#senticnet['concept_name'] = ['pleasantness_value', 'attention_value', 'sensitivity_value', 'aptitude_value', 'primary_mood', 'secondary_mood', 'polarity_label', 'polarity_value', 'semantics1', 'semantics2', 'semantics3', 'semantics4', 'semantics5']
senticnet = {}
senticnet['a_little'] = ['-0.99', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.84', 'least', 'little', 'small_amount', 'shortage', 'scarce']
senticnet['a_little_hungry'] = ['0.757', '0', '0', '0', '#joy', '#joy', 'positive', '0.757', 'get_full', 'hunger_go_away', 'feel_full', 'hunger', 'full']
senticnet['a_little_specific'] = ['0.089', '0.133', '-0.10', '0.126', '#interest', '#admiration', 'positive', '0.06', 'clan', 'happy_together', 'many_people', 'aunt_uncle', 'human_group']
senticnet['a_lot'] = ['0.277', '0.161', '0', '0.337', '#joy', '#admiration', 'positive', '0.258', 'many', 'plenty', 'big_amount', 'abundant', 'good_number']
senticnet['a_lot_of_books'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'library', 'old_testament', 'cook_book', 'magazine', 'librarian']
senticnet['a_lot_of_energy'] = ['0', '0', '0', '0.766', '#admiration', '#admiration', 'positive', '0.766', 'effort', 'determination', 'discipline', 'good_condition', 'stamina']
senticnet['a_lot_of_fat'] = ['0', '0', '0.95', '-0.95', '#anger', '#disgust', 'negative', '-0.95', 'overweight', 'obese', 'gross', 'roly-poly', 'heavy']
senticnet['a_lot_of_flowers'] = ['0.027', '0.093', '0.025', '0.071', '#interest', '#admiration', 'positive', '0.041', 'flower', 'love', 'show_love', 'rose', 'give_flower']
senticnet['a_lot_of_food'] = ['0.043', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'apple_pie', 'lasagna', 'jelly', 'frozen_food', 'cottage_cheese']
senticnet['a_lot_of_fun'] = ['0.814', '0', '0', '0.856', '#joy', '#admiration', 'positive', '0.835', 'radical', 'enjoyable', 'just_fun', 'good_mental_health', 'fun_play']
senticnet['a_lot_of_information'] = ['0', '0.219', '-0.05', '0.144', '#interest', '#admiration', 'positive', '0.104', 'information', 'encyclopedia', 'amount_charge', 'amount', 'book']
senticnet['a_lot_of_money'] = ['0', '0.131', '0', '0', '#interest', '#interest', 'positive', '0.131', 'money_bank', 'bring_wallet', 'much_money', 'wealthy', 'down_payment']
senticnet['a_lot_of_noise'] = ['0', '0', '0.896', '-0.96', '#anger', '#disgust', 'negative', '-0.92', 'back_pain', 'dog_bark', 'sprain_ankle', 'backache', 'broken_leg']
senticnet['a_lot_of_people'] = ['0.08', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.054', 'big_group', 'multitude', 'bunch_person', 'group_of_people', 'several_people']
senticnet['a_lot_of_practice'] = ['0.808', '0', '0', '0.943', '#joy', '#admiration', 'positive', '0.875', 'elastic', 'flexibility', 'take_lesson', 'coordination', 'air_tank']
senticnet['a_lot_of_sex'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'prosper', 'sexual_intercourse', 'warm_fuzzy_feeling', 'woman', 'give_birth_child']
senticnet['a_lot_of_space'] = ['0.941', '0.76', '0', '0', '#joy', '#interest', 'positive', '0.851', 'room', 'space', 'capacity', 'house', 'universe']
senticnet['a_lot_of_stress'] = ['-0.18', '-0.01', '-0.10', '-0.16', '#sadness', '#disgust', 'negative', '-0.10', 'get_grade', 'failure', 'flunk', 'stress', 'worry']
senticnet['a_lot_of_study'] = ['0', '0', '0.969', '-0.84', '#anger', '#disgust', 'negative', '-0.90', 'study_lot', 'stress', 'worry', 'study_material', 'anxiety']
senticnet['a_lot_of_time'] = ['0.918', '0.988', '0', '0', '#joy', '#interest', 'positive', '0.953', 'learn_program_language', 'write_code', 'learn_program', 'debug', 'learn_computer_language']
senticnet['a_lot_of_work'] = ['0.742', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.848', 'work', 'produce_value', 'prepare_event', 'accomplish', 'finish_work']
senticnet['aa_gill'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'rascal', 'rapscallion', 'scalawag', 'holy_terror', 'monkey']
senticnet['aa_gun'] = ['-0.90', '0', '0.517', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'outdated', 'noncurrent', 'disused', 'obsolete', 'superannuated']
senticnet['abandon'] = ['-0.99', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.84', 'leave_alone', 'smelly_foot', 'unattractive', 'lose_team', 'leave_out']
senticnet['abandoned'] = ['0', '0', '-0.84', '-0.85', '#fear', '#disgust', 'negative', '-0.85', 'give_up', 'forsake', 'desolate', 'derelict', 'deserted']
senticnet['abandoned_person'] = ['-0.67', '0', '0.838', '-0.86', '#anger', '#disgust', 'negative', '-0.79', 'unsuccessful_person', 'loser', 'unfortunate', 'desperate', 'orphan']
senticnet['abandoned_quarry'] = ['0', '0', '-0.85', '-0.71', '#fear', '#disgust', 'negative', '-0.78', 'fair_game', 'pit', 'stone_pit', 'target', 'prey']
senticnet['abandonment'] = ['-0.91', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.82', 'need_break', 'need_to_rest', 'leg_tire', 'catch_breath', 'weary']
senticnet['abase'] = ['-0.84', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.90', 'mortify', 'embarrassed', 'self-hatred', 'self-disgust', 'humiliated']
senticnet['abasement'] = ['-0.84', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.90', 'mortify', 'embarrassed', 'self-hatred', 'self-disgust', 'humiliated']
senticnet['abash'] = ['-0.71', '0', '0.836', '0', '#sadness', '#anger', 'negative', '-0.77', 'awkward', 'unenviable', 'embarrassingly', 'embarrassing', 'sheepish']
senticnet['abashed'] = ['-0.86', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.92', 'chagrined', 'disconcert', 'chagrin', 'abash', 'embarrassingly']
senticnet['abashment'] = ['-0.76', '0', '0.773', '0', '#sadness', '#anger', 'negative', '-0.76', 'bashfulness', 'humility', 'timidity', 'abash', 'reservation']
senticnet['abasia'] = ['-0.75', '0', '0.504', '-0.77', '#sadness', '#disgust', 'negative', '-0.67', 'disablement', 'anorgasmia', 'hearing_impairment', 'hearing_disorder', 'bow_leg']
senticnet['abate'] = ['-0.92', '0', '0.757', '-0.90', '#sadness', '#disgust', 'negative', '-0.86', 'let_up', 'far', 'slack_off', 'die_away', 'die']
senticnet['abatement'] = ['0', '0', '0.904', '-0.81', '#anger', '#disgust', 'negative', '-0.85', 'defervescence', 'subsidence', 'break', 'decline', 'remittal']
senticnet['abattoir'] = ['-0.67', '0', '0.938', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'massacre', 'mass_murder', 'carnage', 'bloodbath', 'butchery']
senticnet['abaxial_surface'] = ['-0.66', '0', '0.809', '-0.61', '#sadness', '#anger', 'negative', '-0.69', 'face', 'base', 'underbelly', 'sole', 'bilge']
senticnet['abaxially'] = ['-0.86', '0', '0.511', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'separate', 'move', 'branch', 'diverge', 'fork']
senticnet['abba_kovner'] = ['-0.97', '0', '0.62', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'obstructor', 'obstructer', 'technophobe', 'obstructionist', 'naysayer']
senticnet['abbandono'] = ['0.502', '-0.82', '0', '0.779', '#surprise', '#admiration', 'positive', '0.703', 'mortal', 'person', 'individual', 'free_liver', 'agent']
senticnet['abbreviate'] = ['-0.04', '0.127', '-0.12', '0.223', '#interest', '#admiration', 'positive', '0.046', 'shorten', 'abridge', 'curtail', 'cut_short', 'shortcut']
senticnet['abc'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'kit_fox', 'prairie_fox', 'white_fox']
senticnet['abc_dancing_with'] = ['0.977', '-0.93', '0', '0.583', '#joy', '#surprise', 'positive', '0.831', 'giant', 'shinny', 'supernova', 'supergiant', 'binary_star']
senticnet['abc_drama'] = ['0.54', '-0.64', '0', '0.566', '#surprise', '#admiration', 'positive', '0.582', 'pleaser', 'entertainer', 'mortal', 'busker', 'drawing_card']
senticnet['abc_paramount'] = ['0', '0.115', '0.036', '0.207', '#interest', '#admiration', 'positive', '0.095', 'total', 'amount', 'merit', 'award', 'excellence']
senticnet['abc_television'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'theater_seat', 'theater_box', 'sticky_floor', 'stage_entrance', 'balcony']
senticnet['abc_tv'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'theater_seat', 'theater_box', 'sticky_floor', 'stage_entrance', 'balcony']
senticnet['abdel'] = ['0.798', '-0.51', '0', '0.973', '#joy', '#admiration', 'positive', '0.763', 'inexperienced_person', 'boy_scout', 'dear', 'virgin', 'mortal']
senticnet['abdicate'] = ['-0.78', '0', '0.801', '-0.87', '#anger', '#disgust', 'negative', '-0.81', 'resign', 'renounce', 'step_down', 'vacate', 'leave_office']
senticnet['abdication'] = ['0', '0', '-0.83', '-0.78', '#fear', '#disgust', 'negative', '-0.80', 'renunciation', 'apostasy', 'quitclaim', 'reject', 'repudiation']
senticnet['abdomen'] = ['0.902', '0.772', '0', '0', '#joy', '#interest', 'positive', '0.837', 'body', 'two_ears', 'dog_pet', 'pain', 'midsection']
senticnet['abdominal'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'anus', 'scrotum', 'shoulder', 'liver', 'waist']
senticnet['abdominal_aortic'] = ['-0.70', '0', '0.639', '-0.56', '#sadness', '#anger', 'negative', '-0.63', 'fibroma', 'adenoma', 'chondroma', 'enchondroma', 'benign_tumor']
senticnet['abdominal_cavity'] = ['-0.99', '0', '0.871', '-0.55', '#sadness', '#anger', 'negative', '-0.80', 'endure', 'brook', 'tolerate', 'bear_up', 'abide']
senticnet['abdominal_discomfort'] = ['-0.06', '0', '-0.15', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'severe_pain', 'discomfort', 'physical_reaction', 'choke_death', 'embarrassment']
senticnet['abdominal_distension'] = ['0.759', '-0.72', '0', '0.893', '#joy', '#admiration', 'positive', '0.793', 'manifestation', 'cry', 'reflection', 'acting_out', 'explosion']
senticnet['abdominal_distention'] = ['-0.58', '0', '0.773', '-0.79', '#anger', '#disgust', 'negative', '-0.71', 'perspiration', 'sweating', 'polyhidrosis', 'sudation', 'hidrosis']
senticnet['abdominal_ectopic_gestation'] = ['-0.75', '0', '0.712', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'pregnancy', 'ovarian_ectopic_gestation', 'tubal_ectopic_gestation', 'ectopic_gestation', 'gestation']
senticnet['abdominal_incision'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
senticnet['abdominal_muscle'] = ['0', '0', '0', '0.106', '#admiration', '#admiration', 'positive', '0.106', 'vocal_chord', 'mouth_noise', 'vocal_sound', 'human_sound', 'vocalization']
senticnet['abdominal_pain'] = ['-0.20', '0', '-0.22', '-0.19', '#sadness', '#fear', 'negative', '-0.20', 'go_hospital', 'death', 'die', 'take_aspirin', 'heart_attack']
senticnet['abdominal_pregnancy'] = ['-0.75', '0', '0.715', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'ectopic_gestation', 'ovarian_pregnancy', 'tubal_pregnancy', 'disease', 'pain']
senticnet['abdominal_viscera'] = ['-0.78', '0', '0.855', '-0.99', '#anger', '#disgust', 'negative', '-0.87', 'blood_blister', 'pustule', 'water_blister', 'vesicle', 'cyst']
senticnet['abdominal_wall'] = ['0', '0', '0.021', '-0.06', '#anger', '#disgust', 'negative', '-0.04', 'skin', 'skeletal_system', 'shoulder', 'cuticle', 'groin']
senticnet['abdominoplasty'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
senticnet['abducens_nerve'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['abduct'] = ['-0.89', '-0.71', '-0.76', '-0.93', '#sadness', '#disgust', 'negative', '-0.47', 'bigot', 'censorship', 'jail_time', 'reprimand', 'intolerance']
senticnet['abduction'] = ['0', '0', '-0.76', '-0.97', '#fear', '#disgust', 'negative', '-0.86', 'reaper', 'intolerance', 'danger', 'illiteracy', 'kidnap']
senticnet['abductor'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'deltoid', 'pterygoid_muscle', 'skeletal_muscle', 'muscle']
senticnet['abductor_digiti_minimi'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'skeletal_muscle', 'pterygoid_muscle', 'abductor', 'abductor_muscle']
senticnet['abductor_muscle'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'deltoid', 'muscle', 'abductor', 'skeletal_muscle']
senticnet['abelisauridae'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'hag', 'osteostracan', 'cephalaspid', 'ostracoderm', 'jawless_fish']
senticnet['aberdeen_angus_cattle'] = ['0.546', '-0.96', '0', '0.533', '#joy', '#surprise', 'positive', '0.683', 'dairy_cattle', 'dairy_cow', 'milch_cow', 'ayrshire', 'milcher']
senticnet['aberrance'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'deflection', 'warp', 'chromosomal_aberration']
senticnet['aberrancy'] = ['-0.55', '0', '0.911', '-0.98', '#anger', '#disgust', 'negative', '-0.81', 'abnormalcy', 'abnormality', 'deflection', 'warp', 'chromosomal_aberration']
senticnet['aberrant'] = ['-0.88', '0', '0.882', '-0.58', '#sadness', '#anger', 'negative', '-0.78', 'mortal', 'behemoth', 'colossus', 'unusual_person', 'giant']
senticnet['aberrantly'] = ['0.921', '0', '0', '0.793', '#joy', '#admiration', 'positive', '0.857', 'maternally', 'dominantly', 'inherited', 'paternally', 'ubiquitously']
senticnet['aberration'] = ['-0.68', '0', '0.946', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'abnormality', 'abnormalcy', 'birth_defect', 'congenital_anomaly', 'congenital_defect']
senticnet['abetalipoproteinemia'] = ['-0.54', '0', '0.92', '-0.84', '#anger', '#disgust', 'negative', '-0.76', 'dearth', 'demand', 'mineral_deficiency', 'famine', 'need']
senticnet['abhominable'] = ['-0.52', '0', '0.542', '-0.93', '#anger', '#disgust', 'negative', '-0.67', 'intoxication', 'drunkenness', 'inebriation', 'inebriety', 'tipsiness']
senticnet['abhor'] = ['-0.06', '0.183', '0.651', '-0.64', '#anger', '#disgust', 'negative', '-0.29', 'abhorrence', 'abomination', 'detestation', 'execration', 'loathing']
senticnet['abhorrence'] = ['-0.06', '0.183', '0.651', '-0.64', '#anger', '#disgust', 'negative', '-0.29', 'abomination', 'detestation', 'execration', 'loathing', 'odium']
senticnet['abhorrent'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['abhorreth'] = ['-0.89', '0.161', '-0.61', '0.053', '#sadness', '#fear', 'negative', '-0.32', 'heartrending', 'heartbreaking', 'grievous', 'grievously', 'brokenheartedness']
senticnet['abidance'] = ['0.614', '-0.95', '0', '0.768', '#surprise', '#admiration', 'positive', '0.778', 'keeping', 'formality', 'honoring', 'line', 'observance']
senticnet['abide'] = ['0.038', '0', '0', '0.097', '#joy', '#admiration', 'positive', '0.068', 'rule', 'decision', 'follow', 'discipline', 'acceptance']
senticnet['abide_by'] = ['0.731', '-0.59', '0', '0.634', '#joy', '#admiration', 'positive', '0.652', 'accommodate', 'obey', 'conform_to', 'toe_line', 'oblige']
senticnet['abided_by'] = ['0.731', '-0.59', '0', '0.634', '#joy', '#admiration', 'positive', '0.652', 'accommodate', 'obey', 'conform_to', 'toe_line', 'oblige']
senticnet['abideth'] = ['0.741', '-0.84', '0', '0.543', '#joy', '#surprise', 'positive', '0.711', 'integrity', 'righteousness', 'scrupulousness', 'incorruptness', 'honestness']
senticnet['abiding_love'] = ['0.505', '-0.85', '0', '0.94', '#surprise', '#admiration', 'positive', '0.767', 'infatuation', 'ardor', 'fervor', 'fervency', 'fervidness']
senticnet['abience'] = ['-0.85', '0', '0.949', '0', '#sadness', '#anger', 'negative', '-0.90', 'motive', 'need', 'spur', 'impulse', 'adience']
senticnet['ability'] = ['0', '0.273', '-0.16', '0.277', '#interest', '#admiration', 'positive', '0.129', 'skill', 'competence', 'talent', 'expert', 'possible']
senticnet['ability_communicate'] = ['0', '0.713', '0', '0.984', '#interest', '#admiration', 'positive', '0.849', 'convey_information', 'know_language', 'fact_straight', 'know_talk', 'use_language']
senticnet['ability_hear'] = ['0.708', '0', '0', '0', '#joy', '#joy', 'positive', '0.708', 'use_ear', 'ear_activity', 'hear', 'audible', 'hear_sound']
senticnet['ability_read'] = ['0.989', '0', '0', '0.999', '#joy', '#admiration', 'positive', '0.994', 'eye', 'information', 'choose_book', 'good_book', 'hold_book']
senticnet['ability_see'] = ['0.837', '0', '0', '0', '#joy', '#joy', 'positive', '0.837', 'wear_glass', 'recognize', 'open_eye', 'turn_on_tv', 'regard']
senticnet['abimelech'] = ['-0.62', '0', '0.645', '-0.67', '#anger', '#disgust', 'negative', '-0.65', 'lady_maid', 'handmaiden', 'chambermaid', 'maid', 'parlormaid']
senticnet['abject'] = ['-0.83', '0', '0', '-0.81', '#sadness', '#disgust', 'negative', '-0.82', 'resignedly', 'unhopeful', 'abjectly', 'resignation', 'reconcile']
senticnet['abject_misery'] = ['-0.77', '0', '0.797', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'unhappiness', 'sorrow', 'remorse', 'melancholy', 'guilt']
senticnet['abject_poverty'] = ['-0.56', '0', '-0.49', '-0.09', '#sadness', '#fear', 'negative', '-0.38', 'lack', 'absence', 'poverty', 'emptiness', 'hunger']
senticnet['abjection'] = ['-0.56', '0', '0.512', '-0.50', '#sadness', '#anger', 'negative', '-0.52', 'depth', 'humiliation', 'degeneration', 'decadence', 'decadency']
senticnet['abjectly'] = ['-0.81', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.76', 'resignedly', 'abject', 'unhopeful', 'resignation', 'reconcile']
senticnet['abjure'] = ['-0.97', '0', '0.616', '-0.58', '#sadness', '#anger', 'negative', '-0.72', 'repudiate', 'recant', 'forswear', 'deny', 'reject']
senticnet['ablate'] = ['-0.97', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.83', 'wear_out', 'wear_off', 'melt', 'vaporize', 'wear_thin']
senticnet['ablate_down'] = ['-0.98', '0', '0.539', '-0.55', '#sadness', '#disgust', 'negative', '-0.69', 'ablate', 'ablate_off', 'ablate_out', 'wear', 'ablate_thin']
senticnet['ablate_off'] = ['-0.97', '0', '0.534', '-0.55', '#sadness', '#disgust', 'negative', '-0.68', 'ablate', 'wear', 'ablate_out', 'ablate_down', 'ablate_thin']
senticnet['ablate_out'] = ['-0.98', '0', '0.537', '-0.55', '#sadness', '#disgust', 'negative', '-0.69', 'ablate', 'ablate_off', 'wear', 'ablate_down', 'ablate_thin']
senticnet['ablate_thin'] = ['-0.97', '0', '0.532', '-0.56', '#sadness', '#disgust', 'negative', '-0.69', 'ablate', 'ablate_off', 'ablate_out', 'ablate_down', 'wear']
senticnet['ablaut'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'pronunciation', 'sibilation', 'mispronunciation', 'homophony']
senticnet['ablaze'] = ['0.641', '-0.73', '0', '0.798', '#surprise', '#admiration', 'positive', '0.724', 'lighted', 'aflame', 'afire', 'aflare', 'alight']
senticnet['able'] = ['0.982', '-0.67', '0', '0.682', '#joy', '#admiration', 'positive', '0.778', 'workmanlike', 'effective', 'efficient', 'competent', 'capcompetent']
senticnet['able_eat'] = ['0.727', '0.786', '0', '0', '#joy', '#interest', 'positive', '0.756', 'munch', 'satisfy_appetite', 'nourishment', 'replenish_energy', 'sustenance']
senticnet['able_hear'] = ['0.814', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.847', 'go_rock_concert', 'attend_concert', 'sensibility', 'audible', 'turn_radio']
senticnet['able_read'] = ['0.972', '0.895', '0', '0.727', '#joy', '#interest', 'positive', '0.865', 'good_sense_of_humor', 'good_eyesight', 'taken_seriously', 'complement', 'much_need']
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senticnet['absent-minded'] = ['-0.89', '0', '0.749', '0', '#sadness', '#anger', 'negative', '-0.82', 'dim-witted', 'scatterbrained', 'accident-prone', 'dimwitted', 'klutzy']
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senticnet['absolute_alcohol'] = ['0', '0', '0.992', '-0.76', '#anger', '#disgust', 'negative', '-0.87', 'ethyl_alcohol', 'spirits_of_wine', 'absolute_vodka', 'fermentation_alcohol', 'grain_alcohol']
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senticnet['absolute_liability'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'culpable_negligence', 'evasion', 'dereliction', 'negligence']
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senticnet['absolute_perfection'] = ['0', '0.706', '0', '0.934', '#interest', '#admiration', 'positive', '0.82', 'value', 'good_value', 'ideal', 'flawless', 'ultimate']
senticnet['absolute_pitch'] = ['0.785', '-0.99', '0', '0.981', '#surprise', '#admiration', 'positive', '0.92', 'ear', 'audition', 'perfect_pitch', 'modality', 'sense_modality']
senticnet['absolute_power'] = ['-0.90', '0', '0.855', '0', '#sadness', '#anger', 'negative', '-0.87', 'tyranny', 'despotism', 'absolutism', 'monopoly', 'autocracy']
senticnet['absolute_prohibition'] = ['0', '0', '-0.9', '-0.79', '#fear', '#disgust', 'negative', '-0.84', 'forbid', 'prohibit', 'veto', 'disallow', 'interdict']
senticnet['absolute_refractory_period'] = ['-0.87', '0', '0.915', '-0.95', '#anger', '#disgust', 'negative', '-0.91', 'inactivity', 'inactiveness', 'slumber', 'inaction', 'hibernation']
senticnet['absolute_rubbish'] = ['0.739', '-0.89', '0', '0.702', '#joy', '#surprise', 'positive', '0.78', 'pap', 'content', 'pablum', 'folderol', 'drivel']
senticnet['absolute_ruler'] = ['0.029', '0.079', '0', '0.063', '#interest', '#admiration', 'positive', '0.057', 'king', 'duchess', 'sultan', 'royal_leader', 'emperor']
senticnet['absolute_silence'] = ['0.449', '0.305', '-0.19', '0.222', '#joy', '#interest', 'positive', '0.196', 'calm', 'peace', 'serenity', 'quiet', 'tranquility']
senticnet['absolute_truth'] = ['0.295', '0.239', '0.049', '0.346', '#joy', '#admiration', 'positive', '0.208', 'full', 'replicate', 'infinite', 'total', 'unending']
senticnet['absolute_value'] = ['0.037', '0', '0.026', '0.129', '#joy', '#admiration', 'positive', '0.047', 'measurement_unit', 'many_items', 'long', 'circle_part', 'narrow']
senticnet['absolute_veto'] = ['-0.81', '0', '0.513', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'ballot', 'vote', 'voting', 'balloting', 'veto']
senticnet['absolute_vodka'] = ['0', '0', '0.99', '-0.76', '#anger', '#disgust', 'negative', '-0.87', 'ethyl_alcohol', 'spirits_of_wine', 'absolute_alcohol', 'fermentation_alcohol', 'grain_alcohol']
senticnet['absolutely'] = ['0.77', '0', '0', '0.996', '#joy', '#admiration', 'positive', '0.883', 'bind_agreement', 'need_money', 'completely', 'earn_wage', 'truly']
senticnet['absolutely_brilliant'] = ['0.88', '0.86', '0', '0', '#joy', '#interest', 'positive', '0.87', 'amaze', 'grand', 'wonderful', 'pose', 'howling']
senticnet['absolutely_clear'] = ['-0.66', '0', '0.721', '-0.53', '#sadness', '#anger', 'negative', '-0.64', 'unlimited', 'outright', 'unconditional', 'unqualified', 'unqualifiedal']
senticnet['absolutely_critical'] = ['0.062', '0.335', '0', '0.36', '#interest', '#admiration', 'positive', '0.252', 'religious', 'religious_ceremony', 'religious_service', 'church_service', 'sacred']
senticnet['absolutely_crucial'] = ['0.062', '0.335', '0', '0.36', '#interest', '#admiration', 'positive', '0.252', 'religious', 'religious_ceremony', 'religious_service', 'church_service', 'sacred']
senticnet['absolutely_essential'] = ['0.062', '0.335', '0', '0.36', '#interest', '#admiration', 'positive', '0.252', 'religious', 'religious_ceremony', 'religious_service', 'church_service', 'sacred']
senticnet['absolutely_false'] = ['-0.24', '0.147', '-0.25', '0.215', '#sadness', '#fear', 'negative', '-0.03', 'dishonest', 'against_law', 'falsehood', 'deceit', 'deception']
senticnet['absolutely_fantastic'] = ['0.88', '0.86', '0', '0', '#joy', '#interest', 'positive', '0.87', 'amaze', 'grand', 'wonderful', 'pose', 'howling']
senticnet['absolutely_impossible'] = ['-0.80', '0', '-0.80', '-0.96', '#sadness', '#disgust', 'negative', '-0.85', 'unable', 'absurd', 'difficult', 'impractical', 'unimaginable']
senticnet['absolutely_incapable'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'blatancy', 'patency', 'obviousness']
senticnet['absolutely_necessary'] = ['-0.97', '0', '0.84', '-0.52', '#sadness', '#anger', 'negative', '-0.78', 'pis_aller', 'expedient', 'temporary_expedient', 'makeshift', 'stopgap']
senticnet['absolutely_perfect'] = ['0', '0.794', '0', '0.941', '#interest', '#admiration', 'positive', '0.867', 'best', 'excellent', 'goodness', 'greatness', 'great']
senticnet['absolutely_stunning'] = ['0.88', '0.86', '0', '0', '#joy', '#interest', 'positive', '0.87', 'amaze', 'grand', 'wonderful', 'pose', 'howling']
senticnet['absolutely_vital'] = ['0.062', '0.335', '0', '0.36', '#interest', '#admiration', 'positive', '0.252', 'religious', 'religious_ceremony', 'religious_service', 'church_service', 'sacred']
senticnet['absolutely_wonderful'] = ['0.88', '0.86', '0', '0', '#joy', '#interest', 'positive', '0.87', 'amaze', 'grand', 'wonderful', 'pose', 'howling']
senticnet['absolution'] = ['0.901', '0', '0', '0.916', '#joy', '#admiration', 'positive', '0.909', 'get_away', 'bread', 'glory', 'counsel', 'vow']
senticnet['absolutism'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'ascendance', 'autocracy', 'ascendancy', 'tyrant']
senticnet['absolutist'] = ['-0.89', '0', '0.678', '-0.62', '#sadness', '#anger', 'negative', '-0.73', 'undisciplined', 'anarchic', 'uncontrolled', 'irrepressible', 'uncontrollable']
senticnet['absolutist_rule'] = ['0.059', '0.092', '0', '0.107', '#interest', '#admiration', 'positive', '0.086', 'royalty', 'monarch', 'throne', 'emperor', 'king_queen']
senticnet['absolutive'] = ['-0.82', '0', '0.81', '-0.61', '#sadness', '#anger', 'negative', '-0.75', 'oblique', 'genitive', 'envy', 'possessive', 'jealous']
senticnet['absolve'] = ['0.901', '0', '0', '0.916', '#joy', '#admiration', 'positive', '0.909', 'get_away', 'bread', 'glory', 'counsel', 'vow']
senticnet['absolved'] = ['0.908', '-0.75', '0', '0.699', '#joy', '#surprise', 'positive', '0.786', 'guiltless', 'exculpated', 'exonerated', 'vindicated', 'clear']
senticnet['absolvitory'] = ['0.579', '-0.54', '0', '0.784', '#joy', '#admiration', 'positive', '0.635', 'forgiving', 'vindicatory', 'exculpatory', 'exonerative', 'extenuating']
senticnet['absorb'] = ['0.869', '-0.59', '0', '0.66', '#joy', '#admiration', 'positive', '0.709', 'assimilate', 'take_up', 'reabsorb', 'resorb', 'imbibe']
senticnet['absorb_carbon_dioxide'] = ['0.053', '0.264', '-0.11', '0.23', '#interest', '#admiration', 'positive', '0.107', 'natural_resource', 'petroleum', 'fossil_fuel', 'energy_source', 'potential_energy']
senticnet['absorb_heat'] = ['0', '0.073', '-0.05', '0.093', '#interest', '#admiration', 'positive', '0.038', 'thicken', 'condense_water', 'condensed_milk', 'weather_condition', 'condensation']
senticnet['absorb_information'] = ['0.757', '0.896', '0', '0.538', '#joy', '#interest', 'positive', '0.73', 'ask_expert', 'remember_fact', 'remember_information', 'research', 'seek_info']
senticnet['absorb_moisture'] = ['0', '0.799', '0', '0', '#interest', '#interest', 'positive', '0.799', 'iron_shirt', 'sit_on_beach', 'color_clothes', 'iron_clothes', 'shine_shoes']
senticnet['absorb_oxygen'] = ['-0.57', '0', '0.767', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'verboseness', 'verbosity', 'prolixity', 'turgidness', 'flatulence']
senticnet['absorb_water'] = ['0.781', '0.845', '0', '0', '#joy', '#interest', 'positive', '0.813', 'hygroscope', 'hold_water', 'pure_water', 'molecule', 'rain_water']
senticnet['absorbable_suture'] = ['-0.58', '0', '0.715', '-0.59', '#anger', '#disgust', 'negative', '-0.63', 'fly', 'overlap', 'barndoor', 'coattail', 'codpiece']
senticnet['absorbent'] = ['0.085', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.074', 'soap', 'drizzle', 'bubbly', 'solid_water', 'ivory']
senticnet['absorbent_cotton'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'hankie', 'kleenex', 'hanky', 'handkerchief', 'tissue']
senticnet['absorbent_paper'] = ['0.029', '-0.03', '0.066', '-0.08', '#anger', '#disgust', 'negative', '-0.02', 'sheet', 'drawer', 'file_box', 'desk_tray', 'letter_scale']
senticnet['absorbing'] = ['0.566', '-0.91', '0', '0.599', '#surprise', '#admiration', 'positive', '0.692', 'intriguing', 'interesting', 'engrossing', 'fascinating', 'gripping']
senticnet['absorbing_carbon_dioxide'] = ['0.053', '0.264', '-0.11', '0.23', '#interest', '#admiration', 'positive', '0.107', 'natural_resource', 'petroleum', 'fossil_fuel', 'energy_source', 'potential_energy']
senticnet['absorbs_moisture'] = ['-0.57', '0', '0.767', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'verboseness', 'verbosity', 'prolixity', 'turgidness', 'flatulence']
senticnet['absorption'] = ['0.633', '-0.96', '0', '0.612', '#joy', '#surprise', 'positive', '0.738', 'particularism', 'centering', 'concentration', 'engrossment', 'immersion']
senticnet['abstain_from'] = ['-0.91', '0', '0.576', '-0.95', '#sadness', '#disgust', 'negative', '-0.81', 'stop', 'halt', 'lock_in', 'lock', 'block']
senticnet['abstaining_from'] = ['0', '0', '0.973', '-0.78', '#anger', '#disgust', 'negative', '-0.87', 'chastity', 'ignore_conception', 'restrain', 'abortion', 'stop']
senticnet['abstemious'] = ['0', '0.731', '0', '0.757', '#interest', '#admiration', 'positive', '0.744', 'ascetic', 'ascetical', 'spartan', 'austere', 'abstinent']
senticnet['abstemiousal'] = ['0.547', '-0.92', '0', '0.6', '#surprise', '#admiration', 'positive', '0.69', 'abstemious', 'ascetic', 'spartan', 'austere', 'abstinent']
senticnet['abstemiousness'] = ['-0.74', '0', '0.585', '-0.58', '#sadness', '#anger', 'negative', '-0.63', 'lack_of_quality', 'meagreness', 'poorness', 'scantiness', 'scantness']
senticnet['abstention'] = ['0.63', '0.622', '-0.29', '0.365', '#joy', '#interest', 'positive', '0.332', 'joyful', 'favorable', 'favorably', 'agree', 'jubilantly']
senticnet['absterge'] = ['0.89', '0', '0', '0.698', '#joy', '#admiration', 'positive', '0.794', 'rake', 'hackle', 'purge', 'cleanse', 'shampoo']
senticnet['abstinance'] = ['-0.74', '0', '0.677', '-0.77', '#sadness', '#disgust', 'negative', '-0.73', 'whore', 'jazz', 'hump', 'bonk', 'screw']
senticnet['abstinence'] = ['0', '0', '0.973', '-0.78', '#anger', '#disgust', 'negative', '-0.87', 'chastity', 'ignore_conception', 'restrain', 'abortion', 'stop']
senticnet['abstinence_education'] = ['0', '0', '0.973', '-0.78', '#anger', '#disgust', 'negative', '-0.87', 'chastity', 'ignore_conception', 'restrain', 'abortion', 'stop']
senticnet['abstinence_only'] = ['0', '0', '0.973', '-0.78', '#anger', '#disgust', 'negative', '-0.87', 'chastity', 'ignore_conception', 'restrain', 'abortion', 'stop']
senticnet['abstinent'] = ['-0.71', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.77', 'ascetic', 'ascetical', 'spartan', 'austere', 'abstemious']
senticnet['abstract'] = ['0', '0.309', '-0.10', '0.266', '#interest', '#admiration', 'positive', '0.157', 'subjective', 'notional', 'choice', 'academic', 'imaginary']
senticnet['abstract_mathematical'] = ['-0.99', '0', '0.878', '-0.95', '#sadness', '#disgust', 'negative', '-0.94', 'adequacy', 'satisfactoriness', 'acceptability', 'acceptableness', 'quality']
senticnet['abstract_think'] = ['0', '0.977', '0', '0.901', '#interest', '#admiration', 'positive', '0.939', 'new_think', 'brain_function', 'make_right_decision', 'brainstorm', 'think']
senticnet['abstract_thought'] = ['0.725', '-0.76', '0', '0.51', '#joy', '#surprise', 'positive', '0.665', 'ratiocination', 'logical_argument', 'line_of_reasoning', 'thinking', 'thought']
senticnet['abstraction'] = ['0.144', '0.291', '-0.07', '0.296', '#interest', '#admiration', 'positive', '0.164', 'description', 'reliable', 'social_creature', 'imaginary', 'sentient']
senticnet['abstractionism'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'native_person', 'revolutionary', 'republic', 'revolution', 'look_ahead']
senticnet['abstractly'] = ['0.945', '0', '0', '0.736', '#joy', '#admiration', 'positive', '0.841', 'metaphorically', 'symbolically', 'figuratively', 'prophetically', 'allegorically']
senticnet['abstruse'] = ['0', '0.72', '0', '0.937', '#interest', '#admiration', 'positive', '0.829', 'exquisite', 'pleasing', 'exquisitely', 'exceptional', 'beauty']
senticnet['abstruseness'] = ['0', '0', '-0.99', '-0.83', '#fear', '#disgust', 'negative', '-0.91', 'abstrusity', 'reconditeness', 'astuteness', 'profundity', 'profoundness']
senticnet['abstrusity'] = ['0', '0', '-0.99', '-0.83', '#fear', '#disgust', 'negative', '-0.91', 'profundity', 'reconditeness', 'astuteness', 'abstruseness', 'profoundness']
senticnet['absurd'] = ['0', '0', '0.93', '-0.70', '#anger', '#disgust', 'negative', '-0.81', 'illogical', 'nonrational', 'preposterous', 'ridiculous', 'ludicrous']
senticnet['absurdism'] = ['0.807', '-0.83', '0', '0.688', '#joy', '#surprise', 'positive', '0.776', 'folly', 'lunacy', 'craziness', 'indulgence', 'foolery']
senticnet['absurdist'] = ['0', '-0.03', '0', '0.094', '#surprise', '#admiration', 'positive', '0.065', 'commercial', 'sitcom', 'presentation', 'joke', 'funny']
senticnet['absurdist_comedy'] = ['0.807', '-0.83', '0', '0.688', '#joy', '#surprise', 'positive', '0.776', 'folly', 'lunacy', 'craziness', 'indulgence', 'foolery']
senticnet['absurdist_humor'] = ['-0.9', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.80', 'filth', 'smut', 'vulgarism', 'dirt', 'obscenity']
senticnet['absurdity'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'nonsense', 'meaninglessness', 'bunk', 'nonsensicality', 'hokum']
senticnet['absurdly'] = ['-0.77', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.8', 'malapropism', 'absurd', 'irrationally', 'clownishly', 'ridicule']
senticnet['abugida'] = ['-0.86', '0', '0.619', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'character', 'logogram', 'logograph', 'graph', 'grapheme']
senticnet['abundance'] = ['0', '0.184', '-0.05', '0.179', '#interest', '#admiration', 'positive', '0.104', 'rich', 'a_lot', 'abundant', 'wealth', 'plentiful']
senticnet['abundant'] = ['-0.08', '0.168', '-0.06', '0.189', '#interest', '#admiration', 'positive', '0.051', 'abundance', 'a_lot', 'rich', 'plenty', 'plentiful']
senticnet['abundant_fruit'] = ['0.851', '-0.67', '0', '0.854', '#joy', '#admiration', 'positive', '0.792', 'fertile', 'breeding', 'prolific', 'bountiful', 'productive']
senticnet['abundant_wildlife'] = ['0.896', '0', '0', '0.709', '#joy', '#admiration', 'positive', '0.802', 'perspective', 'vista', 'view', 'aspect', 'see_view']
senticnet['abundantly'] = ['0.851', '-0.67', '0', '0.854', '#joy', '#admiration', 'positive', '0.792', 'fertile', 'breeding', 'prolific', 'bountiful', 'productive']
senticnet['abusable'] = ['-0.92', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.94', 'addictive', 'mind-altering', 'sedating', 'addicting', 'habit-forming']
senticnet['abuse'] = ['-0.25', '-0.03', '-0.15', '-0.28', '#sadness', '#disgust', 'negative', '-0.16', 'feel_stressed', 'parking_ticket', 'without_money', 'mock', 'reprimand']
senticnet['abuse_neglect'] = ['-0.94', '0', '0.923', '-0.98', '#sadness', '#disgust', 'negative', '-0.95', 'heap', 'push', 'accept', 'bring', 'lunge']
senticnet['abused_alcohol'] = ['0', '0.931', '0', '0', '#interest', '#interest', 'positive', '0.931', 'football', 'rake_leave', 'spray_paint_grafitti', 'juggle', 'play_hide_seek']
senticnet['abusing_alcohol'] = ['0', '0.931', '0', '0', '#interest', '#interest', 'positive', '0.931', 'football', 'rake_leave', 'spray_paint_grafitti', 'juggle', 'play_hide_seek']
senticnet['abusive'] = ['-0.99', '0', '0.709', '0', '#sadness', '#anger', 'negative', '-0.84', 'billingsgate', 'contumely', 'epithet', 'fishwife', 'vitriol']
senticnet['abusive_behavior'] = ['-0.56', '0', '0.743', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'negligent', 'derelict', 'inattentive', 'delinquent', 'lax']
senticnet['abusive_boyfriend'] = ['0.864', '-0.54', '0', '0.613', '#joy', '#admiration', 'positive', '0.675', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['abusive_conduct'] = ['0', '0', '0.853', '-0.84', '#anger', '#disgust', 'negative', '-0.84', 'flagrant', 'cry', 'glaring', 'gross', 'rank']
senticnet['abusive_father'] = ['0', '0', '0.821', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'student', 'serious_student', 'addiction', 'graduate_student', 'hard_worker']
senticnet['abusive_husband'] = ['0', '0', '0.821', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'student', 'serious_student', 'addiction', 'graduate_student', 'hard_worker']
senticnet['abusive_parent'] = ['-0.56', '0', '0.743', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'negligent', 'derelict', 'inattentive', 'delinquent', 'lax']
senticnet['abusive_stepfather'] = ['0', '0', '0.821', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'student', 'serious_student', 'addiction', 'graduate_student', 'hard_worker']
senticnet['abusive_treatment'] = ['-0.79', '0', '0.691', '-0.88', '#sadness', '#disgust', 'negative', '-0.79', 'cold', 'pitiless', 'cruel', 'unkind', 'vicious']
senticnet['abusively'] = ['0', '0.149', '0.719', '-0.66', '#anger', '#disgust', 'negative', '-0.41', 'belligerently', 'antagonism', 'class_feeling', 'aggressiveness', 'animus']
senticnet['abut'] = ['0.812', '0', '0', '0.783', '#joy', '#admiration', 'positive', '0.798', 'adjoin', 'coadunate', 'juxtapose', 'melt', 'weave']
senticnet['abuzz'] = ['0', '0', '-0.82', '-0.71', '#fear', '#disgust', 'negative', '-0.77', 'bombinate', 'bombilate', 'hum', 'seethe', 'buzzing']
senticnet['abysmal'] = ['0.787', '0', '0', '0.904', '#joy', '#admiration', 'positive', '0.846', 'illimitable', 'limitless', 'measureless', 'unmeasured', 'immeasurable']
senticnet['abysmally'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'lamentably', 'deplorably', 'bereaved', 'bereft', 'grief-stricken']
senticnet['abyss'] = ['-0.78', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.84', 'depravity', 'escutcheon', 'profound', 'infinite', 'void']
senticnet['abyssal_plain'] = ['-0.04', '-0.01', '0.103', '0', '#sadness', '#anger', 'negative', '-0.04', 'near_beach', 'along_beach', 'full_water', 'ocean_ground', 'salty']
senticnet['abyssinicum'] = ['-0.50', '0', '0.573', '-0.89', '#anger', '#disgust', 'negative', '-0.65', 'petallike', 'bowery', 'leafy', 'unifoliate', 'bifoliate']
senticnet['ac_adapter'] = ['0', '-0.12', '0.011', '0.089', '#surprise', '#admiration', 'positive', '0.067', 'cellular_phone', 'telephone', 'wireless', 'connect', 'message']
senticnet['ac_adaptor'] = ['0', '-0.12', '0.011', '0.089', '#surprise', '#admiration', 'positive', '0.067', 'cellular_phone', 'telephone', 'wireless', 'connect', 'message']
senticnet['ac_chart'] = ['0.094', '0.091', '0', '0.125', '#joy', '#admiration', 'positive', '0.103', 'diagram', 'graph', 'medium', 'fragment', 'representation']
senticnet['ac_impedance'] = ['0.965', '-0.84', '0', '0.599', '#joy', '#surprise', 'positive', '0.802', 'mental_faculty', 'faculty', 'module', 'remembrance', 'anamnesis']
senticnet['ac_power'] = ['0', '-0.03', '0', '0.093', '#surprise', '#admiration', 'positive', '0.063', 'work_well', 'internal_combustion_engine', 'broken_car', 'mode_transportation', 'machine']
senticnet['ac_traction'] = ['0.547', '-0.69', '0', '0.52', '#joy', '#surprise', 'positive', '0.586', 'vivification', 'invigoration', 'activity', 'animation', 'activating']
senticnet['academe'] = ['0', '0.941', '0', '0.897', '#interest', '#admiration', 'positive', '0.919', 'academia', 'industry', 'research', 'career', 'workshop']
senticnet['academese'] = ['-0.89', '0', '0.987', '-0.90', '#anger', '#disgust', 'negative', '-0.93', 'cacoethes', 'logomania', 'agromania', 'pyromania', 'necrophilia']
senticnet['academia'] = ['0', '0.942', '0', '0.905', '#interest', '#admiration', 'positive', '0.923', 'academe', 'industry', 'research', 'career', 'workshop']
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senticnet['accurate_estimate'] = ['-0.02', '0.248', '0', '0.31', '#interest', '#admiration', 'positive', '0.178', 'assurance', 'total', 'quality', 'importance', 'amount']
senticnet['accurate_estimation'] = ['0.83', '-0.83', '0', '0.872', '#surprise', '#admiration', 'positive', '0.846', 'overappraisal', 'overvaluation', 'overestimate', 'overestimation', 'commercial_document']
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senticnet['accurately_estimate'] = ['-0.02', '0.248', '0', '0.31', '#interest', '#admiration', 'positive', '0.178', 'assurance', 'total', 'quality', 'importance', 'amount']
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senticnet['accurately_predict'] = ['-0.80', '0', '0.945', '-0.63', '#sadness', '#anger', 'negative', '-0.79', 'fancy', 'conceive_of', 'fantasy', 'daydream', 'woolgather']
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senticnet['acetaminophen_tylenol'] = ['0', '0', '-0.70', '-0.76', '#fear', '#disgust', 'negative', '-0.73', 'choke', 'become_full', 'get_full', 'burp', 'sickness']
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senticnet['acetic_acid'] = ['0', '0.104', '-0.09', '0.146', '#interest', '#admiration', 'positive', '0.052', 'thaw', 'slush', 'water_inside', 'give_water', 'obtain_water']
senticnet['acetic_anhydride'] = ['-0.81', '0', '0.774', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'acid', 'hypophosphoric_acid', 'periodic_acid', 'oxyacid', 'phosphorous_acid']
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senticnet['acetone'] = ['0.843', '-0.95', '0', '0.648', '#joy', '#surprise', 'positive', '0.816', 'medium', 'dissolvent', 'propanone', 'dimethyl_ketone', 'chlorobenzene']
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senticnet['acetylacetone'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'carboxylic_acid', 'succinic_acid', 'valeric_acid']
senticnet['acetylcholine_ach'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'gastrointestinal_hormone', 'secretin', 'ghrelin']
senticnet['acetylcholine_release'] = ['0.698', '-0.61', '0', '0.619', '#joy', '#admiration', 'positive', '0.644', 'sensitivity', 'sensitiveness', 'irritability', 'sensibility', 'reactivity']
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senticnet['ach_credit'] = ['-0.05', '0.26', '-0.08', '0.142', '#interest', '#admiration', 'positive', '0.068', 'expense', 'afford', 'payment', 'cost', 'owe']
senticnet['ach_debit'] = ['-0.05', '0.26', '-0.08', '0.142', '#interest', '#admiration', 'positive', '0.068', 'expense', 'afford', 'payment', 'cost', 'owe']
senticnet['ach_tha'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
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senticnet['achalasia'] = ['-0.56', '0', '0.806', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
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senticnet['achromat'] = ['0.057', '0', '0.012', '0.08', '#joy', '#admiration', 'positive', '0.042', 'see_better', 'glasses', 'become_gigantic', 'magnify', 'contact_lens']
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senticnet['achromatopsia'] = ['-0.82', '0', '0.996', '-0.61', '#sadness', '#anger', 'negative', '-0.81', 'visual_impairment', 'visual_defect', 'vision_defect', 'visual_disorder', 'avitaminosis']
senticnet['achy'] = ['0.815', '-0.75', '0', '0.744', '#joy', '#surprise', 'positive', '0.772', 'perk', 'convalesce', 'improve', 'look_better', 'meliorate']
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senticnet['acicular'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'harsh', 'common', 'rough-cut', 'uncouth', 'vulgar']
senticnet['acid'] = ['0.071', '0', '-0.08', '-0.09', '#fear', '#disgust', 'negative', '-0.03', 'gunk', 'rotten_food', 'sour_cream', 'glass_of_milk', 'lemon_juice']
senticnet['acid-forming'] = ['0', '0', '0.825', '-0.74', '#anger', '#disgust', 'negative', '-0.78', 'alkaline-forming', 'acid-producing', 'alkalizing', 'potassium-rich', 'gas-forming']
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senticnet['acidify'] = ['-0.70', '0', '0.518', '-0.51', '#sadness', '#anger', 'negative', '-0.58', 'bitter', 'modify', 'acidulate', 'change_taste', 'acetify']
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senticnet['acknowledge_receipt'] = ['0.862', '0.709', '0', '0', '#joy', '#interest', 'positive', '0.785', 'invoice', 'bill', 'check', 'mastercard', 'payment_counter']
senticnet['acknowledgeable'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'identifiable', 'specifiable', 'recognizable', 'diagnosable', 'classifiable']
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senticnet['acknowledgment'] = ['0.98', '0.857', '0', '0.966', '#joy', '#admiration', 'positive', '0.934', 'sincere', 'brownie_point', 'positive_reinforcement', 'acceptance', 'mutual_understand']
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senticnet['acleistoceratidae'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'seed_fern', 'gymnosperm']
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senticnet['acm_digital'] = ['0.81', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.774', 'available', 'public', 'population', 'crowd', 'majority']
senticnet['acml'] = ['0', '0', '0.737', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'annoy', 'skin_rash', 'bother', 'irritation', 'great_dane']
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senticnet['acne_medication'] = ['-0.84', '0', '0', '-0.96', '#sadness', '#disgust', 'negative', '-0.90', 'skin_condition', 'adolescent', 'pimple', 'medical_treatment', 'prescription']
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senticnet['acorn_squash'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'squash', 'winter_squash', 'hubbard_squash', 'turban_squash', 'winter_squash_plant']
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senticnet['acoustic_aphasia'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'aphasia', 'brain_damage', 'nominal_aphasia', 'anomic_aphasia']
senticnet['acoustic_noise'] = ['0', '0', '0.735', '-0.70', '#anger', '#disgust', 'negative', '-0.72', 'swing_set', 'roadway_square', 'fresh_air', 'dog_poop', 'wildflower']
senticnet['acoustic_power'] = ['0.695', '-0.85', '0', '0.501', '#joy', '#surprise', 'positive', '0.684', 'sound_power', 'light_intensity', 'intensity', 'sound_pressure_level', 'threshold_level']
senticnet['acoustic_signal'] = ['-0.95', '0', '-0.73', '-0.73', '#sadness', '#fear', 'negative', '-0.80', 'decibel', 'noise', 'loud_soft', 'sound', 'audio']
senticnet['acoustic_trio'] = ['0.899', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.813', 'concert', 'measure_angle', 'make_drawing', 'make_beautiful_music', 'listen_heart']
senticnet['acoustic_version'] = ['0.773', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.844', 'favorite', 'crowd', 'acclamation', 'bunch', 'large_crowd']
senticnet['acoustical'] = ['-0.98', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.91', 'reverberant', 'reverberation', 'soundproof', 'anechoic', 'attenuation']
senticnet['acp_cartridge'] = ['0.887', '-0.95', '0', '0.913', '#surprise', '#admiration', 'positive', '0.917', 'high_quality', 'superiority', 'level', 'grade', 'degree']
senticnet['acquaint'] = ['0.649', '-0.61', '0', '0.723', '#joy', '#admiration', 'positive', '0.664', 'bring_out', 'present', 'reintroduce', 'introduce', 'inform']
senticnet['acquaint_student_with'] = ['0', '0.948', '0', '0.948', '#interest', '#admiration', 'positive', '0.948', 'cope', 'farewell', 'incident', 'propose', 'welcome']
senticnet['acquaintance'] = ['0.814', '-0.50', '0', '0.622', '#joy', '#admiration', 'positive', '0.648', 'friend', 'champion', 'supporter', 'protagonist', 'endorser']
senticnet['acquaintance_rape'] = ['-0.64', '0', '0.854', '-0.83', '#anger', '#disgust', 'negative', '-0.77', 'crime', 'bigamy', 'regulatory_offence', 'sexual_abuse', 'sex_crime']
senticnet['acquaintanceship'] = ['0.608', '0.647', '-0.26', '0.325', '#joy', '#interest', 'positive', '0.328', 'amicably', 'amicable', 'amicableness', 'brotherly', 'friendly']
senticnet['acquainted_with'] = ['-0.10', '0.127', '-0.11', '0.174', '#interest', '#admiration', 'positive', '0.022', 'query', 'response', 'respond', 'interrogation', 'inquiry']
senticnet['acquiesce'] = ['-0.54', '0', '0.815', '-0.83', '#anger', '#disgust', 'negative', '-0.73', 'brave_out', 'endure', 'weather', 'withstand', 'stand_firm']
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senticnet['acquire'] = ['-0.04', '0.18', '-0.09', '0.247', '#interest', '#admiration', 'positive', '0.071', 'obtain', 'satisfy_need', 'business_transaction', 'get_stuff', 'return_goods']
senticnet['acquire_goods'] = ['0', '0.078', '0', '0.099', '#interest', '#admiration', 'positive', '0.088', 'exchange_money', 'buy_sell', 'sell', 'make_rich', 'sell_goods']
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senticnet['acquire_knowledge'] = ['0.719', '0.757', '0', '0', '#joy', '#interest', 'positive', '0.738', 'get_knowledge', 'gain_knowledge', 'increase_knowledge', 'expand_knowledge', 'become_intelligent']
senticnet['acquire_wealth'] = ['0.881', '0.737', '0', '0.851', '#joy', '#admiration', 'positive', '0.823', 'financial_security', 'take_vacation', 'accomplish', 'human_race_survive', 'positive_reinforcement']
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senticnet['activeness'] = ['0.681', '-0.93', '0', '0.78', '#surprise', '#admiration', 'positive', '0.799', 'behavior', 'state', 'play', 'agency', 'busyness']
senticnet['activewear'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
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senticnet['activity_described'] = ['-0.96', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.93', 'innovationpfi', 'month', 'partnrships', 'hurt', 'number']
senticnet['actor'] = ['0', '0.797', '0', '0.804', '#interest', '#admiration', 'positive', '0.8', 'director', 'shooting_movie', 'director_chair', 'important_person', 'movie']
senticnet['actor_chow_yun'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'obese', 'worse', 'health_issue', 'clinical_problem']
senticnet['actor_comedian'] = ['0', '0.797', '0', '0.804', '#interest', '#admiration', 'positive', '0.8', 'director', 'shooting_movie', 'director_chair', 'important_person', 'movie']
senticnet['actor_equity'] = ['0.967', '-0.98', '0', '0.64', '#joy', '#surprise', 'positive', '0.863', 'club', 'social_club', 'society', 'jockey_club', 'gild']
senticnet['actorly'] = ['-0.69', '0', '0.597', '-0.56', '#sadness', '#anger', 'negative', '-0.62', 'improperness', 'impropriety', 'indelicacy', 'spiciness', 'gaminess']
senticnet['actress'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'movie_star', 'actor', 'entertainer', 'performer', 'preview']
senticnet['actress_singer'] = ['0.882', '0.901', '0', '0', '#joy', '#interest', 'positive', '0.891', 'songwriter', 'vip', 'rock_group', 'guitar_player', 'violinist']
senticnet['actress_vivica'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'kit_fox', 'prairie_fox', 'white_fox']
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senticnet['actual_eviction'] = ['-0.87', '0', '0.765', '-0.82', '#sadness', '#disgust', 'negative', '-0.82', 'due_process', 'due_process_of_law', 'ouster', 'retaliatory_eviction', 'dispossession']
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senticnet['actuality'] = ['0.295', '0.239', '0.049', '0.346', '#joy', '#admiration', 'positive', '0.208', 'full', 'replicate', 'infinite', 'total', 'unending']
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senticnet['actuated'] = ['0.884', '0', '0', '0.849', '#joy', '#admiration', 'positive', '0.867', 'hydraulically', 'pneumatically', 'mechanically', 'electrically', 'pneumatic']
senticnet['actus_reus'] = ['-0.63', '0', '0.728', '-0.58', '#sadness', '#anger', 'negative', '-0.65', 'misconduct', 'inroad', 'wrongdoing', 'wrongful_conduct', 'usurpation']
senticnet['acuity'] = ['0.959', '-0.97', '0', '0.633', '#joy', '#surprise', 'positive', '0.856', 'oxyopia', 'sight', 'top', 'vision', 'visual_sense']
senticnet['acumen'] = ['0', '-0.71', '0', '0.89', '#surprise', '#admiration', 'positive', '0.804', 'perceptiveness', 'cleverness', 'sharp', 'knowledge', 'learning']
senticnet['acupuncture'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
senticnet['acupuncture_oriental_medicine'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
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senticnet['acutance'] = ['0.973', '-0.77', '0', '0.983', '#joy', '#admiration', 'positive', '0.911', 'opaqueness', 'faintness', 'dimness', 'opacity', 'vagueness']
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senticnet['acute_accent'] = ['-0.61', '0', '0.721', '-0.68', '#anger', '#disgust', 'negative', '-0.67', 'character', 'logogram', 'logograph', 'graph', 'ideogram']
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senticnet['acute_asthma'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchitis', 'chronic_bronchitis', 'respiratory_disease', 'respiratory_illness', 'respiratory_disorder']
senticnet['acute_bacterial'] = ['0.998', '-0.54', '0', '0.957', '#joy', '#admiration', 'positive', '0.834', 'contagious_disease', 'contagion', 'swine_influenza', 'swine_flu', 'respiratory_disease']
senticnet['acute_care_nurse_practitioner'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'infant_feeding', 'schedule_feeding', 'demand_feeding', 'breast_feeding', 'feeding']
senticnet['acute_chest_pain'] = ['-0.20', '0', '-0.22', '-0.19', '#sadness', '#fear', 'negative', '-0.20', 'go_hospital', 'death', 'die', 'take_aspirin', 'heart_attack']
senticnet['acute_coronary_syndrome'] = ['-0.20', '0', '-0.22', '-0.19', '#sadness', '#fear', 'negative', '-0.20', 'go_hospital', 'death', 'die', 'take_aspirin', 'heart_attack']
senticnet['acute_dehydration'] = ['-0.72', '0', '0.762', '-0.79', '#anger', '#disgust', 'negative', '-0.75', 'heat_hyperpyrexia', 'heat_prostration', 'prostration', 'sunstroke', 'thermic_fever']
senticnet['acute_diarrhea'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'inflammatory_disease', 'stomach_flu', 'intestinal_flu', 'pelvic_inflammatory_disease']
senticnet['acute_diarrhoea'] = ['-0.71', '0', '0.74', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'malnutrition', 'marasmus', 'avitaminosis', 'hypovitaminosis', 'deficiency_disease']
senticnet['acute_exacerbation'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchitis', 'chronic_bronchitis', 'respiratory_disease', 'respiratory_illness', 'respiratory_disorder']
senticnet['acute_fever'] = ['0', '0', '0', '-0.96', '#disgust', '#disgust', 'negative', '-0.96', 'thick_substance', 'viscous_substance', 'mucous_substance', 'disgusting', 'disgust']
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senticnet['acute_glossitis'] = ['-0.63', '0', '0.961', '-0.70', '#anger', '#disgust', 'negative', '-0.76', 'glossitis', 'glossodynia_exfoliativa', 'rubor', 'redness', 'inflammation']
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senticnet['acute_heart_failure'] = ['-0.98', '0', '0.827', '-0.94', '#sadness', '#disgust', 'negative', '-0.91', 'congestive_heart_failure', 'heart_attack', 'failure', 'heart_disease', 'cardiopathy']
senticnet['acute_infectious_disease'] = ['-0.95', '0', '0.876', '-0.89', '#sadness', '#disgust', 'negative', '-0.90', 'bubonic_plague', 'pestis_bubonica', 'glandular_plague', 'septicemic_plague', 'pestilence']
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senticnet['acute_intermittent_porphyria'] = ['-0.57', '0', '0.973', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['acute_kidney_failure'] = ['-0.60', '0', '0.544', '-0.53', '#sadness', '#anger', 'negative', '-0.56', 'acute_renal_failure', 'kidney_failure', 'chronic_renal_failure', 'chronic_kidney_failure', 'nephrosis']
senticnet['acute_leukemia'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['acute_lymphoblastic_leukemia'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['acute_lymphocytic_leukemia'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['acute_malnutrition'] = ['-0.71', '0', '0.74', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'malnutrition', 'marasmus', 'avitaminosis', 'hypovitaminosis', 'deficiency_disease']
senticnet['acute_myelogenous_leukemia'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['acute_myeloid_leukaemia'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['acute_myeloid_leukemia'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['acute_onset'] = ['-0.65', '0', '0.873', '-0.63', '#sadness', '#anger', 'negative', '-0.72', 'neuritis', 'multiple_neuritis', 'rubor', 'redness', 'inflammation']
senticnet['acute_otitis_media'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchitis', 'chronic_bronchitis', 'respiratory_disease', 'respiratory_illness', 'respiratory_disorder']
senticnet['acute_phase'] = ['-0.53', '0', '0.608', '-0.93', '#anger', '#disgust', 'negative', '-0.69', 'reprieve', 'respite', 'hiatus', 'abatement', 'suspension']
senticnet['acute_pneumonia'] = ['-0.09', '0', '-0.16', '-0.21', '#fear', '#disgust', 'negative', '-0.15', 'sickness', 'malady', 'polio', 'disease', 'ailment']
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senticnet['acute_pulmonary'] = ['-0.95', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.89', 'chronic_gastritis', 'rubor', 'redness', 'gastritis', 'inflammation']
senticnet['acute_rehabilitation'] = ['0.641', '-0.78', '0', '0.721', '#surprise', '#admiration', 'positive', '0.715', 'correctional_rehabilitation', 'physical_rehabilitation', 'physical_restoration', 'therapeutic_rehabilitation', 'vocational_rehabilitation']
senticnet['acute_renal_failure'] = ['-0.60', '0', '0.544', '-0.53', '#sadness', '#anger', 'negative', '-0.56', 'kidney_failure', 'acute_kidney_failure', 'chronic_renal_failure', 'chronic_kidney_failure', 'nephrosis']
senticnet['acute_respiratory'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchiolitis', 'chronic_bronchitis', 'respiratory_disease', 'bronchitis', 'respiratory_disorder']
senticnet['acute_respiratory_syndrome'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchiolitis', 'chronic_bronchitis', 'respiratory_disease', 'bronchitis', 'respiratory_disorder']
senticnet['acute_rheumatic_fever'] = ['-0.54', '0', '0.67', '-0.92', '#anger', '#disgust', 'negative', '-0.71', 'pericarditis', 'rubor', 'redness', 'endocarditis', 'inflammation']
senticnet['acute_shortage'] = ['-0.83', '0', '-0.88', '-0.78', '#sadness', '#fear', 'negative', '-0.83', 'little', 'sadness', 'small_amount', 'lack', 'absence']
senticnet['acute_trauma'] = ['-0.88', '0', '-0.79', '-0.85', '#sadness', '#disgust', 'negative', '-0.84', 'pain', 'severe_pain', 'agony', 'physical_pain', 'suffer']
senticnet['acute_triangle'] = ['0.763', '-0.61', '0', '0.777', '#joy', '#admiration', 'positive', '0.717', 'triangle', 'wedge_shape', 'cuneus', 'wedge', 'oblique_triangle']
senticnet['acute_urinary_retention'] = ['-0.52', '0', '0.839', '-0.86', '#anger', '#disgust', 'negative', '-0.74', 'folie', 'upset', 'psychosomatic_disorder', 'conversion_disorder', 'conversion_reaction']
senticnet['acute_viral_disease'] = ['-0.75', '0', '0.69', '-0.69', '#sadness', '#disgust', 'negative', '-0.71', 'rubor', 'redness', 'sleeping_sickness', 'sleepy_sickness', 'cephalitis']
senticnet['acutely'] = ['-0.82', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'aggressively', 'crisply', 'sharp', 'precipitously', 'sharply']
senticnet['acutely_toxic'] = ['-0.18', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.16', 'hazardous_health', 'dangerous_health', 'bad_health', 'gross', 'harmful']
senticnet['acvp'] = ['0.102', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.076', 'adjust_volume', 'tap_toe', 'enter_contest', 'tap_foot', 'dance_well']
senticnet['acyclovir_resistant'] = ['-0.73', '-0.72', '-0.86', '-0.79', '#fear', '#disgust', 'negative', '-0.41', 'venereal_disease', 'overpopulation', 'std', 'loss_offreedom', 'lower_standard_live']
senticnet['acyl_coa'] = ['-0.52', '0', '0.681', '-0.76', '#anger', '#disgust', 'negative', '-0.65', 'triose', 'tetrose', 'pentose', 'simple_sugar', 'aldose']
senticnet['acyltransferase'] = ['-0.52', '0', '0.681', '-0.76', '#anger', '#disgust', 'negative', '-0.65', 'triose', 'tetrose', 'pentose', 'simple_sugar', 'aldose']
senticnet['ad'] = ['-0.78', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.81', 'annoy', 'advertising', 'annoy_user', 'useless_info', 'useless_information']
senticnet['ad_blocker'] = ['-0.09', '-0.04', '0.039', '-0.24', '#sadness', '#disgust', 'negative', '-0.08', 'learn_new_idea', 'learn_quickly', 'point_out', 'look_picture', 'jot_down_note']
senticnet['ad_campaign'] = ['0.808', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.903', 'marketing', 'sale', 'announcement', 'billboard', 'promotion']
senticnet['ad_exec'] = ['-0.80', '0', '0.56', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'gossip', 'gossiper', 'blabbermouth', 'talkative', 'rumormonger']
senticnet['ad_hd'] = ['-0.91', '0', '0.508', '-0.89', '#sadness', '#disgust', 'negative', '-0.77', 'mental_disorder', 'mental_disturbance', 'psychological_disorder', 'folie', 'disturbance']
senticnet['ad_hoc'] = ['0.096', '0.182', '-0.09', '0.198', '#interest', '#admiration', 'positive', '0.094', 'female_parent', 'relative', 'kin', 'relate_person', 'aunt_uncle']
senticnet['ad_hominem'] = ['0.715', '-0.82', '0', '0.659', '#joy', '#surprise', 'positive', '0.734', 'reasoning', 'logical_thinking', 'abstract_thought', 'casuistry', 'line_of_inquiry']
senticnet['ad_hominem_argument'] = ['0.715', '-0.82', '0', '0.659', '#joy', '#surprise', 'positive', '0.734', 'reasoning', 'logical_thinking', 'abstract_thought', 'casuistry', 'line_of_inquiry']
senticnet['ad_hominem_attack'] = ['-0.84', '0', '0.572', '-0.68', '#sadness', '#disgust', 'negative', '-0.69', 'reply', 'response', 'sass', 'sassing', 'backtalk']
senticnet['ad_nauseam'] = ['0.022', '0.123', '0.038', '0.129', '#interest', '#admiration', 'positive', '0.059', 'hymn', 'written_music', 'walk_beach', 'lyric', 'piece_of_music']
senticnet['ad_revenue'] = ['0.886', '-0.77', '0', '0.815', '#joy', '#admiration', 'positive', '0.826', 'countenance', 'legitimate', 'permit', 'allow', 'let']
senticnet['ad_valorem'] = ['-0.08', '0.358', '-0.20', '0.237', '#interest', '#admiration', 'positive', '0.077', 'extortion', 'money_due', 'afford', 'bribe', 'owe']
senticnet['ad_valorem_tax'] = ['0.922', '0.848', '0', '0', '#joy', '#interest', 'positive', '0.885', 'tub', 'value-added_tax', 'vat', 'president', 'level']
senticnet['adage'] = ['0.022', '0.123', '0.038', '0.129', '#interest', '#admiration', 'positive', '0.059', 'hymn', 'written_music', 'walk_beach', 'lyric', 'piece_of_music']
senticnet['adagio'] = ['0.54', '-0.57', '0', '0.678', '#surprise', '#admiration', 'positive', '0.598', 'musical_composition', 'opus', 'composition', 'piece', 'piece_of_music']
senticnet['adamance'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['adamance_of_purpose'] = ['-0.97', '0', '0.541', '-0.98', '#sadness', '#disgust', 'negative', '-0.83', 'adamance', 'resolve', 'resoluteness', 'firmness', 'resolution']
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senticnet['adamantine'] = ['0', '0.839', '0', '0.963', '#interest', '#admiration', 'positive', '0.901', 'imputrescible', 'undestroyable', 'imperishable', 'durable', 'perdurable']
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senticnet['adaptively'] = ['0.766', '0.908', '0', '0', '#joy', '#interest', 'positive', '0.837', 'adapted', 'adaptiveness', 'adaptive', 'adapt', 'adaptability']
senticnet['adaptiveness'] = ['0.767', '0.905', '0', '0', '#joy', '#interest', 'positive', '0.836', 'adapted', 'adaptive', 'adaptively', 'adapt', 'adaptability']
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senticnet['adaxial_surface'] = ['-0.66', '0', '0.809', '-0.61', '#sadness', '#anger', 'negative', '-0.69', 'face', 'base', 'underbelly', 'sole', 'bilge']
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senticnet['add-on'] = ['0', '0', '0.869', '-0.97', '#anger', '#disgust', 'negative', '-0.92', 'periphery', 'peripheral', 'scanner', 'peripherally', 'polling']
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senticnet['add_broth'] = ['-0.66', '0', '0.992', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'cold', 'boil', 'temperature', 'low_temperature', 'frigidity']
senticnet['add_flavor'] = ['0.04', '0', '0', '0.067', '#joy', '#admiration', 'positive', '0.054', 'cook', 'food', 'taste', 'ingredient', 'tongue']
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senticnet['add_knowledge'] = ['0.757', '0', '0', '0', '#joy', '#joy', 'positive', '0.757', 'become_smart', 'learn', 'increase_knowledge', 'gain_knowledge', 'great_understand']
senticnet['add_number'] = ['0', '0.951', '0', '0', '#interest', '#interest', 'positive', '0.951', 'enter_data_computer', 'train_open_mind', 'write_computer_program', 'add_two_numbers_together', 'calculation']
senticnet['add_number_together'] = ['0', '0.27', '0.014', '0.133', '#interest', '#admiration', 'positive', '0.13', 'add_two_numbers_together', 'learn_add', 'get_total', 'get_answer', 'sharpen_pencil']
senticnet['add_on'] = ['0.846', '-0.76', '0', '0.688', '#joy', '#surprise', 'positive', '0.766', 'further', 'promote', 'encourage', 'boost', 'advance']
senticnet['add_onion'] = ['-0.66', '0', '0.992', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'cold', 'boil', 'temperature', 'low_temperature', 'frigidity']
senticnet['add_spice'] = ['0', '-0.01', '0.014', '-0.10', '#anger', '#disgust', 'negative', '-0.03', 'burp', 'prepare_ingredient', 'say_grace', 'measure_ingredient', 'swallow_food']
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senticnet['add_two_numbers'] = ['0', '0.068', '0', '0', '#interest', '#interest', 'positive', '0.068', 'delight_audience', 'amuse_child', 'bounce_up_down', 'go_game', 'play_on_grass']
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senticnet['add_up_column_number'] = ['0', '0.083', '0.039', '0.074', '#interest', '#admiration', 'positive', '0.039', 'compute_sum', 'add_up_number', 'calculate_quickly', 'program_computer', 'write_program']
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senticnet['added_value'] = ['0', '0.197', '0.056', '0.177', '#interest', '#admiration', 'positive', '0.106', 'advance_career', 'total', 'four_quarters', 'root_evil', 'total_number']
senticnet['addendum'] = ['0', '0', '0', '0.091', '#admiration', '#admiration', 'positive', '0.091', 'correspondence', 'handwritten', 'written_message', 'written_communication', 'write_note']
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senticnet['addicting'] = ['-0.91', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.93', 'addictive', 'mind-altering', 'sedating', 'habit-forming', 'abusable']
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senticnet['additional'] = ['0.295', '0.239', '0.049', '0.346', '#joy', '#admiration', 'positive', '0.208', 'absolute', 'complete', 'infinite', 'total', 'stuffed']
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senticnet['additive_color'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'indigo', 'yellow', 'water_color', 'red', 'violet']
senticnet['additivity'] = ['-0.63', '0', '0.822', '-0.95', '#anger', '#disgust', 'negative', '-0.80', 'uniformness', 'homogeneousness', 'consistency', 'consistence', 'similarity']
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senticnet['addlehead'] = ['-0.99', '0', '0.804', '-0.52', '#sadness', '#anger', 'negative', '-0.77', 'misfit', 'birdbrain', 'ape', 'loon', 'mortal']
senticnet['addleshaw_goddard'] = ['0.841', '0.721', '0', '0.74', '#joy', '#admiration', 'positive', '0.767', 'good_manner', 'two_people', 'love', 'guardian', 'guy']
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senticnet['address'] = ['0.038', '0', '0', '0.097', '#joy', '#admiration', 'positive', '0.068', 'place', 'wigwam', 'abide', 'post', 'property']
senticnet['address_book'] = ['0.038', '0', '0', '0.097', '#joy', '#admiration', 'positive', '0.068', 'blank_check', 'glue_stick', 'post-it_note', 'ball_point_pen', 'paper_clip']
senticnet['address_label'] = ['0.058', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.054', 'letter_slot', 'envelope', 'pamphlet', 'mailman', 'postage_stamp']
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senticnet['adducing_evidence'] = ['-0.97', '0', '0.781', '-0.91', '#sadness', '#disgust', 'negative', '-0.88', 'weakness', 'fault', 'flaw', 'defect', 'wart']
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senticnet['adelic'] = ['0.875', '-0.65', '0', '0.861', '#joy', '#admiration', 'positive', '0.797', 'completeness', 'work', 'whole_shebang', 'essential', 'whole']
senticnet['adelphi_theatre'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'dress', 'pinny', 'dirndl', 'gown', 'muumuu']
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senticnet['adenosine'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'biochemistry', 'water', 'make_up_water', 'large_body', 'water_spill']
senticnet['adenosis'] = ['-0.53', '0', '0.667', '-0.55', '#anger', '#disgust', 'negative', '-0.58', 'exophthalmic_goiter', 'glandular_disease', 'gland_disease', 'glandular_disorder', 'thyrotoxicosis']
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senticnet['adenozine'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'water', 'make_up_water', 'large_body', 'water_spill', 'cover_water']
senticnet['adept'] = ['0', '0.787', '0', '0.802', '#interest', '#admiration', 'positive', '0.794', 'virtuoso', 'genius', 'expert', 'proficient', 'skillful']
senticnet['adept_at'] = ['0.028', '0.138', '-0.04', '0.215', '#interest', '#admiration', 'positive', '0.083', 'skilled', 'expert', 'tin', 'dense_water', 'metal_container']
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senticnet['adeptness'] = ['0.703', '-0.8', '0', '0.691', '#joy', '#surprise', 'positive', '0.731', 'skillfulness', 'touch', 'dexterity', 'manual_dexterity', 'sleight']
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senticnet['adequate_documentation'] = ['0.84', '-0.97', '0', '0.713', '#joy', '#surprise', 'positive', '0.844', 'proof', 'bed_check', 'crosscheck', 'parity_check', 'redundancy_check']
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senticnet['adequate_moisture'] = ['0', '-0.03', '0.05', '-0.14', '#anger', '#disgust', 'negative', '-0.05', 'humidity', 'wetness', 'damp', 'humid', 'dank']
senticnet['adequate_pain_relief'] = ['-0.03', '-0.02', '-0.08', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'ibuprofen', 'phenobarbital', 'analgesic', 'sleep_drug', 'pill']
senticnet['adequate_remedy'] = ['0.931', '0.947', '0', '0', '#joy', '#interest', 'positive', '0.939', 'antidote', 'cure', 'therapeutic', 'curative', 'repair']
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senticnet['adequate_to'] = ['0.889', '-0.90', '0', '0.559', '#joy', '#surprise', 'positive', '0.784', 'competent', 'up_to', 'adequate', 'equal_to', 'capable']
senticnet['adequate_ventilation'] = ['0.033', '0.186', '0', '0', '#joy', '#interest', 'positive', '0.11', 'bank', 'bank_vault', 'wallet', 'government_agreement', 'coffer']
senticnet['adequately'] = ['0.021', '0.167', '0', '0.207', '#interest', '#admiration', 'positive', '0.132', 'size_up', 'measure_length', 'scope', 'distant', 'extension']
senticnet['adequately_capture'] = ['-0.84', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'brag', 'embellish', 'overemphasize', 'overstress', 'vaunt']
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senticnet['adhesive_bandage'] = ['-0.81', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.85', 'bandage', 'painkiller', 'gauze', 'aspirin', 'contraceptive_device']
senticnet['adhesive_strength'] = ['-0.52', '0', '0.596', '-0.76', '#anger', '#disgust', 'negative', '-0.62', 'retention', 'retentiveness', 'retentivity', 'solidness', 'nonabsorbency']
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senticnet['adhesive_tape'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'foil', 'tin_foil', 'gold_foil']
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senticnet['adiabatic_expansion'] = ['-0.71', '0', '0.509', '-0.92', '#sadness', '#disgust', 'negative', '-0.71', 'natural_process', 'natural_action', 'activity', 'curing', 'congelation']
senticnet['adiabatic_process'] = ['-0.64', '0', '0.938', '-0.52', '#sadness', '#anger', 'negative', '-0.70', 'extinction', 'thermodynamic_process', 'hardening', 'decay', 'absorption']
senticnet['adiantum'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'clematis', 'legume', 'leguminous_plant', 'climber', 'root_climber']
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senticnet['adience'] = ['-0.63', '0', '0.677', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'motive', 'need', 'motivation', 'abience', 'impulse']
senticnet['adieu'] = ['0', '0.145', '0', '0.2', '#interest', '#admiration', 'positive', '0.172', 'hand_gesture', 'arm_movement', 'water_splash', 'hand_motion', 'water_form']
senticnet['adipic_acid'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'carboxylic_acid', 'succinic_acid', 'valeric_acid']
senticnet['adiponectin'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'cholecystokinin', 'secretin', 'gastrointestinal_hormone']
senticnet['adiponitrile'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'carboxylic_acid', 'succinic_acid', 'valeric_acid']
senticnet['adirondack'] = ['0.725', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.839', 'island-dweller', 'island', 'islander', 'isle', 'think']
senticnet['adirondack_chair'] = ['0.074', '0', '0', '0', '#joy', '#joy', 'positive', '0.074', 'hike_trail', 'ant_hill', 'compost', 'picnic', 'soccer_field']
senticnet['adisson'] = ['0.502', '-0.82', '0', '0.779', '#surprise', '#admiration', 'positive', '0.703', 'mortal', 'person', 'individual', 'free_liver', 'agent']
senticnet['adjacent'] = ['0', '0', '-0.92', '-0.96', '#fear', '#disgust', 'negative', '-0.94', 'side_by_side', 'succeeding', 'following', 'next', 'future']
senticnet['adjacent_parking_lot'] = ['0', '0', '0', '-0.09', '#disgust', '#disgust', 'negative', '-0.09', 'subway', 'bus', 'train_station', 'railway_station', 'bus_stop']
senticnet['adjectival'] = ['-0.57', '0', '0.713', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'assured', 'conclusion', 'certainty', 'determinateness', 'predictability']
senticnet['adjectival_suffix'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicado', 'incommunicative', 'impassive', 'inexpressive', 'silent']
senticnet['adjective'] = ['0', '0.115', '-0.10', '0.249', '#interest', '#admiration', 'positive', '0.087', 'idiom', 'remark', 'grammar_part', 'speech_part', 'sentence']
senticnet['adjoin'] = ['-0.74', '0', '0.627', '-0.58', '#sadness', '#anger', 'negative', '-0.65', 'touch', 'contact', 'meet', 'neighbor', 'butt_against']
senticnet['adjoining_bathroom'] = ['0', '0.171', '-0.05', '0.102', '#interest', '#admiration', 'positive', '0.072', 'bed', 'mattress', 'bead', 'bed_cover', 'seduce']
senticnet['adjoining_cemetery'] = ['-0.50', '0', '0.762', '-0.89', '#anger', '#disgust', 'negative', '-0.71', 'cemetery', 'graveyard', 'burial_site', 'grassland', 'burying_ground']
senticnet['adjoining_parish'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'bodyguard', 'outrider', 'bodyguard_of_guard', 'beefeater', 'defender']
senticnet['adjourn'] = ['-0.57', '0', '0.576', '-0.78', '#anger', '#disgust', 'negative', '-0.64', 'reprieve', 'respite', 'suspend', 'call', 'delay']
senticnet['adjourned_sine_die'] = ['-0.81', '0', '0.513', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'ballot', 'vote', 'voting', 'balloting', 'veto']
senticnet['adjournment'] = ['-0.81', '0', '0.513', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'ballot', 'vote', 'voting', 'balloting', 'veto']
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senticnet['adjudged_guilty'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'negligence', 'evasion', 'dereliction', 'contributory_negligence']
senticnet['adjunct'] = ['-0.70', '0', '0.733', '0', '#sadness', '#anger', 'negative', '-0.72', 'lecturer', 'associate', 'professor', 'assistant', 'faculty']
senticnet['adjunct_assistant_professor'] = ['0', '0', '0', '0.97', '#admiration', '#admiration', 'positive', '0.97', 'lecturer', 'teacher', 'instructor', 'college_teacher', 'teach_lesson']
senticnet['adjunct_associate_professor'] = ['0', '0', '0', '0.97', '#admiration', '#admiration', 'positive', '0.97', 'lecturer', 'teacher', 'instructor', 'college_teacher', 'teach_lesson']
senticnet['adjunct_faculty'] = ['0.102', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.076', 'adjust_volume', 'tap_toe', 'enter_contest', 'tap_foot', 'dance_well']
senticnet['adjunct_faculty_member'] = ['0.102', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.076', 'adjust_volume', 'tap_toe', 'enter_contest', 'tap_foot', 'dance_well']
senticnet['adjunct_instructor'] = ['0.102', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.076', 'adjust_volume', 'tap_toe', 'enter_contest', 'tap_foot', 'dance_well']
senticnet['adjunct_lecturer'] = ['0', '0', '0', '0.97', '#admiration', '#admiration', 'positive', '0.97', 'lecturer', 'teacher', 'instructor', 'college_teacher', 'teach_lesson']
senticnet['adjunct_professor'] = ['0.102', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.076', 'adjust_volume', 'tap_toe', 'enter_contest', 'tap_foot', 'dance_well']
senticnet['adjunctive'] = ['0.785', '-0.99', '0', '0.981', '#surprise', '#admiration', 'positive', '0.92', 'ear', 'absolute_pitch', 'perfect_pitch', 'audition', 'sense_audition']
senticnet['adjunctive_therapy'] = ['-0.05', '0', '-0.22', '-0.06', '#fear', '#disgust', 'negative', '-0.11', 'medicine', 'cure', 'medical_procedure', 'sleep_drug', 'nurse']
senticnet['adjust'] = ['0.033', '0.234', '-0.08', '0.274', '#interest', '#admiration', 'positive', '0.114', 'change', 'transform', 'modifier', 'precise', 'versatile']
senticnet['adjust_clothes'] = ['-0.09', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.07', 'plan_day', 'get_stick_traffic', 'rush_work', 'out_car', 'meet_old_friend']
senticnet['adjust_seat'] = ['0', '0.098', '0', '0.107', '#interest', '#admiration', 'positive', '0.103', 'check_mirror', 'fasten_seatbelt', 'turn_engine', 'change_gear', 'put_key_ignition']
senticnet['adjust_volume'] = ['0', '0', '0.954', '-0.81', '#anger', '#disgust', 'negative', '-0.88', 'sound_level', 'speaker', 'noisy', 'making_a_lot_of_noise', 'hear_song']
senticnet['adjust_water_temperature'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'water_garden', 'water_lawn', 'heater', 'shower', 'cook']
senticnet['adjustable'] = ['0.102', '0.078', '0', '0.087', '#joy', '#admiration', 'positive', '0.089', 'gold', 'platinum', 'iron_ore', 'silver', 'magnetic']
senticnet['adjustable_wrench'] = ['0', '0.098', '0', '0.107', '#interest', '#admiration', 'positive', '0.103', 'power_drill', 'air_pump', 'drill_bite', 'type_of_machine', 'drill']
senticnet['adjusted'] = ['-0.63', '0', '0.653', '-0.96', '#anger', '#disgust', 'negative', '-0.74', 'altered', 'paraphrastic', 'changed', 'emended', 'edited']
senticnet['adjusted_gross_income_agi'] = ['0.763', '-0.67', '0', '0.945', '#joy', '#admiration', 'positive', '0.795', 'set', 'income_bracket', 'bracket', 'income', 'price_bracket']
senticnet['adjustment'] = ['0.735', '-0.71', '0', '0.566', '#joy', '#surprise', 'positive', '0.672', 'improvement', 'betterment', 'habituation', 'domestication', 'shakedown']
senticnet['adjutant'] = ['0.045', '0', '0', '0.088', '#joy', '#admiration', 'positive', '0.067', 'leader', 'seaman', 'captain', 'officer', 'traitor']
senticnet['adlerangriff'] = ['0', '0.105', '0', '0', '#interest', '#interest', 'positive', '0.105', 'beak', 'many_feathers', 'big_eye', 'bird', 'bird_mouth']
senticnet['adm_chegwidden'] = ['0', '0', '0.785', '-0.72', '#anger', '#disgust', 'negative', '-0.75', 'sharp_nail', 'sharp', 'sharp_end', 'sharp_point', 'pointy']
senticnet['admin'] = ['0.92', '0.794', '0', '0', '#joy', '#interest', 'positive', '0.857', 'move_mouse', 'reboot', 'type_of_keyboard', 'enter_address', 'keep_track_appointment']
senticnet['admin_intervention'] = ['0.533', '0.818', '0', '0.683', '#interest', '#admiration', 'positive', '0.678', 'gain_information', 'report_news', 'solve', 'seek_answer', 'gather_information']
senticnet['administer'] = ['0.995', '-0.78', '0', '0', '#joy', '#surprise', 'positive', '0.889', 'openly', 'freely', 'open', 'fairly', 'implicit']
senticnet['administer_corporal_punishment'] = ['-0.83', '0', '0.924', '-0.79', '#sadness', '#anger', 'negative', '-0.85', 'kick_in_butt', 'licking', 'whacking', 'stoning', 'lapidation']
senticnet['administer_oxygen'] = ['-0.57', '0', '0.737', '-0.85', '#anger', '#disgust', 'negative', '-0.72', 'emergency_procedure', 'emergency', 'external_respiration', 'respiration', 'ventilation']
senticnet['administered_intramuscularly'] = ['-0.74', '0', '0.766', '0', '#sadness', '#anger', 'negative', '-0.75', 'use_needle', 'clothes_repair', 'woven', 'embroidery', 'thread_needle']
senticnet['administered_orally'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'spironolactone', 'adrenal_cortical_steroid', 'antihypertensive', 'antihypertensive_drug']
senticnet['administered_parenterally'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'spironolactone', 'adrenal_cortical_steroid', 'antihypertensive', 'antihypertensive_drug']
senticnet['administered_subcutaneously'] = ['0', '0', '-0.95', '-0.87', '#fear', '#disgust', 'negative', '-0.91', 'sharp_weapon', 'bayonet', 'ammunition', 'spear', 'arrow']
senticnet['administering_oxygen'] = ['-0.57', '0', '0.737', '-0.85', '#anger', '#disgust', 'negative', '-0.72', 'emergency_procedure', 'emergency', 'external_respiration', 'respiration', 'ventilation']
senticnet['administering_poison'] = ['-0.89', '0.161', '-0.61', '0.053', '#sadness', '#fear', 'negative', '-0.32', 'heartrending', 'heartbreaking', 'grievous', 'grievously', 'brokenheartedness']
senticnet['administrability'] = ['0.5', '-0.9', '0', '0.525', '#surprise', '#admiration', 'positive', '0.642', 'manageable', 'controllable', 'governable', 'steerable', 'dirigible']
senticnet['administrable'] = ['0.5', '-0.9', '0', '0.525', '#surprise', '#admiration', 'positive', '0.642', 'manageable', 'controllable', 'governable', 'steerable', 'dirigible']
senticnet['administrate'] = ['0.781', '-0.79', '0', '0.816', '#surprise', '#admiration', 'positive', '0.798', 'deliver_goods', 'succeed', 'win', 'bring_home_bacon', 'come_through']
senticnet['administrated_by'] = ['0.851', '-0.65', '0', '0.66', '#joy', '#admiration', 'positive', '0.723', 'omen', 'portent', 'presage', 'prognostic', 'prognostication']
senticnet['administration'] = ['0.039', '0.145', '0', '0.142', '#interest', '#admiration', 'positive', '0.109', 'hierarchy', 'position', 'order', 'bureaucracy', 'rule']
senticnet['administration_mba'] = ['0.036', '0.111', '-0.05', '0.054', '#interest', '#admiration', 'positive', '0.038', 'despot', 'supervisor', 'chief', 'commander', 'earl']
senticnet['administration_mba_degree'] = ['0.036', '0.111', '-0.05', '0.054', '#interest', '#admiration', 'positive', '0.038', 'despot', 'supervisor', 'chief', 'commander', 'earl']
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senticnet['administrative'] = ['0.977', '0.927', '0', '0', '#joy', '#interest', 'positive', '0.952', 'supervision', 'supervisory', 'managerial', 'oversight', 'responsibility']
senticnet['administrative_assistant'] = ['0.102', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.076', 'adjust_volume', 'tap_toe', 'enter_contest', 'tap_foot', 'dance_well']
senticnet['administrative_burden'] = ['-0.03', '0.269', '-0.06', '0.136', '#interest', '#admiration', 'positive', '0.078', 'cost', 'money_payment', 'debit', 'price', 'money_charge']
senticnet['administrative_district'] = ['0.858', '-0.89', '0', '0.719', '#joy', '#surprise', 'positive', '0.823', 'mandatory', 'district', 'administrative_division', 'territorial_division', 'borderland']
senticnet['administrative_division'] = ['0.858', '-0.89', '0', '0.719', '#joy', '#surprise', 'positive', '0.823', 'mandatory', 'administrative_district', 'district', 'territorial_division', 'borderland']
senticnet['administrative_overhead'] = ['-0.03', '0.269', '-0.06', '0.136', '#interest', '#admiration', 'positive', '0.078', 'cost', 'money_payment', 'debit', 'price', 'money_charge']
senticnet['administrator'] = ['0.841', '0', '0', '0.941', '#joy', '#admiration', 'positive', '0.891', 'head', 'executive', 'executive_director', 'chief', 'academic_administrator']
senticnet['admirable'] = ['0.575', '0.636', '-0.26', '0.318', '#joy', '#interest', 'positive', '0.315', 'laudably', 'admirably', 'praiseworthily', 'commendable', 'look_up_to']
senticnet['admirably'] = ['0.575', '0.636', '-0.26', '0.318', '#joy', '#interest', 'positive', '0.315', 'laudably', 'praiseworthily', 'commendable', 'look_up_to', 'admirable']
senticnet['admiral'] = ['0.688', '-0.90', '0', '0.877', '#surprise', '#admiration', 'positive', '0.824', 'fleet_flag_officer', 'rear_flag_officer', 'flag_officer', 'full_flag_officer', 'vice_flag_officer']
senticnet['admiral_general_aladeen'] = ['-0.73', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.72', 'royal_leader', 'despot', 'emperor', 'monarch', 'king']
senticnet['admiral_graf_spee'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'submarine', 'lifeboat', 'titanic', 'large_ship', 'canoe']
senticnet['admiral_nimitz'] = ['0.084', '-0.05', '0.013', '0', '#joy', '#surprise', 'positive', '0.043', 'carnival_ride', 'carny', 'fortune_teller', 'horseback', 'ferris_wheel']
senticnet['admiral_sir'] = ['0.688', '-0.90', '0', '0.877', '#surprise', '#admiration', 'positive', '0.824', 'fleet_admiral', 'rear_admiral', 'admiral', 'full_admiral', 'flag_officer']
senticnet['admiration'] = ['0', '0', '0', '1', '#admiration', '#admiration', 'positive', '1', 'admirable', 'adorable', 'worship', 'fancy', 'devotion']
senticnet['admire'] = ['0', '0', '0', '0.705', '#admiration', '#admiration', 'positive', '0.705', 'positive', 'confidence', 'sympathetic', 'regard', 'fellow_feeling']
senticnet['admired'] = ['0.586', '-0.81', '0', '0.568', '#joy', '#surprise', 'positive', '0.657', 'preferred', 'wanted', 'darling', 'loved', 'precious']
senticnet['admirer'] = ['0.814', '-0.50', '0', '0.622', '#joy', '#admiration', 'positive', '0.648', 'friend', 'champion', 'supporter', 'protagonist', 'endorser']
senticnet['admiringly'] = ['-0.89', '0.168', '-0.60', '0.05', '#sadness', '#fear', 'negative', '-0.32', 'remorsefully', 'contritely', 'contrition', 'contriteness', 'attrition']
senticnet['admissible'] = ['0.817', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.824', 'witness', 'prosecutor', 'legal_process', 'lawyer', 'jury']
senticnet['admissible_evidence'] = ['-0.70', '0', '0.874', '-0.75', '#anger', '#disgust', 'negative', '-0.77', 'blame', 'accusation', 'inculpation', 'complaint', 'imprecation']
senticnet['admission'] = ['0', '0.175', '-0.13', '0.204', '#interest', '#admiration', 'positive', '0.083', 'door', 'control_access', 'gate', 'corridor', 'let_in']
senticnet['admission_tutor'] = ['0', '0.084', '0', '0', '#interest', '#interest', 'positive', '0.084', 'call_restaurant', 'reservation', 'reserve', 'dinner', 'reserved']
senticnet['admit'] = ['0', '0', '0', '0.852', '#admiration', '#admiration', 'positive', '0.852', 'aye', 'allow', 'promise', 'cope', 'accept']
senticnet['admit_defeat'] = ['-0.06', '0.109', '-0.05', '0.158', '#interest', '#admiration', 'positive', '0.036', 'elasticity', 'stretch_out', 'grow_long', 'widen', 'advance_civilization']
senticnet['admittedly'] = ['0', '0', '-0.86', '-0.99', '#fear', '#disgust', 'negative', '-0.93', 'niggle', 'quiddity', 'bicker', 'pettifog', 'cavil']
senticnet['admixture'] = ['-0.22', '0', '-0.09', '-0.16', '#sadness', '#disgust', 'negative', '-0.16', 'inequality', 'reject', 'discard', 'difference', 'gap']
senticnet['admonish'] = ['-0.73', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.81', 'admonishment', 'mane', 'admonitory', 'monition', 'reproving']
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senticnet['admonishment'] = ['0.609', '-0.68', '0', '0.951', '#surprise', '#admiration', 'positive', '0.749', 'rebuke', 'reproof', 'reproval', 'reprehension', 'reprimand']
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senticnet['adnan_akmal'] = ['-0.62', '0', '0.713', '-0.98', '#anger', '#disgust', 'negative', '-0.77', 'muddy_up', 'muck_up', 'foul', 'blemish', 'smear']
senticnet['adnate'] = ['0', '0', '0.064', '-0.04', '#anger', '#disgust', 'negative', '-0.05', 'bird_food', 'funiculus', 'plant_product', 'fuzz', 'agricultural_product']
senticnet['adnyamathanha'] = ['-0.95', '0', '0.624', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['adobe'] = ['0.962', '-0.70', '0', '0.522', '#joy', '#surprise', 'positive', '0.729', 'brick', 'stack_brick', 'clinker', 'clinker_brick', 'firebrick']
senticnet['adobe_acrobat_plugin'] = ['0.929', '0.507', '-0.17', '0.396', '#joy', '#interest', 'positive', '0.415', 'plume', 'congratulate', 'smugness', 'smug', 'proudly']
senticnet['adobe_brick'] = ['0.962', '-0.70', '0', '0.522', '#joy', '#surprise', 'positive', '0.729', 'adobe', 'brick', 'clinker', 'clinker_brick', 'firebrick']
senticnet['adobe_rgb'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'indigo', 'yellow', 'water_color', 'red', 'violet']
senticnet['adolescence'] = ['0', '0.788', '0', '0.86', '#interest', '#admiration', 'positive', '0.824', 'child', 'enjoy_time', 'youth', 'smile_child', 'open_mind']
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senticnet['adverse_possession'] = ['0.621', '-0.60', '0', '0.545', '#joy', '#surprise', 'positive', '0.59', 'privilege', 'perquisite', 'public_easement', 'right_of_way', 'prerogative']
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senticnet['adverse_yaw'] = ['-0.97', '0', '0.831', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'skewness', 'irregularity']
senticnet['adversely_affect'] = ['-0.62', '0', '0.756', '-0.62', '#anger', '#disgust', 'negative', '-0.66', 'vitiate', 'ruin', 'maul', 'mangle', 'deface']
senticnet['adversely_impact'] = ['-0.62', '0', '0.756', '-0.62', '#anger', '#disgust', 'negative', '-0.66', 'vitiate', 'ruin', 'maul', 'mangle', 'deface']
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senticnet['advert'] = ['-0.97', '0', '0.692', '-0.64', '#sadness', '#anger', 'negative', '-0.77', 'introduce', 'bring_up', 'mention', 'drag_up', 'cite']
senticnet['advertise'] = ['0.808', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.903', 'marketing', 'sale', 'announcement', 'billboard', 'promotion']
senticnet['advertise_company'] = ['0.856', '0', '0', '0.963', '#joy', '#admiration', 'positive', '0.909', 'advertise_product', 'media', 'advertise_panel', 'money', 'company']
senticnet['advertise_heavily'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'profitable', 'moneymaking', 'gainful', 'paying', 'paid']
senticnet['advertise_panel'] = ['0.087', '0.08', '0.013', '0', '#joy', '#interest', 'positive', '0.051', 'advertise_company', 'billboard', 'underground_map', 'neighborhood_map', 'restaurant_entrance']
senticnet['advertise_product'] = ['0.866', '0', '0', '0', '#joy', '#joy', 'positive', '0.866', 'report_crime', 'page_through_book', 'check_mail', 'find_book_library', 'receive_email']
senticnet['advertisement'] = ['0.808', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.903', 'marketing', 'sale', 'announcement', 'billboard', 'promotion']
senticnet['advertiser'] = ['0.808', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.903', 'marketing', 'sale', 'announcement', 'billboard', 'promotion']
senticnet['advertising'] = ['-0.78', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.80', 'annoy', 'ad', 'annoy_user', 'useless_info', 'useless_information']
senticnet['advertising_blitz'] = ['-0.81', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.81', 'take_charge', 'anti', 'go_against', 'death_wish', 'file_complaint']
senticnet['advertising_campaign'] = ['0.808', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.903', 'marketing', 'sale', 'announcement', 'billboard', 'promotion']
senticnet['advertisment'] = ['-0.02', '0', '-0.10', '0', '#sadness', '#fear', 'negative', '-0.06', 'ads', 'boring', 'bother', 'stop_watch', 'annoying']
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senticnet['advice'] = ['0.986', '0', '0', '0.862', '#joy', '#admiration', 'positive', '0.924', 'plea', 'charity', 'agreement', 'contribution', 'offer']
senticnet['advil'] = ['0.969', '0.817', '0', '0.828', '#joy', '#admiration', 'positive', '0.871', 'ibuprofen', 'painkiller', 'medicine', 'analgesic', 'phenobarbital']
senticnet['advisable'] = ['0.724', '0.73', '0', '0', '#joy', '#interest', 'positive', '0.727', 'capable', 'recommended', 'advise', 'willing', 'advisably']
senticnet['advisably'] = ['0.714', '0.725', '0', '0', '#joy', '#interest', 'positive', '0.72', 'capable', 'recommended', 'advise', 'willing', 'advisable']
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senticnet['advisor'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'sexual_relationship', 'affair', 'involvement']
senticnet['advisory'] = ['0.795', '-0.97', '0', '0.679', '#joy', '#surprise', 'positive', '0.817', 'informatory', 'consultative', 'consultatory', 'consultive', 'revealing']
senticnet['advisory_board'] = ['0', '0', '0.88', '-0.92', '#anger', '#disgust', 'negative', '-0.90', 'sofa_bed', 'small_seat', 'beanbag_airline_seat', 'hide-a-bed_sofa', 'airline_seat']
senticnet['advisory_board_member'] = ['-0.77', '0', '0.511', '-0.53', '#sadness', '#disgust', 'negative', '-0.60', 'go_under', 'subside', 'fall', 'settle', 'submerge']
senticnet['advisory_body'] = ['0.795', '-0.97', '0', '0.679', '#joy', '#surprise', 'positive', '0.817', 'informatory', 'consultative', 'consultatory', 'consultive', 'revealing']
senticnet['advisory_capacity'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'sexual_relationship', 'affair', 'involvement']
senticnet['advisory_committee'] = ['0', '0', '0.88', '-0.92', '#anger', '#disgust', 'negative', '-0.90', 'sofa_bed', 'small_seat', 'beanbag_airline_seat', 'hide-a-bed_sofa', 'airline_seat']
senticnet['advisory_council'] = ['0', '0', '0.88', '-0.92', '#anger', '#disgust', 'negative', '-0.90', 'sofa_bed', 'small_seat', 'beanbag_airline_seat', 'hide-a-bed_sofa', 'airline_seat']
senticnet['advisory_group'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'sexual_relationship', 'affair', 'involvement']
senticnet['advisory_panel'] = ['0.795', '-0.97', '0', '0.679', '#joy', '#surprise', 'positive', '0.817', 'informatory', 'consultative', 'consultatory', 'consultive', 'revealing']
senticnet['advisory_role'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'baddie', 'theatrical_baddie', 'part', 'persona']
senticnet['advocacy'] = ['0.098', '0.165', '-0.13', '0.284', '#interest', '#admiration', 'positive', '0.103', 'see_different', 'eye_strain', 'know', 'learn', 'insight']
senticnet['advocate'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'economic_libertarian', 'civil_libertarian', 'libertarian', 'advocator', 'proponent']
senticnet['advocate_depute'] = ['0.053', '0.122', '-0.07', '0.086', '#interest', '#admiration', 'positive', '0.046', 'attorney', 'lawyer', 'jury', 'decide_guilt', 'prosecutor']
senticnet['advocating'] = ['0.714', '0.779', '0', '0', '#joy', '#interest', 'positive', '0.746', 'implying', 'treason', 'tantamount', 'committing', 'equating']
senticnet['advocation'] = ['0.778', '-0.73', '0', '0.559', '#joy', '#surprise', 'positive', '0.689', 'sex', 'inversion', 'sexual_inversion', 'lesbianism', 'sapphism']
senticnet['advocator'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'economic_libertarian', 'civil_libertarian', 'advocate', 'libertarian', 'proponent']
senticnet['advowson'] = ['0.049', '0.16', '0', '0.155', '#interest', '#admiration', 'positive', '0.121', 'castle', 'palace', 'medieval', 'aristocrat', 'throne']
senticnet['adze'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'useful_tool']
senticnet['aec'] = ['-0.90', '0', '0.748', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'alternative_energy', 'activation_energy', 'energy_of_activation', 'energy', 'nuclear_energy']
senticnet['aec_chairman'] = ['-0.90', '0', '0.748', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'alternative_energy', 'activation_energy', 'energy_of_activation', 'energy', 'nuclear_energy']
senticnet['aedicula'] = ['-0.92', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.91', 'bury_body', 'coffin', 'headstone', 'morgue', 'tombstone']
senticnet['aega'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'sedum', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['aeonian'] = ['-0.95', '0', '0.808', '-0.61', '#sadness', '#anger', 'negative', '-0.79', 'everlasting', 'standing', 'imperishable', 'enduring', 'lasting']
senticnet['aerator'] = ['0.939', '0.939', '0', '0', '#joy', '#interest', 'positive', '0.939', 'puddle', 'sop', 'liquid_water', 'finger_paint', 'bread']
senticnet['aerial'] = ['0.16', '0.067', '0', '0.083', '#joy', '#admiration', 'positive', '0.103', 'antenna', 'sensory', 'arthropod', 'insect', 'connection']
senticnet['aerial_bomb'] = ['0', '0', '-0.93', '-0.73', '#fear', '#disgust', 'negative', '-0.83', 'grenade', 'weapon', 'bomb', 'cannon', 'machine_gun']
senticnet['aerial_gunnery'] = ['0.93', '-0.95', '0', '0.909', '#joy', '#surprise', 'positive', '0.93', 'acquirement', 'skill', 'accomplishment', 'acquisition', 'attainment']
senticnet['aerial_resupply'] = ['-0.07', '0.069', '-0.18', '0', '#sadness', '#fear', 'negative', '-0.06', 'medical_procedure', 'cure', 'doctor_surgery', 'treatment', 'doctor_work']
senticnet['aerial_view'] = ['0.838', '0.828', '0', '0', '#joy', '#interest', 'positive', '0.833', 'vista', 'spectacular', 'perspective', 'image', 'scenic']
senticnet['aerialist'] = ['0.9', '-0.88', '0', '0.687', '#joy', '#surprise', 'positive', '0.823', 'conjurer', 'escape_expert', 'mind_reader', 'telepathist', 'performer']
senticnet['aerobic_activity'] = ['0.094', '0', '0', '0', '#joy', '#joy', 'positive', '0.094', 'hooker', 'registry_office', 'restaurant_entrance', 'elevator', 'exit']
senticnet['aerobic_capacity'] = ['0.603', '-0.77', '0', '0.641', '#surprise', '#admiration', 'positive', '0.674', 'suitability', 'suitableness', 'habitability', 'habitableness', 'qualification']
senticnet['aerobic_dance'] = ['0.603', '-0.77', '0', '0.641', '#surprise', '#admiration', 'positive', '0.674', 'suitability', 'suitableness', 'habitability', 'habitableness', 'qualification']
senticnet['aerobic_exercise'] = ['0', '0.08', '0', '0.117', '#interest', '#admiration', 'positive', '0.099', 'exercise', 'healthy_exercise', 'good_environment', 'make_strong', 'stay_fit']
senticnet['aerobic_fermentation'] = ['0', '0.104', '-0.09', '0.146', '#interest', '#admiration', 'positive', '0.052', 'thaw', 'slush', 'water_inside', 'give_water', 'obtain_water']
senticnet['aerobic_treatment'] = ['-0.53', '0', '0.671', '-0.55', '#anger', '#disgust', 'negative', '-0.58', 'dirty', 'putrefactive', 'putrefacient', 'abscessed', 'contaminative']
senticnet['aerobic_workout'] = ['0.094', '0', '0', '0', '#joy', '#joy', 'positive', '0.094', 'hooker', 'registry_office', 'restaurant_entrance', 'elevator', 'exit']
senticnet['aerodrome'] = ['-0.70', '0', '0.621', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'flare_path', 'airfield', 'grassland', 'runway', 'road']
senticnet['aerodynamic_drag'] = ['0', '0', '-0.11', '0', '#fear', '#fear', 'negative', '-0.11', 'chemical_reaction', 'set_fire', 'grow_yard', 'heat_light', 'burn_down_house']
senticnet['aerodynamic_lift'] = ['0.676', '0.63', '-0.23', '0.31', '#joy', '#interest', 'positive', '0.345', 'perkiness', 'walk_on_air', 'exultingly', 'exultantly', 'jubilance']
senticnet['aerofoil'] = ['-0.97', '0', '0.831', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'skewness', 'irregularity']
senticnet['aeronaut'] = ['0.756', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.742', 'handbill', 'fly', 'flier', 'airman', 'flyer']
senticnet['aeronomy'] = ['0', '0.731', '0', '0.897', '#interest', '#admiration', 'positive', '0.814', 'space', 'atmospheric_science', 'award', 'continue_grant', 'sponsor']
senticnet['aerophobia'] = ['-0.79', '-0.71', '-0.96', '-0.95', '#fear', '#disgust', 'negative', '-0.49', 'fear', 'caution', 'deep_sadness', 'bad_feeling', 'psychoneurosis']
senticnet['aerophone'] = ['0.821', '-0.53', '0', '0.785', '#joy', '#admiration', 'positive', '0.712', 'fife', 'nose_flute', 'piccolo', 'woodwind', 'flute']
senticnet['aeroplane'] = ['-0.04', '0.105', '-0.06', '0.115', '#interest', '#admiration', 'positive', '0.028', 'airplane', 'pilot', 'air_traffic', 'sky', 'jet']
senticnet['aeroscraft'] = ['-0.04', '0.092', '0', '0.062', '#interest', '#admiration', 'positive', '0.036', 'hangar', 'aircraft_carrier', 'hot_air_balloon', 'war_museum', 'spaceship']
senticnet['aerosol'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'radioactive_dust', 'dust', 'dust_matter']
senticnet['aerosol_dissemination'] = ['-0.95', '0', '0.876', '-0.89', '#sadness', '#disgust', 'negative', '-0.90', 'bubonic_plague', 'pestis_bubonica', 'glandular_plague', 'septicemic_plague', 'pestilence']
senticnet['aerosol_formation'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'radioactive_dust', 'dust', 'dust_matter']
senticnet['aerosol_transmission'] = ['-0.95', '0', '0.876', '-0.89', '#sadness', '#disgust', 'negative', '-0.90', 'bubonic_plague', 'pestis_bubonica', 'glandular_plague', 'septicemic_plague', 'pestilence']
senticnet['aerostat'] = ['-0.04', '0.092', '0', '0.062', '#interest', '#admiration', 'positive', '0.036', 'hangar', 'aircraft_carrier', 'hot_air_balloon', 'war_museum', 'spaceship']
senticnet['aerozine'] = ['0', '0', '0', '0.073', '#admiration', '#admiration', 'positive', '0.073', 'fire_starter', 'organic_substance', 'renewable_resource', 'use_fuel', 'tree_product']
senticnet['aeschylus_agamemnon'] = ['-0.81', '0', '0.52', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'affray', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['aeschylus_oresteia'] = ['0.089', '-0.01', '0.033', '0.082', '#joy', '#admiration', 'positive', '0.039', 'informative', 'informational', 'ephemeral', 'fun_read', 'ads']
senticnet['aesculetin'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'capsaicin', 'chemical_irritant', 'piperine', 'pepper_spray']
senticnet['aesculi'] = ['-0.93', '0', '0.699', '-0.91', '#sadness', '#disgust', 'negative', '-0.84', 'onion_smut', 'head_smut', 'stinking_smut', 'flag_smut_fungus', 'covered_smut']
senticnet['aesculus_hippocastanum'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'bottlebrush_buckeye', 'red_buckeye', 'particolored_buckeye']
senticnet['aesculus_parviflora'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'horse_chestnut', 'red_buckeye', 'particolored_buckeye']
senticnet['aesop'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'narrative', 'bedtime', 'bed_time', 'long_story', 'saga']
senticnet['aesop_fable'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'narrative', 'bedtime', 'bed_time', 'long_story', 'saga']
senticnet['aesopic'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'narrative', 'bedtime', 'bed_time', 'long_story', 'saga']
senticnet['aesthesia'] = ['0.638', '-0.99', '0', '0.631', '#joy', '#surprise', 'positive', '0.755', 'awareness', 'ego', 'state_of_mind', 'conscious', 'consciousness']
senticnet['aesthesis'] = ['-0.58', '0', '0.568', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'masking', 'taste', 'threshold', 'perception', 'limen']
senticnet['aesthete'] = ['0.814', '-0.50', '0', '0.622', '#joy', '#admiration', 'positive', '0.648', 'friend', 'champion', 'supporter', 'protagonist', 'endorser']
senticnet['aesthetic'] = ['0.737', '-0.85', '0', '0.623', '#joy', '#surprise', 'positive', '0.738', 'understated', 'esthetic', 'tasteful', 'artistic', 'unostentatious']
senticnet['aesthetic_appeal'] = ['0.817', '-0.62', '0', '0.62', '#joy', '#surprise', 'positive', '0.688', 'quality', 'fascination', 'allurement', 'allure', 'temptingness']
senticnet['aesthetic_beauty'] = ['-0.63', '0', '0.694', '-0.66', '#anger', '#disgust', 'negative', '-0.66', 'corporality', 'tangibleness', 'palpability', 'reality', 'solidness']
senticnet['aesthetic_contemplation'] = ['-0.73', '0', '0.732', '-0.53', '#sadness', '#anger', 'negative', '-0.66', 'insurmountable', 'insuperable', 'unconquerable', 'indomitable', 'unsubduable']
senticnet['aesthetic_judgement'] = ['0.828', '-0.90', '0', '0.874', '#surprise', '#admiration', 'positive', '0.869', 'perspicacity', 'judgment', 'fair', 'sound_judgment', 'fair_judgment']
senticnet['aesthetic_pleasure'] = ['-0.63', '0', '0.694', '-0.66', '#anger', '#disgust', 'negative', '-0.66', 'corporality', 'tangibleness', 'palpability', 'reality', 'solidness']
senticnet['aesthetic_sensibility'] = ['0.607', '-0.99', '0', '0.847', '#surprise', '#admiration', 'positive', '0.817', 'eristic', 'puppetry', 'homiletics', 'aviation', 'airmanship']
senticnet['aesthetic_sensitivity'] = ['0.794', '-0.61', '0', '0.675', '#joy', '#admiration', 'positive', '0.694', 'fruitfulness', 'genius', 'fecundity', 'ability', 'wizardry']
senticnet['aesthetic_surgery'] = ['0.745', '-0.55', '0', '0.597', '#joy', '#admiration', 'positive', '0.633', 'lift', 'phalloplasty', 'abdominoplasty', 'tummy_tuck', 'autoplasty']
senticnet['aesthetic_value'] = ['0.817', '-0.62', '0', '0.62', '#joy', '#surprise', 'positive', '0.688', 'quality', 'fascination', 'allurement', 'allure', 'temptingness']
senticnet['aesthetical'] = ['0.596', '-0.64', '0', '0.945', '#surprise', '#admiration', 'positive', '0.727', 'cosmetic', 'painterly', 'sensuous', 'artistic', 'enhancive']
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senticnet['afternoon_nap'] = ['0.026', '0.069', '0', '0', '#joy', '#interest', 'positive', '0.048', 'eat_food', 'eat_beef', 'chew_food', 'espresso', 'eat_much']
senticnet['afternoon_snack'] = ['0.715', '0.785', '0', '0', '#joy', '#interest', 'positive', '0.75', 'scrumble_egg', 'heat_meal', 'pan', 'beat_egg', 'food']
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senticnet['afterparty'] = ['0.763', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.75', 'pub', 'disco', 'dance_club', 'cafe', 'tavern']
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senticnet['afterword'] = ['-0.04', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.08', 'data', 'awareness', 'information', 'saga', 'knowledge']
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senticnet['against'] = ['0', '0', '0.706', '-0.74', '#anger', '#disgust', 'negative', '-0.72', 'uprise', 'quarrel', 'opposition', 'riot', 'dispute']
senticnet['against_corruption_icac'] = ['0.845', '0.916', '0', '0', '#joy', '#interest', 'positive', '0.881', 'question', 'answer', 'query', 'question_start', 'question_answering']
senticnet['against_democratic_challenger'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['against_law'] = ['-0.28', '0.214', '-0.35', '0.206', '#sadness', '#fear', 'negative', '-0.05', 'felony', 'dishonest', 'bad_action', 'bad_behavior', 'deception']
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senticnet['agamid_lizard_endemic'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'death_adder', 'ophidian', 'serpent', 'elapid', 'taipan']
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senticnet['agapic'] = ['-0.52', '0', '0.604', '-0.82', '#anger', '#disgust', 'negative', '-0.65', 'discernment', 'sagacity', 'sagaciousness', 'confidentiality', 'judgment']
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senticnet['agave'] = ['0.864', '0.846', '0', '0', '#joy', '#interest', 'positive', '0.855', 'nectar', 'honey', 'juice', 'syrup', 'bee']
senticnet['agave_syrup'] = ['-0.08', '0.106', '0', '0.125', '#interest', '#admiration', 'positive', '0.047', 'sugar', 'treacle', 'black_treacle', 'add_sugar', 'sugar_substitute']
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senticnet['age_prematurely'] = ['-0.95', '0', '0.924', '-0.93', '#sadness', '#disgust', 'negative', '-0.93', 'cause_mental_illness', 'catch_fire', 'smoke_cigarette', 'light_cigar', 'light_pipe']
senticnet['age_skin'] = ['-0.06', '0', '-0.08', '-0.08', '#fear', '#disgust', 'negative', '-0.07', 'dry_wet_clothes', 'make_drunk', 'grow_plant', 'warm_hand', 'burn_trash']
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senticnet['agedashi_tofu'] = ['0.084', '-0.05', '0', '-0.04', '#joy', '#surprise', 'positive', '0.031', 'entree', 'feast', 'satiation', 'breakfast', 'supper']
senticnet['ageing_squad'] = ['0.857', '-0.68', '0', '0.971', '#joy', '#admiration', 'positive', '0.839', 'revitalize', 'refurbish', 'reconstruct', 'renovate', 'renew']
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senticnet['agency_darpa'] = ['-0.04', '0', '-0.06', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'make_war', 'conquer_enemy', 'use_military', 'gain_territory', 'keep_peace']
senticnet['agency_dera'] = ['-0.85', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.86', 'attack', 'raid', 'fight_against', 'conflict', 'opposition']
senticnet['agenda'] = ['0.055', '-0.01', '0', '0.106', '#joy', '#admiration', 'positive', '0.059', 'attendee', 'pencil_pen', 'learn_write', 'find_paper', 'paper_plane']
senticnet['agent'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'infective_agent', 'satisfier', 'quickener', 'invigorator']
senticnet['agent_mta'] = ['-0.1', '0.273', '-0.09', '0.219', '#interest', '#admiration', 'positive', '0.074', 'money_exchange', 'money_transfer', 'financial_transaction', 'owe_money', 'money']
senticnet['ageratina'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'carnauba_palm', 'carnauba', 'wax_palm', 'palmetto', 'palmyra']
senticnet['aggie_stadium'] = ['0', '-0.84', '0', '0.723', '#surprise', '#admiration', 'positive', '0.785', 'circular_route', 'stadium', 'memorial', 'station', 'row_house']
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senticnet['agglomeration'] = ['-0.63', '0', '0.862', '-0.52', '#sadness', '#anger', 'negative', '-0.67', 'knot', 'swad', 'tuft', 'tussock', 'bunch']
senticnet['aggradation'] = ['0.614', '-0.95', '0', '0.584', '#joy', '#surprise', 'positive', '0.718', 'mother_lode', 'champion_lode', 'deposit', 'sedimentation', 'lode']
senticnet['aggrandise'] = ['0.54', '-0.97', '0', '0.569', '#surprise', '#admiration', 'positive', '0.695', 'overstate', 'hyperbolize', 'glorify', 'exaggerate', 'overdraw']
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senticnet['aggrandizement'] = ['0.916', '0', '0', '0.749', '#joy', '#admiration', 'positive', '0.833', 'self-aggrandizement', 'self-fulfillment', 'gratification', 'self-indulgence', 'self-gratification']
senticnet['aggravate'] = ['-0.12', '0.069', '0.703', '-0.67', '#anger', '#disgust', 'negative', '-0.35', 'exacerbate', 'provoked', 'aggravated', 'exasperation', 'displeased']
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senticnet['aggravated_asthma'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchiolitis', 'chronic_bronchitis', 'respiratory_disease', 'bronchitis', 'respiratory_disorder']
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senticnet['aggravated_indecent_assault'] = ['0.542', '-0.83', '0', '0.927', '#surprise', '#admiration', 'positive', '0.769', 'incest', 'extramarital_sex', 'free_love', 'sexual_intercourse', 'intercourse']
senticnet['aggravated_manslaughter'] = ['0.654', '-0.59', '0', '0.774', '#joy', '#admiration', 'positive', '0.673', 'instrument', 'indictment', 'murder_indictment', 'true_bill', 'legal_document']
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senticnet['aggregate_amount'] = ['-0.04', '0.268', '0', '0.159', '#interest', '#admiration', 'positive', '0.127', 'total', 'amount', 'a_lot', 'aggregate', 'bill']
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senticnet['aggregate_website_metacritic'] = ['0.926', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.916', 'score_goal', 'accomplish', 'finalize', 'pot', 'make_point']
senticnet['aggregation'] = ['0.553', '-0.8', '0', '0.908', '#surprise', '#admiration', 'positive', '0.754', 'strong_suit', 'collection', 'long_suit', 'accumulation', 'assemblage']
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senticnet['aghast'] = ['0.839', '0', '0', '0.775', '#joy', '#admiration', 'positive', '0.807', 'agog', 'dumbfounded', 'transfixed', 'dumbstruck', 'flabbergasted']
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senticnet['agitate'] = ['-0.98', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.93', 'foment', 'agitated', 'tumult', 'stir_up', 'earnestness']
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senticnet['agnate'] = ['0', '0.769', '0', '0', '#interest', '#interest', 'positive', '0.769', 'get_rich', 'get_money', 'inherit_property', 'benefit', 'enjoy_life']
senticnet['agnathan'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'hag', 'osteostracan', 'cephalaspid', 'ostracoderm', 'jawless_fish']
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senticnet['algebra_equation'] = ['0.725', '0.825', '0', '0', '#joy', '#interest', 'positive', '0.775', 'calculus', 'algebra', 'algebraematics', 'math', 'algebraematical_function']
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senticnet['algorithmic_rule'] = ['0', '0.878', '0', '0.776', '#interest', '#admiration', 'positive', '0.827', 'algorithm', 'algorithmic_program', 'research', 'project', 'development']
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senticnet['alias'] = ['0.056', '0.183', '-0.12', '0.188', '#interest', '#admiration', 'positive', '0.075', 'kingdom', 'royal', 'monarchy', 'throne', 'noble']
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senticnet['alien-like'] = ['0', '-0.85', '0', '0.854', '#surprise', '#admiration', 'positive', '0.853', 'bedfellow', 'bed-fellows', 'counter-part', 'bedfellow', 'uncongenial']
senticnet['alien_abduction'] = ['0', '0.871', '0', '0.861', '#interest', '#admiration', 'positive', '0.866', 'predict_future', 'psychic_phenomenon', 'parapsychology', 'clairvoyance', 'foreknowledge']
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senticnet['alien_invasion'] = ['-0.96', '0', '0.501', '-0.59', '#sadness', '#disgust', 'negative', '-0.68', 'calamity', 'catastrophe', 'tragedy', 'disaster', 'cataclysm']
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senticnet['alien_planet'] = ['0.884', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.919', 'ufo', 'milky', 'space_junk', 'galaxy', 'solar_system']
senticnet['alien_race'] = ['0.086', '-0.10', '0.064', '0', '#joy', '#surprise', 'positive', '0.043', 'art_piece', 'lifetime', 'museum', 'vintage', 'collectible']
senticnet['alien_registration'] = ['0.931', '-0.52', '0', '0.67', '#joy', '#admiration', 'positive', '0.709', 'document', 'certificate_of_incorporation', 'credential', 'diploma', 'sheepskin']
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senticnet['alien_weaponry'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
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senticnet['alienate'] = ['-0.10', '0.176', '0.629', '-0.64', '#anger', '#disgust', 'negative', '-0.3', 'alienated', 'alienation', 'disaffection', 'estrange', 'disoriented']
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senticnet['alienee'] = ['0', '0.88', '0', '0.864', '#interest', '#admiration', 'positive', '0.872', 'grantee', 'research', 'award', 'project', 'cost_sharing']
senticnet['alienism'] = ['-0.86', '0', '0.941', '-0.62', '#sadness', '#anger', 'negative', '-0.80', 'alienage', 'foreignness', 'exoticism', 'exoticness', 'exotism']
senticnet['alight'] = ['0.641', '-0.73', '0', '0.798', '#surprise', '#admiration', 'positive', '0.724', 'ablaze', 'aflame', 'afire', 'aflare', 'lighted']
senticnet['align'] = ['-0.75', '0', '0.514', '-0.67', '#sadness', '#disgust', 'negative', '-0.64', 'realign', 'realine', 'focus', 'true', 'address']
senticnet['aligned_parallel'] = ['-0.97', '0', '0.865', '-0.54', '#sadness', '#anger', 'negative', '-0.79', 'piping', 'lace', 'lacing', 'cord', 'slack']
senticnet['alignment'] = ['0', '0', '-0.72', '-0.73', '#fear', '#disgust', 'negative', '-0.73', 'misalignment', 'misaligned', 'misalignment', 'mis-alignment', 'slippage']
senticnet['alikeness'] = ['-0.89', '0', '0.58', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'resemblance', 'comparison', 'equivalence', 'reflection', 'spitting_image']
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senticnet['alimony'] = ['-0.79', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.86', 'allowance', 'provision', 'divorce', 'bill', 'due']
senticnet['alinement'] = ['0.974', '0.973', '0', '0', '#joy', '#interest', 'positive', '0.974', 'alliance', 'coalition', 'confederation', 'alignment', 'bond']
senticnet['aliphatic_side'] = ['0.096', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.057', 'platform_rail', 'rail', 'catwalk', 'column', 'token_turnstile']
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senticnet['aliquid'] = ['0.654', '-0.75', '0', '0.78', '#surprise', '#admiration', 'positive', '0.729', 'suit', 'cause', 'lawsuit', 'occurrence', 'countersuit']
senticnet['aliquippa'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
senticnet['alison_lohman'] = ['0.794', '-0.50', '0', '0.531', '#joy', '#admiration', 'positive', '0.611', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['alison_suttie'] = ['0', '0', '0', '0.876', '#admiration', '#admiration', 'positive', '0.876', 'good_manner', 'courteous', 'good_behavior', 'handle_carefully', 'politeness']
senticnet['alisoun'] = ['0.539', '-0.96', '0', '0.745', '#surprise', '#admiration', 'positive', '0.751', 'spouse', 'partner', 'married_person', 'mate', 'better_half']
senticnet['alistair_mcgowan'] = ['-0.89', '0', '0.58', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'resemblance', 'comparison', 'equivalence', 'reflection', 'alikeness']
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senticnet['alitalia_azpia'] = ['0', '0.189', '0.04', '0.091', '#interest', '#admiration', 'positive', '0.08', 'combination', 'margin', 'teller', 'merge', 'finite']
senticnet['alito'] = ['-0.74', '0', '0.748', '-0.81', '#anger', '#disgust', 'negative', '-0.77', 'coral', 'caviar', 'caviare', 'shad_roe', 'seafood']
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senticnet['alive'] = ['0.027', '0.132', '-0.06', '0.182', '#interest', '#admiration', 'positive', '0.07', 'life_form', 'breathe', 'poisonous_human', 'live_organism', 'live_creature']
senticnet['aliyah'] = ['0.682', '-0.70', '0', '0.98', '#surprise', '#admiration', 'positive', '0.789', 'prize', 'seal_of_approval', 'honorable_mention', 'laurel', 'cachet']
senticnet['alizarin'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['alj'] = ['0', '0.146', '0', '0.077', '#interest', '#admiration', 'positive', '0.111', 'negotiator', 'judge_jury', 'arbiter', 'decide_guilt', 'become_judge']
senticnet['alkahest'] = ['0', '0.887', '0', '0.973', '#interest', '#admiration', 'positive', '0.93', 'servant', 'art_of_transmuting_metal', 'stone', 'alchemy', 'philosopher']
senticnet['alkali'] = ['0', '0', '0.748', '-0.93', '#anger', '#disgust', 'negative', '-0.84', 'vitriolic', 'mordant', 'acrid', 'caustic', 'biting']
senticnet['alkali_metal'] = ['0.032', '-0.02', '-0.04', '-0.08', '#fear', '#disgust', 'negative', '-0.01', 'liquid', 'carbonate_drink', 'chemical', 'soda', 'bromide']
senticnet['alkali_poisoning'] = ['-0.72', '0', '0.884', '-0.75', '#anger', '#disgust', 'negative', '-0.78', 'ergotism', 'paraquat_poisoning', 'poisoning', 'caffeinism', 'caffeine_intoxication']
senticnet['alkaline'] = ['0.045', '0', '0', '0.051', '#joy', '#admiration', 'positive', '0.048', 'science', 'battery', 'chemistry', 'ph_level', 'chemical']
senticnet['alkaline-forming'] = ['0', '0', '0.824', '-0.74', '#anger', '#disgust', 'negative', '-0.78', 'acid-forming', 'acid-producing', 'alkalizing', 'potassium-rich', 'gas-forming']
senticnet['alkaline_environment'] = ['0.071', '0', '-0.08', '-0.09', '#fear', '#disgust', 'negative', '-0.03', 'gunk', 'rotten_food', 'sour_cream', 'glass_of_milk', 'lemon_juice']
senticnet['alkaline_paper'] = ['0.022', '-0.05', '0.079', '-0.11', '#anger', '#disgust', 'negative', '-0.02', 'stationery', 'ink', 'ball_point_pen', 'refill', 'note']
senticnet['alkaline_solution'] = ['0.071', '0', '-0.08', '-0.09', '#fear', '#disgust', 'negative', '-0.03', 'gunk', 'rotten_food', 'sour_cream', 'glass_of_milk', 'lemon_juice']
senticnet['alkalizer'] = ['-0.53', '0', '0.954', '-0.54', '#anger', '#disgust', 'negative', '-0.67', 'antacid', 'histamine_blocker', 'gastric_antacid', 'ranitidine', 'antiacid']
senticnet['alkalizing'] = ['0', '0', '0.821', '-0.75', '#anger', '#disgust', 'negative', '-0.78', 'alkaline-forming', 'acid-producing', 'acid-forming', 'potassium-rich', 'gas-forming']
senticnet['alkaloid_isolated_from'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['alkanet'] = ['0.632', '-0.93', '0', '0.928', '#surprise', '#admiration', 'positive', '0.831', 'herb', 'oregano', 'marjoram', 'pot_marjoram', 'origanum']
senticnet['alkaptonuria'] = ['-0.57', '0', '0.973', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['alky'] = ['0.556', '-0.79', '0', '0.755', '#surprise', '#admiration', 'positive', '0.701', 'lush', 'alcoholic', 'rummy', 'dipsomaniac', 'boozer']
senticnet['alkyl_group'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
senticnet['alkylidene'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
senticnet['alkyne'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
senticnet['all'] = ['-0.78', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.76', 'entirely', 'whole', 'only', 'exclusively', 'wholly']
senticnet['all-cause'] = ['-0.89', '0', '-0.89', '0', '#sadness', '#fear', 'negative', '-0.89', 'non-fatal', 'nonfatal', 'infarction', 'hospitalization', 'nonlethal']
senticnet['all-destroying'] = ['0', '0', '0.968', '-0.97', '#anger', '#disgust', 'negative', '-0.97', 'hydra-headed', 'all-devouring', 'supineness', 'incontrollable', 'always-evolving']
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senticnet['all-out'] = ['0', '0', '0.819', '-0.73', '#anger', '#disgust', 'negative', '-0.77', 'large-scale', 'full-blown', 'full-fledged', 'full-scale', 'full-on']
senticnet['all_dolled_up'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'hankie', 'kleenex', 'facial_tissue', 'handkerchief', 'tissue']
senticnet['all_important'] = ['0.86', '-0.85', '0', '0.864', '#joy', '#admiration', 'positive', '0.86', 'of_import', 'of_essence', 'outstanding', 'master', 'chief']
senticnet['all_in_vain'] = ['-0.98', '0', '0.004', '-0.93', '#sadness', '#disgust', 'negative', '-0.64', 'come_to_nothing', 'waste_of_time', 'failure', 'unsuccessful', 'without_end_product']
senticnet['all_right'] = ['0.065', '0.351', '-0.17', '0.395', '#interest', '#admiration', 'positive', '0.16', 'let', 'suggest', 'plea', 'request', 'permission']
senticnet['allactaga'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'carbon_monoxide', 'tree_animal']
senticnet['allah'] = ['0.71', '0.882', '0', '0.9', '#interest', '#admiration', 'positive', '0.831', 'positive_reinforcement', 'brownie_point', 'benefit', 'give', 'gratification']
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senticnet['allay'] = ['0.615', '0.643', '-0.24', '0.29', '#joy', '#interest', 'positive', '0.325', 'relieve', 'easiness', 'pacifically', 'peaceable', 'repose']
senticnet['allayer'] = ['0.81', '-0.69', '0', '0.741', '#joy', '#admiration', 'positive', '0.748', 'mortal', 'set_to_rest', 'reduce_intensity', 'calm', 'pacify']
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senticnet['alleged_abuse'] = ['-0.8', '0', '0.875', '-0.74', '#sadness', '#anger', 'negative', '-0.80', 'inhuman_treatment', 'cruelty', 'child_neglect', 'mistreatment', 'child_abuse']
senticnet['alleged_adultery'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'spousal_relationship', 'wedlock']
senticnet['alleged_affair'] = ['0.54', '-0.64', '0', '0.566', '#surprise', '#admiration', 'positive', '0.582', 'pleaser', 'entertainer', 'mortal', 'busker', 'drawing_card']
senticnet['alleged_assailant'] = ['-0.60', '0', '0.777', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'batrachomyomachia', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['alleged_assault'] = ['-0.60', '0', '0.777', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'batrachomyomachia', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['alleged_bribe'] = ['-0.95', '0', '-0.70', '-0.96', '#sadness', '#disgust', 'negative', '-0.87', 'corrupt', 'bribe_police', 'debt', 'tax', 'persuade']
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senticnet['alleged_copyright_infringement'] = ['0.682', '-0.67', '0', '0.588', '#joy', '#surprise', 'positive', '0.648', 'lawful', 'civil_action', 'counterclaim', 'occurrence', 'li_pendens']
senticnet['alleged_corruption'] = ['0', '0', '-0.77', '-0.86', '#fear', '#disgust', 'negative', '-0.82', 'abduction', 'punishment', 'emergency', 'illiteracy', 'heal']
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senticnet['alleged_fraud'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'mail_fraud', 'swindle', 'rig']
senticnet['alleged_gunman'] = ['-0.86', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.79', 'gun', 'firearm', 'handgun', 'rifle', 'pistol']
senticnet['alleged_incident'] = ['-0.60', '0', '0.777', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'batrachomyomachia', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['alleged_infringement'] = ['0.682', '-0.67', '0', '0.588', '#joy', '#surprise', 'positive', '0.648', 'lawful', 'civil_action', 'counterclaim', 'occurrence', 'li_pendens']
senticnet['alleged_intentional_infliction'] = ['-0.71', '0', '-0.75', '-0.90', '#fear', '#disgust', 'negative', '-0.79', 'convent', 'monastery', 'capital_build', 'confession', 'synagogue']
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senticnet['alleged_misconduct'] = ['0.845', '0.916', '0', '0', '#joy', '#interest', 'positive', '0.881', 'question', 'answer', 'query', 'question_start', 'question_answering']
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senticnet['alleged_rapist'] = ['0.864', '-0.54', '0', '0.613', '#joy', '#admiration', 'positive', '0.675', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['alleged_robber'] = ['0.964', '-0.57', '0', '0.827', '#joy', '#admiration', 'positive', '0.789', 'mortal', 'murder_suspect', 'suspect', 'rape_suspect', 'person']
senticnet['alleged_sexual_assault'] = ['-0.64', '0', '0.854', '-0.83', '#anger', '#disgust', 'negative', '-0.77', 'crime', 'bigamy', 'regulatory_offence', 'sexual_abuse', 'sex_crime']
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senticnet['alleged_stalker'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'addicted', 'obsessed_love', 'addiction', 'obsession', 'weird']
senticnet['alleged_thief'] = ['0.964', '-0.57', '0', '0.827', '#joy', '#admiration', 'positive', '0.789', 'mortal', 'murder_suspect', 'suspect', 'rape_suspect', 'person']
senticnet['alleged_violation'] = ['-0.89', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.93', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['alleged_violator'] = ['-0.89', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.93', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['allegedly'] = ['0.931', '-0.71', '0', '0.63', '#joy', '#surprise', 'positive', '0.758', 'hustle', 'con', 'bunco', 'sting', 'bunco_game']
senticnet['allegedly_defective'] = ['-0.92', '0', '0.256', '-0.98', '#sadness', '#disgust', 'negative', '-0.72', 'lousy', 'bad_quality', 'failure', 'poor_quality', 'low_quality']
senticnet['allegedly_obscene'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'indecency']
senticnet['allegheny'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
senticnet['allegheny_county'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
senticnet['allegiance'] = ['0.997', '0.796', '0', '0.89', '#joy', '#admiration', 'positive', '0.894', 'devotion', 'bravery', 'adoration', 'zeal', 'charm']
senticnet['allegorical'] = ['-0.73', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.79', 'mythical', 'mythology', 'mythological', 'deity', 'archetypal']
senticnet['allegorical_interpretation'] = ['-0.57', '0', '0.791', '-0.74', '#anger', '#disgust', 'negative', '-0.70', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['allegorical_poem'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'narrative', 'bedtime', 'bed_time', 'long_story', 'saga']
senticnet['allegorically'] = ['0.952', '0', '0', '0.74', '#joy', '#admiration', 'positive', '0.846', 'metaphorically', 'symbolically', 'figuratively', 'prophetically', 'abstractly']
senticnet['allegory'] = ['0.844', '-0.57', '0', '0.576', '#joy', '#admiration', 'positive', '0.664', 'blue_ribbon', 'cordon_bleu', 'badge', 'stripe', 'merit_badge']
senticnet['allentown'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
senticnet['allergen'] = ['-0.15', '0.162', '0.596', '-0.62', '#anger', '#disgust', 'negative', '-0.30', 'hackle', 'umbrage', 'offense', 'ire', 'see_red']
senticnet['allergen_avoidance'] = ['-0.96', '0', '0.848', '-0.82', '#sadness', '#anger', 'negative', '-0.87', 'sneeze', 'sign_sickness', 'runny_nose', 'flu', 'common_cold']
senticnet['allergen_specific_ige'] = ['-0.66', '0', '0.877', '-0.64', '#sadness', '#anger', 'negative', '-0.72', 'somatic_cell', 'mastocyte', 'labrocyte', 'target_cell', 'phagocyte']
senticnet['allergic'] = ['-0.74', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.77', 'allergy', 'reaction', 'hypersensitivity', 'asthma', 'dermatitis']
senticnet['allergic_asthma'] = ['-0.96', '0', '0.848', '-0.82', '#sadness', '#anger', 'negative', '-0.87', 'sneeze', 'sign_sickness', 'runny_nose', 'flu', 'common_cold']
senticnet['allergic_disease'] = ['-0.96', '0', '0.848', '-0.82', '#sadness', '#anger', 'negative', '-0.87', 'sneeze', 'sign_sickness', 'runny_nose', 'flu', 'common_cold']
senticnet['allergic_inflammation'] = ['-0.66', '0', '0.877', '-0.64', '#sadness', '#anger', 'negative', '-0.72', 'somatic_cell', 'mastocyte', 'labrocyte', 'target_cell', 'phagocyte']
senticnet['allergic_reaction'] = ['-0.06', '0.183', '0.651', '-0.64', '#anger', '#disgust', 'negative', '-0.29', 'runny_nose', 'chew', 'pierce', 'mastication', 'put_mouth']
senticnet['allergic_response'] = ['-0.53', '0', '0.86', '-0.65', '#anger', '#disgust', 'negative', '-0.68', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['allergic_rhinitis'] = ['-0.60', '0', '0.931', '-0.92', '#anger', '#disgust', 'negative', '-0.82', 'rhinitis', 'coryza', 'hay_fever', 'pollinosis', 'hypersensitivity_reaction']
senticnet['allergic_skin'] = ['-0.70', '0', '-0.83', '-0.98', '#fear', '#disgust', 'negative', '-0.84', 'body_hurt', 'ache', 'ouch', 'cut_skin', 'scar']
senticnet['allergist'] = ['-0.96', '0', '0.848', '-0.82', '#sadness', '#anger', 'negative', '-0.87', 'sneeze', 'sign_sickness', 'runny_nose', 'flu', 'common_cold']
senticnet['allergy'] = ['-0.96', '0', '0.848', '-0.82', '#sadness', '#anger', 'negative', '-0.87', 'sneeze', 'sign_sickness', 'runny_nose', 'flu', 'common_cold']
senticnet['allergy-inducing'] = ['0', '0', '0.823', '-0.92', '#anger', '#disgust', 'negative', '-0.87', 'alcohol-soluble', 'oxygen-bearing', 'energy-production', 'extra-national', 'exudation']
senticnet['allergy_asthma'] = ['-0.96', '0', '0.848', '-0.82', '#sadness', '#anger', 'negative', '-0.87', 'sneeze', 'sign_sickness', 'runny_nose', 'flu', 'common_cold']
senticnet['allergy_clinic'] = ['-0.96', '0', '0.848', '-0.82', '#sadness', '#anger', 'negative', '-0.87', 'sneeze', 'sign_sickness', 'runny_nose', 'flu', 'common_cold']
senticnet['allergy_immunotherapy'] = ['-0.96', '0', '0.848', '-0.82', '#sadness', '#anger', 'negative', '-0.87', 'sneeze', 'sign_sickness', 'runny_nose', 'flu', 'common_cold']
senticnet['allergy_skin'] = ['0.928', '-0.87', '0', '0.679', '#joy', '#surprise', 'positive', '0.829', 'examine', 'experiment', 'alpha_test', 'beta_test', 'road_test']
senticnet['allergy_specialist'] = ['-0.96', '0', '0.848', '-0.82', '#sadness', '#anger', 'negative', '-0.87', 'sneeze', 'sign_sickness', 'runny_nose', 'flu', 'common_cold']
senticnet['allergy_symptom'] = ['-0.94', '0', '0.891', '-0.93', '#sadness', '#disgust', 'negative', '-0.92', 'allergy', 'symptom', 'need_cure', 'sick', 'cause_allergy']
senticnet['allethrin'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'giant_hornet', 'hornet', 'hornet_wasp']
senticnet['alleviate'] = ['0.676', '-0.70', '0', '0.8', '#surprise', '#admiration', 'positive', '0.727', 'better', 'soothe', 'improve', 'alleviate_pain', 'meliorate']
senticnet['alleviate_anxiety'] = ['0.376', '0.271', '-0.19', '0.258', '#joy', '#interest', 'positive', '0.177', 'pleasant_emotion', 'bliss', 'good_feeling', 'confirm', 'healthful']
senticnet['alleviate_boredom'] = ['0.738', '0.961', '0', '0', '#joy', '#interest', 'positive', '0.849', 'amusement', 'entertainment', 'fun', 'play', 'game']
senticnet['alleviate_pain'] = ['0.676', '-0.70', '0', '0.8', '#surprise', '#admiration', 'positive', '0.727', 'better', 'soothe', 'improve', 'alleviate', 'meliorate']
senticnet['alleviating_pain'] = ['-0.03', '-0.02', '-0.08', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'ibuprofen', 'phenobarbital', 'analgesic', 'sleep_drug', 'pill']
senticnet['alleviation'] = ['0.676', '-0.70', '0', '0.8', '#surprise', '#admiration', 'positive', '0.727', 'better', 'soothe', 'improve', 'alleviate_pain', 'meliorate']
senticnet['alleviative'] = ['0.761', '-0.75', '0', '0.77', '#joy', '#admiration', 'positive', '0.761', 'analgesic', 'analgetic', 'anodyne', 'weakening', 'moderating']
senticnet['alley'] = ['0.749', '0', '0', '0', '#joy', '#joy', 'positive', '0.749', 'dark', 'road', 'street', 'direction', 'alleyway']
senticnet['alley_behind'] = ['0.758', '0.878', '0', '0', '#joy', '#interest', 'positive', '0.818', 'tavern', 'bar', 'pub', 'martini', 'brewery']
senticnet['alley_cat'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'rascal', 'rapscallion', 'scalawag', 'holy_terror', 'monkey']
senticnet['alley_oop_dunk'] = ['-0.76', '0', '0.794', '-0.91', '#anger', '#disgust', 'negative', '-0.82', 'discharge', 'spray', 'spritz', 'outpouring', 'run']
senticnet['alleyway'] = ['0.749', '0', '0', '0', '#joy', '#joy', 'positive', '0.749', 'dark', 'road', 'street', 'direction', 'alley']
senticnet['alliance'] = ['0.977', '0.982', '0', '0', '#joy', '#interest', 'positive', '0.98', 'alinement', 'coalition', 'confederation', 'alignment', 'bond']
senticnet['allicin'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'chemical_irritant', 'gingerol', 'piperine', 'pepper_spray']
senticnet['allicrocodilian'] = ['-0.85', '0', '0.684', '-0.84', '#sadness', '#disgust', 'negative', '-0.79', 'gator', 'crocodile', 'false_gavial', 'crocodilian', 'caiman']
senticnet['allie'] = ['-0.74', '0', '0.877', '-0.97', '#anger', '#disgust', 'negative', '-0.86', 'miss', 'girl', 'chachka', 'young_lady', 'young_woman']
senticnet['allied_fleet'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['allied_health'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'infant_feeding', 'schedule_feeding', 'demand_feeding', 'breast_feeding', 'feeding']
senticnet['allied_occupation'] = ['0', '0.112', '-0.04', '0.099', '#interest', '#admiration', 'positive', '0.054', 'profession', 'career', 'vocation', 'important_job', 'laborer']
senticnet['allied_offensive'] = ['0.737', '-0.96', '0', '0.552', '#joy', '#surprise', 'positive', '0.75', 'prominence', 'hump', 'bump', 'swelling', 'frontal_eminence']
senticnet['alligator'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'reptile', 'animal', 'predator', 'meat_eater', 'river']
senticnet['alligator_farm'] = ['-0.85', '0', '0.685', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'allicrocodilian', 'crocodile', 'false_gavial', 'crocodilian', 'caiman']
senticnet['alligator_hunt'] = ['-0.85', '0', '0.685', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'allicrocodilian', 'crocodile', 'false_gavial', 'crocodilian', 'caiman']
senticnet['alligator_skin'] = ['0.706', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.806', 'shoe', 'flip_flop', 'slipper', 'casual_shoes', 'footwear']
senticnet['alligator_snapping_turtle'] = ['0.03', '0.068', '-0.04', '0.093', '#interest', '#admiration', 'positive', '0.037', 'beast', 'equine', 'live_creature', 'furry_creature', 'domesticate_animal']
senticnet['alliin'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'capsaicin', 'chemical_irritant', 'piperine', 'pepper_spray']
senticnet['alliterative_poem'] = ['-0.7', '0', '0.746', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'insufficiency', 'sparseness', 'sparsity', 'abstemiousness', 'lack_of_quantity']
senticnet['allmendinger_certainly'] = ['-0.92', '0', '0.783', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'clot', 'coagulum', 'gob', 'clew', 'agglomeration']
senticnet['allocable'] = ['0', '0', '0.943', '-0.95', '#anger', '#disgust', 'negative', '-0.94', 'taxable', 'non-taxable', 'nontaxable', 'chargeable', 'tax-free']
senticnet['allocasuarina'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['allocation'] = ['0.732', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.71', 'share', 'reallocation', 'quota', 'portion', 'part']
senticnet['allocative_efficiency'] = ['-0.54', '0', '0.739', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'unremunerative', 'idle', 'lean', 'dead', 'unprofitable']
senticnet['alloderm'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'hankie', 'kleenex', 'hanky', 'handkerchief', 'tissue']
senticnet['allodynia'] = ['-0.78', '0', '0.669', '-0.89', '#sadness', '#disgust', 'negative', '-0.78', 'pain_sensation', 'painful_sensation', 'prickling', 'tingling', 'pain']
senticnet['allogeneic_blood'] = ['0.79', '-0.62', '0', '0.674', '#joy', '#admiration', 'positive', '0.695', 'add', 'contribute', 'lend', 'breathe', 'impart']
senticnet['allograft'] = ['0', '0', '-0.77', '-0.86', '#fear', '#disgust', 'negative', '-0.82', 'abduction', 'punishment', 'emergency', 'illiteracy', 'heal']
senticnet['allomerism'] = ['-0.83', '0', '0.546', '-0.88', '#sadness', '#disgust', 'negative', '-0.75', 'difference', 'variation', 'deviation', 'divergence', 'departure']
senticnet['allopathic_medicine'] = ['-0.60', '0', '0.544', '-0.62', '#sadness', '#disgust', 'negative', '-0.59', 'remedy', 'cure', 'therapeutic', 'antidote', 'atropine']
senticnet['allophonic'] = ['-0.71', '0', '0.998', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'tone', 'quality', 'twang', 'nasal_twang', 'timber']
senticnet['allot'] = ['0.519', '-0.99', '0', '0.741', '#surprise', '#admiration', 'positive', '0.75', 'earmark', 'appropriate', 'set_aside', 'reserve', 'reserve_table']
senticnet['allotment'] = ['0', '0.108', '0.029', '0.09', '#interest', '#admiration', 'positive', '0.056', 'quantity', 'amount', 'rate', 'a_lot', 'pound']
senticnet['allow'] = ['0', '0', '0', '0.964', '#admiration', '#admiration', 'positive', '0.964', 'let', 'permission', 'accept', 'come', 'continue']
senticnet['allow_student'] = ['0', '0.856', '0', '0.79', '#interest', '#admiration', 'positive', '0.823', 'student', 'project', 'different_application', 'lab_improve', 'laboratory']
senticnet['allowable'] = ['0', '-0.93', '0', '0.715', '#surprise', '#admiration', 'positive', '0.825', 'satisfactory', 'allowably', 'consideration', 'praiseworthy', 'probable']
senticnet['allowably'] = ['0', '-0.93', '0', '0.707', '#surprise', '#admiration', 'positive', '0.823', 'satisfactory', 'allowable', 'consideration', 'praiseworthy', 'probable']
senticnet['allowance'] = ['0.924', '0.968', '0', '0.876', '#joy', '#interest', 'positive', '0.923', 'live_simply', 'help_out', 'money', 'accolade', 'generous']
senticnet['allowance_payable'] = ['0.924', '0.968', '0', '0.876', '#joy', '#interest', 'positive', '0.923', 'live_simply', 'help_out', 'money', 'accolade', 'generous']
senticnet['alloy'] = ['0.04', '0', '0.017', '0.056', '#joy', '#admiration', 'positive', '0.026', 'platinum', 'silver', 'mixed_metal', 'titanium', 'durable']
senticnet['alloy_cast_cast_iron'] = ['0.7', '-0.94', '0', '0.615', '#joy', '#surprise', 'positive', '0.753', 'alloy_cast_iron', 'iron', 'pot_metal', 'cast_iron', 'metal']
senticnet['alloy_cast_iron'] = ['0.706', '-0.94', '0', '0.622', '#joy', '#surprise', 'positive', '0.756', 'alloy_iron', 'cast_iron', 'pot_metal', 'iron', 'metal']
senticnet['alloy_iron'] = ['0.706', '-0.94', '0', '0.622', '#joy', '#surprise', 'positive', '0.756', 'cast_iron', 'alloy_cast_iron', 'pot_metal', 'iron', 'metal']
senticnet['alloy_steel'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'gold', 'bronze', 'alloy', 'medal', 'copper']
senticnet['alloyed'] = ['-0.82', '0', '0.664', '-0.88', '#sadness', '#disgust', 'negative', '-0.79', 'dirty', 'dingy', 'muddied', 'impure', 'adulterate']
senticnet['allspice'] = ['0.1', '0', '0', '0', '#joy', '#joy', 'positive', '0.1', 'burn_material', 'ash', 'stay_warm', 'bonfire', 'campfire']
senticnet['allude'] = ['0', '0', '0.93', '-0.92', '#anger', '#disgust', 'negative', '-0.92', 'insinuation', 'insinuative', 'imply', 'imperceptibly', 'insinuate']
senticnet['allure'] = ['0.817', '-0.62', '0', '0.62', '#joy', '#surprise', 'positive', '0.688', 'quality', 'fascination', 'attractivenessment', 'attractiveness', 'temptingness']
senticnet['allurement'] = ['0.817', '-0.62', '0', '0.62', '#joy', '#surprise', 'positive', '0.688', 'quality', 'fascination', 'attractiveness', 'allure', 'temptingness']
senticnet['alluring'] = ['0.969', '-0.84', '0', '0.837', '#joy', '#surprise', 'positive', '0.885', 'seductive', 'enticing', 'tempting', 'beguiling', 'corrupting']
senticnet['alluringly'] = ['-0.94', '0', '0.779', '-0.85', '#sadness', '#disgust', 'negative', '-0.86', 'eremitic', 'eremitical', 'cloistered', 'anchoritic', 'hermitic']
senticnet['allusion'] = ['-0.96', '0', '0.715', '0', '#sadness', '#anger', 'negative', '-0.83', 'derogatory', 'implication', 'insinuation', 'hint', 'innuendo']
senticnet['allusive'] = ['0.023', '0.235', '-0.06', '0.243', '#interest', '#admiration', 'positive', '0.11', 'creative', 'wall_hang', 'symbolic', 'artwork', 'craft']
senticnet['allusiveness'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'mediacy', 'mediateness', 'characteristic', 'indirectness']
senticnet['alluvial'] = ['-0.79', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.83', 'impudent', 'deposit', 'rock', 'insensitive', 'brash']
senticnet['alluvial_basin'] = ['0.614', '-0.95', '0', '0.584', '#joy', '#surprise', 'positive', '0.718', 'mother_lode', 'champion_lode', 'deposit', 'sedimentation', 'lode']
senticnet['alluvial_flood_plain'] = ['0', '0.08', '0.032', '0.096', '#interest', '#admiration', 'positive', '0.048', 'land_form', 'land_formation', 'land_feature', 'geographic_feature', 'landscape_feature']
senticnet['alluvial_gold'] = ['-0.62', '0', '0.504', '-0.79', '#sadness', '#disgust', 'negative', '-0.64', 'green_gold', 'pure_gold', 'gold', 'guinea_gold', 'noble_metal']
senticnet['alluvial_plain'] = ['0.614', '-0.95', '0', '0.584', '#joy', '#surprise', 'positive', '0.718', 'mother_lode', 'champion_lode', 'deposit', 'sedimentation', 'lode']
senticnet['alluvial_soil'] = ['0.702', '-0.95', '0', '0.997', '#surprise', '#admiration', 'positive', '0.883', 'fertilizable', 'fecund', 'conceptive', 'impregnable', 'rank']
senticnet['alluvial_valley'] = ['0.614', '-0.95', '0', '0.584', '#joy', '#surprise', 'positive', '0.718', 'mother_lode', 'champion_lode', 'deposit', 'sedimentation', 'lode']
senticnet['alluviation'] = ['0.614', '-0.95', '0', '0.584', '#joy', '#surprise', 'positive', '0.718', 'mother_lode', 'champion_lode', 'deposit', 'sedimentation', 'lode']
senticnet['alluvium'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'fossil', 'oil', 'dark_liquid', 'smithsonian_institution', 'inert_gas']
senticnet['allworthy'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitentially', 'penitent', 'repentantly', 'penitence', 'repentant']
senticnet['ally'] = ['0.716', '-0.51', '0', '0.647', '#joy', '#admiration', 'positive', '0.624', 'state', 'nation', 'country', 'land', 'commonwealth']
senticnet['ally_sloper_half'] = ['0.908', '0.991', '0', '0', '#joy', '#interest', 'positive', '0.95', 'party_time', 'new_baby', 'special_day', 'birthday', 'annual_celebration']
senticnet['allyl_alcohol'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'carboxylic_acid', 'succinic_acid', 'valeric_acid']
senticnet['allyl_isothiocyanate'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'chemical_irritant', 'gingerol', 'piperine', 'pepper_spray']
senticnet['almarhum_tuanku'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['almighty'] = ['0.064', '0.225', '-0.09', '0.232', '#interest', '#admiration', 'positive', '0.108', 'lord', 'baron', 'omniscient', 'nobility', 'deity']
senticnet['almighty_creator'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
senticnet['almighty_god'] = ['0.488', '0.661', '-0.25', '0.244', '#joy', '#interest', 'positive', '0.284', 'appreciativeness', 'gratefulness', 'unafraid', 'intrepidly', 'audacious']
senticnet['almond'] = ['0.128', '0', '0', '0.092', '#joy', '#admiration', 'positive', '0.11', 'face_feature', 'near_nose', 'facial', 'facial_part', 'socket']
senticnet['almond_butter'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'lemon_juice', 'chocolate_pudding', 'grape_juice', 'orange_juice', 'juice']
senticnet['almond_cream'] = ['0', '0.066', '-0.05', '0.089', '#interest', '#admiration', 'positive', '0.032', 'pineapple', 'kale', 'banana', 'pickle', 'tomato']
senticnet['almond_extract'] = ['-0.08', '0.106', '0', '0.125', '#interest', '#admiration', 'positive', '0.047', 'sugar', 'treacle', 'black_treacle', 'add_sugar', 'sugar_substitute']
senticnet['almond_milk'] = ['0.026', '0.08', '0.043', '0.064', '#interest', '#admiration', 'positive', '0.032', 'eat_rice', 'white_brown', 'asian', 'eat_pasta', 'staple_food']
senticnet['almond_oil'] = ['-0.81', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.82', 'peanut', 'leave_over', 'floppy', 'circular_motion', 'store_vegetable']
senticnet['almond_paste'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'roll', 'custard', 'bread', 'biscuit']
senticnet['almond_tree'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'black_locust']
senticnet['almoner'] = ['0.053', '0.248', '-0.18', '0.258', '#interest', '#admiration', 'positive', '0.093', 'slogan', 'find_money', 'wealthy', 'much_money', 'donation']
senticnet['alms'] = ['0.053', '0.248', '-0.18', '0.258', '#interest', '#admiration', 'positive', '0.093', 'slogan', 'find_money', 'wealthy', 'much_money', 'donation']
senticnet['alms_food'] = ['0.053', '0.248', '-0.18', '0.258', '#interest', '#admiration', 'positive', '0.093', 'slogan', 'find_money', 'wealthy', 'much_money', 'donation']
senticnet['almshouse'] = ['0', '0', '0.962', '-0.81', '#anger', '#disgust', 'negative', '-0.88', 'asylum', 'poorhouse', 'rat-race', 'gaol', 'workhouse']
senticnet['alnifolia'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'horse_chestnut', 'red_buckeye', 'particolored_buckeye']
senticnet['alnitak'] = ['0.977', '-0.93', '0', '0.583', '#joy', '#surprise', 'positive', '0.831', 'giant', 'shinny', 'supernova', 'supergiant', 'binary_star']
senticnet['aloba'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'head_hurt', 'introspection', 'lifestyle_change', 'headache', 'move_eye']
senticnet['alocasia'] = ['0.826', '0.787', '0', '0', '#joy', '#interest', 'positive', '0.806', 'fig', 'baby_flower', 'carpel', 'strawberry', 'star_anise']
senticnet['aloe'] = ['0.722', '-0.60', '0', '0.596', '#joy', '#surprise', 'positive', '0.641', 'medicament', 'medicinal_drug', 'medication', 'cathartic', 'bitter_cathartic']
senticnet['aloft'] = ['0.877', '0', '0', '0.783', '#joy', '#admiration', 'positive', '0.83', 'high', 'superior', 'above', 'above_all', 'atop']
senticnet['aloin'] = ['0.049', '0', '0', '0.113', '#joy', '#admiration', 'positive', '0.081', 'solid', 'hard_material', 'construction_material', 'hard', 'calcium']
senticnet['alone'] = ['-0.81', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.85', 'lonely', 'feel_lonely', 'entire', 'body_type', 'live_in_mountain']
senticnet['alone-time'] = ['0.743', '0', '0', '0.702', '#joy', '#admiration', 'positive', '0.722', 'conjugal', 'yawn-inducing', 'blamelessness', 'nuptial', 'connubial']
senticnet['alone_long_time'] = ['-0.92', '0', '0.779', '-0.70', '#sadness', '#anger', 'negative', '-0.80', 'loneliness', 'disappointment', 'stupidity', 'feel_lonely', 'act_stupid']
senticnet['aloneness'] = ['-0.58', '0', '0.925', '-0.63', '#anger', '#disgust', 'negative', '-0.71', 'ill_nature', 'disagreeableness', 'unfriendliness', 'bloodthirstiness', 'disposition']
senticnet['along_beach'] = ['-0.06', '0.07', '0.03', '0.138', '#interest', '#admiration', 'positive', '0.028', 'depth', 'salty', 'vast', 'oceanside', 'full_water']
senticnet['alongside'] = ['0.671', '-0.57', '0', '0.629', '#joy', '#admiration', 'positive', '0.625', 'board_boat', 'near_destination', 'board_ship', 'return_home', 'appear_beautiful']
senticnet['alongside_compatriot'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'nazi', 'man', 'pervert', 'healthy_person']
senticnet['aloo_paratha'] = ['0.084', '-0.05', '0', '-0.04', '#joy', '#surprise', 'positive', '0.031', 'entree', 'feast', 'satiation', 'breakfast', 'supper']
senticnet['aloof'] = ['-0.96', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.95', 'standoffish', 'unapproachable', 'uncommunicative', 'inscrutable', 'ineffable']
senticnet['aloofness'] = ['-0.93', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.82', 'withdrawal', 'detachment', 'indifferently', 'indifferent', 'apathetic']
senticnet['alors_que_se'] = ['0', '0.189', '0.04', '0.091', '#interest', '#admiration', 'positive', '0.08', 'combination', 'margin', 'teller', 'merge', 'finite']
senticnet['alot'] = ['-0.04', '-0.04', '0.056', '-0.1', '#anger', '#disgust', 'negative', '-0.03', 'exercise', 'exercise_regularly', 'need_fresh_air', 'car_repair', 'strong_muscle']
senticnet['alouatta'] = ['0.921', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.825', 'monkey', 'fiance', 'lemur', 'beaver', 'koala']
senticnet['alp'] = ['0', '0.069', '0', '0.065', '#interest', '#admiration', 'positive', '0.067', 'canyon', 'high_mountain', 'mountainside', 'ski_resort', 'high_place']
senticnet['alp_candidate'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'couch', 'stool', 'coach_seat']
senticnet['alp_preselection'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'couch', 'stool', 'coach_seat']
senticnet['alpaca'] = ['-0.53', '0', '0.751', '-0.97', '#anger', '#disgust', 'negative', '-0.75', 'guanaco', 'domestic_llama', 'llama', 'artiodactyl', 'artiodactyl_mammal']
senticnet['alpaca_fiber'] = ['-0.53', '0', '0.751', '-0.97', '#anger', '#disgust', 'negative', '-0.75', 'guanaco', 'domestic_llama', 'llama', 'artiodactyl', 'artiodactyl_mammal']
senticnet['alpaca_fleece'] = ['-0.53', '0', '0.751', '-0.97', '#anger', '#disgust', 'negative', '-0.75', 'guanaco', 'domestic_llama', 'llama', 'artiodactyl', 'artiodactyl_mammal']
senticnet['alpaca_wool'] = ['-0.53', '0', '0.751', '-0.97', '#anger', '#disgust', 'negative', '-0.75', 'guanaco', 'domestic_llama', 'llama', 'artiodactyl', 'artiodactyl_mammal']
senticnet['alpha'] = ['0.053', '0.248', '-0.18', '0.258', '#interest', '#admiration', 'positive', '0.093', 'first', 'dominant_role', 'alpha_male', 'master', 'in_charge']
senticnet['alpha_adrenergic_activity'] = ['-0.73', '0', '0.904', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'unshapely', 'shapeless', 'lumpy', 'deformed', 'malformed']
senticnet['alpha_agonist'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['alpha_blocker'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'spironolactone', 'spironolactone_drug']
senticnet['alpha_decay'] = ['-0.77', '0', '0.764', '-0.54', '#sadness', '#anger', 'negative', '-0.69', 'pullulation', 'apomixis', 'blastogenesis', 'budding', 'asexual_reproduction']
senticnet['alpha_helical'] = ['-0.02', '0.095', '0', '0.117', '#interest', '#admiration', 'positive', '0.063', 'identification', 'paper_square', 'carry_money', 'money_hold', 'credit']
senticnet['alpha_lipoic_acid'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucose', 'corn_sugar', 'dextrose', 'dextroglucose', 'grape_sugar']
senticnet['alpha_male'] = ['0', '0', '-0.12', '0', '#fear', '#fear', 'negative', '-0.12', 'male', 'male_person', 'masculine', 'man_child', 'boy']
senticnet['alpha_persei'] = ['0.977', '-0.93', '0', '0.583', '#joy', '#surprise', 'positive', '0.831', 'giant', 'shinny', 'supernova', 'supergiant', 'binary_star']
senticnet['alpha_pinene'] = ['-0.67', '0', '0.503', '-0.67', '#sadness', '#disgust', 'negative', '-0.61', 'stink_up', 'smell_up', 'stink_out', 'incense', 'perfume']
senticnet['alpha_radiation'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'ionizing_radiation', 'alpha_ray', 'beta_radiation', 'beta_ray', 'electron_radiation']
senticnet['alpha_ray'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'alpha_radiation', 'ionizing_radiation', 'beta_radiation', 'beta_ray', 'electron_radiation']
senticnet['alpha_rhythm'] = ['-0.56', '0', '0.849', '-0.61', '#anger', '#disgust', 'negative', '-0.67', 'convulsive', 'unsteady', 'jerky', 'spasmodic', 'spastic']
senticnet['alpha_test'] = ['0.928', '-0.87', '0', '0.679', '#joy', '#surprise', 'positive', '0.829', 'examine', 'experiment', 'test', 'beta_test', 'road_test']
senticnet['alphabet'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'part_of_speech', 'set_of_letter', 'ideograph', 'vowel', 'news_story']
senticnet['alphabet_soup'] = ['-0.62', '0', '0.988', '-0.59', '#sadness', '#anger', 'negative', '-0.73', 'motley', 'mixture', 'variety', 'assortment', 'mixed_bag']
senticnet['alphabetic'] = ['0.049', '0.113', '0.107', '0.162', '#interest', '#admiration', 'positive', '0.054', 'list_number', 'ordinal', 'count', 'numerical', 'enumeration']
senticnet['alphabetic_character'] = ['0.049', '0.113', '0.107', '0.162', '#interest', '#admiration', 'positive', '0.054', 'list_number', 'ordinal', 'count', 'numerical', 'enumeration']
senticnet['alphabetic_list'] = ['0.113', '0.126', '0', '0.11', '#joy', '#interest', 'positive', '0.116', 'file', 'pencil_pen', 'document', 'note_pad', 'scrap_paper']
senticnet['alphabetic_order'] = ['0.113', '0.126', '0', '0.11', '#joy', '#interest', 'positive', '0.116', 'file', 'pencil_pen', 'document', 'note_pad', 'scrap_paper']
senticnet['alphabetical_list'] = ['0.113', '0.126', '0', '0.11', '#joy', '#interest', 'positive', '0.116', 'file', 'pencil_pen', 'document', 'note_pad', 'scrap_paper']
senticnet['alphabetical_order'] = ['0.113', '0.126', '0', '0.11', '#joy', '#interest', 'positive', '0.116', 'file', 'pencil_pen', 'document', 'note_pad', 'scrap_paper']
senticnet['alphabetically'] = ['0', '0.721', '0', '0.896', '#interest', '#admiration', 'positive', '0.808', 'thematically', 'chronological', 'chronologically', 'grouped', 'logically']
senticnet['alphanumeric'] = ['0.049', '0.113', '0.107', '0.162', '#interest', '#admiration', 'positive', '0.054', 'list_number', 'ordinal', 'count', 'numerical', 'enumeration']
senticnet['alpheus_babcock'] = ['-0.75', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.80', 'high-strung', 'uptight', 'restive', 'nervy', 'jumpy']
senticnet['alphonse_gangitano'] = ['-0.54', '0', '0.845', '-0.95', '#anger', '#disgust', 'negative', '-0.78', 'face', 'base', 'underside', 'sole', 'bilge']
senticnet['alpine_downhill'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'identifiable', 'specifiable', 'recognizable', 'diagnosable', 'classifiable']
senticnet['alpine_ibex'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'argali', 'argal', 'urial', 'white_sheep', 'wild_sheep']
senticnet['alpine_meadow'] = ['-0.86', '0', '0.665', '-0.61', '#sadness', '#anger', 'negative', '-0.71', 'brambly', 'braky', 'wooded', 'scrubby', 'forested']
senticnet['alpine_milk_vetch'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'wild_licorice', 'wild_liquorice', 'milk_vetch', 'purple_milk_vetch', 'woody_plant']
senticnet['alpine_tundra'] = ['-0.04', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.04', 'bad_luck', 'damn', 'artic', 'mammal_family', 'bad_mood']
senticnet['alpine_vegetation'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'woodland', 'timberland']
senticnet['alpine_zone'] = ['-0.04', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.04', 'bad_luck', 'damn', 'artic', 'mammal_family', 'bad_mood']
senticnet['alpinist'] = ['0.518', '-0.51', '0', '0.65', '#surprise', '#admiration', 'positive', '0.562', 'clematis', 'legume', 'leguminous_plant', 'climbing_fern', 'root_climber']
senticnet['alport_syndrome'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'myotonic_muscular_dystrophy', 'myotonic_dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['already'] = ['0.702', '-0.65', '0', '0.788', '#joy', '#admiration', 'positive', '0.713', 'bonk', 'pair', 'take', 'couple', 'fornicate']
senticnet['already_reeling_from'] = ['0', '0.909', '0', '0.948', '#interest', '#admiration', 'positive', '0.928', 'set_off', 'shake_up', 'scandalmongering', 'sensationalistic', 'sensational']
senticnet['already_stretched_thin'] = ['0.814', '-0.87', '0', '0.918', '#surprise', '#admiration', 'positive', '0.87', 'ship_company', 'company', 'work_force', 'workforce', 'full_complement']
senticnet['alright'] = ['0.766', '-1.0', '0', '0', '#joy', '#surprise', 'positive', '0.883', 'okay', 'yeah', 'gonna', 'gotta', 'hey']
senticnet['alroy'] = ['-0.71', '0', '0.836', '0', '#sadness', '#anger', 'negative', '-0.77', 'awkward', 'unenviable', 'embarrassingly', 'embarrassing', 'sheepish']
senticnet['alsoe'] = ['-0.69', '0', '0.883', '-0.91', '#anger', '#disgust', 'negative', '-0.82', 'stick_in', 'enclose', 'insert', 'feed_in', 'put_in']
senticnet['alstrm_syndrome'] = ['-0.50', '0', '0.542', '-0.51', '#anger', '#disgust', 'negative', '-0.51', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'hirschsprung_disease']
senticnet['alstrom_syndrome'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'dystrophy', 'myotonic_dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['alt_rock_band'] = ['0.797', '-0.95', '0', '0.921', '#surprise', '#admiration', 'positive', '0.891', 'lush', 'alcoholic', 'alky', 'dipsomaniac', 'boozer']
senticnet['altar'] = ['0.027', '0.24', '0', '0.226', '#interest', '#admiration', 'positive', '0.164', 'chapel', 'confessional', 'christianity', 'lectern', 'temple']
senticnet['altar_boy'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'acolyte', 'reverend', 'thurifer', 'clergyman', 'man_of_cloth']
senticnet['altar_cloth'] = ['0.849', '-0.50', '0', '0.845', '#joy', '#admiration', 'positive', '0.732', 'robe', 'gown', 'veil', 'vest', 'cassock']
senticnet['altar_rail'] = ['0.023', '0.218', '0.01', '0.235', '#interest', '#admiration', 'positive', '0.116', 'confessional', 'altar', 'chapel', 'temple', 'christianity']
senticnet['alter'] = ['-0.03', '0.224', '0', '0.221', '#interest', '#admiration', 'positive', '0.135', 'small_coin', 'small_money', 'money_coin', 'american_coin', 'change']
senticnet['alter_state_consciousness'] = ['0', '0', '-0.87', '-0.96', '#fear', '#disgust', 'negative', '-0.91', 'change_mind', 'think_sex', 'invigorate', 'hallucinate', 'loss_of_consciousness']
senticnet['alterable'] = ['0.623', '-0.56', '0', '0.974', '#joy', '#admiration', 'positive', '0.721', 'commutable', 'convertible', 'translatable', 'transformable', 'transmutable']
senticnet['alteration'] = ['0.772', '-0.92', '0', '0.524', '#joy', '#surprise', 'positive', '0.74', 'change', 'modification', 'disembarrassment', 'breath_of_fresh_air', 'easing']
senticnet['altercate'] = ['0.915', '-0.84', '0', '0.778', '#joy', '#surprise', 'positive', '0.847', 'fall_out', 'debate', 'wrangle', 'fight', 'brawl']
senticnet['altercation'] = ['-0.60', '0', '0.777', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'batrachomyomachia', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['altercation_between'] = ['-0.60', '0', '0.777', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'batrachomyomachia', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['altered'] = ['-0.62', '0', '0.655', '-0.95', '#anger', '#disgust', 'negative', '-0.74', 'paraphrastic', 'adjusted', 'changed', 'emended', 'edited']
senticnet['altered_mental'] = ['0.03', '0.167', '-0.10', '0.19', '#interest', '#admiration', 'positive', '0.071', 'designation', 'social_class', 'classification', 'military_order', 'champion']
senticnet['alterity'] = ['0.122', '0.159', '-0.05', '0.217', '#interest', '#admiration', 'positive', '0.111', 'profile_picture', 'unity', 'country_person', 'unite', 'signature']
senticnet['alternaria'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'apple_rust', 'fungus']
senticnet['alternate'] = ['0.963', '-0.85', '0', '0.941', '#joy', '#admiration', 'positive', '0.919', 'match', 'double', 'equal', 'reliever', 'stunt_man']
senticnet['alternate_costume'] = ['0.021', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'plastic_bottle', 'party', 'use_party', 'lighter_air', 'compress_air']
senticnet['alternate_dimension'] = ['0.086', '-0.10', '0.064', '0', '#joy', '#surprise', 'positive', '0.043', 'art_piece', 'lifetime', 'museum', 'vintage', 'collectible']
senticnet['alternate_juror'] = ['0', '0.104', '0', '0.075', '#interest', '#admiration', 'positive', '0.089', 'judge', 'negotiator', 'jury', 'arbiter', 'member_jury']
senticnet['alternate_payee'] = ['0.851', '0.708', '0', '0', '#joy', '#interest', 'positive', '0.779', 'devisee', 'legatee', 'recipient', 'receiver', 'pensioner']
senticnet['alternate_plumage'] = ['0.022', '-0.11', '0.014', '0.057', '#surprise', '#admiration', 'positive', '0.045', 'wood', 'many_steps', 'franchise', 'stump', 'timber']
senticnet['alternately_arranged_along'] = ['-0.50', '0', '0.573', '-0.89', '#anger', '#disgust', 'negative', '-0.65', 'petallike', 'bowery', 'leafy', 'unifoliate', 'bifoliate']
senticnet['alternating_current'] = ['-0.86', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.85', 'static_electricity', 'shock', 'medical_condition', 'painful', 'sudden']
senticnet['alternative'] = ['0.02', '0.277', '-0.11', '0.336', '#interest', '#admiration', 'positive', '0.129', 'different', 'similar', 'substitute', 'option', 'exception']
senticnet['alternative_energy'] = ['-0.79', '0', '0.997', '-0.68', '#sadness', '#anger', 'negative', '-0.82', 'energy', 'solar_energy', 'solar_power', 'wind_generation', 'wind_power']
senticnet['alternative_explanation'] = ['-0.11', '0.283', '-0.16', '0.331', '#interest', '#admiration', 'positive', '0.084', 'conclude', 'question_answering', 'solution', 'jutification', 'question_starter']
senticnet['alternatively'] = ['0.02', '0.277', '-0.11', '0.336', '#interest', '#admiration', 'positive', '0.129', 'different', 'similar', 'substitute', 'option', 'exception']
senticnet['altier'] = ['0.746', '-0.88', '0', '0.723', '#joy', '#surprise', 'positive', '0.783', 'strike', 'run_into', 'hit', 'impinge_on', 'collide_with']
senticnet['altimeter'] = ['0.023', '0.113', '0.053', '0.153', '#interest', '#admiration', 'positive', '0.059', 'high', 'high_land', 'incline_plane', 'top', 'diagonal']
senticnet['altimur'] = ['-0.66', '0', '0.789', '-0.63', '#sadness', '#anger', 'negative', '-0.69', 'humbug', 'play_trick', 'cozen', 'pull_fast_one_on', 'play_trick_on']
senticnet['altitude'] = ['0.023', '0.113', '0.053', '0.153', '#interest', '#admiration', 'positive', '0.059', 'high', 'high_land', 'incline_plane', 'top', 'diagonal']
senticnet['altitude_above'] = ['0.023', '0.113', '0.053', '0.153', '#interest', '#admiration', 'positive', '0.059', 'high', 'high_land', 'incline_plane', 'top', 'diagonal']
senticnet['altitude_above_sea'] = ['0.023', '0.113', '0.053', '0.153', '#interest', '#admiration', 'positive', '0.059', 'high', 'high_land', 'incline_plane', 'top', 'diagonal']
senticnet['altitude_between'] = ['0.023', '0.113', '0.053', '0.153', '#interest', '#admiration', 'positive', '0.059', 'high', 'high_land', 'incline_plane', 'top', 'diagonal']
senticnet['altitude_ranging_from'] = ['0.023', '0.113', '0.053', '0.153', '#interest', '#admiration', 'positive', '0.059', 'high', 'high_land', 'incline_plane', 'top', 'diagonal']
senticnet['alto'] = ['0.034', '0', '0.01', '0.087', '#joy', '#admiration', 'positive', '0.037', 'music', 'voice', 'bell', 'singing', 'highest_pitch']
senticnet['altogether'] = ['-0.94', '0', '0.991', '-0.65', '#sadness', '#anger', 'negative', '-0.86', 'birthday_suit', 'raw', 'nakedness', 'nude', 'undress']
senticnet['altogether_incapable'] = ['-0.95', '0', '0.703', '-0.83', '#sadness', '#disgust', 'negative', '-0.83', 'raid', 'come_in', 'foray_into', 'enter', 'get_into']
senticnet['altoona'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
senticnet['altruism'] = ['-0.81', '0', '0.931', '-0.77', '#sadness', '#anger', 'negative', '-0.84', 'mutualness', 'mutuality', 'complementarity', 'correlation', 'correlativity']
senticnet['altruist'] = ['0.977', '0.764', '0', '0', '#joy', '#interest', 'positive', '0.871', 'misogyny', 'misandry', 'altruistically', 'altruistic', 'philanthropic']
senticnet['altruistic'] = ['0.075', '0.252', '-0.14', '0.186', '#interest', '#admiration', 'positive', '0.091', 'tall_play_basketball', 'niceness', 'magnanimous', 'kindness', 'type_of_bear']
senticnet['altruistic_behavior'] = ['-0.93', '0', '0.586', '-0.66', '#sadness', '#disgust', 'negative', '-0.73', 'camp', 'pack', 'clique', 'coterie', 'brain_trust']
senticnet['altruistically'] = ['0.982', '0.771', '0', '0', '#joy', '#interest', 'positive', '0.877', 'misogyny', 'misandry', 'altruist', 'altruistic', 'philanthropic']
senticnet['aluminium'] = ['0.057', '0', '0.012', '0.08', '#joy', '#admiration', 'positive', '0.042', 'rust', 'foil', 'tin', 'thin_metal', 'magnet']
senticnet['aluminium_alloy'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'gold', 'bronze', 'alloy', 'medal', 'copper']
senticnet['aluminium_chloride'] = ['0.032', '-0.02', '-0.04', '-0.08', '#fear', '#disgust', 'negative', '-0.01', 'liquid', 'carbonate_drink', 'chemical', 'soda', 'bromide']
senticnet['aluminium_foil'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'foil', 'tin_foil', 'gold_foil']
senticnet['aluminium_frame'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'carbon_fiber', 'aluminium', 'light', 'thin', 'performant']
senticnet['aluminium_hydroxide'] = ['-0.81', '0', '0.774', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'acid', 'hypophosphoric_acid', 'periodic_acid', 'hypooxyacid', 'oxyacid']
senticnet['aluminium_wire'] = ['0.085', '0', '0', '0', '#joy', '#joy', 'positive', '0.085', 'wire', 'several_types', 'gas_stove', 'tape_player', 'control_room']
senticnet['aluminum'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'gold', 'bronze', 'alloy', 'medal', 'copper']
senticnet['aluminum_alloy'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'gold', 'bronze', 'alloy', 'medal', 'copper']
senticnet['aluminum_alloy_frame'] = ['0.033', '0', '0', '0.081', '#joy', '#admiration', 'positive', '0.057', 'door', 'door_lock', 'window_door', 'wall', 'doorway']
senticnet['aluminum_foil'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'foil', 'tin_foil', 'gold_foil']
senticnet['aluminum_frame'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'performant']
senticnet['aluminum_honeycomb'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'performant']
senticnet['aluminum_plate'] = ['0.022', '0.069', '0.027', '0.059', '#interest', '#admiration', 'positive', '0.031', 'perfect_surface', 'plane', 'flat_land', 'flat', 'floor']
senticnet['aluminum_toxicity'] = ['-0.03', '0', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.06', 'lithium', 'mineral', 'potassium', 'iodine', 'magnesium']
senticnet['aluminum_wire'] = ['0.085', '0', '0', '0', '#joy', '#joy', 'positive', '0.085', 'wire', 'several_types', 'gas_stove', 'tape_player', 'control_room']
senticnet['alumnus'] = ['0.053', '-0.12', '0.015', '-0.03', '#joy', '#surprise', 'positive', '0.03', 'student_place', 'learning_place', 'school_session', 'after_school', 'professor']
senticnet['alumnus_association'] = ['0.02', '0.24', '-0.11', '0.267', '#interest', '#admiration', 'positive', '0.103', 'organize_group', 'come_together', 'bring_together', 'business_group', 'business_firm']
senticnet['alumnus_reunion'] = ['0.119', '-0.13', '-0.06', '0', '#joy', '#surprise', 'positive', '0.063', 'gala', 'celebration', 'special_event', 'special_happen', 'special_moment']
senticnet['alun_hoddinott'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'band_play', 'songwriter', 'rock_band']
senticnet['alunite'] = ['-0.03', '0', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.06', 'lithium', 'mineral', 'potassium', 'iodine', 'magnesium']
senticnet['alvaradoa'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'cabbage', 'lettuce', 'broccoli']
senticnet['alveolar_bone'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'plaque']
senticnet['alveolar_hemorrhage'] = ['0.777', '-0.89', '0', '0.508', '#joy', '#surprise', 'positive', '0.727', 'stretching', 'enlarge', 'enlargement', 'get_bigger', 'distension']
senticnet['alveolar_trill'] = ['-0.71', '0', '0.998', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'tone', 'quality', 'twang', 'nasal_twang', 'timber']
senticnet['alveolata'] = ['0.807', '-0.86', '0', '0.611', '#joy', '#surprise', 'positive', '0.762', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['alveolus'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'hip_socket', 'tooth_socket', 'socket', 'acetabulum', 'cotyloid_cavity']
senticnet['alveston'] = ['0.049', '0.16', '0', '0.155', '#interest', '#admiration', 'positive', '0.121', 'castle', 'palace', 'medieval', 'aristocrat', 'throne']
senticnet['alvis_saladin'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'tank', 'tank_destroyer', 'armored_car', 'armored_vehicle', 'armored_personnel_carrier']
senticnet['always-evolving'] = ['0', '0', '0.968', '-0.98', '#anger', '#disgust', 'negative', '-0.97', 'hydra-headed', 'all-destroying', 'supineness', 'incontrollable', 'all-devouring']
senticnet['always_read'] = ['-0.04', '0.226', '-0.14', '0.255', '#interest', '#admiration', 'positive', '0.074', 'alive', 'plot', 'personality_trait', 'animate', 'play_role']
senticnet['always_smile'] = ['0.733', '0.964', '0', '0.944', '#interest', '#admiration', 'positive', '0.88', 'positive_attitude', 'comedian', 'happy_person', 'spread_happiness', 'release_tension']
senticnet['alyngton'] = ['0', '0.918', '0', '0.958', '#interest', '#admiration', 'positive', '0.938', 'marry', 'get_married', 'love', 'join', 'hook_up_with']
senticnet['alyssa_reid'] = ['-0.73', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'unwed', 'unwedded', 'divorced', 'unmarried', 'widowed']
senticnet['alyssum'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'livelong', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['alzheimer'] = ['-0.95', '0', '0.621', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'disease', 'brain_disorder', 'brain_disease', 'ill', 'epilepsy']
senticnet['alzheimer_dementia'] = ['-0.94', '0', '0.821', '-0.75', '#sadness', '#anger', 'negative', '-0.84', 'predementedness', 'insanity', 'dementedness', 'senile_psychosis', 'alcoholic_dementia']
senticnet['alzheimer_disease'] = ['-0.95', '0', '0.621', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'disease', 'brain_disorder', 'brain_disease', 'ill', 'epilepsy']
senticnet['alzheimer_disease_ad'] = ['-0.94', '0', '0.821', '-0.75', '#sadness', '#anger', 'negative', '-0.84', 'predementedness', 'insanity', 'dementedness', 'senile_psychosis', 'alcoholic_dementia']
senticnet['alzheimer_society'] = ['-0.95', '0', '0.621', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'disease', 'brain_disorder', 'brain_disease', 'ill', 'epilepsy']
senticnet['alzheimers_disease'] = ['-0.95', '0', '0.621', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'disease', 'brain_disorder', 'brain_disease', 'ill', 'epilepsy']
senticnet['am_fm_tuner'] = ['0.036', '-0.04', '0.027', '0.063', '#surprise', '#admiration', 'positive', '0.028', 'radio', 'speaker', 'channel_selector', 'power_switch', 'female_television_connector']
senticnet['am_glad'] = ['0', '0', '0', '0.869', '#admiration', '#admiration', 'positive', '0.869', 'prosper', 'succeed', 'realize', 'fly_high', 'thrive']
senticnet['am_latex'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'wall_cover', 'two-dimensional', 'cover_wall', 'pigment', 'dye']
senticnet['am_measg'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['am_plana'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['ama_dablam'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['amadeus_cho'] = ['-0.88', '0', '0.882', '-0.58', '#sadness', '#anger', 'negative', '-0.78', 'mortal', 'behemoth', 'unusual_person', 'aberrant', 'giant']
senticnet['amador_county'] = ['-0.06', '0.21', '0', '0.076', '#interest', '#admiration', 'positive', '0.074', 'new_think', 'figure_out', 'brain_activity', 'idea', 'come_up_idea']
senticnet['amai'] = ['0.973', '-0.50', '0', '0.612', '#joy', '#admiration', 'positive', '0.698', 'chic', 'chicness', 'jauntiness', 'modishness', 'stylishness']
senticnet['amale'] = ['-0.84', '0', '0.579', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'squiggle', 'hand', 'chicken_scratch', 'handwriting', 'script']
senticnet['amalek'] = ['-0.52', '0', '0.701', '-0.50', '#sadness', '#anger', 'negative', '-0.57', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['amalgamate'] = ['0', '0', '-0.83', '-0.90', '#fear', '#disgust', 'negative', '-0.87', 'mingle', 'coalesced', 'commix', 'mix', 'unify']
senticnet['amalgamated_transit_union'] = ['-0.77', '0', '0.505', '-0.50', '#sadness', '#anger', 'negative', '-0.59', 'wildcat_strike', 'sympathy_strike', 'sympathetic_strike', 'walkout', 'job_action']
senticnet['amalgamation'] = ['0', '0.981', '0', '0.851', '#interest', '#admiration', 'positive', '0.916', 'uniting', 'fusion', 'unification', 'merger', 'deal']
senticnet['amanar'] = ['0.02', '0.205', '0', '0', '#joy', '#interest', 'positive', '0.112', 'safe', 'bank_vault', 'bank', 'piggy_bank', 'bank_account']
senticnet['amaretto'] = ['0.02', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'many_fruits', 'peach', 'buy_fruit', 'pineapple', 'tomato']
senticnet['amarg'] = ['0.024', '0.17', '0', '0.113', '#interest', '#admiration', 'positive', '0.102', 'load_dock', 'storage_place', 'lock_box', 'government_office', 'treasure_chest']
senticnet['amas_air'] = ['-0.91', '0', '0.769', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'break', 'good_luck', 'chance_eventpy_chance', 'chance_event', 'coincidence']
senticnet['amass'] = ['-0.59', '0', '0.604', '-0.95', '#anger', '#disgust', 'negative', '-0.71', 'pile_up', 'roll_up', 'collect', 'accumulate', 'run_up']
senticnet['amass_wealth'] = ['0.54', '-0.97', '0', '0.569', '#surprise', '#admiration', 'positive', '0.695', 'overstate', 'hyperbolize', 'glorify', 'exaggerate', 'overdraw']
senticnet['amassed_considerable'] = ['0.976', '0.811', '0', '0', '#joy', '#interest', 'positive', '0.893', 'deposit', 'bank_loan', 'wealth', 'finance', 'account']
senticnet['amateur'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'sciolist', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
senticnet['amateur_astronomer'] = ['-0.64', '0', '0.524', '-0.59', '#sadness', '#disgust', 'negative', '-0.58', 'bum', 'dreamer', 'daydreamer', 'escapist', 'wishful_thinker']
senticnet['amateur_athlete'] = ['0', '0', '0', '0.97', '#admiration', '#admiration', 'positive', '0.97', 'increase_strength', 'keep_fit', 'fit_person', 'exercise', 'strengthen_heart']
senticnet['amateur_athletic_association'] = ['-0.68', '0', '0.923', '-0.68', '#sadness', '#anger', 'negative', '-0.76', 'negative', 'veto', 'refute', 'rebut', 'controvert']
senticnet['amateur_career'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'sciolist', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
senticnet['amateur_champion'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'sciolist', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
senticnet['amateur_championship'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'sciolist', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
senticnet['amateur_cup'] = ['0.093', '-0.02', '0', '0.056', '#joy', '#admiration', 'positive', '0.057', 'pitcher', 'carafe', 'container_pitcher', 'mug', 'glass_of_water']
senticnet['amateur_football'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'sciolist', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
senticnet['amateur_football_club'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'sciolist', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
senticnet['amateur_golf'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'sciolist', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
senticnet['amateur_golf_tournament'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'sciolist', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
senticnet['amateur_golfer'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'sciolist', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
senticnet['amateur_musician'] = ['0.592', '-0.93', '0', '0.935', '#surprise', '#admiration', 'positive', '0.819', 'child', 'small_fry', 'tyke', 'tike', 'kid']
senticnet['amateurish'] = ['-0.88', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.89', 'disrespectful', 'unethical', 'dishonest', 'unprofessional', 'rude']
senticnet['amateurishness'] = ['0.983', '-0.63', '0', '0.697', '#joy', '#admiration', 'positive', '0.772', 'unskillfulness', 'inability', 'ineptness', 'clumsiness', 'awkwardness']
senticnet['amative'] = ['0.531', '0.673', '-0.27', '0.275', '#joy', '#interest', 'positive', '0.301', 'amorous', 'amorousness', 'amatory', 'enamoredness', 'puppy_love']
senticnet['amativeness'] = ['0.982', '-0.72', '0', '0.615', '#joy', '#surprise', 'positive', '0.772', 'sexual_desire', 'concupiscence', 'physical_attraction', 'prurience', 'pruriency']
senticnet['amatory'] = ['0.531', '0.673', '-0.27', '0.275', '#joy', '#interest', 'positive', '0.301', 'amorous', 'amorousness', 'enamoredness', 'amative', 'puppy_love']
senticnet['amavasya'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['amaze'] = ['0', '-0.73', '0', '0', '#surprise', '#surprise', 'positive', '0.735', 'fantastic', 'grand', 'pose', 'marvel', 'wonderful']
senticnet['amaze_friend'] = ['-0.05', '-0.19', '-0.17', '0.219', '#surprise', '#admiration', 'positive', '0.046', 'amuse_child', 'entertain_child', 'add_two_numbers', 'delight_child', 'make_child_laugh']
senticnet['amazed'] = ['0.043', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'astonishment', 'astonished', 'surprise', 'astound', 'astonish']
senticnet['amazement'] = ['0', '-1', '0', '0', '#surprise', '#surprise', 'positive', '1', 'amazed', 'shock', 'astonished', 'mind-blowing', 'speechless']
senticnet['amazing'] = ['0.045', '-0.66', '0', '0.363', '#surprise', '#admiration', 'positive', '0.357', 'astounded', 'astonished', 'astound', 'surprise', 'amazed']
senticnet['amazing_feat'] = ['0.749', '0', '0', '0.952', '#joy', '#admiration', 'positive', '0.851', 'high_class', 'citation', 'suspicion', 'goodness', 'prosperity']
senticnet['amazingly'] = ['0.045', '-0.66', '0', '0.363', '#surprise', '#admiration', 'positive', '0.357', 'astonishingly', 'surprised', 'surprising', 'surprisedly', 'surprisingly']
senticnet['amazo'] = ['-0.58', '0', '0.661', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'explosive', 'cordite', 'dynamite', 'plastic_explosive', 'explosive_compound']
senticnet['amazon'] = ['-0.68', '0', '0.947', '-0.83', '#anger', '#disgust', 'negative', '-0.82', 'popinjay', 'poll', 'bird', 'lovebird', 'parrot']
senticnet['amazon_deforestation'] = ['0', '-0.02', '0.085', '-0.09', '#anger', '#disgust', 'negative', '-0.05', 'tree_wood', 'forest', 'sideways', 'dead_tree', 'wood']
senticnet['amazon_forest'] = ['0', '-0.02', '0.085', '-0.09', '#anger', '#disgust', 'negative', '-0.05', 'tree_wood', 'forest', 'sideways', 'dead_tree', 'wood']
senticnet['amazon_marketplace'] = ['-0.03', '0.139', '0', '0.109', '#interest', '#admiration', 'positive', '0.07', 'exchange_money', 'purchase', 'sell_goods', 'make_rich', 'incur_debt']
senticnet['amazon_rain_forest'] = ['0', '-0.02', '0.085', '-0.09', '#anger', '#disgust', 'negative', '-0.05', 'tree_wood', 'forest', 'sideways', 'dead_tree', 'wood']
senticnet['amazonia'] = ['0', '-0.02', '0.085', '-0.09', '#anger', '#disgust', 'negative', '-0.05', 'tree_wood', 'forest', 'sideways', 'dead_tree', 'wood']
senticnet['amazonian'] = ['-0.76', '0', '0.97', '0', '#sadness', '#anger', 'negative', '-0.86', 'warlike', 'hawkish', 'martial', 'bellicose', 'belligerent']
senticnet['ambasjoyfulor'] = ['0', '0.85', '0', '0.879', '#interest', '#admiration', 'positive', '0.865', 'management', 'policeman', 'authority', 'duty', 'construction_worker']
senticnet['ambassador'] = ['0.029', '0.157', '-0.07', '0.12', '#interest', '#admiration', 'positive', '0.058', 'supervisor', 'management', 'master', 'president', 'despot']
senticnet['ambiance'] = ['0.733', '0', '0', '0.801', '#joy', '#admiration', 'positive', '0.767', 'serenity', 'recreate', 'peace_of_mind', 'ease', 'serene']
senticnet['ambidextrous'] = ['0', '0', '-0.74', '-0.93', '#fear', '#disgust', 'negative', '-0.83', 'duplicitous', 'deceitful', 'double-dealing', 'janus-faced', 'two-handed']
senticnet['ambience'] = ['-0.05', '0.096', '0', '0.066', '#interest', '#admiration', 'positive', '0.037', 'hue_tint', 'spectrum', 'red_blue', 'green_yellow', 'tint']
senticnet['ambient_air'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'radioactive_dust', 'dust', 'dust_matter']
senticnet['ambient_air_pressure'] = ['-0.12', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.09', 'breeze', 'move_air', 'air_movement', 'gape', 'force']
senticnet['ambient_humidity'] = ['0', '-0.03', '0.05', '-0.14', '#anger', '#disgust', 'negative', '-0.05', 'humidity', 'wetness', 'damp', 'humid', 'dank']
senticnet['ambient_illumination'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'incandescence', 'luminescence', 'brightness_level']
senticnet['ambient_light'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'incandescence', 'luminescence', 'brightness_level']
senticnet['ambient_lighting'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'incandescence', 'luminescence', 'brightness_level']
senticnet['ambient_moisture'] = ['0', '-0.03', '0.05', '-0.14', '#anger', '#disgust', 'negative', '-0.05', 'humidity', 'wetness', 'damp', 'humid', 'dank']
senticnet['ambient_music'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'drink', 'party', 'friend_gathering', 'rest_area', 'relax_time']
senticnet['ambient_noise'] = ['-0.95', '0', '-0.73', '-0.73', '#sadness', '#fear', 'negative', '-0.80', 'decibel', 'noise', 'loud_soft', 'sound', 'audio']
senticnet['ambient_pressure'] = ['0.676', '0.63', '-0.23', '0.31', '#joy', '#interest', 'positive', '0.345', 'perkiness', 'walk_on_air', 'exultingly', 'exultantly', 'jubilance']
senticnet['ambigram'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'foot_motion', 'foot_action', 'foot_movement', 'stride', 'pace']
senticnet['ambiguity'] = ['0', '0', '0.749', '-0.80', '#anger', '#disgust', 'negative', '-0.77', 'amphibology', 'amphiboly', 'parisology', 'ambivalence', 'equivocation']
senticnet['ambiguous'] = ['-0.79', '0', '0', '-0.91', '#sadness', '#disgust', 'negative', '-0.85', 'ambiguity', 'homonym', 'confusing', 'interpretation', 'ambiguously']
senticnet['ambiguous_sexuality'] = ['0.947', '-0.99', '0', '0.936', '#joy', '#surprise', 'positive', '0.958', 'utterer', 'deipnosophist', 'interlocutor', 'conversational_partner', 'speaker']
senticnet['ambiguously'] = ['-0.79', '0', '0', '-0.91', '#sadness', '#disgust', 'negative', '-0.85', 'ambiguity', 'homonym', 'confusing', 'interpretation', 'ambiguous']
senticnet['ambition'] = ['0.053', '0.207', '-0.07', '0.165', '#interest', '#admiration', 'positive', '0.088', 'motivation', 'go_job', 'reinforce', 'resume', 'effort']
senticnet['ambitious'] = ['0.913', '-0.83', '0', '0.739', '#joy', '#surprise', 'positive', '0.828', 'pushful', 'pushy', 'aspirant', 'aspiring', 'wishful']
senticnet['ambivalence'] = ['0', '0', '0.748', '-0.81', '#anger', '#disgust', 'negative', '-0.78', 'amphibology', 'amphiboly', 'parisology', 'ambiguity', 'equivocation']
senticnet['ambivalence_toward'] = ['-0.10', '0.176', '0.629', '-0.64', '#anger', '#disgust', 'negative', '-0.3', 'alienated', 'alienation', 'disaffection', 'estrange', 'alienate']
senticnet['ambivalent'] = ['-0.97', '0', '0.712', '-0.78', '#sadness', '#disgust', 'negative', '-0.82', 'discrepant', 'inconsistent', 'scratchy', 'uneven', 'contradictory']
senticnet['ambiversion'] = ['-0.64', '0', '0.574', '-0.89', '#sadness', '#disgust', 'negative', '-0.70', 'gregariousness', 'sociability', 'openness', 'nakedness', 'extraversion']
senticnet['amble'] = ['-0.83', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.77', 'saunter', 'mosey', 'traipse', 'moseyed', 'ambled']
senticnet['amble_along'] = ['0.876', '0.883', '0', '0', '#joy', '#interest', 'positive', '0.879', 'walk_along', 'gait', 'take_step', 'run_slowly', 'locomotion']
senticnet['amble_through'] = ['-0.77', '0', '0.855', '-0.57', '#sadness', '#anger', 'negative', '-0.73', 'unhurried', 'easygoing', 'deliberate', 'easy', 'careful']
senticnet['ambled'] = ['-0.83', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.77', 'saunter', 'amble', 'traipse', 'moseyed', 'mosey']
senticnet['ambling_through'] = ['0.047', '-0.01', '0.033', '0.071', '#joy', '#admiration', 'positive', '0.024', 'get_place', 'dawdle_around', 'get_around_town', 'go_to_place', 'stroll']
senticnet['amblyomma_americanum'] = ['0.579', '-0.81', '0', '0.833', '#surprise', '#admiration', 'positive', '0.741', 'typhus', 'rat_typhus', 'urban_typhus', 'endemic_typhus', 'rickettsial_disease']
senticnet['amblyopia'] = ['-0.88', '0', '0.762', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'divergent_strabismus', 'exotropia', 'walleye', 'abnormality', 'abnormalcy']
senticnet['amblyopia_lazy_eye'] = ['-0.88', '0', '0.762', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'divergent_strabismus', 'exotropia', 'walleye', 'abnormality', 'abnormalcy']
senticnet['ambracia'] = ['0.8', '-0.79', '0', '0.751', '#joy', '#surprise', 'positive', '0.782', 'elder_statesman', 'stateswoman', 'politician', 'politico', 'pol']
senticnet['ambrosia'] = ['-0.55', '0', '0.5', '-0.55', '#sadness', '#disgust', 'negative', '-0.53', 'remedy', 'cure', 'catholicon', 'therapeutic', 'curative']
senticnet['ambrosial'] = ['0.933', '-0.87', '0', '0.766', '#joy', '#surprise', 'positive', '0.858', 'scent', 'aroma', 'olfactory_property', 'smell', 'odor']
senticnet['ambu_bag'] = ['-0.57', '0', '0.737', '-0.85', '#anger', '#disgust', 'negative', '-0.72', 'emergency_procedure', 'emergency', 'external_respiration', 'respiration', 'ventilation']
senticnet['ambulance'] = ['0', '0', '-0.13', '-0.14', '#fear', '#disgust', 'negative', '-0.14', 'dentist_office', 'hospital', 'doctor_office', 'emergency_ward', 'prosthesis']
senticnet['ambulance_ride'] = ['0', '0', '-0.73', '0', '#fear', '#fear', 'negative', '-0.73', 'clinic', 'emergency_ward', 'hospital', 'dentist_office', 'medical_office']
senticnet['ambulance_service'] = ['0', '0', '-0.82', '0', '#fear', '#fear', 'negative', '-0.82', 'sickness', 'polio', 'malady', 'ailment', 'treatment']
senticnet['ambulant_plague'] = ['-0.86', '0', '0.825', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'pestis', 'plague', 'pestilence', 'pest', 'bubonic_plague']
senticnet['ambulation'] = ['0.84', '0.76', '0', '0.894', '#joy', '#admiration', 'positive', '0.831', 'keep_skin_healthy', 'stay_healthy', 'stay_fit', 'maintain_good_health', 'eat_healthily']
senticnet['ambulatory'] = ['-0.96', '0', '0.742', '0', '#sadness', '#anger', 'negative', '-0.85', 'inpatient', 'out-patient', 'in-patient', 'outpatient', 'psychiatric']
senticnet['ambuscade'] = ['0', '0', '-0.90', '-0.87', '#fear', '#disgust', 'negative', '-0.88', 'waylay', 'lurk', 'ambush', 'scupper', 'bushwhack']
senticnet['ambush'] = ['0.02', '0.162', '0', '0.062', '#interest', '#admiration', 'positive', '0.081', 'catch', 'trap', 'bait_trap', 'lure', 'decoy']
senticnet['ambush_predator'] = ['0.812', '0.872', '0', '0', '#joy', '#interest', 'positive', '0.842', 'insect', 'fly_away', 'small_insect', 'maggot', 'chihuahua']
senticnet['ambush_prey'] = ['0.812', '0.872', '0', '0', '#joy', '#interest', 'positive', '0.842', 'insect', 'fly_away', 'small_insect', 'maggot', 'chihuahua']
senticnet['amc_gremlin'] = ['-0.73', '0', '0.72', '-0.93', '#sadness', '#disgust', 'negative', '-0.79', 'imp', 'elf', 'hob', 'leprechaun', 'pixie']
senticnet['amc_hornet'] = ['-0.73', '0', '0.72', '-0.93', '#sadness', '#disgust', 'negative', '-0.79', 'imp', 'elf', 'hob', 'leprechaun', 'pixie']
senticnet['amc_pacer'] = ['-0.73', '0', '0.72', '-0.93', '#sadness', '#disgust', 'negative', '-0.79', 'imp', 'elf', 'hob', 'leprechaun', 'pixie']
senticnet['amc_theater'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'theater', 'drive-in_movie', 'watch_movie', 'cinema', 'movie']
senticnet['ame_church'] = ['0.081', '0.146', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'cult', 'sect', 'conservative', 'worshiper', 'pastor']
senticnet['amebic_dysentery'] = ['-0.89', '0', '0.702', '-0.67', '#sadness', '#anger', 'negative', '-0.75', 'dysentery', 'amoebic_dysentery', 'shigellosis', 'bacillary_dysentery', 'infectious_disease']
senticnet['amegilla'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'hornet', 'vespid', 'vespid_wasp']
senticnet['ameiva'] = ['-0.03', '-0.01', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'shit', 'wildflower', 'poop', 'wild_flower', 'dog_poop']
senticnet['amelanchier_alnifolia'] = ['-0.79', '0', '0.906', '-0.67', '#sadness', '#anger', 'negative', '-0.79', 'water_dropwort', 'hemlock_water_dropwort', 'water_hemlock', 'plant_life', 'poisonous_plant']
senticnet['amelia'] = ['-0.57', '0', '0.888', '-0.88', '#anger', '#disgust', 'negative', '-0.78', 'birth_defect', 'congenital_anomaly', 'congenital_defect', 'congenital_disorder', 'congenital_abnormality']
senticnet['ameliorate'] = ['0.911', '0', '0', '0.779', '#joy', '#admiration', 'positive', '0.845', 'mend', 'compose', 'renew', 'alleviate', 'treat']
senticnet['ameliorating'] = ['0.618', '-0.60', '0', '0.747', '#joy', '#admiration', 'positive', '0.656', 'corrective', 'remedial', 'bettering', 'ameliorative', 'amelioratory']
senticnet['ameliorative'] = ['0.618', '-0.60', '0', '0.747', '#joy', '#admiration', 'positive', '0.656', 'corrective', 'remedial', 'ameliorating', 'bettering', 'amelioratory']
senticnet['amelioratory'] = ['0.618', '-0.60', '0', '0.747', '#joy', '#admiration', 'positive', '0.656', 'corrective', 'remedial', 'ameliorating', 'ameliorative', 'bettering']
senticnet['amen'] = ['0', '0.738', '0', '0.804', '#interest', '#admiration', 'positive', '0.771', 'indeed', 'frank', 'truly', 'faithfully', 'accurately']
senticnet['amenability'] = ['0.637', '-0.91', '0', '0.757', '#surprise', '#admiration', 'positive', '0.768', 'manageability', 'manageableness', 'docility', 'obedience', 'tractability']
senticnet['amenable'] = ['0.933', '-0.75', '0', '0.89', '#joy', '#admiration', 'positive', '0.859', 'susceptible', 'tractable', 'pliable', 'capable', 'vulnerable']
senticnet['amend'] = ['0.881', '-0.86', '0', '0.688', '#joy', '#surprise', 'positive', '0.813', 'better', 'improve', 'make_look_better', 'meliorate', 'enrich']
senticnet['amendable'] = ['0.779', '-0.88', '0', '0.521', '#joy', '#surprise', 'positive', '0.727', 'corrigible', 'correctable', 'improvable', 'reformable', 'redeemable']
senticnet['amended'] = ['-0.95', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.83', 'irreparable', 'cured', 'irretrievable', 'repaired', 'rectified']
senticnet['amended_birth_certificate'] = ['0.931', '-0.52', '0', '0.67', '#joy', '#admiration', 'positive', '0.709', 'document', 'certificate_of_incorporation', 'credential', 'diploma', 'sheepskin']
senticnet['amending_formula'] = ['0.779', '-0.88', '0', '0.521', '#joy', '#surprise', 'positive', '0.727', 'corrigible', 'correctable', 'improvable', 'reformable', 'redeemable']
senticnet['amending_legislation'] = ['0.779', '-0.88', '0', '0.521', '#joy', '#surprise', 'positive', '0.727', 'corrigible', 'correctable', 'improvable', 'reformable', 'redeemable']
senticnet['amendment'] = ['0', '0.173', '-0.06', '0.126', '#interest', '#admiration', 'positive', '0.077', 'democracy', 'empire', 'realm', 'unite', 'international']
senticnet['amendment_en_bloc'] = ['0.779', '-0.88', '0', '0.521', '#joy', '#surprise', 'positive', '0.727', 'corrigible', 'correctable', 'improvable', 'reformable', 'redeemable']
senticnet['amends'] = ['-0.75', '0', '0.805', '-0.70', '#sadness', '#anger', 'negative', '-0.75', 'atonement', 'expiation', 'punitive_damage', 'exemplary_damage', 'compensation']
senticnet['amenorrhea'] = ['-0.73', '0', '0.555', '-0.90', '#sadness', '#disgust', 'negative', '-0.73', 'aching', 'hurting', 'labor_pain', 'neuralgia', 'neuralgy']
senticnet['amenorrhoea'] = ['-0.73', '0', '0.555', '-0.90', '#sadness', '#disgust', 'negative', '-0.73', 'aching', 'hurting', 'labor_pain', 'neuralgia', 'neuralgy']
senticnet['amentia'] = ['-0.60', '0', '0.827', '-0.76', '#anger', '#disgust', 'negative', '-0.73', 'slowness', 'mental_retardation', 'backwardness', 'subnormality', 'retardation']
senticnet['america'] = ['0.838', '0.707', '0', '0.904', '#joy', '#admiration', 'positive', '0.816', 'natural_order', 'universe', 'existence', 'creation', 'cosmos']
senticnet['america_alba'] = ['0', '0', '0', '0.119', '#admiration', '#admiration', 'positive', '0.119', 'planet', 'earth', 'spherical', 'planet_solar_system', 'terrain']
senticnet['america_heartland'] = ['-0.55', '0', '0.912', '-0.65', '#anger', '#disgust', 'negative', '-0.70', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['america_hosa'] = ['0', '0.112', '-0.04', '0.099', '#interest', '#admiration', 'positive', '0.054', 'profession', 'career', 'vocation', 'important_job', 'laborer']
senticnet['american_alligator'] = ['-0.85', '0', '0.685', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'allicrocodilian', 'crocodile', 'false_gavial', 'crocodilian', 'caiman']
senticnet['american_archivist_saa'] = ['0.02', '0.24', '-0.11', '0.267', '#interest', '#admiration', 'positive', '0.103', 'organize_group', 'come_together', 'bring_together', 'business_group', 'business_firm']
senticnet['american_axle'] = ['-0.77', '0', '0.505', '-0.50', '#sadness', '#anger', 'negative', '-0.59', 'wildcat_strike', 'sympathy_strike', 'sympathetic_strike', 'walkout', 'job_action']
senticnet['american_ballet_theater'] = ['0.12', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.09', 'ballroom', 'perform_art', 'nightclub', 'ballerina', 'venue']
senticnet['american_ballet_theatre'] = ['0.12', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.09', 'ballroom', 'perform_art', 'nightclub', 'ballerina', 'venue']
senticnet['american_bmxer'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'wildcat', 'informal', 'loose', 'drumhead']
senticnet['american_businessman'] = ['0', '0', '-0.99', '-0.73', '#fear', '#disgust', 'negative', '-0.86', 'king', 'baron', 'business_leader', 'mogul', 'big_businessman']
senticnet['american_chicle'] = ['0.965', '0.775', '0', '0.968', '#joy', '#admiration', 'positive', '0.903', 'snot', 'bad_taste', 'mouth_part', 'beautiful_smile', 'near_mouth']
senticnet['american_cigar_slang'] = ['0', '0.721', '0', '0.731', '#interest', '#admiration', 'positive', '0.726', 'dimension', 'measure', 'tackle', 'length', 'measurement']
senticnet['american_civil_war'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'dictatorship', 'dictator', 'colony', 'world_domination', 'oppression']
senticnet['american_coin'] = ['0.799', '0.84', '0', '0', '#joy', '#interest', 'positive', '0.82', 'small_coin', 'monetary_unit', 'currency_unit', 'form_of_money', 'small_currency']
senticnet['american_exceptionalism'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'motherland', 'homeland']
senticnet['american_express'] = ['0.74', '0.973', '0', '0', '#joy', '#interest', 'positive', '0.857', 'credit_card', 'profit', 'mastercard', 'audit', 'back_to_black']
senticnet['american_flag'] = ['0.076', '0.067', '0', '0.056', '#joy', '#interest', 'positive', '0.066', 'rubble', 'ground', 'ground_material', 'abrasive', 'asphalt']
senticnet['american_folk_singer'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
senticnet['american_folklife'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'institution', 'tundra', 'child_story', 'natural_history_museum', 'cartoon']
senticnet['american_foreign_policy'] = ['0', '0.098', '0', '0.08', '#interest', '#admiration', 'positive', '0.089', 'talk_person', 'wisdom', 'tact', 'statecraft', 'greet_person']
senticnet['american_forest'] = ['0.03', '0.068', '-0.06', '0.062', '#interest', '#admiration', 'positive', '0.025', 'pacific_northwest', 'thermal', 'treetop', 'animal', 'zoo']
senticnet['american_glam_metal'] = ['0.542', '-0.83', '0', '0.927', '#surprise', '#admiration', 'positive', '0.769', 'incest', 'extramarital_sex', 'free_love', 'sexual_intercourse', 'intercourse']
senticnet['american_gospel_singer'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
senticnet['american_hegemony'] = ['0.828', '-0.67', '0', '0.811', '#joy', '#admiration', 'positive', '0.771', 'foolhardiness', 'brashness', 'thoughtlessness', 'desperation', 'unthoughtfulness']
senticnet['american_history_nmah'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'institution', 'tundra', 'child_story', 'natural_history_museum', 'cartoon']
senticnet['american_horror_film'] = ['-0.79', '0', '0.885', '-0.76', '#sadness', '#anger', 'negative', '-0.81', 'infernal', 'mortal', 'dead_soul', 'dead_soule', 'living_dead']
senticnet['american_horticultural_society'] = ['0.427', '0.227', '0', '0.37', '#joy', '#admiration', 'positive', '0.341', 'taken_seriously', 'complement', 'much_need', 'remission', 'knowledgeable']
senticnet['american_ice_dancer'] = ['0.841', '0.721', '0', '0.74', '#joy', '#admiration', 'positive', '0.767', 'good_manner', 'two_people', 'love', 'guardian', 'guy']
senticnet['american_idol'] = ['0.761', '0.566', '-0.21', '0.293', '#joy', '#interest', 'positive', '0.352', 'gratify', 'jubilation', 'gloat', 'gloating', 'gloatingly']
senticnet['american_imperialism'] = ['0.828', '-0.67', '0', '0.811', '#joy', '#admiration', 'positive', '0.771', 'foolhardiness', 'brashness', 'thoughtlessness', 'desperation', 'unthoughtfulness']
senticnet['american_indian_nmai'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'institution', 'tundra', 'child_story', 'natural_history_museum', 'cartoon']
senticnet['american_indian_tribe'] = ['0.989', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.97', 'committee', 'council', 'caste', 'association', 'organization']
senticnet['american_kestrel'] = ['-0.74', '0', '0.963', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['american_money'] = ['0', '0.112', '0.071', '0.089', '#interest', '#admiration', 'positive', '0.043', 'currency_unit', 'american_coin', 'money_unit', 'form_of_money', 'dollar_change']
senticnet['american_musical_theater'] = ['0.022', '0', '0', '0.109', '#joy', '#admiration', 'positive', '0.066', 'instrumental', 'singer', 'hip_hop', 'melodic_noise', 'music_player']
senticnet['american_pair_skater'] = ['0.841', '0.721', '0', '0.74', '#joy', '#admiration', 'positive', '0.767', 'good_manner', 'two_people', 'love', 'guardian', 'guy']
senticnet['american_politician'] = ['0', '0.868', '0', '0.853', '#interest', '#admiration', 'positive', '0.861', 'country_leader', 'prime_minister', 'president', 'elect_official', 'leader']
senticnet['american_polo_player'] = ['0.941', '-0.83', '0', '0.973', '#joy', '#admiration', 'positive', '0.917', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['american_privateer'] = ['0', '0', '0.08', '0', '#anger', '#anger', 'negative', '-0.08', 'tree_animal', 'small_furry', 'rodent', 'big_mouse', 'small_rodent']
senticnet['american_psyche'] = ['0', '0.827', '0', '0.846', '#interest', '#admiration', 'positive', '0.837', 'inexterminable', 'inextirpable', 'inexpungible', 'inexpungeable', 'ineradicable']
senticnet['american_rapper'] = ['0', '0.712', '0', '0.844', '#interest', '#admiration', 'positive', '0.778', 'dollar_change', 'small_currency', 'currency_unit', 'metal_money', 'american_money']
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senticnet['american_songbag'] = ['0', '0.176', '0', '0.216', '#interest', '#admiration', 'positive', '0.196', 'choir_stall', 'worshiper', 'pew', 'fundamentalist', 'pulpit']
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senticnet['american_soul_singer'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
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senticnet['americorps_education'] = ['0', '0.893', '0', '0', '#interest', '#interest', 'positive', '0.893', 'salary', 'wage', 'send_money', 'earnings', 'pay']
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senticnet['amiably'] = ['0', '0.764', '0', '0.845', '#interest', '#admiration', 'positive', '0.804', 'empathetically', 'empathic', 'empathetic', 'kind-heartedness', 'kindheartedness']
senticnet['amicability'] = ['0.608', '0.647', '-0.26', '0.325', '#joy', '#interest', 'positive', '0.328', 'amicably', 'amicable', 'amicableness', 'brotherly', 'friendly']
senticnet['amicable'] = ['0.608', '0.647', '-0.26', '0.325', '#joy', '#interest', 'positive', '0.328', 'amicably', 'amicableness', 'amicability', 'brotherly', 'friendly']
senticnet['amicableness'] = ['0.608', '0.647', '-0.26', '0.325', '#joy', '#interest', 'positive', '0.328', 'amicably', 'amicable', 'amicability', 'brotherly', 'friendly']
senticnet['amicably'] = ['0.608', '0.647', '-0.26', '0.325', '#joy', '#interest', 'positive', '0.328', 'amicable', 'amicableness', 'amicability', 'brotherly', 'friendly']
senticnet['amice'] = ['0.849', '-0.50', '0', '0.845', '#joy', '#admiration', 'positive', '0.732', 'robe', 'gown', 'veil', 'vest', 'cassock']
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senticnet['amide'] = ['0.775', '0.759', '0', '0', '#joy', '#interest', 'positive', '0.767', 'endorphin', 'fibrinopeptide', 'polypeptide', 'peptide', 'protein']
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senticnet['amidst'] = ['0', '0', '-0.70', '-0.95', '#fear', '#disgust', 'negative', '-0.82', 'unceasing', 'never-ending', 'constant', 'ceaseless', 'perpetual']
senticnet['aminoaciduria'] = ['-0.60', '0', '0.544', '-0.53', '#sadness', '#anger', 'negative', '-0.56', 'acute_renal_failure', 'acute_kidney_failure', 'chronic_renal_failure', 'chronic_kidney_failure', 'kidney_failure']
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senticnet['amitabha'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['amitabha_buddha'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['amitbha'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['amite'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
senticnet['amitriptyline_elavil'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['amlodipine'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'spironolactone', 'spironolactone_drug']
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senticnet['ammon_hennacy'] = ['-0.97', '0', '0.62', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'obstructor', 'obstructer', 'technophobe', 'obstructionist', 'naysayer']
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senticnet['ammonification'] = ['0.789', '-0.92', '0', '0.964', '#surprise', '#admiration', 'positive', '0.893', 'permeation', 'carbonation', 'impregnation', 'saturation', 'diffusion']
senticnet['ammonium'] = ['-0.79', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.89', 'carbonate', 'phosphate', 'phosphate', 'calcium', 'sulfate']
senticnet['ammonium_bicarbonate'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
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senticnet['ammonium_salt'] = ['0.766', '-0.87', '0', '0.666', '#joy', '#surprise', 'positive', '0.769', 'reactive', 'unstable', 'labile', 'activated', 'excited']
senticnet['ammonoid_cephalopod'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'gymnosperm', 'pteridosperm']
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senticnet['ammunition'] = ['0', '0', '-0.92', '-0.91', '#fear', '#disgust', 'negative', '-0.92', 'automatic', 'firearm', 'gun', 'weapon', 'projectile']
senticnet['ammunition_storage'] = ['0', '0', '-0.92', '-0.91', '#fear', '#disgust', 'negative', '-0.92', 'automatic', 'firearm', 'gun', 'weapon', 'projectile']
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senticnet['amnestic'] = ['-0.58', '0', '0.722', '-0.51', '#sadness', '#anger', 'negative', '-0.60', 'dissociative_disorder', 'depersonalization_neurosis', 'depersonalization', 'mental_disorder', 'mental_disturbance']
senticnet['amnesty'] = ['0.73', '-0.56', '0', '0.621', '#joy', '#admiration', 'positive', '0.64', 'exemption', 'unsusceptibility', 'diplomatic_immunity', 'indemnity', 'impunity']
senticnet['amnh'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'institution', 'tundra', 'child_story', 'natural_history_museum', 'cartoon']
senticnet['amniotic'] = ['0.922', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.957', 'placenta', 'placental', 'fetal', 'fetus', 'umbilical']
senticnet['amniotic_sac'] = ['0.706', '0', '0', '0', '#joy', '#joy', 'positive', '0.706', 'birth', 'new_baby', 'born', 'birthday', 'thanksgiving']
senticnet['amoeba'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['amoebic'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['amoebic_dysentery'] = ['-0.89', '0', '0.702', '-0.67', '#sadness', '#anger', 'negative', '-0.75', 'amebic_dysentery', 'dysentery', 'shigellosis', 'bacillary_dysentery', 'infectious_disease']
senticnet['amoeboid'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'heron', 'single_celled']
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senticnet['amorous'] = ['0.531', '0.673', '-0.27', '0.275', '#joy', '#interest', 'positive', '0.301', 'amorousness', 'amatory', 'enamoredness', 'amative', 'puppy_love']
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senticnet['amount'] = ['0', '0.236', '0.031', '0.158', '#interest', '#admiration', 'positive', '0.121', 'quantity', 'rate', 'price', 'aggregate', 'amount_charge']
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senticnet['amount_due'] = ['-0.04', '0.268', '0', '0.158', '#interest', '#admiration', 'positive', '0.126', 'debt', 'payment_amount', 'amount_pay', 'value_amount', 'amount_money']
senticnet['amount_equal'] = ['0', '0.992', '0', '0.703', '#interest', '#admiration', 'positive', '0.847', 'award', 'contribute', 'project', 'student', 'fund']
senticnet['amount_money'] = ['-0.02', '0.254', '0', '0.147', '#interest', '#admiration', 'positive', '0.125', 'value', 'cost', 'worth', 'sticker', 'amount_due']
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senticnet['amount_paid'] = ['-0.89', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.80', 'account_indebtedness', 'indebtedness', 'limited_liability', 'debt', 'arrears']
senticnet['amount_pay'] = ['-0.04', '0.263', '0', '0.159', '#interest', '#admiration', 'positive', '0.127', 'debt', 'money', 'cost', 'value', 'tag']
senticnet['amount_payable'] = ['-0.04', '0.268', '0', '0.159', '#interest', '#admiration', 'positive', '0.127', 'total', 'amount', 'a_lot', 'aggregate', 'bill']
senticnet['amount_water'] = ['0.988', '0.898', '0', '0', '#joy', '#interest', 'positive', '0.943', 'liquid_unit', 'liquid_measure', 'small_size', 'liquid_measurement', 'small_amount']
senticnet['amount_waterment'] = ['0.824', '0.719', '0', '0', '#joy', '#interest', 'positive', '0.771', 'liquid_unit', 'amount_water', 'small_size', 'liquid_measure', 'small_amount']
senticnet['amounts_payable'] = ['-0.89', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.80', 'account_indebtedness', 'indebtedness', 'limited_liability', 'debt', 'arrears']
senticnet['amour'] = ['0.982', '0.851', '0', '0.902', '#joy', '#admiration', 'positive', '0.912', 'passion', 'beloved', 'human_emotion', 'wonderful_feeling', 'pleasant_emotion']
senticnet['amour_propre'] = ['0.469', '0.661', '-0.24', '0.23', '#joy', '#interest', 'positive', '0.278', 'self-love', 'conceit', 'self-pride', 'egotism', 'self-importance']
senticnet['amoureux'] = ['-0.85', '0', '0.968', '-0.89', '#anger', '#disgust', 'negative', '-0.90', 'family', 'household', 'house', 'home', 'broken_home']
senticnet['amp_hour'] = ['0', '0', '-1', '-1', '#fear', '#disgust', 'negative', '-1.0', 'moon_gravity', 'force', 'coercion', 'watt', 'power_source']
senticnet['amped_up'] = ['-0.89', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.85', 'high-strung', 'uptight', 'restive', 'overstrung', 'nervy']
senticnet['ampeg'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'lever']
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senticnet['ampere'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'electricity', 'electrical_unit', 'voltage', 'relevant', 'contemporary']
senticnet['amphiapteral'] = ['-0.66', '0', '0.877', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'amphistylar', 'amphiprostylar', 'prostyle', 'porticoed', 'apteral']
senticnet['amphibian'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'frog', 'aquatic']
senticnet['amphibious_assault'] = ['-0.07', '0.069', '-0.18', '0', '#sadness', '#fear', 'negative', '-0.06', 'medical_procedure', 'cure', 'doctor_surgery', 'treatment', 'doctor_work']
senticnet['amphibious_caterpillar'] = ['0.967', '-0.98', '0', '0.64', '#joy', '#surprise', 'positive', '0.863', 'club', 'social_club', 'society', 'jockey_club', 'gild']
senticnet['amphibious_operation'] = ['-0.07', '0.069', '-0.18', '0', '#sadness', '#fear', 'negative', '-0.06', 'medical_procedure', 'cure', 'doctor_surgery', 'treatment', 'doctor_work']
senticnet['amphibology'] = ['0.633', '-0.67', '0', '0.776', '#surprise', '#admiration', 'positive', '0.693', 'double_entendre', 'loophole', 'ambiguity', 'amphiboly', 'parisology']
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senticnet['ancient_artifact'] = ['0.086', '-0.10', '0.064', '0', '#joy', '#surprise', 'positive', '0.043', 'art_piece', 'lifetime', 'museum', 'vintage', 'collectible']
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senticnet['android_gingerbread'] = ['0.027', '-0.09', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'cookie', 'pudding', 'custard', 'candy', 'muffin']
senticnet['android_honeycomb'] = ['0.027', '-0.09', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'cookie', 'pudding', 'custard', 'candy', 'muffin']
senticnet['android_ice_cream'] = ['0.027', '-0.09', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'cookie', 'pudding', 'custard', 'candy', 'muffin']
senticnet['android_jelly_bean'] = ['0.027', '-0.09', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'cookie', 'pudding', 'custard', 'candy', 'muffin']
senticnet['android_kitkat'] = ['0.027', '-0.09', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'cookie', 'pudding', 'custard', 'candy', 'muffin']
senticnet['android_lollipop'] = ['0.027', '-0.09', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'cookie', 'pudding', 'custard', 'candy', 'muffin']
senticnet['android_marshmallow'] = ['0.027', '-0.09', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'cookie', 'pudding', 'custard', 'candy', 'muffin']
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senticnet['anencephaly'] = ['-0.91', '0', '0.961', '-0.55', '#sadness', '#anger', 'negative', '-0.81', 'hypopigmentation', 'albinism', 'congenital_anomaly', 'congenital_defect', 'congenital_disorder']
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senticnet['anesthetic'] = ['-0.61', '0', '0.572', '-0.83', '#sadness', '#disgust', 'negative', '-0.67', 'numb', 'benumbed', 'insensible', 'emotionless', 'asleep']
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senticnet['anethole'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
senticnet['aneuploid'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'deflection', 'warp', 'aberrance']
senticnet['aneurysm'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'chest', 'artery', 'vein', 'internal_organ', 'under_head']
senticnet['aneurysm_formation'] = ['-0.81', '0', '0.684', '-0.81', '#sadness', '#disgust', 'negative', '-0.77', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['aneurysmal'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'blatancy', 'obviousness']
senticnet['anew'] = ['0.734', '-0.64', '0', '0.933', '#joy', '#admiration', 'positive', '0.771', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['anfractuous'] = ['-0.93', '0', '0.861', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'knotty', 'contorted', 'writhed', 'writhen', 'crooked']
senticnet['ang_tharkay'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['angel'] = ['0.99', '0', '0', '0.982', '#joy', '#admiration', 'positive', '0.986', 'god', 'spiritual', 'saint', 'prayer', 'dogma']
senticnet['angelfish'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'autumn_leaf', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['angelic'] = ['0.936', '-0.81', '0', '0.83', '#joy', '#admiration', 'positive', '0.86', 'cherubic', 'lovely', 'adorable', 'endearing', 'lovable']
senticnet['angelman_syndrome'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'myotonic_muscular_dystrophy', 'dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['angelo_taylor'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'nazi', 'man', 'pervert', 'healthy_person']
senticnet['anger'] = ['0', '0', '0.66', '0', '#anger', '#anger', 'negative', '-0.66', 'angry', 'pissed', 'mad', 'bad_temper', 'boiling']
senticnet['angered'] = ['-0.13', '0.142', '0.616', '-0.63', '#anger', '#disgust', 'negative', '-0.31', 'enraged', 'infuriated', 'maddened', 'furious', 'indignantly']
senticnet['angharradh'] = ['-0.04', '0.166', '0.684', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'malign', 'malefic', 'maliciously', 'spitefulness', 'maliciousness']
senticnet['anghelu'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'head_hurt', 'introspection', 'lifestyle_change', 'headache', 'move_eye']
senticnet['angie'] = ['0.033', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.058', 'baby_dog', 'sweet', 'puppet', 'dog', 'love']
senticnet['angina'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'inflammatory_disease', 'gastroenteritis', 'stomach_flu', 'intestinal_flu', 'pelvic_inflammatory_disease']
senticnet['angina_pain'] = ['-0.20', '0', '-0.22', '-0.19', '#sadness', '#fear', 'negative', '-0.20', 'go_hospital', 'death', 'die', 'take_aspirin', 'heart_attack']
senticnet['angioedema'] = ['-0.99', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.78', 'dropsy', 'atrophedema', 'giant_hive', 'periodic_edema', 'lymphedema']
senticnet['angiofibroma'] = ['-0.88', '0', '0.774', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fibroid_tumor', 'fibroid', 'benign_tumor', 'nonmalignant_tumor', 'nonmalignant_neoplasm']
senticnet['angiography'] = ['0', '0', '-0.77', '-0.99', '#fear', '#disgust', 'negative', '-0.88', 'non-invasive', 'noninvasive', 'tomography', 'invasive', 'non-intrusive']
senticnet['angiologist'] = ['0', '0.948', '0', '0.77', '#interest', '#admiration', 'positive', '0.859', 'primary_care_physician', 'abortionist', 'doc', 'commerce', 'intern']
senticnet['angioplasty'] = ['-0.50', '0', '0.596', '-0.76', '#anger', '#disgust', 'negative', '-0.62', 'operation', 'surgery', 'surgical_operation', 'surgical_procedure', 'surgical_process']
senticnet['angiosperm'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'seed_fern', 'pteridosperm']
senticnet['angle'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'incline_plane', 'mountain_side', 'facet', 'apex', 'height']
senticnet['angle_grinder'] = ['0', '0', '-0.79', '-0.72', '#fear', '#disgust', 'negative', '-0.75', 'hatchet', 'sword', 'knife', 'nuclear_bomb', 'atomic_bomb']
senticnet['angler'] = ['0.073', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'fisherman', 'informal_rod', 'archaic_fisher', 'rare_piscator', 'fishing_line']
senticnet['angler_fish'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'catch_fish', 'fishing', 'patience', 'fish_lake', 'bait']
senticnet['angler_fishing'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'divergent_strabismus', 'exotropia', 'squint', 'abnormality', 'abnormalcy']
senticnet['anglerfish'] = ['-0.79', '0', '0.709', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'animal', 'animate_being', 'ctenophore', 'invertebrate', 'beast']
senticnet['anglican_cemetery'] = ['-0.50', '0', '0.762', '-0.89', '#anger', '#disgust', 'negative', '-0.71', 'cemetery', 'graveyard', 'burial_site', 'grassland', 'burying_ground']
senticnet['anglican_church'] = ['0.081', '0.146', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'cult', 'sect', 'conservative', 'worshiper', 'pastor']
senticnet['anglican_clergy'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'nonconformity', 'iconoclasm', 'heterodoxy', 'nonconformance', 'orientation']
senticnet['anglican_cleric'] = ['-0.85', '0', '0.749', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'hairsplitter', 'fight', 'quarrel', 'obstructor', 'obstructer']
senticnet['anglican_congregation'] = ['0.081', '0.146', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'cult', 'sect', 'conservative', 'worshiper', 'pastor']
senticnet['anglican_divine'] = ['-0.85', '0', '0.749', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'hairsplitter', 'fight', 'quarrel', 'obstructor', 'obstructer']
senticnet['anglican_faith'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'nonconformity', 'iconoclasm', 'heterodoxy', 'nonconformance', 'orientation']
senticnet['anglican_theologian'] = ['-0.85', '0', '0.749', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'hairsplitter', 'fight', 'quarrel', 'obstructor', 'obstructer']
senticnet['anglicanism'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'nonconformity', 'iconoclasm', 'heterodoxy', 'nonconformance', 'orientation']
senticnet['anglicized_version'] = ['-0.84', '0', '0.579', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'squiggle', 'hand', 'chicken_scratch', 'handwriting', 'script']
senticnet['anglo_catholic'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'nonconformity', 'iconoclasm', 'heterodoxy', 'nonconformance', 'orientation']
senticnet['anglo_catholicism'] = ['0.563', '-0.72', '0', '0.644', '#surprise', '#admiration', 'positive', '0.645', 'idiom', 'idiomatic_expression', 'phrasal_idiom', 'set_phrase', 'phrase']
senticnet['anglo_dutch_war'] = ['0.582', '-0.97', '0', '0.802', '#surprise', '#admiration', 'positive', '0.788', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['anglo_saxon_poem'] = ['-0.81', '0', '0.52', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'affray', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['anglo_saxon_protestant'] = ['0.748', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.873', 'butterfly', 'moth', 'parrot', 'beetle', 'falcon']
senticnet['anglophile'] = ['0.814', '-0.50', '0', '0.622', '#joy', '#admiration', 'positive', '0.648', 'friend', 'champion', 'supporter', 'protagonist', 'endorser']
senticnet['anglophobe'] = ['-0.55', '0', '0.689', '-0.80', '#anger', '#disgust', 'negative', '-0.68', 'abominator', 'loather', 'mortal', 'hater', 'person']
senticnet['angora_goat'] = ['-0.53', '0', '0.751', '-0.97', '#anger', '#disgust', 'negative', '-0.75', 'guanaco', 'domestic_llama', 'llama', 'artiodactyl', 'artiodactyl_mammal']
senticnet['angora_rabbit'] = ['-0.53', '0', '0.751', '-0.97', '#anger', '#disgust', 'negative', '-0.75', 'guanaco', 'domestic_llama', 'llama', 'artiodactyl', 'artiodactyl_mammal']
senticnet['angora_wool'] = ['-0.53', '0', '0.751', '-0.97', '#anger', '#disgust', 'negative', '-0.75', 'guanaco', 'domestic_llama', 'llama', 'artiodactyl', 'artiodactyl_mammal']
senticnet['angrily'] = ['-0.16', '0.179', '0.568', '-0.61', '#anger', '#disgust', 'negative', '-0.29', 'choler', 'ire', 'see_red', 'dander', 'hackle']
senticnet['angry'] = ['0', '0', '0.869', '0', '#anger', '#anger', 'negative', '-0.86', 'anger', 'hate', 'mad', 'hatred', 'resentment']
senticnet['angry_behavior'] = ['-0.10', '-0.04', '0', '-0.14', '#sadness', '#disgust', 'negative', '-0.06', 'adjust_seat', 'road_rage', 'fasten_seatbelt', 'traffic_jam', 'skid_mark']
senticnet['angry_confrontation'] = ['-0.99', '0', '0.603', '-0.82', '#sadness', '#disgust', 'negative', '-0.80', 'batrachomyomachia', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['angry_emotion'] = ['0', '0', '0.959', '0', '#anger', '#anger', 'negative', '-0.95', 'arrogance', 'dislike', 'psychosis', 'anger', 'hurt_feeling']
senticnet['angry_man'] = ['-0.94', '0', '0.129', '-0.97', '#sadness', '#disgust', 'negative', '-0.68', 'trouble', 'anger', 'fury', 'rage', 'fight']
senticnet['angry_mob'] = ['-0.21', '0.078', '-0.14', '-0.11', '#sadness', '#fear', 'negative', '-0.09', 'picket', 'rally', 'against', 'protest', 'riot']
senticnet['angry_outburst'] = ['0', '0', '0.781', '-0.82', '#anger', '#disgust', 'negative', '-0.80', 'cry', 'acting_out', 'explosion', 'halloo', 'yell']
senticnet['angry_person'] = ['0', '-0.00', '-0.04', '-0.15', '#fear', '#disgust', 'negative', '-0.06', 'victim', 'news_reporter', 'fire_weapon', 'use_weapon', 'use_gun']
senticnet['angry_reaction'] = ['0', '0', '0.781', '-0.82', '#anger', '#disgust', 'negative', '-0.80', 'cry', 'acting_out', 'explosion', 'halloo', 'yell']
senticnet['angst'] = ['-0.87', '0', '0.924', '-0.96', '#anger', '#disgust', 'negative', '-0.92', 'embarrassment', 'consternation', 'humility', 'hysteria', 'nervousness']
senticnet['anguish'] = ['-0.58', '0', '-0.39', '-0.18', '#sadness', '#fear', 'negative', '-0.38', 'feeling_guilt', 'feel_bad', 'bad_feeling', 'death_love', 'get_fired']
senticnet['anguished'] = ['0', '0', '0.796', '-0.77', '#anger', '#disgust', 'negative', '-0.78', 'pain', 'hurt', 'tormented', 'tortured', 'rage']
senticnet['angular'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'harsh', 'common', 'rough-cut', 'uncouth', 'vulgar']
senticnet['angular_acceleration'] = ['-0.03', '0.178', '0', '0.168', '#interest', '#admiration', 'positive', '0.104', 'speed', 'fast', 'fast_and_furious', 'speedy', 'fleet']
senticnet['angular_deflection'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'aberrance', 'warp', 'chromosomal_aberration']
senticnet['angular_dependence'] = ['-0.97', '0', '0.831', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'skewness', 'irregularity']
senticnet['angular_diameter'] = ['0', '-0.72', '0', '0.712', '#surprise', '#admiration', 'positive', '0.72', 'heavenly_body', 'around_earth', 'source_light', 'sol', 'celestial_body']
senticnet['angular_frequency'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'newton', 'sthene', 'force_unit']
senticnet['angular_leaf_spot'] = ['-0.83', '0', '0.754', '-0.97', '#sadness', '#disgust', 'negative', '-0.85', 'brown_rot_gummosis', 'ring_rot', 'ring_disease', 'tobacco_wilt', 'gummosis']
senticnet['angular_momentum'] = ['-0.98', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.87', 'become_fat', 'flatulence', 'feel_rejuvenated', 'tan_skin', 'make_angry']
senticnet['angular_shape'] = ['0.601', '-0.87', '0', '0.5', '#joy', '#surprise', 'positive', '0.66', 'fold', 'crease', 'plication', 'crimp', 'flexure']
senticnet['angular_velocity'] = ['-0.03', '0.178', '0', '0.168', '#interest', '#admiration', 'positive', '0.104', 'speed', 'fast', 'fast_and_furious', 'speedy', 'fleet']
senticnet['angularity'] = ['0.957', '-0.74', '0', '0.987', '#joy', '#admiration', 'positive', '0.898', 'fold', 'crease', 'plication', 'crimp', 'flexure']
senticnet['angulation'] = ['0.592', '-0.91', '0', '0.61', '#surprise', '#admiration', 'positive', '0.705', 'folding', 'change_of_shape', 'narrowing', 'elongation', 'fold']
senticnet['angus_burger'] = ['0.965', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.951', 'delicious_food', 'hot_meal', 'good_food', 'fresh_produce', 'tasty_food']
senticnet['angus_cattle'] = ['0.546', '-0.96', '0', '0.533', '#joy', '#surprise', 'positive', '0.683', 'dairy_cattle', 'dairy_cow', 'milch_cow', 'ayrshire', 'milcher']
senticnet['angus_deayton'] = ['-0.89', '0', '0.58', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'resemblance', 'comparison', 'equivalence', 'reflection', 'alikeness']
senticnet['anharmonic'] = ['0.807', '-0.88', '0', '0.718', '#joy', '#surprise', 'positive', '0.802', 'sex', 'gayness', 'sexual_gayness', 'lesbianism', 'sapphism']
senticnet['anhedonia'] = ['0.995', '-0.73', '0', '0.83', '#joy', '#admiration', 'positive', '0.852', 'spiritism', 'otherworldliness', 'spirituality', 'spiritualism', 'internality']
senticnet['anhydride'] = ['0.032', '-0.02', '-0.04', '-0.08', '#fear', '#disgust', 'negative', '-0.01', 'liquid', 'carbonate_drink', 'chemical', 'soda', 'bromide']
senticnet['ani_complaint'] = ['0.533', '0.818', '0', '0.683', '#interest', '#admiration', 'positive', '0.678', 'gain_information', 'report_news', 'solve', 'seek_answer', 'gather_information']
senticnet['anicca'] = ['-0.87', '0', '0.744', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['anicca_impermanence'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'metastability', 'monotony', 'invariance', 'constancy', 'unchangeability']
senticnet['anilca'] = ['0', '0.089', '0', '0.129', '#interest', '#admiration', 'positive', '0.109', 'passageway', 'connect_room', 'entrance_room', 'corridor', 'walk_through']
senticnet['animadversion'] = ['-0.65', '0', '0.716', '-0.94', '#anger', '#disgust', 'negative', '-0.77', 'bad_hat', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['animal'] = ['0', '-0.10', '0', '0', '#surprise', '#surprise', 'positive', '0.101', 'mammal', 'cage', 'reptile', 'wildlife', 'zoo']
senticnet['animal_behavior'] = ['0', '0', '-0.86', '-0.90', '#fear', '#disgust', 'negative', '-0.88', 'animal_instinct', 'fixed_pattern_of_behavior', 'overeact', 'instinct', 'impulse']
senticnet['animal_dander'] = ['-0.15', '0.162', '0.596', '-0.62', '#anger', '#disgust', 'negative', '-0.30', 'hackle', 'umbrage', 'offense', 'ire', 'see_red']
senticnet['animal_domestication'] = ['0.735', '-0.71', '0', '0.566', '#joy', '#surprise', 'positive', '0.672', 'improvement', 'betterment', 'habituation', 'adjustment', 'shakedown']
senticnet['animal_dung'] = ['0.037', '0.084', '0', '0', '#joy', '#interest', 'positive', '0.06', 'leave_over', 'leftover', 'cellulose', 'cane', 'remainder']
senticnet['animal_dwell'] = ['-0.86', '0', '0.84', '0', '#sadness', '#anger', 'negative', '-0.85', 'animal_house', 'animal_home', 'animal_live', 'acreage', 'animal_place']
senticnet['animal_eat'] = ['0', '0.105', '0.015', '0.068', '#interest', '#admiration', 'positive', '0.053', 'make_meal', 'able_eat', 'bait_trap', 'eat_rice', 'provide_food']
senticnet['animal_enclosure'] = ['0', '0.073', '0.025', '0.131', '#interest', '#admiration', 'positive', '0.06', 'animal_place', 'animal_home', 'animal_house', 'homestead', 'crop_place']
senticnet['animal_fancier'] = ['-0.61', '0', '0.606', '-0.53', '#sadness', '#anger', 'negative', '-0.58', 'enthusiast', 'cat_fancier', 'horseman', 'horse_fancier', 'fancier']
senticnet['animal_flesh'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'veal', 'beef', 'food_group', 'cereal', 'curry']
senticnet['animal_fodder'] = ['0.061', '0.093', '0.037', '0.114', '#interest', '#admiration', 'positive', '0.058', 'whole_grain', 'food_ingredient', 'staple_food', 'starchy_food', 'food_grain']
senticnet['animal_fur'] = ['0', '0', '0.07', '0', '#anger', '#anger', 'negative', '-0.07', 'animal_hair', 'animal_skin', 'dog_fur', 'fluffy_animal', 'furry']
senticnet['animal_furry'] = ['-0.03', '0.11', '0', '0.177', '#interest', '#admiration', 'positive', '0.084', 'domestic_pet', 'cute', 'woolly_animal', 'hairy', 'fuzzy_animal']
senticnet['animal_glue'] = ['0.037', '0.084', '0', '0', '#joy', '#interest', 'positive', '0.06', 'leave_over', 'leftover', 'cellulose', 'cane', 'remainder']
senticnet['animal_group'] = ['-0.85', '0', '0.683', '-0.84', '#sadness', '#disgust', 'negative', '-0.79', 'pack', 'strain', 'biological_group', 'brood', 'litter']
senticnet['animal_hair'] = ['0', '-0.10', '0', '0', '#surprise', '#surprise', 'positive', '0.101', 'animal_fur', 'animal_skin', 'dog_fur', 'fluffy_animal', 'furry']
senticnet['animal_hide'] = ['-0.10', '-0.03', '0.057', '-0.05', '#sadness', '#anger', 'negative', '-0.04', 'animal_skin', 'striped', 'animal_pelt', 'animal_fur', 'waterproof']
senticnet['animal_home'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'lair', 'habitat', 'beehive']
senticnet['animal_house'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'animal_dwell', 'animal_home', 'animal_place', 'cow_home', 'homestead']
senticnet['animal_instinct'] = ['0', '0', '-0.86', '-0.89', '#fear', '#disgust', 'negative', '-0.88', 'animal_behavior', 'fixed_pattern_of_behavior', 'overeact', 'instinct', 'impulse']
senticnet['animal_live'] = ['0', '0.088', '0', '0.088', '#interest', '#admiration', 'positive', '0.088', 'animal_house', 'animal_place', 'animal_dwell', 'animal_home', 'cow_home']
senticnet['animal_order'] = ['-0.92', '0', '0.64', '-0.52', '#sadness', '#anger', 'negative', '-0.69', 'perching_bird', 'percher', 'thyreophoran', 'marginocephalian', 'euronithopod']
senticnet['animal_part'] = ['0', '-0.01', '0.041', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'claw', 'paw', 'long_tail', 'sharp_tooth', 'fang']
senticnet['animal_pelt'] = ['-0.10', '-0.03', '0.057', '-0.05', '#sadness', '#anger', 'negative', '-0.04', 'animal_skin', 'striped', 'animal_hide', 'animal_fur', 'endangered']
senticnet['animal_person'] = ['0.847', '0.791', '0', '0', '#joy', '#interest', 'positive', '0.819', 'responsibility', 'obligation', 'ask_nicely', 'help_person', 'meet_need']
senticnet['animal_physiology'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'college_major', 'geology', 'physic', 'chemistry', 'subject_school']
senticnet['animal_place'] = ['0', '0.1', '0.023', '0.122', '#interest', '#admiration', 'positive', '0.066', 'cow_home', 'crop_place', 'raise_animal', 'shelter', 'animal_house']
senticnet['animal_plant'] = ['0.054', '0', '0', '0.056', '#joy', '#admiration', 'positive', '0.055', 'plant_animal', 'fauna', 'live_organism', 'flora', 'grow_place']
senticnet['animal_product'] = ['0.911', '0', '0', '0.949', '#joy', '#admiration', 'positive', '0.93', 'food', 'biodegradable', 'pet', 'organic', 'toy']
senticnet['animal_protein'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'grill', 'cook_grill', 'barbecue', 'cook', 'grilled_vegetable']
senticnet['animal_sacrifice'] = ['-0.17', '0', '-0.16', '-0.09', '#sadness', '#fear', 'negative', '-0.14', 'murder', 'hurt_others', 'death_penalty', 'death_destruction', 'end_live']
senticnet['animal_shelter'] = ['0.749', '0', '0', '0', '#joy', '#joy', 'positive', '0.749', 'warm_bed', 'alleyway', 'doghouse', 'veterinarian_office', 'cage']
senticnet['animal_skin'] = ['-0.02', '0', '0.05', '0', '#sadness', '#anger', 'negative', '-0.03', 'animal_fur', 'animal_hide', 'waterproof', 'animal_pelt', 'striped']
senticnet['animal_sound'] = ['0.029', '0', '0', '0.112', '#joy', '#admiration', 'positive', '0.071', 'sound', 'noise', 'decibel', 'loudness', 'audio']
senticnet['animal_trainer'] = ['0', '0', '0', '0.987', '#admiration', '#admiration', 'positive', '0.987', 'emote', 'bold', 'form_of_social_interaction', 'act', 'stage_play']
senticnet['animal_type'] = ['0.052', '0.119', '0', '0.141', '#interest', '#admiration', 'positive', '0.104', 'neigh', 'domestic_animal', 'rover', 'wild_animal', 'mammary_gland']
senticnet['animalism'] = ['-0.77', '0', '0.809', '-0.89', '#anger', '#disgust', 'negative', '-0.82', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['animalistic'] = ['-0.61', '0', '0.572', '-0.83', '#sadness', '#disgust', 'negative', '-0.67', 'numb', 'benumbed', 'anesthetic', 'insensible', 'asleep']
senticnet['animality'] = ['0.03', '0', '0', '0.119', '#joy', '#admiration', 'positive', '0.074', 'notional', 'abstract', 'wow', 'made_out_of_plastic', 'make_paper']
senticnet['animals'] = ['0.03', '0.068', '-0.06', '0.062', '#interest', '#admiration', 'positive', '0.025', 'pacific_northwest', 'thermal', 'treetop', 'american_forest', 'zoo']
senticnet['animate'] = ['0.056', '0.165', '-0.11', '0.228', '#interest', '#admiration', 'positive', '0.083', 'always_read', 'bold', 'option', 'activity', 'game_action']
senticnet['animate_being'] = ['-0.97', '0', '0.677', '-0.95', '#sadness', '#disgust', 'negative', '-0.86', 'stunt', 'game', 'mutant', 'invertebrate', 'varmint']
senticnet['animated'] = ['0.743', '0.79', '0', '0', '#joy', '#interest', 'positive', '0.766', 'vivacious', 'vivace', 'airy', 'elastic', 'smart']
senticnet['animated_adaptation'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'child_movie', 'child_story', 'funny_moment', 'childhood']
senticnet['animated_cartoon'] = ['0.789', '-0.51', '0', '0.947', '#joy', '#admiration', 'positive', '0.751', 'flick', 'cartoon', 'short_subject', 'toon', 'newsreel']
senticnet['animated_character'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'child_movie', 'child_story', 'funny_moment', 'childhood']
senticnet['animated_comedy'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'kit_fox', 'prairie_fox', 'white_fox']
senticnet['animated_disney'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'child_movie', 'child_story', 'funny_moment', 'childhood']
senticnet['animated_feature'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'child_movie', 'child_story', 'funny_moment', 'childhood']
senticnet['animated_feature_film'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'child_movie', 'child_story', 'funny_moment', 'childhood']
senticnet['animated_film'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'child_movie', 'child_story', 'funny_moment', 'childhood']
senticnet['animated_film_kung_fu'] = ['0.02', '0.071', '0', '0.069', '#interest', '#admiration', 'positive', '0.053', 'many_animals', 'donkey', 'chimpanzee', 'cute_animal', 'creature']
senticnet['animated_movie'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'child_movie', 'child_story', 'funny_moment', 'childhood']
senticnet['animated_musical'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'child_movie', 'child_story', 'funny_moment', 'childhood']
senticnet['animated_version'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'child_movie', 'child_story', 'funny_moment', 'childhood']
senticnet['animatedly'] = ['-0.85', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.88', 'oppression', 'enslavement', 'slave', 'captivity', 'misery']
senticnet['animating'] = ['0.843', '-0.81', '0', '0.624', '#joy', '#surprise', 'positive', '0.762', 'exhilarating', 'invigorating', 'enlivening', 'refreshing', 'restorative']
senticnet['animation'] = ['0.895', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.823', 'liveliness', 'excitement', 'art_form', 'art', 'film']
senticnet['animation_studio'] = ['0.895', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.823', 'liveliness', 'excitement', 'art_form', 'art', 'film']
senticnet['animator'] = ['0.895', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.823', 'liveliness', 'excitement', 'art_form', 'art', 'film']
senticnet['anime'] = ['0.757', '-0.70', '0', '0', '#joy', '#surprise', 'positive', '0.732', 'manga', 'cartoon', 'japanese_animation', 'movie', 'film']
senticnet['anime_adaptation'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['animistic_belief'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
senticnet['animosity'] = ['-0.98', '0', '0.871', '-0.97', '#sadness', '#disgust', 'negative', '-0.94', 'grudge', 'hostility', 'loathe', 'bitterness', 'aggressive']
senticnet['animosity_between'] = ['0', '0.151', '0.717', '-0.66', '#anger', '#disgust', 'negative', '-0.41', 'class_feeling', 'belligerently', 'hostilely', 'aggressiveness', 'bad_blood']
senticnet['animosity_toward'] = ['-0.12', '0.193', '0.593', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'distaste', 'aversion', 'disapproval', 'disapprove', 'disapproving']
senticnet['animus'] = ['0', '0.146', '0.723', '-0.66', '#anger', '#disgust', 'negative', '-0.41', 'bad_blood', 'belligerently', 'hostilely', 'aggressiveness', 'antagonism']
senticnet['animus_toward'] = ['-0.12', '0.193', '0.593', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'distaste', 'aversion', 'disapproval', 'disapprove', 'disapproving']
senticnet['anion_exchange_chromatography'] = ['0.838', '-0.61', '0', '0.556', '#joy', '#surprise', 'positive', '0.671', 'activity', 'natural_process', 'natural_action', 'column_chromatography', 'paper_chromatography']
senticnet['aniridia'] = ['-0.88', '0', '0.762', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'divergent_strabismus', 'exotropia', 'walleye', 'abnormality', 'abnormalcy']
senticnet['anise'] = ['0.1', '0', '0', '0', '#joy', '#joy', 'positive', '0.1', 'burn_material', 'ash', 'stay_warm', 'bonfire', 'campfire']
senticnet['anise_hyssop'] = ['0.598', '-0.72', '0', '0.941', '#surprise', '#admiration', 'positive', '0.753', 'sicklepod', 'tower_rock_cress', 'tower_mustard', 'rock_cress', 'rock_cress_plant']
senticnet['aniseikonia'] = ['-0.88', '0', '0.762', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'divergent_strabismus', 'exotropia', 'walleye', 'abnormality', 'abnormalcy']
senticnet['anisette'] = ['0.693', '-0.94', '0', '0.713', '#surprise', '#admiration', 'positive', '0.784', 'cordial', 'maraschino', 'ratafia', 'pastis', 'ratafee']
senticnet['anishinaabe'] = ['0.052', '0.099', '0', '0.067', '#interest', '#admiration', 'positive', '0.073', 'police_rank', 'head_guy', 'chief', 'leader', 'commander']
senticnet['anishinaabeg'] = ['0.989', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.97', 'committee', 'council', 'caste', 'association', 'organization']
senticnet['anishinabe'] = ['0.052', '0.099', '0', '0.067', '#interest', '#admiration', 'positive', '0.073', 'police_rank', 'head_guy', 'chief', 'leader', 'commander']
senticnet['anisometropia'] = ['-0.88', '0', '0.762', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'divergent_strabismus', 'exotropia', 'walleye', 'abnormality', 'abnormalcy']
senticnet['anisotropic'] = ['0', '0', '0.783', '-0.89', '#anger', '#disgust', 'negative', '-0.83', 'isotropic', 'inhomogeneous', 'dielectric', 'nonlinear', 'anisotropy']
senticnet['anisotropy'] = ['0', '0', '0.777', '-0.89', '#anger', '#disgust', 'negative', '-0.83', 'isotropic', 'inhomogeneous', 'dielectric', 'nonlinear', 'anisotropic']
senticnet['anither'] = ['0.947', '0.914', '0', '0', '#joy', '#interest', 'positive', '0.93', 'ball_bounce', 'bicycle_tire', 'tire', 'increase_price', 'gyroscope']
senticnet['ankh_morpork_city'] = ['0', '0.081', '0', '0', '#interest', '#interest', 'positive', '0.081', 'favorite', 'guard', 'crowd', 'several_people', 'clique']
senticnet['ankle'] = ['-0.03', '-0.02', '0.065', '-0.02', '#sadness', '#anger', 'negative', '-0.02', 'leg', 'toe', 'hold_canvas', 'foot', 'heel']
senticnet['ankle-high'] = ['0', '0', '-0.90', '-0.86', '#fear', '#disgust', 'negative', '-0.88', 'thigh-high', 'over-the-knee', 'mid-calf', 'heeled', 'knee-high']
senticnet['ankle_bone'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'socket', 'tooth_socket', 'alveolus', 'acetabulum', 'cotyloid_cavity']
senticnet['ankle_boot'] = ['-0.54', '0', '0.612', '-0.98', '#anger', '#disgust', 'negative', '-0.71', 'scuff', 'mule', 'bootee', 'carpet_slipper', 'footwear']
senticnet['ankle_bracelet'] = ['0.789', '-0.51', '0', '0.947', '#joy', '#admiration', 'positive', '0.751', 'jewelry', 'jewellery', 'anklet', 'bangle', 'armilla']
senticnet['ankle_deep'] = ['-0.02', '-0.01', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.03', 'dung', 'manure', 'shit', 'dog_poop', 'crap']
senticnet['ankle_fracture'] = ['-0.90', '0', '-0.91', '-0.73', '#sadness', '#fear', 'negative', '-0.85', 'broken_window', 'step_dog_shit', 'broken_leg', 'brain_tumor', 'bad_luck']
senticnet['ankle_injury'] = ['-0.78', '0', '0.554', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'rubor', 'redness', 'inflammation', 'tennis_elbow', 'lateral_epicondylitis']
senticnet['ankle_joint'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'socket', 'tooth_socket', 'alveolus', 'acetabulum', 'cotyloid_cavity']
senticnet['ankle_ligament'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'deltoid', 'muscle', 'abductor', 'skeletal_muscle']
senticnet['ankle_monitor'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'bangle', 'ankle_bracelet', 'armilla']
senticnet['ankle_problem'] = ['-0.69', '0', '0.768', '-0.67', '#sadness', '#anger', 'negative', '-0.71', 'dither', 'pother', 'worry', 'flap', 'scruple']
senticnet['ankle_sprain'] = ['-0.78', '0', '0.554', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'rubor', 'redness', 'inflammation', 'tennis_elbow', 'lateral_epicondylitis']
senticnet['ankle_strap'] = ['-0.54', '0', '0.612', '-0.98', '#anger', '#disgust', 'negative', '-0.71', 'scuff', 'mule', 'bootee', 'carpet_slipper', 'footwear']
senticnet['ankle_tendon'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'deltoid', 'muscle', 'abductor', 'skeletal_muscle']
senticnet['anklet'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'bangle', 'ankle_bracelet', 'armilla']
senticnet['anmu'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['annabel'] = ['-0.03', '-0.00', '0.087', '-0.05', '#anger', '#disgust', 'negative', '-0.04', 'dog_fur', 'dog_cat', 'dog_hair', 'pet', 'animal_hair']
senticnet['annabel_kantaria'] = ['0.071', '0', '0.031', '0.064', '#joy', '#admiration', 'positive', '0.035', 'far_from_home', 'immigrant', 'live_abroad', 'connoisseur', 'work_abroad']
senticnet['annal'] = ['0', '0.105', '0', '0.084', '#interest', '#admiration', 'positive', '0.095', 'archive', 'print_medium', 'quiet_place', 'register', 'media']
senticnet['annales_cambriae'] = ['0', '0.105', '0', '0.084', '#interest', '#admiration', 'positive', '0.095', 'archive', 'print_medium', 'quiet_place', 'register', 'media']
senticnet['annales_regni_francorum'] = ['0', '0.105', '0', '0.084', '#interest', '#admiration', 'positive', '0.095', 'archive', 'print_medium', 'quiet_place', 'register', 'media']
senticnet['annalist'] = ['0', '0.105', '0', '0.084', '#interest', '#admiration', 'positive', '0.095', 'archive', 'print_medium', 'quiet_place', 'register', 'media']
senticnet['annalistic'] = ['0', '0.105', '0', '0.084', '#interest', '#admiration', 'positive', '0.095', 'archive', 'print_medium', 'quiet_place', 'register', 'media']
senticnet['annals'] = ['0', '0.105', '0', '0.084', '#interest', '#admiration', 'positive', '0.095', 'archive', 'print_medium', 'quiet_place', 'register', 'media']
senticnet['annapurna_range'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['anneal'] = ['0', '0', '0.885', '-0.86', '#anger', '#disgust', 'negative', '-0.87', 'mood', 'humor', 'irritation', 'chasten', 'temper']
senticnet['annelid'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'leech', 'leech_worm', 'segmented_worm']
senticnet['annelid_worm'] = ['-0.10', '-0.04', '0', '-0.14', '#sadness', '#disgust', 'negative', '-0.06', 'medicinal_leech', 'horseleech', 'annelid', 'leech', 'segmented_worm']
senticnet['annex'] = ['0', '0.31', '-0.25', '0.286', '#interest', '#admiration', 'positive', '0.113', 'appoint', 'contribution', 'demand', 'elect', 'receive']
senticnet['annihilate'] = ['0', '0', '0.759', '-0.85', '#anger', '#disgust', 'negative', '-0.80', 'breach', 'enfeeble', 'declass', 'disconfirm', 'discontinue']
senticnet['annihilating'] = ['0.518', '-0.63', '0', '0.529', '#surprise', '#admiration', 'positive', '0.561', 'disrespectful', 'undeferential', 'devastating', 'insolent', 'undutiful']
senticnet['annihilation'] = ['-0.98', '0', '0.516', '-0.90', '#sadness', '#disgust', 'negative', '-0.80', 'devastation', 'atomization', 'pulverization', 'vaporization', 'destruction']
senticnet['annika_smethurst'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'concupiscence', 'lustfulness', 'amorousness']
senticnet['anniversary'] = ['0.124', '-0.19', '-0.12', '0.063', '#joy', '#surprise', 'positive', '0.065', 'birthday', 'occasion', 'new_baby', 'holiday', 'cake_candle']
senticnet['anniversary_celebration'] = ['0.119', '-0.13', '-0.06', '0', '#joy', '#surprise', 'positive', '0.063', 'gala', 'celebration', 'special_event', 'special_happen', 'special_moment']
senticnet['anniversary_concert'] = ['0.119', '-0.13', '-0.06', '0', '#joy', '#surprise', 'positive', '0.063', 'gala', 'celebration', 'special_event', 'special_happen', 'special_moment']
senticnet['anniversary_gift'] = ['0.997', '-0.88', '0', '0', '#joy', '#surprise', 'positive', '0.942', 'birthday_gift', 'christmas_present', 'gift', 'gamble_game', 'greeting']
senticnet['anniversary_reunion'] = ['0.119', '-0.13', '-0.06', '0', '#joy', '#surprise', 'positive', '0.063', 'gala', 'celebration', 'special_event', 'special_happen', 'special_moment']
senticnet['annona_reticulata'] = ['0.861', '-0.87', '0', '0.597', '#joy', '#surprise', 'positive', '0.779', 'herb', 'cultivated_parsnip', 'wild_parsnip', 'madnep', 'herbaceous_plant']
senticnet['annona_squamosa'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'nut', 'palm_kernel', 'edible_nut', 'cembra_nut', 'cedar_nut']
senticnet['annotation'] = ['0.041', '0.16', '0', '0.3', '#interest', '#admiration', 'positive', '0.167', 'instruction', 'summary', 'same', 'narrate', 'explaination']
senticnet['announce'] = ['0', '0', '-0.78', '-0.99', '#fear', '#disgust', 'negative', '-0.89', 'forerunner', 'harbinger', 'precursor', 'predecessor', 'herald']
senticnet['announced'] = ['0.838', '-0.93', '0', '0.947', '#surprise', '#admiration', 'positive', '0.907', 'asserted', 'declared', 'professed', 'alleged', 'avowed']
senticnet['announced_shortly'] = ['0', '0.067', '0', '0.062', '#interest', '#admiration', 'positive', '0.065', 'social_skill', 'show_up', 'go_abroad', 'go_new_place', 'visit']
senticnet['announcement'] = ['0.849', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.803', 'remark', 'message', 'reply', 'report', 'sender']
senticnet['announcement_come_amid'] = ['0', '0.909', '0', '0.948', '#interest', '#admiration', 'positive', '0.928', 'set_off', 'shake_up', 'scandalmongering', 'sensationalistic', 'sensational']
senticnet['annoy'] = ['0', '0', '0.784', '0', '#anger', '#anger', 'negative', '-0.78', 'annoyance', 'irritate', 'bother', 'fury', 'bothersome']
senticnet['annoy_bug'] = ['-0.91', '0', '0.563', '-0.93', '#sadness', '#disgust', 'negative', '-0.80', 'flea', 'bug', 'disturb', 'fly', 'insect']
senticnet['annoy_mom'] = ['0', '-0.05', '0.087', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'play_orchestra', 'play_tune', 'make_musical_sound', 'play_music', 'perform_of_music']
senticnet['annoy_neighbor'] = ['0', '0', '0.762', '0', '#anger', '#anger', 'negative', '-0.76', 'create_music', 'make_music', 'play_music', 'produce_musical_sound', 'perform_of_music']
senticnet['annoy_person'] = ['-0.90', '0', '0.877', '-0.91', '#sadness', '#disgust', 'negative', '-0.89', 'talkative', 'bored_person', 'tired_person', 'boring_movie', 'boring_show']
senticnet['annoy_sound'] = ['-0.17', '-0.04', '0.035', '-0.08', '#sadness', '#disgust', 'negative', '-0.06', 'bad_music', 'squeak', 'high_music_note', 'bang', 'loud']
senticnet['annoy_user'] = ['-0.78', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.80', 'annoy', 'ad', 'advertising', 'useless_info', 'useless_information']
senticnet['annoyance'] = ['0', '0.33', '0', '0', '#interest', '#interest', 'negative', '-0.33', 'annoyed', 'irritated', 'vexed', 'upset', 'crabby']
senticnet['annoyed'] = ['0', '0.33', '0', '0', '#interest', '#interest', 'negative', '0.33', 'annoyance', 'irritated', 'vexed', 'upset', 'crabby']
senticnet['annoying'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'vexatious', 'teasing']
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senticnet['antarctic_glaciology'] = ['0', '0.977', '0', '0.736', '#interest', '#admiration', 'positive', '0.857', 'polar_programsrelated', 'office', 'award_support', 'support', 'year']
senticnet['antarctic_plate'] = ['-0.87', '0', '0.818', '0', '#sadness', '#anger', 'negative', '-0.84', 'slow_speed', 'wifi', 'computer_internet_connection', 'internet_connection', 'boot_up_computer']
senticnet['antarctica'] = ['-0.96', '0', '0.962', '0', '#sadness', '#anger', 'negative', '-0.96', 'polar_programsrelated', 'antarctic_geology_geophysics', 'transantarctic_mountain', 'award_provided', 'geophysics']
senticnet['ante_babaja'] = ['0.725', '-0.66', '0', '0.906', '#joy', '#admiration', 'positive', '0.765', 'set', 'pose', 'lay', 'put', 'place']
senticnet['ante_natal'] = ['0.1', '0.216', '-0.18', '0.22', '#interest', '#admiration', 'positive', '0.088', 'female', 'maternity_ward', 'motherhood', 'woman', 'mother']
senticnet['ante_natal_care'] = ['0.1', '0.216', '-0.18', '0.22', '#interest', '#admiration', 'positive', '0.088', 'female', 'maternity_ward', 'motherhood', 'woman', 'mother']
senticnet['antec'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'computer_part', 'system', 'cpu', 'pencil_pen', 'monitor']
senticnet['antecedent'] = ['-0.57', '0', '0.713', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'assured', 'conclusion', 'certainty', 'determinateness', 'predictability']
senticnet['antecedently'] = ['-0.87', '0', '0.858', '-0.77', '#sadness', '#anger', 'negative', '-0.83', 'pass_off', 'fall_out', 'occur', 'happen', 'hap']
senticnet['antechamber'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'vestibule', 'door', 'central_passage', 'gate', 'door_handle']
senticnet['antedate'] = ['-0.57', '0', '0.954', '-0.81', '#anger', '#disgust', 'negative', '-0.78', 'foregoing', 'above', 'old', 'preceding', 'introductory']
senticnet['antegrade'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'blatancy', 'obviousness']
senticnet['antelope'] = ['0', '-0.97', '0', '0', '#surprise', '#surprise', 'positive', '0.977', 'cougar', 'skunk', 'lion', 'tiger', 'lynx']
senticnet['antemedial'] = ['-0.95', '0', '0.892', '-0.81', '#sadness', '#anger', 'negative', '-0.88', 'blurry', 'foggy', 'hazy', 'muzzy', 'blurred']
senticnet['antenatal'] = ['0.1', '0.216', '-0.18', '0.22', '#interest', '#admiration', 'positive', '0.088', 'female', 'maternity_ward', 'motherhood', 'woman', 'mother']
senticnet['antenna'] = ['0.16', '0.067', '0', '0.083', '#joy', '#admiration', 'positive', '0.103', 'aerial', 'sensory', 'arthropod', 'insect', 'connection']
senticnet['antenna_beam'] = ['0', '0.175', '0', '0.231', '#interest', '#admiration', 'positive', '0.203', 'compass_direction', 'go_straight', 'end_point', 'eight_sides', 'red_light']
senticnet['antenna_filiform'] = ['0.969', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.772', 'aspheric', 'aspherical', 'prolate', 'rounded', 'crescent']
senticnet['antenna_tower'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'castle', 'bell_tower', 'spire', 'steeple', 'religious_place']
senticnet['antero_temporal'] = ['0.719', '-0.57', '0', '0.651', '#joy', '#admiration', 'positive', '0.647', 'chap', 'break', 'slit', 'gap', 'shift']
senticnet['anterograde_amnesia'] = ['0.998', '-0.63', '0', '0.574', '#joy', '#surprise', 'positive', '0.736', 'cleavable', 'dissociable', 'separable', 'severable', 'divisible']
senticnet['anteroom'] = ['0.797', '0.781', '0', '0', '#joy', '#interest', 'positive', '0.789', 'residence_hall', 'charles_francis_hall', 'marguerite_radclyffe_hall', 'hall', 'manse']
senticnet['anteroposteriorly'] = ['0.886', '-0.99', '0', '0.542', '#joy', '#surprise', 'positive', '0.808', 'jowl', 'lower_jaw', 'mandible', 'lantern_jaw', 'lantern_jawr_bone']
senticnet['anthelmintic'] = ['0.701', '-0.76', '0', '0.904', '#surprise', '#admiration', 'positive', '0.791', 'curative', 'heal', 'medicine', 'get_cured', 'take_care']
senticnet['anthill'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'lair', 'habitat', 'animal_home']
senticnet['anthology'] = ['-0.04', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.08', 'data', 'awareness', 'information', 'saga', 'knowledge']
senticnet['anthracnose'] = ['-0.83', '0', '0.754', '-0.97', '#sadness', '#disgust', 'negative', '-0.85', 'brown_rot_gummosis', 'ring_rot', 'ring_disease', 'tobacco_wilt', 'gummosis']
senticnet['anthralin'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'radiation_therapy', 'radiation', 'actinotherapy', 'irradiation']
senticnet['anthranilic_acid'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'capsaicin', 'chemical_irritant', 'piperine', 'pepper_spray']
senticnet['anthrax'] = ['-0.87', '0', '0.936', '-0.98', '#anger', '#disgust', 'negative', '-0.93', 'diabetes', 'chicken_pox', 'out_work', 'schizophrenia', 'ill_health']
senticnet['anthrax_infection'] = ['-0.95', '0', '0.876', '-0.89', '#sadness', '#disgust', 'negative', '-0.90', 'bubonic_plague', 'pestis_bubonica', 'glandular_plague', 'septicemic_plague', 'pestilence']
senticnet['anthrax_killer'] = ['0', '0', '-0.88', '-0.99', '#fear', '#disgust', 'negative', '-0.93', 'murder_suspect', 'robbery_suspect', 'rape_suspect', 'murder', 'doubt']
senticnet['anthrax_mailer'] = ['0', '0', '-0.88', '-0.99', '#fear', '#disgust', 'negative', '-0.93', 'murder_suspect', 'robbery_suspect', 'rape_suspect', 'murder', 'doubt']
senticnet['anthrax_vaccine'] = ['-0.07', '0', '-0.05', '-0.18', '#sadness', '#disgust', 'negative', '-0.10', 'medical_therapy', 'vaccination', 'doctor', 'medicine', 'hospital']
senticnet['anthropogenic_climate_change'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'weather', 'sunny', 'spring', 'snow', 'four_seasons']
senticnet['anthropoid'] = ['-0.93', '0', '0.508', '-0.58', '#sadness', '#disgust', 'negative', '-0.67', 'inhuman', 'bloodless', 'apelike', 'nonhuman', 'nonhumanal']
senticnet['anthropoid_ape'] = ['-0.93', '0', '0.508', '-0.58', '#sadness', '#disgust', 'negative', '-0.67', 'inhuman', 'bloodless', 'apelike', 'nonhuman', 'nonhumanal']
senticnet['anthropoidal'] = ['-0.93', '0', '0.508', '-0.58', '#sadness', '#disgust', 'negative', '-0.67', 'inhuman', 'bloodless', 'apelike', 'anthropoid', 'nonhuman']
senticnet['anthropologist'] = ['0', '0.909', '0', '0.799', '#interest', '#admiration', 'positive', '0.854', 'research', 'research_topic', 'research_subject', 'search', 'student']
senticnet['anthropology'] = ['0.825', '0.992', '0', '0', '#joy', '#interest', 'positive', '0.908', 'ethnology', 'social_anthropology', 'archeology', 'physical_anthropology', 'social_science']
senticnet['anthropomorphic_cat'] = ['0.931', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.855', 'crystal_clear', 'blue_sky', 'imaginary', 'water_sky', 'blue_color']
senticnet['anthropomorphism'] = ['-0.77', '0', '0.885', '-0.73', '#sadness', '#anger', 'negative', '-0.8', 'fallacy', 'misconception', 'paralogism', 'logical_fallacy', 'pseudoscience']
senticnet['anthropophagite'] = ['-0.73', '0', '0.734', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'primitive', 'headhunter', 'cannibal', 'anthropophagus', 'barbarian']
senticnet['anthropophagus'] = ['-0.73', '0', '0.734', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'primitive', 'headhunter', 'cannibal', 'barbarian', 'anthropophagite']
senticnet['anthropophagy'] = ['-0.17', '0', '-0.16', '-0.09', '#sadness', '#fear', 'negative', '-0.14', 'murder', 'hurt_others', 'death_penalty', 'death_destruction', 'end_live']
senticnet['anthroposophy'] = ['0.661', '-0.66', '0', '0.685', '#surprise', '#admiration', 'positive', '0.671', 'ethical_code', 'theosophy', 'system_of_rules', 'logic', 'method']
senticnet['anti'] = ['-0.12', '0.115', '-0.06', '-0.11', '#sadness', '#disgust', 'negative', '-0.04', 'opposition', 'fight_against', 'rival', 'uprise', 'go_against']
senticnet['anti-aircraft'] = ['0', '0', '0.973', '-0.79', '#anger', '#disgust', 'negative', '-0.88', 'anti-tank', 'antiaircraft', 'antitank', 'anti-armor', 'howitzer']
senticnet['anti-armor'] = ['0', '0', '0.966', '-0.79', '#anger', '#disgust', 'negative', '-0.88', 'anti-tank', 'antiaircraft', 'anti-aircraft', 'antitank', 'howitzer']
senticnet['anti-bacterial'] = ['0.858', '0.981', '0', '0', '#joy', '#interest', 'positive', '0.919', 'antibacterial', 'antiseptic', 'astringent', 'disinfectant', 'anti-fungal']
senticnet['anti-fungal'] = ['0.859', '0.979', '0', '0', '#joy', '#interest', 'positive', '0.919', 'antibacterial', 'anti-bacterial', 'astringent', 'disinfectant', 'antiseptic']
senticnet['anti-hero'] = ['-0.75', '0', '0', '-0.95', '#sadness', '#disgust', 'negative', '-0.85', 'demigod', 'antihero', 'narrator', 'hero', 'everyman']
senticnet['anti-national'] = ['0', '0', '0.715', '-0.71', '#anger', '#disgust', 'negative', '-0.71', 'counter-revolutionary', 'anti-revolutionary', 'counterrevolutionary', 'anti-state', 'anti-soviet']
senticnet['anti-revolutionary'] = ['0', '0', '0.714', '-0.71', '#anger', '#disgust', 'negative', '-0.71', 'counter-revolutionary', 'counterrevolutionary', 'anti-national', 'anti-state', 'anti-soviet']
senticnet['anti-soviet'] = ['0', '0', '0.719', '-0.71', '#anger', '#disgust', 'negative', '-0.71', 'counter-revolutionary', 'anti-revolutionary', 'anti-national', 'anti-state', 'counterrevolutionary']
senticnet['anti-state'] = ['0', '0', '0.712', '-0.71', '#anger', '#disgust', 'negative', '-0.71', 'counter-revolutionary', 'anti-revolutionary', 'anti-national', 'counterrevolutionary', 'anti-soviet']
senticnet['anti-tank'] = ['0', '0', '0.972', '-0.79', '#anger', '#disgust', 'negative', '-0.88', 'antitank', 'antiaircraft', 'anti-aircraft', 'anti-armor', 'howitzer']
senticnet['anti-woman'] = ['0', '0', '-0.86', '-0.79', '#fear', '#disgust', 'negative', '-0.82', 'eugenics', 'eugenicist', 'eugenicists', 'socialistic', 'eugenic']
senticnet['anti_abortion'] = ['-0.90', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.86', 'death', 'feticide', 'sickness', 'miscarriage', 'spontaneous_abortion']
senticnet['anti_aircraft_ammunition'] = ['0', '0', '-0.92', '-0.91', '#fear', '#disgust', 'negative', '-0.92', 'automatic', 'firearm', 'gun', 'weapon', 'projectile']
senticnet['anti_aircraft_armament'] = ['-0.90', '0', '0.517', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'outdated', 'noncurrent', 'disused', 'obsolete', 'superannuated']
senticnet['anti_aircraft_weapon'] = ['-0.90', '0', '0.517', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'outdated', 'noncurrent', 'disused', 'obsolete', 'superannuated']
senticnet['anti_arrhythmic'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'spironolactone', 'spironolactone_drug']
senticnet['anti_authoritarianism'] = ['-0.50', '0', '0.733', '-0.55', '#anger', '#disgust', 'negative', '-0.59', 'originality', 'novelty', 'innovativeness', 'freshness', 'ability']
senticnet['anti_avoidance'] = ['0', '0', '0.76', '-0.92', '#anger', '#disgust', 'negative', '-0.84', 'ambiguity', 'prevarication', 'equivocation', 'unclearness', 'equivocalness']
senticnet['anti_bacterial'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['anti_calvinist'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'heterodoxy', 'iconoclasm', 'nonconformism', 'nonconformance', 'orientation']
senticnet['anti_cellulite'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'beauty_parlor', 'lady_hair', 'beauty_shop', 'beauty_salon', 'make_up']
senticnet['anti_clericalism'] = ['-0.91', '0', '0.688', '-0.57', '#sadness', '#anger', 'negative', '-0.72', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['anti_climactic'] = ['-0.83', '0', '0.912', '-0.67', '#sadness', '#anger', 'negative', '-0.80', 'dissatisfaction', 'displeasure', 'disappointment', 'boredom', 'ennui']
senticnet['anti_climax'] = ['-0.83', '0', '0.912', '-0.67', '#sadness', '#anger', 'negative', '-0.80', 'dissatisfaction', 'displeasure', 'disappointment', 'boredom', 'ennui']
senticnet['anti_comintern_pact'] = ['0.397', '0.245', '-0.13', '0.224', '#joy', '#interest', 'positive', '0.183', 'agreement', 'provide', 'advice', 'contribution', 'offer']
senticnet['anti_communism'] = ['0.946', '-0.54', '0', '0.774', '#joy', '#admiration', 'positive', '0.754', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['anti_communist'] = ['0', '0', '-0.70', '-0.83', '#fear', '#disgust', 'negative', '-0.76', 'settle_disagreement', 'hitler', 'republican', 'conservative', 'unchanging']
senticnet['anti_communist_resistance'] = ['-0.97', '0', '0.62', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'obstructor', 'obstructer', 'technophobe', 'obstructionist', 'naysayer']
senticnet['anti_corruption'] = ['0', '0', '-0.77', '-0.86', '#fear', '#disgust', 'negative', '-0.82', 'abduction', 'punishment', 'emergency', 'illiteracy', 'heal']
senticnet['anti_corruption_agency'] = ['0', '0', '-0.77', '-0.86', '#fear', '#disgust', 'negative', '-0.82', 'abduction', 'punishment', 'emergency', 'illiteracy', 'heal']
senticnet['anti_corruption_campaign'] = ['0', '0', '-0.77', '-0.86', '#fear', '#disgust', 'negative', '-0.82', 'abduction', 'punishment', 'emergency', 'illiteracy', 'heal']
senticnet['anti_corruption_drive'] = ['0', '0', '-0.77', '-0.86', '#fear', '#disgust', 'negative', '-0.82', 'abduction', 'punishment', 'emergency', 'illiteracy', 'heal']
senticnet['anti_death_penalty'] = ['0', '0', '0.869', '0', '#anger', '#anger', 'negative', '-0.86', 'convoke', 'loose_end', 'car_race', 'auction', 'mortuary']
senticnet['anti_depressive'] = ['0.95', '-0.95', '0', '0.769', '#joy', '#surprise', 'positive', '0.89', 'adrenocorticotropic', 'adrenocorticotrophic', 'stimulant', 'stimulating', 'stimulative']
senticnet['anti_diabetic'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'spironolactone', 'spironolactone_drug']
senticnet['anti_discrimination'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
senticnet['anti_establishment'] = ['-0.61', '0', '0.738', '-0.80', '#anger', '#disgust', 'negative', '-0.71', 'dissident', 'beat', 'bohemian', 'maverick', 'recusant']
senticnet['anti_fascist_resistance'] = ['-0.97', '0', '0.62', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'obstructor', 'obstructer', 'technophobe', 'obstructionist', 'naysayer']
senticnet['anti_feminist'] = ['0.585', '-0.7', '0', '0.586', '#surprise', '#admiration', 'positive', '0.624', 'jingoistic', 'ultranationalistic', 'superpatriotic', 'nationalistic', 'patriotic']
senticnet['anti_fraud'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'mail_fraud', 'swindle', 'rig']
senticnet['anti_fungal'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['anti_gay'] = ['0.778', '-0.73', '0', '0.559', '#joy', '#surprise', 'positive', '0.689', 'sex', 'inversion', 'sexual_inversion', 'lesbianism', 'sapphism']
senticnet['anti_gay_rhetoric'] = ['-0.76', '0', '0.519', '-0.56', '#sadness', '#disgust', 'negative', '-0.61', 'scurrility', 'billingsgate', 'disrespect', 'discourtesy', 'vituperation']
senticnet['anti_glare'] = ['0.057', '0', '0.012', '0.08', '#joy', '#admiration', 'positive', '0.042', 'see_better', 'glasses', 'become_gigantic', 'magnify', 'contact_lens']
senticnet['anti_graffiti'] = ['-0.12', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.07', 'bum', 'homeless_people', 'homeless_person', 'panhandler', 'homeless']
senticnet['anti_graft'] = ['0', '0', '-0.77', '-0.86', '#fear', '#disgust', 'negative', '-0.82', 'abduction', 'punishment', 'emergency', 'illiteracy', 'heal']
senticnet['anti_graft_body'] = ['0', '0', '-0.77', '-0.86', '#fear', '#disgust', 'negative', '-0.82', 'abduction', 'punishment', 'emergency', 'illiteracy', 'heal']
senticnet['anti_hero'] = ['-0.95', '0', '0.774', '0', '#sadness', '#anger', 'negative', '-0.86', 'argue', 'rival', 'disagreement', 'opposition', 'anti']
senticnet['anti_hiv'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'indinavir', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['anti_homosexual'] = ['0.778', '-0.73', '0', '0.559', '#joy', '#surprise', 'positive', '0.689', 'sex', 'inversion', 'sexual_inversion', 'lesbianism', 'sapphism']
senticnet['anti_imperialism'] = ['0', '0', '0', '0.986', '#admiration', '#admiration', 'positive', '0.986', 'idea', 'decision', 'consider', 'polarize', 'make_right_decision']
senticnet['anti_imperialist'] = ['0.946', '-0.54', '0', '0.774', '#joy', '#admiration', 'positive', '0.754', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['anti_inflammatory'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'chemical_irritant', 'gingerol', 'piperine', 'pepper_spray']
senticnet['anti_inflammatory_activity'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['anti_inflammatory_agent'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'chemical_irritant', 'gingerol', 'piperine', 'pepper_spray']
senticnet['anti_inflammatory_drug'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucose', 'corn_sugar', 'dextrose', 'dextroglucose', 'grape_sugar']
senticnet['anti_inflammatory_effect'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'chemical_irritant', 'gingerol', 'piperine', 'pepper_spray']
senticnet['anti_inflammatory_medication'] = ['-0.98', '0', '0.678', '-0.95', '#sadness', '#disgust', 'negative', '-0.87', 'rubor', 'redness', 'inflammation', 'epicondylitis', 'lateral_epicondylitis']
senticnet['anti_inflammatory_medicine'] = ['-0.93', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.92', 'pull_muscle', 'become_exhausted', 'increased_heart_rate', 'breathlessness', 'tired_leg']
senticnet['anti_intellectualism'] = ['-0.83', '0', '0.904', '0', '#sadness', '#anger', 'negative', '-0.87', 'disappointed', 'thwarted', 'frustrated', 'foiled', 'discomfited']
senticnet['anti_ischemic'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'spironolactone', 'spironolactone_drug']
senticnet['anti_jacobin'] = ['0.058', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.054', 'letter_slot', 'envelope', 'address_label', 'mailman', 'postage_stamp']
senticnet['anti_jewish'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'racism', 'intolerant', 'xenophobic', 'racist', 'nazism']
senticnet['anti_judaism'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'racism', 'intolerant', 'xenophobic', 'racist', 'nazism']
senticnet['anti_malaria'] = ['-0.88', '0', '-0.84', '-0.95', '#sadness', '#disgust', 'negative', '-0.89', 'polio', 'infectious_disease', 'malady', 'sickness', 'ailment']
senticnet['anti_malarial'] = ['-0.88', '0', '-0.84', '-0.95', '#sadness', '#disgust', 'negative', '-0.89', 'polio', 'infectious_disease', 'malady', 'sickness', 'ailment']
senticnet['anti_malware_software'] = ['0.857', '0', '0', '0.968', '#joy', '#admiration', 'positive', '0.912', 'inerrable', 'flawless', 'unfailing', 'reliable', 'sure']
senticnet['anti_markovnikov'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
senticnet['anti_materialist'] = ['0.95', '-0.77', '0', '0.578', '#joy', '#surprise', 'positive', '0.767', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['anti_microbial'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['anti_militarism'] = ['-0.91', '0', '0.688', '-0.57', '#sadness', '#anger', 'negative', '-0.72', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['anti_missile_defense'] = ['-0.04', '0', '-0.06', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'make_war', 'conquer_enemy', 'use_military', 'gain_territory', 'keep_peace']
senticnet['anti_modernist'] = ['0.563', '-0.72', '0', '0.644', '#surprise', '#admiration', 'positive', '0.645', 'idiom', 'idiomatic_expression', 'phrasal_idiom', 'set_phrase', 'phrase']
senticnet['anti_monarchical'] = ['0.059', '0.092', '0', '0.107', '#interest', '#admiration', 'positive', '0.086', 'royalty', 'monarch', 'throne', 'emperor', 'king_queen']
senticnet['anti_monarchist'] = ['0.059', '0.092', '0', '0.107', '#interest', '#admiration', 'positive', '0.086', 'royalty', 'monarch', 'throne', 'emperor', 'king_queen']
senticnet['anti_mutant'] = ['0.02', '0.131', '0.749', '-0.67', '#anger', '#disgust', 'negative', '-0.31', 'belligerence', 'belligerency', 'belligerent', 'war', 'rancor']
senticnet['anti_nausea'] = ['0.969', '0.817', '0', '0.828', '#joy', '#admiration', 'positive', '0.871', 'ibuprofen', 'painkiller', 'medicine', 'analgesic', 'phenobarbital']
senticnet['anti_nazi'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'healthy_person', 'man', 'pervert', 'compatriot']
senticnet['anti_obscenity'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'indecency']
senticnet['anti_polygamy'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'spousal_relationship', 'wedlock']
senticnet['anti_poverty'] = ['0.812', '-0.91', '0', '0.647', '#joy', '#surprise', 'positive', '0.79', 'assistance', 'relief', 'economic_aid', 'financial_aid', 'social_insurance']
senticnet['anti_realist'] = ['-0.99', '0', '0.878', '-0.95', '#sadness', '#disgust', 'negative', '-0.94', 'adequacy', 'satisfactoriness', 'acceptability', 'acceptableness', 'quality']
senticnet['anti_secession'] = ['0', '0.089', '0', '0.129', '#interest', '#admiration', 'positive', '0.109', 'passageway', 'connect_room', 'entrance_room', 'corridor', 'walk_through']
senticnet['anti_semetic'] = ['-0.21', '0', '-0.19', '-0.18', '#sadness', '#fear', 'negative', '-0.2', 'censorship', 'bad_word', 'kidnap', 'intolerance', 'sectarian']
senticnet['anti_semite'] = ['-0.91', '0', '0.794', '-0.93', '#sadness', '#disgust', 'negative', '-0.88', 'fundamentalist', 'extremist', 'obsession', 'stubborn', 'violent']
senticnet['anti_semitic'] = ['-0.21', '0', '-0.19', '-0.18', '#sadness', '#fear', 'negative', '-0.2', 'censorship', 'bad_word', 'kidnap', 'intolerance', 'sectarian']
senticnet['anti_semitic_propaganda'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'racism', 'intolerant', 'xenophobic', 'racist', 'nazism']
senticnet['anti_semitic_rhetoric'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'racism', 'intolerant', 'xenophobic', 'racist', 'nazism']
senticnet['anti_semitic_sentiment'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'racism', 'intolerant', 'xenophobic', 'racist', 'nazism']
senticnet['anti_semitic_violence'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'racism', 'intolerant', 'xenophobic', 'racist', 'nazism']
senticnet['anti_semitism'] = ['-0.81', '-0.71', '0.843', '-0.72', '#sadness', '#anger', 'negative', '-0.41', 'lack_of_rules', 'fascism', 'despot', 'tyrant', 'anarchism']
senticnet['anti_slavery_campaigner'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['anti_spam'] = ['-0.05', '-0.03', '0.023', '-0.09', '#sadness', '#disgust', 'negative', '-0.03', 'moldy_food', 'celery', 'paper_product', 'orange_juice', 'meat']
senticnet['anti_spam_software'] = ['-0.05', '-0.03', '0.023', '-0.09', '#sadness', '#disgust', 'negative', '-0.03', 'moldy_food', 'celery', 'paper_product', 'orange_juice', 'meat']
senticnet['anti_spasmodic'] = ['0.701', '-0.76', '0', '0.904', '#surprise', '#admiration', 'positive', '0.791', 'curative', 'heal', 'medicine', 'get_cured', 'take_care']
senticnet['anti_stalinist'] = ['0', '0', '-0.70', '-0.83', '#fear', '#disgust', 'negative', '-0.76', 'settle_disagreement', 'hitler', 'republican', 'conservative', 'unchanging']
senticnet['anti_statist'] = ['0.633', '-0.92', '0', '0.869', '#surprise', '#admiration', 'positive', '0.808', 'advocate', 'bigot', 'dogmatist', 'partisan', 'advocator']
senticnet['anti_submarine_weapon'] = ['0', '0', '-0.83', '-0.8', '#fear', '#disgust', 'negative', '-0.81', 'armory', 'tank', 'hydrogen_bomb', 'rifle', 'use_hunt']
senticnet['anti_tank_battery'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'start_over', 'queue', 'find_line', 'take_turn', 'long_line']
senticnet['anti_tank_grenade'] = ['0', '0', '0.818', '-0.99', '#anger', '#disgust', 'negative', '-0.90', 'weapon', 'aerial_bomb', 'machine_gun', 'cannon', 'handgun']
senticnet['anti_tank_gun'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'tank', 'tank_destroyer', 'armored_car', 'armored_vehicle', 'armored_personnel_carrier']
senticnet['anti_tank_weapon'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'tank', 'tank_destroyer', 'armored_car', 'armored_vehicle', 'armored_personnel_carrier']
senticnet['anti_theft'] = ['0.857', '0', '0', '0.968', '#joy', '#admiration', 'positive', '0.912', 'inerrable', 'flawless', 'unfailing', 'reliable', 'sure']
senticnet['anti_theist'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'non-believer', 'doubter', 'agnostic', 'freethinker', 'sceptic']
senticnet['anti_tobacco'] = ['-0.99', '0', '0.056', '-0.98', '#sadness', '#disgust', 'negative', '-0.67', 'smoke', 'cigarette', 'bad_air', 'pollution', 'cigar']
senticnet['anti_zionism'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'racism', 'intolerant', 'xenophobic', 'racist', 'nazism']
senticnet['anti_zionist'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'racism', 'intolerant', 'xenophobic', 'racist', 'nazism']
senticnet['antiacid'] = ['-0.53', '0', '0.954', '-0.54', '#anger', '#disgust', 'negative', '-0.67', 'antacid', 'histamine_blocker', 'gastric_antacid', 'alkalizer', 'ranitidine']
senticnet['antiaircraft'] = ['0', '0', '0.968', '-0.79', '#anger', '#disgust', 'negative', '-0.88', 'anti-tank', 'antitank', 'anti-aircraft', 'anti-armor', 'howitzer']
senticnet['antialias'] = ['0.93', '0.833', '0', '0', '#joy', '#interest', 'positive', '0.881', 'benday_process', 'technique', 'musketry', 'simulation', 'proficiency']
senticnet['antiallergic'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['antianxiety_drug'] = ['-0.86', '0', '0.646', '-0.88', '#sadness', '#disgust', 'negative', '-0.8', 'minor_tranquilizer', 'put_to_sleep', 'tranquilizer', 'anxiolytic', 'anxiolytic_drug']
senticnet['antiarrhythmic'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'spironolactone', 'spironolactone_drug']
senticnet['antibacterial'] = ['0.768', '-0.63', '0', '0.717', '#joy', '#admiration', 'positive', '0.705', 'antibiotic_drug', 'medication', 'kill_germ', 'antibiotic', 'bacitracin']
senticnet['antibacterial_action'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['antibacterial_activity'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['antibacterial_activity_against'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['antibacterial_agent'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['antibacterial_ointment'] = ['-0.81', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.82', 'peanut', 'leave_over', 'floppy', 'circular_motion', 'store_vegetable']
senticnet['antibacterial_soap'] = ['0', '0.086', '-0.06', '0.12', '#interest', '#admiration', 'positive', '0.046', 'gel_toothpaste', 'tartar_control_toothpaste', 'fluoride_mouthwash', 'toothpaste', 'hair_lotion']
senticnet['antibiotic'] = ['-0.99', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.90', 'medicine', 'analgesic', 'ibuprofen', 'painkiller', 'pain_reliever']
senticnet['antibiotic_drug'] = ['0.768', '-0.63', '0', '0.717', '#joy', '#admiration', 'positive', '0.705', 'antibacterial', 'medication', 'kill_germ', 'antibiotic', 'bacitracin']
senticnet['antibiotic_ointment'] = ['-0.81', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.82', 'peanut', 'leave_over', 'floppy', 'circular_motion', 'store_vegetable']
senticnet['antibiotic_penicillin'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['antibiotic_residue'] = ['0', '0', '0.709', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'debasement', 'dirtiness', 'pollution', 'taint', 'impureness']
senticnet['antibiotic_resistant_strain'] = ['-0.85', '0', '0.549', '-0.93', '#sadness', '#disgust', 'negative', '-0.78', 'coccidioidomycosis', 'coccidiomycosis', 'mycosis', 'desert_rheumatism', 'cryptococcosis']
senticnet['antiblack'] = ['0.779', '-0.82', '0', '0.66', '#joy', '#surprise', 'positive', '0.753', 'homophobic', 'loaded', 'racist', 'jaundiced', 'discriminatory']
senticnet['antic'] = ['0.9', '-0.80', '0', '0.568', '#joy', '#surprise', 'positive', '0.757', 'joke', 'dirty_trick', 'prank', 'trick', 'caper']
senticnet['anticholinergic'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['antichrist'] = ['-0.96', '0', '0.501', '-0.59', '#sadness', '#disgust', 'negative', '-0.68', 'calamity', 'catastrophe', 'tragedy', 'disaster', 'cataclysm']
senticnet['anticipant'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'expectant', 'expectantly', 'anticipative', 'fevered', 'hectic']
senticnet['anticipate'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'hopeful', 'hopefully', 'look_forward', 'probable', 'optimism']
senticnet['anticipated'] = ['0.896', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.829', 'upcoming', 'forthcoming', 'debut', 'release', 'imminent']
senticnet['anticipation'] = ['0', '0.66', '0', '0', '#interest', '#interest', 'positive', '0.66', 'hope', 'hopeful', 'ambitious', 'desire', 'faithful']
senticnet['anticipative'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'expectant', 'expectantly', 'anticipant', 'fevered', 'hectic']
senticnet['anticipator'] = ['-0.98', '0', '0.599', '-0.93', '#sadness', '#disgust', 'negative', '-0.83', 'diviner', 'intellect', 'anticipant', 'intellectual', 'illusionist']
senticnet['anticipatory_anxiety'] = ['-0.79', '-0.71', '-0.96', '-0.95', '#fear', '#disgust', 'negative', '-0.49', 'fear', 'caution', 'deep_sadness', 'bad_feeling', 'psychoneurosis']
senticnet['anticipatory_breach'] = ['-0.10', '0', '-0.05', '-0.24', '#sadness', '#disgust', 'negative', '-0.13', 'breach_of_warranty', 'breach_of_trust', 'partial_breach', 'breach_of_contract', 'constructive_breach']
senticnet['anticlimactic'] = ['-0.83', '0', '0.912', '-0.67', '#sadness', '#anger', 'negative', '-0.80', 'dissatisfaction', 'displeasure', 'disappointment', 'boredom', 'ennui']
senticnet['anticlimax'] = ['-0.83', '0', '0.912', '-0.67', '#sadness', '#anger', 'negative', '-0.80', 'dissatisfaction', 'displeasure', 'disappointment', 'boredom', 'ennui']
senticnet['anticommunist'] = ['0', '0', '-0.70', '-0.83', '#fear', '#disgust', 'negative', '-0.76', 'settle_disagreement', 'hitler', 'republican', 'conservative', 'unchanging']
senticnet['anticompetitive'] = ['0.658', '-0.52', '0', '0.626', '#joy', '#admiration', 'positive', '0.602', 'point_in_time', 'threshold', 'birth', 'commencement', 'incipience']
senticnet['anticompetitive_behavior'] = ['0.658', '-0.52', '0', '0.626', '#joy', '#admiration', 'positive', '0.602', 'point_in_time', 'threshold', 'birth', 'commencement', 'incipience']
senticnet['anticompetitive_conduct'] = ['0.658', '-0.52', '0', '0.626', '#joy', '#admiration', 'positive', '0.602', 'point_in_time', 'threshold', 'birth', 'commencement', 'incipience']
senticnet['anticompetitive_effect'] = ['0.658', '-0.52', '0', '0.626', '#joy', '#admiration', 'positive', '0.602', 'point_in_time', 'threshold', 'birth', 'commencement', 'incipience']
senticnet['anticon_label'] = ['-0.93', '0', '0.861', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'knotty', 'contorted', 'writhed', 'crooked', 'anfractuous']
senticnet['anticonvulsant_drug'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['antideficiency_act'] = ['-0.88', '0', '0.977', '-0.81', '#sadness', '#anger', 'negative', '-0.89', 'execute', 'run', 'compel', 'oblige', 'enforce']
senticnet['antidemocratic'] = ['-0.89', '0', '0.678', '-0.62', '#sadness', '#anger', 'negative', '-0.73', 'undisciplined', 'anarchic', 'uncontrolled', 'irrepressible', 'uncontrollable']
senticnet['antidiarrheal'] = ['0.701', '-0.76', '0', '0.904', '#surprise', '#admiration', 'positive', '0.791', 'curative', 'heal', 'medicine', 'get_cured', 'take_care']
senticnet['antidiscrimination_law'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
senticnet['antidiuretic_hormone'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'keep_alive', 'body_system', 'body_working', 'blood_pressure', 'heart']
senticnet['antidiuretic_hormone_adh'] = ['-0.74', '0', '0.723', '-0.59', '#sadness', '#anger', 'negative', '-0.68', 'tone', 'tautness', 'tonus', 'condition', 'status']
senticnet['antidote'] = ['-0.60', '0', '0.544', '-0.62', '#sadness', '#disgust', 'negative', '-0.59', 'remedy', 'cure', 'therapeutic', 'curative', 'atropine']
senticnet['antient'] = ['-0.99', '0', '0.947', '-0.88', '#sadness', '#anger', 'negative', '-0.94', 'atrociousness', 'inhumanity', 'heinousness', 'atrocity', 'barbarity']
senticnet['antifascist'] = ['-0.97', '0', '0.62', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'obstructor', 'obstructer', 'technophobe', 'obstructionist', 'naysayer']
senticnet['antifeedant'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['antifeminism'] = ['-0.68', '0', '0.949', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'chauvinism', 'male_chauvinism', 'sexism', 'sexual_discrimination', 'discrimination']
senticnet['antifeminist'] = ['-0.68', '0', '0.949', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'chauvinism', 'sexism', 'antifeminism', 'sexual_discrimination', 'discrimination']
senticnet['antiferromagnet'] = ['0.038', '0', '0.031', '0.124', '#joy', '#admiration', 'positive', '0.044', 'rotate', 'rotate_movement', 'change_direction', 'move', 'go_around']
senticnet['antifungal'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['antifungal_activity'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['antifungal_agent'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'antifungal', 'lamisil', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['antifungal_cream'] = ['-0.81', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.82', 'peanut', 'leave_over', 'floppy', 'circular_motion', 'store_vegetable']
senticnet['antifungal_drug'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['antigenicity'] = ['-0.78', '0', '0.6', '-0.61', '#sadness', '#disgust', 'negative', '-0.66', 'suggestibility', 'sensitivity', 'capacity', 'reactivity', 'predisposition']
senticnet['antigraft'] = ['0', '0', '-0.77', '-0.86', '#fear', '#disgust', 'negative', '-0.82', 'abduction', 'punishment', 'emergency', 'illiteracy', 'heal']
senticnet['antihero'] = ['-0.76', '0', '0', '-0.95', '#sadness', '#disgust', 'negative', '-0.85', 'demigod', 'everyman', 'narrator', 'hero', 'anti-hero']
senticnet['antihistamine'] = ['0.969', '0.817', '0', '0.828', '#joy', '#admiration', 'positive', '0.871', 'ibuprofen', 'painkiller', 'medicine', 'analgesic', 'phenobarbital']
senticnet['antihypertensive'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'spironolactone', 'spironolactone_drug']
senticnet['antihypertensive_agent'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['antihypertensive_drug'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'antihypertensive', 'spironolactone']
senticnet['antiinflammatory'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'chemical_irritant', 'gingerol', 'piperine', 'pepper_spray']
senticnet['antimalarial'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['antimension'] = ['0.849', '-0.50', '0', '0.845', '#joy', '#admiration', 'positive', '0.732', 'robe', 'gown', 'veil', 'vest', 'cassock']
senticnet['antimicrobial'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['antimicrobial_activity'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['antimicrobial_activity_against'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['antimicrobial_soap'] = ['-0.60', '0', '0.992', '-0.94', '#anger', '#disgust', 'negative', '-0.84', 'cleanse', 'lather', 'floss', 'decontaminate', 'bathe']
senticnet['antimissile'] = ['-0.04', '0', '-0.06', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'make_war', 'conquer_enemy', 'use_military', 'gain_territory', 'keep_peace']
senticnet['antimycotic'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'antifungal', 'antifungal_agent', 'fungicide', 'lamisil', 'lamisil_agent']
senticnet['antimycotic_agent'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'antifungal', 'antifungal_agent', 'fungicide', 'antimycotic', 'lamisil']
senticnet['antineutron'] = ['0.713', '0.738', '0', '0', '#joy', '#interest', 'positive', '0.726', 'nucleon', 'baryon', 'proton', 'antiproton', 'neutron']
senticnet['antinociception'] = ['0.95', '-0.95', '0', '0.769', '#joy', '#surprise', 'positive', '0.89', 'adrenocorticotropic', 'adrenocorticotrophic', 'stimulant', 'stimulating', 'stimulative']
senticnet['antinociceptive'] = ['0.95', '-0.95', '0', '0.769', '#joy', '#surprise', 'positive', '0.89', 'adrenocorticotropic', 'adrenocorticotrophic', 'stimulant', 'stimulating', 'stimulative']
senticnet['antinociceptive_effect'] = ['0.95', '-0.95', '0', '0.769', '#joy', '#surprise', 'positive', '0.89', 'adrenocorticotropic', 'adrenocorticotrophic', 'stimulant', 'stimulating', 'stimulative']
senticnet['antinomian'] = ['0.889', '-0.69', '0', '0.908', '#joy', '#admiration', 'positive', '0.83', 'pharisaic', 'pious', 'sanctimonious', 'reverent', 'worshipful']
senticnet['antinomianism'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'heterodoxy', 'iconoclasm', 'nonconformism', 'nonconformance', 'orientation']
senticnet['antinomy'] = ['-0.56', '0', '0.593', '-0.59', '#anger', '#disgust', 'negative', '-0.58', 'contradiction_in_term', 'paradox', 'falsehood', 'falsity', 'untruth']
senticnet['antioxidant'] = ['-0.02', '0', '0.052', '0', '#sadness', '#anger', 'negative', '-0.04', 'exercise', 'exercise_regularly', 'physical_exercise', 'moderation', 'drink_plenty_water']
senticnet['antioxidant_activity'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'chemical_irritant', 'gingerol', 'piperine', 'pepper_spray']
senticnet['antiparallel'] = ['0', '0', '-0.73', '-0.79', '#fear', '#disgust', 'negative', '-0.76', 'analogue', 'analog', 'line_of_latitude', 'latitude', 'collimate']
senticnet['antiparasitic'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['antipasti'] = ['0.062', '0', '0.027', '0.055', '#joy', '#admiration', 'positive', '0.03', 'porridge', 'meal', 'broth', 'hamburger', 'hearty']
senticnet['antipasto'] = ['0.763', '0.9', '0', '0', '#joy', '#interest', 'positive', '0.832', 'breakfast', 'box_cereal', 'calcium', 'drink', 'carton_milk']
senticnet['antipathize'] = ['0', '0', '0.725', '-0.95', '#anger', '#disgust', 'negative', '-0.84', 'eschew', 'mind', 'detest', 'shun', 'deplore']
senticnet['antipathy'] = ['-0.12', '0.193', '0.593', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'distaste', 'aversion', 'disapproval', 'disapprove', 'disapproving']
senticnet['antipathy_toward'] = ['-0.12', '0.193', '0.593', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'distaste', 'aversion', 'disapproval', 'disapprove', 'disapproving']
senticnet['antiperiplanar'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
senticnet['antiphospholipid_syndrome'] = ['-0.50', '0', '0.542', '-0.51', '#anger', '#disgust', 'negative', '-0.51', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'hirschsprung_disease']
senticnet['antiplatelet'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'spironolactone', 'spironolactone_drug']
senticnet['antiplatelet_agent'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'spironolactone', 'spironolactone_drug']
senticnet['antipode'] = ['-0.83', '0', '0.716', '0', '#sadness', '#anger', 'negative', '-0.77', 'diametrically', 'antonymous', 'diametric', 'antipolar', 'antipodean']
senticnet['antipodean'] = ['-0.83', '0', '0.712', '0', '#sadness', '#anger', 'negative', '-0.77', 'antipode', 'antonymous', 'diametric', 'antipolar', 'diametrically']
senticnet['antipolar'] = ['-0.83', '0', '0.712', '0', '#sadness', '#anger', 'negative', '-0.77', 'antipode', 'antonymous', 'diametric', 'diametrically', 'antipodean']
senticnet['antiprism'] = ['0.641', '-0.6', '0', '0.957', '#joy', '#admiration', 'positive', '0.733', 'regular_tetrahedron', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'ideal_solid']
senticnet['antiproton'] = ['0.713', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.726', 'antineutron', 'baryon', 'proton', 'nucleon', 'neutron']
senticnet['antiprotozoal'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['antiquate'] = ['-0.81', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.79', 'stifle', 'forgotten', 'old', 'touch_ground', 'seldom']
senticnet['antiquated'] = ['0', '-0.85', '0', '0.946', '#surprise', '#admiration', 'positive', '0.9', 'venerable', 'dignity', 'goodness', 'reverence', 'patriarchal']
senticnet['antique'] = ['0.07', '0.177', '0.028', '0.133', '#interest', '#admiration', 'positive', '0.088', 'worth', 'shilling', 'merit', 'monetary_value', 'value']
senticnet['antique_car'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'fancy_car', 'car_model', 'expensive_car', 'old_car', 'car']
senticnet['antique_jewellery'] = ['0.532', '-0.60', '0', '0.875', '#surprise', '#admiration', 'positive', '0.67', 'gem', 'adornment', 'jewel', 'bead', 'bangle']
senticnet['antique_shop'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'card_deck', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['antique_store'] = ['0', '0.093', '0', '0.066', '#interest', '#admiration', 'positive', '0.08', 'gallery_open', 'museum', 'art_show', 'collectible', 'vintage']
senticnet['antiqued'] = ['-0.75', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.83', 'plated', 'pewter', 'brass', 'silver', 'pendant']
senticnet['antiquity'] = ['-0.69', '0', '0.957', '-0.92', '#anger', '#disgust', 'negative', '-0.85', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['antisemite'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'racism', 'intolerant', 'xenophobic', 'racist', 'nazism']
senticnet['antisemitic'] = ['0.778', '-0.73', '0', '0.559', '#joy', '#surprise', 'positive', '0.689', 'sex', 'inversion', 'sexual_inversion', 'lesbianism', 'sapphism']
senticnet['antisemitic_propaganda'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'racism', 'intolerant', 'xenophobic', 'racist', 'nazism']
senticnet['antisemitic_rhetoric'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'racism', 'intolerant', 'xenophobic', 'racist', 'nazism']
senticnet['antisemitic_violence'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'racism', 'intolerant', 'xenophobic', 'racist', 'nazism']
senticnet['antisemitism'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'racism', 'intolerant', 'xenophobic', 'racist', 'nazism']
senticnet['antiseptic'] = ['0.864', '0.989', '0', '0', '#joy', '#interest', 'positive', '0.926', 'antibacterial', 'anti-bacterial', 'astringent', 'disinfectant', 'anti-fungal']
senticnet['antisocial'] = ['0', '0', '0.896', '-0.96', '#anger', '#disgust', 'negative', '-0.92', 'personality_disorder', 'sociopathic', 'psychopathic_personality', 'disturbance', 'schizoid']
senticnet['antisocial_personality'] = ['0', '0', '0.896', '-0.96', '#anger', '#disgust', 'negative', '-0.92', 'antisocial', 'sociopathic', 'psychopathic_personality', 'disturbance', 'schizoid']
senticnet['antispam'] = ['-0.05', '-0.03', '0.023', '-0.09', '#sadness', '#disgust', 'negative', '-0.03', 'moldy_food', 'celery', 'paper_product', 'orange_juice', 'meat']
senticnet['antispasmodic'] = ['0.701', '-0.76', '0', '0.904', '#surprise', '#admiration', 'positive', '0.791', 'curative', 'heal', 'medicine', 'get_cured', 'take_care']
senticnet['antistia'] = ['-0.76', '0', '0.995', '-0.94', '#anger', '#disgust', 'negative', '-0.90', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'excretion']
senticnet['antisymmetric'] = ['0.807', '-0.88', '0', '0.718', '#joy', '#surprise', 'positive', '0.802', 'sex', 'gayness', 'sexual_gayness', 'lesbianism', 'sapphism']
senticnet['antitank'] = ['0', '0', '0.975', '-0.78', '#anger', '#disgust', 'negative', '-0.88', 'anti-tank', 'antiaircraft', 'anti-aircraft', 'anti-armor', 'howitzer']
senticnet['antitheist'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'non-believer', 'doubter', 'agnostic', 'freethinker', 'sceptic']
senticnet['antithetical'] = ['0', '0', '0.75', '-0.77', '#anger', '#disgust', 'negative', '-0.76', 'congruent', 'incongruous', 'contradictory', 'equivocal', 'incongruent']
senticnet['antitrust_immunity'] = ['0.658', '-0.52', '0', '0.626', '#joy', '#admiration', 'positive', '0.602', 'point_in_time', 'threshold', 'birth', 'commencement', 'incipience']
senticnet['antitrust_liability'] = ['0.658', '-0.52', '0', '0.626', '#joy', '#admiration', 'positive', '0.602', 'point_in_time', 'threshold', 'birth', 'commencement', 'incipience']
senticnet['antituberculous_therapy'] = ['-0.53', '0', '0.608', '-0.93', '#anger', '#disgust', 'negative', '-0.69', 'reprieve', 'respite', 'hiatus', 'abatement', 'suspension']
senticnet['antitumor'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['antitumor_activity'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'imperium', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['antitussive'] = ['0.701', '-0.76', '0', '0.904', '#surprise', '#admiration', 'positive', '0.791', 'curative', 'heal', 'medicine', 'get_cured', 'take_care']
senticnet['antivenin'] = ['0', '0', '-0.73', '-0.81', '#fear', '#disgust', 'negative', '-0.77', 'flab', 'bad_hair_day', 'lose_friend', 'alcoholism', 'brain_tumor']
senticnet['antiviral'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'protease_inhibitor', 'indinavir', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['antiviral_activity'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'imperium', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['antiviral_effect'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'imperium', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['antler'] = ['0.063', '0.089', '0.036', '0.146', '#interest', '#admiration', 'positive', '0.066', 'horn', 'jew_harp', 'brass_instrument', 'trumpet', 'crash_cymbal']
senticnet['antler_growth'] = ['0.063', '0.089', '0.036', '0.146', '#interest', '#admiration', 'positive', '0.066', 'horn', 'jew_harp', 'brass_instrument', 'trumpet', 'crash_cymbal']
senticnet['antler_moth'] = ['-0.10', '-0.04', '0', '-0.14', '#sadness', '#disgust', 'negative', '-0.06', 'armyworm', 'cerapteryx_graminis', 'noctuid', 'heliothis_moth', 'moth']
senticnet['antoine_crozat'] = ['-0.78', '0', '0.944', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'loser', 'gambler', 'mortal', 'better', 'bettor']
senticnet['anton_dolin'] = ['0.12', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.09', 'ballroom', 'perform_art', 'nightclub', 'ballerina', 'venue']
senticnet['anton_siluanov'] = ['0.911', '-0.71', '0', '0.552', '#joy', '#surprise', 'positive', '0.726', 'deliver_goods', 'succeed', 'win', 'bring_home_bacon', 'come_through']
senticnet['antonn_dvok'] = ['0.591', '-0.91', '0', '0.605', '#surprise', '#admiration', 'positive', '0.704', 'farandole', 'folk_dance', 'polka', 'dance', 'typical_dance']
senticnet['antony_tudor'] = ['0.12', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.09', 'ballroom', 'perform_art', 'nightclub', 'ballerina', 'venue']
senticnet['antonymous'] = ['-0.82', '0', '0.719', '0', '#sadness', '#anger', 'negative', '-0.77', 'antipode', 'diametrically', 'diametric', 'antipolar', 'antipodean']
senticnet['antorbital'] = ['0.886', '-0.99', '0', '0.542', '#joy', '#surprise', 'positive', '0.808', 'jowl', 'lower_jaw', 'mandible', 'lantern_jaw', 'lantern_jawr_bone']
senticnet['antrectomy'] = ['-0.70', '0', '0.639', '-0.56', '#sadness', '#anger', 'negative', '-0.63', 'fibroma', 'adenoma', 'chondroma', 'enchondroma', 'benign_tumor']
senticnet['antsy'] = ['-0.7', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.80', 'fretful', 'fidgetiness', 'fidgety', 'stewing', 'impatiently']
senticnet['antwerp'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'feudal_lord', 'lord', 'master', 'overlord', 'king']
senticnet['anunnaki'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
senticnet['anus'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'bowel', 'scrotum', 'shoulder', 'liver', 'waist']
senticnet['anveena'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'animal_home', 'habitat', 'beehive']
senticnet['anxiety'] = ['0', '0.33', '-1', '0', '#interest', '#fear', 'negative', '-0.33', 'anxious', 'worried', 'worry', 'stressed', 'stress']
senticnet['anxiety_disorder'] = ['-0.86', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.90', 'illiteracy', 'schizophrenia', 'censorship', 'ill_health', 'big_monster']
senticnet['anxiety_hysteria'] = ['-0.55', '0', '0.736', '-0.71', '#anger', '#disgust', 'negative', '-0.66', 'neurosis', 'neuroticism', 'psychoneurosis', 'hysterocatalepsy', 'hysterical_neurosis']
senticnet['anxiogenic'] = ['0.95', '-0.95', '0', '0.769', '#joy', '#surprise', 'positive', '0.89', 'adrenocorticotropic', 'adrenocorticotrophic', 'stimulant', 'stimulating', 'stimulative']
senticnet['anxiolytic'] = ['-0.86', '0', '0.646', '-0.88', '#sadness', '#disgust', 'negative', '-0.8', 'minor_tranquilizer', 'put_to_sleep', 'antianxiety_drug', 'tranquilizer', 'tranquilizer_drug']
senticnet['anxiolytic_drug'] = ['-0.86', '0', '0.646', '-0.88', '#sadness', '#disgust', 'negative', '-0.8', 'minor_tranquilizer', 'put_to_sleep', 'antianxiety_drug', 'anxiolytic', 'tranquilizer']
senticnet['anxiolytic_effect'] = ['0.95', '-0.95', '0', '0.769', '#joy', '#surprise', 'positive', '0.89', 'adrenocorticotropic', 'adrenocorticotrophic', 'stimulant', 'stimulating', 'stimulative']
senticnet['anxious'] = ['-0.74', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.76', 'hysteria', 'concerned', 'distressfully', 'perturbing', 'disturbing']
senticnet['anxious_wait'] = ['0', '0', '0', '-0.70', '#disgust', '#disgust', 'negative', '-0.70', 'uncertainty', 'uncertain_environment', 'insecure', 'blur', 'anxiety']
senticnet['anxiously'] = ['-0.95', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.86', 'uneasily', 'apprehensively', 'apprehensive', 'worried', 'solicitousness']
senticnet['anxiouslyly'] = ['-0.94', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.87', 'uneasily', 'apprehensive', 'anxiously', 'worried', 'solicitousness']
senticnet['anxiousness'] = ['-0.80', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.89', 'worried', 'solicitous', 'solicitously', 'solicitude', 'solicitousness']
senticnet['anyhoo'] = ['-0.94', '0', '0.557', '-0.71', '#sadness', '#disgust', 'negative', '-0.74', 'sad', 'disappointing', 'unlucky', 'disengaged', 'cry']
senticnet['anyhow'] = ['-0.99', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.93', 'dispose', 'ditch', 'exchange_goods', 'discard', 'delete']
senticnet['anymore'] = ['-0.99', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.93', 'dispose', 'ditch', 'exchange_goods', 'discard', 'delete']
senticnet['anyway'] = ['-0.95', '0', '0.721', '-0.75', '#sadness', '#disgust', 'negative', '-0.81', 'cook_up', 'ready', 'make', 'cook', 'prepare']
senticnet['aopen'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'computer_part', 'system', 'cpu', 'pencil_pen', 'monitor']
senticnet['aorta'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'chest', 'artery', 'vein', 'internal_organ', 'under_head']
senticnet['aortic'] = ['0.777', '-0.89', '0', '0.508', '#joy', '#surprise', 'positive', '0.727', 'stretching', 'enlarge', 'enlargement', 'get_bigger', 'distension']
senticnet['aortic_insufficiency'] = ['-0.81', '0', '0.684', '-0.81', '#sadness', '#disgust', 'negative', '-0.77', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['aortic_regurgitation'] = ['-0.54', '0', '0.67', '-0.92', '#anger', '#disgust', 'negative', '-0.71', 'pericarditis', 'rubor', 'redness', 'endocarditis', 'inflammation']
senticnet['aortic_root'] = ['-0.81', '0', '0.684', '-0.81', '#sadness', '#disgust', 'negative', '-0.77', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['aortic_stenosis'] = ['-0.81', '0', '0.684', '-0.81', '#sadness', '#disgust', 'negative', '-0.77', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'stenosis', 'mitral_stenosis']
senticnet['aoul'] = ['-0.93', '0', '0.791', '0', '#sadness', '#anger', 'negative', '-0.86', 'hopelessness', 'hopelessly', 'despairing', 'pessimistically', 'pessimistic']
senticnet['aouw'] = ['0.605', '0.647', '-0.26', '0.323', '#joy', '#interest', 'positive', '0.327', 'friendly', 'brotherly', 'amicableness', 'amicably', 'amicable']
senticnet['ap_photo_orlando_sentinel'] = ['0', '0', '0', '0.964', '#admiration', '#admiration', 'positive', '0.964', 'swimming_pool', 'swim', 'swimsuit', 'lake', 'have_fun']
senticnet['ap_photo_winslow_townson'] = ['0.609', '0.597', '-0.23', '0.398', '#joy', '#interest', 'positive', '0.341', 'near', 'pick_up', 'togetherness', 'gay', 'capture']
senticnet['apa_membership'] = ['0.02', '0.24', '-0.11', '0.267', '#interest', '#admiration', 'positive', '0.103', 'organize_group', 'come_together', 'bring_together', 'business_group', 'business_firm']
senticnet['apace'] = ['0', '0.933', '0', '0.784', '#interest', '#admiration', 'positive', '0.859', 'chop-chop', 'speedily', 'rapidly', 'quickly', 'study']
senticnet['apache_solr'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'carbon_fiber']
senticnet['apache_tribe'] = ['0.052', '0.099', '0', '0.067', '#interest', '#admiration', 'positive', '0.073', 'police_rank', 'head_guy', 'chief', 'leader', 'commander']
senticnet['apachecon'] = ['-0.79', '0', '0.948', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['apalachicola_bay'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'harpoon', 'armory', 'lake', 'river', 'bay']
senticnet['apart'] = ['-0.18', '0', '-0.10', '0', '#sadness', '#fear', 'negative', '-0.14', 'tear_apart', 'time_out', 'painful_injury', 'break_apart', 'rest_time']
senticnet['apartment'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'home', 'furniture', 'kitchen', 'flat']
senticnet['apartment_block'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'home', 'furniture', 'kitchen', 'flat']
senticnet['apartment_building'] = ['0.591', '-0.74', '0', '0.672', '#surprise', '#admiration', 'positive', '0.669', 'room', 'chillax', 'hotel_room', 'furniture_store', 'living_room']
senticnet['apartment_complex'] = ['0', '0', '0', '-0.09', '#disgust', '#disgust', 'negative', '-0.09', 'subway', 'bus', 'train_station', 'railway_station', 'bus_stop']
senticnet['apartment_house'] = ['0.868', '0.863', '0', '0', '#joy', '#interest', 'positive', '0.865', 'capitol', 'government_building', 'house', 'white_house', 'government']
senticnet['apartment_next_door'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'home', 'furniture', 'kitchen', 'flat']
senticnet['apatetic_coloration'] = ['0.62', '-0.59', '0', '0.967', '#joy', '#admiration', 'positive', '0.728', 'aposematic_coloration', 'warning_coloration', 'protective_coloration', 'cryptic_coloration', 'coloration']
senticnet['apatheia'] = ['0.971', '-0.71', '0', '0.656', '#joy', '#surprise', 'positive', '0.781', 'wellbeing', 'human_flourishing', 'successfulness', 'upbeat', 'strength']
senticnet['apathetic'] = ['-0.83', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.87', 'unconcern', 'nonchalance', 'unconcernedly', 'apathetically', 'indifferently']
senticnet['apathetically'] = ['-0.81', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.86', 'impassiveness', 'impassivity', 'emotionlessness', 'unemotionality', 'stolidity']
senticnet['apathy'] = ['-0.79', '0', '0.759', '-0.70', '#sadness', '#anger', 'negative', '-0.75', 'emotionlessness', 'impassivity', 'impassiveness', 'phlegm', 'stolidity']
senticnet['ape'] = ['-0.62', '0', '0.667', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'copycat', 'emulator', 'epigonr', 'imitator', 'epigon']
senticnet['apelike'] = ['-0.5', '0', '0.97', '-0.52', '#anger', '#disgust', 'negative', '-0.66', 'simulated', 'apish', 'parrotlike', 'imitative', 'mimic']
senticnet['aper'] = ['-0.67', '0', '0.759', '-0.80', '#anger', '#disgust', 'negative', '-0.74', 'epigone', 'epigon', 'parrot', 'mortal', 'person']
senticnet['aperient'] = ['-0.50', '0', '0.582', '-0.57', '#anger', '#disgust', 'negative', '-0.55', 'purgative', 'cathartic', 'physic', 'laxative', 'phenolphthalein']
senticnet['aperiodic'] = ['0.09', '0.07', '0', '0.246', '#joy', '#admiration', 'positive', '0.135', 'hand_motion', 'water_form', 'water_relate', 'hand_gesture', 'arm_movement']
senticnet['aperitif'] = ['0.032', '0.075', '0', '0', '#joy', '#interest', 'positive', '0.053', 'alcohol', 'stimulate_appetite', 'beverage', 'adult_drink', 'bartender']
senticnet['apertural_view'] = ['0.969', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.772', 'aspheric', 'aspherical', 'prolate', 'rounded', 'crescent']
senticnet['aperture'] = ['-0.03', '0.079', '0', '0.142', '#interest', '#admiration', 'positive', '0.064', 'build_open', 'use_farm', 'house_open', 'open', 'home_entrance']
senticnet['aperture_diaphragm'] = ['-0.82', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.84', 'block', 'obstruct', 'close', 'clogged', 'occluded']
senticnet['aperture_priority'] = ['0.021', '0.081', '0.017', '0.113', '#interest', '#admiration', 'positive', '0.05', 'aspect', 'perspective', 'look_round', 'scenic', 'look_through']
senticnet['aperture_ring'] = ['-0.82', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.84', 'block', 'obstruct', 'close', 'clogged', 'occluded']
senticnet['aperture_telescope'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'binoculars', 'read_glass', 'eyeglass', 'camera', 'monocle']
senticnet['apex'] = ['0.062', '0.1', '0.074', '0.117', '#interest', '#admiration', 'positive', '0.051', 'angle', 'semicircle', 'facet', 'edge', 'altitude']
senticnet['apex_angle'] = ['0.06', '-0.03', '0.037', '0.075', '#joy', '#admiration', 'positive', '0.034', 'semicircle', 'trapezoid', 'circle', 'triangular', 'facet']
senticnet['apex_predator'] = ['-0.53', '0', '0.648', '-0.70', '#anger', '#disgust', 'negative', '-0.63', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['aphakia'] = ['-0.79', '0', '0.927', '-0.95', '#anger', '#disgust', 'negative', '-0.89', 'disability', 'disablement', 'vision_defect', 'color_blindness', 'color_vision_deficiency']
senticnet['aphakic'] = ['-0.63', '0', '0.625', '-0.98', '#sadness', '#disgust', 'negative', '-0.75', 'visually_impaired_person', 'blind_person', 'myope', 'mortal', 'hyperope']
senticnet['aphasia'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'nominal_aphasia', 'anomic_aphasia']
senticnet['aphasic'] = ['-0.56', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.63', 'inarticulate', 'aphonic', 'speechless', 'unarticulated', 'dumb']
senticnet['aphasiology'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'nominal_aphasia', 'anomic_aphasia']
senticnet['aphek'] = ['0.849', '-0.89', '0', '0.924', '#surprise', '#admiration', 'positive', '0.89', 'nonintellectual', 'lowbrow', 'lowbrowed', 'mindless', 'uncultivated']
senticnet['aphelinidae'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'giant_hornet', 'hornet', 'hornet_wasp']
senticnet['aphid'] = ['0', '-0.09', '0.123', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'gypsy_moth', 'japanese_beetle', 'cucumber_beetle', 'mosquito', 'earwig']
senticnet['aphis_gossypii'] = ['0', '-0.09', '0.123', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'gypsy_moth', 'japanese_beetle', 'cucumber_beetle', 'mosquito', 'earwig']
senticnet['aphonia'] = ['-0.84', '0', '0.829', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'upset', 'dysarthria', 'defect_of_speech', 'disorder', 'lallation']
senticnet['aphonic'] = ['-0.56', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.63', 'aphasic', 'inarticulate', 'speechless', 'unarticulated', 'dumb']
senticnet['aphorism'] = ['0', '0', '-0.89', '-0.91', '#fear', '#disgust', 'negative', '-0.90', 'laconic', 'concise', 'laconically', 'spartan', 'few']
senticnet['aphoristic'] = ['-0.67', '0', '0.936', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'unprepared', 'extemporary', 'extempore', 'impromptu', 'unrehearsed']
senticnet['aphrodisia'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'concupiscence', 'lecherousness', 'lustfulness', 'amorousness']
senticnet['aphrodisiac'] = ['0.807', '0.761', '0', '0', '#joy', '#interest', 'positive', '0.784', 'stimulant', 'sexual_desire', 'melodic_noise', 'loud', 'lol']
senticnet['api'] = ['-0.95', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.84', 'hold_pencil', 'food_scrap', 'paper_product', 'waste_paper', 'cover']
senticnet['apiarist'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'lair', 'habitat', 'animal_home']
senticnet['apiary'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'lair', 'habitat', 'animal_home']
senticnet['apical_cilium'] = ['-0.95', '0', '0.892', '-0.81', '#sadness', '#anger', 'negative', '-0.88', 'blurry', 'foggy', 'hazy', 'muzzy', 'blurred']
senticnet['apical_dominance'] = ['-0.57', '0', '0.767', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'verboseness', 'verbosity', 'prolixity', 'turgidness', 'flatulence']
senticnet['apical_fourth'] = ['-0.95', '0', '0.892', '-0.81', '#sadness', '#anger', 'negative', '-0.88', 'blurry', 'foggy', 'hazy', 'muzzy', 'blurred']
senticnet['apical_impulse'] = ['-0.73', '0', '0.904', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'unshapely', 'shapeless', 'lumpy', 'deformed', 'malformed']
senticnet['apical_line'] = ['-0.95', '0', '0.892', '-0.81', '#sadness', '#anger', 'negative', '-0.88', 'blurry', 'foggy', 'hazy', 'muzzy', 'blurred']
senticnet['apical_margin'] = ['0.942', '-0.61', '0', '0.908', '#joy', '#admiration', 'positive', '0.82', 'adornment', 'cigar_band', 'hatband', 'neckband', 'pinstripe']
senticnet['apical_portion'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expandible', 'expansive', 'erectile', 'cavernous']
senticnet['apical_third'] = ['-0.7', '0', '0.617', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'silvery', 'achromatic', 'bluish_black', 'hueless', 'pitchy']
senticnet['apico_alveolar'] = ['-0.56', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.63', 'aphasic', 'inarticulate', 'speechless', 'unarticulated', 'dumb']
senticnet['apiece'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'hundred_year', 'twelve', 'day_month', 'time_measurement']
senticnet['apiece_draw'] = ['-0.09', '0.147', '-0.11', '0.205', '#interest', '#admiration', 'positive', '0.036', 'question_starter', 'resolution', 'available', 'explanation', 'request']
senticnet['apio'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'cabbage', 'lettuce', 'broccoli']
senticnet['apis_cerana'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'hornet', 'vespid', 'vespid_wasp']
senticnet['apis_mellifera'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'hornet', 'vespid', 'vespid_wasp']
senticnet['apish'] = ['-0.5', '0', '0.97', '-0.52', '#anger', '#disgust', 'negative', '-0.66', 'simulated', 'imitative', 'parrotlike', 'apelike', 'mimic']
senticnet['aplastic_anemia'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['aplenty'] = ['0.935', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.967', 'galore', 'abound', 'goody', 'everywhere', 'lot']
senticnet['aplomado_falcon'] = ['-0.74', '0', '0.963', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['aplomb'] = ['0.724', '-0.92', '0', '0.854', '#surprise', '#admiration', 'positive', '0.833', 'tranquility', 'serenity', 'disposition', 'repose', 'placidity']
senticnet['apnea'] = ['-0.03', '-0.03', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'eat_poison', 'commit_suicide', 'drug_addiction', 'drunk_drive', 'jump_off_cliff']
senticnet['apneic'] = ['-0.03', '-0.03', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'eat_poison', 'commit_suicide', 'drug_addiction', 'drunk_drive', 'jump_off_cliff']
senticnet['apnoea'] = ['-0.03', '-0.03', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'eat_poison', 'commit_suicide', 'drug_addiction', 'drunk_drive', 'jump_off_cliff']
senticnet['apocalypse'] = ['-0.96', '0', '0.501', '-0.59', '#sadness', '#disgust', 'negative', '-0.68', 'calamity', 'catastrophe', 'tragedy', 'disaster', 'cataclysm']
senticnet['apocalypse_trilogy'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amuck', 'murderousness', 'misocainea', 'misogyny']
senticnet['apocalyptic'] = ['-0.95', '0', '0.739', '-0.59', '#sadness', '#anger', 'negative', '-0.76', 'foreboding', 'precursory', 'premonitory', 'prophetic', 'prophetical']
senticnet['apocalyptic_literature'] = ['-0.96', '0', '0.501', '-0.59', '#sadness', '#disgust', 'negative', '-0.68', 'calamity', 'catastrophe', 'tragedy', 'disaster', 'cataclysm']
senticnet['apocalyptic_vision'] = ['0.84', '-0.56', '0', '0.561', '#joy', '#surprise', 'positive', '0.654', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['apocalyptical'] = ['-0.95', '0', '0.739', '-0.59', '#sadness', '#anger', 'negative', '-0.76', 'foreboding', 'precursory', 'premonitory', 'apocalyptic', 'prophetic']
senticnet['apocalypticism'] = ['-0.99', '0', '0.835', '-0.58', '#sadness', '#anger', 'negative', '-0.80', 'beginning', 'origin', 'root', 'wellspring', 'source']
senticnet['apocope'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'pronunciation', 'sibilation', 'mispronunciation', 'homophony']
senticnet['apocrypha'] = ['0.802', '0.878', '0', '0', '#joy', '#interest', 'positive', '0.84', 'gospel', 'apocryphal', 'gnostic', 'non-canonical', 'epistle']
senticnet['apocryphal'] = ['0.809', '0.886', '0', '0', '#joy', '#interest', 'positive', '0.848', 'gospel', 'apocrypha', 'gnostic', 'non-canonical', 'epistle']
senticnet['apocryphal_book'] = ['0', '0.106', '-0.05', '0.163', '#interest', '#admiration', 'positive', '0.072', 'band', 'kit', 'oblivion', 'rock_band', 'high_school_band']
senticnet['apocryphon'] = ['0', '0.106', '-0.05', '0.163', '#interest', '#admiration', 'positive', '0.072', 'band', 'kit', 'oblivion', 'rock_band', 'high_school_band']
senticnet['apollonian'] = ['0.84', '-0.56', '0', '0.561', '#joy', '#surprise', 'positive', '0.654', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['apollyon'] = ['-0.66', '0', '0.859', '-0.82', '#anger', '#disgust', 'negative', '-0.78', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['apologetic'] = ['-0.84', '0', '0.572', '-0.68', '#sadness', '#disgust', 'negative', '-0.69', 'reply', 'response', 'sass', 'sassing', 'backtalk']
senticnet['apologetically'] = ['0', '-0.95', '0', '0.701', '#surprise', '#admiration', 'positive', '0.829', 'sheepishly', 'shyly', 'condescendingly', 'coyly', 'mockingly']
senticnet['apologia'] = ['-0.84', '0', '0.572', '-0.68', '#sadness', '#disgust', 'negative', '-0.69', 'reply', 'response', 'sass', 'sassing', 'backtalk']
senticnet['apologie'] = ['0.058', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.054', 'letter_slot', 'envelope', 'address_label', 'mailman', 'postage_stamp']
senticnet['apologist'] = ['-0.67', '0', '0.715', '-0.89', '#anger', '#disgust', 'negative', '-0.76', 'obstinate', 'unregenerated', 'impenitent', 'unconverted', 'unpersuaded']
senticnet['apologize'] = ['-0.82', '0', '-0.98', '0', '#sadness', '#fear', 'negative', '-0.90', 'emotion', 'regret', 'forgiveness', 'pardon', 'beg']
senticnet['apologue'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'narrative', 'bedtime', 'bed_time', 'long_story', 'saga']
senticnet['apology'] = ['0.875', '0', '0', '0.802', '#joy', '#admiration', 'positive', '0.839', 'polite', 'care', 'ask_for_forgiveness', 'forgive', 'take_care']
senticnet['apomictic'] = ['-0.68', '0', '0.779', '-0.86', '#anger', '#disgust', 'negative', '-0.77', 'agamic', 'agamous', 'agamogenetic', 'parthenogenetic', 'asexual']
senticnet['apomixis'] = ['-0.77', '0', '0.764', '-0.54', '#sadness', '#anger', 'negative', '-0.69', 'pullulation', 'asexual_reproduction', 'blastogenesis', 'budding', 'fission']
senticnet['apomorphic'] = ['-0.68', '0', '0.956', '-0.90', '#anger', '#disgust', 'negative', '-0.84', 'giant_moa', 'anomalopteryx', 'moa', 'moa_bird', 'flightless_bird']
senticnet['apomorphine'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['apophatic'] = ['0.995', '-0.69', '0', '0.528', '#joy', '#surprise', 'positive', '0.74', 'grasp', 'hug', 'nestle', 'snuggle', 'grip']
senticnet['apophatic_theology'] = ['0.779', '-0.75', '0', '0.526', '#joy', '#surprise', 'positive', '0.687', 'privateness', 'covertness', 'bosom', 'isolation', 'confidentiality']
senticnet['apophenia'] = ['-0.77', '0', '0.981', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'trip_up', 'stumble', 'misjudge', 'err', 'fall_for']
senticnet['apoplectic'] = ['-0.91', '0', '0.704', '-0.70', '#sadness', '#anger', 'negative', '-0.77', 'rant', 'declamation', 'harangue', 'ranting', 'tirade']
senticnet['aposematic'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'blatancy', 'patency', 'obviousness']
senticnet['aposematic_coloration'] = ['0.62', '-0.59', '0', '0.967', '#joy', '#admiration', 'positive', '0.728', 'protective_coloration', 'warning_coloration', 'apatetic_coloration', 'cryptic_coloration', 'coloration']
senticnet['aposematism'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'blatancy', 'patency', 'obviousness']
senticnet['apostacy'] = ['0', '0', '0.753', '-0.72', '#anger', '#disgust', 'negative', '-0.73', 'sinful', 'human_emotion', 'call', 'demand', 'yearn']
senticnet['apostasy'] = ['-0.80', '0', '0', '-0.81', '#sadness', '#disgust', 'negative', '-0.80', 'irreverence', 'disrespect', 'impiety', 'atheism', 'sacrilege']
senticnet['apostate'] = ['-0.66', '0', '0.709', '-0.88', '#anger', '#disgust', 'negative', '-0.75', 'untrue', 'unfaithful', 'treacherous', 'perfidious', 'punic']
senticnet['apostelic_father'] = ['0.87', '0', '0', '0.976', '#joy', '#admiration', 'positive', '0.923', 'apostle', 'act', 'church', 'god', 'disciple']
senticnet['apostle'] = ['0.874', '0', '0', '0.979', '#joy', '#admiration', 'positive', '0.926', 'apostelic_father', 'act', 'church', 'god', 'disciple']
senticnet['apostle_saint'] = ['0', '0.248', '-0.08', '0.173', '#interest', '#admiration', 'positive', '0.113', 'god', 'minister', 'evangelist', 'rector', 'goddess']
senticnet['apostolic_administration'] = ['0.8', '-0.84', '0', '0.868', '#surprise', '#admiration', 'positive', '0.837', 'commonwealth', 'province', 'administrative_district', 'administrative_division', 'territorial_division']
senticnet['apostolic_chancery'] = ['-0.81', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.79', 'stifle', 'forgotten', 'old', 'touch_ground', 'seldom']
senticnet['apostolic_exarch'] = ['0.8', '-0.84', '0', '0.868', '#surprise', '#admiration', 'positive', '0.837', 'commonwealth', 'province', 'administrative_district', 'administrative_division', 'territorial_division']
senticnet['apostolic_exarchate'] = ['0.8', '-0.84', '0', '0.868', '#surprise', '#admiration', 'positive', '0.837', 'commonwealth', 'province', 'administrative_district', 'administrative_division', 'territorial_division']
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senticnet['apostrophe'] = ['-0.97', '0', '-0.98', '0', '#sadness', '#fear', 'negative', '-0.98', 'apostrophe', 'hyphen', 'punctuation', 'comma', 'semicolon']
senticnet['apothecary'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'pharmaceutical_chemist', 'health_professional', 'primary_care_provider', 'health_care_provider']
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senticnet['apotheosis'] = ['0.84', '-0.56', '0', '0.561', '#joy', '#surprise', 'positive', '0.654', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
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senticnet['appanage'] = ['0.639', '-0.97', '0', '0.648', '#surprise', '#admiration', 'positive', '0.755', 'benefit', 'fringe_benefit', 'provision', 'gratuity', 'tip']
senticnet['apparatus'] = ['0', '0.955', '0', '0.982', '#interest', '#admiration', 'positive', '0.968', 'luxurious', 'opulent', 'magnificent', 'sumptuousness', 'sumptuous']
senticnet['apparel'] = ['0.032', '0.075', '0', '0', '#joy', '#interest', 'positive', '0.053', 'nightgown', 'garment', 'clothes', 'dress', 'shirt']
senticnet['apparel_brand'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['apparel_manufacturer'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['apparel_retailer'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['apparent'] = ['0.958', '-0.83', '0', '0.81', '#joy', '#surprise', 'positive', '0.869', 'discernible', 'perceptible', 'noticeable', 'detectable', 'observable']
senticnet['apparent_inability'] = ['-0.78', '0', '0.707', '-0.86', '#sadness', '#disgust', 'negative', '-0.78', 'mental_block', 'uncreativeness', 'stupidity', 'illiteracy', 'analphabetism']
senticnet['apparent_indifference'] = ['0', '0', '0.709', '-0.92', '#anger', '#disgust', 'negative', '-0.81', 'apathetic', 'unemotionality', 'passionless', 'stolidity', 'phlegm']
senticnet['apparent_lack'] = ['-0.50', '0', '0.807', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'powerlessness', 'impotence', 'impotency', 'inefficacy', 'inefficaciousness']
senticnet['apparent_magnitude'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['apparent_motion'] = ['-0.94', '0', '0.866', '-0.91', '#sadness', '#disgust', 'negative', '-0.90', 'movement', 'optical_illusion', 'motion', 'apparent_movement', 'mirage']
senticnet['apparent_movement'] = ['-0.94', '0', '0.866', '-0.91', '#sadness', '#disgust', 'negative', '-0.90', 'movement', 'apparent_motion', 'motion', 'optical_illusion', 'mirage']
senticnet['apparent_overdose'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'use_drug', 'do_drug', 'drug_use', 'drug', 'addiction']
senticnet['apparent_robbery'] = ['0.964', '-0.57', '0', '0.827', '#joy', '#admiration', 'positive', '0.789', 'mortal', 'murder_suspect', 'suspect', 'rape_suspect', 'person']
senticnet['apparently'] = ['0', '0.928', '0', '0.813', '#interest', '#admiration', 'positive', '0.871', 'patently', 'plainly', 'ostensibly', 'plain', 'seemingly']
senticnet['apparently_insurmountable'] = ['-0.73', '0', '0.732', '-0.53', '#sadness', '#anger', 'negative', '-0.66', 'unconquerable', 'insuperable', 'inexpugnable', 'indomitable', 'unsubduable']
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senticnet['appeal'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'witness', 'new_law', 'make_judgment', 'court_action', 'contact_lawyer']
senticnet['appeal_court_decision'] = ['-0.06', '0.32', '-0.10', '0.248', '#interest', '#admiration', 'positive', '0.101', 'decide', 'select', 'selection', 'make_choice', 'choice']
senticnet['appealing'] = ['0.956', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.976', 'foot-tapping', 'catchy', 'memorable', 'hook', 'jingle']
senticnet['appealingly'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicado', 'unpronounceable', 'impassive', 'incommunicative', 'silent']
senticnet['appear'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'come', 'heap', 'arrive', 'accept', 'tow']
senticnet['appear_beautiful'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'hot_girl', 'good_looking', 'good_appearance', 'beauty', 'enjoy_view']
senticnet['appear_dumb'] = ['0', '0', '0.87', '-0.87', '#anger', '#disgust', 'negative', '-0.87', 'mess_up', 'dumb', 'mess', 'goof', 'bungle']
senticnet['appear_smart'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'smart', 'intelligent', 'smart_person', 'become_better', 'wisdom']
senticnet['appear_suddenly'] = ['-0.10', '0', '-0.09', '0.067', '#sadness', '#fear', 'negative', '-0.04', 'corner_well', 'gain_speed', 'travel_road', 'go_fast', 'drive_down_street']
senticnet['appearance'] = ['0.769', '0', '0', '0', '#joy', '#joy', 'positive', '0.769', 'look', 'glimpse', 'ship_part', 'quick_look', 'vision']
senticnet['appease'] = ['0.683', '-0.70', '0', '0.527', '#joy', '#surprise', 'positive', '0.638', 'tranquilize', 'tranquillize', 'calm_down', 'quieten', 'calm']
senticnet['appeasement'] = ['-0.83', '0', '0.904', '0', '#sadness', '#anger', 'negative', '-0.87', 'disappointed', 'thwarted', 'frustrated', 'foiled', 'discomfited']
senticnet['appeasing'] = ['-0.60', '0', '0.694', '-0.65', '#anger', '#disgust', 'negative', '-0.65', 'conciliative', 'placating', 'placative', 'placatory', 'pacific']
senticnet['appellant'] = ['0.817', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.824', 'witness', 'prosecutor', 'legal_process', 'lawyer', 'jury']
senticnet['appellant_argument'] = ['-0.50', '0', '0.89', '-0.78', '#anger', '#disgust', 'negative', '-0.72', 'excess', 'degree', 'grade', 'level', 'immoderateness']
senticnet['appellate'] = ['-0.78', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.75', 'irrelevant', 'debatable', 'dismissed', 'arguable', 'moot']
senticnet['appellate_attorney'] = ['0', '0.146', '-0.05', '0.077', '#interest', '#admiration', 'positive', '0.057', 'lawyer', 'negotiator', 'arbiter', 'judge_jury', 'decide_guilt']
senticnet['appellate_body'] = ['0', '0.146', '0', '0.077', '#interest', '#admiration', 'positive', '0.111', 'negotiator', 'judge_jury', 'arbiter', 'decide_guilt', 'become_judge']
senticnet['appellate_court'] = ['-0.06', '0.32', '-0.10', '0.248', '#interest', '#admiration', 'positive', '0.101', 'decide', 'select', 'selection', 'make_choice', 'choice']
senticnet['appellate_court_decision'] = ['-0.06', '0.32', '-0.10', '0.248', '#interest', '#admiration', 'positive', '0.101', 'decide', 'select', 'selection', 'make_choice', 'choice']
senticnet['appellate_decision'] = ['-0.06', '0.32', '-0.10', '0.248', '#interest', '#admiration', 'positive', '0.101', 'decide', 'select', 'selection', 'make_choice', 'choice']
senticnet['appellate_jurisdiction'] = ['0.657', '-0.88', '0', '0.631', '#joy', '#surprise', 'positive', '0.723', 'cognisance', 'knowingness', 'awareness', 'feel', 'consciousness']
senticnet['appellate_lawyer'] = ['0', '0.146', '-0.05', '0.077', '#interest', '#admiration', 'positive', '0.057', 'lawyer', 'negotiator', 'arbiter', 'judge_jury', 'decide_guilt']
senticnet['appellation'] = ['0.072', '0.244', '-0.20', '0.252', '#interest', '#admiration', 'positive', '0.091', 'doe', 'sweet', 'live_organism', 'love', 'alive']
senticnet['appellative'] = ['0.904', '-0.58', '0', '0.736', '#joy', '#admiration', 'positive', '0.743', 'naming', 'denotative', 'designative', 'extensional', 'referent']
senticnet['append'] = ['0.737', '0', '0', '0.728', '#joy', '#admiration', 'positive', '0.732', 'fasten', 'entwine', 'blend', 'combine', 'consort']
senticnet['appendage'] = ['0', '0.103', '0', '0.111', '#interest', '#admiration', 'positive', '0.107', 'body_extremity', 'projection', 'limb', 'organ', 'tailpiece']
senticnet['appendant'] = ['0.993', '-0.89', '0', '0.891', '#joy', '#surprise', 'positive', '0.926', 'mounted', 'affixed', 'basifixed', 'glued', 'pasted']
senticnet['appendix'] = ['0', '0.136', '0', '0.196', '#interest', '#admiration', 'positive', '0.166', 'describe', 'explain', 'answer', 'description', 'example']
senticnet['apperceptive_agnosia'] = ['-0.73', '0', '0.955', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'visual_agnosia', 'auditory_agnosia', 'brain_disorder', 'encephalopathy', 'brain_disease']
senticnet['appertain'] = ['0.657', '-0.88', '0', '0.631', '#joy', '#surprise', 'positive', '0.723', 'cognisance', 'knowingness', 'awareness', 'feel', 'consciousness']
senticnet['appetence'] = ['-0.87', '0', '0.919', '-0.89', '#anger', '#disgust', 'negative', '-0.89', 'appetite', 'appetency', 'craving', 'addiction', 'desire']
senticnet['appetency'] = ['-0.87', '0', '0.919', '-0.89', '#anger', '#disgust', 'negative', '-0.89', 'appetite', 'craving', 'appetence', 'addiction', 'desire']
senticnet['appetent'] = ['0.919', '-0.71', '0', '0.569', '#joy', '#surprise', 'positive', '0.735', 'desirous', 'avid', 'thirsty', 'devouring', 'envious']
senticnet['appetite'] = ['0.769', '0', '0', '0', '#joy', '#joy', 'positive', '0.769', 'make_breakfast', 'enjoy_meal', 'put_food_into_mouth', 'prepare_meal', 'prepare_breakfast']
senticnet['appetite_stimulant'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'infective_agent', 'satisfier', 'quickener', 'agent']
senticnet['appetite_stimulation'] = ['0.95', '-0.95', '0', '0.769', '#joy', '#surprise', 'positive', '0.89', 'adrenocorticotropic', 'adrenocorticotrophic', 'stimulant', 'stimulating', 'stimulative']
senticnet['appetite_suppressant'] = ['-0.72', '0', '0.973', '-0.57', '#sadness', '#anger', 'negative', '-0.76', 'corpulency', 'blubber', 'fat', 'embonpoint', 'stoutness']
senticnet['appetite_suppression'] = ['0.795', '0', '0', '0.787', '#joy', '#admiration', 'positive', '0.791', 'entree', 'soup', 'wine_glass', 'salt_pepper_shaker', 'plate_silverware']
senticnet['appetitive'] = ['0.795', '0', '0', '0.787', '#joy', '#admiration', 'positive', '0.791', 'entree', 'soup', 'wine_glass', 'salt_pepper_shaker', 'plate_silverware']
senticnet['appetitive_behavior'] = ['0.795', '0', '0', '0.787', '#joy', '#admiration', 'positive', '0.791', 'entree', 'soup', 'wine_glass', 'salt_pepper_shaker', 'plate_silverware']
senticnet['appetizer'] = ['0.062', '0', '0.027', '0.055', '#joy', '#admiration', 'positive', '0.03', 'porridge', 'meal', 'broth', 'hamburger', 'hearty']
senticnet['appetizingness'] = ['-0.79', '0', '0.992', '-0.88', '#anger', '#disgust', 'negative', '-0.89', 'flavorsomeness', 'flavoursomeness', 'savoriness', 'succulence', 'succulency']
senticnet['applaud'] = ['0.354', '-0.23', '-0.05', '0', '#joy', '#surprise', 'positive', '0.178', 'happiness', 'emotional_reaction', 'appreciation', 'clap', 'hypnotic_effect']
senticnet['applaudable'] = ['0.754', '0', '0', '0.736', '#joy', '#admiration', 'positive', '0.745', 'noteworthy', 'valuable', 'deserve', 'meritable', 'sacred']
senticnet['applauded_enthusiastically'] = ['0', '0', '-0.76', '-0.82', '#fear', '#disgust', 'negative', '-0.79', 'stage', 'thespian', 'actor', 'theatre', 'drama']
senticnet['applause'] = ['0.973', '0', '0', '0.878', '#joy', '#admiration', 'positive', '0.925', 'praise', 'perform_on_stage', 'enjoy_show', 'giggle', 'clap']
senticnet['apple'] = ['0', '0.085', '0', '0.146', '#interest', '#admiration', 'positive', '0.115', 'food', 'garden', 'fruit', 'tree', 'outdoor']
senticnet['apple_brandy'] = ['0.899', '0.991', '0', '0', '#joy', '#interest', 'positive', '0.945', 'bourbon', 'scotch', 'vodka', 'brandy', 'wine']
senticnet['apple_cider'] = ['0.079', '-0.02', '0.023', '0', '#joy', '#surprise', 'positive', '0.027', 'wine', 'lemonade', 'juice', 'whisky', 'soft_drink']
senticnet['apple_cider_vinegar'] = ['0.95', '-0.86', '0', '0.947', '#joy', '#admiration', 'positive', '0.92', 'content', 'lore', 'folklore', 'cognitive_content', 'story']
senticnet['apple_crumble'] = ['0.644', '-0.94', '0', '0.729', '#surprise', '#admiration', 'positive', '0.773', 'gem', 'banana_bread', 'date_bread', 'nut_bread', 'quick_bread']
senticnet['apple_inc_iphone'] = ['0.807', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.881', 'fruit', 'mobile_phone', 'drinkable', 'savory', 'eat_fruit']
senticnet['apple_iphone'] = ['0.807', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.881', 'fruit', 'mobile_phone', 'drinkable', 'savory', 'eat_fruit']
senticnet['apple_itunes_store'] = ['0.101', '-0.04', '0.036', '0', '#joy', '#surprise', 'positive', '0.038', 'song', 'rap', 'jingle', 'chord', 'lyric']
senticnet['apple_juice'] = ['0.087', '-0.00', '0', '0', '#joy', '#surprise', 'positive', '0.045', 'juice', 'soft_drink', 'orange_juice', 'ice_tea', 'cider']
senticnet['apple_maggot'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'nuisance', 'annoyance', 'irritant', 'pain_in_ass', 'troublesome']
senticnet['apple_orchard'] = ['0.053', '0.094', '0', '0.136', '#interest', '#admiration', 'positive', '0.094', 'barn_animal', 'domesticate', 'grassland', 'sheep', 'cow']
senticnet['apple_pie'] = ['0.079', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.072', 'ice_cream', 'a_lot_of_food', 'pumpkin_pie', 'muffin', 'cupcake']
senticnet['apple_polisher'] = ['-0.55', '0', '0.588', '-0.65', '#anger', '#disgust', 'negative', '-0.59', 'crawler', 'bootlicker', 'fawner', 'web', 'groveler']
senticnet['apple_rust'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'rust_fungus', 'fungus']
senticnet['apple_strudel'] = ['0.092', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.071', 'bake_treat', 'round_pastry', 'bake_good', 'bake_goods', 'sweet_food']
senticnet['apple_tart'] = ['0.092', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.071', 'bake_treat', 'round_pastry', 'bake_good', 'bake_goods', 'sweet_food']
senticnet['apple_tree'] = ['0', '0.081', '0.03', '0.126', '#interest', '#admiration', 'positive', '0.059', 'plant', 'tree', 'fruit', 'apple', 'red']
senticnet['apple_weblog'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'wildcat', 'informal', 'loose', 'drumhead']
senticnet['apple_wwdc'] = ['-0.79', '0', '0.948', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['applesauce'] = ['0.056', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.053', 'leftover', 'dusty', 'mayonnaise', 'leave_over', 'savory']
senticnet['appliance_maker'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'builder']
senticnet['appliance_manufacturer'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'builder']
senticnet['appliance_store'] = ['0.101', '0.099', '0', '0.169', '#joy', '#admiration', 'positive', '0.123', 'family_home', 'apartment', 'renovation', 'kitchen', 'electronics']
senticnet['applicability'] = ['0', '0.813', '0', '0.955', '#interest', '#admiration', 'positive', '0.884', 'pertinence', 'pertinency', 'research', 'study', 'project']
senticnet['applicable'] = ['0.966', '0.896', '0', '0', '#joy', '#interest', 'positive', '0.931', 'pertinent', 'relevant', 'hydride', 'rider', 'germane']
senticnet['applicant'] = ['0.862', '0.709', '0', '0', '#joy', '#interest', 'positive', '0.785', 'invoice', 'bill', 'check', 'mastercard', 'payment_counter']
senticnet['application'] = ['0', '0', '0', '0.075', '#admiration', '#admiration', 'positive', '0.075', 'job_application', 'resume', 'respond', 'questionnaire', 'help_understand']
senticnet['application_lepa'] = ['0', '-0.90', '0', '0.791', '#surprise', '#admiration', 'positive', '0.849', 'precise', 'accuracy', 'correct', 'performance', 'measure']
senticnet['application_type_award'] = ['0', '0.92', '0', '0.881', '#interest', '#admiration', 'positive', '0.901', 'award', 'mathematical_science', 'standard_grant', 'continue_grant', 'different_application']
senticnet['applied_externally'] = ['-0.81', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.82', 'peanut', 'leave_over', 'floppy', 'circular_motion', 'store_vegetable']
senticnet['applied_kinesiology'] = ['-0.84', '0', '0.585', '-0.85', '#sadness', '#disgust', 'negative', '-0.76', 'subject', 'discipline', 'study', 'concern', 'research']
senticnet['applied_math'] = ['0', '0.867', '0', '0.87', '#interest', '#admiration', 'positive', '0.869', 'statistic', 'applied_mathematics', 'mathematics', 'linear_programming', 'probability_theory']
senticnet['applied_mathematics'] = ['0', '0.87', '0', '0.871', '#interest', '#admiration', 'positive', '0.871', 'statistic', 'applied_math', 'mathematics', 'linear_programming', 'probability_theory']
senticnet['applied_topically'] = ['0.701', '-0.76', '0', '0.904', '#surprise', '#admiration', 'positive', '0.791', 'curative', 'heal', 'medicine', 'get_cured', 'take_care']
senticnet['applique'] = ['0.548', '-0.61', '0', '0.556', '#surprise', '#admiration', 'positive', '0.572', 'patchwork', 'darn', 'mend', 'stitch', 'stitchery']
senticnet['apply'] = ['0', '0.196', '-0.13', '0.192', '#interest', '#admiration', 'positive', '0.086', 'employ', 'attempt', 'resume', 'labor', 'police_work']
senticnet['apply_sunscreen'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'change_tire', 'fix_flat_tire', 'play_rugby', 'wear_silly_hat', 'become_adult']
senticnet['applying_fertilizer'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'mop_floor', 'absorb_moisture', 'sit_on_beach', 'scrub_floor', 'cut_grass']
senticnet['applying_sunscreen'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'change_tire', 'fix_flat_tire', 'play_rugby', 'wear_silly_hat', 'become_adult']
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senticnet['aristocratic'] = ['0.952', '0.844', '0', '0.96', '#joy', '#admiration', 'positive', '0.919', 'fashion', 'demand', 'kindness', 'better_person', 'warm_feeling']
senticnet['aristocratic_privilege'] = ['0.059', '0.092', '0', '0.107', '#interest', '#admiration', 'positive', '0.086', 'royalty', 'monarch', 'throne', 'emperor', 'king_queen']
senticnet['aristocratical'] = ['0.028', '0.12', '0.068', '0.176', '#interest', '#admiration', 'positive', '0.064', 'elite', 'number_person', 'count_person', 'small_group', 'enumeration']
senticnet['aristotle'] = ['0.971', '-0.71', '0', '0.656', '#joy', '#surprise', 'positive', '0.781', 'wellbeing', 'human_flourishing', 'successfulness', 'upbeat', 'strength']
senticnet['arithmetic'] = ['0.076', '0.096', '-0.08', '0.167', '#interest', '#admiration', 'positive', '0.063', 'statistic', 'mathematical_function', 'calculation', 'list_number', 'calculate']
senticnet['arithmetical'] = ['0.076', '0.096', '-0.08', '0.167', '#interest', '#admiration', 'positive', '0.063', 'statistic', 'mathematical_function', 'calculation', 'list_number', 'calculate']
senticnet['arithmometer'] = ['-0.03', '0.201', '-0.13', '0.171', '#interest', '#admiration', 'positive', '0.049', 'scientific_statement', 'hypothetical', 'conjecture', 'theory', 'fact']
senticnet['arjan_singh'] = ['0.74', '-0.53', '0', '0.641', '#joy', '#admiration', 'positive', '0.64', 'marshal', 'sky_marshal', 'lawman', 'law_officer', 'peace_officer']
senticnet['ark_angel'] = ['-0.69', '0', '0.756', '-0.99', '#anger', '#disgust', 'negative', '-0.81', 'waster', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['arkadia_jazz_all'] = ['0.977', '-0.93', '0', '0.583', '#joy', '#surprise', 'positive', '0.831', 'giant', 'shinny', 'supernova', 'supergiant', 'binary_star']
senticnet['arkansaw'] = ['0.934', '-0.57', '0', '0.767', '#joy', '#admiration', 'positive', '0.76', 'morosoph', 'simpleton', 'flibbertigibbet', 'foolish_woman', 'simple']
senticnet['arkham_asylum'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'only_bird']
senticnet['arkham_knight'] = ['-0.86', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.86', 'demon', 'fool', 'cannibalism', 'jolly', 'treachery']
senticnet['arkon'] = ['-0.04', '-0.02', '0.027', '-0.03', '#sadness', '#disgust', 'negative', '-0.01', 'precious_metal', 'olympic_medal', 'shiny_metal', 'many_different_kinds', 'valuable_metal']
senticnet['arlecchino'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'lordosis', 'kyphosis', 'humpback', 'spinal_curvature', 'abnormality']
senticnet['arleigh_burke'] = ['0.688', '-0.90', '0', '0.877', '#surprise', '#admiration', 'positive', '0.824', 'flag_officer', 'rear_admiral', 'admiral', 'full_admiral', 'vice_admiral']
senticnet['arlesburgh'] = ['-0.50', '0', '0.764', '-0.61', '#anger', '#disgust', 'negative', '-0.62', 'doctor', 'mountebank', 'charlatan', 'quack', 'physician']
senticnet['arm'] = ['0', '0', '-0.06', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'elbow', 'bandit', 'wrist', 'five_fingers', 'bendable']
senticnet['arm-in-arm'] = ['0', '0.8', '0', '0.79', '#interest', '#admiration', 'positive', '0.795', 'lock-step', 'corporatist', 'two-party', 'blindly', 'lockstep']
senticnet['arm_aloft'] = ['0.653', '0.635', '-0.24', '0.294', '#joy', '#interest', 'positive', '0.335', 'merrily', 'happily', 'mirthfully', 'gayly', 'blithely']
senticnet['arm_around_neck'] = ['0.177', '0.083', '0', '0.166', '#joy', '#admiration', 'positive', '0.142', 'keep_clothes_clean', 'soft_skin', 'make_pretty', 'cover_table', 'clean_cloth']
senticnet['arm_atomic_bomb'] = ['-0.82', '0', '0.839', '-0.72', '#sadness', '#anger', 'negative', '-0.79', 'disastrous', 'hunt_game', 'commit_genocide', 'kill', 'corner_prey']
senticnet['arm_bomb'] = ['-0.93', '0', '0.352', '-0.93', '#sadness', '#disgust', 'negative', '-0.74', 'bomb', 'war', 'weapon', 'soldier', 'gun']
senticnet['arm_buildup'] = ['0.942', '-0.79', '0', '0.901', '#joy', '#admiration', 'positive', '0.88', 'backup', 'accretion', 'deposition', 'deposit', 'increase']
senticnet['arm_candy'] = ['0.824', '0.86', '0', '0.983', '#interest', '#admiration', 'positive', '0.889', 'look_smart', 'social_acceptance', 'improve_appearance', 'improve_look', 'look_good']
senticnet['arm_control'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'dynamite', 'gunshot', 'missile', 'nuclear_bomb', 'fall_sky']
senticnet['arm_dealer'] = ['-0.87', '0', '0.574', '-0.73', '#sadness', '#disgust', 'negative', '-0.72', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['arm_force'] = ['0', '0.906', '0', '0.732', '#interest', '#admiration', 'positive', '0.819', 'military', 'attacker', 'army', 'soldier_group', 'samurai']
senticnet['arm_man'] = ['0', '0', '0.868', '-0.89', '#anger', '#disgust', 'negative', '-0.88', 'steal', 'commit_crime', 'storm_fortress', 'defence', 'go_after']
senticnet['arm_movement'] = ['0', '0.098', '-0.04', '0.265', '#interest', '#admiration', 'positive', '0.105', 'hand_gesture', 'water_motion', 'hand_motion', 'water_action', 'water_relate']
senticnet['arm_part'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'body_member', 'body_extremity', 'body_appendage', 'appendage', 'above_leg']
senticnet['arm_pub'] = ['0.853', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.916', 'tavern', 'cafe', 'nightclub', 'bar', 'liquor_store']
senticnet['arm_smuggler'] = ['-0.87', '0', '0.574', '-0.73', '#sadness', '#disgust', 'negative', '-0.72', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['arm_weapon'] = ['-0.96', '0', '0.761', '-0.96', '#sadness', '#disgust', 'negative', '-0.89', 'notice_problem', 'smoke_joint', 'shovel_snow', 'war', 'fly_around']
senticnet['arm_wrestle'] = ['-0.18', '-0.00', '-0.18', '0', '#sadness', '#fear', 'negative', '-0.11', 'pulverize', 'violent_action', 'smash', 'hit_hard', 'melt_away']
senticnet['arm_wrestler'] = ['0.833', '-0.97', '0', '0.91', '#surprise', '#admiration', 'positive', '0.906', 'combatant', 'fencer', 'belligerent', 'fighter', 'scrapper']
senticnet['armageddon'] = ['-0.96', '0', '0.501', '-0.59', '#sadness', '#disgust', 'negative', '-0.68', 'calamity', 'catastrophe', 'tragedy', 'disaster', 'cataclysm']
senticnet['armament'] = ['-0.90', '0', '0.517', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'outdated', 'noncurrent', 'disused', 'obsolete', 'superannuated']
senticnet['armament_industry'] = ['0.814', '-0.87', '0', '0.918', '#surprise', '#admiration', 'positive', '0.87', 'ship_company', 'company', 'work_force', 'workforce', 'full_complement']
senticnet['armani_suit'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'hankie', 'kleenex', 'facial_tissue', 'handkerchief', 'tissue']
senticnet['armchair'] = ['0.912', '0.946', '0', '0', '#joy', '#interest', 'positive', '0.929', 'sofa_bed', 'club_chair', 'sofa_hide_bed', 'chair', 'rock_chair']
senticnet['armed'] = ['-0.04', '0', '-0.08', '-0.13', '#fear', '#disgust', 'negative', '-0.08', 'carry_gun', 'find_victim', 'buy_weapon', 'bomb', 'buy_gun']
senticnet['armed_confrontation'] = ['-0.2', '-0.03', '0', '-0.23', '#sadness', '#disgust', 'negative', '-0.13', 'beat_up', 'fight', 'conflict', 'violence', 'fight_against']
senticnet['armed_cutter'] = ['0', '0', '0.08', '0', '#anger', '#anger', 'negative', '-0.08', 'tree_animal', 'small_furry', 'rodent', 'big_mouse', 'small_rodent']
senticnet['armed_force'] = ['-0.97', '0', '0.84', '-0.52', '#sadness', '#anger', 'negative', '-0.78', 'pis_aller', 'expedient', 'temporary_expedient', 'makeshift', 'stopgap']
senticnet['armed_forces'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'military', 'military_reserve', 'armed_service', 'military_machine', 'war_machine']
senticnet['armed_insurrection'] = ['-0.84', '0', '0.744', '-0.58', '#sadness', '#anger', 'negative', '-0.72', 'subversion', 'subversive_activity', 'termination', 'ending', 'conclusion']
senticnet['armed_men'] = ['-0.93', '0', '0.722', '-0.66', '#sadness', '#anger', 'negative', '-0.77', 'penetration', 'air_raid', 'air_attack', 'swoop', 'incursion']
senticnet['armed_rebellion'] = ['-0.84', '0', '0.744', '-0.58', '#sadness', '#anger', 'negative', '-0.72', 'subversion', 'subversive_activity', 'termination', 'ending', 'conclusion']
senticnet['armed_rebellion_against'] = ['0.887', '0.748', '0', '0.954', '#joy', '#admiration', 'positive', '0.863', 'political_action', 'political', 'mediocrity', 'discrimination', 'independent']
senticnet['armed_resistance'] = ['0', '0.112', '-0.04', '0.099', '#interest', '#admiration', 'positive', '0.054', 'profession', 'career', 'vocation', 'important_job', 'laborer']
senticnet['armed_revolt'] = ['-0.84', '0', '0.744', '-0.58', '#sadness', '#anger', 'negative', '-0.72', 'subversion', 'subversive_activity', 'termination', 'ending', 'conclusion']
senticnet['armed_robbery'] = ['-0.75', '0', '0.94', '0', '#sadness', '#anger', 'negative', '-0.84', 'steal_money', 'theft', 'stick_up', 'robbery', 'rob']
senticnet['armed_service'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'military', 'armed_forces', 'military_reserve', 'military_machine', 'war_machine']
senticnet['armed_standoff'] = ['-0.2', '-0.03', '0', '-0.23', '#sadness', '#disgust', 'negative', '-0.13', 'beat_up', 'fight', 'conflict', 'violence', 'fight_against']
senticnet['armed_struggle_against'] = ['0', '0.112', '-0.04', '0.099', '#interest', '#admiration', 'positive', '0.054', 'profession', 'career', 'vocation', 'important_job', 'laborer']
senticnet['armet'] = ['-0.59', '0', '0.887', '-0.71', '#anger', '#disgust', 'negative', '-0.73', 'helmet', 'basinet', 'casque', 'heaume', 'morion']
senticnet['armhole'] = ['-0.04', '-0.01', '0.026', '-0.12', '#sadness', '#disgust', 'negative', '-0.04', 'embroidery', 'cover_wind', 'use_needle', 'bandage', 'joint']
senticnet['armilla'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'anklet', 'ankle_bracelet', 'bangle']
senticnet['armillaria_mellea'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'apple_rust', 'fungus']
senticnet['armillaria_root_rot'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['armin_luistro'] = ['0.104', '0', '0', '0.088', '#joy', '#admiration', 'positive', '0.096', 'baby_flower', 'wee', 'bud', 'part_of_plant', 'leafy']
senticnet['arming_sword'] = ['0.93', '-0.78', '0', '0.873', '#joy', '#admiration', 'positive', '0.861', 'blade', 'brand', 'steel', 'claymore', 'sword']
senticnet['armless'] = ['0', '0', '-0.81', '-0.86', '#fear', '#disgust', 'negative', '-0.83', 'limbless', 'legless', 'white-haired', 'lanky', 'lizard']
senticnet['armlet'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'bangle', 'ankle_bracelet', 'armilla']
senticnet['armoire'] = ['0', '0.092', '0.016', '0.074', '#interest', '#admiration', 'positive', '0.05', 'beach_towel', 'pillowcase', 'bed_sheet', 'duvet_cover', 'comforter']
senticnet['armond_white'] = ['0.811', '-0.63', '0', '0.959', '#joy', '#admiration', 'positive', '0.8', 'drama_critic', 'theater_critic', 'professional', 'art_critic', 'literary_critic']
senticnet['armor'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'coat', 'suit', 'leather', 'sweater', 'belt']
senticnet['armor_penetration'] = ['0.887', '-0.95', '0', '0.913', '#surprise', '#admiration', 'positive', '0.917', 'high_quality', 'superiority', 'level', 'grade', 'degree']
senticnet['armor_piercing_ammunition'] = ['0', '0', '-0.92', '-0.91', '#fear', '#disgust', 'negative', '-0.92', 'automatic', 'firearm', 'gun', 'weapon', 'projectile']
senticnet['armored'] = ['0.778', '0.808', '0', '0', '#joy', '#interest', 'positive', '0.793', 'incontrovertible', 'bulletproof', 'incontestable', 'unassailable', 'unshakable']
senticnet['armored_car'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'tank', 'tank_destroyer', 'armored_vehicle', 'armoured_car', 'armored_personnel_carrier']
senticnet['armored_cruiser'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'submarine', 'lifeboat', 'titanic', 'large_ship', 'canoe']
senticnet['armored_frigate'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'submarine', 'lifeboat', 'titanic', 'large_ship', 'canoe']
senticnet['armored_limousine'] = ['0', '0', '-0.04', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'good_band', 'band', 'kit', 'procession', 'oblivion']
senticnet['armored_personnel_carrier'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'tank', 'tank_destroyer', 'armored_car', 'armoured_car', 'armored_vehicle']
senticnet['armored_suv'] = ['0', '0', '-0.04', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'good_band', 'band', 'kit', 'procession', 'oblivion']
senticnet['armored_vehicle'] = ['-0.71', '0', '0.585', '-0.51', '#sadness', '#anger', 'negative', '-0.60', 'tank', 'tank_destroyer', 'armored_car', 'armoured_car', 'armored_personnel_carrier']
senticnet['armorer'] = ['0', '0.115', '0', '0.105', '#interest', '#admiration', 'positive', '0.11', 'farrier', 'miner', 'horseshoer', 'blacksmith', 'smith']
senticnet['armory'] = ['0.85', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.887', 'harpoon', 'gulf', 'lake', 'river', 'bay']
senticnet['armour_protection'] = ['-0.90', '0', '0.517', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'outdated', 'noncurrent', 'disused', 'obsolete', 'superannuated']
senticnet['armoured_car'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'tank', 'tank_destroyer', 'armored_car', 'armored_vehicle', 'armored_personnel_carrier']
senticnet['armoured_cruiser'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'submarine', 'lifeboat', 'titanic', 'large_ship', 'canoe']
senticnet['armoured_fighting_vehicle'] = ['-0.56', '0', '0.714', '-0.89', '#anger', '#disgust', 'negative', '-0.72', 'tank', 'tank_destroyer', 'armored_car', 'armoured_car', 'armored_personnel_carrier']
senticnet['armoured_patrol'] = ['-0.56', '0', '0.714', '-0.89', '#anger', '#disgust', 'negative', '-0.72', 'tank', 'tank_destroyer', 'armored_car', 'armoured_car', 'armored_personnel_carrier']
senticnet['armoured_personnel_carrier'] = ['-0.56', '0', '0.714', '-0.89', '#anger', '#disgust', 'negative', '-0.72', 'tank', 'tank_destroyer', 'armored_car', 'armoured_car', 'armored_personnel_carrier']
senticnet['armoured_personnel_carrier_apc'] = ['-0.56', '0', '0.714', '-0.89', '#anger', '#disgust', 'negative', '-0.72', 'tank', 'tank_destroyer', 'armored_car', 'armoured_car', 'armored_personnel_carrier']
senticnet['armoured_recovery'] = ['-0.56', '0', '0.714', '-0.89', '#anger', '#disgust', 'negative', '-0.72', 'tank', 'tank_destroyer', 'armored_car', 'armoured_car', 'armored_personnel_carrier']
senticnet['armoured_vehicle'] = ['-0.56', '0', '0.714', '-0.89', '#anger', '#disgust', 'negative', '-0.72', 'tank', 'tank_destroyer', 'armored_car', 'armoured_car', 'armored_personnel_carrier']
senticnet['armourer'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'bodyguard', 'outrider', 'bodyguard_of_guard', 'beefeater', 'defender']
senticnet['armpit'] = ['0', '0', '0', '-0.06', '#disgust', '#disgust', 'negative', '-0.06', 'body', 'nervous_system', 'normal_human_body', 'human_body', 'head_shoulder']
senticnet['armstrong_siddeley_viper'] = ['0.526', '-0.99', '0', '0.673', '#surprise', '#admiration', 'positive', '0.732', 'faery', 'faerie', 'fairy', 'leprechaun', 'sandman']
senticnet['armstrong_whitworth_aircraft'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'cheapjack', 'pitchman']
senticnet['army'] = ['-0.04', '0', '-0.07', '-0.16', '#fear', '#disgust', 'negative', '-0.09', 'infantry', 'attacker', 'soldier', 'military', 'samurai']
senticnet['army_airfield'] = ['-0.97', '0', '0.737', '-0.97', '#sadness', '#disgust', 'negative', '-0.89', 'flare_path', 'airstrip', 'take_off', 'runway', 'grassland']
senticnet['army_boss'] = ['0', '0.121', '0', '0.068', '#interest', '#admiration', 'positive', '0.095', 'rank', 'military_rank', 'military_order', 'general', 'sergeant']
senticnet['army_checkpoint'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'corner_two_streets', 'street_corner', 'roadside', 'city_street', 'schoolyard']
senticnet['army_cutworm'] = ['-0.02', '-0.09', '0.11', '-0.13', '#anger', '#disgust', 'negative', '-0.04', 'beetle', 'gypsy_moth', 'mole_cricket', 'grasshopper', 'many_insects']
senticnet['army_fighter'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'man', 'army_person', 'sailor', 'fighter', 'workfighter']
senticnet['army_leader'] = ['0.023', '0.079', '0', '0', '#joy', '#interest', 'positive', '0.051', 'army_officer', 'army_rank', 'high_rank', 'military_position', 'army_boss']
senticnet['army_man'] = ['0', '0', '-0.07', '-0.03', '#fear', '#disgust', 'negative', '-0.05', 'army_person', 'fighter', 'police_man', 'warrior', 'sergent']
senticnet['army_navy'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'army_rank', 'army_leader', 'navy_rank', 'navy', 'navy_leader']
senticnet['army_officer'] = ['0.028', '0.081', '0', '0', '#joy', '#interest', 'positive', '0.054', 'military_position', 'army_rank', 'military_officer', 'military_rank', 'high_rank']
senticnet['army_person'] = ['0', '0.965', '0', '0.821', '#interest', '#admiration', 'positive', '0.893', 'soldier', 'fighter', 'military', 'sergent', 'warrior']
senticnet['army_position'] = ['0', '0.09', '0', '0', '#interest', '#interest', 'positive', '0.09', 'military_position', 'military_rank', 'high_rank', 'army_rank', 'army_boss']
senticnet['army_rank'] = ['0', '0.07', '0', '0', '#interest', '#interest', 'positive', '0.07', 'army_leader', 'military_rank', 'high_rank', 'military_position', 'army_officer']
senticnet['army_title'] = ['0.031', '0.079', '0', '0', '#joy', '#interest', 'positive', '0.055', 'police_rank', 'military_rank', 'head_guy', 'army_boss', 'high_rank']
senticnet['army_unit'] = ['0.031', '0.079', '0', '0', '#joy', '#interest', 'positive', '0.055', 'military_force', 'attacker', 'soldier_group', 'army', 'military']
senticnet['armyworm'] = ['-0.53', '0', '0.8', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'noctuid', 'underwing', 'antler_moth', 'heliothis_moth', 'moth']
senticnet['arnim_zola'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
senticnet['aroma'] = ['0.966', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.968', 'odor', 'fragrance', 'scent', 'smell_good', 'good_smell']
senticnet['aromatic_compound'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'enhance', 'intensify', 'deepen', 'potentiate', 'heighten']
senticnet['aromatic_ring'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
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senticnet['ascitic_fluid'] = ['0.79', '-0.62', '0', '0.674', '#joy', '#admiration', 'positive', '0.695', 'add', 'contribute', 'lend', 'breathe', 'impart']
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senticnet['ascription'] = ['0.534', '-0.86', '0', '0.676', '#surprise', '#admiration', 'positive', '0.692', 'suitability', 'suitableness', 'habitability', 'habitableness', 'qualification']
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senticnet['asian_tsunami'] = ['-0.17', '0', '-0.25', '-0.13', '#sadness', '#fear', 'negative', '-0.18', 'destruction', 'bigot', 'kidnap', 'torture', 'censorship']
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senticnet['ask_a_lot_of_question'] = ['-0.13', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'curious', 'determine_need', 'excited', 'discuss_friend', 'come_warranty']
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senticnet['ask_money'] = ['-0.82', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.82', 'moolah', 'plan_advance', 'bind_agreement', 'money_credit', 'bring_wallet']
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senticnet['asturias_prize'] = ['0.051', '0.097', '0', '0.065', '#interest', '#admiration', 'positive', '0.071', 'king', 'queen', 'duchess', 'throne', 'lord']
senticnet['astute'] = ['0', '-0.93', '0', '0.873', '#surprise', '#admiration', 'positive', '0.903', 'perceptive', 'prescient', 'trenchant', 'prophetic', 'far-sighted']
senticnet['astuteness'] = ['0.784', '-0.59', '0', '0.846', '#joy', '#admiration', 'positive', '0.742', 'foxiness', 'slyness', 'wiliness', 'craftiness', 'cunning']
senticnet['asunder'] = ['0.776', '0', '0', '0.945', '#joy', '#admiration', 'positive', '0.861', 'doggedness', 'mobility', 'adhere', 'tenacity', 'bear']
senticnet['asura'] = ['-0.09', '0.085', '-0.08', '-0.11', '#sadness', '#disgust', 'negative', '-0.05', 'hitler', 'treachery', 'good_man', 'cannibalism', 'sad_person']
senticnet['aswan'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['aswan_dam'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['asylum'] = ['-0.65', '0', '0.514', '-0.69', '#sadness', '#disgust', 'negative', '-0.62', 'booby_hatch', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['asymmetric'] = ['0.84', '-0.64', '0', '0.912', '#joy', '#admiration', 'positive', '0.799', 'received', 'customary', 'formulaic', 'stuffy', 'stodgy']
senticnet['asymmetric_cryptography'] = ['0.584', '-0.72', '0', '0.742', '#surprise', '#admiration', 'positive', '0.684', 'decompression', 'cryptography', 'coding', 'secret_writing', 'steganography']
senticnet['asymmetric_encryption'] = ['0.584', '-0.72', '0', '0.742', '#surprise', '#admiration', 'positive', '0.684', 'decompression', 'cryptography', 'coding', 'secret_writing', 'steganography']
senticnet['asymmetric_warfare'] = ['0.833', '-0.97', '0', '0.91', '#surprise', '#admiration', 'positive', '0.906', 'fencer', 'battler', 'belligerent', 'fighter', 'scrapper']
senticnet['asymmetrical'] = ['-0.80', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.77', 'contrary', 'contradiction', 'lie', 'liar', 'bad_person']
senticnet['asymmetrical_warfare'] = ['-0.82', '0', '0.739', '-0.50', '#sadness', '#anger', 'negative', '-0.69', 'fake', 'juke', 'maneuver', 'manoeuvre', 'feint']
senticnet['asymmetry'] = ['-0.59', '0', '0.672', '-0.83', '#anger', '#disgust', 'negative', '-0.7', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'skewness', 'irregularity']
senticnet['asymmetry_between'] = ['-0.97', '0', '0.781', '-0.91', '#sadness', '#disgust', 'negative', '-0.88', 'weakness', 'fault', 'flaw', 'defect', 'wart']
senticnet['asymptotic_behavior'] = ['-0.75', '0', '0.679', '-0.77', '#sadness', '#disgust', 'negative', '-0.73', 'unvariedness', 'changelessness', 'unchangeability', 'unchangeableness', 'unchangingness']
senticnet['asymptotic_optimality'] = ['-0.96', '0', '0.603', '-0.91', '#sadness', '#disgust', 'negative', '-0.82', 'potential', 'potency', 'conceivableness', 'conceivability', 'possibleness']
senticnet['asynchronously'] = ['-0.15', '0', '0.029', '-0.11', '#sadness', '#disgust', 'negative', '-0.1', 'start_over', 'front_line', 'find_line', 'take_turn', 'long_line']
senticnet['asystole'] = ['-0.03', '-0.03', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'eat_poison', 'commit_suicide', 'drug_addiction', 'drunk_drive', 'jump_off_cliff']
senticnet['at_arm_length'] = ['0.609', '0.597', '-0.23', '0.398', '#joy', '#interest', 'positive', '0.341', 'near', 'pick_up', 'togetherness', 'gay', 'capture']
senticnet['at_bletchley_park'] = ['-0.05', '-0.19', '-0.17', '0.219', '#surprise', '#admiration', 'positive', '0.046', 'riddle', 'mystery', 'obituary', 'editorial', 'book_genre']
senticnet['at_brookvale_oval'] = ['0', '0.996', '0', '0.85', '#interest', '#admiration', 'positive', '0.923', 'strength', 'strong', 'physical_strength', 'athlete', 'muscle']
senticnet['at_btig'] = ['0.711', '0', '0', '0', '#joy', '#joy', 'positive', '0.711', 'chemist', 'expert', 'scientist', 'esaminatore', 'physicist']
senticnet['at_coventry_ricoh'] = ['0.026', '-0.03', '0', '0.091', '#surprise', '#admiration', 'positive', '0.051', 'stadium', 'auditorium', 'television_station', 'music_hall', 'movie_theater']
senticnet['at_craigs_investment'] = ['0.841', '0.721', '0', '0.74', '#joy', '#admiration', 'positive', '0.767', 'good_manner', 'two_people', 'love', 'guardian', 'guy']
senticnet['at_ease'] = ['0.325', '0', '0', '0', '#joy', '#joy', 'positive', '0.325', 'serene', 'serenity', 'comfort', 'comfy', 'blessed']
senticnet['at_edgewood_tahoe'] = ['0.758', '0.719', '0', '0', '#joy', '#interest', 'positive', '0.738', 'plan_party', 'dance_music', 'become_adult', 'stay_fit', 'drive_crazy']
senticnet['at_full_gallop'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'hardy', 'fearlessness', 'fearlessly', 'unafraid', 'unfearing']
senticnet['at_gunpoint'] = ['-0.89', '-0.71', '-0.76', '-0.93', '#sadness', '#disgust', 'negative', '-0.47', 'bigot', 'censorship', 'jail_time', 'reprimand', 'intolerance']
senticnet['at_hanamaki_station'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'aridness', 'dryness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['at_least'] = ['-0.61', '0', '0.856', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'unfixed', 'indefinite', 'nebulous', 'indecisive', 'coy']
senticnet['at_leichhardt_oval'] = ['0', '0.996', '0', '0.85', '#interest', '#admiration', 'positive', '0.923', 'strength', 'strong', 'physical_strength', 'athlete', 'muscle']
senticnet['at_loss'] = ['-0.83', '0', '0.925', '-0.99', '#anger', '#disgust', 'negative', '-0.91', 'perplexed', 'nonplused', 'confused', 'puzzled', 'bewildered']
senticnet['at_low_tide'] = ['0.84', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.798', 'big_breadbox', 'water_vehicle', 'military_ship', 'ship', 'small_boat']
senticnet['at_munich_allianz'] = ['0.026', '-0.03', '0', '0.091', '#surprise', '#admiration', 'positive', '0.051', 'stadium', 'auditorium', 'television_station', 'music_hall', 'movie_theater']
senticnet['at_rosehill'] = ['0.744', '-0.77', '0', '0.661', '#joy', '#surprise', 'positive', '0.725', 'sugarcoat', 'honey', 'glaze', 'change_taste', 'sugar']
senticnet['at_semple_stadium'] = ['-0.09', '0.147', '-0.11', '0.205', '#interest', '#admiration', 'positive', '0.036', 'question_starter', 'resolution', 'available', 'explanation', 'request']
senticnet['at_stadion_poljud'] = ['0.909', '0.986', '0', '0', '#joy', '#interest', 'positive', '0.948', 'separation', 'divide', 'division', 'slash', 'bagle']
senticnet['at_the_same_time'] = ['0.8', '0', '0', '0.799', '#joy', '#admiration', 'positive', '0.8', 'concurrently', 'research', 'student', 'first', 'faculty']
senticnet['at_variance'] = ['-0.93', '0', '0.515', '-0.78', '#sadness', '#disgust', 'negative', '-0.74', 'discrepant', 'discordant', 'dissentious', 'divisive', 'factious']
senticnet['at_warwick_farm'] = ['0.744', '-0.77', '0', '0.661', '#joy', '#surprise', 'positive', '0.725', 'sugarcoat', 'honey', 'glaze', 'change_taste', 'sugar']
senticnet['atacama_desert_trek'] = ['0.141', '0', '-0.06', '0.086', '#joy', '#admiration', 'positive', '0.055', 'dancer', 'stout', 'old_dance', 'climb_rock', 'jump_around']
senticnet['ataraxia'] = ['0.503', '-0.82', '0', '0.681', '#surprise', '#admiration', 'positive', '0.671', 'tranquility', 'serenity', 'repose', 'placidity', 'quiet']
senticnet['ataraxis'] = ['0.621', '0.648', '-0.25', '0.296', '#joy', '#interest', 'positive', '0.328', 'pacifically', 'sereneness', 'tranquility', 'harmony', 'peace']
senticnet['atari_teenage_riot'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amuck', 'murderousness', 'misocainea', 'misogyny']
senticnet['atasteful'] = ['0.733', '-0.86', '0', '0.619', '#joy', '#surprise', 'positive', '0.738', 'understated', 'tasteful', 'esthetic', 'artistic', 'unostentatious']
senticnet['ataxia'] = ['-0.29', '0.095', '-0.11', '-0.13', '#sadness', '#disgust', 'negative', '-0.11', 'say_aha', 'think_hard', 'think_past', 'scratch_head', 'head_hurt']
senticnet['ataxia_telangiectasia'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'dystrophy', 'myotonic_dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['ataxic'] = ['0.722', '-0.87', '0', '0.696', '#joy', '#surprise', 'positive', '0.763', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['atazanavir'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'indinavir', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['atbara'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['atbara_river'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['atcc_cip'] = ['-0.08', '-0.03', '-0.07', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'injury', 'soreness', 'pain', 'hurt', 'dislocation']
senticnet['atchafalaya_basin'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
senticnet['atef_crown'] = ['-0.62', '0', '0.961', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'crown', 'jeweled_headdress', 'tiara', 'headdress', 'headgear']
senticnet['ateji'] = ['-0.86', '0', '0.619', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'character', 'logogram', 'logograph', 'graph', 'grapheme']
senticnet['atelic'] = ['0.633', '-0.67', '0', '0.776', '#surprise', '#admiration', 'positive', '0.693', 'double_entendre', 'loophole', 'ambiguity', 'amphiboly', 'parisology']
senticnet['atemporal'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'metastability', 'monotony', 'invariance', 'constancy', 'unchangeability']
senticnet['atenolol'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'spironolactone', 'spironolactone_drug']
senticnet['aterazawa_line'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'concupiscence', 'lustfulness', 'amorousness']
senticnet['athame'] = ['0.105', '-0.17', '0.057', '0.079', '#joy', '#surprise', 'positive', '0.076', 'cue', 'twig', 'long_stick', 'stick', 'adhere']
senticnet['athanor'] = ['0', '0.887', '0', '0.973', '#interest', '#admiration', 'positive', '0.93', 'servant', 'art_of_transmuting_metal', 'stone', 'alchemy', 'philosopher']
senticnet['atheia'] = ['-0.66', '0', '0.859', '-0.82', '#anger', '#disgust', 'negative', '-0.78', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['atheism'] = ['-0.79', '0', '0', '-0.81', '#sadness', '#disgust', 'negative', '-0.80', 'irreverence', 'disrespect', 'apostasy', 'impiety', 'sacrilege']
senticnet['atheist'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'non-believer', 'doubter', 'agnostic', 'freethinker', 'sceptic']
senticnet['atheist_activist'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'non-believer', 'doubter', 'agnostic', 'freethinker', 'sceptic']
senticnet['atheistic'] = ['-0.98', '0', '0.891', '-0.60', '#sadness', '#anger', 'negative', '-0.82', 'exorcism', 'dispossession', 'magic', 'thaumaturgy', 'belief']
senticnet['atheistic_communism'] = ['0.045', '0.286', '-0.14', '0.229', '#interest', '#admiration', 'positive', '0.104', 'socialism', 'parliament', 'elect', 'government', 'elect_president']
senticnet['atheistical'] = ['0.922', '0.762', '0', '0', '#joy', '#interest', 'positive', '0.842', 'controvert', 'refute', 'confutation', 'negate', 'rebut']
senticnet['athelete'] = ['0', '0', '0', '0.97', '#admiration', '#admiration', 'positive', '0.97', 'increase_strength', 'keep_fit', 'fit_person', 'exercise', 'strengthen_heart']
senticnet['athenian'] = ['0.8', '-0.79', '0', '0.751', '#joy', '#surprise', 'positive', '0.782', 'elder_statesman', 'stateswoman', 'politician', 'politico', 'pol']
senticnet['athenian_comic'] = ['-0.81', '0', '0.52', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'affray', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['athenian_lawgiver'] = ['0.8', '-0.79', '0', '0.751', '#joy', '#surprise', 'positive', '0.782', 'elder_statesman', 'stateswoman', 'politician', 'politico', 'pol']
senticnet['atheoretical'] = ['-0.91', '0', '0.598', '-0.52', '#sadness', '#anger', 'negative', '-0.68', 'stingy', 'penurious', 'scrimy', 'beggarly', 'chintzy']
senticnet['atherosclerotic_disease'] = ['-0.81', '0', '0.684', '-0.81', '#sadness', '#disgust', 'negative', '-0.77', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['atherosclerotic_plaque'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'chest', 'artery', 'vein', 'internal_organ', 'under_head']
senticnet['athiest'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'non-believer', 'doubter', 'agnostic', 'freethinker', 'sceptic']
senticnet['athirst'] = ['-0.87', '0', '-0.71', '-0.96', '#sadness', '#disgust', 'negative', '-0.85', 'avid', 'eagerly', 'eagerness', 'avidity', 'avidness']
senticnet['athleta'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['athlete'] = ['0', '0.818', '0', '0.994', '#interest', '#admiration', 'positive', '0.906', 'strength', 'strong', 'manly', 'physical_strength', 'muscle']
senticnet['athlete_foot'] = ['-0.96', '0', '0.694', '-0.62', '#sadness', '#anger', 'negative', '-0.76', 'fungal_infection', 'mycosis', 'kerion', 'tinea_pedis', 'ringworm']
senticnet['athletic'] = ['0.844', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.904', 'fun_exercise', 'keep_fit', 'recreational', 'get_physically_fit', 'fit_person']
senticnet['athletic_ability'] = ['0', '0.127', '-0.04', '0.194', '#interest', '#admiration', 'positive', '0.092', 'quick', 'fast', 'speedy', 'quick_movement', 'speed']
senticnet['athletic_activity'] = ['0.032', '0', '0.024', '0.176', '#joy', '#admiration', 'positive', '0.061', 'healthy_activity', 'human_motion', 'water_activity', 'good_body', 'athletic']
senticnet['athletic_apparel'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['athletic_department'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['athletic_eligibility'] = ['-0.60', '0', '0.832', '-0.53', '#sadness', '#anger', 'negative', '-0.65', 'unsuitable', 'undesirable', 'unqualified', 'disqualified', 'unentitled']
senticnet['athletic_event'] = ['0.889', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.897', 'sport_play', 'excite_game', 'sport', 'rough_game', 'verbosity']
senticnet['athletic_footwear'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['athletic_game'] = ['0.7', '-0.79', '0', '0.873', '#surprise', '#admiration', 'positive', '0.79', 'sport', 'game', 'ice_hockey', 'hockey', 'hockey_game']
senticnet['athletic_mascot'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'unofficial', 'informal', 'loose', 'drumhead']
senticnet['athletic_shoe'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['athletic_trainer'] = ['0', '0', '0', '0.97', '#admiration', '#admiration', 'positive', '0.97', 'increase_strength', 'keep_fit', 'fit_person', 'exercise', 'strengthen_heart']
senticnet['athletic_training'] = ['0.767', '-0.81', '0', '0.714', '#joy', '#surprise', 'positive', '0.764', 'practice', 'activity', 'discipline', 'grooming', 'drill']
senticnet['athletic_wear'] = ['-0.94', '0', '0.639', '-0.75', '#sadness', '#disgust', 'negative', '-0.78', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['athleticism'] = ['-0.70', '0', '0.703', '-0.53', '#sadness', '#anger', 'negative', '-0.64', 'vigor', 'strenuosity', 'forcefulness', 'force', 'strength']
senticnet['athletics'] = ['0', '-0.92', '0', '0.834', '#surprise', '#admiration', 'positive', '0.877', 'olympic_event', 'train_hard', 'shape', 'form_of_exercise', 'good_shape']
senticnet['atlantic'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'pacific_ocean', 'small_lake', 'mere']
senticnet['atlantic_bottlenose_dolphin'] = ['0.024', '0.102', '-0.06', '0.074', '#interest', '#admiration', 'positive', '0.033', 'marine_mammal', 'large_mammal', 'pack_animal', 'mammal', 'smart_creature']
senticnet['atlantic_coast'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'pacific_ocean', 'small_lake', 'mere']
senticnet['atlantic_coastline'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'pacific_ocean', 'small_lake', 'mere']
senticnet['atlantic_hurricane'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'atmospheric_phenomenon', 'natural_disaster', 'flood', 'destruction', 'tropical_storm']
senticnet['atlantic_hurricane_activity'] = ['-0.69', '0', '0.835', '-0.56', '#sadness', '#anger', 'negative', '-0.69', 'roughness', 'inclemency', 'bad_weather', 'inclementness', 'windiness']
senticnet['atlantic_ocean'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'pacific_ocean', 'small_lake', 'mere']
senticnet['atlantic_ocean_off'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'pacific_ocean', 'small_lake', 'mere']
senticnet['atlantic_seaboard'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'pacific_ocean', 'small_lake', 'mere']
senticnet['atlantic_spotted_dolphin'] = ['0.024', '0.102', '-0.06', '0.074', '#interest', '#admiration', 'positive', '0.033', 'marine_mammal', 'large_mammal', 'pack_animal', 'mammal', 'smart_creature']
senticnet['atlantic_tropical_cyclone'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'atmospheric_phenomenon', 'natural_disaster', 'flood', 'destruction', 'tropical_storm']
senticnet['atlanticism'] = ['0.946', '-0.54', '0', '0.774', '#joy', '#admiration', 'positive', '0.754', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['atm'] = ['0', '0.186', '0', '0.075', '#interest', '#admiration', 'positive', '0.131', 'money_inside', 'check_account', 'savings', 'money_storage', 'store_money']
senticnet['atm_card'] = ['-0.05', '0.14', '0.015', '0.082', '#interest', '#admiration', 'positive', '0.039', 'cash', 'cash_pay', 'need_earn_money', 'open_wallet', 'earn_wage']
senticnet['atm_debit_card'] = ['-0.05', '0.14', '0.015', '0.082', '#interest', '#admiration', 'positive', '0.039', 'cash', 'cash_pay', 'need_earn_money', 'open_wallet', 'earn_wage']
senticnet['atm_gene'] = ['-0.87', '0', '0.873', '-0.62', '#sadness', '#anger', 'negative', '-0.78', 'pycnodysostosis', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'dwarfism']
senticnet['atm_machine'] = ['0', '0', '0', '-0.06', '#disgust', '#disgust', 'negative', '-0.06', 'newsstand', 'bookshop', 'walmart', 'shoe_store', 'hooker']
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senticnet['atomic_scale'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'amoeba', 'tiny_organism', 'microbe', 'heron', 'single_celled']
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senticnet['atop_rockefeller_center'] = ['0.03', '0.085', '0', '0', '#joy', '#interest', 'positive', '0.058', 'favorite_color', 'colorful', 'different_color', 'pink', 'happy']
senticnet['atp_funded_technology'] = ['0', '0.243', '0', '0.218', '#interest', '#admiration', 'positive', '0.23', 'ware', 'goods', 'result', 'good_price', 'bargain']
senticnet['atque'] = ['0.654', '-0.75', '0', '0.78', '#surprise', '#admiration', 'positive', '0.729', 'suit', 'cause', 'lawsuit', 'occurrence', 'countersuit']
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senticnet['atrial_fibrillation'] = ['-0.83', '0', '0.987', '-0.50', '#sadness', '#anger', 'negative', '-0.77', 'heart_block', 'atrioventricular_block', 'bradycardia', 'ventricular_fibrillation', 'tachycardia']
senticnet['atrial_fibrillation_af'] = ['-0.83', '0', '0.987', '-0.50', '#sadness', '#anger', 'negative', '-0.77', 'heart_block', 'atrioventricular_block', 'bradycardia', 'ventricular_fibrillation', 'tachycardia']
senticnet['atrial_flutter'] = ['-0.83', '0', '0.987', '-0.50', '#sadness', '#anger', 'negative', '-0.77', 'heart_block', 'atrioventricular_block', 'bradycardia', 'ventricular_fibrillation', 'tachycardia']
senticnet['atrial_tachycardia'] = ['-0.83', '0', '0.987', '-0.50', '#sadness', '#anger', 'negative', '-0.77', 'heart_block', 'atrioventricular_block', 'bradycardia', 'ventricular_fibrillation', 'tachycardia']
senticnet['atrioventricular'] = ['-0.56', '0', '0.849', '-0.61', '#anger', '#disgust', 'negative', '-0.67', 'convulsive', 'unsteady', 'jerky', 'spasmodic', 'spastic']
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senticnet['attendant'] = ['0', '0.837', '0', '0.932', '#interest', '#admiration', 'positive', '0.885', 'personnel', 'employee', 'manager', 'servant', 'helper']
senticnet['attended_elementary_school'] = ['0.706', '0', '0', '0', '#joy', '#joy', 'positive', '0.706', 'birth', 'new_baby', 'mother_womb', 'birthday', 'thanksgiving']
senticnet['attended_kingswood_school'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
senticnet['attendee'] = ['0.055', '-0.01', '0', '0.106', '#joy', '#admiration', 'positive', '0.059', 'agenda', 'pencil_pen', 'learn_write', 'find_paper', 'paper_plane']
senticnet['attendence'] = ['0.723', '0', '0', '0.762', '#joy', '#admiration', 'positive', '0.742', 'presence', 'feel_important', 'existence', 'feel_satisfied', 'feel_presence']
senticnet['attention'] = ['0', '0.33', '0', '0', '#interest', '#interest', 'positive', '0.33', 'interest', 'anticipation', 'vigilance', 'hope', 'optimism']
senticnet['attention_deficit'] = ['0.89', '-0.70', '0', '0.643', '#joy', '#surprise', 'positive', '0.745', 'dyslexia', 'dyscalculia', 'learning_disability', 'disorder', 'upset']
senticnet['attention_deficit_disorder'] = ['0.89', '-0.70', '0', '0.643', '#joy', '#surprise', 'positive', '0.745', 'dyslexia', 'dyscalculia', 'learning_disability', 'disorder', 'upset']
senticnet['attentional'] = ['0.971', '0.751', '0', '0.896', '#joy', '#admiration', 'positive', '0.873', 'new_friendship', 'foreplay', 'warm_fuzzy_feeling', 'become_aroused', 'erection']
senticnet['attentional_control'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'excitation', 'excitement', 'arousal', 'anger']
senticnet['attentive'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'express_feeling', 'understand', 'end_conversation', 'empathy', 'mutual_understand']
senticnet['attentively'] = ['-0.95', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.86', 'uneasily', 'apprehensively', 'apprehensive', 'worried', 'solicitousness']
senticnet['attenuation'] = ['-0.78', '0', '0.764', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'withering', 'downfall', 'atrophy', 'fading', 'weakening']
senticnet['attenuation_coefficient'] = ['-0.93', '0', '0.698', '-0.85', '#sadness', '#disgust', 'negative', '-0.83', 'candlepower', 'light_intensity', 'acoustic_power', 'sound_pressure_level', 'threshold_level']
senticnet['attest'] = ['0.749', '-0.79', '0', '0.768', '#surprise', '#admiration', 'positive', '0.771', 'prove', 'show', 'condemn', 'authenticate', 'reflect']
senticnet['attestation'] = ['0.036', '0.292', '0', '0.337', '#interest', '#admiration', 'positive', '0.222', 'importance', 'quality', 'total', 'excellence', 'frivolity']
senticnet['attested'] = ['0.853', '0', '0', '0.875', '#joy', '#admiration', 'positive', '0.864', 'documented', 'echt', 'veritable', 'authenticated', 'genuine']
senticnet['attic'] = ['-0.05', '-0.04', '0.017', '-0.05', '#sadness', '#disgust', 'negative', '-0.02', 'shoe_shop', 'shoe_store', 'hamper', 'obstruct', 'closet']
senticnet['attic_floor'] = ['0', '0.171', '-0.05', '0.102', '#interest', '#admiration', 'positive', '0.072', 'bed', 'mattress', 'bead', 'bed_cover', 'seduce']
senticnet['attic_room'] = ['0', '0.171', '-0.05', '0.102', '#interest', '#admiration', 'positive', '0.072', 'bed', 'mattress', 'bead', 'bed_cover', 'seduce']
senticnet['attire'] = ['-0.68', '0', '0.947', '-0.95', '#anger', '#disgust', 'negative', '-0.86', 'finery', 'sportswear', 'athletic_wear', 'activewear', 'costume']
senticnet['attitude'] = ['0.197', '0.374', '-0.27', '0.384', '#interest', '#admiration', 'positive', '0.17', 'thoughtful', 'certainty', 'similarity', 'yearn', 'prefer']
senticnet['attitude_towards_homosexuality'] = ['0', '0.985', '0', '0.794', '#interest', '#admiration', 'positive', '0.889', 'patient', 'bear_up', 'tolerate', 'endurable', 'swallow']
senticnet['attitude_towards_woman'] = ['-0.68', '0', '0.949', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'chauvinism', 'sexism', 'antifeminism', 'sexual_discrimination', 'discrimination']
senticnet['attorney'] = ['0', '0.146', '-0.05', '0.077', '#interest', '#admiration', 'positive', '0.057', 'lawyer', 'negotiator', 'arbiter', 'judge_jury', 'decide_guilt']
senticnet['attorney_general'] = ['0', '0.146', '-0.05', '0.077', '#interest', '#admiration', 'positive', '0.057', 'lawyer', 'negotiator', 'arbiter', 'judge_jury', 'decide_guilt']
senticnet['attorney_general_nicola_roxon'] = ['0.865', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.887', 'manager', 'colleague', 'office_worker', 'coworker', 'secretary']
senticnet['attorney_office'] = ['0', '0.146', '-0.05', '0.077', '#interest', '#admiration', 'positive', '0.057', 'lawyer', 'negotiator', 'arbiter', 'judge_jury', 'decide_guilt']
senticnet['attorneyclient_privilege'] = ['0', '0', '0', '0.816', '#admiration', '#admiration', 'positive', '0.816', 'resistance', 'protection', 'untouchable', 'invulnerable', 'sacrosanct']
senticnet['attract'] = ['0.92', '-0.88', '0', '0.695', '#joy', '#surprise', 'positive', '0.833', 'get', 'catch', 'arrest', 'tug', 'retract']
senticnet['attract_attention'] = ['0', '0.87', '0', '0.882', '#interest', '#admiration', 'positive', '0.876', 'give_hug', 'cordial', 'take_movie', 'find_partner', 'shake_hand']
senticnet['attract_dog'] = ['0.076', '0', '0', '0', '#joy', '#joy', 'positive', '0.076', 'dog_food', 'toy', 'lure', 'treat', 'whistle']
senticnet['attract_foreign'] = ['0.92', '-0.88', '0', '0.695', '#joy', '#surprise', 'positive', '0.833', 'get', 'catch', 'arrest', 'tug', 'retract']
senticnet['attract_mate'] = ['0.166', '-0.05', '0.044', '-0.04', '#joy', '#surprise', 'positive', '0.032', 'impress_girl', 'relieve_stress', 'enjoy_outdoors', 'social_bond', 'become_popular']
senticnet['attract_opposite_sex'] = ['0.879', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.895', 'love', 'become_better', 'sing_dance', 'great_joy', 'listen']
senticnet['attract_top_notch'] = ['0.92', '-0.88', '0', '0.695', '#joy', '#surprise', 'positive', '0.833', 'get', 'catch', 'arrest', 'tug', 'retract']
senticnet['attractant'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'fishing_rod', 'fishing', 'patience', 'fish_lake', 'catch_fish']
senticnet['attraction'] = ['0.908', '0.971', '0', '0', '#joy', '#interest', 'positive', '0.94', 'care_deeply', 'attachment', 'lure', 'show_empathy', 'find_woman']
senticnet['attraction_include'] = ['0.034', '0.13', '-0.08', '0.258', '#interest', '#admiration', 'positive', '0.085', 'tourism', 'nice_view', 'big_monument', 'temple', 'sightsee']
senticnet['attractive'] = ['0.111', '0.121', '-0.09', '0.246', '#interest', '#admiration', 'positive', '0.096', 'nice_look', 'lovely', 'good_look', 'gorgeous', 'nice']
senticnet['attractive_alternative'] = ['0.554', '-0.72', '0', '0.651', '#surprise', '#admiration', 'positive', '0.644', 'reasonableness', 'modestness', 'cost', 'cut_rate', 'cut_price']
senticnet['attractive_valuation'] = ['0', '0.994', '0', '0.88', '#interest', '#admiration', 'positive', '0.937', 'exceed', 'outshout', 'outstrip', 'outshine', 'outsail']
senticnet['attractiveness'] = ['0.817', '-0.62', '0', '0.62', '#joy', '#surprise', 'positive', '0.688', 'quality', 'fascination', 'allurement', 'allure', 'temptingness']
senticnet['attractivenessment'] = ['0.809', '-0.62', '0', '0.611', '#joy', '#surprise', 'positive', '0.682', 'quality', 'fascination', 'allure', 'attractiveness', 'temptingness']
senticnet['attributable'] = ['0.511', '-0.59', '0', '0.576', '#surprise', '#admiration', 'positive', '0.561', 'due', 'traceable', 'ascribable', 'imputable', 'referable']
senticnet['attribute'] = ['-0.55', '0', '0.882', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'bugger_all', 'zero', 'naught', 'zip', 'nothing']
senticnet['attribution'] = ['0.964', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.846', 'bulletin', 'replicate', 'current_event', 'computer_command', 'paper_write']
senticnet['attribution_error'] = ['-0.93', '0', '0.586', '-0.66', '#sadness', '#disgust', 'negative', '-0.73', 'camp', 'pack', 'clique', 'coterie', 'brain_trust']
senticnet['attributive'] = ['-0.57', '0', '0.713', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'assured', 'conclusion', 'certainty', 'determinateness', 'predictability']
senticnet['attrition'] = ['-0.89', '0.168', '-0.60', '0.05', '#sadness', '#fear', 'negative', '-0.32', 'remorsefully', 'contritely', 'contrition', 'contriteness', 'ruefully']
senticnet['attrition_rate'] = ['-0.89', '0.168', '-0.60', '0.05', '#sadness', '#fear', 'negative', '-0.32', 'remorsefully', 'contritely', 'contrition', 'contriteness', 'ruefully']
senticnet['attrition_warfare'] = ['-0.82', '0', '0.739', '-0.50', '#sadness', '#anger', 'negative', '-0.69', 'fake', 'juke', 'maneuver', 'manoeuvre', 'feint']
senticnet['attunement'] = ['0.668', '-0.87', '0', '0.606', '#joy', '#surprise', 'positive', '0.715', 'adeptness', 'adroitness', 'deftness', 'quickness', 'expertness']
senticnet['atv_accident'] = ['-0.92', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.93', 'serious_accident', 'accident', 'hit_car', 'loss_ofawareness', 'bang_head']
senticnet['atwitter'] = ['-0.85', '0', '-0.70', '0', '#sadness', '#fear', 'negative', '-0.78', 'nervous', 'fumble', 'jumpily', 'nervousness', 'nervously']
senticnet['atypical'] = ['-0.58', '0', '0.814', '-0.54', '#sadness', '#anger', 'negative', '-0.64', 'abnormal', 'irregular', 'vicarious', 'supernormal', 'subnormal']
senticnet['atypical_depression'] = ['-0.78', '0', '0.918', '-0.96', '#anger', '#disgust', 'negative', '-0.88', 'major_depressive_episode', 'depression', 'depressive_disorder', 'clinical_depression', 'involutional_depression']
senticnet['atypical_pneumonia'] = ['-0.95', '0', '0.876', '-0.89', '#sadness', '#disgust', 'negative', '-0.90', 'bubonic_plague', 'pestis_bubonica', 'glandular_plague', 'septicemic_plague', 'pestilence']
senticnet['au_coeur'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'sexual_relationship', 'liaison', 'affair', 'involvement']
senticnet['au_dem'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['au_der'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['au_pair'] = ['0.875', '0', '0', '0.802', '#joy', '#admiration', 'positive', '0.839', 'foreigner', 'exile', 'deportee', 'gringo', 'import']
senticnet['au_pair_placement'] = ['0.875', '0', '0', '0.802', '#joy', '#admiration', 'positive', '0.839', 'foreigner', 'exile', 'deportee', 'gringo', 'import']
senticnet['au_plus_haut'] = ['0.911', '-0.94', '0', '0.953', '#surprise', '#admiration', 'positive', '0.935', 'common_sense', 'good_sense', 'horse_sense', 'mother_wit', 'gumption']
senticnet['au_revoir'] = ['0', '0.145', '0', '0.2', '#interest', '#admiration', 'positive', '0.172', 'hand_gesture', 'arm_movement', 'water_splash', 'hand_motion', 'water_form']
senticnet['auberon_waugh'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'rascal', 'rapscallion', 'scalawag', 'holy_terror', 'monkey']
senticnet['auburn_hair'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'hair_color', 'purple', 'violet', 'dark_light', 'hue']
senticnet['auburn_plainsman'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['auburn_quarterback_cam'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'force_unit', 'sthene', 'poundal']
senticnet['auchentoshan'] = ['0.877', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.919', 'score', 'economical', 'scotch_whiskey', 'spoil', 'scottish']
senticnet['auckland_rugby_football'] = ['-0.68', '0', '0.923', '-0.68', '#sadness', '#anger', 'negative', '-0.76', 'negative', 'veto', 'refute', 'rebut', 'controvert']
senticnet['auckland_rugby_union'] = ['-0.68', '0', '0.923', '-0.68', '#sadness', '#anger', 'negative', '-0.76', 'negative', 'veto', 'refute', 'rebut', 'controvert']
senticnet['auction'] = ['0.122', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.085', 'banquet', 'rave', 'buffet', 'limbo', 'loose_end']
senticnet['auctoritate'] = ['0.654', '-0.75', '0', '0.78', '#surprise', '#admiration', 'positive', '0.729', 'suit', 'cause', 'lawsuit', 'occurrence', 'countersuit']
senticnet['aucune_diffrence'] = ['0.502', '-0.82', '0', '0.779', '#surprise', '#admiration', 'positive', '0.703', 'mortal', 'person', 'individual', 'free_liver', 'agent']
senticnet['audacious'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'intrepidly', 'hardy', 'fearlessness', 'fearlessly', 'unafraid']
senticnet['audaciously'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'intrepidly', 'hardy', 'fearlessness', 'unafraid', 'unfearing']
senticnet['audaciousness'] = ['0.775', '-0.97', '0', '0.731', '#joy', '#surprise', 'positive', '0.828', 'daredevilry', 'daredeviltry', 'temerity', 'audacity', 'boldness']
senticnet['audacity'] = ['0.775', '-0.97', '0', '0.731', '#joy', '#surprise', 'positive', '0.828', 'daredevilry', 'daredeviltry', 'temerity', 'boldness', 'audaciousness']
senticnet['audibility'] = ['0.685', '-0.91', '0', '0.566', '#joy', '#surprise', 'positive', '0.721', 'perceptibility', 'audibleness', 'visibleness', 'visibility', 'physical_property']
senticnet['audible'] = ['0.778', '0', '0', '0.706', '#joy', '#admiration', 'positive', '0.742', 'loud_soft', 'vocalization', 'mouth_noise', 'human_sound', 'human_noise']
senticnet['audible_frequency'] = ['-0.95', '0', '-0.73', '-0.73', '#sadness', '#fear', 'negative', '-0.80', 'decibel', 'noise', 'loud_soft', 'sound', 'audio']
senticnet['audible_noise'] = ['0.048', '0.084', '0', '0.209', '#interest', '#admiration', 'positive', '0.114', 'move', 'motion', 'motility', 'movement', 'flicker']
senticnet['audible_voice'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'reverence', 'genuflection', 'obeisance', 'kowtow', 'mafia']
senticnet['audibleness'] = ['0.685', '-0.91', '0', '0.566', '#joy', '#surprise', 'positive', '0.721', 'audibility', 'perceptibility', 'visibleness', 'visibility', 'physical_property']
senticnet['audibly'] = ['-0.94', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.94', 'drive_recklessly', 'discord', 'deaf', 'deny', 'silent']
senticnet['audience'] = ['0.875', '0', '0', '0.859', '#joy', '#admiration', 'positive', '0.867', 'public', 'crowd', 'entertain_crowd', 'committee', 'large_crowd']
senticnet['audio'] = ['0.055', '0', '0.03', '0.136', '#joy', '#admiration', 'positive', '0.054', 'loud_soft', 'decibel', 'loudness', 'sound', 'melodic_noise']
senticnet['audio_amplifier'] = ['0.036', '-0.04', '0.027', '0.063', '#surprise', '#admiration', 'positive', '0.028', 'radio', 'speaker', 'channel_selector', 'power_switch', 'female_television_connector']
senticnet['audio_cassette'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['audio_clip'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'paperclip', 'clasp', 'glue_stick', 'clothespin', 'clamp']
senticnet['audio_commentary_track'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['audio_compression'] = ['0.055', '0', '0.03', '0.136', '#joy', '#admiration', 'positive', '0.054', 'loud_soft', 'decibel', 'loudness', 'sound', 'melodic_noise']
senticnet['audio_mixer'] = ['0.055', '0', '0.03', '0.136', '#joy', '#admiration', 'positive', '0.054', 'loud_soft', 'decibel', 'loudness', 'sound', 'melodic_noise']
senticnet['audio_output'] = ['0.055', '0', '0.03', '0.136', '#joy', '#admiration', 'positive', '0.054', 'loud_soft', 'decibel', 'loudness', 'sound', 'melodic_noise']
senticnet['audio_playback'] = ['0.055', '0', '0.03', '0.136', '#joy', '#admiration', 'positive', '0.054', 'loud_soft', 'decibel', 'loudness', 'sound', 'melodic_noise']
senticnet['audio_recorder'] = ['0', '0', '0', '-0.17', '#disgust', '#disgust', 'negative', '-0.17', 'medicine', 'aspirin', 'blank_check', 'painkiller', 'gauze']
senticnet['audio_signal'] = ['-0.95', '0', '-0.73', '-0.73', '#sadness', '#fear', 'negative', '-0.80', 'decibel', 'noise', 'loud_soft', 'sound', 'audio']
senticnet['audio_stream'] = ['0.055', '0', '0.03', '0.136', '#joy', '#admiration', 'positive', '0.054', 'loud_soft', 'decibel', 'loudness', 'sound', 'melodic_noise']
senticnet['audio_tape'] = ['0', '0', '0', '-0.17', '#disgust', '#disgust', 'negative', '-0.17', 'medicine', 'aspirin', 'blank_check', 'painkiller', 'gauze']
senticnet['audio_video'] = ['0.055', '0', '0.03', '0.136', '#joy', '#admiration', 'positive', '0.054', 'loud_soft', 'decibel', 'loudness', 'sound', 'melodic_noise']
senticnet['audio_visual'] = ['0.055', '0', '0.03', '0.136', '#joy', '#admiration', 'positive', '0.054', 'loud_soft', 'decibel', 'loudness', 'sound', 'melodic_noise']
senticnet['audio_visual_room'] = ['0.872', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.811', 'lecture_theater', 'lecture', 'conference_auditorium', 'read_book', 'text_book']
senticnet['audiocassette'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['audiotape'] = ['0', '0', '0', '-0.17', '#disgust', '#disgust', 'negative', '-0.17', 'medicine', 'aspirin', 'blank_check', 'painkiller', 'gauze']
senticnet['audiovisual_equipment'] = ['-0.02', '-0.05', '0', '0.069', '#surprise', '#admiration', 'positive', '0.032', 'fixture', 'endowment', 'material', 'stuff', 'kit']
senticnet['audit'] = ['0.801', '0.86', '0', '0', '#joy', '#interest', 'positive', '0.831', 'credit_card', 'profit', 'mastercard', 'american_express', 'back_to_black']
senticnet['audit_report'] = ['0.801', '0.86', '0', '0', '#joy', '#interest', 'positive', '0.831', 'credit_card', 'profit', 'mastercard', 'american_express', 'back_to_black']
senticnet['auditability'] = ['0', '0', '0', '0.817', '#admiration', '#admiration', 'positive', '0.817', 'truthful', 'honesty', 'honest', 'unity', 'good_character']
senticnet['audition'] = ['0.785', '-0.99', '0', '0.981', '#surprise', '#admiration', 'positive', '0.92', 'ear', 'absolute_pitch', 'perfect_pitch', 'modality', 'sense_modality']
senticnet['auditor'] = ['-0.83', '0', '0.665', '-0.51', '#sadness', '#anger', 'negative', '-0.67', 'percipient', 'eavesdropper', 'perceiver', 'observer', 'beholder']
senticnet['auditor_general'] = ['0.801', '0.86', '0', '0', '#joy', '#interest', 'positive', '0.831', 'credit_card', 'profit', 'mastercard', 'american_express', 'back_to_black']
senticnet['auditoria'] = ['0.872', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.811', 'lecture_theater', 'lecture', 'conference_auditorium', 'read_book', 'text_book']
senticnet['auditorily'] = ['-0.56', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.63', 'aphasic', 'inarticulate', 'speechless', 'unarticulated', 'dumb']
senticnet['auditorium'] = ['0.719', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.755', 'theater', 'lecture_theater', 'tv_studio', 'movie_theater', 'opus']
senticnet['auditory'] = ['0.862', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.861', 'aural', 'visual', 'sensory', 'tactile', 'kinesthetic']
senticnet['auditory_agnosia'] = ['-0.73', '0', '0.955', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'visual_agnosia', 'agnosia', 'brain_disorder', 'encephalopathy', 'brain_disease']
senticnet['auditory_aphasia'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'aphasia', 'acoustic_aphasia', 'brain_damage', 'nominal_aphasia', 'anomic_aphasia']
senticnet['auditory_bulla'] = ['0.1', '-0.12', '0.05', '-0.03', '#joy', '#surprise', 'positive', '0.034', 'fancy_dress', 'blouse', 'fashion', 'dress', 'skirt']
senticnet['auditory_comprehension'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'nominal_aphasia', 'anomic_aphasia']
senticnet['auditory_cue'] = ['0.067', '-0.2', '0.064', '0', '#joy', '#surprise', 'positive', '0.068', 'wand', 'long_stick', 'branch', 'twig', 'stick']
senticnet['auditory_discrimination'] = ['-0.02', '0.067', '-0.04', '0.192', '#interest', '#admiration', 'positive', '0.047', 'verbalize', 'orate', 'say', 'talkative', 'discourse']
senticnet['auditory_input'] = ['-0.56', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.63', 'inarticulate', 'aphonic', 'speechless', 'unarticulated', 'dumb']
senticnet['auditory_memory'] = ['0.89', '-0.70', '0', '0.643', '#joy', '#surprise', 'positive', '0.745', 'dyslexia', 'dyscalculia', 'learning_disability', 'disorder', 'upset']
senticnet['auditory_oral'] = ['-0.94', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.94', 'drive_recklessly', 'discord', 'mute', 'deny', 'helpless']
senticnet['auditory_processing_disorder'] = ['0.89', '-0.70', '0', '0.643', '#joy', '#surprise', 'positive', '0.745', 'dyslexia', 'dyscalculia', 'learning_disability', 'disorder', 'upset']
senticnet['auditory_sensitivity'] = ['-0.95', '0', '-0.73', '-0.73', '#sadness', '#fear', 'negative', '-0.80', 'decibel', 'noise', 'loud_soft', 'sound', 'audio']
senticnet['auditory_signal'] = ['0.067', '-0.2', '0.064', '0', '#joy', '#surprise', 'positive', '0.068', 'wand', 'long_stick', 'branch', 'twig', 'stick']
senticnet['auditory_stimulus'] = ['0.067', '-0.2', '0.064', '0', '#joy', '#surprise', 'positive', '0.068', 'wand', 'long_stick', 'branch', 'twig', 'stick']
senticnet['auditory_system'] = ['0', '0.131', '-0.08', '0.239', '#interest', '#admiration', 'positive', '0.096', 'human_sense', 'ear_activity', 'vibration', 'five_senses', 'sensor']
senticnet['auditory_verbal_agnosia'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'nominal_aphasia', 'anomic_aphasia']
senticnet['audix'] = ['0.164', '0.13', '0.031', '0.209', '#joy', '#admiration', 'positive', '0.118', 'leave_message', 'write_note', 'leave_document', 'send_email', 'message']
senticnet['audre_lorde'] = ['-0.97', '0', '0.971', '-0.84', '#sadness', '#anger', 'negative', '-0.93', 'fail', 'betray', 'disappoint', 'cross', 'disillusion']
senticnet['auf_den'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['aught'] = ['-0.04', '0.268', '-0.06', '0.227', '#interest', '#admiration', 'positive', '0.097', 'proposition', 'abstract', 'get_rid', 'theoretical', 'business_transaction']
senticnet['aught_else'] = ['0.633', '-0.50', '0', '0.59', '#joy', '#admiration', 'positive', '0.575', 'craftsman', 'manicurist', 'artisan', 'journeyman', 'beautician']
senticnet['augment'] = ['0', '0.262', '-0.12', '0.399', '#interest', '#admiration', 'positive', '0.18', 'grow_large', 'proceed', 'increase', 'inclusion', 'plus']
senticnet['augmented_reality'] = ['0.026', '0.14', '-0.13', '0.31', '#interest', '#admiration', 'positive', '0.085', 'teach_new', 'share_idea', 'gesture', 'start_talk', 'exchange_idea']
senticnet['augmented_triad'] = ['-0.65', '0', '0.681', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'tone', 'whole_tone', 'step', 'whole_step', 'musical_interval']
senticnet['augmentin'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['augur'] = ['0.686', '-0.51', '0', '0.829', '#joy', '#admiration', 'positive', '0.676', 'diviner', 'oracle', 'auspex', 'prophetess', 'sibyl']
senticnet['augustan'] = ['0.708', '0.804', '0', '0', '#joy', '#interest', 'positive', '0.756', 'poetry', 'poem', 'poet', 'poetically', 'poetic']
senticnet['augustan_period'] = ['-0.03', '0.092', '0.07', '0.1', '#interest', '#admiration', 'positive', '0.021', 'gladiator', 'civilization', 'church_build', 'coliseum', 'every_town']
senticnet['augustan_poet_ovid'] = ['-0.87', '0', '0.599', '-0.71', '#sadness', '#disgust', 'negative', '-0.73', 'hysteria', 'fury', 'delirium', 'frenzy', 'craze']
senticnet['augustan_rome'] = ['-0.69', '0', '0.957', '-0.92', '#anger', '#disgust', 'negative', '-0.85', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['auguste_escoffier'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'blue_ribbon', 'badge', 'allegory', 'stripe', 'merit_badge']
senticnet['aulularia'] = ['-0.81', '0', '0.52', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'affray', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['aumf'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'address_book', 'get_rich', 'ladle', 'swiss_army_knife', 'whisk']
senticnet['aunt'] = ['0.02', '0.209', '-0.14', '0.202', '#interest', '#admiration', 'positive', '0.071', 'familiar', 'mum', 'familiarity_uncle', 'care_giver', 'familiarity']
senticnet['aunt_em'] = ['0.931', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.855', 'crystal_clear', 'blue_sky', 'imaginary', 'water_sky', 'blue_color']
senticnet['aunt_jamesina'] = ['0.591', '-0.76', '0', '0.66', '#surprise', '#admiration', 'positive', '0.673', 'darling', 'pet', 'favorite', 'first_choice', 'macushla']
senticnet['aunt_plenty'] = ['0.591', '-0.76', '0', '0.66', '#surprise', '#admiration', 'positive', '0.673', 'darling', 'pet', 'favorite', 'first_choice', 'macushla']
senticnet['aunt_uncle'] = ['0.949', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.973', 'brother_sister', 'relative', 'relate_person', 'female_sibling', 'mom_dad']
senticnet['auntie'] = ['0.02', '0.209', '-0.14', '0.202', '#interest', '#admiration', 'positive', '0.071', 'familiar', 'mum', 'familiarity_uncle', 'care_giver', 'familiarity']
senticnet['auntie_em'] = ['0.931', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.855', 'crystal_clear', 'blue_sky', 'imaginary', 'water_sky', 'blue_color']
senticnet['aupair'] = ['0.875', '0', '0', '0.802', '#joy', '#admiration', 'positive', '0.839', 'foreigner', 'exile', 'deportee', 'gringo', 'import']
senticnet['aura'] = ['0', '0.842', '0', '0.83', '#interest', '#admiration', 'positive', '0.836', 'charm', 'presence', 'charisma', 'personality', 'strength_of_character']
senticnet['aural'] = ['0.867', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.862', 'auditory', 'visual', 'sensory', 'tactile', 'kinesthetic']
senticnet['aural_equivalent'] = ['-0.89', '0', '0.722', '-0.94', '#sadness', '#disgust', 'negative', '-0.85', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['aureate'] = ['0.918', '-0.71', '0', '0', '#joy', '#surprise', 'positive', '0.816', 'splashy', 'royal_poinciana', 'florid', 'showy', 'flamboyant']
senticnet['aureole'] = ['0.931', '-0.82', '0', '0.709', '#joy', '#surprise', 'positive', '0.822', 'light', 'radiance', 'corona', 'visible_light', 'visible_radiation']
senticnet['auricle_setose'] = ['-0.50', '0', '0.573', '-0.89', '#anger', '#disgust', 'negative', '-0.65', 'petallike', 'bowery', 'leafy', 'unifoliate', 'bifoliate']
senticnet['auricular_confession'] = ['0.901', '0', '0', '0.916', '#joy', '#admiration', 'positive', '0.909', 'get_away', 'bread', 'glory', 'counsel', 'vow']
senticnet['auriculata'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['auriculotemporal_nerve'] = ['-0.41', '0', '-0.28', '-0.05', '#sadness', '#fear', 'negative', '-0.25', 'numbness', 'carpal_tunnel_syndrome', 'weakness', 'pain', 'muscle_damage']
senticnet['auriform'] = ['0.969', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.772', 'aspheric', 'aspherical', 'prolate', 'rounded', 'crescent']
senticnet['aurora'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'hurricane', 'natural_disaster', 'flood', 'atmospheric_pressure', 'tropical_storm']
senticnet['auroral'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'hurricane', 'natural_disaster', 'flood', 'atmospheric_pressure', 'tropical_storm']
senticnet['auroral_activity'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'hurricane', 'natural_disaster', 'flood', 'atmospheric_pressure', 'tropical_storm']
senticnet['auroral_oval'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'hurricane', 'natural_disaster', 'flood', 'atmospheric_pressure', 'tropical_storm']
senticnet['auroral_zone'] = ['-0.68', '0', '0.946', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'abnormality', 'abnormalcy', 'birth_defect', 'congenital_anomaly', 'congenital_defect']
senticnet['aurore'] = ['-0.81', '0', '0.885', '-0.92', '#anger', '#disgust', 'negative', '-0.87', 'miss', 'girl', 'damsel', 'maiden', 'damoiselle']
senticnet['auschwitz_birkenau_death'] = ['0', '0.156', '-0.06', '0.198', '#interest', '#admiration', 'positive', '0.098', 'grassland', 'camping', 'camp_out', 'campsite', 'campground']
senticnet['ausiello'] = ['0.54', '-0.64', '0', '0.566', '#surprise', '#admiration', 'positive', '0.582', 'pleaser', 'entertainer', 'mortal', 'busker', 'drawing_card']
senticnet['auspex'] = ['0.686', '-0.51', '0', '0.829', '#joy', '#admiration', 'positive', '0.676', 'diviner', 'augur', 'oracle', 'prophetess', 'sibyl']
senticnet['auspicate'] = ['0.636', '-0.54', '0', '0.673', '#joy', '#admiration', 'positive', '0.617', 'foreshow', 'signal', 'threaten', 'bespeak', 'betoken']
senticnet['auspice'] = ['0.851', '-0.65', '0', '0.66', '#joy', '#admiration', 'positive', '0.723', 'omen', 'portent', 'presage', 'prognostic', 'prognostication']
senticnet['auspicious'] = ['0.997', '-0.65', '0', '0.569', '#joy', '#surprise', 'positive', '0.74', 'fortunate', 'rosy', 'promising', 'bright', 'hopeful']
senticnet['auspicious_occasion'] = ['0.701', '0.867', '0', '0', '#joy', '#interest', 'positive', '0.784', 'festivity', 'special_day', 'birthday', 'special_time', 'cake_candle']
senticnet['auspiciously'] = ['0.914', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.91', 'fairly', 'fully', 'plainly', 'somewhat', 'neutrally']
senticnet['auspiciousness'] = ['0.854', '-0.79', '0', '0.977', '#joy', '#admiration', 'positive', '0.875', 'profitableness', 'favorableness', 'luck', 'advantageousness', 'positivity']
senticnet['austen_pride'] = ['-0.94', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.92', 'insanity', 'angry_emotion', 'dislike', 'psychosis', 'fight']
senticnet['austere'] = ['0', '0', '0.712', '-0.97', '#anger', '#disgust', 'negative', '-0.84', 'spartan', 'harsh', 'rigid', 'strict', 'severe']
senticnet['austere_beauty'] = ['-0.56', '0', '0.746', '-0.62', '#anger', '#disgust', 'negative', '-0.64', 'contrariness', 'fractiousness', 'intractability', 'intractableness', 'perversity']
senticnet['austerity_budget'] = ['0.911', '-0.71', '0', '0.552', '#joy', '#surprise', 'positive', '0.726', 'deliver_goods', 'succeed', 'win', 'bring_home_bacon', 'come_through']
senticnet['australasian_association'] = ['0.856', '0', '0', '0.963', '#joy', '#admiration', 'positive', '0.909', 'progress', 'get_better_job', 'further_career', 'graduate_school', 'get_diploma']
senticnet['australia'] = ['0', '0.766', '0', '0', '#interest', '#interest', 'positive', '0.766', 'abroad', 'foreign', 'study_abroad', 'elsewhere', 'live_abroad']
senticnet['australian_aviator'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'cheapjack', 'pitchman']
senticnet['australian_defence'] = ['-0.85', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.86', 'attack', 'raid', 'fight_against', 'conflict', 'opposition']
senticnet['australian_magpie'] = ['-0.84', '0', '0.655', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'piping_crow', 'butcherbird', 'currawong', 'bell_magpie', 'oscine']
senticnet['australian_masked_owl'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'lovebird', 'parroket', 'paraquet', 'paroquet']
senticnet['australian_outback'] = ['0.141', '0', '-0.06', '0.086', '#joy', '#admiration', 'positive', '0.055', 'dancer', 'stout', 'old_dance', 'climb_rock', 'jump_around']
senticnet['australian_swimmer'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'chlorine', 'lifeguard', 'still_water', 'large_body', 'shallow_water']
senticnet['australopith'] = ['-0.93', '0', '0.508', '-0.58', '#sadness', '#disgust', 'negative', '-0.67', 'inhuman', 'bloodless', 'apelike', 'nonhuman', 'nonhumanal']
senticnet['australopithecine'] = ['0.537', '-0.51', '0', '0.807', '#joy', '#admiration', 'positive', '0.619', 'lesbian', 'fairy', 'fagot', 'faggot', 'fag']
senticnet['austudy'] = ['0.924', '0.968', '0', '0.876', '#joy', '#interest', 'positive', '0.923', 'live_simply', 'help_out', 'money', 'accolade', 'generous']
senticnet['autarchy'] = ['-0.89', '0', '0.678', '-0.62', '#sadness', '#anger', 'negative', '-0.73', 'undisciplined', 'anarchic', 'uncontrolled', 'irrepressible', 'uncontrollable']
senticnet['autem'] = ['0.654', '-0.75', '0', '0.78', '#surprise', '#admiration', 'positive', '0.729', 'suit', 'cause', 'lawsuit', 'occurrence', 'countersuit']
senticnet['auteur'] = ['-0.61', '0', '0.738', '-0.80', '#anger', '#disgust', 'negative', '-0.71', 'dissident', 'beat', 'bohemian', 'maverick', 'recusant']
senticnet['authentic'] = ['0.832', '0.747', '0', '0', '#joy', '#interest', 'positive', '0.789', 'reality', 'canon', 'genuine', 'final', 'authentically']
senticnet['authentically'] = ['0.83', '0.745', '0', '0', '#joy', '#interest', 'positive', '0.787', 'reality', 'canon', 'genuine', 'final', 'authentic']
senticnet['authenticate'] = ['0.749', '-0.79', '0', '0.768', '#surprise', '#admiration', 'positive', '0.771', 'prove', 'show', 'condemn', 'attest', 'reflect']
senticnet['authenticated'] = ['0.857', '0', '0', '0.867', '#joy', '#admiration', 'positive', '0.862', 'documented', 'echt', 'attested', 'veritable', 'genuine']
senticnet['authenticity'] = ['0.726', '-0.82', '0', '0.966', '#surprise', '#admiration', 'positive', '0.839', 'original', 'real_stuff', 'credibility', 'credibleness', 'believability']
senticnet['author'] = ['0', '0.745', '0', '0.913', '#interest', '#admiration', 'positive', '0.829', 'source', 'writer', 'generator', 'well', 'write']
senticnet['author_suggest'] = ['-0.03', '0.323', '-0.23', '0.437', '#interest', '#admiration', 'positive', '0.124', 'request', 'permission', 'proclamation', 'plea', 'ask']
senticnet['authorial'] = ['0.751', '-0.98', '0', '0.989', '#surprise', '#admiration', 'positive', '0.909', 'okay', 'approval', 'commendation', 'okey', 'okeh']
senticnet['authorial_voice'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'mediacy', 'mediateness', 'characteristic', 'indirectness']
senticnet['authoring_language'] = ['-0.84', '0', '0.689', '-0.90', '#sadness', '#disgust', 'negative', '-0.81', 'software', 'software_program', 'computer_software', 'software_system', 'software_package']
senticnet['authoring_language_package'] = ['-0.85', '0', '0.686', '-0.91', '#sadness', '#disgust', 'negative', '-0.81', 'authoring_language', 'authoring_language_program', 'computer_authoring_language', 'authoring_language_system', 'software']
senticnet['authoring_language_program'] = ['-0.84', '0', '0.688', '-0.91', '#sadness', '#disgust', 'negative', '-0.81', 'authoring_language', 'software', 'computer_authoring_language', 'authoring_language_system', 'authoring_language_package']
senticnet['authoring_language_system'] = ['-0.85', '0', '0.682', '-0.90', '#sadness', '#disgust', 'negative', '-0.81', 'authoring_language', 'authoring_language_program', 'computer_authoring_language', 'software', 'authoring_language_package']
senticnet['authorised_biography'] = ['0.701', '0.939', '0', '0.718', '#interest', '#admiration', 'positive', '0.786', 'literature', 'bed_time_story', 'book_story', 'fiction', 'life_story']
senticnet['authorised_overdraft'] = ['0.54', '-0.97', '0', '0.569', '#surprise', '#admiration', 'positive', '0.695', 'overstate', 'hyperbolize', 'glorify', 'exaggerate', 'aggrandize']
senticnet['authoritarian'] = ['-0.55', '0', '0.931', '-0.74', '#anger', '#disgust', 'negative', '-0.74', 'totalitarian', 'undemocratic', 'dictatorial', 'despotic', 'monarchal']
senticnet['authoritarian_leader'] = ['-0.73', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.72', 'royal_leader', 'despot', 'emperor', 'monarch', 'king']
senticnet['authoritarian_regime'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'ascendance', 'absolutism', 'ascendancy', 'tyrant']
senticnet['authoritarian_rule'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'ascendance', 'absolutism', 'ascendancy', 'tyrant']
senticnet['authoritarianism'] = ['0', '0.877', '0', '0.963', '#interest', '#admiration', 'positive', '0.92', 'paternalistic', 'paternalism', 'collectivism', 'individualism', 'libertarianism']
senticnet['authoritative'] = ['0.923', '-0.54', '0', '0.633', '#joy', '#admiration', 'positive', '0.701', 'important', 'prestigious', 'powerful', 'influential', 'potent']
senticnet['authoritative_guide'] = ['-0.03', '0.168', '-0.07', '0.139', '#interest', '#admiration', 'positive', '0.049', 'command', 'direct', 'follow', 'take_charge', 'govern']
senticnet['authoritative_reference'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'hardcore', 'declared', 'explicit', 'graphic']
senticnet['authoritative_source'] = ['0.964', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.846', 'bulletin', 'replicate', 'current_event', 'computer_command', 'paper_write']
senticnet['authoritie'] = ['0.621', '0.648', '-0.25', '0.296', '#joy', '#interest', 'positive', '0.328', 'pacifically', 'repose', 'peace-loving', 'peaceable', 'peace_of_mind']
senticnet['authority'] = ['0.954', '0.996', '0', '0.781', '#joy', '#interest', 'positive', '0.91', 'officer', 'command', 'policeman', 'military_order', 'supervisor']
senticnet['authority_ukaea'] = ['-0.90', '0', '0.748', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'alternative_energy', 'activation_energy', 'energy_of_activation', 'energy', 'nuclear_energy']
senticnet['authorization'] = ['0', '0', '0', '0.075', '#admiration', '#admiration', 'positive', '0.075', 'job_application', 'resume', 'respond', 'questionnaire', 'help_understand']
senticnet['authorize'] = ['-0.95', '0', '0.735', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'novice', 'beginner', 'tyro', 'tiro', 'cub']
senticnet['authorize_payment'] = ['-0.04', '0.277', '-0.10', '0.11', '#interest', '#admiration', 'positive', '0.059', 'owe', 'tax', 'debit', 'afford', 'wage']
senticnet['autism'] = ['-0.91', '0', '0.508', '-0.89', '#sadness', '#disgust', 'negative', '-0.77', 'mental_disorder', 'mental_disturbance', 'psychological_disorder', 'folie', 'disturbance']
senticnet['autism_spectrum'] = ['-0.91', '0', '0.508', '-0.89', '#sadness', '#disgust', 'negative', '-0.77', 'mental_disorder', 'mental_disturbance', 'psychological_disorder', 'folie', 'disturbance']
senticnet['autism_spectrum_disorder'] = ['-0.91', '0', '0.508', '-0.89', '#sadness', '#disgust', 'negative', '-0.77', 'mental_disorder', 'mental_disturbance', 'psychological_disorder', 'folie', 'disturbance']
senticnet['autistic'] = ['-0.91', '0', '0.508', '-0.89', '#sadness', '#disgust', 'negative', '-0.77', 'mental_disorder', 'mental_disturbance', 'psychological_disorder', 'folie', 'disturbance']
senticnet['autistic_disorder'] = ['-0.91', '0', '0.508', '-0.89', '#sadness', '#disgust', 'negative', '-0.77', 'mental_disorder', 'mental_disturbance', 'psychological_disorder', 'folie', 'disturbance']
senticnet['autistic_spectrum'] = ['-0.91', '0', '0.508', '-0.89', '#sadness', '#disgust', 'negative', '-0.77', 'mental_disorder', 'mental_disturbance', 'psychological_disorder', 'folie', 'disturbance']
senticnet['autistic_spectrum_disorder'] = ['-0.91', '0', '0.508', '-0.89', '#sadness', '#disgust', 'negative', '-0.77', 'mental_disorder', 'mental_disturbance', 'psychological_disorder', 'folie', 'disturbance']
senticnet['auto'] = ['-0.51', '0', '0.552', '-0.65', '#anger', '#disgust', 'negative', '-0.57', 'car', 'gas_guzzler', 'gas_guzzlermobile', 'machine', 'motorcar']
senticnet['auto_accident'] = ['-0.92', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.93', 'serious_accident', 'accident', 'hit_car', 'loss_ofawareness', 'bang_head']
senticnet['auto_analyst'] = ['0.711', '0', '0', '0', '#joy', '#joy', 'positive', '0.711', 'chemist', 'expert', 'scientist', 'esaminatore', 'physicist']
senticnet['auto_correlation'] = ['-0.55', '0', '0.965', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'interjection', 'delay', 'insert', 'interposition', 'disruption']
senticnet['auto_dealer'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'sell_car', 'truck_stop', 'vehicle', 'insurance', 'car']
senticnet['auto_dealership'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'clean', 'polish', 'shiny', 'water', 'sexy_girl']
senticnet['auto_immune'] = ['-0.75', '0', '0.515', '-0.93', '#sadness', '#disgust', 'negative', '-0.73', 'sclerosis', 'induration', 'degenerative_disorder', 'autoimmune_disease', 'disseminated_sclerosis']
senticnet['auto_immune_disease'] = ['-0.71', '0', '0.85', '-0.82', '#anger', '#disgust', 'negative', '-0.79', 'sclerosis', 'induration', 'degenerative_disorder', 'autoimmune_disease', 'autoimmune_disorder']
senticnet['auto_immune_disorder'] = ['-0.71', '0', '0.85', '-0.82', '#anger', '#disgust', 'negative', '-0.79', 'sclerosis', 'induration', 'degenerative_disorder', 'autoimmune_disease', 'autoimmune_disorder']
senticnet['auto_mechanic'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'lubricant', 'oil', 'fuel', 'motor', 'car_gas']
senticnet['auto_part_store'] = ['0', '0', '0.044', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'large_supermarket', 'food_store', 'large_food_store', 'corner_shop', 'convenience_food_store']
senticnet['auto_pilen'] = ['0.087', '0.113', '-0.08', '0', '#joy', '#interest', 'positive', '0.038', 'find_match', 'strike_against', 'cigarette_lighter', 'match', 'use_lighter']
senticnet['auto_repair_shop'] = ['-0.05', '-0.04', '0.017', '-0.05', '#sadness', '#disgust', 'negative', '-0.02', 'shoe_shop', 'hamper', 'attic', 'obstruct', 'closet']
senticnet['auto_reply'] = ['0.949', '0.866', '0', '0', '#joy', '#interest', 'positive', '0.907', 'snail_mail', 'postage', 'send_package', 'buy_stamp', 'send_letter']
senticnet['auto_shop'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'clean', 'polish', 'shiny', 'water', 'sexy_girl']
senticnet['auto_showroom'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'sell_car', 'truck_stop', 'vehicle', 'insurance', 'car']
senticnet['auto_theft'] = ['-0.47', '0.174', '-0.44', '0.105', '#sadness', '#fear', 'negative', '-0.16', 'wrong', 'wrongdoing', 'bad_choice', 'harmful_human', 'bad_behavior']
senticnet['autobahn'] = ['0.869', '-0.58', '0', '0.824', '#joy', '#admiration', 'positive', '0.76', 'expressway', 'freeway', 'motorway', 'state_highway', 'throughway']
senticnet['autobahnen'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'expressway', 'turnpike', 'toll_road', 'highway']
senticnet['autobiographical'] = ['0.701', '0.939', '0', '0.718', '#interest', '#admiration', 'positive', '0.786', 'literature', 'bed_time_story', 'book_story', 'fiction', 'life_story']
senticnet['autobiographical_account'] = ['0.701', '0.939', '0', '0.718', '#interest', '#admiration', 'positive', '0.786', 'literature', 'bed_time_story', 'book_story', 'fiction', 'life_story']
senticnet['autobiographical_book'] = ['-0.04', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.08', 'data', 'awareness', 'information', 'saga', 'knowledge']
senticnet['autobiographical_memoir'] = ['0.701', '0.939', '0', '0.718', '#interest', '#admiration', 'positive', '0.786', 'literature', 'bed_time_story', 'book_story', 'fiction', 'life_story']
senticnet['autobiographical_sketch'] = ['0.701', '0.939', '0', '0.718', '#interest', '#admiration', 'positive', '0.786', 'literature', 'bed_time_story', 'book_story', 'fiction', 'life_story']
senticnet['autobiography'] = ['-0.04', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.08', 'data', 'awareness', 'information', 'saga', 'knowledge']
senticnet['autobot_ship'] = ['-0.84', '0', '0.96', '-0.91', '#anger', '#disgust', 'negative', '-0.90', 'housewrecker', 'housebreaker', 'knacker', 'laborer', 'manual_laborer']
senticnet['autocorrelation'] = ['-0.59', '0', '0.672', '-0.83', '#anger', '#disgust', 'negative', '-0.7', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'asymmetry', 'irregularity']
senticnet['autocracy'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'ascendance', 'absolutism', 'ascendancy', 'tyrant']
senticnet['autocrat'] = ['-0.68', '0', '0.597', '-0.84', '#sadness', '#disgust', 'negative', '-0.71', 'dominance', 'tyrant', 'despot', 'despotism', 'dictator']
senticnet['autocratic'] = ['-0.95', '0', '0.74', '0', '#sadness', '#anger', 'negative', '-0.84', 'autocracy', 'autocratically', 'arbitrary', 'despotic', 'draconian']
senticnet['autocratic_regime'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'ascendance', 'absolutism', 'ascendancy', 'tyrant']
senticnet['autocratic_rule'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'ascendance', 'absolutism', 'ascendancy', 'tyrant']
senticnet['autocratically'] = ['-0.96', '0', '0.74', '0', '#sadness', '#anger', 'negative', '-0.85', 'autocracy', 'autocratic', 'arbitrary', 'despotic', 'draconian']
senticnet['autoerotic'] = ['0', '0.095', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'fragrant', 'use_decoration', 'colorful', 'green', 'cultivate_yard']
senticnet['autofocus_system'] = ['-0.82', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.84', 'block', 'obstruct', 'close', 'clogged', 'occluded']
senticnet['autograft'] = ['0', '0', '-0.77', '-0.86', '#fear', '#disgust', 'negative', '-0.82', 'abduction', 'punishment', 'emergency', 'illiteracy', 'heal']
senticnet['autograph'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'discuss', 'communication', 'communicate_person', 'signature', 'discussion']
senticnet['autographion'] = ['0', '0', '0', '0.073', '#admiration', '#admiration', 'positive', '0.073', 'autograph', 'communication', 'communicate_person', 'share', 'discuss']
senticnet['autoignition'] = ['-0.90', '0', '0.825', '-0.61', '#sadness', '#anger', 'negative', '-0.78', 'flaming', 'combustion', 'fire', 'flame', 'internal_combustion']
senticnet['autoignition_temperature'] = ['-0.90', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.85', 'flame', 'combustible', 'dangerous', 'incendiary', 'heat']
senticnet['autoimmune'] = ['0', '0', '0.74', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'rheumatism', 'arthritis', 'arthritic', 'rheumatoid', 'rheumatic']
senticnet['autoimmune_attack'] = ['0.079', '-0.10', '0.046', '0', '#joy', '#surprise', 'positive', '0.047', 'nerve', 'liver', 'groin', 'scrotum', 'diaphragm']
senticnet['autoimmune_condition'] = ['-0.75', '0', '0.515', '-0.93', '#sadness', '#disgust', 'negative', '-0.73', 'sclerosis', 'induration', 'degenerative_disorder', 'autoimmune_disease', 'disseminated_sclerosis']
senticnet['autoimmune_disease'] = ['-0.75', '0', '0.515', '-0.93', '#sadness', '#disgust', 'negative', '-0.73', 'sclerosis', 'induration', 'degenerative_disorder', 'disseminated_sclerosis', 'autoimmune_disorder']
senticnet['autoimmune_disorder'] = ['-0.75', '0', '0.515', '-0.93', '#sadness', '#disgust', 'negative', '-0.73', 'sclerosis', 'induration', 'degenerative_disorder', 'autoimmune_disease', 'disseminated_sclerosis']
senticnet['autoimmune_hemolytic_anemia'] = ['-0.87', '0', '0.531', '-0.68', '#sadness', '#disgust', 'negative', '-0.69', 'rubor', 'redness', 'dermatomyositis', 'inclusion_body_myositis', 'inflammation']
senticnet['autologous_blood'] = ['0.79', '-0.62', '0', '0.674', '#joy', '#admiration', 'positive', '0.695', 'add', 'contribute', 'lend', 'breathe', 'impart']
senticnet['autologous_donation'] = ['0.79', '-0.62', '0', '0.674', '#joy', '#admiration', 'positive', '0.695', 'add', 'contribute', 'lend', 'breathe', 'impart']
senticnet['autologous_fat'] = ['0', '0', '-0.77', '-0.86', '#fear', '#disgust', 'negative', '-0.82', 'abduction', 'punishment', 'emergency', 'illiteracy', 'heal']
senticnet['automate'] = ['0.781', '-0.79', '0', '0.816', '#surprise', '#admiration', 'positive', '0.798', 'deliver_goods', 'succeed', 'win', 'bring_home_bacon', 'come_through']
senticnet['automated_backup'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_musical_sound', 'play_orchestra', 'make_noise', 'play_music', 'make_drawing']
senticnet['automated_clearing_house'] = ['-0.05', '0.26', '-0.08', '0.142', '#interest', '#admiration', 'positive', '0.068', 'expense', 'afford', 'payment', 'cost', 'owe']
senticnet['automated_planet_finder'] = ['-0.64', '0', '0.524', '-0.59', '#sadness', '#disgust', 'negative', '-0.58', 'bum', 'dreamer', 'daydreamer', 'escapist', 'wishful_thinker']
senticnet['automated_teller'] = ['0', '0.186', '0', '0.075', '#interest', '#admiration', 'positive', '0.131', 'money_inside', 'check_account', 'savings', 'money_storage', 'store_money']
senticnet['automated_teller_machine'] = ['0', '0.186', '0', '0.075', '#interest', '#admiration', 'positive', '0.131', 'money_inside', 'check_account', 'savings', 'money_storage', 'store_money']
senticnet['automatic'] = ['0.911', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.881', 'eletronic', 'robotic', 'pistol', 'firearm', 'shoot_weapon']
senticnet['automatic_backup'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_musical_sound', 'play_orchestra', 'make_noise', 'play_music', 'make_drawing']
senticnet['automatic_car'] = ['0.027', '0', '0.026', '0.073', '#joy', '#admiration', 'positive', '0.025', 'large_automobile', 'expensive_car', 'limousine', 'means_of_transport', 'roulotte']
senticnet['automatic_excommunication'] = ['0.901', '0', '0', '0.916', '#joy', '#admiration', 'positive', '0.909', 'get_away', 'bread', 'glory', 'counsel', 'vow']
senticnet['automatic_firearm'] = ['0.978', '-0.57', '0', '0.793', '#joy', '#admiration', 'positive', '0.782', 'pistol', 'handgun', 'side_arm', 'shooting_iron', 'semiautomatic']
senticnet['automatic_handgun'] = ['0', '0', '0.729', '-0.84', '#anger', '#disgust', 'negative', '-0.78', 'rifle', 'pistol', 'revolver', 'hydrogen_bomb', 'firearm']
senticnet['automatic_pistol'] = ['-0.75', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.75', 'hydrogen_bomb', 'rifle', 'revolver', 'pistol', 'gun']
senticnet['automatic_renewal'] = ['0.857', '-0.68', '0', '0.971', '#joy', '#admiration', 'positive', '0.839', 'revitalize', 'refurbish', 'reconstruct', 'renovate', 'freshen_up']
senticnet['automatic_reply'] = ['0.949', '0.866', '0', '0', '#joy', '#interest', 'positive', '0.907', 'snail_mail', 'postage', 'send_package', 'buy_stamp', 'send_letter']
senticnet['automatic_rifle'] = ['0', '0', '0.818', '-0.99', '#anger', '#disgust', 'negative', '-0.90', 'weapon', 'aerial_bomb', 'machine_gun', 'cannon', 'handgun']
senticnet['automatic_suspension'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'steer_wheel', 'gear_shift', 'chassis', 'brake_pedal', 'air_vent']
senticnet['automatic_teller_machine'] = ['-0.05', '0.14', '0.015', '0.082', '#interest', '#admiration', 'positive', '0.039', 'cash', 'cash_pay', 'need_earn_money', 'open_wallet', 'earn_wage']
senticnet['automatic_weapon_fire'] = ['0', '0', '-0.85', '-0.99', '#fear', '#disgust', 'negative', '-0.92', 'onslaught', 'battery', 'shelling', 'bombardment', 'outpouring']
senticnet['automatically'] = ['0', '0.79', '0', '0.849', '#interest', '#admiration', 'positive', '0.82', 'mechanically', 'research', 'project', 'system', 'developed']
senticnet['automatically_activate'] = ['-0.99', '0', '0.651', '-0.51', '#sadness', '#anger', 'negative', '-0.72', 'sending', 'compulsion', 'influence', 'inducement', 'causation']
senticnet['automatically_debited_from'] = ['0', '0.192', '0', '0.052', '#interest', '#admiration', 'positive', '0.122', 'bank_vault', 'wallet', 'billfold', 'hold_change', 'bank']
senticnet['automatically_detect'] = ['-0.08', '0', '0.011', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'deflate', 'read_cover', 'obtain_information', 'upgrade', 'spread_out']
senticnet['automatically_disconnect'] = ['-0.08', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.09', 'mediocrity', 'effect', 'suspicion', 'exclusion', 'pugilist']
senticnet['automatically_expire'] = ['0.857', '-0.68', '0', '0.971', '#joy', '#admiration', 'positive', '0.839', 'revitalize', 'refurbish', 'reconstruct', 'renovate', 'freshen_up']
senticnet['automatically_reconnect'] = ['-0.08', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.09', 'mediocrity', 'effect', 'suspicion', 'exclusion', 'pugilist']
senticnet['automatically_renew'] = ['0.857', '-0.68', '0', '0.971', '#joy', '#admiration', 'positive', '0.839', 'revitalize', 'refurbish', 'reconstruct', 'renovate', 'freshen_up']
senticnet['automatically_shuts_off'] = ['0', '0', '0', '0.109', '#admiration', '#admiration', 'positive', '0.109', 'knob', 'door_knob', 'door_handle', 'handle', 'hinge']
senticnet['automation'] = ['0.083', '0.144', '-0.04', '0.165', '#interest', '#admiration', 'positive', '0.086', 'golf_place', 'nanotechnology', 'developement', 'school_course', 'school_class']
senticnet['automatism'] = ['-0.05', '0.122', '-0.12', '0.129', '#interest', '#admiration', 'positive', '0.02', 'bad_odor', 'smell_odor', 'inhale', 'whine', 'blow_nose']
senticnet['automaton'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'biological_machine', 'social_creature', 'live_creature', 'powerful', 'alive']
senticnet['automobil_gesellschaft'] = ['0', '0', '0.737', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'annoy', 'skin_rash', 'bother', 'irritation', 'great_dane']
senticnet['automobile'] = ['-0.04', '-0.16', '0', '0.056', '#surprise', '#admiration', 'positive', '0.059', 'car_part', 'under_car', 'car_wheel', 'wheel', 'car']
senticnet['automobile_accident'] = ['-0.92', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.93', 'serious_accident', 'accident', 'hit_car', 'loss_ofawareness', 'bang_head']
senticnet['automobile_collision'] = ['-0.92', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.93', 'serious_accident', 'accident', 'hit_car', 'loss_ofawareness', 'bang_head']
senticnet['automobile_crash'] = ['-0.92', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.93', 'serious_accident', 'accident', 'hit_car', 'loss_ofawareness', 'bang_head']
senticnet['automobile_dealership'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'sell_car', 'truck_stop', 'vehicle', 'insurance', 'car']
senticnet['automobile_exhaust'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'rodent', 'tree_animal']
senticnet['automobile_industry'] = ['-0.04', '-0.16', '0', '0.056', '#surprise', '#admiration', 'positive', '0.059', 'car_part', 'under_car', 'car_wheel', 'wheel', 'car']
senticnet['automobile_liability_insurance'] = ['0', '-0.01', '0', '0.064', '#surprise', '#admiration', 'positive', '0.042', 'car_gas', 'car_power', 'fossil_fuel', 'run_car', 'car_engine']
senticnet['automobile_manufacturer'] = ['-0.04', '-0.16', '0', '0.056', '#surprise', '#admiration', 'positive', '0.059', 'car_part', 'under_car', 'car_wheel', 'wheel', 'car']
senticnet['automobile_model'] = ['0', '0', '0', '0.102', '#admiration', '#admiration', 'positive', '0.102', 'fancy_car', 'old_car', 'expensive_car', 'car_model', 'car']
senticnet['automobile_tire'] = ['0', '-0.07', '0', '0', '#surprise', '#surprise', 'positive', '0.075', 'gyroscope', 'air_pump', 'ball_bounce', 'motor_oil', 'tire']
senticnet['automobile_traffic'] = ['-0.98', '0', '0.989', '-0.93', '#sadness', '#anger', 'negative', '-0.97', 'flight_accident', 'dangerous', 'danger', 'air_traffic', 'air_traffic_jam']
senticnet['automorphic'] = ['0.875', '-0.65', '0', '0.861', '#joy', '#admiration', 'positive', '0.797', 'completeness', 'work', 'whole_shebang', 'essential', 'whole']
senticnet['automotive'] = ['-0.04', '-0.16', '0', '0.056', '#surprise', '#admiration', 'positive', '0.059', 'car_part', 'under_car', 'car_wheel', 'wheel', 'car']
senticnet['automotive_analyst'] = ['0.711', '0', '0', '0', '#joy', '#joy', 'positive', '0.711', 'chemist', 'expert', 'scientist', 'esaminatore', 'physicist']
senticnet['automotive_fuel'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'gas', 'inert_gas', 'diesel', 'oil', 'combustible']
senticnet['automotive_gasoline'] = ['0.855', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.86', 'ticket_counter', 'newspaper_stand', 'luggage_carrier', 'aircraft_roadway', 'timetable']
senticnet['automotive_navigation'] = ['0', '0.071', '0', '0.143', '#interest', '#admiration', 'positive', '0.107', 'post_notice', 'indicator', 'way', 'one_way', 'arrow']
senticnet['autonomic_arousal'] = ['0.971', '0.751', '0', '0.896', '#joy', '#admiration', 'positive', '0.873', 'new_friendship', 'foreplay', 'warm_fuzzy_feeling', 'become_aroused', 'erection']
senticnet['autonomic_dysfunction'] = ['-0.62', '0', '0.901', '-0.71', '#anger', '#disgust', 'negative', '-0.74', 'pressure', 'osmotic_pressure', 'hypotonicity', 'pressure_level', 'force']
senticnet['autonomously'] = ['0', '0.717', '0', '0.765', '#interest', '#admiration', 'positive', '0.741', 'helper', 'artificial_intelligence', 'goal_driven', 'proactive', 'intelligent']
senticnet['autonomy'] = ['-0.52', '0', '0.561', '-0.90', '#anger', '#disgust', 'negative', '-0.66', 'difference', 'tolerance', 'allowance', 'discrepancy', 'margin']
senticnet['autophagic'] = ['-0.84', '0', '0.8', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'rigor_mortis', 'natural_process', 'natural_action', 'activity', 'action']
senticnet['autoplasty'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'abdominoplasty', 'tummy_tuck', 'plastic_surgery']
senticnet['autopsy'] = ['-0.06', '0.09', '-0.14', '-0.15', '#fear', '#disgust', 'negative', '-0.06', 'casket', 'coffin', 'grave', 'headstone', 'bury_person']
senticnet['autoresponder'] = ['0.031', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.062', 'letter_slot', 'postal_counter', 'mailbox', 'letter_scale', 'envelope']
senticnet['autosomal_dominant_disorder'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'dystrophy', 'myotonic_dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['autosomal_dominant_inheritance'] = ['0.504', '-0.93', '0', '0.797', '#surprise', '#admiration', 'positive', '0.747', 'diabetes', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['autosomal_recessive'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'myotonic_muscular_dystrophy', 'dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['autosomal_recessive_disease'] = ['-0.50', '0', '0.542', '-0.51', '#anger', '#disgust', 'negative', '-0.51', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'hirschsprung_disease']
senticnet['autosomal_recessive_disorder'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'dystrophy', 'myotonic_dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['autostrada'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'expressway', 'turnpike', 'toll_road', 'highway']
senticnet['autostrada_del_sole'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'expressway', 'turnpike', 'toll_road', 'highway']
senticnet['autostrade'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'expressway', 'turnpike', 'toll_road', 'highway']
senticnet['autosub'] = ['-0.04', '-0.01', '0.103', '0', '#sadness', '#anger', 'negative', '-0.04', 'near_beach', 'along_beach', 'full_water', 'ocean_ground', 'salty']
senticnet['autotopagnosia'] = ['-0.73', '0', '0.955', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'visual_agnosia', 'auditory_agnosia', 'brain_disorder', 'encephalopathy', 'brain_disease']
senticnet['autrefois_acquit'] = ['-0.70', '0', '0.874', '-0.75', '#anger', '#disgust', 'negative', '-0.77', 'blame', 'accusation', 'inculpation', 'complaint', 'imprecation']
senticnet['autumn'] = ['0', '-0.03', '-0.10', '-0.07', '#fear', '#disgust', 'negative', '-0.04', 'avalanche', 'winter', 'skier', 'snow_fall', 'volcano']
senticnet['autumn_equinox'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['autumn_foliage'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'tropical_fish', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['autumn_gentian'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
senticnet['autumn_leaf'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'tropical_fish', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['autumn_olive'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'locust_tree']
senticnet['autumn_sneezeweed'] = ['-0.77', '0', '0.755', '-0.67', '#sadness', '#anger', 'negative', '-0.73', 'herb', 'owlclaws', 'orange_sneezeweed', 'sneezeweed', 'rosilla']
senticnet['autumnal'] = ['-0.93', '0', '0.906', '-0.53', '#sadness', '#anger', 'negative', '-0.79', 'juvenility', 'state', 'immatureness', 'callowness', 'jejuneness']
senticnet['autumnal_equinox'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['auxiliary'] = ['-0.82', '0', '-0.84', '0', '#sadness', '#fear', 'negative', '-0.83', 'subsidiary', 'supplementary', 'subsidiary_company', 'subordinate', 'foot_soldier']
senticnet['auxiliary_equipment'] = ['-0.02', '-0.05', '0', '0.069', '#surprise', '#admiration', 'positive', '0.032', 'fixture', 'endowment', 'material', 'stuff', 'kit']
senticnet['auxiliary_gym'] = ['0.041', '0.086', '0.016', '0', '#joy', '#interest', 'positive', '0.037', 'team', 'play_sport', 'game', 'exercise', 'match']
senticnet['auxiliary_verb'] = ['0.732', '0.8', '0', '0', '#joy', '#interest', 'positive', '0.766', 'fast', 'place', 'substance', 'substantive', 'person']
senticnet['av_conduction'] = ['-0.56', '0', '0.849', '-0.61', '#anger', '#disgust', 'negative', '-0.67', 'convulsive', 'unsteady', 'jerky', 'spasmodic', 'spastic']
senticnet['avail'] = ['0.951', '-0.96', '0', '0.707', '#joy', '#surprise', 'positive', '0.873', 'helpfulness', 'help', 'usefulness', 'utility', 'service']
senticnet['avail_able'] = ['-0.05', '0.161', '-0.12', '0.181', '#interest', '#admiration', 'positive', '0.039', 'usable', 'redo', 'timeliness', 'question_starter', 'replay']
senticnet['availability'] = ['0.781', '0.956', '0', '0.839', '#interest', '#admiration', 'positive', '0.859', 'ornament', 'centennial', 'present', 'card', 'lifetime']
senticnet['availability_heuristic'] = ['0.628', '-0.96', '0', '0.682', '#surprise', '#admiration', 'positive', '0.759', 'taboo', 'tabu', 'tendentiousness', 'irrational_hostility', 'bias']
senticnet['available'] = ['-0.05', '0.161', '-0.12', '0.181', '#interest', '#admiration', 'positive', '0.039', 'usable', 'redo', 'timeliness', 'question_starter', 'replay']
senticnet['available_electronically'] = ['0.81', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.774', 'available', 'public', 'population', 'crowd', 'majority']
senticnet['avait_connu'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'sexual_relationship', 'liaison', 'affair', 'involvement']
senticnet['avalanche'] = ['0', '-0.03', '-0.10', '-0.07', '#fear', '#disgust', 'negative', '-0.04', 'autumn', 'winter', 'skier', 'snow_fall', 'volcano']
senticnet['avalanche_breakdown'] = ['-0.09', '-0.01', '-0.10', '-0.20', '#fear', '#disgust', 'negative', '-0.09', 'violence', 'siege', 'conflict', 'bloodshed', 'fight']
senticnet['avalanche_danger'] = ['0', '-0.03', '-0.10', '-0.07', '#fear', '#disgust', 'negative', '-0.04', 'autumn', 'winter', 'skier', 'snow_fall', 'volcano']
senticnet['avalanche_hazard'] = ['0', '-0.03', '-0.10', '-0.07', '#fear', '#disgust', 'negative', '-0.04', 'autumn', 'winter', 'skier', 'snow_fall', 'volcano']
senticnet['avalanche_occur'] = ['0', '-0.03', '-0.10', '-0.07', '#fear', '#disgust', 'negative', '-0.04', 'autumn', 'winter', 'skier', 'snow_fall', 'volcano']
senticnet['avalokiteshvara'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['avalokitesvara'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['avalokitevara'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['avant-garde'] = ['0.766', '0', '0', '0.793', '#joy', '#admiration', 'positive', '0.78', 'caravan', 'vanguard', 'van', 'new_wave', 'bos']
senticnet['avant_garde'] = ['-0.61', '0', '0.738', '-0.80', '#anger', '#disgust', 'negative', '-0.71', 'dissident', 'beat', 'bohemian', 'maverick', 'recusant']
senticnet['avant_garde_art'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'native_person', 'revolutionary', 'republic', 'revolution', 'look_ahead']
senticnet['avant_garde_artist'] = ['-0.61', '0', '0.738', '-0.80', '#anger', '#disgust', 'negative', '-0.71', 'dissident', 'beat', 'bohemian', 'maverick', 'recusant']
senticnet['avant_garde_cinema'] = ['-0.85', '0', '0.514', '-0.73', '#sadness', '#disgust', 'negative', '-0.70', 'flick', 'movie', 'film', 'picture', 'moving_picture']
senticnet['avant_garde_composer'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'band_play', 'songwriter', 'rock_band']
senticnet['avant_garde_filmmaker'] = ['-0.85', '0', '0.514', '-0.73', '#sadness', '#disgust', 'negative', '-0.70', 'flick', 'movie', 'film', 'picture', 'moving_picture']
senticnet['avant_garde_jazz'] = ['0.901', '0', '0', '0', '#joy', '#joy', 'positive', '0.901', 'music', 'vapor', 'blues', 'megrim', 'demoralization']
senticnet['avant_garde_movement'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'native_person', 'revolutionary', 'republic', 'revolution', 'look_ahead']
senticnet['avant_gardism'] = ['-0.54', '0', '0.979', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'unlikeness', 'dissimilitude', 'difference', 'heterology', 'dissimilarity']
senticnet['avant_gardist'] = ['-0.61', '0', '0.738', '-0.80', '#anger', '#disgust', 'negative', '-0.71', 'dissident', 'beat', 'bohemian', 'maverick', 'recusant']
senticnet['avarice'] = ['-0.5', '0', '0.939', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'greed', 'covetousness', 'possessiveness', 'desire', 'acquisitiveness']
senticnet['avaricious'] = ['0.098', '0.084', '0.819', '-0.68', '#anger', '#disgust', 'negative', '-0.33', 'grasp', 'covet', 'covetousness', 'prehensile', 'grabby']
senticnet['avariciousness'] = ['0', '0', '0.91', '-0.75', '#anger', '#disgust', 'negative', '-0.83', 'garrulousness', 'illiberality', 're-localization', 'over-specialization', 'over-zealousness']
senticnet['avascular'] = ['-0.53', '0', '0.982', '-0.67', '#anger', '#disgust', 'negative', '-0.73', 'meniscus', 'semilunar_cartilage', 'gristle', 'hyaline_cartilage', 'arytenoid']
senticnet['avce'] = ['-0.04', '0.097', '0.06', '0.146', '#interest', '#admiration', 'positive', '0.036', 'vector', 'ratio', 'length_measurement', 'measurement_unit', 'unit_of_measurement']
senticnet['avdo'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'white_animal', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
senticnet['avec_qui'] = ['0.911', '-0.94', '0', '0.953', '#surprise', '#admiration', 'positive', '0.935', 'common_sense', 'good_sense', 'horse_sense', 'mother_wit', 'gumption']
senticnet['aveeno'] = ['0', '0.086', '-0.06', '0.12', '#interest', '#admiration', 'positive', '0.046', 'gel_toothpaste', 'tartar_control_toothpaste', 'fluoride_mouthwash', 'toothpaste', 'hair_lotion']
senticnet['avena_sativa'] = ['0.878', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.883', 'wheat', 'grain', 'rye', 'white_rice', 'rice']
senticnet['avenae'] = ['-0.93', '0', '0.699', '-0.91', '#sadness', '#disgust', 'negative', '-0.84', 'onion_smut', 'head_smut', 'stinking_smut', 'flag_smut_fungus', 'covered_smut']
senticnet['avenge'] = ['0', '0.762', '0', '0.895', '#interest', '#admiration', 'positive', '0.829', 'avenger', 'vindicate', 'clear', 'evidence', 'vindictive']
senticnet['avenger'] = ['0', '0.763', '0', '0.89', '#interest', '#admiration', 'positive', '0.827', 'vindicate', 'avenge', 'clear', 'evidence', 'vindictive']
senticnet['aventador'] = ['0.755', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.728', 'high_class', 'expensive_car', 'ferrari', 'fancy_car', 'sports_car']
senticnet['avenue'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'boulevard', 'lane', 'thoroughfare', 'intersection', 'curb']
senticnet['aver'] = ['0.565', '-0.73', '0', '0.542', '#joy', '#surprise', 'positive', '0.615', 'say', 'asseverate', 'predicate', 'insist', 'take_firm_stand']
senticnet['average'] = ['0.031', '0.125', '0.081', '0.138', '#interest', '#admiration', 'positive', '0.053', 'equal', 'add', 'tally', 'multiplication', 'mathematical_operation']
senticnet['average_altitude'] = ['0.023', '0.113', '0.053', '0.153', '#interest', '#admiration', 'positive', '0.059', 'high', 'high_land', 'incline_plane', 'top', 'diagonal']
senticnet['average_annual_precipitation'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'condensation', 'water_fall', 'falling_water', 'frozen_water', 'water_drop']
senticnet['average_annual_rainfall'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'condensation', 'water_fall', 'falling_water', 'frozen_water', 'water_drop']
senticnet['average_annual_salary'] = ['0', '0.893', '0', '0', '#interest', '#interest', 'positive', '0.893', 'salary', 'wage', 'send_money', 'earnings', 'pay']
senticnet['average_attendance'] = ['0.723', '0', '0', '0.762', '#joy', '#admiration', 'positive', '0.742', 'presence', 'feel_important', 'existence', 'feel_satisfied', 'feel_presence']
senticnet['average_density'] = ['0.728', '0.977', '0', '0.939', '#interest', '#admiration', 'positive', '0.881', 'air_pressure', 'altitude', 'air', 'heavy_air', 'air_density']
senticnet['average_hourly_wage'] = ['0', '0.923', '0', '0', '#interest', '#interest', 'positive', '0.923', 'salary', 'remittance', 'owe', 'payment', 'severance']
senticnet['average_iq'] = ['-0.98', '0', '0.674', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'slow', 'slow_pace', 'stupid', 'slow_car', 'slow_vehicle']
senticnet['average_life_span'] = ['0.097', '0.099', '-0.09', '0.137', '#interest', '#admiration', 'positive', '0.059', 'live_long', 'long_life', 'live_healthy_lifestyle', 'increase_muscle_mass', 'feel_healthy']
senticnet['average_lifespan'] = ['0.097', '0.099', '-0.09', '0.137', '#interest', '#admiration', 'positive', '0.059', 'live_long', 'long_life', 'live_healthy_lifestyle', 'increase_muscle_mass', 'feel_healthy']
senticnet['average_out'] = ['-0.52', '0', '0.821', '-0.65', '#anger', '#disgust', 'negative', '-0.66', 'work_out', 'reckon', 'calculate', 'cipher', 'cypher']
senticnet['average_precipitation'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'condensation', 'water_fall', 'falling_water', 'frozen_water', 'water_drop']
senticnet['average_rainfall'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'condensation', 'water_fall', 'falling_water', 'frozen_water', 'water_drop']
senticnet['average_score'] = ['0.926', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.916', 'score_goal', 'accomplish', 'finalize', 'pot', 'make_point']
senticnet['average_wage'] = ['0', '0.923', '0', '0', '#interest', '#interest', 'positive', '0.923', 'salary', 'remittance', 'owe', 'payment', 'severance']
senticnet['average_yearly_rainfall'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'condensation', 'water_fall', 'falling_water', 'frozen_water', 'water_drop']
senticnet['averageness'] = ['0.798', '-0.51', '0', '0.519', '#joy', '#admiration', 'positive', '0.611', 'normalcy', 'commonness', 'expectedness', 'typicality', 'condition']
senticnet['averment'] = ['0.976', '-0.68', '0', '0.672', '#joy', '#surprise', 'positive', '0.778', 'testimony', 'claim', 'charge', 'contention', 'accusation']
senticnet['averse'] = ['-0.98', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.85', 'abhor', 'adverse', 'aversion', 'avert', 'averseness']
senticnet['aversely'] = ['-0.84', '0', '0.814', '0', '#sadness', '#anger', 'negative', '-0.83', 'cooperate', 'unwillingly', 'disgorge', 'loathly', 'unwilling']
senticnet['averseness'] = ['-0.99', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.85', 'abhor', 'adverse', 'aversion', 'avert', 'averse']
senticnet['aversion'] = ['-0.12', '0.193', '0.593', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'distaste', 'antipathy', 'disapproval', 'disapprove', 'disapproving']
senticnet['aversion_therapy'] = ['0.542', '-0.63', '0', '0.912', '#surprise', '#admiration', 'positive', '0.695', 'implosion_therapy', 'flooding', 'reciprocal_inhibition', 'behavior_modification', 'desensitization_technique']
senticnet['aversive'] = ['0.971', '0.751', '0', '0.896', '#joy', '#admiration', 'positive', '0.873', 'new_friendship', 'foreplay', 'warm_fuzzy_feeling', 'become_aroused', 'erection']
senticnet['aversive_racism'] = ['-0.93', '0', '0.586', '-0.66', '#sadness', '#disgust', 'negative', '-0.73', 'camp', 'pack', 'clique', 'coterie', 'brain_trust']
senticnet['aversive_stimulation'] = ['0.583', '-0.82', '0', '0.574', '#joy', '#surprise', 'positive', '0.661', 'stimulant', 'positive_reinforcing_stimulus', 'positive_reinforcer', 'negative_reinforcing_stimulus', 'reinforcer']
senticnet['aversive_stimulus'] = ['0.722', '-0.76', '0', '0.946', '#surprise', '#admiration', 'positive', '0.811', 'arouse', 'ask_for', 'enkindle', 'kindle', 'evoke']
senticnet['aversive_therapy'] = ['0.542', '-0.63', '0', '0.912', '#surprise', '#admiration', 'positive', '0.695', 'implosion_therapy', 'flooding', 'reciprocal_inhibition', 'behavior_modification', 'desensitization_technique']
senticnet['avert'] = ['-0.98', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.85', 'abhor', 'adverse', 'aversion', 'averse', 'averseness']
senticnet['avert_automatic'] = ['0', '0', '-0.87', '-0.84', '#fear', '#disgust', 'negative', '-0.85', 'seize', 'seclude', 'attach', 'impound', 'confiscate']
senticnet['avert_bankruptcy'] = ['0.961', '-0.84', '0', '0.958', '#joy', '#admiration', 'positive', '0.923', 'pause', 'break', 'intermission', 'suspension', 'rest_period']
senticnet['averted_vision'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'eye', 'peeper', 'oculus_sinister']
senticnet['averting'] = ['0.649', '-0.85', '0', '0.947', '#surprise', '#admiration', 'positive', '0.818', 'rejection', 'escape', 'abscond', 'aversion', 'avoidance']
senticnet['avialae'] = ['-0.68', '0', '0.956', '-0.90', '#anger', '#disgust', 'negative', '-0.84', 'giant_moa', 'anomalopteryx', 'moa', 'moa_bird', 'flightless_bird']
senticnet['avian'] = ['0.05', '-0.01', '0', '0.058', '#joy', '#admiration', 'positive', '0.041', 'wing_insect', 'flap_wing', 'aviation', 'wing_animal', 'wing_creature']
senticnet['avian_conservation'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'hold_egg', 'baby_bird', 'hatch', 'lay_egg', 'bird_home']
senticnet['avian_influenza'] = ['-0.96', '0', '0.43', '-0.90', '#sadness', '#disgust', 'negative', '-0.76', 'disease', 'doctor', 'sickness', 'illness', 'sick']
senticnet['avian_pox'] = ['0.688', '-0.94', '0', '0.925', '#surprise', '#admiration', 'positive', '0.852', 'flatworm', 'platyhelminth', 'trematode', 'schistosome', 'blood_fluke']
senticnet['aviation'] = ['0.05', '-0.01', '0', '0.058', '#joy', '#admiration', 'positive', '0.041', 'wing_insect', 'flap_wing', 'avian', 'wing_animal', 'wing_creature']
senticnet['aviation_biofuel'] = ['0.855', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.86', 'ticket_counter', 'newspaper_stand', 'luggage_carrier', 'aircraft_roadway', 'timetable']
senticnet['aviation_fuel'] = ['0.855', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.86', 'ticket_counter', 'newspaper_stand', 'luggage_carrier', 'aircraft_roadway', 'timetable']
senticnet['aviation_gasoline'] = ['0.855', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.86', 'ticket_counter', 'newspaper_stand', 'luggage_carrier', 'aircraft_roadway', 'timetable']
senticnet['aviation_turbine'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'gas', 'inert_gas', 'diesel', 'oil', 'combustible']
senticnet['aviator'] = ['0.946', '0', '0', '0.77', '#joy', '#admiration', 'positive', '0.858', 'shade', 'drive_quickly', 'drive', 'pilot', 'fast_travel']
senticnet['avicularia'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'annelid', 'annelid_worm', 'leech']
senticnet['avid'] = ['0.864', '0', '0', '0.991', '#joy', '#admiration', 'positive', '0.927', 'thirst', 'eagerly', 'eagerness', 'avidity', 'avidness']
senticnet['avid_golfer'] = ['0.758', '0.719', '0', '0', '#joy', '#interest', 'positive', '0.738', 'plan_party', 'dance_music', 'become_adult', 'stay_fit', 'drive_crazy']
senticnet['avidity'] = ['-0.87', '0', '-0.71', '-0.96', '#sadness', '#disgust', 'negative', '-0.85', 'avid', 'eagerly', 'eagerness', 'thirstily', 'avidness']
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senticnet['avuncular'] = ['0', '0.723', '0', '0.78', '#interest', '#admiration', 'positive', '0.752', 'grandfatherly', 'fatherly', 'affable', 'stolid', 'supercilious']
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senticnet['award-winning'] = ['0.865', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.912', 'acclaimed', 'renowned', 'world-renowned', 'best-selling', 'innovative']
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senticnet['axe_blade'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'useful_tool']
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senticnet['axial_force'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'aberrance', 'warp', 'chromosomal_aberration']
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senticnet['axial_rotation'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotate_movement', 'rotate', 'go_around', 'manoeuvre', 'full_circle']
senticnet['axial_strain'] = ['-0.08', '-0.03', '-0.07', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'injury', 'soreness', 'pain', 'hurt', 'dislocation']
senticnet['axially'] = ['0.867', '0.779', '0', '0', '#joy', '#interest', 'positive', '0.823', 'radially', 'circumferentially', 'longitudinally', 'laterally', 'transversely']
senticnet['axially_symmetric'] = ['-0.97', '0', '0.831', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'skewness', 'irregularity']
senticnet['axillary_dissection'] = ['-0.99', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.78', 'angioedema', 'atrophedema', 'giant_hive', 'periodic_edema', 'dropsy']
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senticnet['axiom'] = ['0', '0.274', '-0.06', '0.282', '#interest', '#admiration', 'positive', '0.163', 'principle', 'tenet', 'govern', 'confirmation', 'supreme']
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senticnet['axiomatic_approach'] = ['-0.99', '0', '0.878', '-0.95', '#sadness', '#disgust', 'negative', '-0.94', 'adequacy', 'satisfactoriness', 'acceptability', 'acceptableness', 'quality']
senticnet['axiomatize'] = ['-0.99', '0', '0.878', '-0.95', '#sadness', '#disgust', 'negative', '-0.94', 'adequacy', 'satisfactoriness', 'acceptability', 'acceptableness', 'quality']
senticnet['axis_occupation'] = ['0', '0.112', '-0.04', '0.099', '#interest', '#admiration', 'positive', '0.054', 'profession', 'career', 'vocation', 'important_job', 'laborer']
senticnet['axis_parallel'] = ['-0.97', '0', '0.865', '-0.54', '#sadness', '#anger', 'negative', '-0.79', 'piping', 'lace', 'lacing', 'cord', 'slack']
senticnet['axis_theorem'] = ['0', '0', '-0.73', '-0.79', '#fear', '#disgust', 'negative', '-0.76', 'analogue', 'analog', 'line_of_latitude', 'latitude', 'collimate']
senticnet['axisymmetric'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'assumption', 'precondition', 'provision', 'constraint', 'premiss']
senticnet['axisymmetry'] = ['0.807', '-0.88', '0', '0.718', '#joy', '#surprise', 'positive', '0.802', 'sex', 'gayness', 'sexual_gayness', 'lesbianism', 'sapphism']
senticnet['axle'] = ['0.041', '-0.08', '0', '0.056', '#surprise', '#admiration', 'positive', '0.061', 'engine', 'wheel', 'motor', 'steer_wheel', 'two_wheels']
senticnet['axonal_damage'] = ['-0.78', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.80', 'deterioration', 'decline', 'decay', 'degradation', 'slide']
senticnet['axonal_degeneration'] = ['-0.78', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.80', 'deterioration', 'decline', 'decay', 'degradation', 'slide']
senticnet['axonometric_projection'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'arm_movement', 'hand_gesture', 'water_motion', 'hand_signal', 'sea_movement']
senticnet['axotomy'] = ['-0.78', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.80', 'deterioration', 'decline', 'decay', 'degradation', 'slide']
senticnet['ay'] = ['-0.83', '0', '0.87', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'precipitate', 'rain_down', 'shower_down', 'spit', 'patter']
senticnet['ayahuasca'] = ['-0.73', '0', '0.944', '-0.94', '#anger', '#disgust', 'negative', '-0.87', 'lysergic_acid_diethylamide', 'mescaline', 'peyote', 'phencyclidine', 'drug']
senticnet['ayane'] = ['0.021', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'plastic_bottle', 'party', 'use_party', 'lighter_air', 'compress_air']
senticnet['ayasofya'] = ['0', '0.224', '0', '0.197', '#interest', '#admiration', 'positive', '0.211', 'church', 'temple', 'cathedral', 'chapel', 'confession']
senticnet['aye'] = ['0', '0.779', '0', '0', '#interest', '#interest', 'positive', '0.779', 'salutation', 'welcome', 'please', 'nod', 'give']
senticnet['ayers_rock'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'beachcomber', 'bum', 'drifter']
senticnet['ayrshire'] = ['0.546', '-0.96', '0', '0.533', '#joy', '#surprise', 'positive', '0.683', 'dairy_cattle', 'dairy_cow', 'milch_cow', 'milk_cow', 'milcher']
senticnet['aysn_region'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'dryness', 'thirstiness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['ayurveda'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
senticnet['ayurvedic'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
senticnet['ayurvedic_medical'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
senticnet['ayurvedic_medicine'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
senticnet['azadirachta_indica'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'nut', 'palm_kernel', 'edible_nut', 'cembra_nut', 'cedar_nut']
senticnet['azazel'] = ['-0.96', '0', '0.501', '-0.59', '#sadness', '#disgust', 'negative', '-0.68', 'calamity', 'catastrophe', 'tragedy', 'disaster', 'cataclysm']
senticnet['aziz_dweik'] = ['0.034', '-0.02', '0', '0.057', '#joy', '#admiration', 'positive', '0.039', 'female_television_connector', 'channel_selector', 'power_switch', 'tuner', 'radio']
senticnet['azo_dye'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['azole'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['azoth'] = ['0', '0.887', '0', '0.973', '#interest', '#admiration', 'positive', '0.93', 'servant', 'art_of_transmuting_metal', 'stone', 'alchemy', 'philosopher']
senticnet['azure_blue'] = ['0.049', '0.145', '0', '0.171', '#interest', '#admiration', 'positive', '0.122', 'kingly', 'govern', 'head_wear', 'chivalry', 'blue_blood']
senticnet['azure_sea'] = ['0.98', '-0.74', '0', '0.984', '#joy', '#admiration', 'positive', '0.903', 'prosperous', 'flourishing', 'successful', 'prospering', 'thriving']
senticnet['azure_sky'] = ['0.085', '0.071', '0.01', '0.085', '#joy', '#admiration', 'positive', '0.058', 'blue_color', 'munchkin', 'imaginary', 'crystal_clear', 'water_sky']
senticnet['azurite'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['azz'] = ['-0.02', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.04', 'butt', 'foreign_country', 'menagerie', 'way', 'out_door']
senticnet['b_degree'] = ['-0.75', '0', '0.589', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'lowness', 'grind', 'immoderation', 'immoderateness', 'amplitude_level']
senticnet['ba_degree'] = ['-0.75', '0', '0.589', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'lowness', 'grind', 'immoderation', 'immoderateness', 'amplitude_level']
senticnet['ba_lot_of_out'] = ['0', '0', '-0.90', '-0.82', '#fear', '#disgust', 'negative', '-0.86', 'obliterate', 'kill', 'wipe_out', 'veil', 'obscure']
senticnet['ba_relief'] = ['0.085', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.079', 'sculpture', 'creative', 'yellow_ribbon', 'artwork', 'monument']
senticnet['baal_peor'] = ['-0.88', '0', '0.929', '-0.84', '#sadness', '#anger', 'negative', '-0.88', 'paynim', 'nonreligious_person', 'idolizer', 'gentile', 'idol_worshiper']
senticnet['baalim'] = ['-0.52', '0', '0.701', '-0.50', '#sadness', '#anger', 'negative', '-0.57', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['bab_el'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['baba_yaga'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'jawless_fish', 'osteostracan', 'cephalaspid', 'ostracoderm', 'heterostracan']
senticnet['babassu'] = ['0.627', '-0.84', '0', '0.845', '#surprise', '#admiration', 'positive', '0.773', 'feather_palm', 'oil_palm', 'babassu_palm', 'palm', 'wax_palm']
senticnet['babassu_oil'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'palm_nut', 'nut', 'edible_nut', 'cembra_nut', 'cedar_nut']
senticnet['babassu_palm'] = ['0.635', '-0.84', '0', '0.849', '#surprise', '#admiration', 'positive', '0.777', 'feather_palm', 'babassu', 'oil_palm', 'palm', 'wax_palm']
senticnet['babble'] = ['-0.61', '0', '0.738', '-0.80', '#anger', '#disgust', 'negative', '-0.71', 'babbling', 'double_talk', 'mumbo_jumbo', 'gibber', 'lallation']
senticnet['babbling'] = ['-0.61', '0', '0.738', '-0.80', '#anger', '#disgust', 'negative', '-0.71', 'gibber', 'double_talk', 'mumbo_jumbo', 'babble', 'lallation']
senticnet['babe'] = ['0', '0', '0', '0.939', '#admiration', '#admiration', 'positive', '0.939', 'large_family', 'small_child', 'youth', 'quiet_baby', 'deliver_baby']
senticnet['babe_ruth'] = ['0.048', '0', '0', '0.092', '#joy', '#admiration', 'positive', '0.07', 'friendly', 'match', 'step_up_plate', 'contest', 'game_chess']
senticnet['babek_rayon'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'head_hurt', 'introspection', 'lifestyle_change', 'headache', 'move_eye']
senticnet['babesia'] = ['0.995', '-0.71', '0', '0.647', '#joy', '#surprise', 'positive', '0.784', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['babesia_microti'] = ['0.579', '-0.81', '0', '0.833', '#surprise', '#admiration', 'positive', '0.741', 'typhus', 'rat_typhus', 'urban_typhus', 'endemic_typhus', 'rickettsial_disease']
senticnet['babinski'] = ['0.89', '-0.82', '0', '0.776', '#joy', '#surprise', 'positive', '0.829', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['babinski_reflex'] = ['0.68', '-0.82', '0', '0.574', '#joy', '#surprise', 'positive', '0.693', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['babinski_sign'] = ['0.722', '-0.87', '0', '0.696', '#joy', '#surprise', 'positive', '0.763', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['baby'] = ['0', '0', '0', '0.939', '#admiration', '#admiration', 'positive', '0.939', 'large_family', 'small_child', 'youth', 'quiet_baby', 'deliver_baby']
senticnet['baby_action'] = ['0.97', '0', '0', '0', '#joy', '#joy', 'positive', '0.97', 'eye_tear', 'reaction', 'shed_tear', 'bawl', 'body_action']
senticnet['baby_bird'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'chick', 'wild_bird', 'hatch', 'lay_egg', 'bird_home']
senticnet['baby_born'] = ['0.032', '0.261', '-0.15', '0.198', '#interest', '#admiration', 'positive', '0.084', 'carry_baby', 'ejaculate', 'herpes', 'cum', 'change_diaper']
senticnet['baby_born_prematurely'] = ['-0.51', '0', '0.747', '-0.94', '#anger', '#disgust', 'negative', '-0.73', 'stillborn_infant', 'neonate', 'preterm_baby', 'premature_infant', 'preterm_infant']
senticnet['baby_bottle'] = ['0', '-0.02', '0.027', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'shoulder_bag', 'satchel', 'satchel_bag', 'tote_bag', 'carry']
senticnet['baby_boy'] = ['0', '0', '0', '0.939', '#admiration', '#admiration', 'positive', '0.939', 'large_family', 'small_child', 'youth', 'quiet_baby', 'deliver_baby']
senticnet['baby_brine_shrimp'] = ['-0.83', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.78', 'picaninny', 'kid', 'minor', 'tike', 'small_picaninny']
senticnet['baby_bump'] = ['0.54', '-0.64', '0', '0.566', '#surprise', '#admiration', 'positive', '0.582', 'pleaser', 'entertainer', 'mortal', 'busker', 'drawing_card']
senticnet['baby_carriage'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'super_market', 'salad_bar', 'fruit_stand', 'candy_store', 'produce_store']
senticnet['baby_cat'] = ['0.959', '0', '0', '0.929', '#joy', '#admiration', 'positive', '0.944', 'small_cat', 'cute', 'pet', 'small_animal', 'curious_animal']
senticnet['baby_chick'] = ['0.042', '0.088', '0', '0', '#joy', '#interest', 'positive', '0.065', 'jump_over_obstacle', 'attempt_fly', 'aircraft', 'pilot', 'wing']
senticnet['baby_chicken'] = ['0.053', '0.091', '0', '0.152', '#interest', '#admiration', 'positive', '0.099', 'inside_egg', 'hen_baby', 'oval_shape', 'lay_egg', 'hatch']
senticnet['baby_cow'] = ['0', '-0.02', '0', '0.06', '#surprise', '#admiration', 'positive', '0.041', 'male_cow', 'donkey', 'little_horse', 'hoof_animal', 'animal']
senticnet['baby_crib'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'lavatory', 'commode', 'potty_chair']
senticnet['baby_cry'] = ['-0.96', '0', '0.915', '0', '#sadness', '#anger', 'negative', '-0.94', 'hear_sound', 'disturb', 'make_noise', 'listen_news', 'mess_up']
senticnet['baby_daddy'] = ['0.033', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.058', 'baby_dog', 'sweet', 'puppet', 'dog', 'love']
senticnet['baby_deer'] = ['0', '-0.10', '0', '0', '#surprise', '#surprise', 'positive', '0.101', 'mammal', 'type_of_animal', 'animal', 'sea_mammal', 'water_mammal']
senticnet['baby_dog'] = ['0.144', '0.134', '-0.04', '0.238', '#joy', '#admiration', 'positive', '0.118', 'puppy', 'vulnerable', 'need_care', 'young_animal', 'newborn']
senticnet['baby_flower'] = ['0.088', '0', '0', '0.072', '#joy', '#admiration', 'positive', '0.08', 'wee', 'poppy', 'yellow_flower', 'rose', 'green_plant']
senticnet['baby_formula'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'drinking_water', 'sugar_water']
senticnet['baby_girl'] = ['0', '0', '0', '0.939', '#admiration', '#admiration', 'positive', '0.939', 'large_family', 'small_child', 'youth', 'quiet_baby', 'deliver_baby']
senticnet['baby_mama'] = ['-0.90', '0.152', '-0.62', '0.061', '#sadness', '#fear', 'negative', '-0.33', 'guilt_feeling', 'guilty', 'dolor', 'grieve', 'penitentially']
senticnet['baby_mammoth'] = ['0.969', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.96', 'extinct', 'animal', 'ice', 'elephant', 'giant']
senticnet['baby_panda'] = ['0.02', '0.071', '0', '0.069', '#interest', '#admiration', 'positive', '0.053', 'many_animals', 'donkey', 'chimpanzee', 'cute_animal', 'creature']
senticnet['baby_powder'] = ['0', '0.095', '0', '0.074', '#interest', '#admiration', 'positive', '0.084', 'buy_wash_powder', 'water_soap', 'turn_faucet', 'take_off', 'fill_bathtub_water']
senticnet['baby_shampoo'] = ['0', '0.095', '0', '0.074', '#interest', '#admiration', 'positive', '0.084', 'buy_wash_powder', 'water_soap', 'turn_faucet', 'take_off', 'fill_bathtub_water']
senticnet['baby_shower'] = ['0.161', '0.155', '-0.08', '0.223', '#joy', '#admiration', 'positive', '0.114', 'family', 'happy_moment', 'aunt_uncle', 'brother_sister', 'relation']
senticnet['baby_sitter'] = ['0', '0.827', '0', '0.84', '#interest', '#admiration', 'positive', '0.833', 'take_care', 'baby', 'free_time', 'child_in_safe_hand', 'free_night']
senticnet['baby_spinach'] = ['0.904', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.907', 'eden', 'garden', 'usual_green', 'apple', 'leafy']
senticnet['baby_stroller'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'super_market', 'salad_bar', 'fruit_stand', 'candy_store', 'produce_store']
senticnet['baby_teeth'] = ['0', '0', '0.813', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'ice_cream_melt', 'allergy', 'toothache', 'gonorrhea', 'tastebud']
senticnet['babycall'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'concupiscence', 'lustfulness', 'amorousness']
senticnet['babygap'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['babyish'] = ['-0.29', '0.2', '0.49', '-0.61', '#anger', '#disgust', 'negative', '-0.3', 'queasy', 'outrageous', 'horrid', 'angrily', 'see_red']
senticnet['babysit'] = ['0.798', '0.915', '0', '0', '#joy', '#interest', 'positive', '0.857', 'part_of_family', 'father_child', 'take_out_trash', 'mother_child', 'relationship']
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senticnet['bacteria_order'] = ['0.888', '-0.81', '0', '0.617', '#joy', '#surprise', 'positive', '0.773', 'ordering', 'sequence', 'genetic_code', 'genome', 'series']
senticnet['bacteriae'] = ['0.901', '-0.66', '0', '0.842', '#joy', '#admiration', 'positive', '0.802', 'gonococcus', 'bacteria', 'acidophil', 'probiotic', 'probiotic_bacterium']
senticnet['bacterial_blight'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'apple_rust', 'fungus']
senticnet['bacterial_canker'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'rust_fungus', 'fungus']
senticnet['bacterial_colonization'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'plaque']
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senticnet['bacterial_disease'] = ['0.579', '-0.81', '0', '0.833', '#surprise', '#admiration', 'positive', '0.741', 'typhus', 'rat_typhus', 'urban_typhus', 'endemic_typhus', 'rickettsial_disease']
senticnet['bacterial_fermentation'] = ['0', '0.104', '-0.09', '0.146', '#interest', '#admiration', 'positive', '0.052', 'thaw', 'slush', 'water_inside', 'give_water', 'obtain_water']
senticnet['bacterial_growth'] = ['-0.08', '-0.04', '0.023', '-0.07', '#sadness', '#disgust', 'negative', '-0.03', 'tier', 'make_toast', 'house_smell_good', 'get_hot', 'bread_crumb']
senticnet['bacterial_infection'] = ['-0.71', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.73', 'pain', 'gape', 'sunburn', 'ouch', 'cut_skin']
senticnet['bacterial_leaf_spot'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'rust_fungus', 'fungus']
senticnet['bacterial_overgrowth'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'anus', 'scrotum', 'shoulder', 'liver', 'waist']
senticnet['bacterial_plaque'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'plaque']
senticnet['bacterial_pneumonia'] = ['-0.99', '0', '0.964', '-0.70', '#sadness', '#anger', 'negative', '-0.88', 'blepharitis', 'diverticulitis', 'inflammation', 'orchitis', 'prostatitis']
senticnet['bacterial_soft_rot'] = ['0', '0', '-0.94', '-0.96', '#fear', '#disgust', 'negative', '-0.95', 'say_prayer', 'fall_off_chair', 'put_box', 'feel_terrible', 'lift_leg']
senticnet['bacterial_spot'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'rust_fungus', 'fungus']
senticnet['bacterial_toxin'] = ['0.772', '-0.56', '0', '0.824', '#joy', '#admiration', 'positive', '0.719', 'neutrophil', 'leukocyte', 'leucocyte', 'white_blood_cell', 'white_cell']
senticnet['bacterial_vaginosis_bv'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'gastroenteritis', 'stomach_flu', 'intestinal_flu', 'inflammatory_disease']
senticnet['bactericidal'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['bacteriocidal'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['bacteriocin'] = ['0.772', '-0.56', '0', '0.824', '#joy', '#admiration', 'positive', '0.719', 'neutrophil', 'leukocyte', 'leucocyte', 'white_blood_cell', 'white_cell']
senticnet['bacteriologist'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'druggist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['bacteriology'] = ['-0.59', '0', '0.911', '-0.87', '#anger', '#disgust', 'negative', '-0.79', 'osteosclerosis', 'sclerosis', 'disseminated_sclerosis', 'multiple_sclerosis', 'induration']
senticnet['bacteriostatic'] = ['0.701', '-0.76', '0', '0.904', '#surprise', '#admiration', 'positive', '0.791', 'curative', 'heal', 'medicine', 'get_cured', 'take_care']
senticnet['bacterium'] = ['-0.94', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.94', 'earthworm', 'microorganism', 'germ', 'bug', 'cutworm']
senticnet['bacterium_rickettsia'] = ['-0.11', '0', '-0.15', '-0.24', '#fear', '#disgust', 'negative', '-0.17', 'disease', 'infectious_disease', 'polio', 'malady', 'sickness']
senticnet['bactrian_camel'] = ['-0.53', '0', '0.751', '-0.97', '#anger', '#disgust', 'negative', '-0.75', 'guanaco', 'domestic_llama', 'llama', 'artiodactyl', 'artiodactyl_mammal']
senticnet['bactrim'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['baculum'] = ['0.886', '-0.99', '0', '0.542', '#joy', '#surprise', 'positive', '0.808', 'jowl', 'lower_jaw', 'mandible', 'lantern_jaw', 'lantern_jawr_bone']
senticnet['bad'] = ['-0.75', '0.154', '-0.64', '0.142', '#sadness', '#fear', 'negative', '-0.27', 'evil', 'awful', 'bad_person', 'smell_bad', 'harmful_human']
senticnet['bad-smelling'] = ['0', '-0.97', '0', '-0.82', '#surprise', '#disgust', 'positive', '0.075', 'bad_smell', 'odoriferous', 'strong-smelling', 'disgusting', 'foul-smelling']
senticnet['bad_action'] = ['-0.76', '0', '0.85', '-0.77', '#anger', '#disgust', 'negative', '-0.79', 'violent_action', 'bad_behavior', 'consequence', 'judge_decision', 'badness']
senticnet['bad_air'] = ['-0.99', '0', '0.056', '-0.98', '#sadness', '#disgust', 'negative', '-0.67', 'smoke', 'cigarette', 'tobacco', 'pollution', 'cigar']
senticnet['bad_airette'] = ['-1.0', '0', '0.05', '-0.98', '#sadness', '#disgust', 'negative', '-0.67', 'smoke', 'cigar', 'tobacco', 'pollution', 'bad_air']
senticnet['bad_attitude'] = ['0', '0.322', '0.995', '0', '#interest', '#anger', 'negative', '-0.33', 'aggressive', 'angry', 'aggression', 'bad_temper', 'aggressiveness']
senticnet['bad_behavior'] = ['-0.45', '0.154', '-0.39', '0.094', '#sadness', '#fear', 'negative', '-0.15', 'bad_choice', 'wrongdoing', 'misbehavior', 'wrong', 'retribution']
senticnet['bad_blood'] = ['0', '0.146', '0.723', '-0.66', '#anger', '#disgust', 'negative', '-0.41', 'animus', 'belligerently', 'hostilely', 'aggressiveness', 'antagonism']
senticnet['bad_boy'] = ['-0.19', '0', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.15', 'bad_child', 'misbehavior', 'bad_kid', 'bad_action', 'difficulty']
senticnet['bad_breath'] = ['0', '0', '0.955', '-0.78', '#anger', '#disgust', 'negative', '-0.87', 'worm_intestine', 'fur_keep_warm', 'unpleasant-smelling', 'halitosis', 'asthama']
senticnet['bad_camera'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'slow', 'long_focus', 'focus_time', 'long_focus_time', 'low_quality_photo']
senticnet['bad_camera_time'] = ['-0.92', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'slow', 'bad_camera', 'focus_time', 'long_focus', 'low_quality_photo']
senticnet['bad_character'] = ['-0.94', '0', '0.105', '-0.96', '#sadness', '#disgust', 'negative', '-0.67', 'bad_person', 'bad', 'evil_man', 'evil_eye', 'bad_action']
senticnet['bad_child'] = ['-0.2', '0.081', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.08', 'bad_boy', 'misbehavior', 'bad_action', 'badness', 'difficulty']
senticnet['bad_choice'] = ['-0.95', '0', '0.42', '-0.91', '#sadness', '#disgust', 'negative', '-0.76', 'mistake', 'bad', 'error', 'danger', 'trouble']
senticnet['bad_citizen'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'steal', 'rob', 'leave_without_pay', 'shoplift', 'robbery']
senticnet['bad_climate'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'storm', 'heavy_rain', 'rain', 'snow', 'become_sick']
senticnet['bad_communication'] = ['-0.91', '0', '0.833', '-0.94', '#sadness', '#disgust', 'negative', '-0.89', 'curse_word', 'bad_word', 'rude', 'rude_man', 'curse']
senticnet['bad_company'] = ['-0.94', '0', '0.622', '-0.91', '#sadness', '#disgust', 'negative', '-0.82', 'bad_team', 'terrorist_group', 'terrorist_corporation', 'terrorism', 'bad_guy']
senticnet['bad_connection'] = ['-0.09', '-0.03', '0.061', '0', '#sadness', '#anger', 'negative', '-0.04', 'bad_reception', 'wifi', 'internet', 'phone_call', 'underground']
senticnet['bad_couple'] = ['-0.97', '0', '0.893', '-0.99', '#sadness', '#disgust', 'negative', '-0.95', 'heart_broken', 'wrong_person', 'fake_love', 'bad_person', 'bad_marriage']
senticnet['bad_credit'] = ['-0.17', '0.095', '0', '-0.16', '#sadness', '#disgust', 'negative', '-0.08', 'bank', 'piggy_bank', 'checkbook', 'government_agreement', 'wallet']
senticnet['bad_customer'] = ['-0.92', '0', '0.784', '-0.91', '#sadness', '#disgust', 'negative', '-0.87', 'rude_man', 'disloyal', 'make_noise', 'bad_communication', 'buy_cheaper']
senticnet['bad_dream'] = ['-0.97', '0', '0.502', '-0.97', '#sadness', '#disgust', 'negative', '-0.81', 'nightmare', 'worry', 'stress', 'anxiety', 'bad_rest']
senticnet['bad_drug'] = ['-0.18', '-0.07', '0', '-0.20', '#sadness', '#disgust', 'negative', '-0.10', 'water_spill', 'water', 'atmosphere', 'cool_water', 'obtain_water']
senticnet['bad_drug_spill'] = ['-0.19', '-0.08', '0', '-0.21', '#sadness', '#disgust', 'negative', '-0.10', 'water', 'bad_drug', 'atmosphere', 'cool_bad_drug', 'obtain_bad_drug']
senticnet['bad_egg'] = ['-0.93', '0', '0.238', '-0.90', '#sadness', '#disgust', 'negative', '-0.69', 'expired', 'poisoning', 'bad_taste', 'spoil', 'expired_food']
senticnet['bad_experience'] = ['0', '0', '-0.73', '0', '#fear', '#fear', 'negative', '-0.73', 'bad_haircut', 'further_knowledge', 'join_navy', 'explain_problem', 'live_castle']
senticnet['bad_eyesight'] = ['-0.13', '0', '0.013', '-0.08', '#sadness', '#disgust', 'negative', '-0.07', 'blind', 'dig_tunnel', 'underground_animal', 'big_mouse', 'small_rodent']
senticnet['bad_fairy'] = ['-0.67', '0', '0.752', '-0.89', '#anger', '#disgust', 'negative', '-0.77', 'evil_spirit', 'fiend', 'demon', 'devil', 'daimon']
senticnet['bad_feeling'] = ['-0.83', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.83', 'feel_stressed', 'complication', 'anguish', 'death_love', 'screw_up']
senticnet['bad_flatmate'] = ['-0.93', '0', '0.578', '-0.90', '#sadness', '#disgust', 'negative', '-0.80', 'lazy', 'slack_off', 'careless', 'terrible', 'slack']
senticnet['bad_food'] = ['0', '0', '-0.84', '0', '#fear', '#fear', 'negative', '-0.84', 'bad_karma', 'spoil_food', 'anxiety_disorder', 'anthrax', 'tape_machine_room']
senticnet['bad_girl'] = ['-0.97', '0', '0.795', '-0.84', '#sadness', '#disgust', 'negative', '-0.87', 'hooker', 'prostitute', 'working_girl', 'mistress', 'pay_for_sex']
senticnet['bad_grade'] = ['-0.3', '-0.03', '-0.09', '-0.05', '#sadness', '#fear', 'negative', '-0.10', 'loss', 'flunk', 'lose_memory', 'loose', 'lose_war']
senticnet['bad_guy'] = ['0', '0', '0.813', '-0.82', '#anger', '#disgust', 'negative', '-0.81', 'opposition', 'rival', 'conflict', 'uprise', 'foe']
senticnet['bad_habit'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'vapor', 'gaseous', 'haze', 'smoke', 'smog']
senticnet['bad_hair_day'] = ['-0.49', '-0.14', '-0.08', '-0.37', '#sadness', '#disgust', 'negative', '-0.20', 'brain_tumor', 'lyme_disease', 'bad_luck', 'flab', 'suffer_fool']
senticnet['bad_haircut'] = ['-0.91', '0', '0.869', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'talk_hairdresser', 'hair_get_short', 'hair_become_short', 'keep_hair_short', 'hair_wash']
senticnet['bad_hat'] = ['-0.65', '0', '0.716', '-0.94', '#anger', '#disgust', 'negative', '-0.77', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['bad_health'] = ['-0.38', '0.108', '-0.38', '0.072', '#sadness', '#fear', 'negative', '-0.14', 'unhealthy', 'dangerous_health', 'hazardous_health', 'harmful_human', 'inherit']
senticnet['bad_influence'] = ['-0.92', '0', '0.095', '-0.91', '#sadness', '#disgust', 'negative', '-0.64', 'mess', 'trouble', 'bad_company', 'wrong_person', 'awful_event']
senticnet['bad_injury'] = ['-0.78', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.80', 'minor_injury', 'get_injured', 'injure', 'accident', 'become_exhausted']
senticnet['bad_joke'] = ['0.867', '0.949', '0', '0', '#joy', '#interest', 'positive', '0.908', 'tell_funny_joke', 'tell_joke', 'funny_joke', 'jest', 'witticism']
senticnet['bad_karma'] = ['-0.39', '-0.11', '-0.17', '-0.30', '#sadness', '#disgust', 'negative', '-0.19', 'physical_pain', 'screw_up', 'bad_feeling', 'schizophrenia', 'disrespect']
senticnet['bad_kid'] = ['-0.89', '0', '0.972', '0', '#sadness', '#anger', 'negative', '-0.93', 'bad_child', 'bad_boy', 'calamity', 'badness', 'bad_action']
senticnet['bad_knee'] = ['-0.16', '-0.06', '-0.06', '-0.26', '#sadness', '#disgust', 'negative', '-0.10', 'broken_bone', 'poke_pin', 'bruise', 'physical_pain', 'earache']
senticnet['bad_liar'] = ['-0.24', '0.086', '-0.17', '0', '#sadness', '#fear', 'negative', '-0.11', 'correct_mistake', 'face_music', 'wait_forever', 'keep_promise', 'state_obvious']
senticnet['bad_luck'] = ['-0.43', '-0.14', '-0.06', '-0.33', '#sadness', '#disgust', 'negative', '-0.17', 'brain_tumor', 'lyme_disease', 'cause_suffer', 'lose_team', 'bad_hair_day']
senticnet['bad_man'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'lifeboat', 'mast', 'pirate', 'keel', 'yacht']
senticnet['bad_management'] = ['-0.94', '0', '0.708', '0', '#sadness', '#anger', 'negative', '-0.82', 'minority', 'unfair', 'illegal', 'underrepresented_group', 'discrimination']
senticnet['bad_manner'] = ['-0.06', '0', '0', '-0.16', '#sadness', '#disgust', 'negative', '-0.11', 'inferno', 'big_problem', 'smoke_pipe', 'combustion', 'fire_product']
senticnet['bad_manners'] = ['0', '0', '0', '-0.76', '#disgust', '#disgust', 'negative', '-0.76', 'burp', 'rude', 'fart', 'shame', 'get_angry']
senticnet['bad_marriage'] = ['-0.97', '0', '0.893', '-0.99', '#sadness', '#disgust', 'negative', '-0.95', 'heart_broken', 'wrong_person', 'fake_love', 'bad_person', 'bad_couple']
senticnet['bad_meal'] = ['-0.92', '0', '0.784', '-0.91', '#sadness', '#disgust', 'negative', '-0.87', 'eat_fast_food', 'eat_snack', 'eat_unhealthy_food', 'eat_expired_food', 'eat_quickly']
senticnet['bad_memory'] = ['-0.95', '0', '0.549', '-0.99', '#sadness', '#disgust', 'negative', '-0.83', 'lose_track_of_time', 'memory_loss', 'lose_consciousness', 'sickness', 'become_old']
senticnet['bad_mood'] = ['-0.05', '-0.00', '0.086', '-0.18', '#anger', '#disgust', 'negative', '-0.07', 'playful_mood', 'rain_forest', 'sadness', 'hole_ground', 'feel_down']
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senticnet['bad_news'] = ['-0.88', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.83', 'calamity', 'big_monster', 'set_fire', 'sick', 'anxiety_disorder']
senticnet['bad_nightmare'] = ['0', '0', '-0.90', '0', '#fear', '#fear', 'negative', '-0.90', 'comfortable_position', 'asleep', 'take_nap', 'lie_back', 'miss_work']
senticnet['bad_odor'] = ['-0.03', '0.152', '-0.10', '0.188', '#interest', '#admiration', 'positive', '0.051', 'mouth_action', 'smelly', 'five_senses', 'human_sense', 'exhalation']
senticnet['bad_omen'] = ['0', '0', '-0.99', '0', '#fear', '#fear', 'negative', '-0.99', 'morality', 'worse', 'inherit', 'hazardous', 'badness']
senticnet['bad_organization'] = ['-0.94', '0', '0.622', '-0.91', '#sadness', '#disgust', 'negative', '-0.82', 'terrorist_organization', 'terrorist_group', 'bad_company', 'terrorism', 'terrorist_corporation']
senticnet['bad_person'] = ['-0.09', '0', '-0.09', '-0.09', '#sadness', '#disgust', 'negative', '-0.09', 'felon', 'freak', 'busy_person', 'delusional', 'good_man']
senticnet['bad_pillow'] = ['-0.92', '0', '0.022', '-0.97', '#sadness', '#disgust', 'negative', '-0.63', 'problem', 'headache', 'sick', 'sickness', 'ill']
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senticnet['bad_quality'] = ['-0.06', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.04', 'poor', 'bad', 'dirt', 'terrible', 'easy_to_break']
senticnet['bad_read'] = ['-0.06', '-0.02', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.03', 'choose_book', 'purchase_book', 'store_book', 'sell_book', 'hold_book']
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senticnet['bad_service'] = ['-0.98', '0', '0.058', '-0.92', '#sadness', '#disgust', 'negative', '-0.65', 'bad_staff', 'cheap_hotel', 'cheap_restaurant', 'unfriendly_staff', 'long_waiting_time']
senticnet['bad_situation'] = ['0', '0', '-0.94', '-0.92', '#fear', '#disgust', 'negative', '-0.93', 'difficulty', 'dilemma', 'odds', 'misplace', 'sadness']
senticnet['bad_smell'] = ['0', '0', '0.83', '-0.93', '#anger', '#disgust', 'negative', '-0.88', 'nasty', 'smelly', 'dirty_sock', 'stinky', 'durian']
senticnet['bad_staff'] = ['-0.98', '0', '0.058', '-0.92', '#sadness', '#disgust', 'negative', '-0.65', 'bad_service', 'cheap_hotel', 'cheap_restaurant', 'unfriendly_staff', 'long_waiting_time']
senticnet['bad_student'] = ['-0.11', '0.19', '0.604', '-0.63', '#anger', '#disgust', 'negative', '-0.29', 'disinclination', 'disinclined', 'indispose', 'unfriendly', 'inimical']
senticnet['bad_surprise'] = ['-0.73', '0', '0.865', '-0.87', '#anger', '#disgust', 'negative', '-0.82', 'jeer', 'shout', 'abrupt', 'rude', 'mocking']
senticnet['bad_taste'] = ['0', '0', '0', '-0.81', '#disgust', '#disgust', 'negative', '-0.81', 'snot', 'beautiful_smile', 'mouth_part', 'volume_control', 'near_mouth']
senticnet['bad_team'] = ['-0.94', '0', '0.622', '-0.91', '#sadness', '#disgust', 'negative', '-0.82', 'bad_company', 'terrorist_group', 'terrorist_corporation', 'terrorism', 'bad_guy']
senticnet['bad_temper'] = ['-0.13', '0.151', '0.613', '-0.62', '#anger', '#disgust', 'negative', '-0.30', 'ill_temper', 'umbrage', 'offense', 'outraged', 'indignant']
senticnet['bad_tempered'] = ['-0.91', '0', '-0.73', '-1.0', '#sadness', '#disgust', 'negative', '-0.88', 'irritable', 'nervous', 'neurasthenic', 'cranky', 'testy']
senticnet['bad_time'] = ['-0.39', '0', '-0.32', '0', '#sadness', '#fear', 'negative', '-0.36', 'deliberate', 'unwanted_outcome', 'say_eureka', 'look_fact', 'rational']
senticnet['bad_vision'] = ['-0.32', '-0.19', '0.049', '-0.49', '#sadness', '#disgust', 'negative', '-0.16', 'lay_off', 'poor_eyesight', 'migraine', 'forget_everything', 'cross_eye']
senticnet['bad_weather'] = ['-0.98', '0', '0.96', '-0.92', '#sadness', '#anger', 'negative', '-0.95', 'thunderstorm', 'storm', 'gale', 'heavy_rain', 'hurricane']
senticnet['bad_word'] = ['0', '0', '0.764', '-0.70', '#anger', '#disgust', 'negative', '-0.73', 'bigot', 'intolerance', 'censorship', 'kidnap', 'insane']
senticnet['bad_writing'] = ['-0.76', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.74', 'mistake', 'error', 'grammar_mistake', 'poor_english', 'misspell']
senticnet['badaling_expressway'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'artery', 'skeletal_system', 'groin', 'neck', 'muscle']
senticnet['badbadnotgood'] = ['0.946', '-0.91', '0', '0.664', '#joy', '#surprise', 'positive', '0.841', 'pretender', 'phony', 'phoney', 'hypocrite', 'dissembler']
senticnet['badboy'] = ['-0.19', '0', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.15', 'bad_child', 'misbehavior', 'bad_kid', 'bad_action', 'difficulty']
senticnet['baddie'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'role', 'theatrical_role', 'part', 'persona']
senticnet['baddy'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'role', 'theatrical_role', 'part', 'persona']
senticnet['bade_farewell'] = ['0.037', '0.161', '-0.15', '0.203', '#interest', '#admiration', 'positive', '0.062', 'wave', 'flow', 'salute', 'multiple_purposes', 'wave_wind']
senticnet['badge'] = ['0.844', '-0.57', '0', '0.576', '#joy', '#admiration', 'positive', '0.664', 'blue_ribbon', 'cordon_bleu', 'allegory', 'stripe', 'merit_badge']
senticnet['badger'] = ['0', '0', '0.784', '-0.73', '#anger', '#disgust', 'negative', '-0.75', 'displease', 'bug', 'nettle', 'chafe', 'ruffle']
senticnet['badger_herald'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['badger_skunk'] = ['-0.54', '0', '0.768', '-0.50', '#sadness', '#anger', 'negative', '-0.60', 'striped_skunk', 'hooded_skunk', 'hognosed_skunk', 'polecat', 'rooter_skunk']
senticnet['badinage'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'witty_remark', 'cross_talking', 'swordplay', 'wordplay', 'jester']
senticnet['badly'] = ['0', '0', '-0.90', '-0.93', '#fear', '#disgust', 'negative', '-0.92', 'luckless', 'ill-omened', 'ill-starred', 'doomed', 'ill-fated']
senticnet['badly_hit'] = ['-0.92', '0', '0.946', '-0.56', '#sadness', '#anger', 'negative', '-0.81', 'forcefulness', 'intensiveness', 'impulse', 'vigor', 'energy']
senticnet['badly_hurt'] = ['-0.95', '0', '0.712', '-0.52', '#sadness', '#anger', 'negative', '-0.73', 'uncut', 'unharmed', 'unscathed', 'unwounded', 'uninjured']
senticnet['badly_injured'] = ['-0.16', '0', '-0.18', '-0.23', '#fear', '#disgust', 'negative', '-0.19', 'attack_enemy', 'cause_death', 'die', 'lose_war', 'imprisonment']
senticnet['badlydrawnjeff'] = ['0.533', '0.818', '0', '0.683', '#interest', '#admiration', 'positive', '0.678', 'gain_information', 'report_news', 'solve', 'seek_answer', 'gather_information']
senticnet['badminton'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'golf', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['badminton_association'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'golf', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['badminton_club'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'golf', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['badness'] = ['-0.39', '0.149', '-0.16', '0.062', '#sadness', '#fear', 'negative', '-0.08', 'bad_action', 'bad_behavior', 'negative', 'inherit', 'evil']
senticnet['badugi'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'jewelry_store', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['baffle'] = ['-0.39', '0.149', '-0.16', '0.062', '#sadness', '#fear', 'negative', '-0.08', 'block', 'confuse', 'confound', 'frustrate', 'impede']
senticnet['bafflement'] = ['-0.50', '0', '0.85', '-0.82', '#anger', '#disgust', 'negative', '-0.72', 'confusion', 'disarray', 'mental_confusion', 'confusedness', 'muddiness']
senticnet['bafflingly'] = ['0.706', '0.945', '0', '0', '#joy', '#interest', 'positive', '0.825', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['bafta'] = ['0.232', '0.178', '-0.17', '0.223', '#joy', '#admiration', 'positive', '0.115', 'fame', 'alms', 'find_money', 'better_life', 'small_detail']
senticnet['bafta_fellowship'] = ['0.232', '0.178', '-0.17', '0.223', '#joy', '#admiration', 'positive', '0.115', 'fame', 'alms', 'find_money', 'better_life', 'small_detail']
senticnet['bag'] = ['0', '-0.02', '0', '0.051', '#surprise', '#admiration', 'positive', '0.037', 'cardboard_box', 'box', 'package', 'box_cereal', 'bin']
senticnet['bag_lunch'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'cafe', 'buy_sandwich', 'eat_food', 'buy_food', 'finish_job']
senticnet['bag_pack'] = ['0.041', '-0.04', '0.003', '0.049', '#joy', '#admiration', 'positive', '0.032', 'travel_long_distance', 'go_from_place_to_place', 'transport_person', 'move', 'transportation']
senticnet['bag_stuffed_with'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'sports_equipment', 'sport_stuff', 'golf', 'sport_store', 'bag']
senticnet['bag_valve'] = ['-0.57', '0', '0.737', '-0.85', '#anger', '#disgust', 'negative', '-0.72', 'emergency_procedure', 'emergency', 'external_respiration', 'respiration', 'ventilation']
senticnet['bagel'] = ['0.041', '-0.07', '0.034', '0', '#joy', '#surprise', 'positive', '0.026', 'butter', 'mayonaise', 'pie', 'noodle', 'muffin']
senticnet['bagel_shop'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'candy', 'sweet', 'sugar', 'sweet_tooth', 'chocolate']
senticnet['baggage'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'hand_luggage', 'luggage', 'passenger_ticket', 'ticket_counter', 'baggage_trunk']
senticnet['baggage_allowance'] = ['-0.07', '0.207', '-0.05', '0.228', '#interest', '#admiration', 'positive', '0.076', 'free', 'gift', 'abundance', 'plus', 'plentiful']
senticnet['baggage_car'] = ['-0.58', '0', '0.714', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'waster', 'diversionist', 'destroyer', 'ruiner', 'undoer']
senticnet['baggage_carousel'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'hand_baggage', 'baggage', 'passenger_ticket', 'ticket_counter', 'baggage_trunk']
senticnet['baggage_claim'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'hand_baggage', 'baggage', 'passenger_ticket', 'ticket_counter', 'baggage_trunk']
senticnet['baggage_compartment'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'train_station', 'government_office', 'cable_car', 'sport_stadium', 'terminal']
senticnet['baggage_handling_system'] = ['0', '0.147', '0', '0.099', '#interest', '#admiration', 'positive', '0.123', 'airliner', 'airport', 'baggage_compartment', 'aircraft', 'cable_car']
senticnet['baggage_trunk'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'hand_luggage', 'luggage', 'passenger_ticket', 'ticket_counter', 'travel']
senticnet['baggie'] = ['0', '-0.02', '0.027', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'shoulder_bag', 'satchel', 'satchel_bag', 'tote_bag', 'carry']
senticnet['baghdad_international_airport'] = ['-0.66', '0', '0.789', '-0.63', '#sadness', '#anger', 'negative', '-0.69', 'humbug', 'play_trick', 'cozen', 'pull_fast_one_on', 'play_trick_on']
senticnet['bagle'] = ['-0.87', '0', '0.8', '0', '#sadness', '#anger', 'negative', '-0.83', 'separation', 'divide', 'division', 'slash', 'split']
senticnet['bagman'] = ['-0.95', '0', '-0.70', '-0.96', '#sadness', '#disgust', 'negative', '-0.87', 'corrupt', 'bribe_police', 'debt', 'tax', 'persuade']
senticnet['bagnio'] = ['0.754', '-0.82', '0', '0.947', '#surprise', '#admiration', 'positive', '0.841', 'whorehouse', 'brothel', 'bordello', 'massage_parlor', 'house_of_prostitution']
senticnet['bagoong'] = ['0.9', '0.963', '0', '0', '#joy', '#interest', 'positive', '0.931', 'form_of_food', 'japanese_food', 'uncooked', 'healthy', 'food']
senticnet['bagram'] = ['-0.66', '0', '0.789', '-0.63', '#sadness', '#anger', 'negative', '-0.69', 'humbug', 'play_trick', 'cozen', 'pull_fast_one_on', 'play_trick_on']
senticnet['bagram_airfield'] = ['-0.66', '0', '0.789', '-0.63', '#sadness', '#anger', 'negative', '-0.69', 'humbug', 'play_trick', 'cozen', 'pull_fast_one_on', 'play_trick_on']
senticnet['bagratid'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
senticnet['bagua_province'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'head_hurt', 'introspection', 'lifestyle_change', 'headache', 'move_eye']
senticnet['bah'] = ['0', '0', '0.941', '-0.93', '#anger', '#disgust', 'negative', '-0.93', 'boredom', 'ugh', 'disgust', 'repugnance', 'tiredness']
senticnet['bah_humbug'] = ['-0.90', '0.152', '-0.62', '0.061', '#sadness', '#fear', 'negative', '-0.33', 'guilt_feeling', 'guilty', 'dolor', 'grieve', 'penitentially']
senticnet['bahna_river'] = ['-0.95', '0', '0.845', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'grapefruit_peel', 'lemon_peel', 'orange_peel', 'candied_fruit', 'succade']
senticnet['bai_yun'] = ['-0.95', '0', '0.735', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'novice', 'beginner', 'tyro', 'tiro', 'initiate']
senticnet['baijiu'] = ['0', '0', '0', '-0.91', '#disgust', '#disgust', 'negative', '-0.91', 'alcoholic_drink', 'get_drunk', 'ferment_beverage', 'adult_drink', 'booze']
senticnet['bail'] = ['-0.08', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.052', 'bequest', 'excise', 'bribe', 'afford', 'owe']
senticnet['bail_awaiting_trial'] = ['-0.08', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.052', 'bequest', 'excise', 'bribe', 'afford', 'owe']
senticnet['bail_bond'] = ['-0.08', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.052', 'bequest', 'excise', 'bribe', 'afford', 'owe']
senticnet['bail_hostel'] = ['-0.64', '0', '0.879', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'drone', 'poke', 'dawdler', 'laggard', 'straggler']
senticnet['bail_out'] = ['-0.12', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'escape', 'escape_situation', 'cancel_meeting', 'absent', 'cancel_appointment']
senticnet['bail_pending_trial'] = ['-0.08', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.052', 'bequest', 'excise', 'bribe', 'afford', 'owe']
senticnet['bail_water'] = ['-0.03', '0.146', '-0.08', '0.208', '#interest', '#admiration', 'positive', '0.058', 'sail_ocean', 'hold_flower', 'carry_book', 'carry_liquid', 'hold_stuff']
senticnet['bailable'] = ['0.866', '-0.52', '0', '0.529', '#joy', '#admiration', 'positive', '0.641', 'worthy', 'desirable', 'suitable', 'eligible', 'entitled']
senticnet['bailed_out'] = ['-0.12', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'escape', 'escape_situation', 'cancel_meeting', 'absent', 'cancel_appointment']
senticnet['bailey'] = ['-0.20', '-0.10', '0.07', '-0.24', '#sadness', '#disgust', 'negative', '-0.10', 'flipper', 'sea_life', 'seafood', 'swim_animal', 'fillet']
senticnet['bailing_out'] = ['-0.12', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'escape', 'escape_situation', 'cancel_meeting', 'absent', 'cancel_appointment']
senticnet['bailing_wire'] = ['0', '0', '-0.91', '-0.70', '#fear', '#disgust', 'negative', '-0.81', 'wax_dental_floss', 'tartar_control_toothpaste', 'toothpaste', 'gel_toothpaste', 'toothbrush']
senticnet['bailout'] = ['-0.12', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'escape', 'escape_situation', 'cancel_meeting', 'absent', 'cancel_appointment']
senticnet['bailout_fund'] = ['-0.12', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'escape', 'escape_situation', 'cancel_meeting', 'absent', 'cancel_appointment']
senticnet['bailout_money'] = ['-0.12', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'escape', 'escape_situation', 'cancel_meeting', 'absent', 'cancel_appointment']
senticnet['bailout_package'] = ['-0.12', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'escape', 'escape_situation', 'cancel_meeting', 'absent', 'cancel_appointment']
senticnet['bailout_plan'] = ['-0.12', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'escape', 'escape_situation', 'cancel_meeting', 'absent', 'cancel_appointment']
senticnet['bainouk'] = ['-0.84', '0', '0.579', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'squiggle', 'hand', 'chicken_scratch', 'handwriting', 'script']
senticnet['bairn'] = ['0.947', '0.914', '0', '0', '#joy', '#interest', 'positive', '0.93', 'ball_bounce', 'bicycle_tire', 'tire', 'increase_price', 'gyroscope']
senticnet['bairstow'] = ['-0.62', '0', '0.713', '-0.98', '#anger', '#disgust', 'negative', '-0.77', 'muddy_up', 'muck_up', 'foul', 'blemish', 'smear']
senticnet['bait'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'fishing_rod', 'fishing', 'patience', 'fish_lake', 'catch_fish']
senticnet['bait_fish'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'fishing_rod', 'fishing', 'patience', 'fish_lake', 'catch_fish']
senticnet['bait_trap'] = ['0.02', '0.162', '0', '0.062', '#interest', '#admiration', 'positive', '0.081', 'catch', 'trap', 'ambush', 'lure', 'decoy']
senticnet['baited_hook'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'fishing_rod', 'fishing', 'patience', 'fish_lake', 'catch_fish']
senticnet['baitfish'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'fishing_rod', 'fishing', 'patience', 'fish_lake', 'catch_fish']
senticnet['bajaur'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'webpage', 'store_record', 'ideal']
senticnet['baji'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'white_animal', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
senticnet['bajram_curri'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
senticnet['bajur'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'webpage', 'store_record', 'ideal']
senticnet['bake'] = ['0.032', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.05', 'prepare_food', 'food', 'make_flour', 'bake_bread', 'bread']
senticnet['bake_bean'] = ['0.098', '0.103', '-0.06', '0.134', '#interest', '#admiration', 'positive', '0.068', 'candy', 'bake_cake', 'foodstuff', 'pop', 'eat_food']
senticnet['bake_bread'] = ['0.032', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.05', 'prepare_food', 'food', 'make_flour', 'bake', 'bread']
senticnet['bake_cake'] = ['0.731', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.837', 'make_bread', 'cook_dinner', 'cook_meal', 'cook_curry', 'follow_recipe']
senticnet['bake_cookie'] = ['0.86', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.928', 'make_flour', 'bake_bread', 'bake_food', 'bake', 'pot_roast']
senticnet['bake_food'] = ['0.036', '-0.06', '0.037', '0.05', '#surprise', '#admiration', 'positive', '0.027', 'bake', 'make_grain', 'make_flour', 'pot_roast', 'eat']
senticnet['bake_good'] = ['0.077', '-0.01', '0.024', '0.095', '#joy', '#admiration', 'positive', '0.041', 'bake_product', 'cook_good', 'bake_treat', 'round_pastry', 'sweet_food']
senticnet['bake_goods'] = ['0.078', '-0.02', '0.012', '0.11', '#joy', '#admiration', 'positive', '0.05', 'round_pastry', 'bake_good', 'bake_treat', 'cold', 'cover_ice']
senticnet['bake_oven'] = ['0.061', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.055', 'cook', 'stick_butter', 'find_recipe', 'grill', 'food_bake']
senticnet['bake_pan'] = ['0.044', '0.08', '0', '-0.03', '#joy', '#interest', 'positive', '0.031', 'cook', 'festival', 'dinner', 'restaurant', 'food']
senticnet['bake_product'] = ['0.07', '-0.01', '0.026', '0.108', '#joy', '#admiration', 'positive', '0.042', 'bake_good', 'sweet_food', 'eatable', 'soft', 'morning_food']
senticnet['bake_sale'] = ['0.048', '0', '0', '0.092', '#joy', '#admiration', 'positive', '0.07', 'gym_locker', 'observatory', 'prison_cell', 'luggage_store', 'luggage_compartment']
senticnet['bake_shop'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'candy', 'sweet', 'sugar', 'sweet_tooth', 'chocolate']
senticnet['bake_treat'] = ['0.076', '-0.00', '0.019', '0.102', '#joy', '#admiration', 'positive', '0.041', 'cooked', 'bake_good', 'round_pastry', 'bake_goods', 'bake_product']
senticnet['baked_bean'] = ['0.914', '-0.56', '0', '0.869', '#joy', '#admiration', 'positive', '0.781', 'tabula_rasa', 'ego', 'subconscious', 'unconscious', 'psyche']
senticnet['baked_chicken'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'grocery_cart', 'super_market', 'fruit_stand', 'candy_store', 'produce_store']
senticnet['baked_goods'] = ['0.96', '-0.82', '0', '0', '#joy', '#surprise', 'positive', '0.893', 'home_made', 'bread', 'container', 'big_breadbox', 'eat_bread']
senticnet['baked_potato'] = ['0.028', '-0.07', '0', '0', '#joy', '#surprise', 'positive', '0.05', 'casserole', 'delicious_meal', 'hot_dog_bun', 'hamburger', 'hot_dog']
senticnet['baker'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'cook_meat', 'cook_hamburger', 'cook_food', 'brown_meat', 'eat_outdoors']
senticnet['baker_hostetler'] = ['0', '0.146', '-0.05', '0.077', '#interest', '#admiration', 'positive', '0.057', 'lawyer', 'negotiator', 'arbiter', 'judge_jury', 'decide_guilt']
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senticnet['ballot_envelope'] = ['-0.94', '0', '0.569', '-0.72', '#sadness', '#disgust', 'negative', '-0.74', 'bitumastic', 'veneer', 'veneering', 'adhesive', 'coating']
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senticnet['balmy'] = ['0.898', '0.751', '0', '0', '#joy', '#interest', 'positive', '0.825', 'daft', 'kooky', 'barmy', 'bonkers', 'loony']
senticnet['balmy_night'] = ['0.98', '-0.74', '0', '0.984', '#joy', '#admiration', 'positive', '0.903', 'prosperous', 'flourishing', 'successful', 'prospering', 'thriving']
senticnet['balmy_weather'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'weather', 'four_seasons', 'fall_spring', 'rain_snow', 'climate']
senticnet['balneology'] = ['-0.60', '0', '0.544', '-0.62', '#sadness', '#disgust', 'negative', '-0.59', 'remedy', 'cure', 'therapeutic', 'antidote', 'atropine']
senticnet['balneotherapy'] = ['-0.60', '0', '0.544', '-0.62', '#sadness', '#disgust', 'negative', '-0.59', 'remedy', 'cure', 'therapeutic', 'antidote', 'atropine']
senticnet['baloney'] = ['-0.61', '0', '0.981', '-0.98', '#anger', '#disgust', 'negative', '-0.85', 'pious_platitude', 'nothingness', 'jazz', 'talk', 'malarky']
senticnet['balor'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'jawless_fish', 'osteostracan', 'cephalaspid', 'ostracoderm', 'heterostracan']
senticnet['balsam'] = ['0.863', '0', '0', '0.805', '#joy', '#admiration', 'positive', '0.834', 'fir', 'spruce', 'pine', 'hemlock', 'eucalyptus']
senticnet['balsam_woolly_adelgid'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['balsamic'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'mayonnaise', 'box_cereal', 'bottle_water']
senticnet['balsamic_glaze'] = ['-0.69', '0', '0.597', '-0.56', '#sadness', '#anger', 'negative', '-0.62', 'improperness', 'impropriety', 'raciness', 'spiciness', 'indelicacy']
senticnet['balsamic_vinaigrette'] = ['0.043', '0.091', '0', '0.113', '#interest', '#admiration', 'positive', '0.082', 'sweet_sour', 'tasty', 'yummy', 'popular_food', 'form_of_food']
senticnet['balsamorhiza'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
senticnet['balto_slavic'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'pronunciation', 'sibilation', 'mispronunciation', 'homophony']
senticnet['balustrade'] = ['0', '0', '0.986', '-0.76', '#anger', '#disgust', 'negative', '-0.87', 'parapet', 'battlement', 'parapet', 'railing', 'cornice']
senticnet['balwant'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['balzan_prize'] = ['0.894', '0.881', '0', '0.803', '#joy', '#interest', 'positive', '0.859', 'remittance', 'afford', 'award', 'money_due', 'amount_owe']
senticnet['bam'] = ['0', '0', '-0.86', '-0.74', '#fear', '#disgust', 'negative', '-0.80', 'extravasation', 'eruption', 'eructation', 'irruption', 'bang']
senticnet['bamboo'] = ['0', '0.773', '0', '0.895', '#interest', '#admiration', 'positive', '0.834', 'wooden_board', 'make_wood', 'wooden_box', 'wooden_floor', 'long_wood']
senticnet['bamboo_cane'] = ['0.609', '-0.74', '0', '0.716', '#surprise', '#admiration', 'positive', '0.69', 'malacca', 'sword', 'swagger_stick', 'sword_cane', 'sword_stick']
senticnet['bamboo_curtain'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['bamboo_pit_viper'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'death_adder', 'ophidian', 'serpent', 'elapid', 'taipan']
senticnet['bamboo_stick'] = ['-0.71', '0', '0.954', '-0.56', '#sadness', '#anger', 'negative', '-0.74', 'jackknife', 'pocket_knife', 'penknife', 'switchblade', 'switchblade_knife']
senticnet['bambu'] = ['0.973', '-0.50', '0', '0.612', '#joy', '#admiration', 'positive', '0.698', 'chic', 'chicness', 'jauntiness', 'modishness', 'stylishness']
senticnet['ban'] = ['0', '0', '0.917', '-0.75', '#anger', '#disgust', 'negative', '-0.83', 'dismiss', 'bust', 'dispossess', 'evacuate', 'eliminate']
senticnet['ban_abortion'] = ['-0.90', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.86', 'death', 'feticide', 'sickness', 'miscarriage', 'spontaneous_abortion']
senticnet['ban_corporal_punishment'] = ['-0.83', '0', '0.924', '-0.79', '#sadness', '#anger', 'negative', '-0.85', 'kick_in_butt', 'licking', 'whacking', 'stoning', 'lapidation']
senticnet['ban_junk_food'] = ['-0.98', '0', '0.594', '-0.98', '#sadness', '#disgust', 'negative', '-0.85', 'fast_food', 'deep_fried', 'unhealthy_food', 'reuse_oil', 'fattening_food']
senticnet['banachek'] = ['0.818', '0.913', '0', '0.825', '#interest', '#admiration', 'positive', '0.852', 'greeting', 'blackjack', 'pass_card', 'credit', 'play_blackjack']
senticnet['banal'] = ['-0.85', '0', '0', '-0.96', '#sadness', '#disgust', 'negative', '-0.90', 'dull', 'ordinary', 'unoriginal', 'murky', 'lack_originality']
senticnet['banality'] = ['0.524', '-0.64', '0', '0.675', '#surprise', '#admiration', 'positive', '0.613', 'truth', 'platitude', 'cliche', 'truism', 'commonplace']
senticnet['banana'] = ['0', '0.066', '-0.05', '0.089', '#interest', '#admiration', 'positive', '0.032', 'pineapple', 'kale', 'raisin', 'pickle', 'tomato']
senticnet['banana_bread'] = ['0.644', '-0.94', '0', '0.729', '#surprise', '#admiration', 'positive', '0.773', 'gem', 'quick_bread', 'date_bread', 'nut_bread', 'oatcake']
senticnet['banana_foster'] = ['0.092', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.071', 'bake_treat', 'round_pastry', 'bake_good', 'bake_goods', 'sweet_food']
senticnet['banana_tree'] = ['0', '0', '-0.14', '-0.03', '#fear', '#disgust', 'negative', '-0.09', 'pine_cone', 'fallen_leaf', 'poison_ivy', 'detritus', 'tress']
senticnet['banarasi_babu'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['band'] = ['0.023', '0.303', '-0.08', '0.261', '#interest', '#admiration', 'positive', '0.125', 'high_school_choral', 'drum_set', 'choral', 'string_quartet', 'school_choral']
senticnet['band_debut_ep'] = ['-0.73', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'unwed', 'unwedded', 'divorced', 'unmarried', 'widowed']
senticnet['band_eq'] = ['0.036', '-0.04', '0.027', '0.063', '#surprise', '#admiration', 'positive', '0.028', 'radio', 'speaker', 'channel_selector', 'power_switch', 'female_television_connector']
senticnet['band_mate'] = ['0', '-0.02', '0.055', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'djembe', 'make_musicr', 'use_jazz_music', 'tune_instrument', 'percussion']
senticnet['band_play'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'songwriter', 'rock_band']
senticnet['band_room'] = ['0.709', '0.996', '0', '0', '#joy', '#interest', 'positive', '0.853', 'string_quartet', 'music_store', 'musical_instrument_store', 'music_shop', 'high_school_band']
senticnet['band_setlist'] = ['0.773', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.844', 'favorite', 'crowd', 'acclamation', 'bunch', 'large_crowd']
senticnet['band_together'] = ['0.098', '0.254', '-0.16', '0.297', '#interest', '#admiration', 'positive', '0.121', 'make_right', 'same', 'similar', 'commence', 'proposition']
senticnet['band_westlife'] = ['-0.86', '0', '0.675', '-0.84', '#sadness', '#disgust', 'negative', '-0.79', 'shenanigan', 'chicanery', 'wile', 'dupery', 'trickery']
senticnet['bandage'] = ['-0.06', '-0.03', '0', '-0.17', '#sadness', '#disgust', 'negative', '-0.06', 'seam', 'needle_thread', 'dressing', 'stitch', 'medical_kit']
senticnet['bandanna'] = ['-0.99', '0', '0.806', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'wear', 'biohazard_suit', 'coverall', 'protective_garment', 'wearable']
senticnet['banded_krait'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'death_adder', 'ophidian', 'serpent', 'elapid', 'taipan']
senticnet['banded_water_snake'] = ['-0.51', '0', '0.625', '-0.98', '#anger', '#disgust', 'negative', '-0.70', 'common_water_snake', 'water_snake', 'water_moccasin', 'grass_snake', 'ring_snake']
senticnet['bandersnatchi'] = ['0.651', '-0.73', '0', '0.975', '#surprise', '#admiration', 'positive', '0.788', 'profoundness', 'profundity', 'sagacity', 'sagaciousness', 'discernment']
senticnet['bandit'] = ['0', '0', '-0.06', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'elbow', 'arm', 'wrist', 'five_fingers', 'bendable']
senticnet['bandit_gang'] = ['0', '0', '-0.06', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'elbow', 'arm', 'wrist', 'five_fingers', 'bendable']
senticnet['bandit_leader'] = ['0', '0', '-0.06', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'elbow', 'arm', 'wrist', 'five_fingers', 'bendable']
senticnet['bandmate'] = ['0', '-0.02', '0.055', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'djembe', 'make_musicr', 'use_jazz_music', 'tune_instrument', 'percussion']
senticnet['bandpass_filter'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'water_inside', 'water_spray', 'teapot', 'bodily_waste', 'different_type']
senticnet['bandsaw'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'useful_tool']
senticnet['bandwagon_effect'] = ['0.894', '-0.51', '0', '0.813', '#joy', '#admiration', 'positive', '0.741', 'activity', 'effect', 'consequence', 'outcome', 'result']
senticnet['bandwidth'] = ['-0.9', '0', '0.841', '-0.73', '#sadness', '#anger', 'negative', '-0.82', 'nubbiness', 'tweediness', 'harshness', 'graininess', 'raggedness']
senticnet['baneful'] = ['-0.63', '0', '0.674', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'maleficent', 'evil', 'baleful', 'malevolent', 'malign']
senticnet['baneful_influence'] = ['-0.99', '0', '0.658', '-0.73', '#sadness', '#disgust', 'negative', '-0.79', 'adrift', 'afloat', 'aimless', 'directionless', 'purposeless']
senticnet['bang'] = ['0.444', '0.367', '-0.11', '0.188', '#joy', '#interest', 'positive', '0.222', 'strike', 'soothe', 'quiet', 'squeak', 'annoy_sound']
senticnet['bang-on'] = ['0', '-0.98', '0', '0.922', '#surprise', '#admiration', 'positive', '0.954', 'spot-on', 'pitch-perfect', 'dead-on', 'uncannily', 'impersonation']
senticnet['bang-up'] = ['0.745', '0.79', '0', '0', '#joy', '#interest', 'positive', '0.768', 'nifty', 'dandy', 'corking', 'cracking', 'bully']
senticnet['bang_gavel'] = ['-0.73', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.79', 'serve_jury', 'verdict', 'register_vote', 'mallet', 'convict_criminal']
senticnet['bang_head'] = ['-0.25', '-0.09', '-0.09', '-0.27', '#sadness', '#disgust', 'negative', '-0.13', 'loss_ofawareness', 'serious_accident', 'broken_limb', 'concussion', 'brain_damage']
senticnet['bang_head_against_wall'] = ['0', '0', '-0.97', '-0.92', '#fear', '#disgust', 'negative', '-0.94', 'feel_guilty', 'anguish', 'feeling_guilt', 'end_relationship', 'feel_anger']
senticnet['banger'] = ['-0.94', '0', '0.492', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'make_noise', 'disturb', 'confusion', 'hustle_and_bustle', 'bustle']
senticnet['bangle'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'anklet', 'ankle_bracelet', 'armilla']
senticnet['banish'] = ['-0.74', '0', '0.966', '0', '#sadness', '#anger', 'negative', '-0.85', 'move', 'displace', 'fire', 'chase_away', 'ostracise']
senticnet['banished'] = ['0', '0', '0.876', '-0.78', '#anger', '#disgust', 'negative', '-0.82', 'abject', 'outcast', 'castaway', 'pariah', 'lumpen']
senticnet['banjo_player'] = ['-0.04', '-0.04', '-0.04', '-0.04', '#fear', '#disgust', 'negative', '-0.02', 'music_shop', 'music_store', 'musical_instrument_store', 'string_quartet', 'high_school_band']
senticnet['banjoist'] = ['-0.04', '-0.04', '-0.04', '-0.04', '#fear', '#disgust', 'negative', '-0.02', 'music_shop', 'music_store', 'musical_instrument_store', 'string_quartet', 'high_school_band']
senticnet['bank'] = ['0.031', '0.176', '0', '0', '#joy', '#interest', 'positive', '0.103', 'government_agreement', 'bank_vault', 'wallet', 'safe', 'coffer']
senticnet['bank_account'] = ['0', '0.192', '0', '0.052', '#interest', '#admiration', 'positive', '0.122', 'bank_vault', 'wallet', 'billfold', 'hold_change', 'bank']
senticnet['bank_bailout'] = ['-0.12', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'escape', 'escape_situation', 'cancel_meeting', 'absent', 'cancel_appointment']
senticnet['bank_cashier'] = ['0', '0.186', '0', '0.075', '#interest', '#admiration', 'positive', '0.131', 'money_inside', 'check_account', 'savings', 'money_storage', 'store_money']
senticnet['bank_heist'] = ['-0.57', '0', '0.721', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'bush', 'native_pomegranate', 'caper_tree', 'native_orange', 'common_caper']
senticnet['bank_holiday'] = ['0.908', '0.991', '0', '0', '#joy', '#interest', 'positive', '0.95', 'party_time', 'new_baby', 'special_day', 'birthday', 'annual_celebration']
senticnet['bank_holiday_weekend'] = ['0', '0.734', '0', '0', '#interest', '#interest', 'positive', '0.734', 'sleep', 'go_sleep', 'relax', 'go_out', 'sleep_night']
senticnet['bank_loan'] = ['0', '0.33', '-0.06', '0.107', '#interest', '#admiration', 'positive', '0.125', 'bank_money', 'vault', 'account', 'checkbook', 'dividend']
senticnet['bank_machine'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'bank_vault', 'wallet', 'bank', 'credit_card_wallet', 'billfold']
senticnet['bank_money'] = ['0', '0.304', '-0.07', '0.102', '#interest', '#admiration', 'positive', '0.11', 'bank_loan', 'store_money', 'check_account', 'savings_account', 'require_signature']
senticnet['bank_robber'] = ['-0.83', '0', '0.831', '-0.69', '#sadness', '#anger', 'negative', '-0.78', 'toughie', 'accessory', 'highbinder', 'hoodlum', 'thug']
senticnet['bank_robbery_suspect'] = ['0.964', '-0.57', '0', '0.827', '#joy', '#admiration', 'positive', '0.789', 'mortal', 'murder_suspect', 'suspect', 'rape_suspect', 'person']
senticnet['bank_safe_deposit'] = ['0.02', '0.205', '0', '0', '#joy', '#interest', 'positive', '0.112', 'safe', 'bank_vault', 'bank', 'piggy_bank', 'bank_account']
senticnet['bank_statement'] = ['0.671', '-0.57', '0', '0.879', '#joy', '#admiration', 'positive', '0.708', 'financial_statement', 'bill', 'account', 'invoice', 'accounting']
senticnet['bank_teller'] = ['0', '0.186', '0', '0.075', '#interest', '#admiration', 'positive', '0.131', 'money_inside', 'check_account', 'savings', 'money_storage', 'store_money']
senticnet['bank_vault'] = ['0.031', '0.171', '0', '0', '#joy', '#interest', 'positive', '0.101', 'bank_account', 'bank', 'wallet', 'safe', 'government_agreement']
senticnet['bankai'] = ['-0.04', '0.138', '-0.08', '0.111', '#interest', '#admiration', 'positive', '0.031', 'geometry', 'craft', 'blade_geometry', 'blade', 'collect_item']
senticnet['bankcard'] = ['-0.05', '0.14', '0.015', '0.082', '#interest', '#admiration', 'positive', '0.039', 'cash', 'cash_pay', 'need_earn_money', 'open_wallet', 'earn_wage']
senticnet['banker'] = ['0', '0.807', '0', '0.819', '#interest', '#admiration', 'positive', '0.813', 'money', 'treasurer', 'account', 'dividend', 'piggy_bank']
senticnet['bankim_chandra_chattopadhyay'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['banking'] = ['0', '0.995', '0', '0.96', '#interest', '#admiration', 'positive', '0.978', 'rely', 'swear', 'deposit', 'trust', 'bank']
senticnet['banking_analyst'] = ['0.711', '0', '0', '0', '#joy', '#joy', 'positive', '0.711', 'chemist', 'expert', 'scientist', 'esaminatore', 'physicist']
senticnet['banking_bailout'] = ['-0.12', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'escape', 'escape_situation', 'cancel_meeting', 'absent', 'cancel_appointment']
senticnet['banking_industry'] = ['0.033', '0.247', '0', '0.13', '#interest', '#admiration', 'positive', '0.137', 'accountant_office', 'deposit', 'account_department', 'checkbook', 'bank_loan']
senticnet['banking_magnate'] = ['0', '0', '-0.99', '-0.73', '#fear', '#disgust', 'negative', '-0.86', 'king', 'baron', 'business_leader', 'mogul', 'big_businessman']
senticnet['banking_meltdown'] = ['-0.13', '-0.05', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'monitor_heart_rate', 'feel_tired', 'bump_head', 'exhaustion', 'become_fatigued']
senticnet['banking_sector'] = ['0.033', '0.247', '0', '0.13', '#interest', '#admiration', 'positive', '0.137', 'accountant_office', 'deposit', 'account_department', 'checkbook', 'bank_loan']
senticnet['banking_system'] = ['0.067', '0.172', '0', '0.121', '#interest', '#admiration', 'positive', '0.12', 'economic_activity', 'business', 'support_family', 'work_done', 'earn_money']
senticnet['banknote'] = ['0.81', '0.763', '0', '0', '#joy', '#interest', 'positive', '0.786', 'dollar_bill', 'franc', 'euro', 'dollar', 'cent']
senticnet['banknote_paper'] = ['0.81', '0.763', '0', '0', '#joy', '#interest', 'positive', '0.786', 'dollar_bill', 'franc', 'euro', 'dollar', 'cent']
senticnet['bankroll'] = ['0', '0', '-0.81', '-0.92', '#fear', '#disgust', 'negative', '-0.87', 'wager', 'bankroll', 'winning', 'poker', 'bet']
senticnet['bankrupt'] = ['-0.94', '0', '0.551', '-0.98', '#sadness', '#disgust', 'negative', '-0.82', 'unfortunate', 'insolvent', 'underdog', 'nonstarter', 'dud']
senticnet['bankruptcy'] = ['0', '0', '0.779', '-0.82', '#anger', '#disgust', 'negative', '-0.80', 'go_broke', 'receive_change', 'run_out_money', 'compare_price', 'incur_debt']
senticnet['bankruptcy_fraud'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'fraud', 'swindle', 'rig']
senticnet['banksia_hill'] = ['0.816', '-0.91', '0', '0.944', '#surprise', '#admiration', 'positive', '0.891', 'jail', 'jailhouse', 'gaol', 'poky', 'clink']
senticnet['banksia_hill_detention_centre'] = ['0.816', '-0.91', '0', '0.944', '#surprise', '#admiration', 'positive', '0.891', 'jail', 'jailhouse', 'gaol', 'poky', 'clink']
senticnet['bankster'] = ['-0.78', '0', '0.915', '-0.99', '#anger', '#disgust', 'negative', '-0.89', 'bodge', 'botch_up', 'flub', 'ball_up', 'go_wrong']
senticnet['banksy'] = ['-0.12', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.07', 'bum', 'homeless_people', 'homeless_person', 'panhandler', 'homeless']
senticnet['banned_corporal_punishment'] = ['-0.83', '0', '0.924', '-0.79', '#sadness', '#anger', 'negative', '-0.85', 'kick_in_butt', 'licking', 'whacking', 'stoning', 'lapidation']
senticnet['banned_user'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['banner'] = ['0.041', '0.077', '0', '0', '#joy', '#interest', 'positive', '0.059', 'post_notice', 'emblem', 'placard', 'car_path', 'asphalt']
senticnet['banner_headline'] = ['0.088', '-0.00', '0', '0', '#joy', '#surprise', 'positive', '0.045', 'look_up_information', 'news', 'find_newspaper', 'enter_contest', 'latest_news']
senticnet['banner_tailed_kangaroo'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'carbon_monoxide', 'tree_animal']
senticnet['banning_abortion'] = ['-0.90', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.86', 'death', 'feticide', 'sickness', 'miscarriage', 'spontaneous_abortion']
senticnet['banning_junk_food'] = ['-0.98', '0', '0.594', '-0.98', '#sadness', '#disgust', 'negative', '-0.85', 'fast_food', 'deep_fried', 'unhealthy_food', 'reuse_oil', 'fattening_food']
senticnet['banquet'] = ['0.128', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.085', 'buffet', 'rave', 'auction', 'limbo', 'loose_end']
senticnet['banquet_hall'] = ['0', '0', '-0.76', '-0.82', '#fear', '#disgust', 'negative', '-0.79', 'stage', 'thespian', 'actor', 'theatre', 'drama']
senticnet['banquet_room'] = ['0', '0', '-0.76', '-0.82', '#fear', '#disgust', 'negative', '-0.79', 'stage', 'thespian', 'actor', 'theatre', 'drama']
senticnet['banqueting_hall'] = ['0', '0.105', '0', '0.076', '#interest', '#admiration', 'positive', '0.09', 'big_house', 'mansion', 'castle', 'block_flat', 'medieval']
senticnet['banqueting_house'] = ['0', '0.105', '0', '0.076', '#interest', '#admiration', 'positive', '0.09', 'big_house', 'mansion', 'castle', 'block_flat', 'medieval']
senticnet['banquette'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'coach_seat', 'stool', 'seat']
senticnet['banshee'] = ['0.02', '0.131', '0.749', '-0.67', '#anger', '#disgust', 'negative', '-0.31', 'belligerence', 'belligerency', 'belligerent', 'war', 'rancor']
senticnet['bantam'] = ['0', '0', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.05', 'human', 'delusional', 'use_illegal_drug', 'busy_person', 'carpenter']
senticnet['bantamweight'] = ['-0.79', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'featherweight', 'welterweight', 'middleweight', 'flyweight', 'heavyweight']
senticnet['banter'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'witty_remark', 'cross_talking', 'swordplay', 'wordplay', 'jester']
senticnet['banter_between'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'witty_remark', 'cross_talking', 'swordplay', 'wordplay', 'jester']
senticnet['banty'] = ['0.934', '-0.57', '0', '0.767', '#joy', '#admiration', 'positive', '0.76', 'morosoph', 'simpleton', 'flibbertigibbet', 'foolish_woman', 'simple']
senticnet['baptism'] = ['0.84', '0', '0', '0.913', '#joy', '#admiration', 'positive', '0.877', 'god', 'faith', 'baby', 'power', 'christ']
senticnet['baptismal_certificate'] = ['0.931', '-0.52', '0', '0.67', '#joy', '#admiration', 'positive', '0.709', 'document', 'certificate_of_incorporation', 'credential', 'diploma', 'sheepskin']
senticnet['baptismal_record'] = ['0.931', '-0.52', '0', '0.67', '#joy', '#admiration', 'positive', '0.709', 'document', 'certificate_of_incorporation', 'credential', 'diploma', 'sheepskin']
senticnet['baptist'] = ['0', '0.849', '0', '0.715', '#interest', '#admiration', 'positive', '0.782', 'adhesive', 'adherently', 'adherent', 'coherent', 'disciple']
senticnet['baptist_cemetery'] = ['-0.50', '0', '0.762', '-0.89', '#anger', '#disgust', 'negative', '-0.71', 'cemetery', 'graveyard', 'burial_site', 'grassland', 'burying_ground']
senticnet['baptist_church'] = ['0.081', '0.146', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'cult', 'sect', 'conservative', 'worshiper', 'pastor']
senticnet['baptist_congregation'] = ['0.081', '0.146', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'cult', 'sect', 'conservative', 'worshiper', 'pastor']
senticnet['baptist_encampment'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'common_bean', 'green_bean', 'wax_bean']
senticnet['baptize'] = ['0.693', '-0.69', '0', '0.714', '#surprise', '#admiration', 'positive', '0.702', 'vow', 'dedicate', 'apply', 'rededicate', 'use']
senticnet['baptized_jew'] = ['0', '-0.00', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'hot_chick', 'atheist', 'computer_user', 'fastener_binder', 'monitor']
senticnet['bar'] = ['0.829', '-0.74', '0', '0', '#joy', '#surprise', 'positive', '0.784', 'liquor_store', 'tavern', 'pub', 'internet_site', 'club']
senticnet['bar_brawl'] = ['-0.60', '0', '0.777', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'batrachomyomachia', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['bar_coziba'] = ['-0.52', '0', '0.701', '-0.50', '#sadness', '#anger', 'negative', '-0.57', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['bar_crawl'] = ['0', '-0.08', '0', '0', '#surprise', '#surprise', 'positive', '0.082', 'yo-yo', 'playroom', 'backgammon', 'action_figure', 'board_game']
senticnet['bar_grill'] = ['0.061', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.055', 'cook', 'stick_butter', 'find_recipe', 'bake_oven', 'food_bake']
senticnet['bar_grille'] = ['0.914', '-0.56', '0', '0.869', '#joy', '#admiration', 'positive', '0.781', 'tabula_rasa', 'ego', 'subconscious', 'unconscious', 'psyche']
senticnet['bar_kokhba_rebellion'] = ['-0.52', '0', '0.701', '-0.50', '#sadness', '#anger', 'negative', '-0.57', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['bar_lounge'] = ['0.841', '0', '0', '0.71', '#joy', '#admiration', 'positive', '0.775', 'waiter', 'bar_service', 'beverage_service', 'concierge', 'minibar']
senticnet['bar_service'] = ['0.89', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.825', 'porter', 'beverage_service', 'luggage_trolley', 'concierge', 'minibar']
senticnet['bar_stool'] = ['0.903', '-0.72', '0', '0', '#joy', '#surprise', 'positive', '0.814', 'alcohol', 'bar', 'high_chair', 'beer_mug', 'minibar']
senticnet['bara_brith'] = ['0.644', '-0.94', '0', '0.729', '#surprise', '#admiration', 'positive', '0.773', 'gem', 'banana_bread', 'date_bread', 'nut_bread', 'quick_bread']
senticnet['barasingha'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheep', 'argal', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['barb'] = ['0', '0', '0', '0.071', '#admiration', '#admiration', 'positive', '0.071', 'two_horses', 'horse', 'mule', 'buddhist', 'ride_horse']
senticnet['barbarian'] = ['-0.73', '0', '0.734', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'primitive', 'headhunter', 'cannibal', 'anthropophagus', 'anthropophagite']
senticnet['barbarianism'] = ['-0.86', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.91', 'simplistically', 'simplistic', 'simple', 'reductive', 'simpleton']
senticnet['barbaric'] = ['0', '0', '0.795', '-0.74', '#anger', '#disgust', 'negative', '-0.76', 'barbarous', 'inhuman', 'barbarity', 'savage', 'uncouth']
senticnet['barbaric_practice'] = ['-0.79', '0', '0.691', '-0.88', '#sadness', '#disgust', 'negative', '-0.79', 'cold', 'pitiless', 'cruel', 'unkind', 'vicious']
senticnet['barbarically'] = ['-0.87', '0', '0.53', '-0.65', '#sadness', '#disgust', 'negative', '-0.68', 'malevolence', 'malevolency', 'malice', 'murderousness', 'ferociousness']
senticnet['barbarism'] = ['-0.85', '0', '0.75', '-0.95', '#sadness', '#disgust', 'negative', '-0.85', 'reign', 'legislation', 'rebel', 'legislature', 'decree']
senticnet['barbarity'] = ['-0.99', '0', '0.947', '-0.88', '#sadness', '#anger', 'negative', '-0.94', 'atrociousness', 'barbarousness', 'heinousness', 'atrocity', 'inhumanity']
senticnet['barbarous'] = ['-0.52', '0', '0.896', '-0.76', '#anger', '#disgust', 'negative', '-0.72', 'noncivilized', 'primitive', 'savage', 'wild', 'preliterate']
senticnet['barbarously'] = ['-0.87', '0', '0.53', '-0.65', '#sadness', '#disgust', 'negative', '-0.68', 'malevolence', 'malevolency', 'malice', 'murderousness', 'ferociousness']
senticnet['barbarousness'] = ['-0.99', '0', '0.947', '-0.88', '#sadness', '#anger', 'negative', '-0.94', 'atrociousness', 'inhumanity', 'heinousness', 'atrocity', 'barbarity']
senticnet['barbary_sheep'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'argali', 'argal', 'urial', 'white_sheep', 'wild_sheep']
senticnet['barbate'] = ['-0.94', '0', '0.84', '-0.91', '#sadness', '#disgust', 'negative', '-0.90', 'bearded', 'unshaved', 'bewhiskered', 'whiskered', 'whiskery']
senticnet['barbecue'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'grill', 'red_meat', 'cook_grill', 'cook', 'grilled_vegetable']
senticnet['barbecue_chicken'] = ['0.061', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.055', 'cook', 'stick_butter', 'find_recipe', 'bake_oven', 'food_bake']
senticnet['barbecue_dinner'] = ['0.044', '0.08', '0', '-0.03', '#joy', '#interest', 'positive', '0.031', 'cook', 'festival', 'bake_pan', 'restaurant', 'food']
senticnet['barbecue_grill'] = ['0.074', '0', '0', '0', '#joy', '#joy', 'positive', '0.074', 'hike_trail', 'ant_hill', 'compost', 'picnic', 'soccer_field']
senticnet['barbecue_pit'] = ['0.074', '0', '0', '0', '#joy', '#joy', 'positive', '0.074', 'hike_trail', 'ant_hill', 'compost', 'picnic', 'soccer_field']
senticnet['barbecue_restaurant'] = ['0.061', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.055', 'cook', 'stick_butter', 'find_recipe', 'bake_oven', 'food_bake']
senticnet['barbecue_sauce'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'horseradish', 'box_cereal', 'bottle_water']
senticnet['barbecued_chicken'] = ['0.061', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.055', 'cook', 'stick_butter', 'find_recipe', 'bake_oven', 'food_bake']
senticnet['barbed'] = ['0', '0', '-0.94', '-0.82', '#fear', '#disgust', 'negative', '-0.88', 'barbellate', 'briery', 'bristled', 'briary', 'thorny']
senticnet['barbed_spear'] = ['0', '0', '-0.83', '-0.8', '#fear', '#disgust', 'negative', '-0.81', 'armory', 'tank', 'hydrogen_bomb', 'rifle', 'use_hunt']
senticnet['barbed_wire_barricade'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'corner_two_streets', 'street_corner', 'roadside', 'city_street', 'schoolyard']
senticnet['barbellate'] = ['0', '0', '-0.94', '-0.82', '#fear', '#disgust', 'negative', '-0.88', 'thorny', 'briery', 'bristled', 'briary', 'barbed']
senticnet['barbeque'] = ['0.061', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.055', 'cook', 'stick_butter', 'find_recipe', 'bake_oven', 'food_bake']
senticnet['barbeque_pork'] = ['0.084', '-0.05', '0', '-0.04', '#joy', '#surprise', 'positive', '0.031', 'entree', 'feast', 'satiation', 'breakfast', 'supper']
senticnet['barber'] = ['-0.72', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.76', 'talk', 'chatter', 'meaningless', 'sound', 'unimportant']
senticnet['barber_comb'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'hair_roller', 'hairdresser_chair', 'hair_accessory', 'sculpt_gel', 'barrette']
senticnet['barber_shop'] = ['0.949', '0', '0', '0.925', '#joy', '#admiration', 'positive', '0.937', 'barbershop', 'beauty_salon', 'hair_salon', 'salon', 'hairdressing_salon']
senticnet['barbershop'] = ['-0.02', '0.122', '0', '0.137', '#interest', '#admiration', 'positive', '0.078', 'barber_shop', 'beauty_salon', 'hair_salon', 'salon', 'hairdressing_salon']
senticnet['barbershop_quartet'] = ['0.552', '-0.61', '0', '0.588', '#surprise', '#admiration', 'positive', '0.583', 'quartette', 'string_quartet', 'string_quartette', 'musical_organization', 'musical_group']
senticnet['barbie_doll'] = ['0.039', '-0.08', '0', '0.098', '#surprise', '#admiration', 'positive', '0.073', 'slinky', 'toy', 'yo-yo', 'action_figure', 'stuff_animal']
senticnet['barbossa'] = ['0', '0', '0.034', '-0.08', '#anger', '#disgust', 'negative', '-0.06', 'lifeboat', 'mast', 'bad_man', 'keel', 'yacht']
senticnet['barcalounger'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'coach_seat', 'stool', 'seat']
senticnet['barcelona_defender_gerard'] = ['-0.13', '0.083', '0.682', '-0.66', '#anger', '#disgust', 'negative', '-0.35', 'frustrating', 'dun', 'crucify', 'bedevil', 'displeasingly']
senticnet['barclay_tagg'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'coach', 'instructor', 'personal_trainer', 'fit_person', 'train']
senticnet['barclays_center'] = ['0.026', '-0.03', '0', '0.091', '#surprise', '#admiration', 'positive', '0.051', 'stadium', 'auditorium', 'television_station', 'music_hall', 'movie_theater']
senticnet['bard'] = ['0.836', '-0.60', '0', '0.845', '#joy', '#admiration', 'positive', '0.761', 'poetess', 'poet', 'elegist', 'odist', 'poet_laureate']
senticnet['bardet_biedl_syndrome'] = ['0.504', '-0.93', '0', '0.797', '#surprise', '#admiration', 'positive', '0.747', 'diabetes', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['bardiche'] = ['0.93', '-0.78', '0', '0.873', '#joy', '#admiration', 'positive', '0.861', 'blade', 'brand', 'steel', 'claymore', 'sword']
senticnet['bare'] = ['-0.75', '0', '0.869', '-0.73', '#sadness', '#anger', 'negative', '-0.78', 'spare', 'unembellished', 'unornamented', 'unadorned', 'plain']
senticnet['bare-bones'] = ['0', '0', '0.94', '-0.77', '#anger', '#disgust', 'negative', '-0.85', 'no-frills', 'unpretentious', 'stripped-down', 'low-cost', 'no-nonsense']
senticnet['bare_assertion'] = ['0.84', '-0.97', '0', '0.713', '#joy', '#surprise', 'positive', '0.844', 'proof', 'bed_check', 'crosscheck', 'parity_check', 'redundancy_check']
senticnet['bare_bone'] = ['0.645', '-0.76', '0', '0.961', '#surprise', '#admiration', 'positive', '0.791', 'content', 'quintessence', 'stuff', 'essence', 'hypostasis']
senticnet['bare_concrete'] = ['-0.03', '-0.00', '0.066', '0', '#sadness', '#anger', 'negative', '-0.03', 'dirt', 'allergy', 'dust', 'wet_floor', 'insect']
senticnet['bare_copper'] = ['0.085', '0', '0', '0', '#joy', '#joy', 'positive', '0.085', 'wire', 'several_types', 'gas_stove', 'tape_player', 'control_room']
senticnet['bare_feet'] = ['-0.06', '-0.01', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'low', 'ground', 'look_down', 'ground_level', 'under_house']
senticnet['bare_ground'] = ['-0.93', '0', '0.906', '-0.53', '#sadness', '#anger', 'negative', '-0.79', 'juvenility', 'state', 'immatureness', 'callowness', 'jejuneness']
senticnet['bare_knuckle_fighter'] = ['0.9', '-0.88', '0', '0.687', '#joy', '#surprise', 'positive', '0.823', 'conjurer', 'escape_expert', 'mind_reader', 'telepathist', 'performer']
senticnet['bare_lightbulb'] = ['0', '0.171', '-0.05', '0.102', '#interest', '#admiration', 'positive', '0.072', 'bed', 'mattress', 'bead', 'bed_cover', 'seduce']
senticnet['bare_majority'] = ['0.63', '0.622', '-0.29', '0.365', '#joy', '#interest', 'positive', '0.332', 'joyful', 'favorable', 'favorably', 'agree', 'jubilantly']
senticnet['bare_metal_recovery'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_musical_sound', 'play_orchestra', 'make_noise', 'play_music', 'make_drawing']
senticnet['bare_metal_restore'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_musical_sound', 'play_orchestra', 'make_noise', 'play_music', 'make_drawing']
senticnet['bare_midriff'] = ['0', '0', '0', '-0.06', '#disgust', '#disgust', 'negative', '-0.06', 'body', 'nervous_system', 'normal_human_body', 'human_body', 'head_shoulder']
senticnet['bare_rock'] = ['-0.98', '0', '0.441', '-0.96', '#sadness', '#disgust', 'negative', '-0.79', 'desert_land', 'infertile', 'sand', 'barren_land', 'desert']
senticnet['bare_subsistence'] = ['-0.91', '0', '0.598', '-0.52', '#sadness', '#anger', 'negative', '-0.68', 'parsimonious', 'stingy', 'scrimy', 'beggarly', 'chintzy']
senticnet['bareback_rider'] = ['0', '0', '0', '0.987', '#admiration', '#admiration', 'positive', '0.987', 'emote', 'bold', 'form_of_social_interaction', 'act', 'stage_play']
senticnet['bareface'] = ['0', '0', '0.82', '-0.74', '#anger', '#disgust', 'negative', '-0.78', 'undeferential', 'disrespectful', 'undutiful', 'insolent', 'brazen']
senticnet['barefaced'] = ['-0.70', '0', '0.856', '-0.92', '#anger', '#disgust', 'negative', '-0.82', 'bald', 'raw', 'overt', 'visible', 'naked']
senticnet['barehanded'] = ['-0.57', '0', '0.653', '-0.99', '#anger', '#disgust', 'negative', '-0.74', 'defenseless', 'unarmed', 'defenceless', 'clean', 'weaponless']
senticnet['barely'] = ['0', '0', '-0.77', '-1', '#fear', '#disgust', 'negative', '-0.88', 'marginal', 'sufficiently', 'margin', 'marginally', 'slightly']
senticnet['barely_adequate'] = ['-0.94', '0', '0.952', '-0.83', '#sadness', '#anger', 'negative', '-0.91', 'miserable', 'scanty', 'exiguous', 'spare', 'hardscrabble']
senticnet['barely_afford'] = ['0', '0.799', '0', '0', '#interest', '#interest', 'positive', '0.799', 'iron_shirt', 'sit_on_beach', 'dry_clothes', 'iron_clothes', 'absorb_moisture']
senticnet['barely_audible'] = ['-0.94', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.94', 'drive_recklessly', 'discord', 'deaf', 'deny', 'silent']
senticnet['barely_comprehensible'] = ['-0.52', '0', '0.71', '-0.94', '#anger', '#disgust', 'negative', '-0.72', 'atrocious', 'horrific', 'menacing', 'minacious', 'alarming']
senticnet['barely_detectable'] = ['-0.76', '0', '0.573', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'gossip', 'gossiper', 'gossipmonger', 'talk_back', 'rumormonger']
senticnet['barely_discernible'] = ['-0.50', '0', '0.85', '-0.82', '#anger', '#disgust', 'negative', '-0.72', 'confusion', 'disarray', 'mental_confusion', 'confusedness', 'bafflement']
senticnet['barely_literate'] = ['0.724', '-0.70', '0', '0.571', '#joy', '#surprise', 'positive', '0.666', 'spinnable', 'susceptible', 'easy', 'pliant', 'plastic']
senticnet['barely_recognisable'] = ['-0.82', '0', '0.758', '-0.70', '#sadness', '#anger', 'negative', '-0.76', 'unclassifiable', 'unrecognizable', 'unidentifiable', 'intangible', 'undiagnosable']
senticnet['barely_recognizable'] = ['-0.82', '0', '0.758', '-0.70', '#sadness', '#anger', 'negative', '-0.76', 'unclassifiable', 'unrecognizable', 'unidentifiable', 'intangible', 'undiagnosable']
senticnet['barely_visible'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'eye', 'peeper', 'oculus_sinister']
senticnet['bareness'] = ['-0.93', '0', '0.508', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'nakedness', 'cloud', 'bleakness', 'desolation', 'glumness']
senticnet['barest'] = ['-0.94', '0', '0.952', '-0.83', '#sadness', '#anger', 'negative', '-0.91', 'miserable', 'scanty', 'exiguous', 'spare', 'hardscrabble']
senticnet['barf'] = ['-0.26', '-0.07', '-0.06', '-0.28', '#sadness', '#disgust', 'negative', '-0.13', 'go_insane', 'punch_face', 'ice_cream_melt', 'bang_head', 'suffer_fool']
senticnet['barfly'] = ['0.797', '-0.95', '0', '0.921', '#surprise', '#admiration', 'positive', '0.891', 'lush', 'alcoholic', 'alky', 'dipsomaniac', 'boozer']
senticnet['bargain'] = ['-0.03', '0.306', '-0.07', '0.241', '#interest', '#admiration', 'positive', '0.11', 'sale', 'good_buy', 'cheap_price', 'good_price', 'business_deal']
senticnet['bargain-basement'] = ['0', '0', '-0.74', '-0.72', '#fear', '#disgust', 'negative', '-0.73', 'cut-rate', 'low-priced', 'dirt-cheap', 'cut-price', 'high-priced']
senticnet['bargain_basement'] = ['-0.04', '0.201', '-0.06', '0.312', '#interest', '#admiration', 'positive', '0.101', 'save_money', 'good_price', 'inexpensive', 'bargain', 'discount']
senticnet['bargain_finder'] = ['0.94', '0.804', '0', '0', '#joy', '#interest', 'positive', '0.872', 'spend_a_lot_of_money', 'window_shop', 'spend_much_money', 'check_price', 'compare_price']
senticnet['bargain_hunter'] = ['0.875', '0.706', '0', '0', '#joy', '#interest', 'positive', '0.79', 'customer', 'clerk', 'patron', 'vintner', 'hairdresser']
senticnet['bargain_price'] = ['-0.03', '0.139', '0', '0.109', '#interest', '#admiration', 'positive', '0.07', 'exchange_money', 'purchase', 'sell_goods', 'make_rich', 'incur_debt']
senticnet['bargainer'] = ['-0.71', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.79', 'trader', 'principal', 'dealer', 'monger', 'problem']
senticnet['bargaining_power'] = ['-0.94', '0', '0.863', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'poke', 'knockout_punch', 'lick', 'punch', 'biff']
senticnet['bargaining_session'] = ['-0.77', '0', '0.505', '-0.50', '#sadness', '#anger', 'negative', '-0.59', 'wildcat_strike', 'sympathy_strike', 'sympathetic_strike', 'walkout', 'job_action']
senticnet['barge'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'push', 'ram', 'shove_around', 'bang', 'jostle']
senticnet['barge_in'] = ['-0.92', '0', '0.722', '-0.55', '#sadness', '#anger', 'negative', '-0.73', 'block', 'disrupt', 'jam', 'interject', 'throw_in']
senticnet['barge_traffic'] = ['0.933', '0.795', '0', '0', '#joy', '#interest', 'positive', '0.864', 'water_passage', 'small_stream', 'water_flow', 'flowing_water', 'water_stream']
senticnet['baritone'] = ['-0.82', '0', '0.928', '0', '#sadness', '#anger', 'negative', '-0.87', 'tenor', 'saxophone', 'soprano', 'sax', 'clarinet']
senticnet['baritone_sax'] = ['0.049', '0.167', '-0.05', '0.295', '#interest', '#admiration', 'positive', '0.115', 'four_sides', 'sound_system', 'dolby_surround', 'movie', 'surround']
senticnet['baritone_saxophone'] = ['0.049', '0.167', '-0.05', '0.295', '#interest', '#admiration', 'positive', '0.115', 'four_sides', 'sound_system', 'dolby_surround', 'movie', 'surround']
senticnet['baritone_voice'] = ['-0.71', '0', '0.998', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'tone', 'quality', 'twang', 'nasality', 'timber']
senticnet['barium_ferrite'] = ['0.965', '-0.84', '0', '0.599', '#joy', '#surprise', 'positive', '0.802', 'mental_faculty', 'faculty', 'module', 'remembrance', 'anamnesis']
senticnet['bark'] = ['-0.95', '0', '0.892', '-0.77', '#sadness', '#anger', 'negative', '-0.87', 'cork', 'cassia_bark', 'canella', 'canella_bark', 'white_cinnamon']
senticnet['bark_beetle'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['bark_cloth'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_fabric']
senticnet['bark_mulch'] = ['-0.12', '0', '-0.10', '-0.03', '#sadness', '#fear', 'negative', '-0.08', 'dog_poop', 'picnic', 'hike_trail', 'detritus', 'wildlife']
senticnet['bark_scorpion'] = ['-0.25', '0.081', '-0.19', '0', '#sadness', '#fear', 'negative', '-0.12', 'hairy', 'kind_animal', 'poisonous', 'big_fish', 'graceful']
senticnet['barkcloth'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_fabric']
senticnet['barkeep'] = ['-0.81', '0', '0.553', '-0.92', '#sadness', '#disgust', 'negative', '-0.76', 'bar', 'sawdust_saloon', 'saloon', 'ginmill', 'taproom']
senticnet['barley_bread'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'green_bread', 'rotten', 'disgusting', 'taste_awful', 'mouldy']
senticnet['barley_grain'] = ['0.061', '0.093', '0.037', '0.114', '#interest', '#admiration', 'positive', '0.058', 'whole_grain', 'food_ingredient', 'staple_food', 'starchy_food', 'food_grain']
senticnet['barley_mow'] = ['0.853', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.916', 'tavern', 'cafe', 'nightclub', 'bar', 'liquor_store']
senticnet['barley_scab_initiative'] = ['-0.56', '0', '0.655', '-0.85', '#anger', '#disgust', 'negative', '-0.69', 'slovenly_woman', 'slut', 'trollop', 'slattern', 'slob']
senticnet['barley_wine'] = ['0', '0.091', '-0.05', '0.073', '#interest', '#admiration', 'positive', '0.037', 'privately', 'hold_liquid', 'make_wish', 'rinse_dish', 'find_toilet']
senticnet['barleywine'] = ['0', '0.091', '-0.05', '0.073', '#interest', '#admiration', 'positive', '0.037', 'privately', 'hold_liquid', 'make_wish', 'rinse_dish', 'find_toilet']
senticnet['barmaid'] = ['0.99', '0', '0', '0.721', '#joy', '#admiration', 'positive', '0.855', 'hair', 'haircut', 'hairdresser_chair', 'scissor', 'hairstyle']
senticnet['barmy'] = ['0.896', '0.744', '0', '0', '#joy', '#interest', 'positive', '0.82', 'daft', 'balmy', 'kooky', 'bonkers', 'loony']
senticnet['barn'] = ['0.034', '0', '0', '0.064', '#joy', '#admiration', 'positive', '0.049', 'ground', 'shed', 'grassland', 'stable', 'farmyard']
senticnet['barn_animal'] = ['0.05', '0.088', '0', '0.133', '#interest', '#admiration', 'positive', '0.09', 'farm', 'domesticate', 'grassland', 'sheep', 'cow']
senticnet['barn_door'] = ['0.979', '-0.69', '0', '0.576', '#joy', '#surprise', 'positive', '0.749', 'big_cheese', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['barn_owl'] = ['-0.02', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.04', 'wren', 'bird', 'quail', 'sparrow', 'chick']
senticnet['barn_owl_tyto_alba'] = ['-0.53', '0', '0.648', '-0.70', '#anger', '#disgust', 'negative', '-0.63', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['barn_yard'] = ['0.525', '-0.85', '0', '0.599', '#surprise', '#admiration', 'positive', '0.66', 'frolic', 'coquetry', 'romp', 'play', 'caper']
senticnet['barnacle'] = ['0', '0', '-0.06', '0', '#fear', '#fear', 'negative', '-0.06', 'mussel', 'fish', 'urchin', 'scuba_diver', 'seal']
senticnet['barnard_star'] = ['0', '-0.72', '0', '0.712', '#surprise', '#admiration', 'positive', '0.72', 'heavenly_body', 'around_earth', 'source_light', 'sol', 'celestial_body']
senticnet['barndoor'] = ['-0.58', '0', '0.715', '-0.59', '#anger', '#disgust', 'negative', '-0.63', 'fly', 'overlap', 'flap', 'coattail', 'codpiece']
senticnet['barnehurst'] = ['0.623', '-0.56', '0', '0.974', '#joy', '#admiration', 'positive', '0.721', 'alterable', 'convertible', 'translatable', 'transformable', 'transmutable']
senticnet['barnsley_seam'] = ['-0.04', '-0.01', '0.026', '-0.12', '#sadness', '#disgust', 'negative', '-0.04', 'embroidery', 'cover_wind', 'use_needle', 'bandage', 'joint']
senticnet['barnum'] = ['0.563', '-0.81', '0', '0.577', '#surprise', '#admiration', 'positive', '0.65', 'mortal', 'person', 'individual', 'performer', 'artist']
senticnet['barnum_bailey'] = ['0.954', '0', '0', '0', '#joy', '#joy', 'positive', '0.954', 'clown', 'halloween', 'entertainment', 'fun', 'happiness']
senticnet['barnum_bailey_clown'] = ['0.954', '0', '0', '0', '#joy', '#joy', 'positive', '0.954', 'clown', 'halloween', 'entertainment', 'fun', 'happiness']
senticnet['barnyard_animal'] = ['0.032', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.041', 'bird_animal', 'wing', 'farm_bird', 'feather', 'hen_baby']
senticnet['barometer'] = ['0', '0.721', '0', '0.731', '#interest', '#admiration', 'positive', '0.726', 'dimension', 'measure', 'tackle', 'length', 'measurement']
senticnet['baron'] = ['0.066', '0.163', '-0.06', '0.163', '#interest', '#admiration', 'positive', '0.083', 'lord', 'royal_title', 'almighty', 'duchess', 'omniscient']
senticnet['baron_bergavenny'] = ['0.975', '-0.78', '0', '0.969', '#joy', '#admiration', 'positive', '0.911', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['baron_dacre'] = ['0.066', '0.163', '-0.06', '0.163', '#interest', '#admiration', 'positive', '0.083', 'lord', 'royal_title', 'almighty', 'duchess', 'omniscient']
senticnet['baron_delamere'] = ['0.066', '0.163', '-0.06', '0.163', '#interest', '#admiration', 'positive', '0.083', 'lord', 'royal_title', 'almighty', 'duchess', 'omniscient']
senticnet['baron_mowbray'] = ['0.066', '0.163', '-0.06', '0.163', '#interest', '#admiration', 'positive', '0.083', 'lord', 'royal_title', 'almighty', 'duchess', 'omniscient']
senticnet['baron_ponsonby'] = ['0.066', '0.163', '-0.06', '0.163', '#interest', '#admiration', 'positive', '0.083', 'lord', 'royal_title', 'almighty', 'duchess', 'omniscient']
senticnet['baron_samedi'] = ['-0.78', '0', '0.74', '-0.94', '#sadness', '#disgust', 'negative', '-0.82', 'eject', 'chuck_out', 'exclude', 'turf_out', 'boot_out']
senticnet['baron_segrave'] = ['0.066', '0.163', '-0.06', '0.163', '#interest', '#admiration', 'positive', '0.083', 'lord', 'royal_title', 'almighty', 'duchess', 'omniscient']
senticnet['baron_slane'] = ['0.975', '-0.78', '0', '0.969', '#joy', '#admiration', 'positive', '0.911', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['baron_stafford'] = ['0.066', '0.163', '-0.06', '0.163', '#interest', '#admiration', 'positive', '0.083', 'lord', 'royal_title', 'almighty', 'duchess', 'omniscient']
senticnet['baron_stourton'] = ['0.066', '0.163', '-0.06', '0.163', '#interest', '#admiration', 'positive', '0.083', 'lord', 'royal_title', 'almighty', 'duchess', 'omniscient']
senticnet['baron_tuchet'] = ['0.066', '0.163', '-0.06', '0.163', '#interest', '#admiration', 'positive', '0.083', 'lord', 'royal_title', 'almighty', 'duchess', 'omniscient']
senticnet['baron_von'] = ['0.066', '0.163', '-0.06', '0.163', '#interest', '#admiration', 'positive', '0.083', 'lord', 'royal_title', 'almighty', 'duchess', 'omniscient']
senticnet['baroness_lane'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'kit_fox', 'prairie_fox', 'white_fox']
senticnet['baroness_taylor'] = ['-0.85', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.86', 'attack', 'raid', 'fight_against', 'conflict', 'opposition']
senticnet['baronet'] = ['0.066', '0.163', '-0.06', '0.163', '#interest', '#admiration', 'positive', '0.083', 'lord', 'royal_title', 'almighty', 'duchess', 'omniscient']
senticnet['baronetcy'] = ['0.847', '0.716', '0', '0', '#joy', '#interest', 'positive', '0.781', 'dukedom', 'barony', 'earldom', 'duchy', 'fief']
senticnet['barony'] = ['0.975', '-0.78', '0', '0.969', '#joy', '#admiration', 'positive', '0.911', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['baroque'] = ['-0.54', '0', '0.7', '-0.64', '#anger', '#disgust', 'negative', '-0.62', 'flourish', 'rant', 'bombast', 'fustian', 'claptrap']
senticnet['baroque_architectural'] = ['-0.54', '0', '0.7', '-0.64', '#anger', '#disgust', 'negative', '-0.62', 'flourish', 'rant', 'bombast', 'fustian', 'claptrap']
senticnet['baroque_architecture'] = ['0.578', '-0.95', '0', '0.979', '#surprise', '#admiration', 'positive', '0.838', 'perpendicular', 'perpendicular_style', 'gothic_architecture', 'style_of_architecture', 'type_of_architecture']
senticnet['baroque_composer'] = ['0.033', '0', '0.009', '0.057', '#joy', '#admiration', 'positive', '0.027', 'triangle', 'baton', 'bugle', 'type_of_bow', 'horn']
senticnet['baroque_decoration'] = ['-0.54', '0', '0.7', '-0.64', '#anger', '#disgust', 'negative', '-0.62', 'flourish', 'rant', 'bombast', 'fustian', 'claptrap']
senticnet['baroque_guitar'] = ['0.033', '0', '0.009', '0.057', '#joy', '#admiration', 'positive', '0.027', 'triangle', 'baton', 'bugle', 'type_of_bow', 'horn']
senticnet['baroque_lute'] = ['0.033', '0', '0.009', '0.057', '#joy', '#admiration', 'positive', '0.027', 'triangle', 'baton', 'bugle', 'type_of_bow', 'horn']
senticnet['baroque_music'] = ['0.99', '0', '0', '0.719', '#joy', '#admiration', 'positive', '0.855', 'instrumental', 'lyric', 'lyrics', 'sing', 'melody']
senticnet['baroque_oboe'] = ['0.033', '0', '0.009', '0.057', '#joy', '#admiration', 'positive', '0.027', 'triangle', 'baton', 'bugle', 'type_of_bow', 'horn']
senticnet['baroque_orchestra'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'classical_music', 'make_beautiful_music', 'create_music', 'part_of_band', 'make_music']
senticnet['baroque_palace'] = ['0', '0.105', '0', '0.076', '#interest', '#admiration', 'positive', '0.09', 'big_house', 'mansion', 'castle', 'block_flat', 'medieval']
senticnet['baroque_violin'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'classical_music', 'make_beautiful_music', 'create_music', 'part_of_band', 'make_music']
senticnet['barra_brava'] = ['-0.89', '0', '0.933', '-0.93', '#anger', '#disgust', 'negative', '-0.92', 'bullyboy', 'muscleman', 'muscle', 'skinhead', 'skin']
senticnet['barrack'] = ['0.627', '0.61', '-0.24', '0.234', '#joy', '#interest', 'positive', '0.308', 'pacifically', 'peaceably', 'peace-loving', 'peaceable', 'peace_of_mind']
senticnet['barraclough'] = ['-0.20', '-0.10', '0.07', '-0.24', '#sadness', '#disgust', 'negative', '-0.10', 'flipper', 'sea_life', 'seafood', 'swim_animal', 'fillet']
senticnet['barrage'] = ['0', '0', '-0.85', '-0.99', '#fear', '#disgust', 'negative', '-0.92', 'onslaught', 'battery', 'shelling', 'bombardment', 'outpouring']
senticnet['barrel'] = ['0.153', '-0.15', '0.023', '-0.11', '#joy', '#surprise', 'positive', '0.042', 'keg', 'reception', 'champagne', 'beer', 'wine']
senticnet['barrel-chested'] = ['0.729', '0.98', '0', '0', '#joy', '#interest', 'positive', '0.855', 'thick-set', 'stocky', 'corpulent', 'thickset', 'broad-shouldered']
senticnet['barrel_distortion'] = ['0.906', '-0.60', '0', '0.653', '#joy', '#admiration', 'positive', '0.72', 'opaqueness', 'faintness', 'dimness', 'opacity', 'vagueness']
senticnet['barrel_length'] = ['0.887', '-0.95', '0', '0.913', '#surprise', '#admiration', 'positive', '0.917', 'high_quality', 'superiority', 'level', 'grade', 'degree']
senticnet['barrel_per_day_bpd'] = ['0', '0', '-0.06', '0', '#fear', '#fear', 'negative', '-0.06', 'greasy', 'car_gas', 'fossil_fuel', 'liquid_fuel', 'lubrication']
senticnet['barrelene'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
senticnet['barren'] = ['-0.98', '0', '0.441', '-0.96', '#sadness', '#disgust', 'negative', '-0.79', 'desert_land', 'infertile', 'sand', 'barren_land', 'desert']
senticnet['barren_desert'] = ['-0.98', '0', '0.441', '-0.96', '#sadness', '#disgust', 'negative', '-0.79', 'desert_land', 'infertile', 'sand', 'barren_land', 'desert']
senticnet['barren_ground'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'cheetah', 'giraffe', 'kangaroo', 'mule', 'horse']
senticnet['barren_land'] = ['-0.98', '0', '0.441', '-0.96', '#sadness', '#disgust', 'negative', '-0.79', 'desert_land', 'infertile', 'sand', 'barren', 'desert']
senticnet['barren_landscape'] = ['-0.98', '0', '0.441', '-0.96', '#sadness', '#disgust', 'negative', '-0.79', 'desert_land', 'infertile', 'sand', 'barren_land', 'desert']
senticnet['barren_rock'] = ['-0.98', '0', '0.441', '-0.96', '#sadness', '#disgust', 'negative', '-0.79', 'desert_land', 'infertile', 'sand', 'barren_land', 'desert']
senticnet['barren_soil'] = ['-0.98', '0', '0.441', '-0.96', '#sadness', '#disgust', 'negative', '-0.79', 'desert_land', 'infertile', 'sand', 'barren_land', 'desert']
senticnet['barren_strawberry'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'horse_chestnut', 'red_buckeye', 'particolored_buckeye']
senticnet['barren_terrain'] = ['-0.56', '0', '0.814', '-0.88', '#anger', '#disgust', 'negative', '-0.75', 'unfriendly', 'barren', 'bleak', 'desolate', 'wild']
senticnet['barren_waste'] = ['-0.98', '0', '0.441', '-0.96', '#sadness', '#disgust', 'negative', '-0.79', 'desert_land', 'infertile', 'sand', 'barren_land', 'desert']
senticnet['barrenness'] = ['-0.73', '0', '0.634', '-0.85', '#sadness', '#disgust', 'negative', '-0.74', 'cacogenesis', 'impotence', 'impotency', 'infertility', 'dysgenesis']
senticnet['barrett'] = ['-0.69', '0', '0.89', '-0.70', '#anger', '#disgust', 'negative', '-0.76', 'still', 'suppress', 'moderate', 'hush_up', 'quell']
senticnet['barrette'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'sculpt_gel', 'haircut_scissor', 'hair_dryer', 'pin', 'hair_gel']
senticnet['barricade'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'corner_two_streets', 'street_corner', 'roadside', 'city_street', 'schoolyard']
senticnet['barrick'] = ['-0.92', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.85', 'brex', 'indonesian_government', 'indonesia', 'busang', 'work']
senticnet['barrier'] = ['-0.93', '0', '-0.99', '-0.92', '#sadness', '#fear', 'negative', '-0.95', 'language_barrier', 'shut', 'shut_down', 'close_down', 'obstacle']
senticnet['barrier_contraception'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'birth_control_pill', 'condom', 'contraceptive_pill', 'sex_toy', 'prevent_pregnancy']
senticnet['barrier_contraceptive'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'birth_control_pill', 'condom', 'contraceptive_pill', 'sex_toy', 'prevent_pregnancy']
senticnet['barrier_cream'] = ['0', '0.086', '-0.06', '0.12', '#interest', '#admiration', 'positive', '0.046', 'gel_toothpaste', 'tartar_control_toothpaste', 'fluoride_mouthwash', 'toothpaste', 'hair_lotion']
senticnet['barrier_layer'] = ['-0.93', '0', '-0.99', '-0.92', '#sadness', '#fear', 'negative', '-0.95', 'language_barrier', 'shut', 'shut_down', 'close_down', 'obstacle']
senticnet['barrister'] = ['0.053', '0.122', '-0.07', '0.086', '#interest', '#admiration', 'positive', '0.046', 'attorney', 'lawyer', 'jury', 'decide_guilt', 'prosecutor']
senticnet['barroom'] = ['-0.81', '0', '0.553', '-0.92', '#sadness', '#disgust', 'negative', '-0.76', 'bar', 'sawdust_saloon', 'saloon', 'ginmill', 'taproom']
senticnet['barstool'] = ['-0.81', '0', '0.553', '-0.92', '#sadness', '#disgust', 'negative', '-0.76', 'bar', 'sawdust_saloon', 'saloon', 'ginmill', 'taproom']
senticnet['barstools'] = ['0', '0', '-0.91', '-0.70', '#fear', '#disgust', 'negative', '-0.81', 'strapless', 'upholstered', 'stool', 'sleeveless', 'backless']
senticnet['bart'] = ['0.159', '-0.07', '0.024', '0', '#joy', '#surprise', 'positive', '0.069', 'club', 'metal_stick', 'hangout', 'organize_group', 'staff']
senticnet['bartender'] = ['0.787', '0', '0', '0.98', '#joy', '#admiration', 'positive', '0.883', 'drunkard', 'drunk_person', 'whisky', 'beer', 'alcoholic']
senticnet['barter'] = ['-0.05', '0.215', '-0.08', '0.293', '#interest', '#admiration', 'positive', '0.092', 'swap', 'give_take', 'exchange_goods', 'transaction', 'increase_money']
senticnet['barter_economy'] = ['-0.05', '0.215', '-0.08', '0.293', '#interest', '#admiration', 'positive', '0.092', 'swap', 'give_take', 'exchange_goods', 'transaction', 'increase_money']
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senticnet['bass_guitar'] = ['0.049', '0.167', '-0.05', '0.295', '#interest', '#admiration', 'positive', '0.115', 'four_sides', 'sound_system', 'dolby_surround', 'movie', 'surround']
senticnet['bass_guitar_player'] = ['0', '-0.02', '0.055', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'djembe', 'make_musicr', 'use_jazz_music', 'tune_instrument', 'percussion']
senticnet['bass_guitarist'] = ['0', '-0.02', '0.055', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'djembe', 'make_musicr', 'use_jazz_music', 'tune_instrument', 'percussion']
senticnet['bass_player'] = ['0.049', '0.167', '-0.05', '0.295', '#interest', '#admiration', 'positive', '0.115', 'four_sides', 'sound_system', 'dolby_surround', 'movie', 'surround']
senticnet['bass_reflex'] = ['0.056', '-0.01', '0.036', '0.073', '#joy', '#admiration', 'positive', '0.028', 'loud_sound', 'stereo', 'tuner', 'speaker', 'steam_engine']
senticnet['bass_solo'] = ['0.773', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.844', 'favorite', 'crowd', 'acclamation', 'bunch', 'large_crowd']
senticnet['bass_viol'] = ['0.033', '0', '0.009', '0.057', '#joy', '#admiration', 'positive', '0.027', 'triangle', 'baton', 'bugle', 'type_of_bow', 'horn']
senticnet['basset_horn'] = ['-0.75', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.80', 'high-strung', 'uptight', 'restive', 'nervy', 'jumpy']
senticnet['basset_hound'] = ['-0.72', '0', '0.619', '-0.86', '#sadness', '#disgust', 'negative', '-0.73', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['bassike'] = ['0', '-0.03', '0.022', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'shoulder_bag', 'satchel', 'water_bottle', 'tote_bag', 'carry']
senticnet['bassinet'] = ['0.697', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.779', 'bedding', 'crib', 'toddler', 'cot', 'bunk']
senticnet['bassist'] = ['0.049', '0.167', '-0.05', '0.295', '#interest', '#admiration', 'positive', '0.115', 'four_sides', 'sound_system', 'dolby_surround', 'movie', 'surround']
senticnet['bassist_jah_wobble'] = ['-0.73', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'unwed', 'unwedded', 'divorced', 'unmarried', 'widowed']
senticnet['bassist_vocalist'] = ['0', '-0.02', '0.055', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'djembe', 'make_musicr', 'use_jazz_music', 'tune_instrument', 'percussion']
senticnet['bassline'] = ['0.857', '-0.77', '0', '0.878', '#joy', '#admiration', 'positive', '0.836', 'hand', 'round', 'approval', 'commendation', 'clapping']
senticnet['basso_brooke'] = ['0', '-0.03', '0.022', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'shoulder_bag', 'satchel', 'water_bottle', 'tote_bag', 'carry']
senticnet['bassoon'] = ['0.73', '0', '0', '0.933', '#joy', '#admiration', 'positive', '0.832', 'descant', 'soprano', 'oboe', 'recorder', 'treble']
senticnet['basswood_tilia_americana'] = ['0.776', '-0.6', '0', '0.568', '#joy', '#surprise', 'positive', '0.648', 'swamp_chestnut_oak', 'chinquapin_oak', 'chinkapin_oak', 'yellow_chestnut_oak', 'basket_oak']
senticnet['bastard'] = ['-0.35', '-0.13', '-0.05', '-0.38', '#sadness', '#disgust', 'negative', '-0.16', 'dick', 'idiot', 'asshole', 'fuck', 'motherfucker']
senticnet['bastard_child'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'spousal_relationship', 'bigamy']
senticnet['bastard_son'] = ['-0.35', '-0.13', '-0.05', '-0.38', '#sadness', '#disgust', 'negative', '-0.16', 'dick', 'idiot', 'asshole', 'fuck', 'motherfucker']
senticnet['bastard_toadflax'] = ['-0.79', '0', '0.837', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'sunray', 'wilding', 'sagebrush_buttercup', 'wildflower', 'pasque_flower']
senticnet['bastardly'] = ['0', '0', '-0.82', '-0.90', '#fear', '#disgust', 'negative', '-0.86', 'misbegotten', 'inauthentic', 'misbegot', 'spurious', 'specious']
senticnet['bastille'] = ['0.977', '-0.55', '0', '0.679', '#joy', '#admiration', 'positive', '0.737', 'lockup', 'holding_cell', 'workhouse', 'gaol', 'hoosegow']
senticnet['bastinado'] = ['-0.83', '0', '0.924', '-0.79', '#sadness', '#anger', 'negative', '-0.85', 'kick_in_butt', 'licking', 'whacking', 'stoning', 'lapidation']
senticnet['bastogne'] = ['0.737', '-0.96', '0', '0.552', '#joy', '#surprise', 'positive', '0.75', 'prominence', 'hump', 'bump', 'swelling', 'frontal_eminence']
senticnet['bastyr_university'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
senticnet['bat'] = ['0.723', '0.717', '0', '0', '#joy', '#interest', 'positive', '0.72', 'lick', 'drub', 'squash_racket', 'clobber', 'chiropteran']
senticnet['bat_boy'] = ['0.575', '-0.62', '0', '0.596', '#surprise', '#admiration', 'positive', '0.598', 'altar_boy', 'cub', 'ball_boy', 'boy', 'lad']
senticnet['bat_cave'] = ['-0.02', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'tunnel', 'dark_place', 'ski_resort', 'top_tree', 'compost_pile']
senticnet['bat_eared_fox'] = ['0', '0', '-0.95', '-0.81', '#fear', '#disgust', 'negative', '-0.88', 'herb', 'plant', 'poisonous_plant', 'yellow_flower', 'lichen']
senticnet['bat_mite'] = ['-0.86', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.86', 'demon', 'fool', 'cannibalism', 'jolly', 'treachery']
senticnet['batallion'] = ['0', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.09', 'army', 'attacker', 'military', 'soldier', 'infantry']
senticnet['batarang'] = ['0.757', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.831', 'baseball_bat', 'water_gun', 'goalie', 'hockey_puck', 'hockey_stick']
senticnet['batcave'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'only_bird']
senticnet['bateleur'] = ['0', '0.105', '0', '0', '#interest', '#interest', 'positive', '0.105', 'beak', 'many_feathers', 'big_eye', 'bird', 'bird_mouth']
senticnet['batesian_mimic'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'blatancy', 'patency', 'obviousness']
senticnet['batesian_mimicry'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'blatancy', 'patency', 'obviousness']
senticnet['batfe'] = ['-0.84', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.86', 'handgun', 'rifle', 'weapon', 'pistol', 'gun']
senticnet['batgirl'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'only_bird']
senticnet['bath'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
senticnet['bath_abbey'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
senticnet['bath_cb'] = ['0.756', '0', '0', '0', '#joy', '#joy', 'positive', '0.756', 'companionship', 'cherish', 'lover', 'bond', 'buddy']
senticnet['bath_robe'] = ['0.761', '0', '0', '0.719', '#joy', '#admiration', 'positive', '0.74', 'washroom', 'bathroom', 'toilet', 'hotel_bathroom', 'bathroom_drawer']
senticnet['bath_shower'] = ['0.041', '0.086', '0.016', '0', '#joy', '#interest', 'positive', '0.037', 'separate_shower', 'shower_head', 'shampoo_conditioner', 'plunger', 'tub']
senticnet['bath_soap'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'sexual_excitement', 'sexy', 'wet_lip', 'kiss', 'clean_up']
senticnet['bath_spa'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
senticnet['bath_towel'] = ['0.024', '0', '0', '0.074', '#joy', '#admiration', 'positive', '0.049', 'sheet', 'cover', 'quilt', 'comforter', 'modesty']
senticnet['bath_toy'] = ['0', '-0.10', '0', '0', '#surprise', '#surprise', 'positive', '0.106', 'toy', 'rubber_duck', 'toy_soldier', 'action_figure', 'old_game']
senticnet['bath_tub'] = ['0', '-0.02', '0.041', '-0.10', '#anger', '#disgust', 'negative', '-0.03', 'separate_shower', 'shampoo_conditioner', 'towel_bar', 'shower_head', 'toilet_brush']
senticnet['bathe'] = ['-0.60', '0', '0.992', '-0.94', '#anger', '#disgust', 'negative', '-0.84', 'wash', 'lather', 'floss', 'decontaminate', 'cleanse']
senticnet['batheaston'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
senticnet['bathed_therewith'] = ['-0.16', '0.179', '0.568', '-0.61', '#anger', '#disgust', 'negative', '-0.29', 'angrily', 'ire', 'see_red', 'dander', 'hackle']
senticnet['bathery'] = ['0.8', '-0.84', '0', '0.868', '#surprise', '#admiration', 'positive', '0.837', 'commonwealth', 'province', 'administrative_district', 'administrative_division', 'territorial_division']
senticnet['bathetic'] = ['0', '0', '-0.73', '-0.90', '#fear', '#disgust', 'negative', '-0.82', 'maudlin', 'hokey', 'drippy', 'mawkish', 'soupy']
senticnet['bathing'] = ['0.785', '0', '0', '0.841', '#joy', '#admiration', 'positive', '0.813', 'take_bath', 'wash_up', 'hygiene', 'wash_body', 'clean_self']
senticnet['bathing_cap'] = ['0', '0.092', '0.016', '0.074', '#interest', '#admiration', 'positive', '0.05', 'armoire', 'pillowcase', 'bed_sheet', 'duvet_cover', 'comforter']
senticnet['bathing_suit'] = ['-0.96', '0', '0.636', '-0.64', '#sadness', '#disgust', 'negative', '-0.75', 'burn', 'tan', 'hyperpigmentation', 'sunburn', 'physical_condition']
senticnet['bathrobe'] = ['0', '-0.02', '0.041', '-0.10', '#anger', '#disgust', 'negative', '-0.03', 'separate_shower', 'shampoo_conditioner', 'towel_bar', 'shower_head', 'toilet_brush']
senticnet['bathroom'] = ['0', '-0.10', '0', '0', '#surprise', '#surprise', 'positive', '0.106', 'washroom', 'bath_robe', 'toilet', 'hotel_bathroom', 'bathroom_drawer']
senticnet['bathroom_cabinet'] = ['0.711', '0.79', '0', '0', '#joy', '#interest', 'positive', '0.75', 'hotel_bathroom', 'medicine_cabinet', 'first-aid_kit', 'towel', 'toothbrush']
senticnet['bathroom_door'] = ['0', '0', '0', '0.077', '#admiration', '#admiration', 'positive', '0.077', 'glass_front', 'gate', 'doorway', 'use_farm', 'entranceway']
senticnet['bathroom_drawer'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom', 'beauty_parlor', 'hotel_bathroom', 'bath']
senticnet['bathroom_drawerroom'] = ['0.033', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.047', 'toilet', 'bath', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
senticnet['bathroom_floor'] = ['-0.04', '0.066', '0', '0.149', '#interest', '#admiration', 'positive', '0.057', 'first_aid_kit', 'floor', 'restroom', 'floorboard', 'fish_tackle']
senticnet['bathroom_humor'] = ['0.827', '-0.55', '0', '0.679', '#joy', '#admiration', 'positive', '0.687', 'spirit', 'spirit_up', 'inspirit', 'energize', 'jazz_up']
senticnet['bathroom_mirror'] = ['0', '-0.02', '0.041', '-0.10', '#anger', '#disgust', 'negative', '-0.03', 'separate_shower', 'shampoo_conditioner', 'towel_bar', 'shower_head', 'toilet_brush']
senticnet['bathroom_shower'] = ['0', '0.171', '-0.05', '0.102', '#interest', '#admiration', 'positive', '0.072', 'bed', 'mattress', 'bead', 'bed_cover', 'seduce']
senticnet['bathroom_sink'] = ['0', '0.171', '-0.05', '0.102', '#interest', '#admiration', 'positive', '0.072', 'bed', 'mattress', 'bead', 'bed_cover', 'seduce']
senticnet['bathroom_stall'] = ['0', '0.072', '0', '0', '#interest', '#interest', 'positive', '0.072', 'metal_box', 'cash_drawer', 'keyhole', 'clutch_bag', 'safe']
senticnet['bathroom_tissue'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'hankie', 'kleenex', 'hanky', 'handkerchief', 'tissue']
senticnet['bathroom_wall'] = ['0', '0.952', '0', '0', '#interest', '#interest', 'positive', '0.952', 'tile', 'hair', 'design', 'water_resistance', 'algae']
senticnet['bathroom_window'] = ['0', '0.171', '-0.05', '0.102', '#interest', '#admiration', 'positive', '0.072', 'bed', 'mattress', 'bead', 'bed_cover', 'seduce']
senticnet['bathsheba'] = ['-0.62', '0', '0.645', '-0.67', '#anger', '#disgust', 'negative', '-0.65', 'lady_maid', 'handmaiden', 'chambermaid', 'maid', 'parlormaid']
senticnet['bathtub'] = ['0', '-0.02', '0.041', '-0.10', '#anger', '#disgust', 'negative', '-0.03', 'separate_shower', 'shampoo_conditioner', 'towel_bar', 'shower_head', 'toilet_brush']
senticnet['bathwick'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
senticnet['bathwick_hill'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
senticnet['batiri'] = ['-0.57', '0', '0.571', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['batizado'] = ['-0.06', '-0.00', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.05', 'madman', 'trouble', 'agony', 'cause_problem', 'river_stream']
senticnet['batman'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'only_bird']
senticnet['batman_costume'] = ['-0.76', '0', '0.672', '-0.88', '#sadness', '#disgust', 'negative', '-0.77', 'attire', 'garb', 'dress', 'fancy_dress', 'masquerade']
senticnet['batman_forever'] = ['0.705', '-0.60', '0', '0.913', '#joy', '#admiration', 'positive', '0.741', 'lick', 'clout', 'poke', 'punch', 'biff']
senticnet['batman_rogue_gallery'] = ['-0.86', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.86', 'demon', 'fool', 'cannibalism', 'jolly', 'treachery']
senticnet['batman_villain'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'only_bird']
senticnet['batmobile'] = ['-0.73', '0', '0.72', '-0.93', '#sadness', '#disgust', 'negative', '-0.79', 'imp', 'elf', 'hob', 'leprechaun', 'pixie']
senticnet['baton'] = ['-0.91', '0', '0.931', '-0.72', '#sadness', '#anger', 'negative', '-0.85', 'hit_with_baton', 'get_hurt', 'whip', 'arrow', 'hurt']
senticnet['baton_rouge'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
senticnet['baton_rouge_advocate'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
senticnet['batou'] = ['-0.61', '0', '0.572', '-0.83', '#sadness', '#disgust', 'negative', '-0.67', 'numb', 'benumbed', 'anesthetic', 'insensible', 'asleep']
senticnet['batrachomyomachia'] = ['-0.81', '0', '0.52', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'affray', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['batroc'] = ['-0.58', '0', '0.661', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'explosive', 'cordite', 'dynamite', 'plastic_explosive', 'explosive_compound']
senticnet['batshit'] = ['-0.76', '0', '0.904', '-0.80', '#anger', '#disgust', 'negative', '-0.82', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['batsman'] = ['-0.13', '0', '-0.11', '-0.04', '#sadness', '#fear', 'negative', '-0.1', 'concern_health', 'medical_problem', 'strengthful', 'surgery', 'injury']
senticnet['batsuit'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'only_bird']
senticnet['battalion'] = ['0', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.09', 'army', 'attacker', 'military', 'soldier', 'infantry']
senticnet['battalion_hq'] = ['0', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.09', 'army', 'attacker', 'military', 'soldier', 'infantry']
senticnet['batten_disease'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'dystrophy', 'myotonic_dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['battened_down'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'chick', 'wild_bird', 'baby_bird', 'lay_egg', 'bird_home']
senticnet['batter'] = ['0', '0', '0.793', '-0.88', '#anger', '#disgust', 'negative', '-0.84', 'humiliate', 'separate', 'crush', 'bust', 'trash']
senticnet['batter_eye'] = ['0.953', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.929', 'hockey_fan', 'goalie', 'competitor', 'spectator', 'hockey_stick']
senticnet['battered'] = ['-0.76', '0', '0.677', '-0.6', '#sadness', '#anger', 'negative', '-0.67', 'derelict', 'bedraggled', 'hurt', 'damaged', 'dilapidated']
senticnet['battered_economy'] = ['-0.07', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.07', 'use_needle', 'doctor_tool', 'fix', 'embroidery', 'seam']
senticnet['battered_reputation'] = ['-0.07', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.07', 'use_needle', 'doctor_tool', 'fix', 'embroidery', 'seam']
senticnet['battered_suitcase'] = ['0', '-0.02', '0.018', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'shoulder_bag', 'water_bottle', 'satchel_bag', 'tote_bag', 'carry']
senticnet['batterer'] = ['-0.06', '0', '-0.15', '0', '#sadness', '#fear', 'negative', '-0.10', 'heroism', 'role_play', 'repeat', 'act', 'part_of_movie']
senticnet['battering_ram'] = ['0.085', '0', '-0.05', '0.116', '#joy', '#admiration', 'positive', '0.05', 'dance_well', 'water_faucet', 'enter_contest', 'dance_type', 'dance_music']
senticnet['battersea_power_station'] = ['-0.81', '0', '0.543', '-0.63', '#sadness', '#disgust', 'negative', '-0.66', 'caper', 'pickle', 'relish', 'dill_pickle', 'sweet_pickle']
senticnet['battery'] = ['0.045', '0', '0', '0.051', '#joy', '#admiration', 'positive', '0.048', 'science', 'alkaline', 'chemistry', 'ph_level', 'chemical']
senticnet['battery_acid'] = ['0.071', '0', '-0.08', '-0.09', '#fear', '#disgust', 'negative', '-0.03', 'gunk', 'rotten_food', 'sour_cream', 'glass_of_milk', 'lemon_juice']
senticnet['battery_backup'] = ['0', '-0.03', '0', '0.093', '#surprise', '#admiration', 'positive', '0.063', 'work_well', 'internal_combustion_engine', 'broken_car', 'mode_transportation', 'machine']
senticnet['battery_compartment'] = ['0', '-0.03', '0.043', '-0.11', '#anger', '#disgust', 'negative', '-0.03', 'torch', 'safety_pin', 'laser', 'flare', 'firebomb']
senticnet['battery_operated_device'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'smart_technology', 'electric_shaver', 'hair_dryer', 'stay_healthy', 'wash_tooth']
senticnet['battery_pack'] = ['0', '0', '-1', '-1', '#fear', '#disgust', 'negative', '-1.0', 'moon_gravity', 'force', 'coercion', 'watt', 'power_source']
senticnet['battiato'] = ['0.107', '0.11', '-0.08', '0.21', '#interest', '#admiration', 'positive', '0.086', 'live_healthy_life', 'forever_young', 'live_forever', 'live_long', 'live_good_life']
senticnet['battle'] = ['-0.05', '0', '-0.08', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'siege', 'big_fight', 'war', 'declare_war', 'combat']
senticnet['battle_axe'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'bayonet', 'spear', 'shotgun', 'bullet', 'hydrogen_bomb']
senticnet['battle_cancer'] = ['-0.78', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.85', 'lower_standard', 'fight', 'hang_picture_on_wall', 'gather_berry', 'wave_flag']
senticnet['battle_disease'] = ['-0.94', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.83', 'fear_death', 'condition', 'infection', 'battle_cancer', 'health']
senticnet['battle_dress'] = ['-0.79', '0', '0.599', '-0.93', '#sadness', '#disgust', 'negative', '-0.77', 'uniform', 'military_uniform', 'dress_uniform', 'fatigue', 'khaki']
senticnet['battle_fatigue'] = ['-0.75', '0', '0.87', '-0.51', '#sadness', '#anger', 'negative', '-0.71', 'posttraumatic_stress_disorder', 'combat_fatigue', 'combat_neurosis', 'shell_shock', 'anxiety_disorder']
senticnet['battle_illness'] = ['-0.14', '0', '-0.05', '-0.03', '#sadness', '#fear', 'negative', '-0.07', 'laugh', 'throw_fit', 'smile', 'laugh_clown', 'honor_parent']
senticnet['battlecruiser'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'submarine', 'lifeboat', 'titanic', 'large_ship', 'canoe']
senticnet['battled_spider_man'] = ['-0.98', '0', '0.714', '-0.90', '#sadness', '#disgust', 'negative', '-0.86', 'stupid', 'stupid_person', 'stupe', 'dullard', 'dolt']
senticnet['battlefield'] = ['-0.02', '0.067', '-0.11', '-0.19', '#fear', '#disgust', 'negative', '-0.06', 'war_museum', 'underworld', 'police_station', 'prisoner_war_camp', 'war_zone']
senticnet['battlefront'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'start_over', 'queue', 'find_line', 'take_turn', 'long_line']
senticnet['battlement'] = ['0', '0', '0.993', '-0.76', '#anger', '#disgust', 'negative', '-0.87', 'parapet', 'parapet', 'balustrade', 'railing', 'cornice']
senticnet['battler'] = ['0.833', '-0.97', '0', '0.91', '#surprise', '#admiration', 'positive', '0.906', 'combatant', 'fencer', 'belligerent', 'fighter', 'scrapper']
senticnet['battleship'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'submarine', 'lifeboat', 'titanic', 'large_ship', 'canoe']
senticnet['battleship_division'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'submarine', 'lifeboat', 'titanic', 'large_ship', 'canoe']
senticnet['battleship_hm_iron'] = ['0.033', '0.088', '-0.06', '0.068', '#interest', '#admiration', 'positive', '0.032', 'earl', 'noble', 'king', 'sultan', 'royal_title']
senticnet['battleship_squadron'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'submarine', 'lifeboat', 'titanic', 'large_ship', 'canoe']
senticnet['battlestar_galactica_blood'] = ['0.02', '0.085', '-0.05', '0.085', '#interest', '#admiration', 'positive', '0.034', 'gold', 'silver', 'medal', 'carat', 'medallion']
senticnet['battlesuit'] = ['-0.73', '0', '0.72', '-0.93', '#sadness', '#disgust', 'negative', '-0.79', 'imp', 'elf', 'hob', 'leprechaun', 'pixie']
senticnet['battling_cancer'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['battuta'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['batty'] = ['0', '0', '0.714', '-0.76', '#anger', '#disgust', 'negative', '-0.73', 'crazy', 'mad', 'buttock', 'bat', 'anus']
senticnet['batuque'] = ['-0.06', '-0.00', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.05', 'madman', 'trouble', 'agony', 'cause_problem', 'river_stream']
senticnet['batwoman'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'only_bird']
senticnet['batzarro'] = ['-0.86', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.86', 'demon', 'fool', 'cannibalism', 'jolly', 'treachery']
senticnet['bauble'] = ['0', '0', '-0.86', '-0.85', '#fear', '#disgust', 'negative', '-0.86', 'gaud', 'novelty', 'gewgaw', 'bangle', 'trinket']
senticnet['baudrillard'] = ['-0.94', '0', '0.925', '-0.97', '#sadness', '#disgust', 'negative', '-0.95', 'nonexistence', 'nonentity', 'cloud', 'falseness', 'falsity']
senticnet['baulk'] = ['-0.71', '0', '0.932', '-0.50', '#sadness', '#anger', 'negative', '-0.71', 'refuse', 'subvert', 'decline', 'balk', 'disobey']
senticnet['bawd'] = ['-0.91', '0', '0.974', '-0.61', '#sadness', '#anger', 'negative', '-0.83', 'slattern', 'camp_follower', 'call_girl', 'comfort_woman', 'ianfu']
senticnet['bawdily'] = ['0', '0.79', '0', '0.936', '#interest', '#admiration', 'positive', '0.863', 'humor', 'humorous', 'bawdy', 'laughter', 'humorously']
senticnet['bawdy'] = ['0', '0.788', '0', '0.936', '#interest', '#admiration', 'positive', '0.862', 'humor', 'bawdily', 'humorous', 'laughter', 'humorously']
senticnet['bawdy_house'] = ['0.754', '-0.82', '0', '0.947', '#surprise', '#admiration', 'positive', '0.841', 'whorehouse', 'brothel', 'bordello', 'massage_parlor', 'house_of_prostitution']
senticnet['bawl'] = ['-0.22', '0', '-0.2', '0', '#sadness', '#fear', 'negative', '-0.21', 'weep', 'sob', 'shed_tear', 'wail', 'eye_tear']
senticnet['bawl_out'] = ['-0.99', '0', '0.545', '-0.55', '#sadness', '#disgust', 'negative', '-0.69', 'castigate', 'tell_off', 'brush_down', 'chasten', 'objurgate']
senticnet['bawling_out'] = ['-0.64', '0', '0.859', '-0.60', '#sadness', '#anger', 'negative', '-0.7', 'reprimand', 'rebuke', 'reproof', 'reproval', 'reprehension']
senticnet['bay'] = ['0.859', '-0.89', '0', '0', '#joy', '#surprise', 'positive', '0.875', 'estuary', 'harpoon', 'gulf', 'lake', 'river']
senticnet['bay_colt'] = ['0', '0', '0', '0.071', '#admiration', '#admiration', 'positive', '0.071', 'two_barbs', 'barb', 'mule', 'buddhist', 'ride_barb']
senticnet['bay_leaf'] = ['-0.66', '0', '0.992', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'cold', 'boil', 'temperature', 'low_temperature', 'frigidity']
senticnet['bay_mare'] = ['0', '0', '0', '0.071', '#admiration', '#admiration', 'positive', '0.071', 'two_barbs', 'barb', 'mule', 'buddhist', 'ride_barb']
senticnet['bay_realty_trust'] = ['0.866', '0.82', '0', '0.713', '#joy', '#interest', 'positive', '0.8', 'titanium', 'bronze', 'alloy', 'medal', 'copper']
senticnet['bay_scallop'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'seal', 'walrus', 'otter', 'alligator', 'whale']
senticnet['bay_window'] = ['0.046', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'breeze', 'sea_view', 'natural_air', 'seashore', 'sand']
senticnet['bayer_designation'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['bayern_munich_butt'] = ['0.939', '-0.71', '0', '0.775', '#joy', '#admiration', 'positive', '0.81', 'vox_angelica', 'voix_celeste', 'organ_stop', 'pipe', 'diapason']
senticnet['bayesian'] = ['0.83', '-0.83', '0', '0.872', '#surprise', '#admiration', 'positive', '0.846', 'overappraisal', 'overvaluation', 'overestimate', 'overestimation', 'commercial_document']
senticnet['bayesian_approach'] = ['0.83', '-0.83', '0', '0.872', '#surprise', '#admiration', 'positive', '0.846', 'overappraisal', 'overvaluation', 'overestimate', 'overestimation', 'commercial_document']
senticnet['bayesian_decision'] = ['-0.99', '0', '0.878', '-0.95', '#sadness', '#disgust', 'negative', '-0.94', 'adequacy', 'satisfactoriness', 'acceptability', 'acceptableness', 'quality']
senticnet['bayesian_estimation'] = ['0.83', '-0.83', '0', '0.872', '#surprise', '#admiration', 'positive', '0.846', 'overappraisal', 'overvaluation', 'overestimate', 'overestimation', 'commercial_document']
senticnet['bayesian_model'] = ['-0.91', '0', '0.598', '-0.52', '#sadness', '#anger', 'negative', '-0.68', 'stingy', 'penurious', 'scrimy', 'beggarly', 'chintzy']
senticnet['bayley_mental'] = ['0.874', '-0.73', '0', '0.608', '#joy', '#surprise', 'positive', '0.74', 'growth', 'growing', 'maturation', 'development', 'ontogeny']
senticnet['bayonet'] = ['0', '0', '-0.99', '-0.85', '#fear', '#disgust', 'negative', '-0.92', 'shotgun', 'knife', 'stab', 'hydrogen_bomb', 'dangerous_weapon']
senticnet['bayonet_attack'] = ['0.957', '-0.93', '0', '0.795', '#joy', '#surprise', 'positive', '0.896', 'outdo', 'outstrip', 'outmatch', 'outgo', 'outperform']
senticnet['bayonet_charge'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['bayou'] = ['0.85', '0.96', '0', '0', '#joy', '#interest', 'positive', '0.905', 'flatness', 'boring', 'flat_tire', 'flat_foot', 'small_breast']
senticnet['bayou_chene'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
senticnet['bayou_teche'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
senticnet['bayshore_mall'] = ['-0.06', '0.21', '0', '0.076', '#interest', '#admiration', 'positive', '0.074', 'new_think', 'figure_out', 'brain_activity', 'idea', 'come_up_idea']
senticnet['bazaar'] = ['0', '0.113', '0.041', '0.058', '#interest', '#admiration', 'positive', '0.043', 'corner_shop', 'general_store', 'many_shops', 'large_supermarket', 'goods']
senticnet['bazar'] = ['0', '0.113', '0.041', '0.058', '#interest', '#admiration', 'positive', '0.043', 'corner_shop', 'general_store', 'many_shops', 'large_supermarket', 'goods']
senticnet['bazentin_le_petit'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'start_over', 'queue', 'find_line', 'take_turn', 'long_line']
senticnet['bazi'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'white_animal', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
senticnet['bazooka'] = ['0.757', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.831', 'baseball_bat', 'water_gun', 'goalie', 'hockey_puck', 'hockey_stick']
senticnet['bb_cream'] = ['-0.93', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.85', 'protect_from_sun', 'shade', 'meaning', 'corrector', 'sunglasses']
senticnet['bb_gun'] = ['-0.75', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.75', 'hydrogen_bomb', 'rifle', 'revolver', 'pistol', 'gun']
senticnet['bb_operating_system'] = ['0.807', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.881', 'fruit', 'mobile_phone', 'drinkable', 'savory', 'eat_fruit']
senticnet['bba_degree'] = ['-0.75', '0', '0.589', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'lowness', 'grind', 'immoderation', 'immoderateness', 'amplitude_level']
senticnet['bbc_comedy'] = ['-0.89', '0', '0.58', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'resemblance', 'comparison', 'equivalence', 'reflection', 'alikeness']
senticnet['bbc_documentary'] = ['0', '0.762', '0', '0', '#interest', '#interest', 'positive', '0.762', 'encyclopaedia', 'tropical_rainforest', 'learn_new_information', 'tropical_forest', 'child_story']
senticnet['bbc_matt_danzico'] = ['0.502', '-0.82', '0', '0.779', '#surprise', '#admiration', 'positive', '0.703', 'mortal', 'person', 'individual', 'free_liver', 'agent']
senticnet['bbc_natalia_antelava'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'dejectedness', 'low-spiritedness', 'lowness', 'downheartedness', 'cheerless']
senticnet['bbc_ni_dublin'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'adventurer', 'humankind']
senticnet['bbc_radio_comedy'] = ['-0.89', '0', '0.58', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'resemblance', 'comparison', 'equivalence', 'reflection', 'alikeness']
senticnet['bbc_scotland_pundit'] = ['-0.79', '0', '0.992', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['bbc_sport_west_ham'] = ['0.941', '-0.83', '0', '0.973', '#joy', '#admiration', 'positive', '0.917', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['bburago'] = ['0.087', '0.113', '-0.08', '0', '#joy', '#interest', 'positive', '0.038', 'find_match', 'strike_against', 'cigarette_lighter', 'match', 'use_lighter']
senticnet['bcg_vaccination'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'medical_therapy', 'vaccine', 'doctor', 'medicine', 'hospital']
senticnet['bchamel'] = ['0.059', '-0.02', '0.025', '0.109', '#joy', '#admiration', 'positive', '0.043', 'tasty', 'bake_treat', 'bake_goods', 'bake_good', 'loaf']
senticnet['bdeni_river'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'aphasia', 'anomic_aphasia']
senticnet['bea_benaderet'] = ['-0.71', '0', '0.972', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'scold', 'nag', 'scolder', 'harridan', 'common_scold']
senticnet['beach'] = ['0.03', '0.117', '0', '0.084', '#interest', '#admiration', 'positive', '0.077', 'ocean_beach', 'waterfront', 'ocean', 'coast', 'sea']
senticnet['beach_bag'] = ['0', '0.092', '0.016', '0.074', '#interest', '#admiration', 'positive', '0.05', 'walmart', 'drugstore', 'own_bathroom', 'pharmacy', 'car_trunk']
senticnet['beach_ball'] = ['0', '0.952', '0', '0', '#interest', '#interest', 'positive', '0.952', 'volleyball', 'game', 'sun', 'sand', 'fun']
senticnet['beach_bum'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'drifter', 'bum', 'sundowner']
senticnet['beach_flea'] = ['0', '0', '-0.94', '-0.98', '#fear', '#disgust', 'negative', '-0.96', 'whale_louse', 'amphipod', 'sand_hopper', 'sandhopper', 'sand_flea']
senticnet['beach_getaway'] = ['0.024', '0.152', '-0.06', '0.236', '#interest', '#admiration', 'positive', '0.087', 'vacation', 'exit', 'stance', 'take_break', 'holiday']
senticnet['beach_house'] = ['0.844', '0', '0', '0', '#joy', '#joy', 'positive', '0.844', 'front_build', 'television_station', 'football_stadium', 'football_game', 'back_door']
senticnet['beach_hut'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'drifter', 'bum', 'sundowner']
senticnet['beach_promenade'] = ['0.203', '0.077', '0', '0.189', '#joy', '#admiration', 'positive', '0.156', 'good_life', 'coast', 'relax', 'sediment', 'shell']
senticnet['beach_resort'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'lurking_place', 'spot', 'drop', 'hiding_place', 'den']
senticnet['beach_sand'] = ['-0.06', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.04', 'friction', 'lack_of_water', 'dirt', 'abrasive', 'ground_material']
senticnet['beach_shack'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'lurking_place', 'spot', 'drop', 'hiding_place', 'den']
senticnet['beach_shooting_brake'] = ['-0.54', '0', '0.9', '-0.58', '#anger', '#disgust', 'negative', '-0.67', 'wagon', 'station_shooting_brake', 'shooting_brake', 'estate_car', 'beach_waggon']
senticnet['beach_soccer_league'] = ['0.771', '0.846', '0', '0', '#joy', '#interest', 'positive', '0.808', 'dollar_bill', 'franc', 'banknote', 'dollar', 'cent']
senticnet['beach_towel'] = ['0', '0.092', '0.016', '0.074', '#interest', '#admiration', 'positive', '0.05', 'armoire', 'pillowcase', 'bed_sheet', 'duvet_cover', 'comforter']
senticnet['beach_volley'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'golf', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['beach_volleyball'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'golf', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['beach_waggon'] = ['0.975', '0.835', '0', '0', '#joy', '#interest', 'positive', '0.905', 'beach_wagon', 'station_wagon', 'wagon', 'estate_car', 'shooting_brake']
senticnet['beach_wagon'] = ['0.999', '0.961', '0', '0', '#joy', '#interest', 'positive', '0.98', 'shooting_brake', 'station_wagon', 'wagon', 'estate_car', 'beach_waggon']
senticnet['beachcomber'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'drifter', 'bum', 'sundowner']
senticnet['beachdan'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['beached'] = ['-0.98', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.96', 'sea-worthy', 'maneuverable', 'seagoing', 'roadworthy', 'seaworthy']
senticnet['beachfront'] = ['0.203', '0.077', '0', '0.189', '#joy', '#admiration', 'positive', '0.156', 'good_life', 'coast', 'relax', 'sediment', 'shell']
senticnet['beachfront_resort'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'lurking_place', 'spot', 'drop', 'hiding_place', 'den']
senticnet['beachside'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'lurking_place', 'spot', 'drop', 'hiding_place', 'den']
senticnet['beachwear'] = ['0.053', '-0.01', '-0.05', '0.121', '#joy', '#admiration', 'positive', '0.034', 'coat', 'winter_clothes', 'stylish', 'adorn', 'protective_cover']
senticnet['bead'] = ['0', '0.171', '-0.05', '0.102', '#interest', '#admiration', 'positive', '0.072', 'bed', 'mattress', 'linen_cupboard', 'bed_cover', 'seduce']
senticnet['bead_cover'] = ['0', '0.167', '-0.05', '0.098', '#interest', '#admiration', 'positive', '0.069', 'bead', 'mattress', 'linen_cupboard', 'bed', 'seduce']
senticnet['bead_necklace'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'anklet', 'ankle_bracelet', 'armilla']
senticnet['beadboard'] = ['0', '0.089', '0', '0', '#interest', '#interest', 'positive', '0.089', 'ground', 'basement', 'base', 'foundation', 'basis']
senticnet['beagle_mix'] = ['-0.72', '0', '0.619', '-0.86', '#sadness', '#disgust', 'negative', '-0.73', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['beagle_voyage'] = ['-0.04', '0.192', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.054', 'hypothetical', 'supposition', 'scientific_statement', 'psychology', 'educate_guess']
senticnet['beak'] = ['0', '0.105', '0', '0', '#interest', '#interest', 'positive', '0.105', 'big_eye', 'many_feathers', 'predator', 'bird', 'eagle']
senticnet['beaked_hazelnut'] = ['0.773', '-0.55', '0', '0.949', '#joy', '#admiration', 'positive', '0.757', 'cornel', 'dwarf_cornel', 'crackerberry', 'pudding_berry', 'common_white_dogwood']
senticnet['beaked_whale'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'many_fishes', 'seahorse', 'starfish', 'plankton', 'sea_wee']
senticnet['beaker'] = ['0.026', '0.067', '0', '0', '#joy', '#interest', 'positive', '0.046', 'vial', 'glass_container', 'bottle', 'drink_holder', 'flask']
senticnet['beam'] = ['0.846', '0.744', '0', '0', '#joy', '#interest', 'positive', '0.795', 'cork', 'rod', 'nest_table', 'wand', 'i-beam']
senticnet['beam_of_light'] = ['0.575', '-0.62', '0', '0.596', '#surprise', '#admiration', 'positive', '0.598', 'beam', 'sunbeam', 'light_beam', 'ray', 'ray_of_light']
senticnet['beam_pass_through'] = ['-0.50', '0', '0.75', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'beam', 'beam_of_light', 'sunbeam', 'ray', 'ray_of_light']
senticnet['beam_splitter'] = ['-0.50', '0', '0.75', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'beam', 'beam_of_light', 'sunbeam', 'ray', 'ray_of_light']
senticnet['beamer'] = ['0', '0', '0.71', '-0.71', '#anger', '#disgust', 'negative', '-0.71', 'presumption', 'presumable', 'presumptuous', 'presume', 'presumptive']
senticnet['beaming'] = ['0.635', '0.639', '-0.24', '0.291', '#joy', '#interest', 'positive', '0.331', 'gladsomeness', 'gladfulness', 'gladness', 'gladsome', 'gladden']
senticnet['beaming_smile'] = ['0.895', '0.712', '0', '0', '#joy', '#interest', 'positive', '0.803', 'timorousness', 'timidness', 'timidity', 'shyly', 'timidly']
senticnet['beamsplitter'] = ['-0.50', '0', '0.75', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'beam', 'beam_of_light', 'sunbeam', 'ray', 'ray_of_light']
senticnet['bean'] = ['-0.70', '0', '0.606', '-0.72', '#sadness', '#disgust', 'negative', '-0.67', 'common_bean', 'soya', 'goa_bean', 'mung_bean', 'mung']
senticnet['bean_curd'] = ['0', '-0.03', '0.046', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'pasta', 'green_bean', 'mayonnaise', 'frozen_food', 'sour_milk']
senticnet['bean_goose'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'hold_egg', 'baby_bird', 'hatch', 'lay_egg', 'bird_home']
senticnet['bean_paste'] = ['0', '-0.03', '0.046', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'pasta', 'green_bean', 'mayonnaise', 'frozen_food', 'sour_milk']
senticnet['beanbag'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'collectible', 'teddy_bear', 'paper_airplane', 'barbie_doll', 'beanie_baby']
senticnet['beanbag_airline_seat'] = ['0', '0', '0.874', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'sofa_bed', 'small_seat', 'chair', 'hide-a-bed_sofa', 'airline_seat']
senticnet['beanbag_chair'] = ['0', '0.952', '0', '0', '#interest', '#interest', 'positive', '0.952', 'soft', 'small_seat', 'relax', 'bean', 'chair']
senticnet['beanie_baby'] = ['0.703', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.717', 'action_figure', 'toy_car', 'toy_ball', 'toy_soldier', 'yo-yo']
senticnet['bear'] = ['0.835', '-0.94', '0', '0.537', '#joy', '#surprise', 'positive', '0.773', 'fluster', 'hold', 'assert', 'deport', 'move']
senticnet['bear_arm'] = ['0.972', '0.896', '0', '0', '#joy', '#interest', 'positive', '0.934', 'right', 'people', 'well', 'security', 'constitution']
senticnet['bear_claw'] = ['0.793', '0.847', '0', '0', '#joy', '#interest', 'positive', '0.82', 'cross_bun', 'hot_cross_bun', 'bear_paw', 'coffee_roll', 'bun']
senticnet['bear_cub'] = ['0', '-0.97', '0', '0', '#surprise', '#surprise', 'positive', '0.977', 'cougar', 'skunk', 'lion', 'tiger', 'lynx']
senticnet['bear_fruit'] = ['0.81', '0', '0', '0.703', '#joy', '#admiration', 'positive', '0.756', 'harvest', 'crop', 'grow_crop', 'plant_crop', 'graze_cattle']
senticnet['bear_in_mind'] = ['0.541', '-0.86', '0', '0.85', '#surprise', '#admiration', 'positive', '0.753', 'qualify', 'think_of', 'mind', 'keep_note', 'characterize']
senticnet['bear_on'] = ['0.874', '-0.61', '0', '0.845', '#joy', '#admiration', 'positive', '0.778', 'matter_to', 'interest', 'hold', 'go_for', 'regard']
senticnet['bear_out'] = ['0.853', '-0.97', '0', '0.855', '#surprise', '#admiration', 'positive', '0.893', 'check', 'match', 'correspond', 'jibe', 'tally']
senticnet['bear_paw'] = ['0.886', '0.838', '0', '0', '#joy', '#interest', 'positive', '0.862', 'cross_bun', 'hot_cross_bun', 'coffee_roll', 'bear_claw', 'bun']
senticnet['bear_stearns_collapse'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'fragmentation', 'discharge', 'backfire', 'blast']
senticnet['bear_up'] = ['-0.99', '0', '0.871', '-0.55', '#sadness', '#anger', 'negative', '-0.80', 'endure', 'brook', 'tolerate', 'stomach', 'abide']
senticnet['bear_upon'] = ['0.976', '-0.80', '0', '0.557', '#joy', '#surprise', 'positive', '0.78', 'color', 'treat', 'strike_blow', 'expose', 'influence']
senticnet['bearable'] = ['0.525', '-0.79', '0', '0.593', '#surprise', '#admiration', 'positive', '0.639', 'tolerant', 'resistant', 'tolerable', 'endurable', 'sufferable']
senticnet['bearberry'] = ['-0.79', '0', '0.906', '-0.67', '#sadness', '#anger', 'negative', '-0.79', 'water_dropwort', 'hemlock_water_dropwort', 'water_hemlock', 'plant_life', 'poisonous_plant']
senticnet['bearcat'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'unofficial', 'informal', 'loose', 'drumhead']
senticnet['beard'] = ['-0.10', '0', '-0.05', '-0.11', '#sadness', '#disgust', 'negative', '-0.08', 'jaw', 'cheek', 'dimple', 'forehead', 'pimple']
senticnet['beard_cut'] = ['0.575', '-0.62', '0', '0.596', '#surprise', '#admiration', 'positive', '0.598', 'go_barber', 'visit_barber', 'go_barber_shop', 'go_hair_salon', 'haircut']
senticnet['beard_trimmer'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'smart_technology', 'electric_shaver', 'hair_dryer', 'stay_healthy', 'wash_tooth']
senticnet['bearded'] = ['-0.64', '0', '0.63', '-0.61', '#sadness', '#anger', 'negative', '-0.63', 'unshaven', 'barbate', 'bewhiskered', 'whiskered', 'whiskery']
senticnet['bearded_figure'] = ['0.026', '0.18', '-0.11', '0.18', '#interest', '#admiration', 'positive', '0.069', 'deity', 'god', 'angel', 'goddess', 'rabbi']
senticnet['bearded_lady'] = ['0', '0', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.05', 'human', 'delusional', 'use_illegal_drug', 'busy_person', 'carpenter']
senticnet['bearded_seal'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'seal', 'otter', 'mussel', 'alligator', 'whale']
senticnet['bearden_josey_center'] = ['-0.63', '0', '0.559', '-0.82', '#sadness', '#disgust', 'negative', '-0.67', 'take_bull_by_horns', 'confront', 'front', 'encounter', 'oppose']
senticnet['bearer'] = ['-0.98', '0', '0.664', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'unfortunate', 'unfortunate_person', 'weeper', 'pallgriever', 'griever']
senticnet['bearish'] = ['-0.98', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.87', 'rough', 'bearishly', 'bearishness', 'surly', 'clumsy']
senticnet['bearishly'] = ['-0.99', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.88', 'rough', 'bearish', 'bearishness', 'surly', 'clumsy']
senticnet['bearishness'] = ['-0.99', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.88', 'rough', 'bearishly', 'bearish', 'surly', 'clumsy']
senticnet['bearkat'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'unofficial', 'informal', 'loose', 'drumhead']
senticnet['bearskin'] = ['-0.58', '0', '0.632', '-0.63', '#anger', '#disgust', 'negative', '-0.61', 'skimmer', 'trilby', 'chapeau', 'busby', 'shako']
senticnet['beast'] = ['0.03', '0.068', '-0.04', '0.093', '#interest', '#admiration', 'positive', '0.037', 'critter', 'equine', 'live_creature', 'furry_creature', 'domesticate_animal']
senticnet['beastie'] = ['0.03', '0.068', '-0.04', '0.093', '#interest', '#admiration', 'positive', '0.037', 'beast', 'equine', 'live_creature', 'furry_creature', 'domesticate_animal']
senticnet['beastie_boy_grand'] = ['0.059', '0.076', '-0.05', '0.079', '#interest', '#admiration', 'positive', '0.039', 'noble', 'king', 'earl', 'duchess', 'duke']
senticnet['beastliness'] = ['0', '0', '0.962', '-0.96', '#anger', '#disgust', 'negative', '-0.96', 'abominable', 'nature', 'bestial', 'led', 'beastly']
senticnet['beastly'] = ['0', '0', '0.965', '-0.95', '#anger', '#disgust', 'negative', '-0.96', 'abominable', 'nature', 'bestial', 'led', 'beastliness']
senticnet['beastman'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amuck', 'murderousness', 'misocainea', 'misogyny']
senticnet['beastmen'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'feudal_lord', 'lord']
senticnet['beat'] = ['0.062', '-0.60', '0', '0.289', '#surprise', '#admiration', 'positive', '0.318', 'perplex', 'amaze', 'marvel', 'pose', 'grand']
senticnet['beat-up'] = ['-0.85', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.80', 'well-worn', 'ill-fitting', 'worn-down', 'ratty', 'worn-out']
senticnet['beat_box'] = ['0.779', '-0.72', '0', '0', '#joy', '#surprise', 'positive', '0.753', 'sound_control_room', 'microphone_boom', 'mouth', 'speaker', 'beat']
senticnet['beat_compatriot'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'nazi', 'man', 'pervert', 'healthy_person']
senticnet['beat_egg'] = ['0.719', '-0.70', '0', '0', '#joy', '#surprise', 'positive', '0.714', 'scrumble_egg', 'heat_meal', 'pan', 'breakfast', 'food']
senticnet['beat_enemy'] = ['0', '0.827', '0', '0.84', '#interest', '#admiration', 'positive', '0.833', 'win_war', 'win_battle', 'kill_enemy', 'become_strong', 'become_best']
senticnet['beat_generation'] = ['0.52', '-0.91', '0', '0.65', '#surprise', '#admiration', 'positive', '0.694', 'flower_people', 'hippy', 'hipster', 'youth_subculture', 'beat']
senticnet['beat_off'] = ['0', '0.724', '0', '0.929', '#interest', '#admiration', 'positive', '0.827', 'beat', 'defeat', 'punch', 'fight', 'masturbation']
senticnet['beat_opponent'] = ['0.816', '0.756', '0', '0.747', '#joy', '#interest', 'positive', '0.773', 'teamwork', 'fight', 'win_hand', 'defeat', 'winner']
senticnet['beat_out'] = ['0.595', '-0.5', '0', '0.712', '#joy', '#admiration', 'positive', '0.602', 'master', 'walk_over', 'checkmate', 'outfight', 'screw']
senticnet['beat_person'] = ['-0.91', '0', '0.513', '-0.93', '#sadness', '#disgust', 'negative', '-0.78', 'fight', 'cause_bleeding', 'cut_finger', 'kill_person', 'cause_strong_pain']
senticnet['beat_to_bleeding'] = ['-0.97', '0', '0.676', '-0.96', '#sadness', '#disgust', 'negative', '-0.87', 'commit_crime', 'criminal', 'kill_person', 'deceive', 'torture']
senticnet['beat_up'] = ['-0.94', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.97', 'lose_battle', 'confrontation', 'fight', 'victory_defeat', 'military_action']
senticnet['beat_violently'] = ['-0.97', '0', '0.676', '-0.96', '#sadness', '#disgust', 'negative', '-0.87', 'violent', 'beat', 'thrash', 'merciless', 'thief']
senticnet['beatable'] = ['0.702', '-0.94', '0', '0.582', '#joy', '#surprise', 'positive', '0.743', 'conquerable', 'vanquishable', 'vincible', 'subduable', 'subjugable']
senticnet['beaten'] = ['0', '0', '-0.75', '0', '#fear', '#fear', 'negative', '-0.75', 'lose_war', 'loss', 'lose_battle', 'military_action', 'defeat']
senticnet['beaten-down'] = ['-0.94', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.89', 'beat-up', 'beaten-up', 'weather-beaten', 'broken-down', 'rusted-out']
senticnet['beaten-up'] = ['-0.94', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.89', 'beat-up', 'beaten-down', 'weather-beaten', 'broken-down', 'rusted-out']
senticnet['beaten_egg'] = ['0.03', '-0.05', '0', '0.059', '#surprise', '#admiration', 'positive', '0.046', 'platter', 'fork', 'spoon', 'tablespoon', 'bowl']
senticnet['beaten_path'] = ['0.626', '-0.78', '0', '0.611', '#joy', '#surprise', 'positive', '0.675', 'audacious', 'swaggering', 'daring', 'venturesome', 'venturous']
senticnet['beaten_track'] = ['0.626', '-0.78', '0', '0.611', '#joy', '#surprise', 'positive', '0.675', 'audacious', 'swaggering', 'daring', 'venturesome', 'venturous']
senticnet['beater_bar'] = ['-0.04', '-0.02', '0.089', '-0.14', '#anger', '#disgust', 'negative', '-0.06', 'steam_locomotive', 'loudspeaker', 'collector', 'siren', 'activist']
senticnet['beatific'] = ['0.504', '0.156', '-0.17', '0.193', '#joy', '#admiration', 'positive', '0.169', 'good_emotion', 'happy', 'happy_emotion', 'happy_feeling', 'pleasant_emotion']
senticnet['beatific_vision'] = ['0.732', '0', '0', '0.706', '#joy', '#admiration', 'positive', '0.719', 'pleasant_emotion', 'happy_emotion', 'happy_feeling', 'show_affection', 'good_emotion']
senticnet['beatify'] = ['0.645', '0.637', '-0.23', '0.299', '#joy', '#interest', 'positive', '0.336', 'gladdened', 'stimulating', 'tickle_pink', 'inebriate', 'exalt']
senticnet['beatitude'] = ['0.732', '0', '0', '0.706', '#joy', '#admiration', 'positive', '0.719', 'pleasant_emotion', 'happy_emotion', 'happy_feeling', 'show_affection', 'good_emotion']
senticnet['beatle_esque'] = ['-0.89', '0', '0.722', '-0.94', '#sadness', '#disgust', 'negative', '-0.85', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['beatles_song'] = ['0.029', '0.189', '-0.09', '0.306', '#interest', '#admiration', 'positive', '0.106', 'woolly_animal', 'goofy', 'big_person', 'melodious', 'song']
senticnet['beatmaker'] = ['0', '0.712', '0', '0.844', '#interest', '#admiration', 'positive', '0.778', 'dollar_change', 'small_currency', 'currency_unit', 'metal_money', 'american_money']
senticnet['beau'] = ['0.818', '0', '0', '0.763', '#joy', '#admiration', 'positive', '0.79', 'dandy', 'male_admirer', 'boyfriend', 'rich', 'young_man']
senticnet['beau_art_architecture'] = ['0.578', '-0.95', '0', '0.979', '#surprise', '#admiration', 'positive', '0.838', 'perpendicular', 'perpendicular_style', 'gothic_architecture', 'style_of_architecture', 'type_of_architecture']
senticnet['beau_nash'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
senticnet['beaucaire'] = ['0.754', '-0.82', '0', '0.947', '#surprise', '#admiration', 'positive', '0.841', 'whorehouse', 'brothel', 'bordello', 'massage_parlor', 'house_of_prostitution']
senticnet['beauce'] = ['-0.71', '0', '0.917', '-0.51', '#sadness', '#anger', 'negative', '-0.71', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['beaucoup_ont'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'indweller', 'invillager', 'villager', 'dweller', 'denizen']
senticnet['beauport'] = ['-0.71', '0', '0.917', '-0.51', '#sadness', '#anger', 'negative', '-0.71', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['beaupr'] = ['-0.71', '0', '0.917', '-0.51', '#sadness', '#anger', 'negative', '-0.71', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['beaut'] = ['0.986', '-0.60', '0', '0.568', '#joy', '#surprise', 'positive', '0.721', 'exemplar', 'ideal', 'exemplary', 'pattern', 'pacesetter']
senticnet['beautician'] = ['0.633', '-0.50', '0', '0.59', '#joy', '#admiration', 'positive', '0.575', 'craftsman', 'manicurist', 'artisan', 'journeyman', 'artificer']
senticnet['beautification'] = ['0.944', '-0.60', '0', '0.756', '#joy', '#admiration', 'positive', '0.768', 'makeover', 'beauty_treatment', 'manicure', 'pedicure', 'facial']
senticnet['beautiful'] = ['0.091', '0.121', '0', '0.229', '#interest', '#admiration', 'positive', '0.147', 'pretty', 'delicate', 'elegant', 'big', 'fair']
senticnet['beautiful_blonde'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'hair_color', 'purple', 'violet', 'dark_light', 'hue']
senticnet['beautiful_car'] = ['0.992', '0.728', '0', '0.834', '#joy', '#admiration', 'positive', '0.851', 'famous_brand', 'car', 'high_class', 'expensive_car', 'limousine']
senticnet['beautiful_creature'] = ['0.816', '0.756', '0', '0.747', '#joy', '#interest', 'positive', '0.773', 'attractive', 'become_extinct', 'exotic', 'nocturnal', 'unique_animal']
senticnet['beautiful_dangerous'] = ['-0.09', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.11', 'destructive', 'violent', 'dangerous_health', 'illegal', 'dangerous']
senticnet['beautiful_day'] = ['0.984', '0.789', '0', '0', '#joy', '#interest', 'positive', '0.887', 'attract_opposite_sex', 'have_fun', 'popularity', 'feel_pleasure', 'humor']
senticnet['beautiful_girl'] = ['0.902', '0.995', '0', '0', '#joy', '#interest', 'positive', '0.949', 'hot_babe', 'hen_baby', 'pretty_girl', 'hen_child', 'bird']
senticnet['beautiful_handwriting'] = ['0.839', '0.993', '0', '0', '#joy', '#interest', 'positive', '0.916', 'sharpen_pencil', 'knowledge_subject', 'research_topic', 'study_for_exam', 'add_number_together']
senticnet['beautiful_look'] = ['0.062', '0.076', '0.054', '0.171', '#interest', '#admiration', 'positive', '0.064', 'pretty', 'tall', 'large', 'really_big', 'high_land']
senticnet['beautiful_maiden'] = ['0.756', '0', '0', '0', '#joy', '#joy', 'positive', '0.756', 'companionship', 'cherish', 'lover', 'bond', 'buddy']
senticnet['beautiful_music'] = ['0.994', '0.924', '0', '0.926', '#joy', '#admiration', 'positive', '0.948', 'stressful_situation', 'chamber_music', 'store_photo', 'music', 'part_of_band']
senticnet['beautiful_place'] = ['0.089', '0', '0', '0.13', '#joy', '#admiration', 'positive', '0.11', 'metropolis', 'county', 'good_view', 'complex_structure', 'populate_area']
senticnet['beautiful_scenery'] = ['0.877', '0.832', '0', '0.791', '#joy', '#interest', 'positive', '0.833', 'enjoy_scenery', 'beautiful_view', 'enjoy_nature', 'relax', 'tourist_attraction']
senticnet['beautiful_smile'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'teeth_whitening', 'smile', 'clean_teeth', 'white_tooth', 'beautiful']
senticnet['beautiful_sunset'] = ['0.865', '0', '0', '0.984', '#joy', '#admiration', 'positive', '0.924', 'adorable', 'gas_stove', 'attain_goal', 'sate', 'taken_seriously']
senticnet['beautiful_view'] = ['0.877', '0.832', '0', '0.791', '#joy', '#interest', 'positive', '0.833', 'enjoy_scenery', 'beautiful_scenery', 'enjoy_nature', 'relax', 'tourist_attraction']
senticnet['beautiful_well_useful'] = ['-0.04', '0.12', '-0.07', '0.199', '#interest', '#admiration', 'positive', '0.049', 'brown', 'attractive', 'graceful', 'majestic', 'opaque']
senticnet['beautiful_woman'] = ['0', '0.098', '0', '0.107', '#interest', '#admiration', 'positive', '0.103', 'interjection', 'pretty_girl', 'policy', 'traffic_cop', 'adjust_seat']
senticnet['beautifully'] = ['0.512', '0.658', '-0.25', '0.256', '#joy', '#interest', 'positive', '0.293', 'riotously', 'expansively', 'ebulliently', 'zestfulness', 'gusto']
senticnet['beautifully_shot'] = ['0.881', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.937', 'granular', 'gamy', 'spirited', 'farinaceous', 'coarse-grained']
senticnet['beautify'] = ['0.903', '-0.61', '0', '0.736', '#joy', '#admiration', 'positive', '0.751', 'suit', 'better', 'improve', 'make_look_better', 'meliorate']
senticnet['beauty'] = ['0.868', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.929', 'pretty', 'nice_look', 'gorgeous', 'please_eye', 'good_look']
senticnet['beauty_parlor'] = ['0.816', '0.756', '0', '0.747', '#joy', '#interest', 'positive', '0.773', 'beauty_shop', 'lady_hair', 'barber_shop', 'hair_salon', 'beauty_salon']
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senticnet['become_sick'] = ['0', '-0.73', '-0.77', '-0.80', '#fear', '#disgust', 'negative', '-0.28', 'suffer_fool', 'punch_face', 'std', 'toothache', 'go_insane']
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senticnet['become_smart'] = ['0', '0', '0', '0.879', '#admiration', '#admiration', 'positive', '0.879', 'gain_knowledge', 'increase_knowledge', 'add_knowledge', 'learn_information', 'learn']
senticnet['become_stressed'] = ['0', '0', '0', '-0.78', '#disgust', '#disgust', 'negative', '-0.78', 'stress', 'tension', 'fail', 'complete_course', 'lose_qualification']
senticnet['become_strong'] = ['0.879', '0.884', '0', '0.979', '#interest', '#admiration', 'positive', '0.914', 'live_healthy_lifestyle', 'long_life', 'exercise', 'feel_fit', 'stay_shape']
senticnet['become_tired'] = ['0', '0', '0', '-0.83', '#disgust', '#disgust', 'negative', '-0.83', 'leg_cramp', 'stare', 'fatigue', 'sore_foot', 'tiredness']
senticnet['become_unbearable'] = ['-0.17', '0.079', '-0.18', '0', '#sadness', '#fear', 'negative', '-0.09', 'terminate', 'bloody', 'maim', 'hurtful', 'severe']
senticnet['become_unconscious'] = ['-0.81', '0', '0', '-0.96', '#sadness', '#disgust', 'negative', '-0.88', 'stop_move', 'senseless', 'twitch', 'loss_of_consciousness', 'lose_consciousness']
senticnet['become_unusable'] = ['-0.77', '0', '0.644', '-0.68', '#sadness', '#disgust', 'negative', '-0.70', 'unobtainable', 'unprocurable', 'out_of_stock', 'bunk', 'untouchable']
senticnet['become_unwieldy'] = ['0', '0', '0.99', '-0.72', '#anger', '#disgust', 'negative', '-0.85', 'cumbrous', 'ungainly', 'clumsy', 'cumbersome', 'awkward']
senticnet['become_wage-slave'] = ['-0.73', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'employment', 'workaholic', 'arduous', 'work', 'salary']
senticnet['become_wealthy'] = ['0.986', '0', '0', '0', '#joy', '#joy', 'positive', '0.986', 'become_rich', 'gain_wealth', 'buy_land', 'need_job', 'need_make_money']
senticnet['become_wet'] = ['0', '0', '0.883', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'shower', 'rain', 'wash_away_dirt', 'wet_clothes', 'run_out_soap']
senticnet['become_wind'] = ['0', '0', '-0.74', '-0.80', '#fear', '#disgust', 'negative', '-0.77', 'get_out_breath', 'run_out_breath', 'breathlessness', 'breaheavily', 'shin_splint']
senticnet['become_wise'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'enlightenment', 'smartness', 'curious', 'intelligent', 'comprehend']
senticnet['become_younger'] = ['0.855', '0.741', '0', '0.862', '#joy', '#admiration', 'positive', '0.819', 'feel_rejuvenated', 'feel_fresher', 'youth', 'recover', 'feel_regenerate']
senticnet['becomes_brittle'] = ['-0.11', '0.095', '-0.11', '0.071', '#sadness', '#fear', 'negative', '-0.01', 'violent_action', 'pulverize', 'break_glass', 'tear_apart', 'hit_hard']
senticnet['becomes_concurrent'] = ['0.032', '0.116', '0.016', '0', '#joy', '#interest', 'positive', '0.044', 'curb', 'crossroad', 'ring_road', 'lane', 'interstate']
senticnet['becomes_dormant'] = ['-0.75', '0', '0.512', '-0.75', '#sadness', '#disgust', 'negative', '-0.67', 'inactivity', 'inactiveness', 'slumber', 'inaction', 'hibernation']
senticnet['becomes_increasingly_difficult'] = ['0', '0', '-0.80', '-0.94', '#fear', '#disgust', 'negative', '-0.87', 'tough', 'difficulty', 'impossible', 'tedious', 'scientific_method']
senticnet['becomes_increasingly_paranoid'] = ['0.72', '-0.72', '0', '0.506', '#joy', '#surprise', 'positive', '0.651', 'standoffishness', 'withdrawnness', 'closeness', 'secretiveness', 'remoteness']
senticnet['becomes_ineligible'] = ['-0.60', '0', '0.832', '-0.53', '#sadness', '#anger', 'negative', '-0.65', 'unsuitable', 'undesirable', 'unqualified', 'disqualified', 'unentitled']
senticnet['becomes_infatuated_with'] = ['0.023', '0.085', '0', '0', '#joy', '#interest', 'positive', '0.054', 'public_place', 'front_build', 'rest_area', 'memorial', 'nuclear_power_plant']
senticnet['becomes_irritable'] = ['-0.71', '0', '0.972', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'scold', 'nag', 'scolder', 'harridan', 'common_scold']
senticnet['becomes_unavailable'] = ['-0.77', '0', '0.644', '-0.68', '#sadness', '#disgust', 'negative', '-0.70', 'unobtainable', 'unprocurable', 'out_of_stock', 'bunk', 'untouchable']
senticnet['becomes_unstable'] = ['-0.11', '0.095', '-0.11', '0.071', '#sadness', '#fear', 'negative', '-0.01', 'violent_action', 'pulverize', 'break_glass', 'tear_apart', 'hit_hard']
senticnet['becometh'] = ['0.887', '-0.84', '0', '0.819', '#joy', '#surprise', 'positive', '0.851', 'arouse', 'elicit', 'enkindle', 'kindle', 'evoke']
senticnet['becoming'] = ['0.784', '0.876', '0', '0', '#joy', '#interest', 'positive', '0.83', 'comme_il_faut', 'comely', 'decorous', 'seemly', 'decent']
senticnet['becoming_commonplace'] = ['-0.95', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.96', 'cliche', 'banality', 'prosaic', 'unexciting', 'humdrum']
senticnet['becoming_complacent'] = ['-0.99', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.93', 'complacency', 'self-complacency', 'complacence', 'self_satisfaction', 'self_complacent']
senticnet['becoming_obese'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'fatty', 'worse', 'health_issue', 'clinical_problem']
senticnet['becoming_obsolete'] = ['-0.90', '0', '0.517', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'outdated', 'noncurrent', 'disused', 'obsolete', 'superannuated']
senticnet['becoming_overweight'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'fatty', 'worse', 'health_issue', 'clinical_problem']
senticnet['becoming_pregnant'] = ['0.97', '0.729', '0', '0.946', '#joy', '#admiration', 'positive', '0.882', 'make_baby', 'give_birth_child', 'sexual_intercourse', 'find_partner', 'make_love']
senticnet['becoming_unwell'] = ['-0.71', '0', '-0.96', '-0.94', '#fear', '#disgust', 'negative', '-0.87', 'ill', 'unconscious', 'sick', 'feel_sick', 'sleep_apnea']
senticnet['bed'] = ['0', '0.171', '-0.05', '0.102', '#interest', '#admiration', 'positive', '0.072', 'bead', 'mattress', 'linen_cupboard', 'bead_cover', 'seduce']
senticnet['bed-fellows'] = ['0', '-0.84', '0', '0.86', '#surprise', '#admiration', 'positive', '0.855', 'bedfellow', 'bedfellow', 'counter-part', 'alien-like', 'uncongenial']
senticnet['bed-ridden'] = ['-0.71', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.84', 'catatonic', 'bedridden', 'paralyzed', 'comatose', 'coma']
senticnet['bed_bath_beyond'] = ['0.912', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.917', 'carpet_store', 'lumberyard', 'furniture_store', 'front_porch', 'shoe_shop']
senticnet['bed_bug'] = ['0', '-0.07', '0.137', '-0.12', '#anger', '#disgust', 'negative', '-0.06', 'gnat', 'mite', 'small_fly', 'flea', 'fly_away']
senticnet['bed_check'] = ['0.84', '-0.97', '0', '0.713', '#joy', '#surprise', 'positive', '0.844', 'proof', 'substantiation', 'crosscheck', 'parity_check', 'redundancy_check']
senticnet['bed_cover'] = ['0.049', '0', '0', '0.052', '#joy', '#admiration', 'positive', '0.051', 'sleep_under', 'bed', 'berth', 'cover_furniture', 'mattress']
senticnet['bed_covering'] = ['0.883', '-0.91', '0', '0.991', '#surprise', '#admiration', 'positive', '0.929', 'spread', 'bedspread', 'bedcover', 'bed_cover', 'quilted_bedspread']
senticnet['bed_linen'] = ['0', '0', '0', '0.068', '#admiration', '#admiration', 'positive', '0.068', 'become_clean', 'wash_clothes', 'take_shower', 'get_clean', 'clean_body']
senticnet['bed_pisser'] = ['-0.53', '0', '0.506', '-0.74', '#sadness', '#disgust', 'negative', '-0.59', 'bedpisser', 'wetter', 'mortal', 'pisser', 'person']
senticnet['bed_sheet'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'duvet_cover', 'pillow', 'comforter', 'pillowcase', 'blanket']
senticnet['bed_sleep'] = ['0.299', '0.126', '0', '0.207', '#joy', '#admiration', 'positive', '0.211', 'inactivity', 'count_sheep', 'shut_eye', 'rest_night', 'lay_down']
senticnet['bed_time'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'narrative', 'bedtime', 'fable', 'long_story', 'saga']
senticnet['bed_time_story'] = ['0.701', '0.939', '0', '0.718', '#interest', '#admiration', 'positive', '0.786', 'literature', 'book_story', 'fairy_tale', 'fiction', 'childhood']
senticnet['bed_wetter'] = ['-0.53', '0', '0.508', '-0.74', '#sadness', '#disgust', 'negative', '-0.59', 'bedwetter', 'pisser', 'mortal', 'wetter', 'person']
senticnet['bed_wetting'] = ['-0.52', '0', '0.839', '-0.86', '#anger', '#disgust', 'negative', '-0.74', 'folie', 'upset', 'psychosomatic_disorder', 'conversion_disorder', 'conversion_reaction']
senticnet['bedamn'] = ['-0.71', '0', '0.792', '-0.95', '#anger', '#disgust', 'negative', '-0.82', 'call_down', 'stir', 'call_forth', 'conjure', 'bring_up']
senticnet['bedarra'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'drifter', 'bum', 'sundowner']
senticnet['bedaze'] = ['0', '0', '0.731', '-0.91', '#anger', '#disgust', 'negative', '-0.82', 'confuse', 'fulgurant', 'eye-popping', 'fulgurous', 'dazzling']
senticnet['bedazzle'] = ['0.891', '0.875', '0', '0', '#joy', '#interest', 'positive', '0.883', 'blender', 'bright', 'sight', 'dazzle', 'dazzling']
senticnet['bedbug'] = ['0', '0', '-0.94', '-0.90', '#fear', '#disgust', 'negative', '-0.92', 'eight_legs', 'dog_hair', 'parasite', 'dog_fur', 'bite_dog']
senticnet['bedcover'] = ['0.883', '-0.91', '0', '0.991', '#surprise', '#admiration', 'positive', '0.929', 'spread', 'bedspread', 'quilted_bedspread', 'bed_cover', 'bed_covering']
senticnet['bedding'] = ['0.694', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.773', 'crib', 'bassinet', 'toddler', 'cot', 'bunk']
senticnet['bedding_plane'] = ['-0.04', '-0.01', '0.026', '-0.12', '#sadness', '#disgust', 'negative', '-0.04', 'embroidery', 'cover_wind', 'use_needle', 'bandage', 'joint']
senticnet['bedding_plant'] = ['-0.12', '0.066', '-0.08', '-0.06', '#sadness', '#fear', 'negative', '-0.05', 'garden_yard', 'see_sky', 'plant_tree', 'hair', 'seed_lawn']
senticnet['bedeutung'] = ['0.633', '-0.67', '0', '0.776', '#surprise', '#admiration', 'positive', '0.693', 'double_entendre', 'loophole', 'ambiguity', 'amphiboly', 'parisology']
senticnet['bedevil'] = ['-0.13', '0.071', '0.694', '-0.67', '#anger', '#disgust', 'negative', '-0.35', 'crucify', 'dun', 'frustrating', 'aggravate', 'exasperation']
senticnet['bedfellow'] = ['0', '-0.84', '0', '0.863', '#surprise', '#admiration', 'positive', '0.853', 'bedfellow', 'bed-fellows', 'counter-part', 'alien-like', 'uncongenial']
senticnet['bedizen'] = ['-0.99', '0', '0.687', '-0.86', '#sadness', '#disgust', 'negative', '-0.84', 'ornament', 'grace', 'decorate', 'adorn', 'embellish']
senticnet['bedlam'] = ['-0.99', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.90', 'chaos', 'madhouse', 'confusion', 'chaotic', 'prevail']
senticnet['bedpisser'] = ['-0.53', '0', '0.501', '-0.75', '#sadness', '#disgust', 'negative', '-0.59', 'wetter', 'bed_pisser', 'mortal', 'pisser', 'person']
senticnet['bedraggle'] = ['-0.80', '0', '0.962', '-0.55', '#sadness', '#anger', 'negative', '-0.77', 'sop', 'douse', 'dowse', 'drench', 'soak']
senticnet['bedraggled'] = ['-0.75', '0', '0.671', '-0.60', '#sadness', '#anger', 'negative', '-0.67', 'battered', 'derelict', 'hurt', 'damaged', 'dilapidated']
senticnet['bedrest'] = ['0.84', '0.76', '0', '0.894', '#joy', '#admiration', 'positive', '0.831', 'keep_skin_healthy', 'stay_healthy', 'stay_fit', 'maintain_good_health', 'eat_healthily']
senticnet['bedri'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'white_animal', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
senticnet['bedridden'] = ['-0.71', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.85', 'catatonic', 'comatose', 'paralyzed', 'bed-ridden', 'coma']
senticnet['bedrock'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'fossil', 'oil', 'dark_liquid', 'smithsonian_institution', 'inert_gas']
senticnet['bedroom'] = ['0', '0.171', '-0.05', '0.102', '#interest', '#admiration', 'positive', '0.072', 'bed', 'mattress', 'bead', 'bed_cover', 'seduce']
senticnet['bedroom_apartment'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'home', 'furniture', 'kitchen', 'flat']
senticnet['bedroom_closet'] = ['0', '0.072', '0', '0', '#interest', '#interest', 'positive', '0.072', 'metal_box', 'cash_drawer', 'keyhole', 'clutch_bag', 'safe']
senticnet['bedroom_door'] = ['0', '0', '0', '0.077', '#admiration', '#admiration', 'positive', '0.077', 'glass_front', 'gate', 'doorway', 'use_farm', 'entranceway']
senticnet['bedroom_house'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'apartment', 'furniture', 'kitchen', 'flat']
senticnet['bedroom_ranch'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'apartment', 'furniture', 'kitchen', 'flat']
senticnet['bedroom_rental'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'apartment', 'furniture', 'kitchen', 'flat']
senticnet['bedroom_suite'] = ['0', '-0.02', '-0.08', '-0.05', '#fear', '#disgust', 'negative', '-0.03', 'mattress', 'sleep_furniture', 'cradle', 'small_bed', 'pillow']
senticnet['bedroom_wall'] = ['0.024', '0', '0', '0.074', '#joy', '#admiration', 'positive', '0.049', 'sheet', 'cover', 'quilt', 'comforter', 'modesty']
senticnet['bedroom_window'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'stage_border', 'row_seat', 'stage_entrance', 'theater_seat', 'theater_box']
senticnet['bedsheet'] = ['0.024', '0', '0', '0.074', '#joy', '#admiration', 'positive', '0.049', 'sheet', 'cover', 'quilt', 'comforter', 'modesty']
senticnet['bedside_lamp'] = ['0', '-0.03', '0.043', '-0.11', '#anger', '#disgust', 'negative', '-0.03', 'torch', 'safety_pin', 'laser', 'flare', 'firebomb']
senticnet['bedside_table'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'lavatory', 'commode', 'potty_chair']
senticnet['bedspread'] = ['0', '0.141', '-0.06', '0.215', '#interest', '#admiration', 'positive', '0.097', 'concealment', 'front_page', 'conceal', 'lid', 'surround']
senticnet['bedstead'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'wallet', 'billfold', 'bank', 'piggy_bank', 'bank_vault']
senticnet['bedtime'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'narrative', 'bed_time', 'fable', 'long_story', 'saga']
senticnet['bedwetter'] = ['-0.53', '0', '0.508', '-0.74', '#sadness', '#disgust', 'negative', '-0.59', 'pisser', 'bed_wetter', 'mortal', 'wetter', 'person']
senticnet['bee'] = ['0', '0', '0', '0.869', '#admiration', '#admiration', 'positive', '0.869', 'fly_animal', 'insect', 'mosquito', 'bug', 'fly_insect']
senticnet['bee_colony'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'lair', 'habitat', 'animal_home']
senticnet['bee_hive'] = ['0', '0', '0', '0.087', '#admiration', '#admiration', 'positive', '0.087', 'windsor', 'lair', 'habitat', 'honey', 'pet_store']
senticnet['bee_home'] = ['0.028', '0', '0.027', '0.101', '#joy', '#admiration', 'positive', '0.034', 'animal_dwell', 'mew', 'live_place', 'dwell_place', 'place_to_live']
senticnet['bee_house'] = ['-0.08', '0.232', '0', '0.099', '#interest', '#admiration', 'positive', '0.084', 'bee_product', 'gooey', 'red_skin', 'really_hot', 'crispy']
senticnet['bee_keeper'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'lair', 'habitat', 'animal_home']
senticnet['bee_larva'] = ['0.866', '-0.91', '0', '0.893', '#surprise', '#admiration', 'positive', '0.892', 'mooch', 'cadge', 'freeload', 'sponge', 'bum']
senticnet['bee_nest'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'lair', 'habitat', 'animal_home']
senticnet['bee_orchid'] = ['-0.79', '0', '0.837', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'sunray', 'wilding', 'sagebrush_buttercup', 'pasqueflower', 'pasque_flower']
senticnet['bee_product'] = ['0.072', '0.065', '-0.06', '0.144', '#joy', '#admiration', 'positive', '0.053', 'gooey', 'paraffin', 'slick', 'goo', 'fuel_source']
senticnet['bee_sting'] = ['0', '0', '-0.73', '-0.81', '#fear', '#disgust', 'negative', '-0.77', 'flab', 'bad_hair_day', 'lose_friend', 'alcoholism', 'brain_tumor']
senticnet['bee_swarm'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'lair', 'habitat', 'animal_home']
senticnet['bee_venom'] = ['0', '0', '-0.73', '-0.81', '#fear', '#disgust', 'negative', '-0.77', 'flab', 'bad_hair_day', 'lose_friend', 'alcoholism', 'brain_tumor']
senticnet['beech_bark_disease'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['beech_marten'] = ['0', '0', '-0.95', '-0.81', '#fear', '#disgust', 'negative', '-0.88', 'herb', 'plant', 'poisonous_plant', 'yellow_flower', 'lichen']
senticnet['beech_tree'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'black_locust']
senticnet['beech_wood'] = ['-0.69', '0', '0.725', '-0.89', '#anger', '#disgust', 'negative', '-0.77', 'ponderosa', 'ponderosa_pine', 'western_yellow_pine', 'bull_pine', 'longleaf_pine']
senticnet['beech_woodland'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'biome', 'timberland']
senticnet['beechcraft_king_air'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'atmosphere', 'wing', 'nest', 'feather', 'bird_fly']
senticnet['beechen_cliff_school'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
senticnet['beef'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'veal', 'animal_flesh', 'food_group', 'cereal', 'curry']
senticnet['beef_bourguignon'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'blue_ribbon', 'badge', 'allegory', 'stripe', 'merit_badge']
senticnet['beef_broth'] = ['0.07', '0.084', '0.023', '0', '#joy', '#interest', 'positive', '0.044', 'hearty', 'porridge', 'hot_liquid', 'liquid_food', 'meat_vegetable']
senticnet['beef_carpaccio'] = ['0.783', '-0.63', '0', '0.829', '#joy', '#admiration', 'positive', '0.749', 'leg_of_lamb', 'gigot', 'joint', 'roast', 'rack_of_lamb']
senticnet['beef_cattle'] = ['0.037', '0', '0', '0.067', '#joy', '#admiration', 'positive', '0.052', 'veal', 'milk_product', 'salad_dress', 'ranch', 'food_group']
senticnet['beef_cattle_production'] = ['0.037', '0', '0', '0.067', '#joy', '#admiration', 'positive', '0.052', 'veal', 'milk_product', 'salad_dress', 'ranch', 'food_group']
senticnet['beef_cheek'] = ['0.783', '-0.63', '0', '0.829', '#joy', '#admiration', 'positive', '0.749', 'leg_of_lamb', 'gigot', 'joint', 'roast', 'rack_of_lamb']
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senticnet['beef_filet'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'saltine', 'soda_cracker', 'cracker', 'water_biscuit', 'graham_cracker']
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senticnet['beef_sausage'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'roll', 'custard', 'bread', 'biscuit']
senticnet['beef_stew'] = ['0.028', '-0.07', '0', '0', '#joy', '#surprise', 'positive', '0.05', 'casserole', 'delicious_meal', 'hot_dog_bun', 'hamburger', 'hot_dog']
senticnet['beef_stroganoff'] = ['0.028', '-0.07', '0', '0', '#joy', '#surprise', 'positive', '0.05', 'casserole', 'delicious_meal', 'hot_dog_bun', 'hamburger', 'hot_dog']
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senticnet['beef_tapeworm'] = ['0.509', '-0.9', '0', '0.507', '#joy', '#surprise', 'positive', '0.639', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['beef_tea'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'green_bread', 'rotten', 'disgusting', 'taste_awful', 'mouldy']
senticnet['beef_tenderloin'] = ['0.062', '0', '0.027', '0.055', '#joy', '#admiration', 'positive', '0.03', 'porridge', 'meal', 'broth', 'hamburger', 'hearty']
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senticnet['beefed-up'] = ['0.935', '-0.89', '0', '0', '#joy', '#surprise', 'positive', '0.915', 'beefed', 'beefier', 'stepped-up', 'beefing', 'much-improved']
senticnet['beefier'] = ['0.925', '-0.9', '0', '0', '#joy', '#surprise', 'positive', '0.913', 'beefed', 'beefed-up', 'stepped-up', 'beefing', 'much-improved']
senticnet['beefing'] = ['0.928', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.915', 'beefed', 'beefier', 'stepped-up', 'beefed-up', 'much-improved']
senticnet['beehive'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'lair', 'habitat', 'animal_home']
senticnet['beekeeper'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'lair', 'habitat', 'animal_home']
senticnet['beekman_place'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'condominium', 'rent_apartment', 'high_rise', 'condo', 'row_house']
senticnet['beelzebub'] = ['-0.52', '0', '0.701', '-0.50', '#sadness', '#anger', 'negative', '-0.57', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
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senticnet['beeping_sound'] = ['0.067', '-0.2', '0.064', '0', '#joy', '#surprise', 'positive', '0.068', 'wand', 'long_stick', 'branch', 'twig', 'stick']
senticnet['beer'] = ['0.942', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.968', 'whisky', 'drink', 'alcohol', 'vodka', 'wine']
senticnet['beer_barrel'] = ['0.161', '-0.14', '0.031', '-0.10', '#joy', '#surprise', 'positive', '0.043', 'barrel', 'reception', 'champagne', 'beer', 'wine']
senticnet['beer_battered_onion'] = ['0.052', '0', '0', '0.118', '#joy', '#admiration', 'positive', '0.085', 'diamond_ring', 'piece_of_jewelry', 'bell_sound', 'wedding_band', 'engagement']
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senticnet['beer_container'] = ['0.03', '0', '0.022', '0.062', '#joy', '#admiration', 'positive', '0.023', 'large_cup', 'hold_drink', 'cool_drink', 'store_ice_cream', 'drink_holder']
senticnet['beer_garden'] = ['0.853', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.916', 'tavern', 'cafe', 'nightclub', 'bar', 'liquor_store']
senticnet['beer_keg'] = ['0.161', '-0.14', '0.031', '-0.10', '#joy', '#surprise', 'positive', '0.043', 'barrel', 'reception', 'champagne', 'beer', 'wine']
senticnet['beer_mug'] = ['0.986', '0.832', '0', '0', '#joy', '#interest', 'positive', '0.909', 'liquor_glass', 'flask', 'beer', 'refill', 'bartender']
senticnet['beer_stein'] = ['0.214', '0.258', '-0.09', '0.2', '#joy', '#interest', 'positive', '0.146', 'cup', 'carafe', 'pitcher', 'container_cup', 'glasses']
senticnet['beer_tent'] = ['-0.72', '0', '0.978', '-0.86', '#anger', '#disgust', 'negative', '-0.85', 'amusement_park', 'common', 'pleasure_ground', 'village_green', 'tract']
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senticnet['beet_armyworm'] = ['0', '-0.09', '0.123', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'gypsy_moth', 'japanese_beetle', 'cucumber_beetle', 'mosquito', 'earwig']
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senticnet['beet_sugar'] = ['-0.08', '0.106', '0', '0.125', '#interest', '#admiration', 'positive', '0.047', 'sugar', 'treacle', 'black_treacle', 'add_sugar', 'sugar_substitute']
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senticnet['beetle_infestation'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['beetle_larva'] = ['0.866', '-0.91', '0', '0.893', '#surprise', '#admiration', 'positive', '0.892', 'mooch', 'cadge', 'freeload', 'sponge', 'bum']
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senticnet['befriend'] = ['0.774', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.885', 'ski_bind', 'bind', 'three-quarter_bind', 'keep_paper_together', 'fasten']
senticnet['befuddle'] = ['-0.87', '0', '0.787', '-0.71', '#sadness', '#anger', 'negative', '-0.79', 'demoralize', 'pose', 'disorient', 'disorientate', 'amaze']
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senticnet['beggar_banquet'] = ['0', '0.115', '0.036', '0.207', '#interest', '#admiration', 'positive', '0.095', 'total', 'amount', 'merit', 'award', 'excellence']
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senticnet['begin_day'] = ['0.02', '0.067', '0', '0', '#joy', '#interest', 'positive', '0.044', 'sunrise', 'light_in_sky', 'dawn', 'daylight', 'sun']
senticnet['begin_end'] = ['0', '0.069', '0.009', '0.173', '#interest', '#admiration', 'positive', '0.078', 'start_end', 'omega', 'infinite', 'plot', 'always_read']
senticnet['begin_pro'] = ['0.038', '0.259', '0', '0.195', '#interest', '#admiration', 'positive', '0.164', 'money_maker', 'bank', 'savings_account', 'save', 'check_account']
senticnet['begin_shortly'] = ['0', '0.067', '0', '0.062', '#interest', '#admiration', 'positive', '0.065', 'social_skill', 'show_up', 'go_abroad', 'go_new_place', 'visit']
senticnet['begin_work'] = ['0.229', '0.127', '0', '0', '#joy', '#interest', 'positive', '0.178', 'return_work', 'start_project', 'work_box_office', 'write_program', 'go_work']
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senticnet['begotten'] = ['0.706', '0', '0', '0', '#joy', '#joy', 'positive', '0.706', 'birth', 'new_baby', 'born', 'birthday', 'thanksgiving']
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senticnet['beguiling'] = ['0.969', '-0.84', '0', '0.837', '#joy', '#surprise', 'positive', '0.885', 'alluring', 'enticing', 'tempting', 'seductive', 'corrupting']
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senticnet['behave'] = ['0', '0.801', '0', '0.767', '#interest', '#admiration', 'positive', '0.784', 'show', 'emote', 'act', 'imitate', 'stage_play']
senticnet['behave_aggressively'] = ['-0.75', '0', '0.886', '-0.72', '#sadness', '#anger', 'negative', '-0.78', 'harass', 'move', 'check', 'fork', 'pin']
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senticnet['behave_intelligently'] = ['0', '0.717', '0', '0.765', '#interest', '#admiration', 'positive', '0.741', 'helper', 'artificial_intelligence', 'goal_driven', 'proactive', 'intelligent']
senticnet['behave_properly'] = ['0', '0.801', '0', '0.767', '#interest', '#admiration', 'positive', '0.784', 'show', 'emote', 'act', 'imitate', 'stage_play']
senticnet['behave_responsibly'] = ['0.36', '0.248', '-0.08', '0.278', '#joy', '#admiration', 'positive', '0.201', 'feel_important', 'strong_relationship', 'eat_together', 'world_peace', 'express_gratitude']
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senticnet['behavior_modification'] = ['0.542', '-0.63', '0', '0.912', '#surprise', '#admiration', 'positive', '0.695', 'implosion_therapy', 'flooding', 'reciprocal_inhibition', 'aversion_therapy', 'desensitization_technique']
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senticnet['behavioral_activation'] = ['0.43', '0.657', '-0.28', '0.244', '#joy', '#interest', 'positive', '0.262', 'expectation', 'look_for', 'amour_propre', 'self-love', 'conceit']
senticnet['behavioral_disorder'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'acknowledgeable', 'specifiable', 'recognizable', 'identifiable', 'classifiable']
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senticnet['behead'] = ['0', '0', '-0.96', '-0.91', '#fear', '#disgust', 'negative', '-0.93', 'kill', 'french_revolution', 'die', 'seek_revenge', 'murder']
senticnet['beheld'] = ['-0.71', '0', '0.836', '0', '#sadness', '#anger', 'negative', '-0.77', 'awkward', 'unenviable', 'embarrassingly', 'embarrassing', 'sheepish']
senticnet['behemoth'] = ['-0.88', '0', '0.882', '-0.58', '#sadness', '#anger', 'negative', '-0.78', 'mortal', 'unusual_person', 'colossus', 'aberrant', 'giant']
senticnet['behind'] = ['-0.89', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.84', 'down', 'back', 'out', 'away', 'put']
senticnet['behind_house'] = ['0.741', '-0.78', '0', '0', '#joy', '#surprise', 'positive', '0.761', 'outside_house', 'back_yard', 'yard', 'backyard', 'back_house']
senticnet['behind_pierro'] = ['-0.79', '0', '0.992', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['behold'] = ['-0.87', '0', '0.882', '-0.56', '#sadness', '#anger', 'negative', '-0.77', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['beholden'] = ['-0.94', '0', '0.863', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'poke', 'knockout_punch', 'lick', 'punch', 'biff']
senticnet['beholder'] = ['-0.83', '0', '0.665', '-0.51', '#sadness', '#anger', 'negative', '-0.67', 'percipient', 'eavesdropper', 'perceiver', 'observer', 'auditor']
senticnet['behring_center'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'institution', 'tundra', 'child_story', 'natural_history_museum', 'cartoon']
senticnet['beige'] = ['0', '0', '0', '-0.72', '#disgust', '#disgust', 'negative', '-0.72', 'dreary', 'gloominess', 'darkness', 'gloomful', 'gloomily']
senticnet['beige_box'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'clip', 'clasp', 'glue_stick', 'clothespin', 'clamp']
senticnet['beige_brick'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'condominium', 'rent_apartment', 'high_rise', 'condo', 'row_house']
senticnet['beijing'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'newyork', 'london', 'big_city', 'rome', 'tokyo']
senticnet['beilschmiedia'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'seed_fern', 'pteridosperm']
senticnet['being_burnt_alive'] = ['-0.73', '0', '0.641', '-0.74', '#sadness', '#disgust', 'negative', '-0.70', 'treated', 'burned', 'proofed', 'doped', 'activated']
senticnet['being_morally_obligatory'] = ['-0.69', '0', '0.806', '-0.74', '#anger', '#disgust', 'negative', '-0.74', 'hypothesis', 'theory', 'base', 'possibility', 'presumption']
senticnet['being_overweight'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'fatty', 'worse', 'health_issue', 'clinical_problem']
senticnet['being_physically_active'] = ['0.84', '0.76', '0', '0.894', '#joy', '#admiration', 'positive', '0.831', 'keep_skin_healthy', 'stay_healthy', 'stay_fit', 'maintain_good_health', 'maintain_muscle_strength']
senticnet['being_unfaithful'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'liaison', 'affair', 'involvement']
senticnet['beit_hanina'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['beit_sahour'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['beit_shemesh'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['bej'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'white_animal', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
senticnet['bekenstein_hawking_entropy'] = ['-0.73', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'unwed', 'unwedded', 'divorced', 'unmarried', 'widowed']
senticnet['bela_karolyi'] = ['0.964', '0.713', '0', '0', '#joy', '#interest', 'positive', '0.839', 'martial_art', 'judo', 'jump_around', 'go_underwater', 'athletics']
senticnet['belabor'] = ['0', '0', '-0.86', '-0.99', '#fear', '#disgust', 'negative', '-0.93', 'niggle', 'quiddity', 'bicker', 'pettifog', 'cavil']
senticnet['belated_honeymoon'] = ['0.878', '0.84', '0', '0.91', '#joy', '#admiration', 'positive', '0.876', 'stumble', 'slip', 'fall', 'trip', 'tumble']
senticnet['belaying_pin'] = ['-0.71', '0', '0.932', '-0.50', '#sadness', '#anger', 'negative', '-0.71', 'refuse', 'subvert', 'decline', 'balk', 'disobey']
senticnet['belch'] = ['0', '0', '-0.86', '-0.77', '#fear', '#disgust', 'negative', '-0.81', 'acid_indigestion', 'burp', 'gurk', 'hunger_go_away', 'choke']
senticnet['beldam'] = ['0.52', '-0.77', '0', '0.921', '#surprise', '#admiration', 'positive', '0.738', 'oldster', 'old_person', 'senior_citizen', 'golden_ager', 'old_woman']
senticnet['beleaguer'] = ['-0.64', '0', '0.881', '-0.82', '#anger', '#disgust', 'negative', '-0.78', 'torment', 'bedevil', 'rag', 'dun', 'crucify']
senticnet['beleeve'] = ['-0.79', '0', '0.607', '-0.92', '#sadness', '#disgust', 'negative', '-0.77', 'stubbornness', 'obstinacy', 'obstinance', 'bullheadedness', 'pigheadedness']
senticnet['belfer'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['belgarath'] = ['0.594', '-0.54', '0', '0.518', '#joy', '#surprise', 'positive', '0.554', 'exorcist', 'enchanter', 'magus', 'sorceress', 'witch_doctor']
senticnet['belgrave_road'] = ['0.623', '-0.56', '0', '0.974', '#joy', '#admiration', 'positive', '0.721', 'alterable', 'convertible', 'translatable', 'transformable', 'transmutable']
senticnet['belie'] = ['-0.59', '0', '0.537', '-0.53', '#sadness', '#anger', 'negative', '-0.55', 'murder', 'mutilate', 'mangle', 'misrepresent', 'falsify']
senticnet['belief'] = ['0.086', '0.471', '-0.12', '0.411', '#interest', '#admiration', 'positive', '0.21', 'faith', 'dogma', 'belief_god', 'spiritual', 'religious']
senticnet['belief_god'] = ['-0.02', '0.347', '-0.12', '0.305', '#interest', '#admiration', 'positive', '0.126', 'faith', 'religious_purpose', 'spiritual', 'religious', 'belief']
senticnet['belief_system'] = ['0', '0.356', '-0.07', '0.327', '#interest', '#admiration', 'positive', '0.201', 'belief_god', 'religious_action', 'faith', 'belief', 'sect']
senticnet['believability'] = ['0.726', '-0.82', '0', '0.966', '#surprise', '#admiration', 'positive', '0.839', 'original', 'real_stuff', 'credibility', 'credibleness', 'authenticity']
senticnet['believable'] = ['0.744', '-0.76', '0', '0.669', '#joy', '#surprise', 'positive', '0.726', 'accessible', 'comprehendible', 'approachable', 'perceivable', 'intelligible']
senticnet['believably'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicado', 'unpronounceable', 'impassive', 'incommunicative', 'silent']
senticnet['believe'] = ['0.06', '0.422', '-0.17', '0.41', '#interest', '#admiration', 'positive', '0.179', 'faith', 'enter_contract', 'belief', 'belief_god', 'religious_belief']
senticnet['believe_god'] = ['-0.03', '0.284', '-0.11', '0.211', '#interest', '#admiration', 'positive', '0.087', 'plan_wedding', 'trust_god', 'kneel', 'belief_god', 'wish']
senticnet['believe_in'] = ['0.105', '-0.17', '0.057', '0.079', '#joy', '#surprise', 'positive', '0.076', 'trust', 'believe', 'pray', 'religion', 'church']
senticnet['believer'] = ['0', '0.231', '-0.06', '0.216', '#interest', '#admiration', 'positive', '0.128', 'true_believer', 'worshiper', 'cleric', 'religious_person', 'vicar']
senticnet['belike'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'wallet', 'billfold', 'bank', 'piggy_bank', 'bank_vault']
senticnet['belittle'] = ['-0.82', '0', '0.835', '-0.67', '#sadness', '#anger', 'negative', '-0.77', 'disparage', 'depreciate', 'deflate', 'puncture', 'pick_at']
senticnet['bell'] = ['0.034', '0', '0.01', '0.087', '#joy', '#admiration', 'positive', '0.037', 'chime', 'bell_noise', 'alto', 'piano', 'ring_loudly']
senticnet['bell_cat'] = ['-0.05', '0.072', '0.012', '0.066', '#interest', '#admiration', 'positive', '0.018', 'pet', 'leash', 'animal', 'dog_tag', 'fur']
senticnet['bell_foundry'] = ['0.034', '0', '0.01', '0.087', '#joy', '#admiration', 'positive', '0.037', 'chime', 'bell_noise', 'alto', 'piano', 'ring_loudly']
senticnet['bell_inn'] = ['0.853', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.916', 'tavern', 'cafe', 'nightclub', 'bar', 'liquor_store']
senticnet['bell_magpie'] = ['0.993', '0.866', '0', '0', '#joy', '#interest', 'positive', '0.929', 'bird', 'piping_crow', 'butcherbird', 'currawong', 'australian_magpie']
senticnet['bell_noise'] = ['0.02', '0', '0', '0.123', '#joy', '#admiration', 'positive', '0.071', 'bell_sound', 'clang', 'chime', 'jingle', 'piece_of_jewelry']
senticnet['bell_pepper'] = ['0.1', '0', '0', '0', '#joy', '#joy', 'positive', '0.1', 'burn_material', 'ash', 'stay_warm', 'bonfire', 'campfire']
senticnet['bell_ring'] = ['0.999', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.952', 'loud', 'remember', 'amplitude', 'step', 'move']
senticnet['bell_rope'] = ['0.979', '-0.69', '0', '0.576', '#joy', '#surprise', 'positive', '0.749', 'big_cheese', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['bell_sound'] = ['0.034', '0', '0', '0.19', '#joy', '#admiration', 'positive', '0.112', 'piece_of_jewelry', 'ring', 'wedding_band', 'engagement', 'make_gold']
senticnet['bell_tower'] = ['0', '0.199', '0.036', '0.199', '#interest', '#admiration', 'positive', '0.121', 'temple', 'catholic_church', 'church', 'cathedral', 'chapel']
senticnet['bellaert'] = ['0.036', '0.111', '-0.05', '0.054', '#interest', '#admiration', 'positive', '0.038', 'despot', 'supervisor', 'chief', 'commander', 'earl']
senticnet['bellagio_hotel'] = ['0.057', '0.13', '0', '0.119', '#interest', '#admiration', 'positive', '0.102', 'friend_wedding', 'circular_route', 'spanish_restaurant', 'altar', 'monastery']
senticnet['belle'] = ['0.858', '0', '0', '0.874', '#joy', '#admiration', 'positive', '0.866', 'pretty', 'gorgeous', 'nice_look', 'beautiful', 'sophisticated']
senticnet['belle_epoque'] = ['0.952', '0.844', '0', '0.96', '#joy', '#admiration', 'positive', '0.919', 'fashion', 'demand', 'kindness', 'better_person', 'warm_feeling']
senticnet['belle_poque'] = ['-0.96', '0', '0.615', '-0.73', '#sadness', '#disgust', 'negative', '-0.77', 'flush', 'blossom', 'prime', 'peak', 'efflorescence']
senticnet['belle_starr'] = ['0', '0', '-0.11', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'hunting_season', 'hound', 'cougar', 'hunter', 'dragon']
senticnet['belleau_wood'] = ['0.737', '-0.96', '0', '0.552', '#joy', '#surprise', 'positive', '0.75', 'prominence', 'hump', 'bump', 'swelling', 'frontal_eminence']
senticnet['bellechasse'] = ['-0.71', '0', '0.917', '-0.51', '#sadness', '#anger', 'negative', '-0.71', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['bellhop'] = ['0.023', '0.085', '0', '0', '#joy', '#interest', 'positive', '0.054', 'public_place', 'front_build', 'rest_area', 'memorial', 'nuclear_power_plant']
senticnet['bellicose'] = ['0', '0', '0.813', '-0.99', '#anger', '#disgust', 'negative', '-0.90', 'warlike', 'aggressive', 'belligerent', 'martian', 'bellicoseness']
senticnet['bellicoseness'] = ['-0.50', '0', '0.817', '-0.59', '#anger', '#disgust', 'negative', '-0.63', 'disagreeableness', 'truculence', 'truculency', 'bellicosity', 'pugnacity']
senticnet['bellicosity'] = ['-0.50', '0', '0.817', '-0.59', '#anger', '#disgust', 'negative', '-0.63', 'disagreeableness', 'truculence', 'truculency', 'pugnacity', 'bellicoseness']
senticnet['belligerence'] = ['0', '0.139', '0.736', '-0.66', '#anger', '#disgust', 'negative', '-0.42', 'belligerent', 'belligerency', 'bad_blood', 'animus', 'warpath']
senticnet['belligerency'] = ['0', '0.139', '0.736', '-0.66', '#anger', '#disgust', 'negative', '-0.42', 'belligerent', 'belligerence', 'bad_blood', 'animus', 'warpath']
senticnet['belligerent'] = ['0', '0.139', '0.736', '-0.66', '#anger', '#disgust', 'negative', '-0.42', 'belligerence', 'belligerency', 'bad_blood', 'animus', 'warpath']
senticnet['belligerently'] = ['0', '0.149', '0.719', '-0.66', '#anger', '#disgust', 'negative', '-0.41', 'hostilely', 'antagonism', 'class_feeling', 'aggressiveness', 'animus']
senticnet['bellville_sassoon'] = ['0', '-0.03', '0.022', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'shoulder_bag', 'satchel', 'water_bottle', 'tote_bag', 'carry']
senticnet['bellwort'] = ['-0.52', '0', '0.527', '-0.78', '#anger', '#disgust', 'negative', '-0.61', 'herb', 'black_snakeroot', 'heartleaf', 'asarabacca', 'herbaceous_plant']
senticnet['belly'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'digestive_system', 'nervous_system', 'human_body', 'stomach', 'tummy']
senticnet['belly-down'] = ['0', '0.948', '0', '0.833', '#interest', '#admiration', 'positive', '0.89', 'spread-eagled', 'spreadeagled', 'spread-eagle', 'half-sitting', 'cross-legged']
senticnet['belly_button'] = ['0', '0.102', '0', '0', '#interest', '#interest', 'positive', '0.102', 'two_arms', 'body_part', 'two_ears', 'five_fingers_hand', 'liver']
senticnet['belly_fat'] = ['-0.42', '-0.14', '-0.06', '-0.37', '#sadness', '#disgust', 'negative', '-0.18', 'brain_tumor', 'bad_hair_day', 'lyme_disease', 'diarrhoea', 'suffer_fool']
senticnet['belly_flop'] = ['-0.05', '0.066', '0', '0.079', '#interest', '#admiration', 'positive', '0.029', 'skip', 'jump_high', 'float_through_air', 'jump_over', 'bail_water']
senticnet['belly_laugh'] = ['0.739', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.747', 'giggle', 'make_fun', 'make_others_laugh', 'hear_joke', 'laughter']
senticnet['belly_out'] = ['-0.76', '0', '0.656', '-0.72', '#sadness', '#disgust', 'negative', '-0.71', 'tumefy', 'swell_up', 'tumesce', 'intumesce', 'swell']
senticnet['bellyache'] = ['-0.74', '0', '0.872', '-0.61', '#sadness', '#anger', 'negative', '-0.74', 'hurting', 'aching', 'pain', 'headache', 'toothache']
senticnet['bellybutton'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'naked_eye', 'eye', 'oculus_sinister']
senticnet['belmont_avenue'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'boulevard', 'lane', 'thoroughfare', 'intersection', 'curb']
senticnet['beloch'] = ['-0.87', '0', '0.599', '-0.71', '#sadness', '#disgust', 'negative', '-0.73', 'hysteria', 'fury', 'delirium', 'frenzy', 'craze']
senticnet['belong'] = ['0.064', '0.2', '-0.19', '0.292', '#interest', '#admiration', 'positive', '0.091', 'ownership', 'possessive', 'possess', 'possession', 'own']
senticnet['belong_club'] = ['0', '0.086', '-0.06', '0.12', '#interest', '#admiration', 'positive', '0.046', 'miss_bus', 'study_library', 'gain_knowledge', 'learn_new_idea', 'point_out']
senticnet['belong_person'] = ['0', '0.221', '-0.13', '0.249', '#interest', '#admiration', 'positive', '0.113', 'belong', 'possessive', 'part_of_group', 'bring_together', 'inclusion']
senticnet['belong_unto'] = ['-0.95', '0', '0.703', '-0.83', '#sadness', '#disgust', 'negative', '-0.83', 'raid', 'come_in', 'foray_into', 'enter', 'get_into']
senticnet['belonging'] = ['0.632', '0.645', '-0.24', '0.295', '#joy', '#interest', 'positive', '0.333', 'elated', 'gleeful', 'gladden', 'gladsome', 'gladness']
senticnet['belongings'] = ['0.057', '0.141', '-0.06', '0.162', '#interest', '#admiration', 'positive', '0.074', 'elated', 'singer', 'gladden', 'guarantee', 'stuff']
senticnet['belousov_zhabotinsky_reaction'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'constancy', 'monotony', 'invariance', 'changelessness', 'unchangeability']
senticnet['beloved'] = ['0.981', '0.851', '0', '0.893', '#joy', '#admiration', 'positive', '0.908', 'passion', 'human_emotion', 'amour', 'wonderful_feeling', 'pleasant_emotion']
senticnet['beloved_aunt'] = ['0.02', '0.209', '-0.14', '0.202', '#interest', '#admiration', 'positive', '0.071', 'familiar', 'mum', 'familiarity_uncle', 'care_giver', 'familiarity']
senticnet['beloved_homeland'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'patriotism', 'homeland']
senticnet['beloved_wife'] = ['-0.87', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.91', 'loss_oflife', 'chest_pain', 'die', 'tumor', 'end_life']
senticnet['below_average_rainfall'] = ['-0.98', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.89', 'dry', 'lose_team', 'leave_out', 'disrespect', 'cause_suffer']
senticnet['below_belt'] = ['-0.74', '0', '0.934', '-0.85', '#anger', '#disgust', 'negative', '-0.84', 'dirty', 'unjust', 'foul', 'cheating', 'raw']
senticnet['below_degree_fahrenheit'] = ['-0.64', '0', '0.612', '-0.84', '#sadness', '#disgust', 'negative', '-0.7', 'chilling', 'frost', 'lyophilization', 'icing', 'cooling']
senticnet['below_ghz'] = ['-0.07', '0.078', '0', '0.078', '#interest', '#admiration', 'positive', '0.028', 'red_blue', 'red_orange', 'green_yellow', 'hue_tint', 'yellow_green']
senticnet['below_neck'] = ['0', '0.072', '0', '0.064', '#interest', '#admiration', 'positive', '0.068', 'physique', 'leg_arm', 'anatomy', 'human_part', 'hand_foot']
senticnet['below_zero_fahrenheit'] = ['-0.56', '0', '0.578', '-0.74', '#anger', '#disgust', 'negative', '-0.62', 'cold', 'unloving', 'loveless', 'detached', 'unaffectionate']
senticnet['belstaff'] = ['0.053', '-0.01', '-0.05', '0.121', '#joy', '#admiration', 'positive', '0.034', 'coat', 'winter_clothes', 'stylish', 'adorn', 'protective_cover']
senticnet['belt'] = ['0', '0.072', '0', '0.064', '#interest', '#admiration', 'positive', '0.068', 'zipper', 'pant', 'trousers', 'cowboy_boot', 'accessory']
senticnet['belt_buckle'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'sports_equipment', 'sport_stuff', 'golf', 'sport_store', 'bag']
senticnet['belt_clip'] = ['0', '-0.12', '0.011', '0.089', '#surprise', '#admiration', 'positive', '0.067', 'cellular_phone', 'telephone', 'wireless', 'connect', 'message']
senticnet['belt_loop'] = ['0', '0.072', '0', '0.064', '#interest', '#admiration', 'positive', '0.068', 'zipper', 'pant', 'trousers', 'cowboy_boot', 'accessory']
senticnet['belting_out'] = ['-0.89', '0', '0.722', '-0.94', '#sadness', '#disgust', 'negative', '-0.85', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['beltline'] = ['0', '0', '0', '0.085', '#admiration', '#admiration', 'positive', '0.085', 'use_farm', 'gate', 'door', 'doorway', 'entranceway']
senticnet['beluga_whale'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'many_fishes', 'seahorse', 'starfish', 'plankton', 'sea_wee']
senticnet['belzec_death'] = ['0', '0.156', '-0.06', '0.198', '#interest', '#admiration', 'positive', '0.098', 'grassland', 'camping', 'camp_out', 'campsite', 'campground']
senticnet['bemire'] = ['-0.97', '0', '0.873', '-0.66', '#sadness', '#anger', 'negative', '-0.83', 'muddy_up', 'muck_up', 'foul', 'blemish', 'smear']
senticnet['bemoan'] = ['-0.90', '0', '0.725', '-0.86', '#sadness', '#disgust', 'negative', '-0.83', 'quetch', 'kvetch', 'complain', 'sound_off', 'plain']
senticnet['bemock'] = ['-0.55', '0', '0.947', '-0.77', '#anger', '#disgust', 'negative', '-0.75', 'blackguard', 'deride', 'twit', 'tease', 'razz']
senticnet['bemuddy'] = ['-0.93', '0', '0.895', '-0.93', '#sadness', '#disgust', 'negative', '-0.91', 'dirty', 'soil', 'grime', 'muddy', 'colly']
senticnet['bemuse'] = ['-0.95', '0', '0.683', '-0.57', '#sadness', '#anger', 'negative', '-0.73', 'upset', 'disconcert', 'discompose', 'discomfit', 'untune']
senticnet['bemused'] = ['0', '0', '-0.81', '-0.99', '#fear', '#disgust', 'negative', '-0.90', 'preoccupation', 'preoccupied', 'preoccupy', 'harried', 'prepossess']
senticnet['bemusement'] = ['-0.84', '0', '0.855', '-0.71', '#sadness', '#anger', 'negative', '-0.80', 'confusion', 'disarray', 'mental_confusion', 'confusedness', 'muddiness']
senticnet['benadryl'] = ['0.969', '0.817', '0', '0.828', '#joy', '#admiration', 'positive', '0.871', 'ibuprofen', 'painkiller', 'medicine', 'analgesic', 'phenobarbital']
senticnet['bench'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'coach_seat', 'couch', 'stool', 'seat']
senticnet['bench_clearing_brawl'] = ['-0.60', '0', '0.777', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'batrachomyomachia', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['bench_press'] = ['0.642', '-0.58', '0', '0.751', '#joy', '#admiration', 'positive', '0.659', 'jerk', 'snatch', 'military_press', 'clean', 'weightlift']
senticnet['bench_seat'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'couch', 'stool', 'coach_seat']
senticnet['bench_sit'] = ['0', '-0.12', '0', '0', '#surprise', '#surprise', 'positive', '0.122', 'rest', 'wooden_chair', 'park', 'garden', 'outdoor']
senticnet['bench_warrant'] = ['-0.08', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.052', 'bequest', 'excise', 'bribe', 'afford', 'owe']
senticnet['benchmade'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'useful_tool']
senticnet['benchmark'] = ['0', '0.203', '0.036', '0.204', '#interest', '#admiration', 'positive', '0.124', 'amount', 'count', 'total', 'quantity', 'total_amount']
senticnet['benchmark_suite'] = ['0.558', '-0.60', '0', '0.779', '#surprise', '#admiration', 'positive', '0.646', 'benefit', 'pianism', 'show', 'routine', 'histrionics']
senticnet['benchmark_wti'] = ['0', '0', '-0.06', '0', '#fear', '#fear', 'negative', '-0.06', 'greasy', 'car_gas', 'fossil_fuel', 'liquid_fuel', 'lubrication']
senticnet['bend'] = ['0', '0', '0', '0.078', '#admiration', '#admiration', 'positive', '0.078', 'rotate', 'turn_around', 'change_direction', 'revolve', 'rotate_movement']
senticnet['bend_knee'] = ['0', '0', '-0.90', '-0.87', '#fear', '#disgust', 'negative', '-0.89', 'knee_bend', 'submit', 'bend_over', 'find_chair', 'keep_balance']
senticnet['bend_leg'] = ['0', '0', '0.89', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'walk_upon', 'stand_upon', 'stand', 'keep_foot_warm', 'lower_body']
senticnet['bend_over'] = ['-0.08', '-0.02', '0.042', '-0.11', '#sadness', '#disgust', 'negative', '-0.05', 'stop_walk', 'bend_knee', 'coordination', 'manual_dexterity', 'break_half']
senticnet['bend_spear'] = ['-0.93', '0', '0.794', '-0.99', '#sadness', '#disgust', 'negative', '-0.90', 'break', 'smash', 'damage', 'fracture', 'ruin']
senticnet['bendability'] = ['-0.54', '0', '0.909', '-0.97', '#anger', '#disgust', 'negative', '-0.81', 'suppleness', 'pliancy', 'pliantness', 'flexibility', 'flexibleness']
senticnet['bendable'] = ['0', '0', '-0.06', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'elbow', 'arm', 'wrist', 'five_fingers', 'bandit']
senticnet['benday_process'] = ['0.924', '0.832', '0', '0', '#joy', '#interest', 'positive', '0.878', 'technique', 'antialias', 'musketry', 'simulation', 'proficiency']
senticnet['bender'] = ['-0.83', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.78', 'picaninny', 'kid', 'minor', 'tike', 'small_picaninny']
senticnet['bending_forward'] = ['-0.68', '0', '0.545', '-0.68', '#sadness', '#disgust', 'negative', '-0.64', 'stunt', 'tumble', 'split', 'walkover', 'acrobatic_feat']
senticnet['bending_moment'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'aberrance', 'warp', 'chromosomal_aberration']
senticnet['beneath'] = ['-0.06', '-0.01', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'low', 'ground', 'look_down', 'ground_level', 'under_house']
senticnet['beneath_contempt'] = ['0', '0', '-0.90', '-0.90', '#fear', '#disgust', 'negative', '-0.90', 'parody', 'farce', 'lampoon', 'farce_comedy', 'spoof']
senticnet['benedict_nightingale'] = ['0.811', '-0.63', '0', '0.959', '#joy', '#admiration', 'positive', '0.8', 'drama_critic', 'critic', 'professional', 'art_critic', 'literary_critic']
senticnet['benedicta'] = ['-0.76', '0', '0.995', '-0.94', '#anger', '#disgust', 'negative', '-0.90', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'excretion']
senticnet['benediction'] = ['0.638', '-0.83', '0', '0.852', '#surprise', '#admiration', 'positive', '0.776', 'blessing', 'grace', 'commination', 'orison', 'supplication']
senticnet['benedictory'] = ['0', '0', '0.839', '-0.96', '#anger', '#disgust', 'negative', '-0.90', 'hastily-written', 'connectors-electric', 'checks-printing', 're-articulation', 'curtain-call']
senticnet['benefic'] = ['0.578', '0.672', '-0.28', '0.305', '#joy', '#interest', 'positive', '0.317', 'beneficence', 'beneficent', 'beneficed', 'beneficially', 'kindly']
senticnet['beneficed'] = ['0', '0.901', '0', '0.981', '#interest', '#admiration', 'positive', '0.941', 'beneficence', 'beneficially', 'beneficent', 'zeal', 'benefic']
senticnet['beneficence'] = ['0.578', '0.672', '-0.28', '0.305', '#joy', '#interest', 'positive', '0.317', 'beneficent', 'beneficed', 'benefic', 'beneficially', 'kindly']
senticnet['beneficent'] = ['0.578', '0.672', '-0.28', '0.305', '#joy', '#interest', 'positive', '0.317', 'beneficence', 'beneficed', 'benefic', 'beneficially', 'kindly']
senticnet['beneficial'] = ['0.53', '-0.55', '0', '0.912', '#surprise', '#admiration', 'positive', '0.667', 'advantageous', 'positive', 'good', 'plus', 'discriminatory']
senticnet['beneficial_effect'] = ['0.53', '-0.55', '0', '0.912', '#surprise', '#admiration', 'positive', '0.667', 'advantageous', 'positive', 'good', 'plus', 'discriminatory']
senticnet['beneficial_insect'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'hornet', 'vespid', 'vespid_wasp']
senticnet['beneficial_owner'] = ['0.964', '0.931', '0', '0', '#joy', '#interest', 'positive', '0.948', 'heel', 'foot_part', 'type_of_shoes', 'shoe_type', 'high_heel']
senticnet['beneficial_ownership'] = ['0.069', '0.07', '0', '0.116', '#interest', '#admiration', 'positive', '0.085', 'entrepreneur', 'invest_money', 'move_around', 'traveling_person', 'guilty_person']
senticnet['beneficially'] = ['0.578', '0.672', '-0.28', '0.305', '#joy', '#interest', 'positive', '0.317', 'beneficence', 'beneficent', 'beneficed', 'benefic', 'kindly']
senticnet['beneficially_owned_by'] = ['0.069', '0.07', '0', '0.116', '#interest', '#admiration', 'positive', '0.085', 'entrepreneur', 'invest_money', 'move_around', 'traveling_person', 'guilty_person']
senticnet['beneficiary'] = ['0.851', '0.708', '0', '0', '#joy', '#interest', 'positive', '0.779', 'devisee', 'legatee', 'recipient', 'receiver', 'pensioner']
senticnet['beneficiary_association_veba'] = ['-0.02', '0.1', '-0.07', '0.089', '#interest', '#admiration', 'positive', '0.023', 'functionary', 'employment', 'labor', 'employ', 'toil']
senticnet['benefit'] = ['0.085', '0.365', '-0.20', '0.362', '#interest', '#admiration', 'positive', '0.153', 'bonus', 'monetary_gain', 'financial_gain', 'return', 'give']
senticnet['benefit_accrue'] = ['0.085', '0.365', '-0.20', '0.362', '#interest', '#admiration', 'positive', '0.153', 'bonus', 'monetary_gain', 'financial_gain', 'return', 'give']
senticnet['benefit_greatly'] = ['0.085', '0.365', '-0.20', '0.362', '#interest', '#admiration', 'positive', '0.153', 'bonus', 'monetary_gain', 'financial_gain', 'return', 'give']
senticnet['benefit_payable'] = ['0.924', '0.968', '0', '0.876', '#joy', '#interest', 'positive', '0.923', 'live_simply', 'help_out', 'money', 'accolade', 'generous']
senticnet['benefiting_from'] = ['0.81', '0', '0', '0.703', '#joy', '#admiration', 'positive', '0.756', 'harvest', 'crop', 'grow_crop', 'plant_crop', 'graze_cattle']
senticnet['benevolence'] = ['0.547', '0.674', '-0.28', '0.286', '#joy', '#interest', 'positive', '0.306', 'benevolently', 'amorous', 'amative', 'enamoredness', 'amorousness']
senticnet['benevolent'] = ['0.552', '0.681', '-0.29', '0.297', '#joy', '#interest', 'positive', '0.309', 'charitable', 'good-hearted', 'large-hearted', 'openhearted', 'kindly']
senticnet['benevolent_creator'] = ['0.071', '0.222', '-0.10', '0.234', '#interest', '#admiration', 'positive', '0.105', 'baron', 'almighty', 'deity', 'nobility', 'god']
senticnet['benevolent_despot'] = ['0.029', '0.079', '0', '0.063', '#interest', '#admiration', 'positive', '0.057', 'king', 'duchess', 'sultan', 'royal_leader', 'emperor']
senticnet['benevolent_god'] = ['0.071', '0.222', '-0.10', '0.234', '#interest', '#admiration', 'positive', '0.105', 'baron', 'almighty', 'deity', 'nobility', 'god']
senticnet['benevolent_ruler'] = ['0.702', '-0.94', '0', '0.582', '#joy', '#surprise', 'positive', '0.743', 'beatable', 'vanquishable', 'vincible', 'subduable', 'subjugable']
senticnet['benevolent_sexism'] = ['-0.93', '0', '0.586', '-0.66', '#sadness', '#disgust', 'negative', '-0.73', 'camp', 'pack', 'clique', 'coterie', 'brain_trust']
senticnet['benevolent_society'] = ['0.605', '0.647', '-0.26', '0.323', '#joy', '#interest', 'positive', '0.327', 'friendly', 'brotherly', 'amicableness', 'amicably', 'amicable']
senticnet['benevolently'] = ['0.547', '0.674', '-0.28', '0.286', '#joy', '#interest', 'positive', '0.306', 'benevolence', 'amorous', 'amative', 'enamoredness', 'amorousness']
senticnet['benifit'] = ['0', '0.959', '0', '0.792', '#interest', '#admiration', 'positive', '0.875', 'altruistic', 'better', 'advantage', 'tall_play_basketball', 'precise']
senticnet['benight'] = ['0.627', '-0.65', '0', '0.968', '#surprise', '#admiration', 'positive', '0.749', 'overwhelm', 'overpower', 'sweep_over', 'whelm', 'overcome']
senticnet['benighted'] = ['-0.79', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.79', 'reverse', 'retrograde', 'retral', 'primitive', 'backward']
senticnet['benign'] = ['0.795', '0', '0', '0.705', '#joy', '#admiration', 'positive', '0.75', 'caring', 'benevolent', 'warmer', 'fatherly', 'motherly']
senticnet['benign_cyst'] = ['-0.88', '0', '0.774', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fibroma', 'fibroid', 'benign_tumor', 'nonmalignant_tumor', 'nonmalignant_neoplasm']
senticnet['benign_neglect'] = ['-0.94', '0', '0.923', '-0.98', '#sadness', '#disgust', 'negative', '-0.95', 'heap', 'push', 'accept', 'bring', 'lunge']
senticnet['benign_neoplasm'] = ['-0.88', '0', '0.774', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fibroid_tumor', 'fibroid', 'benign_tumor', 'nonmalignant_tumor', 'nonmalignant_neoplasm']
senticnet['benign_polyp'] = ['-0.88', '0', '0.774', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fibroma', 'fibroid', 'benign_tumor', 'nonmalignant_tumor', 'nonmalignant_neoplasm']
senticnet['benign_tumor'] = ['-0.70', '0', '0.639', '-0.56', '#sadness', '#anger', 'negative', '-0.63', 'fibroma', 'adenoma', 'chondroma', 'enchondroma', 'myoma']
senticnet['benignant'] = ['0.655', '0.635', '-0.23', '0.3', '#joy', '#interest', 'positive', '0.338', 'cheerfully', 'cheerful', 'jolly_up', 'hearten', 'chirk_up']
senticnet['benignity'] = ['0.646', '-0.69', '0', '0.609', '#joy', '#surprise', 'positive', '0.651', 'good_manner', 'personal_manner', 'courtesy', 'comportment', 'mien']
senticnet['benk'] = ['0.975', '-0.66', '0', '0.813', '#joy', '#admiration', 'positive', '0.816', 'cry', 'yell', 'scream', 'shrill', 'call']
senticnet['benn_mcleod'] = ['-0.79', '0', '0.992', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['bennett'] = ['-0.69', '0', '0.89', '-0.70', '#anger', '#disgust', 'negative', '-0.76', 'still', 'suppress', 'moderate', 'hush_up', 'quell']
senticnet['benny'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'impaired', 'dickey', 'lessened', 'diminished']
senticnet['benny_hill'] = ['-0.89', '0', '0.58', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'resemblance', 'comparison', 'equivalence', 'reflection', 'alikeness']
senticnet['bentel'] = ['0.746', '-0.88', '0', '0.723', '#joy', '#surprise', 'positive', '0.783', 'strike', 'run_into', 'hit', 'impinge_on', 'collide_with']
senticnet['benthic_fauna'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'frog', 'amphibian']
senticnet['bentley_continental'] = ['0.755', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.728', 'high_class', 'expensive_car', 'ferrari', 'fancy_car', 'sports_car']
senticnet['bentley_continental_gt'] = ['0.755', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.728', 'high_class', 'expensive_car', 'ferrari', 'fancy_car', 'sports_car']
senticnet['benton'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['benumb'] = ['-0.58', '0', '0.703', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'numb', 'desensitize', 'modify', 'deaf', 'bedaze']
senticnet['benumbed'] = ['-0.61', '0', '0.571', '-0.84', '#sadness', '#disgust', 'negative', '-0.67', 'numb', 'emotionless', 'anesthetic', 'insensible', 'asleep']
senticnet['benzalkonium_chloride'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['benzimidazole'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['benzo_fury'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'use_drug', 'do_drug', 'drug_use', 'drug', 'addiction']
senticnet['benzofuran'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'enhance', 'intensify', 'deepen', 'potentiate', 'heighten']
senticnet['benzoic_acid'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
senticnet['benzopyrene'] = ['-0.67', '0', '0.503', '-0.67', '#sadness', '#disgust', 'negative', '-0.61', 'stink_up', 'smell_up', 'stink_out', 'incense', 'perfume']
senticnet['benzoyl_chloride'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'carboxylic_acid', 'succinic_acid', 'valeric_acid']
senticnet['benzoyl_peroxide'] = ['-0.81', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.82', 'peanut', 'leave_over', 'floppy', 'circular_motion', 'store_vegetable']
senticnet['benzphetamine'] = ['-0.72', '0', '0.973', '-0.57', '#sadness', '#anger', 'negative', '-0.76', 'corpulency', 'blubber', 'fat', 'embonpoint', 'stoutness']
senticnet['benzyl'] = ['-0.81', '0', '0.774', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'acid', 'hypophosphoric_acid', 'periodic_acid', 'hypooxyacid', 'oxyacid']
senticnet['benzyl_alcohol'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
senticnet['benzyl_chloride'] = ['0.032', '-0.02', '-0.04', '-0.08', '#fear', '#disgust', 'negative', '-0.01', 'liquid', 'carbonate_drink', 'chemical', 'soda', 'bromide']
senticnet['benzylpenicillin'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['bequeath'] = ['0.83', '0.757', '0', '0', '#joy', '#interest', 'positive', '0.793', 'unforced', 'leave', 'willing', 'volition', 'uncoerced']
senticnet['bequest'] = ['-0.06', '0.247', '-0.20', '0.205', '#interest', '#admiration', 'positive', '0.047', 'bail', 'excise', 'bribe', 'divorce_money', 'return']
senticnet['ber_den'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['berat'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
senticnet['berate'] = ['-0.90', '0', '0.841', '-0.65', '#sadness', '#anger', 'negative', '-0.8', 'castigate', 'tell_off', 'brush_down', 'chasten', 'objurgate']
senticnet['berating'] = ['-0.86', '0', '0.953', '-0.96', '#anger', '#disgust', 'negative', '-0.92', 'reprimand', 'rebuke', 'reproof', 'reproval', 'reprehension']
senticnet['berberine'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['berchta'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'jawless_fish', 'osteostracan', 'cephalaspid', 'ostracoderm', 'heterostracan']
senticnet['berdyaev'] = ['0.95', '-0.77', '0', '0.578', '#joy', '#surprise', 'positive', '0.767', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['bereave'] = ['-0.98', '0', '0.654', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'deprive', 'expropriate', 'disarm', 'unclothe', 'take']
senticnet['bereaved'] = ['-0.88', '0.175', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'grief-stricken', 'sorrowing', 'bereft', 'grieving', 'mourning']
senticnet['bereavement'] = ['0', '0', '-0.75', '-0.99', '#fear', '#disgust', 'negative', '-0.87', 'loss', 'deprivation', 'dispossession', 'privation', 'demise']
senticnet['bereavement_note'] = ['0.944', '0.704', '0', '0', '#joy', '#interest', 'positive', '0.824', 'hold_document', 'write', 'continue_story', 'make_paper_airplane', 'bereavement']
senticnet['bereft'] = ['-0.88', '0.175', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'bereaved', 'grief-stricken', 'sorrowing', 'grieving', 'mourning']
senticnet['bereshit'] = ['0', '0.106', '-0.05', '0.163', '#interest', '#admiration', 'positive', '0.072', 'band', 'kit', 'oblivion', 'rock_band', 'high_school_band']
senticnet['beret'] = ['0.08', '0.106', '-0.04', '0.112', '#interest', '#admiration', 'positive', '0.063', 'helmet', 'head_gear', 'cap', 'head_wear', 'head_protection']
senticnet['beretta_pistol'] = ['-0.75', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.75', 'hydrogen_bomb', 'rifle', 'revolver', 'pistol', 'gun']
senticnet['bergamot'] = ['0.863', '0.861', '0', '0', '#joy', '#interest', 'positive', '0.862', 'aroma', 'scent', 'smell', 'odor', 'sniff']
senticnet['bergapten'] = ['0.049', '0', '0', '0.113', '#joy', '#admiration', 'positive', '0.081', 'solid', 'hard_material', 'construction_material', 'hard', 'calcium']
senticnet['bergenia'] = ['0.632', '-0.93', '0', '0.928', '#surprise', '#admiration', 'positive', '0.831', 'herb', 'oregano', 'marjoram', 'pot_marjoram', 'origanum']
senticnet['berghain'] = ['0.966', '0.921', '0', '0.892', '#joy', '#interest', 'positive', '0.926', 'disco', 'nightclub', 'shop_complex', 'dance_club', 'fast_food_restaurant']
senticnet['beriberi'] = ['0', '0', '0.82', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'debility', 'hypochondria', 'neurosis', 'neurasthenia', 'pellagra']
senticnet['berimbau'] = ['-0.06', '-0.00', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.05', 'madman', 'trouble', 'agony', 'cause_problem', 'river_stream']
senticnet['berkaitan'] = ['-0.56', '0', '0.617', '-0.80', '#anger', '#disgust', 'negative', '-0.66', 'prolixity', 'prolixness', 'wordiness', 'repetitiveness', 'repetitiousness']
senticnet['berkeleyan'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['berkeleyi'] = ['-0.60', '0', '0.541', '-0.52', '#sadness', '#anger', 'negative', '-0.55', 'swoon', 'pass_out', 'faint', 'zonk_out', 'change_state']
senticnet['berm'] = ['0.766', '0.968', '0', '0', '#joy', '#interest', 'positive', '0.867', 'hob', 'mantel', 'support', 'overmantel', 'shelf']
senticnet['bernborough'] = ['0.744', '-0.77', '0', '0.661', '#joy', '#surprise', 'positive', '0.725', 'sugarcoat', 'honey', 'glaze', 'change_taste', 'sugar']
senticnet['bernie_faloney'] = ['-0.97', '0', '0.728', '-0.78', '#sadness', '#disgust', 'negative', '-0.82', 'beguiler', 'slicker', 'phony', 'faker', 'deceiver']
senticnet['bernie_madoff'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'gouger', 'swindler', 'confidence_man', 'conman', 'fraud']
senticnet['bernie_wrightson'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'encyclopaedia', 'child_story', 'comic', 'disney_cartoon']
senticnet['bernoulli_principle'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotate_movement', 'rotate', 'go_around', 'manoeuvre', 'full_circle']
senticnet['beroe_ovata'] = ['-0.79', '0', '0.709', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'animal', 'animate_being', 'ctenophore', 'invertebrate', 'beast']
senticnet['berry'] = ['0.845', '-0.72', '0', '0', '#joy', '#surprise', 'positive', '0.783', 'juicy_fruit', 'fruit', 'prune', 'fig', 'sweet_fruit']
senticnet['berry_bros_rudd'] = ['0.331', '0.119', '0.022', '0.229', '#joy', '#admiration', 'positive', '0.164', 'spice_life', 'red_light_district', 'fresh_produce', 'enough_food', 'knowledgeable']
senticnet['berserk'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amok', 'murderousness', 'misocainea', 'misogyny']
senticnet['berserker'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
senticnet['bert'] = ['0', '0', '0.837', '-0.99', '#anger', '#disgust', 'negative', '-0.91', 'discourteous', 'brusque', 'brusk', 'abrupt', 'unceremonious']
senticnet['bert_hinkler'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'cheapjack', 'pitchman']
senticnet['berth'] = ['0.049', '0', '0', '0.052', '#joy', '#admiration', 'positive', '0.051', 'sleep_under', 'bed', 'bed_cover', 'cover_furniture', 'mattress']
senticnet['bertie'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'impaired', 'dickey', 'lessened', 'diminished']
senticnet['bertie_wooster'] = ['0.556', '-0.79', '0', '0.755', '#surprise', '#admiration', 'positive', '0.701', 'lush', 'alcoholic', 'alky', 'rummy', 'boozer']
senticnet['beryllium_copper'] = ['0.72', '0', '0', '0.733', '#joy', '#admiration', 'positive', '0.726', 'spanner', 'gold', 'strong_material', 'platinum', 'durable']
senticnet['beseech'] = ['0', '0.725', '0', '0.968', '#interest', '#admiration', 'positive', '0.847', 'bidding', 'invite', 'offer', 'wish', 'bid']
senticnet['beseech_god'] = ['0.903', '0', '0', '0', '#joy', '#joy', 'positive', '0.903', 'gospel', 'religion', 'prayer', 'faith', 'world_religion']
senticnet['beseem'] = ['0.59', '-0.54', '0', '0.766', '#joy', '#admiration', 'positive', '0.634', 'check', 'match', 'correspond', 'jibe', 'tally']
senticnet['beset'] = ['-0.59', '0.162', '0.221', '-0.50', '#sadness', '#disgust', 'negative', '-0.28', 'annoy', 'harass', 'provoke', 'poke', 'chivvy']
senticnet['besetting_sin'] = ['0.702', '-0.94', '0', '0.582', '#joy', '#surprise', 'positive', '0.743', 'beatable', 'vanquishable', 'vincible', 'subduable', 'subjugable']
senticnet['beshrew'] = ['-0.72', '0', '0.847', '-0.97', '#anger', '#disgust', 'negative', '-0.84', 'call_down', 'stir', 'call_forth', 'conjure', 'bring_up']
senticnet['beside'] = ['0.071', '0.127', '0', '0.197', '#interest', '#admiration', 'positive', '0.132', 'place_work', 'workplace', 'office', 'work', 'hang_wall']
senticnet['besiege'] = ['-0.78', '0', '0.725', '-0.95', '#sadness', '#disgust', 'negative', '-0.81', 'assail', 'blockade', 'seal_off', 'ebb', 'attack']
senticnet['besmirch'] = ['-0.95', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.90', 'charge', 'accuse', 'malign', 'libel', 'slander']
senticnet['besot'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'nonplus', 'baffle', 'stupefy', 'dumbfound', 'stupefaction']
senticnet['besought'] = ['0.962', '-0.73', '0', '0.995', '#joy', '#admiration', 'positive', '0.899', 'lenify', 'placate', 'gruntle', 'pacify', 'conciliate']
senticnet['bespeak'] = ['0.636', '-0.54', '0', '0.673', '#joy', '#admiration', 'positive', '0.617', 'foreshow', 'signal', 'threaten', 'auspicate', 'betoken']
senticnet['bespectacled_man'] = ['-0.64', '0', '0.632', '-0.61', '#sadness', '#anger', 'negative', '-0.62', 'bearded', 'barbate', 'bewhiskered', 'whiskered', 'whiskery']
senticnet['bess_kinder'] = ['-0.24', '0', '-0.19', '0', '#sadness', '#fear', 'negative', '-0.22', 'mock', 'prison_sentence', 'wrongdoing', 'jail_time', 'violent_action']
senticnet['bessy'] = ['0.947', '0.914', '0', '0', '#joy', '#interest', 'positive', '0.93', 'ball_bounce', 'bicycle_tire', 'tire', 'increase_price', 'gyroscope']
senticnet['best'] = ['0.135', '0.225', '-0.12', '0.432', '#interest', '#admiration', 'positive', '0.167', 'excellent', 'goodness', 'well_done', 'great', 'good_quality']
senticnet['best-known'] = ['-0.72', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.76', 'best-loved', 'well-known', 'lesser-known', 'famous', 'renowned']
senticnet['best-loved'] = ['-0.72', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.77', 'best-known', 'well-known', 'lesser-known', 'famous', 'renowned']
senticnet['best-selling'] = ['0.857', '-0.72', '0', '0', '#joy', '#surprise', 'positive', '0.792', 'bestselling', 'top-selling', 'acclaimed', 'bestseller', 'award-winning']
senticnet['best_bet'] = ['0.135', '0.225', '-0.12', '0.432', '#interest', '#admiration', 'positive', '0.167', 'excellent', 'goodness', 'well_done', 'great', 'good_quality']
senticnet['best_defensive_player'] = ['0.135', '0.225', '-0.12', '0.432', '#interest', '#admiration', 'positive', '0.167', 'excellent', 'goodness', 'well_done', 'great', 'good_quality']
senticnet['best_ever'] = ['0.106', '-0.05', '0', '0.437', '#joy', '#admiration', 'positive', '0.199', 'high_quality', 'really_good', 'well_done', 'high_standard', 'great']
senticnet['best_friend'] = ['0.727', '0', '0', '0.705', '#joy', '#admiration', 'positive', '0.716', 'loyal', 'household_animal', 'house_pet', 'good_pet', 'home_animal']
senticnet['best_life'] = ['0.924', '0', '0', '0', '#joy', '#joy', 'positive', '0.924', 'knowledgeable', 'live_ripe_old_age', 'complement', 'develop_talent', 'live_well']
senticnet['best_moment'] = ['0.642', '-0.58', '0', '0.751', '#joy', '#admiration', 'positive', '0.659', 'highlight', 'sport_highlight', 'football_highlight', 'television_news', 'put_in_evidence']
senticnet['best_opponent'] = ['-0.13', '0', '-0.09', '-0.12', '#sadness', '#disgust', 'negative', '-0.11', 'die_in_war', 'hit', 'dribble', 'beat', 'experience_fear']
senticnet['best_seller'] = ['-0.04', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.08', 'data', 'awareness', 'information', 'saga', 'knowledge']
senticnet['best_selling_book'] = ['-0.04', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.08', 'data', 'awareness', 'information', 'saga', 'knowledge']
senticnet['bestial'] = ['0', '0', '0.965', '-0.96', '#anger', '#disgust', 'negative', '-0.96', 'abominable', 'nature', 'beastly', 'led', 'beastliness']
senticnet['bestiality'] = ['0.542', '-0.83', '0', '0.927', '#surprise', '#admiration', 'positive', '0.769', 'incest', 'extramarital_sex', 'free_love', 'sexual_intercourse', 'intercourse']
senticnet['bestow'] = ['-0.53', '0', '0.612', '-0.79', '#anger', '#disgust', 'negative', '-0.64', 'impart', 'lend', 'contribute', 'add', 'throw_in']
senticnet['bestow_grace'] = ['-0.69', '0', '0.806', '-0.74', '#anger', '#disgust', 'negative', '-0.74', 'hypothesis', 'theory', 'base', 'possibility', 'presumption']
senticnet['bestow_upon'] = ['0.586', '-0.96', '0', '0.9', '#surprise', '#admiration', 'positive', '0.818', 'kick_upstairs', 'gentle', 'prefer', 'spot_promote', 'favor']
senticnet['bestowed'] = ['0', '0.836', '0', '0.743', '#interest', '#admiration', 'positive', '0.789', 'epithet', 'honorific', 'pejorative', 'honorific', 'euphemistic']
senticnet['bestowed_annually'] = ['0.258', '0.35', '-0.23', '0.318', '#interest', '#admiration', 'positive', '0.173', 'brownie_point', 'honor', 'reinforcement', 'positive_reinforcement', 'fame']
senticnet['bestowed_upon'] = ['-0.98', '0', '0.664', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'unfortunate', 'unfortunate_person', 'weeper', 'pallgriever', 'griever']
senticnet['bestower'] = ['0.883', '-0.66', '0', '0.732', '#joy', '#admiration', 'positive', '0.76', 'giver', 'donor', 'presenter', 'subsidizer', 'conferrer']
senticnet['bestride'] = ['-0.88', '0', '0.882', '-0.58', '#sadness', '#anger', 'negative', '-0.78', 'mortal', 'behemoth', 'unusual_person', 'aberrant', 'giant']
senticnet['bestrode'] = ['-0.88', '0', '0.882', '-0.58', '#sadness', '#anger', 'negative', '-0.78', 'mortal', 'behemoth', 'unusual_person', 'aberrant', 'giant']
senticnet['bestseller'] = ['0.855', '-0.72', '0', '0', '#joy', '#surprise', 'positive', '0.791', 'bestselling', 'top-selling', 'acclaimed', 'best-selling', 'award-winning']
senticnet['bestselling'] = ['0.852', '-0.72', '0', '0', '#joy', '#surprise', 'positive', '0.79', 'best-selling', 'top-selling', 'acclaimed', 'bestseller', 'award-winning']
senticnet['bestselling_album'] = ['0.625', '-0.90', '0', '0.54', '#joy', '#surprise', 'positive', '0.69', 'smash', 'bang', 'smasher', 'strike', 'hit']
senticnet['bestselling_book'] = ['-0.04', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.08', 'data', 'awareness', 'information', 'saga', 'knowledge']
senticnet['bet'] = ['0', '0', '-0.86', '-0.86', '#fear', '#disgust', 'negative', '-0.86', 'gamble', 'casino', 'hazard', 'poker_chip', 'risk']
senticnet['bet_hillel'] = ['-0.22', '0', '-0.09', '-0.16', '#sadness', '#disgust', 'negative', '-0.16', 'inequality', 'reject', 'discard', 'difference', 'gap']
senticnet['beta_barrel'] = ['0', '0', '0.019', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'worm_intestine', 'bristle', 'fur_keep_warm', 'animal', 'whisker']
senticnet['beta_caryophyllene'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'capsaicin', 'chemical_irritant', 'piperine', 'pepper_spray']
senticnet['beta_decay'] = ['-0.77', '0', '0.764', '-0.54', '#sadness', '#anger', 'negative', '-0.69', 'pullulation', 'apomixis', 'blastogenesis', 'budding', 'asexual_reproduction']
senticnet['beta_endorphin'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'gastrointestinal_hormone', 'secretin', 'ghrelin']
senticnet['beta_helix'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['beta_lyrae'] = ['0', '-0.72', '0', '0.712', '#surprise', '#admiration', 'positive', '0.72', 'heavenly_body', 'around_earth', 'source_light', 'sol', 'celestial_body']
senticnet['beta_myosin_heavy'] = ['0.096', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.057', 'platform_rail', 'rail', 'catwalk', 'column', 'token_turnstile']
senticnet['beta_propeller'] = ['-0.02', '0.095', '0', '0.117', '#interest', '#admiration', 'positive', '0.063', 'identification', 'paper_square', 'carry_money', 'money_hold', 'credit']
senticnet['beta_radiation'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'alpha_radiation', 'alpha_ray', 'ionizing_radiation', 'beta_ray', 'electron_radiation']
senticnet['beta_ray'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'alpha_radiation', 'alpha_ray', 'beta_radiation', 'ionizing_radiation', 'electron_radiation']
senticnet['beta_sheet'] = ['-0.02', '0.095', '0', '0.117', '#interest', '#admiration', 'positive', '0.063', 'identification', 'paper_square', 'carry_money', 'money_hold', 'credit']
senticnet['beta_sp'] = ['0', '0', '0', '-0.17', '#disgust', '#disgust', 'negative', '-0.17', 'medicine', 'aspirin', 'blank_check', 'painkiller', 'gauze']
senticnet['beta_test'] = ['0.928', '-0.87', '0', '0.679', '#joy', '#surprise', 'positive', '0.829', 'examine', 'experiment', 'alpha_test', 'test', 'road_test']
senticnet['betadine'] = ['-0.81', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.85', 'bandage', 'painkiller', 'gauze', 'aspirin', 'contraceptive_device']
senticnet['betake'] = ['-0.91', '0', '0.816', '0', '#sadness', '#anger', 'negative', '-0.86', 'recur', 'appeal', 'recourse', 'resort', 'recurrence']
senticnet['betamethasone'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'spironolactone', 'adrenal_cortical_steroid', 'antihypertensive', 'antihypertensive_drug']
senticnet['betamethasone_dipropionate'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'spironolactone', 'adrenal_cortical_steroid', 'antihypertensive', 'antihypertensive_drug']
senticnet['bethel_park'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
senticnet['bethlem'] = ['-0.67', '0', '0.931', '-0.77', '#anger', '#disgust', 'negative', '-0.79', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['bethlem_hospital'] = ['-0.67', '0', '0.931', '-0.77', '#anger', '#disgust', 'negative', '-0.79', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['bethlem_royal_hospital'] = ['-0.67', '0', '0.931', '-0.77', '#anger', '#disgust', 'negative', '-0.79', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['betide'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitentially', 'penitent', 'penitence', 'repentant']
senticnet['betoken'] = ['0.636', '-0.54', '0', '0.673', '#joy', '#admiration', 'positive', '0.617', 'foreshow', 'signal', 'threaten', 'bespeak', 'auspicate']
senticnet['betony'] = ['0.598', '-0.72', '0', '0.941', '#surprise', '#admiration', 'positive', '0.753', 'sicklepod', 'tower_rock_cress', 'tower_mustard', 'rock_cress', 'rock_cress_plant']
senticnet['betray'] = ['-0.50', '-0.18', '0.038', '-0.34', '#sadness', '#disgust', 'negative', '-0.17', 'leave_out', 'unattractive', 'cause_suffer', 'loathing', 'lose_team']
senticnet['betrayal'] = ['-0.22', '0.154', '-0.24', '0', '#sadness', '#fear', 'negative', '-0.10', 'abduction', 'kidnap', 'sell_out', 'sleepless_night', 'sin']
senticnet['betrayer'] = ['-0.67', '0', '0.546', '-0.63', '#sadness', '#disgust', 'negative', '-0.61', 'fink', 'snitch', 'snitcher', 'sneak', 'stoolpigeon']
senticnet['betrayerer'] = ['-0.67', '0', '0.544', '-0.64', '#sadness', '#disgust', 'negative', '-0.62', 'fink', 'betrayer', 'snitch', 'sneak', 'stoolpigeon']
senticnet['betrothal'] = ['0', '0.819', '0', '0.987', '#interest', '#admiration', 'positive', '0.903', 'celebrate', 'wedding_ceremony', 'propose_marriage', 'engage', 'engagement']
senticnet['betrothal_ceremony'] = ['0', '0.819', '0', '0.987', '#interest', '#admiration', 'positive', '0.903', 'celebrate', 'wedding_ceremony', 'propose_marriage', 'engage', 'engagement']
senticnet['betsy_bobbin'] = ['0.931', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.855', 'crystal_clear', 'blue_sky', 'imaginary', 'water_sky', 'blue_color']
senticnet['better'] = ['0.993', '0', '0', '0.863', '#joy', '#admiration', 'positive', '0.928', 'improve', 'tall_play_basketball', 'reliable', 'best', 'good_quality']
senticnet['better-funded'] = ['0', '0', '-0.83', '-0.78', '#fear', '#disgust', 'negative', '-0.80', 'lesser-known', 'best-known', 'higher-profile', 'better-known', 'pricier']
senticnet['better-known'] = ['0', '0', '-0.83', '-0.77', '#fear', '#disgust', 'negative', '-0.8', 'lesser-known', 'best-known', 'higher-profile', 'better-funded', 'pricier']
senticnet['better-looking'] = ['0.908', '0.723', '0', '0', '#joy', '#interest', 'positive', '0.816', 'big', 'well-favored', 'good-looking', 'handsome', 'bighearted']
senticnet['better-off'] = ['0', '0.896', '0', '0.928', '#interest', '#admiration', 'positive', '0.912', 'well-to-do', 'affluent', 'wealthier', 'well-off', 'middle-class']
senticnet['better_cardiovascular_health'] = ['0.698', '-0.54', '0', '0.995', '#joy', '#admiration', 'positive', '0.747', 'fitter', 'increased_heart_rate', 'increase_stamina', 'weight_loss', 'get_strong']
senticnet['better_cold'] = ['0.121', '0.113', '-0.04', '0.114', '#joy', '#admiration', 'positive', '0.075', 'gulp', 'potable_liquid', 'sip', 'liquid_beverage', 'ingest']
senticnet['better_good'] = ['0.123', '-0.33', '-0.04', '0.493', '#surprise', '#admiration', 'positive', '0.226', 'great', 'really_good', 'high_quality', 'exceptional', 'wonderful']
senticnet['better_grade'] = ['0.769', '0', '0', '0', '#joy', '#joy', 'positive', '0.769', 'become_educated', 'learn', 'learn_information', 'desire_education', 'get_smart']
senticnet['better_half'] = ['0.905', '-0.51', '0', '0.913', '#joy', '#admiration', 'positive', '0.778', 'spouse', 'partner', 'married_person', 'mate', 'helpmate']
senticnet['better_health'] = ['0.769', '0', '0', '0', '#joy', '#joy', 'positive', '0.769', 'health', 'become_strong', 'feel_better', 'sporty_person', 'increase_muscle_mass']
senticnet['better_job'] = ['0.957', '0', '0', '0', '#joy', '#joy', 'positive', '0.957', 'earn_degree', 'graduate_school', 'get_better_job', 'good_job', 'personal_improvement']
senticnet['better_life'] = ['0.808', '0', '0', '0.975', '#joy', '#admiration', 'positive', '0.891', 'happiness', 'accomplish_goal', 'self_esteem', 'make_world_better_place', 'help_others']
senticnet['better_off'] = ['0.943', '-0.58', '0', '0.694', '#joy', '#admiration', 'positive', '0.74', 'fortunate', 'happy', 'lucky', 'well', 'felicitous']
senticnet['better_others'] = ['0.913', '0.822', '0', '0', '#joy', '#interest', 'positive', '0.867', 'medical', 'medical_person', 'trustworthy', 'doctor_patient', 'treat_illness']
senticnet['better_others_person'] = ['0.924', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.945', 'better_others', 'medical', 'trustworthy', 'doctor_patient', 'treat_illness']
senticnet['better_person'] = ['0.235', '0.271', '-0.18', '0.199', '#joy', '#interest', 'positive', '0.131', 'demand', 'pleasant_emotion', 'good_feeling', 'human_emotion', 'end_argument']
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senticnet['bettie_page'] = ['-0.98', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.89', 'fairy', 'genus', 'honey_bee', 'infant', 'dove']
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senticnet['betulaceae'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'seed_fern', 'pteridosperm']
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senticnet['beverage_alcohol'] = ['0', '0', '0', '-0.91', '#disgust', '#disgust', 'negative', '-0.91', 'alcoholic_drink', 'get_drunk', 'ferment_beverage', 'adult_drink', 'booze']
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senticnet['beverage_service'] = ['0.993', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.872', 'waiter', 'bar_service', 'lounge', 'concierge', 'minibar']
senticnet['bevy'] = ['0', '0', '0.988', '-0.89', '#anger', '#disgust', 'negative', '-0.94', 'smattering', 'handful', 'plethora', 'smattering', 'sprinkling']
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senticnet['beware'] = ['0', '0', '-0.81', '-0.75', '#fear', '#disgust', 'negative', '-0.78', 'watch_out', 'keep_one_eye_peeled', 'keep_one_eye_skinned', 'keep_one_eye_open', 'aware']
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senticnet['bewildering_array'] = ['-0.85', '0', '0.583', '-0.71', '#sadness', '#disgust', 'negative', '-0.71', 'muddle', 'fuddle', 'mare_nest', 'rummage', 'clutter']
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senticnet['bewilderment'] = ['-0.50', '-0.18', '0.038', '-0.34', '#sadness', '#disgust', 'negative', '-0.17', 'crucifix', 'religion', 'world_religion', 'religious_icon', 'faith']
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senticnet['bewitching'] = ['0.724', '0', '0', '0.961', '#joy', '#admiration', 'positive', '0.843', 'captivated', 'charmed', 'captivate', 'bewitch', 'enthrallment']
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senticnet['beyonc_pulse'] = ['0.863', '0.861', '0', '0', '#joy', '#interest', 'positive', '0.862', 'aroma', 'scent', 'smell', 'odor', 'sniff']
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senticnet['beyond_repair'] = ['0', '0', '0.758', '-0.78', '#anger', '#disgust', 'negative', '-0.77', 'irreparable', 'irremediable', 'irreversible', 'unrectifiable', 'irrevocable']
senticnet['beyond_reproach'] = ['0.837', '-0.99', '0', '0.559', '#joy', '#surprise', 'positive', '0.797', 'disinterested', 'dispassionate', 'indifferent', 'unbiased', 'fair']
senticnet['bezan_text'] = ['-0.22', '0', '-0.09', '-0.16', '#sadness', '#disgust', 'negative', '-0.16', 'inequality', 'reject', 'discard', 'difference', 'gap']
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senticnet['bezoar_goat'] = ['0.963', '0.827', '0', '0', '#joy', '#interest', 'positive', '0.895', 'wild_goat', 'pasang', 'markhor', 'goat', 'ibex']
senticnet['bfa'] = ['-0.02', '0', '-0.04', '-0.03', '#fear', '#disgust', 'negative', '-0.03', 'studio', 'art_piece', 'art_gallery', 'artist', 'museum']
senticnet['bfoe'] = ['0', '0', '-0.06', '0', '#fear', '#fear', 'negative', '-0.06', 'greasy', 'car_gas', 'fossil_fuel', 'liquid_fuel', 'lubrication']
senticnet['bga_package'] = ['-0.83', '0', '0.521', '-0.71', '#sadness', '#disgust', 'negative', '-0.69', 'stool', 'dejection', 'droppings', 'cow_droppings', 'cow_dung']
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senticnet['bhaanu_prasad'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['bhabha_atomic_research'] = ['-0.90', '0', '0.748', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'alternative_energy', 'activation_energy', 'energy_of_activation', 'energy', 'nuclear_energy']
senticnet['bhagora'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['bhang'] = ['-0.94', '0', '0.517', '-0.75', '#sadness', '#disgust', 'negative', '-0.74', 'narcotic', 'soft_drug', 'cannabis', 'marijuana', 'marihuana']
senticnet['bhanu_pratap'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
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senticnet['bhaspati'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
senticnet['bhatt_murphy'] = ['0.053', '0.122', '-0.07', '0.086', '#interest', '#admiration', 'positive', '0.046', 'attorney', 'lawyer', 'jury', 'decide_guilt', 'prosecutor']
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senticnet['bheda'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
senticnet['bheireadh'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['bhel_puri'] = ['-0.80', '0', '0.718', '-0.79', '#sadness', '#disgust', 'negative', '-0.77', 'walk_exercise', 'lose_wallet', 'go_debt', 'pay_dinner', 'spend_much_money']
senticnet['bhiknur'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['bhiksha'] = ['0.053', '0.248', '-0.18', '0.258', '#interest', '#admiration', 'positive', '0.093', 'slogan', 'find_money', 'wealthy', 'much_money', 'donation']
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senticnet['bhoopathi'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['bhreatainn'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['bhuidhinn'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['bhut_jolokia'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'high_temperature', 'hot', 'hot_weather', 'fire_product', 'heat']
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senticnet['bi-annual'] = ['0.763', '0.849', '0', '0', '#joy', '#interest', 'positive', '0.806', 'biennial', 'triennial', 'biannual', 'semiannual', 'juried']
senticnet['bi_annually'] = ['0', '0.152', '0', '0.119', '#interest', '#admiration', 'positive', '0.136', 'replicate', 'start_end', 'everyday', 'description', 'precise']
senticnet['bi_directional_communication'] = ['0.63', '-0.55', '0', '0.946', '#joy', '#admiration', 'positive', '0.711', 'complect', 'connect', 'link', 'link_up', 'join']
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senticnet['bi_linear_interpolation'] = ['-0.55', '0', '0.965', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'interjection', 'delay', 'insert', 'interposition', 'disruption']
senticnet['bi_monthly'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'current_event', 'headline', 'paper_write', 'paper_page', 'many_pages']
senticnet['bi_monthly_journal'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'current_event', 'headline', 'paper_write', 'paper_page', 'many_pages']
senticnet['bi_monthly_magazine'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'library', 'old_testament', 'cook_book', 'a_lot_of_books', 'librarian']
senticnet['bi_monthly_newsletter'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'current_event', 'headline', 'paper_write', 'paper_page', 'many_pages']
senticnet['bi_monthly_publication'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'current_event', 'headline', 'paper_write', 'paper_page', 'many_pages']
senticnet['bi_plane'] = ['-0.04', '0.092', '0', '0.062', '#interest', '#admiration', 'positive', '0.036', 'hangar', 'aircraft_carrier', 'hot_air_balloon', 'war_museum', 'spaceship']
senticnet['bi_polar_disorder'] = ['-0.99', '0', '0.982', '-0.62', '#sadness', '#anger', 'negative', '-0.86', 'dysthymia', 'dysthymic_depression', 'affective_disorder', 'major_affective_disorder', 'emotional_disorder']
senticnet['bi_sexual'] = ['-0.97', '0', '0.971', '-0.84', '#sadness', '#anger', 'negative', '-0.93', 'fail', 'betray', 'disappoint', 'cross', 'disillusion']
senticnet['bi_sn'] = ['0.965', '-0.84', '0', '0.599', '#joy', '#surprise', 'positive', '0.802', 'mental_faculty', 'faculty', 'module', 'remembrance', 'anamnesis']
senticnet['bi_weekly'] = ['0', '0.152', '0', '0.119', '#interest', '#admiration', 'positive', '0.136', 'replicate', 'start_end', 'everyday', 'description', 'precise']
senticnet['bi_weekly_newspaper'] = ['-0.05', '0.106', '0', '0.06', '#interest', '#admiration', 'positive', '0.036', 'buy', 'buy_newspaper', 'read_buy_newspaper', 'buy_book', 'read_news']
senticnet['biannual'] = ['0.766', '0.847', '0', '0', '#joy', '#interest', 'positive', '0.806', 'biennial', 'bi-annual', 'triennial', 'semiannual', 'juried']
senticnet['biannual_conference'] = ['0', '0.214', '0.012', '0.208', '#interest', '#admiration', 'positive', '0.137', 'lecture_hall', 'lecture_theater', 'lesson', 'lecture', 'school_session']
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senticnet['biannual_newsletter'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'current_event', 'headline', 'paper_write', 'paper_page', 'many_pages']
senticnet['biannually'] = ['0', '0.152', '0', '0.119', '#interest', '#admiration', 'positive', '0.136', 'replicate', 'start_end', 'everyday', 'description', 'precise']
senticnet['biap'] = ['-0.66', '0', '0.789', '-0.63', '#sadness', '#anger', 'negative', '-0.69', 'humbug', 'play_trick', 'cozen', 'pull_fast_one_on', 'play_trick_on']
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senticnet['bias_toward'] = ['0.63', '0.622', '-0.29', '0.365', '#joy', '#interest', 'positive', '0.332', 'joyful', 'favorable', 'favorably', 'agree', 'jubilantly']
senticnet['bibb_lettuce'] = ['0.904', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.907', 'eden', 'garden', 'usual_green', 'apple', 'leafy']
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senticnet['bible_prophecy'] = ['-0.96', '0', '0.501', '-0.59', '#sadness', '#disgust', 'negative', '-0.68', 'calamity', 'catastrophe', 'tragedy', 'disaster', 'cataclysm']
senticnet['bible_story'] = ['0.916', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.935', 'bible', 'popular_hobby', 'big_boat', 'song_lyric', 'religion']
senticnet['bible_thumper'] = ['-0.64', '0', '0.781', '-0.74', '#anger', '#disgust', 'negative', '-0.72', 'weirdo', 'crazy', 'loony', 'lunatic', 'nutcase']
senticnet['biblical'] = ['0', '0.106', '-0.05', '0.163', '#interest', '#admiration', 'positive', '0.072', 'band', 'kit', 'oblivion', 'rock_band', 'high_school_band']
senticnet['biblical_account'] = ['0', '0.106', '-0.05', '0.163', '#interest', '#admiration', 'positive', '0.072', 'band', 'kit', 'oblivion', 'rock_band', 'high_school_band']
senticnet['biblical_chronology'] = ['0', '0.106', '-0.05', '0.163', '#interest', '#admiration', 'positive', '0.072', 'band', 'kit', 'oblivion', 'rock_band', 'high_school_band']
senticnet['biblical_criticism'] = ['-0.83', '0', '0.648', '-0.60', '#sadness', '#anger', 'negative', '-0.69', 'infallibility', 'dependability', 'dependableness', 'reliability', 'reliableness']
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senticnet['biblical_inerrancy'] = ['-0.83', '0', '0.648', '-0.60', '#sadness', '#anger', 'negative', '-0.69', 'infallibility', 'dependability', 'dependableness', 'reliability', 'reliableness']
senticnet['biblical_infallibility'] = ['-0.83', '0', '0.648', '-0.60', '#sadness', '#anger', 'negative', '-0.69', 'infallibility', 'dependability', 'dependableness', 'reliability', 'reliableness']
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senticnet['biblical_literalism'] = ['-0.98', '0', '0.891', '-0.60', '#sadness', '#anger', 'negative', '-0.82', 'exorcism', 'dispossession', 'magic', 'thaumaturgy', 'belief']
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senticnet['biblical_passage'] = ['0.075', '-0.01', '0.019', '0.067', '#joy', '#admiration', 'positive', '0.034', 'rhyme', 'paragraph', 'song_lyric', 'lyrics', 'stanza']
senticnet['biblical_revelation'] = ['-0.57', '0', '0.791', '-0.74', '#anger', '#disgust', 'negative', '-0.70', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['biblical_scholar_believe'] = ['0', '0.106', '-0.05', '0.163', '#interest', '#admiration', 'positive', '0.072', 'band', 'kit', 'oblivion', 'rock_band', 'high_school_band']
senticnet['biblical_scholarship'] = ['-0.83', '0', '0.648', '-0.60', '#sadness', '#anger', 'negative', '-0.69', 'infallibility', 'dependability', 'dependableness', 'reliability', 'reliableness']
senticnet['biblical_story'] = ['0', '0.106', '-0.05', '0.163', '#interest', '#admiration', 'positive', '0.072', 'band', 'kit', 'oblivion', 'rock_band', 'high_school_band']
senticnet['biblical_text'] = ['0', '0.106', '-0.05', '0.163', '#interest', '#admiration', 'positive', '0.072', 'band', 'kit', 'oblivion', 'rock_band', 'high_school_band']
senticnet['biblical_theological'] = ['-0.99', '0', '0.835', '-0.58', '#sadness', '#anger', 'negative', '-0.80', 'beginning', 'origin', 'root', 'wellspring', 'source']
senticnet['biblical_theology'] = ['0.664', '-0.57', '0', '0.981', '#joy', '#admiration', 'positive', '0.741', 'theodicy', 'homiletics', 'discipline', 'study', 'subject']
senticnet['biblical_verse'] = ['0', '0.106', '-0.05', '0.163', '#interest', '#admiration', 'positive', '0.072', 'band', 'kit', 'oblivion', 'rock_band', 'high_school_band']
senticnet['biblical_zoo'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['biblicism'] = ['0.995', '-0.69', '0', '0.528', '#joy', '#surprise', 'positive', '0.74', 'grasp', 'hug', 'nestle', 'snuggle', 'grip']
senticnet['bibliographic'] = ['-0.05', '0.084', '0', '0.062', '#interest', '#admiration', 'positive', '0.03', 'vocabulary', 'translation', 'book_library', 'book_poetry', 'book_genre']
senticnet['bibliographic_control'] = ['-0.97', '0', '0.885', '-0.97', '#sadness', '#disgust', 'negative', '-0.94', 'protection', 'conservation', 'environmentalism', 'reservation', 'immobilization']
senticnet['bibliographic_coverage'] = ['0.812', '-0.91', '0', '0.647', '#joy', '#surprise', 'positive', '0.79', 'assistance', 'relief', 'economic_aid', 'financial_aid', 'social_insurance']
senticnet['bibliographic_essay'] = ['0', '0.154', '-0.05', '0.17', '#interest', '#admiration', 'positive', '0.089', 'court_action', 'accessory', 'legal_document', 'short', 'protective_cover']
senticnet['bibliographic_instruction'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'a_lot_of_books', 'old_testament', 'cook_book', 'magazine', 'librarian']
senticnet['bibliographical'] = ['0.587', '-0.94', '0', '0.659', '#surprise', '#admiration', 'positive', '0.731', 'critique', 'review', 'criticism', 'literary_criticism', 'new_criticism']
senticnet['bibliotheca_historica'] = ['-0.7', '0', '0.746', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'insufficiency', 'sparseness', 'sparsity', 'abstemiousness', 'lack_of_quantity']
senticnet['bibtex'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'wall_cover', 'two-dimensional', 'cover_wall', 'pigment', 'dye']
senticnet['bicause'] = ['-0.11', '0.112', '0.674', '-0.66', '#anger', '#disgust', 'negative', '-0.33', 'wrathfully', 'wrathful', 'wrothful', 'lividly', 'lividity']
senticnet['biceps_brachii'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'skeletal_muscle', 'pterygoid_muscle', 'abductor', 'abductor_muscle']
senticnet['biceps_tendon'] = ['-0.53', '0', '0.982', '-0.67', '#anger', '#disgust', 'negative', '-0.73', 'gristle', 'semilunar_cartilage', 'fibrocartilage', 'hyaline_cartilage', 'arytenoid']
senticnet['bichon_frise'] = ['-0.72', '0', '0.619', '-0.86', '#sadness', '#disgust', 'negative', '-0.73', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['bicker'] = ['-0.83', '0', '0.714', '-0.72', '#sadness', '#disgust', 'negative', '-0.75', 'wrangle', 'dustup', 'quarrel', 'row', 'argue']
senticnet['bicker_over'] = ['-0.63', '0', '0.982', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'wrangle', 'dustup', 'quarrel', 'row', 'argument']
senticnet['bickering'] = ['-0.83', '0', '0.735', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'wrangle', 'dustup', 'quarrel', 'row', 'argue']
senticnet['bickleigh'] = ['-0.64', '0', '0.879', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'drone', 'poke', 'dawdler', 'laggard', 'straggler']
senticnet['bicubic_interpolation'] = ['-0.55', '0', '0.965', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'interjection', 'delay', 'insert', 'interposition', 'disruption']
senticnet['bicycle'] = ['0.701', '0.939', '0', '0.718', '#interest', '#admiration', 'positive', '0.786', 'exercise', 'keep_fit', 'healthy_life', 'sport', 'bicycle_riding']
senticnet['bicycle_accident'] = ['-0.92', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.93', 'serious_accident', 'accident', 'hit_car', 'loss_ofawareness', 'bang_head']
senticnet['bicycle_across_town'] = ['0.985', '0.803', '0', '0', '#joy', '#interest', 'positive', '0.894', 'cycle', 'pedal_cycle', 'bike', 'transportation', 'city']
senticnet['bicycle_area'] = ['0.038', '0', '0.01', '0.059', '#joy', '#admiration', 'positive', '0.029', 'traffic_rule', 'develop_city', 'control_road', 'red_light', 'yellow_line']
senticnet['bicycle_courier'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'mailman', 'package', 'mail', 'parcel', 'letter_slot']
senticnet['bicycle_craze'] = ['-0.04', '-0.16', '0', '0.056', '#surprise', '#admiration', 'positive', '0.059', 'car_part', 'under_car', 'car_wheel', 'wheel', 'car']
senticnet['bicycle_ergometer'] = ['0.094', '0', '0', '0', '#joy', '#joy', 'positive', '0.094', 'hooker', 'registry_office', 'restaurant_entrance', 'elevator', 'exit']
senticnet['bicycle_frame'] = ['0', '-0.07', '0', '0', '#surprise', '#surprise', 'positive', '0.075', 'gyroscope', 'air_pump', 'ball_bounce', 'motor_oil', 'tire']
senticnet['bicycle_part'] = ['0.069', '0.072', '0', '0.124', '#interest', '#admiration', 'positive', '0.088', 'door_opener', 'broomstick', 'carry_suitcase', 'threshold', 'slam']
senticnet['bicycle_pump'] = ['0', '-0.07', '0', '0', '#surprise', '#surprise', 'positive', '0.075', 'gyroscope', 'air_pump', 'ball_bounce', 'motor_oil', 'tire']
senticnet['bicycle_riding'] = ['0.701', '0.939', '0', '0.718', '#interest', '#admiration', 'positive', '0.786', 'exercise', 'keep_fit', 'healthy_life', 'sport', 'bicycle']
senticnet['bicycle_school'] = ['-0.16', '0', '-0.05', '-0.06', '#sadness', '#disgust', 'negative', '-0.09', 'roll_ball', 'laugh_clown', 'thank_parent', 'throw_fit', 'cycle']
senticnet['bicycle_shop'] = ['0', '-0.02', '0.038', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'bicycle', 'repair_shop', 'yellow_line', 'big_road', 'schoolyard']
senticnet['bicycle_store'] = ['-0.07', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.07', 'enter_door', 'grunt', 'open', 'hang_door', 'control_access']
senticnet['bicycle_tire'] = ['0', '-0.07', '0', '0', '#surprise', '#surprise', 'positive', '0.075', 'gyroscope', 'air_pump', 'ball_bounce', 'motor_oil', 'tire']
senticnet['bicycle_wheel'] = ['0', '-0.09', '0.044', '0.055', '#surprise', '#admiration', 'positive', '0.036', 'two_wheels', 'gear', 'axle', 'engine', 'steer_wheel']
senticnet['bicycle_work'] = ['-0.10', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.08', 'travel_work', 'fly_plane', 'hurry_work', 'push_limit', 'rush_work']
senticnet['bicyclist'] = ['-0.10', '-0.04', '0', '-0.14', '#sadness', '#disgust', 'negative', '-0.06', 'adjust_seat', 'angry_behavior', 'fasten_seatbelt', 'traffic_jam', 'skid_mark']
senticnet['bicyle'] = ['0.128', '-0.11', '0', '-0.05', '#joy', '#surprise', 'positive', '0.061', 'chesterfield', 'stool', 'tape_machine_room', 'chair', 'tripod_dolly']
senticnet['bid'] = ['0.917', '0.74', '0', '0', '#joy', '#interest', 'positive', '0.829', 'receive', 'price', 'limit_time', 'offer', 'return']
senticnet['bid_farewell'] = ['0', '0.152', '-0.08', '0.208', '#interest', '#admiration', 'positive', '0.091', 'departure', 'leave', 'colored_green', 'green_color', 'make_big']
senticnet['bid_goodbye'] = ['0.037', '0.161', '-0.15', '0.203', '#interest', '#admiration', 'positive', '0.062', 'wave', 'flow', 'salute', 'multiple_purposes', 'wave_wind']
senticnet['bid_ifb'] = ['0', '0.852', '0', '0', '#interest', '#interest', 'positive', '0.852', 'streamer', 'tribute', 'salutation', 'gift', 'anniversary_gift']
senticnet['biddable'] = ['0.953', '0', '0', '0.94', '#joy', '#admiration', 'positive', '0.946', 'compliant', 'rule', 'obey', 'docile', 'compliantly']
senticnet['bidden'] = ['-0.04', '0.268', '-0.06', '0.227', '#interest', '#admiration', 'positive', '0.097', 'proposition', 'abstract', 'get_rid', 'theoretical', 'business_transaction']
senticnet['bidder'] = ['-0.76', '0', '0.783', '0', '#sadness', '#anger', 'negative', '-0.77', 'nonpaying', 'unpaid', 'non-paying', 'low-paying', 'non-paid']
senticnet['bidding'] = ['0', '0.734', '0', '0.975', '#interest', '#admiration', 'positive', '0.855', 'beseech', 'invite', 'offer', 'wish', 'bid']
senticnet['bidding_farewell'] = ['0.037', '0.161', '-0.15', '0.203', '#interest', '#admiration', 'positive', '0.062', 'wave', 'flow', 'salute', 'multiple_purposes', 'wave_wind']
senticnet['biddy'] = ['-0.56', '0', '0.738', '-0.61', '#anger', '#disgust', 'negative', '-0.64', 'mix_up', 'muddle', 'puddle', 'assemble', 'piece']
senticnet['bidet'] = ['0', '0', '-0.84', '-0.77', '#fear', '#disgust', 'negative', '-0.80', 'dragon', 'pigeon', 'dragoon', 'cavalryman', 'horse']
senticnet['bidirectional_connection'] = ['0.078', '0.103', '-0.09', '0.191', '#interest', '#admiration', 'positive', '0.069', 'connect', 'aunt_uncle', 'short_form', 'relationship', 'kinship']
senticnet['bids_farewell'] = ['-0.12', '0', '-0.05', '-0.02', '#sadness', '#fear', 'negative', '-0.06', 'throw_fit', 'bye', 'greet', 'battle_illness', 'honor_parent']
senticnet['biennial'] = ['0.758', '0.847', '0', '0', '#joy', '#interest', 'positive', '0.802', 'triennial', 'bi-annual', 'biannual', 'semiannual', 'juried']
senticnet['biennial_budget'] = ['-0.05', '0.282', '-0.04', '0.107', '#interest', '#admiration', 'positive', '0.072', 'expense', 'dollar_cent', 'dollar_sign', 'amount_pay', 'payment_amount']
senticnet['biennial_conference'] = ['0', '0.214', '0.012', '0.208', '#interest', '#admiration', 'positive', '0.137', 'lecture_hall', 'lecture_theater', 'lesson', 'lecture', 'school_session']
senticnet['biennial_herb'] = ['-0.80', '0', '0.555', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'bush', 'wormwood', 'milkwort', 'germander', 'centaury']
senticnet['biennial_meeting'] = ['0', '0.214', '0.012', '0.208', '#interest', '#admiration', 'positive', '0.137', 'lecture_hall', 'lecture_theater', 'lesson', 'lecture', 'school_session']
senticnet['biennial_prize'] = ['0.894', '0.881', '0', '0.803', '#joy', '#interest', 'positive', '0.859', 'remittance', 'afford', 'award', 'money_due', 'amount_owe']
senticnet['biennium'] = ['-0.05', '0.282', '-0.04', '0.107', '#interest', '#admiration', 'positive', '0.072', 'expense', 'dollar_cent', 'dollar_sign', 'amount_pay', 'payment_amount']
senticnet['biface'] = ['0.063', '0.089', '0.036', '0.146', '#interest', '#admiration', 'positive', '0.066', 'horn', 'jew_harp', 'brass_instrument', 'trumpet', 'crash_cymbal']
senticnet['bifacial'] = ['0.063', '0.089', '0.036', '0.146', '#interest', '#admiration', 'positive', '0.066', 'horn', 'jew_harp', 'brass_instrument', 'trumpet', 'crash_cymbal']
senticnet['biff'] = ['-0.94', '0', '0.863', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'poke', 'clout', 'lick', 'punch', 'knockout_punch']
senticnet['biflagellate'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'immobile', 'immotile']
senticnet['biflora'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
senticnet['bifoliate'] = ['-0.50', '0', '0.573', '-0.89', '#anger', '#disgust', 'negative', '-0.65', 'petallike', 'bowery', 'foliolate', 'unifoliate', 'leafy']
senticnet['bifurcate'] = ['-0.86', '0', '0.511', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'separate', 'move', 'branch', 'diverge', 'fork']
senticnet['bifurcation'] = ['-0.67', '0', '0.97', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'joint', 'node', 'articulation', 'join', 'connexion']
senticnet['big'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'large', 'huge', 'great', 'really_big', 'tall']
senticnet['big-ticket'] = ['0', '0.903', '0', '0.862', '#interest', '#admiration', 'positive', '0.883', 'high-ticket', 'high-margin', 'high-price', 'bigger-ticket', 'low-ticket']
senticnet['big-time'] = ['-0.79', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.82', 'smalltime', 'small-time', 'two-bit', 'wanna-be', 'ex-con']
senticnet['big_amount'] = ['0.272', '0.153', '0', '0.334', '#joy', '#admiration', 'positive', '0.253', 'many', 'plenty', 'a_lot', 'abundant', 'good_number']
senticnet['big_animal'] = ['0.029', '0', '0.018', '0.059', '#joy', '#admiration', 'positive', '0.023', 'domestic_animal', 'wild_animal', 'hoof_mammal', 'large_mammal', 'animal']
senticnet['big_bang'] = ['0.763', '0', '0', '0.719', '#joy', '#admiration', 'positive', '0.741', 'hypothesis', 'test_hypothesis', 'theory', 'prove_theory', 'experiment']
senticnet['big_blue'] = ['0', '0.854', '0', '0.897', '#interest', '#admiration', 'positive', '0.875', 'vast', 'along_beach', 'near_beach', 'land_sea', 'full_water']
senticnet['big_boat'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'boat', 'large_boat', 'rowboat', 'ship', 'military_ship']
senticnet['big_breadbox'] = ['0.968', '-0.82', '0', '0', '#joy', '#surprise', 'positive', '0.896', 'home_made', 'bread', 'container', 'baked_goods', 'eat_bread']
senticnet['big_bro'] = ['0.104', '0', '0', '0.088', '#joy', '#admiration', 'positive', '0.096', 'baby_flower', 'wee', 'bud', 'part_of_plant', 'leafy']
senticnet['big_brother'] = ['0', '0.269', '-0.17', '0.182', '#interest', '#admiration', 'positive', '0.094', 'kinship', 'sibling', 'elder', 'family', 'best_friend']
senticnet['big_build'] = ['0', '0.126', '0.032', '0.167', '#interest', '#admiration', 'positive', '0.087', 'edifice', 'storage_facility', 'manufacture_plant', 'make_stuff', 'storage_space']
senticnet['big_business'] = ['0.973', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.985', 'large_business', 'business_organization', 'partnership', 'business_firm', 'company']
senticnet['big_businessman'] = ['0', '0', '-1.0', '-0.74', '#fear', '#disgust', 'negative', '-0.87', 'king', 'baron', 'business_leader', 'mogul', 'magnate']
senticnet['big_cap'] = ['0.033', '0.075', '0.029', '0.074', '#interest', '#admiration', 'positive', '0.038', 'hat', 'country_side', 'large_hat', 'rural_area', 'big_hat']
senticnet['big_car'] = ['0.028', '0', '0.013', '0.061', '#joy', '#admiration', 'positive', '0.025', 'truck', 'vehicle', 'limousine', 'car', 'lorry']
senticnet['big_car_accident'] = ['-0.92', '0', '0.833', '-0.97', '#sadness', '#disgust', 'negative', '-0.90', 'accident', 'drive_fast', 'car_accident', 'miss_red_light', 'break_rule']
senticnet['big_cat'] = ['0.028', '0', '0.013', '0.061', '#joy', '#admiration', 'positive', '0.025', 'cute_cat', 'domesticate_animal', 'cat', 'big_animal', 'big']
senticnet['big_cheese'] = ['0.979', '-0.69', '0', '0.576', '#joy', '#surprise', 'positive', '0.749', 'knocker', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['big_christmas_tree'] = ['0.856', '0', '0', '0', '#joy', '#joy', 'positive', '0.856', 'holiday', 'jingle', 'tree', 'festive_season', 'christmas']
senticnet['big_chunk'] = ['0', '-0.86', '0', '0.762', '#surprise', '#admiration', 'positive', '0.815', 'big', 'large', 'really_big', 'wide', 'tall']
senticnet['big_city'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'newyork', 'london', 'beijing', 'rome', 'tokyo']
senticnet['big_city_life'] = ['0', '-0.01', '0.046', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'pressure', 'busy', 'system', 'fast_pace', 'subway']
senticnet['big_country'] = ['0', '0.769', '0', '0.736', '#interest', '#admiration', 'positive', '0.752', 'country', 'democratic_country', 'free_country', 'unite', 'power']
senticnet['big_daddy_kane'] = ['0.081', '-0.05', '0.052', '0.072', '#joy', '#admiration', 'positive', '0.039', 'rhythm', 'type_of_music', 'instrumental', 'lyric', 'melody']
senticnet['big_deal'] = ['-0.04', '0.198', '-0.13', '0.185', '#interest', '#admiration', 'positive', '0.051', 'complete', 'assign', 'factory_output', 'creation', 'make_happen']
senticnet['big_dog'] = ['0.731', '0', '0', '0.843', '#joy', '#admiration', 'positive', '0.787', 'breed', 'domestic_pet', 'smart_dog', 'wild_wolf', 'fierce']
senticnet['big_enchilada'] = ['0.571', '-0.68', '0', '0.769', '#surprise', '#admiration', 'positive', '0.676', 'knocker', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['big_event'] = ['0.838', '0.75', '0', '0.708', '#joy', '#interest', 'positive', '0.765', 'festival', 'party', 'banquet', 'marketplace', 'rock_concert']
senticnet['big_eye'] = ['0', '0.105', '0', '0', '#interest', '#interest', 'positive', '0.105', 'beak', 'many_feathers', 'predator', 'bird', 'eagle']
senticnet['big_fight'] = ['-0.08', '0', '-0.08', '-0.17', '#sadness', '#disgust', 'negative', '-0.11', 'conflict', 'combat', 'battle', 'war', 'siege']
senticnet['big_fire'] = ['-0.02', '-0.01', '-0.07', '-0.12', '#fear', '#disgust', 'negative', '-0.05', 'grow_yard', 'stain_clothes', 'burn', 'set_on_fire', 'chemical_process']
senticnet['big_fish'] = ['-0.04', '0.184', '-0.08', '0.172', '#interest', '#admiration', 'positive', '0.055', 'fish', 'animal', 'big_animal', 'big_cat', 'big']
senticnet['big_game'] = ['-0.04', '0.184', '-0.08', '0.172', '#interest', '#admiration', 'positive', '0.055', 'winner_loser', 'passive', 'competitive', 'fun', 'profitable']
senticnet['big_glasses'] = ['-0.96', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.83', 'nerd', 'wear_glasses', 'computer_lover', 'geek', 'blind']
senticnet['big_group'] = ['0.066', '0.069', '0', '0.086', '#interest', '#admiration', 'positive', '0.074', 'multitude', 'clique', 'several_people', 'large_group', 'group']
senticnet['big_gulp'] = ['0', '0', '0', '-0.06', '#disgust', '#disgust', 'negative', '-0.06', 'newsstand', 'bookshop', 'walmart', 'shoe_store', 'hooker']
senticnet['big_gun'] = ['-0.03', '-0.01', '0', '-0.16', '#sadness', '#disgust', 'negative', '-0.05', 'weapon', 'cannon', 'firearm', 'machine_gun', 'handgun']
senticnet['big_guy'] = ['0.034', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.05', 'strong_person', 'strength', 'fearless', 'wrestler', 'body_building']
senticnet['big_hat'] = ['0.038', '0.079', '0.03', '0.083', '#interest', '#admiration', 'positive', '0.042', 'hat', 'country_side', 'large_hat', 'rural_area', 'big_cap']
senticnet['big_heavy'] = ['0.026', '0.141', '0.031', '0.2', '#interest', '#admiration', 'positive', '0.084', 'really_big', 'large', 'big', 'huge', 'massive']
senticnet['big_home'] = ['0.231', '0', '0', '0.109', '#joy', '#admiration', 'positive', '0.17', 'watch_comedy', 'dinner_party', 'opera', 'family_activity', 'anarchy']
senticnet['big_horn'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['big_house'] = ['0', '0.105', '0', '0.076', '#interest', '#admiration', 'positive', '0.09', 'palace', 'mansion', 'castle', 'bungalow', 'terrace']
senticnet['big_lake'] = ['-0.03', '0.085', '0', '0.127', '#interest', '#admiration', 'positive', '0.061', 'small_stream', 'riverside', 'reservoir', 'dam', 'oceanside']
senticnet['big_land'] = ['0', '0.07', '0.018', '0.069', '#interest', '#admiration', 'positive', '0.04', 'large_land', 'vast_land', 'immense', 'large', 'land']
senticnet['big_leaguer'] = ['0', '0.075', '0', '0', '#interest', '#interest', 'positive', '0.075', 'cup', 'carafe', 'container_cup', 'mug', 'glasses']
senticnet['big_meal'] = ['0.799', '-0.92', '0', '0.896', '#surprise', '#admiration', 'positive', '0.874', 'size_up', 'upsize', 'big_size', 'big', 'quantity']
senticnet['big_mistake'] = ['-0.56', '0.156', '-0.43', '0.122', '#sadness', '#fear', 'negative', '-0.17', 'bad_behavior', 'hard_to_define', 'treatable', 'harmful_human', 'mock']
senticnet['big_money'] = ['0.223', '0', '0.055', '0.157', '#joy', '#admiration', 'positive', '0.108', 'part_of_team', 'rich', 'athletic', 'increase_strength', 'keep_fit']
senticnet['big_monster'] = ['-0.34', '0', '-0.39', '-0.09', '#sadness', '#fear', 'negative', '-0.27', 'caution', 'unsafe', 'psychoneurosis', 'severe_pain', 'anxiety_disorder']
senticnet['big_monument'] = ['0.034', '0.13', '-0.08', '0.258', '#interest', '#admiration', 'positive', '0.085', 'tourism', 'nice_view', 'tourist_attraction', 'temple', 'sightsee']
senticnet['big_mouse'] = ['-0.13', '0', '0.013', '-0.08', '#sadness', '#disgust', 'negative', '-0.07', 'mouse', 'dig_tunnel', 'underground_animal', 'rat', 'small_rodent']
senticnet['big_mouth'] = ['0', '0.068', '0', '0', '#interest', '#interest', 'positive', '0.068', 'piece_of_meat', 'flavorful', 'choose_eat', 'mouth', 'masticate']
senticnet['big_muscle'] = ['0.076', '0', '0', '0', '#joy', '#joy', 'positive', '0.076', 'increase_metabolism', 'feel_fit', 'become_healthy', 'exercise', 'weight_lift']
senticnet['big_ol'] = ['-0.94', '0', '0.557', '-0.71', '#sadness', '#disgust', 'negative', '-0.74', 'sad', 'disappointing', 'unlucky', 'disengaged', 'cry']
senticnet['big_paycheck'] = ['0.163', '0.142', '0.024', '0.184', '#joy', '#admiration', 'positive', '0.116', 'self_confidence', 'look_beautiful', 'take_out_loan', 'down_payment', 'much_money']
senticnet['big_payday'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'profitable', 'moneymaking', 'gainful', 'paying', 'paid']
senticnet['big_person'] = ['0', '0.073', '0.009', '0.122', '#interest', '#admiration', 'positive', '0.062', 'smart_animal', 'person', 'domestic_animal', 'human', 'large_breadbox']
senticnet['big_picture'] = ['0', '0.211', '-0.12', '0.286', '#interest', '#admiration', 'positive', '0.125', 'bring_together', 'fuse', 'redo', 'fuse_together', 'request']
senticnet['big_place'] = ['0.034', '0.09', '0.021', '0.186', '#interest', '#admiration', 'positive', '0.072', 'location', 'place', 'scope', 'big', 'tourist_attraction']
senticnet['big_problem'] = ['-0.17', '0', '-0.20', '0', '#sadness', '#fear', 'negative', '-0.19', 'radiation', 'risk', 'problem', 'calamity', 'difficult_problem']
senticnet['big_rat'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'helpless', 'unwanted', 'rabies', 'small_cat', 'freckle']
senticnet['big_rig'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'trailer', 'trail_car', 'camion', 'wagon', 'towboat']
senticnet['big_road'] = ['0', '-0.02', '0.038', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'expressway', 'repair_shop', 'yellow_line', 'car_workshop', 'schoolyard']
senticnet['big_rock'] = ['0.057', '0.075', '0.039', '0.059', '#interest', '#admiration', 'positive', '0.038', 'summit', 'cliff', 'high_land', 'peak', 'mountain_side']
senticnet['big_sagebrush'] = ['0.045', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.356', 'ponderosa', 'bull_pine', 'western_yellow_pine', 'yellow_pine', 'longleaf_pine']
senticnet['big_science'] = ['0.757', '0.761', '0', '0', '#joy', '#interest', 'positive', '0.759', 'research_project', 'research', 'experiment', 'biological_research', 'scientific_research']
senticnet['big_screen'] = ['-0.85', '0', '0.514', '-0.73', '#sadness', '#disgust', 'negative', '-0.70', 'cinema_verite', 'movie', 'film', 'picture', 'moving_picture']
senticnet['big_shot'] = ['0.925', '-0.52', '0', '0.691', '#joy', '#admiration', 'positive', '0.713', 'knocker', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['big_size'] = ['0', '0.14', '0.06', '0.237', '#interest', '#admiration', 'positive', '0.106', 'size_up', 'upsize', 'big_meal', 'big', 'quantity']
senticnet['big_snowstorm'] = ['0', '0', '-0.93', '-0.82', '#fear', '#disgust', 'negative', '-0.88', 'thunderstorm', 'snow_storm', 'storm', 'weather', 'rainy']
senticnet['big_softie'] = ['-0.91', '0', '0.681', '-0.81', '#sadness', '#disgust', 'negative', '-0.80', 'chicken', 'doormat', 'softie', 'crybaby', 'wimp']
senticnet['big_stick'] = ['0.134', '-0.11', '0.066', '0.054', '#joy', '#surprise', 'positive', '0.058', 'wooden_stick', 'long_stick', 'tree_limb', 'tree_part', 'twig']
senticnet['big_toe'] = ['0', '0', '-0.06', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'bandit', 'arm', 'wrist', 'five_fingers', 'bendable']
senticnet['big_tooth'] = ['0', '-0.01', '0.041', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'long_tooth', 'fang', 'tail', 'sharp_tooth', 'animal_part']
senticnet['big_tree'] = ['0.856', '0', '0', '0', '#joy', '#joy', 'positive', '0.856', 'tree', 'evergreen_tree', 'christmas_tree', 'forest', 'christmas']
senticnet['big_wheel'] = ['0.749', '-0.97', '0', '0.992', '#surprise', '#admiration', 'positive', '0.905', 'knocker', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['bigamist'] = ['-0.54', '0', '0.691', '-0.96', '#anger', '#disgust', 'negative', '-0.73', 'slut', 'trollop', 'libertine', 'adulteress', 'fornicatress']
senticnet['bigamous_marriage'] = ['-0.76', '0', '0.995', '-0.94', '#anger', '#disgust', 'negative', '-0.90', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'excretion']
senticnet['bigamously'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'spousal_relationship', 'wedlock']
senticnet['bigamy'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'spousal_relationship', 'wedlock']
senticnet['bigcompany'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'identifiable', 'specifiable', 'recognizable', 'diagnosable', 'classifiable']
senticnet['bigger'] = ['0', '-0.86', '0', '0.762', '#surprise', '#admiration', 'positive', '0.815', 'big', 'large', 'really_big', 'wide', 'tall']
senticnet['bigger-ticket'] = ['0', '0.906', '0', '0.857', '#interest', '#admiration', 'positive', '0.881', 'big-ticket', 'high-margin', 'high-price', 'high-ticket', 'low-ticket']
senticnet['bigger_slice'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'profitable', 'moneymaking', 'gainful', 'paying', 'paid']
senticnet['biggest'] = ['0.738', '0', '0', '0.766', '#joy', '#admiration', 'positive', '0.752', 'heavy', 'liberal', 'braggart', 'full-grown', 'boastful']
senticnet['biggest_disappointment'] = ['-0.83', '0', '0.912', '-0.67', '#sadness', '#anger', 'negative', '-0.80', 'dissatisfaction', 'displeasure', 'disappointment', 'boredom', 'ennui']
senticnet['biggest_hurdle'] = ['-0.04', '0', '0.023', '0', '#sadness', '#anger', 'negative', '-0.03', 'golf_place', 'college_class', 'school_study', 'school_class', 'school_course']
senticnet['biggest_maker'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'builder']
senticnet['biggest_obstacle'] = ['-0.73', '0', '0.732', '-0.53', '#sadness', '#anger', 'negative', '-0.66', 'unconquerable', 'insuperable', 'inexpugnable', 'indomitable', 'unsubduable']
senticnet['biggest_surprise'] = ['-0.83', '0', '0.912', '-0.67', '#sadness', '#anger', 'negative', '-0.80', 'dissatisfaction', 'displeasure', 'disappointment', 'boredom', 'ennui']
senticnet['bighead'] = ['-0.89', '0', '0.853', '0', '#sadness', '#anger', 'negative', '-0.87', 'arrogantly', 'arrogant', 'chutzpah', 'presumer', 'prideful']
senticnet['bigheaded'] = ['0', '0', '-0.94', '-0.88', '#fear', '#disgust', 'negative', '-0.91', 'snooty', 'uppish', 'arrogant', 'persnickety', 'stuck-up']
senticnet['bighearted'] = ['0.916', '0.727', '0', '0', '#joy', '#interest', 'positive', '0.822', 'big', 'well-favored', 'good-looking', 'better-looking', 'handsome']
senticnet['bighorn'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['bighorn_sheep'] = ['0', '-0.97', '0', '0', '#surprise', '#surprise', 'positive', '0.977', 'cougar', 'skunk', 'lion', 'tiger', 'lynx']
senticnet['bigleaf_maple'] = ['0.776', '-0.6', '0', '0.568', '#joy', '#surprise', 'positive', '0.648', 'swamp_chestnut_oak', 'chinquapin_oak', 'chinkapin_oak', 'yellow_chestnut_oak', 'basket_oak']
senticnet['bigmouthed'] = ['-0.92', '0', '0.873', '-0.96', '#sadness', '#disgust', 'negative', '-0.92', 'indiscreet', 'blabby', 'blabbermouthed', 'talkative', 'imprudent']
senticnet['bigot'] = ['-0.21', '0', '-0.19', '-0.18', '#sadness', '#fear', 'negative', '-0.2', 'censorship', 'bad_word', 'kidnap', 'intolerance', 'sectarian']
senticnet['bigotry'] = ['0', '0', '0.854', '-0.85', '#anger', '#disgust', 'negative', '-0.85', 'fight_enemy', 'conquer_nation', 'destroy_enemy', 'genocide', 'wage_war']
senticnet['bigwig'] = ['0.534', '-0.69', '0', '0.724', '#surprise', '#admiration', 'positive', '0.65', 'knocker', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['bike'] = ['0.05', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.046', 'have_fun', 'motorcycle', 'roller_coaster', 'sports_car', 'scooter']
senticnet['bike_ride'] = ['-0.08', '0.179', '-0.06', '0.096', '#interest', '#admiration', 'positive', '0.034', 'ride_subway', 'buy_vending_machine', 'travel', 'buy_lottery_ticket', 'acquire']
senticnet['bikeboat'] = ['-0.95', '0', '0.793', '0', '#sadness', '#anger', 'negative', '-0.87', 'block', 'obstacle', 'nodus', 'impediment', 'obstruction']
senticnet['biker'] = ['0.995', '-0.72', '0', '0', '#joy', '#surprise', 'positive', '0.859', 'cowgirl', 'cowboy', 'rodeo', 'boot', 'cowboy']
senticnet['biker_gang'] = ['0.867', '-0.84', '0', '0.876', '#joy', '#admiration', 'positive', '0.863', 'meet', 'team', 'team_up', 'embed', 'aggroup']
senticnet['biker_jacket'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'elegant', 'stylish', 'smart', 'posh', 'sleek']
senticnet['bikini'] = ['0.842', '-0.70', '0', '0', '#joy', '#surprise', 'positive', '0.773', 'swimming_costume', 'shorts', 'jeans', 'pair_of_pants', 'dirty_magazine']
senticnet['bikini_clad'] = ['-0.94', '0', '0.991', '-0.65', '#sadness', '#anger', 'negative', '-0.86', 'birthday_suit', 'raw', 'altogether', 'nakedness', 'undress']
senticnet['bikini_contest'] = ['0', '-0.08', '0', '0', '#surprise', '#surprise', 'positive', '0.082', 'yo-yo', 'playroom', 'backgammon', 'action_figure', 'board_game']
senticnet['bikini_pants'] = ['0.822', '0.867', '0', '0', '#joy', '#interest', 'positive', '0.845', 'bikini', 'pants', 'underpants', 'bloomer', 'swimming_costume']
senticnet['bikini_top'] = ['0', '0', '0', '-0.06', '#disgust', '#disgust', 'negative', '-0.06', 'body', 'nervous_system', 'normal_human_body', 'human_body', 'head_shoulder']
senticnet['bikram_yoga'] = ['0', '0', '0', '0.93', '#admiration', '#admiration', 'positive', '0.93', 'lift_weight', 'release_stress', 'move_body', 'take_hike', 'change_color']
senticnet['bilabial'] = ['-0.71', '0', '0.998', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'tone', 'quality', 'twang', 'nasal_twang', 'timber']
senticnet['bilateral'] = ['0', '0', '0', '0.881', '#admiration', '#admiration', 'positive', '0.881', 'educate_others', 'share_knowledge', 'share_opinion', 'discover_new', 'collaboration']
senticnet['bilateral_accord'] = ['0.397', '0.245', '-0.13', '0.224', '#joy', '#interest', 'positive', '0.183', 'agreement', 'provide', 'advice', 'contribution', 'offer']
senticnet['bilateral_agreement'] = ['0.397', '0.245', '-0.13', '0.224', '#joy', '#interest', 'positive', '0.183', 'agreement', 'provide', 'advice', 'contribution', 'offer']
senticnet['bilateral_agreement_between'] = ['0.073', '0.256', '-0.19', '0.371', '#interest', '#admiration', 'positive', '0.127', 'good_etiquette', 'okay', 'niceness', 'needful', 'need']
senticnet['bilateral_cooperation'] = ['0', '0', '0', '0.881', '#admiration', '#admiration', 'positive', '0.881', 'educate_others', 'share_knowledge', 'share_opinion', 'discover_new', 'collaboration']
senticnet['bilateral_diplomacy'] = ['0', '0.098', '0', '0.08', '#interest', '#admiration', 'positive', '0.089', 'talk_person', 'wisdom', 'tact', 'statecraft', 'greet_person']
senticnet['bilateral_pact'] = ['0.397', '0.245', '-0.13', '0.224', '#joy', '#interest', 'positive', '0.183', 'agreement', 'provide', 'advice', 'contribution', 'offer']
senticnet['bilateral_retinoblastoma'] = ['-0.95', '0', '0.684', '-0.76', '#sadness', '#disgust', 'negative', '-0.79', 'innate', 'nurtural', 'inborn', 'nontransmissible', 'nonheritable']
senticnet['bilateral_treaty'] = ['0.397', '0.245', '-0.13', '0.224', '#joy', '#interest', 'positive', '0.183', 'agreement', 'provide', 'advice', 'contribution', 'offer']
senticnet['bilayer'] = ['0', '0', '0.019', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'worm_intestine', 'bristle', 'fur_keep_warm', 'animal', 'whisker']
senticnet['bilberry'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['bilbie_family'] = ['0.034', '0', '0.01', '0.087', '#joy', '#admiration', 'positive', '0.037', 'chime', 'bell_noise', 'alto', 'piano', 'ring_loudly']
senticnet['bile'] = ['0', '0', '0', '-0.96', '#disgust', '#disgust', 'negative', '-0.96', 'thick_substance', 'viscous_substance', 'mucous_substance', 'disgusting', 'disgust']
senticnet['bile_duct'] = ['-0.56', '0', '0.806', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['bile_duct_cancer'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['bilge'] = ['-0.54', '0', '0.845', '-0.95', '#anger', '#disgust', 'negative', '-0.78', 'face', 'base', 'underbelly', 'sole', 'underside']
senticnet['bilge_water'] = ['-0.58', '0', '0.714', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'diversionist', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['bilhah'] = ['-0.62', '0', '0.645', '-0.67', '#anger', '#disgust', 'negative', '-0.65', 'lady_maid', 'handmaiden', 'chambermaid', 'maid', 'parlormaid']
senticnet['bilharzia'] = ['-0.55', '0', '0.757', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'rubor', 'redness', 'cephalitis', 'sleepy_sickness', 'epidemic_encephalitis']
senticnet['biliary_atresia'] = ['-0.50', '0', '0.542', '-0.51', '#anger', '#disgust', 'negative', '-0.51', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'hirschsprung_disease']
senticnet['biliary_colic'] = ['-0.99', '0', '0.964', '-0.70', '#sadness', '#anger', 'negative', '-0.88', 'blepharitis', 'inflammation', 'epiglottitis', 'orchitis', 'prostatitis']
senticnet['biliary_dyskinesia'] = ['-0.95', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.89', 'chronic_gastritis', 'rubor', 'redness', 'gastritis', 'inflammation']
senticnet['biliary_excretion'] = ['0.992', '-0.80', '0', '0.584', '#joy', '#surprise', 'positive', '0.795', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'incontinence']
senticnet['biliary_obstruction'] = ['-0.56', '0', '0.906', '-0.80', '#anger', '#disgust', 'negative', '-0.76', 'jaundice_of_newborn', 'disease', 'icterus_neonatorum', 'kernicterus', 'symptom']
senticnet['bilinear'] = ['0.506', '-0.56', '0', '0.788', '#surprise', '#admiration', 'positive', '0.62', 'backward', 'reverse', 'retrograde', 'reflexive', 'retral']
senticnet['bilinear_interpolation'] = ['-0.55', '0', '0.965', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'interjection', 'delay', 'insert', 'interposition', 'disruption']
senticnet['bilingual'] = ['-0.93', '0', '-0.99', '-0.92', '#sadness', '#fear', 'negative', '-0.95', 'barrier', 'shut', 'shut_down', 'close_down', 'obstacle']
senticnet['bilingual_esl'] = ['-0.69', '0', '0.659', '-0.69', '#sadness', '#disgust', 'negative', '-0.68', 'help', 'paralegal', 'legal_assistant', 'paramedic', 'paramedical']
senticnet['bilious_fever'] = ['0.709', '-0.67', '0', '0.969', '#joy', '#admiration', 'positive', '0.784', 'murine_typhus', 'rat_typhus', 'urban_typhus', 'endemic_typhus', 'rickettsial_disease']
senticnet['biliousness'] = ['-0.59', '0', '0.911', '-0.83', '#anger', '#disgust', 'negative', '-0.78', 'querulousness', 'peevishness', 'pettishness', 'snappishness', 'surliness']
senticnet['bilk'] = ['0', '0', '-0.89', '-0.92', '#fear', '#disgust', 'negative', '-0.91', 'thwart', 'spoil', 'queer', 'foil', 'scotch']
senticnet['bill'] = ['-0.03', '0.249', '-0.06', '0.102', '#interest', '#admiration', 'positive', '0.063', 'yellowstone', 'invoice', 'expense', 'owe', 'mastercard']
senticnet['bill_barretta'] = ['-0.93', '0', '0.77', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'hothead', 'unpleasant_person', 'disagreeable_person', 'crabby_person', 'crab']
senticnet['bill_doolin'] = ['0', '0', '-0.06', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'elbow', 'arm', 'wrist', 'five_fingers', 'bendable']
senticnet['bill_granger'] = ['0.783', '-0.63', '0', '0.829', '#joy', '#admiration', 'positive', '0.749', 'leg_of_lamb', 'gigot', 'joint', 'roast', 'rack_of_lamb']
senticnet['bill_haley_rock'] = ['0', '0', '0.95', '-0.86', '#anger', '#disgust', 'negative', '-0.90', 'watch', 'stopwatch', 'timepiece', 'wristwatch', 'find_pawnshop']
senticnet['bill_maher'] = ['-0.02', '-0.02', '0.062', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'jump_up_down', 'tie_shoelace', 'jump_rope', 'hike', 'skate']
senticnet['bill_mcgarry'] = ['-0.9', '0', '0.749', '-0.71', '#sadness', '#anger', 'negative', '-0.78', 'chute', 'slide', 'slideway', 'sloping_trough', 'gutter']
senticnet['bill_mcvicar'] = ['0.053', '0.122', '-0.07', '0.086', '#interest', '#admiration', 'positive', '0.046', 'attorney', 'lawyer', 'jury', 'decide_guilt', 'prosecutor']
senticnet['bill_monroe'] = ['-0.04', '-0.04', '-0.04', '-0.04', '#fear', '#disgust', 'negative', '-0.02', 'music_shop', 'music_store', 'musical_instrument_store', 'string_quartet', 'high_school_band']
senticnet['bill_moseley'] = ['-0.03', '-0.01', '-0.16', '-0.06', '#fear', '#disgust', 'negative', '-0.06', 'sticky_floor', 'theater_box', 'projector', 'row_seat', 'theater_seat']
senticnet['bill_mott'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'coach', 'instructor', 'personal_trainer', 'fit_person', 'train']
senticnet['bill_pay'] = ['-0.13', '0.251', '-0.13', '0.146', '#interest', '#admiration', 'positive', '0.032', 'give_cash', 'owe_money', 'send_money', 'money_exchange', 'money']
senticnet['bill_tilman'] = ['0', '0.076', '0', '0.063', '#interest', '#admiration', 'positive', '0.07', 'up_hill', 'elevation', 'up_mountain', 'ascend', 'upwards']
senticnet['billboard'] = ['0.997', '0.892', '0', '0', '#joy', '#interest', 'positive', '0.945', 'advertisement', 'large_outdoor_board', 'motorway', 'expressway', 'train_track']
senticnet['billboard_album_chart'] = ['0.997', '0.892', '0', '0', '#joy', '#interest', 'positive', '0.945', 'advertisement', 'large_outdoor_board', 'motorway', 'expressway', 'train_track']
senticnet['billboard_bubbling_under'] = ['0.094', '0.091', '0', '0.125', '#joy', '#admiration', 'positive', '0.103', 'diagram', 'graph', 'medium', 'fragment', 'representation']
senticnet['billboard_chart'] = ['0.997', '0.892', '0', '0', '#joy', '#interest', 'positive', '0.945', 'advertisement', 'large_outdoor_board', 'motorway', 'expressway', 'train_track']
senticnet['billboard_hot'] = ['0.997', '0.892', '0', '0', '#joy', '#interest', 'positive', '0.945', 'advertisement', 'large_outdoor_board', 'motorway', 'expressway', 'train_track']
senticnet['billboard_hot_chart'] = ['0.997', '0.892', '0', '0', '#joy', '#interest', 'positive', '0.945', 'advertisement', 'large_outdoor_board', 'motorway', 'expressway', 'train_track']
senticnet['billboard_hot_single_chart'] = ['0.997', '0.892', '0', '0', '#joy', '#interest', 'positive', '0.945', 'advertisement', 'large_outdoor_board', 'motorway', 'expressway', 'train_track']
senticnet['billboard_magazine'] = ['0.997', '0.892', '0', '0', '#joy', '#interest', 'positive', '0.945', 'advertisement', 'large_outdoor_board', 'motorway', 'expressway', 'train_track']
senticnet['billboard_pop'] = ['0.997', '0.892', '0', '0', '#joy', '#interest', 'positive', '0.945', 'advertisement', 'large_outdoor_board', 'motorway', 'expressway', 'train_track']
senticnet['billboard_top'] = ['0.997', '0.892', '0', '0', '#joy', '#interest', 'positive', '0.945', 'advertisement', 'large_outdoor_board', 'motorway', 'expressway', 'train_track']
senticnet['billboard_top_ten'] = ['0.625', '-0.90', '0', '0.54', '#joy', '#surprise', 'positive', '0.69', 'smash', 'bang', 'smasher', 'strike', 'hit']
senticnet['billboards_hot'] = ['0.997', '0.892', '0', '0', '#joy', '#interest', 'positive', '0.945', 'advertisement', 'large_outdoor_board', 'motorway', 'expressway', 'train_track']
senticnet['billee_taylor'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'dress', 'pinny', 'dirndl', 'gown', 'muumuu']
senticnet['billetdoux'] = ['-0.93', '0', '0.861', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'knotty', 'contorted', 'writhed', 'crooked', 'anfractuous']
senticnet['billfold'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'wallet', 'piggy_bank', 'coffer', 'bank_vault', 'credit_card_wallet']
senticnet['billhook'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'useful_tool']
senticnet['billiard_ball'] = ['0', '-0.03', '0.042', '-0.07', '#anger', '#disgust', 'negative', '-0.02', 'ball', 'ping_pong_show', 'dandruff', 'solid_water', 'empty_bottle']
senticnet['billiards'] = ['-0.03', '0.085', '-0.07', '0.114', '#interest', '#admiration', 'positive', '0.022', 'many_games', 'poker', 'form_of_gambling', 'card_game', 'backgammon']
senticnet['billiards_champion'] = ['-0.68', '0', '0.923', '-0.68', '#sadness', '#anger', 'negative', '-0.76', 'negative', 'veto', 'refute', 'rebut', 'controvert']
senticnet['billiards_player'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'sciolist', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
senticnet['billina'] = ['0.039', '0.089', '-0.08', '0.143', '#interest', '#admiration', 'positive', '0.048', 'bird', 'nightingale', 'stork', 'eagle', 'rooster']
senticnet['billing-related'] = ['-0.98', '0', '0.788', '0', '#sadness', '#anger', 'negative', '-0.88', 'non-legal', 'non-diplomatic', 'nonlegal', 'public-law', 'court-related']
senticnet['billing_act_fcba'] = ['0.713', '0', '0', '0.746', '#joy', '#admiration', 'positive', '0.73', 'carnival', 'circus', 'fairground', 'rodeo', 'disneyland']
senticnet['billingsgate'] = ['-0.89', '0', '0.554', '-0.71', '#sadness', '#disgust', 'negative', '-0.72', 'abuse', 'vilification', 'insult', 'revilement', 'contumely']
senticnet['billion'] = ['0.787', '-0.52', '0', '0.806', '#joy', '#admiration', 'positive', '0.707', 'gross', 'century', 'grand', 'million', 'large_integer']
senticnet['billion_bailout'] = ['-0.12', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'escape', 'escape_situation', 'cancel_meeting', 'absent', 'cancel_appointment']
senticnet['billion_bailout_package'] = ['-0.12', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'escape', 'escape_situation', 'cancel_meeting', 'absent', 'cancel_appointment']
senticnet['billion_billion_bailout'] = ['-0.12', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'escape', 'escape_situation', 'cancel_meeting', 'absent', 'cancel_appointment']
senticnet['billion_dollar'] = ['0.522', '-0.88', '0', '0.965', '#surprise', '#admiration', 'positive', '0.789', 'overage', 'asset', 'security', 'business', 'hole_card']
senticnet['billion_dollar_bailout'] = ['-0.12', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'escape', 'escape_situation', 'cancel_meeting', 'absent', 'cancel_appointment']
senticnet['billion_dollar_fortune'] = ['0.976', '0.811', '0', '0', '#joy', '#interest', 'positive', '0.893', 'deposit', 'bank_loan', 'wealth', 'finance', 'account']
senticnet['billion_dollar_troubled_asset'] = ['0.732', '0.729', '0', '0', '#joy', '#interest', 'positive', '0.73', 'feel_relief', 'good_feeling', 'feel_ok', 'feel_relieved', 'feel_better']
senticnet['billion_euro_billion_bailout'] = ['-0.12', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'escape', 'escape_situation', 'cancel_meeting', 'absent', 'cancel_appointment']
senticnet['billion_rescue_package'] = ['-0.12', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'escape', 'escape_situation', 'cancel_meeting', 'absent', 'cancel_appointment']
senticnet['billion_takeover'] = ['-0.04', '0.274', '-0.15', '0.278', '#interest', '#admiration', 'positive', '0.089', 'business_deal', 'trade', 'exchange', 'bargain', 'offer']
senticnet['billion_troubled_asset'] = ['0.732', '0.729', '0', '0', '#joy', '#interest', 'positive', '0.73', 'feel_relief', 'good_feeling', 'feel_ok', 'feel_relieved', 'feel_better']
senticnet['billionaire'] = ['0', '0.778', '0', '0.866', '#interest', '#admiration', 'positive', '0.822', 'philanthropist', 'magnate', 'industrialist', 'businessman', 'manufacturer']
senticnet['billionaire_businessman'] = ['0', '0', '-0.99', '-0.73', '#fear', '#disgust', 'negative', '-0.86', 'king', 'baron', 'business_leader', 'mogul', 'big_businessman']
senticnet['billionaire_industrialist'] = ['0', '0', '-0.99', '-0.73', '#fear', '#disgust', 'negative', '-0.86', 'king', 'baron', 'business_leader', 'mogul', 'big_businessman']
senticnet['billionaire_raj_rajaratnam'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'busy_bee', 'gopher', 'actor', 'doer', 'worker']
senticnet['billowing'] = ['0', '0', '-0.93', '-0.71', '#fear', '#disgust', 'negative', '-0.82', 'heave', 'inflate', 'balloon', 'surge', 'wallow']
senticnet['billowing_smoke'] = ['-0.08', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.08', 'turn_key', 'plan_route', 'open_car_door', 'book_flight', 'turn_car_off']
senticnet['billquick'] = ['-0.03', '0.112', '-0.08', '0.164', '#interest', '#admiration', 'positive', '0.04', 'run', 'jump_over', 'quick', 'sprint', 'flee']
senticnet['biloxi'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
senticnet['bimah'] = ['0', '0.209', '0.026', '0.184', '#interest', '#admiration', 'positive', '0.122', 'church', 'confession', 'temple', 'cathedral', 'monastery']
senticnet['bimolecular'] = ['0.695', '-0.85', '0', '0.501', '#joy', '#surprise', 'positive', '0.684', 'alternative_energy', 'energy', 'energy_of_activation', 'atomic_energy', 'nuclear_energy']
senticnet['bimonthly'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'current_event', 'headline', 'paper_write', 'paper_page', 'many_pages']
senticnet['bimonthly_magazine'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'library', 'old_testament', 'cook_book', 'a_lot_of_books', 'librarian']
senticnet['bimonthly_publication'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'current_event', 'headline', 'paper_write', 'paper_page', 'many_pages']
senticnet['bin'] = ['0', '-0.02', '0', '0.051', '#surprise', '#admiration', 'positive', '0.037', 'cardboard_box', 'bag', 'package', 'box_cereal', 'box']
senticnet['bin_bag'] = ['-0.04', '0', '0.041', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'back_sofa', 'home_improvement_store', 'closet', 'under_carpet', 'shoe_shop']
senticnet['bin_directory'] = ['0', '0.175', '0', '0.231', '#interest', '#admiration', 'positive', '0.203', 'list', 'schedule', 'index', 'agenda', 'register']
senticnet['bin_liner'] = ['0', '0.082', '0.009', '0.135', '#interest', '#admiration', 'positive', '0.069', 'duvet_cover', 'pillow', 'mattress', 'bed_sheet', 'duvet']
senticnet['binary'] = ['0.773', '0.89', '0', '0', '#joy', '#interest', 'positive', '0.832', 'binary_star', 'binary_program', 'double_star', 'picture', 'erotica']
senticnet['binary_arithmetic'] = ['0.076', '0.096', '-0.08', '0.167', '#interest', '#admiration', 'positive', '0.063', 'statistic', 'mathematical_function', 'calculation', 'list_number', 'calculate']
senticnet['binary_classification'] = ['0.929', '-0.71', '0', '0.605', '#joy', '#surprise', 'positive', '0.748', 'incisiveness', 'trenchancy', 'efficacy', 'efficaciousness', 'power']
senticnet['binary_program'] = ['0.769', '0.88', '0', '0', '#joy', '#interest', 'positive', '0.825', 'binary_star', 'binary', 'double_star', 'picture', 'erotica']
senticnet['binary_star'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['binary_star_system'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['bind'] = ['0.784', '0.998', '0', '0', '#joy', '#interest', 'positive', '0.891', 'ski_bind', 'befriend', 'three-quarter_bind', 'keep_paper_together', 'fasten']
senticnet['bind_agreement'] = ['0', '0.104', '-0.04', '0.092', '#interest', '#admiration', 'positive', '0.05', 'earn_money', 'need_earn_money', 'support_family', 'responsible', 'ask_money']
senticnet['bind_book'] = ['-0.06', '0', '0.013', '0', '#sadness', '#anger', 'negative', '-0.03', 'dry_hair', 'refrigerate', 'scrub_floor', 'remove_paint', 'mow_grass']
senticnet['bind_paper_together'] = ['0.025', '0.083', '0', '0.06', '#interest', '#admiration', 'positive', '0.056', 'organize_document', 'carry_document', 'keep_paper_together', 'carry_paper', 'store_document']
senticnet['bind_rune'] = ['-0.52', '0', '0.506', '-0.94', '#sadness', '#disgust', 'negative', '-0.65', 'gouger', 'swindler', 'defrauder', 'fraud', 'welcher']
senticnet['bind_together'] = ['0.769', '0.727', '0', '0', '#joy', '#interest', 'positive', '0.748', 'fasten_two_objects_together', 'hold_together', 'build_bridge', 'teamwork', 'tow']
senticnet['binder_clip'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'clip', 'clasp', 'glue_stick', 'clothespin', 'clamp']
senticnet['binding'] = ['0.717', '0', '0', '0.875', '#joy', '#admiration', 'positive', '0.796', 'ski_binding', 'back', 'obligate', 'truss', 'book_binding']
senticnet['binding_acknowledgment'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'identifiable', 'specifiable', 'recognizable', 'diagnosable', 'classifiable']
senticnet['binding_acknowledgment_message'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'identifiable', 'specifiable', 'recognizable', 'diagnosable', 'classifiable']
senticnet['binding_affinity'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'imperium', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['binding_arbitration'] = ['0', '0.146', '0', '0.077', '#interest', '#admiration', 'positive', '0.111', 'negotiator', 'judge_jury', 'arbiter', 'decide_guilt', 'become_judge']
senticnet['bindley_bioscience_center'] = ['0.774', '-0.63', '0', '0.567', '#joy', '#surprise', 'positive', '0.658', 'catching', 'breakthrough', 'spying', 'determination', 'tracing']
senticnet['binge'] = ['-0.70', '0', '0.704', '-0.55', '#sadness', '#anger', 'negative', '-0.65', 'whoopee', 'merrymaking', 'jollification', 'revel', 'riot']
senticnet['binge_drink'] = ['0', '0.931', '0', '0', '#interest', '#interest', 'positive', '0.931', 'football', 'rake_leave', 'spray_paint_grafitti', 'juggle', 'play_hide_seek']
senticnet['binge_drinking_culture'] = ['0.706', '0.945', '0', '0', '#joy', '#interest', 'positive', '0.825', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['binge_eat'] = ['-0.52', '0', '0.786', '-0.92', '#anger', '#disgust', 'negative', '-0.74', 'upset', 'bulimarexia', 'bulima_nervosa', 'eating_disorder', 'disorder']
senticnet['binge_eating_disorder'] = ['-0.52', '0', '0.786', '-0.92', '#anger', '#disgust', 'negative', '-0.74', 'upset', 'bulimarexia', 'bulima_nervosa', 'eating_disorder', 'disorder']
senticnet['bingham_canyon'] = ['-0.98', '0', '0.521', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'battery', 'stamp_battery', 'quartz_battery', 'stamp_mill', 'factory']
senticnet['bingo'] = ['0.801', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.796', 'verbosity', 'sport_play', 'excite_game', 'contest', 'fun_game']
senticnet['binman'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'mailman', 'package', 'mail', 'parcel', 'letter_slot']
senticnet['binnacle'] = ['0.865', '0.84', '0', '0', '#joy', '#interest', 'positive', '0.853', 'travel', 'find_way', 'help_travelling', 'compass_direction', 'use_compass']
senticnet['binocular_disparity'] = ['0.685', '-0.91', '0', '0.566', '#joy', '#surprise', 'positive', '0.721', 'audibility', 'audibleness', 'visibleness', 'visibility', 'physical_property']
senticnet['binocular_rivalry'] = ['0.685', '-0.91', '0', '0.566', '#joy', '#surprise', 'positive', '0.721', 'audibility', 'audibleness', 'visibleness', 'visibility', 'physical_property']
senticnet['binocular_vision'] = ['-0.88', '0', '0.762', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'divergent_strabismus', 'exotropia', 'walleye', 'abnormality', 'abnormalcy']
senticnet['binocularity'] = ['-0.88', '0', '0.762', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'divergent_strabismus', 'exotropia', 'walleye', 'abnormality', 'abnormalcy']
senticnet['binocularly'] = ['0.685', '-0.91', '0', '0.566', '#joy', '#surprise', 'positive', '0.721', 'audibility', 'audibleness', 'visibleness', 'visibility', 'physical_property']
senticnet['binoculars'] = ['0', '-0.02', '0.048', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'telescope', 'read_glass', 'eyeglass', 'camera', 'monocle']
senticnet['binprolog'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'carbon_fiber']
senticnet['binthar'] = ['-0.09', '0', '-0.05', '-0.24', '#sadness', '#disgust', 'negative', '-0.13', 'war', 'enemy', 'arsenal', 'big_fight', 'conflict']
senticnet['binucleate'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'nonmotile', 'immobile']
senticnet['bio_chemistry'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'college_major', 'geology', 'physic', 'chemistry', 'subject_school']
senticnet['bio_degradable'] = ['0', '0.065', '0', '0.146', '#interest', '#admiration', 'positive', '0.105', 'drink_container', 'storage_container', 'opaque', 'drink_holder', 'food_container']
senticnet['bio_fertiliser'] = ['0.071', '0', '0.031', '0.064', '#joy', '#admiration', 'positive', '0.035', 'organic_matter', 'decompose', 'recycle', 'soil', 'compost_pile']
senticnet['bio_hazard'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'wear', 'protective_garment', 'coverall', 'shoulder_pad', 'wearable']
senticnet['bio_jet_fuel'] = ['0.855', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.86', 'ticket_counter', 'newspaper_stand', 'luggage_carrier', 'aircraft_roadway', 'timetable']
senticnet['bioactivity'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'imperium', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['bioavailability'] = ['0.992', '-0.80', '0', '0.584', '#joy', '#surprise', 'positive', '0.795', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'incontinence']
senticnet['biobehavioral'] = ['0.627', '-0.65', '0', '0.968', '#surprise', '#admiration', 'positive', '0.749', 'overwhelm', 'overpower', 'sweep_over', 'whelm', 'overcome']
senticnet['biochemical'] = ['0', '0', '-0.90', '-0.85', '#fear', '#disgust', 'negative', '-0.88', 'reductionist', 'empirical', 'elucidate', 'mechanistic', 'causal']
senticnet['biochemical_cascade'] = ['-0.99', '0', '0.651', '-0.51', '#sadness', '#anger', 'negative', '-0.72', 'sending', 'compulsion', 'influence', 'inducement', 'causation']
senticnet['biochemically'] = ['0', '0.851', '0', '0.905', '#interest', '#admiration', 'positive', '0.878', 'mechanistically', 'sociologically', 'phonologically', 'analytically', 'semantically']
senticnet['biochemist'] = ['0', '0.909', '0', '0.799', '#interest', '#admiration', 'positive', '0.854', 'research', 'research_topic', 'research_subject', 'search', 'student']
senticnet['biochemistry'] = ['0.76', '0.946', '0', '0', '#joy', '#interest', 'positive', '0.853', 'pharmacology', 'microbiology', 'toxicology', 'toxicological', 'pathology']
senticnet['biochemistry_molecular_biology'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'college_major', 'geology', 'physic', 'chemistry', 'subject_school']
senticnet['biocide'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
senticnet['biocompatibility'] = ['0.8', '-0.90', '0', '0.675', '#joy', '#surprise', 'positive', '0.794', 'indestructibility', 'tensile_strength', 'continuity', 'persistence', 'changelessness']
senticnet['biocomplexity'] = ['0', '0.986', '0', '0.792', '#interest', '#admiration', 'positive', '0.889', 'project', 'standard_grant', 'award', 'workshop', 'research']
senticnet['biodegradable'] = ['0.085', '0', '0.035', '0.147', '#joy', '#admiration', 'positive', '0.066', 'soft', 'material_type', 'fabric_type', 'type_of_cloth', 'cloth_fabric']
senticnet['biodiversity'] = ['0', '0.926', '0', '0.832', '#interest', '#admiration', 'positive', '0.879', 'study', 'project', 'research', 'award', 'specie']
senticnet['biodiversity_conservation'] = ['0', '0', '0', '0.857', '#admiration', '#admiration', 'positive', '0.857', 'say', 'beckon', 'call', 'conserve', 'retort']
senticnet['biodiversity_hotspot'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'woodland', 'timberland']
senticnet['biodynamic'] = ['0.035', '0.088', '0.031', '0.113', '#interest', '#admiration', 'positive', '0.051', 'cultivate_yard', 'same_answer', 'poisonous_human', 'juicy', 'natural_material']
senticnet['biodynamically'] = ['0.035', '0.088', '0.031', '0.113', '#interest', '#admiration', 'positive', '0.051', 'cultivate_yard', 'same_answer', 'poisonous_human', 'juicy', 'natural_material']
senticnet['bioethics'] = ['0.601', '-0.84', '0', '0.872', '#surprise', '#admiration', 'positive', '0.773', 'moral_philosophy', 'hedonism', 'philosophy', 'casuistry', 'eudemonism']
senticnet['biofeedback'] = ['0.542', '-0.63', '0', '0.912', '#surprise', '#admiration', 'positive', '0.695', 'implosion_therapy', 'flooding', 'reciprocal_inhibition', 'aversion_therapy', 'desensitization_technique']
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senticnet['birch'] = ['0.043', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.041', 'willow', 'cedar', 'oak', 'elm', 'lumber']
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senticnet['bird'] = ['-0.54', '0', '0.811', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'bird_of_prey', 'raptor', 'raptorial_bird', 'parrot', 'cock']
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senticnet['bird_eye_view'] = ['0', '0.095', '0', '0.112', '#interest', '#admiration', 'positive', '0.104', 'stare', 'look', 'observation', 'ship_part', 'see']
senticnet['bird_flu_vaccine'] = ['-0.07', '0', '-0.05', '-0.18', '#sadness', '#disgust', 'negative', '-0.10', 'medical_therapy', 'vaccination', 'doctor', 'medicine', 'hospital']
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senticnet['bitter_flavor'] = ['-0.82', '0', '0.901', '-0.65', '#sadness', '#anger', 'negative', '-0.79', 'taste', 'taste_sensation', 'gustatory_sensation', 'taste_perception', 'gustatory_perception']
senticnet['bitter_gourd'] = ['0.035', '0.104', '0', '0.12', '#interest', '#admiration', 'positive', '0.086', 'fruit_vegetable', 'vegetable', 'vegetable_fruit', 'fresh_vegetable', 'vegetarian_food']
senticnet['bitter_grief'] = ['-0.95', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.95', 'unhappy', 'dysphoric', 'forlornly', 'desolation', 'forlornness']
senticnet['bitter_herb'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'aridness', 'dryness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['bitter_hostility'] = ['-0.12', '0.193', '0.593', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'distaste', 'aversion', 'disapproval', 'disapprove', 'disapproving']
senticnet['bitter_resentment'] = ['-0.77', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.84', 'jumpiness', 'jitteriness', 'restive', 'nervy', 'jumpy']
senticnet['bitter_rot'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'rust_fungus', 'fungus']
senticnet['bitter_struggle'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'ascendance', 'autocracy', 'absolutism', 'tyrant']
senticnet['bitter_taste'] = ['0.112', '0', '0', '0.141', '#joy', '#admiration', 'positive', '0.127', 'sweet', 'sweet_food', 'bake_good', 'round_pastry', 'eatable']
senticnet['bitterest'] = ['-0.87', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'disgraceful', 'shameful', 'dishonorably', 'dishonored', 'shame']
senticnet['bitterly'] = ['0.75', '-0.87', '0', '0.603', '#joy', '#surprise', 'positive', '0.743', 'quality', 'futility', 'impracticality', 'worthlessness', 'impracticability']
senticnet['bitterly_cold'] = ['0.953', '0.792', '0', '0', '#joy', '#interest', 'positive', '0.873', 'snowy', 'cool_temperature', 'coldness', 'ice_rain', 'season_year']
senticnet['bitterness'] = ['0', '0', '0', '-0.87', '#disgust', '#disgust', 'negative', '-0.87', 'grievance', 'heartburning', 'enviable', 'grudging', 'enviably']
senticnet['bitternut_hickory'] = ['0.776', '-0.6', '0', '0.568', '#joy', '#surprise', 'positive', '0.648', 'swamp_chestnut_oak', 'chinquapin_oak', 'chinkapin_oak', 'yellow_chestnut_oak', 'basket_oak']
senticnet['bitterroot'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['bittersweet'] = ['0', '0', '0.772', '-0.94', '#anger', '#disgust', 'negative', '-0.85', 'semi-sweet', 'unsweetened', 'unsalted', 'pecan', 'semisweet']
senticnet['bittersweet_feeling'] = ['-0.89', '0', '0.722', '-0.94', '#sadness', '#disgust', 'negative', '-0.85', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['bittorrent_peer'] = ['0.848', '0.735', '0', '0', '#joy', '#interest', 'positive', '0.791', 'observer', 'peek', 'appearance', 'seer', 'glimpse']
senticnet['bittorrent_protocol'] = ['0.848', '0.735', '0', '0', '#joy', '#interest', 'positive', '0.791', 'observer', 'peek', 'appearance', 'seer', 'glimpse']
senticnet['bitty'] = ['0', '0', '0', '0.869', '#admiration', '#admiration', 'positive', '0.869', 'fluffy_white', 'white', 'pillow', 'mattress', 'silky']
senticnet['bitumastic'] = ['-0.94', '0', '0.569', '-0.72', '#sadness', '#disgust', 'negative', '-0.74', 'coating', 'veneer', 'veneering', 'paint', 'seal']
senticnet['bituminous_surface'] = ['0', '-0.01', '0.046', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'cement', 'tar', 'concrete', 'big_city_life', 'popular_building_material']
senticnet['bivalence'] = ['-0.92', '0', '0.547', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'assumption', 'boundary_condition', 'provision', 'proviso', 'precondition']
senticnet['bivalent'] = ['0', '0', '0.928', '-0.75', '#anger', '#disgust', 'negative', '-0.84', 'univalent', 'multivalent', 'divalent', 'meromorphic', 'monovalent']
senticnet['bivalve'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'seal', 'walrus', 'otter', 'alligator', 'whale']
senticnet['bivariate'] = ['-0.59', '0', '0.672', '-0.83', '#anger', '#disgust', 'negative', '-0.7', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'asymmetry', 'irregularity']
senticnet['bivouac'] = ['0', '0.156', '-0.06', '0.198', '#interest', '#admiration', 'positive', '0.098', 'grassland', 'camping', 'camp_out', 'campsite', 'campground']
senticnet['biweekly_magazine'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'library', 'old_testament', 'cook_book', 'a_lot_of_books', 'librarian']
senticnet['biweekly_newsletter'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'current_event', 'headline', 'paper_write', 'paper_page', 'many_pages']
senticnet['biweekly_newspaper'] = ['-0.05', '0.106', '0', '0.06', '#interest', '#admiration', 'positive', '0.036', 'buy', 'buy_newspaper', 'read_buy_newspaper', 'buy_book', 'read_news']
senticnet['biweekly_publication'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'current_event', 'headline', 'paper_write', 'paper_page', 'many_pages']
senticnet['bixa_orellana'] = ['0.046', '0', '0.041', '0.102', '#joy', '#admiration', 'positive', '0.036', 'good_eat', 'delicious', 'yummy', 'tasty', 'mouth_watering']
senticnet['bizarre'] = ['0.879', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.938', 'freaky', 'outlandish', 'off-the-wall', 'flaky', 'gonzo']
senticnet['bizarre_behavior'] = ['0.929', '-0.79', '0', '0.915', '#joy', '#admiration', 'positive', '0.88', 'privacy', 'seclusion', 'privateness', 'solitariness', 'loneliness']
senticnet['bizarre_love_triangle'] = ['0.542', '-0.83', '0', '0.927', '#surprise', '#admiration', 'positive', '0.769', 'incest', 'extramarital_sex', 'free_love', 'sexual_intercourse', 'intercourse']
senticnet['bizarrely'] = ['-0.82', '0', '0.758', '-0.70', '#sadness', '#anger', 'negative', '-0.76', 'unclassifiable', 'unrecognizable', 'unidentifiable', 'intangible', 'undiagnosable']
senticnet['bjelopavlii'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
senticnet['blab'] = ['-0.98', '0', '0.93', '-0.50', '#sadness', '#anger', 'negative', '-0.80', 'fink', 'snitch', 'snitcher', 'sneak', 'stoolpigeon']
senticnet['blabber'] = ['-0.98', '0', '0.93', '-0.50', '#sadness', '#anger', 'negative', '-0.80', 'fink', 'snitch', 'snitcher', 'sneak', 'stoolpigeon']
senticnet['blabbermouth'] = ['-0.80', '0', '0.56', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'gossip', 'gossiper', 'gossipmonger', 'talkative', 'rumormonger']
senticnet['blabbermouthed'] = ['-0.60', '0', '0.523', '-0.95', '#sadness', '#disgust', 'negative', '-0.69', 'communicatory', 'expansive', 'heraldic', 'talkative', 'leaky']
senticnet['blabby'] = ['-0.92', '0', '0.876', '-0.96', '#sadness', '#disgust', 'negative', '-0.92', 'bigmouthed', 'indiscreet', 'blabbermouthed', 'talkative', 'imprudent']
senticnet['blachernitissa'] = ['0.055', '0.117', '0', '0.133', '#interest', '#admiration', 'positive', '0.102', 'graphical_representation', 'pencil_pen', 'note_pad', 'marker', 'stationery']
senticnet['black'] = ['-0.60', '0', '0.895', '-0.54', '#sadness', '#anger', 'negative', '-0.68', 'coal_black', 'jet_black', 'pitch_black', 'soot_black', 'achromatic_color']
senticnet['black-rimmed'] = ['0', '-0.94', '0', '0.904', '#surprise', '#admiration', 'positive', '0.924', 'horn-rimmed', 'owl-like', 'heavy-lidded', 'owlish', 'half-lidded']
senticnet['black_angus_cattle'] = ['0.546', '-0.96', '0', '0.533', '#joy', '#surprise', 'positive', '0.683', 'dairy_cattle', 'dairy_cow', 'milch_cow', 'ayrshire', 'milcher']
senticnet['black_art'] = ['-0.84', '0', '0.833', '-0.66', '#sadness', '#anger', 'negative', '-0.78', 'magic', 'thaumaturgy', 'enchantment', 'witchcraft', 'witchery']
senticnet['black_bear'] = ['0', '-0.97', '0', '0', '#surprise', '#surprise', 'positive', '0.977', 'cougar', 'skunk', 'lion', 'tiger', 'lynx']
senticnet['black_bear_cub'] = ['-0.95', '0', '0.735', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'novice', 'beginner', 'tyro', 'tiro', 'initiate']
senticnet['black_bile'] = ['-0.12', '0.13', '0.646', '-0.64', '#anger', '#disgust', 'negative', '-0.32', 'irascibility', 'quick_temper', 'short_temper', 'irascible', 'ill_temper']
senticnet['black_billed_magpie'] = ['-0.74', '0', '0.963', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['black_body'] = ['0.566', '-0.55', '0', '0.776', '#joy', '#admiration', 'positive', '0.632', 'whole', 'dark', 'blackbody', 'full_radiator', 'stone']
senticnet['black_bread'] = ['0.52', '-0.69', '0', '0.639', '#surprise', '#admiration', 'positive', '0.617', 'rye_bread', 'pumpernickel', 'limpa', 'bread', 'breadstuff']
senticnet['black_bugbane'] = ['-0.52', '0', '0.518', '-0.78', '#anger', '#disgust', 'negative', '-0.61', 'herb', 'fairy_candle', 'actaea_racemosa', 'black_snakeroot', 'black_cohosh']
senticnet['black_bullhead'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'divergent_strabismus', 'exotropia', 'squint', 'abnormality', 'abnormalcy']
senticnet['black_cab'] = ['0.04', '0.144', '-0.07', '0.122', '#interest', '#admiration', 'positive', '0.058', 'travel', 'abroad', 'foreign_country', 'transit', 'elsewhere']
senticnet['black_cadillac_escalade'] = ['0.028', '0', '0.013', '0.061', '#joy', '#admiration', 'positive', '0.025', 'truck', 'vehicle', 'big_car', 'car', 'lorry']
senticnet['black_canary'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'only_bird']
senticnet['black_cassock'] = ['0.849', '-0.50', '0', '0.845', '#joy', '#admiration', 'positive', '0.732', 'robe', 'gown', 'veil', 'vest', 'cassock']
senticnet['black_cat'] = ['0.813', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.891', 'fisher', 'martes_pennanti', 'fisherman', 'fisher_cat', 'pekan']
senticnet['black_cherry'] = ['0.02', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'many_fruits', 'peach', 'buy_fruit', 'pineapple', 'tomato']
senticnet['black_chiffon'] = ['0.735', '-0.54', '0', '0.897', '#joy', '#admiration', 'positive', '0.727', 'net', 'network', 'mesh', 'meshing', 'meshwork']
senticnet['black_cod'] = ['-0.02', '-0.02', '0.064', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'ocean_water', 'fillet_fish', 'chowder', 'port', 'underwater']
senticnet['black_cohosh'] = ['-0.53', '0', '0.518', '-0.78', '#anger', '#disgust', 'negative', '-0.61', 'herb', 'fairy_candle', 'actaea_racemosa', 'black_bugbane', 'black_snakeroot']
senticnet['black_crappie'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'divergent_strabismus', 'exotropia', 'squint', 'abnormality', 'abnormalcy']
senticnet['black_crayon'] = ['0', '0.068', '0', '0', '#interest', '#interest', 'positive', '0.068', 'artist', 'oil_paint', 'painter', 'creative_person', 'make_paint']
senticnet['black_currant'] = ['0.784', '-0.59', '0', '0.846', '#joy', '#admiration', 'positive', '0.742', 'astuteness', 'slyness', 'wiliness', 'craftiness', 'cunning']
senticnet['black_cutworm'] = ['-0.02', '-0.09', '0.11', '-0.13', '#anger', '#disgust', 'negative', '-0.04', 'beetle', 'gypsy_moth', 'mole_cricket', 'grasshopper', 'many_insects']
senticnet['black_discoloration'] = ['-0.60', '0', '0.541', '-0.52', '#sadness', '#anger', 'negative', '-0.55', 'swoon', 'pass_out', 'faint', 'zonk_out', 'change_state']
senticnet['black_dot'] = ['-0.02', '0', '0.048', '0', '#sadness', '#anger', 'negative', '-0.03', 'furry_creature', 'skin_impurity', 'big_mouse', 'underground_animal', 'small_rodent']
senticnet['black_dress'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'sexy', 'elegant', 'classy', 'slim', 'beautiful']
senticnet['black_eye'] = ['0', '0', '0.974', '-0.94', '#anger', '#disgust', 'negative', '-0.95', 'broken_finger', 'schizophrenia', 'bloody_nose', 'complication', 'censorship']
senticnet['black_eyeliner'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'sexual_excitement', 'sexy', 'wet_lip', 'kiss', 'clean_up']
senticnet['black_faced_spoonbill'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'hold_egg', 'baby_bird', 'hatch', 'lay_egg', 'bird_home']
senticnet['black_footed_ferret'] = ['0', '0', '-0.70', '-0.77', '#fear', '#disgust', 'negative', '-0.73', 'animal', 'brown_bear', 'forest', 'creature', 'polar_bear']
senticnet['black_frock_coat'] = ['-0.84', '0', '0.633', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'grizzle', 'postiche', 'horsehair_wig', 'wig', 'peruke']
senticnet['black_goo'] = ['0.024', '0.073', '0', '0.101', '#interest', '#admiration', 'positive', '0.066', 'polish', 'black_liquid', 'greasy', 'slick', 'slippery']
senticnet['black_gown'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'pinafore', 'pinny', 'dirndl', 'dress', 'muumuu']
senticnet['black_green'] = ['-0.04', '0.079', '0', '0.073', '#interest', '#admiration', 'positive', '0.035', 'red_green', 'red_orange', 'spectrum', 'yellow_green', 'red_blue']
senticnet['black_grouper'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'crab', 'swimming_crab', 'rock_crab', 'fiddler_crab', 'pea_crab']
senticnet['black_grouse'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['black_gum'] = ['-0.60', '0', '0.787', '-0.90', '#anger', '#disgust', 'negative', '-0.76', 'pain', 'tooth_disease', 'need_dentist', 'discomfort', 'ugly_smile']
senticnet['black_hair'] = ['0', '0', '0.841', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'ancient', 'many_years', 'year', 'old', 'teen']
senticnet['black_headed_gull'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'amoeba', 'single_celled']
senticnet['black_hole'] = ['0', '0.701', '0', '0.963', '#interest', '#admiration', 'positive', '0.832', 'region', 'universe', 'star', 'study', 'gravitational_wave']
senticnet['black_hooded_sweatshirt'] = ['0.024', '0', '0', '0.074', '#joy', '#admiration', 'positive', '0.049', 'sheet', 'cover', 'quilt', 'comforter', 'modesty']
senticnet['black_huckleberry_gaylussacia'] = ['-0.79', '0', '0.906', '-0.67', '#sadness', '#anger', 'negative', '-0.79', 'water_dropwort', 'hemlock_water_dropwort', 'water_hemlock', 'plant_life', 'poisonous_plant']
senticnet['black_inferiority'] = ['-0.57', '0', '0.53', '-0.63', '#sadness', '#disgust', 'negative', '-0.58', 'low_status', 'lowness', 'backseat', 'lowliness', 'subordinateness']
senticnet['black_ink'] = ['0.031', '0.08', '0.023', '0.069', '#interest', '#admiration', 'positive', '0.039', 'marker', 'graphite_pencil', 'pen', 'mechanical_graphite_pencil', 'stamp_pad']
senticnet['black_labrador'] = ['-0.72', '0', '0.619', '-0.86', '#sadness', '#disgust', 'negative', '-0.73', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['black_leather'] = ['-0.99', '0', '0.806', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'wear', 'biohazard_suit', 'coverall', 'protective_garment', 'wearable']
senticnet['black_leather_belt'] = ['0', '0.072', '0', '0.064', '#interest', '#admiration', 'positive', '0.068', 'zipper', 'pant', 'trousers', 'cowboy_boot', 'accessory']
senticnet['black_leather_coat'] = ['0', '-0.08', '0.105', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'underwear', 'shirt', 'panties', 'robe', 'slack']
senticnet['black_leather_vest'] = ['-0.99', '0', '0.806', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'wear', 'biohazard_suit', 'coverall', 'protective_garment', 'wearable']
senticnet['black_licorice'] = ['0.965', '0.775', '0', '0.968', '#joy', '#admiration', 'positive', '0.903', 'snot', 'bad_taste', 'mouth_part', 'beautiful_smile', 'near_mouth']
senticnet['black_limousine'] = ['0', '0', '-0.04', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'good_band', 'band', 'kit', 'procession', 'oblivion']
senticnet['black_liquid'] = ['-0.11', '0', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.10', 'black_water', 'dirt', 'energy_source', 'dark_liquid', 'greasy']
senticnet['black_locust'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'locust_tree']
senticnet['black_mage'] = ['0.021', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'plastic_bottle', 'party', 'use_party', 'lighter_air', 'compress_air']
senticnet['black_magic'] = ['-0.72', '0', '0.759', '-0.87', '#anger', '#disgust', 'negative', '-0.78', 'magic', 'thaumaturgy', 'enchantment', 'witchcraft', 'witchery']
senticnet['black_mamba'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'elapid', 'ophidian', 'serpent', 'krait', 'taipan']
senticnet['black_mane'] = ['-0.60', '0', '0.895', '-0.54', '#sadness', '#anger', 'negative', '-0.68', 'coal_black', 'black', 'pitch_black', 'soot_black', 'achromatic_color']
senticnet['black_manta'] = ['-0.98', '0', '0.714', '-0.90', '#sadness', '#disgust', 'negative', '-0.86', 'stupid', 'stupid_person', 'stupe', 'dullard', 'dolt']
senticnet['black_mask'] = ['-0.86', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.86', 'demon', 'fool', 'cannibalism', 'jolly', 'treachery']
senticnet['black_moustache'] = ['0.209', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.118', 'old_dance', 'dance', 'slow_dance', 'dancer', 'enter_contest']
senticnet['black_mustache'] = ['0', '0', '0.841', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'ancient', 'many_years', 'year', 'old', 'teen']
senticnet['black_nazarene'] = ['0', '0.82', '0', '0.847', '#interest', '#admiration', 'positive', '0.833', 'reverently', 'hero-worship', 'idolize', 'veneration', 'reverentially']
senticnet['black_nightshade'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'meadow_grass', 'grass', 'manna_grass', 'sweet_grass', 'wood_meadowgrass']
senticnet['black_ooze'] = ['-0.09', '0', '-0.07', '-0.06', '#sadness', '#fear', 'negative', '-0.07', 'blow', 'gape', 'move_air', 'mud', 'gash']
senticnet['black_orchid'] = ['0', '0', '-0.14', '-0.03', '#fear', '#disgust', 'negative', '-0.09', 'pine_cone', 'fallen_leaf', 'poison_ivy', 'detritus', 'tress']
senticnet['black_overcoat'] = ['0', '-0.08', '0.105', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'underwear', 'shirt', 'panties', 'robe', 'slack']
senticnet['black_pavement'] = ['0', '0', '0.078', '0', '#anger', '#anger', 'negative', '-0.07', 'street_city', 'small_street', 'walkway', 'pave', 'dark_road']
senticnet['black_pepper'] = ['0.1', '0', '0', '0', '#joy', '#joy', 'positive', '0.1', 'burn_material', 'ash', 'stay_warm', 'bonfire', 'campfire']
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senticnet['black_tupelo'] = ['0.958', '-0.82', '0', '0.924', '#joy', '#admiration', 'positive', '0.901', 'bunchberry', 'dwarf_cornel', 'crackerberry', 'pudding_berry', 'common_white_dogwood']
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senticnet['black_widow'] = ['0', '0', '-0.87', '0', '#fear', '#fear', 'negative', '-0.87', 'beetle', 'many_insects', 'grasshopper', 'firefly', 'mouse']
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senticnet['blackberry_maker'] = ['0.807', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.881', 'fruit', 'mobile_phone', 'drinkable', 'savory', 'eat_fruit']
senticnet['blackberry_maker_research'] = ['0.807', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.881', 'fruit', 'mobile_phone', 'drinkable', 'savory', 'eat_fruit']
senticnet['blackberry_operating_system'] = ['0.807', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.881', 'fruit', 'mobile_phone', 'drinkable', 'savory', 'eat_fruit']
senticnet['blackberry_phone'] = ['0.807', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.881', 'fruit', 'mobile_phone', 'drinkable', 'savory', 'eat_fruit']
senticnet['blackberry_platform'] = ['0.807', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.881', 'fruit', 'mobile_phone', 'drinkable', 'savory', 'eat_fruit']
senticnet['blackberry_smartphone'] = ['0.807', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.881', 'fruit', 'mobile_phone', 'drinkable', 'savory', 'eat_fruit']
senticnet['blackberry_storm'] = ['0.807', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.881', 'fruit', 'mobile_phone', 'drinkable', 'savory', 'eat_fruit']
senticnet['blackberry_torch'] = ['0.027', '-0.09', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'cookie', 'pudding', 'custard', 'candy', 'muffin']
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senticnet['blackbuck_antelope'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'argali', 'argal', 'urial', 'white_sheep', 'mountain_sheep']
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senticnet['blacktail'] = ['0', '0', '-0.95', '-0.81', '#fear', '#disgust', 'negative', '-0.88', 'herb', 'plant', 'poisonous_plant', 'yellow_flower', 'lichen']
senticnet['blacktail_deer'] = ['0', '-0.97', '0', '0', '#surprise', '#surprise', 'positive', '0.977', 'cougar', 'skunk', 'lion', 'tiger', 'lynx']
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senticnet['blade_trilogy'] = ['0', '0', '-0.11', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'hunting_season', 'hound', 'cougar', 'outlaw', 'dragon']
senticnet['blade_weapon'] = ['-0.91', '0', '0.718', '-0.94', '#sadness', '#disgust', 'negative', '-0.85', 'knife', 'blade', 'cut', 'sword', 'weapon']
senticnet['bladecenter'] = ['0.637', '-0.91', '0', '0.757', '#surprise', '#admiration', 'positive', '0.768', 'tractability', 'manageableness', 'docility', 'obedience', 'amenability']
senticnet['bladed_knife'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'useful_tool']
senticnet['bladed_weapon'] = ['0.93', '-0.78', '0', '0.873', '#joy', '#admiration', 'positive', '0.861', 'blade', 'brand', 'steel', 'claymore', 'sword']
senticnet['bladelogic'] = ['-0.63', '0', '0.862', '-0.52', '#sadness', '#anger', 'negative', '-0.67', 'knot', 'bunch', 'tuft', 'tussock', 'agglomeration']
senticnet['blah'] = ['-0.91', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.84', 'boredom', 'depression', 'drivel', 'dissatisfaction', 'derogatory']
senticnet['blaine_anderson'] = ['0.761', '0.566', '-0.21', '0.293', '#joy', '#interest', 'positive', '0.352', 'gratify', 'jubilation', 'gloat', 'gloating', 'gloatingly']
senticnet['blair_witch_project'] = ['-0.08', '-0.01', '-0.16', '-0.13', '#fear', '#disgust', 'negative', '-0.09', 'hockey_game', 'movie', 'scary_movie', 'cinema', 'cctv']
senticnet['blak'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['blake_shelton_barn'] = ['0.061', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.055', 'cook', 'stick_butter', 'find_recipe', 'bake_oven', 'food_bake']
senticnet['blakean'] = ['-0.77', '0', '0.885', '-0.73', '#sadness', '#anger', 'negative', '-0.8', 'fallacy', 'misconception', 'paralogism', 'logical_fallacy', 'pseudoscience']
senticnet['blamable'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'forgivable', 'excusable', 'mercifully', 'merciful', 'mercifulness']
senticnet['blame'] = ['-0.42', '0', '-0.21', '-0.20', '#sadness', '#fear', 'negative', '-0.28', 'injustice', 'put_in_jail', 'bad_feeling', 'complication', 'imprisonment']
senticnet['blame_squarely'] = ['-0.81', '0', '0.979', '-0.88', '#anger', '#disgust', 'negative', '-0.89', 'vexation', 'dead_weight', 'imposition', 'worry', 'concern']
senticnet['blameable'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'forgivable', 'excusable', 'mercifully', 'merciful', 'mercifulness']
senticnet['blameless_life'] = ['-0.74', '0', '0.585', '-0.58', '#sadness', '#anger', 'negative', '-0.63', 'lack_of_quality', 'meagreness', 'poorness', 'scantiness', 'scantness']
senticnet['blamelessness'] = ['0.742', '0', '0', '0.706', '#joy', '#admiration', 'positive', '0.724', 'conjugal', 'yawn-inducing', 'connubial', 'nuptial', 'alone-time']
senticnet['blameworthiness'] = ['-0.98', '0', '0.657', '-0.95', '#sadness', '#disgust', 'negative', '-0.86', 'impeachability', 'indictability', 'guiltiness', 'culpability', 'culpableness']
senticnet['blameworthy'] = ['0', '0', '-0.88', '-0.94', '#fear', '#disgust', 'negative', '-0.91', 'blameworthiness', 'censurable', 'culpable', 'reprehensible', 'blame']
senticnet['blanc_et'] = ['-0.58', '0', '0.661', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'explosive', 'cordite', 'dynamite', 'plastic_explosive', 'explosive_compound']
senticnet['blanchimont'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['bland'] = ['0', '0', '0.773', '-0.93', '#anger', '#disgust', 'negative', '-0.85', 'uninteresting', 'dull', 'boring', 'banal', 'tasteless']
senticnet['bland_diet'] = ['-0.03', '0.146', '-0.08', '0.208', '#interest', '#admiration', 'positive', '0.058', 'light_diet', 'soft_diet', 'spoon_food', 'diet', 'ulcer_diet']
senticnet['blandish'] = ['-0.10', '0', '-0.05', '-0.24', '#sadness', '#disgust', 'negative', '-0.13', 'adulate', 'suck_up', 'toady', 'bootlick', 'kowtow']
senticnet['blandly'] = ['-0.86', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.92', 'dully', 'mutely', 'wearily', 'faintly', 'cheerily']
senticnet['blank'] = ['0', '0', '-0.91', '-0.98', '#fear', '#disgust', 'negative', '-0.95', 'space', 'lacuna', 'dummy', 'clean', 'vacuous']
senticnet['blank_cartridge'] = ['0.978', '-0.57', '0', '0.793', '#joy', '#admiration', 'positive', '0.782', 'pistol', 'handgun', 'side_arm', 'shooting_iron', 'semiautomatic']
senticnet['blank_check'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'address_book', 'get_rich', 'ladle', 'swiss_army_knife', 'whisk']
senticnet['blank_cheque'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'address_book', 'get_rich', 'ladle', 'swiss_army_knife', 'whisk']
senticnet['blank_piece'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'gift_wrap', 'kraft', 'kraft_paper', 'wrapping_paper', 'paper']
senticnet['blank_sheet'] = ['0.022', '-0.05', '0.079', '-0.11', '#anger', '#disgust', 'negative', '-0.02', 'stationery', 'ink', 'ball_point_pen', 'refill', 'note']
senticnet['blank_slate'] = ['0.992', '-0.66', '0', '0.536', '#joy', '#surprise', 'positive', '0.729', 'nous', 'mind', 'brain', 'psyche', 'head']
senticnet['blank_space'] = ['-0.98', '0', '0.959', '-0.68', '#sadness', '#anger', 'negative', '-0.87', 'expanse', 'margin', 'indentation', 'indention', 'indent']
senticnet['blank_spaceation'] = ['-0.98', '0', '0.955', '-0.69', '#sadness', '#anger', 'negative', '-0.87', 'expanse', 'margin', 'indent', 'blank_spaceion', 'blank_space']
senticnet['blank_spaceion'] = ['-0.98', '0', '0.951', '-0.69', '#sadness', '#anger', 'negative', '-0.87', 'expanse', 'margin', 'blank_spaceation', 'indent', 'blank_space']
senticnet['blank_stare'] = ['-0.16', '0', '-0.14', '-0.09', '#sadness', '#fear', 'negative', '-0.13', 'mind_boggle', 'illogical', 'emotional_response', 'epiphany', 'introspection']
senticnet['blank_tile'] = ['0.801', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.796', 'verbosity', 'sport_play', 'excite_game', 'contest', 'fun_game']
senticnet['blank_wall'] = ['0.952', '-0.52', '0', '0.835', '#joy', '#admiration', 'positive', '0.771', 'billboard', 'hoarding', 'sandwich_board', 'scoreboard', 'shingle']
senticnet['blanket'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'duvet_cover', 'pillow', 'comforter', 'pillowcase', 'bed_sheet']
senticnet['blanket_immunity'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'address_book', 'get_rich', 'ladle', 'swiss_army_knife', 'whisk']
senticnet['blanket_prohibition'] = ['0', '0', '-0.9', '-0.79', '#fear', '#disgust', 'negative', '-0.84', 'forbid', 'prohibit', 'veto', 'disallow', 'interdict']
senticnet['blanket_stitch'] = ['-0.03', '-0.00', '-0.05', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'bandage', 'needle_thread', 'sew', 'spasm', 'suture']
senticnet['blanket_waiver'] = ['0', '0', '-0.75', '-0.71', '#fear', '#disgust', 'negative', '-0.73', 'foreswear', 'relinquish', 'dispense_with', 'forgo', 'throw_overboard']
senticnet['blankie'] = ['0.032', '0', '0', '0.077', '#joy', '#admiration', 'positive', '0.054', 'embrace', 'hug', 'caress', 'kiss', 'love']
senticnet['blankly'] = ['0', '-0.87', '0', '-0.93', '#surprise', '#disgust', 'negative', '-0.02', 'mutely', 'numbly', 'dumbly', 'glumly', 'distractedly']
senticnet['blanton'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'dicky', 'impaired', 'lessened', 'diminished']
senticnet['blare'] = ['-0.03', '-0.05', '0.071', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'chamber_music', 'stressful_situation', 'make_sound', 'low_note', 'annoy_neighbor']
senticnet['blaring'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'noisy', 'noise_pollution', 'make_sound', 'shouting', 'loud']
senticnet['blaring_music'] = ['-0.03', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.04', 'rabbit', 'squirrel', 'mole', 'chipmunk', 'nymph']
senticnet['blarney'] = ['0', '0', '-0.83', '-0.74', '#fear', '#disgust', 'negative', '-0.79', 'wheedle', 'palaver', 'cajole', 'coax', 'inveigle']
senticnet['blaspheme'] = ['-0.89', '0.161', '-0.61', '0.053', '#sadness', '#fear', 'negative', '-0.32', 'heartrending', 'heartbreaking', 'grievous', 'grievously', 'brokenheartedness']
senticnet['blaspheme_god'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitentially', 'penitent', 'penitence', 'repentant']
senticnet['blasphemed_against'] = ['-0.88', '0', '0.929', '-0.84', '#sadness', '#anger', 'negative', '-0.88', 'paynim', 'nonreligious_person', 'idolizer', 'gentile', 'idol_worshiper']
senticnet['blasphemous'] = ['0.615', '-0.57', '0', '0.502', '#joy', '#surprise', 'positive', '0.563', 'aweless', 'awless', 'disrespectful', 'sacrilegious', 'irreverent']
senticnet['blasphemy'] = ['-0.84', '0', '0.882', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'perversion', 'fetishism', 'paraphilia', 'sexual_perversion', 'buggery']
senticnet['blasphemy_against'] = ['-0.76', '0', '0.519', '-0.56', '#sadness', '#disgust', 'negative', '-0.61', 'scurrility', 'billingsgate', 'disrespect', 'discourtesy', 'vituperation']
senticnet['blast'] = ['0', '-0.05', '-0.12', '-0.12', '#fear', '#disgust', 'negative', '-0.06', 'invade', 'combat', 'fight_against', 'use_military', 'lose_war']
senticnet['blast_overpressure'] = ['-0.84', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.85', 'inferno', 'forest_fire', 'fire_light', 'dismissal', 'wash_mouth_soap']
senticnet['blast_wave'] = ['-0.90', '0', '0.825', '-0.61', '#sadness', '#anger', 'negative', '-0.78', 'flaming', 'combustion', 'fire', 'flame', 'internal_combustion']
senticnet['blastema'] = ['-0.81', '0', '0.504', '-0.96', '#sadness', '#disgust', 'negative', '-0.76', 'freshen', 'air_out', 'air', 'renew', 'vent']
senticnet['blaster_fire'] = ['0.955', '-0.93', '0', '0', '#joy', '#surprise', 'positive', '0.943', 'tack', 'foot_part', 'casual_shoes', 'spike', 'pin']
senticnet['blasting_cap'] = ['0.13', '0.097', '-0.07', '0.198', '#joy', '#admiration', 'positive', '0.087', 'jiggle', 'back_forth', 'vibrate', 'blend', 'mixture']
senticnet['blastocyst'] = ['0', '0.067', '0', '0.07', '#interest', '#admiration', 'positive', '0.069', 'peck', 'bushel', 'chicken_egg', 'ovum', 'egg']
senticnet['blastocyst_stage'] = ['0', '0.067', '0', '0.07', '#interest', '#admiration', 'positive', '0.069', 'peck', 'bushel', 'chicken_egg', 'ovum', 'egg']
senticnet['blastogenesis'] = ['-0.77', '0', '0.764', '-0.54', '#sadness', '#anger', 'negative', '-0.69', 'pullulation', 'apomixis', 'asexual_reproduction', 'budding', 'fission']
senticnet['blastula'] = ['0', '0.067', '0', '0.07', '#interest', '#admiration', 'positive', '0.069', 'peck', 'bushel', 'chicken_egg', 'ovum', 'egg']
senticnet['blastula_stage'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'nonmotile', 'immobile']
senticnet['blatancy'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'patency', 'obviousness']
senticnet['blatant'] = ['0', '0', '-0.78', '-0.9', '#fear', '#disgust', 'negative', '-0.84', 'clamant', 'strident', 'conspicuous', 'blazing', 'clamorous']
senticnet['blatant_advertisement'] = ['-0.02', '0', '-0.10', '0', '#sadness', '#fear', 'negative', '-0.06', 'ads', 'boring', 'bother', 'stop_watch', 'annoying']
senticnet['blatant_discrimination'] = ['-0.96', '0', '0.766', '-0.96', '#sadness', '#disgust', 'negative', '-0.89', 'calumniation', 'traducement', 'hatchet_job', 'defamation', 'calumny']
senticnet['blatant_disregard'] = ['0', '0', '0.853', '-0.84', '#anger', '#disgust', 'negative', '-0.84', 'flagrant', 'cry', 'glaring', 'gross', 'rank']
senticnet['blatant_interference'] = ['-0.99', '0', '0.581', '-0.97', '#sadness', '#disgust', 'negative', '-0.84', 'obstruct_justice', 'football_rule', 'block_road', 'hinder', 'obstruction']
senticnet['blatant_lie'] = ['0.739', '-0.89', '0', '0.702', '#joy', '#surprise', 'positive', '0.78', 'pap', 'content', 'pablum', 'folderol', 'drivel']
senticnet['blatant_racism'] = ['-0.96', '0', '0.766', '-0.96', '#sadness', '#disgust', 'negative', '-0.89', 'calumniation', 'traducement', 'hatchet_job', 'defamation', 'calumny']
senticnet['blatantly'] = ['0', '0', '0.853', '-0.84', '#anger', '#disgust', 'negative', '-0.84', 'flagrant', 'cry', 'glaring', 'gross', 'rank']
senticnet['blatantly_discriminatory'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
senticnet['blatantly_racist'] = ['0.585', '-0.7', '0', '0.586', '#surprise', '#admiration', 'positive', '0.624', 'jingoistic', 'ultranationalistic', 'superpatriotic', 'nationalistic', 'patriotic']
senticnet['blate'] = ['0.691', '-0.97', '0', '0.771', '#surprise', '#admiration', 'positive', '0.812', 'squeal', 'let_loose', 'churr', 'honk', 'sibilate']
senticnet['blatella_germanica'] = ['0', '0', '-0.92', '-0.97', '#fear', '#disgust', 'negative', '-0.95', 'annoy_bug', 'insect', 'bug', 'hare', 'nymph']
senticnet['blatent'] = ['0', '0', '-0.78', '-0.9', '#fear', '#disgust', 'negative', '-0.84', 'clamant', 'strident', 'conspicuous', 'blazing', 'clamorous']
senticnet['blather'] = ['0', '0', '0.919', '-0.74', '#anger', '#disgust', 'negative', '-0.83', 'stupid', 'silly', 'goofy', 'dumb', 'foolish']
senticnet['blattella_germanica'] = ['0', '0', '-0.92', '-0.97', '#fear', '#disgust', 'negative', '-0.95', 'annoy_bug', 'insect', 'bug', 'hare', 'nymph']
senticnet['blaze'] = ['0.983', '0', '0', '0.781', '#joy', '#admiration', 'positive', '0.882', 'brightness', 'glare', 'brilliance', 'glitter', 'glister']
senticnet['blazing'] = ['0', '0', '-0.79', '-0.90', '#fear', '#disgust', 'negative', '-0.84', 'clamant', 'strident', 'conspicuous', 'blatant', 'clamorous']
senticnet['blazing_fire'] = ['0.641', '-0.73', '0', '0.798', '#surprise', '#admiration', 'positive', '0.724', 'ablaze', 'aflame', 'afire', 'aflare', 'lighted']
senticnet['blazing_sun'] = ['0', '0', '0.943', '-0.8', '#anger', '#disgust', 'negative', '-0.87', 'feel_dirty', 'unclean', 'dirty', 'yucky', 'filthy']
senticnet['bleach_blonde_hair'] = ['-0.91', '0', '0.869', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'talk_hairdresser', 'hair_get_short', 'hair_become_short', 'keep_hair_short', 'hair_wash']
senticnet['bleached_blond'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'hair_color', 'purple', 'violet', 'dark_light', 'hue']
senticnet['bleached_blond_hair'] = ['-0.91', '0', '0.869', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'talk_hairdresser', 'hair_get_short', 'hair_become_short', 'keep_hair_short', 'hair_wash']
senticnet['bleached_hair'] = ['-0.96', '0', '0.636', '-0.64', '#sadness', '#disgust', 'negative', '-0.75', 'burn', 'tan', 'hyperpigmentation', 'sunburn', 'physical_condition']
senticnet['bleached_pulp'] = ['0.024', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'stump', 'plank', 'timber', 'paper', 'mahogany']
senticnet['bleacher'] = ['0.953', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.929', 'hockey_fan', 'goalie', 'competitor', 'spectator', 'hockey_stick']
senticnet['bleacher_section'] = ['0.831', '-0.82', '0', '0.826', '#joy', '#admiration', 'positive', '0.826', 'deck', 'freeboard_deck', 'weather_deck', 'shelter_deck', 'boat_deck']
senticnet['bleaching_clay'] = ['-0.71', '0', '0.704', '-0.89', '#sadness', '#disgust', 'negative', '-0.76', 'soil', 'mire', 'dirt', 'mud', 'bleaching_earth']
senticnet['bleaching_earth'] = ['-0.71', '0', '0.704', '-0.89', '#sadness', '#disgust', 'negative', '-0.76', 'soil', 'mire', 'dirt', 'bleaching_clay', 'mud']
senticnet['bleah'] = ['0', '0', '0', '-0.66', '#disgust', '#disgust', 'negative', '-0.66', 'disgusting', 'fed_up', 'disgusted', 'disgust', 'dishonor']
senticnet['bleak'] = ['-0.04', '-0.02', '0.062', '-0.1', '#anger', '#disgust', 'negative', '-0.04', 'darkness', 'dark', 'dull', 'moon_star', 'cold_day']
senticnet['bleak_future'] = ['0', '0', '0', '-0.70', '#disgust', '#disgust', 'negative', '-0.70', 'uncertainty', 'uncertain_environment', 'insecure', 'blur', 'anxiety']
senticnet['bleak_landscape'] = ['-0.55', '0', '0.912', '-0.65', '#anger', '#disgust', 'negative', '-0.70', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['bleak_outlook'] = ['0', '0', '0', '-0.70', '#disgust', '#disgust', 'negative', '-0.70', 'uncertainty', 'uncertain_environment', 'insecure', 'blur', 'anxiety']
senticnet['bleak_prospect'] = ['0', '0', '0', '-0.70', '#disgust', '#disgust', 'negative', '-0.70', 'uncertainty', 'uncertain_environment', 'insecure', 'blur', 'anxiety']
senticnet['bleaker'] = ['-0.79', '0.262', '-0.45', '-0.04', '#sadness', '#fear', 'negative', '-0.25', 'downhearted', 'down_in_mouth', 'depressed', 'downcast', 'low-spirited']
senticnet['bleakest'] = ['-0.79', '0.262', '-0.45', '-0.04', '#sadness', '#fear', 'negative', '-0.25', 'downhearted', 'down_in_mouth', 'depressed', 'downcast', 'low-spirited']
senticnet['bleakly'] = ['-0.93', '0', '0.508', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'nakedness', 'cloud', 'glumness', 'desolation', 'bareness']
senticnet['bleakness'] = ['-0.93', '0', '0.508', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'nakedness', 'cloud', 'glumness', 'desolation', 'bareness']
senticnet['bleary'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'hazy', 'blur', 'blurry', 'blurred', 'foggy']
senticnet['bleary-eyed'] = ['0.767', '-0.89', '0', '0', '#joy', '#surprise', 'positive', '0.833', 'groggy', 'jet-lagged', 'clear-headed', 'wide-awake', 'hung-over']
senticnet['bleat'] = ['0', '0', '0.802', '-0.75', '#anger', '#disgust', 'negative', '-0.78', 'cry', 'yell', 'scream', 'hollo', 'call']
senticnet['bleb'] = ['-0.78', '0', '0.855', '-0.99', '#anger', '#disgust', 'negative', '-0.87', 'blood_blister', 'pustule', 'water_blister', 'vesicle', 'cyst']
senticnet['blechnum'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['bleed'] = ['0', '0', '-0.75', '-0.74', '#fear', '#disgust', 'negative', '-0.75', 'bloody', 'hurt', 'suffer', 'damage', 'physical_pain']
senticnet['bleed_profusely'] = ['-0.10', '-0.04', '-0.05', '-0.15', '#sadness', '#disgust', 'negative', '-0.06', 'acid_indigestion', 'get_full', 'burp', 'indigestion', 'become_full']
senticnet['bleedin'] = ['-0.89', '0', '0.739', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'rot', 'bunk', 'bunkum', 'buncombe', 'guff']
senticnet['bleeding'] = ['-0.93', '0', '0.945', '-0.93', '#anger', '#disgust', 'negative', '-0.93', 'break_window', 'break_window_shop', 'accident', 'break_mirror', 'damage']
senticnet['bleeding_badly'] = ['-0.95', '0', '0.712', '-0.52', '#sadness', '#anger', 'negative', '-0.73', 'uncut', 'unharmed', 'unscathed', 'unwounded', 'uninjured']
senticnet['bleeding_heavily'] = ['-0.95', '0', '0.712', '-0.52', '#sadness', '#anger', 'negative', '-0.73', 'uncut', 'unharmed', 'unscathed', 'unwounded', 'uninjured']
senticnet['bleeding_profusely'] = ['-0.52', '0', '0.785', '-0.61', '#anger', '#disgust', 'negative', '-0.64', 'gap', 'rathole', 'aperture', 'hole', 'cranny']
senticnet['bleeding_ulcer'] = ['0.934', '-0.88', '0', '0.797', '#joy', '#surprise', 'positive', '0.872', 'scan', 'checkup', 'scrutiny', 'medical_examination', 'medical_exam']
senticnet['bleep'] = ['-0.76', '0', '-0.89', '0', '#sadness', '#fear', 'negative', '-0.82', 'blip', 'blip', 'insignificant', 'beep', 'lag']
senticnet['blemish'] = ['-0.22', '-0.03', '0.026', '-0.13', '#sadness', '#disgust', 'negative', '-0.08', 'burn_toast', 'bad_eyesight', 'water_bubble', 'dig_tunnel', 'vermin']
senticnet['blemished'] = ['-0.85', '0', '0.799', '-0.86', '#sadness', '#disgust', 'negative', '-0.83', 'scarred', 'flyblown', 'marred', 'acned', 'pimpled']
senticnet['blend'] = ['0.13', '0.097', '-0.07', '0.198', '#joy', '#admiration', 'positive', '0.087', 'jiggle', 'back_forth', 'vibrate', 'fuse', 'mixture']
senticnet['blended_scotch_whisky'] = ['0.877', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.919', 'score', 'economical', 'scotch_whiskey', 'spoil', 'scottish']
senticnet['blended_whisky'] = ['0.877', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.919', 'score', 'economical', 'scotch_whiskey', 'spoil', 'scottish']
senticnet['blender'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'salad_shaker', 'store_shelf', 'kitchen_tool', 'pliers', 'ladle']
senticnet['blender_jar'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'salad_shaker', 'store_shelf', 'kitchen_tool', 'pliers', 'ladle']
senticnet['blending'] = ['-0.77', '0', '0.914', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'confluence', 'conflux', 'merging', 'homogenization', 'combination']
senticnet['blendtec'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'salad_shaker', 'store_shelf', 'kitchen_tool', 'pliers', 'ladle']
senticnet['blepharism'] = ['-0.69', '0', '0.849', '-0.71', '#anger', '#disgust', 'negative', '-0.75', 'muscle_spasm', 'spasm', 'cramp', 'tic', 'twitch']
senticnet['blepharitis'] = ['-0.99', '0', '0.964', '-0.70', '#sadness', '#anger', 'negative', '-0.88', 'inflammation', 'diverticulitis', 'epiglottitis', 'orchitis', 'prostatitis']
senticnet['blepharoplasty'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
senticnet['blepharospasm'] = ['-0.56', '0', '0.981', '-0.67', '#anger', '#disgust', 'negative', '-0.73', 'tenesmus', 'muscle_spasm', 'twitch', 'twitching', 'vellication']
senticnet['bleskiver'] = ['0', '-0.11', '0', '0', '#surprise', '#surprise', 'positive', '0.113', 'cake', 'cookie', 'mousse', 'doughnut', 'brownie']
senticnet['bless'] = ['0.903', '0', '0', '0', '#joy', '#joy', 'positive', '0.903', 'gospel', 'religion', 'prayer', 'faith', 'world_religion']
senticnet['bless_god'] = ['-0.98', '0', '0.654', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'deprive', 'expropriate', 'disarm', 'unclothe', 'take']
senticnet['blessed'] = ['0', '-0.94', '0', '0.763', '#surprise', '#admiration', 'positive', '0.853', 'sanctity', 'desecrate', 'holiness', 'sacred', 'saintliness']
senticnet['blessed_augustine'] = ['-0.81', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.79', 'stifle', 'forgotten', 'old', 'touch_ground', 'seldom']
senticnet['blessed_virgin'] = ['0', '0.82', '0', '0.847', '#interest', '#admiration', 'positive', '0.833', 'reverently', 'hero-worship', 'idolize', 'veneration', 'reverentially']
senticnet['blessedly'] = ['-0.65', '0', '0.858', '-0.83', '#anger', '#disgust', 'negative', '-0.78', 'turmoil', 'convulsion', 'tempest', 'disorder', 'earthquake']
senticnet['blessing'] = ['0.577', '-0.77', '0', '0.517', '#joy', '#surprise', 'positive', '0.621', 'admiration', 'appreciation', 'reward', 'acceptation', 'support']
senticnet['blessing_ceremony'] = ['0.82', '0', '0', '0.885', '#joy', '#admiration', 'positive', '0.853', 'religious_event', 'sunday', 'happy_event', 'tradition', 'wedding']
senticnet['blest'] = ['0.969', '-0.86', '0', '0.631', '#joy', '#surprise', 'positive', '0.823', 'assuage', 'perfect', 'make_look_better', 'relieve', 'sublimate']
senticnet['bletchley_park'] = ['-0.05', '-0.19', '-0.17', '0.219', '#surprise', '#admiration', 'positive', '0.046', 'riddle', 'mystery', 'obituary', 'editorial', 'book_genre']
senticnet['bletso'] = ['0.066', '0.163', '-0.06', '0.163', '#interest', '#admiration', 'positive', '0.083', 'lord', 'royal_title', 'almighty', 'duchess', 'omniscient']
senticnet['bleu_cheese'] = ['0', '0', '0.066', '-0.07', '#anger', '#disgust', 'negative', '-0.07', 'container_cup', 'cup', 'grease_trap', 'dish', 'open_container']
senticnet['bleue'] = ['-0.85', '0', '0.968', '-0.89', '#anger', '#disgust', 'negative', '-0.90', 'family', 'household', 'house', 'home', 'broken_home']
senticnet['blifil'] = ['-0.98', '0', '0.537', '-0.72', '#sadness', '#disgust', 'negative', '-0.75', 'reprimand', 'wigging', 'rebuke', 'reproof', 'reproval']
senticnet['blight'] = ['-0.87', '0', '0.889', '-0.62', '#sadness', '#anger', 'negative', '-0.79', 'ruination', 'ruin', 'deterioration', 'devastation', 'impairment']
senticnet['blighted'] = ['-0.62', '0', '0.854', '-0.99', '#anger', '#disgust', 'negative', '-0.82', 'destroyed', 'wrecked', 'impoverished', 'spoilt', 'extinguished']
senticnet['blighter'] = ['-0.99', '0', '0.636', '-0.80', '#sadness', '#disgust', 'negative', '-0.81', 'pest', 'dullard', 'curse', 'nudnick', 'cuss']
senticnet['blimey'] = ['0.746', '-0.67', '0', '0.529', '#joy', '#surprise', 'positive', '0.651', 'labor', 'cinch', 'undertaking', 'project', 'task']
senticnet['blimp'] = ['-0.04', '0.092', '0', '0.062', '#interest', '#admiration', 'positive', '0.036', 'hangar', 'aircraft_carrier', 'hot_air_balloon', 'war_museum', 'spaceship']
senticnet['blind'] = ['-0.22', '-0.03', '0.026', '-0.13', '#sadness', '#disgust', 'negative', '-0.08', 'blind_man', 'blind_person', 'shade', 'need_help', 'shadow']
senticnet['blind_arcade'] = ['0.029', '0.087', '0.065', '0.137', '#interest', '#admiration', 'positive', '0.047', 'archway', 'aisle', 'mat', 'passageway', 'right_angle']
senticnet['blind_man'] = ['-0.97', '0', '0.165', '-0.93', '#sadness', '#disgust', 'negative', '-0.69', 'shade', 'need_help', 'blind', 'shadow', 'conceal']
senticnet['blind_nfb'] = ['-0.22', '-0.03', '0.026', '-0.13', '#sadness', '#disgust', 'negative', '-0.08', 'blind_man', 'blind_person', 'shade', 'need_help', 'shadow']
senticnet['blind_obedience'] = ['0.909', '-0.85', '0', '0.846', '#joy', '#surprise', 'positive', '0.87', 'respectability', 'reputability', 'venerability', 'venerableness', 'righteousness']
senticnet['blind_person'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'walking_stick', 'sunglasses', 'disabled', 'poor_visual_perception', 'vision_loss']
senticnet['blind_pig'] = ['0.797', '-0.95', '0', '0.921', '#surprise', '#admiration', 'positive', '0.891', 'lush', 'alcoholic', 'alky', 'dipsomaniac', 'boozer']
senticnet['blind_rage'] = ['0', '0', '1', '0', '#anger', '#anger', 'negative', '-1', 'furious', 'raging', 'stormy', 'roaring', 'wrathful']
senticnet['blind_snake'] = ['-0.57', '0', '0.571', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'ophidian']
senticnet['blindfold'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'surprise', 'suspense', 'bandage', 'muffle', 'blindfolded']
senticnet['blindfolded'] = ['0.786', '0.93', '0', '0.847', '#interest', '#admiration', 'positive', '0.854', 'surprise', 'suspense', 'bandage', 'muffle', 'blindfold']
senticnet['blinding'] = ['0', '0', '0.837', '-0.87', '#anger', '#disgust', 'negative', '-0.85', 'fulgent', 'dazzling', 'egregious', 'glaring', 'glower']
senticnet['blinding_flash'] = ['0', '-0.00', '-0.07', '-0.06', '#fear', '#disgust', 'negative', '-0.04', 'lighten', 'tornado', 'dynamite_explosion', 'forest_fire', 'lightning']
senticnet['blinding_glare'] = ['0.988', '0', '0', '0.789', '#joy', '#admiration', 'positive', '0.889', 'brightness', 'blaze', 'brilliance', 'glitter', 'glister']
senticnet['blinding_light'] = ['0.54', '-0.64', '0', '0.728', '#surprise', '#admiration', 'positive', '0.638', 'gleaming', 'luster', 'polish', 'glow', 'burnish']
senticnet['blinding_snow'] = ['0', '0', '-0.93', '-0.82', '#fear', '#disgust', 'negative', '-0.88', 'thunderstorm', 'snow_storm', 'storm', 'weather', 'rainy']
senticnet['blinding_white'] = ['0', '-0.00', '-0.07', '-0.06', '#fear', '#disgust', 'negative', '-0.04', 'lighten', 'tornado', 'dynamite_explosion', 'forest_fire', 'lightning']
senticnet['blindly'] = ['0', '0.804', '0', '0.792', '#interest', '#admiration', 'positive', '0.798', 'lock-step', 'corporatist', 'two-party', 'lockstep', 'arm-in-arm']
senticnet['blindness'] = ['-0.13', '-0.01', '0.014', '-0.1', '#sadness', '#disgust', 'negative', '-0.05', 'big_tooth', 'fang', 'sharp_tooth', 'long_tail', 'blind']
senticnet['blindside'] = ['-0.63', '0', '0.796', '-0.77', '#anger', '#disgust', 'negative', '-0.73', 'assail', 'profane', 'assault', 'savage', 'violate']
senticnet['blinged_out'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'clothes_style', 'style', 'glamour', 'model', 'formal_clothes']
senticnet['blingy'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['blink'] = ['-0.15', '-0.00', '0', '-0.10', '#sadness', '#disgust', 'negative', '-0.08', 'take_picture', 'try_understand', 'take_shit', 'point_out', 'check_mail']
senticnet['blink_eye'] = ['0', '0.709', '0', '0', '#interest', '#interest', 'positive', '0.709', 'eye_sense', 'sight', 'wink', 'vision', 'eye_function']
senticnet['blinking_light'] = ['-0.76', '0', '0.573', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'gossip', 'gossiper', 'gossipmonger', 'talk_back', 'rumormonger']
senticnet['blinking_red'] = ['-0.15', '-0.00', '0', '-0.10', '#sadness', '#disgust', 'negative', '-0.08', 'take_picture', 'try_understand', 'take_shit', 'point_out', 'check_mail']
senticnet['bliny'] = ['0.52', '-0.69', '0', '0.639', '#surprise', '#admiration', 'positive', '0.617', 'black_bread', 'pumpernickel', 'limpa', 'bread', 'breadstuff']
senticnet['blip'] = ['-0.75', '0', '-0.89', '0', '#sadness', '#fear', 'negative', '-0.82', 'blip', 'bleep', 'insignificant', 'beep', 'lag']
senticnet['blir'] = ['0', '0', '0.785', '-0.72', '#anger', '#disgust', 'negative', '-0.75', 'sharp_nail', 'sharp', 'sharp_end', 'sharp_point', 'pointy']
senticnet['blisco'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['bliss'] = ['0.732', '0', '0', '0.706', '#joy', '#admiration', 'positive', '0.719', 'pleasant_emotion', 'happy_emotion', 'happy_feeling', 'show_affection', 'good_emotion']
senticnet['blissed_out'] = ['-0.60', '0', '0.541', '-0.52', '#sadness', '#anger', 'negative', '-0.55', 'zonk_out', 'pass_out', 'faint', 'conk', 'change_state']
senticnet['blissful'] = ['0', '0.901', '0', '0.827', '#interest', '#admiration', 'positive', '0.864', 'glorified', 'blissfulness', 'elysium', 'bliss', 'elysian']
senticnet['blissfully'] = ['0.732', '0', '0', '0.706', '#joy', '#admiration', 'positive', '0.719', 'pleasant_emotion', 'happy_emotion', 'happy_feeling', 'show_affection', 'good_emotion']
senticnet['blissfulness'] = ['0', '0.895', '0', '0.824', '#interest', '#admiration', 'positive', '0.859', 'glorified', 'blissful', 'elysium', 'bliss', 'elysian']
senticnet['blister'] = ['-0.11', '-0.08', '0.028', '-0.18', '#sadness', '#disgust', 'negative', '-0.06', 'injury', 'contusion', 'broken_bone', 'leg_hurt', 'muscle_pain']
senticnet['blister_agent'] = ['-0.90', '0', '0.529', '-0.84', '#sadness', '#disgust', 'negative', '-0.75', 'poison_gas', 'nerve_agent', 'mustard_gas', 'mustard_agent', 'blistering_agent']
senticnet['blister_foot'] = ['0', '0', '-0.80', '-0.71', '#fear', '#disgust', 'negative', '-0.76', 'swelling', 'breahard', 'trip_fall', 'breafast', 'run_out_breath']
senticnet['blister_rust'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['blistering'] = ['0', '0', '-0.95', '-0.87', '#fear', '#disgust', 'negative', '-0.91', 'deadly', 'acerbic', 'virulent', 'acid', 'venomous']
senticnet['blistering_agent'] = ['-0.90', '0', '0.529', '-0.84', '#sadness', '#disgust', 'negative', '-0.75', 'nerve_gas', 'nerve_agent', 'mustard_gas', 'mustard_agent', 'poison_gas']
senticnet['blistering_heat'] = ['-0.56', '0', '0.578', '-0.74', '#anger', '#disgust', 'negative', '-0.62', 'cold', 'unloving', 'loveless', 'detached', 'unaffectionate']
senticnet['blistering_pace'] = ['-0.04', '0.123', '0', '0.237', '#interest', '#admiration', 'positive', '0.107', 'foot_motion', 'foot_movement', 'foot_action', 'move_ahead', 'stride']
senticnet['blistering_start'] = ['0.034', '-0.02', '0.054', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'compass', 'swiss_army_knife', 'bake_pan', 'coffee_mug', 'wine_opener']
senticnet['blisteringly_fast'] = ['0.746', '-0.67', '0', '0.529', '#joy', '#surprise', 'positive', '0.651', 'labor', 'cinch', 'undertaking', 'project', 'task']
senticnet['blisteringly_hot'] = ['0', '0', '0.954', '-0.76', '#anger', '#disgust', 'negative', '-0.85', 'fervid', 'ardent', 'impassioned', 'fervent', 'fiery']
senticnet['blithe'] = ['0.783', '-0.51', '0', '0.584', '#joy', '#admiration', 'positive', '0.627', 'appetent', 'avid', 'thirsty', 'devouring', 'envious']
senticnet['blithely'] = ['0.653', '0.635', '-0.24', '0.294', '#joy', '#interest', 'positive', '0.335', 'merrily', 'jubilantly', 'happily', 'mirthfully', 'gayly']
senticnet['blitheness'] = ['0.655', '0.635', '-0.23', '0.3', '#joy', '#interest', 'positive', '0.338', 'cheerfully', 'cheerful', 'jolly_up', 'hearten', 'chirk_up']
senticnet['blither'] = ['-0.87', '0', '0.838', '-0.69', '#sadness', '#anger', 'negative', '-0.80', 'prate', 'prattle', 'gibber', 'blabber', 'gabble']
senticnet['blithering'] = ['-0.90', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.89', 'babble', 'incoherently', 'rave', 'incoherent', 'ramble']
senticnet['blitter'] = ['0.865', '-0.80', '0', '0.52', '#joy', '#surprise', 'positive', '0.73', 'set', 'pose', 'lay', 'intersperse', 'put']
senticnet['blizzard'] = ['0', '0', '-0.93', '-0.82', '#fear', '#disgust', 'negative', '-0.88', 'thunderstorm', 'snow_storm', 'storm', 'weather', 'rainy']
senticnet['bloat'] = ['-0.73', '0', '0.94', '-0.87', '#anger', '#disgust', 'negative', '-0.85', 'edema', 'oedema', 'hydrops', 'dropsy', 'puffiness']
senticnet['bloated'] = ['-0.92', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fat', 'heavy_dinner', 'enlarged', 'expanded', 'bulging']
senticnet['bloated_bureaucracy'] = ['0', '0', '0', '-0.94', '#disgust', '#disgust', 'negative', '-0.94', 'boring', 'fill_form', 'annoying', 'form', 'fill_out_form']
senticnet['bloatware'] = ['-0.73', '0', '0.94', '-0.87', '#anger', '#disgust', 'negative', '-0.85', 'edema', 'oedema', 'hydrops', 'dropsy', 'puffiness']
senticnet['blob'] = ['-0.92', '0', '0.783', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'clot', 'coagulum', 'gob', 'clew', 'agglomeration']
senticnet['bloc'] = ['0.397', '0.245', '-0.13', '0.224', '#joy', '#interest', 'positive', '0.183', 'agreement', 'provide', 'advice', 'contribution', 'offer']
senticnet['block'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'building', 'piece', 'cube', 'dice', 'ping_pong_paddle']
senticnet['block-shaped'] = ['0', '-0.85', '0', '0.79', '#surprise', '#admiration', 'positive', '0.824', 'diamond-shaped', 'egg-shaped', 'butterfly-like', 'ovular', 'saw-tooth']
senticnet['block_away'] = ['0', '0', '0', '-0.09', '#disgust', '#disgust', 'negative', '-0.09', 'subway', 'bus', 'train_station', 'railway_station', 'bus_stop']
senticnet['block_blood_flow'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'chest', 'artery', 'vein', 'internal_organ', 'under_head']
senticnet['block_cipher'] = ['-0.52', '0', '0.821', '-0.65', '#anger', '#disgust', 'negative', '-0.66', 'work_out', 'reckon', 'calculate', 'average_out', 'cypher']
senticnet['block_diagram'] = ['0', '0.15', '0.067', '0.118', '#interest', '#admiration', 'positive', '0.067', 'representation', 'outline', 'arithmetic', 'click', 'list_number']
senticnet['block_evasion'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['block_flat'] = ['0', '0.105', '0', '0.076', '#interest', '#admiration', 'positive', '0.09', 'house', 'mansion', 'high_rise_building', 'home', 'modern_home']
senticnet['block_light'] = ['-0.95', '0', '0.485', '-0.93', '#sadness', '#disgust', 'negative', '-0.79', 'shade', 'block', 'shadow', 'dark', 'blind']
senticnet['block_litton_weekend'] = ['0.704', '-0.80', '0', '0', '#joy', '#surprise', 'positive', '0.755', 'quest', 'stunt', 'illusion', 'journey', 'activity']
senticnet['block_off'] = ['-0.57', '0', '0.814', '-0.80', '#anger', '#disgust', 'negative', '-0.73', 'block', 'jam', 'close_up', 'obstruct', 'obturate']
senticnet['block_rain'] = ['0.106', '0.162', '0.037', '0.32', '#interest', '#admiration', 'positive', '0.138', 'block_sun', 'waterproof_jacket', 'waterproof_boot', 'rain_equipment', 'umbrella']
senticnet['block_road'] = ['0', '0', '0.895', '-0.83', '#anger', '#disgust', 'negative', '-0.86', 'roadblock', 'stop', 'block_sunlight', 'shade', 'cover_window']
senticnet['block_sun'] = ['0', '0.071', '0', '0.078', '#interest', '#admiration', 'positive', '0.074', 'sun_cover', 'eclipse', 'cool_temperature', 'nice_day', 'overcast']
senticnet['block_sunlight'] = ['0.939', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.922', 'sun_block', 'shadow', 'block_light', 'parasol', 'eclipse']
senticnet['block_up'] = ['-0.79', '0', '0.925', '-0.87', '#anger', '#disgust', 'negative', '-0.86', 'block', 'jam', 'block_off', 'close', 'close_up']
senticnet['block_vote'] = ['0.611', '-0.82', '0', '0.737', '#surprise', '#admiration', 'positive', '0.726', 'choice', 'option', 'veto', 'multiple_voting', 'balloting']
senticnet['blockade'] = ['-0.78', '0', '0.725', '-0.95', '#sadness', '#disgust', 'negative', '-0.81', 'assail', 'besiege', 'seal_off', 'ebb', 'attack']
senticnet['blockade_runner'] = ['0', '0', '0.08', '0', '#anger', '#anger', 'negative', '-0.08', 'tree_animal', 'small_furry', 'rodent', 'big_mouse', 'small_rodent']
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senticnet['blow_out'] = ['0.991', '0.925', '0', '0', '#joy', '#interest', 'positive', '0.958', 'light_pia_lot_of_light', 'create_light', 'burn_wood', 'burn_paper', 'heat_light']
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senticnet['blow_torch'] = ['0.886', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.881', 'fire_hydrant', 'stair_rail', 'christian_fundamentalist', 'extinguisher', 'reception_desk']
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senticnet['blow_up_balloon'] = ['0', '0.754', '0', '0', '#interest', '#interest', 'positive', '0.754', 'start_computer', 'make_error', 'type_of_keyboard', 'train_open_mind', 'gather_data']
senticnet['blower'] = ['0.628', '-0.96', '0', '0.682', '#surprise', '#admiration', 'positive', '0.759', 'electric_fan', 'fan', 'exhaust_fan', 'punkah', 'device']
senticnet['blowfish'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'puffer', 'pufferfish', 'plectognath', 'triggerfish', 'filefish']
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senticnet['blowing_sand'] = ['-0.06', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.04', 'friction', 'lack_of_water', 'dirt', 'abrasive', 'ground_material']
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senticnet['blowup'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'fragmentation', 'discharge', 'backfire', 'blast']
senticnet['blu_ray'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['blu_ray_box'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['blu_ray_disc'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['blu_ray_dvd'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['blu_ray_dvd_combo'] = ['0', '0.11', '-0.05', '0.149', '#interest', '#admiration', 'positive', '0.067', 'pack_clothes', 'hold_stuff', 'carry_stuff', 'carry_book', 'carry_belongings']
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senticnet['blu_ray_release'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['blu_ray_version'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['blub'] = ['0', '0', '-0.76', '-0.99', '#fear', '#disgust', 'negative', '-0.87', 'whine', 'blubber', 'sniffle', 'snuffle', 'sniveling']
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senticnet['blue-sky'] = ['0.817', '0', '0', '0.922', '#joy', '#admiration', 'positive', '0.869', 'sunshiny', 'pie-in-the-sky', 'drizzly', 'cloudless', 'sun-soaked']
senticnet['blue_bead_lily'] = ['0.773', '-0.55', '0', '0.949', '#joy', '#admiration', 'positive', '0.757', 'cornel', 'dwarf_cornel', 'crackerberry', 'pudding_berry', 'common_white_dogwood']
senticnet['blue_beetle'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'only_bird']
senticnet['blue_blazer'] = ['-0.79', '0', '0.599', '-0.93', '#sadness', '#disgust', 'negative', '-0.77', 'uniform', 'battle_dress', 'dress_uniform', 'fatigue', 'military_uniform']
senticnet['blue_blood'] = ['0.064', '0.087', '0', '0.135', '#interest', '#admiration', 'positive', '0.095', 'kingly', 'king_queen', 'royalty', 'monarch', 'regal']
senticnet['blue_catfish'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'divergent_strabismus', 'exotropia', 'squint', 'abnormality', 'abnormalcy']
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senticnet['blue_collar'] = ['0', '0.108', '0', '0.127', '#interest', '#admiration', 'positive', '0.117', 'laborer', 'work_person', 'occupation', 'worker', 'land_owner']
senticnet['blue_color'] = ['0', '0.108', '0', '0.127', '#interest', '#admiration', 'positive', '0.117', 'color', 'blue', 'blue_sky', 'sea', 'blue_green']
senticnet['blue_color_scheme'] = ['-0.45', '0.081', '-0.25', '0', '#sadness', '#fear', 'negative', '-0.20', 'green_yellow', 'color', 'blue_green', 'cyan', 'purple']
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senticnet['blue_dress_shirt'] = ['-0.79', '0', '0.599', '-0.93', '#sadness', '#disgust', 'negative', '-0.77', 'uniform', 'battle_dress', 'dress_uniform', 'fatigue', 'military_uniform']
senticnet['blue_duiker'] = ['0', '0', '-0.95', '-0.81', '#fear', '#disgust', 'negative', '-0.88', 'herb', 'plant', 'poisonous_plant', 'yellow_flower', 'lichen']
senticnet['blue_enamel'] = ['0.586', '-0.65', '0', '0.503', '#joy', '#surprise', 'positive', '0.58', 'decoration', 'distinguished_service_cross', 'medal', 'medallion', 'palm']
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senticnet['blue_faced_malkoha'] = ['-0.02', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.04', 'wren', 'bird', 'quail', 'sparrow', 'chick']
senticnet['blue_fairy'] = ['0.967', '0.768', '0', '0', '#joy', '#interest', 'positive', '0.867', 'centerfold', 'genus', 'honey_bee', 'infant', 'dove']
senticnet['blue_glow'] = ['0.988', '0', '0', '0.789', '#joy', '#admiration', 'positive', '0.889', 'brightness', 'blaze', 'brilliance', 'glitter', 'glister']
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senticnet['blue_grouse'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['blue_haze'] = ['0', '0', '-0.94', '-0.91', '#fear', '#disgust', 'negative', '-0.92', 'night', 'blackout', 'darkness', 'dark', 'obscure']
senticnet['blue_hue'] = ['-0.93', '0', '0.906', '-0.53', '#sadness', '#anger', 'negative', '-0.79', 'juvenility', 'state', 'immatureness', 'callowness', 'jejuneness']
senticnet['blue_ivy'] = ['0', '0', '0', '0.939', '#admiration', '#admiration', 'positive', '0.939', 'large_family', 'small_child', 'youth', 'quiet_baby', 'deliver_baby']
senticnet['blue_joke'] = ['0.949', '-0.64', '0', '0.937', '#joy', '#admiration', 'positive', '0.842', 'joke', 'jape', 'jest', 'gag', 'laugh']
senticnet['blue_merle'] = ['0.022', '-0.11', '0.014', '0.057', '#surprise', '#admiration', 'positive', '0.045', 'wood', 'many_steps', 'franchise', 'stump', 'timber']
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senticnet['blue_plaque'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'dental_plaque']
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senticnet['blue_rectangle'] = ['0.891', '0.961', '0', '0', '#joy', '#interest', 'positive', '0.926', 'square', 'four_sides', 'rhombus', 'four_corners', 'cube']
senticnet['blue_rgb'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'indigo', 'yellow', 'water_color', 'red', 'violet']
senticnet['blue_ribbon'] = ['0.064', '0.203', '0.014', '0.202', '#interest', '#admiration', 'positive', '0.114', 'compete', 'compete_against', 'score_home_run', 'play_baseball', 'play_hockey']
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senticnet['blue_sash'] = ['-0.53', '0', '0.682', '-0.74', '#anger', '#disgust', 'negative', '-0.65', 'girdle', 'cincture', 'cummerbund', 'waistband', 'waistcloth']
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senticnet['blue_silk'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'pinafore', 'pinny', 'dirndl', 'dress', 'muumuu']
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senticnet['blue_steel'] = ['0.727', '0.783', '0', '0.913', '#interest', '#admiration', 'positive', '0.808', 'find_flower', 'shoe_horse', 'seed_lawn', 'plant_tree', 'dig_grave']
senticnet['blue_story'] = ['0.641', '-0.87', '0', '0.637', '#joy', '#surprise', 'positive', '0.717', 'joke', 'jape', 'jest', 'gag', 'laugh']
senticnet['blue_stripe'] = ['0.942', '-0.61', '0', '0.908', '#joy', '#admiration', 'positive', '0.82', 'adornment', 'cigar_band', 'hatband', 'neckband', 'pinstripe']
senticnet['blue_tarp'] = ['0.024', '0', '0', '0.074', '#joy', '#admiration', 'positive', '0.049', 'sheet', 'cover', 'quilt', 'comforter', 'modesty']
senticnet['blue_tit'] = ['0.039', '0.089', '-0.08', '0.143', '#interest', '#admiration', 'positive', '0.048', 'bird', 'nightingale', 'stork', 'eagle', 'rooster']
senticnet['blue_tongued_skink'] = ['-0.03', '-0.01', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'shit', 'wildflower', 'poop', 'wild_flower', 'dog_poop']
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senticnet['blue_trim'] = ['-0.45', '0.081', '-0.25', '0', '#sadness', '#fear', 'negative', '-0.20', 'green_yellow', 'color', 'blue_green', 'cyan', 'purple']
senticnet['blue_winged_goose'] = ['0.797', '-0.71', '0', '0.736', '#joy', '#admiration', 'positive', '0.751', 'wood_ibis', 'wood_stork', 'ibis', 'wading_bird', 'wader']
senticnet['blue_yellow'] = ['-0.05', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.04', 'yellow_blue', 'hue', 'tint', 'red_blue', 'hue_tint']
senticnet['blueback'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'many_fishes', 'seahorse', 'starfish', 'plankton', 'sea_wee']
senticnet['bluebeard'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'lordosis', 'kyphosis', 'humpback', 'spinal_curvature', 'abnormality']
senticnet['blueberry'] = ['0.753', '-0.78', '0', '0', '#joy', '#surprise', 'positive', '0.77', 'raspberry', 'strawberry', 'herb', 'rhubarb', 'custard']
senticnet['blueberry_juice'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'lemon_juice', 'chocolate_pudding', 'grape_juice', 'orange_juice', 'juice']
senticnet['blueberry_muffin'] = ['0.547', '-0.96', '0', '0.89', '#surprise', '#admiration', 'positive', '0.799', 'pudding', 'ice_cream', 'chocolate', 'chocolate_pudding', 'chocolate_dessert']
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senticnet['bluegrass_band'] = ['-0.04', '-0.04', '-0.04', '-0.04', '#fear', '#disgust', 'negative', '-0.02', 'music_shop', 'music_store', 'musical_instrument_store', 'string_quartet', 'high_school_band']
senticnet['bluegrass_legend'] = ['-0.04', '-0.04', '-0.04', '-0.04', '#fear', '#disgust', 'negative', '-0.02', 'music_shop', 'music_store', 'musical_instrument_store', 'string_quartet', 'high_school_band']
senticnet['bluegrass_music'] = ['-0.04', '-0.04', '-0.04', '-0.04', '#fear', '#disgust', 'negative', '-0.02', 'music_shop', 'music_store', 'musical_instrument_store', 'string_quartet', 'high_school_band']
senticnet['bluegrass_musician'] = ['-0.04', '-0.04', '-0.04', '-0.04', '#fear', '#disgust', 'negative', '-0.02', 'music_shop', 'music_store', 'musical_instrument_store', 'string_quartet', 'high_school_band']
senticnet['blueish'] = ['-0.66', '0', '0.782', '-0.52', '#sadness', '#anger', 'negative', '-0.65', 'lividness', 'pallidness', 'pallor', 'complexion', 'achromasia']
senticnet['blueish-green'] = ['0', '0', '0.841', '-0.71', '#anger', '#disgust', 'negative', '-0.77', 'golden-green', 'greenish-gold', 'ruby-brown', 'orange-copper', 'deep-brown']
senticnet['bluekai'] = ['0.886', '-0.77', '0', '0.815', '#joy', '#admiration', 'positive', '0.826', 'countenance', 'legitimate', 'permit', 'allow', 'let']
senticnet['blueprint'] = ['-0.03', '0.267', '-0.05', '0.21', '#interest', '#admiration', 'positive', '0.097', 'plan', 'draw_blueprint', 'outline', 'forethought', 'create_form']
senticnet['blues'] = ['0.901', '0', '0', '0', '#joy', '#joy', 'positive', '0.901', 'music', 'vapor', 'jazz', 'megrim', 'demoralization']
senticnet['blues_band'] = ['-0.04', '-0.04', '-0.04', '-0.04', '#fear', '#disgust', 'negative', '-0.02', 'music_shop', 'music_store', 'musical_instrument_store', 'string_quartet', 'high_school_band']
senticnet['bluesy_rock'] = ['0.7', '-0.62', '0', '0.58', '#joy', '#surprise', 'positive', '0.636', 'white_magic', 'thaumaturgy', 'juju', 'conjuration', 'supernaturalism']
senticnet['bluesy_sound'] = ['0.584', '-0.62', '0', '0.815', '#surprise', '#admiration', 'positive', '0.675', 'squiggle', 'hand', 'cacography', 'handwriting', 'script']
senticnet['bluetooth'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'run_treadmill', 'complete_thesis', 'dwarf_planet', 'insure', 'ridgeback']
senticnet['bluetooth_capability'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'run_treadmill', 'complete_thesis', 'dwarf_planet', 'insure', 'ridgeback']
senticnet['bluetooth_connection'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'run_treadmill', 'complete_thesis', 'dwarf_planet', 'insure', 'ridgeback']
senticnet['bluetooth_connectivity'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'run_treadmill', 'complete_thesis', 'dwarf_planet', 'insure', 'ridgeback']
senticnet['bluetooth_device'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'run_treadmill', 'complete_thesis', 'dwarf_planet', 'insure', 'ridgeback']
senticnet['bluetooth_headset'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'run_treadmill', 'complete_thesis', 'dwarf_planet', 'insure', 'ridgeback']
senticnet['bluetooth_keyboard'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'run_treadmill', 'complete_thesis', 'dwarf_planet', 'insure', 'ridgeback']
senticnet['bluetooth_phone'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'run_treadmill', 'complete_thesis', 'dwarf_planet', 'insure', 'ridgeback']
senticnet['bluetooth_smart'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'run_treadmill', 'complete_thesis', 'dwarf_planet', 'insure', 'ridgeback']
senticnet['bluetooth_specification'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'run_treadmill', 'complete_thesis', 'dwarf_planet', 'insure', 'ridgeback']
senticnet['bluetooth_technology'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'run_treadmill', 'complete_thesis', 'dwarf_planet', 'insure', 'ridgeback']
senticnet['bluetooth_wireless_technology'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'run_treadmill', 'complete_thesis', 'dwarf_planet', 'insure', 'ridgeback']
senticnet['bluff'] = ['-0.81', '0', '0.595', '-0.88', '#sadness', '#disgust', 'negative', '-0.76', 'deceit', 'deception', 'misrepresentation', 'pretext', 'dissembling']
senticnet['bluff_erosion'] = ['-0.88', '0', '0.907', '-0.52', '#sadness', '#anger', 'negative', '-0.77', 'wearing', 'corrasion', 'eroding', 'eating_away', 'wearing_away']
senticnet['blugold_card'] = ['0.739', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.814', 'receipt', 'bet', 'electric_bill', 'letter_slot', 'deal']
senticnet['bluish'] = ['-0.95', '0', '0.892', '-0.81', '#sadness', '#anger', 'negative', '-0.88', 'blurry', 'foggy', 'hazy', 'muzzy', 'blurred']
senticnet['bluish_black'] = ['-0.7', '0', '0.617', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'silvery', 'blackish', 'achromatic', 'hueless', 'pitchy']
senticnet['bluish_color'] = ['-0.66', '0', '0.782', '-0.52', '#sadness', '#anger', 'negative', '-0.65', 'lividness', 'pallidness', 'pallor', 'complexion', 'achromasia']
senticnet['bluish_discoloration'] = ['-0.66', '0', '0.782', '-0.52', '#sadness', '#anger', 'negative', '-0.65', 'lividness', 'pallidness', 'complexion', 'wanness', 'achromasia']
senticnet['bluish_green'] = ['0', '0.19', '-0.09', '0.175', '#interest', '#admiration', 'positive', '0.089', 'red', 'green_black', 'green_red', 'peelable', 'precious_jewel']
senticnet['bluish_light'] = ['0', '-0.00', '-0.07', '-0.06', '#fear', '#disgust', 'negative', '-0.04', 'lighten', 'tornado', 'dynamite_explosion', 'forest_fire', 'lightning']
senticnet['bluish_purple'] = ['-0.02', '0.068', '0.04', '0.084', '#interest', '#admiration', 'positive', '0.022', 'peelable', 'green_black', 'purple', 'red', 'edible_human']
senticnet['bluish_red'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'helpless', 'unwanted', 'rabies', 'small_cat', 'big_rat']
senticnet['bluish_tint'] = ['-0.66', '0', '0.782', '-0.52', '#sadness', '#anger', 'negative', '-0.65', 'lividness', 'pallidness', 'pallor', 'complexion', 'achromasia']
senticnet['blunder'] = ['-0.84', '0', '0.707', '-0.75', '#sadness', '#disgust', 'negative', '-0.77', 'bobble', 'error', 'misstep', 'fumble', 'stumble']
senticnet['blunderer'] = ['-0.77', '0', '0.503', '-0.93', '#sadness', '#disgust', 'negative', '-0.73', 'butcher', 'fuckup', 'slouch', 'bungler', 'incompetent']
senticnet['blunt'] = ['-0.04', '-0.04', '-0.04', '-0.04', '#fear', '#disgust', 'negative', '-0.02', 'blunted', 'dull', 'unsharpened', 'insensitive', 'edgeless']
senticnet['blunt_trauma'] = ['-0.12', '-0.04', '0', '-0.17', '#sadness', '#disgust', 'negative', '-0.08', 'cause_bleed', 'open_wind', 'gash', 'lacerate', 'painful_injury']
senticnet['blunted'] = ['-0.05', '-0.05', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.02', 'insensitive', 'dull', 'unsharpened', 'blunt', 'edgeless']
senticnet['blunted_affect'] = ['0.998', '-0.63', '0', '0.574', '#joy', '#surprise', 'positive', '0.736', 'cleavable', 'dissociable', 'separable', 'severable', 'divisible']
senticnet['bluntness'] = ['0', '0', '0.804', '-0.95', '#anger', '#disgust', 'negative', '-0.87', 'concision', 'conciseness', 'terseness', 'succinctness', 'brevity']
senticnet['blur'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'hazy', 'blurry', 'bleary', 'blurryred', 'foggy']
senticnet['blur_vision'] = ['-0.08', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.08', 'near_tv', 'wear_glass', 'switch_tv', 'turn_on_tv', 'eyesight']
senticnet['blurred'] = ['-0.96', '0', '0.887', '-0.81', '#sadness', '#anger', 'negative', '-0.89', 'blurry', 'foggy', 'hazy', 'muzzy', 'indistinct']
senticnet['blurred_image'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'hazy', 'blurry', 'bleary', 'blurryred', 'foggy']
senticnet['blurred_vision'] = ['-0.72', '0', '-0.89', '0', '#sadness', '#fear', 'negative', '-0.80', 'unclear', 'dark', 'shadow', 'unseen', 'without_light']
senticnet['blurrier'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'hazy', 'blurry', 'bleary', 'blurryred', 'foggy']
senticnet['blurriness'] = ['0.906', '-0.60', '0', '0.653', '#joy', '#admiration', 'positive', '0.72', 'opaqueness', 'faintness', 'dimness', 'opacity', 'vagueness']
senticnet['blurry'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'hazy', 'blur', 'bleary', 'blurred', 'foggy']
senticnet['blurry_vision'] = ['-0.13', '0', '0.013', '-0.08', '#sadness', '#disgust', 'negative', '-0.07', 'blind', 'dig_tunnel', 'underground_animal', 'big_mouse', 'small_rodent']
senticnet['blurryred'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'hazy', 'blurry', 'bleary', 'blur', 'foggy']
senticnet['blurt'] = ['-0.82', '0', '0.754', '0', '#sadness', '#anger', 'negative', '-0.78', 'divulge', 'thought', 'inconsiderately', 'unadvisedly', 'flirt']
senticnet['blurt_out'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'annoy', 'irritate', 'trouble', 'nag', 'bother']
senticnet['blurts_out'] = ['-0.89', '0.168', '-0.60', '0.05', '#sadness', '#fear', 'negative', '-0.32', 'contritely', 'contrition', 'contriteness', 'ruefully', 'attrition']
senticnet['blush'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'embarrass', 'embarrassed', 'embarrassing', 'embarrassingly', 'embarassament']
senticnet['blusher'] = ['-0.93', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.85', 'protect_from_sun', 'shade', 'meaning', 'corrector', 'sunglasses']
senticnet['blushful'] = ['0.882', '0', '0', '0.868', '#joy', '#admiration', 'positive', '0.875', 'fortunate', 'rose_colored', 'rose_cheeked', 'rosy', 'flushed']
senticnet['blushing'] = ['-0.99', '0', '0.858', '-0.59', '#sadness', '#anger', 'negative', '-0.81', 'embarrassed', 'abashed', 'chagrined', 'discomposed', 'bothered']
senticnet['bluster'] = ['0.957', '-0.57', '0', '0.857', '#joy', '#admiration', 'positive', '0.797', 'gust', 'puff', 'sandblast', 'puff_of_air', 'whiff']
senticnet['blustery'] = ['-0.91', '0', '0.521', '-0.81', '#sadness', '#disgust', 'negative', '-0.74', 'bullying', 'peremptory', 'domineering', 'roughshod', 'cavalier']
senticnet['blustery_day'] = ['0', '-0.01', '0.048', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'mild', 'breeze', 'tropical_storm', 'wind', 'air_movement']
senticnet['blynken'] = ['0.021', '0.179', '-0.13', '0.164', '#interest', '#admiration', 'positive', '0.058', 'nod_head', 'point_finger', 'upgrade', 'ask', 'put_bid']
senticnet['bmi_songwriter'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
senticnet['bmp_format'] = ['-0.55', '0', '0.912', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'wrangle', 'dustup', 'quarrel', 'row', 'squabble']
senticnet['bmw'] = ['0.755', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.728', 'high_class', 'expensive_car', 'ferrari', 'fancy_car', 'sports_car']
senticnet['bmw_motorcycle'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'fuel', 'car', 'fossil_fuel', 'transport', 'natural_resource']
senticnet['bmx_bike'] = ['-0.18', '0', '-0.14', '-0.11', '#sadness', '#fear', 'negative', '-0.14', 'fly_kite', 'walk_dog', 'ride_roller_coaster', 'rake_leave', 'jump_up_down']
senticnet['bo_quote'] = ['0', '0.189', '0.04', '0.091', '#interest', '#admiration', 'positive', '0.08', 'combination', 'margin', 'teller', 'merge', 'finite']
senticnet['boa_constrictor'] = ['-0.68', '0', '0.611', '-0.59', '#sadness', '#anger', 'negative', '-0.63', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['boar'] = ['0.746', '0.769', '0', '0', '#joy', '#interest', 'positive', '0.758', 'yak', 'sheep', 'cow', 'camel', 'yak']
senticnet['boar_sus_scrofa'] = ['-0.73', '0', '0.55', '-0.75', '#sadness', '#disgust', 'negative', '-0.68', 'mortal', 'person', 'individual', 'mangler', 'mutilator']
senticnet['board'] = ['0.819', '0.791', '0', '0', '#joy', '#interest', 'positive', '0.805', 'plank', 'lumber', 'mahogany', 'hardwood', 'stump']
senticnet['board_airplane'] = ['0.838', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.887', 'board_bus', 'travel', 'board_plane', 'travel_abroad', 'board_boat']
senticnet['board_boat'] = ['0.838', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.887', 'travel', 'board_airplane', 'board_ship', 'board_plane', 'travel_abroad']
senticnet['board_bus'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'board_airplane', 'board_plane', 'board_boat', 'board_ship', 'travel_abroad']
senticnet['board_game'] = ['0', '-0.07', '-0.04', '0.063', '#surprise', '#admiration', 'positive', '0.029', 'yo-yo', 'playroom', 'game', 'backgammon', 'play_game']
senticnet['board_interactive_whiteboard'] = ['0.995', '0.801', '0', '0.968', '#joy', '#admiration', 'positive', '0.921', 'acquire_knowledge', 'comprehend', 'great', 'accumulate_knowledge', 'increase_knowledge']
senticnet['board_of_director'] = ['0', '0.914', '0', '0.722', '#interest', '#admiration', 'positive', '0.818', 'directorate', 'division', 'award', 'physical_science', 'research']
senticnet['board_plane'] = ['0.838', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.887', 'board_bus', 'travel', 'board_airplane', 'travel_abroad', 'board_boat']
senticnet['board_room'] = ['0.053', '0.1', '0', '0', '#joy', '#interest', 'positive', '0.076', 'registry_office', 'lodging', 'telephone_booth', 'cubicle', 'reception_area']
senticnet['board_ship'] = ['0.79', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.816', 'board_boat', 'visit_grandmother', 'market_product', 'bridge_difference', 'rush_job']
senticnet['board_subway_train'] = ['-0.04', '-0.01', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.03', 'wait_train', 'public_transport', 'wait', 'find_end_line', 'travel']
senticnet['boarded_up'] = ['-0.02', '-0.02', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.02', 'empty_glass', 'empty_space', 'worthless', 'empty_stomach', 'universe']
senticnet['boarding_gate'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'hand_baggage', 'baggage', 'passenger_ticket', 'ticket_counter', 'baggage_trunk']
senticnet['boarding_school'] = ['-0.04', '0.088', '0.013', '0.069', '#interest', '#admiration', 'positive', '0.024', 'kindergarden', 'daycare', 'nursery', 'child', 'asylum']
senticnet['boardroom_coup'] = ['-0.98', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.92', 'thrilling', 'electrifying', 'sensational', 'sensationally', 'scandalmongering']
senticnet['boardwalk'] = ['-0.09', '-0.02', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.05', 'stupid_person', 'tourist', 'pass_exam', 'apple_tree', 'learn_fact']
senticnet['boast'] = ['0.838', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.887', 'proud', 'glory', 'brag', 'vaunt', 'pride']
senticnet['boaster'] = ['0', '0', '0.986', '-0.70', '#anger', '#disgust', 'negative', '-0.84', 'blowhard', 'braggart', 'vaunter', 'line-shooter', 'bragger']
senticnet['boastful'] = ['-0.94', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.85', 'brag', 'cocky', 'loudmouth', 'cock', 'self-aggrandizing']
senticnet['boastfulness'] = ['-0.84', '0', '0.969', '-0.68', '#sadness', '#anger', 'negative', '-0.83', 'posturing', 'swelled_head', 'egotism', 'conceitedness', 'vainglory']
senticnet['boat'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'big_boat', 'rowboat', 'large_boat', 'ship', 'sailboat']
senticnet['boat_builder'] = ['0.073', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'angler', 'informal_rod', 'archaic_fisher', 'rare_piscator', 'fishing_line']
senticnet['boat_deck'] = ['0.831', '-0.82', '0', '0.826', '#joy', '#admiration', 'positive', '0.826', 'deck', 'freeboard_deck', 'weather_deck', 'shelter_deck', 'upper_deck']
senticnet['boat_launching_ramp'] = ['0.84', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.798', 'big_breadbox', 'water_vehicle', 'military_ship', 'ship', 'small_boat']
senticnet['boat_part'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'propeller', 'hull', 'cleat', 'gunwale', 'stern']
senticnet['boat_ride'] = ['-0.08', '0.179', '-0.06', '0.096', '#interest', '#admiration', 'positive', '0.034', 'ride_subway', 'buy_vending_machine', 'travel', 'buy_lottery_ticket', 'acquire']
senticnet['boat_trip'] = ['-0.08', '0.179', '-0.06', '0.096', '#interest', '#admiration', 'positive', '0.034', 'ride_subway', 'buy_vending_machine', 'travel', 'buy_lottery_ticket', 'acquire']
senticnet['boat_type'] = ['0', '0', '0', '0.135', '#admiration', '#admiration', 'positive', '0.135', 'kayak', 'ferry', 'canoe', 'towboat', 'yacht']
senticnet['boat_upper_deck'] = ['0.829', '-0.82', '0', '0.818', '#joy', '#admiration', 'positive', '0.824', 'upper_deck', 'freeboard_upper_deck', 'weather_upper_deck', 'shelter_upper_deck', 'deck']
senticnet['boating_accident'] = ['-0.73', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.79', 'die_painfully', 'arrest_police', 'get_accident', 'fall_head', 'skin_knee']
senticnet['boating_lake'] = ['-0.72', '0', '0.978', '-0.86', '#anger', '#disgust', 'negative', '-0.85', 'amusement_park', 'common', 'pleasure_ground', 'village_green', 'tract']
senticnet['boating_safety'] = ['0.764', '-0.96', '0', '0.883', '#surprise', '#admiration', 'positive', '0.87', 'boatmanship', 'acquirement', 'skill', 'accomplishment', 'acquisition']
senticnet['boatman'] = ['0.073', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'angler', 'informal_rod', 'archaic_fisher', 'rare_piscator', 'fishing_line']
senticnet['boatmanship'] = ['0.764', '-0.96', '0', '0.883', '#surprise', '#admiration', 'positive', '0.87', 'seamanship', 'acquirement', 'skill', 'accomplishment', 'acquisition']
senticnet['bobbed_hair'] = ['0', '0.097', '0', '0.146', '#interest', '#admiration', 'positive', '0.121', 'hair_style', 'hair', 'appearance', 'hold', 'look']
senticnet['bobber'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'catch_fish', 'fishing', 'patience', 'fish_lake', 'bait']
senticnet['bobbi_brown'] = ['-0.93', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.85', 'protect_from_sun', 'shade', 'meaning', 'corrector', 'sunglasses']
senticnet['bobbin'] = ['-0.03', '-0.00', '-0.05', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'bandage', 'needle_thread', 'sew', 'spasm', 'suture']
senticnet['bobble'] = ['-0.96', '0', '0.636', '-0.67', '#sadness', '#disgust', 'negative', '-0.75', 'blooper', 'error', 'misstep', 'fumble', 'stumble']
senticnet['bobbled'] = ['0', '0', '0.898', '-0.96', '#anger', '#disgust', 'negative', '-0.93', 'misplayed', 'miscue', 'misplay', 'misjudgment', 'misplay']
senticnet['bobcat'] = ['-0.03', '0', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.06', 'lion', 'leopard', 'mountain_lion', 'craven', 'wolf']
senticnet['bobcat_goldthwait'] = ['-0.93', '0', '0.77', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'hothead', 'unpleasant_person', 'disagreeable_person', 'crabby_person', 'crab']
senticnet['bobwhite'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['bocce'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'golf', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['bocce_court'] = ['0.074', '0', '0', '0', '#joy', '#joy', 'positive', '0.074', 'hike_trail', 'ant_hill', 'compost', 'picnic', 'soccer_field']
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senticnet['boilersuit'] = ['-0.67', '0', '0.688', '-0.89', '#anger', '#disgust', 'negative', '-0.75', 'protective_garment', 'coverall', 'boiler_suit', 'overall', 'duster']
senticnet['boiling'] = ['-0.72', '0', '0.883', '0', '#sadness', '#anger', 'negative', '-0.80', 'stewing', 'simmering', 'churn', 'moil', 'seethe']
senticnet['boiling_oil'] = ['-0.73', '0', '0.641', '-0.74', '#sadness', '#disgust', 'negative', '-0.70', 'treated', 'burned', 'proofed', 'doped', 'activated']
senticnet['boiling_salted_water'] = ['-0.66', '0', '0.992', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'cold', 'boil', 'temperature', 'low_temperature', 'frigidity']
senticnet['boingo'] = ['-0.87', '0', '0.818', '0', '#sadness', '#anger', 'negative', '-0.84', 'slow_speed', 'wifi', 'computer_internet_connection', 'internet_connection', 'boot_up_computer']
senticnet['bois_guilbert'] = ['0', '-0.01', '-0.08', '-0.16', '#fear', '#disgust', 'negative', '-0.07', 'siege', 'big_fight', 'battle', 'declare_war', 'conflict']
senticnet['boisterous'] = ['-0.64', '0', '0.941', '-0.66', '#anger', '#disgust', 'negative', '-0.74', 'rowdy', 'disorderly', 'unruly', 'rambunctious', 'robustious']
senticnet['boisterous_crowd'] = ['-0.71', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.84', 'traffic_jam', 'accumulate_stress', 'stress', 'accumulate_stress_city', 'bad_service']
senticnet['bok_choy'] = ['0', '0.067', '-0.09', '-0.09', '#fear', '#disgust', 'negative', '-0.04', 'cotton_boll', 'asbestos', 'corn', 'opium', 'rice']
senticnet['bokeh'] = ['-0.05', '0.07', '0', '0.062', '#interest', '#admiration', 'positive', '0.027', 'salty', 'fine_grain', 'powder', 'granulate', 'food_flavor']
senticnet['bokken'] = ['-0.04', '0.138', '-0.08', '0.111', '#interest', '#admiration', 'positive', '0.031', 'geometry', 'craft', 'blade_geometry', 'blade', 'collect_item']
senticnet['bold'] = ['0', '0.852', '0', '0.947', '#interest', '#admiration', 'positive', '0.899', 'courageous', 'brave', 'play_role', 'imitate', 'personality_trait']
senticnet['bold_move'] = ['-0.98', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.92', 'thrilling', 'electrifying', 'sensational', 'sensationally', 'scandalmongering']
senticnet['bold_vision'] = ['-0.03', '0.081', '0', '0.095', '#interest', '#admiration', 'positive', '0.047', 'sight', 'eye_function', 'eye_sense', 'see_ability', 'aim']
senticnet['bolder'] = ['0.17', '0', '0.081', '0.184', '#joy', '#admiration', 'positive', '0.091', 'food_holder', 'food_server', 'crockery', 'flattery', 'round_flat']
senticnet['boldest'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'intrepidly', 'hardy', 'fearlessness', 'fearlessly', 'unafraid']
senticnet['boldly'] = ['0', '0.826', '0', '0.802', '#interest', '#admiration', 'positive', '0.814', 'assertive', 'avow', 'confidence', 'courageously', 'straight-faced']
senticnet['boldness'] = ['0.775', '-0.97', '0', '0.731', '#joy', '#surprise', 'positive', '0.828', 'daredevilry', 'daredeviltry', 'temerity', 'audacity', 'audaciousness']
senticnet['bole'] = ['0.891', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.922', 'body', 'luggage_compartment', 'proboscis', 'trunk', 'tree_trunk']
senticnet['bolero'] = ['0.116', '-0.02', '0.025', '0.156', '#joy', '#admiration', 'positive', '0.069', 'dance_move', 'latin_dance', 'body_movement', 'waltz', 'dance']
senticnet['bolivar_trask'] = ['-0.74', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.80', 'casualty', 'extermination', 'cause_death', 'abduction', 'die']
senticnet['bolivarian_alliance'] = ['0', '0', '0', '0.119', '#admiration', '#admiration', 'positive', '0.119', 'planet', 'earth', 'spherical', 'planet_solar_system', 'terrain']
senticnet['boll'] = ['0.069', '0.112', '0', '0.167', '#interest', '#admiration', 'positive', '0.116', 'plant_material', 'clothes_material', 'natural_fiber', 'natural_fabric', 'fabric_type']
senticnet['bollettieri_prediction'] = ['-0.10', '0', '-0.05', '-0.24', '#sadness', '#disgust', 'negative', '-0.13', 'adulate', 'suck_up', 'toady', 'blandish', 'kowtow']
senticnet['bollix'] = ['-0.78', '0', '0.915', '-0.99', '#anger', '#disgust', 'negative', '-0.89', 'bodge', 'botch_up', 'flub', 'ball_up', 'go_wrong']
senticnet['bollywood_dance'] = ['-0.95', '0', '0.992', '-0.63', '#sadness', '#anger', 'negative', '-0.86', 'interpretive_dance', 'interpretive_dancing', 'modern_dance', 'interpretative_dancing', 'stage_dancing']
senticnet['bolognese_sauce'] = ['0.763', '0.9', '0', '0', '#joy', '#interest', 'positive', '0.832', 'breakfast', 'box_cereal', 'calcium', 'drink', 'carton_milk']
senticnet['bolsa_chica_ecological'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['bolshevik'] = ['0', '0', '-0.70', '-0.83', '#fear', '#disgust', 'negative', '-0.76', 'settle_disagreement', 'hitler', 'republican', 'conservative', 'unchanging']
senticnet['bolshevik_coup'] = ['0', '0.793', '0', '0.847', '#interest', '#admiration', 'positive', '0.82', 'disagreement', 'fight_against', 'opposition', 'against', 'go_against']
senticnet['bolshevism'] = ['-0.81', '-0.71', '0.843', '-0.72', '#sadness', '#anger', 'negative', '-0.41', 'lack_of_rules', 'fascism', 'despot', 'tyrant', 'anarchism']
senticnet['bolshevist'] = ['-0.67', '0', '0.85', '-0.59', '#sadness', '#anger', 'negative', '-0.70', 'rack', 'boot', 'whip', 'iron_boot', 'instrument_of_torture']
senticnet['bolshy'] = ['-0.96', '0', '0.858', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'contrarious', 'dogged', 'pertinacious', 'cantankerous', 'unregenerate']
senticnet['bolster'] = ['0.846', '-0.94', '0', '0.797', '#joy', '#surprise', 'positive', '0.861', 'strengthen', 'buttress', 'support', 'line', 'reenforce']
senticnet['bolt_carrier'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'peavey']
senticnet['bolted_shut'] = ['0.829', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.908', 'cut_food', 'drink_beer', 'serve_drink', 'drink_much', 'drink_beverage']
senticnet['bolthole'] = ['0.071', '0', '0.031', '0.064', '#joy', '#admiration', 'positive', '0.035', 'far_from_home', 'immigrant', 'live_abroad', 'connoisseur', 'work_abroad']
senticnet['boltzmann'] = ['-0.98', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.87', 'become_fat', 'flatulence', 'feel_rejuvenated', 'tan_skin', 'make_angry']
senticnet['boltzmann_equation'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'assumption', 'precondition', 'provision', 'constraint', 'premiss']
senticnet['bolus'] = ['-0.81', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.82', 'crystal_ball', 'ball', 'spherule', 'globe', 'celestial_globe']
senticnet['bolus_injection'] = ['0', '0', '-0.95', '-0.87', '#fear', '#disgust', 'negative', '-0.91', 'sharp_weapon', 'bayonet', 'ammunition', 'spear', 'arrow']
senticnet['bomb'] = ['0', '-0.03', '-0.04', '-0.19', '#fear', '#disgust', 'negative', '-0.06', 'nuclear_bomb', 'explosive', 'grenade', 'nuclear_weapon', 'aerial_bomb']
senticnet['bomb-grade'] = ['0', '0', '0.729', '-0.76', '#anger', '#disgust', 'negative', '-0.74', 'weapons-grade', 'plutonium', 'weapon-grade', 'fissile', 'uranium']
senticnet['bomb-thrower'] = ['0.917', '0', '0', '0.731', '#joy', '#admiration', 'positive', '0.824', 'polemicist', 'rabble-rouser', 'tree-hugger', 'demagogue', 'hanger-on']
senticnet['bomb_explode'] = ['-0.57', '0', '0.56', '-0.87', '#sadness', '#disgust', 'negative', '-0.67', 'reprimand', 'rebuke', 'reproof', 'reproval', 'reprehension']
senticnet['bomb_explosion'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'firework', 'bright_flash', 'nuclear_weapon', 'earthquake', 'nuclear_bomb']
senticnet['bomb_grade_plutonium'] = ['0', '0', '0.9', '-0.84', '#anger', '#disgust', 'negative', '-0.87', 'bomb', 'atomic_bomb', 'nuclear_weapon', 'explosive', 'grenade']
senticnet['bomb_grade_uranium'] = ['-0.03', '-0.03', '-0.05', '-0.20', '#fear', '#disgust', 'negative', '-0.06', 'nuclear_bomb', 'bomb', 'explosive', 'atomic_bomb', 'grenade']
senticnet['bombard'] = ['0', '0', '-0.85', '-0.99', '#fear', '#disgust', 'negative', '-0.92', 'onslaught', 'battery', 'shelling', 'bombardment', 'outpouring']
senticnet['bombardier_crj_aircraft'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['bombardment'] = ['0', '0', '-0.86', '-0.99', '#fear', '#disgust', 'negative', '-0.93', 'onslaught', 'battery', 'shelling', 'barrage', 'outpouring']
senticnet['bombast'] = ['-0.54', '0', '0.7', '-0.64', '#anger', '#disgust', 'negative', '-0.62', 'flourish', 'rant', 'ornateness', 'fustian', 'claptrap']
senticnet['bombastic'] = ['-0.99', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.96', 'declamatory', 'bombast', 'bombastically', 'showy', 'flowery']
senticnet['bombastically'] = ['-0.99', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.96', 'declamatory', 'bombast', 'bombastic', 'showy', 'flowery']
senticnet['bombay_jayashri'] = ['-0.56', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.63', 'aphasic', 'inarticulate', 'speechless', 'unarticulated', 'dumb']
senticnet['bombed_hiroshima'] = ['0', '0', '-0.72', '-0.94', '#fear', '#disgust', 'negative', '-0.83', 'pistol', 'gun', 'hydrogen_bomb', 'handgun', 'revolver']
senticnet['bomber'] = ['-0.03', '0', '-0.06', '-0.18', '#fear', '#disgust', 'negative', '-0.09', 'arsenal', 'war_zone', 'bomb', 'gun_shop', 'war']
senticnet['bomber_airfield'] = ['-0.97', '0', '0.737', '-0.97', '#sadness', '#disgust', 'negative', '-0.89', 'flare_path', 'airstrip', 'take_off', 'runway', 'grassland']
senticnet['bomber_jacket'] = ['-0.02', '0.256', '0', '0.166', '#interest', '#admiration', 'positive', '0.132', 'coat', 'shirt', 'garment', 'dress', 'vest']
senticnet['bomberman'] = ['0.757', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.831', 'baseball_bat', 'water_gun', 'goalie', 'hockey_puck', 'hockey_stick']
senticnet['bombilate'] = ['0', '0', '-0.82', '-0.71', '#fear', '#disgust', 'negative', '-0.77', 'bombinate', 'buzzing', 'hum', 'seethe', 'abuzz']
senticnet['bombinate'] = ['0', '0', '-0.83', '-0.72', '#fear', '#disgust', 'negative', '-0.77', 'buzzing', 'bombilate', 'hum', 'seethe', 'abuzz']
senticnet['bombora'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'beachcomber', 'bum', 'drifter']
senticnet['bombproof'] = ['-0.58', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.64', 'burial_chamber', 'sepulcher', 'bunker', 'sepulture', 'chamber']
senticnet['bombshell'] = ['0.54', '-0.64', '0', '0.566', '#surprise', '#admiration', 'positive', '0.582', 'pleaser', 'entertainer', 'mortal', 'busker', 'drawing_card']
senticnet['bombshell_announcement'] = ['0.54', '-0.64', '0', '0.566', '#surprise', '#admiration', 'positive', '0.582', 'pleaser', 'entertainer', 'mortal', 'busker', 'drawing_card']
senticnet['bon_jovi_livin'] = ['0.023', '0.295', '-0.05', '0.283', '#interest', '#admiration', 'positive', '0.136', 'religion', 'religious_purpose', 'world_religion', 'gospel', 'judaism']
senticnet['bon_mot'] = ['-0.84', '0', '0.572', '-0.68', '#sadness', '#disgust', 'negative', '-0.69', 'reply', 'response', 'sass', 'sassing', 'backtalk']
senticnet['bon_vivant'] = ['0.563', '-0.81', '0', '0.577', '#surprise', '#admiration', 'positive', '0.65', 'mortal', 'person', 'individual', 'performer', 'artist']
senticnet['bon_viveur'] = ['0.556', '-0.79', '0', '0.755', '#surprise', '#admiration', 'positive', '0.701', 'lush', 'alcoholic', 'alky', 'rummy', 'boozer']
senticnet['bon_voyage'] = ['0', '0.145', '0', '0.2', '#interest', '#admiration', 'positive', '0.172', 'hand_gesture', 'arm_movement', 'water_splash', 'hand_motion', 'water_form']
senticnet['bona-fide'] = ['0', '0.986', '0', '0.704', '#interest', '#admiration', 'positive', '0.845', 'dyed-in-the-wool', 'red-blooded', 'self-professed', 'die-hard', 'true-blue']
senticnet['bonanza'] = ['0.808', '-0.83', '0', '0.548', '#joy', '#surprise', 'positive', '0.73', 'successfulness', 'luckiness', 'prosperity', 'circumstance', 'blessing']
senticnet['bonapartism'] = ['0.127', '0.069', '0.029', '0.16', '#joy', '#admiration', 'positive', '0.082', 'basic_human_need', 'construct', 'set_down', 'make', 'build_process']
senticnet['bonbon'] = ['0.037', '-0.00', '0', '0.12', '#joy', '#admiration', 'positive', '0.055', 'bread_crumb', 'candy', 'bun', 'sweet', 'gush']
senticnet['bond'] = ['0.228', '0.123', '-0.09', '0', '#joy', '#interest', 'positive', '0.084', 'spend_time_with_child', 'social', 'friendship', 'companionship', 'get_close']
senticnet['bond_servant'] = ['0.585', '-0.69', '0', '0.909', '#surprise', '#admiration', 'positive', '0.731', 'thrall', 'slave', 'bondman', 'bondsman', 'bondwoman']
senticnet['bond_villain'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'role', 'theatrical_role', 'part', 'persona']
senticnet['bondage'] = ['-0.86', '0', '0.947', '0', '#sadness', '#anger', 'negative', '-0.90', 'punk', 'thralldom', 'clothing', 'goth', 'sexual']
senticnet['bonded_debt'] = ['-0.82', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.78', 'go_broke', 'receive_change', 'run_out_money', 'compare_price', 'bankruptcy']
senticnet['bonded_labor'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'vassalage']
senticnet['bonded_labour_system'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'vassalage']
senticnet['bonding'] = ['0.988', '-0.60', '0', '0.7', '#joy', '#admiration', 'positive', '0.766', 'indebtedness', 'personal_relation', 'obligation', 'relationship', 'human_relationship']
senticnet['bondman'] = ['0.585', '-0.69', '0', '0.909', '#surprise', '#admiration', 'positive', '0.731', 'thrall', 'slave', 'bond_servant', 'bondsman', 'bondwoman']
senticnet['bondsman'] = ['0.585', '-0.69', '0', '0.909', '#surprise', '#admiration', 'positive', '0.731', 'thrall', 'slave', 'bondman', 'bond_servant', 'bondwoman']
senticnet['bondwoman'] = ['0.585', '-0.69', '0', '0.909', '#surprise', '#admiration', 'positive', '0.731', 'thrall', 'slave', 'bondman', 'bondsman', 'bond_servant']
senticnet['bone'] = ['0.228', '0.123', '-0.09', '0', '#joy', '#interest', 'positive', '0.084', 'muscle', 'neck', 'shoulder', 'skeletal_system', 'bone_human']
senticnet['bone-in'] = ['-0.73', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.78', 'skinless', 'boneless', 'sirloin', 'marinated', 'loin']
senticnet['bone_bruise'] = ['-0.78', '0', '0.554', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'rubor', 'redness', 'inflammation', 'tennis_elbow', 'lateral_epicondylitis']
senticnet['bone_cancer'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['bone_cement'] = ['-0.03', '0', '-0.09', '-0.14', '#fear', '#disgust', 'negative', '-0.09', 'x-ray_machine', 'artificial_arm', 'ambulance', 'syringe', 'latex_glove']
senticnet['bone_chicken'] = ['-0.08', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.05', 'salt_food', 'chicken', 'brown_hamburger', 'fry_chicken', 'make_tea']
senticnet['bone_contention'] = ['-0.93', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.86', 'argument', 'disagreement', 'prejudice', 'opposition', 'insanity']
senticnet['bone_formation'] = ['0', '0', '0.025', '-0.09', '#anger', '#disgust', 'negative', '-0.06', 'body_fluid', 'artery', 'skeletal_system', 'bodily_fluid', 'near_mouth']
senticnet['bone_graft'] = ['-0.03', '0', '-0.09', '-0.14', '#fear', '#disgust', 'negative', '-0.09', 'x-ray_machine', 'artificial_arm', 'ambulance', 'syringe', 'latex_glove']
senticnet['bone_human'] = ['0.228', '0.123', '-0.09', '0', '#joy', '#interest', 'positive', '0.084', 'muscle', 'neck', 'shoulder', 'skeletal_system', 'bone']
senticnet['bone_marrow'] = ['0.73', '-0.72', '0', '0.653', '#joy', '#surprise', 'positive', '0.703', 'red_marrow', 'red_bone_marrow', 'yellow_marrow', 'yellow_bone_marrow', 'connective_tissue']
senticnet['bone_marrow_cancer'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['bone_marrow_disorder'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['bone_marrow_transplant'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'displace', 'move', 'move_away', 'bump', 'dislocate']
senticnet['bone_maturation'] = ['0.995', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.918', 'tell_everyone', 'reason', 'clarify', 'forethought', 'opinion']
senticnet['bone_pain'] = ['-0.59', '0', '0.911', '-0.87', '#anger', '#disgust', 'negative', '-0.79', 'induration', 'sclerosis', 'disseminated_sclerosis', 'multiple_sclerosis', 'pathology']
senticnet['bone_spavin'] = ['0.789', '-0.99', '0', '0.54', '#joy', '#surprise', 'positive', '0.776', 'imperfection', 'imperfectness', 'bugginess', 'defectiveness', 'sickness']
senticnet['bone_tissue'] = ['0', '0', '0.025', '-0.09', '#anger', '#disgust', 'negative', '-0.06', 'body_fluid', 'artery', 'skeletal_system', 'bodily_fluid', 'near_mouth']
senticnet['bonehead'] = ['-0.68', '0', '0.533', '-0.60', '#sadness', '#disgust', 'negative', '-0.60', 'stupid', 'stupid_person', 'stupe', 'dullard', 'dolt']
senticnet['boneless'] = ['-0.73', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.77', 'skinless', 'bone-in', 'sirloin', 'marinated', 'loin']
senticnet['boneless_chicken'] = ['0.965', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.951', 'delicious_food', 'hot_meal', 'good_food', 'fresh_produce', 'tasty_food']
senticnet['bonelli_eagle'] = ['0.797', '-0.71', '0', '0.736', '#joy', '#admiration', 'positive', '0.751', 'wood_ibis', 'wood_stork', 'ibis', 'wading_bird', 'wader']
senticnet['boner'] = ['-0.14', '-0.00', '-0.04', '-0.09', '#sadness', '#disgust', 'negative', '-0.07', 'master_new_skill', 'sleep_bed', 'study_library', 'finish_test', 'hard_on']
senticnet['boneset'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['boney'] = ['0', '0', '0.971', '-0.85', '#anger', '#disgust', 'negative', '-0.91', 'scraggy', 'scrawny', 'underweight', 'skinny', 'weedy']
senticnet['bonfire'] = ['0.1', '0', '0', '0', '#joy', '#joy', 'positive', '0.1', 'burn_material', 'ash', 'stay_warm', 'oregano', 'campfire']
senticnet['bonheur'] = ['0.611', '0.644', '-0.23', '0.282', '#joy', '#interest', 'positive', '0.325', 'gleeful', 'gladden', 'joyousness', 'joyfulness', 'gleefully']
senticnet['bonhomie'] = ['0.702', '-0.93', '0', '0.719', '#surprise', '#admiration', 'positive', '0.786', 'naturalness', 'condescension', 'condescendingness', 'mellowness', 'friendliness']
senticnet['boniad'] = ['0.228', '0.123', '-0.09', '0', '#joy', '#interest', 'positive', '0.084', 'spend_time_with_child', 'social', 'friendship', 'companionship', 'get_close']
senticnet['boning_knife'] = ['0.818', '-0.95', '0', '0.695', '#joy', '#surprise', 'positive', '0.823', 'jackknife', 'clasp_knife', 'penknife', 'switchblade', 'switchblade_knife']
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senticnet['bonkers'] = ['0.903', '0.748', '0', '0', '#joy', '#interest', 'positive', '0.826', 'daft', 'balmy', 'barmy', 'kooky', 'loony']
senticnet['bonniest'] = ['0.947', '0.914', '0', '0', '#joy', '#interest', 'positive', '0.93', 'ball_bounce', 'bicycle_tire', 'tire', 'increase_price', 'gyroscope']
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senticnet['bonus'] = ['0.785', '0', '0', '0', '#joy', '#joy', 'positive', '0.785', 'monetary_gain', 'money_pay', 'financial_gain', 'earnings', 'compensation']
senticnet['bonus_feature'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['bonus_footage'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['bonus_multiplier'] = ['0.757', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.831', 'baseball_bat', 'water_gun', 'goalie', 'hockey_puck', 'hockey_stick']
senticnet['bonwit_teller'] = ['0', '0.081', '0', '0.071', '#interest', '#admiration', 'positive', '0.076', 'thrift_store', 'type_of_store', 'record_store', 'many_shops', 'clothes_store']
senticnet['bony'] = ['0', '0', '-0.87', '-0.78', '#fear', '#disgust', 'negative', '-0.82', 'cadaverous', 'gaunt', 'haggard', 'pinched', 'skeletal']
senticnet['bony_bream'] = ['0', '-0.02', '0.044', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'haddock', 'mermaid', 'fish_rod', 'tidal_wave', 'dryad']
senticnet['bony_crest'] = ['0.1', '-0.12', '0.05', '-0.03', '#joy', '#surprise', 'positive', '0.034', 'fancy_dress', 'blouse', 'fashion', 'dress', 'skirt']
senticnet['bony_projection'] = ['0.719', '-0.57', '0', '0.651', '#joy', '#admiration', 'positive', '0.647', 'chap', 'break', 'slit', 'gap', 'shift']
senticnet['bony_tissue'] = ['0', '0', '0.025', '-0.09', '#anger', '#disgust', 'negative', '-0.06', 'body_fluid', 'artery', 'skeletal_system', 'bodily_fluid', 'near_mouth']
senticnet['bonzer'] = ['0.728', '-0.72', '0', '0.881', '#joy', '#admiration', 'positive', '0.779', 'howling', 'wonderful', 'wondrous', 'extraordinary', 'rattling']
senticnet['bonzo'] = ['-0.93', '0', '0.77', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'hothead', 'unpleasant_person', 'disagreeable_person', 'crabby_person', 'crab']
senticnet['boo'] = ['0', '0', '-0.87', '-0.88', '#fear', '#disgust', 'negative', '-0.88', 'exclamation', 'thumb_down', 'complaint', 'bad', 'dislike']
senticnet['boo_berry'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'veal', 'beef', 'food_group', 'animal_flesh', 'curry']
senticnet['boob'] = ['0', '-0.73', '0', '0.75', '#surprise', '#admiration', 'positive', '0.74', 'cleavage', 'breast', 'bikini', 'bra', 'tit']
senticnet['booby'] = ['0.776', '-0.53', '0', '0.728', '#joy', '#admiration', 'positive', '0.678', 'saphead', 'muggins', 'tomfool', 'fool', 'simpleton']
senticnet['booby_hatch'] = ['-0.65', '0', '0.514', '-0.69', '#sadness', '#disgust', 'negative', '-0.62', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['booby_trap'] = ['0', '0', '0.818', '-0.99', '#anger', '#disgust', 'negative', '-0.90', 'weapon', 'aerial_bomb', 'machine_gun', 'cannon', 'handgun']
senticnet['boogaloo'] = ['0.116', '-0.02', '0.025', '0.156', '#joy', '#admiration', 'positive', '0.069', 'dance_move', 'latin_dance', 'body_movement', 'waltz', 'dance']
senticnet['book'] = ['-0.04', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.08', 'data', 'awareness', 'information', 'saga', 'knowledge']
senticnet['book_appointment'] = ['-0.09', '0', '-0.08', '-0.06', '#sadness', '#fear', 'negative', '-0.08', 'close_together', 'feel_heat', 'book_reservation', 'wear_clothes', 'write_computer_program']
senticnet['book_bible'] = ['0.031', '0.178', '-0.10', '0.183', '#interest', '#admiration', 'positive', '0.072', 'omega', 'begin_end', 'first', 'sacred', 'book']
senticnet['book_binding'] = ['0.714', '0', '0', '0.872', '#joy', '#admiration', 'positive', '0.793', 'ski_binding', 'back', 'obligate', 'truss', 'binding']
senticnet['book_content'] = ['0.044', '0', '0', '0.206', '#joy', '#admiration', 'positive', '0.125', 'begin_end', 'book', 'title_page', 'book_end', 'frontspiece']
senticnet['book_end'] = ['0.044', '0', '0', '0.206', '#joy', '#admiration', 'positive', '0.125', 'cookbook', 'begin_end', 'good_book', 'novel', 'book']
senticnet['book_flight'] = ['0', '0.882', '0', '0', '#interest', '#interest', 'positive', '0.882', 'book_hotel_room', 'travel_by_airplane', 'board_boat', 'board_plane', 'board_airplane']
senticnet['book_genre'] = ['-0.05', '-0.19', '-0.17', '0.219', '#surprise', '#admiration', 'positive', '0.046', 'classic', 'novel', 'long_story', 'fiction', 'literature']
senticnet['book_holiday'] = ['0.939', '-0.63', '0', '0.653', '#joy', '#admiration', 'positive', '0.741', 'buy_new_car', 'buy_ice_cream', 'happiness', 'book_flight', 'buy_happiness']
senticnet['book_hotel'] = ['0', '0.084', '0', '0', '#interest', '#interest', 'positive', '0.084', 'reservation', 'online_booking', 'staycation', 'rate', 'currency']
senticnet['book_hotel_room'] = ['0', '0.788', '0', '0.787', '#interest', '#admiration', 'positive', '0.788', 'book_flight', 'look_around', 'board_boat', 'decide_to_go_first', 'go_town']
senticnet['book_library'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'vocabulary', 'read_material', 'book_genre', 'many_words', 'definition']
senticnet['book_magazine'] = ['-0.05', '0.084', '0', '0.062', '#interest', '#admiration', 'positive', '0.03', 'book', 'gossip', 'beauty_section', 'fashion', 'visual_communication']
senticnet['book_newspaper'] = ['0.067', '0.153', '-0.05', '0.111', '#interest', '#admiration', 'positive', '0.069', 'many_pages', 'record_event', 'current_affair', 'world_event', 'current_event']
senticnet['book_of_leviticus'] = ['0', '0.846', '0', '0.953', '#interest', '#admiration', 'positive', '0.899', 'leviticus', 'law', 'christian', 'god', 'think']
senticnet['book_office'] = ['0.077', '0.073', '0.013', '-0.02', '#joy', '#interest', 'positive', '0.027', 'registry_office', 'radio_studio', 'book', 'conference_auditorium', 'mezzanine']
senticnet['book_page'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'book_paper', 'book_content', 'readable', 'book_write', 'print_medium']
senticnet['book_paper'] = ['0.026', '0', '0', '0.084', '#joy', '#admiration', 'positive', '0.055', 'write_material', 'thin_wood', 'book', 'paper', 'book_content']
senticnet['book_part'] = ['0.053', '0', '0.025', '0.118', '#joy', '#admiration', 'positive', '0.049', 'paper_square', 'book', 'part_of_book', 'book_end', 'flat_thin']
senticnet['book_poetry'] = ['0', '0.865', '0', '0.855', '#interest', '#admiration', 'positive', '0.86', 'book_genre', 'book_library', 'vocabulary', 'translation', 'thesaurus']
senticnet['book_read'] = ['0.725', '0', '0', '0', '#joy', '#joy', 'positive', '0.725', 'buy_book', 'open_book', 'choose_book', 'find_book', 'book_subject']
senticnet['book_relate'] = ['0.762', '0.746', '0', '0', '#joy', '#interest', 'positive', '0.754', 'book', 'store_knowledge', 'find_book', 'use_book', 'bookmark']
senticnet['book_reservation'] = ['-0.10', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.10', 'write_computer_program', 'reserve', 'make_card', 'mind_store', 'come_house']
senticnet['book_review'] = ['0.725', '-0.94', '0', '0.792', '#surprise', '#admiration', 'positive', '0.822', 'rave', 'literary_criticism', 'criticism', 'critique', 'notice']
senticnet['book_store'] = ['0.228', '0.123', '-0.09', '0', '#joy', '#interest', 'positive', '0.084', 'book', 'shop', 'book_library', 'library', 'bookshop']
senticnet['book_story'] = ['0.701', '0.939', '0', '0.718', '#interest', '#admiration', 'positive', '0.786', 'literature', 'bed_time_story', 'biography', 'fiction', 'horror']
senticnet['book_subject'] = ['0.725', '0', '0', '0', '#joy', '#joy', 'positive', '0.725', 'intelligence', 'buy_textbook', 'school', 'study', 'student']
senticnet['book_ticket'] = ['-0.04', '0', '0.014', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'purchase_ticket', 'buy_ticket', 'dress_nicely', 'plan_time', 'find_out_play']
senticnet['book_token'] = ['0.921', '-0.57', '0', '0.962', '#joy', '#admiration', 'positive', '0.818', 'coupon', 'meal_ticket', 'luncheon_voucher', 'twofer', 'commercial_document']
senticnet['book_worm'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'nerd', 'computer_lover', 'loser', 'underdog', 'geek']
senticnet['book_write'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'book_paper', 'book_content', 'readable', 'book_page', 'print_medium']
senticnet['bookhand'] = ['-0.95', '0', '0.517', '-0.53', '#sadness', '#disgust', 'negative', '-0.67', 'copperplate', 'hand', 'minuscule', 'italic', 'round_hand']
senticnet['bookie'] = ['-0.78', '0', '0.944', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'loser', 'speculator', 'mortal', 'better', 'gambler']
senticnet['booking_fee'] = ['-0.03', '0.29', '-0.12', '0.203', '#interest', '#admiration', 'positive', '0.083', 'excise', 'severance', 'wage', 'afford', 'revenue']
senticnet['bookish'] = ['0.71', '-0.81', '0', '0.937', '#surprise', '#admiration', 'positive', '0.819', 'erudite', 'donnish', 'pedantic', 'learned', 'scholarly']
senticnet['bookkeeper'] = ['-0.07', '0.228', '0.019', '0.089', '#interest', '#admiration', 'positive', '0.056', 'football_coach', 'little_child', 'accountant', 'visitor', 'child']
senticnet['booklet'] = ['-0.03', '0.168', '-0.07', '0.139', '#interest', '#admiration', 'positive', '0.049', 'command', 'direct', 'follow', 'take_charge', 'govern']
senticnet['bookmaker'] = ['-0.78', '0', '0.944', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'loser', 'speculator', 'mortal', 'better', 'gambler']
senticnet['bookmark'] = ['0.762', '0.746', '0', '0', '#joy', '#interest', 'positive', '0.754', 'book', 'store_knowledge', 'find_book', 'use_book', 'book_relate']
senticnet['bookmark_bar'] = ['-0.09', '-0.04', '0.039', '-0.24', '#sadness', '#disgust', 'negative', '-0.08', 'learn_new_idea', 'learn_quickly', 'point_out', 'look_picture', 'jot_down_note']
senticnet['bookshop'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'book', 'shop', 'book_library', 'library', 'book_store']
senticnet['bookstore'] = ['0.026', '0.082', '0', '0', '#joy', '#interest', 'positive', '0.054', 'account_department', 'office', 'accountant_office', 'shirt_pocket', 'desk_drawer']
senticnet['boolean'] = ['0', '0.817', '0', '0.866', '#interest', '#admiration', 'positive', '0.841', 'real-world', 'verified', 'verifiableness', 'confirmed', 'verifiable']
senticnet['boolean_expression'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly']
senticnet['boolean_formula'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly']
senticnet['boolean_function'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly']
senticnet['boolean_satisfiability_problem'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly']
senticnet['boolean_search'] = ['-0.05', '0.084', '0', '0.062', '#interest', '#admiration', 'positive', '0.03', 'vocabulary', 'translation', 'book_library', 'book_poetry', 'book_genre']
senticnet['boolean_value'] = ['-0.55', '0', '0.882', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'bugger_all', 'zero', 'naught', 'zip', 'nothing']
senticnet['boom'] = ['0.045', '0', '0', '0.088', '#joy', '#admiration', 'positive', '0.067', 'loud_sound', 'thunder', 'rain_storm', 'sound_wave', 'rain_wind']
senticnet['boom_bap'] = ['0.857', '-0.77', '0', '0.878', '#joy', '#admiration', 'positive', '0.836', 'hand', 'round', 'approval', 'commendation', 'clapping']
senticnet['boomerang'] = ['-0.50', '0', '0.762', '-0.89', '#anger', '#disgust', 'negative', '-0.72', 'error', 'fault', 'backfire', 'mistake', 'miscalculation']
senticnet['booming_demand'] = ['0.749', '0.764', '0', '0', '#joy', '#interest', 'positive', '0.756', 'obedience', 'order', 'requirement', 'permission', 'necessary']
senticnet['booming_economy'] = ['0.067', '0.172', '0', '0.121', '#interest', '#admiration', 'positive', '0.12', 'economic_activity', 'business', 'support_family', 'work_done', 'earn_money']
senticnet['boomtown'] = ['-0.55', '0', '0.912', '-0.65', '#anger', '#disgust', 'negative', '-0.70', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['boon'] = ['0.808', '-0.83', '0', '0.548', '#joy', '#surprise', 'positive', '0.73', 'successfulness', 'luckiness', 'prosperity', 'circumstance', 'blessing']
senticnet['boondoggle'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'address_book', 'get_rich', 'ladle', 'swiss_army_knife', 'whisk']
senticnet['boonshoft_museum'] = ['0.774', '-0.63', '0', '0.567', '#joy', '#surprise', 'positive', '0.658', 'catching', 'breakthrough', 'spying', 'determination', 'tracing']
senticnet['boor'] = ['0', '0', '-0.91', '-0.91', '#fear', '#disgust', 'negative', '-0.91', 'tyke', 'barbarian', 'peasant', 'churl', 'goth']
senticnet['boorish'] = ['-0.78', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.82', 'neandertal', 'loutish', 'swinish', 'oafish', 'neanderthal']
senticnet['boorish_behavior'] = ['-0.69', '0', '0.792', '-0.98', '#anger', '#disgust', 'negative', '-0.82', 'outcry', 'call', 'cry', 'yell', 'shout']
senticnet['boost'] = ['0.66', '-0.85', '0', '0.744', '#surprise', '#admiration', 'positive', '0.752', 'further', 'promote', 'encourage', 'conduce', 'advance']
senticnet['boost_economic_growth'] = ['0.66', '-0.85', '0', '0.744', '#surprise', '#admiration', 'positive', '0.752', 'further', 'promote', 'encourage', 'conduce', 'advance']
senticnet['booster'] = ['0', '0', '-0.99', '-0.97', '#fear', '#disgust', 'negative', '-0.98', 'champion', 'admirer', 'protagonist', 'friend', 'supporter']
senticnet['booster_vaccination'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'medical_therapy', 'vaccine', 'doctor', 'medicine', 'hospital']
senticnet['boosting_demand'] = ['0.66', '-0.85', '0', '0.744', '#surprise', '#admiration', 'positive', '0.752', 'further', 'promote', 'encourage', 'conduce', 'advance']
senticnet['boot'] = ['0.306', '0.228', '0', '0.087', '#joy', '#interest', 'positive', '0.207', 'shoe', 'moccasin', 'cowboy_boot', 'casual_shoes', 'slipper']
senticnet['boot_disk'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotary_file', 'cd-rom', 'database', 'business_card', 'calendar_book']
senticnet['boot_licker'] = ['0', '0', '0.743', '-0.94', '#anger', '#disgust', 'negative', '-0.84', 'stupid', 'increase_body_temperature', 'idiot', 'get_excited', 'sense_accomplishment']
senticnet['boot_lid'] = ['0.725', '0.796', '0', '0', '#joy', '#interest', 'positive', '0.76', 'car_stereo', 'battery', 'seatbelt', 'spare_tire', 'brake_pedal']
senticnet['boot_out'] = ['-0.71', '0', '0.954', '-0.56', '#sadness', '#anger', 'negative', '-0.74', 'eject', 'chuck_out', 'exclude', 'turf_out', 'evict']
senticnet['boot_trunk'] = ['0', '0.248', '-0.07', '0.121', '#interest', '#admiration', 'positive', '0.099', 'gym_locker', 'observatory', 'prison_cell', 'luggage_store', 'garage_sale']
senticnet['boot_up_computer'] = ['0', '0.201', '0.018', '0.077', '#interest', '#admiration', 'positive', '0.087', 'restart', 'start_up', 'connect_internet', 'buy_computer', 'shut_down']
senticnet['bootable'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotary_file', 'cd-rom', 'database', 'business_card', 'calendar_book']
senticnet['booted'] = ['-0.79', '0', '0.861', '-0.89', '#anger', '#disgust', 'negative', '-0.85', 'shodden', 'slippered', 'roughshod', 'sandaled', 'wear_sandal']
senticnet['booted_eagle'] = ['0.797', '-0.71', '0', '0.736', '#joy', '#admiration', 'positive', '0.751', 'wood_ibis', 'wood_stork', 'ibis', 'wading_bird', 'wader']
senticnet['bootee'] = ['-0.54', '0', '0.612', '-0.98', '#anger', '#disgust', 'negative', '-0.71', 'scuff', 'mule', 'carpet_slipper', 'bootie', 'footwear']
senticnet['boothia'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'harpoon', 'armory', 'lake', 'river', 'bay']
senticnet['bootie'] = ['-0.54', '0', '0.612', '-0.98', '#anger', '#disgust', 'negative', '-0.71', 'scuff', 'mule', 'bootee', 'carpet_slipper', 'footwear']
senticnet['bootlace'] = ['-0.71', '0', '0.539', '-0.88', '#sadness', '#disgust', 'negative', '-0.71', 'shoe_string', 'shoestring', 'cord', 'lace', 'shoelace']
senticnet['bootless'] = ['-0.60', '0', '0.761', '-0.84', '#anger', '#disgust', 'negative', '-0.73', 'vain', 'unproductive', 'fruitless', 'futile', 'sleeveless']
senticnet['bootlick'] = ['-0.10', '0', '-0.05', '-0.24', '#sadness', '#disgust', 'negative', '-0.13', 'adulate', 'suck_up', 'toady', 'blandish', 'kowtow']
senticnet['bootlicker'] = ['-0.55', '0', '0.588', '-0.65', '#anger', '#disgust', 'negative', '-0.59', 'apple_polisher', 'crawler', 'fawner', 'web', 'groveler']
senticnet['bootmaker'] = ['-0.07', '0.228', '0.019', '0.089', '#interest', '#admiration', 'positive', '0.056', 'home_depot', 'lumberyard', 'furniture_store', 'front_porch', 'carpet_store']
senticnet['bootsie'] = ['-0.71', '0', '0.972', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'scold', 'nag', 'scolder', 'harridan', 'common_scold']
senticnet['booty'] = ['0.09', '0.135', '-0.05', '0.091', '#interest', '#admiration', 'positive', '0.065', 'self_confidence', 'find_money', 'down_payment', 'money', 'wealthy']
senticnet['booty_shaking'] = ['-0.95', '0', '0.992', '-0.63', '#sadness', '#anger', 'negative', '-0.86', 'interpretive_dance', 'interpretive_dancing', 'modern_dance', 'interpretative_dancing', 'stage_dancing']
senticnet['boovi'] = ['0.954', '-0.90', '0', '0.867', '#joy', '#surprise', 'positive', '0.91', 'friend', 'sports_fan', 'rooter', 'fan', 'nut']
senticnet['booze'] = ['0.11', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'alcoholic_beverage', 'adult_drink', 'alcoholic_drink', 'drink_alcohol', 'alcohol']
senticnet['boozed_up'] = ['0.884', '0', '0', '0', '#joy', '#joy', 'positive', '0.884', 'egalitarian', 'enjoy', 'drink', 'have_fun', 'senator']
senticnet['boozer'] = ['0.556', '-0.79', '0', '0.755', '#surprise', '#admiration', 'positive', '0.701', 'lush', 'alcoholic', 'alky', 'dipsomaniac', 'rummy']
senticnet['boozing'] = ['0.987', '-0.74', '0', '0.695', '#joy', '#surprise', 'positive', '0.809', 'vice', 'drink', 'drinking', 'intemperateness', 'drunkenness']
senticnet['boozy'] = ['0.951', '0.763', '0', '0', '#joy', '#interest', 'positive', '0.857', 'pull_muscle', 'heart_rate_increase', 'heavy_breathe', 'get_exhausted', 'tire']
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senticnet['borscht'] = ['0.52', '-0.69', '0', '0.639', '#surprise', '#admiration', 'positive', '0.617', 'black_bread', 'pumpernickel', 'limpa', 'bread', 'breadstuff']
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senticnet['boss'] = ['0.036', '0.129', '0', '0.06', '#interest', '#admiration', 'positive', '0.075', 'ceo', 'officer', 'supervisor', 'head_guy', 'head_person']
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senticnet['bottle_soda'] = ['0', '-0.01', '0.025', '-0.06', '#anger', '#disgust', 'negative', '-0.02', 'chef_special', 'hostess', 'dispense', 'bottle_wine', 'waiter']
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senticnet['bottle_water'] = ['0.804', '0.903', '0', '0', '#joy', '#interest', 'positive', '0.854', 'water', 'sweet_drink', 'beer', 'plastic_container', 'convenience_store']
senticnet['bottle_wine'] = ['0.921', '0.992', '0', '0', '#joy', '#interest', 'positive', '0.957', 'ferment_grape_juice', 'chef_special', 'cuisine', 'fine_dining', 'decanter']
senticnet['bottle_without_cap'] = ['0', '-0.09', '0', '0', '#surprise', '#surprise', 'positive', '0.094', 'insure', 'ridgeback', 'endanger', 'complete_thesis', 'bad_omen']
senticnet['bottlebrush_buckeye'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'horse_chestnut', 'red_buckeye', 'particolored_buckeye']
senticnet['bottled_beer'] = ['0.161', '-0.14', '0.031', '-0.10', '#joy', '#surprise', 'positive', '0.043', 'barrel', 'reception', 'champagne', 'beer', 'wine']
senticnet['bottled_drinking_water'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'bottled_water', 'sugar_water']
senticnet['bottled_mineral_water'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'drinking_water', 'sugar_water']
senticnet['bottled_spring'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'drinking_water', 'sugar_water']
senticnet['bottled_water'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'drinking_water', 'sugar_water']
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senticnet['bottling_plant'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'still', 'wine_maker', 'winery', 'mint', 'plant']
senticnet['bottom'] = ['0.908', '-0.74', '0', '0', '#joy', '#surprise', 'positive', '0.827', 'plate', 'platter', 'mounted', 'tray', 'plate']
senticnet['bottom-up'] = ['0', '0.855', '0', '0.806', '#interest', '#admiration', 'positive', '0.831', 'top-down', 'participatory', 'decentralized', 'grass-roots', 'participative']
senticnet['bottom_edge'] = ['0.891', '0.961', '0', '0', '#joy', '#interest', 'positive', '0.926', 'square', 'four_sides', 'rhombus', 'four_corners', 'cube']
senticnet['bottom_fermentation'] = ['0.833', '-0.50', '0', '0.519', '#joy', '#admiration', 'positive', '0.62', 'zymosis', 'top_fermentation', 'vinification', 'chemical_process', 'chemical_change']
senticnet['bottom_line'] = ['0', '0.298', '-0.06', '0.305', '#interest', '#admiration', 'positive', '0.18', 'reason_why', 'correct', 'punctuation', 'exclamation', 'end_point']
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senticnet['bottom_shoes'] = ['0', '-0.02', '0.11', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'shoe', 'flip_flop', 'shoe_type', 'casual_shoes', 'moccasin']
senticnet['bottomless'] = ['0.782', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.877', 'inexhaustible', 'abyss', 'endless', 'fathomless', 'pit']
senticnet['bottomless_cup'] = ['0.807', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.859', 'bubble', 'steam', 'hot', 'boil_water', 'tea']
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senticnet['botulism'] = ['-0.53', '0', '0.678', '-0.73', '#anger', '#disgust', 'negative', '-0.64', 'malady', 'mushroom_poisoning', 'food_poisoning', 'ptomaine', 'ptomaine_poisoning']
senticnet['boudoir'] = ['0.625', '-0.88', '0', '0.782', '#surprise', '#admiration', 'positive', '0.762', 'judicious', 'prudent', 'prudential', 'heady', 'circumspect']
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senticnet['bouffant'] = ['0', '0.097', '0', '0.146', '#interest', '#admiration', 'positive', '0.121', 'hair_style', 'hair', 'appearance', 'hold', 'look']
senticnet['bouffant_hair'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'sexual_excitement', 'sexy', 'wet_lip', 'kiss', 'clean_up']
senticnet['bouillabaisse'] = ['0.897', '-0.96', '0', '0.785', '#joy', '#surprise', 'positive', '0.881', 'erotic', 'voluptuary', 'epicure', 'gourmet', 'sensualist']
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senticnet['boulevard'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'curb', 'lane', 'thoroughfare', 'intersection', 'avenue']
senticnet['bounce'] = ['0.793', '0.915', '0', '0', '#joy', '#interest', 'positive', '0.854', 'ball', 'ball_bounce', 'football', 'soccer', 'basketball']
senticnet['bounce_back'] = ['0.793', '0.915', '0', '0', '#joy', '#interest', 'positive', '0.854', 'ball', 'ball_bounce', 'football', 'soccer', 'basketball']
senticnet['bounce_ball'] = ['-0.09', '0', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.07', 'roll_downhill', 'rebound', 'shovel_snow', 'fly_around', 'spring_up']
senticnet['bounce_check'] = ['-0.32', '-0.00', '-0.11', '-0.15', '#sadness', '#disgust', 'negative', '-0.14', 'become_poor', 'go_bankrupt', 'pay_fine', 'go_debt', 'go_broke']
senticnet['bounce_off'] = ['0.793', '0.915', '0', '0', '#joy', '#interest', 'positive', '0.854', 'ball', 'ball_bounce', 'football', 'soccer', 'basketball']
senticnet['bounce_up_down'] = ['0.987', '0', '0', '0.868', '#joy', '#admiration', 'positive', '0.927', 'bounce', 'throw', 'play_catch', 'ball_game', 'game']
senticnet['bounced'] = ['0', '0.752', '0', '0.835', '#interest', '#admiration', 'positive', '0.793', 'ricocheted', 'deflected', 'harmlessly', 'crossbar', 'uselessly']
senticnet['bounced_back'] = ['0', '0', '-0.94', '-0.77', '#fear', '#disgust', 'negative', '-0.85', 'need_information', 'need_help', 'need_know', 'ignorant', 'needy_person']
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senticnet['bounced_mail'] = ['0.031', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.062', 'letter_slot', 'postal_counter', 'mailbox', 'letter_scale', 'envelope']
senticnet['bouncer'] = ['-0.90', '0', '0.943', '-0.98', '#anger', '#disgust', 'negative', '-0.94', 'kick_out', 'push_out', 'keep_distant', 'get_out', 'push']
senticnet['bounces_off'] = ['0.793', '0.915', '0', '0', '#joy', '#interest', 'positive', '0.854', 'ball', 'ball_bounce', 'football', 'soccer', 'basketball']
senticnet['bouncing'] = ['0.65', '-0.61', '0', '0.711', '#joy', '#admiration', 'positive', '0.658', 'lively', 'scintillating', 'spirited', 'peppy', 'zippy']
senticnet['bouncing_ball'] = ['0.793', '0.915', '0', '0', '#joy', '#interest', 'positive', '0.854', 'ball', 'ball_bounce', 'football', 'soccer', 'basketball']
senticnet['bouncing_off'] = ['0.793', '0.915', '0', '0', '#joy', '#interest', 'positive', '0.854', 'ball', 'ball_bounce', 'football', 'soccer', 'basketball']
senticnet['bouncy'] = ['0.75', '0.71', '0', '0', '#joy', '#interest', 'positive', '0.73', 'spherical_shape', 'use_sport', 'flexible', 'ball', 'fill_air']
senticnet['bouncy_castle'] = ['0', '-0.08', '0', '0', '#surprise', '#surprise', 'positive', '0.082', 'yo-yo', 'playroom', 'backgammon', 'action_figure', 'board_game']
senticnet['bouncy_seat'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'lavatory', 'commode', 'potty_chair']
senticnet['bound'] = ['-0.51', '0', '0.946', '-0.99', '#anger', '#disgust', 'negative', '-0.82', 'trammel', 'limit', 'restrict', 'clamp_down', 'confine']
senticnet['boundary'] = ['0.034', '0.09', '0.039', '0.061', '#interest', '#admiration', 'positive', '0.036', 'border', 'limit', 'edge', 'indoor', 'side']
senticnet['boundary_between'] = ['0.034', '0.09', '0.039', '0.061', '#interest', '#admiration', 'positive', '0.036', 'border', 'limit', 'edge', 'indoor', 'side']
senticnet['boundary_condition'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'assumption', 'precondition', 'provision', 'constraint', 'premiss']
senticnet['boundary_line'] = ['0.034', '0.09', '0.039', '0.061', '#interest', '#admiration', 'positive', '0.036', 'border', 'limit', 'edge', 'indoor', 'side']
senticnet['boundary_rope'] = ['-0.13', '0', '-0.11', '-0.04', '#sadness', '#fear', 'negative', '-0.1', 'concern_health', 'medical_problem', 'strengthful', 'surgery', 'injury']
senticnet['bounded'] = ['0.896', '0.861', '0', '0', '#joy', '#interest', 'positive', '0.879', 'finite', 'limited', 'finite_state_machine', 'wall', 'boundary']
senticnet['bounded_posteriorly'] = ['-0.74', '0', '0.598', '-0.75', '#sadness', '#disgust', 'negative', '-0.69', 'lunate_bone', 'semilunar_bone', 'carpal_bone', 'carpal', 'bone']
senticnet['bounden'] = ['0.924', '-0.55', '0', '0.756', '#joy', '#admiration', 'positive', '0.744', 'obligatory', 'incumbent_on', 'obligated', 'compulsory', 'mandatory']
senticnet['bounder'] = ['-0.88', '0', '0.806', '-0.63', '#sadness', '#anger', 'negative', '-0.77', 'blackguard', 'cad', 'scoundrel', 'rogue', 'hound']
senticnet['bounding_box'] = ['0.891', '0.961', '0', '0', '#joy', '#interest', 'positive', '0.926', 'square', 'four_sides', 'rhombus', 'four_corners', 'cube']
senticnet['bounding_rectangle'] = ['0.891', '0.961', '0', '0', '#joy', '#interest', 'positive', '0.926', 'square', 'four_sides', 'rhombus', 'four_corners', 'cube']
senticnet['boundless'] = ['0', '0.132', '0', '0.176', '#interest', '#admiration', 'positive', '0.154', 'infinity', 'interval', 'infinite', 'endless', 'dimension']
senticnet['boundless_energy'] = ['0.812', '0.937', '0', '0', '#joy', '#interest', 'positive', '0.875', 'retentive', 'long', 'dogged', 'dour', 'recollective']
senticnet['boundless_enthusiasm'] = ['0.887', '0', '0', '0', '#joy', '#joy', 'positive', '0.887', 'thankful', 'openhearted', 'good-hearted', 'kindly', 'benevolent']
senticnet['boundless_ocean'] = ['0.98', '-0.74', '0', '0.984', '#joy', '#admiration', 'positive', '0.903', 'prosperous', 'flourishing', 'successful', 'prospering', 'thriving']
senticnet['bountiful'] = ['0.851', '-0.67', '0', '0.854', '#joy', '#admiration', 'positive', '0.792', 'fertile', 'breeding', 'prolific', 'fruitful', 'productive']
senticnet['bountifully'] = ['0.902', '-0.72', '0', '0.919', '#joy', '#admiration', 'positive', '0.849', 'preciousness', 'costliness', 'expensiveness', 'cost', 'lavishness']
senticnet['bounty'] = ['0.023', '0.233', '-0.06', '0.16', '#interest', '#admiration', 'positive', '0.087', 'money_earn', 'financial_gain', 'pay_monthly', 'monetary_gain', 'extra_money']
senticnet['bounty_hunter'] = ['-0.87', '0', '0.574', '-0.73', '#sadness', '#disgust', 'negative', '-0.72', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['bouquet'] = ['0.081', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.057', 'blossom', 'bloom', 'pollen', 'poppy', 'many_plants']
senticnet['bourbon'] = ['0.975', '0.801', '0', '0', '#joy', '#interest', 'positive', '0.888', 'scotch', 'ale', 'vodka', 'brandy', 'whiskey']
senticnet['bourbon_dynasty'] = ['0.059', '0.092', '0', '0.107', '#interest', '#admiration', 'positive', '0.086', 'royalty', 'monarch', 'throne', 'emperor', 'king_queen']
senticnet['bourbon_monarchy'] = ['0.059', '0.092', '0', '0.107', '#interest', '#admiration', 'positive', '0.086', 'royalty', 'monarch', 'throne', 'emperor', 'king_queen']
senticnet['bourbon_whiskey'] = ['0.899', '0.991', '0', '0', '#joy', '#interest', 'positive', '0.945', 'bourbon', 'scotch', 'vodka', 'brandy', 'wine']
senticnet['bourboulenc'] = ['0.784', '-0.59', '0', '0.846', '#joy', '#admiration', 'positive', '0.742', 'astuteness', 'slyness', 'wiliness', 'craftiness', 'cunning']
senticnet['bourgeois'] = ['0.127', '0.069', '0.029', '0.16', '#joy', '#admiration', 'positive', '0.082', 'basic_human_need', 'construct', 'set_down', 'make', 'build_process']
senticnet['bourgeois_democracy'] = ['0.127', '0.069', '0.029', '0.16', '#joy', '#admiration', 'positive', '0.082', 'basic_human_need', 'construct', 'set_down', 'make', 'build_process']
senticnet['bourgeois_ideology'] = ['0.127', '0.069', '0.029', '0.16', '#joy', '#admiration', 'positive', '0.082', 'basic_human_need', 'construct', 'set_down', 'make', 'build_process']
senticnet['bourgeois_liberal'] = ['0.127', '0.069', '0.029', '0.16', '#joy', '#admiration', 'positive', '0.082', 'basic_human_need', 'construct', 'set_down', 'make', 'build_process']
senticnet['bourgeois_liberalism'] = ['0.95', '-0.77', '0', '0.578', '#joy', '#surprise', 'positive', '0.767', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['bourgeois_morality'] = ['0.95', '-0.77', '0', '0.578', '#joy', '#surprise', 'positive', '0.767', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['bourgeois_society'] = ['0.127', '0.069', '0.029', '0.16', '#joy', '#admiration', 'positive', '0.082', 'basic_human_need', 'construct', 'set_down', 'make', 'build_process']
senticnet['bourgeoisie'] = ['-0.65', '0', '0.691', '-0.54', '#sadness', '#anger', 'negative', '-0.63', 'moronity', 'mental_deficiency', 'imbecility', 'stupidity', 'mental_defectiveness']
senticnet['bourre'] = ['0.591', '-0.91', '0', '0.605', '#surprise', '#admiration', 'positive', '0.704', 'farandole', 'folk_dance', 'polka', 'dance', 'typical_dance']
senticnet['boursault'] = ['-0.81', '0', '0.885', '-0.92', '#anger', '#disgust', 'negative', '-0.87', 'miss', 'girl', 'damsel', 'maiden', 'damoiselle']
senticnet['bout'] = ['0.944', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.967', 'meeting', 'meet', 'match', 'encounter', 'round']
senticnet['bout_via_tko'] = ['0.823', '0.982', '0', '0', '#joy', '#interest', 'positive', '0.902', 'spherical', 'three_dimensions', 'round_orb', 'coil', 'fill_with_sand']
senticnet['boutique'] = ['0.822', '0', '0', '0.927', '#joy', '#admiration', 'positive', '0.875', 'chic', 'luxurious', 'luxury', 'stylish', 'luxe']
senticnet['boutique_firm'] = ['0.841', '0.721', '0', '0.74', '#joy', '#admiration', 'positive', '0.767', 'good_manner', 'two_people', 'love', 'guardian', 'guy']
senticnet['boutique_hotel'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'lurking_place', 'spot', 'drop', 'hiding_place', 'den']
senticnet['boutique_label'] = ['0', '0.115', '0.036', '0.207', '#interest', '#admiration', 'positive', '0.095', 'total', 'amount', 'merit', 'award', 'excellence']
senticnet['boutique_resort'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'lurking_place', 'spot', 'drop', 'hiding_place', 'den']
senticnet['boutonneuse_fever'] = ['-0.73', '0', '0.599', '-0.96', '#sadness', '#disgust', 'negative', '-0.76', 'spotted_fever', 'rickettsial_disease', 'rickettsialpox', 'typhus', 'typhus_fever']
senticnet['bouyancy'] = ['0.676', '0.63', '-0.23', '0.31', '#joy', '#interest', 'positive', '0.345', 'perkiness', 'walk_on_air', 'exultingly', 'exultantly', 'jubilance']
senticnet['bovatec'] = ['-0.73', '0', '0.94', '-0.87', '#anger', '#disgust', 'negative', '-0.85', 'edema', 'oedema', 'hydrops', 'dropsy', 'puffiness']
senticnet['bovine'] = ['0.027', '0.077', '0', '0.138', '#interest', '#admiration', 'positive', '0.081', 'hoof_mammal', 'hoof_animal', 'neigh', 'smart_animal', 'critter']
senticnet['bovine_serum_albumin'] = ['-0.04', '0.069', '0', '0.075', '#interest', '#admiration', 'positive', '0.035', 'sculpt_gel', 'hairstyle', 'hairdresser', 'cosmetic', 'hair_sculpt_gel']
senticnet['bow'] = ['0.937', '0.847', '0', '0', '#joy', '#interest', 'positive', '0.892', 'arrow', 'spear', 'buy_wood', 'baton', 'archery']
senticnet['bow_leg'] = ['-0.75', '0', '0.504', '-0.77', '#sadness', '#disgust', 'negative', '-0.67', 'astasia', 'anorgasmia', 'hearing_impairment', 'hearing_disorder', 'disablement']
senticnet['bow_string'] = ['0.037', '-0.11', '0.144', '-0.18', '#anger', '#disgust', 'negative', '-0.04', 'pluck', 'string_break', 'play_instrument', 'rock_out', 'record_song']
senticnet['bow_tie'] = ['0', '-0.02', '0.048', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'telescope', 'read_glass', 'eyeglass', 'camera', 'binoculars']
senticnet['bowdlerization'] = ['0', '0', '0.967', '-0.72', '#anger', '#disgust', 'negative', '-0.84', 'bowdlerize', 'alter', 'unseemly', 'vulgar', 'remove']
senticnet['bowdlerize'] = ['0', '0', '0.972', '-0.71', '#anger', '#disgust', 'negative', '-0.84', 'bowdlerization', 'alter', 'unseemly', 'vulgar', 'remove']
senticnet['bowdoin_orient'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['bowed_down'] = ['-0.93', '0', '0.807', '-0.95', '#sadness', '#disgust', 'negative', '-0.89', 'heatstroke', 'heat_hyperpyrexia', 'malady', 'breakdown', 'shock']
senticnet['bowed_instrument'] = ['0.821', '-0.53', '0', '0.785', '#joy', '#admiration', 'positive', '0.712', 'fife', 'nose_flute', 'piccolo', 'woodwind', 'flute']
senticnet['bowed_string_instrument'] = ['0.821', '-0.53', '0', '0.785', '#joy', '#admiration', 'positive', '0.712', 'fife', 'nose_flute', 'piccolo', 'woodwind', 'flute']
senticnet['bowel'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'anus', 'scrotum', 'shoulder', 'liver', 'waist']
senticnet['bowel_control'] = ['-0.05', '0', '-0.05', '-0.04', '#sadness', '#fear', 'negative', '-0.05', 'empty_bladder', 'pee', 'go_potty', 'go_pee', 'go_toilet']
senticnet['bowel_dysfunction'] = ['0.992', '-0.80', '0', '0.584', '#joy', '#surprise', 'positive', '0.795', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'excretion']
senticnet['bowel_function'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'anus', 'scrotum', 'shoulder', 'liver', 'waist']
senticnet['bowel_incontinence'] = ['-0.52', '0', '0.839', '-0.86', '#anger', '#disgust', 'negative', '-0.74', 'folie', 'upset', 'psychosomatic_disorder', 'conversion_disorder', 'conversion_reaction']
senticnet['bowel_inflammation'] = ['-0.75', '0', '0.868', '-0.99', '#anger', '#disgust', 'negative', '-0.87', 'regional_enteritis', 'regional_ileitis', 'ulcerative_colitis', 'rubor', 'redness']
senticnet['bowel_movement'] = ['-0.89', '0', '0.925', '-0.58', '#sadness', '#anger', 'negative', '-0.80', 'shit', 'laxation', 'movement', 'dump', 'asshole']
senticnet['bowel_movement_per'] = ['-0.52', '0', '0.839', '-0.86', '#anger', '#disgust', 'negative', '-0.74', 'folie', 'upset', 'psychosomatic_disorder', 'conversion_disorder', 'conversion_reaction']
senticnet['bowel_obstruction'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'anus', 'scrotum', 'shoulder', 'liver', 'waist']
senticnet['bowel_perforation'] = ['-0.56', '0', '0.806', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['bowel_resection'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'anus', 'scrotum', 'shoulder', 'liver', 'waist']
senticnet['bowel_wall'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'anus', 'scrotum', 'shoulder', 'liver', 'waist']
senticnet['bowelless'] = ['0.971', '-0.92', '0', '0.617', '#joy', '#surprise', 'positive', '0.838', 'mortal', 'cutthroat', 'merciless', 'tigerish', 'pitiless']
senticnet['bowery'] = ['-0.50', '0', '0.573', '-0.89', '#anger', '#disgust', 'negative', '-0.65', 'petallike', 'leafy', 'foliolate', 'unifoliate', 'bifoliate']
senticnet['bowhead'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'seal', 'otter', 'mussel', 'alligator', 'whale']
senticnet['bowhead_whale'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'seal', 'otter', 'mussel', 'alligator', 'whale']
senticnet['bowhunter'] = ['0', '-0.07', '0.091', '-0.19', '#anger', '#disgust', 'negative', '-0.07', 'projectile', 'use_hunt', 'weapon', 'ammunition', 'firearm']
senticnet['bowie_knife'] = ['0.986', '0.979', '0', '0', '#joy', '#interest', 'positive', '0.982', 'hatchet', 'gun_knife', 'sword', 'bayonet', 'sharp_weapon']
senticnet['bowing'] = ['0.679', '-0.94', '0', '0.661', '#joy', '#surprise', 'positive', '0.762', 'submissive', 'cringing', 'groveling', 'sub', 'wormlike']
senticnet['bowing_down'] = ['-0.93', '0', '0.807', '-0.95', '#sadness', '#disgust', 'negative', '-0.89', 'heatstroke', 'heat_hyperpyrexia', 'malady', 'breakdown', 'shock']
senticnet['bowl'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'mazer']
senticnet['bowl_alley'] = ['0.101', '0.074', '0.03', '0', '#joy', '#interest', 'positive', '0.048', 'bowling', 'pin', 'sports', 'challenge', 'bowling_center']
senticnet['bowl_alley_center'] = ['0.092', '0.072', '0.024', '0', '#joy', '#interest', 'positive', '0.047', 'bowl_alley', 'pin', 'sports', 'challenge', 'bowling']
senticnet['bowl_club'] = ['-0.68', '0', '0.923', '-0.68', '#sadness', '#anger', 'negative', '-0.76', 'negative', 'veto', 'refute', 'rebut', 'controvert']
senticnet['bowler_hat'] = ['0.08', '0.106', '-0.04', '0.112', '#interest', '#admiration', 'positive', '0.063', 'helmet', 'head_gear', 'cap', 'head_wear', 'head_protection']
senticnet['bowling'] = ['0.101', '0.074', '0.03', '0', '#joy', '#interest', 'positive', '0.048', 'bowl_alley', 'pin', 'sports', 'challenge', 'bowl_alley_center']
senticnet['bowling_alley'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'theater', 'drive-in_movie', 'watch_movie', 'cinema', 'movie']
senticnet['bowling_ball'] = ['0.793', '0.915', '0', '0', '#joy', '#interest', 'positive', '0.854', 'ball', 'ball_bounce', 'football', 'soccer', 'basketball']
senticnet['bowling_center'] = ['0', '0.827', '0', '0.84', '#interest', '#admiration', 'positive', '0.833', 'bowling', 'sport', 'excise', 'challenge', 'competition']
senticnet['bowling_club'] = ['-0.68', '0', '0.923', '-0.68', '#sadness', '#anger', 'negative', '-0.76', 'negative', 'veto', 'refute', 'rebut', 'controvert']
senticnet['bowling_pin'] = ['0.757', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.831', 'baseball_bat', 'water_gun', 'goalie', 'hockey_puck', 'hockey_stick']
senticnet['bowling_spell'] = ['0.034', '-0.02', '0.054', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'compass', 'swiss_army_knife', 'bake_pan', 'coffee_mug', 'wine_opener']
senticnet['bowsprit'] = ['0.937', '0.847', '0', '0', '#joy', '#interest', 'positive', '0.892', 'arrow', 'spear', 'buy_wood', 'baton', 'archery']
senticnet['box'] = ['0', '-0.02', '0', '0.051', '#surprise', '#admiration', 'positive', '0.037', 'cardboard_box', 'bag', 'package', 'box_cereal', 'bin']
senticnet['box_canyon'] = ['0', '0.069', '0', '0.065', '#interest', '#admiration', 'positive', '0.067', 'alp', 'high_mountain', 'pass', 'ski_resort', 'high_place']
senticnet['box_cereal'] = ['0.738', '-0.71', '0', '0', '#joy', '#surprise', 'positive', '0.728', 'breakfast', 'dry_fruit', 'carton_milk', 'wheat', 'morning']
senticnet['box_cutter'] = ['0.818', '-0.95', '0', '0.695', '#joy', '#surprise', 'positive', '0.823', 'jackknife', 'clasp_knife', 'penknife', 'switchblade', 'switchblade_knife']
senticnet['box_ear'] = ['0', '0', '0.833', '-0.96', '#anger', '#disgust', 'negative', '-0.9', 'bridge_difference', 'lower_standard', 'market_product', 'deal_card_player', 'stand_alone']
senticnet['box_girder_bridge'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'suspension_bridge', 'under_bridge', 'roadway', 'county_highway', 'long_road']
senticnet['box_match'] = ['0.938', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.958', 'competition', 'contest', 'sports', 'match', 'boxing']
senticnet['box_office'] = ['0', '0', '-0.1', '-0.04', '#fear', '#disgust', 'negative', '-0.07', 'ticket_box', 'stage_entrance', 'movie', 'ticket_booth', 'sticky_floor']
senticnet['box_office_hit'] = ['0.625', '-0.90', '0', '0.54', '#joy', '#surprise', 'positive', '0.69', 'smash', 'bang', 'smasher', 'strike', 'hit']
senticnet['box_office_revenue'] = ['-0.62', '0', '0.7', '-0.83', '#anger', '#disgust', 'negative', '-0.72', 'take', 'pull', 'return', 'payback', 'proceeds']
senticnet['box_ring'] = ['0', '0', '-0.77', '-0.98', '#fear', '#disgust', 'negative', '-0.88', 'convention', 'prisoner_war_camp', 'rave', 'gift_shop', 'dark_alley']
senticnet['box_tissue'] = ['0', '-0.04', '0.068', '-0.06', '#anger', '#disgust', 'negative', '-0.02', 'side_chair', 'stapler', 'tape_dispenser', 'box', 'ergonomic_chair']
senticnet['boxed_set'] = ['-0.73', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'unwed', 'unwedded', 'divorced', 'unmarried', 'widowed']
senticnet['boxer'] = ['0.833', '-0.97', '0', '0.91', '#surprise', '#admiration', 'positive', '0.906', 'combatant', 'fencer', 'belligerent', 'fighter', 'scrapper']
senticnet['boxing'] = ['0.929', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.955', 'competition', 'contest', 'sports', 'match', 'box_match']
senticnet['boxing_glove'] = ['0.783', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.766', 'baseball_bat', 'water_gun', 'goalie', 'hockey_stick', 'bullseye']
senticnet['boxing_match'] = ['0', '-0.01', '-0.08', '-0.16', '#fear', '#disgust', 'negative', '-0.07', 'siege', 'big_fight', 'battle', 'declare_war', 'conflict']
senticnet['boxset'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['boxy'] = ['-0.76', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.81', 'slouchy', 'squarish', 'low-slung', 'blocky', 'cropped']
senticnet['boy'] = ['0.575', '-0.62', '0', '0.596', '#surprise', '#admiration', 'positive', '0.598', 'altar_boy', 'cub', 'ball_boy', 'bat_boy', 'lad']
senticnet['boy_girl'] = ['0.025', '0.073', '-0.05', '0.1', '#interest', '#admiration', 'positive', '0.037', 'little_person', 'small_kid', 'small_boy', 'young_kid', 'young_people']
senticnet['boy_next_door'] = ['-0.19', '0', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.15', 'bad_child', 'misbehavior', 'bad_kid', 'bad_action', 'difficulty']
senticnet['boy_racer'] = ['-0.10', '-0.04', '0', '-0.14', '#sadness', '#disgust', 'negative', '-0.06', 'adjust_seat', 'angry_behavior', 'fasten_seatbelt', 'traffic_jam', 'skid_mark']
senticnet['boy_scout'] = ['0.798', '-0.51', '0', '0.973', '#joy', '#admiration', 'positive', '0.763', 'naif', 'inexperienced_person', 'dear', 'virgin', 'mortal']
senticnet['boy_scout_camp'] = ['0', '0.156', '-0.06', '0.198', '#interest', '#admiration', 'positive', '0.098', 'grassland', 'camping', 'camp_out', 'campsite', 'campground']
senticnet['boy_toy'] = ['-0.19', '0', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.15', 'bad_child', 'misbehavior', 'bad_kid', 'bad_action', 'difficulty']
senticnet['boy_varsity_soccer'] = ['0.035', '0.155', '0', '0', '#joy', '#interest', 'positive', '0.095', 'basketball_court', 'basketball_game', 'basketball', 'ball', 'sport']
senticnet['boyce_thompson_arboretum'] = ['-0.79', '0', '0.837', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'sunray', 'wilding', 'sagebrush_buttercup', 'pasqueflower', 'pasque_flower']
senticnet['boycott'] = ['-0.82', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.87', 'protest', 'buying', 'using', 'blockade', 'expression']
senticnet['boyfriend'] = ['0.033', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.058', 'baby_dog', 'sweet', 'puppet', 'dog', 'love']
senticnet['boyhood'] = ['-0.95', '0', '0.735', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'novice', 'beginner', 'cub', 'tiro', 'initiate']
senticnet['boyish'] = ['0.963', '-0.98', '0', '0.514', '#joy', '#surprise', 'positive', '0.821', 'warm', 'flighty', 'nervous', 'skittish', 'spooky']
senticnet['boyish_charm'] = ['0.85', '-0.71', '0', '0.617', '#joy', '#surprise', 'positive', '0.728', 'pretender', 'phony', 'phoney', 'hypocrite', 'dissembler']
senticnet['boyish_face'] = ['0', '0', '0.841', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'ancient', 'many_years', 'year', 'old', 'teen']
senticnet['boyle'] = ['-0.69', '0', '0.89', '-0.70', '#anger', '#disgust', 'negative', '-0.76', 'still', 'suppress', 'moderate', 'hush_up', 'quell']
senticnet['boys'] = ['0.967', '0.886', '0', '0', '#joy', '#interest', 'positive', '0.926', 'bloke', 'male', 'male_person', 'man_child', 'dude']
senticnet['boysenberry'] = ['0.02', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'many_fruits', 'peach', 'buy_fruit', 'pineapple', 'tomato']
senticnet['bpm_beat_per'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'watch', 'clock', 'hour', 'hourglass', 'stopwatch']
senticnet['br_er_bear'] = ['0.934', '-0.57', '0', '0.767', '#joy', '#admiration', 'positive', '0.76', 'morosoph', 'simpleton', 'flibbertigibbet', 'foolish_woman', 'simple']
senticnet['br_er_fox'] = ['0.934', '-0.57', '0', '0.767', '#joy', '#admiration', 'positive', '0.76', 'morosoph', 'simpleton', 'flibbertigibbet', 'foolish_woman', 'simple']
senticnet['br_er_rabbit'] = ['0.934', '-0.57', '0', '0.767', '#joy', '#admiration', 'positive', '0.76', 'morosoph', 'simpleton', 'flibbertigibbet', 'foolish_woman', 'simple']
senticnet['bra'] = ['0', '0.151', '-0.08', '0.232', '#interest', '#admiration', 'positive', '0.098', 'good_human', 'active', 'healthy_activity', 'push_up', 'result_injury']
senticnet['bra_strap'] = ['0', '0', '0', '-0.06', '#disgust', '#disgust', 'negative', '-0.06', 'body', 'nervous_system', 'normal_human_body', 'human_body', 'head_shoulder']
senticnet['braathens_helikopter'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['braathens_rederi'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['braathens_safe'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['brabant'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'feudal_lord', 'lord', 'master', 'overlord', 'king']
senticnet['brabazon_trophy'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'sciolist', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
senticnet['brabham_straight'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['brace'] = ['0.8', '-0.97', '0', '0.512', '#joy', '#surprise', 'positive', '0.763', 'energize', 'perk_up', 'reanimate', 'stimulate', 'arouse']
senticnet['bracelet'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'anklet', 'ankle_bracelet', 'armilla']
senticnet['bracer'] = ['-0.50', '0', '0.937', '-0.59', '#anger', '#disgust', 'negative', '-0.67', 'restorative', 'medicine', 'medication', 'medicament', 'medicinal_drug']
senticnet['brachiation'] = ['0', '0', '-0.81', '-0.92', '#fear', '#disgust', 'negative', '-0.87', 'tree-living', 'dendroidal', 'arborous', 'arboriform', 'arborary']
senticnet['brachioplasty'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
senticnet['brachycephaly'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'lordosis', 'spinal_curvature', 'humpback', 'hunchback', 'abnormality']
senticnet['brachydactyly'] = ['0.582', '-0.66', '0', '0.661', '#surprise', '#admiration', 'positive', '0.637', 'talipes_equinus', 'deformity', 'malformation', 'talipes_valgus', 'talipes_calcaneus']
senticnet['brachyuran'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'stone_crab', 'swimming_crab', 'rock_crab', 'crab', 'pea_crab']
senticnet['bracing_wit'] = ['0.947', '-0.99', '0', '0.936', '#joy', '#surprise', 'positive', '0.958', 'utterer', 'deipnosophist', 'interlocutor', 'conversational_partner', 'speaker']
senticnet['bracingly'] = ['0.741', '-0.74', '0', '0.666', '#joy', '#surprise', 'positive', '0.718', 'commonplaceness', 'mundanity', 'prosiness', 'prosaicness', 'ordinariness']
senticnet['bracken_fern'] = ['-0.79', '0', '0.906', '-0.67', '#sadness', '#anger', 'negative', '-0.79', 'water_dropwort', 'hemlock_water_dropwort', 'water_hemlock', 'plant_life', 'poisonous_plant']
senticnet['bracket'] = ['0.763', '-0.67', '0', '0.945', '#joy', '#admiration', 'positive', '0.795', 'set', 'income_bracket', 'tax_bracket', 'income_tax_bracket', 'price_bracket']
senticnet['bracket_fungi'] = ['-0.60', '0', '0.541', '-0.52', '#sadness', '#anger', 'negative', '-0.55', 'swoon', 'pass_out', 'faint', 'zonk_out', 'change_state']
senticnet['bracket_repechage'] = ['-0.80', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.77', 'contrary', 'contradiction', 'lie', 'liar', 'bad_person']
senticnet['brackish'] = ['-0.81', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.90', 'seawater', 'estuary', 'freshwater', 'saltwater', 'salt-water']
senticnet['braconid_wasp'] = ['0', '-0.09', '0.123', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'gypsy_moth', 'japanese_beetle', 'cucumber_beetle', 'mosquito', 'earwig']
senticnet['bract'] = ['0', '0', '0.064', '-0.04', '#anger', '#disgust', 'negative', '-0.05', 'bird_food', 'funiculus', 'plant_product', 'fuzz', 'agricultural_product']
senticnet['bradford_washburn'] = ['0', '0.076', '0', '0.063', '#interest', '#admiration', 'positive', '0.07', 'up_hill', 'elevation', 'up_mountain', 'ascend', 'upwards']
senticnet['bradycardia'] = ['-0.83', '0', '0.987', '-0.50', '#sadness', '#anger', 'negative', '-0.77', 'heart_block', 'atrioventricular_block', 'arrhythmia', 'ventricular_fibrillation', 'tachycardia']
senticnet['bradykinesia'] = ['-0.75', '0', '0.504', '-0.77', '#sadness', '#disgust', 'negative', '-0.67', 'disablement', 'anorgasmia', 'hearing_impairment', 'hearing_disorder', 'bow_leg']
senticnet['brag'] = ['0', '0', '0', '-0.73', '#disgust', '#disgust', 'negative', '-0.73', 'convince', 'talk_much', 'discuss', 'transfer_knowledge', 'communicate']
senticnet['bragelonne'] = ['-0.90', '0', '0.865', '-0.77', '#sadness', '#anger', 'negative', '-0.84', 'nosiness', 'snoopiness', 'prying', 'curiosity', 'wonder']
senticnet['braggadocio'] = ['-0.54', '0', '0.7', '-0.64', '#anger', '#disgust', 'negative', '-0.62', 'flourish', 'rant', 'ornateness', 'fustian', 'claptrap']
senticnet['braggart'] = ['0', '0', '-0.83', '-0.84', '#fear', '#disgust', 'negative', '-0.84', 'blowhard', 'vaunter', 'boaster', 'line-shooter', 'bragger']
senticnet['bragger'] = ['0', '0', '0.982', '-0.71', '#anger', '#disgust', 'negative', '-0.84', 'boaster', 'braggart', 'vaunter', 'line-shooter', 'blowhard']
senticnet['bragging'] = ['-0.88', '0', '0.666', '-0.90', '#sadness', '#disgust', 'negative', '-0.81', 'crow', 'crowing', 'bluster', 'jactitation', 'vaporing']
senticnet['brahma_vihara'] = ['-0.85', '0', '0.968', '-0.75', '#sadness', '#anger', 'negative', '-0.86', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['brahman_atman'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
senticnet['brahminy_kite'] = ['0', '0.105', '0', '0', '#interest', '#interest', 'positive', '0.105', 'beak', 'many_feathers', 'big_eye', 'bird', 'bird_mouth']
senticnet['brahmo'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['brahmo_leader'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['brahmo_samaj'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['braid'] = ['-0.06', '-0.01', '0.05', '-0.07', '#sadness', '#disgust', 'negative', '-0.04', 'embroidery', 'woven', 'cover_wind', 'torn', 'use_needle']
senticnet['braided_channel'] = ['0.97', '0.758', '0', '0', '#joy', '#interest', 'positive', '0.864', 'trot', 'step_out', 'step_aside', 'progress', 'enter_place']
senticnet['braided_hair'] = ['-0.06', '-0.01', '0.05', '-0.07', '#sadness', '#disgust', 'negative', '-0.04', 'embroidery', 'woven', 'cover_wind', 'torn', 'use_needle']
senticnet['braille'] = ['-0.22', '-0.03', '0.026', '-0.13', '#sadness', '#disgust', 'negative', '-0.08', 'blind_man', 'blind_person', 'shade', 'need_help', 'shadow']
senticnet['braille_keyboard'] = ['0.893', '0.979', '0', '0', '#joy', '#interest', 'positive', '0.936', 'find_phone_number', 'dial_phone_number', 'pick_up_telephone_receiver', 'dial', 'dial_telephone']
senticnet['brain'] = ['0', '0.786', '0', '0.728', '#interest', '#admiration', 'positive', '0.757', 'hold_brain', 'inside_head', 'cranium', 'skull', 'mind']
senticnet['brain-washing'] = ['0', '0', '0.796', '-0.75', '#anger', '#disgust', 'negative', '-0.77', 'indoctrinate', 'rudiment', 'science', 'principle', 'indoctrination']
senticnet['brain_activity'] = ['0.892', '0.935', '0', '0', '#joy', '#interest', 'positive', '0.913', 'recall', 'analysis', 'new_think', 'recollection', 'put_two_together']
senticnet['brain_aneurysm'] = ['-0.73', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.79', 'die_painfully', 'arrest_police', 'get_accident', 'fall_head', 'skin_knee']
senticnet['brain_atrophy'] = ['-0.94', '0', '0.821', '-0.75', '#sadness', '#anger', 'negative', '-0.84', 'predementedness', 'insanity', 'dementedness', 'senile_psychosis', 'alcoholic_dementia']
senticnet['brain_behind'] = ['0.534', '-0.69', '0', '0.724', '#surprise', '#admiration', 'positive', '0.65', 'knocker', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['brain_cancer'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['brain_circuitry'] = ['0', '0.786', '0', '0.728', '#interest', '#admiration', 'positive', '0.757', 'hold_brain', 'inside_head', 'cranium', 'skull', 'mind']
senticnet['brain_contusion'] = ['-0.03', '-0.00', '-0.07', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'injury', 'get_hurt', 'bruise', 'lose_game', 'get_injured']
senticnet['brain_corpus_callosum'] = ['-0.02', '0.103', '0.069', '0.151', '#interest', '#admiration', 'positive', '0.039', 'single', 'unit_of_measurement', 'do_it_yourself', 'enumeration', 'alone']
senticnet['brain_cortex'] = ['0', '0.786', '0', '0.728', '#interest', '#admiration', 'positive', '0.757', 'hold_brain', 'inside_head', 'cranium', 'skull', 'mind']
senticnet['brain_damage'] = ['-0.17', '-0.02', '-0.11', '-0.15', '#sadness', '#disgust', 'negative', '-0.10', 'serious_accident', 'bang_head', 'loss_ofawareness', 'concussion', 'get_hurt']
senticnet['brain_disease'] = ['-0.95', '0', '0.621', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'disease', 'brain_disorder', 'parkinson', 'ill', 'epilepsy']
senticnet['brain_disorder'] = ['-0.94', '0', '0.868', '-0.56', '#sadness', '#anger', 'negative', '-0.79', 'disease', 'brain_disease', 'parkinson', 'ill', 'epilepsy']
senticnet['brain_dysfunction'] = ['-0.94', '0', '0.821', '-0.75', '#sadness', '#anger', 'negative', '-0.84', 'predementedness', 'insanity', 'dementedness', 'senile_psychosis', 'alcoholic_dementia']
senticnet['brain_failure'] = ['-0.52', '0', '0.785', '-0.61', '#anger', '#disgust', 'negative', '-0.64', 'disease', 'feel_sick', 'brain_disease', 'eye_failure', 'epilepsy']
senticnet['brain_fever'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'leptomeningitis', 'cerebrospinal_meningitis', 'epidemic_meningitis', 'meningitis', 'cerebrospinal_fever']
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senticnet['branded_jewellery'] = ['0.532', '-0.60', '0', '0.875', '#surprise', '#admiration', 'positive', '0.67', 'gem', 'adornment', 'jewel', 'bead', 'bangle']
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senticnet['bransdicke_theory'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'general_relativity', 'general_theory_of_relativity', 'relativity', 'special_relativity', 'special_theory_of_relativity']
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senticnet['brawl'] = ['0.915', '-0.84', '0', '0.778', '#joy', '#surprise', 'positive', '0.847', 'fall_out', 'debate', 'wrangle', 'fight', 'altercate']
senticnet['brawl_broke_out'] = ['-0.60', '0', '0.777', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'batrachomyomachia', 'wrangle', 'dustup', 'quarrel', 'row']
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senticnet['breach'] = ['-0.70', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.78', 'break', 'opening', 'surge', 'leap', 'falling-out']
senticnet['breach_of_contract'] = ['-0.10', '0', '-0.05', '-0.24', '#sadness', '#disgust', 'negative', '-0.13', 'breach_of_warranty', 'breach_of_trust', 'partial_breach', 'anticipatory_breach', 'constructive_breach']
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senticnet['bread'] = ['0.901', '0', '0', '0.916', '#joy', '#admiration', 'positive', '0.909', 'get_away', 'absolution', 'glory', 'counsel', 'vow']
senticnet['bread-like'] = ['-0.85', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'pizza-like', 'pancake-like', 'crepe-like', 'donut-like', 'yeast-risen']
senticnet['bread_crumb'] = ['-0.08', '-0.04', '0.023', '-0.07', '#sadness', '#disgust', 'negative', '-0.03', 'toast', 'make_toast', 'messy', 'bread', 'spoilage']
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senticnet['bread_knife'] = ['-0.71', '0', '0.954', '-0.56', '#sadness', '#anger', 'negative', '-0.74', 'jackknife', 'pocket_knife', 'penknife', 'switchblade', 'switchblade_knife']
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senticnet['bread_sauce'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'cook_meat', 'cook_hamburger', 'cook_food', 'cook_rice', 'brown_meat']
senticnet['bread_wheat'] = ['0.061', '0.093', '0.037', '0.114', '#interest', '#admiration', 'positive', '0.058', 'whole_grain', 'food_ingredient', 'staple_food', 'starchy_food', 'food_grain']
senticnet['breadbox'] = ['0', '-0.02', '0', '0.07', '#surprise', '#admiration', 'positive', '0.046', 'pastry', 'christmas_stock', 'bakery', 'sundae', 'freezer']
senticnet['breadcrumb_mixture'] = ['-0.83', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.78', 'picaninny', 'kid', 'minor', 'tike', 'small_picaninny']
senticnet['breaded_chicken'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'obese', 'worse', 'health_issue', 'clinical_problem']
senticnet['breadeeply'] = ['0', '0', '-0.81', '-0.97', '#fear', '#disgust', 'negative', '-0.89', 'take_breath', 'renew_energy', 'late_work', 'feel_refreshed', 'stop_activity']
senticnet['breadstuff'] = ['0.52', '-0.69', '0', '0.639', '#surprise', '#admiration', 'positive', '0.617', 'black_bread', 'pumpernickel', 'limpa', 'bread', 'rye_bread']
senticnet['breadth'] = ['0', '0.098', '-0.06', '0.118', '#interest', '#admiration', 'positive', '0.051', 'egret', 'tall_man', 'wide', 'wood_structure', 'large']
senticnet['breadwinner'] = ['0.051', '0.112', '0', '0', '#joy', '#interest', 'positive', '0.082', 'neighbor', 'family_reunion', 'clan', 'desire', 'little_specific']
senticnet['breafast'] = ['0', '0', '-0.79', '-0.74', '#fear', '#disgust', 'negative', '-0.77', 'blister_foot', 'trip_fall', 'unfit', 'run_out_breath', 'breahard']
senticnet['breahard'] = ['0', '0', '-0.72', '-0.99', '#fear', '#disgust', 'negative', '-0.85', 'blister_foot', 'trip_fall', 'out_breath', 'pull_muscle', 'breafast']
senticnet['breaheavily'] = ['0', '0', '-0.73', '-0.74', '#fear', '#disgust', 'negative', '-0.73', 'get_exhausted', 'run_out_breath', 'heavy_breathe', 'become_wind', 'blister_foot']
senticnet['break'] = ['-0.05', '-0.04', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.03', 'tear_apart', 'painful_injury', 'smash', 'dislocation', 'open_wind']
senticnet['break_ankle'] = ['0', '0', '-0.92', '-0.82', '#fear', '#disgust', 'negative', '-0.87', 'twist_ankle', 'out_breath', 'trip_fall', 'blister_foot', 'minor_injury']
senticnet['break_apart'] = ['-0.11', '0.095', '-0.11', '0.071', '#sadness', '#fear', 'negative', '-0.01', 'violent_action', 'pulverize', 'break_glass', 'tear_apart', 'hit_hard']
senticnet['break_apparatus'] = ['-0.87', '0', '0.682', '-0.53', '#sadness', '#anger', 'negative', '-0.69', 'break', 'crash_cymbal', 'broken_guitar', 'break_instrument', 'damage']
senticnet['break_arm'] = ['-0.74', '-0.81', '-0.83', '-0.98', '#fear', '#disgust', 'negative', '-0.43', 'get_injured', 'fall_down', 'breafast', 'tackle', 'pass_out']
senticnet['break_arrow'] = ['-0.96', '0', '0.704', '-0.97', '#sadness', '#disgust', 'negative', '-0.87', 'break', 'crash', 'smash', 'damage', 'hunting_problem']
senticnet['break_bad'] = ['-0.19', '0', '-0.15', '-0.1', '#sadness', '#fear', 'negative', '-0.15', 'break_law', 'send_to_prison', 'revenge', 'rape', 'use_illegal_drug']
senticnet['break_bone'] = ['-0.91', '-0.72', '-0.84', '-0.80', '#sadness', '#fear', 'negative', '-0.46', 'see_dog', 'go_downhill', 'hit_tree', 'break_leg', 'score_touchdown']
senticnet['break_chair'] = ['0.732', '-0.81', '0', '0', '#joy', '#surprise', 'positive', '0.772', 'break', 'rejuvenation', 'lie_back', 'slow_heart_beat', 'fat']
senticnet['break_door'] = ['-0.93', '0', '0.945', '-0.93', '#anger', '#disgust', 'negative', '-0.93', 'break_into_house', 'break_window_shop', 'accident', 'robbery', 'force_enter']
senticnet['break_dormancy'] = ['-0.75', '0', '0.512', '-0.75', '#sadness', '#disgust', 'negative', '-0.67', 'inactivity', 'inactiveness', 'slumber', 'inaction', 'hibernation']
senticnet['break_down'] = ['-0.09', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.07', 'collide', 'crash', 'turn_steer_wheel', 'near_destination', 'get_accident']
senticnet['break_dunk'] = ['-0.76', '0', '0.794', '-0.91', '#anger', '#disgust', 'negative', '-0.82', 'discharge', 'spray', 'spritz', 'outpouring', 'run']
senticnet['break_easily'] = ['-0.73', '0', '0', '-0.86', '#sadness', '#disgust', 'negative', '-0.80', 'shatter', 'see_better', 'open', 'magnify', 'shard']
senticnet['break_fingernail'] = ['-0.06', '0', '0.078', '-0.12', '#anger', '#disgust', 'negative', '-0.09', 'stardom', 'cramp', 'accomplishment', 'knee_injury', 'sore_foot']
senticnet['break_free'] = ['0.601', '0.647', '-0.26', '0.322', '#joy', '#interest', 'positive', '0.326', 'liberate', 'set_free', 'relief', 'freedom', 'release']
senticnet['break_glass'] = ['-0.11', '0.095', '-0.11', '0.071', '#sadness', '#fear', 'negative', '-0.01', 'violent_action', 'pulverize', 'break_apart', 'tear_apart', 'hit_hard']
senticnet['break_half'] = ['-0.10', '-0.08', '0.106', '-0.15', '#anger', '#disgust', 'negative', '-0.07', 'broken', 'cut_finger', 'cut_up', 'scrape', 'karate']
senticnet['break_in'] = ['-0.81', '0', '0.849', '-0.53', '#sadness', '#anger', 'negative', '-0.73', 'break', 'change', 'alter', 'modify', 'domesticize']
senticnet['break_instrument'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'break', 'crash_cymbal', 'broken_guitar', 'break_apparatus', 'damage']
senticnet['break_into_house'] = ['-0.93', '0', '0.945', '-0.93', '#anger', '#disgust', 'negative', '-0.93', 'break_door', 'break_window_shop', 'accident', 'robbery', 'force_enter']
senticnet['break_law'] = ['0', '-0.86', '0.992', '-0.92', '#anger', '#disgust', 'negative', '-0.34', 'commit_crime', 'send_to_jail', 'send_to_prison', 'feel_remorse', 'go_to_prison']
senticnet['break_leg'] = ['-0.07', '-0.05', '0', '-0.14', '#sadness', '#disgust', 'negative', '-0.05', 'loss_ofbreath', 'see_dog', 'hurt_back', 'fall_ground', 'fall_dead']
senticnet['break_mirror'] = ['-0.93', '0', '0.945', '-0.93', '#anger', '#disgust', 'negative', '-0.93', 'break_window', 'break_window_shop', 'accident', 'bleeding', 'damage']
senticnet['break_neck'] = ['-0.86', '0', '-0.84', '0', '#sadness', '#fear', 'negative', '-0.85', 'fall_ground', 'fall', 'fall_over', 'take_photo', 'crash_car']
senticnet['break_record'] = ['0', '-0.75', '0', '0.766', '#surprise', '#admiration', 'positive', '0.762', 'play_rugby', 'hit_ball_hard', 'play_volleyball', 'run_basis', 'play_basketball']
senticnet['break_rule'] = ['-0.93', '0', '0.945', '-0.93', '#anger', '#disgust', 'negative', '-0.93', 'prison_breaker', 'escape_prison', 'illegal', 'escape_justice', 'cross_red_light']
senticnet['break_silence'] = ['-0.03', '-0.10', '-0.09', '0.114', '#surprise', '#admiration', 'positive', '0.022', 'speak_up', 'cry', 'laugh_joke', 'thank', 'howl_laughter']
senticnet['break_tooth'] = ['0', '-0.84', '-0.88', '-0.96', '#fear', '#disgust', 'negative', '-0.33', 'toothache', 'numbness', 'ice_cream_melt', 'mad_cow_disease', 'car_wreck']
senticnet['break_up'] = ['-0.80', '0', '-0.77', '-0.99', '#sadness', '#disgust', 'negative', '-0.85', 'break', 'amount_nothing', 'ease_pain', 'need_companionship', 'wait_forever']
senticnet['break_window'] = ['-0.93', '0', '0.945', '-0.93', '#anger', '#disgust', 'negative', '-0.93', 'break_mirror', 'break_window_shop', 'accident', 'accident_game', 'break_door']
senticnet['break_window_shop'] = ['-0.93', '0', '0.945', '-0.93', '#anger', '#disgust', 'negative', '-0.93', 'break_mirror', 'break_window', 'accident', 'robbery', 'thief']
senticnet['break_work'] = ['0.897', '0.959', '0', '0', '#joy', '#interest', 'positive', '0.928', 'spend_time_with_friend', 'show_off', 'enjoy_person', 'fun', 'win_money']
senticnet['breakable'] = ['-0.08', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.09', 'fragile', 'brittle', 'frail', 'easy_to_break', 'flimsy']
senticnet['breakage'] = ['-0.69', '0', '0.74', '-0.66', '#sadness', '#anger', 'negative', '-0.70', 'large_indefinite_quantity', 'large_indefinite_amount', 'small_indefinite_quantity', 'small_indefinite_amount', 'indefinite_quantity']
senticnet['breakaway_dunk'] = ['-0.81', '0', '0.747', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'vagrant', 'drifter', 'hobo', 'vagabond', 'dosser']
senticnet['breakaway_faction'] = ['-0.50', '0', '0.989', '-0.91', '#anger', '#disgust', 'negative', '-0.80', 'camp', 'pack', 'clique', 'coterie', 'ingroup']
senticnet['breakaway_layup'] = ['-0.81', '0', '0.747', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'vagrant', 'drifter', 'hobo', 'vagabond', 'dosser']
senticnet['breakbone_fever'] = ['-0.75', '0', '0.69', '-0.69', '#sadness', '#disgust', 'negative', '-0.71', 'rubor', 'redness', 'sleeping_sickness', 'sleepy_sickness', 'cephalitis']
senticnet['breakdown'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'crack', 'cracked_screen', 'cracked_car', 'break', 'cracked_phone']
senticnet['breakfast'] = ['0.715', '0.785', '0', '0', '#joy', '#interest', 'positive', '0.75', 'scrumble_egg', 'heat_meal', 'pan', 'beat_egg', 'food']
senticnet['breakfast_bed'] = ['0.279', '0.113', '-0.11', '0.209', '#joy', '#admiration', 'positive', '0.121', 'cherish', 'eat_together', 'a_lot_of_sex', 'feel_pleasure', 'good_morning']
senticnet['breakfast_buffet'] = ['0.715', '0.785', '0', '0', '#joy', '#interest', 'positive', '0.75', 'scrumble_egg', 'heat_meal', 'pan', 'beat_egg', 'food']
senticnet['breakfast_cereal'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'veal', 'beef', 'food_group', 'animal_flesh', 'curry']
senticnet['breakfast_food'] = ['0.772', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.858', 'chewy', 'morning_food', 'food_bake', 'healthy', 'round_bread']
senticnet['breakfast_sandwich'] = ['-0.92', '0', '0.609', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'puffed_rice', 'granola', 'raisin_bran', 'corn_flake', 'bran_flake']
senticnet['breaking_apart'] = ['-0.11', '0.095', '-0.11', '0.071', '#sadness', '#fear', 'negative', '-0.01', 'violent_action', 'pulverize', 'break_glass', 'tear_apart', 'hit_hard']
senticnet['breaking_away'] = ['-0.66', '0', '0.585', '-0.91', '#sadness', '#disgust', 'negative', '-0.72', 'withdrawal', 'exit', 'dispatch', 'going', 'farewell']
senticnet['breaking_loose'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amok', 'murderousness', 'misocainea', 'misogyny']
senticnet['breaking_news'] = ['-0.08', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.09', 'crossword', 'grade_paper', 'type_of_paper', 'hit_headlines', 'narrative']
senticnet['breakneck'] = ['0', '0', '-0.86', '-0.86', '#fear', '#disgust', 'negative', '-0.86', 'amusement_park', 'have_fun', 'drag_race', 'pia_lot_of_seat', 'roller_coaster']
senticnet['breakneck_pace'] = ['-0.04', '0.123', '0', '0.237', '#interest', '#admiration', 'positive', '0.107', 'foot_motion', 'foot_movement', 'foot_action', 'move_ahead', 'stride']
senticnet['breakout'] = ['0', '0', '-0.88', '-0.94', '#fear', '#disgust', 'negative', '-0.91', 'mold', 'prison', 'confining', 'restrictiveness', 'restrictive']
senticnet['breakout_box'] = ['0.036', '-0.04', '0.027', '0.063', '#surprise', '#admiration', 'positive', '0.028', 'radio', 'speaker', 'channel_selector', 'power_switch', 'female_television_connector']
senticnet['breakout_hit'] = ['0.625', '-0.90', '0', '0.54', '#joy', '#surprise', 'positive', '0.69', 'smash', 'bang', 'smasher', 'strike', 'hit']
senticnet['breakout_role'] = ['0', '0.793', '0', '0.91', '#interest', '#admiration', 'positive', '0.852', 'superstar', 'vip', 'accomplishment', 'performance', 'concert']
senticnet['breaks_apart'] = ['-0.12', '0', '-0.09', '-0.04', '#sadness', '#fear', 'negative', '-0.08', 'hold_two_together', 'install_window', 'float_through_air', 'spring_up', 'collision']
senticnet['breakstone'] = ['-0.56', '0', '0.848', '-0.80', '#anger', '#disgust', 'negative', '-0.73', 'herb', 'yellow_mountain_saxifrage', 'meadow_saxifrage', 'mossy_saxifrage', 'western_saxifrage']
senticnet['breakthrough'] = ['0.774', '-0.63', '0', '0.567', '#joy', '#surprise', 'positive', '0.658', 'catching', 'discovery', 'spying', 'determination', 'tracing']
senticnet['breakup'] = ['0.909', '0.986', '0', '0', '#joy', '#interest', 'positive', '0.948', 'separation', 'divide', 'division', 'slash', 'bagle']
senticnet['breakwater'] = ['0', '0', '-0.87', '-0.78', '#fear', '#disgust', 'negative', '-0.82', 'wall', 'mole', 'groin', 'rampart', 'bulwark']
senticnet['bream'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['breaout'] = ['-0.12', '0.085', '-0.14', '0.087', '#sadness', '#fear', 'negative', '-0.02', 'breath_difficulty', 'exhalation', 'push_wind', 'mouth_action', 'move_air']
senticnet['breasigh_relief'] = ['-0.07', '0', '-0.17', '-0.05', '#sadness', '#fear', 'negative', '-0.10', 'feel_relieved', 'feel_relief', 'jump_off_bridge', 'act_foolish', 'act_silly']
senticnet['breast'] = ['-0.63', '0', '0.559', '-0.82', '#sadness', '#disgust', 'negative', '-0.67', 'take_bull_by_horns', 'confront', 'front', 'encounter', 'oppose']
senticnet['breast-feeding'] = ['0.842', '0.867', '0', '0', '#joy', '#interest', 'positive', '0.855', 'child-bearing', 'pregnancy', 'child-rearing', 'adulthood', 'childbearing']
senticnet['breast_augmentation'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
senticnet['breast_bone'] = ['-0.63', '0', '0.559', '-0.82', '#sadness', '#disgust', 'negative', '-0.67', 'take_bull_by_horns', 'confront', 'front', 'encounter', 'oppose']
senticnet['breast_cancer'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['breast_cancer_surgery'] = ['-0.99', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.78', 'angioedema', 'atrophedema', 'giant_hive', 'periodic_edema', 'dropsy']
senticnet['breast_enlargement'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
senticnet['breast_feeding'] = ['0.798', '-0.51', '0', '0.973', '#joy', '#admiration', 'positive', '0.763', 'nursing', 'schedule_feeding', 'demand_feeding', 'infant_feeding', 'feeding']
senticnet['breast_infant_feeding'] = ['-0.80', '0', '0.724', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'nursing', 'schedule_infant_feeding', 'demand_infant_feeding', 'feeding', 'infant_feeding']
senticnet['breast_lift'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
senticnet['breast_lump'] = ['-0.88', '0', '0.774', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fibroma', 'fibroid', 'benign_tumor', 'nonmalignant_tumor', 'nonmalignant_neoplasm']
senticnet['breast_pain'] = ['-0.73', '0', '0.555', '-0.90', '#sadness', '#disgust', 'negative', '-0.73', 'aching', 'hurting', 'labor_pain', 'neuralgia', 'neuralgy']
senticnet['breast_plate'] = ['0.92', '-0.57', '0', '0.758', '#joy', '#admiration', 'positive', '0.751', 'bail_out', 'unspell', 'bail', 'unchain', 'run']
senticnet['breast_pocket'] = ['0', '0', '0.733', '-0.98', '#anger', '#disgust', 'negative', '-0.86', 'lymph', 'exoskeleton', 'forehead', 'toward', 'squint']
senticnet['breast_reconstruction'] = ['0.745', '-0.55', '0', '0.597', '#joy', '#admiration', 'positive', '0.633', 'lift', 'phalloplasty', 'abdominoplasty', 'tummy_tuck', 'autoplasty']
senticnet['breast_reconstruction_surgery'] = ['-0.88', '0', '0.774', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fibroma', 'fibroid', 'benign_tumor', 'nonmalignant_tumor', 'nonmalignant_neoplasm']
senticnet['breast_reduction'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
senticnet['breast_stroke'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'chlorine', 'lifeguard', 'still_water', 'large_body', 'shallow_water']
senticnet['breast_surgeon'] = ['-0.88', '0', '0.774', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fibroma', 'fibroid', 'benign_tumor', 'nonmalignant_tumor', 'nonmalignant_neoplasm']
senticnet['breast_surgery'] = ['0.745', '-0.55', '0', '0.597', '#joy', '#admiration', 'positive', '0.633', 'lift', 'phalloplasty', 'abdominoplasty', 'tummy_tuck', 'autoplasty']
senticnet['breast_tissue'] = ['-0.63', '0', '0.559', '-0.82', '#sadness', '#disgust', 'negative', '-0.67', 'take_bull_by_horns', 'confront', 'front', 'encounter', 'oppose']
senticnet['breastfeed'] = ['0', '0.804', '0', '0.695', '#interest', '#admiration', 'positive', '0.75', 'weaning', 'weaned', 'weening', 'wean', 'medicate']
senticnet['breastfeeding_jaundice'] = ['-0.72', '0', '0.778', '-0.98', '#anger', '#disgust', 'negative', '-0.82', 'actinic_dermatitis', 'hypericism', 'diaper_rash', 'dermatitis', 'seborrheic_dermatitis']
senticnet['breastplate'] = ['-0.50', '0', '0.937', '-0.59', '#anger', '#disgust', 'negative', '-0.67', 'restorative', 'medicine', 'medication', 'medicament', 'medicinal_drug']
senticnet['breaststroke'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'chlorine', 'lifeguard', 'still_water', 'large_body', 'shallow_water']
senticnet['breaststroke_swimmer'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'chlorine', 'lifeguard', 'still_water', 'large_body', 'shallow_water']
senticnet['breaststroker'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'chlorine', 'lifeguard', 'still_water', 'large_body', 'shallow_water']
senticnet['breath'] = ['0.776', '0', '0', '0.862', '#joy', '#admiration', 'positive', '0.819', 'gasp', 'wind', 'respite', 'blow', 'whiff']
senticnet['breath_alcohol'] = ['0.928', '-0.87', '0', '0.679', '#joy', '#surprise', 'positive', '0.829', 'examine', 'experiment', 'alpha_test', 'beta_test', 'road_test']
senticnet['breath_difficulty'] = ['0', '0', '0.933', '-0.86', '#anger', '#disgust', 'negative', '-0.89', 'feel_out_of_breath', 'muscle_cramp', 'throat_injury', 'asthma', 'epileptic_seizure']
senticnet['breath_freshener'] = ['0.965', '0.775', '0', '0.968', '#joy', '#admiration', 'positive', '0.903', 'snot', 'bad_taste', 'mouth_part', 'beautiful_smile', 'near_mouth']
senticnet['breath_heavily'] = ['0', '0', '-0.92', '-0.89', '#fear', '#disgust', 'negative', '-0.90', 'breathlessness', 'heart_rate_increase', 'become_wind', 'increased_heart_rate', 'get_out_breath']
senticnet['breath_mint'] = ['0.965', '0.775', '0', '0.968', '#joy', '#admiration', 'positive', '0.903', 'snot', 'bad_taste', 'mouth_part', 'beautiful_smile', 'near_mouth']
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senticnet['brick_ranch'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'apartment', 'furniture', 'kitchen', 'flat']
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senticnet['bricklayer'] = ['0', '0.934', '0', '0.876', '#interest', '#admiration', 'positive', '0.905', 'carpentry', 'plastering', 'bricklaying', 'masonry', 'tiling']
senticnet['bricklaying'] = ['0', '0.938', '0', '0.878', '#interest', '#admiration', 'positive', '0.908', 'carpentry', 'plastering', 'bricklayer', 'masonry', 'tiling']
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senticnet['bridal_chamber'] = ['-0.62', '0', '0.645', '-0.67', '#anger', '#disgust', 'negative', '-0.65', 'lady_maid', 'handmaiden', 'chambermaid', 'maid', 'parlormaid']
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senticnet['bride'] = ['0.151', '0', '-0.14', '0.157', '#joy', '#admiration', 'positive', '0.053', 'bride_groom', 'wedding_cake', 'flower_girl', 'bride_groomsmaid', 'alcohol']
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senticnet['bridegroom'] = ['0.869', '-0.53', '0', '0.772', '#joy', '#admiration', 'positive', '0.725', 'bride', 'groom', 'honeymooner', 'spouse', 'partner']
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senticnet['bring_around'] = ['0.658', '-0.60', '0', '0.691', '#joy', '#admiration', 'positive', '0.651', 'bring_up', 'revive', 'resurrect', 'boot', 'reboot']
senticnet['bring_back_home'] = ['0', '0.779', '0', '0.775', '#interest', '#admiration', 'positive', '0.777', 'bring_home', 'take_home', 'take_care', 'help', 'sidekick']
senticnet['bring_bad_luck'] = ['-0.43', '-0.14', '-0.06', '-0.33', '#sadness', '#disgust', 'negative', '-0.17', 'brain_tumor', 'lyme_disease', 'cause_suffer', 'lose_team', 'bad_hair_day']
senticnet['bring_book'] = ['-0.89', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.80', 'enroll_class', 'enroll', 'matriculate', 'plan_time', 'go_meet']
senticnet['bring_cash'] = ['0.799', '-0.92', '0', '0.896', '#surprise', '#admiration', 'positive', '0.874', 'find_shop', 'shopping', 'bring_money', 'go_supermarket', 'enough_money']
senticnet['bring_dishonor'] = ['-0.93', '0', '0.936', '-0.88', '#sadness', '#anger', 'negative', '-0.91', 'lose_team', 'cause_suffer', 'leave_out', 'loathing', 'lyme_disease']
senticnet['bring_dog'] = ['-0.07', '-0.07', '0.082', '-0.06', '#sadness', '#anger', 'negative', '-0.03', 'put_sneaker', 'move_leg', 'put_jogging_shoes', 'put_shoes', 'stretch_muscle']
senticnet['bring_food'] = ['0.722', '0', '0', '0.735', '#joy', '#admiration', 'positive', '0.728', 'mow_grass', 'meet_need', 'clean_sink', 'help_person', 'mop_floor']
senticnet['bring_forth'] = ['0.851', '-0.67', '0', '0.854', '#joy', '#admiration', 'positive', '0.792', 'fertile', 'breeding', 'prolific', 'bountiful', 'productive']
senticnet['bring_friend'] = ['-0.92', '0', '0.807', '0', '#sadness', '#anger', 'negative', '-0.86', 'enjoy_art', 'go_symphony', 'buy_popcorn', 'understand_plot', 'insert_dvd_in_player']
senticnet['bring_gift'] = ['0.719', '0.988', '0', '0', '#joy', '#interest', 'positive', '0.853', 'anniversary', 'visit_friend', 'present', 'birthday', 'surprise']
senticnet['bring_good_luck'] = ['-0.43', '-0.14', '-0.06', '-0.33', '#sadness', '#disgust', 'negative', '-0.17', 'brain_tumor', 'lyme_disease', 'cause_suffer', 'lose_team', 'bad_hair_day']
senticnet['bring_happiness'] = ['0.8', '0', '0', '0.845', '#joy', '#admiration', 'positive', '0.823', 'happy', 'good_feeling', 'make_happy', 'express_love', 'have_fun']
senticnet['bring_home'] = ['0.935', '-0.66', '0', '0.93', '#joy', '#admiration', 'positive', '0.842', 'demo', 'show', 'demonstrate', 'exhibit', 'present']
senticnet['bring_home_bacon'] = ['0.945', '-0.73', '0', '0.779', '#joy', '#admiration', 'positive', '0.819', 'clear', 'pan_out', 'reach', 'bring_off', 'luck_out']
senticnet['bring_joy'] = ['-0.17', '-0.01', '-0.12', '-0.12', '#sadness', '#fear', 'negative', '-0.10', 'make_cry', 'laugh', 'amount_nothing', 'play_hard', 'act_foolish']
senticnet['bring_lasting_peace'] = ['0.497', '0.453', '-0.23', '0.189', '#joy', '#interest', 'positive', '0.226', 'calm', 'calmness', 'stillness', 'truce', 'love']
senticnet['bring_money'] = ['0.799', '-0.92', '0', '0.896', '#surprise', '#admiration', 'positive', '0.874', 'bring_cash', 'pay_bill', 'shopping', 'purchase_ticket', 'rich']
senticnet['bring_off'] = ['0.718', '-0.72', '0', '0.928', '#surprise', '#admiration', 'positive', '0.792', 'deliver_goods', 'succeed', 'win', 'bring_home_bacon', 'come_through']
senticnet['bring_out'] = ['0.58', '-0.82', '0', '0.77', '#surprise', '#admiration', 'positive', '0.724', 'show', 'expose', 'trot_out', 'unfold', 'disclose']
senticnet['bring_present'] = ['0', '0.076', '-0.06', '0.205', '#interest', '#admiration', 'positive', '0.072', 'cover_body', 'put_clothes', 'christmas', 'carry_book', 'move_away']
senticnet['bring_together'] = ['0.065', '0.209', '-0.11', '0.297', '#interest', '#admiration', 'positive', '0.113', 'come_together', 'put_together', 'gather', 'fuse_together', 'inclusion']
senticnet['bring_up'] = ['0', '0.296', '-0.14', '0.359', '#interest', '#admiration', 'positive', '0.169', 'pull_up', 'rise_up', 'raise_up', 'upward_movement', 'go_up']
senticnet['bring_wallet'] = ['0.956', '-0.82', '0', '0', '#joy', '#surprise', 'positive', '0.889', 'bring_money', 'pay_bill', 'go_shopping', 'need_money', 'credit_card']
senticnet['bringeth'] = ['-0.79', '0', '0.607', '-0.92', '#sadness', '#disgust', 'negative', '-0.77', 'stubbornness', 'obstinacy', 'obstinance', 'bullheadedness', 'pigheadedness']
senticnet['bringing_forth'] = ['0.851', '-0.67', '0', '0.854', '#joy', '#admiration', 'positive', '0.792', 'fertile', 'breeding', 'prolific', 'bountiful', 'productive']
senticnet['bringing_heavy_rain'] = ['-0.78', '0', '0.906', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'rainstorm', 'atmospheric_phenomenon', 'northeaster', 'noreaster', 'hailstorm']
senticnet['bringing_together'] = ['0.53', '-0.64', '0', '0.76', '#surprise', '#admiration', 'positive', '0.646', 'focus', 'get_focused', 'focusing', 'centering', 'particularism']
senticnet['bringing_up'] = ['0.902', '-0.5', '0', '0.716', '#joy', '#admiration', 'positive', '0.706', 'breeding', 'fostering', 'fosterage', 'cultivation', 'enculturation']
senticnet['brings_good_luck'] = ['-0.43', '-0.14', '-0.06', '-0.33', '#sadness', '#disgust', 'negative', '-0.17', 'brain_tumor', 'lyme_disease', 'cause_suffer', 'lose_team', 'bad_hair_day']
senticnet['brinjal'] = ['0.859', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.888', 'green_apple', 'apricot', 'lime', 'peach', 'cherry']
senticnet['brink'] = ['-0.76', '0', '0.86', '0', '#sadness', '#anger', 'negative', '-0.81', 'agony', 'midst', 'agonizing', 'throe', 'protracted']
senticnet['brinkmanship'] = ['-0.91', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.86', 'impasse', 'deadlock', 'gridlock', 'negotiation', 'stalemate']
senticnet['briny'] = ['0', '0', '-0.78', '-0.85', '#fear', '#disgust', 'negative', '-0.81', 'liquid', 'crystal_clear', 'pellucid', 'crystalline', 'lucid']
senticnet['brio'] = ['0.586', '-0.51', '0', '0.949', '#joy', '#admiration', 'positive', '0.685', 'chirpiness', 'spirit', 'sprightliness', 'liveliness', 'activity']
senticnet['brioche'] = ['0.059', '-0.02', '0.025', '0.109', '#joy', '#admiration', 'positive', '0.043', 'tasty', 'bake_treat', 'bake_goods', 'bake_good', 'loaf']
senticnet['brique'] = ['-0.58', '0', '0.661', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'explosive', 'cordite', 'dynamite', 'plastic_explosive', 'explosive_compound']
senticnet['briquette'] = ['0.037', '0.084', '0', '0', '#joy', '#interest', 'positive', '0.06', 'leave_over', 'leftover', 'cellulose', 'cane', 'remainder']
senticnet['brisbane_architect'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'maker']
senticnet['brise_soleil'] = ['0.046', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'light', 'bay_window', 'air_conditioner', 'sky', 'lamp']
senticnet['brisk'] = ['0.954', '0.876', '0', '0', '#joy', '#interest', 'positive', '0.915', 'lively', 'merry', 'rattling', 'snappy', 'fresh']
senticnet['brisk_pace'] = ['-0.04', '0.123', '0', '0.237', '#interest', '#admiration', 'positive', '0.107', 'foot_motion', 'foot_movement', 'foot_action', 'move_ahead', 'stride']
senticnet['brisk_trot'] = ['0', '0', '0.752', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'get_hit_by_car', 'meander', 'full_moon', 'step_dog_poop', 'kick_ass']
senticnet['brisk_wind'] = ['-0.87', '0', '0.91', '-0.78', '#sadness', '#anger', 'negative', '-0.85', 'storm', 'rainstorm', 'rain', 'line_storm', 'equinoctial_storm']
senticnet['brisket'] = ['0', '0.08', '0', '0', '#interest', '#interest', 'positive', '0.08', 'beef', 'pork', 'french_toast', 'steak', 'ham']
senticnet['briskly'] = ['-0.77', '0', '0.855', '-0.57', '#sadness', '#anger', 'negative', '-0.73', 'unhurried', 'easygoing', 'deliberate', 'easy', 'careful']
senticnet['bristle'] = ['-0.02', '-0.05', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.02', 'motor_oil', 'make_toy', 'engine', 'emission', 'castor']
senticnet['bristle_brush'] = ['0', '0.068', '0', '0', '#interest', '#interest', 'positive', '0.068', 'artist', 'oil_paint', 'painter', 'creative_person', 'make_paint']
senticnet['bristled'] = ['0', '0', '-0.94', '-0.82', '#fear', '#disgust', 'negative', '-0.88', 'barbellate', 'briery', 'thorny', 'briary', 'barbed']
senticnet['bristled_at'] = ['-0.02', '-0.05', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.02', 'motor_oil', 'make_toy', 'engine', 'emission', 'castor']
senticnet['bristletail'] = ['-0.70', '0', '0.994', '-0.99', '#anger', '#disgust', 'negative', '-0.89', 'insect', 'thysanuran_insect', 'silverfish', 'jumping_thysanuran_insect', 'machilid']
senticnet['bristly'] = ['0', '0', '-0.99', '-0.83', '#fear', '#disgust', 'negative', '-0.91', 'barbed', 'splenetic', 'prickly', 'barbellate', 'waspish']
senticnet['bristol'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
senticnet['bristol_aeroplane_company'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'cheapjack', 'pitchman']
senticnet['bristol_fighter'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'cheapjack', 'pitchman']
senticnet['british_actress_thandie'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'force_unit', 'sthene', 'poundal']
senticnet['british_adventurer'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'correspondent', 'humankind']
senticnet['british_afro_pop'] = ['0.542', '-0.83', '0', '0.927', '#surprise', '#admiration', 'positive', '0.769', 'incest', 'extramarital_sex', 'free_love', 'sexual_intercourse', 'intercourse']
senticnet['british_airway_cabin_crew'] = ['-0.77', '0', '0.505', '-0.50', '#sadness', '#anger', 'negative', '-0.59', 'wildcat_strike', 'sympathy_strike', 'sympathetic_strike', 'walkout', 'job_action']
senticnet['british_aristocrat'] = ['0.066', '0.163', '-0.06', '0.163', '#interest', '#admiration', 'positive', '0.083', 'lord', 'royal_title', 'almighty', 'duchess', 'omniscient']
senticnet['british_artist_damien'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'hip', 'joint', 'above_leg', 'knee']
senticnet['british_biochemist'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'druggist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['british_blue_ensign'] = ['0.053', '0.068', '-0.05', '0.06', '#interest', '#admiration', 'positive', '0.032', 'linen', 'star_stripe', 'velvet', 'nation', 'burlap']
senticnet['british_chocolate_maker'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'wrapping_paper', 'wrap_gift', 'butcher_paper', 'paper']
senticnet['british_chocolatier_cadbury'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'wrapping_paper', 'wrap_gift', 'butcher_paper', 'paper']
senticnet['british_colonial_rule'] = ['0.887', '0.748', '0', '0.954', '#joy', '#admiration', 'positive', '0.863', 'political_action', 'political', 'mediocrity', 'discrimination', 'independent']
senticnet['british_confectioner'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'wrapping_paper', 'wrap_gift', 'butcher_paper', 'paper']
senticnet['british_confectioner_cadbury'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'wrapping_paper', 'wrap_gift', 'butcher_paper', 'paper']
senticnet['british_electropop_band'] = ['0.028', '0', '0.027', '0.101', '#joy', '#admiration', 'positive', '0.034', 'animal_dwell', 'bee_home', 'live_place', 'dwell_place', 'place_to_live']
senticnet['british_expat'] = ['0.071', '0', '0.031', '0.064', '#joy', '#admiration', 'positive', '0.035', 'far_from_home', 'immigrant', 'live_abroad', 'connoisseur', 'work_abroad']
senticnet['british_heavyweight_champion'] = ['0', '0', '0', '0.834', '#admiration', '#admiration', 'positive', '0.834', 'royal_person', 'royal_title', 'top', 'master', 'rank']
senticnet['british_hereditary_peer'] = ['0.066', '0.163', '-0.06', '0.163', '#interest', '#admiration', 'positive', '0.083', 'lord', 'royal_title', 'almighty', 'duchess', 'omniscient']
senticnet['british_horror_film'] = ['-0.79', '0', '0.885', '-0.76', '#sadness', '#anger', 'negative', '-0.81', 'infernal', 'mortal', 'dead_soul', 'dead_soule', 'living_dead']
senticnet['british_light_middleweight'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['british_monarchy'] = ['0.059', '0.092', '0', '0.107', '#interest', '#admiration', 'positive', '0.086', 'royalty', 'monarch', 'throne', 'emperor', 'king_queen']
senticnet['british_mountaineer'] = ['0.518', '-0.51', '0', '0.65', '#surprise', '#admiration', 'positive', '0.562', 'clematis', 'legume', 'leguminous_plant', 'climbing_fern', 'root_climber']
senticnet['british_nationality'] = ['0', '-0.91', '0', '0.806', '#surprise', '#admiration', 'positive', '0.861', 'clean_shoes', 'black_stuff', 'paint_nail', 'ethnicity', 'black_goo']
senticnet['british_naval_officer'] = ['0.688', '-0.90', '0', '0.877', '#surprise', '#admiration', 'positive', '0.824', 'fleet_admiral', 'rear_admiral', 'admiral', 'full_admiral', 'flag_officer']
senticnet['british_ornithologist_club'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'current_event', 'headline', 'paper_write', 'paper_page', 'many_pages']
senticnet['british_physiologist'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'druggist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['british_rapper_dizzee'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'holy_terror', 'rapscallion', 'scalawag', 'scallywag', 'monkey']
senticnet['british_sloop'] = ['0', '0', '0.08', '0', '#anger', '#anger', 'negative', '-0.08', 'tree_animal', 'small_furry', 'rodent', 'big_mouse', 'small_rodent']
senticnet['british_sport_shooter'] = ['-0.64', '0', '0.879', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'drone', 'poke', 'dawdler', 'laggard', 'straggler']
senticnet['britney'] = ['0.056', '0.139', '-0.07', '0.154', '#interest', '#admiration', 'positive', '0.07', 'belongings', 'material', 'fabric', 'guarantee', 'stuff']
senticnet['briton'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'nazi', 'man', 'pervert', 'healthy_person']
senticnet['britovi'] = ['0.995', '-0.71', '0', '0.647', '#joy', '#surprise', 'positive', '0.784', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['britpop'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'hazy', 'blurry', 'bleary', 'blurryred', 'foggy']
senticnet['britt_ekland'] = ['0.794', '-0.50', '0', '0.531', '#joy', '#admiration', 'positive', '0.611', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['brittanica'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'comic_book', 'child_book', 'child_story', 'comic', 'disney_cartoon']
senticnet['brittle'] = ['0.704', '-0.78', '0', '0', '#joy', '#surprise', 'positive', '0.746', 'translucent', 'clear_solid', 'see_thru', 'drink_container', 'transparent']
senticnet['brittle_failure'] = ['-0.52', '0', '0.596', '-0.76', '#anger', '#disgust', 'negative', '-0.62', 'retention', 'retentiveness', 'retentivity', 'solidness', 'nonabsorbency']
senticnet['brittle_hair'] = ['0.048', '-0.00', '0.011', '0.064', '#joy', '#admiration', 'positive', '0.027', 'become_wet', 'wash_away_dirt', 'run_out_soap', 'cleanliness', 'clean_skin']
senticnet['brittle_star'] = ['-0.79', '0', '0.709', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'animal', 'animate_being', 'ctenophore', 'invertebrate', 'beast']
senticnet['brittlegill'] = ['-0.60', '0', '0.541', '-0.52', '#sadness', '#anger', 'negative', '-0.55', 'swoon', 'pass_out', 'faint', 'zonk_out', 'change_state']
senticnet['brixton_prison'] = ['-0.78', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.87', 'jailbird', 'captive', 'imprisoned', 'convict', 'inmate']
senticnet['brno_conservatory'] = ['0.536', '-0.56', '0', '0.567', '#surprise', '#admiration', 'positive', '0.557', 'recreation', 'diversion', 'move', 'motility', 'movement']
senticnet['bro'] = ['0.104', '0', '0', '0.088', '#joy', '#admiration', 'positive', '0.096', 'baby_flower', 'wee', 'bud', 'part_of_plant', 'leafy']
senticnet['broacher'] = ['0', '-0.75', '0', '0.902', '#surprise', '#admiration', 'positive', '0.827', 'catalysis', 'promoter', 'promote', 'bug', 'auspice']
senticnet['broad'] = ['0', '0.098', '-0.06', '0.118', '#interest', '#admiration', 'positive', '0.051', 'egret', 'tall_man', 'wide', 'wood_structure', 'large']
senticnet['broad-ranging'] = ['0', '-0.96', '0', '0.855', '#surprise', '#admiration', 'positive', '0.909', 'thorough-going', 'doctrinaire', 'thoroughgoing', 'trenchant', 'thorough']
senticnet['broad-shouldered'] = ['0.734', '0.981', '0', '0', '#joy', '#interest', 'positive', '0.857', 'thick-set', 'stocky', 'corpulent', 'barrel-chested', 'thickset']
senticnet['broad_applicability'] = ['0.637', '-0.91', '0', '0.757', '#surprise', '#admiration', 'positive', '0.768', 'manageability', 'manageableness', 'docility', 'obedience', 'tractability']
senticnet['broad_array'] = ['0.057', '0', '0', '0.153', '#joy', '#admiration', 'positive', '0.105', 'multiple', 'cute', 'variety', 'hairy', 'large_breadbox']
senticnet['broad_bean'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'cabbage', 'lettuce', 'broccoli']
senticnet['broad_brimmed_hat'] = ['0.08', '0.106', '-0.04', '0.112', '#interest', '#admiration', 'positive', '0.063', 'helmet', 'head_gear', 'cap', 'head_wear', 'head_protection']
senticnet['broad_definition'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'vocabulary', 'read_material', 'book_genre', 'many_words', 'book_library']
senticnet['broad_discretion'] = ['-0.52', '0', '0.561', '-0.90', '#anger', '#disgust', 'negative', '-0.66', 'difference', 'tolerance', 'allowance', 'discrepancy', 'margin']
senticnet['broad_forehead'] = ['0.352', '0.142', '0', '0.281', '#joy', '#admiration', 'positive', '0.258', 'knowledgeable', 'complement', 'much_need', 'see_ocean', 'good_sense_of_humor']
senticnet['broad_latitude'] = ['-0.52', '0', '0.561', '-0.90', '#anger', '#disgust', 'negative', '-0.66', 'difference', 'tolerance', 'allowance', 'discrepancy', 'margin']
senticnet['broad_leaved_centaury'] = ['-0.90', '0', '0.722', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'tufted_centaury', 'rosita', 'lesser_centaury', 'centaury', 'seaside_centaury']
senticnet['broad_leaved_woodland'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'biome', 'timberland']
senticnet['broad_leeway'] = ['-0.52', '0', '0.561', '-0.90', '#anger', '#disgust', 'negative', '-0.66', 'difference', 'tolerance', 'allowance', 'discrepancy', 'margin']
senticnet['broad_mite'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'rust_fungus', 'fungus']
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senticnet['broncho_pneumonia'] = ['-0.92', '0', '0.824', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'bronchopneumonia', 'bronchial_pneumonia', 'pneumonia', 'pleuropneumonia', 'pneumococcal_pneumonia']
senticnet['bronchopneumonia'] = ['-0.92', '0', '0.824', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'pneumonia', 'bronchial_pneumonia', 'viral_pneumonia', 'pleuropneumonia', 'pneumococcal_pneumonia']
senticnet['bronchopulmonary_dysplasia'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchitis', 'chronic_bronchitis', 'respiratory_disease', 'respiratory_illness', 'respiratory_disorder']
senticnet['bronchopulmonary_dysplasia_bpd'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchitis', 'chronic_bronchitis', 'respiratory_disease', 'respiratory_illness', 'respiratory_disorder']
senticnet['bronchoscope'] = ['0.255', '0.182', '-0.06', '0.314', '#joy', '#admiration', 'positive', '0.173', 'doctor_office', 'urethroscope', 'sigmoidoscope', 'hospital', 'check_health']
senticnet['bronchospasm'] = ['-0.99', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.78', 'dropsy', 'atrophedema', 'giant_hive', 'periodic_edema', 'lymphedema']
senticnet['bronco'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'unofficial', 'informal', 'loose', 'drumhead']
senticnet['bronislava_nijinska'] = ['0.12', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.09', 'ballroom', 'perform_art', 'nightclub', 'ballerina', 'venue']
senticnet['brontoscorpio'] = ['0', '0', '-0.87', '0', '#fear', '#fear', 'negative', '-0.87', 'beetle', 'many_insects', 'grasshopper', 'firefly', 'mouse']
senticnet['bronx_apartment'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'home', 'furniture', 'kitchen', 'flat']
senticnet['bronx_cheer'] = ['-0.86', '0', '0.516', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'outcry', 'call', 'cry', 'yell', 'shout']
senticnet['bronze'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'gold', 'medal', 'alloy', 'titanium', 'copper']
senticnet['bronze_age_burial_mound'] = ['-0.59', '0', '0.506', '-0.52', '#sadness', '#disgust', 'negative', '-0.54', 'dump', 'garbage_dump', 'trash_dump', 'rubbish_dump', 'wasteyard']
senticnet['bronze_age_pottery'] = ['0.099', '-0.07', '-0.08', '0.195', '#joy', '#admiration', 'positive', '0.071', 'art', 'exhibit', 'craft', 'fine_art', 'artwork']
senticnet['bronze_birch_borer'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['bronze_bust'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'dental_plaque']
senticnet['bronze_dagger'] = ['0.099', '-0.07', '-0.08', '0.195', '#joy', '#admiration', 'positive', '0.071', 'art', 'exhibit', 'craft', 'fine_art', 'artwork']
senticnet['bronze_equestrian_statue'] = ['0.085', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.079', 'sculpture', 'creative', 'yellow_ribbon', 'artwork', 'monument']
senticnet['bronze_fennel'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'tropical_fish', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['bronze_marker'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'dental_plaque']
senticnet['bronze_medal_respectively'] = ['0.866', '0.82', '0', '0.713', '#joy', '#interest', 'positive', '0.8', 'titanium', 'bronze', 'alloy', 'medal', 'copper']
senticnet['bronze_medallist'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'nazi', 'man', 'pervert', 'healthy_person']
senticnet['bronze_memorial_plaque'] = ['0.072', '0.126', '0', '0', '#joy', '#interest', 'positive', '0.099', 'subway_station', 'war_memorial', 'utopia', 'mortuary', 'hero']
senticnet['bronze_monument'] = ['0.085', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.079', 'sculpture', 'creative', 'yellow_ribbon', 'artwork', 'monument']
senticnet['bronze_plaque'] = ['0.072', '0.126', '0', '0', '#joy', '#interest', 'positive', '0.099', 'subway_station', 'war_memorial', 'utopia', 'mortuary', 'hero']
senticnet['bronze_replica'] = ['0.085', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.079', 'sculpture', 'creative', 'yellow_ribbon', 'artwork', 'monument']
senticnet['bronze_sculpture'] = ['0.085', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.079', 'sculpture', 'creative', 'yellow_ribbon', 'artwork', 'monument']
senticnet['bronze_statue'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'dental_plaque']
senticnet['bronze_tablet'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'dental_plaque']
senticnet['bronzing'] = ['-0.77', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.85', 'tanning', 'self-tanning', 'lotion', 'tanner', 'sunless']
senticnet['brooch'] = ['-0.97', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.95', 'cameo', 'silver', 'sterling', 'gem', 'cameo']
senticnet['brood'] = ['-0.04', '0.174', '0.679', '-0.65', '#anger', '#disgust', 'negative', '-0.3', 'maleficence', 'malefic', 'malign', 'spitefulness', 'maliciousness']
senticnet['brood_cell'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'lair', 'habitat', 'animal_home']
senticnet['brood_comb'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'lair', 'habitat', 'animal_home']
senticnet['brood_nest'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'lair', 'habitat', 'animal_home']
senticnet['brooded_over'] = ['-0.69', '0.32', '-0.38', '-0.08', '#sadness', '#fear', 'negative', '-0.21', 'forlornly', 'desolation', 'forlorn', 'weepiness', 'tearful']
senticnet['brooding'] = ['-0.83', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.90', 'pondering', 'ruminative', 'broody', 'pensiveness', 'meditative']
senticnet['broody'] = ['0.948', '0.871', '0', '0', '#joy', '#interest', 'positive', '0.909', 'pondering', 'cogitative', 'contemplative', 'meditative', 'think']
senticnet['brook'] = ['-0.99', '0', '0.871', '-0.55', '#sadness', '#anger', 'negative', '-0.80', 'endure', 'bear_up', 'tolerate', 'stomach', 'abide']
senticnet['brook_atkinson'] = ['0.811', '-0.63', '0', '0.959', '#joy', '#admiration', 'positive', '0.8', 'drama_critic', 'critic', 'professional', 'art_critic', 'literary_critic']
senticnet['brook_flow_through'] = ['-0.91', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.85', 'play', 'perpetrator', 'role_play', 'act_part', 'terrible_event']
senticnet['brook_lamprey'] = ['0', '-0.02', '0.044', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'haddock', 'mermaid', 'fish_rod', 'tidal_wave', 'dryad']
senticnet['brook_takahashi'] = ['0.077', '0', '0', '0', '#joy', '#joy', 'positive', '0.077', 'dip', 'garlic', 'oat', 'savory', 'rye']
senticnet['brook_trout'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'divergent_strabismus', 'exotropia', 'squint', 'abnormality', 'abnormalcy']
senticnet['brook_utley'] = ['0.76', '0.919', '0', '0', '#joy', '#interest', 'positive', '0.84', 'chore', 'make_happen', 'pick_up_trash', 'intuition', 'stop_procrastinate']
senticnet['brooklyn_apartment'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'home', 'furniture', 'kitchen', 'flat']
senticnet['brooklyn_borough'] = ['0.706', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.768', 'train_station', 'subway_stop', 'parking_lot', 'subway_station', 'park_structure']
senticnet['brooklyn_botanic_garden'] = ['0.427', '0.227', '0', '0.37', '#joy', '#admiration', 'positive', '0.341', 'taken_seriously', 'complement', 'much_need', 'remission', 'knowledgeable']
senticnet['brooklyn_eagle'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquisitor', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['brooklyn_handicap'] = ['-0.76', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.78', 'strangle', 'bloodshed', 'cripple', 'hear_bad_news', 'sexually_transmit_disease']
senticnet['brooklyn_rapper'] = ['0', '0.712', '0', '0.844', '#interest', '#admiration', 'positive', '0.778', 'dollar_change', 'small_currency', 'currency_unit', 'metal_money', 'american_money']
senticnet['brooklyn_subway_station'] = ['0.706', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.768', 'train_station', 'subway_stop', 'parking_lot', 'subway_station', 'park_structure']
senticnet['broom'] = ['-0.84', '0', '0.987', '-0.77', '#sadness', '#anger', 'negative', '-0.87', 'thorn', 'sticker', 'glochid', 'prickle', 'spikelet']
senticnet['broom_closet'] = ['0', '0.072', '0', '0', '#interest', '#interest', 'positive', '0.072', 'metal_box', 'cash_drawer', 'keyhole', 'clutch_bag', 'safe']
senticnet['broom_handle'] = ['-0.71', '0', '0.954', '-0.56', '#sadness', '#anger', 'negative', '-0.74', 'jackknife', 'pocket_knife', 'penknife', 'switchblade', 'switchblade_knife']
senticnet['broomstick'] = ['0.048', '-0.10', '0', '0.18', '#surprise', '#admiration', 'positive', '0.111', 'door_knob', 'door_opener', 'carry_suitcase', 'handle', 'bicycle_part']
senticnet['broon'] = ['0.027', '0', '0.012', '0.063', '#joy', '#admiration', 'positive', '0.026', 'vegetation', 'tree_flower', 'green_growth', 'small', 'greenery']
senticnet['broth'] = ['0.07', '0.084', '0.023', '0', '#joy', '#interest', 'positive', '0.044', 'hearty', 'porridge', 'hot_liquid', 'liquid_food', 'meat_vegetable']
senticnet['brothel'] = ['0.754', '-0.82', '0', '0.947', '#surprise', '#admiration', 'positive', '0.841', 'whorehouse', 'massage_parlor', 'bordello', 'bagnio', 'house_of_prostitution']
senticnet['brothel_keeper'] = ['0.754', '-0.82', '0', '0.947', '#surprise', '#admiration', 'positive', '0.841', 'whorehouse', 'brothel', 'bordello', 'massage_parlor', 'house_of_prostitution']
senticnet['brothel_madam'] = ['-0.80', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.79', 'whorehouse', 'brothel', 'massage_parlor', 'bagnio', 'house_of_prostitution']
senticnet['brothel_owner'] = ['-0.80', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.79', 'whorehouse', 'brothel', 'massage_parlor', 'bagnio', 'house_of_prostitution']
senticnet['brother'] = ['0', '0.727', '0', '0.858', '#interest', '#admiration', 'positive', '0.792', 'family_member', 'dad', 'daddy', 'mother_daddy', 'uncle']
senticnet['brother_grimm'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'mythical', 'tale', 'dictionary', 'magic', 'perfect_world']
senticnet['brother_grimm_fairy_tale'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'mythical', 'tale', 'dictionary', 'magic', 'perfect_world']
senticnet['brother_sister'] = ['0', '0.998', '0', '0.835', '#interest', '#admiration', 'positive', '0.916', 'mom_dad', 'female_sibling', 'cousin', 'mother_father', 'relation']
senticnet['brotherhood'] = ['0.648', '0.627', '-0.26', '0.329', '#joy', '#interest', 'positive', '0.334', 'brotherly', 'friendly', 'fraternal', 'amicability', 'amicable']
senticnet['brotherly'] = ['0.605', '0.647', '-0.26', '0.323', '#joy', '#interest', 'positive', '0.327', 'friendly', 'fraternal', 'amicableness', 'amicably', 'amicable']
senticnet['brotherly_affection'] = ['0.608', '0.647', '-0.26', '0.325', '#joy', '#interest', 'positive', '0.328', 'amicably', 'amicable', 'amicableness', 'brotherly', 'friendly']
senticnet['brotherly_love'] = ['0.605', '0.647', '-0.26', '0.323', '#joy', '#interest', 'positive', '0.327', 'friendly', 'brotherly', 'amicableness', 'amicably', 'amicable']
senticnet['brouhaha'] = ['0', '0', '0.94', '0', '#anger', '#anger', 'negative', '-0.94', 'feel_angry', 'hurt_feeling', 'feel_hurt', 'ridicule', 'vengeance']
senticnet['brouhaha_over'] = ['-0.87', '0', '0.762', '-0.92', '#sadness', '#disgust', 'negative', '-0.85', 'misreading', 'misconstrual', 'misconstruction', 'misinterpretation', 'interpretation']
senticnet['brow_lift'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
senticnet['browband'] = ['0', '-0.05', '0.079', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'c_clamp', 'thumb_tack', 'pin', 'pliers', 'without_space']
senticnet['browbeat'] = ['-0.91', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.94', 'browbeaten', 'browbeating', 'bossy', 'bully', 'intimidating']
senticnet['browbeaten'] = ['-0.92', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.94', 'browbeat', 'browbeating', 'bossy', 'bully', 'intimidating']
senticnet['browbeating'] = ['-0.92', '0', '-0.98', '0', '#sadness', '#fear', 'negative', '-0.95', 'browbeaten', 'browbeat', 'bossy', 'bully', 'intimidating']
senticnet['browght'] = ['-0.11', '0.112', '0.674', '-0.66', '#anger', '#disgust', 'negative', '-0.33', 'wrathfully', 'wrathful', 'wrothful', 'lividly', 'lividity']
senticnet['browlift'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
senticnet['brown'] = ['-0.65', '0', '0.523', '-0.73', '#sadness', '#disgust', 'negative', '-0.64', 'opaque', 'color', 'bear', 'poo', 'poop']
senticnet['brown_ale'] = ['0', '0.091', '-0.05', '0.073', '#interest', '#admiration', 'positive', '0.037', 'privately', 'hold_liquid', 'make_wish', 'rinse_dish', 'find_toilet']
senticnet['brown_alga'] = ['-0.89', '0', '0.556', '-0.72', '#sadness', '#disgust', 'negative', '-0.72', 'fucus', 'rockweed', 'black_rockweed', 'bladder_fucus', 'tang']
senticnet['brown_algae'] = ['-0.65', '0', '0.523', '-0.73', '#sadness', '#disgust', 'negative', '-0.64', 'confervoid_algae', 'seaweed', 'golden_algae', 'diatom', 'alga']
senticnet['brown_bear'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'animal', 'grizzly_bear', 'forest', 'creature', 'polar_bear']
senticnet['brown_discoloration'] = ['0', '0.141', '0.01', '0.115', '#interest', '#admiration', 'positive', '0.082', 'sepal', 'achene', 'funiculus', 'seed', 'pip']
senticnet['brown_forman'] = ['0.951', '-0.97', '0', '0.639', '#joy', '#surprise', 'positive', '0.853', 'smoothie', 'drinking_water', 'nutrient', 'milk', 'refresher']
senticnet['brown_fur'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'brown_bear', 'woodland_creature', 'hairy_animal', 'huge_animal', 'brown_hair']
senticnet['brown_gravy'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'obese', 'worse', 'health_issue', 'clinical_problem']
senticnet['brown_hair'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'plato', 'natural_world', 'hair', 'feature', 'stick_out']
senticnet['brown_hamburger'] = ['-0.08', '0', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.06', 'salt_food', 'chicken', 'make_tea', 'fry_chicken', 'bone_chicken']
senticnet['brown_leaf'] = ['0.034', '0.111', '-0.09', '0.148', '#interest', '#admiration', 'positive', '0.05', 'ant_hill', 'hike_trail', 'tress', 'stake', 'oak']
senticnet['brown_leather'] = ['0.028', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.047', 'handkerchief', 'kerchief', 'hanky', 'facial_tissue', 'zipper']
senticnet['brown_meat'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'cook_meat', 'cook_hamburger', 'cook_food', 'baker', 'eat_outdoors']
senticnet['brown_pigment'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['brown_plumage'] = ['-0.59', '0', '0.887', '-0.71', '#anger', '#disgust', 'negative', '-0.73', 'armet', 'basinet', 'helmet', 'heaume', 'morion']
senticnet['brown_recluse_spider'] = ['0', '0', '-0.73', '-0.81', '#fear', '#disgust', 'negative', '-0.77', 'flab', 'bad_hair_day', 'lose_friend', 'alcoholism', 'brain_tumor']
senticnet['brown_rot'] = ['-0.83', '0', '0.754', '-0.97', '#sadness', '#disgust', 'negative', '-0.85', 'brown_rot_gummosis', 'ring_rot', 'ring_disease', 'tobacco_wilt', 'gummosis']
senticnet['brown_rot_brown_rot'] = ['-0.83', '0', '0.75', '-0.98', '#sadness', '#disgust', 'negative', '-0.85', 'gummosis', 'ring_rot', 'ring_disease', 'tobacco_wilt', 'brown_rot']
senticnet['brown_rot_gummosis'] = ['-0.83', '0', '0.754', '-0.97', '#sadness', '#disgust', 'negative', '-0.85', 'brown_rot', 'ring_rot', 'ring_disease', 'tobacco_wilt', 'gummosis']
senticnet['brown_sauce'] = ['0.644', '-0.94', '0', '0.729', '#surprise', '#admiration', 'positive', '0.773', 'gem', 'banana_bread', 'date_bread', 'nut_bread', 'quick_bread']
senticnet['brown_stripe'] = ['0.942', '-0.61', '0', '0.908', '#joy', '#admiration', 'positive', '0.82', 'adornment', 'cigar_band', 'hatband', 'neckband', 'pinstripe']
senticnet['brown_sugar_macchiato'] = ['0.023', '0.141', '-0.13', '0.174', '#interest', '#admiration', 'positive', '0.052', 'candy', 'push_wind', 'exhalation', 'breaout', 'mouth_action']
senticnet['brown_trout'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'divergent_strabismus', 'exotropia', 'squint', 'abnormality', 'abnormalcy']
senticnet['brown_trout_salmo_trutta'] = ['0', '-0.02', '0.044', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'haddock', 'mermaid', 'fish_rod', 'tidal_wave', 'dryad']
senticnet['brown_wrapping_paper'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'gift_wrap', 'kraft', 'kraft_paper', 'wrapping_paper', 'paper']
senticnet['brownie'] = ['0.084', '-0.11', '-0.04', '0', '#joy', '#surprise', 'positive', '0.052', 'mousse', 'cookie', 'cake', 'custard', 'doughnut']
senticnet['brownie_point'] = ['0.874', '0.867', '0', '0', '#joy', '#interest', 'positive', '0.871', 'gratification', 'good_deed', 'appreciate', 'recognition', 'acknowledgment']
senticnet['brownie_wise'] = ['0.348', '0.23', '-0.15', '0.294', '#joy', '#admiration', 'positive', '0.18', 'happy_emotion', 'niceness', 'contagious', 'good_emotion', 'savor']
senticnet['browning_hi_power'] = ['0', '0', '0.954', '-0.74', '#anger', '#disgust', 'negative', '-0.85', 'gun', 'pistol', 'revolver', 'firearm', 'handgun']
senticnet['brownish'] = ['-0.92', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.85', 'splotchy', 'blotch', 'splotch', 'blotchy', 'discolored']
senticnet['brownish_black'] = ['-0.7', '0', '0.617', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'silvery', 'achromatic', 'bluish_black', 'hueless', 'pitchy']
senticnet['brownish_grey'] = ['-0.7', '0', '0.617', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'silvery', 'achromatic', 'bluish_black', 'hueless', 'pitchy']
senticnet['brownish_olive'] = ['0.942', '-0.61', '0', '0.908', '#joy', '#admiration', 'positive', '0.82', 'adornment', 'cigar_band', 'hatband', 'neckband', 'pinstripe']
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senticnet['brutal_treatment'] = ['-0.79', '0', '0.691', '-0.88', '#sadness', '#disgust', 'negative', '-0.79', 'cold', 'pitiless', 'cruel', 'unkind', 'vicious']
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senticnet['bubba'] = ['0.17', '0.125', '-0.04', '0.196', '#joy', '#admiration', 'positive', '0.111', 'pal', 'single_person', 'comrade', 'good', 'companion']
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senticnet['bubbly'] = ['0.982', '0.945', '0', '0', '#joy', '#interest', 'positive', '0.964', 'clean_agent', 'foam', 'lather', 'wash_self', 'cleanser']
senticnet['bubonic'] = ['-0.95', '0', '0.876', '-0.89', '#sadness', '#disgust', 'negative', '-0.90', 'bubonic_plague', 'pestis_bubonica', 'glandular_plague', 'septicemic_plague', 'pestilence']
senticnet['bubonic_plague'] = ['-0.86', '0', '0.825', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'pestis', 'plague', 'pestilence', 'pest', 'ambulant_plague']
senticnet['bubonic_plague_epidemic'] = ['-0.86', '0', '0.825', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'pestis', 'plague', 'pestilence', 'pest', 'ambulant_plague']
senticnet['buccaneer'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'unofficial', 'informal', 'loose', 'drumhead']
senticnet['bucchero'] = ['-0.06', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.04', 'polish', 'magnify', 'see_out', 'sill', 'pane']
senticnet['bucconidae_family'] = ['-0.92', '0', '0.64', '-0.52', '#sadness', '#anger', 'negative', '-0.69', 'animal_order', 'percher', 'thyreophoran', 'marginocephalian', 'euronithopod']
senticnet['bucerotidae'] = ['-0.92', '0', '0.64', '-0.52', '#sadness', '#anger', 'negative', '-0.69', 'animal_order', 'percher', 'thyreophoran', 'marginocephalian', 'euronithopod']
senticnet['buck'] = ['0', '0.144', '0.031', '0.129', '#interest', '#admiration', 'positive', '0.081', 'money_unit', 'currency_unit', 'form_of_money', 'monetary_unit', 'paper_currency']
senticnet['buck_fever'] = ['-0.98', '0', '0', '-0.86', '#sadness', '#disgust', 'negative', '-0.92', 'fevered', 'feverishly', 'feverish', 'hectic', 'anticipant']
senticnet['buck_knife'] = ['0.818', '-0.95', '0', '0.695', '#joy', '#surprise', 'positive', '0.823', 'jackknife', 'clasp_knife', 'penknife', 'switchblade', 'switchblade_knife']
senticnet['buck_naked'] = ['0.132', '0.115', '-0.06', '0.121', '#joy', '#admiration', 'positive', '0.076', 'make_person', 'survival_species', 'make_baby', 'lover', 'man_wife']
senticnet['buckaroo'] = ['-0.64', '0', '0.667', '-0.82', '#anger', '#disgust', 'negative', '-0.71', 'novice', 'beginner', 'tyro', 'tiro', 'initiate']
senticnet['buckboard'] = ['0.064', '0', '0', '0.123', '#joy', '#admiration', 'positive', '0.093', 'horse', 'passenger_car', 'car_action', 'conveyance', 'car_travel']
senticnet['buckbrush'] = ['-0.79', '0', '0.906', '-0.67', '#sadness', '#anger', 'negative', '-0.79', 'water_dropwort', 'hemlock_water_dropwort', 'water_hemlock', 'plant_life', 'poisonous_plant']
senticnet['bucked_up'] = ['0.485', '0.672', '-0.26', '0.255', '#joy', '#interest', 'positive', '0.288', 'encouraged', 'encouragingly', 'encouraging', 'optimistic', 'optimistically']
senticnet['bucket'] = ['0', '0', '0.795', '-0.81', '#anger', '#disgust', 'negative', '-0.80', 'shoot', 'trematode', 'octopus', 'sucker', 'draw']
senticnet['bucket_overflow'] = ['0', '0', '0.865', '-0.95', '#anger', '#disgust', 'negative', '-0.90', 'flat', 'dirty_floor', 'spill_over', 'float', 'spoil_wood_floor']
senticnet['bucket_shop'] = ['-0.78', '0', '0.944', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'loser', 'gambler', 'mortal', 'better', 'bettor']
senticnet['bucking_horse'] = ['0.938', '0.779', '0', '0', '#joy', '#interest', 'positive', '0.859', 'carnival', 'circus', 'fairground', 'fair', 'disneyland']
senticnet['buckingham'] = ['0.033', '0.088', '-0.06', '0.068', '#interest', '#admiration', 'positive', '0.032', 'earl', 'noble', 'king', 'sultan', 'royal_title']
senticnet['buckland_warricombe'] = ['-0.75', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.83', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['buckle'] = ['0', '0.068', '-0.06', '0.081', '#interest', '#admiration', 'positive', '0.028', 'pyramid', 'japanese_person', 'mexican_restaurant', 'anemone', 'herringbone']
senticnet['buckle_down'] = ['0', '0.082', '0', '0.077', '#interest', '#admiration', 'positive', '0.08', 'drink_tea', 'absorb_moisture', 'sit_on_beach', 'procrastination', 'wave_goodbye']
senticnet['buckle_under'] = ['0.839', '-0.88', '0', '0.957', '#surprise', '#admiration', 'positive', '0.895', 'consent', 'give_in', 'go_for', 'submit', 'accept']
senticnet['buckling'] = ['-0.80', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.84', 'warp', 'buckle', 'heave', 'crumple', 'clasp']
senticnet['buckminster_fuller_challenge'] = ['0.894', '0.881', '0', '0.803', '#joy', '#interest', 'positive', '0.859', 'remittance', 'afford', 'award', 'money_due', 'amount_owe']
senticnet['buckshee'] = ['-0.97', '0', '0.53', '-0.66', '#sadness', '#disgust', 'negative', '-0.72', 'uncompensated', 'unsalaried', 'unpaid', 'undischarged', 'complimentary']
senticnet['buckshot'] = ['-0.97', '0', '0.677', '-0.95', '#sadness', '#disgust', 'negative', '-0.86', 'stunt', 'game', 'mutant', 'invertebrate', 'animate_being']
senticnet['buckskin'] = ['-0.97', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.96', 'gelding', 'roan', 'palomino', 'mare', 'sorrel']
senticnet['bucktail'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['buckweed_fire'] = ['0.057', '0', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'huge_fire', 'fire', 'rapid_oxidation', 'heat_light', 'flame']
senticnet['bucolic'] = ['0.793', '0', '0', '0.834', '#joy', '#admiration', 'positive', '0.814', 'tranquil', 'picturesque', 'idyllic', 'secluded', 'unspoilt']
senticnet['bud'] = ['0.104', '0', '0', '0.088', '#joy', '#admiration', 'positive', '0.096', 'baby_flower', 'wee', 'bro', 'part_of_plant', 'leafy']
senticnet['bud_break'] = ['-0.75', '0', '0.512', '-0.75', '#sadness', '#disgust', 'negative', '-0.67', 'inactivity', 'inactiveness', 'slumber', 'inaction', 'hibernation']
senticnet['bud_fisher'] = ['-0.72', '0', '0.619', '-0.86', '#sadness', '#disgust', 'negative', '-0.73', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['bud_jamison'] = ['-0.53', '0', '0.591', '-0.59', '#anger', '#disgust', 'negative', '-0.57', 'mug', 'sitting_duck', 'easy_mark', 'dupe', 'mortal']
senticnet['bud_light'] = ['0.964', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.964', 'carbonated_drink', 'sugar_free', 'coke', 'diet_coke', 'beverage']
senticnet['bud_light_lime'] = ['0.964', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.964', 'carbonated_drink', 'sugar_free', 'coke', 'diet_coke', 'beverage']
senticnet['bud_mite'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'rust_fungus', 'fungus']
senticnet['bud_vase'] = ['0.937', '0', '0', '0.828', '#joy', '#admiration', 'positive', '0.883', 'bakery', 'produce_section', 'thanksgiving_dinner', 'dinner_meal', 'fast_food']
senticnet['buddha'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['buddha_bar'] = ['0.966', '0.921', '0', '0.892', '#joy', '#interest', 'positive', '0.926', 'disco', 'nightclub', 'shop_complex', 'dance_club', 'fast_food_restaurant']
senticnet['buddha_nature'] = ['-0.87', '0', '0.744', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['buddha_sakyamuni'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['buddha_shakyamuni'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['buddhism'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['buddhist'] = ['0', '0', '0', '0.066', '#admiration', '#admiration', 'positive', '0.066', 'two_barbs', 'barb', 'mule', 'horse', 'ride_barb']
senticnet['buddhist_meditation'] = ['0.088', '0.135', '0', '0', '#joy', '#interest', 'positive', '0.112', 'watch_others', 'meditate', 'imagine', 'mantra', 'dance_music']
senticnet['buddhist_tradition'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['budding'] = ['-0.77', '0', '0.764', '-0.54', '#sadness', '#anger', 'negative', '-0.69', 'pullulation', 'apomixis', 'blastogenesis', 'asexual_reproduction', 'fission']
senticnet['buddy'] = ['0.17', '0.125', '-0.04', '0.196', '#joy', '#admiration', 'positive', '0.111', 'pal', 'single_person', 'comrade', 'good', 'companion']
senticnet['buddy_comedy'] = ['-0.90', '0.152', '-0.62', '0.061', '#sadness', '#fear', 'negative', '-0.33', 'guilt_feeling', 'guilty', 'dolor', 'grieve', 'penitentially']
senticnet['buddy_list'] = ['-0.51', '0', '0.545', '-0.91', '#anger', '#disgust', 'negative', '-0.65', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['budgerigar'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet']
senticnet['budget'] = ['-0.05', '0.282', '-0.04', '0.107', '#interest', '#admiration', 'positive', '0.072', 'expense', 'dollar_cent', 'dollar_sign', 'amount_pay', 'payment_amount']
senticnet['budget_allocation'] = ['-0.05', '0.282', '-0.04', '0.107', '#interest', '#admiration', 'positive', '0.072', 'expense', 'dollar_cent', 'dollar_sign', 'amount_pay', 'payment_amount']
senticnet['budget_deficit'] = ['-0.05', '0.282', '-0.04', '0.107', '#interest', '#admiration', 'positive', '0.072', 'expense', 'dollar_cent', 'dollar_sign', 'amount_pay', 'payment_amount']
senticnet['budget_horror_movie'] = ['-0.08', '-0.01', '-0.16', '-0.13', '#fear', '#disgust', 'negative', '-0.09', 'hockey_game', 'movie', 'scary_movie', 'cinema', 'cctv']
senticnet['budget_proposal'] = ['-0.05', '0.282', '-0.04', '0.107', '#interest', '#admiration', 'positive', '0.072', 'expense', 'dollar_cent', 'dollar_sign', 'amount_pay', 'payment_amount']
senticnet['budget_request'] = ['-0.05', '0.282', '-0.04', '0.107', '#interest', '#admiration', 'positive', '0.072', 'expense', 'dollar_cent', 'dollar_sign', 'amount_pay', 'payment_amount']
senticnet['budget_sequestration'] = ['0', '0', '-0.87', '-0.84', '#fear', '#disgust', 'negative', '-0.85', 'seize', 'seclude', 'attach', 'impound', 'confiscate']
senticnet['budget_shortfall'] = ['-0.05', '0.282', '-0.04', '0.107', '#interest', '#admiration', 'positive', '0.072', 'expense', 'dollar_cent', 'dollar_sign', 'amount_pay', 'payment_amount']
senticnet['budgetary'] = ['-0.05', '0.282', '-0.04', '0.107', '#interest', '#admiration', 'positive', '0.072', 'expense', 'dollar_cent', 'dollar_sign', 'amount_pay', 'payment_amount']
senticnet['budgetary_self_reliance'] = ['0.856', '0', '0', '0.963', '#joy', '#admiration', 'positive', '0.909', 'progress', 'get_better_job', 'further_career', 'graduate_school', 'get_diploma']
senticnet['budgeted_amount'] = ['-0.04', '0.268', '0', '0.159', '#interest', '#admiration', 'positive', '0.127', 'total', 'amount', 'a_lot', 'aggregate', 'bill']
senticnet['budunost'] = ['0.954', '-0.90', '0', '0.867', '#joy', '#surprise', 'positive', '0.91', 'friend', 'sports_fan', 'rooter', 'fan', 'nut']
senticnet['budunost_podgorica'] = ['0.954', '-0.90', '0', '0.867', '#joy', '#surprise', 'positive', '0.91', 'friend', 'sports_fan', 'rooter', 'fan', 'nut']
senticnet['budweiser'] = ['0.964', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.964', 'carbonated_drink', 'sugar_free', 'coke', 'diet_coke', 'beverage']
senticnet['budweiser_beer'] = ['0.964', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.964', 'carbonated_drink', 'sugar_free', 'coke', 'diet_coke', 'beverage']
senticnet['budwood'] = ['0.826', '0.787', '0', '0', '#joy', '#interest', 'positive', '0.806', 'fig', 'baby_flower', 'carpel', 'strawberry', 'star_anise']
senticnet['buelow'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['buen_retiro'] = ['0', '0.105', '0', '0.076', '#interest', '#admiration', 'positive', '0.09', 'big_house', 'mansion', 'castle', 'block_flat', 'medieval']
senticnet['buff'] = ['-0.74', '0', '0.585', '-0.61', '#sadness', '#disgust', 'negative', '-0.64', 'brownness', 'chestnut', 'chocolate', 'coffee', 'deep_brown']
senticnet['buff_orpington'] = ['0.039', '0.089', '-0.08', '0.143', '#interest', '#admiration', 'positive', '0.048', 'bird', 'nightingale', 'stork', 'eagle', 'rooster']
senticnet['buffalo_coach_lindy'] = ['0.022', '-0.11', '0.014', '0.057', '#surprise', '#admiration', 'positive', '0.045', 'wood', 'many_steps', 'franchise', 'stump', 'timber']
senticnet['buffalo_hide'] = ['0.028', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.047', 'handkerchief', 'kerchief', 'hanky', 'facial_tissue', 'zipper']
senticnet['buffalo_horn'] = ['0.028', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.047', 'handkerchief', 'kerchief', 'hanky', 'facial_tissue', 'zipper']
senticnet['buffalo_milk'] = ['0.057', '0', '0.018', '0.058', '#joy', '#admiration', 'positive', '0.032', 'porridge', 'food', 'milk', 'healthy', 'morning_food']
senticnet['buffalo_robe'] = ['0.028', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.047', 'handkerchief', 'kerchief', 'hanky', 'facial_tissue', 'zipper']
senticnet['buffer'] = ['-0.15', '0', '0.029', '-0.11', '#sadness', '#disgust', 'negative', '-0.1', 'start_over', 'front_line', 'find_line', 'take_turn', 'long_line']
senticnet['buffer_overflow'] = ['-0.02', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.07', 'army', 'military', 'infantry', 'samurai', 'soldier']
senticnet['buffer_overflow_attack'] = ['-0.02', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.07', 'army', 'military', 'infantry', 'samurai', 'soldier']
senticnet['buffer_overflow_vulnerability'] = ['-0.02', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.07', 'army', 'military', 'infantry', 'samurai', 'soldier']
senticnet['buffer_overrun'] = ['-0.02', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.07', 'army', 'military', 'infantry', 'samurai', 'soldier']
senticnet['buffer_zone'] = ['0.034', '0.09', '0.039', '0.061', '#interest', '#admiration', 'positive', '0.036', 'border', 'limit', 'edge', 'indoor', 'side']
senticnet['buffering_agent'] = ['0.071', '0', '-0.08', '-0.09', '#fear', '#disgust', 'negative', '-0.03', 'gunk', 'rotten_food', 'sour_cream', 'glass_of_milk', 'lemon_juice']
senticnet['buffet'] = ['0.128', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.085', 'banquet', 'rave', 'auction', 'limbo', 'loose_end']
senticnet['buffet_breakfast'] = ['0.715', '0.785', '0', '0', '#joy', '#interest', 'positive', '0.75', 'scrumble_egg', 'heat_meal', 'pan', 'beat_egg', 'food']
senticnet['buffet_car'] = ['-0.58', '0', '0.714', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'waster', 'diversionist', 'destroyer', 'ruiner', 'undoer']
senticnet['buffet_dinner'] = ['0.715', '0.785', '0', '0', '#joy', '#interest', 'positive', '0.75', 'scrumble_egg', 'heat_meal', 'pan', 'beat_egg', 'food']
senticnet['buffet_lunch'] = ['0.715', '0.785', '0', '0', '#joy', '#interest', 'positive', '0.75', 'scrumble_egg', 'heat_meal', 'pan', 'beat_egg', 'food']
senticnet['buffet_restaurant'] = ['0.128', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.085', 'banquet', 'rave', 'auction', 'limbo', 'loose_end']
senticnet['buffet_style'] = ['0.128', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.085', 'banquet', 'rave', 'auction', 'limbo', 'loose_end']
senticnet['buffet_table'] = ['0.128', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.085', 'banquet', 'rave', 'auction', 'limbo', 'loose_end']
senticnet['buffeted_by'] = ['0.126', '0', '0', '0', '#joy', '#joy', 'positive', '0.126', 'natural_phenomenon', 'rain_wind', 'rain_storm', 'weather_phenomenon', 'weather_type']
senticnet['bufflehead'] = ['-0.50', '0', '0.764', '-0.61', '#anger', '#disgust', 'negative', '-0.62', 'doctor', 'mountebank', 'charlatan', 'quack', 'physician']
senticnet['buffoon'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'dvd', 'clown']
senticnet['buffoonery'] = ['0.931', '-0.84', '0', '0.829', '#joy', '#surprise', 'positive', '0.869', 'folly', 'lunacy', 'craziness', 'indulgence', 'shtik']
senticnet['buffoonish'] = ['-0.76', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.81', 'intractability', 'unruliness', 'fractiousness', 'willfulness', 'against_rule']
senticnet['bufotenin'] = ['0.049', '0', '0', '0.113', '#joy', '#admiration', 'positive', '0.081', 'solid', 'hard_material', 'construction_material', 'hard', 'calcium']
senticnet['bug'] = ['-0.02', '-0.05', '0.082', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'insect', 'beetle', 'cutworm', 'annoy_bug', 'pest']
senticnet['bug_bite'] = ['-0.10', '0', '-0.05', '-0.11', '#sadness', '#disgust', 'negative', '-0.08', 'jaw', 'cheek', 'dimple', 'forehead', 'beard']
senticnet['bug_bunny'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'child_movie', 'child_story', 'funny_moment', 'childhood']
senticnet['bug_bunny_cartoon'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'child_movie', 'child_story', 'funny_moment', 'childhood']
senticnet['bug_fix'] = ['-0.95', '0', '0.721', '-0.75', '#sadness', '#disgust', 'negative', '-0.81', 'cook_up', 'ready', 'make', 'cook', 'prepare']
senticnet['bug_person'] = ['0.818', '0.98', '0', '0', '#joy', '#interest', 'positive', '0.899', 'swim_ocean', 'smoke_joint', 'fly_around', 'swim_lake', 'shovel_snow']
senticnet['bug_repellent'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['bug_spray'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['bugatti_veyron'] = ['0.755', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.728', 'high_class', 'expensive_car', 'ferrari', 'fancy_car', 'sports_car']
senticnet['bugfix'] = ['-0.58', '0', '0.561', '-0.62', '#sadness', '#disgust', 'negative', '-0.58', 'spot', 'speckle', 'dapple', 'mark', 'fleck']
senticnet['bugger'] = ['-0.35', '-0.13', '-0.05', '-0.38', '#sadness', '#disgust', 'negative', '-0.16', 'dick', 'idiot', 'asshole', 'fuck', 'motherfucker']
senticnet['bugger_all'] = ['-0.55', '0', '0.882', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'null', 'zero', 'naught', 'zip', 'nothing']
senticnet['bugger_off'] = ['-0.92', '0', '0.458', '-0.92', '#sadness', '#disgust', 'negative', '-0.76', 'rude_person', 'rude', 'impolite', 'discourteous', 'bad_word']
senticnet['buggery'] = ['-0.84', '0', '0.882', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'perversion', 'fetishism', 'paraphilia', 'sodomy', 'sexual_perversion']
senticnet['buggery_act'] = ['-0.84', '0', '0.882', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'perversion', 'fetishism', 'paraphilia', 'sexual_perversion', 'buggery']
senticnet['bugginess'] = ['0.789', '-0.99', '0', '0.54', '#joy', '#surprise', 'positive', '0.776', 'imperfection', 'imperfectness', 'defectiveness', 'lameness', 'sickness']
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senticnet['bump_head'] = ['-0.16', '-0.09', '0', '-0.20', '#sadness', '#disgust', 'negative', '-0.09', 'pass_out', 'collapse', 'dog_bark', 'breahard', 'become_fatigued']
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senticnet['bumping_into'] = ['0.03', '0', '0.017', '0.138', '#joy', '#admiration', 'positive', '0.05', 'up_down', 'curl', 'crest', 'hand_gesture', 'goodbye']
senticnet['bumpkin'] = ['0.806', '-0.70', '0', '0.687', '#joy', '#surprise', 'positive', '0.734', 'saphead', 'muggins', 'tomfool', 'fool', 'sap']
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senticnet['bunac'] = ['0', '0.766', '0', '0', '#interest', '#interest', 'positive', '0.766', 'abroad', 'foreign', 'study_abroad', 'elsewhere', 'live_abroad']
senticnet['bunch'] = ['-0.63', '0', '0.862', '-0.52', '#sadness', '#anger', 'negative', '-0.67', 'knot', 'swad', 'tuft', 'tussock', 'agglomeration']
senticnet['bunch_person'] = ['0.078', '0', '0.014', '0.095', '#joy', '#admiration', 'positive', '0.053', 'big_group', 'large_group', 'large_number', 'mass_person', 'push_together']
senticnet['bunchberry'] = ['0.773', '-0.55', '0', '0.949', '#joy', '#admiration', 'positive', '0.757', 'cornel', 'dwarf_cornel', 'crackerberry', 'pudding_berry', 'common_white_dogwood']
senticnet['bunched_up'] = ['-0.1', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.1', 'deflate', 'read_cover', 'look_behind', 'upgrade', 'scan']
senticnet['bunco'] = ['0.931', '-0.71', '0', '0.63', '#joy', '#surprise', 'positive', '0.758', 'hustle', 'con', 'sting_operation', 'sting', 'sting_operation_game']
senticnet['bunco_game'] = ['0.931', '-0.71', '0', '0.63', '#joy', '#surprise', 'positive', '0.758', 'hustle', 'con', 'bunco', 'sting', 'sting_operation']
senticnet['buncombe'] = ['-0.89', '0', '0.739', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'rot', 'bunk', 'bunkum', 'dogshit', 'guff']
senticnet['bundahishn'] = ['0', '0.106', '-0.05', '0.163', '#interest', '#admiration', 'positive', '0.072', 'band', 'kit', 'oblivion', 'rock_band', 'high_school_band']
senticnet['bundesadler'] = ['0', '0.105', '0', '0', '#interest', '#interest', 'positive', '0.105', 'beak', 'many_feathers', 'big_eye', 'bird', 'bird_mouth']
senticnet['bundjalung'] = ['-0.95', '0', '0.624', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['bundle'] = ['0', '0.11', '-0.05', '0.149', '#interest', '#admiration', 'positive', '0.067', 'pack_clothes', 'hold_stuff', 'carry_stuff', 'carry_book', 'carry_belongings']
senticnet['bundt_cake'] = ['0.731', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.837', 'make_bread', 'cook_dinner', 'cook_meal', 'cook_curry', 'follow_recipe']
senticnet['bundt_pan'] = ['0.731', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.837', 'make_bread', 'cook_dinner', 'cook_meal', 'cook_curry', 'follow_recipe']
senticnet['buner'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'webpage', 'store_record', 'ideal']
senticnet['buner_district'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'webpage', 'store_record', 'ideal']
senticnet['bung_hole'] = ['0.161', '-0.14', '0.031', '-0.10', '#joy', '#surprise', 'positive', '0.043', 'barrel', 'reception', 'champagne', 'beer', 'wine']
senticnet['bungalow'] = ['0', '0.105', '0', '0.076', '#interest', '#admiration', 'positive', '0.09', 'palace', 'mansion', 'castle', 'big_house', 'terrace']
senticnet['bungee'] = ['-0.88', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.90', 'undergarment', 'outerwear', 'lady_underwear', 'under_garment', 'slip']
senticnet['bungee_jump'] = ['0.748', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.849', 'fun', 'exciting', 'exhilarating', 'jump', 'adrenaline']
senticnet['bungee_jump_from_high_bridge'] = ['0.842', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.894', 'jump_out_of_window', 'get_involved_in_accident', 'jump_off_cliff', 'storm_castle', 'advance_battle']
senticnet['bungee_jump_high_bridge'] = ['0', '-0.00', '-0.08', '-0.08', '#fear', '#disgust', 'negative', '-0.05', 'lose_consciousness', 'get_involved_in_accident', 'go_coma', 'jump_out_of_bridge', 'heart_attack']
senticnet['bungle'] = ['-0.84', '0', '0.828', '-0.57', '#sadness', '#anger', 'negative', '-0.75', 'bobble', 'error', 'misstep', 'fumble', 'stumble']
senticnet['bungled'] = ['-0.98', '0', '0.499', '-0.84', '#sadness', '#disgust', 'negative', '-0.77', 'lowly', 'fumbling', 'botched', 'unskilled', 'bungling']
senticnet['bungler'] = ['-0.77', '0', '0.503', '-0.93', '#sadness', '#disgust', 'negative', '-0.73', 'butcher', 'fuckup', 'slouch', 'incompetent', 'blunderer']
senticnet['bunglesome'] = ['-0.96', '0', '0.636', '-0.85', '#sadness', '#disgust', 'negative', '-0.81', 'awkward', 'unmanageable', 'clumsy', 'cumbersome', 'ungainly']
senticnet['bungling'] = ['-0.61', '0', '0.533', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'maladroit', 'bumbling', 'butterfingered', 'inept', 'handless']
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senticnet['bunk_bed'] = ['0.716', '0.96', '0', '0', '#joy', '#interest', 'positive', '0.838', 'airline_seat', 'small_seat', 'beanbag_chair', 'hide-a-bed_sofa', 'chair']
senticnet['bunker'] = ['-0.58', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.64', 'burial_chamber', 'sepulcher', 'chamber', 'sepulture', 'bombproof']
senticnet['bunker_fuel'] = ['0.855', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.86', 'ticket_counter', 'newspaper_stand', 'luggage_carrier', 'aircraft_roadway', 'timetable']
senticnet['bunkum'] = ['-0.89', '0', '0.739', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'rot', 'bunk', 'dogshit', 'buncombe', 'guff']
senticnet['bunny'] = ['0', '0', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.05', 'human', 'delusional', 'use_illegal_drug', 'busy_person', 'carpenter']
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senticnet['bunny_rabbit'] = ['0.03', '0.068', '-0.04', '0.093', '#interest', '#admiration', 'positive', '0.037', 'beast', 'equine', 'live_creature', 'furry_creature', 'domesticate_animal']
senticnet['bunsen'] = ['0.032', '0', '0', '0.052', '#joy', '#admiration', 'positive', '0.042', 'test_tube', 'chemistry_equipment', 'test', 'chemistry', 'tube']
senticnet['buntline_hitch'] = ['-0.95', '0', '0.793', '0', '#sadness', '#anger', 'negative', '-0.87', 'block', 'obstacle', 'nodus', 'impediment', 'obstruction']
senticnet['bunyip'] = ['-0.95', '0', '0.624', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['buoyancy'] = ['0.676', '0.63', '-0.23', '0.31', '#joy', '#interest', 'positive', '0.345', 'perkiness', 'walk_on_air', 'exultingly', 'exultantly', 'jubilance']
senticnet['buoyancy_aid'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'lifebelt', 'save_life', 'lifeguard', 'life_jacket', 'safety_equipment']
senticnet['buoyancy_compensator'] = ['0.676', '0.63', '-0.23', '0.31', '#joy', '#interest', 'positive', '0.345', 'perkiness', 'walk_on_air', 'exultingly', 'exultantly', 'jubilance']
senticnet['buoyancy_force'] = ['0.676', '0.63', '-0.23', '0.31', '#joy', '#interest', 'positive', '0.345', 'perkiness', 'walk_on_air', 'exultingly', 'exultantly', 'jubilance']
senticnet['buoyant'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'lively', 'vivacious', 'joyful', 'cheerful', 'happy']
senticnet['buoyant_force'] = ['0.676', '0.63', '-0.23', '0.31', '#joy', '#interest', 'positive', '0.345', 'perkiness', 'walk_on_air', 'exultingly', 'exultantly', 'jubilance']
senticnet['buoyant_mood'] = ['0', '0', '0.954', '-0.76', '#anger', '#disgust', 'negative', '-0.85', 'fervid', 'ardent', 'impassioned', 'fervent', 'fiery']
senticnet['buoyed_by'] = ['-0.11', '0.188', '0.608', '-0.63', '#anger', '#disgust', 'negative', '-0.29', 'contempt', 'disincline', 'disinclination', 'disinclined', 'indispose']
senticnet['bupivacaine'] = ['0', '0', '-0.95', '-0.87', '#fear', '#disgust', 'negative', '-0.91', 'sharp_weapon', 'bayonet', 'ammunition', 'spear', 'arrow']
senticnet['bur_oak'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'locust_tree']
senticnet['burden'] = ['0', '-0.00', '-0.08', '-0.08', '#fear', '#disgust', 'negative', '-0.05', 'duty', 'tax', 'concern', 'responsibility', 'onus']
senticnet['burdensome'] = ['0', '0', '-0.76', '-0.79', '#fear', '#disgust', 'negative', '-0.77', 'burden', 'demanding', 'burdensomely', 'tax', 'burdensomeness']
senticnet['burdensomely'] = ['0', '0', '-0.76', '-0.79', '#fear', '#disgust', 'negative', '-0.78', 'burden', 'demanding', 'burdensome', 'tax', 'burdensomeness']
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senticnet['burdock'] = ['0.683', '-0.54', '0', '0.568', '#joy', '#admiration', 'positive', '0.599', 'subshrub', 'suffrutex', 'common_burdock', 'lesser_burdock', 'great_burdock']
senticnet['burdock_root'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'mayonnaise', 'box_cereal', 'bottle_water']
senticnet['bureacracy'] = ['0', '0', '0', '-0.94', '#disgust', '#disgust', 'negative', '-0.94', 'boring', 'fill_form', 'annoying', 'form', 'fill_out_form']
senticnet['bureacratic'] = ['0', '0', '0', '-0.94', '#disgust', '#disgust', 'negative', '-0.94', 'boring', 'fill_form', 'annoying', 'form', 'fill_out_form']
senticnet['bureau'] = ['0.685', '-0.84', '0', '0.903', '#surprise', '#admiration', 'positive', '0.812', 'agency', 'federal_agency', 'government_agency', 'bureau_of_diplomatic_security', 'office']
senticnet['bureau_chief'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'adventurer', 'humankind']
senticnet['bureau_of_diplomatic_security'] = ['0.685', '-0.84', '0', '0.903', '#surprise', '#admiration', 'positive', '0.812', 'agency', 'federal_agency', 'government_agency', 'bureau', 'office']
senticnet['bureaucracy'] = ['0', '0', '0', '-0.94', '#disgust', '#disgust', 'negative', '-0.94', 'boring', 'fill_form', 'annoying', 'form', 'fill_out_form']
senticnet['bureaucrat'] = ['0', '0', '0', '-0.94', '#disgust', '#disgust', 'negative', '-0.94', 'boring', 'fill_form', 'annoying', 'form', 'fill_out_form']
senticnet['bureaucratese'] = ['-0.61', '0', '0.73', '-0.85', '#anger', '#disgust', 'negative', '-0.73', 'diction', 'phrasing', 'verbalization', 'wording', 'phraseology']
senticnet['bureaucratic'] = ['0', '0.868', '0', '0.978', '#interest', '#admiration', 'positive', '0.923', 'bottom-up', 'hierarchical', 'command-and-control', 'decentralized', 'top-down']
senticnet['bureaucratic_collectivism'] = ['0.95', '-0.77', '0', '0.578', '#joy', '#surprise', 'positive', '0.767', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['bureaucratic_elite'] = ['0.028', '0.12', '0.068', '0.176', '#interest', '#admiration', 'positive', '0.064', 'elite', 'number_person', 'count_person', 'small_group', 'enumeration']
senticnet['bureaucratic_inertia'] = ['-0.70', '0', '0.914', '-0.72', '#anger', '#disgust', 'negative', '-0.78', 'unadaptability', 'rigidity', 'rigidness', 'unfitness', 'inability']
senticnet['bureaucratic_interference'] = ['-0.70', '0', '0.914', '-0.72', '#anger', '#disgust', 'negative', '-0.78', 'unadaptability', 'rigidity', 'rigidness', 'unfitness', 'inability']
senticnet['bureaucratic_red_tape'] = ['0', '0', '0', '-0.94', '#disgust', '#disgust', 'negative', '-0.94', 'boring', 'fill_form', 'annoying', 'form', 'fill_out_form']
senticnet['bureaucratic_structure'] = ['0', '0', '0', '-0.94', '#disgust', '#disgust', 'negative', '-0.94', 'boring', 'fill_form', 'annoying', 'form', 'fill_out_form']
senticnet['bureaucratically'] = ['0', '0', '0', '-0.94', '#disgust', '#disgust', 'negative', '-0.94', 'boring', 'fill_form', 'annoying', 'form', 'fill_out_form']
senticnet['bureaucratism'] = ['-0.89', '0', '0.678', '-0.62', '#sadness', '#anger', 'negative', '-0.73', 'undisciplined', 'anarchic', 'uncontrolled', 'irrepressible', 'uncontrollable']
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senticnet['bureucracy'] = ['0', '-0.53', '0', '-0.99', '#surprise', '#disgust', 'negative', '-0.23', 'pay_insurance', 'tax', 'useless', 'waste_money', 'premium']
senticnet['burgage'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'bodyguard', 'outrider', 'bodyguard_of_guard', 'beefeater', 'defender']
senticnet['burgeoning'] = ['0.732', '0.785', '0', '0', '#joy', '#interest', 'positive', '0.758', 'new-found', 'enthusiasm', 'newfound', 'fascination', 'hard-won']
senticnet['burgeoning_demand'] = ['0.749', '0.764', '0', '0', '#joy', '#interest', 'positive', '0.756', 'obedience', 'order', 'requirement', 'permission', 'necessary']
senticnet['burger'] = ['0.711', '0', '0', '0', '#joy', '#joy', 'positive', '0.711', 'cheeseburger', 'appetizer', 'chinese_food', 'porridge', 'hearty']
senticnet['burger_bar'] = ['0.06', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'eat_food', 'fast_food', 'pizza_parlor', 'pizza_store', 'kitchen_cupboard']
senticnet['burger_chain'] = ['0.965', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.951', 'delicious_food', 'hot_meal', 'good_food', 'fresh_produce', 'tasty_food']
senticnet['burger_equation'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'assumption', 'precondition', 'provision', 'constraint', 'premiss']
senticnet['burger_king'] = ['0.135', '0.065', '0', '0.082', '#joy', '#admiration', 'positive', '0.094', 'breakfast', 'delicious_food', 'dinner_meal', 'sandwich', 'dine']
senticnet['burglar'] = ['0.057', '0', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'huge_fire', 'fire', 'rapid_oxidation', 'heat_light', 'flame']
senticnet['burglar_alarm'] = ['0', '0.115', '0', '0.105', '#interest', '#admiration', 'positive', '0.11', 'farrier', 'miner', 'horseshoer', 'blacksmith', 'smith']
senticnet['burglarize'] = ['-0.75', '0', '0.94', '0', '#sadness', '#anger', 'negative', '-0.84', 'steal_money', 'theft', 'stick_up', 'robbery', 'rob']
senticnet['burglary'] = ['-0.75', '0', '0.94', '0', '#sadness', '#anger', 'negative', '-0.84', 'steal_money', 'theft', 'stick_up', 'robbery', 'rob']
senticnet['burglary_charge'] = ['0.654', '-0.59', '0', '0.774', '#joy', '#admiration', 'positive', '0.673', 'instrument', 'indictment', 'murder_indictment', 'true_bill', 'legal_document']
senticnet['burglary_suspect'] = ['0.964', '-0.57', '0', '0.827', '#joy', '#admiration', 'positive', '0.789', 'mortal', 'murder_suspect', 'suspect', 'rape_suspect', 'person']
senticnet['burgle'] = ['-0.75', '0', '0.94', '0', '#sadness', '#anger', 'negative', '-0.84', 'steal_money', 'theft', 'stick_up', 'robbery', 'rob']
senticnet['burgoon'] = ['0.627', '-0.65', '0', '0.968', '#surprise', '#admiration', 'positive', '0.749', 'overwhelm', 'overpower', 'sweep_over', 'whelm', 'overcome']
senticnet['burgundy_butterfly'] = ['0.033', '0.088', '-0.06', '0.068', '#interest', '#admiration', 'positive', '0.032', 'earl', 'noble', 'king', 'sultan', 'royal_title']
senticnet['burgundy_hamearis_lucina'] = ['0.033', '0.088', '-0.06', '0.068', '#interest', '#admiration', 'positive', '0.032', 'earl', 'noble', 'king', 'sultan', 'royal_title']
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senticnet['bus_driver'] = ['0.969', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.936', 'attendant', 'manager', 'public_servant', 'specialist', 'accomplice']
senticnet['bus_interchange'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'train_station', 'bus', 'subway_station', 'public_place', 'pier']
senticnet['bus_pass'] = ['0', '0.093', '0', '0', '#interest', '#interest', 'positive', '0.093', 'subway_pass', 'train_ticket', 'transit_ticket', 'passenger_ticket', 'transit_card']
senticnet['bus_person'] = ['-0.71', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'glide', 'cool_engine', 'go_stall', 'land_runway', 'buzz']
senticnet['bus_ply'] = ['0', '0.147', '0', '0.099', '#interest', '#admiration', 'positive', '0.123', 'airport', 'subway', 'bus_stop', 'bus', 'control_tower']
senticnet['bus_ride'] = ['-0.08', '0.179', '-0.06', '0.096', '#interest', '#admiration', 'positive', '0.034', 'ride_subway', 'buy_vending_machine', 'travel', 'buy_lottery_ticket', 'acquire']
senticnet['bus_ride_away'] = ['-0.08', '0.179', '-0.06', '0.096', '#interest', '#admiration', 'positive', '0.034', 'ride_subway', 'buy_vending_machine', 'travel', 'buy_lottery_ticket', 'acquire']
senticnet['bus_ridership'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'seeker', 'chairperson']
senticnet['bus_route'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'train_station', 'bus', 'subway_station', 'public_place', 'pier']
senticnet['bus_route_serve'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'seeker', 'chairperson']
senticnet['bus_service'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'seeker', 'chairperson']
senticnet['bus_shelter'] = ['0.952', '-0.52', '0', '0.835', '#joy', '#admiration', 'positive', '0.771', 'billboard', 'hoarding', 'sandwich_board', 'scoreboard', 'shingle']
senticnet['bus_station'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'train_station', 'bus', 'subway_station', 'public_place', 'pier']
senticnet['bus_stop'] = ['0', '0', '0', '-0.09', '#disgust', '#disgust', 'negative', '-0.09', 'subway', 'bus', 'train_station', 'railway_station', 'parking_lot']
senticnet['bus_stop_chicane'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['bus_table'] = ['0', '-0.88', '0', '0.817', '#surprise', '#admiration', 'positive', '0.853', 'work_team', 'help', 'move_furniture', 'lend_hand', 'win_award']
senticnet['bus_terminal'] = ['0', '0.147', '0', '0.099', '#interest', '#admiration', 'positive', '0.123', 'airport', 'subway', 'bus_stop', 'bus', 'control_tower']
senticnet['busang'] = ['-0.92', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.84', 'brex', 'indonesian_government', 'indonesia', 'barrick', 'work']
senticnet['busboy'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'kitchen_cabinet', 'kitchen_cupboard', 'kitchen', 'cupboard', 'own_kitchen']
senticnet['busby'] = ['-0.58', '0', '0.632', '-0.63', '#anger', '#disgust', 'negative', '-0.61', 'skimmer', 'trilby', 'bearskin', 'chapeau', 'shako']
senticnet['busch_beer'] = ['0.964', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.964', 'carbonated_drink', 'sugar_free', 'coke', 'diet_coke', 'beverage']
senticnet['busch_garden_williamsburg'] = ['0.056', '-0.01', '0.036', '0.073', '#joy', '#admiration', 'positive', '0.028', 'thrill_ride', 'have_fun', 'drag_race', 'enjoyment', 'roller_coaster']
senticnet['bush'] = ['-0.57', '0', '0.721', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'caper', 'native_pomegranate', 'caper_tree', 'native_orange', 'common_caper']
senticnet['bush_administration'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'address_book', 'get_rich', 'ladle', 'swiss_army_knife', 'whisk']
senticnet['bush_hater'] = ['-0.97', '0', '0.62', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'obstructor', 'obstructer', 'technophobe', 'obstructionist', 'resister']
senticnet['bush_honeysuckle'] = ['-0.92', '0', '0.912', '-0.94', '#sadness', '#disgust', 'negative', '-0.92', 'true_blackberry', 'sand_blackberry', 'blackberry_bush', 'blackberry', 'running_blackberry']
senticnet['bush_white_house'] = ['0', '0.709', '0', '0.933', '#interest', '#admiration', 'positive', '0.821', 'capitol', 'government_building', 'president', 'apartment_house', 'government']
senticnet['bushbaby'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'kangaroo', 'giraffe', 'caribou', 'mule', 'horse']
senticnet['bushbuck'] = ['0', '0', '-0.95', '-0.81', '#fear', '#disgust', 'negative', '-0.88', 'herb', 'plant', 'poisonous_plant', 'yellow_flower', 'lichen']
senticnet['bushcraft'] = ['0.93', '-0.95', '0', '0.909', '#joy', '#surprise', 'positive', '0.93', 'acquirement', 'skill', 'accomplishment', 'acquisition', 'attainment']
senticnet['bushel'] = ['0', '0.067', '0', '0.07', '#interest', '#admiration', 'positive', '0.069', 'peck', 'embryo', 'chicken_egg', 'ovum', 'egg']
senticnet['bushland_reserve'] = ['-0.95', '0', '0.624', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['bushwhack'] = ['0', '0', '-0.90', '-0.87', '#fear', '#disgust', 'negative', '-0.88', 'ambuscade', 'lurk', 'ambush', 'scupper', 'waylay']
senticnet['bushy'] = ['0', '0', '-0.85', '-0.93', '#fear', '#disgust', 'negative', '-0.89', 'hairy', 'shrub', 'wiry', 'erect', 'curly']
senticnet['bushy_black'] = ['-0.64', '0', '0.632', '-0.61', '#sadness', '#anger', 'negative', '-0.62', 'bearded', 'barbate', 'bewhiskered', 'whiskered', 'whiskery']
senticnet['bushy_hair'] = ['0', '0', '0.841', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'ancient', 'many_years', 'year', 'old', 'teen']
senticnet['bushy_shrub'] = ['-0.80', '0', '0.555', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'bush', 'wormwood', 'milkwort', 'germander', 'centaury']
senticnet['bushy_tail'] = ['-0.58', '0', '0.66', '-0.8', '#anger', '#disgust', 'negative', '-0.68', 'bald', 'depilous', 'glabrescent', 'glabrous', 'balding']
senticnet['busier'] = ['0', '0', '0', '-0.76', '#disgust', '#disgust', 'negative', '-0.76', 'fevered', 'feverish', 'feverishly', 'buck_fever', 'expectantly']
senticnet['busiest_port'] = ['-0.02', '-0.02', '0.064', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'ocean_water', 'fillet_fish', 'chowder', 'fillet', 'underwater']
senticnet['busiest_shopping'] = ['0.623', '-0.56', '0', '0.974', '#joy', '#admiration', 'positive', '0.721', 'alterable', 'convertible', 'translatable', 'transformable', 'transmutable']
senticnet['busily'] = ['0.788', '-0.60', '0', '0.884', '#joy', '#admiration', 'positive', '0.759', 'gap', 'rathole', 'aperture', 'bullet_hole', 'hole']
senticnet['business'] = ['0.052', '0.206', '0', '0.153', '#interest', '#admiration', 'positive', '0.137', 'enterprise', 'corporation', 'company', 'money_maker', 'industry']
senticnet['business-like'] = ['0', '0.705', '0', '0.89', '#interest', '#admiration', 'positive', '0.797', 'businesslike', 'matter-of-fact', 'tactful', 'non-confrontational', 'gentlemanly']
senticnet['business_account'] = ['-0.04', '0.28', '-0.05', '0.077', '#interest', '#admiration', 'positive', '0.065', 'finance', 'business', 'profit', 'account', 'bank_account']
senticnet['business_activity'] = ['0.842', '-0.73', '0', '0.662', '#joy', '#surprise', 'positive', '0.745', 'labor', 'racket', 'forlorn_hope', 'fraudulent_scheme', 'endeavor']
senticnet['business_acumen'] = ['0.073', '0.228', '-0.10', '0.263', '#interest', '#admiration', 'positive', '0.114', 'creativity', 'blueprint', 'skill', 'expressive', 'competence']
senticnet['business_administration_bsba'] = ['0.036', '0.111', '-0.05', '0.054', '#interest', '#admiration', 'positive', '0.038', 'despot', 'supervisor', 'chief', 'commander', 'earl']
senticnet['business_agreement'] = ['0.984', '0.758', '0', '0', '#joy', '#interest', 'positive', '0.871', 'transact_business', 'bankruptcy', 'business_transaction', 'receive_change', 'give_change']
senticnet['business_attire'] = ['0', '0', '0', '0.99', '#admiration', '#admiration', 'positive', '0.99', 'politeness', 'good_behavior', 'good_etiquette', 'manner', 'hint']
senticnet['business_card'] = ['0.735', '-0.70', '0', '0', '#joy', '#surprise', 'positive', '0.721', 'socialize', 'formal_introduction', 'database', 'professional', 'communicate']
senticnet['business_cycle'] = ['-0.77', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.80', 'deviation', 'allomerism', 'fluctuation', 'variation', 'wave']
senticnet['business_deal'] = ['-0.04', '0.345', '-0.15', '0.259', '#interest', '#admiration', 'positive', '0.101', 'money_exchange', 'bargain', 'financial_transaction', 'deal', 'sale']
senticnet['business_education_iacbe'] = ['0.055', '0.072', '-0.06', '0.094', '#interest', '#admiration', 'positive', '0.038', 'big_group', 'large_group', 'bunch_person', 'large_number', 'clique']
senticnet['business_executive'] = ['0.703', '-0.59', '0', '0.842', '#joy', '#admiration', 'positive', '0.714', 'rainmaker', 'minister', 'administrator', 'corporate_executive', 'executive']
senticnet['business_firm'] = ['0.094', '0.198', '0', '0.23', '#interest', '#admiration', 'positive', '0.174', 'large_business', 'partnership', 'business_organization', 'business_group', 'company']
senticnet['business_group'] = ['0.08', '0.186', '0', '0.217', '#interest', '#admiration', 'positive', '0.161', 'business_firm', 'large_business', 'partnership', 'business_organization', 'work_group']
senticnet['business_interruption'] = ['-0.81', '0', '0.884', '-0.87', '#anger', '#disgust', 'negative', '-0.85', 'lose_war', 'beaten', 'cost_fortune', 'bad_grade', 'military_action']
senticnet['business_leader'] = ['0', '0', '-1.0', '-0.74', '#fear', '#disgust', 'negative', '-0.87', 'king', 'baron', 'magnate', 'mogul', 'big_businessman']
senticnet['business_magnate'] = ['0', '0', '-0.99', '-0.73', '#fear', '#disgust', 'negative', '-0.86', 'king', 'baron', 'business_leader', 'mogul', 'big_businessman']
senticnet['business_man'] = ['0', '0.105', '0', '0', '#interest', '#interest', 'positive', '0.105', 'boss', 'visitor', 'business_person', 'defendant', 'dolt']
senticnet['business_organization'] = ['0.093', '0.174', '0', '0.2', '#interest', '#admiration', 'positive', '0.156', 'large_business', 'partnership', 'business_firm', 'company', 'business_group']
senticnet['business_person'] = ['-0.06', '0.148', '0.08', '0.111', '#interest', '#admiration', 'positive', '0.03', 'busy', 'a_lot_of_work', 'business_man', 'business', 'undertake']
senticnet['business_plan'] = ['0', '0.225', '-0.05', '0.106', '#interest', '#admiration', 'positive', '0.093', 'money_make', 'business', 'work_hard', 'produce_goods', 'workplace']
senticnet['business_report'] = ['0', '0.164', '0', '0.058', '#interest', '#admiration', 'positive', '0.111', 'restaurant_bill', 'bill', 'high_cost', 'debit', 'cost']
senticnet['business_transaction'] = ['-0.06', '0.282', '-0.11', '0.292', '#interest', '#admiration', 'positive', '0.099', 'transaction', 'obtain', 'financial_transaction', 'acquire', 'good_price']
senticnet['business_tycoon'] = ['0', '0', '-0.99', '-0.73', '#fear', '#disgust', 'negative', '-0.86', 'king', 'baron', 'business_leader', 'mogul', 'big_businessman']
senticnet['businesslike'] = ['0', '0.714', '0', '0.896', '#interest', '#admiration', 'positive', '0.805', 'business-like', 'matter-of-fact', 'tactful', 'non-confrontational', 'gentlemanly']
senticnet['businessman'] = ['0', '0.123', '-0.07', '0.097', '#interest', '#admiration', 'positive', '0.05', 'capitalist', 'make_rich', 'buyer', 'consumer', 'broker']
senticnet['busker'] = ['0.54', '-0.64', '0', '0.566', '#surprise', '#admiration', 'positive', '0.582', 'pleaser', 'bombshell', 'mortal', 'entertainer', 'drawing_card']
senticnet['buspirone'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['bust'] = ['-0.57', '0', '0.852', '-0.74', '#anger', '#disgust', 'negative', '-0.72', 'tease', 'rip_up', 'tear', 'rupture', 'snap']
senticnet['bustard'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'amoeba', 'single_celled']
senticnet['buster'] = ['0.757', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.831', 'baseball_bat', 'water_gun', 'goalie', 'hockey_puck', 'hockey_stick']
senticnet['bustier'] = ['0.923', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.939', 'under_garment', 'undergarment', 'underwear', 'woman_clothes', 'lady_underwear']
senticnet['bustle'] = ['-0.94', '0', '0.492', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'make_noise', 'disturb', 'confusion', 'hustle', 'hustle_and_bustle']
senticnet['bustling'] = ['-0.99', '0', '-0.73', '0', '#sadness', '#fear', 'negative', '-0.86', 'metropolis', 'vibrant', 'glamorous', 'cosmopolitan', 'nightlife']
senticnet['busty'] = ['0', '0', '-0.82', '-0.79', '#fear', '#disgust', 'negative', '-0.81', 'bosomy', 'curvy', 'buxom', 'curvaceous', 'full-bosomed']
senticnet['busty_blonde'] = ['0.54', '-0.64', '0', '0.566', '#surprise', '#admiration', 'positive', '0.582', 'pleaser', 'entertainer', 'mortal', 'busker', 'drawing_card']
senticnet['busy'] = ['-0.77', '0', '0.761', '-0.86', '#sadness', '#disgust', 'negative', '-0.79', 'business_firm', 'come_together', 'business_organization', 'large_business', 'large_group']
senticnet['busy_bee'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['busy_commuter'] = ['0.623', '-0.56', '0', '0.974', '#joy', '#admiration', 'positive', '0.721', 'alterable', 'convertible', 'translatable', 'transformable', 'transmutable']
senticnet['busy_intersection'] = ['0', '0', '0', '-0.09', '#disgust', '#disgust', 'negative', '-0.09', 'subway', 'bus', 'train_station', 'railway_station', 'bus_stop']
senticnet['busy_person'] = ['-0.04', '-0.02', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.02', 'use_illegal_drug', 'delusional', 'human', 'cold_person', 'carpenter']
senticnet['busy_schedule'] = ['0', '0', '0', '-0.76', '#disgust', '#disgust', 'negative', '-0.76', 'fevered', 'feverish', 'feverishly', 'buck_fever', 'expectantly']
senticnet['busy_signal'] = ['-0.07', '-0.02', '0.034', '0', '#sadness', '#anger', 'negative', '-0.02', 'call_back', 'busy', 'cut_off', 'engaged', 'hang_up']
senticnet['busy_street'] = ['0.035', '0.065', '0.025', '0.127', '#interest', '#admiration', 'positive', '0.051', 'city_street', 'departmental_highway', 'center', 'central', 'main']
senticnet['busy_thoroughfare'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'artery', 'skeletal_system', 'groin', 'neck', 'muscle']
senticnet['busybodied'] = ['-0.99', '0', '0.846', '-0.90', '#sadness', '#disgust', 'negative', '-0.91', 'interfering', 'meddlesome', 'meddling', 'intrusive', 'officious']
senticnet['busybody'] = ['-0.64', '0', '0.71', '-0.95', '#anger', '#disgust', 'negative', '-0.76', 'buttinsky', 'unwelcome_person', 'killjoy', 'backseat_driver', 'meddler']
senticnet['busyness'] = ['0.681', '-0.93', '0', '0.78', '#surprise', '#admiration', 'positive', '0.799', 'behavior', 'state', 'play', 'agency', 'activeness']
senticnet['busywork'] = ['0', '0.082', '0', '0.077', '#interest', '#admiration', 'positive', '0.08', 'drink_tea', 'absorb_moisture', 'sit_on_beach', 'procrastination', 'wave_goodbye']
senticnet['butane'] = ['0', '0', '-0.08', '0', '#fear', '#fear', 'negative', '-0.08', 'petrol', 'diesel', 'kerosene', 'fuel', 'gasoline']
senticnet['butch_cassidy_wild_bunch'] = ['0', '0', '-0.11', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'hunting_season', 'hound', 'cougar', 'hunter', 'dragon']
senticnet['butch_femme'] = ['-0.97', '0', '0.971', '-0.84', '#sadness', '#anger', 'negative', '-0.93', 'fail', 'betray', 'disappoint', 'cross', 'disillusion']
senticnet['butch_harmon'] = ['0.758', '0.719', '0', '0', '#joy', '#interest', 'positive', '0.738', 'plan_party', 'dance_music', 'become_adult', 'stay_fit', 'drive_crazy']
senticnet['butcher'] = ['-0.77', '0', '0.503', '-0.93', '#sadness', '#disgust', 'negative', '-0.73', 'incompetent', 'fuckup', 'slouch', 'bungler', 'blunderer']
senticnet['butcher_block'] = ['0', '0.171', '-0.05', '0.102', '#interest', '#admiration', 'positive', '0.072', 'bed', 'mattress', 'bead', 'bed_cover', 'seduce']
senticnet['butcher_boy'] = ['-0.53', '0', '0.508', '-0.74', '#sadness', '#disgust', 'negative', '-0.59', 'bedwetter', 'bed_wetter', 'mortal', 'wetter', 'person']
senticnet['butcher_knife'] = ['-0.71', '0', '0.954', '-0.56', '#sadness', '#anger', 'negative', '-0.74', 'jackknife', 'pocket_knife', 'penknife', 'switchblade', 'switchblade_knife']
senticnet['butcher_meat'] = ['0.902', '-0.72', '0', '0.919', '#joy', '#admiration', 'positive', '0.849', 'preciousness', 'costliness', 'expensiveness', 'cost', 'lavishness']
senticnet['butcher_paper'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'gift_wrap', 'kraft', 'kraft_paper', 'wrapping_paper', 'paper']
senticnet['butcher_shop'] = ['0', '0', '0.044', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'large_supermarket', 'food_store', 'large_food_store', 'corner_shop', 'convenience_food_store']
senticnet['butcherbird'] = ['-0.84', '0', '0.655', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'piping_crow', 'australian_magpie', 'currawong', 'bell_magpie', 'oscine']
senticnet['butcherly'] = ['0', '0', '-0.92', '-0.94', '#fear', '#disgust', 'negative', '-0.93', 'bloodthirsty', 'slaughterous', 'gory', 'sanguinary', 'sanguineous']
senticnet['butchers_shop'] = ['-0.07', '0.228', '0.019', '0.089', '#interest', '#admiration', 'positive', '0.056', 'home_depot', 'lumberyard', 'furniture_store', 'front_porch', 'carpet_store']
senticnet['butchery'] = ['-0.67', '0', '0.938', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'massacre', 'mass_murder', 'carnage', 'slaughter', 'bloodbath']
senticnet['buteo'] = ['-0.74', '0', '0.963', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['buthidae_family'] = ['0', '0', '-0.87', '0', '#fear', '#fear', 'negative', '-0.87', 'beetle', 'many_insects', 'grasshopper', 'firefly', 'mouse']
senticnet['butler'] = ['-0.69', '0', '0.89', '-0.70', '#anger', '#disgust', 'negative', '-0.76', 'still', 'suppress', 'moderate', 'hush_up', 'quell']
senticnet['butler_pantry'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'kitchen_cabinet', 'kitchen_cupboard', 'dishwasher', 'cupboard', 'own_kitchen']
senticnet['butlerian_jihad'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['butlins_holiday_camp'] = ['-0.72', '0', '0.978', '-0.86', '#anger', '#disgust', 'negative', '-0.85', 'amusement_park', 'common', 'pleasure_ground', 'village_green', 'tract']
senticnet['butorphanol'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['butt'] = ['-0.05', '-0.00', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.04', 'ass', 'rut', 'foreign_country', 'dog_show', 'asshole']
senticnet['butt_against'] = ['-0.74', '0', '0.627', '-0.58', '#sadness', '#anger', 'negative', '-0.65', 'touch', 'contact', 'meet', 'neighbor', 'adjoin']
senticnet['butt_rot'] = ['-0.60', '0', '0.541', '-0.52', '#sadness', '#anger', 'negative', '-0.55', 'swoon', 'pass_out', 'faint', 'zonk_out', 'change_state']
senticnet['butte_county'] = ['-0.06', '0.21', '0', '0.076', '#interest', '#admiration', 'positive', '0.074', 'new_think', 'figure_out', 'brain_activity', 'idea', 'come_up_idea']
senticnet['butte_silver'] = ['0.937', '0.847', '0', '0', '#joy', '#interest', 'positive', '0.892', 'arrow', 'spear', 'buy_wood', 'baton', 'archery']
senticnet['butter'] = ['0.041', '-0.07', '0.034', '0', '#joy', '#surprise', 'positive', '0.026', 'bagel', 'mayonaise', 'pie', 'noodle', 'muffin']
senticnet['butter_bean'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'cabbage', 'lettuce', 'broccoli']
senticnet['butter_bread'] = ['-0.14', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.11', 'fill_bottle', 'bread', 'butter_toast', 'feel_heat', 'quiet_child']
senticnet['butter_cookie'] = ['0.731', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.837', 'make_bread', 'cook_dinner', 'cook_meal', 'cook_curry', 'follow_recipe']
senticnet['butter_cream'] = ['0.731', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.837', 'make_bread', 'cook_dinner', 'cook_meal', 'cook_curry', 'follow_recipe']
senticnet['butter_factory'] = ['0.053', '0.085', '0.019', '0', '#joy', '#interest', 'positive', '0.04', 'farm', 'countryside', 'farm_animal', 'cow', 'healthy']
senticnet['butter_knife'] = ['0.035', '0.065', '0.025', '0.127', '#interest', '#admiration', 'positive', '0.051', 'butter', 'kitchen_tool', 'breakfast', 'butter_toast', 'butter_bread']
senticnet['butter_puff_pastry'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'saltine', 'soda_cracker', 'cracker', 'water_biscuit', 'graham_cracker']
senticnet['butter_sauce'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'mayonnaise', 'box_cereal', 'bottle_water']
senticnet['butter_toast'] = ['0.71', '0.754', '0', '0', '#joy', '#interest', 'positive', '0.732', 'feel_grateful', 'taste_food', 'eat_animal', 'yolo', 'live_apartment']
senticnet['buttercream'] = ['0.059', '-0.02', '0.025', '0.109', '#joy', '#admiration', 'positive', '0.043', 'tasty', 'bake_treat', 'bake_goods', 'bake_good', 'loaf']
senticnet['buttercup'] = ['-0.80', '0', '0.632', '-0.71', '#sadness', '#disgust', 'negative', '-0.71', 'herb', 'meadow_buttercup', 'tall_buttercup', 'tall_crowfoot', 'common_buttercup']
senticnet['buttercup_family'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'clematis', 'legume', 'leguminous_plant', 'climber', 'root_climber']
senticnet['buttered_popcorn'] = ['-0.74', '0', '0.872', '-0.61', '#sadness', '#anger', 'negative', '-0.74', 'hurting', 'aching', 'pain', 'headache', 'toothache']
senticnet['butterfat'] = ['0.057', '0', '0.018', '0.058', '#joy', '#admiration', 'positive', '0.032', 'porridge', 'food', 'milk', 'healthy', 'morning_food']
senticnet['butterfingered'] = ['-0.61', '0', '0.523', '-0.98', '#sadness', '#disgust', 'negative', '-0.70', 'bungling', 'maladroit', 'bumbling', 'inept', 'handless']
senticnet['butterfingers'] = ['-0.71', '0', '0.845', '-0.69', '#sadness', '#anger', 'negative', '-0.75', 'lump', 'goon', 'mortal', 'duffer', 'clumsy_person']
senticnet['butterfish'] = ['0.709', '0.964', '0', '0', '#joy', '#interest', 'positive', '0.837', 'fish_trout', 'catch_butterfly', 'fish_lake', 'fish_compliment', 'fish_stream']
senticnet['butterflower'] = ['-0.67', '0', '0.515', '-0.65', '#sadness', '#disgust', 'negative', '-0.61', 'herb', 'meadow_buttercup', 'tall_buttercup', 'tall_crowfoot', 'common_buttercup']
senticnet['butterfly'] = ['0', '0', '-0.80', '-0.82', '#fear', '#disgust', 'negative', '-0.81', 'wasp', 'moth', 'parrot', 'beetle', 'falcon']
senticnet['butterfly-like'] = ['0', '-0.86', '0', '0.794', '#surprise', '#admiration', 'positive', '0.829', 'diamond-shaped', 'egg-shaped', 'ovular', 'block-shaped', 'saw-tooth']
senticnet['butterfly_collection'] = ['0.673', '-0.66', '0', '0.921', '#joy', '#admiration', 'positive', '0.753', 'coin', 'collection', 'coin_collection', 'insect_collection', 'album_collection']
senticnet['butterfly_nebula'] = ['-0.64', '0', '0.524', '-0.59', '#sadness', '#disgust', 'negative', '-0.58', 'bum', 'dreamer', 'daydreamer', 'escapist', 'wishful_thinker']
senticnet['butterflyfish'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'puffer', 'pufferfish', 'blowfish', 'triggerfish', 'plectognath']
senticnet['butterhead'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'cabbage', 'broccoli', 'pea']
senticnet['buttermilk'] = ['-0.08', '0.106', '0', '0.125', '#interest', '#admiration', 'positive', '0.047', 'sugar', 'treacle', 'black_treacle', 'add_sugar', 'sugar_substitute']
senticnet['butternut_squash'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'squash', 'acorn_squash', 'hubbard_squash', 'turban_squash', 'winter_squash']
senticnet['butternut_squash_soup'] = ['0.783', '-0.63', '0', '0.829', '#joy', '#admiration', 'positive', '0.749', 'leg_of_lamb', 'gigot', 'joint', 'roast', 'rack_of_lamb']
senticnet['butternut_winter_squash'] = ['-0.56', '0', '0.693', '-0.96', '#anger', '#disgust', 'negative', '-0.74', 'winter_squash', 'acorn_winter_squash', 'hubbard_winter_squash', 'turban_winter_squash', 'squash']
senticnet['butterscotch'] = ['-0.71', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.74', 'toffee', 'caramel', 'pecan', 'fudge', 'praline']
senticnet['buttery'] = ['0', '0', '-0.96', '-0.87', '#fear', '#disgust', 'negative', '-0.91', 'pantry', 'larder', 'fulsome', 'oily', 'oleaginous']
senticnet['buttery_flavor'] = ['0.054', '0.068', '0', '0.074', '#interest', '#admiration', 'positive', '0.065', 'eat_food', 'sweetness', 'food_flavor', 'eatable', 'tasty']
senticnet['buttinsky'] = ['-0.64', '0', '0.71', '-0.95', '#anger', '#disgust', 'negative', '-0.76', 'meddler', 'unwelcome_person', 'killjoy', 'backseat_driver', 'busybody']
senticnet['buttock'] = ['-0.04', '0.226', '-0.20', '0.209', '#interest', '#admiration', 'positive', '0.045', 'rear', 'elongation', 'go_backwards', 'rear_end', 'hind']
senticnet['button'] = ['0.743', '0.929', '0', '0', '#joy', '#interest', 'positive', '0.836', 'clit', 'clitoris', 'release', 'push', 'push_button']
senticnet['button-down'] = ['0', '0', '-0.90', '-0.80', '#fear', '#disgust', 'negative', '-0.85', 'double-breasted', 'button-front', 'high-necked', 'collarless', 'long-sleeved']
senticnet['button-front'] = ['0', '0', '-0.90', '-0.8', '#fear', '#disgust', 'negative', '-0.85', 'double-breasted', 'collarless', 'high-necked', 'button-down', 'long-sleeved']
senticnet['button_widget'] = ['0.777', '-0.85', '0', '0.615', '#joy', '#surprise', 'positive', '0.748', 'think', 'play', 'trifle', 'abstract', 'warm_to']
senticnet['buttonbush'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'cattail', 'nailrod', 'reed_mace', 'reedmace']
senticnet['buttoned-down'] = ['0', '0.878', '0', '0.737', '#interest', '#admiration', 'positive', '0.807', 'buttoned-up', 'straight-laced', 'staid', 'starched', 'stodgy']
senticnet['buttoned-up'] = ['0', '0.885', '0', '0.743', '#interest', '#admiration', 'positive', '0.814', 'buttoned-down', 'straight-laced', 'staid', 'starched', 'stodgy']
senticnet['buttonhole'] = ['0.857', '-0.56', '0', '0.721', '#joy', '#admiration', 'positive', '0.716', 'ask_for', 'lobby', 'quest', 'canvass', 'solicit']
senticnet['buttonhole_stitch'] = ['-0.06', '-0.01', '0.05', '-0.07', '#sadness', '#disgust', 'negative', '-0.04', 'embroidery', 'woven', 'cover_wind', 'torn', 'use_needle']
senticnet['buttony'] = ['0.613', '-0.53', '0', '0.932', '#joy', '#admiration', 'positive', '0.692', 'adorned', 'ruffled', 'fringed', 'tessellated', 'mounted']
senticnet['buttress'] = ['0.846', '-0.94', '0', '0.797', '#joy', '#surprise', 'positive', '0.861', 'strengthen', 'reenforce', 'support', 'line', 'bolster']
senticnet['buttstock'] = ['0', '0', '0.954', '-0.74', '#anger', '#disgust', 'negative', '-0.85', 'gun', 'pistol', 'revolver', 'firearm', 'handgun']
senticnet['butylone'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'infective_agent', 'satisfier', 'quickener', 'agent']
senticnet['butyraldehyde'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'carboxylic_acid', 'succinic_acid', 'valeric_acid']
senticnet['buxom'] = ['0.636', '-0.95', '0', '0.664', '#surprise', '#admiration', 'positive', '0.751', 'voluptuous', 'statuesque', 'sculptured', 'shapely', 'sculptural']
senticnet['buy'] = ['0.06', '-0.12', '0', '0', '#joy', '#surprise', 'positive', '0.093', 'car', 'buy_car_motorbike', 'buy_car', 'purchase_car', 'obtain']
senticnet['buy_airline_ticket'] = ['0.741', '0.898', '0', '0.734', '#joy', '#interest', 'positive', '0.791', 'plan_trip', 'desire_social_interaction', 'see_different', 'proselytize', 'improve_knowledge']
senticnet['buy_airplane_ticket'] = ['-0.06', '0', '0.011', '-0.02', '#sadness', '#disgust', 'negative', '-0.03', 'buy_plane_ticket', 'talk_to_travel_agent', 'travel', 'plan_time', 'decide_to_go']
senticnet['buy_alcohol'] = ['0.06', '-0.12', '0', '0', '#joy', '#surprise', 'positive', '0.093', 'drink_a_lot_of_booze', 'drink_excess', 'consume_alcohol', 'drink_a_lot_of_alcohol', 'open_bottle']
senticnet['buy_ball'] = ['0.06', '-0.12', '0', '0', '#joy', '#surprise', 'positive', '0.093', 'buy_game', 'play_game', 'enjoy_game', 'fun', 'have_fun']
senticnet['buy_bed'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'home_renovation', 'buy_pillow', 'buy_home_appliance', 'buy_bedroom_sheet', 'buy_bedroom']
senticnet['buy_bed_sheet'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'home_renovation', 'buy_pillow', 'buy_home_appliance', 'buy_bedroom', 'buy_bed']
senticnet['buy_bedroom'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'home_renovation', 'buy_pillow', 'buy_home_appliance', 'buy_bed_sheet', 'buy_bed']
senticnet['buy_bedroom_sheet'] = ['0.28', '0.199', '-0.08', '0.181', '#joy', '#interest', 'positive', '0.145', 'home_renovation', 'buy_pillow', 'buy_home_appliance', 'buy_bed', 'buy_bedroom']
senticnet['buy_book'] = ['0.06', '-0.12', '0', '0', '#joy', '#surprise', 'positive', '0.093', 'open_book', 'go_bookshop', 'visit_library', 'book', 'read_book']
senticnet['buy_bottled_water'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'drinking_water', 'sugar_water']
senticnet['buy_bread'] = ['0.06', '-0.12', '0', '0', '#joy', '#surprise', 'positive', '0.093', 'buy_cheese', 'eat', 'go_supermarket', 'buy_pasta', 'buy_goods']
senticnet['buy_camera'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'watch_tv', 'buy_happiness', 'buy', 'turn_on_tv', 'buy_tv']
senticnet['buy_candy'] = ['0.896', '0', '0', '0', '#joy', '#joy', 'positive', '0.896', 'walk_exercise', 'pay_dinner', 'subscribe_magazine', 'gain_interest', 'buy_soda']
senticnet['buy_car'] = ['0.06', '-0.12', '0', '0', '#joy', '#surprise', 'positive', '0.093', 'car', 'buy_motorbike', 'buy', 'purchase_car', 'buy_object']
senticnet['buy_car_motorbike'] = ['0.058', '-0.12', '0', '0', '#joy', '#surprise', 'positive', '0.093', 'car', 'buy', 'buy_car', 'purchase_car', 'obtain']
senticnet['buy_cartoon_character'] = ['-0.07', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.07', 'cartoon_character', 'use_tool', 'mould', 'use_oil_cartoon_character', 'paint']
senticnet['buy_cheaper'] = ['0.799', '-0.92', '0', '0.896', '#surprise', '#admiration', 'positive', '0.874', 'save_money', 'good_customer', 'better_price', 'sale', 'on_sale']
senticnet['buy_cheese'] = ['0', '0.71', '0', '0.799', '#interest', '#admiration', 'positive', '0.754', 'buy_grocery', 'do_shopping', 'go_supermarket', 'buy_food', 'buy_ingredient']
senticnet['buy_chip'] = ['-0.85', '0', '0.814', '-0.91', '#sadness', '#disgust', 'negative', '-0.86', 'lose_money', 'shuffle_card', 'go_broke', 'lose', 'go_debt']
senticnet['buy_chips'] = ['0', '0.71', '0', '0.799', '#interest', '#admiration', 'positive', '0.754', 'buy_grocery', 'do_shopping', 'go_supermarket', 'buy_food', 'buy_ingredient']
senticnet['buy_christmas_present'] = ['0.796', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.881', 'buy_present', 'christmas_shop', 'holiday_shop', 'christmas_present', 'buy_product']
senticnet['buy_clothes'] = ['-0.04', '0', '0.03', '0', '#sadness', '#anger', 'negative', '-0.03', 'shopping', 'spend_money', 'get_grocery', 'buy_goods', 'buy_stuff']
senticnet['buy_coffee'] = ['-0.06', '-0.01', '0.027', '-0.06', '#sadness', '#disgust', 'negative', '-0.03', 'donate_charity', 'impulse_shop', 'spend_money', 'window_shop', 'use_atm']
senticnet['buy_computer'] = ['0.89', '-0.61', '0', '0.757', '#joy', '#admiration', 'positive', '0.754', 'turn_computer_on', 'find_computer', 'find_address', 'go_online', 'internet_connection']
senticnet['buy_concert_ticket'] = ['0.938', '-0.74', '0', '0', '#joy', '#surprise', 'positive', '0.842', 'purchase_ticket', 'attend_concert', 'pay_ticket', 'go_rock_concert', 'buy_ticket_theater']
senticnet['buy_condom'] = ['0', '0.143', '-0.05', '0.073', '#interest', '#admiration', 'positive', '0.055', 'spend_much_money', 'spend_a_lot_of_money', 'buy_dinner', 'make_payment', 'drive_home']
senticnet['buy_credit'] = ['-0.04', '0.232', '-0.11', '0.183', '#interest', '#admiration', 'positive', '0.064', 'postpone_payment', 'bill_pay', 'spend', 'cashless', 'owe_money']
senticnet['buy_diamond_ring'] = ['0.984', '0.71', '0', '0', '#joy', '#interest', 'positive', '0.847', 'present', 'go_shopping', 'drive_to_store', 'go_department_store', 'expensive_gift']
senticnet['buy_dinner'] = ['0', '0', '0', '0.713', '#admiration', '#admiration', 'positive', '0.713', 'invite_person', 'invite_friend_over', 'invite_guest', 'spend_much_money', 'make_payment']
senticnet['buy_drink'] = ['0', '0.709', '0', '0', '#interest', '#interest', 'positive', '0.709', 'serve_beer', 'drink_beer', 'invite_friend', 'pay_tab', 'order_beer']
senticnet['buy_flower'] = ['0.871', '0.987', '0', '0', '#joy', '#interest', 'positive', '0.929', 'buy_condom', 'drive_home', 'greet', 'wear_protection', 'love_back']
senticnet['buy_food'] = ['0.17', '-0.01', '0', '0.184', '#joy', '#admiration', 'positive', '0.121', 'go_grocery_shop', 'buy_grocery', 'stock_merchandise', 'buy_fresh_fruit_vegetable', 'buy_vegetable']
senticnet['buy_fresh_fruit_vegetable'] = ['0', '0.886', '0', '0', '#interest', '#interest', 'positive', '0.886', 'go_grocery_shop', 'buy_grocery', 'stock_merchandise', 'buy_food', 'buy_vegetable']
senticnet['buy_fruit'] = ['0.02', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'many_fruits', 'peach', 'cherry', 'pineapple', 'tomato']
senticnet['buy_furniture'] = ['-0.10', '0.235', '-0.12', '0.246', '#interest', '#admiration', 'positive', '0.063', 'love', 'settle_down', 'build_house', 'family_nest', 'make_home']
senticnet['buy_game'] = ['0.06', '-0.12', '0', '0', '#joy', '#surprise', 'positive', '0.093', 'buy_ball', 'play_game', 'enjoy_game', 'fun', 'have_fun']
senticnet['buy_gas'] = ['0.968', '0.715', '0', '0', '#joy', '#interest', 'positive', '0.841', 'collect_debt', 'buy_tool', 'negotiate_contract', 'make_work', 'increase_net_worth']
senticnet['buy_gift'] = ['-0.08', '0', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.06', 'spend_money', 'compare_price', 'comparison_shop', 'wrap_gift', 'give_present']
senticnet['buy_goods'] = ['0.841', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.892', 'buy_stuff', 'buy_gum', 'shop', 'purchase', 'acquire_goods']
senticnet['buy_goods_service'] = ['0.985', '0.941', '0', '0', '#joy', '#interest', 'positive', '0.963', 'go_cashless', 'pay_goods', 'incur_debt', 'postpone_payment', 'acquire_goods']
senticnet['buy_grocery'] = ['0.76', '0.782', '0', '0', '#joy', '#interest', 'positive', '0.771', 'purchase_grocery', 'buy_vegetable', 'shop_food', 'purchase_food', 'stock_merchandise']
senticnet['buy_guitar'] = ['0.768', '0', '0', '0', '#joy', '#joy', 'positive', '0.768', 'music_store', 'music_lover', 'music', 'buy_music_album', 'music_shop']
senticnet['buy_gum'] = ['0', '0.095', '-0.05', '0.132', '#interest', '#admiration', 'positive', '0.058', 'purchase_goods', 'buy', 'shop', 'fresh_breathe', 'spread_love']
senticnet['buy_gun'] = ['-0.09', '-0.00', '0', '-0.22', '#sadness', '#disgust', 'negative', '-0.10', 'buy_weapon', 'plan_attack', 'grenade', 'weapon', 'join_military']
senticnet['buy_happiness'] = ['0', '0.095', '-0.05', '0.132', '#interest', '#admiration', 'positive', '0.058', 'buy_new_car', 'buy_ice_cream', 'happiness', 'book_flight', 'book_holiday']
senticnet['buy_home'] = ['0.601', '0.647', '-0.26', '0.322', '#joy', '#interest', 'positive', '0.326', 'family_nest', 'love_back', 'protection', 'shelter', 'purchase_house']
senticnet['buy_home_appliance'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'home_renovation', 'buy_pillow', 'buy_bedroom', 'buy_bed_sheet', 'buy_bed']
senticnet['buy_ice_cream'] = ['0.768', '0', '0', '0', '#joy', '#joy', 'positive', '0.768', 'buy_new_car', 'buy_happiness', 'happiness', 'book_flight', 'book_holiday']
senticnet['buy_ingredient'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'buy_grocery', 'do_shopping', 'go_supermarket', 'buy_food', 'buy_cheese']
senticnet['buy_land'] = ['0.971', '0.984', '0', '0', '#joy', '#interest', 'positive', '0.978', 'pay_money', 'establish_credit', 'compare_price', 'bankruptcy', 'cash_check']
senticnet['buy_lottery_ticket'] = ['-0.08', '0.179', '-0.06', '0.096', '#interest', '#admiration', 'positive', '0.034', 'gamble', 'trying_luck', 'spend_money', 'hope', 'finger_crossed']
senticnet['buy_love'] = ['0', '0', '0', '-0.71', '#disgust', '#disgust', 'negative', '-0.71', 'buy_present_for_others', 'give_gift', 'buy_christmas_present', 'buy_present', 'give_flower']
senticnet['buy_lunch'] = ['-0.08', '0.179', '-0.06', '0.096', '#interest', '#admiration', 'positive', '0.034', 'purchase_food', 'buy_food', 'provide_family', 'get_food', 'buy_grocery']
senticnet['buy_magazine'] = ['-0.07', '0', '0.009', '-0.12', '#sadness', '#disgust', 'negative', '-0.07', 'sell_book', 'check_out', 'learn_read', 'visit_library', 'buy_book']
senticnet['buy_market'] = ['-0.07', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.07', 'market', 'refrigerate', 'hammer_nail', 'put_napkin_lap', 'defrost']
senticnet['buy_meat'] = ['0.747', '0.783', '0', '0', '#joy', '#interest', 'positive', '0.765', 'supermarket', 'buy_grocery', 'compare_price', 'purchase_grocery', 'make_dinner']
senticnet['buy_merrill_lynch'] = ['-0.04', '0.274', '-0.15', '0.278', '#interest', '#admiration', 'positive', '0.089', 'business_deal', 'trade', 'exchange', 'bargain', 'offer']
senticnet['buy_milk'] = ['0.85', '0.947', '0', '0', '#joy', '#interest', 'positive', '0.898', 'buy_grocery', 'buy_bread', 'do_shopping', 'buy_pizza', 'buy_pasta']
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senticnet['buy_weapon'] = ['-0.16', '0', '-0.11', '-0.20', '#sadness', '#disgust', 'negative', '-0.16', 'buy_gun', 'join_military', 'arm', 'declare_war', 'find_enemy']
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senticnet['by_user_tobacman'] = ['0.81', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.774', 'available', 'public', 'population', 'crowd', 'majority']
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senticnet['cab_ride'] = ['-0.08', '0.179', '-0.06', '0.096', '#interest', '#admiration', 'positive', '0.034', 'ride_subway', 'buy_vending_machine', 'travel', 'buy_lottery_ticket', 'acquire']
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senticnet['cabaret_singer'] = ['0.882', '0.901', '0', '0', '#joy', '#interest', 'positive', '0.891', 'songwriter', 'vip', 'rock_group', 'guitar_player', 'violinist']
senticnet['cabaret_venue'] = ['0.763', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.75', 'pub', 'disco', 'dance_club', 'cafe', 'tavern']
senticnet['cabbage'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'broccoli', 'lettuce', 'pea']
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senticnet['cabdriver'] = ['-0.80', '0', '0.718', '-0.79', '#sadness', '#disgust', 'negative', '-0.77', 'walk_exercise', 'lose_wallet', 'go_debt', 'pay_dinner', 'spend_much_money']
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senticnet['cabin'] = ['0', '0', '0.012', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'shack', 'college_dorm', 'hostel', 'dormitory', 'student_home']
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senticnet['cabin_baggage'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'hand_baggage', 'baggage', 'passenger_ticket', 'ticket_counter', 'baggage_trunk']
senticnet['cabin_cruiser'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'boat', 'yacht', 'big_boat', 'large_boat']
senticnet['cabin_door'] = ['0', '0', '0', '0.077', '#admiration', '#admiration', 'positive', '0.077', 'glass_front', 'gate', 'doorway', 'use_farm', 'entranceway']
senticnet['cabin_luggage'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'hand_baggage', 'baggage', 'passenger_ticket', 'ticket_counter', 'baggage_trunk']
senticnet['cabinet_maker'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'pharmaceutical_chemist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['cabinet_minister'] = ['0', '0.868', '0', '0.853', '#interest', '#admiration', 'positive', '0.861', 'country_leader', 'prime_minister', 'president', 'elect_official', 'leader']
senticnet['cabinetmaker'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'maker']
senticnet['cable'] = ['0.986', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.907', 'cable_television', 'wire', 'telegraph', 'cable_television_service', 'transmission_line']
senticnet['cable_car'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'mini_van', 'baggage_compartment', 'subway', 'amusement_park', 'terminal']
senticnet['cable_channel_fx'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'kit_fox', 'prairie_fox', 'white_fox']
senticnet['cable_knit'] = ['-0.54', '0', '0.612', '-0.98', '#anger', '#disgust', 'negative', '-0.71', 'scuff', 'mule', 'bootee', 'carpet_slipper', 'footwear']
senticnet['cable_operator'] = ['0.687', '-0.50', '0', '0.8', '#joy', '#admiration', 'positive', '0.663', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['cable_television'] = ['0.983', '0.822', '0', '0', '#joy', '#interest', 'positive', '0.902', 'cable', 'wire', 'telegraph', 'cable_television_service', 'transmission_line']
senticnet['cable_television_channel'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'theater_seat', 'theater_box', 'sticky_floor', 'stage_entrance', 'balcony']
senticnet['cable_television_operator'] = ['0.687', '-0.50', '0', '0.8', '#joy', '#admiration', 'positive', '0.663', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['cable_television_service'] = ['0.983', '0.822', '0', '0', '#joy', '#interest', 'positive', '0.902', 'cable_television', 'wire', 'telegraph', 'cable', 'transmission_line']
senticnet['cable_tv'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'theater_seat', 'theater_box', 'sticky_floor', 'stage_entrance', 'balcony']
senticnet['cable_tv_channel'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'theater_seat', 'theater_box', 'sticky_floor', 'stage_entrance', 'balcony']
senticnet['cable_tv_network'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'theater_seat', 'theater_box', 'sticky_floor', 'stage_entrance', 'balcony']
senticnet['cabman'] = ['0', '0', '-0.06', '-0.11', '#fear', '#disgust', 'negative', '-0.09', 'elderly_person', 'hide-a-bed', 'homosexual', 'linen_chest', 'dust_mite']
senticnet['cabochon'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'art', 'art_piece', 'museum', 'artist', 'jewel']
senticnet['cabochon_cut'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'art', 'art_piece', 'museum', 'artist', 'jewel']
senticnet['cabover'] = ['0.035', '0.112', '-0.07', '0.195', '#interest', '#admiration', 'positive', '0.067', 'car_action', 'car_travel', 'steer_car', 'make_go', 'transportation']
senticnet['cacac'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'wildcat', 'informal', 'loose', 'drumhead']
senticnet['cacao_theobroma_cacao'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'aridness', 'dryness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['cacapon_river'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['cacatua'] = ['-0.84', '0', '0.655', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'piping_crow', 'butcherbird', 'currawong', 'bell_magpie', 'oscine']
senticnet['cache'] = ['0.922', '0.945', '0', '0', '#joy', '#interest', 'positive', '0.933', 'lay_away', 'stash', 'squirrel_away', 'hive_up', 'memory_cache']
senticnet['cache_coherence'] = ['0.792', '-0.83', '0', '0.575', '#joy', '#surprise', 'positive', '0.734', 'connectedness', 'connection', 'consistency', 'continuity', 'link']
senticnet['cache_coherence_protocol'] = ['0.792', '-0.83', '0', '0.575', '#joy', '#surprise', 'positive', '0.734', 'connectedness', 'connection', 'consistency', 'continuity', 'link']
senticnet['cache_coherency'] = ['0.792', '-0.83', '0', '0.575', '#joy', '#surprise', 'positive', '0.734', 'connectedness', 'connection', 'consistency', 'continuity', 'link']
senticnet['cache_coherent'] = ['0.792', '-0.83', '0', '0.575', '#joy', '#surprise', 'positive', '0.734', 'connectedness', 'connection', 'consistency', 'continuity', 'link']
senticnet['cache_consistency'] = ['0.792', '-0.83', '0', '0.575', '#joy', '#surprise', 'positive', '0.734', 'connectedness', 'connection', 'consistency', 'continuity', 'link']
senticnet['cache_locality'] = ['0.792', '-0.83', '0', '0.575', '#joy', '#surprise', 'positive', '0.734', 'connectedness', 'connection', 'consistency', 'continuity', 'link']
senticnet['cachet'] = ['0.682', '-0.70', '0', '0.98', '#surprise', '#admiration', 'positive', '0.789', 'prize', 'seal_of_approval', 'honorable_mention', 'aliyah', 'laurel']
senticnet['cachexia'] = ['0.576', '-0.95', '0', '0.998', '#surprise', '#admiration', 'positive', '0.844', 'debility', 'cachexy', 'wasting', 'softness', 'unfitness']
senticnet['cachexy'] = ['0.576', '-0.95', '0', '0.998', '#surprise', '#admiration', 'positive', '0.844', 'cachexia', 'debility', 'wasting', 'softness', 'unfitness']
senticnet['cackle'] = ['-0.70', '0', '0.869', '0', '#sadness', '#anger', 'negative', '-0.78', 'hen', 'laying', 'cackly', 'cackler', 'gaggle']
senticnet['cackler'] = ['-0.70', '0', '0.861', '0', '#sadness', '#anger', 'negative', '-0.78', 'hen', 'laying', 'cackly', 'cackle', 'gaggle']
senticnet['cackly'] = ['-0.70', '0', '0.865', '0', '#sadness', '#anger', 'negative', '-0.78', 'hen', 'laying', 'cackle', 'cackler', 'gaggle']
senticnet['cacoethes'] = ['-0.89', '0', '0.987', '-0.90', '#anger', '#disgust', 'negative', '-0.93', 'logorrhea', 'logomania', 'agromania', 'pyromania', 'necrophilia']
senticnet['cacogenesis'] = ['-0.73', '0', '0.634', '-0.85', '#sadness', '#disgust', 'negative', '-0.74', 'infertility', 'impotence', 'impotency', 'barrenness', 'dysgenesis']
senticnet['cacography'] = ['-0.84', '0', '0.579', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'squiggle', 'hand', 'chicken_scratch', 'handwriting', 'script']
senticnet['cacophonic'] = ['-0.93', '0', '0.576', '-0.54', '#sadness', '#anger', 'negative', '-0.68', 'grating', 'rasping', 'raspy', 'jarring', 'gruff']
senticnet['cacophonous'] = ['-0.65', '0', '0.578', '-0.76', '#sadness', '#disgust', 'negative', '-0.66', 'grating', 'rasping', 'raspy', 'jarring', 'gruff']
senticnet['cacophony'] = ['-0.70', '0', '0.706', '0', '#sadness', '#anger', 'negative', '-0.70', 'dissonance', 'conflict', 'assonance', 'resolution', 'harsh']
senticnet['cactus_fruit'] = ['0.05', '0', '0.047', '0.068', '#joy', '#admiration', 'positive', '0.024', 'tree_fruit', 'yellow_fruit', 'small_fruit', 'vine_fruit', 'red_fruit']
senticnet['cad'] = ['-0.88', '0', '0.806', '-0.63', '#sadness', '#anger', 'negative', '-0.77', 'blackguard', 'scoundrel', 'bounder', 'rogue', 'hound']
senticnet['cadasil'] = ['-0.95', '0', '0.684', '-0.76', '#sadness', '#disgust', 'negative', '-0.79', 'innate', 'nurtural', 'inborn', 'nontransmissible', 'nonheritable']
senticnet['cadaver'] = ['0', '0', '-0.75', '-0.74', '#fear', '#disgust', 'negative', '-0.75', 'dead_person', 'corpse', 'in_person', 'human_chest', 'below_neck']
senticnet['cadaver_dog_picked_up'] = ['0.843', '0', '0', '0.971', '#joy', '#admiration', 'positive', '0.907', 'aroma', 'fragrance', 'smell', 'smell_good', 'odor']
senticnet['cadaverous'] = ['0', '0', '-0.90', '-0.86', '#fear', '#disgust', 'negative', '-0.88', 'bony', 'gaunt', 'emaciated', 'pinched', 'haggard']
senticnet['cadbury'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'wrapping_paper', 'wrap_gift', 'butcher_paper', 'paper']
senticnet['cadbury_chairman'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'wrapping_paper', 'wrap_gift', 'butcher_paper', 'paper']
senticnet['cadbury_chief_executive'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'wrapping_paper', 'wrap_gift', 'butcher_paper', 'paper']
senticnet['cadbury_chocolate'] = ['0.547', '-0.96', '0', '0.89', '#surprise', '#admiration', 'positive', '0.799', 'pudding', 'ice_cream', 'chocolate', 'chocolate_pudding', 'chocolate_dessert']
senticnet['cadbury_dairy_milk'] = ['0.547', '-0.96', '0', '0.89', '#surprise', '#admiration', 'positive', '0.799', 'pudding', 'ice_cream', 'chocolate', 'chocolate_pudding', 'chocolate_dessert']
senticnet['cadbury_plc'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'wrapping_paper', 'wrap_gift', 'butcher_paper', 'paper']
senticnet['caddesi'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['caddie'] = ['-0.71', '0', '0.845', '-0.69', '#sadness', '#anger', 'negative', '-0.75', 'lump', 'goon', 'mortal', 'clumsy_person', 'butterfingers']
senticnet['caddisfly_larva'] = ['-0.87', '0', '0.983', '-0.91', '#anger', '#disgust', 'negative', '-0.92', 'thorn', 'sticker', 'pricker', 'prickle', 'spikelet']
senticnet['caddish'] = ['0', '0', '0.733', '-0.98', '#anger', '#disgust', 'negative', '-0.85', 'fetishist', 'degenerate', 'lech', 'letch', 'pervert']
senticnet['caddy'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'sports_equipment', 'sport_stuff', 'golf', 'sport_store', 'bag']
senticnet['cadenced'] = ['-0.84', '0', '0.564', '-0.67', '#sadness', '#disgust', 'negative', '-0.69', 'lilting', 'cadent', 'metrical', 'metric', 'rhythmical']
senticnet['cadent'] = ['-0.84', '0', '0.574', '-0.66', '#sadness', '#disgust', 'negative', '-0.69', 'cadenced', 'rhythmical', 'metrical', 'metric', 'lilting']
senticnet['cadential'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['cadet'] = ['0.754', '0', '0', '0.864', '#joy', '#admiration', 'positive', '0.809', 'midshipman', 'lieutenant', 'lieutenant-colonel', 'navy', 'brigadier']
senticnet['cadet_branch'] = ['0.975', '-0.78', '0', '0.969', '#joy', '#admiration', 'positive', '0.911', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['cadge'] = ['0.866', '-0.91', '0', '0.893', '#surprise', '#admiration', 'positive', '0.892', 'mooch', 'freeload', 'grub', 'sponge', 'bum']
senticnet['cadillac'] = ['0.028', '0', '0.013', '0.061', '#joy', '#admiration', 'positive', '0.025', 'truck', 'vehicle', 'big_car', 'car', 'lorry']
senticnet['cadillac_dealership'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'sell_car', 'truck_stop', 'vehicle', 'insurance', 'car']
senticnet['cadillac_eldorado'] = ['-0.05', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'plane_land', 'floss', 'floss_tooth', 'ornament', 'wear_dress']
senticnet['cadillac_escalade'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'cable_car', 'baggage_compartment', 'subway', 'amusement_park', 'terminal']
senticnet['cadillacs_nightclub'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
senticnet['cadmium_cd'] = ['-0.03', '0', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.06', 'lithium', 'mineral', 'potassium', 'iodine', 'magnesium']
senticnet['cadoc'] = ['0', '0.248', '-0.08', '0.173', '#interest', '#admiration', 'positive', '0.113', 'god', 'minister', 'evangelist', 'rector', 'goddess']
senticnet['cadre'] = ['0.942', '-0.79', '0', '0.561', '#joy', '#surprise', 'positive', '0.767', 'sleeper_cell', 'terrorist_cell', 'radical_cell', 'political_unit', 'political_entity']
senticnet['cadwell'] = ['-0.69', '0', '0.692', '-0.66', '#sadness', '#anger', 'negative', '-0.68', 'wrinkle', 'crinkle', 'crease', 'crisp', 'ruck_up']
senticnet['caecum'] = ['0.04', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.055', 'kidney', 'pancreas', 'liver', 'bone', 'neck']
senticnet['caenogenesis'] = ['-0.59', '0', '0.983', '-0.67', '#anger', '#disgust', 'negative', '-0.75', 'growth', 'growing', 'maturation', 'development', 'ontogeny']
senticnet['caesalpinia'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'carnauba_palm', 'carnauba', 'wax_palm', 'palmetto', 'palmyra']
senticnet['caesar_cipher'] = ['-0.52', '0', '0.821', '-0.65', '#anger', '#disgust', 'negative', '-0.66', 'work_out', 'reckon', 'calculate', 'average_out', 'cypher']
senticnet['caesar_palace'] = ['0.057', '0.13', '0', '0.119', '#interest', '#admiration', 'positive', '0.102', 'friend_wedding', 'circular_route', 'spanish_restaurant', 'altar', 'monastery']
senticnet['caesar_salad'] = ['0.062', '0', '0.027', '0.055', '#joy', '#admiration', 'positive', '0.03', 'porridge', 'meal', 'broth', 'hamburger', 'hearty']
senticnet['caesarea_maritima'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['caesarean'] = ['-0.51', '0', '0.747', '-0.94', '#anger', '#disgust', 'negative', '-0.73', 'stillborn_infant', 'neonate', 'preterm_baby', 'premature_infant', 'preterm_infant']
senticnet['caesarean_section'] = ['-0.51', '0', '0.747', '-0.94', '#anger', '#disgust', 'negative', '-0.73', 'stillborn_infant', 'neonate', 'preterm_baby', 'premature_infant', 'preterm_infant']
senticnet['caesarian_section'] = ['0.706', '0', '0', '0', '#joy', '#joy', 'positive', '0.706', 'birth', 'new_baby', 'born', 'birthday', 'thanksgiving']
senticnet['caesium'] = ['-0.02', '0', '0.047', '-0.11', '#anger', '#disgust', 'negative', '-0.06', 'hold_trash', 'put_garbage', 'rubbish', 'put_trash', 'store_trash']
senticnet['caesium_hydroxide'] = ['0.766', '-0.87', '0', '0.666', '#joy', '#surprise', 'positive', '0.769', 'reactive', 'unstable', 'labile', 'activated', 'excited']
senticnet['caespitosa'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'carnauba_palm', 'carnauba', 'wax_palm', 'palmetto', 'palmyra']
senticnet['caf'] = ['0.841', '0', '0', '0.71', '#joy', '#admiration', 'positive', '0.775', 'waiter', 'bar_service', 'beverage_service', 'concierge', 'minibar']
senticnet['caf_bar'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'buy_sandwich', 'lunch', 'eat_food', 'buy_food', 'finish_job']
senticnet['cafe'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'buy_sandwich', 'lunch', 'eat_food', 'buy_food', 'finish_job']
senticnet['cafe_bar'] = ['0.841', '0', '0', '0.71', '#joy', '#admiration', 'positive', '0.775', 'waiter', 'bar_service', 'beverage_service', 'concierge', 'minibar']
senticnet['cafe_restaurant'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'buy_sandwich', 'lunch', 'eat_food', 'buy_food', 'finish_job']
senticnet['cafergot'] = ['0.969', '0.817', '0', '0.828', '#joy', '#admiration', 'positive', '0.871', 'ibuprofen', 'painkiller', 'medicine', 'analgesic', 'phenobarbital']
senticnet['cafeteria'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'grocery_cart', 'super_market', 'fruit_stand', 'candy_store', 'produce_store']
senticnet['cafeteria_style'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'grocery_cart', 'super_market', 'fruit_stand', 'candy_store', 'produce_store']
senticnet['caffe'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'buy_sandwich', 'lunch', 'eat_food', 'buy_food', 'finish_job']
senticnet['caffeic_acid'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'capsaicin', 'chemical_irritant', 'piperine', 'pepper_spray']
senticnet['caffeinated_beverage'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'wooden_spoon', 'cereal_bowl', 'teaspoon', 'container_cup', 'large_container']
senticnet['caffeinated_coffee'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'wooden_spoon', 'cereal_bowl', 'teaspoon', 'container_cup', 'large_container']
senticnet['caffeinated_energy'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'wooden_spoon', 'cereal_bowl', 'teaspoon', 'container_cup', 'large_container']
senticnet['caffeine'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'wooden_spoon', 'cereal_bowl', 'teaspoon', 'container_cup', 'large_container']
senticnet['caffeine_consumption'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'wooden_spoon', 'cereal_bowl', 'teaspoon', 'container_cup', 'large_container']
senticnet['caffeine_content'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'wooden_spoon', 'cereal_bowl', 'teaspoon', 'container_cup', 'large_container']
senticnet['caffeine_intake'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'wooden_spoon', 'cereal_bowl', 'teaspoon', 'container_cup', 'large_container']
senticnet['caffeine_intoxication'] = ['-0.72', '0', '0.884', '-0.75', '#anger', '#disgust', 'negative', '-0.78', 'ergotism', 'paraquat_poisoning', 'alkali_poisoning', 'caffeinism', 'poisoning']
senticnet['caffeine_withdrawal'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'wooden_spoon', 'cereal_bowl', 'teaspoon', 'container_cup', 'large_container']
senticnet['caffeinism'] = ['-0.72', '0', '0.884', '-0.75', '#anger', '#disgust', 'negative', '-0.78', 'ergotism', 'paraquat_poisoning', 'alkali_poisoning', 'poisoning', 'caffeine_intoxication']
senticnet['caffiene'] = ['-0.04', '-0.04', '0.056', '-0.1', '#anger', '#disgust', 'negative', '-0.03', 'exercise', 'exercise_regularly', 'need_fresh_air', 'car_repair', 'strong_muscle']
senticnet['caffine'] = ['-0.71', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.77', 'spill_milk', 'read_paper', 'drop_food', 'smell_food', 'choke']
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senticnet['campaign_rally'] = ['0.669', '-0.58', '0', '0.803', '#joy', '#admiration', 'positive', '0.685', 'pep_rally', 'revival', 'revival_meeting', 'gathering', 'assemblage']
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senticnet['campbell_soup'] = ['0.348', '0.23', '-0.15', '0.294', '#joy', '#admiration', 'positive', '0.18', 'happy_emotion', 'niceness', 'contagious', 'good_emotion', 'savor']
senticnet['campbell_soup_company'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'wrapping_paper', 'wrap_gift', 'butcher_paper', 'paper']
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senticnet['campephaga'] = ['-0.92', '0', '0.64', '-0.52', '#sadness', '#anger', 'negative', '-0.69', 'animal_order', 'percher', 'thyreophoran', 'marginocephalian', 'euronithopod']
senticnet['campephagidae'] = ['-0.92', '0', '0.64', '-0.52', '#sadness', '#anger', 'negative', '-0.69', 'animal_order', 'percher', 'thyreophoran', 'marginocephalian', 'euronithopod']
senticnet['camper'] = ['0', '0.156', '-0.06', '0.198', '#interest', '#admiration', 'positive', '0.098', 'grassland', 'camping', 'camp_out', 'campsite', 'campground']
senticnet['camper_van'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'fuel', 'car', 'fossil_fuel', 'transport', 'natural_resource']
senticnet['campesterol'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucose', 'corn_sugar', 'dextrose', 'dextroglucose', 'grape_sugar']
senticnet['campfire'] = ['0.1', '0', '0', '0', '#joy', '#joy', 'positive', '0.1', 'burn_material', 'ash', 'stay_warm', 'oregano', 'bonfire']
senticnet['campground'] = ['0', '0.156', '-0.06', '0.198', '#interest', '#admiration', 'positive', '0.098', 'grassland', 'camping', 'camp_out', 'campsite', 'camp']
senticnet['camphor'] = ['0.827', '0', '0', '0', '#joy', '#joy', 'positive', '0.827', 'wrathful', 'wrathfully', 'wroth', 'wrothful', 'lividity']
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senticnet['camping_gear'] = ['0', '0.11', '-0.05', '0.149', '#interest', '#admiration', 'positive', '0.067', 'pack_clothes', 'hold_stuff', 'carry_stuff', 'carry_book', 'carry_belongings']
senticnet['camping_ground'] = ['0', '0.156', '-0.06', '0.198', '#interest', '#admiration', 'positive', '0.098', 'grassland', 'camping', 'camp_out', 'campsite', 'campground']
senticnet['camping_stove'] = ['0.088', '0.076', '0.016', '0.094', '#joy', '#admiration', 'positive', '0.06', 'copper_wire', 'stove', 'several_types', 'oven', 'suspect']
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senticnet['campsite'] = ['0.786', '0', '0', '0', '#joy', '#joy', 'positive', '0.786', 'camping', 'outdoor', 'camp_out', 'camp', 'enjoy_nature']
senticnet['camptodactyly'] = ['0.582', '-0.66', '0', '0.661', '#surprise', '#admiration', 'positive', '0.637', 'talipes_equinus', 'deformity', 'malformation', 'talipes_valgus', 'talipes_calcaneus']
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senticnet['campus_residence_hall'] = ['0.053', '-0.12', '0.015', '-0.03', '#joy', '#surprise', 'positive', '0.03', 'student_place', 'learning_place', 'school_session', 'after_school', 'professor']
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senticnet['canaanite_city'] = ['0.849', '-0.89', '0', '0.924', '#surprise', '#admiration', 'positive', '0.89', 'nonintellectual', 'lowbrow', 'lowbrowed', 'mindless', 'uncultivated']
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senticnet['canal_lock'] = ['-0.77', '0', '0.583', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'rain', 'rain_down', 'sheet', 'rain_bucket', 'rain_buckets_down']
senticnet['canaliculus'] = ['-0.86', '0', '0.511', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'separate', 'move', 'branch', 'diverge', 'fork']
senticnet['canalization'] = ['-0.7', '0', '0.77', '-0.75', '#anger', '#disgust', 'negative', '-0.74', 'fake', 'unnatural', 'man_made', 'artificial_sea', 'artificial_taste']
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senticnet['canary_wharf'] = ['-0.81', '0', '0.543', '-0.63', '#sadness', '#disgust', 'negative', '-0.66', 'caper', 'pickle', 'relish', 'dill_pickle', 'sweet_pickle']
senticnet['canary_wharf_tower'] = ['-0.81', '0', '0.543', '-0.63', '#sadness', '#disgust', 'negative', '-0.66', 'caper', 'pickle', 'relish', 'dill_pickle', 'sweet_pickle']
senticnet['canary_yellow'] = ['0.065', '-0.02', '0.009', '0', '#joy', '#surprise', 'positive', '0.026', 'brownish_yellow', 'yellowness', 'old_gold', 'gold', 'canary']
senticnet['canasta'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'jewelry_store', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['canberra_golf_club'] = ['0.059', '0.076', '-0.05', '0.079', '#interest', '#admiration', 'positive', '0.039', 'noble', 'king', 'earl', 'duchess', 'duke']
senticnet['canberra_speculative_fiction'] = ['0.967', '-0.98', '0', '0.64', '#joy', '#surprise', 'positive', '0.863', 'club', 'social_club', 'society', 'jockey_club', 'gild']
senticnet['cancel'] = ['-0.22', '0', '-0.11', '-0.12', '#sadness', '#disgust', 'negative', '-0.15', 'give_up', 'failure', 'military_action', 'beaten', 'cost_fortune']
senticnet['cancel_appointment'] = ['-0.95', '0', '0.475', '-0.91', '#sadness', '#disgust', 'negative', '-0.78', 'escape', 'escape_situation', 'cancel_meeting', 'abscond', 'bail_out']
senticnet['cancel_meeting'] = ['-0.95', '0', '0.475', '-0.91', '#sadness', '#disgust', 'negative', '-0.78', 'escape', 'escape_situation', 'bail_out', 'abscond', 'cancel_appointment']
senticnet['cancellous_bone'] = ['-0.53', '0', '0.982', '-0.67', '#anger', '#disgust', 'negative', '-0.73', 'meniscus', 'semilunar_cartilage', 'gristle', 'hyaline_cartilage', 'arytenoid']
senticnet['cancer'] = ['0', '0', '-0.87', '-0.81', '#fear', '#disgust', 'negative', '-0.84', 'infection', 'get_lung_cancer', 'nuclear_war', 'bigot', 'torture']
senticnet['cancer_of_liver'] = ['-0.91', '0', '0.974', '-0.61', '#sadness', '#anger', 'negative', '-0.83', 'disease', 'fatty_liver', 'hepatitis', 'liver_cancer', 'liver_disease']
senticnet['cancer_telethon'] = ['-0.02', '-0.02', '0.062', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'jump_up_down', 'tie_shoelace', 'jump_rope', 'hike', 'skate']
senticnet['cancernet'] = ['0', '0.08', '0', '0', '#interest', '#interest', 'positive', '0.08', 'internet', 'go_online', 'visit_web_site', 'find_information', 'choose_book']
senticnet['cancerous_brain_tumor'] = ['0.934', '-0.88', '0', '0.797', '#joy', '#surprise', 'positive', '0.872', 'scan', 'checkup', 'scrutiny', 'medical_examination', 'medical_exam']
senticnet['cancerous_lump'] = ['-0.63', '0', '0.559', '-0.82', '#sadness', '#disgust', 'negative', '-0.67', 'take_bull_by_horns', 'confront', 'front', 'encounter', 'oppose']
senticnet['cancerous_tumor'] = ['-0.88', '0', '0.774', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fibroma', 'fibroid', 'benign_tumor', 'nonmalignant_tumor', 'nonmalignant_neoplasm']
senticnet['cancri'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['candela'] = ['-0.63', '0', '0.897', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'cd-rom', 'international_candle', 'candle', 'candlepower_unit', 'standard_candle']
senticnet['candela_cd'] = ['-0.63', '0', '0.897', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'cd-rom', 'international_candle', 'candle', 'candlepower_unit', 'standard_candle']
senticnet['candid'] = ['0.05', '0.166', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.074', 'direct', 'person', 'outspoken', 'frank', 'direct_person']
senticnet['candid_interview'] = ['0.54', '-0.64', '0', '0.566', '#surprise', '#admiration', 'positive', '0.582', 'pleaser', 'entertainer', 'mortal', 'busker', 'drawing_card']
senticnet['candida_infection'] = ['-0.72', '0', '0.778', '-0.98', '#anger', '#disgust', 'negative', '-0.82', 'actinic_dermatitis', 'hypericism', 'diaper_rash', 'dermatitis', 'seborrheic_dermatitis']
senticnet['candidacy'] = ['-0.79', '0', '0.525', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'campaigning', 'whispering_campaign', 'candidature', 'electioneering', 'political_campaign']
senticnet['candidacy_exam'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'organize_thought', 'scratch_head', 'know_answer', 'speak_out', 'know_talk']
senticnet['candidacy_examination'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'organize_thought', 'scratch_head', 'know_answer', 'speak_out', 'know_talk']
senticnet['candidal'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'plaque']
senticnet['candidal_infection'] = ['-0.72', '0', '0.778', '-0.98', '#anger', '#disgust', 'negative', '-0.82', 'actinic_dermatitis', 'hypericism', 'diaper_rash', 'dermatitis', 'seborrheic_dermatitis']
senticnet['candidate'] = ['0', '0.232', '-0.10', '0.156', '#interest', '#admiration', 'positive', '0.094', 'election', 'elect', 'government', 'president', 'vote']
senticnet['candidature'] = ['-0.79', '0', '0.525', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'campaigning', 'candidacy', 'whispering_campaign', 'electioneering', 'political_campaign']
senticnet['candidly'] = ['0', '0.764', '0', '0.845', '#interest', '#admiration', 'positive', '0.804', 'empathetically', 'empathic', 'empathetic', 'kind-heartedness', 'kindheartedness']
senticnet['candied_citrus_peel'] = ['-0.95', '0', '0.845', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'grapefruit_peel', 'lemon_peel', 'orange_peel', 'candied_fruit', 'succade']
senticnet['candied_fruit'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'grapefruit_peel', 'lemon_peel', 'orange_peel', 'candied_citrus_peel', 'succade']
senticnet['candied_ginger'] = ['0.077', '0', '0', '0', '#joy', '#joy', 'positive', '0.077', 'dip', 'garlic', 'oat', 'savory', 'rye']
senticnet['candied_peel'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'grapefruit_peel', 'lemon_peel', 'orange_peel', 'candied_citrus_peel', 'succade']
senticnet['candle'] = ['0', '0.232', '-0.10', '0.156', '#interest', '#admiration', 'positive', '0.094', 'light', 'eat_cake', 'birthday_party', 'candle_light', 'romantic_atmosphere']
senticnet['candle_flame'] = ['0', '0.232', '-0.10', '0.156', '#interest', '#admiration', 'positive', '0.094', 'light', 'eat_cake', 'birthday_party', 'candle_light', 'romantic_atmosphere']
senticnet['candle_holder'] = ['0', '0.232', '-0.10', '0.156', '#interest', '#admiration', 'positive', '0.094', 'light', 'eat_cake', 'birthday_party', 'candle_light', 'romantic_atmosphere']
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senticnet['candle_wax'] = ['0', '0.232', '-0.10', '0.156', '#interest', '#admiration', 'positive', '0.094', 'light', 'eat_cake', 'birthday_party', 'candle_light', 'romantic_atmosphere']
senticnet['candle_wick'] = ['0', '0.232', '-0.10', '0.156', '#interest', '#admiration', 'positive', '0.094', 'light', 'eat_cake', 'birthday_party', 'candle_light', 'romantic_atmosphere']
senticnet['candleholder'] = ['0', '0.232', '-0.10', '0.156', '#interest', '#admiration', 'positive', '0.094', 'light', 'eat_cake', 'birthday_party', 'candle_light', 'romantic_atmosphere']
senticnet['candlelight'] = ['0', '0.232', '-0.10', '0.156', '#interest', '#admiration', 'positive', '0.094', 'light', 'eat_cake', 'birthday_party', 'candle_light', 'romantic_atmosphere']
senticnet['candlelight_dinner'] = ['0.044', '0.08', '0', '-0.03', '#joy', '#interest', 'positive', '0.031', 'cook', 'festival', 'bake_pan', 'restaurant', 'food']
senticnet['candlelight_vigil'] = ['0.669', '-0.58', '0', '0.803', '#joy', '#admiration', 'positive', '0.685', 'pep_rally', 'revival', 'revival_meeting', 'gathering', 'assemblage']
senticnet['candlenut'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'palm_nut', 'nut', 'edible_nut', 'cembra_nut', 'cedar_nut']
senticnet['candlepower'] = ['-0.93', '0', '0.698', '-0.85', '#sadness', '#disgust', 'negative', '-0.83', 'intensity', 'light_intensity', 'acoustic_power', 'sound_pressure_level', 'threshold_level']
senticnet['candlepower_unit'] = ['-0.63', '0', '0.897', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'cd-rom', 'international_candle', 'candle', 'candela', 'standard_candle']
senticnet['candlestick'] = ['0', '0.232', '-0.10', '0.156', '#interest', '#admiration', 'positive', '0.094', 'light', 'eat_cake', 'birthday_party', 'candle_light', 'romantic_atmosphere']
senticnet['candombe'] = ['-0.06', '-0.00', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.05', 'madman', 'trouble', 'agony', 'cause_problem', 'river_stream']
senticnet['candombl'] = ['-0.06', '-0.00', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.05', 'madman', 'trouble', 'agony', 'cause_problem', 'river_stream']
senticnet['candor'] = ['0.734', '0.751', '0', '0', '#joy', '#interest', 'positive', '0.742', 'impartiality', 'disinterest', 'justice', 'unbiasedness', 'fairness']
senticnet['candy'] = ['0.066', '-0.07', '0', '0', '#joy', '#surprise', 'positive', '0.073', 'ice_cream', 'muffin', 'chocolate_dessert', 'pudding', 'chocolate']
senticnet['candy_bar'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'candy', 'sweet', 'sugar', 'sweet_tooth', 'chocolate']
senticnet['candy_cane'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'ripe_apple', 'custard', 'cookie', 'candy', 'orange_fruit']
senticnet['candy_corn'] = ['0.274', '-0.01', '0.012', '0.132', '#joy', '#admiration', 'positive', '0.102', 'dance_type', 'swirl', 'throw_football', 'jump_around', 'dance_move']
senticnet['candy_factory'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'candy', 'sweet', 'sugar', 'sweet_tooth', 'chocolate']
senticnet['candy_machine'] = ['0.709', '0.771', '0', '0', '#joy', '#interest', 'positive', '0.74', 'candy', 'park_meter', 'coin_purse', 'pocket', 'purse']
senticnet['candy_shop'] = ['0', '0.136', '-0.09', '0.213', '#interest', '#admiration', 'positive', '0.084', 'good', 'tasty_good', 'uncommon', 'make_fruit', 'serve_cold']
senticnet['candy_store'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'candy', 'sweet', 'sugar', 'sweet_tooth', 'chocolate']
senticnet['candytuft'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'livelong', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['cane'] = ['0.609', '-0.74', '0', '0.716', '#surprise', '#admiration', 'positive', '0.69', 'malacca', 'sword', 'swagger_stick', 'sword_cane', 'sword_stick']
senticnet['cane_juice'] = ['-0.08', '0.106', '0', '0.125', '#interest', '#admiration', 'positive', '0.047', 'sugar', 'treacle', 'black_treacle', 'add_sugar', 'sugar_substitute']
senticnet['cane_stalk'] = ['0.609', '-0.74', '0', '0.716', '#surprise', '#admiration', 'positive', '0.69', 'malacca', 'sword', 'swagger_stick', 'sword_cane', 'sword_stick']
senticnet['cane_sugar'] = ['-0.08', '0.106', '0', '0.125', '#interest', '#admiration', 'positive', '0.047', 'sugar', 'treacle', 'black_treacle', 'add_sugar', 'sugar_substitute']
senticnet['cane_syrup'] = ['-0.08', '0.106', '0', '0.125', '#interest', '#admiration', 'positive', '0.047', 'sugar', 'treacle', 'black_treacle', 'add_sugar', 'sugar_substitute']
senticnet['canella'] = ['0', '0.158', '-0.12', '0.138', '#interest', '#admiration', 'positive', '0.057', 'spice', 'sweet', 'sweetener', 'smell_good', 'white_cinnamon']
senticnet['canella_bark'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'cork', 'cassia_bark', 'canella', 'bark', 'white_cinnamon']
senticnet['canescent'] = ['0', '0', '-0.79', '-0.84', '#fear', '#disgust', 'negative', '-0.81', 'gray-haired', 'gray', 'rusty', 'hoary', 'grizzly']
senticnet['canid'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'animal', 'animate_being', 'pet', 'creature']
senticnet['canidae'] = ['-0.68', '0', '0.956', '-0.90', '#anger', '#disgust', 'negative', '-0.84', 'giant_moa', 'anomalopteryx', 'moa', 'moa_bird', 'flightless_bird']
senticnet['canine'] = ['0.039', '0', '0', '0.059', '#joy', '#admiration', 'positive', '0.049', 'big_dog', 'dog_cat', 'smart_dog', 'pet', 'domesticated_animal']
senticnet['canine_genome'] = ['0.039', '0', '0', '0.059', '#joy', '#admiration', 'positive', '0.049', 'big_dog', 'dog_cat', 'smart_dog', 'pet', 'domesticated_animal']
senticnet['canine_teeth'] = ['0', '-0.02', '0.033', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'tooth', 'teeth', 'dimple', 'mouth', 'cliche']
senticnet['caning'] = ['-0.76', '-0.83', '-0.89', '-0.96', '#fear', '#disgust', 'negative', '-0.44', 'capital_punishment', 'death_destruction', 'kill', 'murder', 'get_rid_of_enemy']
senticnet['canis_majoris_cma'] = ['0.977', '-0.93', '0', '0.583', '#joy', '#surprise', 'positive', '0.831', 'giant', 'shinny', 'supernova', 'supergiant', 'binary_star']
senticnet['canister'] = ['0.886', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.881', 'fire_hydrant', 'stair_rail', 'christian_fundamentalist', 'extinguisher', 'reception_desk']
senticnet['canister_shot'] = ['0.957', '-0.93', '0', '0.795', '#joy', '#surprise', 'positive', '0.896', 'outdo', 'outstrip', 'outmatch', 'outgo', 'outperform']
senticnet['canister_vacuum'] = ['-0.04', '-0.02', '0.089', '-0.14', '#anger', '#disgust', 'negative', '-0.06', 'steam_locomotive', 'loudspeaker', 'collector', 'siren', 'activist']
senticnet['canker'] = ['-0.86', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.85', 'activity', 'reddish', 'rust', 'rusty', 'recent']
senticnet['canker_disease'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['cannabalism'] = ['-0.17', '0', '-0.16', '-0.09', '#sadness', '#fear', 'negative', '-0.14', 'murder', 'hurt_others', 'death_penalty', 'death_destruction', 'end_live']
senticnet['cannabinol'] = ['0.049', '0', '0', '0.113', '#joy', '#admiration', 'positive', '0.081', 'solid', 'hard_material', 'construction_material', 'hard', 'calcium']
senticnet['cannabis'] = ['0.962', '0', '0', '0', '#joy', '#joy', 'positive', '0.962', 'marijuana', 'pleasure_garden', 'trellis', 'mushroom', 'cabbage']
senticnet['cannabis_smoke'] = ['0.047', '0', '-0.10', '-0.06', '#fear', '#disgust', 'negative', '-0.03', 'smoke', 'tobacco_product', 'smoke_tobacco', 'lung_cancer', 'cigar']
senticnet['canned_food'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'drinking_water', 'sugar_water']
senticnet['canned_pineapple'] = ['0.52', '-0.69', '0', '0.639', '#surprise', '#admiration', 'positive', '0.617', 'black_bread', 'pumpernickel', 'limpa', 'bread', 'rye_bread']
senticnet['canned_pumpkin'] = ['0', '-0.01', '0.037', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'pear', 'green_apple', 'apricot', 'cherry', 'lime']
senticnet['canned_soup'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'apple_pie', 'lasagna', 'jelly', 'a_lot_of_food', 'cottage_cheese']
senticnet['cannelloni'] = ['0.041', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.043', 'meatloaf', 'salad', 'delicious_meal', 'casserole', 'apple_pie']
senticnet['cannery_row'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'drifter', 'bum', 'sundowner']
senticnet['cannibal'] = ['-0.73', '0', '0.734', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'primitive', 'headhunter', 'barbarian', 'anthropophagus', 'anthropophagite']
senticnet['cannibal_ferox'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amuck', 'murderousness', 'misocainea', 'misogyny']
senticnet['cannibal_holocaust'] = ['-0.73', '0', '0.734', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'primitive', 'headhunter', 'barbarian', 'anthropophagus', 'anthropophagite']
senticnet['cannibal_tribe'] = ['-0.73', '0', '0.734', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'primitive', 'headhunter', 'barbarian', 'anthropophagus', 'anthropophagite']
senticnet['cannibalism'] = ['-0.17', '0', '-0.16', '-0.09', '#sadness', '#fear', 'negative', '-0.14', 'murder', 'hurt_others', 'death_penalty', 'death_destruction', 'end_live']
senticnet['cannibalistic'] = ['-0.73', '0', '0.734', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'primitive', 'headhunter', 'barbarian', 'anthropophagus', 'anthropophagite']
senticnet['cannibalistic_behavior'] = ['-0.17', '0', '-0.16', '-0.09', '#sadness', '#fear', 'negative', '-0.14', 'murder', 'hurt_others', 'death_penalty', 'death_destruction', 'end_live']
senticnet['cannibalize'] = ['-0.99', '0', '0.553', '-0.52', '#sadness', '#anger', 'negative', '-0.69', 'take_in', 'take', 'ingest', 'have', 'consume']
senticnet['cannon'] = ['0', '0', '0.806', '-0.77', '#anger', '#disgust', 'negative', '-0.79', 'machine_gun', 'handgun', 'big_gun', 'weapon', 'grenade']
senticnet['cannon_fire'] = ['0', '0', '-0.85', '-0.99', '#fear', '#disgust', 'negative', '-0.92', 'onslaught', 'battery', 'shelling', 'bombardment', 'outpouring']
senticnet['cannonball'] = ['0', '0', '0.917', '-0.71', '#anger', '#disgust', 'negative', '-0.81', 'aerial_bomb', 'grenade', 'cannon', 'weapon', 'machine_gun']
senticnet['cannula'] = ['0.255', '0.182', '-0.06', '0.314', '#joy', '#admiration', 'positive', '0.173', 'doctor_office', 'urethroscope', 'sigmoidoscope', 'hospital', 'check_health']
senticnet['cannulation'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'blatancy', 'obviousness']
senticnet['canny'] = ['0', '-0.98', '0', '0.738', '#surprise', '#admiration', 'positive', '0.863', 'shrewd', 'clever', 'sharp', 'astute', 'wily']
senticnet['canoe'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'submarine', 'lifeboat', 'titanic', 'large_ship', 'battleship']
senticnet['canon'] = ['0.028', '0.138', '-0.04', '0.215', '#interest', '#admiration', 'positive', '0.083', 'fee', 'rule', 'standard', 'criterion', 'tenet']
senticnet['canon_lawyer'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquirer', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['canon_powershot'] = ['0', '-0.02', '0.048', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'telescope', 'read_glass', 'eyeglass', 'binoculars', 'monocle']
senticnet['canon_residentiary'] = ['0.031', '0.272', '0', '0.231', '#interest', '#admiration', 'positive', '0.178', 'minister', 'church_head', 'pastor', 'protestant', 'evangelist']
senticnet['canon_theologian'] = ['0.031', '0.272', '0', '0.231', '#interest', '#admiration', 'positive', '0.178', 'minister', 'church_head', 'pastor', 'protestant', 'evangelist']
senticnet['canonic'] = ['0.136', '-0.11', '0.031', '0', '#joy', '#surprise', 'positive', '0.075', 'rap', 'popular_music', 'music', 'band_play', 'rock_roll']
senticnet['canonical'] = ['0', '0', '-0.90', '-0.75', '#fear', '#disgust', 'negative', '-0.83', 'statute', 'provision', 'contractual', 'applicable', 'statutory']
senticnet['canonical_ensemble'] = ['-0.98', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.87', 'become_fat', 'flatulence', 'feel_rejuvenated', 'tan_skin', 'make_angry']
senticnet['canonical_monte_carlo'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'fantastic', 'amaze', 'pose', 'wondrous', 'wonderfully']
senticnet['canonize'] = ['0', '0.82', '0', '0.847', '#interest', '#admiration', 'positive', '0.833', 'reverently', 'hero-worship', 'idolize', 'veneration', 'reverentially']
senticnet['canonsburg'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
senticnet['canopic_branch'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['canopic_chest'] = ['-0.09', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.10', 'funeral_celebration', 'memorial', 'funeral', 'memento', 'cry']
senticnet['canopus'] = ['-0.87', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.81', 'afraid', 'extravagantly', 'prodigality', 'extravagant', 'extravagance']
senticnet['cantaloupe'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'cabbage', 'broccoli', 'pea']
senticnet['cantando'] = ['-0.56', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.63', 'aphasic', 'inarticulate', 'speechless', 'unarticulated', 'dumb']
senticnet['cantankerous'] = ['-0.96', '0', '0.858', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'contrarious', 'dogged', 'pertinacious', 'unregenerate', 'bolshy']
senticnet['cantankerous_old'] = ['0.795', '-0.85', '0', '0.973', '#surprise', '#admiration', 'positive', '0.873', 'centenarian', 'nonagenarian', 'octogenarian', 'septuagenarian', 'sexagenarian']
senticnet['cantata'] = ['0.514', '-0.58', '0', '0.927', '#surprise', '#admiration', 'positive', '0.676', 'genre', 'fugue', 'chamber_music', 'opera', 'serious_music']
senticnet['canteen'] = ['0.619', '-0.83', '0', '0.912', '#surprise', '#admiration', 'positive', '0.788', 'hipflask', 'pocket_flask', 'ampulla', 'flask', 'vacuum_flask']
senticnet['canter'] = ['0', '0', '-0.81', '-0.93', '#fear', '#disgust', 'negative', '-0.87', 'trot', 'gallop', 'galloping', 'dart', 'trotting']
senticnet['canterbury_rugby_union'] = ['-0.68', '0', '0.923', '-0.68', '#sadness', '#anger', 'negative', '-0.76', 'negative', 'veto', 'refute', 'rebut', 'controvert']
senticnet['canthigaster'] = ['0.96', '-0.77', '0', '0.929', '#joy', '#admiration', 'positive', '0.887', 'whale_louse', 'beach_flea', 'sand_hopper', 'sandhopper', 'amphipod']
senticnet['cantilever'] = ['0', '0.958', '0', '0.915', '#interest', '#admiration', 'positive', '0.936', 'truss', 'girder', 'joist', 'truss', 'screw']
senticnet['cantilever_beam'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'aberrance', 'warp', 'chromosomal_aberration']
senticnet['cantilever_bridge'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'suspension_bridge', 'under_bridge', 'roadway', 'county_highway', 'long_road']
senticnet['cantina'] = ['-0.80', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.79', 'whorehouse', 'brothel', 'massage_parlor', 'bagnio', 'house_of_prostitution']
senticnet['cantina_band'] = ['0.71', '0.92', '0', '0', '#joy', '#interest', 'positive', '0.815', 'toy_car', 'toy_ball', 'action_figure', 'toy_marble', 'beanie_baby']
senticnet['cantlon'] = ['0.627', '-0.65', '0', '0.968', '#surprise', '#admiration', 'positive', '0.749', 'overwhelm', 'overpower', 'sweep_over', 'whelm', 'overcome']
senticnet['cantor_set'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'fragmentation', 'discharge', 'backfire', 'blast']
senticnet['canute'] = ['-0.92', '0', '0.659', '-0.81', '#sadness', '#disgust', 'negative', '-0.8', 'unripe', 'napping', 'off_guard', 'off_one_guard', 'unprepared']
senticnet['canvas'] = ['0.041', '0.112', '0', '0.077', '#interest', '#admiration', 'positive', '0.077', 'oil_paint', 'artist', 'brush', 'palette', 'artist_tool']
senticnet['canvas_bag'] = ['0', '0.072', '0', '0', '#interest', '#interest', 'positive', '0.072', 'metal_box', 'cash_drawer', 'keyhole', 'clutch_bag', 'safe']
senticnet['canvas_sack'] = ['0', '-0.02', '0.018', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'shoulder_bag', 'water_bottle', 'satchel_bag', 'tote_bag', 'carry']
senticnet['canvas_tent'] = ['-0.97', '0', '0.84', '-0.52', '#sadness', '#anger', 'negative', '-0.78', 'pis_aller', 'last_resort', 'temporary_expedient', 'expedient', 'stopgap']
senticnet['canvass'] = ['0.722', '-0.87', '0', '0.9', '#surprise', '#admiration', 'positive', '0.831', 'study', 'explore', 'analyze', 'examine', 'diagnose']
senticnet['canyon'] = ['0', '0.069', '0', '0.065', '#interest', '#admiration', 'positive', '0.067', 'alp', 'high_mountain', 'pass', 'ski_resort', 'high_place']
senticnet['canyon_creek'] = ['0', '0.069', '0', '0.065', '#interest', '#admiration', 'positive', '0.067', 'alp', 'high_mountain', 'pass', 'ski_resort', 'high_place']
senticnet['canyon_floor'] = ['0', '0.069', '0', '0.065', '#interest', '#admiration', 'positive', '0.067', 'alp', 'high_mountain', 'pass', 'ski_resort', 'high_place']
senticnet['canyon_rim'] = ['0', '0.069', '0', '0.065', '#interest', '#admiration', 'positive', '0.067', 'alp', 'high_mountain', 'pass', 'ski_resort', 'high_place']
senticnet['canyon_trail'] = ['0', '0.069', '0', '0.065', '#interest', '#admiration', 'positive', '0.067', 'alp', 'high_mountain', 'pass', 'ski_resort', 'high_place']
senticnet['canyon_wall'] = ['0', '0.069', '0', '0.065', '#interest', '#admiration', 'positive', '0.067', 'alp', 'high_mountain', 'pass', 'ski_resort', 'high_place']
senticnet['canzona'] = ['0.99', '0', '0', '0.719', '#joy', '#admiration', 'positive', '0.855', 'instrumental', 'lyric', 'lyrics', 'sing', 'melody']
senticnet['caoshu'] = ['-0.95', '0', '0.517', '-0.53', '#sadness', '#disgust', 'negative', '-0.67', 'copperplate', 'hand', 'minuscule', 'italic', 'round_hand']
senticnet['cap'] = ['0.179', '0.164', '-0.05', '0.163', '#joy', '#interest', 'positive', '0.113', 'shower_cover_hair', 'hat', 'cover_hair', 'head_wear', 'scarf']
senticnet['cap-like'] = ['-0.82', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.83', 'sheet-like', 'sheetlike', 'semi-conducting', 'water-impermeable', 'screen-like']
senticnet['cap_anson'] = ['-0.58', '0', '0.757', '-0.67', '#anger', '#disgust', 'negative', '-0.67', 'breech', 'knee_breech', 'knee_pants', 'trunk_hose', 'knickers']
senticnet['cap_lamp'] = ['0', '-0.03', '0.043', '-0.11', '#anger', '#disgust', 'negative', '-0.03', 'torch', 'safety_pin', 'laser', 'flare', 'firebomb']
senticnet['capability'] = ['0.515', '-0.54', '0', '0.579', '#surprise', '#admiration', 'positive', '0.546', 'executability', 'ability', 'associability', 'associableness', 'strength']
senticnet['capable'] = ['0.028', '0.138', '-0.04', '0.215', '#interest', '#admiration', 'positive', '0.083', 'skilled', 'expert', 'tin', 'dense_water', 'metal_container']
senticnet['capableness'] = ['0.946', '-0.64', '0', '0.602', '#joy', '#surprise', 'positive', '0.73', 'grasp', 'perfectibility', 'imperfectibility', 'reach', 'aptitude']
senticnet['capacious'] = ['0', '0.798', '0', '0.711', '#interest', '#admiration', 'positive', '0.754', 'voluminous', 'commodious', 'roomy', 'spacious', 'ample']
senticnet['capacitor_bank'] = ['0', '-0.03', '0', '0.093', '#surprise', '#admiration', 'positive', '0.063', 'work_well', 'internal_combustion_engine', 'broken_car', 'mode_transportation', 'machine']
senticnet['capacity'] = ['-0.82', '0', '0.948', '-0.85', '#anger', '#disgust', 'negative', '-0.87', 'resistance', 'potential', 'reluctance', 'distortion', 'electrical_phenomenon']
senticnet['capacity_building'] = ['0.952', '-0.84', '0', '0.875', '#joy', '#admiration', 'positive', '0.89', 'administration', 'judiciary', 'governing_body', 'establishment', 'brass']
senticnet['capacity_crowd'] = ['-0.71', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.84', 'traffic_jam', 'accumulate_stress', 'stress', 'accumulate_stress_city', 'bad_service']
senticnet['capacity_utilisation'] = ['0.749', '0.764', '0', '0', '#joy', '#interest', 'positive', '0.756', 'obedience', 'order', 'requirement', 'permission', 'necessary']
senticnet['caparisoned'] = ['-0.54', '0', '0.935', '-0.81', '#anger', '#disgust', 'negative', '-0.76', 'gowned', 'petticoated', 'surpliced', 'arrayed', 'clad']
senticnet['capcompetent'] = ['0.975', '-0.67', '0', '0.673', '#joy', '#admiration', 'positive', '0.773', 'workmanlike', 'effective', 'efficient', 'competent', 'able']
senticnet['cape'] = ['0', '0', '0.893', '-0.85', '#anger', '#disgust', 'negative', '-0.87', 'vest', 'suit', 'garment', 'outer_wear', 'kimono']
senticnet['cape_cod_style'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'condominium', 'rent_apartment', 'high_rise', 'condo', 'row_house']
senticnet['cape_esperance'] = ['0.084', '-0.05', '0.013', '0', '#joy', '#surprise', 'positive', '0.043', 'carnival_ride', 'carny', 'fortune_teller', 'horseback', 'ferris_wheel']
senticnet['caped_crusader'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'only_bird']
senticnet['caper'] = ['-0.57', '0', '0.721', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'bush', 'native_pomegranate', 'caper_tree', 'native_orange', 'common_caper']
senticnet['caper_comedy'] = ['-0.57', '0', '0.721', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'bush', 'native_pomegranate', 'caper_tree', 'native_orange', 'common_caper']
senticnet['caper_film'] = ['-0.57', '0', '0.721', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'bush', 'native_pomegranate', 'caper_tree', 'native_orange', 'common_caper']
senticnet['caper_flick'] = ['-0.57', '0', '0.721', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'bush', 'native_pomegranate', 'caper_tree', 'native_orange', 'common_caper']
senticnet['caper_movie'] = ['-0.57', '0', '0.721', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'bush', 'native_pomegranate', 'caper_tree', 'native_orange', 'common_caper']
senticnet['caper_tree'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'bush', 'native_pomegranate', 'caper', 'native_orange', 'common_caper']
senticnet['capercailzie'] = ['-0.54', '0', '0.811', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'bird_of_prey', 'raptor', 'raptorial_bird', 'parrot', 'bird']
senticnet['capgras_delusion'] = ['-0.58', '0', '0.722', '-0.51', '#sadness', '#anger', 'negative', '-0.60', 'dissociative_disorder', 'depersonalization_neurosis', 'depersonalization', 'mental_disorder', 'mental_disturbance']
senticnet['capillary'] = ['0', '0', '0.019', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'worm_intestine', 'bristle', 'fur_keep_warm', 'animal', 'whisker']
senticnet['capillary_bed'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'keep_alive', 'body_system', 'body_working', 'blood_pressure', 'heart']
senticnet['capillary_electrophoresis_ce'] = ['0.838', '-0.61', '0', '0.556', '#joy', '#surprise', 'positive', '0.671', 'activity', 'natural_process', 'natural_action', 'column_chromatography', 'paper_chromatography']
senticnet['capillary_fracture'] = ['-0.81', '0', '0.745', '-0.94', '#sadness', '#disgust', 'negative', '-0.83', 'injury', 'hairline_fracture', 'fracture', 'incomplete_fracture', 'simple_fracture']
senticnet['capillary_hemangioma'] = ['-0.66', '0', '0.911', '-0.99', '#anger', '#disgust', 'negative', '-0.85', 'liver_spot', 'spot', 'freckle', 'macule', 'plague_spot']
senticnet['capillary_leak_syndrome'] = ['-0.99', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.78', 'dropsy', 'atrophedema', 'giant_hive', 'periodic_edema', 'lymphedema']
senticnet['capillary_malformation'] = ['-0.66', '0', '0.911', '-0.99', '#anger', '#disgust', 'negative', '-0.85', 'liver_spot', 'spot', 'freckle', 'macule', 'plague_spot']
senticnet['capillary_network'] = ['-0.86', '0', '0.511', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'separate', 'move', 'branch', 'diverge', 'fork']
senticnet['capillary_permeability'] = ['-0.57', '0', '0.767', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'verboseness', 'verbosity', 'prolixity', 'turgidness', 'flatulence']
senticnet['capital'] = ['0.059', '0.143', '0', '0', '#joy', '#interest', 'positive', '0.101', 'capitol', 'feudalism', 'district', 'citizen', 'empire']
senticnet['capital_build'] = ['0.02', '0.222', '0.023', '0.176', '#interest', '#admiration', 'positive', '0.099', 'monastery', 'cathedral', 'temple', 'church', 'synagogue']
senticnet['capital_fm_jingle'] = ['0.034', '0', '0.01', '0.087', '#joy', '#admiration', 'positive', '0.037', 'chime', 'bell_noise', 'alto', 'piano', 'ring_loudly']
senticnet['capital_gain_rate'] = ['0.763', '-0.67', '0', '0.945', '#joy', '#admiration', 'positive', '0.795', 'set', 'income_bracket', 'bracket', 'income', 'price_bracket']
senticnet['capital_gain_tax'] = ['-0.83', '0', '0.555', '-0.64', '#sadness', '#disgust', 'negative', '-0.67', 'single_tax', 'income_tax', 'taxation', 'capital_levy', 'departure_tax']
senticnet['capital_gain_tax_cgt'] = ['-0.83', '0', '0.555', '-0.64', '#sadness', '#disgust', 'negative', '-0.67', 'single_tax', 'income_tax', 'taxation', 'capital_levy', 'departure_tax']
senticnet['capital_gain_tax_liability'] = ['-0.83', '0', '0.555', '-0.64', '#sadness', '#disgust', 'negative', '-0.67', 'single_tax', 'income_tax', 'taxation', 'capital_levy', 'departure_tax']
senticnet['capital_gain_tax_relief'] = ['-0.83', '0', '0.555', '-0.64', '#sadness', '#disgust', 'negative', '-0.67', 'single_tax', 'income_tax', 'taxation', 'capital_levy', 'departure_tax']
senticnet['capital_gain_taxation'] = ['-0.83', '0', '0.555', '-0.64', '#sadness', '#disgust', 'negative', '-0.67', 'single_tax', 'income_tax', 'taxation', 'capital_levy', 'departure_tax']
senticnet['capital_intensive'] = ['-0.12', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.09', 'painful', 'uncomfortable', 'violent', 'fun_learn_play', 'expensive']
senticnet['capital_investment'] = ['0', '0.312', '-0.10', '0.207', '#interest', '#admiration', 'positive', '0.137', 'infuse', 'loan', 'lend', 'increase_money', 'wealth']
senticnet['capital_iq'] = ['-0.63', '0', '0.862', '-0.52', '#sadness', '#anger', 'negative', '-0.67', 'knot', 'bunch', 'tuft', 'tussock', 'agglomeration']
senticnet['capital_levy'] = ['-0.83', '0', '0.555', '-0.64', '#sadness', '#disgust', 'negative', '-0.67', 'single_tax', 'income_tax', 'capital_gain_tax', 'taxation', 'departure_tax']
senticnet['capital_messuage'] = ['0.049', '0.16', '0', '0.155', '#interest', '#admiration', 'positive', '0.121', 'castle', 'palace', 'medieval', 'aristocrat', 'throne']
senticnet['capital_punishment'] = ['-0.19', '0', '-0.17', '-0.14', '#sadness', '#fear', 'negative', '-0.17', 'death_penalty', 'die', 'death_destruction', 'kill', 'murder']
senticnet['capitalise_on'] = ['-0.75', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.77', 'avail', 'use', 'harness', 'utilise', 'overexploit']
senticnet['capitalism'] = ['0', '0.224', '-0.05', '0.128', '#interest', '#admiration', 'positive', '0.1', 'save', 'receive_service', 'money_maker', 'commerce', 'private']
senticnet['capitalist'] = ['0.022', '0', '0.074', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'sell_goods', 'buy_cheese', 'buy_sell', 'impulse_shop', 'make_rich']
senticnet['capitalist_economy'] = ['0.067', '0.172', '0', '0.121', '#interest', '#admiration', 'positive', '0.12', 'economic_activity', 'business', 'support_family', 'work_done', 'earn_money']
senticnet['capitalist_exploitation'] = ['-0.65', '0', '0.691', '-0.54', '#sadness', '#anger', 'negative', '-0.63', 'moronity', 'mental_deficiency', 'imbecility', 'stupidity', 'mental_defectiveness']
senticnet['capitalist_ideology'] = ['0.127', '0.069', '0.029', '0.16', '#joy', '#admiration', 'positive', '0.082', 'basic_human_need', 'construct', 'set_down', 'make', 'build_process']
senticnet['capitalist_society'] = ['0.127', '0.069', '0.029', '0.16', '#joy', '#admiration', 'positive', '0.082', 'basic_human_need', 'construct', 'set_down', 'make', 'build_process']
senticnet['capitalist_system'] = ['0.127', '0.069', '0.029', '0.16', '#joy', '#admiration', 'positive', '0.082', 'basic_human_need', 'construct', 'set_down', 'make', 'build_process']
senticnet['capitalistic'] = ['0', '0', '-0.87', '-0.89', '#fear', '#disgust', 'negative', '-0.88', 'socialistic', 'fascistic', 'communistic', 'collectivist', 'theocratic']
senticnet['capitalize'] = ['0.101', '0.275', '-0.11', '0.376', '#interest', '#admiration', 'positive', '0.16', 'important', 'good_etiquette', 'good_quality', 'earn_money', 'good']
senticnet['capitalize_on'] = ['0.81', '0', '0', '0.703', '#joy', '#admiration', 'positive', '0.756', 'harvest', 'crop', 'grow_crop', 'plant_crop', 'graze_cattle']
senticnet['capitalizing_on'] = ['-0.75', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.77', 'avail', 'use', 'harness', 'utilise', 'overexploit']
senticnet['capitated_rate'] = ['0.687', '-0.98', '0', '0.666', '#joy', '#surprise', 'positive', '0.778', 'scale', 'scale_of_measurement', 'graduated_table', 'ordered_series', 'wage_scale']
senticnet['capitation'] = ['0.687', '-0.98', '0', '0.666', '#joy', '#surprise', 'positive', '0.778', 'scale', 'scale_of_measurement', 'graduated_table', 'ordered_series', 'wage_scale']
senticnet['capitation_rate'] = ['0.687', '-0.98', '0', '0.666', '#joy', '#surprise', 'positive', '0.778', 'scale', 'scale_of_measurement', 'graduated_table', 'ordered_series', 'wage_scale']
senticnet['capitation_tax'] = ['-0.08', '0', '-0.15', '-0.20', '#fear', '#disgust', 'negative', '-0.14', 'ask_money', 'extortion', 'wringing', 'intimidate', 'psychological_pressure']
senticnet['capitol'] = ['0.066', '0.179', '0', '0.066', '#joy', '#interest', 'positive', '0.104', 'capital', 'citizen', 'district', 'feudalism', 'empire']
senticnet['capitol_avenue'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'boulevard', 'lane', 'thoroughfare', 'intersection', 'curb']
senticnet['capitol_build'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'sumo_wrestler', 'tall_man', 'egret', 'capital_build']
senticnet['capitol_hill'] = ['0', '0.709', '0', '0.933', '#interest', '#admiration', 'positive', '0.821', 'capitol', 'government_building', 'president', 'apartment_house', 'government']
senticnet['capitulate'] = ['-0.75', '0', '-1.0', '0', '#sadness', '#fear', 'negative', '-0.87', 'give_in', 'submit', 'accept', 'defer', 'yield']
senticnet['caplet'] = ['-0.98', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.87', 'spoliation', 'tobacco', 'tool', 'untampered', 'tamper']
senticnet['capoeira'] = ['-0.06', '-0.00', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.05', 'madman', 'trouble', 'agony', 'cause_problem', 'river_stream']
senticnet['capoeira_regional'] = ['-0.06', '-0.00', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.05', 'madman', 'trouble', 'agony', 'cause_problem', 'river_stream']
senticnet['capoeirista'] = ['-0.06', '-0.00', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.05', 'madman', 'trouble', 'agony', 'cause_problem', 'river_stream']
senticnet['capoiera'] = ['-0.06', '-0.00', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.05', 'madman', 'trouble', 'agony', 'cause_problem', 'river_stream']
senticnet['cappa_magna'] = ['0.849', '-0.50', '0', '0.845', '#joy', '#admiration', 'positive', '0.732', 'robe', 'gown', 'veil', 'vest', 'cassock']
senticnet['cappella'] = ['0.023', '0.303', '-0.08', '0.261', '#interest', '#admiration', 'positive', '0.125', 'school_choral', 'drum_set', 'choral', 'string_quartet', 'band']
senticnet['cappella_rendition'] = ['0.023', '0.303', '-0.08', '0.261', '#interest', '#admiration', 'positive', '0.125', 'school_choral', 'drum_set', 'choral', 'string_quartet', 'band']
senticnet['cappella_version'] = ['0.773', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.844', 'favorite', 'crowd', 'acclamation', 'bunch', 'large_crowd']
senticnet['cappuccino'] = ['-0.71', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'latte', 'mocha', 'espresso', 'caramel', 'vanilla']
senticnet['cappucino'] = ['0.807', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.859', 'bubble', 'steam', 'hot', 'boil_water', 'tea']
senticnet['capri_sun'] = ['0.951', '-0.97', '0', '0.639', '#joy', '#surprise', 'positive', '0.853', 'smoothie', 'drinking_water', 'nutrient', 'milk', 'refresher']
senticnet['capricious'] = ['0', '0', '-0.70', '-0.84', '#fear', '#disgust', 'negative', '-0.77', 'whim', 'whim', 'dictated', 'dictate', 'whatever']
senticnet['caprolactam'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'carboxylic_acid', 'succinic_acid', 'valeric_acid']
senticnet['caprolactone'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'carboxylic_acid', 'succinic_acid', 'valeric_acid']
senticnet['capsaicin'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'chemical_irritant', 'gingerol', 'piperine', 'pepper_spray']
senticnet['capsaicin_cream'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'chemical_irritant', 'gingerol', 'piperine', 'pepper_spray']
senticnet['capsaicin_receptor'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'chemical_irritant', 'gingerol', 'piperine', 'pepper_spray']
senticnet['capsicum_annuum'] = ['0.035', '0.104', '0', '0.12', '#interest', '#admiration', 'positive', '0.086', 'fruit_vegetable', 'vegetable', 'vegetable_fruit', 'fresh_vegetable', 'vegetarian_food']
senticnet['capsize'] = ['0', '0', '0.877', '-0.87', '#anger', '#disgust', 'negative', '-0.87', 'preponderate', 'outweigh', 'predominate', 'overbalance', 'upset']
senticnet['capsized_boat'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'lifebelt', 'save_life', 'lifeguard', 'life_jacket', 'safety_equipment']
senticnet['capstan'] = ['0', '0.142', '0', '0.125', '#interest', '#admiration', 'positive', '0.134', 'weight_machine', 'power_drill', 'screw', 'screwdriver', 'air_pump']
senticnet['capsule'] = ['0', '0', '0.868', '-0.84', '#anger', '#disgust', 'negative', '-0.85', 'aspirin', 'scalpel', 'ibuprofen', 'pushpin', 'palliative']
senticnet['capsule_collection'] = ['0', '-0.03', '0.022', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'shoulder_bag', 'satchel', 'water_bottle', 'tote_bag', 'carry']
senticnet['captain'] = ['0.045', '0', '0', '0.088', '#joy', '#admiration', 'positive', '0.067', 'leader', 'seaman', 'lieutenant', 'officer', 'traitor']
senticnet['captain_ahab'] = ['-0.93', '0', '0.544', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'mutant', 'variation', 'sport', 'lusus_naturae', 'mutation']
senticnet['captain_barbossa'] = ['0', '0', '0.034', '-0.08', '#anger', '#disgust', 'negative', '-0.06', 'lifeboat', 'mast', 'bad_man', 'keel', 'yacht']
senticnet['captain_belmein'] = ['-0.56', '0', '0.828', '-0.68', '#anger', '#disgust', 'negative', '-0.69', 'abruptness', 'precipitateness', 'precipitousness', 'hastiness', 'precipitancy']
senticnet['captain_boat'] = ['-0.10', '0', '-0.07', '0', '#sadness', '#fear', 'negative', '-0.08', 'needy', 'party', 'welcome_friend', 'come_house', 'page_person']
senticnet['captain_boomerang'] = ['0', '0.081', '0', '0', '#interest', '#interest', 'positive', '0.081', 'favorite', 'guard', 'crowd', 'several_people', 'clique']
senticnet['captain_corcoran'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'dress', 'pinny', 'dirndl', 'gown', 'muumuu']
senticnet['captain_kidd'] = ['0', '0', '0.034', '-0.08', '#anger', '#disgust', 'negative', '-0.06', 'lifeboat', 'mast', 'bad_man', 'keel', 'yacht']
senticnet['captain_kronos_vampire'] = ['0', '0', '-0.11', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'hunting_season', 'hound', 'cougar', 'outlaw', 'dragon']
senticnet['captain_nemo'] = ['-0.93', '0', '0.544', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'mutant', 'variation', 'sport', 'lusus_naturae', 'mutation']
senticnet['captain_pugwash'] = ['-0.89', '0', '0.58', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'resemblance', 'comparison', 'equivalence', 'reflection', 'alikeness']
senticnet['captain_ship'] = ['-0.99', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.94', 'chair_committee', 'sail_around_world', 'voice_concern', 'age_gracefully', 'storm_out_room']
senticnet['captain_whiddon'] = ['-0.80', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'difficulty', 'awkwardness', 'flea_bite', 'difficultness', 'cumbersomeness']
senticnet['caption'] = ['0', '0.137', '0', '0.133', '#interest', '#admiration', 'positive', '0.135', 'sign', 'indication', 'post_notice', 'notice', 'red_octagon']
senticnet['captioned_media'] = ['-0.94', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.94', 'drive_recklessly', 'discord', 'mute', 'deny', 'helpless']
senticnet['captionist'] = ['0', '0.202', '-0.06', '0.291', '#interest', '#admiration', 'positive', '0.142', 'vocabulary', 'translate', 'dictionary', 'translation', 'thesaurus']
senticnet['captivate'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'captivated', 'charmed', 'bewitch', 'enthrallment', 'enchantment']
senticnet['captivated'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.356', 'captivate', 'charmed', 'bewitch', 'enthrallment', 'enchantment']
senticnet['captivating'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'captivated', 'charmed', 'captivate', 'bewitch', 'enthrallment']
senticnet['captivation'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'captivated', 'charmed', 'captivate', 'bewitch', 'enthrallment']
senticnet['captive'] = ['-0.98', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.9', 'jailbird', 'prisoner', 'imprisoned', 'convict', 'inmate']
senticnet['captivity'] = ['0', '0', '0.703', '-0.98', '#anger', '#disgust', 'negative', '-0.84', 'oppression', 'enslavement', 'slave', 'caged_lion', 'misery']
senticnet['captor'] = ['-0.98', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.9', 'jailbird', 'prisoner', 'imprisoned', 'convict', 'inmate']
senticnet['capture'] = ['0.619', '0.663', '-0.28', '0.362', '#joy', '#interest', 'positive', '0.341', 'captivated', 'charmed', 'beguiled', 'enthralling', 'bewitching']
senticnet['capture_prey'] = ['0.812', '0.872', '0', '0', '#joy', '#interest', 'positive', '0.842', 'insect', 'fly_away', 'small_insect', 'maggot', 'chihuahua']
senticnet['capturing_prey'] = ['0.812', '0.872', '0', '0', '#joy', '#interest', 'positive', '0.842', 'insect', 'fly_away', 'small_insect', 'maggot', 'chihuahua']
senticnet['capybara'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'carbon_monoxide', 'tree_animal']
senticnet['car'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'safety', 'traffic_light', 'slow_speed', 'safety_belt', 'avoid_crash']
senticnet['car_accident'] = ['-0.73', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.79', 'die_painfully', 'arrest_police', 'get_accident', 'fall_head', 'skin_knee']
senticnet['car_action'] = ['0.024', '0.097', '-0.06', '0.235', '#interest', '#admiration', 'positive', '0.074', 'passenger_car', 'car_travel', 'steer_car', 'car_movement', 'conveyance']
senticnet['car_bomb'] = ['0', '-0.03', '-0.04', '-0.19', '#fear', '#disgust', 'negative', '-0.06', 'nuclear_bomb', 'explosive', 'grenade', 'nuclear_weapon', 'aerial_bomb']
senticnet['car_boot'] = ['0.048', '0', '0', '0.092', '#joy', '#admiration', 'positive', '0.07', 'gym_locker', 'observatory', 'prison_cell', 'luggage_store', 'luggage_compartment']
senticnet['car_break_down'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'receive_paycheck', 'present_bill', 'car_crash', 'dig_ground', 'work']
senticnet['car_bus'] = ['0', '0', '0', '0.089', '#admiration', '#admiration', 'positive', '0.089', 'car_truck', 'vehicle', 'car_drive', 'big_car', 'truck']
senticnet['car_charger'] = ['0', '-0.12', '0.011', '0.089', '#surprise', '#admiration', 'positive', '0.067', 'cellular_phone', 'telephone', 'wireless', 'connect', 'message']
senticnet['car_chase'] = ['0', '0.786', '0', '0.917', '#interest', '#admiration', 'positive', '0.852', 'act', 'portray', 'play_ping_pong', 'school_play', 'dangerous']
senticnet['car_collided_with'] = ['-0.90', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.86', 'trauma', 'car_wreck', 'broken_bone', 'bad_knee', 'wreck']
senticnet['car_crash'] = ['0', '0', '-0.98', '-0.73', '#fear', '#disgust', 'negative', '-0.85', 'accident', 'skid_mark', 'automobile_accident', 'speeding_ticket', 'traffic_jam']
senticnet['car_crusher'] = ['-0.84', '0', '0.96', '-0.91', '#anger', '#disgust', 'negative', '-0.90', 'housewrecker', 'housebreaker', 'knacker', 'laborer', 'manual_laborer']
senticnet['car_dashboard'] = ['0.725', '0.796', '0', '0', '#joy', '#interest', 'positive', '0.76', 'car_stereo', 'battery', 'seatbelt', 'spare_tire', 'brake_pedal']
senticnet['car_dealer'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'sell_car', 'truck_stop', 'vehicle', 'insurance', 'car']
senticnet['car_dealership'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'sell_car', 'truck_stop', 'vehicle', 'insurance', 'car']
senticnet['car_drive'] = ['0', '0', '0', '0.089', '#admiration', '#admiration', 'positive', '0.089', 'car_truck', 'vehicle', 'car_bus', 'big_car', 'truck']
senticnet['car_engine'] = ['0', '-0.01', '0', '0.064', '#surprise', '#admiration', 'positive', '0.042', 'car_gas', 'car_power', 'fossil_fuel', 'run_car', 'motor_vehicle']
senticnet['car_exhaust'] = ['0.047', '0', '-0.10', '-0.06', '#fear', '#disgust', 'negative', '-0.03', 'smoke', 'tobacco_product', 'smoke_tobacco', 'lung_cancer', 'cigar']
senticnet['car_gas'] = ['0', '-0.01', '0', '0.064', '#surprise', '#admiration', 'positive', '0.042', 'car_power', 'car_engine', 'fossil_fuel', 'oil', 'fuel']
senticnet['car_go'] = ['0', '0', '0', '0.165', '#admiration', '#admiration', 'positive', '0.165', 'get_around', 'passenger_car', 'transportation', 'car_travel', 'use_vehicle']
senticnet['car_home'] = ['0', '0.281', '0', '0.206', '#interest', '#admiration', 'positive', '0.243', 'type_of_car', 'car', 'theater', 'movie_theater', 'back_airplane']
senticnet['car_key'] = ['0', '0.757', '0', '0.811', '#interest', '#admiration', 'positive', '0.784', 'find_car_key', 'car', 'drive_license', 'start_car_engine', 'transportation']
senticnet['car_mechanic'] = ['0.969', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.936', 'attendant', 'manager', 'public_servant', 'specialist', 'accomplice']
senticnet['car_mirror'] = ['0.584', '-0.62', '0', '0.815', '#surprise', '#admiration', 'positive', '0.675', 'cheval_glass', 'mirror', 'hand_glass', 'hand_mirror', 'looking_glass']
senticnet['car_model'] = ['0', '0', '0', '0.103', '#admiration', '#admiration', 'positive', '0.103', 'fancy_car', 'old_car', 'expensive_car', 'automobile_model', 'car']
senticnet['car_movement'] = ['0', '0', '0', '0.196', '#admiration', '#admiration', 'positive', '0.196', 'steer_car', 'car_action', 'car_travel', 'car_steer', 'control_car']
senticnet['car_park'] = ['0.043', '0.087', '0', '0', '#joy', '#interest', 'positive', '0.065', 'station', 'park_structure', 'taxiway', 'parking_lot', 'park_zone']
senticnet['car_part'] = ['0.873', '0.873', '0', '0', '#joy', '#interest', 'positive', '0.873', 'gear', 'two_wheels', 'wheel', 'car', 'windshield']
senticnet['car_path'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'path', 'roadside', 'trail', 'intersection', 'under_bridge']
senticnet['car_power'] = ['0', '-0.01', '0', '0.064', '#surprise', '#admiration', 'positive', '0.042', 'car_gas', 'car_engine', 'fossil_fuel', 'run_car', 'crude']
senticnet['car_race'] = ['-0.71', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.71', 'death_row', 'sad', 'loose_end', 'funeral_home', 'mortuary']
senticnet['car_repair'] = ['-0.04', '-0.04', '0.056', '-0.1', '#anger', '#disgust', 'negative', '-0.03', 'exercise', 'exercise_regularly', 'need_fresh_air', 'excercise', 'strong_muscle']
senticnet['car_repair_shop'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'clean', 'polish', 'shiny', 'water', 'sexy_girl']
senticnet['car_salesman'] = ['0.969', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.936', 'attendant', 'manager', 'public_servant', 'specialist', 'accomplice']
senticnet['car_showroom'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'sell_car', 'truck_stop', 'vehicle', 'insurance', 'car']
senticnet['car_steer'] = ['0', '0', '0', '0.177', '#admiration', '#admiration', 'positive', '0.177', 'car_movement', 'make_go', 'car_bus', 'run_car', 'turn_car']
senticnet['car_stereo'] = ['0.945', '0.848', '0', '0', '#joy', '#interest', 'positive', '0.896', 'dashboard', 'battery', 'seatbelt', 'spare_tire', 'brake_pedal']
senticnet['car_theft'] = ['-0.75', '0', '0.94', '0', '#sadness', '#anger', 'negative', '-0.84', 'steal_money', 'theft', 'stick_up', 'robbery', 'rob']
senticnet['car_thief'] = ['0.057', '0', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'huge_fire', 'fire', 'rapid_oxidation', 'heat_light', 'flame']
senticnet['car_travel'] = ['0.025', '0', '0', '0.171', '#joy', '#admiration', 'positive', '0.098', 'transportation', 'trucking', 'steer_car', 'car_action', 'passenger_car']
senticnet['car_truck'] = ['0', '-0.04', '0', '0.053', '#surprise', '#admiration', 'positive', '0.048', 'vehicle', 'transportation', 'big_car', 'car', 'truck']
senticnet['car_trunk'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'boot', 'put_groceries', 'extra_tyre', 'store', 'storage_place']
senticnet['car_type'] = ['0.027', '0', '0', '0.146', '#joy', '#admiration', 'positive', '0.086', 'long_car', 'big_car', 'transportation', 'large_car', 'work_well']
senticnet['car_wash'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'clean', 'polish', 'shiny', 'water', 'sexy_girl']
senticnet['car_wheel'] = ['-0.04', '-0.16', '0', '0.056', '#surprise', '#admiration', 'positive', '0.059', 'car_part', 'under_car', 'automobile', 'wheel', 'car']
senticnet['car_windscreen'] = ['0.754', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.867', 'american_flag', 'asphalt', 'rubble', 'pavement', 'pave']
senticnet['car_windshield'] = ['0.754', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.867', 'american_flag', 'asphalt', 'rubble', 'pavement', 'pave']
senticnet['car_workshop'] = ['0', '0.71', '0', '0.799', '#interest', '#admiration', 'positive', '0.754', 'repair_car', 'repair_shop', 'workshop', 'repair_motorcycle', 'check_up_car']
senticnet['car_wreck'] = ['0', '0', '0.96', '-0.76', '#anger', '#disgust', 'negative', '-0.86', 'physical_pain', 'screw_up', 'broken_finger', 'meanness', 'bloody_nose']
senticnet['carabiner'] = ['0', '0', '0', '0.09', '#admiration', '#admiration', 'positive', '0.09', 'keep_paper_together', 'bind', 'frighten_person', 'carry_paper', 'beatles_song']
senticnet['carabosse'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'lordosis', 'kyphosis', 'humpback', 'spinal_curvature', 'abnormality']
senticnet['caracal'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'kangaroo', 'giraffe', 'caribou', 'mule', 'horse']
senticnet['carafe'] = ['0.214', '0.258', '-0.09', '0.2', '#joy', '#interest', 'positive', '0.146', 'cup', 'container_cup', 'pitcher', 'mug', 'glasses']
senticnet['caramel'] = ['-0.72', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.78', 'latte', 'cappuccino', 'espresso', 'mocha', 'vanilla']
senticnet['caramel_apple'] = ['0', '-0.01', '0.037', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'pear', 'green_apple', 'apricot', 'cherry', 'lime']
senticnet['caramel_corn'] = ['0', '-0.01', '0.037', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'pear', 'green_apple', 'apricot', 'cherry', 'lime']
senticnet['caramelise'] = ['-0.18', '-0.00', '-0.18', '0', '#sadness', '#fear', 'negative', '-0.11', 'pulverize', 'violent_action', 'smash', 'hit_hard', 'melt_away']
senticnet['caramelized_onion'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'mayonnaise', 'box_cereal', 'bottle_water']
senticnet['caramelized_sugar'] = ['-0.06', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.04', 'polish', 'magnify', 'see_out', 'sill', 'pane']
senticnet['carat'] = ['-0.02', '0.143', '-0.05', '0.082', '#interest', '#admiration', 'positive', '0.037', 'medallion', 'merit', 'value', 'price', 'prize']
senticnet['carat_diamond'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'art', 'art_piece', 'museum', 'artist', 'jewel']
senticnet['carat_diamond_ring'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'card_deck', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['carat_gold'] = ['0.866', '0.82', '0', '0.713', '#joy', '#interest', 'positive', '0.8', 'titanium', 'bronze', 'alloy', 'medal', 'copper']
senticnet['caravan'] = ['0.766', '0', '0', '0.802', '#joy', '#admiration', 'positive', '0.784', 'van', 'vanguard', 'avant-garde', 'new_wave', 'bos']
senticnet['caravan_inn'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'caravansary', 'caravanserai', 'khan', 'hostel', 'imaret']
senticnet['caravansary'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['caravanserai'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'caravansary', 'hostel', 'khan', 'caravan_inn', 'imaret']
senticnet['caraway_seed'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'roll', 'custard', 'bread', 'biscuit']
senticnet['carbachol'] = ['0.95', '-0.95', '0', '0.769', '#joy', '#surprise', 'positive', '0.89', 'adrenocorticotropic', 'adrenocorticotrophic', 'stimulant', 'stimulating', 'stimulative']
senticnet['carbidopa_levodopa'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['carbine'] = ['-0.97', '0', '0.677', '-0.95', '#sadness', '#disgust', 'negative', '-0.86', 'stunt', 'game', 'mutant', 'invertebrate', 'animate_being']
senticnet['carbody'] = ['0.035', '0.112', '-0.07', '0.195', '#interest', '#admiration', 'positive', '0.067', 'car_action', 'car_travel', 'steer_car', 'make_go', 'transportation']
senticnet['carbohydrate'] = ['0.037', '0', '0', '0.063', '#joy', '#admiration', 'positive', '0.05', 'food_type', 'food_group', 'lard', 'starch', 'staple_food']
senticnet['carbol_fuchsin'] = ['0', '-0.01', '0.046', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'varnish', 'brush', 'dye', 'pigment', 'canvas']
senticnet['carbon'] = ['0.053', '0.264', '-0.11', '0.23', '#interest', '#admiration', 'positive', '0.107', 'natural_resource', 'petroleum', 'fossil_fuel', 'energy_source', 'potential_energy']
senticnet['carbon_adsorption'] = ['-0.58', '0', '0.714', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'diversionist', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['carbon_backbone'] = ['0.096', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.057', 'platform_rail', 'rail', 'catwalk', 'column', 'token_turnstile']
senticnet['carbon_composite'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'performant']
senticnet['carbon_dioxide'] = ['0.053', '0.264', '-0.11', '0.23', '#interest', '#admiration', 'positive', '0.107', 'natural_resource', 'petroleum', 'fossil_fuel', 'energy_source', 'potential_energy']
senticnet['carbon_fiber'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'performant']
senticnet['carbon_fiber_composite'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'gold', 'bronze', 'alloy', 'medal', 'copper']
senticnet['carbon_fibre'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'gold', 'bronze', 'alloy', 'medal', 'copper']
senticnet['carbon_fibre_composite'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'performant']
senticnet['carbon_fluorine'] = ['0.228', '0.123', '-0.09', '0', '#joy', '#interest', 'positive', '0.084', 'spend_time_with_child', 'social', 'friendship', 'companionship', 'get_close']
senticnet['carbon_flux'] = ['0.053', '0.264', '-0.11', '0.23', '#interest', '#admiration', 'positive', '0.107', 'natural_resource', 'petroleum', 'fossil_fuel', 'energy_source', 'potential_energy']
senticnet['carbon_kevlar'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'performant']
senticnet['carbon_monoxide'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'rodent', 'tree_animal']
senticnet['carbon_monoxide_detector'] = ['0.02', '0.076', '0', '0', '#joy', '#interest', 'positive', '0.048', 'land_earth', 'tidal_wave', 'puddle', 'brine', 'spurt']
senticnet['carbon_monoxide_exposure'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'rodent', 'tree_animal']
senticnet['carbon_sequestration'] = ['0.053', '0.264', '-0.11', '0.23', '#interest', '#admiration', 'positive', '0.107', 'natural_resource', 'petroleum', 'fossil_fuel', 'energy_source', 'potential_energy']
senticnet['carbon_sink'] = ['0.053', '0.264', '-0.11', '0.23', '#interest', '#admiration', 'positive', '0.107', 'natural_resource', 'petroleum', 'fossil_fuel', 'energy_source', 'potential_energy']
senticnet['carbon_steel'] = ['0.72', '0', '0', '0.733', '#joy', '#admiration', 'positive', '0.726', 'spanner', 'gold', 'strong_material', 'platinum', 'durable']
senticnet['carbon_storage'] = ['0.053', '0.264', '-0.11', '0.23', '#interest', '#admiration', 'positive', '0.107', 'natural_resource', 'petroleum', 'fossil_fuel', 'energy_source', 'potential_energy']
senticnet['carbon_tetrafluoride'] = ['-0.67', '0', '0.503', '-0.67', '#sadness', '#disgust', 'negative', '-0.61', 'stink_up', 'smell_up', 'stink_out', 'incense', 'perfume']
senticnet['carbon_uptake'] = ['-0.93', '0', '0.906', '-0.53', '#sadness', '#anger', 'negative', '-0.79', 'juvenility', 'state', 'immatureness', 'callowness', 'jejuneness']
senticnet['carbonate'] = ['-0.79', '0', '-1', '0', '#sadness', '#fear', 'negative', '-0.89', 'phosphate', 'ammonium', 'phosphate', 'calcium', 'sulfate']
senticnet['carbonate_bedrock'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'fossil', 'oil', 'dark_liquid', 'smithsonian_institution', 'inert_gas']
senticnet['carbonate_drink'] = ['0.032', '-0.02', '-0.04', '-0.08', '#fear', '#disgust', 'negative', '-0.01', 'liquid', 'sparkling_water', 'sweet', 'soda', 'drink']
senticnet['carbonated_beverage'] = ['0.951', '-0.97', '0', '0.639', '#joy', '#surprise', 'positive', '0.853', 'smoothie', 'drinking_water', 'nutrient', 'milk', 'refresher']
senticnet['carbonated_drink'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'diet_coke', 'sugar_free', 'energy_drink', 'drink', 'beverage']
senticnet['carbonated_soft_drink'] = ['0.951', '-0.97', '0', '0.639', '#joy', '#surprise', 'positive', '0.853', 'smoothie', 'drinking_water', 'nutrient', 'milk', 'refresher']
senticnet['carbonation'] = ['0.789', '-0.92', '0', '0.964', '#surprise', '#admiration', 'positive', '0.893', 'ammonification', 'permeation', 'impregnation', 'saturation', 'diffusion']
senticnet['carbonization'] = ['0.037', '0.084', '0', '0', '#joy', '#interest', 'positive', '0.06', 'leave_over', 'leftover', 'cellulose', 'cane', 'remainder']
senticnet['carbonless_paper'] = ['0.022', '-0.05', '0.079', '-0.11', '#anger', '#disgust', 'negative', '-0.02', 'stationery', 'ink', 'ball_point_pen', 'refill', 'note']
senticnet['carbonyl_carbon'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
senticnet['carbonyl_oxygen'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
senticnet['carboxamide'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'enhance', 'intensify', 'deepen', 'potentiate', 'heighten']
senticnet['carboxyhemoglobin'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'rodent', 'tree_animal']
senticnet['carboxylic_acid'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'adipic_acid', 'succinic_acid', 'valeric_acid']
senticnet['carboy'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'drink_container', 'liquid_container', 'drink_cup', 'drink_holder', 'type_of_container']
senticnet['carbylamine'] = ['-0.55', '0', '0.736', '-0.71', '#anger', '#disgust', 'negative', '-0.66', 'neurosis', 'neuroticism', 'psychoneurosis', 'hysterocatalepsy', 'anxiety_hysteria']
senticnet['carcase'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'hip', 'joint', 'above_leg', 'knee']
senticnet['carceral'] = ['0.654', '-0.71', '0', '0.624', '#joy', '#surprise', 'positive', '0.664', 'viewable', 'gross', 'telescopic', 'seeable', 'visual']
senticnet['carcinoma'] = ['0', '0.707', '0', '0.939', '#interest', '#admiration', 'positive', '0.823', 'hyperplasia', 'nodule', 'goiter', 'lesion', 'nodular']
senticnet['card'] = ['0.953', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.911', 'habit_form', 'poker_chip', 'bet', 'chance', 'letter']
senticnet['card_deck'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'jewelry_store', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['card_game'] = ['-0.03', '0.085', '-0.07', '0.114', '#interest', '#admiration', 'positive', '0.022', 'many_games', 'poker', 'form_of_gambling', 'billiards', 'backgammon']
senticnet['card_holder'] = ['-0.05', '0.26', '-0.08', '0.142', '#interest', '#admiration', 'positive', '0.068', 'expense', 'afford', 'payment', 'cost', 'owe']
senticnet['card_issuer'] = ['-0.05', '0.26', '-0.08', '0.142', '#interest', '#admiration', 'positive', '0.068', 'expense', 'afford', 'payment', 'cost', 'owe']
senticnet['card_piquet_pack'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'jewelry_store', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['card_poker'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'jewelry_store', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['card_stud'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'jewelry_store', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['card_type'] = ['0.07', '0', '0', '0.086', '#joy', '#admiration', 'positive', '0.078', 'ephemeral', 'enjoyable_activity', 'good_sport', 'competitive', 'medical_test']
senticnet['cardamine'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
senticnet['cardamom'] = ['0', '0', '-0.76', '-0.72', '#fear', '#disgust', 'negative', '-0.74', 'cumin', 'cilantro', 'coriander', 'turmeric', 'clove']
senticnet['cardboard'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'gift_wrap', 'kraft', 'kraft_paper', 'wrapping_paper', 'paper']
senticnet['cardboard_box'] = ['0', '-0.02', '0', '0.051', '#surprise', '#admiration', 'positive', '0.037', 'box', 'storage', 'package', 'gift_box', 'bin']
senticnet['cardboard_container'] = ['0', '-0.02', '0', '0.051', '#surprise', '#admiration', 'positive', '0.037', 'cardboard_box', 'bag', 'package', 'box_cereal', 'bin']
senticnet['cardboard_sign'] = ['0', '0.137', '0', '0.133', '#interest', '#admiration', 'positive', '0.135', 'sign', 'indication', 'post_notice', 'notice', 'red_octagon']
senticnet['cardboard_sleeve'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['cardboard_tube'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'aluminium_foil', 'foil', 'gold_foil']
senticnet['cardcaptor_sakura'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['cardholder'] = ['-0.05', '0.14', '0.015', '0.082', '#interest', '#admiration', 'positive', '0.039', 'cash', 'cash_pay', 'need_earn_money', 'open_wallet', 'earn_wage']
senticnet['cardiac'] = ['-0.98', '0', '0.827', '-0.94', '#sadness', '#disgust', 'negative', '-0.91', 'congestive_heart_failure', 'heart_attack', 'failure', 'heart_disease', 'cardiopathy']
senticnet['cardiac_arrest'] = ['-0.98', '0', '0.827', '-0.94', '#sadness', '#disgust', 'negative', '-0.91', 'congestive_heart_failure', 'heart_attack', 'failure', 'heart_disease', 'cardiopathy']
senticnet['cardiac_arrhythmia'] = ['-0.83', '0', '0.667', '-0.71', '#sadness', '#disgust', 'negative', '-0.74', 'heart_block', 'atrioventricular_block', 'bradycardia', 'ventricular_fibrillation', 'tachycardia']
senticnet['cardiac_contraction'] = ['-0.56', '0', '0.849', '-0.61', '#anger', '#disgust', 'negative', '-0.67', 'convulsive', 'unsteady', 'jerky', 'spasmodic', 'spastic']
senticnet['cardiac_disease'] = ['-0.98', '0', '0.827', '-0.94', '#sadness', '#disgust', 'negative', '-0.91', 'congestive_heart_failure', 'heart_attack', 'failure', 'heart_disease', 'cardiopathy']
senticnet['cardiac_drug'] = ['-0.95', '0', '0.475', '-0.91', '#sadness', '#disgust', 'negative', '-0.78', 'heart_disease', 'heart_attack', 'cholesterol', 'blocked_artery', 'need_medicine']
senticnet['cardiac_failure'] = ['-0.98', '0', '0.827', '-0.94', '#sadness', '#disgust', 'negative', '-0.91', 'congestive_heart_failure', 'heart_attack', 'failure', 'heart_disease', 'cardiopathy']
senticnet['cardiac_glycoside'] = ['0.701', '-0.76', '0', '0.904', '#surprise', '#admiration', 'positive', '0.791', 'curative', 'heal', 'medicine', 'get_cured', 'take_care']
senticnet['cardiac_involvement'] = ['-0.87', '0', '0.531', '-0.68', '#sadness', '#disgust', 'negative', '-0.69', 'rubor', 'redness', 'dermatomyositis', 'inclusion_body_myositis', 'inflammation']
senticnet['cardiac_massage'] = ['-0.57', '0', '0.737', '-0.85', '#anger', '#disgust', 'negative', '-0.72', 'emergency_procedure', 'emergency', 'external_respiration', 'respiration', 'ventilation']
senticnet['cardiac_monitor'] = ['-0.57', '0', '0.737', '-0.85', '#anger', '#disgust', 'negative', '-0.72', 'emergency_procedure', 'emergency', 'external_respiration', 'respiration', 'ventilation']
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senticnet['cardiomegaly'] = ['0.777', '-0.89', '0', '0.508', '#joy', '#surprise', 'positive', '0.727', 'stretching', 'enlarge', 'enlargement', 'get_bigger', 'distension']
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senticnet['cardiovascular_pathology'] = ['-0.59', '0', '0.911', '-0.87', '#anger', '#disgust', 'negative', '-0.79', 'osteosclerosis', 'sclerosis', 'disseminated_sclerosis', 'multiple_sclerosis', 'induration']
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senticnet['care_child'] = ['0.909', '0', '0', '0.859', '#joy', '#admiration', 'positive', '0.884', 'responsibility', 'love_child', 'quiet_cry_baby', 'quiet_child', 'care']
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senticnet['career_demand'] = ['0.91', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.916', 'graduate_education', 'igert', 'engineer', 'challenge', 'training']
senticnet['career_grand_slam'] = ['0.749', '0', '0', '0.952', '#joy', '#admiration', 'positive', '0.851', 'high_class', 'citation', 'suspicion', 'goodness', 'prosperity']
senticnet['career_ladder'] = ['0.907', '0', '0', '0.79', '#joy', '#admiration', 'positive', '0.849', 'work', 'task', 'hard_work', 'accomplish', 'find_accomplish']
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senticnet['careful_observation'] = ['0', '0.811', '0', '0', '#interest', '#interest', 'positive', '0.811', 'observe', 'glimpse', 'look', 'stare', 'glance']
senticnet['careful_plan'] = ['0.751', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.871', 'plan', 'eat_potato_chip', 'intelligent', 'healthy_food', 'stuff_face']
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senticnet['careful_stewardship'] = ['0.545', '-0.59', '0', '0.865', '#surprise', '#admiration', 'positive', '0.67', 'farseeing', 'longsighted', 'telescopic', 'hyperopic', 'hypermetropic']
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senticnet['carefully-chosen'] = ['0', '-0.90', '0', '0.989', '#surprise', '#admiration', 'positive', '0.947', 'well-chosen', 'well-selected', 'well-crafted', 'well-placed', 'well-constructed']
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senticnet['carefully_proofread'] = ['0.642', '-0.53', '0', '0.575', '#joy', '#admiration', 'positive', '0.583', 'check', 'assure', 'see', 'insure', 'examine']
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senticnet['caressing'] = ['0', '0', '0.807', '-0.78', '#anger', '#disgust', 'negative', '-0.79', 'groped', 'fondling', 'grope', 'fumbling', 'groping']
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senticnet['cargo_bay'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'chick', 'wild_bird', 'baby_bird', 'lay_egg', 'bird_home']
senticnet['cargo_carrier'] = ['0', '0.089', '0.01', '0.118', '#interest', '#admiration', 'positive', '0.066', 'crate', 'mom_house', 'sack', 'poker_game', 'safekeep']
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senticnet['cargo_hold'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'train_station', 'government_office', 'cable_car', 'sport_stadium', 'terminal']
senticnet['cargo_ship'] = ['-0.78', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.78', 'merchant_marine', 'transportation', 'merchant_vessel', 'shipping', 'embark']
senticnet['cargo_terminal'] = ['0', '0.147', '0', '0.099', '#interest', '#admiration', 'positive', '0.123', 'airliner', 'airport', 'baggage_compartment', 'aircraft', 'cable_car']
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senticnet['caribbean_carnival'] = ['0.957', '0.7', '0', '0', '#joy', '#interest', 'positive', '0.829', 'ballpark', 'coven', 'disneyland', 'hockey_game', 'symposium']
senticnet['caribbean_getaway'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'lurking_place', 'spot', 'drop', 'hiding_place', 'den']
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senticnet['caribbean_sea'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'pacific_ocean', 'small_lake', 'mere']
senticnet['caribbean_vacation'] = ['0.024', '0.152', '-0.06', '0.236', '#interest', '#admiration', 'positive', '0.087', 'vacation', 'exit', 'stance', 'take_break', 'holiday']
senticnet['caribou'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'cheetah', 'giraffe', 'kangaroo', 'mule', 'horse']
senticnet['caribou_herd'] = ['0', '0', '-0.70', '-0.77', '#fear', '#disgust', 'negative', '-0.73', 'animal', 'brown_bear', 'forest', 'creature', 'polar_bear']
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senticnet['caribou_migration'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'cheetah', 'giraffe', 'kangaroo', 'mule', 'horse']
senticnet['caricature'] = ['0.788', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.779', 'manga', 'anime', 'radio-control_helicopter', 'beat_off', 'cartoon']
senticnet['carillon'] = ['0.034', '0', '0.01', '0.087', '#joy', '#admiration', 'positive', '0.037', 'chime', 'bell_noise', 'alto', 'piano', 'ring_loudly']
senticnet['carinate'] = ['0.969', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.772', 'aspheric', 'aspherical', 'prolate', 'rounded', 'crescent']
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senticnet['caring_adult'] = ['0.36', '0.248', '-0.08', '0.278', '#joy', '#admiration', 'positive', '0.201', 'feel_important', 'strong_relationship', 'eat_together', 'world_peace', 'express_gratitude']
senticnet['caring_attitude'] = ['0.701', '0.874', '0', '0.752', '#interest', '#admiration', 'positive', '0.776', 'self_satisfaction', 'enjoy_life', 'good_feeling', 'good_attitude', 'enjoy']
senticnet['cariogenic'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'plaque']
senticnet['cariogenic_bacteria'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'plaque']
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senticnet['carline_thistle'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
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senticnet['carling_cup_fourth'] = ['0.823', '0.982', '0', '0', '#joy', '#interest', 'positive', '0.902', 'spherical', 'three_dimensions', 'round_orb', 'coil', 'fill_with_sand']
senticnet['carlo_carr'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'native_person', 'revolutionary', 'republic', 'revolution', 'look_ahead']
senticnet['carlo_collodi'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'lordosis', 'kyphosis', 'humpback', 'spinal_curvature', 'abnormality']
senticnet['carmarthen_town_west'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'couch', 'stool', 'coach_seat']
senticnet['carmex'] = ['0.03', '0', '0.07', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'checkbook_holder', 'account_book', 'checkbook_cover', 'highlighter_pen', 'shine']
senticnet['carmike'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'theater', 'drive-in_movie', 'watch_movie', 'cinema', 'movie']
senticnet['carminative'] = ['0.701', '-0.76', '0', '0.904', '#surprise', '#admiration', 'positive', '0.791', 'curative', 'heal', 'medicine', 'get_cured', 'take_care']
senticnet['carmine_infantino'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'encyclopaedia', 'child_story', 'comic', 'disney_cartoon']
senticnet['carnage'] = ['-0.79', '0', '0.624', '-0.61', '#sadness', '#anger', 'negative', '-0.67', 'murder', 'slaying', 'bloodbath', 'bloodshed', 'bloodletting']
senticnet['carnal'] = ['-0.74', '0', '0.677', '-0.77', '#sadness', '#disgust', 'negative', '-0.73', 'whore', 'jazz', 'hump', 'bonk', 'screw']
senticnet['carnal_concupiscence'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'lecherousness', 'lustfulness', 'amorousness']
senticnet['carnal_desire'] = ['0.509', '-0.86', '0', '0.691', '#surprise', '#admiration', 'positive', '0.688', 'amorousness', 'sexiness', 'amativeness', 'eroticism', 'erotism']
senticnet['carnal_intercourse'] = ['0.531', '0.673', '-0.27', '0.275', '#joy', '#interest', 'positive', '0.301', 'amorous', 'amorousness', 'amatory', 'enamoredness', 'puppy_love']
senticnet['carnal_knowledge'] = ['0.542', '-0.83', '0', '0.927', '#surprise', '#admiration', 'positive', '0.769', 'incest', 'extramarital_sex', 'free_love', 'sexual_intercourse', 'intercourse']
senticnet['carnal_lust'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'lecherousness', 'lustfulness', 'amorousness']
senticnet['carnal_mind'] = ['0.673', '-0.66', '0', '0.566', '#joy', '#surprise', 'positive', '0.634', 'wickedness', 'impiety', 'impiousness', 'unjustness', 'dishonor']
senticnet['carnality'] = ['0.509', '-0.86', '0', '0.691', '#surprise', '#admiration', 'positive', '0.688', 'amorousness', 'sexiness', 'amativeness', 'eroticism', 'erotism']
senticnet['carnauba'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'palm', 'fan_palm', 'wax_palm', 'palmetto', 'palmyra']
senticnet['carnauba_palm'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'fan_palm', 'carnauba', 'wax_palm', 'palmetto', 'palmyra']
senticnet['carnaval'] = ['0.957', '0.7', '0', '0', '#joy', '#interest', 'positive', '0.829', 'ballpark', 'coven', 'disneyland', 'hockey_game', 'symposium']
senticnet['carne_asada'] = ['0.116', '-0.02', '0.025', '0.156', '#joy', '#admiration', 'positive', '0.069', 'dance_move', 'latin_dance', 'body_movement', 'waltz', 'dance']
senticnet['carnegie_institution'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'institution', 'tundra', 'child_story', 'natural_history_museum', 'cartoon']
senticnet['carnegie_library'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'a_lot_of_books', 'old_testament', 'cook_book', 'magazine', 'librarian']
senticnet['carnelian'] = ['0.037', '0.073', '0.027', '0.072', '#interest', '#admiration', 'positive', '0.039', 'sapphire', 'birth_stone', 'turquoise', 'gemstone', 'amethyst']
senticnet['carnival'] = ['0.957', '0.7', '0', '0', '#joy', '#interest', 'positive', '0.829', 'ballpark', 'coven', 'disneyland', 'hockey_game', 'symposium']
senticnet['carnival_celebration'] = ['0.957', '0.7', '0', '0', '#joy', '#interest', 'positive', '0.829', 'ballpark', 'coven', 'disneyland', 'hockey_game', 'symposium']
senticnet['carnival_mask'] = ['0.108', '-0.04', '0.043', '0.065', '#joy', '#admiration', 'positive', '0.043', 'sunglasses', 'glass', 'mask', 'drink_cup', 'focus_light']
senticnet['carnival_parade'] = ['0.957', '0.7', '0', '0', '#joy', '#interest', 'positive', '0.829', 'ballpark', 'coven', 'disneyland', 'hockey_game', 'symposium']
senticnet['carnival_procession'] = ['0.957', '0.7', '0', '0', '#joy', '#interest', 'positive', '0.829', 'ballpark', 'coven', 'disneyland', 'hockey_game', 'symposium']
senticnet['carnival_queen'] = ['0.957', '0.7', '0', '0', '#joy', '#interest', 'positive', '0.829', 'ballpark', 'coven', 'disneyland', 'hockey_game', 'symposium']
senticnet['carnival_ride'] = ['0.086', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'carny', 'fortune_teller', 'midway', 'ferris_wheel', 'roller-coaster']
senticnet['carnival_take_place'] = ['0.957', '0.7', '0', '0', '#joy', '#interest', 'positive', '0.829', 'ballpark', 'coven', 'disneyland', 'hockey_game', 'symposium']
senticnet['carnival_triumph_cruise_ship'] = ['-0.99', '0', '0.658', '-0.73', '#sadness', '#disgust', 'negative', '-0.79', 'purposeless', 'afloat', 'aimless', 'directionless', 'planless']
senticnet['carnivalesque'] = ['0.84', '-0.56', '0', '0.561', '#joy', '#surprise', 'positive', '0.654', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['carnivore'] = ['-0.02', '0', '0.044', '0', '#sadness', '#anger', 'negative', '-0.03', 'large_cat', 'fast_person', 'household_pet', 'big_cat', 'good_pet']
senticnet['carnivorous_dinosaur'] = ['0.969', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.96', 'extinct', 'animal', 'ice', 'elephant', 'giant']
senticnet['carnot_engine'] = ['-0.71', '0', '0.509', '-0.92', '#sadness', '#disgust', 'negative', '-0.71', 'natural_process', 'natural_action', 'activity', 'curing', 'congelation']
senticnet['carnotite'] = ['-0.05', '-0.00', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'pigment', 'many_shades', 'cyan', 'green_yellow', 'material']
senticnet['carny'] = ['0.082', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.057', 'carnival_ride', 'fortune_teller', 'midway', 'ferris_wheel', 'roller-coaster']
senticnet['carob_tree'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'black_locust']
senticnet['carolingian_minuscule'] = ['-0.95', '0', '0.517', '-0.53', '#sadness', '#disgust', 'negative', '-0.67', 'copperplate', 'hand', 'minuscule', 'italic', 'round_hand']
senticnet['caroll_spinney'] = ['-0.93', '0', '0.77', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'hothead', 'unpleasant_person', 'disagreeable_person', 'crabby_person', 'crab']
senticnet['carolus_disease'] = ['-0.85', '0', '0.657', '-0.86', '#sadness', '#disgust', 'negative', '-0.79', 'pulseless_disease', 'temporal_arteritis', 'rubor', 'redness', 'arteritis']
senticnet['carom'] = ['0.793', '0.915', '0', '0', '#joy', '#interest', 'positive', '0.854', 'ball', 'ball_bounce', 'football', 'soccer', 'basketball']
senticnet['carotenemia'] = ['-0.56', '0', '0.906', '-0.80', '#anger', '#disgust', 'negative', '-0.76', 'jaundice_of_newborn', 'disease', 'icterus_neonatorum', 'kernicterus', 'symptom']
senticnet['carotid'] = ['-0.81', '0', '0.684', '-0.81', '#sadness', '#disgust', 'negative', '-0.77', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['carotid_artery'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'chest', 'artery', 'vein', 'internal_organ', 'under_head']
senticnet['carotid_canal'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['carouse'] = ['-0.97', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.94', 'bacchanalia', 'bacchanalian', 'drunken', 'dipsomaniac', 'drunkenness']
senticnet['carp_fishing'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'catch_fish', 'fishing', 'patience', 'fish_lake', 'bait']
senticnet['carpal'] = ['-0.74', '0', '0.598', '-0.75', '#sadness', '#disgust', 'negative', '-0.69', 'lunate_bone', 'semilunar_bone', 'carpal_bone', 'triquetral', 'triquetral_bone']
senticnet['carpal_bone'] = ['-0.66', '0', '0.947', '-0.74', '#anger', '#disgust', 'negative', '-0.78', 'lunate_bone', 'semilunar_bone', 'bone', 'triquetral', 'triquetral_bone']
senticnet['carpal_tunnel'] = ['-0.98', '0', '0.678', '-0.95', '#sadness', '#disgust', 'negative', '-0.87', 'rubor', 'redness', 'inflammation', 'epicondylitis', 'lateral_epicondylitis']
senticnet['carpal_tunnel_syndrome'] = ['-0.41', '0', '-0.28', '-0.05', '#sadness', '#fear', 'negative', '-0.25', 'numbness', 'median_nerve', 'weakness', 'pain', 'muscle_damage']
senticnet['carpel'] = ['0.967', '0.9', '0', '0', '#joy', '#interest', 'positive', '0.933', 'fruit_tree', 'baby_flower', 'fig', 'strawberry', 'star_anise']
senticnet['carpenter'] = ['-0.04', '-0.02', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.02', 'use_illegal_drug', 'delusional', 'human', 'cold_person', 'busy_person']
senticnet['carpenter_ant'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'giant_hornet', 'hornet', 'hornet_wasp']
senticnet['carpentry'] = ['0', '0.934', '0', '0.872', '#interest', '#admiration', 'positive', '0.903', 'bricklaying', 'plastering', 'bricklayer', 'masonry', 'tiling']
senticnet['carpet'] = ['0.778', '0.804', '0', '0', '#joy', '#interest', 'positive', '0.791', 'rug', 'walk_closet', 'linoleum', 'mat', 'floor']
senticnet['carpet_beetle'] = ['-0.15', '0.162', '0.596', '-0.62', '#anger', '#disgust', 'negative', '-0.30', 'hackle', 'umbrage', 'offense', 'ire', 'see_red']
senticnet['carpet_python'] = ['-0.85', '0', '0.685', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'alligator', 'crocodilian', 'false_gavial', 'gator', 'caiman']
senticnet['carpet_shark'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'plectognath', 'plectognathfish', 'blowfish', 'triggerfish', 'filefish']
senticnet['carpet_slipper'] = ['-0.54', '0', '0.612', '-0.98', '#anger', '#disgust', 'negative', '-0.71', 'scuff', 'mule', 'bootee', 'bootie', 'footwear']
senticnet['carpet_store'] = ['-0.07', '0.228', '0.019', '0.089', '#interest', '#admiration', 'positive', '0.056', 'home_depot', 'lumberyard', 'furniture_store', 'front_porch', 'shoe_shop']
senticnet['carpet_sweeper'] = ['-0.04', '-0.02', '0.089', '-0.14', '#anger', '#disgust', 'negative', '-0.06', 'steam_locomotive', 'loudspeaker', 'collector', 'siren', 'activist']
senticnet['carpeted_floor'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'coach_seat', 'stool', 'seat']
senticnet['carpool_lane'] = ['0.825', '0', '0', '0.703', '#joy', '#admiration', 'positive', '0.764', 'pay_toll', 'get_fast', 'access', 'fast_travel', 'access_quickly']
senticnet['carriage_accident'] = ['-0.87', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.91', 'loss_oflife', 'chest_pain', 'die', 'tumor', 'end_life']
senticnet['carriage_house'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'condominium', 'rent_apartment', 'high_rise', 'condo', 'row_house']
senticnet['carriage_maker'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'pharmaceutical_chemist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['carried_out'] = ['0', '0.067', '0', '0.062', '#interest', '#admiration', 'positive', '0.065', 'social_skill', 'show_up', 'go_abroad', 'go_new_place', 'visit']
senticnet['carried_unanimously'] = ['0.779', '-0.88', '0', '0.521', '#joy', '#surprise', 'positive', '0.727', 'corrigible', 'correctable', 'improvable', 'reformable', 'redeemable']
senticnet['carrier'] = ['0', '0.089', '0.01', '0.118', '#interest', '#admiration', 'positive', '0.066', 'crate', 'mom_house', 'sack', 'poker_game', 'safekeep']
senticnet['carrier_bag'] = ['-0.04', '0', '0.041', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'back_sofa', 'home_improvement_store', 'closet', 'under_carpet', 'shoe_shop']
senticnet['carrier_grade'] = ['0.637', '-0.91', '0', '0.757', '#surprise', '#admiration', 'positive', '0.768', 'tractability', 'manageableness', 'docility', 'obedience', 'amenability']
senticnet['carrizo_citrange'] = ['-0.69', '0', '0.516', '-0.94', '#sadness', '#disgust', 'negative', '-0.71', 'insubordinate', 'recalcitrant', 'obstreperous', 'defiant', 'resistive']
senticnet['carroll'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
senticnet['carrom'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'golf', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['carron_oil'] = ['-0.64', '0', '0.941', '-0.92', '#anger', '#disgust', 'negative', '-0.83', 'unction', 'salve', 'ointment', 'unguent', 'balm']
senticnet['carrot_cake'] = ['0.937', '0', '0', '0.828', '#joy', '#admiration', 'positive', '0.883', 'bakery', 'produce_section', 'thanksgiving_dinner', 'dinner_meal', 'fast_food']
senticnet['carrot_ginger'] = ['0.084', '-0.05', '0', '-0.04', '#joy', '#surprise', 'positive', '0.031', 'entree', 'feast', 'satiation', 'breakfast', 'supper']
senticnet['carrot_juice'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'lemon_juice', 'chocolate_pudding', 'grape_juice', 'orange_juice', 'juice']
senticnet['carrozzeria'] = ['0.755', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.728', 'high_class', 'expensive_car', 'ferrari', 'fancy_car', 'sports_car']
senticnet['carry'] = ['-0.03', '0.178', '-0.12', '0.279', '#interest', '#admiration', 'positive', '0.075', 'load', 'haul', 'fetch', 'spring_leak', 'carry_belongings']
senticnet['carry_around'] = ['0', '0.13', '-0.05', '0.184', '#interest', '#admiration', 'positive', '0.085', 'portable', 'hold_place', 'carry_belongings', 'carry_clothes', 'carry_stuff']
senticnet['carry_baby'] = ['0.108', '-0.04', '0.043', '0.065', '#joy', '#admiration', 'positive', '0.043', 'family', 'love_back', 'mother', 'father', 'change_diaper']
senticnet['carry_belongings'] = ['0', '0.148', '-0.09', '0.282', '#interest', '#admiration', 'positive', '0.112', 'carry_clothes', 'carry_book', 'transfer', 'carry_stuff', 'fetch']
senticnet['carry_blood'] = ['0', '0.1', '-0.06', '0.171', '#interest', '#admiration', 'positive', '0.069', 'carry_belongings', 'trucking', 'carry_clothes', 'transport_person', 'haul']
senticnet['carry_book'] = ['-0.04', '0.149', '-0.09', '0.236', '#interest', '#admiration', 'positive', '0.063', 'carry_stuff', 'carry_belongings', 'carry_clothes', 'shift', 'carry_grocery']
senticnet['carry_briefcase'] = ['-0.75', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.78', 'hurry_work', 'voice_concern', 'sail_around_world', 'storm_out_room', 'age_gracefully']
senticnet['carry_clothes'] = ['0', '0.108', '-0.05', '0.209', '#interest', '#admiration', 'positive', '0.087', 'carry_belongings', 'carry_book', 'transport', 'carry_stuff', 'carry_laundry']
senticnet['carry_credit_card'] = ['-0.04', '0', '0.03', '0', '#sadness', '#anger', 'negative', '-0.03', 'use_atm', 'carry_wallet', 'take_out_wallet', 'decide_buy', 'look_price']
senticnet['carry_disease'] = ['-0.11', '0', '0.03', '-0.07', '#sadness', '#disgust', 'negative', '-0.07', 'scratch_back', 'train_open_mind', 'unhealthy', 'lose_key', 'voice_opinion']
senticnet['carry_document'] = ['0.023', '0.085', '0', '0.061', '#interest', '#admiration', 'positive', '0.056', 'carry_paper', 'store_document', 'organize_document', 'store_checkbook', 'keep_paper_together']
senticnet['carry_food'] = ['0', '-0.17', '0', '0.244', '#surprise', '#admiration', 'positive', '0.209', 'obtain_food', 'serve_food', 'take_order', 'consumption', 'make_dinner']
senticnet['carry_goods'] = ['-0.04', '0.066', '0', '0.149', '#interest', '#admiration', 'positive', '0.057', 'carry_laundry', 'shift', 'hold_grocery', 'hold_flower', 'carry_grocery']
senticnet['carry_grocery'] = ['-0.04', '0.166', '-0.10', '0.207', '#interest', '#admiration', 'positive', '0.056', 'carry_stuff', 'carry_book', 'carry_laundry', 'carry_clothes', 'transport']
senticnet['carry_gun'] = ['-0.04', '0', '-0.08', '-0.12', '#fear', '#disgust', 'negative', '-0.08', 'armed', 'find_victim', 'buy_weapon', 'bomb', 'buy_gun']
senticnet['carry_hand'] = ['0.937', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.844', 'shift', 'carry_goods', 'fetch', 'carry', 'upkeep']
senticnet['carry_heavy_load'] = ['-0.14', '0', '-0.07', '0', '#sadness', '#fear', 'negative', '-0.11', 'read_science_book', 'study_chemistry', 'study_biology', 'finish_test', 'pull_wagon']
senticnet['carry_home'] = ['-0.88', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.87', 'pay_dinner', 'go_debt', 'count_change', 'gain_interest', 'receive_change']
senticnet['carry_laundry'] = ['-0.02', '0.139', '-0.08', '0.24', '#interest', '#admiration', 'positive', '0.068', 'move', 'carry_clothes', 'hold_stuff', 'carry_belongings', 'carry_goods']
senticnet['carry_liquid'] = ['-0.03', '0.146', '-0.08', '0.208', '#interest', '#admiration', 'positive', '0.058', 'sail_ocean', 'hold_flower', 'carry_book', 'bail_water', 'hold_stuff']
senticnet['carry_luggage'] = ['0', '0', '0.764', '-0.84', '#anger', '#disgust', 'negative', '-0.80', 'pull_off_road', 'locate_place', 'discover_new_place', 'pack_luggage', 'drive_destination']
senticnet['carry_lunch'] = ['0.853', '0', '0', '0.84', '#joy', '#admiration', 'positive', '0.847', 'super', 'better_good', 'great', 'wonderful', 'better']
senticnet['carry_money'] = ['0', '0.147', '0', '0.099', '#interest', '#admiration', 'positive', '0.123', 'hold_money', 'put_money', 'store_money', 'keep_money', 'money_holder']
senticnet['carry_off'] = ['0.701', '-0.56', '0', '0.921', '#joy', '#admiration', 'positive', '0.728', 'deliver_goods', 'succeed', 'win', 'bring_home_bacon', 'come_through']
senticnet['carry_on'] = ['-0.29', '-0.00', '0', '-0.16', '#sadness', '#disgust', 'negative', '-0.14', 'act_up', 'move', 'fall_from_grace', 'misdemean', 'act']
senticnet['carry_out'] = ['-0.02', '0.087', '0', '0.107', '#interest', '#admiration', 'positive', '0.056', 'illustrate', 'act', 'perform', 'fulfill', 'execute']
senticnet['carry_paper'] = ['0.025', '0.083', '0', '0.06', '#interest', '#admiration', 'positive', '0.056', 'organize_document', 'carry_document', 'keep_paper_together', 'bind_paper_together', 'store_document']
senticnet['carry_person'] = ['0', '0', '0', '0.077', '#admiration', '#admiration', 'positive', '0.077', 'transport_person', 'corner_well', 'travel_road', 'move_person', 'travel_place']
senticnet['carry_stuff'] = ['-0.04', '0.133', '-0.07', '0.218', '#interest', '#admiration', 'positive', '0.058', 'carry_book', 'move', 'carry_clothes', 'carry_belongings', 'put_stuff']
senticnet['carry_suitcase'] = ['0.741', '0.792', '0', '0', '#joy', '#interest', 'positive', '0.766', 'enter_door', 'bicycle_store', 'turn_knob', 'take_responsibility', 'broomstick']
senticnet['carry_through'] = ['-0.29', '-0.00', '0', '-0.16', '#sadness', '#disgust', 'negative', '-0.14', 'move', 'execute', 'fulfill', 'consummate', 'accomplish']
senticnet['carry_umbrella'] = ['-0.02', '-0.02', '0.076', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'wear_raincoat', 'wet', 'feel_cold', 'camp_out', 'splash_water']
senticnet['carry_vessel'] = ['0', '0', '0.075', '0', '#anger', '#anger', 'negative', '-0.07', 'ship_container', 'covenant', 'big_boat', 'military_ship', 'water_vehicle']
senticnet['carry_wallet'] = ['-0.06', '0', '0.018', '0', '#sadness', '#anger', 'negative', '-0.04', 'out_money', 'decide_buy', 'look_price', 'use_atm', 'establish_credit']
senticnet['carry_water'] = ['0.025', '-0.06', '-0.05', '0.161', '#surprise', '#admiration', 'positive', '0.048', 'cloudy', 'up_high', 'weather_condition', 'dew', 'hazy']
senticnet['carry_weight'] = ['0.933', '-0.83', '0', '0.826', '#joy', '#surprise', 'positive', '0.865', 'influence', 'determine', 'shape', 'mold', 'regulate']
senticnet['carryall'] = ['0.041', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.043', 'fit_room', 'clothes_store_change_room', 'dress_room', 'clothes_shelf', 'changing_room']
senticnet['carrying_out'] = ['0.955', '0.945', '0', '0.835', '#joy', '#interest', 'positive', '0.912', 'support_family', 'provide_service', 'accomplish', 'find_job', 'pay_well']
senticnet['carrying_strap'] = ['0.067', '0.088', '-0.07', '0.246', '#interest', '#admiration', 'positive', '0.081', 'multiple_purposes', 'revolve', 'start_run', 'wiggle', 'twist']
senticnet['carryon'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'hand_baggage', 'baggage', 'passenger_ticket', 'ticket_counter', 'baggage_trunk']
senticnet['carryon_luggage'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'hand_baggage', 'baggage', 'passenger_ticket', 'ticket_counter', 'baggage_trunk']
senticnet['carseat'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'lavatory', 'commode', 'potty_chair']
senticnet['carsick'] = ['-0.91', '0', '-0.89', '0', '#sadness', '#fear', 'negative', '-0.90', 'seasick', 'homesick', 'fidgety', 'crabby', 'cranky']
senticnet['cart_horse'] = ['0', '0', '0', '0.071', '#admiration', '#admiration', 'positive', '0.071', 'two_barbs', 'barb', 'mule', 'buddhist', 'ride_barb']
senticnet['cart_wheel'] = ['0', '-0.02', '-0.08', '-0.05', '#fear', '#disgust', 'negative', '-0.03', 'mattress', 'sleep_furniture', 'master_bedroom', 'small_bed', 'pillow']
senticnet['carta_brava'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'find_out', 'dig_out', 'unearth', 'become_aware', 'dish_out']
senticnet['carte_blanche'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'imperium', 'lordship', 'power_of_appointment', 'potency', 'command']
senticnet['carted_away'] = ['-0.87', '0', '0.682', '-0.91', '#sadness', '#disgust', 'negative', '-0.82', 'dump', 'garbage_dump', 'trash_dump', 'rubbish_dump', 'wasteyard']
senticnet['cartel'] = ['0.776', '-0.86', '0', '0.893', '#surprise', '#admiration', 'positive', '0.843', 'trust', 'combine', 'corporate_trust', 'consortium', 'association']
senticnet['carter_handicap'] = ['-0.76', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.78', 'strangle', 'bloodshed', 'cripple', 'hear_bad_news', 'sexually_transmit_disease']
senticnet['cartesian_dualism'] = ['0.992', '-0.66', '0', '0.536', '#joy', '#surprise', 'positive', '0.729', 'nous', 'mind', 'brain', 'psyche', 'head']
senticnet['cartesianism'] = ['-0.98', '0', '0.891', '-0.60', '#sadness', '#anger', 'negative', '-0.82', 'exorcism', 'dispossession', 'magic', 'thaumaturgy', 'belief']
senticnet['cartilage'] = ['0', '0', '0.025', '-0.09', '#anger', '#disgust', 'negative', '-0.06', 'body_fluid', 'artery', 'skeletal_system', 'bodily_fluid', 'near_mouth']
senticnet['cartilage_damage'] = ['-0.83', '0', '0.509', '-0.84', '#sadness', '#disgust', 'negative', '-0.72', 'wrench', 'injury', 'harm', 'hurt', 'trauma']
senticnet['cartilage_formation'] = ['0', '0', '0.025', '-0.09', '#anger', '#disgust', 'negative', '-0.06', 'body_fluid', 'artery', 'skeletal_system', 'bodily_fluid', 'near_mouth']
senticnet['cartilage_hair_hypoplasia'] = ['-0.87', '0', '0.873', '-0.62', '#sadness', '#anger', 'negative', '-0.78', 'dwarfism', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['cartilage_tissue'] = ['0', '0', '0.025', '-0.09', '#anger', '#disgust', 'negative', '-0.06', 'body_fluid', 'artery', 'skeletal_system', 'bodily_fluid', 'near_mouth']
senticnet['cartload'] = ['0.054', '0.237', '-0.15', '0.33', '#interest', '#admiration', 'positive', '0.117', 'retrieve', 'bring', 'tote', 'take', 'go_get']
senticnet['cartographic_projection'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'arm_movement', 'hand_gesture', 'water_motion', 'hand_signal', 'sea_movement']
senticnet['carton'] = ['0.035', '-0.01', '0.009', '0.063', '#joy', '#admiration', 'positive', '0.026', 'container', 'jar', 'contain', 'hold_cupcake', 'store_dish']
senticnet['carton_milk'] = ['0.954', '0.963', '0', '0', '#joy', '#interest', 'positive', '0.958', 'breakfast', 'box_cereal', 'calcium', 'drink', 'pasta']
senticnet['cartoon'] = ['0.789', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.779', 'manga', 'anime', 'radio-control_helicopter', 'beat_off', 'caricature']
senticnet['cartoon_animation'] = ['0.895', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.823', 'liveliness', 'excitement', 'art_form', 'art', 'film']
senticnet['cartoon_character'] = ['-0.07', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.07', 'paint', 'use_tool', 'mould', 'use_oil_paint', 'buy_paint']
senticnet['cartoon_sketch'] = ['0', '0.074', '0', '0.06', '#interest', '#admiration', 'positive', '0.067', 'use_crayon', 'draw', 'pencil_art', 'make_sketch', 'outline']
senticnet['cartoonish'] = ['0', '-0.73', '0', '0.787', '#surprise', '#admiration', 'positive', '0.763', 'brassy', 'flashy', 'gaudy', 'cheap', 'flash']
senticnet['cartoonishly'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicado', 'unpronounceable', 'impassive', 'incommunicative', 'silent']
senticnet['cartridge_belt'] = ['0', '0.072', '0', '0.064', '#interest', '#admiration', 'positive', '0.068', 'zipper', 'pant', 'trousers', 'cowboy_boot', 'accessory']
senticnet['cartwheel'] = ['-0.68', '0', '0.545', '-0.68', '#sadness', '#disgust', 'negative', '-0.64', 'stunt', 'tumble', 'split', 'walkover', 'acrobatic_feat']
senticnet['carucate'] = ['0.049', '0.16', '0', '0.155', '#interest', '#admiration', 'positive', '0.121', 'castle', 'palace', 'medieval', 'aristocrat', 'throne']
senticnet['carve'] = ['0.045', '0', '0', '0.051', '#joy', '#admiration', 'positive', '0.048', 'tree', 'forest', 'wood', 'find_forest', 'mass_production']
senticnet['carve_niche'] = ['0', '0', '0', '-0.09', '#disgust', '#disgust', 'negative', '-0.09', 'stand_queue', 'wait_line', 'stand_in_line', 'wait_turn', 'get_line']
senticnet['carved_decoration'] = ['0.876', '-0.76', '0', '0.963', '#joy', '#admiration', 'positive', '0.867', 'housecraft', 'tradecraft', 'watercraft', 'acquirement', 'skill']
senticnet['carved_wooden'] = ['0', '0.773', '0', '0.895', '#interest', '#admiration', 'positive', '0.834', 'wooden_board', 'make_wood', 'wooden_box', 'wooden_floor', 'long_wood']
senticnet['carvedilol'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'spironolactone', 'spironolactone_drug']
senticnet['carving'] = ['0.969', '-0.68', '0', '0.756', '#joy', '#admiration', 'positive', '0.804', 'vermiculation', 'cinquefoil', 'glyptic_art', 'glyptography', 'scrimshaw']
senticnet['carving_fork'] = ['-0.71', '0', '0.954', '-0.56', '#sadness', '#anger', 'negative', '-0.74', 'jackknife', 'pocket_knife', 'penknife', 'switchblade', 'switchblade_knife']
senticnet['carving_knife'] = ['-0.71', '0', '0.954', '-0.56', '#sadness', '#anger', 'negative', '-0.74', 'jackknife', 'pocket_knife', 'penknife', 'switchblade', 'switchblade_knife']
senticnet['carwash'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'clean', 'polish', 'shiny', 'water', 'sexy_girl']
senticnet['casanova_lerrone'] = ['0.939', '-0.71', '0', '0.775', '#joy', '#admiration', 'positive', '0.81', 'vox_angelica', 'voix_celeste', 'organ_stop', 'pipe', 'diapason']
senticnet['cascade_effect'] = ['0.729', '-0.71', '0', '0', '#joy', '#surprise', 'positive', '0.72', 'compel', 'cause_change', 'change_life', 'extinction', 'eco_system']
senticnet['cascara'] = ['-0.50', '0', '0.582', '-0.57', '#anger', '#disgust', 'negative', '-0.55', 'purgative', 'cathartic', 'physic', 'aperient', 'phenolphthalein']
senticnet['cascara_sagrada'] = ['-0.50', '0', '0.582', '-0.57', '#anger', '#disgust', 'negative', '-0.55', 'purgative', 'cathartic', 'physic', 'aperient', 'phenolphthalein']
senticnet['caseload'] = ['-0.88', '0', '-0.73', '0', '#sadness', '#fear', 'negative', '-0.81', 'backlog', 'backlogged', 'backlog', 'paperwork', 'pending']
senticnet['casevac'] = ['-0.16', '0', '-0.18', '-0.23', '#fear', '#disgust', 'negative', '-0.19', 'attack_enemy', 'cause_death', 'die', 'lose_war', 'imprisonment']
senticnet['cash'] = ['-0.05', '0.14', '0.015', '0.082', '#interest', '#admiration', 'positive', '0.039', 'debit_card', 'cash_pay', 'need_earn_money', 'open_wallet', 'earn_wage']
senticnet['cash_bill'] = ['0.905', '0.902', '0', '0', '#joy', '#interest', 'positive', '0.903', 'dollar_bill', 'paper_bill', 'cash_coin', 'check', 'euro']
senticnet['cash_check'] = ['0', '0.224', '-0.04', '0.098', '#interest', '#admiration', 'positive', '0.092', 'purchase', 'cash', 'establish_credit', 'bankruptcy', 'incur_debt']
senticnet['cash_coin'] = ['0.812', '0.911', '0', '0', '#joy', '#interest', 'positive', '0.862', 'cash_bill', 'paper_bill', 'debit_card', 'dollar_bill', 'piece_of_change']
senticnet['cash_cow'] = ['0.685', '-0.58', '0', '0.56', '#joy', '#surprise', 'positive', '0.608', 'profit', 'net_income', 'net', 'net_profit', 'lucre']
senticnet['cash_disbursement'] = ['0', '0.119', '0', '0', '#interest', '#interest', 'positive', '0.119', 'ledger', 'computer_disk', 'memo_pad', 'intern', 'graphite_pencil']
senticnet['cash_disbursement_journal'] = ['0', '0.119', '0', '0', '#interest', '#interest', 'positive', '0.119', 'ledger', 'computer_disk', 'memo_pad', 'intern', 'graphite_pencil']
senticnet['cash_dispenser'] = ['0', '0.186', '0', '0.075', '#interest', '#admiration', 'positive', '0.131', 'money_inside', 'check_account', 'savings', 'money_storage', 'store_money']
senticnet['cash_drawer'] = ['0.021', '0.139', '0', '0', '#joy', '#interest', 'positive', '0.08', 'bank', 'wallet', 'safe', 'bank_vault', 'coffer']
senticnet['cash_flow'] = ['0', '0.298', '-0.06', '0.305', '#interest', '#admiration', 'positive', '0.18', 'reason_why', 'correct', 'punctuation', 'exclamation', 'end_point']
senticnet['cash_in_hand'] = ['0.534', '-0.86', '0', '0.736', '#surprise', '#admiration', 'positive', '0.713', 'fund', 'finance', 'monetary_resource', 'escrow_fund', 'pecuniary_resource']
senticnet['cash_injection'] = ['0.961', '-0.84', '0', '0.958', '#joy', '#admiration', 'positive', '0.923', 'pause', 'break', 'intermission', 'suspension', 'rest_period']
senticnet['cash_outlay'] = ['-0.02', '0.249', '0', '0.116', '#interest', '#admiration', 'positive', '0.112', 'price', 'expense', 'bill', 'value', 'worth']
senticnet['cash_pay'] = ['0.858', '0.927', '0', '0', '#joy', '#interest', 'positive', '0.893', 'give_money', 'work_for_money', 'pay_money', 'extra_money', 'money_earn']
senticnet['cash_payment'] = ['-0.04', '0.277', '-0.10', '0.11', '#interest', '#admiration', 'positive', '0.059', 'owe', 'tax', 'debit', 'afford', 'wage']
senticnet['cash_prize'] = ['0.894', '0.881', '0', '0.803', '#joy', '#interest', 'positive', '0.859', 'remittance', 'afford', 'award', 'money_due', 'amount_owe']
senticnet['cash_receipt_journal'] = ['0', '0.119', '0', '0', '#interest', '#interest', 'positive', '0.119', 'ledger', 'computer_disk', 'memo_pad', 'intern', 'graphite_pencil']
senticnet['cash_register'] = ['0.05', '0.139', '0.037', '0', '#joy', '#interest', 'positive', '0.051', 'bank_machine', 'piggy_bank', 'billfold', 'spare_change', 'bank_account']
senticnet['cashew'] = ['0.859', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.888', 'green_apple', 'apricot', 'lime', 'peach', 'cherry']
senticnet['cashew_nut'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'palm_nut', 'nut', 'edible_nut', 'cembra_nut', 'cedar_nut']
senticnet['cashier'] = ['0.038', '0.155', '0', '0.074', '#interest', '#admiration', 'positive', '0.089', 'merchandise', 'trolley', 'goods', 'shopper', 'grocery']
senticnet['cashier_check'] = ['0', '0.192', '0', '0.052', '#interest', '#admiration', 'positive', '0.122', 'bank_vault', 'wallet', 'billfold', 'hold_change', 'bank']
senticnet['cashiers_check'] = ['0', '0.192', '0', '0.052', '#interest', '#admiration', 'positive', '0.122', 'bank_vault', 'wallet', 'billfold', 'hold_change', 'bank']
senticnet['cashless'] = ['-0.05', '0.17', '0', '0.091', '#interest', '#admiration', 'positive', '0.07', 'postpone_payment', 'credit_card', 'buy_goods_service', 'pay_goods', 'debit_card']
senticnet['cashmere'] = ['0', '0.992', '0', '0.963', '#interest', '#admiration', 'positive', '0.978', 'wife', 'superfine', 'merino', 'personal', 'wool']
senticnet['cashmere_cardigan'] = ['-0.54', '0', '0.612', '-0.98', '#anger', '#disgust', 'negative', '-0.71', 'scuff', 'mule', 'bootee', 'carpet_slipper', 'footwear']
senticnet['cashmere_goat'] = ['-0.53', '0', '0.751', '-0.97', '#anger', '#disgust', 'negative', '-0.75', 'guanaco', 'domestic_llama', 'llama', 'artiodactyl', 'artiodactyl_mammal']
senticnet['cashmere_wool'] = ['-0.53', '0', '0.751', '-0.97', '#anger', '#disgust', 'negative', '-0.75', 'guanaco', 'domestic_llama', 'llama', 'artiodactyl', 'artiodactyl_mammal']
senticnet['casimir_force'] = ['-0.97', '0', '0.831', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'skewness', 'irregularity']
senticnet['casino'] = ['0.021', '0', '0', '0.115', '#joy', '#admiration', 'positive', '0.068', 'display_wealth', 'propose_marriage', 'shoe_lace', 'time_tracker', 'use_finger']
senticnet['casino_hotel'] = ['0.057', '0.13', '0', '0.119', '#interest', '#admiration', 'positive', '0.102', 'friend_wedding', 'circular_route', 'spanish_restaurant', 'altar', 'monastery']
senticnet['cask'] = ['0.161', '-0.14', '0.031', '-0.10', '#joy', '#surprise', 'positive', '0.043', 'barrel', 'reception', 'champagne', 'beer', 'wine']
senticnet['casket'] = ['-0.84', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.85', 'morgue', 'coffin', 'grave', 'corpse', 'dead_person']
senticnet['casotte'] = ['-0.84', '0', '0.987', '-0.77', '#sadness', '#anger', 'negative', '-0.87', 'thorn', 'sticker', 'glochid', 'prickle', 'spikelet']
senticnet['caspian_tern'] = ['0.797', '-0.71', '0', '0.736', '#joy', '#admiration', 'positive', '0.751', 'wood_ibis', 'wood_stork', 'ibis', 'wading_bird', 'wader']
senticnet['casque'] = ['-0.59', '0', '0.887', '-0.71', '#anger', '#disgust', 'negative', '-0.73', 'armet', 'basinet', 'helmet', 'heaume', 'morion']
senticnet['cassava_bread'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'nut', 'palm_kernel', 'edible_nut', 'cembra_nut', 'cedar_nut']
senticnet['cassava_flour'] = ['-0.08', '0.106', '0', '0.125', '#interest', '#admiration', 'positive', '0.047', 'sugar', 'treacle', 'black_treacle', 'add_sugar', 'sugar_substitute']
senticnet['cassava_root'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'nut', 'palm_kernel', 'edible_nut', 'cembra_nut', 'cedar_nut']
senticnet['cassava_starch'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'palm_nut', 'nut', 'edible_nut', 'cembra_nut', 'cedar_nut']
senticnet['cassegrain'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'binoculars', 'read_glass', 'eyeglass', 'camera', 'monocle']
senticnet['cassegrain_telescope'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'binoculars', 'read_glass', 'eyeglass', 'camera', 'monocle']
senticnet['casserole'] = ['0.041', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.043', 'meatloaf', 'salad', 'delicious_meal', 'garden_salad', 'apple_pie']
senticnet['casserole_dish'] = ['0.03', '-0.05', '0', '0.059', '#surprise', '#admiration', 'positive', '0.046', 'platter', 'fork', 'spoon', 'tablespoon', 'bowl']
senticnet['cassette'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['cassette_deck'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['cassette_format'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['cassette_player'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['cassette_recorder'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['cassette_tape'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['cassia_bark'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'cork', 'bark', 'canella', 'canella_bark', 'white_cinnamon']
senticnet['cassia_fistula'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'black_locust']
senticnet['cassie'] = ['-0.74', '0', '0.877', '-0.97', '#anger', '#disgust', 'negative', '-0.86', 'miss', 'girl', 'chachka', 'young_lady', 'young_woman']
senticnet['cassini_orbiter'] = ['0', '0.825', '0', '0', '#interest', '#interest', 'positive', '0.825', 'query', 'interrogation', 'inquire', 'response', 'solution']
senticnet['cassiopea'] = ['-0.64', '0', '0.524', '-0.59', '#sadness', '#disgust', 'negative', '-0.58', 'bum', 'dreamer', 'daydreamer', 'escapist', 'wishful_thinker']
senticnet['cassock'] = ['0.849', '-0.50', '0', '0.845', '#joy', '#admiration', 'positive', '0.732', 'robe', 'gown', 'veil', 'vest', 'vestment']
senticnet['cassoulet'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'blue_ribbon', 'badge', 'allegory', 'stripe', 'merit_badge']
senticnet['casspir'] = ['-0.56', '0', '0.714', '-0.89', '#anger', '#disgust', 'negative', '-0.72', 'tank', 'tank_destroyer', 'armored_car', 'armoured_car', 'armored_personnel_carrier']
senticnet['cast'] = ['0', '0', '0', '0.068', '#admiration', '#admiration', 'positive', '0.068', 'dark_corner', 'child_game', 'play_person', 'role_play', 'part_of_play']
senticnet['cast_adrift'] = ['-0.99', '0', '0.658', '-0.73', '#sadness', '#disgust', 'negative', '-0.79', 'purposeless', 'afloat', 'aimless', 'directionless', 'planless']
senticnet['cast_aluminium'] = ['0.72', '0', '0', '0.733', '#joy', '#admiration', 'positive', '0.726', 'spanner', 'gold', 'strong_material', 'platinum', 'durable']
senticnet['cast_aluminum'] = ['0.72', '0', '0', '0.733', '#joy', '#admiration', 'positive', '0.726', 'spanner', 'gold', 'strong_material', 'platinum', 'durable']
senticnet['cast_aside'] = ['-0.54', '0', '0.815', '-0.83', '#anger', '#disgust', 'negative', '-0.73', 'brave_out', 'endure', 'weather', 'withstand', 'stand_firm']
senticnet['cast_down'] = ['-0.81', '0.239', '-0.44', '-0.06', '#sadness', '#fear', 'negative', '-0.27', 'feel_down', 'deject', 'get_down', 'dispirit', 'demoralize']
senticnet['cast_iron'] = ['0.706', '-0.94', '0', '0.622', '#joy', '#surprise', 'positive', '0.756', 'alloy_iron', 'alloy_cast_iron', 'pot_metal', 'iron', 'metal']
senticnet['cast_iron_bridge'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'suspension_bridge', 'under_bridge', 'roadway', 'county_highway', 'long_road']
senticnet['cast_iron_stove'] = ['-0.61', '0', '0.732', '-0.59', '#sadness', '#anger', 'negative', '-0.64', 'leaky', 'draughty', 'porous', 'drippy', 'oozing']
senticnet['cast_shadow'] = ['-0.08', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.08', 'blow_horn', 'weather_storm', 'cross_bridge', 'jump_trampoline', 'box_ear']
senticnet['castaneum'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'giant_hornet', 'hornet', 'hornet_wasp']
senticnet['castaway'] = ['0', '0', '0.877', '-0.78', '#anger', '#disgust', 'negative', '-0.83', 'abject', 'banished', 'outcast', 'pariah', 'lumpen']
senticnet['caste'] = ['0.025', '0.176', '-0.06', '0.114', '#interest', '#admiration', 'positive', '0.063', 'tribe', 'hierarchy', 'council', 'social_status', 'social_rank']
senticnet['caste_discrimination'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'vassalage']
senticnet['casteless'] = ['-0.82', '0', '0.85', '-0.70', '#sadness', '#anger', 'negative', '-0.79', 'outcaste', 'unwanted', 'unclaimed', 'unwelcome', 'undesired']
senticnet['castigate'] = ['-0.81', '0', '0.967', '-0.96', '#anger', '#disgust', 'negative', '-0.91', 'call_on_carpet', 'take_to_task', 'chide', 'berate', 'bawl_out']
senticnet['castigation'] = ['-0.89', '0', '0.974', '-0.98', '#anger', '#disgust', 'negative', '-0.95', 'reprimand', 'rebuke', 'reproof', 'reproval', 'reprehension']
senticnet['casting'] = ['0', '0', '0.775', '-0.93', '#anger', '#disgust', 'negative', '-0.85', 'overcast', 'adumbrate', 'eclipse', 'overshadowed', 'overshadow']
senticnet['castle'] = ['0.021', '0.119', '0', '0.129', '#interest', '#admiration', 'positive', '0.09', 'manor', 'palace', 'medieval', 'tower', 'chess_game']
senticnet['castle_bravo'] = ['-0.84', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.85', 'inferno', 'forest_fire', 'fire_light', 'dismissal', 'wash_mouth_soap']
senticnet['castle_burg'] = ['0.021', '0.119', '0', '0.129', '#interest', '#admiration', 'positive', '0.09', 'manor', 'palace', 'medieval', 'tower', 'chess_game']
senticnet['castle_ruin'] = ['0.021', '0.119', '0', '0.129', '#interest', '#admiration', 'positive', '0.09', 'manor', 'palace', 'medieval', 'tower', 'chess_game']
senticnet['castor'] = ['-0.02', '-0.05', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.02', 'motor_oil', 'make_toy', 'engine', 'emission', 'bristle']
senticnet['castor_oil'] = ['-0.81', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.82', 'peanut', 'leave_over', 'floppy', 'circular_motion', 'store_vegetable']
senticnet['castrate'] = ['-0.78', '0', '0.91', '-0.91', '#anger', '#disgust', 'negative', '-0.87', 'desex', 'neuter', 'alter', 'emasculate', 'spay']
senticnet['castration'] = ['0.542', '-0.63', '0', '0.912', '#surprise', '#admiration', 'positive', '0.695', 'implosion_therapy', 'flooding', 'reciprocal_inhibition', 'behavior_modification', 'desensitization_technique']
senticnet['castration_anxiety'] = ['0.833', '0', '0', '0.807', '#joy', '#admiration', 'positive', '0.82', 'lust', 'amiable', 'give_hug', 'giggle', 'smile']
senticnet['casual'] = ['-0.15', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.09', 'use_leg', 'walk_quickly', 'gait', 'take_step', 'strut']
senticnet['casual_apparel'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['casual_attire'] = ['0', '0.144', '-0.07', '0.222', '#interest', '#admiration', 'positive', '0.096', 'hit_tree', 'perspire', 'enjoy_fresh_air', 'catch_ball', 'work_up_sweat']
senticnet['casual_chic'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'elegant', 'stylish', 'smart', 'posh', 'sleek']
senticnet['casual_clothing'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['casual_racism'] = ['-0.96', '0', '0.766', '-0.96', '#sadness', '#disgust', 'negative', '-0.89', 'calumniation', 'traducement', 'hatchet_job', 'defamation', 'calumny']
senticnet['casual_reader'] = ['0.026', '0', '0', '0.084', '#joy', '#admiration', 'positive', '0.055', 'book_write', 'make_book', 'written_text', 'paragraph', 'book_paper']
senticnet['casual_restaurant'] = ['0.061', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.055', 'cook', 'stick_butter', 'find_recipe', 'bake_oven', 'food_bake']
senticnet['casual_sex'] = ['0.081', '0.095', '0', '0', '#joy', '#interest', 'positive', '0.088', 'build_tower_out_toy_block', 'a_lot_of_sex', 'play_hockey', 'play_baseball', 'play_football']
senticnet['casual_shoes'] = ['-0.05', '0.068', '0', '0.102', '#interest', '#admiration', 'positive', '0.038', 'shoe', 'moccasin', 'flip_flop', 'footwear', 'shoe_type']
senticnet['casual_sportswear'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['casual_wear'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['casually'] = ['-0.79', '0', '-0.70', '0', '#sadness', '#fear', 'negative', '-0.75', 'hesitance', 'hesitancy', 'hesitatingly', 'unassertive', 'unassertively']
senticnet['casually_elegant'] = ['0', '0', '0', '0.963', '#admiration', '#admiration', 'positive', '0.963', 'wear', 'furnishing', 'wearable_technology', 'wearable', 'belt']
senticnet['casualty'] = ['-0.16', '0', '-0.18', '-0.23', '#fear', '#disgust', 'negative', '-0.19', 'attack_enemy', 'cause_death', 'die', 'lose_war', 'imprisonment']
senticnet['casualty_evacuation'] = ['-0.07', '0.069', '-0.18', '0', '#sadness', '#fear', 'negative', '-0.06', 'medical_procedure', 'cure', 'doctor_surgery', 'treatment', 'doctor_work']
senticnet['casualty_rate'] = ['-0.16', '0', '-0.18', '-0.23', '#fear', '#disgust', 'negative', '-0.19', 'attack_enemy', 'cause_death', 'die', 'lose_war', 'imprisonment']
senticnet['casuistical'] = ['-0.70', '0', '0.767', '-0.93', '#anger', '#disgust', 'negative', '-0.80', 'report', 'composition', 'paper', 'paragraph', 'essay']
senticnet['casuistry'] = ['-0.69', '0', '0.697', '-0.60', '#sadness', '#anger', 'negative', '-0.66', 'argumentation', 'argument', 'logical_argument', 'line_of_reasoning', 'line']
senticnet['casus_belli'] = ['-0.61', '0', '0.606', '-0.53', '#sadness', '#anger', 'negative', '-0.58', 'establish', 'start', 'beginning', 'emergence', 'egress']
senticnet['cat'] = ['0.028', '0', '0.013', '0.061', '#joy', '#admiration', 'positive', '0.025', 'cute_big_cat', 'domestibig_cate_animal', 'big_cat', 'big_animal', 'big']
senticnet['cat_allergen'] = ['-0.15', '0.162', '0.596', '-0.62', '#anger', '#disgust', 'negative', '-0.30', 'hackle', 'umbrage', 'offense', 'ire', 'see_red']
senticnet['cat_burglar'] = ['0.9', '-0.88', '0', '0.687', '#joy', '#surprise', 'positive', '0.823', 'conjurer', 'escape_expert', 'mind_reader', 'telepathist', 'performer']
senticnet['cat_dander'] = ['-0.15', '0.162', '0.596', '-0.62', '#anger', '#disgust', 'negative', '-0.30', 'hackle', 'umbrage', 'offense', 'ire', 'see_red']
senticnet['cat_fancier'] = ['0.534', '-0.86', '0', '0.736', '#surprise', '#admiration', 'positive', '0.713', 'enthusiast', 'animal_fancier', 'horseman', 'horse_fancier', 'fancier']
senticnet['cat_food'] = ['0.076', '0', '0', '0', '#joy', '#joy', 'positive', '0.076', 'attract_dog', 'food', 'dog', 'dog_food', 'pet_food']
senticnet['cat_fur'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'animal_hair', 'dog_hair', 'animal_fur', 'dog_cat', 'bite_dog']
senticnet['cat_hair'] = ['0', '-0.08', '0.125', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'sleep_cat', 'cookie_crumb', 'dust_mite', 'coffee_table', 'dirty_sock']
senticnet['cat_litter'] = ['0', '0.095', '0', '0.074', '#interest', '#admiration', 'positive', '0.084', 'buy_wash_powder', 'water_soap', 'turn_faucet', 'take_off', 'fill_bathtub_water']
senticnet['cat_litter_box'] = ['0.822', '0.802', '0', '0', '#joy', '#interest', 'positive', '0.812', 'electric_toothbrush', 'towel', 't_shirt', 'heat_towel_rack', 'hair_brush']
senticnet['cat_lover'] = ['0', '0', '0.022', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'kind_animal', 'wild_animal', 'predator', 'hound', 'fang']
senticnet['cat_nap'] = ['0.051', '0.074', '-0.05', '0.084', '#interest', '#admiration', 'positive', '0.04', 'rest_night', 'lie_down', 'shut_eye', 'lay_down', 'count_sheep']
senticnet['cat_purr'] = ['0.967', '0.945', '0', '0', '#joy', '#interest', 'positive', '0.956', 'express_love', 'receive_thank', 'get_present', 'make_happy', 'celebrate_special_occasion']
senticnet['cat_scratch_disease'] = ['-0.73', '0', '0.599', '-0.96', '#sadness', '#disgust', 'negative', '-0.76', 'spotted_fever', 'trench_fever', 'rickettsial_disease', 'typhus', 'typhus_fever']
senticnet['cat_thyme'] = ['0.076', '0', '0', '0', '#joy', '#joy', 'positive', '0.076', 'subshrub', 'suffrutex', 'wood_sage', 'wall_germander', 'germander']
senticnet['cat_urine'] = ['0', '0', '0.943', '-0.8', '#anger', '#disgust', 'negative', '-0.87', 'fight_fire', 'filthy', 'dirty', 'stink', 'fall_mud']
senticnet['catabolism'] = ['0.992', '-0.80', '0', '0.584', '#joy', '#surprise', 'positive', '0.795', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'incontinence']
senticnet['cataclysm'] = ['-0.96', '0', '0.501', '-0.59', '#sadness', '#disgust', 'negative', '-0.68', 'calamity', 'catastrophe', 'tragedy', 'disaster', 'apocalypse']
senticnet['cataclysmic'] = ['0', '0', '0', '-0.78', '#disgust', '#disgust', 'negative', '-0.78', 'suddenly', 'precipitous', 'immediate', 'unexpected', 'sudden_asthma_attack']
senticnet['catafalque'] = ['-0.88', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.90', 'coffin', 'casket', 'morgue', 'headstone', 'burial']
senticnet['catalan_cuisine'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'artistic_creation', 'fabrication', 'carving', 'decoupage', 'ceramic']
senticnet['catalase_negative'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'immobile', 'immotile']
senticnet['catalase_positive'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'immobile', 'immotile']
senticnet['catalepsy'] = ['-0.56', '0', '0.577', '-0.61', '#anger', '#disgust', 'negative', '-0.58', 'foggy', 'groggy', 'logy', 'lethargic', 'dazed']
senticnet['cataleptic'] = ['-0.60', '0', '0.892', '-0.92', '#anger', '#disgust', 'negative', '-0.80', 'schizophrenic', 'psycho', 'paranoid', 'paranoiac', 'sick_person']
senticnet['catalyse'] = ['0.918', '0.864', '0', '0', '#joy', '#interest', 'positive', '0.891', 'consequence', 'cause', 'bad_action', 'problem', 'political_statement']
senticnet['catalysis'] = ['0.903', '0.983', '0', '0', '#joy', '#interest', 'positive', '0.943', 'contact_action', 'molecule', 'study', 'award', 'result']
senticnet['catalyst'] = ['0.918', '0.864', '0', '0', '#joy', '#interest', 'positive', '0.891', 'consequence', 'cause', 'bad_action', 'problem', 'political_statement']
senticnet['catalyst_deactivation'] = ['0.918', '0.864', '0', '0', '#joy', '#interest', 'positive', '0.891', 'consequence', 'cause', 'bad_action', 'problem', 'political_statement']
senticnet['catalytic'] = ['-0.94', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.89', 'oxidation', 'catalyst', 'converter', 'hydrogenation', 'exhaust']
senticnet['catalytic_cycle'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
senticnet['catalytic_effect'] = ['0.918', '0.864', '0', '0', '#joy', '#interest', 'positive', '0.891', 'consequence', 'cause', 'bad_action', 'problem', 'political_statement']
senticnet['catalytic_hydrogenation'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'carboxylic_acid', 'succinic_acid', 'valeric_acid']
senticnet['catalytic_role'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'baddie', 'theatrical_baddie', 'part', 'persona']
senticnet['catalytically'] = ['0.918', '0.864', '0', '0', '#joy', '#interest', 'positive', '0.891', 'consequence', 'cause', 'bad_action', 'problem', 'political_statement']
senticnet['catalyze'] = ['0.918', '0.864', '0', '0', '#joy', '#interest', 'positive', '0.891', 'consequence', 'cause', 'bad_action', 'problem', 'political_statement']
senticnet['catamaran'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'boat', 'yacht', 'big_boat', 'large_boat']
senticnet['catamite'] = ['-0.62', '0', '0.98', '-0.81', '#anger', '#disgust', 'negative', '-0.80', 'tart', 'prostitute', 'harlot', 'bawd', 'sporting_lady']
senticnet['catamount'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'unofficial', 'informal', 'loose', 'drumhead']
senticnet['cataplasm'] = ['-0.81', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.82', 'peanut', 'leave_over', 'floppy', 'circular_motion', 'store_vegetable']
senticnet['cataract'] = ['-0.89', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.87', 'blind', 'disease', 'poor_sight', 'poor_vision', 'handicap']
senticnet['catarrh'] = ['0', '0', '0', '-0.96', '#disgust', '#disgust', 'negative', '-0.96', 'thick_substance', 'viscous_substance', 'mucous_substance', 'disgusting', 'disgust']
senticnet['catarrhal'] = ['-0.97', '0', '0.584', '-0.78', '#sadness', '#disgust', 'negative', '-0.78', 'decreased', 'diminished', 'weakened', 'faded', 'small']
senticnet['catarrhal_stage'] = ['-0.53', '0', '0.608', '-0.93', '#anger', '#disgust', 'negative', '-0.69', 'reprieve', 'respite', 'hiatus', 'abatement', 'suspension']
senticnet['catastrophe'] = ['-0.15', '-0.01', '-0.07', '-0.21', '#sadness', '#disgust', 'negative', '-0.10', 'disaster', 'broken_finger', 'censorship', 'destruction', 'schizophrenia']
senticnet['catastrophic'] = ['0', '0', '-0.80', '-0.71', '#fear', '#disgust', 'negative', '-0.75', 'disaster', 'catastrophe', 'disastrous', 'iceberg', 'tragedy']
senticnet['catastrophic_collapse'] = ['-0.13', '-0.05', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'monitor_heart_rate', 'feel_tired', 'bump_head', 'exhaustion', 'become_fatigued']
senticnet['catastrophic_damage'] = ['-0.87', '0', '0.889', '-0.62', '#sadness', '#anger', 'negative', '-0.79', 'ruination', 'ruin', 'deterioration', 'blight', 'impairment']
senticnet['catastrophic_destruction'] = ['-0.87', '0', '0.889', '-0.62', '#sadness', '#anger', 'negative', '-0.79', 'ruination', 'ruin', 'deterioration', 'blight', 'impairment']
senticnet['catastrophic_disaster'] = ['-0.17', '0', '-0.25', '-0.13', '#sadness', '#fear', 'negative', '-0.18', 'destruction', 'bigot', 'kidnap', 'torture', 'censorship']
senticnet['catastrophic_earthquake'] = ['-0.87', '0', '0.889', '-0.62', '#sadness', '#anger', 'negative', '-0.79', 'ruination', 'ruin', 'deterioration', 'blight', 'impairment']
senticnet['catastrophic_event'] = ['-0.17', '0', '-0.25', '-0.13', '#sadness', '#fear', 'negative', '-0.18', 'destruction', 'bigot', 'kidnap', 'torture', 'censorship']
senticnet['catastrophic_failure'] = ['-0.91', '0', '0.905', '-0.78', '#sadness', '#anger', 'negative', '-0.86', 'pity', 'shame', 'misfortune', 'mischance', 'misadventure']
senticnet['catastrophic_incident'] = ['0', '0', '-0.82', '0', '#fear', '#fear', 'negative', '-0.82', 'sickness', 'polio', 'malady', 'ailment', 'treatment']
senticnet['catastrophically'] = ['-0.13', '-0.05', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'monitor_heart_rate', 'feel_tired', 'bump_head', 'exhaustion', 'become_fatigued']
senticnet['catatonic'] = ['-0.71', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.84', 'comatose', 'bedridden', 'paralyzed', 'bed-ridden', 'coma']
senticnet['catatonic_schizophrenia'] = ['-0.74', '0', '0.893', '-0.58', '#sadness', '#anger', 'negative', '-0.73', 'encopresis', 'upset', 'psychosomatic_disorder', 'conversion_disorder', 'conversion_reaction']
senticnet['catatonic_state'] = ['0.8', '-0.97', '0', '0.512', '#joy', '#surprise', 'positive', '0.763', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['catbird'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet']
senticnet['catcall'] = ['-0.69', '0', '0.792', '-0.98', '#anger', '#disgust', 'negative', '-0.82', 'outcry', 'call', 'cry', 'yell', 'shout']
senticnet['catch'] = ['0.378', '0.388', '-0.21', '0.203', '#joy', '#interest', 'positive', '0.188', 'thrill', 'encourage', 'console', 'exhilaration', 'strike']
senticnet['catch-all'] = ['-0.73', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.82', 'indiscriminate', 'equal-opportunity', 'indiscriminately', 'indistinguishably', 'wholesale']
senticnet['catch_ball'] = ['0.885', '0', '0', '0', '#joy', '#joy', 'positive', '0.885', 'ball_game', 'strike_out', 'throw_ball', 'play', 'toss_ball']
senticnet['catch_baseball'] = ['0', '-0.05', '0.066', '-0.13', '#anger', '#disgust', 'negative', '-0.05', 'strike_out', 'throw_ball', 'hit_ball', 'hit_ball_with_bat', 'run_first_base']
senticnet['catch_bass'] = ['0', '0', '0.895', '-0.84', '#anger', '#disgust', 'negative', '-0.86', 'catch_catfish', 'experience_joy', 'fish_trout', 'fish_compliment', 'catch_butterfly']
senticnet['catch_breath'] = ['0.818', '0.757', '0', '0', '#joy', '#interest', 'positive', '0.787', 'exercise', 'breathing', 'relax', 'rest_up', 'regain_energy']
senticnet['catch_bus'] = ['-0.10', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.08', 'board_boat', 'ride_bus', 'travel_by_airplane', 'near_destination', 'push_limit']
senticnet['catch_butterfly'] = ['-0.06', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.05', 'fish_lake', 'fish_trout', 'fish_compliment', 'fish_stream', 'catch_fly']
senticnet['catch_catfish'] = ['0', '0', '0.886', '-0.96', '#anger', '#disgust', 'negative', '-0.92', 'catch_bass', 'catch_shark', 'catch_fish', 'catch_butterfly', 'fish_lake']
senticnet['catch_cold'] = ['-0.1', '0', '-0.07', '-0.09', '#sadness', '#disgust', 'negative', '-0.08', 'trade_stock', 'become_depressed', 'absorb_moisture', 'catch_fire', 'part_of_hair']
senticnet['catch_fire'] = ['0', '0', '-0.06', '-0.05', '#fear', '#disgust', 'negative', '-0.06', 'light_candle', 'light_cigar', 'light_pipe', 'age_prematurely', 'light_cigarette']
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senticnet['catch_rain'] = ['0.072', '0.071', '0', '0.053', '#joy', '#interest', 'positive', '0.065', 'cold', 'coldness', 'icy', 'cold_outside', 'ice_crystal']
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senticnet['catered_lunch'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'cafe', 'buy_sandwich', 'eat_food', 'buy_food', 'finish_job']
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senticnet['catfish'] = ['0.853', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.891', 'mullet', 'mackerel', 'goatee', 'mullet', 'flounder']
senticnet['catgirl'] = ['-0.73', '0', '0.72', '-0.93', '#sadness', '#disgust', 'negative', '-0.79', 'imp', 'elf', 'hob', 'leprechaun', 'pixie']
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senticnet['cathartic'] = ['0.722', '-0.60', '0', '0.596', '#joy', '#surprise', 'positive', '0.641', 'medicament', 'medicinal_drug', 'medication', 'aloe', 'bitter_aloe']
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senticnet['cathedral'] = ['0.971', '0', '0', '0.752', '#joy', '#admiration', 'positive', '0.861', 'church', 'temple', 'chapel', 'catholic_church', 'confession']
senticnet['cathedral_episcopal'] = ['0.8', '-0.84', '0', '0.868', '#surprise', '#admiration', 'positive', '0.837', 'commonwealth', 'province', 'administrative_district', 'administrative_division', 'territorial_division']
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senticnet['catheter'] = ['-0.02', '0', '-0.12', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'x-ray_machine', 'medical_record', 'latex_glove', 'medicine', 'doctor_examination_room']
senticnet['catheter_ablation'] = ['-0.83', '0', '0.987', '-0.50', '#sadness', '#anger', 'negative', '-0.77', 'heart_block', 'atrioventricular_block', 'bradycardia', 'ventricular_fibrillation', 'tachycardia']
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senticnet['cathine'] = ['0.049', '0', '0', '0.113', '#joy', '#admiration', 'positive', '0.081', 'solid', 'hard_material', 'construction_material', 'hard', 'calcium']
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senticnet['cathode_ray_tube_crt'] = ['0.044', '0.08', '0', '-0.03', '#joy', '#interest', 'positive', '0.031', 'screen', 'pixel', 'visual_information', 'image', 'computer_monitor']
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senticnet['catholic_cemetery'] = ['-0.50', '0', '0.762', '-0.89', '#anger', '#disgust', 'negative', '-0.71', 'cemetery', 'graveyard', 'burial_site', 'grassland', 'burying_ground']
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senticnet['catlinite'] = ['0.706', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.806', 'shoe', 'flip_flop', 'slipper', 'casual_shoes', 'footwear']
senticnet['catnap'] = ['-0.62', '0', '0.944', '-0.72', '#anger', '#disgust', 'negative', '-0.76', 'relax', 'sleep_late', 'sleep_in', 'rest', 'practice_bundling']
senticnet['catsuit'] = ['0', '0.075', '0', '0.2', '#interest', '#admiration', 'positive', '0.138', 'stride', 'foot_motion', 'gait', 'foot_action', 'foot_movement']
senticnet['catsup'] = ['-0.81', '0', '0.543', '-0.63', '#sadness', '#disgust', 'negative', '-0.66', 'caper', 'gherkin', 'relish', 'dill_pickle', 'sweet_pickle']
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senticnet['cattle'] = ['0.834', '0.765', '0', '0', '#joy', '#interest', 'positive', '0.799', 'bull', 'cow', 'bison', 'mammal', 'animal']
senticnet['cattle_breeder'] = ['-0.08', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.11', 'serf', 'peasant', 'servant', 'poor', 'villein']
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senticnet['cattle_graze'] = ['0.966', '0.881', '0', '0', '#joy', '#interest', 'positive', '0.923', 'harvest', 'crop', 'grow_crop', 'plant_crop', 'reap']
senticnet['cattle_herd'] = ['0.546', '-0.96', '0', '0.533', '#joy', '#surprise', 'positive', '0.683', 'dairy_cattle', 'dairy_cow', 'milch_cow', 'ayrshire', 'milcher']
senticnet['cattle_pasture'] = ['0.966', '0.881', '0', '0', '#joy', '#interest', 'positive', '0.923', 'harvest', 'crop', 'grow_crop', 'plant_crop', 'reap']
senticnet['cattle_prod'] = ['-0.13', '0.113', '-0.16', '0', '#sadness', '#fear', 'negative', '-0.06', 'question_witness', 'legal_process', 'find_guilty', 'convict_criminal', 'custody']
senticnet['cattle_rancher'] = ['-0.08', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.11', 'serf', 'peasant', 'servant', 'poor', 'villein']
senticnet['cattle_rustler'] = ['-0.83', '0', '0.831', '-0.69', '#sadness', '#anger', 'negative', '-0.78', 'toughie', 'accessory', 'highbinder', 'hoodlum', 'thug']
senticnet['cattle_slaughter'] = ['-0.67', '0', '0.938', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'massacre', 'mass_murder', 'carnage', 'bloodbath', 'butchery']
senticnet['cattleman'] = ['-0.08', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.11', 'serf', 'peasant', 'servant', 'poor', 'villein']
senticnet['cattrall'] = ['0.54', '-0.64', '0', '0.566', '#surprise', '#admiration', 'positive', '0.582', 'pleaser', 'entertainer', 'mortal', 'busker', 'drawing_card']
senticnet['catty'] = ['-0.88', '0', '0.864', '-0.72', '#sadness', '#anger', 'negative', '-0.82', 'vixenish', 'venomous', 'bitchy', 'malicious', 'cattish']
senticnet['catwalk'] = ['0.096', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.057', 'chain', 'rail', 'platform_rail', 'column', 'token_turnstile']
senticnet['catwoman'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'only_bird']
senticnet['caubeen'] = ['-0.58', '0', '0.632', '-0.63', '#anger', '#disgust', 'negative', '-0.61', 'skimmer', 'trilby', 'chapeau', 'busby', 'shako']
senticnet['caucasian'] = ['-0.93', '0', '0.77', '-0.79', '#sadness', '#disgust', 'negative', '-0.83', 'whitey', 'honky', 'honkey', 'honkie', 'mortal']
senticnet['cauchy_distribution'] = ['-0.59', '0', '0.672', '-0.83', '#anger', '#disgust', 'negative', '-0.7', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'asymmetry', 'irregularity']
senticnet['cauchyschwarz_inequality'] = ['0.875', '-0.65', '0', '0.861', '#joy', '#admiration', 'positive', '0.797', 'completeness', 'work', 'whole_shebang', 'essential', 'whole']
senticnet['cauda_equina_syndrome'] = ['-0.65', '0', '0.873', '-0.63', '#sadness', '#anger', 'negative', '-0.72', 'polyneuritis', 'multiple_neuritis', 'rubor', 'redness', 'inflammation']
senticnet['caudillo'] = ['-0.73', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.72', 'royal_leader', 'despot', 'emperor', 'monarch', 'king']
senticnet['cauleen_smith'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'indirectness', 'mediateness', 'characteristic', 'allusiveness']
senticnet['cauliflower'] = ['0', '0.067', '-0.09', '-0.09', '#fear', '#disgust', 'negative', '-0.04', 'cotton_boll', 'asbestos', 'corn', 'opium', 'rice']
senticnet['caulk_gun'] = ['0.76', '0.919', '0', '0', '#joy', '#interest', 'positive', '0.84', 'chore', 'make_happen', 'pick_up_trash', 'intuition', 'stop_procrastinate']
senticnet['causa'] = ['0.654', '-0.75', '0', '0.78', '#surprise', '#admiration', 'positive', '0.729', 'suit', 'cause', 'lawsuit', 'occurrence', 'countersuit']
senticnet['causal'] = ['-0.82', '0', '-0.73', '0', '#sadness', '#fear', 'negative', '-0.78', 'causality', 'causation', 'inference', 'cause-and-effect', 'causative']
senticnet['causal_association'] = ['-0.64', '0', '0.576', '-0.83', '#sadness', '#disgust', 'negative', '-0.68', 'nisi', 'indeterminate', 'equivocal', 'ambiguous', 'indecisive']
senticnet['causal_attribution'] = ['0.854', '-0.79', '0', '0.977', '#joy', '#admiration', 'positive', '0.875', 'profitableness', 'auspiciousness', 'luck', 'advantageousness', 'positivity']
senticnet['causal_chain'] = ['-0.91', '0', '0.795', '-0.94', '#sadness', '#disgust', 'negative', '-0.88', 'ineluctability', 'assured', 'predictability', 'slam_dunk', 'inevitability']
senticnet['causal_efficacy'] = ['-0.87', '0', '0.858', '-0.77', '#sadness', '#anger', 'negative', '-0.83', 'pass_off', 'fall_out', 'occur', 'happen', 'hap']
senticnet['causal_explanation'] = ['-0.91', '0', '0.795', '-0.94', '#sadness', '#disgust', 'negative', '-0.88', 'ineluctability', 'assured', 'predictability', 'slam_dunk', 'inevitability']
senticnet['causal_interaction'] = ['-0.87', '0', '0.858', '-0.77', '#sadness', '#anger', 'negative', '-0.83', 'pass_off', 'fall_out', 'occur', 'happen', 'hap']
senticnet['causal_mechanism'] = ['0', '0.137', '-0.10', '0.197', '#interest', '#admiration', 'positive', '0.076', 'boom', 'circumstance', 'loud', 'stormy', 'procedure']
senticnet['causal_principle'] = ['-0.92', '0', '0.547', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'assumption', 'boundary_condition', 'provision', 'proviso', 'precondition']
senticnet['causal_relation'] = ['-0.87', '0', '0.858', '-0.77', '#sadness', '#anger', 'negative', '-0.83', 'pass_off', 'fall_out', 'occur', 'happen', 'hap']
senticnet['causal_relevance'] = ['0.534', '-0.86', '0', '0.676', '#surprise', '#admiration', 'positive', '0.692', 'suitability', 'suitableness', 'habitability', 'habitableness', 'qualification']
senticnet['causal_role'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'baddie', 'theatrical_baddie', 'part', 'persona']
senticnet['causal_theory'] = ['0.693', '-0.52', '0', '0.82', '#joy', '#admiration', 'positive', '0.681', 'embodiment', 'shape', 'representation', 'concrete_representation', 'internal_representation']
senticnet['causality'] = ['-0.82', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.78', 'causal', 'causation', 'inference', 'cause-and-effect', 'causative']
senticnet['causally'] = ['-0.87', '0', '0.858', '-0.77', '#sadness', '#anger', 'negative', '-0.83', 'pass_off', 'fall_out', 'occur', 'happen', 'hap']
senticnet['causally_relevant'] = ['-0.87', '0', '0.858', '-0.77', '#sadness', '#anger', 'negative', '-0.83', 'pass_off', 'fall_out', 'occur', 'happen', 'hap']
senticnet['causally_responsible'] = ['-0.87', '0', '0.858', '-0.77', '#sadness', '#anger', 'negative', '-0.83', 'pass_off', 'fall_out', 'occur', 'happen', 'hap']
senticnet['causantn'] = ['0', '0.105', '0', '0.084', '#interest', '#admiration', 'positive', '0.095', 'archive', 'print_medium', 'quiet_place', 'register', 'media']
senticnet['causation'] = ['-0.99', '0', '0.651', '-0.51', '#sadness', '#anger', 'negative', '-0.72', 'sending', 'compulsion', 'influence', 'inducement', 'trigger']
senticnet['causative'] = ['0.59', '-0.59', '0', '0.527', '#joy', '#surprise', 'positive', '0.572', 'responsible', 'inducive', 'activating', 'tributary', 'motivative']
senticnet['causative_gene'] = ['-0.95', '0', '0.684', '-0.76', '#sadness', '#disgust', 'negative', '-0.79', 'innate', 'nurtural', 'inborn', 'nontransmissible', 'nonheritable']
senticnet['causative_organism'] = ['-0.73', '0', '0.599', '-0.96', '#sadness', '#disgust', 'negative', '-0.76', 'spotted_fever', 'rickettsial_disease', 'rickettsialpox', 'typhus', 'typhus_fever']
senticnet['cause'] = ['0.888', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.843', 'effect', 'consequence', 'impact', 'outcome', 'conclusion']
senticnet['cause-and-effect'] = ['-0.82', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.78', 'causality', 'causation', 'inference', 'causal', 'causative']
senticnet['cause_accident'] = ['-0.12', '0', '-0.06', '-0.03', '#sadness', '#fear', 'negative', '-0.07', 'follow_person', 'stay_in_house', 'mark_territory', 'sense_fear', 'guide_blind']
senticnet['cause_allergy'] = ['-0.94', '0', '0.891', '-0.93', '#sadness', '#disgust', 'negative', '-0.92', 'peanut', 'seafood', 'wrong_food', 'sick', 'allergy_symptom']
senticnet['cause_bleed'] = ['-0.13', '-0.02', '-0.05', '-0.15', '#sadness', '#disgust', 'negative', '-0.08', 'open_wind', 'laceration', 'gash', 'injury', 'painful_injury']
senticnet['cause_bleeding'] = ['-0.91', '0', '0.513', '-0.93', '#sadness', '#disgust', 'negative', '-0.78', 'beat_to_bleeding', 'cause_strong_pain', 'cut_finger', 'kill_person', 'beat_person']
senticnet['cause_bse'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'agent', 'satisfier', 'quickener', 'invigorator']
senticnet['cause_cancer'] = ['-0.13', '-0.02', '-0.05', '-0.15', '#sadness', '#disgust', 'negative', '-0.08', 'cancer', 'smoke', 'smoke_cigarette', 'smoke_cigar', 'eat_unhealthy_food']
senticnet['cause_change'] = ['0.781', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.825', 'compel', 'cascade_effect', 'change_life', 'permission', 'take']
senticnet['cause_chickenpox'] = ['-0.75', '0', '0.69', '-0.69', '#sadness', '#disgust', 'negative', '-0.71', 'rubor', 'redness', 'sleeping_sickness', 'sleepy_sickness', 'cephalitis']
senticnet['cause_cold'] = ['0', '0', '0', '-0.96', '#disgust', '#disgust', 'negative', '-0.96', 'reproduce', 'start_family', 'procreate', 'copulate', 'give_birth_child']
senticnet['cause_confusion'] = ['-0.81', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'shame', 'dread', 'consternation', 'terrible', 'embarrass']
senticnet['cause_constipation'] = ['0', '0', '-0.70', '-0.76', '#fear', '#disgust', 'negative', '-0.73', 'choke', 'become_full', 'get_full', 'burp', 'sickness']
senticnet['cause_death'] = ['-0.17', '0', '-0.18', '-0.18', '#fear', '#disgust', 'negative', '-0.18', 'take_life', 'lose_war', 'attack_enemy', 'die', 'enslave_person']
senticnet['cause_dehydration'] = ['-0.84', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.85', 'feel_faint', 'lack_of_water', 'much_exercise', 'weak', 'feel_lousy']
senticnet['cause_diarrhea'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'inflammatory_disease', 'stomach_flu', 'intestinal_flu', 'pelvic_inflammatory_disease']
senticnet['cause_diarrhoea'] = ['-0.99', '0', '0.859', '-0.62', '#sadness', '#anger', 'negative', '-0.82', 'lax', 'loose', 'diarrheal', 'diarrhoeal', 'unconstipated']
senticnet['cause_effect'] = ['-0.73', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.76', 'careless', 'scare_dog', 'startle', 'accident', 'embarrassment']
senticnet['cause_fire'] = ['0', '0', '0.779', '-0.72', '#anger', '#disgust', 'negative', '-0.75', 'destroy_home', 'danger', 'kill_life', 'destroy_property', 'destroy_city']
senticnet['cause_harm'] = ['-0.04', '0.164', '0.685', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maliciousness', 'spitefulness', 'maleficence', 'malefic', 'malign']
senticnet['cause_headache'] = ['0', '0.099', '-0.05', '0.113', '#interest', '#admiration', 'positive', '0.053', 'make_throw_up', 'blind', 'big_rat', 'grizzly', 'hare']
senticnet['cause_heart_disease'] = ['0', '0', '0.752', '-0.74', '#anger', '#disgust', 'negative', '-0.75', 'kill_person', 'fat', 'unhealthy_food', 'kill', 'blocked_artery']
senticnet['cause_hemolytic_uremic'] = ['-0.99', '0', '0.859', '-0.62', '#sadness', '#anger', 'negative', '-0.82', 'lax', 'loose', 'diarrheal', 'diarrhoeal', 'unconstipated']
senticnet['cause_illness'] = ['-0.12', '-0.02', '-0.09', '-0.14', '#sadness', '#disgust', 'negative', '-0.08', 'get_rid_of_enemy', 'hurt_others', 'end_live', 'destroy_live', 'murder']
senticnet['cause_infertility'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'gastroenteritis', 'stomach_flu', 'intestinal_flu', 'inflammatory_disease']
senticnet['cause_injury'] = ['0', '0', '0.783', '-0.86', '#anger', '#disgust', 'negative', '-0.82', 'stab', 'maim', 'injure', 'damage', 'destroy']
senticnet['cause_irritation'] = ['-0.53', '0', '0.86', '-0.65', '#anger', '#disgust', 'negative', '-0.68', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['cause_kidney_failure'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'bowel', 'scrotum', 'shoulder', 'anus', 'waist']
senticnet['cause_mental_illness'] = ['-0.02', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.04', 'age_prematurely', 'catch_fire', 'light_pipe', 'light_cigar', 'smoke_cigarette']
senticnet['cause_nausea'] = ['-0.06', '0', '-0.15', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'severe_pain', 'discomfort', 'physical_reaction', 'choke_death', 'embarrassment']
senticnet['cause_pain'] = ['-0.21', '-0.16', '-0.14', '-0.20', '#sadness', '#disgust', 'negative', '-0.10', 'physical_pain', 'poke_pin', 'suffer', 'broken_finger', 'black_eye']
senticnet['cause_paralytic_shellfish'] = ['-0.99', '0', '0.859', '-0.62', '#sadness', '#anger', 'negative', '-0.82', 'lax', 'loose', 'diarrheal', 'diarrhoeal', 'unconstipated']
senticnet['cause_pelvic_inflammatory'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'gastroenteritis', 'stomach_flu', 'intestinal_flu', 'inflammatory_disease']
senticnet['cause_powdery_mildew'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'rust_fungus', 'fungus']
senticnet['cause_problem'] = ['-0.34', '0.102', '-0.36', '0', '#sadness', '#fear', 'negative', '-0.20', 'trouble', 'caution', 'unsafe', 'reprimand', 'big_monster']
senticnet['cause_sars_severe'] = ['0.998', '-0.54', '0', '0.957', '#joy', '#admiration', 'positive', '0.834', 'contagious_disease', 'contagion', 'swine_influenza', 'swine_flu', 'respiratory_disease']
senticnet['cause_serious_injury'] = ['0.888', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.843', 'effect', 'consequence', 'impact', 'outcome', 'conclusion']
senticnet['cause_severe'] = ['0.888', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.843', 'effect', 'consequence', 'impact', 'outcome', 'conclusion']
senticnet['cause_severe_diarrhea'] = ['-0.99', '0', '0.859', '-0.62', '#sadness', '#anger', 'negative', '-0.82', 'lax', 'loose', 'diarrheal', 'diarrhoeal', 'unconstipated']
senticnet['cause_sickness'] = ['-0.17', '0', '-0.10', '-0.12', '#sadness', '#disgust', 'negative', '-0.13', 'infection', 'cause_illness', 'end_life', 'kill_self', 'threaten']
senticnet['cause_side_effect'] = ['-0.61', '0', '0.606', '-0.53', '#sadness', '#anger', 'negative', '-0.58', 'birth_control_pill', 'condom', 'contraceptive_device', 'contraceptive_pill', 'prevent_pregnancy']
senticnet['cause_skin_irritation'] = ['-0.53', '0', '0.86', '-0.65', '#anger', '#disgust', 'negative', '-0.68', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['cause_spoilage'] = ['-0.08', '-0.04', '0.023', '-0.07', '#sadness', '#disgust', 'negative', '-0.03', 'tier', 'make_toast', 'house_smell_good', 'get_hot', 'bread_crumb']
senticnet['cause_stress'] = ['-0.67', '0.233', '-0.46', '0.222', '#sadness', '#fear', 'negative', '-0.17', 'confuse', 'feel_sorry', 'guilt', 'say_sorry', 'regret']
senticnet['cause_strong_pain'] = ['-0.91', '0', '0.513', '-0.93', '#sadness', '#disgust', 'negative', '-0.78', 'struggle', 'cause_bleeding', 'strong_pain', 'kill_person', 'beat_person']
senticnet['cause_suffer'] = ['0', '0', '0.754', '-0.78', '#anger', '#disgust', 'negative', '-0.77', 'lose_team', 'leave_out', 'loathing', 'disrespect', 'lyme_disease']
senticnet['cause_to_perceive'] = ['0.84', '-0.83', '0', '0.528', '#joy', '#surprise', 'positive', '0.735', 'sound', 'smart', 'sensation', 'scent', 'ache']
senticnet['cause_tooth_decay'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'plaque']
senticnet['cause_undue'] = ['0.676', '-0.70', '0', '0.8', '#surprise', '#admiration', 'positive', '0.727', 'better', 'soothe', 'improve', 'alleviate_pain', 'meliorate']
senticnet['cause_urinary_retention'] = ['-0.77', '0', '0.966', '-0.51', '#sadness', '#anger', 'negative', '-0.75', 'enuresis', 'incontinency', 'incontinence', 'stress_incontinence', 'urge_incontinence']
senticnet['cause_vasoconstriction'] = ['0.95', '-0.95', '0', '0.769', '#joy', '#surprise', 'positive', '0.89', 'adrenocorticotropic', 'adrenocorticotrophic', 'stimulant', 'stimulating', 'stimulative']
senticnet['causes_irritation'] = ['-0.53', '0', '0.86', '-0.65', '#anger', '#disgust', 'negative', '-0.68', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['causes_severe_diarrhea'] = ['-0.99', '0', '0.859', '-0.62', '#sadness', '#anger', 'negative', '-0.82', 'lax', 'loose', 'diarrheal', 'diarrhoeal', 'unconstipated']
senticnet['causeth'] = ['-0.72', '0', '0.737', '-0.60', '#sadness', '#anger', 'negative', '-0.68', 'call_on_carpet', 'take_to_task', 'chide', 'berate', 'bawl_out']
senticnet['causeway'] = ['0.84', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.798', 'big_breadbox', 'water_vehicle', 'military_ship', 'ship', 'small_boat']
senticnet['causing'] = ['-0.94', '0', '0.709', '-0.97', '#sadness', '#disgust', 'negative', '-0.87', 'sending', 'compulsion', 'influence', 'inducement', 'trigger']
senticnet['causing_bodily_harm'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'negligence', 'evasion', 'dereliction', 'contributory_negligence']
senticnet['causing_damage'] = ['-0.98', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.97', 'harm', 'hurt', 'cut_skin', 'bloody', 'ouch']
senticnet['causing_harm'] = ['-0.04', '0.164', '0.685', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maliciousness', 'spitefulness', 'maleficence', 'malefic', 'malign']
senticnet['causing_irritation'] = ['-0.53', '0', '0.86', '-0.65', '#anger', '#disgust', 'negative', '-0.68', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['causing_trouble'] = ['-0.84', '0', '0.506', '-0.62', '#sadness', '#disgust', 'negative', '-0.66', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['causing_vasodilation'] = ['-0.73', '0', '0.904', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'unshapely', 'shapeless', 'lumpy', 'deformed', 'malformed']
senticnet['caustic'] = ['0', '0', '0.748', '-0.92', '#anger', '#disgust', 'negative', '-0.83', 'vitriolic', 'mordant', 'acrid', 'alkali', 'biting']
senticnet['caustic_wit'] = ['-0.12', '0.13', '0.646', '-0.64', '#anger', '#disgust', 'negative', '-0.32', 'quick_temper', 'short_temper', 'irascible', 'choleric', 'ill_temper']
senticnet['caution'] = ['-0.32', '0.133', '-0.38', '0', '#sadness', '#fear', 'negative', '-0.19', 'big_monster', 'unsafe', 'phobia', 'mock', 'feel_scared']
senticnet['cautionary'] = ['-0.87', '0', '-0.70', '0', '#sadness', '#fear', 'negative', '-0.78', 'preventive', 'warn', 'admonitory', 'caution', 'hostage']
senticnet['cautionary_note'] = ['-0.32', '0.133', '-0.38', '0', '#sadness', '#fear', 'negative', '-0.19', 'big_monster', 'unsafe', 'phobia', 'mock', 'feel_scared']
senticnet['cautionary_tale'] = ['-0.96', '0', '0.108', '-0.90', '#sadness', '#disgust', 'negative', '-0.66', 'evil', 'darkness', 'dark', 'sun_down', 'shadow']
senticnet['cautious'] = ['-0.70', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.74', 'leery', 'concern', 'nervous', 'reservation', 'leeriness']
senticnet['cautious_approach'] = ['-0.32', '0.133', '-0.38', '0', '#sadness', '#fear', 'negative', '-0.19', 'big_monster', 'unsafe', 'phobia', 'mock', 'feel_scared']
senticnet['cautious_welcome'] = ['0.961', '-0.84', '0', '0.958', '#joy', '#admiration', 'positive', '0.923', 'pause', 'break', 'intermission', 'suspension', 'rest_period']
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senticnet['cautiousness'] = ['-0.78', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.81', 'precautionary', 'avoid', 'risk', 'vigilance', 'wariness']
senticnet['cavalier'] = ['-0.91', '0', '0.521', '-0.81', '#sadness', '#disgust', 'negative', '-0.74', 'bullying', 'peremptory', 'blustery', 'roughshod', 'domineering']
senticnet['cavalier_daily'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['cavalierly'] = ['0.653', '0.635', '-0.24', '0.294', '#joy', '#interest', 'positive', '0.335', 'merrily', 'jubilantly', 'happily', 'mirthfully', 'gayly']
senticnet['cavalry'] = ['0', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.09', 'army', 'attacker', 'military', 'soldier', 'infantry']
senticnet['cavalry_battalion'] = ['-0.9', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.9', 'withdrawal', 'indifferently', 'apathetic', 'indifferent', 'concerned']
senticnet['cavalry_detachment'] = ['-0.9', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.9', 'withdrawal', 'indifferently', 'apathetic', 'indifferent', 'concerned']
senticnet['cavalry_officer'] = ['0.045', '0', '0', '0.088', '#joy', '#admiration', 'positive', '0.067', 'leader', 'seaman', 'captain', 'officer', 'traitor']
senticnet['cavalry_regiment'] = ['0', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.09', 'army', 'attacker', 'military', 'soldier', 'infantry']
senticnet['cavalry_skirmish'] = ['-0.05', '0', '-0.08', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'siege', 'big_fight', 'war', 'declare_war', 'combat']
senticnet['cavalry_squadron'] = ['0', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.09', 'army', 'attacker', 'military', 'soldier', 'infantry']
senticnet['cavalry_troop'] = ['0', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.09', 'army', 'attacker', 'military', 'soldier', 'infantry']
senticnet['cavalry_unit'] = ['0', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.09', 'army', 'attacker', 'military', 'soldier', 'infantry']
senticnet['cavalryman'] = ['0', '0', '-0.84', '-0.76', '#fear', '#disgust', 'negative', '-0.80', 'dragon', 'pigeon', 'bidet', 'dragoon', 'horse']
senticnet['cavanaugh'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
senticnet['cavatina'] = ['0.037', '0.161', '-0.15', '0.203', '#interest', '#admiration', 'positive', '0.062', 'wave', 'flow', 'salute', 'multiple_purposes', 'wave_wind']
senticnet['cavcw_prince_park'] = ['0.552', '-0.61', '0', '0.588', '#surprise', '#admiration', 'positive', '0.583', 'barbershop_quartet', 'string_quartet', 'quartette', 'musical_organization', 'musical_group']
senticnet['cave'] = ['-0.02', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'tunnel', 'dark_place', 'ski_resort', 'top_tree', 'compost_pile']
senticnet['cave_bear'] = ['0.969', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.96', 'extinct', 'animal', 'ice', 'elephant', 'giant']
senticnet['cave_diver'] = ['0', '0.07', '0', '0', '#interest', '#interest', 'positive', '0.07', 'sunken_treasure', 'real_duck', 'urchin', 'mussel', 'gold_fish']
senticnet['cave_entrance'] = ['-0.02', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'tunnel', 'dark_place', 'ski_resort', 'top_tree', 'compost_pile']
senticnet['caveat'] = ['-0.32', '0.133', '-0.38', '0', '#sadness', '#fear', 'negative', '-0.19', 'big_monster', 'unsafe', 'phobia', 'mock', 'feel_scared']
senticnet['caveat_emptor'] = ['0.841', '-0.65', '0', '0.645', '#joy', '#surprise', 'positive', '0.714', 'moral_principle', 'rule', 'precept', 'higher_law', 'hypothetical_imperative']
senticnet['caveman'] = ['-0.76', '0', '0.816', '0', '#sadness', '#anger', 'negative', '-0.78', 'cruel', 'brute', 'brutish', 'ogre', 'sensibility']
senticnet['cavern'] = ['-0.96', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.96', 'tunnel', 'international_space_station', 'stick_wall', 'car_race', 'auction']
senticnet['cavern_beneath'] = ['0.086', '-0.10', '0.064', '0', '#joy', '#surprise', 'positive', '0.043', 'art_piece', 'lifetime', 'museum', 'vintage', 'collectible']
senticnet['cavernous'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expandible', 'expansible', 'erectile', 'expansive']
senticnet['cavernous_room'] = ['0.788', '-0.60', '0', '0.884', '#joy', '#admiration', 'positive', '0.759', 'gap', 'rathole', 'aperture', 'bullet_hole', 'hole']
senticnet['cavernous_space'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'empty_room', 'a_lot_of_space', 'empty', 'empty_house', 'universe']
senticnet['caverns_beneath'] = ['-0.96', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.96', 'tunnel', 'international_space_station', 'stick_wall', 'car_race', 'auction']
senticnet['cavesson'] = ['0', '-0.05', '0.079', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'c_clamp', 'thumb_tack', 'pin', 'pliers', 'without_space']
senticnet['cavge_corio_oval'] = ['0.552', '-0.61', '0', '0.588', '#surprise', '#admiration', 'positive', '0.583', 'barbershop_quartet', 'string_quartet', 'quartette', 'musical_organization', 'musical_group']
senticnet['caviar'] = ['0.061', '0.07', '-0.05', '0.073', '#interest', '#admiration', 'positive', '0.037', 'italian_food', 'eat_food', 'fine_dining', 'expensive_food', 'lobster']
senticnet['caviare'] = ['-0.52', '0', '0.748', '-0.92', '#anger', '#disgust', 'negative', '-0.73', 'coral', 'caviar', 'hard_roe', 'shad_roe', 'seafood']
senticnet['cavil'] = ['0', '0', '-0.87', '-1', '#fear', '#disgust', 'negative', '-0.93', 'niggle', 'quiddity', 'bicker', 'pettifog', 'quibble']
senticnet['cavity'] = ['-0.05', '0.068', '-0.10', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'tooth_decay', 'body_ache', 'symptom', 'pierce', 'tooth']
senticnet['cavolo_nero'] = ['0.636', '-0.79', '0', '0.552', '#joy', '#surprise', 'positive', '0.661', 'lettuce', 'endive', 'green', 'salad_green', 'food']
senticnet['cavort'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'push', 'ram', 'barge', 'bang', 'shove_around']
senticnet['cavum'] = ['0.718', '-0.88', '0', '0.83', '#surprise', '#admiration', 'positive', '0.81', 'throat', 'pharynx', 'cavity', 'bodily_cavity', 'tubular_cavity']
senticnet['cayman_trough'] = ['-0.02', '-0.01', '0.03', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'ocean_beach', 'beach', 'water_sea', 'shore', 'ground_ocean']
senticnet['cb_cbe'] = ['0.74', '-0.53', '0', '0.641', '#joy', '#admiration', 'positive', '0.64', 'marshal', 'sky_marshal', 'lawman', 'law_officer', 'peace_officer']
senticnet['cbc_count'] = ['0.79', '-0.62', '0', '0.674', '#joy', '#admiration', 'positive', '0.695', 'add', 'contribute', 'lend', 'breathe', 'impart']
senticnet['cboe_vix_index'] = ['0', '0.721', '0', '0.731', '#interest', '#admiration', 'positive', '0.726', 'dimension', 'measure', 'tackle', 'length', 'measurement']
senticnet['cbot'] = ['0.868', '-0.84', '0', '0.673', '#joy', '#surprise', 'positive', '0.796', 'instrument', 'option', 'derivative_instrument', 'legal_document', 'legal_instrument']
senticnet['cbot_corn'] = ['0.868', '-0.84', '0', '0.673', '#joy', '#surprise', 'positive', '0.796', 'instrument', 'option', 'derivative_instrument', 'legal_document', 'legal_instrument']
senticnet['cbs_crime_drama'] = ['0', '0', '0.785', '-0.72', '#anger', '#disgust', 'negative', '-0.75', 'sharp_nail', 'sharp', 'sharp_end', 'sharp_point', 'pointy']
senticnet['cbs_drama'] = ['0', '0', '0.785', '-0.72', '#anger', '#disgust', 'negative', '-0.75', 'sharp_nail', 'sharp', 'sharp_end', 'sharp_point', 'pointy']
senticnet['cbs_news_mark_knoller'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'wildcat', 'informal', 'loose', 'drumhead']
senticnet['ccd_camera'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'binoculars', 'read_glass', 'eyeglass', 'camera', 'monocle']
senticnet['ccheekiness'] = ['-0.89', '0', '-0.92', '-0.82', '#sadness', '#fear', 'negative', '-0.88', 'rudeness', 'discourtesy', 'insolence', 'hutzpah', 'cheekiness']
senticnet['cchem'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'druggist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['ccleaner'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'car_trunk', 'walmart', 'warehouse', 'store', 'storage_place']
senticnet['cctv'] = ['-0.03', '-0.01', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.04', 'movie_theater', 'theater', 'cinema', 'movie', 'drive-in_movie']
senticnet['cd'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['cd-rom'] = ['-0.63', '0', '0.897', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'candlepower_unit', 'international_candle', 'candle', 'candela', 'standard_candle']
senticnet['cd_compilation'] = ['0', '0.176', '0', '0.216', '#interest', '#admiration', 'positive', '0.196', 'choir_stall', 'worshiper', 'pew', 'fundamentalist', 'pulpit']
senticnet['cd_format'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['cd_rom'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'slot', 'box', 'load_dock', 'safe_deposit_box', 'emporium']
senticnet['cd_rom_drive'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotary_file', 'cd-rom', 'database', 'business_card', 'calendar_book']
senticnet['cd_rom_format'] = ['-0.05', '0.161', '-0.12', '0.181', '#interest', '#admiration', 'positive', '0.039', 'usable', 'redo', 'timeliness', 'question_starter', 'replay']
senticnet['cd_tray'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'clip', 'clasp', 'glue_stick', 'clothespin', 'clamp']
senticnet['cdrom'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotary_file', 'cd-rom', 'database', 'business_card', 'calendar_book']
senticnet['cease'] = ['-0.91', '0', '0.576', '-0.95', '#sadness', '#disgust', 'negative', '-0.81', 'stop', 'halt', 'lock_in', 'lock', 'block']
senticnet['cease_fire'] = ['0.299', '0.299', '-0.15', '0.233', '#joy', '#interest', 'positive', '0.168', 'desirable', 'stay_calm', 'hold', 'stillness', 'get_along']
senticnet['cease_fire_agreement'] = ['0.397', '0.245', '-0.13', '0.224', '#joy', '#interest', 'positive', '0.183', 'agreement', 'provide', 'advice', 'contribution', 'offer']
senticnet['ceased_production'] = ['0', '0.164', '-0.08', '0.215', '#interest', '#admiration', 'positive', '0.098', 'manufacture', 'factory_output', 'creation', 'assembly_line', 'make']
senticnet['ceasefire'] = ['0.263', '0.196', '-0.13', '0', '#joy', '#interest', 'positive', '0.109', 'peace', 'respite', 'truce', 'treaty', 'civil_war']
senticnet['ceasefire_deal'] = ['0.397', '0.245', '-0.13', '0.224', '#joy', '#interest', 'positive', '0.183', 'agreement', 'provide', 'advice', 'contribution', 'offer']
senticnet['ceaseless'] = ['0', '0', '-0.70', '-0.95', '#fear', '#disgust', 'negative', '-0.83', 'unceasing', 'never-ending', 'constant', 'incessant', 'perpetual']
senticnet['ceaselessly'] = ['0', '0.824', '0', '0.857', '#interest', '#admiration', 'positive', '0.841', 'incessantly', 'continuously', 'unceasingly', 'endlessly', 'unendingly']
senticnet['ceaseth'] = ['-0.79', '0', '0.607', '-0.92', '#sadness', '#disgust', 'negative', '-0.77', 'stubbornness', 'obstinacy', 'obstinance', 'bullheadedness', 'pigheadedness']
senticnet['cebu_apella'] = ['0.921', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.825', 'monkey', 'fiance', 'lemur', 'beaver', 'koala']
senticnet['cecchetti_method'] = ['0.536', '-0.56', '0', '0.567', '#surprise', '#admiration', 'positive', '0.557', 'recreation', 'diversion', 'move', 'motility', 'movement']
senticnet['cecum'] = ['0.04', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.055', 'kidney', 'pancreas', 'liver', 'bone', 'neck']
senticnet['cedar'] = ['0.048', '-0.09', '0', '0.086', '#surprise', '#admiration', 'positive', '0.077', 'elm', 'tree', 'willow', 'pine', 'timber']
senticnet['cedar_apple_rust'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'rust_fungus', 'fungus']
senticnet['cedar_avenue'] = ['0.914', '-0.56', '0', '0.869', '#joy', '#admiration', 'positive', '0.781', 'tabula_rasa', 'ego', 'subconscious', 'unconscious', 'psyche']
senticnet['cedar_bark'] = ['0.793', '0.847', '0', '0', '#joy', '#interest', 'positive', '0.82', 'cross_bun', 'hot_cross_bun', 'bear_paw', 'coffee_roll', 'bun']
senticnet['cedar_creek_ecosystem'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['cedar_fair_entertainment'] = ['0.056', '-0.01', '0.036', '0.073', '#joy', '#admiration', 'positive', '0.028', 'thrill_ride', 'have_fun', 'drag_race', 'enjoyment', 'roller_coaster']
senticnet['cedar_nut'] = ['0.534', '-0.86', '0', '0.736', '#surprise', '#admiration', 'positive', '0.713', 'palm_nut', 'palm_kernel', 'edible_nut', 'cembra_nut', 'nut']
senticnet['cedar_tree'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'black_locust']
senticnet['cedar_wood'] = ['0.827', '0', '0', '0', '#joy', '#joy', 'positive', '0.827', 'wrathful', 'wrathfully', 'wroth', 'wrothful', 'lividity']
senticnet['cede'] = ['0.914', '-0.95', '0', '0', '#joy', '#surprise', 'positive', '0.932', 'proposal', 'assent', 'comply', 'assume', 'accede']
senticnet['cede_power'] = ['-0.89', '0', '0.925', '-0.84', '#sadness', '#anger', 'negative', '-0.88', 'vacate', 'resign', 'give_up', 'renounce', 'depart']
senticnet['cedilla'] = ['0.902', '-0.85', '0', '0.898', '#joy', '#admiration', 'positive', '0.885', 'discerning', 'diacritic', 'discriminating', 'discriminative', 'selective']
senticnet['ceelanguage'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'carbon_fiber']
senticnet['cefazolin'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['cefixime'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['cefoperazone'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['cefotetan'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['cefoxitin'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['cefuroxime'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['ceiba_pentandra'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'nut', 'palm_kernel', 'edible_nut', 'cembra_nut', 'cedar_nut']
senticnet['ceiling'] = ['0', '0.128', '-0.12', '0.183', '#interest', '#admiration', 'positive', '0.063', 'support_roof', 'interior', 'furnish', 'window_door', 'frame']
senticnet['ceiling_fan'] = ['0.063', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.057', 'indoor', 'ventilation', 'breeze', 'cool_air', 'fan']
senticnet['ceiling_mounted_projector'] = ['0.872', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.811', 'lecture_theater', 'lecture', 'conference_auditorium', 'read_book', 'text_book']
senticnet['ceiling_tile'] = ['0', '0.089', '0', '0', '#interest', '#interest', 'positive', '0.089', 'ground', 'basement', 'base', 'foundation', 'basis']
senticnet['cel_animation'] = ['0.895', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.823', 'liveliness', 'excitement', 'art_form', 'art', 'film']
senticnet['cela_serum'] = ['0', '0.919', '0', '0.888', '#interest', '#admiration', 'positive', '0.903', 'surprise', 'eye-popping', 'fulgurous', 'dazzling', 'bedaze']
senticnet['celandine'] = ['-0.66', '0', '0.859', '-0.82', '#anger', '#disgust', 'negative', '-0.78', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['celebrant'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'reverence', 'obeisance', 'genuflexion', 'kowtow', 'mafia']
senticnet['celebrate'] = ['0.719', '0', '0', '0', '#joy', '#joy', 'positive', '0.719', 'go_party', 'enjoy_company_friend', 'friend_over', 'drink_a_lot_of_beer', 'entertain_person']
senticnet['celebrate_anniversary'] = ['0.859', '0', '0', '0', '#joy', '#joy', 'positive', '0.859', 'win_lottery', 'have_fun', 'birthday', 'christmas', 'make_happy']
senticnet['celebrate_birthday'] = ['0.812', '0', '0', '0', '#joy', '#joy', 'positive', '0.812', 'child_birthday', 'special_occasion', 'celebrate_special_occasion', 'special_event', 'show_care']
senticnet['celebrate_event'] = ['0.799', '0', '0', '0', '#joy', '#joy', 'positive', '0.799', 'celebrate_birthday', 'special_occasion', 'celebrate_holiday', 'child_birthday', 'show_care']
senticnet['celebrate_holiday'] = ['0.172', '-0.20', '0.015', '-0.14', '#joy', '#surprise', 'positive', '0.053', 'celebrate_occasion', 'celebrate_special_occasion', 'amuse', 'hospitable', 'good_time']
senticnet['celebrate_occasion'] = ['0.7', '0', '0', '0', '#joy', '#joy', 'positive', '0.7', 'celebrate_holiday', 'celebrate_special_occasion', 'see_friend', 'celebrate_wedding', 'good_time']
senticnet['celebrate_special_occasion'] = ['0.78', '0.739', '0', '0.798', '#joy', '#admiration', 'positive', '0.772', 'friend_wedding', 'wedding', 'cake', 'celebrate_wedding', 'wedding_cake']
senticnet['celebrate_wedding'] = ['0.7', '0', '0', '0', '#joy', '#joy', 'positive', '0.7', 'celebrate_holiday', 'celebrate_special_occasion', 'see_friend', 'celebrate_occasion', 'good_time']
senticnet['celebrated'] = ['0.788', '0.855', '0', '0', '#joy', '#interest', 'positive', '0.822', 'notable', 'far-famed', 'famous', 'famed', 'illustrious']
senticnet['celebration'] = ['0.816', '0', '0', '0', '#joy', '#joy', 'positive', '0.816', 'special_occasion', 'special_event', 'special_time', 'party_time', 'special_day']
senticnet['celebratory'] = ['-0.85', '0', '0.691', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'jinks', 'high_jinks', 'hijinks', 'high_jinx', 'revel']
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senticnet['celebrity'] = ['0', '0', '0', '0.993', '#admiration', '#admiration', 'positive', '0.993', 'senator', 'ceo', 'egalitarian', 'champion', 'vip']
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senticnet['celebrity_hairdresser'] = ['0.99', '0', '0', '0.721', '#joy', '#admiration', 'positive', '0.855', 'hair', 'haircut', 'hairdresser_chair', 'scissor', 'hairstyle']
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senticnet['celerity'] = ['0.548', '-0.62', '0', '0.695', '#surprise', '#admiration', 'positive', '0.622', 'quickness', 'instancy', 'rapidity', 'rapidness', 'speediness']
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senticnet['celery_salad'] = ['0.084', '-0.05', '0', '-0.04', '#joy', '#surprise', 'positive', '0.031', 'entree', 'feast', 'satiation', 'breakfast', 'supper']
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senticnet['celestial_body'] = ['0', '-0.71', '0', '0.884', '#surprise', '#admiration', 'positive', '0.8', 'heavenly_body', 'around_earth', 'source_light', 'sol', 'celestial_object']
senticnet['celestial_globe'] = ['-0.80', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.81', 'crystal_ball', 'ball', 'spherule', 'bolus', 'globe']
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senticnet['celestial_navigation'] = ['0.865', '0.84', '0', '0', '#joy', '#interest', 'positive', '0.853', 'travel', 'find_way', 'help_travelling', 'compass_direction', 'use_compass']
senticnet['celestial_neighbor'] = ['0.916', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.929', 'galaxy', 'universe', 'planetary', 'sun', 'dwarf_planet']
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senticnet['celestial_phenomenon'] = ['0', '0.811', '0', '0', '#interest', '#interest', 'positive', '0.811', 'observe', 'glimpse', 'look', 'stare', 'glance']
senticnet['celestial_pole'] = ['0', '-0.72', '0', '0.712', '#surprise', '#admiration', 'positive', '0.72', 'heavenly_body', 'around_earth', 'source_light', 'sol', 'celestial_body']
senticnet['celestron'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'binoculars', 'read_glass', 'eyeglass', 'camera', 'monocle']
senticnet['celgene'] = ['0.559', '0.656', '-0.26', '0.285', '#joy', '#interest', 'positive', '0.31', 'zealous', 'avidity', 'eagerness', 'avidness', 'thirstily']
senticnet['celia_cruz'] = ['0.116', '-0.02', '0.025', '0.156', '#joy', '#admiration', 'positive', '0.069', 'dance_move', 'latin_dance', 'body_movement', 'waltz', 'dance']
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senticnet['celibacy'] = ['0', '0', '-0.72', '-0.93', '#fear', '#disgust', 'negative', '-0.83', 'celibate', 'vow', 'monogamy', 'priesthood', 'ordination']
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senticnet['cell-to-cell'] = ['0.842', '0.876', '0', '0', '#joy', '#interest', 'positive', '0.859', 'peer-to-peer', 'human-to-human', 'skin-to-skin', 'person-to-person', 'fecal-oral']
senticnet['cell_activation'] = ['-0.66', '0', '0.877', '-0.64', '#sadness', '#anger', 'negative', '-0.72', 'somatic_cell', 'mastocyte', 'labrocyte', 'target_cell', 'phagocyte']
senticnet['cell_biologist'] = ['0', '0.909', '0', '0.799', '#interest', '#admiration', 'positive', '0.854', 'research', 'research_topic', 'research_subject', 'search', 'student']
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senticnet['cell_degeneration'] = ['-0.78', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.80', 'deterioration', 'decline', 'decay', 'degradation', 'slide']
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senticnet['cell_elongation'] = ['-0.84', '0', '0.8', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'rigor_mortis', 'natural_process', 'natural_action', 'activity', 'action']
senticnet['cell_embryo'] = ['0', '0.067', '0', '0.07', '#interest', '#admiration', 'positive', '0.069', 'peck', 'bushel', 'chicken_egg', 'ovum', 'egg']
senticnet['cell_mate'] = ['-0.78', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.87', 'jailbird', 'captive', 'imprisoned', 'convict', 'inmate']
senticnet['cell_mediated_immunity'] = ['0', '0', '0', '0.816', '#admiration', '#admiration', 'positive', '0.816', 'resistance', 'protection', 'untouchable', 'invulnerable', 'sacrosanct']
senticnet['cell_membrane'] = ['0', '0', '0.019', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'worm_intestine', 'bristle', 'fur_keep_warm', 'animal', 'whisker']
senticnet['cell_phone'] = ['0.035', '-0.01', '0.027', '0.085', '#joy', '#admiration', 'positive', '0.026', 'telephone', 'find_phone_number', 'telephone_room', 'phone', 'use_telephone']
senticnet['cell_phone_charger'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'smart_technology', 'electric_shaver', 'hair_dryer', 'stay_healthy', 'wash_tooth']
senticnet['cell_phone_conversation'] = ['0.716', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.733', 'call', 'use_phone', 'orate', 'use_telephone', 'speech']
senticnet['cell_phone_etiquette'] = ['0', '0', '0', '0.99', '#admiration', '#admiration', 'positive', '0.99', 'politeness', 'good_behavior', 'good_etiquette', 'manner', 'hint']
senticnet['cell_proliferate'] = ['0.772', '-0.56', '0', '0.824', '#joy', '#admiration', 'positive', '0.719', 'neutrophil', 'leukocyte', 'leucocyte', 'white_blood_cell', 'white_cell']
senticnet['cell_saver'] = ['0.79', '-0.62', '0', '0.674', '#joy', '#admiration', 'positive', '0.695', 'add', 'contribute', 'lend', 'breathe', 'impart']
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senticnet['cell_wall'] = ['0', '0.097', '0', '0.085', '#interest', '#admiration', 'positive', '0.091', 'lunula', 'arm_part', 'leave_hand', 'ten_fingers', 'five_fingers']
senticnet['cellar'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'poison', 'rotten', 'disgusting', 'taste_awful', 'green_bread']
senticnet['cellarer'] = ['0.053', '0.248', '-0.18', '0.258', '#interest', '#admiration', 'positive', '0.093', 'slogan', 'find_money', 'wealthy', 'much_money', 'donation']
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senticnet['cellophane_tape'] = ['0.852', '0.818', '0', '0', '#joy', '#interest', 'positive', '0.835', 'handout', 'fountain_pen', 'pen', 'stamp_pad', 'scrap_paper']
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senticnet['cellular_bioscience'] = ['0.774', '0.907', '0', '0', '#joy', '#interest', 'positive', '0.841', 'molecular', 'molecular_biochemistry', 'life_science_biological', 'award', 'latest']
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senticnet['cellular_telephone_service'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'long_distance_relationship', 'distant', 'kilometer', 'length_measurement', 'distance_measure']
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senticnet['celluloid'] = ['0', '0.827', '0', '0.846', '#interest', '#admiration', 'positive', '0.837', 'inexterminable', 'inextirpable', 'inexpungible', 'inexpungeable', 'ineradicable']
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senticnet['cent_apiece'] = ['0.987', '0.902', '0', '0', '#joy', '#interest', 'positive', '0.945', 'franc', 'dime', 'dollar', 'shilling', 'banknote']
senticnet['cent_coin'] = ['0.81', '0.763', '0', '0', '#joy', '#interest', 'positive', '0.786', 'dollar_bill', 'franc', 'euro', 'dollar', 'cent']
senticnet['cent_per'] = ['0.987', '0.902', '0', '0', '#joy', '#interest', 'positive', '0.945', 'franc', 'dime', 'dollar', 'shilling', 'banknote']
senticnet['cent_per_dollar'] = ['0.987', '0.902', '0', '0', '#joy', '#interest', 'positive', '0.945', 'franc', 'dime', 'dollar', 'shilling', 'banknote']
senticnet['cent_per_minute'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'long_distance_relationship', 'distant', 'kilometer', 'length_measurement', 'distance_measure']
senticnet['cent_per_ounce'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'coke', 'sprite', 'cola', 'beverage', 'soft_drink']
senticnet['cent_piece'] = ['0.987', '0.902', '0', '0', '#joy', '#interest', 'positive', '0.945', 'franc', 'dime', 'dollar', 'shilling', 'banknote']
senticnet['cent_postage_stamp'] = ['0.058', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.054', 'letter_slot', 'envelope', 'pamphlet', 'mailman', 'address_label']
senticnet['cent_stamp'] = ['0.058', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.054', 'letter_slot', 'envelope', 'pamphlet', 'mailman', 'address_label']
senticnet['centamin_plc_cey_lse'] = ['0.025', '0.115', '0.06', '0.183', '#interest', '#admiration', 'positive', '0.066', 'volume_measurement', 'liquid_measurement', 'mass_measure', 'geometry', 'size']
senticnet['centaur_upper_stage'] = ['0', '0', '0', '0.073', '#admiration', '#admiration', 'positive', '0.073', 'fire_starter', 'organic_substance', 'renewable_resource', 'use_fuel', 'tree_product']
senticnet['centaurea_jacea'] = ['0.861', '-0.87', '0', '0.597', '#joy', '#surprise', 'positive', '0.779', 'herb', 'cultivated_parsnip', 'wild_parsnip', 'madnep', 'herbaceous_plant']
senticnet['centaurea_nigra'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
senticnet['centauri_republic'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['centaury'] = ['-0.90', '0', '0.726', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'broad_leaved_centaury', 'rosita', 'lesser_centaury', 'tufted_centaury', 'seaside_centaury']
senticnet['centavo'] = ['-0.02', '0.264', '0', '0.095', '#interest', '#admiration', 'positive', '0.111', 'finance', 'franc', 'cent', 'banknote', 'mastercard']
senticnet['centenarian'] = ['0.556', '-0.64', '0', '0.64', '#surprise', '#admiration', 'positive', '0.612', 'golden_ager', 'nonagenarian', 'octogenarian', 'septuagenarian', 'sexagenarian']
senticnet['centennial'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'yearly_celebration', 'special_day', 'age', 'occasion', 'annual']
senticnet['center'] = ['0.043', '0.14', '0.01', '0.245', '#interest', '#admiration', 'positive', '0.104', 'in_between', 'middle', 'in_middle', 'downtown', 'placement']
senticnet['center_armrest'] = ['0', '0.248', '-0.07', '0.121', '#interest', '#admiration', 'positive', '0.099', 'gym_locker', 'observatory', 'prison_cell', 'luggage_store', 'garage_sale']
senticnet['center_attention'] = ['0.704', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.727', 'orgy', 'pia_lot_of_seat', 'seat_chair', 'loose_end', 'death_row']
senticnet['center_conduct_research'] = ['0.887', '0.877', '0', '0', '#joy', '#interest', 'positive', '0.882', 'ask_expert', 'absorb_information', 'analyze', 'learn_computer', 'search_information']
senticnet['center_divider'] = ['0.754', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.867', 'american_flag', 'asphalt', 'rubble', 'pavement', 'pave']
senticnet['center_fielder'] = ['0', '0.075', '0', '0', '#interest', '#interest', 'positive', '0.075', 'cup', 'carafe', 'container_cup', 'mug', 'glasses']
senticnet['center_mehmet_okur'] = ['0.556', '-0.79', '0', '0.755', '#surprise', '#admiration', 'positive', '0.701', 'lush', 'alcoholic', 'alky', 'dipsomaniac', 'rummy']
senticnet['center_passage'] = ['0.076', '0.094', '-0.06', '0', '#joy', '#interest', 'positive', '0.036', 'center', 'control_room', 'graffiti', 'passage', 'ticket_box']
senticnet['centered_around'] = ['0.53', '-0.64', '0', '0.76', '#surprise', '#admiration', 'positive', '0.646', 'focus', 'get_focused', 'focusing', 'centering', 'particularism']
senticnet['centerfold'] = ['-0.98', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.89', 'fairy', 'genus', 'honey_bee', 'infant', 'dove']
senticnet['centering'] = ['0.633', '-0.96', '0', '0.612', '#joy', '#surprise', 'positive', '0.738', 'particularism', 'absorption', 'concentration', 'engrossment', 'immersion']
senticnet['centerline'] = ['-0.04', '0.074', '0.019', '0.071', '#interest', '#admiration', 'positive', '0.021', 'sharpness', 'sharp_end', 'sharp_corner', 'near_end', 'edge']
senticnet['centerpiece'] = ['0.53', '-0.64', '0', '0.76', '#surprise', '#admiration', 'positive', '0.646', 'focus', 'get_focused', 'focusing', 'centering', 'particularism']
senticnet['centime'] = ['0.987', '0.902', '0', '0', '#joy', '#interest', 'positive', '0.945', 'franc', 'dime', 'dollar', 'shilling', 'banknote']
senticnet['centimeter_gram_second'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'newton', 'sthene', 'force_unit']
senticnet['centimetre'] = ['0', '0.082', '0.076', '0.111', '#interest', '#admiration', 'positive', '0.039', 'measurement_unit', 'measure', 'imperial_measurement', 'length', 'measurement']
senticnet['centimetre_gram_second'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'newton', 'sthene', 'force_unit']
senticnet['centipede'] = ['0', '0', '-0.87', '0', '#fear', '#fear', 'negative', '-0.87', 'beetle', 'many_insects', 'grasshopper', 'firefly', 'mouse']
senticnet['central'] = ['0.035', '0.065', '0.025', '0.127', '#interest', '#admiration', 'positive', '0.051', 'vicinity', 'asset', 'center', 'busy_street', 'main']
senticnet['central_america'] = ['0', '0', '0', '0.119', '#admiration', '#admiration', 'positive', '0.119', 'planet', 'earth', 'spherical', 'planet_solar_system', 'terrain']
senticnet['central_arctic_caribou'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'cheetah', 'giraffe', 'kangaroo', 'mule', 'horse']
senticnet['central_atrium'] = ['0.046', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'light', 'bay_window', 'air_conditioner', 'sky', 'lamp']
senticnet['central_bank'] = ['0', '0.79', '0', '0.837', '#interest', '#admiration', 'positive', '0.814', 'bank', 'central_government', 'hub', 'manage', 'central_hub']
senticnet['central_brick_chimney'] = ['0.027', '0.139', '0', '0.203', '#interest', '#admiration', 'positive', '0.123', 'horse_shoes', 'four_leaf_clover', 'rotary_phone', 'ping_pong', 'billiards']
senticnet['central_bulge'] = ['0.737', '-0.96', '0', '0.552', '#joy', '#surprise', 'positive', '0.75', 'prominence', 'hump', 'bump', 'swelling', 'frontal_eminence']
senticnet['central_bus_terminal'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'train_station', 'bus', 'subway_station', 'public_place', 'pier']
senticnet['central_clearinghouse'] = ['0.53', '-0.64', '0', '0.76', '#surprise', '#admiration', 'positive', '0.646', 'focus', 'get_focused', 'focusing', 'centering', 'particularism']
senticnet['central_equatorial_pacific'] = ['-0.69', '0', '0.835', '-0.56', '#sadness', '#anger', 'negative', '-0.69', 'roughness', 'inclemency', 'bad_weather', 'inclementness', 'windiness']
senticnet['central_excise'] = ['-0.08', '0.358', '-0.20', '0.237', '#interest', '#admiration', 'positive', '0.077', 'extortion', 'money_due', 'afford', 'bribe', 'owe']
senticnet['central_government'] = ['0', '0.783', '0', '0.828', '#interest', '#admiration', 'positive', '0.805', 'bank', 'central_bank', 'hub', 'manage', 'central_hub']
senticnet['central_hallway'] = ['0', '0', '0.095', '0', '#anger', '#anger', 'negative', '-0.09', 'build_part', 'flat', 'step', 'large_rock', 'hard_surface']
senticnet['central_highland_coffee'] = ['0', '0.072', '0', '0.064', '#interest', '#admiration', 'positive', '0.068', 'zipper', 'pant', 'trousers', 'cowboy_boot', 'accessory']
senticnet['central_hub'] = ['0', '0.781', '0', '0.832', '#interest', '#admiration', 'positive', '0.806', 'bank', 'central_government', 'hub', 'manage', 'central_bank']
senticnet['central_kalahari_game'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['central_medallion'] = ['0.586', '-0.65', '0', '0.503', '#joy', '#surprise', 'positive', '0.58', 'decoration', 'distinguished_service_cross', 'medal', 'medallion', 'palm']
senticnet['central_nervous_system'] = ['0', '0.786', '0', '0.728', '#interest', '#admiration', 'positive', '0.757', 'hold_brain', 'inside_head', 'cranium', 'skull', 'mind']
senticnet['central_otago_goldrush'] = ['-0.7', '0', '0.746', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'insufficiency', 'sparseness', 'sparsity', 'abstemiousness', 'lack_of_quantity']
senticnet['central_part'] = ['0.889', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.917', 'govern_city', 'large_town', 'city_life', 'large_building', 'downtown']
senticnet['central_passage'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'vestibule', 'door', 'doorway', 'gate', 'door_handle']
senticnet['central_sleep_apnea'] = ['-0.03', '-0.03', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'eat_poison', 'commit_suicide', 'drug_addiction', 'drunk_drive', 'jump_off_cliff']
senticnet['centralisation'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'blowup', 'discharge', 'backfire', 'blast']
senticnet['centralism'] = ['-0.54', '0', '0.979', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'unlikeness', 'dissimilitude', 'difference', 'heterology', 'dissimilarity']
senticnet['centrality'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'baddie', 'theatrical_baddie', 'part', 'persona']
senticnet['centralization'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'blowup', 'discharge', 'backfire', 'blast']
senticnet['centralized_bureaucracy'] = ['0', '0', '0', '-0.94', '#disgust', '#disgust', 'negative', '-0.94', 'boring', 'fill_form', 'annoying', 'form', 'fill_out_form']
senticnet['centrally_manage'] = ['0.781', '-0.79', '0', '0.816', '#surprise', '#admiration', 'positive', '0.798', 'deliver_goods', 'succeed', 'win', 'bring_home_bacon', 'come_through']
senticnet['centre'] = ['0.994', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.98', 'concentrate', 'focus', 'pore', 'riveting', 'engrossing']
senticnet['centre_barc'] = ['-0.90', '0', '0.748', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'alternative_energy', 'activation_energy', 'energy_of_activation', 'energy', 'nuclear_energy']
senticnet['centre_manu_tuilagi'] = ['-0.69', '0', '0.768', '-0.67', '#sadness', '#anger', 'negative', '-0.71', 'dither', 'pother', 'worry', 'flap', 'scruple']
senticnet['centred_around'] = ['0.53', '-0.64', '0', '0.76', '#surprise', '#admiration', 'positive', '0.646', 'focus', 'get_focused', 'focusing', 'centering', 'particularism']
senticnet['centrefold'] = ['-0.98', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.89', 'fairy', 'genus', 'honey_bee', 'infant', 'dove']
senticnet['centric_episode'] = ['0.918', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.827', 'encounter', 'reveal', 'programme', 'lose', 'sequel']
senticnet['centrifugal'] = ['-0.85', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.84', 'impeller', 'centripetal', 'self-priming', 'spiral', 'pump']
senticnet['centrifugal_clutch'] = ['0', '-0.09', '0.044', '0.055', '#surprise', '#admiration', 'positive', '0.036', 'two_wheels', 'gear', 'axle', 'engine', 'steer_wheel']
senticnet['centrifugal_force'] = ['0.038', '0', '0.031', '0.124', '#joy', '#admiration', 'positive', '0.044', 'rotate', 'rotate_movement', 'change_direction', 'move', 'go_around']
senticnet['centrifugal_pump'] = ['0.843', '-0.99', '0', '0.706', '#joy', '#surprise', 'positive', '0.849', 'gadgetry', 'gimbal', 'contrivance', 'mod_con', 'device']
senticnet['centrifuge_tube'] = ['-0.86', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.80', 'riverbed', 'bottom_ocean', 'waterfall', 'down_drain', 'septic_tank']
senticnet['centripetal'] = ['-0.86', '0', '-0.84', '0', '#sadness', '#fear', 'negative', '-0.85', 'impeller', 'centrifugal', 'self-priming', 'spiral', 'pump']
senticnet['centripetal_force'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotate_movement', 'rotate', 'go_around', 'manoeuvre', 'full_circle']
senticnet['centrist'] = ['0', '-0.87', '0', '0.974', '#surprise', '#admiration', 'positive', '0.922', 'middle-of-the-road', 'middling', 'inoffensive', 'wishy-washy', 'run-of-the-mill']
senticnet['centrist_politician'] = ['0', '0.868', '0', '0.853', '#interest', '#admiration', 'positive', '0.861', 'country_leader', 'prime_minister', 'president', 'elect_official', 'leader']
senticnet['cents_per'] = ['0.987', '0.902', '0', '0', '#joy', '#interest', 'positive', '0.945', 'franc', 'dime', 'dollar', 'shilling', 'banknote']
senticnet['centuriate_assembly'] = ['0.774', '-0.82', '0', '0.63', '#joy', '#surprise', 'positive', '0.744', 'quality', 'perishability', 'perishableness', 'unsatisfactoriness', 'inadequacy']
senticnet['centurion_tank'] = ['-0.56', '0', '0.714', '-0.89', '#anger', '#disgust', 'negative', '-0.72', 'tank', 'tank_destroyer', 'armored_car', 'armoured_car', 'armored_personnel_carrier']
senticnet['century'] = ['0.978', '-0.60', '0', '0.895', '#joy', '#admiration', 'positive', '0.825', 'period', 'quattrocento', 'twentieth_century', 'time_period', 'period_of_time']
senticnet['century_ad'] = ['0.978', '-0.60', '0', '0.895', '#joy', '#admiration', 'positive', '0.825', 'period', 'quattrocento', 'twentieth_century', 'time_period', 'period_of_time']
senticnet['century_fox'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'kit_fox', 'prairie_fox', 'white_fox']
senticnet['century_fox_television'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'kit_fox', 'prairie_fox', 'white_fox']
senticnet['century_old'] = ['0', '0.915', '0', '0.821', '#interest', '#admiration', 'positive', '0.868', 'culture', 'special_day', 'holiday', 'party_time', 'yearly_celebration']
senticnet['century_onward'] = ['0.978', '-0.60', '0', '0.895', '#joy', '#admiration', 'positive', '0.825', 'period', 'quattrocento', 'twentieth_century', 'time_period', 'period_of_time']
senticnet['centurythe'] = ['-0.96', '0', '0.615', '-0.73', '#sadness', '#disgust', 'negative', '-0.77', 'flush', 'blossom', 'prime', 'peak', 'efflorescence']
senticnet['cenvat'] = ['-0.08', '0.358', '-0.20', '0.237', '#interest', '#admiration', 'positive', '0.077', 'extortion', 'money_due', 'afford', 'bribe', 'owe']
senticnet['ceo'] = ['0.046', '0.116', '-0.05', '0.076', '#interest', '#admiration', 'positive', '0.045', 'head_guy', 'boss', 'officer', 'leader', 'supervisor']
senticnet['ceorl'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'bodyguard', 'outrider', 'bodyguard_of_guard', 'beefeater', 'defender']
senticnet['cepacol'] = ['0.878', '0.869', '0', '0', '#joy', '#interest', 'positive', '0.873', 'fluoride_mouthwash', 'hair_conditioner', 'sculpt_gel', 'dandruff_shampoo', 'hair_gel']
senticnet['cephalaspid'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'hag', 'osteostracan', 'jawless_fish', 'ostracoderm', 'heterostracan']
senticnet['cephalitis'] = ['-0.55', '0', '0.757', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'rubor', 'redness', 'sleeping_sickness', 'sleepy_sickness', 'epidemic_encephalitis']
senticnet['cephalopod'] = ['-0.52', '0', '0.728', '-0.92', '#anger', '#disgust', 'negative', '-0.72', 'nautilus', 'octopod', 'octopod_mollusk', 'paper_nautilus', 'octopus']
senticnet['cephalopod_mollusk'] = ['0.076', '0', '0', '0', '#joy', '#joy', 'positive', '0.076', 'nautilus', 'cephalopod', 'octopod', 'paper_nautilus', 'octopus']
senticnet['cephalosporium'] = ['-0.93', '0', '0.699', '-0.91', '#sadness', '#disgust', 'negative', '-0.84', 'onion_smut', 'head_smut', 'stinking_smut', 'flag_smut_fungus', 'covered_smut']
senticnet['ceradyne'] = ['0.939', '-0.63', '0', '0.653', '#joy', '#admiration', 'positive', '0.741', 'vest', 'waistcoat', 'bulletproof_vest', 'suit_of_armor', 'coat_of_mail']
senticnet['ceramic'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'fabrication', 'carving', 'decoupage', 'artistic_creation']
senticnet['ceramic_armor'] = ['0.939', '-0.63', '0', '0.653', '#joy', '#admiration', 'positive', '0.741', 'vest', 'waistcoat', 'bulletproof_vest', 'suit_of_armor', 'coat_of_mail']
senticnet['ceramic_art'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'fabrication', 'carving', 'decoupage', 'artistic_creation']
senticnet['ceramic_artist'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'fabrication', 'carving', 'decoupage', 'artistic_creation']
senticnet['ceramic_assemblage'] = ['-0.68', '0', '0.517', '-0.59', '#sadness', '#disgust', 'negative', '-0.59', 'dump', 'garbage_dump', 'trash_dump', 'rubbish_dump', 'wasteyard']
senticnet['ceramic_bowl'] = ['0.035', '-0.01', '0.009', '0.063', '#joy', '#admiration', 'positive', '0.026', 'container', 'carton', 'contain', 'hold_cupcake', 'store_dish']
senticnet['ceramic_composite'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'gold', 'bronze', 'alloy', 'medal', 'copper']
senticnet['ceramic_decoration'] = ['0.876', '-0.76', '0', '0.963', '#joy', '#admiration', 'positive', '0.867', 'housecraft', 'tradecraft', 'watercraft', 'acquirement', 'skill']
senticnet['ceramic_glaze'] = ['-0.06', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.04', 'polish', 'magnify', 'see_out', 'sill', 'pane']
senticnet['ceramic_jar'] = ['0.035', '-0.01', '0.009', '0.063', '#joy', '#admiration', 'positive', '0.026', 'container', 'carton', 'contain', 'hold_cupcake', 'store_dish']
senticnet['ceramic_mug'] = ['0.214', '0.258', '-0.09', '0.2', '#joy', '#interest', 'positive', '0.146', 'cup', 'carafe', 'pitcher', 'container_cup', 'glasses']
senticnet['ceramic_pot'] = ['-0.52', '0', '0.978', '-0.55', '#anger', '#disgust', 'negative', '-0.68', 'dump', 'garbage_dump', 'trash_dump', 'rubbish_dump', 'wasteyard']
senticnet['ceramic_sculpture'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'fabrication', 'carving', 'decoupage', 'artistic_creation']
senticnet['ceramic_vessel'] = ['0.099', '-0.07', '-0.08', '0.195', '#joy', '#admiration', 'positive', '0.071', 'art', 'exhibit', 'craft', 'fine_art', 'artwork']
senticnet['ceramic_ware'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'fabrication', 'carving', 'decoupage', 'artistic_creation']
senticnet['ceramicrete'] = ['0', '0.127', '-0.04', '0.194', '#interest', '#admiration', 'positive', '0.092', 'concrete', 'tar', 'pavement', 'big_city_life', 'popular_building_material']
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senticnet['cerapteryx_graminis'] = ['-0.10', '-0.05', '0', '-0.15', '#sadness', '#disgust', 'negative', '-0.06', 'armyworm', 'antler_moth', 'noctuid', 'heliothis_moth', 'moth']
senticnet['ceratitic'] = ['0.969', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.772', 'aspheric', 'aspherical', 'prolate', 'rounded', 'crescent']
senticnet['ceratopogonidae'] = ['-0.77', '0', '0.576', '-0.92', '#sadness', '#disgust', 'negative', '-0.75', 'insect', 'firebug', 'water_bug', 'assassin_bug', 'heteropterous_insect']
senticnet['cercaria'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'annelid', 'annelid_worm', 'leech']
senticnet['cercospora'] = ['-0.93', '0', '0.699', '-0.91', '#sadness', '#disgust', 'negative', '-0.84', 'onion_smut', 'head_smut', 'stinking_smut', 'flag_smut_fungus', 'covered_smut']
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senticnet['cere'] = ['-0.59', '0', '0.887', '-0.71', '#anger', '#disgust', 'negative', '-0.73', 'armet', 'basinet', 'helmet', 'heaume', 'morion']
senticnet['cereal'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'veal', 'beef', 'food_group', 'animal_flesh', 'curry']
senticnet['cereal_bowl'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'wooden_spoon', 'caffeine', 'teaspoon', 'container_cup', 'large_container']
senticnet['cereal_box'] = ['0', '0', '0', '0.93', '#admiration', '#admiration', 'positive', '0.93', 'yogurt', 'ferment_beverage', 'healthy_drink', 'ice_cream', 'healthy_snack']
senticnet['cereal_crop'] = ['0', '0.082', '0.052', '0.08', '#interest', '#admiration', 'positive', '0.037', 'starch', 'food_type', 'farm_plant', 'farm_crop', 'staple_food']
senticnet['cereal_grain'] = ['0.061', '0.093', '0.037', '0.114', '#interest', '#admiration', 'positive', '0.058', 'whole_grain', 'food_ingredient', 'staple_food', 'starchy_food', 'food_grain']
senticnet['cereal_leaf_beetle'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'nuisance', 'annoyance', 'irritant', 'pain_in_ass', 'troublesome']
senticnet['cereal_malt_beverage'] = ['0', '0', '0', '-0.91', '#disgust', '#disgust', 'negative', '-0.91', 'alcoholic_drink', 'get_drunk', 'ferment_beverage', 'adult_drink', 'booze']
senticnet['cerebellar_ataxia'] = ['-0.65', '0', '0.873', '-0.63', '#sadness', '#anger', 'negative', '-0.72', 'neuritis', 'multiple_neuritis', 'rubor', 'redness', 'inflammation']
senticnet['cerebellar_hypoplasia'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'myotonic_muscular_dystrophy', 'myotonic_dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['cerebellum'] = ['0', '0.786', '0', '0.728', '#interest', '#admiration', 'positive', '0.757', 'hold_brain', 'inside_head', 'cranium', 'skull', 'mind']
senticnet['cerebral_achromatopsia'] = ['-0.73', '0', '0.955', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'visual_agnosia', 'auditory_agnosia', 'brain_disorder', 'encephalopathy', 'brain_disease']
senticnet['cerebral_atrophy'] = ['-0.78', '0', '0.764', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'withering', 'downfall', 'weakening', 'fading', 'attenuation']
senticnet['cerebral_cortex'] = ['0', '0.786', '0', '0.728', '#interest', '#admiration', 'positive', '0.757', 'hold_brain', 'inside_head', 'cranium', 'skull', 'mind']
senticnet['cerebral_function'] = ['-0.78', '0', '0.669', '-0.89', '#sadness', '#disgust', 'negative', '-0.78', 'pain_sensation', 'painful_sensation', 'prickling', 'tingling', 'pain']
senticnet['cerebral_hemorrhage'] = ['-0.98', '0', '0.827', '-0.94', '#sadness', '#disgust', 'negative', '-0.91', 'congestive_heart_failure', 'heart_attack', 'failure', 'heart_disease', 'cardiopathy']
senticnet['cerebral_pathway'] = ['-0.74', '0', '0.888', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'optic_radiation', 'radiatio_optica', 'pathway', 'peduncle']
senticnet['cerebral_peduncle'] = ['0.7', '0', '0', '0', '#joy', '#joy', 'positive', '0.7', 'commissure', 'optic_radiation', 'radiatio_optica', 'peduncle', 'pathway']
senticnet['cerebral_spinal_fluid'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'keep_alive', 'body_system', 'body_working', 'blood_pressure', 'heart']
senticnet['cerebral_vascular'] = ['-0.73', '0', '0.904', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'unshapely', 'shapeless', 'lumpy', 'deformed', 'malformed']
senticnet['cerebration'] = ['0.621', '-0.60', '0', '0.53', '#joy', '#surprise', 'positive', '0.585', 'reasoning', 'consideration', 'logical_thinking', 'abstract_thought', 'excogitation']
senticnet['cerebrospinal_fever'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'leptomeningitis', 'cerebrospinal_meningitis', 'epidemic_meningitis', 'brain_fever', 'meningitis']
senticnet['cerebrospinal_meningitis'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'leptomeningitis', 'meningitis', 'epidemic_meningitis', 'brain_fever', 'cerebrospinal_fever']
senticnet['ceremonial'] = ['-0.71', '0', '0.757', '0', '#sadness', '#anger', 'negative', '-0.73', 'receding', 'recessional', 'recession', 'bankruptcy', 'depressed']
senticnet['ceremonial_baton'] = ['-0.91', '0', '0.931', '-0.72', '#sadness', '#anger', 'negative', '-0.85', 'hit_with_baton', 'get_hurt', 'whip', 'arrow', 'hurt']
senticnet['ceremonial_mace'] = ['-0.91', '0', '0.931', '-0.72', '#sadness', '#anger', 'negative', '-0.85', 'hit_with_baton', 'get_hurt', 'whip', 'arrow', 'hurt']
senticnet['ceremonial_magic'] = ['-0.51', '0', '0.979', '-0.89', '#anger', '#disgust', 'negative', '-0.79', 'invisible', 'concealed', 'nonvisual', 'hidden', 'out_of_sight']
senticnet['ceremonial_magician'] = ['-0.51', '0', '0.979', '-0.89', '#anger', '#disgust', 'negative', '-0.79', 'invisible', 'concealed', 'nonvisual', 'hidden', 'out_of_sight']
senticnet['ceremonially'] = ['0.82', '0', '0', '0.885', '#joy', '#admiration', 'positive', '0.853', 'religious_event', 'sunday', 'happy_event', 'tradition', 'wedding']
senticnet['ceremonially_unclean'] = ['-0.69', '0', '0.858', '-0.83', '#anger', '#disgust', 'negative', '-0.79', 'draggled', 'befouled', 'filthy', 'nasty', 'scummy']
senticnet['ceremoniously'] = ['0.82', '0', '0', '0.885', '#joy', '#admiration', 'positive', '0.853', 'religious_event', 'sunday', 'happy_event', 'tradition', 'wedding']
senticnet['ceremony'] = ['0.82', '0', '0', '0.885', '#joy', '#admiration', 'positive', '0.853', 'religious_event', 'sunday', 'happy_event', 'tradition', 'wedding']
senticnet['ceremony_take_place'] = ['0.82', '0', '0', '0.885', '#joy', '#admiration', 'positive', '0.853', 'religious_event', 'sunday', 'happy_event', 'tradition', 'wedding']
senticnet['cerh_european_league'] = ['0', '0', '0', '0.879', '#admiration', '#admiration', 'positive', '0.879', 'sport_competition', 'win_competition', 'race', 'match', 'championship']
senticnet['cerium'] = ['-0.05', '-0.00', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'pigment', 'many_shades', 'cyan', 'green_yellow', 'material']
senticnet['cert_nly'] = ['-0.83', '0', '0.87', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'precipitate', 'rain_down', 'shower_down', 'spit', 'patter']
senticnet['certain'] = ['0', '0', '0', '0.884', '#admiration', '#admiration', 'positive', '0.884', 'aware', 'truth', 'discovery', 'fact', 'help_find']
senticnet['certain_amount'] = ['-0.07', '0.207', '-0.05', '0.228', '#interest', '#admiration', 'positive', '0.076', 'free', 'gift', 'abundance', 'plus', 'plentiful']
senticnet['certain_extent'] = ['0.021', '0.167', '0', '0.207', '#interest', '#admiration', 'positive', '0.132', 'size_up', 'measure_length', 'scope', 'distant', 'extension']
senticnet['certain_threshold'] = ['0', '0.098', '0', '0.089', '#interest', '#admiration', 'positive', '0.093', 'wood_panel', 'wooden_structure', 'hinge', 'entrance_room', 'corridor']
senticnet['certainly'] = ['-0.70', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.71', 'devout', 'earnestness', 'earnestly', 'seriousness', 'in_earnest']
senticnet['certainty'] = ['0', '0.793', '0', '0', '#interest', '#interest', 'positive', '0.793', 'attitude', 'prefer', 'thoughtful', 'strong_feeling', 'wish']
senticnet['certificate'] = ['0', '0.155', '0', '0', '#interest', '#interest', 'positive', '0.155', 'diploma', 'celsius', 'qualification', 'temperature_measure', 'temperature_measurement']
senticnet['certificate_of_incorporation'] = ['0.931', '-0.52', '0', '0.67', '#joy', '#admiration', 'positive', '0.709', 'document', 'credential', 'birth_certificate', 'diploma', 'sheepskin']
senticnet['certification'] = ['-0.02', '0.102', '0', '0.07', '#interest', '#admiration', 'positive', '0.049', 'qualification', 'grade', 'diploma', 'pass_fail', 'graduate']
senticnet['certificatory'] = ['0.9', '-0.74', '0', '0.721', '#joy', '#surprise', 'positive', '0.788', 'accessary', 'demonstrative_of', 'encouraging', 'supportive', 'supporting']
senticnet['certified_birth_certificate'] = ['0.931', '-0.52', '0', '0.67', '#joy', '#admiration', 'positive', '0.709', 'document', 'certificate_of_incorporation', 'credential', 'diploma', 'sheepskin']
senticnet['certified_copy'] = ['0.931', '-0.52', '0', '0.67', '#joy', '#admiration', 'positive', '0.709', 'document', 'certificate_of_incorporation', 'credential', 'diploma', 'sheepskin']
senticnet['certified_diver'] = ['0', '0.07', '0', '0', '#interest', '#interest', 'positive', '0.07', 'sunken_treasure', 'real_duck', 'urchin', 'mussel', 'gold_fish']
senticnet['certified_mail'] = ['0.862', '0.709', '0', '0', '#joy', '#interest', 'positive', '0.785', 'invoice', 'bill', 'check', 'mastercard', 'payment_counter']
senticnet['certified_organic'] = ['0.035', '0.088', '0.031', '0.113', '#interest', '#admiration', 'positive', '0.051', 'cultivate_yard', 'same_answer', 'poisonous_human', 'juicy', 'natural_material']
senticnet['certified_personal_trainer'] = ['0.603', '-0.77', '0', '0.641', '#surprise', '#admiration', 'positive', '0.674', 'suitability', 'suitableness', 'habitability', 'habitableness', 'qualification']
senticnet['certified_scuba_diver'] = ['0', '0.07', '0', '0', '#interest', '#interest', 'positive', '0.07', 'sunken_treasure', 'real_duck', 'urchin', 'mussel', 'gold_fish']
senticnet['certified_trainer'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'coach', 'instructor', 'personal_trainer', 'fit_person', 'train']
senticnet['certified_yoga_instructor'] = ['0', '0', '0', '0.93', '#admiration', '#admiration', 'positive', '0.93', 'lift_weight', 'release_stress', 'move_body', 'take_hike', 'change_color']
senticnet['certify'] = ['0', '0', '-0.75', '-0.71', '#fear', '#disgust', 'negative', '-0.73', 'foreswear', 'relinquish', 'dispense_with', 'forgo', 'throw_overboard']
senticnet['certitude'] = ['0.795', '-0.67', '0', '0.934', '#joy', '#admiration', 'positive', '0.8', 'appreciation', 'grasp', 'comprehension', 'brainstorm', 'hold']
senticnet['cerulean_blue'] = ['-0.45', '0.081', '-0.25', '0', '#sadness', '#fear', 'negative', '-0.20', 'green_yellow', 'color', 'blue_green', 'cyan', 'purple']
senticnet['cerussite'] = ['0.555', '-0.99', '0', '0.851', '#surprise', '#admiration', 'positive', '0.8', 'quartz', 'mineral', 'white_lead_ore', 'cobaltite', 'molybdenite']
senticnet['cervarix'] = ['-0.07', '0', '-0.05', '-0.18', '#sadness', '#disgust', 'negative', '-0.10', 'medical_therapy', 'vaccination', 'doctor', 'medicine', 'hospital']
senticnet['cervical_cap'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'birth_control_pill', 'condom', 'contraceptive_pill', 'sex_toy', 'prevent_pregnancy']
senticnet['cervical_lymphadenopathy'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema', 'erythema_multiforme', 'windburn', 'hickey']
senticnet['cervical_radiculopathy'] = ['0.789', '-0.99', '0', '0.54', '#joy', '#surprise', 'positive', '0.776', 'imperfection', 'imperfectness', 'bugginess', 'defectiveness', 'sickness']
senticnet['cervical_vertebra'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'above_leg', 'joint', 'hip', 'knee']
senticnet['cesarean_section'] = ['-0.51', '0', '0.747', '-0.94', '#anger', '#disgust', 'negative', '-0.73', 'stillborn_infant', 'neonate', 'preterm_baby', 'premature_infant', 'preterm_infant']
senticnet['cesium'] = ['-0.02', '0', '0.047', '-0.11', '#anger', '#disgust', 'negative', '-0.06', 'hold_trash', 'put_garbage', 'rubbish', 'put_trash', 'store_trash']
senticnet['cespitosa'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'meadow_grass', 'grass', 'manna_grass', 'sweet_grass', 'wood_meadowgrass']
senticnet['cessation'] = ['-0.91', '0', '0.576', '-0.95', '#sadness', '#disgust', 'negative', '-0.81', 'stop', 'halt', 'lock_in', 'lock', 'block']
senticnet['cessationist'] = ['-0.83', '0', '0.648', '-0.60', '#sadness', '#anger', 'negative', '-0.69', 'infallibility', 'dependability', 'dependableness', 'reliability', 'reliableness']
senticnet['cesspool'] = ['-0.02', '-0.01', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.03', 'dung', 'manure', 'shit', 'dog_poop', 'crap']
senticnet['cestoda'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'annelid', 'annelid_worm', 'leech']
senticnet['cestode'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'annelid', 'annelid_worm', 'leech']
senticnet['cet_pisode'] = ['0', '0.919', '0', '0.888', '#interest', '#admiration', 'positive', '0.903', 'surprise', 'eye-popping', 'fulgurous', 'dazzling', 'bedaze']
senticnet['cetacean'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'lordosis', 'kyphosis', 'spinal_curvature', 'hunchback', 'abnormality']
senticnet['cetasika'] = ['-0.85', '0', '0.968', '-0.75', '#sadness', '#anger', 'negative', '-0.86', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['cfo'] = ['0.016', '0.127', '-0.05', '0.071', '#interest', '#admiration', 'positive', '0.039', 'prime_minister', 'country_leader', 'elect_official', 'ceo', 'officer']
senticnet['cg_animation'] = ['0.895', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.823', 'liveliness', 'excitement', 'art_form', 'art', 'film']
senticnet['cgi_animation'] = ['0.895', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.823', 'liveliness', 'excitement', 'art_form', 'art', 'film']
senticnet['cgmp_pde_inhibitor'] = ['0.701', '-0.76', '0', '0.904', '#surprise', '#admiration', 'positive', '0.791', 'curative', 'heal', 'medicine', 'get_cured', 'take_care']
senticnet['cgs_centimeter_gram'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'newton', 'sthene', 'force_unit']
senticnet['cgs_unit'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'newton', 'sthene', 'force_unit']
senticnet['cgt_liability'] = ['-0.83', '0', '0.555', '-0.64', '#sadness', '#disgust', 'negative', '-0.67', 'single_tax', 'income_tax', 'taxation', 'capital_levy', 'departure_tax']
senticnet['ch_iqun'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'head_hurt', 'introspection', 'lifestyle_change', 'headache', 'move_eye']
senticnet['chachas_district'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'head_hurt', 'introspection', 'lifestyle_change', 'headache', 'move_eye']
senticnet['chachka'] = ['-0.74', '0', '0.877', '-0.97', '#anger', '#disgust', 'negative', '-0.86', 'miss', 'girl', 'missy', 'young_lady', 'young_woman']
senticnet['chacma'] = ['-0.52', '0', '0.604', '-0.82', '#anger', '#disgust', 'negative', '-0.65', 'discernment', 'sagacity', 'sagaciousness', 'confidentiality', 'judgment']
senticnet['chaconne'] = ['0.591', '-0.91', '0', '0.605', '#surprise', '#admiration', 'positive', '0.704', 'farandole', 'folk_dance', 'polka', 'dance', 'typical_dance']
senticnet['chafe'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['chaff'] = ['0', '0', '0.753', '-0.97', '#anger', '#disgust', 'negative', '-0.86', 'rubbish', 'refuse', 'waste', 'garbage', 'litter']
senticnet['chaga_disease'] = ['-0.55', '0', '0.757', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'rubor', 'redness', 'cephalitis', 'sleepy_sickness', 'epidemic_encephalitis']
senticnet['chagrin'] = ['-0.9', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.86', 'abashed', 'chagrined', 'abash', 'embarrassingly', 'unenviable']
senticnet['chagrined'] = ['-0.85', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.92', 'disconcert', 'abashed', 'chagrin', 'abash', 'embarrassingly']
senticnet['chai_chidchob'] = ['0.034', '-0.02', '0', '0.057', '#joy', '#admiration', 'positive', '0.039', 'female_television_connector', 'channel_selector', 'power_switch', 'tuner', 'radio']
senticnet['chain'] = ['0.096', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.057', 'platform_rail', 'rail', 'catwalk', 'column', 'token_turnstile']
senticnet['chain_reaction'] = ['-0.99', '0', '0.651', '-0.51', '#sadness', '#anger', 'negative', '-0.72', 'sending', 'compulsion', 'influence', 'inducement', 'causation']
senticnet['chain_reaction_accident'] = ['-0.90', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.86', 'trauma', 'car_wreck', 'broken_bone', 'bad_knee', 'wreck']
senticnet['chain_stitch'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_cloth']
senticnet['chain_up'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'hold', 'prisoner', 'prison', 'tie_down', 'enchain']
senticnet['chain_wrapped_around'] = ['-0.13', '0.113', '-0.16', '0', '#sadness', '#fear', 'negative', '-0.06', 'question_witness', 'legal_process', 'find_guilty', 'convict_criminal', 'custody']
senticnet['chainsaw'] = ['0', '0', '-0.93', '-0.78', '#fear', '#disgust', 'negative', '-0.85', 'handgun', 'firearm', 'cannon', 'weapon', 'machine_gun']
senticnet['chair'] = ['0', '0', '0.88', '-0.92', '#anger', '#disgust', 'negative', '-0.90', 'sofa_bed', 'small_seat', 'beanbag_airline_seat', 'hide-a-bed_sofa', 'airline_seat']
senticnet['chair_committee'] = ['-0.84', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.85', 'voice_concern', 'sail_around_world', 'age_gracefully', 'lose_key', 'storm_out_room']
senticnet['chair_conformation'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
senticnet['chair_elect'] = ['0', '0', '0.88', '-0.92', '#anger', '#disgust', 'negative', '-0.90', 'sofa_bed', 'small_seat', 'beanbag_airline_seat', 'hide-a-bed_sofa', 'airline_seat']
senticnet['chair_meet'] = ['-0.79', '0', '-0.84', '0', '#sadness', '#fear', 'negative', '-0.82', 'age_gracefully', 'interaction', 'storm_out_room', 'sail_around_world', 'voice_concern']
senticnet['chairholder'] = ['0', '0', '0.88', '-0.92', '#anger', '#disgust', 'negative', '-0.90', 'sofa_bed', 'small_seat', 'beanbag_airline_seat', 'hide-a-bed_sofa', 'airline_seat']
senticnet['chairlift_fixed_grip'] = ['0', '0.142', '-0.09', '0.183', '#interest', '#admiration', 'positive', '0.078', 'arise', 'rise', 'raise_up', 'go_up', 'go_upstairs']
senticnet['chairman'] = ['0.027', '0.074', '0', '0.087', '#interest', '#admiration', 'positive', '0.063', 'president', 'use_school', 'wood_plank', 'chairperson', 'high_post']
senticnet['chairman_mao_zedong'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['chairperson'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'seeker', 'commuter']
senticnet['chaise'] = ['0.979', '-0.69', '0', '0.576', '#joy', '#surprise', 'positive', '0.749', 'big_cheese', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['chaise_longue'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'coach_seat', 'stool', 'seat']
senticnet['chaise_lounge'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'coach_seat', 'stool', 'seat']
senticnet['chaitra_month'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['chal'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'white_animal', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
senticnet['chalcedony'] = ['0.037', '0.073', '0.027', '0.072', '#interest', '#admiration', 'positive', '0.039', 'sapphire', 'birth_stone', 'turquoise', 'gemstone', 'amethyst']
senticnet['chalcone'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'capsaicin', 'chemical_irritant', 'piperine', 'pepper_spray']
senticnet['chaldean_catholic_eparchy'] = ['0.8', '-0.84', '0', '0.868', '#surprise', '#admiration', 'positive', '0.837', 'commonwealth', 'province', 'administrative_district', 'administrative_division', 'territorial_division']
senticnet['chalet_style'] = ['-0.96', '0', '0.924', '-0.86', '#sadness', '#anger', 'negative', '-0.91', 'redneck', 'yokel', 'rube', 'hick', 'hayseed']
senticnet['chalice'] = ['0', '0.082', '-0.09', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'coffin', 'headstone', 'morgue', 'bury_body', 'casket']
senticnet['chalk_board'] = ['0', '-0.01', '0.027', '-0.1', '#anger', '#disgust', 'negative', '-0.03', 'sand_paper', 'obstacle', 'golf_place', 'paper', 'school_class']
senticnet['chalk_downland'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'biome', 'timberland']
senticnet['chalk_dust'] = ['-0.02', '0.087', '0', '0.107', '#interest', '#admiration', 'positive', '0.056', 'fallout', 'dust', 'radioactive_dust', 'particulate', 'particulate_matter']
senticnet['chalk_grassland'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'biome', 'timberland']
senticnet['chalk_pit'] = ['0', '0', '-0.85', '-0.71', '#fear', '#disgust', 'negative', '-0.78', 'fair_game', 'pit', 'stone_pit', 'target', 'prey']
senticnet['chalk_quarry'] = ['0', '0', '-0.85', '-0.71', '#fear', '#disgust', 'negative', '-0.78', 'fair_game', 'pit', 'stone_pit', 'target', 'prey']
senticnet['chalkboard'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'gift_wrap', 'kraft', 'kraft_paper', 'wrapping_paper', 'paper']
senticnet['chalked_off'] = ['-0.71', '0', '0.83', '-0.68', '#sadness', '#anger', 'negative', '-0.74', 'gag', 'jest', 'jape', 'joke', 'laugh']
senticnet['chalked_up'] = ['0.568', '-0.99', '0', '0.638', '#surprise', '#admiration', 'positive', '0.734', 'push_aside', 'brush_off', 'disregard', 'ignore', 'dismiss']
senticnet['challenge'] = ['0.104', '-0.06', '0.057', '0', '#joy', '#surprise', 'positive', '0.038', 'enjoy_game', 'competition', 'match', 'recreation', 'enjoy_challenge']
senticnet['challenge_cup'] = ['0.093', '-0.02', '0', '0.056', '#joy', '#admiration', 'positive', '0.057', 'pitcher', 'carafe', 'container_pitcher', 'mug', 'glass_of_water']
senticnet['challengeable'] = ['0.544', '-0.69', '0', '0.658', '#surprise', '#admiration', 'positive', '0.632', 'shakable', 'shakeable', 'contestable', 'disputable', 'debatable']
senticnet['challenger'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['challenger_deep'] = ['-0.02', '-0.01', '0.03', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'ocean_beach', 'beach', 'water_sea', 'shore', 'ground_ocean']
senticnet['challenger_tank'] = ['-0.56', '0', '0.714', '-0.89', '#anger', '#disgust', 'negative', '-0.72', 'tank', 'tank_destroyer', 'armored_car', 'armoured_car', 'armored_personnel_carrier']
senticnet['challenging_task'] = ['0.028', '0.138', '-0.04', '0.215', '#interest', '#admiration', 'positive', '0.083', 'skilled', 'expert', 'tin', 'dense_water', 'metal_container']
senticnet['chalukya_dynasty'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
senticnet['chalumeau'] = ['0.033', '0', '0.009', '0.057', '#joy', '#admiration', 'positive', '0.027', 'triangle', 'baton', 'bugle', 'type_of_bow', 'horn']
senticnet['chalumeaux'] = ['-0.75', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.80', 'high-strung', 'uptight', 'restive', 'nervy', 'jumpy']
senticnet['chamaecyparis_lawsoniana'] = ['0.776', '-0.6', '0', '0.568', '#joy', '#surprise', 'positive', '0.648', 'swamp_chestnut_oak', 'chinquapin_oak', 'chinkapin_oak', 'yellow_chestnut_oak', 'basket_oak']
senticnet['chamaedorea'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'carnauba_palm', 'carnauba', 'wax_palm', 'palmetto', 'palmyra']
senticnet['chamanthedon'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'head_hurt', 'introspection', 'lifestyle_change', 'headache', 'move_eye']
senticnet['chamber'] = ['-0.58', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.64', 'burial_chamber', 'sepulcher', 'bunker', 'sepulture', 'bombproof']
senticnet['chamber_choir'] = ['0.506', '0.659', '-0.24', '0.242', '#joy', '#interest', 'positive', '0.29', 'sing', 'music_studio', 'music_room', 'choir', 'symphony']
senticnet['chamber_ensemble'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'classical_music', 'make_beautiful_music', 'create_music', 'part_of_band', 'make_music']
senticnet['chamber_maid'] = ['-0.62', '0', '0.649', '-0.67', '#anger', '#disgust', 'negative', '-0.64', 'handmaid', 'handmaiden', 'chambermaid', 'maid', 'parlormaid']
senticnet['chamber_music'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'classical_music', 'make_beautiful_music', 'create_music', 'part_of_band', 'make_music']
senticnet['chamber_music_ensemble'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'classical_music', 'make_beautiful_music', 'create_music', 'part_of_band', 'make_music']
senticnet['chamber_music_festival'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'classical_music', 'make_beautiful_music', 'create_music', 'part_of_band', 'make_music']
senticnet['chamber_music_repertoire'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'classical_music', 'make_beautiful_music', 'create_music', 'part_of_band', 'make_music']
senticnet['chamber_music_society'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'classical_music', 'make_beautiful_music', 'create_music', 'part_of_band', 'make_music']
senticnet['chamber_musician'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'classical_music', 'make_beautiful_music', 'create_music', 'part_of_band', 'make_music']
senticnet['chamber_orchestra'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'classical_music', 'make_beautiful_music', 'create_music', 'part_of_band', 'make_music']
senticnet['chamber_pot'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'commode', 'potty_seat', 'potty_chair']
senticnet['chamber_tpc'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'arm_movement', 'hand_gesture', 'water_motion', 'hand_signal', 'sea_movement']
senticnet['chambermaid'] = ['-0.62', '0', '0.649', '-0.67', '#anger', '#disgust', 'negative', '-0.64', 'handmaid', 'handmaiden', 'maid', 'lady_maid', 'parlormaid']
senticnet['chambersburg_pike'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['chambly'] = ['-0.71', '0', '0.917', '-0.51', '#sadness', '#anger', 'negative', '-0.71', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['chambray'] = ['0', '-0.03', '0.022', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'shoulder_bag', 'satchel', 'water_bottle', 'tote_bag', 'carry']
senticnet['chameleon'] = ['0.63', '-0.88', '0', '0.503', '#joy', '#surprise', 'positive', '0.674', 'mortal', 'person', 'individual', 'animal', 'color']
senticnet['chamomile_tea'] = ['0.95', '-0.86', '0', '0.947', '#joy', '#admiration', 'positive', '0.92', 'content', 'lore', 'folklore', 'cognitive_content', 'story']
senticnet['champ'] = ['-0.91', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.84', 'welterweight', 'middleweight', 'champion', 'heavyweight', 'featherweight']
senticnet['champagne'] = ['0.153', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.105', 'beer', 'wine', 'vodka', 'whiskey', 'whisky']
senticnet['champagne_bar'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'drink', 'party', 'friend_gathering', 'rest_area', 'relax_time']
senticnet['champagne_breakfast'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'beachcomber', 'bum', 'drifter']
senticnet['champagne_reception'] = ['0.044', '0.08', '0', '-0.03', '#joy', '#interest', 'positive', '0.031', 'cook', 'festival', 'bake_pan', 'restaurant', 'food']
senticnet['champion'] = ['0', '0', '0', '0.834', '#admiration', '#admiration', 'positive', '0.834', 'royal_person', 'royal_title', 'top', 'master', 'rank']
senticnet['champion_ari_vatanen'] = ['0.669', '-0.58', '0', '0.803', '#joy', '#admiration', 'positive', '0.685', 'pep_rally', 'revival', 'revival_meeting', 'gathering', 'assemblage']
senticnet['champion_cup'] = ['0.093', '-0.02', '0', '0.056', '#joy', '#admiration', 'positive', '0.057', 'pitcher', 'carafe', 'container_pitcher', 'mug', 'glass_of_water']
senticnet['champion_hurdle_winner'] = ['0.744', '-0.77', '0', '0.661', '#joy', '#surprise', 'positive', '0.725', 'sugarcoat', 'honey', 'glaze', 'change_taste', 'sugar']
senticnet['champion_league_qualification'] = ['0.844', '0', '0', '0', '#joy', '#joy', 'positive', '0.844', 'straight', 'late_time', 'preparedness', 'past_present', 'wait']
senticnet['champion_lode'] = ['0.614', '-0.95', '0', '0.584', '#joy', '#surprise', 'positive', '0.718', 'mother_lode', 'lode', 'deposit', 'sedimentation', 'alluviation']
senticnet['champion_runner_up'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'home', 'position', 'time_limit', 'monthly', 'modern_home']
senticnet['champion_stayer'] = ['0.744', '-0.77', '0', '0.661', '#joy', '#surprise', 'positive', '0.725', 'sugarcoat', 'honey', 'glaze', 'change_taste', 'sugar']
senticnet['champion_swimmer'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'chlorine', 'lifeguard', 'still_water', 'large_body', 'shallow_water']
senticnet['championship'] = ['0', '0', '0', '0.879', '#admiration', '#admiration', 'positive', '0.879', 'sport_competition', 'win_competition', 'race', 'match', 'competition']
senticnet['championship_belt'] = ['0.052', '0', '0', '0.118', '#joy', '#admiration', 'positive', '0.085', 'diamond_ring', 'piece_of_jewelry', 'bell_sound', 'wedding_band', 'engagement']
senticnet['championship_bout'] = ['0.944', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.967', 'meeting', 'meet', 'match', 'encounter', 'round']
senticnet['championship_golf_course'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'table_tennis', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['chan_son_egocentrique'] = ['0.107', '0.11', '-0.08', '0.21', '#interest', '#admiration', 'positive', '0.086', 'live_healthy_life', 'forever_young', 'live_forever', 'live_long', 'live_good_life']
senticnet['chanaan'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'dryness', 'thirstiness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['chance'] = ['0.737', '-0.78', '0', '0', '#joy', '#surprise', 'positive', '0.76', 'bet', 'card', 'dare', 'cope', 'jeopardy']
senticnet['chance_encounter'] = ['0.86', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.798', 'director_chair', 'alcohol', 'bride_groom', 'attendee', 'coworker']
senticnet['chance_event'] = ['-0.91', '0', '0.769', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'break', 'good_luck', 'happy_chance', 'hap', 'coincidence']
senticnet['chance_eventpy_chance'] = ['-0.92', '0', '0.768', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'break', 'good_luck', 'hap', 'chance_event', 'coincidence']
senticnet['chancellor'] = ['0.964', '0.817', '0', '0', '#joy', '#interest', 'positive', '0.89', 'provost', 'vice-chancellor', 'vice-president', 'vice', 'faculty']
senticnet['chancellor_rolin'] = ['0.056', '0.139', '-0.07', '0.154', '#interest', '#admiration', 'positive', '0.07', 'belongings', 'material', 'fabric', 'guarantee', 'stuff']
senticnet['chand_bibi'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['chandan_singh'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['chandelier'] = ['0.046', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'light', 'bay_window', 'air_conditioner', 'sky', 'lamp']
senticnet['chanderpaul'] = ['-0.13', '0', '-0.11', '-0.04', '#sadness', '#fear', 'negative', '-0.1', 'concern_health', 'medical_problem', 'strengthful', 'surgery', 'injury']
senticnet['chandni_chowk'] = ['0', '0.113', '0.041', '0.058', '#interest', '#admiration', 'positive', '0.043', 'corner_shop', 'general_store', 'many_shops', 'large_supermarket', 'goods']
senticnet['chandos_label'] = ['0.805', '-0.58', '0', '0.525', '#joy', '#surprise', 'positive', '0.637', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['chandrachur_singh'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['chanel_allure'] = ['0.863', '0.861', '0', '0', '#joy', '#interest', 'positive', '0.862', 'aroma', 'scent', 'smell', 'odor', 'sniff']
senticnet['change'] = ['-0.03', '0.212', '0', '0.093', '#interest', '#admiration', 'positive', '0.09', 'account', 'bank_loan', 'deposit', 'spare_change', 'banknote']
senticnet['change_appearance'] = ['0', '-0.05', '0.016', '0.053', '#surprise', '#admiration', 'positive', '0.03', 'dress_nice', 'improve_image', 'straighten_hair', 'haircut', 'dye_hair']
senticnet['change_aspect'] = ['0.642', '-0.58', '0', '0.751', '#joy', '#admiration', 'positive', '0.659', 'change', 'become_better', 'metamorphosis', 'look_better', 'become_bigger']
senticnet['change_attitude'] = ['-0.02', '-0.02', '-0.08', '-0.04', '#fear', '#disgust', 'negative', '-0.03', 'new_information', 'misunderstanding', 'exchange_idea', 'find_solution', 'get_know_better']
senticnet['change_behavior'] = ['0.849', '0.899', '0', '0', '#joy', '#interest', 'positive', '0.874', 'felony', 'vengeance', 'restrict_freedom', 'revenge', 'commit_crime']
senticnet['change_channel'] = ['0', '0', '0.906', '-0.77', '#anger', '#disgust', 'negative', '-0.83', 'see_commercial', 'laugh', 'channel_surf', 'entertain', 'kill_brain_cell']
senticnet['change_clothes'] = ['0.928', '0.778', '0', '0', '#joy', '#interest', 'positive', '0.853', 'nice_clothes', 'put_clean_clothes', 'put_nice_clothes', 'take_bath', 'take_shower']
senticnet['change_color'] = ['0', '0.073', '0', '0.056', '#interest', '#admiration', 'positive', '0.065', 'yoga', 'eat_healthy_diet', 'exercise_much', 'chameleon', 'make_look_better']
senticnet['change_diaper'] = ['0.762', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.795', 'make_love', 'family', 'baby', 'mother', 'father']
senticnet['change_direction'] = ['0', '0.114', '-0.05', '0.207', '#interest', '#admiration', 'positive', '0.09', 'rotate', 'go_around', 'rotate_movement', 'turn_around', 'revolve']
senticnet['change_gear'] = ['0', '0', '0', '0.093', '#admiration', '#admiration', 'positive', '0.093', 'steer_car', 'car_travel', 'control_car', 'transportation', 'car_movement']
senticnet['change_government'] = ['0.966', '0.985', '0', '0', '#joy', '#interest', 'positive', '0.976', 'change_law', 'change', 'documentation', 'recollection', 'recall']
senticnet['change_hair'] = ['0.05', '-0.02', '0', '0.111', '#joy', '#admiration', 'positive', '0.063', 'grow_beard', 'makeover', 'haircut', 'dye_hair', 'get_haircut']
senticnet['change_hair_style'] = ['0.065', '0', '0.01', '0.049', '#joy', '#admiration', 'positive', '0.035', 'look_smart', 'improve_appearance', 'short_hair', 'look_neat', 'get_new_hairstyle']
senticnet['change_hairstyle'] = ['0.642', '-0.58', '0', '0.751', '#joy', '#admiration', 'positive', '0.659', 'dye_hair', 'change_look', 'consult_expert', 'cut_hair', 'get_manicure']
senticnet['change_idea'] = ['-0.04', '0.343', '-0.05', '0.179', '#interest', '#admiration', 'positive', '0.106', 'change_law', 'idea', 'make_right_decision', 'new_think', 'change_government']
senticnet['change_job'] = ['0.928', '0.778', '0', '0', '#joy', '#interest', 'positive', '0.853', 'look_for_job', 'change_work', 'change_life', 'go_interview', 'new_job']
senticnet['change_law'] = ['-0.04', '0.343', '-0.05', '0.179', '#interest', '#admiration', 'positive', '0.106', 'change_idea', 'idea', 'make_right_decision', 'new_think', 'change_government']
senticnet['change_life'] = ['-0.10', '0.068', '-0.12', '-0.11', '#fear', '#disgust', 'negative', '-0.07', 'mentor', 'housewife', 'know_well', 'finish_college', 'get_high_education']
senticnet['change_lifestyle'] = ['-0.13', '0.098', '-0.15', '0', '#sadness', '#fear', 'negative', '-0.06', 'pour_water', 'remove_clothes', 'wear_bath_suit', 'malfunction', 'use_shower']
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senticnet['charm'] = ['0', '0', '0', '0.751', '#admiration', '#admiration', 'positive', '0.751', 'enchant', 'favorable', 'favorably', 'charmy', 'comfortableness']
senticnet['charm_bracelet'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'anklet', 'ankle_bracelet', 'armilla']
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senticnet['charm_quark'] = ['0.022', '0.073', '0', '0.14', '#interest', '#admiration', 'positive', '0.078', 'elementary_particle', 'electron', 'proton_neutron', 'neutron', 'proton']
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senticnet['charming'] = ['0.779', '0.992', '0', '0', '#joy', '#interest', 'positive', '0.885', 'capture', 'enamour', 'trance', 'catch', 'becharm']
senticnet['charming_personality'] = ['0.769', '0', '0', '0.879', '#joy', '#admiration', 'positive', '0.824', 'enviousness', 'enviably', 'preferred', 'lovable', 'desired']
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senticnet['charreada'] = ['0.938', '0.779', '0', '0', '#joy', '#interest', 'positive', '0.859', 'carnival', 'circus', 'fairground', 'fair', 'disneyland']
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senticnet['charred_wood'] = ['0.037', '0.084', '0', '0', '#joy', '#interest', 'positive', '0.06', 'leave_over', 'leftover', 'cellulose', 'cane', 'remainder']
senticnet['chart'] = ['0.094', '0.091', '0', '0.125', '#joy', '#admiration', 'positive', '0.103', 'diagram', 'graph', 'medium', 'fragment', 'representation']
senticnet['chart_topper'] = ['0.625', '-0.90', '0', '0.54', '#joy', '#surprise', 'positive', '0.69', 'smash', 'bang', 'smasher', 'strike', 'hit']
senticnet['chart_topping_hit'] = ['0.625', '-0.90', '0', '0.54', '#joy', '#surprise', 'positive', '0.69', 'smash', 'bang', 'smasher', 'strike', 'hit']
senticnet['chart_topping_single'] = ['0.625', '-0.90', '0', '0.54', '#joy', '#surprise', 'positive', '0.69', 'smash', 'bang', 'smasher', 'strike', 'hit']
senticnet['charter_airline'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['charter_member'] = ['-0.77', '0', '0.511', '-0.53', '#sadness', '#disgust', 'negative', '-0.60', 'go_under', 'subside', 'fall', 'settle', 'submerge']
senticnet['chartism'] = ['-0.71', '0', '0.928', '-0.74', '#anger', '#disgust', 'negative', '-0.79', 'set_off', 'shake_up', 'stir_up', 'instigate', 'inflame']
senticnet['chartist_movement'] = ['-0.71', '0', '0.928', '-0.74', '#anger', '#disgust', 'negative', '-0.79', 'set_off', 'shake_up', 'stir_up', 'instigate', 'inflame']
senticnet['charvel'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'lever']
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senticnet['chase'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'go_away', 'ask_to_leave', 'anger', 'back_off', 'hold_onto']
senticnet['chase_away'] = ['0.705', '-0.55', '0', '0.684', '#joy', '#admiration', 'positive', '0.647', 'move', 'displace', 'clear_air', 'fire', 'banish']
senticnet['chase_ball'] = ['-0.09', '-0.04', '0.081', '-0.06', '#sadness', '#anger', 'negative', '-0.04', 'run_quickly', 'run_fast', 'monitor_heart_rate', 'bring_dog', 'strike_out']
senticnet['chase_dog'] = ['-0.05', '-0.02', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.03', 'feel_out_breath', 'muscle_cramp', 'cramp', 'sore_muscle', 'stiffness']
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senticnet['chasing_pack'] = ['0.844', '0', '0', '0', '#joy', '#joy', 'positive', '0.844', 'straight', 'late_time', 'preparedness', 'past_present', 'wait']
senticnet['chasing_prey'] = ['0.812', '0.872', '0', '0', '#joy', '#interest', 'positive', '0.842', 'insect', 'fly_away', 'small_insect', 'maggot', 'chihuahua']
senticnet['chasm'] = ['0', '0', '0.869', '-0.92', '#anger', '#disgust', 'negative', '-0.89', 'uncrossable', 'unbridgeable', 'irreconcilable', 'irresolvable', 'gulf']
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senticnet['chaste'] = ['0.839', '-0.70', '0', '0.93', '#joy', '#admiration', 'positive', '0.824', 'virtuous', 'celibate', 'pure', 'continent', 'virginal']
senticnet['chastely'] = ['0.539', '-0.96', '0', '0.745', '#surprise', '#admiration', 'positive', '0.751', 'spouse', 'partner', 'married_person', 'mate', 'better_half']
senticnet['chasten'] = ['-0.72', '0', '0.737', '-0.60', '#sadness', '#anger', 'negative', '-0.68', 'call_on_carpet', 'take_to_task', 'chide', 'berate', 'bawl_out']
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senticnet['chastening'] = ['-0.61', '0', '0.719', '-0.71', '#anger', '#disgust', 'negative', '-0.68', 'reprimand', 'rebuke', 'reproof', 'reproval', 'reprehension']
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senticnet['chastisement'] = ['-0.82', '0', '0.879', '0', '#sadness', '#anger', 'negative', '-0.85', 'chastising', 'castigation', 'rebuke', 'corrigible', 'punishment']
senticnet['chastising'] = ['-0.83', '0', '0.876', '0', '#sadness', '#anger', 'negative', '-0.85', 'chastisement', 'castigation', 'rebuke', 'corrigible', 'punishment']
senticnet['chastity'] = ['0', '0.83', '0', '0.728', '#interest', '#admiration', 'positive', '0.779', 'sexual_morality', 'sexual_abstention', 'celibacy', 'virtue', 'intellect']
senticnet['chasuble'] = ['0.849', '-0.50', '0', '0.845', '#joy', '#admiration', 'positive', '0.732', 'robe', 'gown', 'veil', 'vest', 'cassock']
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senticnet['chat_friend'] = ['0.828', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.901', 'conversation', 'take_phone_call', 'communicate', 'talk_far_away', 'chat']
senticnet['chat_room'] = ['-0.51', '0', '0.545', '-0.91', '#anger', '#disgust', 'negative', '-0.65', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
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senticnet['chat_up'] = ['0', '0', '0.759', '-0.78', '#anger', '#disgust', 'negative', '-0.77', 'dawdle', 'dally', 'toy', 'play', 'flirt']
senticnet['chat_window'] = ['-0.51', '0', '0.545', '-0.91', '#anger', '#disgust', 'negative', '-0.65', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['chatbot'] = ['0', '0.717', '0', '0.765', '#interest', '#admiration', 'positive', '0.741', 'helper', 'artificial_intelligence', 'goal_driven', 'proactive', 'intelligent']
senticnet['chateau_lafite'] = ['0.331', '0.119', '0.022', '0.229', '#joy', '#admiration', 'positive', '0.164', 'spice_life', 'red_light_district', 'fresh_produce', 'enough_food', 'knowledgeable']
senticnet['chateau_lafite_rothschild'] = ['0.331', '0.119', '0.022', '0.229', '#joy', '#admiration', 'positive', '0.164', 'spice_life', 'red_light_district', 'fresh_produce', 'enough_food', 'knowledgeable']
senticnet['chateau_marmont'] = ['0.962', '0', '0', '0', '#joy', '#joy', 'positive', '0.962', 'interest_person', 'social_occasion', 'become_intoxicated', 'network', 'get_sex']
senticnet['chateau_mouton_rothschild'] = ['0.331', '0.119', '0.022', '0.229', '#joy', '#admiration', 'positive', '0.164', 'spice_life', 'red_light_district', 'fresh_produce', 'enough_food', 'knowledgeable']
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senticnet['chatspeak'] = ['-0.04', '-0.02', '0.041', '-0.03', '#sadness', '#anger', 'negative', '-0.02', 'jargon', 'lingo', 'microtext', 'slang', 'unusual_language']
senticnet['chattel'] = ['0', '0', '0.8', '-0.82', '#anger', '#disgust', 'negative', '-0.81', 'personnel', 'attendant', 'manager', 'elect_official', 'gentleman']
senticnet['chattel_slave'] = ['0', '0', '0.8', '-0.82', '#anger', '#disgust', 'negative', '-0.81', 'personnel', 'attendant', 'manager', 'elect_official', 'gentleman']
senticnet['chattel_slavery'] = ['-0.75', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.82', 'oppression', 'caged_lion', 'slave', 'captivity', 'misery']
senticnet['chatter'] = ['-0.71', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.76', 'talk', 'barber', 'meaningless', 'sound', 'unimportant']
senticnet['chatter_tooth'] = ['0.773', '-0.72', '0', '0', '#joy', '#surprise', 'positive', '0.748', 'remote_control', 'air_pump', 'safety_pin', 'slinky', 'typewriter_ribbon']
senticnet['chatterbot'] = ['0', '0.717', '0', '0.765', '#interest', '#admiration', 'positive', '0.741', 'helper', 'artificial_intelligence', 'goal_driven', 'proactive', 'intelligent']
senticnet['chatterbox'] = ['0.949', '-0.69', '0', '0.992', '#joy', '#admiration', 'positive', '0.878', 'voluble', 'gabby', 'garrulous', 'loquacious', 'talky']
senticnet['chatting_online'] = ['-0.51', '0', '0.545', '-0.91', '#anger', '#disgust', 'negative', '-0.65', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['chatty'] = ['0.949', '-0.69', '0', '0.992', '#joy', '#admiration', 'positive', '0.878', 'voluble', 'gabby', 'garrulous', 'loquacious', 'talky']
senticnet['chaturdashi'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['chatzilla'] = ['-0.89', '0', '0.672', '-0.69', '#sadness', '#disgust', 'negative', '-0.75', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['chauffeur'] = ['0.066', '0', '-0.05', '0.066', '#joy', '#admiration', 'positive', '0.024', 'cyclist', 'smoker', 'homeowner', 'programmer', 'stalactite']
senticnet['chauffeur_license'] = ['0', '-0.01', '0', '0.064', '#surprise', '#admiration', 'positive', '0.042', 'car_gas', 'car_power', 'fossil_fuel', 'run_car', 'car_engine']
senticnet['chauffeured_around'] = ['0.028', '0', '0.013', '0.061', '#joy', '#admiration', 'positive', '0.025', 'truck', 'vehicle', 'big_car', 'car', 'lorry']
senticnet['chauffeured_car'] = ['0.028', '0', '0.013', '0.061', '#joy', '#admiration', 'positive', '0.025', 'truck', 'vehicle', 'big_car', 'car', 'lorry']
senticnet['chaunsa'] = ['0.859', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.888', 'green_apple', 'apricot', 'lime', 'peach', 'cherry']
senticnet['chausingha'] = ['-0.73', '0', '0.55', '-0.75', '#sadness', '#disgust', 'negative', '-0.68', 'mortal', 'person', 'individual', 'mangler', 'mutilator']
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senticnet['chauvinism'] = ['0.553', '-0.72', '0', '0.752', '#surprise', '#admiration', 'positive', '0.678', 'jingoism', 'trueness', 'loyalty', 'nationalism', 'superpatriotism']
senticnet['chauvinist'] = ['0', '0', '-0.75', '-0.79', '#fear', '#disgust', 'negative', '-0.77', 'jingo', 'hundred-percenter', 'flag-waver', 'patrioteer', 'jingoist']
senticnet['chauvinistic'] = ['0.585', '-0.7', '0', '0.586', '#surprise', '#admiration', 'positive', '0.624', 'jingoistic', 'ultranationalistic', 'superpatriotic', 'nationalistic', 'patriotic']
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senticnet['chawley'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'bodyguard', 'outrider', 'bodyguard_of_guard', 'beefeater', 'defender']
senticnet['chayote'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'cabbage', 'lettuce', 'broccoli']
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senticnet['cheap_beer'] = ['0.11', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'alcoholic_beverage', 'adult_drink', 'alcoholic_drink', 'drink_alcohol', 'alcohol']
senticnet['cheap_booze'] = ['0.11', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'alcoholic_beverage', 'adult_drink', 'alcoholic_drink', 'drink_alcohol', 'alcohol']
senticnet['cheap_buy'] = ['-0.03', '0.292', '-0.09', '0.35', '#interest', '#admiration', 'positive', '0.128', 'good_price', 'business_transaction', 'get_rid', 'cheap_price', 'good_buy']
senticnet['cheap_camera'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'slow', 'long_focus', 'focus_time', 'bad_camera', 'long_focus_time']
senticnet['cheap_chic'] = ['0.875', '0.706', '0', '0', '#joy', '#interest', 'positive', '0.79', 'customer', 'clerk', 'patron', 'vintner', 'hairdresser']
senticnet['cheap_cider'] = ['0.11', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'alcoholic_beverage', 'adult_drink', 'alcoholic_drink', 'drink_alcohol', 'alcohol']
senticnet['cheap_food'] = ['0.043', '0', '0.041', '0.086', '#joy', '#admiration', 'positive', '0.029', 'starchy_food', 'food_ingredient', 'whole_grain', 'white_brown', 'staple_food']
senticnet['cheap_hotel'] = ['-0.98', '0', '0.058', '-0.92', '#sadness', '#disgust', 'negative', '-0.65', 'bad_staff', 'bad_service', 'cheap_restaurant', 'unfriendly_staff', 'long_waiting_time']
senticnet['cheap_labor'] = ['-0.04', '0.201', '-0.06', '0.312', '#interest', '#admiration', 'positive', '0.101', 'save_money', 'good_price', 'inexpensive', 'bargain', 'discount']
senticnet['cheap_liquor'] = ['0.11', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'alcoholic_beverage', 'adult_drink', 'alcoholic_drink', 'drink_alcohol', 'alcohol']
senticnet['cheap_motel'] = ['0.023', '0.085', '0', '0', '#joy', '#interest', 'positive', '0.054', 'public_place', 'front_build', 'rest_area', 'memorial', 'nuclear_power_plant']
senticnet['cheap_price'] = ['-0.03', '0.273', '-0.04', '0.244', '#interest', '#admiration', 'positive', '0.109', 'good_price', 'bargain', 'good_buy', 'sale', 'good_deal']
senticnet['cheap_restaurant'] = ['-0.98', '0', '0.058', '-0.92', '#sadness', '#disgust', 'negative', '-0.65', 'bad_staff', 'cheap_hotel', 'bad_service', 'unfriendly_staff', 'long_waiting_time']
senticnet['cheap_shot'] = ['0.695', '-0.52', '0', '0.957', '#joy', '#admiration', 'positive', '0.726', 'jibe', 'slam', 'gibe', 'shot', 'shaft']
senticnet['cheap_stuff'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'discount_store', 'save_money', 'cheap_food', 'sale', 'on_sale']
senticnet['cheap_vodka'] = ['0.11', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'alcoholic_beverage', 'adult_drink', 'alcoholic_drink', 'drink_alcohol', 'alcohol']
senticnet['cheap_whiskey'] = ['0.11', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'alcoholic_beverage', 'adult_drink', 'alcoholic_drink', 'drink_alcohol', 'alcohol']
senticnet['cheapair'] = ['-0.65', '0', '0.943', '-0.75', '#anger', '#disgust', 'negative', '-0.78', 'rubberneck', 'sightseer', 'excursionist', 'tripper', 'holidaymaker']
senticnet['cheapen'] = ['-0.64', '0', '0.805', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'worsen', 'aggravate', 'exacerbate', 'devalue', 'devaluate']
senticnet['cheaper'] = ['0', '0', '0.944', '-0.78', '#anger', '#disgust', 'negative', '-0.86', 'tatty', 'cheap', 'cheesy', 'brassy', 'flash']
senticnet['cheaper_alternative'] = ['-0.78', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.86', 'high_cost', 'spend_money', 'waste_money', 'high_price', 'unaffordable']
senticnet['cheapest'] = ['-0.72', '0', '-0.73', '0', '#sadness', '#fear', 'negative', '-0.72', 'highest', 'lowest', 'low', 'price', 'rate']
senticnet['cheapest_fare'] = ['-0.03', '0.29', '-0.12', '0.203', '#interest', '#admiration', 'positive', '0.083', 'excise', 'severance', 'wage', 'afford', 'revenue']
senticnet['cheapest_option'] = ['-0.78', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.86', 'high_cost', 'spend_money', 'waste_money', 'high_price', 'unaffordable']
senticnet['cheapest_price'] = ['0.94', '0.804', '0', '0', '#joy', '#interest', 'positive', '0.872', 'spend_a_lot_of_money', 'window_shop', 'spend_much_money', 'check_price', 'compare_price']
senticnet['cheapjack'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['cheaply'] = ['-0.04', '0.201', '-0.06', '0.312', '#interest', '#admiration', 'positive', '0.101', 'save_money', 'good_price', 'inexpensive', 'bargain', 'discount']
senticnet['cheapside_hoard'] = ['0', '0.177', '0', '0.126', '#interest', '#admiration', 'positive', '0.151', 'treasure', 'storage_facility', 'shrine', 'lock_box', 'jewelry_box']
senticnet['cheapskate'] = ['-0.89', '0', '0.783', '-0.89', '#sadness', '#disgust', 'negative', '-0.85', 'dodge', 'plant', 'scheme', 'strategy', 'wangling']
senticnet['chearful'] = ['0.655', '0.635', '-0.23', '0.3', '#joy', '#interest', 'positive', '0.338', 'cheerfully', 'cheerful', 'jolly_up', 'hearten', 'chirk_up']
senticnet['cheat'] = ['-0.98', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.97', 'flunk', 'a_lot_of_stress', 'further_goal', 'failure', 'shuffle_card']
senticnet['cheat_on'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'unfaithful', 'cheat_partner', 'plagiarism', 'deceive', 'trick']
senticnet['cheat_partner'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'unfaithful', 'cheat_on', 'plagiarism', 'deceive', 'trick']
senticnet['cheat_spouse'] = ['0', '0', '0.828', '-0.93', '#anger', '#disgust', 'negative', '-0.88', 'ridicule', 'need_help', 'vengeance', 'infidelity', 'caution']
senticnet['cheater'] = ['-0.97', '0', '0.549', '-0.80', '#sadness', '#disgust', 'negative', '-0.77', 'beguiler', 'slicker', 'deceiver', 'cheat', 'dodger']
senticnet['cheating'] = ['-0.90', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.86', 'gerrymander', 'dissimulation', 'dissembling', 'deception', 'deceit']
senticnet['cheating_husband'] = ['-0.94', '0', '0.944', '-0.90', '#sadness', '#anger', 'negative', '-0.93', 'lust', 'pregnant', 'desire', 'care_deeply', 'love']
senticnet['check'] = ['-0.02', '0.193', '0.013', '0.053', '#interest', '#admiration', 'positive', '0.051', 'dollar_bill', 'cash_bill', 'piece_of_change', 'debit_card', 'dollar']
senticnet['check_account'] = ['0', '0.218', '0', '0.074', '#interest', '#admiration', 'positive', '0.146', 'store_money', 'savings', 'money_inside', 'save_money', 'money_storage']
senticnet['check_balance'] = ['-0.02', '0.207', '0.028', '0.094', '#interest', '#admiration', 'positive', '0.062', 'money_hold', 'money_place', 'store_money', 'money_storage', 'check_account']
senticnet['check_blood_pressure'] = ['-0.12', '0', '-0.12', '-0.08', '#sadness', '#fear', 'negative', '-0.11', 'hospital', 'check_up', 'check_doctor', 'check_pulse', 'healthcare']
senticnet['check_body'] = ['0.642', '-0.58', '0', '0.751', '#joy', '#admiration', 'positive', '0.659', 'syringe', 'latex_glove', 'x-ray_machine', 'wait_room', 'novocaine']
senticnet['check_breathe'] = ['0', '0', '-0.97', '-0.83', '#fear', '#disgust', 'negative', '-0.90', 'stop_bleed', 'blood_pressure', 'desire_help', 'look_out_window', 'fee_animal']
senticnet['check_clock'] = ['0', '-0.03', '0.078', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'time_pass', 'watch_clock', 'tie_score', 'day_period', 'keep_track_time']
senticnet['check_doctor'] = ['-0.06', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.06', 'go_gym', 'join_gym', 'workout', 'lift_weight', 'eat_balance_diet']
senticnet['check_email'] = ['-0.06', '0.224', '0', '0.133', '#interest', '#admiration', 'positive', '0.096', 'check_mail', 'search_information', 'use_search_engine', 'look_for_information', 'phone_line_busy']
senticnet['check_health'] = ['-0.85', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.83', 'diagnose_illness', 'eat_well', 'stay_healthy', 'do_exercise', 'maintain_good_health']
senticnet['check_into'] = ['0.738', '-0.75', '0', '0.854', '#surprise', '#admiration', 'positive', '0.783', 'watch_over', 'keep_an_eye_on', 'watch', 'study', 'observe']
senticnet['check_listing'] = ['-0.79', '0', '-0.84', '0', '#sadness', '#fear', 'negative', '-0.82', 'go_cinema', 'go_to_theater', 'go_movie_theater', 'pay_ticket', 'buy_popcorn']
senticnet['check_mail'] = ['-0.12', '0', '-0.05', '-0.02', '#sadness', '#fear', 'negative', '-0.06', 'page_through_book', 'open_letter', 'crowd_elevator', 'examine', 'wind_watch']
senticnet['check_mirror'] = ['-0.06', '-0.00', '0.016', '0', '#sadness', '#anger', 'negative', '-0.02', 'adjust_seat', 'fasten_seatbelt', 'turn_engine', 'mirror', 'put_key_ignition']
senticnet['check_off'] = ['0.547', '-0.96', '0', '0.89', '#surprise', '#admiration', 'positive', '0.799', 'check', 'support', 'assure', 'affirm', 'verify']
senticnet['check_out'] = ['-0.07', '0', '0.009', '-0.12', '#sadness', '#disgust', 'negative', '-0.07', 'hotel', 'travel', 'holiday', 'go_home', 'staycation']
senticnet['check_over'] = ['0.934', '-0.82', '0', '0.729', '#joy', '#surprise', 'positive', '0.829', 'watch_over', 'keep_an_eye_on', 'watch', 'study', 'observe']
senticnet['check_price'] = ['-0.07', '0', '0', '-0.02', '#sadness', '#disgust', 'negative', '-0.05', 'buy_stuff', 'acquire_goods', 'procure', 'make_purchase', 'buy_goods']
senticnet['check_pulse'] = ['-0.13', '-0.03', '0', '-0.10', '#sadness', '#disgust', 'negative', '-0.06', 'become_fatigued', 'leg_cramp', 'become_tired', 'sore_foot', 'tiredness']
senticnet['check_spelling'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'review', 'double_check', 'spelling', 'correct_homework', 'mark_exam']
senticnet['check_time'] = ['-0.07', '-0.00', '0.079', '-0.20', '#sadness', '#anger', 'negative', '-0.09', 'act_bored', 'think_dog', 'finish_test', 'get_bored', 'watch']
senticnet['check_tire'] = ['-0.07', '0', '0.009', '0', '#sadness', '#anger', 'negative', '-0.04', 'drive_license', 'start_car_engine', 'pressure', 'find_car_key', 'put_key_ignition']
senticnet['check_traffic'] = ['0', '0', '0.943', '-0.91', '#anger', '#disgust', 'negative', '-0.92', 'check', 'relieve_boredom', 'find_out_weather', 'learn_news', 'date_activity']
senticnet['check_up'] = ['-0.12', '0', '-0.12', '-0.08', '#sadness', '#fear', 'negative', '-0.11', 'talk_doctor', 'check_blood_pressure', 'check_doctor', 'check_pulse', 'bladder_full']
senticnet['check_up_car'] = ['0.642', '-0.58', '0', '0.751', '#joy', '#admiration', 'positive', '0.659', 'repair_car', 'repair_shop', 'workshop', 'repair_motorcycle', 'car_workshop']
senticnet['check_up_on'] = ['0.936', '-0.72', '0', '0.894', '#joy', '#admiration', 'positive', '0.851', 'watch_over', 'keep_an_eye_on', 'watch', 'study', 'observe']
senticnet['check_vital_sign'] = ['-0.07', '0', '-0.13', '0', '#sadness', '#fear', 'negative', '-0.10', 'know_healthy', 'nervous_breakdown', 'physical_exam', 'examination', 'epileptic_fit']
senticnet['check_wallet'] = ['-0.04', '0', '0.011', '-0.02', '#sadness', '#disgust', 'negative', '-0.03', 'look_price', 'decide_buy', 'compare_price', 'take_out_wallet', 'pay_food']
senticnet['check_weather'] = ['-0.09', '-0.12', '0.126', '-0.23', '#anger', '#disgust', 'negative', '-0.08', 'go_park', 'put_shoes', 'put_jogging_shoes', 'put_running_shoes', 'go_outside']
senticnet['checkbook'] = ['0.752', '0.932', '0', '0', '#joy', '#interest', 'positive', '0.842', 'spare_change', 'checkbook_cover', 'savings', 'banknote', 'billfold']
senticnet['checkbook_cover'] = ['0.022', '-0.03', '0.07', '-0.13', '#anger', '#disgust', 'negative', '-0.03', 'checkbook_holder', 'lip_balm', 'account_book', 'sleeve', 'cover']
senticnet['checkbook_holder'] = ['0', '0', '0.036', '-0.11', '#anger', '#disgust', 'negative', '-0.07', 'credit_card_wallet', 'clutch_bag', 'pouch', 'checkbook_cover', 'coin_purse']
senticnet['checked_bag'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'hand_baggage', 'baggage', 'passenger_ticket', 'ticket_counter', 'baggage_trunk']
senticnet['checked_baggage'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'hand_baggage', 'baggage', 'passenger_ticket', 'ticket_counter', 'baggage_trunk']
senticnet['checked_luggage'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'hand_baggage', 'baggage', 'passenger_ticket', 'ticket_counter', 'baggage_trunk']
senticnet['checker'] = ['0.934', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.895', 'ball_game', 'verbosity', 'scrabble', 'sport_play', 'many_sports']
senticnet['checking_account'] = ['-0.05', '0.14', '0.015', '0.082', '#interest', '#admiration', 'positive', '0.039', 'cash', 'cash_pay', 'need_earn_money', 'open_wallet', 'earn_wage']
senticnet['checklist'] = ['-0.03', '0.168', '-0.07', '0.139', '#interest', '#admiration', 'positive', '0.049', 'command', 'direct', 'follow', 'take_charge', 'govern']
senticnet['checkmark'] = ['0.055', '0.117', '0', '0.133', '#interest', '#admiration', 'positive', '0.102', 'graphical_representation', 'pencil_pen', 'note_pad', 'marker', 'stationery']
senticnet['checkmate'] = ['0.595', '-0.5', '0', '0.712', '#joy', '#admiration', 'positive', '0.602', 'master', 'walk_over', 'beat_out', 'outfight', 'screw']
senticnet['checkout_clerk'] = ['0', '0', '0.044', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'large_supermarket', 'food_store', 'large_food_store', 'corner_shop', 'convenience_food_store']
senticnet['checkout_counter'] = ['0.03', '0.073', '0.027', '0.084', '#interest', '#admiration', 'positive', '0.04', 'grocery', 'store_vegetable', 'fresh_vegetable', 'vendor', 'fruit_vegetable']
senticnet['checkout_lane'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'super_market', 'salad_bar', 'fruit_stand', 'candy_store', 'produce_store']
senticnet['checkout_line'] = ['0', '0', '0.044', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'large_supermarket', 'food_store', 'large_food_store', 'corner_shop', 'convenience_food_store']
senticnet['checkpoint'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'corner_two_streets', 'street_corner', 'roadside', 'city_street', 'schoolyard']
senticnet['checkpoint_manned_by'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'corner_two_streets', 'street_corner', 'roadside', 'city_street', 'schoolyard']
senticnet['checkpoint_near'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'corner_two_streets', 'street_corner', 'roadside', 'city_street', 'schoolyard']
senticnet['checks-printing'] = ['0', '0', '0.83', '-0.96', '#anger', '#disgust', 'negative', '-0.89', 'hastily-written', 'connectors-electric', 'benedictory', 're-articulation', 'curtain-call']
senticnet['checksum'] = ['0.022', '-0.13', '0.153', '-0.31', '#anger', '#disgust', 'negative', '-0.07', 'codeine', 'morphine', 'infection', 'cocaine', 'nuclear_war']
senticnet['checkup'] = ['0.934', '-0.88', '0', '0.797', '#joy', '#surprise', 'positive', '0.872', 'scan', 'scrutiny', 'medical_scrutiny', 'medical_examination', 'medical_exam']
senticnet['checkuser'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['checkuser_request'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['cheek'] = ['0', '0', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.05', 'jaw', 'mouth', 'dimple', 'nose', 'mucus']
senticnet['cheek_augmentation'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'part_of_face', 'facial_organ', 'face_feature', 'facial_part', 'near_nose']
senticnet['cheek_bone'] = ['0', '-0.02', '0.033', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'tooth', 'teeth', 'dimple', 'mouth', 'cliche']
senticnet['cheek_patch'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'neck', 'body_part', 'henchman', 'shoulder', 'head']
senticnet['cheek_teeth'] = ['0.1', '-0.12', '0.05', '-0.03', '#joy', '#surprise', 'positive', '0.034', 'fancy_dress', 'blouse', 'fashion', 'dress', 'skirt']
senticnet['cheekbone'] = ['0', '-0.02', '0.033', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'tooth', 'teeth', 'dimple', 'mouth', 'cliche']
senticnet['cheekily'] = ['0', '-0.63', '-1', '0', '#surprise', '#fear', 'negative', '-0.18', 'disingenuously', 'lamely', 'coyly', 'apologetically', 'unconvincingly']
senticnet['cheekiness'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'rudeness', 'discourtesy', 'insolence', 'chutzpah', 'hutzpah']
senticnet['cheekteeth'] = ['0.886', '-0.99', '0', '0.542', '#joy', '#surprise', 'positive', '0.808', 'jowl', 'lower_jaw', 'mandible', 'lantern_jaw', 'lantern_jawr_bone']
senticnet['cheeky'] = ['0', '0', '-0.79', '-0.80', '#fear', '#disgust', 'negative', '-0.79', 'impertinence', 'impudent', 'impertinent', 'pert', 'pertinent']
senticnet['cheeky_chappie'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'rascal', 'holy_terror', 'scalawag', 'scallywag', 'monkey']
senticnet['cheeky_chappy'] = ['-0.91', '0', '0.681', '-0.81', '#sadness', '#disgust', 'negative', '-0.80', 'chicken', 'doormat', 'softie', 'crybaby', 'wimp']
senticnet['cheer'] = ['0.703', '0.534', '-0.21', '0.243', '#joy', '#interest', 'positive', '0.316', 'euphoria', 'glee', 'contentment', 'jubilation', 'closeness']
senticnet['cheer_team'] = ['0', '0', '0', '0.717', '#admiration', '#admiration', 'positive', '0.717', 'enjoyment', 'have_fun', 'motivate', 'spend_time_with_friend', 'eat_hot_dog']
senticnet['cheer_up'] = ['0.841', '0', '0', '0', '#joy', '#joy', 'positive', '0.841', 'great_time', 'glee', 'have_fun', 'crowd_cheer', 'joke_around']
senticnet['cheered_wildly'] = ['0.653', '0.635', '-0.24', '0.294', '#joy', '#interest', 'positive', '0.335', 'merrily', 'happily', 'mirthfully', 'gayly', 'blithely']
senticnet['cheerer'] = ['0.574', '-0.85', '0', '0.668', '#surprise', '#admiration', 'positive', '0.699', 'looker', 'spectator', 'viewer', 'watcher', 'witness']
senticnet['cheerful'] = ['0.655', '0.635', '-0.23', '0.3', '#joy', '#interest', 'positive', '0.338', 'cheerfully', 'jolly_up', 'hearten', 'chirk_up', 'embolden']
senticnet['cheerfully'] = ['0.655', '0.635', '-0.23', '0.3', '#joy', '#interest', 'positive', '0.338', 'cheerful', 'jolly_up', 'hearten', 'chirk_up', 'embolden']
senticnet['cheerfulness'] = ['0.655', '0.635', '-0.23', '0.3', '#joy', '#interest', 'positive', '0.338', 'cheerfully', 'cheerful', 'jolly_up', 'hearten', 'chirk_up']
senticnet['cheerier'] = ['-0.79', '0.262', '-0.45', '-0.04', '#sadness', '#fear', 'negative', '-0.25', 'downhearted', 'down_in_mouth', 'depressed', 'downcast', 'low-spirited']
senticnet['cheerily'] = ['-0.86', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.91', 'blandly', 'mutely', 'wearily', 'faintly', 'dully']
senticnet['cheering'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly']
senticnet['cheering_audience'] = ['0.653', '0.635', '-0.24', '0.294', '#joy', '#interest', 'positive', '0.335', 'merrily', 'happily', 'mirthfully', 'gayly', 'blithely']
senticnet['cheering_crowd'] = ['0.653', '0.635', '-0.24', '0.294', '#joy', '#interest', 'positive', '0.335', 'merrily', 'happily', 'mirthfully', 'gayly', 'blithely']
senticnet['cheerleader'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'supporter', 'cheer', 'cheer_up', 'cheer_team', 'cheerful']
senticnet['cheerleading_team'] = ['0.035', '0.155', '0', '0', '#joy', '#interest', 'positive', '0.095', 'basketball_court', 'basketball_game', 'basketball', 'ball', 'sport']
senticnet['cheerless'] = ['-0.75', '0.282', '-0.42', '-0.06', '#sadness', '#fear', 'negative', '-0.24', 'depressing', 'uncheerful', 'low-spiritedness', 'lowness', 'dispiritedness']
senticnet['cheerlessly'] = ['-0.71', '0.307', '-0.40', '-0.07', '#sadness', '#fear', 'negative', '-0.22', 'uncheerfulness', 'cheerlessness', 'lachrymose', 'weeping', 'dolorous']
senticnet['cheerlessness'] = ['-0.71', '0.307', '-0.40', '-0.07', '#sadness', '#fear', 'negative', '-0.22', 'uncheerfulness', 'cheerlessly', 'lachrymose', 'weeping', 'dolorous']
senticnet['cheery'] = ['0.918', '0.476', '-0.19', '0.417', '#joy', '#interest', 'positive', '0.404', 'happy', 'gaiety', 'festal', 'playfulness', 'pridefulness']
senticnet['cheese'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'cheese_platter', 'cold_cuts', 'cottage_cheese_platter', 'platter', 'appetizer']
senticnet['cheese_factory'] = ['0.037', '0', '0', '0.067', '#joy', '#admiration', 'positive', '0.052', 'veal', 'milk_product', 'salad_dress', 'ranch', 'food_group']
senticnet['cheese_plate'] = ['0.076', '0', '0.032', '0.106', '#joy', '#admiration', 'positive', '0.05', 'delicious', 'good_eat', 'yummy', 'edible', 'eatable']
senticnet['cheese_platter'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'cheese', 'cold_cuts', 'cottage_cheese', 'platter', 'appetizer']
senticnet['cheese_sauce'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'horseradish', 'box_cereal', 'bottle_water']
senticnet['cheese_steak'] = ['0.274', '-0.01', '0.012', '0.132', '#joy', '#admiration', 'positive', '0.102', 'dance_type', 'swirl', 'throw_football', 'jump_around', 'dance_move']
senticnet['cheeseburger'] = ['0.711', '0', '0', '0', '#joy', '#joy', 'positive', '0.711', 'burger', 'appetizer', 'chinese_food', 'porridge', 'hearty']
senticnet['cheesecake'] = ['0.092', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.071', 'bake_treat', 'round_pastry', 'bake_good', 'bake_goods', 'sweet_food']
senticnet['cheesecloth'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'aluminium_foil', 'foil', 'gold_foil']
senticnet['cheesegrater'] = ['-0.81', '0', '0.543', '-0.63', '#sadness', '#disgust', 'negative', '-0.66', 'caper', 'pickle', 'relish', 'dill_pickle', 'sweet_pickle']
senticnet['cheesy'] = ['0', '0', '0.936', '-0.78', '#anger', '#disgust', 'negative', '-0.86', 'tatty', 'cheap', 'cheaper', 'brassy', 'flash']
senticnet['cheetah'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'kangaroo', 'giraffe', 'caribou', 'mule', 'horse']
senticnet['cheetah_conservation_fund'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'kangaroo', 'giraffe', 'caribou', 'mule', 'horse']
senticnet['cheetah_cub'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'kangaroo', 'giraffe', 'caribou', 'mule', 'horse']
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senticnet['chest_tube'] = ['-0.56', '0', '0.806', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['chest_tube_drainage'] = ['-0.53', '0', '0.671', '-0.55', '#anger', '#disgust', 'negative', '-0.58', 'dirty', 'putrefactive', 'putrefacient', 'abscessed', 'contaminative']
senticnet['chesterfield'] = ['0.128', '-0.11', '0', '-0.05', '#joy', '#surprise', 'positive', '0.061', 'tripod_dolly', 'stool', 'tape_machine_room', 'chair', 'bicyle']
senticnet['chesterfield_canal'] = ['-0.77', '0', '0.583', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'rain', 'rain_down', 'sheet', 'rain_bucket', 'rain_buckets_down']
senticnet['chestnut'] = ['-0.74', '0', '0.585', '-0.61', '#sadness', '#disgust', 'negative', '-0.64', 'buff', 'brownness', 'chocolate', 'coffee', 'deep_brown']
senticnet['chestnut_blight'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['chestnut_brown'] = ['-0.58', '0', '0.66', '-0.8', '#anger', '#disgust', 'negative', '-0.68', 'bald', 'depilous', 'glabrescent', 'glabrous', 'balding']
senticnet['chestnut_crown'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'neck', 'body_part', 'henchman', 'shoulder', 'head']
senticnet['chestnut_hair'] = ['0', '0', '0.841', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'ancient', 'many_years', 'year', 'old', 'teen']
senticnet['chestnut_oak'] = ['0.776', '-0.6', '0', '0.568', '#joy', '#surprise', 'positive', '0.648', 'swamp_chestnut_oak', 'chinquapin_oak', 'chinkapin_oak', 'yellow_chestnut_oak', 'basket_oak']
senticnet['chestnut_street'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'boulevard', 'lane', 'thoroughfare', 'intersection', 'curb']
senticnet['chestnut_teal'] = ['-0.84', '0', '0.655', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'piping_crow', 'butcherbird', 'currawong', 'bell_magpie', 'oscine']
senticnet['chesty'] = ['-0.53', '0', '0.798', '-0.73', '#anger', '#disgust', 'negative', '-0.68', 'husky', 'hardy', 'sturdy', 'robust', 'stalwart']
senticnet['chet_faker'] = ['0.946', '-0.91', '0', '0.664', '#joy', '#surprise', 'positive', '0.841', 'pretender', 'phony', 'phoney', 'hypocrite', 'dissembler']
senticnet['cheval_glass'] = ['0.584', '-0.62', '0', '0.815', '#surprise', '#admiration', 'positive', '0.675', 'mirror', 'car_mirror', 'hand_glass', 'hand_mirror', 'looking_glass']
senticnet['chevrolet'] = ['0.909', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.952', 'displace', 'swing', 'snowshoe', 'shuttle', 'bang']
senticnet['chevrolet_bel_air'] = ['-0.05', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'plane_land', 'floss', 'floss_tooth', 'ornament', 'wear_dress']
senticnet['chevrolet_blazer'] = ['-0.05', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'plane_land', 'floss', 'floss_tooth', 'ornament', 'wear_dress']
senticnet['chevrolet_cadillac'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'sell_car', 'truck_stop', 'vehicle', 'insurance', 'car']
senticnet['chevrolet_dealer'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'sell_car', 'truck_stop', 'vehicle', 'insurance', 'car']
senticnet['chevrolet_dealership'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'sell_car', 'truck_stop', 'vehicle', 'insurance', 'car']
senticnet['chevrolet_gmc'] = ['0.035', '0.112', '-0.07', '0.195', '#interest', '#admiration', 'positive', '0.067', 'car_action', 'car_travel', 'steer_car', 'make_go', 'transportation']
senticnet['chevrolet_impala'] = ['-0.05', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'plane_land', 'floss', 'floss_tooth', 'ornament', 'wear_dress']
senticnet['chevrolet_nova'] = ['-0.05', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'plane_land', 'floss', 'floss_tooth', 'ornament', 'wear_dress']
senticnet['chevrolet_pickup'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'trailer', 'trail_car', 'camion', 'wagon', 'towboat']
senticnet['chevrolet_suburban'] = ['-0.05', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'plane_land', 'floss', 'floss_tooth', 'ornament', 'wear_dress']
senticnet['chevrolet_volt'] = ['0', '0', '-1', '-1', '#fear', '#disgust', 'negative', '-1.0', 'moon_gravity', 'force', 'coercion', 'watt', 'power_source']
senticnet['chevrotain'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'death_adder', 'ophidian', 'serpent', 'elapid', 'taipan']
senticnet['chevvy'] = ['-0.60', '0', '0.517', '-0.76', '#sadness', '#disgust', 'negative', '-0.62', 'torment', 'nettle', 'bedevil', 'bother', 'devil']
senticnet['chevy'] = ['-0.52', '0', '0.562', '-0.72', '#anger', '#disgust', 'negative', '-0.60', 'torment', 'nettle', 'bedevil', 'bother', 'devil']
senticnet['chevy_cavalier'] = ['-0.10', '-0.07', '0.032', '-0.15', '#sadness', '#disgust', 'negative', '-0.05', 'crash_tree', 'press_accelerator', 'stop_traffic_light', 'accident', 'tire_puncture']
senticnet['chevy_impala'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'cable_car', 'baggage_compartment', 'subway', 'amusement_park', 'terminal']
senticnet['chevy_malibu'] = ['0.909', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.952', 'displace', 'swing', 'snowshoe', 'shuttle', 'bang']
senticnet['chevy_pickup'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'trailer', 'trail_car', 'camion', 'wagon', 'towboat']
senticnet['chevy_rock'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'poppy_seed', 'custard', 'bread', 'biscuit']
senticnet['chevy_suburban'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'cable_car', 'baggage_compartment', 'subway', 'amusement_park', 'terminal']
senticnet['chevy_tahoe'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'cable_car', 'baggage_compartment', 'subway', 'amusement_park', 'terminal']
senticnet['chevy_volt'] = ['0', '0', '-1', '-1', '#fear', '#disgust', 'negative', '-1.0', 'moon_gravity', 'force', 'coercion', 'watt', 'power_source']
senticnet['chew'] = ['-0.06', '0.183', '0.651', '-0.64', '#anger', '#disgust', 'negative', '-0.29', 'runny_nose', 'allergic_reaction', 'pierce', 'mastication', 'put_mouth']
senticnet['chew_bone'] = ['0.11', '0', '0', '0.095', '#joy', '#admiration', 'positive', '0.103', 'play_fetch', 'ride_car', 'dog', 'play_human', 'run']
senticnet['chew_fat'] = ['0.651', '-0.92', '0', '0.657', '#surprise', '#admiration', 'positive', '0.746', 'schmooze', 'schmoose', 'shmooze', 'shmoose', 'jawbone']
senticnet['chew_food'] = ['0.026', '0.069', '0', '0', '#joy', '#interest', 'positive', '0.048', 'eat_food', 'eat_beef', 'eat_dinner', 'put_food_mouth', 'eat_much']
senticnet['chew_gum'] = ['-0.09', '0', '0.023', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'read_paper', 'use_logic', 'order_food', 'tell_secret', 'find_book_library']
senticnet['chew_out'] = ['-0.55', '0', '0.577', '-0.50', '#sadness', '#anger', 'negative', '-0.54', 'castigate', 'tell_off', 'brush_down', 'chasten', 'objurgate']
senticnet['chew_over'] = ['-0.73', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.73', 'think_over', 'ruminate', 'ponder', 'meditate', 'excogitate']
senticnet['chew_pencil'] = ['-0.88', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.87', 'get_nervous', 'write_essay', 'scratch_head', 'write_down', 'look_clock']
senticnet['chew_tobacco'] = ['-0.10', '-0.06', '0.075', '-0.28', '#sadness', '#disgust', 'negative', '-0.1', 'make_anger', 'shit', 'fall_down_flight_stair', 'lean_over', 'ooze']
senticnet['chew_toy'] = ['-0.03', '-0.01', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'shit', 'wildflower', 'skink', 'wild_flower', 'dog_poop']
senticnet['chew_up'] = ['-0.64', '0', '0.716', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'castigate', 'tell_off', 'brush_down', 'chasten', 'objurgate']
senticnet['chewable'] = ['0.965', '0.775', '0', '0.968', '#joy', '#admiration', 'positive', '0.903', 'snot', 'bad_taste', 'mouth_part', 'beautiful_smile', 'near_mouth']
senticnet['chewable_tablet'] = ['0.969', '0.817', '0', '0.828', '#joy', '#admiration', 'positive', '0.871', 'ibuprofen', 'painkiller', 'medicine', 'analgesic', 'phenobarbital']
senticnet['chewer'] = ['0.965', '0.775', '0', '0.968', '#joy', '#admiration', 'positive', '0.903', 'snot', 'bad_taste', 'mouth_part', 'beautiful_smile', 'near_mouth']
senticnet['chewing_gum'] = ['0.023', '0.141', '-0.13', '0.174', '#interest', '#admiration', 'positive', '0.052', 'candy', 'push_wind', 'exhalation', 'breaout', 'mouth_action']
senticnet['chewing_out'] = ['-0.94', '0', '0.808', '-0.63', '#sadness', '#anger', 'negative', '-0.79', 'reprimand', 'rebuke', 'reproof', 'reproval', 'reprehension']
senticnet['chewing_tobacco'] = ['0.965', '0.775', '0', '0.968', '#joy', '#admiration', 'positive', '0.903', 'snot', 'bad_taste', 'mouth_part', 'beautiful_smile', 'near_mouth']
senticnet['chewy'] = ['0', '0', '0.753', '-0.73', '#anger', '#disgust', 'negative', '-0.74', 'gummy_food', 'bad_food', 'hard_to_chew', 'unpleasant', 'tough']
senticnet['chewy_texture'] = ['0.043', '0.091', '0', '0.113', '#interest', '#admiration', 'positive', '0.082', 'sweet_sour', 'tasty', 'yummy', 'popular_food', 'form_of_food']
senticnet['chez_barclays_bourse'] = ['0.865', '-0.84', '0', '0.915', '#joy', '#admiration', 'positive', '0.875', 'contract', 'sale_in_gross', 'contract_of_hazard', 'adhesion_contract', 'contract_of_adhesion']
senticnet['chez_panisse'] = ['0.897', '-0.96', '0', '0.785', '#joy', '#surprise', 'positive', '0.881', 'erotic', 'voluptuary', 'epicure', 'gourmet', 'sensualist']
senticnet['chi_square'] = ['-0.59', '0', '0.672', '-0.83', '#anger', '#disgust', 'negative', '-0.7', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'asymmetry', 'irregularity']
senticnet['chiang_kai_shek_nationalist'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['chiang_pin_kung'] = ['0', '0.072', '0.068', '0.074', '#interest', '#admiration', 'positive', '0.026', 'part_of_book', 'book_part', 'publication', 'book_content', 'book_paper']
senticnet['chiang_wei_kuo'] = ['-0.65', '0', '0.505', '-0.91', '#sadness', '#disgust', 'negative', '-0.69', 'hellhound', 'mythical_being', 'dragon', 'mythical_monster', 'amphisbaena']
senticnet['chianina'] = ['0.546', '-0.96', '0', '0.533', '#joy', '#surprise', 'positive', '0.683', 'dairy_cattle', 'dairy_cow', 'milch_cow', 'ayrshire', 'milcher']
senticnet['chic'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'elegant', 'stylish', 'smart', 'posh', 'sleek']
senticnet['chic_restaurant'] = ['0.06', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'eat_food', 'fast_food', 'pizza_parlor', 'pizza_store', 'kitchen_cupboard']
senticnet['chicane'] = ['-0.82', '0', '0.872', '0', '#sadness', '#anger', 'negative', '-0.84', 'cheat', 'chouse', 'screw', 'shaft', 'jockeying']
senticnet['chicanery'] = ['-0.60', '0', '0.982', '-0.56', '#sadness', '#anger', 'negative', '-0.71', 'humbug', 'deceit', 'deception', 'fraudulence', 'dissembling']
senticnet['chicest'] = ['0.952', '0.844', '0', '0.96', '#joy', '#admiration', 'positive', '0.919', 'fashion', 'demand', 'kindness', 'better_person', 'warm_feeling']
senticnet['chichi'] = ['0.973', '-0.50', '0', '0.612', '#joy', '#admiration', 'positive', '0.698', 'chic', 'chicness', 'jauntiness', 'modishness', 'stylishness']
senticnet['chick'] = ['0.042', '0.088', '0', '0', '#joy', '#interest', 'positive', '0.065', 'jump_over_obstacle', 'attempt_fly', 'aircraft', 'pilot', 'wing']
senticnet['chick-flick'] = ['-0.79', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.84', 'tear-jerker', 'tear-jerking', 'heartwarming', 'tearjerker', 'crowd-pleaser']
senticnet['chick_flick'] = ['0.807', '-0.83', '0', '0.688', '#joy', '#surprise', 'positive', '0.776', 'folly', 'lunacy', 'craziness', 'indulgence', 'foolery']
senticnet['chick_hatch'] = ['0.042', '0.088', '0', '0', '#joy', '#interest', 'positive', '0.065', 'jump_over_obstacle', 'attempt_fly', 'aircraft', 'pilot', 'wing']
senticnet['chickadee'] = ['0.042', '0.088', '0', '0', '#joy', '#interest', 'positive', '0.065', 'jump_over_obstacle', 'attempt_fly', 'aircraft', 'pilot', 'wing']
senticnet['chicken'] = ['0.026', '0.069', '0', '0', '#joy', '#interest', 'positive', '0.048', 'chicken_egg', 'chicken_soup', 'chick', 'male_chicken', 'quail']
senticnet['chicken_breast'] = ['0.584', '-0.62', '0', '0.815', '#surprise', '#admiration', 'positive', '0.675', 'affliction', 'pigeon_breast', 'deformity', 'clubfoot', 'talipes']
senticnet['chicken_broth'] = ['0.07', '0.084', '0.023', '0', '#joy', '#interest', 'positive', '0.044', 'hearty', 'porridge', 'hot_liquid', 'liquid_food', 'meat_vegetable']
senticnet['chicken_bushel'] = ['0', '0.058', '0', '0.064', '#interest', '#admiration', 'positive', '0.061', 'peck', 'embryo', 'egg', 'ovum', 'bushel']
senticnet['chicken_dinner'] = ['0', '0', '0.044', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'large_supermarket', 'food_store', 'large_food_store', 'corner_shop', 'convenience_food_store']
senticnet['chicken_egg'] = ['-0.10', '0', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.1', 'quail', 'wren', 'sparrow', 'chicken', 'stork']
senticnet['chicken_embryo'] = ['-0.86', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.80', 'riverbed', 'bottom_ocean', 'waterfall', 'down_drain', 'septic_tank']
senticnet['chicken_farm'] = ['0.053', '0.094', '0', '0.136', '#interest', '#admiration', 'positive', '0.094', 'barn_animal', 'domesticate', 'grassland', 'sheep', 'cow']
senticnet['chicken_fried_steak'] = ['0.132', '0.1', '0.063', '0.159', '#joy', '#admiration', 'positive', '0.082', 'chime', 'school_end', 'ring_loudly', 'noise_maker', 'vital']
senticnet['chicken_liver'] = ['-0.69', '0', '0.597', '-0.56', '#sadness', '#anger', 'negative', '-0.62', 'improperness', 'impropriety', 'raciness', 'spiciness', 'indelicacy']
senticnet['chicken_pot_pie'] = ['0.043', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'apple_pie', 'a_lot_of_food', 'jelly', 'frozen_food', 'cottage_cheese']
senticnet['chicken_pox'] = ['0', '0', '0.753', '-0.73', '#anger', '#disgust', 'negative', '-0.74', 'diabetes', 'influenza', 'cripple', 'disability', 'smelly_foot']
senticnet['chicken_rice'] = ['0.711', '0', '0', '0', '#joy', '#joy', 'positive', '0.711', 'noodles', 'dumpling', 'chinese_food', 'porridge', 'rice']
senticnet['chicken_salad'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'grocery_cart', 'super_market', 'fruit_stand', 'candy_store', 'produce_store']
senticnet['chicken_sandwich'] = ['0.965', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.951', 'delicious_food', 'hot_meal', 'good_food', 'fresh_produce', 'tasty_food']
senticnet['chicken_satay'] = ['0.076', '0', '0.037', '0.065', '#joy', '#admiration', 'positive', '0.035', 'serve_cold', 'tasty_good', 'tasty', 'sour', 'high_calorie']
senticnet['chicken_sausage'] = ['0.783', '-0.63', '0', '0.829', '#joy', '#admiration', 'positive', '0.749', 'leg_of_lamb', 'gigot', 'joint', 'roast', 'rack_of_lamb']
senticnet['chicken_scratch'] = ['0.584', '-0.62', '0', '0.815', '#surprise', '#admiration', 'positive', '0.675', 'squiggle', 'hand', 'cacography', 'handwriting', 'script']
senticnet['chicken_soup'] = ['0.957', '0.818', '0', '0.919', '#joy', '#admiration', 'positive', '0.898', 'healthy_food', 'groceries', 'fresh_produce', 'delicious_food', 'food']
senticnet['chicken_stew'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'cook_meat', 'cook_hamburger', 'cook_food', 'cook_rice', 'brown_meat']
senticnet['chicken_stock'] = ['0.07', '0.084', '0.023', '0', '#joy', '#interest', 'positive', '0.044', 'hearty', 'porridge', 'hot_liquid', 'liquid_food', 'meat_vegetable']
senticnet['chicken_wire'] = ['0', '-0.00', '0', '0.088', '#surprise', '#admiration', 'positive', '0.048', 'heavy_paper', 'lightweight_metal', 'wizard', 'metal_type', 'soft_metal']
senticnet['chickenhawk'] = ['-0.86', '0', '0.675', '-0.84', '#sadness', '#disgust', 'negative', '-0.79', 'shenanigan', 'chicanery', 'wile', 'dupery', 'trickery']
senticnet['chickenpox_vaccine'] = ['-0.07', '0', '-0.05', '-0.18', '#sadness', '#disgust', 'negative', '-0.10', 'medical_therapy', 'vaccination', 'doctor', 'medicine', 'hospital']
senticnet['chickpea'] = ['0.878', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.883', 'wheat', 'grain', 'rye', 'white_rice', 'rice']
senticnet['chickweed'] = ['-0.64', '0', '0.552', '-0.63', '#sadness', '#disgust', 'negative', '-0.61', 'herb', 'common_chickweed', 'stitchwort', 'greater_stitchwort', 'starwort']
senticnet['chicle'] = ['0.965', '0.775', '0', '0.968', '#joy', '#admiration', 'positive', '0.903', 'snot', 'bad_taste', 'mouth_part', 'beautiful_smile', 'near_mouth']
senticnet['chicness'] = ['0.853', '-0.76', '0', '0.966', '#joy', '#admiration', 'positive', '0.861', 'elegance', 'jauntiness', 'rakishness', 'nattiness', 'dapperness']
senticnet['chicory'] = ['0.636', '-0.79', '0', '0.552', '#joy', '#surprise', 'positive', '0.661', 'lettuce', 'endive', 'green', 'salad_green', 'food']
senticnet['chide'] = ['-0.95', '0', '0.52', '-0.70', '#sadness', '#disgust', 'negative', '-0.72', 'castigate', 'tell_off', 'brush_down', 'chasten', 'objurgate']
senticnet['chiding'] = ['-0.99', '0', '0.709', '-0.60', '#sadness', '#anger', 'negative', '-0.76', 'reprimand', 'wigging', 'rebuke', 'reproof', 'reproval']
senticnet['chief'] = ['0.84', '0', '0', '0.707', '#joy', '#admiration', 'positive', '0.773', 'head', 'executive', 'executive_director', 'administrator', 'academic_administrator']
senticnet['chief_antagonist'] = ['-0.95', '0', '0.774', '0', '#sadness', '#anger', 'negative', '-0.86', 'argue', 'rival', 'disagreement', 'opposition', 'anti']
senticnet['chief_boatswain_mate'] = ['0.045', '0', '0', '0.088', '#joy', '#admiration', 'positive', '0.067', 'leader', 'seaman', 'captain', 'officer', 'traitor']
senticnet['chief_constable'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'law_enforcement_agency', 'law', 'agency']
senticnet['chief_counsel'] = ['0', '0.146', '-0.05', '0.077', '#interest', '#admiration', 'positive', '0.057', 'lawyer', 'negotiator', 'arbiter', 'judge_jury', 'decide_guilt']
senticnet['chief_dull_knife'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['chief_economist'] = ['0.711', '0', '0', '0', '#joy', '#joy', 'positive', '0.711', 'chemist', 'expert', 'scientist', 'esaminatore', 'physicist']
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senticnet['chief_film_critic'] = ['0.811', '-0.63', '0', '0.959', '#joy', '#admiration', 'positive', '0.8', 'drama_critic', 'critic', 'professional', 'art_critic', 'literary_critic']
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senticnet['chief_foreign_correspondent'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'adventurer', 'humankind']
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senticnet['chief_justice'] = ['0.865', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.887', 'manager', 'colleague', 'office_worker', 'coworker', 'secretary']
senticnet['chief_justice_rehnquist'] = ['-0.74', '0', '0.748', '-0.81', '#anger', '#disgust', 'negative', '-0.77', 'coral', 'caviar', 'caviare', 'shad_roe', 'seafood']
senticnet['chief_justiciar'] = ['0.059', '0.076', '-0.05', '0.079', '#interest', '#admiration', 'positive', '0.039', 'noble', 'king', 'earl', 'duchess', 'duke']
senticnet['chief_keef'] = ['0', '0.712', '0', '0.844', '#interest', '#admiration', 'positive', '0.778', 'dollar_change', 'small_currency', 'currency_unit', 'metal_money', 'american_money']
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senticnet['chief_operating_officer'] = ['0.046', '0.116', '-0.05', '0.076', '#interest', '#admiration', 'positive', '0.045', 'head_guy', 'boss', 'officer', 'leader', 'supervisor']
senticnet['chief_shigeru_ishiba'] = ['-0.04', '0', '-0.06', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'make_war', 'conquer_enemy', 'use_military', 'gain_territory', 'keep_peace']
senticnet['chief_sitting_bull'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
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senticnet['chiesi'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'busy_bee', 'gopher', 'actor', 'doer', 'worker']
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senticnet['chiffon_gown'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'pinafore', 'pinny', 'dirndl', 'dress', 'muumuu']
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senticnet['chignon'] = ['-0.06', '-0.01', '0.05', '-0.07', '#sadness', '#disgust', 'negative', '-0.04', 'embroidery', 'woven', 'cover_wind', 'torn', 'use_needle']
senticnet['chihuahua'] = ['0.059', '0.073', '0', '0', '#joy', '#interest', 'positive', '0.066', 'bloodhound', 'dog', 'domesticate_dog', 'domesticate_animal', 'truffle_dog']
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senticnet['child-bearing'] = ['0.847', '0.867', '0', '0', '#joy', '#interest', 'positive', '0.857', 'childbearing', 'pregnancy', 'child-rearing', 'adulthood', 'breast-feeding']
senticnet['child-rearing'] = ['0.844', '0.865', '0', '0', '#joy', '#interest', 'positive', '0.855', 'child-bearing', 'pregnancy', 'childbearing', 'adulthood', 'breast-feeding']
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senticnet['child_birth'] = ['0.95', '0', '0', '0', '#joy', '#joy', 'positive', '0.95', 'born', 'new_baby', 'birthday', 'thanksgiving', 'anniversary']
senticnet['child_birth_certificate'] = ['0.931', '-0.52', '0', '0.67', '#joy', '#admiration', 'positive', '0.709', 'document', 'certificate_of_incorporation', 'credential', 'diploma', 'sheepskin']
senticnet['child_birthday'] = ['0.776', '0', '0', '0.853', '#joy', '#admiration', 'positive', '0.815', 'celebrate_birthday', 'special_occasion', 'special_event', 'cake_eat', 'celebrate_event']
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senticnet['child_care'] = ['0.063', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.065', 'look_catalog', 'make_selection', 'good_credit', 'menu', 'save_money']
senticnet['child_cry'] = ['-0.88', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.88', 'moan', 'common_emotion', 'depress_person', 'smart_man', 'severe_pain']
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senticnet['child_entering_kindergarten'] = ['0.063', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.065', 'look_catalog', 'make_selection', 'good_credit', 'menu', 'save_money']
senticnet['child_funtime'] = ['0.711', '0', '0', '0', '#joy', '#joy', 'positive', '0.711', 'child_toy', 'stacking_game', 'play', 'build_tower_out_toy_block', 'playroom']
senticnet['child_game'] = ['0.839', '-0.93', '0', '0', '#joy', '#surprise', 'positive', '0.885', 'form_of_social_interaction', 'kid_activity', 'have_fun', 'fun_activity', 'fun_game']
senticnet['child_growth'] = ['0.776', '0', '0', '0.853', '#joy', '#admiration', 'positive', '0.815', 'get_high_education', 'learn_evolution', 'teach_person', 'child', 'finish_college']
senticnet['child_in_safe_hand'] = ['0.833', '-0.50', '0', '0.519', '#joy', '#admiration', 'positive', '0.62', 'take_care', 'baby', 'free_time', 'baby_sitter', 'free_night']
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senticnet['child_molestation'] = ['0', '0.942', '0', '0', '#interest', '#interest', 'positive', '0.942', 'guy', 'special_person', 'guardian', 'boy', 'male']
senticnet['child_molester'] = ['0.864', '-0.54', '0', '0.613', '#joy', '#admiration', 'positive', '0.675', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
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senticnet['child_movie'] = ['0.928', '0.778', '0', '0', '#joy', '#interest', 'positive', '0.853', 'child_book', 'disney_cartoon', 'child_story', 'funny_moment', 'childhood']
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senticnet['child_play'] = ['0.9', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.903', 'child_plaything', 'kid_play', 'easy', 'plaything', 'play_device']
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senticnet['child_pornographer'] = ['0', '0.942', '0', '0', '#interest', '#interest', 'positive', '0.942', 'guy', 'special_person', 'guardian', 'boy', 'male']
senticnet['child_pornography'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'indecency']
senticnet['child_prodigy'] = ['0.592', '-0.93', '0', '0.935', '#surprise', '#admiration', 'positive', '0.819', 'child', 'small_fry', 'tyke', 'tike', 'kid']
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senticnet['child_ren'] = ['0.026', '0.069', '0', '0', '#joy', '#interest', 'positive', '0.048', 'young_child', 'childhood', 'boy', 'girl', 'smile_child']
senticnet['child_sexual_abuse'] = ['-0.8', '0', '0.875', '-0.74', '#sadness', '#anger', 'negative', '-0.80', 'inhuman_treatment', 'cruelty', 'child_neglect', 'mistreatment', 'child_abuse']
senticnet['child_smile'] = ['0.833', '-0.50', '0', '0.519', '#joy', '#admiration', 'positive', '0.62', 'child', 'youth', 'young_child', 'kid', 'childhood']
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senticnet['childbearing'] = ['0.852', '0.87', '0', '0', '#joy', '#interest', 'positive', '0.861', 'child-bearing', 'pregnancy', 'child-rearing', 'adulthood', 'breast-feeding']
senticnet['childbearing_age'] = ['0.97', '0.729', '0', '0.946', '#joy', '#admiration', 'positive', '0.882', 'make_baby', 'give_birth_child', 'sexual_intercourse', 'find_partner', 'make_love']
senticnet['childbearing_potential'] = ['0.97', '0.729', '0', '0.946', '#joy', '#admiration', 'positive', '0.882', 'make_baby', 'give_birth_child', 'sexual_intercourse', 'find_partner', 'make_love']
senticnet['childbed_fever'] = ['0.579', '-0.81', '0', '0.833', '#surprise', '#admiration', 'positive', '0.741', 'murine_typhus', 'rat_typhus', 'urban_typhus', 'endemic_typhus', 'rickettsial_disease']
senticnet['childbirth'] = ['0.1', '0.216', '-0.18', '0.22', '#interest', '#admiration', 'positive', '0.088', 'female', 'maternity_ward', 'motherhood', 'woman', 'mother']
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senticnet['childcare_centre'] = ['-0.04', '0.088', '0.013', '0.069', '#interest', '#admiration', 'positive', '0.024', 'kindergarden', 'daycare', 'nursery', 'child', 'asylum']
senticnet['childcare_worker'] = ['0.875', '0', '0', '0.802', '#joy', '#admiration', 'positive', '0.839', 'foreigner', 'exile', 'deportee', 'gringo', 'import']
senticnet['childhood'] = ['-0.06', '0.19', '0.039', '0.103', '#interest', '#admiration', 'positive', '0.047', 'young_child', 'child', 'baby', 'small_child', 'playtime']
senticnet['childhood_abuse'] = ['-0.88', '0', '-0.79', '-0.85', '#sadness', '#disgust', 'negative', '-0.84', 'pain', 'severe_pain', 'agony', 'physical_pain', 'suffer']
senticnet['childhood_acute_lymphoblastic'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['childhood_adhd'] = ['0.504', '-0.93', '0', '0.797', '#surprise', '#admiration', 'positive', '0.747', 'diabetes', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['childhood_asthma'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchiolitis', 'chronic_bronchitis', 'respiratory_disease', 'bronchitis', 'respiratory_disorder']
senticnet['childhood_cancer'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['childhood_diarrhea'] = ['-0.71', '0', '0.74', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'kwashiorkor', 'marasmus', 'malnutrition', 'hypovitaminosis', 'deficiency_disease']
senticnet['childhood_fascination'] = ['0.601', '0.647', '-0.26', '0.322', '#joy', '#interest', 'positive', '0.326', 'captivated', 'charmed', 'captivate', 'bewitch', 'enthrallment']
senticnet['childhood_friend'] = ['0.86', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.798', 'director_chair', 'alcohol', 'bride_groom', 'attendee', 'coworker']
senticnet['childhood_innocence'] = ['-0.68', '0', '0.863', '-0.69', '#anger', '#disgust', 'negative', '-0.74', 'pretension', 'artificiality', 'affectedness', 'supernaturalness', 'largeness']
senticnet['childhood_leukemia'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['childhood_malnutrition'] = ['-0.71', '0', '0.74', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'malnutrition', 'marasmus', 'avitaminosis', 'hypovitaminosis', 'deficiency_disease']
senticnet['childhood_maltreatment'] = ['-0.8', '0', '0.875', '-0.74', '#sadness', '#anger', 'negative', '-0.80', 'inhuman_treatment', 'cruelty', 'child_neglect', 'mistreatment', 'child_abuse']
senticnet['childhood_nickname'] = ['0.072', '0.244', '-0.20', '0.252', '#interest', '#admiration', 'positive', '0.091', 'doe', 'sweet', 'live_organism', 'love', 'alive']
senticnet['childhood_onset'] = ['0.504', '-0.93', '0', '0.797', '#surprise', '#admiration', 'positive', '0.747', 'diabetes', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['childhood_trauma'] = ['-0.88', '0', '-0.79', '-0.85', '#sadness', '#disgust', 'negative', '-0.84', 'pain', 'severe_pain', 'agony', 'physical_pain', 'suffer']
senticnet['childish'] = ['0', '0', '0.888', '-0.73', '#anger', '#disgust', 'negative', '-0.81', 'puerile', 'infantile', 'immature', 'spastic', 'narcissistic']
senticnet['childish_innocence'] = ['-0.90', '0.138', '-0.63', '0.071', '#sadness', '#fear', 'negative', '-0.33', 'woeful', 'execrable', 'deplorable', 'woebegone', 'plaintiveness']
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senticnet['childless_marriage'] = ['-0.70', '0', '0.919', '-0.73', '#anger', '#disgust', 'negative', '-0.78', 'unfruitful', 'unsuccessful', 'acarpous', 'abortive', 'stillborn']
senticnet['childlike'] = ['0', '0', '0.957', '-0.85', '#anger', '#disgust', 'negative', '-0.90', 'ingenuous', 'gullible', 'artless', 'naive', 'guileless']
senticnet['childlike_behavior'] = ['0.72', '-0.72', '0', '0.506', '#joy', '#surprise', 'positive', '0.651', 'standoffishness', 'withdrawnness', 'closeness', 'secretiveness', 'remoteness']
senticnet['childlike_innocence'] = ['0.758', '0.818', '0', '0', '#joy', '#interest', 'positive', '0.788', 'little_boy', 'youngster', 'youth', 'brat', 'lad']
senticnet['childlike_wonder'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'captivated', 'charmed', 'captivate', 'bewitch', 'enthrallment']
senticnet['childminder'] = ['0', '0.864', '0', '0.751', '#interest', '#admiration', 'positive', '0.807', 'family_member', 'sibling', 'parent', 'female_relative', 'relative']
senticnet['childproof'] = ['0.033', '0.186', '0', '0', '#joy', '#interest', 'positive', '0.11', 'bank', 'bank_vault', 'wallet', 'government_agreement', 'coffer']
senticnet['children'] = ['0.029', '0.076', '0', '0', '#joy', '#interest', 'positive', '0.052', 'youngster', 'adolescent', 'grandchild', 'lad', 'two_children']
senticnet['children_toy'] = ['0.081', '0.095', '0', '0', '#joy', '#interest', 'positive', '0.088', 'build_tower_out_toy_block', 'stacking_game', 'play', 'child_funtime', 'playroom']
senticnet['childrens_playground'] = ['0.076', '0.079', '0.027', '0.118', '#interest', '#admiration', 'positive', '0.061', 'outside', 'plant_place', 'plant_ground', 'ground', 'cultivate_yard']
senticnet['childrenswear'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['childs_birth_certificate'] = ['0.931', '-0.52', '0', '0.67', '#joy', '#admiration', 'positive', '0.709', 'document', 'certificate_of_incorporation', 'credential', 'diploma', 'sheepskin']
senticnet['chili'] = ['0.973', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.908', 'tomato', 'soup', 'sauce', 'chicken', 'garlic']
senticnet['chili_cookoff'] = ['0.938', '0.779', '0', '0', '#joy', '#interest', 'positive', '0.859', 'carnival', 'circus', 'fairground', 'fair', 'disneyland']
senticnet['chili_dog'] = ['0.132', '0.1', '0.063', '0.159', '#joy', '#admiration', 'positive', '0.082', 'chime', 'school_end', 'ring_loudly', 'noise_maker', 'vital']
senticnet['chili_oil'] = ['0.076', '0', '0.037', '0.065', '#joy', '#admiration', 'positive', '0.035', 'serve_cold', 'tasty_good', 'tasty', 'sour', 'high_calorie']
senticnet['chili_paste'] = ['0.979', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.918', 'make_curry', 'cook_dinner', 'cook_meal', 'cook_curry', 'powder']
senticnet['chili_powder'] = ['0.979', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.918', 'make_curry', 'cook_dinner', 'cook_meal', 'cook_curry', 'powder']
senticnet['chili_sauce'] = ['0.076', '0', '0', '0', '#joy', '#joy', 'positive', '0.076', 'taco_sauce', 'condiment', 'steak_sauce', 'duck_sauce', 'hoisin_sauce']
senticnet['chill'] = ['0.981', '0.891', '0', '0', '#joy', '#interest', 'positive', '0.936', 'bitterness', 'nip', 'bite', 'sting', 'sharpness']
senticnet['chill_drink'] = ['0', '0.091', '-0.06', '0.114', '#interest', '#admiration', 'positive', '0.048', 'put_liquid', 'drink_liquid', 'hold_coffee', 'serve_beer', 'store_liquid']
senticnet['chill_out'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'drink', 'party', 'friend_gathering', 'rest_area', 'relax_time']
senticnet['chillax'] = ['-0.04', '0.139', '-0.07', '0.106', '#interest', '#admiration', 'positive', '0.032', 'garden', 'relax', 'use_garden', 'chill_out', 'outdoor']
senticnet['chilled'] = ['-0.77', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.86', 'icy-cold', 'ice-cold', 'phosphate-buffered', 'margarita', 'sipping']
senticnet['chilli_powder'] = ['0.979', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.918', 'make_curry', 'cook_dinner', 'cook_meal', 'cook_curry', 'powder']
senticnet['chilli_sauce'] = ['0.076', '0', '0.037', '0.065', '#joy', '#admiration', 'positive', '0.035', 'serve_cold', 'tasty_good', 'tasty', 'sour', 'high_calorie']
senticnet['chillier'] = ['0.953', '0.792', '0', '0', '#joy', '#interest', 'positive', '0.873', 'snowy', 'cool_temperature', 'coldness', 'ice_rain', 'season_year']
senticnet['chilliness'] = ['-0.92', '0', '0.972', '-0.67', '#sadness', '#anger', 'negative', '-0.85', 'cold', 'low_temperature', 'frigidity', 'frigidness', 'coldness']
senticnet['chilling'] = ['-0.78', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.83', 'shivery', 'shuddery', 'apprehension', 'apprehensiveness', 'scare_off']
senticnet['chilling_effect'] = ['0.033', '-0.19', '0.203', '-0.22', '#anger', '#disgust', 'negative', '-0.05', 'antiquate', 'optional', 'kleenex', 'key_chain', 'sachet']
senticnet['chillingly'] = ['0', '0', '-0.78', '-0.81', '#fear', '#disgust', 'negative', '-0.8', 'blunt', 'stern', 'crude', 'austere', 'severe']
senticnet['chillout'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'drink', 'party', 'friend_gathering', 'rest_area', 'relax_time']
senticnet['chilly'] = ['0.953', '0.792', '0', '0', '#joy', '#interest', 'positive', '0.873', 'snowy', 'cool_temperature', 'coldness', 'ice_rain', 'season_year']
senticnet['chilly_night'] = ['0.953', '0.792', '0', '0', '#joy', '#interest', 'positive', '0.873', 'snowy', 'cool_temperature', 'coldness', 'ice_rain', 'season_year']
senticnet['chilly_weather'] = ['-0.09', '0', '-0.15', '-0.16', '#fear', '#disgust', 'negative', '-0.13', 'emotional_pain', 'desire_entertain', 'feel_depressed', 'desire_entertainment', 'embarrassment']
senticnet['chilly_wind'] = ['0.981', '0.891', '0', '0', '#joy', '#interest', 'positive', '0.936', 'bitterness', 'nip', 'bite', 'sting', 'sharpness']
senticnet['chimaera'] = ['0', '0', '-0.87', '0', '#fear', '#fear', 'negative', '-0.87', 'beetle', 'many_insects', 'grasshopper', 'firefly', 'mouse']
senticnet['chimayo'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'common_bean', 'green_bean', 'wax_bean']
senticnet['chime'] = ['0', '0.086', '0.019', '0.182', '#interest', '#admiration', 'positive', '0.083', 'ring_loudly', 'school_end', 'bell_noise', 'bell', 'bell_sound']
senticnet['chimera_ant'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'hornet', 'vespid', 'vespid_wasp']
senticnet['chimeran'] = ['-0.74', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.80', 'casualty', 'extermination', 'cause_death', 'abduction', 'die']
senticnet['chimerical'] = ['-0.86', '0', '0.632', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'airy', 'shadowy', 'stringy', 'wraithlike', 'ethereal']
senticnet['chimerism'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'deflection', 'warp', 'aberrance']
senticnet['chimney'] = ['0', '0', '0.794', '-0.93', '#anger', '#disgust', 'negative', '-0.86', 'soot', 'buildup', 'creosote', 'thatch', 'build-up']
senticnet['chimney_piece'] = ['0.979', '-0.69', '0', '0.576', '#joy', '#surprise', 'positive', '0.749', 'big_cheese', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['chimpanzee'] = ['0.02', '0.071', '0', '0.069', '#interest', '#admiration', 'positive', '0.053', 'many_animals', 'donkey', 'panda', 'cute_animal', 'creature']
senticnet['chin'] = ['0', '-0.02', '0.033', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'tooth', 'teeth', 'dimple', 'mouth', 'cliche']
senticnet['chin_augmentation'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
senticnet['chin_implant'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
senticnet['chin_strap'] = ['0', '0.072', '0', '0.064', '#interest', '#admiration', 'positive', '0.068', 'zipper', 'pant', 'trousers', 'cowboy_boot', 'accessory']
senticnet['china'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['china_cultural_revolution'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['china_manufacturing_sector'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'mathematical', 'muscle', 'math_function', 'feel_sick', 'separation']
senticnet['china_prc'] = ['-0.65', '0', '0.505', '-0.91', '#sadness', '#disgust', 'negative', '-0.69', 'hellhound', 'mythical_being', 'dragon', 'mythical_monster', 'amphisbaena']
senticnet['china_roc'] = ['-0.65', '0', '0.505', '-0.91', '#sadness', '#disgust', 'negative', '-0.69', 'hellhound', 'mythical_being', 'dragon', 'mythical_monster', 'amphisbaena']
senticnet['chinaman'] = ['-0.57', '0', '0.803', '-0.77', '#anger', '#disgust', 'negative', '-0.71', 'au_pair', 'exile', 'deportee', 'gringo', 'import']
senticnet['chinatown'] = ['-0.06', '-0.02', '-0.05', '-0.11', '#sadness', '#disgust', 'negative', '-0.05', 'radio_station', 'old_movie', 'movie', 'pirate_ship', 'military_base']
senticnet['chinaware'] = ['0.022', '0.069', '0.027', '0.059', '#interest', '#admiration', 'positive', '0.031', 'perfect_surface', 'flat_land', 'flat_surface', 'bowl', 'plate']
senticnet['chinawhite'] = ['0.763', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.75', 'pub', 'disco', 'dance_club', 'cafe', 'tavern']
senticnet['chinese'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['chinese_bamboo_partridge'] = ['-0.73', '0', '0.55', '-0.75', '#sadness', '#disgust', 'negative', '-0.68', 'mortal', 'person', 'individual', 'mangler', 'mutilator']
senticnet['chinese_cabbage'] = ['0.035', '0.104', '0', '0.12', '#interest', '#admiration', 'positive', '0.086', 'fruit_vegetable', 'vegetable', 'vegetable_fruit', 'fresh_vegetable', 'vegetarian_food']
senticnet['chinese_communist'] = ['0', '0', '-0.70', '-0.83', '#fear', '#disgust', 'negative', '-0.76', 'settle_disagreement', 'hitler', 'republican', 'conservative', 'unchanging']
senticnet['chinese_communist_party'] = ['0', '0', '-0.70', '-0.83', '#fear', '#disgust', 'negative', '-0.76', 'settle_disagreement', 'hitler', 'republican', 'conservative', 'unchanging']
senticnet['chinese_communist_revolution'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['chinese_cuisine'] = ['0.882', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.822', 'ferment_grape_juice', 'chef_special', 'bottle_wine', 'fine_dining', 'decanter']
senticnet['chinese_food'] = ['0.711', '0', '0', '0', '#joy', '#joy', 'positive', '0.711', 'noodles', 'appetizer', 'chicken_rice', 'porridge', 'hearty']
senticnet['chinese_hanzi'] = ['-0.86', '0', '0.619', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'character', 'logogram', 'logograph', 'graph', 'grapheme']
senticnet['chinese_herbal'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'infective_agent', 'satisfier', 'quickener', 'agent']
senticnet['chinese_herbal_medicine'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
senticnet['chinese_herbology'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
senticnet['chinese_medicinal_herb'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'infective_agent', 'satisfier', 'quickener', 'agent']
senticnet['chinese_medicine'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
senticnet['chinese_nationalism'] = ['-0.54', '0', '0.979', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'unlikeness', 'dissimilitude', 'difference', 'heterology', 'dissimilarity']
senticnet['chinese_premier_zhou'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['chinese_restaurant'] = ['0.06', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'eat_food', 'fast_food', 'pizza_parlor', 'pizza_store', 'kitchen_cupboard']
senticnet['chinese_rule'] = ['0', '0.799', '0', '0.867', '#interest', '#admiration', 'positive', '0.833', 'year', 'beijing', 'territory', 'return', 'freedom']
senticnet['chinese_takeaway'] = ['0.059', '0.212', '0', '0.143', '#interest', '#admiration', 'positive', '0.138', 'christmas_shop', 'go_grocery_shop', 'pay_credit_card', 'pay_cash', 'holiday_shop']
senticnet['chinese_takeout'] = ['0', '0', '0.044', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'large_supermarket', 'food_store', 'large_food_store', 'corner_shop', 'convenience_food_store']
senticnet['ching_chuan_kang'] = ['-0.65', '0', '0.505', '-0.91', '#sadness', '#disgust', 'negative', '-0.69', 'hellhound', 'mythical_being', 'dragon', 'mythical_monster', 'amphisbaena']
senticnet['chinkapin_oak'] = ['0.076', '0', '0', '0', '#joy', '#joy', 'positive', '0.076', 'swamp_chestnut_oak', 'chinquapin_oak', 'chestnut_oak', 'yellow_chestnut_oak', 'basket_oak']
senticnet['chinkara'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheep', 'argal', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['chinnagottigallu'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['chinoiserie'] = ['0.952', '0.844', '0', '0.96', '#joy', '#admiration', 'positive', '0.919', 'fashion', 'demand', 'kindness', 'better_person', 'warm_feeling']
senticnet['chinquapin_oak'] = ['0.076', '0', '0', '0', '#joy', '#joy', 'positive', '0.076', 'swamp_chestnut_oak', 'chestnut_oak', 'chinkapin_oak', 'yellow_chestnut_oak', 'basket_oak']
senticnet['chintz'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dirtiness', 'dinge']
senticnet['chintzy'] = ['-0.91', '0', '0.598', '-0.52', '#sadness', '#anger', 'negative', '-0.68', 'parsimonious', 'penurious', 'scrimy', 'beggarly', 'stingy']
senticnet['chip'] = ['-0.83', '0', '0.521', '-0.71', '#sadness', '#disgust', 'negative', '-0.69', 'stool', 'dejection', 'droppings', 'cow_droppings', 'cow_dung']
senticnet['chip_away'] = ['-0.63', '0', '0.712', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'upset', 'disconcert', 'discompose', 'discomfit', 'untune']
senticnet['chip_soc'] = ['-0.83', '0', '0.521', '-0.71', '#sadness', '#disgust', 'negative', '-0.69', 'stool', 'dejection', 'droppings', 'cow_droppings', 'cow_dung']
senticnet['chiplunkar'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['chipmunk'] = ['0.026', '0.082', '-0.04', '0.068', '#interest', '#admiration', 'positive', '0.032', 'hare', 'bear', 'big_rat', 'omnivore', 'grizzly']
senticnet['chipped_flint'] = ['0.063', '0.089', '0.036', '0.146', '#interest', '#admiration', 'positive', '0.066', 'horn', 'jew_harp', 'brass_instrument', 'trumpet', 'crash_cymbal']
senticnet['chipped_stone'] = ['0.063', '0.089', '0.036', '0.146', '#interest', '#admiration', 'positive', '0.066', 'horn', 'jew_harp', 'brass_instrument', 'trumpet', 'crash_cymbal']
senticnet['chipper_shredder'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'mop_floor', 'absorb_moisture', 'sit_on_beach', 'scrub_floor', 'cut_grass']
senticnet['chippewa'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['chips'] = ['0.532', '-0.68', '0', '0.984', '#surprise', '#admiration', 'positive', '0.733', 'potato', 'white_potato', 'murphy', 'spud', 'tater']
senticnet['chipset'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'computer_part', 'system', 'cpu', 'pencil_pen', 'monitor']
senticnet['chirimoya'] = ['0.859', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.888', 'green_apple', 'apricot', 'lime', 'peach', 'cherry']
senticnet['chirk_up'] = ['0.655', '0.635', '-0.23', '0.3', '#joy', '#interest', 'positive', '0.338', 'cheerfully', 'cheerful', 'jolly_up', 'hearten', 'embolden']
senticnet['chiromachla'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'saltine', 'soda_cracker', 'cracker', 'water_biscuit', 'graham_cracker']
senticnet['chiropodist'] = ['0.738', '-0.53', '0', '0.856', '#joy', '#admiration', 'positive', '0.71', 'doctor', 'country_doctor', 'family_doctor', 'doctor_office', 'physician']
senticnet['chiropractic'] = ['-0.87', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.87', 'manipulate', 'manipulation', 'manipulatory', 'psychopath', 'taxi']
senticnet['chiropractic_college'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
senticnet['chiropractic_medicine'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
senticnet['chiropractor'] = ['0', '0.862', '0', '0.942', '#interest', '#admiration', 'positive', '0.902', 'podiatry', 'dermatologist', 'podiatrist', 'physiotherapist', 'foot']
senticnet['chiropteran'] = ['0.718', '0.715', '0', '0', '#joy', '#interest', 'positive', '0.716', 'lick', 'drub', 'squash_racket', 'clobber', 'bat']
senticnet['chirpiness'] = ['0.975', '-0.98', '0', '0.988', '#surprise', '#admiration', 'positive', '0.982', 'spiritedness', 'brio', 'invigoration', 'vivification', 'animation']
senticnet['chirpy'] = ['0.65', '-0.61', '0', '0.711', '#joy', '#admiration', 'positive', '0.658', 'bouncing', 'scintillating', 'spirited', 'lively', 'zippy']
senticnet['chisato_okai'] = ['0.028', '0', '0.027', '0.101', '#joy', '#admiration', 'positive', '0.034', 'animal_dwell', 'bee_home', 'live_place', 'dwell_place', 'place_to_live']
senticnet['chisel'] = ['0', '0', '0.898', '-0.73', '#anger', '#disgust', 'negative', '-0.81', 'air_pump', 'drill_bite', 'power_drill', 'adjustable_wrench', 'screwdriver']
senticnet['chisel-like'] = ['-0.70', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.85', 'chisel-shaped', 'shovel-like', 'scythe-like', 'saw-like', 'teeth-like']
senticnet['chisel-shaped'] = ['-0.70', '0', '0', '-1', '#sadness', '#disgust', 'negative', '-0.85', 'chisel-like', 'shovel-like', 'scythe-like', 'saw-like', 'teeth-like']
senticnet['chisel_plow'] = ['0.689', '-0.56', '0', '0.74', '#joy', '#admiration', 'positive', '0.665', 'crease', 'crinkle', 'wrinkle', 'heart_line', 'seam']
senticnet['chiseler'] = ['-0.52', '0', '0.506', '-0.94', '#sadness', '#disgust', 'negative', '-0.65', 'gouger', 'swindler', 'defrauder', 'fraud', 'welcher']
senticnet['chisholm_trail'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'common_bean', 'green_bean', 'wax_bean']
senticnet['chit'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'lath', 'shoot', 'memorandum', 'pert', 'sprout']
senticnet['chit-chat'] = ['-0.91', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.95', 'showoff', 'flaunt', 'show-off', 'brag', 'showboating']
senticnet['chit_chat'] = ['0.048', '-0.08', '0.036', '0', '#joy', '#surprise', 'positive', '0.031', 'meet_person', 'meet_friend', 'make_friend', 'meet_girl', 'socialize']
senticnet['chitchat'] = ['-0.51', '0', '0.545', '-0.91', '#anger', '#disgust', 'negative', '-0.65', 'gossip', 'chat', 'chit_chat', 'gab', 'small_talk']
senticnet['chitin'] = ['0', '0.097', '0', '0.085', '#interest', '#admiration', 'positive', '0.091', 'lunula', 'arm_part', 'leave_hand', 'ten_fingers', 'five_fingers']
senticnet['chitrangi'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['chittagong_port'] = ['-0.02', '-0.02', '0.064', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'ocean_water', 'fillet_fish', 'chowder', 'fillet', 'underwater']
senticnet['chittlehampton'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'bodyguard', 'outrider', 'bodyguard_of_guard', 'beefeater', 'defender']
senticnet['chivalric_order'] = ['0.059', '0.076', '-0.05', '0.079', '#interest', '#admiration', 'positive', '0.039', 'noble', 'king', 'earl', 'duchess', 'duke']
senticnet['chivalric_romance'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'narrative', 'bedtime', 'bed_time', 'long_story', 'saga']
senticnet['chivalrous'] = ['0.944', '0', '0', '0.757', '#joy', '#admiration', 'positive', '0.851', 'dandy', 'clotheshorse', 'fop', 'proud', 'gallant']
senticnet['chivalry'] = ['0.268', '0.252', '-0.13', '0.332', '#joy', '#admiration', 'positive', '0.179', 'kingly', 'blue_blood', 'regal', 'royal_person', 'royal_title']
senticnet['chive'] = ['0.726', '-0.71', '0', '0', '#joy', '#surprise', 'positive', '0.72', 'parsley', 'cilantro', 'scallion', 'tarragon', 'shallot']
senticnet['chivvy'] = ['-0.83', '0', '0.978', '-0.72', '#sadness', '#anger', 'negative', '-0.84', 'torment', 'nettle', 'bedevil', 'bother', 'devil']
senticnet['chivy'] = ['-0.99', '0', '0.604', '-0.91', '#sadness', '#disgust', 'negative', '-0.83', 'torment', 'nettle', 'bedevil', 'bother', 'devil']
senticnet['chiyonofuji'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['chiyotaikai'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['chlamydia'] = ['-0.73', '-0.72', '-0.86', '-0.79', '#fear', '#disgust', 'negative', '-0.41', 'venereal_disease', 'overpopulation', 'std', 'loss_offreedom', 'lower_standard_live']
senticnet['chlamydia_infection'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'gastroenteritis', 'stomach_flu', 'intestinal_flu', 'inflammatory_disease']
senticnet['chlamydia_psittaci'] = ['-0.73', '0', '0.599', '-0.96', '#sadness', '#disgust', 'negative', '-0.76', 'spotted_fever', 'trench_fever', 'rickettsial_disease', 'typhus', 'typhus_fever']
senticnet['chlamydial_infection'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'gastroenteritis', 'stomach_flu', 'intestinal_flu', 'inflammatory_disease']
senticnet['chloe'] = ['-0.03', '-0.00', '0.087', '-0.05', '#anger', '#disgust', 'negative', '-0.04', 'dog_fur', 'dog_cat', 'dog_hair', 'pet', 'animal_hair']
senticnet['chloe_jasmine'] = ['-0.08', '-0.03', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'graduate_student', 'student', 'serious_student', 'competition', 'hard_worker']
senticnet['chloramination'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
senticnet['chloramine'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
senticnet['chloraseptic'] = ['0', '0', '-0.70', '-0.76', '#fear', '#disgust', 'negative', '-0.73', 'choke', 'become_full', 'get_full', 'burp', 'sickness']
senticnet['chlordiazepoxide'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['chlorhexidine_gluconate'] = ['0.878', '0.869', '0', '0', '#joy', '#interest', 'positive', '0.873', 'fluoride_mouthwash', 'hair_conditioner', 'sculpt_gel', 'dandruff_shampoo', 'hair_gel']
senticnet['chloride'] = ['0.032', '-0.02', '-0.04', '-0.08', '#fear', '#disgust', 'negative', '-0.01', 'liquid', 'carbonate_drink', 'chemical', 'soda', 'bromide']
senticnet['chlorinate'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
senticnet['chlorinated_water'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
senticnet['chlorination'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
senticnet['chlorine'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
senticnet['chlorine_bleach'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
senticnet['chlorine_concentration'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
senticnet['chlorine_dioxide'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
senticnet['chlorine_disinfection'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
senticnet['chlorine_smell'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
senticnet['chloro'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'enhance', 'intensify', 'deepen', 'potentiate', 'heighten']
senticnet['chloroacetic_acid'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'carboxylic_acid', 'succinic_acid', 'valeric_acid']
senticnet['chlorobenzene'] = ['0.843', '-0.95', '0', '0.648', '#joy', '#surprise', 'positive', '0.816', 'medium', 'acetone', 'propanone', 'dimethyl_ketone', 'dissolvent']
senticnet['chlorogenic_acid'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'capsaicin', 'chemical_irritant', 'piperine', 'pepper_spray']
senticnet['chloromethyl'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'carboxylic_acid', 'succinic_acid', 'valeric_acid']
senticnet['chlorophyll_content'] = ['-0.93', '0', '0.906', '-0.53', '#sadness', '#anger', 'negative', '-0.79', 'juvenility', 'state', 'immatureness', 'callowness', 'jejuneness']
senticnet['chloroplast'] = ['0.993', '0.832', '0', '0', '#joy', '#interest', 'positive', '0.912', 'plastid', 'protein', 'mitochondrion', 'cytoplasm', 'cell']
senticnet['chlorpromazine'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['chlorthalidone'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'spironolactone', 'spironolactone_drug']
senticnet['cho_oyu'] = ['0.518', '-0.51', '0', '0.65', '#surprise', '#admiration', 'positive', '0.562', 'clematis', 'legume', 'leguminous_plant', 'climbing_fern', 'root_climber']
senticnet['choana'] = ['-0.93', '0', '0.607', '-0.67', '#sadness', '#disgust', 'negative', '-0.73', 'spiracle', 'stigma', 'orifice', 'opening', 'porta']
senticnet['choccolocco_creek'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'dirtiness', 'befoulment', 'defilement', 'dinginess', 'dinge']
senticnet['chock'] = ['0', '0', '0.052', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'garden_tool', 'straight_line', 'sharp', 'razor_blade', 'wedge']
senticnet['choclo'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'cabbage', 'lettuce', 'broccoli']
senticnet['chocolate'] = ['0.088', '-0.08', '0', '0', '#joy', '#surprise', 'positive', '0.084', 'ice_cream', 'biscuit', 'candy', 'pudding', 'bagel']
senticnet['chocolate-box'] = ['0.841', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.807', 'specular', 'unpicturesque', 'picturesque', 'idyllic', 'painting']
senticnet['chocolate_bar'] = ['0.077', '-0.11', '0.031', '-0.04', '#joy', '#surprise', 'positive', '0.027', 'unhealthy_food', 'carton_milk', 'candy', 'pumpkin_pie', 'leftover']
senticnet['chocolate_biscuit'] = ['0.547', '-0.96', '0', '0.89', '#surprise', '#admiration', 'positive', '0.799', 'pudding', 'ice_cream', 'chocolate', 'chocolate_pudding', 'chocolate_dessert']
senticnet['chocolate_brown'] = ['-0.02', '0.068', '0.04', '0.084', '#interest', '#admiration', 'positive', '0.022', 'peelable', 'green_black', 'purple', 'red', 'edible_human']
senticnet['chocolate_brownie'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'relax', 'eat_food', 'eat_snack', 'eat_sweet', 'childhood']
senticnet['chocolate_buttercream'] = ['0.731', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.837', 'make_bread', 'cook_dinner', 'cook_meal', 'cook_curry', 'follow_recipe']
senticnet['chocolate_cake'] = ['0.092', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.071', 'bake_treat', 'round_pastry', 'bake_good', 'bake_goods', 'sweet_food']
senticnet['chocolate_caramel'] = ['0.037', '-0.00', '0', '0.12', '#joy', '#admiration', 'positive', '0.055', 'bread_crumb', 'candy', 'bun', 'sweet', 'gush']
senticnet['chocolate_chip'] = ['0.274', '-0.01', '0.012', '0.132', '#joy', '#admiration', 'positive', '0.102', 'dance_type', 'swirl', 'throw_football', 'jump_around', 'dance_move']
senticnet['chocolate_chip_cookie'] = ['0.274', '-0.01', '0.012', '0.132', '#joy', '#admiration', 'positive', '0.102', 'dance_type', 'swirl', 'throw_football', 'jump_around', 'dance_move']
senticnet['chocolate_chip_cookie_dough'] = ['-0.92', '0', '0.609', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'puffed_rice', 'granola', 'raisin_bran', 'corn_flake', 'bran_flake']
senticnet['chocolate_chocolate_chip'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'aridness', 'dryness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['chocolate_dessert'] = ['0.081', '-0.08', '0', '0', '#joy', '#surprise', 'positive', '0.085', 'chocolate', 'pumpkin_pie', 'candy', 'pudding', 'ice_cream']
senticnet['chocolate_drizzle'] = ['0.084', '-0.05', '0', '-0.04', '#joy', '#surprise', 'positive', '0.031', 'entree', 'feast', 'satiation', 'breakfast', 'supper']
senticnet['chocolate_flavor'] = ['0.112', '0', '0', '0.141', '#joy', '#admiration', 'positive', '0.127', 'sweet', 'sweet_food', 'bake_good', 'round_pastry', 'eatable']
senticnet['chocolate_fondue'] = ['0.092', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.071', 'bake_treat', 'round_pastry', 'bake_good', 'bake_goods', 'sweet_food']
senticnet['chocolate_fountain'] = ['0', '-0.08', '0', '0', '#surprise', '#surprise', 'positive', '0.082', 'yo-yo', 'playroom', 'backgammon', 'action_figure', 'board_game']
senticnet['chocolate_fudge'] = ['0.037', '-0.00', '0', '0.12', '#joy', '#admiration', 'positive', '0.055', 'bread_crumb', 'candy', 'bun', 'sweet', 'gush']
senticnet['chocolate_ganache'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'grapefruit_peel', 'lemon_peel', 'orange_peel', 'candied_citrus_peel', 'succade']
senticnet['chocolate_ice_cream'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'relax', 'eat_food', 'eat_snack', 'eat_sweet', 'childhood']
senticnet['chocolate_lava'] = ['0.029', '-0.16', '0', '0', '#joy', '#surprise', 'positive', '0.095', 'mousse', 'brownie', 'pancake', 'custard', 'doughnut']
senticnet['chocolate_layer_cake'] = ['0.092', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.071', 'bake_treat', 'round_pastry', 'bake_good', 'bake_goods', 'sweet_food']
senticnet['chocolate_maker'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'wrapping_paper', 'wrap_gift', 'butcher_paper', 'paper']
senticnet['chocolate_milk'] = ['0', '0', '0', '-0.06', '#disgust', '#disgust', 'negative', '-0.06', 'newsstand', 'bookshop', 'walmart', 'shoe_store', 'hooker']
senticnet['chocolate_mousse'] = ['0.092', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.071', 'bake_treat', 'round_pastry', 'bake_good', 'bake_goods', 'sweet_food']
senticnet['chocolate_peanut_butter'] = ['0.547', '-0.96', '0', '0.89', '#surprise', '#admiration', 'positive', '0.799', 'pudding', 'ice_cream', 'chocolate', 'chocolate_pudding', 'chocolate_dessert']
senticnet['chocolate_pudding'] = ['0.081', '-0.08', '0', '0', '#joy', '#surprise', 'positive', '0.085', 'pudding', 'ice_cream', 'chocolate', 'chocolate_biscuit', 'chocolate_dessert']
senticnet['chocolate_sauce'] = ['0', '0', '-0.72', '-0.92', '#fear', '#disgust', 'negative', '-0.82', 'bee_product', 'flammable', 'remove_hair', 'slick', 'really_hot']
senticnet['chocolate_shop'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'candy', 'sweet', 'sugar', 'sweet_tooth', 'chocolate']
senticnet['chocolate_sponge'] = ['0.029', '-0.16', '0', '0', '#joy', '#surprise', 'positive', '0.095', 'mousse', 'brownie', 'pancake', 'custard', 'doughnut']
senticnet['chocolate_syrup'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'dainty', 'delicacy', 'treat', 'jelly', 'aspic']
senticnet['chocolate_treat'] = ['0.136', '-0.11', '0', '0', '#joy', '#surprise', 'positive', '0.124', 'chocolate_dessert', 'chocolate', 'candy', 'pudding', 'pumpkin_pie']
senticnet['chocolaty'] = ['0', '0', '-0.72', '-0.92', '#fear', '#disgust', 'negative', '-0.82', 'bee_product', 'flammable', 'remove_hair', 'slick', 'really_hot']
senticnet['chogha'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'white_animal', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
senticnet['choice'] = ['0', '0.35', '-0.09', '0.31', '#interest', '#admiration', 'positive', '0.188', 'decision', 'conclude', 'explanation', 'reason', 'think_process']
senticnet['choiceness'] = ['0.757', '-0.82', '0', '0.982', '#surprise', '#admiration', 'positive', '0.855', 'first_class', 'superiority', 'first_water', 'fineness', 'quality']
senticnet['choicest'] = ['0', '-0.75', '0', '0.794', '#surprise', '#admiration', 'positive', '0.772', 'solid', 'brown', 'red_stone', 'opaque', 'generic']
senticnet['choir'] = ['0.506', '0.659', '-0.24', '0.242', '#joy', '#interest', 'positive', '0.29', 'sing', 'music_studio', 'music_room', 'symphony_orchestra', 'symphony']
senticnet['choir_loft'] = ['0.023', '0.218', '0.01', '0.235', '#interest', '#admiration', 'positive', '0.116', 'confessional', 'altar', 'chapel', 'temple', 'christianity']
senticnet['choir_orchestra'] = ['0.445', '0.37', '-0.14', '0.483', '#joy', '#admiration', 'positive', '0.289', 'sing', 'instrument', 'music_room', 'symphony', 'choir']
senticnet['choir_stall'] = ['0.024', '0.211', '0', '0.209', '#interest', '#admiration', 'positive', '0.148', 'pulpit', 'rastafarian', 'fundamentalist', 'pew', 'altar']
senticnet['choirboy'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'acolyte', 'reverend', 'thurifer', 'clergyman', 'man_of_cloth']
senticnet['choirmaster'] = ['0', '-0.86', '0', '0.845', '#surprise', '#admiration', 'positive', '0.856', 'chorister', 'organist', 'seminarian', 'choir', 'chorister']
senticnet['choke'] = ['-0.10', '-0.04', '-0.05', '-0.15', '#sadness', '#disgust', 'negative', '-0.06', 'acid_indigestion', 'get_full', 'burp', 'indigestion', 'become_full']
senticnet['choke_death'] = ['-0.07', '-0.00', '-0.14', '-0.2', '#fear', '#disgust', 'negative', '-0.10', 'stomach_ache', 'indigestion', 'get_sick', 'sick', 'embarrassment']
senticnet['choke_food'] = ['-0.08', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.07', 'drink_tea', 'suck', 'spill_food_clothes', 'open_close_mouth', 'wind_watch']
senticnet['choke_hold'] = ['-0.10', '-0.04', '-0.05', '-0.15', '#sadness', '#disgust', 'negative', '-0.06', 'acid_indigestion', 'get_full', 'burp', 'indigestion', 'become_full']
senticnet['choke_off'] = ['0.033', '-0.19', '0.203', '-0.22', '#anger', '#disgust', 'negative', '-0.05', 'antiquate', 'optional', 'kleenex', 'key_chain', 'sachet']
senticnet['chokecherry'] = ['0.773', '-0.55', '0', '0.949', '#joy', '#admiration', 'positive', '0.757', 'cornel', 'dwarf_cornel', 'crackerberry', 'pudding_berry', 'common_white_dogwood']
senticnet['choked_up'] = ['-0.71', '0.312', '-0.4', '-0.08', '#sadness', '#fear', 'negative', '-0.22', 'lachrymose', 'weeping', 'dolorous', 'tearfulness', 'weepiness']
senticnet['chokehold'] = ['-0.13', '0.113', '-0.16', '0', '#sadness', '#fear', 'negative', '-0.06', 'question_witness', 'legal_process', 'find_guilty', 'convict_criminal', 'custody']
senticnet['choker'] = ['-0.62', '0', '0.94', '-0.99', '#anger', '#disgust', 'negative', '-0.85', 'poisoner', 'slayer', 'mortal', 'executioner', 'public_executioner']
senticnet['chokeslam'] = ['-0.94', '0', '0.863', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'poke', 'clout', 'lick', 'punch', 'biff']
senticnet['choking_dust'] = ['-0.06', '-0.01', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'low', 'ground', 'look_down', 'ground_level', 'under_house']
senticnet['cholecystokinin'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'gastrointestinal_hormone', 'secretin', 'ghrelin']
senticnet['cholecystokinin_cck'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'gastrointestinal_hormone', 'secretin', 'ghrelin']
senticnet['choler'] = ['-0.16', '0.179', '0.568', '-0.61', '#anger', '#disgust', 'negative', '-0.29', 'angrily', 'ire', 'see_red', 'dander', 'hackle']
senticnet['cholera'] = ['0', '0', '-0.82', '-0.96', '#fear', '#disgust', 'negative', '-0.89', 'tuberculosis', 'leper', 'leprosy', 'syphilis', 'smallpox']
senticnet['cholera_bacteria'] = ['-0.99', '0', '0.859', '-0.62', '#sadness', '#anger', 'negative', '-0.82', 'lax', 'loose', 'diarrheal', 'diarrhoeal', 'unconstipated']
senticnet['cholera_broke'] = ['0.579', '-0.81', '0', '0.833', '#surprise', '#admiration', 'positive', '0.741', 'murine_typhus', 'rat_typhus', 'urban_typhus', 'endemic_typhus', 'rickettsial_disease']
senticnet['cholera_epidemic'] = ['-0.86', '0', '0.825', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'pestis', 'plague', 'pestilence', 'pest', 'ambulant_plague']
senticnet['cholera_outbreak'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'leptomeningitis', 'meningitis', 'epidemic_meningitis', 'brain_fever', 'cerebrospinal_fever']
senticnet['cholera_vaccine'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'medical_therapy', 'vaccine', 'doctor', 'medicine', 'hospital']
senticnet['choleric'] = ['-0.12', '0.13', '0.646', '-0.64', '#anger', '#disgust', 'negative', '-0.32', 'irascibility', 'quick_temper', 'short_temper', 'irascible', 'ill_temper']
senticnet['cholestatic'] = ['-0.85', '0', '0.657', '-0.86', '#sadness', '#disgust', 'negative', '-0.79', 'pulseless_disease', 'temporal_arteritis', 'rubor', 'redness', 'arteritis']
senticnet['cholestatic_jaundice'] = ['-0.53', '0', '0.608', '-0.93', '#anger', '#disgust', 'negative', '-0.69', 'reprieve', 'respite', 'hiatus', 'abatement', 'suspension']
senticnet['cholestatic_liver_disease'] = ['-0.60', '0', '0.544', '-0.53', '#sadness', '#anger', 'negative', '-0.56', 'acute_renal_failure', 'acute_kidney_failure', 'chronic_renal_failure', 'chronic_kidney_failure', 'kidney_failure']
senticnet['cholesterol'] = ['-0.12', '0.13', '0.646', '-0.64', '#anger', '#disgust', 'negative', '-0.32', 'high_cholesterol', 'high_blood_pressure', 'disease', 'health_issue', 'unhealthy']
senticnet['cholesterol_laden'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'obese', 'worse', 'health_issue', 'clinical_problem']
senticnet['cholic'] = ['0', '0', '0', '-0.96', '#disgust', '#disgust', 'negative', '-0.96', 'thick_substance', 'viscous_substance', 'mucous_substance', 'disgusting', 'disgust']
senticnet['cholinergic'] = ['0.698', '-0.61', '0', '0.619', '#joy', '#admiration', 'positive', '0.644', 'sensitivity', 'sensitiveness', 'irritability', 'sensibility', 'reactivity']
senticnet['cholinergic_activity'] = ['0.698', '-0.61', '0', '0.619', '#joy', '#admiration', 'positive', '0.644', 'sensitivity', 'sensitiveness', 'irritability', 'sensibility', 'reactivity']
senticnet['cholinergic_urticaria'] = ['-0.55', '0', '0.536', '-0.72', '#sadness', '#disgust', 'negative', '-0.60', 'hypersensitivity_reaction', 'rash', 'efflorescence', 'roseola', 'skin_rash']
senticnet['cholinomimetic'] = ['0.95', '-0.95', '0', '0.769', '#joy', '#surprise', 'positive', '0.89', 'adrenocorticotropic', 'adrenocorticotrophic', 'stimulant', 'stimulating', 'stimulative']
senticnet['chomhairle'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['chomper'] = ['-0.73', '0', '0.72', '-0.93', '#sadness', '#disgust', 'negative', '-0.79', 'imp', 'elf', 'hob', 'leprechaun', 'pixie']
senticnet['chondriosome'] = ['0.721', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.837', 'sarcosome', 'mitochondrion', 'organelle', 'protein', 'chloroplast']
senticnet['chondrodysplasia'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'myotonic_muscular_dystrophy', 'dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['chondroitin'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucose', 'corn_sugar', 'dextrose', 'dextroglucose', 'grape_sugar']
senticnet['chondroitin_sulfate'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucose', 'corn_sugar', 'dextrose', 'dextroglucose', 'grape_sugar']
senticnet['chondroma'] = ['-0.70', '0', '0.639', '-0.56', '#sadness', '#anger', 'negative', '-0.63', 'fibroma', 'adenoma', 'benign_tumor', 'enbenign_tumor', 'myoma']
senticnet['chong_mong_ju'] = ['-0.76', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'disgraceful', 'shameful', 'dishonorably', 'inglorious', 'dishonored']
senticnet['chook'] = ['0.866', '-0.91', '0', '0.893', '#surprise', '#admiration', 'positive', '0.892', 'mooch', 'cadge', 'freeload', 'sponge', 'bum']
senticnet['choose'] = ['-0.04', '0.339', '-0.13', '0.252', '#interest', '#admiration', 'positive', '0.104', 'decide', 'select', 'selection', 'decision', 'make_choice']
senticnet['choose_book'] = ['0.906', '0.788', '0', '0', '#joy', '#interest', 'positive', '0.847', 'book_library', 'find_book', 'hold_book', 'ability_read', 'good_book']
senticnet['choose_color'] = ['0.848', '0.935', '0', '0', '#joy', '#interest', 'positive', '0.891', 'squeeze', 'charge_credit_card', 'sit_toilet', 'ship_goods', 'transport']
senticnet['choose_destination'] = ['-0.08', '0', '0.032', '0', '#sadness', '#anger', 'negative', '-0.05', 'good_shoes', 'travel', 'decide_to_go_first', 'talk_to_travel_agent', 'decide_to_go']
senticnet['choose_eat'] = ['-0.03', '-0.00', '0.043', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'decide_eat', 'fix_breakfast', 'prepare_breakfast', 'piece_of_meat', 'sell_vegetable']
senticnet['choose_game'] = ['0', '0.086', '-0.06', '0.134', '#interest', '#admiration', 'positive', '0.053', 'find_partner', 'win_hand', 'shake_hand', 'shuffle_card', 'select_game']
senticnet['choose_option'] = ['0', '0.142', '0', '0.125', '#interest', '#admiration', 'positive', '0.134', 'change_direction', 'rotate', 'turn_around', 'turn', 'politician']
senticnet['choose_research_environment'] = ['0', '0.929', '0', '0.787', '#interest', '#admiration', 'positive', '0.858', 'fellowship', 'research', 'mathematics', 'award', 'permit_awardees']
senticnet['choose_seat'] = ['-0.04', '-0.08', '0.092', '-0.25', '#anger', '#disgust', 'negative', '-0.07', 'change_opinion', 'fall_asleep', 'drink_cup_tea', 'learn_new_idea', 'look_picture']
senticnet['choose_subject'] = ['-0.07', '0', '0.02', '-0.09', '#sadness', '#disgust', 'negative', '-0.06', 'review_material', 'knowledge_subject', 'revise', 'study_material', 'go_college']
senticnet['choose_topic'] = ['0.932', '0.974', '0', '0', '#joy', '#interest', 'positive', '0.953', 'speak_up', 'use_language', 'think_first', 'know_talk', 'think_say']
senticnet['choose_victim'] = ['0', '0', '0.905', '-0.78', '#anger', '#disgust', 'negative', '-0.84', 'murder_weapon', 'alibi', 'shoot_person', 'hide_evidence', 'shoot_target']
senticnet['choose_whichever'] = ['0.054', '0.221', '-0.12', '0.236', '#interest', '#admiration', 'positive', '0.098', 'abstract', 'exclamation', 'versatile', 'nice', 'subjective']
senticnet['choosy'] = ['0.68', '-0.53', '0', '0.61', '#joy', '#admiration', 'positive', '0.608', 'meticulous', 'pernickety', 'fastidious', 'fussy', 'dainty']
senticnet['chop'] = ['-0.04', '-0.03', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.02', 'cut_wood', 'cut', 'slit', 'cover_wound', 'split']
senticnet['chop-chop'] = ['0', '0.934', '0', '0.791', '#interest', '#admiration', 'positive', '0.863', 'rapidly', 'speedily', 'apace', 'quickly', 'study']
senticnet['chop_down_tree'] = ['-0.88', '0', '0.778', '-0.79', '#sadness', '#disgust', 'negative', '-0.82', 'climb_mountain', 'skate', 'prune_tree', 'ski', 'jump_out_of_window']
senticnet['chop_off'] = ['-0.18', '-0.00', '-0.18', '0', '#sadness', '#fear', 'negative', '-0.11', 'pulverize', 'violent_action', 'smash', 'hit_hard', 'melt_away']
senticnet['chop_onion'] = ['-0.05', '0', '-0.07', '0', '#sadness', '#fear', 'negative', '-0.06', 'cry', 'sad_movie', 'watch_sad_film', 'see_sad_show', 'see_sad_movie']
senticnet['chop_tree'] = ['-0.03', '-0.03', '0.026', '-0.06', '#sadness', '#disgust', 'negative', '-0.02', 'cut_tree', 'cut_wood', 'cut_lumber', 'timber', 'cellulose']
senticnet['chop_vegetable'] = ['-0.06', '-0.01', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.04', 'stir_pot', 'brown_hamburger', 'prepare_breakfast', 'open_package', 'cut_food']
senticnet['chop_wood'] = ['0', '0', '0.719', '-0.74', '#anger', '#disgust', 'negative', '-0.73', 'lacerate', 'gash', 'slash', 'cut', 'cover_wound']
senticnet['chopped_cilantro'] = ['0.041', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.043', 'meatloaf', 'salad', 'delicious_meal', 'casserole', 'apple_pie']
senticnet['chopped_coriander'] = ['0', '0', '0.066', '-0.07', '#anger', '#disgust', 'negative', '-0.07', 'container_cup', 'cup', 'grease_trap', 'dish', 'open_container']
senticnet['chopped_fresh'] = ['-0.05', '-0.03', '0.023', '-0.09', '#sadness', '#disgust', 'negative', '-0.03', 'moldy_food', 'spam', 'paper_product', 'orange_juice', 'plant']
senticnet['chopped_garlic'] = ['0.1', '0', '0', '0', '#joy', '#joy', 'positive', '0.1', 'burn_material', 'ash', 'stay_warm', 'bonfire', 'campfire']
senticnet['chopped_parsley'] = ['0.979', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.918', 'make_curry', 'cook_dinner', 'cook_meal', 'cook_curry', 'powder']
senticnet['chopped_salad'] = ['0.041', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.043', 'meatloaf', 'salad', 'delicious_meal', 'casserole', 'apple_pie']
senticnet['chopped_tbsp'] = ['0.636', '-0.79', '0', '0.552', '#joy', '#surprise', 'positive', '0.661', 'lettuce', 'endive', 'green', 'salad_green', 'food']
senticnet['chopped_tsp'] = ['0.636', '-0.79', '0', '0.552', '#joy', '#surprise', 'positive', '0.661', 'lettuce', 'endive', 'green', 'salad_green', 'food']
senticnet['chopping_board'] = ['-0.71', '0', '0.954', '-0.56', '#sadness', '#anger', 'negative', '-0.74', 'jackknife', 'pocket_knife', 'penknife', 'switchblade', 'switchblade_knife']
senticnet['chopstick'] = ['0.722', '0.783', '0', '0', '#joy', '#interest', 'positive', '0.752', 'good_meal', 'hot_table', 'special', 'plate_silverware', 'chef_special']
senticnet['choral'] = ['0.023', '0.303', '-0.08', '0.261', '#interest', '#admiration', 'positive', '0.125', 'high_school_band', 'drum_set', 'band', 'string_quartet', 'school_band']
senticnet['choral_arrangement'] = ['0.023', '0.303', '-0.08', '0.261', '#interest', '#admiration', 'positive', '0.125', 'school_choral', 'drum_set', 'choral', 'string_quartet', 'band']
senticnet['choral_conductor'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'band_play', 'songwriter', 'rock_band']
senticnet['choral_ensemble'] = ['0.99', '0', '0', '0.719', '#joy', '#admiration', 'positive', '0.855', 'instrumental', 'lyric', 'lyrics', 'sing', 'melody']
senticnet['choral_literature'] = ['0.99', '0', '0', '0.719', '#joy', '#admiration', 'positive', '0.855', 'instrumental', 'lyric', 'lyrics', 'sing', 'melody']
senticnet['choral_music'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'classical_music', 'make_beautiful_music', 'create_music', 'part_of_band', 'make_music']
senticnet['choral_repertoire'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'classical_music', 'make_beautiful_music', 'create_music', 'part_of_band', 'make_music']
senticnet['choral_society'] = ['0.506', '0.659', '-0.24', '0.242', '#joy', '#interest', 'positive', '0.29', 'sing', 'music_studio', 'music_room', 'choir', 'symphony']
senticnet['chorale_melody'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['chorale_tune'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['chord'] = ['0.056', '-0.01', '0.036', '0.073', '#joy', '#admiration', 'positive', '0.028', 'song', 'musical_note', 'amplifier', 'music_note', 'lyrics']
senticnet['chorda_tympani'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['chordae_tendineae'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'chest', 'artery', 'vein', 'internal_organ', 'under_head']
senticnet['chordal'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['chordophone'] = ['0.821', '-0.53', '0', '0.785', '#joy', '#admiration', 'positive', '0.712', 'fife', 'nose_flute', 'piccolo', 'woodwind', 'flute']
senticnet['chore'] = ['-0.05', '0.197', '-0.11', '0.184', '#interest', '#admiration', 'positive', '0.053', 'question_ask', 'office_job', 'task', 'obligation', 'day_job']
senticnet['choreograph'] = ['-0.95', '0', '0.992', '-0.63', '#sadness', '#anger', 'negative', '-0.86', 'modern_dance', 'interpretive_dancing', 'interpretative_dance', 'interpretative_dancing', 'stage_dancing']
senticnet['choreographed_dance'] = ['-0.95', '0', '0.992', '-0.63', '#sadness', '#anger', 'negative', '-0.86', 'modern_dance', 'interpretive_dancing', 'interpretative_dance', 'interpretative_dancing', 'stage_dancing']
senticnet['choreographer'] = ['0.12', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.09', 'ballroom', 'perform_art', 'nightclub', 'ballerina', 'venue']
senticnet['choreographic'] = ['0.12', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.09', 'ballroom', 'perform_art', 'nightclub', 'ballerina', 'venue']
senticnet['choreography'] = ['0', '-0.90', '0', '0.752', '#surprise', '#admiration', 'positive', '0.83', 'quickness', 'athleticism', 'explosiveness', 'drill', 'footwork']
senticnet['chorister'] = ['0', '-0.85', '0', '0.852', '#surprise', '#admiration', 'positive', '0.855', 'chorister', 'organist', 'seminarian', 'choir', 'choirmaster']
senticnet['chorley_fc'] = ['0.941', '-0.83', '0', '0.973', '#joy', '#admiration', 'positive', '0.917', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['choro'] = ['-0.06', '-0.00', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.05', 'madman', 'trouble', 'agony', 'cause_problem', 'river_stream']
senticnet['chortle'] = ['-0.71', '0', '0.83', '-0.68', '#sadness', '#anger', 'negative', '-0.74', 'gag', 'jest', 'jape', 'joke', 'laugh']
senticnet['chorus'] = ['0.023', '0.303', '-0.08', '0.261', '#interest', '#admiration', 'positive', '0.125', 'school_choral', 'drum_set', 'choral', 'string_quartet', 'band']
senticnet['chou_pastry'] = ['0', '-0.11', '0', '0', '#surprise', '#surprise', 'positive', '0.113', 'cake', 'cookie', 'mousse', 'doughnut', 'brownie']
senticnet['chouse'] = ['-0.81', '0', '0.875', '0', '#sadness', '#anger', 'negative', '-0.84', 'cheat', 'jockeying', 'screw', 'shaft', 'chicane']
senticnet['chow'] = ['0.061', '0', '0', '0.052', '#joy', '#admiration', 'positive', '0.056', 'noodle', 'result_cook', 'biscuit', 'nutrient', 'cream']
senticnet['chow_down'] = ['0.866', '-0.91', '0', '0.893', '#surprise', '#admiration', 'positive', '0.892', 'mooch', 'cadge', 'freeload', 'sponge', 'bum']
senticnet['chowder'] = ['-0.02', '-0.02', '0.064', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'ocean_water', 'fillet_fish', 'fillet', 'port', 'underwater']
senticnet['chowed_down'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'relax', 'eat_food', 'eat_snack', 'eat_sweet', 'childhood']
senticnet['chowing_down'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'relax', 'eat_food', 'eat_snack', 'eat_sweet', 'childhood']
senticnet['chrissy'] = ['-0.74', '0', '0.877', '-0.97', '#anger', '#disgust', 'negative', '-0.86', 'miss', 'girl', 'chachka', 'young_lady', 'young_woman']
senticnet['christ'] = ['0', '0', '0', '0.984', '#admiration', '#admiration', 'positive', '0.984', 'spiritual', 'religious_purpose', 'prayer', 'religious', 'faith']
senticnet['christ_ascension'] = ['-0.69', '0', '0.806', '-0.74', '#anger', '#disgust', 'negative', '-0.74', 'hypothesis', 'theory', 'base', 'possibility', 'presumption']
senticnet['christ_church'] = ['0.031', '0.272', '0', '0.231', '#interest', '#admiration', 'positive', '0.178', 'minister', 'church_head', 'pastor', 'protestant', 'evangelist']
senticnet['christ_death'] = ['-0.82', '0', '0.672', '-0.54', '#sadness', '#anger', 'negative', '-0.68', 'amends', 'damage', 'indemnification', 'redress', 'restitution']
senticnet['christ_hath'] = ['0.741', '-0.84', '0', '0.543', '#joy', '#surprise', 'positive', '0.711', 'integrity', 'righteousness', 'scrupulousness', 'incorruptness', 'honestness']
senticnet['christ_love'] = ['0.914', '0', '0', '0.954', '#joy', '#admiration', 'positive', '0.934', 'spiritual', 'religious', 'religious_purpose', 'religion', 'prayer']
senticnet['christendom'] = ['0', '0.08', '0', '0.117', '#interest', '#admiration', 'positive', '0.099', 'stand_still', 'go_home', 'pull_out', 'stretch_out', 'advance_civilization']
senticnet['christening'] = ['0.713', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.744', 'baptism', 'invitation', 'keepsake', 'communion', 'wedding']
senticnet['christian'] = ['0.795', '0', '0', '0.724', '#joy', '#admiration', 'positive', '0.76', 'church_head', 'protestant', 'holy_man', 'religious_official', 'goddess']
senticnet['christian_fundamentalist'] = ['0.885', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.881', 'fire_hydrant', 'stair_rail', 'fire_extinguisher', 'extinguisher', 'reception_desk']
senticnet['christianist'] = ['-0.30', '0.083', '-0.21', '-0.17', '#sadness', '#fear', 'negative', '-0.15', 'end_life', 'death', 'seek_revenge', 'kill_self', 'kill']
senticnet['christianity'] = ['0.012', '0.189', '0.028', '0.163', '#interest', '#admiration', 'positive', '0.084', 'church', 'confession', 'catholic_church', 'spirituality', 'good_catholic']
senticnet['christmas'] = ['0.855', '0', '0', '0', '#joy', '#joy', 'positive', '0.855', 'birthday', 'occasion', 'holiday', 'new_baby', 'celebrate_anniversary']
senticnet['christmas_blizzard'] = ['0', '0', '-0.93', '-0.82', '#fear', '#disgust', 'negative', '-0.88', 'thunderstorm', 'snow_storm', 'storm', 'weather', 'rainy']
senticnet['christmas_break'] = ['0.977', '0.828', '0', '0', '#joy', '#interest', 'positive', '0.902', 'hunt_fish', 'out_place', 'getaway', 'outdoor_activity', 'vacation']
senticnet['christmas_cake'] = ['0.029', '-0.16', '0', '0', '#joy', '#surprise', 'positive', '0.095', 'mousse', 'brownie', 'pancake', 'custard', 'doughnut']
senticnet['christmas_celebration'] = ['0.856', '0', '0', '0', '#joy', '#joy', 'positive', '0.856', 'tree', 'evergreen_tree', 'big_tree', 'santa_claus', 'christmas']
senticnet['christmas_decoration'] = ['0.856', '0', '0', '0', '#joy', '#joy', 'positive', '0.856', 'tree', 'evergreen_tree', 'big_tree', 'santa_claus', 'christmas']
senticnet['christmas_dinner'] = ['0.044', '0.08', '0', '-0.03', '#joy', '#interest', 'positive', '0.031', 'cook', 'festival', 'bake_pan', 'restaurant', 'food']
senticnet['christmas_eve'] = ['0.908', '0.991', '0', '0', '#joy', '#interest', 'positive', '0.95', 'party_time', 'new_baby', 'special_day', 'birthday', 'annual_celebration']
senticnet['christmas_eve_dinner'] = ['0.937', '0', '0', '0.828', '#joy', '#admiration', 'positive', '0.883', 'bakery', 'produce_section', 'fruit_bowl', 'dinner_meal', 'fast_food']
senticnet['christmas_gift'] = ['0.088', '-0.09', '-0.07', '0.141', '#surprise', '#admiration', 'positive', '0.065', 'play_device', 'surprise', 'plaything', 'child_plaything', 'kid_game']
senticnet['christmas_holiday'] = ['0.856', '0', '0', '0', '#joy', '#joy', 'positive', '0.856', 'christmas_tree', 'christmas_gift', 'family_time', 'family_gathering', 'christmas']
senticnet['christmas_holiday_season'] = ['0.908', '0.991', '0', '0', '#joy', '#interest', 'positive', '0.95', 'party_time', 'new_baby', 'special_day', 'birthday', 'annual_celebration']
senticnet['christmas_hurdle'] = ['0.744', '-0.77', '0', '0.661', '#joy', '#surprise', 'positive', '0.725', 'sugarcoat', 'honey', 'glaze', 'change_taste', 'sugar']
senticnet['christmas_ornament'] = ['0.71', '0.92', '0', '0', '#joy', '#interest', 'positive', '0.815', 'toy_car', 'toy_ball', 'action_figure', 'toy_marble', 'beanie_baby']
senticnet['christmas_parade'] = ['0.957', '0.7', '0', '0', '#joy', '#interest', 'positive', '0.829', 'ballpark', 'coven', 'disneyland', 'hockey_game', 'symposium']
senticnet['christmas_present'] = ['0.087', '-0.28', '-0.09', '0.108', '#surprise', '#admiration', 'positive', '0.094', 'anniversary_gift', 'play_device', 'christmas_gift', 'make_child_laugh', 'stuff_animal']
senticnet['christmas_shop'] = ['0.802', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.78', 'buy_present', 'holiday_shop', 'buy_christmas_present', 'christmas_present', 'buy_product']
senticnet['christmas_shopping'] = ['0.908', '0.991', '0', '0', '#joy', '#interest', 'positive', '0.95', 'party_time', 'new_baby', 'special_day', 'birthday', 'annual_celebration']
senticnet['christmas_song'] = ['0.758', '0.856', '0', '0', '#joy', '#interest', 'positive', '0.807', 'hymn', 'chant', 'sing_together', 'melodic_noise', 'common_language']
senticnet['christmas_stock'] = ['0', '-0.02', '0', '0.07', '#surprise', '#admiration', 'positive', '0.046', 'pastry', 'bakery', 'breadbox', 'sundae', 'freezer']
senticnet['christmas_time'] = ['0.976', '0', '0', '0', '#joy', '#joy', 'positive', '0.976', 'happy_occasion', 'happy_event', 'gala', 'special_occasion', 'birthday_party']
senticnet['christmas_tree'] = ['0.856', '0', '0', '0', '#joy', '#joy', 'positive', '0.856', 'tree', 'evergreen_tree', 'big_tree', 'santa_claus', 'christmas']
senticnet['christmas_vacation'] = ['0.977', '0.828', '0', '0', '#joy', '#interest', 'positive', '0.902', 'hunt_fish', 'out_place', 'getaway', 'outdoor_activity', 'vacation']
senticnet['christmas_wreath'] = ['0.856', '0', '0', '0', '#joy', '#joy', 'positive', '0.856', 'tree', 'evergreen_tree', 'big_tree', 'santa_claus', 'christmas']
senticnet['christmassy'] = ['0.699', '0.62', '-0.23', '0.323', '#joy', '#interest', 'positive', '0.353', 'joviality', 'jolity', 'josh', 'chaff', 'banter']
senticnet['christmastime'] = ['0.856', '0', '0', '0', '#joy', '#joy', 'positive', '0.856', 'tree', 'evergreen_tree', 'big_tree', 'santa_claus', 'christmas']
senticnet['christminster'] = ['-0.94', '0', '0.779', '-0.85', '#sadness', '#disgust', 'negative', '-0.86', 'eremitic', 'eremitical', 'cloistered', 'anchoritic', 'unworldly']
senticnet['christological'] = ['-0.83', '0', '0.648', '-0.60', '#sadness', '#anger', 'negative', '-0.69', 'infallibility', 'dependability', 'dependableness', 'reliability', 'reliableness']
senticnet['chroma'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'indigo', 'yellow', 'water_color', 'red', 'violet']
senticnet['chromate'] = ['-0.03', '0', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.06', 'lithium', 'mineral', 'potassium', 'iodine', 'magnesium']
senticnet['chromatic_aberration'] = ['0.906', '-0.60', '0', '0.653', '#joy', '#admiration', 'positive', '0.72', 'opaqueness', 'faintness', 'dimness', 'opacity', 'vagueness']
senticnet['chromatic_dispersion'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'short_circuit', 'error', 'mistake', 'undesired', 'trouble']
senticnet['chromatic_harmony'] = ['0.045', '0', '0.033', '0.089', '#joy', '#admiration', 'positive', '0.034', 'spanish', 'vocalization', 'verbal_communication', 'vocal', 'mouth_noise']
senticnet['chromatic_scale'] = ['-0.65', '0', '0.681', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'tone', 'whole_tone', 'step', 'whole_step', 'musical_interval']
senticnet['chromatic_semitone'] = ['-0.65', '0', '0.681', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'tone', 'whole_tone', 'step', 'whole_step', 'musical_interval']
senticnet['chromaticism'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['chromatin_condensation'] = ['-0.03', '0.169', '-0.07', '0.218', '#interest', '#admiration', 'positive', '0.071', 'elongate', 'elasticity', 'extension', 'grow_long', 'widen']
senticnet['chromatographic'] = ['0.838', '-0.61', '0', '0.556', '#joy', '#surprise', 'positive', '0.671', 'activity', 'natural_process', 'natural_action', 'column_chromatography', 'paper_chromatography']
senticnet['chromatographic_separation'] = ['0.838', '-0.61', '0', '0.556', '#joy', '#surprise', 'positive', '0.671', 'activity', 'natural_process', 'natural_action', 'column_chromatography', 'paper_chromatography']
senticnet['chromatography'] = ['0.838', '-0.61', '0', '0.556', '#joy', '#surprise', 'positive', '0.671', 'activity', 'natural_process', 'natural_action', 'column_chromatography', 'paper_chromatography']
senticnet['chrome'] = ['0.02', '0.085', '-0.05', '0.085', '#interest', '#admiration', 'positive', '0.034', 'gold', 'silver', 'medal', 'carat', 'medallion']
senticnet['chrome_finish'] = ['0.02', '0.085', '-0.05', '0.085', '#interest', '#admiration', 'positive', '0.034', 'gold', 'silver', 'medal', 'carat', 'medallion']
senticnet['chrome_grille'] = ['0.02', '0.085', '-0.05', '0.085', '#interest', '#admiration', 'positive', '0.034', 'gold', 'silver', 'medal', 'carat', 'medallion']
senticnet['chrome_trim'] = ['0.02', '0.085', '-0.05', '0.085', '#interest', '#admiration', 'positive', '0.034', 'gold', 'silver', 'medal', 'carat', 'medallion']
senticnet['chromene'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'enhance', 'intensify', 'deepen', 'potentiate', 'heighten']
senticnet['chromite'] = ['-0.05', '-0.00', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'pigment', 'many_shades', 'cyan', 'green_yellow', 'material']
senticnet['chromium'] = ['-0.05', '-0.00', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'pigment', 'many_shades', 'cyan', 'green_yellow', 'material']
senticnet['chromium_cr'] = ['-0.05', '-0.00', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'pigment', 'many_shades', 'cyan', 'green_yellow', 'material']
senticnet['chromium_dioxide'] = ['0.965', '-0.84', '0', '0.599', '#joy', '#surprise', 'positive', '0.802', 'mental_faculty', 'faculty', 'module', 'remembrance', 'anamnesis']
senticnet['chromium_picolinate'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucose', 'corn_sugar', 'dextrose', 'dextroglucose', 'grape_sugar']
senticnet['chromium_vi'] = ['-0.18', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.16', 'hazardous_health', 'dangerous_health', 'bad_health', 'gross', 'harmful']
senticnet['chromogen'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['chromolaena'] = ['-0.52', '0', '0.527', '-0.78', '#anger', '#disgust', 'negative', '-0.61', 'herb', 'black_snakeroot', 'wild_ginger', 'asarabacca', 'herbaceous_plant']
senticnet['chromosomal_aberration'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'deflection', 'warp', 'aberrance']
senticnet['chromosomal_abnormality'] = ['-0.87', '0', '0.873', '-0.62', '#sadness', '#anger', 'negative', '-0.78', 'pycnodysostosis', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'dwarfism']
senticnet['chromosomal_damage'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'deflection', 'warp', 'aberrance']
senticnet['chromosomal_deletion'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'deflection', 'warp', 'aberrance']
senticnet['chromosomal_disorder'] = ['-0.91', '0', '0.961', '-0.55', '#sadness', '#anger', 'negative', '-0.81', 'hypopigmentation', 'albinism', 'congenital_anomaly', 'congenital_defect', 'congenital_disorder']
senticnet['chromosomal_translocation'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'deflection', 'warp', 'aberrance']
senticnet['chromosome'] = ['0', '0.882', '0', '0.772', '#interest', '#admiration', 'positive', '0.827', 'gene', 'study', 'project', 'specie', 'determine']
senticnet['chromosome_aberration'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'deflection', 'warp', 'aberrance']
senticnet['chromosome_breakage'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'deflection', 'warp', 'aberrance']
senticnet['chromosphere'] = ['0.931', '-0.82', '0', '0.709', '#joy', '#surprise', 'positive', '0.822', 'light', 'aureole', 'radiance', 'visible_light', 'visible_radiation']
senticnet['chromospheric'] = ['0.931', '-0.82', '0', '0.709', '#joy', '#surprise', 'positive', '0.822', 'light', 'aureole', 'radiance', 'visible_light', 'visible_radiation']
senticnet['chronic_alcoholic'] = ['0.645', '0.637', '-0.23', '0.299', '#joy', '#interest', 'positive', '0.336', 'gladdened', 'stimulating', 'tickle_pink', 'exalt', 'beatify']
senticnet['chronic_anemia'] = ['-0.71', '0', '0.74', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'kwashiorkor', 'marasmus', 'malnutrition', 'hypovitaminosis', 'deficiency_disease']
senticnet['chronic_anxiety'] = ['-0.58', '0', '0.722', '-0.51', '#sadness', '#anger', 'negative', '-0.60', 'dissociative_disorder', 'depersonalization_neurosis', 'depersonalization', 'mental_disorder', 'mental_disturbance']
senticnet['chronic_asthma'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchiolitis', 'bronchitis', 'respiratory_disease', 'respiratory_illness', 'respiratory_disorder']
senticnet['chronic_autoimmune'] = ['-0.71', '0', '0.74', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'kwashiorkor', 'marasmus', 'malnutrition', 'hypovitaminosis', 'deficiency_disease']
senticnet['chronic_bronchitis'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchiolitis', 'bronchitis', 'respiratory_disease', 'respiratory_illness', 'respiratory_disorder']
senticnet['chronic_constipation'] = ['-0.52', '0', '0.839', '-0.86', '#anger', '#disgust', 'negative', '-0.74', 'folie', 'upset', 'psychosomatic_disorder', 'conversion_disorder', 'conversion_reaction']
senticnet['chronic_cough'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchiolitis', 'bronchitis', 'respiratory_disease', 'respiratory_illness', 'respiratory_disorder']
senticnet['chronic_depression'] = ['-0.78', '0', '0.918', '-0.96', '#anger', '#disgust', 'negative', '-0.88', 'major_depressive_episode', 'depression', 'depressive_disorder', 'clinical_depression', 'involutional_depression']
senticnet['chronic_diarrhea'] = ['-0.56', '0', '0.906', '-0.80', '#anger', '#disgust', 'negative', '-0.76', 'jaundice_of_newborn', 'disease', 'icterus_neonatorum', 'kernicterus', 'symptom']
senticnet['chronic_diarrhoea'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'inflammatory_disease', 'stomach_flu', 'intestinal_flu', 'pelvic_inflammatory_disease']
senticnet['chronic_disease'] = ['-0.38', '-0.12', '-0.06', '-0.37', '#sadness', '#disgust', 'negative', '-0.17', 'chicken_pox', 'influenza', 'disability', 'brain_tumor', 'lyme_disease']
senticnet['chronic_disease_management'] = ['-0.12', '0', '-0.12', '-0.08', '#sadness', '#fear', 'negative', '-0.11', 'hospital', 'check_up', 'check_doctor', 'check_pulse', 'check_blood_pressure']
senticnet['chronic_fatigue'] = ['-0.80', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'fatigue', 'exhaustion', 'become_tired', 'get_tired', 'feel_tired']
senticnet['chronic_fatigue_syndrome'] = ['-0.71', '0', '0.85', '-0.82', '#anger', '#disgust', 'negative', '-0.79', 'sclerosis', 'induration', 'degenerative_disorder', 'autoimmune_disease', 'autoimmune_disorder']
senticnet['chronic_gastritis'] = ['-0.95', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.89', 'gastritis', 'rubor', 'redness', 'acute_gastritis', 'inflammation']
senticnet['chronic_granulomatous_disease'] = ['-0.50', '0', '0.542', '-0.51', '#anger', '#disgust', 'negative', '-0.51', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'hirschsprung_disease']
senticnet['chronic_heart_failure'] = ['-0.98', '0', '0.827', '-0.94', '#sadness', '#disgust', 'negative', '-0.91', 'congestive_heart_failure', 'heart_attack', 'failure', 'heart_disease', 'cardiopathy']
senticnet['chronic_heartburn'] = ['0', '0', '-0.70', '-0.76', '#fear', '#disgust', 'negative', '-0.73', 'choke', 'become_full', 'get_full', 'burp', 'sickness']
senticnet['chronic_hypertension'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'illness', 'ill', 'tension', 'bad_health', 'malignant_hypertension']
senticnet['chronic_idiopathic'] = ['-0.75', '0', '0.515', '-0.93', '#sadness', '#disgust', 'negative', '-0.73', 'sclerosis', 'induration', 'degenerative_disorder', 'autoimmune_disease', 'disseminated_sclerosis']
senticnet['chronic_idiopathic_urticaria'] = ['-0.55', '0', '0.536', '-0.72', '#sadness', '#disgust', 'negative', '-0.60', 'hypersensitivity_reaction', 'rash', 'efflorescence', 'roseola', 'skin_rash']
senticnet['chronic_inability'] = ['-0.78', '0', '0.707', '-0.86', '#sadness', '#disgust', 'negative', '-0.78', 'mental_block', 'uncreativeness', 'stupidity', 'illiteracy', 'analphabetism']
senticnet['chronic_indecision'] = ['-0.78', '0', '0.915', '-0.99', '#anger', '#disgust', 'negative', '-0.89', 'bodge', 'botch_up', 'flub', 'ball_up', 'go_wrong']
senticnet['chronic_indigestion'] = ['-0.59', '0', '0.911', '-0.83', '#anger', '#disgust', 'negative', '-0.78', 'querulousness', 'peevishness', 'pettishness', 'snappishness', 'surliness']
senticnet['chronic_instability'] = ['-0.84', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.82', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecure']
senticnet['chronic_kidney'] = ['-0.09', '0', '-0.16', '-0.21', '#fear', '#disgust', 'negative', '-0.15', 'sickness', 'malady', 'polio', 'disease', 'ailment']
senticnet['chronic_kidney_disease'] = ['-0.38', '-0.12', '-0.06', '-0.37', '#sadness', '#disgust', 'negative', '-0.17', 'chicken_pox', 'influenza', 'disability', 'brain_tumor', 'lyme_disease']
senticnet['chronic_kidney_failure'] = ['-0.60', '0', '0.544', '-0.53', '#sadness', '#anger', 'negative', '-0.56', 'acute_renal_failure', 'acute_kidney_failure', 'chronic_renal_failure', 'kidney_failure', 'nephrosis']
senticnet['chronic_lung_disease'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchitis', 'chronic_bronchitis', 'respiratory_disease', 'respiratory_illness', 'respiratory_disorder']
senticnet['chronic_lung_disease_cld'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchitis', 'chronic_bronchitis', 'respiratory_disease', 'respiratory_illness', 'respiratory_disorder']
senticnet['chronic_lymphedema'] = ['-0.99', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.78', 'angioedema', 'atrophedema', 'giant_hive', 'periodic_edema', 'dropsy']
senticnet['chronic_lymphocytic_leukemia'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['chronic_malaria'] = ['-0.98', '0', '0.355', '-0.93', '#sadness', '#disgust', 'negative', '-0.75', 'malaria', 'sick', 'sickness', 'ill', 'need_cure']
senticnet['chronic_malnutrition'] = ['-0.71', '0', '0.74', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'malnutrition', 'marasmus', 'avitaminosis', 'hypovitaminosis', 'deficiency_disease']
senticnet['chronic_migraine'] = ['-0.73', '0', '-0.85', '-0.72', '#sadness', '#fear', 'negative', '-0.77', 'lay_off', 'hurt', 'pain_suffer', 'lose_mind', 'forget_everything']
senticnet['chronic_myelogenous_leukemia'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['chronic_otitis_media'] = ['-0.87', '0', '0.773', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'rubor', 'redness', 'inflammation', 'otitis', 'otitis_interna']
senticnet['chronic_paronychia'] = ['-0.95', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.89', 'chronic_gastritis', 'rubor', 'redness', 'gastritis', 'inflammation']
senticnet['chronic_pelvic_pain'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'gastroenteritis', 'stomach_flu', 'intestinal_flu', 'inflammatory_disease']
senticnet['chronic_poverty'] = ['-0.84', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.82', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecure']
senticnet['chronic_progressive'] = ['-0.87', '0', '0.531', '-0.68', '#sadness', '#disgust', 'negative', '-0.69', 'rubor', 'redness', 'dermatomyositis', 'inclusion_body_myositis', 'inflammation']
senticnet['chronic_pulmonary'] = ['-0.95', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.89', 'chronic_gastritis', 'rubor', 'redness', 'gastritis', 'inflammation']
senticnet['chronic_renal_disease'] = ['-0.60', '0', '0.544', '-0.53', '#sadness', '#anger', 'negative', '-0.56', 'acute_renal_failure', 'acute_kidney_failure', 'chronic_renal_failure', 'chronic_kidney_failure', 'kidney_failure']
senticnet['chronic_renal_failure'] = ['-0.60', '0', '0.544', '-0.53', '#sadness', '#anger', 'negative', '-0.56', 'acute_renal_failure', 'acute_kidney_failure', 'kidney_failure', 'chronic_kidney_failure', 'nephrosis']
senticnet['chronic_respiratory_disease'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchiolitis', 'chronic_bronchitis', 'respiratory_disease', 'bronchitis', 'respiratory_disorder']
senticnet['chronic_rheumatism'] = ['-0.69', '0', '0.852', '-0.69', '#sadness', '#anger', 'negative', '-0.74', 'conjunctivitis', 'ophthalmitis', 'rubor', 'redness', 'inflammation']
senticnet['chronic_shortage'] = ['-0.83', '0', '-0.88', '-0.78', '#sadness', '#fear', 'negative', '-0.83', 'little', 'sadness', 'small_amount', 'lack', 'absence']
senticnet['chronic_undernutrition'] = ['-0.71', '0', '0.74', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'malnutrition', 'marasmus', 'avitaminosis', 'hypovitaminosis', 'deficiency_disease']
senticnet['chronic_unemployment'] = ['-0.61', '0', '0.712', '-0.86', '#anger', '#disgust', 'negative', '-0.73', 'impoverishment', 'deprivation', 'indigence', 'penury', 'pauperism']
senticnet['chronic_urticaria'] = ['-0.99', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.78', 'dropsy', 'atrophedema', 'giant_hive', 'periodic_edema', 'lymphedema']
senticnet['chronic_venous_insufficiency'] = ['-0.99', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.78', 'angioedema', 'atrophedema', 'giant_hive', 'periodic_edema', 'dropsy']
senticnet['chronically_homeless_people'] = ['-0.88', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.81', 'bum', 'red_light', 'petition', 'take', 'violent_action']
senticnet['chronically_ill'] = ['0', '0', '0', '0.7', '#admiration', '#admiration', 'positive', '0.7', 'old', 'year', 'many_years', 'teen', 'annual']
senticnet['chronically_ill_elderly'] = ['0', '0', '0', '0.7', '#admiration', '#admiration', 'positive', '0.7', 'old', 'year', 'many_years', 'teen', 'annual']
senticnet['chronically_late'] = ['0.723', '0.72', '0', '0', '#joy', '#interest', 'positive', '0.722', 'late', 'get_dark', 'rainy_day', 'split_end', 'house_look_better']
senticnet['chronically_sick'] = ['0', '0', '0', '0.7', '#admiration', '#admiration', 'positive', '0.7', 'old', 'year', 'many_years', 'teen', 'annual']
senticnet['chronicity'] = ['0.798', '-0.51', '0', '0.519', '#joy', '#admiration', 'positive', '0.611', 'averageness', 'commonness', 'normalcy', 'typicality', 'condition']
senticnet['chronicle'] = ['0', '0.105', '0', '0.084', '#interest', '#admiration', 'positive', '0.095', 'archive', 'print_medium', 'quiet_place', 'register', 'media']
senticnet['chronicon'] = ['0', '0.105', '0', '0.084', '#interest', '#admiration', 'positive', '0.095', 'archive', 'print_medium', 'quiet_place', 'register', 'media']
senticnet['chronicon_scotorum'] = ['0', '0.105', '0', '0.084', '#interest', '#admiration', 'positive', '0.095', 'archive', 'print_medium', 'quiet_place', 'register', 'media']
senticnet['chronoamperometry'] = ['0.965', '-0.84', '0', '0.599', '#joy', '#surprise', 'positive', '0.802', 'mental_faculty', 'faculty', 'module', 'remembrance', 'anamnesis']
senticnet['chronograph'] = ['0', '0', '0.95', '-0.86', '#anger', '#disgust', 'negative', '-0.90', 'watch', 'stopwatch', 'timepiece', 'wristwatch', 'find_pawnshop']
senticnet['chronological'] = ['0', '0.715', '0', '0.889', '#interest', '#admiration', 'positive', '0.802', 'thematically', 'chronologically', 'alphabetically', 'grouped', 'logically']
senticnet['chronological_age'] = ['0', '0.075', '0', '0.159', '#interest', '#admiration', 'positive', '0.117', 'many_plants', 'blossom', 'development', 'poppy', 'adult']
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senticnet['cinema_multiplex'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'theater', 'drive-in_movie', 'watch_movie', 'cinema', 'movie']
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senticnet['cinematic_technique'] = ['-0.85', '0', '0.514', '-0.73', '#sadness', '#disgust', 'negative', '-0.70', 'flick', 'movie', 'film', 'picture', 'moving_picture']
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senticnet['cineplex'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'theater', 'drive-in_movie', 'watch_movie', 'cinema', 'movie']
senticnet['cinerary_urn'] = ['0', '0.082', '-0.09', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'coffin', 'headstone', 'morgue', 'bury_body', 'casket']
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senticnet['cinnabon'] = ['0.274', '-0.01', '0.012', '0.132', '#joy', '#admiration', 'positive', '0.102', 'dance_type', 'swirl', 'throw_football', 'jump_around', 'dance_move']
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senticnet['cinnamomi'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'apple_rust', 'fungus']
senticnet['cinnamomum_camphora'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'carnauba_palm', 'carnauba', 'wax_palm', 'palmetto', 'palmyra']
senticnet['cinnamon'] = ['0.077', '0', '0', '0', '#joy', '#joy', 'positive', '0.077', 'dip', 'garlic', 'oat', 'savory', 'rye']
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senticnet['cinnarizine'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'spironolactone', 'adrenal_cortical_steroid', 'antihypertensive', 'antihypertensive_drug']
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senticnet['cipher'] = ['-0.52', '0', '0.821', '-0.65', '#anger', '#disgust', 'negative', '-0.66', 'work_out', 'reckon', 'calculate', 'average_out', 'cypher']
senticnet['cipher_machine'] = ['-0.52', '0', '0.821', '-0.65', '#anger', '#disgust', 'negative', '-0.66', 'work_out', 'reckon', 'calculate', 'average_out', 'cypher']
senticnet['cipher_text'] = ['-0.52', '0', '0.821', '-0.65', '#anger', '#disgust', 'negative', '-0.66', 'work_out', 'reckon', 'calculate', 'average_out', 'cypher']
senticnet['ciphertext'] = ['0.022', '-0.13', '0.153', '-0.31', '#anger', '#disgust', 'negative', '-0.07', 'codeine', 'morphine', 'infection', 'cocaine', 'nuclear_war']
senticnet['ciphertext_block'] = ['0.584', '-0.72', '0', '0.742', '#surprise', '#admiration', 'positive', '0.684', 'decompression', 'cryptography', 'coding', 'secret_writing', 'steganography']
senticnet['ciprofloxacin_cipro'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
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senticnet['circular_buffer'] = ['0.865', '-0.80', '0', '0.52', '#joy', '#surprise', 'positive', '0.73', 'set', 'pose', 'lay', 'intersperse', 'put']
senticnet['circular_cylinder'] = ['-0.97', '0', '0.831', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'skewness', 'irregularity']
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senticnet['circular_path'] = ['-0.04', '0.074', '0.019', '0.071', '#interest', '#admiration', 'positive', '0.021', 'sharpness', 'sharp_end', 'sharp_corner', 'near_end', 'edge']
senticnet['circular_pattern'] = ['0.932', '0.753', '0', '0', '#joy', '#interest', 'positive', '0.843', 'circle', 'loop', 'arc', 'coil', 'trapezoid']
senticnet['circular_route'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'row_house', 'human_hair', 'semi-truck', 'expressway', 'park_structure']
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senticnet['circular_staircase'] = ['0.046', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'light', 'bay_window', 'air_conditioner', 'sky', 'lamp']
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senticnet['circular_tower'] = ['0.026', '0.219', '0', '0.16', '#interest', '#admiration', 'positive', '0.135', 'large_build', 'palace', 'high_rise', 'structure', 'fortification']
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senticnet['circumspect'] = ['0.625', '-0.88', '0', '0.782', '#surprise', '#admiration', 'positive', '0.762', 'judicious', 'discreet', 'prudential', 'heady', 'prudent']
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senticnet['circus_act'] = ['0.106', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.062', 'western', 'sumo', 'rodeo', 'grand_canyon', 'have_fun']
senticnet['circus_circus_hotel'] = ['0.057', '0.13', '0', '0.119', '#interest', '#admiration', 'positive', '0.102', 'friend_wedding', 'circular_route', 'spanish_restaurant', 'altar', 'monastery']
senticnet['circus_clown'] = ['0.9', '-0.88', '0', '0.687', '#joy', '#surprise', 'positive', '0.823', 'conjurer', 'escape_expert', 'mind_reader', 'telepathist', 'performer']
senticnet['circus_parade'] = ['0.954', '0', '0', '0', '#joy', '#joy', 'positive', '0.954', 'clown', 'halloween', 'entertainment', 'fun', 'happiness']
senticnet['circus_performer'] = ['0', '0', '0', '0.987', '#admiration', '#admiration', 'positive', '0.987', 'emote', 'bold', 'form_of_social_interaction', 'act', 'stage_play']
senticnet['circus_ring'] = ['0.954', '0', '0', '0', '#joy', '#joy', 'positive', '0.954', 'clown', 'halloween', 'entertainment', 'fun', 'happiness']
senticnet['circus_sideshow'] = ['0.954', '0', '0', '0', '#joy', '#joy', 'positive', '0.954', 'clown', 'halloween', 'entertainment', 'fun', 'happiness']
senticnet['circus_strongman'] = ['0', '0', '0', '0.987', '#admiration', '#admiration', 'positive', '0.987', 'emote', 'bold', 'form_of_social_interaction', 'act', 'stage_play']
senticnet['circus_tent'] = ['0.954', '0', '0', '0', '#joy', '#joy', 'positive', '0.954', 'clown', 'halloween', 'entertainment', 'fun', 'happiness']
senticnet['circus_troupe'] = ['0.9', '-0.88', '0', '0.687', '#joy', '#surprise', 'positive', '0.823', 'conjurer', 'escape_expert', 'mind_reader', 'telepathist', 'performer']
senticnet['cirque_dream_jungle'] = ['0.747', '0.85', '0', '0', '#joy', '#interest', 'positive', '0.798', 'night_vision', 'mystery', 'plot', 'imagine', 'myth']
senticnet['cirrhimuraena'] = ['-0.56', '0', '0.617', '-0.80', '#anger', '#disgust', 'negative', '-0.66', 'prolixity', 'prolixness', 'wordiness', 'repetitiveness', 'repetitiousness']
senticnet['cirrhosa'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'annelid', 'annelid_worm', 'leech']
senticnet['cirrus_cloud'] = ['-0.93', '0', '0.508', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'nakedness', 'glumness', 'bleakness', 'desolation', 'bareness']
senticnet['cirsium_arvense'] = ['-0.02', '-0.09', '0.11', '-0.13', '#anger', '#disgust', 'negative', '-0.04', 'beetle', 'gypsy_moth', 'mole_cricket', 'grasshopper', 'many_insects']
senticnet['cirsium_vulgare'] = ['0.861', '-0.87', '0', '0.597', '#joy', '#surprise', 'positive', '0.779', 'herb', 'cultivated_parsnip', 'wild_parsnip', 'madnep', 'herbaceous_plant']
senticnet['cisapride_propulsid'] = ['-0.72', '0', '0.973', '-0.57', '#sadness', '#anger', 'negative', '-0.76', 'corpulency', 'blubber', 'fat', 'embonpoint', 'stoutness']
senticnet['cisco_unified_callmanager'] = ['0.749', '0.724', '0', '0', '#joy', '#interest', 'positive', '0.736', 'mouth_noise', 'vocal_cord', 'vocalization', 'vocal_sound', 'loud_soft']
senticnet['cissy'] = ['-0.63', '0', '0.986', '-0.94', '#anger', '#disgust', 'negative', '-0.85', 'womanish', 'effeminate', 'emasculate', 'unmanful', 'sissified']
senticnet['cista'] = ['0', '0.082', '-0.09', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'coffin', 'headstone', 'morgue', 'bury_body', 'casket']
senticnet['citation'] = ['0.866', '0', '0', '0.712', '#joy', '#admiration', 'positive', '0.789', 'suspicion', 'effect', 'cite', 'demand', 'permit']
senticnet['citationality'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'nonsense', 'meaninglessness', 'bunk', 'absurdity', 'hokum']
senticnet['cite'] = ['0', '-0.77', '0', '0.81', '#surprise', '#admiration', 'positive', '0.792', 'suspicion', 'effect', 'citation', 'demand', 'permit']
senticnet['citizen'] = ['0', '0.826', '0', '0.79', '#interest', '#admiration', 'positive', '0.808', 'feudalism', 'nation', 'capitol', 'capital', 'democracy']
senticnet['citizen_arrest'] = ['-0.13', '0.113', '-0.16', '0', '#sadness', '#fear', 'negative', '-0.06', 'question_witness', 'legal_process', 'find_guilty', 'convict_criminal', 'custody']
senticnet['citizen_committee'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'denomination', 'committee', 'ngo', 'mission', 'missionary_post']
senticnet['citizen_participation'] = ['0.952', '-0.84', '0', '0.875', '#joy', '#admiration', 'positive', '0.89', 'administration', 'judiciary', 'governing_body', 'establishment', 'brass']
senticnet['citizenry'] = ['0', '0', '0.695', '-0.82', '#anger', '#disgust', 'negative', '-0.75', 'tax-paying', 'law-abiding', 'tax-payers', 'hard-working', 'taxpaying']
senticnet['citizenship'] = ['0', '-0.91', '0', '0.806', '#surprise', '#admiration', 'positive', '0.861', 'clean_shoes', 'black_stuff', 'paint_nail', 'ethnicity', 'black_goo']
senticnet['citric'] = ['-0.82', '0', '0.901', '-0.65', '#sadness', '#anger', 'negative', '-0.79', 'taste', 'taste_sensation', 'gustatory_sensation', 'taste_perception', 'gustatory_perception']
senticnet['citrobacter'] = ['-0.60', '0', '0.721', '-0.57', '#sadness', '#anger', 'negative', '-0.63', 'tubercle_bacillus', 'leprosy_bacillus', 'eubacteria', 'eubacterium', 'true_bacteria']
senticnet['citronella'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['citrophthora'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'rust_fungus', 'fungus']
senticnet['citrucel'] = ['-0.50', '0', '0.582', '-0.57', '#anger', '#disgust', 'negative', '-0.55', 'purgative', 'cathartic', 'physic', 'aperient', 'phenolphthalein']
senticnet['citrus'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'citrus_fruit', 'orange_fruit', 'orange_color', 'hair_color', 'fruit']
senticnet['citrus_aroma'] = ['0.933', '-0.87', '0', '0.766', '#joy', '#surprise', 'positive', '0.858', 'scent', 'aroma', 'olfactory_property', 'smell', 'odor']
senticnet['citrus_fruit'] = ['0.106', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.062', 'citrus', 'orange_fruit', 'orange_color', 'fruit', 'healthy']
senticnet['citrus_juice'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'lemon_juice', 'chocolate_pudding', 'grape_juice', 'orange_juice', 'juice']
senticnet['citrus_leafminer'] = ['-0.77', '0', '0.576', '-0.92', '#sadness', '#disgust', 'negative', '-0.75', 'insect', 'firebug', 'water_bug', 'assassin_bug', 'heteropterous_insect']
senticnet['citrus_maximum'] = ['0.859', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.888', 'green_apple', 'apricot', 'lime', 'peach', 'cherry']
senticnet['citrus_scent'] = ['0.933', '-0.87', '0', '0.766', '#joy', '#surprise', 'positive', '0.858', 'scent', 'aroma', 'olfactory_property', 'smell', 'odor']
senticnet['cittern'] = ['0.033', '0', '0.009', '0.057', '#joy', '#admiration', 'positive', '0.027', 'triangle', 'baton', 'bugle', 'type_of_bow', 'horn']
senticnet['citty'] = ['-0.69', '0', '0.883', '-0.91', '#anger', '#disgust', 'negative', '-0.82', 'stick_in', 'enclose', 'insert', 'feed_in', 'put_in']
senticnet['city'] = ['0', '0', '0', '0.104', '#admiration', '#admiration', 'positive', '0.104', 'downtown', 'govern_city', 'large_town', 'govern', 'small_govern_city']
senticnet['city-dwelling'] = ['0.761', '-0.89', '0', '0', '#joy', '#surprise', 'positive', '0.829', 'sin-cursed', 'newly-made', 'soccer-loving', 'new-made', 'newly-christened']
senticnet['city-state'] = ['0', '0.925', '0', '0.955', '#interest', '#admiration', 'positive', '0.94', 'fiefdom', 'fief', 'oligarchy', 'manorial', 'fiefdom']
senticnet['city_block'] = ['0.106', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.062', 'street_corner', 'corner_two_streets', 'city_street', 'city_road', 'junk_yard']
senticnet['city_center'] = ['-0.03', '0.099', '0.017', '0.102', '#interest', '#admiration', 'positive', '0.038', 'every_town', 'downtown', 'monument', 'cathedral', 'town_center']
senticnet['city_country'] = ['0.02', '0', '0', '0.081', '#joy', '#admiration', 'positive', '0.051', 'grocery_store', 'supermarket', 'fast_food', 'restaurant', 'retail_store']
senticnet['city_dweller'] = ['-0.55', '0', '0.912', '-0.65', '#anger', '#disgust', 'negative', '-0.70', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['city_hall'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'town_hall', 'courthouse', 'subway_station', 'public_place', 'bus_station']
senticnet['city_life'] = ['0.891', '-0.93', '0', '0', '#joy', '#surprise', 'positive', '0.913', 'govern_city', 'large_town', 'central_part', 'large_building', 'downtown']
senticnet['city_main_thoroughfare'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'artery', 'skeletal_system', 'groin', 'neck', 'muscle']
senticnet['city_park'] = ['0.048', '0.077', '0', '-0.03', '#joy', '#interest', 'positive', '0.031', 'soccer_field', 'outhouse', 'memorial', 'ground', 'garden']
senticnet['city_road'] = ['0.048', '0.077', '0', '-0.03', '#joy', '#interest', 'positive', '0.031', 'street_corner', 'corner_two_streets', 'city_block', 'city_street', 'junk_yard']
senticnet['city_skyline'] = ['0.838', '0.828', '0', '0', '#joy', '#interest', 'positive', '0.833', 'vista', 'spectacular', 'perspective', 'image', 'scenic']
senticnet['city_slicker'] = ['-0.96', '0', '0.924', '-0.86', '#sadness', '#anger', 'negative', '-0.91', 'redneck', 'rustic', 'rube', 'hick', 'hayseed']
senticnet['city_street'] = ['0', '0', '0.021', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'street_corner', 'corner_two_streets', 'city_block', 'city_road', 'junk_yard']
senticnet['cityjet'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['cityscape'] = ['0.838', '0.828', '0', '0', '#joy', '#interest', 'positive', '0.833', 'vista', 'spectacular', 'perspective', 'image', 'scenic']
senticnet['civet_cat'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'animal_hair', 'dog_hair', 'animal_fur', 'dog_cat', 'bite_dog']
senticnet['civic'] = ['0.123', '0.19', '-0.11', '0.183', '#interest', '#admiration', 'positive', '0.096', 'local_food', 'zone', 'headquarters', 'regional', 'urban']
senticnet['civic_activist'] = ['0', '0.868', '0', '0.853', '#interest', '#admiration', 'positive', '0.861', 'country_leader', 'prime_minister', 'president', 'elect_official', 'leader']
senticnet['civic_arena'] = ['0', '-0.84', '0', '0.723', '#surprise', '#admiration', 'positive', '0.785', 'circular_route', 'stadium', 'memorial', 'station', 'row_house']
senticnet['civic_auditorium'] = ['0', '-0.84', '0', '0.723', '#surprise', '#admiration', 'positive', '0.785', 'circular_route', 'stadium', 'memorial', 'station', 'row_house']
senticnet['civic_ballet'] = ['0.12', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.09', 'ballroom', 'perform_art', 'nightclub', 'ballerina', 'venue']
senticnet['civic_center'] = ['0.048', '0.077', '0', '-0.03', '#joy', '#interest', 'positive', '0.031', 'house', 'city_center', 'city_street', 'city_block', 'city_park']
senticnet['civic_centre'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'city_hall', 'courthouse', 'subway_station', 'public_place', 'bus_station']
senticnet['civic_duty'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'motherland', 'homeland']
senticnet['civic_symphony'] = ['0.506', '0.659', '-0.24', '0.242', '#joy', '#interest', 'positive', '0.29', 'sing', 'music_studio', 'music_room', 'choir', 'symphony']
senticnet['civil'] = ['-0.78', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.81', 'complaisant', 'discourteous', 'uncivilly', 'uncivil', 'savage']
senticnet['civil_action'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'secular', 'action_at_law', 'counterclaim', 'final_decision', 'li_pendens']
senticnet['civil_ceremony'] = ['0.057', '0.13', '0', '0.119', '#interest', '#admiration', 'positive', '0.102', 'friend_wedding', 'circular_route', 'spanish_restaurant', 'altar', 'monastery']
senticnet['civil_conflict'] = ['-0.92', '0', '0.546', '-0.52', '#sadness', '#anger', 'negative', '-0.66', 'irresponsibility', 'irresponsibleness', 'undependability', 'irreproducibility', 'fallibility']
senticnet['civil_discord'] = ['-0.65', '0', '0.716', '-0.94', '#anger', '#disgust', 'negative', '-0.77', 'bad_hat', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['civil_disobedience'] = ['-0.90', '0', '0.771', '-0.99', '#sadness', '#disgust', 'negative', '-0.89', 'fight', 'battle', 'war', 'protest', 'attack']
senticnet['civil_disobedience_movement'] = ['-0.71', '0', '0.928', '-0.74', '#anger', '#disgust', 'negative', '-0.79', 'set_off', 'shake_up', 'stir_up', 'instigate', 'inflame']
senticnet['civil_engineer'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'maker']
senticnet['civil_engineering_contractor'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'maker']
senticnet['civil_ensign'] = ['0.053', '0.068', '-0.05', '0.06', '#interest', '#admiration', 'positive', '0.032', 'linen', 'star_stripe', 'velvet', 'nation', 'burlap']
senticnet['civil_forfeiture'] = ['-0.62', '0', '0.7', '-0.83', '#anger', '#disgust', 'negative', '-0.72', 'take', 'pull', 'return', 'payback', 'proceeds']
senticnet['civil_infraction'] = ['-0.89', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.93', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['civil_lawsuit'] = ['-0.91', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.85', 'lawyer', 'take_court', 'call_lawyer', 'determine_guilt', 'go_to_court']
senticnet['civil_lawsuit_against'] = ['-0.91', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.85', 'lawyer', 'take_court', 'call_lawyer', 'determine_guilt', 'go_to_court']
senticnet['civil_liability'] = ['0', '0', '0', '0.816', '#admiration', '#admiration', 'positive', '0.816', 'resistance', 'protection', 'untouchable', 'invulnerable', 'sacrosanct']
senticnet['civil_libertarian'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'economic_libertarian', 'libertarian', 'advocate', 'advocator', 'proponent']
senticnet['civil_liberty'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'economic_libertarian', 'libertarian', 'advocate', 'advocator', 'proponent']
senticnet['civil_magistrate'] = ['0.621', '0.648', '-0.25', '0.296', '#joy', '#interest', 'positive', '0.328', 'pacifically', 'repose', 'peace-loving', 'peaceable', 'peace_of_mind']
senticnet['civil_marriage'] = ['0', '0.819', '0', '0.987', '#interest', '#admiration', 'positive', '0.903', 'celebrate', 'wedding_ceremony', 'propose_marriage', 'engage', 'engagement']
senticnet['civil_penalty'] = ['-0.89', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.93', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['civil_registry'] = ['0.931', '-0.52', '0', '0.67', '#joy', '#admiration', 'positive', '0.709', 'document', 'certificate_of_incorporation', 'credential', 'diploma', 'sheepskin']
senticnet['civil_right_lawsuit'] = ['-0.91', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.85', 'lawyer', 'take_court', 'call_lawyer', 'determine_guilt', 'go_to_court']
senticnet['civil_right_lawsuit_against'] = ['-0.91', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.85', 'lawyer', 'take_court', 'call_lawyer', 'determine_guilt', 'go_to_court']
senticnet['civil_servant'] = ['0', '0.868', '0', '0.853', '#interest', '#admiration', 'positive', '0.861', 'country_leader', 'prime_minister', 'president', 'elect_official', 'leader']
senticnet['civil_society'] = ['-0.54', '0', '0.863', '-0.76', '#anger', '#disgust', 'negative', '-0.72', 'denomination', 'committee', 'citizen_committee', 'mission', 'missionary_post']
senticnet['civil_society_participation'] = ['0.952', '-0.84', '0', '0.875', '#joy', '#admiration', 'positive', '0.89', 'administration', 'judiciary', 'governing_body', 'establishment', 'brass']
senticnet['civil_strife'] = ['-0.84', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.82', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecure']
senticnet['civil_suit'] = ['-0.91', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.85', 'lawyer', 'take_court', 'call_lawyer', 'determine_guilt', 'go_to_court']
senticnet['civil_unrest'] = ['-0.92', '0', '0.546', '-0.52', '#sadness', '#anger', 'negative', '-0.66', 'irresponsibility', 'irresponsibleness', 'undependability', 'irreproducibility', 'fallibility']
senticnet['civil_war'] = ['-0.99', '0', '0.738', '-0.94', '#sadness', '#disgust', 'negative', '-0.89', 'war', 'battle', 'soldier', 'fight', 'weapon']
senticnet['civilian'] = ['-0.04', '0', '-0.06', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'make_war', 'conquer_enemy', 'use_military', 'gain_territory', 'keep_peace']
senticnet['civilian_nuclear'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'dynamite', 'gunshot', 'missile', 'nuclear_bomb', 'fall_sky']
senticnet['civilian_nuclear_power'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'dynamite', 'gunshot', 'missile', 'nuclear_bomb', 'fall_sky']
senticnet['civilian_personnel'] = ['0.814', '-0.87', '0', '0.918', '#surprise', '#admiration', 'positive', '0.87', 'ship_company', 'company', 'work_force', 'workforce', 'full_complement']
senticnet['civilian_population'] = ['-0.88', '0', '0.733', '-0.60', '#sadness', '#anger', 'negative', '-0.74', 'vengeance', 'payback', 'retribution', 'retaliation', 'return']
senticnet['civilisation'] = ['-0.65', '0', '0.691', '-0.54', '#sadness', '#anger', 'negative', '-0.63', 'moronity', 'mental_deficiency', 'imbecility', 'stupidity', 'mental_defectiveness']
senticnet['civility'] = ['0', '0.128', '-0.04', '0.064', '#interest', '#admiration', 'positive', '0.048', 'line_up', 'find_end_line', 'come_early', 'find_line', 'wait_patiently']
senticnet['civilization'] = ['0', '0.08', '0', '0.117', '#interest', '#admiration', 'positive', '0.099', 'stand_still', 'go_home', 'pull_out', 'stretch_out', 'advance_civilization']
senticnet['civilizational'] = ['-0.67', '0', '0.97', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'joint', 'node', 'articulation', 'join', 'connexion']
senticnet['civilize'] = ['0.601', '0.647', '-0.26', '0.322', '#joy', '#interest', 'positive', '0.326', 'break_free', 'set_free', 'relief', 'freedom', 'liberty']
senticnet['civilized_man'] = ['0.967', '-0.69', '0', '0.52', '#joy', '#surprise', 'positive', '0.729', 'marksmanship', 'oarsmanship', 'seamanship', 'swordsmanship', 'numeracy']
senticnet['civilized_manner'] = ['0.911', '-0.71', '0', '0.552', '#joy', '#surprise', 'positive', '0.726', 'deliver_goods', 'succeed', 'win', 'bring_home_bacon', 'come_through']
senticnet['civilized_world'] = ['0', '0.08', '0', '0.117', '#interest', '#admiration', 'positive', '0.099', 'stand_still', 'go_home', 'pull_out', 'stretch_out', 'advance_civilization']
senticnet['civilizing_mission'] = ['0.757', '-0.82', '0', '0.982', '#surprise', '#admiration', 'positive', '0.855', 'first_class', 'choiceness', 'first_water', 'fineness', 'quality']
senticnet['civill_power'] = ['-0.81', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.79', 'stifle', 'forgotten', 'old', 'touch_ground', 'seldom']
senticnet['civilly'] = ['0', '0.149', '0.719', '-0.66', '#anger', '#disgust', 'negative', '-0.41', 'belligerently', 'antagonism', 'class_feeling', 'aggressiveness', 'animus']
senticnet['clad'] = ['-0.53', '0', '0.937', '-0.80', '#anger', '#disgust', 'negative', '-0.75', 'gowned', 'petticoated', 'surpliced', 'arrayed', 'caparisoned']
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senticnet['classification_scheme'] = ['-0.05', '0.084', '0', '0.062', '#interest', '#admiration', 'positive', '0.03', 'vocabulary', 'translation', 'book_library', 'book_poetry', 'book_genre']
senticnet['classified_ad'] = ['0.808', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.903', 'marketing', 'sale', 'announcement', 'billboard', 'promotion']
senticnet['classified_advertisement'] = ['0.808', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.903', 'marketing', 'sale', 'announcement', 'billboard', 'promotion']
senticnet['classify'] = ['-0.07', '0.331', '-0.11', '0.275', '#interest', '#admiration', 'positive', '0.105', 'intend', 'let', 'right', 'determine', 'selection']
senticnet['classify_ad'] = ['0', '-0.08', '-0.05', '0.099', '#surprise', '#admiration', 'positive', '0.043', 'phone_book', 'side_build', 'city_center', 'letter_alphabet', 'ads']
senticnet['classmate'] = ['0.05', '0.141', '-0.07', '0.132', '#interest', '#admiration', 'positive', '0.063', 'public_servant', 'worker', 'manager', 'laborer', 'office_job']
senticnet['classroom'] = ['0.87', '0.817', '0', '0', '#joy', '#interest', 'positive', '0.843', 'room', 'home_room', 'study_hall', 'lecture_room', 'schoolroom']
senticnet['classroom_bulletin_board'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'gift_wrap', 'kraft', 'kraft_paper', 'wrapping_paper', 'paper']
senticnet['classroom_chair'] = ['0', '0', '0.99', '-0.73', '#anger', '#disgust', 'negative', '-0.86', 'text_book', 'exam_table', 'lead_pencil', 'eraser_holder', 'pencil_sharpener']
senticnet['classroom_instruction'] = ['0.633', '-0.96', '0', '0.612', '#joy', '#surprise', 'positive', '0.738', 'particularism', 'absorption', 'concentration', 'engrossment', 'centering']
senticnet['classroom_teacher'] = ['-0.69', '0', '0.659', '-0.69', '#sadness', '#disgust', 'negative', '-0.68', 'help', 'paralegal', 'legal_assistant', 'paramedic', 'paramedical']
senticnet['classwork'] = ['-0.12', '0', '0', '-0.15', '#sadness', '#disgust', 'negative', '-0.13', 'go_college', 'study_test', 'sharpen_pencil', 'revise', 'take_test']
senticnet['classy'] = ['0', '-0.94', '0', '0.85', '#surprise', '#admiration', 'positive', '0.898', 'elegant', 'use_binoculars', 'sophisticated', 'pretty', 'female_person']
senticnet['classy_restaurant'] = ['0.084', '-0.05', '0', '-0.04', '#joy', '#surprise', 'positive', '0.031', 'entree', 'feast', 'satiation', 'breakfast', 'supper']
senticnet['clastogenic'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'deflection', 'warp', 'aberrance']
senticnet['clatter'] = ['-0.84', '0', '0', '-0.95', '#sadness', '#disgust', 'negative', '-0.89', 'thud', 'thudding', 'thump', 'thud', 'thwack']
senticnet['clattering'] = ['0.701', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.849', 'thud', 'thumping', 'thudded', 'thudding', 'thrumming']
senticnet['claudication'] = ['-0.88', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.85', 'gimpiness', 'lameness', 'limping', 'gimp', 'gameness']
senticnet['claudio_baglioni'] = ['0.107', '0.11', '-0.08', '0.21', '#interest', '#admiration', 'positive', '0.086', 'live_healthy_life', 'forever_young', 'live_forever', 'live_long', 'live_good_life']
senticnet['claudio_castagnoli'] = ['-0.57', '0', '0.571', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['claudio_fragasso'] = ['-0.79', '0', '0.885', '-0.76', '#sadness', '#anger', 'negative', '-0.81', 'infernal', 'mortal', 'dead_soul', 'dead_soule', 'living_dead']
senticnet['claudio_guidetti'] = ['0.107', '0.11', '-0.08', '0.21', '#interest', '#admiration', 'positive', '0.086', 'live_healthy_life', 'forever_young', 'live_forever', 'live_long', 'live_good_life']
senticnet['clausal'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'integrative', 'consolidative', 'unifying', 'combinatorial']
senticnet['clause'] = ['-0.04', '0.183', '-0.16', '0.33', '#interest', '#admiration', 'positive', '0.075', 'question_starter', 'inquiry', 'question_start', 'question', 'ask']
senticnet['claustrophobe'] = ['-0.70', '0', '0.624', '-0.65', '#sadness', '#disgust', 'negative', '-0.66', 'sick_person', 'diseased_person', 'sufferer', 'ache', 'hysteric']
senticnet['claustrophobia'] = ['0', '0', '-0.91', '-0.82', '#fear', '#disgust', 'negative', '-0.86', 'agoraphobia', 'claustrophobic', 'acrophobia', 'phobia', 'aversion']
senticnet['claustrophobic'] = ['0', '0', '-0.91', '-0.82', '#fear', '#disgust', 'negative', '-0.87', 'agoraphobia', 'claustrophobia', 'acrophobia', 'phobia', 'aversion']
senticnet['claustrophobic_atmosphere'] = ['-0.83', '0', '0.703', '-0.85', '#sadness', '#disgust', 'negative', '-0.79', 'hurly_burly', 'kerfuffle', 'commotion', 'disruption', 'disturbance']
senticnet['claver'] = ['0.882', '-0.67', '0', '0.769', '#joy', '#admiration', 'positive', '0.776', 'schmooze', 'schmoose', 'shmooze', 'shmoose', 'jawbone']
senticnet['clavichord'] = ['0.033', '0', '0.009', '0.057', '#joy', '#admiration', 'positive', '0.027', 'triangle', 'baton', 'bugle', 'type_of_bow', 'horn']
senticnet['clavicular'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'skeletal_muscle', 'pterygoid_muscle', 'abductor', 'abductor_muscle']
senticnet['claw'] = ['0', '-0.03', '0.08', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'tic', 'whisker', 'paw', 'fur_keep_warm', 'worm_intestine']
senticnet['claw_back'] = ['0', '0.263', '-0.14', '0.295', '#interest', '#admiration', 'positive', '0.139', 'keep', 'hold', 'retain', 'hold_stuff', 'upkeep']
senticnet['claw_foot_tub'] = ['0', '-0.02', '-0.08', '-0.05', '#fear', '#disgust', 'negative', '-0.03', 'mattress', 'sleep_furniture', 'cradle', 'small_bed', 'pillow']
senticnet['claw_hammer'] = ['0.818', '-0.95', '0', '0.695', '#joy', '#surprise', 'positive', '0.823', 'jackknife', 'clasp_knife', 'penknife', 'switchblade', 'switchblade_knife']
senticnet['clawhammer_banjo'] = ['-0.04', '-0.04', '-0.04', '-0.04', '#fear', '#disgust', 'negative', '-0.02', 'music_shop', 'music_store', 'musical_instrument_store', 'string_quartet', 'high_school_band']
senticnet['claws'] = ['0', '-0.01', '0.041', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'animal_part', 'paw', 'long_tail', 'sharp_tooth', 'fang']
senticnet['clay_animation'] = ['0.895', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.823', 'liveliness', 'excitement', 'art_form', 'art', 'film']
senticnet['clay_brick'] = ['0', '0', '-0.95', '-0.72', '#fear', '#disgust', 'negative', '-0.83', 'cannon', 'explosive', 'firearm', 'pistol', 'machine_gun']
senticnet['clay_extraction'] = ['0', '0', '-0.85', '-0.71', '#fear', '#disgust', 'negative', '-0.78', 'fair_game', 'pit', 'stone_pit', 'target', 'prey']
senticnet['clay_jar'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'wallet', 'billfold', 'bank', 'piggy_bank', 'bank_vault']
senticnet['clay_pit'] = ['0', '0', '-0.85', '-0.71', '#fear', '#disgust', 'negative', '-0.78', 'fair_game', 'pit', 'stone_pit', 'target', 'prey']
senticnet['clay_pot'] = ['0', '0.082', '-0.09', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'coffin', 'headstone', 'morgue', 'bury_body', 'casket']
senticnet['clay_pottery'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'fabrication', 'carving', 'decoupage', 'artistic_creation']
senticnet['clay_quarry'] = ['0', '0', '-0.85', '-0.71', '#fear', '#disgust', 'negative', '-0.78', 'fair_game', 'pit', 'stone_pit', 'target', 'prey']
senticnet['clay_tablet'] = ['0.719', '0.757', '0', '0', '#joy', '#interest', 'positive', '0.738', 'matriculate', 'enroll_class', 'enroll_school', 'enroll', 'register']
senticnet['clayey'] = ['-0.74', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'silty', 'loam', 'loamy', 'calcareous', 'subsoil']
senticnet['clayface'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'only_bird']
senticnet['claymation'] = ['0.895', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.823', 'liveliness', 'excitement', 'art_form', 'art', 'film']
senticnet['claymore'] = ['0.93', '-0.78', '0', '0.873', '#joy', '#admiration', 'positive', '0.861', 'blade', 'brand', 'steel', 'broadsword', 'sword']
senticnet['clazomenae'] = ['0.8', '-0.79', '0', '0.751', '#joy', '#surprise', 'positive', '0.782', 'elder_statesman', 'stateswoman', 'politician', 'politico', 'pol']
senticnet['clean'] = ['0.986', '0', '0', '0', '#joy', '#joy', 'positive', '0.986', 'personal_cleanliness', 'cleanness', 'wipe_up_spill', 'smell_good', 'clean_carpet']
senticnet['clean-living'] = ['0', '0.843', '0', '0.877', '#interest', '#admiration', 'positive', '0.86', 'wasp-y', 'honest-hearted', 'serious-minded', 'god-loving', 'levelheaded']
senticnet['clean_agent'] = ['0', '-0.91', '0', '0.943', '#surprise', '#admiration', 'positive', '0.93', 'hand_wash', 'hygiene', 'cleanser', 'body_care', 'foam']
senticnet['clean_air'] = ['0.85', '0', '0', '0', '#joy', '#joy', 'positive', '0.85', 'eat_fruit', 'accomplish_goal', 'healthy_environment', 'nice_place_to_live', 'calm_mind']
senticnet['clean_apartment'] = ['0.714', '0', '0', '0', '#joy', '#joy', 'positive', '0.714', 'call_up', 'kill_spider', 'clean_sink', 'clean_floor', 'tidy_up']
senticnet['clean_bathroom'] = ['0', '0', '0', '-0.82', '#disgust', '#disgust', 'negative', '-0.82', 'clean_house', 'clean_carpet', 'clean', 'clean_room', 'wipe_up_spill']
senticnet['clean_between_tooth'] = ['-0.05', '-0.01', '0.012', '-0.04', '#sadness', '#disgust', 'negative', '-0.02', 'remove_plaque', 'keep_tooth_clean', 'close_mouth', 'chew', 'suck']
senticnet['clean_body'] = ['0.076', '-0.00', '0', '0', '#joy', '#surprise', 'positive', '0.042', 'personal_hygiene', 'fresh', 'remove_dirt_body', 'shower', 'feel_refreshed']
senticnet['clean_car'] = ['0.769', '0', '0', '0.832', '#joy', '#admiration', 'positive', '0.8', 'clean_skin', 'run_out_soap', 'remove_dirt', 'clean_hair', 'cleanliness']
senticnet['clean_carpet'] = ['0', '0.733', '0', '0.784', '#interest', '#admiration', 'positive', '0.758', 'clean_floor', 'vacuum', 'keep_house_clean', 'expect_guest', 'clean_bathroom']
senticnet['clean_cloth'] = ['0.159', '0', '0.041', '0.174', '#joy', '#admiration', 'positive', '0.097', 'clean_hand', 'use_clean', 'clean_supply', 'cleanse', 'sweeper']
senticnet['clean_clothes'] = ['0', '0', '0', '0.068', '#admiration', '#admiration', 'positive', '0.068', 'become_clean', 'wash_clothes', 'take_shower', 'get_clean', 'clean_body']
senticnet['clean_dish'] = ['0', '0.784', '0', '0.818', '#interest', '#admiration', 'positive', '0.801', 'wash_utensil', 'wash_up', 'hygiene', 'wash_body', 'clean_self']
senticnet['clean_drinking_water'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'bottled_water', 'sugar_water']
senticnet['clean_energy'] = ['-0.79', '0', '0.997', '-0.68', '#sadness', '#anger', 'negative', '-0.82', 'energy', 'alternative_energy', 'solar_power', 'wind_generation', 'wind_power']
senticnet['clean_fish'] = ['0.029', '0', '0.042', '-0.07', '#anger', '#disgust', 'negative', '-0.02', 'fry_fish', 'make_coffee', 'make_ice', 'piss', 'relieve']
senticnet['clean_floor'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'cleanness', 'dust_furniture', 'sweep', 'vacuum_floor', 'clean']
senticnet['clean_hair'] = ['0.986', '0', '0', '0', '#joy', '#joy', 'positive', '0.986', 'clean_skin', 'cleanliness', 'run_out_soap', 'remove_dirt', 'clean_kitchen']
senticnet['clean_hand'] = ['0', '0', '0', '0.09', '#admiration', '#admiration', 'positive', '0.09', 'remove_dirt', 'wash_behind_ear', 'cleanse_body', 'clean_skin', 'cleaner']
senticnet['clean_house'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'clean_bathroom', 'clean_carpet', 'clean', 'clean_room', 'wipe_up_spill']
senticnet['clean_jerk'] = ['-0.75', '0', '0.794', '-0.58', '#sadness', '#anger', 'negative', '-0.71', 'jerk', 'weightlift', 'military_press', 'clean', 'bench_press']
senticnet['clean_kitchen'] = ['0.986', '0', '0', '0', '#joy', '#joy', 'positive', '0.986', 'cleanness', 'kill_germ', 'personal_cleanliness', 'clean_hair', 'clean']
senticnet['clean_lady'] = ['0', '0.073', '-0.08', '-0.08', '#fear', '#disgust', 'negative', '-0.03', 'cleaning', 'maid', 'lady', 'servant', 'staff']
senticnet['clean_laundry'] = ['0', '0.133', '0', '0.206', '#interest', '#admiration', 'positive', '0.169', 'clean_clothes', 'cleanse', 'washing_machine', 'remove_dirt', 'laundry']
senticnet['clean_linen'] = ['0', '0', '0', '0.068', '#admiration', '#admiration', 'positive', '0.068', 'become_clean', 'wash_clothes', 'take_shower', 'get_clean', 'clean_body']
senticnet['clean_mess'] = ['0.101', '0', '0.015', '0.063', '#joy', '#admiration', 'positive', '0.05', 'wipe_up_spill', 'kill_germ', 'clean_floor', 'clean', 'personal_cleanliness']
senticnet['clean_mouth'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'wipe', 'wipe_mouth', 'napkin', 'oral_cleaning', 'toothbrush']
senticnet['clean_often'] = ['-0.02', '0.165', '-0.08', '0.14', '#interest', '#admiration', 'positive', '0.05', 'maintenance', 'vacuum_carpet', 'use_vacuum_cleaner', 'tidy_up', 'vacuum_floor']
senticnet['clean_product'] = ['0.078', '0', '0', '0.06', '#joy', '#admiration', 'positive', '0.069', 'clean_agent', 'cleanser', 'hand_wash', 'use_clean', 'tv_drama']
senticnet['clean_room'] = ['0', '0', '0', '-0.82', '#disgust', '#disgust', 'negative', '-0.82', 'clean_house', 'clean_carpet', 'clean', 'clean_bathroom', 'wipe_up_spill']
senticnet['clean_saucepan'] = ['0.07', '0.084', '0.023', '0', '#joy', '#interest', 'positive', '0.044', 'hearty', 'porridge', 'hot_liquid', 'liquid_food', 'meat_vegetable']
senticnet['clean_self'] = ['0.783', '0', '0', '0.837', '#joy', '#admiration', 'positive', '0.81', 'take_bath', 'wash_up', 'hygiene', 'wash_body', 'bathing']
senticnet['clean_shaven'] = ['-0.64', '0', '0.632', '-0.61', '#sadness', '#anger', 'negative', '-0.62', 'bearded', 'barbate', 'bewhiskered', 'whiskered', 'whiskery']
senticnet['clean_shoes'] = ['0', '-0.95', '0', '0.87', '#surprise', '#admiration', 'positive', '0.914', 'paint_nail', 'make_pretty', 'remove_paint', 'find_home', 'sanitary']
senticnet['clean_sink'] = ['0', '0', '0', '-0.97', '#disgust', '#disgust', 'negative', '-0.97', 'sit_on_beach', 'pet_cat', 'absorb_moisture', 'mow_grass', 'wash_window']
senticnet['clean_skin'] = ['0.025', '-0.00', '0.013', '0.061', '#joy', '#admiration', 'positive', '0.019', 'run_out_soap', 'remove_dirt', 'wash_away_dirt', 'cleanse_body', 'cleanliness']
senticnet['clean_supply'] = ['0.159', '0', '0.041', '0.174', '#joy', '#admiration', 'positive', '0.097', 'clean_hand', 'use_clean', 'clean_cloth', 'cleanse', 'soap']
senticnet['clean_sweep'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'cleanness', 'dust_furniture', 'clean_floor', 'vacuum_floor', 'clean']
senticnet['clean_teeth'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'teeth_whitening', 'brush_tooth', 'clean_mouth', 'after_meal', 'dentist']
senticnet['clean_toilet'] = ['-0.12', '-0.01', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.06', 'clean_sink', 'dust_furniture', 'clean_floor', 'sit_on_beach', 'wash_window']
senticnet['clean_tooth'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'teeth_whitening', 'brush_tooth', 'clean_mouth', 'after_meal', 'dentist']
senticnet['clean_towel'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'hankie', 'kleenex', 'hanky', 'handkerchief', 'tissue']
senticnet['clean_underwear'] = ['0.998', '0', '0', '0', '#joy', '#joy', 'positive', '0.998', 'panties', 'lingerie', 'pantyhose', 'slack', 'prescription_medication']
senticnet['clean_up'] = ['0.935', '0', '0', '0', '#joy', '#joy', 'positive', '0.935', 'clean_up_kitchen', 'feel_dirty', 'remove_stain', 'stink', 'stink_mess']
senticnet['clean_up_kitchen'] = ['0.935', '0', '0', '0', '#joy', '#joy', 'positive', '0.935', 'clean_up', 'eat_sushi', 'make_dinner', 'eat_pudding', 'enjoy_meal']
senticnet['clean_up_mess'] = ['0.813', '0.832', '0', '0.874', '#interest', '#admiration', 'positive', '0.84', 'remove_stain', 'clean', 'clean_up', 'relief', 'clean_house']
senticnet['clean_water'] = ['0.718', '0', '0', '0', '#joy', '#joy', 'positive', '0.718', 'water_container', 'still_water', 'dampness', 'water', 'cool_water']
senticnet['clean_water_clean_air'] = ['0.228', '0.123', '-0.09', '0', '#joy', '#interest', 'positive', '0.084', 'spend_time_with_child', 'social', 'friendship', 'companionship', 'get_close']
senticnet['clean_water_supply'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'bottled_water', 'sugar_water']
senticnet['clean_window'] = ['-0.06', '-0.02', '0.021', '-0.06', '#sadness', '#disgust', 'negative', '-0.03', 'feel_dirty', 'remove_stain', 'clean_agent', 'cleanser', 'remove_dirt']
senticnet['cleanability'] = ['0.8', '-0.90', '0', '0.675', '#joy', '#surprise', 'positive', '0.794', 'indestructibility', 'tensile_strength', 'continuity', 'persistence', 'changelessness']
senticnet['cleanable'] = ['0.986', '0', '0', '0', '#joy', '#joy', 'positive', '0.986', 'personal_cleanliness', 'cleanness', 'wipe_up_spill', 'smell_good', 'clean_carpet']
senticnet['cleaned'] = ['0', '0.873', '0', '0', '#interest', '#interest', 'positive', '0.873', 'relax_time', 'reinvigorated', 'shower', 'feel_refreshed', 'recharge']
senticnet['cleaned_regularly'] = ['0.986', '0', '0', '0', '#joy', '#joy', 'positive', '0.986', 'personal_cleanliness', 'cleanness', 'wipe_up_spill', 'smell_good', 'clean_carpet']
senticnet['cleaned_up'] = ['0.72', '-0.76', '0', '0.695', '#joy', '#surprise', 'positive', '0.726', 'suck_up', 'blot', 'wipe_up', 'mop', 'sponge_up']
senticnet['cleaner'] = ['0.035', '0', '0', '0.057', '#joy', '#admiration', 'positive', '0.046', 'clean_hand', 'clean_product', 'remove_dirt', 'clean_skin', 'soapy']
senticnet['cleaner_fuel'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'gas', 'inert_gas', 'diesel', 'oil', 'combustible']
senticnet['cleanest'] = ['0.986', '0', '0', '0', '#joy', '#joy', 'positive', '0.986', 'personal_cleanliness', 'cleanness', 'wipe_up_spill', 'smell_good', 'clean_carpet']
senticnet['cleaning'] = ['0', '0.073', '-0.08', '-0.08', '#fear', '#disgust', 'negative', '-0.03', 'clean_lady', 'maid', 'lady', 'servant', 'staff']
senticnet['cleaning_fluid'] = ['0.939', '0.939', '0', '0', '#joy', '#interest', 'positive', '0.939', 'puddle', 'sop', 'liquid_water', 'finger_paint', 'bread']
senticnet['cleaning_lady'] = ['0.875', '0', '0', '0.802', '#joy', '#admiration', 'positive', '0.839', 'foreigner', 'exile', 'deportee', 'gringo', 'import']
senticnet['cleaning_up'] = ['0.72', '-0.76', '0', '0.695', '#joy', '#surprise', 'positive', '0.726', 'suck_up', 'blot', 'wipe_up', 'mop', 'sponge_up']
senticnet['cleanliness'] = ['0.033', '0', '0', '0.087', '#joy', '#admiration', 'positive', '0.06', 'wash_away_dirt', 'cleanness', 'personal_cleanliness', 'run_out_soap', 'get_rid_of_dirt']
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senticnet['cleanness'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'personal_cleanliness', 'cleanliness', 'clean', 'run_out_soap', 'wash_away_dirt']
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senticnet['climatic_variation'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'weather', 'sunny', 'spring', 'snow', 'four_seasons']
senticnet['climatically'] = ['0', '-0.80', '0', '0.784', '#surprise', '#admiration', 'positive', '0.792', 'geologically', 'morphologically', 'scenically', 'topographically', 'spatially']
senticnet['climatological'] = ['0', '0', '-0.72', '-0.94', '#fear', '#disgust', 'negative', '-0.83', 'hydrological', 'oceanographic', 'meteorology', 'climatic', 'meteorological']
senticnet['climax'] = ['0.655', '-0.87', '0', '0.595', '#joy', '#surprise', 'positive', '0.709', 'orgasm', 'climactic', 'peak', 'denouement', 'summit']
senticnet['climb'] = ['0', '0.166', '-0.13', '0.275', '#interest', '#admiration', 'positive', '0.102', 'mountain_climb_mountain', 'climb_mountain', 'chop_down_tree', 'reflect_water', 'store_garage']
senticnet['climb_aboard'] = ['-0.95', '0', '0.793', '0', '#sadness', '#anger', 'negative', '-0.87', 'block', 'obstacle', 'nodus', 'impediment', 'obstruction']
senticnet['climb_everest'] = ['0.518', '-0.51', '0', '0.65', '#surprise', '#admiration', 'positive', '0.562', 'clematis', 'legume', 'leguminous_plant', 'climbing_fern', 'root_climber']
senticnet['climb_hill'] = ['-0.05', '-0.04', '0.091', '-0.05', '#sadness', '#anger', 'negative', '-0.04', 'enjoy_nature', 'view_nature', 'expend_energy', 'little_pollution', 'strain_muscle']
senticnet['climb_ladder'] = ['-0.06', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.06', 'picture_scene', 'take_photograph', 'paint_ceiling', 'frame_picture', 'staircase']
senticnet['climb_mount'] = ['0', '0.076', '0', '0.063', '#interest', '#admiration', 'positive', '0.07', 'up_hill', 'elevation', 'up_mountain', 'ascend', 'upwards']
senticnet['climb_mount_everest'] = ['0.518', '-0.51', '0', '0.65', '#surprise', '#admiration', 'positive', '0.562', 'clematis', 'legume', 'leguminous_plant', 'climbing_fern', 'root_climber']
senticnet['climb_mountain'] = ['0', '0.166', '-0.13', '0.275', '#interest', '#admiration', 'positive', '0.102', 'mountain_climb', 'climb', 'chop_down_tree', 'reflect_water', 'store_garage']
senticnet['climb_onto'] = ['-0.05', '0.066', '0', '0.079', '#interest', '#admiration', 'positive', '0.029', 'skip', 'jump_high', 'float_through_air', 'jump_over', 'bail_water']
senticnet['climb_rock'] = ['0', '0', '-0.75', '0', '#fear', '#fear', 'negative', '-0.75', 'exercise_much', 'play_saxophone', 'dance_well', 'feel_faint', 'eat_healthy_diet']
senticnet['climb_stair'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'take_hike', 'exercise_daily', 'stay_up_night', 'skydive', 'staircase']
senticnet['climb_to_top'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'run', 'meet_greet', 'hot_summer', 'price_goods', 'appear_beautiful']
senticnet['climb_tree'] = ['0', '0.095', '0', '0.127', '#interest', '#admiration', 'positive', '0.111', 'upside_down', 'fun_activity', 'swing', 'nocturnal', 'tree_house']
senticnet['climb_up'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'walk_up', 'stroll', 'go_upstairs', 'go_up_down', 'move_between_floor_building']
senticnet['climbable'] = ['-0.99', '0', '0.641', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'navigable', 'negotiable', 'surmountable', 'passable', 'traversable']
senticnet['climbed_everest'] = ['0.518', '-0.51', '0', '0.65', '#surprise', '#admiration', 'positive', '0.562', 'clematis', 'legume', 'leguminous_plant', 'climbing_fern', 'root_climber']
senticnet['climbed_mount'] = ['0', '0.076', '0', '0.063', '#interest', '#admiration', 'positive', '0.07', 'up_hill', 'elevation', 'up_mountain', 'ascend', 'upwards']
senticnet['climber'] = ['0.518', '-0.51', '0', '0.65', '#surprise', '#admiration', 'positive', '0.562', 'clematis', 'legume', 'leguminous_plant', 'climbing_fern', 'root_climber']
senticnet['climbing_fern'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'clematis', 'legume', 'leguminous_plant', 'climber', 'root_climber']
senticnet['climbing_gear'] = ['0.518', '-0.51', '0', '0.65', '#surprise', '#admiration', 'positive', '0.562', 'clematis', 'legume', 'leguminous_plant', 'climbing_fern', 'root_climber']
senticnet['climbing_rope'] = ['0.518', '-0.51', '0', '0.65', '#surprise', '#admiration', 'positive', '0.562', 'clematis', 'legume', 'leguminous_plant', 'climbing_fern', 'root_climber']
senticnet['climbing_vine'] = ['0', '0', '-0.14', '-0.03', '#fear', '#disgust', 'negative', '-0.09', 'pine_cone', 'fallen_leaf', 'poison_ivy', 'detritus', 'tress']
senticnet['climbing_wall'] = ['0.076', '0.079', '0.027', '0.118', '#interest', '#admiration', 'positive', '0.061', 'outside', 'plant_place', 'plant_ground', 'ground', 'cultivate_yard']
senticnet['clinch'] = ['0.733', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.861', 'clinched', 'clinching', 'clinch', 'win', 'playoff']
senticnet['clinched'] = ['0.724', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.861', 'clinch', 'clinching', 'clinch', 'win', 'playoff']
senticnet['clinching'] = ['0.727', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.862', 'clinched', 'clinch', 'clinch', 'win', 'playoff']
senticnet['cling'] = ['0.51', '-0.56', '0', '0.768', '#surprise', '#admiration', 'positive', '0.613', 'hold', 'clasp', 'hold_on', 'latch_on', 'hang']
senticnet['cling_film'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'aluminium_foil', 'foil', 'gold_foil']
senticnet['cling_onto'] = ['0', '0.263', '-0.14', '0.295', '#interest', '#admiration', 'positive', '0.139', 'keep', 'hold', 'retain', 'hold_stuff', 'upkeep']
senticnet['cling_wrap'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'foil', 'tin_foil', 'gold_foil']
senticnet['clingfilm'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'foil', 'tin_foil', 'gold_foil']
senticnet['clinic'] = ['-0.03', '0', '-0.15', '-0.15', '#fear', '#disgust', 'negative', '-0.11', 'medical_office', 'hospital', 'doctor_office', 'emergency_room', 'nurse_home']
senticnet['clinical'] = ['0.85', '0.896', '0', '0', '#joy', '#interest', 'positive', '0.873', 'diagnosis', 'diagnostics', 'test', 'diagnostic', 'evaluation']
senticnet['clinical_depression'] = ['-0.99', '0', '0.982', '-0.62', '#sadness', '#anger', 'negative', '-0.86', 'dysthymia', 'dysthymic_depression', 'affective_disorder', 'major_affective_disorder', 'emotional_disorder']
senticnet['clinical_manifestation'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema', 'erythema_multiforme', 'windburn', 'hickey']
senticnet['clinical_medicine'] = ['-0.59', '0', '0.911', '-0.87', '#anger', '#disgust', 'negative', '-0.79', 'osteosclerosis', 'sclerosis', 'disseminated_sclerosis', 'multiple_sclerosis', 'induration']
senticnet['clinical_nurse'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'infant_feeding', 'schedule_feeding', 'demand_feeding', 'breast_feeding', 'feeding']
senticnet['clinical_nurse_leader'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'infant_feeding', 'schedule_feeding', 'demand_feeding', 'breast_feeding', 'feeding']
senticnet['clinical_nurse_specialist'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'infant_feeding', 'schedule_feeding', 'demand_feeding', 'breast_feeding', 'feeding']
senticnet['clinical_pathology'] = ['-0.59', '0', '0.911', '-0.87', '#anger', '#disgust', 'negative', '-0.79', 'osteosclerosis', 'sclerosis', 'disseminated_sclerosis', 'multiple_sclerosis', 'induration']
senticnet['clinical_pharmacy'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'infant_feeding', 'schedule_feeding', 'demand_feeding', 'breast_feeding', 'feeding']
senticnet['clinical_practice'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'infant_feeding', 'schedule_feeding', 'demand_feeding', 'breast_feeding', 'feeding']
senticnet['clinical_presentation'] = ['-0.58', '0', '0.814', '-0.54', '#sadness', '#anger', 'negative', '-0.64', 'abnormal', 'irregular', 'vicarious', 'supernormal', 'subnormal']
senticnet['clinical_problem'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'disease', 'heart_attack', 'worse', 'health_issue', 'obese']
senticnet['clinical_research'] = ['0.887', '0.877', '0', '0', '#joy', '#interest', 'positive', '0.882', 'ask_expert', 'absorb_information', 'analyze', 'learn_computer', 'search_information']
senticnet['clinical_supervision'] = ['-0.69', '0', '0.659', '-0.69', '#sadness', '#disgust', 'negative', '-0.68', 'help', 'paralegal', 'legal_assistant', 'paramedic', 'paramedical']
senticnet['clinical_syndrome'] = ['-0.58', '0', '0.814', '-0.54', '#sadness', '#anger', 'negative', '-0.64', 'abnormal', 'irregular', 'vicarious', 'supernormal', 'subnormal']
senticnet['clinically_obese'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'fatty', 'worse', 'health_issue', 'clinical_problem']
senticnet['clinically_overweight'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'fatty', 'worse', 'health_issue', 'clinical_problem']
senticnet['clinically_useful'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'hardcore', 'declared', 'explicit', 'graphic']
senticnet['clinician'] = ['0.072', '0', '0', '0.078', '#joy', '#admiration', 'positive', '0.075', 'participate', 'examine', 'voyeur', 'influence_public_decision', 'question_everything']
senticnet['clinicopathologic'] = ['0.798', '-0.51', '0', '0.519', '#joy', '#admiration', 'positive', '0.611', 'averageness', 'commonness', 'normalcy', 'typicality', 'condition']
senticnet['clinique'] = ['-0.93', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.85', 'protect_from_sun', 'shade', 'meaning', 'corrector', 'sunglasses']
senticnet['clink'] = ['-0.60', '0', '0.866', '-0.61', '#anger', '#disgust', 'negative', '-0.69', 'pokey', 'holding_cell', 'slammer', 'jail', 'jailhouse']
senticnet['clink_prison'] = ['0.754', '-0.82', '0', '0.947', '#surprise', '#admiration', 'positive', '0.841', 'whorehouse', 'brothel', 'bordello', 'massage_parlor', 'house_of_prostitution']
senticnet['clinker'] = ['0.962', '-0.70', '0', '0.522', '#joy', '#surprise', 'positive', '0.729', 'adobe', 'adobe_brick', 'brick', 'building_block', 'firebrick']
senticnet['clinker_brick'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'adobe', 'adobe_brick', 'clinker', 'brick', 'firebrick']
senticnet['clinodactyly'] = ['0.582', '-0.66', '0', '0.661', '#surprise', '#admiration', 'positive', '0.637', 'talipes_equinus', 'deformity', 'malformation', 'talipes_valgus', 'talipes_calcaneus']
senticnet['clinometer'] = ['0', '0.795', '0', '0.948', '#interest', '#admiration', 'positive', '0.871', 'protractor', 'inclinometer', 'accelerometer', 'inclinometer', 'magnetometer']
senticnet['clint_eastwood_million_dollar'] = ['0', '0', '0', '0.939', '#admiration', '#admiration', 'positive', '0.939', 'large_family', 'small_child', 'youth', 'quiet_baby', 'deliver_baby']
senticnet['clintonian'] = ['0.783', '-0.51', '0', '0.584', '#joy', '#admiration', 'positive', '0.627', 'appetent', 'avid', 'thirsty', 'devouring', 'envious']
senticnet['clip'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'paperclip', 'clasp', 'glue_stick', 'clothespin', 'clamp']
senticnet['clip_clop'] = ['0', '0', '0.752', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'get_hit_by_car', 'meander', 'full_moon', 'step_dog_poop', 'kick_ass']
senticnet['clipboard'] = ['0', '0', '0.98', '-0.78', '#anger', '#disgust', 'negative', '-0.88', 'eraser_holder', 'pencil_sharpener', 'stick_eraser', 'intern', 'electric_pencil_sharpener']
senticnet['clipped_by'] = ['0.777', '-0.85', '0', '0.615', '#joy', '#surprise', 'positive', '0.748', 'think', 'play', 'trifle', 'abstract', 'warm_to']
senticnet['clipper'] = ['0', '0', '0.74', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'scissor', 'razor', 'pair_of_scissors', 'haircut_scissor', 'double_edge_razor']
senticnet['clipper_card'] = ['-0.03', '0.29', '-0.12', '0.203', '#interest', '#admiration', 'positive', '0.083', 'excise', 'severance', 'wage', 'afford', 'revenue']
senticnet['clipper_chip'] = ['-0.97', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.90', 'chip', 'serial_number', 'clipper', 'court_order', 'escrow_agency']
senticnet['clipping_plane'] = ['0.777', '-0.85', '0', '0.615', '#joy', '#surprise', 'positive', '0.748', 'think', 'play', 'trifle', 'abstract', 'warm_to']
senticnet['clippy'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'clip', 'clasp', 'glue_stick', 'clothespin', 'clamp']
senticnet['clique'] = ['0.042', '0', '0', '0.067', '#joy', '#admiration', 'positive', '0.055', 'big_group', 'several_people', 'multitude', 'group', 'favorite']
senticnet['cliquishness'] = ['-0.94', '0', '0.687', '-0.75', '#sadness', '#disgust', 'negative', '-0.79', 'clannishness', 'snobbery', 'exclusiveness', 'arrogance', 'haughtiness']
senticnet['clit'] = ['0.739', '0.923', '0', '0', '#joy', '#interest', 'positive', '0.831', 'button', 'clitoris', 'release', 'push', 'push_button']
senticnet['clitic'] = ['-0.82', '0', '0.81', '-0.61', '#sadness', '#anger', 'negative', '-0.75', 'oblique', 'genitive', 'envy', 'possessive', 'jealous']
senticnet['clitoral'] = ['0', '-0.89', '0', '0.873', '#surprise', '#admiration', 'positive', '0.884', 'g-spot', 'clitoris', 'erogenous', 'perineum', 'genitals']
senticnet['clitoral_hood'] = ['0', '0', '0.021', '-0.06', '#anger', '#disgust', 'negative', '-0.04', 'skin', 'skeletal_system', 'shoulder', 'cuticle', 'groin']
senticnet['clitoral_orgasm'] = ['0.655', '-0.87', '0', '0.595', '#joy', '#surprise', 'positive', '0.709', 'erection', 'excitement', 'arousal', 'kill_brain_cell', 'feel_relieved']
senticnet['clitoral_stimulation'] = ['0.655', '-0.87', '0', '0.595', '#joy', '#surprise', 'positive', '0.709', 'erection', 'excitement', 'arousal', 'kill_brain_cell', 'feel_relieved']
senticnet['clitoris'] = ['0', '-0.89', '0', '0.874', '#surprise', '#admiration', 'positive', '0.883', 'g-spot', 'erogenous', 'clitoral', 'perineum', 'genitals']
senticnet['clive_barker'] = ['-0.79', '0', '0.885', '-0.76', '#sadness', '#anger', 'negative', '-0.81', 'infernal', 'mortal', 'dead_soul', 'dead_soule', 'living_dead']
senticnet['clive_brittain'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'coach', 'instructor', 'personal_trainer', 'fit_person', 'train']
senticnet['cloaca'] = ['0.947', '-0.81', '0', '0.656', '#joy', '#surprise', 'positive', '0.805', 'sexual_intercourse', 'intercourse', 'sex_act', 'hank_panky', 'coitus']
senticnet['cloak'] = ['0', '0', '0.955', '-0.76', '#anger', '#disgust', 'negative', '-0.86', 'winter_clothes', 'outerwear', 'coat', 'jacket', 'shirt']
senticnet['cloak-and-dagger'] = ['-0.89', '0', '0.816', '0', '#sadness', '#anger', 'negative', '-0.85', 'melodramatic', 'melodramatically', 'opera', 'cheese', 'melodrama']
senticnet['clobber'] = ['0.719', '0.71', '0', '0', '#joy', '#interest', 'positive', '0.714', 'lick', 'drub', 'squash_racket', 'bat', 'chiropteran']
senticnet['clock'] = ['0', '0', '0.95', '-0.86', '#anger', '#disgust', 'negative', '-0.90', 'watch', 'stopwatch', 'timepiece', 'wristwatch', 'find_pawnshop']
senticnet['clock_face'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'time', 'swiss_watch', 'tell_time', 'timekeeper', 'device_measure_time']
senticnet['clock_out'] = ['-0.04', '0', '-0.07', '-0.10', '#fear', '#disgust', 'negative', '-0.07', 'put_clothes_back', 'sit_back_relax', 'quit_work', 'make_sex', 'stop_work']
senticnet['clock_pulse'] = ['0', '0', '0.95', '-0.86', '#anger', '#disgust', 'negative', '-0.90', 'watch', 'stopwatch', 'timepiece', 'wristwatch', 'find_pawnshop']
senticnet['clock_signal'] = ['0', '0', '0.95', '-0.86', '#anger', '#disgust', 'negative', '-0.90', 'watch', 'stopwatch', 'timepiece', 'wristwatch', 'find_pawnshop']
senticnet['clock_synchronization'] = ['0.792', '-0.83', '0', '0.575', '#joy', '#surprise', 'positive', '0.734', 'connectedness', 'connection', 'consistency', 'continuity', 'link']
senticnet['clock_tick'] = ['0', '0', '0.95', '-0.86', '#anger', '#disgust', 'negative', '-0.90', 'watch', 'stopwatch', 'timepiece', 'wristwatch', 'find_pawnshop']
senticnet['clock_tower'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'city_hall', 'courthouse', 'subway_station', 'public_place', 'bus_station']
senticnet['clockface'] = ['0', '0', '0.95', '-0.86', '#anger', '#disgust', 'negative', '-0.90', 'watch', 'stopwatch', 'timepiece', 'wristwatch', 'find_pawnshop']
senticnet['clockmaker'] = ['0', '0.115', '0', '0.105', '#interest', '#admiration', 'positive', '0.11', 'farrier', 'miner', 'horseshoer', 'blacksmith', 'smith']
senticnet['clocktower'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'castle', 'bell_tower', 'spire', 'steeple', 'religious_place']
senticnet['clockwise_direction'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotate_movement', 'rotate', 'go_around', 'manoeuvre', 'full_circle']
senticnet['clockwise_motion'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotate_movement', 'rotate', 'go_around', 'manoeuvre', 'full_circle']
senticnet['clockwise_rotation'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotate_movement', 'rotate', 'go_around', 'manoeuvre', 'full_circle']
senticnet['clockwork_universe'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'force_unit', 'sthene', 'poundal']
senticnet['clod'] = ['0', '0', '-0.87', '-0.99', '#fear', '#disgust', 'negative', '-0.93', 'oaf', 'lummox', 'goon', 'stumblebum', 'lout']
senticnet['clog'] = ['0.092', '-0.19', '0.053', '0.089', '#joy', '#surprise', 'positive', '0.081', 'hairball', 'waste_water', 'false_tooth', 'still_water', 'go_lake']
senticnet['clog_artery'] = ['0', '0', '-0.96', '0', '#fear', '#fear', 'negative', '-0.96', 'world_hunger', 'eat', 'full_stomach', 'salivate', 'satisfy_crave']
senticnet['clogged'] = ['-0.83', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.84', 'block', 'obstruct', 'close', 'shutter', 'occluded']
senticnet['cloistered'] = ['-0.94', '0', '0.77', '-0.86', '#sadness', '#disgust', 'negative', '-0.86', 'eremitic', 'eremitical', 'unworldly', 'anchoritic', 'hermitic']
senticnet['cloistered_life'] = ['-0.94', '0', '0.779', '-0.85', '#sadness', '#disgust', 'negative', '-0.86', 'eremitic', 'eremitical', 'cloistered', 'anchoritic', 'unworldly']
senticnet['clon'] = ['0.027', '0.111', '-0.05', '0.167', '#interest', '#admiration', 'positive', '0.063', 'price_product', 'draw', 'fillet_fish', 'design', 'create']
senticnet['clone'] = ['0', '0.143', '0', '0.264', '#interest', '#admiration', 'positive', '0.204', 'replicate', 'imitation', 'duplicate', 'jot', 'make_copy']
senticnet['clone_stamp'] = ['0.883', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.864', 'stick_eraser', 'ballpoint_pen', 'intern', 'paper_clip', 'eraser_holder']
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senticnet['cloudiness'] = ['-0.92', '0', '0.929', '-0.56', '#sadness', '#anger', 'negative', '-0.80', 'overcast', 'weather', 'raw_weather', 'storminess', 'inclemency']
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senticnet['cloudless_sky'] = ['0.042', '0.126', '-0.06', '0.165', '#interest', '#admiration', 'positive', '0.067', 'sparkle', 'shiny', 'shinny', 'gray', 'shine']
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senticnet['cloudy_sky'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'hazy', 'blur', 'bleary', 'blurred', 'blurry']
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senticnet['clout'] = ['-0.94', '0', '0.863', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'poke', 'knockout_punch', 'lick', 'punch', 'biff']
senticnet['clove'] = ['0', '0', '-0.76', '-0.71', '#fear', '#disgust', 'negative', '-0.73', 'cumin', 'cilantro', 'cardamom', 'turmeric', 'coriander']
senticnet['clove_garlic'] = ['-0.05', '-0.03', '0.023', '-0.09', '#sadness', '#disgust', 'negative', '-0.03', 'moldy_food', 'spam', 'paper_product', 'orange_juice', 'plant']
senticnet['clove_oil'] = ['0.701', '-0.76', '0', '0.904', '#surprise', '#admiration', 'positive', '0.791', 'curative', 'heal', 'medicine', 'get_cured', 'take_care']
senticnet['clover'] = ['0', '0.067', '0', '0.05', '#interest', '#admiration', 'positive', '0.059', 'bloom', 'flower', 'tulip', 'rose', 'daffodil']
senticnet['clover_hill'] = ['0', '0.113', '0', '0.111', '#interest', '#admiration', 'positive', '0.112', 'animal_house', 'cow_home', 'animal_place', 'crop_place', 'acreage']
senticnet['clover_trifolium'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'meadow_grass', 'grass', 'manna_grass', 'sweet_grass', 'wood_meadowgrass']
senticnet['cloverleaf_interchange'] = ['0.032', '0.116', '0.016', '0', '#joy', '#interest', 'positive', '0.044', 'curb', 'crossroad', 'ring_road', 'lane', 'interstate']
senticnet['clown'] = ['0.954', '0', '0', '0', '#joy', '#joy', 'positive', '0.954', 'circus', 'halloween', 'entertainment', 'fun', 'happiness']
senticnet['clown_nose'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'hankie', 'kleenex', 'facial_tissue', 'handkerchief', 'tissue']
senticnet['clown_suit'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'naked_eye', 'eye', 'oculus_sinister']
senticnet['clowning'] = ['0.662', '-0.80', '0', '0.651', '#joy', '#surprise', 'positive', '0.707', 'folly', 'lunacy', 'craziness', 'indulgence', 'shtik']
senticnet['clownish'] = ['-0.77', '0', '0.626', '-0.78', '#sadness', '#disgust', 'negative', '-0.72', 'bumpy', 'unsmooth', 'lepidote', 'leprose', 'roughish']
senticnet['clownishly'] = ['-0.78', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.80', 'malapropism', 'absurd', 'irrationally', 'absurdly', 'ridicule']
senticnet['cloying'] = ['0.789', '0', '0', '0.839', '#joy', '#admiration', 'positive', '0.814', 'sickeningly', 'overpoweringly', 'cloyingly', 'overbearingly', 'treacly']
senticnet['cloyingly'] = ['0.792', '0', '0', '0.847', '#joy', '#admiration', 'positive', '0.82', 'sickeningly', 'overpoweringly', 'cloying', 'overbearingly', 'treacly']
senticnet['cloze_procedure'] = ['0.747', '-0.58', '0', '0.92', '#joy', '#admiration', 'positive', '0.749', 'assay', 'biopsy', 'diagnostic_assay', 'cloze_test', 'fecal_occult_test']
senticnet['cloze_test'] = ['0.747', '-0.58', '0', '0.92', '#joy', '#admiration', 'positive', '0.749', 'assay', 'biopsy', 'cloze_procedure', 'diagnostic_assay', 'fecal_occult_test']
senticnet['club'] = ['0.202', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.124', 'bart', 'bar', 'fraternity', 'metal_stick', 'tavern']
senticnet['club_chair'] = ['0.918', '0.756', '0', '0', '#joy', '#interest', 'positive', '0.837', 'sofa_bed', 'armchair', 'sofa_hide_bed', 'chair', 'rock_chair']
senticnet['clubber'] = ['0.884', '0', '0', '0', '#joy', '#joy', 'positive', '0.884', 'egalitarian', 'enjoy', 'drink', 'have_fun', 'senator']
senticnet['clubbing'] = ['-0.72', '0', '-0.98', '0', '#sadness', '#fear', 'negative', '-0.85', 'nightclub', 'attraction', 'restaurant', 'pub', 'nightlife']
senticnet['clubbing_baby'] = ['-0.94', '0', '0.569', '-0.72', '#sadness', '#disgust', 'negative', '-0.74', 'bitumastic', 'veneer', 'veneering', 'adhesive', 'coating']
senticnet['clubface'] = ['0', '0', '0.061', '-0.03', '#anger', '#disgust', 'negative', '-0.04', 'garden_tool', 'straight_line', 'sharp', 'razor_blade', 'chock']
senticnet['clubfoot'] = ['-0.85', '0', '0.695', '-0.65', '#sadness', '#anger', 'negative', '-0.73', 'affliction', 'pigeon_breast', 'chicken_breast', 'deformity', 'talipes']
senticnet['clubgoer'] = ['0.884', '0', '0', '0', '#joy', '#joy', 'positive', '0.884', 'egalitarian', 'enjoy', 'drink', 'have_fun', 'senator']
senticnet['clubhead'] = ['0', '0', '0.061', '-0.03', '#anger', '#disgust', 'negative', '-0.04', 'garden_tool', 'straight_line', 'sharp', 'razor_blade', 'chock']
senticnet['cluck'] = ['0.039', '0.089', '-0.08', '0.143', '#interest', '#admiration', 'positive', '0.048', 'bird', 'nightingale', 'stork', 'eagle', 'rooster']
senticnet['clue'] = ['0', '0.161', '-0.11', '0.189', '#interest', '#admiration', 'positive', '0.079', 'find_play', 'verbosity', 'hint', 'opponent', 'option']
senticnet['clued_up'] = ['0.746', '-0.67', '0', '0.529', '#joy', '#surprise', 'positive', '0.651', 'labor', 'cinch', 'undertaking', 'project', 'task']
senticnet['cluelessly'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['cluemaster'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'only_bird']
senticnet['clump'] = ['-0.51', '0', '0.551', '-0.81', '#anger', '#disgust', 'negative', '-0.62', 'knot', 'swad', 'tuft', 'tussock', 'agglomeration']
senticnet['clump_together'] = ['-0.11', '0.095', '-0.11', '0.071', '#sadness', '#fear', 'negative', '-0.01', 'violent_action', 'pulverize', 'break_glass', 'tear_apart', 'hit_hard']
senticnet['clumped_together'] = ['-0.55', '0', '0.869', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'air_bubble', 'foam', 'froth', 'soap_bubble', 'bubble']
senticnet['clumping_habit'] = ['-0.56', '0', '0.617', '-0.80', '#anger', '#disgust', 'negative', '-0.66', 'prolixity', 'prolixness', 'wordiness', 'repetitiveness', 'repetitiousness']
senticnet['clumsiness'] = ['0.983', '-0.63', '0', '0.697', '#joy', '#admiration', 'positive', '0.772', 'amateurishness', 'inability', 'ineptness', 'unskillfulness', 'awkwardness']
senticnet['clumsy'] = ['-0.76', '0', '0.994', '-0.60', '#sadness', '#anger', 'negative', '-0.78', 'unfortunate', 'inapt', 'inept', 'awkward', 'infelicitous']
senticnet['clumsy_person'] = ['-0.71', '0', '0.845', '-0.69', '#sadness', '#anger', 'negative', '-0.75', 'lump', 'goon', 'mortal', 'duffer', 'butterfingers']
senticnet['clunker'] = ['0', '0', '-0.97', '-0.81', '#fear', '#disgust', 'negative', '-0.89', 'decrepit', 'creaky', 'flea-bitten', 'crazy', 'mangy']
senticnet['clunky'] = ['0', '0', '0.99', '-0.72', '#anger', '#disgust', 'negative', '-0.85', 'cumbrous', 'ungainly', 'clumsy', 'cumbersome', 'awkward']
senticnet['clusia'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'seed_fern', 'pteridosperm']
senticnet['cluster'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'bunch', 'several_people', 'clique', 'big_group', 'group']
senticnet['cluster_headache'] = ['-0.73', '0', '-0.85', '-0.72', '#sadness', '#fear', 'negative', '-0.77', 'lay_off', 'hurt', 'pain_suffer', 'lose_mind', 'forget_everything']
senticnet['clustered_lady_slipper'] = ['0.277', '0.161', '0', '0.337', '#joy', '#admiration', 'positive', '0.258', 'moccasin_flower', 'nerveroot', 'showy_lady_slipper', 'yellow_lady_slipper', 'lady_slipper']
senticnet['clustered_together'] = ['-0.1', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.1', 'deflate', 'read_cover', 'look_behind', 'upgrade', 'scan']
senticnet['clustering'] = ['-0.64', '0', '0.637', '-0.88', '#sadness', '#disgust', 'negative', '-0.72', 'knot', 'swad', 'tuft', 'tussock', 'agglomeration']
senticnet['clutch'] = ['0', '-0.03', '0', '0.064', '#surprise', '#admiration', 'positive', '0.049', 'bumper', 'fan_belt', 'brake_pedal', 'emergency_brake', 'windshield']
senticnet['clutch_bag'] = ['0', '0.084', '0', '0', '#interest', '#interest', 'positive', '0.084', 'credit_card_wallet', 'checkbook_holder', 'pouch', 'bag', 'coin_purse']
senticnet['clutter'] = ['0.035', '0.076', '0', '0.067', '#interest', '#admiration', 'positive', '0.059', 'postal_counter', 'paperwork', 'coworker', 'white-out', 'stapler']
senticnet['clybourne_park'] = ['0', '0.066', '-0.05', '0.089', '#interest', '#admiration', 'positive', '0.032', 'pineapple', 'kale', 'banana', 'pickle', 'tomato']
senticnet['clyse'] = ['-0.77', '0', '0.583', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'rain', 'rain_down', 'sheet', 'rain_bucket', 'rain_buckets_down']
senticnet['cm_inch_tall'] = ['-0.80', '0', '0.555', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'bush', 'wormwood', 'milkwort', 'germander', 'centaury']
senticnet['cmdicely'] = ['0', '0', '-0.86', '-0.99', '#fear', '#disgust', 'negative', '-0.93', 'niggle', 'quiddity', 'bicker', 'pettifog', 'cavil']
senticnet['cmll_anniversary_show'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'find_out', 'dig_out', 'unearth', 'become_aware', 'dish_out']
senticnet['cmon'] = ['0', '0', '-0.87', '-0.88', '#fear', '#disgust', 'negative', '-0.88', 'exclamation', 'thumb_down', 'complaint', 'bad', 'dislike']
senticnet['cnen'] = ['-0.90', '0', '0.748', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'alternative_energy', 'activation_energy', 'energy_of_activation', 'energy', 'nuclear_energy']
senticnet['cnf_formula'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly']
senticnet['cnidarian'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'annelid', 'annelid_worm', 'leech']
senticnet['cnidocil'] = ['-0.99', '0', '0.651', '-0.51', '#sadness', '#anger', 'negative', '-0.72', 'sending', 'compulsion', 'influence', 'inducement', 'causation']
senticnet['cnn'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'tv_program', 'daily', 'eat_breakfast', 'wake_up', 'medium']
senticnet['cnn_alla_eshchenko'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'dejectedness', 'low-spiritedness', 'lowness', 'downheartedness', 'cheerless']
senticnet['cnn_gabriella_schwarz'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'dejectedness', 'low-spiritedness', 'lowness', 'downheartedness', 'cheerless']
senticnet['cnn_gul_tuysuz'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'dejectedness', 'low-spiritedness', 'lowness', 'downheartedness', 'cheerless']
senticnet['cnn_jomana_karadsheh'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'dejectedness', 'low-spiritedness', 'lowness', 'downheartedness', 'cheerless']
senticnet['cnn_mohammed_jamjoom'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'dejectedness', 'low-spiritedness', 'lowness', 'downheartedness', 'cheerless']
senticnet['cnn_mohammed_tawfeeq'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'dejectedness', 'low-spiritedness', 'lowness', 'downheartedness', 'cheerless']
senticnet['cnn_roba_alhenawi'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'dejectedness', 'low-spiritedness', 'lowness', 'downheartedness', 'cheerless']
senticnet['cnn_zain_verjee'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'dejectedness', 'low-spiritedness', 'lowness', 'downheartedness', 'cheerless']
senticnet['cnngo'] = ['0.071', '0', '0.031', '0.064', '#joy', '#admiration', 'positive', '0.035', 'far_from_home', 'immigrant', 'live_abroad', 'connoisseur', 'work_abroad']
senticnet['cns_depression'] = ['-0.58', '0', '0.773', '-0.79', '#anger', '#disgust', 'negative', '-0.71', 'perspiration', 'sweating', 'polyhidrosis', 'sudation', 'hidrosis']
senticnet['cns_involvement'] = ['-0.60', '0', '0.544', '-0.53', '#sadness', '#anger', 'negative', '-0.56', 'acute_renal_failure', 'acute_kidney_failure', 'chronic_renal_failure', 'chronic_kidney_failure', 'kidney_failure']
senticnet['cns_stimulant'] = ['0.672', '0.632', '-0.23', '0.312', '#joy', '#interest', 'positive', '0.344', 'euphoria', 'buoyancy', 'perkiness', 'jubilance', 'ecstasy']
senticnet['cnut'] = ['-0.92', '0', '0.659', '-0.81', '#sadness', '#disgust', 'negative', '-0.8', 'unripe', 'napping', 'off_guard', 'off_one_guard', 'unprepared']
senticnet['co-occurrence'] = ['0.922', '0.721', '0', '0', '#joy', '#interest', 'positive', '0.822', 'junction', 'continuative', 'concurrence', 'conjunction', 'conjugation']
senticnet['co-op'] = ['0', '0', '-0.91', '-0.89', '#fear', '#disgust', 'negative', '-0.90', 'cooperative', 'concerted', 'conjunct', 'conjunctive', 'accommodative']
senticnet['co-star'] = ['0.985', '-0.78', '0', '0', '#joy', '#surprise', 'positive', '0.887', 'co-star', 'costars', 'actor', 'co-starring', 'actress']
senticnet['co-starring'] = ['0.984', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.887', 'co-star', 'costars', 'actor', 'co-star', 'actress']
senticnet['co-worker'] = ['0.158', '0.137', '-0.11', '0.184', '#joy', '#admiration', 'positive', '0.092', 'two_people', 'partner', 'group_of_people', 'husband_wife', 'sing_microphone']
senticnet['coach'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'trainer', 'instructor', 'personal_trainer', 'fit_person', 'train']
senticnet['coach_geoff_toovey'] = ['0', '0.996', '0', '0.85', '#interest', '#admiration', 'positive', '0.923', 'strength', 'strong', 'physical_strength', 'athlete', 'muscle']
senticnet['coach_graham_steadman'] = ['-0.85', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.86', 'attack', 'raid', 'fight_against', 'conflict', 'opposition']
senticnet['coach_seat'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'couch', 'stool', 'seat']
senticnet['coachman'] = ['0.979', '-0.69', '0', '0.576', '#joy', '#surprise', 'positive', '0.749', 'big_cheese', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['coachwhip'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'elapid', 'ophidian', 'serpent', 'krait', 'taipan']
senticnet['coadministration'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'protease_inhibitor', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['coadunate'] = ['0.812', '0', '0', '0.781', '#joy', '#admiration', 'positive', '0.796', 'adjoin', 'abut', 'juxtapose', 'melt', 'weave']
senticnet['coagulate'] = ['0', '0', '0.961', '-0.95', '#anger', '#disgust', 'negative', '-0.95', 'curdled', 'curdle', 'congeal', 'thicken', 'curdles']
senticnet['coagulum'] = ['-0.92', '0', '0.783', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'clot', 'lump', 'gob', 'clew', 'agglomeration']
senticnet['coal'] = ['0', '0', '-0.72', '-0.83', '#fear', '#disgust', 'negative', '-0.78', 'diesel', 'fuel', 'kerosene', 'gas', 'inert_gas']
senticnet['coal_ash'] = ['-0.81', '0', '0.987', '-0.64', '#sadness', '#anger', 'negative', '-0.81', 'burn_garbage', 'eliminate_garbage', 'incinerate_waste', 'dump', 'garbage_dump']
senticnet['coal_black'] = ['-0.60', '0', '0.895', '-0.54', '#sadness', '#anger', 'negative', '-0.68', 'black', 'jet_black', 'pitch_black', 'soot_black', 'achromatic_color']
senticnet['coal_fired_plant'] = ['-0.81', '0', '0.987', '-0.64', '#sadness', '#anger', 'negative', '-0.81', 'burn_garbage', 'eliminate_garbage', 'incinerate_waste', 'dump', 'garbage_dump']
senticnet['coal_magnate'] = ['0', '0', '-0.99', '-0.73', '#fear', '#disgust', 'negative', '-0.86', 'king', 'baron', 'business_leader', 'mogul', 'big_businessman']
senticnet['coal_mining_town'] = ['-0.55', '0', '0.912', '-0.65', '#anger', '#disgust', 'negative', '-0.70', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['coal_pit'] = ['-0.78', '0', '0.699', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'battery', 'stamp_battery', 'quartz_battery', 'quartz_mill', 'factory']
senticnet['coal_scuttle'] = ['0.979', '-0.69', '0', '0.576', '#joy', '#surprise', 'positive', '0.749', 'big_cheese', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['coal_seam'] = ['-0.04', '-0.01', '0.026', '-0.12', '#sadness', '#disgust', 'negative', '-0.04', 'embroidery', 'cover_wind', 'use_needle', 'bandage', 'joint']
senticnet['coal_stove'] = ['0.088', '0.076', '0.016', '0.094', '#joy', '#admiration', 'positive', '0.06', 'copper_wire', 'stove', 'several_types', 'oven', 'suspect']
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senticnet['coarse'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'harsh', 'common', 'rough-cut', 'uncouth', 'vulgar']
senticnet['coarse-grained'] = ['0', '0', '-0.76', '-0.78', '#fear', '#disgust', 'negative', '-0.77', 'mealy', 'gritty', 'grainy', 'farinaceous', 'granular']
senticnet['coarse_bread'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'green_bread', 'rotten', 'disgusting', 'taste_awful', 'mouldy']
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senticnet['coarse_gravel'] = ['-0.06', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.04', 'friction', 'lack_of_water', 'dirt', 'abrasive', 'ground_material']
senticnet['coarse_hair'] = ['0.352', '0.142', '0', '0.281', '#joy', '#admiration', 'positive', '0.258', 'knowledgeable', 'complement', 'much_need', 'see_ocean', 'good_sense_of_humor']
senticnet['coarse_sand'] = ['-0.06', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.04', 'friction', 'lack_of_water', 'dirt', 'abrasive', 'ground_material']
senticnet['coarse_texture'] = ['-0.50', '0', '0.85', '-0.82', '#anger', '#disgust', 'negative', '-0.72', 'confusion', 'disarray', 'mental_confusion', 'confusedness', 'bafflement']
senticnet['coarse_woollen'] = ['-0.54', '0', '0.7', '-0.64', '#anger', '#disgust', 'negative', '-0.62', 'flourish', 'rant', 'bombast', 'ornateness', 'claptrap']
senticnet['coarsely'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'harsh', 'common', 'rough-cut', 'uncouth', 'vulgar']
senticnet['coarsely_granular'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'harsh', 'common', 'rough-cut', 'uncouth', 'vulgar']
senticnet['coarsely_toothed_margin'] = ['-0.50', '0', '0.573', '-0.89', '#anger', '#disgust', 'negative', '-0.65', 'petallike', 'bowery', 'leafy', 'unifoliate', 'bifoliate']
senticnet['coarseness'] = ['0.989', '-0.99', '0', '0.728', '#joy', '#surprise', 'positive', '0.905', 'quality', 'tastelessness', 'sleaziness', 'inelegance', 'commonness']
senticnet['coarser_material'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'harsh', 'common', 'rough-cut', 'uncouth', 'vulgar']
senticnet['coarsest'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'harsh', 'common', 'rough-cut', 'uncouth', 'vulgar']
senticnet['coarticulation'] = ['-0.67', '0', '0.97', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'joint', 'node', 'connexion', 'join', 'juncture']
senticnet['coast'] = ['0.03', '0.117', '0', '0.084', '#interest', '#admiration', 'positive', '0.077', 'ocean_beach', 'waterfront', 'ocean', 'beach', 'sea']
senticnet['coast_aero'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['coastal_erosion'] = ['-0.88', '0', '0.907', '-0.52', '#sadness', '#anger', 'negative', '-0.77', 'wearing', 'corrasion', 'eroding', 'eating_away', 'wearing_away']
senticnet['coastal_fog'] = ['-0.09', '0', '-0.05', '-0.24', '#sadness', '#disgust', 'negative', '-0.13', 'smoke', 'smog', 'haze', 'mist', 'cloudy']
senticnet['coastal_headland'] = ['0', '0.08', '0.032', '0.096', '#interest', '#admiration', 'positive', '0.048', 'land_form', 'land_formation', 'land_feature', 'geographic_feature', 'landscape_feature']
senticnet['coastal_highway'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'artery', 'skeletal_system', 'groin', 'neck', 'muscle']
senticnet['coastal_ocean'] = ['0.078', '-0.03', '-0.04', '0.105', '#joy', '#admiration', 'positive', '0.043', 'aquarium', 'underwater', 'pacific_ocean', 'sea', 'ocean_wave']
senticnet['coastal_resort'] = ['0.203', '0.077', '0', '0.189', '#joy', '#admiration', 'positive', '0.156', 'good_life', 'coast', 'relax', 'sediment', 'shell']
senticnet['coastal_scenery'] = ['0.896', '0', '0', '0.709', '#joy', '#admiration', 'positive', '0.802', 'perspective', 'vista', 'view', 'aspect', 'see_view']
senticnet['coastal_town'] = ['0.203', '0.077', '0', '0.189', '#joy', '#admiration', 'positive', '0.156', 'good_life', 'coast', 'relax', 'sediment', 'shell']
senticnet['coaster'] = ['0', '-0.04', '0.061', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'computer_monitor', 'address_book', 'post_note', 'glue_stick', 'post-it_note']
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senticnet['coat'] = ['0.053', '-0.01', '-0.05', '0.121', '#joy', '#admiration', 'positive', '0.034', 'adorn', 'winter_clothes', 'stylish', 'outerwear', 'protective_cover']
senticnet['coat_closet'] = ['0', '0.072', '0', '0', '#interest', '#interest', 'positive', '0.072', 'metal_box', 'cash_drawer', 'keyhole', 'clutch_bag', 'safe']
senticnet['coat_collar'] = ['0.028', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.047', 'cowhide', 'kerchief', 'hanky', 'facial_tissue', 'zipper']
senticnet['coat_color'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'blonde', 'purple', 'violet', 'dark_light', 'hue']
senticnet['coat_hanger'] = ['0', '0', '0.875', '-0.87', '#anger', '#disgust', 'negative', '-0.87', 'gay_person', 'hanger', 'hook', 'slipper', 'bikini']
senticnet['coat_of_mail'] = ['0.939', '-0.63', '0', '0.653', '#joy', '#admiration', 'positive', '0.741', 'vest', 'waistcoat', 'body_armor', 'suit_of_armor', 'bulletproof_vest']
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senticnet['coat_sleeve'] = ['-0.71', '0', '0.954', '-0.56', '#sadness', '#anger', 'negative', '-0.74', 'jackknife', 'pocket_knife', 'penknife', 'switchblade', 'switchblade_knife']
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senticnet['coat_wall'] = ['0.035', '-0.05', '0', '0.101', '#surprise', '#admiration', 'positive', '0.063', 'dry', 'coat_surface', 'artist_tool', 'palette', 'paint']
senticnet['coated_paper'] = ['0.022', '-0.05', '0.079', '-0.11', '#anger', '#disgust', 'negative', '-0.02', 'stationery', 'ink', 'ball_point_pen', 'refill', 'note']
senticnet['coating'] = ['-0.94', '0', '0.569', '-0.72', '#sadness', '#disgust', 'negative', '-0.74', 'bitumastic', 'veneer', 'veneering', 'covering', 'seal']
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senticnet['coax'] = ['0', '0', '-0.83', '-0.74', '#fear', '#disgust', 'negative', '-0.78', 'wheedle', 'palaver', 'cajole', 'inveigle', 'blarney']
senticnet['coaxial_cable'] = ['0.085', '0', '0', '0', '#joy', '#joy', 'positive', '0.085', 'wire', 'several_types', 'gas_stove', 'tape_player', 'control_room']
senticnet['coaxially'] = ['-0.05', '0.068', '-0.10', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'tooth_decay', 'body_ache', 'symptom', 'pierce', 'tooth']
senticnet['cobalamin'] = ['-0.54', '0', '0.92', '-0.84', '#anger', '#disgust', 'negative', '-0.76', 'dearth', 'demand', 'mineral_deficiency', 'famine', 'need']
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senticnet['cobalt_oxide'] = ['-0.05', '-0.00', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'pigment', 'many_shades', 'cyan', 'green_yellow', 'material']
senticnet['cobaltite'] = ['0.555', '-0.99', '0', '0.851', '#surprise', '#admiration', 'positive', '0.8', 'quartz', 'cerussite', 'white_lead_ore', 'mineral', 'molybdenite']
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senticnet['cobble_together'] = ['0', '0.211', '-0.12', '0.286', '#interest', '#admiration', 'positive', '0.125', 'bring_together', 'fuse', 'redo', 'fuse_together', 'request']
senticnet['cobbled_together'] = ['0', '0.211', '-0.12', '0.286', '#interest', '#admiration', 'positive', '0.125', 'bring_together', 'fuse', 'redo', 'fuse_together', 'request']
senticnet['cobbler'] = ['-0.07', '0.228', '0.019', '0.089', '#interest', '#admiration', 'positive', '0.056', 'home_depot', 'lumberyard', 'furniture_store', 'front_porch', 'carpet_store']
senticnet['cobbling_together'] = ['0', '0.211', '-0.12', '0.286', '#interest', '#admiration', 'positive', '0.125', 'bring_together', 'fuse', 'redo', 'fuse_together', 'request']
senticnet['cobham_park'] = ['0', '0.179', '-0.05', '0.162', '#interest', '#admiration', 'positive', '0.095', 'house_land', 'real_estate', 'acreage', 'house_place', 'good_investment']
senticnet['cobol'] = ['0', '-0.99', '0', '0.862', '#surprise', '#admiration', 'positive', '0.928', 'helpful_tool', 'woodworking_tool', 'helpful', 'type_of_fruit', 'hand_tool']
senticnet['cobol_code'] = ['0', '-0.99', '0', '0.862', '#surprise', '#admiration', 'positive', '0.928', 'helpful_tool', 'woodworking_tool', 'helpful', 'type_of_fruit', 'hand_tool']
senticnet['cobol_compiler'] = ['0', '-0.99', '0', '0.862', '#surprise', '#admiration', 'positive', '0.928', 'helpful_tool', 'woodworking_tool', 'helpful', 'type_of_fruit', 'hand_tool']
senticnet['cobra'] = ['0', '0.11', '0', '0.077', '#interest', '#admiration', 'positive', '0.093', 'snake', 'python', 'reptile', 'animal', 'rattlesnake']
senticnet['cobra_island'] = ['-0.98', '0', '0.714', '-0.90', '#sadness', '#disgust', 'negative', '-0.86', 'stupid', 'stupid_person', 'stupe', 'dullard', 'dolt']
senticnet['cobweb'] = ['0', '0', '-0.75', '-0.74', '#fear', '#disgust', 'negative', '-0.75', 'storage_box', 'undead', 'dust', 'wine_bottle', 'read']
senticnet['coca_cola'] = ['0.964', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.964', 'carbonated_drink', 'sugar_free', 'coke', 'diet_coke', 'beverage']
senticnet['coca_cola_classic'] = ['0.964', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.964', 'carbonated_drink', 'sugar_free', 'coke', 'diet_coke', 'beverage']
senticnet['coca_cola_company'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'glass_water', 'refreshed', 'smoothie', 'lemonade', 'cold_water']
senticnet['coca_cola_zero'] = ['0.964', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.964', 'carbonated_drink', 'sugar_free', 'coke', 'diet_coke', 'beverage']
senticnet['cocaethylene'] = ['0.95', '-0.95', '0', '0.769', '#joy', '#surprise', 'positive', '0.89', 'adrenocorticotropic', 'adrenocorticotrophic', 'stimulant', 'stimulating', 'stimulative']
senticnet['cocaine'] = ['0.022', '-0.13', '0.153', '-0.31', '#anger', '#disgust', 'negative', '-0.07', 'codeine', 'morphine', 'infection', 'hash', 'nuclear_war']
senticnet['cocaine_abstinence'] = ['0', '0', '0.973', '-0.78', '#anger', '#disgust', 'negative', '-0.87', 'chastity', 'ignore_conception', 'restrain', 'abortion', 'stop']
senticnet['cocaine_addict'] = ['0', '0', '0.821', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'student', 'serious_student', 'addiction', 'graduate_student', 'hard_worker']
senticnet['cocaine_dealer'] = ['0.931', '-0.71', '0', '0.63', '#joy', '#surprise', 'positive', '0.758', 'hustle', 'con', 'bunco', 'sting', 'bunco_game']
senticnet['cocaine_habit'] = ['0', '0', '0.821', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'student', 'serious_student', 'addiction', 'graduate_student', 'hard_worker']
senticnet['cocaine_hcl'] = ['-0.63', '0', '0.819', '-0.54', '#sadness', '#anger', 'negative', '-0.66', 'smack', 'hell_dust', 'nose_drop', 'thunder', 'skag']
senticnet['cocaine_hydrochloride'] = ['-0.63', '0', '0.819', '-0.54', '#sadness', '#anger', 'negative', '-0.66', 'smack', 'hell_dust', 'nose_drop', 'thunder', 'skag']
senticnet['cocaine_powder'] = ['-0.63', '0', '0.819', '-0.54', '#sadness', '#anger', 'negative', '-0.66', 'smack', 'hell_dust', 'nose_drop', 'thunder', 'skag']
senticnet['cocci'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'immobile', 'immotile']
senticnet['coccidian_parasite'] = ['0.995', '-0.71', '0', '0.647', '#joy', '#surprise', 'positive', '0.784', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['coccidioidomycosis'] = ['-0.85', '0', '0.549', '-0.93', '#sadness', '#disgust', 'negative', '-0.78', 'mycosis', 'coccidiomycosis', 'valley_fever', 'desert_rheumatism', 'cryptococcosis']
senticnet['coccidiomycosis'] = ['-0.85', '0', '0.549', '-0.93', '#sadness', '#disgust', 'negative', '-0.78', 'coccidioidomycosis', 'mycosis', 'valley_fever', 'desert_rheumatism', 'cryptococcosis']
senticnet['coccinella_septempunctata'] = ['0', '-0.09', '0.123', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'gypsy_moth', 'japanese_beetle', 'cucumber_beetle', 'mosquito', 'earwig']
senticnet['coccobacillary'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'immobile', 'immotile']
senticnet['coccobacilli'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'immobile', 'immotile']
senticnet['coccoid'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['cochair'] = ['0', '0', '0.88', '-0.92', '#anger', '#disgust', 'negative', '-0.90', 'sofa_bed', 'small_seat', 'beanbag_airline_seat', 'hide-a-bed_sofa', 'airline_seat']
senticnet['cochlear_nerve'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['cock'] = ['-0.54', '0', '0.811', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'bird_of_prey', 'raptor', 'raptorial_bird', 'parrot', 'bird']
senticnet['cock_inn'] = ['0.853', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.916', 'tavern', 'cafe', 'nightclub', 'bar', 'liquor_store']
senticnet['cock_rock'] = ['-0.05', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.05', 'masturbate', 'jerk', 'jerk_off', 'watch_porn', 'porn']
senticnet['cockamamie'] = ['0.764', '-0.66', '0', '0.898', '#joy', '#admiration', 'positive', '0.776', 'farfetched', 'unbelievable', 'implausible', 'cockamamy', 'unlikely']
senticnet['cockamamy'] = ['0.764', '-0.66', '0', '0.898', '#joy', '#admiration', 'positive', '0.776', 'farfetched', 'unbelievable', 'cockamamie', 'implausible', 'unlikely']
senticnet['cockatiel'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet']
senticnet['cockchafer'] = ['0.748', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.873', 'butterfly', 'moth', 'parrot', 'beetle', 'falcon']
senticnet['cocked_hammer'] = ['-0.99', '0', '0.651', '-0.51', '#sadness', '#anger', 'negative', '-0.72', 'sending', 'compulsion', 'influence', 'inducement', 'causation']
senticnet['cocked_hat'] = ['0.08', '0.106', '-0.04', '0.112', '#interest', '#admiration', 'positive', '0.063', 'helmet', 'head_gear', 'cap', 'head_wear', 'head_protection']
senticnet['cocker'] = ['0', '0', '-0.75', '-0.78', '#fear', '#disgust', 'negative', '-0.77', 'coddle', 'baby', 'cosset', 'featherbed', 'pamper']
senticnet['cocker_spaniel'] = ['-0.72', '0', '0.619', '-0.86', '#sadness', '#disgust', 'negative', '-0.73', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['cockerel'] = ['-0.54', '0', '0.811', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'bird_of_prey', 'raptor', 'raptorial_bird', 'parrot', 'bird']
senticnet['cockersand_abbey'] = ['0.049', '0.16', '0', '0.155', '#interest', '#admiration', 'positive', '0.121', 'castle', 'palace', 'medieval', 'aristocrat', 'throne']
senticnet['cockeyed'] = ['0', '0', '0.952', '-0.79', '#anger', '#disgust', 'negative', '-0.87', 'ridiculous', 'ludicrous', 'derisory', 'absurd', 'farcical']
senticnet['cocking_handle'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'peavey']
senticnet['cocking_lever'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'peavey']
senticnet['cockle'] = ['0.996', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.978', 'heart-strings', 'tugged', 'tugging', 'tug', 'heartstrings']
senticnet['cockle_shell'] = ['0', '-0.02', '-0.08', '-0.05', '#fear', '#disgust', 'negative', '-0.03', 'mattress', 'sleep_furniture', 'master_bedroom', 'small_bed', 'pillow']
senticnet['cocklebur'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'meadow_grass', 'grass', 'manna_grass', 'sweet_grass', 'wood_meadowgrass']
senticnet['cockney'] = ['-0.71', '0', '0.998', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'tone', 'quality', 'twang', 'nasality', 'timber']
senticnet['cockney_accent'] = ['-0.71', '0', '0.998', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'tone', 'quality', 'twang', 'nasality', 'timber']
senticnet['cockney_rhyming_slang'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'rascal', 'rapscallion', 'scalawag', 'holy_terror', 'monkey']
senticnet['cockpit'] = ['-0.08', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.08', 'turn_key', 'plan_route', 'open_car_door', 'book_flight', 'turn_car_off']
senticnet['cockpit_canopy'] = ['-0.58', '0', '0.715', '-0.59', '#anger', '#disgust', 'negative', '-0.63', 'fly', 'overlap', 'barndoor', 'coattail', 'codpiece']
senticnet['cockpit_crew'] = ['-0.08', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.08', 'turn_key', 'plan_route', 'open_car_door', 'book_flight', 'turn_car_off']
senticnet['cockpit_window'] = ['-0.08', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.08', 'turn_key', 'plan_route', 'open_car_door', 'book_flight', 'turn_car_off']
senticnet['cockroach'] = ['0', '0', '-0.92', '-0.97', '#fear', '#disgust', 'negative', '-0.95', 'annoy_bug', 'insect', 'bug', 'hare', 'nymph']
senticnet['cockroach_antigen'] = ['-0.15', '0.162', '0.596', '-0.62', '#anger', '#disgust', 'negative', '-0.30', 'hackle', 'umbrage', 'offense', 'ire', 'see_red']
senticnet['cocksucker'] = ['-0.72', '0', '0.728', '-0.68', '#sadness', '#anger', 'negative', '-0.71', 'sap', 'fool', 'saphead', 'muggins', 'tomfool']
senticnet['cocksure'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'intrepidly', 'hardy', 'fearlessness', 'fearlessly', 'unafraid']
senticnet['cocktail'] = ['0.122', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.067', 'bourbon', 'alcoholic_beverage', 'booze', 'vodka', 'beverage']
senticnet['cocktail_bar'] = ['0.841', '0', '0', '0.71', '#joy', '#admiration', 'positive', '0.775', 'waiter', 'bar_service', 'beverage_service', 'concierge', 'minibar']
senticnet['cocktail_cabinet'] = ['0.705', '0.875', '0', '0', '#joy', '#interest', 'positive', '0.79', 'rest_room', 'stool', 'chair', 'coffee_table', 'bar_stool']
senticnet['cocktail_hour'] = ['0.044', '0.08', '0', '-0.03', '#joy', '#interest', 'positive', '0.031', 'cook', 'festival', 'bake_pan', 'restaurant', 'food']
senticnet['cocktail_lounge'] = ['0.841', '0', '0', '0.71', '#joy', '#admiration', 'positive', '0.775', 'waiter', 'bar_service', 'beverage_service', 'concierge', 'minibar']
senticnet['cocktail_napkin'] = ['0', '0.074', '0', '0.06', '#interest', '#admiration', 'positive', '0.067', 'use_crayon', 'draw', 'pencil_art', 'make_sketch', 'outline']
senticnet['cocktail_party'] = ['0.962', '0', '0', '0', '#joy', '#joy', 'positive', '0.962', 'interest_person', 'social_occasion', 'become_intoxicated', 'network', 'get_sex']
senticnet['cocktail_party_effect'] = ['0.727', '-0.62', '0', '0.57', '#joy', '#surprise', 'positive', '0.64', 'utterer', 'deipnosophist', 'interlocutor', 'conversationalist', 'speaker']
senticnet['cocktail_reception'] = ['0.044', '0.08', '0', '-0.03', '#joy', '#interest', 'positive', '0.031', 'cook', 'festival', 'bake_pan', 'restaurant', 'food']
senticnet['cocktail_sauce'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'horseradish', 'box_cereal', 'bottle_water']
senticnet['cockup'] = ['-0.84', '0', '0.828', '-0.57', '#sadness', '#anger', 'negative', '-0.75', 'bobble', 'error', 'misstep', 'fumble', 'stumble']
senticnet['cocky'] = ['0', '-0.77', '0', '-0.82', '#surprise', '#disgust', 'negative', '-0.02', 'arrogant', 'brash', 'smug', 'conceited', 'pompous']
senticnet['cocky_young'] = ['-0.64', '0', '0.667', '-0.82', '#anger', '#disgust', 'negative', '-0.71', 'novice', 'beginner', 'tyro', 'tiro', 'initiate']
senticnet['coclass_graph'] = ['-0.53', '0', '0.993', '-0.88', '#anger', '#disgust', 'negative', '-0.80', 'expansiveness', 'impressiveness', 'stateliness', 'loftiness', 'excellence']
senticnet['coco'] = ['0.973', '-0.50', '0', '0.612', '#joy', '#admiration', 'positive', '0.698', 'chic', 'chicness', 'jauntiness', 'modishness', 'stylishness']
senticnet['coco_montrese'] = ['-0.95', '0', '0.992', '-0.63', '#sadness', '#anger', 'negative', '-0.86', 'interpretive_dance', 'interpretive_dancing', 'modern_dance', 'interpretative_dancing', 'stage_dancing']
senticnet['cocoa'] = ['0.799', '-0.95', '0', '0', '#joy', '#surprise', 'positive', '0.875', 'drinking_chocolate', 'hot_chocolate', 'chocolate', 'season', 'stock']
senticnet['coconut'] = ['0.081', '0', '0', '0', '#joy', '#joy', 'positive', '0.081', 'bottle_wine', 'table_chair', 'human_intelligence', 'chef_special', 'oatmeal']
senticnet['coconut_meat'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'palm_nut', 'nut', 'edible_nut', 'cembra_nut', 'cedar_nut']
senticnet['coconut_oil'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'palm_nut', 'nut', 'edible_nut', 'cembra_nut', 'cedar_nut']
senticnet['coconut_palm'] = ['0.826', '0.787', '0', '0', '#joy', '#interest', 'positive', '0.806', 'fig', 'baby_flower', 'carpel', 'strawberry', 'star_anise']
senticnet['coconut_rice'] = ['0.076', '0', '0.037', '0.065', '#joy', '#admiration', 'positive', '0.035', 'serve_cold', 'tasty_good', 'tasty', 'sour', 'high_calorie']
senticnet['coconut_shrimp'] = ['0.062', '0', '0.027', '0.055', '#joy', '#admiration', 'positive', '0.03', 'porridge', 'meal', 'broth', 'hamburger', 'hearty']
senticnet['coconut_shy'] = ['-0.72', '0', '0.978', '-0.86', '#anger', '#disgust', 'negative', '-0.85', 'amusement_park', 'common', 'pleasure_ground', 'village_green', 'tract']
senticnet['cocotte'] = ['-0.67', '0', '0.589', '-0.96', '#sadness', '#disgust', 'negative', '-0.74', 'slattern', 'camp_follower', 'call_girl', 'comfort_woman', 'ianfu']
senticnet['cocycle'] = ['0.875', '-0.65', '0', '0.861', '#joy', '#admiration', 'positive', '0.797', 'completeness', 'work', 'whole_shebang', 'essential', 'whole']
senticnet['cod'] = ['-0.62', '0', '0.9', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'dupe', 'slang', 'put_on', 'pull_leg_of', 'take_in']
senticnet['cod_gwirfoddol'] = ['-0.56', '0', '0.617', '-0.80', '#anger', '#disgust', 'negative', '-0.66', 'prolixity', 'prolixness', 'wordiness', 'repetitiveness', 'repetitiousness']
senticnet['coddle'] = ['0', '0', '-0.75', '-0.78', '#fear', '#disgust', 'negative', '-0.76', 'cocker', 'baby', 'cosset', 'featherbed', 'pamper']
senticnet['code'] = ['-0.03', '0.201', '-0.04', '0.201', '#interest', '#admiration', 'positive', '0.08', 'plan', 'oversee', 'decision', 'skill', 'make_decision']
senticnet['code_adjudicator'] = ['0.03', '0.073', '0.027', '0.084', '#interest', '#admiration', 'positive', '0.04', 'grocery', 'store_vegetable', 'fresh_vegetable', 'vendor', 'fruit_vegetable']
senticnet['code_bloat'] = ['-0.73', '0', '0.94', '-0.87', '#anger', '#disgust', 'negative', '-0.85', 'edema', 'oedema', 'hydrops', 'dropsy', 'puffiness']
senticnet['code_duello'] = ['0', '-0.01', '-0.08', '-0.16', '#fear', '#disgust', 'negative', '-0.07', 'siege', 'big_fight', 'battle', 'declare_war', 'conflict']
senticnet['code_upc'] = ['0.196', '0.129', '-0.06', '0.184', '#joy', '#admiration', 'positive', '0.111', 'pleasant', 'pleasurable', 'bourgeois', 'important_human', 'good']
senticnet['codebreaker'] = ['-0.05', '-0.19', '-0.17', '0.219', '#surprise', '#admiration', 'positive', '0.046', 'riddle', 'mystery', 'obituary', 'editorial', 'book_genre']
senticnet['codefendant'] = ['0.817', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.824', 'witness', 'prosecutor', 'legal_process', 'lawyer', 'jury']
senticnet['codeine'] = ['0.047', '-0.01', '-0.08', '-0.21', '#fear', '#disgust', 'negative', '-0.05', 'morphine', 'phenobarbital', 'drug', 'analgesic', 'palliative']
senticnet['codger'] = ['-0.64', '0', '0.667', '-0.82', '#anger', '#disgust', 'negative', '-0.71', 'novice', 'beginner', 'tyro', 'tiro', 'initiate']
senticnet['codie_award_finalist'] = ['0', '0', '0', '0.879', '#admiration', '#admiration', 'positive', '0.879', 'sport_competition', 'win_competition', 'race', 'match', 'championship']
senticnet['codify'] = ['-0.91', '0', '0.637', '-0.59', '#sadness', '#anger', 'negative', '-0.71', 'gauge', 'govern', 'regulate', 'standardize', 'order']
senticnet['coding'] = ['0.634', '-0.73', '0', '0.974', '#surprise', '#admiration', 'positive', '0.782', 'decompression', 'cryptography', 'decipherment', 'secret_writing', 'steganography']
senticnet['codings'] = ['0', '0', '0', '0.074', '#admiration', '#admiration', 'positive', '0.074', 'write_message', 'grammar', 'computer_language', 'english', 'program_language']
senticnet['codium_fragile'] = ['-0.89', '0', '0.556', '-0.72', '#sadness', '#disgust', 'negative', '-0.72', 'fucus', 'rockweed', 'black_rockweed', 'bladder_fucus', 'tang']
senticnet['codominium'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['codpiece'] = ['-0.58', '0', '0.715', '-0.59', '#anger', '#disgust', 'negative', '-0.63', 'fly', 'overlap', 'barndoor', 'coattail', 'flap']
senticnet['codswallop'] = ['0.739', '-0.89', '0', '0.702', '#joy', '#surprise', 'positive', '0.78', 'pap', 'content', 'pablum', 'folderol', 'drivel']
senticnet['coeducation'] = ['0.702', '-0.60', '0', '0.784', '#joy', '#admiration', 'positive', '0.698', 'continuing_education', 'activity', 'course', 'extracurricular_activity', 'didactics']
senticnet['coeducational_school'] = ['-0.04', '0.088', '0.013', '0.069', '#interest', '#admiration', 'positive', '0.024', 'kindergarden', 'daycare', 'nursery', 'child', 'asylum']
senticnet['coefficient'] = ['0.728', '0.977', '0', '0.939', '#interest', '#admiration', 'positive', '0.881', 'air_pressure', 'altitude', 'air', 'heavy_air', 'air_density']
senticnet['coefficient_theorem'] = ['0.196', '0.129', '-0.06', '0.184', '#joy', '#admiration', 'positive', '0.111', 'pleasant', 'pleasurable', 'bourgeois', 'important_human', 'good']
senticnet['coeliac_disease'] = ['-0.86', '0', '0.76', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['coendou'] = ['0', '0', '-0.95', '-0.81', '#fear', '#disgust', 'negative', '-0.88', 'herb', 'plant', 'poisonous_plant', 'yellow_flower', 'lichen']
senticnet['coenen'] = ['0.746', '-0.88', '0', '0.723', '#joy', '#surprise', 'positive', '0.783', 'strike', 'run_into', 'hit', 'impinge_on', 'collide_with']
senticnet['coequal'] = ['0.964', '0.931', '0', '0', '#joy', '#interest', 'positive', '0.948', 'heel', 'foot_part', 'type_of_shoes', 'shoe_type', 'high_heel']
senticnet['coerce'] = ['-0.79', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'turn_up_heat', 'turn_up_pressure', 'steamroller', 'steamroll', 'sandbag']
senticnet['coerced_confession'] = ['-0.70', '0', '0.874', '-0.75', '#anger', '#disgust', 'negative', '-0.77', 'blame', 'accusation', 'inculpation', 'complaint', 'imprecation']
senticnet['coercion'] = ['0', '0', '-1', '-0.66', '#fear', '#disgust', 'negative', '-0.83', 'obligation', 'obligated', 'force', 'force_feed', 'enslave']
senticnet['coercive'] = ['-0.87', '0', '-0.8', '0', '#sadness', '#fear', 'negative', '-0.83', 'coerce', 'coerciveness', 'sanction', 'coercively', 'anarchy']
senticnet['coercive_diplomacy'] = ['0', '0.098', '0', '0.08', '#interest', '#admiration', 'positive', '0.089', 'talk_person', 'wisdom', 'tact', 'statecraft', 'greet_person']
senticnet['coercive_force'] = ['-0.89', '0', '0.678', '-0.62', '#sadness', '#anger', 'negative', '-0.73', 'undisciplined', 'anarchic', 'uncontrolled', 'irrepressible', 'uncontrollable']
senticnet['coercive_power'] = ['0', '0.325', '-0.18', '0.335', '#interest', '#admiration', 'positive', '0.16', 'get_rid', 'cause_change', 'interaction', 'suggest', 'movement_towards']
senticnet['coercive_sex'] = ['-0.75', '0', '0.886', '-0.72', '#sadness', '#anger', 'negative', '-0.78', 'harass', 'move', 'check', 'fork', 'pin']
senticnet['coercively'] = ['-0.87', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.84', 'coerce', 'coerciveness', 'sanction', 'coercive', 'anarchy']
senticnet['coerciveness'] = ['-0.87', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.84', 'coerce', 'coercive', 'sanction', 'coercively', 'anarchy']
senticnet['coercivity'] = ['0.965', '-0.84', '0', '0.599', '#joy', '#surprise', 'positive', '0.802', 'mental_faculty', 'faculty', 'module', 'remembrance', 'anamnesis']
senticnet['coerne'] = ['-0.93', '0', '0.861', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'knotty', 'contorted', 'writhed', 'crooked', 'anfractuous']
senticnet['coeternal'] = ['-0.90', '0', '0.653', '-0.99', '#sadness', '#disgust', 'negative', '-0.85', 'unalterable', 'static', 'changeless', 'confirmed', 'fixed']
senticnet['coevolutionary'] = ['-0.89', '0', '0.972', '-0.50', '#sadness', '#anger', 'negative', '-0.79', 'unsteadiness', 'haphazardness', 'unregularity', 'variability', 'fitfulness']
senticnet['coexist'] = ['-0.12', '0', '-0.09', '-0.04', '#sadness', '#fear', 'negative', '-0.08', 'hold_two_together', 'install_window', 'float_through_air', 'spring_up', 'collision']
senticnet['coexistence'] = ['0.826', '-0.71', '0', '0.913', '#joy', '#admiration', 'positive', '0.819', 'true', 'pure', 'symphonious', 'harmonical', 'harmonized']
senticnet['coffe'] = ['0.807', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.859', 'bubble', 'steam', 'hot', 'boil_water', 'tea']
senticnet['coffee'] = ['0.807', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.859', 'bubble', 'steam', 'hot', 'boil_water', 'tea']
senticnet['coffee_bar'] = ['0.841', '0', '0', '0.71', '#joy', '#admiration', 'positive', '0.775', 'waiter', 'bar_service', 'beverage_service', 'concierge', 'minibar']
senticnet['coffee_bean'] = ['0.807', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.859', 'bubble', 'steam', 'hot', 'boil_water', 'tea']
senticnet['coffee_break'] = ['0.251', '0.207', '-0.08', '0.18', '#joy', '#interest', 'positive', '0.139', 'middle_day', 'work_break', 'inactivity', 'recover', 'take_break']
senticnet['coffee_cake'] = ['0.092', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.071', 'bake_treat', 'round_pastry', 'bake_good', 'bake_goods', 'sweet_food']
senticnet['coffee_cart'] = ['-0.80', '0', '0.718', '-0.79', '#sadness', '#disgust', 'negative', '-0.77', 'walk_exercise', 'lose_wallet', 'go_debt', 'pay_dinner', 'spend_much_money']
senticnet['coffee_cup'] = ['0.095', '0', '0', '0', '#joy', '#joy', 'positive', '0.095', 'coffee', 'drink_coffee', 'coffee_shop', 'caffeine', 'drink_tea']
senticnet['coffee_drinker'] = ['0.807', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.859', 'bubble', 'steam', 'hot', 'boil_water', 'tea']
senticnet['coffee_grinder'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'salad_shaker', 'store_shelf', 'kitchen_tool', 'pliers', 'ladle']
senticnet['coffee_house'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'drink_coffee', 'meet_up', 'internet_cafe', 'backpack', 'coffee']
senticnet['coffee_intake'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'wooden_spoon', 'cereal_bowl', 'teaspoon', 'container_cup', 'large_container']
senticnet['coffee_kiosk'] = ['0.813', '0.832', '0', '0.874', '#interest', '#admiration', 'positive', '0.84', 'ferris_wheel', 'concession_stand', 'food', 'carnival_ride', 'theme_park']
senticnet['coffee_maker'] = ['0.866', '0.833', '0', '0', '#joy', '#interest', 'positive', '0.849', 'alcohol', 'porter', 'concierge', 'beer_mug', 'bar_stool']
senticnet['coffee_mug'] = ['0.095', '0', '0', '0', '#joy', '#joy', 'positive', '0.095', 'coffee', 'drink_coffee', 'coffee_shop', 'caffeine', 'drink_tea']
senticnet['coffee_pot'] = ['0', '0', '0', '-0.06', '#disgust', '#disgust', 'negative', '-0.06', 'newsstand', 'bookshop', 'walmart', 'shoe_store', 'hooker']
senticnet['coffee_roll'] = ['-0.84', '0', '0.713', '-0.63', '#sadness', '#anger', 'negative', '-0.73', 'cross_bun', 'hot_cross_bun', 'bear_paw', 'bear_claw', 'bun']
senticnet['coffee_shop'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'drink_coffee', 'beverage', 'food_and_beverage', 'kitchen', 'eat_dinner']
senticnet['coffee_table'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'coffee', 'table', 'coffee_shop', 'coffee_house', 'hot_chocolate']
senticnet['coffee_tea'] = ['0.866', '0.833', '0', '0', '#joy', '#interest', 'positive', '0.849', 'alcohol', 'porter', 'concierge', 'beer_mug', 'bar_stool']
senticnet['coffeehouse'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'buy_sandwich', 'lunch', 'eat_food', 'buy_food', 'finish_job']
senticnet['coffeemaker'] = ['0.866', '0.833', '0', '0', '#joy', '#interest', 'positive', '0.849', 'alcohol', 'porter', 'concierge', 'beer_mug', 'bar_stool']
senticnet['coffeepot'] = ['0.214', '0.258', '-0.09', '0.2', '#joy', '#interest', 'positive', '0.146', 'cup', 'carafe', 'pitcher', 'container_cup', 'glasses']
senticnet['coffer'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'wallet', 'billfold', 'bank', 'piggy_bank', 'bank_vault']
senticnet['coffey'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
senticnet['coffin'] = ['0', '0', '-0.87', '-0.75', '#fear', '#disgust', 'negative', '-0.81', 'grave', 'casket', 'morgue', 'headstone', 'graveyard']
senticnet['cofor'] = ['0.063', '0', '0.01', '0.16', '#joy', '#admiration', 'positive', '0.071', 'goodbye', 'hand_gesture', 'arm_movement', 'crest', 'water_splash']
senticnet['cofounder'] = ['0.046', '0.116', '-0.05', '0.076', '#interest', '#admiration', 'positive', '0.045', 'head_guy', 'boss', 'officer', 'leader', 'supervisor']
senticnet['cog'] = ['-0.04', '-0.07', '0.049', '0.085', '#surprise', '#admiration', 'positive', '0.017', 'wheel', 'two_wheels', 'gear', 'bumper', 'steer_wheel']
senticnet['cog_belt'] = ['0.825', '0', '0', '0.703', '#joy', '#admiration', 'positive', '0.764', 'pay_toll', 'get_fast', 'access', 'fast_travel', 'access_quickly']
senticnet['cogad_gedel_re_gallaib'] = ['0', '0.105', '0', '0.084', '#interest', '#admiration', 'positive', '0.095', 'archive', 'print_medium', 'quiet_place', 'register', 'media']
senticnet['cogency'] = ['0', '0.803', '0', '0.71', '#interest', '#admiration', 'positive', '0.756', 'novelty', 'innovativeness', 'freshness', 'ability', 'power']
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senticnet['cold_night'] = ['-0.29', '0.076', '-0.11', '0.104', '#sadness', '#fear', 'negative', '-0.05', 'windy', 'breeze', 'jacket', 'chilly', 'cuddle']
senticnet['cold_outside'] = ['0.157', '0', '0', '0', '#joy', '#joy', 'positive', '0.157', 'icy', 'cold', 'coldness', 'cold_weather', 'catch_rain']
senticnet['cold_person'] = ['0', '0', '0.988', '-0.71', '#anger', '#disgust', 'negative', '-0.84', 'human', 'delusional', 'use_illegal_drug', 'busy_person', 'smart_man']
senticnet['cold_shoulder'] = ['-0.75', '0', '0.666', '-0.51', '#sadness', '#anger', 'negative', '-0.64', 'discourtesy', 'snub', 'rebuff', 'offense', 'cut']
senticnet['cold_snap'] = ['0.15', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.087', 'winter', 'winter_time', 'snow_fall', 'snowy', 'put_warm_clothes']
senticnet['cold_sore'] = ['-0.10', '0', '-0.05', '-0.11', '#sadness', '#disgust', 'negative', '-0.08', 'jaw', 'cheek', 'dimple', 'forehead', 'beard']
senticnet['cold_spell'] = ['0.15', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.087', 'winter', 'winter_time', 'snow_fall', 'snowy', 'put_warm_clothes']
senticnet['cold_storage'] = ['0.045', '-0.05', '0', '-0.02', '#joy', '#surprise', 'positive', '0.023', 'refrigerator', 'store_refrigerator', 'icebox', 'frozen', 'icy']
senticnet['cold_storage_facility'] = ['0.024', '0.17', '0', '0.113', '#interest', '#admiration', 'positive', '0.102', 'load_dock', 'storage_place', 'lock_box', 'government_office', 'treasure_chest']
senticnet['cold_sweat'] = ['-0.80', '0', '-0.8', '0', '#sadness', '#fear', 'negative', '-0.80', 'sinking', 'misgiving', 'qualm', 'scruple', 'insecurely']
senticnet['cold_tolerant'] = ['0', '0.985', '0', '0.794', '#interest', '#admiration', 'positive', '0.889', 'patient', 'bear_up', 'tolerate', 'endurable', 'swallow']
senticnet['cold_turkey'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'vapor', 'gaseous', 'haze', 'smoke', 'smog']
senticnet['cold_urticaria'] = ['-0.55', '0', '0.536', '-0.72', '#sadness', '#disgust', 'negative', '-0.60', 'hypersensitivity_reaction', 'rash', 'efflorescence', 'roseola', 'skin_rash']
senticnet['cold_war'] = ['-0.95', '0', '0.84', '-0.97', '#sadness', '#disgust', 'negative', '-0.92', 'war', 'battle', 'fight', 'conflict', 'soldier']
senticnet['cold_water'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'glass_water', 'refreshed', 'smoothie', 'lemonade', 'coke']
senticnet['cold_weather'] = ['0.15', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.087', 'winter', 'winter_time', 'snow_fall', 'snowy', 'put_warm_clothes']
senticnet['cold_winter'] = ['-0.96', '0', '0.172', '-0.99', '#sadness', '#disgust', 'negative', '-0.71', 'severe_cold', 'cold', 'hypothermia', 'possibility_to_die', 'freeze']
senticnet['colder'] = ['0', '0.094', '0', '0', '#interest', '#interest', 'positive', '0.094', 'cold', 'cavern', 'under_umbrella', 'parasol', 'stick_wall']
senticnet['colder_water'] = ['0', '0.094', '0', '0', '#interest', '#interest', 'positive', '0.094', 'cold', 'cavern', 'under_umbrella', 'parasol', 'stick_wall']
senticnet['colder_weather'] = ['0.15', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.087', 'winter', 'winter_time', 'snow_fall', 'snowy', 'put_warm_clothes']
senticnet['colder_winter'] = ['0.15', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.087', 'winter', 'winter_time', 'snow_fall', 'snowy', 'put_warm_clothes']
senticnet['coldest'] = ['0', '0.772', '0', '0.924', '#interest', '#admiration', 'positive', '0.848', 'ice-covered', 'snow-free', 'arctic', 'ice-free', 'inhospitable']
senticnet['coldest_spring'] = ['0', '0', '0.954', '-0.76', '#anger', '#disgust', 'negative', '-0.85', 'fervid', 'ardent', 'impassioned', 'fervent', 'fiery']
senticnet['coldest_weather'] = ['0.15', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.087', 'winter', 'winter_time', 'snow_fall', 'snowy', 'put_warm_clothes']
senticnet['coldframe'] = ['-0.12', '0.066', '-0.08', '-0.06', '#sadness', '#fear', 'negative', '-0.05', 'garden_yard', 'see_sky', 'plant_tree', 'hair', 'seed_lawn']
senticnet['coldheartedness'] = ['-0.77', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.84', 'hardheartedness', 'heartlessly', 'heartlessness', 'heartless', 'hardhearted']
senticnet['colditz_castle'] = ['-0.78', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.87', 'jailbird', 'captive', 'imprisoned', 'convict', 'inmate']
senticnet['coldly'] = ['0', '0.764', '0', '0.845', '#interest', '#admiration', 'positive', '0.804', 'empathetically', 'empathic', 'empathetic', 'kind-heartedness', 'kindheartedness']
senticnet['coldness'] = ['0.094', '0', '0', '0', '#joy', '#joy', 'positive', '0.094', 'ice_rain', 'cold_weather', 'snowy', 'cold', 'chilly']
senticnet['cole'] = ['-0.56', '0', '0.892', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'borecrucifer', 'cruciferwort', 'kail', 'crucifer', 'kale']
senticnet['cole_haan'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['cole_slaw'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'grocery_cart', 'super_market', 'fruit_stand', 'candy_store', 'produce_store']
senticnet['coleman'] = ['0.532', '-0.68', '0', '0.984', '#surprise', '#admiration', 'positive', '0.733', 'potato', 'white_potato', 'chips', 'spud', 'tater']
senticnet['coleoptera_curculionidae'] = ['0', '-0.09', '0.123', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'gypsy_moth', 'japanese_beetle', 'cucumber_beetle', 'mosquito', 'earwig']
senticnet['coleoptile'] = ['-0.03', '0.169', '-0.07', '0.218', '#interest', '#admiration', 'positive', '0.071', 'elongate', 'elasticity', 'extension', 'grow_long', 'widen']
senticnet['colewort'] = ['-0.56', '0', '0.892', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'borecole', 'crucifer', 'kail', 'cole', 'kale']
senticnet['colfer'] = ['0.761', '0.566', '-0.21', '0.293', '#joy', '#interest', 'positive', '0.352', 'gratify', 'jubilation', 'gloat', 'gloating', 'gloatingly']
senticnet['coli_ehec'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'leptomeningitis', 'cerebrospinal_meningitis', 'meningitis', 'brain_fever', 'cerebrospinal_fever']
senticnet['colicky'] = ['-0.84', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.85', 'constipated', 'gassy', 'fussy', 'bloated', 'nauseous']
senticnet['colin'] = ['-0.20', '-0.10', '0.07', '-0.24', '#sadness', '#disgust', 'negative', '-0.10', 'flipper', 'sea_life', 'seafood', 'swim_animal', 'fillet']
senticnet['colin_bell'] = ['-0.9', '0', '0.749', '-0.71', '#sadness', '#anger', 'negative', '-0.78', 'chute', 'slide', 'slideway', 'sloping_trough', 'gutter']
senticnet['colin_ebelthite'] = ['-0.10', '0', '-0.05', '-0.24', '#sadness', '#disgust', 'negative', '-0.13', 'adulate', 'suck_up', 'toady', 'blandish', 'kowtow']
senticnet['colin_quinn'] = ['-0.02', '-0.02', '0.062', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'jump_up_down', 'tie_shoelace', 'jump_rope', 'hike', 'skate']
senticnet['coliseum'] = ['0', '-0.84', '0', '0.723', '#surprise', '#admiration', 'positive', '0.785', 'circular_route', 'stadium', 'memorial', 'station', 'row_house']
senticnet['coliseum_arena'] = ['0', '-0.84', '0', '0.723', '#surprise', '#admiration', 'positive', '0.785', 'circular_route', 'stadium', 'memorial', 'station', 'row_house']
senticnet['colistin'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['colitis'] = ['-0.75', '0', '0.868', '-0.99', '#anger', '#disgust', 'negative', '-0.87', 'regional_enteritis', 'regional_ileitis', 'ulcerative_colitis', 'rubor', 'redness']
senticnet['collaborate'] = ['0.979', '0.93', '0', '0.723', '#joy', '#interest', 'positive', '0.877', 'information_transfer', 'tranfer', 'inform_person', 'inform_others', 'information']
senticnet['collaborate_closely'] = ['0.979', '0.93', '0', '0.723', '#joy', '#interest', 'positive', '0.877', 'information_transfer', 'tranfer', 'inform_person', 'inform_others', 'information']
senticnet['collaborates_with'] = ['0.979', '0.93', '0', '0.723', '#joy', '#interest', 'positive', '0.877', 'information_transfer', 'tranfer', 'inform_person', 'inform_others', 'information']
senticnet['collaboration'] = ['0', '0', '0', '0.881', '#admiration', '#admiration', 'positive', '0.881', 'educate_others', 'share_knowledge', 'share_opinion', 'discover_new', 'cooperation']
senticnet['collaboration_between'] = ['0.979', '0.93', '0', '0.723', '#joy', '#interest', 'positive', '0.877', 'information_transfer', 'tranfer', 'inform_person', 'inform_others', 'information']
senticnet['collaboration_expertise'] = ['0', '0.726', '0', '0.992', '#interest', '#admiration', 'positive', '0.859', 'university', 'western_europe', 'research', 'french_expertise', 'project']
senticnet['collaborationism'] = ['-0.97', '0', '0.62', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'obstructor', 'obstructer', 'technophobe', 'obstructionist', 'naysayer']
senticnet['collaborationist'] = ['0.916', '0', '0', '0.72', '#joy', '#admiration', 'positive', '0.818', 'partner_in_crime', 'henchman', 'pardner', 'quisling', 'collaborator']
senticnet['collaborations_between'] = ['0.979', '0.93', '0', '0.723', '#joy', '#interest', 'positive', '0.877', 'information_transfer', 'tranfer', 'inform_person', 'inform_others', 'information']
senticnet['collaborative'] = ['0.754', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.836', 'participative', 'community-based', 'participatory', 'bottom-up', 'governance']
senticnet['collaborative_approach'] = ['0', '0', '0', '0.881', '#admiration', '#admiration', 'positive', '0.881', 'educate_others', 'share_knowledge', 'share_opinion', 'discover_new', 'cooperation']
senticnet['collaborative_effort'] = ['0', '0.097', '0', '0.092', '#interest', '#admiration', 'positive', '0.095', 'urgent', 'classify_ad', 'important', 'review', 'repetition']
senticnet['collaborative_effort_between'] = ['0', '0', '0', '0.881', '#admiration', '#admiration', 'positive', '0.881', 'educate_others', 'share_knowledge', 'share_opinion', 'discover_new', 'cooperation']
senticnet['collaborative_partnership'] = ['0', '0', '0', '0.881', '#admiration', '#admiration', 'positive', '0.881', 'educate_others', 'share_knowledge', 'share_opinion', 'discover_new', 'cooperation']
senticnet['collaborative_project'] = ['0', '0.959', '0', '0.76', '#interest', '#admiration', 'positive', '0.859', 'university', 'support', 'different_application', 'sponsor', 'project']
senticnet['collaborative_research'] = ['0', '0.763', '0', '0.994', '#interest', '#admiration', 'positive', '0.879', 'award', 'graduate_education', 'university', 'research', 'standard_grant']
senticnet['collaborative_venture'] = ['0', '0.097', '0', '0.092', '#interest', '#admiration', 'positive', '0.095', 'urgent', 'classify_ad', 'important', 'review', 'repetition']
senticnet['collaboratively'] = ['0.979', '0.93', '0', '0.723', '#joy', '#interest', 'positive', '0.877', 'information_transfer', 'tranfer', 'inform_person', 'inform_others', 'information']
senticnet['collaborator'] = ['0.917', '0', '0', '0.724', '#joy', '#admiration', 'positive', '0.821', 'partner_in_crime', 'henchman', 'pardner', 'quisling', 'collaborationist']
senticnet['collabra'] = ['0.749', '0.724', '0', '0', '#joy', '#interest', 'positive', '0.736', 'mouth_noise', 'vocal_cord', 'vocalization', 'vocal_sound', 'loud_soft']
senticnet['collage'] = ['0.043', '0.112', '0', '0', '#joy', '#interest', 'positive', '0.077', 'collection', 'drawing', 'artwork', 'gallery', 'sculpture']
senticnet['collage_film'] = ['0.571', '-0.70', '0', '0.941', '#surprise', '#admiration', 'positive', '0.738', 'flick', 'movie', 'film', 'picture', 'moving_picture']
senticnet['collagen_alpha'] = ['0.096', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.057', 'platform_rail', 'rail', 'catwalk', 'column', 'token_turnstile']
senticnet['collapse'] = ['-0.13', '-0.05', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'monitor_heart_rate', 'feel_tired', 'bump_head', 'exhaustion', 'become_fatigued']
senticnet['collapsed_lung'] = ['-0.03', '-0.00', '-0.07', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'injury', 'get_hurt', 'bruise', 'lose_game', 'get_injured']
senticnet['collar'] = ['0', '0.072', '0', '0.064', '#interest', '#admiration', 'positive', '0.068', 'zipper', 'pant', 'trousers', 'cowboy_boot', 'accessory']
senticnet['collar_rot'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'apple_rust', 'fungus']
senticnet['collar_shirt'] = ['-0.02', '0.256', '0', '0.166', '#interest', '#admiration', 'positive', '0.132', 'coat', 'shirt', 'garment', 'dress', 'vest']
senticnet['collard'] = ['0.928', '-0.78', '0', '0', '#joy', '#surprise', 'positive', '0.855', 'collard', 'kale', 'chard', 'turnip', 'spinach']
senticnet['collared_dove'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'hold_egg', 'baby_bird', 'hatch', 'lay_egg', 'bird_home']
senticnet['collared_peccary'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'fox', 'prairie_fox', 'white_fox']
senticnet['collared_pratincole'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet']
senticnet['collared_shirt'] = ['-0.79', '0', '0.599', '-0.93', '#sadness', '#disgust', 'negative', '-0.77', 'uniform', 'battle_dress', 'dress_uniform', 'fatigue', 'military_uniform']
senticnet['collarless'] = ['0', '0', '-0.9', '-0.79', '#fear', '#disgust', 'negative', '-0.85', 'double-breasted', 'button-front', 'high-necked', 'button-down', 'long-sleeved']
senticnet['collate'] = ['0.048', '0.092', '-0.08', '0.216', '#interest', '#admiration', 'positive', '0.067', 'gather', 'group', 'organization', 'clique', 'collection']
senticnet['collateral_attack'] = ['-0.55', '0', '0.741', '-0.76', '#anger', '#disgust', 'negative', '-0.68', 'illogicality', 'illogicalness', 'illogic', 'inconsequence', 'fallaciousness']
senticnet['collateral_circulation'] = ['-0.81', '0', '0.684', '-0.81', '#sadness', '#disgust', 'negative', '-0.77', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['collateral_ligament'] = ['-0.53', '0', '0.982', '-0.67', '#anger', '#disgust', 'negative', '-0.73', 'gristle', 'semilunar_cartilage', 'fibrocartilage', 'hyaline_cartilage', 'arytenoid']
senticnet['collation'] = ['0.789', '-0.68', '0', '0.729', '#joy', '#admiration', 'positive', '0.733', 'refreshment', 'nosh', 'coffee_break', 'tea_break', 'meal']
senticnet['colleague'] = ['0.05', '0.141', '-0.07', '0.132', '#interest', '#admiration', 'positive', '0.063', 'public_servant', 'worker', 'manager', 'laborer', 'office_job']
senticnet['collect'] = ['0.757', '0.777', '0', '0', '#joy', '#interest', 'positive', '0.767', 'add_two_numbers', 'delight_audience', 'amuse_child', 'bounce_up_down', 'make_move']
senticnet['collect_crap'] = ['0', '0', '0.982', '-0.90', '#anger', '#disgust', 'negative', '-0.94', 'junk', 'waste', 'store_junk', 'waste_space', 'stupid']
senticnet['collect_data'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'listen_heart', 'make_drawing', 'make_noise', 'backup', 'make_a_lot_of_noise']
senticnet['collect_debt'] = ['0', '0.117', '-0.04', '0.089', '#interest', '#admiration', 'positive', '0.053', 'get_money', 'get_paid', 'receive_money', 'collect_money', 'pay_service']
senticnet['collect_goods'] = ['0', '0.13', '-0.04', '0.216', '#interest', '#admiration', 'positive', '0.1', 'store_stuff', 'store_dry_goods', 'hold', 'store_goods', 'hold_stuff']
senticnet['collect_information'] = ['0.574', '0.561', '0', '0.795', '#joy', '#admiration', 'positive', '0.643', 'take_note', 'transfer_information', 'take_note_class', 'search_information', 'listen_lecture']
senticnet['collect_item'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'collector', 'numismatics', 'coin', 'collect', 'stamp']
senticnet['collect_money'] = ['-0.1', '0.272', '-0.14', '0.15', '#interest', '#admiration', 'positive', '0.044', 'give_change', 'settle_bill', 'pay_back', 'owe_money', 'make_change']
senticnet['collect_pollen'] = ['0', '0', '0', '0.869', '#admiration', '#admiration', 'positive', '0.869', 'fly_animal', 'insect', 'mosquito', 'bug', 'fly_insect']
senticnet['collect_ticket'] = ['-0.09', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.07', 'buy_ticket', 'purchase_ticket', 'ticket', 'sell_ticket', 'pay_ticket']
senticnet['collectable'] = ['0.031', '-0.10', '0', '0', '#joy', '#surprise', 'positive', '0.07', 'teddy_bear', 'beanie_baby', 'yo-yo', 'board_game', 'toy']
senticnet['collectible'] = ['0.031', '-0.10', '0', '0', '#joy', '#surprise', 'positive', '0.07', 'teddy_bear', 'beanie_baby', 'yo-yo', 'board_game', 'toy']
senticnet['collectible_toy'] = ['0.031', '-0.10', '0', '0', '#joy', '#surprise', 'positive', '0.07', 'teddy_bear', 'beanie_baby', 'yo-yo', 'board_game', 'toy']
senticnet['collectibles'] = ['0.086', '-0.10', '0.064', '0', '#joy', '#surprise', 'positive', '0.043', 'art_piece', 'lifetime', 'museum', 'vintage', 'ancient_artifact']
senticnet['collecting_antique'] = ['0.891', '0.835', '0', '0.721', '#joy', '#interest', 'positive', '0.816', 'hobby', 'diversion', 'challenge', 'enjoy_game', 'leisure']
senticnet['collecting_duct'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'keep_alive', 'body_system', 'body_working', 'blood_pressure', 'heart']
senticnet['collecting_trash'] = ['-0.70', '0', '0.611', '-0.69', '#sadness', '#disgust', 'negative', '-0.67', 'dump', 'garbage_dump', 'garbage_heap', 'rubbish_dump', 'wasteyard']
senticnet['collection'] = ['0', '0.188', '-0.08', '0.137', '#interest', '#admiration', 'positive', '0.081', 'clique', 'several_people', 'big_group', 'group', 'mob']
senticnet['collective_gasp'] = ['-0.86', '0', '0.516', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'outcry', 'call', 'cry', 'yell', 'shout']
senticnet['collective_groan'] = ['-0.86', '0', '0.516', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'outcry', 'call', 'cry', 'yell', 'shout']
senticnet['collective_guilt'] = ['-0.89', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.84', 'remorse', 'grief', 'regret', 'sorrow', 'heartache']
senticnet['collective_identity'] = ['0.122', '0.159', '-0.05', '0.217', '#interest', '#admiration', 'positive', '0.111', 'profile_picture', 'unity', 'country_person', 'unite', 'signature']
senticnet['collective_intelligence'] = ['0.487', '0.675', '-0.26', '0.266', '#joy', '#interest', 'positive', '0.29', 'solve_big_problem', 'gather_intelligence', 'intelligent', 'team_work', 'clever']
senticnet['collective_memory'] = ['0', '0.827', '0', '0.846', '#interest', '#admiration', 'positive', '0.837', 'inexterminable', 'inextirpable', 'inexpungible', 'inexpungeable', 'ineradicable']
senticnet['collective_narcissism'] = ['-0.93', '0', '0.586', '-0.66', '#sadness', '#disgust', 'negative', '-0.73', 'camp', 'pack', 'clique', 'coterie', 'brain_trust']
senticnet['collective_punishment'] = ['-0.79', '0', '0.691', '-0.88', '#sadness', '#disgust', 'negative', '-0.79', 'cold', 'pitiless', 'cruel', 'unkind', 'vicious']
senticnet['collective_sigh'] = ['-0.19', '-0.06', '-0.21', '0', '#sadness', '#fear', 'negative', '-0.11', 'breath', 'smell_odor', 'exhale', 'drool', 'say_excuse']
senticnet['collective_wisdom'] = ['0.798', '0.754', '0', '0.779', '#joy', '#admiration', 'positive', '0.777', 'know_world', 'find_out_truth', 'knowledge', 'honesty', 'experience']
senticnet['collectively'] = ['0.789', '0.95', '0', '0', '#joy', '#interest', 'positive', '0.869', 'together_with', 'jointly', 'conjointly', 'graduate_education', 'award']
senticnet['collectively_span'] = ['0.786', '0.898', '0', '0', '#joy', '#interest', 'positive', '0.842', 'engineering_support', 'collectively', 'graduate_education', 'program', 'cultural_change']
senticnet['collectivism'] = ['0', '0.883', '0', '0.958', '#interest', '#admiration', 'positive', '0.92', 'paternalistic', 'authoritarianism', 'paternalism', 'individualism', 'libertarianism']
senticnet['collectivist'] = ['0', '0', '-0.87', '-0.89', '#fear', '#disgust', 'negative', '-0.88', 'socialistic', 'fascistic', 'capitalistic', 'communistic', 'theocratic']
senticnet['collectivization'] = ['0', '0.77', '0', '0.758', '#interest', '#admiration', 'positive', '0.764', 'slope', 'natural_depression', 'wall', 'formation', 'massif']
senticnet['collector'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'child', 'young_child', 'numismatics', 'collect_item', 'stamp']
senticnet['collector_edition'] = ['0.031', '-0.10', '0', '0', '#joy', '#surprise', 'positive', '0.07', 'teddy_bear', 'beanie_baby', 'yo-yo', 'board_game', 'toy']
senticnet['college'] = ['-0.93', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.86', 'school', 'school_system', 'class', 'time_waster', 'time_waster_class']
senticnet['college-level'] = ['0', '0.824', '0', '0.829', '#interest', '#admiration', 'positive', '0.827', 'lower-level', 'upper-division', 'upper-level', 'graduate-level', 'mid-level']
senticnet['college_affordability'] = ['-0.07', '0', '0.073', '-0.26', '#sadness', '#disgust', 'negative', '-0.13', 'tuition', 'private_tuition', 'pay_tuition', 'enroll_class', 'enroll_school']
senticnet['college_basketball'] = ['0.035', '0.155', '0', '0', '#joy', '#interest', 'positive', '0.095', 'basketball_court', 'basketball_game', 'basketball', 'ball', 'sport']
senticnet['college_class'] = ['0.2', '0.154', '-0.08', '0.166', '#joy', '#admiration', 'positive', '0.11', 'laboratory', 'elementary_school', 'science_class', 'lab', 'school_session']
senticnet['college_course'] = ['0.93', '0.734', '0', '0.958', '#joy', '#admiration', 'positive', '0.874', 'scholar', 'school_division', 'group_student', 'pupil', 'learning_place']
senticnet['college_degree'] = ['0.059', '0', '0', '0.076', '#joy', '#admiration', 'positive', '0.068', 'college_student', 'teacher', 'graduate_student', 'professor', 'student']
senticnet['college_dorm'] = ['0', '0', '0.012', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'shack', 'cabin', 'hostel', 'dormitory', 'student_home']
senticnet['college_entrance_exam'] = ['-0.22', '-0.00', '-0.09', '-0.03', '#sadness', '#fear', 'negative', '-0.08', 'failure', 'bad_grade', 'a_lot_of_stress', 'fail', 'further_goal']
senticnet['college_football'] = ['0.728', '0.83', '0', '0', '#joy', '#interest', 'positive', '0.779', 'ball', 'bounce', 'ball_bounce', 'soccer', 'play_football']
senticnet['college_girl'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lowerclassman', 'underclassman', 'senior', 'undergrad', 'collegian']
senticnet['college_major'] = ['0', '0.084', '0', '0.065', '#interest', '#admiration', 'positive', '0.075', 'physic', 'chemistry', 'science', 'geology', 'school_course']
senticnet['college_professor'] = ['0.904', '0.932', '0', '0.976', '#interest', '#admiration', 'positive', '0.937', 'reliability', 'husband_wife', 'good_teacher', 'smart_man', 'voyeur']
senticnet['college_sandhurst'] = ['0.059', '0.076', '-0.05', '0.079', '#interest', '#admiration', 'positive', '0.039', 'noble', 'king', 'earl', 'duchess', 'duke']
senticnet['college_student'] = ['0.059', '0', '0', '0.076', '#joy', '#admiration', 'positive', '0.068', 'college_degree', 'teacher', 'graduate_student', 'professor', 'student']
senticnet['college_teacher'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lecturer', 'teacher', 'instructor', 'professor', 'teach_lesson']
senticnet['college_tuition'] = ['-0.07', '0', '0.073', '-0.26', '#sadness', '#disgust', 'negative', '-0.13', 'tuition', 'private_tuition', 'pay_tuition', 'enroll_class', 'enroll_school']
senticnet['collegia'] = ['0.605', '0.647', '-0.26', '0.323', '#joy', '#interest', 'positive', '0.327', 'friendly', 'brotherly', 'amicableness', 'amicably', 'amicable']
senticnet['collegial'] = ['0.096', '0.182', '-0.09', '0.198', '#interest', '#admiration', 'positive', '0.094', 'female_parent', 'relative', 'kin', 'relate_person', 'aunt_uncle']
senticnet['collegiality'] = ['0.768', '0.792', '0', '0', '#joy', '#interest', 'positive', '0.78', 'win', 'strategize', 'try_best', 'catch_ball', 'lose_game']
senticnet['collegian'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['collegiate_athlete'] = ['0', '0', '0', '0.97', '#admiration', '#admiration', 'positive', '0.97', 'increase_strength', 'keep_fit', 'fit_person', 'exercise', 'strengthen_heart']
senticnet['collegiate_basketball'] = ['0.035', '0.155', '0', '0', '#joy', '#interest', 'positive', '0.095', 'basketball_court', 'basketball_game', 'basketball', 'ball', 'sport']
senticnet['collegiate_football'] = ['0.728', '0.83', '0', '0', '#joy', '#interest', 'positive', '0.779', 'ball', 'bounce', 'ball_bounce', 'soccer', 'play_football']
senticnet['collegium_musicum'] = ['0.99', '0', '0', '0.719', '#joy', '#admiration', 'positive', '0.855', 'instrumental', 'lyric', 'lyrics', 'sing', 'melody']
senticnet['colletotrichum'] = ['-0.93', '0', '0.699', '-0.91', '#sadness', '#disgust', 'negative', '-0.84', 'onion_smut', 'head_smut', 'stinking_smut', 'flag_smut_fungus', 'covered_smut']
senticnet['collide'] = ['-0.12', '0', '-0.09', '-0.04', '#sadness', '#fear', 'negative', '-0.08', 'hold_two_together', 'install_window', 'float_through_air', 'spring_up', 'collision']
senticnet['collide_with'] = ['-0.53', '0', '0.57', '-0.73', '#anger', '#disgust', 'negative', '-0.61', 'run_into', 'strike', 'hit', 'impinge_on', 'bump_into']
senticnet['collided'] = ['-0.80', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.83', 'head-on', 'collide', 'collision', 'collides', 'colliding']
senticnet['collided_head'] = ['-0.90', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.86', 'trauma', 'car_wreck', 'broken_bone', 'bad_knee', 'wreck']
senticnet['collides'] = ['-0.79', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.82', 'collided', 'collide', 'collision', 'head-on', 'colliding']
senticnet['collides_with'] = ['-0.12', '0', '-0.09', '-0.04', '#sadness', '#fear', 'negative', '-0.08', 'hold_two_together', 'install_window', 'float_through_air', 'spring_up', 'collision']
senticnet['colliding'] = ['-0.79', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.82', 'collided', 'collide', 'collision', 'collides', 'head-on']
senticnet['collimate'] = ['0', '0', '-0.74', '-0.79', '#fear', '#disgust', 'negative', '-0.76', 'analogue', 'analog', 'line_of_latitude', 'latitude', 'parallel']
senticnet['collimated_beam'] = ['-0.50', '0', '0.75', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'beam', 'beam_of_light', 'sunbeam', 'ray', 'ray_of_light']
senticnet['collimated_light'] = ['-0.50', '0', '0.75', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'beam', 'beam_of_light', 'sunbeam', 'ray', 'ray_of_light']
senticnet['collin_backowski'] = ['0', '-0.03', '-0.10', '-0.07', '#fear', '#disgust', 'negative', '-0.04', 'autumn', 'winter', 'skier', 'snow_fall', 'volcano']
senticnet['collinearity'] = ['-0.54', '0', '0.979', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'unlikeness', 'dissimilitude', 'difference', 'heterology', 'disparateness']
senticnet['collision'] = ['-0.90', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.86', 'trauma', 'car_wreck', 'broken_bone', 'bad_knee', 'wreck']
senticnet['collision_between'] = ['-0.12', '0', '-0.09', '-0.04', '#sadness', '#fear', 'negative', '-0.08', 'hold_two_together', 'install_window', 'float_through_air', 'spring_up', 'collision']
senticnet['collision_detection_csma_cd'] = ['0', '0.089', '0.01', '0.118', '#interest', '#admiration', 'positive', '0.066', 'crate', 'mom_house', 'sack', 'poker_game', 'safekeep']
senticnet['collision_induced_dissociation'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'blowup', 'discharge', 'backfire', 'blast']
senticnet['colloidal'] = ['0.976', '0.786', '0', '0', '#joy', '#interest', 'positive', '0.881', 'combustible', 'state_matter', 'natural_resource', 'liquid_fuel', 'fossil_fuel']
senticnet['colloidal_suspension'] = ['-0.78', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.83', 'break', 'end', 'die', 'fall_apart', 'go_to_piece']
senticnet['colloquial'] = ['-0.72', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.79', 'non-standard', 'ordinal', 'domain-specific', 'nonstandard', 'syntactical']
senticnet['colloquial_english'] = ['0', '0.156', '-0.06', '0.241', '#interest', '#admiration', 'positive', '0.11', 'mixture', 'noisy_place', 'make_go', 'move_vehicle', 'big_car']
senticnet['colloquial_speech'] = ['0', '0.156', '-0.06', '0.241', '#interest', '#admiration', 'positive', '0.11', 'mixture', 'noisy_place', 'make_go', 'move_vehicle', 'big_car']
senticnet['colloquial_style'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'mediacy', 'mediateness', 'characteristic', 'indirectness']
senticnet['colloquial_usage'] = ['0', '0.115', '-0.10', '0.249', '#interest', '#admiration', 'positive', '0.087', 'idiom', 'remark', 'grammar_part', 'speech_part', 'sentence']
senticnet['colloquialism'] = ['0', '0.115', '-0.10', '0.249', '#interest', '#admiration', 'positive', '0.087', 'idiom', 'remark', 'grammar_part', 'speech_part', 'sentence']
senticnet['colloquially'] = ['0.072', '0.244', '-0.20', '0.252', '#interest', '#admiration', 'positive', '0.091', 'doe', 'sweet', 'live_organism', 'love', 'alive']
senticnet['colloquium'] = ['0', '0.214', '0.012', '0.208', '#interest', '#admiration', 'positive', '0.137', 'lecture_hall', 'lecture_theater', 'lesson', 'lecture', 'school_session']
senticnet['collotype'] = ['0', '0.067', '0', '0.077', '#interest', '#admiration', 'positive', '0.072', 'pencil_mark', 'use_ink', 'script', 'pencil_paper', 'written_text']
senticnet['collusion'] = ['0', '0', '0.832', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'secret', 'conspiracy', 'purpose', 'prevarication', 'suborn']
senticnet['collusive'] = ['-0.94', '0', '0.701', '0', '#sadness', '#anger', 'negative', '-0.82', 'connive', 'conniving', 'intrigue', 'calculating', 'scheme']
senticnet['colluvium'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'fossil', 'oil', 'dark_liquid', 'smithsonian_institution', 'inert_gas']
senticnet['colly'] = ['-0.62', '0', '0.713', '-0.98', '#anger', '#disgust', 'negative', '-0.77', 'muddy_up', 'muck_up', 'foul', 'blemish', 'smear']
senticnet['colmar_pocket'] = ['0.737', '-0.96', '0', '0.552', '#joy', '#surprise', 'positive', '0.75', 'prominence', 'hump', 'bump', 'swelling', 'frontal_eminence']
senticnet['colmn_mr'] = ['0', '0.105', '0', '0.084', '#interest', '#admiration', 'positive', '0.095', 'archive', 'print_medium', 'quiet_place', 'register', 'media']
senticnet['coloboma'] = ['-0.88', '0', '0.762', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'divergent_strabismus', 'exotropia', 'walleye', 'abnormality', 'abnormalcy']
senticnet['colocasia'] = ['0.826', '0.787', '0', '0', '#joy', '#interest', 'positive', '0.806', 'fig', 'baby_flower', 'carpel', 'strawberry', 'star_anise']
senticnet['cologne'] = ['0.053', '0.078', '0', '0', '#joy', '#interest', 'positive', '0.066', 'wash_clothes', 'wash_body', 'clean_clothes', 'get_clean', 'wash_hand']
senticnet['colombe'] = ['-0.81', '0', '0.885', '-0.92', '#anger', '#disgust', 'negative', '-0.87', 'miss', 'girl', 'damsel', 'maiden', 'damoiselle']
senticnet['colonel'] = ['0.045', '0', '0', '0.088', '#joy', '#admiration', 'positive', '0.067', 'leader', 'seaman', 'captain', 'officer', 'traitor']
senticnet['colonial_conquest'] = ['-0.75', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.82', 'oppression', 'caged_lion', 'slave', 'captivity', 'misery']
senticnet['colonial_domination'] = ['-0.75', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.82', 'oppression', 'caged_lion', 'slave', 'captivity', 'misery']
senticnet['colonial_exploitation'] = ['-0.75', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.82', 'oppression', 'caged_lion', 'slave', 'captivity', 'misery']
senticnet['colonial_occupation'] = ['0', '0.112', '-0.04', '0.099', '#interest', '#admiration', 'positive', '0.054', 'profession', 'career', 'vocation', 'important_job', 'laborer']
senticnet['colonial_oppression'] = ['-0.75', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.82', 'oppression', 'caged_lion', 'slave', 'captivity', 'misery']
senticnet['colonial_period'] = ['0.978', '-0.60', '0', '0.895', '#joy', '#admiration', 'positive', '0.825', 'period', 'quattrocento', 'twentieth_century', 'time_period', 'period_of_time']
senticnet['colonial_regime'] = ['0.887', '0.748', '0', '0.954', '#joy', '#admiration', 'positive', '0.863', 'political_action', 'political', 'mediocrity', 'discrimination', 'independent']
senticnet['colonial_rule'] = ['0.887', '0.748', '0', '0.954', '#joy', '#admiration', 'positive', '0.863', 'political_action', 'political', 'mediocrity', 'discrimination', 'independent']
senticnet['colonialism'] = ['-0.65', '0', '0.691', '-0.54', '#sadness', '#anger', 'negative', '-0.63', 'moronity', 'mental_deficiency', 'imbecility', 'stupidity', 'mental_defectiveness']
senticnet['colonic'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'anus', 'scrotum', 'shoulder', 'liver', 'waist']
senticnet['colonization'] = ['0', '0', '-0.99', '-0.77', '#fear', '#disgust', 'negative', '-0.88', 'conquest', 'empire', 'conquest', 'conquered', 'subjugation']
senticnet['colonnade'] = ['0.029', '0.087', '0.065', '0.137', '#interest', '#admiration', 'positive', '0.047', 'archway', 'aisle', 'mat', 'passageway', 'right_angle']
senticnet['colonnaded'] = ['0.894', '-0.98', '0', '0.684', '#joy', '#surprise', 'positive', '0.855', 'amphistylar', 'columnar', 'pillared', 'columned', 'columniform']
senticnet['colonoscope'] = ['0.255', '0.182', '-0.06', '0.314', '#joy', '#admiration', 'positive', '0.173', 'doctor_office', 'urethroscope', 'sigmoidoscope', 'hospital', 'check_health']
senticnet['colony'] = ['0', '0', '0', '0.744', '#admiration', '#admiration', 'positive', '0.744', 'committee', 'association', 'egalitarian_society', 'organization', 'population']
senticnet['color'] = ['0.053', '0.078', '0', '0', '#joy', '#interest', 'positive', '0.066', 'colorful', 'bright_color', 'secondary_color', 'color_rainbow', 'primary_color']
senticnet['color_bar'] = ['-0.07', '0', '0.073', '-0.26', '#sadness', '#disgust', 'negative', '-0.13', 'iron_curtain', 'bamboo_curtain', 'ideological_barrier', 'color_line', 'barrier']
senticnet['color_blindness'] = ['-0.74', '0', '0.793', '-0.62', '#sadness', '#anger', 'negative', '-0.72', 'visual_impairment', 'visual_defect', 'vision_defect', 'visual_disorder', 'monochromacy']
senticnet['color_cable_tv'] = ['0.866', '0.833', '0', '0', '#joy', '#interest', 'positive', '0.849', 'alcohol', 'porter', 'concierge', 'beer_mug', 'bar_stool']
senticnet['color_clothes'] = ['0', '0.798', '0', '0', '#interest', '#interest', 'positive', '0.798', 'iron_shirt', 'sit_on_beach', 'absorb_moisture', 'iron_clothes', 'shine_shoes']
senticnet['color_composite'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'indigo', 'yellow', 'water_color', 'red', 'violet']
senticnet['color_constancy'] = ['0.685', '-0.91', '0', '0.566', '#joy', '#surprise', 'positive', '0.721', 'audibility', 'audibleness', 'visibleness', 'visibility', 'physical_property']
senticnet['color_cyan'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'indigo', 'yellow', 'water_color', 'red', 'violet']
senticnet['color_glass'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'see_out', 'house_open', 'stain_glass', 'open_close', 'look_out']
senticnet['color_line'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'bamboo_curtain', 'color_bar', 'ideological_barrier', 'barrier']
senticnet['color_liquid_crystal'] = ['0.044', '0.08', '0', '-0.03', '#joy', '#interest', 'positive', '0.031', 'screen', 'pixel', 'visual_information', 'image', 'computer_monitor']
senticnet['color_map'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'illuminate_room', 'give_light', 'light', 'light_bulb', 'lantern']
senticnet['color_paint'] = ['0', '0', '0.046', '-0.03', '#anger', '#disgust', 'negative', '-0.04', 'many_shades', 'paint', 'bright_color', 'color', 'secondary_color']
senticnet['color_paper'] = ['0.982', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.954', 'photo_copy', 'writing_paper', 'printing_paper', 'note_book', 'white_paper']
senticnet['color_rainbow'] = ['0.714', '0.924', '0', '0', '#joy', '#interest', 'positive', '0.819', 'colorful', 'color', 'bright_color', 'happiness', 'primary_color']
senticnet['color_red'] = ['-0.02', '0', '0.068', '0', '#sadness', '#anger', 'negative', '-0.04', 'finite', 'danger', 'attention', 'stop', 'bright_red']
senticnet['color_saturation'] = ['-0.05', '0.07', '0', '0.062', '#interest', '#admiration', 'positive', '0.027', 'salty', 'fine_grain', 'powder', 'granulate', 'food_flavor']
senticnet['color_scanner'] = ['-0.08', '0', '0.011', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'deflate', 'read_cover', 'obtain_information', 'upgrade', 'spread_out']
senticnet['color_vision_deficiency'] = ['-0.72', '0', '0.736', '-0.95', '#anger', '#disgust', 'negative', '-0.80', 'visual_impairment', 'visual_defect', 'vision_defect', 'visual_disorder', 'monochromacy']
senticnet['color_wheel'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'indigo', 'yellow', 'water_color', 'red', 'violet']
senticnet['colorable_claim'] = ['-0.50', '0', '0.89', '-0.78', '#anger', '#disgust', 'negative', '-0.72', 'excess', 'degree', 'grade', 'level', 'immoderateness']
senticnet['coloradillo'] = ['0.938', '-0.77', '0', '0.944', '#joy', '#admiration', 'positive', '0.885', 'bush', 'scarlet_bush', 'scarlet_hamelia', 'hamelia', 'shrub']
senticnet['colorant'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['coloration'] = ['0.62', '-0.59', '0', '0.967', '#joy', '#admiration', 'positive', '0.728', 'rainbow', 'color', 'paint', 'colorful', 'protective_coloration']
senticnet['coloratura'] = ['0.824', '-0.95', '0', '0.513', '#joy', '#surprise', 'positive', '0.765', 'clearcutness', 'explicitness', 'preciseness', 'perspicuity', 'perspicuousness']
senticnet['colordef'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'illuminate_room', 'give_light', 'light', 'light_bulb', 'lantern']
senticnet['colored'] = ['0', '0', '0.974', '-0.73', '#anger', '#disgust', 'negative', '-0.85', 'pervasively', 'pervading', 'wall-to-wall', 'affecting', 'pervasive']
senticnet['colored_chalk'] = ['0.031', '0.08', '0.023', '0.069', '#interest', '#admiration', 'positive', '0.039', 'marker', 'graphite_pencil', 'pen', 'mechanical_graphite_pencil', 'stamp_pad']
senticnet['colored_green'] = ['0', '0.152', '-0.08', '0.208', '#interest', '#admiration', 'positive', '0.091', 'to_go', 'plant', 'depart', 'green_color', 'tree']
senticnet['colored_ink'] = ['0.022', '-0.05', '0.079', '-0.11', '#anger', '#disgust', 'negative', '-0.02', 'stationery', 'ink', 'ball_point_pen', 'refill', 'note']
senticnet['colored_pencil'] = ['0.031', '0.08', '0.023', '0.069', '#interest', '#admiration', 'positive', '0.039', 'marker', 'graphite_pencil', 'pen', 'mechanical_graphite_pencil', 'stamp_pad']
senticnet['colored_silk'] = ['-0.06', '-0.01', '0.05', '-0.07', '#sadness', '#disgust', 'negative', '-0.04', 'embroidery', 'woven', 'cover_wind', 'torn', 'use_needle']
senticnet['colored_yellow'] = ['-0.45', '0.081', '-0.25', '0', '#sadness', '#fear', 'negative', '-0.20', 'green_yellow', 'color', 'blue_green', 'cyan', 'purple']
senticnet['colorful'] = ['0.043', '0.084', '0', '0.139', '#interest', '#admiration', 'positive', '0.089', 'sexual', 'pink', 'fragrant', 'purple', 'green']
senticnet['colorful_foliage'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'tropical_fish', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['colorful_personality'] = ['0.563', '-0.81', '0', '0.577', '#surprise', '#admiration', 'positive', '0.65', 'mortal', 'person', 'individual', 'performer', 'artist']
senticnet['colorful_reef_fish'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'autumn_leaf', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['colorfully'] = ['0.512', '0.658', '-0.25', '0.256', '#joy', '#interest', 'positive', '0.293', 'riotously', 'expansively', 'ebulliently', 'zestfulness', 'gusto']
senticnet['coloring_agent'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['colorless_liquid'] = ['-0.67', '0', '0.503', '-0.67', '#sadness', '#disgust', 'negative', '-0.61', 'stink_up', 'smell_up', 'stink_out', 'incense', 'perfume']
senticnet['colorless_solid'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'enhance', 'intensify', 'deepen', 'potentiate', 'heighten']
senticnet['colorspec'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'illuminate_room', 'give_light', 'light', 'light_bulb', 'lantern']
senticnet['colossal'] = ['0.76', '0', '0', '0.725', '#joy', '#admiration', 'positive', '0.742', 'colossus', 'extremely', 'great', 'large', 'prodigious']
senticnet['colossal_statue'] = ['-0.88', '0', '0.882', '-0.58', '#sadness', '#anger', 'negative', '-0.78', 'mortal', 'behemoth', 'unusual_person', 'aberrant', 'giant']
senticnet['colossally'] = ['-0.71', '0', '0.995', '0', '#sadness', '#anger', 'negative', '-0.85', 'horrifying', 'atrocious', 'dreadfully', 'horrifyingly', 'horrified']
senticnet['colossus'] = ['-0.88', '0', '0.882', '-0.58', '#sadness', '#anger', 'negative', '-0.78', 'mortal', 'behemoth', 'unusual_person', 'aberrant', 'giant']
senticnet['colostomy'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'anus', 'scrotum', 'shoulder', 'liver', 'waist']
senticnet['colourant'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['coloured_pencil'] = ['0.031', '0.08', '0.023', '0.069', '#interest', '#admiration', 'positive', '0.039', 'marker', 'graphite_pencil', 'pen', 'mechanical_graphite_pencil', 'stamp_pad']
senticnet['coloured_red'] = ['-0.45', '0.081', '-0.25', '0', '#sadness', '#fear', 'negative', '-0.20', 'green_yellow', 'color', 'blue_green', 'cyan', 'purple']
senticnet['coloured_silk'] = ['0.035', '0', '0', '0.116', '#joy', '#admiration', 'positive', '0.076', 'maroon', 'color', 'bright_color', 'hair_colored', 'cool_color']
senticnet['colourful'] = ['0.65', '-0.61', '0', '0.711', '#joy', '#admiration', 'positive', '0.658', 'bouncing', 'scintillating', 'spirited', 'peppy', 'zippy']
senticnet['colourless_liquid'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
senticnet['colourless_solid'] = ['-0.81', '0', '0.774', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'acid', 'hypophosphoric_acid', 'periodic_acid', 'oxyacid', 'phosphorous_acid']
senticnet['colposcope'] = ['0.255', '0.182', '-0.06', '0.314', '#joy', '#admiration', 'positive', '0.173', 'doctor_office', 'urethroscope', 'sigmoidoscope', 'hospital', 'check_health']
senticnet['colt'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'big_gun', 'explosive_device', 'firearm', 'cannon', 'civil_war']
senticnet['colt_pistol'] = ['0', '0', '0.729', '-0.84', '#anger', '#disgust', 'negative', '-0.78', 'rifle', 'pistol', 'revolver', 'hydrogen_bomb', 'firearm']
senticnet['colt_revolver'] = ['0', '0', '-0.87', '-0.82', '#fear', '#disgust', 'negative', '-0.84', 'pistol', 'gun', 'hydrogen_bomb', 'rifle', 'shotgun']
senticnet['coltish'] = ['0.582', '-0.78', '0', '0.795', '#surprise', '#admiration', 'positive', '0.72', 'elfish', 'elvish', 'playful', 'frisky', 'sportive']
senticnet['coluber'] = ['-0.68', '0', '0.611', '-0.59', '#sadness', '#anger', 'negative', '-0.63', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['colubrid_snake'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'death_adder', 'ophidian', 'serpent', 'elapid', 'taipan']
senticnet['colubridae_family'] = ['-0.68', '0', '0.611', '-0.59', '#sadness', '#anger', 'negative', '-0.63', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['columbian_mammoth'] = ['0.969', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.96', 'extinct', 'animal', 'ice', 'elephant', 'giant']
senticnet['columbidae'] = ['-0.68', '0', '0.956', '-0.90', '#anger', '#disgust', 'negative', '-0.84', 'giant_moa', 'anomalopteryx', 'moa', 'moa_bird', 'flightless_bird']
senticnet['columbus'] = ['0.731', '0', '0', '0.843', '#joy', '#admiration', 'positive', '0.787', 'find_country', 'learn_much', 'know', 'grasp_knowledge', 'become_knowledgeable']
senticnet['columbus_international_airport'] = ['-0.02', '-0.02', '0.064', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'ocean_water', 'fillet_fish', 'chowder', 'fillet', 'underwater']
senticnet['column'] = ['0.076', '0', '0', '0.104', '#joy', '#admiration', 'positive', '0.09', 'post', 'newspaper_article', 'rail', 'paper_sign', 'peg']
senticnet['column_chromatography'] = ['0.838', '-0.61', '0', '0.556', '#joy', '#surprise', 'positive', '0.671', 'activity', 'natural_process', 'natural_action', 'chromatography', 'paper_chromatography']
senticnet['column_width'] = ['0.734', '0.884', '0', '0', '#joy', '#interest', 'positive', '0.809', 'dimension', 'size', 'mass_measure', 'length_dimension', 'gauge']
senticnet['columnar'] = ['0.897', '-0.98', '0', '0.692', '#joy', '#surprise', 'positive', '0.858', 'amphistylar', 'colonnaded', 'pillared', 'columned', 'columniform']
senticnet['columned'] = ['0.889', '-0.98', '0', '0.688', '#joy', '#surprise', 'positive', '0.854', 'amphistylar', 'colonnaded', 'pillared', 'columnar', 'columniform']
senticnet['columniform'] = ['0.897', '-0.98', '0', '0.692', '#joy', '#surprise', 'positive', '0.858', 'amphistylar', 'colonnaded', 'pillared', 'columnar', 'columned']
senticnet['columnist'] = ['0.811', '-0.63', '0', '0.959', '#joy', '#admiration', 'positive', '0.8', 'drama_critic', 'theater_critic', 'professional', 'art_critic', 'literary_critic']
senticnet['coma'] = ['-0.10', '0.068', '-0.15', '-0.13', '#fear', '#disgust', 'negative', '-0.08', 'loss_of_consciousness', 'concussion', 'control_machine', 'sick', 'crowd_room']
senticnet['comatose'] = ['-0.70', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.84', 'catatonic', 'bedridden', 'paralyzed', 'bed-ridden', 'coma']
senticnet['comatose_body'] = ['0.8', '-0.97', '0', '0.512', '#joy', '#surprise', 'positive', '0.763', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['comb'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'barrette', 'haircut_scissor', 'sculpt_gel', 'hair_shape_gel', 'hair_gel']
senticnet['comb_hair'] = ['0', '0.152', '-0.08', '0.208', '#interest', '#admiration', 'positive', '0.091', 'make_hair_look_nice', 'arrange_hair', 'fix_hair', 'groom_hair', 'style_hair']
senticnet['comb_jelly'] = ['-0.79', '0', '0.709', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'animal', 'animate_being', 'ctenophore', 'invertebrate', 'beast']
senticnet['combat'] = ['-0.06', '0', '-0.09', '-0.18', '#fear', '#disgust', 'negative', '-0.11', 'big_fight', 'battle', 'fight', 'war', 'conflict']
senticnet['combat_corruption'] = ['0', '0', '-0.77', '-0.86', '#fear', '#disgust', 'negative', '-0.82', 'abduction', 'punishment', 'emergency', 'illiteracy', 'heal']
senticnet['combat_fatigue'] = ['-0.75', '0', '0.87', '-0.51', '#sadness', '#anger', 'negative', '-0.71', 'battle_fatigue', 'posttraumatic_stress_disorder', 'combat_neurosis', 'shell_shock', 'anxiety_disorder']
senticnet['combat_helmet'] = ['0.939', '-0.63', '0', '0.653', '#joy', '#admiration', 'positive', '0.741', 'vest', 'waistcoat', 'bulletproof_vest', 'suit_of_armor', 'coat_of_mail']
senticnet['combat_illegal_immigration'] = ['-0.91', '0', '0.637', '-0.59', '#sadness', '#anger', 'negative', '-0.71', 'gauge', 'govern', 'regulate', 'standardize', 'order']
senticnet['combat_malaria'] = ['-0.88', '0', '-0.84', '-0.95', '#sadness', '#disgust', 'negative', '-0.89', 'polio', 'infectious_disease', 'malady', 'sickness', 'ailment']
senticnet['combat_neurosis'] = ['-0.75', '0', '0.87', '-0.51', '#sadness', '#anger', 'negative', '-0.71', 'battle_fatigue', 'combat_fatigue', 'posttraumatic_stress_disorder', 'shell_shock', 'anxiety_disorder']
senticnet['combat_organized_crime'] = ['-0.94', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.88', 'combat', 'uprise', 'big_fight', 'opposition', 'violence']
senticnet['combat_outpost'] = ['-0.66', '0', '0.789', '-0.63', '#sadness', '#anger', 'negative', '-0.69', 'humbug', 'play_trick', 'cozen', 'pull_fast_one_on', 'play_trick_on']
senticnet['combat_terrorism'] = ['-0.94', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.88', 'combat', 'uprise', 'big_fight', 'opposition', 'violence']
senticnet['combat_valor'] = ['0.827', '0.85', '0', '0.743', '#joy', '#interest', 'positive', '0.807', 'chivalry', 'honor', 'almighty', 'admire', 'respect']
senticnet['combat_zone'] = ['-0.03', '0', '-0.08', '-0.20', '#fear', '#disgust', 'negative', '-0.10', 'gun_shop', 'arsenal', 'holster', 'armory', 'gun_store']
senticnet['combatant'] = ['0.833', '-0.97', '0', '0.91', '#surprise', '#admiration', 'positive', '0.906', 'fencer', 'battler', 'belligerent', 'fighter', 'scrapper']
senticnet['combating_crime'] = ['-0.94', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.88', 'combat', 'uprise', 'big_fight', 'opposition', 'violence']
senticnet['combating_fraud'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'mail_fraud', 'swindle', 'rig']
senticnet['combating_organized_crime'] = ['-0.94', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.88', 'combat', 'uprise', 'big_fight', 'opposition', 'violence']
senticnet['combating_poverty'] = ['-0.94', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.88', 'combat', 'uprise', 'big_fight', 'opposition', 'violence']
senticnet['combating_terror'] = ['-0.94', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.88', 'combat', 'uprise', 'big_fight', 'opposition', 'violence']
senticnet['combating_terrorism'] = ['-0.94', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.88', 'combat', 'uprise', 'big_fight', 'opposition', 'violence']
senticnet['combative'] = ['-0.75', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.73', 'combatively', 'litigious', 'adversarial', 'pugnacious', 'combat']
senticnet['combative_approach'] = ['0', '0', '-0.85', '-0.93', '#fear', '#disgust', 'negative', '-0.89', 'anger', 'animosity', 'hostility', 'malice', 'grudge']
senticnet['combative_spirit'] = ['0.827', '0.913', '0', '0.867', '#interest', '#admiration', 'positive', '0.869', 'good_habit', 'rivalry', 'competitive', 'competitive_nature', 'determination']
senticnet['combatively'] = ['-0.75', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'combative', 'litigious', 'adversarial', 'pugnacious', 'combat']
senticnet['combe_down'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
senticnet['combed_cotton'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'white_cotton', 'sheepskin_material', 'woven_sheepskin', 'sheepskin', 'clothes_type']
senticnet['combination'] = ['0.03', '0.215', '-0.06', '0.18', '#interest', '#admiration', 'positive', '0.09', 'combo', 'hold_coin', 'money_storage', 'money_place', 'money_holder']
senticnet['combination_therapy'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'indinavir', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['combinative'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'integrative', 'combinatory', 'consolidative', 'unifying', 'combinatorial']
senticnet['combinatorial'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'combinatory', 'consolidative', 'unifying', 'integrative']
senticnet['combinatorial_geometry'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'combinatory', 'consolidative', 'unifying', 'integrative']
senticnet['combinatorial_optimization'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'combinatory', 'consolidative', 'unifying', 'integrative']
senticnet['combinatorial_structure'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'combinatory', 'consolidative', 'unifying', 'integrative']
senticnet['combinatorially'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'combinatory', 'consolidative', 'unifying', 'integrative']
senticnet['combinatory'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'integrative', 'consolidative', 'unifying', 'combinatorial']
senticnet['combine'] = ['0.884', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.874', 'join_together', 'final', 'include', 'math_function', 'bring_together']
senticnet['combine_harvester'] = ['-0.99', '0', '0.827', '-0.54', '#sadness', '#anger', 'negative', '-0.78', 'treat', 'cover', 'handle', 'theologize', 'deal']
senticnet['combine_sound'] = ['0.067', '0.193', '0', '0.294', '#interest', '#admiration', 'positive', '0.185', 'compose_music', 'placement', 'small_group', 'mix_track', 'location']
senticnet['combined_apparent_magnitude'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['combing'] = ['-0.96', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.84', 'scouring', 'scoured', 'scour', 'scour', 'trawled']
senticnet['comblike'] = ['-0.02', '-0.05', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.02', 'motor_oil', 'make_toy', 'engine', 'emission', 'castor']
senticnet['combo'] = ['0.03', '0.215', '-0.06', '0.18', '#interest', '#admiration', 'positive', '0.09', 'combination', 'hold_coin', 'money_storage', 'money_place', 'money_holder']
senticnet['combo_amp'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'lever']
senticnet['combo_box'] = ['0.146', '0.095', '-0.04', '0', '#joy', '#interest', 'positive', '0.065', 'paper_menu', 'diner', 'cafe', 'restaurant', 'dine_room']
senticnet['combo_pass'] = ['0.709', '0', '0', '0.989', '#joy', '#admiration', 'positive', '0.849', 'theme_park', 'short_cut', 'save_time', 'multiple_entry', 'ticket']
senticnet['combustible'] = ['-0.97', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.86', 'flame', 'flammable', 'dangerous', 'incendiary', 'heat']
senticnet['combustible_liquid'] = ['-0.90', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.85', 'flame', 'combustible', 'dangerous', 'incendiary', 'heat']
senticnet['combustible_material'] = ['-0.76', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.77', 'fire', 'wood', 'wood_pulp', 'steel_wool', 'campfire']
senticnet['combustion'] = ['-0.90', '0', '0.825', '-0.61', '#sadness', '#anger', 'negative', '-0.78', 'flaming', 'deflagration', 'fire', 'flame', 'internal_combustion']
senticnet['combustion_chamber'] = ['0.042', '-0.02', '0.035', '0.065', '#joy', '#admiration', 'positive', '0.023', 'food_container', 'metal_cylinder', 'metal_container', 'small_ball', 'storage_container']
senticnet['combustion_reaction'] = ['-0.90', '0', '0.825', '-0.61', '#sadness', '#anger', 'negative', '-0.78', 'flaming', 'combustion', 'fire', 'flame', 'internal_combustion']
senticnet['combustion_zone'] = ['-0.90', '0', '0.825', '-0.61', '#sadness', '#anger', 'negative', '-0.78', 'flaming', 'combustion', 'fire', 'flame', 'internal_combustion']
senticnet['combustor'] = ['0.843', '-0.99', '0', '0.706', '#joy', '#surprise', 'positive', '0.849', 'gadgetry', 'gimbal', 'contrivance', 'mod_con', 'device']
senticnet['comcast_interactive'] = ['-0.63', '0', '0.862', '-0.52', '#sadness', '#anger', 'negative', '-0.67', 'knot', 'bunch', 'tuft', 'tussock', 'agglomeration']
senticnet['comdex'] = ['-0.79', '0', '0.948', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['come'] = ['-0.03', '0.212', '-0.17', '0.33', '#interest', '#admiration', 'positive', '0.084', 'arrive', 'tow', 'take', 'permission', 'continue']
senticnet['come-from-behind'] = ['0', '-0.84', '0', '0.981', '#surprise', '#admiration', 'positive', '0.914', 'three-set', 'hard-fought', 'victory', 'low-scoring', 'nail-biting']
senticnet['come_across'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'stumble', 'tripped_over', 'lose_balance', 'stammer', 'stutter']
senticnet['come_amid'] = ['0', '0.909', '0', '0.948', '#interest', '#admiration', 'positive', '0.928', 'set_off', 'shake_up', 'scandalmongering', 'sensationalistic', 'sensational']
senticnet['come_back'] = ['0.04', '0.269', '-0.20', '0.251', '#interest', '#admiration', 'positive', '0.089', 'donation', 'investment_return', 'extra_money', 'pay_back', 'return']
senticnet['come_back_home'] = ['0', '0', '0', '0.087', '#admiration', '#admiration', 'positive', '0.087', 'travel', 'get_plane', 'get_bus', 'go_new_place', 'reach_destination']
senticnet['come_conclusion'] = ['-0.09', '0.106', '0', '0.093', '#interest', '#admiration', 'positive', '0.036', 'figure_out', 'revelation', 'arrive_conclusion', 'resolve_problem', 'deduce']
senticnet['come_crashing_down'] = ['-0.64', '0', '0.717', '-0.98', '#anger', '#disgust', 'negative', '-0.78', 'debacle', 'licking', 'whitewash', 'finish', 'failure']
senticnet['come_decision'] = ['0.703', '0.748', '0', '0', '#joy', '#interest', 'positive', '0.726', 'consider_fact', 'look_evidence', 'sit_jury', 'find_out_fact', 'make_decision']
senticnet['come_down'] = ['0.959', '-0.81', '0', '0', '#joy', '#surprise', 'positive', '0.885', 'down', 'snow', 'water_fall', 'water_drop', 'water_droplet']
senticnet['come_early'] = ['0', '0.994', '0', '0.775', '#interest', '#admiration', 'positive', '0.885', 'wait_patiently', 'learn_patience', 'conformist_attitude', 'line_up', 'stand_queue']
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senticnet['commence_immediately'] = ['0', '0.067', '0', '0.062', '#interest', '#admiration', 'positive', '0.065', 'social_skill', 'show_up', 'go_abroad', 'go_new_place', 'visit']
senticnet['commencement'] = ['0.658', '-0.52', '0', '0.626', '#joy', '#admiration', 'positive', '0.602', 'point_in_time', 'threshold', 'birth', 'incipiency', 'incipience']
senticnet['commencement_ceremony'] = ['0.82', '0', '0', '0.885', '#joy', '#admiration', 'positive', '0.853', 'religious_event', 'sunday', 'happy_event', 'tradition', 'wedding']
senticnet['commend'] = ['0.62', '-0.99', '0', '0.621', '#surprise', '#admiration', 'positive', '0.745', 'hand', 'entrust', 'reach', 'charge', 'give']
senticnet['commendable'] = ['0.575', '0.636', '-0.26', '0.318', '#joy', '#interest', 'positive', '0.315', 'laudably', 'admirably', 'praiseworthily', 'look_up_to', 'admirable']
senticnet['commendation'] = ['0.857', '-0.77', '0', '0.878', '#joy', '#admiration', 'positive', '0.836', 'hand', 'round', 'approval', 'clapping', 'handclap']
senticnet['commendatory'] = ['0.722', '-0.98', '0', '0.832', '#surprise', '#admiration', 'positive', '0.845', 'laudatory', 'praiseful', 'praising', 'encomiastic', 'complimentary']
senticnet['commensal_bacteria'] = ['0.702', '-0.52', '0', '0.958', '#joy', '#admiration', 'positive', '0.728', 'somatic_cell', 'vegetative_cell', 'fixed_phagocyte', 'macrophage', 'free_phagocyte']
senticnet['commensurability'] = ['0.789', '-0.99', '0', '0.54', '#joy', '#surprise', 'positive', '0.776', 'imperfection', 'imperfectness', 'bugginess', 'lameness', 'sickness']
senticnet['commensurable'] = ['0.902', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.925', 'answerable', 'commensurate', 'measurable', 'standard', 'adjust']
senticnet['commensurate'] = ['0.907', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.925', 'answerable', 'commensurable', 'measurable', 'standard', 'adjust']
senticnet['comment'] = ['-0.07', '0.112', '-0.06', '0.191', '#interest', '#admiration', 'positive', '0.043', 'response', 'remark', 'answer_back', 'retort', 'explain']
senticnet['commentary'] = ['0', '0.857', '0', '0.929', '#interest', '#admiration', 'positive', '0.893', 'comment', 'beijing', 'daily', 'taiwan', 'government']
senticnet['commentator'] = ['0.811', '-0.63', '0', '0.959', '#joy', '#admiration', 'positive', '0.8', 'drama_critic', 'theater_critic', 'professional', 'art_critic', 'literary_critic']
senticnet['commerce'] = ['0', '0.148', '0', '0.167', '#interest', '#admiration', 'positive', '0.158', 'economics', 'economic_activity', 'exchange_goods', 'make_rich', 'sell']
senticnet['commerce_raider'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'submarine', 'lifeboat', 'titanic', 'large_ship', 'canoe']
senticnet['commercial'] = ['0', '-0.01', '-0.06', '-0.07', '#fear', '#disgust', 'negative', '-0.04', 'satire', 'presentation', 'circus_performer', 'demonstrate', 'ads']
senticnet['commercial-grade'] = ['0', '0', '0.914', '-0.81', '#anger', '#disgust', 'negative', '-0.86', 'professional-grade', 'industrial-grade', 'heavy-duty', 'commercial-quality', 'weather-resistant']
senticnet['commercial-quality'] = ['0', '0', '0.911', '-0.82', '#anger', '#disgust', 'negative', '-0.86', 'professional-grade', 'industrial-grade', 'heavy-duty', 'commercial-grade', 'weather-resistant']
senticnet['commercial_document'] = ['0.921', '-0.57', '0', '0.962', '#joy', '#admiration', 'positive', '0.818', 'book_token', 'meal_ticket', 'luncheon_voucher', 'twofer', 'coupon']
senticnet['commercial_driver_license'] = ['0', '-0.01', '0', '0.064', '#surprise', '#admiration', 'positive', '0.042', 'car_gas', 'car_power', 'fossil_fuel', 'run_car', 'car_engine']
senticnet['commercial_fisherman'] = ['0.073', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'angler', 'informal_rod', 'archaic_fisher', 'rare_piscator', 'fishing_line']
senticnet['commercial_intercourse'] = ['0.621', '0.648', '-0.25', '0.296', '#joy', '#interest', 'positive', '0.328', 'pacifically', 'repose', 'peace-loving', 'peaceably', 'peace_of_mind']
senticnet['commercial_motor_vehicle'] = ['0', '-0.01', '0', '0.064', '#surprise', '#admiration', 'positive', '0.042', 'car_gas', 'car_power', 'fossil_fuel', 'run_car', 'car_engine']
senticnet['commercial_sexual_exploitation'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'vassalage']
senticnet['commercial_thoroughfare'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'artery', 'skeletal_system', 'groin', 'neck', 'muscle']
senticnet['commercial_traveller'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'pharmaceutical_chemist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['commercialisation'] = ['0', '0.871', '0', '0.966', '#interest', '#admiration', 'positive', '0.918', 'commercialization', 'phase', 'development', 'research', 'project']
senticnet['commercialism'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'nonsense', 'meaninglessness', 'bunk', 'absurdity', 'hokum']
senticnet['commercialization'] = ['0', '0.881', '0', '0.969', '#interest', '#admiration', 'positive', '0.925', 'commercialisation', 'phase', 'development', 'research', 'project']
senticnet['commercially_available'] = ['-0.05', '0.161', '-0.12', '0.181', '#interest', '#admiration', 'positive', '0.039', 'usable', 'redo', 'timeliness', 'question_starter', 'replay']
senticnet['commercially_successful'] = ['0.98', '-0.74', '0', '0.984', '#joy', '#admiration', 'positive', '0.903', 'prosperous', 'flourishing', 'palmy', 'prospering', 'thriving']
senticnet['commie'] = ['-0.53', '0', '0.591', '-0.59', '#anger', '#disgust', 'negative', '-0.57', 'mug', 'sitting_duck', 'easy_mark', 'dupe', 'mortal']
senticnet['commination'] = ['0.638', '-0.83', '0', '0.852', '#surprise', '#admiration', 'positive', '0.776', 'blessing', 'grace', 'orison', 'benediction', 'supplication']
senticnet['commingle'] = ['0', '0', '0.966', '-0.91', '#anger', '#disgust', 'negative', '-0.94', 'flux', 'mix', 'blend', 'conflate', 'immix']
senticnet['commiserate'] = ['-0.86', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.81', 'sympathize', 'commiseration', 'feel_sorry', 'ruth', 'pathos']
senticnet['commiseration'] = ['0.702', '0', '0', '0.928', '#joy', '#admiration', 'positive', '0.815', 'sympathize', 'commiserate', 'feel_sorry', 'ruth', 'pathos']
senticnet['commission'] = ['0.845', '0.916', '0', '0', '#joy', '#interest', 'positive', '0.881', 'question', 'answer', 'query', 'question_start', 'question_answering']
senticnet['commission_paec'] = ['-0.90', '0', '0.748', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'alternative_energy', 'activation_energy', 'energy_of_activation', 'energy', 'nuclear_energy']
senticnet['commissioned_officer_nco'] = ['-0.53', '0', '0.977', '-0.54', '#anger', '#disgust', 'negative', '-0.68', 'rank', 'surpass', 'shine_at', 'excel_at', 'exceed']
senticnet['commissioner'] = ['0.865', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.887', 'manager', 'colleague', 'office_worker', 'coworker', 'secretary']
senticnet['commissure'] = ['0.683', '-0.91', '0', '0.679', '#joy', '#surprise', 'positive', '0.76', 'pathway', 'corpus_callosum', 'nerve_pathway', 'tract', 'nerve_tract']
senticnet['commit'] = ['0.702', '0', '0', '0.928', '#joy', '#admiration', 'positive', '0.815', 'propose_woman', 'kiss', 'commit_to_memory', 'love', 'marriage']
senticnet['commit_adultery'] = ['-0.74', '0', '0.677', '-0.77', '#sadness', '#disgust', 'negative', '-0.73', 'whore', 'jazz', 'hump', 'bonk', 'screw']
senticnet['commit_aggression'] = ['-0.61', '0', '0.817', '-0.78', '#anger', '#disgust', 'negative', '-0.73', 'pretence', 'feigning', 'dissembling', 'pretense', 'putoff']
senticnet['commit_arson'] = ['-0.14', '0', '-0.21', '-0.07', '#sadness', '#fear', 'negative', '-0.14', 'heat_light', 'burn_down_house', 'set_on_fire', 'rapid_oxidation', 'flame']
senticnet['commit_burglary'] = ['-0.75', '0', '0.94', '0', '#sadness', '#anger', 'negative', '-0.84', 'steal_money', 'theft', 'stick_up', 'robbery', 'rob']
senticnet['commit_crime'] = ['-0.93', '0', '0.408', '-0.92', '#sadness', '#disgust', 'negative', '-0.75', 'kill', 'murder', 'kill_person', 'violation', 'crime']
senticnet['commit_extortion'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'fraud', 'swindle', 'rig']
senticnet['commit_fornication'] = ['-0.74', '0', '0.677', '-0.77', '#sadness', '#disgust', 'negative', '-0.73', 'whore', 'jazz', 'hump', 'bonk', 'screw']
senticnet['commit_fraud'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'mail_fraud', 'swindle', 'rig']
senticnet['commit_genocide'] = ['-0.11', '0', '-0.09', '-0.08', '#sadness', '#fear', 'negative', '-0.09', 'hunt_game', 'arm_bomb', 'kill_man', 'guard_bank', 'drug_addiction']
senticnet['commit_murder'] = ['-0.72', '0', '0.623', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'eliminate', 'kill_person', 'kill', 'take_life', 'kill_man']
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senticnet['commit_sin'] = ['0.673', '-0.66', '0', '0.566', '#joy', '#surprise', 'positive', '0.634', 'wickedness', 'impiety', 'impiousness', 'unjustness', 'dishonor']
senticnet['commit_suicide'] = ['-0.03', '-0.05', '-0.06', '-0.09', '#fear', '#disgust', 'negative', '-0.03', 'apnea', 'eat_poison', 'drunk_drive', 'drug_addiction', 'die']
senticnet['commit_to_memory'] = ['0', '0.148', '0', '0.167', '#interest', '#admiration', 'positive', '0.158', 'memorize', 'learn_science', 'learn_subject', 'commit', 'study']
senticnet['commit_wire_fraud'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'fraud', 'swindle', 'rig']
senticnet['commitment'] = ['0', '0', '0', '0.704', '#admiration', '#admiration', 'positive', '0.704', 'engage', 'vow', 'determination', 'demand', 'holy_matrimony']
senticnet['commitment-free'] = ['-0.80', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'free-and-easy', 'six-question', 'one-camera', 'three-camera', 'self-forgetful']
senticnet['commits_adultery'] = ['-0.54', '0', '0.691', '-0.96', '#anger', '#disgust', 'negative', '-0.73', 'slut', 'trollop', 'libertine', 'adulteress', 'fornicatress']
senticnet['commits_arson'] = ['0.057', '0', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'huge_fire', 'fire', 'rapid_oxidation', 'heat_light', 'flame']
senticnet['commits_suicide'] = ['-0.03', '0.157', '0.698', '-0.66', '#anger', '#disgust', 'negative', '-0.30', 'vengefulness', 'vindictively', 'revengefully', 'vindictive', 'spiteful']
senticnet['committal'] = ['-0.89', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.80', 'crime', 'indictment', 'trespass', 'contravention', 'offence']
senticnet['committed_adultery'] = ['-0.54', '0', '0.691', '-0.96', '#anger', '#disgust', 'negative', '-0.73', 'slut', 'trollop', 'libertine', 'adulteress', 'fornicatress']
senticnet['committed_fornication'] = ['-0.88', '0', '0.929', '-0.84', '#sadness', '#anger', 'negative', '-0.88', 'paynim', 'nonreligious_person', 'idolizer', 'gentile', 'idol_worshiper']
senticnet['committed_incest'] = ['-0.76', '0', '0.995', '-0.94', '#anger', '#disgust', 'negative', '-0.90', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'excretion']
senticnet['committed_pacifist'] = ['-0.97', '0', '0.62', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'obstructor', 'obstructer', 'technophobe', 'obstructionist', 'naysayer']
senticnet['committed_perjury'] = ['-0.73', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'obstruct_justice', 'get_away_crime', 'punish', 'hurt_else', 'commit_murder']
senticnet['committed_suicide'] = ['-0.87', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.91', 'loss_oflife', 'chest_pain', 'die', 'tumor', 'end_life']
senticnet['committed_treason'] = ['0', '0', '0.85', '-0.88', '#anger', '#disgust', 'negative', '-0.86', 'rogue', 'seaman', 'lieutenant', 'officer', 'captain']
senticnet['committee'] = ['0.051', '0.154', '-0.06', '0.07', '#interest', '#admiration', 'positive', '0.052', 'organization', 'association', 'tribe', 'majority', 'egalitarian_society']
senticnet['committee_chair'] = ['0', '0', '0.88', '-0.92', '#anger', '#disgust', 'negative', '-0.90', 'sofa_bed', 'small_seat', 'beanbag_airline_seat', 'hide-a-bed_sofa', 'airline_seat']
senticnet['committee_chairperson'] = ['0', '0', '0.88', '-0.92', '#anger', '#disgust', 'negative', '-0.90', 'sofa_bed', 'small_seat', 'beanbag_airline_seat', 'hide-a-bed_sofa', 'airline_seat']
senticnet['committeth'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitentially', 'penitent', 'penitence', 'repentant']
senticnet['committing'] = ['0.716', '0.782', '0', '0', '#joy', '#interest', 'positive', '0.749', 'implying', 'treason', 'advocating', 'tantamount', 'equating']
senticnet['committing_adultery'] = ['-0.54', '0', '0.691', '-0.96', '#anger', '#disgust', 'negative', '-0.73', 'slut', 'trollop', 'libertine', 'adulteress', 'fornicatress']
senticnet['committing_arson'] = ['-0.14', '0', '-0.21', '-0.07', '#sadness', '#fear', 'negative', '-0.14', 'heat_light', 'burn_down_house', 'set_on_fire', 'rapid_oxidation', 'flame']
senticnet['committing_fraud'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'mail_fraud', 'swindle', 'rig']
senticnet['committing_murder'] = ['-0.06', '0', '-0.15', '0', '#sadness', '#fear', 'negative', '-0.10', 'heroism', 'role_play', 'repeat', 'act', 'part_of_movie']
senticnet['committing_perjury'] = ['-0.73', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'obstruct_justice', 'get_away_crime', 'punish', 'hurt_else', 'commit_murder']
senticnet['committing_suicide'] = ['-0.87', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.91', 'loss_oflife', 'chest_pain', 'die', 'tumor', 'end_life']
senticnet['committing_treason'] = ['0', '0', '0.85', '-0.88', '#anger', '#disgust', 'negative', '-0.86', 'rogue', 'seaman', 'lieutenant', 'officer', 'captain']
senticnet['commix'] = ['0', '0', '-0.84', '-0.91', '#fear', '#disgust', 'negative', '-0.87', 'mingle', 'coalesced', 'amalgamate', 'mix', 'unify']
senticnet['commixture'] = ['0.668', '-0.88', '0', '0.893', '#surprise', '#admiration', 'positive', '0.815', 'chirpiness', 'spirit', 'sprightliness', 'liveliness', 'activity']
senticnet['commode'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'lavatory', 'potty_seat', 'potty_chair']
senticnet['commodification'] = ['0', '0', '-0.74', '-0.93', '#fear', '#disgust', 'negative', '-0.84', 'generosity', 'oral_sex', 'win_lottery', 'gift_wrap', 'give_away']
senticnet['commodious'] = ['0', '0.858', '0', '0.895', '#interest', '#admiration', 'positive', '0.877', 'broad', 'roomy', 'spacious', 'suitable', 'comfortable']
senticnet['commodity'] = ['0', '0.243', '0', '0.218', '#interest', '#admiration', 'positive', '0.23', 'ware', 'goods', 'result', 'good_price', 'bargain']
senticnet['commodity_bubble'] = ['-0.10', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.10', 'read_cover', 'spread_out', 'upgrade', 'look_behind', 'scan']
senticnet['commodity_fetishism'] = ['0', '0', '-0.74', '-0.93', '#fear', '#disgust', 'negative', '-0.84', 'generosity', 'oral_sex', 'win_lottery', 'gift_wrap', 'give_away']
senticnet['common'] = ['0', '0.212', '-0.06', '0.138', '#interest', '#admiration', 'positive', '0.095', 'ordinary', 'usual', 'typical', 'normal', 'usual_ordinary']
senticnet['common_bean'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'frijole', 'green_bean', 'wax_bean']
senticnet['common_beane'] = ['0.859', '-0.55', '0', '0.968', '#joy', '#admiration', 'positive', '0.792', 'kidney_bean', 'common_bean', 'frijol', 'green_bean', 'wax_bean']
senticnet['common_bile_duct'] = ['-0.56', '0', '0.806', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['common_birth_defect'] = ['-0.91', '0', '0.961', '-0.55', '#sadness', '#anger', 'negative', '-0.81', 'hypopigmentation', 'albinism', 'congenital_anomaly', 'congenital_defect', 'congenital_disorder']
senticnet['common_bottlenose_dolphin'] = ['0.024', '0.102', '-0.06', '0.074', '#interest', '#admiration', 'positive', '0.033', 'marine_mammal', 'large_mammal', 'pack_animal', 'mammal', 'smart_creature']
senticnet['common_burdock'] = ['0.683', '-0.54', '0', '0.568', '#joy', '#admiration', 'positive', '0.599', 'subshrub', 'suffrutex', 'burdock', 'lesser_burdock', 'great_burdock']
senticnet['common_buttercup'] = ['-0.74', '0', '0.6', '-0.77', '#sadness', '#disgust', 'negative', '-0.70', 'herb', 'meadow_buttercup', 'tall_buttercup', 'tall_crowfoot', 'crowfoot']
senticnet['common_buzzard'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['common_caper'] = ['0.683', '-0.54', '0', '0.568', '#joy', '#admiration', 'positive', '0.599', 'bush', 'native_pomegranate', 'caper_tree', 'native_orange', 'caper']
senticnet['common_carrier'] = ['0', '0.089', '0.01', '0.118', '#interest', '#admiration', 'positive', '0.066', 'crate', 'mom_house', 'sack', 'poker_game', 'safekeep']
senticnet['common_chickweed'] = ['-0.64', '0', '0.554', '-0.62', '#sadness', '#disgust', 'negative', '-0.61', 'herb', 'greater_stitchwort', 'stitchwort', 'chickweed', 'starwort']
senticnet['common_chimpanzee'] = ['0.537', '-0.51', '0', '0.807', '#joy', '#admiration', 'positive', '0.619', 'lesbian', 'fairy', 'fagot', 'faggot', 'fag']
senticnet['common_cold'] = ['0', '0', '-0.85', '-0.93', '#fear', '#disgust', 'negative', '-0.89', 'runny_nose', 'allergy', 'savor', 'flu', 'sign_sickness']
senticnet['common_decency'] = ['0.879', '0.97', '0', '0.921', '#interest', '#admiration', 'positive', '0.923', 'courteous', 'aide', 'compliment', 'politeness', 'handle_carefully']
senticnet['common_emotion'] = ['-0.18', '-0.13', '-0.22', '-0.11', '#sadness', '#fear', 'negative', '-0.09', 'psychoneurosis', 'paper_cut', 'feel_hurt', 'ridicule', 'startle']
senticnet['common_goldeneye'] = ['-0.50', '0', '0.764', '-0.61', '#anger', '#disgust', 'negative', '-0.62', 'doctor', 'mountebank', 'charlatan', 'quack', 'physician']
senticnet['common_good'] = ['0.701', '0.939', '0', '0.718', '#interest', '#admiration', 'positive', '0.786', 'same_interest', 'group_of_people', 'community', 'similar', 'common_interest']
senticnet['common_greenshank'] = ['0.022', '-0.11', '0.014', '0.057', '#surprise', '#admiration', 'positive', '0.045', 'wood', 'many_steps', 'franchise', 'stump', 'timber']
senticnet['common_hangman'] = ['-0.73', '0', '0.641', '-0.74', '#sadness', '#disgust', 'negative', '-0.70', 'treated', 'burned', 'proofed', 'doped', 'activated']
senticnet['common_housefly'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'annelid', 'annelid_worm', 'leech']
senticnet['common_interest'] = ['0.701', '0.939', '0', '0.718', '#interest', '#admiration', 'positive', '0.786', 'same_interest', 'group_of_people', 'community', 'similar', 'common_good']
senticnet['common_knowledge'] = ['0.745', '-0.77', '0', '0.655', '#joy', '#surprise', 'positive', '0.724', 'light', 'open', 'general_knowledge', 'surface', 'cognition']
senticnet['common_krait'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'death_adder', 'ophidian', 'serpent', 'elapid', 'taipan']
senticnet['common_land'] = ['-0.90', '0', '0.765', '-0.98', '#sadness', '#disgust', 'negative', '-0.88', 'pastureland', 'grazing_land', 'ley', 'pasture', 'lea']
senticnet['common_landland'] = ['-0.90', '0', '0.763', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'pasture', 'grazing_land', 'ley', 'common_land', 'lea']
senticnet['common_language'] = ['0.097', '-0.01', '0.026', '0.144', '#joy', '#admiration', 'positive', '0.056', 'universal_language', 'melodic_noise', 'audio', 'instrumental', 'human_sound']
senticnet['common_mallow'] = ['0.683', '-0.54', '0', '0.568', '#joy', '#admiration', 'positive', '0.599', 'bush', 'musk_mallow', 'mu_rose', 'mallow', 'tall_mallow']
senticnet['common_man'] = ['0.759', '0.862', '0', '0', '#joy', '#interest', 'positive', '0.81', 'sophisticated', 'lovely', 'elegant', 'contrary', 'feminine']
senticnet['common_marmoset'] = ['0.921', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.825', 'monkey', 'fiance', 'lemur', 'beaver', 'koala']
senticnet['common_marsh_bedstraw'] = ['-0.79', '0', '0.906', '-0.67', '#sadness', '#anger', 'negative', '-0.79', 'poisonous_plant', 'hemlock_poisonous_plant', 'water_hemlock', 'plant_life', 'poison_sumac']
senticnet['common_milkwort'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
senticnet['common_occurrence'] = ['0', '0.188', '-0.04', '0.191', '#interest', '#admiration', 'positive', '0.111', 'usual_ordinary', 'normal', 'typical', 'usual', 'daily_routine']
senticnet['common_parlance'] = ['0', '0.115', '-0.10', '0.249', '#interest', '#admiration', 'positive', '0.087', 'idiom', 'remark', 'grammar_part', 'speech_part', 'sentence']
senticnet['common_periwinkle'] = ['-0.89', '0', '0.556', '-0.72', '#sadness', '#disgust', 'negative', '-0.72', 'fucus', 'rockweed', 'black_rockweed', 'bladder_fucus', 'tang']
senticnet['common_pet'] = ['0', '0', '0', '-0.87', '#disgust', '#disgust', 'negative', '-0.87', 'domestic_pet', 'popular_pet', 'similar', 'nothing_special', 'common']
senticnet['common_practice'] = ['0.974', '0.789', '0', '0.828', '#joy', '#admiration', 'positive', '0.864', 'daily_routine', 'eternal', 'eat_breakfast', 'routine', 'good_trait']
senticnet['common_presenting_symptom'] = ['-0.84', '0', '0.829', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'upset', 'dysarthria', 'defect_of_speech', 'disorder', 'lallation']
senticnet['common_redshank'] = ['0.797', '-0.71', '0', '0.736', '#joy', '#admiration', 'positive', '0.751', 'wood_ibis', 'wood_stork', 'ibis', 'wading_bird', 'wader']
senticnet['common_sandpiper'] = ['0.797', '-0.71', '0', '0.736', '#joy', '#admiration', 'positive', '0.751', 'wood_ibis', 'wood_stork', 'ibis', 'wading_bird', 'wader']
senticnet['common_scold'] = ['-0.71', '0', '0.972', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'scold', 'nag', 'scolder', 'nagger', 'harridan']
senticnet['common_sense'] = ['0', '0', '0', '0.951', '#admiration', '#admiration', 'positive', '0.951', 'wit', 'culture', 'eat_fruit', 'take_ticket', 'adapt']
senticnet['common_sense_approach'] = ['0.843', '-0.6', '0', '0.698', '#joy', '#admiration', 'positive', '0.714', 'levelheaded', 'sound', 'healthy', 'sensible', 'commonsensible']
senticnet['common_sensical'] = ['0.843', '-0.6', '0', '0.698', '#joy', '#admiration', 'positive', '0.714', 'levelheaded', 'sound', 'healthy', 'sensible', 'commonsensible']
senticnet['common_shiner'] = ['0', '-0.02', '0.044', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'haddock', 'mermaid', 'fish_rod', 'tidal_wave', 'dryad']
senticnet['common_snipe'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['common_spotted_orchid'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
senticnet['common_stock'] = ['0.069', '0.07', '0', '0.116', '#interest', '#admiration', 'positive', '0.085', 'entrepreneur', 'invest_money', 'move_around', 'traveling_person', 'guilty_person']
senticnet['common_usage'] = ['0', '0.115', '-0.10', '0.249', '#interest', '#admiration', 'positive', '0.087', 'idiom', 'remark', 'grammar_part', 'speech_part', 'sentence']
senticnet['common_wart'] = ['-0.58', '0', '0.727', '-0.59', '#anger', '#disgust', 'negative', '-0.63', 'blemish', 'keratosis', 'infection', 'defect', 'verruca']
senticnet['common_water_snake'] = ['-0.51', '0', '0.625', '-0.98', '#anger', '#disgust', 'negative', '-0.70', 'water_snake', 'banded_water_snake', 'water_moccasin', 'grass_snake', 'ring_snake']
senticnet['common_watercress'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'marsh_cress', 'yellow_watercress', 'mountain_watercress', 'watercress', 'great_yellowcress']
senticnet['common_weal'] = ['0.926', '0.937', '0', '0.975', '#interest', '#admiration', 'positive', '0.946', 'monument', 'statue', 'freedom', 'idol', 'sculpture']
senticnet['common_white_dogwood'] = ['0.773', '-0.55', '0', '0.949', '#joy', '#admiration', 'positive', '0.757', 'bunchberry', 'dwarf_cornel', 'crackerberry', 'pudding_berry', 'cornel']
senticnet['commonality'] = ['-0.63', '0', '0.822', '-0.95', '#anger', '#disgust', 'negative', '-0.80', 'uniformness', 'homogeneousness', 'consistency', 'consistence', 'similarity']
senticnet['commonest'] = ['-0.95', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.96', 'cliche', 'banality', 'prosaic', 'unexciting', 'humdrum']
senticnet['commonness'] = ['0.989', '-0.99', '0', '0.728', '#joy', '#surprise', 'positive', '0.905', 'quality', 'tastelessness', 'sleaziness', 'coarseness', 'inelegance']
senticnet['commonplace'] = ['-0.95', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.96', 'cliche', 'banality', 'prosaic', 'unexciting', 'humdrum']
senticnet['commonplaceness'] = ['0.741', '-0.74', '0', '0.666', '#joy', '#surprise', 'positive', '0.718', 'mundaneness', 'mundanity', 'prosiness', 'prosaicness', 'ordinariness']
senticnet['commons'] = ['-0.72', '0', '0.978', '-0.86', '#anger', '#disgust', 'negative', '-0.85', 'amusement_park', 'funfair', 'pleasure_ground', 'village_green', 'tract']
senticnet['commonsense'] = ['0.843', '-0.6', '0', '0.698', '#joy', '#admiration', 'positive', '0.714', 'levelheaded', 'sound', 'healthy', 'sensible', 'commonsensible']
senticnet['commonsense_approach'] = ['0.843', '-0.6', '0', '0.698', '#joy', '#admiration', 'positive', '0.714', 'levelheaded', 'sound', 'healthy', 'sensible', 'commonsensible']
senticnet['commonsensible'] = ['0.843', '-0.6', '0', '0.698', '#joy', '#admiration', 'positive', '0.714', 'levelheaded', 'sound', 'healthy', 'commonsense', 'sensible']
senticnet['commonsensical'] = ['0.843', '-0.6', '0', '0.698', '#joy', '#admiration', 'positive', '0.714', 'levelheaded', 'sound', 'healthy', 'sensible', 'commonsensible']
senticnet['commonwealth'] = ['0.716', '-0.51', '0', '0.647', '#joy', '#admiration', 'positive', '0.624', 'state', 'nation', 'country', 'land', 'ally']
senticnet['commotion'] = ['-0.70', '0', '0.725', '-0.52', '#sadness', '#anger', 'negative', '-0.65', 'turmoil', 'convulsion', 'tempest', 'disorder', 'earthquake']
senticnet['commove'] = ['-0.74', '0', '0.69', '-0.68', '#sadness', '#anger', 'negative', '-0.70', 'upset', 'hype_up', 'psych_up', 'bother', 'trouble']
senticnet['communal'] = ['0.563', '-0.72', '0', '0.644', '#surprise', '#admiration', 'positive', '0.645', 'idiom', 'idiomatic_expression', 'phrasal_idiom', 'set_phrase', 'phrase']
senticnet['communal_bathroom'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'commode', 'potty_seat', 'potty_chair']
senticnet['communal_grave'] = ['-0.88', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.90', 'coffin', 'casket', 'morgue', 'headstone', 'burial']
senticnet['communal_identity'] = ['0.122', '0.159', '-0.05', '0.217', '#interest', '#admiration', 'positive', '0.111', 'profile_picture', 'unity', 'country_person', 'unite', 'signature']
senticnet['communal_kitchen'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'kitchen_cabinet', 'kitchen_cupboard', 'dishwasher', 'cupboard', 'own_kitchen']
senticnet['communal_prayer'] = ['0.023', '0.295', '-0.05', '0.283', '#interest', '#admiration', 'positive', '0.136', 'religion', 'religious_purpose', 'world_religion', 'gospel', 'judaism']
senticnet['communalist'] = ['-0.89', '0', '0.678', '-0.62', '#sadness', '#anger', 'negative', '-0.73', 'undisciplined', 'anarchic', 'uncontrolled', 'irrepressible', 'uncontrollable']
senticnet['communality'] = ['-0.64', '0', '0.574', '-0.89', '#sadness', '#disgust', 'negative', '-0.70', 'gregariousness', 'ambiversion', 'openness', 'nakedness', 'extraversion']
senticnet['communicable'] = ['-0.70', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.72', 'infectious', 'contagious', 'epidemic', 'pandemic', 'epizootic']
senticnet['communicable_disease'] = ['-0.11', '0', '-0.15', '-0.24', '#fear', '#disgust', 'negative', '-0.17', 'disease', 'infectious_disease', 'polio', 'malady', 'sickness']
senticnet['communicate'] = ['0.764', '-0.70', '0', '0', '#joy', '#surprise', 'positive', '0.733', 'dictation', 'verbal', 'beckon', 'call', 'phone_conversation']
senticnet['communicate_effectively'] = ['0.749', '0.724', '0', '0', '#joy', '#interest', 'positive', '0.736', 'mouth_noise', 'vocal_cord', 'vocalization', 'vocal_sound', 'loud_soft']
senticnet['communicate_family'] = ['0.881', '0', '0', '0.724', '#joy', '#admiration', 'positive', '0.802', 'communicate_with_others', 'communicate_idea', 'exchange_information', 'social_life', 'keep_in_touch']
senticnet['communicate_fluently'] = ['-0.93', '0', '-0.99', '-0.92', '#sadness', '#fear', 'negative', '-0.95', 'barrier', 'shut', 'shut_down', 'close_down', 'obstacle']
senticnet['communicate_friend'] = ['0.072', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.046', 'communicate', 'teach_new', 'talk', 'engage_conversation', 'exchange_information']
senticnet['communicate_idea'] = ['0.881', '0', '0', '0.724', '#joy', '#admiration', 'positive', '0.802', 'communicate_with_others', 'communicate_family', 'exchange_information', 'social_life', 'keep_in_touch']
senticnet['communicate_person'] = ['0.849', '0', '0', '0.861', '#joy', '#admiration', 'positive', '0.855', 'exchange_idea', 'social_life', 'keep_in_touch', 'give_information', 'exchange_information']
senticnet['communicate_thought'] = ['0.963', '0.606', '0', '0.955', '#joy', '#admiration', 'positive', '0.841', 'represent_idea', 'convey_message', 'communicate', 'send_message', 'record_data']
senticnet['communicate_wirelessly'] = ['0.63', '-0.55', '0', '0.946', '#joy', '#admiration', 'positive', '0.711', 'complect', 'connect', 'link', 'link_up', 'join']
senticnet['communicate_with_others'] = ['0.881', '0', '0', '0.724', '#joy', '#admiration', 'positive', '0.802', 'communicate_family', 'communicate_idea', 'exchange_information', 'social_life', 'keep_in_touch']
senticnet['communication'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'discuss', 'autograph', 'communicate_person', 'exchange_word', 'discussion']
senticnet['communication_channel'] = ['0.911', '0.853', '0', '0', '#joy', '#interest', 'positive', '0.882', 'duct', 'tv_channel', 'channel', 'convey', 'carry']
senticnet['communication_decency_act'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'indecency']
senticnet['communication_infrastructure'] = ['0.021', '0', '0', '0.058', '#joy', '#admiration', 'positive', '0.04', 'architecture', 'route', 'structure', 'car_path', 'building']
senticnet['communication_method'] = ['0', '0.077', '0', '0.154', '#interest', '#admiration', 'positive', '0.115', 'form_of_communication', 'note_down', 'written_language', 'written_communication', 'message']
senticnet['communication_protocol'] = ['0.89', '0.912', '0', '0', '#joy', '#interest', 'positive', '0.901', 'protocol', 'project', 'research', 'development', 'design']
senticnet['communication_sys'] = ['0', '0.846', '0', '0.724', '#interest', '#admiration', 'positive', '0.785', 'award', 'standard_grant', 'telecommunication', 'elect_photonics_device_tec', 'continue_grant']
senticnet['communication_system'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'land_fish', 'fish', 'catch_fish', 'triathlon', 'bait']
senticnet['communicative'] = ['0.727', '-0.62', '0', '0.57', '#joy', '#surprise', 'positive', '0.64', 'utterer', 'deipnosophist', 'interlocutor', 'conversationalist', 'speaker']
senticnet['communicative_approach'] = ['0.633', '-0.96', '0', '0.612', '#joy', '#surprise', 'positive', '0.738', 'particularism', 'absorption', 'concentration', 'engrossment', 'centering']
senticnet['communicative_function'] = ['0.727', '-0.62', '0', '0.57', '#joy', '#surprise', 'positive', '0.64', 'utterer', 'deipnosophist', 'interlocutor', 'conversationalist', 'speaker']
senticnet['communicative_intent'] = ['0.727', '-0.62', '0', '0.57', '#joy', '#surprise', 'positive', '0.64', 'utterer', 'deipnosophist', 'interlocutor', 'conversationalist', 'speaker']
senticnet['communicative_proficiency'] = ['0.633', '-0.96', '0', '0.612', '#joy', '#surprise', 'positive', '0.738', 'particularism', 'absorption', 'concentration', 'engrossment', 'centering']
senticnet['communicativeness'] = ['0.664', '-0.94', '0', '0.613', '#joy', '#surprise', 'positive', '0.739', 'outspokenness', 'frankness', 'garrulity', 'garrulousness', 'loquaciousness']
senticnet['communicator'] = ['0.749', '0.724', '0', '0', '#joy', '#interest', 'positive', '0.736', 'mouth_noise', 'vocal_cord', 'vocalization', 'vocal_sound', 'loud_soft']
senticnet['communicator_mpi_comm_world'] = ['0.749', '0.724', '0', '0', '#joy', '#interest', 'positive', '0.736', 'mouth_noise', 'vocal_cord', 'vocalization', 'vocal_sound', 'loud_soft']
senticnet['communicatory'] = ['-0.60', '0', '0.523', '-0.95', '#sadness', '#disgust', 'negative', '-0.69', 'blabbermouthed', 'expansive', 'heraldic', 'talkative', 'leaky']
senticnet['communigate_pro'] = ['0.031', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.062', 'letter_slot', 'postal_counter', 'mailbox', 'letter_scale', 'envelope']
senticnet['communion'] = ['0.705', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.74', 'baptism', 'invitation', 'keepsake', 'christening', 'wedding']
senticnet['communion_rail'] = ['0.023', '0.218', '0.01', '0.235', '#interest', '#admiration', 'positive', '0.116', 'confessional', 'altar', 'chapel', 'temple', 'christianity']
senticnet['communion_table'] = ['0.023', '0.218', '0.01', '0.235', '#interest', '#admiration', 'positive', '0.116', 'confessional', 'altar', 'chapel', 'temple', 'christianity']
senticnet['communism'] = ['0.045', '0.286', '-0.14', '0.229', '#interest', '#admiration', 'positive', '0.104', 'socialism', 'parliament', 'elect', 'government', 'elect_president']
senticnet['communist'] = ['0', '0', '-0.70', '-0.83', '#fear', '#disgust', 'negative', '-0.76', 'settle_disagreement', 'hitler', 'republican', 'conservative', 'unchanging']
senticnet['communist_bloc'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['communist_dictator'] = ['-0.73', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.72', 'royal_leader', 'despot', 'emperor', 'monarch', 'king']
senticnet['communist_dictatorship'] = ['0.045', '0.286', '-0.14', '0.229', '#interest', '#admiration', 'positive', '0.104', 'socialism', 'parliament', 'elect', 'government', 'elect_president']
senticnet['communist_faction'] = ['-0.50', '0', '0.989', '-0.91', '#anger', '#disgust', 'negative', '-0.80', 'camp', 'pack', 'clique', 'coterie', 'ingroup']
senticnet['communist_ideology'] = ['0', '0', '0', '0.986', '#admiration', '#admiration', 'positive', '0.986', 'idea', 'decision', 'consider', 'polarize', 'make_right_decision']
senticnet['communist_leader'] = ['0', '0', '-0.70', '-0.83', '#fear', '#disgust', 'negative', '-0.76', 'settle_disagreement', 'hitler', 'republican', 'conservative', 'unchanging']
senticnet['communist_movement'] = ['0', '0', '-0.70', '-0.83', '#fear', '#disgust', 'negative', '-0.76', 'settle_disagreement', 'hitler', 'republican', 'conservative', 'unchanging']
senticnet['communist_oppression'] = ['0.045', '0.286', '-0.14', '0.229', '#interest', '#admiration', 'positive', '0.104', 'socialism', 'parliament', 'elect', 'government', 'elect_president']
senticnet['communist_party'] = ['0', '0', '-0.70', '-0.83', '#fear', '#disgust', 'negative', '-0.76', 'settle_disagreement', 'hitler', 'republican', 'conservative', 'unchanging']
senticnet['communist_politician'] = ['0', '0.868', '0', '0.853', '#interest', '#admiration', 'positive', '0.861', 'country_leader', 'prime_minister', 'president', 'elect_official', 'leader']
senticnet['communist_regime'] = ['0.045', '0.286', '-0.14', '0.229', '#interest', '#admiration', 'positive', '0.104', 'socialism', 'parliament', 'elect', 'government', 'elect_president']
senticnet['communist_revolution'] = ['0.045', '0.286', '-0.14', '0.229', '#interest', '#admiration', 'positive', '0.104', 'socialism', 'parliament', 'elect', 'government', 'elect_president']
senticnet['communist_revolutionary'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'patriotism', 'homeland']
senticnet['communist_rule'] = ['0.045', '0.286', '-0.14', '0.229', '#interest', '#admiration', 'positive', '0.104', 'socialism', 'parliament', 'elect', 'government', 'elect_president']
senticnet['communist_spy'] = ['-0.69', '0', '0.756', '-0.99', '#anger', '#disgust', 'negative', '-0.81', 'waster', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['communist_takeover'] = ['0', '0', '-0.70', '-0.83', '#fear', '#disgust', 'negative', '-0.76', 'settle_disagreement', 'hitler', 'republican', 'conservative', 'unchanging']
senticnet['communist_totalitarianism'] = ['0.045', '0.286', '-0.14', '0.229', '#interest', '#admiration', 'positive', '0.104', 'socialism', 'parliament', 'elect', 'government', 'elect_president']
senticnet['communist_tyranny'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'patriotism', 'homeland']
senticnet['communistic'] = ['0', '0', '-0.86', '-0.89', '#fear', '#disgust', 'negative', '-0.87', 'socialistic', 'fascistic', 'capitalistic', 'collectivist', 'theocratic']
senticnet['communitarian'] = ['-0.80', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.82', 'pluralistic', 'egalitarian', 'pluralism', 'pluralist', 'individualistic']
senticnet['community'] = ['0.701', '0.939', '0', '0.718', '#interest', '#admiration', 'positive', '0.786', 'common_interest', 'group_of_people', 'urban', 'many_people', 'metropolis']
senticnet['community-based'] = ['0.75', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.834', 'participative', 'participatory', 'collaborative', 'bottom-up', 'governance']
senticnet['community_acquired_methicillin'] = ['-0.85', '0', '0.549', '-0.93', '#sadness', '#disgust', 'negative', '-0.78', 'coccidioidomycosis', 'coccidiomycosis', 'mycosis', 'desert_rheumatism', 'cryptococcosis']
senticnet['community_cohesion'] = ['-0.25', '0', '-0.17', '-0.08', '#sadness', '#fear', 'negative', '-0.16', 'exclusion', 'exclusion_zone', 'isolation', 'detach', 'except']
senticnet['community_college'] = ['0.81', '0.893', '0', '0', '#joy', '#interest', 'positive', '0.852', 'junior_college', 'student', 'faculty', 'education', 'university']
senticnet['community_involvement'] = ['0.053', '0.136', '0', '0.057', '#interest', '#admiration', 'positive', '0.082', 'boss', 'supervisor', 'leader', 'head_person', 'head_guy']
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senticnet['condemning_homosexuality'] = ['0.778', '-0.73', '0', '0.559', '#joy', '#surprise', 'positive', '0.689', 'sex', 'inversion', 'sexual_inversion', 'lesbianism', 'sapphism']
senticnet['condensable'] = ['0', '0.073', '-0.05', '0.093', '#interest', '#admiration', 'positive', '0.038', 'vapor', 'water_vapor', 'evaporate', 'condense_water', 'bubble']
senticnet['condensation'] = ['0', '0.073', '-0.05', '0.093', '#interest', '#admiration', 'positive', '0.038', 'thicken', 'condense_water', 'condensed_milk', 'weather_condition', 'condense']
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senticnet['condense_water'] = ['0', '0.073', '-0.05', '0.093', '#interest', '#admiration', 'positive', '0.038', 'vapor', 'water_vapor', 'evaporate', 'gaseous', 'bubble']
senticnet['condensed_matter_physic'] = ['0', '0.728', '0', '0.808', '#interest', '#admiration', 'positive', '0.768', 'study', 'research', 'project', 'award', 'magnetic_property']
senticnet['condensed_milk'] = ['0.051', '0.074', '-0.05', '0.084', '#interest', '#admiration', 'positive', '0.04', 'thicken', 'condense_water', 'condense', 'weather_condition', 'condensation']
senticnet['condenser_microphone'] = ['0.056', '-0.01', '0.036', '0.073', '#joy', '#admiration', 'positive', '0.028', 'loud_sound', 'stereo', 'tuner', 'speaker', 'steam_engine']
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senticnet['condensing_boiler'] = ['0.02', '0.076', '0', '0', '#joy', '#interest', 'positive', '0.048', 'land_earth', 'tidal_wave', 'puddle', 'brine', 'spurt']
senticnet['condescend'] = ['0', '0', '0.982', '-0.71', '#anger', '#disgust', 'negative', '-0.84', 'condescension', 'inferior', 'high-hat', 'stooping', 'condescending']
senticnet['condescending'] = ['-0.97', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.96', 'snobby', 'whiny', 'snooty', 'snotty', 'stuck-up']
senticnet['condescendingly'] = ['0', '-0.96', '0', '0.699', '#surprise', '#admiration', 'positive', '0.831', 'sheepishly', 'shyly', 'apologetically', 'coyly', 'mockingly']
senticnet['condescendingness'] = ['0.593', '-0.65', '0', '0.711', '#surprise', '#admiration', 'positive', '0.654', 'naturalness', 'condescension', 'affability', 'mellowness', 'friendliness']
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senticnet['condign_punishment'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'forgivable', 'excusable', 'mercifully', 'merciful', 'mercifulness']
senticnet['condillac'] = ['0.992', '-0.66', '0', '0.536', '#joy', '#surprise', 'positive', '0.729', 'nous', 'mind', 'brain', 'psyche', 'head']
senticnet['condiment'] = ['-0.75', '0', '0.593', '-0.59', '#sadness', '#anger', 'negative', '-0.64', 'taco_sauce', 'chili_sauce', 'steak_sauce', 'duck_sauce', 'hoisin_sauce']
senticnet['condition'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'diagnose', 'criterion', 'medical', 'sleep_disorder', 'ailment']
senticnet['condition_precedent'] = ['-0.99', '0', '0.712', '-0.85', '#sadness', '#disgust', 'negative', '-0.85', 'need', 'obligation', 'requirement', 'desideratum', 'important']
senticnet['condition_set_forth'] = ['-0.68', '0', '0.967', '-0.65', '#sadness', '#anger', 'negative', '-0.76', 'depart', 'start', 'part', 'start_out', 'sally_forth']
senticnet['conditional_bail'] = ['-0.08', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.052', 'bequest', 'excise', 'bribe', 'afford', 'owe']
senticnet['conditional_mood'] = ['0', '0.156', '-0.06', '0.241', '#interest', '#admiration', 'positive', '0.11', 'mixture', 'noisy_place', 'make_go', 'move_vehicle', 'big_car']
senticnet['conditional_relation'] = ['0.786', '0.859', '0', '0', '#joy', '#interest', 'positive', '0.823', 'entailment', 'implication', 'deduction', 'logical_implication', 'significance']
senticnet['conditioned_emotion'] = ['0.708', '-0.60', '0', '0.661', '#joy', '#admiration', 'positive', '0.659', 'love', 'emotional_response', 'emotion', 'fright', 'reverence']
senticnet['conditioned_emotional_response'] = ['0.708', '-0.60', '0', '0.661', '#joy', '#admiration', 'positive', '0.659', 'love', 'emotion', 'conditioned_emotion', 'fright', 'reverence']
senticnet['conditioned_fear'] = ['0.971', '0.751', '0', '0.896', '#joy', '#admiration', 'positive', '0.873', 'new_friendship', 'foreplay', 'warm_fuzzy_feeling', 'become_aroused', 'erection']
senticnet['conditioned_phenomenon'] = ['-0.87', '0', '0.744', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['conditioned_reinforcement'] = ['0.583', '-0.82', '0', '0.574', '#joy', '#surprise', 'positive', '0.661', 'stimulant', 'positive_reinforcing_stimulus', 'positive_reinforcer', 'negative_reinforcing_stimulus', 'reinforcer']
senticnet['conditioned_reinforcer'] = ['0.583', '-0.82', '0', '0.574', '#joy', '#surprise', 'positive', '0.661', 'stimulant', 'positive_reinforcing_stimulus', 'positive_reinforcer', 'negative_reinforcing_stimulus', 'reinforcer']
senticnet['conditioned_suppression'] = ['0.583', '-0.82', '0', '0.574', '#joy', '#surprise', 'positive', '0.661', 'stimulant', 'positive_reinforcing_stimulus', 'reinforcer', 'negative_reinforcing_stimulus', 'negative_reinforcer']
senticnet['conditioning'] = ['0.887', '-0.98', '0', '0.611', '#joy', '#surprise', 'positive', '0.829', 'education', 'internalization', 'incorporation', 'study', 'learning']
senticnet['conditioning_paradigm'] = ['0.583', '-0.82', '0', '0.574', '#joy', '#surprise', 'positive', '0.661', 'stimulant', 'positive_reinforcing_stimulus', 'reinforcer', 'negative_reinforcing_stimulus', 'negative_reinforcer']
senticnet['condo'] = ['0.878', '0.932', '0', '0', '#joy', '#interest', 'positive', '0.905', 'brownstone', 'rent_apartment', 'high_rise', 'condominium', 'row_house']
senticnet['condole_with'] = ['-0.97', '0', '0.892', '0', '#sadness', '#anger', 'negative', '-0.93', 'sympathize_with', 'compassionateness', 'feel_for', 'sympathize', 'commiserate']
senticnet['condom'] = ['0.734', '0.947', '0', '0', '#joy', '#interest', 'positive', '0.841', 'birth_control_pill', 'prevent_disease', 'prevent_pregnancy', 'safety', 'safe_sex']
senticnet['condom_distribution'] = ['0', '0', '0.973', '-0.78', '#anger', '#disgust', 'negative', '-0.87', 'chastity', 'ignore_conception', 'restrain', 'abortion', 'stop']
senticnet['condom_promotion'] = ['0', '0', '0.973', '-0.78', '#anger', '#disgust', 'negative', '-0.87', 'chastity', 'ignore_conception', 'restrain', 'abortion', 'stop']
senticnet['condom_usage'] = ['0', '0', '0.973', '-0.78', '#anger', '#disgust', 'negative', '-0.87', 'chastity', 'ignore_conception', 'restrain', 'abortion', 'stop']
senticnet['condom_use'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'safe_sex', 'sex', 'prevent_pregnancy', 'avoid_pregnancy', 'become_aroused']
senticnet['condom_use_among'] = ['0', '0', '0.973', '-0.78', '#anger', '#disgust', 'negative', '-0.87', 'chastity', 'ignore_conception', 'restrain', 'abortion', 'stop']
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senticnet['condominium_building'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'condominium', 'rent_apartment', 'high_rise', 'condo', 'row_house']
senticnet['condonation'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'negligence', 'evasion', 'dereliction', 'contributory_negligence']
senticnet['condone'] = ['0.785', '-0.88', '0', '0', '#joy', '#surprise', 'positive', '0.833', 'condoner', 'allow', 'approval', 'overlook', 'excuse']
senticnet['condoner'] = ['0.777', '-0.88', '0', '0', '#joy', '#surprise', 'positive', '0.831', 'condone', 'allow', 'approval', 'overlook', 'excuse']
senticnet['conduce'] = ['0.66', '-0.85', '0', '0.744', '#surprise', '#admiration', 'positive', '0.752', 'further', 'promote', 'encourage', 'boost', 'advance']
senticnet['conducive'] = ['0.711', '0', '0', '0.798', '#joy', '#admiration', 'positive', '0.754', 'conduciveness', 'tend', 'unfavorable', 'contribute', 'encourage']
senticnet['conduciveness'] = ['0.703', '0', '0', '0.796', '#joy', '#admiration', 'positive', '0.75', 'conducive', 'tend', 'unfavorable', 'contribute', 'encourage']
senticnet['conduct'] = ['0.203', '0.241', '-0.19', '0.343', '#interest', '#admiration', 'positive', '0.147', 'honor', 'mandatory', 'courtesy', 'good_etiquette', 'behavior']
senticnet['conduct_business'] = ['0', '0.926', '0', '0.824', '#interest', '#admiration', 'positive', '0.875', 'see_know', 'talk_person', 'make_hotel_reservation', 'make_deal', 'send_information']
senticnet['conduct_detrimental'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'steer_wheel', 'gear_shift', 'chassis', 'brake_pedal', 'air_vent']
senticnet['conduct_disorder'] = ['0', '0', '0.896', '-0.96', '#anger', '#disgust', 'negative', '-0.92', 'antisocial', 'sociopathic', 'psychopathic_personality', 'disturbance', 'schizoid']
senticnet['conduct_experiment'] = ['-0.07', '0', '-0.05', '-0.08', '#sadness', '#disgust', 'negative', '-0.06', 'head_class', 'crowd_room', 'seat_chair', 'leave_table', 'page_through_book']
senticnet['conduct_periodic'] = ['0.09', '0.07', '0', '0.246', '#joy', '#admiration', 'positive', '0.135', 'hand_motion', 'water_form', 'water_relate', 'hand_gesture', 'arm_movement']
senticnet['conduct_regular'] = ['0', '0.152', '0', '0.119', '#interest', '#admiration', 'positive', '0.136', 'replicate', 'start_end', 'everyday', 'description', 'precise']
senticnet['conduct_research'] = ['0.504', '0.674', '0', '0.641', '#interest', '#admiration', 'positive', '0.606', 'gather_information', 'search_information', 'experimentation', 'read_out_loud', 'learn_much']
senticnet['conducting_research'] = ['0.887', '0.877', '0', '0', '#joy', '#interest', 'positive', '0.882', 'ask_expert', 'absorb_information', 'analyze', 'learn_computer', 'search_information']
senticnet['conduction_aphasia'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'nominal_aphasia', 'anomic_aphasia']
senticnet['conduction_band_offset'] = ['-0.54', '0', '0.909', '-0.97', '#anger', '#disgust', 'negative', '-0.81', 'suppleness', 'pliancy', 'pliantness', 'flexibility', 'flexibleness']
senticnet['conduction_velocity'] = ['0.698', '-0.61', '0', '0.619', '#joy', '#admiration', 'positive', '0.644', 'sensitivity', 'sensitiveness', 'irritability', 'sensibility', 'reactivity']
senticnet['conductive_afm'] = ['-0.93', '0', '0.739', '-0.96', '#sadness', '#disgust', 'negative', '-0.88', 'prevarication', 'fairytale', 'fairy_tale', 'fairy_story', 'play']
senticnet['conductive_material'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'foil', 'tin_foil', 'gold_foil']
senticnet['conductivity'] = ['0.728', '0.977', '0', '0.939', '#interest', '#admiration', 'positive', '0.881', 'air_pressure', 'altitude', 'air', 'heavy_air', 'air_density']
senticnet['conductor'] = ['-0.91', '0', '0', '-0.81', '#sadness', '#disgust', 'negative', '-0.86', 'silicon', 'semiconductor_device', 'semiconductor', 'chip', 'transistor']
senticnet['conductor_baton'] = ['-0.91', '0', '0.931', '-0.72', '#sadness', '#anger', 'negative', '-0.85', 'hit_with_baton', 'get_hurt', 'whip', 'arrow', 'hurt']
senticnet['condyle'] = ['0', '-0.02', '0.033', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'tooth', 'teeth', 'dimple', 'mouth', 'cliche']
senticnet['cone'] = ['0.06', '-0.03', '0.037', '0.075', '#joy', '#admiration', 'positive', '0.034', 'semicircle', 'trapezoid', 'circle', 'triangular', 'facet']
senticnet['cone_biopsy'] = ['-0.88', '0', '0.774', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fibroma', 'fibroid', 'benign_tumor', 'nonmalignant_tumor', 'nonmalignant_neoplasm']
senticnet['cone_bra'] = ['0.056', '0.139', '-0.07', '0.154', '#interest', '#admiration', 'positive', '0.07', 'belongings', 'material', 'fabric', 'guarantee', 'stuff']
senticnet['cone_dystrophy'] = ['-0.82', '0', '0.996', '-0.61', '#sadness', '#anger', 'negative', '-0.81', 'visual_impairment', 'visual_defect', 'vision_defect', 'visual_disorder', 'avitaminosis']
senticnet['cone_shape'] = ['0.06', '-0.03', '0.037', '0.075', '#joy', '#admiration', 'positive', '0.034', 'semicircle', 'trapezoid', 'circle', 'triangular', 'facet']
senticnet['conestoga_wagon'] = ['0', '0', '0', '0.071', '#admiration', '#admiration', 'positive', '0.071', 'two_horses', 'horse', 'barb', 'buddhist', 'ride_horse']
senticnet['coney_island'] = ['0.089', '-0.18', '0.037', '-0.06', '#joy', '#surprise', 'positive', '0.043', 'balloon', 'ballon', 'fielder', 'kite', 'blimp']
senticnet['coney_island_amusement_park'] = ['0.089', '-0.18', '0.037', '-0.06', '#joy', '#surprise', 'positive', '0.043', 'balloon', 'ballon', 'fielder', 'kite', 'blimp']
senticnet['coney_island_ice_cream'] = ['0.977', '-0.93', '0', '0.583', '#joy', '#surprise', 'positive', '0.831', 'giant', 'shinny', 'supernova', 'supergiant', 'binary_star']
senticnet['confab'] = ['-0.79', '0', '0.948', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['confabulate'] = ['0.542', '-0.60', '0', '0.757', '#surprise', '#admiration', 'positive', '0.636', 'fabricate', 'manufacture', 'cook_up', 'invent', 'make_up']
senticnet['confabulation'] = ['-0.60', '0', '0.837', '-0.58', '#sadness', '#anger', 'negative', '-0.67', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['confarreatio'] = ['-0.76', '0', '0.995', '-0.94', '#anger', '#disgust', 'negative', '-0.90', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'excretion']
senticnet['confection'] = ['-0.97', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.97', 'contrive', 'ingenious', 'pretext', 'contrived', 'contriver']
senticnet['confectionery_shop'] = ['0.026', '0.082', '0', '0', '#joy', '#interest', 'positive', '0.054', 'account_department', 'office', 'accountant_office', 'shirt_pocket', 'desk_drawer']
senticnet['confectionery_store'] = ['0.026', '0.082', '0', '0', '#joy', '#interest', 'positive', '0.054', 'account_department', 'office', 'accountant_office', 'shirt_pocket', 'desk_drawer']
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senticnet['confederate_flag'] = ['0.053', '0.068', '-0.05', '0.06', '#interest', '#admiration', 'positive', '0.032', 'linen', 'star_stripe', 'velvet', 'nation', 'burlap']
senticnet['confederate_prisoner'] = ['0', '0.156', '-0.06', '0.198', '#interest', '#admiration', 'positive', '0.098', 'grassland', 'camping', 'camp_out', 'campsite', 'campground']
senticnet['confederation'] = ['0.97', '0.975', '0', '0', '#joy', '#interest', 'positive', '0.972', 'alinement', 'coalition', 'alliance', 'alignment', 'bond']
senticnet['confer'] = ['0.737', '-0.93', '0', '0', '#joy', '#surprise', 'positive', '0.835', 'accolade', 'bestow', 'conferment', 'crescent', 'honorific']
senticnet['confer_immunity'] = ['0', '0', '0', '0.816', '#admiration', '#admiration', 'positive', '0.816', 'resistance', 'protection', 'untouchable', 'invulnerable', 'sacrosanct']
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senticnet['conference'] = ['0', '0.214', '0.012', '0.208', '#interest', '#admiration', 'positive', '0.137', 'lecture_hall', 'lecture_theater', 'lesson', 'lecture', 'school_session']
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senticnet['conference_expo'] = ['-0.79', '0', '0.948', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['conferencethe'] = ['0', '0.214', '0.012', '0.208', '#interest', '#admiration', 'positive', '0.137', 'lecture_hall', 'lecture_theater', 'lesson', 'lecture', 'school_session']
senticnet['conferment'] = ['0.729', '-0.93', '0', '0', '#joy', '#surprise', 'positive', '0.833', 'accolade', 'bestow', 'confer', 'crescent', 'honorific']
senticnet['conferrer'] = ['0.883', '-0.66', '0', '0.732', '#joy', '#admiration', 'positive', '0.76', 'giver', 'donor', 'presenter', 'bestower', 'subsidizer']
senticnet['confers_resistance'] = ['-0.69', '0', '0.516', '-0.94', '#sadness', '#disgust', 'negative', '-0.71', 'insubordinate', 'recalcitrant', 'obstreperous', 'defiant', 'resistive']
senticnet['confervoid_algae'] = ['-0.65', '0', '0.523', '-0.73', '#sadness', '#disgust', 'negative', '-0.64', 'alga', 'seaweed', 'golden_algae', 'diatom', 'brown_algae']
senticnet['confess_sin'] = ['-0.11', '0.205', '-0.10', '0.219', '#interest', '#admiration', 'positive', '0.051', 'confessional', 'confession', 'judaism', 'christianity', 'church']
senticnet['confesse'] = ['-0.87', '0', '0.838', '-0.69', '#sadness', '#anger', 'negative', '-0.80', 'blither', 'prattle', 'gibber', 'blabber', 'gabble']
senticnet['confessedly'] = ['0.565', '-0.73', '0', '0.542', '#joy', '#surprise', 'positive', '0.615', 'say', 'asseverate', 'predicate', 'insist', 'take_firm_stand']
senticnet['confession'] = ['0', '0.235', '0.012', '0.222', '#interest', '#admiration', 'positive', '0.148', 'church', 'cathedral', 'catholic_church', 'christianity', 'temple']
senticnet['confessional'] = ['-0.05', '0.22', '-0.04', '0.233', '#interest', '#admiration', 'positive', '0.089', 'christianity', 'chapel', 'altar', 'church', 'catholic_church']
senticnet['confessor'] = ['0.901', '0', '0', '0.916', '#joy', '#admiration', 'positive', '0.909', 'get_away', 'bread', 'glory', 'counsel', 'vow']
senticnet['confetti'] = ['0.104', '-0.11', '0', '-0.04', '#joy', '#surprise', 'positive', '0.057', 'reception', 'disco', 'manhole_cover', 'dance_club', 'balloon']
senticnet['conficker'] = ['0', '0', '-0.98', '-0.85', '#fear', '#disgust', 'negative', '-0.92', 'contagious_disease', 'infectious_disease', 'sexually_transmit_disease', 'disease', 'malady']
senticnet['confidant'] = ['0.814', '-0.50', '0', '0.622', '#joy', '#admiration', 'positive', '0.648', 'friend', 'champion', 'supporter', 'protagonist', 'endorser']
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senticnet['confidence-building'] = ['0.947', '-0.78', '0', '0', '#joy', '#surprise', 'positive', '0.865', 'last-ditch', 'quick-and-dirty', 'middle-ground', 'stopgap', 'face-saving']
senticnet['confidence_man'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'gouger', 'swindler', 'defrauder', 'conman', 'fraud']
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senticnet['considerable_expertise'] = ['0', '0.983', '0', '0', '#interest', '#interest', 'positive', '0.983', 'find_problem', 'analyze', 'expert_report', 'examination', 'check']
senticnet['considerable_extent'] = ['0.021', '0.167', '0', '0.207', '#interest', '#admiration', 'positive', '0.132', 'size_up', 'measure_length', 'scope', 'distant', 'extension']
senticnet['considerable_flexibility'] = ['-0.52', '0', '0.561', '-0.90', '#anger', '#disgust', 'negative', '-0.66', 'difference', 'tolerance', 'allowance', 'discrepancy', 'margin']
senticnet['considerable_fortune'] = ['0.976', '0.811', '0', '0', '#joy', '#interest', 'positive', '0.893', 'deposit', 'bank_loan', 'wealth', 'finance', 'account']
senticnet['considerable_inconvenience'] = ['-0.89', '0', '0.712', '-0.80', '#sadness', '#disgust', 'negative', '-0.80', 'difficultness', 'severity', 'severeness', 'troublesomeness', 'worriment']
senticnet['considerable_latitude'] = ['-0.52', '0', '0.561', '-0.90', '#anger', '#disgust', 'negative', '-0.66', 'difference', 'tolerance', 'allowance', 'discrepancy', 'margin']
senticnet['considerable_leeway'] = ['-0.52', '0', '0.561', '-0.90', '#anger', '#disgust', 'negative', '-0.66', 'difference', 'tolerance', 'allowance', 'discrepancy', 'margin']
senticnet['considerable_overlap'] = ['-0.58', '0', '0.715', '-0.59', '#anger', '#disgust', 'negative', '-0.63', 'fly', 'flap', 'barndoor', 'coattail', 'codpiece']
senticnet['considerable_prestige'] = ['0.751', '-0.98', '0', '0.989', '#surprise', '#admiration', 'positive', '0.909', 'okay', 'approval', 'commendation', 'okey', 'okeh']
senticnet['considerable_resemblance'] = ['-0.88', '0', '0.813', '-0.57', '#sadness', '#anger', 'negative', '-0.75', 'affinity', 'mutual_resemblance', 'alikeness', 'likeness', 'similitude']
senticnet['considerable_skepticism'] = ['0', '0', '0.928', '-0.70', '#anger', '#disgust', 'negative', '-0.81', 'doubt', 'incertitude', 'dubiety', 'doubtfulness', 'dubiousness']
senticnet['considerable_skill'] = ['0.682', '-0.52', '0', '0.613', '#joy', '#admiration', 'positive', '0.607', 'nice', 'fastidious', 'dainty', 'prissy', 'pernickety']
senticnet['considerable_sum'] = ['0.685', '-0.58', '0', '0.56', '#joy', '#surprise', 'positive', '0.608', 'profit', 'net_income', 'net', 'net_profit', 'fast_buck']
senticnet['considerable_uncertainty'] = ['0', '0', '0', '-0.70', '#disgust', '#disgust', 'negative', '-0.70', 'uncertain_future', 'uncertain_environment', 'insecure', 'blur', 'anxiety']
senticnet['considerable_variation'] = ['0.295', '0.072', '0', '0.136', '#joy', '#admiration', 'positive', '0.168', 'live_environment', 'beautiful', 'lovely', 'important_man', 'peaceful']
senticnet['considerable_wealth'] = ['0.976', '0.811', '0', '0', '#joy', '#interest', 'positive', '0.893', 'deposit', 'bank_loan', 'wealth', 'finance', 'account']
senticnet['considerably'] = ['0', '0.931', '0', '0.839', '#interest', '#admiration', 'positive', '0.885', 'substantially', 'well', 'project', 'research', 'development']
senticnet['considerably_cheaper'] = ['-0.78', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.86', 'high_cost', 'spend_money', 'waste_money', 'high_price', 'unaffordable']
senticnet['considerably_faster'] = ['0.729', '0.974', '0', '0', '#joy', '#interest', 'positive', '0.851', 'travel', 'fast', 'travel_fast', 'take_airplane', 'drive_fast']
senticnet['considerably_higher'] = ['0', '0.075', '0', '0.159', '#interest', '#admiration', 'positive', '0.117', 'maximum', 'max', 'utmost', 'top', 'extreme']
senticnet['considerably_less_expensive'] = ['-0.78', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.86', 'high_cost', 'spend_money', 'waste_money', 'high_price', 'unaffordable']
senticnet['considerably_longer'] = ['0', '0', '-0.78', '-0.71', '#fear', '#disgust', 'negative', '-0.74', 'unretentive', 'poor', 'inadequate', 'little', 'forgetful']
senticnet['considerably_shorter'] = ['0', '0', '-0.78', '-0.71', '#fear', '#disgust', 'negative', '-0.74', 'unretentive', 'poor', 'inadequate', 'little', 'forgetful']
senticnet['considerably_smaller'] = ['-0.05', '0', '-0.12', '-0.16', '#fear', '#disgust', 'negative', '-0.11', 'reduced', 'tiny', 'small_size', 'narrow', 'poor']
senticnet['considerate'] = ['0', '0.954', '0', '0.766', '#interest', '#admiration', 'positive', '0.86', 'circumspect', 'caring', 'consider', 'considerately', 'observant']
senticnet['considerately'] = ['0', '0.946', '0', '0.76', '#interest', '#admiration', 'positive', '0.853', 'circumspect', 'caring', 'consider', 'considerate', 'observant']
senticnet['considerateness'] = ['0.594', '-0.60', '0', '0.813', '#surprise', '#admiration', 'positive', '0.67', 'tact', 'delicacy', 'thoughtfulness', 'consideration', 'finesse']
senticnet['consideration'] = ['0.024', '0.167', '-0.04', '0.123', '#interest', '#admiration', 'positive', '0.067', 'cum', 'find_mate', 'lower_standard_live', 'ovary', 'pregnant']
senticnet['considered_impolite'] = ['0.102', '0.262', '-0.24', '0.412', '#interest', '#admiration', 'positive', '0.132', 'good_behavior', 'courtesy', 'good_etiquette', 'courteous', 'please']
senticnet['considered_indecent'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'indecency']
senticnet['considered_obsolete'] = ['-0.90', '0', '0.517', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'outdated', 'noncurrent', 'disused', 'obsolete', 'superannuated']
senticnet['considered_unclean'] = ['-0.69', '0', '0.858', '-0.83', '#anger', '#disgust', 'negative', '-0.79', 'draggled', 'befouled', 'filthy', 'nasty', 'scummy']
senticnet['considr_comme'] = ['0.667', '-0.98', '0', '0.61', '#joy', '#surprise', 'positive', '0.753', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['consign'] = ['-0.74', '0', '0.966', '0', '#sadness', '#anger', 'negative', '-0.85', 'move', 'displace', 'fire', 'chase_away', 'ostracise']
senticnet['consignment'] = ['0.857', '0.884', '0', '0', '#joy', '#interest', 'positive', '0.871', 'mission', 'expedition', 'delivery', 'shipment', 'dispatch']
senticnet['consignment_shop'] = ['0.026', '0.082', '0', '0', '#joy', '#interest', 'positive', '0.054', 'account_department', 'office', 'accountant_office', 'shirt_pocket', 'desk_drawer']
senticnet['consignment_store'] = ['0.048', '0', '0', '0.092', '#joy', '#admiration', 'positive', '0.07', 'gym_locker', 'observatory', 'prison_cell', 'luggage_store', 'luggage_compartment']
senticnet['consilii'] = ['-0.81', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.79', 'stifle', 'forgotten', 'old', 'touch_ground', 'seldom']
senticnet['consist'] = ['0.04', '0.22', '-0.17', '0.362', '#interest', '#admiration', 'positive', '0.113', 'come', 'inside', 'allow', 'tow', 'accept']
senticnet['consist_primarily'] = ['0.04', '0.22', '-0.17', '0.362', '#interest', '#admiration', 'positive', '0.113', 'come', 'inside', 'allow', 'tow', 'accept']
senticnet['consisted_chiefly'] = ['-0.99', '0', '0.947', '-0.88', '#sadness', '#anger', 'negative', '-0.94', 'atrociousness', 'inhumanity', 'heinousness', 'atrocity', 'barbarity']
senticnet['consistence'] = ['-0.67', '0', '0.863', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'incompressibility', 'substance', 'firmness', 'consistency', 'hardness']
senticnet['consistency'] = ['0', '0', '0', '0.76', '#admiration', '#admiration', 'positive', '0.76', 'big_paycheck', 'self_confidence', 'balance_check_account', 'look_beautiful', 'much_money']
senticnet['consistent'] = ['0.726', '-0.88', '0', '0.529', '#joy', '#surprise', 'positive', '0.714', 'reconciled', 'accordant', 'agreeable', 'conformable', 'pursuant']
senticnet['consistent_condom_use'] = ['0', '0', '0.973', '-0.78', '#anger', '#disgust', 'negative', '-0.87', 'chastity', 'ignore_conception', 'restrain', 'abortion', 'stop']
senticnet['consistent_manner'] = ['0', '0', '0', '0.76', '#admiration', '#admiration', 'positive', '0.76', 'big_paycheck', 'self_confidence', 'balance_check_account', 'look_beautiful', 'much_money']
senticnet['consistently'] = ['0', '0.819', '0', '0.927', '#interest', '#admiration', 'positive', '0.873', 'systematically', 'phase', 'consistent', 'higher', 'data']
senticnet['consistently_outperform'] = ['0', '0.994', '0', '0.88', '#interest', '#admiration', 'positive', '0.937', 'exceed', 'outshout', 'outstrip', 'outshine', 'outsail']
senticnet['consisteth'] = ['0.657', '-0.56', '0', '0.749', '#joy', '#admiration', 'positive', '0.655', 'naturalness', 'condescension', 'condescendingness', 'mellowness', 'friendliness']
senticnet['consolamentum'] = ['0.901', '0', '0', '0.916', '#joy', '#admiration', 'positive', '0.909', 'get_away', 'bread', 'glory', 'counsel', 'vow']
senticnet['consolation'] = ['0.488', '0.661', '-0.25', '0.244', '#joy', '#interest', 'positive', '0.284', 'appreciativeness', 'gratefulness', 'unafraid', 'intrepidly', 'audacious']
senticnet['consolation_bracket'] = ['0.763', '-0.67', '0', '0.945', '#joy', '#admiration', 'positive', '0.795', 'set', 'income_bracket', 'tax_bracket', 'income_tax_bracket', 'price_bracket']
senticnet['consolation_round'] = ['0.763', '-0.67', '0', '0.945', '#joy', '#admiration', 'positive', '0.795', 'set', 'income_bracket', 'tax_bracket', 'income_tax_bracket', 'price_bracket']
senticnet['console'] = ['0.686', '0.621', '-0.24', '0.335', '#joy', '#interest', 'positive', '0.35', 'closeness', 'joyful', 'intoxicate', 'cheer_up', 'solace']
senticnet['consolidant'] = ['-0.52', '0', '0.596', '-0.76', '#anger', '#disgust', 'negative', '-0.62', 'retention', 'retentiveness', 'retentivity', 'solidness', 'nonabsorbency']
senticnet['consolidate'] = ['-0.04', '0.153', '-0.12', '0.232', '#interest', '#admiration', 'positive', '0.055', 'reach', 'elasticity', 'grow_long', 'extend_arm', 'tow']
senticnet['consolidation'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'blowup', 'discharge', 'backfire', 'blast']
senticnet['consolidative'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'combinatory', 'integrative', 'unifying', 'combinatorial']
senticnet['consonant'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'letter', 'vowel', 'alphabet', 'basic_speech', 'kind_gesture']
senticnet['consonant_cluster'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'letter', 'vowel', 'alphabet', 'basic_speech', 'kind_gesture']
senticnet['consonant_gradation'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'pronunciation', 'sibilation', 'mispronunciation', 'homophony']
senticnet['consonantal'] = ['0.705', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.836', 'fricative', 'plosive', 'vowel', 'vocalic', 'consonant']
senticnet['consort'] = ['0.728', '0', '0', '0.722', '#joy', '#admiration', 'positive', '0.725', 'fasten', 'entwine', 'blend', 'combine', 'append']
senticnet['consorted_with'] = ['0.754', '-0.82', '0', '0.947', '#surprise', '#admiration', 'positive', '0.841', 'whorehouse', 'brothel', 'bordello', 'massage_parlor', 'house_of_prostitution']
senticnet['consortium'] = ['0.776', '-0.86', '0', '0.893', '#surprise', '#admiration', 'positive', '0.843', 'trust', 'combine', 'corporate_trust', 'cartel', 'association']
senticnet['conspicuous'] = ['0.916', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.935', 'prominent', 'vivid', 'striking', 'dramatic', 'flagrant']
senticnet['conspicuous_bravery'] = ['0.827', '0.85', '0', '0.743', '#joy', '#interest', 'positive', '0.807', 'chivalry', 'honor', 'almighty', 'admire', 'respect']
senticnet['conspicuous_consumption'] = ['0', '0.095', '0', '0.05', '#interest', '#admiration', 'positive', '0.073', 'need_earn_money', 'earn_money', 'responsible', 'perform_service', 'go_work_to_earn_money']
senticnet['conspicuous_courage'] = ['0', '0', '0.764', '-0.77', '#anger', '#disgust', 'negative', '-0.77', 'restriction', 'tragedy', 'perpetrator', 'dominate', 'control']
senticnet['conspicuously'] = ['-0.78', '0', '0.836', '0', '#sadness', '#anger', 'negative', '-0.80', 'conspicuous', 'display', 'markedly', 'signally', 'pectoral']
senticnet['conspiracy'] = ['-0.75', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.73', 'conspirator', 'raven', 'conspiratorial', 'conspiring', 'intrigue']
senticnet['conspiracy_charge'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'fraud', 'swindle', 'rig']
senticnet['conspiracy_nut'] = ['0', '0', '0.743', '-0.94', '#anger', '#disgust', 'negative', '-0.84', 'stupid', 'increase_body_temperature', 'idiot', 'get_excited', 'sense_accomplishment']
senticnet['conspiracy_theorist'] = ['-0.91', '0', '0.794', '-0.93', '#sadness', '#disgust', 'negative', '-0.88', 'fundamentalist', 'extremist', 'obsession', 'stubborn', 'violent']
senticnet['conspiracy_theory'] = ['-0.72', '0', '0.755', '0', '#sadness', '#anger', 'negative', '-0.73', 'disgrace', 'shameful', 'dishonorably', 'inglorious', 'dishonored']
senticnet['conspiracy_thriller'] = ['-0.54', '0', '0.845', '-0.95', '#anger', '#disgust', 'negative', '-0.78', 'face', 'base', 'underside', 'sole', 'bilge']
senticnet['conspirator'] = ['-0.75', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.73', 'conspiracy', 'raven', 'conspiratorial', 'conspiring', 'intrigue']
senticnet['conspiratorial'] = ['-0.75', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.73', 'conspirator', 'raven', 'conspiracy', 'conspiring', 'intrigue']
senticnet['conspiratorially'] = ['0.776', '0.783', '0', '0', '#joy', '#interest', 'positive', '0.78', 'naughty', 'point_finger', 'nod', 'sexual_attraction', 'unscrew']
senticnet['conspiring'] = ['-0.75', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.74', 'conspirator', 'raven', 'conspiratorial', 'conspiracy', 'intrigue']
senticnet['const_char'] = ['-0.04', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.06', 'chemical_reaction', 'burn', 'chemical_process', 'get_burnt', 'set_on_fire']
senticnet['constabulary'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'law_enforcement_agency', 'law', 'agency']
senticnet['constabulary_hmic'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'law_enforcement_agency', 'law', 'agency']
senticnet['constancy'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'metastability', 'monotony', 'invariance', 'changelessness', 'unchangeability']
senticnet['constant'] = ['0', '0.1', '0', '0.131', '#interest', '#admiration', 'positive', '0.116', 'good_trait', 'unending', 'time_frame', 'short_time', 'time_length']
senticnet['constant_barrage'] = ['0', '0', '-0.85', '-0.99', '#fear', '#disgust', 'negative', '-0.92', 'onslaught', 'battery', 'shelling', 'bombardment', 'outpouring']
senticnet['constant_bombardment'] = ['0', '0', '-0.85', '-0.99', '#fear', '#disgust', 'negative', '-0.92', 'onslaught', 'battery', 'shelling', 'bombardment', 'outpouring']
senticnet['constant_companion'] = ['0.756', '0', '0', '0', '#joy', '#joy', 'positive', '0.756', 'companionship', 'cherish', 'lover', 'bond', 'buddy']
senticnet['constant_dread'] = ['-0.84', '0', '0.827', '-0.81', '#sadness', '#anger', 'negative', '-0.83', 'sop', 'douse', 'dowse', 'drench', 'soak']
senticnet['constant_fear'] = ['-0.17', '0.079', '-0.18', '0', '#sadness', '#fear', 'negative', '-0.09', 'terminate', 'bloody', 'maim', 'hurtful', 'severe']
senticnet['constant_hum'] = ['-0.03', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.04', 'rabbit', 'squirrel', 'mole', 'chipmunk', 'nymph']
senticnet['constant_irritation'] = ['-0.93', '0', '0.75', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'invalidism', 'injury', 'affliction', 'biliousness', 'harm']
senticnet['constant_pressure'] = ['-0.12', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.09', 'breeze', 'move_air', 'air_movement', 'gape', 'force']
senticnet['constant_reassurance'] = ['0.376', '0.271', '-0.19', '0.258', '#joy', '#interest', 'positive', '0.177', 'pleasant_emotion', 'bliss', 'good_feeling', 'confirm', 'healthful']
senticnet['constant_repetition'] = ['0.022', '0.123', '0.038', '0.129', '#interest', '#admiration', 'positive', '0.059', 'hymn', 'written_music', 'walk_beach', 'lyric', 'piece_of_music']
senticnet['constant_scrutiny'] = ['-0.79', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.75', 'scan', 'checkup', 'medical_checkup', 'medical_examination', 'medical_exam']
senticnet['constant_tension_between'] = ['-0.54', '0', '0.979', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'unlikeness', 'dissimilitude', 'difference', 'heterology', 'dissimilarity']
senticnet['constant_velocity'] = ['-0.04', '0.074', '0.019', '0.071', '#interest', '#admiration', 'positive', '0.021', 'sharpness', 'sharp_end', 'sharp_corner', 'near_end', 'edge']
senticnet['constant_vigilance'] = ['0', '1', '0', '0', '#interest', '#interest', 'positive', '1', 'confident', 'confidence', 'optimistic', 'assured', 'engrossed']
senticnet['constantinopolitan'] = ['-0.69', '0', '0.957', '-0.92', '#anger', '#disgust', 'negative', '-0.85', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['constantly'] = ['0', '0.093', '0', '0.053', '#interest', '#admiration', 'positive', '0.073', 'underground', 'under_ground', 'environment', 'outpost', 'dark_place']
senticnet['constantly_monitor'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'computer_part', 'system', 'motherboard', 'pencil_pen', 'cpu']
senticnet['constative'] = ['0.633', '-0.67', '0', '0.776', '#surprise', '#admiration', 'positive', '0.693', 'double_entendre', 'loophole', 'ambiguity', 'amphiboly', 'parisology']
senticnet['constellate'] = ['0', '-0.94', '0', '0.876', '#surprise', '#admiration', 'positive', '0.91', 'stud', 'doting', 'disperse', 'adoring', 'fond']
senticnet['constellation'] = ['0.054', '0.098', '0', '0.076', '#interest', '#admiration', 'positive', '0.076', 'sky', 'heaven', 'paradise', 'thunder_storm', 'orbit_around_earth']
senticnet['constellation_andromeda'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['constellation_cancer'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['constellation_canis_major'] = ['0.977', '-0.93', '0', '0.583', '#joy', '#surprise', 'positive', '0.831', 'giant', 'shinny', 'supernova', 'supergiant', 'binary_star']
senticnet['constellation_carina'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['constellation_cassiopeia'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['constellation_hydra'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['constellation_orion'] = ['0.977', '-0.93', '0', '0.583', '#joy', '#surprise', 'positive', '0.831', 'giant', 'shinny', 'supernova', 'supergiant', 'binary_star']
senticnet['constellation_sagitta'] = ['0.977', '-0.93', '0', '0.583', '#joy', '#surprise', 'positive', '0.831', 'giant', 'shinny', 'supernova', 'supergiant', 'binary_star']
senticnet['constellation_scutum'] = ['0.977', '-0.93', '0', '0.583', '#joy', '#surprise', 'positive', '0.831', 'giant', 'shinny', 'supernova', 'supergiant', 'binary_star']
senticnet['constellation_telescopium'] = ['0.977', '-0.93', '0', '0.583', '#joy', '#surprise', 'positive', '0.831', 'giant', 'shinny', 'supernova', 'supergiant', 'binary_star']
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senticnet['contribute_significantly'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'compound', 'intensify', 'deepen', 'potentiate', 'heighten']
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senticnet['coolness'] = ['0.497', '0.594', '-0.2', '0.263', '#joy', '#interest', 'positive', '0.288', 'imperturbability', 'imperturbableness', 'placid', 'placidness', 'placidly']
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senticnet['cooper_hawk_accipiter_cooperii'] = ['-0.74', '0', '0.963', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['cooperate'] = ['0.741', '0.898', '0', '0.734', '#joy', '#interest', 'positive', '0.791', 'interact', 'deal', 'connect', 'face_to_face', 'interaction']
senticnet['cooperate_closely'] = ['0', '0', '0', '0.881', '#admiration', '#admiration', 'positive', '0.881', 'educate_others', 'share_knowledge', 'share_opinion', 'discover_new', 'collaboration']
senticnet['cooperates_closely_with'] = ['0.043', '0.109', '0', '0.061', '#interest', '#admiration', 'positive', '0.071', 'nation_state', 'democratic_country', 'free_country', 'large_country', 'republic']
senticnet['cooperation'] = ['0', '0', '0', '0.881', '#admiration', '#admiration', 'positive', '0.881', 'educate_others', 'share_knowledge', 'share_opinion', 'discover_new', 'collaboration']
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senticnet['cooperative'] = ['0', '0', '-0.90', '-0.88', '#fear', '#disgust', 'negative', '-0.89', 'co-op', 'concerted', 'conjunct', 'conjunctive', 'accommodative']
senticnet['cooperative_agreement'] = ['0', '0.709', '0', '0.96', '#interest', '#admiration', 'positive', '0.835', 'award', 'education', 'university', 'different_application', 'human_resource_development']
senticnet['cooperative_education'] = ['0', '0.098', '0.012', '0.276', '#interest', '#admiration', 'positive', '0.121', 'position', 'job', 'find_own_place_in_world', 'character_trait', 'place']
senticnet['cooperative_effort'] = ['0', '0', '0', '0.881', '#admiration', '#admiration', 'positive', '0.881', 'educate_others', 'share_knowledge', 'share_opinion', 'discover_new', 'collaboration']
senticnet['cooperative_relationship'] = ['0', '0', '0', '0.881', '#admiration', '#admiration', 'positive', '0.881', 'educate_others', 'share_knowledge', 'share_opinion', 'discover_new', 'collaboration']
senticnet['cooperatively'] = ['0.979', '0.93', '0', '0.723', '#joy', '#interest', 'positive', '0.877', 'information_transfer', 'tranfer', 'inform_person', 'inform_others', 'information']
senticnet['cooperativity'] = ['-0.84', '0', '0.8', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'rigor_mortis', 'natural_process', 'natural_action', 'activity', 'action']
senticnet['coopt'] = ['0.54', '-0.97', '0', '0.569', '#surprise', '#admiration', 'positive', '0.695', 'overstate', 'hyperbolize', 'glorify', 'exaggerate', 'overdraw']
senticnet['coordinate'] = ['0', '0.115', '0', '0', '#interest', '#interest', 'positive', '0.115', 'ledge', 'middle_road', 'mountain_top', 'coordinator', 'tennis_match']
senticnet['coordinated'] = ['0.989', '0.712', '0', '0.765', '#joy', '#admiration', 'positive', '0.822', 'highly_skilled', 'coordination', 'find_ball', 'cooperation', 'teamwork']
senticnet['coordinating_committee'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'sexual_relationship', 'affair', 'involvement']
senticnet['coordination'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'highly_skilled', 'coordinated', 'find_ball', 'cooperation', 'teamwork']
senticnet['coordinator'] = ['0', '0.115', '0', '0', '#interest', '#interest', 'positive', '0.115', 'ledge', 'middle_road', 'mountain_top', 'coordinate', 'tennis_match']
senticnet['coors_light'] = ['0.964', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.964', 'carbonated_drink', 'sugar_free', 'coke', 'diet_coke', 'beverage']
senticnet['coorse'] = ['0.027', '0', '0.012', '0.063', '#joy', '#admiration', 'positive', '0.026', 'vegetation', 'tree_flower', 'green_growth', 'small', 'greenery']
senticnet['cop'] = ['0.968', '0.791', '0', '0', '#joy', '#interest', 'positive', '0.879', 'nab', 'pick_up', 'thieve', 'glom', 'copper']
senticnet['cop_drama'] = ['-0.54', '0', '0.845', '-0.95', '#anger', '#disgust', 'negative', '-0.78', 'face', 'base', 'underside', 'sole', 'bilge']
senticnet['copa_del_generalsimo_final'] = ['0.093', '-0.02', '0', '0.056', '#joy', '#admiration', 'positive', '0.057', 'pitcher', 'carafe', 'container_pitcher', 'mug', 'glass_of_water']
senticnet['copal_incense'] = ['0.827', '0', '0', '0', '#joy', '#joy', 'positive', '0.827', 'wrathful', 'wrathfully', 'wroth', 'wrothful', 'lividity']
senticnet['copaxone'] = ['-0.71', '0', '0.85', '-0.82', '#anger', '#disgust', 'negative', '-0.79', 'sclerosis', 'induration', 'degenerative_disorder', 'autoimmune_disease', 'autoimmune_disorder']
senticnet['copayment'] = ['0.687', '-0.98', '0', '0.666', '#joy', '#surprise', 'positive', '0.778', 'scale', 'scale_of_measurement', 'graduated_table', 'ordered_series', 'wage_scale']
senticnet['cope'] = ['0', '0.917', '0', '0.789', '#interest', '#admiration', 'positive', '0.853', 'offer', 'receive', 'contribution', 'give', 'propose']
senticnet['cope_with'] = ['-0.03', '0.086', '0', '0.114', '#interest', '#admiration', 'positive', '0.054', 'deduce', 'figure_out', 'arrive_conclusion', 'come_conclusion', 'come_up_new_idea']
senticnet['copepod'] = ['-0.79', '0', '0.709', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'animal', 'animate_being', 'ctenophore', 'invertebrate', 'beast']
senticnet['copepoda'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'annelid', 'annelid_worm', 'leech']
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senticnet['copernicia'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'carnauba_palm', 'carnauba', 'wax_palm', 'palmetto', 'palmyra']
senticnet['copied_onto'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'slot', 'box', 'load_dock', 'safe_deposit_box', 'emporium']
senticnet['copier_paper'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'kraft', 'kraft_paper', 'butcher_paper', 'paper']
senticnet['copieux'] = ['0.146', '0.095', '-0.04', '0', '#joy', '#interest', 'positive', '0.065', 'paper_menu', 'diner', 'cafe', 'restaurant', 'dine_room']
senticnet['copious'] = ['0', '-0.91', '0', '0.903', '#surprise', '#admiration', 'positive', '0.909', 'profuse', 'exuberant', 'lavish', 'squander', 'effuse']
senticnet['copolymer'] = ['-0.04', '0.069', '0', '0.075', '#interest', '#admiration', 'positive', '0.035', 'sculpt_gel', 'hairstyle', 'hairdresser', 'cosmetic', 'hair_sculpt_gel']
senticnet['coppelia'] = ['0.12', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.09', 'ballroom', 'perform_art', 'nightclub', 'ballerina', 'venue']
senticnet['copper'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'gold', 'bronze', 'alloy', 'titanium', 'medal']
senticnet['copper_cable'] = ['0.085', '0', '0', '0', '#joy', '#joy', 'positive', '0.085', 'wire', 'several_types', 'gas_stove', 'tape_player', 'control_room']
senticnet['copper_cu'] = ['-0.05', '-0.00', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'pigment', 'many_shades', 'cyan', 'green_yellow', 'material']
senticnet['copper_deficiency'] = ['-0.54', '0', '0.92', '-0.84', '#anger', '#disgust', 'negative', '-0.76', 'dearth', 'demand', 'mineral_deficiency', 'famine', 'need']
senticnet['copper_inuit'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'cheetah', 'giraffe', 'kangaroo', 'mule', 'horse']
senticnet['copper_iud'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'birth_control_pill', 'condom', 'contraceptive_pill', 'sex_toy', 'prevent_pregnancy']
senticnet['copper_metabolism'] = ['-0.57', '0', '0.973', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['copper_mining_company'] = ['-0.98', '0', '0.521', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'battery', 'stamp_battery', 'quartz_battery', 'stamp_mill', 'factory']
senticnet['copper_ore'] = ['-0.78', '0', '0.699', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'battery', 'stamp_battery', 'quartz_battery', 'quartz_mill', 'factory']
senticnet['copper_smelter'] = ['-0.78', '0', '0.699', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'battery', 'stamp_battery', 'quartz_battery', 'quartz_mill', 'factory']
senticnet['copper_tube'] = ['0.72', '0', '0', '0.733', '#joy', '#admiration', 'positive', '0.726', 'spanner', 'gold', 'strong_material', 'platinum', 'durable']
senticnet['copper_wire'] = ['0.085', '0', '0', '0', '#joy', '#joy', 'positive', '0.085', 'wire', 'several_types', 'gas_stove', 'tape_player', 'control_room']
senticnet['copper_zinc'] = ['-0.05', '-0.00', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'pigment', 'many_shades', 'cyan', 'green_yellow', 'material']
senticnet['copperplate'] = ['-0.95', '0', '0.517', '-0.53', '#sadness', '#disgust', 'negative', '-0.67', 'cursive', 'hand', 'minuscule', 'italic', 'round_hand']
senticnet['copplia'] = ['0.12', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.09', 'ballroom', 'perform_art', 'nightclub', 'ballerina', 'venue']
senticnet['copra'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'palm_nut', 'nut', 'edible_nut', 'cembra_nut', 'cedar_nut']
senticnet['copra_dried_coconut'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'palm_nut', 'nut', 'edible_nut', 'cembra_nut', 'cedar_nut']
senticnet['copra_production'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'palm_nut', 'nut', 'edible_nut', 'cembra_nut', 'cedar_nut']
senticnet['coprincipal'] = ['0', '0.775', '0', '0.809', '#interest', '#admiration', 'positive', '0.792', 'award', 'different_application', 'standard_grant', 'sponsor', 'project']
senticnet['coprophagia'] = ['-0.17', '0', '-0.16', '-0.09', '#sadness', '#fear', 'negative', '-0.14', 'murder', 'hurt_others', 'death_penalty', 'death_destruction', 'end_live']
senticnet['coprophilia'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'concupiscence', 'lustfulness', 'amorousness']
senticnet['coprosma'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['copter'] = ['-0.04', '0.092', '0', '0.062', '#interest', '#admiration', 'positive', '0.036', 'hangar', 'aircraft_carrier', 'hot_air_balloon', 'war_museum', 'spaceship']
senticnet['coptic_translation'] = ['-0.87', '0', '0.599', '-0.71', '#sadness', '#disgust', 'negative', '-0.73', 'hysteria', 'fury', 'delirium', 'frenzy', 'craze']
senticnet['copula'] = ['-0.82', '0', '0.81', '-0.61', '#sadness', '#anger', 'negative', '-0.75', 'oblique', 'genitive', 'envy', 'possessive', 'jealous']
senticnet['copular_verb'] = ['0.904', '-0.58', '0', '0.736', '#joy', '#admiration', 'positive', '0.743', 'naming', 'appellative', 'denotative', 'extensional', 'referent']
senticnet['copulate'] = ['0.049', '0.098', '-0.07', '0.121', '#interest', '#admiration', 'positive', '0.047', 'procreate', 'reproduce', 'start_family', 'cause_cold', 'unprotect_sex']
senticnet['copulation'] = ['0.947', '-0.81', '0', '0.656', '#joy', '#surprise', 'positive', '0.805', 'sexual_intercourse', 'intercourse', 'sex_act', 'hank_panky', 'coitus']
senticnet['copulation_take_place'] = ['0.947', '-0.81', '0', '0.656', '#joy', '#surprise', 'positive', '0.805', 'sexual_intercourse', 'intercourse', 'sex_act', 'hank_panky', 'coitus']
senticnet['copulatory'] = ['0.049', '0.098', '-0.07', '0.121', '#interest', '#admiration', 'positive', '0.047', 'procreate', 'reproduce', 'start_family', 'cause_cold', 'unprotect_sex']
senticnet['copy'] = ['-0.02', '0.114', '0', '0.204', '#interest', '#admiration', 'positive', '0.098', 'mimic', 'halve', 'separate', 'divide', 'make_copy']
senticnet['copy_paste'] = ['-0.10', '0', '0.032', '-0.16', '#sadness', '#disgust', 'negative', '-0.10', 'slit', 'sever', 'open_wind', 'cause_bleed', 'draw_blood']
senticnet['copycat'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'intend', 'mimic', 'imitation', 'copy', 'heap']
senticnet['copyedited_manuscript'] = ['0.642', '-0.53', '0', '0.575', '#joy', '#admiration', 'positive', '0.583', 'check', 'assure', 'see', 'insure', 'examine']
senticnet['copyhold'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'bodyguard', 'outrider', 'bodyguard_of_guard', 'beefeater', 'defender']
senticnet['copying_machine'] = ['0', '0', '0.128', '0', '#anger', '#anger', 'negative', '-0.12', 'grid', 'efficient', 'calculator', 'computer', 'personal_computer']
senticnet['copyright'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'write_down', 'run_out_paper', 'legal_right', 'write_essay', 'realization']
senticnet['copyright_infringement'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'intend', 'mimic', 'imitation', 'copy', 'heap']
senticnet['copystructure'] = ['0.146', '0.095', '-0.04', '0', '#joy', '#interest', 'positive', '0.065', 'paper_menu', 'diner', 'cafe', 'restaurant', 'dine_room']
senticnet['coq_au_vin'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'blue_ribbon', 'badge', 'allegory', 'stripe', 'merit_badge']
senticnet['coquetry'] = ['0.525', '-0.85', '0', '0.599', '#surprise', '#admiration', 'positive', '0.66', 'frolic', 'gambol', 'romp', 'play', 'caper']
senticnet['coquette'] = ['0.531', '0.673', '-0.27', '0.275', '#joy', '#interest', 'positive', '0.301', 'amorousness', 'amatory', 'enamoredness', 'amative', 'puppy_love']
senticnet['coquettish'] = ['0.828', '-0.88', '0', '0.551', '#joy', '#surprise', 'positive', '0.755', 'superiority', 'pride', 'superbia', 'imperiousness', 'domineeringness']
senticnet['coracoid'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'above_leg', 'joint', 'hip', 'knee']
senticnet['coral'] = ['0.053', '0.073', '-0.06', '0.104', '#interest', '#admiration', 'positive', '0.041', 'aquatic_life', 'small_fish', 'horizon', 'ocean', 'wave']
senticnet['coral_reef'] = ['0', '0', '-0.06', '-0.03', '#fear', '#disgust', 'negative', '-0.05', 'tidal_pool', 'saltwater', 'fish_market', 'sea', 'reef']
senticnet['coral_sand'] = ['-0.06', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.04', 'friction', 'lack_of_water', 'dirt', 'abrasive', 'ground_material']
senticnet['coral_sea'] = ['0.084', '-0.05', '0.013', '0', '#joy', '#surprise', 'positive', '0.043', 'carnival_ride', 'carny', 'fortune_teller', 'horseback', 'ferris_wheel']
senticnet['coral_snake'] = ['-0.68', '0', '0.611', '-0.59', '#sadness', '#anger', 'negative', '-0.63', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['coralberry'] = ['0.958', '-0.82', '0', '0.924', '#joy', '#admiration', 'positive', '0.901', 'bunchberry', 'dwarf_cornel', 'crackerberry', 'pudding_berry', 'common_white_dogwood']
senticnet['corangamite'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'couch', 'stool', 'coach_seat']
senticnet['corbett_tiger'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['corcoran_gallery'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'institution', 'tundra', 'child_story', 'natural_history_museum', 'cartoon']
senticnet['cord'] = ['-0.97', '0', '0.865', '-0.54', '#sadness', '#anger', 'negative', '-0.79', 'piping', 'lace', 'lacing', 'perpendicular', 'slack']
senticnet['cord_blood_transplant'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'displace', 'move', 'move_away', 'bump', 'dislocate']
senticnet['cordate'] = ['-0.75', '0', '0.734', '-0.82', '#sadness', '#disgust', 'negative', '-0.77', 'puberulent', 'haired', 'furlike', 'pappose', 'pilous']
senticnet['cordial'] = ['0.693', '-0.94', '0', '0.713', '#surprise', '#admiration', 'positive', '0.784', 'anisette', 'maraschino', 'ratafia', 'pastis', 'ratafee']
senticnet['cordial_invitation'] = ['0', '0.852', '0', '0', '#interest', '#interest', 'positive', '0.852', 'streamer', 'tribute', 'salutation', 'gift', 'anniversary_gift']
senticnet['cordial_welcome'] = ['0.736', '-0.66', '0', '0.777', '#joy', '#admiration', 'positive', '0.725', 'lucky', 'propitious', 'prosperous', 'favorable', 'good_luck']
senticnet['cordiality'] = ['0.655', '0.635', '-0.23', '0.3', '#joy', '#interest', 'positive', '0.338', 'cheerfully', 'cheerful', 'jolly_up', 'hearten', 'chirk_up']
senticnet['cordially'] = ['0.621', '0.648', '-0.25', '0.296', '#joy', '#interest', 'positive', '0.328', 'pacifically', 'repose', 'peace-loving', 'peaceable', 'peace_of_mind']
senticnet['cordifolia'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
senticnet['cordite'] = ['-0.58', '0', '0.661', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'explosive', 'explosive_compound', 'dynamite', 'plastic_explosive', 'plastique']
senticnet['cordless'] = ['0', '-0.12', '0.011', '0.089', '#surprise', '#admiration', 'positive', '0.067', 'cellular_phone', 'telephone', 'wireless', 'connect', 'message']
senticnet['cordless_drill'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'smart_technology', 'electric_shaver', 'hair_dryer', 'stay_healthy', 'wash_tooth']
senticnet['cordless_mouse'] = ['0', '-0.12', '0.011', '0.089', '#surprise', '#admiration', 'positive', '0.067', 'cellular_phone', 'telephone', 'wireless', 'connect', 'message']
senticnet['cordless_phone'] = ['0', '-0.12', '0.011', '0.089', '#surprise', '#admiration', 'positive', '0.067', 'cellular_phone', 'telephone', 'wireless', 'connect', 'message']
senticnet['cordless_telephone'] = ['0', '-0.12', '0.011', '0.089', '#surprise', '#admiration', 'positive', '0.067', 'cellular_phone', 'telephone', 'wireless', 'connect', 'message']
senticnet['cordon'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'corner_two_streets', 'street_corner', 'roadside', 'city_street', 'schoolyard']
senticnet['cordon_around'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'corner_two_streets', 'street_corner', 'roadside', 'city_street', 'schoolyard']
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senticnet['corporate_trust'] = ['0.776', '-0.86', '0', '0.893', '#surprise', '#admiration', 'positive', '0.843', 'trust', 'combine', 'consortium', 'cartel', 'association']
senticnet['corporate_welfare'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'address_book', 'get_rich', 'ladle', 'swiss_army_knife', 'whisk']
senticnet['corporation'] = ['0.08', '0.195', '0', '0.137', '#interest', '#admiration', 'positive', '0.137', 'company', 'enterprise', 'business', 'partnership', 'large_business']
senticnet['corporation_counsel'] = ['0', '0.146', '-0.05', '0.077', '#interest', '#admiration', 'positive', '0.057', 'lawyer', 'negotiator', 'arbiter', 'judge_jury', 'decide_guilt']
senticnet['corporation_ltd'] = ['0.59', '-0.75', '0', '0.543', '#joy', '#surprise', 'positive', '0.63', 'venture', 'sally', 'labor', 'cause', 'risk']
senticnet['corporatist'] = ['0', '0.796', '0', '0.791', '#interest', '#admiration', 'positive', '0.794', 'lock-step', 'lockstep', 'two-party', 'blindly', 'arm-in-arm']
senticnet['corporeal'] = ['0.03', '0', '0', '0.119', '#joy', '#admiration', 'positive', '0.074', 'notional', 'abstract', 'wow', 'made_out_of_plastic', 'make_paper']
senticnet['corporeal_form'] = ['-0.53', '0', '0.637', '-0.92', '#anger', '#disgust', 'negative', '-0.69', 'discorporate', 'unembodied', 'disembodied', 'incorporeal', 'unbodied']
senticnet['corporeal_nature'] = ['0.668', '-0.88', '0', '0.893', '#surprise', '#admiration', 'positive', '0.815', 'chirpiness', 'spirit', 'sprightliness', 'liveliness', 'activity']
senticnet['corporeality'] = ['-0.76', '0', '0.821', '-0.71', '#sadness', '#anger', 'negative', '-0.76', 'tangibility', 'tangibleness', 'palpability', 'reality', 'solidness']
senticnet['corporeity'] = ['0.668', '-0.88', '0', '0.893', '#surprise', '#admiration', 'positive', '0.815', 'chirpiness', 'spirit', 'sprightliness', 'liveliness', 'activity']
senticnet['corpse'] = ['-0.04', '0.077', '-0.09', '-0.08', '#fear', '#disgust', 'negative', '-0.03', 'body', 'inside_body', 'normal_human_body', 'dead_person', 'nervous_system']
senticnet['corpulence'] = ['-0.93', '0', '0.75', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'invalidism', 'injury', 'affliction', 'biliousness', 'harm']
senticnet['corpulency'] = ['-0.72', '0', '0.973', '-0.57', '#sadness', '#anger', 'negative', '-0.76', 'exogenous_obesity', 'blubber', 'fat', 'embonpoint', 'stoutness']
senticnet['corpulent'] = ['0.734', '0.976', '0', '0', '#joy', '#interest', 'positive', '0.855', 'thick-set', 'stocky', 'thickset', 'barrel-chested', 'broad-shouldered']
senticnet['corpus_callosum'] = ['0.683', '-0.91', '0', '0.679', '#joy', '#surprise', 'positive', '0.76', 'pathway', 'commissure', 'nerve_pathway', 'tract', 'nerve_tract']
senticnet['corpus_cavernosa'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expandible', 'expansive', 'erectile', 'cavernous']
senticnet['corpus_luteum'] = ['-0.87', '0', '0.915', '-0.95', '#anger', '#disgust', 'negative', '-0.91', 'inactivity', 'inactiveness', 'slumber', 'inaction', 'hibernation']
senticnet['corpuscle'] = ['-0.85', '0', '0.743', '-0.83', '#sadness', '#disgust', 'negative', '-0.81', 'atom', 'molecule', 'particle', 'grinding', 'mote']
senticnet['corrasion'] = ['-0.88', '0', '0.907', '-0.52', '#sadness', '#anger', 'negative', '-0.77', 'wearing', 'erosion', 'eroding', 'eating_away', 'wearing_away']
senticnet['corrasion_away'] = ['-0.88', '0', '0.903', '-0.52', '#sadness', '#anger', 'negative', '-0.77', 'corrasion', 'erosion', 'eroding', 'eating_away', 'wearing']
senticnet['correct'] = ['0.026', '0.25', '-0.04', '0.28', '#interest', '#admiration', 'positive', '0.127', 'make_better', 'bottom_line', 'tell_everyone', 'make_right', 'band_together']
senticnet['correct_answer'] = ['-0.08', '-0.03', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'graduate_student', 'student', 'serious_student', 'competition', 'hard_worker']
senticnet['correct_behavior'] = ['0', '0', '0', '0.881', '#admiration', '#admiration', 'positive', '0.881', 'retribution', 'end_marriage', 'vengeance', 'misbehavior', 'feel_bad']
senticnet['correct_grammar'] = ['0.642', '-0.53', '0', '0.575', '#joy', '#admiration', 'positive', '0.583', 'check', 'assure', 'see', 'insure', 'examine']
senticnet['correct_homework'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'review', 'double_check', 'spelling', 'check_spelling', 'mark_exam']
senticnet['correct_mistake'] = ['-0.12', '0.089', '-0.10', '0', '#sadness', '#fear', 'negative', '-0.04', 'wave_flag', 'kill_specific_group', 'age_gracefully', 'hurt_animal', 'bicycle_across_town']
senticnet['correct_posture'] = ['0.84', '0.76', '0', '0.894', '#joy', '#admiration', 'positive', '0.831', 'keep_skin_healthy', 'stay_healthy', 'stay_fit', 'maintain_good_health', 'eat_healthily']
senticnet['correct_pronunciation'] = ['-0.02', '0.186', '0', '0.213', '#interest', '#admiration', 'positive', '0.125', 'description', 'mumble', 'bummer', 'numerical', 'proverb']
senticnet['correct_vision'] = ['0', '0', '0', '0.881', '#admiration', '#admiration', 'positive', '0.881', 'vision', 'eye', 'contact_lens', 'use_eye', 'improve_vision']
senticnet['correctable'] = ['0.779', '-0.88', '0', '0.521', '#joy', '#surprise', 'positive', '0.727', 'amendable', 'corrigible', 'improvable', 'reformable', 'redeemable']
senticnet['correction'] = ['0.982', '0', '0', '0.901', '#joy', '#admiration', 'positive', '0.942', 'restore', 'rehabilitative', 'rehabilitate', 'rehab', 'habitable']
senticnet['correction_fluid'] = ['0.883', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.864', 'stick_eraser', 'ballpoint_pen', 'intern', 'paper_clip', 'eraser_holder']
senticnet['correctional'] = ['-0.75', '0', '0.981', '-0.58', '#sadness', '#anger', 'negative', '-0.77', 'retaliatory', 'penitentiary', 'punitive', 'retributive', 'retributory']
senticnet['correctional_center'] = ['0.816', '-0.91', '0', '0.944', '#surprise', '#admiration', 'positive', '0.891', 'jail', 'jailhouse', 'gaol', 'poky', 'clink']
senticnet['correctional_centre'] = ['0.816', '-0.91', '0', '0.944', '#surprise', '#admiration', 'positive', '0.891', 'jail', 'jailhouse', 'gaol', 'poky', 'clink']
senticnet['correctional_facility'] = ['0.816', '-0.91', '0', '0.944', '#surprise', '#admiration', 'positive', '0.891', 'jail', 'jailhouse', 'gaol', 'poky', 'clink']
senticnet['correctional_rehabilitation'] = ['0.641', '-0.78', '0', '0.721', '#surprise', '#admiration', 'positive', '0.715', 'rehabilitation', 'physical_rehabilitation', 'physical_restoration', 'therapeutic_rehabilitation', 'vocational_rehabilitation']
senticnet['correctional_supervision'] = ['-0.43', '0.107', '-0.42', '-0.03', '#sadness', '#fear', 'negative', '-0.19', 'send_to_jail', 'go_jail', 'jail_time', 'go_to_prison', 'injustice']
senticnet['correctional_system'] = ['-0.43', '0.107', '-0.42', '-0.03', '#sadness', '#fear', 'negative', '-0.19', 'send_to_jail', 'go_jail', 'jail_time', 'go_to_prison', 'injustice']
senticnet['correctitude'] = ['0.85', '-0.85', '0', '0.712', '#joy', '#surprise', 'positive', '0.807', 'decency', 'decorum', 'decorousness', 'correctness', 'seemliness']
senticnet['corrective'] = ['-0.62', '0', '0.996', '-0.68', '#anger', '#disgust', 'negative', '-0.76', 'pack', 'face_pack', 'pickup', 'prosthesis', 'prosthetic_device']
senticnet['corrective_action'] = ['0.931', '0.947', '0', '0', '#joy', '#interest', 'positive', '0.939', 'antidote', 'cure', 'therapeutic', 'curative', 'repair']
senticnet['corrective_feedback'] = ['0.347', '0.348', '-0.13', '0.394', '#interest', '#admiration', 'positive', '0.239', 'good_deed', 'benefit', 'gratification', 'self_respect', 'bonus']
senticnet['corrective_surgery'] = ['0.745', '-0.55', '0', '0.597', '#joy', '#admiration', 'positive', '0.633', 'lift', 'phalloplasty', 'abdominoplasty', 'tummy_tuck', 'autoplasty']
senticnet['correctly'] = ['0.865', '0.978', '0', '0', '#joy', '#interest', 'positive', '0.921', 'english', 'orthodoxly', 'rightly', 'rightfully', 'aright']
senticnet['correctly_pointed_out'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'rudeness', 'discourtesy', 'insolence', 'ccheekiness', 'cheekiness']
senticnet['correctly_pronounce'] = ['-0.02', '0.186', '0', '0.213', '#interest', '#admiration', 'positive', '0.125', 'description', 'mumble', 'bummer', 'numerical', 'proverb']
senticnet['correctness'] = ['0.85', '-0.85', '0', '0.712', '#joy', '#surprise', 'positive', '0.807', 'decency', 'decorum', 'decorousness', 'correctitude', 'seemliness']
senticnet['corrector'] = ['0', '0', '-0.86', '-0.89', '#fear', '#disgust', 'negative', '-0.87', 'protect_from_sun', 'shade', 'meaning', 'concealer', 'sunglasses']
senticnet['correlate'] = ['-0.98', '0', '0.737', '-0.92', '#sadness', '#disgust', 'negative', '-0.88', 'separate', 'severalize', 'contrast', 'decouple', 'dissociate']
senticnet['correlation'] = ['-0.81', '0', '0.931', '-0.77', '#sadness', '#anger', 'negative', '-0.84', 'mutualness', 'mutuality', 'complementarity', 'reciprocity', 'correlativity']
senticnet['correlation_between'] = ['0.118', '0.181', '-0.07', '0.374', '#interest', '#admiration', 'positive', '0.149', 'compare', 'confrontation', 'collation', 'comparative', 'paragon']
senticnet['correlation_coefficient'] = ['-0.59', '0', '0.672', '-0.83', '#anger', '#disgust', 'negative', '-0.7', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'asymmetry', 'irregularity']
senticnet['correlation_length'] = ['0.965', '-0.84', '0', '0.599', '#joy', '#surprise', 'positive', '0.802', 'mental_faculty', 'faculty', 'module', 'remembrance', 'anamnesis']
senticnet['correlational'] = ['0.628', '-0.96', '0', '0.682', '#surprise', '#admiration', 'positive', '0.759', 'taboo', 'tabu', 'tendentiousness', 'irrational_hostility', 'bias']
senticnet['correlations_between'] = ['0.118', '0.181', '-0.07', '0.374', '#interest', '#admiration', 'positive', '0.149', 'compare', 'confrontation', 'collation', 'comparative', 'paragon']
senticnet['correlativity'] = ['-0.81', '0', '0.931', '-0.77', '#sadness', '#anger', 'negative', '-0.84', 'mutualness', 'mutuality', 'complementarity', 'correlation', 'reciprocity']
senticnet['correspond'] = ['0.521', '-0.51', '0', '0.814', '#joy', '#admiration', 'positive', '0.615', 'suit', 'befit', 'beseem', 'adhere', 'support']
senticnet['correspondence'] = ['0', '0', '0', '0.12', '#admiration', '#admiration', 'positive', '0.12', 'written_communication', 'written_message', 'handwritten', 'message', 'correlation']
senticnet['correspondence_between'] = ['0', '0', '0', '0.12', '#admiration', '#admiration', 'positive', '0.12', 'written_communication', 'written_message', 'handwritten', 'message', 'correlation']
senticnet['correspondent'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'adventurer', 'humankind']
senticnet['corresponding_adjective'] = ['0.633', '-0.67', '0', '0.776', '#surprise', '#admiration', 'positive', '0.693', 'double_entendre', 'loophole', 'ambiguity', 'amphiboly', 'parisology']
senticnet['corresponding_pixel'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'illuminate_room', 'give_light', 'light', 'light_bulb', 'lantern']
senticnet['correspondingly'] = ['-0.54', '0', '0.739', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'unremunerative', 'idle', 'lean', 'dead', 'unprofitable']
senticnet['corridor'] = ['0', '0', '0', '0.085', '#admiration', '#admiration', 'positive', '0.085', 'use_farm', 'gate', 'door', 'doorway', 'entranceway']
senticnet['corrigible'] = ['0.779', '-0.88', '0', '0.521', '#joy', '#surprise', 'positive', '0.727', 'amendable', 'correctable', 'improvable', 'reformable', 'redeemable']
senticnet['corrino'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['corroborate'] = ['0', '0', '-0.79', '-0.81', '#fear', '#disgust', 'negative', '-0.80', 'affirm', 'support', 'confirm', 'sustain', 'substantiate']
senticnet['corroborating_evidence'] = ['0.84', '-0.97', '0', '0.713', '#joy', '#surprise', 'positive', '0.844', 'proof', 'bed_check', 'crosscheck', 'parity_check', 'redundancy_check']
senticnet['corroboration'] = ['0.84', '-0.97', '0', '0.713', '#joy', '#surprise', 'positive', '0.844', 'proof', 'bed_check', 'crosscheck', 'parity_check', 'redundancy_check']
senticnet['corroborative_evidence'] = ['0.84', '-0.97', '0', '0.713', '#joy', '#surprise', 'positive', '0.844', 'proof', 'bed_check', 'crosscheck', 'parity_check', 'redundancy_check']
senticnet['corrode'] = ['-0.06', '0', '-0.07', '-0.03', '#sadness', '#fear', 'negative', '-0.05', 'hold_two_together', 'deteriorate', 'burn_up', 'travel_through_space', 'float_through_air']
senticnet['corrosion'] = ['-0.97', '0', '0.752', '-0.83', '#sadness', '#disgust', 'negative', '-0.85', 'decay', 'wear', 'deterioration', 'devastation', 'dilapidation']
senticnet['corrosion-resistant'] = ['0', '0', '0.843', '-0.76', '#anger', '#disgust', 'negative', '-0.80', 'high-strength', 'non-corrosive', 'rustproof', 'wear-resistant', 'powder-coated']
senticnet['corrosion_protection'] = ['0.8', '-0.90', '0', '0.675', '#joy', '#surprise', 'positive', '0.794', 'indestructibility', 'tensile_strength', 'continuity', 'persistence', 'changelessness']
senticnet['corrosion_resistance'] = ['0.8', '-0.90', '0', '0.675', '#joy', '#surprise', 'positive', '0.794', 'indestructibility', 'tensile_strength', 'continuity', 'persistence', 'changelessness']
senticnet['corrosion_resistant'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'gold', 'bronze', 'alloy', 'medal', 'copper']
senticnet['corrosive'] = ['-0.85', '0', '0.817', '0', '#sadness', '#anger', 'negative', '-0.83', 'destroy', 'substance', 'corrosively', 'fret', 'undermining']
senticnet['corrosively'] = ['-0.85', '0', '0.815', '0', '#sadness', '#anger', 'negative', '-0.83', 'destroy', 'substance', 'corrosive', 'fret', 'undermining']
senticnet['corrosiveness'] = ['0', '0', '-0.89', '-0.94', '#fear', '#disgust', 'negative', '-0.92', 'dissolve', 'decompose', 'detritus', 'disunite', 'disintegrate']
senticnet['corrugated_cardboard'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'kraft', 'kraft_paper', 'butcher_paper', 'paper']
senticnet['corrugated_metal'] = ['0', '-0.00', '0', '0.088', '#surprise', '#admiration', 'positive', '0.048', 'heavy_paper', 'lightweight_metal', 'wizard', 'metal_type', 'soft_metal']
senticnet['corrugated_steel'] = ['0', '0.773', '0', '0.895', '#interest', '#admiration', 'positive', '0.834', 'wooden_board', 'make_wood', 'wooden_box', 'wooden_floor', 'long_wood']
senticnet['corrugated_tin'] = ['0', '-0.00', '0', '0.088', '#surprise', '#admiration', 'positive', '0.048', 'heavy_paper', 'lightweight_metal', 'wizard', 'metal_type', 'soft_metal']
senticnet['corrugating_medium'] = ['0.024', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'stump', 'plank', 'timber', 'paper', 'mahogany']
senticnet['corrupt'] = ['-0.85', '0', '0.872', '-0.94', '#anger', '#disgust', 'negative', '-0.89', 'political_body', 'cheat', 'take_over', 'vote', 'status']
senticnet['corrupt_bureaucracy'] = ['0', '0', '0', '-0.94', '#disgust', '#disgust', 'negative', '-0.94', 'boring', 'fill_form', 'annoying', 'form', 'fill_out_form']
senticnet['corrupt_policeman'] = ['0.794', '-0.50', '0', '0.531', '#joy', '#admiration', 'positive', '0.611', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['corrupt_regime'] = ['-0.73', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.72', 'royal_leader', 'despot', 'emperor', 'monarch', 'king']
senticnet['corrupt_sheriff'] = ['0', '0', '-0.06', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'elbow', 'arm', 'wrist', 'five_fingers', 'bendable']
senticnet['corrupter'] = ['-0.65', '0', '0.716', '-0.94', '#anger', '#disgust', 'negative', '-0.77', 'bad_hat', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['corruptibility'] = ['-0.69', '0', '0.89', '-0.98', '#anger', '#disgust', 'negative', '-0.85', 'evil', 'wrongness', 'putrefaction', 'wantonness', 'immorality']
senticnet['corruptible'] = ['0.741', '-0.84', '0', '0.543', '#joy', '#surprise', 'positive', '0.711', 'integrity', 'righteousness', 'scrupulousness', 'incorruptness', 'honestness']
senticnet['corruptible_body'] = ['0.741', '-0.84', '0', '0.543', '#joy', '#surprise', 'positive', '0.711', 'integrity', 'righteousness', 'scrupulousness', 'incorruptness', 'honestness']
senticnet['corrupting'] = ['-0.86', '0', '0', '-0.86', '#sadness', '#disgust', 'negative', '-0.86', 'seductive', 'pervert', 'debauch', 'deprave', 'warp']
senticnet['corruption'] = ['-0.95', '0', '-0.70', '-0.96', '#sadness', '#disgust', 'negative', '-0.87', 'corrupt', 'bribe', 'bribe_police', 'illegal', 'persuade']
senticnet['corruption_allegation_against'] = ['0', '0', '-0.77', '-0.86', '#fear', '#disgust', 'negative', '-0.82', 'abduction', 'punishment', 'emergency', 'illiteracy', 'heal']
senticnet['corruption_probe'] = ['0', '0.825', '0', '0', '#interest', '#interest', 'positive', '0.825', 'query', 'interrogation', 'inquire', 'response', 'solution']
senticnet['corruptly'] = ['-0.95', '0', '-0.70', '-0.96', '#sadness', '#disgust', 'negative', '-0.87', 'corrupt', 'bribe_police', 'debt', 'tax', 'persuade']
senticnet['corsage'] = ['0.857', '-0.56', '0', '0.721', '#joy', '#admiration', 'positive', '0.716', 'ask_for', 'lobby', 'quest', 'canvass', 'solicit']
senticnet['corselet'] = ['-0.58', '0', '0.632', '-0.63', '#anger', '#disgust', 'negative', '-0.61', 'skimmer', 'trilby', 'chapeau', 'busby', 'shako']
senticnet['corset'] = ['-0.86', '0', '0', '-0.91', '#sadness', '#disgust', 'negative', '-0.89', 'underskirt', 'petticoat', 'tulle', 'camisole', 'crinoline']
senticnet['corson_county'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['cortana'] = ['0', '0.081', '0', '0', '#interest', '#interest', 'positive', '0.081', 'favorite', 'guard', 'crowd', 'several_people', 'clique']
senticnet['cortege'] = ['0', '0', '-0.04', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'good_band', 'band', 'kit', 'procession', 'oblivion']
senticnet['cortex'] = ['0', '0.786', '0', '0.728', '#interest', '#admiration', 'positive', '0.757', 'hold_brain', 'inside_head', 'cranium', 'skull', 'mind']
senticnet['cortical_activity'] = ['0.971', '0.751', '0', '0.896', '#joy', '#admiration', 'positive', '0.873', 'new_friendship', 'foreplay', 'warm_fuzzy_feeling', 'become_aroused', 'erection']
senticnet['cortical_arousal'] = ['0.971', '0.751', '0', '0.896', '#joy', '#admiration', 'positive', '0.873', 'new_friendship', 'foreplay', 'warm_fuzzy_feeling', 'become_aroused', 'erection']
senticnet['cortical_bone'] = ['-0.53', '0', '0.982', '-0.67', '#anger', '#disgust', 'negative', '-0.73', 'meniscus', 'semilunar_cartilage', 'gristle', 'hyaline_cartilage', 'arytenoid']
senticnet['cortical_layer'] = ['-0.86', '0', '0.511', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'separate', 'move', 'branch', 'diverge', 'fork']
senticnet['corticoid'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'spironolactone', 'adrenal_cortical_steroid', 'antihypertensive', 'antihypertensive_drug']
senticnet['corticospinal_tract'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['corticosteroid'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'spironolactone', 'corticoid', 'adrenal_cortical_steroid', 'antihypertensive', 'antihypertensive_drug']
senticnet['corticosteroid_injection'] = ['-0.98', '0', '0.678', '-0.95', '#sadness', '#disgust', 'negative', '-0.87', 'rubor', 'redness', 'inflammation', 'epicondylitis', 'lateral_epicondylitis']
senticnet['corticosteroid_therapy'] = ['-0.05', '0', '-0.22', '-0.06', '#fear', '#disgust', 'negative', '-0.11', 'medicine', 'cure', 'medical_procedure', 'sleep_drug', 'nurse']
senticnet['corticosterone'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'cholecystokinin', 'secretin', 'gastrointestinal_hormone']
senticnet['corticotroph'] = ['0.905', '-0.65', '0', '0.851', '#joy', '#admiration', 'positive', '0.805', 'gonococcus', 'bacteria', 'bacteriae', 'probiotic', 'probiotic_bacterium']
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senticnet['cortisone_injection'] = ['-0.93', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.92', 'pull_muscle', 'become_exhausted', 'increased_heart_rate', 'breathlessness', 'tired_leg']
senticnet['cortisone_shot'] = ['-0.93', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.92', 'pull_muscle', 'become_exhausted', 'increased_heart_rate', 'breathlessness', 'tired_leg']
senticnet['coruscant'] = ['0', '0', '0.794', '-0.81', '#anger', '#disgust', 'negative', '-0.80', 'glisten', 'scintillating', 'fulgid', 'glittering', 'sparkly']
senticnet['coruscate'] = ['0', '0.908', '0', '0.978', '#interest', '#admiration', 'positive', '0.943', 'flash', 'glistening', 'glisten', 'glittering', 'glister']
senticnet['coruscation'] = ['-0.66', '0', '0.616', '-0.70', '#sadness', '#disgust', 'negative', '-0.66', 'flicker', 'flash', 'streak', 'glint', 'gleam']
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senticnet['corynebacterium'] = ['-0.60', '0', '0.721', '-0.57', '#sadness', '#anger', 'negative', '-0.63', 'tubercle_bacillus', 'leprosy_bacillus', 'eubacteria', 'eubacterium', 'true_bacteria']
senticnet['corynebacterium_diphtheriae'] = ['-0.73', '0', '0.599', '-0.96', '#sadness', '#disgust', 'negative', '-0.76', 'spotted_fever', 'rickettsial_disease', 'rickettsialpox', 'typhus', 'typhus_fever']
senticnet['corythucha'] = ['-0.77', '0', '0.576', '-0.92', '#sadness', '#disgust', 'negative', '-0.75', 'insect', 'firebug', 'water_bug', 'assassin_bug', 'heteropterous_insect']
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senticnet['cosh'] = ['0', '0', '-0.75', '-0.92', '#fear', '#disgust', 'negative', '-0.84', 'tomfool', 'fool', 'sap', 'blackjack', 'muggins']
senticnet['cosign'] = ['0.867', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.871', 'endorsement', 'endorse', 'notation', 'insurance', 'recommendation']
senticnet['cosiness'] = ['0.101', '0', '0', '0', '#joy', '#joy', 'positive', '0.101', 'keep_warm', 'keep_warm_winter', 'provide_warmth', 'provide_heat', 'stay_warm']
senticnet['cosmetic'] = ['0', '0.957', '0', '0.973', '#interest', '#admiration', 'positive', '0.965', 'hair_accessory', 'hair_roller', 'hairdresser_chair', 'straighten_hair', 'sculpt_gel']
senticnet['cosmetic_brand'] = ['0.863', '0.861', '0', '0', '#joy', '#interest', 'positive', '0.862', 'aroma', 'scent', 'smell', 'odor', 'sniff']
senticnet['cosmetic_counter'] = ['0.875', '0.706', '0', '0', '#joy', '#interest', 'positive', '0.79', 'customer', 'clerk', 'patron', 'vintner', 'hairdresser']
senticnet['cosmetic_damage'] = ['-0.98', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.97', 'harm', 'hurt', 'cut_skin', 'bloody', 'ouch']
senticnet['cosmetic_procedure'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
senticnet['cosmetic_surgery'] = ['0', '-0.86', '0', '0.912', '#surprise', '#admiration', 'positive', '0.889', 'looking_old', 'aging', 'looking_good', 'accepting_change', 'age_gracefully']
senticnet['cosmetician'] = ['0.814', '-0.58', '0', '0.646', '#joy', '#admiration', 'positive', '0.681', 'seller', 'marketer', 'vender', 'vendor', 'trafficker']
senticnet['cosmetologist'] = ['0.99', '0', '0', '0.721', '#joy', '#admiration', 'positive', '0.855', 'hair', 'haircut', 'hairdresser_chair', 'scissor', 'hairstyle']
senticnet['cosmic'] = ['0.106', '0.162', '0.037', '0.32', '#interest', '#admiration', 'positive', '0.138', 'superlative', 'best', 'mass_measure', 'a_lot', 'plenty']
senticnet['cosmic_cube'] = ['-0.88', '0', '0.882', '-0.58', '#sadness', '#anger', 'negative', '-0.78', 'mortal', 'behemoth', 'unusual_person', 'aberrant', 'giant']
senticnet['cosmic_entity'] = ['0', '0.081', '0', '0', '#interest', '#interest', 'positive', '0.081', 'favorite', 'guard', 'crowd', 'several_people', 'clique']
senticnet['cosmic_ray'] = ['0', '0', '-0.07', '-0.16', '#fear', '#disgust', 'negative', '-0.12', 'radio_wave', 'sun', 'solar_cell', 'x-ray', 'gamma_ray']
senticnet['cosmo_girl'] = ['-0.98', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.89', 'fairy', 'genus', 'honey_bee', 'infant', 'dove']
senticnet['cosmogonical'] = ['0', '0.106', '-0.05', '0.163', '#interest', '#admiration', 'positive', '0.072', 'band', 'kit', 'oblivion', 'rock_band', 'high_school_band']
senticnet['cosmogony'] = ['0.738', '0.825', '0', '0', '#joy', '#interest', 'positive', '0.781', 'astrophysics', 'cosmological', 'astronomy', 'relativity', 'cosmology']
senticnet['cosmological'] = ['0.734', '0.823', '0', '0', '#joy', '#interest', 'positive', '0.778', 'astrophysics', 'cosmology', 'astronomy', 'relativity', 'cosmogony']
senticnet['cosmological_argument'] = ['0.715', '-0.82', '0', '0.659', '#joy', '#surprise', 'positive', '0.734', 'reasoning', 'logical_thinking', 'abstract_thought', 'casuistry', 'line_of_inquiry']
senticnet['cosmological_model'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'general_relativity', 'general_theory_of_relativity', 'relativity', 'special_relativity', 'special_theory_of_relativity']
senticnet['cosmology'] = ['0.74', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.784', 'astrophysics', 'cosmological', 'astronomy', 'relativity', 'cosmogony']
senticnet['cosmopolitan'] = ['-0.99', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.86', 'metropolis', 'vibrant', 'glamorous', 'bustling', 'nightlife']
senticnet['cosmopolitan_atmosphere'] = ['-0.05', '0.096', '0', '0.066', '#interest', '#admiration', 'positive', '0.037', 'hue_tint', 'spectrum', 'red_blue', 'green_yellow', 'tint']
senticnet['cosmopolitan_city'] = ['-0.05', '0.096', '0', '0.066', '#interest', '#admiration', 'positive', '0.037', 'hue_tint', 'spectrum', 'red_blue', 'green_yellow', 'tint']
senticnet['cosmopolitan_magazine'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'library', 'old_testament', 'cook_book', 'a_lot_of_books', 'librarian']
senticnet['cosmopolitanism'] = ['0.946', '-0.54', '0', '0.774', '#joy', '#admiration', 'positive', '0.754', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['cosmos'] = ['0.814', '-0.58', '0', '0.646', '#joy', '#admiration', 'positive', '0.681', 'world', 'universe', 'existence', 'creation', 'natural_order']
senticnet['cosplay_contest'] = ['0.925', '0.993', '0', '0.832', '#joy', '#interest', 'positive', '0.917', 'dance_samba', 'dance', 'skate', 'jump_rope', 'play_frisbee']
senticnet['cosset'] = ['0', '0', '-0.75', '-0.78', '#fear', '#disgust', 'negative', '-0.77', 'cocker', 'baby', 'coddle', 'featherbed', 'pamper']
senticnet['cost'] = ['-0.02', '0.249', '0', '0.116', '#interest', '#admiration', 'positive', '0.112', 'price', 'expense', 'bill', 'value', 'worth']
senticnet['cost-efficient'] = ['0.907', '0.73', '0', '0', '#joy', '#interest', 'positive', '0.819', 'expedient', 'speedier', 'expeditious', 'expeditiously', 'orderly']
senticnet['cost_a_lot_of_money'] = ['-0.13', '0', '-0.10', '0', '#sadness', '#fear', 'negative', '-0.12', 'enter_door', 'grunt', 'find_church', 'bicycle_store', 'hang_door']
senticnet['cost_allowance_aca'] = ['0.924', '0.968', '0', '0.876', '#joy', '#interest', 'positive', '0.923', 'live_simply', 'help_out', 'money', 'accolade', 'generous']
senticnet['cost_attributable'] = ['-0.02', '0.249', '0', '0.116', '#interest', '#admiration', 'positive', '0.112', 'price', 'expense', 'bill', 'value', 'worth']
senticnet['cost_cutting_drive'] = ['-0.98', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.92', 'thrilling', 'electrifying', 'sensational', 'sensationally', 'scandalmongering']
senticnet['cost_effective'] = ['0.085', '0.365', '-0.20', '0.362', '#interest', '#admiration', 'positive', '0.153', 'bonus', 'monetary_gain', 'financial_gain', 'return', 'give']
senticnet['cost_effective_alternative'] = ['0.02', '0.277', '-0.11', '0.336', '#interest', '#admiration', 'positive', '0.129', 'different', 'similar', 'substitute', 'option', 'exception']
senticnet['cost_effective_manner'] = ['0.982', '-0.67', '0', '0.682', '#joy', '#admiration', 'positive', '0.778', 'workmanlike', 'competent', 'efficient', 'able', 'capable']
senticnet['cost_effective_method'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'woodworking_tool']
senticnet['cost_effective_solution'] = ['-0.04', '0.172', '-0.05', '0.057', '#interest', '#admiration', 'positive', '0.034', 'carry_money', 'put_money', 'require_signature', 'store_money', 'keep_money']
senticnet['cost_effective_way'] = ['-0.04', '0.172', '-0.05', '0.057', '#interest', '#admiration', 'positive', '0.034', 'carry_money', 'put_money', 'require_signature', 'store_money', 'keep_money']
senticnet['cost_fortune'] = ['-0.19', '-0.00', '-0.09', '-0.13', '#sadness', '#disgust', 'negative', '-0.10', 'loss', 'loose', 'lose_game', 'beaten', 'exhilarate']
senticnet['cost_little_money'] = ['0.045', '0', '0.017', '0.052', '#joy', '#admiration', 'positive', '0.027', 'inexpensive', 'good_deal', 'cutlass', 'cheap', 'integer']
senticnet['cost_lot'] = ['0.835', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.797', 'care_patient', 'make_drunk', 'burn_up', 'crispy', 'rude']
senticnet['cost_money'] = ['0.82', '0.925', '0', '0', '#joy', '#interest', 'positive', '0.873', 'first_class', 'shopping', 'travel_across_world', 'spending', 'run_out_fuel']
senticnet['cost_nothing'] = ['0', '0.157', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.078', 'inexpensive', 'unbearable', 'liberate', 'physical_reaction', 'three_syllables']
senticnet['cost_per_ton'] = ['-0.02', '0.249', '0', '0.116', '#interest', '#admiration', 'positive', '0.112', 'price', 'expense', 'bill', 'value', 'worth']
senticnet['cost_prohibitive'] = ['-0.12', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.09', 'painful', 'uncomfortable', 'violent', 'fun_learn_play', 'expensive']
senticnet['cost_sharing'] = ['0', '0.996', '0', '0.972', '#interest', '#admiration', 'positive', '0.984', 'project', 'research', 'teacher', 'award', 'science']
senticnet['costal_edge'] = ['-0.7', '0', '0.617', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'silvery', 'achromatic', 'bluish_black', 'hueless', 'pitchy']
senticnet['costal_spot'] = ['-0.7', '0', '0.617', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'silvery', 'achromatic', 'bluish_black', 'hueless', 'pitchy']
senticnet['costard'] = ['0.934', '-0.57', '0', '0.767', '#joy', '#admiration', 'positive', '0.76', 'morosoph', 'simpleton', 'flibbertigibbet', 'foolish_woman', 'simple']
senticnet['costars'] = ['0.98', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.889', 'co-star', 'co-star', 'actor', 'co-starring', 'actress']
senticnet['costco'] = ['0.912', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.917', 'carpet_store', 'lumberyard', 'furniture_store', 'front_porch', 'shoe_shop']
senticnet['costco_wholesale'] = ['0.912', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.917', 'carpet_store', 'lumberyard', 'furniture_store', 'front_porch', 'shoe_shop']
senticnet['costia'] = ['0.807', '-0.86', '0', '0.611', '#joy', '#surprise', 'positive', '0.762', 'flagellate', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['costless'] = ['0', '0', '0.765', '-0.84', '#anger', '#disgust', 'negative', '-0.80', 'gratuitous', 'complimentary', 'needless', 'gratis', 'free']
senticnet['costlier'] = ['-0.12', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.09', 'painful', 'uncomfortable', 'violent', 'fun_learn_play', 'expensive']
senticnet['costliness'] = ['0.902', '-0.72', '0', '0.919', '#joy', '#admiration', 'positive', '0.849', 'preciousness', 'expensiveness', 'dearness', 'cost', 'lavishness']
senticnet['costly'] = ['-0.12', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.09', 'painful', 'uncomfortable', 'violent', 'fun_learn_play', 'expensive']
senticnet['costochondral'] = ['-0.74', '0', '0.598', '-0.75', '#sadness', '#disgust', 'negative', '-0.69', 'lunate_bone', 'semilunar_bone', 'carpal_bone', 'carpal', 'bone']
senticnet['costume'] = ['-0.76', '0', '0.672', '-0.88', '#sadness', '#disgust', 'negative', '-0.77', 'attire', 'garb', 'dress', 'fancy_dress', 'masquerade']
senticnet['costume_designer'] = ['-0.76', '0', '0.672', '-0.88', '#sadness', '#disgust', 'negative', '-0.77', 'attire', 'garb', 'dress', 'fancy_dress', 'masquerade']
senticnet['costume_jewellery'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'anklet', 'ankle_bracelet', 'armilla']
senticnet['costume_jewelry'] = ['0.532', '-0.60', '0', '0.875', '#surprise', '#admiration', 'positive', '0.67', 'gem', 'adornment', 'jewel', 'bead', 'bangle']
senticnet['costume_parade'] = ['0.957', '0.7', '0', '0', '#joy', '#interest', 'positive', '0.829', 'ballpark', 'coven', 'disneyland', 'hockey_game', 'symposium']
senticnet['costumed_mascot'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'unofficial', 'informal', 'loose', 'drumhead']
senticnet['costuming'] = ['0', '0.962', '0', '0.75', '#interest', '#admiration', 'positive', '0.856', 'dramaturgy', 'theatricality', 'stagecraft', 'theatrics', 'puppetry']
senticnet['cosy'] = ['0.157', '0.126', '0', '0.166', '#joy', '#admiration', 'positive', '0.15', 'comfortable', 'sleep_place', 'rest_place', 'restful', 'occur']
senticnet['cot'] = ['0.695', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.775', 'bedding', 'bassinet', 'toddler', 'crib', 'bunk']
senticnet['coterie'] = ['-0.93', '0', '0.586', '-0.66', '#sadness', '#disgust', 'negative', '-0.73', 'camp', 'pack', 'clique', 'brain_trust', 'ingroup']
senticnet['coterminous_with'] = ['0.595', '-0.61', '0', '0.826', '#surprise', '#admiration', 'positive', '0.679', 'completeness', 'work', 'whole_shebang', 'absolutely', 'wholly']
senticnet['cotinus_coggygria'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'black_locust']
senticnet['cotoneaster'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'bottlebrush_buckeye', 'red_buckeye', 'particolored_buckeye']
senticnet['cottage'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'apartment', 'furniture', 'kitchen', 'flat']
senticnet['cottage_cheese'] = ['0.043', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'dairy_product', 'savory', 'cheese_platter', 'biscuit', 'appetizer']
senticnet['cottage_cheese_platter'] = ['0.039', '-0.66', '0', '0.353', '#surprise', '#admiration', 'positive', '0.351', 'cheese_platter', 'cold_cuts', 'cheese', 'platter', 'appetizer']
senticnet['cotton'] = ['-0.03', '0', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'asbestos', 'silk', 'wool', 'asbestos_boll', 'velvet']
senticnet['cotton_acreage'] = ['0', '0.122', '0', '0.123', '#interest', '#admiration', 'positive', '0.122', 'acre', 'cow_home', 'crop_place', 'big_land', 'animal_house']
senticnet['cotton_ball'] = ['0.606', '-0.50', '0', '0.795', '#joy', '#admiration', 'positive', '0.635', 'plant_disease', 'pinkroot', 'disease', 'rust', 'scorch']
senticnet['cotton_boll'] = ['0.731', '0', '0', '0.974', '#joy', '#admiration', 'positive', '0.853', 'cotton', 'silk', 'wool', 'fleece', 'plant']
senticnet['cotton_candy'] = ['0.149', '-0.06', '0.014', '0.064', '#joy', '#admiration', 'positive', '0.065', 'carnival_ride', 'carny', 'candy', 'fortune_teller', 'ice_cream']
senticnet['cotton_cloth'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_fabric']
senticnet['cotton_fabric'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_fabric']
senticnet['cotton_farmer'] = ['-0.08', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.11', 'serf', 'peasant', 'servant', 'poor', 'villein']
senticnet['cotton_flannel'] = ['0.028', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.047', 'handkerchief', 'kerchief', 'hanky', 'facial_tissue', 'zipper']
senticnet['cotton_gauze'] = ['0', '0', '0', '-0.17', '#disgust', '#disgust', 'negative', '-0.17', 'medicine', 'aspirin', 'blank_check', 'painkiller', 'video_tape']
senticnet['cotton_knit_fabric'] = ['-0.04', '-0.01', '0.026', '-0.12', '#sadness', '#disgust', 'negative', '-0.04', 'embroidery', 'cover_wind', 'use_needle', 'bandage', 'joint']
senticnet['cotton_rat'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'carbon_monoxide', 'tree_animal']
senticnet['cotton_swab'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'hankie', 'kleenex', 'hanky', 'handkerchief', 'tissue']
senticnet['cotton_thread'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_fabric']
senticnet['cotton_top_tamarin'] = ['0.921', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.825', 'monkey', 'fiance', 'lemur', 'beaver', 'koala']
senticnet['cotton_underwear'] = ['0.998', '0', '0', '0', '#joy', '#joy', 'positive', '0.998', 'panties', 'lingerie', 'pantyhose', 'slack', 'prescription_medication']
senticnet['cotton_up'] = ['0.985', '-0.75', '0', '0.894', '#joy', '#admiration', 'positive', '0.879', 'cozy_up', 'ingratiate', 'shine_up', 'sidle_up', 'play_up']
senticnet['cotton_voile'] = ['0', '-0.03', '0.022', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'shoulder_bag', 'satchel', 'water_bottle', 'tote_bag', 'carry']
senticnet['cotton_wool'] = ['0', '0', '0', '-0.17', '#disgust', '#disgust', 'negative', '-0.17', 'medicine', 'aspirin', 'blank_check', 'painkiller', 'video_tape']
senticnet['cottonelle'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'hankie', 'kleenex', 'hanky', 'handkerchief', 'tissue']
senticnet['cottontail'] = ['0.941', '0.987', '0', '0', '#joy', '#interest', 'positive', '0.964', 'monkey', 'marmoset', 'lemur', 'fiance', 'koala']
senticnet['cottontail_rabbit'] = ['0', '-0.97', '0', '0', '#surprise', '#surprise', 'positive', '0.977', 'cougar', 'skunk', 'lion', 'tiger', 'lynx']
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senticnet['cracked_screen'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'crack', 'cracked_phone', 'cracked_car', 'break', 'breakdown']
senticnet['cracked_skin'] = ['0.048', '-0.00', '0.011', '0.064', '#joy', '#admiration', 'positive', '0.027', 'become_wet', 'wash_away_dirt', 'run_out_soap', 'cleanliness', 'clean_skin']
senticnet['cracked_wheat'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'roll', 'custard', 'bread', 'biscuit']
senticnet['cracked_windshield'] = ['0.754', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.867', 'american_flag', 'asphalt', 'rubble', 'pavement', 'pave']
senticnet['cracker'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'saltine', 'soda_cracker', 'oyster_cracker', 'water_biscuit', 'graham_cracker']
senticnet['crackerberry'] = ['0.773', '-0.55', '0', '0.949', '#joy', '#admiration', 'positive', '0.757', 'bunchberry', 'dwarf_cornel', 'cornel', 'pudding_berry', 'common_white_dogwood']
senticnet['crackerjack'] = ['0.59', '-0.90', '0', '0.54', '#joy', '#surprise', 'positive', '0.679', 'nonesuch', 'nonsuch', 'ideal', 'nonpareil', 'apotheosis']
senticnet['crackhead'] = ['0', '0', '0.821', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'student', 'serious_student', 'addiction', 'graduate_student', 'hard_worker']
senticnet['cracking'] = ['0.74', '0.784', '0', '0', '#joy', '#interest', 'positive', '0.762', 'bang-up', 'dandy', 'corking', 'nifty', 'bully']
senticnet['cracking_down'] = ['-0.51', '0', '0.946', '-0.99', '#anger', '#disgust', 'negative', '-0.82', 'trammel', 'limit', 'restrict', 'bound', 'confine']
senticnet['cracking_down_on'] = ['-0.53', '0', '0.825', '-0.87', '#anger', '#disgust', 'negative', '-0.74', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['cracking_wise'] = ['-0.94', '0', '0.492', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'make_noise', 'disturb', 'confusion', 'hustle_and_bustle', 'bustle']
senticnet['crackling_noise'] = ['0', '-0.00', '-0.07', '-0.06', '#fear', '#disgust', 'negative', '-0.04', 'lighten', 'tornado', 'dynamite_explosion', 'forest_fire', 'lightning']
senticnet['crackly'] = ['-0.73', '0', '0', '-0.95', '#sadness', '#disgust', 'negative', '-0.84', 'muffled', 'tinny', 'sounding', 'screechy', 'shrill']
senticnet['crackpot'] = ['-0.66', '0', '0.918', '-0.62', '#sadness', '#anger', 'negative', '-0.73', 'beguiler', 'slicker', 'hidden', 'ringer', 'deceiver']
senticnet['cradle'] = ['0', '-0.02', '-0.08', '-0.05', '#fear', '#disgust', 'negative', '-0.03', 'mattress', 'sleep_furniture', 'master_bedroom', 'small_bed', 'pillow']
senticnet['cradle_cap'] = ['0.048', '-0.00', '0.011', '0.064', '#joy', '#admiration', 'positive', '0.027', 'become_wet', 'wash_away_dirt', 'run_out_soap', 'cleanliness', 'clean_skin']
senticnet['craft'] = ['0.08', '0', '0', '0.106', '#joy', '#admiration', 'positive', '0.093', 'design', 'wall_hang', 'artwork', 'activity', 'painting']
senticnet['craft_knife'] = ['-0.08', '0.232', '0', '0.099', '#interest', '#admiration', 'positive', '0.084', 'wax_dental_floss', 'fluoride_toothpaste', 'toilet_paper', 'dental_floss', 'tartar_control_toothpaste']
senticnet['craft_shop'] = ['0.877', '-0.80', '0', '0', '#joy', '#surprise', 'positive', '0.843', 'convention', 'prisoner_war_camp', 'rave', 'box_ring', 'dark_alley']
senticnet['craftily'] = ['0', '0', '-0.83', '-0.73', '#fear', '#disgust', 'negative', '-0.78', 'wheedle', 'palaver', 'cajole', 'coax', 'blarney']
senticnet['craftiness'] = ['0.784', '-0.59', '0', '0.846', '#joy', '#admiration', 'positive', '0.742', 'foxiness', 'slyness', 'wiliness', 'astuteness', 'cunning']
senticnet['craftmanship'] = ['0.876', '-0.76', '0', '0.963', '#joy', '#admiration', 'positive', '0.867', 'housecraft', 'tradecraft', 'watercraft', 'acquirement', 'skill']
senticnet['craftsman'] = ['0.633', '-0.50', '0', '0.59', '#joy', '#admiration', 'positive', '0.575', 'beautician', 'manicurist', 'artisan', 'journeyman', 'artificer']
senticnet['craftsman_style'] = ['0.578', '-0.95', '0', '0.979', '#surprise', '#admiration', 'positive', '0.838', 'perpendicular', 'perpendicular_style', 'gothic_architecture', 'style_of_architecture', 'type_of_architecture']
senticnet['craftsmanship'] = ['0.921', '-0.61', '0', '0.91', '#joy', '#admiration', 'positive', '0.815', 'housecraft', 'tradecraft', 'watercraft', 'acquirement', 'skill']
senticnet['craftsmen'] = ['0.876', '-0.76', '0', '0.963', '#joy', '#admiration', 'positive', '0.867', 'housecraft', 'tradecraft', 'watercraft', 'acquirement', 'skill']
senticnet['craftsperson'] = ['0.633', '-0.50', '0', '0.59', '#joy', '#admiration', 'positive', '0.575', 'craftsman', 'manicurist', 'artisan', 'journeyman', 'beautician']
senticnet['crafty'] = ['0', '0', '-0.98', '-0.82', '#fear', '#disgust', 'negative', '-0.90', 'cunning', 'dodgy', 'guileful', 'knavish', 'tricksy']
senticnet['crag'] = ['0.057', '0.075', '0.039', '0.059', '#interest', '#admiration', 'positive', '0.038', 'summit', 'big_rock', 'high_land', 'peak', 'mountain_side']
senticnet['craigiebank'] = ['-0.85', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.86', 'attack', 'raid', 'fight_against', 'conflict', 'opposition']
senticnet['crammed_into'] = ['-0.97', '0', '0.84', '-0.52', '#sadness', '#anger', 'negative', '-0.78', 'pis_aller', 'last_resort', 'temporary_expedient', 'expedient', 'stopgap']
senticnet['crammed_together'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'push', 'ram', 'barge', 'bang', 'shove_around']
senticnet['cramp'] = ['-0.76', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'become_exhausted', 'breathlessness', 'become_wind', 'pull_muscle', 'muscle_cramp']
senticnet['cramped'] = ['-0.96', '0', '0.156', '-0.94', '#sadness', '#disgust', 'negative', '-0.68', 'narrow_road', 'poky', 'tight', 'clenched', 'narrow']
senticnet['cramped_apartment'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'home', 'furniture', 'kitchen', 'flat']
senticnet['cramped_kitchen'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'kitchen_cabinet', 'kitchen_cupboard', 'dishwasher', 'cupboard', 'own_kitchen']
senticnet['cranberry'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'cherry', 'tomato', 'plum', 'apricot', 'pineapple']
senticnet['cranberry_juice'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'lemon_juice', 'chocolate_pudding', 'grape_juice', 'orange_juice', 'juice']
senticnet['cranberry_sauce'] = ['0.937', '0', '0', '0.828', '#joy', '#admiration', 'positive', '0.883', 'bakery', 'produce_section', 'fruit_bowl', 'dinner_meal', 'fast_food']
senticnet['cranbourne_railway'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'aridness', 'dryness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['crane_boom'] = ['0', '0.142', '-0.09', '0.183', '#interest', '#admiration', 'positive', '0.078', 'arise', 'rise', 'raise_up', 'go_up', 'go_upstairs']
senticnet['crane_operator'] = ['0.969', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.936', 'attendant', 'manager', 'public_servant', 'specialist', 'accomplice']
senticnet['cranial'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'part_of_face', 'facial_organ', 'face_feature', 'facial_part', 'near_nose']
senticnet['cranial_nerve_involvement'] = ['0.722', '-0.87', '0', '0.696', '#joy', '#surprise', 'positive', '0.763', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['craniofacial'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'part_of_face', 'facial_organ', 'face_feature', 'facial_part', 'near_nose']
senticnet['craniofacial_research_nidcr'] = ['-0.03', '0.079', '0.024', '0.091', '#interest', '#admiration', 'positive', '0.029', 'toothpaste', 'toothbrush', 'mouth', 'gel_toothpaste', 'tooth']
senticnet['craniofacial_surgery'] = ['0.745', '-0.55', '0', '0.597', '#joy', '#admiration', 'positive', '0.633', 'lift', 'phalloplasty', 'abdominoplasty', 'tummy_tuck', 'autoplasty']
senticnet['craniologist'] = ['0.969', '-0.88', '0', '0.736', '#joy', '#surprise', 'positive', '0.863', 'slicker', 'beguiler', 'quack', 'phrenologist', 'charlatan']
senticnet['craniotomy'] = ['-0.65', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'put_to_sleep', 'operation', 'general_anesthesia', 'surgery', 'anesthesia']
senticnet['cranium'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'hold_brain', 'inside_head', 'brain', 'skull', 'mind']
senticnet['crank'] = ['-0.05', '-0.01', '-0.05', '-0.06', '#sadness', '#disgust', 'negative', '-0.04', 'dynamo', 'air_vent', 'emergency_brake', 'brake_pedal', 'grunt']
senticnet['crank_handle'] = ['0', '0.142', '0', '0.125', '#interest', '#admiration', 'positive', '0.134', 'weight_machine', 'power_drill', 'screw', 'screwdriver', 'air_pump']
senticnet['crank_up'] = ['0.815', '-0.75', '0', '0.744', '#joy', '#surprise', 'positive', '0.772', 'perk', 'convalesce', 'improve', 'look_better', 'meliorate']
senticnet['crankbait'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'catch_fish', 'fishing', 'patience', 'fish_lake', 'bait']
senticnet['crankcase'] = ['0.042', '-0.02', '0.035', '0.065', '#joy', '#admiration', 'positive', '0.023', 'food_container', 'metal_cylinder', 'metal_container', 'small_ball', 'storage_container']
senticnet['crankiness'] = ['-0.95', '0', '0.694', '-0.94', '#sadness', '#disgust', 'negative', '-0.86', 'sullenness', 'moroseness', 'crabbiness', 'crabbedness', 'ill_nature']
senticnet['crankshaft'] = ['0.042', '-0.02', '0.035', '0.065', '#joy', '#admiration', 'positive', '0.023', 'food_container', 'metal_cylinder', 'metal_container', 'small_ball', 'storage_container']
senticnet['cranky'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'bad_tempered', 'nervous', 'neurasthenic', 'irritable', 'testy']
senticnet['cranky_old'] = ['-0.95', '0', '0.772', '-0.89', '#sadness', '#disgust', 'negative', '-0.87', 'smart_aleck', 'upstart', 'snob', 'snooty', 'unpleasant_person']
senticnet['cranny'] = ['0.788', '-0.60', '0', '0.884', '#joy', '#admiration', 'positive', '0.759', 'gap', 'rathole', 'aperture', 'bullet_hole', 'hole']
senticnet['crap'] = ['0', '0', '0', '-0.88', '#disgust', '#disgust', 'negative', '-0.88', 'hunt_fox', 'software_company', 'waste', 'last_forever', 'useless']
senticnet['crap_table'] = ['-0.78', '0', '0.944', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'loser', 'speculator', 'mortal', 'better', 'gambler']
senticnet['crapola'] = ['-0.61', '0', '0.738', '-0.80', '#anger', '#disgust', 'negative', '-0.71', 'babbling', 'double_talk', 'gibber', 'babble', 'lallation']
senticnet['crappie'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'divergent_strabismus', 'exotropia', 'squint', 'abnormality', 'abnormalcy']
senticnet['crappie_fishing'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'divergent_strabismus', 'exotropia', 'squint', 'abnormality', 'abnormalcy']
senticnet['crappy'] = ['0', '0', '-0.77', '-0.92', '#fear', '#disgust', 'negative', '-0.84', 'icky', 'rotten', 'lousy', 'stinking', 'shitty']
senticnet['crapshooter'] = ['0.755', '0', '0', '0.962', '#joy', '#admiration', 'positive', '0.859', 'early-ripening', 'late-flowering', 'quick-growing', 'non-blooming', 'early-blooming']
senticnet['craquelure'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'wall_decoration', 'renovation', 'painting', 'artwork', 'new_house']
senticnet['crash'] = ['-0.08', '-0.03', '0', '-0.14', '#sadness', '#disgust', 'negative', '-0.06', 'accident', 'stop_red_light', 'get_accident', 'break_down', 'automobile_accident']
senticnet['crash_car'] = ['-0.83', '0', '-0.70', '0', '#sadness', '#fear', 'negative', '-0.76', 'accident', 'fall_ground', 'loss_ofawareness', 'broken_limb', 'hit_car']
senticnet['crash_cymbal'] = ['-0.04', '-0.01', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.02', 'break', 'break_instrument', 'broken_guitar', 'trumpet', 'antler']
senticnet['crash_pad'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'lurking_place', 'spot', 'drop', 'hiding_place', 'den']
senticnet['crash_tree'] = ['0', '0', '-0.74', '0', '#fear', '#fear', 'negative', '-0.74', 'fall_butt', 'accident', 'hit_tree', 'fall_over', 'take_photo']
senticnet['crashed_heavily'] = ['0', '0', '0.873', '-0.92', '#anger', '#disgust', 'negative', '-0.89', 'punch', 'sting', 'disable', 'ache', 'vex']
senticnet['crashed_into'] = ['-0.90', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.86', 'trauma', 'car_wreck', 'broken_bone', 'bad_knee', 'wreck']
senticnet['crashed_spaceship'] = ['-0.03', '0.118', '0.472', '-0.46', '#anger', '#disgust', 'negative', '-0.21', 'detached', 'set-apart', 'separated', 'isolated', 'sickening']
senticnet['crashing_into'] = ['-0.12', '0', '-0.09', '-0.04', '#sadness', '#fear', 'negative', '-0.08', 'hold_two_together', 'install_window', 'float_through_air', 'spring_up', 'collision']
senticnet['crashingly'] = ['0', '0', '-0.81', '-0.83', '#fear', '#disgust', 'negative', '-0.82', 'terribly', 'very', 'terror', 'awfully', 'awe']
senticnet['crass'] = ['-0.88', '0', '0.984', '0', '#sadness', '#anger', 'negative', '-0.93', 'dense', 'crassness', 'refined', 'coarse', 'crude']
senticnet['crass_materialism'] = ['0', '0', '-0.74', '-0.93', '#fear', '#disgust', 'negative', '-0.84', 'generosity', 'oral_sex', 'win_lottery', 'gift_wrap', 'give_away']
senticnet['crassly'] = ['0', '0', '0.841', '-0.95', '#anger', '#disgust', 'negative', '-0.89', 'garish', 'flashy', 'trashy', 'cheap', 'brassy']
senticnet['crassness'] = ['-0.88', '0', '0.982', '0', '#sadness', '#anger', 'negative', '-0.93', 'dense', 'crass', 'refined', 'coarse', 'crude']
senticnet['crassostrea_virginica'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'seal', 'walrus', 'otter', 'alligator', 'whale']
senticnet['crassulaceae'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'seed_fern', 'pteridosperm']
senticnet['crataegus_monogyna'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'black_locust']
senticnet['crate'] = ['0', '0.088', '0.022', '0.099', '#interest', '#admiration', 'positive', '0.055', 'storage_place', 'storage', 'sack', 'carrier', 'stick_wall']
senticnet['crate_barrel'] = ['0.041', '0.098', '0', '0', '#joy', '#interest', 'positive', '0.07', 'utopia', 'java', 'memorial', 'nowhere', 'coffee']
senticnet['crater_tycho'] = ['0.916', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.929', 'galaxy', 'universe', 'planetary', 'sun', 'dwarf_planet']
senticnet['cravat'] = ['0.08', '0.106', '-0.04', '0.112', '#interest', '#admiration', 'positive', '0.063', 'helmet', 'head_gear', 'cap', 'head_wear', 'head_protection']
senticnet['crave'] = ['0.942', '0.792', '0', '0', '#joy', '#interest', 'positive', '0.867', 'hunger', 'hungry', 'survive', 'stay_alive', 'desire']
senticnet['crave_pardon'] = ['-0.64', '0', '0.625', '-0.88', '#sadness', '#disgust', 'negative', '-0.71', 'difficulty', 'awkwardness', 'flea_bite', 'difficultness', 'cumbersomeness']
senticnet['craven'] = ['-0.03', '0', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.06', 'lion', 'leopard', 'mountain_lion', 'bobcat', 'wolf']
senticnet['cravenly'] = ['-0.85', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.89', 'humbling', 'demeaning', 'humbleness', 'humiliating', 'demeaningly']
senticnet['craving'] = ['-0.87', '0', '0.919', '-0.89', '#anger', '#disgust', 'negative', '-0.89', 'appetite', 'appetency', 'appetence', 'addiction', 'desire']
senticnet['crawl'] = ['-0.70', '0', '0.801', '0', '#sadness', '#anger', 'negative', '-0.75', 'dash', 'uneasiness', 'fright', 'disquiet', 'upset']
senticnet['crawler'] = ['-0.55', '0', '0.588', '-0.65', '#anger', '#disgust', 'negative', '-0.59', 'apple_polisher', 'bootlicker', 'fawner', 'web', 'groveler']
senticnet['crayfish'] = ['0', '-0.95', '0', '0.875', '#surprise', '#admiration', 'positive', '0.913', 'lobster', 'spiky', 'crab', 'spiny', 'urchin']
senticnet['crayon'] = ['0.031', '0.103', '0', '0.06', '#interest', '#admiration', 'positive', '0.065', 'graphical_representation', 'photo', 'artwork', 'photograph', 'varnish']
senticnet['craze'] = ['-0.87', '0', '0.599', '-0.71', '#sadness', '#disgust', 'negative', '-0.73', 'hysteria', 'fury', 'delirium', 'frenzy', 'nympholepsy']
senticnet['crazed_killer'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'addicted', 'obsessed_love', 'addiction', 'obsession', 'weird']
senticnet['craziness'] = ['-0.78', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.74', 'folly', 'lunacy', 'buffoonery', 'shtik', 'insanity']
senticnet['crazy'] = ['-0.15', '0', '-0.12', '-0.09', '#sadness', '#fear', 'negative', '-0.12', 'feel_ashamed', 'hunt_animal', 'play_dead', 'feel_confident', 'vicious']
senticnet['crazy_frog'] = ['0.023', '0.141', '-0.13', '0.174', '#interest', '#admiration', 'positive', '0.052', 'candy', 'push_wind', 'exhalation', 'breaout', 'mouth_action']
senticnet['crazy_golf_course'] = ['-0.72', '0', '0.978', '-0.86', '#anger', '#disgust', 'negative', '-0.85', 'amusement_park', 'common', 'pleasure_ground', 'village_green', 'tract']
senticnet['crazy_horse'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['crazy_house'] = ['-0.55', '0', '0.517', '-0.83', '#sadness', '#disgust', 'negative', '-0.63', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['crazy_mind'] = ['-0.95', '0', '0.902', '-0.91', '#sadness', '#disgust', 'negative', '-0.92', 'sadness', 'mental_disease', 'mental_problem', 'depression', 'disease']
senticnet['crazy_moment'] = ['0.819', '-0.88', '0', '0', '#joy', '#surprise', 'positive', '0.853', 'funny_moment', 'dance_in_rain', 'sing_in_rain', 'playful', 'dance']
senticnet['crazy_person'] = ['-0.04', '-0.03', '0', '-0.16', '#sadness', '#disgust', 'negative', '-0.05', 'crazy', 'damage_body', 'marathon_runner', 'threat', 'max']
senticnet['crazy_quilt'] = ['-0.44', '-0.13', '-0.08', '-0.40', '#sadness', '#disgust', 'negative', '-0.19', 'disorder', 'untidy', 'mess', 'heap', 'chaos']
senticnet['crche'] = ['-0.04', '0.088', '0.013', '0.069', '#interest', '#admiration', 'positive', '0.024', 'kindergarden', 'daycare', 'nursery', 'child', 'asylum']
senticnet['creak'] = ['-0.06', '-0.01', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'low', 'ground', 'look_down', 'ground_level', 'under_house']
senticnet['creaky'] = ['0', '0', '-0.97', '-0.81', '#fear', '#disgust', 'negative', '-0.89', 'clunker', 'decrepit', 'flea-bitten', 'crazy', 'mangy']
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senticnet['creepily'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicado', 'unpronounceable', 'impassive', 'incommunicative', 'silent']
senticnet['creepiness'] = ['-0.94', '0', '0.827', '-0.55', '#sadness', '#anger', 'negative', '-0.77', 'cutaneous_sensation', 'haptic_sensation', 'skin_sensation', 'perception', 'tactual_sensation']
senticnet['creeping_phlox'] = ['-0.92', '0', '0.912', '-0.94', '#sadness', '#disgust', 'negative', '-0.92', 'true_blackberry', 'sand_blackberry', 'blackberry_bush', 'blackberry', 'running_blackberry']
senticnet['creeping_thyme'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'livelong', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['creeps'] = ['-0.74', '0', '0', '-0.81', '#sadness', '#disgust', 'negative', '-0.78', 'cringe', 'grovel', 'creep', 'cower', 'hysterically']
senticnet['creepy'] = ['0.052', '0', '0', '0.131', '#joy', '#admiration', 'positive', '0.091', 'run_treadmill', 'dangerous_drive', 'helpful_tool', 'profitable', 'conscious']
senticnet['creepy_atmosphere'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'anxiety', 'apprehension', 'insecurity', 'tension', 'horror_movie']
senticnet['creepy_crawly'] = ['0.03', '0.068', '-0.04', '0.093', '#interest', '#admiration', 'positive', '0.037', 'beast', 'equine', 'live_creature', 'furry_creature', 'domesticate_animal']
senticnet['creige'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'saltine', 'soda_cracker', 'cracker', 'water_biscuit', 'graham_cracker']
senticnet['cremaster_muscle'] = ['0.087', '-0.03', '0', '0.063', '#joy', '#admiration', 'positive', '0.062', 'puppet', 'bouncy', 'curly', 'throw_object', 'glove_leather']
senticnet['cremate'] = ['0', '0', '0', '-0.78', '#disgust', '#disgust', 'negative', '-0.78', 'find_farm', 'find_bank', 'find_subway_stop', 'dispose', 'warm_hand']
senticnet['cremated_bone'] = ['0.099', '-0.07', '-0.08', '0.195', '#joy', '#admiration', 'positive', '0.071', 'art', 'exhibit', 'craft', 'fine_art', 'artwork']
senticnet['cremation'] = ['-0.12', '0.091', '-0.19', '-0.10', '#sadness', '#fear', 'negative', '-0.08', 'headstone', 'grave', 'bury_body', 'bury', 'bury_dead']
senticnet['cremation_urn'] = ['0', '0.082', '-0.09', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'coffin', 'headstone', 'morgue', 'bury_body', 'casket']
senticnet['crematory'] = ['-0.70', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.70', 'death_row', 'sad', 'loose_end', 'car_race', 'mortuary']
senticnet['creme'] = ['0.028', '0.138', '-0.04', '0.215', '#interest', '#admiration', 'positive', '0.083', 'water', 'dense', 'thick', 'milk', 'dense_water']
senticnet['creme_brulee'] = ['0.041', '0.069', '0.032', '0.13', '#interest', '#admiration', 'positive', '0.052', 'edible_human', 'round_fruit', 'peelable', 'green_fruit', 'nutritious']
senticnet['creme_egg'] = ['0.547', '-0.96', '0', '0.89', '#surprise', '#admiration', 'positive', '0.799', 'pudding', 'ice_cream', 'chocolate', 'chocolate_pudding', 'chocolate_dessert']
senticnet['crenate'] = ['-0.75', '0', '0.734', '-0.82', '#sadness', '#disgust', 'negative', '-0.77', 'puberulent', 'haired', 'furlike', 'pappose', 'pilous']
senticnet['crenellated_tower'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'castle', 'bell_tower', 'spire', 'steeple', 'religious_place']
senticnet['crenulate'] = ['-0.95', '0', '0.892', '-0.81', '#sadness', '#anger', 'negative', '-0.88', 'blurry', 'foggy', 'hazy', 'muzzy', 'blurred']
senticnet['creole_cuisine'] = ['0.882', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.822', 'ferment_grape_juice', 'chef_special', 'bottle_wine', 'fine_dining', 'decanter']
senticnet['creosol'] = ['-0.67', '0', '0.503', '-0.67', '#sadness', '#disgust', 'negative', '-0.61', 'stink_up', 'smell_up', 'stink_out', 'incense', 'perfume']
senticnet['creosote'] = ['0', '0', '0.797', '-0.92', '#anger', '#disgust', 'negative', '-0.86', 'soot', 'buildup', 'chimney', 'thatch', 'build-up']
senticnet['crepe-like'] = ['-0.85', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.85', 'pizza-like', 'bread-like', 'pancake-like', 'donut-like', 'yeast-risen']
senticnet['crepe_paper'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'kraft', 'kraft_paper', 'butcher_paper', 'paper']
senticnet['crepuscular'] = ['0.967', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.91', 'furry_creature', 'hairy', 'furry', 'woodland_creature', 'animal_furry']
senticnet['crepuscular_activity'] = ['0.967', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.91', 'furry_creature', 'hairy', 'furry', 'woodland_creature', 'animal_furry']
senticnet['crescent'] = ['0.09', '0', '0', '0.094', '#joy', '#admiration', 'positive', '0.092', 'heavenly_body', 'celestial_body', 'around_earth', 'celestial_object', 'bright_yellow']
senticnet['crescent_avenue'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'boulevard', 'lane', 'thoroughfare', 'intersection', 'curb']
senticnet['crescent_moon'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['crescent_shape'] = ['0.027', '0.139', '0', '0.203', '#interest', '#admiration', 'positive', '0.123', 'horse_shoes', 'four_leaf_clover', 'rotary_phone', 'ping_pong', 'billiards']
senticnet['cress'] = ['0.598', '-0.72', '0', '0.941', '#surprise', '#admiration', 'positive', '0.753', 'sicklepod', 'tower_rock_cress', 'tower_mustard', 'rock_cress', 'rock_cress_plant']
senticnet['cress_plant'] = ['0.598', '-0.72', '0', '0.941', '#surprise', '#admiration', 'positive', '0.753', 'sicklepod', 'tower_cress', 'tower_mustard', 'cress', 'rock_cress']
senticnet['cressbrook'] = ['0', '0.113', '0', '0.111', '#interest', '#admiration', 'positive', '0.112', 'animal_house', 'cow_home', 'animal_place', 'crop_place', 'acreage']
senticnet['crest'] = ['0.03', '0', '0.017', '0.138', '#joy', '#admiration', 'positive', '0.05', 'up_down', 'curl', 'bump', 'hand_gesture', 'goodbye']
senticnet['crest_coronet'] = ['0.03', '0', '0.017', '0.138', '#joy', '#admiration', 'positive', '0.05', 'up_down', 'curl', 'bump', 'hand_gesture', 'goodbye']
senticnet['crest_syndrome'] = ['-0.87', '0', '0.531', '-0.68', '#sadness', '#disgust', 'negative', '-0.69', 'rubor', 'redness', 'dermatomyositis', 'inclusion_body_myositis', 'inflammation']
senticnet['crest_toothpaste'] = ['0', '0.095', '0', '0.074', '#interest', '#admiration', 'positive', '0.084', 'buy_wash_powder', 'water_soap', 'turn_faucet', 'take_off', 'fill_bathtub_water']
senticnet['crested_helmet'] = ['-0.62', '0', '0.961', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'crown', 'jeweled_headdress', 'tiara', 'headdress', 'headgear']
senticnet['crested_serpent_eagle'] = ['0.797', '-0.71', '0', '0.736', '#joy', '#admiration', 'positive', '0.751', 'wood_ibis', 'wood_stork', 'ibis', 'wading_bird', 'wader']
senticnet['crestfallen'] = ['-0.71', '0', '0.906', '-0.93', '#anger', '#disgust', 'negative', '-0.85', 'absent', 'forgetful', 'distracted', 'inattentive', 'scatty']
senticnet['crestron'] = ['0', '0', '0', '0.109', '#admiration', '#admiration', 'positive', '0.109', 'knob', 'door_knob', 'door_handle', 'handle', 'hinge']
senticnet['cretin'] = ['-0.98', '0', '0.674', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'slow', 'slow_pace', 'stupid', 'slow_car', 'slow_vehicle']
senticnet['cretinism'] = ['-0.98', '0', '0.674', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'slow', 'slow_pace', 'stupid', 'slow_car', 'slow_vehicle']
senticnet['creutzfeldt_jakob_disease_cjd'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'agent', 'satisfier', 'quickener', 'invigorator']
senticnet['crevasse'] = ['0.057', '0.075', '0.039', '0.059', '#interest', '#admiration', 'positive', '0.038', 'summit', 'big_rock', 'high_land', 'peak', 'mountain_side']
senticnet['crevice'] = ['-0.02', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'tunnel', 'dark_place', 'ski_resort', 'top_tree', 'compost_pile']
senticnet['crew_cabin'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'chick', 'wild_bird', 'baby_bird', 'lay_egg', 'bird_home']
senticnet['crew_compartment'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'chick', 'wild_bird', 'baby_bird', 'lay_egg', 'bird_home']
senticnet['crewcut'] = ['-0.91', '0', '0.869', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'talk_hairdresser', 'hair_get_short', 'hair_become_short', 'keep_hair_short', 'hair_wash']
senticnet['crib'] = ['0.702', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.777', 'bedding', 'bassinet', 'toddler', 'cot', 'bunk']
senticnet['cribbage'] = ['0.801', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.796', 'verbosity', 'sport_play', 'excite_game', 'contest', 'fun_game']
senticnet['cricket'] = ['0.728', '0.83', '0', '0', '#joy', '#interest', 'positive', '0.779', 'ball', 'bounce', 'ball_bounce', 'soccer', 'play_football']
senticnet['cried_aloud'] = ['-0.87', '0', '0.882', '-0.56', '#sadness', '#anger', 'negative', '-0.77', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['cried_out'] = ['-0.87', '0', '0.882', '-0.56', '#sadness', '#anger', 'negative', '-0.77', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['crieth_out'] = ['0.622', '0.641', '-0.24', '0.285', '#joy', '#interest', 'positive', '0.327', 'gleeful', 'gladsome', 'happiness', 'gladness', 'gladfulness']
senticnet['crigler_najjar_syndrome'] = ['-0.57', '0', '0.973', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['crikey'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'rascal', 'rapscallion', 'scalawag', 'holy_terror', 'monkey']
senticnet['crime'] = ['-0.79', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.79', 'felony', 'revenge', 'theft', 'retribution', 'criminal']
senticnet['crime_caper'] = ['-0.85', '0', '0.514', '-0.73', '#sadness', '#disgust', 'negative', '-0.70', 'cinema_verite', 'movie', 'film', 'picture', 'moving_picture']
senticnet['crime_drama'] = ['-0.54', '0', '0.845', '-0.95', '#anger', '#disgust', 'negative', '-0.78', 'face', 'base', 'underside', 'sole', 'bilge']
senticnet['crime_fiction'] = ['0.036', '0.219', '0', '0.19', '#interest', '#admiration', 'positive', '0.148', 'book_genre', 'literature', 'concoction', 'untruth', 'falsehood']
senticnet['crime_punishable'] = ['-0.84', '0', '0.745', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'crime', 'against_law', 'revenge', 'vengeance', 'theft']
senticnet['crime_scene'] = ['-0.03', '-0.00', '0', '-0.20', '#sadness', '#disgust', 'negative', '-0.07', 'military_base', 'holster', 'gun_store', 'gun_shop', 'roadblock']
senticnet['crime_thriller'] = ['-0.54', '0', '0.845', '-0.95', '#anger', '#disgust', 'negative', '-0.78', 'face', 'base', 'underside', 'sole', 'bilge']
senticnet['crimean_war'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'dictatorship', 'dictator', 'colony', 'world_domination', 'oppression']
senticnet['criminal'] = ['-0.85', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.86', 'murderer', 'thief', 'crime', 'harden_criminal', 'revenge']
senticnet['criminal_activity'] = ['-0.16', '-0.01', '-0.17', '0.062', '#sadness', '#fear', 'negative', '-0.06', 'heart_attack', 'sign_paper', 'understand_fact', 'freedom', 'sign_document']
senticnet['criminal_barrister'] = ['0.053', '0.122', '-0.07', '0.086', '#interest', '#admiration', 'positive', '0.046', 'attorney', 'lawyer', 'jury', 'decide_guilt', 'prosecutor']
senticnet['criminal_charge_against'] = ['0.682', '-0.67', '0', '0.588', '#joy', '#surprise', 'positive', '0.648', 'lawful', 'civil_action', 'counterclaim', 'occurrence', 'li_pendens']
senticnet['criminal_conduct'] = ['-0.61', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.69', 'irresponsibility', 'untrustiness', 'undependability', 'undependableness', 'unreliability']
senticnet['criminal_congress'] = ['0.752', '-0.57', '0', '0.967', '#joy', '#admiration', 'positive', '0.765', 'incest', 'extramarital_sex', 'free_love', 'sexual_intercourse', 'intercourse']
senticnet['criminal_conversation'] = ['-0.98', '0', '0.662', '-0.73', '#sadness', '#disgust', 'negative', '-0.79', 'fornication', 'unlawful_carnal_knowledge', 'criminal_congress', 'adultery', 'extramarital_sex']
senticnet['criminal_culpability'] = ['-0.61', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.69', 'irresponsibility', 'untrustiness', 'undependability', 'undependableness', 'unreliability']
senticnet['criminal_damage'] = ['-0.14', '0', '-0.21', '-0.07', '#sadness', '#fear', 'negative', '-0.14', 'heat_light', 'burn_down_house', 'set_on_fire', 'rapid_oxidation', 'flame']
senticnet['criminal_defendant'] = ['0.817', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.824', 'witness', 'prosecutor', 'legal_process', 'lawyer', 'jury']
senticnet['criminal_defense_attorney'] = ['0', '0.146', '-0.05', '0.077', '#interest', '#admiration', 'positive', '0.057', 'lawyer', 'negotiator', 'arbiter', 'judge_jury', 'decide_guilt']
senticnet['criminal_gang'] = ['0.867', '-0.84', '0', '0.876', '#joy', '#admiration', 'positive', '0.863', 'meet', 'team', 'team_up', 'embed', 'aggroup']
senticnet['criminal_homicide'] = ['0', '0', '-0.95', '-0.85', '#fear', '#disgust', 'negative', '-0.90', 'choose_victim', 'murder_weapon', 'hide_evidence', 'murder', 'shoot_gun']
senticnet['criminal_intent'] = ['-0.92', '0', '0.682', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'contrariness', 'wildness', 'intractability', 'intractableness', 'perversity']
senticnet['criminal_investigation'] = ['0.845', '0.916', '0', '0', '#joy', '#interest', 'positive', '0.881', 'question', 'answer', 'query', 'question_start', 'question_answering']
senticnet['criminal_investigation_ci'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'constabulary', 'law', 'agency']
senticnet['criminal_investigation_into'] = ['0', '0.825', '0', '0', '#interest', '#interest', 'positive', '0.825', 'query', 'interrogation', 'inquire', 'response', 'solution']
senticnet['criminal_investigative'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'constabulary', 'law', 'agency']
senticnet['criminal_jurisdiction'] = ['0.657', '-0.88', '0', '0.631', '#joy', '#surprise', 'positive', '0.723', 'cognisance', 'knowingness', 'awareness', 'feel', 'consciousness']
senticnet['criminal_justice_system'] = ['-0.43', '0.107', '-0.42', '-0.03', '#sadness', '#fear', 'negative', '-0.19', 'send_to_jail', 'go_jail', 'jail_time', 'go_to_prison', 'injustice']
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senticnet['criminal_liability'] = ['-0.72', '0', '0.742', '-0.80', '#anger', '#disgust', 'negative', '-0.75', 'illicitness', 'illegality', 'illegitimacy', 'shadiness', 'quality']
senticnet['criminal_mastermind'] = ['-0.69', '0', '0.756', '-0.99', '#anger', '#disgust', 'negative', '-0.81', 'waster', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['criminal_negligence'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'negligence', 'culpable_negligence', 'evasion', 'dereliction', 'contributory_negligence']
senticnet['criminal_offense'] = ['-0.95', '0', '0.434', '-0.98', '#sadness', '#disgust', 'negative', '-0.79', 'crime', 'criminal', 'thief', 'murder', 'kill']
senticnet['criminal_probe'] = ['0', '0.825', '0', '0', '#interest', '#interest', 'positive', '0.825', 'query', 'interrogation', 'inquire', 'response', 'solution']
senticnet['criminal_prosecution'] = ['0.682', '-0.67', '0', '0.588', '#joy', '#surprise', 'positive', '0.648', 'lawful', 'civil_action', 'counterclaim', 'occurrence', 'li_pendens']
senticnet['criminal_punishment'] = ['-0.75', '0', '0.981', '-0.58', '#sadness', '#anger', 'negative', '-0.77', 'retaliatory', 'penitentiary', 'correctional', 'retributive', 'retributory']
senticnet['criminal_recidivism'] = ['-0.43', '0.107', '-0.42', '-0.03', '#sadness', '#fear', 'negative', '-0.19', 'send_to_jail', 'go_jail', 'jail_time', 'go_to_prison', 'injustice']
senticnet['criminal_sanction'] = ['-0.72', '0', '0.742', '-0.80', '#anger', '#disgust', 'negative', '-0.75', 'illicitness', 'unlawfulness', 'illegitimacy', 'shadiness', 'quality']
senticnet['criminal_underworld'] = ['0.867', '-0.84', '0', '0.876', '#joy', '#admiration', 'positive', '0.863', 'meet', 'team', 'team_up', 'embed', 'aggroup']
senticnet['criminalise'] = ['0', '0', '-0.9', '-0.79', '#fear', '#disgust', 'negative', '-0.84', 'forbid', 'prohibit', 'veto', 'disallow', 'interdict']
senticnet['criminality'] = ['0', '0', '0.756', '-0.73', '#anger', '#disgust', 'negative', '-0.74', 'chicanery', 'intimidation', 'thuggery', 'opportunism', 'thuggish']
senticnet['criminalization'] = ['-0.72', '0', '0.742', '-0.80', '#anger', '#disgust', 'negative', '-0.75', 'illicitness', 'unlawfulness', 'illegitimacy', 'shadiness', 'quality']
senticnet['criminalize'] = ['0.778', '-0.96', '0', '0.702', '#joy', '#surprise', 'positive', '0.815', 'require', 'bar', 'debar', 'forbid', 'outlaw']
senticnet['criminally_insane'] = ['0.203', '0.077', '0', '0.189', '#joy', '#admiration', 'positive', '0.156', 'sea_shell', 'coast', 'seaside', 'sediment', 'shell']
senticnet['criminally_liable'] = ['0.511', '-0.59', '0', '0.576', '#surprise', '#admiration', 'positive', '0.561', 'due', 'traceable', 'ascribable', 'attributable', 'referable']
senticnet['criminate'] = ['-0.70', '0', '0.905', '-0.84', '#anger', '#disgust', 'negative', '-0.81', 'reproach', 'upbraid', 'arraign', 'charge', 'accuse']
senticnet['crimp'] = ['0.688', '-0.73', '0', '0.955', '#surprise', '#admiration', 'positive', '0.792', 'angular_shape', 'angularity', 'ruck', 'pucker', 'pleat']
senticnet['crimper'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'smart_technology', 'electric_toothbrush', 'hair_dryer', 'stay_healthy', 'wash_tooth']
senticnet['crimson'] = ['0.049', '0.145', '0', '0.171', '#interest', '#admiration', 'positive', '0.122', 'kingly', 'govern', 'head_wear', 'chivalry', 'blue_blood']
senticnet['crimson_avenger'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
senticnet['crimson_livery'] = ['0.028', '0.138', '-0.04', '0.215', '#interest', '#admiration', 'positive', '0.083', 'water', 'dense', 'thick', 'milk', 'dense_water']
senticnet['crimson_red'] = ['-0.45', '0.081', '-0.25', '0', '#sadness', '#fear', 'negative', '-0.20', 'green_yellow', 'color', 'blue_green', 'cyan', 'purple']
senticnet['crimson_silk'] = ['0.035', '0', '0', '0.116', '#joy', '#admiration', 'positive', '0.076', 'maroon', 'color', 'bright_color', 'hair_colored', 'cool_color']
senticnet['crimson_velvet'] = ['0.967', '-0.98', '0', '0.64', '#joy', '#surprise', 'positive', '0.863', 'club', 'social_club', 'society', 'guild', 'jockey_club']
senticnet['cringe'] = ['-0.73', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.72', 'shrink', 'grovel', 'creep', 'cower', 'hysterically']
senticnet['cringe_worthy'] = ['-0.69', '0', '0.941', '-0.75', '#anger', '#disgust', 'negative', '-0.8', 'flub', 'blunder', 'blooper', 'bungle', 'botch']
senticnet['cringeworthy'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'wet', 'sticky', 'gluey', 'viscous', 'gooey']
senticnet['cringing'] = ['0.679', '-0.94', '0', '0.661', '#joy', '#surprise', 'positive', '0.762', 'bowing', 'submissive', 'groveling', 'sub', 'wormlike']
senticnet['crinkle'] = ['0.689', '-0.56', '0', '0.74', '#joy', '#admiration', 'positive', '0.665', 'crease', 'heart_line', 'wrinkle', 'furrow', 'seam']
senticnet['crinkly'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'facial_tissue', 'kleenex', 'hanky', 'handkerchief', 'tissue']
senticnet['crinoline'] = ['-0.86', '0', '0', '-0.91', '#sadness', '#disgust', 'negative', '-0.88', 'underskirt', 'petticoat', 'tulle', 'camisole', 'corset']
senticnet['cripple'] = ['-0.90', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.92', 'influenza', 'chicken_pox', 'sexually_transmit_disease', 'overpay', 'diabetes']
senticnet['cripple_creek_district'] = ['-0.98', '0', '0.521', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'battery', 'stamp_battery', 'quartz_battery', 'stamp_mill', 'factory']
senticnet['crippled'] = ['0', '0', '0.838', '-0.73', '#anger', '#disgust', 'negative', '-0.78', 'gimpy', 'feeble', 'lame', 'square', 'halt']
senticnet['crippling_depression'] = ['-0.99', '0', '0.982', '-0.62', '#sadness', '#anger', 'negative', '-0.86', 'dysthymia', 'dysthymic_depression', 'affective_disorder', 'major_affective_disorder', 'emotional_disorder']
senticnet['crippling_strike'] = ['-0.77', '0', '0.505', '-0.50', '#sadness', '#anger', 'negative', '-0.59', 'wildcat_strike', 'sympathy_strike', 'sympathetic_strike', 'walkout', 'job_action']
senticnet['crisis'] = ['-0.99', '0', '0.691', '-0.65', '#sadness', '#anger', 'negative', '-0.78', 'depression', 'economic_crisis', 'exigency', 'slump', 'juncture']
senticnet['crism'] = ['0.884', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.919', 'ufo', 'milky', 'space_junk', 'galaxy', 'solar_system']
senticnet['crisp'] = ['0.059', '-0.04', '0.015', '0.064', '#joy', '#admiration', 'positive', '0.038', 'cold_water', 'dampness', 'crunchy', 'cover_water', 'give_water']
senticnet['crisp_bacon'] = ['-0.92', '0', '0.609', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'puffed_rice', 'granola', 'raisin_bran', 'corn_flake', 'bran_flake']
senticnet['crisp_bread'] = ['0.52', '-0.69', '0', '0.639', '#surprise', '#admiration', 'positive', '0.617', 'black_bread', 'pumpernickel', 'limpa', 'bread', 'breadstuff']
senticnet['crisp_crust'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'obese', 'worse', 'health_issue', 'clinical_problem']
senticnet['crisp_skin'] = ['-0.02', '-0.02', '0.064', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'ocean_water', 'fillet_fish', 'chowder', 'port', 'underwater']
senticnet['crisp_tender'] = ['0', '0.067', '-0.09', '-0.09', '#fear', '#disgust', 'negative', '-0.04', 'cotton_boll', 'asbestos', 'corn', 'opium', 'rice']
senticnet['crisp_white'] = ['-0.79', '0', '0.599', '-0.93', '#sadness', '#disgust', 'negative', '-0.77', 'uniform', 'battle_dress', 'dress_uniform', 'fatigue', 'military_uniform']
senticnet['crispbread'] = ['0.52', '-0.69', '0', '0.639', '#surprise', '#admiration', 'positive', '0.617', 'black_bread', 'pumpernickel', 'limpa', 'bread', 'breadstuff']
senticnet['crisper'] = ['-0.05', '0.07', '0', '0.062', '#interest', '#admiration', 'positive', '0.027', 'salty', 'fine_grain', 'powder', 'granulate', 'food_flavor']
senticnet['crisphead_lettuce'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'cabbage', 'broccoli', 'pea']
senticnet['crisply'] = ['-0.82', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'aggressively', 'sharply', 'sharp', 'precipitously', 'acutely']
senticnet['crispy'] = ['-0.08', '0.232', '0', '0.099', '#interest', '#admiration', 'positive', '0.084', 'bee_product', 'gooey', 'red_skin', 'really_hot', 'bee_house']
senticnet['crispy_duck'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'obese', 'worse', 'health_issue', 'clinical_problem']
senticnet['crispy_pork'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'obese', 'worse', 'health_issue', 'clinical_problem']
senticnet['crispy_salmon'] = ['0.084', '-0.05', '0', '-0.04', '#joy', '#surprise', 'positive', '0.031', 'entree', 'feast', 'satiation', 'breakfast', 'supper']
senticnet['crispy_skin'] = ['0.041', '0.069', '0.032', '0.13', '#interest', '#admiration', 'positive', '0.052', 'edible_human', 'round_fruit', 'peelable', 'green_fruit', 'nutritious']
senticnet['criss_angel'] = ['0.818', '0.913', '0', '0.825', '#interest', '#admiration', 'positive', '0.852', 'greeting', 'blackjack', 'pass_card', 'credit', 'play_blackjack']
senticnet['crisscross'] = ['-0.78', '0', '0.754', '-0.96', '#sadness', '#disgust', 'negative', '-0.83', 'tramp', 'cut_across', 'course', 'take', 'stride']
senticnet['cristata'] = ['-0.64', '0', '0.903', '-0.63', '#sadness', '#anger', 'negative', '-0.72', 'jaundice_of_newborn', 'disease', 'icterus_neonatorum', 'kernicterus', 'symptom']
senticnet['cristin_milioti'] = ['0', '0', '0', '0.068', '#admiration', '#admiration', 'positive', '0.068', 'dark_corner', 'child_game', 'play_person', 'role_play', 'part_of_play']
senticnet['cristofori'] = ['-0.75', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.80', 'high-strung', 'uptight', 'restive', 'nervy', 'jumpy']
senticnet['criteria'] = ['0.57', '0.754', '-0.32', '0.464', '#joy', '#interest', 'positive', '0.366', 'esteem', 'optimism', 'anticipation', 'adoration', 'confidence']
senticnet['criterion'] = ['0', '0.225', '-0.15', '0.236', '#interest', '#admiration', 'positive', '0.101', 'condition', 'diagnose', 'circumstance', 'axiom', 'tv_channel']
senticnet['criterion_referenced_test'] = ['0.928', '-0.87', '0', '0.679', '#joy', '#surprise', 'positive', '0.829', 'examine', 'experiment', 'alpha_test', 'beta_test', 'road_test']
senticnet['criterion_set_forth'] = ['-0.68', '0', '0.967', '-0.65', '#sadness', '#anger', 'negative', '-0.76', 'depart', 'start', 'part', 'start_out', 'sally_forth']
senticnet['critic'] = ['0.811', '-0.63', '0', '0.959', '#joy', '#admiration', 'positive', '0.8', 'drama_critic', 'theater_critic', 'professional', 'art_critic', 'literary_critic']
senticnet['critic_association_galeca'] = ['0.951', '0', '0', '0.731', '#joy', '#admiration', 'positive', '0.841', 'pastime', 'enjoyment', 'pleasure', 'enjoy', 'fun']
senticnet['critical'] = ['-0.11', '0', '-0.20', '0', '#sadness', '#fear', 'negative', '-0.16', 'liberate', 'smartness', 'trustworthy', 'notoriety', 'judgment']
senticnet['critical_acclaim'] = ['0.853', '0.828', '0', '0.98', '#joy', '#admiration', 'positive', '0.887', 'endorse', 'recommend', 'stimulate_mind', 'acculturation', 'support']
senticnet['critical_acumen'] = ['0.968', '-0.87', '0', '0.992', '#joy', '#admiration', 'positive', '0.946', 'sagacity', 'sagaciousness', 'care', 'caution', 'discernment']
senticnet['critical_component'] = ['0.943', '-0.78', '0', '0.571', '#joy', '#surprise', 'positive', '0.767', 'master', 'ignition_key', 'latchkey', 'passkey', 'master_key']
senticnet['critical_habitat'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'panda', 'hunting', 'air_pollution', 'global_warming', 'sea_pollution']
senticnet['critical_importance'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'baddie', 'theatrical_baddie', 'part', 'persona']
senticnet['critical_juncture'] = ['-0.67', '0', '0.97', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'joint', 'node', 'articulation', 'join', 'connexion']
senticnet['critical_praise'] = ['0.853', '0.828', '0', '0.98', '#joy', '#admiration', 'positive', '0.887', 'endorse', 'recommend', 'stimulate_mind', 'acculturation', 'support']
senticnet['critical_reflection'] = ['0.795', '-0.67', '0', '0.934', '#joy', '#admiration', 'positive', '0.8', 'appreciation', 'grasp', 'comprehension', 'brainstorm', 'hold']
senticnet['critical_regionalism'] = ['0.563', '-0.72', '0', '0.644', '#surprise', '#admiration', 'positive', '0.645', 'idiom', 'idiomatic_expression', 'phrasal_idiom', 'set_phrase', 'phrase']
senticnet['critical_shortage'] = ['-0.83', '0', '-0.88', '-0.78', '#sadness', '#fear', 'negative', '-0.83', 'little', 'sadness', 'small_amount', 'lack', 'absence']
senticnet['critical_wildlife_habitat'] = ['0', '0.243', '-0.11', '0.201', '#interest', '#admiration', 'positive', '0.108', 'upkeep', 'contain', 'keep_safe', 'keep', 'prevent']
senticnet['critically_analyze'] = ['0.868', '-0.95', '0', '0.548', '#joy', '#surprise', 'positive', '0.791', 'find', 'design', 'preconceive', 'discover', 'create_by_mental_act']
senticnet['critically_evaluate'] = ['0.056', '-0.03', '0', '0.054', '#joy', '#admiration', 'positive', '0.048', 'analyze', 'stargazing', 'influence_public_decision', 'question_everything', 'see_artifact']
senticnet['critically_examine'] = ['0.868', '-0.95', '0', '0.548', '#joy', '#surprise', 'positive', '0.791', 'find', 'design', 'preconceive', 'discover', 'create_by_mental_act']
senticnet['critically_important'] = ['0.943', '-0.78', '0', '0.571', '#joy', '#surprise', 'positive', '0.767', 'master', 'ignition_key', 'latchkey', 'passkey', 'master_key']
senticnet['critically_injured'] = ['-0.95', '0', '0.712', '-0.52', '#sadness', '#anger', 'negative', '-0.73', 'uncut', 'unharmed', 'unscathed', 'unwounded', 'uninjured']
senticnet['critically_reflect'] = ['0.117', '-0.00', '0', '0', '#joy', '#surprise', 'positive', '0.062', 'leisure_activity', 'web_surf', 'hobby', 'pastime', 'exploration']
senticnet['criticise'] = ['0', '0', '0', '-0.87', '#disgust', '#disgust', 'negative', '-0.87', 'move_eye', 'learn_current_event', 'become_informed', 'time_waste', 'eye_strain']
senticnet['criticism'] = ['-0.29', '-0.12', '0', '-0.29', '#sadness', '#disgust', 'negative', '-0.15', 'complication', 'bad_feeling', 'screw_up', 'make_mistake', 'black_eye']
senticnet['criticize'] = ['0', '0', '0', '-0.87', '#disgust', '#disgust', 'negative', '-0.87', 'move_eye', 'learn_current_event', 'become_informed', 'time_waste', 'eye_strain']
senticnet['criticize_bush'] = ['0', '0', '0', '-0.87', '#disgust', '#disgust', 'negative', '-0.87', 'move_eye', 'learn_current_event', 'become_informed', 'time_waste', 'eye_strain']
senticnet['critique'] = ['0.725', '-0.94', '0', '0.792', '#surprise', '#admiration', 'positive', '0.822', 'rave', 'literary_criticism', 'criticism', 'book_review', 'notice']
senticnet['critize'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'rudeness', 'discourtesy', 'insolence', 'ccheekiness', 'cheekiness']
senticnet['critter'] = ['0.03', '0.068', '-0.04', '0.093', '#interest', '#admiration', 'positive', '0.037', 'beast', 'equine', 'live_creature', 'furry_creature', 'domesticate_animal']
senticnet['crixivan'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'protease_inhibitor', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['crm_excellence'] = ['0.135', '0.225', '-0.12', '0.432', '#interest', '#admiration', 'positive', '0.167', 'excellent', 'goodness', 'well_done', 'great', 'good_quality']
senticnet['crm_software'] = ['0.971', '0.76', '0', '0', '#joy', '#interest', 'positive', '0.865', 'shopper', 'clerk', 'patron', 'vintner', 'hairdresser']
senticnet['crm_solution'] = ['0.971', '0.76', '0', '0', '#joy', '#interest', 'positive', '0.865', 'shopper', 'clerk', 'patron', 'vintner', 'hairdresser']
senticnet['crme'] = ['0.028', '0.138', '-0.04', '0.215', '#interest', '#admiration', 'positive', '0.083', 'water', 'dense', 'thick', 'milk', 'dense_water']
senticnet['crme_anglaise'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'grapefruit_peel', 'lemon_peel', 'orange_peel', 'candied_citrus_peel', 'succade']
senticnet['crme_brle'] = ['0.092', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.071', 'bake_treat', 'round_pastry', 'bake_good', 'bake_goods', 'sweet_food']
senticnet['crme_frache'] = ['-0.95', '0', '0.845', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'grapefruit_peel', 'candied_citrus_peel', 'orange_peel', 'candied_fruit', 'succade']
senticnet['cro_magnon'] = ['0.537', '-0.51', '0', '0.807', '#joy', '#admiration', 'positive', '0.619', 'lesbian', 'fairy', 'fagot', 'faggot', 'fag']
senticnet['croak'] = ['-0.10', '-0.01', '-0.05', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'clamor', 'roar', 'groan', 'squeak', 'loud_noise']
senticnet['crobar'] = ['0.763', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.75', 'pub', 'disco', 'dance_club', 'cafe', 'tavern']
senticnet['croc'] = ['-0.85', '0', '0.685', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'alligator', 'crocodilian', 'false_gavial', 'gator', 'caiman']
senticnet['crochet'] = ['-0.05', '0', '0.01', '-0.02', '#sadness', '#disgust', 'negative', '-0.03', 'shovel_snow', 'fly_around', 'blow_away', 'smoke_joint', 'hold_two_together']
senticnet['crochet_hook'] = ['-0.03', '-0.00', '-0.05', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'bandage', 'needle_thread', 'sew', 'spasm', 'suture']
senticnet['crock_pot'] = ['-0.04', '-0.00', '0.049', '0', '#sadness', '#anger', 'negative', '-0.02', 'janitor_room', 'buy_gum', 'shop_grocery', 'buy_clothes', 'find_pantry']
senticnet['crockery'] = ['0.022', '0.069', '0.027', '0.059', '#interest', '#admiration', 'positive', '0.031', 'perfect_surface', 'flat_land', 'flat_surface', 'bowl', 'plate']
senticnet['crocodile'] = ['-0.85', '0', '0.685', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'alligator', 'crocodilian', 'false_gavial', 'gator', 'caiman']
senticnet['crocodile_farm'] = ['-0.85', '0', '0.685', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'alligator', 'crocodilian', 'false_gavial', 'gator', 'caiman']
senticnet['crocodile_skin'] = ['0.022', '-0.01', '0.062', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'electric_toothbrush', 'moistener', 'hairbrush', 'shampoo_conditioner', 'shave_kit']
senticnet['crocodile_tear'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'pretense', 'feigning', 'dissembling', 'pretence', 'hypocrisy']
senticnet['crocodilian'] = ['-0.85', '0', '0.685', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'alligator', 'crocodile', 'false_gavial', 'gator', 'caiman']
senticnet['crocodilian_reptile'] = ['-0.52', '0', '0.981', '-0.65', '#anger', '#disgust', 'negative', '-0.71', 'alligator', 'crocodile', 'false_gavial', 'gator', 'caiman']
senticnet['crocodyliform'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'hag', 'osteostracan', 'cephalaspid', 'ostracoderm', 'jawless_fish']
senticnet['crocosmia'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'livelong', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['crohn'] = ['-0.75', '0', '0.868', '-0.99', '#anger', '#disgust', 'negative', '-0.87', 'regional_enteritis', 'regional_ileitis', 'ulcerative_colitis', 'rubor', 'redness']
senticnet['crohn_disease'] = ['-0.75', '0', '0.868', '-0.99', '#anger', '#disgust', 'negative', '-0.87', 'regional_enteritis', 'regional_ileitis', 'ulcerative_colitis', 'rubor', 'redness']
senticnet['crohn_disease_cd'] = ['-0.75', '0', '0.868', '-0.99', '#anger', '#disgust', 'negative', '-0.87', 'regional_enteritis', 'regional_ileitis', 'ulcerative_colitis', 'rubor', 'redness']
senticnet['crohns_disease'] = ['-0.75', '0', '0.868', '-0.99', '#anger', '#disgust', 'negative', '-0.87', 'regional_enteritis', 'regional_ileitis', 'ulcerative_colitis', 'rubor', 'redness']
senticnet['cromer_lifeboat'] = ['0', '-0.02', '0.046', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'sail_ship', 'canoe', 'ship', 'submarine', 'yacht']
senticnet['cronbach_alpha'] = ['0.854', '-0.79', '0', '0.977', '#joy', '#admiration', 'positive', '0.875', 'profitableness', 'auspiciousness', 'luck', 'advantageousness', 'positivity']
senticnet['crone'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'jawless_fish', 'osteostracan', 'cephalaspid', 'ostracoderm', 'heterostracan']
senticnet['cronulla'] = ['0', '0.996', '0', '0.85', '#interest', '#admiration', 'positive', '0.923', 'strength', 'strong', 'physical_strength', 'athlete', 'muscle']
senticnet['crony'] = ['-0.85', '0', '0.938', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'menial', 'lackey', 'servant', 'flunkey', 'familiar']
senticnet['cronyism'] = ['0', '0', '-0.77', '-0.86', '#fear', '#disgust', 'negative', '-0.82', 'abduction', 'punishment', 'emergency', 'illiteracy', 'heal']
senticnet['crook'] = ['-0.83', '0', '0.831', '-0.69', '#sadness', '#anger', 'negative', '-0.78', 'toughie', 'accessory', 'highbinder', 'hoodlum', 'thug']
senticnet['crooked'] = ['-0.93', '0', '0.853', '-0.51', '#sadness', '#anger', 'negative', '-0.76', 'knotty', 'contorted', 'writhed', 'writhen', 'anfractuous']
senticnet['crooked_creek'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['crooked_leg'] = ['-0.18', '-0.12', '0.049', '-0.24', '#sadness', '#disgust', 'negative', '-0.09', 'tree_trunk', 'plank', 'artificial_leg', 'hardwood', 'tree_wood']
senticnet['crooked_nose'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'helpless', 'unwanted', 'rabies', 'small_cat', 'big_rat']
senticnet['crookneck'] = ['0', '-0.01', '0.037', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'pear', 'green_apple', 'apricot', 'cherry', 'lime']
senticnet['croon'] = ['0.65', '-0.61', '0', '0.711', '#joy', '#admiration', 'positive', '0.658', 'bouncing', 'scintillating', 'spirited', 'lively', 'zippy']
senticnet['crooner'] = ['0.882', '0.901', '0', '0', '#joy', '#interest', 'positive', '0.891', 'songwriter', 'vip', 'rock_group', 'guitar_player', 'violinist']
senticnet['crooning'] = ['0.984', '-0.81', '0', '0', '#joy', '#surprise', 'positive', '0.898', 'breathy', 'falsetto', 'raspy', 'harmony', 'vocal']
senticnet['crop'] = ['-0.03', '0.079', '0.013', '0.05', '#interest', '#admiration', 'positive', '0.021', 'maize', 'wheat_corn', 'wheat', 'reap', 'wheat_rice']
senticnet['crop-dusting'] = ['0.778', '-0.82', '0', '0', '#joy', '#surprise', 'positive', '0.799', 'diffusion', 'dispersal', 'dissemination', 'dispersion', 'spread']
senticnet['crop_acreage'] = ['0', '0.122', '0', '0.123', '#interest', '#admiration', 'positive', '0.122', 'acre', 'cow_home', 'crop_place', 'big_land', 'animal_house']
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senticnet['cross-legged'] = ['0', '0.948', '0', '0.834', '#interest', '#admiration', 'positive', '0.891', 'spread-eagled', 'spreadeagled', 'belly-down', 'half-sitting', 'spread-eagle']
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senticnet['cross_dresser'] = ['0.82', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.862', 'pervert', 'healthy_person', 'philanthropist', 'arsonist', 'nazi']
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senticnet['crossbow'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'bayonet', 'spear', 'shotgun', 'bullet', 'hydrogen_bomb']
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senticnet['crossover_episode'] = ['0.918', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.827', 'encounter', 'reveal', 'programme', 'lose', 'sequel']
senticnet['crossover_hit'] = ['0.625', '-0.90', '0', '0.54', '#joy', '#surprise', 'positive', '0.69', 'smash', 'bang', 'smasher', 'strike', 'hit']
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senticnet['crown_solicitor'] = ['0.053', '0.122', '-0.07', '0.086', '#interest', '#admiration', 'positive', '0.046', 'attorney', 'lawyer', 'jury', 'decide_guilt', 'prosecutor']
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senticnet['cryogenic_liquid'] = ['0', '0', '-0.06', '0', '#fear', '#fear', 'negative', '-0.06', 'greasy', 'car_gas', 'fossil_fuel', 'crude', 'lubrication']
senticnet['cryogenic_upper'] = ['0', '0', '0', '0.073', '#admiration', '#admiration', 'positive', '0.073', 'fire_starter', 'organic_substance', 'renewable_resource', 'use_fuel', 'tree_product']
senticnet['cryogenically'] = ['-0.03', '0', '0.014', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'ice_crystal', 'frozen_water', 'wet_cold', 'coldness', 'wetness']
senticnet['cryogenically_frozen'] = ['0.8', '-0.97', '0', '0.512', '#joy', '#surprise', 'positive', '0.763', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['cryogenics'] = ['-0.97', '0', '0.852', '0', '#sadness', '#anger', 'negative', '-0.91', 'cryogenic', 'high-temperature', 'pressurized', 'propellant', 'gaseous']
senticnet['cryoglobulinemia'] = ['-0.60', '0', '0.544', '-0.53', '#sadness', '#anger', 'negative', '-0.56', 'acute_renal_failure', 'acute_kidney_failure', 'chronic_renal_failure', 'chronic_kidney_failure', 'kidney_failure']
senticnet['cryomodule'] = ['0.843', '-0.99', '0', '0.706', '#joy', '#surprise', 'positive', '0.849', 'gadgetry', 'gimbal', 'contrivance', 'mod_con', 'device']
senticnet['cryopathy'] = ['-0.50', '0', '0.737', '-0.74', '#anger', '#disgust', 'negative', '-0.66', 'injury', 'harm', 'hurt', 'trench_foot', 'immersion_foot']
senticnet['cryovolcanism'] = ['0.095', '0', '-0.04', '0.062', '#joy', '#admiration', 'positive', '0.037', 'photographer', 'water_spray', 'moisture', 'rise_water', 'droplet']
senticnet['crypt_beneath'] = ['0.02', '0.205', '0', '0', '#joy', '#interest', 'positive', '0.112', 'safe', 'bank_vault', 'bank', 'piggy_bank', 'bank_account']
senticnet['cryptanalyst'] = ['0.615', '-0.93', '0', '0.812', '#surprise', '#admiration', 'positive', '0.787', 'intellectual', 'intellect', 'decipherer', 'cryptographer', 'cryptologist']
senticnet['cryptic'] = ['-0.98', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.95', 'inscrutable', 'cabalistic', 'deep', 'mysterious', 'mystifying']
senticnet['cryptic_coloration'] = ['0.62', '-0.59', '0', '0.967', '#joy', '#admiration', 'positive', '0.728', 'aposematic_coloration', 'warning_coloration', 'apatetic_coloration', 'protective_coloration', 'coloration']
senticnet['cryptic_female'] = ['0', '0.35', '-0.09', '0.31', '#interest', '#admiration', 'positive', '0.188', 'decision', 'conclude', 'explanation', 'reason', 'think_process']
senticnet['crypto_fascist'] = ['-0.66', '0', '0.918', '-0.62', '#sadness', '#anger', 'negative', '-0.73', 'beguiler', 'slicker', 'hidden', 'ringer', 'deceiver']
senticnet['cryptocarya'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['cryptococcosis'] = ['-0.85', '0', '0.549', '-0.93', '#sadness', '#disgust', 'negative', '-0.78', 'coccidioidomycosis', 'coccidiomycosis', 'valley_fever', 'desert_rheumatism', 'mycosis']
senticnet['cryptogram'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'foot_motion', 'foot_action', 'foot_movement', 'stride', 'pace']
senticnet['cryptograph'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'foot_motion', 'foot_action', 'foot_movement', 'stride', 'pace']
senticnet['cryptographer'] = ['0.615', '-0.93', '0', '0.812', '#surprise', '#admiration', 'positive', '0.787', 'intellectual', 'intellect', 'cryptanalyst', 'decipherer', 'cryptologist']
senticnet['cryptographic'] = ['-0.52', '0', '0.821', '-0.65', '#anger', '#disgust', 'negative', '-0.66', 'work_out', 'reckon', 'calculate', 'average_out', 'cypher']
senticnet['cryptographic_algorithm'] = ['-0.52', '0', '0.821', '-0.65', '#anger', '#disgust', 'negative', '-0.66', 'work_out', 'reckon', 'calculate', 'average_out', 'cypher']
senticnet['cryptographic_hardware'] = ['0.943', '-0.78', '0', '0.571', '#joy', '#surprise', 'positive', '0.767', 'master', 'ignition_key', 'latchkey', 'passkey', 'master_key']
senticnet['cryptographic_hash'] = ['0.022', '-0.13', '0.153', '-0.31', '#anger', '#disgust', 'negative', '-0.07', 'codeine', 'morphine', 'infection', 'cocaine', 'nuclear_war']
senticnet['cryptographic_hash_function'] = ['0.022', '-0.13', '0.153', '-0.31', '#anger', '#disgust', 'negative', '-0.07', 'codeine', 'morphine', 'infection', 'cocaine', 'nuclear_war']
senticnet['cryptographic_key'] = ['-0.83', '0', '0.665', '-0.51', '#sadness', '#anger', 'negative', '-0.67', 'percipient', 'auditor', 'perceiver', 'observer', 'beholder']
senticnet['cryptographic_protection'] = ['-0.79', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.79', 'misgiving', 'qualm', 'scruple', 'insecurity', 'insecure']
senticnet['cryptographically'] = ['-0.83', '0', '0.665', '-0.51', '#sadness', '#anger', 'negative', '-0.67', 'percipient', 'auditor', 'perceiver', 'observer', 'beholder']
senticnet['cryptography'] = ['0.634', '-0.73', '0', '0.974', '#surprise', '#admiration', 'positive', '0.782', 'decompression', 'decipherment', 'coding', 'secret_writing', 'steganography']
senticnet['cryptologist'] = ['0.615', '-0.93', '0', '0.812', '#surprise', '#admiration', 'positive', '0.787', 'intellectual', 'intellect', 'cryptanalyst', 'cryptographer', 'decipherer']
senticnet['cryptomeria_japonica'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'bottlebrush_buckeye', 'red_buckeye', 'particolored_buckeye']
senticnet['cryptomonad'] = ['0.807', '-0.86', '0', '0.611', '#joy', '#surprise', 'positive', '0.762', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['cryptorchidism'] = ['-0.99', '0', '0.964', '-0.70', '#sadness', '#anger', 'negative', '-0.88', 'blepharitis', 'diverticulitis', 'epiglottitis', 'inflammation', 'prostatitis']
senticnet['cryptosporidium'] = ['-0.62', '0', '0.987', '-0.74', '#anger', '#disgust', 'negative', '-0.78', 'medical_aid', 'treatment', 'hospice', 'digitalization', 'bloodletting']
senticnet['cryptosystem'] = ['-0.52', '0', '0.821', '-0.65', '#anger', '#disgust', 'negative', '-0.66', 'work_out', 'reckon', 'calculate', 'average_out', 'cypher']
senticnet['crystal'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'art', 'art_piece', 'museum', 'artist', 'jewel']
senticnet['crystal_ball'] = ['-0.80', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.81', 'globe', 'ball', 'spherule', 'bolus', 'celestial_globe']
senticnet['crystal_cave'] = ['-0.02', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'tunnel', 'dark_place', 'ski_resort', 'top_tree', 'compost_pile']
senticnet['crystal_chandelier'] = ['0.046', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'light', 'bay_window', 'air_conditioner', 'sky', 'lamp']
senticnet['crystal_clear'] = ['0.522', '-0.88', '0', '0.965', '#surprise', '#admiration', 'positive', '0.789', 'blue_color', 'munchkin', 'imaginary', 'blue_sky', 'water_sky']
senticnet['crystal_cove'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'drifter', 'bum', 'sundowner']
senticnet['crystal_gazing'] = ['0.958', '-0.50', '0', '0.527', '#joy', '#admiration', 'positive', '0.664', 'divination', 'anticipation', 'prevision', 'prophecy', 'foretelling']
senticnet['crystal_growth'] = ['0.976', '0.786', '0', '0', '#joy', '#interest', 'positive', '0.881', 'combustible', 'state_matter', 'natural_resource', 'liquid_fuel', 'fossil_fuel']
senticnet['crystal_meth'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'use_drug', 'do_drug', 'drug_use', 'drug', 'addiction']
senticnet['crystal_methamphetamine'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'use_drug', 'do_drug', 'drug_use', 'drug', 'addiction']
senticnet['crystal_oscillator'] = ['0', '0', '0.95', '-0.86', '#anger', '#disgust', 'negative', '-0.90', 'watch', 'stopwatch', 'timepiece', 'wristwatch', 'find_pawnshop']
senticnet['crystal_palace_fan'] = ['-0.89', '0', '0.933', '-0.93', '#anger', '#disgust', 'negative', '-0.92', 'bullyboy', 'muscleman', 'muscle', 'skinhead', 'skin']
senticnet['crystal_pepsi'] = ['0.964', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.964', 'carbonated_drink', 'sugar_free', 'coke', 'diet_coke', 'beverage']
senticnet['crystal_shard'] = ['0', '0.081', '0', '0', '#interest', '#interest', 'positive', '0.081', 'favorite', 'guard', 'crowd', 'several_people', 'clique']
senticnet['crystal_violet'] = ['0', '-0.01', '0.046', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'varnish', 'brush', 'dye', 'pigment', 'canvas']
senticnet['crystalline'] = ['0', '0', '-0.79', '-0.85', '#fear', '#disgust', 'negative', '-0.82', 'liquid', 'crystal_clear', 'pellucid', 'limpid', 'lucid']
senticnet['crystalline_basement'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'fossil', 'oil', 'dark_liquid', 'smithsonian_institution', 'inert_gas']
senticnet['crystalline_bedrock'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'fossil', 'oil', 'dark_liquid', 'smithsonian_institution', 'inert_gas']
senticnet['crystalline_rock'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'fossil', 'oil', 'dark_liquid', 'smithsonian_institution', 'inert_gas']
senticnet['crystalline_solid'] = ['0.036', '0', '0.033', '0.091', '#joy', '#admiration', 'positive', '0.031', 'tasty', 'white_color', 'fluid', 'flavor', 'white_stuff']
senticnet['crystalline_substance'] = ['0.049', '0', '0', '0.113', '#joy', '#admiration', 'positive', '0.081', 'solid', 'hard_material', 'construction_material', 'hard', 'calcium']
senticnet['crystallize'] = ['0.548', '-0.86', '0', '0.938', '#surprise', '#admiration', 'positive', '0.784', 'elaborate', 'clear', 'elucidate', 'clear_up', 'make_clear']
senticnet['crystalloid'] = ['0.79', '-0.62', '0', '0.674', '#joy', '#admiration', 'positive', '0.695', 'add', 'contribute', 'lend', 'breathe', 'impart']
senticnet['csak'] = ['0.975', '-0.66', '0', '0.813', '#joy', '#admiration', 'positive', '0.816', 'cry', 'yell', 'scream', 'shrill', 'call']
senticnet['csf_drainage'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'blatancy', 'obviousness']
senticnet['csik'] = ['0.975', '-0.66', '0', '0.813', '#joy', '#admiration', 'positive', '0.816', 'cry', 'yell', 'scream', 'shrill', 'call']
senticnet['csist'] = ['-0.65', '0', '0.505', '-0.91', '#sadness', '#disgust', 'negative', '-0.69', 'hellhound', 'mythical_being', 'dragon', 'mythical_monster', 'amphisbaena']
senticnet['csloat'] = ['0.533', '0.818', '0', '0.683', '#interest', '#admiration', 'positive', '0.678', 'gain_information', 'report_news', 'solve', 'seek_answer', 'gather_information']
senticnet['css_financial_aid'] = ['0', '0.189', '0.04', '0.091', '#interest', '#admiration', 'positive', '0.08', 'combination', 'margin', 'teller', 'merge', 'finite']
senticnet['css_stylesheet'] = ['-0.89', '0', '0.672', '-0.69', '#sadness', '#disgust', 'negative', '-0.75', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['ctenophore'] = ['-0.79', '0', '0.709', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'animal', 'animate_being', 'invertebrate', 'comb_jelly', 'beast']
senticnet['cto'] = ['0.046', '0.116', '-0.05', '0.076', '#interest', '#admiration', 'positive', '0.045', 'head_guy', 'boss', 'officer', 'leader', 'supervisor']
senticnet['cub'] = ['-0.95', '0', '0.735', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'novice', 'beginner', 'tyro', 'tiro', 'initiate']
senticnet['cub_pitcher'] = ['0', '0.075', '0', '0', '#interest', '#interest', 'positive', '0.075', 'cup', 'carafe', 'container_cup', 'mug', 'glasses']
senticnet['cuban_cigar'] = ['0.031', '0', '-0.08', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'smoke_cigarette', 'smoke', 'cinder', 'tobacco_product', 'smoke_pipe']
senticnet['cuban_peso'] = ['-0.02', '0.264', '0', '0.095', '#interest', '#admiration', 'positive', '0.111', 'finance', 'franc', 'cent', 'banknote', 'mastercard']
senticnet['cuban_rum'] = ['0.031', '0', '-0.08', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'smoke_cigarette', 'smoke', 'cinder', 'tobacco_product', 'smoke_pipe']
senticnet['cuban_salsa'] = ['0.116', '-0.02', '0.025', '0.156', '#joy', '#admiration', 'positive', '0.069', 'dance_move', 'latin_dance', 'body_movement', 'waltz', 'dance']
senticnet['cuban_sandwich'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'crab', 'swimming_crab', 'rock_crab', 'fiddler_crab', 'pea_crab']
senticnet['cube'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'block', 'dice', 'ping_pong_paddle', 'ball_player', 'trapezoid']
senticnet['cube_map'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'illuminate_room', 'give_light', 'light', 'light_bulb', 'lantern']
senticnet['cubed'] = ['-0.75', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'week-old', 'month-old', 'day-old', 'good-quality', 'wholemeal']
senticnet['cubic'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'block', 'dice', 'ping_pong_paddle', 'ball_player', 'trapezoid']
senticnet['cubic_hermite'] = ['-0.55', '0', '0.965', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'interjection', 'delay', 'insert', 'interposition', 'disruption']
senticnet['cubic_honeycomb'] = ['0.641', '-0.6', '0', '0.957', '#joy', '#admiration', 'positive', '0.733', 'ideal_solid', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'regular_octahedron']
senticnet['cubic_inch'] = ['0.068', '0', '0.034', '0.076', '#joy', '#admiration', 'positive', '0.037', 'gallon', 'liquid_measurement', 'ounce', 'measurement', 'quart']
senticnet['cubic_interpolation'] = ['-0.55', '0', '0.965', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'interjection', 'delay', 'insert', 'interposition', 'disruption']
senticnet['cubic_spline'] = ['-0.55', '0', '0.965', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'interjection', 'delay', 'insert', 'interposition', 'disruption']
senticnet['cubic_surface'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'fragmentation', 'discharge', 'backfire', 'blast']
senticnet['cubic_zirconia'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'art', 'art_piece', 'museum', 'artist', 'jewel']
senticnet['cubical'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'block', 'dice', 'ping_pong_paddle', 'ball_player', 'trapezoid']
senticnet['cubicle'] = ['0.053', '0.1', '0', '0', '#joy', '#interest', 'positive', '0.076', 'registry_office', 'lodging', 'telephone_booth', 'board_room', 'reception_area']
senticnet['cubism'] = ['0', '0.965', '0', '0.876', '#interest', '#admiration', 'positive', '0.92', 'expressionism', 'fauvism', 'impressionism', 'post-impressionism', 'surrealism']
senticnet['cubital_tunnel_syndrome'] = ['-0.98', '0', '0.678', '-0.95', '#sadness', '#disgust', 'negative', '-0.87', 'rubor', 'redness', 'inflammation', 'tennis_elbow', 'lateral_epicondylitis']
senticnet['cuboctahedra'] = ['0.641', '-0.6', '0', '0.957', '#joy', '#admiration', 'positive', '0.733', 'regular_tetrahedron', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'ideal_solid']
senticnet['cuboctahedron'] = ['0.641', '-0.6', '0', '0.957', '#joy', '#admiration', 'positive', '0.733', 'ideal_solid', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'regular_octahedron']
senticnet['cuboid'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'block', 'dice', 'ping_pong_paddle', 'ball_player', 'trapezoid']
senticnet['cuboid_bone'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'socket', 'tooth_socket', 'alveolus', 'acetabulum', 'cotyloid_cavity']
senticnet['cuckold'] = ['-0.75', '0', '0.547', '-0.78', '#sadness', '#disgust', 'negative', '-0.69', 'cheat_on', 'play_around', 'wander', 'betray', 'cheat']
senticnet['cuckolded_husband'] = ['0', '0', '0.733', '-0.98', '#anger', '#disgust', 'negative', '-0.85', 'fetishist', 'degenerate', 'lech', 'letch', 'pervert']
senticnet['cuckoo'] = ['-0.82', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.80', 'gawk', 'stupidly', 'gawker', 'simpleton', 'conspicuous']
senticnet['cuckoo_nest'] = ['-0.60', '0', '0.716', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['cuckoo_wasp'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'giant_hornet', 'hornet', 'hornet_wasp']
senticnet['cuckooland'] = ['-0.10', '0', '-0.05', '-0.24', '#sadness', '#disgust', 'negative', '-0.13', 'adulate', 'suck_up', 'toady', 'blandish', 'kowtow']
senticnet['cucumber'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'broccoli', 'cabbage', 'lettuce', 'pea']
senticnet['cucumber_beetle'] = ['0', '-0.09', '0.119', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'japanese_beetle', 'mole_cricket', 'gypsy_moth', 'mosquito', 'earwig']
senticnet['cucurbitaceae'] = ['0.826', '0.787', '0', '0', '#joy', '#interest', 'positive', '0.806', 'fig', 'baby_flower', 'carpel', 'strawberry', 'star_anise']
senticnet['cuddle'] = ['0.032', '0', '0', '0.077', '#joy', '#admiration', 'positive', '0.054', 'embrace', 'hug', 'caress', 'kiss', 'love']
senticnet['cuddle_up'] = ['0.032', '0', '0', '0.077', '#joy', '#admiration', 'positive', '0.054', 'embrace', 'hug', 'caress', 'kiss', 'love']
senticnet['cuddled_up'] = ['0.032', '0', '0', '0.077', '#joy', '#admiration', 'positive', '0.054', 'embrace', 'hug', 'caress', 'kiss', 'love']
senticnet['cuddly'] = ['0.936', '-0.81', '0', '0.83', '#joy', '#admiration', 'positive', '0.86', 'cherubic', 'lovely', 'lovable', 'endearing', 'angelic']
senticnet['cue'] = ['0.067', '-0.2', '0.064', '0', '#joy', '#surprise', 'positive', '0.068', 'wand', 'long_stick', 'branch', 'twig', 'stick']
senticnet['cue_ball'] = ['0', '-0.03', '0.042', '-0.07', '#anger', '#disgust', 'negative', '-0.02', 'ball', 'ping_pong_show', 'dandruff', 'solid_water', 'empty_bottle']
senticnet['cue_reactivity'] = ['0.627', '-0.65', '0', '0.968', '#surprise', '#admiration', 'positive', '0.749', 'overwhelm', 'overpower', 'sweep_over', 'whelm', 'overcome']
senticnet['cue_stick'] = ['0', '-0.03', '0.042', '-0.07', '#anger', '#disgust', 'negative', '-0.02', 'ball', 'ping_pong_show', 'dandruff', 'solid_water', 'empty_bottle']
senticnet['cued_recall'] = ['0.067', '-0.2', '0.064', '0', '#joy', '#surprise', 'positive', '0.068', 'wand', 'long_stick', 'branch', 'twig', 'stick']
senticnet['cued_speech'] = ['-0.94', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.94', 'drive_recklessly', 'discord', 'mute', 'deny', 'helpless']
senticnet['cuerda'] = ['-0.63', '0', '0.897', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'cd-rom', 'international_candle', 'candle', 'candlepower_unit', 'standard_candle']
senticnet['cugel'] = ['0.594', '-0.54', '0', '0.518', '#joy', '#surprise', 'positive', '0.554', 'exorcist', 'necromancer', 'magus', 'sorceress', 'witch_doctor']
senticnet['cuireadh'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['cuisinart'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'salad_shaker', 'store_shelf', 'kitchen_tool', 'pliers', 'ladle']
senticnet['cuisine'] = ['0.882', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.822', 'ferment_grape_juice', 'chef_special', 'bottle_wine', 'fine_dining', 'decanter']
senticnet['cuisinier'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'blue_ribbon', 'badge', 'allegory', 'stripe', 'merit_badge']
senticnet['culainn'] = ['-0.73', '0', '0.55', '-0.75', '#sadness', '#disgust', 'negative', '-0.68', 'mortal', 'person', 'individual', 'mangler', 'mutilator']
senticnet['culantro'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'cabbage', 'lettuce', 'broccoli']
senticnet['culinary'] = ['0.882', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.822', 'ferment_grape_juice', 'chef_special', 'bottle_wine', 'fine_dining', 'decanter']
senticnet['culinary_creativity'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'artistic_creation', 'fabrication', 'carving', 'decoupage', 'ceramic']
senticnet['culinary_herb'] = ['0.1', '0', '0', '0', '#joy', '#joy', 'positive', '0.1', 'burn_material', 'ash', 'stay_warm', 'bonfire', 'campfire']
senticnet['culinary_heritage'] = ['0.882', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.822', 'ferment_grape_juice', 'chef_special', 'bottle_wine', 'fine_dining', 'decanter']
senticnet['culinary_scene'] = ['0.882', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.822', 'ferment_grape_juice', 'chef_special', 'bottle_wine', 'fine_dining', 'decanter']
senticnet['culinary_tradition'] = ['0.882', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.822', 'ferment_grape_juice', 'chef_special', 'bottle_wine', 'fine_dining', 'decanter']
senticnet['cullen'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
senticnet['cullenbone'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'lovebird', 'parroket', 'paraquet', 'paroquet']
senticnet['cullis'] = ['-0.9', '0', '0.749', '-0.71', '#sadness', '#anger', 'negative', '-0.78', 'chute', 'slide', 'slideway', 'sloping_trough', 'gutter']
senticnet['culm_sheath_blade_lanceolate'] = ['-0.50', '0', '0.573', '-0.89', '#anger', '#disgust', 'negative', '-0.65', 'petallike', 'bowery', 'leafy', 'unifoliate', 'bifoliate']
senticnet['culminate'] = ['0', '0.067', '0', '0.062', '#interest', '#admiration', 'positive', '0.065', 'social_skill', 'show_up', 'go_abroad', 'go_new_place', 'visit']
senticnet['culo'] = ['-0.63', '0', '0.791', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'little', 'pinch', 'modicum', 'small_indefinite_amount', 'indefinite_quantity']
senticnet['culpability'] = ['-0.98', '0', '0.657', '-0.95', '#sadness', '#disgust', 'negative', '-0.86', 'impeachability', 'indictability', 'blameworthiness', 'guiltiness', 'culpableness']
senticnet['culpable'] = ['0', '0', '-0.97', '-0.95', '#fear', '#disgust', 'negative', '-0.96', 'blameworthy', 'blame', 'culpableness', 'culpability', 'injurious']
senticnet['culpable_negligence'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'negligence', 'evasion', 'dereliction', 'contributory_negligence']
senticnet['culpableness'] = ['-0.98', '0', '0.657', '-0.95', '#sadness', '#disgust', 'negative', '-0.86', 'impeachability', 'indictability', 'blameworthiness', 'culpability', 'guiltiness']
senticnet['culpably'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'negligence', 'evasion', 'dereliction', 'contributory_negligence']
senticnet['culprit'] = ['0', '0', '-0.88', '-0.99', '#fear', '#disgust', 'negative', '-0.93', 'murder_suspect', 'robbery_suspect', 'rape_suspect', 'murder', 'doubt']
senticnet['cult'] = ['0.052', '0.223', '-0.06', '0.163', '#interest', '#admiration', 'positive', '0.092', 'congregation', 'committee', 'majority', 'gang', 'bunch']
senticnet['cult_favourite'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'important', 'universal', 'vip', 'prestige', 'status']
senticnet['cult_figure'] = ['-0.61', '0', '0.738', '-0.80', '#anger', '#disgust', 'negative', '-0.71', 'dissident', 'beat', 'bohemian', 'maverick', 'recusant']
senticnet['cult_hit'] = ['0.625', '-0.90', '0', '0.54', '#joy', '#surprise', 'positive', '0.69', 'smash', 'bang', 'smasher', 'strike', 'hit']
senticnet['cultic'] = ['0.807', '-0.92', '0', '0.796', '#joy', '#surprise', 'positive', '0.841', 'agape', 'confirmation', 'libation', 'rite', 'service']
senticnet['cultipacker'] = ['0.689', '-0.56', '0', '0.74', '#joy', '#admiration', 'positive', '0.665', 'crease', 'crinkle', 'wrinkle', 'heart_line', 'seam']
senticnet['cultish'] = ['-0.91', '0', '0.794', '-0.93', '#sadness', '#disgust', 'negative', '-0.88', 'fundamentalist', 'extremist', 'obsession', 'stubborn', 'violent']
senticnet['cultist'] = ['-0.91', '0', '0.794', '-0.93', '#sadness', '#disgust', 'negative', '-0.88', 'fundamentalist', 'extremist', 'obsession', 'stubborn', 'violent']
senticnet['cultivable_land'] = ['0.716', '-0.51', '0', '0.647', '#joy', '#admiration', 'positive', '0.624', 'state', 'nation', 'country', 'ally', 'commonwealth']
senticnet['cultivate'] = ['0', '0.111', '0', '0.116', '#interest', '#admiration', 'positive', '0.114', 'plough', 'plantation', 'edible_plant', 'farm_vegetable', 'plant_food']
senticnet['cultivate_yard'] = ['0.101', '0.137', '0', '0.193', '#interest', '#admiration', 'positive', '0.144', 'sexual', 'use_decoration', 'organic', 'juicy', 'nutritious']
senticnet['cultivated'] = ['0.021', '0.059', '-0.04', '0.092', '#interest', '#admiration', 'positive', '0.032', 'domesticate_animal', 'tamed', 'live_creature', 'furry_creature', 'farm']
senticnet['cultivated_land'] = ['0.958', '-0.50', '0', '0.527', '#joy', '#admiration', 'positive', '0.664', 'fallow', 'farmland', 'plowland', 'ploughland', 'tilled_land']
senticnet['cultivated_parsnip'] = ['0.861', '-0.87', '0', '0.597', '#joy', '#surprise', 'positive', '0.779', 'herb', 'pastinaca_sativa', 'wild_parsnip', 'madnep', 'herbaceous_plant']
senticnet['cultivation'] = ['0', '0.105', '0', '0.1', '#interest', '#admiration', 'positive', '0.103', 'agricultural', 'grow_food', 'harvest_food', 'grow_stuff', 'grow_crop']
senticnet['cultivator'] = ['-0.99', '0', '0.827', '-0.54', '#sadness', '#anger', 'negative', '-0.78', 'treat', 'cover', 'handle', 'theologize', 'deal']
senticnet['cultural'] = ['-0.05', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.05', 'traditional', 'political_state', 'large_land', 'large_state', 'political_unit']
senticnet['cultural_anthropology'] = ['-0.98', '0', '0', '-0.91', '#sadness', '#disgust', 'negative', '-0.94', 'dissertation_research', 'anthropology', 'number', 'role', 'forest']
senticnet['cultural_appropriation'] = ['-0.96', '0', '0.766', '-0.96', '#sadness', '#disgust', 'negative', '-0.89', 'calumniation', 'traducement', 'hatchet_job', 'defamation', 'calumny']
senticnet['cultural_assimilation'] = ['-0.77', '0', '0.914', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'confluence', 'conflux', 'merging', 'blending', 'combination']
senticnet['cultural_change'] = ['0', '0.881', '0', '0.742', '#interest', '#admiration', 'positive', '0.811', 'funny_look', 'soldier_group', 'rival', 'anti', 'military_force']
senticnet['cultural_context'] = ['0', '0.139', '0', '0.18', '#interest', '#admiration', 'positive', '0.16', 'aspect', 'vista', 'scenic', 'spectacular', 'view']
senticnet['cultural_critic'] = ['0.811', '-0.63', '0', '0.959', '#joy', '#admiration', 'positive', '0.8', 'drama_critic', 'theater_critic', 'professional', 'art_critic', 'literary_critic']
senticnet['cultural_difference_between'] = ['-0.93', '0', '-0.99', '-0.92', '#sadness', '#fear', 'negative', '-0.95', 'barrier', 'shut', 'shut_down', 'close_down', 'obstacle']
senticnet['cultural_diversity'] = ['0.057', '0', '0', '0.153', '#joy', '#admiration', 'positive', '0.105', 'multiple', 'cute', 'variety', 'hairy', 'large_breadbox']
senticnet['cultural_enrichment'] = ['0.881', '-0.86', '0', '0.688', '#joy', '#surprise', 'positive', '0.813', 'better', 'improve', 'make_look_better', 'meliorate', 'amend']
senticnet['cultural_eutrophication'] = ['-0.80', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.84', 'popularization', 'profanation', 'demoralization', 'corruption', 'humiliation']
senticnet['cultural_experience'] = ['0.973', '0.822', '0', '0.735', '#joy', '#interest', 'positive', '0.843', 'acculturation', 'golfer', 'stimulate_mind', 'pastime', 'kill_time']
senticnet['cultural_genocide'] = ['-0.96', '0', '0.766', '-0.96', '#sadness', '#disgust', 'negative', '-0.89', 'calumniation', 'traducement', 'hatchet_job', 'defamation', 'calumny']
senticnet['cultural_hegemony'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'absolutism', 'autocracy', 'ascendancy', 'tyrant']
senticnet['cultural_heritage'] = ['0.953', '0.958', '0', '0', '#joy', '#interest', 'positive', '0.956', 'cultural', 'heritage', 'ethnicity', 'nationality', 'tradition']
senticnet['cultural_homogeneity'] = ['-0.63', '0', '0.822', '-0.95', '#anger', '#disgust', 'negative', '-0.80', 'uniformness', 'homogeneousness', 'consistency', 'consistence', 'similarity']
senticnet['cultural_homogenization'] = ['-0.54', '0', '0.979', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'unlikeness', 'dissimilitude', 'difference', 'heterology', 'dissimilarity']
senticnet['cultural_hybridity'] = ['0.122', '0.159', '-0.05', '0.217', '#interest', '#admiration', 'positive', '0.111', 'profile_picture', 'unity', 'country_person', 'unite', 'signature']
senticnet['cultural_icon'] = ['0.055', '0.117', '0', '0.133', '#interest', '#admiration', 'positive', '0.102', 'graphical_representation', 'pencil_pen', 'note_pad', 'marker', 'stationery']
senticnet['cultural_identity'] = ['0.122', '0.159', '-0.05', '0.217', '#interest', '#admiration', 'positive', '0.111', 'profile_picture', 'unity', 'country_person', 'unite', 'signature']
senticnet['cultural_immersion'] = ['0.633', '-0.96', '0', '0.612', '#joy', '#surprise', 'positive', '0.738', 'particularism', 'absorption', 'concentration', 'engrossment', 'centering']
senticnet['cultural_immersion_program'] = ['0.633', '-0.96', '0', '0.612', '#joy', '#surprise', 'positive', '0.738', 'particularism', 'absorption', 'concentration', 'engrossment', 'centering']
senticnet['cultural_imperialism'] = ['0', '0', '-0.74', '-0.93', '#fear', '#disgust', 'negative', '-0.84', 'generosity', 'oral_sex', 'win_lottery', 'gift_wrap', 'give_away']
senticnet['cultural_inferiority'] = ['-0.57', '0', '0.53', '-0.63', '#sadness', '#disgust', 'negative', '-0.58', 'low_status', 'lowness', 'backseat', 'lowliness', 'subordinateness']
senticnet['cultural_melting_pot'] = ['-0.05', '0.096', '0', '0.066', '#interest', '#admiration', 'positive', '0.037', 'hue_tint', 'spectrum', 'red_blue', 'green_yellow', 'tint']
senticnet['cultural_phenomena'] = ['0', '0.137', '-0.10', '0.197', '#interest', '#admiration', 'positive', '0.076', 'boom', 'circumstance', 'loud', 'stormy', 'procedure']
senticnet['cultural_phenomenon'] = ['0', '0.137', '-0.10', '0.197', '#interest', '#admiration', 'positive', '0.076', 'boom', 'circumstance', 'loud', 'stormy', 'procedure']
senticnet['cultural_superiority'] = ['0.757', '-0.82', '0', '0.982', '#surprise', '#admiration', 'positive', '0.855', 'first_class', 'choiceness', 'first_water', 'fineness', 'quality']
senticnet['cultural_theorist'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'indirectness', 'mediateness', 'characteristic', 'allusiveness']
senticnet['cultural_vitality'] = ['-0.05', '0.096', '0', '0.066', '#interest', '#admiration', 'positive', '0.037', 'hue_tint', 'spectrum', 'red_blue', 'green_yellow', 'tint']
senticnet['culturally_diverse'] = ['0.057', '0', '0', '0.153', '#joy', '#admiration', 'positive', '0.105', 'multiple', 'cute', 'variety', 'hairy', 'large_breadbox']
senticnet['culturally_insensitive'] = ['-0.04', '-0.04', '-0.04', '-0.04', '#fear', '#disgust', 'negative', '-0.02', 'blunted', 'dull', 'unsharpened', 'blunt', 'edgeless']
senticnet['culturally_rich'] = ['0.843', '0.977', '0', '0', '#joy', '#interest', 'positive', '0.91', 'business', 'finance', 'revenue', 'wealth', 'income']
senticnet['culturally_vibrant'] = ['-0.05', '0.096', '0', '0.066', '#interest', '#admiration', 'positive', '0.037', 'hue_tint', 'spectrum', 'red_blue', 'green_yellow', 'tint']
senticnet['culture'] = ['0.096', '0.097', '-0.07', '0', '#joy', '#interest', 'positive', '0.039', 'craft', 'design', 'religion', 'artist', 'art']
senticnet['culture_shock'] = ['-0.93', '0', '-0.99', '-0.92', '#sadness', '#fear', 'negative', '-0.95', 'barrier', 'shut', 'shut_down', 'close_down', 'obstacle']
senticnet['culture_supernatant'] = ['0.772', '-0.56', '0', '0.824', '#joy', '#admiration', 'positive', '0.719', 'neutrophil', 'leukocyte', 'leucocyte', 'white_blood_cell', 'white_cell']
senticnet['cultured_milk'] = ['0.057', '0', '0.018', '0.058', '#joy', '#admiration', 'positive', '0.032', 'porridge', 'food', 'milk', 'healthy', 'morning_food']
senticnet['culver_root'] = ['0.632', '-0.93', '0', '0.928', '#surprise', '#admiration', 'positive', '0.831', 'herb', 'oregano', 'marjoram', 'pot_marjoram', 'origanum']
senticnet['culvert'] = ['-0.77', '0', '0.583', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'rain', 'rain_down', 'sheet', 'rain_bucket', 'rain_buckets_down']
senticnet['cum'] = ['0', '0.079', '0', '0', '#interest', '#interest', 'positive', '0.079', 'change_diaper', 'lower_standard_live', 'become_pregnant', 'find_mate', 'make_baby']
senticnet['cum_dividend'] = ['-0.05', '0.241', '-0.06', '0.286', '#interest', '#admiration', 'positive', '0.103', 'give_take', 'barter', 'exchange_goods', 'transaction', 'lower_price']
senticnet['cumaean'] = ['-0.87', '0', '0.599', '-0.71', '#sadness', '#disgust', 'negative', '-0.73', 'hysteria', 'fury', 'delirium', 'frenzy', 'craze']
senticnet['cumberland_avenue'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'boulevard', 'lane', 'thoroughfare', 'intersection', 'curb']
senticnet['cumberland_sausage'] = ['0.644', '-0.94', '0', '0.729', '#surprise', '#admiration', 'positive', '0.773', 'gem', 'banana_bread', 'date_bread', 'nut_bread', 'quick_bread']
senticnet['cumbersome'] = ['-0.96', '0', '0.636', '-0.85', '#sadness', '#disgust', 'negative', '-0.81', 'awkward', 'bunglesome', 'clumsy', 'unmanageable', 'ungainly']
senticnet['cumbersomeness'] = ['-0.64', '0', '0.625', '-0.88', '#sadness', '#disgust', 'negative', '-0.71', 'difficulty', 'awkwardness', 'flea_bite', 'difficultness', 'troublesomeness']
senticnet['cumbia'] = ['0.116', '-0.02', '0.025', '0.156', '#joy', '#admiration', 'positive', '0.069', 'dance_move', 'latin_dance', 'body_movement', 'waltz', 'dance']
senticnet['cumbria_constabulary'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'law_enforcement_agency', 'law', 'agency']
senticnet['cumbrous'] = ['0.834', '-0.71', '0', '0.649', '#joy', '#surprise', 'positive', '0.733', 'unwieldy', 'ungainly', 'clumsy', 'cumbersome', 'awkward']
senticnet['cumin'] = ['0', '0', '-0.76', '-0.72', '#fear', '#disgust', 'negative', '-0.74', 'coriander', 'cilantro', 'cardamom', 'turmeric', 'clove']
senticnet['cummerbund'] = ['-0.53', '0', '0.682', '-0.74', '#anger', '#disgust', 'negative', '-0.65', 'girdle', 'cincture', 'sash', 'waistband', 'waistcloth']
senticnet['cumulation'] = ['-0.77', '0', '0.588', '-0.80', '#sadness', '#disgust', 'negative', '-0.72', 'heap', 'agglomerate', 'piles', 'mound', 'funeral_pyre']
senticnet['cumulative'] = ['0.037', '0', '0.026', '0.129', '#joy', '#admiration', 'positive', '0.047', 'measurement_unit', 'many_items', 'long', 'circle_part', 'narrow']
senticnet['cumulative_patch'] = ['-0.58', '0', '0.561', '-0.62', '#sadness', '#disgust', 'negative', '-0.58', 'spot', 'speckle', 'dapple', 'mark', 'fleck']
senticnet['cumulative_score'] = ['0.926', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.916', 'score_goal', 'accomplish', 'finalize', 'pot', 'make_point']
senticnet['cumulative_total'] = ['-0.04', '0.268', '0', '0.159', '#interest', '#admiration', 'positive', '0.127', 'total', 'amount', 'a_lot', 'aggregate', 'bill']
senticnet['cumulative_trauma'] = ['-0.50', '0', '0.681', '-0.89', '#anger', '#disgust', 'negative', '-0.69', 'sweat', 'effort', 'elbow_grease', 'exertion', 'travail']
senticnet['cumuliform'] = ['-0.93', '0', '0.508', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'nakedness', 'glumness', 'bleakness', 'desolation', 'bareness']
senticnet['cumulonimbus_cloud'] = ['0', '-0.41', '-0.95', '0', '#surprise', '#fear', 'negative', '-0.26', 'blizzard', 'snow_storm', 'storm', 'danger', 'rainy']
senticnet['cumulus'] = ['-0.93', '0', '0.508', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'nakedness', 'glumness', 'bleakness', 'desolation', 'bareness']
senticnet['cumulus_cloud'] = ['0', '-0.41', '-0.95', '0', '#surprise', '#fear', 'negative', '-0.26', 'blizzard', 'snow_storm', 'storm', 'danger', 'rainy']
senticnet['cumulus_convection'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'hurricane', 'natural_disaster', 'flood', 'atmospheric_pressure', 'tropical_storm']
senticnet['cuneiform'] = ['0', '0.877', '0', '0.861', '#interest', '#admiration', 'positive', '0.869', 'demotic', 'sacred', 'hieroglyph', 'priest', 'hieratic']
senticnet['cuneiform_sign'] = ['0.049', '0.113', '0.107', '0.162', '#interest', '#admiration', 'positive', '0.054', 'list_number', 'ordinal', 'count', 'numerical', 'enumeration']
senticnet['cuneus'] = ['0.763', '-0.61', '0', '0.777', '#joy', '#admiration', 'positive', '0.717', 'acute_triangle', 'wedge_shape', 'triangle', 'wedge', 'oblique_triangle']
senticnet['cunina'] = ['0', '-0.02', '-0.08', '-0.05', '#fear', '#disgust', 'negative', '-0.03', 'mattress', 'sleep_furniture', 'master_bedroom', 'small_bed', 'pillow']
senticnet['cunning'] = ['0.738', '-0.96', '0', '0.727', '#joy', '#surprise', 'positive', '0.81', 'ingenious', 'clever', 'adroit', 'clean', 'neat']
senticnet['cunning_folk'] = ['-0.79', '0', '0.506', '-0.93', '#sadness', '#disgust', 'negative', '-0.74', 'magic', 'thaumaturgy', 'enchantment', 'witchcraft', 'witchery']
senticnet['cunning_linguist'] = ['0.807', '-0.83', '0', '0.688', '#joy', '#surprise', 'positive', '0.776', 'folly', 'lunacy', 'craziness', 'indulgence', 'foolery']
senticnet['cunningham'] = ['-0.69', '0', '0.89', '-0.70', '#anger', '#disgust', 'negative', '-0.76', 'still', 'suppress', 'moderate', 'hush_up', 'quell']
senticnet['cunningly_wrought'] = ['0.967', '-0.98', '0', '0.64', '#joy', '#surprise', 'positive', '0.863', 'club', 'social_club', 'society', 'guild', 'jockey_club']
senticnet['cunoniaceae'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'seed_fern', 'gymnosperm']
senticnet['cunt'] = ['-0.11', '0', '-0.16', '0.062', '#sadness', '#fear', 'negative', '-0.07', 'tile_floor', 'wicker', 'miss', 'short_form', 'happy_together']
senticnet['cup'] = ['0.093', '-0.02', '0', '0.056', '#joy', '#admiration', 'positive', '0.057', 'pitcher', 'carafe', 'container_pitcher', 'mug', 'glass_of_water']
senticnet['cup_breast'] = ['-0.79', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.75', 'wave_hello', 'wedge_door_open', 'wank', 'tear_paper', 'write_computer_program']
senticnet['cup_champion'] = ['0', '0', '0', '0.834', '#admiration', '#admiration', 'positive', '0.834', 'royal_person', 'royal_title', 'top', 'master', 'rank']
senticnet['cup_coffee'] = ['0.911', '0', '0', '0', '#joy', '#joy', 'positive', '0.911', 'read_book', 'stay_awake', 'cafe', 'peace_quiet', 'find_quiet_place']
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senticnet['curl_up'] = ['0.804', '0', '0', '0', '#joy', '#joy', 'positive', '0.804', 'fall_asleep', 'relax_mind', 'snuggle', 'make_out', 'bed_time']
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senticnet['curling_free_kick'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'watch', 'clock', 'hour', 'hourglass', 'stopwatch']
senticnet['curling_hair'] = ['0.209', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.118', 'old_dance', 'dance', 'slow_dance', 'dancer', 'enter_contest']
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senticnet['curly_black'] = ['0', '0', '0.841', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'ancient', 'many_years', 'year', 'old', 'teen']
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senticnet['curly_kale'] = ['0.636', '-0.79', '0', '0.552', '#joy', '#surprise', 'positive', '0.661', 'lettuce', 'endive', 'green', 'salad_green', 'food']
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senticnet['currency_bloc'] = ['0.771', '0.846', '0', '0', '#joy', '#interest', 'positive', '0.808', 'dollar_bill', 'franc', 'banknote', 'dollar', 'cent']
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senticnet['curry_favor'] = ['0.632', '-0.86', '0', '0.799', '#surprise', '#admiration', 'positive', '0.765', 'suck_up', 'toady', 'bootlick', 'kowtow', 'mafia']
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senticnet['cut_tree'] = ['-0.04', '0', '0.026', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'chop_tree', 'cut_wood', 'cut_lumber', 'cellulose', 'timber']
senticnet['cut_up'] = ['-0.06', '-0.02', '0.068', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'slice', 'sever', 'slash', 'cut', 'separation']
senticnet['cut_vegetable'] = ['-0.04', '-0.02', '0', '-0.10', '#sadness', '#disgust', 'negative', '-0.04', 'stir_pot', 'chop_vegetable', 'open_package', 'cut_food', 'heat_pot_water']
senticnet['cut_wood'] = ['-0.07', '-0.02', '0.036', '-0.12', '#sadness', '#disgust', 'negative', '-0.05', 'cut_tree', 'chop_tree', 'cut_lumber', 'chop', 'chop_wood']
senticnet['cutaneous_anthrax'] = ['-0.95', '0', '0.876', '-0.89', '#sadness', '#disgust', 'negative', '-0.90', 'bubonic_plague', 'pestis_bubonica', 'glandular_plague', 'septicemic_plague', 'pestilence']
senticnet['cutaneous_condition'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema', 'erythema_multiforme', 'windburn', 'hickey']
senticnet['cutaneous_involvement'] = ['-0.58', '0', '0.814', '-0.54', '#sadness', '#anger', 'negative', '-0.64', 'abnormal', 'irregular', 'vicarious', 'supernormal', 'subnormal']
senticnet['cutaneous_reaction'] = ['-0.55', '0', '0.536', '-0.72', '#sadness', '#disgust', 'negative', '-0.60', 'hypersensitivity_reaction', 'rash', 'efflorescence', 'roseola', 'skin_rash']
senticnet['cutaneous_sensation'] = ['-0.77', '0', '0.895', '-0.73', '#sadness', '#anger', 'negative', '-0.8', 'itch', 'urtication', 'itchiness', 'itching', 'tickle']
senticnet['cute'] = ['0.057', '0', '0.023', '0.124', '#joy', '#admiration', 'positive', '0.053', 'hairy', 'nocturnal', 'furry', 'domestic_pet', 'animal_furry']
senticnet['cute_animal'] = ['0.02', '0.071', '0', '0.069', '#interest', '#admiration', 'positive', '0.053', 'many_animals', 'little_animal', 'chihuahua', 'panda', 'small_creature']
senticnet['cute_baby'] = ['0.03', '0.068', '-0.04', '0.093', '#interest', '#admiration', 'positive', '0.037', 'beast', 'equine', 'live_creature', 'furry_creature', 'domesticate_animal']
senticnet['cute_big_cat'] = ['0.028', '0', '0.006', '0.054', '#joy', '#admiration', 'positive', '0.025', 'cat', 'domestibig_cate_animal', 'big_cat', 'big_animal', 'big']
senticnet['cute_cat'] = ['0.636', '-0.79', '0', '0.552', '#joy', '#surprise', 'positive', '0.661', 'big_cat', 'domesticate_animal', 'cat', 'big_animal', 'big']
senticnet['cute_kid'] = ['0.568', '-0.79', '0', '0.804', '#surprise', '#admiration', 'positive', '0.723', 'age', 'get_on', 'senesce', 'mature', 'maturate']
senticnet['cute_little'] = ['0.03', '0.068', '-0.04', '0.093', '#interest', '#admiration', 'positive', '0.037', 'beast', 'equine', 'live_creature', 'furry_creature', 'domesticate_animal']
senticnet['cutely'] = ['0.974', '0', '0', '0.723', '#joy', '#admiration', 'positive', '0.849', 'winningly', 'winsomely', 'sinisterly', 'engagingly', 'movingly']
senticnet['cutest'] = ['0.936', '-0.81', '0', '0.83', '#joy', '#admiration', 'positive', '0.86', 'cherubic', 'lovely', 'lovable', 'endearing', 'angelic']
senticnet['cutesy'] = ['0.65', '-0.61', '0', '0.711', '#joy', '#admiration', 'positive', '0.658', 'bouncing', 'scintillating', 'spirited', 'lively', 'zippy']
senticnet['cutflower'] = ['0', '0.086', '0', '0', '#interest', '#interest', 'positive', '0.086', 'swim_ocean', 'weight_lift', 'sit_in_shade', 'cut_grass', 'trust_friend']
senticnet['cuti_citta'] = ['-0.85', '0', '0.968', '-0.75', '#sadness', '#anger', 'negative', '-0.86', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['cuticle'] = ['0', '0.097', '0', '0.085', '#interest', '#admiration', 'positive', '0.091', 'lunula', 'arm_part', 'leave_hand', 'ten_fingers', 'five_fingers']
senticnet['cuticular'] = ['0', '0.097', '0', '0.085', '#interest', '#admiration', 'positive', '0.091', 'lunula', 'arm_part', 'leave_hand', 'ten_fingers', 'five_fingers']
senticnet['cutin'] = ['0', '0.097', '0', '0.085', '#interest', '#admiration', 'positive', '0.091', 'lunula', 'arm_part', 'leave_hand', 'ten_fingers', 'five_fingers']
senticnet['cutis_laxa'] = ['-0.87', '0', '0.531', '-0.68', '#sadness', '#disgust', 'negative', '-0.69', 'rubor', 'redness', 'dermatomyositis', 'inclusion_body_myositis', 'inflammation']
senticnet['cutlass'] = ['0.045', '0', '0.017', '0.052', '#joy', '#admiration', 'positive', '0.027', 'inexpensive', 'good_deal', 'cost_little_money', 'cheap', 'integer']
senticnet['cutleaf'] = ['-0.92', '0', '0.912', '-0.94', '#sadness', '#disgust', 'negative', '-0.92', 'true_blackberry', 'sand_blackberry', 'blackberry_bush', 'blackberry', 'running_blackberry']
senticnet['cutlery'] = ['0.054', '-0.07', '0.025', '0', '#joy', '#surprise', 'positive', '0.035', 'table', 'dine_room_table', 'dine_table', 'restaurant', 'cafe']
senticnet['cutlery_drawer'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'neat', 'kitchen_drawer', 'silverware_drawer', 'dine_table', 'table']
senticnet['cutlet'] = ['0.52', '-0.69', '0', '0.639', '#surprise', '#admiration', 'positive', '0.617', 'black_bread', 'pumpernickel', 'limpa', 'bread', 'breadstuff']
senticnet['cutoff'] = ['-0.76', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.77', 'cut-off', 'threshold', 'cut-offs', 'ending', 'deadline']
senticnet['cutoff_point'] = ['0', '0.098', '0', '0.089', '#interest', '#admiration', 'positive', '0.093', 'wood_panel', 'wooden_structure', 'hinge', 'entrance_room', 'corridor']
senticnet['cutout_animation'] = ['0.895', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.823', 'liveliness', 'excitement', 'art_form', 'art', 'film']
senticnet['cutthroat'] = ['0.971', '-0.92', '0', '0.617', '#joy', '#surprise', 'positive', '0.838', 'mortal', 'merciless', 'bowelless', 'tigerish', 'pitiless']
senticnet['cutting-edge'] = ['0', '0.807', '0', '0.71', '#interest', '#admiration', 'positive', '0.758', 'hi-tech', 'high-tech', 'high-technology', 'technology', 'state-of-the-art']
senticnet['cutting_blade'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'useful_tool']
senticnet['cutting_cane'] = ['0.609', '-0.74', '0', '0.716', '#surprise', '#admiration', 'positive', '0.69', 'malacca', 'sword', 'swagger_stick', 'sword_cane', 'sword_stick']
senticnet['cutting_edge'] = ['0.083', '0.144', '-0.04', '0.165', '#interest', '#admiration', 'positive', '0.086', 'golf_place', 'nanotechnology', 'developement', 'school_course', 'school_class']
senticnet['cutting_edge_research'] = ['0.887', '0.877', '0', '0', '#joy', '#interest', 'positive', '0.882', 'ask_expert', 'absorb_information', 'analyze', 'learn_computer', 'search_information']
senticnet['cutting_edge_technology'] = ['0.083', '0.144', '-0.04', '0.165', '#interest', '#admiration', 'positive', '0.086', 'golf_place', 'nanotechnology', 'developement', 'school_course', 'school_class']
senticnet['cutting_torch'] = ['0.886', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.881', 'fire_hydrant', 'stair_rail', 'christian_fundamentalist', 'extinguisher', 'reception_desk']
senticnet['cutworm'] = ['-0.02', '-0.09', '0.11', '-0.13', '#anger', '#disgust', 'negative', '-0.04', 'beetle', 'gypsy_moth', 'mole_cricket', 'grasshopper', 'many_insects']
senticnet['cuyahoga_county_jail'] = ['0.816', '-0.91', '0', '0.944', '#surprise', '#admiration', 'positive', '0.891', 'jail', 'jailhouse', 'gaol', 'poky', 'clink']
senticnet['cuz'] = ['0', '0', '0.741', '-0.8', '#anger', '#disgust', 'negative', '-0.77', 'sex', 'make_love', 'safe_sex', 'sexual_intercourse', 'become_aroused']
senticnet['cwmcarn'] = ['-0.64', '0', '0.879', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'drone', 'poke', 'dawdler', 'laggard', 'straggler']
senticnet['cwvca_prince_park'] = ['0.552', '-0.61', '0', '0.588', '#surprise', '#admiration', 'positive', '0.583', 'barbershop_quartet', 'string_quartet', 'quartette', 'musical_organization', 'musical_group']
senticnet['cy_coleman'] = ['0.022', '0', '0', '0.109', '#joy', '#admiration', 'positive', '0.066', 'instrumental', 'singer', 'hip_hop', 'melodic_noise', 'music_player']
senticnet['cy_kill'] = ['-0.84', '0', '0.96', '-0.91', '#anger', '#disgust', 'negative', '-0.90', 'housewrecker', 'housebreaker', 'knacker', 'laborer', 'manual_laborer']
senticnet['cyan'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'indigo', 'yellow', 'water_color', 'red', 'violet']
senticnet['cyan_dye'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'indigo', 'yellow', 'water_color', 'red', 'violet']
senticnet['cyanide'] = ['-0.98', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.88', 'arsenic', 'poisoning', 'strychnine', 'sulfide', 'monoxide']
senticnet['cyanine'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'indigo', 'yellow', 'water_color', 'red', 'violet']
senticnet['cyanoacrylate'] = ['-0.04', '0.069', '0', '0.075', '#interest', '#admiration', 'positive', '0.035', 'sculpt_gel', 'hairstyle', 'hairdresser', 'cosmetic', 'hair_sculpt_gel']
senticnet['cyanobacterium'] = ['0.807', '-0.86', '0', '0.611', '#joy', '#surprise', 'positive', '0.762', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['cyanotype'] = ['0', '0.067', '0', '0.077', '#interest', '#admiration', 'positive', '0.072', 'pencil_mark', 'use_ink', 'script', 'pencil_paper', 'written_text']
senticnet['cyathea'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['cyatheaceae'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'gymnosperm', 'pteridosperm']
senticnet['cyber_caf'] = ['0.813', '0.832', '0', '0.874', '#interest', '#admiration', 'positive', '0.84', 'ferris_wheel', 'concession_stand', 'food', 'carnival_ride', 'theme_park']
senticnet['cyber_command'] = ['-0.04', '0', '-0.06', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'make_war', 'conquer_enemy', 'use_military', 'gain_territory', 'keep_peace']
senticnet['cyber_defense'] = ['-0.04', '0', '-0.06', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'make_war', 'conquer_enemy', 'use_military', 'gain_territory', 'keep_peace']
senticnet['cyber_punk'] = ['0.725', '-0.66', '0', '0.906', '#joy', '#admiration', 'positive', '0.765', 'set', 'pose', 'lay', 'put', 'place']
senticnet['cyber_stalker'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'addicted', 'obsessed_love', 'addiction', 'obsession', 'weird']
senticnet['cybercafe'] = ['0.813', '0.832', '0', '0.874', '#interest', '#admiration', 'positive', '0.84', 'ferris_wheel', 'concession_stand', 'food', 'carnival_ride', 'theme_park']
senticnet['cyberface'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'feudal_lord', 'lord']
senticnet['cyberglove'] = ['0.026', '0.14', '-0.13', '0.31', '#interest', '#admiration', 'positive', '0.085', 'teach_new', 'share_idea', 'gesture', 'start_talk', 'exchange_idea']
senticnet['cybernetic'] = ['-0.7', '0', '0.77', '-0.75', '#anger', '#disgust', 'negative', '-0.74', 'fake', 'unnatural', 'man_made', 'artificial_sea', 'artificial_taste']
senticnet['cyberpunk_novel'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amuck', 'murderousness', 'misocainea', 'misogyny']
senticnet['cybersex'] = ['0.081', '0.095', '0', '0', '#joy', '#interest', 'positive', '0.088', 'build_tower_out_toy_block', 'a_lot_of_sex', 'play_hockey', 'play_baseball', 'play_football']
senticnet['cyberspace'] = ['0', '0', '-0.04', '-0.04', '#fear', '#disgust', 'negative', '-0.04', 'bad_luck', 'wrong', 'hockey_rink', 'wrong_place', 'hard_luck']
senticnet['cyberstalker'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'addicted', 'obsessed_love', 'addiction', 'obsession', 'weird']
senticnet['cybex'] = ['0.094', '0', '0', '0', '#joy', '#joy', 'positive', '0.094', 'hooker', 'registry_office', 'restaurant_entrance', 'elevator', 'exit']
senticnet['cyborg'] = ['-0.58', '0', '0.661', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'explosive', 'cordite', 'dynamite', 'plastic_explosive', 'explosive_compound']
senticnet['cycad'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'gymnosperm', 'seed_fern', 'pteridosperm']
senticnet['cycadaceae'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'gymnosperm', 'pteridosperm']
senticnet['cycadophyta'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'seed_fern', 'pteridosperm']
senticnet['cycladic'] = ['-0.69', '0', '0.957', '-0.92', '#anger', '#disgust', 'negative', '-0.85', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['cycle'] = ['0', '0', '0', '0.106', '#admiration', '#admiration', 'positive', '0.106', 'stay_young', 'spin', 'move_quickly', 'jump_up', 'athletics']
senticnet['cycle_ergometer'] = ['0.094', '0', '0', '0', '#joy', '#joy', 'positive', '0.094', 'hooker', 'registry_office', 'restaurant_entrance', 'elevator', 'exit']
senticnet['cyclecar'] = ['-0.04', '-0.16', '0', '0.056', '#surprise', '#admiration', 'positive', '0.059', 'car_part', 'under_car', 'car_wheel', 'wheel', 'car']
senticnet['cyclic'] = ['0.09', '0.07', '0', '0.246', '#joy', '#admiration', 'positive', '0.135', 'hand_motion', 'water_form', 'water_relate', 'hand_gesture', 'arm_movement']
senticnet['cyclic_voltammetry'] = ['0.965', '-0.84', '0', '0.599', '#joy', '#surprise', 'positive', '0.802', 'mental_faculty', 'faculty', 'module', 'remembrance', 'anamnesis']
senticnet['cyclically'] = ['0.923', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.944', 'sequentially', 'synchronously', 'successively', 'serially', 'outputted']
senticnet['cyclicity'] = ['-0.68', '0', '0.946', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'abnormality', 'abnormalcy', 'birth_defect', 'congenital_anomaly', 'congenital_defect']
senticnet['cyclist'] = ['0.073', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'chauffeur', 'angler', 'homeowner', 'stalactite', 'smoker']
senticnet['cyclohexane_ring'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
senticnet['cyclohexanone'] = ['0.032', '-0.02', '-0.04', '-0.08', '#fear', '#disgust', 'negative', '-0.01', 'liquid', 'carbonate_drink', 'chemical', 'soda', 'bromide']
senticnet['cyclohexene'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'carboxylic_acid', 'succinic_acid', 'valeric_acid']
senticnet['cyclone'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'atmospheric_phenomenon', 'natural_disaster', 'flood', 'destruction', 'tropical_storm']
senticnet['cyclone_roller_coaster'] = ['0.089', '-0.18', '0.037', '-0.06', '#joy', '#surprise', 'positive', '0.043', 'balloon', 'ballon', 'fielder', 'kite', 'blimp']
senticnet['cyclonic_activity'] = ['-0.69', '0', '0.835', '-0.56', '#sadness', '#anger', 'negative', '-0.69', 'roughness', 'inclemency', 'bad_weather', 'inclementness', 'windiness']
senticnet['cyclopaedia'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'comic_book', 'child_book', 'child_story', 'comic', 'disney_cartoon']
senticnet['cyclopedia'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'comic_book', 'child_book', 'child_story', 'comic', 'disney_cartoon']
senticnet['cyclopentene'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
senticnet['cyclorama'] = ['0.838', '0.828', '0', '0', '#joy', '#interest', 'positive', '0.833', 'vista', 'spectacular', 'perspective', 'image', 'scenic']
senticnet['cycloramphidae_family'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'amphibian', 'aquatic']
senticnet['cyclothymia'] = ['-0.97', '0', '0.793', '-0.68', '#sadness', '#anger', 'negative', '-0.81', 'affective_disorder', 'major_affective_disorder', 'emotional_disorder', 'emotional_disturbance', 'bipolar_disorder']
senticnet['cyclothymic'] = ['-0.78', '0', '0.918', '-0.96', '#anger', '#disgust', 'negative', '-0.88', 'major_depressive_episode', 'depression', 'depressive_disorder', 'clinical_depression', 'involutional_depression']
senticnet['cyclotron_resonance'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'emotional_arousal', 'excitement', 'arousal', 'anger']
senticnet['cyder'] = ['0.877', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.919', 'score', 'economical', 'scotch_whiskey', 'spoil', 'scottish']
senticnet['cydonia_oblonga'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
senticnet['cylinder'] = ['0.042', '-0.02', '0.035', '0.065', '#joy', '#admiration', 'positive', '0.023', 'food_container', 'metal_cylinder', 'metal_container', 'small_ball', 'storage_container']
senticnet['cylinder_block'] = ['0.042', '-0.02', '0.035', '0.065', '#joy', '#admiration', 'positive', '0.023', 'food_container', 'metal_cylinder', 'metal_container', 'small_ball', 'storage_container']
senticnet['cylinder_head'] = ['0.042', '-0.02', '0.035', '0.065', '#joy', '#admiration', 'positive', '0.023', 'food_container', 'metal_cylinder', 'metal_container', 'small_ball', 'storage_container']
senticnet['cylinder_pratt_whitney'] = ['0.748', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.873', 'butterfly', 'moth', 'parrot', 'beetle', 'falcon']
senticnet['cylindric'] = ['0.022', '0', '0.015', '0.096', '#joy', '#admiration', 'positive', '0.034', 'write_tool', 'write_implement', 'cylindrical', 'write_device', 'write_paper']
senticnet['cylindrical'] = ['0.022', '0', '0.015', '0.096', '#joy', '#admiration', 'positive', '0.034', 'write_tool', 'write_implement', 'cylindric', 'write_device', 'write_paper']
senticnet['cylindrical_body'] = ['0.022', '0', '0.015', '0.096', '#joy', '#admiration', 'positive', '0.034', 'write_tool', 'write_implement', 'cylindric', 'write_device', 'write_paper']
senticnet['cylindrical_metal'] = ['0', '-0.07', '0', '0', '#surprise', '#surprise', 'positive', '0.075', 'gyroscope', 'air_pump', 'ball_bounce', 'motor_oil', 'tire']
senticnet['cylindrical_projection'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'arm_movement', 'hand_gesture', 'water_motion', 'hand_signal', 'sea_movement']
senticnet['cylindrical_shape'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'finger', 'cylindric', 'thumb', 'five_digits', 'cylinder']
senticnet['cylindrical_tower'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'castle', 'bell_tower', 'spire', 'steeple', 'religious_place']
senticnet['cylindrical_tube'] = ['-0.05', '0.068', '-0.10', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'tooth_decay', 'body_ache', 'symptom', 'pierce', 'tooth']
senticnet['cylindrically'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expandible', 'expansive', 'erectile', 'cavernous']
senticnet['cylindrically_symmetric'] = ['-0.97', '0', '0.831', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'skewness', 'irregularity']
senticnet['cyndi_lauper_true'] = ['0.03', '0.085', '0', '0', '#joy', '#interest', 'positive', '0.058', 'favorite_color', 'colorful', 'different_color', 'pink', 'happy']
senticnet['cynic'] = ['0.899', '-0.77', '0', '0.866', '#joy', '#admiration', 'positive', '0.847', 'unpleasant_person', 'disagreeable_person', 'detractor', 'disparager', 'depreciator']
senticnet['cynical'] = ['-0.98', '0', '0.957', '0', '#sadness', '#anger', 'negative', '-0.97', 'cynicism', 'pessimistically', 'pessimism', 'pessimistic', 'despairing']
senticnet['cynical_ploy'] = ['-0.12', '0.13', '0.647', '-0.64', '#anger', '#disgust', 'negative', '-0.32', 'exasperating', 'infuriating', 'maddening', 'furiously', 'dudgeon']
senticnet['cynically'] = ['-0.98', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.85', 'scornfully', 'mockingly', 'sarcastically', 'sardonic', 'sardonically']
senticnet['cynicism'] = ['-0.72', '0', '0.749', '0', '#sadness', '#anger', 'negative', '-0.73', 'cynical', 'pessimistically', 'pessimism', 'pessimistic', 'despairing']
senticnet['cynicism_toward'] = ['0', '0', '0', '-0.90', '#disgust', '#disgust', 'negative', '-0.90', 'cause_suffer', 'disrespect', 'lose_team', 'bad_luck', 'leave_out']
senticnet['cynophobia'] = ['-0.57', '0', '0.814', '-0.93', '#anger', '#disgust', 'negative', '-0.77', 'phobia', 'acarophobia', 'fear', 'arachnophobia', 'zoophobia']
senticnet['cynosure'] = ['0.724', '-0.84', '0', '0.919', '#surprise', '#admiration', 'positive', '0.83', 'guidance', 'counsel', 'counseling', 'mentor', 'direction']
senticnet['cypher'] = ['-0.52', '0', '0.821', '-0.65', '#anger', '#disgust', 'negative', '-0.66', 'work_out', 'reckon', 'calculate', 'cipher', 'average_out']
senticnet['cypress_swamp'] = ['0.85', '0.96', '0', '0', '#joy', '#interest', 'positive', '0.905', 'flatness', 'boring', 'flat_tire', 'flat_foot', 'small_breast']
senticnet['cypress_tree'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'black_locust']
senticnet['cypress_wood'] = ['0', '0.773', '0', '0.895', '#interest', '#admiration', 'positive', '0.834', 'wooden_board', 'make_wood', 'wooden_box', 'wooden_floor', 'long_wood']
senticnet['cyprian'] = ['-0.86', '0', '0.783', '-0.57', '#sadness', '#anger', 'negative', '-0.74', 'slattern', 'camp_follower', 'call_girl', 'comfort_woman', 'ianfu']
senticnet['cyprinella'] = ['0', '-0.02', '0.044', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'haddock', 'mermaid', 'fish_rod', 'tidal_wave', 'dryad']
senticnet['cyproheptadine'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['cypselae'] = ['0', '0.141', '0.01', '0.115', '#interest', '#admiration', 'positive', '0.082', 'fruit', 'rind', 'funiculus', 'seed', 'pip']
senticnet['cyrillic_letter'] = ['0.049', '0.113', '0.107', '0.162', '#interest', '#admiration', 'positive', '0.054', 'list_number', 'ordinal', 'count', 'numerical', 'enumeration']
senticnet['cyrtoconic'] = ['0.969', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.772', 'aspheric', 'aspherical', 'prolate', 'rounded', 'crescent']
senticnet['cyrus_imap'] = ['0.031', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.062', 'letter_slot', 'postal_counter', 'mailbox', 'letter_scale', 'envelope']
senticnet['cyst'] = ['-0.78', '0', '0.855', '-0.99', '#anger', '#disgust', 'negative', '-0.87', 'blood_blister', 'pustule', 'water_blister', 'vesicle', 'bleb']
senticnet['cyst_formation'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'nonmotile', 'immobile']
senticnet['cystadenoma'] = ['-0.88', '0', '0.774', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fibroid_tumor', 'fibroid', 'benign_tumor', 'nonmalignant_tumor', 'nonmalignant_neoplasm']
senticnet['cystectomy'] = ['-0.70', '0', '0.639', '-0.56', '#sadness', '#anger', 'negative', '-0.63', 'fibroma', 'adenoma', 'chondroma', 'enchondroma', 'benign_tumor']
senticnet['cystic_dilation'] = ['0.905', '-0.65', '0', '0.851', '#joy', '#admiration', 'positive', '0.805', 'gonococcus', 'bacteria', 'bacteriae', 'probiotic', 'probiotic_bacterium']
senticnet['cystic_hygroma'] = ['-0.75', '0', '0.715', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'abdominal_pregnancy', 'ectopic_gestation', 'tubal_pregnancy', 'pregnancy', 'gestation']
senticnet['cystoid_macular_edema'] = ['-0.86', '0', '0.858', '-0.91', '#sadness', '#disgust', 'negative', '-0.87', 'edema', 'oedema', 'hydrops', 'dropsy', 'eye_disease']
senticnet['cystoscope'] = ['0.255', '0.182', '-0.06', '0.314', '#joy', '#admiration', 'positive', '0.173', 'doctor_office', 'urethroscope', 'sigmoidoscope', 'hospital', 'check_health']
senticnet['cystoscopy'] = ['0.255', '0.182', '-0.06', '0.314', '#joy', '#admiration', 'positive', '0.173', 'doctor_office', 'urethroscope', 'sigmoidoscope', 'hospital', 'check_health']
senticnet['cytogenetic_abnormality'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'deflection', 'warp', 'aberrance']
senticnet['cytokine_release'] = ['-0.66', '0', '0.877', '-0.64', '#sadness', '#anger', 'negative', '-0.72', 'somatic_cell', 'mastocyte', 'labrocyte', 'target_cell', 'phagocyte']
senticnet['cytolytic'] = ['0.772', '-0.56', '0', '0.824', '#joy', '#admiration', 'positive', '0.719', 'neutrophil', 'leukocyte', 'leucocyte', 'white_blood_cell', 'white_cell']
senticnet['cytopathology'] = ['-0.59', '0', '0.911', '-0.87', '#anger', '#disgust', 'negative', '-0.79', 'osteosclerosis', 'sclerosis', 'disseminated_sclerosis', 'multiple_sclerosis', 'induration']
senticnet['cytoplasm'] = ['0', '0.095', '0.016', '0.106', '#interest', '#admiration', 'positive', '0.062', 'blood_plasma', 'plasma', 'germplasm', 'nucleoplasm', 'protoplasm']
senticnet['cytoplasmic_membrane'] = ['0', '0', '0.019', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'worm_intestine', 'bristle', 'fur_keep_warm', 'animal', 'whisker']
senticnet['cytoplasmic_side'] = ['0', '0', '0.019', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'worm_intestine', 'bristle', 'fur_keep_warm', 'animal', 'whisker']
senticnet['cytospora_canker'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['cytotoxic_edema'] = ['-0.74', '0', '0.723', '-0.59', '#sadness', '#anger', 'negative', '-0.68', 'tone', 'tautness', 'tonus', 'condition', 'status']
senticnet['czarism'] = ['-0.67', '0', '0.85', '-0.59', '#sadness', '#anger', 'negative', '-0.70', 'rack', 'boot', 'whip', 'iron_boot', 'instrument_of_torture']
senticnet['czesaw'] = ['0', '0', '0', '0.1', '#admiration', '#admiration', 'positive', '0.1', 'find_earth', 'wax', 'plutonium', 'shine', 'melt']
senticnet['d_egress_node'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'identifiable', 'specifiable', 'recognizable', 'diagnosable', 'classifiable']
senticnet['d_mardi'] = ['0.667', '-0.98', '0', '0.61', '#joy', '#surprise', 'positive', '0.753', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['da_capo_aria'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['da_capo_form'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['da_ist'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['da_leben'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['da_qin'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['dab_radio'] = ['0.746', '-0.67', '0', '0.529', '#joy', '#surprise', 'positive', '0.651', 'labor', 'cinch', 'undertaking', 'project', 'task']
senticnet['dabba'] = ['-0.80', '0', '0.718', '-0.79', '#sadness', '#disgust', 'negative', '-0.77', 'walk_exercise', 'lose_wallet', 'go_debt', 'pay_dinner', 'spend_much_money']
senticnet['dabbaba'] = ['0', '0', '0', '0.115', '#admiration', '#admiration', 'positive', '0.115', 'chess_knight', 'strategy', 'pawn', 'chess_queen', 'chess_rook']
senticnet['dabbler'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'sciolist', 'amateur', 'mortal', 'dilettante', 'bird_watcher']
senticnet['dabbling_duck'] = ['-0.50', '0', '0.764', '-0.61', '#anger', '#disgust', 'negative', '-0.62', 'doctor', 'mountebank', 'charlatan', 'quack', 'physician']
senticnet['daboia_russelii'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'death_adder', 'ophidian', 'serpent', 'elapid', 'taipan']
senticnet['dachshund'] = ['0.039', '0', '0', '0.059', '#joy', '#admiration', 'positive', '0.049', 'big_dog', 'dog_cat', 'smart_dog', 'pet', 'domesticated_animal']
senticnet['dacron'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_cloth']
senticnet['dactylis_glomerata'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'meadow_grass', 'grass', 'manna_grass', 'sweet_grass', 'wood_meadowgrass']
senticnet['dactylorhiza_maculata'] = ['-0.79', '0', '0.837', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'sunray', 'wilding', 'sagebrush_buttercup', 'wildflower', 'pasque_flower']
senticnet['dad'] = ['0', '0.756', '0', '0.936', '#interest', '#admiration', 'positive', '0.846', 'family_member', 'daddy', 'brother', 'mother_brother', 'uncle']
senticnet['dada'] = ['0', '0', '-0.94', '-0.72', '#fear', '#disgust', 'negative', '-0.83', 'unreason', 'irrational', 'faculty', 'fatuity', 'irrationality']
senticnet['dada_movement'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'native_person', 'revolutionary', 'republic', 'revolution', 'look_ahead']
senticnet['dadaism'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'native_person', 'revolutionary', 'republic', 'revolution', 'look_ahead']
senticnet['dadaist'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'native_person', 'revolutionary', 'republic', 'revolution', 'look_ahead']
senticnet['daddy'] = ['0', '0.765', '0', '0.757', '#interest', '#admiration', 'positive', '0.761', 'family_member', 'dad', 'brother', 'mother_brother', 'uncle']
senticnet['daemon'] = ['-0.79', '0', '0.92', '-0.75', '#sadness', '#anger', 'negative', '-0.82', 'evil_spirit', 'incubus', 'succubus', 'succuba', 'dybbuk']
senticnet['daemonic'] = ['-0.78', '0', '0.74', '-0.94', '#sadness', '#disgust', 'negative', '-0.82', 'eject', 'chuck_out', 'exclude', 'turf_out', 'boot_out']
senticnet['daemonite'] = ['-0.04', '-0.02', '0.027', '-0.03', '#sadness', '#disgust', 'negative', '-0.01', 'precious_metal', 'olympic_medal', 'shiny_metal', 'many_different_kinds', 'valuable_metal']
senticnet['daewon_song'] = ['-0.18', '0', '-0.14', '-0.11', '#sadness', '#fear', 'negative', '-0.14', 'fly_kite', 'walk_dog', 'ride_roller_coaster', 'rake_leave', 'jump_up_down']
senticnet['daffodil'] = ['0.776', '0.876', '0', '0', '#joy', '#interest', 'positive', '0.826', 'blossom', 'dandelion', 'flower', 'bloom', 'pollen']
senticnet['daffy'] = ['-0.50', '0', '0.764', '-0.61', '#anger', '#disgust', 'negative', '-0.62', 'doctor', 'mountebank', 'charlatan', 'quack', 'physician']
senticnet['daft'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'simple', 'stupid', 'idiotic', 'moron', 'imbecile']
senticnet['dagda'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'jawless_fish', 'osteostracan', 'cephalaspid', 'ostracoderm', 'heterostracan']
senticnet['dagesh'] = ['0.902', '-0.85', '0', '0.898', '#joy', '#admiration', 'positive', '0.885', 'discerning', 'diacritic', 'discriminating', 'discriminative', 'selective']
senticnet['dagger'] = ['-0.73', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.83', 'machete', 'vaccination', 'cut_tool', 'knife', 'blade']
senticnet['daggerboard'] = ['0', '-0.05', '0.079', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'c_clamp', 'thumb_tack', 'pin', 'pliers', 'without_space']
senticnet['dai_dai_shogi'] = ['0.042', '-0.00', '0.027', '0.099', '#joy', '#admiration', 'positive', '0.031', 'chess_knight', 'chess_queen', 'chess_pawn', 'pawn', 'chess_bishop']
senticnet['daie'] = ['-0.11', '0.112', '0.674', '-0.66', '#anger', '#disgust', 'negative', '-0.33', 'wrathfully', 'wrathful', 'wrothful', 'lividly', 'lividity']
senticnet['daikon'] = ['0', '-0.03', '0.046', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'pasta', 'green_bean', 'mayonnaise', 'frozen_food', 'sour_milk']
senticnet['daikon_radish'] = ['0', '-0.03', '0.046', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'pasta', 'green_bean', 'mayonnaise', 'frozen_food', 'sour_milk']
senticnet['dailie'] = ['-0.11', '0.112', '0.674', '-0.66', '#anger', '#disgust', 'negative', '-0.33', 'wrathfully', 'wrathful', 'wrothful', 'lividly', 'lividity']
senticnet['daily'] = ['0.899', '0.801', '0', '0', '#joy', '#interest', 'positive', '0.85', 'tv_program', 'cnn', 'eat_breakfast', 'wake_up', 'medium']
senticnet['daily_activity'] = ['-0.03', '0.157', '-0.15', '0.22', '#interest', '#admiration', 'positive', '0.048', 'habit', 'wake_up', 'form_of_social_interaction', 'act', 'behave']
senticnet['daily_bread'] = ['0.053', '0.248', '-0.18', '0.258', '#interest', '#admiration', 'positive', '0.093', 'slogan', 'find_money', 'wealthy', 'much_money', 'donation']
senticnet['daily_bruin'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['daily_caffeine_consumption'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'wooden_spoon', 'cereal_bowl', 'teaspoon', 'container_cup', 'large_container']
senticnet['daily_californian'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['daily_calorie_intake'] = ['-0.92', '0', '0.784', '-0.91', '#sadness', '#disgust', 'negative', '-0.87', 'bad_meal', 'eat_snack', 'eat_unhealthy_food', 'eat_expired_food', 'eat_quickly']
senticnet['daily_chronicle'] = ['-0.05', '0.106', '0', '0.06', '#interest', '#admiration', 'positive', '0.036', 'buy', 'buy_newspaper', 'read_buy_newspaper', 'buy_book', 'read_news']
senticnet['daily_collegian'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['daily_continental_breakfast'] = ['0.722', '-0.98', '0', '0.832', '#surprise', '#admiration', 'positive', '0.845', 'laudatory', 'praiseful', 'praising', 'encomiastic', 'eulogistic']
senticnet['daily_drudgery'] = ['-0.30', '0.089', '-0.10', '-0.03', '#sadness', '#fear', 'negative', '-0.08', 'hardship', 'office_job', 'work_much', 'day_job', 'toil']
senticnet['daily_emerald'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['daily_gazette'] = ['-0.05', '0.106', '0', '0.06', '#interest', '#admiration', 'positive', '0.036', 'buy', 'buy_newspaper', 'read_buy_newspaper', 'buy_book', 'read_news']
senticnet['daily_grind'] = ['0.681', '-0.93', '0', '0.78', '#surprise', '#admiration', 'positive', '0.799', 'behavior', 'state', 'play', 'agency', 'activeness']
senticnet['daily_herald'] = ['-0.05', '0.106', '0', '0.06', '#interest', '#admiration', 'positive', '0.036', 'buy', 'buy_newspaper', 'read_buy_newspaper', 'buy_book', 'read_news']
senticnet['daily_illini'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['daily_intelligencer'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquisitor', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['daily_iowan'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['daily_mail_columnist'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'rascal', 'rapscallion', 'scalawag', 'holy_terror', 'monkey']
senticnet['daily_mirror'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'rascal', 'rapscallion', 'scalawag', 'holy_terror', 'monkey']
senticnet['daily_moisturizer'] = ['-0.93', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.85', 'protect_from_sun', 'shade', 'meaning', 'corrector', 'sunglasses']
senticnet['daily_nebraskan'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['daily_news'] = ['0.03', '0', '0.01', '0.071', '#joy', '#admiration', 'positive', '0.03', 'thin_wood', 'book_content', 'flat_thin', 'print_medium', 'book_page']
senticnet['daily_newspaper'] = ['-0.05', '0.106', '0', '0.06', '#interest', '#admiration', 'positive', '0.036', 'buy', 'buy_newspaper', 'read_buy_newspaper', 'buy_book', 'read_news']
senticnet['daily_passenger_train'] = ['0.861', '-0.52', '0', '0.598', '#joy', '#admiration', 'positive', '0.663', 'exit', 'get_off', 'deplane', 'go_out', 'get_out']
senticnet['daily_pennsylvanian'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquisitor', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['daily_physical_activity'] = ['-0.06', '-0.02', '0.021', '-0.06', '#sadness', '#disgust', 'negative', '-0.03', 'clean_window', 'vacuum_carpet', 'repetitive_task', 'run_long_time', 'exercise_daily']
senticnet['daily_picayune'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
senticnet['daily_princetonian'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['daily_ration'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'green_bread', 'rotten', 'disgusting', 'taste_awful', 'mouldy']
senticnet['daily_reveille'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['daily_routine'] = ['0', '0.188', '-0.04', '0.191', '#interest', '#admiration', 'positive', '0.111', 'usual_ordinary', 'normal', 'typical', 'usual', 'frequent']
senticnet['daily_tabloid'] = ['-0.05', '0.106', '0', '0.06', '#interest', '#admiration', 'positive', '0.036', 'buy', 'buy_newspaper', 'read_buy_newspaper', 'buy_book', 'read_news']
senticnet['daily_tar_heel'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['daily_targum'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['daily_texan'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['daily_toil'] = ['-0.30', '0.089', '-0.10', '-0.03', '#sadness', '#fear', 'negative', '-0.08', 'hardship', 'office_job', 'work_much', 'day_job', 'toil']
senticnet['daily_torpor'] = ['0.967', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.91', 'furry_creature', 'hairy', 'furry', 'woodland_creature', 'animal_furry']
senticnet['daimler_ag_quote'] = ['0', '0.189', '0.04', '0.091', '#interest', '#admiration', 'positive', '0.08', 'combination', 'margin', 'teller', 'merge', 'finite']
senticnet['daimoku'] = ['0.022', '0.123', '0.038', '0.129', '#interest', '#admiration', 'positive', '0.059', 'hymn', 'written_music', 'walk_beach', 'lyric', 'piece_of_music']
senticnet['daimon'] = ['-0.86', '0', '0.914', '-0.69', '#sadness', '#anger', 'negative', '-0.82', 'evil_spirit', 'incubus', 'succubus', 'succuba', 'dybbuk']
senticnet['daimon_hellstrom'] = ['-0.04', '-0.02', '0.027', '-0.03', '#sadness', '#disgust', 'negative', '-0.01', 'precious_metal', 'olympic_medal', 'shiny_metal', 'many_different_kinds', 'valuable_metal']
senticnet['daimy'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'overlord', 'lord']
senticnet['daintily'] = ['0.525', '-0.85', '0', '0.599', '#surprise', '#admiration', 'positive', '0.66', 'frolic', 'coquetry', 'romp', 'play', 'caper']
senticnet['daintiness'] = ['0.639', '-0.70', '0', '0.507', '#joy', '#surprise', 'positive', '0.616', 'tastefulness', 'elegance', 'eclat', 'breeding', 'genteelness']
senticnet['daintree_national'] = ['-0.95', '0', '0.624', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['dainty'] = ['0.682', '-0.52', '0', '0.613', '#joy', '#admiration', 'positive', '0.607', 'nice', 'meticulous', 'fastidious', 'prissy', 'pernickety']
senticnet['dainty_little'] = ['0.022', '-0.01', '0.062', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'electric_toothbrush', 'moistener', 'hairbrush', 'shampoo_conditioner', 'shave_kit']
senticnet['daiquiri'] = ['0.031', '0', '-0.08', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'smoke_cigarette', 'smoke', 'cinder', 'tobacco_product', 'smoke_pipe']
senticnet['dairy'] = ['0.037', '0', '0', '0.067', '#joy', '#admiration', 'positive', '0.052', 'veal', 'milk_product', 'salad_dress', 'ranch', 'food_group']
senticnet['dairy-free'] = ['-0.92', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.89', 'vegetarian', 'gluten-free', 'gluten', 'veggie', 'vegan']
senticnet['dairy_barn'] = ['0.053', '0.094', '0', '0.136', '#interest', '#admiration', 'positive', '0.094', 'barn_animal', 'domesticate', 'grassland', 'sheep', 'cow']
senticnet['dairy_cattle'] = ['0.546', '-0.96', '0', '0.533', '#joy', '#surprise', 'positive', '0.683', 'ayrshire', 'dairy_cow', 'milch_cow', 'milk_cow', 'milcher']
senticnet['dairy_cooperative'] = ['0.037', '0', '0', '0.067', '#joy', '#admiration', 'positive', '0.052', 'veal', 'milk_product', 'salad_dress', 'ranch', 'food_group']
senticnet['dairy_cow'] = ['0.546', '-0.96', '0', '0.533', '#joy', '#surprise', 'positive', '0.683', 'dairy_cattle', 'ayrshire', 'milch_cow', 'milk_cow', 'milcher']
senticnet['dairy_factory'] = ['0.037', '0', '0', '0.067', '#joy', '#admiration', 'positive', '0.052', 'veal', 'milk_product', 'salad_dress', 'ranch', 'food_group']
senticnet['dairy_farm'] = ['0.053', '0.094', '0', '0.136', '#interest', '#admiration', 'positive', '0.094', 'barn_animal', 'domesticate', 'grassland', 'sheep', 'cow']
senticnet['dairy_farmer'] = ['-0.08', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.11', 'serf', 'peasant', 'servant', 'poor', 'villein']
senticnet['dairy_goat'] = ['0.546', '-0.96', '0', '0.533', '#joy', '#surprise', 'positive', '0.683', 'dairy_cattle', 'dairy_cow', 'milch_cow', 'ayrshire', 'milcher']
senticnet['dairy_herd'] = ['0.053', '0.094', '0', '0.136', '#interest', '#admiration', 'positive', '0.094', 'barn_animal', 'domesticate', 'grassland', 'sheep', 'cow']
senticnet['dairy_industry'] = ['0.057', '0', '0.018', '0.058', '#joy', '#admiration', 'positive', '0.032', 'porridge', 'food', 'milk', 'healthy', 'morning_food']
senticnet['dairy_milk'] = ['-0.03', '0.086', '0', '0.076', '#interest', '#admiration', 'positive', '0.042', 'drink_water', 'drink_espresso', 'drink', 'drink_coffee', 'drink_hot_chocolate']
senticnet['dairy_milk_chocolate'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'builder']
senticnet['dairy_producer'] = ['0.037', '0', '0', '0.067', '#joy', '#admiration', 'positive', '0.052', 'veal', 'milk_product', 'salad_dress', 'ranch', 'food_group']
senticnet['dairy_product'] = ['0.057', '0', '0.018', '0.058', '#joy', '#admiration', 'positive', '0.032', 'porridge', 'food', 'milk', 'healthy', 'morning_food']
senticnet['dairy_queen'] = ['0.274', '-0.01', '0.012', '0.132', '#joy', '#admiration', 'positive', '0.102', 'dance_type', 'swirl', 'throw_football', 'jump_around', 'dance_move']
senticnet['dairy_sector'] = ['0.053', '0.085', '0.019', '0', '#joy', '#interest', 'positive', '0.04', 'farm', 'countryside', 'farm_animal', 'cow', 'healthy']
senticnet['dairy_sheep'] = ['0.546', '-0.96', '0', '0.533', '#joy', '#surprise', 'positive', '0.683', 'dairy_cattle', 'dairy_cow', 'milch_cow', 'ayrshire', 'milcher']
senticnet['dairymaid'] = ['-0.62', '0', '0.649', '-0.67', '#anger', '#disgust', 'negative', '-0.64', 'handmaid', 'handmaiden', 'chambermaid', 'maid', 'parlormaid']
senticnet['dairyman'] = ['0.053', '0.085', '0.019', '0', '#joy', '#interest', 'positive', '0.04', 'farm', 'countryside', 'farm_animal', 'cow', 'healthy']
senticnet['dairyman_association'] = ['0.037', '0', '0', '0.067', '#joy', '#admiration', 'positive', '0.052', 'veal', 'milk_product', 'salad_dress', 'ranch', 'food_group']
senticnet['dais'] = ['0', '0', '-0.76', '-0.82', '#fear', '#disgust', 'negative', '-0.79', 'stage', 'thespian', 'actor', 'theatre', 'drama']
senticnet['daizyuzin'] = ['-0.88', '0', '0.882', '-0.58', '#sadness', '#anger', 'negative', '-0.78', 'mortal', 'behemoth', 'unusual_person', 'aberrant', 'giant']
senticnet['dakini'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['dakotah'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['dakuten'] = ['-0.86', '0', '0.619', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'character', 'logogram', 'logograph', 'graph', 'grapheme']
senticnet['dalabon'] = ['-0.95', '0', '0.624', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['dalcroze'] = ['0.536', '-0.56', '0', '0.567', '#surprise', '#admiration', 'positive', '0.557', 'recreation', 'diversion', 'move', 'motility', 'movement']
senticnet['dalcroze_method'] = ['0.536', '-0.56', '0', '0.567', '#surprise', '#admiration', 'positive', '0.557', 'recreation', 'diversion', 'move', 'motility', 'movement']
senticnet['dalit_woman'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'vassalage']
senticnet['dalkon_shield'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'birth_control_pill', 'condom', 'contraceptive_pill', 'sex_toy', 'prevent_pregnancy']
senticnet['dall_sheep'] = ['0', '0', '-0.70', '-0.77', '#fear', '#disgust', 'negative', '-0.73', 'animal', 'brown_bear', 'forest', 'creature', 'polar_bear']
senticnet['dallas_dirk_nowitzki'] = ['0.556', '-0.79', '0', '0.755', '#surprise', '#admiration', 'positive', '0.701', 'lush', 'alcoholic', 'alky', 'dipsomaniac', 'rummy']
senticnet['dallben'] = ['0.594', '-0.54', '0', '0.518', '#joy', '#surprise', 'positive', '0.554', 'exorcist', 'necromancer', 'magus', 'sorceress', 'witch_doctor']
senticnet['dalliance'] = ['0', '0', '-0.77', '-0.91', '#fear', '#disgust', 'negative', '-0.84', 'trifling', 'piddle', 'wanton_away', 'wanton', 'dawdling']
senticnet['dally'] = ['0', '0', '0.762', '-0.78', '#anger', '#disgust', 'negative', '-0.77', 'dawdle', 'chat_up', 'toy', 'play', 'flirt']
senticnet['dalmatic'] = ['0.849', '-0.50', '0', '0.845', '#joy', '#admiration', 'positive', '0.732', 'robe', 'gown', 'veil', 'vest', 'cassock']
senticnet['dalton_gang'] = ['0', '0', '-0.11', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'hunting_season', 'hound', 'cougar', 'hunter', 'dragon']
senticnet['daly'] = ['-0.69', '0', '0.89', '-0.70', '#anger', '#disgust', 'negative', '-0.76', 'still', 'suppress', 'moderate', 'hush_up', 'quell']
senticnet['daly_theatre'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'dress', 'pinny', 'dirndl', 'gown', 'muumuu']
senticnet['dam'] = ['-0.03', '0.085', '0', '0.127', '#interest', '#admiration', 'positive', '0.061', 'small_stream', 'riverside', 'reservoir', 'big_lake', 'oceanside']
senticnet['dam_spillway'] = ['-0.77', '0', '0.583', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'rain', 'rain_down', 'sheet', 'rain_bucket', 'rain_buckets_down']
senticnet['damage'] = ['-0.98', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.97', 'harm', 'hurt', 'cut_skin', 'bloody', 'ouch']
senticnet['damage_arising_from'] = ['0.73', '-0.56', '0', '0.621', '#joy', '#admiration', 'positive', '0.64', 'amnesty', 'unsusceptibility', 'diplomatic_immunity', 'exemption', 'impunity']
senticnet['damage_arising_out'] = ['0.73', '-0.56', '0', '0.621', '#joy', '#admiration', 'positive', '0.64', 'amnesty', 'unsusceptibility', 'diplomatic_immunity', 'exemption', 'impunity']
senticnet['damage_body'] = ['-0.94', '0', '0.434', '-0.94', '#sadness', '#disgust', 'negative', '-0.77', 'damage', 'injury', 'hurt', 'physical_pain', 'painful']
senticnet['damage_car'] = ['-0.16', '-0.07', '-0.08', '-0.16', '#sadness', '#disgust', 'negative', '-0.08', 'broken_car', 'road_rage', 'adjust_seat', 'car_crash', 'skid_mark']
senticnet['damage_caused_by'] = ['-0.98', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.97', 'harm', 'hurt', 'cut_skin', 'bloody', 'ouch']
senticnet['damage_ear'] = ['-0.08', '-0.04', '0.201', '-0.41', '#anger', '#disgust', 'negative', '-0.16', 'hear_loss', 'earring', 'tinnitus', 'loud_music', 'damaged_eardrum']
senticnet['damaged'] = ['-0.75', '0', '0.68', '-0.59', '#sadness', '#anger', 'negative', '-0.67', 'battered', 'bedraggled', 'hurt', 'derelict', 'dilapidated']
senticnet['damaged_eardrum'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'hear_loss', 'high_music', 'deaf', 'loud_music', 'damage_ear']
senticnet['damaged_tissue'] = ['-0.81', '0', '0.504', '-0.96', '#sadness', '#disgust', 'negative', '-0.76', 'freshen', 'air_out', 'air', 'renew', 'vent']
senticnet['damages'] = ['-0.82', '0', '0.672', '-0.54', '#sadness', '#anger', 'negative', '-0.68', 'amends', 'atonement', 'indemnification', 'redress', 'restitution']
senticnet['damaging'] = ['-0.86', '0', '0.77', '0', '#sadness', '#anger', 'negative', '-0.81', 'defamatory', 'reputation', 'backbite', 'untrue', 'defame']
senticnet['damaging_hail'] = ['0.898', '-0.70', '0', '0.712', '#joy', '#admiration', 'positive', '0.773', 'wish', 'reception', 'welcome', 'greeting', 'regard']
senticnet['damascus_gate'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['damashii'] = ['0.021', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'plastic_bottle', 'party', 'use_party', 'lighter_air', 'compress_air']
senticnet['damask'] = ['0.967', '-0.98', '0', '0.64', '#joy', '#surprise', 'positive', '0.863', 'club', 'social_club', 'society', 'guild', 'jockey_club']
senticnet['dame'] = ['0.824', '-0.71', '0', '0.732', '#joy', '#admiration', 'positive', '0.757', 'girl', 'miss', 'missy', 'young_lady', 'young_woman']
senticnet['dame_dash'] = ['-0.65', '0', '0.505', '-0.91', '#sadness', '#disgust', 'negative', '-0.69', 'hellhound', 'mythical_being', 'dragon', 'mythical_monster', 'amphisbaena']
senticnet['damien_hirst'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'hip', 'joint', 'above_leg', 'knee']
senticnet['damien_jurado'] = ['0.304', '0.339', '-0.19', '0.333', '#interest', '#admiration', 'positive', '0.195', 'careful', 'reinforce', 'load_bear', 'go_outdoors', 'allowance']
senticnet['dammit'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amok', 'murderousness', 'misocainea', 'misogyny']
senticnet['damn'] = ['-0.04', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.04', 'bad_luck', 'tundra', 'artic', 'mammal_family', 'bad_mood']
senticnet['damn_good'] = ['0.744', '-0.76', '0', '0.669', '#joy', '#surprise', 'positive', '0.726', 'accessible', 'comprehendible', 'approachable', 'perceivable', 'intelligible']
senticnet['damnable'] = ['-0.62', '0', '0.627', '-0.59', '#sadness', '#anger', 'negative', '-0.61', 'execrable', 'blame', 'cursed', 'maledict', 'damn']
senticnet['damnably'] = ['-0.44', '-0.15', '0', '-0.36', '#sadness', '#disgust', 'negative', '-0.21', 'baffle', 'stupefy', 'dumbfound', 'stupefaction', 'confuse']
senticnet['damnation'] = ['0', '0', '0.968', '-0.76', '#anger', '#disgust', 'negative', '-0.86', 'condemnation', 'damned', 'perdition', 'disapprobation', 'nation']
senticnet['damned'] = ['0', '0', '0.964', '-0.77', '#anger', '#disgust', 'negative', '-0.86', 'condemnation', 'damnation', 'perdition', 'disapprobation', 'nation']
senticnet['damoiselle'] = ['-0.81', '0', '0.885', '-0.92', '#anger', '#disgust', 'negative', '-0.87', 'miss', 'girl', 'damsel', 'demoiselle', 'maiden']
senticnet['damon_holt'] = ['-0.58', '0', '0.561', '-0.62', '#sadness', '#disgust', 'negative', '-0.58', 'spot', 'speckle', 'dapple', 'mark', 'fleck']
senticnet['damon_sharpe'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'cur', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['damp'] = ['0', '-0.03', '0.05', '-0.14', '#anger', '#disgust', 'negative', '-0.05', 'humidity', 'wetness', 'moisture', 'humid', 'dank']
senticnet['damp_basement'] = ['-0.61', '0', '0.732', '-0.59', '#sadness', '#anger', 'negative', '-0.64', 'leaky', 'draughty', 'porous', 'drippy', 'oozing']
senticnet['damp_cloth'] = ['0.986', '0', '0', '0', '#joy', '#joy', 'positive', '0.986', 'personal_cleanliness', 'cleanness', 'wipe_up_spill', 'smell_good', 'clean_carpet']
senticnet['damp_hair'] = ['0.063', '0', '0.01', '0.16', '#joy', '#admiration', 'positive', '0.071', 'goodbye', 'hand_gesture', 'arm_movement', 'crest', 'water_splash']
senticnet['damp_sand'] = ['-0.06', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.04', 'friction', 'lack_of_water', 'dirt', 'abrasive', 'ground_material']
senticnet['damp_sponge'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'wall_decoration', 'renovation', 'painting', 'artwork', 'new_house']
senticnet['damp_squib'] = ['-0.83', '0', '0.912', '-0.67', '#sadness', '#anger', 'negative', '-0.80', 'dissatisfaction', 'displeasure', 'disappointment', 'boredom', 'ennui']
senticnet['damp_towel'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'toilet_paper', 'beach_towel', 'face_towel', 'hand_towel', 'tissue']
senticnet['damp_weather'] = ['-0.02', '-0.02', '0.076', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'wear_raincoat', 'carry_umbrella', 'feel_cold', 'camp_out', 'splash_water']
senticnet['dampen'] = ['0', '0', '0.968', '-0.95', '#anger', '#disgust', 'negative', '-0.96', 'benumb', 'congeal', 'chill', 'inhibit', 'nip']
senticnet['damphreux'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'aphasia', 'anomic_aphasia']
senticnet['damply'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'facial_tissue', 'kleenex', 'hanky', 'handkerchief', 'tissue']
senticnet['dampness'] = ['0.718', '0', '0', '0', '#joy', '#joy', 'positive', '0.718', 'water_container', 'still_water', 'clean_water', 'water', 'cool_water']
senticnet['damsel'] = ['0', '0', '-0.84', '-0.85', '#fear', '#disgust', 'negative', '-0.84', 'female_human', 'female_person', 'niece', 'female', 'woman']
senticnet['damsel_human'] = ['0.772', '0', '0', '0.892', '#joy', '#admiration', 'positive', '0.832', 'female', 'damsel_person', 'niece', 'damsel', 'woman']
senticnet['damsel_person'] = ['0.772', '0', '0', '0.884', '#joy', '#admiration', 'positive', '0.828', 'damsel_human', 'female', 'niece', 'damsel', 'woman']
senticnet['damselfish'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'puffer', 'pufferfish', 'blowfish', 'triggerfish', 'plectognath']
senticnet['damson'] = ['0.02', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'many_fruits', 'peach', 'buy_fruit', 'pineapple', 'tomato']
senticnet['danbury_ice'] = ['0.026', '-0.03', '0', '0.091', '#surprise', '#admiration', 'positive', '0.051', 'stadium', 'auditorium', 'television_station', 'music_hall', 'movie_theater']
senticnet['danbury_mint'] = ['0.031', '-0.10', '0', '0', '#joy', '#surprise', 'positive', '0.07', 'teddy_bear', 'beanie_baby', 'yo-yo', 'board_game', 'toy']
senticnet['dance'] = ['0.922', '0', '0', '0.762', '#joy', '#admiration', 'positive', '0.842', 'slow_dance', 'old_dance', 'have_fun', 'dancer', 'ballroom_dance']
senticnet['dance_around'] = ['0.02', '0.071', '0', '0.069', '#interest', '#admiration', 'positive', '0.053', 'make_face', 'wave_hand', 'dancing_in_rain', 'playful', 'studio']
senticnet['dance_choreography'] = ['-0.95', '0', '0.992', '-0.63', '#sadness', '#anger', 'negative', '-0.86', 'interpretive_dance', 'interpretive_dancing', 'modern_dance', 'interpretative_dancing', 'stage_dancing']
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senticnet['dance_routine'] = ['0', '-0.08', '0', '0', '#surprise', '#surprise', 'positive', '0.082', 'yo-yo', 'playroom', 'backgammon', 'action_figure', 'board_game']
senticnet['dance_samba'] = ['0.925', '0.993', '0', '0.832', '#joy', '#interest', 'positive', '0.917', 'karaoke', 'dance', 'skate', 'jump_rope', 'play_frisbee']
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senticnet['danceable'] = ['0.75', '0.71', '0', '0', '#joy', '#interest', 'positive', '0.73', 'spherical_shape', 'use_sport', 'flexible', 'ball', 'fill_air']
senticnet['dancefloor'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'drink', 'party', 'friend_gathering', 'rest_area', 'relax_time']
senticnet['dancehall'] = ['0.081', '-0.05', '0.052', '0.072', '#joy', '#admiration', 'positive', '0.039', 'rhythm', 'type_of_music', 'instrumental', 'lyric', 'melody']
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senticnet['dancer_choreographer'] = ['-0.95', '0', '0.992', '-0.63', '#sadness', '#anger', 'negative', '-0.86', 'modern_dance', 'interpretive_dancing', 'interpretative_dance', 'interpretative_dancing', 'stage_dancing']
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senticnet['danceteria'] = ['0.763', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.75', 'pub', 'disco', 'dance_club', 'cafe', 'tavern']
senticnet['dancing'] = ['0.584', '-0.78', '0', '0.546', '#joy', '#surprise', 'positive', '0.638', 'acting', 'musical_performance', 'performing_art', 'dance', 'terpsichore']
senticnet['dancing_in_rain'] = ['0.546', '-0.96', '0', '0.533', '#joy', '#surprise', 'positive', '0.683', 'make_face', 'wave_hand', 'dance_around', 'playful', 'studio']
senticnet['dandelion'] = ['0.818', '0.843', '0', '0', '#joy', '#interest', 'positive', '0.831', 'blossom', 'flower', 'daffodil', 'bloom', 'pollen']
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senticnet['dandruff_shampoo'] = ['0.702', '0', '0', '0.703', '#joy', '#admiration', 'positive', '0.702', 'sculpt_gel', 'prevent_dandruff', 'hair_care_article', 'get_clean', 'hair_conditioner']
senticnet['dandy'] = ['0.741', '0.79', '0', '0', '#joy', '#interest', 'positive', '0.766', 'bang-up', 'nifty', 'corking', 'cracking', 'bully']
senticnet['dandyish'] = ['0.952', '0.844', '0', '0.96', '#joy', '#admiration', 'positive', '0.919', 'fashion', 'demand', 'kindness', 'better_person', 'warm_feeling']
senticnet['dandyism'] = ['0.952', '0.844', '0', '0.96', '#joy', '#admiration', 'positive', '0.919', 'fashion', 'demand', 'kindness', 'better_person', 'warm_feeling']
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senticnet['dangerman'] = ['0.034', '-0.02', '0.054', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'compass', 'swiss_army_knife', 'bake_pan', 'coffee_mug', 'wine_opener']
senticnet['dangerous'] = ['-0.09', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.11', 'destructive', 'violent', 'dangerous_health', 'illegal', 'danger']
senticnet['dangerous_activity'] = ['0.094', '0.14', '-0.05', '-0.04', '#joy', '#interest', 'positive', '0.034', 'danger', 'matter_lot', 'dangerous', 'currency_system', 'person']
senticnet['dangerous_animal'] = ['-0.96', '0', '0.108', '-0.90', '#sadness', '#disgust', 'negative', '-0.66', 'danger', 'dangerous_insect', 'dangerous_fly', 'shark', 'snake']
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senticnet['dangerous_person'] = ['-0.98', '0', '0.677', '-0.91', '#sadness', '#disgust', 'negative', '-0.85', 'evil_man', 'bad_person', 'murderer', 'killer', 'thief']
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senticnet['dangerous_to_person'] = ['-0.94', '0', '0.427', '-0.90', '#sadness', '#disgust', 'negative', '-0.75', 'electricity', 'poison', 'accident', 'fire', 'gun']
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senticnet['dangerouspanda'] = ['0.533', '0.818', '0', '0.683', '#interest', '#admiration', 'positive', '0.678', 'gain_information', 'report_news', 'solve', 'seek_answer', 'gather_information']
senticnet['dangle'] = ['0.918', '-0.88', '0', '0', '#joy', '#surprise', 'positive', '0.901', 'earring', 'dangles', 'dangling', 'necklace', 'pendant']
senticnet['dangles'] = ['0.918', '-0.89', '0', '0', '#joy', '#surprise', 'positive', '0.905', 'earring', 'dangle', 'dangling', 'necklace', 'pendant']
senticnet['dangling'] = ['0.913', '-0.88', '0', '0', '#joy', '#surprise', 'positive', '0.898', 'earring', 'dangles', 'dangle', 'necklace', 'pendant']
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senticnet['daniele_gatti'] = ['-0.91', '0', '0.931', '-0.72', '#sadness', '#anger', 'negative', '-0.85', 'hit_with_baton', 'get_hurt', 'whip', 'arrow', 'hurt']
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senticnet['dank'] = ['0', '-0.03', '0.05', '-0.14', '#anger', '#disgust', 'negative', '-0.05', 'humidity', 'wetness', 'damp', 'humid', 'moisture']
senticnet['dans_lequel'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'sexual_relationship', 'liaison', 'affair', 'involvement']
senticnet['dans_son_rquisitoire'] = ['-0.85', '0', '0.968', '-0.89', '#anger', '#disgust', 'negative', '-0.90', 'family', 'household', 'house', 'home', 'broken_home']
senticnet['dante_autullo'] = ['0', '0.078', '0.027', '0.196', '#interest', '#admiration', 'positive', '0.082', 'heel', 'ankle', 'toe', 'foot_massage', 'foot']
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senticnet['daphne_akhurst_memorial'] = ['0.093', '-0.02', '0', '0.056', '#joy', '#admiration', 'positive', '0.057', 'pitcher', 'carafe', 'container_pitcher', 'mug', 'glass_of_water']
senticnet['daphne_mezereum'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'clematis', 'legume', 'leguminous_plant', 'climber', 'root_climber']
senticnet['dapper'] = ['0', '0', '-0.76', '-0.78', '#fear', '#disgust', 'negative', '-0.77', 'dashing', 'jaunty', 'natty', 'raffish', 'spiffy']
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senticnet['dapple'] = ['-0.58', '0', '0.561', '-0.62', '#sadness', '#disgust', 'negative', '-0.58', 'spot', 'speckle', 'mark', 'patch', 'fleck']
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senticnet['dare_say'] = ['0.565', '-0.73', '0', '0.542', '#joy', '#surprise', 'positive', '0.615', 'say', 'asseverate', 'predicate', 'insist', 'take_firm_stand']
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senticnet['daredevilry'] = ['0.775', '-0.97', '0', '0.731', '#joy', '#surprise', 'positive', '0.828', 'boldness', 'daredeviltry', 'temerity', 'audacity', 'audaciousness']
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senticnet['daringly'] = ['0.891', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.917', 'impudently', 'daring', 'intrepidly', 'venturously', 'boldly']
senticnet['dario_argento'] = ['-0.79', '0', '0.885', '-0.76', '#sadness', '#anger', 'negative', '-0.81', 'infernal', 'mortal', 'dead_soul', 'dead_soule', 'living_dead']
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senticnet['dark_blond_hair'] = ['0', '0', '0.841', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'ancient', 'many_years', 'year', 'old', 'teen']
senticnet['dark_blotch'] = ['0.942', '-0.61', '0', '0.908', '#joy', '#admiration', 'positive', '0.82', 'adornment', 'cigar_band', 'hatband', 'neckband', 'pinstripe']
senticnet['dark_blue'] = ['-0.45', '0.081', '-0.25', '0', '#sadness', '#fear', 'negative', '-0.20', 'green_yellow', 'color', 'blue_green', 'cyan', 'purple']
senticnet['dark_blue_background'] = ['-0.45', '0.081', '-0.25', '0', '#sadness', '#fear', 'negative', '-0.20', 'green_yellow', 'color', 'blue_green', 'cyan', 'purple']
senticnet['dark_blue_sky'] = ['0.085', '0.071', '0.01', '0.085', '#joy', '#admiration', 'positive', '0.058', 'blue_color', 'munchkin', 'imaginary', 'crystal_clear', 'water_sky']
senticnet['dark_bluish'] = ['0', '0.19', '-0.09', '0.175', '#interest', '#admiration', 'positive', '0.089', 'red', 'green_black', 'green_red', 'peelable', 'precious_jewel']
senticnet['dark_brown'] = ['-0.7', '0', '0.617', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'silvery', 'achromatic', 'bluish_black', 'hueless', 'pitchy']
senticnet['dark_brown_dehaired_seal'] = ['-0.71', '0', '0.932', '-0.50', '#sadness', '#anger', 'negative', '-0.71', 'refuse', 'subvert', 'decline', 'balk', 'disobey']
senticnet['dark_brown_fur'] = ['-0.58', '0', '0.66', '-0.8', '#anger', '#disgust', 'negative', '-0.68', 'bald', 'depilous', 'glabrescent', 'glabrous', 'balding']
senticnet['dark_brown_hair'] = ['0', '0', '0.841', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'ancient', 'many_years', 'year', 'old', 'teen']
senticnet['dark_cavern'] = ['-0.96', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.96', 'tunnel', 'international_space_station', 'stick_wall', 'car_race', 'auction']
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senticnet['dark_color'] = ['-0.10', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.10', 'cool_color', 'primary_color', 'darkness', 'color', 'secondary_color']
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senticnet['dark_fuscous_irroration'] = ['-0.95', '0', '0.892', '-0.81', '#sadness', '#anger', 'negative', '-0.88', 'blurry', 'foggy', 'hazy', 'muzzy', 'blurred']
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senticnet['dark_green_foliage'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'tropical_fish', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['dark_greyish'] = ['-0.59', '0', '0.887', '-0.71', '#anger', '#disgust', 'negative', '-0.73', 'armet', 'basinet', 'helmet', 'heaume', 'morion']
senticnet['dark_hair'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'hair_color', 'purple', 'violet', 'dark_light', 'hue']
senticnet['dark_humor'] = ['-0.93', '0', '0.508', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'nakedness', 'cloud', 'glumness', 'desolation', 'bareness']
senticnet['dark_jedi'] = ['0', '0.081', '0', '0', '#interest', '#interest', 'positive', '0.081', 'favorite', 'guard', 'crowd', 'several_people', 'clique']
senticnet['dark_light'] = ['0', '0.114', '0', '0.08', '#interest', '#admiration', 'positive', '0.097', 'inky', 'black', 'purple', 'dark', 'hue_tint']
senticnet['dark_liquid'] = ['0', '0', '-0.09', '0', '#fear', '#fear', 'negative', '-0.09', 'black_liquid', 'oil', 'fossil', 'liquid_fuel', 'inert_gas']
senticnet['dark_magic'] = ['-0.88', '0', '0.589', '-0.85', '#sadness', '#disgust', 'negative', '-0.77', 'phantom', 'shadow', 'disbelief', 'illusion', 'fool']
senticnet['dark_olive'] = ['-0.02', '0.068', '0.04', '0.084', '#interest', '#admiration', 'positive', '0.022', 'peelable', 'green_black', 'purple', 'red', 'edible_human']
senticnet['dark_outside'] = ['-0.08', '0', '-0.04', '0', '#sadness', '#fear', 'negative', '-0.06', 'get_dark', 'night_time', 'day_end', 'sundown', 'dusk']
senticnet['dark_pigment'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['dark_pigmentation'] = ['-0.64', '0', '0.903', '-0.63', '#sadness', '#anger', 'negative', '-0.72', 'jaundice_of_newborn', 'disease', 'icterus_neonatorum', 'kernicterus', 'symptom']
senticnet['dark_place'] = ['-0.02', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'tunnel', 'cave', 'underground', 'top_tree', 'compost_pile']
senticnet['dark_plumage'] = ['0.041', '0', '0', '0.058', '#joy', '#admiration', 'positive', '0.05', 'bird_animal', 'wing', 'farm_bird', 'barnyard_animal', 'hen_baby']
senticnet['dark_purple'] = ['-0.02', '0.068', '0.04', '0.084', '#interest', '#admiration', 'positive', '0.022', 'peelable', 'green_black', 'purple', 'red', 'edible_human']
senticnet['dark_red'] = ['0.59', '-0.86', '0', '0.561', '#joy', '#surprise', 'positive', '0.672', 'claret', 'wine', 'oxblood_red', 'red', 'redness']
senticnet['dark_road'] = ['-0.95', '0', '0.159', '-0.94', '#sadness', '#disgust', 'negative', '-0.68', 'road', 'narrow', 'poky', 'narrow_road', 'rough_road']
senticnet['dark_roast'] = ['0.807', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.859', 'bubble', 'steam', 'hot', 'boil_water', 'tea']
senticnet['dark_rye_bread'] = ['0.52', '-0.69', '0', '0.639', '#surprise', '#admiration', 'positive', '0.617', 'black_bread', 'pumpernickel', 'limpa', 'bread', 'breadstuff']
senticnet['dark_shadow'] = ['-0.82', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.80', 'evil', 'darkness', 'dark', 'sun_down', 'dark_side']
senticnet['dark_side'] = ['-0.96', '0', '0.108', '-0.90', '#sadness', '#disgust', 'negative', '-0.66', 'evil', 'darkness', 'dark', 'sun_down', 'shadow']
senticnet['dark_silhouette'] = ['-0.82', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.80', 'evil', 'darkness', 'dark', 'sun_down', 'dark_side']
senticnet['dark_skin'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'blonde', 'purple', 'violet', 'dark_light', 'hue']
senticnet['dark_soy_sauce'] = ['0.07', '0.084', '0.023', '0', '#joy', '#interest', 'positive', '0.044', 'hearty', 'porridge', 'hot_liquid', 'liquid_food', 'meat_vegetable']
senticnet['dark_stain'] = ['0', '-0.01', '0.046', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'varnish', 'brush', 'dye', 'pigment', 'canvas']
senticnet['dark_stripe'] = ['0.942', '-0.61', '0', '0.908', '#joy', '#admiration', 'positive', '0.82', 'adornment', 'cigar_band', 'hatband', 'neckband', 'pinstripe']
senticnet['dark_underbelly'] = ['-0.54', '0', '0.845', '-0.95', '#anger', '#disgust', 'negative', '-0.78', 'face', 'base', 'underside', 'sole', 'bilge']
senticnet['dark_urine'] = ['-0.56', '0', '0.906', '-0.80', '#anger', '#disgust', 'negative', '-0.76', 'jaundice_of_newborn', 'disease', 'icterus_neonatorum', 'kernicterus', 'symptom']
senticnet['darken'] = ['0', '0', '0.701', '-0.81', '#anger', '#disgust', 'negative', '-0.75', 'dark', 'dark_energy', 'evil', 'bad', 'dangerous']
senticnet['darkening_sky'] = ['0.085', '0.071', '0.01', '0.085', '#joy', '#admiration', 'positive', '0.058', 'blue_color', 'munchkin', 'imaginary', 'crystal_clear', 'water_sky']
senticnet['darker'] = ['-0.54', '0', '0.845', '-0.95', '#anger', '#disgust', 'negative', '-0.78', 'face', 'base', 'underside', 'sole', 'bilge']
senticnet['darker_blue'] = ['-0.45', '0.081', '-0.25', '0', '#sadness', '#fear', 'negative', '-0.20', 'green_yellow', 'color', 'blue_green', 'cyan', 'purple']
senticnet['darker_brown'] = ['-0.7', '0', '0.617', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'silvery', 'achromatic', 'bluish_black', 'hueless', 'pitchy']
senticnet['darker_shade'] = ['-0.45', '0.081', '-0.25', '0', '#sadness', '#fear', 'negative', '-0.20', 'green_yellow', 'color', 'blue_green', 'cyan', 'purple']
senticnet['darker_side'] = ['-0.54', '0', '0.845', '-0.95', '#anger', '#disgust', 'negative', '-0.78', 'face', 'base', 'underside', 'sole', 'bilge']
senticnet['darker_skin'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'blonde', 'purple', 'violet', 'dark_light', 'hue']
senticnet['darkest'] = ['-0.11', '0.074', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.05', 'sun_down', 'night', 'night_time', 'moon_star', 'without_light']
senticnet['darkest_corner'] = ['-0.75', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.83', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['darkest_faerie'] = ['-0.63', '0', '0.674', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'baneful', 'evil', 'baleful', 'malevolent', 'malign']
senticnet['darkfield'] = ['0.057', '0', '0.012', '0.08', '#joy', '#admiration', 'positive', '0.042', 'see_better', 'glasses', 'become_gigantic', 'magnify', 'contact_lens']
senticnet['darkforce'] = ['0.8', '-0.97', '0', '0.512', '#joy', '#surprise', 'positive', '0.763', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['darkhaven'] = ['-0.66', '0', '0.859', '-0.82', '#anger', '#disgust', 'negative', '-0.78', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['darkhawk'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
senticnet['darkhold'] = ['0.594', '-0.54', '0', '0.518', '#joy', '#surprise', 'positive', '0.554', 'exorcist', 'enchanter', 'magus', 'sorceress', 'witch_doctor']
senticnet['darklord'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'feudal_lord', 'lord']
senticnet['darkly'] = ['-0.79', '0.262', '-0.45', '-0.04', '#sadness', '#fear', 'negative', '-0.25', 'downhearted', 'down_in_mouth', 'depressed', 'downcast', 'low-spirited']
senticnet['darkly_comic'] = ['-0.54', '0', '0.845', '-0.95', '#anger', '#disgust', 'negative', '-0.78', 'face', 'base', 'underside', 'sole', 'bilge']
senticnet['darkly_funny'] = ['0.881', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.937', 'granular', 'gamy', 'spirited', 'farinaceous', 'coarse-grained']
senticnet['darkness'] = ['-0.11', '0.074', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.05', 'sun_down', 'night', 'night_time', 'moon_star', 'without_light']
senticnet['darkspawn'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'elapid', 'ophidian', 'serpent', 'krait', 'taipan']
senticnet['darkstar'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
senticnet['darlin'] = ['0.591', '-0.76', '0', '0.66', '#surprise', '#admiration', 'positive', '0.673', 'darling', 'pet', 'favorite', 'first_choice', 'macushla']
senticnet['darling'] = ['0.59', '-0.81', '0', '0.571', '#joy', '#surprise', 'positive', '0.659', 'preferred', 'wanted', 'admired', 'loved', 'precious']
senticnet['darlint'] = ['-0.83', '0', '0.87', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'precipitate', 'rain_down', 'shower_down', 'spit', 'patter']
senticnet['darn'] = ['-0.56', '0', '0.917', '-0.88', '#anger', '#disgust', 'negative', '-0.79', 'troubleshoot', 'fiddle', 'fill', 'furbish_up', 'sole']
senticnet['dart'] = ['0', '0', '0.919', '-0.74', '#anger', '#disgust', 'negative', '-0.83', 'knife_sharp', 'decline', 'dullness', 'unpleasantness', 'rim']
senticnet['dart_board'] = ['0.035', '-0.10', '0.026', '0', '#joy', '#surprise', 'positive', '0.039', 'helium_balloon', 'round', 'ball', 'roller_coaster', 'horseshoe']
senticnet['dart_gun'] = ['0.757', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.831', 'baseball_bat', 'water_gun', 'goalie', 'hockey_puck', 'hockey_stick']
senticnet['dartboard'] = ['0.744', '0.743', '0', '0', '#joy', '#interest', 'positive', '0.744', 'loop', 'horse_shoes', 'ring', 'gray_hair', 'rotary_phone']
senticnet['darted_forward'] = ['-0.75', '0', '0.794', '-0.58', '#sadness', '#anger', 'negative', '-0.71', 'jerk', 'weightlift', 'military_press', 'clean', 'bench_press']
senticnet['darth_vader'] = ['-0.96', '0', '0.108', '-0.90', '#sadness', '#disgust', 'negative', '-0.66', 'evil', 'darkness', 'dark', 'sun_down', 'shadow']
senticnet['darunavir'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'indinavir', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['darvocet'] = ['0.969', '0.817', '0', '0.828', '#joy', '#admiration', 'positive', '0.871', 'ibuprofen', 'painkiller', 'medicine', 'analgesic', 'phenobarbital']
senticnet['darvon'] = ['0.969', '0.817', '0', '0.828', '#joy', '#admiration', 'positive', '0.871', 'ibuprofen', 'painkiller', 'medicine', 'analgesic', 'phenobarbital']
senticnet['darwin'] = ['-0.04', '0.192', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.054', 'hypothetical', 'supposition', 'scientific_statement', 'psychology', 'educate_guess']
senticnet['darwin_bulldog'] = ['-0.04', '0.192', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.054', 'hypothetical', 'supposition', 'scientific_statement', 'psychology', 'educate_guess']
senticnet['darwin_theory'] = ['-0.04', '0.192', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.054', 'hypothetical', 'supposition', 'scientific_statement', 'psychology', 'educate_guess']
senticnet['darwinian'] = ['-0.04', '0.192', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.054', 'hypothetical', 'supposition', 'scientific_statement', 'psychology', 'educate_guess']
senticnet['darwinian_evolution'] = ['-0.04', '0.192', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.054', 'hypothetical', 'supposition', 'scientific_statement', 'psychology', 'educate_guess']
senticnet['darwinian_survival'] = ['0.988', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.98', 'sustain_life', 'sustenance', 'nutrition', 'keep_alive', 'satisfy_hunger']
senticnet['darwinian_theory'] = ['-0.04', '0.192', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.054', 'hypothetical', 'supposition', 'scientific_statement', 'psychology', 'educate_guess']
senticnet['darwinism'] = ['-0.04', '0.192', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.054', 'hypothetical', 'supposition', 'scientific_statement', 'psychology', 'educate_guess']
senticnet['darwinist'] = ['-0.04', '0.192', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.054', 'hypothetical', 'supposition', 'scientific_statement', 'psychology', 'educate_guess']
senticnet['darwins_theory'] = ['-0.04', '0.192', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.054', 'hypothetical', 'supposition', 'scientific_statement', 'psychology', 'educate_guess']
senticnet['dasani'] = ['0.964', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.964', 'carbonated_drink', 'sugar_free', 'coke', 'diet_coke', 'beverage']
senticnet['dash'] = ['-0.89', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.87', 'dreadful', 'disquiet', 'scary', 'fearful', 'frighteningly']
senticnet['dashboard'] = ['0.725', '0.796', '0', '0', '#joy', '#interest', 'positive', '0.76', 'car_stereo', 'battery', 'seatbelt', 'spare_tire', 'brake_pedal']
senticnet['dashboard_display'] = ['0.725', '0.796', '0', '0', '#joy', '#interest', 'positive', '0.76', 'car_stereo', 'battery', 'seatbelt', 'spare_tire', 'brake_pedal']
senticnet['dashed_arrow'] = ['0', '0.15', '0.067', '0.118', '#interest', '#admiration', 'positive', '0.067', 'representation', 'outline', 'arithmetic', 'click', 'list_number']
senticnet['dashed_curve'] = ['0.854', '0.76', '0', '0', '#joy', '#interest', 'positive', '0.807', 'geometric_figure', 'polygon', 'form', 'parallelogram', 'hexagon']
senticnet['dashed_forward'] = ['0.957', '-0.93', '0', '0.795', '#joy', '#surprise', 'positive', '0.896', 'outdo', 'outstrip', 'outmatch', 'outgo', 'outperform']
senticnet['dashed_line'] = ['0.854', '0.76', '0', '0', '#joy', '#interest', 'positive', '0.807', 'geometric_figure', 'polygon', 'form', 'parallelogram', 'hexagon']
senticnet['dasher_hatfield'] = ['-0.57', '0', '0.571', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['dashing'] = ['0.716', '-0.57', '0', '0.681', '#joy', '#admiration', 'positive', '0.656', 'vibrant', 'vivacious', 'con_brio', 'lively', 'spirited']
senticnet['dashing_young'] = ['0.814', '-0.50', '0', '0.622', '#joy', '#admiration', 'positive', '0.648', 'friend', 'champion', 'supporter', 'protagonist', 'endorser']
senticnet['dasht_kuh'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'white_animal', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
senticnet['dassault_breguet'] = ['-0.94', '0', '0.866', '-0.91', '#sadness', '#disgust', 'negative', '-0.90', 'movement', 'apparent_motion', 'motion', 'apparent_movement', 'optical_illusion']
senticnet['dassault_mirage'] = ['-0.94', '0', '0.866', '-0.91', '#sadness', '#disgust', 'negative', '-0.90', 'movement', 'apparent_motion', 'motion', 'apparent_movement', 'optical_illusion']
senticnet['dastard'] = ['-0.95', '0', '0.825', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'cur', 'coward', 'craven', 'recreant', 'mortal']
senticnet['dastardliness'] = ['-0.83', '0', '0.943', '0', '#sadness', '#anger', 'negative', '-0.88', 'pusillanimous', 'dastard', 'treacherous', 'dastardly', 'cowardice']
senticnet['dastardly'] = ['-0.83', '0', '0.947', '0', '#sadness', '#anger', 'negative', '-0.89', 'pusillanimous', 'dastard', 'treacherous', 'dastardliness', 'cowardice']
senticnet['dat_nigger'] = ['-0.93', '0', '0.791', '0', '#sadness', '#anger', 'negative', '-0.86', 'hopelessness', 'hopelessly', 'despairing', 'pessimistically', 'pessimistic']
senticnet['data'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'fact', 'scientific_statement', 'report', 'educate_guess', 'hypothesis']
senticnet['data-based'] = ['0', '0.749', '0', '0.987', '#interest', '#admiration', 'positive', '0.868', 'observational', 'experimental', 'research', 'award', 'study']
senticnet['data_analysis'] = ['0', '0.859', '0', '0.975', '#interest', '#admiration', 'positive', '0.917', 'student', 'project', 'well', 'lab_improve', 'different_application']
senticnet['data_center_dmdc'] = ['0.814', '-0.87', '0', '0.918', '#surprise', '#admiration', 'positive', '0.87', 'ship_company', 'company', 'work_force', 'workforce', 'full_complement']
senticnet['data_collection'] = ['-0.06', '0.18', '0', '0.143', '#interest', '#admiration', 'positive', '0.086', 'grade', 'multiple_choices', 'final', 'final_exam', 'school_grade']
senticnet['data_compression'] = ['0', '0', '-0.1', '0', '#fear', '#fear', 'negative', '-0.1', 'grow_yard', 'heat', 'burn', 'corrode', 'stain_clothes']
senticnet['data_format'] = ['0', '0.93', '0', '0.733', '#interest', '#admiration', 'positive', '0.832', 'format', 'initialise', 'formatting', 'data_formatting', 'initialize']
senticnet['data_formatting'] = ['0', '0.934', '0', '0.728', '#interest', '#admiration', 'positive', '0.831', 'data_format', 'initialise', 'formatting', 'format', 'initialize']
senticnet['data_management_mdm'] = ['0.036', '0.111', '-0.05', '0.054', '#interest', '#admiration', 'positive', '0.038', 'despot', 'supervisor', 'chief', 'commander', 'earl']
senticnet['data_mining_kdd'] = ['0.774', '-0.63', '0', '0.567', '#joy', '#surprise', 'positive', '0.658', 'catching', 'breakthrough', 'spying', 'determination', 'tracing']
senticnet['data_processing'] = ['0.783', '-0.60', '0', '0.512', '#joy', '#surprise', 'positive', '0.634', 'refinement', 'blowing', 'processing', 'development', 'developing']
senticnet['data_set'] = ['0', '0.962', '0', '0.897', '#interest', '#admiration', 'positive', '0.929', 'data', 'project', 'study', 'well', 'analysis']
senticnet['database'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'socialize', 'formal_introduction', 'business_card', 'professional', 'collection']
senticnet['database_kdd'] = ['0.774', '-0.63', '0', '0.567', '#joy', '#surprise', 'positive', '0.658', 'catching', 'breakthrough', 'spying', 'determination', 'tracing']
senticnet['datacenter'] = ['0.637', '-0.91', '0', '0.757', '#surprise', '#admiration', 'positive', '0.768', 'tractability', 'manageableness', 'docility', 'obedience', 'amenability']
senticnet['dataglove'] = ['0.026', '0.14', '-0.13', '0.31', '#interest', '#admiration', 'positive', '0.085', 'teach_new', 'share_idea', 'gesture', 'start_talk', 'exchange_idea']
senticnet['datalist_control'] = ['-0.78', '0', '0.915', '-0.99', '#anger', '#disgust', 'negative', '-0.89', 'bodge', 'botch_up', 'flub', 'ball_up', 'go_wrong']
senticnet['dataport'] = ['0.866', '0.833', '0', '0', '#joy', '#interest', 'positive', '0.849', 'alcohol', 'porter', 'concierge', 'beer_mug', 'bar_stool']
senticnet['dataset'] = ['0.74', '0.898', '0', '0', '#joy', '#interest', 'positive', '0.819', 'discipline', 'scientific_knowledge', 'region', 'knowledge_base', 'content']
senticnet['datatype'] = ['-0.55', '0', '0.882', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'bugger_all', 'zero', 'naught', 'zip', 'nothing']
senticnet['date'] = ['0.863', '-0.55', '0', '0.653', '#joy', '#admiration', 'positive', '0.689', 'day', 'maturity', 'maturity_date', 'due_date', 'birthday']
senticnet['date_activity'] = ['0.793', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.766', 'recreation', 'pastime', 'leisure', 'leisure_time', 'enjoy_game']
senticnet['date_bread'] = ['0.644', '-0.94', '0', '0.729', '#surprise', '#admiration', 'positive', '0.773', 'gem', 'banana_bread', 'quick_bread', 'nut_bread', 'oatcake']
senticnet['date_girl'] = ['-0.10', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.07', 'fancy_girl', 'kiss_girl', 'wedge_door', 'jump_off_bridge', 'rake_leave']
senticnet['date_man'] = ['-0.79', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'date_woman', 'marry_man', 'service_car', 'oil_hinge', 'voice_concern']
senticnet['date_rape'] = ['-0.64', '0', '0.854', '-0.83', '#anger', '#disgust', 'negative', '-0.77', 'crime', 'bigamy', 'regulatory_offence', 'sexual_abuse', 'sex_crime']
senticnet['date_woman'] = ['-0.84', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.83', 'marry_man', 'service_car', 'date_man', 'oil_hinge', 'man_post']
senticnet['dated'] = ['-0.88', '0', '0.822', '-0.61', '#sadness', '#anger', 'negative', '-0.77', 'out', 'unfashionable', 'stodgy', 'prehistoric', 'antique']
senticnet['dateless'] = ['0', '-0.84', '0', '0.892', '#surprise', '#admiration', 'positive', '0.868', 'friendless', 'sexless', 'hung-over', 'druggie', 'obdurate']
senticnet['dating'] = ['0.577', '-0.50', '0', '0.901', '#joy', '#admiration', 'positive', '0.66', 'show_ticket', 'dress_nicely', 'dinner', 'find_place_to_sit', 'restaurant']
senticnet['dating_violence'] = ['-0.64', '0', '0.854', '-0.83', '#anger', '#disgust', 'negative', '-0.77', 'crime', 'bigamy', 'regulatory_offence', 'sexual_abuse', 'sex_crime']
senticnet['dative'] = ['-0.82', '0', '0.81', '-0.61', '#sadness', '#anger', 'negative', '-0.75', 'oblique', 'genitive', 'envy', 'possessive', 'jealous']
senticnet['datura_stramonium'] = ['0.95', '-0.86', '0', '0.947', '#joy', '#admiration', 'positive', '0.92', 'content', 'lore', 'folklore', 'cognitive_content', 'story']
senticnet['daub'] = ['0.707', '-0.52', '0', '0.634', '#joy', '#admiration', 'positive', '0.623', 'spot', 'smirch', 'blotch', 'smudge', 'blot']
senticnet['daucus_carota'] = ['0.861', '-0.87', '0', '0.597', '#joy', '#surprise', 'positive', '0.779', 'herb', 'cultivated_parsnip', 'wild_parsnip', 'madnep', 'herbaceous_plant']
senticnet['daughter'] = ['0', '0.964', '0', '0.974', '#interest', '#admiration', 'positive', '0.969', 'foster_kid', 'adopt', 'relation', 'sister', 'cousin']
senticnet['daughterboard'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'computer_part', 'system', 'cpu', 'pencil_pen', 'monitor']
senticnet['daunt'] = ['-0.88', '0', '-0.9', '0', '#sadness', '#fear', 'negative', '-0.89', 'scarily', 'scared', 'frighten_away', 'scare_away', 'frighten_off']
senticnet['daunting'] = ['0', '0', '0.804', '-0.83', '#anger', '#disgust', 'negative', '-0.82', 'forbidding', 'redoubtable', 'formidable', 'alarming', 'frightening']
senticnet['daunting_challenge'] = ['0', '0', '0', '-0.70', '#disgust', '#disgust', 'negative', '-0.70', 'uncertainty', 'uncertain_environment', 'insecure', 'blur', 'anxiety']
senticnet['daunting_task'] = ['-0.03', '0.136', '-0.09', '0.105', '#interest', '#admiration', 'positive', '0.028', 'chore', 'hire_help', 'police_work', 'career', 'job']
senticnet['dauntless'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'intrepidly', 'hardy', 'fearlessness', 'fearlessly', 'unafraid']
senticnet['dauntless_courage'] = ['0', '0', '0.764', '-0.77', '#anger', '#disgust', 'negative', '-0.77', 'restriction', 'tragedy', 'perpetrator', 'dominate', 'control']
senticnet['dauntlessly'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'intrepidly', 'hardy', 'fearlessness', 'fearlessly', 'unafraid']
senticnet['dauntlessness'] = ['-0.94', '0', '0.745', '-0.97', '#sadness', '#disgust', 'negative', '-0.89', 'nerve', 'fearlessness', 'heart', 'braveness', 'intrepidity']
senticnet['davien'] = ['0.943', '-0.78', '0', '0.571', '#joy', '#surprise', 'positive', '0.767', 'master', 'ignition_key', 'key', 'passkey', 'master_key']
senticnet['davis_brody'] = ['0.228', '0.123', '-0.09', '0', '#joy', '#interest', 'positive', '0.084', 'spend_time_with_child', 'social', 'friendship', 'companionship', 'get_close']
senticnet['davos_forum'] = ['-0.79', '0', '0.948', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['davy_jones_locker'] = ['0', '0', '0.034', '-0.08', '#anger', '#disgust', 'negative', '-0.06', 'lifeboat', 'mast', 'bad_man', 'keel', 'yacht']
senticnet['dawdle'] = ['0', '0', '-0.95', '-0.77', '#fear', '#disgust', 'negative', '-0.86', 'dawdler', 'loaf', 'unfruitfully', 'wander', 'drawl']
senticnet['dawdle_around'] = ['0.039', '-0.02', '0.03', '0.068', '#joy', '#admiration', 'positive', '0.024', 'get_place', 'wander_around', 'get_around_town', 'go_to_place', 'stroll']
senticnet['dawdler'] = ['-0.64', '0', '0.879', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'drone', 'poke', 'straggler', 'laggard', 'lagger']
senticnet['dawdling'] = ['0', '0', '-0.77', '-0.90', '#fear', '#disgust', 'negative', '-0.83', 'dalliance', 'piddle', 'wanton_away', 'wanton', 'trifling']
senticnet['dawg'] = ['-0.85', '0', '0.685', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'allicrocodilian', 'crocodile', 'false_gavial', 'crocodilian', 'caiman']
senticnet['dawn'] = ['0.02', '0.067', '0', '0', '#joy', '#interest', 'positive', '0.044', 'sunrise', 'light_in_sky', 'begin_day', 'daylight', 'sun']
senticnet['dawned_bright'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'weather', 'four_seasons', 'fall_spring', 'rain_snow', 'climate']
senticnet['dawson'] = ['-0.69', '0', '0.89', '-0.70', '#anger', '#disgust', 'negative', '-0.76', 'still', 'suppress', 'moderate', 'hush_up', 'quell']
senticnet['daxam'] = ['-0.03', '0.118', '0.472', '-0.46', '#anger', '#disgust', 'negative', '-0.21', 'detached', 'set-apart', 'separated', 'isolated', 'sickening']
senticnet['day'] = ['0.936', '-0.79', '0', '0.552', '#joy', '#surprise', 'positive', '0.761', 'today', 'day_of_month', 'date', 'tomorrow', 'yesterday']
senticnet['day-in-day-out'] = ['0', '0', '-0.75', '-0.86', '#fear', '#disgust', 'negative', '-0.80', 'paycheck-to-paycheck', 'moment-to-moment', 'hermit-like', 'eking', 'hand-to-mouth']
senticnet['day-neutral'] = ['0.832', '0', '0', '0.802', '#joy', '#admiration', 'positive', '0.817', 'winter-flowering', 'spring-blooming', 'winter-blooming', 'summer-flowering', 'summer-blooming']
senticnet['day-old'] = ['-0.74', '0', '0', '-0.81', '#sadness', '#disgust', 'negative', '-0.78', 'week-old', 'month-old', 'cubed', 'good-quality', 'wholemeal']
senticnet['day-to-day'] = ['-0.87', '0', '0.767', '0', '#sadness', '#anger', 'negative', '-0.82', 'monotonic', 'variety', 'monotonous', 'humdrum', 'tedious']
senticnet['day_care_provider'] = ['0.943', '-0.78', '0', '0.571', '#joy', '#surprise', 'positive', '0.767', 'master', 'ignition_key', 'key', 'passkey', 'master_key']
senticnet['day_end'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'dusk', 'nightfall', 'sundown', 'sunset', 'twilight']
senticnet['day_glo'] = ['0', '-0.73', '0', '0.787', '#surprise', '#admiration', 'positive', '0.763', 'brassy', 'flashy', 'gaudy', 'cheap', 'flash']
senticnet['day_holiday_weekend'] = ['0', '0.734', '0', '0', '#interest', '#interest', 'positive', '0.734', 'sleep', 'go_sleep', 'relax', 'go_out', 'sleep_night']
senticnet['day_job'] = ['-0.05', '0.197', '-0.11', '0.184', '#interest', '#admiration', 'positive', '0.053', 'question_ask', 'office_job', 'task', 'obligation', 'chore']
senticnet['day_month'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'date', 'calendar', 'day', 'time_measurement']
senticnet['day_of_month'] = ['0.585', '-0.92', '0', '0.508', '#joy', '#surprise', 'positive', '0.674', 'day', 'maturity', 'maturity_date', 'due_date', 'birthday']
senticnet['day_of_week'] = ['0.73', '0.789', '0', '0', '#joy', '#interest', 'positive', '0.76', 'friday', 'monday', 'fortnight', 'weekend', 'midweek']
senticnet['day_off'] = ['0.981', '0.791', '0', '0', '#joy', '#interest', 'positive', '0.886', 'retirement', 'holiday', 'time_relax', 'make_others_happy', 'win_lottery']
senticnet['day_part'] = ['-0.04', '0.088', '0.013', '0.069', '#interest', '#admiration', 'positive', '0.024', 'nightfall', 'sunrise', 'time_day', 'day_end', 'late_time']
senticnet['day_period'] = ['0', '-0.02', '0.085', '-0.09', '#anger', '#disgust', 'negative', '-0.05', 'time_pass', 'watch_clock', 'tie_score', 'keep_track_time', 'time_day']
senticnet['day_walkout'] = ['-0.77', '0', '0.505', '-0.50', '#sadness', '#anger', 'negative', '-0.59', 'wildcat_strike', 'sympathy_strike', 'sympathetic_strike', 'walkout', 'job_action']
senticnet['daybreak'] = ['-0.70', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.72', 'blackness', 'blackout', 'darkness', 'dark', 'obscure']
senticnet['daycare'] = ['-0.04', '0.088', '0.013', '0.069', '#interest', '#admiration', 'positive', '0.024', 'kindergarden', 'nursery_school', 'nursery', 'child', 'asylum']
senticnet['daycare_center'] = ['0.956', '-0.70', '0', '0.653', '#joy', '#surprise', 'positive', '0.771', 'centenarian', 'nonagenarian', 'octogenarian', 'septuagenarian', 'sexagenarian']
senticnet['daycare_centre'] = ['-0.04', '0.088', '0.013', '0.069', '#interest', '#admiration', 'positive', '0.024', 'kindergarden', 'daycare', 'nursery', 'child', 'asylum']
senticnet['daycare_facility'] = ['0.956', '-0.70', '0', '0.653', '#joy', '#surprise', 'positive', '0.771', 'centenarian', 'nonagenarian', 'octogenarian', 'septuagenarian', 'sexagenarian']
senticnet['daydream'] = ['-0.09', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.10', 'desire_travel', 'think_critically', 'trance', 'act_out', 'turn_light']
senticnet['daydreamer'] = ['-0.64', '0', '0.524', '-0.59', '#sadness', '#disgust', 'negative', '-0.58', 'bum', 'dreamer', 'stargazer', 'escapist', 'wishful_thinker']
senticnet['daydreaming'] = ['0.846', '-0.92', '0', '0.504', '#joy', '#surprise', 'positive', '0.759', 'woolgathering', 'revery', 'daydream', 'reverie', 'dreaming']
senticnet['dayglo'] = ['0', '-0.73', '0', '0.787', '#surprise', '#admiration', 'positive', '0.763', 'brassy', 'flashy', 'gaudy', 'cheap', 'flash']
senticnet['daylength'] = ['-0.75', '0', '0.512', '-0.75', '#sadness', '#disgust', 'negative', '-0.67', 'inactivity', 'inactiveness', 'slumber', 'inaction', 'hibernation']
senticnet['daylight'] = ['0.02', '0.067', '0', '0', '#joy', '#interest', 'positive', '0.044', 'sunrise', 'light_in_sky', 'dawn', 'begin_day', 'sun']
senticnet['daylong_conference'] = ['0', '0.214', '0.012', '0.208', '#interest', '#admiration', 'positive', '0.137', 'lecture_hall', 'lecture_theater', 'lesson', 'lecture', 'school_session']
senticnet['daymark'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'castle', 'bell_tower', 'spire', 'steeple', 'religious_place']
senticnet['daypack'] = ['0', '-0.02', '0.027', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'shoulder_bag', 'satchel', 'satchel_bag', 'tote_bag', 'carry']
senticnet['dayquil'] = ['-0.53', '0', '0.954', '-0.54', '#anger', '#disgust', 'negative', '-0.67', 'ranitidine', 'histamine_blocker', 'gastric_ranitidine', 'alkalizer', 'antiacid']
senticnet['daytime'] = ['-0.70', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.72', 'blackness', 'blackout', 'darkness', 'dark', 'obscure']
senticnet['daytime_fatigue'] = ['-0.80', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'fatigue', 'exhaustion', 'become_tired', 'get_tired', 'feel_tired']
senticnet['daytime_sky'] = ['0', '-0.72', '0', '0.712', '#surprise', '#admiration', 'positive', '0.72', 'heavenly_body', 'around_earth', 'source_light', 'sol', 'celestial_body']
senticnet['daytrip'] = ['-0.08', '0.179', '-0.06', '0.096', '#interest', '#admiration', 'positive', '0.034', 'ride_subway', 'buy_vending_machine', 'travel', 'buy_lottery_ticket', 'acquire']
senticnet['daywear'] = ['0.053', '-0.01', '-0.05', '0.121', '#joy', '#admiration', 'positive', '0.034', 'coat', 'winter_clothes', 'stylish', 'adorn', 'protective_cover']
senticnet['daze'] = ['-0.83', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.80', 'scared', 'fulgurant', 'eye_popping', 'fulgurous', 'dazzling']
senticnet['dazed'] = ['0', '0', '0.803', '-0.89', '#anger', '#disgust', 'negative', '-0.85', 'stun', 'stunned', 'stupefied', 'stupefy', 'dumbstricken']
senticnet['dazedly'] = ['0', '0', '0.728', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'glumly', 'mutely', 'dumbly', 'distractedly', 'numbly']
senticnet['dazzle'] = ['0.892', '0.88', '0', '0', '#joy', '#interest', 'positive', '0.886', 'blender', 'bright', 'sight', 'bedazzle', 'dazzling']
senticnet['dazzling'] = ['0.864', '-0.81', '0', '0', '#joy', '#surprise', 'positive', '0.837', 'surprise', 'fulgurant', 'eye-popping', 'fulgurous', 'bedaze']
senticnet['dazzling_display'] = ['0.794', '0', '0', '0.76', '#joy', '#admiration', 'positive', '0.777', 'stupefy', 'daze', 'sandbag', 'arresting', 'bedaze']
senticnet['dazzlingly'] = ['-0.95', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.96', 'daze', 'fulgurant', 'eye-popping', 'fulgurous', 'dazzling']
senticnet['dbanj'] = ['0', '0', '0.876', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'untune', 'discompose', 'discomfit', 'disconcert', 'upset']
senticnet['dbut_album'] = ['0.946', '-0.91', '0', '0.664', '#joy', '#surprise', 'positive', '0.841', 'pretender', 'phony', 'phoney', 'hypocrite', 'dissembler']
senticnet['dbutsu'] = ['-0.73', '0', '0.55', '-0.75', '#sadness', '#disgust', 'negative', '-0.68', 'mortal', 'person', 'individual', 'mangler', 'mutilator']
senticnet['dc_brau'] = ['0.161', '-0.14', '0.031', '-0.10', '#joy', '#surprise', 'positive', '0.043', 'barrel', 'reception', 'champagne', 'beer', 'wine']
senticnet['dc_comic'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'encyclopaedia', 'child_story', 'comic', 'disney_cartoon']
senticnet['dc_dc_converter'] = ['0', '-0.03', '0', '0.093', '#surprise', '#admiration', 'positive', '0.063', 'work_well', 'internal_combustion_engine', 'broken_car', 'mode_transportation', 'machine']
senticnet['dc_voltage'] = ['0', '-0.03', '0', '0.093', '#surprise', '#admiration', 'positive', '0.063', 'work_well', 'internal_combustion_engine', 'broken_car', 'mode_transportation', 'machine']
senticnet['dcalculable'] = ['0.979', '-0.81', '0', '0.963', '#joy', '#admiration', 'positive', '0.92', 'estimable', 'computable', 'countable', 'enumerable', 'calculable']
senticnet['dcbteach'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'neat', 'kitchen_drawer', 'cutlery_drawer', 'dine_table', 'table']
senticnet['dcc_send'] = ['0.028', '0', '0.027', '0.101', '#joy', '#admiration', 'positive', '0.034', 'animal_dwell', 'bee_home', 'live_place', 'dwell_place', 'place_to_live']
senticnet['dce_rpc'] = ['-0.95', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.84', 'hold_pencil', 'food_scrap', 'paper_product', 'waste_paper', 'cover']
senticnet['dclar_lundi'] = ['0.667', '-0.98', '0', '0.61', '#joy', '#surprise', 'positive', '0.753', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['dcolletage'] = ['-0.88', '0', '0.864', '-0.72', '#sadness', '#anger', 'negative', '-0.82', 'malicious', 'venomous', 'bitchy', 'catty', 'cattish']
senticnet['dcor'] = ['0.737', '-0.85', '0', '0.623', '#joy', '#surprise', 'positive', '0.738', 'understated', 'esthetic', 'aesthetic', 'artistic', 'unostentatious']
senticnet['dcr_license'] = ['-0.97', '0', '0.781', '-0.91', '#sadness', '#disgust', 'negative', '-0.88', 'weakness', 'fault', 'flaw', 'defect', 'wart']
senticnet['dd_form'] = ['0.862', '0.709', '0', '0', '#joy', '#interest', 'positive', '0.785', 'invoice', 'bill', 'check', 'mastercard', 'payment_counter']
senticnet['ddavp'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'spironolactone', 'adrenal_cortical_steroid', 'antihypertensive', 'antihypertensive_drug']
senticnet['de_cinq'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'indweller', 'invillager', 'villager', 'dweller', 'denizen']
senticnet['de_hasler'] = ['0', '0.996', '0', '0.85', '#interest', '#admiration', 'positive', '0.923', 'strength', 'strong', 'physical_strength', 'athlete', 'muscle']
senticnet['de_lupeaulx'] = ['0.857', '-0.65', '0', '0.869', '#joy', '#admiration', 'positive', '0.795', 'slicker', 'beguiler', 'quack', 'phrenologist', 'craniologist']
senticnet['de_oiseaux'] = ['-0.81', '0', '0.885', '-0.92', '#anger', '#disgust', 'negative', '-0.87', 'miss', 'girl', 'damsel', 'maiden', 'damoiselle']
senticnet['de_poux'] = ['-0.85', '0', '0.968', '-0.89', '#anger', '#disgust', 'negative', '-0.90', 'family', 'household', 'house', 'home', 'broken_home']
senticnet['dea_agent'] = ['-0.87', '0', '0.574', '-0.73', '#sadness', '#disgust', 'negative', '-0.72', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['deacon'] = ['0.546', '-0.76', '0', '0.74', '#surprise', '#admiration', 'positive', '0.685', 'exorcist', 'holy_order', 'acolyte', 'anagnost', 'doorkeeper']
senticnet['deactivate'] = ['0', '0', '0.706', '-0.71', '#anger', '#disgust', 'negative', '-0.71', 'inactivating', 'inactivates', 'inactivate', 'neutralize', 'inhibit']
senticnet['deactivated'] = ['-0.86', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.85', 'inoperable', 'unusable', 'malfunctioning', 'non-functioning', 'inoperative']
senticnet['dead'] = ['0', '0', '-0.75', '-0.74', '#fear', '#disgust', 'negative', '-0.75', 'dead_person', 'death', 'die_in_war', 'dead_leaf', 'dead_cow']
senticnet['dead-on'] = ['0', '-0.98', '0', '0.931', '#surprise', '#admiration', 'positive', '0.956', 'spot-on', 'pitch-perfect', 'bang-on', 'uncannily', 'impersonation']
senticnet['dead-set'] = ['0', '0.902', '0', '0.773', '#interest', '#admiration', 'positive', '0.838', 'hellbent', 'hell-bent', 'fixated', 'wreaking', 'bloodthirsty']
senticnet['dead-stroke'] = ['0', '0', '0.709', '-0.95', '#anger', '#disgust', 'negative', '-0.83', 'defeated', 'needle', 'deadbeat', 'exhausted', 'lazy']
senticnet['dead_animal'] = ['-0.88', '0', '0.589', '-0.85', '#sadness', '#disgust', 'negative', '-0.77', 'dead_person', 'death', 'dead_cow', 'dead_leaf', 'dead']
senticnet['dead_body'] = ['-0.09', '0', '-0.1', '-0.19', '#fear', '#disgust', 'negative', '-0.12', 'dead_person', 'morgue', 'coffin', 'headstone', 'bury_dead_person']
senticnet['dead_cow'] = ['0', '0', '-0.75', '-0.74', '#fear', '#disgust', 'negative', '-0.75', 'dead_person', 'death', 'dead_animal', 'dead_leaf', 'dead']
senticnet['dead_fish'] = ['0', '0', '0.021', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'driftwood', 'tidal_wave', 'seashell', 'titanic', 'jelly_fish']
senticnet['dead_hungry'] = ['-0.84', '0', '0', '-0.80', '#sadness', '#disgust', 'negative', '-0.82', 'need_food', 'a_little_hungry', 'snack', 'prepare_food', 'dead']
senticnet['dead_leaf'] = ['-0.78', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.84', 'fallen_leaf', 'pine_cone', 'detritus', 'autumn', 'fall']
senticnet['dead_milkman'] = ['0.797', '-0.95', '0', '0.921', '#surprise', '#admiration', 'positive', '0.891', 'lush', 'alcoholic', 'alky', 'dipsomaniac', 'boozer']
senticnet['dead_organic_matter'] = ['-0.12', '0', '-0.10', '-0.03', '#sadness', '#fear', 'negative', '-0.08', 'dog_poop', 'picnic', 'hike_trail', 'compost', 'wildlife']
senticnet['dead_person'] = ['-0.09', '0.069', '-0.14', '-0.14', '#fear', '#disgust', 'negative', '-0.07', 'dead_body', 'morgue', 'corpse', 'cadaver', 'casket']
senticnet['dead_place'] = ['-0.14', '0.123', '-0.15', '-0.04', '#sadness', '#fear', 'negative', '-0.05', 'death_place', 'burial_place', 'headstone', 'bury_dead', 'bury']
senticnet['dead_planet'] = ['0', '0', '-0.71', '-0.89', '#fear', '#disgust', 'negative', '-0.80', 'global_climate_change', 'end_world', 'apocalypse', 'kill_earth', 'climate_change']
senticnet['dead_president'] = ['0', '0.237', '0.035', '0.099', '#interest', '#admiration', 'positive', '0.1', 'dollar_cent', 'paper_currency', 'four_quarters', 'denomination', 'dollar_sign']
senticnet['dead_reckoning'] = ['-0.04', '0.127', '-0.12', '0.223', '#interest', '#admiration', 'positive', '0.046', 'underestimation', 'underrating', 'underreckoning', 'approximation', 'underestimate']
senticnet['dead_skin'] = ['-0.04', '-0.01', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.03', 'sweat', 'feces', 'urine', 'bodily_waste', 'shit']
senticnet['dead_soul'] = ['-0.79', '0', '0.885', '-0.76', '#sadness', '#anger', 'negative', '-0.81', 'infernal', 'mortal', 'zombi', 'zombie', 'living_dead']
senticnet['dead_soule'] = ['-0.79', '0', '0.875', '-0.77', '#sadness', '#anger', 'negative', '-0.81', 'infernal', 'mortal', 'dead_soul', 'zombi', 'living_dead']
senticnet['dead_stay_dead'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'dead', 'sentence_end', 'reason_why', 'negative', 'stationary']
senticnet['dead_tired'] = ['0', '0', '0.928', '-0.86', '#anger', '#disgust', 'negative', '-0.89', 'tired', 'energy_exhaustion', 'need_rest', 'exhausted', 'lack_of_rest']
senticnet['dead_tree'] = ['0', '-0.02', '0.085', '-0.09', '#anger', '#disgust', 'negative', '-0.05', 'tree_wood', 'forest', 'sideways', 'deforestation', 'wood']
senticnet['dead_weight'] = ['-0.93', '0', '0.922', '-0.79', '#sadness', '#anger', 'negative', '-0.88', 'onus', 'burden', 'encumbrance', 'incumbrance', 'load']
senticnet['deadbeat'] = ['0', '0', '0.714', '-0.94', '#anger', '#disgust', 'negative', '-0.83', 'defeated', 'needle', 'dead-stroke', 'exhausted', 'lazy']
senticnet['deadbolt'] = ['0', '0', '0', '0.109', '#admiration', '#admiration', 'positive', '0.109', 'knob', 'door_knob', 'door_handle', 'handle', 'hinge']
senticnet['deadbolt_lock'] = ['0', '0.115', '0', '0.105', '#interest', '#admiration', 'positive', '0.11', 'farrier', 'miner', 'horseshoer', 'blacksmith', 'smith']
senticnet['deaden'] = ['0.734', '-0.64', '0', '0.933', '#joy', '#admiration', 'positive', '0.771', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['deadening'] = ['-0.66', '0', '0.594', '-0.67', '#sadness', '#disgust', 'negative', '-0.64', 'boring', 'uninteresting', 'irksome', 'tiresome', 'wearisome']
senticnet['deadeye'] = ['0.801', '-0.87', '0', '0.916', '#surprise', '#admiration', 'positive', '0.864', 'rifleman', 'crack_shot', 'sniper', 'shot', 'shooter']
senticnet['deadite'] = ['0.8', '-0.97', '0', '0.512', '#joy', '#surprise', 'positive', '0.763', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['deadlier'] = ['0', '0', '-0.71', '0', '#fear', '#fear', 'negative', '-0.71', 'hazardous', 'violent', 'take_life', 'threat', 'lethal']
senticnet['deadliest'] = ['0', '0', '-0.71', '0', '#fear', '#fear', 'negative', '-0.71', 'hazardous', 'violent', 'take_life', 'threat', 'lethal']
senticnet['deadliest_avalanche'] = ['0', '-0.03', '-0.10', '-0.07', '#fear', '#disgust', 'negative', '-0.04', 'autumn', 'winter', 'skier', 'snow_fall', 'volcano']
senticnet['deadline'] = ['0.83', '0.95', '0', '0', '#joy', '#interest', 'positive', '0.89', 'amount_owe', 'percentage', 'money_payment', 'money_owe', 'due']
senticnet['deadlock'] = ['-0.91', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.86', 'impasse', 'stalemate', 'gridlock', 'negotiation', 'brinkmanship']
senticnet['deadly'] = ['0', '0', '-0.71', '0', '#fear', '#fear', 'negative', '-0.71', 'hazardous', 'violent', 'take_life', 'threat', 'lethal']
senticnet['deadly_avalanche'] = ['0', '-0.03', '-0.10', '-0.07', '#fear', '#disgust', 'negative', '-0.04', 'autumn', 'winter', 'skier', 'snow_fall', 'volcano']
senticnet['deadly_carbon_monoxide'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'rodent', 'tree_animal']
senticnet['deadly_confrontation'] = ['-0.2', '-0.03', '0', '-0.23', '#sadness', '#disgust', 'negative', '-0.13', 'beat_up', 'fight', 'conflict', 'violence', 'fight_against']
senticnet['deadly_crash'] = ['-0.90', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.86', 'trauma', 'car_wreck', 'broken_bone', 'bad_knee', 'wreck']
senticnet['deadly_disease'] = ['0.713', '0', '0', '0.766', '#joy', '#admiration', 'positive', '0.74', 'doctor', 'nurse', 'medical_procedure', 'medicine', 'doctor_surgery']
senticnet['deadly_nerve_agent'] = ['-0.90', '0', '0.529', '-0.84', '#sadness', '#disgust', 'negative', '-0.75', 'poison_gas', 'nerve_agent', 'mustard_gas', 'mustard_agent', 'blistering_agent']
senticnet['deadly_nightshade'] = ['0.95', '-0.86', '0', '0.947', '#joy', '#admiration', 'positive', '0.92', 'content', 'lore', 'folklore', 'cognitive_content', 'story']
senticnet['deadly_pale'] = ['-0.75', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.83', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['deadly_pandemic'] = ['-0.07', '0', '-0.22', '-0.15', '#fear', '#disgust', 'negative', '-0.15', 'gonorrhea', 'infectious_disease', 'psychoneurosis', 'sickness', 'polio']
senticnet['deadly_poison'] = ['-0.55', '0', '0.5', '-0.55', '#sadness', '#disgust', 'negative', '-0.53', 'remedy', 'cure', 'catholicon', 'therapeutic', 'curative']
senticnet['deadly_sin'] = ['0', '0', '0.753', '-0.72', '#anger', '#disgust', 'negative', '-0.73', 'sinful', 'human_emotion', 'call', 'demand', 'yearn']
senticnet['deadly_venom'] = ['-0.68', '0', '0.611', '-0.59', '#sadness', '#anger', 'negative', '-0.63', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['deadness'] = ['-0.81', '0', '0.83', '-0.97', '#anger', '#disgust', 'negative', '-0.87', 'inelasticity', 'rigidity', 'rigidness', 'stiffness', 'physical_property']
senticnet['deadpan'] = ['0.741', '-0.74', '0', '0.666', '#joy', '#surprise', 'positive', '0.718', 'commonplaceness', 'mundanity', 'prosiness', 'prosaicness', 'ordinariness']
senticnet['deadpan_delivery'] = ['-0.92', '0', '0.66', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'clown', 'buffoon', 'goof', 'zany', 'stupid']
senticnet['deadpan_humor'] = ['0.741', '-0.74', '0', '0.666', '#joy', '#surprise', 'positive', '0.718', 'commonplaceness', 'mundanity', 'prosiness', 'prosaicness', 'ordinariness']
senticnet['deadpool'] = ['0.757', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.831', 'baseball_bat', 'water_gun', 'goalie', 'hockey_puck', 'hockey_stick']
senticnet['deadshot'] = ['0.757', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.831', 'baseball_bat', 'water_gun', 'goalie', 'hockey_puck', 'hockey_stick']
senticnet['deadweight'] = ['-0.92', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.85', 'hindrance', 'cushion', 'useless', 'weight', 'burden']
senticnet['deadwood'] = ['-0.77', '0', '0.9', '-0.79', '#anger', '#disgust', 'negative', '-0.82', 'embarrassment', 'fifth_wheel', 'redundance', 'overplus', 'plethora']
senticnet['deaf'] = ['-0.94', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.94', 'drive_recklessly', 'discord', 'mute', 'deny', 'helpless']
senticnet['deaf-mute'] = ['0', '0', '0.953', '-0.73', '#anger', '#disgust', 'negative', '-0.84', 'mentally-challenged', 'twelve-year-old', 'blond-haired', 'eleven-year-old', 'thirteen-year-old']
senticnet['deaf_blind'] = ['-0.22', '-0.03', '0.026', '-0.13', '#sadness', '#disgust', 'negative', '-0.08', 'blind_man', 'blind_person', 'shade', 'need_help', 'shadow']
senticnet['deaf_culture'] = ['-0.94', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.94', 'drive_recklessly', 'discord', 'mute', 'deny', 'helpless']
senticnet['deaf_dumb'] = ['-0.22', '-0.03', '0.026', '-0.13', '#sadness', '#disgust', 'negative', '-0.08', 'blind_man', 'blind_person', 'shade', 'need_help', 'shadow']
senticnet['deaf_person'] = ['-0.69', '0', '0.99', '-0.71', '#anger', '#disgust', 'negative', '-0.79', 'mute', 'mortal', 'lip_reader', 'person', 'individual']
senticnet['deafblind'] = ['-0.94', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.94', 'drive_recklessly', 'discord', 'mute', 'deny', 'helpless']
senticnet['deafening'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'hear_loud_sound', 'roaring', 'ear_splitting', 'rowdy', 'noisy']
senticnet['deafening_explosion'] = ['0', '-0.00', '-0.07', '-0.06', '#fear', '#disgust', 'negative', '-0.04', 'lighten', 'tornado', 'dynamite_explosion', 'forest_fire', 'lightning']
senticnet['deafening_noise'] = ['0', '-0.00', '-0.07', '-0.06', '#fear', '#disgust', 'negative', '-0.04', 'lighten', 'tornado', 'dynamite_explosion', 'forest_fire', 'lightning']
senticnet['deafening_roar'] = ['0', '0', '-0.91', '-0.72', '#fear', '#disgust', 'negative', '-0.81', 'loudness', 'vibration', 'echo', 'detect', 'decibel']
senticnet['deafferentation'] = ['-0.78', '0', '0.669', '-0.89', '#sadness', '#disgust', 'negative', '-0.78', 'pain_sensation', 'painful_sensation', 'prickling', 'tingling', 'pain']
senticnet['deafness'] = ['0.914', '-0.80', '0', '0', '#joy', '#surprise', 'positive', '0.862', 'have_fun', 'change_mood', 'enjoy_music', 'hear_loss', 'earring']
senticnet['deal'] = ['-0.04', '0.274', '-0.15', '0.278', '#interest', '#admiration', 'positive', '0.089', 'business_deal', 'trade', 'exchange', 'bargain', 'offer']
senticnet['deal_card'] = ['-0.81', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.87', 'smoke_cigar', 'deal_card_player', 'smoke_joint', 'stand_alone', 'explain_everything']
senticnet['deal_card_player'] = ['-0.84', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.86', 'forward_email', 'stand_alone', 'explain_everything', 'bridge_difference', 'mind_store']
senticnet['deal_constructively'] = ['-0.63', '0', '0.559', '-0.82', '#sadness', '#disgust', 'negative', '-0.67', 'take_bull_by_horns', 'breast', 'front', 'encounter', 'oppose']
senticnet['deal_drug'] = ['0', '0', '0.937', '-0.87', '#anger', '#disgust', 'negative', '-0.90', 'make_porn', 'sell_new_book', 'go_off_strike', 'go_public', 'open_business']
senticnet['deal_gently'] = ['0.76', '-0.59', '0', '0.713', '#joy', '#admiration', 'positive', '0.689', 'spare_enemy', 'magnanimous', 'generous', 'forgiving', 'spareness']
senticnet['deal_harshly'] = ['-0.51', '0', '0.946', '-0.99', '#anger', '#disgust', 'negative', '-0.82', 'trammel', 'limit', 'restrict', 'bound', 'confine']
senticnet['dealer'] = ['-0.71', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.79', 'trader', 'principal', 'bargainer', 'monger', 'problem']
senticnet['dealership'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'sell_car', 'truck_stop', 'vehicle', 'insurance', 'car']
senticnet['dealing_with'] = ['-0.04', '0.274', '-0.15', '0.278', '#interest', '#admiration', 'positive', '0.089', 'business_deal', 'trade', 'exchange', 'bargain', 'offer']
senticnet['dealing_with_situation'] = ['0.87', '0.877', '0', '0', '#joy', '#interest', 'positive', '0.873', 'quick_look', 'approach', 'solution', 'proposal', 'proceed']
senticnet['deamidation'] = ['-0.84', '0', '0.8', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'rigor_mortis', 'natural_process', 'natural_action', 'activity', 'action']
senticnet['deanne_bray'] = ['-0.94', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.94', 'drive_recklessly', 'discord', 'mute', 'deny', 'helpless']
senticnet['deap_vally'] = ['0.946', '-0.91', '0', '0.664', '#joy', '#surprise', 'positive', '0.841', 'pretender', 'phony', 'phoney', 'hypocrite', 'dissembler']
senticnet['dear'] = ['0.773', '0.727', '0', '0', '#joy', '#interest', 'positive', '0.75', 'respect', 'expect', 'kind', 'affection', 'regard']
senticnet['dear_brother'] = ['0.773', '0.727', '0', '0', '#joy', '#interest', 'positive', '0.75', 'respect', 'expect', 'kind', 'affection', 'regard']
senticnet['dear_colleague_letter'] = ['0', '0', '0', '0.091', '#admiration', '#admiration', 'positive', '0.091', 'correspondence', 'handwritten', 'written_message', 'written_communication', 'write_note']
senticnet['dear_friend'] = ['0.05', '0.141', '-0.07', '0.132', '#interest', '#admiration', 'positive', '0.063', 'public_servant', 'worker', 'manager', 'laborer', 'office_job']
senticnet['dear_lady'] = ['0.773', '0.727', '0', '0', '#joy', '#interest', 'positive', '0.75', 'respect', 'expect', 'kind', 'affection', 'regard']
senticnet['dear_lord'] = ['0.903', '0', '0', '0', '#joy', '#joy', 'positive', '0.903', 'gospel', 'religion', 'prayer', 'faith', 'world_religion']
senticnet['dear_reader'] = ['0', '0.074', '0.026', '0.105', '#interest', '#admiration', 'positive', '0.051', 'handwritten', 'scribble', 'music_part', 'written_message', 'calligraphy']
senticnet['dear_sir'] = ['-0.69', '0', '0.883', '-0.91', '#anger', '#disgust', 'negative', '-0.82', 'stick_in', 'enclose', 'insert', 'feed_in', 'put_in']
senticnet['dear_sister'] = ['0.773', '0.727', '0', '0', '#joy', '#interest', 'positive', '0.75', 'respect', 'expect', 'kind', 'affection', 'regard']
senticnet['dearer'] = ['0.902', '-0.72', '0', '0.919', '#joy', '#admiration', 'positive', '0.849', 'preciousness', 'costliness', 'expensiveness', 'cost', 'lavishness']
senticnet['dearest'] = ['0.773', '0.727', '0', '0', '#joy', '#interest', 'positive', '0.75', 'respect', 'expect', 'kind', 'affection', 'regard']
senticnet['dearie'] = ['0.591', '-0.76', '0', '0.66', '#surprise', '#admiration', 'positive', '0.673', 'darling', 'pet', 'favorite', 'first_choice', 'macushla']
senticnet['dearness'] = ['0.902', '-0.72', '0', '0.919', '#joy', '#admiration', 'positive', '0.849', 'preciousness', 'costliness', 'expensiveness', 'cost', 'lavishness']
senticnet['dearth'] = ['-0.54', '0', '0.92', '-0.84', '#anger', '#disgust', 'negative', '-0.76', 'deficiency', 'demand', 'mineral_deficiency', 'famine', 'need']
senticnet['death'] = ['-0.87', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.91', 'loss_oflife', 'chest_pain', 'die', 'tumor', 'end_life']
senticnet['death_adder'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'elapid', 'ophidian', 'serpent', 'krait', 'taipan']
senticnet['death_bed'] = ['-0.95', '0', '0.198', '-0.96', '#sadness', '#disgust', 'negative', '-0.70', 'grave', 'corpse', 'coffin', 'tomb', 'dead_person']
senticnet['death_certificate'] = ['0.931', '-0.52', '0', '0.67', '#joy', '#admiration', 'positive', '0.709', 'document', 'certificate_of_incorporation', 'credential', 'diploma', 'sheepskin']
senticnet['death_destruction'] = ['-0.78', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.84', 'kill', 'murder', 'destroy_live', 'demolition', 'get_rid_of_enemy']
senticnet['death_knell'] = ['-0.52', '0', '0.986', '-0.94', '#anger', '#disgust', 'negative', '-0.81', 'omen', 'portent', 'presage', 'prognostic', 'prognostication']
senticnet['death_love'] = ['-0.59', '-0.06', '-0.35', '-0.25', '#sadness', '#fear', 'negative', '-0.28', 'anguish', 'bad_feeling', 'get_fired', 'disgrace', 'feel_stressed']
senticnet['death_penalty'] = ['-0.30', '-0.01', '-0.18', '-0.18', '#sadness', '#fear', 'negative', '-0.16', 'capital_punishment', 'send_to_prison', 'imprison', 'die', 'feel_remorse']
senticnet['death_penalty_moratorium'] = ['-0.19', '0', '-0.17', '-0.14', '#sadness', '#fear', 'negative', '-0.17', 'death_penalty', 'die', 'death_destruction', 'kill', 'murder']
senticnet['death_place'] = ['0', '0', '-0.75', '-0.74', '#fear', '#disgust', 'negative', '-0.75', 'dead_place', 'burial_place', 'headstone', 'bury_dead', 'bury']
senticnet['death_row'] = ['0', '0', '0.869', '0', '#anger', '#anger', 'negative', '-0.86', 'convoke', 'loose_end', 'car_race', 'auction', 'mortuary']
senticnet['death_row_inmate'] = ['0', '0', '0.869', '0', '#anger', '#anger', 'negative', '-0.86', 'convoke', 'loose_end', 'car_race', 'auction', 'mortuary']
senticnet['death_row_prisoner'] = ['0', '0', '0.869', '0', '#anger', '#anger', 'negative', '-0.86', 'convoke', 'loose_end', 'car_race', 'auction', 'mortuary']
senticnet['death_sentence'] = ['-0.93', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.93', 'revenge', 'harden_criminal', 'arrest', 'send_to_jail', 'felony']
senticnet['death_wish'] = ['-0.81', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.81', 'take_charge', 'anti', 'go_against', 'campaign', 'file_complaint']
senticnet['deathbird'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'feudal_lord', 'lord']
senticnet['deathcore'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'explicit', 'declared', 'definitive', 'graphic']
senticnet['deathless'] = ['0', '0', '0', '0.808', '#admiration', '#admiration', 'positive', '0.808', 'immortality', 'live_forever', 'immortal', 'eternal_life', 'everlasting_life']
senticnet['deathlike'] = ['-0.72', '0', '-0.7', '0', '#sadness', '#fear', 'negative', '-0.71', 'impassiveness', 'impassivity', 'apathetically', 'unemotionality', 'stolidity']
senticnet['deathly'] = ['-0.63', '0', '0.674', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'baneful', 'evil', 'maleficent', 'malevolent', 'malign']
senticnet['deathly_pale'] = ['-0.66', '0', '0.782', '-0.52', '#sadness', '#anger', 'negative', '-0.65', 'lividness', 'pallidness', 'complexion', 'wanness', 'achromasia']
senticnet['deathstroke'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'only_bird']
senticnet['deathurge'] = ['0', '0.081', '0', '0', '#interest', '#interest', 'positive', '0.081', 'favorite', 'guard', 'crowd', 'several_people', 'clique']
senticnet['debacle'] = ['-0.64', '0', '0.717', '-0.98', '#anger', '#disgust', 'negative', '-0.78', 'licking', 'lurch', 'whitewash', 'finish', 'failure']
senticnet['debar'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
senticnet['debase'] = ['-0.60', '0', '0.872', '-0.56', '#sadness', '#anger', 'negative', '-0.68', 'suborn', 'modify', 'poison', 'lead_astray', 'alter']
senticnet['debasement'] = ['0', '0', '0.773', '-0.74', '#anger', '#disgust', 'negative', '-0.75', 'impureness', 'dirtiness', 'pollution', 'taint', 'adulteration']
senticnet['debasing'] = ['-0.91', '0', '0.907', '-0.60', '#sadness', '#anger', 'negative', '-0.80', 'opprobrious', 'dishonorable', 'disgraceful', 'degrading', 'inglorious']
senticnet['debatable'] = ['0.544', '-0.69', '0', '0.658', '#surprise', '#admiration', 'positive', '0.632', 'shakable', 'shakeable', 'challengeable', 'disputable', 'contestable']
senticnet['debate'] = ['0.915', '-0.84', '0', '0.778', '#joy', '#surprise', 'positive', '0.847', 'fall_out', 'altercate', 'wrangle', 'fight', 'brawl']
senticnet['debate_moderator'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquirer', 'enquirer', 'inquisitor', 'querier', 'asker']
senticnet['debate_politics'] = ['0', '0', '0.933', '-0.88', '#anger', '#disgust', 'negative', '-0.91', 'opinioned', 'point', 'conquer_opponent', 'dicussion', 'debate']
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senticnet['decide_to_go_first'] = ['0', '0', '0', '0.703', '#admiration', '#admiration', 'positive', '0.703', 'choose_destination', 'book_hotel_room', 'talk_to_travel_agent', 'make_reservation', 'pay_fee']
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senticnet['decidedly'] = ['0.807', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.832', 'apparently', 'clearly', 'pregnantly', 'evidently', 'obviously']
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senticnet['decimal'] = ['0.049', '0.113', '0.107', '0.162', '#interest', '#admiration', 'positive', '0.054', 'list_number', 'ordinal', 'count', 'numerical', 'enumeration']
senticnet['decimal_arithmetic'] = ['0.076', '0.096', '-0.08', '0.167', '#interest', '#admiration', 'positive', '0.063', 'statistic', 'mathematical_function', 'calculation', 'list_number', 'calculate']
senticnet['decimal_integer'] = ['-0.04', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.06', 'chemical_reaction', 'burn', 'chemical_process', 'get_burnt', 'set_on_fire']
senticnet['decimal_number'] = ['0.049', '0.113', '0.107', '0.162', '#interest', '#admiration', 'positive', '0.054', 'list_number', 'ordinal', 'count', 'numerical', 'enumeration']
senticnet['decimal_point'] = ['0.049', '0.113', '0.107', '0.162', '#interest', '#admiration', 'positive', '0.054', 'list_number', 'ordinal', 'count', 'numerical', 'enumeration']
senticnet['decimation'] = ['-0.14', '-0.00', '-0.12', '-0.21', '#sadness', '#disgust', 'negative', '-0.11', 'torture', 'demolish', 'maim', 'terminate', 'hurt_person']
senticnet['decipher'] = ['0.987', '0', '0', '0.899', '#joy', '#admiration', 'positive', '0.943', 'cut_down', 'analyze', 'reduce', 'disinvolve', 'abridge']
senticnet['decipherer'] = ['0.615', '-0.93', '0', '0.812', '#surprise', '#admiration', 'positive', '0.787', 'intellectual', 'intellect', 'cryptanalyst', 'cryptographer', 'cryptologist']
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senticnet['decision'] = ['-0.06', '0.32', '-0.10', '0.248', '#interest', '#admiration', 'positive', '0.101', 'decide', 'select', 'selection', 'make_choice', 'choice']
senticnet['decision_maker'] = ['0', '0.35', '-0.09', '0.31', '#interest', '#admiration', 'positive', '0.188', 'decision', 'conclude', 'explanation', 'reason', 'think_process']
senticnet['decision_making'] = ['0', '0.197', '-0.06', '0.233', '#interest', '#admiration', 'positive', '0.12', 'make_rule', 'decision', 'stubborn', 'advise', 'make_decision']
senticnet['decisional'] = ['0.929', '-0.71', '0', '0.605', '#joy', '#surprise', 'positive', '0.748', 'incisiveness', 'trenchancy', 'efficacy', 'efficaciousness', 'power']
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senticnet['decisive_action'] = ['0', '0', '0.808', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'idle', 'lazy', 'stay', 'take_easy', 'recline']
senticnet['decisive_battle'] = ['-0.05', '0', '-0.08', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'siege', 'big_fight', 'war', 'declare_war', 'combat']
senticnet['decisive_engagement'] = ['-0.05', '0', '-0.08', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'siege', 'big_fight', 'war', 'declare_war', 'combat']
senticnet['decisive_moment'] = ['-0.67', '0', '0.97', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'joint', 'node', 'articulation', 'join', 'connexion']
senticnet['decisive_naval'] = ['-0.05', '0', '-0.08', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'siege', 'big_fight', 'war', 'declare_war', 'combat']
senticnet['decisive_role'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'baddie', 'theatrical_baddie', 'part', 'persona']
senticnet['decisive_shift'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'absolutism', 'autocracy', 'ascendancy', 'tyrant']
senticnet['decisive_victory'] = ['-0.05', '0', '-0.08', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'siege', 'big_fight', 'war', 'declare_war', 'combat']
senticnet['decisively_defeat'] = ['-0.82', '0', '0.739', '-0.50', '#sadness', '#anger', 'negative', '-0.69', 'fake', 'juke', 'maneuver', 'manoeuvre', 'feint']
senticnet['decisiveness'] = ['0.96', '-0.92', '0', '0.606', '#joy', '#surprise', 'positive', '0.831', 'resoluteness', 'firmness_of_purpose', 'firmness', 'resolve', 'resolution']
senticnet['deck'] = ['0.831', '-0.82', '0', '0.826', '#joy', '#admiration', 'positive', '0.826', 'upper_deck', 'freeboard_upper_deck', 'weather_upper_deck', 'shelter_upper_deck', 'boat_upper_deck']
senticnet['deck_card'] = ['0', '-0.03', '0', '0.075', '#surprise', '#admiration', 'positive', '0.056', 'poker_chip', 'go_casino', 'toy_car', 'yo-yo', 'poker']
senticnet['deck_lid'] = ['0.02', '0.085', '-0.05', '0.085', '#interest', '#admiration', 'positive', '0.034', 'gold', 'silver', 'medal', 'carat', 'medallion']
senticnet['deck_seamanship'] = ['0.764', '-0.96', '0', '0.883', '#surprise', '#admiration', 'positive', '0.87', 'boatmanship', 'acquirement', 'skill', 'accomplishment', 'acquisition']
senticnet['deckchair'] = ['0', '0.092', '0.016', '0.074', '#interest', '#admiration', 'positive', '0.05', 'armoire', 'pillowcase', 'bed_sheet', 'duvet_cover', 'comforter']
senticnet['decked_out'] = ['0', '0', '0', '0.963', '#admiration', '#admiration', 'positive', '0.963', 'wear', 'furnishing', 'wearable_technology', 'wearable', 'belt']
senticnet['deckhand'] = ['0.073', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'angler', 'informal_rod', 'archaic_fisher', 'rare_piscator', 'fishing_line']
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senticnet['declaimer'] = ['0.584', '-0.93', '0', '0.927', '#surprise', '#admiration', 'positive', '0.816', 'elocute', 'perorate', 'perform', 'orate', 'spout']
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senticnet['declarable'] = ['0.683', '-0.55', '0', '0.552', '#joy', '#surprise', 'positive', '0.596', 'accepted', 'recognized', 'acknowledged', 'give', 'putative']
senticnet['declaration'] = ['0', '0.954', '0', '0.877', '#interest', '#admiration', 'positive', '0.915', 'announcement', 'resolve', 'proclamation', 'contract', 'resolution']
senticnet['declaratory_statement'] = ['-0.81', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.79', 'stifle', 'forgotten', 'old', 'touch_ground', 'seldom']
senticnet['declare'] = ['0', '-0.81', '0', '0.718', '#surprise', '#admiration', 'positive', '0.766', 'remark', 'reply', 'oath', 'announcement', 'mention']
senticnet['declare_null'] = ['-0.91', '0', '-0.77', '-0.75', '#sadness', '#fear', 'negative', '-0.81', 'emptiness', 'star_planet', 'nothingness', 'outer', 'around_earth']
senticnet['declare_victory'] = ['-0.06', '0.109', '-0.05', '0.158', '#interest', '#admiration', 'positive', '0.036', 'elasticity', 'stretch_out', 'grow_long', 'widen', 'advance_civilization']
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senticnet['declared_independence'] = ['0.887', '0.748', '0', '0.954', '#joy', '#admiration', 'positive', '0.863', 'political_action', 'political', 'mediocrity', 'discrimination', 'independent']
senticnet['declared_ineligible'] = ['-0.60', '0', '0.832', '-0.53', '#sadness', '#anger', 'negative', '-0.65', 'unsuitable', 'undesirable', 'unqualified', 'disqualified', 'unentitled']
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senticnet['declared_obsolete'] = ['-0.90', '0', '0.517', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'outdated', 'noncurrent', 'disused', 'obsolete', 'superannuated']
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senticnet['declass'] = ['0', '0', '0.931', '-0.96', '#anger', '#disgust', 'negative', '-0.94', 'enfeeble', 'disconfirm', 'discontinue', 'befall', 'crack']
senticnet['declassification'] = ['0', '-0.86', '0', '0.906', '#surprise', '#admiration', 'positive', '0.887', 'declassifying', 'declassify', 'declassified', 'redact', 'unseal']
senticnet['declassified'] = ['0', '-0.86', '0', '0.901', '#surprise', '#admiration', 'positive', '0.883', 'declassifying', 'declassification', 'declassify', 'redact', 'unseal']
senticnet['declassify'] = ['0', '-0.85', '0', '0.906', '#surprise', '#admiration', 'positive', '0.882', 'declassifying', 'declassification', 'declassified', 'redact', 'unseal']
senticnet['declassifying'] = ['0', '-0.86', '0', '0.897', '#surprise', '#admiration', 'positive', '0.881', 'declassify', 'declassification', 'declassified', 'redact', 'unseal']
senticnet['declination'] = ['-0.63', '0', '0.663', '-0.82', '#anger', '#disgust', 'negative', '-0.70', 'wreck', 'twilight', 'neglect', 'disuse', 'condition']
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senticnet['decline_in_quality'] = ['-0.95', '0', '0.862', '-0.97', '#sadness', '#disgust', 'negative', '-0.92', 'decline', 'slack', 'slump', 'falloff', 'falling_off']
senticnet['declining_trend'] = ['0.05', '0.151', '0', '0.159', '#interest', '#admiration', 'positive', '0.12', 'glamour', 'stylish', 'style', 'clothes_style', 'fancy_clothes']
senticnet['declutter'] = ['0', '0', '0', '0.897', '#admiration', '#admiration', 'positive', '0.897', 'organize_life', 'clear_think', 'neatness', 'unambiguous', 'organizational_skill']
senticnet['decoction'] = ['-0.81', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.82', 'peanut', 'leave_over', 'floppy', 'circular_motion', 'store_vegetable']
senticnet['decoction_thereof'] = ['-0.16', '0.179', '0.568', '-0.61', '#anger', '#disgust', 'negative', '-0.29', 'angrily', 'ire', 'see_red', 'dander', 'hackle']
senticnet['decoding'] = ['0.972', '-0.86', '0', '0.588', '#joy', '#surprise', 'positive', '0.808', 'decompression', 'cryptography', 'coding', 'secret_writing', 'steganography']
senticnet['decoherence'] = ['-0.98', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.87', 'become_fat', 'flatulence', 'feel_rejuvenated', 'tan_skin', 'make_angry']
senticnet['decompose'] = ['-0.22', '0', '-0.19', '0', '#sadness', '#fear', 'negative', '-0.21', 'collide', 'lift_leg', 'feel_terrible', 'travel_through_space', 'fly_around']
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senticnet['decomposing_flesh'] = ['0', '0.073', '0', '0.092', '#interest', '#admiration', 'positive', '0.082', 'false_tooth', 'scent', 'waste_water', 'sniff', 'odor']
senticnet['decomposing_plant'] = ['-0.12', '0', '-0.10', '-0.03', '#sadness', '#fear', 'negative', '-0.08', 'dog_poop', 'picnic', 'hike_trail', 'compost', 'wildlife']
senticnet['decomposition'] = ['-0.47', '0', '-0.46', '-0.04', '#sadness', '#fear', 'negative', '-0.32', 'sadness', 'deep_sadness', 'psychoneurosis', 'lament', 'feel_sorry']
senticnet['decomposition_theorem'] = ['0.875', '-0.65', '0', '0.861', '#joy', '#admiration', 'positive', '0.797', 'completeness', 'work', 'whole_shebang', 'essential', 'whole']
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senticnet['decongestant'] = ['0.969', '0.817', '0', '0.828', '#joy', '#admiration', 'positive', '0.871', 'ibuprofen', 'painkiller', 'medicine', 'analgesic', 'phenobarbital']
senticnet['deconsecrate'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'profane', 'desecrate', 'outrage', 'unhallow', 'violate']
senticnet['deconstruct'] = ['0.868', '-0.95', '0', '0.548', '#joy', '#surprise', 'positive', '0.791', 'find', 'design', 'preconceive', 'discover', 'create_by_mental_act']
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senticnet['decontamination'] = ['-0.77', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.76', 'contamination', 'blend', 'contaminator', 'impurity', 'sophistication']
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senticnet['decor'] = ['0.737', '-0.85', '0', '0.623', '#joy', '#surprise', 'positive', '0.738', 'understated', 'esthetic', 'aesthetic', 'artistic', 'unostentatious']
senticnet['decorate'] = ['0.092', '0', '-0.05', '0.158', '#joy', '#admiration', 'positive', '0.064', 'home_improvement', 'please_eye', 'decoration', 'decorate_room', 'cover_wall']
senticnet['decorate_home'] = ['0.875', '0', '0', '0.962', '#joy', '#admiration', 'positive', '0.918', 'make_easy', 'strum', 'sing_dance', 'play_song', 'backup']
senticnet['decorate_house'] = ['0.216', '0.154', '-0.05', '0.342', '#joy', '#admiration', 'positive', '0.164', 'own_space', 'nice_house', 'rich_house', 'embellish', 'place_to_live']
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senticnet['decorate_yard'] = ['0', '-0.09', '0.035', '0.1', '#surprise', '#admiration', 'positive', '0.054', 'dig_dirt', 'plant_flower', 'cut_grass', 'plant_garden', 'grow_flower']
senticnet['decorated_christmas_tree'] = ['0.856', '0', '0', '0', '#joy', '#joy', 'positive', '0.856', 'tree', 'evergreen_tree', 'big_tree', 'santa_claus', 'christmas']
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senticnet['decorative_detail'] = ['-0.54', '0', '0.7', '-0.64', '#anger', '#disgust', 'negative', '-0.62', 'flourish', 'rant', 'bombast', 'fustian', 'claptrap']
senticnet['decorative_lighting'] = ['-0.90', '0', '0.209', '-0.92', '#sadness', '#disgust', 'negative', '-0.68', 'sun_burn', 'erythema', 'fire', 'burn_skin', 'hurt_skin']
senticnet['decorative_plaster'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'adobe', 'adobe_brick', 'clinker', 'brick', 'firebrick']
senticnet['decorator'] = ['0', '0.098', '0', '0.144', '#interest', '#admiration', 'positive', '0.121', 'wall_cover', 'cover_wall', 'latex', 'two-dimensional', 'home_improvement']
senticnet['decorous'] = ['0.783', '0.884', '0', '0', '#joy', '#interest', 'positive', '0.834', 'comme_il_faut', 'comely', 'seemly', 'becoming', 'decent']
senticnet['decorously'] = ['0.625', '-0.88', '0', '0.782', '#surprise', '#admiration', 'positive', '0.762', 'judicious', 'prudent', 'prudential', 'heady', 'circumspect']
senticnet['decorousness'] = ['0.85', '-0.85', '0', '0.712', '#joy', '#surprise', 'positive', '0.807', 'decency', 'decorum', 'correctitude', 'correctness', 'seemliness']
senticnet['decorum'] = ['0.85', '-0.85', '0', '0.712', '#joy', '#surprise', 'positive', '0.807', 'decency', 'correctitude', 'decorousness', 'correctness', 'seemliness']
senticnet['decoupage'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'building', 'carving', 'artistic_creation', 'ceramic']
senticnet['decouple'] = ['-0.98', '0', '0.737', '-0.92', '#sadness', '#disgust', 'negative', '-0.88', 'separate', 'severalize', 'contrast', 'differentiate', 'dissociate']
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senticnet['decrease'] = ['-0.12', '-0.05', '0', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'diminish', 'reduce', 'lower', 'lower_music', 'low_level']
senticnet['decrease_hunger'] = ['0.896', '0', '0', '0.809', '#joy', '#admiration', 'positive', '0.853', 'get_fat', 'eat_food', 'feel_satisfied', 'obesity', 'eat_better']
senticnet['decrease_music'] = ['-0.13', '-0.06', '0', '-0.13', '#sadness', '#disgust', 'negative', '-0.07', 'diminish', 'reduce', 'decrease', 'lower', 'low_level']
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senticnet['decreased_cardiac_output'] = ['-0.62', '0', '0.901', '-0.71', '#anger', '#disgust', 'negative', '-0.74', 'pressure', 'osmotic_pressure', 'hypotonicity', 'pressure_level', 'force']
senticnet['decreased_lung_function'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchiolitis', 'chronic_bronchitis', 'respiratory_disease', 'bronchitis', 'respiratory_disorder']
senticnet['decreased_salivary_flow'] = ['0', '0', '0.813', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'ice_cream_melt', 'allergy', 'toothache', 'gonorrhea', 'tastebud']
senticnet['decreased_sensitivity'] = ['0', '0', '-0.66', '0', '#fear', '#fear', 'negative', '-0.66', 'anger', 'rage', 'annoyance', 'apprehension', 'fear']
senticnet['decreased_urine_output'] = ['-0.58', '0', '0.773', '-0.79', '#anger', '#disgust', 'negative', '-0.71', 'perspiration', 'sweating', 'polyhidrosis', 'sudation', 'hidrosis']
senticnet['decreased_visual_acuity'] = ['-0.86', '0', '0.858', '-0.91', '#sadness', '#disgust', 'negative', '-0.87', 'edema', 'oedema', 'hydrops', 'dropsy', 'eye_disease']
senticnet['decreasing'] = ['-0.98', '0', '0.962', '-0.95', '#sadness', '#anger', 'negative', '-0.96', 'depreciative', 'depreciatory', 'falling', 'depreciating', 'detractive']
senticnet['decree'] = ['0', '0.173', '-0.09', '0.092', '#interest', '#admiration', 'positive', '0.058', 'regulation', 'respect_law', 'legislation', 'edict', 'law']
senticnet['decrement'] = ['-0.12', '-0.05', '0', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'reduction', 'diminution', 'decrease', 'inferior', 'reduce']
senticnet['decrepit'] = ['0', '0', '-0.96', '-0.81', '#fear', '#disgust', 'negative', '-0.88', 'clunker', 'creaky', 'flea-bitten', 'crazy', 'mangy']
senticnet['decrepitude'] = ['-0.77', '0', '0.5', '-0.75', '#sadness', '#disgust', 'negative', '-0.67', 'apathy', 'indifference', 'passivity', 'inertia', 'inactiveness']
senticnet['decretal'] = ['-0.81', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.79', 'stifle', 'forgotten', 'old', 'touch_ground', 'seldom']
senticnet['decrier'] = ['0', '0', '-0.95', '-0.83', '#fear', '#disgust', 'negative', '-0.89', 'blame', 'rogue', 'decry', 'denounce', 'harmful']
senticnet['decry'] = ['0', '0', '-0.95', '-0.82', '#fear', '#disgust', 'negative', '-0.89', 'blame', 'rogue', 'decrier', 'denounce', 'harmful']
senticnet['decrypt'] = ['-0.52', '0', '0.821', '-0.65', '#anger', '#disgust', 'negative', '-0.66', 'work_out', 'reckon', 'calculate', 'average_out', 'cypher']
senticnet['decryption'] = ['0.584', '-0.72', '0', '0.742', '#surprise', '#admiration', 'positive', '0.684', 'decompression', 'cryptography', 'coding', 'secret_writing', 'steganography']
senticnet['decryption_algorithm'] = ['0.584', '-0.72', '0', '0.742', '#surprise', '#admiration', 'positive', '0.684', 'decompression', 'cryptography', 'coding', 'secret_writing', 'steganography']
senticnet['decryption_key'] = ['-0.83', '0', '0.665', '-0.51', '#sadness', '#anger', 'negative', '-0.67', 'percipient', 'auditor', 'perceiver', 'observer', 'beholder']
senticnet['decryptor'] = ['-0.83', '0', '0.665', '-0.51', '#sadness', '#anger', 'negative', '-0.67', 'percipient', 'auditor', 'perceiver', 'observer', 'beholder']
senticnet['decumbent'] = ['-0.80', '0', '0.555', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'bush', 'wormwood', 'milkwort', 'germander', 'centaury']
senticnet['decussation'] = ['-0.86', '0', '0.511', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'separate', 'move', 'branch', 'diverge', 'fork']
senticnet['dedicate'] = ['0.693', '-0.69', '0', '0.714', '#surprise', '#admiration', 'positive', '0.702', 'vow', 'consecrate', 'apply', 'rededicate', 'use']
senticnet['dedication'] = ['0', '0', '0', '0.946', '#admiration', '#admiration', 'positive', '0.946', 'persistence', 'effort', 'eat_properly', 'healthy_environment', 'physical_effort']
senticnet['dedication_ceremony'] = ['0.072', '0.126', '0', '0', '#joy', '#interest', 'positive', '0.099', 'subway_station', 'war_memorial', 'utopia', 'mortuary', 'hero']
senticnet['dedication_plaque'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'dental_plaque']
senticnet['dedicatory_epistle'] = ['0.722', '-0.98', '0', '0.832', '#surprise', '#admiration', 'positive', '0.845', 'laudatory', 'praiseful', 'praising', 'encomiastic', 'complimentary']
senticnet['dedit'] = ['0.654', '-0.75', '0', '0.78', '#surprise', '#admiration', 'positive', '0.729', 'suit', 'cause', 'lawsuit', 'occurrence', 'countersuit']
senticnet['deduce'] = ['-0.09', '0.106', '0', '0.093', '#interest', '#admiration', 'positive', '0.036', 'figure_out', 'revelation', 'arrive_conclusion', 'resolve_problem', 'come_conclusion']
senticnet['deducible_from'] = ['-0.87', '0', '0.858', '-0.77', '#sadness', '#anger', 'negative', '-0.83', 'pass_off', 'fall_out', 'occur', 'happen', 'hap']
senticnet['deduct'] = ['-0.82', '0', '0.835', '-0.67', '#sadness', '#anger', 'negative', '-0.77', 'disparage', 'belittle', 'deflate', 'puncture', 'pick_at']
senticnet['deduction'] = ['0.064', '0.4', '-0.15', '0.308', '#interest', '#admiration', 'positive', '0.154', 'honesty', 'determine', 'compare', 'depend', 'right']
senticnet['deductive'] = ['0', '0.772', '0', '0.723', '#interest', '#admiration', 'positive', '0.748', 'syllogism', 'syllogistic', 'fallacious', 'syllogism', 'disjunctive']
senticnet['deductive_approach'] = ['-0.99', '0', '0.878', '-0.95', '#sadness', '#disgust', 'negative', '-0.94', 'adequacy', 'satisfactoriness', 'acceptability', 'acceptableness', 'quality']
senticnet['deductive_argument'] = ['0.715', '-0.82', '0', '0.659', '#joy', '#surprise', 'positive', '0.734', 'reasoning', 'logical_thinking', 'abstract_thought', 'casuistry', 'line_of_inquiry']
senticnet['deductive_reasoning'] = ['0', '0.705', '0', '0.713', '#interest', '#admiration', 'positive', '0.709', 'deduction', 'synthesis', 'synthetic_thinking', 'research', 'chemistry']
senticnet['deductive_system'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'integrative', 'consolidative', 'unifying', 'combinatorial']
senticnet['deductively'] = ['-0.99', '0', '0.878', '-0.95', '#sadness', '#disgust', 'negative', '-0.94', 'adequacy', 'satisfactoriness', 'acceptability', 'acceptableness', 'quality']
senticnet['dee_dee_ramone'] = ['0.797', '-0.95', '0', '0.921', '#surprise', '#admiration', 'positive', '0.891', 'lush', 'alcoholic', 'alky', 'dipsomaniac', 'boozer']
senticnet['deed'] = ['-0.02', '0.202', '-0.15', '0.266', '#interest', '#admiration', 'positive', '0.071', 'heap', 'let', 'allow', 'come', 'mimic']
senticnet['deed_restriction'] = ['0.621', '-0.60', '0', '0.545', '#joy', '#surprise', 'positive', '0.59', 'privilege', 'perquisite', 'public_easement', 'right_of_way', 'prerogative']
senticnet['deem'] = ['0.862', '-0.61', '0', '0.549', '#joy', '#surprise', 'positive', '0.674', 'consider', 'view', 'regard', 'reckon', 'see']
senticnet['deemed_appropriate'] = ['0', '0.81', '0', '0.825', '#interest', '#admiration', 'positive', '0.818', 'prudence', 'frugality', 'frugalness', 'discernment', 'natural_virtue']
senticnet['deemed_confidential'] = ['-0.02', '0.175', '-0.11', '0.148', '#interest', '#admiration', 'positive', '0.045', 'tolerant', 'phrase', 'inquiry', 'explain', 'question']
senticnet['deemed_harmful'] = ['0.051', '0.165', '-0.06', '0.174', '#interest', '#admiration', 'positive', '0.08', 'control_access', 'prevent', 'latch', 'lock', 'gate']
senticnet['deemed_indecent'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'indecency']
senticnet['deemed_ineligible'] = ['-0.60', '0', '0.832', '-0.53', '#sadness', '#anger', 'negative', '-0.65', 'unsuitable', 'undesirable', 'unqualified', 'disqualified', 'unentitled']
senticnet['deemed_necessary'] = ['-0.95', '0', '0.735', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'novice', 'beginner', 'tyro', 'tiro', 'cub']
senticnet['deemed_null'] = ['-0.91', '0', '-0.77', '-0.75', '#sadness', '#fear', 'negative', '-0.81', 'emptiness', 'star_planet', 'nothingness', 'outer', 'around_earth']
senticnet['deemed_obscene'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'indecency']
senticnet['deemed_offensive'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['deemed_pornographic'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['deemed_safe'] = ['-0.74', '0', '0.781', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'reprimand', 'caution', 'big_monster', 'kidnap', 'mock']
senticnet['deemed_sufficient'] = ['-0.99', '0', '0.712', '-0.85', '#sadness', '#disgust', 'negative', '-0.85', 'need', 'obligation', 'requirement', 'desideratum', 'important']
senticnet['deemed_unsafe'] = ['-0.74', '0', '0.781', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'reprimand', 'caution', 'big_monster', 'kidnap', 'mock']
senticnet['deemphasize'] = ['-0.81', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.80', 'overstate', 'hyperbolize', 'exaggerate', 'overdraw', 'magnify']
senticnet['deems_appropriate'] = ['0', '0.81', '0', '0.825', '#interest', '#admiration', 'positive', '0.818', 'prudence', 'frugality', 'frugalness', 'discernment', 'natural_virtue']
senticnet['deems_reasonable'] = ['0', '0', '-0.75', '-0.71', '#fear', '#disgust', 'negative', '-0.73', 'foreswear', 'relinquish', 'dispense_with', 'forgo', 'throw_overboard']
senticnet['deena_kastor'] = ['-0.04', '-0.03', '0', '-0.16', '#sadness', '#disgust', 'negative', '-0.05', 'crazy', 'damage_body', 'crazy_person', 'threat', 'max']
senticnet['deep'] = ['-0.02', '0.202', '-0.15', '0.266', '#interest', '#admiration', 'positive', '0.071', 'sea', 'ocean', 'color', 'deep_blue', 'blue']
senticnet['deep-brown'] = ['0', '0', '0.837', '-0.71', '#anger', '#disgust', 'negative', '-0.77', 'blueish-green', 'greenish-gold', 'ruby-brown', 'orange-copper', 'golden-green']
senticnet['deep_admiration'] = ['0.488', '0.661', '-0.25', '0.244', '#joy', '#interest', 'positive', '0.284', 'appreciativeness', 'thankfulness', 'unafraid', 'intrepidly', 'audacious']
senticnet['deep_affection'] = ['0.488', '0.661', '-0.25', '0.244', '#joy', '#interest', 'positive', '0.284', 'appreciativeness', 'thankfulness', 'unafraid', 'intrepidly', 'audacious']
senticnet['deep_appreciation'] = ['0.798', '0.754', '0', '0.779', '#joy', '#admiration', 'positive', '0.777', 'affection', 'joy', 'glad', 'admire', 'sympathetic']
senticnet['deep_blue'] = ['-0.02', '0.202', '-0.15', '0.266', '#interest', '#admiration', 'positive', '0.071', 'sea', 'ocean', 'color', 'deep', 'blue']
senticnet['deep_blue_sky'] = ['0.042', '0.126', '-0.06', '0.165', '#interest', '#admiration', 'positive', '0.067', 'sparkle', 'shiny', 'shinny', 'gray', 'shine']
senticnet['deep_breath'] = ['0.757', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.754', 'inhale', 'cough', 'respiration', 'involuntary', 'smell_odor']
senticnet['deep_brown'] = ['0', '0', '-0.72', '-0.83', '#fear', '#disgust', 'negative', '-0.77', 'buff', 'chestnut', 'chocolate', 'coffee', 'brownness']
senticnet['deep_bruise'] = ['0.94', '0.78', '0', '0', '#joy', '#interest', 'positive', '0.86', 'breaheavily', 'heavy_breathe', 'get_exhausted', 'exhaustion', 'see_neighbor']
senticnet['deep_canyon'] = ['0', '0.069', '0', '0.065', '#interest', '#admiration', 'positive', '0.067', 'alp', 'high_mountain', 'pass', 'ski_resort', 'high_place']
senticnet['deep_coma'] = ['-0.56', '0', '0.577', '-0.61', '#anger', '#disgust', 'negative', '-0.58', 'foggy', 'groggy', 'logy', 'lethargic', 'dazed']
senticnet['deep_convection'] = ['-0.12', '-0.05', '0', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'diminish', 'reduce', 'lower', 'lower_music', 'decrease']
senticnet['deep_crimson'] = ['0.577', '-0.69', '0', '0.645', '#surprise', '#admiration', 'positive', '0.639', 'shining', 'polished', 'shiny', 'bright', 'burnished']
senticnet['deep_disappointment'] = ['-0.66', '0', '0.721', '-0.53', '#sadness', '#anger', 'negative', '-0.64', 'unlimited', 'outright', 'unconditional', 'unqualified', 'unqualifiedal']
senticnet['deep_discount'] = ['-0.03', '0.139', '0', '0.109', '#interest', '#admiration', 'positive', '0.07', 'exchange_money', 'purchase', 'sell_goods', 'make_rich', 'incur_debt']
senticnet['deep_distrust'] = ['0', '0', '0', '-0.90', '#disgust', '#disgust', 'negative', '-0.90', 'cause_suffer', 'disrespect', 'lose_team', 'bad_luck', 'leave_out']
senticnet['deep_fat_fryer'] = ['-0.83', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.78', 'picaninny', 'kid', 'minor', 'tike', 'small_picaninny']
senticnet['deep_freeze'] = ['-0.64', '0', '0.612', '-0.84', '#sadness', '#disgust', 'negative', '-0.7', 'chilling', 'frost', 'lyophilization', 'icing', 'cooling']
senticnet['deep_fried'] = ['-0.98', '0', '0.591', '-0.99', '#sadness', '#disgust', 'negative', '-0.85', 'fast_food', 'junk_food', 'unhealthy_food', 'reuse_oil', 'fattening_food']
senticnet['deep_fry'] = ['-0.83', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.78', 'picaninny', 'kid', 'minor', 'tike', 'small_picaninny']
senticnet['deep_fryer'] = ['0.061', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.055', 'cook', 'stick_butter', 'find_recipe', 'bake_oven', 'food_bake']
senticnet['deep_furrow'] = ['0.689', '-0.56', '0', '0.74', '#joy', '#admiration', 'positive', '0.665', 'crease', 'crinkle', 'wrinkle', 'heart_line', 'seam']
senticnet['deep_gash'] = ['-0.12', '-0.02', '0', '-0.15', '#sadness', '#disgust', 'negative', '-0.08', 'incision', 'laceration', 'abrasion', 'cut_skin', 'painful_injury']
senticnet['deep_gloom'] = ['-0.79', '0.262', '-0.45', '-0.04', '#sadness', '#fear', 'negative', '-0.25', 'downhearted', 'down_in_mouth', 'depressed', 'downcast', 'low-spirited']
senticnet['deep_gorge'] = ['0.97', '0.758', '0', '0', '#joy', '#interest', 'positive', '0.864', 'trot', 'step_out', 'step_aside', 'progress', 'enter_place']
senticnet['deep_gratitude'] = ['0.798', '0.754', '0', '0.779', '#joy', '#admiration', 'positive', '0.777', 'affection', 'joy', 'glad', 'admire', 'sympathetic']
senticnet['deep_hole'] = ['-0.93', '0', '0.955', '-0.90', '#sadness', '#anger', 'negative', '-0.93', 'grave', 'hole', 'burial', 'corpse', 'deep']
senticnet['deep_humility'] = ['0.488', '0.661', '-0.25', '0.244', '#joy', '#interest', 'positive', '0.284', 'appreciativeness', 'gratefulness', 'unafraid', 'intrepidly', 'audacious']
senticnet['deep_inferior_epigastric'] = ['-0.58', '0', '0.715', '-0.59', '#anger', '#disgust', 'negative', '-0.63', 'fly', 'overlap', 'barndoor', 'coattail', 'codpiece']
senticnet['deep_inside'] = ['0.788', '-0.60', '0', '0.884', '#joy', '#admiration', 'positive', '0.759', 'gap', 'rathole', 'aperture', 'bullet_hole', 'hole']
senticnet['deep_insight_into'] = ['-0.06', '-0.02', '0.02', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'eye_strain', 'new_perspective', 'move_eye', 'gain_insight', 'great_understand']
senticnet['deep_laceration'] = ['-0.12', '-0.02', '0', '-0.15', '#sadness', '#disgust', 'negative', '-0.08', 'incision', 'laceration', 'abrasion', 'cut_skin', 'painful_injury']
senticnet['deep_maroon'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'tropical_fish', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['deep_meditation'] = ['0.088', '0.135', '0', '0', '#joy', '#interest', 'positive', '0.112', 'watch_others', 'meditate', 'imagine', 'mantra', 'dance_music']
senticnet['deep_melancholy'] = ['-0.69', '0.32', '-0.38', '-0.08', '#sadness', '#fear', 'negative', '-0.21', 'forlornly', 'desolation', 'forlorn', 'weepiness', 'tearful']
senticnet['deep_mid_wicket'] = ['-0.13', '0', '-0.11', '-0.04', '#sadness', '#fear', 'negative', '-0.1', 'concern_health', 'medical_problem', 'strengthful', 'surgery', 'injury']
senticnet['deep_midwicket'] = ['-0.13', '0', '-0.11', '-0.04', '#sadness', '#fear', 'negative', '-0.1', 'concern_health', 'medical_problem', 'strengthful', 'surgery', 'injury']
senticnet['deep_mistrust'] = ['0', '0', '0', '-0.90', '#disgust', '#disgust', 'negative', '-0.90', 'cause_suffer', 'disrespect', 'lose_team', 'bad_luck', 'leave_out']
senticnet['deep_ocean'] = ['-0.02', '-0.01', '0.03', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'ocean_beach', 'beach', 'water_sea', 'shore', 'ground_ocean']
senticnet['deep_ocean_floor'] = ['-0.02', '-0.01', '0.03', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'ocean_beach', 'beach', 'water_sea', 'shore', 'ground_ocean']
senticnet['deep_penetration'] = ['0.89', '-0.62', '0', '0.548', '#joy', '#surprise', 'positive', '0.686', 'breakthrough', 'interpenetration', 'foray', 'maraud', 'raid']
senticnet['deep_pessimism'] = ['-0.70', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.75', 'pessimism', 'pessimistically', 'dispiritedly', 'hopelessness', 'hopelessly']
senticnet['deep_pit'] = ['-0.96', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.96', 'tunnel', 'international_space_station', 'stick_wall', 'car_race', 'auction']
senticnet['deep_ravine'] = ['0.057', '0.075', '0.039', '0.059', '#interest', '#admiration', 'positive', '0.038', 'summit', 'big_rock', 'high_land', 'peak', 'mountain_side']
senticnet['deep_relaxation'] = ['0.088', '0.135', '0', '0', '#joy', '#interest', 'positive', '0.112', 'watch_others', 'meditate', 'imagine', 'mantra', 'dance_music']
senticnet['deep_remorse'] = ['-0.89', '0.168', '-0.60', '0.05', '#sadness', '#fear', 'negative', '-0.32', 'remorsefully', 'contritely', 'contriteness', 'ruefully', 'attrition']
senticnet['deep_resentment'] = ['-0.90', '0', '0.48', '-0.99', '#sadness', '#disgust', 'negative', '-0.79', 'strong_dislike', 'hate', 'hatred', 'enemy', 'animosity']
senticnet['deep_reverence'] = ['0', '0.767', '0', '0.711', '#interest', '#admiration', 'positive', '0.739', 'reverently', 'hero-worship', 'venerate', 'idolize', 'veneration']
senticnet['deep_rooted_corruption'] = ['0', '0', '-0.77', '-0.86', '#fear', '#disgust', 'negative', '-0.82', 'abduction', 'punishment', 'emergency', 'illiteracy', 'heal']
senticnet['deep_sadness'] = ['-0.85', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.85', 'feel_sorry', 'sadness', 'bad_feeling', 'negative_feeling', 'lament']
senticnet['deep_sea'] = ['-0.02', '-0.01', '0.03', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'ocean_beach', 'beach', 'water_sea', 'shore', 'ground_ocean']
senticnet['deep_sea_container'] = ['-0.02', '-0.02', '0.064', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'ocean_water', 'fillet_fish', 'chowder', 'fillet', 'underwater']
senticnet['deep_sea_diver'] = ['0', '0.07', '0', '0', '#interest', '#interest', 'positive', '0.07', 'sunken_treasure', 'real_duck', 'urchin', 'mussel', 'gold_fish']
senticnet['deep_sea_fish'] = ['-0.79', '0', '0.709', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'animal', 'animate_being', 'ctenophore', 'invertebrate', 'beast']
senticnet['deep_sea_floor'] = ['-0.02', '-0.01', '0.03', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'ocean_beach', 'beach', 'water_sea', 'shore', 'ground_ocean']
senticnet['deep_sea_port'] = ['-0.02', '-0.02', '0.064', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'ocean_water', 'fillet_fish', 'chowder', 'fillet', 'underwater']
senticnet['deep_seafloor'] = ['-0.02', '-0.01', '0.03', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'ocean_beach', 'beach', 'water_sea', 'shore', 'ground_ocean']
senticnet['deep_seated_fear'] = ['0.72', '-0.72', '0', '0.506', '#joy', '#surprise', 'positive', '0.651', 'standoffishness', 'withdrawnness', 'closeness', 'secretiveness', 'remoteness']
senticnet['deep_sedation'] = ['-0.65', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'put_to_sleep', 'operation', 'anesthesia', 'surgery', 'local_anesthesia']
senticnet['deep_shade'] = ['-0.93', '0', '0.906', '-0.53', '#sadness', '#anger', 'negative', '-0.79', 'juvenility', 'state', 'immatureness', 'callowness', 'jejuneness']
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senticnet['degree_av_block'] = ['-0.83', '0', '0.987', '-0.50', '#sadness', '#anger', 'negative', '-0.77', 'heart_block', 'atrioventricular_block', 'bradycardia', 'ventricular_fibrillation', 'tachycardia']
senticnet['degree_burglary'] = ['-0.75', '0', '0.94', '0', '#sadness', '#anger', 'negative', '-0.84', 'steal_money', 'theft', 'stick_up', 'robbery', 'rob']
senticnet['degree_cooler'] = ['0', '0.094', '0', '0', '#interest', '#interest', 'positive', '0.094', 'cold', 'cavern', 'under_umbrella', 'parasol', 'stick_wall']
senticnet['degree_felony'] = ['-0.84', '0', '0.745', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'crime', 'against_law', 'revenge', 'vengeance', 'theft']
senticnet['degree_felony_murder'] = ['0', '0', '-0.95', '-0.85', '#fear', '#disgust', 'negative', '-0.90', 'choose_victim', 'murder_weapon', 'hide_evidence', 'murder', 'shoot_gun']
senticnet['degree_granting_program'] = ['0.087', '0.248', '-0.19', '0.31', '#interest', '#admiration', 'positive', '0.113', 'enroll', 'enroll_school', 'enroll_class', 'pay_tuition', 'start_college']
senticnet['degree_murder'] = ['0.654', '-0.59', '0', '0.774', '#joy', '#admiration', 'positive', '0.673', 'instrument', 'indictment', 'murder_indictment', 'true_bill', 'legal_document']
senticnet['degree_premeditated_murder'] = ['0.654', '-0.59', '0', '0.774', '#joy', '#admiration', 'positive', '0.673', 'instrument', 'indictment', 'murder_indictment', 'true_bill', 'legal_document']
senticnet['degustation'] = ['0.897', '-0.96', '0', '0.785', '#joy', '#surprise', 'positive', '0.881', 'erotic', 'voluptuary', 'epicure', 'gourmet', 'sensualist']
senticnet['deh_chenar'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'identifiable', 'specifiable', 'recognizable', 'diagnosable', 'classifiable']
senticnet['dehumanization'] = ['-0.99', '0', '0.947', '-0.88', '#sadness', '#anger', 'negative', '-0.94', 'atrociousness', 'barbarousness', 'heinousness', 'atrocity', 'barbarity']
senticnet['dehumanize'] = ['-0.89', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.84', 'degrade', 'demean', 'disgrace', 'undignified', 'inglorious']
senticnet['dehumidifier'] = ['0.628', '-0.96', '0', '0.682', '#surprise', '#admiration', 'positive', '0.759', 'electric_fan', 'blower', 'fan', 'punkah', 'device']
senticnet['dehydrase'] = ['-0.52', '0', '0.681', '-0.76', '#anger', '#disgust', 'negative', '-0.65', 'triose', 'tetrose', 'pentose', 'simple_sugar', 'aldose']
senticnet['dehydration'] = ['-0.84', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.90', 'tired_leg', 'heavy_breathe', 'sweat', 'shin_splint', 'get_sweaty']
senticnet['dehydrogenase_deficiency'] = ['-0.57', '0', '0.973', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['dehydrogenation'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'carboxylic_acid', 'succinic_acid', 'valeric_acid']
senticnet['deictic'] = ['0.904', '-0.58', '0', '0.736', '#joy', '#admiration', 'positive', '0.743', 'naming', 'appellative', 'denotative', 'extensional', 'referent']
senticnet['deification'] = ['0', '-0.72', '0', '0.895', '#surprise', '#admiration', 'positive', '0.807', 'exalt', 'elevation', 'ecstasy', 'exaltation', 'self-exaltation']
senticnet['deify'] = ['0', '0.82', '0', '0.847', '#interest', '#admiration', 'positive', '0.833', 'reverently', 'hero-worship', 'idolize', 'veneration', 'reverentially']
senticnet['deighton_pierce_glynn'] = ['0.053', '0.122', '-0.07', '0.086', '#interest', '#admiration', 'positive', '0.046', 'attorney', 'lawyer', 'jury', 'decide_guilt', 'prosecutor']
senticnet['deign'] = ['0.86', '0', '0', '0.97', '#joy', '#admiration', 'positive', '0.915', 'accept', 'acknowledge', 'condescend', 'vouchsafing', 'vouchsafe']
senticnet['deindividuation'] = ['-0.93', '0', '0.586', '-0.66', '#sadness', '#disgust', 'negative', '-0.73', 'camp', 'pack', 'clique', 'coterie', 'brain_trust']
senticnet['deinze'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'feudal_lord', 'lord', 'master', 'overlord', 'king']
senticnet['deionization'] = ['0.581', '-0.88', '0', '0.534', '#joy', '#surprise', 'positive', '0.667', 'better', 'improve', 'pure', 'meliorate', 'amend']
senticnet['deipnosophist'] = ['0.727', '-0.62', '0', '0.57', '#joy', '#surprise', 'positive', '0.64', 'utterer', 'conversationalist', 'interlocutor', 'conversational_partner', 'speaker']
senticnet['deir_el_bahari'] = ['-0.92', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.91', 'bury_body', 'coffin', 'headstone', 'morgue', 'tombstone']
senticnet['deir_el_bahri'] = ['-0.92', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.91', 'bury_body', 'coffin', 'headstone', 'morgue', 'tombstone']
senticnet['deism'] = ['-0.98', '0', '0.891', '-0.60', '#sadness', '#anger', 'negative', '-0.82', 'exorcism', 'dispossession', 'magic', 'thaumaturgy', 'belief']
senticnet['deist'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'non-believer', 'doubter', 'agnostic', 'freethinker', 'sceptic']
senticnet['deity'] = ['0', '0', '0', '0.844', '#admiration', '#admiration', 'positive', '0.844', 'almighty', 'omniscient', 'protector', 'god', 'baron']
senticnet['deja_vu'] = ['-0.80', '0', '-0.8', '0', '#sadness', '#fear', 'negative', '-0.80', 'sinking', 'misgiving', 'qualm', 'scruple', 'insecurely']
senticnet['deject'] = ['-0.81', '0.239', '-0.44', '-0.06', '#sadness', '#fear', 'negative', '-0.27', 'feel_down', 'cast_down', 'get_down', 'dispirit', 'demoralize']
senticnet['dejected'] = ['-0.56', '0', '0.55', '-0.70', '#sadness', '#anger', 'negative', '-0.60', 'downhearted', 'down_in_mouth', 'dispirited', 'grim', 'glum']
senticnet['dejectedly'] = ['-0.81', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.86', 'impassiveness', 'impassivity', 'emotionlessness', 'unemotionality', 'stolidity']
senticnet['dejectedness'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'dispiritedness', 'low-spiritedness', 'lowness', 'downheartedness', 'cheerless']
senticnet['dejection'] = ['-0.83', '0', '0.521', '-0.71', '#sadness', '#disgust', 'negative', '-0.69', 'stool', 'droppings', 'chip', 'cow_chip', 'cow_dung']
senticnet['del_frisco_double'] = ['0', '0.105', '0', '0', '#interest', '#interest', 'positive', '0.105', 'beak', 'many_feathers', 'big_eye', 'bird', 'bird_mouth']
senticnet['del_fuego'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'dryness', 'thirstiness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['del_mccoury'] = ['-0.04', '-0.04', '-0.04', '-0.04', '#fear', '#disgust', 'negative', '-0.02', 'music_shop', 'music_store', 'musical_instrument_store', 'string_quartet', 'high_school_band']
senticnet['del_mccoury_band'] = ['-0.04', '-0.04', '-0.04', '-0.04', '#fear', '#disgust', 'negative', '-0.02', 'music_shop', 'music_store', 'musical_instrument_store', 'string_quartet', 'high_school_band']
senticnet['del_norte_county'] = ['-0.06', '0.21', '0', '0.076', '#interest', '#admiration', 'positive', '0.074', 'new_think', 'figure_out', 'brain_activity', 'idea', 'come_up_idea']
senticnet['del_pezzo_surface'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'fragmentation', 'discharge', 'backfire', 'blast']
senticnet['del_sole'] = ['0.107', '0.11', '-0.08', '0.21', '#interest', '#admiration', 'positive', '0.086', 'live_healthy_life', 'forever_young', 'live_forever', 'live_long', 'live_good_life']
senticnet['delaney'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
senticnet['delay'] = ['0', '0', '0.98', '-0.88', '#anger', '#disgust', 'negative', '-0.93', 'waste_time', 'get_angry', 'become_impatient', 'lose_temper', 'lose_sleep']
senticnet['delay_ejaculation'] = ['-0.74', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.78', 'ejaculate', 'pass_gene', 'ecstasy', 'lower_standard_live', 'survival_species']
senticnet['delay_gratification'] = ['0', '0.082', '0', '0.077', '#interest', '#admiration', 'positive', '0.08', 'drink_tea', 'absorb_moisture', 'sit_on_beach', 'procrastination', 'wave_goodbye']
senticnet['delay_ovulation'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'birth_control_pill', 'condom', 'contraceptive_pill', 'sex_toy', 'prevent_pregnancy']
senticnet['delay_payment'] = ['-0.04', '0', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'debt', 'become_dull', 'delay', 'accumulate_debt', 'owe']
senticnet['delayed_implantation'] = ['0.032', '0.109', '-0.07', '0.085', '#interest', '#admiration', 'positive', '0.037', 'plural', 'kin', 'relate_person', 'younger', 'young_people']
senticnet['delayed_puberty'] = ['0.995', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.918', 'tell_everyone', 'reason', 'clarify', 'forethought', 'opinion']
senticnet['delayed_type_hypersensitivity'] = ['-0.66', '0', '0.877', '-0.64', '#sadness', '#anger', 'negative', '-0.72', 'somatic_cell', 'mastocyte', 'labrocyte', 'target_cell', 'phagocyte']
senticnet['delaying_action'] = ['0.72', '-0.76', '0', '0.695', '#joy', '#surprise', 'positive', '0.726', 'suck_up', 'blot', 'wipe_up', 'mop', 'sponge_up']
senticnet['delaying_ovulation'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'birth_control_pill', 'condom', 'contraceptive_pill', 'sex_toy', 'prevent_pregnancy']
senticnet['delectable'] = ['0.721', '-0.78', '0', '0', '#joy', '#surprise', 'positive', '0.754', 'delicious', 'luscious', 'pleasant-tasting', 'scrumptious', 'toothsome']
senticnet['delegate'] = ['-0.72', '0', '0.918', '-0.54', '#sadness', '#anger', 'negative', '-0.73', 'designate', 'depute', 'reduce', 'sideline', 'demote']
senticnet['delegated_authority'] = ['0', '0.81', '0', '0.825', '#interest', '#admiration', 'positive', '0.818', 'prudence', 'frugality', 'frugalness', 'discernment', 'natural_virtue']
senticnet['delegated_credential'] = ['-0.79', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.79', 'misgiving', 'qualm', 'scruple', 'insecurity', 'insecure']
senticnet['delegated_responsibility'] = ['0.955', '0.945', '0', '0.835', '#joy', '#interest', 'positive', '0.912', 'support_family', 'provide_service', 'accomplish', 'find_job', 'pay_well']
senticnet['delegation'] = ['0', '0.852', '0', '0', '#interest', '#interest', 'positive', '0.852', 'streamer', 'tribute', 'salutation', 'gift', 'anniversary_gift']
senticnet['delegitimize'] = ['0.54', '-0.97', '0', '0.569', '#surprise', '#admiration', 'positive', '0.695', 'overstate', 'hyperbolize', 'glorify', 'exaggerate', 'overdraw']
senticnet['delete'] = ['-0.82', '0', '0.694', '-0.71', '#sadness', '#disgust', 'negative', '-0.74', 'withdraw', 'strike', 'scratch', 'efface', 'take']
senticnet['deleted_scene'] = ['0.918', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.827', 'encounter', 'reveal', 'programme', 'lose', 'sequel']
senticnet['delhi_mumbai'] = ['0', '0', '0', '0.085', '#admiration', '#admiration', 'positive', '0.085', 'use_farm', 'gate', 'door', 'doorway', 'entranceway']
senticnet['deli'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'candy', 'sweet', 'sugar', 'sweet_tooth', 'chocolate']
senticnet['deli_counter'] = ['0', '0', '0.044', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'large_supermarket', 'food_store', 'large_food_store', 'corner_shop', 'convenience_food_store']
senticnet['deli_sandwich'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'grocery_cart', 'super_market', 'fruit_stand', 'candy_store', 'produce_store']
senticnet['deli_store'] = ['0', '0', '0.016', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'dime_store', 'shop_arcade', 'vend_stand', 'bookshop', 'store']
senticnet['deliberate'] = ['-0.32', '0.138', '-0.25', '0.055', '#sadness', '#fear', 'negative', '-0.09', 'bad_time', 'look_fact', 'make_decision', 'investigation', 'rational']
senticnet['deliberate_deception'] = ['0', '0', '0.989', '-0.71', '#anger', '#disgust', 'negative', '-0.85', 'deceive', 'against_law', 'false', 'fool', 'trick']
senticnet['deliberate_destruction'] = ['-0.14', '-0.00', '-0.12', '-0.21', '#sadness', '#disgust', 'negative', '-0.11', 'torture', 'demolish', 'maim', 'terminate', 'hurt_person']
senticnet['deliberate_disregard'] = ['-0.50', '0', '0.89', '-0.78', '#anger', '#disgust', 'negative', '-0.72', 'excess', 'degree', 'grade', 'level', 'immoderateness']
senticnet['deliberate_falsification'] = ['-0.59', '0', '0.537', '-0.53', '#sadness', '#anger', 'negative', '-0.55', 'murder', 'mutilate', 'mangle', 'misrepresent', 'belie']
senticnet['deliberate_fraud'] = ['-0.59', '0', '0.537', '-0.53', '#sadness', '#anger', 'negative', '-0.55', 'murder', 'mutilate', 'mangle', 'misrepresent', 'belie']
senticnet['deliberate_handball'] = ['-0.71', '0', '0.83', '-0.68', '#sadness', '#anger', 'negative', '-0.74', 'gag', 'jest', 'jape', 'joke', 'laugh']
senticnet['deliberate_intent'] = ['-0.04', '0.164', '0.685', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maliciousness', 'spitefulness', 'maleficence', 'malefic', 'malign']
senticnet['deliberate_misrepresentation'] = ['-0.59', '0', '0.537', '-0.53', '#sadness', '#anger', 'negative', '-0.55', 'murder', 'mutilate', 'mangle', 'misrepresent', 'belie']
senticnet['deliberate_obfuscation'] = ['-0.77', '0', '0.981', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'trip_up', 'stumble', 'misjudge', 'err', 'fall_for']
senticnet['deliberately'] = ['-0.04', '0.164', '0.685', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maliciousness', 'spitefulness', 'maleficence', 'malefic', 'malign']
senticnet['deliberately_mislead'] = ['-0.78', '0', '0.704', '-0.91', '#sadness', '#disgust', 'negative', '-0.8', 'direct', 'take', 'lead', 'conduct', 'guide']
senticnet['deliberately_misrepresent'] = ['-0.77', '0', '0.981', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'trip_up', 'stumble', 'misjudge', 'err', 'fall_for']
senticnet['deliberately_vague'] = ['-0.79', '0.241', '-0.52', '-0.11', '#sadness', '#fear', 'negative', '-0.29', 'disoriented', 'obscure', 'unsure', 'uncertain', 'unfair']
senticnet['deliberateness'] = ['-0.68', '0', '0.82', '-0.96', '#anger', '#disgust', 'negative', '-0.82', 'dilatoriness', 'leisureliness', 'procrastination', 'rate', 'pace']
senticnet['deliberation'] = ['0', '0', '0.988', '-0.87', '#anger', '#disgust', 'negative', '-0.93', 'off-the-record', 'closed-door', 'round-table', 'town-hall', 'town-hall-style']
senticnet['delicacy'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'dainty', 'jelly', 'treat', 'gelatin_dessert', 'aspic']
senticnet['delicate'] = ['0.058', '0.141', '0', '0.207', '#interest', '#admiration', 'positive', '0.135', 'versatile', 'beautiful', 'light_colored', 'fragile', 'delicious']
senticnet['delicate_beauty'] = ['0.577', '-0.69', '0', '0.645', '#surprise', '#admiration', 'positive', '0.639', 'shining', 'polished', 'shiny', 'bright', 'burnished']
senticnet['delicate_flavor'] = ['0.043', '0.091', '0', '0.113', '#interest', '#admiration', 'positive', '0.082', 'sweet_sour', 'tasty', 'yummy', 'popular_food', 'form_of_food']
senticnet['delicate_task'] = ['-0.03', '0.136', '-0.09', '0.105', '#interest', '#admiration', 'positive', '0.028', 'chore', 'hire_help', 'police_work', 'career', 'job']
senticnet['delicately'] = ['0', '-0.88', '0', '0.732', '#surprise', '#admiration', 'positive', '0.807', 'subtle', 'subtly', 'cleverly', 'powerfully', 'artfully']
senticnet['delicatessen'] = ['0.059', '0.212', '0', '0.143', '#interest', '#admiration', 'positive', '0.138', 'christmas_shop', 'go_grocery_shop', 'pay_credit_card', 'pay_cash', 'holiday_shop']
senticnet['delicioso'] = ['0.973', '-0.50', '0', '0.612', '#joy', '#admiration', 'positive', '0.698', 'chic', 'chicness', 'jauntiness', 'modishness', 'stylishness']
senticnet['delicious'] = ['0.075', '0.076', '0', '0.166', '#interest', '#admiration', 'positive', '0.106', 'yummy', 'tasty', 'good_eat', 'creamy', 'edible']
senticnet['delicious_aroma'] = ['0.933', '-0.87', '0', '0.766', '#joy', '#surprise', 'positive', '0.858', 'scent', 'aroma', 'olfactory_property', 'smell', 'odor']
senticnet['delicious_breakfast'] = ['0.715', '0.785', '0', '0', '#joy', '#interest', 'positive', '0.75', 'scrumble_egg', 'heat_meal', 'pan', 'beat_egg', 'food']
senticnet['delicious_cake'] = ['0.029', '-0.16', '0', '0', '#joy', '#surprise', 'positive', '0.095', 'mousse', 'brownie', 'pancake', 'custard', 'doughnut']
senticnet['delicious_dinner'] = ['0.026', '0.069', '0', '0', '#joy', '#interest', 'positive', '0.048', 'eat_food', 'eat_beef', 'chew_food', 'espresso', 'eat_much']
senticnet['delicious_dish'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'cook_meat', 'cook_hamburger', 'cook_food', 'cook_rice', 'brown_meat']
senticnet['delicious_food'] = ['0.965', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.951', 'yum', 'hot_meal', 'good_food', 'fresh_produce', 'tasty_food']
senticnet['delicious_fruit'] = ['0.05', '0', '0.047', '0.068', '#joy', '#admiration', 'positive', '0.024', 'tree_fruit', 'yellow_fruit', 'small_fruit', 'vine_fruit', 'red_fruit']
senticnet['delicious_lunch'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'cafe', 'buy_sandwich', 'eat_food', 'buy_food', 'finish_job']
senticnet['delicious_meal'] = ['0.028', '-0.07', '0', '0', '#joy', '#surprise', 'positive', '0.05', 'casserole', 'meatloaf', 'hot_dog_bun', 'hamburger', 'hot_dog']
senticnet['delicious_meal_bun'] = ['0.023', '-0.07', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'casserole', 'meatloaf', 'hot_dog', 'hamburger', 'delicious_meal']
senticnet['delicious_taste'] = ['0.939', '-0.74', '0', '0.57', '#joy', '#surprise', 'positive', '0.751', 'haleness', 'wholeness', 'rosiness', 'health', 'vitality']
senticnet['deliciously'] = ['0.512', '0.658', '-0.25', '0.256', '#joy', '#interest', 'positive', '0.293', 'riotously', 'expansively', 'ebulliently', 'zestfulness', 'gusto']
senticnet['deliciously_comic'] = ['0.807', '-0.83', '0', '0.688', '#joy', '#surprise', 'positive', '0.776', 'folly', 'lunacy', 'craziness', 'indulgence', 'foolery']
senticnet['deliciously_evil'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'role', 'theatrical_role', 'part', 'persona']
senticnet['deliciousness'] = ['-0.83', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.88', 'morsel', 'nibble', 'mouthful', 'morsel', 'delectable']
senticnet['delict'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'culpable_negligence', 'evasion', 'dereliction', 'negligence']
senticnet['delight'] = ['0.709', '0.977', '0', '0.795', '#interest', '#admiration', 'positive', '0.827', 'happy', 'bring_happiness', 'happiness', 'good_feeling', 'feel_glad']
senticnet['delight_audience'] = ['0.986', '0.924', '0', '0', '#joy', '#interest', 'positive', '0.955', 'add_two_numbers', 'play', 'win_sport_event', 'play_cat', 'find_hardware_store']
senticnet['delight_child'] = ['0.933', '0.85', '0', '0.966', '#joy', '#admiration', 'positive', '0.916', 'entertain_child', 'amuse', 'make_laugh', 'amuse_child', 'give_pleasure']
senticnet['delight_sense'] = ['0.884', '0.722', '0', '0', '#joy', '#interest', 'positive', '0.803', 'sound_good', 'great_joy', 'feel_refreshed', 'happiness', 'self_satisfaction']
senticnet['delightedly'] = ['0.655', '0.635', '-0.23', '0.3', '#joy', '#interest', 'positive', '0.338', 'cheerful', 'jolly_up', 'hearten', 'chirk_up', 'embolden']
senticnet['delighteth'] = ['0.622', '0.641', '-0.24', '0.285', '#joy', '#interest', 'positive', '0.327', 'gleeful', 'gladsome', 'happiness', 'gladness', 'gladfulness']
senticnet['delightful'] = ['0.06', '0', '0', '0.115', '#joy', '#admiration', 'positive', '0.087', 'rhyme', 'sonnet', 'poem', 'song_lyric', 'form_of_art']
senticnet['delightful_companion'] = ['0.695', '0.623', '-0.23', '0.319', '#joy', '#interest', 'positive', '0.351', 'uproarious', 'hilariously', 'hilarious', 'screaming', 'uproariously']
senticnet['delightfully'] = ['0.512', '0.658', '-0.25', '0.256', '#joy', '#interest', 'positive', '0.293', 'riotously', 'expansively', 'ebulliently', 'zestfulness', 'gusto']
senticnet['delimitate'] = ['0', '0.85', '0', '0.996', '#interest', '#admiration', 'positive', '0.923', 'set', 'specify', 'determine', 'designate', 'set_apart']
senticnet['delineate'] = ['0.548', '-0.86', '0', '0.938', '#surprise', '#admiration', 'positive', '0.784', 'elaborate', 'clear', 'crystallize', 'clear_up', 'make_clear']
senticnet['delineated'] = ['0.71', '-0.78', '0', '0.531', '#joy', '#surprise', 'positive', '0.675', 'depicted', 'pictured', 'portrayed', 'described', 'diagrammatic']
senticnet['delineation'] = ['0', '0', '-0.99', '-0.87', '#fear', '#disgust', 'negative', '-0.93', 'portrayal', 'picture', 'word-painting', 'depiction', 'characterization']
senticnet['delinquency'] = ['-0.76', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.73', 'overdue', 'debt', 'offense', 'misconduct', 'relict']
senticnet['delinquent'] = ['-0.56', '0', '0.743', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'neglectful', 'derelict', 'inattentive', 'negligent', 'lax']
senticnet['delinquent_youth'] = ['-0.43', '0.107', '-0.42', '-0.03', '#sadness', '#fear', 'negative', '-0.19', 'send_to_jail', 'go_jail', 'jail_time', 'go_to_prison', 'injustice']
senticnet['deliquescence'] = ['-0.57', '0', '0.767', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'verboseness', 'verbosity', 'prolixity', 'turgidness', 'flatulence']
senticnet['deliriant'] = ['-0.73', '0', '0.944', '-0.94', '#anger', '#disgust', 'negative', '-0.87', 'lysergic_acid_diethylamide', 'mescaline', 'peyote', 'phencyclidine', 'drug']
senticnet['delirious'] = ['0', '0', '-0.72', '-0.84', '#fear', '#disgust', 'negative', '-0.78', 'hallucinating', 'excited', 'frantic', 'mad', 'unrestrained']
senticnet['deliriously'] = ['0.512', '0.658', '-0.25', '0.256', '#joy', '#interest', 'positive', '0.293', 'riotously', 'expansively', 'ebulliently', 'zestfulness', 'gusto']
senticnet['delirium'] = ['-0.97', '0', '0.777', '-0.53', '#sadness', '#anger', 'negative', '-0.76', 'manic_disorder', 'mania', 'nympholepsy', 'mass_hysteria', 'epidemic_hysertia']
senticnet['deliver'] = ['0.846', '-0.83', '0', '0', '#joy', '#surprise', 'positive', '0.838', 'send', 'cope', 'give_out', 'express', 'permit']
senticnet['deliver_baby'] = ['-0.03', '0', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.05', 'baby', 'large_family', 'youth', 'small_child', 'birth']
senticnet['deliver_checkmate'] = ['0', '0', '0', '0.115', '#admiration', '#admiration', 'positive', '0.115', 'chess_knight', 'strategy', 'pawn', 'chess_queen', 'chess_rook']
senticnet['deliver_goods'] = ['0.735', '-0.51', '0', '0.66', '#joy', '#admiration', 'positive', '0.637', 'clear', 'pan_out', 'reach', 'bring_off', 'luck_out']
senticnet['deliverance'] = ['0.825', '-0.63', '0', '0.781', '#joy', '#admiration', 'positive', '0.747', 'salvage', 'lifesaving', 'redemption', 'salvation', 'recovery']
senticnet['delivered_prematurely'] = ['-0.51', '0', '0.747', '-0.94', '#anger', '#disgust', 'negative', '-0.73', 'stillborn_infant', 'neonate', 'preterm_baby', 'premature_infant', 'preterm_infant']
senticnet['deliverer'] = ['-0.72', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.85', 'quit', 'wound', 'ooze', 'sore', 'quitter']
senticnet['delivery'] = ['0.782', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.823', 'send', 'mail_delivery', 'mail', 'postal_service', 'package']
senticnet['delivery_network_cdn'] = ['0.886', '-0.77', '0', '0.815', '#joy', '#admiration', 'positive', '0.826', 'countenance', 'legitimate', 'permit', 'allow', 'let']
senticnet['delivery_truck'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'clean', 'polish', 'shiny', 'water', 'sexy_girl']
senticnet['delivery_truck_driver'] = ['0.969', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.936', 'attendant', 'manager', 'public_servant', 'specialist', 'accomplice']
senticnet['deliveryman'] = ['-0.80', '0', '0.718', '-0.79', '#sadness', '#disgust', 'negative', '-0.77', 'walk_exercise', 'lose_wallet', 'go_debt', 'pay_dinner', 'spend_much_money']
senticnet['dell_emc'] = ['-0.63', '0', '0.862', '-0.52', '#sadness', '#anger', 'negative', '-0.67', 'knot', 'bunch', 'tuft', 'tussock', 'agglomeration']
senticnet['delle_dolomiti'] = ['0.107', '0.11', '-0.08', '0.21', '#interest', '#admiration', 'positive', '0.086', 'live_healthy_life', 'forever_young', 'live_forever', 'live_long', 'live_good_life']
senticnet['delocalization'] = ['-0.84', '0', '0.8', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'rigor_mortis', 'natural_process', 'natural_action', 'activity', 'action']
senticnet['delonix_regia'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'carnauba_palm', 'carnauba', 'wax_palm', 'palmetto', 'palmyra']
senticnet['delorean_dmc'] = ['-0.73', '0', '0.72', '-0.93', '#sadness', '#disgust', 'negative', '-0.79', 'imp', 'elf', 'hob', 'leprechaun', 'pixie']
senticnet['delpo'] = ['0', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.91', 'umpire', 'referee', 'legal_proceed', 'clang', 'clamor']
senticnet['delrin'] = ['0.72', '0', '0', '0.733', '#joy', '#admiration', 'positive', '0.726', 'spanner', 'gold', 'strong_material', 'platinum', 'durable']
senticnet['delta_dental'] = ['-0.03', '0.079', '0.024', '0.091', '#interest', '#admiration', 'positive', '0.029', 'toothpaste', 'toothbrush', 'mouth', 'gel_toothpaste', 'tooth']
senticnet['delta_lyrae'] = ['0.977', '-0.93', '0', '0.583', '#joy', '#surprise', 'positive', '0.831', 'giant', 'shinny', 'supernova', 'supergiant', 'binary_star']
senticnet['deltoid'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'skeletal_muscle', 'pterygoid_muscle', 'abductor', 'abductor_muscle']
senticnet['deltoid_muscle'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'skeletal_muscle', 'pterygoid_muscle', 'abductor', 'abductor_muscle']
senticnet['deltoidea'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'carnauba_palm', 'carnauba', 'wax_palm', 'palmetto', 'palmyra']
senticnet['deltopectoral_crest'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'above_leg', 'joint', 'hip', 'knee']
senticnet['delude'] = ['-0.80', '0', '0.654', '-0.74', '#sadness', '#disgust', 'negative', '-0.73', 'humbug', 'play_trick', 'fob', 'pull_fast_one_on', 'play_trick_on']
senticnet['deluded'] = ['0', '0', '-0.83', '-0.86', '#fear', '#disgust', 'negative', '-0.85', 'superstition', 'ignorant', 'superstitious', 'irrational', 'credulous']
senticnet['deluge'] = ['-0.56', '0', '0.944', '-0.89', '#anger', '#disgust', 'negative', '-0.80', 'mess', 'spate', 'stack', 'mass', 'plenty']
senticnet['delusion'] = ['-0.50', '0', '0.641', '-0.89', '#anger', '#disgust', 'negative', '-0.68', 'delusion_of_persecution', 'hallucination', 'delusion_of_grandeur', 'nihilistic_delusion', 'nihilism']
senticnet['delusion_of_grandeur'] = ['-0.50', '0', '0.641', '-0.89', '#anger', '#disgust', 'negative', '-0.68', 'delusion_of_persecution', 'hallucination', 'delusion', 'nihilistic_delusion', 'nihilism']
senticnet['delusion_of_persecution'] = ['-0.50', '0', '0.641', '-0.89', '#anger', '#disgust', 'negative', '-0.68', 'delusion', 'hallucination', 'delusion_of_grandeur', 'nihilistic_delusion', 'nihilism']
senticnet['delusional'] = ['-0.09', '0', '-0.09', '-0.09', '#sadness', '#disgust', 'negative', '-0.09', 'felon', 'freak', 'busy_person', 'bad_person', 'good_man']
senticnet['delusional_disorder'] = ['-0.88', '0', '0.833', '-0.66', '#sadness', '#anger', 'negative', '-0.79', 'mental_disorder', 'mental_disturbance', 'psychological_disorder', 'folie', 'disturbance']
senticnet['delusive'] = ['-0.93', '0', '0.94', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'unrealistic', 'phantasmagoric', 'phantasmagorical', 'surrealistic', 'wild']
senticnet['deluxe'] = ['0.835', '0.747', '0', '0', '#joy', '#interest', 'positive', '0.791', 'luxurious', 'princely', 'gilded', 'grand', 'opulent']
senticnet['deluxe_continental_breakfast'] = ['0.866', '0.833', '0', '0', '#joy', '#interest', 'positive', '0.849', 'alcohol', 'porter', 'concierge', 'beer_mug', 'bar_stool']
senticnet['deluxe_room'] = ['0.716', '0.96', '0', '0', '#joy', '#interest', 'positive', '0.838', 'airline_seat', 'small_seat', 'beanbag_chair', 'hide-a-bed_sofa', 'chair']
senticnet['delve_deeper_into'] = ['0.117', '-0.00', '0', '0', '#joy', '#surprise', 'positive', '0.062', 'leisure_activity', 'web_surf', 'hobby', 'pastime', 'exploration']
senticnet['delve_into'] = ['0.117', '-0.00', '0', '0', '#joy', '#surprise', 'positive', '0.062', 'leisure_activity', 'web_surf', 'hobby', 'pastime', 'exploration']
senticnet['dem_franchize_boyz'] = ['0', '0', '0.876', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'untune', 'discompose', 'discomfit', 'disconcert', 'upset']
senticnet['dem_mann'] = ['0.633', '-0.67', '0', '0.776', '#surprise', '#admiration', 'positive', '0.693', 'double_entendre', 'loophole', 'ambiguity', 'amphiboly', 'parisology']
senticnet['demagogue'] = ['0.919', '0', '0', '0.731', '#joy', '#admiration', 'positive', '0.825', 'polemicist', 'bomb-thrower', 'tree-hugger', 'rabble-rouser', 'hanger-on']
senticnet['demagoguery'] = ['0.68', '-0.96', '0', '0.597', '#joy', '#surprise', 'positive', '0.746', 'flag_waving', 'jingoism', 'appeal', 'prayer', 'entreaty']
senticnet['demagogy'] = ['0.996', '-0.76', '0', '0.798', '#joy', '#admiration', 'positive', '0.854', 'flag_waving', 'jingoism', 'appeal', 'prayer', 'entreaty']
senticnet['demand'] = ['0.749', '0.764', '0', '0', '#joy', '#interest', 'positive', '0.756', 'obedience', 'order', 'requirement', 'permission', 'necessary']
senticnet['demand_feeding'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'nursing', 'schedule_feeding', 'infant_feeding', 'breast_feeding', 'feeding']
senticnet['demand_infant_feeding'] = ['-0.80', '0', '0.72', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'nursing', 'schedule_infant_feeding', 'feeding', 'breast_infant_feeding', 'infant_feeding']
senticnet['demanding'] = ['-0.75', '0', '0.982', '-0.95', '#anger', '#disgust', 'negative', '-0.89', 'needy', 'stringent', 'stern', 'exacting', 'exigent']
senticnet['demanding_ransom'] = ['-0.89', '-0.71', '-0.76', '-0.93', '#sadness', '#disgust', 'negative', '-0.47', 'bigot', 'censorship', 'jail_time', 'reprimand', 'intolerance']
senticnet['demarcating_expansion'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'mathematical', 'muscle', 'math_function', 'feel_sick', 'separation']
senticnet['demarcation'] = ['0.034', '0.09', '0.039', '0.061', '#interest', '#admiration', 'positive', '0.036', 'border', 'limit', 'edge', 'indoor', 'side']
senticnet['demarcation_line'] = ['0.034', '0.09', '0.039', '0.061', '#interest', '#admiration', 'positive', '0.036', 'border', 'limit', 'edge', 'indoor', 'side']
senticnet['demean'] = ['-0.62', '0', '0.631', '-0.67', '#anger', '#disgust', 'negative', '-0.64', 'humiliate', 'humble', 'abase', 'mortify', 'reduce']
senticnet['demeaning'] = ['-0.73', '0', '0.786', '0', '#sadness', '#anger', 'negative', '-0.76', 'humbling', 'humbleness', 'humiliating', 'demeaningly', 'humiliatingly']
senticnet['demeaningly'] = ['-0.73', '0', '0.748', '0', '#sadness', '#anger', 'negative', '-0.74', 'humbling', 'demeaning', 'humbleness', 'humiliating', 'humiliatingly']
senticnet['demeanor'] = ['0.941', '0.822', '0', '0', '#joy', '#interest', 'positive', '0.881', 'melodic_noise', 'universal_language', 'instrumental', 'musical_note', 'audio']
senticnet['demeclocycline'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'spironolactone', 'adrenal_cortical_steroid', 'antihypertensive', 'antihypertensive_drug']
senticnet['dementedness'] = ['-0.94', '0', '0.821', '-0.75', '#sadness', '#anger', 'negative', '-0.84', 'presenile_dementia', 'insanity', 'senile_dementia', 'senile_psychosis', 'alcoholic_dementia']
senticnet['dementia'] = ['-0.60', '0', '0.634', '-0.93', '#anger', '#disgust', 'negative', '-0.72', 'presenile_dementia', 'insanity', 'senile_dementia', 'senile_psychosis', 'alcoholic_dementia']
senticnet['dementia_praecox'] = ['-0.94', '0', '0.821', '-0.75', '#sadness', '#anger', 'negative', '-0.84', 'predementedness', 'insanity', 'dementedness', 'senile_psychosis', 'alcoholic_dementia']
senticnet['demerara_sugar'] = ['0.028', '0.138', '-0.04', '0.215', '#interest', '#admiration', 'positive', '0.083', 'water', 'dense', 'thick', 'milk', 'dense_water']
senticnet['demersal_marine_fish'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'plectognath', 'plectognathfish', 'blowfish', 'triggerfish', 'filefish']
senticnet['demesne'] = ['0.975', '-0.99', '0', '0.644', '#joy', '#surprise', 'positive', '0.871', 'entail', 'barony', 'seigneury', 'seigniory', 'immovable']
senticnet['demi_god'] = ['0', '0', '0', '0.816', '#admiration', '#admiration', 'positive', '0.816', 'immortality', 'live_forever', 'deathless', 'eternal_life', 'everlasting_life']
senticnet['demi_lion'] = ['0.042', '-0.00', '0.027', '0.099', '#joy', '#admiration', 'positive', '0.031', 'chess_knight', 'chess_queen', 'chess_pawn', 'pawn', 'chess_bishop']
senticnet['demi_monde'] = ['-0.80', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.79', 'whorehouse', 'brothel', 'massage_parlor', 'bagnio', 'house_of_prostitution']
senticnet['demi_pointe'] = ['-0.68', '0', '0.545', '-0.68', '#sadness', '#disgust', 'negative', '-0.64', 'stunt', 'tumble', 'split', 'walkover', 'acrobatic_feat']
senticnet['demigod'] = ['0.978', '-0.88', '0', '0.785', '#joy', '#surprise', 'positive', '0.881', 'daemon', 'divinity', 'sea_god', 'sun_god', 'goddess']
senticnet['demijohn'] = ['0.161', '-0.14', '0.031', '-0.10', '#joy', '#surprise', 'positive', '0.043', 'barrel', 'reception', 'champagne', 'beer', 'wine']
senticnet['demilitarise'] = ['-0.98', '0', '0.654', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'bereave', 'expropriate', 'deprive', 'unclothe', 'take']
senticnet['demilitarize'] = ['-0.98', '0', '0.654', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'bereave', 'expropriate', 'deprive', 'unclothe', 'take']
senticnet['demimonde'] = ['-0.80', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.79', 'whorehouse', 'brothel', 'massage_parlor', 'bagnio', 'house_of_prostitution']
senticnet['demise'] = ['0', '0', '-0.75', '-0.99', '#fear', '#disgust', 'negative', '-0.87', 'loss', 'deprivation', 'dispossession', 'privation', 'bereavement']
senticnet['demo'] = ['0', '0', '0.07', '0', '#anger', '#anger', 'negative', '-0.07', 'price_product', 'pottery', 'create_sound', 'music', 'create_new_idea']
senticnet['demobilise'] = ['-0.98', '0', '0.654', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'bereave', 'expropriate', 'deprive', 'unclothe', 'take']
senticnet['demobilize'] = ['-0.98', '0', '0.654', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'bereave', 'expropriate', 'deprive', 'unclothe', 'take']
senticnet['democracy'] = ['0.044', '0.133', '-0.08', '0.087', '#interest', '#admiration', 'positive', '0.044', 'republic', 'nation', 'democratic_country', 'free_country', 'nation_state']
senticnet['democracy_fdd'] = ['-0.85', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.86', 'attack', 'raid', 'fight_against', 'conflict', 'opposition']
senticnet['democrat'] = ['0', '0.852', '0', '0.879', '#interest', '#admiration', 'positive', '0.865', 'detective', 'politician', 'government', 'voter', 'legal_person']
senticnet['democratic'] = ['0.151', '-0.09', '0', '0', '#joy', '#surprise', 'positive', '0.122', 'democratic_country', 'pledge_allegiance', 'nation_state', 'keep_peace', 'nation']
senticnet['democratic_accountability'] = ['0.952', '-0.84', '0', '0.875', '#joy', '#admiration', 'positive', '0.89', 'administration', 'judiciary', 'governing_body', 'establishment', 'brass']
senticnet['democratic_candidate'] = ['0', '0.924', '0', '0.921', '#interest', '#admiration', 'positive', '0.923', 'settle_disagreement', 'hitler', 'communist', 'conservative', 'unchanging']
senticnet['democratic_challenger'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['democratic_congressman'] = ['0', '0.924', '0', '0.921', '#interest', '#admiration', 'positive', '0.923', 'settle_disagreement', 'hitler', 'communist', 'conservative', 'unchanging']
senticnet['democratic_congressmen'] = ['0', '0.924', '0', '0.921', '#interest', '#admiration', 'positive', '0.923', 'settle_disagreement', 'hitler', 'communist', 'conservative', 'unchanging']
senticnet['democratic_country'] = ['0.084', '0.122', '-0.08', '0.137', '#interest', '#admiration', 'positive', '0.064', 'unite', 'pledge_allegiance', 'political_unit', 'state', 'free_country']
senticnet['democratic_governance'] = ['0.952', '-0.84', '0', '0.875', '#joy', '#admiration', 'positive', '0.89', 'administration', 'judiciary', 'governing_body', 'establishment', 'brass']
senticnet['democratic_incumbent'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['democratic_leaning_district'] = ['0', '0.924', '0', '0.921', '#interest', '#admiration', 'positive', '0.923', 'settle_disagreement', 'hitler', 'communist', 'conservative', 'unchanging']
senticnet['democratic_nominee'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['democratic_operative'] = ['0', '0', '0', '0.876', '#admiration', '#admiration', 'positive', '0.876', 'good_manner', 'courteous', 'good_behavior', 'handle_carefully', 'politeness']
senticnet['democratic_party'] = ['0', '0.942', '0', '0.837', '#interest', '#admiration', 'positive', '0.889', 'largest', 'beijing', 'democracy', 'tung', 'people']
senticnet['democratic_politician'] = ['0', '0.868', '0', '0.853', '#interest', '#admiration', 'positive', '0.861', 'country_leader', 'prime_minister', 'president', 'elect_official', 'leader']
senticnet['democratic_primary'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['democratic_process'] = ['0', '0.261', '-0.12', '0.322', '#interest', '#admiration', 'positive', '0.154', 'presidential', 'procedure', 'election', 'posture', 'select']
senticnet['democratic_socialist'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'economic_libertarian', 'civil_libertarian', 'advocate', 'advocator', 'proponent']
senticnet['democratic_socialist_party'] = ['-0.50', '0', '0.989', '-0.91', '#anger', '#disgust', 'negative', '-0.80', 'camp', 'pack', 'clique', 'coterie', 'ingroup']
senticnet['democratic_transition'] = ['0', '0.202', '-0.06', '0.291', '#interest', '#admiration', 'positive', '0.142', 'change_life', 'pocket_money', 'go_forward', 'evolve', 'rotate_movement']
senticnet['democratically'] = ['-0.55', '0', '0.931', '-0.74', '#anger', '#disgust', 'negative', '-0.74', 'totalitarian', 'authoritarian', 'dictatorial', 'despotic', 'monarchal']
senticnet['democratically_elected_leader'] = ['-0.73', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.72', 'royal_leader', 'despot', 'emperor', 'monarch', 'king']
senticnet['democratization'] = ['0', '0.202', '-0.06', '0.291', '#interest', '#admiration', 'positive', '0.142', 'change_life', 'pocket_money', 'go_forward', 'evolve', 'rotate_movement']
senticnet['democratize'] = ['0.7', '-0.79', '0', '0.66', '#joy', '#surprise', 'positive', '0.719', 'influence', 'determine', 'shape', 'mold', 'regulate']
senticnet['demodex'] = ['-0.57', '0', '0.873', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'fungal_infection', 'mycosis', 'kerion', 'tinea_pedis', 'athlete_foot']
senticnet['demodulate'] = ['-0.91', '0', '0.656', '-0.81', '#sadness', '#disgust', 'negative', '-0.79', 'withdraw', 'squeeze_out', 'take', 'wring_out', 'rip_out']
senticnet['demogorgon'] = ['0.826', '-0.65', '0', '0.587', '#joy', '#surprise', 'positive', '0.689', 'scoffer', 'vegetarian', 'eater', 'diner', 'dunker']
senticnet['demographic'] = ['-0.88', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.94', 'demographic', 'socioeconomic', 'socio-economic', 'population', 'geographical']
senticnet['demographic_profile'] = ['0', '0.189', '0.04', '0.091', '#interest', '#admiration', 'positive', '0.08', 'combination', 'margin', 'teller', 'merge', 'finite']
senticnet['demographic_shift'] = ['0.05', '0.151', '0', '0.159', '#interest', '#admiration', 'positive', '0.12', 'glamour', 'stylish', 'style', 'clothes_style', 'fancy_clothes']
senticnet['demographic_trend'] = ['0.05', '0.151', '0', '0.159', '#interest', '#admiration', 'positive', '0.12', 'glamour', 'stylish', 'style', 'clothes_style', 'fancy_clothes']
senticnet['demoiselle'] = ['-0.81', '0', '0.885', '-0.92', '#anger', '#disgust', 'negative', '-0.87', 'miss', 'girl', 'damsel', 'maiden', 'damoiselle']
senticnet['demolish'] = ['-0.10', '0', '-0.11', '-0.04', '#sadness', '#fear', 'negative', '-0.08', 'terminate', 'violent_action', 'destruction', 'eliminate', 'take_life']
senticnet['demolition'] = ['-0.08', '0', '-0.08', '-0.14', '#sadness', '#disgust', 'negative', '-0.10', 'violence', 'assault', 'combat', 'siege', 'use_military']
senticnet['demolition_contractor'] = ['-0.08', '0', '-0.08', '-0.14', '#sadness', '#disgust', 'negative', '-0.10', 'violence', 'assault', 'combat', 'siege', 'use_military']
senticnet['demolition_debris_landfill'] = ['-0.81', '0', '0.987', '-0.64', '#sadness', '#anger', 'negative', '-0.81', 'burn_garbage', 'eliminate_garbage', 'incinerate_waste', 'dump', 'garbage_dump']
senticnet['demolition_derby'] = ['0.086', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'carny', 'fortune_teller', 'midway', 'ferris_wheel', 'roller-coaster']
senticnet['demolition_permit'] = ['-0.08', '0', '-0.08', '-0.14', '#sadness', '#disgust', 'negative', '-0.10', 'violence', 'assault', 'combat', 'siege', 'use_military']
senticnet['demon'] = ['-0.09', '0.085', '-0.08', '-0.11', '#sadness', '#disgust', 'negative', '-0.05', 'hitler', 'treachery', 'good_man', 'cannibalism', 'sad_person']
senticnet['demon_beast'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'elapid', 'ophidian', 'serpent', 'krait', 'taipan']
senticnet['demon_knight'] = ['-0.03', '-0.01', '-0.16', '-0.06', '#fear', '#disgust', 'negative', '-0.06', 'sticky_floor', 'theater_box', 'projector', 'row_seat', 'theater_seat']
senticnet['demon_lord'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'feudal_lord', 'lord']
senticnet['demon_possession'] = ['-0.78', '0', '0.74', '-0.94', '#sadness', '#disgust', 'negative', '-0.82', 'eject', 'chuck_out', 'exclude', 'turf_out', 'boot_out']
senticnet['demon_prince'] = ['0.826', '-0.65', '0', '0.587', '#joy', '#surprise', 'positive', '0.689', 'scoffer', 'vegetarian', 'eater', 'diner', 'dunker']
senticnet['demoniac'] = ['-0.82', '0', '0.635', '-0.56', '#sadness', '#anger', 'negative', '-0.67', 'fiend', 'ogre', 'demon', 'devil', 'monster']
senticnet['demoniacal'] = ['-0.78', '0', '0.74', '-0.94', '#sadness', '#disgust', 'negative', '-0.82', 'eject', 'chuck_out', 'exclude', 'turf_out', 'boot_out']
senticnet['demonic'] = ['-0.04', '0.17', '0.676', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maliciously', 'spitefulness', 'maliciousness', 'maleficence', 'malefic']
senticnet['demonic_entity'] = ['-0.78', '0', '0.74', '-0.94', '#sadness', '#disgust', 'negative', '-0.82', 'eject', 'chuck_out', 'exclude', 'turf_out', 'boot_out']
senticnet['demonic_influence'] = ['-0.78', '0', '0.74', '-0.94', '#sadness', '#disgust', 'negative', '-0.82', 'eject', 'chuck_out', 'exclude', 'turf_out', 'boot_out']
senticnet['demonically'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amok', 'murderousness', 'misocainea', 'misogyny']
senticnet['demonise'] = ['-0.12', '0.13', '0.647', '-0.64', '#anger', '#disgust', 'negative', '-0.32', 'exasperating', 'infuriating', 'maddening', 'furiously', 'dudgeon']
senticnet['demonization'] = ['-0.76', '0', '0.519', '-0.56', '#sadness', '#disgust', 'negative', '-0.61', 'scurrility', 'billingsgate', 'disrespect', 'discourtesy', 'vituperation']
senticnet['demonize'] = ['-0.10', '0.176', '0.629', '-0.64', '#anger', '#disgust', 'negative', '-0.3', 'alienated', 'alienation', 'disaffection', 'estrange', 'disoriented']
senticnet['demonology'] = ['-0.51', '0', '0.979', '-0.89', '#anger', '#disgust', 'negative', '-0.79', 'invisible', 'concealed', 'nonvisual', 'hidden', 'out_of_sight']
senticnet['demonry'] = ['-0.5', '0', '0.982', '-0.85', '#anger', '#disgust', 'negative', '-0.77', 'unpeaceable', 'stormy', 'tempestuous', 'militant', 'belligerent']
senticnet['demonstrability'] = ['0.776', '-0.66', '0', '0.778', '#joy', '#admiration', 'positive', '0.74', 'indisputability', 'provability', 'positivity', 'positiveness', 'certainty']
senticnet['demonstrably'] = ['0.863', '0', '0', '0.946', '#joy', '#admiration', 'positive', '0.904', 'self-evidently', 'patently', 'incontrovertibly', 'palpably', 'manifestly']
senticnet['demonstrably_false'] = ['-0.88', '0', '0.907', '-0.84', '#sadness', '#anger', 'negative', '-0.88', 'intolerable', 'unacceptable', 'bitter', 'impossible', 'insufferable']
senticnet['demonstrably_wrong'] = ['-0.88', '0', '0.907', '-0.84', '#sadness', '#anger', 'negative', '-0.88', 'intolerable', 'unacceptable', 'bitter', 'impossible', 'insufferable']
senticnet['demonstrate'] = ['-0.02', '0', '0', '-0.11', '#sadness', '#disgust', 'negative', '-0.06', 'execute', 'confirm', 'help_understand', 'misunderstand', 'hear_speech']
senticnet['demonstrate_knowledge'] = ['-0.10', '0', '0', '-0.11', '#sadness', '#disgust', 'negative', '-0.10', 'pass_exam', 'seek_understanding', 'learn_fact', 'teach_person', 'further_career']
senticnet['demonstrate_skill'] = ['0.833', '0.906', '0', '0.893', '#interest', '#admiration', 'positive', '0.877', 'raise_self_esteem', 'kill_time', 'pastime', 'execute', 'stimulate_mind']
senticnet['demonstration'] = ['-0.02', '0', '-0.06', '-0.04', '#fear', '#disgust', 'negative', '-0.04', 'sport_stadium', 'marketplace', 'drive-in_movie', 'movie_theater', 'movie_studio']
senticnet['demonstration_project'] = ['0', '0.097', '0', '0.092', '#interest', '#admiration', 'positive', '0.095', 'urgent', 'classify_ad', 'important', 'review', 'repetition']
senticnet['demonstrative'] = ['-0.66', '0', '0.721', '-0.53', '#sadness', '#anger', 'negative', '-0.64', 'unlimited', 'outright', 'unconditional', 'unqualified', 'unqualifiedal']
senticnet['demonstrative_of'] = ['0.9', '-0.74', '0', '0.721', '#joy', '#surprise', 'positive', '0.788', 'accessary', 'supportive', 'encouraging', 'certificatory', 'supporting']
senticnet['demonstrative_pronoun'] = ['0', '0.156', '-0.06', '0.241', '#interest', '#admiration', 'positive', '0.11', 'mixture', 'noisy_place', 'make_go', 'move_vehicle', 'big_car']
senticnet['demonstrator'] = ['-0.93', '0', '0.86', '-0.90', '#sadness', '#disgust', 'negative', '-0.9', 'dry', 'insurgent', 'militant', 'passive_resister', 'crusader']
senticnet['demoralisation'] = ['-0.83', '0', '0.904', '0', '#sadness', '#anger', 'negative', '-0.87', 'disappointed', 'thwarted', 'frustrated', 'foiled', 'discomfited']
senticnet['demoralization'] = ['-0.83', '0.22', '-0.46', '-0.05', '#sadness', '#fear', 'negative', '-0.28', 'disheartening', 'sadness', 'demoralizing', 'feel_down', 'dispiriting']
senticnet['demoralize'] = ['-0.81', '0.239', '-0.44', '-0.06', '#sadness', '#fear', 'negative', '-0.27', 'demoralized', 'deject', 'cast_down', 'get_down', 'dispirit']
senticnet['demoralized'] = ['-0.71', '0.304', '-0.36', '-0.11', '#sadness', '#fear', 'negative', '-0.22', 'despondently', 'disheartened', 'feel_down', 'despairingly', 'forlorn']
senticnet['demoralizing'] = ['-0.83', '0.22', '-0.46', '-0.05', '#sadness', '#fear', 'negative', '-0.28', 'disheartening', 'feel_down', 'demoralization', 'sadness', 'dispiriting']
senticnet['demote'] = ['-0.72', '0', '0.918', '-0.54', '#sadness', '#anger', 'negative', '-0.73', 'designate', 'depute', 'reduce', 'sideline', 'delegate']
senticnet['demotic'] = ['0', '0.886', '0', '0.865', '#interest', '#admiration', 'positive', '0.875', 'hieratic', 'sacred', 'hieroglyph', 'priest', 'cuneiform']
senticnet['demulcent'] = ['0', '0', '-0.81', '-0.73', '#fear', '#disgust', 'negative', '-0.77', 'ointment', 'emollient', 'softening', 'salving', 'cream']
senticnet['demure'] = ['0.828', '-0.88', '0', '0.551', '#joy', '#surprise', 'positive', '0.755', 'superiority', 'pride', 'superbia', 'imperiousness', 'domineeringness']
senticnet['demurely'] = ['0', '-0.72', '0', '0.704', '#surprise', '#admiration', 'positive', '0.713', 'deferentially', 'submissively', 'obsequiously', 'reverentially', 'contemptuously']
senticnet['demureness'] = ['-0.56', '0', '0.757', '-0.99', '#anger', '#disgust', 'negative', '-0.77', 'unnaturalness', 'air', 'pose', 'coyness', 'affectedness']
senticnet['demyelinating_disease'] = ['-0.75', '0', '0.515', '-0.93', '#sadness', '#disgust', 'negative', '-0.73', 'sclerosis', 'induration', 'degenerative_disorder', 'autoimmune_disease', 'disseminated_sclerosis']
senticnet['demyelinating_disorder'] = ['-0.75', '0', '0.69', '-0.69', '#sadness', '#disgust', 'negative', '-0.71', 'rubor', 'redness', 'sleeping_sickness', 'sleepy_sickness', 'cephalitis']
senticnet['demyelination'] = ['-0.78', '0', '0.764', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'withering', 'downfall', 'weakening', 'fading', 'attenuation']
senticnet['demystification'] = ['0', '-0.74', '0', '0.846', '#surprise', '#admiration', 'positive', '0.796', 'demystify', 'remove', 'explain', 'mystery', 'clarify']
senticnet['demystify'] = ['0', '-0.74', '0', '0.855', '#surprise', '#admiration', 'positive', '0.799', 'demystification', 'remove', 'explain', 'mystery', 'clarify']
senticnet['demythologization'] = ['0.779', '-0.75', '0', '0.526', '#joy', '#surprise', 'positive', '0.687', 'privateness', 'covertness', 'bosom', 'isolation', 'confidentiality']
senticnet['den'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'lurking_place', 'spot', 'drop', 'hideaway', 'hiding_place']
senticnet['denali_national_park'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['denationalisation'] = ['-0.90', '0', '0.927', '0', '#sadness', '#anger', 'negative', '-0.91', 'privatization', 'denationalization', 'privatisation', 'lagardere', 'thomson']
senticnet['denationalization'] = ['-0.90', '0', '0.926', '0', '#sadness', '#anger', 'negative', '-0.91', 'privatization', 'privatisation', 'denationalisation', 'lagardere', 'thomson']
senticnet['denatured_alcohol'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'ethyl_alcohol', 'alcohol', 'plant_product']
senticnet['denatured_ethanol'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
senticnet['dendermonde'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'feudal_lord', 'lord', 'master', 'overlord', 'king']
senticnet['dendroidal'] = ['0', '0', '-0.82', '-0.93', '#fear', '#disgust', 'negative', '-0.88', 'tree-living', 'arboreal', 'arborous', 'arboriform', 'arborary']
senticnet['deneb'] = ['0.946', '0', '0', '0.702', '#joy', '#admiration', 'positive', '0.824', 'bright', 'illuminated', 'luminously', 'luminous', 'lambent']
senticnet['denervation'] = ['-0.78', '0', '0.764', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'withering', 'downfall', 'weakening', 'fading', 'attenuation']
senticnet['denesh_ramdin'] = ['-0.13', '0', '-0.11', '-0.04', '#sadness', '#fear', 'negative', '-0.1', 'concern_health', 'medical_problem', 'strengthful', 'surgery', 'injury']
senticnet['deng_era'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'absolutism', 'autocracy', 'ascendancy', 'tyrant']
senticnet['dengue'] = ['-0.88', '0', '-0.84', '-0.95', '#sadness', '#disgust', 'negative', '-0.89', 'polio', 'infectious_disease', 'malady', 'sickness', 'ailment']
senticnet['dengue_hemorrhagic_fever'] = ['-0.75', '0', '0.69', '-0.69', '#sadness', '#disgust', 'negative', '-0.71', 'rubor', 'redness', 'sleeping_sickness', 'sleepy_sickness', 'cephalitis']
senticnet['dengue_infection'] = ['-0.10', '0', '-0.16', '-0.23', '#fear', '#disgust', 'negative', '-0.16', 'contagious_disease', 'infectious_disease', 'polio', 'sickness', 'malady']
senticnet['dengue_shock_syndrome'] = ['-0.99', '0', '0.859', '-0.62', '#sadness', '#anger', 'negative', '-0.82', 'lax', 'loose', 'diarrheal', 'diarrhoeal', 'unconstipated']
senticnet['deniable'] = ['0.906', '-0.62', '0', '0.698', '#joy', '#admiration', 'positive', '0.743', 'disavowable', 'questionable', 'refutable', 'confutable', 'confutative']
senticnet['denial'] = ['0', '0', '0.736', '-0.99', '#anger', '#disgust', 'negative', '-0.86', 'get_fired', 'unbelief', 'extreme_fear', 'psychoneurosis', 'death_love']
senticnet['denie'] = ['0.565', '-0.73', '0', '0.542', '#joy', '#surprise', 'positive', '0.615', 'say', 'asseverate', 'predicate', 'insist', 'take_firm_stand']
senticnet['denied_bail'] = ['-0.08', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.052', 'bequest', 'excise', 'bribe', 'afford', 'owe']
senticnet['denigrate'] = ['0', '0', '-0.81', '-0.73', '#fear', '#disgust', 'negative', '-0.77', 'minimize', 'slander', 'defame', 'derogate', 'belittle']
senticnet['denigration'] = ['0', '0', '0.736', '-0.99', '#anger', '#disgust', 'negative', '-0.86', 'get_fired', 'unbelief', 'extreme_fear', 'psychoneurosis', 'death_love']
senticnet['denim'] = ['0', '-0.05', '0.088', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'clothes', 'pair_of_pants', 'shirt', 'slack', 'jacket']
senticnet['denim_brand'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['denim_jacket'] = ['0', '-0.05', '0.088', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'clothes', 'pair_of_pants', 'shirt', 'slack', 'jacket']
senticnet['denim_shirt'] = ['0', '-0.05', '0.088', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'clothes', 'pair_of_pants', 'shirt', 'slack', 'jacket']
senticnet['denim_skirt'] = ['0.998', '0', '0', '0', '#joy', '#joy', 'positive', '0.998', 'panties', 'lingerie', 'pantyhose', 'slack', 'prescription_medication']
senticnet['denis_smith'] = ['-0.9', '0', '0.749', '-0.71', '#sadness', '#anger', 'negative', '-0.78', 'chute', 'slide', 'slideway', 'sloping_trough', 'gutter']
senticnet['denizen'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'indweller', 'inhabitant', 'habitant', 'dweller', 'villager']
senticnet['denly'] = ['-0.62', '0', '0.713', '-0.98', '#anger', '#disgust', 'negative', '-0.77', 'muddy_up', 'muck_up', 'foul', 'blemish', 'smear']
senticnet['dennehy'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
senticnet['denomination'] = ['0', '0.261', '0.06', '0.182', '#interest', '#admiration', 'positive', '0.128', 'monetary_value', 'four_quarters', 'dollar_change', 'money_value', 'dollar_cent']
senticnet['denominational'] = ['0', '0.878', '0', '0.792', '#interest', '#admiration', 'positive', '0.835', 'parochial', 'parish', 'diocesan', 'catholic', 'school']
senticnet['denotation'] = ['0.633', '-0.67', '0', '0.776', '#surprise', '#admiration', 'positive', '0.693', 'double_entendre', 'loophole', 'ambiguity', 'amphiboly', 'parisology']
senticnet['denotative'] = ['0.904', '-0.58', '0', '0.736', '#joy', '#admiration', 'positive', '0.743', 'naming', 'appellative', 'designative', 'extensional', 'referent']
senticnet['denote'] = ['0.904', '-0.58', '0', '0.736', '#joy', '#admiration', 'positive', '0.743', 'naming', 'appellative', 'denotative', 'extensional', 'referent']
senticnet['denouement'] = ['0.651', '-0.88', '0', '0.586', '#joy', '#surprise', 'positive', '0.706', 'orgasm', 'climactic', 'peak', 'climax', 'summit']
senticnet['denounce'] = ['0', '0', '-0.74', '-0.85', '#fear', '#disgust', 'negative', '-0.79', 'betray', 'stigmatize', 'tell_on', 'brand', 'mark']
senticnet['denouncement'] = ['0', '0', '-0.85', '-0.88', '#fear', '#disgust', 'negative', '-0.86', 'ban', 'denunciative', 'fulminate', 'denunciation', 'diatribe']
senticnet['dense'] = ['0.046', '0', '0.04', '0.109', '#joy', '#admiration', 'positive', '0.038', 'magnetic', 'heavy', 'hard_substance', 'hard_mineral', 'hard_rock']
senticnet['dense_air'] = ['0.728', '0.977', '0', '0.939', '#interest', '#admiration', 'positive', '0.881', 'air_pressure', 'altitude', 'air_density', 'heavy_air', 'soft_metal']
senticnet['dense_cloud'] = ['-0.93', '0', '0.508', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'nakedness', 'glumness', 'bleakness', 'desolation', 'bareness']
senticnet['dense_connective_tissue'] = ['-0.53', '0', '0.982', '-0.67', '#anger', '#disgust', 'negative', '-0.73', 'meniscus', 'semilunar_cartilage', 'gristle', 'hyaline_cartilage', 'arytenoid']
senticnet['dense_fog'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'hazy', 'blur', 'bleary', 'blurred', 'blurry']
senticnet['dense_fur'] = ['0.098', '0.084', '0.819', '-0.68', '#anger', '#disgust', 'negative', '-0.33', 'grasp', 'covet', 'covetousness', 'avaricious', 'grabby']
senticnet['dense_mist'] = ['-0.09', '0', '-0.05', '-0.24', '#sadness', '#disgust', 'negative', '-0.13', 'smoke', 'smog', 'haze', 'mist', 'cloudy']
senticnet['dense_smoke'] = ['0', '-0.03', '-0.05', '-0.06', '#fear', '#disgust', 'negative', '-0.03', 'find_campsite', 'burn_house', 'cause_fire', 'die_out', 'find_wood']
senticnet['dense_water'] = ['0.028', '0.138', '-0.04', '0.215', '#interest', '#admiration', 'positive', '0.083', 'water', 'dense', 'thick', 'milk', 'cream']
senticnet['dense_woodland'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'biome', 'timberland']
senticnet['densely_hairy'] = ['-0.58', '0', '0.66', '-0.8', '#anger', '#disgust', 'negative', '-0.68', 'bald', 'depilous', 'glabrescent', 'glabrous', 'balding']
senticnet['densely_ignorant'] = ['-0.90', '0', '0.76', '-0.87', '#sadness', '#disgust', 'negative', '-0.84', 'analphabet', 'analphabetic', 'functional_illiterate', 'ignoramus', 'know_nothing']
senticnet['densely_pubescent'] = ['-0.75', '0', '0.734', '-0.82', '#sadness', '#disgust', 'negative', '-0.77', 'puberulent', 'haired', 'furlike', 'pappose', 'pilous']
senticnet['densely_tomentose'] = ['-0.75', '0', '0.734', '-0.82', '#sadness', '#disgust', 'negative', '-0.77', 'puberulent', 'haired', 'furlike', 'pappose', 'pilous']
senticnet['densely_woven'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'white_cotton', 'sheepskin_material', 'woven_sheepskin', 'sheepskin', 'clothes_type']
senticnet['density'] = ['0.728', '0.977', '0', '0.939', '#interest', '#admiration', 'positive', '0.881', 'air_pressure', 'altitude', 'air', 'heavy_air', 'air_density']
senticnet['density_altitude'] = ['0.023', '0.113', '0.053', '0.153', '#interest', '#admiration', 'positive', '0.059', 'high', 'high_land', 'incline_plane', 'top', 'diagonal']
senticnet['density_dependence'] = ['-0.89', '0', '0.972', '-0.50', '#sadness', '#anger', 'negative', '-0.79', 'unsteadiness', 'haphazardness', 'unregularity', 'variability', 'fitfulness']
senticnet['density_dependent'] = ['-0.89', '0', '0.972', '-0.50', '#sadness', '#anger', 'negative', '-0.79', 'unsteadiness', 'haphazardness', 'unregularity', 'variability', 'fitfulness']
senticnet['density_estimation'] = ['0.83', '-0.83', '0', '0.872', '#surprise', '#admiration', 'positive', '0.846', 'overappraisal', 'overvaluation', 'overestimate', 'overestimation', 'commercial_document']
senticnet['density_gradient'] = ['0.807', '-0.88', '0', '0.718', '#joy', '#surprise', 'positive', '0.802', 'sex', 'gayness', 'sexual_gayness', 'lesbianism', 'sapphism']
senticnet['density_profile'] = ['0.728', '0.977', '0', '0.939', '#interest', '#admiration', 'positive', '0.881', 'air_pressure', 'altitude', 'air', 'heavy_air', 'air_density']
senticnet['dent'] = ['-0.96', '0', '0.501', '-0.92', '#sadness', '#disgust', 'negative', '-0.79', 'byproduct', 'wallop', 'side_effect', 'effect', 'fallout']
senticnet['dent_corn'] = ['0.878', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.883', 'wheat', 'grain', 'rye', 'white_rice', 'rice']
senticnet['dental'] = ['-0.03', '0.079', '0.024', '0.091', '#interest', '#admiration', 'positive', '0.029', 'toothpaste', 'toothbrush', 'mouth', 'gel_toothpaste', 'tooth']
senticnet['dental_appliance'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'false_teeth', 'bridge', 'bridgework', 'partial_denture', 'dental_plate']
senticnet['dental_arch'] = ['0', '-0.02', '0.033', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'tooth', 'teeth', 'dimple', 'mouth', 'cliche']
senticnet['dental_care'] = ['-0.03', '0.079', '0.024', '0.091', '#interest', '#admiration', 'positive', '0.029', 'toothpaste', 'toothbrush', 'mouth', 'gel_toothpaste', 'tooth']
senticnet['dental_caries_tooth_decay'] = ['0', '0', '0.813', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'ice_cream_melt', 'allergy', 'toothache', 'gonorrhea', 'tastebud']
senticnet['dental_chair'] = ['0', '0', '-0.70', '-0.71', '#fear', '#disgust', 'negative', '-0.70', 'hospital', 'blow_dryer', 'acne_medication', 'medicine', 'x-ray_machine']
senticnet['dental_clinic'] = ['-0.03', '0.079', '0.024', '0.091', '#interest', '#admiration', 'positive', '0.029', 'toothpaste', 'toothbrush', 'mouth', 'gel_toothpaste', 'tooth']
senticnet['dental_decay'] = ['0', '0', '0.813', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'ice_cream_melt', 'allergy', 'toothache', 'gonorrhea', 'tastebud']
senticnet['dental_drill'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'smart_technology', 'electric_shaver', 'hair_dryer', 'stay_healthy', 'wash_tooth']
senticnet['dental_floss'] = ['0', '0', '-0.91', '-0.70', '#fear', '#disgust', 'negative', '-0.81', 'wax_dental_floss', 'tartar_control_toothpaste', 'toothpaste', 'gel_toothpaste', 'toothbrush']
senticnet['dental_hygiene'] = ['-0.03', '0.079', '0.024', '0.091', '#interest', '#admiration', 'positive', '0.029', 'toothpaste', 'toothbrush', 'mouth', 'gel_toothpaste', 'tooth']
senticnet['dental_hygiene_clinic'] = ['-0.03', '0.079', '0.024', '0.091', '#interest', '#admiration', 'positive', '0.029', 'toothpaste', 'toothbrush', 'mouth', 'gel_toothpaste', 'tooth']
senticnet['dental_hygiene_profession'] = ['0', '0.097', '0', '0.058', '#interest', '#admiration', 'positive', '0.077', 'occupation', 'career', 'tradesman', 'vocation', 'important_job']
senticnet['dental_hygiene_program'] = ['-0.03', '0.079', '0.024', '0.091', '#interest', '#admiration', 'positive', '0.029', 'toothpaste', 'toothbrush', 'mouth', 'gel_toothpaste', 'tooth']
senticnet['dental_implant'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'false_teeth', 'bridge', 'dental_plate', 'partial_denture', 'dental_appliance']
senticnet['dental_insurance'] = ['-0.03', '0.079', '0.024', '0.091', '#interest', '#admiration', 'positive', '0.029', 'toothpaste', 'toothbrush', 'mouth', 'gel_toothpaste', 'tooth']
senticnet['dental_office'] = ['0.025', '0', '-0.04', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'dentist_office', 'subway_station', 'office', 'registry_office', 'dentist']
senticnet['dental_phobia'] = ['-0.79', '-0.71', '-0.96', '-0.95', '#fear', '#disgust', 'negative', '-0.49', 'fear', 'caution', 'deep_sadness', 'bad_feeling', 'psychoneurosis']
senticnet['dental_plaque'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'plaque']
senticnet['dental_plate'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'false_teeth', 'bridge', 'bridgework', 'partial_denture', 'dental_appliance']
senticnet['dental_practice'] = ['-0.03', '0.079', '0.024', '0.091', '#interest', '#admiration', 'positive', '0.029', 'toothpaste', 'toothbrush', 'mouth', 'gel_toothpaste', 'tooth']
senticnet['dental_profession'] = ['0', '0.097', '0', '0.058', '#interest', '#admiration', 'positive', '0.077', 'occupation', 'career', 'tradesman', 'vocation', 'important_job']
senticnet['dental_pulp'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'plaque']
senticnet['dental_school'] = ['-0.03', '0.079', '0.024', '0.091', '#interest', '#admiration', 'positive', '0.029', 'toothpaste', 'toothbrush', 'mouth', 'gel_toothpaste', 'tooth']
senticnet['dental_surgeon'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'druggist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['dental_surgery'] = ['-0.03', '0.079', '0.024', '0.091', '#interest', '#admiration', 'positive', '0.029', 'toothpaste', 'toothbrush', 'mouth', 'gel_toothpaste', 'tooth']
senticnet['dental_treatment'] = ['-0.03', '0.079', '0.024', '0.091', '#interest', '#admiration', 'positive', '0.029', 'toothpaste', 'toothbrush', 'mouth', 'gel_toothpaste', 'tooth']
senticnet['dental_work'] = ['-0.11', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.09', 'ninja', 'cavity', 'cover_face', 'false_tooth', 'flowing_water']
senticnet['dentaria'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'clematis', 'legume', 'leguminous_plant', 'climber', 'root_climber']
senticnet['denticulate'] = ['-0.66', '0', '0.809', '-0.61', '#sadness', '#anger', 'negative', '-0.69', 'face', 'base', 'underbelly', 'sole', 'bilge']
senticnet['dentifrice'] = ['0.878', '0.869', '0', '0', '#joy', '#interest', 'positive', '0.873', 'fluoride_mouthwash', 'hair_conditioner', 'sculpt_gel', 'dandruff_shampoo', 'hair_gel']
senticnet['dentist'] = ['0', '0', '-0.13', '-0.15', '#fear', '#disgust', 'negative', '-0.14', 'dentist_office', 'doctor_office', 'hospital', 'clinic', 'medical_office']
senticnet['dentist_chair'] = ['-0.84', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.77', 'magazine_rack', 'sofa', 'couch', 'sofa_bed', 'mattress']
senticnet['dentist_chair_bed'] = ['0.712', '0.721', '0', '0', '#joy', '#interest', 'positive', '0.716', 'magazine_rack', 'dentist_chair', 'couch', 'sofa', 'mattress']
senticnet['dentist_drill'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'smart_technology', 'electric_shaver', 'hair_dryer', 'stay_healthy', 'wash_tooth']
senticnet['dentist_office'] = ['0', '0', '-0.08', '-0.15', '#fear', '#disgust', 'negative', '-0.11', 'dental_office', 'dentist', 'fear', 'remove_tooth', 'pain']
senticnet['dentist_work'] = ['-0.02', '-0.00', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.03', 'pull_tooth', 'wait', 'carve_niche', 'wait_line', 'stand_queue']
senticnet['dentistry'] = ['-0.03', '0.079', '0.024', '0.091', '#interest', '#admiration', 'positive', '0.029', 'toothpaste', 'toothbrush', 'mouth', 'gel_toothpaste', 'tooth']
senticnet['dentition'] = ['-0.88', '0', '0.876', '-0.84', '#sadness', '#anger', 'negative', '-0.86', 'precocious_dentition', 'growth', 'growing', 'maturation', 'development']
senticnet['dentoalveolar'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'false_teeth', 'bridge', 'bridgework', 'dental_plate', 'dental_appliance']
senticnet['denture'] = ['-0.69', '0', '0.678', '-0.92', '#sadness', '#disgust', 'negative', '-0.76', 'false_teeth', 'bridge', 'bridgework', 'partial_denture', 'dental_appliance']
senticnet['dentyne'] = ['0.965', '0.775', '0', '0.968', '#joy', '#admiration', 'positive', '0.903', 'snot', 'bad_taste', 'mouth_part', 'beautiful_smile', 'near_mouth']
senticnet['denumerable'] = ['0.983', '-0.80', '0', '0.966', '#joy', '#admiration', 'positive', '0.919', 'estimable', 'computable', 'countable', 'calculable', 'enumerable']
senticnet['denunciation'] = ['0', '0', '-0.85', '-0.88', '#fear', '#disgust', 'negative', '-0.86', 'ban', 'denunciative', 'fulminate', 'denouncement', 'diatribe']
senticnet['denunciative'] = ['0', '0', '-0.85', '-0.88', '#fear', '#disgust', 'negative', '-0.87', 'ban', 'denunciation', 'fulminate', 'denouncement', 'diatribe']
senticnet['denunciatory'] = ['-0.57', '0', '0.79', '-0.89', '#anger', '#disgust', 'negative', '-0.75', 'abuse', 'vilification', 'insult', 'revilement', 'contumely']
senticnet['deny'] = ['-0.27', '-0.18', '0.02', '-0.36', '#sadness', '#disgust', 'negative', '-0.12', 'interruption', 'criticism', 'anthrax', 'prohibit', 'lay_off']
senticnet['deobligate'] = ['-0.88', '0', '0.977', '-0.81', '#sadness', '#anger', 'negative', '-0.89', 'execute', 'run', 'compel', 'oblige', 'enforce']
senticnet['deobligation'] = ['-0.88', '0', '0.977', '-0.81', '#sadness', '#anger', 'negative', '-0.89', 'execute', 'run', 'compel', 'oblige', 'enforce']
senticnet['deodorant'] = ['0', '0.095', '0', '0.074', '#interest', '#admiration', 'positive', '0.084', 'buy_wash_powder', 'water_soap', 'turn_faucet', 'take_off', 'fill_bathtub_water']
senticnet['deodorize'] = ['0.826', '0', '0', '0.885', '#joy', '#admiration', 'positive', '0.855', 'dredge', 'wipe', 'launder', 'expurgate', 'wash']
senticnet['deontic'] = ['0.734', '-0.56', '0', '0.64', '#joy', '#admiration', 'positive', '0.645', 'inferential', 'suggestive', 'connotative', 'connotational', 'connotative_of']
senticnet['deontic_modality'] = ['0.904', '-0.58', '0', '0.736', '#joy', '#admiration', 'positive', '0.743', 'naming', 'appellative', 'denotative', 'extensional', 'referent']
senticnet['deontological'] = ['-0.91', '0', '0.795', '-0.94', '#sadness', '#disgust', 'negative', '-0.88', 'ineluctability', 'assured', 'predictability', 'slam_dunk', 'inevitability']
senticnet['depart'] = ['0', '0.152', '-0.08', '0.208', '#interest', '#admiration', 'positive', '0.091', 'departure', 'leave', 'colored_green', 'green_color', 'make_big']
senticnet['departed_ulithi'] = ['0.084', '-0.05', '0.013', '0', '#joy', '#surprise', 'positive', '0.043', 'carnival_ride', 'carny', 'fortune_teller', 'horseback', 'ferris_wheel']
senticnet['department'] = ['0.028', '0.146', '0', '0.105', '#interest', '#admiration', 'positive', '0.093', 'association', 'merchant', 'organization', 'retailer', 'company']
senticnet['department_chair'] = ['0', '0', '0.88', '-0.92', '#anger', '#disgust', 'negative', '-0.90', 'sofa_bed', 'small_seat', 'beanbag_airline_seat', 'hide-a-bed_sofa', 'airline_seat']
senticnet['department_chairperson'] = ['-0.04', '-0.02', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.02', 'professor', 'lecturer', 'teacher', 'freshman', 'pupil']
senticnet['department_head'] = ['0', '0.089', '0', '0', '#interest', '#interest', 'positive', '0.089', 'legislature', 'state_head', 'in_charge', 'reign', 'headquarters']
senticnet['department_store'] = ['0', '0.081', '0', '0.071', '#interest', '#admiration', 'positive', '0.076', 'thrift_store', 'type_of_store', 'record_store', 'many_shops', 'clothes_store']
senticnet['department_store_chain'] = ['0', '0.081', '0', '0.071', '#interest', '#admiration', 'positive', '0.076', 'thrift_store', 'type_of_store', 'record_store', 'many_shops', 'clothes_store']
senticnet['departmental'] = ['0', '0', '0.067', '0', '#anger', '#anger', 'negative', '-0.06', 'many_shops', 'many_stores', 'corner_store', 'type_of_store', 'bazaar']
senticnet['departmental_approval'] = ['-0.04', '-0.02', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.02', 'professor', 'lecturer', 'teacher', 'freshman', 'pupil']
senticnet['departmental_highway'] = ['0', '0', '0.822', '-0.81', '#anger', '#disgust', 'negative', '-0.81', 'busy_street', 'city_street', 'highway', 'donut_shop', 'roadblock']
senticnet['departmental_liaison'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'sexual_relationship', 'affair', 'involvement']
senticnet['departure'] = ['0', '0.153', '-0.07', '0.245', '#interest', '#admiration', 'positive', '0.108', 'depart', 'arrival', 'action', 'movement', 'make_big']
senticnet['departure_gate'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'hand_baggage', 'baggage', 'passenger_ticket', 'ticket_counter', 'baggage_trunk']
senticnet['departure_lounge'] = ['0', '0.147', '0', '0.099', '#interest', '#admiration', 'positive', '0.123', 'airliner', 'airport', 'baggage_compartment', 'aircraft', 'cable_car']
senticnet['departure_tax'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'single_tax', 'income_tax', 'capital_gain_tax', 'capital_levy', 'taxation']
senticnet['departure_terminal'] = ['0', '0.147', '0', '0.099', '#interest', '#admiration', 'positive', '0.123', 'airliner', 'airport', 'baggage_compartment', 'aircraft', 'cable_car']
senticnet['depaul_blue'] = ['-0.78', '0', '0.74', '-0.94', '#sadness', '#disgust', 'negative', '-0.82', 'eject', 'chuck_out', 'exclude', 'turf_out', 'boot_out']
senticnet['depauperate'] = ['0', '0.184', '-0.05', '0.179', '#interest', '#admiration', 'positive', '0.104', 'rich', 'a_lot', 'abundant', 'wealth', 'plentiful']
senticnet['depend'] = ['0', '0.387', '-0.23', '0.313', '#interest', '#admiration', 'positive', '0.156', 'intend', 'include', 'let', 'allow', 'determine']
senticnet['depend_critically'] = ['-0.96', '0', '0.603', '-0.91', '#sadness', '#disgust', 'negative', '-0.82', 'potential', 'potency', 'conceivableness', 'conceivability', 'possibleness']
senticnet['depend_on'] = ['-0.04', '0.127', '-0.12', '0.223', '#interest', '#admiration', 'positive', '0.046', 'ride', 'owe', 'build_on', 'depend_upon', 'turn_on']
senticnet['depend_upon'] = ['-0.04', '0.127', '-0.12', '0.223', '#interest', '#admiration', 'positive', '0.046', 'ride', 'owe', 'depend_on', 'build_on', 'turn_on']
senticnet['dependability'] = ['0.512', '-0.52', '0', '0.874', '#surprise', '#admiration', 'positive', '0.635', 'responsibleness', 'reproducibility', 'duplicability', 'solidness', 'responsibility']
senticnet['dependable'] = ['0.947', '-0.79', '0', '0.7', '#joy', '#surprise', 'positive', '0.813', 'tested', 'tried', 'trust', 'sure', 'undeviating']
senticnet['dependableness'] = ['0.584', '-0.61', '0', '0.929', '#surprise', '#admiration', 'positive', '0.71', 'responsibleness', 'reproducibility', 'duplicability', 'solidness', 'responsibility']
senticnet['dependance'] = ['0.633', '-0.63', '0', '0.507', '#joy', '#surprise', 'positive', '0.591', 'drug_addiction', 'white_plague', 'physical_condition', 'physiological_state', 'physiological_condition']
senticnet['dependence'] = ['0.809', '-0.58', '0', '0.702', '#joy', '#admiration', 'positive', '0.7', 'state', 'reliance', 'subordination', 'helplessness', 'contingency']
senticnet['dependency'] = ['0.618', '-0.75', '0', '0.902', '#surprise', '#admiration', 'positive', '0.758', 'state', 'reliance', 'subordination', 'helplessness', 'contingency']
senticnet['dependency_between'] = ['-0.9', '0', '0.841', '-0.73', '#sadness', '#anger', 'negative', '-0.82', 'nubbiness', 'tweediness', 'harshness', 'graininess', 'raggedness']
senticnet['dependent'] = ['0', '0', '-0.77', '-0.97', '#fear', '#disgust', 'negative', '-0.87', 'qualified', 'subordinate', 'pendant', 'subject', 'hooked']
senticnet['dependent_origination'] = ['-0.87', '0', '0.744', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['dependent_personality_disorder'] = ['0', '0', '0.896', '-0.96', '#anger', '#disgust', 'negative', '-0.92', 'antisocial', 'sociopathic', 'psychopathic_personality', 'disturbance', 'schizoid']
senticnet['depending_on'] = ['0.054', '0.221', '-0.12', '0.236', '#interest', '#admiration', 'positive', '0.098', 'abstract', 'exclamation', 'versatile', 'nice', 'subjective']
senticnet['depending_upon'] = ['0.295', '0.072', '0', '0.136', '#joy', '#admiration', 'positive', '0.168', 'live_environment', 'beautiful', 'lovely', 'important_man', 'peaceful']
senticnet['depends'] = ['0', '0', '-0.91', '-0.96', '#fear', '#disgust', 'negative', '-0.94', 'count', 'depend', 'look', 'reckon', 'bet']
senticnet['depends_on'] = ['0.054', '0.221', '-0.12', '0.236', '#interest', '#admiration', 'positive', '0.098', 'abstract', 'exclamation', 'versatile', 'nice', 'subjective']
senticnet['depersonalization'] = ['-0.58', '0', '0.722', '-0.51', '#sadness', '#anger', 'negative', '-0.60', 'disorder', 'dissociative_disorder_neurosis', 'dissociative_disorder', 'mental_disorder', 'mental_disturbance']
senticnet['depersonalization_disorder'] = ['-0.58', '0', '0.722', '-0.51', '#sadness', '#anger', 'negative', '-0.60', 'dissociative_disorder', 'depersonalization_neurosis', 'depersonalization', 'mental_disorder', 'mental_disturbance']
senticnet['depersonalization_neurosis'] = ['-0.58', '0', '0.722', '-0.51', '#sadness', '#anger', 'negative', '-0.60', 'depersonalization_disorder', 'dissociative_disorder', 'depersonalization', 'mental_disorder', 'mental_disturbance']
senticnet['depict'] = ['0.777', '-0.66', '0', '0.839', '#joy', '#admiration', 'positive', '0.76', 'elaborate', 'lucubrate', 'expatiate', 'exposit', 'enlarge']
senticnet['depicted'] = ['0.716', '-0.78', '0', '0.531', '#joy', '#surprise', 'positive', '0.678', 'diagrammatic', 'pictured', 'portrayed', 'described', 'delineated']
senticnet['depiction'] = ['0', '0.15', '0', '0.139', '#interest', '#admiration', 'positive', '0.145', 'wall_hang', 'representation', 'illustration', 'artwork', 'wall_decoration']
senticnet['depigmentation'] = ['-0.89', '0', '0.998', '-0.89', '#sadness', '#anger', 'negative', '-0.93', 'erythema', 'flare', 'skin_disease', 'disease_of_skin', 'skin_disorder']
senticnet['depilous'] = ['-0.58', '0', '0.66', '-0.8', '#anger', '#disgust', 'negative', '-0.68', 'bald', 'hairless', 'glabrescent', 'glabrous', 'balding']
senticnet['deplane'] = ['0.861', '-0.52', '0', '0.598', '#joy', '#admiration', 'positive', '0.663', 'exit', 'detrain', 'get_off', 'go_out', 'get_out']
senticnet['deplete'] = ['0', '0', '-0.82', '-0.83', '#fear', '#disgust', 'negative', '-0.83', 'consume', 'eat', 'use_up', 'eat_up', 'exhaust']
senticnet['depleted'] = ['0', '0.718', '0', '0.808', '#interest', '#admiration', 'positive', '0.763', 'stockpile', 'stockpiling', 'stockpiled', 'stockpile', 'uranium']
senticnet['depleted_uranium_dust'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'dust', 'particulate', 'particulate_matter']
senticnet['depletion'] = ['-0.80', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.84', 'popularization', 'profanation', 'demoralization', 'corruption', 'humiliation']
senticnet['deplorable'] = ['-0.90', '0.138', '-0.63', '0.071', '#sadness', '#fear', 'negative', '-0.33', 'woeful', 'execrable', 'woebegone', 'woefulness', 'plaintiveness']
senticnet['deplorably'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'lamentably', 'woefully', 'bereaved', 'bereft', 'grief-stricken']
senticnet['deplore'] = ['-0.65', '0', '0.754', '-0.93', '#anger', '#disgust', 'negative', '-0.78', 'anathemize', 'pick_apart', 'execrate', 'criticize', 'accurse']
senticnet['deploy'] = ['0.048', '0.092', '-0.08', '0.216', '#interest', '#admiration', 'positive', '0.067', 'gather', 'group', 'organization', 'clique', 'collection']
senticnet['deploy_ballistic_missile'] = ['-0.04', '0', '-0.06', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'make_war', 'conquer_enemy', 'use_military', 'gain_territory', 'keep_peace']
senticnet['deployability'] = ['0', '0', '0', '0.144', '#admiration', '#admiration', 'positive', '0.144', 'go_elsewhere', 'transport_person', 'get_around', 'locomotion', 'trucking']
senticnet['deployment'] = ['-0.07', '0.069', '-0.18', '0', '#sadness', '#fear', 'negative', '-0.06', 'medical_procedure', 'cure', 'doctor_surgery', 'treatment', 'doctor_work']
senticnet['depolarization'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'emotional_arousal', 'excitement', 'arousal', 'anger']
senticnet['depolymerization'] = ['-0.03', '0.169', '-0.07', '0.218', '#interest', '#admiration', 'positive', '0.071', 'elongate', 'elasticity', 'extension', 'grow_long', 'widen']
senticnet['deport'] = ['0.835', '-0.94', '0', '0.537', '#joy', '#surprise', 'positive', '0.773', 'fluster', 'hold', 'assert', 'bear', 'move']
senticnet['deportable'] = ['-0.60', '0', '0.832', '-0.53', '#sadness', '#anger', 'negative', '-0.65', 'unsuitable', 'undesirable', 'unqualified', 'disqualified', 'unentitled']
senticnet['deportee'] = ['-0.57', '0', '0.803', '-0.77', '#anger', '#disgust', 'negative', '-0.71', 'au_pair', 'exile', 'foreigner', 'gringo', 'import']
senticnet['deportment'] = ['0.666', '-0.83', '0', '0.896', '#surprise', '#admiration', 'positive', '0.798', 'nice', 'decent', 'solid', 'presentable', 'upstanding']
senticnet['depose'] = ['0', '0', '0.909', '-0.78', '#anger', '#disgust', 'negative', '-0.84', 'fired', 'replace', 'replaceable', 'remove', 'discard']
senticnet['deposit'] = ['0', '0.306', '-0.05', '0.134', '#interest', '#admiration', 'positive', '0.13', 'finance', 'account', 'bank_loan', 'dividend', 'savings_account']
senticnet['deposit_account'] = ['-0.05', '0.26', '-0.08', '0.142', '#interest', '#admiration', 'positive', '0.068', 'expense', 'afford', 'payment', 'cost', 'owe']
senticnet['deposit_box'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'slot', 'box', 'load_dock', 'diskette', 'emporium']
senticnet['deposition'] = ['0.942', '-0.79', '0', '0.901', '#joy', '#admiration', 'positive', '0.88', 'backup', 'buildup', 'accretion', 'deposit', 'increase']
senticnet['depot'] = ['0', '0.147', '0', '0.099', '#interest', '#admiration', 'positive', '0.123', 'train_station', 'railway_station', 'subway', 'airport_terminal', 'airport']
senticnet['depravation'] = ['0', '0', '-0.97', '-0.72', '#fear', '#disgust', 'negative', '-0.84', 'turpitude', 'putrefaction', 'corruption', 'depravity', 'degeneracy']
senticnet['deprave'] = ['-0.95', '0', '0.636', '-0.85', '#sadness', '#disgust', 'negative', '-0.81', 'suborn', 'modify', 'poison', 'lead_astray', 'alter']
senticnet['depravity'] = ['0', '0', '-0.96', '-0.71', '#fear', '#disgust', 'negative', '-0.84', 'turpitude', 'putrefaction', 'corruption', 'depravation', 'degeneracy']
senticnet['deprecate'] = ['-0.88', '0', '0.756', '0', '#sadness', '#anger', 'negative', '-0.81', 'abash', 'discountenanced', 'disconcert', 'unfavorable', 'discountenance']
senticnet['depreciable'] = ['-0.82', '0', '0.835', '-0.67', '#sadness', '#anger', 'negative', '-0.77', 'disparage', 'belittle', 'deflate', 'puncture', 'pick_at']
senticnet['depreciable_property'] = ['-0.82', '0', '0.835', '-0.67', '#sadness', '#anger', 'negative', '-0.77', 'disparage', 'belittle', 'deflate', 'puncture', 'pick_at']
senticnet['depreciate'] = ['-0.82', '0', '0.835', '-0.67', '#sadness', '#anger', 'negative', '-0.77', 'disparage', 'belittle', 'deflate', 'puncture', 'pick_at']
senticnet['depreciated_replacement_cost'] = ['-0.82', '0', '0.835', '-0.67', '#sadness', '#anger', 'negative', '-0.77', 'disparage', 'belittle', 'deflate', 'puncture', 'pick_at']
senticnet['depreciated_value'] = ['-0.82', '0', '0.835', '-0.67', '#sadness', '#anger', 'negative', '-0.77', 'disparage', 'belittle', 'deflate', 'puncture', 'pick_at']
senticnet['depreciating'] = ['-0.98', '0', '0.962', '-0.95', '#sadness', '#anger', 'negative', '-0.96', 'depreciative', 'depreciatory', 'falling', 'decreasing', 'detractive']
senticnet['depreciating_asset'] = ['-0.82', '0', '0.835', '-0.67', '#sadness', '#anger', 'negative', '-0.77', 'disparage', 'belittle', 'deflate', 'puncture', 'pick_at']
senticnet['depreciation'] = ['-0.82', '0', '0.835', '-0.67', '#sadness', '#anger', 'negative', '-0.77', 'disparage', 'belittle', 'deflate', 'puncture', 'pick_at']
senticnet['depreciation_allowance'] = ['-0.82', '0', '0.835', '-0.67', '#sadness', '#anger', 'negative', '-0.77', 'disparage', 'belittle', 'deflate', 'puncture', 'pick_at']
senticnet['depreciation_deduction'] = ['-0.82', '0', '0.835', '-0.67', '#sadness', '#anger', 'negative', '-0.77', 'disparage', 'belittle', 'deflate', 'puncture', 'pick_at']
senticnet['depreciation_expense'] = ['-0.82', '0', '0.835', '-0.67', '#sadness', '#anger', 'negative', '-0.77', 'disparage', 'belittle', 'deflate', 'puncture', 'pick_at']
senticnet['depreciation_schedule'] = ['-0.82', '0', '0.835', '-0.67', '#sadness', '#anger', 'negative', '-0.77', 'disparage', 'belittle', 'deflate', 'puncture', 'pick_at']
senticnet['depreciative'] = ['-0.98', '0', '0.962', '-0.95', '#sadness', '#anger', 'negative', '-0.96', 'decreasing', 'depreciatory', 'falling', 'depreciating', 'detractive']
senticnet['depreciator'] = ['0.899', '-0.77', '0', '0.866', '#joy', '#admiration', 'positive', '0.847', 'unpleasant_person', 'disagreeable_person', 'detractor', 'disparager', 'cynic']
senticnet['depreciatory'] = ['-0.98', '0', '0.962', '-0.95', '#sadness', '#anger', 'negative', '-0.96', 'depreciative', 'decreasing', 'falling', 'depreciating', 'detractive']
senticnet['depredation'] = ['-0.72', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.82', 'ravager', 'wreak', 'devastation', 'rapid', 'ravage']
senticnet['depress'] = ['-0.84', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.79', 'miserable', 'melancholy', 'grim', 'guilty', 'dolefully']
senticnet['depress_person'] = ['-0.09', '-0.02', '-0.08', '-0.05', '#sadness', '#fear', 'negative', '-0.05', 'use_illegal_drug', 'busy_person', 'delusional', 'human', 'cold_person']
senticnet['depressant'] = ['0.95', '-0.95', '0', '0.769', '#joy', '#surprise', 'positive', '0.89', 'adrenocorticotropic', 'adrenocorticotrophic', 'stimulant', 'stimulating', 'stimulative']
senticnet['depressant_effect'] = ['0.95', '-0.95', '0', '0.769', '#joy', '#surprise', 'positive', '0.89', 'adrenocorticotropic', 'adrenocorticotrophic', 'stimulant', 'stimulating', 'stimulative']
senticnet['depressed'] = ['-0.79', '0.253', '-0.46', '-0.04', '#sadness', '#fear', 'negative', '-0.26', 'downhearted', 'down_in_mouth', 'gloomy', 'downcast', 'low-spirited']
senticnet['depressed_fracture'] = ['-0.12', '-0.02', '0', '-0.15', '#sadness', '#disgust', 'negative', '-0.08', 'incision', 'laceration', 'abrasion', 'cut_skin', 'painful_injury']
senticnet['depressed_person'] = ['-0.97', '0', '0.256', '-0.98', '#sadness', '#disgust', 'negative', '-0.73', 'depression', 'have_problem', 'commit_suicide', 'ill_person', 'sick']
senticnet['depressing'] = ['-0.75', '0.282', '-0.42', '-0.06', '#sadness', '#fear', 'negative', '-0.24', 'uncheerful', 'cheerless', 'low-spiritedness', 'lowness', 'dispiritedness']
senticnet['depressingly'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'lamentably', 'deplorably', 'bereaved', 'bereft', 'grief-stricken']
senticnet['depression'] = ['-0.73', '0', '0', '-0.81', '#sadness', '#disgust', 'negative', '-0.77', 'sadness', 'sad', 'grief', 'unhappiness', 'misery']
senticnet['depression_era'] = ['-0.55', '0', '0.912', '-0.65', '#anger', '#disgust', 'negative', '-0.70', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['depressive'] = ['-0.64', '0', '0.854', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'melancholic', 'sick_person', 'diseased_person', 'sufferer', 'melancholiac']
senticnet['depressive_disorder'] = ['-0.69', '0', '0.655', '-0.86', '#sadness', '#disgust', 'negative', '-0.73', 'dysthymia', 'dysthymic_depression', 'affective_disorder', 'major_affective_disorder', 'emotional_disorder']
senticnet['depressive_episode'] = ['-0.78', '0', '0.918', '-0.96', '#anger', '#disgust', 'negative', '-0.88', 'major_depressive_episode', 'depression', 'depressive_disorder', 'clinical_depression', 'involutional_depression']
senticnet['depressive_symptomatology'] = ['0.627', '-0.65', '0', '0.968', '#surprise', '#admiration', 'positive', '0.749', 'overwhelm', 'overpower', 'sweep_over', 'whelm', 'overcome']
senticnet['deprivation'] = ['-0.61', '0', '0.712', '-0.86', '#anger', '#disgust', 'negative', '-0.73', 'destitution', 'impoverishment', 'indigence', 'penury', 'pauperism']
senticnet['deprive'] = ['-0.98', '0', '0.654', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'bereave', 'expropriate', 'disarm', 'unclothe', 'take']
senticnet['deptford'] = ['0.582', '-0.97', '0', '0.802', '#surprise', '#admiration', 'positive', '0.788', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['depth'] = ['-0.06', '0.07', '0.03', '0.138', '#interest', '#admiration', 'positive', '0.028', 'along_beach', 'small_ocean', 'deep', 'oceanside', 'full_water']
senticnet['depth_exploration'] = ['0.117', '-0.00', '0', '0', '#joy', '#surprise', 'positive', '0.062', 'leisure_activity', 'web_surf', 'hobby', 'pastime', 'exploration']
senticnet['depth_perception'] = ['0.906', '-0.60', '0', '0.653', '#joy', '#admiration', 'positive', '0.72', 'opaqueness', 'faintness', 'dimness', 'opacity', 'vagueness']
senticnet['depurate'] = ['0.716', '0', '0', '0.81', '#joy', '#admiration', 'positive', '0.763', 'rasp', 'whisk', 'edulcorate', 'swab', 'expunge']
senticnet['depute'] = ['-0.72', '0', '0.918', '-0.54', '#sadness', '#anger', 'negative', '-0.73', 'designate', 'demote', 'reduce', 'sideline', 'delegate']
senticnet['deputize'] = ['-0.81', '0', '0.854', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'replace', 'cover', 'supplant', 'supersede', 'supervene_upon']
senticnet['deputy'] = ['0', '0', '0', '0.876', '#admiration', '#admiration', 'positive', '0.876', 'good_manner', 'courteous', 'good_behavior', 'handle_carefully', 'politeness']
senticnet['deputy_associate_director'] = ['-0.04', '0.189', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'label', 'percentage', 'rate', 'money_relate', 'boss']
senticnet['deputy_chair'] = ['0', '0', '0.88', '-0.92', '#anger', '#disgust', 'negative', '-0.90', 'sofa_bed', 'small_seat', 'beanbag_airline_seat', 'hide-a-bed_sofa', 'airline_seat']
senticnet['deputy_chairperson'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'seeker', 'commuter']
senticnet['deputy_collector'] = ['-0.08', '0.358', '-0.20', '0.237', '#interest', '#admiration', 'positive', '0.077', 'extortion', 'money_due', 'afford', 'bribe', 'owe']
senticnet['deputy_director'] = ['-0.04', '0.189', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'label', 'percentage', 'rate', 'money_relate', 'boss']
senticnet['deputy_editor'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'adventurer', 'humankind']
senticnet['deputy_executive_director'] = ['-0.04', '0.189', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'label', 'percentage', 'rate', 'money_relate', 'boss']
senticnet['deputy_managing_editor'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'adventurer', 'humankind']
senticnet['deputy_rector'] = ['0.031', '0.272', '0', '0.231', '#interest', '#admiration', 'positive', '0.178', 'minister', 'church_head', 'pastor', 'protestant', 'evangelist']
senticnet['deputy_speaker'] = ['0.034', '-0.02', '0', '0.057', '#joy', '#admiration', 'positive', '0.039', 'female_television_connector', 'channel_selector', 'power_switch', 'tuner', 'radio']
senticnet['dequeue'] = ['-0.15', '0', '0.029', '-0.11', '#sadness', '#disgust', 'negative', '-0.1', 'start_over', 'front_line', 'find_line', 'take_turn', 'long_line']
senticnet['der_glckner_von'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'lordosis', 'kyphosis', 'humpback', 'spinal_curvature', 'abnormality']
senticnet['der_kleine'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['der_welt'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['der_zeit'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['der_zwerg'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'gnome', 'elf', 'goblin', 'undersized', 'small_person']
senticnet['deracinate'] = ['0', '0', '-0.89', '-0.98', '#fear', '#disgust', 'negative', '-0.94', 'eradicate', 'extirpate', 'root_out', 'exterminate', 'uproot']
senticnet['derail'] = ['-0.57', '0', '0.814', '-0.80', '#anger', '#disgust', 'negative', '-0.73', 'block', 'jam', 'close_up', 'block_off', 'obturate']
senticnet['derailed_train'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'firework', 'bright_flash', 'nuclear_weapon', 'earthquake', 'nuclear_bomb']
senticnet['derailment'] = ['-0.62', '0', '0.507', '-0.72', '#sadness', '#disgust', 'negative', '-0.62', 'accident', 'misfortune', 'bad_luck', 'near_miss', 'misadventure']
senticnet['derandomization'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'combinatory', 'consolidative', 'unifying', 'integrative']
senticnet['derange'] = ['0', '0', '0.716', '-0.75', '#anger', '#disgust', 'negative', '-0.73', 'interfuse', 'interweave', 'snag', 'interrelate', 'tangle']
senticnet['derangement'] = ['-0.57', '0', '0.767', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'verboseness', 'verbosity', 'prolixity', 'turgidness', 'flatulence']
senticnet['derby_hat'] = ['0.08', '0.106', '-0.04', '0.112', '#interest', '#admiration', 'positive', '0.063', 'helmet', 'head_gear', 'cap', 'head_wear', 'head_protection']
senticnet['derealization'] = ['-0.58', '0', '0.722', '-0.51', '#sadness', '#anger', 'negative', '-0.60', 'dissociative_disorder', 'depersonalization_neurosis', 'depersonalization', 'mental_disorder', 'mental_disturbance']
senticnet['deregulate'] = ['-0.71', '0', '0.602', '-0.85', '#sadness', '#disgust', 'negative', '-0.72', 'spare', 'forgive', 'derestrict', 'exempt', 'dispense']
senticnet['deregulation'] = ['-0.73', '0', '0.749', '0', '#sadness', '#anger', 'negative', '-0.74', 'chrysalis', 'constrainedly', 'constrain', 'constraint', 'transcendence']
senticnet['derelict'] = ['-0.75', '0', '0.68', '-0.59', '#sadness', '#anger', 'negative', '-0.67', 'battered', 'bedraggled', 'hurt', 'damaged', 'dilapidated']
senticnet['derelict_ship'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'succourer', 'recoverer', 'salvor', 'mortal']
senticnet['dereliction'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'culpable_negligence', 'evasion', 'negligence', 'contributory_negligence']
senticnet['derequisition'] = ['-0.85', '0', '0.834', '-0.86', '#sadness', '#disgust', 'negative', '-0.85', 'sacrifice', 'give', 'reach', 'hand', 'relinquish']
senticnet['derestrict'] = ['-0.71', '0', '0.602', '-0.85', '#sadness', '#disgust', 'negative', '-0.72', 'spare', 'forgive', 'exempt', 'deregulate', 'dispense']
senticnet['deride'] = ['-0.55', '0', '0.947', '-0.77', '#anger', '#disgust', 'negative', '-0.75', 'blackguard', 'bemock', 'twit', 'tease', 'razz']
senticnet['deridingly'] = ['0', '0', '-0.73', '-0.78', '#fear', '#disgust', 'negative', '-0.76', 'mockingly', 'mock', 'sardonically', 'ridicule', 'derisively']
senticnet['derision'] = ['-0.80', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.83', 'scoff', 'derisive', 'ridicule', 'deride', 'byword']
senticnet['derisive'] = ['-0.84', '0', '0.774', '0', '#sadness', '#anger', 'negative', '-0.80', 'derision', 'mocking', 'derisory', 'deride', 'deserving']
senticnet['derisive_nickname'] = ['0.072', '0.244', '-0.20', '0.252', '#interest', '#admiration', 'positive', '0.091', 'doe', 'sweet', 'live_organism', 'love', 'alive']
senticnet['derisively'] = ['0', '0', '-0.74', '-0.78', '#fear', '#disgust', 'negative', '-0.76', 'deridingly', 'mock', 'sardonically', 'ridicule', 'mockingly']
senticnet['derisory'] = ['-0.84', '0', '0.765', '0', '#sadness', '#anger', 'negative', '-0.80', 'derision', 'mocking', 'derisive', 'deride', 'deserving']
senticnet['derivable'] = ['0.929', '-0.71', '0', '0.605', '#joy', '#surprise', 'positive', '0.748', 'incisiveness', 'trenchancy', 'efficacy', 'efficaciousness', 'power']
senticnet['derivation'] = ['0', '0.178', '0', '0.133', '#interest', '#admiration', 'positive', '0.155', 'mathematical', 'complete', 'compute', 'add', 'math_function']
senticnet['derivative_association_isda'] = ['-0.05', '0.241', '-0.06', '0.286', '#interest', '#admiration', 'positive', '0.103', 'give_take', 'barter', 'exchange_goods', 'transaction', 'lower_price']
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senticnet['desk_tray'] = ['0', '-0.12', '0', '0.105', '#surprise', '#admiration', 'positive', '0.114', 'blotter', 'drawer', 'file_box', 'sheet', 'letter_scale']
senticnet['desktop'] = ['0.941', '0.855', '0', '0', '#joy', '#interest', 'positive', '0.898', 'processor', 'browse_web', 'laptop', 'computer_type', 'language']
senticnet['desktop_computer'] = ['0.113', '0.073', '0.028', '0.107', '#joy', '#admiration', 'positive', '0.066', 'pda', 'telephone_computer', 'browser', 'number_pad', 'modem']
senticnet['desktop_icon'] = ['0.055', '0.117', '0', '0.133', '#interest', '#admiration', 'positive', '0.102', 'graphical_representation', 'pencil_pen', 'note_pad', 'marker', 'stationery']
senticnet['desktop_pc'] = ['0.113', '0.073', '0.028', '0.107', '#joy', '#admiration', 'positive', '0.066', 'pda', 'telephone_computer', 'browser', 'number_pad', 'modem']
senticnet['desktop_scanner'] = ['-0.08', '0', '0.011', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'deflate', 'read_cover', 'obtain_information', 'upgrade', 'spread_out']
senticnet['desmodium'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'meadow_grass', 'grass', 'manna_grass', 'sweet_grass', 'wood_meadowgrass']
senticnet['desmoplastic'] = ['-0.88', '0', '0.774', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fibroid_tumor', 'fibroid', 'benign_tumor', 'nonmalignant_tumor', 'nonmalignant_neoplasm']
senticnet['desolate'] = ['-0.88', '0', '0.693', '-0.83', '#sadness', '#disgust', 'negative', '-0.80', 'maroon', 'strand', 'walk_out', 'expose', 'ditch']
senticnet['desolate_landscape'] = ['0', '0', '-0.78', '-0.81', '#fear', '#disgust', 'negative', '-0.8', 'blunt', 'stern', 'crude', 'austere', 'severe']
senticnet['desolate_planet'] = ['-0.03', '0.118', '0.472', '-0.46', '#anger', '#disgust', 'negative', '-0.21', 'detached', 'set-apart', 'separated', 'isolated', 'sickening']
senticnet['desolation'] = ['-0.69', '0.32', '-0.38', '-0.08', '#sadness', '#fear', 'negative', '-0.21', 'forlornly', 'forlornness', 'forlorn', 'weepiness', 'tearful']
senticnet['desoto_adventurer'] = ['0.909', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.952', 'displace', 'swing', 'snowshoe', 'shuttle', 'bang']
senticnet['despair'] = ['-0.98', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.96', 'humiliation', 'upset', 'shame', 'unhappiness', 'loneliness']
senticnet['despairing'] = ['-0.87', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.88', 'dispiritedly', 'hopelessness', 'hopelessly', 'pessimism', 'pessimistically']
senticnet['despairingly'] = ['-0.70', '0.304', '-0.36', '-0.10', '#sadness', '#fear', 'negative', '-0.21', 'despondently', 'disheartened', 'feel_down', 'demoralized', 'forlorn']
senticnet['despatch'] = ['-0.58', '0', '0.52', '-0.99', '#sadness', '#disgust', 'negative', '-0.69', 'quickness', 'celerity', 'rapidity', 'rapidness', 'speediness']
senticnet['desperado'] = ['0', '0', '-0.11', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'hunting_season', 'hound', 'cougar', 'hunter', 'dragon']
senticnet['desperate'] = ['-0.83', '0', '0.797', '0', '#sadness', '#anger', 'negative', '-0.81', 'hysteria', 'anxious', 'terrible', 'concerned', 'humiliate']
senticnet['desperate_plight'] = ['-0.87', '0', '0.673', '-0.55', '#sadness', '#anger', 'negative', '-0.7', 'hot_water', 'difficulty', 'care', 'corner', 'box']
senticnet['desperate_poverty'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'oppressiveness', 'tyrannical', 'oppressive', 'tyrannous', 'laden']
senticnet['desperate_situation'] = ['-0.87', '0', '0.673', '-0.55', '#sadness', '#anger', 'negative', '-0.7', 'hot_water', 'difficulty', 'care', 'corner', 'box']
senticnet['desperately'] = ['0', '0', '0.768', '-0.79', '#anger', '#disgust', 'negative', '-0.78', 'agony', 'agonize', 'struggle', 'agonizingly', 'violent']
senticnet['desperately_poor'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'oppressiveness', 'tyrannical', 'oppressive', 'tyrannous', 'laden']
senticnet['desperately_unlucky'] = ['0', '0', '-0.90', '-0.93', '#fear', '#disgust', 'negative', '-0.92', 'luckless', 'ill-omened', 'ill-starred', 'doomed', 'ill-fated']
senticnet['desperation'] = ['-0.09', '0.088', '-0.13', '0.061', '#sadness', '#fear', 'negative', '-0.02', 'alcoholism', 'party', 'use_party', 'become_intoxicated', 'company_come']
senticnet['despero'] = ['-0.58', '0', '0.661', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'explosive', 'cordite', 'dynamite', 'plastic_explosive', 'explosive_compound']
senticnet['despicable'] = ['0', '0', '-0.76', '-0.99', '#fear', '#disgust', 'negative', '-0.88', 'unworthy', 'slimy', 'ugly', 'slimed', 'vile']
senticnet['despicable_act'] = ['0', '0', '-0.90', '-0.90', '#fear', '#disgust', 'negative', '-0.90', 'parody', 'farce', 'lampoon', 'farce_comedy', 'spoof']
senticnet['despisal'] = ['-0.07', '0.187', '0.64', '-0.64', '#anger', '#disgust', 'negative', '-0.29', 'despising', 'misocainea', 'amok', 'amuck', 'murderously']
senticnet['despise'] = ['-0.1', '0.158', '0.638', '-0.66', '#anger', '#disgust', 'negative', '-0.31', 'scorn', 'offense', 'disgust', 'loathe', 'offensive']
senticnet['despising'] = ['-0.07', '0.187', '0.64', '-0.64', '#anger', '#disgust', 'negative', '-0.29', 'despisal', 'misocainea', 'amok', 'amuck', 'murderously']
senticnet['despite'] = ['-0.11', '0.188', '0.608', '-0.63', '#anger', '#disgust', 'negative', '-0.29', 'contempt', 'disincline', 'disinclination', 'disinclined', 'indispose']
senticnet['despiteful'] = ['-0.03', '0.157', '0.698', '-0.66', '#anger', '#disgust', 'negative', '-0.30', 'vengefulness', 'vindictively', 'vengefully', 'revengefully', 'vindictive']
senticnet['despoil'] = ['0', '-0.97', '0', '0.83', '#surprise', '#admiration', 'positive', '0.9', 'ransacking', 'ransacked', 'vandalize', 'ransack', 'rummaged']
senticnet['despoiler'] = ['-0.99', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.97', 'plunderer', 'plunder', 'raider', 'pillage', 'shaver']
senticnet['despoliation'] = ['-0.14', '-0.00', '-0.12', '-0.21', '#sadness', '#disgust', 'negative', '-0.11', 'torture', 'demolish', 'maim', 'terminate', 'hurt_person']
senticnet['despond'] = ['-0.71', '0', '0.704', '-0.89', '#sadness', '#disgust', 'negative', '-0.76', 'soil', 'mud', 'dirt', 'bleaching_clay', 'bleaching_earth']
senticnet['despondence'] = ['-0.82', '0.226', '-0.46', '-0.05', '#sadness', '#fear', 'negative', '-0.27', 'heartsickness', 'disconsolateness', 'heartsick', 'despondent', 'despondency']
senticnet['despondency'] = ['-0.82', '0.226', '-0.46', '-0.05', '#sadness', '#fear', 'negative', '-0.27', 'despondence', 'heartsickness', 'disconsolateness', 'heartsick', 'despondent']
senticnet['despondent'] = ['-0.82', '0.226', '-0.46', '-0.05', '#sadness', '#fear', 'negative', '-0.27', 'despondence', 'heartsickness', 'disconsolateness', 'heartsick', 'despondency']
senticnet['despondently'] = ['-0.70', '0.304', '-0.36', '-0.10', '#sadness', '#fear', 'negative', '-0.21', 'despairingly', 'disheartened', 'feel_down', 'demoralized', 'forlorn']
senticnet['despot'] = ['0.029', '0.079', '0', '0.063', '#interest', '#admiration', 'positive', '0.057', 'king', 'duchess', 'sultan', 'royal_leader', 'emperor']
senticnet['despotic'] = ['-0.55', '0', '0.931', '-0.74', '#anger', '#disgust', 'negative', '-0.74', 'totalitarian', 'authoritarian', 'dictatorial', 'undemocratic', 'monarchal']
senticnet['despotic_government'] = ['-0.85', '0', '0.75', '-0.95', '#sadness', '#disgust', 'negative', '-0.85', 'reign', 'legislation', 'rebel', 'legislature', 'decree']
senticnet['despotic_power'] = ['-0.85', '0', '0.75', '-0.95', '#sadness', '#disgust', 'negative', '-0.85', 'reign', 'legislation', 'rebel', 'legislature', 'decree']
senticnet['despotic_rule'] = ['-0.85', '0', '0.75', '-0.95', '#sadness', '#disgust', 'negative', '-0.85', 'reign', 'legislation', 'rebel', 'legislature', 'decree']
senticnet['despotically'] = ['0.029', '0.079', '0', '0.063', '#interest', '#admiration', 'positive', '0.057', 'king', 'duchess', 'sultan', 'royal_leader', 'emperor']
senticnet['despotism'] = ['-0.68', '0', '0.597', '-0.84', '#sadness', '#disgust', 'negative', '-0.71', 'dominance', 'tyrant', 'despot', 'autocrat', 'dictator']
senticnet['desquamation'] = ['-0.89', '0', '0.998', '-0.89', '#sadness', '#anger', 'negative', '-0.93', 'erythema', 'flare', 'skin_disease', 'disease_of_skin', 'skin_disorder']
senticnet['dessert'] = ['0.092', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.071', 'bake_treat', 'round_pastry', 'bake_good', 'bake_goods', 'sweet_food']
senticnet['dessert_menu'] = ['0.062', '0', '0.027', '0.055', '#joy', '#admiration', 'positive', '0.03', 'porridge', 'meal', 'broth', 'hamburger', 'hearty']
senticnet['dessert_plate'] = ['0.937', '0', '0', '0.828', '#joy', '#admiration', 'positive', '0.883', 'bakery', 'produce_section', 'thanksgiving_dinner', 'dinner_meal', 'fast_food']
senticnet['dessert_wine'] = ['0.161', '-0.14', '0.031', '-0.10', '#joy', '#surprise', 'positive', '0.043', 'alcohol', 'reception', 'champagne', 'beer', 'drink']
senticnet['dessication'] = ['-0.57', '0', '0.767', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'verboseness', 'verbosity', 'prolixity', 'turgidness', 'flatulence']
senticnet['destabilisation'] = ['-0.92', '0', '0.546', '-0.52', '#sadness', '#anger', 'negative', '-0.66', 'irresponsibility', 'irresponsibleness', 'undependability', 'irreproducibility', 'fallibility']
senticnet['destabilise'] = ['-0.53', '0', '0.752', '-0.89', '#anger', '#disgust', 'negative', '-0.72', 'fall', 'keel_over', 'descend', 'go_down', 'come_down']
senticnet['destabilization'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'blowup', 'discharge', 'backfire', 'blast']
senticnet['destabilize'] = ['-0.84', '0', '0.744', '-0.58', '#sadness', '#anger', 'negative', '-0.72', 'subversion', 'subversive_activity', 'termination', 'ending', 'conclusion']
senticnet['destabilized'] = ['0', '0.955', '0', '0.937', '#interest', '#admiration', 'positive', '0.946', 'irreparably', 'irrevocably', 'reversibly', 'irretrievably', 'irreversibly']
senticnet['destabilizing_effect'] = ['-0.92', '0', '0.546', '-0.52', '#sadness', '#anger', 'negative', '-0.66', 'irresponsibility', 'irresponsibleness', 'undependability', 'irreproducibility', 'fallibility']
senticnet['destination'] = ['0', '0.14', '0', '0.172', '#interest', '#admiration', 'positive', '0.156', 'last', 'go_on_trip', 'go_abroad', 'result_injury', 'elsewhere']
senticnet['destiny'] = ['0', '0.862', '0', '0.774', '#interest', '#admiration', 'positive', '0.818', 'enter_contract', 'believe', 'give_help', 'karma', 'fate']
senticnet['destitute'] = ['0', '0', '-0.74', '-0.77', '#fear', '#disgust', 'negative', '-0.75', 'institution', 'fakir', 'impoverished', 'destituteness', 'indigent']
senticnet['destituteness'] = ['0', '0', '-0.74', '-0.78', '#fear', '#disgust', 'negative', '-0.76', 'institution', 'fakir', 'impoverished', 'destitute', 'indigent']
senticnet['destitution'] = ['-0.61', '0', '0.712', '-0.86', '#anger', '#disgust', 'negative', '-0.73', 'impoverishment', 'deprivation', 'indigence', 'penury', 'pauperism']
senticnet['destroy'] = ['-0.11', '-0.01', '-0.10', '-0.11', '#sadness', '#disgust', 'negative', '-0.07', 'cause_injury', 'raze', 'hurt_person', 'stab', 'damage']
senticnet['destroy_abnormal_tissue'] = ['-0.64', '0', '0.612', '-0.84', '#sadness', '#disgust', 'negative', '-0.7', 'chilling', 'frost', 'lyophilization', 'icing', 'cooling']
senticnet['destroy_build'] = ['-0.13', '-0.01', '-0.08', '-0.11', '#sadness', '#disgust', 'negative', '-0.07', 'destroy_live', 'death_destruction', 'suffocate', 'hurt_person', 'murder']
senticnet['destroy_building'] = ['-0.96', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.91', 'destroy_city', 'destroy_target', 'kill', 'kill_pest', 'kill_life']
senticnet['destroy_city'] = ['-0.94', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.93', 'destroy_target', 'kill_life', 'kill_pest', 'eliminate', 'kill']
senticnet['destroy_enemy'] = ['0', '0.136', '0', '0.196', '#interest', '#admiration', 'positive', '0.166', 'fight_enemy', 'conquer_nation', 'bigotry', 'genocide', 'wage_war']
senticnet['destroy_evidence'] = ['0', '0', '0.863', '-0.75', '#anger', '#disgust', 'negative', '-0.80', 'break_law', 'hide_evidence', 'take_life', 'commit_crime', 'feel_remorse']
senticnet['destroy_home'] = ['-0.92', '0', '0', '-0.96', '#sadness', '#disgust', 'negative', '-0.94', 'kill_life', 'destroy_city', 'destroy_target', 'kill_people', 'kill_pest']
senticnet['destroy_live'] = ['-0.10', '-0.05', '-0.04', '-0.20', '#sadness', '#disgust', 'negative', '-0.07', 'death_destruction', 'murder', 'get_rid_of_enemy', 'kill', 'parry']
senticnet['destroy_property'] = ['-0.93', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.91', 'destroy_home', 'die_out', 'kill_life', 'cause_fire', 'destroy_city']
senticnet['destroy_target'] = ['-0.87', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.86', 'destroy_city', 'kill', 'kill_pest', 'eliminate', 'murder']
senticnet['destroy_village'] = ['-0.91', '0', '0.045', '-0.90', '#sadness', '#disgust', 'negative', '-0.62', 'fire', 'burn', 'heat', 'flame', 'destroy']
senticnet['destroyed'] = ['-0.63', '0', '0.86', '-0.99', '#anger', '#disgust', 'negative', '-0.82', 'spoilt', 'wrecked', 'impoverished', 'blighted', 'extinguished']
senticnet['destroyed_city'] = ['-0.95', '0', '0.475', '-0.91', '#sadness', '#disgust', 'negative', '-0.78', 'destroyed_home', 'destruction', 'shabby', 'mess', 'count_damage']
senticnet['destroyed_hiroshima'] = ['0', '0', '-0.72', '-0.94', '#fear', '#disgust', 'negative', '-0.83', 'pistol', 'gun', 'hydrogen_bomb', 'handgun', 'revolver']
senticnet['destroyed_home'] = ['-0.95', '0', '0.475', '-0.91', '#sadness', '#disgust', 'negative', '-0.78', 'destruction', 'destroyed_city', 'shabby', 'mess', 'count_damage']
senticnet['destroyer'] = ['0', '0', '-0.13', '-0.13', '#fear', '#disgust', 'negative', '-0.13', 'firebomb', 'nuclear_weapon', 'nuclear_bomb', 'bomb', 'aerial_bomb']
senticnet['destroyeth'] = ['-0.52', '0', '0.542', '-0.93', '#anger', '#disgust', 'negative', '-0.67', 'intoxication', 'drunkenness', 'inebriation', 'inebriety', 'tipsiness']
senticnet['destructible'] = ['0', '0', '0.831', '-0.94', '#anger', '#disgust', 'negative', '-0.88', 'weaken', 'weak', 'degredable', 'vanishing', 'unstable']
senticnet['destruction'] = ['-0.14', '-0.00', '-0.12', '-0.21', '#sadness', '#disgust', 'negative', '-0.11', 'torture', 'demolish', 'maim', 'terminate', 'hurt_person']
senticnet['destruction_wrought_by'] = ['-0.87', '0', '0.889', '-0.62', '#sadness', '#anger', 'negative', '-0.79', 'ruination', 'ruin', 'deterioration', 'blight', 'impairment']
senticnet['destructive'] = ['0', '0', '0.967', '-0.95', '#anger', '#disgust', 'negative', '-0.96', 'dangerous', 'funny_look', 'uncontrollable', 'violent', 'dangerous_health']
senticnet['destructive_behavior'] = ['-0.75', '0', '0.886', '-0.72', '#sadness', '#anger', 'negative', '-0.78', 'harass', 'move', 'check', 'fork', 'pin']
senticnet['destructive_device'] = ['-0.84', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.86', 'handgun', 'rifle', 'weapon', 'pistol', 'gun']
senticnet['destructive_distillation'] = ['0.037', '0.084', '0', '0', '#joy', '#interest', 'positive', '0.06', 'leave_over', 'leftover', 'cellulose', 'cane', 'remainder']
senticnet['destructive_force'] = ['-0.14', '-0.00', '-0.12', '-0.21', '#sadness', '#disgust', 'negative', '-0.11', 'torture', 'demolish', 'maim', 'terminate', 'hurt_person']
senticnet['destructive_hurricane'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'atmospheric_phenomenon', 'natural_disaster', 'flood', 'destruction', 'tropical_storm']
senticnet['desultoriness'] = ['0', '0', '-0.77', '-0.98', '#fear', '#disgust', 'negative', '-0.87', 'maunder', 'connected', 'desultory', 'flitting', 'digression']
senticnet['desultory'] = ['0', '0', '-0.76', '-0.98', '#fear', '#disgust', 'negative', '-0.87', 'maunder', 'connected', 'desultoriness', 'flitting', 'digression']
senticnet['desyreth'] = ['0.947', '-0.99', '0', '0.936', '#joy', '#surprise', 'positive', '0.958', 'utterer', 'deipnosophist', 'interlocutor', 'conversational_partner', 'speaker']
senticnet['det'] = ['0', '0', '0.785', '-0.72', '#anger', '#disgust', 'negative', '-0.75', 'sharp_nail', 'sharp', 'sharp_end', 'sharp_point', 'pointy']
senticnet['detach'] = ['-0.25', '0', '-0.17', '-0.08', '#sadness', '#fear', 'negative', '-0.17', 'exclusion', 'exclusion_zone', 'isolation', 'social_exclusion', 'except']
senticnet['detachable'] = ['0.067', '0.088', '-0.07', '0.246', '#interest', '#admiration', 'positive', '0.081', 'multiple_purposes', 'revolve', 'start_run', 'wiggle', 'twist']
senticnet['detachable_box_magazine'] = ['0.978', '-0.57', '0', '0.793', '#joy', '#admiration', 'positive', '0.782', 'pistol', 'handgun', 'side_arm', 'shooting_iron', 'semiautomatic']
senticnet['detachable_magazine'] = ['0.978', '-0.57', '0', '0.793', '#joy', '#admiration', 'positive', '0.782', 'pistol', 'handgun', 'side_arm', 'shooting_iron', 'semiautomatic']
senticnet['detached'] = ['-0.56', '0', '0.572', '-0.75', '#anger', '#disgust', 'negative', '-0.63', 'cold', 'unloving', 'loveless', 'frigid', 'unaffectionate']
senticnet['detached_objectivity'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'nonsense', 'meaninglessness', 'bunk', 'absurdity', 'hokum']
senticnet['detached_retina'] = ['-0.13', '0', '0.013', '-0.08', '#sadness', '#disgust', 'negative', '-0.07', 'blind', 'dig_tunnel', 'underground_animal', 'big_mouse', 'small_rodent']
senticnet['detachment'] = ['-0.9', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.9', 'withdrawal', 'indifferently', 'apathetic', 'indifferent', 'concerned']
senticnet['detail'] = ['0', '0.136', '0', '0.196', '#interest', '#admiration', 'positive', '0.166', 'describe', 'explain', 'answer', 'description', 'example']
senticnet['detailed'] = ['0.789', '0', '0', '0.964', '#joy', '#admiration', 'positive', '0.877', 'in-depth', 'thorough', 'absolute', 'utter', 'comprehension']
senticnet['detailed_account'] = ['0', '0.154', '-0.05', '0.17', '#interest', '#admiration', 'positive', '0.089', 'court_action', 'accessory', 'legal_document', 'short', 'protective_cover']
senticnet['detailed_description'] = ['0', '0.136', '0', '0.196', '#interest', '#admiration', 'positive', '0.166', 'describe', 'explain', 'answer', 'description', 'example']
senticnet['detailed_discussion'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'discuss', 'communication', 'communicate_person', 'share', 'autograph']
senticnet['detailed_examination'] = ['0.056', '-0.03', '0', '0.054', '#joy', '#admiration', 'positive', '0.048', 'analyze', 'stargazing', 'influence_public_decision', 'question_everything', 'see_artifact']
senticnet['detailed_explanation'] = ['-0.11', '0.283', '-0.16', '0.331', '#interest', '#admiration', 'positive', '0.084', 'conclude', 'question_answering', 'solution', 'jutification', 'question_starter']
senticnet['detailed_information'] = ['-0.05', '0.161', '-0.12', '0.181', '#interest', '#admiration', 'positive', '0.039', 'usable', 'redo', 'timeliness', 'question_starter', 'replay']
senticnet['detailed_itinerary'] = ['0.065', '0', '-0.05', '0.085', '#joy', '#admiration', 'positive', '0.032', 'join_club', 'social_skill', 'go_abroad', 'drive_around', 'ride_train']
senticnet['detailed_rebuttal'] = ['-0.96', '0', '0.885', '-0.75', '#sadness', '#anger', 'negative', '-0.86', 'controvert', 'answer', 'disown', 'renounce', 'repudiate']
senticnet['detained_incommunicado'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicative', 'unpronounceable', 'impassive', 'inexpressive', 'silent']
senticnet['detainee'] = ['-0.78', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.87', 'jailbird', 'captive', 'imprisoned', 'convict', 'inmate']
senticnet['detainee_indefinitely'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicative', 'unpronounceable', 'impassive', 'inexpressive', 'silent']
senticnet['detainment'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicative', 'unpronounceable', 'impassive', 'inexpressive', 'silent']
senticnet['detect'] = ['-0.04', '0.128', '-0.11', '0.191', '#interest', '#admiration', 'positive', '0.04', 'hear_noise', 'hear_voice', 'echo', 'vibration', 'ear_activity']
senticnet['detect_abnormal'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'computer_part', 'system', 'motherboard', 'pencil_pen', 'cpu']
senticnet['detect_fraud'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'mail_fraud', 'swindle', 'rig']
senticnet['detectability'] = ['0', '0', '-0.66', '0', '#fear', '#fear', 'negative', '-0.66', 'anger', 'rage', 'annoyance', 'apprehension', 'fear']
senticnet['detectable'] = ['0.958', '-0.83', '0', '0.81', '#joy', '#surprise', 'positive', '0.869', 'discernible', 'perceptible', 'evident', 'noticeable', 'observable']
senticnet['detecting_deception'] = ['-0.84', '0', '0.585', '-0.85', '#sadness', '#disgust', 'negative', '-0.76', 'subject', 'discipline', 'study', 'concern', 'research']
senticnet['detection'] = ['0.862', '-0.82', '0', '0', '#joy', '#surprise', 'positive', '0.841', 'espial', 'sleuthing', 'spying', 'signal_detection', 'catching']
senticnet['detection_limit'] = ['0', '0.098', '0', '0.089', '#interest', '#admiration', 'positive', '0.093', 'wood_panel', 'wooden_structure', 'hinge', 'entrance_room', 'corridor']
senticnet['detection_sensitivity'] = ['0', '0', '-0.66', '0', '#fear', '#fear', 'negative', '-0.66', 'anger', 'rage', 'annoyance', 'apprehension', 'fear']
senticnet['detection_threshold'] = ['0', '0', '-0.66', '0', '#fear', '#fear', 'negative', '-0.66', 'anger', 'rage', 'annoyance', 'apprehension', 'fear']
senticnet['detective'] = ['0', '0.114', '0', '0.083', '#interest', '#admiration', 'positive', '0.099', 'solicitor', 'attorney', 'legal_person', 'felon', 'liar']
senticnet['detective_fiction'] = ['0.036', '0.219', '0', '0.19', '#interest', '#admiration', 'positive', '0.148', 'book_genre', 'literature', 'concoction', 'untruth', 'falsehood']
senticnet['detective_story'] = ['-0.89', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.80', 'riddle', 'obituary', 'editorial', 'news_story', 'article']
senticnet['detector'] = ['0.048', '0', '0', '0.092', '#joy', '#admiration', 'positive', '0.07', 'radio_wave', 'sun', 'solar_cell', 'x-ray', 'cosmic_ray']
senticnet['detent'] = ['-0.82', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.84', 'block', 'obstruct', 'close', 'clogged', 'occluded']
senticnet['detention'] = ['-0.91', '0', '0.931', '-0.72', '#sadness', '#anger', 'negative', '-0.85', 'get_arrested', 'custody', 'incarceration', 'quarantine', 'prisoner']
senticnet['detention_camp'] = ['-0.78', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.87', 'jailbird', 'captive', 'imprisoned', 'convict', 'inmate']
senticnet['detention_center'] = ['0.816', '-0.91', '0', '0.944', '#surprise', '#admiration', 'positive', '0.891', 'jail', 'jailhouse', 'gaol', 'poky', 'clink']
senticnet['detention_facility'] = ['0.816', '-0.91', '0', '0.944', '#surprise', '#admiration', 'positive', '0.891', 'jail', 'jailhouse', 'gaol', 'poky', 'clink']
senticnet['deter'] = ['0', '0.831', '0', '0.731', '#interest', '#admiration', 'positive', '0.781', 'situation', 'preemptively', 'interfere', 'preemption', 'preemptive']
senticnet['deter_fraud'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'mail_fraud', 'swindle', 'rig']
senticnet['detergent'] = ['0', '0.095', '0', '0.074', '#interest', '#admiration', 'positive', '0.084', 'buy_wash_powder', 'water_soap', 'turn_faucet', 'take_off', 'fill_bathtub_water']
senticnet['detergent_brand'] = ['0', '0.095', '0', '0.074', '#interest', '#admiration', 'positive', '0.084', 'buy_wash_powder', 'water_soap', 'turn_faucet', 'take_off', 'fill_bathtub_water']
senticnet['deteriorate'] = ['-0.51', '0', '0.965', '-0.95', '#anger', '#disgust', 'negative', '-0.81', 'go_to_pot', 'go_to_dog', 'dilapidate', 'decay', 'wear']
senticnet['deteriorating_economy'] = ['0', '0', '0', '-0.70', '#disgust', '#disgust', 'negative', '-0.70', 'uncertainty', 'uncertain_environment', 'insecure', 'blur', 'anxiety']
senticnet['deterioration'] = ['-0.97', '0', '0.752', '-0.83', '#sadness', '#disgust', 'negative', '-0.85', 'decay', 'wear', 'corrosion', 'devastation', 'dilapidation']
senticnet['determinable'] = ['-0.84', '0', '0.627', '-0.76', '#sadness', '#disgust', 'negative', '-0.74', 'difficulty', 'inspiration', 'determinant', 'revelation', 'divine_guidance']
senticnet['determinacy'] = ['0.558', '-0.76', '0', '0.568', '#surprise', '#admiration', 'positive', '0.63', 'indefiniteness', 'indeterminateness', 'indefinity', 'indetermination', 'inconclusiveness']
senticnet['determinant'] = ['-0.84', '0', '0.627', '-0.76', '#sadness', '#disgust', 'negative', '-0.74', 'difficulty', 'inspiration', 'revelation', 'determinative', 'divine_guidance']
senticnet['determinate'] = ['0.866', '0.975', '0', '0', '#joy', '#interest', 'positive', '0.92', 'conclude', 'conclusive', 'definitive', 'inconclusive', 'triumph']
senticnet['determinately'] = ['-0.57', '0', '0.713', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'assured', 'conclusion', 'certainty', 'determinateness', 'predictability']
senticnet['determinateness'] = ['-0.57', '0', '0.713', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'assured', 'conclusion', 'certainty', 'predictability', 'definiteness']
senticnet['determination'] = ['0.75', '0.943', '0', '0', '#joy', '#interest', 'positive', '0.847', 'effort', 'concentration', 'persistence', 'preparation', 'set_goal']
senticnet['determinative'] = ['-0.84', '0', '0.627', '-0.76', '#sadness', '#disgust', 'negative', '-0.74', 'difficulty', 'inspiration', 'determinant', 'revelation', 'divine_guidance']
senticnet['determine'] = ['-0.07', '0.331', '-0.11', '0.275', '#interest', '#admiration', 'positive', '0.105', 'intend', 'let', 'right', 'classify', 'selection']
senticnet['determine_conclusively'] = ['-0.64', '0', '0.576', '-0.83', '#sadness', '#disgust', 'negative', '-0.68', 'nisi', 'indeterminate', 'equivocal', 'ambiguous', 'indecisive']
senticnet['determine_guilt'] = ['-0.07', '0.148', '-0.09', '0.094', '#interest', '#admiration', 'positive', '0.02', 'pass_judgment', 'judgment', 'trial', 'justice', 'punish_criminal']
senticnet['determine_guilt_innocence'] = ['0', '0.823', '0', '0.762', '#interest', '#admiration', 'positive', '0.792', 'become_judge', 'consider_evidence', 'decide_guilt', 'become_lawyer', 'negotiator']
senticnet['determine_health'] = ['-0.15', '0.078', '-0.30', '0', '#sadness', '#fear', 'negative', '-0.12', 'diagnose_illness', 'cure_illness', 'check_health', 'sickness', 'emergency']
senticnet['determine_need'] = ['-0.14', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'demand', 'come_warranty', 'ask', 'discuss_friend', 'ask_a_lot_of_question']
senticnet['determine_size'] = ['-0.07', '0.13', '0.045', '0.149', '#interest', '#admiration', 'positive', '0.04', 'calculation', 'redo', 'addition', 'define', 'multiply']
senticnet['determine_truth'] = ['0', '-0.09', '0', '0.08', '#surprise', '#admiration', 'positive', '0.086', 'examine', 'analyze', 'find_lost_item', 'influence_public_decision', 'find_truth']
senticnet['determined'] = ['0', '-0.91', '0', '0.967', '#surprise', '#admiration', 'positive', '0.942', 'intentional', 'purposive', 'purposeful', 'purposefully', 'purpose']
senticnet['determinedly'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'intrepidly', 'hardy', 'fearlessness', 'unafraid', 'unfearing']
senticnet['determining'] = ['0.698', '-0.98', '0', '0.651', '#joy', '#surprise', 'positive', '0.779', 'fateful', 'deciding', 'determinant', 'determinative', 'decisive']
senticnet['determining_eligibility'] = ['-0.60', '0', '0.832', '-0.53', '#sadness', '#anger', 'negative', '-0.65', 'unsuitable', 'undesirable', 'unqualified', 'disqualified', 'unentitled']
senticnet['determinism'] = ['0.741', '0', '0', '0.702', '#joy', '#admiration', 'positive', '0.722', 'free-will', 'predestination', 'atonement', 'salvation', 'freewill']
senticnet['determinist'] = ['-0.55', '0', '0.58', '-0.98', '#anger', '#disgust', 'negative', '-0.70', 'fatalist', 'necessitarian', 'predestinarian', 'predestinationist', 'philosopher']
senticnet['deterministic'] = ['-0.91', '0', '0.598', '-0.52', '#sadness', '#anger', 'negative', '-0.68', 'stingy', 'penurious', 'scrimy', 'beggarly', 'chintzy']
senticnet['deterministic_approach'] = ['-0.96', '0', '0.603', '-0.91', '#sadness', '#disgust', 'negative', '-0.82', 'potential', 'potency', 'conceivableness', 'conceivability', 'possibleness']
senticnet['deterministic_model'] = ['-0.89', '0', '0.972', '-0.50', '#sadness', '#anger', 'negative', '-0.79', 'unsteadiness', 'haphazardness', 'unregularity', 'variability', 'fitfulness']
senticnet['determynyd'] = ['-0.89', '0.161', '-0.61', '0.053', '#sadness', '#fear', 'negative', '-0.32', 'heartrending', 'heartbreaking', 'grievous', 'grievously', 'brokenheartedness']
senticnet['deterrence'] = ['-0.97', '0', '0.84', '-0.52', '#sadness', '#anger', 'negative', '-0.78', 'pis_aller', 'expedient', 'temporary_expedient', 'makeshift', 'stopgap']
senticnet['deterrent'] = ['-0.96', '0', '0.581', '-0.79', '#sadness', '#disgust', 'negative', '-0.77', 'straitjacket', 'handicap', 'balk', 'baulk', 'impediment']
senticnet['deterrent_effect'] = ['-0.75', '0', '0.981', '-0.58', '#sadness', '#anger', 'negative', '-0.77', 'retaliatory', 'penitentiary', 'correctional', 'retributive', 'retributory']
senticnet['deterrent_example'] = ['-0.66', '0', '0.809', '-0.65', '#sadness', '#anger', 'negative', '-0.70', 'example', 'instruct', 'lesson', 'suggestion', 'advice']
senticnet['deterritorialization'] = ['-0.54', '0', '0.979', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'unlikeness', 'dissimilitude', 'difference', 'heterology', 'dissimilarity']
senticnet['detest'] = ['-0.09', '0.213', '0.596', '-0.61', '#anger', '#disgust', 'negative', '-0.27', 'hatefully', 'hateful', 'misanthropy', 'misopedia', 'misology']
senticnet['detestable'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['detestable_crime'] = ['-0.73', '0', '0.55', '-0.75', '#sadness', '#disgust', 'negative', '-0.68', 'mortal', 'person', 'individual', 'mangler', 'mutilator']
senticnet['detestably'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'abominably', 'odiously', 'detestable']
senticnet['detestation'] = ['-0.06', '0.183', '0.651', '-0.64', '#anger', '#disgust', 'negative', '-0.29', 'abhorrence', 'abomination', 'execration', 'loathing', 'odium']
senticnet['dethrone'] = ['-0.95', '0', '0.673', '-0.85', '#sadness', '#disgust', 'negative', '-0.82', 'discharge', 'free', 'disinvest', 'defrock', 'unfrock']
senticnet['dethronement'] = ['0', '0.793', '0', '0.847', '#interest', '#admiration', 'positive', '0.82', 'disagreement', 'fight_against', 'opposition', 'against', 'go_against']
senticnet['detonate'] = ['-0.96', '0', '0.394', '-0.90', '#sadness', '#disgust', 'negative', '-0.75', 'explosion', 'get_hurt', 'blow_up', 'get_bomb', 'squib_bomb']
senticnet['detonating_cord'] = ['0.13', '0.097', '-0.07', '0.198', '#joy', '#admiration', 'positive', '0.087', 'jiggle', 'back_forth', 'vibrate', 'blend', 'mixture']
senticnet['detonation'] = ['-0.84', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.85', 'inferno', 'forest_fire', 'fire_light', 'dismissal', 'wash_mouth_soap']
senticnet['detonation_transition'] = ['-0.90', '0', '0.825', '-0.61', '#sadness', '#anger', 'negative', '-0.78', 'flaming', 'combustion', 'fire', 'flame', 'internal_combustion']
senticnet['detonation_wave'] = ['-0.90', '0', '0.825', '-0.61', '#sadness', '#anger', 'negative', '-0.78', 'flaming', 'combustion', 'fire', 'flame', 'internal_combustion']
senticnet['detonator'] = ['0.13', '0.097', '-0.07', '0.198', '#joy', '#admiration', 'positive', '0.087', 'jiggle', 'back_forth', 'vibrate', 'blend', 'mixture']
senticnet['detour'] = ['-0.09', '0', '0.03', '-0.04', '#sadness', '#disgust', 'negative', '-0.05', 'efficient', 'browser', 'find_computer', 'go_on-line', 'internet_connection']
senticnet['detox'] = ['-0.76', '0', '0.972', '0', '#sadness', '#anger', 'negative', '-0.86', 'detoxification', 'detoxing', 'detoxify', 'cleanse', 'cleansing']
senticnet['detoxification'] = ['-0.76', '0', '0.981', '0', '#sadness', '#anger', 'negative', '-0.87', 'detox', 'detoxing', 'detoxify', 'cleanse', 'cleansing']
senticnet['detoxify'] = ['0.767', '0', '0', '0.845', '#joy', '#admiration', 'positive', '0.806', 'cleanse', 'detoxing', 'toxin', 'detoxification', 'purify']
senticnet['detoxing'] = ['0.761', '0', '0', '0.841', '#joy', '#admiration', 'positive', '0.801', 'cleanse', 'detoxify', 'toxin', 'detoxification', 'purify']
senticnet['detract_from'] = ['-0.03', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.03', 'cobalt', 'use_construction', 'many_shades', 'dye', 'gray']
senticnet['detracted'] = ['0', '0', '0.956', '-0.71', '#anger', '#disgust', 'negative', '-0.83', 'detraction', 'derogatory', 'slander', 'fault', 'statement']
senticnet['detraction'] = ['0', '0', '0.958', '-0.70', '#anger', '#disgust', 'negative', '-0.82', 'detracted', 'derogatory', 'slander', 'fault', 'statement']
senticnet['detractive'] = ['-0.98', '0', '0.962', '-0.95', '#sadness', '#anger', 'negative', '-0.96', 'depreciative', 'depreciatory', 'falling', 'depreciating', 'decreasing']
senticnet['detractor'] = ['0.899', '-0.77', '0', '0.866', '#joy', '#admiration', 'positive', '0.847', 'unpleasant_person', 'disagreeable_person', 'cynic', 'disparager', 'depreciator']
senticnet['detracts_from'] = ['0.828', '-0.90', '0', '0.874', '#surprise', '#admiration', 'positive', '0.869', 'perspicacity', 'judgment', 'fair', 'sound_judgment', 'fair_judgment']
senticnet['detrain'] = ['0.861', '-0.52', '0', '0.598', '#joy', '#admiration', 'positive', '0.663', 'exit', 'get_off', 'deplane', 'go_out', 'get_out']
senticnet['detriment'] = ['-0.64', '0', '0.731', '-0.52', '#sadness', '#anger', 'negative', '-0.63', 'hurt', 'impairment', 'deformation', 'distortion', 'modification']
senticnet['detrimental'] = ['0', '0', '-0.73', '-0.90', '#fear', '#disgust', 'negative', '-0.82', 'prejudicial', 'unhealthful', 'detriment', 'detrimentally', 'fast']
senticnet['detrimental_effect'] = ['-0.62', '0', '0.756', '-0.62', '#anger', '#disgust', 'negative', '-0.66', 'vitiate', 'ruin', 'maul', 'mangle', 'deface']
senticnet['detrimental_reliance'] = ['-0.55', '0', '0.741', '-0.76', '#anger', '#disgust', 'negative', '-0.68', 'illogicality', 'illogicalness', 'illogic', 'inconsequence', 'fallaciousness']
senticnet['detrimentally'] = ['0', '0', '-0.74', '-0.90', '#fear', '#disgust', 'negative', '-0.82', 'prejudicial', 'unhealthful', 'detriment', 'detrimental', 'fast']
senticnet['detrition'] = ['-0.65', '0', '0.597', '-0.84', '#sadness', '#disgust', 'negative', '-0.69', 'wearing', 'erosion', 'eroding', 'eating_away', 'wearing_away']
senticnet['detritivore'] = ['-0.12', '0', '-0.10', '-0.03', '#sadness', '#fear', 'negative', '-0.08', 'dog_poop', 'picnic', 'hike_trail', 'compost', 'wildlife']
senticnet['detritus'] = ['-0.12', '0', '-0.10', '-0.03', '#sadness', '#fear', 'negative', '-0.08', 'dog_poop', 'picnic', 'hike_trail', 'compost', 'wildlife']
senticnet['detrusor'] = ['-0.62', '0', '0.901', '-0.71', '#anger', '#disgust', 'negative', '-0.74', 'pressure', 'osmotic_pressure', 'hypotonicity', 'pressure_level', 'force']
senticnet['detrusor_contraction'] = ['-0.62', '0', '0.901', '-0.71', '#anger', '#disgust', 'negative', '-0.74', 'pressure', 'osmotic_pressure', 'hypotonicity', 'pressure_level', 'force']
senticnet['detrusor_instability'] = ['-0.77', '0', '0.966', '-0.51', '#sadness', '#anger', 'negative', '-0.75', 'enuresis', 'incontinency', 'incontinence', 'stress_incontinence', 'urge_incontinence']
senticnet['detrusor_muscle'] = ['-0.62', '0', '0.901', '-0.71', '#anger', '#disgust', 'negative', '-0.74', 'pressure', 'osmotic_pressure', 'hypotonicity', 'pressure_level', 'force']
senticnet['detrusor_overactivity'] = ['-0.77', '0', '0.966', '-0.51', '#sadness', '#anger', 'negative', '-0.75', 'enuresis', 'incontinency', 'incontinence', 'stress_incontinence', 'urge_incontinence']
senticnet['deuce'] = ['-0.95', '0', '0.644', '-0.7', '#sadness', '#disgust', 'negative', '-0.76', 'ejaculation', 'interjection', 'devil', 'expostulation', 'exclaiming']
senticnet['deuce-ace'] = ['0', '0.906', '0', '0.792', '#interest', '#admiration', 'positive', '0.849', 'trinity', 'trio', 'triplet', 'sacred_trinity', 'tercet']
senticnet['deum'] = ['0.654', '-0.75', '0', '0.78', '#surprise', '#admiration', 'positive', '0.729', 'suit', 'cause', 'lawsuit', 'occurrence', 'countersuit']
senticnet['deuterium'] = ['-0.77', '0', '0.764', '-0.54', '#sadness', '#anger', 'negative', '-0.69', 'pullulation', 'apomixis', 'blastogenesis', 'budding', 'asexual_reproduction']
senticnet['deuterium_tritium'] = ['-0.77', '0', '0.764', '-0.54', '#sadness', '#anger', 'negative', '-0.69', 'pullulation', 'apomixis', 'blastogenesis', 'budding', 'asexual_reproduction']
senticnet['deuteron'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'elementary_particle', 'neutron', 'electron', 'subatomic_particle', 'proton']
senticnet['deuteronomic'] = ['0', '0.106', '-0.05', '0.163', '#interest', '#admiration', 'positive', '0.072', 'band', 'kit', 'oblivion', 'rock_band', 'high_school_band']
senticnet['deuteronomist'] = ['0', '0.106', '-0.05', '0.163', '#interest', '#admiration', 'positive', '0.072', 'band', 'kit', 'oblivion', 'rock_band', 'high_school_band']
senticnet['deuteronomistic_history'] = ['0', '0.106', '-0.05', '0.163', '#interest', '#admiration', 'positive', '0.072', 'band', 'kit', 'oblivion', 'rock_band', 'high_school_band']
senticnet['deuteronomy'] = ['0', '0.106', '-0.05', '0.163', '#interest', '#admiration', 'positive', '0.072', 'band', 'kit', 'oblivion', 'rock_band', 'high_school_band']
senticnet['deutsche_bank_quote'] = ['0', '0.189', '0.04', '0.091', '#interest', '#admiration', 'positive', '0.08', 'combination', 'margin', 'teller', 'merge', 'finite']
senticnet['deutsche_bundesbahn'] = ['0.547', '-0.69', '0', '0.52', '#joy', '#surprise', 'positive', '0.586', 'vivification', 'invigoration', 'activity', 'animation', 'activating']
senticnet['deutsche_grammophon'] = ['0.805', '-0.58', '0', '0.525', '#joy', '#surprise', 'positive', '0.637', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['deutsche_grammophon_archiv'] = ['0.805', '-0.58', '0', '0.525', '#joy', '#surprise', 'positive', '0.637', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
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senticnet['dextromethorphan'] = ['0.969', '0.817', '0', '0.828', '#joy', '#admiration', 'positive', '0.871', 'ibuprofen', 'painkiller', 'medicine', 'analgesic', 'phenobarbital']
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senticnet['dharma'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['dharmakaya'] = ['-0.87', '0', '0.744', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
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senticnet['dharmata'] = ['-0.87', '0', '0.744', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['dharuk'] = ['-0.95', '0', '0.624', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['dhaulagiri'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['dheginsea'] = ['-0.66', '0', '0.859', '-0.82', '#anger', '#disgust', 'negative', '-0.78', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['dheth'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
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senticnet['dhrupad'] = ['0.99', '0', '0', '0.719', '#joy', '#admiration', 'positive', '0.855', 'instrumental', 'lyric', 'lyrics', 'sing', 'melody']
senticnet['dhu'] = ['-0.71', '0', '0.836', '0', '#sadness', '#anger', 'negative', '-0.77', 'awkward', 'unenviable', 'embarrassingly', 'embarrassing', 'sheepish']
senticnet['dhyana'] = ['0.088', '0.135', '0', '0', '#joy', '#interest', 'positive', '0.112', 'watch_others', 'meditate', 'imagine', 'mantra', 'dance_music']
senticnet['di_quella'] = ['0.502', '-0.82', '0', '0.779', '#surprise', '#admiration', 'positive', '0.703', 'mortal', 'person', 'individual', 'free_liver', 'agent']
senticnet['diabeta'] = ['-0.72', '0', '0.973', '-0.57', '#sadness', '#anger', 'negative', '-0.76', 'corpulency', 'blubber', 'fat', 'embonpoint', 'stoutness']
senticnet['diabetes'] = ['-0.38', '-0.12', '-0.06', '-0.37', '#sadness', '#disgust', 'negative', '-0.17', 'chicken_pox', 'influenza', 'disability', 'brain_tumor', 'lyme_disease']
senticnet['diabetes_association'] = ['-0.38', '-0.12', '-0.06', '-0.37', '#sadness', '#disgust', 'negative', '-0.17', 'chicken_pox', 'influenza', 'disability', 'brain_tumor', 'lyme_disease']
senticnet['diabetes_care'] = ['-0.38', '-0.12', '-0.06', '-0.37', '#sadness', '#disgust', 'negative', '-0.17', 'chicken_pox', 'influenza', 'disability', 'brain_tumor', 'lyme_disease']
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senticnet['diabetes_medicine'] = ['-0.90', '0', '0.143', '-0.90', '#sadness', '#disgust', 'negative', '-0.65', 'disease', 'sickness', 'illness', 'diabetes', 'diabetes_mellitus']
senticnet['diabetes_mellitus'] = ['-0.90', '0', '0.143', '-0.90', '#sadness', '#disgust', 'negative', '-0.65', 'disease', 'sickness', 'illness', 'diabetes', 'diabetes_medicine']
senticnet['diabetes_mellitus_niddm'] = ['-0.38', '-0.12', '-0.06', '-0.37', '#sadness', '#disgust', 'negative', '-0.17', 'chicken_pox', 'influenza', 'disability', 'brain_tumor', 'lyme_disease']
senticnet['diabetes_prevention'] = ['-0.38', '-0.12', '-0.06', '-0.37', '#sadness', '#disgust', 'negative', '-0.17', 'chicken_pox', 'influenza', 'disability', 'brain_tumor', 'lyme_disease']
senticnet['diabetic'] = ['-0.38', '-0.12', '-0.06', '-0.37', '#sadness', '#disgust', 'negative', '-0.17', 'chicken_pox', 'influenza', 'disability', 'brain_tumor', 'lyme_disease']
senticnet['diabetic_macular_edema'] = ['-0.82', '0', '0.996', '-0.61', '#sadness', '#anger', 'negative', '-0.81', 'visual_impairment', 'visual_defect', 'vision_defect', 'visual_disorder', 'avitaminosis']
senticnet['diabetic_nephropathy'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'illness', 'ill', 'tension', 'bad_health', 'malignant_hypertension']
senticnet['diabetic_patient'] = ['-0.38', '-0.12', '-0.06', '-0.37', '#sadness', '#disgust', 'negative', '-0.17', 'chicken_pox', 'influenza', 'disability', 'brain_tumor', 'lyme_disease']
senticnet['diabetic_retinopathy'] = ['-0.38', '-0.12', '-0.06', '-0.37', '#sadness', '#disgust', 'negative', '-0.17', 'chicken_pox', 'influenza', 'disability', 'brain_tumor', 'lyme_disease']
senticnet['diabinese'] = ['-0.72', '0', '0.973', '-0.57', '#sadness', '#anger', 'negative', '-0.76', 'corpulency', 'blubber', 'fat', 'embonpoint', 'stoutness']
senticnet['diabolic'] = ['-0.76', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.80', 'diabolically', 'cruel', 'diabolicalness', 'infernal', 'diabolical']
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senticnet['diabolical_plan'] = ['-0.69', '0', '0.756', '-0.99', '#anger', '#disgust', 'negative', '-0.81', 'waster', 'sleeper', 'destroyer', 'ruiner', 'undoer']
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senticnet['diacetylmorphine'] = ['-0.7', '0', '0.698', '-0.57', '#sadness', '#anger', 'negative', '-0.65', 'smack', 'hell_dust', 'nose_drop', 'thunder', 'skag']
senticnet['diacritic'] = ['0.902', '-0.85', '0', '0.898', '#joy', '#admiration', 'positive', '0.885', 'discerning', 'discriminating', 'discriminatingal', 'discriminative', 'selective']
senticnet['diacritic_mark'] = ['0.049', '0.113', '0.107', '0.162', '#interest', '#admiration', 'positive', '0.054', 'list_number', 'ordinal', 'count', 'numerical', 'enumeration']
senticnet['diacritical'] = ['0.902', '-0.85', '0', '0.898', '#joy', '#admiration', 'positive', '0.885', 'discerning', 'diacritic', 'discriminating', 'discriminative', 'selective']
senticnet['diacritical_mark'] = ['-0.61', '0', '0.721', '-0.68', '#anger', '#disgust', 'negative', '-0.67', 'character', 'logogram', 'logograph', 'graph', 'ideogram']
senticnet['diadegma'] = ['0.748', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.873', 'butterfly', 'moth', 'parrot', 'beetle', 'falcon']
senticnet['diadelia'] = ['-0.56', '0', '0.617', '-0.80', '#anger', '#disgust', 'negative', '-0.66', 'prolixity', 'prolixness', 'wordiness', 'repetitiveness', 'repetitiousness']
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senticnet['diaeretiella_rapae'] = ['-0.77', '0', '0.576', '-0.92', '#sadness', '#disgust', 'negative', '-0.75', 'insect', 'firebug', 'water_bug', 'assassin_bug', 'heteropterous_insect']
senticnet['diagnosable'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'acknowledgeable', 'specifiable', 'recognizable', 'identifiable', 'classifiable']
senticnet['diagnosable_mental_disorder'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'acknowledgeable', 'specifiable', 'recognizable', 'identifiable', 'classifiable']
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senticnet['diagnose_illness'] = ['0', '0', '-0.83', '-0.78', '#fear', '#disgust', 'negative', '-0.81', 'check_health', 'determine_health', 'stay_well', 'maintain_health', 'eat_well']
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senticnet['diagnostic'] = ['0.852', '0.902', '0', '0', '#joy', '#interest', 'positive', '0.877', 'diagnosis', 'diagnostics', 'test', 'clinical', 'evaluation']
senticnet['diagnostic_accuracy'] = ['0', '0', '-0.66', '0', '#fear', '#fear', 'negative', '-0.66', 'anger', 'rage', 'annoyance', 'apprehension', 'fear']
senticnet['diagnostic_assay'] = ['0.747', '-0.58', '0', '0.92', '#joy', '#admiration', 'positive', '0.749', 'assay', 'biopsy', 'cloze_procedure', 'cloze_test', 'fecal_occult_test']
senticnet['diagnostic_equipment'] = ['-0.02', '-0.05', '0', '0.069', '#surprise', '#admiration', 'positive', '0.032', 'fixture', 'endowment', 'material', 'stuff', 'kit']
senticnet['diagnostic_pathology'] = ['-0.59', '0', '0.911', '-0.87', '#anger', '#disgust', 'negative', '-0.79', 'osteosclerosis', 'sclerosis', 'disseminated_sclerosis', 'multiple_sclerosis', 'induration']
senticnet['diagnostic_specificity'] = ['0', '0', '-0.66', '0', '#fear', '#fear', 'negative', '-0.66', 'anger', 'rage', 'annoyance', 'apprehension', 'fear']
senticnet['diagnostic_statistical_manual'] = ['-0.55', '0', '0.736', '-0.71', '#anger', '#disgust', 'negative', '-0.66', 'neurosis', 'neuroticism', 'psychoneurosis', 'hysterocatalepsy', 'anxiety_hysteria']
senticnet['diagnostic_test'] = ['0.909', '-0.81', '0', '0.781', '#joy', '#surprise', 'positive', '0.835', 'assay', 'biopsy', 'cloze_procedure', 'cloze_test', 'fecal_occult_test']
senticnet['diagnostic_tool'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'woodworking_tool']
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senticnet['diagonal'] = ['0.023', '0.113', '0.053', '0.153', '#interest', '#admiration', 'positive', '0.059', 'high', 'high_land', 'incline_plane', 'top', 'altitude']
senticnet['diagonal_line'] = ['-0.04', '0.074', '0.019', '0.071', '#interest', '#admiration', 'positive', '0.021', 'sharpness', 'sharp_end', 'sharp_corner', 'near_end', 'edge']
senticnet['diagonally'] = ['-0.80', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.77', 'contrary', 'contradiction', 'lie', 'liar', 'bad_person']
senticnet['diagonally_forward'] = ['0', '0', '0', '0.115', '#admiration', '#admiration', 'positive', '0.115', 'chess_knight', 'strategy', 'pawn', 'chess_queen', 'chess_rook']
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senticnet['diagram'] = ['0', '0.15', '0.067', '0.118', '#interest', '#admiration', 'positive', '0.067', 'representation', 'outline', 'arithmetic', 'click', 'list_number']
senticnet['diagram_below'] = ['0', '0.15', '0.067', '0.118', '#interest', '#admiration', 'positive', '0.067', 'representation', 'outline', 'arithmetic', 'click', 'list_number']
senticnet['diagram_fig'] = ['0.71', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.85', 'fruit_tree', 'baby_flower', 'carpel', 'strawberry', 'star_anise']
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senticnet['dial'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'find_phone_number', 'dial_phone_number', 'pick_up_telephone_receiver', 'keypad', 'dial_telephone']
senticnet['dial_number'] = ['0.817', '0.747', '0', '0', '#joy', '#interest', 'positive', '0.782', 'communicate', 'telephone', 'make_contact', 'attention', 'ask_question']
senticnet['dial_pad'] = ['0.893', '0.979', '0', '0', '#joy', '#interest', 'positive', '0.936', 'find_phone_number', 'dial_phone_number', 'pick_up_telephone_receiver', 'dial', 'dial_telephone']
senticnet['dial_passcode'] = ['-0.09', '0.147', '-0.11', '0.205', '#interest', '#admiration', 'positive', '0.036', 'question_starter', 'resolution', 'available', 'explanation', 'request']
senticnet['dial_phone'] = ['0.027', '0', '0.01', '0.142', '#joy', '#admiration', 'positive', '0.053', 'dial_telephone', 'phone_conversation', 'speak_loudly', 'telephone_ring', 'use_phone']
senticnet['dial_phone_number'] = ['0.834', '0.896', '0', '0', '#joy', '#interest', 'positive', '0.865', 'find_phone_number', 'dial', 'pick_up_telephone_receiver', 'keypad', 'dial_telephone']
senticnet['dial_plate'] = ['0.865', '0.84', '0', '0', '#joy', '#interest', 'positive', '0.853', 'travel', 'find_way', 'help_travelling', 'compass_direction', 'use_compass']
senticnet['dial_telephone'] = ['0.027', '0', '0.01', '0.142', '#joy', '#admiration', 'positive', '0.053', 'dial_phone', 'phone_conversation', 'speak_loudly', 'telephone_ring', 'use_phone']
senticnet['dialect'] = ['-0.03', '0.118', '-0.11', '0.214', '#interest', '#admiration', 'positive', '0.045', 'snail_mail', 'message', 'grammar_part', 'speech_part', 'unit_language']
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senticnet['dialectically'] = ['-0.67', '0', '0.97', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'joint', 'node', 'articulation', 'join', 'connexion']
senticnet['dialectician'] = ['0.922', '-0.94', '0', '0.965', '#surprise', '#admiration', 'positive', '0.945', 'utilitarian', 'egalitarian', 'equalitarian', 'elitist', 'philosopher']
senticnet['dialkyl'] = ['-0.81', '0', '0.774', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'acid', 'hypophosphoric_acid', 'periodic_acid', 'oxyacid', 'phosphorous_acid']
senticnet['diallyl'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'capsaicin', 'chemical_irritant', 'piperine', 'pepper_spray']
senticnet['diallyl_disulfide'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'capsaicin', 'chemical_irritant', 'piperine', 'pepper_spray']
senticnet['dialog'] = ['0.749', '0.724', '0', '0', '#joy', '#interest', 'positive', '0.736', 'mouth_noise', 'vocal_cord', 'vocalization', 'vocal_sound', 'loud_soft']
senticnet['dialog_box'] = ['-0.09', '-0.04', '0.039', '-0.24', '#sadness', '#disgust', 'negative', '-0.08', 'learn_new_idea', 'learn_quickly', 'point_out', 'look_picture', 'jot_down_note']
senticnet['dialogical_self'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'indirectness', 'mediateness', 'characteristic', 'allusiveness']
senticnet['dialogue'] = ['0.117', '0.066', '0', '0.239', '#joy', '#admiration', 'positive', '0.141', 'verbal', 'orate', 'discourse', 'talkative', 'verbalize']
senticnet['dialogue_box'] = ['-0.09', '-0.04', '0.039', '-0.24', '#sadness', '#disgust', 'negative', '-0.08', 'learn_new_idea', 'learn_quickly', 'point_out', 'look_picture', 'jot_down_note']
senticnet['diameter'] = ['0.707', '0.959', '0', '0', '#joy', '#interest', 'positive', '0.833', 'length_measurement', 'measurement', 'imperial_measurement', 'measure', 'spatial_dimension']
senticnet['diameter_mm'] = ['0.734', '0.884', '0', '0', '#joy', '#interest', 'positive', '0.809', 'dimension', 'size', 'mass_measure', 'length_dimension', 'gauge']
senticnet['diametric'] = ['-0.83', '0', '0.718', '0', '#sadness', '#anger', 'negative', '-0.77', 'antipode', 'antonymous', 'diametrically', 'antipolar', 'antipodean']
senticnet['diametrically'] = ['-0.82', '0', '0.722', '0', '#sadness', '#anger', 'negative', '-0.77', 'antipode', 'antonymous', 'diametric', 'antipolar', 'antipodean']
senticnet['diametrically_opposite'] = ['-0.80', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.77', 'contrary', 'contradiction', 'lie', 'liar', 'bad_person']
senticnet['diamond'] = ['0.021', '0', '0', '0.054', '#joy', '#admiration', 'positive', '0.037', 'gold', 'gem', 'manganese', 'potassium', 'silver']
senticnet['diamond-shaped'] = ['0', '-0.86', '0', '0.792', '#surprise', '#admiration', 'positive', '0.828', 'ovular', 'egg-shaped', 'butterfly-like', 'block-shaped', 'saw-tooth']
senticnet['diamond_ace'] = ['0.022', '-0.11', '0.014', '0.057', '#surprise', '#admiration', 'positive', '0.045', 'wood', 'many_steps', 'franchise', 'stump', 'timber']
senticnet['diamond_bracelet'] = ['0.048', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.071', 'ring', 'ring_loudly', 'diamond_ring', 'necklace', 'jewelry']
senticnet['diamond_brooch'] = ['-0.62', '0', '0.961', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'crown', 'diadem', 'jeweled_headdress', 'headdress', 'headgear']
senticnet['diamond_encrusted_super_bowl'] = ['0.052', '0', '0', '0.118', '#joy', '#admiration', 'positive', '0.085', 'diamond_ring', 'piece_of_jewelry', 'bell_sound', 'wedding_band', 'engagement']
senticnet['diamond_engagement_ring'] = ['-0.62', '0', '0.961', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'crown', 'diadem', 'jeweled_headdress', 'headdress', 'headgear']
senticnet['diamond_finesse'] = ['0.022', '-0.11', '0.014', '0.057', '#surprise', '#admiration', 'positive', '0.045', 'wood', 'many_steps', 'franchise', 'stump', 'timber']
senticnet['diamond_heist'] = ['-0.57', '0', '0.721', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'bush', 'native_pomegranate', 'caper_tree', 'native_orange', 'common_caper']
senticnet['diamond_interchange'] = ['0.032', '0.116', '0.016', '0', '#joy', '#interest', 'positive', '0.044', 'curb', 'crossroad', 'ring_road', 'lane', 'interstate']
senticnet['diamond_jewellery'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'anklet', 'ankle_bracelet', 'armilla']
senticnet['diamond_jewelry'] = ['0.532', '-0.60', '0', '0.875', '#surprise', '#admiration', 'positive', '0.67', 'gem', 'adornment', 'jewel', 'bead', 'bangle']
senticnet['diamond_necklace'] = ['-0.62', '0', '0.961', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'crown', 'diadem', 'jeweled_headdress', 'headdress', 'headgear']
senticnet['diamond_pendant'] = ['-0.62', '0', '0.961', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'crown', 'diadem', 'jeweled_headdress', 'headdress', 'headgear']
senticnet['diamond_ring'] = ['0.786', '-0.82', '0', '0', '#joy', '#surprise', 'positive', '0.806', 'wedding_band', 'bell_sound', 'piece_of_jewelry', 'engage', 'engagement']
senticnet['diamond_ruff'] = ['0.022', '-0.11', '0.014', '0.057', '#surprise', '#admiration', 'positive', '0.045', 'wood', 'many_steps', 'franchise', 'stump', 'timber']
senticnet['diamond_solitaire'] = ['0.048', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.071', 'ring', 'ring_loudly', 'diamond_ring', 'necklace', 'jewelry']
senticnet['diamond_tiara'] = ['-0.62', '0', '0.961', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'crown', 'diadem', 'jeweled_headdress', 'headdress', 'headgear']
senticnet['diamond_wedding_ring'] = ['0.048', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.071', 'ring', 'ring_loudly', 'diamond_ring', 'necklace', 'jewelry']
senticnet['diamondback_moth'] = ['0', '-0.09', '0.123', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'gypsy_moth', 'japanese_beetle', 'cucumber_beetle', 'mosquito', 'earwig']
senticnet['diamondoid'] = ['-0.7', '0', '0.77', '-0.75', '#anger', '#disgust', 'negative', '-0.74', 'fake', 'unnatural', 'man_made', 'artificial_sea', 'artificial_taste']
senticnet['dian_lamitan'] = ['-0.94', '0', '0.745', '-0.97', '#sadness', '#disgust', 'negative', '-0.89', 'nerve', 'fearlessness', 'heart', 'dauntlessness', 'intrepidity']
senticnet['dianoia'] = ['0.911', '-0.94', '0', '0.953', '#surprise', '#admiration', 'positive', '0.935', 'common_sense', 'good_sense', 'horse_sense', 'mother_wit', 'gumption']
senticnet['diapason'] = ['0.939', '-0.71', '0', '0.775', '#joy', '#admiration', 'positive', '0.81', 'vox_angelica', 'voix_celeste', 'dulciana', 'pipe', 'organ_stop']
senticnet['diapause'] = ['-0.75', '0', '0.512', '-0.75', '#sadness', '#disgust', 'negative', '-0.67', 'inactivity', 'inactiveness', 'slumber', 'inaction', 'hibernation']
senticnet['diaper'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'lavatory', 'commode', 'potty_chair']
senticnet['diaper_bag'] = ['0', '-0.02', '0.027', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'shoulder_bag', 'satchel', 'satchel_bag', 'tote_bag', 'carry']
senticnet['diaper_change'] = ['0.032', '0', '0', '0.077', '#joy', '#admiration', 'positive', '0.054', 'embrace', 'hug', 'caress', 'kiss', 'love']
senticnet['diaper_dermatitis'] = ['-0.72', '0', '0.778', '-0.98', '#anger', '#disgust', 'negative', '-0.82', 'actinic_dermatitis', 'hypericism', 'diaper_rash', 'dermatitis', 'seborrheic_dermatitis']
senticnet['diaper_rash'] = ['-0.72', '0', '0.778', '-0.98', '#anger', '#disgust', 'negative', '-0.82', 'actinic_dermatitis', 'hypericism', 'dermatitis', 'diaper_dermatitis', 'seborrheic_dermatitis']
senticnet['diaphanous'] = ['0', '0', '-0.87', '-0.86', '#fear', '#disgust', 'negative', '-0.86', 'gauze-like', 'filmy', 'gauzy', 'gossamer', 'see-through']
senticnet['diaphoresis'] = ['-0.58', '0', '0.773', '-0.79', '#anger', '#disgust', 'negative', '-0.71', 'perspiration', 'sweating', 'polyhidrosis', 'sudation', 'hidrosis']
senticnet['diaphoretic'] = ['-0.59', '0', '0.911', '-0.83', '#anger', '#disgust', 'negative', '-0.78', 'querulousness', 'peevishness', 'pettishness', 'snappishness', 'surliness']
senticnet['diaphragm'] = ['0.021', '0', '0', '0.054', '#joy', '#admiration', 'positive', '0.037', 'nerve', 'liver', 'groin', 'scrotum', 'pancreas']
senticnet['diaphragm_pump'] = ['0.843', '-0.99', '0', '0.706', '#joy', '#surprise', 'positive', '0.849', 'gadgetry', 'gimbal', 'contrivance', 'mod_con', 'device']
senticnet['diaphragmatic'] = ['0.777', '-0.89', '0', '0.508', '#joy', '#surprise', 'positive', '0.727', 'stretching', 'enlarge', 'enlargement', 'get_bigger', 'distension']
senticnet['diaporthe'] = ['-0.93', '0', '0.699', '-0.91', '#sadness', '#disgust', 'negative', '-0.84', 'onion_smut', 'head_smut', 'stinking_smut', 'flag_smut_fungus', 'covered_smut']
senticnet['diarmait_mac_cerbaill'] = ['0', '0.105', '0', '0.084', '#interest', '#admiration', 'positive', '0.095', 'archive', 'print_medium', 'quiet_place', 'register', 'media']
senticnet['diarrhea'] = ['-0.89', '0', '0.925', '-0.58', '#sadness', '#anger', 'negative', '-0.80', 'shit', 'laxation', 'movement', 'dump', 'asshole']
senticnet['diarrheal'] = ['-0.99', '0', '0.859', '-0.62', '#sadness', '#anger', 'negative', '-0.82', 'lax', 'loose', 'unconstipated', 'diarrhoeal', 'diarrhetic']
senticnet['diarrhetic'] = ['-0.99', '0', '0.859', '-0.62', '#sadness', '#anger', 'negative', '-0.82', 'lax', 'loose', 'diarrheal', 'diarrhoeal', 'unconstipated']
senticnet['diarrhoea'] = ['-0.35', '-0.13', '-0.05', '-0.38', '#sadness', '#disgust', 'negative', '-0.16', 'flab', 'toothache', 'brain_tumor', 'suffer_fool', 'bad_hair_day']
senticnet['diarrhoeal'] = ['-0.99', '0', '0.859', '-0.62', '#sadness', '#anger', 'negative', '-0.82', 'lax', 'loose', 'diarrheal', 'unconstipated', 'diarrhetic']
senticnet['diarrhoeal_disease'] = ['-0.71', '0', '0.74', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'malnutrition', 'marasmus', 'avitaminosis', 'hypovitaminosis', 'deficiency_disease']
senticnet['diary'] = ['0.866', '-0.71', '0', '0', '#joy', '#surprise', 'positive', '0.789', 'calendar_book', 'program', 'database', 'schedule', 'timetable']
senticnet['diasporic'] = ['0.122', '0.159', '-0.05', '0.217', '#interest', '#admiration', 'positive', '0.111', 'profile_picture', 'unity', 'country_person', 'unite', 'signature']
senticnet['diastole'] = ['0', '0.098', '-0.09', '0.115', '#interest', '#admiration', 'positive', '0.04', 'push_wind', 'breath', 'breaout', 'body_reaction', 'mouth_action']
senticnet['diastrophic_dysplasia'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'myotonic_muscular_dystrophy', 'dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['diathesis'] = ['0.98', '-0.61', '0', '0.598', '#joy', '#surprise', 'positive', '0.731', 'predisposition', 'hypersensitivity', 'susceptibility', 'susceptibleness', 'habitus']
senticnet['diatom'] = ['-0.65', '0', '0.523', '-0.73', '#sadness', '#disgust', 'negative', '-0.64', 'confervoid_algae', 'seaweed', 'golden_algae', 'alga', 'brown_algae']
senticnet['diatomic'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'elementary_particle', 'neutron', 'electron', 'subatomic_particle', 'proton']
senticnet['diatomic_molecule'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'elementary_particle', 'neutron', 'electron', 'subatomic_particle', 'proton']
senticnet['diatonic'] = ['-0.65', '0', '0.681', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'tone', 'whole_tone', 'step', 'whole_step', 'musical_interval']
senticnet['diatonic_scale'] = ['-0.65', '0', '0.681', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'tone', 'whole_tone', 'step', 'whole_step', 'musical_interval']
senticnet['diatonic_semitone'] = ['-0.65', '0', '0.681', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'tone', 'whole_tone', 'step', 'whole_step', 'musical_interval']
senticnet['diatribe'] = ['-0.80', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.78', 'jeremiad', 'polemic', 'jeremiad', 'tirade', 'chest-thumping']
senticnet['diatribe_against'] = ['-0.57', '0', '0.79', '-0.89', '#anger', '#disgust', 'negative', '-0.75', 'abuse', 'vilification', 'insult', 'revilement', 'contumely']
senticnet['diazomethane'] = ['0.032', '-0.02', '-0.04', '-0.08', '#fear', '#disgust', 'negative', '-0.01', 'liquid', 'carbonate_drink', 'chemical', 'soda', 'bromide']
senticnet['diazonium'] = ['0.032', '-0.02', '-0.04', '-0.08', '#fear', '#disgust', 'negative', '-0.01', 'liquid', 'carbonate_drink', 'chemical', 'soda', 'bromide']
senticnet['diazoxide'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'spironolactone', 'adrenal_cortical_steroid', 'antihypertensive', 'antihypertensive_drug']
senticnet['dibr'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
senticnet['dibromide'] = ['0.032', '-0.02', '-0.04', '-0.08', '#fear', '#disgust', 'negative', '-0.01', 'liquid', 'carbonate_drink', 'chemical', 'soda', 'bromide']
senticnet['dicarboxylic_acid'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'carboxylic_acid', 'succinic_acid', 'valeric_acid']
senticnet['dice'] = ['0.991', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.884', 'cube', 'block', 'ping_pong_paddle', 'ball_player', 'trapezoid']
senticnet['dicey'] = ['-0.82', '0', '0.968', '0', '#sadness', '#anger', 'negative', '-0.89', 'alleged', 'doubtful', 'unclear', 'undecided', 'doubtfulness']
senticnet['dichloride'] = ['0.032', '-0.02', '-0.04', '-0.08', '#fear', '#disgust', 'negative', '-0.01', 'liquid', 'carbonate_drink', 'chemical', 'soda', 'bromide']
senticnet['dichloro'] = ['-0.81', '0', '0.774', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'acid', 'hypophosphoric_acid', 'periodic_acid', 'hypooxyacid', 'oxyacid']
senticnet['dichotomy'] = ['-0.02', '0.114', '0', '0.204', '#interest', '#admiration', 'positive', '0.098', 'mimic', 'halve', 'separate', 'copy', 'make_copy']
senticnet['dichroic'] = ['-0.50', '0', '0.75', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'beam', 'beam_of_light', 'sunbeam', 'ray', 'ray_of_light']
senticnet['dichroic_filter'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'water_inside', 'water_spray', 'teapot', 'bodily_waste', 'different_type']
senticnet['dichroic_mirror'] = ['-0.50', '0', '0.75', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'beam', 'beam_of_light', 'sunbeam', 'ray', 'ray_of_light']
senticnet['dichroism'] = ['0.965', '-0.84', '0', '0.599', '#joy', '#surprise', 'positive', '0.802', 'mental_faculty', 'faculty', 'module', 'remembrance', 'anamnesis']
senticnet['dick'] = ['-0.35', '-0.13', '-0.05', '-0.38', '#sadness', '#disgust', 'negative', '-0.16', 'bastard', 'idiot', 'asshole', 'fuck', 'motherfucker']
senticnet['dick_dastardly'] = ['-0.73', '0', '0.72', '-0.93', '#sadness', '#disgust', 'negative', '-0.79', 'imp', 'elf', 'hob', 'leprechaun', 'pixie']
senticnet['dick_deadeye'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'dress', 'pinny', 'dirndl', 'gown', 'muumuu']
senticnet['dick_emery'] = ['-0.89', '0', '0.58', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'resemblance', 'comparison', 'equivalence', 'reflection', 'alikeness']
senticnet['dick_giordano'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'encyclopaedia', 'child_story', 'comic', 'disney_cartoon']
senticnet['dick_grayson'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'only_bird']
senticnet['dick_groat'] = ['0', '0.075', '0', '0', '#interest', '#interest', 'positive', '0.075', 'cup', 'carafe', 'container_cup', 'mug', 'glasses']
senticnet['dick_swiveller'] = ['-0.56', '0', '0.738', '-0.61', '#anger', '#disgust', 'negative', '-0.64', 'mix_up', 'muddle', 'puddle', 'assemble', 'piece']
senticnet['dickey'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'dicky', 'impaired', 'lessened', 'diminished']
senticnet['dickhead'] = ['-0.66', '0', '0.595', '-0.83', '#sadness', '#disgust', 'negative', '-0.7', 'bastard', 'disagreeable_person', 'mother_fucker', 'motherfucker', 'son_of_bitch']
senticnet['dickie'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'impaired', 'dickey', 'lessened', 'diminished']
senticnet['dickinsonia'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'hag', 'osteostracan', 'cephalaspid', 'ostracoderm', 'jawless_fish']
senticnet['dickon'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'impaired', 'dickey', 'lessened', 'diminished']
senticnet['dicky'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'impaired', 'dickey', 'lessened', 'diminished']
senticnet['dicloxacillin'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['dicroidium'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'gymnosperm', 'pteridosperm']
senticnet['dictate'] = ['0', '0', '-0.71', '-0.84', '#fear', '#disgust', 'negative', '-0.77', 'whim', 'capricious', 'dictated', 'whim', 'whatever']
senticnet['dictated'] = ['0', '0', '-0.71', '-0.84', '#fear', '#disgust', 'negative', '-0.77', 'whim', 'capricious', 'whim', 'dictate', 'whatever']
senticnet['dictation'] = ['0.022', '0', '0', '0.171', '#joy', '#admiration', 'positive', '0.097', 'communicate', 'call', 'answer_back', 'retort', 'say']
senticnet['dictator'] = ['-0.73', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.72', 'royal_leader', 'despot', 'emperor', 'monarch', 'king']
senticnet['dictatorial'] = ['-0.55', '0', '0.931', '-0.74', '#anger', '#disgust', 'negative', '-0.74', 'totalitarian', 'authoritarian', 'undemocratic', 'despotic', 'monarchal']
senticnet['dictatorial_power'] = ['-0.61', '0', '0.817', '-0.78', '#anger', '#disgust', 'negative', '-0.73', 'pretence', 'feigning', 'dissembling', 'pretense', 'putoff']
senticnet['dictatorial_regime'] = ['-0.84', '0', '0.744', '-0.58', '#sadness', '#anger', 'negative', '-0.72', 'subversion', 'subversive_activity', 'termination', 'ending', 'conclusion']
senticnet['dictatorial_rule'] = ['-0.55', '0', '0.931', '-0.74', '#anger', '#disgust', 'negative', '-0.74', 'totalitarian', 'authoritarian', 'dictatorial', 'despotic', 'monarchal']
senticnet['dictatorship'] = ['0', '0', '-0.77', '-0.73', '#fear', '#disgust', 'negative', '-0.75', 'dictator', 'submission', 'fear', 'boss', 'leadership']
senticnet['diction'] = ['-0.61', '0', '0.73', '-0.85', '#anger', '#disgust', 'negative', '-0.73', 'verbalization', 'phrasing', 'verbiage', 'wording', 'phraseology']
senticnet['dictionarie'] = ['-0.93', '0', '0.861', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'knotty', 'contorted', 'writhed', 'crooked', 'anfractuous']
senticnet['dictionary'] = ['0', '0', '-0.08', '0', '#fear', '#fear', 'negative', '-0.08', 'fable', 'story', 'fairy_story', 'saga', 'mythical']
senticnet['dictionary_definition'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'vocabulary', 'read_material', 'book_genre', 'many_words', 'book_library']
senticnet['dictyostelium_discoideum'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['dicussion'] = ['0', '0', '0.933', '-0.88', '#anger', '#disgust', 'negative', '-0.91', 'opinioned', 'argue', 'conquer_opponent', 'debate_politics', 'debate']
senticnet['didactic'] = ['0', '0.79', '0', '0.728', '#interest', '#admiration', 'positive', '0.759', 'moralizer', 'moralize', 'moralizing', 'moral', 'sententious']
senticnet['didactic_instruction'] = ['0.633', '-0.96', '0', '0.612', '#joy', '#surprise', 'positive', '0.738', 'particularism', 'absorption', 'concentration', 'engrossment', 'centering']
senticnet['didactic_poem'] = ['0', '0', '-0.90', '-0.79', '#fear', '#disgust', 'negative', '-0.85', 'encomiastic', 'encomium', 'paean', 'eulogy', 'pean']
senticnet['didacticism'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'nonsense', 'meaninglessness', 'bunk', 'absurdity', 'hokum']
senticnet['didactics'] = ['0.702', '-0.60', '0', '0.784', '#joy', '#admiration', 'positive', '0.698', 'continuing_education', 'activity', 'course', 'extracurricular_activity', 'coeducation']
senticnet['didanosine'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'indinavir', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['didanosine_ddi'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'protease_inhibitor', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['diddle'] = ['-0.78', '0', '0.915', '-0.99', '#anger', '#disgust', 'negative', '-0.89', 'bodge', 'botch_up', 'flub', 'ball_up', 'go_wrong']
senticnet['didna'] = ['0.947', '0.914', '0', '0', '#joy', '#interest', 'positive', '0.93', 'ball_bounce', 'bicycle_tire', 'tire', 'increase_price', 'gyroscope']
senticnet['didrex'] = ['-0.72', '0', '0.973', '-0.57', '#sadness', '#anger', 'negative', '-0.76', 'corpulency', 'blubber', 'fat', 'embonpoint', 'stoutness']
senticnet['die'] = ['-0.33', '-0.03', '-0.26', '-0.14', '#sadness', '#fear', 'negative', '-0.17', 'capital_punishment', 'get_killed', 'death', 'take_life', 'seek_revenge']
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senticnet['diffidently'] = ['-0.87', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.9', 'self-distrust', 'self-doubt', 'diffidence', 'shyness', 'hardheartedness']
senticnet['diffie_hellman'] = ['0.022', '-0.13', '0.153', '-0.31', '#anger', '#disgust', 'negative', '-0.07', 'codeine', 'morphine', 'infection', 'cocaine', 'nuclear_war']
senticnet['diffracted_light'] = ['-0.50', '0', '0.75', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'beam', 'beam_of_light', 'sunbeam', 'ray', 'ray_of_light']
senticnet['diffuse'] = ['0.968', '-0.76', '0', '0.583', '#joy', '#surprise', 'positive', '0.773', 'riddle', 'spiritize', 'permeate', 'pervade', 'penetrate']
senticnet['diffuse_cloud'] = ['-0.55', '0', '0.869', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'air_bubble', 'foam', 'froth', 'soap_bubble', 'bubble']
senticnet['diffuse_light'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'incandescence', 'luminescence', 'brightness_level']
senticnet['diffuse_lighting'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'illuminate_room', 'give_light', 'light', 'light_bulb', 'lantern']
senticnet['diffuse_reflection'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'incandescence', 'luminescence', 'brightness_level']
senticnet['diffusely'] = ['0', '-0.75', '0', '0.846', '#surprise', '#admiration', 'positive', '0.799', 'transiently', 'focally', 'potently', 'reproducibly', 'lastingly']
senticnet['diffusion'] = ['0.223', '-0.09', '0', '0.165', '#joy', '#admiration', 'positive', '0.16', 'dance', 'hook_up', 'go_club', 'old_dance', 'slow_dance']
senticnet['diffusion_barrier'] = ['-0.93', '0', '-0.99', '-0.92', '#sadness', '#fear', 'negative', '-0.95', 'language_barrier', 'shut', 'shut_down', 'close_down', 'obstacle']
senticnet['diffusion_coefficient'] = ['0.728', '0.977', '0', '0.939', '#interest', '#admiration', 'positive', '0.881', 'air_pressure', 'altitude', 'air', 'heavy_air', 'air_density']
senticnet['diffusion_equation'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'assumption', 'precondition', 'provision', 'constraint', 'premiss']
senticnet['diffusion_flame'] = ['-0.90', '0', '0.825', '-0.61', '#sadness', '#anger', 'negative', '-0.78', 'flaming', 'combustion', 'fire', 'flame', 'internal_combustion']
senticnet['diffusional'] = ['-0.98', '0', '0.516', '-0.90', '#sadness', '#disgust', 'negative', '-0.80', 'devastation', 'annihilation', 'pulverization', 'vaporization', 'destruction']
senticnet['diffusivity'] = ['0.728', '0.977', '0', '0.939', '#interest', '#admiration', 'positive', '0.881', 'air_pressure', 'altitude', 'air', 'heavy_air', 'air_density']
senticnet['difreintiedig'] = ['-0.56', '0', '0.617', '-0.80', '#anger', '#disgust', 'negative', '-0.66', 'prolixity', 'prolixness', 'wordiness', 'repetitiveness', 'repetitiousness']
senticnet['dig'] = ['-0.18', '0', '-0.10', '0', '#sadness', '#fear', 'negative', '-0.14', 'dig_hole', 'plant_yard', 'cut_grass', 'hang_ceiling', 'plant_tree']
senticnet['dig_dirt'] = ['-0.08', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.08', 'plant_garden', 'plant_flower', 'plant_seed', 'plant_yard', 'cut_grass']
senticnet['dig_gold'] = ['-0.11', '0.068', '-0.10', '0', '#sadness', '#fear', 'negative', '-0.05', 'close_together', 'cultivate', 'smoke_joint', 'feel_heat', 'bridge_difference']
senticnet['dig_grave'] = ['-0.25', '0', '-0.19', '-0.07', '#sadness', '#fear', 'negative', '-0.17', 'dig_hole', 'eat_animal', 'say_prayer', 'hurt_animal', 'wear_skirt']
senticnet['dig_ground'] = ['-0.09', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.07', 'dig', 'work', 'hard_work', 'settle', 'job']
senticnet['dig_hole'] = ['-0.24', '0', '-0.18', '-0.07', '#sadness', '#fear', 'negative', '-0.17', 'dig_grave', 'eat_animal', 'say_prayer', 'hurt_animal', 'wear_skirt']
senticnet['dig_out'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'find_out', 'unmask', 'unearth', 'become_aware', 'dish_out']
senticnet['dig_tunnel'] = ['0', '0', '0.862', '-0.79', '#anger', '#disgust', 'negative', '-0.82', 'social_insect', 'big_mouse', 'rodent', 'small_animal', 'vermin']
senticnet['dig_up'] = ['-0.08', '0.072', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.04', 'burn_up', 'shine_light', 'dig_gold', 'light_up', 'shine_car']
senticnet['digest'] = ['0.951', '-0.80', '0', '0.501', '#joy', '#surprise', 'positive', '0.753', 'understand', 'twig', 'apprehend', 'tumble', 'intuit']
senticnet['digest_algorithm'] = ['0.022', '-0.13', '0.153', '-0.31', '#anger', '#disgust', 'negative', '-0.07', 'codeine', 'morphine', 'infection', 'cocaine', 'nuclear_war']
senticnet['digest_lactose'] = ['-0.86', '0', '0.76', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['digest_milk'] = ['-0.86', '0', '0.76', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['digested_food'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'belly', 'nervous_system', 'human_body', 'stomach', 'tummy']
senticnet['digestible'] = ['0.515', '-0.80', '0', '0.791', '#surprise', '#admiration', 'positive', '0.703', 'accessible', 'fathomable', 'approachable', 'perceivable', 'intelligible']
senticnet['digesting_food'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'belly', 'nervous_system', 'human_body', 'stomach', 'tummy']
senticnet['digestion'] = ['0.755', '-0.71', '0', '0.972', '#joy', '#admiration', 'positive', '0.813', 'digest', 'metabolism', 'grow_up', 'grow_healthy', 'healthy_lifestyle']
senticnet['digestive'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'belly', 'nervous_system', 'human_body', 'stomach', 'tummy']
senticnet['digestive_aid'] = ['-0.50', '0', '0.582', '-0.57', '#anger', '#disgust', 'negative', '-0.55', 'purgative', 'cathartic', 'physic', 'aperient', 'phenolphthalein']
senticnet['digestive_enzyme'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'belly', 'nervous_system', 'human_body', 'stomach', 'tummy']
senticnet['digestive_fluid'] = ['0', '0', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.05', 'jaw', 'mouth', 'dimple', 'nose', 'cheek']
senticnet['digestive_function'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'belly', 'nervous_system', 'human_body', 'stomach', 'tummy']
senticnet['digestive_system'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'belly', 'nervous_system', 'human_body', 'stomach', 'tummy']
senticnet['digestive_track'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'belly', 'nervous_system', 'human_body', 'stomach', 'tummy']
senticnet['digestive_tract'] = ['0', '0', '-0.74', '-0.89', '#fear', '#disgust', 'negative', '-0.81', 'alimentary_canal', 'alimentary_tract', 'digestive_tube', 'enteron', 'gastrointestinal_tract']
senticnet['digestive_tube'] = ['0', '0', '-0.91', '-0.84', '#fear', '#disgust', 'negative', '-0.88', 'alimentary_canal', 'alimentary_tract', 'enteron', 'digestive_tract', 'gastrointestinal_tract']
senticnet['digestive_upset'] = ['-0.73', '0', '0.94', '-0.87', '#anger', '#disgust', 'negative', '-0.85', 'edema', 'oedema', 'hydrops', 'dropsy', 'puffiness']
senticnet['digger'] = ['-0.84', '0', '0.96', '-0.91', '#anger', '#disgust', 'negative', '-0.90', 'housewrecker', 'housebreaker', 'knacker', 'laborer', 'manual_laborer']
senticnet['diggerland'] = ['-0.72', '0', '0.978', '-0.86', '#anger', '#disgust', 'negative', '-0.85', 'amusement_park', 'common', 'pleasure_ground', 'village_green', 'tract']
senticnet['digibeta'] = ['0', '0', '0', '-0.17', '#disgust', '#disgust', 'negative', '-0.17', 'medicine', 'aspirin', 'blank_check', 'painkiller', 'gauze']
senticnet['digipak_edition'] = ['-0.73', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'unwed', 'unwedded', 'divorced', 'unmarried', 'widowed']
senticnet['digistar'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'floodlight', 'spotlight', 'headlight', 'light', 'teach_lesson']
senticnet['digit'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'figure', 'number', 'numeral', 'cipher', 'amount']
senticnet['digit_number'] = ['0.049', '0.113', '0.107', '0.162', '#interest', '#admiration', 'positive', '0.054', 'list_number', 'ordinal', 'count', 'numerical', 'enumeration']
senticnet['digital'] = ['-0.09', '-0.03', '0.061', '0', '#sadness', '#anger', 'negative', '-0.04', 'bad_reception', 'wifi', 'bad_connection', 'phone_call', 'underground']
senticnet['digital_assistant_pda'] = ['0.946', '0.738', '0', '0', '#joy', '#interest', 'positive', '0.842', 'owner', 'belong', 'own', 'close', 'asset']
senticnet['digital_audio'] = ['0.055', '0', '0.03', '0.136', '#joy', '#admiration', 'positive', '0.054', 'loud_soft', 'decibel', 'loudness', 'sound', 'melodic_noise']
senticnet['digital_audio_workstation_daw'] = ['0.055', '0', '0.03', '0.136', '#joy', '#admiration', 'positive', '0.054', 'loud_soft', 'decibel', 'loudness', 'sound', 'melodic_noise']
senticnet['digital_camera'] = ['0', '-0.02', '0.048', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'telescope', 'read_glass', 'eyeglass', 'binoculars', 'monocle']
senticnet['digital_curation'] = ['-0.97', '0', '0.885', '-0.97', '#sadness', '#disgust', 'negative', '-0.94', 'protection', 'conservation', 'environmentalism', 'reservation', 'immobilization']
senticnet['digital_dashboard'] = ['0.725', '0.796', '0', '0', '#joy', '#interest', 'positive', '0.76', 'car_stereo', 'battery', 'seatbelt', 'spare_tire', 'brake_pedal']
senticnet['digital_entertainment'] = ['0.951', '0', '0', '0.731', '#joy', '#admiration', 'positive', '0.841', 'pastime', 'enjoyment', 'pleasure', 'enjoy', 'fun']
senticnet['digital_library'] = ['0.937', '0.984', '0', '0', '#joy', '#interest', 'positive', '0.961', 'different_application', 'award', 'project', 'standard_grant', 'national_smete_digital_library']
senticnet['digital_media'] = ['-0.02', '0', '-0.04', '-0.03', '#fear', '#disgust', 'negative', '-0.03', 'studio', 'art_piece', 'art_gallery', 'artist', 'museum']
senticnet['digital_preservation'] = ['-0.97', '0', '0.885', '-0.97', '#sadness', '#disgust', 'negative', '-0.94', 'protection', 'conservation', 'environmentalism', 'reservation', 'immobilization']
senticnet['digital_projection'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'floodlight', 'spotlight', 'headlight', 'light', 'teach_lesson']
senticnet['digital_projector'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'floodlight', 'spotlight', 'headlight', 'light', 'teach_lesson']
senticnet['digital_signage'] = ['0.813', '0.832', '0', '0.874', '#interest', '#admiration', 'positive', '0.84', 'ferris_wheel', 'concession_stand', 'food', 'carnival_ride', 'theme_park']
senticnet['digital_signature'] = ['0.584', '-0.72', '0', '0.742', '#surprise', '#admiration', 'positive', '0.684', 'decompression', 'cryptography', 'coding', 'secret_writing', 'steganography']
senticnet['digital_song_chart'] = ['0.997', '0.892', '0', '0', '#joy', '#interest', 'positive', '0.945', 'advertisement', 'large_outdoor_board', 'motorway', 'expressway', 'train_track']
senticnet['digital_speedometer'] = ['0.725', '0.796', '0', '0', '#joy', '#interest', 'positive', '0.76', 'car_stereo', 'battery', 'seatbelt', 'spare_tire', 'brake_pedal']
senticnet['digital_tuner'] = ['0.036', '-0.04', '0.027', '0.063', '#surprise', '#admiration', 'positive', '0.028', 'radio', 'speaker', 'channel_selector', 'power_switch', 'female_television_connector']
senticnet['digital_tv_tuner'] = ['0.036', '-0.04', '0.027', '0.063', '#surprise', '#admiration', 'positive', '0.028', 'radio', 'speaker', 'channel_selector', 'power_switch', 'female_television_connector']
senticnet['digital_video'] = ['0.055', '0', '0.03', '0.136', '#joy', '#admiration', 'positive', '0.054', 'loud_soft', 'decibel', 'loudness', 'sound', 'melodic_noise']
senticnet['digitalis_toxicity'] = ['-0.83', '0', '0.987', '-0.50', '#sadness', '#anger', 'negative', '-0.77', 'heart_block', 'atrioventricular_block', 'bradycardia', 'ventricular_fibrillation', 'tachycardia']
senticnet['digitality'] = ['0.654', '-0.71', '0', '0.624', '#joy', '#surprise', 'positive', '0.664', 'viewable', 'gross', 'telescopic', 'seeable', 'visual']
senticnet['digitalization'] = ['-0.62', '0', '0.987', '-0.74', '#anger', '#disgust', 'negative', '-0.78', 'disinfection', 'treatment', 'hospice', 'medical_aid', 'bloodletting']
senticnet['digitaria'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'meadow_grass', 'grass', 'manna_grass', 'sweet_grass', 'wood_meadowgrass']
senticnet['digitate_date'] = ['0.463', '0.561', '-0.29', '0.281', '#joy', '#interest', 'positive', '0.253', 'write_date', 'impress_date', 'use_computer', 'use_pen', 'date']
senticnet['digitech_whammy'] = ['0.748', '-0.90', '0', '0.99', '#surprise', '#admiration', 'positive', '0.882', 'shakiness', 'quivering', 'shaking', 'vibration', 'quiver']
senticnet['digitigrade'] = ['0.098', '0.084', '0.819', '-0.68', '#anger', '#disgust', 'negative', '-0.33', 'grasp', 'covet', 'covetousness', 'avaricious', 'grabby']
senticnet['digitisation'] = ['-0.97', '0', '0.885', '-0.97', '#sadness', '#disgust', 'negative', '-0.94', 'protection', 'conservation', 'environmentalism', 'reservation', 'immobilization']
senticnet['digitization'] = ['-0.97', '0', '0.885', '-0.97', '#sadness', '#disgust', 'negative', '-0.94', 'protection', 'conservation', 'environmentalism', 'reservation', 'immobilization']
senticnet['digitized_audio'] = ['0.055', '0', '0.03', '0.136', '#joy', '#admiration', 'positive', '0.054', 'loud_soft', 'decibel', 'loudness', 'sound', 'melodic_noise']
senticnet['dignified'] = ['0.891', '-0.56', '0', '0.504', '#joy', '#surprise', 'positive', '0.654', 'stately', 'imposing', 'courtly', 'distinguished', 'grand']
senticnet['dignify'] = ['0', '-0.72', '0', '0.946', '#surprise', '#admiration', 'positive', '0.835', 'dignity', 'exalt', 'dignified', 'honor', 'dowager']
senticnet['dignity'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'world_peace', 'compliment', 'honor', 'prefer', 'kindness']
senticnet['digoxin_lanoxin'] = ['-0.72', '0', '0.973', '-0.57', '#sadness', '#anger', 'negative', '-0.76', 'corpulency', 'blubber', 'fat', 'embonpoint', 'stoutness']
senticnet['digraph'] = ['-0.61', '0', '0.721', '-0.68', '#anger', '#disgust', 'negative', '-0.67', 'character', 'logogram', 'logograph', 'graph', 'ideogram']
senticnet['digress'] = ['0', '0', '-0.92', '-0.83', '#fear', '#disgust', 'negative', '-0.87', 'sidetrack', 'depart', 'wander', 'divagate', 'stray']
senticnet['digression'] = ['-0.95', '0', '0.968', '0', '#sadness', '#anger', 'negative', '-0.96', 'aside', 'away', 'excursus', 'parenthesis', 'divagation']
senticnet['digressive'] = ['-0.89', '0', '0.987', '-0.90', '#anger', '#disgust', 'negative', '-0.93', 'cacoethes', 'logomania', 'agromania', 'pyromania', 'necrophilia']
senticnet['dihydrate'] = ['-0.81', '0', '0.774', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'acid', 'hypophosphoric_acid', 'periodic_acid', 'oxyacid', 'phosphorous_acid']
senticnet['dihydroergotamine'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['diinkarazan'] = ['-0.78', '0', '0.74', '-0.94', '#sadness', '#disgust', 'negative', '-0.82', 'eject', 'chuck_out', 'exclude', 'turf_out', 'boot_out']
senticnet['diirinka'] = ['-0.04', '0.166', '0.684', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'malign', 'malefic', 'maliciously', 'spitefulness', 'maliciousness']
senticnet['dijon_mustard'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'horseradish', 'box_cereal', 'bottle_water']
senticnet['dilapidate'] = ['-0.51', '0', '0.965', '-0.95', '#anger', '#disgust', 'negative', '-0.81', 'go_to_pot', 'go_to_dog', 'deteriorate', 'decay', 'wear']
senticnet['dilapidated'] = ['-0.99', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.95', 'run-down', 'rundown', 'ramshackle', 'decrepit', 'derelict']
senticnet['dilapidation'] = ['-0.97', '0', '0.752', '-0.83', '#sadness', '#disgust', 'negative', '-0.85', 'decay', 'wear', 'corrosion', 'devastation', 'deterioration']
senticnet['dilatant_fluid'] = ['0', '0', '-0.11', '0', '#fear', '#fear', 'negative', '-0.11', 'chemical_reaction', 'set_fire', 'grow_yard', 'heat_light', 'burn_down_house']
senticnet['dilatation'] = ['0.777', '-0.89', '0', '0.508', '#joy', '#surprise', 'positive', '0.727', 'stretching', 'enlarge', 'enlargement', 'get_bigger', 'distension']
senticnet['dilate'] = ['0.773', '0', '0', '0.865', '#joy', '#admiration', 'positive', '0.819', 'spread', 'pullulate', 'supplement', 'triple', 'annex']
senticnet['dilated_cardiomyopathy'] = ['-0.98', '0', '0.827', '-0.94', '#sadness', '#disgust', 'negative', '-0.91', 'congestive_heart_failure', 'heart_attack', 'failure', 'heart_disease', 'cardiopathy']
senticnet['dilation'] = ['0.777', '-0.89', '0', '0.508', '#joy', '#surprise', 'positive', '0.727', 'stretching', 'enlarge', 'enlargement', 'get_bigger', 'distension']
senticnet['dilatoriness'] = ['-0.68', '0', '0.82', '-0.96', '#anger', '#disgust', 'negative', '-0.82', 'deliberateness', 'leisureliness', 'procrastination', 'rate', 'pace']
senticnet['dilatory'] = ['-0.68', '0', '0.82', '-0.96', '#anger', '#disgust', 'negative', '-0.82', 'deliberateness', 'leisureliness', 'procrastination', 'rate', 'pace']
senticnet['dilaudid'] = ['0.138', '0.103', '0', '0.095', '#joy', '#interest', 'positive', '0.112', 'masturbation', 'sexual_intercourse', 'sex', 'warm_fuzzy_feeling', 'fuck']
senticnet['dilbeek'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'feudal_lord', 'lord', 'master', 'overlord', 'king']
senticnet['dildo'] = ['0', '0', '0', '-0.06', '#disgust', '#disgust', 'negative', '-0.06', 'body', 'nervous_system', 'normal_human_body', 'human_body', 'head_shoulder']
senticnet['dilemma'] = ['-0.11', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.08', 'bad_situation', 'anxiety', 'difficult_situation', 'predicament', 'difficulty']
senticnet['dilettante'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'sciolist', 'dabbler', 'mortal', 'amateur', 'bird_watcher']
senticnet['diligence'] = ['0.215', '0.194', '-0.20', '0.279', '#joy', '#admiration', 'positive', '0.121', 'use_match', 'gather_kindle', 'combustible_material', 'rub_two_stick_together', 'lighter']
senticnet['diligent'] = ['0.877', '-0.92', '0', '0.795', '#joy', '#surprise', 'positive', '0.865', 'study_material', 'study_lot', 'study_language', 'go_class', 'good_boy']
senticnet['diligently'] = ['0.215', '0.194', '-0.20', '0.279', '#joy', '#admiration', 'positive', '0.121', 'use_match', 'gather_kindle', 'combustible_material', 'rub_two_stick_together', 'lighter']
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senticnet['diopside'] = ['-0.05', '-0.00', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'pigment', 'many_shades', 'cyan', 'green_yellow', 'material']
senticnet['dioscorea'] = ['0.035', '0.104', '0', '0.12', '#interest', '#admiration', 'positive', '0.086', 'fruit_vegetable', 'vegetable', 'vegetable_fruit', 'fresh_vegetable', 'vegetarian_food']
senticnet['dioxide'] = ['0.053', '0.264', '-0.11', '0.23', '#interest', '#admiration', 'positive', '0.107', 'natural_resource', 'petroleum', 'fossil_fuel', 'energy_source', 'potential_energy']
senticnet['dip'] = ['0.077', '0', '0', '0', '#joy', '#joy', 'positive', '0.077', 'ginger', 'garlic', 'oat', 'savory', 'rye']
senticnet['dipascali'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'busy_bee', 'gopher', 'actor', 'doer', 'worker']
senticnet['diphenhydramine_benadryl'] = ['0.969', '0.817', '0', '0.828', '#joy', '#admiration', 'positive', '0.871', 'ibuprofen', 'painkiller', 'medicine', 'analgesic', 'phenobarbital']
senticnet['diphenyl'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'enhance', 'intensify', 'deepen', 'potentiate', 'heighten']
senticnet['diphtheria'] = ['0', '0', '-0.88', '-0.80', '#fear', '#disgust', 'negative', '-0.84', 'polio', 'measles', 'poliomyelitis', 'smallpox', 'pertussis']
senticnet['diphthong'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'letter', 'vowel', 'alphabet', 'basic_speech', 'kind_gesture']
senticnet['diphthongization'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'pronunciation', 'sibilation', 'mispronunciation', 'homophony']
senticnet['diplodia'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'apple_rust', 'fungus']
senticnet['diplodia_pinea'] = ['-0.75', '0', '0.69', '-0.69', '#sadness', '#disgust', 'negative', '-0.71', 'rubor', 'redness', 'sleeping_sickness', 'sleepy_sickness', 'cephalitis']
senticnet['diploma'] = ['0', '0.155', '0', '0', '#interest', '#interest', 'positive', '0.155', 'certificate', 'celsius', 'qualification', 'temperature_measure', 'temperature_measurement']
senticnet['diplomacy'] = ['0', '0.098', '0', '0.08', '#interest', '#admiration', 'positive', '0.089', 'talk_person', 'wisdom', 'tact', 'statecraft', 'greet_person']
senticnet['diplomat'] = ['0', '0.887', '0', '0.838', '#interest', '#admiration', 'positive', '0.863', 'diplomatist', 'ambassador', 'diplomacy', 'politics', 'keep_peace']
senticnet['diplomatic'] = ['0', '-0.78', '0', '0.942', '#surprise', '#admiration', 'positive', '0.865', 'extraterritoriality', 'government', 'discreet', 'politic', 'conference']
senticnet['diplomatic_clout'] = ['-0.94', '0', '0.863', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'poke', 'knockout_punch', 'lick', 'punch', 'biff']
senticnet['diplomatic_correspondent'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'adventurer', 'humankind']
senticnet['diplomatic_engagement'] = ['0', '0.098', '0', '0.08', '#interest', '#admiration', 'positive', '0.089', 'talk_person', 'wisdom', 'tact', 'statecraft', 'greet_person']
senticnet['diplomatic_immunity'] = ['0.73', '-0.56', '0', '0.621', '#joy', '#admiration', 'positive', '0.64', 'amnesty', 'unsusceptibility', 'exemption', 'indemnity', 'impunity']
senticnet['diplomatic_intrigue'] = ['0.945', '0.996', '0', '0.737', '#joy', '#interest', 'positive', '0.893', 'become_intelligent', 'gain_knowledge', 'increase_knowledge', 'learn', 'desire_education']
senticnet['diplomatic_offensive'] = ['0', '0.098', '0', '0.08', '#interest', '#admiration', 'positive', '0.089', 'talk_person', 'wisdom', 'tact', 'statecraft', 'greet_person']
senticnet['diplomatic_pressure'] = ['-0.12', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.09', 'breeze', 'move_air', 'air_movement', 'gape', 'force']
senticnet['diplomatically'] = ['0', '0.098', '0', '0.08', '#interest', '#admiration', 'positive', '0.089', 'talk_person', 'wisdom', 'tact', 'statecraft', 'greet_person']
senticnet['diplomatist'] = ['0', '0.882', '0', '0.833', '#interest', '#admiration', 'positive', '0.857', 'diplomat', 'ambassador', 'diplomacy', 'politics', 'keep_peace']
senticnet['diplopia'] = ['-0.86', '0', '0.858', '-0.91', '#sadness', '#disgust', 'negative', '-0.87', 'edema', 'oedema', 'hydrops', 'dropsy', 'eye_disease']
senticnet['diplotene'] = ['0.791', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.81', 'dispersing_phase', 'generation', 'leptotene', 'safe_period', 'phase']
senticnet['dipolar'] = ['0.807', '-0.88', '0', '0.718', '#joy', '#surprise', 'positive', '0.802', 'sex', 'gayness', 'sexual_gayness', 'lesbianism', 'sapphism']
senticnet['dipole'] = ['0.807', '-0.88', '0', '0.718', '#joy', '#surprise', 'positive', '0.802', 'sex', 'gayness', 'sexual_gayness', 'lesbianism', 'sapphism']
senticnet['dipotassium'] = ['0.766', '-0.87', '0', '0.666', '#joy', '#surprise', 'positive', '0.769', 'reactive', 'unstable', 'labile', 'activated', 'excited']
senticnet['dipping_sauce'] = ['0.043', '0.091', '0', '0.113', '#interest', '#admiration', 'positive', '0.082', 'sweet_sour', 'tasty', 'yummy', 'popular_food', 'form_of_food']
senticnet['diprivan'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'use_drug', 'do_drug', 'drug_use', 'drug', 'addiction']
senticnet['dipshit'] = ['-0.61', '0', '0.981', '-0.98', '#anger', '#disgust', 'negative', '-0.85', 'pious_platitude', 'nothingness', 'jazz', 'talk', 'malarky']
senticnet['dipsomaniac'] = ['0.556', '-0.79', '0', '0.755', '#surprise', '#admiration', 'positive', '0.701', 'lush', 'alcoholic', 'alky', 'rummy', 'boozer']
senticnet['dipteran'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'giant_hornet', 'hornet', 'hornet_wasp']
senticnet['diptyque'] = ['0.863', '0.861', '0', '0', '#joy', '#interest', 'positive', '0.862', 'aroma', 'scent', 'smell', 'odor', 'sniff']
senticnet['dirac_delta_function'] = ['0.506', '-0.56', '0', '0.788', '#surprise', '#admiration', 'positive', '0.62', 'backward', 'reverse', 'retrograde', 'reflexive', 'retral']
senticnet['dire'] = ['-0.82', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.89', 'fearsome', 'dreaded', 'direful', 'frightening', 'horrendous']
senticnet['dire_poverty'] = ['-0.56', '0', '-0.49', '-0.09', '#sadness', '#fear', 'negative', '-0.38', 'lack', 'absence', 'poverty', 'emptiness', 'hunger']
senticnet['dire_shortage'] = ['-0.83', '0', '-0.88', '-0.78', '#sadness', '#fear', 'negative', '-0.83', 'little', 'sadness', 'small_amount', 'lack', 'absence']
senticnet['dire_situation'] = ['-0.87', '0', '0.673', '-0.55', '#sadness', '#anger', 'negative', '-0.7', 'hot_water', 'difficulty', 'care', 'corner', 'box']
senticnet['direct'] = ['0.05', '0.166', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.074', 'halt', 'red_light', 'director', 'finish', 'direction']
senticnet['direct_confrontation'] = ['-0.82', '0', '0.739', '-0.50', '#sadness', '#anger', 'negative', '-0.69', 'fake', 'juke', 'maneuver', 'manoeuvre', 'feint']
senticnet['direct_connection'] = ['0.078', '0.103', '-0.09', '0.191', '#interest', '#admiration', 'positive', '0.069', 'connect', 'aunt_uncle', 'short_form', 'relationship', 'kinship']
senticnet['direct_descendant'] = ['0.948', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.888', 'relation', 'mom', 'kin', 'cousin', 'granddaughter']
senticnet['direct_injection'] = ['0', '0', '-0.95', '-0.87', '#fear', '#disgust', 'negative', '-0.91', 'sharp_weapon', 'bayonet', 'ammunition', 'spear', 'arrow']
senticnet['direct_investment'] = ['0', '0.312', '-0.10', '0.207', '#interest', '#admiration', 'positive', '0.137', 'infuse', 'loan', 'lend', 'increase_money', 'wealth']
senticnet['direct_observation'] = ['0', '0.811', '0', '0', '#interest', '#interest', 'positive', '0.811', 'observe', 'glimpse', 'look', 'stare', 'glance']
senticnet['direct_person'] = ['0.05', '0.166', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.074', 'direct', 'person', 'outspoken', 'frank', 'candid']
senticnet['direct_play'] = ['0', '0.769', '0', '0', '#interest', '#interest', 'positive', '0.769', 'directing_music', 'quiet_child', 'face_music', 'correct_mistake', 'give_signal']
senticnet['direct_sequel'] = ['-0.06', '0.122', '-0.04', '0.113', '#interest', '#admiration', 'positive', '0.032', 'encounter', 'portray', 'reveal', 'episode', 'next_episode']
senticnet['direct_suffrage'] = ['0.196', '0.129', '-0.06', '0.184', '#joy', '#admiration', 'positive', '0.111', 'pleasant', 'pleasurable', 'bourgeois', 'important_human', 'good']
senticnet['direct_tax_cbdt'] = ['-0.08', '0.358', '-0.20', '0.237', '#interest', '#admiration', 'positive', '0.077', 'extortion', 'money_due', 'afford', 'bribe', 'owe']
senticnet['direct_tide'] = ['0.632', '-0.61', '0', '0.525', '#joy', '#surprise', 'positive', '0.59', 'neap_tide', 'neap', 'springtide', 'high_tide', 'tide']
senticnet['direct_traffic'] = ['0', '0.769', '0', '0', '#interest', '#interest', 'positive', '0.769', 'stop_suspect', 'traffic_police', 'traffic_jam', 'stop_crime', 'fine_violator']
senticnet['directed_toward'] = ['0', '0.21', '-0.11', '0.345', '#interest', '#admiration', 'positive', '0.147', 'direction', 'unto', 'forehead', 'upon', 'go_toward']
senticnet['directing_music'] = ['0', '0.769', '0', '0', '#interest', '#interest', 'positive', '0.769', 'direct_play', 'opera', 'face_music', 'correct_mistake', 'give_signal']
senticnet['direction'] = ['0', '0.175', '0', '0.231', '#interest', '#admiration', 'positive', '0.203', 'compass_direction', 'go_straight', 'end_point', 'eight_sides', 'red_light']
senticnet['direction_perpendicular'] = ['0', '0.175', '0', '0.231', '#interest', '#admiration', 'positive', '0.203', 'compass_direction', 'go_straight', 'end_point', 'eight_sides', 'red_light']
senticnet['directional'] = ['0', '0.175', '0', '0.231', '#interest', '#admiration', 'positive', '0.203', 'compass_direction', 'go_straight', 'end_point', 'eight_sides', 'red_light']
senticnet['directional_pad'] = ['-0.03', '0.11', '0', '0.128', '#interest', '#admiration', 'positive', '0.069', 'freestyle', 'take_leak', 'jump_air', 'backstroke', 'go_underwater']
senticnet['directional_solidification'] = ['0.976', '0.786', '0', '0', '#joy', '#interest', 'positive', '0.881', 'combustible', 'state_matter', 'natural_resource', 'liquid_fuel', 'fossil_fuel']
senticnet['directionality'] = ['-0.59', '0', '0.6', '-0.51', '#sadness', '#anger', 'negative', '-0.57', 'asymmetry', 'dissymmetry', 'imbalance', 'directivity', 'radial_asymmetry']
senticnet['directionallight'] = ['0.777', '-0.85', '0', '0.615', '#joy', '#surprise', 'positive', '0.748', 'think', 'play', 'trifle', 'abstract', 'warm_to']
senticnet['directionless'] = ['-0.99', '0', '0.658', '-0.73', '#sadness', '#disgust', 'negative', '-0.79', 'adrift', 'afloat', 'aimless', 'purposeless', 'planless']
senticnet['directions'] = ['0.078', '0.134', '0.011', '0.108', '#interest', '#admiration', 'positive', '0.077', 'find_house', 'home', 'house', 'find_car', 'find_building']
senticnet['directivity'] = ['-0.59', '0', '0.6', '-0.51', '#sadness', '#anger', 'negative', '-0.57', 'asymmetry', 'dissymmetry', 'imbalance', 'radial_asymmetry', 'directionality']
senticnet['directly'] = ['0', '0.728', '0', '0.874', '#interest', '#admiration', 'positive', '0.801', 'fairly', 'basically', 'apparently', 'plain', 'plainly']
senticnet['directly_affect'] = ['-0.53', '0', '0.57', '-0.73', '#anger', '#disgust', 'negative', '-0.61', 'run_into', 'strike', 'hit', 'bump_into', 'collide_with']
senticnet['directly_comparable'] = ['0.118', '0.181', '-0.07', '0.374', '#interest', '#admiration', 'positive', '0.149', 'compare', 'confrontation', 'collation', 'comparative', 'paragon']
senticnet['directly_opposite'] = ['-0.80', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.77', 'contrary', 'contradiction', 'lie', 'liar', 'bad_person']
senticnet['directness'] = ['0.772', '-0.95', '0', '0.89', '#surprise', '#admiration', 'positive', '0.872', 'downrightness', 'straightforwardness', 'characteristic', 'immediacy', 'immediateness']
senticnet['director'] = ['-0.04', '0.189', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'label', 'percentage', 'rate', 'money_relate', 'boss']
senticnet['director_chair'] = ['0.899', '0', '0', '0.972', '#joy', '#admiration', 'positive', '0.935', 'director', 'shooting_movie', 'actor', 'important_person', 'movie']
senticnet['director_dario_argento'] = ['-0.79', '0', '0.885', '-0.76', '#sadness', '#anger', 'negative', '-0.81', 'infernal', 'mortal', 'dead_soul', 'dead_soule', 'living_dead']
senticnet['director_lucio_fulci'] = ['-0.79', '0', '0.885', '-0.76', '#sadness', '#anger', 'negative', '-0.81', 'infernal', 'mortal', 'dead_soul', 'dead_soule', 'living_dead']
senticnet['director_shami_chakrabarti'] = ['0.926', '0.937', '0', '0.975', '#interest', '#admiration', 'positive', '0.946', 'monument', 'statue', 'freedom', 'idol', 'sculpture']
senticnet['directorate'] = ['0', '0.915', '0', '0.728', '#interest', '#admiration', 'positive', '0.822', 'board_of_director', 'division', 'award', 'physical_science', 'research']
senticnet['directorate_general'] = ['0.851', '-0.65', '0', '0.66', '#joy', '#admiration', 'positive', '0.723', 'omen', 'portent', 'presage', 'prognostic', 'prognostication']
senticnet['directory'] = ['0', '0.175', '0', '0.231', '#interest', '#admiration', 'positive', '0.203', 'list', 'schedule', 'index', 'agenda', 'register']
senticnet['directory_hierarchy'] = ['0', '0.175', '0', '0.231', '#interest', '#admiration', 'positive', '0.203', 'list', 'schedule', 'index', 'agenda', 'register']
senticnet['directory_structure'] = ['0', '0.175', '0', '0.231', '#interest', '#admiration', 'positive', '0.203', 'list', 'schedule', 'index', 'agenda', 'register']
senticnet['directory_tree'] = ['0', '0.175', '0', '0.231', '#interest', '#admiration', 'positive', '0.203', 'list', 'schedule', 'index', 'agenda', 'register']
senticnet['directorythe'] = ['0', '0.175', '0', '0.231', '#interest', '#admiration', 'positive', '0.203', 'list', 'schedule', 'index', 'agenda', 'register']
senticnet['direful'] = ['-0.85', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.86', 'dire', 'fearsome', 'dreaded', 'frightening', 'horrendous']
senticnet['direttissima'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'expressway', 'turnpike', 'toll_road', 'highway']
senticnet['dirichlet_problem'] = ['0.875', '-0.65', '0', '0.861', '#joy', '#admiration', 'positive', '0.797', 'completeness', 'work', 'whole_shebang', 'essential', 'whole']
senticnet['dirige_contre'] = ['0.939', '-0.71', '0', '0.775', '#joy', '#admiration', 'positive', '0.81', 'vox_angelica', 'voix_celeste', 'organ_stop', 'pipe', 'diapason']
senticnet['dirigeant'] = ['0.911', '-0.94', '0', '0.953', '#surprise', '#admiration', 'positive', '0.935', 'common_sense', 'good_sense', 'horse_sense', 'mother_wit', 'gumption']
senticnet['dirigible'] = ['0.5', '-0.9', '0', '0.525', '#surprise', '#admiration', 'positive', '0.642', 'administrable', 'controllable', 'governable', 'steerable', 'manageable']
senticnet['dirk_pitt'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'correspondent', 'humankind']
senticnet['dirndl'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'pinafore', 'pinny', 'dress', 'gown', 'muumuu']
senticnet['dirofilaria'] = ['-0.64', '0', '0.903', '-0.63', '#sadness', '#anger', 'negative', '-0.72', 'jaundice_of_newborn', 'disease', 'icterus_neonatorum', 'kernicterus', 'symptom']
senticnet['dirrell'] = ['-0.94', '0', '0.863', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'poke', 'clout', 'lick', 'punch', 'biff']
senticnet['dirt'] = ['-0.03', '-0.01', '0.026', '-0.04', '#sadness', '#disgust', 'negative', '-0.02', 'soil', 'ground', 'mud', 'ground_cover', 'footprint']
senticnet['dirt-cheap'] = ['0', '0', '-0.74', '-0.72', '#fear', '#disgust', 'negative', '-0.73', 'bargain-basement', 'low-priced', 'cut-rate', 'cut-price', 'high-priced']
senticnet['dirt_airstrip'] = ['-0.70', '0', '0.621', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'flare_path', 'airfield', 'grassland', 'runway', 'road']
senticnet['dirt_bike'] = ['0.05', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.046', 'skate', 'rollerblade', 'skateboard', 'ice_skate', 'skating']
senticnet['dirt_cheap'] = ['-0.04', '0.201', '-0.06', '0.312', '#interest', '#admiration', 'positive', '0.101', 'save_money', 'good_price', 'inexpensive', 'bargain', 'discount']
senticnet['dirt_devil'] = ['-0.04', '-0.02', '0.089', '-0.14', '#anger', '#disgust', 'negative', '-0.06', 'steam_locomotive', 'loudspeaker', 'collector', 'siren', 'activist']
senticnet['dirt_floor'] = ['-0.03', '-0.00', '0.066', '0', '#sadness', '#anger', 'negative', '-0.03', 'dirt', 'allergy', 'dust', 'wet_floor', 'insect']
senticnet['dirt_parking_lot'] = ['0', '0', '0', '-0.09', '#disgust', '#disgust', 'negative', '-0.09', 'subway', 'bus', 'train_station', 'railway_station', 'bus_stop']
senticnet['dirt_poor'] = ['-0.55', '0', '0.912', '-0.65', '#anger', '#disgust', 'negative', '-0.70', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['dirt_runway'] = ['-0.70', '0', '0.621', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'flare_path', 'airfield', 'grassland', 'runway', 'road']
senticnet['dirt_soil'] = ['-0.05', '0.076', '0', '0.107', '#interest', '#admiration', 'positive', '0.042', 'cold_night', 'terrain', 'soil', 'underfoot', 'earth']
senticnet['dirtier'] = ['0', '0', '0.943', '-0.8', '#anger', '#disgust', 'negative', '-0.87', 'fight_fire', 'soapy', 'feel_dirty', 'smell_clean', 'filthy']
senticnet['dirtiest'] = ['0', '0', '0.943', '-0.8', '#anger', '#disgust', 'negative', '-0.87', 'fight_fire', 'soapy', 'feel_dirty', 'smell_clean', 'filthy']
senticnet['dirtiness'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dinge']
senticnet['dirty'] = ['0', '0', '0.943', '-0.8', '#anger', '#disgust', 'negative', '-0.87', 'fight_fire', 'soapy', 'feel_dirty', 'smell_clean', 'filthy']
senticnet['dirty_animal'] = ['-0.99', '0', '0.612', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'dog_flea', 'flea', 'dirty_cat', 'dirty_pet', 'dirty_dog']
senticnet['dirty_blond_hair'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'plato', 'natural_world', 'hair', 'feature', 'stick_out']
senticnet['dirty_bomb'] = ['0', '0', '0.9', '-0.84', '#anger', '#disgust', 'negative', '-0.87', 'bomb', 'atomic_bomb', 'nuclear_weapon', 'explosive', 'grenade']
senticnet['dirty_cat'] = ['-0.99', '0', '0.612', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'dirty_animal', 'flea', 'dog_flea', 'dirty_pet', 'dirty_dog']
senticnet['dirty_clothes'] = ['0', '0', '0.94', '-0.87', '#anger', '#disgust', 'negative', '-0.90', 'sort_clothes', 'lock_front_door', 'dress_occasion', 'basket_laundry', 'laundry']
senticnet['dirty_couch'] = ['0', '0', '0.801', '-0.97', '#anger', '#disgust', 'negative', '-0.88', 'hairloss', 'bald', 'remove_hair', 'dirty_sofa', 'animal_fur']
senticnet['dirty_den'] = ['0.54', '-0.64', '0', '0.566', '#surprise', '#admiration', 'positive', '0.582', 'pleaser', 'entertainer', 'mortal', 'busker', 'drawing_card']
senticnet['dirty_dish'] = ['0', '0', '0.865', '-0.95', '#anger', '#disgust', 'negative', '-0.90', 'faucet_overflow', 'hot_faucet', 'hot_tap', 'faucet', 'washcloth']
senticnet['dirty_dish_overflow'] = ['0', '0', '0.863', '-0.96', '#anger', '#disgust', 'negative', '-0.91', 'faucet', 'hot_dirty_dish', 'hot_tap', 'dirty_dish', 'washcloth']
senticnet['dirty_dog'] = ['-0.99', '0', '0.612', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'dirty_animal', 'flea', 'dirty_cat', 'dirty_pet', 'dog_flea']
senticnet['dirty_floor'] = ['0', '0', '0.865', '-0.95', '#anger', '#disgust', 'negative', '-0.90', 'flat', 'bucket_overflow', 'spill_over', 'float', 'spoil_wood_floor']
senticnet['dirty_house'] = ['0', '0', '0.933', '-0.86', '#anger', '#disgust', 'negative', '-0.89', 'mess', 'disgusting', 'inability_host', 'dirty_kitchen', 'dirty_stuff']
senticnet['dirty_jew'] = ['0', '-0.00', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'hot_chick', 'atheist', 'computer_user', 'fastener_binder', 'monitor']
senticnet['dirty_joke'] = ['0.628', '-0.53', '0', '0.782', '#joy', '#admiration', 'positive', '0.648', 'joke', 'jape', 'jest', 'gag', 'laugh']
senticnet['dirty_kitchen'] = ['0', '0', '0.933', '-0.86', '#anger', '#disgust', 'negative', '-0.89', 'mess', 'trap', 'snake_belly', 'dirty_house', 'cornfield']
senticnet['dirty_laundry'] = ['-0.04', '0', '-0.08', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'wheelchair', 'old_photograph', 'wife', 'hot_chick', 'mommy']
senticnet['dirty_magazine'] = ['0', '-0.06', '0.097', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'slipper', 'old_sock', 'dirty_mind', 'dirty_sock', 'gay_person']
senticnet['dirty_martini'] = ['0.077', '0', '0', '0', '#joy', '#joy', 'positive', '0.077', 'dip', 'garlic', 'oat', 'savory', 'rye']
senticnet['dirty_mind'] = ['0', '-0.07', '0.094', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'slipper', 'old_sock', 'dirty_magazine', 'dirty_sock', 'gay_person']
senticnet['dirty_pet'] = ['-0.99', '0', '0.612', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'dirty_animal', 'flea', 'dirty_cat', 'dog_flea', 'dirty_dog']
senticnet['dirty_pool'] = ['-0.72', '0', '0.778', '-0.98', '#anger', '#disgust', 'negative', '-0.82', 'dirty_trick', 'activity', 'easiness', 'aggression', 'doings']
senticnet['dirty_realism'] = ['0.725', '-0.66', '0', '0.906', '#joy', '#admiration', 'positive', '0.765', 'set', 'pose', 'lay', 'put', 'place']
senticnet['dirty_sock'] = ['0', '0', '0', '-0.92', '#disgust', '#disgust', 'negative', '-0.92', 'dirty_magazine', 'dust_mite', 'cookie_crumb', 'old_sock', 'storage_box']
senticnet['dirty_sofa'] = ['0', '0', '0.799', '-0.97', '#anger', '#disgust', 'negative', '-0.88', 'hairloss', 'bald', 'dirty_couch', 'remove_hair', 'animal_fur']
senticnet['dirty_story'] = ['0.611', '-0.73', '0', '0.572', '#joy', '#surprise', 'positive', '0.638', 'joke', 'jape', 'jest', 'gag', 'laugh']
senticnet['dirty_stuff'] = ['0', '0', '0.933', '-0.86', '#anger', '#disgust', 'negative', '-0.89', 'mess', 'disgusting', 'dirty', 'dirty_kitchen', 'dirty_house']
senticnet['dirty_trick'] = ['0.9', '-0.80', '0', '0.568', '#joy', '#surprise', 'positive', '0.757', 'joke', 'antic', 'prank', 'trick', 'caper']
senticnet['dirty_underwear'] = ['0', '0', '0.943', '-0.8', '#anger', '#disgust', 'negative', '-0.87', 'fight_fire', 'soapy', 'feel_dirty', 'smell_clean', 'filthy']
senticnet['dirty_water'] = ['-0.72', '0', '0.778', '-0.98', '#anger', '#disgust', 'negative', '-0.82', 'squirt', 'puddle', 'mud', 'slosh', 'sludge']
senticnet['dirty_word'] = ['-0.63', '0', '0.712', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'upset', 'disconcert', 'discompose', 'discomfit', 'untune']
senticnet['dis_inhibition'] = ['0.795', '0', '0', '0.787', '#joy', '#admiration', 'positive', '0.791', 'entree', 'soup', 'wine_glass', 'salt_pepper_shaker', 'plate_silverware']
senticnet['disability'] = ['-0.77', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.78', 'chicken_pox', 'diabetes', 'lyme_disease', 'brain_tumor', 'suffer_fool']
senticnet['disability_allowance'] = ['0.924', '0.968', '0', '0.876', '#joy', '#interest', 'positive', '0.923', 'live_simply', 'help_out', 'money', 'accolade', 'generous']
senticnet['disability_check'] = ['0.529', '-0.84', '0', '0.559', '#surprise', '#admiration', 'positive', '0.644', 'pay', 'regular_payment', 'disability_payment', 'wage', 'earnings']
senticnet['disability_discrimination'] = ['-0.77', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.78', 'chicken_pox', 'diabetes', 'lyme_disease', 'brain_tumor', 'suffer_fool']
senticnet['disability_glare'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'incandescence', 'luminescence', 'brightness_level']
senticnet['disability_payment'] = ['0.529', '-0.84', '0', '0.559', '#surprise', '#admiration', 'positive', '0.644', 'pay', 'disability_check', 'regular_payment', 'wage', 'earnings']
senticnet['disable'] = ['-0.95', '0', '0.647', '-0.52', '#sadness', '#anger', 'negative', '-0.70', 'lay_up', 'modify', 'nobble', 'alter', 'change']
senticnet['disabled'] = ['0', '0', '0.914', '-0.79', '#anger', '#disgust', 'negative', '-0.85', 'walking_stick', 'sunglasses', 'poor_visual_perception', 'blind_person', 'vision_loss']
senticnet['disabled_abd'] = ['-0.22', '-0.03', '0.026', '-0.13', '#sadness', '#disgust', 'negative', '-0.08', 'blind_man', 'blind_person', 'shade', 'need_help', 'shadow']
senticnet['disabled_parking_permit'] = ['0', '0.137', '0', '0.133', '#interest', '#admiration', 'positive', '0.135', 'sign', 'indication', 'post_notice', 'notice', 'red_octagon']
senticnet['disabled_parking_placard'] = ['0', '0.137', '0', '0.133', '#interest', '#admiration', 'positive', '0.135', 'sign', 'indication', 'post_notice', 'notice', 'red_octagon']
senticnet['disabled_toilet'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'commode', 'potty_seat', 'potty_chair']
senticnet['disablement'] = ['-0.75', '0', '0.504', '-0.77', '#sadness', '#disgust', 'negative', '-0.67', 'astasia', 'anorgasmia', 'hearing_impairment', 'hearing_disorder', 'bow_leg']
senticnet['disabling_condition'] = ['-0.77', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.78', 'chicken_pox', 'diabetes', 'lyme_disease', 'brain_tumor', 'suffer_fool']
senticnet['disabling_disease'] = ['0.753', '-0.74', '0', '0.587', '#joy', '#surprise', 'positive', '0.695', 'draining', 'exhausting', 'debilitating', 'enervating', 'enfeebling']
senticnet['disabling_injury'] = ['-0.92', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.93', 'serious_accident', 'accident', 'hit_car', 'loss_ofawareness', 'bang_head']
senticnet['disabuse'] = ['0', '-0.83', '0', '0.84', '#surprise', '#admiration', 'positive', '0.836', 'disabused', 'disabusing', 'emasculate', 're-assure', 'dishearten']
senticnet['disabused'] = ['0', '-0.83', '0', '0.84', '#surprise', '#admiration', 'positive', '0.839', 'disabuse', 'disabusing', 'emasculate', 're-assure', 'dishearten']
senticnet['disabusing'] = ['0', '-0.84', '0', '0.837', '#surprise', '#admiration', 'positive', '0.839', 'disabused', 'disabuse', 'emasculate', 're-assure', 'dishearten']
senticnet['disaccord'] = ['0', '0', '0.865', '-0.89', '#anger', '#disgust', 'negative', '-0.88', 'disharmony', 'fail', 'absence', 'reverse', 'accord']
senticnet['disadvantage'] = ['-0.62', '0', '0.708', '-0.61', '#sadness', '#anger', 'negative', '-0.64', 'unfavorableness', 'unlucky', 'unfavorable_position', 'unprofitableness', 'unprofitability']
senticnet['disadvantaged'] = ['0', '0.814', '0', '0.795', '#interest', '#admiration', 'positive', '0.804', 'socio-economically', 'ethnically', 'socioeconomically', 'linguistically', 'educationally']
senticnet['disadvantaged_child'] = ['-0.10', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.08', 'come_house', 'desperate', 'welcome_friend', 'captain_boat', 'receive_email']
senticnet['disadvantaged_youth'] = ['0.025', '0.073', '-0.05', '0.1', '#interest', '#admiration', 'positive', '0.037', 'young_age', 'energy', 'hope', 'young_life', 'healthy']
senticnet['disaffect'] = ['-0.10', '0.176', '0.629', '-0.64', '#anger', '#disgust', 'negative', '-0.3', 'alienated', 'alienation', 'disaffection', 'estrange', 'alienate']
senticnet['disaffected'] = ['-0.88', '0', '0.718', '-0.92', '#sadness', '#disgust', 'negative', '-0.84', 'ungratified', 'discontented', 'malcontent', 'unsatisfied', 'restless']
senticnet['disaffected_young'] = ['0.025', '0.073', '-0.05', '0.1', '#interest', '#admiration', 'positive', '0.037', 'young_age', 'energy', 'hope', 'young_life', 'healthy']
senticnet['disaffection'] = ['-0.10', '0.176', '0.629', '-0.64', '#anger', '#disgust', 'negative', '-0.3', 'alienated', 'alienation', 'estrange', 'alienate', 'disoriented']
senticnet['disaffection_among'] = ['-0.77', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.84', 'jumpiness', 'jitteriness', 'restive', 'nervy', 'jumpy']
senticnet['disaffiliate'] = ['-0.68', '0', '0.923', '-0.68', '#sadness', '#anger', 'negative', '-0.76', 'negative', 'veto', 'refute', 'rebut', 'controvert']
senticnet['disagree'] = ['-0.84', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.79', 'piss_pant', 'rejection', 'argue', 'make_statement', 'become_politician']
senticnet['disagree_strongly'] = ['-0.84', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.79', 'piss_pant', 'rejection', 'argue', 'make_statement', 'become_politician']
senticnet['disagreeable'] = ['-0.86', '0', '0.547', '-0.67', '#sadness', '#disgust', 'negative', '-0.69', 'dreadful', 'vexing', 'harsh', 'stressful', 'trying']
senticnet['disagreeable_odor'] = ['0', '0', '0.717', '-0.84', '#anger', '#disgust', 'negative', '-0.77', 'gymnast', 'bikini', 'underwear', 'basketball_hoop', 'dirty_sock']
senticnet['disagreeable_person'] = ['-0.66', '0', '0.595', '-0.83', '#sadness', '#disgust', 'negative', '-0.7', 'bastard', 'dickhead', 'mother_fucker', 'motherfucker', 'son_of_bitch']
senticnet['disagreeable_smell'] = ['-0.44', '-0.15', '0', '-0.36', '#sadness', '#disgust', 'negative', '-0.21', 'baffle', 'stupefy', 'dumbfound', 'stupefaction', 'confuse']
senticnet['disagreeable_taste'] = ['0.045', '0.141', '-0.05', '0.065', '#interest', '#admiration', 'positive', '0.049', 'kernel', 'starch', 'ginger', 'cereal_crop', 'pea']
senticnet['disagreeable_woman'] = ['-0.59', '0', '0.573', '-0.56', '#sadness', '#anger', 'negative', '-0.57', 'termagant', 'old_bag', 'shrew', 'frump', 'hellcat']
senticnet['disagreeableness'] = ['-0.68', '0', '0.994', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'acridity', 'acridness', 'unpleasantness', 'unpalatability', 'unpalatableness']
senticnet['disagreeably'] = ['0.695', '0.623', '-0.23', '0.319', '#joy', '#interest', 'positive', '0.351', 'uproarious', 'hilariously', 'hilarious', 'screaming', 'uproariously']
senticnet['disagreement'] = ['-0.72', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.85', 'argue', 'rival', 'antagonist', 'opposition', 'anti']
senticnet['disagreement_between'] = ['0', '0', '0.94', '0', '#anger', '#anger', 'negative', '-0.94', 'feel_angry', 'hurt_feeling', 'feel_hurt', 'ridicule', 'vengeance']
senticnet['disallow'] = ['0', '0', '-0.85', '-0.86', '#fear', '#disgust', 'negative', '-0.86', 'deny', 'fraternization', 'disallowed', 'prohibitive', 'reject']
senticnet['disallow_wiki_wikipedia'] = ['0.533', '0.818', '0', '0.683', '#interest', '#admiration', 'positive', '0.678', 'gain_information', 'report_news', 'solve', 'seek_answer', 'gather_information']
senticnet['disallow_wiki_wikipedia_talk'] = ['0.533', '0.818', '0', '0.683', '#interest', '#admiration', 'positive', '0.678', 'gain_information', 'report_news', 'solve', 'seek_answer', 'gather_information']
senticnet['disallowed'] = ['0', '0', '-0.86', '-0.86', '#fear', '#disgust', 'negative', '-0.86', 'deny', 'fraternization', 'disallow', 'prohibitive', 'reject']
senticnet['disappear'] = ['-0.88', '0', '0.682', '-0.60', '#sadness', '#anger', 'negative', '-0.72', 'absent', 'fall_away', 'evanesce', 'blow_over', 'pass_off']
senticnet['disappear_altogether'] = ['-0.88', '0', '0.682', '-0.60', '#sadness', '#anger', 'negative', '-0.72', 'absent', 'fall_away', 'evanesce', 'blow_over', 'pass_off']
senticnet['disappear_completely'] = ['-0.88', '0', '0.682', '-0.60', '#sadness', '#anger', 'negative', '-0.72', 'absent', 'fall_away', 'evanesce', 'blow_over', 'pass_off']
senticnet['disappear_entirely'] = ['-0.88', '0', '0.682', '-0.60', '#sadness', '#anger', 'negative', '-0.72', 'absent', 'fall_away', 'evanesce', 'blow_over', 'pass_off']
senticnet['disappear_forever'] = ['-0.88', '0', '0.682', '-0.60', '#sadness', '#anger', 'negative', '-0.72', 'absent', 'fall_away', 'evanesce', 'blow_over', 'pass_off']
senticnet['disappoint'] = ['-0.97', '0', '0.971', '-0.84', '#sadness', '#anger', 'negative', '-0.93', 'fail', 'betray', 'queer', 'cross', 'disillusion']
senticnet['disappointed'] = ['-0.84', '0', '0.898', '0', '#sadness', '#anger', 'negative', '-0.87', 'defeatism', 'thwarted', 'frustrated', 'foiled', 'discomfited']
senticnet['disappointing'] = ['-0.75', '0', '0.785', '-0.7', '#sadness', '#anger', 'negative', '-0.74', 'unsatisfactory', 'dissatisfactory', 'unsatisfying', 'off', 'unacceptable']
senticnet['disappointingly'] = ['-0.62', '0', '0.988', '-0.59', '#sadness', '#anger', 'negative', '-0.73', 'motley', 'mixture', 'variety', 'assortment', 'alphabet_soup']
senticnet['disappointment'] = ['-0.81', '0', '0.958', '0', '#sadness', '#anger', 'negative', '-0.88', 'sadness', 'disappoint', 'embarrassment', 'silliness', 'anger']
senticnet['disapprobation'] = ['0', '0', '0.964', '-0.77', '#anger', '#disgust', 'negative', '-0.86', 'condemnation', 'damned', 'perdition', 'damnation', 'nation']
senticnet['disapproval'] = ['-1', '-0.33', '0', '0', '#sadness', '#surprise', 'negative', '-0.33', 'disapprove', 'fight', 'opponent', 'against', 'disagree']
senticnet['disapprove'] = ['-0.12', '0.192', '0.596', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'disapproval', 'disapproving', 'unfriendly', 'unfriendliness', 'inimical']
senticnet['disapproving'] = ['-0.12', '0.192', '0.596', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'disapproval', 'disapprove', 'unfriendly', 'unfriendliness', 'inimical']
senticnet['disapproving_father'] = ['-0.71', '0', '0.972', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'scold', 'nag', 'scolder', 'harridan', 'common_scold']
senticnet['disarm'] = ['-0.98', '0', '0.654', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'bereave', 'expropriate', 'deprive', 'unclothe', 'take']
senticnet['disarm_hezbollah'] = ['-0.98', '0', '0.654', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'bereave', 'expropriate', 'deprive', 'unclothe', 'take']
senticnet['disarmament'] = ['-0.98', '0', '0.654', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'bereave', 'expropriate', 'deprive', 'unclothe', 'take']
senticnet['disarmament_agenda'] = ['-0.67', '0', '0.97', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'joint', 'node', 'articulation', 'join', 'connexion']
senticnet['disarmingly'] = ['0.741', '-0.74', '0', '0.666', '#joy', '#surprise', 'positive', '0.718', 'commonplaceness', 'mundanity', 'prosiness', 'prosaicness', 'ordinariness']
senticnet['disarray'] = ['0', '0', '0.922', '-0.95', '#anger', '#disgust', 'negative', '-0.93', 'disorder', 'messy', 'worst', 'mess_up', 'undress']
senticnet['disarticulation'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'blowup', 'discharge', 'backfire', 'blast']
senticnet['disassemble'] = ['0.048', '0.092', '-0.08', '0.216', '#interest', '#admiration', 'positive', '0.067', 'gather', 'group', 'organization', 'clique', 'collection']
senticnet['disassociation'] = ['-0.08', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.09', 'mediocrity', 'effect', 'suspicion', 'exclusion', 'pugilist']
senticnet['disaster'] = ['-0.17', '0', '-0.25', '-0.13', '#sadness', '#fear', 'negative', '-0.18', 'destruction', 'bigot', 'kidnap', 'torture', 'censorship']
senticnet['disaster_assistance'] = ['0.732', '0.729', '0', '0', '#joy', '#interest', 'positive', '0.73', 'feel_relief', 'good_feeling', 'feel_ok', 'feel_relieved', 'feel_better']
senticnet['disaster_recovery'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_musical_sound', 'play_orchestra', 'make_noise', 'play_music', 'make_drawing']
senticnet['disaster_recovery_solution'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_musical_sound', 'play_orchestra', 'make_noise', 'play_music', 'make_drawing']
senticnet['disaster_relief'] = ['0.732', '0.729', '0', '0', '#joy', '#interest', 'positive', '0.73', 'feel_relief', 'good_feeling', 'feel_ok', 'feel_relieved', 'feel_better']
senticnet['disaster_relief_assistance'] = ['0.732', '0.729', '0', '0', '#joy', '#interest', 'positive', '0.73', 'feel_relief', 'good_feeling', 'feel_ok', 'feel_relieved', 'feel_better']
senticnet['disaster_response'] = ['-0.17', '0', '-0.25', '-0.13', '#sadness', '#fear', 'negative', '-0.18', 'destruction', 'bigot', 'kidnap', 'torture', 'censorship']
senticnet['disastrous'] = ['0', '0', '-0.79', '-0.70', '#fear', '#disgust', 'negative', '-0.75', 'disaster', 'catastrophe', 'catastrophic', 'iceberg', 'tragedy']
senticnet['disastrous_battle'] = ['-0.05', '0', '-0.08', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'siege', 'big_fight', 'war', 'declare_war', 'combat']
senticnet['disastrously'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'lamentably', 'deplorably', 'bereaved', 'bereft', 'grief-stricken']
senticnet['disavow'] = ['-0.72', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.82', 'disavowal', 'disavowed', 'denial', 'relationship', 'responsibility']
senticnet['disavowable'] = ['0.906', '-0.62', '0', '0.698', '#joy', '#admiration', 'positive', '0.743', 'deniable', 'questionable', 'refutable', 'confutable', 'confutative']
senticnet['disavowal'] = ['-0.72', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.81', 'disavow', 'disavowed', 'denial', 'relationship', 'responsibility']
senticnet['disavowed'] = ['-0.72', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.82', 'disavow', 'disavowal', 'denial', 'relationship', 'responsibility']
senticnet['disband'] = ['-0.63', '0', '0.872', '-0.88', '#anger', '#disgust', 'negative', '-0.79', 'retreat', 'retrograde', 'back_up', 'move', 'give_up']
senticnet['disbar'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
senticnet['disbelief'] = ['-0.96', '0', '0.866', '0', '#sadness', '#anger', 'negative', '-0.91', 'feel_hurt', 'startle', 'feel_angry', 'become_sad', 'electric_current']
senticnet['disbelieve'] = ['-0.80', '0', '0.517', '-0.75', '#sadness', '#disgust', 'negative', '-0.69', 'suspect', 'reject', 'doubt', 'distrust', 'mistrust']
senticnet['disbelieving'] = ['0', '0', '-0.99', '-1', '#fear', '#disgust', 'negative', '-0.99', 'unbelieving', 'sceptical', 'doubting', 'skeptical', 'questioning']
senticnet['disbud'] = ['0.919', '-0.64', '0', '0.648', '#joy', '#admiration', 'positive', '0.736', 'dress', 'trim', 'cut_back', 'prune', 'snip']
senticnet['disbursement'] = ['0.862', '0.709', '0', '0', '#joy', '#interest', 'positive', '0.785', 'invoice', 'bill', 'check', 'mastercard', 'payment_counter']
senticnet['disc'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'cassette', 'cd-rom', 'music_storage', 'store_music', 'compact_disc']
senticnet['disc_dvd'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['disc_dvd_set'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['disc_harrow'] = ['-0.99', '0', '0.827', '-0.54', '#sadness', '#anger', 'negative', '-0.78', 'treat', 'cover', 'handle', 'theologize', 'deal']
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senticnet['discal_stigma_are'] = ['-0.7', '0', '0.617', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'silvery', 'achromatic', 'bluish_black', 'hueless', 'pitchy']
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senticnet['discard'] = ['-0.22', '0', '-0.09', '-0.16', '#sadness', '#disgust', 'negative', '-0.16', 'inequality', 'reject', 'difference', 'divergency', 'gap']
senticnet['discard_pile'] = ['0.685', '-0.58', '0', '0.56', '#joy', '#surprise', 'positive', '0.608', 'profit', 'net_income', 'net', 'net_profit', 'fast_buck']
senticnet['discarded_cigarette'] = ['-0.14', '0', '-0.21', '-0.07', '#sadness', '#fear', 'negative', '-0.14', 'heat_light', 'burn_down_house', 'set_on_fire', 'rapid_oxidation', 'flame']
senticnet['disceptation'] = ['0.591', '-0.81', '0', '0.589', '#joy', '#surprise', 'positive', '0.663', 'firestorm', 'dispute', 'fight', 'conflict', 'polemic']
senticnet['discern'] = ['0', '0', '-0.86', '-0.85', '#fear', '#disgust', 'negative', '-0.85', 'spot', 'make_out', 'distinguish', 'pick_out', 'recognize']
senticnet['discernability'] = ['0.805', '-0.62', '0', '0.86', '#joy', '#admiration', 'positive', '0.762', 'clearness', 'clarity', 'distinctness', 'uncloudedness', 'legibility']
senticnet['discernible'] = ['0.958', '-0.83', '0', '0.81', '#joy', '#surprise', 'positive', '0.869', 'noticeable', 'perceptible', 'evident', 'detectable', 'observable']
senticnet['discerning'] = ['0.902', '-0.85', '0', '0.898', '#joy', '#admiration', 'positive', '0.885', 'discriminating', 'diacritic', 'diacritical', 'discriminative', 'selective']
senticnet['discernment'] = ['0.795', '-0.67', '0', '0.934', '#joy', '#admiration', 'positive', '0.8', 'appreciation', 'grasp', 'comprehension', 'brainstorm', 'hold']
senticnet['discharge'] = ['-0.71', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.79', 'exhaust', 'unloading', 'drainage', 'waste', 'refuse']
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senticnet['discharge_tube'] = ['-0.03', '-0.04', '0.079', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'string', 'bandage', 'suture', 'thread', 'needle_thread']
senticnet['disciple'] = ['0', '0.978', '0', '0.914', '#interest', '#admiration', 'positive', '0.946', 'adherent', 'follower', 'verse', 'body', 'christ']
senticnet['disciplinary'] = ['0.845', '0.916', '0', '0', '#joy', '#interest', 'positive', '0.881', 'question', 'answer', 'query', 'question_start', 'question_answering']
senticnet['disciplinary_action'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'steer_wheel', 'gear_shift', 'chassis', 'brake_pedal', 'air_vent']
senticnet['disciplinary_action_against'] = ['0.682', '-0.67', '0', '0.588', '#joy', '#surprise', 'positive', '0.648', 'lawful', 'civil_action', 'counterclaim', 'occurrence', 'li_pendens']
senticnet['disciplinary_committee'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'steer_wheel', 'gear_shift', 'chassis', 'brake_pedal', 'air_vent']
senticnet['disciplinary_sanction'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'steer_wheel', 'gear_shift', 'chassis', 'brake_pedal', 'air_vent']
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senticnet['disclaim'] = ['-0.74', '0', '0', '-0.91', '#sadness', '#disgust', 'negative', '-0.83', 'decline', 'abdicate', 'deny', 'abjure', 'refuse']
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senticnet['disclosed_pursuant'] = ['-0.02', '0.175', '-0.11', '0.148', '#interest', '#admiration', 'positive', '0.045', 'tolerant', 'phrase', 'inquiry', 'explain', 'question']
senticnet['disclosing'] = ['0', '0', '-0.74', '-0.86', '#fear', '#disgust', 'negative', '-0.8', 'non-public', 'nonpublic', 'disclose', 'divulge', 'identifiable']
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senticnet['disclosure_requirement'] = ['-0.02', '0.175', '-0.11', '0.148', '#interest', '#admiration', 'positive', '0.045', 'tolerant', 'phrase', 'inquiry', 'explain', 'question']
senticnet['disclosure_statement'] = ['-0.02', '0.175', '-0.11', '0.148', '#interest', '#admiration', 'positive', '0.045', 'tolerant', 'phrase', 'inquiry', 'explain', 'question']
senticnet['disco'] = ['0.799', '0.88', '0', '0', '#joy', '#interest', 'positive', '0.84', 'dance_club', 'nightclub', 'club', 'ballroom', 'pub']
senticnet['disco_ball'] = ['0.075', '0', '0', '0.158', '#joy', '#admiration', 'positive', '0.116', 'sway', 'swirl', 'body_movement', 'move_around', 'dance_move']
senticnet['disco_beat'] = ['0.75', '0.71', '0', '0', '#joy', '#interest', 'positive', '0.73', 'spherical_shape', 'use_sport', 'flexible', 'ball', 'fill_air']
senticnet['disco_funk'] = ['0.857', '-0.77', '0', '0.878', '#joy', '#admiration', 'positive', '0.836', 'hand', 'round', 'approval', 'commendation', 'clapping']
senticnet['disco_tune'] = ['0.65', '-0.61', '0', '0.711', '#joy', '#admiration', 'positive', '0.658', 'bouncing', 'scintillating', 'spirited', 'lively', 'zippy']
senticnet['discoidal'] = ['0.969', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.772', 'aspheric', 'aspherical', 'prolate', 'rounded', 'crescent']
senticnet['discoidea'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
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senticnet['discoloration'] = ['-0.51', '0', '0.937', '-0.92', '#anger', '#disgust', 'negative', '-0.79', 'bloodstain', 'mud_stain', 'iron_mold', 'iron_mould', 'oil_stain']
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senticnet['discolored_varnish'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'wall_decoration', 'renovation', 'painting', 'artwork', 'new_house']
senticnet['discombobulate'] = ['-0.63', '0', '0.712', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'upset', 'disconcert', 'discompose', 'discomfit', 'untune']
senticnet['discombobulation'] = ['-0.84', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.86', 'put_off', 'confusedly', 'disordered', 'confused', 'confusing']
senticnet['discomfit'] = ['-0.92', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'disconcertion', 'untune', 'disconcertment', 'discomfiture', 'discompose']
senticnet['discomfited'] = ['-0.84', '0', '0.9', '0', '#sadness', '#anger', 'negative', '-0.87', 'disappointed', 'thwarted', 'frustrated', 'foiled', 'defeatism']
senticnet['discomfiting'] = ['-0.87', '0', '0', '-1', '#sadness', '#disgust', 'negative', '-0.93', 'congenial', 'uncongenial', 'unpromising', 'unglamorous', 'unexciting']
senticnet['discomfiture'] = ['-0.80', '0', '0.815', '0', '#sadness', '#anger', 'negative', '-0.81', 'disconcertion', 'untune', 'disconcertment', 'discomfit', 'discompose']
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senticnet['discomfort_glare'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'incandescence', 'luminescence', 'brightness_level']
senticnet['discommode'] = ['-0.64', '0', '0.961', '-0.50', '#sadness', '#anger', 'negative', '-0.70', 'distress', 'straiten', 'impact', 'bear_on', 'touch']
senticnet['discompose'] = ['-0.80', '0', '0.911', '0', '#sadness', '#anger', 'negative', '-0.85', 'disconcertion', 'untune', 'disconcertment', 'discomfiture', 'discomfit']
senticnet['discomposed'] = ['-1', '0', '0.853', '-0.6', '#sadness', '#anger', 'negative', '-0.81', 'embarrassed', 'abashed', 'chagrined', 'blushing', 'bothered']
senticnet['discomposure'] = ['-0.96', '0', '0.787', '0', '#sadness', '#anger', 'negative', '-0.87', 'disconcertion', 'untune', 'disconcertment', 'discomfiture', 'discomfit']
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senticnet['disconcertingly'] = ['-0.82', '0', '0.758', '-0.70', '#sadness', '#anger', 'negative', '-0.76', 'unclassifiable', 'unrecognizable', 'unidentifiable', 'intangible', 'undiagnosable']
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senticnet['disconnect_switch'] = ['0.13', '0.097', '-0.07', '0.198', '#joy', '#admiration', 'positive', '0.087', 'jiggle', 'back_forth', 'vibrate', 'blend', 'mixture']
senticnet['disconnected'] = ['-0.96', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.88', 'fall_asleep', 'doze_off', 'boring_talk', 'sleepy', 'distracted']
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senticnet['disconnector'] = ['0', '0.069', '0', '0.083', '#interest', '#admiration', 'positive', '0.076', 'latch', 'restrict_access', 'knob', 'door_knob', 'gate']
senticnet['disconsolate'] = ['-0.79', '0.257', '-0.47', '-0.03', '#sadness', '#fear', 'negative', '-0.26', 'gloomful', 'glooming', 'darkness', 'somberness', 'gloominess']
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senticnet['disconsolateness'] = ['-0.82', '0.226', '-0.46', '-0.05', '#sadness', '#fear', 'negative', '-0.27', 'despondence', 'heartsickness', 'heartsick', 'despondent', 'despondency']
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senticnet['discontent_among'] = ['-0.77', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.84', 'jumpiness', 'jitteriness', 'restive', 'nervy', 'jumpy']
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senticnet['discontentment'] = ['0.997', '-0.78', '0', '0.504', '#joy', '#surprise', 'positive', '0.762', 'dissatisfaction', 'disgruntlement', 'dysphoria', 'longing', 'yearning']
senticnet['discontinuance'] = ['0', '0', '-0.94', '-0.74', '#fear', '#disgust', 'negative', '-0.84', 'non-availability', 'inaccessibility', 'interruption', 'unavailability', 'absence']
senticnet['discontinue'] = ['0', '0', '0.931', '-0.95', '#anger', '#disgust', 'negative', '-0.94', 'declass', 'disconfirm', 'enfeeble', 'befall', 'crack']
senticnet['discontinuity'] = ['-0.83', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.78', 'discontinuous', 'lack', 'break', 'regularity', 'seismic']
senticnet['discontinuous'] = ['-0.84', '0', '-0.73', '0', '#sadness', '#fear', 'negative', '-0.78', 'discontinuity', 'lack', 'break', 'regularity', 'seismic']
senticnet['discontinuous_permafrost'] = ['-0.04', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.04', 'bad_luck', 'damn', 'artic', 'mammal_family', 'bad_mood']
senticnet['discontinuously'] = ['0', '0.939', '0', '0.841', '#interest', '#admiration', 'positive', '0.89', 'sinusoidally', 'modulated', 'oscillates', 'non-linearly', 'oscillated']
senticnet['discord'] = ['-0.84', '0', '0.7', '-0.92', '#sadness', '#disgust', 'negative', '-0.82', 'quarrel', 'fight', 'conflict', 'dispute', 'bloodshed']
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senticnet['discorporate'] = ['-0.53', '0', '0.637', '-0.92', '#anger', '#disgust', 'negative', '-0.69', 'incorporeal', 'unembodied', 'disembodied', 'bodiless', 'unbodied']
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senticnet['discothque'] = ['0.763', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.75', 'pub', 'disco', 'dance_club', 'cafe', 'tavern']
senticnet['discount'] = ['0.95', '0.937', '0', '0', '#joy', '#interest', 'positive', '0.944', 'good_price', 'cheap_price', 'bargain', 'charge_money', 'good_buy']
senticnet['discount_chain'] = ['0', '0.081', '0', '0.071', '#interest', '#admiration', 'positive', '0.076', 'thrift_store', 'type_of_store', 'record_store', 'many_shops', 'clothes_store']
senticnet['discount_fare'] = ['-0.03', '0.29', '-0.12', '0.203', '#interest', '#admiration', 'positive', '0.083', 'excise', 'severance', 'wage', 'afford', 'revenue']
senticnet['discount_store'] = ['0.057', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'cheap_stuff', 'save_money', 'less_quality', 'occasion', 'poor_people']
senticnet['discounted_fare'] = ['-0.03', '0.29', '-0.12', '0.203', '#interest', '#admiration', 'positive', '0.083', 'excise', 'severance', 'wage', 'afford', 'revenue']
senticnet['discounted_fee'] = ['0.687', '-0.98', '0', '0.666', '#joy', '#surprise', 'positive', '0.778', 'scale', 'scale_of_measurement', 'graduated_table', 'ordered_series', 'wage_scale']
senticnet['discountenance'] = ['-0.88', '0', '0.764', '0', '#sadness', '#anger', 'negative', '-0.82', 'abash', 'discountenanced', 'disconcert', 'unfavorable', 'deprecate']
senticnet['discountenanced'] = ['-0.88', '0', '0.761', '0', '#sadness', '#anger', 'negative', '-0.82', 'abash', 'discountenance', 'disconcert', 'unfavorable', 'deprecate']
senticnet['discounter'] = ['0.875', '0.706', '0', '0', '#joy', '#interest', 'positive', '0.79', 'customer', 'clerk', 'patron', 'vintner', 'hairdresser']
senticnet['discourage'] = ['-0.76', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.76', 'disheartenment', 'discouragement', 'mortify', 'abase', 'self-hatred']
senticnet['discourage_innovation'] = ['-0.05', '-0.04', '0.017', '-0.05', '#sadness', '#disgust', 'negative', '-0.02', 'shoe_shop', 'shoe_store', 'attic', 'obstruct', 'closet']
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senticnet['discouragement'] = ['-0.90', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.84', 'disheartenment', 'discourage', 'mortify', 'abase', 'self-hatred']
senticnet['discouraging'] = ['-0.30', '0.294', '0.311', '-0.49', '#anger', '#disgust', 'negative', '-0.20', 'misanthropical', 'misanthropic', 'horrific', 'outrageous', 'horrid']
senticnet['discourse'] = ['0', '0.774', '0', '0.75', '#interest', '#admiration', 'positive', '0.762', 'orate', 'verbalize', 'verbal', 'talkative', 'speech']
senticnet['discourteous'] = ['0.694', '-0.55', '0', '0.894', '#joy', '#admiration', 'positive', '0.714', 'curt', 'brusque', 'brusk', 'abrupt', 'unceremonious']
senticnet['discourteously'] = ['0', '0.149', '0.719', '-0.66', '#anger', '#disgust', 'negative', '-0.41', 'belligerently', 'antagonism', 'class_feeling', 'aggressiveness', 'animus']
senticnet['discourtesy'] = ['-0.71', '0', '0.83', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'scorn', 'abuse', 'vilification', 'contempt', 'insult']
senticnet['discover'] = ['0.99', '0.832', '0', '0', '#joy', '#interest', 'positive', '0.911', 'discovery', 'search', 'review', 'investigation', 'realize']
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senticnet['discoverer'] = ['0.774', '-0.63', '0', '0.567', '#joy', '#surprise', 'positive', '0.658', 'catching', 'breakthrough', 'spying', 'determination', 'tracing']
senticnet['discovereth'] = ['-0.73', '0', '0.732', '-0.53', '#sadness', '#anger', 'negative', '-0.66', 'insurmountable', 'insuperable', 'unconquerable', 'indomitable', 'unsubduable']
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senticnet['discredit'] = ['-0.85', '0', '0.954', '-0.86', '#anger', '#disgust', 'negative', '-0.89', 'refuse_value', 'refuse_dogma', 'disbelieve', 'refuse_religion', 'distrust']
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senticnet['discreetly'] = ['0', '-0.75', '0', '0.815', '#surprise', '#admiration', 'positive', '0.786', 'inconspicuously', 'discretely', 'unobtrusively', 'invisibly', 'stealthily']
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senticnet['discrepancy'] = ['-0.52', '0', '0.561', '-0.90', '#anger', '#disgust', 'negative', '-0.66', 'difference', 'tolerance', 'allowance', 'leeway', 'margin']
senticnet['discrepant'] = ['-0.93', '0', '0.515', '-0.78', '#sadness', '#disgust', 'negative', '-0.74', 'discordant', 'at_variance', 'dissentious', 'divisive', 'factious']
senticnet['discrete'] = ['0.918', '0.795', '0', '0', '#joy', '#interest', 'positive', '0.857', 'distinguishable', 'distinct', 'decided', 'clear-cut', 'trenchant']
senticnet['discrete_fourier_transform'] = ['0.506', '-0.56', '0', '0.788', '#surprise', '#admiration', 'positive', '0.62', 'backward', 'reverse', 'retrograde', 'reflexive', 'retral']
senticnet['discrete_markov'] = ['0.096', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.057', 'platform_rail', 'rail', 'catwalk', 'column', 'token_turnstile']
senticnet['discretely'] = ['0', '-0.76', '0', '0.823', '#surprise', '#admiration', 'positive', '0.794', 'inconspicuously', 'unobtrusively', 'discreetly', 'invisibly', 'stealthily']
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senticnet['discretionary'] = ['0', '0', '-0.81', '-0.74', '#fear', '#disgust', 'negative', '-0.78', 'cost', 'non-discretionary', 'expenditure', 'non-defense', 'fund']
senticnet['discretionary_authority'] = ['-0.52', '0', '0.561', '-0.90', '#anger', '#disgust', 'negative', '-0.66', 'difference', 'tolerance', 'allowance', 'discrepancy', 'margin']
senticnet['discretionary_budget'] = ['-0.05', '0.282', '-0.04', '0.107', '#interest', '#admiration', 'positive', '0.072', 'expense', 'dollar_cent', 'dollar_sign', 'amount_pay', 'payment_amount']
senticnet['discretionary_fund'] = ['0.865', '-0.84', '0', '0.915', '#joy', '#admiration', 'positive', '0.875', 'contract', 'sale_in_gross', 'contract_of_hazard', 'adhesion_contract', 'contract_of_adhesion']
senticnet['discretionary_grant'] = ['0.865', '-0.84', '0', '0.915', '#joy', '#admiration', 'positive', '0.875', 'contract', 'sale_in_gross', 'contract_of_hazard', 'adhesion_contract', 'contract_of_adhesion']
senticnet['discretionary_grant_program'] = ['0.865', '-0.84', '0', '0.915', '#joy', '#admiration', 'positive', '0.875', 'contract', 'sale_in_gross', 'contract_of_hazard', 'adhesion_contract', 'contract_of_adhesion']
senticnet['discretionary_power'] = ['-0.52', '0', '0.561', '-0.90', '#anger', '#disgust', 'negative', '-0.66', 'difference', 'tolerance', 'allowance', 'discrepancy', 'margin']
senticnet['discretionary_relief'] = ['-0.60', '0', '0.832', '-0.53', '#sadness', '#anger', 'negative', '-0.65', 'unsuitable', 'undesirable', 'unqualified', 'disqualified', 'unentitled']
senticnet['discretization'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'assumption', 'precondition', 'provision', 'constraint', 'premiss']
senticnet['discriminability'] = ['0.685', '-0.91', '0', '0.566', '#joy', '#surprise', 'positive', '0.721', 'audibility', 'audibleness', 'visibleness', 'visibility', 'physical_property']
senticnet['discriminable'] = ['0.685', '-0.91', '0', '0.566', '#joy', '#surprise', 'positive', '0.721', 'audibility', 'audibleness', 'visibleness', 'visibility', 'physical_property']
senticnet['discriminant_validity'] = ['0.854', '-0.79', '0', '0.977', '#joy', '#admiration', 'positive', '0.875', 'profitableness', 'auspiciousness', 'luck', 'advantageousness', 'positivity']
senticnet['discriminate'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
senticnet['discriminate_against'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
senticnet['discriminate_against_woman'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
senticnet['discriminate_among'] = ['0.852', '0.864', '0', '0', '#joy', '#interest', 'positive', '0.858', 'police_work', 'career', 'task', 'employment', 'labor']
senticnet['discriminate_between'] = ['-0.98', '0', '0.737', '-0.92', '#sadness', '#disgust', 'negative', '-0.88', 'separate', 'severalize', 'contrast', 'decouple', 'dissociate']
senticnet['discriminated_against'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
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senticnet['discriminates_between'] = ['0.789', '-0.99', '0', '0.54', '#joy', '#surprise', 'positive', '0.776', 'imperfection', 'imperfectness', 'bugginess', 'lameness', 'sickness']
senticnet['discriminating'] = ['0.902', '-0.85', '0', '0.898', '#joy', '#admiration', 'positive', '0.885', 'discerning', 'diacritic', 'diacritical', 'discriminative', 'selective']
senticnet['discriminating_against'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
senticnet['discriminatingal'] = ['0.897', '-0.85', '0', '0.89', '#joy', '#admiration', 'positive', '0.881', 'discerning', 'discriminating', 'diacritic', 'discriminative', 'selective']
senticnet['discrimination'] = ['0', '0', '-0.96', '-0.78', '#fear', '#disgust', 'negative', '-0.87', 'disgust', 'military_force', 'military_division', 'opposition', 'anti']
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senticnet['discrimination_lawsuit_against'] = ['-0.91', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.85', 'lawyer', 'take_court', 'call_lawyer', 'determine_guilt', 'go_to_court']
senticnet['discriminative'] = ['0.902', '-0.85', '0', '0.898', '#joy', '#admiration', 'positive', '0.885', 'discerning', 'diacritic', 'diacritical', 'discriminating', 'selective']
senticnet['discriminatory'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
senticnet['discriminatory_behavior'] = ['-0.94', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.92', 'insanity', 'angry_emotion', 'dislike', 'psychosis', 'fight']
senticnet['discriminatory_conduct'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
senticnet['discriminatory_effect'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
senticnet['discriminatory_employment'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
senticnet['discriminatory_manner'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
senticnet['discriminatory_practice'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
senticnet['discriminatory_taxation'] = ['-0.70', '0', '0.552', '-0.68', '#sadness', '#disgust', 'negative', '-0.64', 'protection', 'regimentation', 'taxation', 'trade_protection', 'reimposition']
senticnet['discriminatory_treatment'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
senticnet['discursive'] = ['0.869', '-0.88', '0', '0.598', '#joy', '#surprise', 'positive', '0.782', 'connectedness', 'connection', 'consistency', 'continuity', 'link']
senticnet['discursive_practice'] = ['0.519', '-0.55', '0', '0.746', '#surprise', '#admiration', 'positive', '0.606', 'narrow_down', 'peg_down', 'nail_down', 'narrow', 'pin_down']
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senticnet['discuss'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'autograph', 'communication', 'communicate_person', 'share', 'autographion']
senticnet['discuss_friend'] = ['-0.09', '0', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.07', 'communicate', 'solution_to_problem', 'say_eureka', 'trivia', 'come_conclusion']
senticnet['discussed_above'] = ['0.021', '0.167', '0', '0.207', '#interest', '#admiration', 'positive', '0.132', 'size_up', 'measure_length', 'scope', 'distant', 'extension']
senticnet['discussed_below'] = ['-0.04', '0.296', '-0.10', '0.291', '#interest', '#admiration', 'positive', '0.109', 'explanation', 'conclusion', 'choice', 'finish', 'tell_why']
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senticnet['discussion_forum'] = ['-0.51', '0', '0.545', '-0.91', '#anger', '#disgust', 'negative', '-0.65', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['discussion_transcluded_from'] = ['0.533', '0.818', '0', '0.683', '#interest', '#admiration', 'positive', '0.678', 'gain_information', 'report_news', 'solve', 'seek_answer', 'gather_information']
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senticnet['disease'] = ['-0.10', '0', '-0.16', '-0.23', '#fear', '#disgust', 'negative', '-0.16', 'contagious_disease', 'infectious_disease', 'polio', 'sickness', 'malady']
senticnet['disease_adpkd'] = ['-0.50', '0', '0.542', '-0.51', '#anger', '#disgust', 'negative', '-0.51', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'hirschsprung_disease']
senticnet['disease_arpkd'] = ['-0.95', '0', '0.684', '-0.76', '#sadness', '#disgust', 'negative', '-0.79', 'innate', 'nurtural', 'inborn', 'nontransmissible', 'nonheritable']
senticnet['disease_of_skin'] = ['-0.89', '0', '0.998', '-0.89', '#sadness', '#anger', 'negative', '-0.93', 'erythema', 'flare', 'skin_disease', 'erythroderma', 'skin_disorder']
senticnet['disease_pid'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'gastroenteritis', 'stomach_flu', 'intestinal_flu', 'inflammatory_disease']
senticnet['disease_severity'] = ['-0.78', '0', '0.6', '-0.61', '#sadness', '#disgust', 'negative', '-0.66', 'suggestibility', 'sensitivity', 'capacity', 'reactivity', 'predisposition']
senticnet['disease_susceptibility'] = ['-0.78', '0', '0.6', '-0.61', '#sadness', '#disgust', 'negative', '-0.66', 'suggestibility', 'sensitivity', 'capacity', 'reactivity', 'predisposition']
senticnet['disease_vcjd'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'agent', 'satisfier', 'quickener', 'invigorator']
senticnet['diseasebackground'] = ['-0.71', '0', '0.74', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'kwashiorkor', 'marasmus', 'malnutrition', 'hypovitaminosis', 'deficiency_disease']
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senticnet['diseased_person'] = ['-0.74', '0', '0.991', '-0.64', '#sadness', '#anger', 'negative', '-0.79', 'unfortunate', 'dyspeptic', 'invalid', 'psychoneurotic', 'psychotic']
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senticnet['disembarrass'] = ['0.939', '-0.50', '0', '0.733', '#joy', '#admiration', 'positive', '0.724', 'smooth_out', 'clear', 'relieve', 'smooth', 'disembody']
senticnet['disembarrassment'] = ['0.772', '-0.92', '0', '0.524', '#joy', '#surprise', 'positive', '0.74', 'change', 'modification', 'easing', 'breath_of_fresh_air', 'alteration']
senticnet['disembodied'] = ['-0.53', '0', '0.637', '-0.93', '#anger', '#disgust', 'negative', '-0.70', 'discorporate', 'unembodied', 'bodiless', 'incorporeal', 'unbodied']
senticnet['disembodied_hand'] = ['0.105', '-0.17', '0.057', '0.079', '#joy', '#surprise', 'positive', '0.076', 'cue', 'twig', 'long_stick', 'stick', 'adhere']
senticnet['disembodied_voice'] = ['-0.94', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.94', 'drive_recklessly', 'discord', 'deaf', 'deny', 'silent']
senticnet['disembodiment'] = ['0.992', '-0.66', '0', '0.536', '#joy', '#surprise', 'positive', '0.729', 'nous', 'mind', 'brain', 'psyche', 'head']
senticnet['disembody'] = ['0.939', '-0.50', '0', '0.733', '#joy', '#admiration', 'positive', '0.724', 'smooth_out', 'clear', 'relieve', 'smooth', 'disembarrass']
senticnet['disembowel'] = ['-0.16', '-0.07', '-0.07', '-0.21', '#sadness', '#disgust', 'negative', '-0.09', 'cause_injury', 'hurt_person', 'maim', 'elephant_sit_chest', 'parry']
senticnet['disempower'] = ['-0.89', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.81', 'leave_out', 'lose_team', 'cause_suffer', 'disrespect', 'loathing']
senticnet['disempowerment'] = ['-0.84', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.82', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecure']
senticnet['disenable'] = ['-0.98', '0', '0.849', '-0.54', '#sadness', '#anger', 'negative', '-0.79', 'lay_up', 'modify', 'nobble', 'alter', 'change']
senticnet['disenchant'] = ['0.894', '0', '0', '0.924', '#joy', '#admiration', 'positive', '0.909', 'captivate', 'belonging', 'comfortably', 'solace', 'comfortableness']
senticnet['disenchantment'] = ['0', '0.951', '0', '0.78', '#interest', '#admiration', 'positive', '0.865', 'disaffection', 'unpopularity', 'dissatisfaction', 'ineptness', 'disillusionment']
senticnet['disenfranchise'] = ['-0.10', '0.176', '0.629', '-0.64', '#anger', '#disgust', 'negative', '-0.3', 'alienated', 'alienation', 'disaffection', 'estrange', 'disoriented']
senticnet['disenfranchised'] = ['-0.84', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.87', 'underclass', 'impoverished', 'ghetto', 'under-class', 'working-class']
senticnet['disengage'] = ['0.693', '-0.52', '0', '0.992', '#joy', '#admiration', 'positive', '0.737', 'release', 'unlock', 'let_go', 'relinquish', 'let_go_of']
senticnet['disengaged'] = ['-0.95', '0', '0.549', '-0.72', '#sadness', '#disgust', 'negative', '-0.74', 'sad', 'disappointing', 'unlucky', 'bummer', 'cry']
senticnet['disentangle'] = ['0', '0.817', '0', '0.769', '#interest', '#admiration', 'positive', '0.793', 'extricate', 'untangle', 'disentangled', 'disentangling', 'elucidate']
senticnet['disentangled'] = ['0', '0.808', '0', '0.768', '#interest', '#admiration', 'positive', '0.788', 'extricate', 'untangle', 'disentangle', 'disentangling', 'elucidate']
senticnet['disentangling'] = ['0', '0.811', '0', '0.766', '#interest', '#admiration', 'positive', '0.788', 'extricate', 'untangle', 'disentangled', 'disentangle', 'elucidate']
senticnet['disequilibrium'] = ['0.984', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.871', 'unequal', 'instability', 'imbalance', 'unbalance', 'astasia']
senticnet['disesteem'] = ['0', '0', '0.748', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'reputation', 'repute', 'blame', 'defame', 'disrepute']
senticnet['disfavor'] = ['0', '0', '0.776', '-0.74', '#anger', '#disgust', 'negative', '-0.76', 'loathe', 'execrate', 'abhor', 'resent', 'condemn']
senticnet['disfigure'] = ['-0.55', '0', '0.81', '-0.71', '#anger', '#disgust', 'negative', '-0.69', 'vitiate', 'ruin', 'maul', 'mangle', 'impair']
senticnet['disfigured_face'] = ['-0.82', '0', '0.758', '-0.70', '#sadness', '#anger', 'negative', '-0.76', 'unclassifiable', 'unidentifiable', 'unrecognisable', 'intangible', 'undiagnosable']
senticnet['disgorge'] = ['0', '0', '0.806', '-0.95', '#anger', '#disgust', 'negative', '-0.87', 'remove', 'deport', 'eruct', 'dislodge', 'erupt']
senticnet['disgrace'] = ['-0.91', '0', '-0.72', '-0.88', '#sadness', '#disgust', 'negative', '-0.84', 'death_love', 'bad_feeling', 'anguish', 'consequence_war', 'feel_stressed']
senticnet['disgraced_financier'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'gouger', 'swindler', 'confidence_man', 'conman', 'fraud']
senticnet['disgraced_former'] = ['0.54', '-0.64', '0', '0.566', '#surprise', '#admiration', 'positive', '0.582', 'pleaser', 'entertainer', 'mortal', 'busker', 'drawing_card']
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senticnet['disgruntle'] = ['-0.71', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.73', 'grunt', 'muss-be', 'disgruntled', 'discontent', 'temper']
senticnet['disgruntled'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'discontent', 'malcontent', 'dissatisfied', 'displeased', 'grouchy']
senticnet['disgruntled_employee'] = ['-0.04', '0.164', '0.685', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maliciousness', 'spitefulness', 'maleficence', 'malefic', 'malign']
senticnet['disgruntlement'] = ['0.938', '-0.55', '0', '0.7', '#joy', '#admiration', 'positive', '0.73', 'dissatisfaction', 'discontent', 'dysphoria', 'longing', 'yearning']
senticnet['disguise'] = ['0', '-0.78', '-0.84', '0', '#surprise', '#fear', 'negative', '-0.03', 'disguise', 'disguised', 'conceal', 'disguising', 'hiding']
senticnet['disguised'] = ['0', '-0.79', '-0.84', '0', '#surprise', '#fear', 'negative', '-0.02', 'disguise', 'disguise', 'conceal', 'disguising', 'hiding']
senticnet['disguising'] = ['0', '-0.78', '-0.85', '0', '#surprise', '#fear', 'negative', '-0.03', 'disguise', 'disguised', 'conceal', 'disguise', 'hiding']
senticnet['disgust'] = ['0', '0', '0', '-0.66', '#disgust', '#disgust', 'negative', '-0.66', 'disgusting', 'fed_up', 'disgusted', 'bleah', 'dishonor']
senticnet['disgusted'] = ['0', '0', '0', '-0.66', '#disgust', '#disgust', 'negative', '-0.66', 'disgusting', 'fed_up', 'disgust', 'bleah', 'dishonor']
senticnet['disgustedly'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'revolting', 'disgusting', 'fed_up', 'sick_of', 'disgustful']
senticnet['disgustful'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['disgusting'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'fed_up', 'sick_of', 'disgustful']
senticnet['disgustingly'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['disgustingness'] = ['-0.52', '0', '0.703', '-0.71', '#anger', '#disgust', 'negative', '-0.64', 'distastefulness', 'taste_property', 'unpalatability', 'unsavoriness', 'nauseatingness']
senticnet['dish'] = ['0', '0', '0.066', '-0.07', '#anger', '#disgust', 'negative', '-0.07', 'container_cup', 'cup', 'grease_trap', 'open_container', 'salad_bowl']
senticnet['dish_detergent'] = ['0', '0.095', '0', '0.074', '#interest', '#admiration', 'positive', '0.084', 'buy_wash_powder', 'water_soap', 'turn_faucet', 'take_off', 'fill_bathtub_water']
senticnet['dish_out'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'find_out', 'unmask', 'unearth', 'become_aware', 'dig_out']
senticnet['dish_soap'] = ['0', '0.095', '0', '0.074', '#interest', '#admiration', 'positive', '0.084', 'buy_wash_powder', 'water_soap', 'turn_faucet', 'take_off', 'fill_bathtub_water']
senticnet['dish_towel'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'facial_tissue', 'kleenex', 'hanky', 'handkerchief', 'tissue']
senticnet['dish_washer'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'kitchen_cabinet', 'kitchen_cupboard', 'kitchen', 'cupboard', 'own_kitchen']
senticnet['dishabille'] = ['0.625', '-0.88', '0', '0.782', '#surprise', '#admiration', 'positive', '0.762', 'judicious', 'prudent', 'prudential', 'heady', 'circumspect']
senticnet['disharmonious'] = ['-0.52', '0', '0.902', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'discordant', 'dissonant', 'unresolved', 'false', 'inharmonious']
senticnet['disharmony'] = ['0', '0', '0.724', '-0.83', '#anger', '#disgust', 'negative', '-0.77', 'divisiveness', 'disunity', 'dissension', 'discord', 'infighting']
senticnet['dishearten'] = ['0', '-0.83', '0', '0.839', '#surprise', '#admiration', 'positive', '0.838', 'disabused', 'disabusing', 'emasculate', 're-assure', 'disabuse']
senticnet['disheartened'] = ['-0.71', '0.3', '-0.37', '-0.11', '#sadness', '#fear', 'negative', '-0.22', 'despondently', 'despairingly', 'feel_down', 'demoralized', 'forlorn']
senticnet['disheartening'] = ['-0.83', '0.22', '-0.46', '-0.05', '#sadness', '#fear', 'negative', '-0.28', 'feel_down', 'demoralizing', 'demoralization', 'sadness', 'dispiriting']
senticnet['disheartenment'] = ['-0.80', '0', '0.898', '0', '#sadness', '#anger', 'negative', '-0.85', 'discouragement', 'discourage', 'mortify', 'abase', 'self-hatred']
senticnet['dished_up'] = ['0.866', '-0.91', '0', '0.893', '#surprise', '#admiration', 'positive', '0.892', 'mooch', 'freeload', 'grub', 'sponge', 'bum']
senticnet['disheveled'] = ['-0.85', '0', '0.982', '-0.72', '#sadness', '#anger', 'negative', '-0.85', 'messy', 'mussy', 'untidy', 'dirty', 'frowzled']
senticnet['dishevelled_hair'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['dishonest'] = ['-0.98', '0', '0.886', '-0.98', '#sadness', '#disgust', 'negative', '-0.95', 'against_law', 'false', 'bad_action', 'consequence', 'judge_decision']
senticnet['dishonest_conduct'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'negligence', 'evasion', 'dereliction', 'contributory_negligence']
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senticnet['dishonesty'] = ['-0.92', '0', '0.862', '-0.90', '#sadness', '#disgust', 'negative', '-0.89', 'untruth', 'falsehood', 'fallacy', 'deceit', 'slumber']
senticnet['dishonor'] = ['-0.93', '0', '0.936', '-0.88', '#sadness', '#anger', 'negative', '-0.91', 'lose_team', 'cause_suffer', 'leave_out', 'loathing', 'lyme_disease']
senticnet['dishonorable'] = ['-0.91', '0', '0.907', '-0.60', '#sadness', '#anger', 'negative', '-0.80', 'opprobrious', 'debasing', 'disgraceful', 'degrading', 'inglorious']
senticnet['dishonorable_conduct'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'negligence', 'evasion', 'dereliction', 'contributory_negligence']
senticnet['dishonorableness'] = ['-0.65', '0', '0.751', '-0.8', '#anger', '#disgust', 'negative', '-0.73', 'ignobleness', 'ignobility', 'sleaziness', 'unrespectability', 'disreputability']
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senticnet['dishonored'] = ['-0.88', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.85', 'disgraceful', 'shameful', 'dishonorably', 'inglorious', 'shamefully']
senticnet['dishonourable'] = ['-0.93', '0', '0.936', '-0.88', '#sadness', '#anger', 'negative', '-0.91', 'lose_team', 'cause_suffer', 'leave_out', 'loathing', 'lyme_disease']
senticnet['dishwasher'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'kitchen_cabinet', 'kitchen_cupboard', 'kitchen', 'cupboard', 'own_kitchen']
senticnet['dishwasher_detergent'] = ['0', '0.095', '0', '0.074', '#interest', '#admiration', 'positive', '0.084', 'buy_wash_powder', 'water_soap', 'turn_faucet', 'take_off', 'fill_bathtub_water']
senticnet['dishwasher_safe'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'kitchen_cabinet', 'kitchen_cupboard', 'kitchen', 'cupboard', 'own_kitchen']
senticnet['dishwashing_detergent'] = ['0.975', '0.941', '0', '0', '#joy', '#interest', 'positive', '0.958', 'clean_agent', 'foam', 'bubbly', 'wash_self', 'cleanser']
senticnet['dishwashing_liquid'] = ['0', '0.095', '0', '0.074', '#interest', '#admiration', 'positive', '0.084', 'buy_wash_powder', 'water_soap', 'turn_faucet', 'take_off', 'fill_bathtub_water']
senticnet['disilicide'] = ['-0.05', '-0.00', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'pigment', 'many_shades', 'cyan', 'green_yellow', 'material']
senticnet['disillusion'] = ['-0.97', '0', '0.971', '-0.84', '#sadness', '#anger', 'negative', '-0.93', 'fail', 'betray', 'queer', 'cross', 'disappoint']
senticnet['disillusionment'] = ['0', '0.955', '0', '0.781', '#interest', '#admiration', 'positive', '0.868', 'disaffection', 'disenchantment', 'dissatisfaction', 'ineptness', 'unpopularity']
senticnet['disincentive'] = ['0.716', '0.813', '0', '0', '#joy', '#interest', 'positive', '0.764', 'bonus', 'reinforce', 'promotion', 'meritocracy', 'gratification']
senticnet['disinclination'] = ['-0.11', '0.19', '0.604', '-0.63', '#anger', '#disgust', 'negative', '-0.29', 'indispose', 'disinclined', 'disincline', 'unfriendly', 'inimical']
senticnet['disincline'] = ['-0.11', '0.19', '0.604', '-0.63', '#anger', '#disgust', 'negative', '-0.29', 'indispose', 'disinclination', 'disinclined', 'unfriendly', 'inimical']
senticnet['disinclined'] = ['-0.12', '0.18', '0.598', '-0.63', '#anger', '#disgust', 'negative', '-0.29', 'disinclination', 'bad_student', 'indispose', 'unfriendly', 'inimical']
senticnet['disinfect'] = ['0.038', '0', '0', '0.09', '#joy', '#admiration', 'positive', '0.064', 'get_rid_of_dirt', 'wash_away_dirt', 'cleanliness', 'keep_clean', 'hygiene']
senticnet['disinfect_water'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
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senticnet['disinfection'] = ['-0.62', '0', '0.987', '-0.74', '#anger', '#disgust', 'negative', '-0.78', 'medical_aid', 'treatment', 'hospice', 'digitalization', 'bloodletting']
senticnet['disinformation'] = ['0', '0', '0.989', '-0.71', '#anger', '#disgust', 'negative', '-0.85', 'deceive', 'against_law', 'false', 'fool', 'trick']
senticnet['disingenuous'] = ['0', '0', '-0.95', '-0.87', '#fear', '#disgust', 'negative', '-0.91', 'beat-up', 'liar', 'languish', 'disingenuously', 'open']
senticnet['disingenuously'] = ['0', '0', '-0.95', '-0.86', '#fear', '#disgust', 'negative', '-0.90', 'beat-up', 'liar', 'languish', 'disingenuous', 'open']
senticnet['disingenuousness'] = ['-0.83', '0', '0.802', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'dishonesty', 'deceitfulness', 'craftiness', 'guile', 'artfulness']
senticnet['disinherit'] = ['0', '0.769', '0', '0', '#interest', '#interest', 'positive', '0.769', 'get_rich', 'get_money', 'inherit_property', 'benefit', 'enjoy_life']
senticnet['disinherited'] = ['-0.86', '0', '0.655', '-0.67', '#sadness', '#disgust', 'negative', '-0.73', 'unloved', 'unbeloved', 'bereft', 'estranged', 'lovelorn']
senticnet['disinhibition'] = ['0.971', '0.751', '0', '0.896', '#joy', '#admiration', 'positive', '0.873', 'new_friendship', 'foreplay', 'warm_fuzzy_feeling', 'become_aroused', 'erection']
senticnet['disintegrate'] = ['0', '0', '-0.89', '-0.93', '#fear', '#disgust', 'negative', '-0.91', 'dissolve', 'decompose', 'detritus', 'disunite', 'corrosiveness']
senticnet['disintegrates_into'] = ['-0.11', '0.095', '-0.11', '0.071', '#sadness', '#fear', 'negative', '-0.01', 'violent_action', 'pulverize', 'break_glass', 'tear_apart', 'hit_hard']
senticnet['disintegration'] = ['0.836', '-0.83', '0', '0.511', '#joy', '#surprise', 'positive', '0.726', 'appearance', 'start', 'casus_belli', 'beginning', 'egress']
senticnet['disinterest'] = ['0.737', '0.745', '0', '0', '#joy', '#interest', 'positive', '0.741', 'candor', 'impartiality', 'justice', 'unbiasedness', 'fairness']
senticnet['disinterested'] = ['0.828', '-1', '0', '0.558', '#joy', '#surprise', 'positive', '0.795', 'dispassionate', 'impartial', 'indifferent', 'unbiased', 'fair']
senticnet['disinterested_friendship'] = ['0.909', '-0.85', '0', '0.846', '#joy', '#surprise', 'positive', '0.87', 'respectability', 'reputability', 'venerability', 'venerableness', 'righteousness']
senticnet['disinterested_third_party'] = ['0.828', '-0.90', '0', '0.874', '#surprise', '#admiration', 'positive', '0.869', 'perspicacity', 'judgment', 'fair', 'sound_judgment', 'fair_judgment']
senticnet['disintergration'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'dejectedness', 'low-spiritedness', 'lowness', 'downheartedness', 'cheerless']
senticnet['disinterment'] = ['-0.12', '0.091', '-0.19', '-0.10', '#sadness', '#fear', 'negative', '-0.08', 'headstone', 'grave', 'bury_body', 'bury', 'bury_dead']
senticnet['disinvest'] = ['-0.95', '0', '0.673', '-0.85', '#sadness', '#disgust', 'negative', '-0.82', 'discharge', 'free', 'dethrone', 'defrock', 'unfrock']
senticnet['disinvolve'] = ['0.983', '0', '0', '0.894', '#joy', '#admiration', 'positive', '0.939', 'cut_down', 'analyze', 'reduce', 'decipher', 'abridge']
senticnet['disjoin'] = ['0', '0', '0.847', '-0.90', '#anger', '#disgust', 'negative', '-0.87', 'dislocate', 'detach', 'luxate', 'incapacitate', 'disintegrate']
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senticnet['disjunction_between'] = ['-0.68', '0', '0.863', '-0.69', '#anger', '#disgust', 'negative', '-0.74', 'pretension', 'artificiality', 'affectedness', 'supernaturalness', 'largeness']
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senticnet['disjunctive_normal_form'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly']
senticnet['disjuncture'] = ['-0.54', '0', '0.979', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'unlikeness', 'dissimilitude', 'difference', 'heterology', 'dissimilarity']
senticnet['disjuncture_between'] = ['-0.54', '0', '0.979', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'unlikeness', 'dissimilitude', 'difference', 'heterology', 'dissimilarity']
senticnet['disk'] = ['0', '0', '0.706', '-0.98', '#anger', '#disgust', 'negative', '-0.84', 'software_crash', 'old_computer', 'seventy', 'music_storage', 'old_technology']
senticnet['disk_backup'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_musical_sound', 'play_orchestra', 'make_noise', 'play_music', 'make_drawing']
senticnet['disk_cleanup'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'car_trunk', 'walmart', 'warehouse', 'store', 'storage_place']
senticnet['disk_drive'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotary_file', 'cd-rom', 'database', 'business_card', 'calendar_book']
senticnet['disk_latency'] = ['0.865', '-0.80', '0', '0.52', '#joy', '#surprise', 'positive', '0.73', 'set', 'pose', 'lay', 'intersperse', 'put']
senticnet['diskeeper'] = ['-0.03', '0.112', '-0.08', '0.164', '#interest', '#admiration', 'positive', '0.04', 'run', 'jump_over', 'quick', 'sprint', 'flee']
senticnet['diskette'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'slot', 'box', 'load_dock', 'safe_deposit_box', 'emporium']
senticnet['diskette_drive'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotary_file', 'cd-rom', 'database', 'business_card', 'calendar_book']
senticnet['dislike'] = ['-0.85', '0', '0.919', '-0.96', '#anger', '#disgust', 'negative', '-0.91', 'disapproval', 'aversion', 'distaste', 'antipathy', 'prejudice']
senticnet['dislike_person'] = ['-0.13', '0', '-0.17', '-0.20', '#fear', '#disgust', 'negative', '-0.17', 'medical_person', 'hate', 'nurse', 'doctor', 'stressful_job']
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senticnet['dislocated_ankle'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'deltoid', 'muscle', 'abductor', 'skeletal_muscle']
senticnet['dislocated_hip'] = ['0.94', '0.78', '0', '0', '#joy', '#interest', 'positive', '0.86', 'breaheavily', 'heavy_breathe', 'get_exhausted', 'exhaustion', 'see_neighbor']
senticnet['dislocated_shoulder'] = ['0.94', '0.78', '0', '0', '#joy', '#interest', 'positive', '0.86', 'breaheavily', 'heavy_breathe', 'get_exhausted', 'exhaustion', 'see_neighbor']
senticnet['dislocation'] = ['-0.09', '-0.04', '-0.06', '-0.18', '#sadness', '#disgust', 'negative', '-0.07', 'ache', 'pain', 'hurt', 'cut_skin', 'ouch']
senticnet['dislodge'] = ['0', '0', '0.798', '-0.95', '#anger', '#disgust', 'negative', '-0.87', 'remove', 'deport', 'eruct', 'disgorge', 'erupt']
senticnet['dislodgement'] = ['0', '0', '0.873', '-0.92', '#anger', '#disgust', 'negative', '-0.89', 'punch', 'sting', 'disable', 'ache', 'vex']
senticnet['dislodgment'] = ['0', '0', '0.873', '-0.92', '#anger', '#disgust', 'negative', '-0.89', 'punch', 'sting', 'disable', 'ache', 'vex']
senticnet['disloyal'] = ['-0.92', '0', '0.784', '-0.91', '#sadness', '#disgust', 'negative', '-0.87', 'enemy', 'bad_customer', 'competitor', 'steal_client', 'buy_cheaper']
senticnet['disloyalty'] = ['-0.98', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.88', 'perfidiousness', 'traitorousness', 'perfidy', 'treachery', 'betrayal']
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senticnet['dismal_swamp'] = ['0.85', '0.96', '0', '0', '#joy', '#interest', 'positive', '0.905', 'flatness', 'boring', 'flat_tire', 'flat_foot', 'small_breast']
senticnet['dismally'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'lamentably', 'deplorably', 'bereaved', 'bereft', 'grief-stricken']
senticnet['dismantle'] = ['0', '0', '0.884', '-0.83', '#anger', '#disgust', 'negative', '-0.85', 'kill', 'nullify', 'vaporize', 'wreck', 'extirpate']
senticnet['dismay'] = ['-0.97', '0', '0.883', '0', '#sadness', '#anger', 'negative', '-0.93', 'distressed', 'shamefacedly', 'sheepishness', 'shamefacedness', 'confusedly']
senticnet['dismembered_body'] = ['-0.06', '0.09', '-0.14', '-0.15', '#fear', '#disgust', 'negative', '-0.06', 'casket', 'coffin', 'grave', 'headstone', 'bury_person']
senticnet['dismiss'] = ['-0.86', '0', '0.897', '-0.81', '#sadness', '#anger', 'negative', '-0.85', 'disregard', 'ignore', 'brush_off', 'push_aside', 'flout']
senticnet['dismissal'] = ['-0.84', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.85', 'inferno', 'forest_fire', 'fire_light', 'detonation', 'wash_mouth_soap']
senticnet['dismissed'] = ['-0.79', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.75', 'irrelevant', 'debatable', 'moot', 'arguable', 'appellate']
senticnet['dismission'] = ['-0.91', '0', '0.521', '-0.81', '#sadness', '#disgust', 'negative', '-0.74', 'bullying', 'domineering', 'blustery', 'roughshod', 'cavalier']
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senticnet['dismissively'] = ['-0.89', '0.168', '-0.60', '0.05', '#sadness', '#fear', 'negative', '-0.32', 'remorsefully', 'contritely', 'contrition', 'contriteness', 'attrition']
senticnet['dismount'] = ['-0.95', '0', '0.793', '0', '#sadness', '#anger', 'negative', '-0.87', 'block', 'obstacle', 'nodus', 'impediment', 'obstruction']
senticnet['disney'] = ['0.053', '0.073', '-0.06', '0.104', '#interest', '#admiration', 'positive', '0.041', 'mermaid', 'autumn_leaf', 'horizon', 'aquatic_life', 'fisherman']
senticnet['disney_aladdin'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'child_movie', 'child_story', 'funny_moment', 'childhood']
senticnet['disney_animated_feature'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'child_movie', 'child_story', 'funny_moment', 'childhood']
senticnet['disney_animated_film'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'child_movie', 'child_story', 'funny_moment', 'childhood']
senticnet['disney_animated_movie'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'child_movie', 'child_story', 'funny_moment', 'childhood']
senticnet['disney_cartoon'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'child_movie', 'child_story', 'funny_moment', 'childhood']
senticnet['disney_hunchback'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'lordosis', 'kyphosis', 'humpback', 'spinal_curvature', 'abnormality']
senticnet['disney_movie'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'child_movie', 'child_story', 'funny_moment', 'childhood']
senticnet['disneyfication'] = ['0', '0', '-0.74', '-0.93', '#fear', '#disgust', 'negative', '-0.84', 'generosity', 'oral_sex', 'win_lottery', 'gift_wrap', 'give_away']
senticnet['disneyland'] = ['0.957', '0.7', '0', '0', '#joy', '#interest', 'positive', '0.829', 'ballpark', 'coven', 'carnival', 'hockey_game', 'symposium']
senticnet['disobedience'] = ['0', '0', '0.854', '-0.89', '#anger', '#disgust', 'negative', '-0.87', 'non-violent', 'nonviolence', 'non-violence', 'nonviolent', 'activism']
senticnet['disobedient'] = ['-0.77', '0', '0.886', '-0.58', '#sadness', '#anger', 'negative', '-0.74', 'refractory', 'obstinate', 'recalcitrant', 'froward', 'headstrong']
senticnet['disobey'] = ['-0.71', '0', '0.932', '-0.50', '#sadness', '#anger', 'negative', '-0.71', 'refuse', 'subvert', 'decline', 'balk', 'baulk']
senticnet['disoblige'] = ['-0.53', '0', '0.549', '-0.88', '#anger', '#disgust', 'negative', '-0.65', 'distress', 'straiten', 'impact', 'bear_on', 'touch']
senticnet['disorder'] = ['0', '0', '0.817', '-0.77', '#anger', '#disgust', 'negative', '-0.79', 'suffer_fool', 'diabetes', 'influenza', 'lyme_disease', 'brain_tumor']
senticnet['disorder_characterized_by'] = ['-0.65', '0', '0.873', '-0.63', '#sadness', '#anger', 'negative', '-0.72', 'neuritis', 'multiple_neuritis', 'rubor', 'redness', 'inflammation']
senticnet['disordered'] = ['-0.80', '0', '0', '-0.95', '#sadness', '#disgust', 'negative', '-0.87', 'put_off', 'confusedly', 'confusing', 'discombobulation', 'confused']
senticnet['disorderliness'] = ['-0.78', '0', '0.854', '-0.9', '#anger', '#disgust', 'negative', '-0.84', 'muss', 'untidiness', 'mess', 'muddle', 'messiness']
senticnet['disorderly'] = ['-0.64', '0', '0.941', '-0.66', '#anger', '#disgust', 'negative', '-0.74', 'rowdy', 'boisterous', 'unruly', 'rambunctious', 'robustious']
senticnet['disorganize'] = ['0', '0', '0.814', '-0.77', '#anger', '#disgust', 'negative', '-0.79', 'office', 'messy', 'workplace', 'business_organization', 'large_business']
senticnet['disorganized'] = ['-0.98', '0', '0.81', '-0.96', '#sadness', '#disgust', 'negative', '-0.92', 'unmethodical', 'upset', 'confused', 'scrambled', 'undone']
senticnet['disorganized_convection'] = ['-0.12', '-0.05', '0', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'diminish', 'reduce', 'lower', 'lower_music', 'decrease']
senticnet['disorient'] = ['-0.50', '0', '0.638', '-0.71', '#anger', '#disgust', 'negative', '-0.61', 'befuddle', 'confound', 'discombobulate', 'bedevil', 'confuse']
senticnet['disorientate'] = ['-0.89', '0', '0.873', '-0.56', '#sadness', '#anger', 'negative', '-0.77', 'befuddle', 'confound', 'discombobulate', 'bedevil', 'confuse']
senticnet['disorientation'] = ['0', '0', '0.958', '-0.82', '#anger', '#disgust', 'negative', '-0.89', 'become_drunk', 'tipsy', 'act_stupid', 'forgetfulness', 'hangover']
senticnet['disoriented'] = ['-0.10', '0.176', '0.629', '-0.64', '#anger', '#disgust', 'negative', '-0.3', 'alienated', 'alienation', 'disaffection', 'estrange', 'alienate']
senticnet['disown'] = ['-0.96', '0', '0.885', '-0.75', '#sadness', '#anger', 'negative', '-0.86', 'controvert', 'answer', 'rebut', 'renounce', 'repudiate']
senticnet['disparage'] = ['-0.76', '0', '0.798', '-0.58', '#sadness', '#anger', 'negative', '-0.71', 'disgrace', 'pick_apart', 'depreciate', 'criticize', 'belittle']
senticnet['disparagement'] = ['-0.10', '0.188', '0.62', '-0.63', '#anger', '#disgust', 'negative', '-0.29', 'contemn', 'contempt', 'despite', 'alienated', 'estranged']
senticnet['disparager'] = ['0.899', '-0.77', '0', '0.866', '#joy', '#admiration', 'positive', '0.847', 'unpleasant_person', 'disagreeable_person', 'detractor', 'cynic', 'depreciator']
senticnet['disparaging'] = ['-0.82', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.90', 'flattering', 'embarrassing', 'unflattering', 'unattractive', 'demeaning']
senticnet['disparate'] = ['0.057', '0', '0', '0.153', '#joy', '#admiration', 'positive', '0.105', 'multiple', 'cute', 'variety', 'hairy', 'large_breadbox']
senticnet['disparate_impact'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
senticnet['disparate_treatment'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
senticnet['disparateness'] = ['-0.54', '0', '0.979', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'unlikeness', 'dissimilitude', 'difference', 'heterology', 'dissimilarity']
senticnet['disparity'] = ['0.855', '-0.84', '0', '0.632', '#joy', '#surprise', 'positive', '0.777', 'disproportion', 'gap', 'spread', 'gulf', 'disconnection']
senticnet['disparity_between'] = ['0.871', '-0.74', '0', '0.981', '#joy', '#admiration', 'positive', '0.867', 'unrighteousness', 'unjustness', 'unfairness', 'wrong', 'wrongfulness']
senticnet['disparity_between_rich'] = ['-0.56', '0', '-0.49', '-0.09', '#sadness', '#fear', 'negative', '-0.38', 'lack', 'absence', 'poverty', 'emptiness', 'hunger']
senticnet['dispassion'] = ['0.08', '0.153', '-0.12', '0.224', '#interest', '#admiration', 'positive', '0.083', 'meditation', 'self_contemplation', 'soul_searching', 'self_examination', 'self_observation']
senticnet['dispassionate'] = ['0.837', '-0.99', '0', '0.559', '#joy', '#surprise', 'positive', '0.797', 'disinterested', 'impartial', 'indifferent', 'unbiased', 'fair']
senticnet['dispassionate_tone'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'nonsense', 'meaninglessness', 'bunk', 'absurdity', 'hokum']
senticnet['dispassionately'] = ['0', '0.764', '0', '0.845', '#interest', '#admiration', 'positive', '0.804', 'empathetically', 'empathic', 'empathetic', 'kind-heartedness', 'kindheartedness']
senticnet['dispatch'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'mission', 'consignment', 'delivery', 'shipment', 'expedition']
senticnet['dispel'] = ['0.838', '-0.84', '0', '0.791', '#joy', '#surprise', 'positive', '0.825', 'move', 'displace', 'clear_air', 'fire', 'banish']
senticnet['dispensable'] = ['-0.85', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.81', 'dispensed', 'dispensation', 'indispensable', 'essential', 'dispense']
senticnet['dispensation'] = ['-0.86', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.82', 'dispensed', 'dispensable', 'indispensable', 'essential', 'dispense']
senticnet['dispensationalism'] = ['-0.83', '0', '0.648', '-0.60', '#sadness', '#anger', 'negative', '-0.69', 'infallibility', 'dependability', 'dependableness', 'reliability', 'reliableness']
senticnet['dispense'] = ['-0.06', '0', '0.023', '0', '#sadness', '#anger', 'negative', '-0.04', 'circulate_blood', 'grind_grain', 'fail_work', 'cremate', 'dry_wet_hair']
senticnet['dispense_medication'] = ['-0.06', '0', '0.023', '0', '#sadness', '#anger', 'negative', '-0.04', 'circulate_blood', 'grind_grain', 'fail_work', 'cremate', 'dry_wet_hair']
senticnet['dispense_water'] = ['-0.07', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.05', 'soap_up', 'turn_water', 'fill_bathtub', 'run_bath', 'launder']
senticnet['dispense_with'] = ['0', '0', '-0.75', '-0.72', '#fear', '#disgust', 'negative', '-0.73', 'foreswear', 'relinquish', 'waive', 'forgo', 'throw_overboard']
senticnet['dispensed'] = ['-0.86', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.82', 'dispensable', 'dispensation', 'indispensable', 'essential', 'dispense']
senticnet['dispensing'] = ['0', '-0.93', '0', '0.703', '#surprise', '#admiration', 'positive', '0.819', 'presentation', 'lecture', 'impression', 'show', 'projector']
senticnet['dispensing_chemist'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'druggist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['dispersal'] = ['0.782', '-0.81', '0', '0', '#joy', '#surprise', 'positive', '0.798', 'diffusion', 'crop-dusting', 'dissemination', 'dispersion', 'spread']
senticnet['disperse'] = ['0', '-0.94', '0', '0.88', '#surprise', '#admiration', 'positive', '0.911', 'stud', 'constellate', 'doting', 'adoring', 'fond']
senticnet['dispersed_throughout'] = ['0.071', '0.115', '-0.08', '0.167', '#interest', '#admiration', 'positive', '0.066', 'live_inside', 'inhabit', 'live_life', 'protection_weather', 'raise_family']
senticnet['dispersing_phase'] = ['0.793', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.811', 'phase', 'generation', 'leptotene', 'safe_period', 'diplotene']
senticnet['dispersion'] = ['0.849', '-0.54', '0', '0.543', '#joy', '#surprise', 'positive', '0.646', 'diffusion', 'spread', 'scattering', 'invasion', 'irradiation']
senticnet['dispersion_relation'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'assumption', 'precondition', 'provision', 'constraint', 'premiss']
senticnet['disphenoid'] = ['0.641', '-0.6', '0', '0.957', '#joy', '#admiration', 'positive', '0.733', 'ideal_solid', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'regular_octahedron']
senticnet['dispirit'] = ['-0.81', '0.239', '-0.44', '-0.06', '#sadness', '#fear', 'negative', '-0.27', 'feel_down', 'deject', 'cast_down', 'get_down', 'demoralize']
senticnet['dispirited'] = ['-0.78', '0.258', '-0.45', '-0.06', '#sadness', '#fear', 'negative', '-0.26', 'downhearted', 'low-spirited', 'depressed', 'down_in_mouth', 'downcast']
senticnet['dispiritedly'] = ['-0.93', '0', '0.791', '0', '#sadness', '#anger', 'negative', '-0.86', 'hopelessness', 'hopelessly', 'despairing', 'pessimistically', 'pessimistic']
senticnet['dispiritedness'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'dejectedness', 'low-spiritedness', 'lowness', 'downheartedness', 'cheerless']
senticnet['dispiriting'] = ['-0.83', '0.22', '-0.46', '-0.05', '#sadness', '#fear', 'negative', '-0.28', 'disheartening', 'feel_down', 'demoralizing', 'demoralization', 'sadness']
senticnet['displace'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'transplant', 'move', 'move_away', 'bump', 'dislocate']
senticnet['displaces_oxygen'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'rodent', 'tree_animal']
senticnet['displacing_agency'] = ['-0.02', '0.119', '-0.06', '0.216', '#interest', '#admiration', 'positive', '0.062', 'go_forward', 'locomotion', 'movement', 'move', 'move_ahead']
senticnet['display'] = ['0.044', '0.08', '0', '-0.03', '#joy', '#interest', 'positive', '0.031', 'screen', 'pixel', 'visual_information', 'image', 'computer_monitor']
senticnet['display_image'] = ['0.076', '0', '0', '0', '#joy', '#joy', 'positive', '0.076', 'send_email', 'post_message', 'access_internet', 'prepare_report', 'enter_data']
senticnet['display_information'] = ['0.686', '0.526', '0', '0.812', '#joy', '#admiration', 'positive', '0.675', 'display', 'sharpen', 'paint_picture', 'letter_sign', 'turner']
senticnet['display_item'] = ['-0.04', '0.151', '-0.09', '0.187', '#interest', '#admiration', 'positive', '0.049', 'store_dry_goods', 'exhibit', 'store_stuff', 'hold_stuff', 'carry_stuff']
senticnet['display_wealth'] = ['0.03', '0', '0', '0.119', '#joy', '#admiration', 'positive', '0.074', 'casino', 'propose_marriage', 'shoe_lace', 'time_tracker', 'use_finger']
senticnet['displayed_argent'] = ['0', '0.105', '0', '0', '#interest', '#interest', 'positive', '0.105', 'beak', 'many_feathers', 'big_eye', 'bird', 'bird_mouth']
senticnet['displayed_sable'] = ['0', '0.105', '0', '0', '#interest', '#interest', 'positive', '0.105', 'beak', 'many_feathers', 'big_eye', 'bird', 'bird_mouth']
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senticnet['displeasing'] = ['-0.12', '0.072', '0.699', '-0.67', '#anger', '#disgust', 'negative', '-0.35', 'displeasingly', 'displeasure', 'displease', 'displeased', 'aggravated']
senticnet['displeasingly'] = ['-0.12', '0.072', '0.699', '-0.67', '#anger', '#disgust', 'negative', '-0.35', 'displeasing', 'displeasure', 'displease', 'displeased', 'aggravated']
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senticnet['disport'] = ['0.795', '0.737', '0', '0', '#joy', '#interest', 'positive', '0.766', 'paddle', 'ludo', 'playful', 'frolic', 'playfully']
senticnet['disposable'] = ['-0.08', '0.232', '0', '0.099', '#interest', '#admiration', 'positive', '0.084', 'wax_dental_floss', 'fluoride_toothpaste', 'toilet_paper', 'dental_floss', 'tartar_control_toothpaste']
senticnet['disposable_blade'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'smart_technology', 'electric_toothbrush', 'hair_dryer', 'stay_healthy', 'wash_tooth']
senticnet['disposable_diaper'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'hankie', 'kleenex', 'hanky', 'handkerchief', 'tissue']
senticnet['disposable_razor'] = ['-0.08', '0.232', '0', '0.099', '#interest', '#admiration', 'positive', '0.084', 'wax_dental_floss', 'fluoride_toothpaste', 'toilet_paper', 'dental_floss', 'tartar_control_toothpaste']
senticnet['disposal'] = ['-0.14', '-0.05', '-0.06', '-0.17', '#sadness', '#disgust', 'negative', '-0.08', 'decompose', 'kill', 'blow_up', 'death_destruction', 'violent_person']
senticnet['disposal_facility'] = ['-0.81', '0', '0.987', '-0.64', '#sadness', '#anger', 'negative', '-0.81', 'burn_garbage', 'eliminate_garbage', 'incinerate_waste', 'dump', 'garbage_dump']
senticnet['dispose'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'throw_out', 'discard', 'get_rid', 'ditch', 'abandon']
senticnet['dispose_waste'] = ['-0.04', '0', '-0.07', '-0.08', '#fear', '#disgust', 'negative', '-0.07', 'put_garbage', 'throw_away_garbage', 'hold_trash', 'barf', 'radioactive']
senticnet['disposeth'] = ['-0.98', '0', '0.654', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'deprive', 'expropriate', 'disarm', 'unclothe', 'take']
senticnet['disposition'] = ['-0.58', '0', '0.925', '-0.63', '#anger', '#disgust', 'negative', '-0.71', 'ill_nature', 'disagreeableness', 'unfriendliness', 'bloodthirstiness', 'aloneness']
senticnet['dispositive'] = ['-0.04', '0.296', '-0.10', '0.291', '#interest', '#admiration', 'positive', '0.109', 'explanation', 'conclusion', 'choice', 'finish', 'tell_why']
senticnet['dispossess'] = ['0', '0', '0.926', '-0.74', '#anger', '#disgust', 'negative', '-0.83', 'dismiss', 'bust', 'ban', 'evacuate', 'eliminate']
senticnet['dispossession'] = ['-0.87', '0', '0.765', '-0.82', '#sadness', '#disgust', 'negative', '-0.82', 'due_process', 'due_process_of_law', 'ouster', 'retaliatory_eviction', 'actual_eviction']
senticnet['dispraise'] = ['0.879', '0', '0', '0.986', '#joy', '#admiration', 'positive', '0.932', 'mercifully', 'merciful', 'ruth', 'commiseration', 'commiserate']
senticnet['disproof'] = ['-0.68', '0', '0.733', '-0.82', '#anger', '#disgust', 'negative', '-0.74', 'controvert', 'answer', 'disown', 'renounce', 'repudiate']
senticnet['disproportion'] = ['0.855', '-0.84', '0', '0.632', '#joy', '#surprise', 'positive', '0.777', 'disparity', 'gap', 'spread', 'gulf', 'disconnection']
senticnet['disproportion_between'] = ['-0.96', '0', '0.91', '-0.61', '#sadness', '#anger', 'negative', '-0.82', 'unpersuadable', 'unsuasible', 'unresponsive', 'resistant', 'incapable']
senticnet['disproportional'] = ['0', '0', '0.865', '-0.79', '#anger', '#disgust', 'negative', '-0.83', 'distemper', 'proportionate', 'unfair', 'proportion', 'disproportionate']
senticnet['disproportionate'] = ['0', '0', '0.872', '-0.78', '#anger', '#disgust', 'negative', '-0.82', 'distemper', 'proportionate', 'unfair', 'proportion', 'disproportional']
senticnet['disproportionate_brunt'] = ['-0.92', '0', '0.946', '-0.56', '#sadness', '#anger', 'negative', '-0.81', 'forcefulness', 'intensiveness', 'impulse', 'vigor', 'energy']
senticnet['disproportionate_burden'] = ['-0.81', '0', '0.979', '-0.88', '#anger', '#disgust', 'negative', '-0.89', 'vexation', 'dead_weight', 'imposition', 'worry', 'concern']
senticnet['disproportionate_influence'] = ['-0.94', '0', '0.863', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'poke', 'knockout_punch', 'lick', 'punch', 'biff']
senticnet['disproportionate_share'] = ['-0.92', '0', '0.946', '-0.56', '#sadness', '#anger', 'negative', '-0.81', 'forcefulness', 'intensiveness', 'impulse', 'vigor', 'energy']
senticnet['disproportionately'] = ['-0.56', '0', '-0.49', '-0.09', '#sadness', '#fear', 'negative', '-0.38', 'lack', 'absence', 'poverty', 'emptiness', 'hunger']
senticnet['disproportionation'] = ['0.695', '-0.85', '0', '0.501', '#joy', '#surprise', 'positive', '0.684', 'alternative_energy', 'energy', 'energy_of_activation', 'atomic_energy', 'nuclear_energy']
senticnet['disprove'] = ['-0.59', '0', '0.824', '-0.81', '#anger', '#disgust', 'negative', '-0.74', 'controvert', 'refute', 'rebut', 'falsify', 'negate']
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senticnet['disputant'] = ['0.644', '-0.86', '0', '0.58', '#joy', '#surprise', 'positive', '0.696', 'hairsplitter', 'quarreler', 'fight', 'obstructor', 'obstructer']
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senticnet['disquieted'] = ['-0.75', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.83', 'distressfully', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['disquietude'] = ['-0.90', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.84', 'inquietude', 'edginess', 'edgy', 'uptight', 'restive']
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senticnet['disrelish'] = ['0', '0', '0.87', '-0.74', '#anger', '#disgust', 'negative', '-0.80', 'disapprove', 'abominate', 'scorn', 'contemn', 'disesteem']
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senticnet['disreputability'] = ['-0.77', '0', '0.748', '-0.60', '#sadness', '#anger', 'negative', '-0.70', 'ignobleness', 'ignobility', 'sleaziness', 'unrespectability', 'dishonorableness']
senticnet['disreputable'] = ['0.725', '-0.88', '0', '0.508', '#joy', '#surprise', 'positive', '0.706', 'louche', 'seedy', 'discredited', 'sleazy', 'discreditable']
senticnet['disrepute'] = ['0', '0', '0.748', '-0.95', '#anger', '#disgust', 'negative', '-0.84', 'reputation', 'repute', 'blame', 'defame', 'disesteem']
senticnet['disrespect'] = ['0', '0', '0.761', '-0.72', '#anger', '#disgust', 'negative', '-0.74', 'cause_suffer', 'lose_team', 'suffer_fool', 'brain_tumor', 'lyme_disease']
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senticnet['disrespectfully'] = ['0', '0.149', '0.719', '-0.66', '#anger', '#disgust', 'negative', '-0.41', 'belligerently', 'antagonism', 'class_feeling', 'aggressiveness', 'animus']
senticnet['disrobe'] = ['-0.04', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.05', 'fill_bathtub', 'fill_bathtub_water', 'run_bath', 'turn_water', 'take_clothes_off']
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senticnet['disrupt_normal'] = ['-0.62', '0', '0.756', '-0.62', '#anger', '#disgust', 'negative', '-0.66', 'vitiate', 'ruin', 'maul', 'mangle', 'deface']
senticnet['disrupt_sleep'] = ['-0.07', '0', '-0.17', '-0.05', '#sadness', '#fear', 'negative', '-0.10', 'night_shift', 'drowsiness', 'drink_coffee', 'need_rest', 'feel_little_tired']
senticnet['disrupted_sleep'] = ['-0.80', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'fatigue', 'exhaustion', 'become_tired', 'get_tired', 'feel_tired']
senticnet['disruption'] = ['-0.55', '0', '0.965', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'interjection', 'delay', 'insert', 'interposition', 'interpolation']
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senticnet['disruptive_behavior'] = ['0.723', '0.72', '0', '0', '#joy', '#interest', 'positive', '0.722', 'late', 'get_dark', 'rainy_day', 'split_end', 'house_look_better']
senticnet['disruptive_mood_dysregulation'] = ['-0.55', '0', '0.736', '-0.71', '#anger', '#disgust', 'negative', '-0.66', 'neurosis', 'neuroticism', 'psychoneurosis', 'hysterocatalepsy', 'anxiety_hysteria']
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senticnet['dissatisfied'] = ['0', '0', '0.991', '-0.66', '#anger', '#disgust', 'negative', '-0.83', 'contempt', 'displeased', 'disrespect', 'heartbroken', 'upset']
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senticnet['dissecting_microscope'] = ['0.057', '0', '0.012', '0.08', '#joy', '#admiration', 'positive', '0.042', 'see_better', 'glasses', 'become_gigantic', 'magnify', 'contact_lens']
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senticnet['dissembling'] = ['-0.81', '0', '0.595', '-0.88', '#sadness', '#disgust', 'negative', '-0.76', 'deceit', 'deception', 'misrepresentation', 'pretext', 'bluff']
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senticnet['disseminate_information'] = ['0.539', '0.874', '0', '0.843', '#interest', '#admiration', 'positive', '0.752', 'gain_information', 'gain_knowledge', 'increase_knowledge', 'learn_much', 'learn']
senticnet['disseminate_knowledge'] = ['0.853', '-0.71', '0', '0.92', '#joy', '#admiration', 'positive', '0.831', 'find_desk', 'learn_information', 'become_educated', 'learn', 'listen_teacher']
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senticnet['disseminating_knowledge'] = ['0', '0.33', '0', '0', '#interest', '#interest', 'positive', '0.33', 'interested', 'focused', 'attentive', 'curious', 'fascinated']
senticnet['dissemination'] = ['0.93', '0', '0', '0.789', '#joy', '#admiration', 'positive', '0.86', 'diffusion', 'dispersal', 'spreading', 'public_exposure', 'dispersion']
senticnet['dissension'] = ['0', '0', '0.983', '-0.75', '#anger', '#disgust', 'negative', '-0.86', 'discord', 'unrest', 'turmoil', 'bloodshed', 'strife']
senticnet['dissension_among'] = ['-0.71', '0', '0.928', '-0.74', '#anger', '#disgust', 'negative', '-0.79', 'set_off', 'shake_up', 'stir_up', 'instigate', 'inflame']
senticnet['dissent'] = ['-0.75', '0', '0.759', '0', '#sadness', '#anger', 'negative', '-0.75', 'expostulation', 'objection', 'protest', 'remonstration', 'remonstrance']
senticnet['dissenter'] = ['-0.93', '0', '0.688', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'dissident', 'nimby', 'protester', 'objector', 'contestant']
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senticnet['dissertation'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'typeface', 'essay', 'thesis', 'hold_information', 'continue_story']
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senticnet['dissertation_committee'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'organize_thought', 'scratch_head', 'know_answer', 'speak_out', 'know_talk']
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senticnet['dissipate'] = ['-0.10', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.10', 'read_cover', 'spread_out', 'upgrade', 'look_behind', 'scan']
senticnet['dissipation'] = ['0', '0', '0.837', '-0.90', '#anger', '#disgust', 'negative', '-0.87', 'lasso', 'corinthian', 'dissolute', 'dissolutely', 'dissoluteness']
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senticnet['dissociate'] = ['0.804', '-0.95', '0', '0.813', '#surprise', '#admiration', 'positive', '0.856', 'break', 'split', 'separate', 'part', 'split_up']
senticnet['dissociation'] = ['0.998', '-0.63', '0', '0.574', '#joy', '#surprise', 'positive', '0.736', 'cleavable', 'dissociable', 'separable', 'severable', 'divisible']
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senticnet['dissociative_disorder'] = ['-0.58', '0', '0.722', '-0.51', '#sadness', '#anger', 'negative', '-0.60', 'depersonalization_disorder', 'depersonalization_neurosis', 'depersonalization', 'mental_disorder', 'mental_disturbance']
senticnet['dissociative_disorder_neurosis'] = ['-0.58', '0', '0.719', '-0.51', '#sadness', '#anger', 'negative', '-0.60', 'disorder', 'depersonalization', 'dissociative_disorder', 'mental_disorder', 'mental_disturbance']
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senticnet['dissociative_phenomenon'] = ['0.998', '-0.63', '0', '0.574', '#joy', '#surprise', 'positive', '0.736', 'cleavable', 'dissociable', 'separable', 'severable', 'divisible']
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senticnet['dissolution'] = ['0.756', '0.866', '0', '0', '#joy', '#interest', 'positive', '0.811', 'annulment', 'annulled', 'invalidity', 'nullity', 'decree']
senticnet['dissolve'] = ['-0.78', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.83', 'break', 'end', 'die', 'fall_apart', 'go_to_piece']
senticnet['dissolve_in_water'] = ['-0.04', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.05', 'chop_vegetable', 'defrost', 'prepare_breakfast', 'brown_hamburger', 'heat_meal']
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senticnet['dissolved_calcium'] = ['-0.03', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'lithium', 'mineral', 'potassium', 'iodine', 'manganese']
senticnet['dissolved_parliament'] = ['-0.57', '0', '0.576', '-0.78', '#anger', '#disgust', 'negative', '-0.64', 'reprieve', 'respite', 'suspend', 'call', 'delay']
senticnet['dissolvent'] = ['0.843', '-0.95', '0', '0.648', '#joy', '#surprise', 'positive', '0.816', 'medium', 'acetone', 'propanone', 'dimethyl_ketone', 'chlorobenzene']
senticnet['dissolver'] = ['0.656', '-0.79', '0', '0.676', '#surprise', '#admiration', 'positive', '0.71', 'medium', 'acetone', 'propanone', 'dimethyl_ketone', 'chlorobenzene']
senticnet['dissolving_agent'] = ['0.641', '-0.77', '0', '0.728', '#surprise', '#admiration', 'positive', '0.714', 'medium', 'acetone', 'propanone', 'dimethyl_ketone', 'chlorobenzene']
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senticnet['dissolving_pulp'] = ['0.024', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'stump', 'plank', 'timber', 'paper', 'mahogany']
senticnet['dissonance'] = ['-0.70', '0', '0.711', '0', '#sadness', '#anger', 'negative', '-0.70', 'cacophony', 'conflict', 'assonance', 'resolution', 'harsh']
senticnet['dissonant'] = ['-0.52', '0', '0.902', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'discordant', 'inharmonious', 'unresolved', 'false', 'disharmonious']
senticnet['dissuade'] = ['0.615', '0.643', '-0.24', '0.29', '#joy', '#interest', 'positive', '0.325', 'relieve', 'easiness', 'pacifically', 'peaceable', 'repose']
senticnet['dissymmetry'] = ['-0.97', '0', '0.831', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'skewness', 'irregularity']
senticnet['distal_colon'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'anus', 'scrotum', 'shoulder', 'liver', 'waist']
senticnet['distal_convoluted_tubule'] = ['-0.74', '0', '0.723', '-0.59', '#sadness', '#anger', 'negative', '-0.68', 'tone', 'tautness', 'tonus', 'condition', 'status']
senticnet['distal_edge'] = ['-0.66', '0', '0.809', '-0.61', '#sadness', '#anger', 'negative', '-0.69', 'face', 'base', 'underbelly', 'sole', 'bilge']
senticnet['distal_end'] = ['-0.03', '0', '0', '-0.11', '#sadness', '#disgust', 'negative', '-0.07', 'store_cloth', 'clothes_factory', 'stock', 'sword', 'gun_store']
senticnet['distal_femoral'] = ['0.719', '-0.57', '0', '0.651', '#joy', '#admiration', 'positive', '0.647', 'chap', 'break', 'slit', 'gap', 'shift']
senticnet['distal_femur'] = ['-0.59', '0', '0.911', '-0.87', '#anger', '#disgust', 'negative', '-0.79', 'induration', 'sclerosis', 'disseminated_sclerosis', 'multiple_sclerosis', 'pathology']
senticnet['distal_fragment'] = ['-0.74', '0', '0.598', '-0.75', '#sadness', '#disgust', 'negative', '-0.69', 'lunate_bone', 'semilunar_bone', 'carpal_bone', 'carpal', 'bone']
senticnet['distal_muscular_dystrophy'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'myotonic_muscular_dystrophy', 'myotonic_dystrophy', 'myotonia_atrophica', 'dystrophy', 'genetic_disease']
senticnet['distal_row'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'hip_socket', 'tooth_socket', 'alveolus', 'socket', 'cotyloid_cavity']
senticnet['distal_tibia'] = ['0.582', '-0.66', '0', '0.661', '#surprise', '#admiration', 'positive', '0.637', 'talipes_equinus', 'deformity', 'malformation', 'talipes_valgus', 'talipes_calcaneus']
senticnet['distal_tubular'] = ['-0.74', '0', '0.723', '-0.59', '#sadness', '#anger', 'negative', '-0.68', 'tone', 'tautness', 'tonus', 'condition', 'status']
senticnet['distal_tubule'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'keep_alive', 'body_system', 'body_working', 'blood_pressure', 'heart']
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senticnet['disturb_sleep'] = ['-0.07', '0', '-0.17', '-0.05', '#sadness', '#fear', 'negative', '-0.10', 'night_shift', 'drowsiness', 'drink_coffee', 'need_rest', 'feel_little_tired']
senticnet['disturb_wildlife'] = ['0', '0', '0.735', '-0.70', '#anger', '#disgust', 'negative', '-0.72', 'swing_set', 'roadway_square', 'fresh_air', 'dog_poop', 'wildflower']
senticnet['disturbance'] = ['-0.74', '0', '0.982', '-0.50', '#sadness', '#anger', 'negative', '-0.74', 'bother', 'trouble', 'fuss', 'hassle', 'activity']
senticnet['disturbed_sleep'] = ['-0.80', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'fatigue', 'exhaustion', 'become_tired', 'get_tired', 'feel_tired']
senticnet['disturber'] = ['-0.65', '0', '0.716', '-0.94', '#anger', '#disgust', 'negative', '-0.77', 'bad_hat', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['disturbing'] = ['-0.85', '0', '0.872', '-0.94', '#anger', '#disgust', 'negative', '-0.89', 'disquieted', 'distressing', 'perturbing', 'troubling', 'worrisome']
senticnet['disturbing_trend'] = ['0.05', '0.151', '0', '0.159', '#interest', '#admiration', 'positive', '0.12', 'glamour', 'stylish', 'style', 'clothes_style', 'fancy_clothes']
senticnet['disturbingly'] = ['0.706', '0.945', '0', '0', '#joy', '#interest', 'positive', '0.825', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['disturbingly_high'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'problem', 'serious_issue', 'overrate', 'prohibitionist_society', 'blade_weapon']
senticnet['disunify'] = ['-0.81', '0', '0.74', '-0.77', '#sadness', '#disgust', 'negative', '-0.77', 'separate', 'split', 'break', 'part', 'split_up']
senticnet['disunion'] = ['-0.55', '0', '0.965', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'interjection', 'delay', 'insert', 'disruption', 'interpolation']
senticnet['disunite'] = ['0', '0', '-0.89', '-0.94', '#fear', '#disgust', 'negative', '-0.92', 'dissolve', 'decompose', 'detritus', 'disintegrate', 'corrosiveness']
senticnet['disunity'] = ['0', '0', '0.732', '-0.82', '#anger', '#disgust', 'negative', '-0.77', 'divisiveness', 'disharmony', 'dissension', 'discord', 'infighting']
senticnet['disuse'] = ['-0.63', '0', '0.663', '-0.82', '#anger', '#disgust', 'negative', '-0.70', 'wreck', 'twilight', 'neglect', 'declination', 'condition']
senticnet['disused'] = ['-0.91', '0', '0.508', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'outdated', 'noncurrent', 'obsolescent', 'obsolete', 'superannuated']
senticnet['disused_airfield'] = ['-0.97', '0', '0.737', '-0.97', '#sadness', '#disgust', 'negative', '-0.89', 'flare_path', 'airstrip', 'take_off', 'runway', 'grassland']
senticnet['disused_quarry'] = ['0', '0', '-0.85', '-0.71', '#fear', '#disgust', 'negative', '-0.78', 'fair_game', 'pit', 'stone_pit', 'target', 'prey']
senticnet['dit_elle'] = ['0', '0.919', '0', '0.888', '#interest', '#admiration', 'positive', '0.903', 'surprise', 'eye-popping', 'fulgurous', 'dazzling', 'bedaze']
senticnet['ditch'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'throw_out', 'discard', 'get_rid', 'dispose', 'abandon']
senticnet['ditch_beside'] = ['-0.11', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.08', 'have_fun', 'spring_up', 'fall_well', 'shovel_snow', 'fly_around']
senticnet['diterpene'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'enhance', 'intensify', 'deepen', 'potentiate', 'heighten']
senticnet['dither'] = ['-0.69', '0', '0.768', '-0.67', '#sadness', '#anger', 'negative', '-0.71', 'niggle', 'pother', 'worry', 'flap', 'scruple']
senticnet['dithyramb'] = ['-0.81', '0', '0.52', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'affray', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['dithyrambic'] = ['-0.81', '0', '0.52', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'affray', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['ditsy'] = ['0.85', '-0.71', '0', '0.617', '#joy', '#surprise', 'positive', '0.728', 'pretender', 'phony', 'phoney', 'hypocrite', 'dissembler']
senticnet['ditthi'] = ['-0.85', '0', '0.968', '-0.75', '#sadness', '#anger', 'negative', '-0.86', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['dittohead'] = ['-0.61', '0', '0.981', '-0.98', '#anger', '#disgust', 'negative', '-0.85', 'pious_platitude', 'nothingness', 'jazz', 'talk', 'malarky']
senticnet['ditz'] = ['-0.91', '0', '0.681', '-0.81', '#sadness', '#disgust', 'negative', '-0.80', 'chicken', 'doormat', 'softie', 'crybaby', 'wimp']
senticnet['ditzy'] = ['-0.88', '0', '0.864', '-0.72', '#sadness', '#anger', 'negative', '-0.82', 'malicious', 'venomous', 'bitchy', 'catty', 'cattish']
senticnet['diuerse'] = ['-0.11', '0.112', '0.674', '-0.66', '#anger', '#disgust', 'negative', '-0.33', 'wrathfully', 'wrathful', 'wrothful', 'lividly', 'lividity']
senticnet['diuretic'] = ['0.045', '0', '-0.05', '-0.08', '#fear', '#disgust', 'negative', '-0.02', 'healthy_drink', 'cola', 'popular_soda', 'beverage', 'soft_drink']
senticnet['diuretic_effect'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'wooden_spoon', 'cereal_bowl', 'teaspoon', 'container_cup', 'large_container']
senticnet['diurnal'] = ['0.967', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.91', 'furry_creature', 'hairy', 'furry', 'woodland_creature', 'animal_furry']
senticnet['diurnal_activity'] = ['0.967', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.91', 'furry_creature', 'hairy', 'furry', 'woodland_creature', 'animal_furry']
senticnet['diva'] = ['-0.95', '0', '0.941', '-0.57', '#sadness', '#anger', 'negative', '-0.82', 'reply', 'response', 'comeback', 'comebacking', 'backtalk']
senticnet['diva_esque'] = ['0', '0', '0.876', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'untune', 'discompose', 'discomfit', 'disconcert', 'upset']
senticnet['divagate'] = ['0', '0', '-0.92', '-0.84', '#fear', '#disgust', 'negative', '-0.88', 'sidetrack', 'depart', 'wander', 'digress', 'stray']
senticnet['divagation'] = ['-0.95', '0', '0.966', '0', '#sadness', '#anger', 'negative', '-0.96', 'digression', 'away', 'excursus', 'parenthesis', 'aside']
senticnet['divalent'] = ['0', '0', '0.931', '-0.75', '#anger', '#disgust', 'negative', '-0.84', 'bivalent', 'multivalent', 'univalent', 'meromorphic', 'monovalent']
senticnet['dive'] = ['-0.90', '0', '0.848', '-0.94', '#sadness', '#disgust', 'negative', '-0.89', 'cabaret', 'nightclub', 'night_club', 'club', 'nightspot']
senticnet['dive-bombed'] = ['-0.93', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.83', 'worn-down', 'beaten-up', 'knocked-out', 'knocked-off', 'reeducated']
senticnet['dive_bar'] = ['0.866', '-0.91', '0', '0.893', '#surprise', '#admiration', 'positive', '0.892', 'mooch', 'freeload', 'grub', 'sponge', 'bum']
senticnet['dive_shop'] = ['0', '0.07', '0', '0', '#interest', '#interest', 'positive', '0.07', 'sunken_treasure', 'real_duck', 'urchin', 'mussel', 'gold_fish']
senticnet['divell'] = ['-0.79', '0', '0.607', '-0.92', '#sadness', '#disgust', 'negative', '-0.77', 'stubbornness', 'obstinacy', 'obstinance', 'bullheadedness', 'pigheadedness']
senticnet['divemaster'] = ['0', '0.07', '0', '0', '#interest', '#interest', 'positive', '0.07', 'sunken_treasure', 'real_duck', 'urchin', 'mussel', 'gold_fish']
senticnet['diverge'] = ['-0.86', '0', '0.511', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'separate', 'move', 'branch', 'ramify', 'fork']
senticnet['divergence'] = ['-0.83', '0', '0.546', '-0.88', '#sadness', '#disgust', 'negative', '-0.75', 'difference', 'allomerism', 'deviation', 'variation', 'departure']
senticnet['divergence_between'] = ['-0.83', '0', '0.546', '-0.88', '#sadness', '#disgust', 'negative', '-0.75', 'difference', 'allomerism', 'deviation', 'variation', 'departure']
senticnet['divergences_between'] = ['-0.83', '0', '0.546', '-0.88', '#sadness', '#disgust', 'negative', '-0.75', 'difference', 'allomerism', 'deviation', 'variation', 'departure']
senticnet['divergency'] = ['-0.22', '0', '-0.09', '-0.16', '#sadness', '#disgust', 'negative', '-0.16', 'inequality', 'reject', 'discard', 'difference', 'gap']
senticnet['divergent'] = ['0', '0', '-0.91', '-0.85', '#fear', '#disgust', 'negative', '-0.88', 'converging', 'diverge', 'trestle', 'series', 'divergence']
senticnet['divergent_strabismus'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'squint', 'exotropia', 'walleye', 'abnormality', 'abnormalcy']
senticnet['diverse'] = ['0.057', '0', '0', '0.153', '#joy', '#admiration', 'positive', '0.105', 'multiple', 'cute', 'variety', 'hairy', 'large_breadbox']
senticnet['diverse_array'] = ['0.057', '0', '0', '0.153', '#joy', '#admiration', 'positive', '0.105', 'multiple', 'cute', 'variety', 'hairy', 'large_breadbox']
senticnet['diverse_group'] = ['0', '0.949', '0', '0.949', '#interest', '#admiration', 'positive', '0.949', 'research', 'student', 'award', 'university', 'project']
senticnet['diverse_mix'] = ['0.057', '0', '0', '0.153', '#joy', '#admiration', 'positive', '0.105', 'multiple', 'cute', 'variety', 'hairy', 'large_breadbox']
senticnet['diverse_range'] = ['0.057', '0', '0', '0.153', '#joy', '#admiration', 'positive', '0.105', 'multiple', 'cute', 'variety', 'hairy', 'large_breadbox']
senticnet['diverse_workforce'] = ['0.032', '0.11', '0', '0.105', '#interest', '#admiration', 'positive', '0.082', 'work', 'place_work', 'implementation', 'disorganize', 'employment']
senticnet['diverseness'] = ['-0.64', '0', '0.773', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'heterogeneity', 'multifariousness', 'diversity', 'variety', 'nonuniformity']
senticnet['diversification'] = ['0', '0.312', '-0.10', '0.207', '#interest', '#admiration', 'positive', '0.137', 'infuse', 'loan', 'lend', 'increase_money', 'wealth']
senticnet['diversified'] = ['-0.72', '0', '-0.76', '0', '#sadness', '#fear', 'negative', '-0.74', 'conglomerate', 'subsidiary', 'multinational', 'multi-national', 'conglomerate']
senticnet['diversified_conglomerate'] = ['0.111', '0.18', '0', '0.128', '#interest', '#admiration', 'positive', '0.14', 'corporation', 'partnership', 'large_business', 'business_organization', 'enterprise']
senticnet['diversified_economy'] = ['0.067', '0.172', '0', '0.121', '#interest', '#admiration', 'positive', '0.12', 'economic_activity', 'business', 'support_family', 'work_done', 'earn_money']
senticnet['diversified_manufacturer'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'builder']
senticnet['diversify'] = ['0', '0.174', '-0.08', '0.302', '#interest', '#admiration', 'positive', '0.131', 'make_big', 'flourish', 'get_large', 'get_big', 'expand']
senticnet['diversion'] = ['0.097', '-0.17', '0.072', '-0.07', '#joy', '#surprise', 'positive', '0.03', 'pastime', 'have_fun', 'fun_activity', 'amusement', 'fun_time']
senticnet['diversion_channel'] = ['0.933', '0.795', '0', '0', '#joy', '#interest', 'positive', '0.864', 'water_passage', 'small_stream', 'water_flow', 'flowing_water', 'water_stream']
senticnet['diversionary'] = ['0', '0', '0.88', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'diversion', 'tactic', 'misdirection', 'propagandistic', 'ploy']
senticnet['diversionary_attack'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['diversionist'] = ['-0.58', '0', '0.714', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'waster', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['diversity'] = ['0', '0.738', '0', '0.769', '#interest', '#admiration', 'positive', '0.754', 'prosper', 'desirable', 'cherish', 'a_lot_of_sex', 'whatever']
senticnet['divert'] = ['0', '0.838', '0', '0.948', '#interest', '#admiration', 'positive', '0.893', 'diverting', 'diverted', 'diverts', 'distract', 'deflect']
senticnet['divert_attention'] = ['0', '0', '-0.97', '-0.86', '#fear', '#disgust', 'negative', '-0.92', 'neglect_chore', 'find_out_new', 'see_ad', 'look_picture', 'eye_strain']
senticnet['divert_attention_away'] = ['0', '0', '-0.97', '-0.86', '#fear', '#disgust', 'negative', '-0.92', 'neglect_chore', 'find_out_new', 'see_ad', 'look_picture', 'eye_strain']
senticnet['divert_attention_from'] = ['0', '0', '-0.97', '-0.86', '#fear', '#disgust', 'negative', '-0.92', 'neglect_chore', 'find_out_new', 'see_ad', 'look_picture', 'eye_strain']
senticnet['diverted'] = ['0', '-0.33', '0', '0', '#surprise', '#surprise', 'positive', '0.336', 'hopeless', 'distracted', 'distraction', 'inattentive', 'confused']
senticnet['diverticular_disease'] = ['-0.99', '0', '0.964', '-0.70', '#sadness', '#anger', 'negative', '-0.88', 'blepharitis', 'inflammation', 'epiglottitis', 'orchitis', 'prostatitis']
senticnet['diverticulitis'] = ['-0.99', '0', '0.964', '-0.70', '#sadness', '#anger', 'negative', '-0.88', 'blepharitis', 'inflammation', 'epiglottitis', 'orchitis', 'prostatitis']
senticnet['diverting'] = ['0', '0.871', '0', '0.811', '#interest', '#admiration', 'positive', '0.841', 'diverts', 'diverted', 'diversion', 'siphoning', 'redirecting']
senticnet['diverting_traffic'] = ['-0.98', '0', '0.989', '-0.93', '#sadness', '#anger', 'negative', '-0.97', 'flight_accident', 'dangerous', 'danger', 'air_traffic', 'air_traffic_jam']
senticnet['divertor'] = ['0.03', '0.068', '-0.06', '0.062', '#interest', '#admiration', 'positive', '0.025', 'pacific_northwest', 'american_forest', 'treetop', 'bath', 'hot_air']
senticnet['diverts'] = ['0', '0.867', '0', '0.811', '#interest', '#admiration', 'positive', '0.839', 'diverting', 'diverted', 'diversion', 'siphoning', 'redirecting']
senticnet['divest'] = ['-0.70', '0', '0.631', '-0.56', '#sadness', '#anger', 'negative', '-0.63', 'bereave', 'expropriate', 'disarm', 'unclothe', 'take']
senticnet['divide'] = ['-0.02', '0.114', '0', '0.204', '#interest', '#admiration', 'positive', '0.098', 'mimic', 'halve', 'separate', 'copy', 'make_copy']
senticnet['divide_between'] = ['-0.02', '0.114', '0', '0.204', '#interest', '#admiration', 'positive', '0.098', 'mimic', 'halve', 'separate', 'copy', 'make_copy']
senticnet['divide_country'] = ['-0.11', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.11', 'enter_door', 'hang_picture', 'turn_knob', 'shut_door', 'bicycle_store']
senticnet['divide_nation'] = ['-0.4', '-0.12', '0.062', '-0.19', '#sadness', '#disgust', 'negative', '-0.13', 'lie_court', 'receive_punishment', 'commit_crime', 'lie', 'tell_lie']
senticnet['divide_person'] = ['0', '0', '0', '0.095', '#admiration', '#admiration', 'positive', '0.095', 'reach_impasse', 'change_society', 'take_oath', 'divide', 'institute_civil_action']
senticnet['divide_property'] = ['0', '-0.01', '0', '0.07', '#surprise', '#admiration', 'positive', '0.043', 'drive_upon', 'fast_travel', 'travel_car', 'drive_quickly', 'partition']
senticnet['divided_between'] = ['0.909', '0.986', '0', '0', '#joy', '#interest', 'positive', '0.948', 'separation', 'divide', 'division', 'slash', 'bagle']
senticnet['divided_christendom'] = ['-0.97', '0', '0.712', '-0.78', '#sadness', '#disgust', 'negative', '-0.82', 'discrepant', 'inconsistent', 'scratchy', 'uneven', 'contradictory']
senticnet['divided_horizontally'] = ['-0.02', '0.103', '0.069', '0.151', '#interest', '#admiration', 'positive', '0.039', 'single', 'unit_of_measurement', 'do_it_yourself', 'enumeration', 'alone']
senticnet['divided_into'] = ['-0.02', '0.114', '0', '0.204', '#interest', '#admiration', 'positive', '0.098', 'mimic', 'halve', 'separate', 'copy', 'make_copy']
senticnet['divided_into_two'] = ['-0.02', '0.114', '0', '0.204', '#interest', '#admiration', 'positive', '0.098', 'mimic', 'halve', 'separate', 'copy', 'make_copy']
senticnet['divided_peninsula'] = ['0', '0.072', '0.068', '0.074', '#interest', '#admiration', 'positive', '0.026', 'part_of_book', 'book_part', 'publication', 'book_content', 'book_paper']
senticnet['divided_vertically'] = ['-0.02', '0.103', '0.069', '0.151', '#interest', '#admiration', 'positive', '0.039', 'single', 'unit_of_measurement', 'do_it_yourself', 'enumeration', 'alone']
senticnet['dividend'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'account', 'banker', 'piggy_bank', 'bank_loan', 'deposit']
senticnet['dividend_income'] = ['-0.83', '0', '0.555', '-0.64', '#sadness', '#disgust', 'negative', '-0.67', 'single_tax', 'income_tax', 'taxation', 'capital_levy', 'departure_tax']
senticnet['dividend_yield'] = ['0', '0.994', '0', '0.88', '#interest', '#admiration', 'positive', '0.937', 'exceed', 'outshout', 'outstrip', 'outshine', 'outsail']
senticnet['dividing_line'] = ['-0.67', '0', '0.97', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'joint', 'node', 'articulation', 'join', 'connexion']
senticnet['divination'] = ['0.861', '-0.55', '0', '0.578', '#joy', '#admiration', 'positive', '0.666', 'conjecture', 'speculation', 'guess', 'surmisal', 'supposition']
senticnet['divinatory'] = ['-0.82', '0', '0.72', '-0.53', '#sadness', '#anger', 'negative', '-0.69', 'invocation', 'charm', 'spell', 'magic_spell', 'magical_spell']
senticnet['divine'] = ['0.128', '0.152', '-0.09', '0.264', '#interest', '#admiration', 'positive', '0.113', 'environment', 'beautiful', 'live_environment', 'really_big', 'important_man']
senticnet['divine_bounty'] = ['0.655', '-0.82', '0', '0.956', '#surprise', '#admiration', 'positive', '0.813', 'superlative', 'compliment', 'testimonial', 'praise', 'approval']
senticnet['divine_compassion'] = ['0.879', '0', '0', '0.986', '#joy', '#admiration', 'positive', '0.932', 'mercifully', 'merciful', 'ruth', 'commiseration', 'commiserate']
senticnet['divine_creator'] = ['0.071', '0.222', '-0.10', '0.234', '#interest', '#admiration', 'positive', '0.105', 'baron', 'almighty', 'deity', 'nobility', 'god']
senticnet['divine_essence'] = ['0', '0', '-0.86', '-0.77', '#fear', '#disgust', 'negative', '-0.81', 'untellable', 'unspeakable', 'indescribable', 'indefinable', 'unutterable']
senticnet['divine_eternity'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'metastability', 'monotony', 'invariance', 'constancy', 'unchangeability']
senticnet['divine_excellency'] = ['0.657', '-0.56', '0', '0.749', '#joy', '#admiration', 'positive', '0.655', 'naturalness', 'condescension', 'condescendingness', 'mellowness', 'friendliness']
senticnet['divine_feminine'] = ['0.037', '0.293', '0', '0.269', '#interest', '#admiration', 'positive', '0.2', 'religious_purpose', 'prayer', 'judaism', 'christianity', 'religion']
senticnet['divine_glory'] = ['0', '0', '-0.86', '-0.77', '#fear', '#disgust', 'negative', '-0.81', 'untellable', 'unspeakable', 'indescribable', 'indefinable', 'unutterable']
senticnet['divine_grace'] = ['0.128', '0.152', '-0.09', '0.264', '#interest', '#admiration', 'positive', '0.113', 'environment', 'beautiful', 'live_environment', 'really_big', 'important_man']
senticnet['divine_guidance'] = ['0.9', '-0.74', '0', '0.721', '#joy', '#surprise', 'positive', '0.788', 'difficulty', 'inspiration', 'determinant', 'determinative', 'motivation']
senticnet['divine_illumination'] = ['0.779', '-0.75', '0', '0.526', '#joy', '#surprise', 'positive', '0.687', 'privateness', 'covertness', 'bosom', 'isolation', 'confidentiality']
senticnet['divine_immanence'] = ['0.779', '-0.75', '0', '0.526', '#joy', '#surprise', 'positive', '0.687', 'privateness', 'covertness', 'bosom', 'isolation', 'confidentiality']
senticnet['divine_judgment'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitentially', 'penitent', 'repentantly', 'repentant']
senticnet['divine_majesty'] = ['0', '0.767', '0', '0.711', '#interest', '#admiration', 'positive', '0.739', 'reverently', 'hero-worship', 'venerate', 'idolize', 'veneration']
senticnet['divine_mercy'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitentially', 'penitent', 'repentantly', 'repentant']
senticnet['divine_messenger'] = ['-0.98', '0', '0.664', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'unfortunate', 'unfortunate_person', 'weeper', 'pallgriever', 'griever']
senticnet['divine_nature'] = ['0.128', '0.152', '-0.09', '0.264', '#interest', '#admiration', 'positive', '0.113', 'environment', 'beautiful', 'live_environment', 'really_big', 'important_man']
senticnet['divine_perfection'] = ['-0.64', '0', '0.87', '-0.74', '#anger', '#disgust', 'negative', '-0.75', 'large_indefinite_amount', 'piles', 'mess', 'large_number', 'tons']
senticnet['divine_presence'] = ['0.128', '0.152', '-0.09', '0.264', '#interest', '#admiration', 'positive', '0.113', 'environment', 'beautiful', 'live_environment', 'really_big', 'important_man']
senticnet['divine_realm'] = ['-0.5', '0', '0.982', '-0.85', '#anger', '#disgust', 'negative', '-0.77', 'unpeaceable', 'stormy', 'tempestuous', 'militant', 'belligerent']
senticnet['divine_retribution'] = ['-0.04', '0.166', '0.684', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'malign', 'malefic', 'maliciously', 'spitefulness', 'maliciousness']
senticnet['divine_revelation'] = ['0.128', '0.152', '-0.09', '0.264', '#interest', '#admiration', 'positive', '0.113', 'environment', 'beautiful', 'live_environment', 'really_big', 'important_man']
senticnet['divine_simplicity'] = ['0.779', '-0.75', '0', '0.526', '#joy', '#surprise', 'positive', '0.687', 'privateness', 'covertness', 'bosom', 'isolation', 'confidentiality']
senticnet['divine_spirit'] = ['0.128', '0.152', '-0.09', '0.264', '#interest', '#admiration', 'positive', '0.113', 'environment', 'beautiful', 'live_environment', 'really_big', 'important_man']
senticnet['divine_transcendence'] = ['0.995', '-0.69', '0', '0.528', '#joy', '#surprise', 'positive', '0.74', 'grasp', 'hug', 'nestle', 'snuggle', 'grip']
senticnet['divine_unity'] = ['0.128', '0.152', '-0.09', '0.264', '#interest', '#admiration', 'positive', '0.113', 'environment', 'beautiful', 'live_environment', 'really_big', 'important_man']
senticnet['divine_vengeance'] = ['-0.78', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.85', 'revenge', 'retribution', 'go_jail', 'sin', 'incarceration']
senticnet['divine_wisdom'] = ['0.128', '0.152', '-0.09', '0.264', '#interest', '#admiration', 'positive', '0.113', 'environment', 'beautiful', 'live_environment', 'really_big', 'important_man']
senticnet['divine_wrath'] = ['0.879', '0', '0', '0.986', '#joy', '#admiration', 'positive', '0.932', 'mercifully', 'merciful', 'ruth', 'commiseration', 'commiserate']
senticnet['divinely'] = ['0.957', '0', '0', '0.873', '#joy', '#admiration', 'positive', '0.915', 'sovereignly', 'supernaturally', 'fortuitously', 'providentially', 'wondrously']
senticnet['diviner'] = ['0.703', '-0.61', '0', '0.637', '#joy', '#admiration', 'positive', '0.651', 'oracle', 'necromancer', 'illusionist', 'visionary', 'geomancer']
senticnet['diving_duck'] = ['-0.50', '0', '0.764', '-0.61', '#anger', '#disgust', 'negative', '-0.62', 'doctor', 'mountebank', 'charlatan', 'quack', 'physician']
senticnet['diving_gear'] = ['0', '0.07', '0', '0', '#interest', '#interest', 'positive', '0.07', 'sunken_treasure', 'real_duck', 'urchin', 'mussel', 'gold_fish']
senticnet['diving_instructor'] = ['0', '0.07', '0', '0', '#interest', '#interest', 'positive', '0.07', 'sunken_treasure', 'real_duck', 'urchin', 'mussel', 'gold_fish']
senticnet['diving_pool'] = ['0', '0', '0', '0.964', '#admiration', '#admiration', 'positive', '0.964', 'swimming_pool', 'swim', 'swimsuit', 'lake', 'have_fun']
senticnet['divinity'] = ['0.978', '-0.88', '0', '0.785', '#joy', '#surprise', 'positive', '0.881', 'daemon', 'demigod', 'sea_god', 'sun_god', 'goddess']
senticnet['divinity_mdiv'] = ['0.036', '0.111', '-0.05', '0.054', '#interest', '#admiration', 'positive', '0.038', 'despot', 'supervisor', 'chief', 'commander', 'earl']
senticnet['divisible'] = ['0.998', '-0.63', '0', '0.574', '#joy', '#surprise', 'positive', '0.736', 'cleavable', 'dissociable', 'separable', 'severable', 'dissociative']
senticnet['division'] = ['0', '0.119', '0', '0.106', '#interest', '#admiration', 'positive', '0.112', 'divide', 'addition', 'subtraction', 'math_action', 'fraction']
senticnet['divisional_artillery'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'start_over', 'queue', 'find_line', 'take_turn', 'long_line']
senticnet['divisive'] = ['-0.93', '0', '0.515', '-0.78', '#sadness', '#disgust', 'negative', '-0.74', 'discrepant', 'at_variance', 'dissentious', 'discordant', 'factious']
senticnet['divisiveness'] = ['0', '0', '0.729', '-0.83', '#anger', '#disgust', 'negative', '-0.78', 'disunity', 'disharmony', 'dissension', 'discord', 'infighting']
senticnet['divorce'] = ['-0.74', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'get_divorce', 'stop_marry', 'break_up', 'institute_civil_action', 'serve_justice']
senticnet['divorce_attorney'] = ['0', '0.146', '-0.05', '0.077', '#interest', '#admiration', 'positive', '0.057', 'lawyer', 'negotiator', 'arbiter', 'judge_jury', 'decide_guilt']
senticnet['divorce_decree'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'bigamy', 'wedlock']
senticnet['divorce_money'] = ['0', '0.369', '-0.16', '0.226', '#interest', '#admiration', 'positive', '0.144', 'payment', 'owe', 'tax', 'bribe', 'afford']
senticnet['divorce_petition'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'bigamy', 'wedlock']
senticnet['divorced'] = ['-0.73', '0', '0.72', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'unwed', 'unwedded', 'mateless', 'unmarried', 'widowed']
senticnet['divorced_dad'] = ['-0.71', '0', '0.972', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'scold', 'nag', 'scolder', 'harridan', 'common_scold']
senticnet['divorced_mother'] = ['0.875', '0', '0', '0.802', '#joy', '#admiration', 'positive', '0.839', 'foreigner', 'exile', 'deportee', 'gringo', 'import']
senticnet['divorced_spouse'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'bigamy', 'wedlock']
senticnet['divulge'] = ['-0.82', '0', '0.754', '0', '#sadness', '#anger', 'negative', '-0.78', 'blurt', 'thought', 'inconsiderately', 'unadvisedly', 'flirt']
senticnet['divya_dham'] = ['-0.95', '0', '0.845', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'grapefruit_peel', 'lemon_peel', 'orange_peel', 'candied_fruit', 'succade']
senticnet['dixie_cup'] = ['0.214', '0.258', '-0.09', '0.2', '#joy', '#interest', 'positive', '0.146', 'cup', 'carafe', 'pitcher', 'container_cup', 'glasses']
senticnet['dixieland'] = ['0.901', '0', '0', '0', '#joy', '#joy', 'positive', '0.901', 'music', 'vapor', 'blues', 'megrim', 'demoralization']
senticnet['dixieland_jazz'] = ['0.901', '0', '0', '0', '#joy', '#joy', 'positive', '0.901', 'music', 'vapor', 'blues', 'megrim', 'demoralization']
senticnet['dizen'] = ['-0.88', '0', '0.896', '-0.79', '#sadness', '#anger', 'negative', '-0.86', 'overdress', 'attire', 'dress_up', 'prink', 'fig_out']
senticnet['dizzily'] = ['0', '0', '-0.73', '-0.79', '#fear', '#disgust', 'negative', '-0.76', 'hard_see', 'rain_fall', 'dew', 'rain', 'mist']
senticnet['dizziness'] = ['-0.11', '-0.10', '0.153', '-0.23', '#anger', '#disgust', 'negative', '-0.1', 'get_tired', 'see_neighbor', 'sore_leg', 'dog_bark', 'exhaustion']
senticnet['dizzy'] = ['0', '0', '-0.73', '-0.79', '#fear', '#disgust', 'negative', '-0.76', 'hard_see', 'rain_fall', 'dew', 'rain', 'mist']
senticnet['dizzying_array'] = ['-0.85', '0', '0.583', '-0.71', '#sadness', '#disgust', 'negative', '-0.71', 'muddle', 'fuddle', 'mare_nest', 'rummage', 'clutter']
senticnet['dj_booth'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'drink', 'party', 'friend_gathering', 'rest_area', 'relax_time']
senticnet['dj_spin'] = ['0.966', '0.921', '0', '0.892', '#joy', '#interest', 'positive', '0.926', 'disco', 'nightclub', 'shop_complex', 'dance_club', 'fast_food_restaurant']
senticnet['djedkare_isesi'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
senticnet['djembe'] = ['0', '-0.02', '0.055', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'percussion', 'make_musicr', 'use_jazz_music', 'tune_instrument', 'drummer']
senticnet['dji_quote'] = ['0', '0.189', '0.04', '0.091', '#interest', '#admiration', 'positive', '0.08', 'combination', 'margin', 'teller', 'merge', 'finite']
senticnet['djinshi'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['djomeh'] = ['0.798', '-0.51', '0', '0.973', '#joy', '#admiration', 'positive', '0.763', 'inexperienced_person', 'boy_scout', 'dear', 'virgin', 'mortal']
senticnet['dkklfar'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'jawless_fish', 'osteostracan', 'cephalaspid', 'ostracoderm', 'heterostracan']
senticnet['dmae'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucose', 'corn_sugar', 'dextrose', 'dextroglucose', 'grape_sugar']
senticnet['dmitri_bashkirov'] = ['0.536', '-0.56', '0', '0.567', '#surprise', '#admiration', 'positive', '0.557', 'recreation', 'diversion', 'move', 'motility', 'movement']
senticnet['dmpa'] = ['0.97', '0.729', '0', '0.946', '#joy', '#admiration', 'positive', '0.882', 'make_baby', 'give_birth_child', 'sexual_intercourse', 'find_partner', 'make_love']
senticnet['dna_strand'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['dnonce_une'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'sexual_relationship', 'liaison', 'affair', 'involvement']
senticnet['dnov'] = ['0.502', '-0.82', '0', '0.779', '#surprise', '#admiration', 'positive', '0.703', 'mortal', 'person', 'individual', 'free_liver', 'agent']
senticnet['do'] = ['0', '-0.94', '0', '0.744', '#surprise', '#admiration', 'positive', '0.846', 'feasibility', 'feasible', 'possible', 'feasibly', 'doable']
senticnet['do-nothing'] = ['-0.94', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.86', 'obstructionist', 'lame-duck', 'know-nothing', 'spineless', 'feckless']
senticnet['do-or-die'] = ['-0.84', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.86', 'must-win', 'make-or-break', 'win-or-go-home', 'nail-biting', 'hard-fought']
senticnet['do_again'] = ['-0.03', '0.213', '0', '0.268', '#interest', '#admiration', 'positive', '0.15', 'repeat', 'duplicate', 'redo', 'finish', 'inclusion']
senticnet['do_attack'] = ['0', '0', '0.736', '-0.99', '#anger', '#disgust', 'negative', '-0.86', 'get_fired', 'unbelief', 'extreme_fear', 'psychoneurosis', 'death_love']
senticnet['do_denial'] = ['-0.02', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.07', 'army', 'military', 'infantry', 'samurai', 'soldier']
senticnet['do_drug'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'use_drug', 'ecstasy_pill', 'drug_use', 'drug', 'addiction']
senticnet['do_exercise'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'go_jogging', 'eat_well', 'stay_healthy', 'check_health', 'maintain_good_health']
senticnet['do_gooder'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'oppressiveness', 'tyrannical', 'oppressive', 'tyrannous', 'laden']
senticnet['do_groceries'] = ['0.463', '0.561', '-0.29', '0.281', '#joy', '#interest', 'positive', '0.253', 'buy_grocery', 'buy_bread', 'buy_milk', 'buy_pizza', 'buy_pasta']
senticnet['do_homework'] = ['0', '-0.88', '0', '0.963', '#surprise', '#admiration', 'positive', '0.924', 'study', 'math_homework', 'student', 'attend_class', 'write']
senticnet['do_it_yourself'] = ['-0.02', '0.103', '0.069', '0.151', '#interest', '#admiration', 'positive', '0.039', 'single', 'unit_of_measurement', 'two_halves', 'enumeration', 'alone']
senticnet['do_shopping'] = ['0.463', '0.561', '-0.29', '0.281', '#joy', '#interest', 'positive', '0.253', 'buy_grocery', 'buy_cheese', 'go_supermarket', 'buy_food', 'buy_ingredient']
senticnet['doable'] = ['0', '-0.94', '0', '0.746', '#surprise', '#admiration', 'positive', '0.843', 'feasibility', 'do', 'possible', 'feasibly', 'feasible']
senticnet['doberman'] = ['0.039', '0', '0', '0.059', '#joy', '#admiration', 'positive', '0.049', 'big_dog', 'dog_cat', 'smart_dog', 'pet', 'domesticated_animal']
senticnet['doberman_pinscher'] = ['-0.72', '0', '0.619', '-0.86', '#sadness', '#disgust', 'negative', '-0.73', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['dobro_player'] = ['-0.04', '-0.04', '-0.04', '-0.04', '#fear', '#disgust', 'negative', '-0.02', 'music_shop', 'music_store', 'musical_instrument_store', 'string_quartet', 'high_school_band']
senticnet['dobsonian'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'binoculars', 'read_glass', 'eyeglass', 'camera', 'monocle']
senticnet['doc'] = ['0', '0.955', '0', '0.778', '#interest', '#admiration', 'positive', '0.867', 'primary_care_physician', 'abortionist', 'angiologist', 'commerce', 'intern']
senticnet['docile'] = ['0.625', '-0.50', '0', '0.822', '#joy', '#admiration', 'positive', '0.649', 'meek', 'tame', 'yielding', 'sheeplike', 'sheepish']
senticnet['docility'] = ['0.637', '-0.91', '0', '0.757', '#surprise', '#admiration', 'positive', '0.768', 'manageability', 'manageableness', 'tractability', 'obedience', 'amenability']
senticnet['dock'] = ['-0.02', '-0.01', '0.045', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'haddryad', 'pier', 'ferry', 'dryad', 'shallow']
senticnet['dock_rumex'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['dock_worker'] = ['0', '0', '-0.73', '-0.82', '#fear', '#disgust', 'negative', '-0.77', 'docker', 'lumper', 'loader', 'longshoreman', 'stevedore']
senticnet['docker'] = ['0', '0', '-0.72', '-0.82', '#fear', '#disgust', 'negative', '-0.77', 'stevedore', 'lumper', 'loader', 'longshoreman', 'dock_worker']
senticnet['docking_hatch'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'chick', 'wild_bird', 'baby_bird', 'lay_egg', 'bird_home']
senticnet['docking_station'] = ['0.113', '0.073', '0.028', '0.107', '#joy', '#admiration', 'positive', '0.066', 'pda', 'telephone_computer', 'browser', 'number_pad', 'modem']
senticnet['docklands_icehouse'] = ['-0.10', '0', '-0.05', '-0.24', '#sadness', '#disgust', 'negative', '-0.13', 'adulate', 'suck_up', 'toady', 'blandish', 'kowtow']
senticnet['doctor'] = ['-0.04', '0', '-0.16', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'nurse', 'cure', 'doctor_office', 'doctor_surgery', 'merlin']
senticnet['doctor_appointment'] = ['-0.04', '0.131', '-0.05', '0.186', '#interest', '#admiration', 'positive', '0.055', 'call_doctor', 'doctor', 'make_doctor_appointment', 'find_doctor', 'appointment']
senticnet['doctor_examination_room'] = ['0', '0', '-0.13', '-0.18', '#fear', '#disgust', 'negative', '-0.15', 'doctor_office', 'therapist', 'nurse', 'syringe', 'emergency_ward']
senticnet['doctor_office'] = ['-0.02', '0', '-0.15', '-0.14', '#fear', '#disgust', 'negative', '-0.10', 'hospital', 'medical_office', 'clinic', 'doctor', 'dentist_office']
senticnet['doctor_patient'] = ['-0.96', '0', '-0.84', '0', '#sadness', '#fear', 'negative', '-0.90', 'medical_person', 'medical', 'doctor_surgery', 'cure', 'medical_procedure']
senticnet['doctor_prescription'] = ['-0.06', '0', '0.023', '0', '#sadness', '#anger', 'negative', '-0.04', 'circulate_blood', 'grind_grain', 'fail_work', 'cremate', 'dry_wet_hair']
senticnet['doctor_sivana'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
senticnet['doctor_surgery'] = ['-0.15', '0.074', '-0.25', '-0.08', '#sadness', '#fear', 'negative', '-0.10', 'doctor', 'nurse', 'cure', 'medical_procedure', 'doctor_office']
senticnet['doctor_tool'] = ['-0.08', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.07', 'stethoscope', 'use_needle', 'mend', 'needle_thread', 'sew']
senticnet['doctor_up'] = ['-0.85', '0', '0.684', '-0.79', '#sadness', '#disgust', 'negative', '-0.77', 'debase', 'adulterate', 'dilute', 'stretch', 'load']
senticnet['doctor_visit'] = ['-0.73', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.72', 'pain', 'ouch', 'hurt', 'ache', 'dislocation']
senticnet['doctor_work'] = ['-0.09', '0', '-0.13', '-0.08', '#sadness', '#fear', 'negative', '-0.10', 'medical_procedure', 'doctor_surgery', 'operation', 'cure', 'treatment']
senticnet['doctoral'] = ['0.963', '0.858', '0', '0', '#joy', '#interest', 'positive', '0.91', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'collegian']
senticnet['doctoral_candidate'] = ['0', '0.909', '0', '0.799', '#interest', '#admiration', 'positive', '0.854', 'research', 'research_topic', 'research_subject', 'search', 'student']
senticnet['doctoral_degree'] = ['0', '0.796', '0', '0.991', '#interest', '#admiration', 'positive', '0.893', 'phd', 'get_phd', 'get_job', 'pursue_research', 'rich']
senticnet['doctoral_dissertation'] = ['0', '0.964', '0', '0.776', '#interest', '#admiration', 'positive', '0.87', 'project', 'sponsor', 'examine', 'change', 'well']
senticnet['doctoral_program'] = ['0.963', '0.858', '0', '0', '#joy', '#interest', 'positive', '0.91', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'collegian']
senticnet['doctoral_research'] = ['0.887', '0.877', '0', '0', '#joy', '#interest', 'positive', '0.882', 'ask_expert', 'absorb_information', 'analyze', 'learn_computer', 'search_information']
senticnet['doctoral_student'] = ['0.05', '0.141', '-0.07', '0.132', '#interest', '#admiration', 'positive', '0.063', 'public_servant', 'worker', 'manager', 'laborer', 'office_job']
senticnet['doctoral_supervisor'] = ['0.05', '0.141', '-0.07', '0.132', '#interest', '#admiration', 'positive', '0.063', 'public_servant', 'worker', 'manager', 'laborer', 'office_job']
senticnet['doctorate_degree'] = ['-0.75', '0', '0.589', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'lowness', 'grind', 'immoderation', 'immoderateness', 'amplitude_level']
senticnet['doctrinaire'] = ['0.633', '-0.92', '0', '0.869', '#surprise', '#admiration', 'positive', '0.808', 'advocate', 'bigot', 'dogmatist', 'partisan', 'advocator']
senticnet['doctrinal'] = ['-0.83', '0', '0.648', '-0.60', '#sadness', '#anger', 'negative', '-0.69', 'infallibility', 'dependability', 'dependableness', 'reliability', 'reliableness']
senticnet['doctrinally'] = ['0', '-0.75', '0', '0.851', '#surprise', '#admiration', 'positive', '0.801', 'educationally', 'esthetically', 'theologically', 'pedagogically', 'tonally']
senticnet['doctrine'] = ['0.95', '-0.77', '0', '0.578', '#joy', '#surprise', 'positive', '0.767', 'philosophy', 'amoralism', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['docu_drama'] = ['0', '0.762', '0', '0', '#interest', '#interest', 'positive', '0.762', 'encyclopaedia', 'tropical_rainforest', 'learn_new_information', 'tropical_forest', 'child_story']
senticnet['docudrama'] = ['0', '0.762', '0', '0', '#interest', '#interest', 'positive', '0.762', 'encyclopaedia', 'tropical_rainforest', 'learn_new_information', 'tropical_forest', 'child_story']
senticnet['docufiction'] = ['-0.85', '0', '0.514', '-0.73', '#sadness', '#disgust', 'negative', '-0.70', 'flick', 'movie', 'film', 'picture', 'moving_picture']
senticnet['document'] = ['0', '0.077', '0', '0.068', '#interest', '#admiration', 'positive', '0.073', 'make_book', 'file', 'paper_write', 'readable', 'newspaper_write']
senticnet['document_feeder'] = ['-0.08', '0', '0.011', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'deflate', 'read_cover', 'obtain_information', 'upgrade', 'spread_out']
senticnet['document_folder'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'car_trunk', 'walmart', 'warehouse', 'store', 'storage_place']
senticnet['document_type'] = ['-0.04', '0.131', '-0.05', '0.186', '#interest', '#admiration', 'positive', '0.055', 'text', 'paragraph', 'typeface', 'hold_information', 'print']
senticnet['documentarian'] = ['0', '0.762', '0', '0', '#interest', '#interest', 'positive', '0.762', 'encyclopaedia', 'tropical_rainforest', 'learn_new_information', 'tropical_forest', 'child_story']
senticnet['documentary'] = ['0', '0.762', '0', '0', '#interest', '#interest', 'positive', '0.762', 'encyclopaedia', 'tropical_rainforest', 'learn_new_information', 'tropical_forest', 'child_story']
senticnet['documentary_evidence'] = ['0.84', '-0.97', '0', '0.713', '#joy', '#surprise', 'positive', '0.844', 'proof', 'bed_check', 'crosscheck', 'parity_check', 'redundancy_check']
senticnet['documentary_feature'] = ['0', '0.762', '0', '0', '#interest', '#interest', 'positive', '0.762', 'encyclopaedia', 'tropical_rainforest', 'learn_new_information', 'tropical_forest', 'child_story']
senticnet['documentary_feature_film'] = ['0', '0.762', '0', '0', '#interest', '#interest', 'positive', '0.762', 'encyclopaedia', 'tropical_rainforest', 'learn_new_information', 'tropical_forest', 'child_story']
senticnet['documentary_film'] = ['0', '0.762', '0', '0', '#interest', '#interest', 'positive', '0.762', 'encyclopaedia', 'tropical_rainforest', 'learn_new_information', 'tropical_forest', 'child_story']
senticnet['documentary_filmmaker'] = ['0', '0.762', '0', '0', '#interest', '#interest', 'positive', '0.762', 'encyclopaedia', 'tropical_rainforest', 'learn_new_information', 'tropical_forest', 'child_story']
senticnet['documentary_footage'] = ['0', '0.762', '0', '0', '#interest', '#interest', 'positive', '0.762', 'encyclopaedia', 'tropical_rainforest', 'learn_new_information', 'tropical_forest', 'child_story']
senticnet['documentary_heritage'] = ['-0.97', '0', '0.885', '-0.97', '#sadness', '#disgust', 'negative', '-0.94', 'protection', 'conservation', 'environmentalism', 'reservation', 'immobilization']
senticnet['documentation'] = ['-0.07', '0.142', '0', '0.095', '#interest', '#admiration', 'positive', '0.053', 'write', 'document', 'record_information', 'misspell', 'use_pencil']
senticnet['documented'] = ['0.854', '0', '0', '0.87', '#joy', '#admiration', 'positive', '0.862', 'veritable', 'echt', 'attested', 'authenticated', 'genuine']
senticnet['docusate_sodium'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
senticnet['dodder'] = ['-0.75', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.81', 'coggle', 'paddle', 'totter', 'toddle', 'waddle']
senticnet['doddering'] = ['0', '0', '0.848', '-0.98', '#anger', '#disgust', 'negative', '-0.91', 'frail', 'ill', 'valetudinarian', 'infirm', 'irresolute']
senticnet['doddle'] = ['0.746', '-0.67', '0', '0.529', '#joy', '#surprise', 'positive', '0.651', 'labor', 'cinch', 'undertaking', 'project', 'task']
senticnet['dodecahedra'] = ['0.641', '-0.6', '0', '0.957', '#joy', '#admiration', 'positive', '0.733', 'ideal_solid', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'regular_octahedron']
senticnet['dodecahedron'] = ['0.641', '-0.6', '0', '0.957', '#joy', '#admiration', 'positive', '0.733', 'ideal_solid', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'regular_octahedron']
senticnet['dodge'] = ['0.909', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.952', 'displace', 'swing', 'snowshoe', 'shuttle', 'bang']
senticnet['dodge_avenger'] = ['0.909', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.952', 'displace', 'swing', 'snowshoe', 'shuttle', 'bang']
senticnet['dodge_ball'] = ['0', '-0.08', '0', '0', '#surprise', '#surprise', 'positive', '0.082', 'yo-yo', 'playroom', 'backgammon', 'action_figure', 'board_game']
senticnet['dodge_caravan'] = ['-0.05', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'plane_land', 'floss', 'floss_tooth', 'ornament', 'wear_dress']
senticnet['dodge_charger'] = ['0.909', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.952', 'displace', 'swing', 'snowshoe', 'shuttle', 'bang']
senticnet['dodge_dealership'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'sell_car', 'truck_stop', 'vehicle', 'insurance', 'car']
senticnet['dodge_intrepid'] = ['0.909', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.952', 'displace', 'swing', 'snowshoe', 'shuttle', 'bang']
senticnet['dodge_magnum'] = ['0.909', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.952', 'displace', 'swing', 'snowshoe', 'shuttle', 'bang']
senticnet['dodge_neon'] = ['0.825', '0', '0', '0.703', '#joy', '#admiration', 'positive', '0.764', 'pay_toll', 'get_fast', 'access', 'fast_travel', 'access_quickly']
senticnet['dodge_pickup'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'cable_car', 'baggage_compartment', 'subway', 'amusement_park', 'terminal']
senticnet['dodge_ram'] = ['0.909', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.952', 'displace', 'swing', 'snowshoe', 'shuttle', 'bang']
senticnet['dodge_ram_pickup'] = ['0.909', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.952', 'displace', 'swing', 'snowshoe', 'shuttle', 'bang']
senticnet['dodge_truck'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'trailer', 'trail_car', 'camion', 'wagon', 'towboat']
senticnet['dodge_van'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'cable_car', 'baggage_compartment', 'subway', 'amusement_park', 'terminal']
senticnet['dodge_viper'] = ['0.755', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.728', 'high_class', 'expensive_car', 'ferrari', 'fancy_car', 'sports_car']
senticnet['dodger'] = ['-0.97', '0', '0.549', '-0.80', '#sadness', '#disgust', 'negative', '-0.77', 'beguiler', 'slicker', 'deceiver', 'cheat', 'cheater']
senticnet['dodger_fyi'] = ['0.663', '-0.68', '0', '0.892', '#surprise', '#admiration', 'positive', '0.747', 'saphead', 'muggins', 'tomfool', 'fool', 'sap']
senticnet['dodger_stadium'] = ['0', '-0.84', '0', '0.723', '#surprise', '#admiration', 'positive', '0.785', 'circular_route', 'stadium', 'memorial', 'station', 'row_house']
senticnet['dodging'] = ['-0.77', '0', '0.504', '-0.91', '#sadness', '#disgust', 'negative', '-0.73', 'carelessness', 'negligence', 'neglect', 'soldiering', 'escape_mechanism']
senticnet['dodgy'] = ['0', '0', '-0.98', '-0.83', '#fear', '#disgust', 'negative', '-0.91', 'cunning', 'crafty', 'guileful', 'knavish', 'tricksy']
senticnet['doe'] = ['0', '0.766', '0', '0.975', '#interest', '#admiration', 'positive', '0.871', 'widow', 'nickname', 'pretty_woman', 'sophisticated', 'woman']
senticnet['doer'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'gopher', 'actor', 'busy_bee', 'worker']
senticnet['doeth'] = ['0.741', '-0.84', '0', '0.543', '#joy', '#surprise', 'positive', '0.711', 'integrity', 'righteousness', 'scrupulousness', 'incorruptness', 'honestness']
senticnet['doeth_evil'] = ['-0.95', '0', '0.703', '-0.83', '#sadness', '#disgust', 'negative', '-0.83', 'raid', 'come_in', 'foray_into', 'enter', 'get_into']
senticnet['doff'] = ['0.08', '0.106', '-0.04', '0.112', '#interest', '#admiration', 'positive', '0.063', 'helmet', 'head_gear', 'cap', 'head_wear', 'head_protection']
senticnet['dog'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'pet', 'best_friend', 'cur', 'domestic_animal', 'domesticated_animal']
senticnet['dog_bark'] = ['-0.13', '-0.11', '0.046', '-0.18', '#sadness', '#disgust', 'negative', '-0.06', 'breaheavily', 'run_out_breath', 'get_exhausted', 'exhaustion', 'blister_foot']
senticnet['dog_bite'] = ['0', '0', '-0.80', '-0.83', '#fear', '#disgust', 'negative', '-0.82', 'leg_hurt', 'strike_ball', 'get_injured', 'become_exhausted', 'broken_leg']
senticnet['dog_cat'] = ['0.731', '0', '0', '0.843', '#joy', '#admiration', 'positive', '0.787', 'pet', 'loyal', 'smart_dog', 'faithful', 'big_dog']
senticnet['dog_chase'] = ['0', '0', '-0.84', '-0.93', '#fear', '#disgust', 'negative', '-0.88', 'get_out_breath', 'sore_leg', 'become_wind', 'see_neighbor', 'dog_bark']
senticnet['dog_collar'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'bangle', 'ankle_bracelet', 'armilla']
senticnet['dog_dander'] = ['-0.15', '0.162', '0.596', '-0.62', '#anger', '#disgust', 'negative', '-0.30', 'hackle', 'umbrage', 'offense', 'ire', 'see_red']
senticnet['dog_die'] = ['-0.79', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.85', 'step_dog_poop', 'broken_window', 'step_dog_shit', 'broken_wrist', 'run_over_car']
senticnet['dog_fetch'] = ['0', '-0.06', '0.046', '0.09', '#surprise', '#admiration', 'positive', '0.038', 'piece_of_wood', 'dog', 'adhere', 'long_wood', 'fetch']
senticnet['dog_flea'] = ['-0.99', '0', '0.612', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'dirty_animal', 'flea', 'dirty_cat', 'dirty_pet', 'dirty_dog']
senticnet['dog_food'] = ['0.076', '0', '0', '0', '#joy', '#joy', 'positive', '0.076', 'attract_dog', 'food', 'dog', 'cat_food', 'pet_food']
senticnet['dog_fur'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'animal_hair', 'dog_hair', 'animal_fur', 'dog_cat', 'bite_dog']
senticnet['dog_groomer'] = ['0.99', '0', '0', '0.721', '#joy', '#admiration', 'positive', '0.855', 'hair', 'haircut', 'hairdresser_chair', 'scissor', 'hairstyle']
senticnet['dog_hair'] = ['-0.03', '-0.00', '0.087', '-0.05', '#anger', '#disgust', 'negative', '-0.04', 'dog_fur', 'dog_cat', 'kitty', 'pet', 'animal_hair']
senticnet['dog_heartworm'] = ['0.579', '-0.81', '0', '0.833', '#surprise', '#admiration', 'positive', '0.741', 'typhus', 'rat_typhus', 'urban_typhus', 'endemic_typhus', 'rickettsial_disease']
senticnet['dog_howl'] = ['-0.36', '-0.04', '-0.41', '-0.11', '#sadness', '#fear', 'negative', '-0.21', 'sadness', 'lament', 'deep_sadness', 'depression', 'shed_tear']
senticnet['dog_pet'] = ['0.902', '0.772', '0', '0', '#joy', '#interest', 'positive', '0.837', 'paw', 'two_ears', 'abdomen', 'two_arms', 'breast']
senticnet['dog_poop'] = ['0', '0', '0.735', '-0.70', '#anger', '#disgust', 'negative', '-0.72', 'crap', 'dung', 'shit', 'noise_pollution', 'compost']
senticnet['dog_shit'] = ['0', '0', '0.943', '-0.8', '#anger', '#disgust', 'negative', '-0.87', 'fight_fire', 'soapy', 'feel_dirty', 'smell_clean', 'filthy']
senticnet['dog_show'] = ['0', '-0.06', '0.046', '0.09', '#surprise', '#admiration', 'positive', '0.038', 'discipline', 'take_care_of_pet', 'circus', 'training', 'animal']
senticnet['dog_tag'] = ['-0.05', '0.072', '0.012', '0.066', '#interest', '#admiration', 'positive', '0.018', 'pet', 'leash', 'animal', 'bell_cat', 'dog']
senticnet['dog_toy'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'dog_treat', 'structural_support', 'human_part', 'human_shape', 'inside_body']
senticnet['dog_trainer'] = ['0.039', '0', '0', '0.059', '#joy', '#admiration', 'positive', '0.049', 'big_dog', 'dog_cat', 'smart_dog', 'pet', 'domesticated_animal']
senticnet['dog_treat'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'dog_toy', 'structural_support', 'human_part', 'human_shape', 'inside_body']
senticnet['dog_turd'] = ['-0.89', '0', '0.739', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'rot', 'bunk', 'bunkum', 'buncombe', 'guff']
senticnet['dog_wag_tail'] = ['0.749', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.772', 'workaholic', 'lonely_person', 'shed_tear', 'laugh', 'friendly_fire']
senticnet['dogbane'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['dogfight'] = ['0', '-0.01', '-0.08', '-0.16', '#fear', '#disgust', 'negative', '-0.07', 'siege', 'big_fight', 'battle', 'declare_war', 'conflict']
senticnet['dogfish_head'] = ['0', '0.091', '-0.05', '0.073', '#interest', '#admiration', 'positive', '0.037', 'privately', 'hold_liquid', 'make_wish', 'rinse_dish', 'find_toilet']
senticnet['dogged'] = ['0.803', '0.932', '0', '0', '#joy', '#interest', 'positive', '0.868', 'retentive', 'long', 'tenacious', 'dour', 'recollective']
senticnet['dogged_determination'] = ['0.812', '0.937', '0', '0', '#joy', '#interest', 'positive', '0.875', 'retentive', 'long', 'dogged', 'dour', 'recollective']
senticnet['doggedly'] = ['0', '0.9', '0', '0.864', '#interest', '#admiration', 'positive', '0.882', 'steadfastly', 'resolutely', 'unwaveringly', 'staunchly', 'obstinately']
senticnet['doggedness'] = ['0', '0.928', '0', '0.925', '#interest', '#admiration', 'positive', '0.927', 'relentlessness', 'single-mindedness', 'earnestness', 'fearlessness', 'obsessiveness']
senticnet['doggie'] = ['-0.72', '0', '0.619', '-0.86', '#sadness', '#disgust', 'negative', '-0.73', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['doggie_bag'] = ['0.866', '-0.91', '0', '0.893', '#surprise', '#admiration', 'positive', '0.892', 'mooch', 'freeload', 'grub', 'sponge', 'bum']
senticnet['dogging'] = ['-0.60', '0', '0.509', '-0.67', '#sadness', '#disgust', 'negative', '-0.59', 'continuous', 'persisting', 'sustained', 'consecutive', 'perpetual']
senticnet['doggy'] = ['0.039', '0', '0', '0.059', '#joy', '#admiration', 'positive', '0.049', 'big_dog', 'dog_cat', 'smart_dog', 'pet', 'domesticated_animal']
senticnet['doghouse'] = ['-0.02', '0', '-0.06', '-0.03', '#fear', '#disgust', 'negative', '-0.04', 'alleyway', 'animal_shelter', 'vet_office', 'home', 'vet']
senticnet['dogleg'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['dogma'] = ['0.99', '0', '0', '0.982', '#joy', '#admiration', 'positive', '0.986', 'god', 'spiritual', 'saint', 'prayer', 'angel']
senticnet['dogmatic'] = ['-0.98', '0', '0.891', '-0.60', '#sadness', '#anger', 'negative', '-0.82', 'exorcism', 'dispossession', 'magic', 'thaumaturgy', 'belief']
senticnet['dogmatic_theology'] = ['0.664', '-0.57', '0', '0.981', '#joy', '#admiration', 'positive', '0.741', 'theodicy', 'homiletics', 'discipline', 'study', 'subject']
senticnet['dogmatical'] = ['0.922', '0.762', '0', '0', '#joy', '#interest', 'positive', '0.842', 'controvert', 'refute', 'confutation', 'negate', 'rebut']
senticnet['dogmatically'] = ['0.633', '-0.92', '0', '0.869', '#surprise', '#admiration', 'positive', '0.808', 'advocate', 'bigot', 'dogmatist', 'partisan', 'advocator']
senticnet['dogmatism'] = ['0', '0', '0.996', '-0.70', '#anger', '#disgust', 'negative', '-0.85', 'fanaticism', 'zealotry', 'obscurantism', 'zealot', 'bigotry']
senticnet['dogmatist'] = ['0.633', '-0.92', '0', '0.869', '#surprise', '#admiration', 'positive', '0.808', 'advocate', 'bigot', 'partisan', 'doctrinaire', 'advocator']
senticnet['dogmatize'] = ['-0.69', '0', '0.806', '-0.74', '#anger', '#disgust', 'negative', '-0.74', 'hypothesis', 'theory', 'base', 'possibility', 'presumption']
senticnet['dogsbody'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'rascal', 'rapscallion', 'scalawag', 'holy_terror', 'monkey']
senticnet['dogshit'] = ['-0.89', '0', '0.739', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'rot', 'bunk', 'bunkum', 'buncombe', 'guff']
senticnet['dogwood'] = ['0.958', '-0.82', '0', '0.924', '#joy', '#admiration', 'positive', '0.901', 'bunchberry', 'dwarf_cornel', 'crackerberry', 'pudding_berry', 'common_white_dogwood']
senticnet['dogwood_anthracnose'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['dogwood_tree'] = ['0.506', '-0.77', '0', '0.543', '#surprise', '#admiration', 'positive', '0.607', 'bunchberry', 'dwarf_cornel', 'crackerberry', 'pudding_berry', 'common_white_dogwood']
senticnet['dohyo'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['doily'] = ['0.976', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.871', 'pinwheel', 'pinwheel', 'snowflake', 'swirl', 'swirly']
senticnet['doing_brisk'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['doing_homework'] = ['0', '0', '0.748', '-0.81', '#anger', '#disgust', 'negative', '-0.78', 'search_information', 'look_for_information', 'check_email', 'phone_line_busy', 'distract']
senticnet['doing_laundry'] = ['0', '0', '0', '0.068', '#admiration', '#admiration', 'positive', '0.068', 'become_clean', 'wash_clothes', 'take_shower', 'get_clean', 'clean_body']
senticnet['doing_penance'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitentially', 'penitent', 'repentantly', 'repentant']
senticnet['doings'] = ['0.577', '-0.82', '0', '0.841', '#surprise', '#admiration', 'positive', '0.748', 'dirty_trick', 'activity', 'easiness', 'aggression', 'dirty_pool']
senticnet['dolby_surround'] = ['0.049', '0.167', '-0.05', '0.295', '#interest', '#admiration', 'positive', '0.115', 'four_sides', 'sound_system', 'bass', 'movie', 'surround']
senticnet['dolby_truehd'] = ['0.055', '0', '0.03', '0.136', '#joy', '#admiration', 'positive', '0.054', 'loud_soft', 'decibel', 'loudness', 'sound', 'melodic_noise']
senticnet['dolce_vita'] = ['0.973', '-0.50', '0', '0.612', '#joy', '#admiration', 'positive', '0.698', 'chic', 'chicness', 'jauntiness', 'modishness', 'stylishness']
senticnet['doldrums'] = ['-0.97', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.85', 'apathy', 'squall', 'malaise', 'boredom', 'ennui']
senticnet['dole_out'] = ['-0.06', '0', '0.023', '0', '#sadness', '#anger', 'negative', '-0.04', 'circulate_blood', 'grind_grain', 'fail_work', 'cremate', 'dry_wet_hair']
senticnet['doleful'] = ['-0.85', '0.207', '-0.56', '0', '#sadness', '#fear', 'negative', '-0.40', 'sorrowfully', 'sadly', 'dolefully', 'ruefulness', 'regretful']
senticnet['dolefully'] = ['-0.84', '0.22', '-0.54', '0', '#sadness', '#fear', 'negative', '-0.39', 'wretched', 'sorrowfully', 'doleful', 'sadly', 'ruefulness']
senticnet['dolefulness'] = ['-0.75', '0.286', '-0.44', '-0.05', '#sadness', '#fear', 'negative', '-0.24', 'joyless', 'joylessly', 'joylessness', 'depressing', 'cheerless']
senticnet['dolichovespula'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'hornet', 'vespid', 'vespid_wasp']
senticnet['doll'] = ['0', '-0.08', '0', '0', '#surprise', '#surprise', 'positive', '0.087', 'toy', 'stuff_animal', 'dummy', 'mannequin', 'action_figure']
senticnet['doll_up'] = ['-0.05', '0.072', '0.012', '0.066', '#interest', '#admiration', 'positive', '0.018', 'night_dress', 'dress', 'make_up', 'fashionable', 'dress_up']
senticnet['dollar'] = ['0', '0.192', '0.019', '0.058', '#interest', '#admiration', 'positive', '0.077', 'franc', 'euro', 'cent', 'dollar_bill', 'banknote']
senticnet['dollar_amount'] = ['-0.04', '0.268', '0', '0.159', '#interest', '#admiration', 'positive', '0.127', 'total', 'amount', 'a_lot', 'aggregate', 'bill']
senticnet['dollar_banknote'] = ['0.81', '0.763', '0', '0', '#joy', '#interest', 'positive', '0.786', 'dollar_bill', 'franc', 'euro', 'dollar', 'cent']
senticnet['dollar_bill'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'cash_bill', 'banknote', 'check', 'euro', 'paper_bill']
senticnet['dollar_cent'] = ['0', '0.27', '0.014', '0.133', '#interest', '#admiration', 'positive', '0.13', 'dollar_sign', 'money_amount', 'value_amount', 'payment_amount', 'money_value']
senticnet['dollar_change'] = ['0', '0.207', '0.063', '0.131', '#interest', '#admiration', 'positive', '0.092', 'small_change', 'currency_unit', 'american_money', 'money_unit', 'euro']
senticnet['dollar_coin'] = ['0.81', '0.763', '0', '0', '#joy', '#interest', 'positive', '0.786', 'dollar_bill', 'franc', 'euro', 'dollar', 'cent']
senticnet['dollar_per_acre'] = ['0', '0.122', '0', '0.123', '#interest', '#admiration', 'positive', '0.122', 'acre', 'cow_home', 'crop_place', 'big_land', 'animal_house']
senticnet['dollar_sign'] = ['0', '0.264', '0.009', '0.144', '#interest', '#admiration', 'positive', '0.133', 'value_amount', 'dollar_cent', 'money_amount', 'sticker', 'payment_amount']
senticnet['dollar_threshold'] = ['0', '0.098', '0', '0.089', '#interest', '#admiration', 'positive', '0.093', 'wood_panel', 'wooden_structure', 'hinge', 'entrance_room', 'corridor']
senticnet['dollar_value'] = ['-0.04', '0.268', '0', '0.159', '#interest', '#admiration', 'positive', '0.127', 'total', 'amount', 'a_lot', 'aggregate', 'bill']
senticnet['dollar_volume'] = ['0', '0.771', '0', '0.711', '#interest', '#admiration', 'positive', '0.741', 'employee_turnover', 'turnover', 'turnover_rate', 'upset', 'overturn']
senticnet['dolled_up'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['dolmabahe_palace'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'caravansary', 'caravanserai', 'khan', 'caravan_inn', 'hostel']
senticnet['dolor'] = ['-0.90', '0.147', '-0.62', '0.064', '#sadness', '#fear', 'negative', '-0.32', 'feel_pain', 'plaintive', 'plaintiveness', 'plaintively', 'woebegone']
senticnet['dolorous'] = ['-0.71', '0.312', '-0.4', '-0.08', '#sadness', '#fear', 'negative', '-0.22', 'lachrymose', 'tearful', 'weeping', 'tearfulness', 'weepiness']
senticnet['dolphin'] = ['0.024', '0.102', '-0.06', '0.074', '#interest', '#admiration', 'positive', '0.033', 'marine_mammal', 'large_mammal', 'pack_animal', 'mammal', 'smart_creature']
senticnet['dolphin_conservation'] = ['0.024', '0.102', '-0.06', '0.074', '#interest', '#admiration', 'positive', '0.033', 'marine_mammal', 'large_mammal', 'pack_animal', 'mammal', 'smart_creature']
senticnet['dolphin_cove'] = ['0.024', '0.102', '-0.06', '0.074', '#interest', '#admiration', 'positive', '0.033', 'marine_mammal', 'large_mammal', 'pack_animal', 'mammal', 'smart_creature']
senticnet['dolphin_quest'] = ['0.024', '0.102', '-0.06', '0.074', '#interest', '#admiration', 'positive', '0.033', 'marine_mammal', 'large_mammal', 'pack_animal', 'mammal', 'smart_creature']
senticnet['dolphin_swim'] = ['0.024', '0.102', '-0.06', '0.074', '#interest', '#admiration', 'positive', '0.033', 'marine_mammal', 'large_mammal', 'pack_animal', 'mammal', 'smart_creature']
senticnet['dolphinfish'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'crab', 'swimming_crab', 'rock_crab', 'fiddler_crab', 'pea_crab']
senticnet['dolt'] = ['-0.79', '0', '0', '-0.95', '#sadness', '#disgust', 'negative', '-0.87', 'idiot', 'douchebag', 'dumb', 'little_minded', 'stupid']
senticnet['doltish'] = ['-0.78', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.82', 'neandertal', 'loutish', 'swinish', 'oafish', 'neanderthal']
senticnet['dom_inspector'] = ['-0.89', '0', '0.672', '-0.69', '#sadness', '#disgust', 'negative', '-0.75', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['dom_joly'] = ['-0.89', '0', '0.58', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'resemblance', 'comparison', 'equivalence', 'reflection', 'alikeness']
senticnet['domain-specific'] = ['-0.72', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.78', 'non-standard', 'ordinal', 'nonstandard', 'colloquial', 'syntactical']
senticnet['domain_circumscription'] = ['-0.99', '0', '0.878', '-0.95', '#sadness', '#disgust', 'negative', '-0.94', 'adequacy', 'satisfactoriness', 'acceptability', 'acceptableness', 'quality']
senticnet['domain_expertise'] = ['0', '0.983', '0', '0', '#interest', '#interest', 'positive', '0.983', 'find_problem', 'analyze', 'expert_report', 'examination', 'check']
senticnet['dome'] = ['0', '0.077', '0.039', '0.154', '#interest', '#admiration', 'positive', '0.064', 'marble', 'round_figure', 'almond', 'round_ball', 'sphere']
senticnet['dome_arch'] = ['0.048', '0.18', '-0.04', '0.169', '#interest', '#admiration', 'positive', '0.087', 'minister', 'pope', 'contradiction', 'preist', 'evangelist']
senticnet['domed_building'] = ['0', '0.224', '0', '0.197', '#interest', '#admiration', 'positive', '0.211', 'church', 'temple', 'cathedral', 'chapel', 'confession']
senticnet['domed_church'] = ['0.026', '0.219', '0', '0.16', '#interest', '#admiration', 'positive', '0.135', 'large_build', 'palace', 'high_rise', 'structure', 'fortification']
senticnet['domed_hall'] = ['0', '0', '-0.76', '-0.82', '#fear', '#disgust', 'negative', '-0.79', 'stage', 'thespian', 'actor', 'theatre', 'drama']
senticnet['domed_mosque'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['domed_roof'] = ['0.026', '0.219', '0', '0.16', '#interest', '#admiration', 'positive', '0.135', 'large_build', 'palace', 'high_rise', 'structure', 'fortification']
senticnet['domed_stadium'] = ['0', '-0.84', '0', '0.723', '#surprise', '#admiration', 'positive', '0.785', 'circular_route', 'stadium', 'memorial', 'station', 'row_house']
senticnet['domed_structure'] = ['0.026', '0.219', '0', '0.16', '#interest', '#admiration', 'positive', '0.135', 'large_build', 'palace', 'high_rise', 'structure', 'fortification']
senticnet['domenico_modugno'] = ['0.107', '0.11', '-0.08', '0.21', '#interest', '#admiration', 'positive', '0.086', 'live_healthy_life', 'forever_young', 'live_forever', 'live_long', 'live_good_life']
senticnet['domesday_survey'] = ['0.049', '0.16', '0', '0.155', '#interest', '#admiration', 'positive', '0.121', 'castle', 'palace', 'medieval', 'aristocrat', 'throne']
senticnet['domestibig_cate_animal'] = ['0.026', '0', '0.01', '0.054', '#joy', '#admiration', 'positive', '0.023', 'cute_big_cat', 'cat', 'big_cat', 'big_animal', 'big']
senticnet['domestic'] = ['-0.75', '0', '0.807', '0', '#sadness', '#anger', 'negative', '-0.77', 'geezer', 'boiler', 'male', 'knacker', 'old']
senticnet['domestic_abuse'] = ['-0.64', '0', '0.854', '-0.83', '#anger', '#disgust', 'negative', '-0.77', 'crime', 'bigamy', 'regulatory_offence', 'sexual_abuse', 'sex_crime']
senticnet['domestic_animal'] = ['0.109', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.087', 'social_animal', 'home_animal', 'household_animal', 'pet', 'herd_sheep']
senticnet['domestic_cat'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'animal', 'animate_being', 'pet', 'creature']
senticnet['domestic_chaplain'] = ['0.031', '0.272', '0', '0.231', '#interest', '#admiration', 'positive', '0.178', 'minister', 'church_head', 'pastor', 'protestant', 'evangelist']
senticnet['domestic_demand'] = ['0.067', '0.172', '0', '0.121', '#interest', '#admiration', 'positive', '0.12', 'economic_activity', 'business', 'support_family', 'work_done', 'earn_money']
senticnet['domestic_dog'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'feist', 'fice', 'pariah_dog', 'cur']
senticnet['domestic_dwell'] = ['0.037', '0.106', '0', '0.14', '#interest', '#admiration', 'positive', '0.094', 'dwell_place', 'live_place', 'place_to_live', 'live_quarter', 'accomodation']
senticnet['domestic_fowl'] = ['-0.90', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.88', 'fowl', 'poultry', 'ban', 'fear', 'reuters']
senticnet['domestic_helper'] = ['0.875', '0', '0', '0.802', '#joy', '#admiration', 'positive', '0.839', 'foreigner', 'exile', 'deportee', 'gringo', 'import']
senticnet['domestic_llama'] = ['0.553', '0.658', '-0.25', '0.271', '#joy', '#interest', 'positive', '0.308', 'guanaco', 'llama', 'alpaca', 'artiodactyl', 'artiodactyl_mammal']
senticnet['domestic_pet'] = ['0.057', '0', '0.023', '0.124', '#joy', '#admiration', 'positive', '0.053', 'hairy', 'cute', 'furry', 'pet', 'animal_furry']
senticnet['domestic_poultry'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'hold_egg', 'baby_bird', 'hatch', 'lay_egg', 'bird_home']
senticnet['domestic_servant'] = ['-0.62', '0', '0.649', '-0.67', '#anger', '#disgust', 'negative', '-0.64', 'handmaid', 'handmaiden', 'chambermaid', 'maid', 'parlormaid']
senticnet['domestic_servitude'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'vassalage']
senticnet['domestic_sheep'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'argali', 'argal', 'urial', 'white_sheep', 'wild_sheep']
senticnet['domestic_surveillance'] = ['-0.53', '0', '0.884', '-0.72', '#anger', '#disgust', 'negative', '-0.71', 'bug', 'tap', 'eavesdrop', 'intercept', 'listen']
senticnet['domestic_unrest'] = ['-0.77', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.84', 'jumpiness', 'jitteriness', 'restive', 'nervy', 'jumpy']
senticnet['domestic_violence'] = ['-0.8', '0', '0.875', '-0.74', '#sadness', '#anger', 'negative', '-0.80', 'inhuman_treatment', 'cruelty', 'child_neglect', 'mistreatment', 'child_abuse']
senticnet['domestic_wastewater'] = ['-0.58', '0', '0.714', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'diversionist', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['domestic_water_supply'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'bottled_water', 'sugar_water']
senticnet['domestically'] = ['0', '0.766', '0', '0', '#interest', '#interest', 'positive', '0.766', 'abroad', 'foreign', 'study_abroad', 'elsewhere', 'live_abroad']
senticnet['domesticate'] = ['0.053', '0.094', '0', '0.136', '#interest', '#admiration', 'positive', '0.094', 'poisonous_human', 'barn_animal', 'stand', 'neigh', 'alive']
senticnet['domesticate_animal'] = ['0.03', '0.068', '-0.04', '0.093', '#interest', '#admiration', 'positive', '0.037', 'cultivated', 'tamed', 'live_creature', 'furry_creature', 'farm']
senticnet['domesticate_cat'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'family_dog', 'domesticate_pet', 'animal', 'domesticate_dog', 'dog']
senticnet['domesticate_dog'] = ['0.056', '0.157', '0', '0.154', '#interest', '#admiration', 'positive', '0.122', 'family_dog', 'domesticate_pet', 'animal', 'domesticate_cat', 'dog']
senticnet['domesticate_pet'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'family_dog', 'domesticate_dog', 'animal', 'domesticate_cat', 'dog']
senticnet['domesticate_plant'] = ['0.057', '0', '0.038', '0.073', '#joy', '#admiration', 'positive', '0.031', 'delicious_fruit', 'small_fruit', 'tree_fruit', 'vine_fruit', 'yellow_fruit']
senticnet['domesticated_animal'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'animal', 'animate_being', 'pet', 'creature']
senticnet['domesticated_cat'] = ['0', '0', '0.022', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'kind_animal', 'wild_animal', 'predator', 'hound', 'fang']
senticnet['domesticated_cattle'] = ['0.735', '-0.71', '0', '0.566', '#joy', '#surprise', 'positive', '0.672', 'improvement', 'betterment', 'habituation', 'adjustment', 'shakedown']
senticnet['domesticated_sheep'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'argali', 'argal', 'urial', 'white_sheep', 'wild_sheep']
senticnet['domestication'] = ['0.735', '-0.71', '0', '0.566', '#joy', '#surprise', 'positive', '0.672', 'improvement', 'betterment', 'habituation', 'adjustment', 'shakedown']
senticnet['domesticize'] = ['0.849', '-0.57', '0', '0.815', '#joy', '#admiration', 'positive', '0.746', 'break', 'change', 'alter', 'modify', 'break_in']
senticnet['domicile'] = ['0', '0.078', '-0.06', '0.096', '#interest', '#admiration', 'positive', '0.037', 'dwell_place', 'abide', 'residence', 'domestic_dwell', 'live_place']
senticnet['domiciled'] = ['-0.99', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.92', 'non-resident', 'non-residents', 'out-of-state', 'foreign', 'nonresident']
senticnet['dominance'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'absolutism', 'ascendance', 'autocracy', 'ascendancy', 'tyrant']
senticnet['dominant'] = ['0', '0.871', '0', '0.861', '#interest', '#admiration', 'positive', '0.866', 'role', 'alpha_male', 'dominant_role', 'master_and_commander', 'sergeant']
senticnet['dominant_canopy'] = ['-0.94', '0', '0.608', '-0.95', '#sadness', '#disgust', 'negative', '-0.83', 'wild_licorice', 'wild_liquorice', 'alpine_milk_vetch', 'purple_milk_vetch', 'milk_vetch']
senticnet['dominant_chord'] = ['-0.65', '0', '0.681', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'tone', 'whole_tone', 'step', 'whole_step', 'musical_interval']
senticnet['dominant_faction'] = ['-0.50', '0', '0.989', '-0.91', '#anger', '#disgust', 'negative', '-0.80', 'camp', 'pack', 'clique', 'coterie', 'ingroup']
senticnet['dominant_force'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'absolutism', 'autocracy', 'ascendancy', 'tyrant']
senticnet['dominant_hemisphere'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'nominal_aphasia', 'anomic_aphasia']
senticnet['dominant_ideology'] = ['0', '0', '0', '0.986', '#admiration', '#admiration', 'positive', '0.986', 'idea', 'decision', 'consider', 'polarize', 'make_right_decision']
senticnet['dominant_role'] = ['0', '0.871', '0', '0.861', '#interest', '#admiration', 'positive', '0.866', 'role', 'alpha_male', 'dominant', 'master_and_commander', 'sergeant']
senticnet['dominant_seventh_chord'] = ['-0.65', '0', '0.681', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'tone', 'whole_tone', 'step', 'whole_step', 'musical_interval']
senticnet['dominant_vegetation'] = ['-0.94', '0', '0.608', '-0.95', '#sadness', '#disgust', 'negative', '-0.83', 'wild_licorice', 'wild_liquorice', 'alpine_milk_vetch', 'purple_milk_vetch', 'milk_vetch']
senticnet['dominantly'] = ['0.918', '0', '0', '0.791', '#joy', '#admiration', 'positive', '0.855', 'maternally', 'paternally', 'inherited', 'aberrantly', 'ubiquitously']
senticnet['dominate'] = ['-0.94', '0', '0.935', '0', '#sadness', '#anger', 'negative', '-0.94', 'rule', 'govern', 'direct', 'control', 'influence']
senticnet['dominating'] = ['0', '0', '0.742', '-0.75', '#anger', '#disgust', 'negative', '-0.74', 'autocratic', 'bossy', 'high-and-mighty', 'magisterial', 'peremptory']
senticnet['domination'] = ['0', '0.884', '0', '0.704', '#interest', '#admiration', 'positive', '0.794', 'authority', 'control', 'supervisor', 'chief', 'president']
senticnet['dominatrix'] = ['0.794', '-0.50', '0', '0.531', '#joy', '#admiration', 'positive', '0.611', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['domine'] = ['-0.52', '0', '0.701', '-0.50', '#sadness', '#anger', 'negative', '-0.57', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['domineer'] = ['0', '0', '-0.77', '-0.85', '#fear', '#disgust', 'negative', '-0.81', 'domineering', 'imperious', 'bossy', 'hectoring', 'lord']
senticnet['domineering'] = ['-0.91', '0', '0.521', '-0.81', '#sadness', '#disgust', 'negative', '-0.74', 'bullying', 'peremptory', 'blustery', 'roughshod', 'cavalier']
senticnet['domineering_father'] = ['0.72', '-0.72', '0', '0.506', '#joy', '#surprise', 'positive', '0.651', 'standoffishness', 'withdrawnness', 'closeness', 'secretiveness', 'remoteness']
senticnet['domineering_mother'] = ['0.584', '-0.59', '0', '0.753', '#surprise', '#admiration', 'positive', '0.643', 'ungracious', 'discourteous', 'impolite', 'brattish', 'unparliamentary']
senticnet['domineering_wife'] = ['0.934', '-0.57', '0', '0.767', '#joy', '#admiration', 'positive', '0.76', 'morosoph', 'simpleton', 'flibbertigibbet', 'foolish_woman', 'simple']
senticnet['domineeringness'] = ['0.968', '-0.88', '0', '0.831', '#joy', '#surprise', 'positive', '0.893', 'superiority', 'pride', 'superbia', 'imperiousness', 'haughtiness']
senticnet['dominic_cooper'] = ['0', '0', '0', '0.068', '#admiration', '#admiration', 'positive', '0.068', 'dark_corner', 'child_game', 'play_person', 'role_play', 'part_of_play']
senticnet['dominion'] = ['0.94', '-0.63', '0', '0.772', '#joy', '#admiration', 'positive', '0.783', 'paramountcy', 'raj', 'reign', 'sovereignty', 'suzerainty']
senticnet['dominion_herbal'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
senticnet['dominionist'] = ['-0.30', '0.083', '-0.21', '-0.17', '#sadness', '#fear', 'negative', '-0.15', 'end_life', 'death', 'seek_revenge', 'kill_self', 'kill']
senticnet['dominium'] = ['0.964', '0.931', '0', '0', '#joy', '#interest', 'positive', '0.948', 'heel', 'foot_part', 'type_of_shoes', 'shoe_type', 'high_heel']
senticnet['domino_mask'] = ['-0.76', '0', '0.672', '-0.88', '#sadness', '#disgust', 'negative', '-0.77', 'attire', 'garb', 'dress', 'fancy_dress', 'masquerade']
senticnet['dominus_vobiscum'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'reverence', 'genuflection', 'obeisance', 'kowtow', 'mafia']
senticnet['domlur_club'] = ['-0.80', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.77', 'contrary', 'contradiction', 'lie', 'liar', 'bad_person']
senticnet['domodossola'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'expressway', 'turnpike', 'toll_road', 'highway']
senticnet['donaldsonville'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
senticnet['donate'] = ['0.809', '0', '0', '0.704', '#joy', '#admiration', 'positive', '0.756', 'hand_over', 'distribute', 'hand_out', 'give', 'spread_love']
senticnet['donate_charity'] = ['0', '0.106', '-0.08', '0.115', '#interest', '#admiration', 'positive', '0.045', 'donate', 'give', 'help_less_fortunate', 'purchase_food', 'help']
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senticnet['donated_kidney'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'displace', 'move', 'move_away', 'bump', 'dislocate']
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senticnet['dongola'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['donjon'] = ['0.021', '0.119', '0', '0.129', '#interest', '#admiration', 'positive', '0.09', 'manor', 'palace', 'medieval', 'tower', 'chess_game']
senticnet['donkey'] = ['0', '-0.02', '0', '0.06', '#surprise', '#admiration', 'positive', '0.041', 'male_cow', 'baby_cow', 'little_horse', 'hoof_animal', 'animal']
senticnet['donkey_cart'] = ['-0.80', '0', '0.718', '-0.79', '#sadness', '#disgust', 'negative', '-0.77', 'walk_exercise', 'lose_wallet', 'go_debt', 'pay_dinner', 'spend_much_money']
senticnet['donkey_derby'] = ['-0.72', '0', '0.978', '-0.86', '#anger', '#disgust', 'negative', '-0.85', 'amusement_park', 'common', 'pleasure_ground', 'village_green', 'tract']
senticnet['donkey_punch'] = ['0.705', '-0.60', '0', '0.913', '#joy', '#admiration', 'positive', '0.741', 'lick', 'clout', 'poke', 'punch', 'biff']
senticnet['donkeywork'] = ['0', '0', '-0.83', '-0.81', '#fear', '#disgust', 'negative', '-0.82', 'grind', 'footslog', 'plodding', 'slog', 'drudgery']
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senticnet['donnish'] = ['0.71', '-0.81', '0', '0.937', '#surprise', '#admiration', 'positive', '0.819', 'erudite', 'scholarly', 'pedantic', 'learned', 'bookish']
senticnet['donohew'] = ['0.627', '-0.65', '0', '0.968', '#surprise', '#admiration', 'positive', '0.749', 'overwhelm', 'overpower', 'sweep_over', 'whelm', 'overcome']
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senticnet['donor_graft'] = ['0', '0', '-0.77', '-0.86', '#fear', '#disgust', 'negative', '-0.82', 'abduction', 'punishment', 'emergency', 'illiteracy', 'heal']
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senticnet['donor_liver'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'displace', 'move', 'move_away', 'bump', 'dislocate']
senticnet['donor_organ'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'displace', 'move', 'move_away', 'bump', 'dislocate']
senticnet['donor_transplant'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'displace', 'move', 'move_away', 'bump', 'dislocate']
senticnet['donst'] = ['-0.57', '0', '0.571', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
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senticnet['donut'] = ['0.051', '-0.10', '0', '0', '#joy', '#surprise', 'positive', '0.079', 'cookie', 'pie', 'doughnut', 'muffin', 'custard']
senticnet['donut-like'] = ['-0.84', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.84', 'pizza-like', 'bread-like', 'crepe-like', 'pancake-like', 'yeast-risen']
senticnet['donut_shop'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'car_dealership', 'truck_stop', 'railway_station', 'city_street', 'donut']
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senticnet['doo_cot'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'lair', 'habitat', 'animal_home']
senticnet['doo_heartbreaker'] = ['-0.86', '0', '0.675', '-0.84', '#sadness', '#disgust', 'negative', '-0.79', 'shenanigan', 'chicanery', 'wile', 'dupery', 'trickery']
senticnet['doodad'] = ['0.971', '-0.83', '0', '0', '#joy', '#surprise', 'positive', '0.901', 'whatsis', 'widget', 'thingumajig', 'thingamabob', 'gimmick']
senticnet['doodle'] = ['-0.02', '0.111', '0', '0.05', '#interest', '#admiration', 'positive', '0.044', 'sketch', 'use_crayon', 'draw', 'make_sketch', 'pencil_art']
senticnet['doodle_dandy'] = ['-0.54', '0', '0.811', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'bird_of_prey', 'raptor', 'raptorial_bird', 'parrot', 'bird']
senticnet['doodle_doo'] = ['-0.54', '0', '0.811', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'bird_of_prey', 'raptor', 'raptorial_bird', 'parrot', 'bird']
senticnet['doolin_dalton_gang'] = ['0', '0', '-0.11', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'hunting_season', 'hound', 'cougar', 'hunter', 'dragon']
senticnet['doom'] = ['0', '0', '-0.79', '-0.88', '#fear', '#disgust', 'negative', '-0.84', 'doomsday', 'destruction', 'universal_death', 'widespread_disease', 'death']
senticnet['doom_laden'] = ['-0.70', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.75', 'pessimism', 'pessimistically', 'dispiritedly', 'hopelessness', 'hopelessly']
senticnet['doomed'] = ['0', '0', '-0.90', '-0.94', '#fear', '#disgust', 'negative', '-0.92', 'luckless', 'ill-omened', 'ill-starred', 'unlucky', 'ill-fated']
senticnet['doomsayer'] = ['-0.97', '0', '0.62', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'obstructor', 'obstructer', 'technophobe', 'obstructionist', 'resister']
senticnet['doomsday'] = ['0', '0', '-0.79', '-0.88', '#fear', '#disgust', 'negative', '-0.84', 'doom', 'destruction', 'universal_death', 'widespread_disease', 'death']
senticnet['doomsday_device'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
senticnet['doomsday_machine'] = ['-0.88', '0', '0.882', '-0.58', '#sadness', '#anger', 'negative', '-0.78', 'mortal', 'behemoth', 'unusual_person', 'aberrant', 'giant']
senticnet['doomsday_seed'] = ['0.02', '0.205', '0', '0', '#joy', '#interest', 'positive', '0.112', 'safe', 'bank_vault', 'bank', 'piggy_bank', 'bank_account']
senticnet['doomwyte'] = ['-0.73', '0', '0.55', '-0.75', '#sadness', '#disgust', 'negative', '-0.68', 'mortal', 'person', 'individual', 'mangler', 'mutilator']
senticnet['doona'] = ['0.087', '-0.07', '0.062', '0', '#joy', '#surprise', 'positive', '0.033', 'pillowcase', 'bed', 'comforter', 'bed_sheet', 'duvet_cover']
senticnet['door'] = ['0', '0', '0', '0.077', '#admiration', '#admiration', 'positive', '0.077', 'glass_front', 'gate', 'doorway', 'use_farm', 'entranceway']
senticnet['door_handle'] = ['0', '-0.01', '0', '0.072', '#surprise', '#admiration', 'positive', '0.042', 'door', 'glass_front', 'gate', 'use_farm', 'doorway']
senticnet['door_jamb'] = ['0', '0.089', '0', '0', '#interest', '#interest', 'positive', '0.089', 'ground', 'basement', 'base', 'foundation', 'basis']
senticnet['door_knob'] = ['0', '0', '0', '0.096', '#admiration', '#admiration', 'positive', '0.096', 'doorknob', 'knob', 'handle', 'lock', 'latch']
senticnet['door_knocker'] = ['0.979', '-0.69', '0', '0.576', '#joy', '#surprise', 'positive', '0.749', 'big_cheese', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['door_latch'] = ['0', '0', '0', '0.077', '#admiration', '#admiration', 'positive', '0.077', 'glass_front', 'gate', 'doorway', 'use_farm', 'entranceway']
senticnet['door_lock'] = ['0.033', '0', '0', '0.081', '#joy', '#admiration', 'positive', '0.057', 'door', 'frame', 'window_door', 'wall', 'doorway']
senticnet['door_open'] = ['-0.10', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.10', 'sell_car', 'unlock_door', 'own_car', 'shine_car', 'shoulder_burden']
senticnet['door_opener'] = ['0', '0', '0', '0.109', '#admiration', '#admiration', 'positive', '0.109', 'knob', 'door_knob', 'door_handle', 'handle', 'hinge']
senticnet['door_salesperson'] = ['0.829', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.908', 'cut_food', 'drink_beer', 'serve_drink', 'drink_much', 'drink_beverage']
senticnet['door_sedan'] = ['0.035', '0.112', '-0.07', '0.195', '#interest', '#admiration', 'positive', '0.067', 'car_action', 'car_travel', 'steer_car', 'make_go', 'transportation']
senticnet['door_shut'] = ['0', '0', '0', '0.077', '#admiration', '#admiration', 'positive', '0.077', 'glass_front', 'gate', 'doorway', 'use_farm', 'entranceway']
senticnet['door_station_wagon'] = ['-0.05', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'plane_land', 'floss', 'floss_tooth', 'ornament', 'wear_dress']
senticnet['door_trim'] = ['0.02', '0.085', '-0.05', '0.085', '#interest', '#admiration', 'positive', '0.034', 'gold', 'silver', 'medal', 'carat', 'medallion']
senticnet['door_wagon'] = ['-0.05', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'plane_land', 'floss', 'floss_tooth', 'ornament', 'wear_dress']
senticnet['doorbell'] = ['0', '0', '0', '0.208', '#admiration', '#admiration', 'positive', '0.208', 'knob', 'mailman', 'door_knob', 'doorknob', 'hinge']
senticnet['doorcase'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'vestibule', 'door', 'central_passage', 'gate', 'door_handle']
senticnet['doorkeeper'] = ['0.546', '-0.76', '0', '0.74', '#surprise', '#admiration', 'positive', '0.685', 'exorcist', 'deacon', 'acolyte', 'anagnost', 'holy_order']
senticnet['doorknob'] = ['0', '0', '0', '0.096', '#admiration', '#admiration', 'positive', '0.096', 'door_knob', 'knob', 'handle', 'lock', 'latch']
senticnet['doormat'] = ['-0.91', '0', '0.681', '-0.81', '#sadness', '#disgust', 'negative', '-0.80', 'chicken', 'softy', 'softie', 'crybaby', 'wimp']
senticnet['doors_shut'] = ['0', '0', '0', '0.077', '#admiration', '#admiration', 'positive', '0.077', 'glass_front', 'gate', 'doorway', 'use_farm', 'entranceway']
senticnet['doorstep'] = ['-0.91', '0', '0.681', '-0.81', '#sadness', '#disgust', 'negative', '-0.80', 'chicken', 'softy', 'softie', 'crybaby', 'wimp']
senticnet['doorway'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'vestibule', 'door', 'central_passage', 'gate', 'door_handle']
senticnet['dopamine_agonist'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['dopamine_antagonist'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['dopamine_da'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'gastrointestinal_hormone', 'secretin', 'ghrelin']
senticnet['dopamine_release'] = ['0.795', '0', '0', '0.787', '#joy', '#admiration', 'positive', '0.791', 'entree', 'soup', 'wine_glass', 'salt_pepper_shaker', 'plate_silverware']
senticnet['dopamine_reuptake_inhibitor'] = ['0.672', '0.632', '-0.23', '0.312', '#joy', '#interest', 'positive', '0.344', 'euphoria', 'buoyancy', 'perkiness', 'jubilance', 'ecstasy']
senticnet['dope'] = ['-0.97', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.86', 'doper', 'drug', 'doped', 'addiction', 'varnish']
senticnet['dope_dealer'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['dope_fiend'] = ['0', '0', '0.821', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'student', 'serious_student', 'addiction', 'graduate_student', 'hard_worker']
senticnet['doped'] = ['-0.74', '0', '0.635', '-0.75', '#sadness', '#disgust', 'negative', '-0.71', 'treated', 'burned', 'proofed', 'burnt', 'activated']
senticnet['doper'] = ['-0.98', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.87', 'dope', 'drug', 'doped', 'addiction', 'varnish']
senticnet['dopey'] = ['0', '0', '0.92', '-0.76', '#anger', '#disgust', 'negative', '-0.84', 'anserine', 'dopy', 'foolish', 'goosy', 'jerky']
senticnet['doping_concentration'] = ['0.89', '-0.62', '0', '0.548', '#joy', '#surprise', 'positive', '0.686', 'breakthrough', 'interpenetration', 'foray', 'maraud', 'raid']
senticnet['doping_violation'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'steer_wheel', 'gear_shift', 'chassis', 'brake_pedal', 'air_vent']
senticnet['doppelganger'] = ['0.849', '-0.50', '0', '0.845', '#joy', '#admiration', 'positive', '0.732', 'mythical_monster', 'mythical_creature', 'abominable_snowman', 'yeti', 'legendary_creature']
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senticnet['doreen_welsh'] = ['-0.08', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.08', 'turn_key', 'plan_route', 'open_car_door', 'book_flight', 'turn_car_off']
senticnet['dorian_mode'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['doric_portico'] = ['0.026', '0.219', '0', '0.16', '#interest', '#admiration', 'positive', '0.135', 'large_build', 'palace', 'high_rise', 'structure', 'fortification']
senticnet['dork'] = ['-0.03', '-0.09', '0.112', '-0.15', '#anger', '#disgust', 'negative', '-0.05', 'remember_childhood', 'make_easy', 'play_tune', 'play_song', 'perform_of_music']
senticnet['dorm_room'] = ['0', '0.072', '0', '0', '#interest', '#interest', 'positive', '0.072', 'metal_box', 'cash_drawer', 'keyhole', 'clutch_bag', 'safe']
senticnet['dormammu'] = ['-0.09', '0.085', '-0.08', '-0.11', '#sadness', '#disgust', 'negative', '-0.05', 'hitler', 'treachery', 'good_man', 'cannibalism', 'sad_person']
senticnet['dormancy'] = ['-0.75', '0', '0.512', '-0.75', '#sadness', '#disgust', 'negative', '-0.67', 'inactivity', 'inactiveness', 'slumber', 'inaction', 'hibernation']
senticnet['dormancy_period'] = ['-0.75', '0', '0.512', '-0.75', '#sadness', '#disgust', 'negative', '-0.67', 'inactivity', 'inactiveness', 'slumber', 'inaction', 'hibernation']
senticnet['dormant'] = ['-0.87', '0', '0.948', '-0.81', '#sadness', '#anger', 'negative', '-0.88', 'neutral', 'noble_gas', 'gas', 'inert_gas', 'argonon']
senticnet['dormer_window'] = ['0.046', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'light', 'bay_window', 'air_conditioner', 'sky', 'lamp']
senticnet['dormitory'] = ['0', '0', '0.012', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'shack', 'cabin', 'hostel', 'college_dorm', 'student_home']
senticnet['dorney_park'] = ['0.056', '-0.01', '0.036', '0.073', '#joy', '#admiration', 'positive', '0.028', 'thrill_ride', 'have_fun', 'drag_race', 'enjoyment', 'roller_coaster']
senticnet['dorsal_carpal_ligament'] = ['-0.74', '0', '0.598', '-0.75', '#sadness', '#disgust', 'negative', '-0.69', 'lunate_bone', 'semilunar_bone', 'carpal_bone', 'carpal', 'bone']
senticnet['dorsal_pattern'] = ['0.942', '-0.61', '0', '0.908', '#joy', '#admiration', 'positive', '0.82', 'adornment', 'cigar_band', 'hatband', 'neckband', 'pinstripe']
senticnet['dorsal_side'] = ['-0.66', '0', '0.809', '-0.61', '#sadness', '#anger', 'negative', '-0.69', 'face', 'base', 'underbelly', 'sole', 'bilge']
senticnet['dorsal_stream'] = ['0.693', '-0.52', '0', '0.82', '#joy', '#admiration', 'positive', '0.681', 'embodiment', 'shape', 'representation', 'concrete_representation', 'internal_representation']
senticnet['dorsal_stripe'] = ['0.942', '-0.61', '0', '0.908', '#joy', '#admiration', 'positive', '0.82', 'adornment', 'cigar_band', 'hatband', 'neckband', 'pinstripe']
senticnet['dorsal_surface'] = ['-0.66', '0', '0.809', '-0.61', '#sadness', '#anger', 'negative', '-0.69', 'face', 'base', 'underbelly', 'sole', 'bilge']
senticnet['dorsetshire'] = ['0.582', '-0.97', '0', '0.802', '#surprise', '#admiration', 'positive', '0.788', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['dorsiflex'] = ['0.582', '-0.66', '0', '0.661', '#surprise', '#admiration', 'positive', '0.637', 'talipes_equinus', 'deformity', 'malformation', 'talipes_valgus', 'talipes_calcaneus']
senticnet['dorsiflexion'] = ['0.582', '-0.66', '0', '0.661', '#surprise', '#admiration', 'positive', '0.637', 'talipes_equinus', 'deformity', 'malformation', 'talipes_valgus', 'talipes_calcaneus']
senticnet['dorsoventrally'] = ['0.886', '-0.99', '0', '0.542', '#joy', '#surprise', 'positive', '0.808', 'jowl', 'lower_jaw', 'mandible', 'lantern_jaw', 'lantern_jawr_bone']
senticnet['dorsum'] = ['-0.7', '0', '0.617', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'silvery', 'achromatic', 'bluish_black', 'hueless', 'pitchy']
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senticnet['dosage'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'imperium', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['dose'] = ['-0.61', '0', '0.899', '-0.62', '#anger', '#disgust', 'negative', '-0.71', 'venereal_disease', 'venereal_infection', 'social_disease', 'sexually_transmitted_disease', 'gonorrhoea']
senticnet['dosser'] = ['-0.81', '0', '0.747', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'vagrant', 'drifter', 'floater', 'vagabond', 'hobo']
senticnet['dot'] = ['0.044', '0', '0.029', '0.064', '#joy', '#admiration', 'positive', '0.026', 'full_stop', 'sentence_end', 'period', 'clock_face', 'point']
senticnet['dot_com_bubble'] = ['-0.55', '0', '0.869', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'air_bubble', 'foam', 'froth', 'soap_bubble', 'globule']
senticnet['dotage'] = ['-0.79', '0', '0.802', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'age', 'old_age', 'year', 'eld', 'geezerhood']
senticnet['dotard'] = ['-0.79', '0', '0.802', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'age', 'old_age', 'year', 'eld', 'geezerhood']
senticnet['dotcom_bubble'] = ['-0.55', '0', '0.869', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'air_bubble', 'foam', 'froth', 'soap_bubble', 'globule']
senticnet['dote'] = ['0.568', '-0.79', '0', '0.804', '#surprise', '#admiration', 'positive', '0.723', 'age', 'get_on', 'senesce', 'mature', 'maturate']
senticnet['doted_on'] = ['0.568', '-0.79', '0', '0.804', '#surprise', '#admiration', 'positive', '0.723', 'age', 'get_on', 'senesce', 'mature', 'maturate']
senticnet['dothistroma'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['doting'] = ['0', '-0.93', '0', '0.88', '#surprise', '#admiration', 'positive', '0.908', 'stud', 'constellate', 'disperse', 'adoring', 'fond']
senticnet['doting_mother'] = ['0.568', '-0.79', '0', '0.804', '#surprise', '#admiration', 'positive', '0.723', 'age', 'get_on', 'senesce', 'mature', 'maturate']
senticnet['dotted_rhythm'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['double'] = ['0.963', '-0.84', '0', '0.947', '#joy', '#admiration', 'positive', '0.919', 'match', 'reliever', 'equal', 'alternate', 'stunt_man']
senticnet['double-book'] = ['0', '0', '0.74', '-0.92', '#anger', '#disgust', 'negative', '-0.83', 'speciously', 'erroneous', 'erroneously', 'error', 'fallaciously']
senticnet['double-breasted'] = ['0', '0', '-0.90', '-0.79', '#fear', '#disgust', 'negative', '-0.85', 'collarless', 'button-front', 'high-necked', 'button-down', 'long-sleeved']
senticnet['double-dealing'] = ['0', '0', '-0.75', '-0.93', '#fear', '#disgust', 'negative', '-0.84', 'duplicitous', 'deceitful', 'ambidextrous', 'janus-faced', 'two-handed']
senticnet['double_agent'] = ['-0.69', '0', '0.756', '-0.99', '#anger', '#disgust', 'negative', '-0.81', 'waster', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['double_backflip'] = ['0.757', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.831', 'baseball_bat', 'water_gun', 'goalie', 'hockey_puck', 'hockey_stick']
senticnet['double_barrel_shotgun'] = ['-0.75', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.75', 'hydrogen_bomb', 'rifle', 'revolver', 'pistol', 'gun']
senticnet['double_barreled_shotgun'] = ['-0.75', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.75', 'hydrogen_bomb', 'rifle', 'revolver', 'pistol', 'gun']
senticnet['double_barrelled_shotgun'] = ['-0.75', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.75', 'hydrogen_bomb', 'rifle', 'revolver', 'pistol', 'gun']
senticnet['double_breasted_blazer'] = ['0', '0', '0', '0.963', '#admiration', '#admiration', 'positive', '0.963', 'wear', 'furnishing', 'wearable_technology', 'wearable', 'belt']
senticnet['double_breasted_jacket'] = ['-0.02', '0.256', '0', '0.166', '#interest', '#admiration', 'positive', '0.132', 'coat', 'shirt', 'garment', 'dress', 'vest']
senticnet['double_breasted_suit'] = ['0', '-0.02', '0.048', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'telescope', 'read_glass', 'eyeglass', 'camera', 'binoculars']
senticnet['double_bypass_surgery'] = ['0.934', '-0.88', '0', '0.797', '#joy', '#surprise', 'positive', '0.872', 'scan', 'checkup', 'scrutiny', 'medical_examination', 'medical_exam']
senticnet['double_cd'] = ['0.805', '-0.58', '0', '0.525', '#joy', '#surprise', 'positive', '0.637', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['double_check'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'review', 'check_spelling', 'spelling', 'correct_homework', 'mark_exam']
senticnet['double_chin'] = ['0', '0', '0.883', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'get_old', 'dry_skin', 'become_wet', 'clothes_shrink', 'wrinkle']
senticnet['double_click'] = ['0.055', '0.117', '0', '0.133', '#interest', '#admiration', 'positive', '0.102', 'graphical_representation', 'pencil_pen', 'note_pad', 'marker', 'stationery']
senticnet['double_cream'] = ['0.028', '0.138', '-0.04', '0.215', '#interest', '#admiration', 'positive', '0.083', 'water', 'dense', 'thick', 'milk', 'dense_water']
senticnet['double_digit_decline'] = ['0.978', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.865', 'tumble', 'dew', 'hailstone', 'rain', 'snowflake']
senticnet['double_digit_unemployment'] = ['0', '0', '0.948', '-0.96', '#anger', '#disgust', 'negative', '-0.95', 'work_much', 'lack_money', 'wage_slavery', 'buy_tool', 'get_paycheck']
senticnet['double_disc'] = ['-0.73', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'unwed', 'unwedded', 'divorced', 'unmarried', 'widowed']
senticnet['double_dissociation'] = ['0.693', '-0.52', '0', '0.82', '#joy', '#admiration', 'positive', '0.681', 'embodiment', 'shape', 'representation', 'concrete_representation', 'internal_representation']
senticnet['double_edge_razor'] = ['0', '0', '-0.92', '-0.95', '#fear', '#disgust', 'negative', '-0.94', 'scissor', 'razor', 'pair_of_scissors', 'haircut_scissor', 'clipper']
senticnet['double_edged_blade'] = ['-0.81', '0', '0.844', '-0.76', '#sadness', '#anger', 'negative', '-0.80', 'hold', 'grip', 'basket_hilt', 'handle', 'handgrip']
senticnet['double_edged_sword'] = ['0.808', '-0.83', '0', '0.548', '#joy', '#surprise', 'positive', '0.73', 'successfulness', 'luckiness', 'prosperity', 'circumstance', 'blessing']
senticnet['double_elimination_bracket'] = ['0.763', '-0.67', '0', '0.945', '#joy', '#admiration', 'positive', '0.795', 'set', 'income_bracket', 'tax_bracket', 'income_tax_bracket', 'price_bracket']
senticnet['double_entendre'] = ['0.88', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.828', 'ambiguity', 'loophole', 'amphibology', 'amphiboly', 'parisology']
senticnet['double_espresso'] = ['0.102', '0.084', '0', '0.076', '#joy', '#interest', 'positive', '0.087', 'better_cold', 'sip', 'potable_liquid', 'ingest', 'liquid_beverage']
senticnet['double_eviction'] = ['-0.08', '-0.03', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'graduate_student', 'student', 'serious_student', 'competition', 'hard_worker']
senticnet['double_handed_backhand'] = ['0', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.91', 'umpire', 'referee', 'legal_proceed', 'clang', 'clamor']
senticnet['double_happiness'] = ['0.044', '0.142', '-0.07', '0.084', '#interest', '#admiration', 'positive', '0.048', 'many_people', 'relate_person', 'human_group', 'happy_together', 'parent_child']
senticnet['double_headed_eagle'] = ['0.03', '0', '0.017', '0.138', '#joy', '#admiration', 'positive', '0.05', 'up_down', 'curl', 'bump', 'hand_gesture', 'goodbye']
senticnet['double_helix_structure'] = ['0.774', '-0.63', '0', '0.567', '#joy', '#surprise', 'positive', '0.658', 'catching', 'breakthrough', 'spying', 'determination', 'tracing']
senticnet['double_homicide'] = ['0', '0', '-0.88', '-0.99', '#fear', '#disgust', 'negative', '-0.93', 'murder_suspect', 'robbery_suspect', 'rape_suspect', 'murder', 'doubt']
senticnet['double_lined_spectroscopic'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['double_lp'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['double_lung_transplant'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'displace', 'move', 'move_away', 'bump', 'dislocate']
senticnet['double_money'] = ['0.807', '0', '0', '0', '#joy', '#joy', 'positive', '0.807', 'buy_stock', 'play_video_game', 'try_win_game', 'lose_money', 'desire_win']
senticnet['double_murderer'] = ['0', '0', '0.869', '0', '#anger', '#anger', 'negative', '-0.86', 'convoke', 'loose_end', 'car_race', 'auction', 'mortuary']
senticnet['double_nature'] = ['0.042', '-0.03', '0', '0.112', '#joy', '#admiration', 'positive', '0.063', 'hybrid', 'clean', 'highway_road', 'go_green', 'car_drive']
senticnet['double_negation'] = ['0.734', '-0.56', '0', '0.64', '#joy', '#admiration', 'positive', '0.645', 'inferential', 'suggestive', 'connotative', 'connotational', 'connotative_of']
senticnet['double_pneumonia'] = ['0', '0.877', '0', '0', '#interest', '#interest', 'positive', '0.877', 'get_rest', 'rejuvenation', 'feel_refreshed', 'take_nap', 'get_sleep']
senticnet['double_seam'] = ['-0.04', '-0.01', '0.026', '-0.12', '#sadness', '#disgust', 'negative', '-0.04', 'embroidery', 'cover_wind', 'use_needle', 'bandage', 'joint']
senticnet['double_sided_tape'] = ['0', '0', '-0.91', '-0.70', '#fear', '#disgust', 'negative', '-0.81', 'wax_dental_floss', 'tartar_control_toothpaste', 'toothpaste', 'gel_toothpaste', 'toothbrush']
senticnet['double_staircase'] = ['0.028', '0', '0', '0.051', '#joy', '#admiration', 'positive', '0.04', 'entranceway', 'doorway', 'entrance', 'entryway', 'corridor']
senticnet['double_star'] = ['0.769', '0.886', '0', '0', '#joy', '#interest', 'positive', '0.828', 'binary_star', 'binary_program', 'binary', 'picture', 'erotica']
senticnet['double_stranded_dna'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['double_talk'] = ['-0.61', '0', '0.738', '-0.80', '#anger', '#disgust', 'negative', '-0.71', 'babbling', 'gibber', 'mumbo_jumbo', 'babble', 'lallation']
senticnet['double_trouble'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'danger', 'dangerous', 'trouble', 'put_at_stake', 'problem']
senticnet['double_twisting_yurchenko'] = ['0.02', '0.205', '0', '0', '#joy', '#interest', 'positive', '0.112', 'safe', 'bank_vault', 'bank', 'piggy_bank', 'bank_account']
senticnet['double_vinyl'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['double_vision_diplopia'] = ['-0.88', '0', '0.762', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'divergent_strabismus', 'exotropia', 'walleye', 'abnormality', 'abnormalcy']
senticnet['double_wishbone_front'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'steer_wheel', 'gear_shift', 'chassis', 'brake_pedal', 'air_vent']
senticnet['doubled_argent'] = ['0.03', '0', '0.017', '0.138', '#joy', '#admiration', 'positive', '0.05', 'up_down', 'curl', 'bump', 'hand_gesture', 'goodbye']
senticnet['doublemint'] = ['0.023', '0.141', '-0.13', '0.174', '#interest', '#admiration', 'positive', '0.052', 'candy', 'push_wind', 'exhalation', 'breaout', 'mouth_action']
senticnet['doublespeak'] = ['0', '0', '0.762', '-0.87', '#anger', '#disgust', 'negative', '-0.81', 'awry', 'distortable', 'distort', 'embellish', 'misshapen']
senticnet['doubleton'] = ['0.022', '-0.11', '0.014', '0.057', '#surprise', '#admiration', 'positive', '0.045', 'wood', 'many_steps', 'franchise', 'stump', 'timber']
senticnet['doubly_periodic'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'fragmentation', 'discharge', 'backfire', 'blast']
senticnet['doubt'] = ['-0.33', '0.111', '-0.32', '0', '#sadness', '#fear', 'negative', '-0.18', 'direct_play', 'care_child', 'keep_promise', 'quiet_child', 'tell_lie']
senticnet['doubt_fud'] = ['0', '0', '0', '-0.70', '#disgust', '#disgust', 'negative', '-0.70', 'uncertain_future', 'uncertain_environment', 'insecure', 'blur', 'anxiety']
senticnet['doubter'] = ['-0.52', '0', '0.95', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'skeptic', 'sceptic', 'pessimist', 'defeatist', 'negativist']
senticnet['doubtful'] = ['-0.82', '0', '0.972', '0', '#sadness', '#anger', 'negative', '-0.89', 'alleged', 'dicey', 'unclear', 'undecided', 'doubtfulness']
senticnet['doubtfully'] = ['-0.78', '0', '-0.84', '0', '#sadness', '#fear', 'negative', '-0.81', 'questionably', 'doubt', 'ambiguity', 'context', 'doubtful']
senticnet['doubtfulness'] = ['0.795', '-0.98', '0', '0.793', '#joy', '#surprise', 'positive', '0.858', 'incredulity', 'mental_rejection', 'skepticism', 'mistrust', 'distrust']
senticnet['doubting'] = ['0.766', '-0.64', '0', '0.526', '#joy', '#surprise', 'positive', '0.647', 'distrustful', 'wary', 'sceptical', 'skeptical', 'overjealous']
senticnet['doubtingly'] = ['-0.80', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'difficulty', 'awkwardness', 'flea_bite', 'difficultness', 'cumbersomeness']
senticnet['douche'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'scumbag', 'bastard', 'asshole', 'motherfucker', 'dick']
senticnet['douchebag'] = ['-0.79', '0', '0', '-0.95', '#sadness', '#disgust', 'negative', '-0.87', 'idiot', 'dolt', 'dumb', 'little_minded', 'stupid']
senticnet['doug'] = ['0', '0', '0.837', '-0.99', '#anger', '#disgust', 'negative', '-0.91', 'discourteous', 'brusque', 'brusk', 'abrupt', 'unceremonious']
senticnet['doug_lebda'] = ['-0.63', '0', '0.862', '-0.52', '#sadness', '#anger', 'negative', '-0.67', 'knot', 'bunch', 'tuft', 'tussock', 'agglomeration']
senticnet['doug_stanhope'] = ['-0.02', '-0.02', '0.062', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'jump_up_down', 'tie_shoelace', 'jump_rope', 'hike', 'skate']
senticnet['doug_varone'] = ['-0.95', '0', '0.992', '-0.63', '#sadness', '#anger', 'negative', '-0.86', 'modern_dance', 'interpretive_dancing', 'interpretative_dance', 'interpretative_dancing', 'stage_dancing']
senticnet['doug_weller_talk'] = ['0.792', '-0.86', '0', '0.888', '#surprise', '#admiration', 'positive', '0.849', 'absurd', 'inconsequential', 'intuitive', 'visceral', 'nonrational']
senticnet['doug_weller_user_talk'] = ['0.792', '-0.86', '0', '0.888', '#surprise', '#admiration', 'positive', '0.849', 'absurd', 'inconsequential', 'intuitive', 'visceral', 'nonrational']
senticnet['dough'] = ['0.033', '0', '0.013', '0.064', '#joy', '#admiration', 'positive', '0.028', 'yeast', 'flour', 'crust', 'sweetness', 'staple_food']
senticnet['dough_hook'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'salad_shaker', 'store_shelf', 'kitchen_tool', 'pliers', 'ladle']
senticnet['dougherty'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
senticnet['doughnut'] = ['0.027', '-0.08', '0', '0', '#joy', '#surprise', 'positive', '0.054', 'cookie', 'mousse', 'custard', 'cake', 'brownie']
senticnet['doughnut_shop'] = ['0', '0', '0.044', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'large_supermarket', 'food_store', 'large_food_store', 'corner_shop', 'convenience_food_store']
senticnet['doughty'] = ['0.97', '0.956', '0', '0', '#joy', '#interest', 'positive', '0.963', 'proud', 'valiantly', 'valiant', 'valediction', 'franc']
senticnet['douglock'] = ['-0.53', '0', '0.637', '-0.92', '#anger', '#disgust', 'negative', '-0.69', 'incorporeal', 'unembodied', 'disembodied', 'bodiless', 'unbodied']
senticnet['doujinshi'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['dour'] = ['0.81', '0.934', '0', '0', '#joy', '#interest', 'positive', '0.872', 'retentive', 'long', 'dogged', 'tenacious', 'recollective']
senticnet['dour_scot'] = ['-0.79', '0', '0.992', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['douse'] = ['-0.80', '0', '0.962', '-0.55', '#sadness', '#anger', 'negative', '-0.77', 'sop', 'bedraggle', 'dowse', 'drench', 'soak']
senticnet['dove'] = ['-0.03', '-0.02', '0.027', '-0.10', '#sadness', '#disgust', 'negative', '-0.03', 'crow', 'bird_type', 'water_bird', 'bird_prey', 'raven']
senticnet['dove_sei'] = ['0.107', '0.11', '-0.08', '0.21', '#interest', '#admiration', 'positive', '0.086', 'live_healthy_life', 'forever_young', 'live_forever', 'live_long', 'live_good_life']
senticnet['dove_streptopelia'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet']
senticnet['dovish'] = ['0.929', '0', '0', '0.962', '#joy', '#admiration', 'positive', '0.946', 'concession', 'conciliate', 'conciliatory', 'conciliative', 'irenic']
senticnet['dowager'] = ['0', '-0.72', '0', '0.94', '#surprise', '#admiration', 'positive', '0.833', 'dignity', 'exalt', 'dignified', 'honor', 'dignify']
senticnet['dowd'] = ['-0.64', '0', '0.781', '-0.74', '#anger', '#disgust', 'negative', '-0.72', 'weirdo', 'crazy', 'loony', 'lunatic', 'nutcase']
senticnet['dowdy'] = ['0', '0', '0', '-0.72', '#disgust', '#disgust', 'negative', '-0.72', 'dreary', 'gloominess', 'darkness', 'gloomful', 'gloomily']
senticnet['dowel'] = ['0', '0', '0.061', '-0.03', '#anger', '#disgust', 'negative', '-0.04', 'garden_tool', 'straight_line', 'sharp', 'razor_blade', 'chock']
senticnet['dower_house'] = ['0.049', '0.16', '0', '0.155', '#interest', '#admiration', 'positive', '0.121', 'castle', 'palace', 'medieval', 'aristocrat', 'throne']
senticnet['down'] = ['-0.74', '0.241', '-0.42', '0', '#sadness', '#fear', 'negative', '-0.31', 'low', 'grim', 'sorry', 'gloominess', 'gloomily']
senticnet['down-and-out'] = ['-0.84', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.80', 'hard-drinking', 'down-on-his-luck', 'washed-up', 'drug-addicted', 'sad-sack']
senticnet['down-home'] = ['0.824', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.832', 'toe-tapping', 'foot-stomping', 'good-time', 'rollicking', 'hard-rocking']
senticnet['down-on-his-luck'] = ['-0.85', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.81', 'hard-drinking', 'down-and-out', 'washed-up', 'drug-addicted', 'sad-sack']
senticnet['down-to-earth'] = ['0', '0', '0.751', '-0.86', '#anger', '#disgust', 'negative', '-0.80', 'no-nonsense', 'straight-talking', 'straight-forward', 'hard-nosed', 'take-no-prisoners']
senticnet['down_comforter'] = ['0.808', '0', '0', '0', '#joy', '#joy', 'positive', '0.808', 'fall_asleep', 'relax_mind', 'curl_up', 'make_out', 'bed_time']
senticnet['down_drain'] = ['0', '0', '0.016', '-0.09', '#anger', '#disgust', 'negative', '-0.05', 'drain', 'skate_rink', 'septic_tank', 'beach', 'swimming_pool']
senticnet['down_feather'] = ['0.814', '-0.53', '0', '0.624', '#joy', '#admiration', 'positive', '0.659', 'feather', 'plume', 'duck_down', 'goose_down', 'swan_down']
senticnet['down_in_mouth'] = ['-0.78', '0.266', '-0.45', '-0.05', '#sadness', '#fear', 'negative', '-0.25', 'downhearted', 'low-spirited', 'depressed', 'dispirited', 'downcast']
senticnet['down_knee'] = ['-0.08', '0.187', '-0.05', '0.108', '#interest', '#admiration', 'positive', '0.038', 'pray', 'trust_god', 'believe_god', 'kneel', 'belief_god']
senticnet['down_payment'] = ['0.973', '0.707', '0', '0', '#joy', '#interest', 'positive', '0.84', 'money', 'wealthy', 'much_money', 'slogan', 'take_out_loan']
senticnet['down_syndrome'] = ['-0.82', '0', '0.582', '-0.74', '#sadness', '#disgust', 'negative', '-0.71', 'trisomy', 'birth_defect', 'congenital_anomaly', 'congenital_defect', 'congenital_disorder']
senticnet['downbeat'] = ['-0.62', '0', '0.988', '-0.59', '#sadness', '#anger', 'negative', '-0.73', 'motley', 'mixture', 'variety', 'assortment', 'alphabet_soup']
senticnet['downcast'] = ['-0.78', '0.266', '-0.45', '-0.05', '#sadness', '#fear', 'negative', '-0.25', 'downhearted', 'low-spirited', 'depressed', 'down_in_mouth', 'dispirited']
senticnet['downfall'] = ['-0.78', '0', '0.764', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'withering', 'weakening', 'atrophy', 'fading', 'attenuation']
senticnet['downgrade'] = ['-0.37', '0.11', '-0.21', '-0.06', '#sadness', '#fear', 'negative', '-0.13', 'military_retreat', 'retreat', 'lose_battle', 'lose_war', 'retrocede']
senticnet['downhearted'] = ['-0.79', '0.256', '-0.46', '-0.04', '#sadness', '#fear', 'negative', '-0.26', 'gloomy', 'down_in_mouth', 'depressed', 'downcast', 'low-spirited']
senticnet['downheartedness'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'dejectedness', 'dispiritedness', 'low-spiritedness', 'lowness', 'cheerless']
senticnet['downhill_ski'] = ['0', '-0.07', '0', '0', '#surprise', '#surprise', 'positive', '0.074', 'door', 'gate', 'doorway', 'glass_front', 'use_farm']
senticnet['downhill_stretch'] = ['-0.11', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.08', 'have_fun', 'spring_up', 'fall_well', 'shovel_snow', 'fly_around']
senticnet['downing_street_spokeswoman'] = ['-0.66', '0', '0.721', '-0.53', '#sadness', '#anger', 'negative', '-0.64', 'unlimited', 'outright', 'unconditional', 'unqualified', 'unqualifiedal']
senticnet['downland'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'biome', 'timberland']
senticnet['download'] = ['-0.08', '0', '0.011', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'deflate', 'read_cover', 'obtain_information', 'upgrade', 'spread_out']
senticnet['downloadable'] = ['0.81', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.774', 'available', 'public', 'population', 'crowd', 'majority']
senticnet['downmarket'] = ['-0.87', '0', '0.83', '-0.61', '#sadness', '#anger', 'negative', '-0.77', 'out', 'dated', 'stodgy', 'prehistoric', 'antique']
senticnet['downplay'] = ['-0.70', '0', '0', '-0.95', '#sadness', '#disgust', 'negative', '-0.82', 'too_much', 'wave_arm', 'show_off', 'laugh', 'applause']
senticnet['downpour'] = ['0.789', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.792', 'torrential', 'rainstorm', 'rain', 'flood', 'storm']
senticnet['downright'] = ['0', '0', '-0.74', '-0.87', '#fear', '#disgust', 'negative', '-0.80', 'absolute', 'out-and-out', 'rank', 'right-down', 'sheer']
senticnet['downright_annoying'] = ['-0.78', '0', '0.915', '-0.99', '#anger', '#disgust', 'negative', '-0.89', 'bodge', 'botch_up', 'flub', 'ball_up', 'go_wrong']
senticnet['downright_disrespectful'] = ['-0.64', '0', '0.625', '-0.88', '#sadness', '#disgust', 'negative', '-0.71', 'difficulty', 'awkwardness', 'flea_bite', 'difficultness', 'cumbersomeness']
senticnet['downright_nasty'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['downrightness'] = ['0.772', '-0.95', '0', '0.89', '#surprise', '#admiration', 'positive', '0.872', 'directness', 'straightforwardness', 'characteristic', 'immediacy', 'immediateness']
senticnet['downrock'] = ['-0.68', '0', '0.545', '-0.68', '#sadness', '#disgust', 'negative', '-0.64', 'stunt', 'tumble', 'split', 'walkover', 'acrobatic_feat']
senticnet['downside'] = ['-0.71', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.82', 'drawback', 'near-term', 'disadvantage', 'upside', 'volatility']
senticnet['downsize'] = ['0.887', '-0.73', '0', '0.603', '#joy', '#surprise', 'positive', '0.742', 'inflate', 'thin', 'trim_back', 'subtract', 'knock_off']
senticnet['downsizing'] = ['0.953', '-0.65', '0', '0.866', '#joy', '#admiration', 'positive', '0.823', 'economy_of_scale', 'retrenchment', 'curtailment', 'saving', 'action']
senticnet['downstairs'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'motel_room', 'appliance_store', 'apartment', 'shack', 'neighbor_house']
senticnet['downstairs_apartment'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'home', 'furniture', 'kitchen', 'flat']
senticnet['downstairs_bar'] = ['0.841', '0', '0', '0.71', '#joy', '#admiration', 'positive', '0.775', 'waiter', 'bar_service', 'beverage_service', 'concierge', 'minibar']
senticnet['downstairs_bathroom'] = ['0', '0.171', '-0.05', '0.102', '#interest', '#admiration', 'positive', '0.072', 'bed', 'mattress', 'bead', 'bed_cover', 'seduce']
senticnet['downstairs_neighbor'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'home', 'furniture', 'kitchen', 'flat']
senticnet['downtempo'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'drink', 'party', 'friend_gathering', 'rest_area', 'relax_time']
senticnet['downtime'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_musical_sound', 'play_orchestra', 'make_noise', 'play_music', 'make_drawing']
senticnet['downtown'] = ['0.043', '0.14', '0.01', '0.245', '#interest', '#admiration', 'positive', '0.104', 'in_between', 'middle', 'in_middle', 'center', 'placement']
senticnet['downtown_lac_mgantic'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'firework', 'bright_flash', 'nuclear_weapon', 'earthquake', 'nuclear_bomb']
senticnet['downtown_pittsburgh'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
senticnet['downtrend'] = ['0.05', '0.151', '0', '0.159', '#interest', '#admiration', 'positive', '0.12', 'glamour', 'stylish', 'style', 'clothes_style', 'fancy_clothes']
senticnet['downtrodden'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'oppressiveness', 'tyrannical', 'oppressive', 'tyrannous', 'laden']
senticnet['downturn'] = ['0', '0.285', '-0.07', '0.163', '#interest', '#admiration', 'positive', '0.124', 'plus', 'importance', 'a_lot', 'bonus', 'extra']
senticnet['downward'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'decline', 'height', 'gravity', 'slant', 'steep']
senticnet['downward_dog'] = ['0', '0', '0', '0.93', '#admiration', '#admiration', 'positive', '0.93', 'lift_weight', 'release_stress', 'move_body', 'take_hike', 'change_color']
senticnet['downward_drift'] = ['0.05', '0.151', '0', '0.159', '#interest', '#admiration', 'positive', '0.12', 'glamour', 'stylish', 'style', 'clothes_style', 'fancy_clothes']
senticnet['downward_motion'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotate_movement', 'rotate', 'go_around', 'manoeuvre', 'full_circle']
senticnet['downward_pressure'] = ['-0.12', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.09', 'breeze', 'move_air', 'air_movement', 'gape', 'force']
senticnet['downward_pressure_on'] = ['-0.12', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.09', 'breeze', 'move_air', 'air_movement', 'gape', 'force']
senticnet['downward_toward'] = ['0', '0.21', '-0.11', '0.345', '#interest', '#admiration', 'positive', '0.147', 'direction', 'unto', 'forehead', 'upon', 'go_toward']
senticnet['downward_trend'] = ['0.05', '0.151', '0', '0.159', '#interest', '#admiration', 'positive', '0.12', 'glamour', 'stylish', 'style', 'clothes_style', 'fancy_clothes']
senticnet['downwardly'] = ['0.833', '0', '0', '0.955', '#joy', '#admiration', 'positive', '0.894', 'inwardly', 'radially', 'upwardly', 'outwardly', 'axially']
senticnet['downwelling_radiation'] = ['-0.97', '0', '0.781', '-0.91', '#sadness', '#disgust', 'negative', '-0.88', 'weakness', 'fault', 'flaw', 'defect', 'wart']
senticnet['downy_mildew'] = ['-0.83', '0', '0.754', '-0.97', '#sadness', '#disgust', 'negative', '-0.85', 'brown_rot_gummosis', 'ring_rot', 'ring_disease', 'tobacco_wilt', 'gummosis']
senticnet['downy_serviceberry'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'horse_chestnut', 'red_buckeye', 'particolored_buckeye']
senticnet['dowry'] = ['0.029', '0.23', '-0.10', '0.196', '#interest', '#admiration', 'positive', '0.088', 'reward', 'bribe', 'welfare', 'remittance', 'owe']
senticnet['dowse'] = ['-0.80', '0', '0.962', '-0.55', '#sadness', '#anger', 'negative', '-0.77', 'sop', 'douse', 'bedraggle', 'drench', 'soak']
senticnet['doxastic'] = ['-0.55', '0', '0.58', '-0.98', '#anger', '#disgust', 'negative', '-0.70', 'fatalist', 'determinist', 'predestinarian', 'predestinationist', 'philosopher']
senticnet['doxazosin'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'spironolactone', 'spironolactone_drug']
senticnet['doyenne'] = ['0.556', '-0.64', '0', '0.64', '#surprise', '#admiration', 'positive', '0.612', 'centenarian', 'nonagenarian', 'golden_ager', 'septuagenarian', 'sexagenarian']
senticnet['doyle'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
senticnet['doze'] = ['0.051', '0.074', '-0.05', '0.084', '#interest', '#admiration', 'positive', '0.04', 'rest_night', 'lie_down', 'shut_eye', 'lay_down', 'count_sheep']
senticnet['doze_off'] = ['-0.84', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.84', 'fall_asleep', 'bed_time', 'become_bored', 'drowsiness', 'turn_off_television']
senticnet['dozed_off'] = ['0', '0.08', '0', '0.179', '#interest', '#admiration', 'positive', '0.13', 'wake_up_morning', 'up_early', 'get_up_early', 'gather_energy_tomorrow', 'sit_chair']
senticnet['dozen'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'hundred_year', 'twelve', 'day_month', 'time_measurement']
senticnet['dozing_off'] = ['0.758', '-0.95', '0', '0', '#joy', '#surprise', 'positive', '0.859', 'boring_talk', 'doze_off', 'drowsiness', 'sleepy', 'tired']
senticnet['dr_kieran_breen'] = ['-0.95', '0', '0.621', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'disease', 'brain_disorder', 'brain_disease', 'ill', 'epilepsy']
senticnet['dr_ng_eng'] = ['0.039', '0.089', '-0.08', '0.143', '#interest', '#admiration', 'positive', '0.048', 'bird', 'nightingale', 'stork', 'eagle', 'rooster']
senticnet['dr_pepper'] = ['0.964', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.964', 'carbonated_drink', 'sugar_free', 'coke', 'diet_coke', 'beverage']
senticnet['dr_sid'] = ['0', '0', '0.876', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'untune', 'discompose', 'discomfit', 'disconcert', 'upset']
senticnet['drab'] = ['0', '0', '0', '-0.72', '#disgust', '#disgust', 'negative', '-0.72', 'dreary', 'gloominess', 'darkness', 'gloomful', 'gloomily']
senticnet['drab_gray'] = ['0', '0', '0', '-0.72', '#disgust', '#disgust', 'negative', '-0.72', 'dreary', 'gloominess', 'darkness', 'gloomful', 'gloomily']
senticnet['drabness'] = ['-1', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.87', 'tastelessness', 'sleaziness', 'coarseness', 'commonness', 'inelegance']
senticnet['dracaena'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'gnome', 'elf', 'goblin', 'undersized', 'small_person']
senticnet['dracaenura'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'aphasia', 'anomic_aphasia']
senticnet['draco'] = ['0', '0', '-0.79', '-0.75', '#fear', '#disgust', 'negative', '-0.77', 'oppressive', 'autocratic', 'severe', 'strict', 'draconian']
senticnet['dracolich'] = ['-0.93', '0', '0.544', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'mutant', 'variation', 'sport', 'lusus_naturae', 'mutation']
senticnet['draconian'] = ['0', '0', '-0.78', '-0.75', '#fear', '#disgust', 'negative', '-0.76', 'oppressive', 'autocratic', 'severe', 'strict', 'draco']
senticnet['draconic'] = ['-0.75', '0', '0.708', '0', '#sadness', '#anger', 'negative', '-0.73', 'hound', 'cage_animal', 'kind_animal', 'hunter', 'animal']
senticnet['draft'] = ['0.051', '0.074', '-0.05', '0.084', '#interest', '#admiration', 'positive', '0.04', 'sketch', 'scheme', 'compose_draft', 'draw_draft', 'draftsman']
senticnet['draft_resister'] = ['-0.97', '0', '0.62', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'obstructor', 'obstructer', 'technophobe', 'obstructionist', 'naysayer']
senticnet['draftsman'] = ['0.051', '0.074', '-0.05', '0.084', '#interest', '#admiration', 'positive', '0.04', 'sketch', 'scheme', 'compose_draft', 'draw_draft', 'draft']
senticnet['draftsmanship'] = ['0.567', '-0.56', '0', '0.724', '#joy', '#admiration', 'positive', '0.617', 'truth', 'accuracy', 'preciseness', 'trueness', 'precision']
senticnet['drafty'] = ['-0.61', '0', '0.732', '-0.59', '#sadness', '#anger', 'negative', '-0.64', 'leaky', 'draughty', 'porous', 'drippy', 'oozing']
senticnet['drag'] = ['0', '0', '0', '-0.72', '#disgust', '#disgust', 'negative', '-0.72', 'impediment', 'obstacle', 'obstruction', 'hindrance', 'annoyance']
senticnet['drag_harrow'] = ['-0.99', '0', '0.827', '-0.54', '#sadness', '#anger', 'negative', '-0.78', 'treat', 'cover', 'handle', 'theologize', 'deal']
senticnet['drag_queen'] = ['0.82', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.862', 'pervert', 'healthy_person', 'philanthropist', 'arsonist', 'nazi']
senticnet['drag_race'] = ['-0.86', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.84', 'dilemma', 'muddle', 'death_row', 'loose_end', 'funeral_home']
senticnet['drag_up'] = ['-0.97', '0', '0.692', '-0.64', '#sadness', '#anger', 'negative', '-0.77', 'moot', 'bring_up', 'mention', 'advert', 'cite']
senticnet['draggable'] = ['0.055', '0.117', '0', '0.133', '#interest', '#admiration', 'positive', '0.102', 'graphical_representation', 'pencil_pen', 'note_pad', 'marker', 'stationery']
senticnet['draggle'] = ['-0.84', '0', '0.827', '-0.81', '#sadness', '#anger', 'negative', '-0.83', 'sop', 'douse', 'dowse', 'drench', 'soak']
senticnet['draggled'] = ['-0.69', '0', '0.857', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'unclean', 'befouled', 'filthy', 'nasty', 'scummy']
senticnet['draggy'] = ['0.741', '-0.74', '0', '0.666', '#joy', '#surprise', 'positive', '0.718', 'commonplaceness', 'mundanity', 'prosiness', 'prosaicness', 'ordinariness']
senticnet['dragon'] = ['-0.75', '0', '0.708', '0', '#sadness', '#anger', 'negative', '-0.73', 'hound', 'cage_animal', 'kind_animal', 'hunter', 'animal']
senticnet['dragon_ball'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['dragon_egg'] = ['-0.75', '0', '0.708', '0', '#sadness', '#anger', 'negative', '-0.73', 'hound', 'cage_animal', 'kind_animal', 'hunter', 'animal']
senticnet['dragon_gate'] = ['-0.57', '0', '0.571', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['dragon_kid'] = ['-0.57', '0', '0.571', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['dragon_slayer'] = ['-0.75', '0', '0.708', '0', '#sadness', '#anger', 'negative', '-0.73', 'hound', 'cage_animal', 'kind_animal', 'hunter', 'animal']
senticnet['dragonball_evolution'] = ['-0.06', '0.122', '-0.04', '0.113', '#interest', '#admiration', 'positive', '0.032', 'encounter', 'portray', 'reveal', 'episode', 'next_episode']
senticnet['dragoncard'] = ['0.739', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.814', 'receipt', 'bet', 'electric_bill', 'letter_slot', 'deal']
senticnet['dragonet'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'plectognath', 'plectognathfish', 'blowfish', 'triggerfish', 'filefish']
senticnet['dragonfly'] = ['0.744', '0', '0', '0.853', '#joy', '#admiration', 'positive', '0.798', 'feather', 'butterfly', 'bird', 'peacock', 'teal']
senticnet['dragonlord'] = ['-0.75', '0', '0.708', '0', '#sadness', '#anger', 'negative', '-0.73', 'hound', 'cage_animal', 'kind_animal', 'hunter', 'animal']
senticnet['dragonslayer'] = ['-0.75', '0', '0.708', '0', '#sadness', '#anger', 'negative', '-0.73', 'hound', 'cage_animal', 'kind_animal', 'hunter', 'animal']
senticnet['dragonstone'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'feudal_lord', 'lord']
senticnet['dragoon'] = ['0', '0', '-0.83', '-0.76', '#fear', '#disgust', 'negative', '-0.8', 'dragon', 'pigeon', 'bidet', 'cavalryman', 'horse']
senticnet['drain'] = ['-0.02', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.04', 'down_drain', 'septic_tank', 'water_fountain', 'sink', 'flea_market']
senticnet['drain_pipe'] = ['0.939', '0.939', '0', '0', '#joy', '#interest', 'positive', '0.939', 'puddle', 'sop', 'liquid_water', 'finger_paint', 'bread']
senticnet['drain_plug'] = ['0.648', '0.618', '-0.26', '0.332', '#joy', '#interest', 'positive', '0.334', 'exhilarate', 'teased', 'titillating', 'titillate', 'titillation']
senticnet['drainage'] = ['0.817', '-0.89', '0', '0', '#joy', '#surprise', 'positive', '0.856', 'exhaust', 'unloading', 'discharge', 'waste', 'refuse']
senticnet['drainage_canal'] = ['0.933', '0.795', '0', '0', '#joy', '#interest', 'positive', '0.864', 'water_passage', 'small_stream', 'water_flow', 'flowing_water', 'water_stream']
senticnet['drainage_channel'] = ['0.933', '0.795', '0', '0', '#joy', '#interest', 'positive', '0.864', 'water_passage', 'small_stream', 'water_flow', 'flowing_water', 'water_stream']
senticnet['drainage_divide'] = ['0.034', '0.09', '0.039', '0.061', '#interest', '#admiration', 'positive', '0.036', 'border', 'limit', 'edge', 'indoor', 'side']
senticnet['drainage_easement'] = ['0.621', '-0.60', '0', '0.545', '#joy', '#surprise', 'positive', '0.59', 'privilege', 'perquisite', 'public_easement', 'right_of_way', 'prerogative']
senticnet['drainer'] = ['0', '0.953', '0', '0.857', '#interest', '#admiration', 'positive', '0.905', 'spontaneously', 'inherently', 'acoustic', 'naturally', 'mineral']
senticnet['draining'] = ['-0.82', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.83', 'debilitating', 'exhausting', 'debilitative', 'enervating', 'enfeebling']
senticnet['drama'] = ['0.734', '-0.60', '0', '0.612', '#joy', '#admiration', 'positive', '0.65', 'theater_of_absurd', 'playlet', 'miracle_play', 'morality_play', 'mystery_play']
senticnet['drama_critic'] = ['0.811', '-0.63', '0', '0.959', '#joy', '#admiration', 'positive', '0.8', 'critic', 'theater_critic', 'professional', 'art_critic', 'literary_critic']
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senticnet['dressing_room_door'] = ['0.085', '0', '-0.05', '0.116', '#joy', '#admiration', 'positive', '0.05', 'dance_well', 'water_faucet', 'enter_contest', 'dance_type', 'dance_music']
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senticnet['dressmaker'] = ['0.99', '0', '0', '0.721', '#joy', '#admiration', 'positive', '0.855', 'hair', 'haircut', 'hairdresser_chair', 'scissor', 'hairstyle']
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senticnet['dribble'] = ['0', '0.093', '0', '0.07', '#interest', '#admiration', 'positive', '0.082', 'play_football', 'football', 'play_team', 'play_game', 'play']
senticnet['dribbling_ability'] = ['0', '0.127', '-0.04', '0.194', '#interest', '#admiration', 'positive', '0.092', 'quick', 'fast', 'speedy', 'quick_movement', 'speed']
senticnet['dried-up'] = ['0', '0', '-0.75', '-0.98', '#fear', '#disgust', 'negative', '-0.86', 'sear', 'withered', 'sere', 'shrivelled', 'shriveled']
senticnet['dried_flower'] = ['0', '0.086', '0', '0', '#interest', '#interest', 'positive', '0.086', 'swim_ocean', 'weight_lift', 'sit_in_shade', 'cut_grass', 'trust_friend']
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senticnet['dried_milk'] = ['0.053', '0.085', '0.019', '0', '#joy', '#interest', 'positive', '0.04', 'farm', 'countryside', 'farm_animal', 'cow', 'healthy']
senticnet['dried_mud'] = ['-0.06', '-0.01', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'low', 'ground', 'look_down', 'ground_level', 'under_house']
senticnet['dried_oregano'] = ['0.1', '0', '0', '0', '#joy', '#joy', 'positive', '0.1', 'burn_material', 'ash', 'stay_warm', 'bonfire', 'campfire']
senticnet['dried_pasta'] = ['0.763', '0.9', '0', '0', '#joy', '#interest', 'positive', '0.832', 'breakfast', 'box_cereal', 'calcium', 'drink', 'carton_milk']
senticnet['dried_shrimp'] = ['0', '-0.03', '0.046', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'pasta', 'green_bean', 'mayonnaise', 'frozen_food', 'sour_milk']
senticnet['dried_squid'] = ['0.9', '0.963', '0', '0', '#joy', '#interest', 'positive', '0.931', 'form_of_food', 'japanese_food', 'uncooked', 'healthy', 'food']
senticnet['drier'] = ['-0.02', '-0.02', '0.076', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'wear_raincoat', 'carry_umbrella', 'feel_cold', 'camp_out', 'splash_water']
senticnet['drier_air'] = ['0', '0.094', '0', '0', '#interest', '#interest', 'positive', '0.094', 'cold', 'cavern', 'under_umbrella', 'parasol', 'stick_wall']
senticnet['driest_desert'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'dryness', 'thirstiness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['driffield_navigation'] = ['-0.77', '0', '0.583', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'rain', 'rain_down', 'sheet', 'rain_bucket', 'rain_buckets_down']
senticnet['drift'] = ['0.908', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.875', 'coyote', 'player', 'prospect', 'hopeful', 'team']
senticnet['drifter'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'beachcomber', 'bum', 'sundowner']
senticnet['drifting_helplessly'] = ['-0.99', '0', '0.658', '-0.73', '#sadness', '#disgust', 'negative', '-0.79', 'purposeless', 'afloat', 'aimless', 'directionless', 'planless']
senticnet['driftwood'] = ['0', '0', '0.021', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'dead_fish', 'tidal_wave', 'seashell', 'titanic', 'jelly_fish']
senticnet['drill'] = ['0.94', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.845', 'power_drill', 'drill_bite', 'air_pump', 'adjustable_wrench', 'screwdriver']
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senticnet['drink_a_lot_of_alcohol'] = ['0', '0', '-0.93', '-0.74', '#fear', '#disgust', 'negative', '-0.84', 'make_potato', 'drink_a_lot_of_booze', 'get_drunk', 'drink_much', 'drunk']
senticnet['drink_a_lot_of_beer'] = ['0.752', '0.911', '0', '0', '#joy', '#interest', 'positive', '0.832', 'go_bar', 'celebrate', 'designate_driver', 'go_party', 'drink_alcohol']
senticnet['drink_a_lot_of_booze'] = ['0.06', '-0.12', '0', '0', '#joy', '#surprise', 'positive', '0.093', 'open_bottle', 'drink_excess', 'consume_alcohol', 'drink_a_lot_of_alcohol', 'buy_alcohol']
senticnet['drink_a_lot_of_water'] = ['-0.03', '0.086', '0', '0.076', '#interest', '#admiration', 'positive', '0.042', 'drink_water', 'stamina', 'eat_properly', 'limber_up', 'yoga']
senticnet['drink_alcohol'] = ['0.081', '-0.05', '0', '-0.03', '#joy', '#surprise', 'positive', '0.036', 'meet_woman', 'consume_alcoholic_beverage', 'drink_much', 'go_party', 'drink_liquor']
senticnet['drink_and_drive'] = ['-0.92', '0', '0.448', '-0.91', '#sadness', '#disgust', 'negative', '-0.76', 'accident', 'illegal', 'death', 'unsafe', 'commit_suicide']
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senticnet['drink_beverage'] = ['0.081', '-0.05', '0', '-0.03', '#joy', '#surprise', 'positive', '0.036', 'cut_food', 'drink_beer', 'serve_drink', 'drink_much', 'pry_open']
senticnet['drink_blood'] = ['-0.23', '0', '-0.14', '-0.11', '#sadness', '#fear', 'negative', '-0.16', 'bite_dog', 'maim', 'pull_trigger', 'feel_remorse', 'stab']
senticnet['drink_booze'] = ['-0.04', '0', '-0.09', '-0.12', '#fear', '#disgust', 'negative', '-0.08', 'drunkenness', 'become_drunk', 'drunk', 'act_stupid', 'intoxication']
senticnet['drink_bottled_water'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'drinking_water', 'sugar_water']
senticnet['drink_champagne'] = ['0.866', '-0.91', '0', '0.893', '#surprise', '#admiration', 'positive', '0.892', 'mooch', 'freeload', 'grub', 'sponge', 'bum']
senticnet['drink_cocktail'] = ['0.074', '0', '0', '0', '#joy', '#joy', 'positive', '0.074', 'alcoholic_drink', 'alcoholic_beverage', 'adult_drink', 'strong_drink', 'ferment_beverage']
senticnet['drink_coffee'] = ['-0.06', '-0.03', '0.013', '-0.15', '#sadness', '#disgust', 'negative', '-0.04', 'drink_cup_coffee', 'drink_tea', 'stay_awake', 'look_out_window', 'drink_soda']
senticnet['drink_coke'] = ['0.789', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.827', 'laugh_out_loud', 'change_channel', 'drink_soda', 'sit_sofa', 'read_credit']
senticnet['drink_container'] = ['0', '0.071', '0', '0.128', '#interest', '#admiration', 'positive', '0.1', 'drink_holder', 'recyclable', 'type_of_container', 'liquid_container', 'storage_container']
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senticnet['drink_cup_coffee'] = ['-0.77', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.83', 'drink_coffee', 'put_feet_up', 'cafe', 'drink_soda', 'fall_asleep']
senticnet['drink_cup_tea'] = ['-0.92', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.92', 'eye_strain', 'see_ad', 'new_perspective', 'time_waste', 'change_opinion']
senticnet['drink_drive'] = ['-0.92', '0', '0.833', '-0.97', '#sadness', '#disgust', 'negative', '-0.90', 'escape_prison', 'drive_fast', 'car_accident', 'cross_red_light', 'break_rule']
senticnet['drink_espresso'] = ['-0.11', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.08', 'deal_card_player', 'speak_english', 'wind_watch', 'forward_email', 'explain_everything']
senticnet['drink_excess'] = ['0', '0', '0.827', '-0.88', '#anger', '#disgust', 'negative', '-0.85', 'drink_a_lot_of_booze', 'open_bottle', 'consume_alcohol', 'drink_a_lot_of_alcohol', 'drunk']
senticnet['drink_excessively'] = ['0.06', '-0.12', '0', '0', '#joy', '#surprise', 'positive', '0.093', 'drink_a_lot_of_booze', 'drink_excess', 'open_bottle', 'drink_a_lot_of_alcohol', 'buy_alcohol']
senticnet['drink_fluid'] = ['0.108', '0.145', '-0.09', '0', '#joy', '#interest', 'positive', '0.054', 'drink_wine', 'choke_food', 'serve_coffee', 'drink_tea', 'serve_drink']
senticnet['drink_glass'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'quench_thirst', 'appear_beautiful', 'forward_email', 'mind_store', 'speak_english']
senticnet['drink_heavily'] = ['0', '0.931', '0', '0', '#interest', '#interest', 'positive', '0.931', 'football', 'rake_leave', 'spray_paint_grafitti', 'juggle', 'play_hide_seek']
senticnet['drink_holder'] = ['0.862', '0.992', '0', '0', '#joy', '#interest', 'positive', '0.927', 'drink_container', 'liquid_container', 'drink_cup', 'glass_container', 'type_of_container']
senticnet['drink_hot_chocolate'] = ['0.887', '0.897', '0', '0', '#joy', '#interest', 'positive', '0.892', 'drink_water', 'drink_espresso', 'drink', 'drink_coffee', 'drink_milk']
senticnet['drink_juice'] = ['-0.08', '0', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.06', 'serve_beer', 'raise_hell', 'chill_drink', 'pay_tab', 'drink_much_beer']
senticnet['drink_liquid'] = ['0', '0.091', '-0.06', '0.114', '#interest', '#admiration', 'positive', '0.048', 'chill_drink', 'put_liquid', 'hold_coffee', 'serve_beer', 'quench_thirst']
senticnet['drink_liquor'] = ['0.081', '-0.05', '0', '-0.03', '#joy', '#surprise', 'positive', '0.036', 'drink_alcohol', 'consume_alcoholic_beverage', 'drink_much', 'hit_bottle', 'meet_woman']
senticnet['drink_lot'] = ['-0.06', '-0.00', '-0.08', '-0.11', '#fear', '#disgust', 'negative', '-0.06', 'find_toilet', 'find_restroom', 'full_bladder', 'serve_beer', 'refreshment']
senticnet['drink_milk'] = ['-0.03', '0.086', '0', '0.076', '#interest', '#admiration', 'positive', '0.042', 'drink_water', 'drink_espresso', 'drink', 'drink_coffee', 'drink_hot_chocolate']
senticnet['drink_moderately'] = ['0.06', '-0.12', '0', '0', '#joy', '#surprise', 'positive', '0.093', 'drink_a_lot_of_booze', 'drink_excess', 'open_bottle', 'drink_a_lot_of_alcohol', 'buy_alcohol']
senticnet['drink_much'] = ['0.081', '-0.05', '0', '-0.03', '#joy', '#surprise', 'positive', '0.036', 'drink_alcohol', 'consume_alcoholic_beverage', 'meet_woman', 'hit_bottle', 'drink_liquor']
senticnet['drink_much_alcohol'] = ['-0.86', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.81', 'drunk', 'drink_much', 'make_potato', 'drink_a_lot_of_alcohol', 'act_stupid']
senticnet['drink_much_beer'] = ['-0.06', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.04', 'eat_salad', 'burp', 'go_restroom', 'serve_beer', 'spill_food_clothes']
senticnet['drink_orange_juice'] = ['-0.71', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.77', 'spill_milk', 'read_paper', 'drop_food', 'smell_food', 'choke']
senticnet['drink_plenty'] = ['-0.71', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.77', 'spill_milk', 'read_paper', 'drop_food', 'smell_food', 'choke']
senticnet['drink_plenty_water'] = ['-0.02', '0', '0.052', '0', '#sadness', '#anger', 'negative', '-0.04', 'exercise', 'exercise_regularly', 'physical_exercise', 'moderation', 'antioxidant']
senticnet['drink_poison'] = ['0', '0', '-0.72', '-0.94', '#fear', '#disgust', 'negative', '-0.83', 'cripple', 'influenza', 'anger', 'overpay', 'meanness']
senticnet['drink_raw_milk'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'aridness', 'dryness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['drink_responsibly'] = ['0.06', '-0.12', '0', '0', '#joy', '#surprise', 'positive', '0.093', 'drink_a_lot_of_booze', 'drink_excess', 'open_bottle', 'drink_a_lot_of_alcohol', 'buy_alcohol']
senticnet['drink_rum'] = ['0.081', '-0.05', '0', '-0.03', '#joy', '#surprise', 'positive', '0.036', 'drink_alcohol', 'consume_alcoholic_beverage', 'drink_much', 'hit_bottle', 'meet_woman']
senticnet['drink_sea_water'] = ['0', '0', '0', '-0.76', '#disgust', '#disgust', 'negative', '-0.76', 'travel_fast_light', 'live_ocean', 'unhealthy', 'sick', 'eat_dog']
senticnet['drink_soda'] = ['-0.75', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.82', 'become_bored', 'turn_off', 'drink', 'get_bored', 'use_electricity']
senticnet['drink_soup'] = ['0.097', '0', '0', '0', '#joy', '#joy', 'positive', '0.097', 'serve_liquid', 'eat_liquid', 'swallow', 'consume_food', 'fill_up']
senticnet['drink_tap_water'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'drinking_water', 'sugar_water']
senticnet['drink_tea'] = ['-0.06', '0', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.05', 'choke_food', 'spill_food_clothes', 'wind_watch', 'date', 'eat_salad']
senticnet['drink_vodka'] = ['0.866', '-0.91', '0', '0.893', '#surprise', '#admiration', 'positive', '0.892', 'mooch', 'freeload', 'grub', 'sponge', 'bum']
senticnet['drink_water'] = ['0.601', '0.647', '-0.26', '0.322', '#joy', '#interest', 'positive', '0.326', 'drink_lot', 'drink_beer', 'refreshment', 'healthy', 'wipe_mouth_napkin']
senticnet['drink_whiskey'] = ['0.081', '-0.05', '0', '-0.03', '#joy', '#surprise', 'positive', '0.036', 'drink_alcohol', 'consume_alcoholic_beverage', 'drink_much', 'hit_bottle', 'meet_woman']
senticnet['drink_wine'] = ['0.108', '0.145', '-0.09', '0', '#joy', '#interest', 'positive', '0.054', 'steak', 'dinner', 'special_occasion', 'decanter', 'wine_opener']
senticnet['drinkable'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'serve_hot', 'tasty', 'liquid_food', 'hearty', 'porridge']
senticnet['drinkable_water'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'bottled_water', 'sugar_water']
senticnet['drinkable_yogurt'] = ['0.057', '0', '0.018', '0.058', '#joy', '#admiration', 'positive', '0.032', 'porridge', 'food', 'milk', 'healthy', 'morning_food']
senticnet['drinker'] = ['0.645', '0.637', '-0.23', '0.299', '#joy', '#interest', 'positive', '0.336', 'gladdened', 'stimulating', 'tickle_pink', 'exalt', 'beatify']
senticnet['drinkin'] = ['0.797', '-0.95', '0', '0.921', '#surprise', '#admiration', 'positive', '0.891', 'lush', 'alcoholic', 'alky', 'dipsomaniac', 'boozer']
senticnet['drinking'] = ['0.987', '-0.74', '0', '0.695', '#joy', '#surprise', 'positive', '0.809', 'vice', 'drink', 'intemperateness', 'boozing', 'drunkenness']
senticnet['drinking_alcohol'] = ['0', '0.931', '0', '0', '#interest', '#interest', 'positive', '0.931', 'football', 'rake_leave', 'spray_paint_grafitti', 'juggle', 'play_hide_seek']
senticnet['drinking_binge'] = ['0.884', '0', '0', '0', '#joy', '#joy', 'positive', '0.884', 'egalitarian', 'enjoy', 'drink', 'have_fun', 'senator']
senticnet['drinking_bottled_water'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'drinking_water', 'sugar_water']
senticnet['drinking_champagne'] = ['0.884', '0', '0', '0', '#joy', '#joy', 'positive', '0.884', 'egalitarian', 'enjoy', 'drink', 'have_fun', 'senator']
senticnet['drinking_chocolate'] = ['0.794', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.877', 'cocoa', 'hot_chocolate', 'chocolate', 'season', 'stock']
senticnet['drinking_den'] = ['0.853', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.916', 'tavern', 'cafe', 'nightclub', 'bar', 'liquor_store']
senticnet['drinking_juice'] = ['-0.71', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.77', 'spill_milk', 'read_paper', 'drop_food', 'smell_food', 'choke']
senticnet['drinking_liquor'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'vapor', 'gaseous', 'haze', 'smoke', 'smog']
senticnet['drinking_milk'] = ['-0.03', '0.086', '0', '0.076', '#interest', '#admiration', 'positive', '0.042', 'drink_water', 'drink_espresso', 'drink', 'drink_coffee', 'drink_hot_chocolate']
senticnet['drinking_place'] = ['-0.86', '0', '0.999', '0', '#sadness', '#anger', 'negative', '-0.93', 'metal_stick', 'tavern', 'place_to_drink', 'water_hole', 'long_tube']
senticnet['drinking_soda'] = ['-0.92', '0', '0.784', '-0.91', '#sadness', '#disgust', 'negative', '-0.87', 'bad_meal', 'eat_snack', 'eat_unhealthy_food', 'eat_expired_food', 'eat_quickly']
senticnet['drinking_straw'] = ['0', '-0.03', '0.042', '-0.07', '#anger', '#disgust', 'negative', '-0.02', 'ball', 'ping_pong_show', 'dandruff', 'solid_water', 'empty_bottle']
senticnet['drinking_tap_water'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'drinking_water', 'sugar_water']
senticnet['drinking_vodka'] = ['0.884', '0', '0', '0', '#joy', '#joy', 'positive', '0.884', 'egalitarian', 'enjoy', 'drink', 'have_fun', 'senator']
senticnet['drinking_water'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'bottled_water', 'sugar_water']
senticnet['drinking_water_disinfection'] = ['-0.62', '0', '0.987', '-0.74', '#anger', '#disgust', 'negative', '-0.78', 'medical_aid', 'treatment', 'hospice', 'digitalization', 'bloodletting']
senticnet['drinking_water_quality'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'bottled_water', 'sugar_water']
senticnet['drinking_water_supply'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'bottled_water', 'sugar_water']
senticnet['drip'] = ['0.718', '-0.89', '0', '0', '#joy', '#surprise', 'positive', '0.806', 'fall', 'water_drip', 'falling_water', 'feel_cold', 'rain_water']
senticnet['drip_coffee'] = ['0.807', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.859', 'bubble', 'steam', 'hot', 'boil_water', 'tea']
senticnet['dripping_blood'] = ['-0.87', '0', '0.682', '-0.53', '#sadness', '#anger', 'negative', '-0.69', 'swash', 'spatter', 'slosh_around', 'plash', 'splash']
senticnet['dripping_paint'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'wall_decoration', 'renovation', 'painting', 'artwork', 'new_house']
senticnet['dripping_wax'] = ['0', '0.232', '-0.10', '0.156', '#interest', '#admiration', 'positive', '0.094', 'light', 'eat_cake', 'birthday_party', 'candle_light', 'romantic_atmosphere']
senticnet['dripping_wet'] = ['0.951', '0.763', '0', '0', '#joy', '#interest', 'positive', '0.857', 'pull_muscle', 'heart_rate_increase', 'heavy_breathe', 'get_exhausted', 'tire']
senticnet['drippy'] = ['-0.61', '0', '0.732', '-0.59', '#sadness', '#anger', 'negative', '-0.64', 'drafty', 'draughty', 'porous', 'leaky', 'oozing']
senticnet['drivable'] = ['-0.99', '0', '0.641', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'navigable', 'negotiable', 'surmountable', 'climbable', 'passable']
senticnet['drive'] = ['0.825', '0', '0', '0.703', '#joy', '#admiration', 'positive', '0.764', 'pay_toll', 'get_fast', 'access', 'fast_travel', 'access_quickly']
senticnet['drive-in_movie'] = ['-0.03', '-0.01', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.04', 'movie_theater', 'theater', 'cinema', 'movie', 'cctv']
senticnet['drive_around'] = ['-0.90', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.84', 'drive_destination', 'find_direction', 'travel_by_airplane', 'near_destination', 'board_boat']
senticnet['drive_away'] = ['0.693', '-0.54', '0', '0.814', '#joy', '#admiration', 'positive', '0.684', 'move', 'displace', 'clear_air', 'fire', 'banish']
senticnet['drive_car'] = ['0.709', '0', '0', '0.989', '#joy', '#admiration', 'positive', '0.849', 'out_car', 'avoid_fall', 'move_car', 'take_car_drive', 'go_drive']
senticnet['drive_crazy'] = ['-0.08', '-0.01', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.04', 'lose_mind', 'love_sick', 'annoy', 'sick', 'stupid']
senticnet['drive_destination'] = ['0', '0.86', '0', '0', '#interest', '#interest', 'positive', '0.86', 'travel_by_airplane', 'board_boat', 'discover_new_place', 'near_destination', 'sleep_late']
senticnet['drive_down_street'] = ['0.709', '0', '0', '0.989', '#joy', '#admiration', 'positive', '0.849', 'appear_suddenly', 'gain_speed', 'travel_road', 'corner_well', 'get_accident']
senticnet['drive_fast'] = ['-0.03', '-0.02', '-0.06', '-0.04', '#fear', '#disgust', 'negative', '-0.03', 'travel_fast', 'drive_quickly', 'get_fast', 'travel_car', 'pay_toll']
senticnet['drive_highway'] = ['-0.08', '0.092', '-0.07', '0.15', '#interest', '#admiration', 'positive', '0.021', 'leave_place', 'push_limit', 'steer_car', 'turn_leave', 'break_down']
senticnet['drive_home'] = ['0', '0.169', '-0.06', '0.077', '#interest', '#admiration', 'positive', '0.06', 'buy_condom', 'wear_protection', 'wrap_present', 'spend_much_money', 'use_credit_card']
senticnet['drive_license'] = ['0.099', '0', '0', '0.168', '#joy', '#admiration', 'positive', '0.134', 'find_car_key', 'start_car_engine', 'permit', 'good_vision', 'license']
senticnet['drive_off'] = ['0.509', '-0.64', '0', '0.942', '#surprise', '#admiration', 'positive', '0.698', 'move', 'displace', 'clear_air', 'fire', 'banish']
senticnet['drive_out'] = ['0.576', '-0.86', '0', '0.531', '#joy', '#surprise', 'positive', '0.657', 'move', 'displace', 'clear_air', 'fire', 'banish']
senticnet['drive_quickly'] = ['0.946', '0', '0', '0.77', '#joy', '#admiration', 'positive', '0.858', 'shade', 'aviator', 'drive', 'hurry', 'fast_travel']
senticnet['drive_recklessly'] = ['0', '0', '-0.80', '-0.88', '#fear', '#disgust', 'negative', '-0.84', 'future_robot', 'type_of_dog', 'dog', 'dangerous', 'drive']
senticnet['drive_safe'] = ['0.822', '0.86', '0', '0.751', '#joy', '#interest', 'positive', '0.811', 'prevent_accident', 'take_nap', 'go_slow', 'feel_little_tired', 'rest_dream']
senticnet['drive_safely'] = ['0.822', '0.86', '0', '0.751', '#joy', '#interest', 'positive', '0.811', 'keep_safe', 'avoid_accident', 'avoid_problem', 'just_in_time', 'good_break']
senticnet['drive_shaft'] = ['0', '0.142', '0', '0.125', '#interest', '#admiration', 'positive', '0.134', 'weight_machine', 'power_drill', 'screw', 'screwdriver', 'air_pump']
senticnet['drive_theater'] = ['-0.06', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.05', 'find_out_play', 'go_cinema', 'go_to_theater', 'go_movie_theater', 'buy_ticket']
senticnet['drive_thru_window'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'clean', 'polish', 'shiny', 'water', 'sexy_girl']
senticnet['drive_to_store'] = ['-0.03', '0', '0.042', '0', '#sadness', '#anger', 'negative', '-0.03', 'go_department_store', 'go_to_shopping_mall', 'find_merchant', 'charge_card', 'go_shopping']
senticnet['drive_to_work'] = ['-0.04', '-0.01', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.03', 'commute', 'transportation', 'go_to_work', 'drive', 'drive_car']
senticnet['drive_train'] = ['0.046', '0.087', '0', '0.153', '#interest', '#admiration', 'positive', '0.095', 'quit_work', 'finish_work', 'work_home', 'accomplish', 'move_furniture']
senticnet['drive_upon'] = ['0', '-0.00', '0', '0.088', '#surprise', '#admiration', 'positive', '0.048', 'fast_travel', 'pay_toll', 'travel_car', 'divide_property', 'drive_quickly']
senticnet['drive_vehicle'] = ['0.026', '-0.03', '0', '0.091', '#surprise', '#admiration', 'positive', '0.051', 'drive_fast', 'travel_car', 'divide_property', 'turn_leave', 'drive_quickly']
senticnet['driveability'] = ['0.558', '-0.60', '0', '0.779', '#surprise', '#admiration', 'positive', '0.646', 'benefit', 'pianism', 'show', 'routine', 'histrionics']
senticnet['drivel'] = ['0.739', '-0.89', '0', '0.702', '#joy', '#surprise', 'positive', '0.78', 'pap', 'content', 'pablum', 'folderol', 'codswallop']
senticnet['driver'] = ['0', '0.352', '-0.23', '0.307', '#interest', '#admiration', 'positive', '0.142', 'operator', 'specialist', 'employee', 'agent', 'surgeon']
senticnet['driver_cab'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'train_station', 'government_office', 'cable_car', 'sport_stadium', 'terminal']
senticnet['driver_compartment'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'train_station', 'government_office', 'cable_car', 'sport_stadium', 'terminal']
senticnet['driver_distraction'] = ['-0.10', '-0.04', '0', '-0.14', '#sadness', '#disgust', 'negative', '-0.06', 'adjust_seat', 'angry_behavior', 'fasten_seatbelt', 'traffic_jam', 'skid_mark']
senticnet['driver_inattention'] = ['-0.10', '-0.04', '0', '-0.14', '#sadness', '#disgust', 'negative', '-0.06', 'adjust_seat', 'angry_behavior', 'fasten_seatbelt', 'traffic_jam', 'skid_mark']
senticnet['driver_licence'] = ['0.673', '-0.66', '0', '0.921', '#joy', '#admiration', 'positive', '0.753', 'instrument', 'building_permit', 'driver_license', 'license', 'driving_license']
senticnet['driver_license'] = ['0.673', '-0.66', '0', '0.921', '#joy', '#admiration', 'positive', '0.753', 'instrument', 'building_permit', 'license', 'driver_licence', 'driving_license']
senticnet['drivers_license'] = ['0.931', '-0.52', '0', '0.67', '#joy', '#admiration', 'positive', '0.709', 'document', 'certificate_of_incorporation', 'credential', 'diploma', 'sheepskin']
senticnet['driveshaft'] = ['0', '-0.09', '0.044', '0.055', '#surprise', '#admiration', 'positive', '0.036', 'two_wheels', 'gear', 'axle', 'engine', 'steer_wheel']
senticnet['driveway'] = ['0.825', '0', '0', '0.703', '#joy', '#admiration', 'positive', '0.764', 'pay_toll', 'get_fast', 'access', 'fast_travel', 'access_quickly']
senticnet['driving'] = ['-0.1', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.10', 'change_tire', 'fix_flat_tire', 'lie_floor', 'workshop', 'car']
senticnet['driving_dangerously'] = ['-0.31', '-0.02', '0', '-0.24', '#sadness', '#disgust', 'negative', '-0.17', 'pollution', 'screw_up', 'car_wreck', 'parking_ticket', 'feel_stressed']
senticnet['driving_drunk'] = ['-0.10', '-0.04', '0', '-0.14', '#sadness', '#disgust', 'negative', '-0.06', 'adjust_seat', 'angry_behavior', 'fasten_seatbelt', 'traffic_jam', 'skid_mark']
senticnet['driving_dunk'] = ['-0.76', '0', '0.794', '-0.91', '#anger', '#disgust', 'negative', '-0.82', 'discharge', 'spray', 'spritz', 'outpouring', 'run']
senticnet['driving_erratically'] = ['-0.31', '-0.02', '0', '-0.24', '#sadness', '#disgust', 'negative', '-0.17', 'pollution', 'screw_up', 'car_wreck', 'parking_ticket', 'feel_stressed']
senticnet['driving_force'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'baddie', 'theatrical_baddie', 'part', 'persona']
senticnet['driving_force_behind'] = ['0.918', '0.864', '0', '0', '#joy', '#interest', 'positive', '0.891', 'consequence', 'cause', 'bad_action', 'problem', 'political_statement']
senticnet['driving_layup'] = ['-0.81', '0', '0.747', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'vagrant', 'drifter', 'hobo', 'vagabond', 'dosser']
senticnet['driving_license'] = ['0.673', '-0.66', '0', '0.921', '#joy', '#admiration', 'positive', '0.753', 'instrument', 'building_permit', 'driver_license', 'driver_licence', 'license']
senticnet['driving_mph'] = ['-0.31', '-0.02', '0', '-0.24', '#sadness', '#disgust', 'negative', '-0.17', 'pollution', 'screw_up', 'car_wreck', 'parking_ticket', 'feel_stressed']
senticnet['driving_rain'] = ['-0.87', '0', '0.91', '-0.78', '#sadness', '#anger', 'negative', '-0.85', 'storm', 'thundershower', 'rain', 'line_storm', 'equinoctial_storm']
senticnet['driving_recklessly'] = ['-0.10', '-0.04', '0', '-0.14', '#sadness', '#disgust', 'negative', '-0.06', 'adjust_seat', 'angry_behavior', 'fasten_seatbelt', 'traffic_jam', 'skid_mark']
senticnet['drizzle'] = ['0.085', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.074', 'soap', 'foam', 'bubbly', 'solid_water', 'ivory']
senticnet['drizzling_rain'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'hazy', 'blur', 'bleary', 'blurred', 'blurry']
senticnet['drizzly'] = ['-0.92', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.83', 'showery', 'muggy', 'blustery', 'squally', 'spring-like']
senticnet['drizzly_day'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'hazy', 'blur', 'bleary', 'blurred', 'blurry']
senticnet['drizzt_do_urden'] = ['0.594', '-0.54', '0', '0.518', '#joy', '#surprise', 'positive', '0.554', 'exorcist', 'enchanter', 'magus', 'sorceress', 'witch_doctor']
senticnet['drogo'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'feudal_lord', 'lord']
senticnet['drogue'] = ['-0.88', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.90', 'undergarment', 'outerwear', 'lady_underwear', 'under_garment', 'slip']
senticnet['drogue_chute'] = ['-0.88', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.90', 'undergarment', 'outerwear', 'lady_underwear', 'under_garment', 'slip']
senticnet['drogue_parachute'] = ['-0.88', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.90', 'undergarment', 'outerwear', 'lady_underwear', 'under_garment', 'slip']
senticnet['droll'] = ['0.879', '0', '0', '0.972', '#joy', '#admiration', 'positive', '0.925', 'fun', 'guffaw', 'make_laugh', 'amuse', 'funny']
senticnet['drollery'] = ['0.918', '0.476', '-0.19', '0.417', '#joy', '#interest', 'positive', '0.404', 'merriment', 'cheery', 'festal', 'playfulness', 'pridefulness']
senticnet['drone'] = ['-0.64', '0', '0.879', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'straggler', 'poke', 'dawdler', 'laggard', 'lagger']
senticnet['drool'] = ['-0.03', '-0.03', '-0.05', '-0.14', '#fear', '#disgust', 'negative', '-0.05', 'saliva', 'start_laugh', 'vomit', 'say_excuse', 'pee_pants']
senticnet['droop'] = ['0', '0', '0.843', '-0.75', '#anger', '#disgust', 'negative', '-0.79', 'reduce', 'devaluate', 'weaken', 'diminish', 'sink']
senticnet['drooping'] = ['-0.78', '0', '0.95', '-0.73', '#sadness', '#anger', 'negative', '-0.82', 'unerect', 'crooked', 'weeping', 'prone', 'supine']
senticnet['drop'] = ['0.978', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.865', 'tumble', 'dew', 'hailstone', 'rain', 'snowflake']
senticnet['drop_airplane'] = ['-0.07', '-0.06', '0', '-0.18', '#sadness', '#disgust', 'negative', '-0.06', 'kill_enemy', 'sink_ship', 'protect_country', 'make_war', 'settle_disagreement']
senticnet['drop_arch'] = ['0.641', '-0.56', '0', '0.748', '#joy', '#admiration', 'positive', '0.653', 'trefoil_arch', 'ogee_arch', 'keel_arch', 'pointed_arch', 'arch']
senticnet['drop_away'] = ['-0.74', '0', '0.749', '-0.82', '#anger', '#disgust', 'negative', '-0.77', 'inflame', 'worsen', 'lose', 'fall_away', 'deteriorate']
senticnet['drop_ball'] = ['-0.64', '0', '0.913', '-0.86', '#anger', '#disgust', 'negative', '-0.80', 'offend', 'violate', 'go_against', 'break', 'lose_game']
senticnet['drop_blood'] = ['-0.05', '0', '-0.15', '-0.12', '#fear', '#disgust', 'negative', '-0.10', 'donor', 'blood', 'disease', 'contagious_disease', 'sick']
senticnet['drop_bomb'] = ['-0.06', '-0.02', '0.034', '-0.11', '#sadness', '#disgust', 'negative', '-0.04', 'land_plane', 'fly_helicopter', 'glide', 'bus_person', 'journey_far']
senticnet['drop_down'] = ['-0.08', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.08', 'fall', 'stumble', 'tumble', 'leak', 'go_down']
senticnet['drop_dramatically'] = ['0.978', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.865', 'tumble', 'dew', 'hailstone', 'rain', 'snowflake']
senticnet['drop_food'] = ['-0.07', '-0.01', '-0.06', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'spill_food_clothes', 'acid_indigestion', 'eat_salad', 'drink_much_beer', 'hunger_go_away']
senticnet['drop_off'] = ['-0.02', '0.141', '-0.09', '0.22', '#interest', '#admiration', 'positive', '0.06', 'take', 'distribute', 'pull_car', 'tote', 'bring']
senticnet['drop_prematurely'] = ['0', '0', '-0.94', '-0.96', '#fear', '#disgust', 'negative', '-0.95', 'say_prayer', 'fall_off_chair', 'put_box', 'feel_terrible', 'lift_leg']
senticnet['drop_sharply'] = ['0.978', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.865', 'tumble', 'dew', 'hailstone', 'rain', 'snowflake']
senticnet['drop_volley'] = ['0', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.91', 'umpire', 'referee', 'legal_proceed', 'clang', 'clamor']
senticnet['drop_zone'] = ['-0.88', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.90', 'undergarment', 'outerwear', 'lady_underwear', 'under_garment', 'slip']
senticnet['dropdown_menu'] = ['0.146', '0.095', '-0.04', '0', '#joy', '#interest', 'positive', '0.065', 'paper_menu', 'diner', 'cafe', 'restaurant', 'dine_room']
senticnet['droped_the_catch'] = ['-0.07', '-0.06', '0.06', '-0.08', '#sadness', '#disgust', 'negative', '-0.04', 'miss_ball', 'try_best', 'loose', 'sore_knee', 'lose_game']
senticnet['droperidol'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['droplet'] = ['0.095', '0', '-0.04', '0.062', '#joy', '#admiration', 'positive', '0.037', 'liquid_water', 'water_spray', 'moisture', 'rise_water', 'photographer']
senticnet['droplet_coalescence'] = ['-0.98', '0', '0.516', '-0.90', '#sadness', '#disgust', 'negative', '-0.80', 'devastation', 'annihilation', 'pulverization', 'vaporization', 'destruction']
senticnet['dropoff'] = ['0.978', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.865', 'tumble', 'dew', 'hailstone', 'rain', 'snowflake']
senticnet['dropout_rate'] = ['-0.89', '0.168', '-0.60', '0.05', '#sadness', '#fear', 'negative', '-0.32', 'remorsefully', 'contritely', 'contrition', 'contriteness', 'ruefully']
senticnet['dropped_anchor'] = ['0', '0', '0.027', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'port', 'sea', 'boat', 'ocean', 'ship']
senticnet['dropper'] = ['-0.06', '-0.00', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'find_out_truth', 'zoology', 'fall', 'prove_theory', 'discovery']
senticnet['droppin'] = ['-0.05', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.05', 'masturbate', 'jerk', 'jerk_off', 'watch_porn', 'porn']
senticnet['droppings'] = ['-0.83', '0', '0.521', '-0.71', '#sadness', '#disgust', 'negative', '-0.69', 'stool', 'dejection', 'chip', 'cow_chip', 'cow_dung']
senticnet['dropsical'] = ['0', '0', '0', '-0.96', '#disgust', '#disgust', 'negative', '-0.96', 'thick_substance', 'viscous_substance', 'mucous_substance', 'disgusting', 'disgust']
senticnet['dropsy'] = ['-0.99', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.78', 'angioedema', 'atrophedema', 'giant_hive', 'periodic_edema', 'lymphedema']
senticnet['dropwort'] = ['0.598', '-0.72', '0', '0.941', '#surprise', '#admiration', 'positive', '0.753', 'sicklepod', 'tower_rock_cress', 'tower_mustard', 'rock_cress', 'rock_cress_plant']
senticnet['drosera_rotundifolia'] = ['-0.79', '0', '0.906', '-0.67', '#sadness', '#anger', 'negative', '-0.79', 'poisonous_plant', 'hemlock_poisonous_plant', 'water_hemlock', 'plant_life', 'poison_sumac']
senticnet['dross'] = ['0.526', '-0.78', '0', '0.869', '#surprise', '#admiration', 'positive', '0.725', 'impurity', 'waste_material', 'exhaust', 'garbage', 'material']
senticnet['drought'] = ['-0.98', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.89', 'dry', 'lose_team', 'leave_out', 'disrespect', 'cause_suffer']
senticnet['drought_relief'] = ['0.732', '0.729', '0', '0', '#joy', '#interest', 'positive', '0.73', 'feel_relief', 'good_feeling', 'feel_ok', 'feel_relieved', 'feel_better']
senticnet['drought_tolerant'] = ['0', '0.985', '0', '0.794', '#interest', '#admiration', 'positive', '0.889', 'patient', 'bear_up', 'tolerate', 'endurable', 'swallow']
senticnet['drouillard'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'common_bean', 'green_bean', 'wax_bean']
senticnet['drown'] = ['-0.73', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.76', 'water_ear', 'wet_hair', 'get_killed', 'get_wet', 'slip_fall']
senticnet['drown_out'] = ['-0.75', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.77', 'scarily', 'scared', 'frighten_away', 'frighten_off', 'scare_off']
senticnet['drowns_out'] = ['-0.65', '0', '0.578', '-0.76', '#sadness', '#disgust', 'negative', '-0.66', 'grating', 'rasping', 'raspy', 'jarring', 'gruff']
senticnet['drowse'] = ['-0.59', '0', '0.799', '-0.69', '#anger', '#disgust', 'negative', '-0.69', 'untroubled', 'undisturbed', 'unmolested', 'unworried', 'unconcerned']
senticnet['drowsily'] = ['0.051', '0.074', '-0.05', '0.084', '#interest', '#admiration', 'positive', '0.04', 'rest_night', 'lie_down', 'shut_eye', 'lay_down', 'count_sheep']
senticnet['drowsiness'] = ['0.869', '-0.72', '0', '0', '#joy', '#surprise', 'positive', '0.796', 'fall_asleep', 'sleepy', 'bed_time', 'drink_cup_tea', 'listen_classical_music']
senticnet['drowsy'] = ['0', '0', '-0.98', '-0.90', '#fear', '#disgust', 'negative', '-0.94', 'oscitancy', 'oscitance', 'gape', 'oscitant', 'yawning']
senticnet['drs_zone'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['drub'] = ['0.893', '-0.56', '0', '0.537', '#joy', '#surprise', 'positive', '0.665', 'vanquish', 'beat', 'beat_out', 'trounce', 'crush']
senticnet['drudge'] = ['0.73', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.851', 'sting', 'nip', 'bite', 'flea', 'least']
senticnet['drudgery'] = ['-0.30', '0.089', '-0.10', '-0.03', '#sadness', '#fear', 'negative', '-0.08', 'hardship', 'office_job', 'work_much', 'day_job', 'toil']
senticnet['drug'] = ['0', '0', '0.868', '-0.89', '#anger', '#disgust', 'negative', '-0.87', 'analgesic', 'phenobarbital', 'trip_acid', 'ibuprofen', 'codeine']
senticnet['drug-addicted'] = ['-0.85', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.81', 'hard-drinking', 'down-on-his-luck', 'washed-up', 'down-and-out', 'sad-sack']
senticnet['drug_abuse'] = ['-0.51', '0', '0.97', '-0.62', '#anger', '#disgust', 'negative', '-0.70', 'abuse', 'misuse', 'alcohol_abuse', 'alcoholic_abuse', 'alcoholism_abuse']
senticnet['drug_addict'] = ['0', '0', '0.821', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'student', 'serious_student', 'addiction', 'graduate_student', 'hard_worker']
senticnet['drug_addicted'] = ['0', '0', '-0.08', '-0.09', '#fear', '#disgust', 'negative', '-0.09', 'ambulance', 'prosthesis', 'old_lady', 'junkie', 'elderly_person']
senticnet['drug_addiction'] = ['-0.09', '-0.06', '-0.10', '-0.17', '#fear', '#disgust', 'negative', '-0.07', 'eat_poison', 'execute', 'apnea', 'drunk_drive', 'old_age']
senticnet['drug_claritin'] = ['-0.96', '0', '0.848', '-0.82', '#sadness', '#anger', 'negative', '-0.87', 'sneeze', 'sign_sickness', 'runny_nose', 'flu', 'common_cold']
senticnet['drug_dealer'] = ['-0.91', '0', '0.129', '-0.96', '#sadness', '#disgust', 'negative', '-0.67', 'drug', 'drug_peddler', 'drug_trafficker', 'break_law', 'harmful']
senticnet['drug_ecstasy'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'use_drug', 'do_drug', 'drug_use', 'drug', 'addiction']
senticnet['drug_fiend'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['drug_ghb'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'use_drug', 'do_drug', 'drug_use', 'drug', 'addiction']
senticnet['drug_habit'] = ['0', '0', '0.821', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'student', 'serious_student', 'addiction', 'graduate_student', 'hard_worker']
senticnet['drug_mule'] = ['-0.87', '0', '0.574', '-0.73', '#sadness', '#disgust', 'negative', '-0.72', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['drug_peddler'] = ['-0.91', '0', '0.129', '-0.96', '#sadness', '#disgust', 'negative', '-0.67', 'drug', 'drug_dealer', 'drug_trafficker', 'break_law', 'harmful']
senticnet['drug_prohibition'] = ['-0.72', '0', '0.71', '-0.9', '#sadness', '#disgust', 'negative', '-0.77', 'perniciousness', 'unwholesomeness', 'toxicity', 'noisomeness', 'noxiousness']
senticnet['drug_pusher'] = ['0', '0', '0.821', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'student', 'serious_student', 'addiction', 'graduate_student', 'hard_worker']
senticnet['drug_resistant'] = ['-0.69', '0', '0.516', '-0.94', '#sadness', '#disgust', 'negative', '-0.71', 'insubordinate', 'recalcitrant', 'obstreperous', 'defiant', 'resistive']
senticnet['drug_smuggler'] = ['-0.87', '0', '0.574', '-0.73', '#sadness', '#disgust', 'negative', '-0.72', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['drug_smuggling_operation'] = ['-0.87', '0', '0.574', '-0.73', '#sadness', '#disgust', 'negative', '-0.72', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['drug_smuggling_ring'] = ['-0.87', '0', '0.574', '-0.73', '#sadness', '#disgust', 'negative', '-0.72', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['drug_therapy'] = ['-0.05', '0', '-0.22', '-0.06', '#fear', '#disgust', 'negative', '-0.11', 'medicine', 'cure', 'medical_procedure', 'sleep_drug', 'nurse']
senticnet['drug_trafficker'] = ['-0.91', '0', '0.129', '-0.96', '#sadness', '#disgust', 'negative', '-0.67', 'drug', 'drug_peddler', 'drug_dealer', 'break_law', 'harmful']
senticnet['drug_treatment'] = ['-0.05', '0', '-0.22', '-0.06', '#fear', '#disgust', 'negative', '-0.11', 'medicine', 'cure', 'medical_procedure', 'sleep_drug', 'nurse']
senticnet['drug_use'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'use_drug', 'ecstasy_pill', 'do_drug', 'drug', 'addiction']
senticnet['druggie'] = ['-0.97', '0', '0', '-0.96', '#sadness', '#disgust', 'negative', '-0.97', 'washed-up', 'has-been', 'talentless', 'has-been', 'wanna-be']
senticnet['druggist'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'pharmaceutical_chemist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['druggy'] = ['0', '0', '0', '-0.87', '#disgust', '#disgust', 'negative', '-0.87', 'convict_crime', 'steal', 'hurt_else', 'get_away_crime', 'obstruct_justice']
senticnet['drugmaker'] = ['0.559', '0.656', '-0.26', '0.285', '#joy', '#interest', 'positive', '0.31', 'zealous', 'avidity', 'eagerness', 'avidness', 'thirstily']
senticnet['drugstore'] = ['0.8', '0.833', '0', '0', '#joy', '#interest', 'positive', '0.817', 'pharmacy', 'walmart', 'bathroom_cabinet', 'shop', 'medicine_cabinet']
senticnet['drum_beat'] = ['0.857', '-0.77', '0', '0.878', '#joy', '#admiration', 'positive', '0.836', 'hand', 'round', 'approval', 'commendation', 'clapping']
senticnet['drum_brake'] = ['0', '-0.09', '0.044', '0.055', '#surprise', '#admiration', 'positive', '0.036', 'two_wheels', 'gear', 'axle', 'engine', 'steer_wheel']
senticnet['drum_major'] = ['-0.91', '0', '0.931', '-0.72', '#sadness', '#anger', 'negative', '-0.85', 'hit_with_baton', 'get_hurt', 'whip', 'arrow', 'hurt']
senticnet['drum_out'] = ['0', '0', '-0.84', '-0.87', '#fear', '#disgust', 'negative', '-0.85', 'throw_out', 'oust', 'kick_out', 'boot_out', 'expel']
senticnet['drum_percussion'] = ['0.049', '0.167', '-0.05', '0.295', '#interest', '#admiration', 'positive', '0.115', 'four_sides', 'sound_system', 'dolby_surround', 'movie', 'surround']
senticnet['drum_riser'] = ['0.26', '0', '0', '0.089', '#joy', '#admiration', 'positive', '0.174', 'balcony_rail', 'movie_theater_seat', 'row_seat', 'usher', 'sticky_floor']
senticnet['drum_set'] = ['0.946', '0', '0', '0.77', '#joy', '#admiration', 'positive', '0.858', 'high_school_band', 'kit', 'string_quartet', 'music_shop', 'battery']
senticnet['drum_solo'] = ['0.773', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.844', 'favorite', 'crowd', 'acclamation', 'bunch', 'large_crowd']
senticnet['drumbeat'] = ['0.862', '0.857', '0', '0', '#joy', '#interest', 'positive', '0.859', 'drumbeat', 'incessant', 'insistent', 'unrelenting', 'harping']
senticnet['drumhead'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'wildcat', 'informal', 'loose', 'unofficial']
senticnet['drummer'] = ['0', '-0.02', '0.055', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'djembe', 'make_musicr', 'use_jazz_music', 'tune_instrument', 'percussion']
senticnet['drummer_percussionist'] = ['0.049', '0.167', '-0.05', '0.295', '#interest', '#admiration', 'positive', '0.115', 'four_sides', 'sound_system', 'dolby_surround', 'movie', 'surround']
senticnet['drummer_rob'] = ['0', '-0.02', '0.055', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'djembe', 'make_musicr', 'use_jazz_music', 'tune_instrument', 'percussion']
senticnet['drunk'] = ['0', '0', '-0.93', '-0.74', '#fear', '#disgust', 'negative', '-0.84', 'make_potato', 'drink_a_lot_of_booze', 'get_drunk', 'drink_much', 'drink_a_lot_of_alcohol']
senticnet['drunk_alcohol'] = ['0', '0.931', '0', '0', '#interest', '#interest', 'positive', '0.931', 'football', 'rake_leave', 'spray_paint_grafitti', 'juggle', 'play_hide_seek']
senticnet['drunk_drive'] = ['-0.72', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.71', 'die', 'eat_poison', 'terrorism', 'commit_suicide', 'kill_man']
senticnet['drunk_driving_accident'] = ['-0.92', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.93', 'serious_accident', 'accident', 'hit_car', 'loss_ofawareness', 'bang_head']
senticnet['drunk_driving_crash'] = ['-0.92', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.93', 'serious_accident', 'accident', 'hit_car', 'loss_ofawareness', 'bang_head']
senticnet['drunk_person'] = ['0.141', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.058', 'drunkard', 'alcohol', 'booze', 'whisky', 'alcoholic_beverage']
senticnet['drunkard'] = ['0', '0', '0.901', '-0.73', '#anger', '#disgust', 'negative', '-0.81', 'drunk_person', 'bartender', 'whisky', 'alcohol', 'guinness']
senticnet['drunken'] = ['-0.97', '0', '0', '-0.91', '#sadness', '#disgust', 'negative', '-0.94', 'bacchanalia', 'bacchanalian', 'carouse', 'dipsomaniac', 'drunkenness']
senticnet['drunken_driver'] = ['-0.92', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.93', 'serious_accident', 'accident', 'hit_car', 'loss_ofawareness', 'bang_head']
senticnet['drunken_night'] = ['0.884', '0', '0', '0', '#joy', '#joy', 'positive', '0.884', 'egalitarian', 'enjoy', 'drink', 'have_fun', 'senator']
senticnet['drunken_stupor'] = ['0.828', '0.832', '0', '0', '#joy', '#interest', 'positive', '0.83', 'daze', 'fulgurant', 'eye-popping', 'fulgurous', 'dazzling']
senticnet['drunkenly'] = ['0.884', '0', '0', '0', '#joy', '#joy', 'positive', '0.884', 'egalitarian', 'enjoy', 'drink', 'have_fun', 'senator']
senticnet['drunkenness'] = ['0', '0', '0.827', '-0.88', '#anger', '#disgust', 'negative', '-0.85', 'drunk', 'become_drunk', 'intoxication', 'get_wasted', 'drink_beer']
senticnet['drupe'] = ['0', '0', '0.064', '-0.04', '#anger', '#disgust', 'negative', '-0.05', 'bird_food', 'funiculus', 'plant_product', 'fuzz', 'agricultural_product']
senticnet['druv'] = ['-0.83', '0', '0.87', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'precipitate', 'rain_down', 'shower_down', 'spit', 'patter']
senticnet['druzil'] = ['0.526', '-0.99', '0', '0.673', '#surprise', '#admiration', 'positive', '0.732', 'faery', 'faerie', 'fairy', 'leprechaun', 'sandman']
senticnet['dry'] = ['0', '-0.04', '0', '0.053', '#surprise', '#admiration', 'positive', '0.051', 'coat_surface', 'dune', 'desert', 'parched', 'dry_paint']
senticnet['dry_amd'] = ['-0.82', '0', '0.996', '-0.61', '#sadness', '#anger', 'negative', '-0.81', 'visual_impairment', 'visual_defect', 'vision_defect', 'visual_disorder', 'avitaminosis']
senticnet['dry_cereal'] = ['-0.78', '0', '0.684', '-0.55', '#sadness', '#anger', 'negative', '-0.67', 'puffed_rice', 'granola', 'raisin_bran', 'corn_flake', 'bran_flake']
senticnet['dry_chemical_extinguisher'] = ['0.886', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.881', 'fire_hydrant', 'stair_rail', 'christian_fundamentalist', 'extinguisher', 'reception_desk']
senticnet['dry_cider'] = ['0.079', '-0.02', '0.023', '0', '#joy', '#surprise', 'positive', '0.027', 'wine', 'lemonade', 'juice', 'whisky', 'soft_drink']
senticnet['dry_clean'] = ['0.761', '0', '0', '0.972', '#joy', '#admiration', 'positive', '0.867', 'soft_fabric', 'fabric_type', 'clothes_fabric', 'cloth_material', 'cloth_fabric']
senticnet['dry_cleaner'] = ['0', '0.081', '0', '0.134', '#interest', '#admiration', 'positive', '0.108', 'washing_machine', 'clean', 'dry', 'clothes', 'piece_of_clothing']
senticnet['dry_cleaning_establishment'] = ['0', '0.081', '0', '0.134', '#interest', '#admiration', 'positive', '0.108', 'washing_machine', 'clean', 'dry', 'clothes', 'piece_of_clothing']
senticnet['dry_cleaning_facility'] = ['0', '0.081', '0', '0.134', '#interest', '#admiration', 'positive', '0.108', 'washing_machine', 'clean', 'dry', 'clothes', 'piece_of_clothing']
senticnet['dry_cleaning_fluid'] = ['-0.67', '0', '0.503', '-0.67', '#sadness', '#disgust', 'negative', '-0.61', 'stink_up', 'smell_up', 'stink_out', 'incense', 'perfume']
senticnet['dry_cleaning_machine'] = ['0', '0.081', '0', '0.134', '#interest', '#admiration', 'positive', '0.108', 'washing_machine', 'clean', 'dry', 'clothes', 'piece_of_clothing']
senticnet['dry_cleaning_shop'] = ['0', '0.081', '0', '0.134', '#interest', '#admiration', 'positive', '0.108', 'washing_machine', 'clean', 'dry', 'clothes', 'piece_of_clothing']
senticnet['dry_cleaning_store'] = ['0', '0.081', '0', '0.134', '#interest', '#admiration', 'positive', '0.108', 'washing_machine', 'clean', 'dry', 'clothes', 'piece_of_clothing']
senticnet['dry_clothes'] = ['-0.06', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.06', 'wash_window', 'sweep_floor', 'wash_dog', 'mop_floor', 'bind_book']
senticnet['dry_cough'] = ['-0.06', '0.183', '0.651', '-0.64', '#anger', '#disgust', 'negative', '-0.29', 'allergic_reaction', 'chew', 'pierce', 'mastication', 'put_mouth']
senticnet['dry_desert'] = ['-0.56', '0', '0.814', '-0.88', '#anger', '#disgust', 'negative', '-0.75', 'unfriendly', 'barren', 'bleak', 'desolate', 'wild']
senticnet['dry_erase_board'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'gift_wrap', 'kraft', 'kraft_paper', 'wrapping_paper', 'paper']
senticnet['dry_fruit'] = ['0.775', '0.94', '0', '0', '#joy', '#interest', 'positive', '0.857', 'coupon', 'box_cereal', 'carton_milk', 'bottle_water', 'pasta']
senticnet['dry_gangrene'] = ['-0.68', '0', '0.916', '-0.91', '#anger', '#disgust', 'negative', '-0.83', 'slough', 'cold_gangrene', 'mumification_necrosis', 'mummification', 'pathology']
senticnet['dry_gin'] = ['0.079', '-0.02', '0.023', '0', '#joy', '#surprise', 'positive', '0.027', 'wine', 'lemonade', 'juice', 'whisky', 'soft_drink']
senticnet['dry_goods_merchant'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'pharmaceutical_chemist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['dry_goods_store'] = ['0.026', '0.082', '0', '0', '#joy', '#interest', 'positive', '0.054', 'account_department', 'office', 'accountant_office', 'shirt_pocket', 'desk_drawer']
senticnet['dry_hair'] = ['-0.08', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.06', 'absorb_moisture', 'sit_on_beach', 'shine_shoes', 'bind_book', 'dry_paint']
senticnet['dry_humor'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'rudeness', 'discourtesy', 'insolence', 'chutzpah', 'hutzpah']
senticnet['dry_land'] = ['-0.68', '0', '0.916', '-0.91', '#anger', '#disgust', 'negative', '-0.83', 'land', 'wonderland', 'earth', 'ground', 'solid_ground']
senticnet['dry_macular_degeneration'] = ['-0.82', '0', '0.996', '-0.61', '#sadness', '#anger', 'negative', '-0.81', 'visual_impairment', 'visual_defect', 'vision_defect', 'visual_disorder', 'avitaminosis']
senticnet['dry_mouth'] = ['-0.07', '-0.05', '0.095', '-0.18', '#anger', '#disgust', 'negative', '-0.07', 'misunderstanding', 'move_tongue', 'persuade', 'look_eye', 'exchange_idea']
senticnet['dry_off'] = ['0', '0.078', '0', '0.065', '#interest', '#admiration', 'positive', '0.072', 'towel_off', 'parched', 'hang_up_towel', 'make_bow', 'dry_off_towel']
senticnet['dry_off_towel'] = ['0', '0.078', '0', '0.065', '#interest', '#admiration', 'positive', '0.072', 'towel_off', 'parched', 'hang_up_towel', 'make_bow', 'dry_off']
senticnet['dry_paint'] = ['-0.05', '0', '0.019', '0', '#sadness', '#anger', 'negative', '-0.03', 'dry_hair', 'remove_paint', 'bind_book', 'paint_nail', 'dry_clothes']
senticnet['dry_pasta'] = ['0.763', '0.9', '0', '0', '#joy', '#interest', 'positive', '0.832', 'breakfast', 'box_cereal', 'calcium', 'drink', 'carton_milk']
senticnet['dry_rot'] = ['0.718', '0', '0', '0', '#joy', '#joy', 'positive', '0.718', 'water_container', 'still_water', 'clean_water', 'water', 'cool_water']
senticnet['dry_sand'] = ['-0.06', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.04', 'friction', 'lack_of_water', 'dirt', 'abrasive', 'ground_material']
senticnet['dry_shampoo'] = ['0.824', '0.86', '0', '0.983', '#interest', '#admiration', 'positive', '0.889', 'look_smart', 'social_acceptance', 'improve_appearance', 'improve_look', 'look_good']
senticnet['dry_skin'] = ['0.048', '-0.00', '0.011', '0.064', '#joy', '#admiration', 'positive', '0.027', 'become_wet', 'wash_away_dirt', 'run_out_soap', 'cleanliness', 'clean_skin']
senticnet['dry_spell'] = ['-0.98', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.89', 'dry', 'lose_team', 'leave_out', 'disrespect', 'cause_suffer']
senticnet['dry_towel'] = ['0.151', '0', '0', '0.055', '#joy', '#admiration', 'positive', '0.103', 'dry_off_towel', 'lock_front_door', 'shrivelled', 'protect_body', 'put_clothes']
senticnet['dry_up'] = ['-0.34', '0.092', '-0.36', '0.079', '#sadness', '#fear', 'negative', '-0.13', 'garden_plant', 'lifeless', 'undesirable', 'kill_self', 'need_water']
senticnet['dry_van_noten'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'elegant', 'stylish', 'smart', 'posh', 'sleek']
senticnet['dry_wall'] = ['0.514', '-0.97', '0', '0.899', '#surprise', '#admiration', 'positive', '0.797', 'board', 'beaver_board', 'fiberboard', 'furniture', 'particle_board']
senticnet['dry_weather'] = ['-0.98', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.89', 'dry', 'lose_team', 'leave_out', 'disrespect', 'cause_suffer']
senticnet['dry_wet_clothes'] = ['-0.08', '0', '-0.11', '-0.10', '#fear', '#disgust', 'negative', '-0.10', 'warm_hand', 'make_drunk', 'warm_room', 'warm_house', 'age_skin']
senticnet['dry_wet_hair'] = ['-0.06', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.06', 'wash_hair', 'dry_hair', 'cremate', 'thrust', 'dispense']
senticnet['dry_wit'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'rudeness', 'discourtesy', 'insolence', 'chutzpah', 'hutzpah']
senticnet['dryad'] = ['-0.02', '-0.01', '0.045', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'haddock', 'pier', 'ferry', 'dock', 'shallow']
senticnet['drycleaner'] = ['0', '0.081', '0', '0.134', '#interest', '#admiration', 'positive', '0.108', 'washing_machine', 'clean', 'dry', 'clothes', 'piece_of_clothing']
senticnet['dryer'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'barrette', 'hair_gel', 'blow_dryer', 'dry_hair', 'wet_hair']
senticnet['dryness'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'aridness', 'thirstiness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['drysuit'] = ['0.676', '0.63', '-0.23', '0.31', '#joy', '#interest', 'positive', '0.345', 'perkiness', 'walk_on_air', 'exultingly', 'exultantly', 'jubilance']
senticnet['drywall'] = ['0', '0.831', '0', '0.916', '#interest', '#admiration', 'positive', '0.873', 'stucco', 'cement', 'plaster', 'lath', 'wall']
senticnet['dsdna'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['dsm_criterion'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'acknowledgeable', 'specifiable', 'recognizable', 'identifiable', 'classifiable']
senticnet['dsm_iv'] = ['0', '0', '0.896', '-0.96', '#anger', '#disgust', 'negative', '-0.92', 'antisocial', 'sociopathic', 'psychopathic_personality', 'disturbance', 'schizoid']
senticnet['dsm_iv_criterion'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'acknowledgeable', 'specifiable', 'recognizable', 'identifiable', 'classifiable']
senticnet['dsw_shoe_warehouse'] = ['0.914', '-0.56', '0', '0.869', '#joy', '#admiration', 'positive', '0.781', 'tabula_rasa', 'ego', 'subconscious', 'unconscious', 'psyche']
senticnet['dtn_meteorlogix'] = ['-0.03', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.04', 'crystal_clear', 'water_sky', 'weather_condition', 'weather_phenomenon', 'sun_cover']
senticnet['dtv_allotment_plan'] = ['0.756', '0', '0', '0', '#joy', '#joy', 'positive', '0.756', 'companionship', 'cherish', 'lover', 'bond', 'buddy']
senticnet['du_bon'] = ['-0.85', '0', '0.968', '-0.89', '#anger', '#disgust', 'negative', '-0.90', 'family', 'household', 'house', 'home', 'broken_home']
senticnet['du_bousquier'] = ['0.089', '0.09', '0.81', '-0.68', '#anger', '#disgust', 'negative', '-0.32', 'covetous', 'envious', 'enviously', 'covetously', 'green-eyed']
senticnet['du_candidat'] = ['0.667', '-0.98', '0', '0.61', '#joy', '#surprise', 'positive', '0.753', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['du_csm'] = ['0.045', '0', '0.033', '0.089', '#joy', '#admiration', 'positive', '0.034', 'spanish', 'vocalization', 'verbal_communication', 'vocal', 'mouth_noise']
senticnet['du_nord'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'indweller', 'invillager', 'villager', 'dweller', 'denizen']
senticnet['du_professeur'] = ['-0.58', '0', '0.661', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'explosive', 'cordite', 'dynamite', 'plastic_explosive', 'explosive_compound']
senticnet['du_roi'] = ['0.059', '0.076', '-0.05', '0.079', '#interest', '#admiration', 'positive', '0.039', 'noble', 'king', 'earl', 'duchess', 'duke']
senticnet['du_septembre'] = ['0.667', '-0.98', '0', '0.61', '#joy', '#surprise', 'positive', '0.753', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['du_taur'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'lordosis', 'kyphosis', 'humpback', 'spinal_curvature', 'abnormality']
senticnet['du_tout'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'sexual_relationship', 'liaison', 'affair', 'involvement']
senticnet['du_trsor_an_reculait'] = ['-0.95', '0', '0.845', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'grapefruit_peel', 'lemon_peel', 'orange_peel', 'candied_fruit', 'succade']
senticnet['dual'] = ['0', '0', '0.738', '-0.76', '#anger', '#disgust', 'negative', '-0.75', 'double', 'twofold', 'treble', 'threefold', 'three-fold']
senticnet['dual_carriageway'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['dual_citizenship'] = ['0', '-0.91', '0', '0.806', '#surprise', '#admiration', 'positive', '0.861', 'clean_shoes', 'black_stuff', 'paint_nail', 'ethnicity', 'black_goo']
senticnet['dual_degree'] = ['0.087', '0.248', '-0.19', '0.31', '#interest', '#admiration', 'positive', '0.113', 'enroll', 'enroll_school', 'enroll_class', 'pay_tuition', 'start_college']
senticnet['dual_flush'] = ['0.939', '0.939', '0', '0', '#joy', '#interest', 'positive', '0.939', 'puddle', 'sop', 'liquid_water', 'finger_paint', 'bread']
senticnet['dual_nationality'] = ['0', '-0.91', '0', '0.806', '#surprise', '#admiration', 'positive', '0.861', 'clean_shoes', 'black_stuff', 'paint_nail', 'ethnicity', 'black_goo']
senticnet['dual_polyhedron'] = ['0.641', '-0.6', '0', '0.957', '#joy', '#admiration', 'positive', '0.733', 'ideal_solid', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'regular_octahedron']
senticnet['dual_role'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'baddie', 'theatrical_baddie', 'part', 'persona']
senticnet['dualism'] = ['0.651', '-0.57', '0', '0.592', '#joy', '#admiration', 'positive', '0.606', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['duality'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'width', 'size', 'mass_measure', 'measure', 'gauge']
senticnet['duality_theory'] = ['0.875', '-0.65', '0', '0.861', '#joy', '#admiration', 'positive', '0.797', 'completeness', 'work', 'whole_shebang', 'essential', 'whole']
senticnet['dubai_burj_khalifa'] = ['-0.81', '0', '0.543', '-0.63', '#sadness', '#disgust', 'negative', '-0.66', 'caper', 'pickle', 'relish', 'dill_pickle', 'sweet_pickle']
senticnet['dubai_international_airport'] = ['0', '0.147', '0', '0.099', '#interest', '#admiration', 'positive', '0.123', 'airliner', 'airport', 'baggage_compartment', 'aircraft', 'cable_car']
senticnet['dubble_bubble'] = ['0.965', '0.775', '0', '0.968', '#joy', '#admiration', 'positive', '0.903', 'snot', 'bad_taste', 'mouth_part', 'beautiful_smile', 'near_mouth']
senticnet['dubby'] = ['0.857', '-0.77', '0', '0.878', '#joy', '#admiration', 'positive', '0.836', 'hand', 'round', 'approval', 'commendation', 'clapping']
senticnet['dubhaltach_mac_fhirbhisigh'] = ['0', '0.105', '0', '0.084', '#interest', '#admiration', 'positive', '0.095', 'archive', 'print_medium', 'quiet_place', 'register', 'media']
senticnet['dubhaltach_macfhirbhisigh'] = ['0', '0.105', '0', '0.084', '#interest', '#admiration', 'positive', '0.095', 'archive', 'print_medium', 'quiet_place', 'register', 'media']
senticnet['dubiety'] = ['0.939', '-0.86', '0', '0.618', '#joy', '#surprise', 'positive', '0.808', 'incredulity', 'mental_rejection', 'skepticism', 'mistrust', 'distrust']
senticnet['dubious_penalty'] = ['0', '0', '-0.90', '-0.93', '#fear', '#disgust', 'negative', '-0.92', 'luckless', 'ill-omened', 'ill-starred', 'doomed', 'ill-fated']
senticnet['dubiousness'] = ['0.536', '-0.71', '0', '0.533', '#joy', '#surprise', 'positive', '0.594', 'incredulity', 'mental_rejection', 'skepticism', 'mistrust', 'distrust']
senticnet['dublin_metropolitan_police'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'law_enforcement_agency', 'law', 'agency']
senticnet['ducal'] = ['0.059', '0.076', '-0.05', '0.079', '#interest', '#admiration', 'positive', '0.039', 'noble', 'king', 'earl', 'duchess', 'duke']
senticnet['ducal_coronet'] = ['-0.62', '0', '0.961', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'jeweled_headdress', 'diadem', 'tiara', 'headdress', 'headgear']
senticnet['ducal_crown'] = ['-0.62', '0', '0.961', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'jeweled_headdress', 'diadem', 'tiara', 'headdress', 'headgear']
senticnet['ducal_family'] = ['0.033', '0.088', '-0.06', '0.068', '#interest', '#admiration', 'positive', '0.032', 'earl', 'noble', 'king', 'sultan', 'royal_title']
senticnet['ducal_palace'] = ['0', '0.105', '0', '0.076', '#interest', '#admiration', 'positive', '0.09', 'big_house', 'mansion', 'castle', 'block_flat', 'medieval']
senticnet['ducal_seat'] = ['0.033', '0.088', '-0.06', '0.068', '#interest', '#admiration', 'positive', '0.032', 'earl', 'noble', 'king', 'sultan', 'royal_title']
senticnet['duchenne_muscular_dystrophy'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'myotonic_muscular_dystrophy', 'dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['duchess'] = ['0.057', '0.096', '0', '0.104', '#interest', '#admiration', 'positive', '0.086', 'king', 'royal_leader', 'noble', 'lord', 'despot']
senticnet['duchess_satin'] = ['0.735', '-0.54', '0', '0.897', '#joy', '#admiration', 'positive', '0.727', 'net', 'network', 'mesh', 'meshing', 'meshwork']
senticnet['duchesse_satin'] = ['0.735', '-0.54', '0', '0.897', '#joy', '#admiration', 'positive', '0.727', 'net', 'network', 'mesh', 'meshing', 'meshwork']
senticnet['duchy'] = ['0.843', '0.709', '0', '0', '#joy', '#interest', 'positive', '0.776', 'dukedom', 'barony', 'baronetcy', 'earldom', 'fief']
senticnet['duck'] = ['-0.50', '0', '0.764', '-0.61', '#anger', '#disgust', 'negative', '-0.62', 'doctor', 'mountebank', 'charlatan', 'quack', 'physician']
senticnet['duck_breast'] = ['0.062', '0', '0.027', '0.055', '#joy', '#admiration', 'positive', '0.03', 'porridge', 'meal', 'broth', 'hamburger', 'hearty']
senticnet['duck_confit'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'mayonnaise', 'box_cereal', 'bottle_water']
senticnet['duck_down'] = ['0.814', '-0.53', '0', '0.624', '#joy', '#admiration', 'positive', '0.659', 'feather', 'plume', 'down_feather', 'goose_down', 'swan_down']
senticnet['duck_fat'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'cook_meat', 'cook_hamburger', 'cook_food', 'cook_rice', 'brown_meat']
senticnet['duck_sauce'] = ['0.641', '-0.56', '0', '0.748', '#joy', '#admiration', 'positive', '0.653', 'taco_sauce', 'chili_sauce', 'steak_sauce', 'condiment', 'hoisin_sauce']
senticnet['duck_sausage'] = ['-0.69', '0', '0.597', '-0.56', '#sadness', '#anger', 'negative', '-0.62', 'improperness', 'impropriety', 'raciness', 'spiciness', 'indelicacy']
senticnet['duck_soup'] = ['0.804', '-0.57', '0', '0.572', '#joy', '#surprise', 'positive', '0.65', 'labor', 'doddle', 'undertaking', 'project', 'task']
senticnet['ducking_stool'] = ['-0.84', '0', '0.987', '-0.77', '#sadness', '#anger', 'negative', '-0.87', 'thorn', 'sticker', 'glochid', 'prickle', 'spikelet']
senticnet['duckling'] = ['-0.80', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.80', 'frog', 'tadpole', 'jellyfish', 'tadpole', 'larva']
senticnet['duct'] = ['0.902', '0.861', '0', '0', '#joy', '#interest', 'positive', '0.881', 'channel', 'tv_channel', 'communication_channel', 'convey', 'carry']
senticnet['ductal'] = ['0.905', '-0.65', '0', '0.851', '#joy', '#admiration', 'positive', '0.805', 'gonococcus', 'bacteria', 'bacteriae', 'probiotic', 'probiotic_bacterium']
senticnet['ductile'] = ['0.841', '-0.57', '0', '0.891', '#joy', '#admiration', 'positive', '0.769', 'tamable', 'tameable', 'manipulable', 'malleable', 'docile']
senticnet['ductility'] = ['0.8', '-0.90', '0', '0.675', '#joy', '#surprise', 'positive', '0.794', 'indestructibility', 'tensile_strength', 'continuity', 'persistence', 'changelessness']
senticnet['ducting'] = ['0.74', '0.813', '0', '0', '#joy', '#interest', 'positive', '0.776', 'plenum', 'manifold', 'ductwork', 'airflow', 'cable']
senticnet['ductopenia'] = ['0.905', '-0.65', '0', '0.851', '#joy', '#admiration', 'positive', '0.805', 'gonococcus', 'bacteria', 'bacteriae', 'probiotic', 'probiotic_bacterium']
senticnet['ductwork'] = ['0.735', '0.806', '0', '0', '#joy', '#interest', 'positive', '0.77', 'ducting', 'manifold', 'plenum', 'airflow', 'cable']
senticnet['dud'] = ['-0.81', '0', '0.849', '-0.53', '#sadness', '#anger', 'negative', '-0.73', 'equipment_failure', 'fault', 'failure', 'brake_failure', 'engine_failure']
senticnet['dude'] = ['0.797', '0', '0', '0.88', '#joy', '#admiration', 'positive', '0.839', 'bloke', 'male', 'male_person', 'man_child', 'boy']
senticnet['dudgeon'] = ['-0.12', '0.13', '0.647', '-0.64', '#anger', '#disgust', 'negative', '-0.32', 'high_dudgeon', 'infuriating', 'exasperating', 'maddening', 'infuriate']
senticnet['due'] = ['0.922', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.947', 'payment', 'tax', 'expense', 'owe', 'cost']
senticnet['due_date'] = ['-0.10', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.10', 'day', 'maturity', 'maturity_date', 'date', 'birthday']
senticnet['due_paid'] = ['0.02', '0.24', '-0.11', '0.267', '#interest', '#admiration', 'positive', '0.103', 'organize_group', 'come_together', 'bring_together', 'business_group', 'business_firm']
senticnet['due_paying_membership'] = ['0.02', '0.24', '-0.11', '0.267', '#interest', '#admiration', 'positive', '0.103', 'organize_group', 'come_together', 'bring_together', 'business_group', 'business_firm']
senticnet['due_payment'] = ['0.02', '0.24', '-0.11', '0.267', '#interest', '#admiration', 'positive', '0.103', 'organize_group', 'come_together', 'bring_together', 'business_group', 'business_firm']
senticnet['due_process'] = ['-0.75', '0', '0.735', '-0.87', '#sadness', '#disgust', 'negative', '-0.78', 'eviction', 'defense', 'plea', 'dispossession', 'proceeding']
senticnet['due_process_of_law'] = ['-0.89', '0', '0.791', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'eviction', 'defense', 'plea', 'dispossession', 'proceeding']
senticnet['duel'] = ['0', '-0.01', '-0.08', '-0.16', '#fear', '#disgust', 'negative', '-0.07', 'siege', 'big_fight', 'battle', 'declare_war', 'conflict']
senticnet['duel_between'] = ['0', '-0.01', '-0.08', '-0.16', '#fear', '#disgust', 'negative', '-0.07', 'siege', 'big_fight', 'battle', 'declare_war', 'conflict']
senticnet['duelist'] = ['0', '-0.01', '-0.08', '-0.16', '#fear', '#disgust', 'negative', '-0.07', 'siege', 'big_fight', 'battle', 'declare_war', 'conflict']
senticnet['duellist'] = ['0', '-0.01', '-0.08', '-0.16', '#fear', '#disgust', 'negative', '-0.07', 'siege', 'big_fight', 'battle', 'declare_war', 'conflict']
senticnet['duergar'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'animal_home', 'habitat', 'beehive']
senticnet['duet_partner'] = ['0.882', '0.901', '0', '0', '#joy', '#interest', 'positive', '0.891', 'songwriter', 'vip', 'rock_group', 'guitar_player', 'violinist']
senticnet['duette'] = ['0.907', '0', '0', '0.736', '#joy', '#admiration', 'positive', '0.822', 'twosome', 'span', 'distich', 'duo', 'couple']
senticnet['duffel'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'sports_equipment', 'sport_stuff', 'golf', 'sport_store', 'bag']
senticnet['duffel_bag'] = ['0', '0.072', '0', '0', '#interest', '#interest', 'positive', '0.072', 'metal_box', 'cash_drawer', 'keyhole', 'clutch_bag', 'safe']
senticnet['duffer'] = ['-0.71', '0', '0.845', '-0.69', '#sadness', '#anger', 'negative', '-0.75', 'lump', 'goon', 'mortal', 'clumsy_person', 'butterfingers']
senticnet['duffle'] = ['0', '-0.02', '0.027', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'shoulder_bag', 'satchel', 'satchel_bag', 'tote_bag', 'carry']
senticnet['duffle_bag'] = ['0', '0.072', '0', '0', '#interest', '#interest', 'positive', '0.072', 'metal_box', 'cash_drawer', 'keyhole', 'clutch_bag', 'safe']
senticnet['duffy'] = ['0.532', '-0.68', '0', '0.984', '#surprise', '#admiration', 'positive', '0.733', 'potato', 'white_potato', 'chips', 'spud', 'tater']
senticnet['dufour_gland'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'giant_hornet', 'hornet', 'hornet_wasp']
senticnet['dugout'] = ['0.782', '0.71', '0', '0', '#joy', '#interest', 'positive', '0.746', 'cave', 'subway', 'bus_stop', 'excavation', 'train_station']
senticnet['duh'] = ['0', '0.1', '0', '0.117', '#interest', '#admiration', 'positive', '0.109', 'cute', 'small_human', 'diverse', 'small_person', 'tiny_animal']
senticnet['dukagjin'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
senticnet['dukagjini'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
senticnet['duke'] = ['0.033', '0.088', '-0.06', '0.068', '#interest', '#admiration', 'positive', '0.032', 'earl', 'noble', 'king', 'sultan', 'royal_title']
senticnet['dukedom'] = ['0.849', '0.709', '0', '0', '#joy', '#interest', 'positive', '0.779', 'earldom', 'barony', 'baronetcy', 'duchy', 'fief']
senticnet['dukkha'] = ['-0.87', '0', '0.744', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['dulcet'] = ['0.704', '-0.50', '0', '0.923', '#joy', '#admiration', 'positive', '0.71', 'mellifluous', 'mellisonant', 'melodic', 'lyrical', 'honeyed']
senticnet['dulciana'] = ['0.939', '-0.71', '0', '0.775', '#joy', '#admiration', 'positive', '0.81', 'vox_angelica', 'voix_celeste', 'organ_stop', 'pipe', 'diapason']
senticnet['dulcify'] = ['0.744', '-0.77', '0', '0.661', '#joy', '#surprise', 'positive', '0.725', 'sugarcoat', 'honey', 'glaze', 'change_taste', 'sugar']
senticnet['dulcolax'] = ['-0.50', '0', '0.582', '-0.57', '#anger', '#disgust', 'negative', '-0.55', 'purgative', 'cathartic', 'physic', 'aperient', 'phenolphthalein']
senticnet['dulcorate'] = ['0.99', '-0.82', '0', '0.691', '#joy', '#surprise', 'positive', '0.837', 'sugarcoat', 'honey', 'glaze', 'change_taste', 'sugar']
senticnet['dull'] = ['0', '0', '0.778', '-0.94', '#anger', '#disgust', 'negative', '-0.86', 'boring_talk', 'yawn', 'talk_lot', 'logorrheic', 'boring_activity']
senticnet['dull_ache'] = ['-0.82', '0.226', '-0.46', '-0.05', '#sadness', '#fear', 'negative', '-0.27', 'heavy_weight', 'heavy', 'heavy_person', 'heavy_breathe', 'heavy_air']
senticnet['dull_gray'] = ['-0.02', '0.068', '0.04', '0.084', '#interest', '#admiration', 'positive', '0.022', 'peelable', 'green_black', 'purple', 'red', 'edible_human']
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senticnet['during_wwi'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'dictatorship', 'dictator', 'colony', 'world_domination', 'oppression']
senticnet['during_wwii'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'dictatorship', 'dictator', 'colony', 'world_domination', 'oppression']
senticnet['durum_wheat'] = ['0.878', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.883', 'wheat', 'grain', 'rye', 'white_rice', 'rice']
senticnet['dusk'] = ['-0.08', '0', '-0.04', '0', '#sadness', '#fear', 'negative', '-0.06', 'get_dark', 'night_time', 'day_end', 'sundown', 'dark_outside']
senticnet['dusky'] = ['0.058', '0.062', '0', '0', '#joy', '#interest', 'positive', '0.06', 'wood_duck', 'dim', 'darkness', 'sunset', 'unlit']
senticnet['dusky_red'] = ['-0.11', '0.115', '0.67', '-0.65', '#anger', '#disgust', 'negative', '-0.33', 'lividity', 'livid', 'wrathfully', 'wrothful', 'wroth']
senticnet['dust'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'radioactive_dust', 'particulate', 'particulate_matter']
senticnet['dust_bin'] = ['0.864', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.924', 'trash', 'trash_container', 'junk_store', 'coffee_cup', 'trash_receptacle']
senticnet['dust_collector'] = ['0.628', '-0.96', '0', '0.682', '#surprise', '#admiration', 'positive', '0.759', 'electric_fan', 'blower', 'fan', 'punkah', 'device']
senticnet['dust_furniture'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'cleanness', 'sweep', 'clean_floor', 'vacuum_floor', 'clean']
senticnet['dust_heap'] = ['0.864', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.924', 'trash', 'trash_container', 'junk_store', 'coffee_cup', 'trash_receptacle']
senticnet['dust_jacket'] = ['-0.04', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.08', 'data', 'awareness', 'information', 'saga', 'knowledge']
senticnet['dust_mask'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'wear', 'protective_garment', 'coverall', 'shoulder_pad', 'wearable']
senticnet['dust_matter'] = ['-0.96', '0', '0.732', '-0.58', '#sadness', '#anger', 'negative', '-0.76', 'fallout', 'chalk_dust', 'radioactive_dust', 'dust', 'particulate']
senticnet['dust_mite'] = ['0', '-0.01', '0.067', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'cookie_crumb', 'storage_box', 'dirty_sock', 'dust', 'louse']
senticnet['dust_mite_allergen'] = ['-0.15', '0.162', '0.596', '-0.62', '#anger', '#disgust', 'negative', '-0.30', 'hackle', 'umbrage', 'offense', 'ire', 'see_red']
senticnet['dust_particle'] = ['0.087', '-0.07', '0.062', '0', '#joy', '#surprise', 'positive', '0.033', 'small_piece', 'spangle', 'stripe', 'particle', 'oxide']
senticnet['dust_ruffle'] = ['0.087', '-0.07', '0.062', '0', '#joy', '#surprise', 'positive', '0.033', 'pillowcase', 'bed', 'comforter', 'bed_sheet', 'duvet_cover']
senticnet['dustbin'] = ['0.864', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.924', 'trash', 'trash_container', 'junk_store', 'coffee_cup', 'trash_receptacle']
senticnet['dustbuster'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'smart_technology', 'electric_shaver', 'hair_dryer', 'stay_healthy', 'wash_tooth']
senticnet['duster'] = ['-0.67', '0', '0.688', '-0.89', '#anger', '#disgust', 'negative', '-0.75', 'protective_garment', 'boilersuit', 'boiler_suit', 'overall', 'coverall']
senticnet['dustjacket'] = ['-0.04', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.08', 'data', 'awareness', 'information', 'saga', 'knowledge']
senticnet['dustpan'] = ['0.022', '-0.01', '0.062', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'electric_toothbrush', 'moistener', 'hairbrush', 'shampoo_conditioner', 'shave_kit']
senticnet['dustup'] = ['-0.82', '0', '0.852', '-0.74', '#sadness', '#anger', 'negative', '-0.80', 'altercation', 'fracas', 'difference', 'affray', 'bickering']
senticnet['dusty'] = ['0', '0', '0.943', '-0.8', '#anger', '#disgust', 'negative', '-0.87', 'feel_dirty', 'unclean', 'dirty', 'yucky', 'filthy']
senticnet['dusty_desert'] = ['0', '0', '0.943', '-0.8', '#anger', '#disgust', 'negative', '-0.87', 'feel_dirty', 'unclean', 'dirty', 'yucky', 'filthy']
senticnet['dusty_disk'] = ['0.737', '-0.96', '0', '0.552', '#joy', '#surprise', 'positive', '0.75', 'prominence', 'hump', 'bump', 'swelling', 'frontal_eminence']
senticnet['dusty_road'] = ['0', '0', '0.943', '-0.8', '#anger', '#disgust', 'negative', '-0.87', 'feel_dirty', 'unclean', 'dirty', 'yucky', 'filthy']
senticnet['dutch_artist_florentijn'] = ['0.036', '0.084', '0', '0', '#joy', '#interest', 'positive', '0.06', 'toy', 'bath_toy', 'yo-yo', 'toy_ball', 'duck']
senticnet['dutch_east_indiaman'] = ['0.582', '-0.97', '0', '0.802', '#surprise', '#admiration', 'positive', '0.788', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['dutch_privateer'] = ['0.582', '-0.97', '0', '0.802', '#surprise', '#admiration', 'positive', '0.788', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['dutie'] = ['-0.89', '0.161', '-0.61', '0.053', '#sadness', '#fear', 'negative', '-0.32', 'heartrending', 'heartbreaking', 'grievous', 'grievously', 'brokenheartedness']
senticnet['dutiful'] = ['0.958', '0', '0', '0.968', '#joy', '#admiration', 'positive', '0.963', 'kind', 'nice', 'respectful', 'meek', 'red_carpet']
senticnet['dutiful_daughter'] = ['0.539', '-0.96', '0', '0.745', '#surprise', '#admiration', 'positive', '0.751', 'spouse', 'partner', 'married_person', 'mate', 'better_half']
senticnet['dutiful_wife'] = ['0.539', '-0.96', '0', '0.745', '#surprise', '#admiration', 'positive', '0.751', 'spouse', 'partner', 'married_person', 'mate', 'better_half']
senticnet['dutifully'] = ['0.653', '0.635', '-0.24', '0.294', '#joy', '#interest', 'positive', '0.335', 'merrily', 'jubilantly', 'happily', 'mirthfully', 'gayly']
senticnet['dutifulness'] = ['0.681', '-0.68', '0', '0.938', '#surprise', '#admiration', 'positive', '0.768', 'righteousness', 'godliness', 'piety', 'devoutness', 'religiousness']
senticnet['dutton_epoch'] = ['0.805', '-0.58', '0', '0.525', '#joy', '#surprise', 'positive', '0.637', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['duty'] = ['0', '0.856', '0', '0.886', '#interest', '#admiration', 'positive', '0.871', 'management', 'policeman', 'authority', 'ambasjoyfulor', 'construction_worker']
senticnet['duty_cycle'] = ['0', '0.098', '-0.06', '0.15', '#interest', '#admiration', 'positive', '0.061', 'make_book', 'use_ink', 'make_newspaper', 'pencil_mark', 'computer_command']
senticnet['duty_drawback'] = ['-0.03', '0.223', '-0.15', '0.35', '#interest', '#admiration', 'positive', '0.097', 'return', 'gain', 'cheap_buy', 'receive', 'bequest']
senticnet['duty_police_officer'] = ['0.964', '-0.57', '0', '0.827', '#joy', '#admiration', 'positive', '0.789', 'mortal', 'murder_suspect', 'suspect', 'rape_suspect', 'person']
senticnet['duvet'] = ['0.087', '-0.07', '0.062', '0', '#joy', '#surprise', 'positive', '0.033', 'pillowcase', 'bed', 'comforter', 'bed_sheet', 'duvet_cover']
senticnet['duvet_cover'] = ['0.087', '-0.07', '0.062', '0', '#joy', '#surprise', 'positive', '0.033', 'pillowcase', 'bed', 'comforter', 'bed_sheet', 'duvet']
senticnet['duz'] = ['-0.63', '0', '0.791', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'little', 'pinch', 'modicum', 'small_indefinite_amount', 'indefinite_quantity']
senticnet['dvd'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['dvd_blu_ray'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['dvd_blu_ray_disc'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['dvd_box'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['dvd_boxed_set'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['dvd_boxset'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['dvd_compilation'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['dvd_format'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['dvd_player'] = ['0.09', '-0.03', '0.015', '0', '#joy', '#surprise', 'positive', '0.036', 'tape_player', 'light_fixture', 'tv_set', 'sit_at_theater', 'technician']
senticnet['dvd_release'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['dwarf'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'gnome', 'elf', 'goblin', 'undersized', 'small_person']
senticnet['dwarf_birch'] = ['-0.04', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.04', 'bad_luck', 'damn', 'artic', 'mammal_family', 'bad_mood']
senticnet['dwarf_buckeye'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'sweet_buckeye', 'horse_chestnut', 'bottlebrush_buckeye', 'red_buckeye', 'particolored_buckeye']
senticnet['dwarf_cornel'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'bunchberry', 'cornel', 'crackerberry', 'pudding_berry', 'common_white_dogwood']
senticnet['dwarf_galaxy'] = ['0', '0.706', '0', '0.847', '#interest', '#admiration', 'positive', '0.776', 'dwarf_planet', 'universe', 'planetary', 'sun', 'moon']
senticnet['dwarf_mistletoe'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['dwarf_planet'] = ['0.834', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.88', 'galaxy', 'universe', 'planetary', 'sun', 'moon']
senticnet['dwarf_star'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['dwarfed_by'] = ['0', '-0.86', '0', '0.762', '#surprise', '#admiration', 'positive', '0.815', 'big', 'large', 'really_big', 'wide', 'tall']
senticnet['dwarfish'] = ['0.795', '0', '0', '0.987', '#joy', '#admiration', 'positive', '0.891', 'undescended', 'rope-like', 'blade-like', 'whip-like', 'retractile']
senticnet['dwarfism'] = ['-0.87', '0', '0.873', '-0.62', '#sadness', '#anger', 'negative', '-0.78', 'pycnodysostosis', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['dweeb'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'book_worm', 'nerd', 'loser', 'underdog', 'computer_lover']
senticnet['dwell'] = ['-0.88', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.86', 'gloat', 'gloating', 'cheer', 'gloatingly', 'schadenfreude']
senticnet['dwell_on'] = ['-0.10', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.10', 'falter', 'waver', 'linger', 'boggle', 'hesitate']
senticnet['dwell_place'] = ['0', '0.096', '-0.05', '0.131', '#interest', '#admiration', 'positive', '0.058', 'domestic_dwell', 'live_place', 'place_to_live', 'live_quarter', 'domicile']
senticnet['dwell_therein'] = ['0.887', '-0.84', '0', '0.819', '#joy', '#surprise', 'positive', '0.851', 'arouse', 'elicit', 'enkindle', 'kindle', 'evoke']
senticnet['dweller'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'invillager', 'inhabitant', 'habitant', 'villager', 'denizen']
senticnet['dwelling'] = ['-0.05', '0.116', '-0.04', '0.102', '#interest', '#admiration', 'positive', '0.029', 'house', 'lease', 'multi-story_build', 'rent_out', 'large_house']
senticnet['dwelling_place'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
senticnet['dwelt'] = ['-0.71', '0', '0.836', '0', '#sadness', '#anger', 'negative', '-0.77', 'awkward', 'unenviable', 'embarrassingly', 'embarrassing', 'sheepish']
senticnet['dweomer'] = ['0.894', '0', '0', '0.924', '#joy', '#admiration', 'positive', '0.909', 'captivate', 'belonging', 'comfortably', 'solace', 'comfortableness']
senticnet['dwindle'] = ['0', '0', '0.758', '-0.77', '#anger', '#disgust', 'negative', '-0.76', 'waste', 'lessen', 'curb', 'deteriorate', 'crumble']
senticnet['dwindle_distribution'] = ['-0.18', '0', '-0.14', '-0.11', '#sadness', '#fear', 'negative', '-0.14', 'fly_kite', 'walk_dog', 'ride_roller_coaster', 'rake_leave', 'jump_up_down']
senticnet['dwindling_supply'] = ['-0.83', '0', '-0.88', '-0.78', '#sadness', '#fear', 'negative', '-0.83', 'little', 'sadness', 'small_amount', 'lack', 'absence']
senticnet['dwinelle_hall'] = ['0.872', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.811', 'lecture_theater', 'lecture', 'conference_auditorium', 'read_book', 'text_book']
senticnet['dwyane'] = ['-0.94', '0', '0.492', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'make_noise', 'disturb', 'confusion', 'hustle_and_bustle', 'bustle']
senticnet['dwyer'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
senticnet['dyadic'] = ['0.073', '0.256', '-0.19', '0.371', '#interest', '#admiration', 'positive', '0.127', 'good_etiquette', 'okay', 'niceness', 'needful', 'need']
senticnet['dybbuk'] = ['-0.79', '0', '0.92', '-0.75', '#sadness', '#anger', 'negative', '-0.82', 'evil_spirit', 'incubus', 'succubus', 'succuba', 'daemon']
senticnet['dydd_llun'] = ['-0.56', '0', '0.617', '-0.80', '#anger', '#disgust', 'negative', '-0.66', 'prolixity', 'prolixness', 'wordiness', 'repetitiveness', 'repetitiousness']
senticnet['dye'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'wall_cover', 'two-dimensional', 'cover_wall', 'pigment', 'latex']
senticnet['dye_hair'] = ['0.05', '-0.02', '0', '0.111', '#joy', '#admiration', 'positive', '0.063', 'change_hair', 'makeover', 'change_hairstyle', 'salon', 'get_haircut']
senticnet['dye_pack'] = ['0', '0.186', '0', '0.075', '#interest', '#admiration', 'positive', '0.131', 'money_inside', 'check_account', 'savings', 'money_storage', 'store_money']
senticnet['dyed-in-the-wool'] = ['-0.96', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.98', 'cold-hearted', 'cold-blooded', 'dispassionate', 'remorseless', 'stone-cold']
senticnet['dyed_blond'] = ['0', '0.097', '0', '0.146', '#interest', '#admiration', 'positive', '0.121', 'hair_style', 'hair', 'appearance', 'hold', 'look']
senticnet['dyed_hair'] = ['-0.91', '0', '0.869', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'talk_hairdresser', 'hair_get_short', 'hair_become_short', 'keep_hair_short', 'hair_wash']
senticnet['dyed_wool'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_fabric']
senticnet['dyin'] = ['-0.66', '0', '0.595', '-0.83', '#sadness', '#disgust', 'negative', '-0.7', 'bastard', 'dickhead', 'disagreeable_person', 'motherfucker', 'son_of_bitch']
senticnet['dying'] = ['0', '0', '0.912', '-0.73', '#anger', '#disgust', 'negative', '-0.82', 'die', 'approaching', 'expiring', 'moribundly', 'moribund']
senticnet['dyk_archive_navigation_templat'] = ['0.92', '0.794', '0', '0', '#joy', '#interest', 'positive', '0.857', 'move_mouse', 'reboot', 'type_of_keyboard', 'enter_address', 'keep_track_appointment']
senticnet['dynamic'] = ['0', '0.144', '-0.09', '0.196', '#interest', '#admiration', 'positive', '0.083', 'capable', 'energetic', 'alter', 'pocket_money', 'change_money']
senticnet['dynamic_range'] = ['0', '0', '-0.66', '0', '#fear', '#fear', 'negative', '-0.66', 'anger', 'rage', 'annoyance', 'apprehension', 'fear']
senticnet['dynamic_recompilation'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'carbon_fiber']
senticnet['dynamical'] = ['0.74', '-0.75', '0', '0.815', '#surprise', '#admiration', 'positive', '0.77', 'propellant', 'propellent', 'propelling', 'changing', 'propulsive']
senticnet['dynamical_behavior'] = ['-0.97', '0', '0.831', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'skewness', 'irregularity']
senticnet['dynamical_phenomenon'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'constancy', 'monotony', 'invariance', 'changelessness', 'unchangeability']
senticnet['dynamical_system'] = ['0', '0.716', '0', '0.847', '#interest', '#admiration', 'positive', '0.781', 'study', 'different_application', 'mathematics', 'theory', 'system']
senticnet['dynamically'] = ['0', '0.887', '0', '0.812', '#interest', '#admiration', 'positive', '0.85', 'research', 'project', 'system', 'provide', 'development']
senticnet['dynamically_adapt'] = ['-0.03', '0.086', '0', '0.114', '#interest', '#admiration', 'positive', '0.054', 'deduce', 'figure_out', 'arrive_conclusion', 'come_conclusion', 'come_up_new_idea']
senticnet['dynamically_configure'] = ['0.63', '-0.55', '0', '0.946', '#joy', '#admiration', 'positive', '0.711', 'complect', 'connect', 'link', 'link_up', 'join']
senticnet['dynamically_discover'] = ['0', '0.717', '0', '0.765', '#interest', '#admiration', 'positive', '0.741', 'helper', 'artificial_intelligence', 'goal_driven', 'proactive', 'intelligent']
senticnet['dynamism'] = ['0.794', '-0.61', '0', '0.675', '#joy', '#admiration', 'positive', '0.694', 'fruitfulness', 'genius', 'fecundity', 'ability', 'wizardry']
senticnet['dynamite'] = ['0', '0', '-0.73', '-0.87', '#fear', '#disgust', 'negative', '-0.80', 'bomb', 'explosive', 'nuclear_bomb', 'projectile', 'aerial_bomb']
senticnet['dynamite_bomb'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'firework', 'bright_flash', 'nuclear_weapon', 'earthquake', 'nuclear_bomb']
senticnet['dynamite_entertainment'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'encyclopaedia', 'child_story', 'comic', 'disney_cartoon']
senticnet['dynamite_explosion'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'firework', 'bright_flash', 'nuclear_weapon', 'earthquake', 'nuclear_bomb']
senticnet['dynamo'] = ['-0.05', '-0.01', '-0.05', '-0.06', '#sadness', '#disgust', 'negative', '-0.04', 'crank', 'air_vent', 'emergency_brake', 'brake_pedal', 'grunt']
senticnet['dynamo_electric'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'biological_machine', 'social_creature', 'live_creature', 'powerful', 'alive']
senticnet['dynamometer'] = ['0.928', '-0.87', '0', '0.679', '#joy', '#surprise', 'positive', '0.829', 'examine', 'experiment', 'alpha_test', 'beta_test', 'road_test']
senticnet['dynastic'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
senticnet['dynastic_rule'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
senticnet['dynasty'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
senticnet['dynasty_pharaoh'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
senticnet['dyne'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'force_unit', 'newton', 'sthene', 'poundal']
senticnet['dynein_light'] = ['0.096', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.057', 'platform_rail', 'rail', 'catwalk', 'column', 'token_turnstile']
senticnet['dynorphin'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'gastrointestinal_hormone', 'secretin', 'ghrelin']
senticnet['dysarthria'] = ['-0.84', '0', '0.829', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'upset', 'defect_of_speech', 'dysphonia', 'disorder', 'lallation']
senticnet['dyscalculia'] = ['0.89', '-0.70', '0', '0.643', '#joy', '#surprise', 'positive', '0.745', 'dyslexia', 'learning_disability', 'dysgraphia', 'disorder', 'upset']
senticnet['dysenteric'] = ['-0.99', '0', '0.859', '-0.62', '#sadness', '#anger', 'negative', '-0.82', 'lax', 'loose', 'diarrheal', 'diarrhoeal', 'unconstipated']
senticnet['dysentery'] = ['-0.89', '0', '0.702', '-0.67', '#sadness', '#anger', 'negative', '-0.75', 'amebic_dysentery', 'amoebic_dysentery', 'shigellosis', 'bacillary_dysentery', 'infectious_disease']
senticnet['dysequilibrium'] = ['-0.87', '0', '0.773', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'rubor', 'redness', 'inflammation', 'otitis', 'otitis_interna']
senticnet['dysesthesia'] = ['-0.58', '0', '0.773', '-0.79', '#anger', '#disgust', 'negative', '-0.71', 'perspiration', 'sweating', 'polyhidrosis', 'sudation', 'hidrosis']
senticnet['dysfunction'] = ['-0.78', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.80', 'deterioration', 'decline', 'decay', 'degradation', 'slide']
senticnet['dysfunctional'] = ['-0.99', '0', '0.658', '-0.73', '#sadness', '#disgust', 'negative', '-0.79', 'adrift', 'afloat', 'aimless', 'purposeless', 'planless']
senticnet['dysgenesis'] = ['-0.73', '0', '0.634', '-0.85', '#sadness', '#disgust', 'negative', '-0.74', 'cacogenesis', 'impotence', 'impotency', 'barrenness', 'infertility']
senticnet['dysgraphia'] = ['0.89', '-0.70', '0', '0.643', '#joy', '#surprise', 'positive', '0.745', 'dyslexia', 'dyscalculia', 'learning_disability', 'disorder', 'upset']
senticnet['dysgraphic'] = ['0.89', '-0.70', '0', '0.643', '#joy', '#surprise', 'positive', '0.745', 'dyslexia', 'dyscalculia', 'learning_disability', 'disorder', 'upset']
senticnet['dyskinetic'] = ['-0.56', '0', '0.849', '-0.61', '#anger', '#disgust', 'negative', '-0.67', 'convulsive', 'unsteady', 'jerky', 'spasmodic', 'spastic']
senticnet['dyslexia'] = ['0.89', '-0.70', '0', '0.643', '#joy', '#surprise', 'positive', '0.745', 'learning_disability', 'dyscalculia', 'dysgraphia', 'disorder', 'upset']
senticnet['dyslexic'] = ['0', '0.943', '0', '0.789', '#interest', '#admiration', 'positive', '0.866', 'energy', 'genius', 'energy_use', 'gas_oil', 'invent']
senticnet['dyslexic_child'] = ['0.89', '-0.70', '0', '0.643', '#joy', '#surprise', 'positive', '0.745', 'dyslexia', 'dyscalculia', 'learning_disability', 'disorder', 'upset']
senticnet['dyslipidemia'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'illness', 'ill', 'tension', 'bad_health', 'malignant_hypertension']
senticnet['dysmenorrhea'] = ['-0.73', '0', '0.555', '-0.90', '#sadness', '#disgust', 'negative', '-0.73', 'aching', 'hurting', 'labor_pain', 'neuralgia', 'neuralgy']
senticnet['dysmenorrhoea'] = ['-0.73', '0', '0.555', '-0.90', '#sadness', '#disgust', 'negative', '-0.73', 'aching', 'hurting', 'labor_pain', 'neuralgia', 'neuralgy']
senticnet['dysmetria'] = ['0.722', '-0.87', '0', '0.696', '#joy', '#surprise', 'positive', '0.763', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['dysmorphic_disorder'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'concupiscence', 'lustfulness', 'amorousness']
senticnet['dyson_dc'] = ['-0.04', '-0.02', '0.089', '-0.14', '#anger', '#disgust', 'negative', '-0.06', 'steam_locomotive', 'loudspeaker', 'collector', 'siren', 'activist']
senticnet['dyson_sphere'] = ['0.045', '0', '0', '0.051', '#joy', '#admiration', 'positive', '0.048', 'perfect_sphere', 'pour_hot_coffee_mug', 'life', 'alkaline', 'wrest']
senticnet['dysostosis_multiplex'] = ['-0.59', '0', '0.911', '-0.87', '#anger', '#disgust', 'negative', '-0.79', 'induration', 'sclerosis', 'disseminated_sclerosis', 'multiple_sclerosis', 'pathology']
senticnet['dyspareunia'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'gastroenteritis', 'stomach_flu', 'intestinal_flu', 'inflammatory_disease']
senticnet['dyspepsia'] = ['0', '0', '-0.70', '-0.76', '#fear', '#disgust', 'negative', '-0.73', 'choke', 'become_full', 'get_full', 'burp', 'sickness']
senticnet['dyspeptic'] = ['-0.74', '0', '0.991', '-0.64', '#sadness', '#anger', 'negative', '-0.79', 'unfortunate', 'diseased_person', 'invalid', 'psychoneurotic', 'psychotic']
senticnet['dysphagia'] = ['-0.84', '0', '0.829', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'upset', 'dysarthria', 'defect_of_speech', 'disorder', 'lallation']
senticnet['dysphemism'] = ['-0.99', '0', '0.597', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'jalopy', 'car', 'auto', 'automobile', 'machine']
senticnet['dysphonia'] = ['-0.84', '0', '0.829', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'upset', 'dysarthria', 'defect_of_speech', 'disorder', 'lallation']
senticnet['dysphonic'] = ['-0.56', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.63', 'aphasic', 'inarticulate', 'speechless', 'unarticulated', 'dumb']
senticnet['dysphoria'] = ['-0.80', '0.247', '-0.44', '-0.06', '#sadness', '#fear', 'negative', '-0.26', 'helplessness', 'feel_down', 'demoralize', 'dispirit', 'get_down']
senticnet['dysphoric'] = ['-0.69', '0.32', '-0.35', '-0.11', '#sadness', '#fear', 'negative', '-0.21', 'despairingly', 'despondently', 'forlornly', 'forlornness', 'desolation']
senticnet['dysphoric_mood'] = ['-0.58', '0', '0.722', '-0.51', '#sadness', '#anger', 'negative', '-0.60', 'dissociative_disorder', 'depersonalization_neurosis', 'depersonalization', 'mental_disorder', 'mental_disturbance']
senticnet['dysplastic'] = ['-0.88', '0', '0.774', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fibroid_tumor', 'fibroid', 'benign_tumor', 'nonmalignant_tumor', 'nonmalignant_neoplasm']
senticnet['dyspnea'] = ['-0.20', '0', '-0.22', '-0.19', '#sadness', '#fear', 'negative', '-0.20', 'go_hospital', 'death', 'die', 'take_aspirin', 'heart_attack']
senticnet['dyspneal'] = ['-0.53', '0', '0.886', '-0.91', '#anger', '#disgust', 'negative', '-0.77', 'dyspneic', 'dyspnoeic', 'asphyxiating', 'dyspnoeal', 'breathless']
senticnet['dyspneic'] = ['-0.53', '0', '0.886', '-0.91', '#anger', '#disgust', 'negative', '-0.77', 'asphyxiating', 'dyspnoeic', 'dyspneal', 'dyspnoeal', 'breathless']
senticnet['dyspnoea'] = ['-0.20', '0', '-0.22', '-0.19', '#sadness', '#fear', 'negative', '-0.20', 'go_hospital', 'death', 'die', 'take_aspirin', 'heart_attack']
senticnet['dyspnoeal'] = ['-0.53', '0', '0.886', '-0.91', '#anger', '#disgust', 'negative', '-0.77', 'dyspneic', 'dyspnoeic', 'dyspneal', 'asphyxiating', 'breathless']
senticnet['dyspnoeic'] = ['-0.53', '0', '0.886', '-0.91', '#anger', '#disgust', 'negative', '-0.77', 'dyspneic', 'asphyxiating', 'dyspneal', 'dyspnoeal', 'breathless']
senticnet['dysport'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'infective_agent', 'satisfier', 'quickener', 'agent']
senticnet['dyspraxia'] = ['0.89', '-0.70', '0', '0.643', '#joy', '#surprise', 'positive', '0.745', 'dyslexia', 'dyscalculia', 'learning_disability', 'disorder', 'upset']
senticnet['dyspraxic'] = ['0.89', '-0.70', '0', '0.643', '#joy', '#surprise', 'positive', '0.745', 'dyslexia', 'dyscalculia', 'learning_disability', 'disorder', 'upset']
senticnet['dysprosium'] = ['-0.05', '-0.00', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'pigment', 'many_shades', 'cyan', 'green_yellow', 'material']
senticnet['dysrhythmia'] = ['-0.56', '0', '0.849', '-0.61', '#anger', '#disgust', 'negative', '-0.67', 'convulsive', 'unsteady', 'jerky', 'spasmodic', 'spastic']
senticnet['dyssynergia'] = ['-0.77', '0', '0.966', '-0.51', '#sadness', '#anger', 'negative', '-0.75', 'enuresis', 'incontinency', 'incontinence', 'stress_incontinence', 'urge_incontinence']
senticnet['dysthymia'] = ['-0.99', '0', '0.982', '-0.62', '#sadness', '#anger', 'negative', '-0.86', 'clinical_depression', 'dysthymic_depression', 'affective_disorder', 'major_affective_disorder', 'emotional_disorder']
senticnet['dysthymic'] = ['-0.78', '0', '0.918', '-0.96', '#anger', '#disgust', 'negative', '-0.88', 'major_depressive_episode', 'depression', 'depressive_disorder', 'clinical_depression', 'involutional_depression']
senticnet['dysthymic_depression'] = ['-0.99', '0', '0.982', '-0.62', '#sadness', '#anger', 'negative', '-0.86', 'dysthymia', 'clinical_depression', 'affective_disorder', 'major_affective_disorder', 'emotional_disorder']
senticnet['dysthymic_disorder'] = ['-0.78', '0', '0.918', '-0.96', '#anger', '#disgust', 'negative', '-0.88', 'major_depressive_episode', 'depression', 'depressive_disorder', 'clinical_depression', 'involutional_depression']
senticnet['dystonic'] = ['-0.89', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.82', 'fretful', 'antsy', 'fidgety', 'stewing', 'impatiently']
senticnet['dystopian_fiction'] = ['0.747', '0.85', '0', '0', '#joy', '#interest', 'positive', '0.798', 'night_vision', 'mystery', 'plot', 'imagine', 'myth']
senticnet['dystopic'] = ['-0.54', '0', '0.845', '-0.95', '#anger', '#disgust', 'negative', '-0.78', 'face', 'base', 'underside', 'sole', 'bilge']
senticnet['dystrophy'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'myotonic_muscular_dystrophy', 'myotonic_dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['dysuria'] = ['-0.05', '0', '-0.05', '-0.04', '#sadness', '#fear', 'negative', '-0.05', 'empty_bladder', 'pee', 'go_potty', 'go_pee', 'go_toilet']
senticnet['dzanga_sangha_special'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['dzielnica_district'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'saltine', 'soda_cracker', 'cracker', 'water_biscuit', 'graham_cracker']
senticnet['each'] = ['-0.61', '0', '0.856', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'unfixed', 'indefinite', 'nebulous', 'indecisive', 'coy']
senticnet['each_attendee'] = ['0.072', '0', '0', '0.078', '#joy', '#admiration', 'positive', '0.075', 'participate', 'examine', 'voyeur', 'influence_public_decision', 'question_everything']
senticnet['each_carpel'] = ['0', '0', '0.064', '-0.04', '#anger', '#disgust', 'negative', '-0.05', 'bird_food', 'funiculus', 'plant_product', 'fuzz', 'agricultural_product']
senticnet['each_guestroom'] = ['0.159', '-0.00', '0.011', '0.079', '#joy', '#admiration', 'positive', '0.058', 'ceiling_fan', 'ipod', 'technologic', 'formula', 'music']
senticnet['each_iteration'] = ['0.037', '0', '0.026', '0.129', '#joy', '#admiration', 'positive', '0.047', 'measurement_unit', 'many_items', 'long', 'circle_part', 'narrow']
senticnet['each_participant'] = ['0.072', '0', '0', '0.078', '#joy', '#admiration', 'positive', '0.075', 'participate', 'examine', 'voyeur', 'influence_public_decision', 'question_everything']
senticnet['each_proglottid'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'annelid', 'annelid_worm', 'leech']
senticnet['each_semester'] = ['-0.04', '-0.02', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.02', 'professor', 'lecturer', 'teacher', 'freshman', 'pupil']
senticnet['each_unexcused_absence'] = ['0.723', '0', '0', '0.762', '#joy', '#admiration', 'positive', '0.742', 'presence', 'feel_important', 'existence', 'feel_satisfied', 'feel_presence']
senticnet['eadric'] = ['-0.92', '0', '0.659', '-0.81', '#sadness', '#disgust', 'negative', '-0.8', 'unripe', 'napping', 'off_guard', 'off_one_guard', 'unprepared']
senticnet['eager'] = ['0.865', '0', '0', '0.901', '#joy', '#admiration', 'positive', '0.883', 'eagerly', 'eagerlyness', 'avidity', 'thirstily', 'avidness']
senticnet['eager_anticipation'] = ['0.772', '0.742', '0', '0', '#joy', '#interest', 'positive', '0.757', 'enjoy', 'satisfaction', 'happiness', 'become_excited', 'joy']
senticnet['eager_beaver'] = ['0.691', '-0.72', '0', '0.673', '#joy', '#surprise', 'positive', '0.698', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['eagerly'] = ['0.979', '0.878', '0', '0', '#joy', '#interest', 'positive', '0.928', 'eager', 'eagerness', 'thirstily', 'impatient', 'hot']
senticnet['eagerlyness'] = ['0.863', '0', '0', '0.894', '#joy', '#admiration', 'positive', '0.879', 'eagerly', 'eager', 'avidity', 'thirstily', 'avidness']
senticnet['eagerness'] = ['0.766', '0', '0', '0.725', '#joy', '#admiration', 'positive', '0.746', 'eagerly', 'avidity', 'thirstily', 'avidness', 'zest']
senticnet['eagle'] = ['0', '0.105', '0', '0', '#interest', '#interest', 'positive', '0.105', 'beak', 'many_feathers', 'big_eye', 'bird', 'bird_mouth']
senticnet['eagle-eyed'] = ['0', '0.732', '0', '0.801', '#interest', '#admiration', 'positive', '0.766', 'apprehensive', 'perceptive', 'knowledgeable', 'locality', 'monochromatic']
senticnet['eagle_butte'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['eagle_crater'] = ['0.052', '0.119', '0', '0.141', '#interest', '#admiration', 'positive', '0.104', 'neigh', 'domestic_animal', 'animal_type', 'animal', 'home_animal']
senticnet['eagle_nest'] = ['0', '0.105', '0', '0', '#interest', '#interest', 'positive', '0.105', 'beak', 'many_feathers', 'big_eye', 'bird', 'bird_mouth']
senticnet['eagle_owl'] = ['-0.53', '0', '0.648', '-0.70', '#anger', '#disgust', 'negative', '-0.63', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['eagle_tavern'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'city_hall', 'courthouse', 'subway_station', 'public_place', 'bus_station']
senticnet['ealdorman'] = ['-0.92', '0', '0.659', '-0.81', '#sadness', '#disgust', 'negative', '-0.8', 'unripe', 'napping', 'off_guard', 'off_one_guard', 'unprepared']
senticnet['ear'] = ['0.785', '-0.99', '0', '0.981', '#surprise', '#admiration', 'positive', '0.92', 'audition', 'absolute_pitch', 'perfect_pitch', 'modality', 'sense_modality']
senticnet['ear-flap'] = ['-0.75', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'large-brimmed', 'low-crowned', 'brimless', 'flat-brimmed', 'brimmed']
senticnet['ear-to-ear'] = ['0.765', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.88', 'grin', 'smile', 'toothy', 'smirk', 'leering']
senticnet['ear_activity'] = ['0.022', '0.098', '0', '0.202', '#interest', '#admiration', 'positive', '0.107', 'use_ear', 'ear_work', 'audible', 'vibration', 'audio']
senticnet['ear_drum'] = ['0', '0', '0', '0.106', '#admiration', '#admiration', 'positive', '0.106', 'vocal_chord', 'mouth_noise', 'vocal_sound', 'human_sound', 'vocalization']
senticnet['ear_money'] = ['0.65', '-0.63', '0', '0.629', '#joy', '#surprise', 'positive', '0.638', 'entertain_crowd', 'performance', 'give_pleasure', 'entertainment', 'use_talent']
senticnet['ear_pad'] = ['0.858', '0.782', '0', '0', '#joy', '#interest', 'positive', '0.82', 'ear', 'enjoy_music', 'sing_along', 'hear_song', 'earring']
senticnet['ear_piece'] = ['0.9', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.865', 'speaker', 'loudspeaker', 'listen_to_music', 'music', 'jazz']
senticnet['ear_rot'] = ['-0.69', '0', '0.516', '-0.94', '#sadness', '#disgust', 'negative', '-0.71', 'insubordinate', 'recalcitrant', 'obstreperous', 'defiant', 'resistive']
senticnet['ear_splitting'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'deafening', 'roaring', 'hear_loud_sound', 'rowdy', 'noisy']
senticnet['ear_wax'] = ['0.95', '-0.86', '0', '0.947', '#joy', '#admiration', 'positive', '0.92', 'content', 'lore', 'folklore', 'cognitive_content', 'story']
senticnet['ear_work'] = ['0.043', '0.107', '0', '0.183', '#interest', '#admiration', 'positive', '0.111', 'use_ear', 'ear_activity', 'hear', 'audible', 'ability_hear']
senticnet['earache'] = ['0', '0', '-0.74', '-0.71', '#fear', '#disgust', 'negative', '-0.73', 'bad_knee', 'physical_pain', 'schizophrenia', 'poor_health', 'poke_pin']
senticnet['earbud'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'smart_technology', 'electric_shaver', 'hair_dryer', 'stay_healthy', 'wash_tooth']
senticnet['earful'] = ['-0.70', '0', '0.965', '-0.53', '#sadness', '#anger', 'negative', '-0.73', 'scuttlebutt', 'malicious_gossip', 'talk_of_town', 'talk', 'scandal']
senticnet['earing'] = ['-0.89', '0', '0.762', '0', '#sadness', '#anger', 'negative', '-0.82', 'topsail', 'transvestite', 'hoax', 'gaff', 'spar']
senticnet['earl'] = ['0.027', '0.106', '-0.05', '0.049', '#interest', '#admiration', 'positive', '0.032', 'despot', 'supervisor', 'chief', 'commander', 'master']
senticnet['earldom'] = ['0.849', '0.718', '0', '0', '#joy', '#interest', 'positive', '0.783', 'dukedom', 'barony', 'baronetcy', 'duchy', 'fief']
senticnet['earliest_mention'] = ['-0.7', '0', '0.746', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'insufficiency', 'sparseness', 'sparsity', 'abstemiousness', 'lack_of_quantity']
senticnet['earlsfield'] = ['0.623', '-0.56', '0', '0.974', '#joy', '#admiration', 'positive', '0.721', 'alterable', 'convertible', 'translatable', 'transformable', 'transmutable']
senticnet['early'] = ['0', '0.071', '0', '0', '#interest', '#interest', 'positive', '0.071', 'soon', 'light_in_sky', 'sunrise', 'day_part', 'part_of_day']
senticnet['early-blooming'] = ['0.756', '0', '0', '0.955', '#joy', '#admiration', 'positive', '0.855', 'early-ripening', 'crapshooter', 'quick-growing', 'non-blooming', 'late-flowering']
senticnet['early-ripening'] = ['0.757', '0', '0', '0.96', '#joy', '#admiration', 'positive', '0.859', 'late-flowering', 'crapshooter', 'quick-growing', 'non-blooming', 'early-blooming']
senticnet['early_adolescence'] = ['0.995', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.918', 'tell_everyone', 'reason', 'clarify', 'forethought', 'opinion']
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senticnet['earth-shaking'] = ['0.871', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.916', 'world-shaking', 'life-shattering', 'world-shattering', 'world-altering', 'earthshaking']
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senticnet['easily_cleanable'] = ['0.986', '0', '0', '0', '#joy', '#joy', 'positive', '0.986', 'personal_cleanliness', 'cleanness', 'wipe_up_spill', 'smell_good', 'clean_carpet']
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senticnet['easily_readable'] = ['0.515', '-0.80', '0', '0.791', '#surprise', '#admiration', 'positive', '0.703', 'accessible', 'fathomable', 'approachable', 'perceivable', 'intelligible']
senticnet['easily_recyclable'] = ['0', '0.065', '0', '0.146', '#interest', '#admiration', 'positive', '0.105', 'drink_container', 'storage_container', 'opaque', 'drink_holder', 'food_container']
senticnet['easily_scalable'] = ['0.085', '0.218', '-0.07', '0.308', '#interest', '#admiration', 'positive', '0.135', 'delicate', 'useful', 'good', 'light_colored', 'beautiful']
senticnet['easily_transmissible'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'leptomeningitis', 'cerebrospinal_meningitis', 'meningitis', 'brain_fever', 'cerebrospinal_fever']
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senticnet['easily_visible'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'eye', 'peeper', 'oculus_sinister']
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senticnet['east_coast_expressway'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
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senticnet['eastward_propagation'] = ['-0.68', '0', '0.946', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'abnormality', 'abnormalcy', 'birth_defect', 'congenital_anomaly', 'congenital_defect']
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senticnet['ecclesiastical_historian'] = ['-0.85', '0', '0.749', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'hairsplitter', 'fight', 'quarrel', 'obstructor', 'obstructer']
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senticnet['ecclesiastical_tribunal'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquirer', 'enquirer', 'questioner', 'querier', 'asker']
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senticnet['ecclesiasticism'] = ['-0.94', '0', '0.687', '-0.75', '#sadness', '#disgust', 'negative', '-0.79', 'clannishness', 'cliquishness', 'snobbery', 'arrogance', 'haughtiness']
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senticnet['ecg'] = ['-0.20', '0', '-0.22', '-0.19', '#sadness', '#fear', 'negative', '-0.20', 'go_hospital', 'death', 'die', 'take_aspirin', 'heart_attack']
senticnet['ecgfrith'] = ['-0.92', '0', '0.659', '-0.81', '#sadness', '#disgust', 'negative', '-0.8', 'unripe', 'napping', 'off_guard', 'off_one_guard', 'unprepared']
senticnet['echo'] = ['0.722', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.726', 'loudness', 'vibration', 'loud_yell', 'detect', 'decibel']
senticnet['echoey'] = ['-0.65', '0', '0.578', '-0.76', '#sadness', '#disgust', 'negative', '-0.66', 'grating', 'rasping', 'raspy', 'jarring', 'gruff']
senticnet['echolalia'] = ['-0.02', '0.067', '-0.04', '0.192', '#interest', '#admiration', 'positive', '0.047', 'verbalize', 'orate', 'say', 'talkative', 'discourse']
senticnet['echosounder'] = ['-0.04', '-0.01', '0.103', '0', '#sadness', '#anger', 'negative', '-0.04', 'near_beach', 'along_beach', 'full_water', 'ocean_ground', 'salty']
senticnet['echt'] = ['-0.64', '0', '0.852', '-0.80', '#anger', '#disgust', 'negative', '-0.76', 'unquestionable', 'documented', 'veritable', 'attested', 'authenticated']
senticnet['ecklonia'] = ['-0.89', '0', '0.556', '-0.72', '#sadness', '#disgust', 'negative', '-0.72', 'fucus', 'rockweed', 'black_rockweed', 'bladder_fucus', 'tang']
senticnet['eclair_big_bang'] = ['0.744', '-0.77', '0', '0.661', '#joy', '#surprise', 'positive', '0.725', 'sugarcoat', 'honey', 'glaze', 'change_taste', 'sugar']
senticnet['eclat'] = ['0.639', '-0.70', '0', '0.507', '#joy', '#surprise', 'positive', '0.616', 'tastefulness', 'daintiness', 'elegance', 'breeding', 'genteelness']
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senticnet['eclectic_style'] = ['0.578', '-0.95', '0', '0.979', '#surprise', '#admiration', 'positive', '0.838', 'perpendicular', 'perpendicular_style', 'gothic_architecture', 'style_of_architecture', 'type_of_architecture']
senticnet['eclipse'] = ['0.935', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.921', 'sun_block', 'shadow', 'block_light', 'parasol', 'block_sunlight']
senticnet['eclipse_plug'] = ['0.868', '0.717', '0', '0', '#joy', '#interest', 'positive', '0.792', 'tool_shed', 'tool', 'garage', 'tool_belt', 'hardware_store']
senticnet['eclipsed_sun'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'eye', 'peeper', 'oculus_sinister']
senticnet['eclipsing_binary'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['eclogue'] = ['0.075', '-0.01', '0.019', '0.067', '#joy', '#admiration', 'positive', '0.034', 'rhyme', 'paragraph', 'song_lyric', 'lyrics', 'stanza']
senticnet['eclosion'] = ['0.642', '-0.58', '0', '0.751', '#joy', '#admiration', 'positive', '0.659', 'change', 'become_better', 'change_aspect', 'look_better', 'become_bigger']
senticnet['eco_adventure'] = ['0.704', '-0.80', '0', '0', '#joy', '#surprise', 'positive', '0.755', 'quest', 'stunt', 'illusion', 'journey', 'activity']
senticnet['eco_nomic'] = ['0.913', '-0.63', '0', '0.961', '#joy', '#admiration', 'positive', '0.836', 'materialistic', 'mercenary', 'mundane', 'material', 'worldly']
senticnet['eco_socialist'] = ['0.563', '-0.72', '0', '0.644', '#surprise', '#admiration', 'positive', '0.645', 'idiom', 'idiomatic_expression', 'phrasal_idiom', 'set_phrase', 'phrase']
senticnet['eco_system'] = ['0.927', '0.742', '0', '0.879', '#joy', '#admiration', 'positive', '0.849', 'compel', 'cause_change', 'change_life', 'extinction', 'cascade_effect']
senticnet['eco_terrorist'] = ['-0.69', '0', '0.756', '-0.99', '#anger', '#disgust', 'negative', '-0.81', 'waster', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['ecoatm'] = ['0.813', '0.832', '0', '0.874', '#interest', '#admiration', 'positive', '0.84', 'ferris_wheel', 'concession_stand', 'food', 'carnival_ride', 'theme_park']
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senticnet['ecological_catastrophe'] = ['-0.14', '-0.00', '-0.12', '-0.21', '#sadness', '#disgust', 'negative', '-0.11', 'torture', 'demolish', 'maim', 'terminate', 'hurt_person']
senticnet['ecological_damage'] = ['0.565', '-0.76', '0', '0.982', '#surprise', '#admiration', 'positive', '0.771', 'risk', 'hazard', 'jeopardy', 'health_hazard', 'peril']
senticnet['ecological_degradation'] = ['0', '-0.02', '0.085', '-0.09', '#anger', '#disgust', 'negative', '-0.05', 'tree_wood', 'forest', 'sideways', 'dead_tree', 'wood']
senticnet['ecological_destruction'] = ['-0.14', '-0.00', '-0.12', '-0.21', '#sadness', '#disgust', 'negative', '-0.11', 'torture', 'demolish', 'maim', 'terminate', 'hurt_person']
senticnet['ecological_disaster'] = ['-0.87', '0', '0.889', '-0.62', '#sadness', '#anger', 'negative', '-0.79', 'devastation', 'ruin', 'deterioration', 'blight', 'impairment']
senticnet['ecological_integrity'] = ['0', '0.243', '-0.11', '0.201', '#interest', '#admiration', 'positive', '0.108', 'upkeep', 'contain', 'keep_safe', 'keep', 'prevent']
senticnet['ecological_restoration'] = ['0', '0', '0', '0.857', '#admiration', '#admiration', 'positive', '0.857', 'say', 'beckon', 'call', 'conserve', 'retort']
senticnet['ecological_study'] = ['0', '0.724', '0', '0.828', '#interest', '#admiration', 'positive', '0.776', 'ecology', 'different_application', 'sponsor', 'standard_grant', 'study']
senticnet['ecological_validity'] = ['0.628', '-0.96', '0', '0.682', '#surprise', '#admiration', 'positive', '0.759', 'taboo', 'tabu', 'tendentiousness', 'irrational_hostility', 'bias']
senticnet['ecology'] = ['-0.77', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.82', 'bionomics', 'environmental_science', 'number', 'environmental_biology', 'forest']
senticnet['ecology_evolutionary_biology'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'college_major', 'geology', 'physic', 'chemistry', 'subject_school']
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senticnet['economic_activity'] = ['0', '0.226', '-0.09', '0.26', '#interest', '#admiration', 'positive', '0.129', 'business', 'corporate', 'negotiate_contract', 'economics', 'enterprise']
senticnet['economic_aid'] = ['0.547', '-0.98', '0', '0.977', '#surprise', '#admiration', 'positive', '0.835', 'grant', 'gift', 'social_welfare', 'public_assistance', 'philanthropy']
senticnet['economic_calamity'] = ['0', '0', '0', '-0.70', '#disgust', '#disgust', 'negative', '-0.70', 'uncertainty', 'uncertain_environment', 'insecure', 'blur', 'anxiety']
senticnet['economic_clout'] = ['-0.94', '0', '0.863', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'poke', 'knockout_punch', 'lick', 'punch', 'biff']
senticnet['economic_collapse'] = ['-0.87', '0', '0.889', '-0.62', '#sadness', '#anger', 'negative', '-0.79', 'devastation', 'ruin', 'deterioration', 'blight', 'impairment']
senticnet['economic_contraction'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'mathematical', 'muscle', 'math_function', 'feel_sick', 'separation']
senticnet['economic_cooperation'] = ['0', '0', '0', '0.881', '#admiration', '#admiration', 'positive', '0.881', 'educate_others', 'share_knowledge', 'share_opinion', 'discover_new', 'collaboration']
senticnet['economic_crisis'] = ['-0.99', '0', '0.691', '-0.65', '#sadness', '#anger', 'negative', '-0.78', 'depression', 'crisis', 'exigency', 'slump', 'juncture']
senticnet['economic_deprivation'] = ['-0.84', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.82', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecure']
senticnet['economic_development'] = ['0.913', '-0.63', '0', '0.961', '#joy', '#admiration', 'positive', '0.836', 'materialistic', 'mercenary', 'mundane', 'material', 'worldly']
senticnet['economic_dislocation'] = ['-0.92', '0', '0.546', '-0.52', '#sadness', '#anger', 'negative', '-0.66', 'irresponsibility', 'irresponsibleness', 'undependability', 'irreproducibility', 'fallibility']
senticnet['economic_disparity'] = ['0.871', '-0.74', '0', '0.981', '#joy', '#admiration', 'positive', '0.867', 'unrighteousness', 'unjustness', 'unfairness', 'wrong', 'wrongfulness']
senticnet['economic_dynamism'] = ['0.067', '0.172', '0', '0.121', '#interest', '#admiration', 'positive', '0.12', 'economic_activity', 'business', 'support_family', 'work_done', 'earn_money']
senticnet['economic_expert'] = ['0', '0.935', '0', '0.794', '#interest', '#admiration', 'positive', '0.865', 'economist', 'economics', 'well', 'research', 'different_application']
senticnet['economic_globalization'] = ['0.836', '-0.83', '0', '0.511', '#joy', '#surprise', 'positive', '0.726', 'appearance', 'start', 'casus_belli', 'beginning', 'egress']
senticnet['economic_growth'] = ['0', '0.312', '-0.10', '0.207', '#interest', '#admiration', 'positive', '0.137', 'infuse', 'loan', 'lend', 'increase_money', 'wealth']
senticnet['economic_hardship'] = ['-0.84', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.82', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecure']
senticnet['economic_inequality'] = ['-0.25', '0', '-0.17', '-0.08', '#sadness', '#fear', 'negative', '-0.16', 'exclusion', 'exclusion_zone', 'isolation', 'detach', 'except']
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senticnet['economic_instability'] = ['-0.84', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.82', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecure']
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senticnet['economic_interdependence'] = ['-0.81', '0', '0.931', '-0.77', '#sadness', '#anger', 'negative', '-0.84', 'mutualness', 'mutuality', 'complementarity', 'correlation', 'correlativity']
senticnet['economic_libertarian'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'libertarian', 'civil_libertarian', 'advocate', 'advocator', 'proponent']
senticnet['economic_meltdown'] = ['-0.99', '0', '0.691', '-0.65', '#sadness', '#anger', 'negative', '-0.78', 'depression', 'economic_crisis', 'exigency', 'slump', 'juncture']
senticnet['economic_prosperity'] = ['0.837', '0', '0', '0', '#joy', '#joy', 'positive', '0.837', 'a_lot_of_sex', 'desirable', 'give_birth_child', 'sexual_intercourse', 'diversity']
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senticnet['economic_recovery'] = ['0.067', '0.172', '0', '0.121', '#interest', '#admiration', 'positive', '0.12', 'economic_activity', 'business', 'support_family', 'work_done', 'earn_money']
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senticnet['economic_science'] = ['0', '0.898', '0', '0.777', '#interest', '#admiration', 'positive', '0.838', 'politics', 'government', 'different_application', 'standard_grant', 'political_science']
senticnet['economic_slowdown'] = ['0', '0.285', '-0.07', '0.163', '#interest', '#admiration', 'positive', '0.124', 'plus', 'importance', 'a_lot', 'bonus', 'extra']
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senticnet['economic_stability'] = ['0.067', '0.172', '0', '0.121', '#interest', '#admiration', 'positive', '0.12', 'economic_activity', 'business', 'support_family', 'work_done', 'earn_money']
senticnet['economic_stagnation'] = ['-0.84', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.82', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecure']
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senticnet['economic_uncertainty'] = ['0', '0', '0', '-0.70', '#disgust', '#disgust', 'negative', '-0.70', 'uncertain_future', 'uncertain_environment', 'insecure', 'blur', 'anxiety']
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senticnet['economical_alternative'] = ['0.02', '0.277', '-0.11', '0.336', '#interest', '#admiration', 'positive', '0.129', 'different', 'similar', 'substitute', 'option', 'exception']
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senticnet['economically_feasible'] = ['0.932', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.959', 'operational', 'functional', 'operable', 'available', 'useable']
senticnet['economically_illiterate'] = ['-0.52', '0', '0.95', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'skeptic', 'sceptic', 'doubter', 'pessimist', 'negativist']
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senticnet['economically_vulnerable'] = ['-0.56', '0', '-0.49', '-0.09', '#sadness', '#fear', 'negative', '-0.38', 'lack', 'absence', 'poverty', 'emptiness', 'hunger']
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senticnet['economics_correspondent'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'adventurer', 'humankind']
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senticnet['economist_expect_growth'] = ['-0.04', '0.123', '0', '0.237', '#interest', '#admiration', 'positive', '0.107', 'foot_motion', 'foot_movement', 'foot_action', 'move_ahead', 'stride']
senticnet['economist_say'] = ['0.067', '0.172', '0', '0.121', '#interest', '#admiration', 'positive', '0.12', 'economic_activity', 'business', 'support_family', 'work_done', 'earn_money']
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senticnet['economy_of_scale'] = ['0.953', '-0.65', '0', '0.866', '#joy', '#admiration', 'positive', '0.823', 'saving', 'retrenchment', 'curtailment', 'downsizing', 'action']
senticnet['ecopsychology'] = ['0.037', '0.293', '0', '0.269', '#interest', '#admiration', 'positive', '0.2', 'religious_purpose', 'prayer', 'judaism', 'christianity', 'religion']
senticnet['ecoregion'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'woodland', 'timberland']
senticnet['ecosphere'] = ['0.045', '0', '0', '0.051', '#joy', '#admiration', 'positive', '0.048', 'perfect_sphere', 'pour_hot_coffee_mug', 'life', 'alkaline', 'wrest']
senticnet['ecospirituality'] = ['0.037', '0.293', '0', '0.269', '#interest', '#admiration', 'positive', '0.2', 'religious_purpose', 'prayer', 'judaism', 'christianity', 'religion']
senticnet['ecosystem'] = ['0', '0.066', '0.048', '0.08', '#interest', '#admiration', 'positive', '0.033', 'many_yards', 'measure_distance', 'distance_measurement', 'mow', 'length']
senticnet['ecosystem_dynamic'] = ['0.731', '0.981', '0', '0', '#joy', '#interest', 'positive', '0.856', 'population', 'ecology', 'specie', 'sponsor', 'environmental_biology']
senticnet['ecosystem_management'] = ['0', '0', '0', '0.857', '#admiration', '#admiration', 'positive', '0.857', 'say', 'beckon', 'call', 'conserve', 'retort']
senticnet['ecosystem_resilience'] = ['0.988', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.98', 'sustain_life', 'sustenance', 'nutrition', 'keep_alive', 'satisfy_hunger']
senticnet['ecosystem_study'] = ['-0.83', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.80', 'forest', 'nitrogen', 'soil', 'structure_function', 'effect']
senticnet['ecotone'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'woodland', 'timberland']
senticnet['ecozone'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'woodland', 'timberland']
senticnet['ecstasy'] = ['1', '0', '0', '0', '#joy', '#joy', 'positive', '1', 'elation', 'euphoria', 'extreme_joy', 'extreme_happiness', 'extreme_pleasure']
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senticnet['educational_place'] = ['0', '-0.02', '0.053', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'place_learn', 'student_study', 'educational_facility', 'student_place', 'educational_institution']
senticnet['educational_reformer'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['educational_underachievement'] = ['-0.25', '0', '-0.17', '-0.08', '#sadness', '#fear', 'negative', '-0.16', 'exclusion', 'exclusion_zone', 'isolation', 'detach', 'except']
senticnet['educationalist'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['educationally'] = ['0', '0.814', '0', '0.795', '#interest', '#admiration', 'positive', '0.804', 'socio-economically', 'ethnically', 'disadvantaged', 'linguistically', 'socioeconomically']
senticnet['educationist'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['educationthe'] = ['0.97', '0', '0.773', '0.878', '#joy', '#admiration', 'positive', '0.358', 'educate', 'get_knowledge', 'new_knowledge', 'expand_knowledge', 'find_out_world']
senticnet['educator'] = ['0.778', '0', '0', '0.856', '#joy', '#admiration', 'positive', '0.817', 'lector', 'academician', 'schoolmaster', 'pedagogue', 'teacher']
senticnet['edulcorate'] = ['0.868', '-0.59', '0', '0.915', '#joy', '#admiration', 'positive', '0.792', 'sugarcoat', 'honey', 'glaze', 'change_taste', 'sugar']
senticnet['edutainment'] = ['0.951', '0', '0', '0.731', '#joy', '#admiration', 'positive', '0.841', 'pastime', 'enjoyment', 'pleasure', 'enjoy', 'fun']
senticnet['edward_angell_palmer'] = ['0.909', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.952', 'displace', 'swing', 'snowshoe', 'shuttle', 'bang']
senticnet['edwardian'] = ['0.952', '0.844', '0', '0.96', '#joy', '#admiration', 'positive', '0.919', 'fashion', 'demand', 'kindness', 'better_person', 'warm_feeling']
senticnet['edwardian_musical_comedy'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'dress', 'pinny', 'dirndl', 'gown', 'muumuu']
senticnet['eea_country'] = ['0.875', '0', '0', '0.802', '#joy', '#admiration', 'positive', '0.839', 'foreigner', 'exile', 'deportee', 'gringo', 'import']
senticnet['eeg_activity'] = ['-0.56', '0', '0.849', '-0.61', '#anger', '#disgust', 'negative', '-0.67', 'convulsive', 'unsteady', 'jerky', 'spasmodic', 'spastic']
senticnet['eel_river_valley'] = ['-0.06', '0.21', '0', '0.076', '#interest', '#admiration', 'positive', '0.074', 'new_think', 'figure_out', 'brain_activity', 'idea', 'come_up_idea']
senticnet['eel_tailed_catfish'] = ['0', '-0.02', '0.044', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'haddock', 'mermaid', 'fish_rod', 'tidal_wave', 'dryad']
senticnet['eeo_activity'] = ['-0.88', '0', '0.733', '-0.60', '#sadness', '#anger', 'negative', '-0.74', 'vengeance', 'payback', 'retribution', 'retaliation', 'return']
senticnet['eerie'] = ['-0.70', '0', '0.757', '0', '#sadness', '#anger', 'negative', '-0.73', 'spooky', 'skittish', 'unpredictably', 'ghost', 'supernatural']
senticnet['eerily'] = ['-0.82', '0', '0.758', '-0.70', '#sadness', '#anger', 'negative', '-0.76', 'unclassifiable', 'unidentifiable', 'unrecognisable', 'intangible', 'undiagnosable']
senticnet['eerily_empty'] = ['-0.03', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.04', 'rabbit', 'squirrel', 'mole', 'chipmunk', 'nymph']
senticnet['eesed_tae'] = ['0.027', '0', '0.012', '0.063', '#joy', '#admiration', 'positive', '0.026', 'vegetation', 'tree_flower', 'green_growth', 'small', 'greenery']
senticnet['eeye'] = ['-0.07', '0.075', '-0.17', '0', '#sadness', '#fear', 'negative', '-0.06', 'protect_family', 'fault', 'mistake', 'expire', 'kill_self']
senticnet['eeye_digital_security'] = ['-0.07', '0.075', '-0.17', '0', '#sadness', '#fear', 'negative', '-0.06', 'protect_family', 'fault', 'mistake', 'expire', 'kill_self']
senticnet['eeyore'] = ['-0.93', '0', '0.77', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'hothead', 'unpleasant_person', 'disagreeable_person', 'crabby_person', 'crab']
senticnet['efavirenz'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'indinavir', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['efavirenz_efv'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'protease_inhibitor', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['efavirenz_sustiva'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'indinavir', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['efendi'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'caravansary', 'caravanserai', 'khan', 'caravan_inn', 'hostel']
senticnet['eff'] = ['0.652', '-0.80', '0', '0.746', '#surprise', '#admiration', 'positive', '0.733', 'have', 'pair', 'take', 'couple', 'fornicate']
senticnet['efface'] = ['-0.82', '0', '0.694', '-0.71', '#sadness', '#disgust', 'negative', '-0.74', 'withdraw', 'strike', 'scratch', 'delete', 'take']
senticnet['effect'] = ['-0.96', '0', '0.501', '-0.92', '#sadness', '#disgust', 'negative', '-0.79', 'byproduct', 'wallop', 'side_effect', 'dent', 'fallout']
senticnet['effect_pedal'] = ['0.748', '-0.90', '0', '0.99', '#surprise', '#admiration', 'positive', '0.882', 'shakiness', 'quivering', 'shaking', 'vibration', 'quiver']
senticnet['effective'] = ['0.982', '-0.67', '0', '0.682', '#joy', '#admiration', 'positive', '0.778', 'workmanlike', 'competent', 'efficient', 'able', 'capable']
senticnet['effective_antidote'] = ['-0.50', '0', '0.807', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'powerlessness', 'impotence', 'impotency', 'ineffectiveness', 'inefficaciousness']
senticnet['effective_communicator'] = ['0.749', '0.724', '0', '0', '#joy', '#interest', 'positive', '0.736', 'mouth_noise', 'vocal_cord', 'vocalization', 'vocal_sound', 'loud_soft']
senticnet['effective_date'] = ['0.862', '0.709', '0', '0', '#joy', '#interest', 'positive', '0.785', 'invoice', 'bill', 'check', 'mastercard', 'payment_counter']
senticnet['effectively'] = ['0', '0.88', '0', '0.822', '#interest', '#admiration', 'positive', '0.851', 'in_effect', 'efficaciously', 'student', 'project', 'research']
senticnet['effectively_communicate'] = ['0.749', '0.724', '0', '0', '#joy', '#interest', 'positive', '0.736', 'mouth_noise', 'vocal_cord', 'vocalization', 'vocal_sound', 'loud_soft']
senticnet['effectively_integrate'] = ['0', '0.276', '-0.12', '0.317', '#interest', '#admiration', 'positive', '0.157', 'make', 'manufacture', 'intend', 'correct', 'design']
senticnet['effectively_manage'] = ['0.781', '-0.79', '0', '0.816', '#surprise', '#admiration', 'positive', '0.798', 'deliver_goods', 'succeed', 'win', 'bring_home_bacon', 'come_through']
senticnet['effectiveness'] = ['0.632', '-0.80', '0', '0.537', '#joy', '#surprise', 'positive', '0.658', 'incisiveness', 'trenchancy', 'efficacy', 'efficaciousness', 'power']
senticnet['effectivity'] = ['0.929', '-0.71', '0', '0.605', '#joy', '#surprise', 'positive', '0.748', 'incisiveness', 'trenchancy', 'efficacy', 'efficaciousness', 'power']
senticnet['effectual'] = ['0.622', '-0.72', '0', '0.799', '#surprise', '#admiration', 'positive', '0.714', 'sound', 'logical', 'validated', 'valid', 'legitimate']
senticnet['effectual_demand'] = ['0.902', '-0.72', '0', '0.919', '#joy', '#admiration', 'positive', '0.849', 'preciousness', 'costliness', 'expensiveness', 'cost', 'lavishness']
senticnet['effectuality'] = ['0.807', '-0.93', '0', '0.621', '#joy', '#surprise', 'positive', '0.789', 'incisiveness', 'trenchancy', 'efficacy', 'efficaciousness', 'power']
senticnet['effectually'] = ['-0.56', '0', '0.505', '-0.95', '#sadness', '#disgust', 'negative', '-0.67', 'unnaturalness', 'artificiality', 'pretension', 'staginess', 'theatricality']
senticnet['effectualness'] = ['0.779', '-0.98', '0', '0.793', '#surprise', '#admiration', 'positive', '0.853', 'incisiveness', 'trenchancy', 'efficacy', 'efficaciousness', 'power']
senticnet['effectuate'] = ['0.658', '-0.52', '0', '0.626', '#joy', '#admiration', 'positive', '0.602', 'point_in_time', 'threshold', 'birth', 'commencement', 'incipience']
senticnet['effeminacy'] = ['-0.57', '0', '0.53', '-0.63', '#sadness', '#disgust', 'negative', '-0.58', 'low_status', 'lowness', 'backseat', 'lowliness', 'subordinateness']
senticnet['effeminate'] = ['-0.63', '0', '0.986', '-0.94', '#anger', '#disgust', 'negative', '-0.85', 'womanish', 'unmanful', 'emasculate', 'cissy', 'sissified']
senticnet['effeminate_male'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'concupiscence', 'lustfulness', 'amorousness']
senticnet['effeminate_men'] = ['0.074', '0.151', '-0.09', '0.119', '#interest', '#admiration', 'positive', '0.062', 'small_man', 'male_person', 'male_child', 'gender', 'man_child']
senticnet['efference_copy'] = ['0', '0.131', '-0.08', '0.239', '#interest', '#admiration', 'positive', '0.096', 'human_sense', 'ear_activity', 'vibration', 'five_senses', 'sensor']
senticnet['effervescence'] = ['0.699', '0.62', '-0.23', '0.323', '#joy', '#interest', 'positive', '0.353', 'joviality', 'jolity', 'josh', 'chaff', 'banter']
senticnet['effervescent'] = ['0.512', '0.658', '-0.25', '0.256', '#joy', '#interest', 'positive', '0.293', 'riotously', 'expansively', 'ebulliently', 'zestfulness', 'gusto']
senticnet['effete'] = ['-0.94', '0', '0.987', '-0.64', '#sadness', '#anger', 'negative', '-0.85', 'unable', 'ineffectual', 'ineffective', 'impuissant', 'impotent']
senticnet['efficaciously'] = ['0', '0.871', '0', '0.815', '#interest', '#admiration', 'positive', '0.843', 'in_effect', 'effectively', 'student', 'project', 'research']
senticnet['efficaciousness'] = ['0.632', '-0.80', '0', '0.537', '#joy', '#surprise', 'positive', '0.658', 'incisiveness', 'trenchancy', 'efficacy', 'effectiveness', 'power']
senticnet['efficacy'] = ['0.632', '-0.80', '0', '0.537', '#joy', '#surprise', 'positive', '0.658', 'incisiveness', 'trenchancy', 'effectiveness', 'efficaciousness', 'power']
senticnet['efficiency'] = ['0.7', '0', '0', '0.979', '#joy', '#admiration', 'positive', '0.839', 'efficient', 'clean_sink', 'maintenance', 'kill_spider', 'mow_grass']
senticnet['efficient'] = ['0.024', '0.089', '0.042', '0.066', '#interest', '#admiration', 'positive', '0.034', 'browser', 'telephone_computer', 'computer', 'modem', 'internet_connection']
senticnet['efficient_algorithm'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'combinatory', 'consolidative', 'unifying', 'integrative']
senticnet['efficient_manner'] = ['0.982', '-0.67', '0', '0.682', '#joy', '#admiration', 'positive', '0.778', 'workmanlike', 'competent', 'efficient', 'able', 'capable']
senticnet['efficiently'] = ['0', '0.981', '0', '0.775', '#interest', '#admiration', 'positive', '0.878', 'expeditiously', 'project', 'research', 'development', 'design']
senticnet['efficiently_manage'] = ['0.781', '-0.79', '0', '0.816', '#surprise', '#admiration', 'positive', '0.798', 'deliver_goods', 'succeed', 'win', 'bring_home_bacon', 'come_through']
senticnet['effie_white'] = ['0', '0.793', '0', '0.91', '#interest', '#admiration', 'positive', '0.852', 'superstar', 'vip', 'accomplishment', 'performance', 'concert']
senticnet['efflorescence'] = ['-0.96', '0', '0.615', '-0.73', '#sadness', '#disgust', 'negative', '-0.77', 'flush', 'blossom', 'prime', 'peak', 'golden_age']
senticnet['effluvium'] = ['-0.86', '0', '0.891', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'baneful', 'pernicious', 'vesicatory', 'vesicant', 'noxious']
senticnet['effort'] = ['-0.10', '0.196', '-0.10', '0.162', '#interest', '#admiration', 'positive', '0.037', 'motivation', 'determination', 'persistence', 'dedication', 'prepare']
senticnet['effortful'] = ['-0.82', '0', '0.58', '-0.53', '#sadness', '#anger', 'negative', '-0.64', 'tiring', 'wearing', 'wearying', 'hard', 'arduous']
senticnet['effortful_control'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'excitation', 'excitement', 'arousal', 'anger']
senticnet['effortfulness'] = ['-0.64', '0', '0.928', '-0.70', '#anger', '#disgust', 'negative', '-0.75', 'difficulty', 'arduousness', 'strenuousness', 'laboriousness', 'operoseness']
senticnet['effortless'] = ['-0.83', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.78', 'friction-free', 'stress-free', 'levitating', 'noiseless', 'frictionless']
senticnet['effortlessly_cool'] = ['0.824', '0.86', '0', '0.983', '#interest', '#admiration', 'positive', '0.889', 'look_smart', 'social_acceptance', 'improve_appearance', 'improve_look', 'look_good']
senticnet['effortlessness'] = ['-0.56', '0', '0.507', '-0.89', '#sadness', '#disgust', 'negative', '-0.65', 'smoothness', 'ease', 'easiness', 'readiness', 'facility']
senticnet['effrontery'] = ['-0.81', '0', '0.825', '0', '#sadness', '#anger', 'negative', '-0.82', 'shameless', 'act', 'insolent', 'temerity', 'brazen']
senticnet['effulgence'] = ['0.65', '-0.61', '0', '0.703', '#joy', '#admiration', 'positive', '0.657', 'gleaming', 'luster', 'polish', 'glow', 'burnish']
senticnet['effulgent'] = ['0.844', '0.953', '0', '0', '#joy', '#interest', 'positive', '0.898', 'lamping', 'brilliant', 'splendor', 'resplendent', 'resplendence']
senticnet['effuse'] = ['0', '-0.91', '0', '0.9', '#surprise', '#admiration', 'positive', '0.907', 'copious', 'exuberant', 'lavish', 'squander', 'profuse']
senticnet['effusion'] = ['0.759', '-0.72', '0', '0.893', '#joy', '#admiration', 'positive', '0.793', 'manifestation', 'cry', 'reflection', 'acting_out', 'explosion']
senticnet['effusive'] = ['0', '-0.82', '0', '0.921', '#surprise', '#admiration', 'positive', '0.873', 'emotional', 'excessive', 'unrestrained', 'gushy', 'maudlin']
senticnet['efreet'] = ['-0.75', '0', '0.708', '0', '#sadness', '#anger', 'negative', '-0.73', 'hound', 'cage_animal', 'kind_animal', 'hunter', 'animal']
senticnet['egalitarian'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'adventurer', 'pioneer', 'correspondent', 'humankind']
senticnet['egalitarian_society'] = ['0', '0', '0', '0.744', '#admiration', '#admiration', 'positive', '0.744', 'committee', 'association', 'colony', 'organization', 'population']
senticnet['egalitarianism'] = ['0.94', '0', '0', '0.805', '#joy', '#admiration', 'positive', '0.873', 'unity', 'equality', 'count_person', 'average', 'median']
senticnet['egalite'] = ['0.942', '-0.82', '0', '0.828', '#joy', '#admiration', 'positive', '0.865', 'egality', 'equivalence', 'tie', 'status', 'position']
senticnet['egality'] = ['0.942', '-0.82', '0', '0.828', '#joy', '#admiration', 'positive', '0.865', 'equivalence', 'egalite', 'tie', 'status', 'position']
senticnet['egan'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
senticnet['egdon'] = ['-0.90', '0', '0.865', '-0.77', '#sadness', '#anger', 'negative', '-0.84', 'nosiness', 'snoopiness', 'prying', 'curiosity', 'wonder']
senticnet['egdon_heath'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'intrepidly', 'fearlessness', 'fearlessly', 'unafraid', 'unfearing']
senticnet['egg'] = ['0', '0.067', '0', '0.07', '#interest', '#admiration', 'positive', '0.069', 'peck', 'embryo', 'chicken_bushel', 'ovum', 'bushel']
senticnet['egg-shaped'] = ['0', '0', '-0.86', '-0.89', '#fear', '#disgust', 'negative', '-0.88', 'elliptical', 'oval', 'ovate', 'ovoid', 'prolate']
senticnet['egg_beater'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'salad_shaker', 'store_shelf', 'kitchen_tool', 'pliers', 'ladle']
senticnet['egg_benedict'] = ['0.062', '0', '0.027', '0.055', '#joy', '#admiration', 'positive', '0.03', 'porridge', 'meal', 'broth', 'hamburger', 'hearty']
senticnet['egg_capsule'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'chick', 'wild_bird', 'baby_bird', 'lay_egg', 'bird_home']
senticnet['egg_cell'] = ['0', '0.067', '0', '0.07', '#interest', '#admiration', 'positive', '0.069', 'peck', 'bushel', 'chicken_egg', 'ovum', 'egg']
senticnet['egg_crate'] = ['0.067', '0.088', '-0.07', '0.246', '#interest', '#admiration', 'positive', '0.081', 'multiple_purposes', 'revolve', 'start_run', 'wiggle', 'twist']
senticnet['egg_custard'] = ['0', '-0.11', '0', '0', '#surprise', '#surprise', 'positive', '0.113', 'cake', 'cookie', 'mousse', 'doughnut', 'brownie']
senticnet['egg_deposition'] = ['0.947', '-0.81', '0', '0.656', '#joy', '#surprise', 'positive', '0.805', 'sexual_intercourse', 'intercourse', 'sex_act', 'hank_panky', 'coitus']
senticnet['egg_hatch'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'chick', 'wild_bird', 'baby_bird', 'lay_egg', 'bird_home']
senticnet['egg_hatch_into'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'chick', 'wild_bird', 'baby_bird', 'lay_egg', 'bird_home']
senticnet['egg_maturation'] = ['0.702', '-0.95', '0', '0.997', '#surprise', '#admiration', 'positive', '0.883', 'fertile', 'fecund', 'conceptive', 'impregnable', 'rank']
senticnet['egg_mixture'] = ['0.03', '-0.05', '0', '0.059', '#surprise', '#admiration', 'positive', '0.046', 'platter', 'fork', 'spoon', 'tablespoon', 'bowl']
senticnet['egg_nog'] = ['0.914', '-0.56', '0', '0.869', '#joy', '#admiration', 'positive', '0.781', 'tabula_rasa', 'ego', 'subconscious', 'unconscious', 'psyche']
senticnet['egg_parasitoid'] = ['0', '-0.09', '0.123', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'gypsy_moth', 'japanese_beetle', 'cucumber_beetle', 'mosquito', 'earwig']
senticnet['egg_sac'] = ['0.049', '0.098', '-0.07', '0.121', '#interest', '#admiration', 'positive', '0.047', 'procreate', 'reproduce', 'start_family', 'cause_cold', 'unprotect_sex']
senticnet['egg_salad'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'grocery_cart', 'super_market', 'fruit_stand', 'candy_store', 'produce_store']
senticnet['egg_shell'] = ['0.953', '-0.71', '0', '0', '#joy', '#surprise', 'positive', '0.832', 'old_paper', 'food_scrap', 'mind_child', 'moldy_food', 'old_newspaper']
senticnet['egg_tooth'] = ['0.042', '0.088', '0', '0', '#joy', '#interest', 'positive', '0.065', 'jump_over_obstacle', 'attempt_fly', 'aircraft', 'pilot', 'wing']
senticnet['eggesford'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'bodyguard', 'outrider', 'bodyguard_of_guard', 'beefeater', 'defender']
senticnet['egglayer'] = ['-0.83', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.78', 'picaninny', 'kid', 'minor', 'tike', 'small_picaninny']
senticnet['eggplant'] = ['0', '0.067', '-0.09', '-0.09', '#fear', '#disgust', 'negative', '-0.04', 'cotton_boll', 'asbestos', 'corn', 'opium', 'rice']
senticnet['egham'] = ['0.623', '-0.56', '0', '0.974', '#joy', '#admiration', 'positive', '0.721', 'alterable', 'convertible', 'translatable', 'transformable', 'transmutable']
senticnet['egils_saga'] = ['0.036', '0.219', '0', '0.19', '#interest', '#admiration', 'positive', '0.148', 'story', 'long_story', 'novel', 'fable', 'fiction']
senticnet['eglantine'] = ['0.967', '-0.98', '0', '0.64', '#joy', '#surprise', 'positive', '0.863', 'club', 'social_club', 'society', 'guild', 'jockey_club']
senticnet['ego'] = ['0.638', '-0.99', '0', '0.631', '#joy', '#surprise', 'positive', '0.755', 'awareness', 'consciousness', 'state_of_mind', 'conscious', 'aesthesia']
senticnet['ego_boost'] = ['0.102', '0', '0', '0', '#joy', '#joy', 'positive', '0.102', 'spread_idea', 'make_world_better_place', 'make_money', 'help_others', 'accomplish_task']
senticnet['egocentric'] = ['0', '0', '-0.73', '-0.71', '#fear', '#disgust', 'negative', '-0.72', 'selfish', 'egotistical', 'egocentrically', 'egocentricity', 'individualistic']
senticnet['egocentrically'] = ['0', '0', '-0.73', '-0.72', '#fear', '#disgust', 'negative', '-0.72', 'selfish', 'egotistical', 'egocentric', 'egocentricity', 'individualistic']
senticnet['egocentricity'] = ['0', '0', '-0.74', '-0.71', '#fear', '#disgust', 'negative', '-0.72', 'selfish', 'egotistical', 'egocentrically', 'egocentric', 'individualistic']
senticnet['egocentrism'] = ['0.995', '-0.73', '0', '0.83', '#joy', '#admiration', 'positive', '0.852', 'spiritism', 'otherworldliness', 'spirituality', 'spiritualism', 'internality']
senticnet['egoism'] = ['0.95', '-0.77', '0', '0.578', '#joy', '#surprise', 'positive', '0.767', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['egoist'] = ['0.95', '-0.77', '0', '0.578', '#joy', '#surprise', 'positive', '0.767', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['egoistic'] = ['0.95', '-0.77', '0', '0.578', '#joy', '#surprise', 'positive', '0.767', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['egoistical'] = ['0', '0', '0.787', '-0.97', '#anger', '#disgust', 'negative', '-0.88', 'swell-headed', 'egotistic', 'conceited', 'egocentric', 'egotistical']
senticnet['egomania'] = ['-0.04', '0.164', '0.685', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maliciousness', 'maliciously', 'maleficence', 'malefic', 'malign']
senticnet['egomaniac'] = ['-0.98', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.95', 'neurotic', 'workaholic', 'hypochondriac', 'hypochondria', 'worrier']
senticnet['egomaniacal'] = ['0', '0', '0.761', '-0.71', '#anger', '#disgust', 'negative', '-0.74', 'impulsive', 'tearaway', 'incautious', 'madcap', 'impetuous']
senticnet['egotism'] = ['0', '0', '0', '-0.95', '#disgust', '#disgust', 'negative', '-0.95', 'self-importance', 'amour_propre', 'conceit', 'self-love', 'self-pride']
senticnet['egotist'] = ['-0.63', '0', '0.88', '-0.94', '#anger', '#disgust', 'negative', '-0.81', 'beguiler', 'slicker', 'perjurer', 'false_witness', 'storyteller']
senticnet['egotistic'] = ['0', '0', '0.795', '-0.98', '#anger', '#disgust', 'negative', '-0.88', 'swell-headed', 'egotistical', 'conceited', 'egocentric', 'egoistical']
senticnet['egotistical'] = ['0', '0', '0.797', '-0.97', '#anger', '#disgust', 'negative', '-0.88', 'swell-headed', 'egotistic', 'conceited', 'egocentric', 'egoistical']
senticnet['egregious'] = ['0', '0', '0.853', '-0.84', '#anger', '#disgust', 'negative', '-0.84', 'flagrant', 'cry', 'glaring', 'gross', 'rank']
senticnet['egregious_behavior'] = ['0', '0', '0.853', '-0.84', '#anger', '#disgust', 'negative', '-0.84', 'flagrant', 'cry', 'glaring', 'gross', 'rank']
senticnet['egregious_conduct'] = ['0', '0', '0.853', '-0.84', '#anger', '#disgust', 'negative', '-0.84', 'flagrant', 'cry', 'glaring', 'gross', 'rank']
senticnet['egregious_misconduct'] = ['0', '0', '0.853', '-0.84', '#anger', '#disgust', 'negative', '-0.84', 'flagrant', 'cry', 'glaring', 'gross', 'rank']
senticnet['egregiously'] = ['0', '0', '0.853', '-0.84', '#anger', '#disgust', 'negative', '-0.84', 'flagrant', 'cry', 'glaring', 'gross', 'rank']
senticnet['egress'] = ['0.836', '-0.83', '0', '0.511', '#joy', '#surprise', 'positive', '0.726', 'emersion', 'start', 'casus_belli', 'emergence', 'beginning']
senticnet['egret'] = ['0', '0.098', '-0.06', '0.118', '#interest', '#admiration', 'positive', '0.051', 'broad', 'tall_man', 'wide', 'wood_structure', 'large']
senticnet['ehealth'] = ['-0.12', '0', '-0.12', '-0.08', '#sadness', '#fear', 'negative', '-0.11', 'hospital', 'check_up', 'check_doctor', 'check_pulse', 'check_blood_pressure']
senticnet['ehealthinsurance'] = ['0.94', '0.804', '0', '0', '#joy', '#interest', 'positive', '0.872', 'spend_a_lot_of_money', 'window_shop', 'spend_much_money', 'check_price', 'compare_price']
senticnet['ehmke'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'dicky', 'impaired', 'lessened', 'diminished']
senticnet['ehnz'] = ['0.049', '0.067', '0', '0.18', '#interest', '#admiration', 'positive', '0.099', 'static', 'unity', 'large_state', 'frequency', 'preparedness']
senticnet['ehrlichia'] = ['0.579', '-0.81', '0', '0.833', '#surprise', '#admiration', 'positive', '0.741', 'typhus', 'rat_typhus', 'urban_typhus', 'endemic_typhus', 'rickettsial_disease']
senticnet['eibon'] = ['0', '0.887', '0', '0.973', '#interest', '#admiration', 'positive', '0.93', 'servant', 'art_of_transmuting_metal', 'stone', 'alchemy', 'philosopher']
senticnet['eiderdown'] = ['0.087', '-0.07', '0.062', '0', '#joy', '#surprise', 'positive', '0.033', 'pillowcase', 'bed', 'comforter', 'bed_sheet', 'duvet_cover']
senticnet['eidgah'] = ['0', '0.224', '0', '0.197', '#interest', '#admiration', 'positive', '0.211', 'church', 'temple', 'cathedral', 'chapel', 'confession']
senticnet['eidolon'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amuck', 'murderousness', 'misocainea', 'misogyny']
senticnet['eiffel_tower'] = ['0', '0.111', '0', '0.07', '#interest', '#admiration', 'positive', '0.09', 'beautiful_view', 'tourist_attraction', 'iconic_place', 'piece_of_story', 'monument']
senticnet['eigenvalue'] = ['0.947', '0', '0', '0.746', '#joy', '#admiration', 'positive', '0.847', 'non-negative', 'nonnegative', 'nonzero', 'hermitian', 'real-valued']
senticnet['eigenvector'] = ['0.506', '-0.56', '0', '0.788', '#surprise', '#admiration', 'positive', '0.62', 'backward', 'reverse', 'retrograde', 'reflexive', 'retral']
senticnet['eight'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'hundred_year', 'twelve', 'day_month', 'time_measurement']
senticnet['eight_lane_highway'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'artery', 'skeletal_system', 'groin', 'neck', 'muscle']
senticnet['eight_legs'] = ['-0.05', '-0.02', '0.062', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'bite_dog', 'tic', 'small_fly', 'itch', 'parasite']
senticnet['eight_sides'] = ['0', '0.171', '-0.07', '0.284', '#interest', '#admiration', 'positive', '0.127', 'red_light', 'direction', 'halt', 'change_direction', 'go_around']
senticnet['eighteen'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'hundred_year', 'dozen', 'day_month', 'time_measurement']
senticnet['eighteen_penny'] = ['-0.54', '0', '0.739', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'unprofitable', 'idle', 'lean', 'dead', 'marginal']
senticnet['eighteenth_century'] = ['0.978', '-0.60', '0', '0.895', '#joy', '#admiration', 'positive', '0.825', 'period', 'quattrocento', 'twentieth_century', 'time_period', 'period_of_time']
senticnet['eighteenth_dynasty'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
senticnet['eightfold'] = ['-0.02', '0.095', '0', '0.117', '#interest', '#admiration', 'positive', '0.063', 'identification', 'paper_square', 'carry_money', 'money_hold', 'credit']
senticnet['eighth_amendment'] = ['-0.19', '0', '-0.17', '-0.14', '#sadness', '#fear', 'negative', '-0.17', 'death_penalty', 'die', 'death_destruction', 'kill', 'murder']
senticnet['eighth_episode'] = ['0.918', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.827', 'encounter', 'reveal', 'programme', 'lose', 'sequel']
senticnet['eighth_grader'] = ['0.994', '0.759', '0', '0', '#joy', '#interest', 'positive', '0.877', 'study', 'do_homework', 'student', 'attend_class', 'write']
senticnet['eighth_installment'] = ['-0.06', '0.122', '-0.04', '0.113', '#interest', '#admiration', 'positive', '0.032', 'encounter', 'portray', 'reveal', 'episode', 'next_episode']
senticnet['eighth_place'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'home', 'position', 'time_limit', 'monthly', 'modern_home']
senticnet['eighth_round_knockout'] = ['0.944', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.967', 'meeting', 'meet', 'match', 'encounter', 'round']
senticnet['eighth_straight_month'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'mathematical', 'muscle', 'math_function', 'feel_sick', 'separation']
senticnet['eightieth_birthday'] = ['0.701', '0.867', '0', '0', '#joy', '#interest', 'positive', '0.784', 'festivity', 'special_day', 'birthday', 'special_time', 'cake_candle']
senticnet['eighty'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'hundred_year', 'twelve', 'day_month', 'time_measurement']
senticnet['eiht'] = ['-0.86', '0', '0.675', '-0.84', '#sadness', '#disgust', 'negative', '-0.79', 'shenanigan', 'chicanery', 'wile', 'dupery', 'trickery']
senticnet['eila'] = ['0.975', '-0.66', '0', '0.813', '#joy', '#admiration', 'positive', '0.816', 'cry', 'yell', 'scream', 'shrill', 'call']
senticnet['einer'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['einfhrung'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['einmal'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['einsatzgruppe'] = ['-0.08', '0', '-0.15', '-0.20', '#fear', '#disgust', 'negative', '-0.14', 'casualty', 'reaper', 'wound', 'cause_death', 'gunshot']
senticnet['einstein'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'force_unit', 'sthene', 'poundal']
senticnet['einstein_equation'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'newton', 'sthene', 'force_unit']
senticnet['einstein_equivalence_principle'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'general_relativity', 'general_theory_of_relativity', 'relativity', 'special_relativity', 'special_theory_of_relativity']
senticnet['einstein_general_relativity'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'general_relativity', 'general_theory_of_relativity', 'relativity', 'special_relativity', 'special_theory_of_relativity']
senticnet['einstein_relativity'] = ['-0.69', '0', '0.83', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'general_relativity', 'general_theory_of_relativity', 'general_relativity_theory', 'relativity', 'special_theory_of_relativity']
senticnet['einstein_ring'] = ['0', '-0.72', '0', '0.712', '#surprise', '#admiration', 'positive', '0.72', 'heavenly_body', 'around_earth', 'source_light', 'sol', 'celestial_body']
senticnet['einstein_theory'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'general_relativity', 'general_theory_of_relativity', 'relativity', 'special_relativity', 'special_theory_of_relativity']
senticnet['einsteins_theory'] = ['-0.69', '0', '0.83', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'general_relativity', 'general_theory_of_relativity', 'general_relativity_theory', 'relativity', 'special_theory_of_relativity']
senticnet['einstrzende_neubauten'] = ['-0.05', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.05', 'masturbate', 'jerk', 'jerk_off', 'watch_porn', 'porn']
senticnet['einval'] = ['-0.98', '0', '0.869', '-0.80', '#sadness', '#anger', 'negative', '-0.88', 'error', 'syntax_error', 'semantic_error', 'programming_error', 'computer_error']
senticnet['eisenhower_trophy'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'sciolist', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
senticnet['eisentrauti'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'carbon_monoxide', 'tree_animal']
senticnet['eismowe'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'glaucous_gull', 'dove', 'egret', 'white_swan', 'white_bird']
senticnet['eispack'] = ['0', '0', '0.737', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'annoy', 'skin_rash', 'bother', 'irritation', 'great_dane']
senticnet['ejaculate'] = ['0.032', '0.261', '-0.15', '0.198', '#interest', '#admiration', 'positive', '0.084', 'carry_baby', 'baby_born', 'herpes', 'cum', 'change_diaper']
senticnet['ejaculation'] = ['-0.74', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.78', 'ejaculate', 'pass_gene', 'ecstasy', 'lower_standard_live', 'survival_species']
senticnet['ejaculatory'] = ['0.655', '-0.87', '0', '0.595', '#joy', '#surprise', 'positive', '0.709', 'erection', 'excitement', 'arousal', 'kill_brain_cell', 'feel_relieved']
senticnet['ejaculatory_control'] = ['0.655', '-0.87', '0', '0.595', '#joy', '#surprise', 'positive', '0.709', 'erection', 'excitement', 'arousal', 'kill_brain_cell', 'feel_relieved']
senticnet['eject'] = ['-0.71', '0', '0.954', '-0.56', '#sadness', '#anger', 'negative', '-0.74', 'evict', 'chuck_out', 'exclude', 'turf_out', 'boot_out']
senticnet['eject_button'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'clip', 'clasp', 'glue_stick', 'clothespin', 'clamp']
senticnet['ejected_material'] = ['-0.55', '0', '0.869', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'air_bubble', 'foam', 'froth', 'soap_bubble', 'bubble']
senticnet['ejection'] = ['0', '0', '0.781', '-0.82', '#anger', '#disgust', 'negative', '-0.80', 'cry', 'acting_out', 'explosion', 'halloo', 'yell']
senticnet['ejection_port'] = ['0', '0.069', '0', '0.083', '#interest', '#admiration', 'positive', '0.076', 'latch', 'restrict_access', 'knob', 'door_knob', 'gate']
senticnet['ejection_seat'] = ['-0.88', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.90', 'undergarment', 'outerwear', 'lady_underwear', 'under_garment', 'slip']
senticnet['ejective'] = ['-0.67', '0', '0.97', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'joint', 'node', 'connexion', 'join', 'juncture']
senticnet['ejector'] = ['0.843', '-0.99', '0', '0.706', '#joy', '#surprise', 'positive', '0.849', 'gadgetry', 'gimbal', 'contrivance', 'mod_con', 'device']
senticnet['ekaggata'] = ['-0.85', '0', '0.968', '-0.75', '#sadness', '#anger', 'negative', '-0.86', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['eke_out'] = ['0.855', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.848', 'stay_alive', 'get_oxygen', 'exist', 'alive', 'crave']
senticnet['eked_out'] = ['-0.69', '0', '0.957', '-0.92', '#anger', '#disgust', 'negative', '-0.85', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['ekg'] = ['-0.20', '0', '-0.22', '-0.19', '#sadness', '#fear', 'negative', '-0.20', 'go_hospital', 'death', 'die', 'take_aspirin', 'heart_attack']
senticnet['eki'] = ['0.973', '-0.50', '0', '0.612', '#joy', '#admiration', 'positive', '0.698', 'chic', 'chicness', 'jauntiness', 'modishness', 'stylishness']
senticnet['eking'] = ['0', '0', '-0.74', '-0.86', '#fear', '#disgust', 'negative', '-0.80', 'paycheck-to-paycheck', 'moment-to-moment', 'hermit-like', 'hand-to-mouth', 'day-in-day-out']
senticnet['eking_out'] = ['-0.69', '0', '0.957', '-0.92', '#anger', '#disgust', 'negative', '-0.85', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['ekron'] = ['0.849', '-0.89', '0', '0.924', '#surprise', '#admiration', 'positive', '0.89', 'nonintellectual', 'lowbrow', 'lowbrowed', 'mindless', 'uncultivated']
senticnet['el_abha'] = ['0.969', '-0.86', '0', '0.631', '#joy', '#surprise', 'positive', '0.823', 'assuage', 'perfect', 'make_look_better', 'relieve', 'sublimate']
senticnet['el_bulli'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'artistic_creation', 'fabrication', 'carving', 'decoupage', 'ceramic']
senticnet['el_cap'] = ['-0.99', '0', '0.641', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'navigable', 'negotiable', 'surmountable', 'passable', 'traversable']
senticnet['el_credito'] = ['0.031', '0', '-0.08', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'smoke_cigarette', 'smoke', 'cinder', 'tobacco_product', 'smoke_pipe']
senticnet['el_faran'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'find_out', 'dig_out', 'unearth', 'become_aware', 'dish_out']
senticnet['el_nino_event'] = ['-0.69', '0', '0.835', '-0.56', '#sadness', '#anger', 'negative', '-0.69', 'roughness', 'inclemency', 'bad_weather', 'inclementness', 'windiness']
senticnet['el_nino_southern_oscillation'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'weather', 'sunny', 'spring', 'snow', 'four_seasons']
senticnet['el_nio_event'] = ['-0.69', '0', '0.835', '-0.56', '#sadness', '#anger', 'negative', '-0.69', 'roughness', 'inclemency', 'bad_weather', 'inclementness', 'windiness']
senticnet['el_pollo_loco'] = ['0.965', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.951', 'delicious_food', 'hot_meal', 'good_food', 'fresh_produce', 'tasty_food']
senticnet['el_questro'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'beachcomber', 'bum', 'drifter']
senticnet['elaborate'] = ['-0.06', '0.108', '-0.08', '0.233', '#interest', '#admiration', 'positive', '0.047', 'response', 'write_back', 'question_answer', 'retort', 'give_answer']
senticnet['elaborate_ceremony'] = ['0.82', '0', '0', '0.885', '#joy', '#admiration', 'positive', '0.853', 'religious_event', 'sunday', 'happy_event', 'tradition', 'wedding']
senticnet['elaborate_costume'] = ['-0.76', '0', '0.672', '-0.88', '#sadness', '#disgust', 'negative', '-0.77', 'attire', 'garb', 'dress', 'fancy_dress', 'masquerade']
senticnet['elaborate_decoration'] = ['-0.54', '0', '0.7', '-0.64', '#anger', '#disgust', 'negative', '-0.62', 'flourish', 'rant', 'bombast', 'fustian', 'claptrap']
senticnet['elaborate_ornamentation'] = ['-0.54', '0', '0.7', '-0.64', '#anger', '#disgust', 'negative', '-0.62', 'flourish', 'rant', 'bombast', 'fustian', 'claptrap']
senticnet['elaborate_plaster'] = ['0', '0.128', '-0.12', '0.183', '#interest', '#admiration', 'positive', '0.063', 'support_roof', 'furnish', 'ceiling', 'window_door', 'frame']
senticnet['elaborate_sting_operation'] = ['0.931', '-0.71', '0', '0.63', '#joy', '#surprise', 'positive', '0.758', 'hustle', 'con', 'bunco', 'sting', 'bunco_game']
senticnet['elaborate_tomb'] = ['-0.92', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.91', 'bury_body', 'coffin', 'headstone', 'morgue', 'tombstone']
senticnet['elaborate_woodwork'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'adobe', 'adobe_brick', 'clinker', 'brick', 'firebrick']
senticnet['elaborately'] = ['0', '0.924', '0', '0.906', '#interest', '#admiration', 'positive', '0.915', 'intricately', 'intricate', 'exquisitely', 'ornately', 'artfully']
senticnet['elaborateness'] = ['0.632', '-0.62', '0', '0.923', '#joy', '#admiration', 'positive', '0.725', 'quality', 'intricacy', 'complexity', 'tapestry', 'complicatedness']
senticnet['elaboration'] = ['-0.81', '0', '0.931', '-0.77', '#sadness', '#anger', 'negative', '-0.84', 'mutualness', 'mutuality', 'reciprocity', 'correlation', 'correlativity']
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senticnet['emotional_attachment'] = ['0.858', '0', '0', '0.8', '#joy', '#admiration', 'positive', '0.829', 'empathize', 'sympathy', 'end_conversation', 'desire', 'human_emotion']
senticnet['emotional_connection'] = ['0.858', '0', '0', '0.8', '#joy', '#admiration', 'positive', '0.829', 'empathize', 'sympathy', 'end_conversation', 'desire', 'human_emotion']
senticnet['emotional_contagion'] = ['-0.93', '0', '0.586', '-0.66', '#sadness', '#disgust', 'negative', '-0.73', 'camp', 'pack', 'clique', 'coterie', 'brain_trust']
senticnet['emotional_disorder'] = ['-0.90', '0', '0.546', '-0.75', '#sadness', '#disgust', 'negative', '-0.73', 'manic_disorder', 'bipolar_disorder', 'manic_depression', 'manic_depressive_illness', 'depression']
senticnet['emotional_distress'] = ['-0.71', '0', '-0.75', '-0.90', '#fear', '#disgust', 'negative', '-0.79', 'convent', 'monastery', 'capital_build', 'confession', 'synagogue']
senticnet['emotional_distress_claim'] = ['-0.91', '0', '0.795', '-0.94', '#sadness', '#disgust', 'negative', '-0.88', 'ineluctability', 'assured', 'predictability', 'slam_dunk', 'inevitability']
senticnet['emotional_disturbance'] = ['-0.89', '0', '0.669', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'manic_disorder', 'bipolar_disorder', 'manic_depression', 'manic_depressive_illness', 'depression']
senticnet['emotional_exhaustion'] = ['-0.97', '0', '0.969', '-0.50', '#sadness', '#anger', 'negative', '-0.81', 'nervousness', 'tenseness', 'tension', 'nerve', 'stress']
senticnet['emotional_expression'] = ['0.708', '-0.60', '0', '0.661', '#joy', '#admiration', 'positive', '0.659', 'love', 'conditioned_emotional_response', 'conditioned_emotion', 'fright', 'reverence']
senticnet['emotional_farewell'] = ['0.037', '0.161', '-0.15', '0.203', '#interest', '#admiration', 'positive', '0.062', 'wave', 'flow', 'salute', 'multiple_purposes', 'wave_wind']
senticnet['emotional_guy'] = ['-0.10', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.10', 'sensitive_skin', 'sensitive_teeth', 'weak', 'sensitive', 'sensitivity']
senticnet['emotional_harm'] = ['0.565', '-0.76', '0', '0.982', '#surprise', '#admiration', 'positive', '0.771', 'risk', 'hazard', 'jeopardy', 'health_hazard', 'peril']
senticnet['emotional_honesty'] = ['0.772', '-0.95', '0', '0.89', '#surprise', '#admiration', 'positive', '0.872', 'downrightness', 'straightforwardness', 'characteristic', 'directness', 'immediateness']
senticnet['emotional_immaturity'] = ['0.604', '-0.57', '0', '0.935', '#joy', '#admiration', 'positive', '0.705', 'affirmativeness', 'deference', 'compliance', 'disposition', 'complaisance']
senticnet['emotional_instability'] = ['-0.97', '0', '0.848', '-0.58', '#sadness', '#anger', 'negative', '-0.80', 'moroseness', 'sullenness', 'ill_humor', 'glumness', 'distemper']
senticnet['emotional_intelligence'] = ['0.858', '0', '0', '0.8', '#joy', '#admiration', 'positive', '0.829', 'empathize', 'sympathy', 'end_conversation', 'desire', 'human_emotion']
senticnet['emotional_intensity'] = ['0.708', '-0.60', '0', '0.661', '#joy', '#admiration', 'positive', '0.659', 'love', 'conditioned_emotional_response', 'conditioned_emotion', 'fright', 'reverence']
senticnet['emotional_intimacy'] = ['0.833', '0', '0', '0.807', '#joy', '#admiration', 'positive', '0.82', 'lust', 'amiable', 'give_hug', 'giggle', 'smile']
senticnet['emotional_lability'] = ['-0.58', '0', '0.722', '-0.51', '#sadness', '#anger', 'negative', '-0.60', 'dissociative_disorder', 'depersonalization_neurosis', 'depersonalization', 'mental_disorder', 'mental_disturbance']
senticnet['emotional_maturity'] = ['0', '0.075', '0', '0.159', '#interest', '#admiration', 'positive', '0.117', 'many_plants', 'blossom', 'development', 'poppy', 'adult']
senticnet['emotional_nuance'] = ['0.76', '-0.59', '0', '0.713', '#joy', '#admiration', 'positive', '0.689', 'spare_enemy', 'magnanimous', 'generous', 'forgiving', 'deal_gently']
senticnet['emotional_outburst'] = ['0', '0', '0.781', '-0.82', '#anger', '#disgust', 'negative', '-0.80', 'cry', 'acting_out', 'explosion', 'halloo', 'yell']
senticnet['emotional_pain'] = ['-0.22', '-0.00', '-0.21', '-0.26', '#sadness', '#disgust', 'negative', '-0.17', 'embarrassment', 'feel_hurt', 'feel_sad', 'cry', 'psychoneurosis']
senticnet['emotional_person'] = ['0.853', '-0.87', '0', '0.973', '#surprise', '#admiration', 'positive', '0.901', 'captive', 'mortal', 'spitfire', 'person', 'individual']
senticnet['emotional_reaction'] = ['0', '-0.06', '-0.14', '-0.14', '#fear', '#disgust', 'negative', '-0.07', 'laughter', 'emotional_response', 'cry', 'make_others_laugh', 'giggle']
senticnet['emotional_reactivity'] = ['0.971', '0.751', '0', '0.896', '#joy', '#admiration', 'positive', '0.873', 'new_friendship', 'foreplay', 'warm_fuzzy_feeling', 'become_aroused', 'erection']
senticnet['emotional_resonance'] = ['0.772', '-0.95', '0', '0.89', '#surprise', '#admiration', 'positive', '0.872', 'downrightness', 'straightforwardness', 'characteristic', 'directness', 'immediateness']
senticnet['emotional_response'] = ['-0.05', '0', '-0.13', '-0.05', '#sadness', '#fear', 'negative', '-0.08', 'emotional_reaction', 'hear_joke', 'blank_stare', 'illogical', 'see_funny']
senticnet['emotional_roller_coaster'] = ['0', '0', '-0.86', '-0.86', '#fear', '#disgust', 'negative', '-0.86', 'amusement_park', 'have_fun', 'drag_race', 'pia_lot_of_seat', 'roller_coaster']
senticnet['emotional_stability'] = ['-0.64', '0', '0.574', '-0.89', '#sadness', '#disgust', 'negative', '-0.70', 'gregariousness', 'ambiversion', 'openness', 'nakedness', 'extraversion']
senticnet['emotional_stimulus'] = ['0.971', '0.751', '0', '0.896', '#joy', '#admiration', 'positive', '0.873', 'new_friendship', 'foreplay', 'warm_fuzzy_feeling', 'become_aroused', 'erection']
senticnet['emotional_strain'] = ['-0.97', '0', '0.969', '-0.50', '#sadness', '#anger', 'negative', '-0.81', 'nervousness', 'tenseness', 'tension', 'nerve', 'stress']
senticnet['emotional_toll'] = ['-0.97', '0', '0.969', '-0.50', '#sadness', '#anger', 'negative', '-0.81', 'nervousness', 'tenseness', 'tension', 'nerve', 'stress']
senticnet['emotional_trauma'] = ['-0.88', '0', '-0.79', '-0.85', '#sadness', '#disgust', 'negative', '-0.84', 'pain', 'severe_pain', 'agony', 'physical_pain', 'suffer']
senticnet['emotional_turmoil'] = ['-0.77', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.78', 'unhappiness', 'melancholy', 'shame', 'unhappy', 'sad']
senticnet['emotionalism'] = ['0.783', '-0.53', '0', '0.937', '#joy', '#admiration', 'positive', '0.752', 'passion', 'affectionateness', 'drama', 'lovingness', 'fondness']
senticnet['emotionality'] = ['0.681', '-0.66', '0', '0.858', '#joy', '#admiration', 'positive', '0.735', 'passion', 'affectionateness', 'drama', 'lovingness', 'fondness']
senticnet['emotionally'] = ['0.944', '0.946', '0', '0', '#joy', '#interest', 'positive', '0.945', 'hormone', 'beautiful_smile', 'emotion', 'competitive_nature', 'two_ears']
senticnet['emotionally_abusive'] = ['-0.56', '0', '0.743', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'negligent', 'derelict', 'inattentive', 'delinquent', 'lax']
senticnet['emotionally_distant'] = ['-0.61', '0', '0.572', '-0.83', '#sadness', '#disgust', 'negative', '-0.67', 'numb', 'benumbed', 'anesthetic', 'insensible', 'asleep']
senticnet['emotionally_disturbed_child'] = ['-0.04', '0.088', '0.013', '0.069', '#interest', '#admiration', 'positive', '0.024', 'kindergarden', 'daycare', 'nursery', 'child', 'asylum']
senticnet['emotionally_fragile'] = ['-0.81', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.89', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecurity']
senticnet['emotionally_honest'] = ['0.904', '0.844', '0', '0', '#joy', '#interest', 'positive', '0.874', 'seriously', 'devout', 'earnestness', 'earnestly', 'seriousness']
senticnet['emotionally_immature'] = ['0.724', '-0.70', '0', '0.571', '#joy', '#surprise', 'positive', '0.666', 'spinnable', 'susceptible', 'easy', 'pliant', 'plastic']
senticnet['emotionally_intense'] = ['0.722', '-0.60', '0', '0.596', '#joy', '#surprise', 'positive', '0.641', 'medicament', 'medicinal_drug', 'medication', 'aloe', 'bitter_aloe']
senticnet['emotionally_powerful'] = ['0.722', '-0.60', '0', '0.596', '#joy', '#surprise', 'positive', '0.641', 'medicament', 'medicinal_drug', 'medication', 'aloe', 'bitter_aloe']
senticnet['emotionally_raw'] = ['0.722', '-0.60', '0', '0.596', '#joy', '#surprise', 'positive', '0.641', 'medicament', 'medicinal_drug', 'medication', 'aloe', 'bitter_aloe']
senticnet['emotionally_stable'] = ['0.992', '0', '0', '0.801', '#joy', '#admiration', 'positive', '0.897', 'feel_content', 'make_feel_better', 'celebrate_special_occasion', 'survival', 'express_appreciation']
senticnet['emotionally_unavailable'] = ['-0.56', '0', '0.743', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'negligent', 'derelict', 'inattentive', 'delinquent', 'lax']
senticnet['emotionally_unstable'] = ['-0.56', '0', '0.743', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'negligent', 'derelict', 'inattentive', 'delinquent', 'lax']
senticnet['emotionless'] = ['-0.61', '0', '0.572', '-0.83', '#sadness', '#disgust', 'negative', '-0.67', 'numb', 'benumbed', 'anesthetic', 'insensible', 'asleep']
senticnet['emotionlessness'] = ['-0.72', '0', '-0.7', '0', '#sadness', '#fear', 'negative', '-0.71', 'impassiveness', 'impassivity', 'apathetically', 'unemotionality', 'stolidity']
senticnet['emotive'] = ['0.461', '0.674', '-0.26', '0.247', '#joy', '#interest', 'positive', '0.28', 'affectionateness', 'warmness', 'philia', 'affectional', 'affective']
senticnet['empathetic'] = ['0.923', '0.93', '0', '0', '#joy', '#interest', 'positive', '0.927', 'empathic', 'sympathetically', 'empathetically', 'kindhearted', 'empathy']
senticnet['empathetically'] = ['0.931', '0.897', '0', '0', '#joy', '#interest', 'positive', '0.914', 'sympathetically', 'empathic', 'empathetic', 'kind', 'kindheartedness']
senticnet['empathic'] = ['0.818', '0.732', '0', '0', '#joy', '#interest', 'positive', '0.775', 'empathetic', 'sympathetically', 'empathetically', 'kindhearted', 'kind']
senticnet['empathic_concern'] = ['-0.93', '0', '0.586', '-0.66', '#sadness', '#disgust', 'negative', '-0.73', 'camp', 'pack', 'clique', 'coterie', 'brain_trust']
senticnet['empathise'] = ['0.858', '0', '0', '0.8', '#joy', '#admiration', 'positive', '0.829', 'empathize', 'sympathy', 'end_conversation', 'desire', 'human_emotion']
senticnet['empathise_with'] = ['-0.99', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.96', 'sympathetic', 'sympathetically', 'pity', 'sympathize_with', 'sympathy']
senticnet['empathize'] = ['0.703', '0.926', '0', '0', '#joy', '#interest', 'positive', '0.815', 'empathy', 'end_conversation', 'find_out_wrong', 'cheer_up', 'share_feeling']
senticnet['empathize_with'] = ['0.858', '0', '0', '0.8', '#joy', '#admiration', 'positive', '0.829', 'empathize', 'sympathy', 'end_conversation', 'desire', 'human_emotion']
senticnet['empathizing_with'] = ['0.858', '0', '0', '0.8', '#joy', '#admiration', 'positive', '0.829', 'empathize', 'sympathy', 'end_conversation', 'desire', 'human_emotion']
senticnet['empathy'] = ['0.858', '0', '0', '0.8', '#joy', '#admiration', 'positive', '0.829', 'empathize', 'sympathy', 'end_conversation', 'desire', 'human_emotion']
senticnet['emperor'] = ['0.054', '0.068', '0', '0.068', '#interest', '#admiration', 'positive', '0.063', 'royal_leader', 'monarch', 'king', 'duchess', 'despot']
senticnet['emperor_hadrian'] = ['-0.03', '0.092', '0.07', '0.1', '#interest', '#admiration', 'positive', '0.021', 'gladiator', 'civilization', 'church_build', 'coliseum', 'every_town']
senticnet['emperor_scorpion'] = ['0', '0', '-0.87', '0', '#fear', '#fear', 'negative', '-0.87', 'beetle', 'many_insects', 'grasshopper', 'firefly', 'mouse']
senticnet['emperor_seamount_chain'] = ['-0.87', '0', '0.818', '0', '#sadness', '#anger', 'negative', '-0.84', 'slow_speed', 'wifi', 'computer_internet_connection', 'internet_connection', 'boot_up_computer']
senticnet['emperor_tamarin'] = ['0.921', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.825', 'monkey', 'fiance', 'lemur', 'beaver', 'koala']
senticnet['emphasis'] = ['0', '0', '-0.73', '-0.97', '#fear', '#disgust', 'negative', '-0.85', 'fury', 'vehemence', 'fierceness', 'wildness', 'ferocity']
senticnet['emphasize'] = ['0.739', '-0.76', '0', '0.977', '#surprise', '#admiration', 'positive', '0.827', 'accent', 'stress', 'wave_off', 'play_down', 'punctuate']
senticnet['emphasize_abstinence'] = ['0', '0', '0.973', '-0.78', '#anger', '#disgust', 'negative', '-0.87', 'chastity', 'ignore_conception', 'restrain', 'abortion', 'stop']
senticnet['emphasize_teamwork'] = ['0.768', '0.792', '0', '0', '#joy', '#interest', 'positive', '0.78', 'win', 'strategize', 'try_best', 'catch_ball', 'lose_game']
senticnet['emphasizes_teamwork'] = ['0.768', '0.792', '0', '0', '#joy', '#interest', 'positive', '0.78', 'win', 'strategize', 'try_best', 'catch_ball', 'lose_game']
senticnet['emphatic'] = ['-0.66', '0', '0.721', '-0.53', '#sadness', '#anger', 'negative', '-0.64', 'unlimited', 'outright', 'unconditional', 'unqualified', 'unqualifiedal']
senticnet['emphatic_victory'] = ['0', '-0.04', '0.085', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'claw', 'fur_keep_warm', 'tic', 'bristle', 'muzzle']
senticnet['emphatically'] = ['-0.90', '0', '0.922', '0', '#sadness', '#anger', 'negative', '-0.91', 'asseverate', 'emphasis', 'resoundingly', 'crush', 'school']
senticnet['emphysema'] = ['-0.98', '0', '0.827', '-0.94', '#sadness', '#disgust', 'negative', '-0.91', 'congestive_heart_failure', 'heart_attack', 'failure', 'heart_disease', 'cardiopathy']
senticnet['empidonax'] = ['-0.92', '0', '0.64', '-0.52', '#sadness', '#anger', 'negative', '-0.69', 'animal_order', 'percher', 'thyreophoran', 'marginocephalian', 'euronithopod']
senticnet['empire'] = ['0', '0.173', '-0.06', '0.126', '#interest', '#admiration', 'positive', '0.077', 'democracy', 'amendment', 'realm', 'unite', 'international']
senticnet['empire_poker_room'] = ['-0.78', '0', '0.944', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'loser', 'speculator', 'mortal', 'better', 'gambler']
senticnet['empire_waist'] = ['0.735', '-0.54', '0', '0.897', '#joy', '#admiration', 'positive', '0.727', 'net', 'network', 'mesh', 'meshing', 'meshwork']
senticnet['empirical'] = ['0', '0', '-0.90', '-0.85', '#fear', '#disgust', 'negative', '-0.87', 'reductionist', 'mechanistic', 'elucidate', 'biochemical', 'causal']
senticnet['empirical_cognition'] = ['0.774', '-0.82', '0', '0.63', '#joy', '#surprise', 'positive', '0.744', 'quality', 'perishability', 'perishableness', 'unsatisfactoriness', 'inadequacy']
senticnet['empirical_ego'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'metastability', 'monotony', 'invariance', 'constancy', 'unchangeability']
senticnet['empirical_evidence'] = ['0.943', '-0.75', '0', '0.515', '#joy', '#surprise', 'positive', '0.736', 'reasonableness', 'tenability', 'tenableness', 'credibility', 'credibleness']
senticnet['empirical_research'] = ['0.887', '0.877', '0', '0', '#joy', '#interest', 'positive', '0.882', 'ask_expert', 'absorb_information', 'analyze', 'learn_computer', 'search_information']
senticnet['empirical_verification'] = ['0.534', '-0.86', '0', '0.676', '#surprise', '#admiration', 'positive', '0.692', 'suitability', 'suitableness', 'habitability', 'habitableness', 'qualification']
senticnet['empirically'] = ['-0.91', '0', '0.598', '-0.52', '#sadness', '#anger', 'negative', '-0.68', 'stingy', 'penurious', 'scrimy', 'beggarly', 'chintzy']
senticnet['empirically_adequate'] = ['-0.97', '0', '0.781', '-0.91', '#sadness', '#disgust', 'negative', '-0.88', 'weakness', 'fault', 'flaw', 'defect', 'wart']
senticnet['empirically_testable'] = ['-0.99', '0', '0.878', '-0.95', '#sadness', '#disgust', 'negative', '-0.94', 'adequacy', 'satisfactoriness', 'acceptability', 'acceptableness', 'quality']
senticnet['empirically_verifiable'] = ['-0.91', '0', '0.795', '-0.94', '#sadness', '#disgust', 'negative', '-0.88', 'ineluctability', 'assured', 'predictability', 'slam_dunk', 'inevitability']
senticnet['empiricism'] = ['-0.98', '0', '0.891', '-0.60', '#sadness', '#anger', 'negative', '-0.82', 'exorcism', 'dispossession', 'magic', 'thaumaturgy', 'belief']
senticnet['empiricist'] = ['0.992', '-0.66', '0', '0.536', '#joy', '#surprise', 'positive', '0.729', 'nous', 'mind', 'brain', 'psyche', 'head']
senticnet['emplace'] = ['0.619', '-0.62', '0', '0.693', '#surprise', '#admiration', 'positive', '0.647', 'set', 'pose', 'lay', 'put', 'place']
senticnet['employ'] = ['0.852', '0.864', '0', '0', '#joy', '#interest', 'positive', '0.858', 'police_work', 'career', 'task', 'employment', 'labor']
senticnet['employ_approximately'] = ['0.814', '-0.87', '0', '0.918', '#surprise', '#admiration', 'positive', '0.87', 'ship_company', 'company', 'work_force', 'workforce', 'manpower']
senticnet['employ_ment'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'lucrative', 'moneymaking', 'profitable', 'paying', 'paid']
senticnet['employability'] = ['0.032', '0.11', '0', '0.105', '#interest', '#admiration', 'positive', '0.082', 'work', 'place_work', 'implementation', 'disorganize', 'employment']
senticnet['employable'] = ['0.783', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.791', 'impracticable', 'functional', 'practicable', 'practical', 'feasible']
senticnet['employee'] = ['0.046', '0.099', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.04', 'vocation', 'secretary', 'public_servant', 'tradesman', 'worker']
senticnet['employee_identification_card'] = ['0.739', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.814', 'receipt', 'bet', 'electric_bill', 'letter_slot', 'deal']
senticnet['employee_productivity'] = ['0', '0.298', '-0.06', '0.305', '#interest', '#admiration', 'positive', '0.18', 'reason_why', 'correct', 'punctuation', 'exclamation', 'end_point']
senticnet['employee_retention'] = ['0.032', '0.11', '0', '0.105', '#interest', '#admiration', 'positive', '0.082', 'work', 'place_work', 'implementation', 'disorganize', 'employment']
senticnet['employee_turnover'] = ['0', '0.767', '0', '0.708', '#interest', '#admiration', 'positive', '0.738', 'turnover', 'dollar_volume', 'turnover_rate', 'upset', 'overturn']
senticnet['employer'] = ['0.071', '0.151', '0', '0.16', '#interest', '#admiration', 'positive', '0.127', 'business_organization', 'large_business', 'partnership', 'business_firm', 'boss']
senticnet['employer_liability'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'culpable_negligence', 'evasion', 'dereliction', 'negligence']
senticnet['employment'] = ['0.85', '0', '0', '0', '#joy', '#joy', 'positive', '0.85', 'task', 'labor', 'police_work', 'employ', 'career']
senticnet['emporium'] = ['0.041', '0.098', '0', '0', '#joy', '#interest', 'positive', '0.07', 'utopia', 'java', 'memorial', 'nowhere', 'coffee']
senticnet['empoverish'] = ['0', '0', '0.916', '-0.74', '#anger', '#disgust', 'negative', '-0.82', 'isolation', 'bankrupt', 'discrimination', 'disjoin', 'brexit']
senticnet['empower'] = ['0.558', '-0.51', '0', '0.901', '#joy', '#admiration', 'positive', '0.659', 'gift', 'change', 'enable', 'invest', 'endow']
senticnet['empower_youth'] = ['0.025', '0.073', '-0.05', '0.1', '#interest', '#admiration', 'positive', '0.037', 'young_age', 'energy', 'hope', 'young_life', 'healthy']
senticnet['empowering_youth'] = ['0.025', '0.073', '-0.05', '0.1', '#interest', '#admiration', 'positive', '0.037', 'young_age', 'energy', 'hope', 'young_life', 'healthy']
senticnet['empowerment'] = ['0.098', '0.165', '-0.13', '0.284', '#interest', '#admiration', 'positive', '0.103', 'see_different', 'eye_strain', 'know', 'learn', 'insight']
senticnet['empta'] = ['-0.90', '0', '0.529', '-0.84', '#sadness', '#disgust', 'negative', '-0.75', 'poison_gas', 'nerve_agent', 'mustard_gas', 'mustard_agent', 'blistering_agent']
senticnet['emptiness'] = ['-0.91', '0', '-0.77', '-0.75', '#sadness', '#fear', 'negative', '-0.81', 'void', 'star_planet', 'nothingness', 'outer', 'around_earth']
senticnet['empty'] = ['-0.02', '-0.02', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.02', 'empty_glass', 'empty_space', 'worthless', 'empty_stomach', 'universe']
senticnet['empty_bladder'] = ['-0.03', '0', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.06', 'urinate', 'pee', 'know_re_healthy', 'soak_hotspring', 'get_front_line']
senticnet['empty_bottle'] = ['-0.87', '-0.79', '0.729', '-0.97', '#sadness', '#disgust', 'negative', '-0.44', 'empty', 'garbage', 'bottle', 'empty_glass', 'thirsty']
senticnet['empty_glass'] = ['-0.61', '0', '0.738', '-0.80', '#anger', '#disgust', 'negative', '-0.71', 'empty', 'empty_space', 'a_lot_of_space', 'empty_stomach', 'universe']
senticnet['empty_house'] = ['-0.61', '0', '0.738', '-0.80', '#anger', '#disgust', 'negative', '-0.71', 'empty_room', 'empty_space', 'empty', 'vacnat', 'aimless']
senticnet['empty_land'] = ['-0.81', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'floss', 'station_wagon', 'land', 'plane_land', 'national']
senticnet['empty_parking_lot'] = ['0', '0', '0', '-0.09', '#disgust', '#disgust', 'negative', '-0.09', 'subway', 'bus', 'train_station', 'railway_station', 'bus_stop']
senticnet['empty_place'] = ['0', '0.086', '0.046', '0.133', '#interest', '#admiration', 'positive', '0.058', 'house_place', 'empty', 'house_location', 'inside_build', 'land_measurement']
senticnet['empty_pocket'] = ['-0.87', '-0.79', '0.729', '-0.97', '#sadness', '#disgust', 'negative', '-0.44', 'bankruptcy', 'lost_money', 'cashless', 'hobo', 'debt']
senticnet['empty_room'] = ['0', '0.09', '0', '0.066', '#interest', '#admiration', 'positive', '0.078', 'lot_of_space', 'above_earth', 'without_light', 'limitless', 'outer']
senticnet['empty_shell'] = ['-0.02', '-0.02', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.02', 'empty_glass', 'empty_space', 'worthless', 'empty_stomach', 'universe']
senticnet['empty_space'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'empty_room', 'a_lot_of_space', 'empty', 'empty_house', 'universe']
senticnet['empty_stomach'] = ['-0.61', '0', '0.738', '-0.80', '#anger', '#disgust', 'negative', '-0.71', 'empty_glass', 'empty_space', 'a_lot_of_space', 'empty', 'universe']
senticnet['empty_talk'] = ['0.89', '-0.73', '0', '0.877', '#joy', '#admiration', 'positive', '0.834', 'nonsense', 'meaninglessness', 'bunk', 'nonsensicality', 'hokum']
senticnet['empty_toilet_paper'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'poppy_seed', 'custard', 'bread', 'biscuit']
senticnet['empty_trash'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'car_trunk', 'walmart', 'warehouse', 'store', 'storage_place']
senticnet['empty_word'] = ['0.55', '-0.58', '0', '0.552', '#surprise', '#admiration', 'positive', '0.561', 'nonsense', 'meaninglessness', 'bunk', 'nonsensicality', 'hokum']
senticnet['empyema'] = ['-0.53', '0', '0.671', '-0.55', '#anger', '#disgust', 'negative', '-0.58', 'dirty', 'putrefactive', 'putrefacient', 'abscessed', 'contaminative']
senticnet['empyreal'] = ['-0.83', '0', '0.896', '-0.95', '#anger', '#disgust', 'negative', '-0.89', 'storied', 'incandescent', 'historied', 'divine', 'glorious']
senticnet['empyrean'] = ['-0.80', '0', '0.761', '-0.70', '#sadness', '#anger', 'negative', '-0.75', 'immeasurable', 'limitless', 'measureless', 'unmeasured', 'abysmal']
senticnet['emtricitabine'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'indinavir', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['emulate'] = ['-0.04', '0.127', '-0.12', '0.223', '#interest', '#admiration', 'positive', '0.046', 'act', 'emote', 'portray', 'shorten', 'bold']
senticnet['emulation_layer'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'carbon_fiber']
senticnet['emulative'] = ['-0.12', '0.193', '0.597', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'upvalue', 'overvalue', 'overprize', 'overstimate', 'overprice']
senticnet['emulator'] = ['-0.83', '0', '0.584', '-0.68', '#sadness', '#disgust', 'negative', '-0.70', 'epigone', 'epigon', 'parrot', 'mortal', 'person']
senticnet['emulsification'] = ['-0.98', '0', '0.516', '-0.90', '#sadness', '#disgust', 'negative', '-0.80', 'devastation', 'annihilation', 'pulverization', 'vaporization', 'destruction']
senticnet['emulsify'] = ['0.581', '-0.88', '0', '0.534', '#joy', '#surprise', 'positive', '0.667', 'better', 'improve', 'pure', 'meliorate', 'amend']
senticnet['emulsion'] = ['-0.04', '0.069', '0', '0.075', '#interest', '#admiration', 'positive', '0.035', 'sculpt_gel', 'hairstyle', 'hairdresser', 'cosmetic', 'hair_sculpt_gel']
senticnet['emusic'] = ['0.101', '-0.04', '0.036', '0', '#joy', '#surprise', 'positive', '0.038', 'song', 'rap', 'jingle', 'chord', 'lyric']
senticnet['en_appel'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'sexual_relationship', 'liaison', 'affair', 'involvement']
senticnet['en_ce_qui_concerne'] = ['0.911', '-0.94', '0', '0.953', '#surprise', '#admiration', 'positive', '0.935', 'common_sense', 'good_sense', 'horse_sense', 'mother_wit', 'gumption']
senticnet['en_dey'] = ['-0.53', '0', '0.508', '-0.74', '#sadness', '#disgust', 'negative', '-0.59', 'bedwetter', 'bed_wetter', 'mortal', 'wetter', 'person']
senticnet['en_examen'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'sexual_relationship', 'liaison', 'affair', 'involvement']
senticnet['en_particulier_dans_le'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'indweller', 'invillager', 'villager', 'dweller', 'denizen']
senticnet['en_passant_capture'] = ['0', '0', '0', '0.115', '#admiration', '#admiration', 'positive', '0.115', 'chess_knight', 'strategy', 'pawn', 'chess_queen', 'chess_rook']
senticnet['en_plastique'] = ['-0.58', '0', '0.661', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'explosive', 'cordite', 'dynamite', 'plastic_explosive', 'explosive_compound']
senticnet['en_pointe'] = ['-0.68', '0', '0.545', '-0.68', '#sadness', '#disgust', 'negative', '-0.64', 'stunt', 'tumble', 'split', 'walkover', 'acrobatic_feat']
senticnet['en_suite'] = ['0.159', '-0.00', '0.011', '0.079', '#joy', '#admiration', 'positive', '0.058', 'ceiling_fan', 'ipod', 'technologic', 'formula', 'music']
senticnet['en_suite_bath'] = ['0', '-0.02', '-0.08', '-0.05', '#fear', '#disgust', 'negative', '-0.03', 'mattress', 'sleep_furniture', 'cradle', 'small_bed', 'pillow']
senticnet['en_suite_bathroom'] = ['0.716', '0.96', '0', '0', '#joy', '#interest', 'positive', '0.838', 'airline_seat', 'small_seat', 'beanbag_chair', 'hide-a-bed_sofa', 'chair']
senticnet['en_suite_shower'] = ['0.159', '-0.00', '0.011', '0.079', '#joy', '#admiration', 'positive', '0.058', 'ceiling_fan', 'ipod', 'technologic', 'formula', 'music']
senticnet['en_toda'] = ['0.502', '-0.82', '0', '0.779', '#surprise', '#admiration', 'positive', '0.703', 'mortal', 'person', 'individual', 'free_liver', 'agent']
senticnet['en_verre'] = ['-0.58', '0', '0.661', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'explosive', 'cordite', 'dynamite', 'plastic_explosive', 'explosive_compound']
senticnet['en_wp_coi_conflict'] = ['0', '0.33', '0', '0', '#interest', '#interest', 'positive', '0.33', 'interested', 'focused', 'attentive', 'curious', 'fascinated']
senticnet['enable'] = ['0.558', '-0.51', '0', '0.901', '#joy', '#admiration', 'positive', '0.659', 'gift', 'change', 'empower', 'invest', 'endow']
senticnet['enable_research'] = ['0', '0.957', '0', '0.821', '#interest', '#admiration', 'positive', '0.889', 'molecule', 'chemist', 'equipment', 'carry', 'chemistry']
senticnet['enables'] = ['0', '0.961', '0', '0.892', '#interest', '#admiration', 'positive', '0.926', 'intl_research_fellow', 'unique', 'research_abroad', 'joint_research', 'abroad']
senticnet['enact'] = ['-0.88', '0', '0.977', '-0.81', '#sadness', '#anger', 'negative', '-0.89', 'execute', 'run', 'compel', 'oblige', 'obligate']
senticnet['enacting_clause'] = ['0.779', '-0.88', '0', '0.521', '#joy', '#surprise', 'positive', '0.727', 'corrigible', 'correctable', 'improvable', 'reformable', 'redeemable']
senticnet['enactment'] = ['-0.70', '0', '0.552', '-0.68', '#sadness', '#disgust', 'negative', '-0.64', 'protection', 'regimentation', 'taxation', 'trade_protection', 'reimposition']
senticnet['enamel'] = ['-0.06', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.04', 'polish', 'magnify', 'see_out', 'sill', 'pane']
senticnet['enamel_paint'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'wall_decoration', 'renovation', 'painting', 'artwork', 'new_house']
senticnet['enamor'] = ['0.601', '0.647', '-0.26', '0.322', '#joy', '#interest', 'positive', '0.326', 'captivated', 'charmed', 'captivate', 'bewitch', 'enthrallment']
senticnet['enamoredness'] = ['0.531', '0.673', '-0.27', '0.275', '#joy', '#interest', 'positive', '0.301', 'amorous', 'amorousness', 'amatory', 'amative', 'puppy_love']
senticnet['enamour'] = ['0.77', '0.992', '0', '0', '#joy', '#interest', 'positive', '0.881', 'capture', 'charming', 'trance', 'catch', 'becharm']
senticnet['enantioselectivity'] = ['0.695', '-0.85', '0', '0.501', '#joy', '#surprise', 'positive', '0.684', 'alternative_energy', 'energy', 'energy_of_activation', 'atomic_energy', 'nuclear_energy']
senticnet['enask_for'] = ['0.718', '-0.76', '0', '0.943', '#surprise', '#admiration', 'positive', '0.81', 'arouse', 'elicit', 'kindle', 'ask_for', 'evoke']
senticnet['enbenign_tumor'] = ['-0.71', '0', '0.634', '-0.56', '#sadness', '#anger', 'negative', '-0.63', 'fibroma', 'adenoma', 'benign_tumor', 'chondroma', 'myoma']
senticnet['encampment'] = ['0', '0.156', '-0.06', '0.198', '#interest', '#admiration', 'positive', '0.098', 'grassland', 'camping', 'camp_out', 'campsite', 'campground']
senticnet['encapsulation'] = ['-0.95', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.84', 'hold_pencil', 'food_scrap', 'paper_product', 'waste_paper', 'cover']
senticnet['encarsia'] = ['0', '-0.09', '0.123', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'gypsy_moth', 'japanese_beetle', 'cucumber_beetle', 'mosquito', 'earwig']
senticnet['encephalitic'] = ['-0.99', '0', '0.859', '-0.62', '#sadness', '#anger', 'negative', '-0.82', 'lax', 'loose', 'diarrheal', 'diarrhoeal', 'unconstipated']
senticnet['encephalitis_lethargica'] = ['0.998', '-0.54', '0', '0.957', '#joy', '#admiration', 'positive', '0.834', 'contagious_disease', 'contagion', 'swine_influenza', 'swine_flu', 'respiratory_disease']
senticnet['encephalopathy'] = ['-0.73', '0', '0.955', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'visual_agnosia', 'auditory_agnosia', 'brain_disorder', 'agnosia', 'brain_disease']
senticnet['enchain'] = ['-0.77', '0', '0.867', '-0.65', '#sadness', '#anger', 'negative', '-0.76', 'hold', 'prisoner', 'prison', 'tie_down', 'chain_up']
senticnet['enchant'] = ['0.894', '0', '0', '0.924', '#joy', '#admiration', 'positive', '0.909', 'captivate', 'belonging', 'comfortably', 'solace', 'comfortableness']
senticnet['enchanted_castle'] = ['0.704', '-0.80', '0', '0', '#joy', '#surprise', 'positive', '0.755', 'quest', 'stunt', 'illusion', 'journey', 'activity']
senticnet['enchanted_forest'] = ['-0.63', '0', '0.674', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'baneful', 'evil', 'baleful', 'malevolent', 'malign']
senticnet['enchanter'] = ['0.594', '-0.54', '0', '0.518', '#joy', '#surprise', 'positive', '0.554', 'exorcist', 'necromancer', 'magus', 'sorceress', 'witch_doctor']
senticnet['enchanting'] = ['0.601', '0.647', '-0.26', '0.322', '#joy', '#interest', 'positive', '0.326', 'captivated', 'charmed', 'captivate', 'bewitch', 'enthrallment']
senticnet['enchantingly'] = ['0.875', '0', '0', '0.747', '#joy', '#admiration', 'positive', '0.811', 'memorably', 'unforgettably', 'terrifically', 'indescribably', 'hauntingly']
senticnet['enchantment'] = ['0.601', '0.647', '-0.26', '0.322', '#joy', '#interest', 'positive', '0.326', 'captivated', 'charmed', 'captivate', 'bewitch', 'enthrallment']
senticnet['enchantress'] = ['0.954', '0', '0', '0.909', '#joy', '#admiration', 'positive', '0.931', 'irresistibleness', 'resistless', 'irresistible', 'ineluctable', 'resist']
senticnet['enchilada'] = ['0.132', '0.1', '0.063', '0.159', '#joy', '#admiration', 'positive', '0.082', 'chime', 'school_end', 'ring_loudly', 'noise_maker', 'vital']
senticnet['enchondroma'] = ['-0.70', '0', '0.639', '-0.56', '#sadness', '#anger', 'negative', '-0.63', 'fibroma', 'adenoma', 'chondroma', 'benign_tumor', 'myoma']
senticnet['encipher'] = ['-0.52', '0', '0.821', '-0.65', '#anger', '#disgust', 'negative', '-0.66', 'work_out', 'reckon', 'calculate', 'average_out', 'cypher']
senticnet['enciphered_message'] = ['-0.52', '0', '0.821', '-0.65', '#anger', '#disgust', 'negative', '-0.66', 'work_out', 'reckon', 'calculate', 'average_out', 'cypher']
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senticnet['enclose'] = ['0.783', '-0.93', '0', '0.585', '#joy', '#surprise', 'positive', '0.768', 'benight', 'engulf', 'hide', 'tube', 'capsule']
senticnet['enclosed_cabin'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'train_station', 'government_office', 'cable_car', 'sport_stadium', 'terminal']
senticnet['enclosed_cockpit'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'train_station', 'government_office', 'cable_car', 'sport_stadium', 'terminal']
senticnet['enclosed_verandah'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'top_floor', 'front_desk', 'concierge', 'exit', 'elevator']
senticnet['enclothe'] = ['-0.63', '0', '0.694', '-0.99', '#anger', '#disgust', 'negative', '-0.77', 'raiment', 'garb', 'dress', 'clothe', 'overclothe']
senticnet['encode'] = ['0', '0', '-0.1', '0', '#fear', '#fear', 'negative', '-0.1', 'grow_yard', 'heat', 'burn', 'corrode', 'stain_clothes']
senticnet['encoder'] = ['0.033', '0', '0', '0.081', '#joy', '#admiration', 'positive', '0.057', 'door', 'door_lock', 'window_door', 'wall', 'doorway']
senticnet['encoding'] = ['0', '0.976', '0', '0.97', '#interest', '#admiration', 'positive', '0.973', 'encryption', 'use', 'government', 'privacy', 'protect']
senticnet['encomia'] = ['0', '0', '-0.90', '-0.79', '#fear', '#disgust', 'negative', '-0.85', 'encomiastic', 'encomium', 'paean', 'eulogy', 'pean']
senticnet['encomiastic'] = ['0.722', '-0.98', '0', '0.832', '#surprise', '#admiration', 'positive', '0.845', 'laudatory', 'praiseful', 'praising', 'complimentary', 'eulogistic']
senticnet['encomium'] = ['0', '0', '-0.90', '-0.80', '#fear', '#disgust', 'negative', '-0.85', 'encomiastic', 'panegyric', 'paean', 'eulogy', 'pean']
senticnet['encompass'] = ['0.077', '0.219', '-0.07', '0.293', '#interest', '#admiration', 'positive', '0.129', 'include', 'comparative', 'totality', 'cover', 'inclusion']
senticnet['encopresis'] = ['-0.52', '0', '0.839', '-0.86', '#anger', '#disgust', 'negative', '-0.74', 'folie', 'upset', 'psychosomatic_disorder', 'conversion_disorder', 'conversion_reaction']
senticnet['encore'] = ['0.773', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.844', 'favorite', 'crowd', 'acclamation', 'bunch', 'large_crowd']
senticnet['encounter'] = ['0.801', '-0.88', '0', '0', '#joy', '#surprise', 'positive', '0.845', 'meeting', 'meet', 'match', 'bout', 'round']
senticnet['encourage'] = ['0', '0.757', '0', '0.84', '#interest', '#admiration', 'positive', '0.798', 'grateful', 'exuberance', 'enthusiasm', 'thankful', 'joyful']
senticnet['encourage_chicano_latino'] = ['0.052', '0.099', '0', '0.067', '#interest', '#admiration', 'positive', '0.073', 'police_rank', 'head_guy', 'chief', 'leader', 'commander']
senticnet['encourage_innovation'] = ['0', '0.757', '0', '0.84', '#interest', '#admiration', 'positive', '0.798', 'grateful', 'exuberance', 'enthusiasm', 'thankful', 'joyful']
senticnet['encouraged'] = ['0.48', '0.667', '-0.26', '0.245', '#joy', '#interest', 'positive', '0.282', 'bucked_up', 'encouraging', 'encouragingly', 'optimistic', 'optimistically']
senticnet['encouragement'] = ['0.531', '0.658', '-0.26', '0.346', '#joy', '#interest', 'positive', '0.318', 'confident', 'fascinate', 'adoration', 'esteem', 'thankful']
senticnet['encourages_creativity'] = ['0.768', '0.792', '0', '0', '#joy', '#interest', 'positive', '0.78', 'win', 'strategize', 'try_best', 'catch_ball', 'lose_game']
senticnet['encouraging'] = ['0.485', '0.672', '-0.26', '0.255', '#joy', '#interest', 'positive', '0.288', 'bucked_up', 'encouraged', 'encouragingly', 'optimistic', 'optimistically']
senticnet['encouraging_abstinence'] = ['0', '0', '0.973', '-0.78', '#anger', '#disgust', 'negative', '-0.87', 'chastity', 'ignore_conception', 'restrain', 'abortion', 'stop']
senticnet['encouragingly'] = ['0.485', '0.672', '-0.26', '0.255', '#joy', '#interest', 'positive', '0.288', 'bucked_up', 'encouraged', 'encouraging', 'optimistic', 'optimistically']
senticnet['encrease'] = ['0.902', '-0.72', '0', '0.919', '#joy', '#admiration', 'positive', '0.849', 'preciousness', 'costliness', 'expensiveness', 'cost', 'lavishness']
senticnet['encroach'] = ['0', '0', '0.809', '-0.94', '#anger', '#disgust', 'negative', '-0.87', 'advance', 'seize', 'encroachment', 'appropriate', 'due']
senticnet['encroach_upon'] = ['-0.93', '0', '0.77', '-0.66', '#sadness', '#anger', 'negative', '-0.79', 'raid', 'come_in', 'foray_into', 'enter', 'get_into']
senticnet['encroachment'] = ['-0.69', '0', '0.752', '-0.82', '#anger', '#disgust', 'negative', '-0.75', 'usurpation', 'trespass', 'inroad', 'intrusion', 'violation']
senticnet['encroachment_upon'] = ['-0.12', '0.193', '0.597', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'unfriendly', 'unfriendliness', 'disapproval', 'disapprove', 'disapproving']
senticnet['encrust'] = ['0', '0', '0.822', '-0.89', '#anger', '#disgust', 'negative', '-0.85', 'foul', 'draggle', 'smirch', 'contaminate', 'smudge']
senticnet['encrypt'] = ['0.022', '-0.13', '0.153', '-0.31', '#anger', '#disgust', 'negative', '-0.07', 'codeine', 'morphine', 'infection', 'cocaine', 'nuclear_war']
senticnet['encrypted_connection'] = ['-0.79', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.79', 'misgiving', 'qualm', 'scruple', 'insecurity', 'insecure']
senticnet['encrypted_data'] = ['-0.83', '0', '0.665', '-0.51', '#sadness', '#anger', 'negative', '-0.67', 'percipient', 'auditor', 'perceiver', 'observer', 'beholder']
senticnet['encrypted_message'] = ['-0.52', '0', '0.821', '-0.65', '#anger', '#disgust', 'negative', '-0.66', 'work_out', 'reckon', 'calculate', 'average_out', 'cypher']
senticnet['encrypted_password'] = ['0.022', '-0.13', '0.153', '-0.31', '#anger', '#disgust', 'negative', '-0.07', 'codeine', 'morphine', 'infection', 'cocaine', 'nuclear_war']
senticnet['encrypted_traffic'] = ['-0.83', '0', '0.665', '-0.51', '#sadness', '#anger', 'negative', '-0.67', 'percipient', 'auditor', 'perceiver', 'observer', 'beholder']
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senticnet['endogenous_depression'] = ['-0.58', '0', '0.722', '-0.51', '#sadness', '#anger', 'negative', '-0.60', 'dissociative_disorder', 'depersonalization_neurosis', 'depersonalization', 'mental_disorder', 'mental_disturbance']
senticnet['endogenous_opioid'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'gastrointestinal_hormone', 'secretin', 'ghrelin']
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senticnet['endorphin'] = ['0.773', '0.76', '0', '0', '#joy', '#interest', 'positive', '0.766', 'peptide', 'fibrinopeptide', 'polypeptide', 'amide', 'protein']
senticnet['endorphin_rush'] = ['0.958', '-0.72', '0', '0.72', '#joy', '#surprise', 'positive', '0.801', 'pressor', 'hormone', 'endocrine', 'internal_secretion', 'catecholamine']
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senticnet['endorser'] = ['0.814', '-0.50', '0', '0.622', '#joy', '#admiration', 'positive', '0.648', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
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senticnet['enter_place'] = ['-0.73', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.85', 'find_garden', 'walk_down', 'walk', 'walk_through', 'progress']
senticnet['enter_puberty'] = ['0.995', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.918', 'tell_everyone', 'reason', 'clarify', 'forethought', 'opinion']
senticnet['enter_room'] = ['0', '0.075', '0', '0.199', '#interest', '#admiration', 'positive', '0.137', 'separate_room', 'hang_door', 'go_build', 'go_through', 'control_access']
senticnet['enter_store'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'pay_cashier', 'go_town', 'live_simply', 'complete_job', 'count_money']
senticnet['enter_theater'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'show_movie', 'watch_movie', 'watch_play', 'see_film', 'record_movie']
senticnet['enter_torpor'] = ['0.967', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.91', 'furry_creature', 'hairy', 'furry', 'woodland_creature', 'animal_furry']
senticnet['enterally'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
senticnet['enteric_fever'] = ['-0.69', '0', '0.852', '-0.69', '#sadness', '#anger', 'negative', '-0.74', 'conjunctivitis', 'ophthalmitis', 'rubor', 'redness', 'inflammation']
senticnet['entering'] = ['-0.50', '0', '0.975', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'entrance', 'irruption', 'entry', 'ingress', 'incoming']
senticnet['entering_freshman'] = ['0.087', '0.248', '-0.19', '0.31', '#interest', '#admiration', 'positive', '0.113', 'enroll', 'enroll_school', 'enroll_class', 'pay_tuition', 'start_college']
senticnet['entering_freshmen'] = ['0.087', '0.248', '-0.19', '0.31', '#interest', '#admiration', 'positive', '0.113', 'enroll', 'enroll_school', 'enroll_class', 'pay_tuition', 'start_college']
senticnet['entering_puberty'] = ['0.995', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.918', 'tell_everyone', 'reason', 'clarify', 'forethought', 'opinion']
senticnet['enterobacter'] = ['-0.60', '0', '0.721', '-0.57', '#sadness', '#anger', 'negative', '-0.63', 'tubercle_bacillus', 'leprosy_bacillus', 'eubacteria', 'eubacterium', 'true_bacteria']
senticnet['enterobacteriaceae'] = ['-0.60', '0', '0.721', '-0.57', '#sadness', '#anger', 'negative', '-0.63', 'tubercle_bacillus', 'leprosy_bacillus', 'eubacteria', 'eubacterium', 'true_bacteria']
senticnet['enterococcus_faecium'] = ['-0.60', '0', '0.721', '-0.57', '#sadness', '#anger', 'negative', '-0.63', 'tubercle_bacillus', 'leprosy_bacillus', 'eubacteria', 'eubacterium', 'true_bacteria']
senticnet['enterocyte'] = ['0.992', '-0.80', '0', '0.584', '#joy', '#surprise', 'positive', '0.795', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'incontinence']
senticnet['enterohepatic_circulation'] = ['0.992', '-0.80', '0', '0.584', '#joy', '#surprise', 'positive', '0.795', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'incontinence']
senticnet['enteron'] = ['-0.82', '0', '0.67', '-0.84', '#sadness', '#disgust', 'negative', '-0.78', 'alimentary_canal', 'alimentary_tract', 'digestive_tube', 'digestive_tract', 'gastrointestinal_tract']
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senticnet['enterostenosis'] = ['-0.81', '0', '0.684', '-0.81', '#sadness', '#disgust', 'negative', '-0.77', 'rhinostenosis', 'stenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
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senticnet['enterprise'] = ['0.059', '0.212', '0', '0.143', '#interest', '#admiration', 'positive', '0.138', 'business', 'corporation', 'money_maker', 'company', 'business_organization']
senticnet['enterprise_wide'] = ['0.781', '-0.79', '0', '0.816', '#surprise', '#admiration', 'positive', '0.798', 'deliver_goods', 'succeed', 'win', 'bring_home_bacon', 'come_through']
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senticnet['enterprising_spirit'] = ['0.837', '0', '0', '0', '#joy', '#joy', 'positive', '0.837', 'a_lot_of_sex', 'desirable', 'give_birth_child', 'sexual_intercourse', 'diversity']
senticnet['enterprisingness'] = ['0.886', '0.854', '0', '0', '#joy', '#interest', 'positive', '0.87', 'maiden', 'opening', 'opening_move', 'initiative', 'inaugural']
senticnet['entertain'] = ['0.875', '0', '0', '0.761', '#joy', '#admiration', 'positive', '0.818', 'mindless_entertainment', 'fun', 'see_actor', 'enjoy', 'relieve_boredom']
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senticnet['entertain_child'] = ['0.738', '0.961', '0', '0', '#joy', '#interest', 'positive', '0.849', 'amuse_child', 'keep_child_occupy', 'alleviate_boredom', 'play_fun', 'entertain_kid']
senticnet['entertain_company'] = ['0.196', '0.077', '0', '0.064', '#joy', '#interest', 'positive', '0.112', 'company', 'give_pleasure', 'use_talent', 'performance', 'entertainment']
senticnet['entertain_crowd'] = ['0.834', '0.789', '0', '0', '#joy', '#interest', 'positive', '0.811', 'ear_money', 'performance', 'give_pleasure', 'entertainment', 'use_talent']
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senticnet['entertain_kid'] = ['0.738', '0.961', '0', '0', '#joy', '#interest', 'positive', '0.849', 'amuse_child', 'keep_child_occupy', 'alleviate_boredom', 'play_fun', 'entertain_child']
senticnet['entertain_others'] = ['0.971', '0', '0', '0', '#joy', '#joy', 'positive', '0.971', 'gain_skill', 'listen', 'tell_joke', 'sound_good', 'appreciate_music']
senticnet['entertain_person'] = ['0', '0', '0', '0.852', '#admiration', '#admiration', 'positive', '0.852', 'crack_joke', 'enjoy_together', 'enjoy_company_friend', 'make_love_spouse', 'laugh_joke']
senticnet['entertainer'] = ['0.54', '-0.64', '0', '0.566', '#surprise', '#admiration', 'positive', '0.582', 'pleaser', 'bombshell', 'mortal', 'busker', 'drawing_card']
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senticnet['entertainment'] = ['0.951', '0', '0', '0.731', '#joy', '#admiration', 'positive', '0.841', 'pastime', 'enjoyment', 'pleasure', 'enjoy', 'fun']
senticnet['entertainment_education'] = ['0.797', '0.928', '0', '0', '#joy', '#interest', 'positive', '0.863', 'cultural_experience', 'acculturation', 'zone_out', 'cultural_change', 'entertainment_learn']
senticnet['entertainment_industry'] = ['0.951', '0', '0', '0.731', '#joy', '#admiration', 'positive', '0.841', 'pastime', 'enjoyment', 'pleasure', 'enjoy', 'fun']
senticnet['entertainment_learn'] = ['0.945', '0.996', '0', '0.737', '#joy', '#interest', 'positive', '0.893', 'escape_reality', 'acculturation', 'enjoy_story', 'stimulate_mind', 'cultural_experience']
senticnet['entertainment_weekly'] = ['0.761', '0.566', '-0.21', '0.293', '#joy', '#interest', 'positive', '0.352', 'gratify', 'jubilation', 'gloat', 'gloating', 'gloatingly']
senticnet['entertainment_weekly_writer'] = ['-0.83', '0', '0.912', '-0.67', '#sadness', '#anger', 'negative', '-0.80', 'dissatisfaction', 'displeasure', 'disappointment', 'boredom', 'ennui']
senticnet['enthalpy'] = ['-0.98', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.87', 'become_fat', 'flatulence', 'feel_rejuvenated', 'tan_skin', 'make_angry']
senticnet['enthalpy_change'] = ['0.695', '-0.85', '0', '0.501', '#joy', '#surprise', 'positive', '0.684', 'alternative_energy', 'energy', 'energy_of_activation', 'atomic_energy', 'nuclear_energy']
senticnet['entheogen'] = ['-0.73', '0', '0.944', '-0.94', '#anger', '#disgust', 'negative', '-0.87', 'lysergic_acid_diethylamide', 'mescaline', 'peyote', 'phencyclidine', 'drug']
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senticnet['enthral'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'captivated', 'charmed', 'bewitch', 'enthrallment', 'enchantment']
senticnet['enthrall'] = ['0.745', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.859', 'mesmerize', 'transfixed', 'mesmerized', 'planer', 'mesmerism']
senticnet['enthralling'] = ['0.721', '0.747', '0', '0', '#joy', '#interest', 'positive', '0.734', 'captivated', 'charmed', 'captivate', 'bewitch', 'enthrallment']
senticnet['enthrallment'] = ['0.601', '0.647', '-0.26', '0.322', '#joy', '#interest', 'positive', '0.326', 'captivated', 'charmed', 'captivate', 'bewitch', 'beguile']
senticnet['enthronement_ceremony'] = ['0.82', '0', '0', '0.885', '#joy', '#admiration', 'positive', '0.853', 'religious_event', 'sunday', 'happy_event', 'tradition', 'wedding']
senticnet['enthuse'] = ['0', '0', '0.773', '-0.78', '#anger', '#disgust', 'negative', '-0.77', 'enthrall', 'motivate', 'infuriate', 'excite', 'titillate']
senticnet['enthusiam'] = ['0', '0', '0', '0.869', '#admiration', '#admiration', 'positive', '0.869', 'prosper', 'succeed', 'realize', 'fly_high', 'thrive']
senticnet['enthusiasm'] = ['0.887', '0', '0', '0', '#joy', '#joy', 'positive', '0.887', 'thankful', 'openhearted', 'good-hearted', 'kindly', 'benevolent']
senticnet['enthusiast'] = ['0.935', '-0.72', '0', '0.747', '#joy', '#admiration', 'positive', '0.801', 'friend', 'sports_fan', 'rooter', 'fan', 'nut']
senticnet['enthusiastic'] = ['0.481', '0.659', '-0.24', '0.236', '#joy', '#interest', 'positive', '0.282', 'exciting', 'enthusiastically', 'unfearing', 'intrepid', 'dauntlessly']
senticnet['enthusiastic_amateur'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'sciolist', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
senticnet['enthusiastic_crowd'] = ['-0.71', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.84', 'traffic_jam', 'accumulate_stress', 'stress', 'accumulate_stress_city', 'bad_service']
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senticnet['entice'] = ['0.731', '0.754', '0', '0', '#joy', '#interest', 'positive', '0.742', 'investment_return', 'reinforce_concrete', 'fish_lake', 'land_fish', 'catch_fish']
senticnet['enticement'] = ['0.854', '-0.95', '0', '0.554', '#joy', '#surprise', 'positive', '0.788', 'forbidden_fruit', 'allurement', 'hook', 'sweetener', 'influence']
senticnet['enticing'] = ['0.969', '-0.84', '0', '0.837', '#joy', '#surprise', 'positive', '0.885', 'alluring', 'seductive', 'tempting', 'beguiling', 'corrupting']
senticnet['entire'] = ['0.956', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.935', 'aggregate', 'total', 'whole', 'complete', 'integral']
senticnet['entire_continent'] = ['0.838', '0.707', '0', '0.904', '#joy', '#admiration', 'positive', '0.816', 'natural_order', 'universe', 'existence', 'creation', 'cosmos']
senticnet['entire_edifice'] = ['0.022', '-0.13', '0', '0', '#joy', '#surprise', 'positive', '0.077', 'amount_nothing', 'feel_heat', 'fall_off_chair', 'pulse', 'blow_up_balloon']
senticnet['entire_hemisphere'] = ['0', '0', '0', '0.119', '#admiration', '#admiration', 'positive', '0.119', 'planet', 'earth', 'spherical', 'planet_solar_system', 'terrain']
senticnet['entire_length'] = ['0.956', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.935', 'aggregate', 'total', 'whole', 'complete', 'integral']
senticnet['entire_spectrum'] = ['0', '0.098', '-0.06', '0.118', '#interest', '#admiration', 'positive', '0.051', 'egret', 'tall_man', 'wide', 'wood_structure', 'large']
senticnet['entirely'] = ['-0.77', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.75', 'all', 'whole', 'only', 'exclusively', 'wholly']
senticnet['entirely_believable'] = ['0.744', '-0.76', '0', '0.669', '#joy', '#surprise', 'positive', '0.726', 'accessible', 'comprehendible', 'approachable', 'perceivable', 'intelligible']
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senticnet['entirely_innocent'] = ['-0.70', '0', '0.874', '-0.75', '#anger', '#disgust', 'negative', '-0.77', 'blame', 'accusation', 'inculpation', 'complaint', 'imprecation']
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senticnet['entirely_voluntary'] = ['-0.02', '0.1', '-0.07', '0.089', '#interest', '#admiration', 'positive', '0.023', 'functionary', 'employment', 'labor', 'employ', 'toil']
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senticnet['entirety'] = ['0.595', '-0.61', '0', '0.826', '#surprise', '#admiration', 'positive', '0.679', 'completeness', 'work', 'whole_shebang', 'absolutely', 'wholly']
senticnet['entitled'] = ['0.861', '-0.52', '0', '0.52', '#joy', '#admiration', 'positive', '0.636', 'worthy', 'desirable', 'suitable', 'eligible', 'bailable']
senticnet['entitlement'] = ['0.924', '0.968', '0', '0.876', '#joy', '#interest', 'positive', '0.923', 'live_simply', 'help_out', 'money', 'accolade', 'generous']
senticnet['entity'] = ['0', '0.093', '0', '0.205', '#interest', '#admiration', 'positive', '0.149', 'form', 'body_type', 'figure', 'hexagon', 'shape']
senticnet['entomology'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'study_person', 'subject_study', 'zoo', 'zoologist', 'geology']
senticnet['entorhinal'] = ['0.998', '-0.63', '0', '0.574', '#joy', '#surprise', 'positive', '0.736', 'cleavable', 'divisible', 'separable', 'severable', 'dissociative']
senticnet['entourage'] = ['0', '0', '-0.04', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'good_band', 'band', 'kit', 'procession', 'oblivion']
senticnet['entr_acte'] = ['0', '0.904', '0', '0', '#interest', '#interest', 'positive', '0.904', 'act', 'part_of_play', 'part_of_movie', 'emote', 'role']
senticnet['entrance'] = ['0.191', '0.227', '-0.09', '0.195', '#interest', '#admiration', 'positive', '0.129', 'entranceway', 'doorway', 'corridor', 'use_farm', 'door']
senticnet['entrance_arch'] = ['0.029', '0.087', '0.065', '0.137', '#interest', '#admiration', 'positive', '0.047', 'archway', 'aisle', 'mat', 'passageway', 'right_angle']
senticnet['entrance_door'] = ['0', '0', '0', '0.077', '#admiration', '#admiration', 'positive', '0.077', 'glass_front', 'gate', 'doorway', 'use_farm', 'entranceway']
senticnet['entrance_examination'] = ['0.087', '0.248', '-0.19', '0.31', '#interest', '#admiration', 'positive', '0.113', 'enroll', 'enroll_school', 'enroll_class', 'pay_tuition', 'start_college']
senticnet['entrance_foyer'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'top_floor', 'front_desk', 'concierge', 'exit', 'elevator']
senticnet['entrance_gate'] = ['0.028', '0', '0', '0.051', '#joy', '#admiration', 'positive', '0.04', 'entranceway', 'doorway', 'entrance', 'entryway', 'corridor']
senticnet['entrance_hall'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'top_floor', 'front_desk', 'concierge', 'exit', 'elevator']
senticnet['entrance_lobby'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'top_floor', 'front_desk', 'concierge', 'exit', 'elevator']
senticnet['entrance_porch'] = ['0.028', '0', '0', '0.051', '#joy', '#admiration', 'positive', '0.04', 'entranceway', 'doorway', 'entrance', 'entryway', 'corridor']
senticnet['entrance_room'] = ['-0.72', '0', '0.803', '0', '#sadness', '#anger', 'negative', '-0.76', 'connect_room', 'home_entrance', 'entry', 'house_front', 'wood_panel']
senticnet['entrance_vestibule'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'top_floor', 'front_desk', 'concierge', 'exit', 'elevator']
senticnet['entranceway'] = ['0.028', '0', '0', '0.075', '#joy', '#admiration', 'positive', '0.051', 'doorway', 'door', 'entryway', 'gate', 'corridor']
senticnet['entrancing'] = ['0.601', '0.647', '-0.26', '0.322', '#joy', '#interest', 'positive', '0.326', 'captivated', 'charmed', 'captivate', 'bewitch', 'enthrallment']
senticnet['entraneur'] = ['0.911', '-0.94', '0', '0.953', '#surprise', '#admiration', 'positive', '0.935', 'common_sense', 'good_sense', 'horse_sense', 'mother_wit', 'gumption']
senticnet['entrant'] = ['0', '0', '-0.75', '-0.98', '#fear', '#disgust', 'negative', '-0.86', 'freshman', 'newcomer', 'starter', 'fledgeling', 'neophyte']
senticnet['entrap'] = ['-0.99', '0', '0.933', '0', '#sadness', '#anger', 'negative', '-0.96', 'trap', 'insidious', 'mantrap', 'morass', 'ensnare']
senticnet['entreat'] = ['0', '-0.77', '0', '0.987', '#surprise', '#admiration', 'positive', '0.879', 'desired', 'solicitation', 'treat', 'urgency', 'implore']
senticnet['entreaty'] = ['0.68', '-0.96', '0', '0.597', '#joy', '#surprise', 'positive', '0.746', 'flag_waving', 'jingoism', 'appeal', 'prayer', 'demagoguery']
senticnet['entred_into'] = ['-0.63', '0', '0.581', '-0.84', '#sadness', '#disgust', 'negative', '-0.68', 'malignment', 'vilification', 'smear', 'character_assassination', 'libel']
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senticnet['epic_scale'] = ['0.089', '-0.01', '0.033', '0.082', '#joy', '#admiration', 'positive', '0.039', 'informative', 'informational', 'ephemeral', 'fun_read', 'ads']
senticnet['epic_struggle'] = ['0.089', '-0.01', '0.033', '0.082', '#joy', '#admiration', 'positive', '0.039', 'informative', 'informational', 'ephemeral', 'fun_read', 'ads']
senticnet['epic_sweep'] = ['0.089', '-0.01', '0.033', '0.082', '#joy', '#admiration', 'positive', '0.039', 'informative', 'informational', 'ephemeral', 'fun_read', 'ads']
senticnet['epic_tale'] = ['0.089', '-0.01', '0.033', '0.082', '#joy', '#admiration', 'positive', '0.039', 'informative', 'informational', 'ephemeral', 'fun_read', 'ads']
senticnet['epic_trilogy'] = ['0.089', '-0.01', '0.033', '0.082', '#joy', '#admiration', 'positive', '0.039', 'informative', 'informational', 'ephemeral', 'fun_read', 'ads']
senticnet['epic_voyage'] = ['0.937', '0.8', '0', '0', '#joy', '#interest', 'positive', '0.869', 'quest', 'stunt', 'illusion', 'adventure', 'activity']
senticnet['epically'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['epicarp'] = ['0', '0.141', '0.01', '0.115', '#interest', '#admiration', 'positive', '0.082', 'sepal', 'achene', 'funiculus', 'seed', 'pip']
senticnet['epicatechin'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'capsaicin', 'chemical_irritant', 'piperine', 'pepper_spray']
senticnet['epicene'] = ['0.806', '-0.70', '0', '0.687', '#joy', '#surprise', 'positive', '0.734', 'saphead', 'muggins', 'tomfool', 'fool', 'sap']
senticnet['epicondylitis'] = ['-0.98', '0', '0.678', '-0.95', '#sadness', '#disgust', 'negative', '-0.87', 'rubor', 'redness', 'inflammation', 'tennis_elbow', 'lateral_epicondylitis']
senticnet['epicure'] = ['0.897', '-0.96', '0', '0.785', '#joy', '#surprise', 'positive', '0.881', 'erotic', 'voluptuary', 'sensualist', 'gourmet', 'gastronome']
senticnet['epicureanism'] = ['0.971', '-0.71', '0', '0.656', '#joy', '#surprise', 'positive', '0.781', 'wellbeing', 'human_flourishing', 'successfulness', 'upbeat', 'strength']
senticnet['epicuticle'] = ['0', '0.097', '0', '0.085', '#interest', '#admiration', 'positive', '0.091', 'lunula', 'arm_part', 'leave_hand', 'ten_fingers', 'five_fingers']
senticnet['epidemic'] = ['-0.37', '0', '-0.34', '-0.15', '#sadness', '#fear', 'negative', '-0.29', 'malady', 'ailment', 'polio', 'emergency', 'illiteracy']
senticnet['epidemic_disease'] = ['-0.11', '0', '-0.15', '-0.24', '#fear', '#disgust', 'negative', '-0.17', 'disease', 'infectious_disease', 'polio', 'malady', 'sickness']
senticnet['epidemic_encephalitis'] = ['-0.75', '0', '0.69', '-0.69', '#sadness', '#disgust', 'negative', '-0.71', 'rubor', 'redness', 'sleeping_sickness', 'sleepy_sickness', 'cephalitis']
senticnet['epidemic_hysertia'] = ['-0.97', '0', '0.777', '-0.53', '#sadness', '#anger', 'negative', '-0.76', 'manic_disorder', 'mania', 'nympholepsy', 'mass_hysteria', 'delirium']
senticnet['epidemic_meningitis'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'leptomeningitis', 'cerebrospinal_meningitis', 'meningitis', 'brain_fever', 'cerebrospinal_fever']
senticnet['epidemical'] = ['-0.74', '0', '0.622', '-0.86', '#sadness', '#disgust', 'negative', '-0.74', 'tribulate', 'upset', 'grieve', 'disconcert', 'aggrieve']
senticnet['epidemiologic_evidence'] = ['-0.64', '0', '0.576', '-0.83', '#sadness', '#disgust', 'negative', '-0.68', 'nisi', 'indeterminate', 'equivocal', 'ambiguous', 'indecisive']
senticnet['epidemiologist'] = ['0.904', '-0.93', '0', '0.639', '#joy', '#surprise', 'positive', '0.824', 'toxicologist', 'medical_scientist', 'immunologist', 'serologist', 'scientist']
senticnet['epidemy'] = ['0.579', '-0.81', '0', '0.833', '#surprise', '#admiration', 'positive', '0.741', 'murine_typhus', 'rat_typhus', 'urban_typhus', 'endemic_typhus', 'rickettsial_disease']
senticnet['epidermal'] = ['0', '0.097', '0', '0.085', '#interest', '#admiration', 'positive', '0.091', 'lunula', 'arm_part', 'leave_hand', 'ten_fingers', 'five_fingers']
senticnet['epidermal_layer'] = ['0', '0.097', '0', '0.085', '#interest', '#admiration', 'positive', '0.091', 'lunula', 'arm_part', 'leave_hand', 'ten_fingers', 'five_fingers']
senticnet['epidermal_tissue'] = ['0', '0.097', '0', '0.085', '#interest', '#admiration', 'positive', '0.091', 'lunula', 'arm_part', 'leave_hand', 'ten_fingers', 'five_fingers']
senticnet['epidermolysis_bullosa_eb'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'dystrophy', 'myotonic_dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['epidermophyton_floccosum'] = ['-0.57', '0', '0.873', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'fungal_infection', 'mycosis', 'kerion', 'tinea_pedis', 'athlete_foot']
senticnet['epidural_anesthesia'] = ['-0.65', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'put_to_sleep', 'operation', 'anesthesia', 'surgery', 'local_anesthesia']
senticnet['epidural_blood'] = ['-0.58', '0', '0.561', '-0.62', '#sadness', '#disgust', 'negative', '-0.58', 'spot', 'speckle', 'dapple', 'mark', 'fleck']
senticnet['epidural_catheter'] = ['-0.65', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'put_to_sleep', 'operation', 'general_anesthesia', 'surgery', 'anesthesia']
senticnet['epidural_hematoma'] = ['-0.12', '-0.04', '0', '-0.17', '#sadness', '#disgust', 'negative', '-0.08', 'cause_bleed', 'open_wind', 'gash', 'lacerate', 'painful_injury']
senticnet['epigastric'] = ['0.777', '-0.89', '0', '0.508', '#joy', '#surprise', 'positive', '0.727', 'stretching', 'enlarge', 'dilatation', 'get_bigger', 'enlargement']
senticnet['epigastric_pain'] = ['-0.58', '0', '0.773', '-0.79', '#anger', '#disgust', 'negative', '-0.71', 'perspiration', 'sweating', 'polyhidrosis', 'sudation', 'hidrosis']
senticnet['epigastrium'] = ['0.777', '-0.89', '0', '0.508', '#joy', '#surprise', 'positive', '0.727', 'stretching', 'enlarge', 'dilatation', 'get_bigger', 'enlargement']
senticnet['epiglottitis'] = ['-0.99', '0', '0.964', '-0.70', '#sadness', '#anger', 'negative', '-0.88', 'blepharitis', 'diverticulitis', 'inflammation', 'orchitis', 'prostatitis']
senticnet['epigon'] = ['-0.62', '0', '0.667', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'copycat', 'emulator', 'aper', 'imitator', 'ape']
senticnet['epigonation'] = ['0.849', '-0.50', '0', '0.845', '#joy', '#admiration', 'positive', '0.732', 'robe', 'gown', 'veil', 'vest', 'cassock']
senticnet['epigone'] = ['-0.86', '0', '0.991', '-0.67', '#sadness', '#anger', 'negative', '-0.84', 'copycat', 'emulator', 'aper', 'imitator', 'ape']
senticnet['epigonr'] = ['-0.63', '0', '0.663', '-0.86', '#anger', '#disgust', 'negative', '-0.72', 'copycat', 'emulator', 'ape', 'imitator', 'epigon']
senticnet['epigram'] = ['0', '-0.89', '0', '0.917', '#surprise', '#admiration', 'positive', '0.904', 'pith', 'sententious', 'concise', 'pithy', 'pithily']
senticnet['epigrammatic'] = ['0.99', '0', '0', '0.716', '#joy', '#admiration', 'positive', '0.853', 'encyclopedism', 'learning', 'scholarship', 'learnedness', 'eruditeness']
senticnet['epigrammatic_style'] = ['0.947', '-0.99', '0', '0.936', '#joy', '#surprise', 'positive', '0.958', 'utterer', 'deipnosophist', 'interlocutor', 'conversational_partner', 'speaker']
senticnet['epigrammatist'] = ['-0.81', '0', '0.52', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'affray', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['epigraph'] = ['0.075', '-0.01', '0.019', '0.067', '#joy', '#admiration', 'positive', '0.034', 'rhyme', 'paragraph', 'song_lyric', 'lyrics', 'stanza']
senticnet['epigraphic'] = ['-0.69', '0', '0.957', '-0.92', '#anger', '#disgust', 'negative', '-0.85', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['epikleroi'] = ['0.8', '-0.79', '0', '0.751', '#joy', '#surprise', 'positive', '0.782', 'elder_statesman', 'stateswoman', 'politician', 'politico', 'pol']
senticnet['epilator'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'smart_technology', 'electric_shaver', 'hair_dryer', 'stay_healthy', 'wash_tooth']
senticnet['epilepsy'] = ['-0.52', '0', '0.785', '-0.61', '#anger', '#disgust', 'negative', '-0.64', 'disease', 'feel_sick', 'brain_disease', 'eye_failure', 'brain_failure']
senticnet['epilepsy_attack'] = ['-0.97', '0', '0.332', '-0.99', '#sadness', '#disgust', 'negative', '-0.76', 'have_epilepsy', 'crowd', 'humanity', 'epilepsy', 'sickness']
senticnet['epileptic'] = ['-0.99', '0', '0.774', '0', '#sadness', '#anger', 'negative', '-0.88', 'seizure', 'epilepsy', 'convulsion', 'psychotic', 'schizophrenic']
senticnet['epileptic_fit'] = ['-0.24', '0', '-0.04', '-0.12', '#sadness', '#disgust', 'negative', '-0.13', 'sickness', 'disease', 'heart_attack', 'convulsion', 'tremor']
senticnet['epileptic_seizure'] = ['-0.97', '0', '0.332', '-0.99', '#sadness', '#disgust', 'negative', '-0.76', 'feel_out_of_breath', 'muscle_cramp', 'neuronal_activity', 'abnormal', 'breath_difficulty']
senticnet['epilobium'] = ['-0.56', '0', '0.892', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'borecole', 'colewort', 'kail', 'cole', 'kale']
senticnet['epimanikia'] = ['0.849', '-0.50', '0', '0.845', '#joy', '#admiration', 'positive', '0.732', 'robe', 'gown', 'veil', 'vest', 'cassock']
senticnet['epimerase'] = ['-0.52', '0', '0.681', '-0.76', '#anger', '#disgust', 'negative', '-0.65', 'triose', 'tetrose', 'pentose', 'simple_sugar', 'aldose']
senticnet['epinephrin'] = ['0.783', '-0.69', '0', '0.827', '#joy', '#admiration', 'positive', '0.768', 'pressor', 'hormone', 'endocrine', 'internal_secretion', 'catecholamine']
senticnet['epinephrine'] = ['0.934', '-0.63', '0', '0.815', '#joy', '#admiration', 'positive', '0.794', 'pressor', 'hormone', 'endocrine', 'internal_secretion', 'catecholamine']
senticnet['epinephrine_adrenaline'] = ['0.958', '-0.72', '0', '0.72', '#joy', '#surprise', 'positive', '0.801', 'pressor', 'hormone', 'endocrine', 'internal_secretion', 'catecholamine']
senticnet['epineurium'] = ['-0.53', '0', '0.982', '-0.67', '#anger', '#disgust', 'negative', '-0.73', 'meniscus', 'semilunar_cartilage', 'gristle', 'hyaline_cartilage', 'arytenoid']
senticnet['epipetric'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['epiphanic'] = ['-0.94', '0', '0.925', '-0.97', '#sadness', '#disgust', 'negative', '-0.95', 'nonexistence', 'nonentity', 'cloud', 'falseness', 'falsity']
senticnet['epiphany'] = ['0.963', '0.606', '0', '0.955', '#joy', '#admiration', 'positive', '0.841', 'revelation', 'think_past', 'resolve_problem', 'arrive_conclusion', 'lifestyle_change']
senticnet['epiphenomenal'] = ['-0.55', '0', '0.58', '-0.98', '#anger', '#disgust', 'negative', '-0.70', 'fatalist', 'determinist', 'predestinarian', 'predestinationist', 'philosopher']
senticnet['epiphenomenalism'] = ['-0.87', '0', '0.858', '-0.77', '#sadness', '#anger', 'negative', '-0.83', 'pass_off', 'fall_out', 'occur', 'happen', 'hap']
senticnet['epiphenomenon'] = ['-0.58', '0', '0.535', '-0.72', '#sadness', '#disgust', 'negative', '-0.61', 'byproduct', 'effect', 'result', 'consequence', 'outcome']
senticnet['epiphone'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'lever']
senticnet['epiphyseal_closure'] = ['0.995', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.918', 'tell_everyone', 'reason', 'clarify', 'forethought', 'opinion']
senticnet['epiphytic'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['epiretinal_membrane'] = ['-0.86', '0', '0.858', '-0.91', '#sadness', '#disgust', 'negative', '-0.87', 'edema', 'oedema', 'hydrops', 'dropsy', 'eye_disease']
senticnet['episcopal_church'] = ['0.081', '0.146', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'cult', 'sect', 'conservative', 'worshiper', 'pastor']
senticnet['episcopal_congregation'] = ['0.081', '0.146', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'cult', 'sect', 'conservative', 'worshiper', 'pastor']
senticnet['episcopal_parish'] = ['0.081', '0.146', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'cult', 'sect', 'conservative', 'worshiper', 'pastor']
senticnet['episcopalianism'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'nonconformity', 'iconoclasm', 'heterodoxy', 'nonconformance', 'orientation']
senticnet['episode'] = ['0.918', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.827', 'encounter', 'reveal', 'programme', 'lose', 'sequel']
senticnet['episode_story_arc'] = ['0.918', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.827', 'encounter', 'reveal', 'programme', 'lose', 'sequel']
senticnet['episodic_ataxia'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'myotonic_muscular_dystrophy', 'dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['episodical'] = ['-0.69', '0', '0.957', '-0.92', '#anger', '#disgust', 'negative', '-0.85', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['epist'] = ['0.055', '0.189', '-0.09', '0.113', '#interest', '#admiration', 'positive', '0.065', 'tame', 'crack', 'sense_fear', 'friend_person', 'good_manner']
senticnet['epistemic'] = ['0.863', '-0.94', '0', '0.986', '#surprise', '#admiration', 'positive', '0.932', 'postulate', 'posit', 'assume', 'presume', 'take_for_granted']
senticnet['epistemological'] = ['0.863', '-0.94', '0', '0.986', '#surprise', '#admiration', 'positive', '0.932', 'postulate', 'posit', 'assume', 'presume', 'take_for_granted']
senticnet['epistemological_theory'] = ['-0.55', '0', '0.58', '-0.98', '#anger', '#disgust', 'negative', '-0.70', 'fatalist', 'determinist', 'predestinarian', 'predestinationist', 'philosopher']
senticnet['epistemologically'] = ['0.863', '-0.94', '0', '0.986', '#surprise', '#admiration', 'positive', '0.932', 'postulate', 'posit', 'assume', 'presume', 'take_for_granted']
senticnet['epistemology'] = ['0', '0.88', '0', '0.983', '#interest', '#admiration', 'positive', '0.931', 'scientific_method', 'methodology', 'methodological_analysis', 'method', 'chemistry']
senticnet['epistle'] = ['0.801', '0.879', '0', '0', '#joy', '#interest', 'positive', '0.84', 'gospel', 'apocrypha', 'gnostic', 'non-canonical', 'apocryphal']
senticnet['epistle_dedicatory'] = ['0.722', '-0.98', '0', '0.832', '#surprise', '#admiration', 'positive', '0.845', 'laudatory', 'praiseful', 'praising', 'encomiastic', 'complimentary']
senticnet['epistolary_style'] = ['0.947', '-0.99', '0', '0.936', '#joy', '#surprise', 'positive', '0.958', 'utterer', 'deipnosophist', 'interlocutor', 'conversational_partner', 'speaker']
senticnet['epitaph'] = ['0.787', '0.905', '0', '0', '#joy', '#interest', 'positive', '0.846', 'poetry', 'ancient_rome', 'memorial', 'every_town', 'poet']
senticnet['epitaxial_film'] = ['-0.54', '0', '0.909', '-0.97', '#anger', '#disgust', 'negative', '-0.81', 'suppleness', 'pliancy', 'pliantness', 'flexibility', 'flexibleness']
senticnet['epithalamium'] = ['0', '0', '-0.90', '-0.79', '#fear', '#disgust', 'negative', '-0.85', 'encomiastic', 'encomium', 'paean', 'eulogy', 'pean']
senticnet['epithelial_layer'] = ['0', '0.097', '0', '0.085', '#interest', '#admiration', 'positive', '0.091', 'lunula', 'arm_part', 'leave_hand', 'ten_fingers', 'five_fingers']
senticnet['epithelial_proliferation'] = ['0.905', '-0.65', '0', '0.851', '#joy', '#admiration', 'positive', '0.805', 'gonococcus', 'bacteria', 'bacteriae', 'probiotic', 'probiotic_bacterium']
senticnet['epithelioid'] = ['-0.88', '0', '0.774', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fibroid_tumor', 'fibroid', 'benign_tumor', 'nonmalignant_tumor', 'nonmalignant_neoplasm']
senticnet['epithelioma'] = ['-0.88', '0', '0.774', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fibroid_tumor', 'fibroid', 'benign_tumor', 'nonmalignant_tumor', 'nonmalignant_neoplasm']
senticnet['epithelium'] = ['0', '0', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.05', 'jaw', 'mouth', 'dimple', 'nose', 'cheek']
senticnet['epithet'] = ['-0.99', '0', '0.707', '0', '#sadness', '#anger', 'negative', '-0.84', 'billingsgate', 'contumely', 'abusive', 'fishwife', 'vitriol']
senticnet['epitome'] = ['0.849', '0', '0', '0.987', '#joy', '#admiration', 'positive', '0.918', 'quintessentially', 'quintessential', 'iconic', 'epitomizes', 'timeless']
senticnet['epitomise'] = ['-0.87', '0', '0.83', '-0.61', '#sadness', '#anger', 'negative', '-0.77', 'out', 'dated', 'stodgy', 'prehistoric', 'antique']
senticnet['epitomizes'] = ['0.854', '0', '0', '0.988', '#joy', '#admiration', 'positive', '0.921', 'quintessentially', 'epitome', 'iconic', 'quintessential', 'timeless']
senticnet['epitrachelion'] = ['0.849', '-0.50', '0', '0.845', '#joy', '#admiration', 'positive', '0.732', 'robe', 'gown', 'veil', 'vest', 'cassock']
senticnet['epivir'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'indinavir', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['epizootic'] = ['-0.71', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.72', 'infectious', 'communicable', 'epidemic', 'pandemic', 'contagious']
senticnet['epoch'] = ['0', '0.104', '0', '0.124', '#interest', '#admiration', 'positive', '0.114', 'good_trait', 'constant', 'time_increment', 'time_designation', 'time_length']
senticnet['epochal'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'absolutism', 'autocracy', 'ascendancy', 'tyrant']
senticnet['eponymous'] = ['-0.96', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.88', 'protagonist', 'villainous', 'nominal', 'titular', 'enigmatic']
senticnet['eponymous_ancestor'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
senticnet['eponymous_hero'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'correspondent', 'humankind']
senticnet['eponymous_restaurant'] = ['0.06', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'eat_food', 'fast_food', 'pizza_parlor', 'pizza_store', 'kitchen_cupboard']
senticnet['epoxy'] = ['-0.82', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.89', 'resin', 'glass', 'acrylic', 'canvas', 'acrylic']
senticnet['eprme'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'white_animal', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
senticnet['eprosartan'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'spironolactone', 'spironolactone_drug']
senticnet['eps_growth'] = ['0', '0.994', '0', '0.88', '#interest', '#admiration', 'positive', '0.937', 'exceed', 'outshout', 'outstrip', 'outshine', 'outsail']
senticnet['epsilon_eridani'] = ['0', '0.706', '0', '0.847', '#interest', '#admiration', 'positive', '0.776', 'dwarf_planet', 'universe', 'planetary', 'sun', 'moon']
senticnet['epsom_handicap'] = ['0.744', '-0.77', '0', '0.661', '#joy', '#surprise', 'positive', '0.725', 'sugarcoat', 'honey', 'glaze', 'change_taste', 'sugar']
senticnet['epyllion'] = ['-0.87', '0', '0.599', '-0.71', '#sadness', '#disgust', 'negative', '-0.73', 'hysteria', 'fury', 'delirium', 'frenzy', 'craze']
senticnet['equable'] = ['0.799', '0', '0', '0.753', '#joy', '#admiration', 'positive', '0.776', 'leisurely', 'unhurried', 'easy', 'easygoing', 'placid']
senticnet['equal'] = ['0.059', '0.171', '0.045', '0.234', '#interest', '#admiration', 'positive', '0.105', 'add', 'average', 'tally', 'multiplication', 'count']
senticnet['equal-opportunity'] = ['-0.73', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.82', 'indiscriminate', 'indiscriminately', 'catch-all', 'indistinguishably', 'wholesale']
senticnet['equal_amount'] = ['0', '0.844', '0', '0.835', '#interest', '#admiration', 'positive', '0.839', 'award', 'investigator_institution', 'fund', 'student', 'university']
senticnet['equal_dignity'] = ['0.324', '0.461', '-0.34', '0.442', '#interest', '#admiration', 'positive', '0.22', 'obedience', 'regard', 'valor', 'affection', 'honor']
senticnet['equal_representation'] = ['0', '0.157', '0', '0.147', '#interest', '#admiration', 'positive', '0.152', 'wall_hang', 'depiction', 'illustration', 'artwork', 'wall_decoration']
senticnet['equal_temperament'] = ['-0.65', '0', '0.681', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'tone', 'whole_tone', 'step', 'whole_step', 'musical_interval']
senticnet['equal_to'] = ['0.889', '-0.90', '0', '0.559', '#joy', '#surprise', 'positive', '0.784', 'competent', 'up_to', 'adequate_to', 'adequate', 'capable']
senticnet['equal_treatment'] = ['0.059', '0.171', '0.045', '0.234', '#interest', '#admiration', 'positive', '0.105', 'add', 'average', 'tally', 'multiplication', 'count']
senticnet['equalising_point'] = ['-0.09', '0.147', '-0.11', '0.205', '#interest', '#admiration', 'positive', '0.036', 'question_starter', 'resolution', 'available', 'explanation', 'request']
senticnet['equalitarian'] = ['0.922', '-0.94', '0', '0.965', '#surprise', '#admiration', 'positive', '0.945', 'utilitarian', 'egalitarian', 'moralist', 'elitist', 'philosopher']
senticnet['equalitarianism'] = ['-0.91', '0', '0.688', '-0.57', '#sadness', '#anger', 'negative', '-0.72', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['equality'] = ['0.182', '0.253', '-0.06', '0.322', '#interest', '#admiration', 'positive', '0.173', 'same', 'equal', 'absolute', 'fair', 'good']
senticnet['equality_everyone'] = ['0.925', '0.768', '0', '0', '#joy', '#interest', 'positive', '0.847', 'freedom', 'rebel', 'glory', 'new_law', 'political']
senticnet['equalize'] = ['0.059', '0.171', '0.045', '0.234', '#interest', '#admiration', 'positive', '0.105', 'add', 'average', 'tally', 'multiplication', 'count']
senticnet['equally'] = ['0.059', '0.171', '0.045', '0.234', '#interest', '#admiration', 'positive', '0.105', 'add', 'average', 'tally', 'multiplication', 'count']
senticnet['equally_adept'] = ['0.085', '0.218', '-0.07', '0.308', '#interest', '#admiration', 'positive', '0.135', 'delicate', 'useful', 'good', 'light_colored', 'beautiful']
senticnet['equally_effective'] = ['0.982', '-0.67', '0', '0.682', '#joy', '#admiration', 'positive', '0.778', 'workmanlike', 'competent', 'efficient', 'able', 'capable']
senticnet['equally_ignorant'] = ['-0.56', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.63', 'aphasic', 'inarticulate', 'speechless', 'unarticulated', 'dumb']
senticnet['equally_important'] = ['0.53', '-0.55', '0', '0.912', '#surprise', '#admiration', 'positive', '0.667', 'advantageous', 'positive', 'good', 'plus', 'discriminatory']
senticnet['equally_impressive'] = ['0.562', '-0.69', '0', '0.883', '#surprise', '#admiration', 'positive', '0.714', 'majestic', 'formidable', 'awesome', 'awing', 'brilliant']
senticnet['equally_passionate'] = ['0.655', '0.635', '-0.23', '0.3', '#joy', '#interest', 'positive', '0.338', 'emotional_response', 'emotional_reaction', 'sentimental', 'sensitive', 'emotional']
senticnet['equanimity'] = ['0.595', '-0.65', '0', '0.89', '#surprise', '#admiration', 'positive', '0.713', 'tranquility', 'serenity', 'disposition', 'repose', 'placidity']
senticnet['equate'] = ['0.84', '-0.53', '0', '0.917', '#joy', '#admiration', 'positive', '0.764', 'correct', 'compensate', 'overcompensate', 'make_up', 'counterbalance']
senticnet['equating'] = ['0.716', '0.775', '0', '0', '#joy', '#interest', 'positive', '0.746', 'implying', 'treason', 'advocating', 'committing', 'tantamount']
senticnet['equation'] = ['0.823', '0.993', '0', '0', '#joy', '#interest', 'positive', '0.908', 'equating', 'par', 'biquadratic_equation', 'differential_equation', 'equivalence']
senticnet['equational'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'integrative', 'consolidative', 'unifying', 'combinatorial']
senticnet['equational_theory'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly']
senticnet['equatorial_disk'] = ['-0.55', '0', '0.869', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'air_bubble', 'foam', 'froth', 'soap_bubble', 'bubble']
senticnet['equatorial_mount'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'binoculars', 'read_glass', 'eyeglass', 'camera', 'monocle']
senticnet['equatorial_orbit'] = ['0.989', '-0.90', '0', '0.876', '#joy', '#surprise', 'positive', '0.923', 'reach', 'range', 'compass', 'scope', 'confines']
senticnet['equatorial_pacific_ocean'] = ['0.078', '-0.03', '-0.04', '0.105', '#joy', '#admiration', 'positive', '0.043', 'aquarium', 'underwater', 'pacific_ocean', 'sea', 'ocean_wave']
senticnet['equestria'] = ['-0.63', '0', '0.674', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'baneful', 'evil', 'baleful', 'malevolent', 'malign']
senticnet['equestrian'] = ['0', '-0.07', '0.091', '-0.19', '#anger', '#disgust', 'negative', '-0.07', 'projectile', 'use_hunt', 'weapon', 'ammunition', 'firearm']
senticnet['equestrian_monument'] = ['0.085', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.079', 'sculpture', 'creative', 'yellow_ribbon', 'artwork', 'monument']
senticnet['equestrian_sport'] = ['-0.02', '-0.05', '0', '0.069', '#surprise', '#admiration', 'positive', '0.032', 'polo', 'soccer', 'game', 'type_of_sport', 'expensive_sport']
senticnet['equestrian_statue'] = ['0.085', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.079', 'sculpture', 'creative', 'yellow_ribbon', 'artwork', 'monument']
senticnet['equestrianism'] = ['0', '-0.07', '0.091', '-0.19', '#anger', '#disgust', 'negative', '-0.07', 'projectile', 'use_hunt', 'weapon', 'ammunition', 'firearm']
senticnet['equicontinuity'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'fragmentation', 'discharge', 'backfire', 'blast']
senticnet['equidistant'] = ['-0.80', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.77', 'contrary', 'contradiction', 'lie', 'liar', 'bad_person']
senticnet['equidistribution'] = ['0.948', '-0.99', '0', '0.549', '#joy', '#surprise', 'positive', '0.83', 'sampling', 'distribution', 'frequency_distribution', 'normal_distribution', 'sample_distribution']
senticnet['equilateral_triangle'] = ['0.891', '0.961', '0', '0', '#joy', '#interest', 'positive', '0.926', 'square', 'four_sides', 'rhombus', 'four_corners', 'cube']
senticnet['equilibrium'] = ['-0.98', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.87', 'become_fat', 'flatulence', 'feel_rejuvenated', 'tan_skin', 'make_angry']
senticnet['equilibrium_constant'] = ['0.695', '-0.85', '0', '0.501', '#joy', '#surprise', 'positive', '0.684', 'alternative_energy', 'energy', 'energy_of_activation', 'atomic_energy', 'nuclear_energy']
senticnet['equine'] = ['0.03', '0.068', '-0.04', '0.093', '#interest', '#admiration', 'positive', '0.037', 'critter', 'beast', 'live_creature', 'furry_creature', 'domesticate_animal']
senticnet['equinoctial_storm'] = ['-0.87', '0', '0.91', '-0.78', '#sadness', '#anger', 'negative', '-0.85', 'storm', 'thundershower', 'rain', 'line_storm', 'rainstorm']
senticnet['equip'] = ['0.852', '0.864', '0', '0', '#joy', '#interest', 'positive', '0.858', 'police_work', 'career', 'task', 'employment', 'labor']
senticnet['equip_ment'] = ['-0.02', '-0.05', '0', '0.069', '#surprise', '#admiration', 'positive', '0.032', 'fixture', 'endowment', 'material', 'stuff', 'kit']
senticnet['equipage'] = ['-0.84', '0', '0.553', '-0.89', '#sadness', '#disgust', 'negative', '-0.76', 'encumbrance', 'incumbrance', 'block', 'bar', 'blockade']
senticnet['equipment'] = ['-0.02', '-0.05', '0', '0.069', '#surprise', '#admiration', 'positive', '0.032', 'fixture', 'endowment', 'material', 'stuff', 'kit']
senticnet['equipment_failure'] = ['-0.81', '0', '0.849', '-0.53', '#sadness', '#anger', 'negative', '-0.73', 'dud', 'fault', 'failure', 'brake_failure', 'engine_failure']
senticnet['equipment_maker'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'builder']
senticnet['equipment_malfunction'] = ['-0.91', '0', '0.905', '-0.78', '#sadness', '#anger', 'negative', '-0.86', 'pity', 'shame', 'misfortune', 'mischance', 'misadventure']
senticnet['equipment_manufacturer'] = ['0.111', '0.18', '0', '0.128', '#interest', '#admiration', 'positive', '0.14', 'corporation', 'partnership', 'large_business', 'business_organization', 'enterprise']
senticnet['equipollence'] = ['0.534', '-0.86', '0', '0.676', '#surprise', '#admiration', 'positive', '0.692', 'suitability', 'suitableness', 'habitability', 'habitableness', 'qualification']
senticnet['equipped'] = ['0.072', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.051', 'precooked', 'fitted_out', 'prepared', 'embattled', 'spread']
senticnet['equipt'] = ['0.61', '-0.88', '0', '0.83', '#surprise', '#admiration', 'positive', '0.776', 'prepared', 'visored', 'armored', 'outfitted', 'helmeted']
senticnet['equisetum'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'seed_fern', 'pteridosperm']
senticnet['equitable'] = ['0.713', '0', '0', '0.746', '#joy', '#admiration', 'positive', '0.73', 'carnival', 'circus', 'fairground', 'rodeo', 'disneyland']
senticnet['equitable_allocation'] = ['0.713', '0', '0', '0.746', '#joy', '#admiration', 'positive', '0.73', 'carnival', 'circus', 'fairground', 'rodeo', 'disneyland']
senticnet['equitable_compensation'] = ['0.713', '0', '0', '0.746', '#joy', '#admiration', 'positive', '0.73', 'carnival', 'circus', 'fairground', 'rodeo', 'disneyland']
senticnet['equitable_distribution'] = ['0.713', '0', '0', '0.746', '#joy', '#admiration', 'positive', '0.73', 'carnival', 'circus', 'fairground', 'rodeo', 'disneyland']
senticnet['equitable_estoppel'] = ['-0.55', '0', '0.741', '-0.76', '#anger', '#disgust', 'negative', '-0.68', 'illogicality', 'illogicalness', 'illogic', 'inconsequence', 'fallaciousness']
senticnet['equitable_manner'] = ['0.713', '0', '0', '0.746', '#joy', '#admiration', 'positive', '0.73', 'carnival', 'circus', 'fairground', 'rodeo', 'disneyland']
senticnet['equitable_relief'] = ['0.931', '0.947', '0', '0', '#joy', '#interest', 'positive', '0.939', 'antidote', 'cure', 'therapeutic', 'curative', 'repair']
senticnet['equitable_representation'] = ['0', '0.157', '0', '0.147', '#interest', '#admiration', 'positive', '0.152', 'wall_hang', 'depiction', 'illustration', 'artwork', 'wall_decoration']
senticnet['equitable_treatment'] = ['0.713', '0', '0', '0.746', '#joy', '#admiration', 'positive', '0.73', 'carnival', 'circus', 'fairground', 'rodeo', 'disneyland']
senticnet['equitably'] = ['0.922', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.947', 'unequally', 'apportioned', 'apportion', 'proportionately', 'unevenly']
senticnet['equities_analyst'] = ['0.711', '0', '0', '0', '#joy', '#joy', 'positive', '0.711', 'chemist', 'expert', 'scientist', 'esaminatore', 'physicist']
senticnet['equity'] = ['0', '0.101', '-0.05', '0.059', '#interest', '#admiration', 'positive', '0.037', 'make_toy', 'frame_picture', 'smoke_alarm', 'buy_paint_brush', 'buy_paint']
senticnet['equity_analyst'] = ['0.711', '0', '0', '0', '#joy', '#joy', 'positive', '0.711', 'chemist', 'expert', 'scientist', 'esaminatore', 'physicist']
senticnet['equity_capital'] = ['0', '0.312', '-0.10', '0.207', '#interest', '#admiration', 'positive', '0.137', 'infuse', 'loan', 'lend', 'increase_money', 'wealth']
senticnet['equity_investment'] = ['0', '0.312', '-0.10', '0.207', '#interest', '#admiration', 'positive', '0.137', 'infuse', 'loan', 'lend', 'increase_money', 'wealth']
senticnet['equity_ownership'] = ['0', '0.33', '0', '0', '#interest', '#interest', 'positive', '0.33', 'interested', 'focused', 'attentive', 'curious', 'fascinated']
senticnet['equity_strategist'] = ['0.711', '0', '0', '0', '#joy', '#joy', 'positive', '0.711', 'chemist', 'expert', 'scientist', 'esaminatore', 'physicist']
senticnet['equity_strategist_at'] = ['0.711', '0', '0', '0', '#joy', '#joy', 'positive', '0.711', 'chemist', 'expert', 'scientist', 'esaminatore', 'physicist']
senticnet['equivalence'] = ['0.942', '-0.82', '0', '0.828', '#joy', '#admiration', 'positive', '0.865', 'egality', 'egalite', 'tie', 'status', 'position']
senticnet['equivalence_principle'] = ['-0.69', '0', '0.83', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'general_relativity', 'general_theory_of_relativity', 'general_relativity_theory', 'relativity', 'special_theory_of_relativity']
senticnet['equivalent'] = ['-0.83', '0', '0.513', '-0.86', '#sadness', '#disgust', 'negative', '-0.73', 'small_indefinite_quantity', 'small_indefinite_amount', 'peak', 'skeleton', 'extremum']
senticnet['equivalently'] = ['0', '0.203', '0.036', '0.204', '#interest', '#admiration', 'positive', '0.124', 'amount', 'count', 'total', 'quantity', 'total_amount']
senticnet['equivariance'] = ['-0.75', '0', '0.679', '-0.77', '#sadness', '#disgust', 'negative', '-0.73', 'unvariedness', 'changelessness', 'unchangeability', 'unchangeableness', 'unchangingness']
senticnet['equivariant_cohomology'] = ['0.875', '-0.65', '0', '0.861', '#joy', '#admiration', 'positive', '0.797', 'completeness', 'work', 'whole_shebang', 'essential', 'whole']
senticnet['equivocal'] = ['-0.64', '0', '0.576', '-0.83', '#sadness', '#disgust', 'negative', '-0.68', 'nisi', 'indeterminate', 'inconclusive', 'ambiguous', 'indistinct']
senticnet['equivocalness'] = ['0.738', '-0.85', '0', '0.89', '#surprise', '#admiration', 'positive', '0.828', 'evasiveness', 'twilight_zone', 'prevarication', 'ambiguous', 'unclearness']
senticnet['equivocate'] = ['-0.89', '0', '0.669', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'deceit', 'deception', 'misrepresentation', 'indirect_expression', 'circumlocution']
senticnet['equivocation'] = ['-0.89', '0', '0.669', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'deceit', 'deception', 'misrepresentation', 'indirect_expression', 'circumlocution']
senticnet['er'] = ['0', '0', '0.785', '-0.72', '#anger', '#disgust', 'negative', '-0.75', 'sharp_nail', 'sharp', 'sharp_end', 'sharp_point', 'pointy']
senticnet['er_membrane'] = ['0', '0', '0.019', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'worm_intestine', 'bristle', 'fur_keep_warm', 'animal', 'whisker']
senticnet['era'] = ['0', '0.104', '0', '0.124', '#interest', '#admiration', 'positive', '0.114', 'good_trait', 'constant', 'time_increment', 'time_designation', 'time_length']
senticnet['eradicate'] = ['0', '0', '0.848', '-0.95', '#anger', '#disgust', 'negative', '-0.89', 'enervate', 'smash', 'transgress', 'humiliate', 'separate']
senticnet['eradicate_malaria'] = ['-0.88', '0', '-0.84', '-0.95', '#sadness', '#disgust', 'negative', '-0.89', 'polio', 'infectious_disease', 'malady', 'sickness', 'ailment']
senticnet['eradicate_smallpox'] = ['-0.07', '0', '-0.22', '-0.15', '#fear', '#disgust', 'negative', '-0.15', 'gonorrhea', 'infectious_disease', 'psychoneurosis', 'sickness', 'polio']
senticnet['eradicate_terrorism'] = ['-0.94', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.88', 'combat', 'uprise', 'big_fight', 'opposition', 'violence']
senticnet['eradicated_worldwide'] = ['-0.07', '0', '-0.22', '-0.15', '#fear', '#disgust', 'negative', '-0.15', 'gonorrhea', 'infectious_disease', 'psychoneurosis', 'sickness', 'polio']
senticnet['eradicating_malaria'] = ['-0.88', '0', '-0.84', '-0.95', '#sadness', '#disgust', 'negative', '-0.89', 'polio', 'infectious_disease', 'malady', 'sickness', 'ailment']
senticnet['erasable'] = ['0.883', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.864', 'stick_eraser', 'ballpoint_pen', 'intern', 'paper_clip', 'eraser_holder']
senticnet['erase'] = ['0.292', '0.328', '-0.09', '0.262', '#joy', '#interest', 'positive', '0.196', 'fold_up', 'turn_knob', 'fill_ink', 'hold_hand', 'shut_door']
senticnet['erase_blackboard'] = ['-0.12', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.08', 'lower_expectation', 'speak_english', 'master_skill', 'stand_alone', 'laugh']
senticnet['eraser'] = ['0.883', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.864', 'stick_eraser', 'ballpoint_pen', 'intern', 'paper_clip', 'eraser_holder']
senticnet['eraser_holder'] = ['0.038', '0', '0.01', '0.059', '#joy', '#admiration', 'positive', '0.029', 'paper_clip', 'computer_desk', 'pencil_box', 'electric_pencil_sharpener', 'folder']
senticnet['eraser_tool'] = ['0.883', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.864', 'stick_eraser', 'ballpoint_pen', 'intern', 'paper_clip', 'eraser_holder']
senticnet['erasmus_darwin'] = ['-0.04', '0.192', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.054', 'hypothetical', 'supposition', 'scientific_statement', 'psychology', 'educate_guess']
senticnet['ere'] = ['-0.04', '0.268', '-0.06', '0.227', '#interest', '#admiration', 'positive', '0.097', 'proposition', 'abstract', 'get_rid', 'theoretical', 'business_transaction']
senticnet['ere_long'] = ['-0.04', '0.268', '-0.06', '0.227', '#interest', '#admiration', 'positive', '0.097', 'proposition', 'abstract', 'get_rid', 'theoretical', 'business_transaction']
senticnet['erect'] = ['-0.10', '0.14', '0', '0.105', '#interest', '#admiration', 'positive', '0.048', 'build_house', 'put_up_construction', 'set_up', 'construct_skyscraper', 'put_up']
senticnet['erect_annual_herb'] = ['-0.80', '0', '0.555', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'bush', 'wormwood', 'milkwort', 'germander', 'centaury']
senticnet['erect_posture'] = ['-0.15', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.09', 'use_leg', 'walk_quickly', 'casual', 'take_step', 'strut']
senticnet['erect_shrub'] = ['-0.80', '0', '0.555', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'bush', 'wormwood', 'milkwort', 'germander', 'centaury']
senticnet['erect_stem'] = ['-0.80', '0', '0.555', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'bush', 'wormwood', 'milkwort', 'germander', 'centaury']
senticnet['erect_stem_up'] = ['-0.80', '0', '0.555', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'bush', 'wormwood', 'milkwort', 'germander', 'centaury']
senticnet['erectile'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expandible', 'expansible', 'expansive', 'cavernous']
senticnet['erectile_dysfunction'] = ['0.992', '-0.80', '0', '0.584', '#joy', '#surprise', 'positive', '0.795', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'excretion']
senticnet['erection'] = ['0.655', '-0.87', '0', '0.595', '#joy', '#surprise', 'positive', '0.709', 'orgasm', 'excitement', 'arousal', 'kill_brain_cell', 'feel_relieved']
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senticnet['ergonomic_chair'] = ['0.036', '0.232', '-0.05', '0.192', '#interest', '#admiration', 'positive', '0.101', 'well_deigned', 'comfortable', 'useful', 'prevent_stress', 'ergonomic_pillow']
senticnet['ergonomic_design'] = ['-0.53', '0', '0.765', '-0.94', '#anger', '#disgust', 'negative', '-0.74', 'jaggedness', 'unregularity', 'unevenness', 'irregularity', 'patchiness']
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senticnet['ergonovine'] = ['0.672', '0.632', '-0.23', '0.312', '#joy', '#interest', 'positive', '0.344', 'euphoria', 'buoyancy', 'perkiness', 'jubilance', 'ecstasy']
senticnet['ergotism'] = ['-0.72', '0', '0.884', '-0.75', '#anger', '#disgust', 'negative', '-0.78', 'poisoning', 'paraquat_poisoning', 'alkali_poisoning', 'caffeinism', 'caffeine_intoxication']
senticnet['erich_kunzel'] = ['0.805', '-0.58', '0', '0.525', '#joy', '#surprise', 'positive', '0.637', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['erickson_beamon'] = ['0.532', '-0.60', '0', '0.875', '#surprise', '#admiration', 'positive', '0.67', 'gem', 'adornment', 'jewel', 'bead', 'bangle']
senticnet['ericsson_mobile'] = ['0.749', '0.724', '0', '0', '#joy', '#interest', 'positive', '0.736', 'mouth_noise', 'vocal_cord', 'vocalization', 'vocal_sound', 'loud_soft']
senticnet['erie'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
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senticnet['eristical'] = ['0.702', '-0.51', '0', '0.658', '#joy', '#admiration', 'positive', '0.625', 'contentious', 'disputatious', 'disputative', 'eristic', 'argumentative']
senticnet['erlenmeyer_flask'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'drink_container', 'liquid_container', 'drink_cup', 'drink_holder', 'type_of_container']
senticnet['ermac'] = ['0.021', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'plastic_bottle', 'party', 'use_party', 'lighter_air', 'compress_air']
senticnet['ermine_mustela_erminea'] = ['0', '0', '-0.95', '-0.81', '#fear', '#disgust', 'negative', '-0.88', 'herb', 'plant', 'poisonous_plant', 'yellow_flower', 'lichen']
senticnet['ermintrude'] = ['0.934', '-0.57', '0', '0.767', '#joy', '#admiration', 'positive', '0.76', 'morosoph', 'simpleton', 'flibbertigibbet', 'foolish_woman', 'simple']
senticnet['ernakulam_angamaly'] = ['0.8', '-0.84', '0', '0.868', '#surprise', '#admiration', 'positive', '0.837', 'commonwealth', 'province', 'administrative_district', 'administrative_division', 'territorial_division']
senticnet['ernesto_natiello'] = ['-0.91', '0', '0.931', '-0.72', '#sadness', '#anger', 'negative', '-0.85', 'hit_with_baton', 'get_hurt', 'whip', 'arrow', 'hurt']
senticnet['ernie'] = ['0.17', '0.125', '-0.04', '0.196', '#joy', '#admiration', 'positive', '0.111', 'pal', 'single_person', 'comrade', 'good', 'companion']
senticnet['ernie_ball'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'lever']
senticnet['ernle'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'bodyguard', 'outrider', 'bodyguard_of_guard', 'beefeater', 'defender']
senticnet['erno'] = ['0.975', '-0.66', '0', '0.813', '#joy', '#admiration', 'positive', '0.816', 'cry', 'yell', 'scream', 'shrill', 'call']
senticnet['ernst_haeckel'] = ['-0.04', '0.192', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.054', 'hypothetical', 'supposition', 'scientific_statement', 'psychology', 'educate_guess']
senticnet['ernst_mayr'] = ['-0.04', '0.192', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.054', 'hypothetical', 'supposition', 'scientific_statement', 'psychology', 'educate_guess']
senticnet['ernst_troeltsch'] = ['-0.99', '0', '0.835', '-0.58', '#sadness', '#anger', 'negative', '-0.80', 'beginning', 'origin', 'root', 'wellspring', 'source']
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senticnet['erodible'] = ['-0.88', '0', '0.907', '-0.52', '#sadness', '#anger', 'negative', '-0.77', 'wearing', 'corrasion', 'eroding', 'eating_away', 'wearing_away']
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senticnet['erogenous'] = ['0', '-0.88', '0', '0.877', '#surprise', '#admiration', 'positive', '0.883', 'g-spot', 'clitoris', 'clitoral', 'perineum', 'genitals']
senticnet['erogenous_zone'] = ['0.982', '-0.72', '0', '0.615', '#joy', '#surprise', 'positive', '0.772', 'sexual_desire', 'concupiscence', 'physical_attraction', 'prurience', 'pruriency']
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senticnet['eros_ramazzotti'] = ['0.107', '0.11', '-0.08', '0.21', '#interest', '#admiration', 'positive', '0.086', 'live_healthy_life', 'forever_young', 'live_forever', 'live_long', 'live_good_life']
senticnet['erosion'] = ['-0.88', '0', '0.907', '-0.52', '#sadness', '#anger', 'negative', '-0.77', 'wearing', 'corrasion', 'eroding', 'eating_away', 'wearing_away']
senticnet['erosion_resistant'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'fossil', 'oil', 'dark_liquid', 'smithsonian_institution', 'inert_gas']
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senticnet['erosive_force'] = ['-0.88', '0', '0.907', '-0.52', '#sadness', '#anger', 'negative', '-0.77', 'wearing', 'corrasion', 'eroding', 'eating_away', 'wearing_away']
senticnet['erotic'] = ['0.229', '0.127', '0', '0', '#joy', '#interest', 'positive', '0.178', 'sense_peace', 'feel_good', 'think_sex', 'feel_relieved', 'alter_state_consciousness']
senticnet['erotic_attraction'] = ['-0.58', '0', '0.814', '-0.54', '#sadness', '#anger', 'negative', '-0.64', 'atypical', 'irregular', 'abnormal', 'supernormal', 'subnormal']
senticnet['erotic_desire'] = ['0.857', '0', '0', '0', '#joy', '#joy', 'positive', '0.857', 'date', 'accuracy', 'sexy_woman', 'woo', 'enjoy_person']
senticnet['erotic_horror'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amuck', 'murderousness', 'misocainea', 'misogyny']
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senticnet['erotic_pleasure'] = ['-0.62', '0', '0.515', '-0.63', '#sadness', '#disgust', 'negative', '-0.59', 'immodesty', 'perversion', 'paraphilia', 'sexual_perversion', 'fetishism']
senticnet['erotica'] = ['0', '0.755', '0', '0.912', '#interest', '#admiration', 'positive', '0.834', 'pornography', 'porno', 'smut', 'porn', 'picture']
senticnet['erotically'] = ['0.857', '0', '0', '0', '#joy', '#joy', 'positive', '0.857', 'date', 'accuracy', 'sexy_woman', 'woo', 'enjoy_person']
senticnet['eroticism'] = ['0.509', '-0.86', '0', '0.691', '#surprise', '#admiration', 'positive', '0.688', 'amorousness', 'sexiness', 'amativeness', 'carnality', 'erotism']
senticnet['erotism'] = ['0.509', '-0.86', '0', '0.691', '#surprise', '#admiration', 'positive', '0.688', 'amorousness', 'sexiness', 'amativeness', 'eroticism', 'carnality']
senticnet['erotomania'] = ['-0.78', '0', '0.542', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'hysterical_neurosis', 'hysteria', 'mental_disorder', 'mental_disturbance', 'psychological_disorder']
senticnet['erp_system'] = ['0.971', '0.76', '0', '0', '#joy', '#interest', 'positive', '0.865', 'shopper', 'clerk', 'patron', 'vintner', 'hairdresser']
senticnet['err'] = ['-0.77', '0', '0.981', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'trip_up', 'stumble', 'misjudge', 'misremember', 'fall_for']
senticnet['errand'] = ['0.054', '0.237', '-0.15', '0.33', '#interest', '#admiration', 'positive', '0.117', 'retrieve', 'bring', 'tote', 'take', 'go_get']
senticnet['errand_boy'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'pharmaceutical_chemist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['errant'] = ['-0.83', '0', '0.962', '-0.62', '#sadness', '#anger', 'negative', '-0.80', 'unreliable', 'undependable', 'weak', 'fallible', 'erring']
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senticnet['erratic_rainfall'] = ['-0.98', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.89', 'dry', 'lose_team', 'leave_out', 'disrespect', 'cause_suffer']
senticnet['erring'] = ['-0.83', '0', '0.962', '-0.62', '#sadness', '#anger', 'negative', '-0.80', 'unreliable', 'undependable', 'weak', 'errant', 'fallible']
senticnet['errno_value'] = ['-0.98', '0', '0.869', '-0.80', '#sadness', '#anger', 'negative', '-0.88', 'error', 'syntax_error', 'semantic_error', 'programming_error', 'computer_error']
senticnet['erroneous'] = ['0', '0', '0.752', '-0.77', '#anger', '#disgust', 'negative', '-0.76', 'signifies', 'error', 'expurgation', 'improper', 'incorrect']
senticnet['erroneous_belief'] = ['-0.70', '0', '0.533', '-0.62', '#sadness', '#disgust', 'negative', '-0.62', 'misconception', 'error', 'misunderstanding', 'hallucination', 'fancy']
senticnet['erroneously'] = ['0', '0', '0.75', '-0.92', '#anger', '#disgust', 'negative', '-0.83', 'speciously', 'erroneous', 'double-book', 'error', 'fallaciously']
senticnet['error'] = ['0', '0', '0', '-0.82', '#disgust', '#disgust', 'negative', '-0.82', 'retribution', 'put_in_jail', 'sleepless_night', 'mistake', 'go_to_jail']
senticnet['error-prone'] = ['0', '-0.84', '-0.82', '0', '#surprise', '#fear', 'positive', '0.013', 'labor-intensive', 'time-consuming', 'tedious', 'time-intensive', 'resource-intensive']
senticnet['error_correction'] = ['0.865', '-0.80', '0', '0.52', '#joy', '#surprise', 'positive', '0.73', 'set', 'pose', 'lay', 'intersperse', 'put']
senticnet['error_handler'] = ['-0.98', '0', '0.869', '-0.80', '#sadness', '#anger', 'negative', '-0.88', 'error', 'syntax_error', 'semantic_error', 'programming_error', 'computer_error']
senticnet['error_message'] = ['-0.98', '0', '0.869', '-0.80', '#sadness', '#anger', 'negative', '-0.88', 'error', 'syntax_error', 'semantic_error', 'programming_error', 'computer_error']
senticnet['error_prone'] = ['0', '0', '0.99', '-0.72', '#anger', '#disgust', 'negative', '-0.85', 'cumbrous', 'ungainly', 'clumsy', 'cumbersome', 'awkward']
senticnet['error_rate'] = ['0', '0.098', '0', '0.089', '#interest', '#admiration', 'positive', '0.093', 'wood_panel', 'wooden_structure', 'hinge', 'entrance_room', 'corridor']
senticnet['error_strewn'] = ['0', '0', '0.954', '-0.76', '#anger', '#disgust', 'negative', '-0.85', 'fervid', 'ardent', 'impassioned', 'fervent', 'fiery']
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senticnet['ertapenem'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['eru_ilvatar'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
senticnet['eruct'] = ['0', '0', '0.8', '-0.95', '#anger', '#disgust', 'negative', '-0.87', 'remove', 'deport', 'disgorge', 'dislodge', 'erupt']
senticnet['eructation'] = ['0', '0', '-0.86', '-0.74', '#fear', '#disgust', 'negative', '-0.80', 'extravasation', 'bam', 'eruption', 'irruption', 'bang']
senticnet['erudite'] = ['0.71', '-0.81', '0', '0.937', '#surprise', '#admiration', 'positive', '0.819', 'scholarly', 'donnish', 'pedantic', 'learned', 'bookish']
senticnet['eruditeness'] = ['0.985', '0', '0', '0.716', '#joy', '#admiration', 'positive', '0.851', 'encyclopedism', 'learning', 'scholarship', 'learnedness', 'erudition']
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senticnet['eruption'] = ['0', '0', '-0.85', '-0.73', '#fear', '#disgust', 'negative', '-0.79', 'extravasation', 'bam', 'eructation', 'irruption', 'bang']
senticnet['erusin'] = ['0', '0.819', '0', '0.987', '#interest', '#admiration', 'positive', '0.903', 'celebrate', 'wedding_ceremony', 'propose_marriage', 'engage', 'engagement']
senticnet['eryngium'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'livelong', 'herb', 'stonecrop', 'orpine', 'orpin']
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senticnet['erysiphe'] = ['-0.93', '0', '0.699', '-0.91', '#sadness', '#disgust', 'negative', '-0.84', 'onion_smut', 'head_smut', 'stinking_smut', 'flag_smut_fungus', 'covered_smut']
senticnet['erythema'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'hickey']
senticnet['erythema_marginatum'] = ['-0.54', '0', '0.67', '-0.92', '#anger', '#disgust', 'negative', '-0.71', 'pericarditis', 'rubor', 'redness', 'endocarditis', 'inflammation']
senticnet['erythema_multiforme'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema', 'windburn', 'hickey']
senticnet['erythema_nodosum'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema', 'erythema_multiforme', 'windburn', 'hickey']
senticnet['erythrina_variegata'] = ['0.826', '0.787', '0', '0', '#joy', '#interest', 'positive', '0.806', 'fig', 'baby_flower', 'carpel', 'strawberry', 'star_anise']
senticnet['erythrocin'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['erythroderma'] = ['-0.89', '0', '0.998', '-0.89', '#sadness', '#anger', 'negative', '-0.93', 'erythema', 'flare', 'skin_disease', 'disease_of_skin', 'skin_disorder']
senticnet['erythrodermic'] = ['-0.72', '0', '0.778', '-0.98', '#anger', '#disgust', 'negative', '-0.82', 'actinic_dermatitis', 'hypericism', 'diaper_rash', 'diaper_dermatitis', 'dermatitis']
senticnet['erythroid_hyperplasia'] = ['0.905', '-0.65', '0', '0.851', '#joy', '#admiration', 'positive', '0.805', 'gonococcus', 'bacteria', 'bacteriae', 'probiotic', 'probiotic_bacterium']
senticnet['erythroplasia'] = ['-0.84', '0', '0.581', '-0.93', '#sadness', '#disgust', 'negative', '-0.78', 'keratosis_blennorrhagica', 'keratoderma_blennorrhagica', 'keratosis_pilaris', 'seborrheic_keratosis', 'verruca']
senticnet['erythropoietic_protoporphyria'] = ['-0.57', '0', '0.973', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['erzherzog_ferdinand_max'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'submarine', 'lifeboat', 'titanic', 'large_ship', 'canoe']
senticnet['esaminatore'] = ['0.711', '0', '0', '0', '#joy', '#joy', 'positive', '0.711', 'analyst', 'expert', 'scientist', 'chemist', 'physicist']
senticnet['esata'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'computer_part', 'system', 'cpu', 'pencil_pen', 'monitor']
senticnet['escalade_hybrid'] = ['0.502', '-0.82', '0', '0.779', '#surprise', '#admiration', 'positive', '0.703', 'mortal', 'person', 'individual', 'free_liver', 'agent']
senticnet['escalate'] = ['0.844', '0', '0', '0.934', '#joy', '#admiration', 'positive', '0.889', 'augment', 'sharpen', 'exaggerate', 'prolong', 'amplify']
senticnet['escalating_conflict'] = ['-0.2', '-0.03', '0', '-0.23', '#sadness', '#disgust', 'negative', '-0.13', 'beat_up', 'fight', 'conflict', 'violence', 'fight_against']
senticnet['escalating_tension'] = ['-0.2', '-0.03', '0', '-0.23', '#sadness', '#disgust', 'negative', '-0.13', 'beat_up', 'fight', 'conflict', 'violence', 'fight_against']
senticnet['escalating_violence'] = ['-0.67', '0', '0.938', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'massacre', 'mass_murder', 'carnage', 'slaughter', 'butchery']
senticnet['escalation'] = ['0.942', '-0.79', '0', '0.901', '#joy', '#admiration', 'positive', '0.88', 'backup', 'accretion', 'deposition', 'deposit', 'increase']
senticnet['escalator'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'top_floor', 'front_desk', 'concierge', 'exit', 'ground_floor']
senticnet['escalatory'] = ['0', '0', '-0.85', '-0.93', '#fear', '#disgust', 'negative', '-0.89', 'anger', 'animosity', 'hostility', 'malice', 'grudge']
senticnet['escapable'] = ['0', '0', '0.93', '-0.84', '#anger', '#disgust', 'negative', '-0.88', 'inevitable', 'inescapably', 'inescapable', 'ineluctable', 'avoided']
senticnet['escapade'] = ['0.954', '-0.87', '0', '0.583', '#joy', '#surprise', 'positive', '0.802', 'penetration', 'air_raid', 'air_attack', 'swoop', 'incursion']
senticnet['escape'] = ['-0.69', '0', '0.998', '-0.84', '#anger', '#disgust', 'negative', '-0.84', 'evasion', 'lam', 'break', 'skedaddle', 'hegira']
senticnet['escape_danger'] = ['0.734', '0', '0', '0.913', '#joy', '#admiration', 'positive', '0.824', 'alive', 'survivor', 'survive', 'avoid_death', 'escape_massacre']
senticnet['escape_detection'] = ['-0.13', '0.07', '-0.10', '0.104', '#sadness', '#fear', 'negative', '-0.01', 'conceal', 'trick', 'concealment', 'hide_body', 'hide_truth']
senticnet['escape_expert'] = ['0.9', '-0.88', '0', '0.687', '#joy', '#surprise', 'positive', '0.823', 'escapologist', 'conjurer', 'mind_reader', 'telepathist', 'performer']
senticnet['escape_fire'] = ['-0.04', '0', '-0.07', '-0.09', '#fear', '#disgust', 'negative', '-0.07', 'run', 'go_up_down', 'climb_up', 'go_upstairs', 'move_between_floor_build']
senticnet['escape_hatch'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'chick', 'wild_bird', 'baby_bird', 'lay_egg', 'bird_home']
senticnet['escape_justice'] = ['-0.93', '0', '0.945', '-0.93', '#anger', '#disgust', 'negative', '-0.93', 'prison_breaker', 'escape_prison', 'illegal', 'break_rule', 'steal']
senticnet['escape_massacre'] = ['0.731', '0', '0', '0.909', '#joy', '#admiration', 'positive', '0.82', 'alive', 'escape_danger', 'survive', 'avoid_death', 'survivor']
senticnet['escape_mechanism'] = ['-0.10', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.10', 'carelessness', 'negligence', 'neglect', 'soldiering', 'dodging']
senticnet['escape_predation'] = ['0.812', '0.872', '0', '0', '#joy', '#interest', 'positive', '0.842', 'insect', 'fly_away', 'small_insect', 'maggot', 'chihuahua']
senticnet['escape_prison'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'prison_breaker', 'break_rule', 'illegal', 'escape_justice', 'prisoner']
senticnet['escape_reality'] = ['0.845', '0', '0', '0', '#joy', '#joy', 'positive', '0.845', 'acculturation', 'entertainment_learn', 'cultural_experience', 'enjoy_story', 'pastime']
senticnet['escape_sere'] = ['0.988', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.98', 'sustain_life', 'sustenance', 'nutrition', 'keep_alive', 'satisfy_hunger']
senticnet['escape_serious_injury'] = ['0', '0', '-0.90', '-0.93', '#fear', '#disgust', 'negative', '-0.92', 'luckless', 'ill-omened', 'ill-starred', 'doomed', 'ill-fated']
senticnet['escape_situation'] = ['-0.12', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'escape', 'bail_out', 'cancel_meeting', 'absent', 'cancel_appointment']
senticnet['escaped_captivity'] = ['-0.98', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.9', 'jailbird', 'prisoner', 'imprisoned', 'convict', 'inmate']
senticnet['escaped_injury'] = ['-0.95', '0', '0.712', '-0.52', '#sadness', '#anger', 'negative', '-0.73', 'uncut', 'unharmed', 'unscathed', 'unwounded', 'uninjured']
senticnet['escaped_prisoner'] = ['0.057', '0', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'huge_fire', 'fire', 'rapid_oxidation', 'heat_light', 'flame']
senticnet['escaped_serious_injury'] = ['-0.95', '0', '0.712', '-0.52', '#sadness', '#anger', 'negative', '-0.73', 'uncut', 'unharmed', 'unscathed', 'unwounded', 'uninjured']
senticnet['escaped_unhurt'] = ['-0.95', '0', '0.712', '-0.52', '#sadness', '#anger', 'negative', '-0.73', 'uncut', 'unharmed', 'unscathed', 'unwounded', 'uninjured']
senticnet['escapism'] = ['0', '0', '0', '-0.33', '#disgust', '#disgust', 'negative', '-0.33', 'bored', 'wearied', 'tired', 'passive', 'inactive']
senticnet['escapist'] = ['-0.64', '0', '0.524', '-0.59', '#sadness', '#disgust', 'negative', '-0.58', 'bum', 'dreamer', 'stargazer', 'daydreamer', 'wishful_thinker']
senticnet['escapist_entertainment'] = ['0', '0.915', '0', '0.704', '#interest', '#admiration', 'positive', '0.809', 'low-brow', 'highbrow', 'high-brow', 'middle-brow', 'lowbrow']
senticnet['escapologist'] = ['0.9', '-0.88', '0', '0.687', '#joy', '#surprise', 'positive', '0.823', 'conjurer', 'escape_expert', 'mind_reader', 'telepathist', 'performer']
senticnet['escapology'] = ['0.9', '-0.88', '0', '0.687', '#joy', '#surprise', 'positive', '0.823', 'conjurer', 'escape_expert', 'mind_reader', 'telepathist', 'performer']
senticnet['escarole'] = ['0.636', '-0.79', '0', '0.552', '#joy', '#surprise', 'positive', '0.661', 'lettuce', 'endive', 'green', 'salad_green', 'food']
senticnet['escarpment'] = ['0', '0.08', '0.032', '0.096', '#interest', '#admiration', 'positive', '0.048', 'land_form', 'land_formation', 'land_feature', 'geographic_feature', 'landscape_feature']
senticnet['eschar'] = ['-0.71', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.73', 'pain', 'gape', 'sunburn', 'ouch', 'cut_skin']
senticnet['escharotic'] = ['0.701', '-0.76', '0', '0.904', '#surprise', '#admiration', 'positive', '0.791', 'curative', 'heal', 'medicine', 'get_cured', 'take_care']
senticnet['eschatological'] = ['-0.96', '0', '0.501', '-0.59', '#sadness', '#disgust', 'negative', '-0.68', 'calamity', 'catastrophe', 'tragedy', 'disaster', 'cataclysm']
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senticnet['eschew'] = ['0', '0', '0.725', '-0.95', '#anger', '#disgust', 'negative', '-0.83', 'antipathize', 'mind', 'detest', 'shun', 'deplore']
senticnet['eschscholzia'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'cabbage', 'lettuce', 'broccoli']
senticnet['escoffier'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'blue_ribbon', 'badge', 'allegory', 'stripe', 'merit_badge']
senticnet['escort'] = ['-0.74', '0', '0.512', '-0.66', '#sadness', '#disgust', 'negative', '-0.64', 'yeoman', 'outrider', 'yeoman_of_guard', 'beefeater', 'defender']
senticnet['escort_carrier'] = ['0.084', '-0.05', '0.013', '0', '#joy', '#surprise', 'positive', '0.043', 'carnival_ride', 'carny', 'fortune_teller', 'horseback', 'ferris_wheel']
senticnet['escrow_agency'] = ['-0.97', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.9', 'chip', 'clipper_chip', 'clipper', 'court_order', 'serial_number']
senticnet['escrow_fund'] = ['0.534', '-0.86', '0', '0.736', '#surprise', '#admiration', 'positive', '0.713', 'fund', 'finance', 'monetary_resource', 'cash_in_hand', 'pecuniary_resource']
senticnet['escutcheon'] = ['-0.79', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.85', 'depravity', 'abyss', 'profound', 'infinite', 'void']
senticnet['eshebala'] = ['-0.04', '0.166', '0.684', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'malign', 'malefic', 'maliciously', 'spitefulness', 'maliciousness']
senticnet['eskimo'] = ['0', '0.102', '0', '0', '#interest', '#interest', 'positive', '0.102', 'frostbite', 'mountain_goat', 'weather_event', 'glacier', 'coldness']
senticnet['eskrima'] = ['-0.06', '-0.00', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.05', 'madman', 'trouble', 'agony', 'cause_problem', 'river_stream']
senticnet['esl'] = ['0.633', '-0.96', '0', '0.612', '#joy', '#surprise', 'positive', '0.738', 'particularism', 'absorption', 'concentration', 'engrossment', 'centering']
senticnet['esl_teacher'] = ['-0.69', '0', '0.659', '-0.69', '#sadness', '#disgust', 'negative', '-0.68', 'help', 'paralegal', 'legal_assistant', 'paramedic', 'paramedical']
senticnet['esmeralda'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'lordosis', 'kyphosis', 'humpback', 'spinal_curvature', 'abnormality']
senticnet['esmerelda'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'lordosis', 'kyphosis', 'humpback', 'spinal_curvature', 'abnormality']
senticnet['esocidae'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['esophageal_balloon'] = ['-0.10', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.10', 'read_cover', 'spread_out', 'upgrade', 'look_behind', 'scan']
senticnet['esophageal_body'] = ['-0.62', '0', '0.901', '-0.71', '#anger', '#disgust', 'negative', '-0.74', 'pressure', 'osmotic_pressure', 'hypotonicity', 'pressure_level', 'force']
senticnet['esophageal_dysmotility'] = ['-0.86', '0', '0.858', '-0.91', '#sadness', '#disgust', 'negative', '-0.87', 'edema', 'oedema', 'hydrops', 'dropsy', 'eye_disease']
senticnet['esophageal_perforation'] = ['-0.56', '0', '0.806', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['esophageal_rupture'] = ['-0.99', '0', '0.964', '-0.70', '#sadness', '#anger', 'negative', '-0.88', 'blepharitis', 'diverticulitis', 'inflammation', 'orchitis', 'prostatitis']
senticnet['esophageal_speech'] = ['0', '0', '0', '0.106', '#admiration', '#admiration', 'positive', '0.106', 'vocal_chord', 'mouth_noise', 'vocal_sound', 'human_sound', 'vocalization']
senticnet['esophageal_wall'] = ['0.777', '-0.89', '0', '0.508', '#joy', '#surprise', 'positive', '0.727', 'stretching', 'enlarge', 'dilatation', 'get_bigger', 'enlargement']
senticnet['esophagoscope'] = ['0.255', '0.182', '-0.06', '0.314', '#joy', '#admiration', 'positive', '0.173', 'doctor_office', 'urethroscope', 'sigmoidoscope', 'hospital', 'check_health']
senticnet['esoteric'] = ['-0.87', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.90', 'esoterica', 'theoretical', 'abstruse', 'obscure', 'geek']
senticnet['esoterica'] = ['-0.88', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.90', 'esoteric', 'theoretical', 'abstruse', 'obscure', 'geek']
senticnet['esotericism'] = ['-0.51', '0', '0.979', '-0.89', '#anger', '#disgust', 'negative', '-0.79', 'invisible', 'concealed', 'nonvisual', 'hidden', 'out_of_sight']
senticnet['esotropia'] = ['-0.88', '0', '0.762', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'divergent_strabismus', 'exotropia', 'walleye', 'abnormality', 'abnormalcy']
senticnet['esox'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['esp'] = ['0.832', '0.861', '0', '0.848', '#interest', '#admiration', 'positive', '0.847', 'extrasensory_perception', 'psychic_phenomenon', 'parapsychology', 'precognition', 'foreknowledge']
senticnet['espadrille'] = ['-0.54', '0', '0.612', '-0.98', '#anger', '#disgust', 'negative', '-0.71', 'scuff', 'mule', 'bootee', 'carpet_slipper', 'footwear']
senticnet['especial'] = ['0.657', '-0.56', '0', '0.749', '#joy', '#admiration', 'positive', '0.655', 'naturalness', 'condescension', 'condescendingness', 'mellowness', 'friendliness']
senticnet['esperanza_fire'] = ['0.057', '0', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'huge_fire', 'fire', 'rapid_oxidation', 'heat_light', 'flame']
senticnet['espial'] = ['0.859', '-0.82', '0', '0', '#joy', '#surprise', 'positive', '0.844', 'detection', 'sleuthing', 'spying', 'signal_detection', 'catching']
senticnet['espionage'] = ['-0.69', '0', '0.756', '-0.99', '#anger', '#disgust', 'negative', '-0.81', 'waster', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['esplanade'] = ['-0.90', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.91', 'seafront', 'stroll', 'promenade', 'ramble', 'boardwalk']
senticnet['espousal'] = ['0.995', '-0.69', '0', '0.528', '#joy', '#surprise', 'positive', '0.74', 'grasp', 'hug', 'nestle', 'snuggle', 'grip']
senticnet['espouse'] = ['0.755', '0.972', '0', '0.775', '#interest', '#admiration', 'positive', '0.834', 'grasp', 'grab', 'hold', 'sensual', 'grip']
senticnet['espresso'] = ['0.832', '0.861', '0', '0.848', '#interest', '#admiration', 'positive', '0.847', 'drink_espresso', 'go_cafe', 'drink_coffee', 'eat_dinner', 'coffee']
senticnet['espresso_bar'] = ['0.041', '0.098', '0', '0', '#joy', '#interest', 'positive', '0.07', 'utopia', 'java', 'memorial', 'nowhere', 'coffee']
senticnet['espy'] = ['0.927', '0', '0', '0.91', '#joy', '#admiration', 'positive', '0.919', 'uncover', 'notice', 'locate', 'unearth', 'observe']
senticnet['espy_award'] = ['0.232', '0.178', '-0.17', '0.223', '#joy', '#admiration', 'positive', '0.115', 'fame', 'alms', 'find_money', 'better_life', 'small_detail']
senticnet['esqr'] = ['-0.69', '0', '0.883', '-0.91', '#anger', '#disgust', 'negative', '-0.82', 'stick_in', 'enclose', 'insert', 'feed_in', 'put_in']
senticnet['esque'] = ['0.021', '0.179', '-0.13', '0.164', '#interest', '#admiration', 'positive', '0.058', 'nod_head', 'point_finger', 'upgrade', 'ask', 'put_bid']
senticnet['esquire_magazine'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'library', 'old_testament', 'cook_book', 'a_lot_of_books', 'librarian']
senticnet['essad_pasha_toptani'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
senticnet['essam_el_erian'] = ['0.648', '0.627', '-0.26', '0.329', '#joy', '#interest', 'positive', '0.334', 'brotherly', 'friendly', 'fraternal', 'amicability', 'amicable']
senticnet['essam_erian'] = ['0.648', '0.627', '-0.26', '0.329', '#joy', '#interest', 'positive', '0.334', 'brotherly', 'friendly', 'fraternal', 'amicability', 'amicable']
senticnet['essay'] = ['-0.70', '0', '0.767', '-0.93', '#anger', '#disgust', 'negative', '-0.80', 'report', 'composition', 'paper', 'paragraph', 'disquisition']
senticnet['essay_assessor'] = ['0.995', '0.801', '0', '0.968', '#joy', '#admiration', 'positive', '0.921', 'smart', 'smartness', 'curious', 'become_wise', 'comprehend']
senticnet['essay_exam'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'organize_thought', 'scratch_head', 'know_answer', 'speak_out', 'know_talk']
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senticnet['esse'] = ['0', '0.079', '0', '0', '#interest', '#interest', 'positive', '0.079', 'change_diaper', 'lower_standard_live', 'become_pregnant', 'find_mate', 'make_baby']
senticnet['essence'] = ['0.645', '-0.76', '0', '0.961', '#surprise', '#admiration', 'positive', '0.791', 'content', 'quintessence', 'stuff', 'bare_bone', 'hypostasis']
senticnet['essential'] = ['0.921', '0', '0', '0.872', '#joy', '#admiration', 'positive', '0.897', 'strong_desire', 'needful', 'necessary', 'compel', 'requirement']
senticnet['essential_condition'] = ['0.695', '-0.85', '0', '0.501', '#joy', '#surprise', 'positive', '0.684', 'prerequisite', 'requirement', 'responsibility', 'precondition', 'academic_requirement']
senticnet['essential_human_life'] = ['0', '0.078', '-0.07', '0.147', '#interest', '#admiration', 'positive', '0.049', 'essential_life', 'necessary_life', 'watery', 'liquid', 'fluid']
senticnet['essential_hypertension'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'illness', 'ill', 'tension', 'bad_health', 'malignant_hypertension']
senticnet['essential_life'] = ['0.047', '0.123', '-0.05', '0.157', '#interest', '#admiration', 'positive', '0.068', 'essential_human_life', 'use_cook', 'translucent', 'liquid_substance', 'transparent']
senticnet['essentially'] = ['0', '0.763', '0', '0.873', '#interest', '#admiration', 'positive', '0.818', 'basically', 'fundamentally', 'project', 'research', 'study']
senticnet['essentially_identical'] = ['0.14', '0.345', '-0.20', '0.378', '#interest', '#admiration', 'positive', '0.165', 'good_etiquette', 'same', 'similarity', 'band_together', 'niceness']
senticnet['esset'] = ['0.654', '-0.75', '0', '0.78', '#surprise', '#admiration', 'positive', '0.729', 'suit', 'cause', 'lawsuit', 'occurrence', 'countersuit']
senticnet['essure'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'birth_control_pill', 'condom', 'contraceptive_pill', 'sex_toy', 'prevent_pregnancy']
senticnet['est_jug'] = ['0', '0.919', '0', '0.888', '#interest', '#admiration', 'positive', '0.903', 'surprise', 'eye-popping', 'fulgurous', 'dazzling', 'bedaze']
senticnet['est_mort'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'sexual_relationship', 'liaison', 'affair', 'involvement']
senticnet['establish'] = ['0.075', '0.177', '0', '0.076', '#interest', '#admiration', 'positive', '0.109', 'institution', 'wonderful_place_visit', 'institute', 'corporation', 'establishment']
senticnet['establish_credit'] = ['0.036', '0.232', '-0.05', '0.192', '#interest', '#admiration', 'positive', '0.101', 'money_spend', 'credit', 'purchase', 'pay_goods', 'pay_debt']
senticnet['establish_new_model'] = ['0.763', '0.886', '0', '0', '#joy', '#interest', 'positive', '0.825', 'graduate_education', 'training', 'collaborative_research', 'innovate', 'fertile_environment']
senticnet['establish_probable_cause'] = ['-0.63', '0', '0.967', '-0.67', '#anger', '#disgust', 'negative', '-0.76', 'condemnatory', 'condemning', 'accusatory', 'accusing', 'accusive']
senticnet['established_scientist'] = ['-0.81', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.83', 'microbial_biology', 'undergraduate', 'research_experience', 'recent_doctoral_recipient', 'provide_opportunity']
senticnet['establishing_religious_freedom'] = ['0', '0.089', '0', '0.129', '#interest', '#admiration', 'positive', '0.109', 'passageway', 'connect_room', 'entrance_room', 'corridor', 'walk_through']
senticnet['establishment'] = ['0.075', '0.177', '0', '0.076', '#interest', '#admiration', 'positive', '0.109', 'institution', 'wonderful_place_visit', 'institute', 'corporation', 'establish']
senticnet['establishmentarian'] = ['0.633', '-0.92', '0', '0.869', '#surprise', '#admiration', 'positive', '0.808', 'advocate', 'bigot', 'dogmatist', 'partisan', 'advocator']
senticnet['estate'] = ['0', '0.179', '-0.05', '0.162', '#interest', '#admiration', 'positive', '0.095', 'house_land', 'real_estate', 'acreage', 'house_place', 'good_investment']
senticnet['estate_car'] = ['0.695', '-0.85', '0', '0.501', '#joy', '#surprise', 'positive', '0.684', 'beach_wagon', 'station_wagon', 'wagon', 'shooting_brake', 'beach_waggon']
senticnet['estate_tax'] = ['-0.83', '0', '0.555', '-0.64', '#sadness', '#disgust', 'negative', '-0.67', 'single_tax', 'income_tax', 'taxation', 'capital_levy', 'departure_tax']
senticnet['estate_tax_exemption'] = ['0.763', '-0.67', '0', '0.945', '#joy', '#admiration', 'positive', '0.795', 'set', 'income_bracket', 'bracket', 'income', 'price_bracket']
senticnet['este_lauder'] = ['0.863', '0.861', '0', '0', '#joy', '#interest', 'positive', '0.862', 'aroma', 'scent', 'smell', 'odor', 'sniff']
senticnet['esteem'] = ['0.859', '0.725', '0', '0.76', '#joy', '#admiration', 'positive', '0.781', 'confident', 'adoration', 'encouragement', 'hero_worship', 'look_up_to']
senticnet['esteemed'] = ['0.984', '-0.99', '0', '0.587', '#joy', '#surprise', 'positive', '0.855', 'estimable', 'respected', 'reputable', 'honored', 'prestigious']
senticnet['esteemed_colleague'] = ['0.05', '0.141', '-0.07', '0.132', '#interest', '#admiration', 'positive', '0.063', 'public_servant', 'worker', 'manager', 'laborer', 'office_job']
senticnet['ester'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'fulmination', 'fulminate', 'salt', 'lightning', 'explode']
senticnet['esterification'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'carboxylic_acid', 'succinic_acid', 'valeric_acid']
senticnet['esthero'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'cur', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['esthesis'] = ['-0.80', '0', '0.885', '-0.53', '#sadness', '#anger', 'negative', '-0.74', 'masking', 'taste', 'threshold', 'perception', 'limen']
senticnet['esthetic'] = ['0.737', '-0.85', '0', '0.623', '#joy', '#surprise', 'positive', '0.738', 'understated', 'tasteful', 'atasteful', 'artistic', 'unostentatious']
senticnet['esthetic_dentistry'] = ['-0.03', '0.079', '0.024', '0.091', '#interest', '#admiration', 'positive', '0.029', 'toothpaste', 'toothbrush', 'mouth', 'gel_toothpaste', 'tooth']
senticnet['esthetical'] = ['0.679', '-0.79', '0', '0.66', '#joy', '#surprise', 'positive', '0.711', 'cosmetic', 'painterly', 'sensuous', 'artistic', 'enhancive']
senticnet['esthetically'] = ['0', '-0.75', '0', '0.845', '#surprise', '#admiration', 'positive', '0.8', 'educationally', 'pedagogically', 'theologically', 'doctrinally', 'tonally']
senticnet['estimable'] = ['0.983', '-0.80', '0', '0.966', '#joy', '#admiration', 'positive', '0.919', 'calculable', 'computable', 'countable', 'denumerable', 'enumerable']
senticnet['estimate'] = ['-0.02', '0.248', '0', '0.31', '#interest', '#admiration', 'positive', '0.178', 'assurance', 'total', 'quality', 'importance', 'amount']
senticnet['estimate_indicate'] = ['-0.02', '0.248', '0', '0.31', '#interest', '#admiration', 'positive', '0.178', 'assurance', 'total', 'quality', 'importance', 'amount']
senticnet['estimate_suggest'] = ['-0.02', '0.248', '0', '0.31', '#interest', '#admiration', 'positive', '0.178', 'assurance', 'total', 'quality', 'importance', 'amount']
senticnet['estimated'] = ['0', '0.992', '0', '0.805', '#interest', '#admiration', 'positive', '0.899', 'calculated', 'degree', 'magnitude', 'inestimable', 'inestimably']
senticnet['estimated_jackpot'] = ['0.858', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.813', 'box_office', 'movie', 'cctv', 'cinema', 'movie_theater']
senticnet['estimation'] = ['0.83', '-0.83', '0', '0.872', '#surprise', '#admiration', 'positive', '0.846', 'overappraisal', 'overvaluation', 'overestimate', 'overestimation', 'commercial_document']
senticnet['estimation_error'] = ['0.83', '-0.83', '0', '0.872', '#surprise', '#admiration', 'positive', '0.846', 'overappraisal', 'overvaluation', 'overestimate', 'overestimation', 'commercial_document']
senticnet['estimation_method'] = ['-0.02', '0.248', '0', '0.31', '#interest', '#admiration', 'positive', '0.178', 'assurance', 'total', 'quality', 'importance', 'amount']
senticnet['estimation_methodology'] = ['-0.02', '0.248', '0', '0.31', '#interest', '#admiration', 'positive', '0.178', 'assurance', 'total', 'quality', 'importance', 'amount']
senticnet['estimation_procedure'] = ['-0.02', '0.248', '0', '0.31', '#interest', '#admiration', 'positive', '0.178', 'assurance', 'total', 'quality', 'importance', 'amount']
senticnet['estimation_technique'] = ['0.83', '-0.83', '0', '0.872', '#surprise', '#admiration', 'positive', '0.846', 'overappraisal', 'overvaluation', 'overestimate', 'overestimation', 'commercial_document']
senticnet['estimator'] = ['0.83', '-0.83', '0', '0.872', '#surprise', '#admiration', 'positive', '0.846', 'overappraisal', 'overvaluation', 'overestimate', 'overestimation', 'commercial_document']
senticnet['estivation'] = ['-0.75', '0', '0.512', '-0.75', '#sadness', '#disgust', 'negative', '-0.67', 'inactivity', 'inactiveness', 'slumber', 'inaction', 'hibernation']
senticnet['estoppel'] = ['-0.55', '0', '0.741', '-0.76', '#anger', '#disgust', 'negative', '-0.68', 'illogicality', 'illogicalness', 'illogic', 'inconsequence', 'fallaciousness']
senticnet['estrange'] = ['-0.54', '0', '0.706', '-0.62', '#anger', '#disgust', 'negative', '-0.62', 'alienated', 'alienation', 'disaffection', 'alienate', 'disoriented']
senticnet['estranged'] = ['-0.86', '0', '0.652', '-0.67', '#sadness', '#disgust', 'negative', '-0.73', 'unloved', 'unbeloved', 'bereft', 'lovelorn', 'disinherited']
senticnet['estranged_father'] = ['0.86', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.798', 'director_chair', 'alcohol', 'bride_groom', 'attendee', 'coworker']
senticnet['estranged_son'] = ['0.86', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.798', 'director_chair', 'alcohol', 'bride_groom', 'attendee', 'coworker']
senticnet['estrangement'] = ['-0.10', '0.176', '0.629', '-0.64', '#anger', '#disgust', 'negative', '-0.3', 'alienated', 'alienation', 'disaffection', 'estrange', 'alienate']
senticnet['estrangement_between'] = ['-0.97', '0', '0.712', '-0.78', '#sadness', '#disgust', 'negative', '-0.82', 'discrepant', 'inconsistent', 'scratchy', 'uneven', 'contradictory']
senticnet['estremadura'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'dryness', 'thirstiness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['estrildidae'] = ['-0.92', '0', '0.64', '-0.52', '#sadness', '#anger', 'negative', '-0.69', 'animal_order', 'percher', 'thyreophoran', 'marginocephalian', 'euronithopod']
senticnet['estrus_cycle'] = ['0.947', '-0.81', '0', '0.656', '#joy', '#surprise', 'positive', '0.805', 'sexual_intercourse', 'intercourse', 'sex_act', 'hank_panky', 'coitus']
senticnet['estuarine'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'frog', 'amphibian']
senticnet['estuarine_ecology'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'frog', 'amphibian']
senticnet['estuary'] = ['0.857', '-0.89', '0', '0', '#joy', '#surprise', 'positive', '0.875', 'bay', 'harpoon', 'gulf', 'lake', 'river']
senticnet['esurience'] = ['0.768', '-0.72', '0', '0.838', '#joy', '#admiration', 'positive', '0.776', 'edacity', 'gluttony', 'voracity', 'voraciousness', 'rapaciousness']
senticnet['esurient'] = ['-0.71', '0', '0.692', '-0.81', '#sadness', '#disgust', 'negative', '-0.74', 'famished', 'starved', 'ravenous', 'hungry', 'peckish']
senticnet['esvcw_east_melbourne'] = ['0.552', '-0.61', '0', '0.588', '#surprise', '#admiration', 'positive', '0.583', 'barbershop_quartet', 'string_quartet', 'quartette', 'musical_organization', 'musical_group']
senticnet['esvfi_east_melbourne'] = ['0.552', '-0.61', '0', '0.588', '#surprise', '#admiration', 'positive', '0.583', 'barbershop_quartet', 'string_quartet', 'quartette', 'musical_organization', 'musical_group']
senticnet['esvsk_east_melbourne'] = ['0.552', '-0.61', '0', '0.588', '#surprise', '#admiration', 'positive', '0.583', 'barbershop_quartet', 'string_quartet', 'quartette', 'musical_organization', 'musical_group']
senticnet['esvsm_east_melbourne'] = ['0.552', '-0.61', '0', '0.588', '#surprise', '#admiration', 'positive', '0.583', 'barbershop_quartet', 'string_quartet', 'quartette', 'musical_organization', 'musical_group']
senticnet['esvun_east_melbourne'] = ['0.552', '-0.61', '0', '0.588', '#surprise', '#admiration', 'positive', '0.583', 'barbershop_quartet', 'string_quartet', 'quartette', 'musical_organization', 'musical_group']
senticnet['etched_into'] = ['0', '0.827', '0', '0.846', '#interest', '#admiration', 'positive', '0.837', 'inexterminable', 'inextirpable', 'inexpungible', 'inexpungeable', 'ineradicable']
senticnet['etched_onto'] = ['0.719', '0.757', '0', '0', '#joy', '#interest', 'positive', '0.738', 'matriculate', 'enroll_class', 'enroll_school', 'enroll', 'register']
senticnet['eternal'] = ['0.051', '0.207', '-0.18', '0.264', '#interest', '#admiration', 'positive', '0.085', 'everlasting', 'forever', 'unending', 'endless', 'eternity']
senticnet['eternal_damnation'] = ['0', '0', '0.753', '-0.72', '#anger', '#disgust', 'negative', '-0.73', 'sinful', 'human_emotion', 'call', 'demand', 'yearn']
senticnet['eternal_fidelity'] = ['0.088', '0.317', '-0.29', '0.379', '#interest', '#admiration', 'positive', '0.122', 'oath', 'commitment', 'marriage', 'obedience', 'enter_contract']
senticnet['eternal_glory'] = ['0.741', '-0.84', '0', '0.543', '#joy', '#surprise', 'positive', '0.711', 'integrity', 'righteousness', 'scrupulousness', 'incorruptness', 'honestness']
senticnet['eternal_gratitude'] = ['0.798', '0.754', '0', '0.779', '#joy', '#admiration', 'positive', '0.777', 'affection', 'joy', 'glad', 'admire', 'sympathetic']
senticnet['eternal_hell'] = ['0', '0', '0.753', '-0.72', '#anger', '#disgust', 'negative', '-0.73', 'sinful', 'human_emotion', 'call', 'demand', 'yearn']
senticnet['eternal_life'] = ['0.695', '-0.85', '0', '0.501', '#joy', '#surprise', 'positive', '0.684', 'immortality', 'live_forever', 'deathless', 'immortal', 'everlasting_life']
senticnet['eternal_peace'] = ['0.497', '0.453', '-0.23', '0.189', '#joy', '#interest', 'positive', '0.226', 'calm', 'calmness', 'stillness', 'truce', 'love']
senticnet['eternal_perdition'] = ['-0.70', '0', '0.566', '-0.95', '#sadness', '#disgust', 'negative', '-0.74', 'hellfire', 'red_region', 'imaginary_place', 'mythical_place', 'fictitious_place']
senticnet['eternal_punishment'] = ['-0.82', '0', '0.672', '-0.54', '#sadness', '#anger', 'negative', '-0.68', 'amends', 'damage', 'indemnification', 'redress', 'restitution']
senticnet['eternal_reward'] = ['0', '0.286', '-0.22', '0.383', '#interest', '#admiration', 'positive', '0.147', 'oath', 'propose', 'suggest', 'ask', 'trust']
senticnet['eternal_salvation'] = ['0.05', '0.363', '-0.09', '0.307', '#interest', '#admiration', 'positive', '0.157', 'faith', 'christ', 'prayer', 'spiritual', 'religious_purpose']
senticnet['eternal_torment'] = ['0', '0.325', '-0.09', '0.21', '#interest', '#admiration', 'positive', '0.146', 'saint', 'priest', 'christian', 'holy_man', 'friar']
senticnet['eternal_truth'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'metastability', 'monotony', 'invariance', 'constancy', 'unchangeability']
senticnet['eternal_youth'] = ['0', '0', '0', '0.816', '#admiration', '#admiration', 'positive', '0.816', 'immortality', 'live_forever', 'deathless', 'eternal_life', 'everlasting_life']
senticnet['eternalism'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'metastability', 'monotony', 'invariance', 'constancy', 'unchangeability']
senticnet['eternality'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'metastability', 'monotony', 'invariance', 'constancy', 'unchangeability']
senticnet['eternally'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'metastability', 'monotony', 'invariance', 'constancy', 'unchangeability']
senticnet['eternally_grateful'] = ['0', '0', '0', '0.869', '#admiration', '#admiration', 'positive', '0.869', 'prosper', 'succeed', 'realize', 'fly_high', 'thrive']
senticnet['eternally_valid'] = ['-0.81', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.79', 'stifle', 'forgotten', 'old', 'touch_ground', 'seldom']
senticnet['eternia'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'feudal_lord', 'lord']
senticnet['eternity'] = ['0', '0.106', '-0.05', '0.116', '#interest', '#admiration', 'positive', '0.056', 'replicate', 'endless', 'live_forever', 'soon', 'general_time']
senticnet['ethacrynic_acid'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'spironolactone', 'adrenal_cortical_steroid', 'antihypertensive', 'antihypertensive_drug']
senticnet['ethafoam'] = ['0.067', '0.088', '-0.07', '0.246', '#interest', '#admiration', 'positive', '0.081', 'multiple_purposes', 'revolve', 'start_run', 'wiggle', 'twist']
senticnet['ethan'] = ['0.699', '0.62', '-0.23', '0.323', '#joy', '#interest', 'positive', '0.353', 'jolliness', 'joviality', 'jolity', 'chaff', 'banter']
senticnet['ethanethiol'] = ['-0.67', '0', '0.503', '-0.67', '#sadness', '#disgust', 'negative', '-0.61', 'stink_up', 'smell_up', 'stink_out', 'incense', 'perfume']
senticnet['ethanol'] = ['0.957', '0.717', '0', '0', '#joy', '#interest', 'positive', '0.837', 'part_of_dinner', 'cola', 'liquid_beverage', 'juice', 'soft_drink']
senticnet['ethanol_future_contract'] = ['0.868', '-0.84', '0', '0.673', '#joy', '#surprise', 'positive', '0.796', 'instrument', 'option', 'derivative_instrument', 'legal_document', 'legal_instrument']
senticnet['ethene'] = ['0.032', '-0.02', '-0.04', '-0.08', '#fear', '#disgust', 'negative', '-0.01', 'liquid', 'carbonate_drink', 'chemical', 'soda', 'bromide']
senticnet['ethereal'] = ['-0.96', '0', '0.939', '-0.60', '#sadness', '#anger', 'negative', '-0.83', 'airy', 'shadowy', 'stringy', 'wraithlike', 'insubstantial']
senticnet['ethereal_beauty'] = ['0.824', '-0.95', '0', '0.513', '#joy', '#surprise', 'positive', '0.765', 'clearcutness', 'explicitness', 'preciseness', 'perspicuity', 'perspicuousness']
senticnet['ethereal_plane'] = ['-0.82', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.80', 'evil', 'darkness', 'dark', 'sun_down', 'dark_side']
senticnet['ethereal_quality'] = ['0.824', '-0.95', '0', '0.513', '#joy', '#surprise', 'positive', '0.765', 'clearcutness', 'explicitness', 'preciseness', 'perspicuity', 'perspicuousness']
senticnet['ethereality'] = ['0.609', '-0.61', '0', '0.592', '#joy', '#surprise', 'positive', '0.605', 'spirit', 'rabidity', 'rabidness', 'sprightliness', 'ebullience']
senticnet['ethereally'] = ['-0.57', '0', '0.841', '-0.50', '#sadness', '#anger', 'negative', '-0.64', 'somberness', 'stodginess', 'gravity', 'soberness', 'graveness']
senticnet['etherial'] = ['-0.81', '0', '0.945', '-0.51', '#sadness', '#anger', 'negative', '-0.75', 'occult', 'concealed', 'nonvisual', 'hidden', 'out_of_sight']
senticnet['etheric'] = ['-0.53', '0', '0.637', '-0.92', '#anger', '#disgust', 'negative', '-0.69', 'discorporate', 'unembodied', 'disembodied', 'incorporeal', 'unbodied']
senticnet['etheric_body'] = ['0.034', '0.161', '-0.05', '0.172', '#interest', '#admiration', 'positive', '0.079', 'mental', 'psychical', 'psychologist', 'mind_power', 'mind']
senticnet['ethernet_lan'] = ['0.078', '0.103', '-0.09', '0.191', '#interest', '#admiration', 'positive', '0.069', 'connect', 'aunt_uncle', 'short_form', 'relationship', 'kinship']
senticnet['ethic'] = ['0.891', '0', '0', '0.821', '#joy', '#admiration', 'positive', '0.856', 'punctual', 'truth', 'certain', 'trait', 'good_character']
senticnet['ethical'] = ['0.772', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.841', 'honorable', 'honest', 'good', 'estimable', 'fair']
senticnet['ethical_behavior'] = ['0', '0', '0', '0.817', '#admiration', '#admiration', 'positive', '0.817', 'truthful', 'honesty', 'honest', 'unity', 'good_character']
senticnet['ethical_code'] = ['0.661', '-0.66', '0', '0.685', '#surprise', '#admiration', 'positive', '0.671', 'system_of_rules', 'theosophy', 'anthroposophy', 'logic', 'method']
senticnet['ethical_conduct'] = ['0', '0', '0', '0.817', '#admiration', '#admiration', 'positive', '0.817', 'truthful', 'honesty', 'honest', 'unity', 'good_character']
senticnet['ethical_consideration'] = ['0', '0.795', '0', '0.961', '#interest', '#admiration', 'positive', '0.878', 'value_study', 'research', 'human_subject', 'science', 'project']
senticnet['ethical_egoism'] = ['0.971', '-0.71', '0', '0.656', '#joy', '#surprise', 'positive', '0.781', 'wellbeing', 'human_flourishing', 'successfulness', 'upbeat', 'strength']
senticnet['ethical_monotheism'] = ['-0.99', '0', '0.835', '-0.58', '#sadness', '#anger', 'negative', '-0.80', 'beginning', 'origin', 'root', 'wellspring', 'source']
senticnet['ethical_motive'] = ['-0.82', '0', '0.599', '-0.72', '#sadness', '#disgust', 'negative', '-0.71', 'motive', 'need', 'motivation', 'conscience', 'scruple']
senticnet['ethically'] = ['0.631', '-0.58', '0', '0.914', '#joy', '#admiration', 'positive', '0.711', 'defendable', 'invulnerable', 'impregnable', 'secure', 'bombproof']
senticnet['ethicism'] = ['-0.98', '0', '0.968', '-0.62', '#sadness', '#anger', 'negative', '-0.86', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['ethmoid_bone'] = ['0.719', '-0.57', '0', '0.651', '#joy', '#admiration', 'positive', '0.647', 'chap', 'break', 'slit', 'gap', 'shift']
senticnet['ethnic_affiliation'] = ['0', '-0.91', '0', '0.806', '#surprise', '#admiration', 'positive', '0.861', 'clean_shoes', 'black_stuff', 'paint_nail', 'ethnicity', 'black_goo']
senticnet['ethnic_ancestry'] = ['0', '-0.91', '0', '0.806', '#surprise', '#admiration', 'positive', '0.861', 'clean_shoes', 'black_stuff', 'paint_nail', 'ethnicity', 'black_goo']
senticnet['ethnic_background'] = ['0', '-0.91', '0', '0.806', '#surprise', '#admiration', 'positive', '0.861', 'clean_shoes', 'black_stuff', 'paint_nail', 'ethnicity', 'black_goo']
senticnet['ethnic_cuisine'] = ['0.882', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.822', 'ferment_grape_juice', 'chef_special', 'bottle_wine', 'fine_dining', 'decanter']
senticnet['ethnic_group'] = ['0', '-0.91', '0', '0.806', '#surprise', '#admiration', 'positive', '0.861', 'clean_shoes', 'black_stuff', 'paint_nail', 'ethnicity', 'black_goo']
senticnet['ethnic_homogeneity'] = ['-0.63', '0', '0.822', '-0.95', '#anger', '#disgust', 'negative', '-0.80', 'uniformness', 'homogeneousness', 'consistency', 'consistence', 'similarity']
senticnet['ethnic_identity'] = ['0.122', '0.159', '-0.05', '0.217', '#interest', '#admiration', 'positive', '0.111', 'profile_picture', 'unity', 'country_person', 'unite', 'signature']
senticnet['ethnic_music'] = ['0', '0.839', '0', '0.692', '#interest', '#admiration', 'positive', '0.765', 'tribe', 'kinfolk', 'family', 'folk', 'family_line']
senticnet['ethnic_origin'] = ['0', '-0.91', '0', '0.806', '#surprise', '#admiration', 'positive', '0.861', 'clean_shoes', 'black_stuff', 'paint_nail', 'ethnicity', 'black_goo']
senticnet['ethnic_prejudice'] = ['-0.94', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.92', 'insanity', 'angry_emotion', 'dislike', 'psychosis', 'fight']
senticnet['ethnic_strife'] = ['-0.92', '0', '0.546', '-0.52', '#sadness', '#anger', 'negative', '-0.66', 'irresponsibility', 'irresponsibleness', 'undependability', 'irreproducibility', 'fallibility']
senticnet['ethnic_tension'] = ['-0.84', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.82', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecure']
senticnet['ethnically'] = ['0', '0.812', '0', '0.796', '#interest', '#admiration', 'positive', '0.804', 'socio-economically', 'socioeconomically', 'disadvantaged', 'linguistically', 'educationally']
senticnet['ethnically_jewish'] = ['0', '-0.00', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'hot_chick', 'atheist', 'computer_user', 'fastener_binder', 'monitor']
senticnet['ethnicity'] = ['0.961', '0.963', '0', '0', '#joy', '#interest', 'positive', '0.962', 'cultural', 'heritage', 'cultural_heritage', 'nationality', 'tradition']
senticnet['ethno_cultural'] = ['0.122', '0.159', '-0.05', '0.217', '#interest', '#admiration', 'positive', '0.111', 'profile_picture', 'unity', 'country_person', 'unite', 'signature']
senticnet['ethno_linguistic'] = ['0', '-0.91', '0', '0.806', '#surprise', '#admiration', 'positive', '0.861', 'clean_shoes', 'black_stuff', 'paint_nail', 'ethnicity', 'black_goo']
senticnet['ethnocentric'] = ['0.585', '-0.7', '0', '0.586', '#surprise', '#admiration', 'positive', '0.624', 'jingoistic', 'ultranationalistic', 'superpatriotic', 'nationalistic', 'patriotic']
senticnet['ethnocentrism'] = ['0', '0', '0.875', '-0.86', '#anger', '#disgust', 'negative', '-0.87', 'parochialism', 'insularity', 'provincialism', 'chauvinism', 'jingoism']
senticnet['ethnocide'] = ['-0.75', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.82', 'oppression', 'caged_lion', 'slave', 'captivity', 'misery']
senticnet['ethnology'] = ['0.818', '0.986', '0', '0', '#joy', '#interest', 'positive', '0.902', 'anthropology', 'social_anthropology', 'archeology', 'physical_anthropology', 'social_science']
senticnet['ethyl_acetate'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
senticnet['ethyl_acetoacetate'] = ['-0.81', '0', '0.774', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'acid', 'hypophosphoric_acid', 'periodic_acid', 'oxyacid', 'phosphorous_acid']
senticnet['ethyl_alcohol'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
senticnet['ethylenediamine'] = ['0.766', '-0.87', '0', '0.666', '#joy', '#surprise', 'positive', '0.769', 'reactive', 'unstable', 'labile', 'activated', 'excited']
senticnet['ethynyl'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
senticnet['eti_regime'] = ['0.5', '-0.9', '0', '0.525', '#surprise', '#admiration', 'positive', '0.642', 'manageable', 'controllable', 'governable', 'steerable', 'dirigible']
senticnet['etiam'] = ['0.654', '-0.75', '0', '0.78', '#surprise', '#admiration', 'positive', '0.729', 'suit', 'cause', 'lawsuit', 'occurrence', 'countersuit']
senticnet['etiologic_agent'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'agent', 'satisfier', 'quickener', 'invigorator']
senticnet['etiological'] = ['0.798', '-0.51', '0', '0.519', '#joy', '#admiration', 'positive', '0.611', 'averageness', 'commonness', 'normalcy', 'typicality', 'condition']
senticnet['etiologically'] = ['0.798', '-0.51', '0', '0.519', '#joy', '#admiration', 'positive', '0.611', 'averageness', 'commonness', 'normalcy', 'typicality', 'condition']
senticnet['etiquette'] = ['0', '0', '0', '0.99', '#admiration', '#admiration', 'positive', '0.99', 'politeness', 'good_behavior', 'good_etiquette', 'manner', 'hint']
senticnet['etiquette_expert'] = ['0', '0', '0', '0.99', '#admiration', '#admiration', 'positive', '0.99', 'politeness', 'good_behavior', 'good_etiquette', 'manner', 'hint']
senticnet['etomidate'] = ['-0.65', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'put_to_sleep', 'operation', 'anesthesia', 'surgery', 'local_anesthesia']
senticnet['etruscan_culture'] = ['-0.03', '0.092', '0.07', '0.1', '#interest', '#admiration', 'positive', '0.021', 'gladiator', 'civilization', 'church_build', 'coliseum', 'every_town']
senticnet['ettan'] = ['0.914', '-0.56', '0', '0.869', '#joy', '#admiration', 'positive', '0.781', 'tabula_rasa', 'ego', 'subconscious', 'unconscious', 'psyche']
senticnet['ettiquette'] = ['0', '0', '0', '0.99', '#admiration', '#admiration', 'positive', '0.99', 'politeness', 'good_behavior', 'good_etiquette', 'manner', 'hint']
senticnet['etymological_dictionary'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'comic_book', 'child_book', 'child_story', 'comic', 'disney_cartoon']
senticnet['etymologically'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
senticnet['eu'] = ['0.771', '0.846', '0', '0', '#joy', '#interest', 'positive', '0.808', 'dollar_bill', 'franc', 'banknote', 'dollar', 'cent']
senticnet['eu_bailout'] = ['-0.12', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'escape', 'escape_situation', 'cancel_meeting', 'absent', 'cancel_appointment']
senticnet['eubacteria'] = ['-0.90', '0', '0.908', '-0.53', '#sadness', '#anger', 'negative', '-0.78', 'tubercle_bacillus', 'leprosy_bacillus', 'mycobacteria', 'eubacterium', 'true_bacteria']
senticnet['eubacterium'] = ['-0.90', '0', '0.908', '-0.53', '#sadness', '#anger', 'negative', '-0.78', 'tubercle_bacillus', 'leprosy_bacillus', 'eubacteria', 'mycobacteria', 'true_bacteria']
senticnet['eucalyptol'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
senticnet['eucalyptus'] = ['0.859', '0', '0', '0.802', '#joy', '#admiration', 'positive', '0.831', 'fir', 'spruce', 'pine', 'hemlock', 'balsam']
senticnet['eucalyptus_oil'] = ['0.701', '-0.76', '0', '0.904', '#surprise', '#admiration', 'positive', '0.791', 'curative', 'heal', 'medicine', 'get_cured', 'take_care']
senticnet['eucerin'] = ['0', '0.086', '-0.06', '0.12', '#interest', '#admiration', 'positive', '0.046', 'gel_toothpaste', 'tartar_control_toothpaste', 'fluoride_mouthwash', 'toothpaste', 'hair_lotion']
senticnet['eucharist'] = ['0.816', '0', '0', '0.751', '#joy', '#admiration', 'positive', '0.783', 'holy', 'liturgy', 'holy_eucharist', 'holy_sacrament', 'sacrament_of_eucharist']
senticnet['eucharistic_celebration'] = ['0.023', '0.295', '-0.05', '0.283', '#interest', '#admiration', 'positive', '0.136', 'religion', 'religious_purpose', 'world_religion', 'gospel', 'judaism']
senticnet['eucharistic_prayer'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'reverence', 'genuflection', 'obeisance', 'kowtow', 'mafia']
senticnet['euchre'] = ['0.934', '-0.57', '0', '0.767', '#joy', '#admiration', 'positive', '0.76', 'morosoph', 'simpleton', 'flibbertigibbet', 'foolish_woman', 'simple']
senticnet['euclidean'] = ['-0.04', '0.138', '-0.08', '0.111', '#interest', '#admiration', 'positive', '0.031', 'blade_geometry', 'craft', 'katana', 'blade', 'collect_item']
senticnet['euclidean_distance'] = ['0', '0.203', '0.036', '0.204', '#interest', '#admiration', 'positive', '0.124', 'amount', 'count', 'total', 'quantity', 'total_amount']
senticnet['euclidean_geometry'] = ['-0.04', '0.138', '-0.08', '0.111', '#interest', '#admiration', 'positive', '0.031', 'blade_geometry', 'craft', 'katana', 'blade', 'collect_item']
senticnet['euclidean_metric'] = ['0', '0.203', '0.036', '0.204', '#interest', '#admiration', 'positive', '0.124', 'amount', 'count', 'total', 'quantity', 'total_amount']
senticnet['euconulidae'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'aridness', 'dryness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['eudaemon'] = ['0.612', '-0.65', '0', '0.941', '#surprise', '#admiration', 'positive', '0.735', 'eudemon', 'guardian_spirit', 'good_spirit', 'genius_locus', 'angel']
senticnet['eudaimonia'] = ['0.971', '-0.71', '0', '0.656', '#joy', '#surprise', 'positive', '0.781', 'wellbeing', 'human_flourishing', 'successfulness', 'upbeat', 'strength']
senticnet['eudaimonism'] = ['0.971', '-0.71', '0', '0.656', '#joy', '#surprise', 'positive', '0.781', 'wellbeing', 'human_flourishing', 'successfulness', 'upbeat', 'strength']
senticnet['eudemon'] = ['0.673', '-0.64', '0', '0.669', '#joy', '#admiration', 'positive', '0.661', 'guardian_angel', 'white_angel', 'good_spirit', 'genius_locus', 'angel']
senticnet['eudemonism'] = ['0.601', '-0.84', '0', '0.872', '#surprise', '#admiration', 'positive', '0.773', 'bioethics', 'hedonism', 'philosophy', 'casuistry', 'moral_philosophy']
senticnet['eudora'] = ['-0.72', '0', '0.619', '-0.86', '#sadness', '#disgust', 'negative', '-0.73', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['euergetism'] = ['-0.69', '0', '0.957', '-0.92', '#anger', '#disgust', 'negative', '-0.85', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['euerie'] = ['-0.11', '0.112', '0.674', '-0.66', '#anger', '#disgust', 'negative', '-0.33', 'wrathfully', 'wrathful', 'wrothful', 'lividly', 'lividity']
senticnet['eugen_bleuler'] = ['-0.55', '0', '0.736', '-0.71', '#anger', '#disgust', 'negative', '-0.66', 'neurosis', 'neuroticism', 'psychoneurosis', 'hysterocatalepsy', 'anxiety_hysteria']
senticnet['eugenic'] = ['0', '0', '-0.85', '-0.78', '#fear', '#disgust', 'negative', '-0.82', 'eugenics', 'eugenicist', 'eugenicists', 'socialistic', 'anti-woman']
senticnet['eugenicist'] = ['0', '0', '-0.86', '-0.79', '#fear', '#disgust', 'negative', '-0.82', 'eugenics', 'eugenic', 'eugenicists', 'socialistic', 'anti-woman']
senticnet['eugenicists'] = ['0', '0', '-0.86', '-0.79', '#fear', '#disgust', 'negative', '-0.83', 'eugenics', 'eugenicist', 'eugenic', 'socialistic', 'anti-woman']
senticnet['eugenics'] = ['0', '0', '-0.86', '-0.79', '#fear', '#disgust', 'negative', '-0.82', 'eugenic', 'eugenicist', 'eugenicists', 'socialistic', 'anti-woman']
senticnet['eugenio_bennato'] = ['0.107', '0.11', '-0.08', '0.21', '#interest', '#admiration', 'positive', '0.086', 'live_healthy_life', 'forever_young', 'live_forever', 'live_long', 'live_good_life']
senticnet['eugenol'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'chemical_irritant', 'gingerol', 'piperine', 'pepper_spray']
senticnet['euglena'] = ['0.807', '-0.86', '0', '0.611', '#joy', '#surprise', 'positive', '0.762', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['euill'] = ['-0.03', '0.157', '0.698', '-0.66', '#anger', '#disgust', 'negative', '-0.30', 'vengefulness', 'vindictively', 'vengefully', 'revengefully', 'vindictive']
senticnet['eukaryote'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'carbon_monoxide', 'endangered_species', 'rodent', 'tree_animal']
senticnet['eulogist'] = ['-0.67', '0', '0.936', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'extemporaneous', 'unprepared', 'extempore', 'impromptu', 'unrehearsed']
senticnet['eulogistic'] = ['0.722', '-0.98', '0', '0.832', '#surprise', '#admiration', 'positive', '0.845', 'laudatory', 'praiseful', 'praising', 'encomiastic', 'complimentary']
senticnet['eulogium'] = ['0', '0', '-0.75', '-0.92', '#fear', '#disgust', 'negative', '-0.84', 'paean', 'eulogy', 'encomium', 'pean', 'panegyric']
senticnet['eulogize'] = ['0', '0.946', '0', '0.724', '#interest', '#admiration', 'positive', '0.835', 'celebrate', 'eulogistic', 'formal', 'eloquent', 'uneulogized']
senticnet['eulogy'] = ['0', '0', '-0.75', '-0.92', '#fear', '#disgust', 'negative', '-0.83', 'paean', 'eulogium', 'encomium', 'pean', 'panegyric']
senticnet['eumelanin'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['euniverse'] = ['-0.63', '0', '0.862', '-0.52', '#sadness', '#anger', 'negative', '-0.67', 'knot', 'bunch', 'tuft', 'tussock', 'agglomeration']
senticnet['euno'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'head_hurt', 'introspection', 'lifestyle_change', 'headache', 'move_eye']
senticnet['eunomian'] = ['-0.22', '0', '-0.09', '-0.16', '#sadness', '#disgust', 'negative', '-0.16', 'inequality', 'reject', 'discard', 'difference', 'gap']
senticnet['eupatorium'] = ['-0.52', '0', '0.527', '-0.78', '#anger', '#disgust', 'negative', '-0.61', 'herb', 'black_snakeroot', 'wild_ginger', 'asarabacca', 'herbaceous_plant']
senticnet['euphemism'] = ['0', '0.115', '-0.10', '0.249', '#interest', '#admiration', 'positive', '0.087', 'idiom', 'remark', 'grammar_part', 'speech_part', 'sentence']
senticnet['euphemistic'] = ['0', '0.832', '0', '0.738', '#interest', '#admiration', 'positive', '0.785', 'epithet', 'honorific', 'pejorative', 'bestowed', 'honorific']
senticnet['euphorbia_corollata'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'clematis', 'legume', 'leguminous_plant', 'climber', 'root_climber']
senticnet['euphoria'] = ['0.973', '0', '0', '0.729', '#joy', '#admiration', 'positive', '0.851', 'cheer', 'contentment', 'elation', 'jubilation', 'glee']
senticnet['euphoriant'] = ['0.672', '0.632', '-0.23', '0.312', '#joy', '#interest', 'positive', '0.344', 'euphoria', 'buoyancy', 'perkiness', 'jubilance', 'ecstasy']
senticnet['euphoric'] = ['0.663', '0.634', '-0.24', '0.303', '#joy', '#interest', 'positive', '0.34', 'euphoria', 'euphoriant', 'mirthfully', 'merrily', 'happily']
senticnet['euphoric_feeling'] = ['0.958', '-0.72', '0', '0.72', '#joy', '#surprise', 'positive', '0.801', 'pressor', 'hormone', 'endocrine', 'internal_secretion', 'catecholamine']
senticnet['eurasian_collared_dove'] = ['-0.66', '0', '0.982', '-0.93', '#anger', '#disgust', 'negative', '-0.86', 'bother', 'barge_in', 'come_in', 'break_in', 'crash']
senticnet['eurasian_coot'] = ['-0.50', '0', '0.764', '-0.61', '#anger', '#disgust', 'negative', '-0.62', 'doctor', 'mountebank', 'charlatan', 'quack', 'physician']
senticnet['eurasian_eagle_owl'] = ['-0.53', '0', '0.648', '-0.70', '#anger', '#disgust', 'negative', '-0.63', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['eurasian_lynx'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheep', 'argal', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['eurasian_woodcock'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['eureka'] = ['-0.06', '0.21', '0', '0.076', '#interest', '#admiration', 'positive', '0.074', 'new_think', 'figure_out', 'brain_activity', 'idea', 'come_up_idea']
senticnet['eureka_flag'] = ['0.053', '0.068', '-0.05', '0.06', '#interest', '#admiration', 'positive', '0.032', 'linen', 'star_stripe', 'velvet', 'nation', 'burlap']
senticnet['eurhythmics'] = ['0.536', '-0.56', '0', '0.567', '#surprise', '#admiration', 'positive', '0.557', 'recreation', 'diversion', 'move', 'motility', 'movement']
senticnet['eurhythmy'] = ['0.964', '-0.86', '0', '0.957', '#joy', '#admiration', 'positive', '0.927', 'recreation', 'diversion', 'move', 'motility', 'movement']
senticnet['euro'] = ['0.771', '0.846', '0', '0', '#joy', '#interest', 'positive', '0.808', 'dollar_bill', 'franc', 'banknote', 'dollar', 'cent']
senticnet['euro_coin'] = ['0.987', '0.902', '0', '0', '#joy', '#interest', 'positive', '0.945', 'franc', 'dime', 'dollar', 'shilling', 'banknote']
senticnet['euro_currency'] = ['0.771', '0.846', '0', '0', '#joy', '#interest', 'positive', '0.808', 'dollar_bill', 'franc', 'banknote', 'dollar', 'cent']
senticnet['euro_en_moyenne'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'indweller', 'invillager', 'villager', 'dweller', 'denizen']
senticnet['euro_exchange_rate'] = ['0.771', '0.846', '0', '0', '#joy', '#interest', 'positive', '0.808', 'dollar_bill', 'franc', 'banknote', 'dollar', 'cent']
senticnet['euro_zone'] = ['0.771', '0.846', '0', '0', '#joy', '#interest', 'positive', '0.808', 'dollar_bill', 'franc', 'banknote', 'dollar', 'cent']
senticnet['euro_zone_recession'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'mathematical', 'muscle', 'math_function', 'feel_sick', 'separation']
senticnet['euro_zone_rescue'] = ['-0.12', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'escape', 'escape_situation', 'cancel_meeting', 'absent', 'cancel_appointment']
senticnet['eurocentrism'] = ['-0.07', '0.195', '0.629', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'misogynic', 'misogyny', 'murderousness', 'amuck', 'amok']
senticnet['euroland'] = ['0.771', '0.846', '0', '0', '#joy', '#interest', 'positive', '0.808', 'dollar_bill', 'franc', 'banknote', 'dollar', 'cent']
senticnet['euronithopod'] = ['-0.92', '0', '0.64', '-0.52', '#sadness', '#anger', 'negative', '-0.69', 'perching_bird', 'percher', 'thyreophoran', 'marginocephalian', 'animal_order']
senticnet['europa_surface'] = ['0.095', '0', '-0.04', '0.062', '#joy', '#admiration', 'positive', '0.037', 'photographer', 'water_spray', 'moisture', 'rise_water', 'droplet']
senticnet['europe'] = ['0.838', '0.707', '0', '0.904', '#joy', '#admiration', 'positive', '0.816', 'natural_order', 'universe', 'existence', 'creation', 'cosmos']
senticnet['european'] = ['0.043', '0.109', '0', '0.061', '#interest', '#admiration', 'positive', '0.071', 'nation_state', 'democratic_country', 'free_country', 'large_country', 'republic']
senticnet['european_central_bank'] = ['0.771', '0.846', '0', '0', '#joy', '#interest', 'positive', '0.808', 'dollar_bill', 'franc', 'banknote', 'dollar', 'cent']
senticnet['european_champion'] = ['0', '0', '0', '0.834', '#admiration', '#admiration', 'positive', '0.834', 'royal_person', 'royal_title', 'top', 'master', 'rank']
senticnet['european_civilisation'] = ['-0.65', '0', '0.691', '-0.54', '#sadness', '#anger', 'negative', '-0.63', 'moronity', 'mental_deficiency', 'imbecility', 'stupidity', 'mental_defectiveness']
senticnet['european_honey_bee'] = ['0', '0', '0', '0.869', '#admiration', '#admiration', 'positive', '0.869', 'fly_animal', 'insect', 'mosquito', 'bug', 'fly_insect']
senticnet['european_monetary'] = ['0.771', '0.846', '0', '0', '#joy', '#interest', 'positive', '0.808', 'dollar_bill', 'franc', 'banknote', 'dollar', 'cent']
senticnet['european_otter'] = ['-0.55', '0', '0.955', '-0.96', '#anger', '#disgust', 'negative', '-0.82', 'house_mouse', 'mouse', 'rat', 'fieldmouse', 'nude_mouse']
senticnet['european_union'] = ['0.043', '0.109', '0', '0.061', '#interest', '#admiration', 'positive', '0.071', 'nation_state', 'democratic_country', 'free_country', 'large_country', 'republic']
senticnet['european_union_weu'] = ['0.851', '-0.65', '0', '0.66', '#joy', '#admiration', 'positive', '0.723', 'omen', 'portent', 'presage', 'prognostic', 'prognostication']
senticnet['eurotrash'] = ['-0.05', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.05', 'masturbate', 'jerk', 'jerk_off', 'watch_porn', 'porn']
senticnet['eurozone'] = ['0.771', '0.846', '0', '0', '#joy', '#interest', 'positive', '0.808', 'dollar_bill', 'franc', 'banknote', 'dollar', 'cent']
senticnet['eurozone_bailout'] = ['-0.12', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'escape', 'escape_situation', 'cancel_meeting', 'absent', 'cancel_appointment']
senticnet['eurozone_debt'] = ['0.771', '0.846', '0', '0', '#joy', '#interest', 'positive', '0.808', 'dollar_bill', 'franc', 'banknote', 'dollar', 'cent']
senticnet['eurozone_rescue'] = ['-0.12', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'escape', 'escape_situation', 'cancel_meeting', 'absent', 'cancel_appointment']
senticnet['eurythmics'] = ['0.556', '-0.86', '0', '0.846', '#surprise', '#admiration', 'positive', '0.757', 'recreation', 'diversion', 'move', 'motility', 'movement']
senticnet['eurythmy'] = ['0.96', '-0.85', '0', '0.934', '#joy', '#admiration', 'positive', '0.917', 'recreation', 'diversion', 'move', 'motility', 'movement']
senticnet['eurytoma'] = ['0.748', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.873', 'butterfly', 'moth', 'parrot', 'beetle', 'falcon']
senticnet['eusapia_palladino'] = ['0.728', '-0.72', '0', '0.881', '#joy', '#admiration', 'positive', '0.779', 'predict_future', 'psychic_phenomenon', 'parapsychology', 'precognition', 'foreknowledge']
senticnet['eustachian_tube_dysfunction'] = ['-0.87', '0', '0.773', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'rubor', 'redness', 'inflammation', 'otitis', 'otitis_interna']
senticnet['eustacia'] = ['-0.75', '0.282', '-0.42', '-0.06', '#sadness', '#fear', 'negative', '-0.24', 'depressing', 'cheerless', 'low-spiritedness', 'lowness', 'dispiritedness']
senticnet['eusuchian'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'hag', 'osteostracan', 'cephalaspid', 'ostracoderm', 'jawless_fish']
senticnet['eutrophication'] = ['-0.80', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.84', 'popularization', 'profanation', 'demoralization', 'corruption', 'humiliation']
senticnet['ev_ry'] = ['0.622', '0.641', '-0.24', '0.285', '#joy', '#interest', 'positive', '0.327', 'gleeful', 'gladsome', 'happiness', 'gladness', 'gladfulness']
senticnet['evacuate'] = ['0', '0', '0.921', '-0.75', '#anger', '#disgust', 'negative', '-0.83', 'dismiss', 'bust', 'ban', 'dispossess', 'eliminate']
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senticnet['evade_detection'] = ['-0.13', '0.07', '-0.10', '0.104', '#sadness', '#fear', 'negative', '-0.01', 'conceal', 'trick', 'concealment', 'hide_body', 'hide_truth']
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senticnet['evaluation'] = ['-0.06', '0.18', '0', '0.143', '#interest', '#admiration', 'positive', '0.086', 'grade', 'multiple_choices', 'final', 'final_exam', 'school_grade']
senticnet['evaluation_criterion'] = ['-0.06', '0.18', '0', '0.143', '#interest', '#admiration', 'positive', '0.086', 'grade', 'multiple_choices', 'final', 'final_exam', 'school_grade']
senticnet['evaluative'] = ['-0.06', '0.18', '0', '0.143', '#interest', '#admiration', 'positive', '0.086', 'grade', 'multiple_choices', 'final', 'final_exam', 'school_grade']
senticnet['evaluator'] = ['-0.06', '0.18', '0', '0.143', '#interest', '#admiration', 'positive', '0.086', 'grade', 'multiple_choices', 'final', 'final_exam', 'school_grade']
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senticnet['evan_slater'] = ['-0.93', '0', '0.791', '0', '#sadness', '#anger', 'negative', '-0.86', 'hopelessness', 'hopelessly', 'despairing', 'pessimistically', 'pessimistic']
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senticnet['evanescent'] = ['-0.63', '0', '0.694', '-0.66', '#anger', '#disgust', 'negative', '-0.66', 'corporality', 'tangibleness', 'palpability', 'reality', 'solidness']
senticnet['evangelical_congregation'] = ['0.081', '0.146', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'cult', 'sect', 'conservative', 'worshiper', 'pastor']
senticnet['evangelical_faith'] = ['0.914', '0', '0', '0.954', '#joy', '#admiration', 'positive', '0.934', 'spiritual', 'religious', 'religious_purpose', 'religion', 'prayer']
senticnet['evangelical_fundamentalism'] = ['0.929', '0.507', '-0.17', '0.396', '#joy', '#interest', 'positive', '0.415', 'plume', 'congratulate', 'smugness', 'smug', 'proudly']
senticnet['evangelical_religion'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'heterodoxy', 'iconoclasm', 'nonconformism', 'nonconformance', 'orientation']
senticnet['evangelical_revival'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'nonconformity', 'iconoclasm', 'heterodoxy', 'nonconformance', 'orientation']
senticnet['evangelical_theology'] = ['0.664', '-0.57', '0', '0.981', '#joy', '#admiration', 'positive', '0.741', 'theodicy', 'homiletics', 'discipline', 'study', 'subject']
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senticnet['evaporate'] = ['0', '0.073', '-0.05', '0.093', '#interest', '#admiration', 'positive', '0.038', 'vapor', 'water_vapor', 'condense_water', 'gaseous', 'bubble']
senticnet['evaporated_milk'] = ['0.053', '0.085', '0.019', '0', '#joy', '#interest', 'positive', '0.04', 'farm', 'countryside', 'farm_animal', 'cow', 'healthy']
senticnet['evaporation'] = ['0', '-0.03', '0.05', '-0.14', '#anger', '#disgust', 'negative', '-0.05', 'humidity', 'wetness', 'damp', 'humid', 'dank']
senticnet['evaporative'] = ['0.03', '0.068', '-0.06', '0.062', '#interest', '#admiration', 'positive', '0.025', 'pacific_northwest', 'american_forest', 'treetop', 'bath', 'hot_air']
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senticnet['evasive'] = ['0', '0', '0.819', '-0.71', '#anger', '#disgust', 'negative', '-0.76', 'waffle', 'avoidance', 'evasively', 'forthright', 'generic']
senticnet['evasively'] = ['0', '0', '0.817', '-0.71', '#anger', '#disgust', 'negative', '-0.76', 'waffle', 'avoidance', 'evasive', 'forthright', 'generic']
senticnet['evasiveness'] = ['0.738', '-0.85', '0', '0.89', '#surprise', '#admiration', 'positive', '0.828', 'equivocalness', 'twilight_zone', 'prevarication', 'elusive', 'unclearness']
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senticnet['eve'] = ['0', '0.085', '0', '0.146', '#interest', '#admiration', 'positive', '0.115', 'party', 'garden', 'apple', 'eden', 'outdoor']
senticnet['eve_lom'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'beauty_parlor', 'lady_hair', 'beauty_shop', 'beauty_salon', 'make_up']
senticnet['evel_knievel'] = ['0', '0.786', '0', '0.917', '#interest', '#admiration', 'positive', '0.852', 'act', 'portray', 'play_ping_pong', 'school_play', 'dangerous']
senticnet['evenhandedly'] = ['0.5', '-0.9', '0', '0.525', '#surprise', '#admiration', 'positive', '0.642', 'manageable', 'controllable', 'governable', 'steerable', 'dirigible']
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senticnet['evening_breeze'] = ['0.98', '-0.74', '0', '0.984', '#joy', '#admiration', 'positive', '0.903', 'prosperous', 'flourishing', 'successful', 'prospering', 'thriving']
senticnet['evening_herald'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'rascal', 'rapscallion', 'scalawag', 'holy_terror', 'monkey']
senticnet['evening_meal'] = ['0.026', '0.069', '0', '0', '#joy', '#interest', 'positive', '0.048', 'eat_food', 'eat_beef', 'chew_food', 'espresso', 'eat_much']
senticnet['evening_prayer'] = ['0.023', '0.295', '-0.05', '0.283', '#interest', '#admiration', 'positive', '0.136', 'religion', 'religious_purpose', 'world_religion', 'gospel', 'judaism']
senticnet['evening_primrose_oil'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucose', 'corn_sugar', 'dextrose', 'dextroglucose', 'grape_sugar']
senticnet['evening_snack'] = ['0.715', '0.785', '0', '0', '#joy', '#interest', 'positive', '0.75', 'scrumble_egg', 'heat_meal', 'pan', 'beat_egg', 'food']
senticnet['evening_stroll'] = ['0.876', '0.883', '0', '0', '#joy', '#interest', 'positive', '0.879', 'walk_along', 'gait', 'take_step', 'run_slowly', 'locomotion']
senticnet['evening_turndown_service'] = ['0.866', '0.833', '0', '0', '#joy', '#interest', 'positive', '0.849', 'alcohol', 'porter', 'concierge', 'beer_mug', 'bar_stool']
senticnet['evening_twilight'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['eveningwear'] = ['0.053', '-0.01', '-0.05', '0.121', '#joy', '#admiration', 'positive', '0.034', 'coat', 'winter_clothes', 'stylish', 'adorn', 'protective_cover']
senticnet['evenness'] = ['0', '0', '0.74', '-0.91', '#anger', '#disgust', 'negative', '-0.83', 'unevenness', 'uniformity', 'roundness', 'smoothness', 'flakiness']
senticnet['event'] = ['0.827', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.776', 'special_time', 'occasion', 'happen', 'incident', 'occurrence']
senticnet['event_horizon'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'empty_room', 'a_lot_of_space', 'empty', 'empty_house', 'universe']
senticnet['event_type'] = ['-0.88', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.90', 'special_event', 'type', 'birthday_party', 'event', 'bachelor_party']
senticnet['eventful'] = ['0', '-0.81', '0', '0.901', '#surprise', '#admiration', 'positive', '0.857', 'activity', 'many', 'event', 'eventfully', 'eventfulness']
senticnet['eventful_journey'] = ['0.937', '0.8', '0', '0', '#joy', '#interest', 'positive', '0.869', 'quest', 'stunt', 'illusion', 'adventure', 'activity']
senticnet['eventfully'] = ['0', '-0.82', '0', '0.891', '#surprise', '#admiration', 'positive', '0.857', 'activity', 'many', 'event', 'eventful', 'eventfulness']
senticnet['eventfulness'] = ['0', '-0.82', '0', '0.897', '#surprise', '#admiration', 'positive', '0.859', 'activity', 'many', 'event', 'eventfully', 'eventful']
senticnet['eventsdescriptor'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'identifiable', 'specifiable', 'recognizable', 'diagnosable', 'classifiable']
senticnet['eventual'] = ['0', '0', '0.078', '0', '#anger', '#anger', 'negative', '-0.07', 'street_city', 'small_street', 'walkway', 'black_pavement', 'dark_road']
senticnet['eventual_acquittal'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'gouger', 'swindler', 'confidence_man', 'conman', 'fraud']
senticnet['eventual_collapse'] = ['-0.13', '-0.05', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'monitor_heart_rate', 'feel_tired', 'bump_head', 'exhaustion', 'become_fatigued']
senticnet['eventual_consistency'] = ['0.792', '-0.83', '0', '0.575', '#joy', '#surprise', 'positive', '0.734', 'connectedness', 'connection', 'consistency', 'continuity', 'link']
senticnet['eventual_demolition'] = ['-0.08', '0', '-0.08', '-0.14', '#sadness', '#disgust', 'negative', '-0.10', 'violence', 'assault', 'combat', 'siege', 'use_military']
senticnet['eventual_destruction'] = ['-0.14', '-0.00', '-0.12', '-0.21', '#sadness', '#disgust', 'negative', '-0.11', 'torture', 'demolish', 'maim', 'terminate', 'hurt_person']
senticnet['eventual_fate'] = ['-0.09', '0', '0', '-0.19', '#sadness', '#disgust', 'negative', '-0.14', 'add_knowledge', 'learn', 'become_smart', 'discover', 'make_out_atom']
senticnet['eventuality'] = ['-0.80', '0', '0.945', '-0.63', '#sadness', '#anger', 'negative', '-0.79', 'fancy', 'conceive_of', 'fantasy', 'daydream', 'woolgather']
senticnet['eventually_coalesce'] = ['0', '0', '-0.89', '-0.79', '#fear', '#disgust', 'negative', '-0.84', 'blend', 'conflate', 'mix', 'flux', 'commingle']
senticnet['eventually_succumb'] = ['-0.75', '0', '-1.0', '0', '#sadness', '#fear', 'negative', '-0.87', 'give_in', 'submit', 'accept', 'defer', 'yield']
senticnet['ever-burning'] = ['0', '0', '-0.76', '-0.98', '#fear', '#disgust', 'negative', '-0.87', 'unquenchable', 'ever-lasting', 'quenchless', 'extinguishes', 'inextinguishable']
senticnet['ever-dwindling'] = ['-0.82', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.87', 'super-abundant', 'superabundance', 'inexhaustible', 'measureless', 'superabundant']
senticnet['ever-lasting'] = ['0', '0', '-0.76', '-0.98', '#fear', '#disgust', 'negative', '-0.87', 'unquenchable', 'inextinguishable', 'quenchless', 'extinguishes', 'ever-burning']
senticnet['ever_beheld'] = ['-0.44', '-0.15', '0', '-0.36', '#sadness', '#disgust', 'negative', '-0.21', 'baffle', 'stupefy', 'dumbfound', 'stupefaction', 'confuse']
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senticnet['ever_watchful'] = ['0', '1', '0', '0', '#interest', '#interest', 'positive', '1', 'confident', 'confidence', 'optimistic', 'assured', 'engrossed']
senticnet['everclear'] = ['0.797', '-0.95', '0', '0.921', '#surprise', '#admiration', 'positive', '0.891', 'lush', 'alcoholic', 'alky', 'dipsomaniac', 'boozer']
senticnet['everest'] = ['0', '0.076', '0', '0.063', '#interest', '#admiration', 'positive', '0.07', 'up_hill', 'elevation', 'up_mountain', 'ascend', 'upwards']
senticnet['everest_expedition'] = ['0.518', '-0.51', '0', '0.65', '#surprise', '#admiration', 'positive', '0.562', 'clematis', 'legume', 'leguminous_plant', 'climbing_fern', 'root_climber']
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senticnet['evergreen'] = ['0.76', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.748', 'stem', 'leaf', 'moss', 'branch', 'leaf_tree']
senticnet['evergreen_cemetery'] = ['-0.70', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.70', 'death_row', 'sad', 'loose_end', 'car_race', 'mortuary']
senticnet['evergreen_christmas_tree'] = ['0.856', '0', '0', '0', '#joy', '#joy', 'positive', '0.856', 'christmas_tree', 'tree', 'big_christmas_tree', 'christmas_holiday', 'christmas']
senticnet['evergreen_foliage'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'tropical_fish', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['evergreen_perennial'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'livelong', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['evergreen_shrub'] = ['-0.80', '0', '0.555', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'bush', 'wormwood', 'milkwort', 'germander', 'centaury']
senticnet['evergreen_tree'] = ['0.856', '0', '0', '0', '#joy', '#joy', 'positive', '0.856', 'tree', 'big_tree', 'christmas_tree', 'evergreen_christmas_tree', 'christmas']
senticnet['everie'] = ['-0.69', '0', '0.883', '-0.91', '#anger', '#disgust', 'negative', '-0.82', 'stick_in', 'enclose', 'insert', 'feed_in', 'put_in']
senticnet['everlasting'] = ['0.882', '0', '0', '0.959', '#joy', '#admiration', 'positive', '0.92', 'forever', 'eternal', 'unending', 'eternity', 'replicate']
senticnet['everlasting_fire'] = ['-0.70', '0', '0.566', '-0.95', '#sadness', '#disgust', 'negative', '-0.74', 'hellfire', 'red_region', 'imaginary_place', 'mythical_place', 'fictitious_place']
senticnet['everlasting_life'] = ['0.661', '-0.66', '0', '0.685', '#surprise', '#admiration', 'positive', '0.671', 'immortality', 'live_forever', 'deathless', 'eternal_life', 'immortal']
senticnet['everlasting_peace'] = ['0.497', '0.453', '-0.23', '0.189', '#joy', '#interest', 'positive', '0.226', 'calm', 'calmness', 'stillness', 'truce', 'love']
senticnet['everlasting_punishment'] = ['-0.70', '0', '0.566', '-0.95', '#sadness', '#disgust', 'negative', '-0.74', 'hellfire', 'red_region', 'imaginary_place', 'mythical_place', 'fictitious_place']
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senticnet['every_cranny'] = ['0.788', '-0.60', '0', '0.884', '#joy', '#admiration', 'positive', '0.759', 'gap', 'rathole', 'aperture', 'bullet_hole', 'hole']
senticnet['every_crevice'] = ['0.788', '-0.60', '0', '0.884', '#joy', '#admiration', 'positive', '0.759', 'gap', 'rathole', 'aperture', 'bullet_hole', 'hole']
senticnet['every_day'] = ['0', '0.142', '-0.06', '0.173', '#interest', '#admiration', 'positive', '0.083', 'common', 'usual', 'circumstance', 'wake_up', 'daily']
senticnet['every_dollar'] = ['0.987', '0.902', '0', '0', '#joy', '#interest', 'positive', '0.945', 'franc', 'dime', 'dollar', 'shilling', 'banknote']
senticnet['every_fortnight'] = ['0', '0.152', '0', '0.119', '#interest', '#admiration', 'positive', '0.136', 'replicate', 'start_end', 'everyday', 'description', 'precise']
senticnet['every_half_hour'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'watch', 'clock', 'hourglass', 'minute', 'stopwatch']
senticnet['every_home'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'appliance_store', 'apartment', 'relative_house', 'kitchen', 'coffee_shop']
senticnet['every_imaginable'] = ['0.857', '0', '0', '0.875', '#joy', '#admiration', 'positive', '0.866', 'documented', 'echt', 'attested', 'authenticated', 'genuine']
senticnet['every_inch'] = ['0.788', '-0.60', '0', '0.884', '#joy', '#admiration', 'positive', '0.759', 'gap', 'rathole', 'aperture', 'bullet_hole', 'hole']
senticnet['every_jot'] = ['0', '0.074', '0.026', '0.105', '#interest', '#admiration', 'positive', '0.051', 'handwritten', 'scribble', 'music_part', 'written_message', 'calligraphy']
senticnet['every_morsel'] = ['-0.88', '0', '0.864', '-0.72', '#sadness', '#anger', 'negative', '-0.82', 'vixenish', 'venomous', 'bitchy', 'catty', 'malicious']
senticnet['every_mouthful'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'green_bread', 'rotten', 'disgusting', 'taste_awful', 'mouldy']
senticnet['every_nook'] = ['0.788', '-0.60', '0', '0.884', '#joy', '#admiration', 'positive', '0.759', 'gap', 'rathole', 'aperture', 'bullet_hole', 'hole']
senticnet['every_ounce'] = ['0.605', '-0.93', '0', '0.742', '#surprise', '#admiration', 'positive', '0.761', 'road_sense', 'sagacity', 'sagaciousness', 'logic', 'nous']
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senticnet['every_square_inch'] = ['0.788', '-0.60', '0', '0.884', '#joy', '#admiration', 'positive', '0.759', 'gap', 'rathole', 'aperture', 'bullet_hole', 'hole']
senticnet['every_town'] = ['-0.03', '0.099', '0.017', '0.102', '#interest', '#admiration', 'positive', '0.038', 'city_center', 'downtown', 'monument', 'cathedral', 'town_center']
senticnet['everybody'] = ['-0.02', '0.087', '0', '0.107', '#interest', '#admiration', 'positive', '0.056', 'say_good_bye', 'step_aside', 'step_out', 'resign', 'open_mailbox']
senticnet['everyday'] = ['-0.07', '0.173', '-0.16', '0.275', '#interest', '#admiration', 'positive', '0.052', 'typical', 'usual_ordinary', 'common', 'repeat', 'normal']
senticnet['everyday_clinical_practice'] = ['0.632', '-0.80', '0', '0.537', '#joy', '#surprise', 'positive', '0.658', 'incisiveness', 'trenchancy', 'efficacy', 'effectiveness', 'power']
senticnet['everyday_conversation'] = ['-0.04', '-0.02', '0.041', '-0.03', '#sadness', '#anger', 'negative', '-0.02', 'slang', 'lingo', 'microtext', 'chatspeak', 'unusual_language']
senticnet['everyday_life'] = ['0', '0.095', '0', '0.05', '#interest', '#admiration', 'positive', '0.073', 'need_earn_money', 'earn_money', 'responsible', 'perform_service', 'go_work_to_earn_money']
senticnet['everyday_speech'] = ['-0.89', '0', '0.669', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'deceit', 'deception', 'misrepresentation', 'indirect_expression', 'equivocation']
senticnet['everyday_wear'] = ['0.053', '-0.01', '-0.05', '0.121', '#joy', '#admiration', 'positive', '0.034', 'coat', 'winter_clothes', 'stylish', 'adorn', 'protective_cover']
senticnet['everydayness'] = ['0.563', '-0.99', '0', '0.779', '#surprise', '#admiration', 'positive', '0.779', 'mundaneness', 'mundanity', 'prosiness', 'prosaicness', 'ordinariness']
senticnet['everyman'] = ['-0.75', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.84', 'demigod', 'antihero', 'narrator', 'hero', 'anti-hero']
senticnet['everyone'] = ['-0.05', '0.178', '-0.12', '0.142', '#interest', '#admiration', 'positive', '0.033', 'recognize', 'investigation', 'deduce', 'aware', 'cognition']
senticnet['everyone_else'] = ['-0.02', '0.087', '0', '0.107', '#interest', '#admiration', 'positive', '0.056', 'say_good_bye', 'step_aside', 'step_out', 'resign', 'open_mailbox']
senticnet['everyone_know'] = ['-0.87', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.92', 'well_known', 'newscaster', 'famous', 'tell_everyone', 'advise']
senticnet['everything'] = ['0.056', '0.174', '-0.08', '0.247', '#interest', '#admiration', 'positive', '0.099', 'encompass', 'infinite', 'unlimited', 'comparative', 'totality']
senticnet['everything_else'] = ['0.057', '0.141', '-0.06', '0.162', '#interest', '#admiration', 'positive', '0.074', 'belongings', 'material', 'fabric', 'guarantee', 'madonna']
senticnet['everytime_tha_beat'] = ['0.978', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.865', 'tumble', 'dew', 'hailstone', 'rain', 'snowflake']
senticnet['everywhere'] = ['0', '0.093', '0', '0.053', '#interest', '#admiration', 'positive', '0.073', 'underground', 'under_ground', 'environment', 'outpost', 'dark_place']
senticnet['everywhere_else'] = ['0', '0.093', '0', '0.053', '#interest', '#admiration', 'positive', '0.073', 'underground', 'under_ground', 'environment', 'outpost', 'dark_place']
senticnet['evict'] = ['-0.71', '0', '0.954', '-0.56', '#sadness', '#anger', 'negative', '-0.74', 'eject', 'chuck_out', 'exclude', 'turf_out', 'boot_out']
senticnet['evicted_housemate'] = ['-0.08', '-0.03', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'graduate_student', 'student', 'serious_student', 'competition', 'hard_worker']
senticnet['evictee'] = ['-0.08', '-0.03', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'graduate_student', 'student', 'serious_student', 'competition', 'hard_worker']
senticnet['eviction'] = ['-0.75', '0', '0.735', '-0.87', '#sadness', '#disgust', 'negative', '-0.78', 'due_process', 'defense', 'plea', 'dispossession', 'proceeding']
senticnet['evidence'] = ['0.059', '0.219', '-0.08', '0.15', '#interest', '#admiration', 'positive', '0.086', 'proof', 'counsel', 'fairness', 'selection', 'confirmation']
senticnet['evidenced_by'] = ['0.958', '-0.83', '0', '0.81', '#joy', '#surprise', 'positive', '0.869', 'discernible', 'perceptible', 'noticeable', 'detectable', 'observable']
senticnet['evident'] = ['0.958', '-0.83', '0', '0.81', '#joy', '#surprise', 'positive', '0.869', 'discernible', 'perceptible', 'noticeable', 'detectable', 'observable']
senticnet['evidential_burden'] = ['-0.81', '0', '0.979', '-0.88', '#anger', '#disgust', 'negative', '-0.89', 'vexation', 'dead_weight', 'imposition', 'worry', 'concern']
senticnet['evidential_support'] = ['0.534', '-0.86', '0', '0.676', '#surprise', '#admiration', 'positive', '0.692', 'suitability', 'suitableness', 'habitability', 'habitableness', 'qualification']
senticnet['evidentialism'] = ['0.715', '-0.82', '0', '0.659', '#joy', '#surprise', 'positive', '0.734', 'reasoning', 'logical_thinking', 'abstract_thought', 'casuistry', 'line_of_inquiry']
senticnet['evidentialist'] = ['-0.98', '0', '0.891', '-0.60', '#sadness', '#anger', 'negative', '-0.82', 'exorcism', 'dispossession', 'magic', 'thaumaturgy', 'belief']
senticnet['evidentiary'] = ['-0.63', '0', '0.967', '-0.67', '#anger', '#disgust', 'negative', '-0.76', 'condemnatory', 'condemning', 'accusatory', 'accusing', 'accusive']
senticnet['evidentiary_burden'] = ['-0.81', '0', '0.979', '-0.88', '#anger', '#disgust', 'negative', '-0.89', 'vexation', 'dead_weight', 'imposition', 'worry', 'concern']
senticnet['evidentiary_support'] = ['-0.04', '0.296', '-0.10', '0.291', '#interest', '#admiration', 'positive', '0.109', 'explanation', 'conclusion', 'choice', 'finish', 'tell_why']
senticnet['evidentiary_value'] = ['-0.63', '0', '0.967', '-0.67', '#anger', '#disgust', 'negative', '-0.76', 'condemnatory', 'condemning', 'accusatory', 'accusing', 'accusive']
senticnet['evidently'] = ['0.81', '0.928', '0', '0', '#joy', '#interest', 'positive', '0.869', 'obviously', 'clearly', 'distinctly', 'manifest', 'pregnantly']
senticnet['evil'] = ['0', '0', '-0.70', '-0.82', '#fear', '#disgust', 'negative', '-0.76', 'hatred', 'hate', 'aggression', 'angry', 'aggressive']
senticnet['evil_arising_from'] = ['-0.97', '0', '0.675', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'infeasibility', 'unfeasibility', 'inutility', 'uselessness', 'unusefulness']
senticnet['evil_deed'] = ['-0.96', '0', '0.313', '-0.93', '#sadness', '#disgust', 'negative', '-0.73', 'bad', 'bad_person', 'sin', 'crime', 'evil_man']
senticnet['evil_doer'] = ['-0.52', '0', '0.542', '-0.93', '#anger', '#disgust', 'negative', '-0.67', 'intoxication', 'drunkenness', 'inebriation', 'inebriety', 'tipsiness']
senticnet['evil_dude'] = ['0', '0', '-0.80', '-0.71', '#fear', '#disgust', 'negative', '-0.76', 'get_rid_of_enemy', 'wicked', 'cause_death', 'murder', 'daemon']
senticnet['evil_eye'] = ['-0.99', '0', '0.522', '-0.98', '#sadness', '#disgust', 'negative', '-0.83', 'evil', 'bad_person', 'evil_man', 'kill', 'fight']
senticnet['evil_fairy'] = ['0.967', '0.768', '0', '0', '#joy', '#interest', 'positive', '0.867', 'centerfold', 'genus', 'honey_bee', 'infant', 'dove']
senticnet['evil_magician'] = ['0.594', '-0.54', '0', '0.518', '#joy', '#surprise', 'positive', '0.554', 'exorcist', 'necromancer', 'magus', 'sorceress', 'witch_doctor']
senticnet['evil_man'] = ['-0.94', '0', '0.105', '-0.96', '#sadness', '#disgust', 'negative', '-0.67', 'bad_person', 'bad', 'bad_character', 'evil_eye', 'bad_action']
senticnet['evil_mastermind'] = ['-0.69', '0', '0.756', '-0.99', '#anger', '#disgust', 'negative', '-0.81', 'waster', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['evil_person'] = ['-0.91', '0', '0.228', '-0.94', '#sadness', '#disgust', 'negative', '-0.69', 'evil_man', 'evil_eye', 'kill_person', 'evil', 'bad_person']
senticnet['evil_queen'] = ['-0.63', '0', '0.674', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'baneful', 'evil', 'baleful', 'malevolent', 'malign']
senticnet['evil_repute'] = ['-0.64', '0', '0.962', '-0.68', '#anger', '#disgust', 'negative', '-0.76', 'beguiler', 'slicker', 'leader', 'deceiver', 'cheat']
senticnet['evil_sorcerer'] = ['0.086', '-0.10', '0.064', '0', '#joy', '#surprise', 'positive', '0.043', 'art_piece', 'lifetime', 'museum', 'vintage', 'collectible']
senticnet['evil_spirit'] = ['-0.67', '0', '0.752', '-0.89', '#anger', '#disgust', 'negative', '-0.77', 'bad_fairy', 'fiend', 'demon', 'devil', 'daimon']
senticnet['evil_stepmother'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'lordosis', 'kyphosis', 'humpback', 'spinal_curvature', 'abnormality']
senticnet['evil_twin'] = ['-0.96', '0', '0.108', '-0.90', '#sadness', '#disgust', 'negative', '-0.66', 'evil', 'darkness', 'dark', 'sun_down', 'shadow']
senticnet['evil_villain'] = ['-0.95', '0', '0.774', '0', '#sadness', '#anger', 'negative', '-0.86', 'argue', 'rival', 'disagreement', 'opposition', 'anti']
senticnet['evil_witch'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'jawless_fish', 'osteostracan', 'cephalaspid', 'ostracoderm', 'heterostracan']
senticnet['evil_wizard'] = ['0.086', '-0.10', '0.064', '0', '#joy', '#surprise', 'positive', '0.043', 'art_piece', 'lifetime', 'museum', 'vintage', 'collectible']
senticnet['evilly'] = ['-0.05', '0.181', '0.656', '-0.64', '#anger', '#disgust', 'negative', '-0.29', 'malignity', 'malevolence', 'loathing', 'execration', 'abhor']
senticnet['evilness'] = ['-0.53', '0', '0.835', '-0.64', '#anger', '#disgust', 'negative', '-0.67', 'malevolence', 'malevolency', 'malice', 'malignity', 'mischief']
senticnet['evince'] = ['0.657', '-0.56', '0', '0.749', '#joy', '#admiration', 'positive', '0.655', 'naturalness', 'condescension', 'condescendingness', 'mellowness', 'friendliness']
senticnet['eviscerate'] = ['-0.63', '0', '0.503', '-0.73', '#sadness', '#disgust', 'negative', '-0.62', 'wounded', 'mangled', 'torn', 'hurt', 'injured']
senticnet['evliya_elebi'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['evocation'] = ['-0.74', '0.292', '-0.42', '-0.06', '#sadness', '#fear', 'negative', '-0.23', 'lowness', 'dispiritedness', 'low-spiritedness', 'dejectedness', 'downheartedness']
senticnet['evocative'] = ['0.709', '-0.61', '0', '0.762', '#joy', '#admiration', 'positive', '0.695', 'careful', 'heedful', 'resonant', 'mindful', 'redolent']
senticnet['evocatively'] = ['0.709', '-0.61', '0', '0.762', '#joy', '#admiration', 'positive', '0.695', 'careful', 'heedful', 'resonant', 'mindful', 'redolent']
senticnet['evoke'] = ['0.722', '-0.76', '0', '0.946', '#surprise', '#admiration', 'positive', '0.811', 'arouse', 'elicit', 'enkindle', 'kindle', 'ask_for']
senticnet['evoke_emotion'] = ['0.047', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.026', 'convey_information', 'tell_story', 'express_idea', 'recite_poem', 'share_thought']
senticnet['evolution'] = ['0', '0.248', '-0.06', '0.222', '#interest', '#admiration', 'positive', '0.135', 'conjecture', 'scientific_statement', 'hypothetical', 'logical', 'theory']
senticnet['evolutionary'] = ['-0.90', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.89', 'vestigial', 'organ', 'ancestor', 'vestige', 'mark']
senticnet['evolutionary_biology'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'college_major', 'geology', 'physic', 'chemistry', 'subject_school']
senticnet['evolutionary_change'] = ['-0.83', '0', '0.546', '-0.88', '#sadness', '#disgust', 'negative', '-0.75', 'difference', 'allomerism', 'deviation', 'variation', 'departure']
senticnet['evolutionary_divergence'] = ['-0.83', '0', '0.546', '-0.88', '#sadness', '#disgust', 'negative', '-0.75', 'difference', 'allomerism', 'deviation', 'variation', 'departure']
senticnet['evolutionary_history'] = ['0', '0.812', '0', '0.996', '#interest', '#admiration', 'positive', '0.904', 'study', 'specie', 'evolution', 'research', 'development']
senticnet['evolutionary_path'] = ['-0.03', '0.128', '-0.09', '0.255', '#interest', '#admiration', 'positive', '0.065', 'live_simply', 'transition', 'change_life', 'backup', 'go_forward']
senticnet['evolutionary_physiology'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'college_major', 'geology', 'physic', 'chemistry', 'subject_school']
senticnet['evolutionary_theory'] = ['-0.04', '0.192', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.054', 'hypothetical', 'supposition', 'scientific_statement', 'psychology', 'educate_guess']
senticnet['evolutionism'] = ['-0.98', '0', '0.891', '-0.60', '#sadness', '#anger', 'negative', '-0.82', 'exorcism', 'dispossession', 'magic', 'thaumaturgy', 'belief']
senticnet['evolutionist'] = ['-0.04', '0.192', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.054', 'hypothetical', 'supposition', 'scientific_statement', 'psychology', 'educate_guess']
senticnet['evolve'] = ['-0.03', '0.128', '-0.09', '0.255', '#interest', '#admiration', 'positive', '0.065', 'live_simply', 'transition', 'change_life', 'backup', 'go_forward']
senticnet['evolve_independently'] = ['-0.03', '0.128', '-0.09', '0.255', '#interest', '#admiration', 'positive', '0.065', 'live_simply', 'transition', 'change_life', 'backup', 'go_forward']
senticnet['evolve_rapidly'] = ['-0.03', '0.086', '0', '0.114', '#interest', '#admiration', 'positive', '0.054', 'deduce', 'figure_out', 'arrive_conclusion', 'come_conclusion', 'come_up_new_idea']
senticnet['evolved_differently'] = ['-0.54', '0', '0.979', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'unlikeness', 'dissimilitude', 'difference', 'heterology', 'dissimilarity']
senticnet['evolved_into'] = ['-0.77', '0', '0.967', '-0.70', '#sadness', '#anger', 'negative', '-0.81', 'champion', 'friend', 'supporter', 'protagonist', 'admirer']
senticnet['evolving_rapidly'] = ['-0.03', '0.128', '-0.09', '0.255', '#interest', '#admiration', 'positive', '0.065', 'live_simply', 'transition', 'change_life', 'backup', 'go_forward']
senticnet['ewca_crim'] = ['0.542', '-0.83', '0', '0.927', '#surprise', '#admiration', 'positive', '0.769', 'incest', 'extramarital_sex', 'free_love', 'sexual_intercourse', 'intercourse']
senticnet['ewe'] = ['0', '-0.03', '0.057', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'lamb_meat', 'bandage', 'shear', 'mutton', 'seam']
senticnet['ewe_milk'] = ['0.053', '0.085', '0.019', '0', '#joy', '#interest', 'positive', '0.04', 'farm', 'countryside', 'farm_animal', 'cow', 'healthy']
senticnet['ewe_weigh'] = ['0', '0.075', '0', '0.159', '#interest', '#admiration', 'positive', '0.117', 'many_plants', 'blossom', 'development', 'poppy', 'adult']
senticnet['eweek_excellence'] = ['0.135', '0.225', '-0.12', '0.432', '#interest', '#admiration', 'positive', '0.167', 'excellent', 'goodness', 'well_done', 'great', 'good_quality']
senticnet['ewing_sarcoma'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['ex-boyfriend'] = ['-0.73', '0', '0.865', '0', '#sadness', '#anger', 'negative', '-0.8', 'ex-husband', 'ex-girlfriend', 'boyfriend', 'ex-lover', 'ex-wife']
senticnet['ex-con'] = ['-0.79', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.82', 'smalltime', 'big-time', 'two-bit', 'wanna-be', 'small-time']
senticnet['ex-girlfriend'] = ['-0.73', '0', '0.859', '0', '#sadness', '#anger', 'negative', '-0.79', 'ex-husband', 'ex-boyfriend', 'boyfriend', 'ex-lover', 'ex-wife']
senticnet['ex-husband'] = ['-0.73', '0', '0.864', '0', '#sadness', '#anger', 'negative', '-0.80', 'ex-boyfriend', 'ex-girlfriend', 'boyfriend', 'ex-lover', 'ex-wife']
senticnet['ex-lover'] = ['-0.73', '0', '0.856', '0', '#sadness', '#anger', 'negative', '-0.79', 'ex-husband', 'ex-girlfriend', 'boyfriend', 'ex-boyfriend', 'ex-wife']
senticnet['ex-wife'] = ['-0.74', '0', '0.855', '0', '#sadness', '#anger', 'negative', '-0.79', 'ex-husband', 'ex-girlfriend', 'boyfriend', 'ex-lover', 'ex-boyfriend']
senticnet['ex_boxer'] = ['0.794', '-0.50', '0', '0.531', '#joy', '#admiration', 'positive', '0.611', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['ex_boyfriend'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'addicted', 'obsessed_love', 'addiction', 'obsession', 'weird']
senticnet['ex_cia_agent'] = ['-0.69', '0', '0.756', '-0.99', '#anger', '#disgust', 'negative', '-0.81', 'waster', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['ex_con'] = ['0.794', '-0.50', '0', '0.531', '#joy', '#admiration', 'positive', '0.611', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['ex_cop'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'addicted', 'obsessed_love', 'addiction', 'obsession', 'weird']
senticnet['ex_fianc'] = ['0.033', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.058', 'baby_dog', 'sweet', 'puppet', 'dog', 'love']
senticnet['ex_fiancee'] = ['0.033', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.058', 'baby_dog', 'sweet', 'puppet', 'dog', 'love']
senticnet['ex_girlfriend'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'addicted', 'obsessed_love', 'addiction', 'obsession', 'weird']
senticnet['ex_gratia'] = ['0.73', '-0.56', '0', '0.621', '#joy', '#admiration', 'positive', '0.64', 'amnesty', 'unsusceptibility', 'diplomatic_immunity', 'exemption', 'impunity']
senticnet['ex_gratia_payment'] = ['0.73', '-0.56', '0', '0.621', '#joy', '#admiration', 'positive', '0.64', 'amnesty', 'unsusceptibility', 'diplomatic_immunity', 'exemption', 'impunity']
senticnet['ex_husband'] = ['0.033', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.058', 'baby_dog', 'sweet', 'puppet', 'dog', 'love']
senticnet['ex_hypothesi'] = ['-0.87', '0', '0.858', '-0.77', '#sadness', '#anger', 'negative', '-0.83', 'pass_off', 'fall_out', 'occur', 'happen', 'hap']
senticnet['ex_junkie'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['ex_lover'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'addicted', 'obsessed_love', 'addiction', 'obsession', 'weird']
senticnet['ex_nihilo'] = ['0.992', '-0.66', '0', '0.536', '#joy', '#surprise', 'positive', '0.729', 'nous', 'mind', 'brain', 'psyche', 'head']
senticnet['ex_officio_capacity'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'seeker', 'commuter']
senticnet['ex_officio_member'] = ['0', '0', '0.88', '-0.92', '#anger', '#disgust', 'negative', '-0.90', 'sofa_bed', 'small_seat', 'beanbag_airline_seat', 'hide-a-bed_sofa', 'airline_seat']
senticnet['ex_parte_application'] = ['-0.88', '0', '0.727', '-0.82', '#sadness', '#disgust', 'negative', '-0.81', 'defervescence', 'subsidence', 'break', 'remission', 'abatement']
senticnet['ex_partner'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'addicted', 'obsessed_love', 'addiction', 'obsession', 'weird']
senticnet['ex_pat'] = ['0.071', '0', '0.031', '0.064', '#joy', '#admiration', 'positive', '0.035', 'far_from_home', 'immigrant', 'live_abroad', 'connoisseur', 'work_abroad']
senticnet['ex_patriate'] = ['0.071', '0', '0.031', '0.064', '#joy', '#admiration', 'positive', '0.035', 'far_from_home', 'immigrant', 'live_abroad', 'connoisseur', 'work_abroad']
senticnet['ex_raf'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'cheapjack', 'pitchman']
senticnet['ex_situ_conservation'] = ['0', '0', '0', '0.857', '#admiration', '#admiration', 'positive', '0.857', 'say', 'beckon', 'call', 'conserve', 'retort']
senticnet['ex_slave'] = ['0', '0', '0.8', '-0.82', '#anger', '#disgust', 'negative', '-0.81', 'personnel', 'attendant', 'manager', 'elect_official', 'gentleman']
senticnet['ex_soldier'] = ['-0.62', '0', '0.98', '-0.81', '#anger', '#disgust', 'negative', '-0.80', 'tart', 'prostitute', 'harlot', 'bawd', 'sporting_lady']
senticnet['ex_vi_terminus'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expandible', 'expansive', 'erectile', 'cavernous']
senticnet['ex_wife'] = ['0.033', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.058', 'baby_dog', 'sweet', 'puppet', 'dog', 'love']
senticnet['exacerbate'] = ['-0.12', '0.069', '0.703', '-0.67', '#anger', '#disgust', 'negative', '-0.35', 'aggravate', 'provoked', 'aggravated', 'exasperation', 'displeased']
senticnet['exacerbate_asthma'] = ['0.047', '0', '-0.10', '-0.06', '#fear', '#disgust', 'negative', '-0.03', 'smoke', 'tobacco_product', 'smoke_tobacco', 'lung_cancer', 'cigar']
senticnet['exacerbating'] = ['0.652', '-0.64', '0', '0.953', '#joy', '#admiration', 'positive', '0.751', 'heightening', 'thickening', 'aggravating', 'intensifying', 'deepening']
senticnet['exact'] = ['0', '0.112', '0', '0.097', '#interest', '#admiration', 'positive', '0.105', 'math_relate', 'math_subject', 'school_study', 'median', 'method']
senticnet['exact_amount'] = ['-0.04', '0.268', '0', '0.159', '#interest', '#admiration', 'positive', '0.127', 'total', 'amount', 'a_lot', 'aggregate', 'bill']
senticnet['exact_cause'] = ['-0.09', '0', '0', '-0.19', '#sadness', '#disgust', 'negative', '-0.14', 'add_knowledge', 'learn', 'become_smart', 'discover', 'make_out_atom']
senticnet['exact_date'] = ['0', '0.112', '0', '0.097', '#interest', '#admiration', 'positive', '0.105', 'math_relate', 'math_subject', 'school_study', 'median', 'method']
senticnet['exact_etiology'] = ['-0.09', '0', '0', '-0.19', '#sadness', '#disgust', 'negative', '-0.14', 'add_knowledge', 'learn', 'become_smart', 'discover', 'make_out_atom']
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senticnet['excessive_urination'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'aridness', 'dryness', 'xerostomia', 'dry_mouth', 'sereness']
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senticnet['exchange_carrier_lec'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'long_distance_relationship', 'distant', 'kilometer', 'length_measurement', 'distance_measure']
senticnet['exchange_gift'] = ['0.123', '0.29', '-0.19', '0.278', '#interest', '#admiration', 'positive', '0.125', 'christmas', 'demand', 'courteous', 'yearn', 'need']
senticnet['exchange_goods'] = ['-0.03', '0.238', '0', '0.228', '#interest', '#admiration', 'positive', '0.144', 'swap', 'give_take', 'transaction', 'barter', 'commerce']
senticnet['exchange_idea'] = ['0.851', '0', '0', '0.806', '#joy', '#admiration', 'positive', '0.829', 'exchange_information', 'share_idea', 'misunderstanding', 'persuade', 'express_opinion']
senticnet['exchange_information'] = ['0.849', '0', '0', '0.861', '#joy', '#admiration', 'positive', '0.855', 'exchange_idea', 'social_life', 'keep_in_touch', 'give_information', 'communicate_person']
senticnet['exchange_knowledge'] = ['0.661', '-0.66', '0', '0.685', '#surprise', '#admiration', 'positive', '0.671', 'share_idea', 'knowledge', 'transfer_knowledge', 'exchange_idea', 'share_knowledge']
senticnet['exchange_money'] = ['-0.04', '0.125', '0', '0.093', '#interest', '#admiration', 'positive', '0.058', 'sell', 'make_rich', 'purchase', 'bankruptcy', 'pay_service']
senticnet['exchange_opinion'] = ['0.875', '0', '0', '0.802', '#joy', '#admiration', 'positive', '0.839', 'meeting', 'attend_conference', 'attend_event', 'attend_meeting', 'exchange_information']
senticnet['exchange_rate'] = ['-0.02', '0.264', '0', '0.095', '#interest', '#admiration', 'positive', '0.111', 'finance', 'franc', 'cent', 'banknote', 'mastercard']
senticnet['exchange_rate_mechanism'] = ['0.771', '0.846', '0', '0', '#joy', '#interest', 'positive', '0.808', 'dollar_bill', 'franc', 'banknote', 'dollar', 'cent']
senticnet['exchange_rate_mechanism_erm'] = ['0.771', '0.846', '0', '0', '#joy', '#interest', 'positive', '0.808', 'dollar_bill', 'franc', 'banknote', 'dollar', 'cent']
senticnet['exchange_transfusion'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'radiation_therapy', 'radiation', 'actinotherapy', 'irradiation']
senticnet['exchange_word'] = ['0', '0', '0', '0.08', '#admiration', '#admiration', 'positive', '0.08', 'discuss', 'autograph', 'communicate_person', 'communication', 'discussion']
senticnet['exchangeability'] = ['0.912', '-0.75', '0', '0.512', '#joy', '#surprise', 'positive', '0.727', 'transferability', 'convertibility', 'changeableness', 'changeability', 'permutability']
senticnet['exchangeable'] = ['0.552', '-0.58', '0', '0.762', '#surprise', '#admiration', 'positive', '0.633', 'transposable', 'permutable', 'fungible', 'commutable', 'substitutable']
senticnet['exchangeable_value'] = ['0.902', '-0.72', '0', '0.919', '#joy', '#admiration', 'positive', '0.849', 'preciousness', 'costliness', 'expensiveness', 'cost', 'lavishness']
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senticnet['excisable'] = ['-0.08', '0.358', '-0.20', '0.237', '#interest', '#admiration', 'positive', '0.077', 'extortion', 'money_due', 'afford', 'bribe', 'owe']
senticnet['excise'] = ['-0.08', '0.358', '-0.20', '0.237', '#interest', '#admiration', 'positive', '0.077', 'extortion', 'money_due', 'afford', 'bribe', 'owe']
senticnet['excise_act'] = ['-0.08', '0.358', '-0.20', '0.237', '#interest', '#admiration', 'positive', '0.077', 'extortion', 'money_due', 'afford', 'bribe', 'owe']
senticnet['excise_duty'] = ['-0.08', '0.358', '-0.20', '0.237', '#interest', '#admiration', 'positive', '0.077', 'extortion', 'money_due', 'afford', 'bribe', 'owe']
senticnet['excise_tariff'] = ['-0.08', '0.358', '-0.20', '0.237', '#interest', '#admiration', 'positive', '0.077', 'extortion', 'money_due', 'afford', 'bribe', 'owe']
senticnet['excise_tax'] = ['-0.08', '0.358', '-0.20', '0.237', '#interest', '#admiration', 'positive', '0.077', 'extortion', 'money_due', 'afford', 'bribe', 'owe']
senticnet['excise_tax_act'] = ['-0.08', '0.358', '-0.20', '0.237', '#interest', '#admiration', 'positive', '0.077', 'extortion', 'money_due', 'afford', 'bribe', 'owe']
senticnet['excitability'] = ['0.698', '-0.61', '0', '0.619', '#joy', '#admiration', 'positive', '0.644', 'sensitivity', 'sensitiveness', 'irritability', 'sensibility', 'reactivity']
senticnet['excitable'] = ['0.963', '-0.98', '0', '0.514', '#joy', '#surprise', 'positive', '0.821', 'warm', 'flighty', 'nervous', 'skittish', 'spooky']
senticnet['excitant'] = ['0.95', '-0.95', '0', '0.769', '#joy', '#surprise', 'positive', '0.89', 'adrenocorticotropic', 'adrenocorticotrophic', 'stimulant', 'stimulating', 'stimulative']
senticnet['excitation'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'emotional_arousal', 'excitement', 'arousal', 'anger']
senticnet['excitation_energy'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'emotional_arousal', 'excitement', 'arousal', 'anger']
senticnet['excitation_light'] = ['-0.50', '0', '0.75', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'beam', 'beam_of_light', 'sunbeam', 'ray', 'ray_of_light']
senticnet['excitation_spectrum'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'emotional_arousal', 'excitement', 'arousal', 'anger']
senticnet['excitatory'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'emotional_arousal', 'excitement', 'arousal', 'anger']
senticnet['excite'] = ['0.847', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.891', 'enjoy', 'excitement', 'feel_different', 'joy', 'funny']
senticnet['excite_game'] = ['0.077', '-0.09', '0.035', '0', '#joy', '#surprise', 'positive', '0.046', 'sport_play', 'rough_game', 'ball_sport', 'team_sport', 'fun_game']
senticnet['excited'] = ['0', '0', '-0.73', '-0.85', '#fear', '#disgust', 'negative', '-0.79', 'hallucinating', 'delirious', 'frantic', 'mad', 'unrestrained']
senticnet['excitedly'] = ['0.653', '0.635', '-0.24', '0.294', '#joy', '#interest', 'positive', '0.335', 'merrily', 'happily', 'mirthfully', 'gayly', 'blithely']
senticnet['excitement'] = ['0.772', '0.742', '0', '0', '#joy', '#interest', 'positive', '0.757', 'enjoy', 'satisfaction', 'happiness', 'become_excited', 'joy']
senticnet['exciting'] = ['0.481', '0.659', '-0.24', '0.236', '#joy', '#interest', 'positive', '0.282', 'enthusiastic', 'enthusiastically', 'unfearing', 'intrepid', 'dauntlessly']
senticnet['exciting_adventure'] = ['0.704', '-0.80', '0', '0', '#joy', '#surprise', 'positive', '0.755', 'quest', 'stunt', 'illusion', 'journey', 'activity']
senticnet['excitment'] = ['0.772', '0.742', '0', '0', '#joy', '#interest', 'positive', '0.757', 'enjoy', 'satisfaction', 'happiness', 'become_excited', 'joy']
senticnet['exciton'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'emotional_arousal', 'excitement', 'arousal', 'anger']
senticnet['exclaim'] = ['0.481', '0.659', '-0.24', '0.236', '#joy', '#interest', 'positive', '0.282', 'express', 'utter', 'gee', 'ooh', 'eureka']
senticnet['exclaiming'] = ['-0.95', '0', '0.644', '-0.7', '#sadness', '#disgust', 'negative', '-0.76', 'ejaculation', 'interjection', 'devil', 'expostulation', 'deuce']
senticnet['exclamation'] = ['0', '0.158', '-0.12', '0.359', '#interest', '#admiration', 'positive', '0.131', 'statement', 'affirmation', 'proclamation', 'clam', 'proposition']
senticnet['exclamatory'] = ['0', '0.156', '-0.06', '0.241', '#interest', '#admiration', 'positive', '0.11', 'mixture', 'noisy_place', 'make_go', 'move_vehicle', 'big_car']
senticnet['exclude'] = ['-0.21', '-0.08', '0.018', '-0.16', '#sadness', '#disgust', 'negative', '-0.07', 'leave_out', 'ridiculous', 'pollute_air', 'kick_out', 'reprimand']
senticnet['exclusion'] = ['-0.25', '0', '-0.17', '-0.08', '#sadness', '#fear', 'negative', '-0.16', 'social_exclusion', 'exclusion_zone', 'isolation', 'detach', 'except']
senticnet['exclusion_zone'] = ['-0.96', '0', '0.414', '-0.94', '#sadness', '#disgust', 'negative', '-0.77', 'social_exclusion', 'exclusion', 'isolation', 'detach', 'except']
senticnet['exclusionary'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
senticnet['exclusionary_conduct'] = ['0.658', '-0.52', '0', '0.626', '#joy', '#admiration', 'positive', '0.602', 'point_in_time', 'threshold', 'birth', 'commencement', 'incipience']
senticnet['exclusive'] = ['-0.77', '0', '0.577', '-0.78', '#sadness', '#disgust', 'negative', '-0.71', 'undivided', 'unshared', 'sole', 'individual', 'single']
senticnet['exclusive_jurisdiction'] = ['0.657', '-0.88', '0', '0.631', '#joy', '#surprise', 'positive', '0.723', 'cognisance', 'knowingness', 'awareness', 'feel', 'consciousness']
senticnet['exclusive_possession'] = ['0.964', '0.931', '0', '0', '#joy', '#interest', 'positive', '0.948', 'heel', 'foot_part', 'type_of_shoes', 'shoe_type', 'high_heel']
senticnet['exclusive_privilege'] = ['0.964', '0.931', '0', '0', '#joy', '#interest', 'positive', '0.948', 'heel', 'foot_part', 'type_of_shoes', 'shoe_type', 'high_heel']
senticnet['exclusive_remedy'] = ['0.964', '0.931', '0', '0', '#joy', '#interest', 'positive', '0.948', 'heel', 'foot_part', 'type_of_shoes', 'shoe_type', 'high_heel']
senticnet['exclusively'] = ['-0.78', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.76', 'all', 'whole', 'only', 'entirely', 'wholly']
senticnet['exclusiveness'] = ['-0.94', '0', '0.687', '-0.75', '#sadness', '#disgust', 'negative', '-0.79', 'clannishness', 'cliquishness', 'snobbery', 'arrogance', 'haughtiness']
senticnet['exclusivism'] = ['0.995', '-0.69', '0', '0.528', '#joy', '#surprise', 'positive', '0.74', 'grasp', 'hug', 'nestle', 'snuggle', 'grip']
senticnet['exclusivist'] = ['-0.94', '0', '0.687', '-0.75', '#sadness', '#disgust', 'negative', '-0.79', 'clannishness', 'cliquishness', 'snobbery', 'arrogance', 'haughtiness']
senticnet['excogitate'] = ['-0.74', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.73', 'think_over', 'chew_over', 'ponder', 'meditate', 'ruminate']
senticnet['excogitation'] = ['0.768', '-0.50', '0', '0.624', '#joy', '#admiration', 'positive', '0.634', 'thinking', 'thought', 'thought_process', 'cerebration', 'intellection']
senticnet['excommunication'] = ['0.901', '0', '0', '0.916', '#joy', '#admiration', 'positive', '0.909', 'get_away', 'bread', 'glory', 'counsel', 'vow']
senticnet['excoriate'] = ['-0.99', '0', '0.869', '-0.60', '#sadness', '#anger', 'negative', '-0.82', 'abrade', 'chafe', 'rasp', 'wear_away', 'wear_off']
senticnet['excrement'] = ['-0.04', '0', '-0.07', '-0.09', '#fear', '#disgust', 'negative', '-0.07', 'go_toilet', 'go_bathroom', 'go_pee', 'read_bedtime_story', 'quiet_cry_baby']
senticnet['excremental'] = ['-0.75', '0', '0.614', '-0.74', '#sadness', '#disgust', 'negative', '-0.70', 'out', 'impermissible', 'untouchable', 'taboo', 'tabu']
senticnet['excrescence'] = ['0.982', '-0.72', '0', '0.615', '#joy', '#surprise', 'positive', '0.772', 'sexual_desire', 'concupiscence', 'physical_attraction', 'prurience', 'pruriency']
senticnet['excreta'] = ['-0.04', '0', '-0.07', '-0.09', '#fear', '#disgust', 'negative', '-0.07', 'go_toilet', 'go_bathroom', 'go_pee', 'read_bedtime_story', 'quiet_cry_baby']
senticnet['excrete'] = ['-0.07', '0', '-0.04', '-0.10', '#sadness', '#disgust', 'negative', '-0.07', 'dream_with_eye_open', 'die', 'form_sentence', 'eat_animal', 'eat_cat_food']
senticnet['excreted_via'] = ['0.992', '-0.80', '0', '0.584', '#joy', '#surprise', 'positive', '0.795', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'incontinence']
senticnet['excreting_salt'] = ['-0.57', '0', '0.767', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'verboseness', 'verbosity', 'prolixity', 'turgidness', 'flatulence']
senticnet['excretion'] = ['0.992', '-0.80', '0', '0.584', '#joy', '#surprise', 'positive', '0.795', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'incontinence']
senticnet['excretory_system'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'belly', 'nervous_system', 'human_body', 'stomach', 'tummy']
senticnet['excruciate'] = ['-0.25', '0', '-0.17', '-0.08', '#sadness', '#fear', 'negative', '-0.16', 'ouch', 'ache', 'cause_bleed', 'cut_skin', 'pain']
senticnet['excruciating'] = ['-0.70', '0', '0.804', '0', '#sadness', '#anger', 'negative', '-0.75', 'agonizing', 'harrowing', 'torturing', 'worry', 'fret']
senticnet['excruciating_pain'] = ['-0.97', '0', '0.969', '-0.50', '#sadness', '#anger', 'negative', '-0.81', 'nervousness', 'tenseness', 'tension', 'nerve', 'stress']
senticnet['excruciatingly'] = ['-0.85', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.81', 'tediously', 'interminably', 'pointlessly', 'agonizingly', 'interminable']
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senticnet['exculpated'] = ['0.909', '-0.75', '0', '0.699', '#joy', '#surprise', 'positive', '0.787', 'absolved', 'guiltless', 'exonerated', 'vindicated', 'clear']
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senticnet['exculpatory'] = ['0.579', '-0.54', '0', '0.784', '#joy', '#admiration', 'positive', '0.635', 'forgiving', 'vindicatory', 'absolvitory', 'exonerative', 'extenuating']
senticnet['exculpatory_evidence'] = ['-0.63', '0', '0.967', '-0.67', '#anger', '#disgust', 'negative', '-0.76', 'condemnatory', 'condemning', 'accusatory', 'accusing', 'accusive']
senticnet['exculpatory_information'] = ['-0.63', '0', '0.967', '-0.67', '#anger', '#disgust', 'negative', '-0.76', 'condemnatory', 'condemning', 'accusatory', 'accusing', 'accusive']
senticnet['excursion'] = ['0', '0', '0.819', '-0.88', '#anger', '#disgust', 'negative', '-0.85', 'outing', 'junket', 'pleasure_trip', 'jaunt', 'expedition']
senticnet['excursionist'] = ['-0.65', '0', '0.943', '-0.75', '#anger', '#disgust', 'negative', '-0.78', 'rubberneck', 'sightseer', 'holidaymaker', 'tripper', 'traveler']
senticnet['excursus'] = ['-0.95', '0', '0.969', '0', '#sadness', '#anger', 'negative', '-0.96', 'digression', 'away', 'aside', 'parenthesis', 'divagation']
senticnet['excusable'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'forgive', 'forgivable', 'mercifully', 'merciful', 'mercifulness']
senticnet['excusable_neglect'] = ['-0.93', '0', '0.624', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'carelessness', 'neglectfulness', 'laxness', 'laxity', 'remissness']
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senticnet['excused_absence'] = ['0.723', '0.72', '0', '0', '#joy', '#interest', 'positive', '0.722', 'late', 'get_dark', 'rainy_day', 'split_end', 'house_look_better']
senticnet['execrable'] = ['-0.90', '0.138', '-0.63', '0.071', '#sadness', '#fear', 'negative', '-0.33', 'woeful', 'deplorable', 'woebegone', 'woefulness', 'plaintiveness']
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senticnet['executability'] = ['0.515', '-0.54', '0', '0.579', '#surprise', '#admiration', 'positive', '0.546', 'capability', 'ability', 'associability', 'associableness', 'strength']
senticnet['executable'] = ['-0.95', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.84', 'hold_pencil', 'food_scrap', 'paper_product', 'waste_paper', 'cover']
senticnet['execute'] = ['-0.02', '0.087', '0', '0.107', '#interest', '#admiration', 'positive', '0.056', 'present', 'act', 'perform', 'fulfill', 'carry_out']
senticnet['execute_arbitrary_code'] = ['-0.02', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.07', 'army', 'military', 'infantry', 'samurai', 'soldier']
senticnet['execute_arbitrary_machine'] = ['-0.75', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.77', 'avail', 'use', 'harness', 'utilise', 'overexploit']
senticnet['execute_malicious_code'] = ['-0.02', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.07', 'army', 'military', 'infantry', 'samurai', 'soldier']
senticnet['execute_well'] = ['0.805', '0.932', '0', '0.927', '#interest', '#admiration', 'positive', '0.888', 'go_rock_concert', 'sit_stand', 'perform_well', 'make_difference', 'enjoy_challenge']
senticnet['executing_innocent'] = ['-0.19', '0', '-0.17', '-0.14', '#sadness', '#fear', 'negative', '-0.17', 'death_penalty', 'die', 'death_destruction', 'kill', 'murder']
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senticnet['execution_chamber'] = ['0', '0', '0.995', '-0.89', '#anger', '#disgust', 'negative', '-0.94', 'shoe_store', 'chess_tournament', 'dress_room', 'front_desk', 'locker_room']
senticnet['executioner'] = ['-0.62', '0', '0.94', '-0.99', '#anger', '#disgust', 'negative', '-0.85', 'poisoner', 'choker', 'mortal', 'slayer', 'public_slayer']
senticnet['executive'] = ['0.703', '-0.59', '0', '0.842', '#joy', '#admiration', 'positive', '0.714', 'rainmaker', 'minister', 'administrator', 'corporate_executive', 'business_executive']
senticnet['executive_assistant'] = ['0', '0', '0', '0.876', '#admiration', '#admiration', 'positive', '0.876', 'good_manner', 'courteous', 'good_behavior', 'handle_carefully', 'politeness']
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senticnet['exocrine'] = ['0.905', '-0.65', '0', '0.851', '#joy', '#admiration', 'positive', '0.805', 'gonococcus', 'bacteria', 'bacteriae', 'probiotic', 'probiotic_bacterium']
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senticnet['exogenous_obesity'] = ['-0.72', '0', '0.973', '-0.57', '#sadness', '#anger', 'negative', '-0.76', 'corpulency', 'blubber', 'fat', 'embonpoint', 'stoutness']
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senticnet['exonerated_death_row'] = ['0', '0', '0.869', '0', '#anger', '#anger', 'negative', '-0.86', 'convoke', 'loose_end', 'car_race', 'auction', 'mortuary']
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senticnet['exonuclease'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['exophthalmic_goiter'] = ['-0.53', '0', '0.667', '-0.55', '#anger', '#disgust', 'negative', '-0.58', 'thyrotoxicosis', 'glandular_disease', 'gland_disease', 'glandular_disorder', 'adenosis']
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senticnet['exotic_pet'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'animal_hair', 'dog_hair', 'animal_fur', 'dog_cat', 'bite_dog']
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senticnet['expand_horizon'] = ['0.868', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.929', 'get_informed', 'stimulate_mind', 'get_news', 'acculturation', 'cultural_experience']
senticnet['expand_knowledge'] = ['0.97', '0', '0.773', '0.878', '#joy', '#admiration', 'positive', '0.358', 'take_up_time', 'get_knowledge', 'expand_mind', 'understand_current_event', 'broaden_horizon']
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senticnet['expand_outward'] = ['0', '0', '-0.89', '-0.79', '#fear', '#disgust', 'negative', '-0.84', 'blend', 'conflate', 'mix', 'flux', 'commingle']
senticnet['expandability'] = ['-0.53', '0', '0.765', '-0.94', '#anger', '#disgust', 'negative', '-0.74', 'jaggedness', 'unregularity', 'unevenness', 'irregularity', 'patchiness']
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senticnet['expanded_polystyrene_foam'] = ['0.085', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.074', 'soap', 'drizzle', 'bubbly', 'solid_water', 'ivory']
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senticnet['expansion_slot'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'computer_part', 'system', 'cpu', 'pencil_pen', 'monitor']
senticnet['expansionism'] = ['-0.54', '0', '0.979', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'unlikeness', 'dissimilitude', 'difference', 'heterology', 'dissimilarity']
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senticnet['expansive_definition'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'vocabulary', 'read_material', 'book_genre', 'many_words', 'book_library']
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senticnet['expect'] = ['-0.9', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.80', 'regard', 'expectation', 'admire', 'desire', 'affection']
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senticnet['expectant'] = ['0.923', '0.818', '0', '0', '#joy', '#interest', 'positive', '0.871', 'expectantly', 'anticipant', 'anticipative', 'fevered', 'hectic']
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senticnet['expectedness'] = ['0.798', '-0.51', '0', '0.519', '#joy', '#admiration', 'positive', '0.611', 'averageness', 'commonness', 'normalcy', 'typicality', 'condition']
senticnet['expectorant'] = ['0.701', '-0.76', '0', '0.904', '#surprise', '#admiration', 'positive', '0.791', 'curative', 'heal', 'medicine', 'get_cured', 'take_care']
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senticnet['expediently'] = ['0', '0.99', '0', '0.964', '#interest', '#admiration', 'positive', '0.977', 'expeditiously', 'speedily', 'satisfactorily', 'swiftly', 'courteously']
senticnet['expedite'] = ['-0.91', '0', '0.637', '-0.59', '#sadness', '#anger', 'negative', '-0.71', 'gauge', 'govern', 'regulate', 'standardize', 'order']
senticnet['expedition'] = ['0.856', '0.834', '0', '0', '#joy', '#interest', 'positive', '0.845', 'mission', 'consignment', 'delivery', 'shipment', 'dispatch']
senticnet['expeditious'] = ['0.908', '0.733', '0', '0', '#joy', '#interest', 'positive', '0.821', 'expedient', 'speedier', 'cost-efficient', 'expeditiously', 'orderly']
senticnet['expeditiously'] = ['0', '0.991', '0', '0.971', '#interest', '#admiration', 'positive', '0.981', 'expediently', 'speedily', 'satisfactorily', 'swiftly', 'courteously']
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senticnet['expence'] = ['0.731', '-0.59', '0', '0.634', '#joy', '#admiration', 'positive', '0.652', 'accommodate', 'obey', 'conform_to', 'toe_line', 'abide_by']
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senticnet['expend_energy'] = ['-0.05', '-0.03', '0.061', '0', '#sadness', '#anger', 'negative', '-0.02', 'lose_energy', 'physical_exertion', 'strain_muscle', 'sweaty', 'expend']
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senticnet['expense_allowance'] = ['0.924', '0.968', '0', '0.876', '#joy', '#interest', 'positive', '0.923', 'live_simply', 'help_out', 'money', 'accolade', 'generous']
senticnet['expense_deduction'] = ['0.924', '0.968', '0', '0.876', '#joy', '#interest', 'positive', '0.923', 'live_simply', 'help_out', 'money', 'accolade', 'generous']
senticnet['expensive'] = ['-0.78', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.86', 'high_cost', 'spend_money', 'waste_money', 'high_price', 'unaffordable']
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senticnet['expensive_food'] = ['0.061', '0.07', '-0.05', '0.073', '#interest', '#admiration', 'positive', '0.037', 'italian_food', 'eat_food', 'fine_dining', 'caviar', 'lobster']
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senticnet['fabricating'] = ['-0.75', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.83', 'fabrication', 'attenuation', 'fabricator', 'figment', 'fabric']
senticnet['fabrication'] = ['-0.74', '0', '-0.89', '0', '#sadness', '#fear', 'negative', '-0.82', 'fabricating', 'attenuation', 'fabricator', 'figment', 'fabric']
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senticnet['fabulous'] = ['0.903', '0.684', '0', '0.791', '#joy', '#admiration', 'positive', '0.793', 'house', 'expensive_house', 'villa', 'fancy_house', 'vip']
senticnet['fabulously'] = ['-0.73', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.79', 'incredibly', 'fantastically', 'improbably', 'implausibly', 'unbelievably']
senticnet['fabulously_rich'] = ['0.843', '0.977', '0', '0', '#joy', '#interest', 'positive', '0.91', 'business', 'finance', 'revenue', 'wealth', 'income']
senticnet['fabulously_wealthy'] = ['0.976', '0.811', '0', '0', '#joy', '#interest', 'positive', '0.893', 'deposit', 'bank_loan', 'wealth', 'finance', 'account']
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senticnet['face'] = ['0.719', '-0.58', '0', '0.739', '#joy', '#admiration', 'positive', '0.679', 'poker_countenance', 'look', 'countenance', 'expression', 'aspect']
senticnet['face-saving'] = ['0.953', '-0.78', '0', '0', '#joy', '#surprise', 'positive', '0.867', 'last-ditch', 'quick-and-dirty', 'middle-ground', 'stopgap', 'confidence-building']
senticnet['face-to-face'] = ['0', '0', '-0.78', '-0.79', '#fear', '#disgust', 'negative', '-0.78', 'in-person', 'one-on-one', 'one-to-one', 'conversation', 'meeting']
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senticnet['face_fierce_competition'] = ['0.887', '0', '0', '0', '#joy', '#joy', 'positive', '0.887', 'fun', 'difficult_play', 'enjoy_sport', 'accept_challenge', 'competition']
senticnet['face_music'] = ['0.861', '0.929', '0', '0.879', '#interest', '#admiration', 'positive', '0.89', 'wait_forever', 'state_obvious', 'direct_play', 'correct_mistake', 'dance']
senticnet['face_pack'] = ['0.825', '0.923', '0', '0.934', '#interest', '#admiration', 'positive', '0.894', 'pack', 'corrective', 'pickup', 'prosthesis', 'prosthetic_device']
senticnet['face_paint'] = ['0.978', '0.834', '0', '0', '#joy', '#interest', 'positive', '0.906', 'face', 'paint', 'fun', 'enjoy_moment', 'carnival']
senticnet['face_part'] = ['0', '0.093', '0', '0.113', '#interest', '#admiration', 'positive', '0.103', 'top_face', 'facial_part', 'near_nose', 'face_feature', 'feature']
senticnet['face_recognition'] = ['0.1', '0.216', '-0.18', '0.22', '#interest', '#admiration', 'positive', '0.088', 'contrast', 'perception', 'seeing', 'recognition', 'visual_space']
senticnet['face_stiff_competition'] = ['0.887', '0', '0', '0', '#joy', '#joy', 'positive', '0.887', 'fun', 'difficult_play', 'enjoy_sport', 'accept_challenge', 'competition']
senticnet['face_time'] = ['0.769', '-0.61', '0', '0.746', '#joy', '#admiration', 'positive', '0.71', 'day', 'period', 'hour', 'work_time', 'workweek']
senticnet['face_to_face'] = ['0.741', '0.898', '0', '0.734', '#joy', '#interest', 'positive', '0.791', 'cooperate', 'communicate', 'connect', 'interact', 'interaction']
senticnet['face_towel'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'toilet_paper', 'beach_towel', 'towel', 'hand_towel', 'tissue']
senticnet['face_transitive'] = ['0.82', '-0.65', '0', '0.826', '#joy', '#admiration', 'positive', '0.766', 'regular_tetrahedron', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'regular_octahedron']
senticnet['face_up'] = ['-0.88', '0', '0.854', '-0.66', '#sadness', '#anger', 'negative', '-0.80', 'approach', 'undertake', 'tackle', 'take_on', 'fight']
senticnet['face_value'] = ['0.686', '0.526', '0', '0.812', '#joy', '#admiration', 'positive', '0.675', 'pretence', 'pretense', 'pretext', 'simulacrum', 'semblance']
senticnet['facebook_source_perthnow'] = ['0.034', '0.075', '0', '0.143', '#interest', '#admiration', 'positive', '0.084', 'photograph', 'art', 'painting', 'artwork', 'mural']
senticnet['facecolor_property'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'illuminate_room', 'give_light', 'light', 'light_bulb', 'lantern']
senticnet['faced_heron'] = ['-0.84', '0', '0.655', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'piping_crow', 'butcherbird', 'currawong', 'bell_magpie', 'oscine']
senticnet['facemask'] = ['-0.99', '0', '0.806', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'wear', 'biohazard_suit', 'coverall', 'protective_garment', 'wearable']
senticnet['faceplate'] = ['0', '-0.02', '0', '0.051', '#surprise', '#admiration', 'positive', '0.037', 'cardboard_box', 'bag', 'package', 'box_cereal', 'bin']
senticnet['facere'] = ['0.654', '-0.75', '0', '0.78', '#surprise', '#admiration', 'positive', '0.729', 'suit', 'cause', 'lawsuit', 'occurrence', 'countersuit']
senticnet['facet'] = ['0.026', '0.086', '0.017', '0.181', '#interest', '#admiration', 'positive', '0.069', 'angle', 'octagon', 'face', 'apex', 'left_right']
senticnet['facetime'] = ['0.769', '-0.61', '0', '0.746', '#joy', '#admiration', 'positive', '0.71', 'day', 'period', 'hour', 'work_time', 'workweek']
senticnet['facetiously'] = ['-0.85', '0', '0.959', '0', '#sadness', '#anger', 'negative', '-0.90', 'frivolous', 'bleep', 'inappropriate', 'familiar', 'off-the-wall']
senticnet['facial'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'part_of_face', 'facial_organ', 'face_feature', 'facial_part', 'near_nose']
senticnet['facial_acne'] = ['-0.73', '0', '0.904', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'unshapely', 'shapeless', 'lumpy', 'deformed', 'malformed']
senticnet['facial_animation'] = ['0.895', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.823', 'liveliness', 'excitement', 'art_form', 'art', 'film']
senticnet['facial_appearance'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'blonde', 'purple', 'violet', 'dark_light', 'hue']
senticnet['facial_asymmetry'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'part_of_face', 'facial_organ', 'face_feature', 'facial_part', 'near_nose']
senticnet['facial_cleanser'] = ['0.03', '0', '0.07', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'checkbook_holder', 'account_book', 'checkbook_cover', 'highlighter_pen', 'shine']
senticnet['facial_dysmorphism'] = ['-0.87', '0', '0.873', '-0.62', '#sadness', '#anger', 'negative', '-0.78', 'dwarfism', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['facial_expression'] = ['-0.97', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.89', 'mouth_movement', 'leer', 'show_tooth', 'expression', 'mouth']
senticnet['facial_expression_movement'] = ['-0.81', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.84', 'mouth', 'kiss', 'show_tooth', 'expression', 'facial_expression']
senticnet['facial_feature'] = ['0', '0.066', '0.01', '0.083', '#interest', '#admiration', 'positive', '0.046', 'face_part', 'facial_part', 'facial_organ', 'near_mouth', 'part_of_face']
senticnet['facial_hair'] = ['0.036', '0', '0.094', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'unaesthetic', 'need_shave', 'need_beauty_center', 'long_beard', 'carelessness']
senticnet['facial_injury'] = ['-0.03', '-0.00', '-0.07', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'injury', 'get_hurt', 'bruise', 'lose_game', 'get_injured']
senticnet['facial_mask'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'sexual_excitement', 'sexy', 'wet_lip', 'kiss', 'clean_up']
senticnet['facial_massage'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'part_of_face', 'facial_organ', 'face_feature', 'facial_part', 'near_nose']
senticnet['facial_moisturizer'] = ['0', '0.086', '-0.06', '0.12', '#interest', '#admiration', 'positive', '0.046', 'gel_toothpaste', 'tartar_control_toothpaste', 'fluoride_mouthwash', 'toothpaste', 'hair_lotion']
senticnet['facial_muscle'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'part_of_face', 'facial_organ', 'face_feature', 'facial_part', 'near_nose']
senticnet['facial_nerve'] = ['-0.41', '0', '-0.28', '-0.05', '#sadness', '#fear', 'negative', '-0.25', 'numbness', 'carpal_tunnel_syndrome', 'weakness', 'pain', 'muscle_damage']
senticnet['facial_nerve_palsy'] = ['-0.84', '0', '0.829', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'upset', 'dysarthria', 'defect_of_speech', 'disorder', 'lallation']
senticnet['facial_nerve_vii'] = ['-0.78', '0', '0.669', '-0.89', '#sadness', '#disgust', 'negative', '-0.78', 'pain_sensation', 'painful_sensation', 'prickling', 'tingling', 'pain']
senticnet['facial_organ'] = ['0', '0.067', '0.024', '0.1', '#interest', '#admiration', 'positive', '0.048', 'facial_part', 'face_feature', 'part_of_face', 'face_part', 'face']
senticnet['facial_pain'] = ['-0.73', '0', '-0.85', '-0.72', '#sadness', '#fear', 'negative', '-0.77', 'lay_off', 'hurt', 'pain_suffer', 'lose_mind', 'forget_everything']
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senticnet['facial_part'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'mouth', 'facial_organ', 'face_feature', 'facial', 'nose']
senticnet['facial_plastic'] = ['0.745', '-0.55', '0', '0.597', '#joy', '#admiration', 'positive', '0.633', 'lift', 'phalloplasty', 'abdominoplasty', 'tummy_tuck', 'autoplasty']
senticnet['facial_plastic_surgery'] = ['0.745', '-0.55', '0', '0.597', '#joy', '#admiration', 'positive', '0.633', 'lift', 'phalloplasty', 'abdominoplasty', 'tummy_tuck', 'autoplasty']
senticnet['facial_reconstructive_surgery'] = ['0.745', '-0.55', '0', '0.597', '#joy', '#admiration', 'positive', '0.633', 'lift', 'phalloplasty', 'abdominoplasty', 'tummy_tuck', 'autoplasty']
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senticnet['facial_skin'] = ['0', '0', '0.883', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'get_old', 'dry_skin', 'become_wet', 'clothes_shrink', 'wrinkle']
senticnet['facial_structure'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'blonde', 'purple', 'violet', 'dark_light', 'hue']
senticnet['facial_stubble'] = ['-0.64', '0', '0.632', '-0.61', '#sadness', '#anger', 'negative', '-0.62', 'bearded', 'barbate', 'bewhiskered', 'whiskered', 'whiskery']
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senticnet['facial_symmetry'] = ['0.817', '-0.62', '0', '0.62', '#joy', '#surprise', 'positive', '0.688', 'quality', 'fascination', 'allurement', 'allure', 'temptingness']
senticnet['facial_tissue'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'hankie', 'kleenex', 'hanky', 'handkerchief', 'tissue']
senticnet['facial_wound'] = ['-0.12', '-0.02', '0', '-0.15', '#sadness', '#disgust', 'negative', '-0.08', 'incision', 'laceration', 'abrasion', 'cut_skin', 'painful_injury']
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senticnet['facilitate'] = ['0', '0.81', '0', '0.938', '#interest', '#admiration', 'positive', '0.874', 'easier', 'mordant', 'facilitatory', 'facility', 'fin']
senticnet['facilitate_communication'] = ['0.979', '0.93', '0', '0.723', '#joy', '#interest', 'positive', '0.877', 'information_transfer', 'tranfer', 'inform_person', 'inform_others', 'information']
senticnet['facilitates_collaboration'] = ['0.979', '0.93', '0', '0.723', '#joy', '#interest', 'positive', '0.877', 'information_transfer', 'tranfer', 'inform_person', 'inform_others', 'information']
senticnet['facilitating_collaboration'] = ['0.979', '0.93', '0', '0.723', '#joy', '#interest', 'positive', '0.877', 'information_transfer', 'tranfer', 'inform_person', 'inform_others', 'information']
senticnet['facilitative_role'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'baddie', 'theatrical_baddie', 'part', 'persona']
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senticnet['facilitatory'] = ['0', '0.809', '0', '0.928', '#interest', '#admiration', 'positive', '0.869', 'easier', 'mordant', 'facilitate', 'facility', 'fin']
senticnet['facility'] = ['0.704', '-0.68', '0', '0.795', '#joy', '#admiration', 'positive', '0.729', 'range', 'utility', 'course', 'airfield', 'advantage']
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senticnet['facing_facade'] = ['0.028', '0', '0', '0.051', '#joy', '#admiration', 'positive', '0.04', 'entranceway', 'doorway', 'entrance', 'entryway', 'corridor']
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senticnet['facit_per_se'] = ['0.511', '-0.59', '0', '0.576', '#surprise', '#admiration', 'positive', '0.561', 'due', 'traceable', 'ascribable', 'attributable', 'referable']
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senticnet['facsimile_edition'] = ['0.025', '0.115', '0.06', '0.183', '#interest', '#admiration', 'positive', '0.066', 'volume_measurement', 'liquid_measurement', 'mass_measure', 'geometry', 'size']
senticnet['fact'] = ['0.839', '0.982', '0', '0', '#joy', '#interest', 'positive', '0.91', 'data', 'scientific_statement', 'report', 'educate_guess', 'hypothesis']
senticnet['fact_finder'] = ['0', '0.146', '0', '0.077', '#interest', '#admiration', 'positive', '0.111', 'negotiator', 'judge_jury', 'arbiter', 'decide_guilt', 'become_judge']
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senticnet['fact_straight'] = ['0.833', '0.875', '0', '0', '#joy', '#interest', 'positive', '0.854', 'know_language', 'ability_communicate', 'know_talk', 'use_language', 'straight_forward']
senticnet['facticity'] = ['0.558', '-0.76', '0', '0.568', '#surprise', '#admiration', 'positive', '0.63', 'indefiniteness', 'indeterminateness', 'indefinity', 'indetermination', 'inconclusiveness']
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senticnet['factional'] = ['0', '0', '0.887', '-0.92', '#anger', '#disgust', 'negative', '-0.90', 'internecine', 'infighting', 'fratricidal', 'squabble', 'in-fighting']
senticnet['factional_strife'] = ['-0.66', '0', '0.649', '-0.76', '#sadness', '#disgust', 'negative', '-0.69', 'contrariness', 'wildness', 'intractability', 'intractableness', 'perversity']
senticnet['factionalism'] = ['0', '0.959', '0', '0.734', '#interest', '#admiration', 'positive', '0.847', 'infighting', 'in-fighting', 'squabbling', 'bickering', 'factional']
senticnet['factious'] = ['-0.93', '0', '0.515', '-0.78', '#sadness', '#disgust', 'negative', '-0.74', 'discrepant', 'at_variance', 'dissentious', 'divisive', 'discordant']
senticnet['factoid'] = ['0.73', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.851', 'sting', 'nip', 'bite', 'flea', 'least']
senticnet['factory'] = ['0.05', '0.184', '0', '0.13', '#interest', '#admiration', 'positive', '0.121', 'industry', 'assembly_line', 'create_product', 'manufacturer', 'corporation']
senticnet['factory_outlet'] = ['0.041', '0.098', '0', '0', '#joy', '#interest', 'positive', '0.07', 'utopia', 'java', 'memorial', 'nowhere', 'coffee']
senticnet['factory_output'] = ['0', '0.184', '-0.06', '0.235', '#interest', '#admiration', 'positive', '0.118', 'creation', 'manufacture', 'make_stuff', 'make', 'make_product']
senticnet['factory_worker'] = ['0.969', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.936', 'attendant', 'manager', 'public_servant', 'specialist', 'accomplice']
senticnet['factual'] = ['0.76', '0.998', '0', '0.75', '#joy', '#interest', 'positive', '0.836', 'honesty', 'honest', 'integrity', 'virtue', 'good_character']
senticnet['factual_accuracy'] = ['0.828', '-0.90', '0', '0.874', '#surprise', '#admiration', 'positive', '0.869', 'perspicacity', 'judgment', 'fair', 'sound_judgment', 'fair_judgment']
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senticnet['factual_knowledge'] = ['0', '0.808', '0', '0.754', '#interest', '#admiration', 'positive', '0.781', 'know', 'understand', 'trivia', 'gain_insight', 'great_understand']
senticnet['factual_situation'] = ['-0.88', '0', '0.727', '-0.82', '#sadness', '#disgust', 'negative', '-0.81', 'defervescence', 'subsidence', 'break', 'remission', 'abatement']
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senticnet['factually'] = ['0.76', '0.998', '0', '0.75', '#joy', '#interest', 'positive', '0.836', 'honesty', 'honest', 'integrity', 'virtue', 'good_character']
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senticnet['faithful'] = ['0.731', '0', '0', '0.843', '#joy', '#admiration', 'positive', '0.787', 'faith', 'loyal', 'smart_dog', 'dog_cat', 'big_dog']
senticnet['faithful_companion'] = ['0.756', '0', '0', '0', '#joy', '#joy', 'positive', '0.756', 'companionship', 'cherish', 'lover', 'bond', 'buddy']
senticnet['faithful_dog'] = ['0.756', '0', '0', '0', '#joy', '#joy', 'positive', '0.756', 'companionship', 'cherish', 'lover', 'bond', 'buddy']
senticnet['faithful_inn'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['faithful_observance'] = ['-0.81', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.79', 'stifle', 'forgotten', 'old', 'touch_ground', 'seldom']
senticnet['faithful_servant'] = ['0.539', '-0.96', '0', '0.745', '#surprise', '#admiration', 'positive', '0.751', 'spouse', 'partner', 'married_person', 'mate', 'better_half']
senticnet['faithfully'] = ['0', '0.732', '0', '0.809', '#interest', '#admiration', 'positive', '0.77', 'indeed', 'frank', 'amen', 'truly', 'accurately']
senticnet['faithfully_execute'] = ['0.594', '-0.81', '0', '0.93', '#surprise', '#admiration', 'positive', '0.78', 'hold', 'hold_on', 'persist', 'hang_on', 'maintain']
senticnet['faithfulness'] = ['0.956', '0', '0', '0.864', '#joy', '#admiration', 'positive', '0.91', 'honesty', 'wear_around_neck', 'type_of_dog', 'future_robot', 'trust']
senticnet['faithlessness'] = ['0.667', '-0.71', '0', '0.775', '#surprise', '#admiration', 'positive', '0.72', 'quality', 'disloyalty', 'inconstancy', 'falseness', 'unfaithfulness']
senticnet['faiyum'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['fajita'] = ['0.132', '0.1', '0.063', '0.159', '#joy', '#admiration', 'positive', '0.082', 'chime', 'school_end', 'ring_loudly', 'noise_maker', 'vital']
senticnet['fake'] = ['-0.09', '0.156', '0.661', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'phony', 'deception', 'false', 'against_law', 'trick']
senticnet['fake_blood'] = ['-0.87', '0', '0.682', '-0.53', '#sadness', '#anger', 'negative', '-0.69', 'swash', 'spatter', 'slosh_around', 'plash', 'splash']
senticnet['fake_certificate'] = ['-0.88', '0', '0.682', '-0.60', '#sadness', '#anger', 'negative', '-0.72', 'false', 'statement', 'fake_document', 'tell_lie', 'false_statement']
senticnet['fake_checkpoint'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'corner_two_streets', 'street_corner', 'roadside', 'city_street', 'schoolyard']
senticnet['fake_diy'] = ['0.7', '-0.62', '0', '0.58', '#joy', '#surprise', 'positive', '0.636', 'white_magic', 'thaumaturgy', 'juju', 'conjuration', 'supernaturalism']
senticnet['fake_document'] = ['-0.88', '0', '0.682', '-0.60', '#sadness', '#anger', 'negative', '-0.72', 'false', 'statement', 'false_statement', 'tell_lie', 'fake_certificate']
senticnet['fake_facebook'] = ['0', '0.189', '0.04', '0.091', '#interest', '#admiration', 'positive', '0.08', 'combination', 'margin', 'teller', 'merge', 'finite']
senticnet['fake_love'] = ['-0.97', '0', '0.893', '-0.99', '#sadness', '#disgust', 'negative', '-0.95', 'heart_broken', 'wrong_person', 'bad_couple', 'bad_person', 'bad_marriage']
senticnet['fake_tan'] = ['-0.96', '0', '0.636', '-0.64', '#sadness', '#disgust', 'negative', '-0.75', 'burn', 'tan', 'hyperpigmentation', 'sunburn', 'physical_condition']
senticnet['fake_tooth'] = ['0', '-0.03', '-0.09', '-0.05', '#fear', '#disgust', 'negative', '-0.03', 'play_in_theater', 'film', 'motion_picture', 'tooth', 'movie']
senticnet['faker'] = ['-0.97', '0', '0.728', '-0.78', '#sadness', '#disgust', 'negative', '-0.82', 'beguiler', 'slicker', 'impostor', 'ringer', 'deceiver']
senticnet['fakhri'] = ['0.798', '-0.51', '0', '0.973', '#joy', '#admiration', 'positive', '0.763', 'inexperienced_person', 'boy_scout', 'dear', 'virgin', 'mortal']
senticnet['fakie'] = ['-0.03', '0.11', '0', '0.128', '#interest', '#admiration', 'positive', '0.069', 'freestyle', 'take_leak', 'jump_air', 'backstroke', 'go_underwater']
senticnet['fakir'] = ['0', '0', '-0.75', '-0.77', '#fear', '#disgust', 'negative', '-0.76', 'institution', 'destitute', 'impoverished', 'destituteness', 'indigent']
senticnet['falaise_gap'] = ['0.737', '-0.96', '0', '0.552', '#joy', '#surprise', 'positive', '0.75', 'prominence', 'hump', 'bump', 'swelling', 'frontal_eminence']
senticnet['falaise_pocket'] = ['0.737', '-0.96', '0', '0.552', '#joy', '#surprise', 'positive', '0.75', 'prominence', 'hump', 'bump', 'swelling', 'frontal_eminence']
senticnet['falcate'] = ['-0.75', '0', '0.734', '-0.82', '#sadness', '#disgust', 'negative', '-0.77', 'puberulent', 'haired', 'furlike', 'pappose', 'pilous']
senticnet['falchion'] = ['0.93', '-0.78', '0', '0.873', '#joy', '#admiration', 'positive', '0.861', 'blade', 'brand', 'steel', 'claymore', 'sword']
senticnet['falcon'] = ['0.752', '-0.93', '0', '0', '#joy', '#surprise', 'positive', '0.842', 'butterfly', 'moth', 'parrot', 'beetle', 'wasp']
senticnet['falcon_falco'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'amoeba', 'single_celled']
senticnet['falcon_lang'] = ['0.129', '0', '0', '0', '#joy', '#joy', 'positive', '0.129', 'incline_plane', 'boulder', 'ramp', 'hill_mountain', 'rock']
senticnet['falconry'] = ['0', '-0.07', '0.091', '-0.19', '#anger', '#disgust', 'negative', '-0.07', 'projectile', 'use_hunt', 'weapon', 'ammunition', 'firearm']
senticnet['falernian'] = ['0.784', '-0.59', '0', '0.846', '#joy', '#admiration', 'positive', '0.742', 'astuteness', 'slyness', 'wiliness', 'craftiness', 'cunning']
senticnet['falkenstein_castle'] = ['0.021', '0.119', '0', '0.129', '#interest', '#admiration', 'positive', '0.09', 'manor', 'palace', 'medieval', 'tower', 'chess_game']
senticnet['fall'] = ['-0.09', '0.156', '0.661', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'fall_down', 'fall_ground', 'break_neck', 'fall_over', 'stumble']
senticnet['fall-blooming'] = ['0', '-0.74', '0', '0.697', '#surprise', '#admiration', 'positive', '0.719', 'spring-blooming', 'summer-blooming', 'summer-flowering', 'shade-loving', 'spring-flowering']
senticnet['fall_apart'] = ['-0.79', '0', '-0.74', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'fall_down_flight_stair', 'collide', 'install_window', 'accident', 'wreck']
senticnet['fall_armyworm'] = ['-0.02', '-0.09', '0.11', '-0.13', '#anger', '#disgust', 'negative', '-0.04', 'beetle', 'gypsy_moth', 'mole_cricket', 'grasshopper', 'many_insects']
senticnet['fall_asleep'] = ['0.758', '-0.95', '0', '0', '#joy', '#surprise', 'positive', '0.859', 'boring_talk', 'doze_off', 'drowsiness', 'sleepy', 'tired']
senticnet['fall_asleep_faster'] = ['-0.07', '0', '-0.17', '-0.05', '#sadness', '#fear', 'negative', '-0.10', 'night_shift', 'drowsiness', 'drink_coffee', 'need_rest', 'feel_little_tired']
senticnet['fall_away'] = ['-0.88', '0', '0.682', '-0.60', '#sadness', '#anger', 'negative', '-0.72', 'absent', 'disappear', 'evanesce', 'blow_over', 'pass_off']
senticnet['fall_back'] = ['-0.77', '0', '0.785', '-0.72', '#sadness', '#anger', 'negative', '-0.76', 'recede', 'worsen', 'decline', 'drop_off', 'retrogress']
senticnet['fall_below'] = ['0', '0.098', '0', '0.089', '#interest', '#admiration', 'positive', '0.093', 'wood_panel', 'wooden_structure', 'hinge', 'entrance_room', 'corridor']
senticnet['fall_butt'] = ['-0.85', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.91', 'fall_over', 'slippery', 'hit_tree', 'break_bone', 'crash_tree']
senticnet['fall_dead'] = ['-0.10', '0', '-0.08', '-0.09', '#sadness', '#disgust', 'negative', '-0.09', 'fall_ground', 'loss_ofawareness', 'break_leg', 'loss_ofbreath', 'memory_loss']
senticnet['fall_disproportionately'] = ['-0.92', '0', '0.946', '-0.56', '#sadness', '#anger', 'negative', '-0.81', 'forcefulness', 'intensiveness', 'impulse', 'vigor', 'energy']
senticnet['fall_down'] = ['-0.99', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.99', 'lose_balance', 'trip_fall', 'fall_over', 'breafast', 'fall']
senticnet['fall_down_flight_stair'] = ['-0.82', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.81', 'experience_pain', 'punch_face', 'go_insane', 'hit_car', 'feel_stupid']
senticnet['fall_downhill'] = ['-0.07', '-0.02', '-0.04', '0', '#sadness', '#fear', 'negative', '-0.03', 'bounce_ball', 'spring_up', 'fall_down', 'roll_downhill', 'float_through_air']
senticnet['fall_floor'] = ['-0.07', '0', '-0.07', '-0.09', '#sadness', '#fear', 'negative', '-0.07', 'stagger', 'fall_ground', 'fall_dead', 'run_out_booze', 'injured']
senticnet['fall_for'] = ['-0.77', '0', '0.981', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'trip_up', 'stumble', 'misjudge', 'misremember', 'err']
senticnet['fall_from_grace'] = ['-0.81', '0', '0.964', '-0.62', '#sadness', '#anger', 'negative', '-0.8', 'act_up', 'move', 'misdemean', 'carry_on', 'act']
senticnet['fall_from_tree'] = ['-0.94', '0', '0.421', '-0.96', '#sadness', '#disgust', 'negative', '-0.77', 'fall_headlong', 'hit', 'strike', 'beat', 'fall']
senticnet['fall_ground'] = ['-0.07', '-0.05', '-0.05', '-0.13', '#sadness', '#disgust', 'negative', '-0.05', 'fall_dead', 'fall_over', 'loss_ofawareness', 'loss_ofbreath', 'fall']
senticnet['fall_head'] = ['-0.94', '0', '0.421', '-0.96', '#sadness', '#disgust', 'negative', '-0.77', 'tired', 'sleepy', 'fall_asleep', 'drowsiness', 'faint']
senticnet['fall_headlong'] = ['-0.91', '0', '0.469', '-0.98', '#sadness', '#disgust', 'negative', '-0.79', 'hit', 'strike', 'beat', 'fall', 'bang']
senticnet['fall_ice'] = ['-0.04', '-0.02', '0.062', '-0.1', '#anger', '#disgust', 'negative', '-0.04', 'slippery', 'fall_down', 'lose_balance', 'fall', 'ice']
senticnet['fall_in_love'] = ['0.738', '-0.53', '0', '0.856', '#joy', '#admiration', 'positive', '0.71', 'enjoy_time_together', 'meet_girl', 'free_sex', 'kiss', 'infatuation']
senticnet['fall_love'] = ['0.865', '0', '0', '0.977', '#joy', '#admiration', 'positive', '0.921', 'attraction', 'find_love', 'become_pregnant', 'make_love', 'care_deeply']
senticnet['fall_madly'] = ['0.568', '-0.79', '0', '0.804', '#surprise', '#admiration', 'positive', '0.723', 'age', 'get_on', 'senesce', 'mature', 'maturate']
senticnet['fall_mud'] = ['-0.06', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.05', 'fight_fire', 'get_dirty', 'become_dirty', 'feel_dirty', 'dirty']
senticnet['fall_off'] = ['-0.74', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.72', 'use_brake', 'fall_down', 'peddle', 'trip_fall', 'fall_over']
senticnet['fall_off_chair'] = ['-0.04', '-0.01', '-0.05', '-0.09', '#fear', '#disgust', 'negative', '-0.04', 'roll_over', 'lift_leg', 'blow_up_balloon', 'travel_through_space', 'fly_around']
senticnet['fall_on_butt'] = ['0', '0', '-0.74', '0', '#fear', '#fear', 'negative', '-0.74', 'accident', 'fall_over', 'butt', 'fall', 'break_bone']
senticnet['fall_out'] = ['0.653', '-0.95', '0', '0.8', '#surprise', '#admiration', 'positive', '0.804', 'argufy', 'altercate', 'dispute', 'scrap', 'quarrel']
senticnet['fall_out_bed'] = ['-0.06', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.06', 'fall_down', 'sleepwalk', 'close_eye', 'take_nap', 'shut_off_alarm_clock']
senticnet['fall_out_of_window'] = ['-0.96', '0', '0.637', '-0.97', '#sadness', '#disgust', 'negative', '-0.85', 'fall_down', 'damage_body', 'injury', 'physical_pain', 'commit_suicide']
senticnet['fall_over'] = ['-0.87', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.82', 'lose_balance', 'fall_down', 'fall', 'skid', 'use_brake']
senticnet['fall_overboard'] = ['-0.97', '0', '-0.89', '0', '#sadness', '#fear', 'negative', '-0.93', 'go_underwater', 'swim', 'scuba_diving', 'dive', 'pool_party']
senticnet['fall_panicum'] = ['0', '0.086', '0', '0', '#interest', '#interest', 'positive', '0.086', 'swim_ocean', 'weight_lift', 'sit_in_shade', 'cut_grass', 'trust_friend']
senticnet['fall_prey'] = ['-0.75', '0', '-1.0', '0', '#sadness', '#fear', 'negative', '-0.87', 'give_in', 'submit', 'accept', 'defer', 'yield']
senticnet['fall_semester'] = ['0.719', '0.757', '0', '0', '#joy', '#interest', 'positive', '0.738', 'matriculate', 'enroll_class', 'enroll_school', 'inscribe', 'register']
senticnet['fall_sick'] = ['-0.81', '0', '0.964', '-0.62', '#sadness', '#anger', 'negative', '-0.8', 'lose_team', 'cause_suffer', 'leave_out', 'disrespect', 'eat_bad_food']
senticnet['fall_sky'] = ['0', '-0.02', '-0.06', '-0.17', '#fear', '#disgust', 'negative', '-0.07', 'forest_fire', 'fire_light', 'dynamite_explosion', 'firebomb', 'killer']
senticnet['fall_spring'] = ['0.093', '0', '0', '0', '#joy', '#joy', 'positive', '0.093', 'snowy', 'season_year', 'summer_winter', 'chilly', 'winter']
senticnet['fall_tree'] = ['-0.21', '-0.07', '0', '-0.23', '#sadness', '#disgust', 'negative', '-0.12', 'cut_finger', 'open_wind', 'draw_blood', 'laceration', 'cause_bleed']
senticnet['fall_unconscious'] = ['-0.99', '0', '0.26', '-0.98', '#sadness', '#disgust', 'negative', '-0.74', 'lose_consciousness', 'fall', 'faint', 'death', 'sleep']
senticnet['fall_victim'] = ['-0.75', '0', '-1.0', '0', '#sadness', '#fear', 'negative', '-0.87', 'give_in', 'submit', 'accept', 'defer', 'yield']
senticnet['fall_well'] = ['-0.10', '0', '-0.04', '0', '#sadness', '#fear', 'negative', '-0.07', 'fly_around', 'shovel_snow', 'blow_up_balloon', 'smoke_joint', 'blow_away']
senticnet['fall_windsor'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'fantastic', 'amaze', 'pose', 'wondrous', 'wonderfully']
senticnet['fallacious'] = ['0', '0', '-0.93', '-0.96', '#fear', '#disgust', 'negative', '-0.95', 'misleading', 'fallacy', 'false', 'falsify', 'illusory']
senticnet['fallaciously'] = ['0', '0', '0.742', '-0.92', '#anger', '#disgust', 'negative', '-0.83', 'speciously', 'erroneous', 'double-book', 'error', 'erroneously']
senticnet['fallaciousness'] = ['-0.55', '0', '0.741', '-0.76', '#anger', '#disgust', 'negative', '-0.68', 'illogicality', 'illogicalness', 'illogic', 'inconsequence', 'invalidity']
senticnet['fallacy'] = ['-0.77', '0', '0.885', '-0.73', '#sadness', '#anger', 'negative', '-0.8', 'pathetic_fallacy', 'misconception', 'paralogism', 'logical_fallacy', 'pseudoscience']
senticnet['fallaway_jumper'] = ['-0.81', '0', '0.747', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'vagrant', 'drifter', 'hobo', 'vagabond', 'dosser']
senticnet['fallback'] = ['-0.97', '0', '0.84', '-0.52', '#sadness', '#anger', 'negative', '-0.78', 'pis_aller', 'expedient', 'temporary_expedient', 'makeshift', 'stopgap']
senticnet['fallen_angel'] = ['0.901', '0', '0', '0.916', '#joy', '#admiration', 'positive', '0.909', 'goddess', 'god', 'saint', 'evangelist', 'angel']
senticnet['fallen_leaf'] = ['-0.87', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.80', 'grass_clipping', 'homeless_people', 'litter', 'streetlight', 'traffic_light']
senticnet['fallen_tree'] = ['-0.17', '-0.07', '0', '-0.17', '#sadness', '#disgust', 'negative', '-0.09', 'rotten_fruit', 'strong_wind', 'compost_pile', 'dangerous', 'bad_weather']
senticnet['falleth'] = ['-0.98', '0', '0.654', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'deprive', 'expropriate', 'disarm', 'unclothe', 'take']
senticnet['fallfish'] = ['0', '-0.02', '0.044', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'haddock', 'mermaid', 'fish_rod', 'tidal_wave', 'dryad']
senticnet['fallibilism'] = ['-0.98', '0', '0.891', '-0.60', '#sadness', '#anger', 'negative', '-0.82', 'exorcism', 'dispossession', 'magic', 'thaumaturgy', 'belief']
senticnet['fallibility'] = ['-0.92', '0', '0.546', '-0.52', '#sadness', '#anger', 'negative', '-0.66', 'irresponsibility', 'irresponsibleness', 'instability', 'irreproducibility', 'undependability']
senticnet['fallible'] = ['-0.83', '0', '0.962', '-0.62', '#sadness', '#anger', 'negative', '-0.80', 'unreliable', 'undependable', 'weak', 'errant', 'erring']
senticnet['falling'] = ['-0.98', '0', '0.962', '-0.95', '#sadness', '#anger', 'negative', '-0.96', 'depreciative', 'depreciatory', 'decreasing', 'depreciating', 'detractive']
senticnet['falling-out'] = ['-0.70', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.78', 'break', 'opening', 'surge', 'leap', 'breach']
senticnet['falling_apart'] = ['-0.07', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.07', 'use_needle', 'doctor_tool', 'fix', 'embroidery', 'seam']
senticnet['falling_asleep'] = ['-0.07', '0', '-0.17', '-0.05', '#sadness', '#fear', 'negative', '-0.10', 'night_shift', 'drowsiness', 'drink_coffee', 'need_rest', 'feel_little_tired']
senticnet['falling_behind'] = ['0.844', '0', '0', '0', '#joy', '#joy', 'positive', '0.844', 'straight', 'late_time', 'preparedness', 'past_present', 'wait']
senticnet['falling_ill'] = ['0', '0.877', '0', '0', '#interest', '#interest', 'positive', '0.877', 'get_rest', 'rejuvenation', 'feel_refreshed', 'take_nap', 'get_sleep']
senticnet['falling_into_disrepair'] = ['-0.08', '0', '-0.08', '-0.14', '#sadness', '#disgust', 'negative', '-0.10', 'violence', 'assault', 'combat', 'siege', 'use_military']
senticnet['falling_off'] = ['-0.95', '0', '0.862', '-0.97', '#sadness', '#disgust', 'negative', '-0.92', 'decline', 'slack', 'slump', 'falloff', 'decline_in_quality']
senticnet['falling_overboard'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'lifebelt', 'save_life', 'lifeguard', 'life_jacket', 'safety_equipment']
senticnet['falling_prey'] = ['-0.75', '0', '-1.0', '0', '#sadness', '#fear', 'negative', '-0.87', 'give_in', 'submit', 'accept', 'defer', 'yield']
senticnet['falling_victim'] = ['-0.75', '0', '-1.0', '0', '#sadness', '#fear', 'negative', '-0.87', 'give_in', 'submit', 'accept', 'defer', 'yield']
senticnet['falling_water'] = ['0.92', '-0.71', '0', '0', '#joy', '#surprise', 'positive', '0.818', 'water_fall', 'come_down', 'water_drop', 'rain_water', 'water']
senticnet['falloff'] = ['-0.95', '0', '0.862', '-0.97', '#sadness', '#disgust', 'negative', '-0.92', 'decline', 'slack', 'slump', 'decline_in_quality', 'falling_off']
senticnet['fallout'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'dust', 'chalk_dust', 'radioactive_dust', 'particulate', 'particulate_matter']
senticnet['fallow'] = ['-0.76', '0', '0.695', '-0.78', '#sadness', '#disgust', 'negative', '-0.75', 'cultivated_land', 'farmland', 'plowland', 'ploughland', 'tilled_land']
senticnet['fallow_land'] = ['0.966', '0.881', '0', '0', '#joy', '#interest', 'positive', '0.923', 'harvest', 'crop', 'grow_crop', 'plant_crop', 'reap']
senticnet['falls_apart'] = ['0.022', '-0.13', '0', '0', '#joy', '#surprise', 'positive', '0.077', 'amount_nothing', 'feel_heat', 'fall_off_chair', 'pulse', 'blow_up_balloon']
senticnet['falls_overboard'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'lifebelt', 'save_life', 'lifeguard', 'life_jacket', 'safety_equipment']
senticnet['falls_prey'] = ['-0.75', '0', '-1.0', '0', '#sadness', '#fear', 'negative', '-0.87', 'give_in', 'submit', 'accept', 'defer', 'yield']
senticnet['fallujah'] = ['-0.03', '0', '-0.08', '-0.20', '#fear', '#disgust', 'negative', '-0.10', 'gun_shop', 'arsenal', 'holster', 'armory', 'gun_store']
senticnet['false'] = ['-0.24', '0.147', '-0.25', '0.215', '#sadness', '#fear', 'negative', '-0.03', 'dishonest', 'against_law', 'falsehood', 'deceit', 'deception']
senticnet['false_accusation'] = ['-0.9', '0', '0.009', '-0.93', '#sadness', '#disgust', 'negative', '-0.61', 'tell_lie', 'lie', 'make_false_accusation', 'tell_untruth', 'dishonesty']
senticnet['false_accusation_against'] = ['-0.03', '0.157', '0.698', '-0.66', '#anger', '#disgust', 'negative', '-0.30', 'vengefulness', 'vengefully', 'revengefully', 'vindictive', 'spiteful']
senticnet['false_accuser'] = ['-0.54', '0', '0.691', '-0.96', '#anger', '#disgust', 'negative', '-0.73', 'slut', 'trollop', 'libertine', 'adulteress', 'fornicatress']
senticnet['false_affidavit'] = ['-0.73', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'obstruct_justice', 'get_away_crime', 'punish', 'hurt_else', 'commit_murder']
senticnet['false_analogy'] = ['-0.88', '0', '0.705', '-0.86', '#sadness', '#disgust', 'negative', '-0.82', 'wrongness', 'inappropriateness', 'indecency', 'improperness', 'indelicacy']
senticnet['false_asphodel'] = ['0', '0', '-0.14', '-0.03', '#fear', '#disgust', 'negative', '-0.09', 'pine_cone', 'fallen_leaf', 'poison_ivy', 'detritus', 'tress']
senticnet['false_assumption'] = ['-0.77', '0', '0.885', '-0.73', '#sadness', '#anger', 'negative', '-0.8', 'pathetic_fallacy', 'misconception', 'paralogism', 'logical_fallacy', 'pseudoscience']
senticnet['false_beard'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'wear', 'protective_garment', 'coverall', 'shoulder_pad', 'wearable']
senticnet['false_belief'] = ['-0.75', '0', '0.624', '-0.98', '#sadness', '#disgust', 'negative', '-0.78', 'pathetic_fallacy', 'misconception', 'paralogism', 'logical_fallacy', 'pseudoscience']
senticnet['false_brother'] = ['-0.64', '0', '0.663', '-0.54', '#sadness', '#anger', 'negative', '-0.61', 'quetch', 'kvetch', 'complain', 'sound_off', 'plain']
senticnet['false_confession'] = ['-0.65', '0', '0.718', '-0.73', '#anger', '#disgust', 'negative', '-0.70', 'inquirer', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['false_dichotomy'] = ['-0.92', '0', '0.952', '-0.67', '#sadness', '#anger', 'negative', '-0.85', 'beguiler', 'slicker', 'deceiver', 'cheat', 'cheater']
senticnet['false_dilemma'] = ['0.715', '-0.82', '0', '0.659', '#joy', '#surprise', 'positive', '0.734', 'reasoning', 'logical_thinking', 'abstract_thought', 'casuistry', 'line_of_inquiry']
senticnet['false_doctrine'] = ['0.889', '-0.69', '0', '0.908', '#joy', '#admiration', 'positive', '0.83', 'pharisaic', 'pious', 'sanctimonious', 'reverent', 'worshipful']
senticnet['false_foxglove'] = ['-0.92', '0', '0.912', '-0.94', '#sadness', '#disgust', 'negative', '-0.92', 'true_blackberry', 'sand_blackberry', 'blackberry_bush', 'blackberry', 'running_blackberry']
senticnet['false_gavial'] = ['-0.85', '0', '0.685', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'alligator', 'crocodile', 'crocodilian', 'gator', 'caiman']
senticnet['false_hellebore'] = ['-0.52', '0', '0.527', '-0.78', '#anger', '#disgust', 'negative', '-0.61', 'herb', 'black_snakeroot', 'wild_ginger', 'asarabacca', 'herbaceous_plant']
senticnet['false_impression'] = ['-0.74', '0', '0.741', '-0.92', '#sadness', '#disgust', 'negative', '-0.80', 'loathing', 'unattractive', 'bad_luck', 'feel_stressed', 'screw_up']
senticnet['false_killer_whale'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'many_fishes', 'seahorse', 'starfish', 'plankton', 'sea_wee']
senticnet['false_modesty'] = ['0', '0', '0', '0.705', '#admiration', '#admiration', 'positive', '0.705', 'positive', 'confidence', 'sympathetic', 'regard', 'fellow_feeling']
senticnet['false_piety'] = ['0.889', '-0.69', '0', '0.908', '#joy', '#admiration', 'positive', '0.83', 'pharisaic', 'pious', 'sanctimonious', 'reverent', 'worshipful']
senticnet['false_positive_rate'] = ['0', '0.098', '0', '0.089', '#interest', '#admiration', 'positive', '0.093', 'wood_panel', 'wooden_structure', 'hinge', 'entrance_room', 'corridor']
senticnet['false_premise'] = ['-0.77', '0', '0.885', '-0.73', '#sadness', '#anger', 'negative', '-0.8', 'pathetic_fallacy', 'misconception', 'paralogism', 'logical_fallacy', 'pseudoscience']
senticnet['false_pretext'] = ['-0.61', '0', '0.817', '-0.78', '#anger', '#disgust', 'negative', '-0.73', 'pretence', 'feigning', 'dissembling', 'pretense', 'putoff']
senticnet['false_reassurance'] = ['0.376', '0.271', '-0.19', '0.258', '#joy', '#interest', 'positive', '0.177', 'pleasant_emotion', 'bliss', 'good_feeling', 'confirm', 'healthful']
senticnet['false_sense'] = ['0.376', '0.271', '-0.19', '0.258', '#joy', '#interest', 'positive', '0.177', 'pleasant_emotion', 'bliss', 'good_feeling', 'confirm', 'healthful']
senticnet['false_statement'] = ['0', '0', '0', '-0.95', '#disgust', '#disgust', 'negative', '-0.95', 'false', 'statement', 'fake_document', 'tell_lie', 'fake_certificate']
senticnet['false_teeth'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'dental_plate', 'bridge', 'bridgework', 'partial_denture', 'dental_appliance']
senticnet['false_testimony'] = ['-0.73', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'obstruct_justice', 'get_away_crime', 'punish', 'hurt_else', 'commit_murder']
senticnet['false_tooth'] = ['0', '0', '0', '-0.80', '#disgust', '#disgust', 'negative', '-0.80', 'hairball', 'clog', 'waste_water', 'stench', 'ugly']
senticnet['false_umbilicate'] = ['0.969', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.772', 'aspheric', 'aspherical', 'prolate', 'rounded', 'crescent']
senticnet['false_witness'] = ['-0.63', '0', '0.88', '-0.94', '#anger', '#disgust', 'negative', '-0.81', 'beguiler', 'slicker', 'perjurer', 'prevaricator', 'storyteller']
senticnet['falsehood'] = ['-0.81', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.89', 'fallacy', 'dishonesty', 'deceit', 'untruth', 'lie']
senticnet['falsely'] = ['-0.80', '0', '0.971', '0', '#sadness', '#anger', 'negative', '-0.89', 'fable', 'forgery', 'oleaginous', 'misspeaking', 'untruthfully']
senticnet['falsely_claim'] = ['-0.78', '0', '0.704', '-0.91', '#sadness', '#disgust', 'negative', '-0.8', 'direct', 'take', 'lead', 'conduct', 'guide']
senticnet['falseness'] = ['-0.94', '0', '0.925', '-0.97', '#sadness', '#disgust', 'negative', '-0.95', 'nonexistence', 'nonentity', 'cloud', 'irreality', 'falsity']
senticnet['falsest'] = ['-0.73', '0', '0.732', '-0.53', '#sadness', '#anger', 'negative', '-0.66', 'insurmountable', 'insuperable', 'unconquerable', 'indomitable', 'unsubduable']
senticnet['falsetto'] = ['0.988', '-0.80', '0', '0', '#joy', '#surprise', 'positive', '0.897', 'breathy', 'crooning', 'raspy', 'harmony', 'vocal']
senticnet['falsification'] = ['-0.24', '0.147', '-0.25', '0.215', '#sadness', '#fear', 'negative', '-0.03', 'dishonest', 'against_law', 'falsehood', 'deceit', 'deception']
senticnet['falsify'] = ['-0.59', '0', '0.537', '-0.53', '#sadness', '#anger', 'negative', '-0.55', 'murder', 'mutilate', 'mangle', 'misrepresent', 'belie']
senticnet['falsity'] = ['-0.56', '0', '0.593', '-0.59', '#anger', '#disgust', 'negative', '-0.58', 'antinomy', 'paradox', 'falsehood', 'contradiction_in_term', 'untruth']
senticnet['falter'] = ['-0.88', '0', '0.925', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'hesitate', 'waver', 'linger', 'boggle', 'dwell_on']
senticnet['fame'] = ['0.874', '0.845', '0', '0.962', '#joy', '#admiration', 'positive', '0.894', 'improve_world', 'accolade', 'alms', 'self_confidence', 'consistency']
senticnet['fame_trainer'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'coach', 'instructor', 'personal_trainer', 'fit_person', 'train']
senticnet['famed'] = ['0.792', '0.854', '0', '0', '#joy', '#interest', 'positive', '0.823', 'notable', 'far-famed', 'famous', 'illustrious', 'celebrated']
senticnet['famed_explorer'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'correspondent', 'humankind']
senticnet['familial'] = ['0.903', '0.684', '0', '0.791', '#joy', '#admiration', 'positive', '0.793', 'relative', 'family_member', 'relationship', 'family', 'mother']
senticnet['familial_alzheimer_disease'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'dystrophy', 'myotonic_dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['familial_connection'] = ['0.903', '0.684', '0', '0.791', '#joy', '#admiration', 'positive', '0.793', 'relative', 'family_member', 'relationship', 'family', 'mother']
senticnet['familial_pattern'] = ['0.504', '-0.93', '0', '0.797', '#surprise', '#admiration', 'positive', '0.747', 'diabetes', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['familial_relationship'] = ['0.903', '0.684', '0', '0.791', '#joy', '#admiration', 'positive', '0.793', 'relative', 'family_member', 'relationship', 'family', 'mother']
senticnet['familial_resemblance'] = ['-0.88', '0', '0.813', '-0.57', '#sadness', '#anger', 'negative', '-0.75', 'affinity', 'mutual_resemblance', 'alikeness', 'likeness', 'similitude']
senticnet['familiar'] = ['0.726', '0.896', '0', '0.757', '#interest', '#admiration', 'positive', '0.793', 'aunt_uncle', 'happy_together', 'relate_person', 'kin', 'relation']
senticnet['familiar_refrain'] = ['0.022', '0.123', '0.038', '0.129', '#interest', '#admiration', 'positive', '0.059', 'hymn', 'written_music', 'walk_beach', 'lyric', 'piece_of_music']
senticnet['familiarise'] = ['0', '0.948', '0', '0.948', '#interest', '#admiration', 'positive', '0.948', 'cope', 'farewell', 'incident', 'propose', 'welcome']
senticnet['familiarity'] = ['0.02', '0.209', '-0.14', '0.202', '#interest', '#admiration', 'positive', '0.071', 'familiar', 'mum', 'aunt_uncle', 'care_giver', 'aunt']
senticnet['familiarity_uncle'] = ['0.016', '0.208', '-0.15', '0.197', '#interest', '#admiration', 'positive', '0.067', 'familiar', 'mum', 'aunt', 'care_giver', 'familiarity']
senticnet['familiarize'] = ['0', '0.948', '0', '0.948', '#interest', '#admiration', 'positive', '0.948', 'cope', 'farewell', 'incident', 'propose', 'welcome']
senticnet['famille_et'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'indweller', 'invillager', 'villager', 'dweller', 'denizen']
senticnet['family'] = ['0', '0.269', '-0.17', '0.182', '#interest', '#admiration', 'positive', '0.094', 'kinship', 'sibling', 'elder', 'big_brother', 'best_friend']
senticnet['family_acf'] = ['0.063', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.065', 'look_catalog', 'make_selection', 'good_credit', 'menu', 'save_money']
senticnet['family_activity'] = ['0.046', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.041', 'anarchy', 'play_marble', 'dinner_party', 'spray_paint_grafitti', 'horseback_ride']
senticnet['family_amphicyonidae_bear'] = ['0.039', '0', '0', '0.059', '#joy', '#admiration', 'positive', '0.049', 'big_dog', 'dog_cat', 'smart_dog', 'pet', 'domesticated_animal']
senticnet['family_apiaceae'] = ['0.632', '-0.93', '0', '0.928', '#surprise', '#admiration', 'positive', '0.831', 'herb', 'oregano', 'marjoram', 'pot_marjoram', 'origanum']
senticnet['family_apidae_subfamily_apinae'] = ['0', '0', '0', '0.869', '#admiration', '#admiration', 'positive', '0.869', 'fly_animal', 'insect', 'mosquito', 'bug', 'fly_insect']
senticnet['family_ardeidae'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'amoeba', 'single_celled']
senticnet['family_artamidae'] = ['-0.84', '0', '0.655', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'piping_crow', 'butcherbird', 'currawong', 'bell_magpie', 'oscine']
senticnet['family_bathyergidae'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'carbon_monoxide', 'tree_animal']
senticnet['family_batrachoididae'] = ['0.96', '-0.77', '0', '0.929', '#joy', '#admiration', 'positive', '0.887', 'whale_louse', 'beach_flea', 'sand_hopper', 'sandhopper', 'amphipod']
senticnet['family_burying_ground'] = ['-0.50', '0', '0.762', '-0.89', '#anger', '#disgust', 'negative', '-0.71', 'cemetery', 'graveyard', 'burial_site', 'grassland', 'burying_ground']
senticnet['family_calappidae'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'saltine', 'soda_cracker', 'cracker', 'water_biscuit', 'graham_cracker']
senticnet['family_carcharhinidae'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'puffer', 'pufferfish', 'blowfish', 'triggerfish', 'plectognath']
senticnet['family_castoridae'] = ['0.941', '0.987', '0', '0', '#joy', '#interest', 'positive', '0.964', 'monkey', 'marmoset', 'lemur', 'fiance', 'koala']
senticnet['family_ciconiidae'] = ['-0.02', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.04', 'wren', 'bird', 'quail', 'sparrow', 'chick']
senticnet['family_corvidae'] = ['-0.84', '0', '0.655', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'piping_crow', 'butcherbird', 'currawong', 'bell_magpie', 'oscine']
senticnet['family_crabronidae'] = ['-0.77', '0', '0.576', '-0.92', '#sadness', '#disgust', 'negative', '-0.75', 'insect', 'firebug', 'water_bug', 'assassin_bug', 'heteropterous_insect']
senticnet['family_dipodidae'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'carbon_monoxide', 'tree_animal']
senticnet['family_doctor'] = ['0.738', '-0.53', '0', '0.856', '#joy', '#admiration', 'positive', '0.71', 'doctor', 'country_doctor', 'general_practitioner', 'doctor_office', 'physician']
senticnet['family_dog'] = ['0.056', '0.157', '0', '0.154', '#interest', '#admiration', 'positive', '0.122', 'domesticate_dog', 'domesticate_pet', 'animal', 'domesticate_cat', 'dog']
senticnet['family_echimyidae'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'carbon_monoxide', 'tree_animal']
senticnet['family_elapidae'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'death_adder', 'ophidian', 'serpent', 'elapid', 'taipan']
senticnet['family_enterobacteriaceae'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'immobile', 'immotile']
senticnet['family_erethizontidae'] = ['0', '0', '-0.95', '-0.81', '#fear', '#disgust', 'negative', '-0.88', 'herb', 'plant', 'poisonous_plant', 'yellow_flower', 'lichen']
senticnet['family_estrildidae'] = ['-0.84', '0', '0.655', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'piping_crow', 'butcherbird', 'currawong', 'bell_magpie', 'oscine']
senticnet['family_etmopteridae'] = ['-0.79', '0', '0.709', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'animal', 'animate_being', 'ctenophore', 'invertebrate', 'beast']
senticnet['family_flaviviridae'] = ['0.995', '-0.71', '0', '0.647', '#joy', '#surprise', 'positive', '0.784', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['family_fun'] = ['0.755', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.728', 'party', 'pool_party', 'play_together', 'family_reunion', 'fun']
senticnet['family_gathering'] = ['0.856', '0', '0', '0', '#joy', '#joy', 'positive', '0.856', 'christmas_tree', 'christmas_gift', 'family_time', 'christmas_holiday', 'christmas']
senticnet['family_gliridae'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'carbon_monoxide', 'tree_animal']
senticnet['family_halictidae'] = ['0', '0', '0', '0.869', '#admiration', '#admiration', 'positive', '0.869', 'fly_animal', 'insect', 'mosquito', 'bug', 'fly_insect']
senticnet['family_heirloom'] = ['0.048', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.071', 'ring', 'ring_loudly', 'diamond_ring', 'necklace', 'jewelry']
senticnet['family_herpesviridae'] = ['0.995', '-0.71', '0', '0.647', '#joy', '#surprise', 'positive', '0.784', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['family_heteromyidae'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'carbon_monoxide', 'tree_animal']
senticnet['family_history'] = ['0.884', '0.777', '0', '0', '#joy', '#interest', 'positive', '0.831', 'origin', 'heritage', 'ancestor', 'family', 'birth_place']
senticnet['family_home'] = ['0', '0', '0', '0.09', '#admiration', '#admiration', 'positive', '0.09', 'small_home', 'residence', 'home_build', 'domicile', 'dwell_place']
senticnet['family_hydrobatidae'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'lovebird', 'parroket', 'paraquet', 'paroquet']
senticnet['family_limapontiidae'] = ['0.969', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.772', 'aspheric', 'aspherical', 'prolate', 'rounded', 'crescent']
senticnet['family_line'] = ['0', '0.832', '0', '0.692', '#interest', '#admiration', 'positive', '0.762', 'tribe', 'kinfolk', 'family', 'ethnic_music', 'folk']
senticnet['family_live'] = ['0.09', '0.197', '-0.13', '0.242', '#interest', '#admiration', 'positive', '0.099', 'live_life', 'accomodate', 'domestic_dwell', 'live_space', 'live_place']
senticnet['family_member'] = ['0.938', '0.762', '0', '0', '#joy', '#interest', 'positive', '0.85', 'relative', 'familial_relationship', 'sibling', 'mother_brother', 'female_relative']
senticnet['family_monacanthidae'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'puffer', 'pufferfish', 'blowfish', 'triggerfish', 'plectognath']
senticnet['family_mycosphaerellaceae'] = ['-0.75', '0', '0.69', '-0.69', '#sadness', '#disgust', 'negative', '-0.71', 'rubor', 'redness', 'sleeping_sickness', 'sleepy_sickness', 'cephalitis']
senticnet['family_myctophidae'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'plectognath', 'plectognathfish', 'blowfish', 'triggerfish', 'filefish']
senticnet['family_mytilidae'] = ['-0.89', '0', '0.556', '-0.72', '#sadness', '#disgust', 'negative', '-0.72', 'fucus', 'rockweed', 'black_rockweed', 'bladder_fucus', 'tang']
senticnet['family_narcinidae'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'plectognath', 'plectognathfish', 'blowfish', 'triggerfish', 'filefish']
senticnet['family_nesomyidae'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'carbon_monoxide', 'tree_animal']
senticnet['family_nest'] = ['-0.10', '0.235', '-0.12', '0.246', '#interest', '#admiration', 'positive', '0.063', 'love', 'settle_down', 'home_sweet_home', 'family', 'happy_family']
senticnet['family_nurse_practitioner'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'infant_feeding', 'schedule_feeding', 'demand_feeding', 'breast_feeding', 'feeding']
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senticnet['fastbreak_layup'] = ['-0.81', '0', '0.747', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'vagrant', 'drifter', 'hobo', 'vagabond', 'dosser']
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senticnet['fasten_seat_belt'] = ['-0.08', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.08', 'turn_key', 'plan_route', 'open_car_door', 'book_flight', 'turn_car_off']
senticnet['fasten_seatbelt'] = ['0.026', '0.077', '0', '0.05', '#interest', '#admiration', 'positive', '0.051', 'adjust_seat', 'check_mirror', 'turn_engine', 'safety', 'put_key_ignition']
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senticnet['fastener'] = ['0.971', '0.897', '0', '0', '#joy', '#interest', 'positive', '0.934', 'bell_sound', 'bell_noise', 'piece_of_jewelry', 'syllable', 'jab']
senticnet['fastener_binder'] = ['-0.85', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.92', 'fix', 'glue', 'super_glue', 'stick_together', 'repair']
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senticnet['faster'] = ['0.729', '0.974', '0', '0', '#joy', '#interest', 'positive', '0.851', 'travel', 'fast', 'travel_fast', 'take_airplane', 'drive_fast']
senticnet['faster_pace'] = ['-0.04', '0.123', '0', '0.237', '#interest', '#admiration', 'positive', '0.107', 'foot_motion', 'foot_movement', 'foot_action', 'move_ahead', 'stride']
senticnet['faster_pussycat'] = ['0.542', '-0.83', '0', '0.927', '#surprise', '#admiration', 'positive', '0.769', 'incest', 'extramarital_sex', 'free_love', 'sexual_intercourse', 'intercourse']
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senticnet['faster_throughput'] = ['0.729', '0.974', '0', '0', '#joy', '#interest', 'positive', '0.851', 'travel', 'fast', 'travel_fast', 'take_airplane', 'drive_fast']
senticnet['fastest'] = ['0.135', '0.225', '-0.12', '0.432', '#interest', '#admiration', 'positive', '0.167', 'excellent', 'goodness', 'well_done', 'great', 'good_quality']
senticnet['fastidious'] = ['0.682', '-0.52', '0', '0.613', '#joy', '#admiration', 'positive', '0.607', 'meticulous', 'pernickety', 'choosy', 'fussy', 'dainty']
senticnet['fastidiously'] = ['0.682', '-0.52', '0', '0.613', '#joy', '#admiration', 'positive', '0.607', 'nice', 'fastidious', 'dainty', 'prissy', 'pernickety']
senticnet['fat'] = ['-0.91', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'flatulence', 'indigestion', 'stomach_ache', 'enjoy_taste', 'eat_lot']
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senticnet['fat_laden'] = ['-0.98', '0', '0.594', '-0.98', '#sadness', '#disgust', 'negative', '-0.85', 'fast_food', 'deep_fried', 'unhealthy_food', 'reuse_oil', 'fattening_food']
senticnet['fat_malabsorption'] = ['-0.86', '0', '0.76', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['fat_milk'] = ['-0.03', '0.086', '0', '0.076', '#interest', '#admiration', 'positive', '0.042', 'drink_water', 'drink_espresso', 'drink', 'drink_coffee', 'drink_hot_chocolate']
senticnet['fat_person'] = ['-0.96', '0', '0.212', '-0.92', '#sadness', '#disgust', 'negative', '-0.70', 'unhealthy_food', 'guzzle', 'greasy_food', 'fast_food', 'lazy']
senticnet['fat_pig'] = ['-0.54', '0', '0.691', '-0.96', '#anger', '#disgust', 'negative', '-0.73', 'slut', 'adulterer', 'libertine', 'adulteress', 'fornicatress']
senticnet['fata_morgana'] = ['-0.94', '0', '0.866', '-0.91', '#sadness', '#disgust', 'negative', '-0.90', 'movement', 'apparent_motion', 'motion', 'apparent_movement', 'optical_illusion']
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senticnet['fatal_bullet'] = ['-0.52', '0', '0.785', '-0.61', '#anger', '#disgust', 'negative', '-0.64', 'gap', 'rathole', 'aperture', 'hole', 'cranny']
senticnet['fatal_bullet_wound'] = ['-0.52', '0', '0.785', '-0.61', '#anger', '#disgust', 'negative', '-0.64', 'gap', 'rathole', 'aperture', 'hole', 'cranny']
senticnet['fatal_car_accident'] = ['-0.92', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.93', 'serious_accident', 'accident', 'hit_car', 'loss_ofawareness', 'bang_head']
senticnet['fatal_car_crash'] = ['-0.73', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.79', 'die_painfully', 'arrest_police', 'get_accident', 'fall_head', 'skin_knee']
senticnet['fatal_collision'] = ['-0.90', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.86', 'trauma', 'car_wreck', 'broken_bone', 'bad_knee', 'wreck']
senticnet['fatal_confrontation'] = ['-0.60', '0', '0.777', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'batrachomyomachia', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['fatal_crash'] = ['-0.91', '0', '0.905', '-0.78', '#sadness', '#anger', 'negative', '-0.86', 'pity', 'shame', 'misfortune', 'mischance', 'misadventure']
senticnet['fatal_disease'] = ['-0.55', '0', '0.757', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'rubor', 'redness', 'cephalitis', 'sleepy_sickness', 'epidemic_encephalitis']
senticnet['fatal_error'] = ['-0.98', '0', '0.869', '-0.80', '#sadness', '#anger', 'negative', '-0.88', 'error', 'syntax_error', 'semantic_error', 'programming_error', 'computer_error']
senticnet['fatal_explosion'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'firework', 'bright_flash', 'nuclear_weapon', 'earthquake', 'nuclear_bomb']
senticnet['fatal_flaw'] = ['-0.56', '0', '0.909', '-0.62', '#anger', '#disgust', 'negative', '-0.69', 'unkemptness', 'disorder', 'slovenliness', 'untidiness', 'disorderliness']
senticnet['fatal_genetic_disorder'] = ['-0.10', '0', '-0.16', '-0.23', '#fear', '#disgust', 'negative', '-0.16', 'contagious_disease', 'infectious_disease', 'polio', 'sickness', 'malady']
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senticnet['fatal_injury'] = ['-0.71', '0', '0.748', '-0.55', '#sadness', '#anger', 'negative', '-0.67', 'casualty', 'fatal_accident', 'killing', 'violent_death', 'death']
senticnet['fatal_lung_disease'] = ['-0.10', '0', '-0.16', '-0.23', '#fear', '#disgust', 'negative', '-0.16', 'contagious_disease', 'infectious_disease', 'polio', 'sickness', 'malady']
senticnet['fatal_overdose'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'use_drug', 'do_drug', 'drug_use', 'drug', 'addiction']
senticnet['fatal_pneumonia'] = ['-0.92', '0', '0.824', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'bronchopneumonia', 'bronchial_pneumonia', 'pneumonia', 'pleuropneumonia', 'pneumococcal_pneumonia']
senticnet['fatal_shot'] = ['-0.08', '-0.02', '0', '-0.19', '#sadness', '#disgust', 'negative', '-0.08', 'wound', 'lethal', 'vaccination', 'destruction', 'fire_bullet']
senticnet['fatalism'] = ['-0.79', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.87', 'fate', 'fatalistic', 'submissive', 'fatalist', 'fatalistically']
senticnet['fatalist'] = ['-0.55', '0', '0.58', '-0.98', '#anger', '#disgust', 'negative', '-0.70', 'necessitarian', 'determinist', 'predestinarian', 'predestinationist', 'philosopher']
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senticnet['fatality'] = ['-0.71', '0', '0.748', '-0.55', '#sadness', '#anger', 'negative', '-0.67', 'casualty', 'fatal_accident', 'killing', 'violent_death', 'death']
senticnet['fatality_rate'] = ['-0.71', '0', '0.748', '-0.55', '#sadness', '#anger', 'negative', '-0.67', 'casualty', 'fatal_accident', 'killing', 'violent_death', 'death']
senticnet['fatally'] = ['-0.99', '0', '0.957', '0', '#sadness', '#anger', 'negative', '-0.97', 'fatal', 'finality', 'doom', 'mortally', 'deadly']
senticnet['fatally_shoot'] = ['0.964', '-0.57', '0', '0.827', '#joy', '#admiration', 'positive', '0.789', 'mortal', 'murder_suspect', 'suspect', 'rape_suspect', 'person']
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senticnet['fate'] = ['0.945', '0.761', '0', '0', '#joy', '#interest', 'positive', '0.853', 'enter_contract', 'believe', 'give_help', 'karma', 'destiny']
senticnet['fateful'] = ['0.698', '-0.98', '0', '0.651', '#joy', '#surprise', 'positive', '0.779', 'decisive', 'deciding', 'determinant', 'determinative', 'determining']
senticnet['fateful_journey'] = ['0.937', '0.8', '0', '0', '#joy', '#interest', 'positive', '0.869', 'quest', 'stunt', 'illusion', 'adventure', 'activity']
senticnet['father'] = ['0.108', '-0.04', '0.043', '0.065', '#joy', '#admiration', 'positive', '0.043', 'family', 'love_back', 'mother', 'carry_baby', 'change_diaper']
senticnet['father_brendan_smyth'] = ['-0.95', '0', '0.673', '-0.85', '#sadness', '#disgust', 'negative', '-0.82', 'discharge', 'free', 'dethrone', 'disinvest', 'unfrock']
senticnet['father_child'] = ['0.798', '0', '0', '0.708', '#joy', '#admiration', 'positive', '0.753', 'mother_child', 'father_offspring', 'father_son', 'kinship', 'take_out_trash']
senticnet['father_maciel'] = ['-0.95', '0', '0.673', '-0.85', '#sadness', '#disgust', 'negative', '-0.82', 'discharge', 'free', 'dethrone', 'disinvest', 'unfrock']
senticnet['father_mother'] = ['0.973', '0', '0', '0.736', '#joy', '#admiration', 'positive', '0.855', 'mom_dad', 'family_relation', 'parent_child', 'human_offspring', 'brother_sister']
senticnet['father_offspring'] = ['0.853', '0.811', '0', '0', '#joy', '#interest', 'positive', '0.832', 'familial_relationship', 'sibling', 'family_relation', 'mother_brother', 'kin']
senticnet['father_son'] = ['0.793', '0', '0', '0.845', '#joy', '#admiration', 'positive', '0.819', 'mommy', 'father_child', 'mother_child', 'kinship', 'adopt_child']
senticnet['fathered_child'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'spousal_relationship', 'bigamy']
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senticnet['fathering'] = ['0.856', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.797', 'motherhood', 'parenting', 'fatherhood', 'dad', 'father']
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senticnet['fatherland'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'motherland', 'homeland']
senticnet['fatherland_reunification'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'patriotism', 'homeland']
senticnet['fatherless'] = ['0', '0', '0.836', '-0.78', '#anger', '#disgust', 'negative', '-0.80', 'motherless', 'destitute', 'widow', 'single-parent', 'friendless']
senticnet['fatherliness'] = ['0.585', '-0.88', '0', '0.615', '#surprise', '#admiration', 'positive', '0.695', 'motherliness', 'maternal_quality', 'quality', 'maternalism', 'parental_quality']
senticnet['fatherly'] = ['0', '0.717', '0', '0.773', '#interest', '#admiration', 'positive', '0.745', 'grandfatherly', 'avuncular', 'affable', 'stolid', 'supercilious']
senticnet['fathom'] = ['0', '0.137', '-0.09', '0.226', '#interest', '#admiration', 'positive', '0.088', 'grab', 'hold', 'grip', 'extend_arm', 'reach']
senticnet['fathomable'] = ['0.744', '-0.76', '0', '0.669', '#joy', '#surprise', 'positive', '0.726', 'accessible', 'comprehendible', 'approachable', 'perceivable', 'intelligible']
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senticnet['fatih'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'caravansary', 'caravanserai', 'khan', 'caravan_inn', 'hostel']
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senticnet['fatih_mosque'] = ['0', '0.224', '0', '0.197', '#interest', '#admiration', 'positive', '0.211', 'church', 'temple', 'cathedral', 'chapel', 'confession']
senticnet['fatima_robinson'] = ['-0.95', '0', '0.992', '-0.63', '#sadness', '#anger', 'negative', '-0.86', 'interpretive_dance', 'interpretive_dancing', 'modern_dance', 'interpretative_dancing', 'stage_dancing']
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senticnet['fattening_food'] = ['-0.98', '0', '0.594', '-0.98', '#sadness', '#disgust', 'negative', '-0.85', 'fast_food', 'deep_fried', 'unhealthy_food', 'reuse_oil', 'junk_food']
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senticnet['fattier'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'obese', 'worse', 'health_issue', 'clinical_problem']
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senticnet['fatty_acyl'] = ['-0.84', '0', '0.8', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'rigor_mortis', 'natural_process', 'natural_action', 'activity', 'action']
senticnet['fatty_finn'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'rascal', 'rapscallion', 'scalawag', 'holy_terror', 'monkey']
senticnet['fatty_fish'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'grill', 'cook_grill', 'barbecue', 'cook', 'grilled_vegetable']
senticnet['fatty_food'] = ['-0.98', '0', '0.594', '-0.98', '#sadness', '#disgust', 'negative', '-0.85', 'fast_food', 'deep_fried', 'unhealthy_food', 'reuse_oil', 'fattening_food']
senticnet['fatty_infiltration'] = ['-0.78', '0', '0.764', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'withering', 'downfall', 'weakening', 'fading', 'attenuation']
senticnet['fatty_liver'] = ['-0.98', '0', '0.594', '-0.98', '#sadness', '#disgust', 'negative', '-0.85', 'disease', 'liver_disease', 'hepatitis', 'liver_cancer', 'cancer_of_liver']
senticnet['fatty_meat'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'grill', 'cook_grill', 'barbecue', 'cook', 'grilled_vegetable']
senticnet['fatty_plaque'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'chest', 'artery', 'vein', 'internal_organ', 'under_head']
senticnet['fatty_substance'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'obese', 'worse', 'health_issue', 'clinical_problem']
senticnet['fatuity'] = ['0.702', '-0.52', '0', '0.981', '#joy', '#admiration', 'positive', '0.737', 'injudiciousness', 'asininity', 'silliness', 'foolishness', 'fatuousness']
senticnet['fatuousness'] = ['0.702', '-0.52', '0', '0.981', '#joy', '#admiration', 'positive', '0.737', 'injudiciousness', 'asininity', 'silliness', 'fatuity', 'foolishness']
senticnet['faucet'] = ['0', '0', '0.865', '-0.95', '#anger', '#disgust', 'negative', '-0.90', 'dirty_dish_overflow', 'hot_dirty_dish', 'hot_tap', 'dirty_dish', 'washcloth']
senticnet['faucet_overflow'] = ['0', '0', '0.865', '-0.95', '#anger', '#disgust', 'negative', '-0.90', 'dirty_dish', 'hot_faucet', 'hot_tap', 'faucet', 'washcloth']
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senticnet['fault_plane'] = ['0', '0', '0.869', '-0.92', '#anger', '#disgust', 'negative', '-0.89', 'accuser', 'fall_down', 'fall_off', 'collapse', 'sore_knee']
senticnet['fault_tolerance'] = ['-0.9', '0', '0.841', '-0.73', '#sadness', '#anger', 'negative', '-0.82', 'nubbiness', 'tweediness', 'harshness', 'graininess', 'raggedness']
senticnet['fault_zone'] = ['0', '0', '0.869', '-0.92', '#anger', '#disgust', 'negative', '-0.89', 'accuser', 'fall_down', 'fall_off', 'collapse', 'sore_knee']
senticnet['faultfinding'] = ['-0.54', '0', '0.884', '-0.71', '#anger', '#disgust', 'negative', '-0.71', 'reprimand', 'static', 'stricture', 'disapproval', 'unfavorable_judgment']
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senticnet['fekner'] = ['-0.12', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.07', 'bum', 'homeless_people', 'homeless_person', 'panhandler', 'homeless']
senticnet['feldman'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['feletti'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquirer', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['felicitous'] = ['0.943', '-0.58', '0', '0.694', '#joy', '#admiration', 'positive', '0.74', 'better_off', 'happy', 'lucky', 'well', 'fortunate']
senticnet['felicity'] = ['0.732', '0', '0', '0.706', '#joy', '#admiration', 'positive', '0.719', 'pleasant_emotion', 'happy_emotion', 'happy_feeling', 'show_affection', 'good_emotion']
senticnet['felid'] = ['-0.02', '0', '0.044', '0', '#sadness', '#anger', 'negative', '-0.03', 'large_cat', 'fast_person', 'household_pet', 'big_cat', 'good_pet']
senticnet['feline'] = ['0', '0', '0.022', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'kind_animal', 'wild_animal', 'predator', 'hound', 'fang']
senticnet['felis_concolor'] = ['0', '-0.97', '0', '0', '#surprise', '#surprise', 'positive', '0.977', 'cougar', 'skunk', 'lion', 'tiger', 'lynx']
senticnet['fella'] = ['0', '0.739', '0', '0.734', '#interest', '#admiration', 'positive', '0.736', 'chap', 'swain', 'feller', 'young_man', 'fellow']
senticnet['fella_def_jam'] = ['-0.65', '0', '0.505', '-0.91', '#sadness', '#disgust', 'negative', '-0.69', 'hellhound', 'mythical_being', 'dragon', 'mythical_monster', 'amphisbaena']
senticnet['fellatio'] = ['0.542', '-0.83', '0', '0.927', '#surprise', '#admiration', 'positive', '0.769', 'incest', 'extramarital_sex', 'free_love', 'sexual_intercourse', 'intercourse']
senticnet['feller'] = ['0', '0.74', '0', '0.738', '#interest', '#admiration', 'positive', '0.739', 'chap', 'swain', 'fellow', 'young_man', 'fella']
senticnet['fellness'] = ['-0.87', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.83', 'reverence', 'solemnity', 'awfulness', 'dreadfulness', 'awe']
senticnet['fellow'] = ['0', '0.748', '0', '0.739', '#interest', '#admiration', 'positive', '0.744', 'chap', 'swain', 'feller', 'young_man', 'fella']
senticnet['fellow_briton'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'nazi', 'man', 'pervert', 'healthy_person']
senticnet['fellow_climber'] = ['0.518', '-0.51', '0', '0.65', '#surprise', '#admiration', 'positive', '0.562', 'clematis', 'legume', 'leguminous_plant', 'climbing_fern', 'root_climber']
senticnet['fellow_conspirator'] = ['0', '0', '-0.92', '-1', '#fear', '#disgust', 'negative', '-0.96', 'crime', 'murder', 'accomplice', 'partner_in_crime', 'offence']
senticnet['fellow_contestant'] = ['-0.08', '-0.03', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'graduate_student', 'student', 'serious_student', 'competition', 'hard_worker']
senticnet['fellow_countryman'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'nazi', 'man', 'pervert', 'healthy_person']
senticnet['fellow_englishman'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'nazi', 'man', 'pervert', 'healthy_person']
senticnet['fellow_feeling'] = ['0.557', '0.715', '-0.53', '0.51', '#joy', '#interest', 'positive', '0.313', 'kind', 'kindheartedness', 'empathy', 'kindhearted', 'compatibly']
senticnet['fellow_frenchman'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'nazi', 'man', 'pervert', 'healthy_person']
senticnet['fellow_inmate'] = ['-0.78', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.87', 'jailbird', 'captive', 'imprisoned', 'convict', 'inmate']
senticnet['fellow_prisoner'] = ['-0.78', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.87', 'jailbird', 'captive', 'imprisoned', 'convict', 'inmate']
senticnet['fellow_rapper'] = ['0', '0.712', '0', '0.844', '#interest', '#admiration', 'positive', '0.778', 'dollar_change', 'small_currency', 'currency_unit', 'metal_money', 'american_money']
senticnet['fellow_spaniard'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'nazi', 'man', 'pervert', 'healthy_person']
senticnet['fellow_tribesman'] = ['0.989', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.97', 'committee', 'council', 'caste', 'association', 'organization']
senticnet['fellowmen'] = ['0.586', '-0.96', '0', '0.9', '#surprise', '#admiration', 'positive', '0.818', 'kick_upstairs', 'gentle', 'prefer', 'spot_promote', 'favor']
senticnet['fellowship'] = ['0', '0.907', '0', '0.788', '#interest', '#admiration', 'positive', '0.848', 'companionship', 'family', 'society', 'company', 'koinonia']
senticnet['fellowship_support'] = ['0', '0.791', '0', '0.757', '#interest', '#admiration', 'positive', '0.774', 'research', 'training_plan', 'fellowship', 'environment', 'sponsoring_laboratory']
senticnet['felon'] = ['-0.79', '0', '0.997', '-0.76', '#sadness', '#anger', 'negative', '-0.85', 'toughie', 'accessory', 'highbinder', 'hoodlum', 'thug']
senticnet['feloniously'] = ['-0.04', '0.164', '0.685', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maliciousness', 'spitefulness', 'maleficence', 'malefic', 'malign']
senticnet['felony'] = ['-0.84', '0', '0.745', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'crime', 'against_law', 'revenge', 'vengeance', 'theft']
senticnet['felony_arson'] = ['-0.14', '0', '-0.21', '-0.07', '#sadness', '#fear', 'negative', '-0.14', 'heat_light', 'burn_down_house', 'set_on_fire', 'rapid_oxidation', 'flame']
senticnet['felony_charge'] = ['-0.84', '0', '0.745', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'crime', 'against_law', 'revenge', 'vengeance', 'theft']
senticnet['felony_child_abuse'] = ['0.654', '-0.59', '0', '0.774', '#joy', '#admiration', 'positive', '0.673', 'instrument', 'indictment', 'murder_indictment', 'true_bill', 'legal_document']
senticnet['felony_conviction'] = ['-0.60', '0', '0.832', '-0.53', '#sadness', '#anger', 'negative', '-0.65', 'unsuitable', 'undesirable', 'unqualified', 'disqualified', 'unentitled']
senticnet['felony_count'] = ['-0.84', '0', '0.745', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'crime', 'against_law', 'revenge', 'vengeance', 'theft']
senticnet['felony_offense'] = ['-0.84', '0', '0.745', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'crime', 'against_law', 'revenge', 'vengeance', 'theft']
senticnet['felony_punishable_by'] = ['-0.84', '0', '0.745', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'crime', 'against_law', 'revenge', 'vengeance', 'theft']
senticnet['felony_violation'] = ['-0.84', '0', '0.745', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'crime', 'against_law', 'revenge', 'vengeance', 'theft']
senticnet['female'] = ['0.779', '0', '0', '0.892', '#joy', '#admiration', 'positive', '0.836', 'damsel_human', 'damsel_person', 'niece', 'damsel', 'woman']
senticnet['female_attire'] = ['-0.04', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.05', 'fill_bathtub', 'fill_bathtub_water', 'run_bath', 'turn_water', 'take_clothes_off']
senticnet['female_child'] = ['-0.76', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.81', 'descendant', 'father_mother', 'parent_child', 'human_offspring', 'family_relation']
senticnet['female_cloaca'] = ['0.049', '0.098', '-0.07', '0.121', '#interest', '#admiration', 'positive', '0.047', 'procreate', 'reproduce', 'start_family', 'cause_cold', 'unprotect_sex']
senticnet['female_cloth'] = ['0.053', '-0.06', '0.047', '0.087', '#surprise', '#admiration', 'positive', '0.04', 'woman_garment', 'skirt', 'woman_dress', 'frilly_design', 'robe']
senticnet['female_companionship'] = ['-0.88', '0', '0.864', '-0.72', '#sadness', '#anger', 'negative', '-0.82', 'vixenish', 'venomous', 'bitchy', 'catty', 'malicious']
senticnet['female_counterpart'] = ['0.779', '0', '0', '0.892', '#joy', '#admiration', 'positive', '0.836', 'damsel_human', 'damsel_person', 'niece', 'damsel', 'woman']
senticnet['female_cub'] = ['0.02', '0.071', '0', '0.069', '#interest', '#admiration', 'positive', '0.053', 'many_animals', 'donkey', 'chimpanzee', 'cute_animal', 'creature']
senticnet['female_dancer'] = ['-0.95', '0', '0.992', '-0.63', '#sadness', '#anger', 'negative', '-0.86', 'modern_dance', 'interpretive_dancing', 'interpretative_dance', 'interpretative_dancing', 'stage_dancing']
senticnet['female_ejaculation'] = ['0.655', '-0.87', '0', '0.595', '#joy', '#surprise', 'positive', '0.709', 'erection', 'excitement', 'arousal', 'kill_brain_cell', 'feel_relieved']
senticnet['female_foeticide'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'vassalage']
senticnet['female_genitalia'] = ['0.779', '0', '0', '0.892', '#joy', '#admiration', 'positive', '0.836', 'damsel_human', 'damsel_person', 'niece', 'damsel', 'woman']
senticnet['female_giant_panda'] = ['-0.95', '0', '0.735', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'novice', 'beginner', 'tyro', 'tiro', 'initiate']
senticnet['female_human'] = ['0.794', '0', '0', '0.724', '#joy', '#admiration', 'positive', '0.759', 'female_person', 'woman_child', 'female', 'female_relative', 'hold_child']
senticnet['female_impersonator'] = ['0', '0', '0', '0.987', '#admiration', '#admiration', 'positive', '0.987', 'emote', 'bold', 'form_of_social_interaction', 'act', 'stage_play']
senticnet['female_infanticide'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'vassalage']
senticnet['female_inferiority'] = ['-0.57', '0', '0.53', '-0.63', '#sadness', '#disgust', 'negative', '-0.58', 'low_status', 'lowness', 'backseat', 'lowliness', 'subordinateness']
senticnet['female_juror'] = ['0', '0.104', '0', '0.075', '#interest', '#admiration', 'positive', '0.089', 'judge', 'negotiator', 'jury', 'arbiter', 'member_jury']
senticnet['female_mate'] = ['0.049', '0.098', '-0.07', '0.121', '#interest', '#admiration', 'positive', '0.047', 'procreate', 'reproduce', 'start_family', 'cause_cold', 'unprotect_sex']
senticnet['female_ninja'] = ['-0.04', '0.138', '-0.08', '0.111', '#interest', '#admiration', 'positive', '0.031', 'geometry', 'craft', 'blade_geometry', 'blade', 'collect_item']
senticnet['female_nude'] = ['-0.94', '0', '0.991', '-0.65', '#sadness', '#anger', 'negative', '-0.86', 'birthday_suit', 'raw', 'altogether', 'nakedness', 'undress']
senticnet['female_orgasm'] = ['0.655', '-0.87', '0', '0.595', '#joy', '#surprise', 'positive', '0.709', 'erection', 'excitement', 'arousal', 'kill_brain_cell', 'feel_relieved']
senticnet['female_panda'] = ['0.02', '0.071', '0', '0.069', '#interest', '#admiration', 'positive', '0.053', 'many_animals', 'donkey', 'chimpanzee', 'cute_animal', 'creature']
senticnet['female_parent'] = ['0.886', '0', '0', '0.957', '#joy', '#admiration', 'positive', '0.921', 'woman_child', 'female_relative', 'kin', 'familial_relationship', 'family_member']
senticnet['female_perfume'] = ['0.066', '0.083', '-0.06', '0.09', '#interest', '#admiration', 'positive', '0.044', 'aroma', 'perfume', 'fragrance', 'bad_odor', 'saliva']
senticnet['female_person'] = ['0.899', '0', '0', '0.772', '#joy', '#admiration', 'positive', '0.836', 'female_human', 'female', 'woman_child', 'hold_child', 'female_relative']
senticnet['female_polar_bear'] = ['-0.95', '0', '0.735', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'novice', 'beginner', 'tyro', 'tiro', 'initiate']
senticnet['female_protester'] = ['-0.93', '0', '0.688', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'dissident', 'dissenter', 'nimby', 'objector', 'contestant']
senticnet['female_rapper'] = ['0', '0.712', '0', '0.844', '#interest', '#admiration', 'positive', '0.778', 'dollar_change', 'small_currency', 'currency_unit', 'metal_money', 'american_money']
senticnet['female_relative'] = ['0.966', '0', '0', '0.99', '#joy', '#admiration', 'positive', '0.978', 'female_sibling', 'woman_child', 'parent', 'sibling', 'kin']
senticnet['female_sexual_arousal'] = ['0.611', '-0.88', '0', '0.883', '#surprise', '#admiration', 'positive', '0.792', 'love', 'aphrodisia', 'lecherousness', 'lustfulness', 'amorousness']
senticnet['female_sexuality'] = ['0.911', '0', '0', '0.85', '#joy', '#admiration', 'positive', '0.881', 'sophisticated', 'lovely', 'elegant', 'contrary', 'common_man']
senticnet['female_sibling'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'family_member', 'relative', 'family_relation', 'sister', 'same_parent']
senticnet['female_singer'] = ['0.882', '0.901', '0', '0', '#joy', '#interest', 'positive', '0.891', 'songwriter', 'vip', 'rock_group', 'guitar_player', 'violinist']
senticnet['female_slave'] = ['-0.91', '0', '0.451', '-0.98', '#sadness', '#disgust', 'negative', '-0.78', 'slave', 'psychological_aggression', 'physical_aggression', 'aggression', 'verbal_aggression']
senticnet['female_sterilization'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'safe_sex', 'sex', 'prevent_pregnancy', 'avoid_pregnancy', 'become_aroused']
senticnet['female_swimmer'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'chlorine', 'lifeguard', 'still_water', 'large_body', 'shallow_water']
senticnet['female_television_connector'] = ['0', '-0.02', '0', '0.052', '#surprise', '#admiration', 'positive', '0.04', 'power_switch', 'channel_selector', 'speaker', 'tuner', 'handset']
senticnet['female_transsexual'] = ['0.779', '0', '0', '0.892', '#joy', '#admiration', 'positive', '0.836', 'damsel_human', 'damsel_person', 'niece', 'damsel', 'woman']
senticnet['female_wasp'] = ['0.748', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.873', 'butterfly', 'moth', 'parrot', 'beetle', 'falcon']
senticnet['feme_covert'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'bigamy', 'wedlock']
senticnet['femicide'] = ['0', '0', '-0.95', '-0.85', '#fear', '#disgust', 'negative', '-0.90', 'choose_victim', 'murder_weapon', 'hide_evidence', 'murder', 'shoot_gun']
senticnet['feminine'] = ['0.911', '0', '0', '0.85', '#joy', '#admiration', 'positive', '0.881', 'sophisticated', 'lovely', 'elegant', 'contrary', 'common_man']
senticnet['feminine_beauty'] = ['0.911', '0', '0', '0.85', '#joy', '#admiration', 'positive', '0.881', 'sophisticated', 'lovely', 'elegant', 'contrary', 'common_man']
senticnet['feminine_charm'] = ['0.585', '-0.88', '0', '0.615', '#surprise', '#admiration', 'positive', '0.695', 'parental_quality', 'maternal_quality', 'quality', 'maternalism', 'fatherliness']
senticnet['feminine_gender'] = ['0.911', '0', '0', '0.85', '#joy', '#admiration', 'positive', '0.881', 'sophisticated', 'lovely', 'elegant', 'contrary', 'common_man']
senticnet['feminine_ideal'] = ['0.911', '0', '0', '0.85', '#joy', '#admiration', 'positive', '0.881', 'sophisticated', 'lovely', 'elegant', 'contrary', 'common_man']
senticnet['feminine_principle'] = ['0.693', '-0.52', '0', '0.82', '#joy', '#admiration', 'positive', '0.681', 'concrete_representation', 'shape', 'representation', 'mental_representation', 'internal_representation']
senticnet['feminine_sexuality'] = ['0.911', '0', '0', '0.85', '#joy', '#admiration', 'positive', '0.881', 'sophisticated', 'lovely', 'elegant', 'contrary', 'common_man']
senticnet['femininity'] = ['-0.68', '0', '0.949', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'chauvinism', 'sexism', 'antifeminism', 'sexual_discrimination', 'discrimination']
senticnet['feminism'] = ['-0.97', '0', '0.971', '-0.84', '#sadness', '#anger', 'negative', '-0.93', 'fail', 'betray', 'disappoint', 'cross', 'disillusion']
senticnet['feminist'] = ['-0.97', '0', '0.971', '-0.84', '#sadness', '#anger', 'negative', '-0.93', 'fail', 'betray', 'disappoint', 'cross', 'disillusion']
senticnet['feminist_criticism'] = ['0.587', '-0.94', '0', '0.659', '#surprise', '#admiration', 'positive', '0.731', 'critique', 'review', 'criticism', 'textual_criticism', 'new_criticism']
senticnet['feminist_critique'] = ['-0.07', '0.195', '0.629', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'misogynic', 'misogyny', 'murderousness', 'amuck', 'amok']
senticnet['feminist_literary'] = ['0.587', '-0.94', '0', '0.659', '#surprise', '#admiration', 'positive', '0.731', 'critique', 'review', 'criticism', 'textual_criticism', 'new_criticism']
senticnet['feminist_science_fiction'] = ['0.036', '0.219', '0', '0.19', '#interest', '#admiration', 'positive', '0.148', 'book_genre', 'literature', 'concoction', 'untruth', 'falsehood']
senticnet['feminist_theology'] = ['0.037', '0.293', '0', '0.269', '#interest', '#admiration', 'positive', '0.2', 'religious_purpose', 'prayer', 'judaism', 'christianity', 'religion']
senticnet['feminist_theory'] = ['0.587', '-0.94', '0', '0.659', '#surprise', '#admiration', 'positive', '0.731', 'critique', 'review', 'criticism', 'textual_criticism', 'new_criticism']
senticnet['feminity'] = ['-0.56', '0', '0.977', '-0.86', '#anger', '#disgust', 'negative', '-0.80', 'strangeness', 'quaintness', 'abnormality', 'queerness', 'crotchet']
senticnet['feminize'] = ['0.911', '0', '0', '0.85', '#joy', '#admiration', 'positive', '0.881', 'sophisticated', 'lovely', 'elegant', 'contrary', 'common_man']
senticnet['femme_au'] = ['-0.58', '0', '0.661', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'explosive', 'cordite', 'dynamite', 'plastic_explosive', 'explosive_compound']
senticnet['femme_fatale'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'concupiscence', 'lustfulness', 'amorousness']
senticnet['femoral_component'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'socket', 'tooth_socket', 'alveolus', 'acetabulum', 'cotyloid_cavity']
senticnet['femoral_head'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'socket', 'tooth_socket', 'alveolus', 'acetabulum', 'cotyloid_cavity']
senticnet['femoral_hernia'] = ['-0.56', '0', '0.806', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['femoral_nerve'] = ['-0.41', '0', '-0.28', '-0.05', '#sadness', '#fear', 'negative', '-0.25', 'numbness', 'carpal_tunnel_syndrome', 'weakness', 'pain', 'muscle_damage']
senticnet['femur'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'above_leg', 'joint', 'hip', 'knee']
senticnet['fence'] = ['-0.93', '0', '-0.99', '-0.92', '#sadness', '#fear', 'negative', '-0.95', 'language_barrier', 'shut', 'shut_down', 'close_down', 'obstacle']
senticnet['fence_yard'] = ['-0.72', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.75', 'seed_lawn', 'protect_place', 'hurry_work', 'air_grievance', 'sail_around_world']
senticnet['fenced_yard'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'apartment', 'furniture', 'kitchen', 'flat']
senticnet['fencer'] = ['0.833', '-0.97', '0', '0.91', '#surprise', '#admiration', 'positive', '0.906', 'combatant', 'battler', 'belligerent', 'fighter', 'scrapper']
senticnet['fend'] = ['-0.79', '0', '0.974', '-0.83', '#anger', '#disgust', 'negative', '-0.86', 'manage', 'make_out', 'cope', 'get_by', 'make_do']
senticnet['fend_off'] = ['0', '0', '0.75', '-0.88', '#anger', '#disgust', 'negative', '-0.81', 'upset', 'faze', 'discompose', 'discomfit', 'disconcert']
senticnet['fend_off_competition'] = ['0.887', '0', '0', '0', '#joy', '#joy', 'positive', '0.887', 'fun', 'difficult_play', 'enjoy_sport', 'accept_challenge', 'competition']
senticnet['fend_off_criticism'] = ['0.65', '-0.61', '0', '0.703', '#joy', '#admiration', 'positive', '0.657', 'gleaming', 'luster', 'polish', 'glow', 'effulgence']
senticnet['fender_bassman'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'lever']
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senticnet['ferris_wheel'] = ['0.086', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.056', 'roller-coaster', 'carnival_ride', 'carny', 'merry_go_round', 'fortune_teller']
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senticnet['ferruginous_duck_aythya_nyroca'] = ['0.797', '-0.71', '0', '0.736', '#joy', '#admiration', 'positive', '0.751', 'wood_ibis', 'wood_stork', 'ibis', 'wading_bird', 'wader']
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senticnet['fertilizer_bomb'] = ['0', '-0.03', '-0.04', '-0.19', '#fear', '#disgust', 'negative', '-0.06', 'nuclear_bomb', 'explosive', 'grenade', 'nuclear_weapon', 'aerial_bomb']
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senticnet['festering_wound'] = ['-0.64', '0', '0.941', '-0.92', '#anger', '#disgust', 'negative', '-0.83', 'unction', 'carron_oil', 'ointment', 'unguent', 'balm']
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senticnet['fig_tree'] = ['0.826', '0.787', '0', '0', '#joy', '#interest', 'positive', '0.806', 'fig', 'baby_flower', 'carpel', 'strawberry', 'star_anise']
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senticnet['fight_disease'] = ['0', '0', '-0.89', '0', '#fear', '#fear', 'negative', '-0.89', 'rescue', 'rejuvenate', 'bank_money', 'rejuvenation', 'efficiency']
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senticnet['fight_enemy'] = ['0', '0', '0.854', '-0.85', '#anger', '#disgust', 'negative', '-0.85', 'destroy_enemy', 'kill', 'fight_war', 'genocide', 'punch']
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senticnet['fight_tooth'] = ['0', '0.078', '0.027', '0.196', '#interest', '#admiration', 'positive', '0.082', 'heel', 'ankle', 'toe', 'foot_massage', 'foot']
senticnet['fight_via_ko'] = ['0.823', '0.982', '0', '0', '#joy', '#interest', 'positive', '0.902', 'spherical', 'three_dimensions', 'round_orb', 'coil', 'fill_with_sand']
senticnet['fight_war'] = ['0', '0', '0', '-0.94', '#disgust', '#disgust', 'negative', '-0.94', 'conquer_nation', 'fight_enemy', 'destroy_enemy', 'kill', 'war']
senticnet['fighter'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'army_man', 'army_person', 'sailor', 'man', 'workman']
senticnet['fighter_airfield'] = ['-0.97', '0', '0.737', '-0.97', '#sadness', '#disgust', 'negative', '-0.89', 'flare_path', 'airstrip', 'take_off', 'runway', 'grassland']
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senticnet['fighting_bravely'] = ['0.827', '0.85', '0', '0.743', '#joy', '#interest', 'positive', '0.807', 'chivalry', 'honor', 'almighty', 'admire', 'respect']
senticnet['fighting_cock'] = ['-0.54', '0', '0.811', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'bird_of_prey', 'raptor', 'raptorial_bird', 'parrot', 'bird']
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senticnet['fighting_spirit'] = ['0.827', '0.913', '0', '0.867', '#interest', '#admiration', 'positive', '0.869', 'good_habit', 'rivalry', 'competitive', 'competitive_nature', 'determination']
senticnet['fighting_terrorism'] = ['-0.94', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.88', 'combat', 'uprise', 'big_fight', 'opposition', 'violence']
senticnet['fighting_tooth'] = ['0', '0.078', '0.027', '0.196', '#interest', '#admiration', 'positive', '0.082', 'heel', 'ankle', 'toe', 'foot_massage', 'foot']
senticnet['figment'] = ['0', '0', '-0.76', '-0.96', '#fear', '#disgust', 'negative', '-0.86', 'fictitious', 'fictitiously', 'phantom', 'pseudonym', 'pseudonymous']
senticnet['figurate_number'] = ['0.042', '-0.00', '0.027', '0.099', '#joy', '#admiration', 'positive', '0.031', 'chess_knight', 'chess_queen', 'chess_pawn', 'pawn', 'chess_bishop']
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senticnet['figure'] = ['0.854', '0.76', '0', '0', '#joy', '#interest', 'positive', '0.807', 'geometric_figure', 'polygon', 'form', 'parallelogram', 'hexagon']
senticnet['figure_colormap'] = ['0.777', '-0.85', '0', '0.615', '#joy', '#surprise', 'positive', '0.748', 'think', 'play', 'trifle', 'abstract', 'warm_to']
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senticnet['figure_skate'] = ['0.877', '0.743', '0', '0', '#joy', '#interest', 'positive', '0.81', 'old_dance', 'dance', 'slow_dance', 'dance_type', 'enter_contest']
senticnet['figure_skating'] = ['0.964', '0.713', '0', '0', '#joy', '#interest', 'positive', '0.839', 'martial_art', 'judo', 'jump_around', 'go_underwater', 'athletics']
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senticnet['figurine'] = ['0', '0', '0', '0.115', '#admiration', '#admiration', 'positive', '0.115', 'chess_knight', 'strategy', 'pawn', 'chess_queen', 'chess_rook']
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senticnet['filbert'] = ['0.02', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'many_fruits', 'peach', 'buy_fruit', 'pineapple', 'tomato']
senticnet['filbert_bayi'] = ['-0.04', '-0.03', '0', '-0.16', '#sadness', '#disgust', 'negative', '-0.05', 'crazy', 'damage_body', 'crazy_person', 'threat', 'max']
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senticnet['file'] = ['0', '0.077', '0', '0.068', '#interest', '#admiration', 'positive', '0.073', 'make_book', 'document', 'paper_write', 'readable', 'newspaper_write']
senticnet['file_bill'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'bill', 'credit_check', 'get_salary', 'get_paid', 'bank']
senticnet['file_box'] = ['0', '0.877', '0', '0', '#interest', '#interest', 'positive', '0.877', 'box', 'home_sweet_home', 'home', 'change_life', 'clean_house']
senticnet['file_chooser_dialog'] = ['0.055', '0.117', '0', '0.133', '#interest', '#admiration', 'positive', '0.102', 'graphical_representation', 'pencil_pen', 'note_pad', 'marker', 'stationery']
senticnet['file_complaint'] = ['0', '0', '0.701', '-0.85', '#anger', '#disgust', 'negative', '-0.78', 'obey_law', 'meet_greet', 'courageous', 'protest', 'box_ear']
senticnet['file_drawer'] = ['0', '0.877', '0', '0', '#interest', '#interest', 'positive', '0.877', 'desk', 'office', 'folder', 'organiser', 'document']
senticnet['file_format'] = ['-0.55', '0', '0.912', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'wrangle', 'dustup', 'quarrel', 'row', 'squabble']
senticnet['file_system'] = ['-0.06', '0.119', '0', '0.096', '#interest', '#admiration', 'positive', '0.05', 'recollection', 'file', 'brain_function', 'rote', 'memory']
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senticnet['filename'] = ['0', '0.175', '0', '0.231', '#interest', '#admiration', 'positive', '0.203', 'list', 'schedule', 'index', 'agenda', 'register']
senticnet['filene_department'] = ['0.914', '-0.56', '0', '0.869', '#joy', '#admiration', 'positive', '0.781', 'tabula_rasa', 'ego', 'subconscious', 'unconscious', 'psyche']
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senticnet['filet_mignon'] = ['0.062', '0', '0.027', '0.055', '#joy', '#admiration', 'positive', '0.03', 'porridge', 'meal', 'broth', 'hamburger', 'hearty']
senticnet['filial_affection'] = ['0', '0.767', '0', '0.711', '#interest', '#admiration', 'positive', '0.739', 'reverently', 'hero-worship', 'venerate', 'idolize', 'veneration']
senticnet['filial_piety'] = ['0.997', '0.796', '0', '0.89', '#joy', '#admiration', 'positive', '0.894', 'devotion', 'bravery', 'adoration', 'zeal', 'charm']
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senticnet['filifera'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'carnauba_palm', 'carnauba', 'wax_palm', 'palmetto', 'palmyra']
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senticnet['filigree'] = ['0.738', '0.996', '0', '0', '#joy', '#interest', 'positive', '0.867', 'enmesh', 'entangle', 'complicate', 'intricate', 'puzzle']
senticnet['filing_cabinet'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'slot', 'box', 'load_dock', 'diskette', 'emporium']
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senticnet['filipino_nanny'] = ['0.875', '0', '0', '0.802', '#joy', '#admiration', 'positive', '0.839', 'foreigner', 'exile', 'deportee', 'gringo', 'import']
senticnet['filippo_tommaso_marinetti'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'native_person', 'revolutionary', 'republic', 'revolution', 'look_ahead']
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senticnet['fill_air'] = ['0.806', '0.73', '0', '0', '#joy', '#interest', 'positive', '0.768', 'spherical_shape', 'use_sport', 'bouncy', 'three_dimensional', 'make_out_rubber']
senticnet['fill_bathtub'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'soap', 'relax', 'relax_time', 'bath', 'bathroom']
senticnet['fill_bathtub_water'] = ['0', '0.877', '0', '0', '#interest', '#interest', 'positive', '0.877', 'fill_bathtub', 'water', 'relax_time', 'relax', 'soap']
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senticnet['fill_bottle'] = ['-0.13', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.13', 'shoulder_responsibility', 'top_up', 'throw_fit', 'fill', 'laugh_clown']
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senticnet['fill_form'] = ['0', '0', '0', '-0.94', '#disgust', '#disgust', 'negative', '-0.94', 'boring', 'bureaucracy', 'annoying', 'form', 'fill_out_form']
senticnet['fill_gas_tank'] = ['-0.1', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.1', 'open_car_door', 'turn_key', 'plan_route', 'put_key_ignition', 'adjust_seat']
senticnet['fill_glass'] = ['0', '0.07', '0', '0.05', '#interest', '#admiration', 'positive', '0.06', 'gulp', 'better_cold', 'ferment', 'potable_liquid', 'sip']
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senticnet['fill_olympic_swimming'] = ['0', '0', '0', '0.964', '#admiration', '#admiration', 'positive', '0.964', 'swimming_pool', 'swim', 'swimsuit', 'lake', 'have_fun']
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senticnet['fill_stomach'] = ['0.72', '0.833', '0', '0', '#joy', '#interest', 'positive', '0.776', 'sustenance', 'enjoy_eat', 'satisfy_hunger', 'nourishment', 'eat_out']
senticnet['fill_up'] = ['0.097', '0', '0', '0', '#joy', '#joy', 'positive', '0.097', 'serve_liquid', 'eat_liquid', 'swallow', 'consume_food', 'drink_soup']
senticnet['fill_with_sand'] = ['-0.73', '0', '0.835', '0', '#sadness', '#anger', 'negative', '-0.78', 'circle_shape', 'ball_shape', 'round_shape', 'container', 'round']
senticnet['fillcolor'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'illuminate_room', 'give_light', 'light', 'light_bulb', 'lantern']
senticnet['filled_cavity'] = ['-0.05', '0.068', '-0.10', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'tooth_decay', 'body_ache', 'symptom', 'pierce', 'tooth']
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senticnet['filled_syringe'] = ['-0.02', '0', '-0.12', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'x-ray_machine', 'medical_record', 'latex_glove', 'medicine', 'doctor_examination_room']
senticnet['filler'] = ['0', '0', '0', '0.869', '#admiration', '#admiration', 'positive', '0.869', 'fluffy_white', 'white', 'pillow', 'mattress', 'silky']
senticnet['fillet'] = ['-0.02', '-0.02', '0.064', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'ocean_water', 'fillet_fish', 'chowder', 'port', 'underwater']
senticnet['fillet_fish'] = ['0.027', '0.111', '-0.05', '0.167', '#interest', '#admiration', 'positive', '0.063', 'price_product', 'food', 'fried_fillet', 'dinner', 'hungry']
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senticnet['filling_station'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'clean', 'polish', 'shiny', 'water', 'sexy_girl']
senticnet['fillip'] = ['0.588', '-0.91', '0', '0.717', '#surprise', '#admiration', 'positive', '0.74', 'positive_stimulus', 'joy', 'bonus', 'stimulant', 'delight']
senticnet['film'] = ['0.837', '0', '0', '0', '#joy', '#joy', 'positive', '0.837', 'movie', 'entertainment', 'show', 'cinema', 'cctv']
senticnet['film_adaptation'] = ['-0.06', '0.122', '-0.04', '0.113', '#interest', '#admiration', 'positive', '0.032', 'encounter', 'portray', 'reveal', 'episode', 'next_episode']
senticnet['film_adaption'] = ['-0.06', '0.122', '-0.04', '0.113', '#interest', '#admiration', 'positive', '0.032', 'encounter', 'portray', 'reveal', 'episode', 'next_episode']
senticnet['film_critic'] = ['0.051', '0', '0', '0.072', '#joy', '#admiration', 'positive', '0.061', 'evaluate', 'wish_entertain', 'enjoyment', 'assess', 'entertainment']
senticnet['film_goldeneye'] = ['0.228', '0.123', '-0.09', '0', '#joy', '#interest', 'positive', '0.084', 'spend_time_with_child', 'social', 'friendship', 'companionship', 'get_close']
senticnet['film_maker'] = ['0', '0.762', '0', '0', '#interest', '#interest', 'positive', '0.762', 'encyclopaedia', 'tropical_rainforest', 'learn_new_information', 'tropical_forest', 'child_story']
senticnet['film_pitch_perfect'] = ['-0.97', '0', '0.728', '-0.78', '#sadness', '#disgust', 'negative', '-0.82', 'beguiler', 'slicker', 'phony', 'faker', 'deceiver']
senticnet['film_producer'] = ['0', '0.797', '0', '0.804', '#interest', '#admiration', 'positive', '0.8', 'director', 'shooting_movie', 'director_chair', 'important_person', 'movie']
senticnet['film_skyfall'] = ['0.228', '0.123', '-0.09', '0', '#joy', '#interest', 'positive', '0.084', 'spend_time_with_child', 'social', 'friendship', 'companionship', 'get_close']
senticnet['film_star_kpac'] = ['-0.56', '0', '0.617', '-0.80', '#anger', '#disgust', 'negative', '-0.66', 'prolixity', 'prolixness', 'wordiness', 'repetitiveness', 'repetitiousness']
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senticnet['finely_chop'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'salad_shaker', 'store_shelf', 'kitchen_tool', 'pliers', 'ladle']
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senticnet['fingermark'] = ['-0.87', '0', '0.5', '-0.90', '#sadness', '#disgust', 'negative', '-0.76', 'smear', 'smudge', 'blot', 'daub', 'smirch']
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senticnet['finish-meal'] = ['0.757', '0', '0', '0', '#joy', '#joy', 'positive', '0.757', 'delicious_food', 'hunger_go_away', 'feel_satisfied', 'full_stomach', 'feel_full']
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senticnet['finite'] = ['0.902', '0.869', '0', '0', '#joy', '#interest', 'positive', '0.885', 'bounded', 'limited', 'finite_state_machine', 'wall', 'boundary']
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senticnet['finite_volume'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'assumption', 'precondition', 'provision', 'constraint', 'premiss']
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senticnet['fipple_flute'] = ['0.821', '-0.53', '0', '0.785', '#joy', '#admiration', 'positive', '0.712', 'fife', 'nose_flute', 'piccolo', 'woodwind', 'flute']
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senticnet['fire-resistant'] = ['0', '0.819', '0', '0.86', '#interest', '#admiration', 'positive', '0.839', 'non-combustible', 'noncombustible', 'non-flammable', 'nonflammable', 'combustible']
senticnet['fire_alarm'] = ['0.886', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.881', 'fire_hydrant', 'stair_rail', 'christian_fundamentalist', 'extinguisher', 'reception_desk']
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senticnet['fire_breathing_dragon'] = ['-0.75', '0', '0.708', '0', '#sadness', '#anger', 'negative', '-0.73', 'hound', 'cage_animal', 'kind_animal', 'hunter', 'animal']
senticnet['fire_bullet'] = ['-0.88', '0', '0', '-0.80', '#sadness', '#disgust', 'negative', '-0.84', 'shoot', 'murder_weapon', 'kill', 'shoot_person', 'choose_victim']
senticnet['fire_burn'] = ['0.86', '0.796', '0', '0', '#joy', '#interest', 'positive', '0.828', 'burn_heat', 'really_hot', 'hot', 'ash', 'flame']
senticnet['fire_eater'] = ['0.9', '-0.88', '0', '0.687', '#joy', '#surprise', 'positive', '0.823', 'conjurer', 'escape_expert', 'mind_reader', 'telepathist', 'performer']
senticnet['fire_employee'] = ['-0.94', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.82', 'receive_punishment', 'market_product', 'ship_goods', 'turn_away', 'make_decision']
senticnet['fire_engine'] = ['0', '0', '0.019', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'fire_truck', 'hardhat', 'stop_sign', 'maroon', 'tangerine']
senticnet['fire_extinguisher'] = ['0.886', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.881', 'fire_hydrant', 'stair_rail', 'christian_fundamentalist', 'extinguisher', 'reception_desk']
senticnet['fire_gun'] = ['-0.07', '-0.02', '-0.04', '-0.17', '#sadness', '#disgust', 'negative', '-0.06', 'shoot_gun', 'shoot', 'fire_weapon', 'use_war', 'lethal']
senticnet['fire_hazard'] = ['0.039', '0', '0', '0.052', '#joy', '#admiration', 'positive', '0.045', 'hot_air', 'liquid_gas', 'type_of_engine', 'warm_air', 'fire_starter']
senticnet['fire_hydrant'] = ['0', '0', '0.027', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'fire_engine', 'stop_sign', 'street_sign', 'streetlight', 'fire_truck']
senticnet['fire_light'] = ['0', '-0.02', '-0.06', '-0.17', '#fear', '#disgust', 'negative', '-0.07', 'forest_fire', 'fall_sky', 'dynamite_explosion', 'firebomb', 'killer']
senticnet['fire_pit'] = ['0.074', '0', '0', '0', '#joy', '#joy', 'positive', '0.074', 'hike_trail', 'ant_hill', 'compost', 'picnic', 'soccer_field']
senticnet['fire_prevention'] = ['0.082', '0.319', '-0.10', '0.147', '#interest', '#admiration', 'positive', '0.11', 'protect', 'defend', 'secure', 'guard', 'watch_over']
senticnet['fire_product'] = ['0', '0', '-0.11', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'scald', 'inferno', 'bright_flash', 'arson', 'high_temperature']
senticnet['fire_resistant'] = ['-0.90', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.85', 'flame', 'combustible', 'dangerous', 'incendiary', 'heat']
senticnet['fire_shotgun'] = ['-0.77', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.87', 'kill_person', 'stab_to_death', 'kill', 'kill_pest', 'socialist']
senticnet['fire_starter'] = ['0', '0', '0', '0.073', '#admiration', '#admiration', 'positive', '0.073', 'solid_fuel', 'organic_substance', 'renewable_resource', 'use_fuel', 'tree_product']
senticnet['fire_suppression_system'] = ['0.886', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.881', 'fire_hydrant', 'stair_rail', 'christian_fundamentalist', 'extinguisher', 'reception_desk']
senticnet['fire_truck'] = ['0', '0', '0.027', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'fire_engine', 'fire_hydrant', 'stop_fire', 'emergency', 'stop_sign']
senticnet['fire_up'] = ['0.894', '0.912', '0', '0', '#joy', '#interest', 'positive', '0.903', 'instigate', 'incite', 'inflame', 'stir_up', 'sensational']
senticnet['fire_up_grill'] = ['0.073', '0.102', '0', '0.151', '#interest', '#admiration', 'positive', '0.109', 'nutritious', 'delicious', 'yummy', 'good_eat', 'tasty']
senticnet['fire_warden'] = ['-0.86', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.84', 'forest_fire_fighter', 'texas_ranger', 'ranger', 'commando', 'game']
senticnet['fire_weapon'] = ['-0.14', '-0.04', '-0.11', '-0.21', '#sadness', '#disgust', 'negative', '-0.10', 'bloodshed', 'use_gun', 'plan_attack', 'use_weapon', 'fire_gun']
senticnet['firearm'] = ['-0.84', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.86', 'handgun', 'rifle', 'weapon', 'pistol', 'gun']
senticnet['firearm_dealer'] = ['-0.84', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.86', 'handgun', 'rifle', 'weapon', 'pistol', 'gun']
senticnet['firearm_instructor'] = ['0.93', '-0.95', '0', '0.909', '#joy', '#surprise', 'positive', '0.93', 'acquirement', 'skill', 'accomplishment', 'acquisition', 'attainment']
senticnet['firearm_licence'] = ['-0.84', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.86', 'handgun', 'rifle', 'weapon', 'pistol', 'gun']
senticnet['firearm_proficiency'] = ['0.93', '-0.95', '0', '0.909', '#joy', '#surprise', 'positive', '0.93', 'acquirement', 'skill', 'accomplishment', 'acquisition', 'attainment']
senticnet['fireball'] = ['0', '-0.00', '-0.07', '-0.06', '#fear', '#disgust', 'negative', '-0.04', 'lighten', 'tornado', 'dynamite_explosion', 'forest_fire', 'lightning']
senticnet['firebase'] = ['-0.66', '0', '0.789', '-0.63', '#sadness', '#anger', 'negative', '-0.69', 'humbug', 'play_trick', 'cozen', 'pull_fast_one_on', 'play_trick_on']
senticnet['fireblight'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'rust_fungus', 'fungus']
senticnet['firebolt'] = ['0.105', '-0.17', '0.057', '0.079', '#joy', '#surprise', 'positive', '0.076', 'cue', 'twig', 'long_stick', 'stick', 'adhere']
senticnet['firebomb'] = ['0', '0', '-0.83', '-0.78', '#fear', '#disgust', 'negative', '-0.80', 'bomb', 'nuclear_weapon', 'explosive', 'nuclear_bomb', 'aerial_bomb']
senticnet['firebrick'] = ['0.962', '-0.70', '0', '0.522', '#joy', '#surprise', 'positive', '0.729', 'adobe', 'adobe_brick', 'clinker', 'clinker_brick', 'brick']
senticnet['firebug'] = ['-0.89', '0', '0.672', '-0.69', '#sadness', '#disgust', 'negative', '-0.75', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['fired'] = ['-0.95', '0', '0.164', '-0.97', '#sadness', '#disgust', 'negative', '-0.69', 'killed', 'unemployed', 'become_poor', 'need_money', 'need_new_job']
senticnet['fired_blast_furnace'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'glass_water', 'refreshed', 'smoothie', 'lemonade', 'cold_water']
senticnet['fired_kiln'] = ['0.037', '0.084', '0', '0', '#joy', '#interest', 'positive', '0.06', 'leave_over', 'leftover', 'cellulose', 'cane', 'remainder']
senticnet['firedamp'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'firework', 'bright_flash', 'nuclear_weapon', 'earthquake', 'nuclear_bomb']
senticnet['firefighter'] = ['0.701', '0.874', '0', '0.752', '#interest', '#admiration', 'positive', '0.776', 'save_life', 'brave', 'fireman', 'strong', 'uniform']
senticnet['firefighter_safety'] = ['0.082', '0.319', '-0.10', '0.147', '#interest', '#admiration', 'positive', '0.11', 'protect', 'defend', 'secure', 'guard', 'watch_over']
senticnet['firefighting_equipment'] = ['0.886', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.881', 'fire_hydrant', 'stair_rail', 'christian_fundamentalist', 'extinguisher', 'reception_desk']
senticnet['firefly'] = ['0', '0', '-0.87', '0', '#fear', '#fear', 'negative', '-0.87', 'beetle', 'many_insects', 'grasshopper', 'scorpion', 'mouse']
senticnet['firefox'] = ['-0.89', '0', '0.672', '-0.69', '#sadness', '#disgust', 'negative', '-0.75', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['firefox_extension'] = ['-0.89', '0', '0.672', '-0.69', '#sadness', '#disgust', 'negative', '-0.75', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['fireftp'] = ['-0.89', '0', '0.672', '-0.69', '#sadness', '#disgust', 'negative', '-0.75', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['firehole_river'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['firelight'] = ['-0.66', '0', '0.616', '-0.70', '#sadness', '#disgust', 'negative', '-0.66', 'flicker', 'coruscation', 'streak', 'glint', 'flash']
senticnet['fireman'] = ['0.031', '0', '-0.13', '-0.08', '#fear', '#disgust', 'negative', '-0.06', 'surgeon', 'connoisseur', 'physician', 'policeman', 'medical_doctor']
senticnet['firepit'] = ['0.074', '0', '0', '0', '#joy', '#joy', 'positive', '0.074', 'hike_trail', 'ant_hill', 'compost', 'picnic', 'soccer_field']
senticnet['fireplace'] = ['0.091', '0', '0.034', '0.049', '#joy', '#admiration', 'positive', '0.035', 'furnace', 'flame', 'huge_fire', 'fire', 'heater']
senticnet['fireplace_poker'] = ['-0.16', '-0.07', '-0.07', '-0.21', '#sadness', '#disgust', 'negative', '-0.09', 'cause_injury', 'hurt_person', 'maim', 'elephant_sit_chest', 'parry']
senticnet['fireproof_box'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'slot', 'box', 'load_dock', 'diskette', 'emporium']
senticnet['fireproof_safe'] = ['0.02', '0.205', '0', '0', '#joy', '#interest', 'positive', '0.112', 'safe', 'bank_vault', 'bank', 'piggy_bank', 'bank_account']
senticnet['fireside'] = ['-0.85', '0', '0.691', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'jinks', 'high_jinks', 'hijinks', 'high_jinx', 'revel']
senticnet['firestone_country'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'table_tennis', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['firestorm'] = ['0.957', '-0.78', '0', '0.658', '#joy', '#surprise', 'positive', '0.801', 'arguing', 'dispute', 'fight', 'conflict', 'polemic']
senticnet['firetruck'] = ['0.073', '0.097', '-0.05', '-0.03', '#joy', '#interest', 'positive', '0.02', 'newspaper_stand', 'jet_fuel', 'ticket_counter', 'aircraft_roadway', 'luggage_carrier']
senticnet['firewater'] = ['0.679', '-0.51', '0', '0.502', '#joy', '#surprise', 'positive', '0.564', 'liquor', 'spirits', 'booze', 'hard_drink', 'hard_liquor']
senticnet['firewire_port'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'computer_part', 'system', 'cpu', 'pencil_pen', 'monitor']
senticnet['firewood'] = ['0.053', '-0.09', '0', '0', '#joy', '#surprise', 'positive', '0.073', 'timber', 'mantle', 'stump', 'kindle', 'lumber']
senticnet['firework'] = ['0.728', '0.77', '0', '0', '#joy', '#interest', 'positive', '0.749', 'dynamite_explosion', 'nuclear_bomb', 'bomb', 'nuclear_weapon', 'atomic_bomb']
senticnet['firework_display'] = ['-0.72', '0', '0.978', '-0.86', '#anger', '#disgust', 'negative', '-0.85', 'amusement_park', 'common', 'pleasure_ground', 'village_green', 'tract']
senticnet['firing_mechanism'] = ['0.13', '0.097', '-0.07', '0.198', '#joy', '#admiration', 'positive', '0.087', 'jiggle', 'back_forth', 'vibrate', 'blend', 'mixture']
senticnet['firing_pin'] = ['-0.99', '0', '0.651', '-0.51', '#sadness', '#anger', 'negative', '-0.72', 'sending', 'compulsion', 'influence', 'inducement', 'causation']
senticnet['firing_pin_block'] = ['-0.99', '0', '0.651', '-0.51', '#sadness', '#anger', 'negative', '-0.72', 'sending', 'compulsion', 'influence', 'inducement', 'causation']
senticnet['firm'] = ['0.966', '0', '0', '0.987', '#joy', '#admiration', 'positive', '0.976', 'large_business', 'business_firm', 'business_organization', 'business_group', 'partnership']
senticnet['firm_grasp'] = ['0', '0.137', '-0.09', '0.226', '#interest', '#admiration', 'positive', '0.088', 'grab', 'hold', 'grip', 'extend_arm', 'reach']
senticnet['firm_kohn_pedersen'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'kit_fox', 'prairie_fox', 'white_fox']
senticnet['firm_tofu'] = ['0', '-0.03', '0.046', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'pasta', 'green_bean', 'mayonnaise', 'frozen_food', 'sour_milk']
senticnet['firmament'] = ['0.65', '-0.61', '0', '0.703', '#joy', '#admiration', 'positive', '0.657', 'gleaming', 'luster', 'polish', 'glow', 'burnish']
senticnet['firmly'] = ['0', '0.9', '0', '0.92', '#interest', '#admiration', 'positive', '0.91', 'firm', 'unwaveringly', 'hard', 'steadfastly', 'securely']
senticnet['firmly_shut'] = ['0.829', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.908', 'cut_food', 'drink_beer', 'serve_drink', 'drink_much', 'drink_beverage']
senticnet['firmness'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'adamance', 'resolve', 'resoluteness', 'adamance_of_purpose', 'resolution']
senticnet['firmness_of_purpose'] = ['0.528', '-0.54', '0', '0.956', '#surprise', '#admiration', 'positive', '0.675', 'willpower', 'will_power', 'decisiveness', 'trait', 'steadiness']
senticnet['firmware'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'computer_part', 'system', 'cpu', 'pencil_pen', 'monitor']
senticnet['firoud'] = ['-0.94', '0', '0.745', '-0.97', '#sadness', '#disgust', 'negative', '-0.89', 'nerve', 'fearlessness', 'heart', 'dauntlessness', 'intrepidity']
senticnet['first'] = ['0.082', '0.191', '0', '0.198', '#interest', '#admiration', 'positive', '0.157', 'regal', 'imperial', 'greatness', 'nobleman', 'magnitude']
senticnet['first-aid_kit'] = ['0', '0', '0', '0.869', '#admiration', '#admiration', 'positive', '0.869', 'first_aid_kit', 'medicine', 'medicine_chest', 'painkiller', 'surgeon']
senticnet['first-class'] = ['0.976', '0', '0', '0.932', '#joy', '#admiration', 'positive', '0.954', 'splendiferous', 'glorious', 'excellent', 'resplendent', 'splendid']
senticnet['first-rate'] = ['0', '0.991', '0', '0.835', '#interest', '#admiration', 'positive', '0.913', 'high-quality', 'top-notch', 'highest-quality', 'top-quality', 'top-of-the-line']
senticnet['first_aid'] = ['-0.06', '-0.03', '0', '-0.17', '#sadness', '#disgust', 'negative', '-0.06', 'seam', 'needle_thread', 'medical_kit', 'stitch', 'bandage']
senticnet['first_aid_kit'] = ['0', '0', '0', '0.869', '#admiration', '#admiration', 'positive', '0.869', 'first-aid_kit', 'dentist_office', 'medicine_chest', 'painkiller', 'toothbrush']
senticnet['first_amendment'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'indecency']
senticnet['first_baseman'] = ['0', '0.075', '0', '0', '#interest', '#interest', 'positive', '0.075', 'cup', 'carafe', 'container_cup', 'mug', 'glasses']
senticnet['first_choice'] = ['0.586', '-0.77', '0', '0.651', '#surprise', '#admiration', 'positive', '0.67', 'darling', 'pet', 'favorite', 'dearie', 'macushla']
senticnet['first_class'] = ['0.899', '0.747', '0', '0', '#joy', '#interest', 'positive', '0.823', 'cost_money', 'shopping', 'travel_across_world', 'spending', 'run_out_fuel']
senticnet['first_class_airplane_seat'] = ['0.912', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.836', 'expensive', 'snack_bar', 'airplane_seat', 'sofa_bed', 'spend_money']
senticnet['first_class_train_seat'] = ['0.723', '-0.82', '0', '0', '#joy', '#surprise', 'positive', '0.774', 'expensive', 'snack_bar', 'train_seat', 'sofa_bed', 'spend_money']
senticnet['first_degree_murder'] = ['0', '0', '-0.95', '-0.85', '#fear', '#disgust', 'negative', '-0.90', 'choose_victim', 'murder_weapon', 'hide_evidence', 'murder', 'shoot_gun']
senticnet['first_floor'] = ['0.039', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'lower_floor', 'floor', 'ground_floor', 'hallway', 'level_one']
senticnet['first_foray'] = ['0.954', '-0.87', '0', '0.583', '#joy', '#surprise', 'positive', '0.802', 'penetration', 'air_raid', 'air_attack', 'swoop', 'incursion']
senticnet['first_foray_into'] = ['0.954', '-0.87', '0', '0.583', '#joy', '#surprise', 'positive', '0.802', 'penetration', 'air_raid', 'air_attack', 'swoop', 'incursion']
senticnet['first_grader'] = ['0.521', '0.677', '-0.27', '0.273', '#joy', '#interest', 'positive', '0.299', 'further_education', 'protect_child', 'look_closely', 'go_to_school', 'register']
senticnet['first_iihf_directorate'] = ['0.031', '0.218', '-0.09', '0.299', '#interest', '#admiration', 'positive', '0.113', 'nominate', 'axiom', 'supreme', 'govern', 'tenet']
senticnet['first_instar'] = ['-0.87', '0', '0.983', '-0.91', '#anger', '#disgust', 'negative', '-0.92', 'thorn', 'sticker', 'pricker', 'prickle', 'spikelet']
senticnet['first_instar_larva'] = ['0', '-0.09', '0.123', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'gypsy_moth', 'japanese_beetle', 'cucumber_beetle', 'mosquito', 'earwig']
senticnet['first_metacarpal_bone'] = ['-0.60', '0', '0.701', '-0.72', '#anger', '#disgust', 'negative', '-0.67', 'lunate_bone', 'semilunar_bone', 'carpal_bone', 'triquetral', 'triquetral_bone']
senticnet['first_period'] = ['0', '0.827', '0', '0.913', '#interest', '#admiration', 'positive', '0.87', 'squeeze_play', 'squeeze', 'scorer', 'power_play', 'second_period']
senticnet['first_permanent_settler'] = ['0', '0.113', '0', '0.111', '#interest', '#admiration', 'positive', '0.112', 'animal_house', 'cow_home', 'animal_place', 'crop_place', 'acreage']
senticnet['first_person_pronoun'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'mediacy', 'mediateness', 'characteristic', 'allusiveness']
senticnet['first_quarter'] = ['0.834', '0.932', '0', '0', '#joy', '#interest', 'positive', '0.883', 'dime', 'nickel', 'metal_money', 'coin', 'money']
senticnet['first_rate'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'top', 'top_class', 'top_notch', 'premium', 'high_class']
senticnet['first_responder'] = ['0', '0', '-0.82', '0', '#fear', '#fear', 'negative', '-0.82', 'sickness', 'polio', 'malady', 'ailment', 'treatment']
senticnet['first_round_knockdown'] = ['0.944', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.967', 'meeting', 'meet', 'match', 'encounter', 'round']
senticnet['first_solo_album'] = ['0.899', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.813', 'concert', 'measure_angle', 'make_drawing', 'make_beautiful_music', 'listen_heart']
senticnet['first_step_toward'] = ['0', '0', '0.078', '0', '#anger', '#anger', 'negative', '-0.07', 'street_city', 'small_street', 'walkway', 'black_pavement', 'dark_road']
senticnet['first_upper_molar'] = ['0.886', '-0.99', '0', '0.542', '#joy', '#surprise', 'positive', '0.808', 'jowl', 'lower_jaw', 'mandible', 'lantern_jaw', 'lantern_jawr_bone']
senticnet['first_water'] = ['0.757', '-0.82', '0', '0.982', '#surprise', '#admiration', 'positive', '0.855', 'first_class', 'choiceness', 'superiority', 'fineness', 'quality']
senticnet['firstcall_sonicwall'] = ['-0.80', '0', '0.815', '-0.98', '#anger', '#disgust', 'negative', '-0.86', 'inalienable', 'nontransferable', 'unassignable', 'untransferable', 'inviolable']
senticnet['firsthand_experience'] = ['0.995', '0.801', '0', '0.968', '#joy', '#admiration', 'positive', '0.921', 'wisdom', 'learn', 'know_world', 'find_out_truth', 'smart']
senticnet['fisa'] = ['-0.53', '0', '0.884', '-0.72', '#anger', '#disgust', 'negative', '-0.71', 'bug', 'tap', 'eavesdrop', 'intercept', 'listen']
senticnet['fisa_court'] = ['-0.53', '0', '0.884', '-0.72', '#anger', '#disgust', 'negative', '-0.71', 'bug', 'tap', 'eavesdrop', 'intercept', 'listen']
senticnet['fisa_law'] = ['-0.53', '0', '0.884', '-0.72', '#anger', '#disgust', 'negative', '-0.71', 'bug', 'tap', 'eavesdrop', 'intercept', 'listen']
senticnet['fisa_warrant'] = ['-0.53', '0', '0.884', '-0.72', '#anger', '#disgust', 'negative', '-0.71', 'bug', 'tap', 'eavesdrop', 'intercept', 'listen']
senticnet['fiscal'] = ['-0.99', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.88', 'prosecution', 'prosecute', 'proceeding', 'persecute', 'prosecutor']
senticnet['fiscal_budget'] = ['-0.05', '0.282', '-0.04', '0.107', '#interest', '#admiration', 'positive', '0.072', 'expense', 'dollar_cent', 'dollar_sign', 'amount_pay', 'payment_amount']
senticnet['fiscal_cliff'] = ['0', '0', '-0.87', '-0.84', '#fear', '#disgust', 'negative', '-0.85', 'seize', 'seclude', 'attach', 'impound', 'confiscate']
senticnet['fiscal_cliff_deal'] = ['0', '0', '-0.87', '-0.84', '#fear', '#disgust', 'negative', '-0.85', 'seize', 'seclude', 'attach', 'impound', 'confiscate']
senticnet['fiscal_decentralization'] = ['0.952', '-0.84', '0', '0.875', '#joy', '#admiration', 'positive', '0.89', 'administration', 'judiciary', 'governing_body', 'establishment', 'brass']
senticnet['fiscal_integrity'] = ['0', '0', '0', '0.817', '#admiration', '#admiration', 'positive', '0.817', 'truthful', 'honesty', 'honest', 'unity', 'good_character']
senticnet['fiscal_irresponsibility'] = ['-0.61', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.69', 'untrustworthiness', 'untrustiness', 'undependability', 'undependableness', 'unreliability']
senticnet['fiscal_pact'] = ['0.397', '0.245', '-0.13', '0.224', '#joy', '#interest', 'positive', '0.183', 'agreement', 'provide', 'advice', 'contribution', 'offer']
senticnet['fiscal_policy'] = ['0.028', '0.153', '-0.05', '0.18', '#interest', '#admiration', 'positive', '0.076', 'nominate', 'administration', 'reign', 'order', 'regulation']
senticnet['fiscal_quarter'] = ['0.834', '0.932', '0', '0', '#joy', '#interest', 'positive', '0.883', 'dime', 'nickel', 'metal_money', 'coin', 'money']
senticnet['fiscal_retrenchment'] = ['0.961', '-0.84', '0', '0.958', '#joy', '#admiration', 'positive', '0.923', 'pause', 'break', 'intermission', 'suspension', 'rest_period']
senticnet['fiscal_year'] = ['-0.04', '0.268', '0', '0.159', '#interest', '#admiration', 'positive', '0.127', 'total', 'amount', 'a_lot', 'aggregate', 'bill']
senticnet['fiscal_year_budget'] = ['-0.05', '0.282', '-0.04', '0.107', '#interest', '#admiration', 'positive', '0.072', 'expense', 'dollar_cent', 'dollar_sign', 'amount_pay', 'payment_amount']
senticnet['fiscally_conservative'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'economic_libertarian', 'civil_libertarian', 'advocate', 'advocator', 'proponent']
senticnet['fischer_tropsch'] = ['0', '0', '-0.06', '0', '#fear', '#fear', 'negative', '-0.06', 'greasy', 'car_gas', 'fossil_fuel', 'crude', 'lubrication']
senticnet['fish'] = ['-0.04', '0.184', '-0.08', '0.172', '#interest', '#admiration', 'positive', '0.055', 'big_fish', 'animal', 'big_animal', 'big_cat', 'big']
senticnet['fish_assemblage'] = ['0', '0.184', '-0.05', '0.179', '#interest', '#admiration', 'positive', '0.104', 'rich', 'a_lot', 'abundant', 'wealth', 'plentiful']
senticnet['fish_bait'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'fishing_rod', 'fishing', 'patience', 'fish_lake', 'catch_fish']
senticnet['fish_biologist'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['fish_boat'] = ['-0.04', '-0.01', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.03', 'sailboat', 'sail_ship', 'battleship', 'oil_tanker', 'lifeboat']
senticnet['fish_bowl'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'bowl', 'goldbowl', 'jorum', 'mazer']
senticnet['fish_chip_shop'] = ['-0.02', '0.122', '0', '0.137', '#interest', '#admiration', 'positive', '0.078', 'barber_shop', 'barbershop', 'hairdressing_salon', 'beauty_salon', 'salon']
senticnet['fish_compliment'] = ['-0.99', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.97', 'fish_trout', 'fish_stream', 'fish_lake', 'forward_email', 'deal_card_player']
senticnet['fish_equipment'] = ['0.079', '-0.10', '0.046', '0', '#joy', '#surprise', 'positive', '0.047', 'fish_rod', 'fish_hook', 'fish_pole', 'bait', 'coral']
senticnet['fish_farm'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'food', 'farm', 'milk_product', 'food_ingredient', 'food_production']
senticnet['fish_fillet'] = ['-0.02', '-0.02', '0.064', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'ocean_water', 'fillet_fish', 'chowder', 'port', 'underwater']
senticnet['fish_food'] = ['-0.09', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.08', 'eat_peach', 'eat_table', 'machine_part', 'eat_pasta', 'bicycle_across_town']
senticnet['fish_hook'] = ['0.079', '-0.10', '0.046', '0', '#joy', '#surprise', 'positive', '0.047', 'fish_rod', 'fish_equipment', 'fish_pole', 'bait', 'coral']
senticnet['fish_lake'] = ['0', '0', '-0.76', '-0.83', '#fear', '#disgust', 'negative', '-0.79', 'fish_trout', 'catch_butterfly', 'sea_bass', 'fish_compliment', 'fish_stream']
senticnet['fish_market'] = ['0', '0', '-0.06', '-0.03', '#fear', '#disgust', 'negative', '-0.05', 'coral_reef', 'saltwater', 'tidal_pool', 'sushi_restaurant', 'reef']
senticnet['fish_pole'] = ['0.079', '-0.10', '0.046', '0', '#joy', '#surprise', 'positive', '0.047', 'fish_rod', 'fish_equipment', 'fish_hook', 'bait', 'coral']
senticnet['fish_rod'] = ['0.079', '-0.10', '0.046', '0', '#joy', '#surprise', 'positive', '0.047', 'fish_hook', 'fish_equipment', 'fish_pole', 'bait', 'coral']
senticnet['fish_soup'] = ['0.9', '0.963', '0', '0', '#joy', '#interest', 'positive', '0.931', 'form_of_food', 'japanese_food', 'uncooked', 'healthy', 'food']
senticnet['fish_species'] = ['0.565', '-0.69', '0', '0.787', '#surprise', '#admiration', 'positive', '0.683', 'species', 'saratoga', 'spotted_barramundi', 'spotted_bonytongue', 'northern_barramundi']
senticnet['fish_stew'] = ['0.897', '-0.96', '0', '0.785', '#joy', '#surprise', 'positive', '0.881', 'erotic', 'voluptuary', 'epicure', 'gourmet', 'sensualist']
senticnet['fish_stream'] = ['0.079', '-0.10', '0.046', '0', '#joy', '#surprise', 'positive', '0.047', 'fish_trout', 'catch_fish', 'fish', 'fish_compliment', 'fish_lake']
senticnet['fish_tackle'] = ['-0.88', '0', '-0.98', '0', '#sadness', '#fear', 'negative', '-0.93', 'fish_lake', 'land_fish', 'catch_fish', 'fish', 'fish_rod']
senticnet['fish_taco'] = ['0.132', '0.1', '0.063', '0.159', '#joy', '#admiration', 'positive', '0.082', 'chime', 'school_end', 'ring_loudly', 'noise_maker', 'vital']
senticnet['fish_tank'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'autumn_leaf', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['fish_trout'] = ['-0.08', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.07', 'fish_compliment', 'fish_stream', 'fish_lake', 'catch_butterfly', 'sail_around_world']
senticnet['fishbowl'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'bowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'mazer']
senticnet['fisher'] = ['0.822', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.895', 'black_cat', 'martes_pennanti', 'fisherman', 'fisher_cat', 'pekan']
senticnet['fisher_cat'] = ['0.817', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.896', 'black_cat', 'martes_pennanti', 'fisherman', 'fisher', 'pekan']
senticnet['fisher_folk'] = ['0.073', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'angler', 'informal_rod', 'archaic_fisher', 'rare_piscator', 'fishing_line']
senticnet['fisher_martes_pennanti'] = ['-0.54', '0', '0.768', '-0.50', '#sadness', '#anger', 'negative', '-0.60', 'polecat', 'hooded_skunk', 'hognosed_skunk', 'badger_skunk', 'rooter_skunk']
senticnet['fisher_price'] = ['0', '-0.10', '0', '0', '#surprise', '#surprise', 'positive', '0.105', 'bath_toy', 'stuff_animal', 'toy_soldier', 'action_figure', 'yo-yo']
senticnet['fisherfolk'] = ['0.073', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'angler', 'informal_rod', 'archaic_fisher', 'rare_piscator', 'fishing_line']
senticnet['fisherian'] = ['-0.91', '0', '0.598', '-0.52', '#sadness', '#anger', 'negative', '-0.68', 'stingy', 'penurious', 'scrimy', 'beggarly', 'chintzy']
senticnet['fisherman'] = ['0.073', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'angler', 'informal_rod', 'archaic_fisher', 'rare_piscator', 'fishing_line']
senticnet['fisherman_cottage'] = ['0.203', '0.077', '0', '0.189', '#joy', '#admiration', 'positive', '0.156', 'good_life', 'coast', 'relax', 'sediment', 'shell']
senticnet['fisherwoman'] = ['0.073', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'angler', 'informal_rod', 'archaic_fisher', 'rare_piscator', 'fishing_line']
senticnet['fisherwomen'] = ['0.073', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'angler', 'informal_rod', 'archaic_fisher', 'rare_piscator', 'fishing_line']
senticnet['fishery_bycatch'] = ['0.565', '-0.76', '0', '0.982', '#surprise', '#admiration', 'positive', '0.771', 'risk', 'hazard', 'jeopardy', 'health_hazard', 'peril']
senticnet['fishhook'] = ['0.793', '0.847', '0', '0', '#joy', '#interest', 'positive', '0.82', 'cross_bun', 'hot_cross_bun', 'bear_paw', 'coffee_roll', 'bun']
senticnet['fishing'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'catch_fish_rod', 'catch_fish', 'patience', 'fish_lake', 'bait']
senticnet['fishing_bait'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'fishing_rod', 'fishing', 'patience', 'fish_lake', 'catch_fish']
senticnet['fishing_boat'] = ['0.073', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'angler', 'informal_rod', 'archaic_fisher', 'rare_piscator', 'fishing_line']
senticnet['fishing_line'] = ['0.073', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'fisherman', 'informal_rod', 'archaic_fisher', 'rare_piscator', 'angler']
senticnet['fishing_lure'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'catch_fish', 'fishing', 'patience', 'fish_lake', 'bait']
senticnet['fishing_pole'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'catch_fish', 'fishing', 'patience', 'fish_lake', 'bait']
senticnet['fishing_port'] = ['-0.02', '-0.02', '0.064', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'ocean_water', 'fillet_fish', 'chowder', 'fillet', 'underwater']
senticnet['fishing_reel'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'catch_fish', 'fishing', 'patience', 'fish_lake', 'bait']
senticnet['fishing_rod'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'catch_fish', 'fishing', 'patience', 'fish_lake', 'bait']
senticnet['fishing_schooner'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'boat', 'yacht', 'big_boat', 'large_boat']
senticnet['fishing_tackle'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'catch_fish', 'fishing', 'patience', 'fish_lake', 'bait']
senticnet['fishing_trawler'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'succourer', 'recoverer', 'salvor', 'mortal']
senticnet['fishing_trip'] = ['0.073', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'angler', 'informal_rod', 'archaic_fisher', 'rare_piscator', 'fishing_line']
senticnet['fishing_vessel'] = ['0.073', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'angler', 'informal_rod', 'archaic_fisher', 'rare_piscator', 'fishing_line']
senticnet['fishing_village'] = ['0.073', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'angler', 'informal_rod', 'archaic_fisher', 'rare_piscator', 'fishing_line']
senticnet['fishkeeping_hobby'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'autumn_leaf', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['fishmonger'] = ['0', '-0.76', '0', '0.703', '#surprise', '#admiration', 'positive', '0.733', 'greengrocer', 'grocer', 'shopkeeper', 'fruiterer', 'newsagent']
senticnet['fishnet'] = ['0', '0', '0.806', '-0.99', '#anger', '#disgust', 'negative', '-0.89', 'tights', 'legging', 'footless', 'hosiery', 'knee-high']
senticnet['fishwife'] = ['-0.99', '0', '0.703', '0', '#sadness', '#anger', 'negative', '-0.84', 'billingsgate', 'contumely', 'epithet', 'abusive', 'vitriol']
senticnet['fishy'] = ['0', '0', '-0.91', '-0.82', '#fear', '#disgust', 'negative', '-0.87', 'suspect', 'shady', 'funny', 'louche', 'fly-by-night']
senticnet['fissile'] = ['0', '0', '0.734', '-0.77', '#anger', '#disgust', 'negative', '-0.75', 'bomb-grade', 'plutonium', 'weapon-grade', 'weapons-grade', 'uranium']
senticnet['fissile_isotope'] = ['-0.77', '0', '0.764', '-0.54', '#sadness', '#anger', 'negative', '-0.69', 'pullulation', 'apomixis', 'blastogenesis', 'budding', 'asexual_reproduction']
senticnet['fissile_material'] = ['-0.03', '-0.03', '-0.05', '-0.20', '#fear', '#disgust', 'negative', '-0.06', 'nuclear_bomb', 'bomb', 'explosive', 'atomic_bomb', 'grenade']
senticnet['fissility'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'harsh', 'common', 'rough-cut', 'uncouth', 'vulgar']
senticnet['fission'] = ['-0.77', '0', '0.764', '-0.54', '#sadness', '#anger', 'negative', '-0.69', 'pullulation', 'apomixis', 'blastogenesis', 'budding', 'asexual_reproduction']
senticnet['fission_bomb'] = ['0', '0', '-0.72', '-0.94', '#fear', '#disgust', 'negative', '-0.83', 'pistol', 'gun', 'hydrogen_bomb', 'handgun', 'revolver']
senticnet['fission_chain_reaction'] = ['-0.77', '0', '0.764', '-0.54', '#sadness', '#anger', 'negative', '-0.69', 'pullulation', 'apomixis', 'blastogenesis', 'budding', 'asexual_reproduction']
senticnet['fission_product'] = ['-0.02', '0', '0.047', '-0.11', '#anger', '#disgust', 'negative', '-0.06', 'hold_trash', 'put_garbage', 'rubbish', 'put_trash', 'store_trash']
senticnet['fission_reaction'] = ['-0.84', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.85', 'inferno', 'forest_fire', 'fire_light', 'dismissal', 'wash_mouth_soap']
senticnet['fissionable'] = ['-0.77', '0', '0.764', '-0.54', '#sadness', '#anger', 'negative', '-0.69', 'pullulation', 'apomixis', 'blastogenesis', 'budding', 'asexual_reproduction']
senticnet['fissured_bark'] = ['-0.75', '0', '0.734', '-0.82', '#sadness', '#disgust', 'negative', '-0.77', 'puberulent', 'haired', 'furlike', 'pappose', 'pilous']
senticnet['fist'] = ['-0.06', '0.068', '-0.08', '-0.07', '#fear', '#disgust', 'negative', '-0.04', 'oppose', 'whack', 'rudeness', 'make_oppose', 'hand']
senticnet['fist_bump'] = ['0.021', '0.179', '-0.13', '0.164', '#interest', '#admiration', 'positive', '0.058', 'nod_head', 'point_finger', 'upgrade', 'ask', 'put_bid']
senticnet['fist_fight'] = ['-0.63', '0', '0.982', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'wrangle', 'dustup', 'quarrel', 'row', 'argument']
senticnet['fist_pump'] = ['-0.86', '0', '0.516', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'outcry', 'call', 'cry', 'yell', 'shout']
senticnet['fistball'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'golf', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['fistfight'] = ['-0.63', '0', '0.982', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'wrangle', 'dustup', 'quarrel', 'row', 'argument']
senticnet['fistfight_between'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'fragmentation', 'discharge', 'backfire', 'blast']
senticnet['fistful'] = ['-0.99', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.91', 'gob', 'stringy', 'wad', 'gluey', 'spit']
senticnet['fistula_formation'] = ['-0.56', '0', '0.806', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['fistula_tract'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'blatancy', 'obviousness']
senticnet['fit'] = ['0.845', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.867', 'lose_weight', 'stay_fit', 'set', 'shape', 'weight_loss']
senticnet['fit_bill'] = ['0.545', '-0.50', '0', '0.591', '#joy', '#admiration', 'positive', '0.548', 'meet', 'satisfy', 'fulfill', 'conform_to', 'fitting']
senticnet['fit_clothes'] = ['0.172', '0', '0.051', '0.175', '#joy', '#admiration', 'positive', '0.099', 'much_energy', 'look_good', 'look_attractive', 'live_healthy_life', 'improve_look']
senticnet['fit_comfortably'] = ['0.845', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.867', 'lose_weight', 'stay_fit', 'set', 'shape', 'weight_loss']
senticnet['fit_into'] = ['0.845', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.867', 'lose_weight', 'stay_fit', 'set', 'shape', 'weight_loss']
senticnet['fit_perfectly'] = ['0.845', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.867', 'lose_weight', 'stay_fit', 'set', 'shape', 'weight_loss']
senticnet['fit_person'] = ['0', '0', '0', '0.97', '#admiration', '#admiration', 'positive', '0.97', 'increase_strength', 'keep_fit', 'professional_athlete', 'exercise', 'strengthen_heart']
senticnet['fit_room'] = ['0', '0.144', '-0.09', '0.196', '#interest', '#admiration', 'positive', '0.083', 'changing_room', 'clothes_store_change_room', 'dress_room', 'clothes_shelf', 'garment_bag']
senticnet['fit_snugly'] = ['0.067', '0.088', '-0.07', '0.246', '#interest', '#admiration', 'positive', '0.081', 'multiple_purposes', 'revolve', 'start_run', 'wiggle', 'twist']
senticnet['fitful'] = ['0', '0', '0.033', '-0.09', '#anger', '#disgust', 'negative', '-0.06', 'need_exercise', 'train_dog', 'tired', 'feel_energetic', 'need_fresh_air']
senticnet['fitful_sleep'] = ['-0.56', '0', '0.577', '-0.61', '#anger', '#disgust', 'negative', '-0.58', 'foggy', 'lethargic', 'logy', 'stuporous', 'dazed']
senticnet['fitfully'] = ['-0.99', '0', '-0.76', '0', '#sadness', '#fear', 'negative', '-0.87', 'unsteadily', 'shake', 'unsteady', 'totter', 'vacillate']
senticnet['fitfulness'] = ['-0.89', '0', '0.972', '-0.50', '#sadness', '#anger', 'negative', '-0.79', 'unsteadiness', 'haphazardness', 'stochasticity', 'variability', 'unregularity']
senticnet['fitly'] = ['0.949', '-0.71', '0', '0', '#joy', '#surprise', 'positive', '0.833', 'conveniently', 'gain', 'handily', 'manner', 'convenient']
senticnet['fitness'] = ['0.603', '-0.77', '0', '0.641', '#surprise', '#admiration', 'positive', '0.674', 'suitability', 'suitableness', 'habitability', 'habitableness', 'qualification']
senticnet['fitness_expert'] = ['0.603', '-0.77', '0', '0.641', '#surprise', '#admiration', 'positive', '0.674', 'suitability', 'suitableness', 'habitability', 'habitableness', 'qualification']
senticnet['fitness_regimen'] = ['-0.06', '-0.02', '0.021', '-0.06', '#sadness', '#disgust', 'negative', '-0.03', 'clean_window', 'vacuum_carpet', 'repetitive_task', 'run_long_time', 'exercise_daily']
senticnet['fitness_routine'] = ['-0.06', '-0.02', '0.021', '-0.06', '#sadness', '#disgust', 'negative', '-0.03', 'clean_window', 'vacuum_carpet', 'repetitive_task', 'run_long_time', 'exercise_daily']
senticnet['fitnessgram'] = ['0.603', '-0.77', '0', '0.641', '#surprise', '#admiration', 'positive', '0.674', 'suitability', 'suitableness', 'habitability', 'habitableness', 'qualification']
senticnet['fitted_bodice'] = ['0.735', '-0.54', '0', '0.897', '#joy', '#admiration', 'positive', '0.727', 'net', 'network', 'mesh', 'meshing', 'meshwork']
senticnet['fitted_out'] = ['0.073', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'precooked', 'equipped', 'prepared', 'embattled', 'spread']
senticnet['fitter'] = ['0.698', '-0.54', '0', '0.995', '#joy', '#admiration', 'positive', '0.747', 'better_cardiovascular_health', 'increase_stamina', 'weight_loss', 'increased_heart_rate', 'become_fit']
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senticnet['flamenco_dance'] = ['-0.06', '-0.00', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.05', 'madman', 'trouble', 'agony', 'cause_problem', 'river_stream']
senticnet['flameout'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'fragmentation', 'discharge', 'backfire', 'blast']
senticnet['flaming'] = ['-0.90', '0', '0.825', '-0.61', '#sadness', '#anger', 'negative', '-0.78', 'combustion', 'deflagration', 'fire', 'flame', 'internal_combustion']
senticnet['flaming_arrow'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'bayonet', 'spear', 'shotgun', 'bullet', 'hydrogen_bomb']
senticnet['flaming_red'] = ['-0.60', '0', '0.895', '-0.54', '#sadness', '#anger', 'negative', '-0.68', 'coal_black', 'black', 'pitch_black', 'soot_black', 'achromatic_color']
senticnet['flammability'] = ['-0.90', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.85', 'flame', 'combustible', 'dangerous', 'incendiary', 'heat']
senticnet['flammable'] = ['-0.90', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.85', 'flame', 'combustible', 'dangerous', 'incendiary', 'heat']
senticnet['flammable_liquid'] = ['-0.90', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.85', 'flame', 'combustible', 'dangerous', 'incendiary', 'heat']
senticnet['flammable_material'] = ['-0.90', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.85', 'flame', 'combustible', 'dangerous', 'incendiary', 'heat']
senticnet['flammable_substance'] = ['-0.90', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.85', 'flame', 'combustible', 'dangerous', 'incendiary', 'heat']
senticnet['flamsteed_designation'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['flanagan'] = ['0.532', '-0.68', '0', '0.984', '#surprise', '#admiration', 'positive', '0.733', 'potato', 'white_potato', 'chips', 'spud', 'tater']
senticnet['flanby'] = ['0.784', '-0.59', '0', '0.846', '#joy', '#admiration', 'positive', '0.742', 'astuteness', 'slyness', 'wiliness', 'craftiness', 'cunning']
senticnet['flandez'] = ['0.761', '0.566', '-0.21', '0.293', '#joy', '#interest', 'positive', '0.352', 'gratify', 'jubilation', 'gloat', 'gloating', 'gloatingly']
senticnet['flange'] = ['0.763', '-0.67', '0', '0.945', '#joy', '#admiration', 'positive', '0.795', 'set', 'income_bracket', 'tax_bracket', 'income_tax_bracket', 'price_bracket']
senticnet['flank_attack'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['flank_pain'] = ['-0.20', '0', '-0.22', '-0.19', '#sadness', '#fear', 'negative', '-0.20', 'go_hospital', 'death', 'die', 'take_aspirin', 'heart_attack']
senticnet['flank_steak'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'obese', 'worse', 'health_issue', 'clinical_problem']
senticnet['flanking_attack'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['flanking_movement'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['flann'] = ['0', '0.105', '0', '0.084', '#interest', '#admiration', 'positive', '0.095', 'archive', 'print_medium', 'quiet_place', 'register', 'media']
senticnet['flannel'] = ['-0.54', '0', '0.7', '-0.64', '#anger', '#disgust', 'negative', '-0.62', 'flourish', 'rant', 'bombast', 'ornateness', 'claptrap']
senticnet['flannel_board'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'gift_wrap', 'kraft', 'kraft_paper', 'wrapping_paper', 'paper']
senticnet['flannel_shirt'] = ['0', '-0.05', '0.088', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'clothes', 'pair_of_pants', 'shirt', 'slack', 'jacket']
senticnet['flap'] = ['-0.58', '0', '0.715', '-0.59', '#anger', '#disgust', 'negative', '-0.63', 'fly', 'overlap', 'barndoor', 'coattail', 'codpiece']
senticnet['flap_wing'] = ['-0.03', '-0.02', '0.043', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'wing_insect', 'aviation', 'fly', 'wing_animal', 'wing_creature']
senticnet['flapdoodle'] = ['-0.61', '0', '0.738', '-0.80', '#anger', '#disgust', 'negative', '-0.71', 'babbling', 'double_talk', 'gibber', 'babble', 'lallation']
senticnet['flare'] = ['0', '0', '-0.81', '-0.90', '#fear', '#disgust', 'negative', '-0.86', 'sword', 'firebomb', 'nuclear_bomb', 'projectile', 'revolver']
senticnet['flare_gun'] = ['0', '0', '-0.83', '-0.8', '#fear', '#disgust', 'negative', '-0.81', 'armory', 'tank', 'hydrogen_bomb', 'rifle', 'use_hunt']
senticnet['flare_path'] = ['-0.97', '0', '0.737', '-0.97', '#sadness', '#disgust', 'negative', '-0.89', 'airstrip', 'airfield', 'blaze', 'runway', 'grassland']
senticnet['flared_skirt'] = ['-0.99', '0', '0.806', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'wear', 'biohazard_suit', 'coverall', 'protective_garment', 'wearable']
senticnet['flash'] = ['-0.66', '0', '0.616', '-0.70', '#sadness', '#disgust', 'negative', '-0.66', 'flicker', 'coruscation', 'streak', 'glint', 'gleam']
senticnet['flash_animation'] = ['0.895', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.823', 'liveliness', 'excitement', 'art_form', 'art', 'film']
senticnet['flash_card'] = ['-0.03', '0.086', '0.01', '0.091', '#interest', '#admiration', 'positive', '0.034', 'information', 'take_exam', 'take_final', 'take_examination', 'take_midterm']
senticnet['flash_cardination'] = ['-0.03', '0.084', '0.008', '0.09', '#interest', '#admiration', 'positive', '0.032', 'write_exam', 'flash_card', 'take_final', 'take_exam', 'take_midterm']
senticnet['flash_chromatography'] = ['0.838', '-0.61', '0', '0.556', '#joy', '#surprise', 'positive', '0.671', 'activity', 'natural_process', 'natural_action', 'column_chromatography', 'paper_chromatography']
senticnet['flash_drive'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotary_file', 'cd-rom', 'database', 'business_card', 'calendar_book']
senticnet['flash_flood'] = ['-0.87', '0', '0.91', '-0.78', '#sadness', '#anger', 'negative', '-0.85', 'storm', 'thundershower', 'rain', 'line_storm', 'equinoctial_storm']
senticnet['flash_frozen'] = ['-0.03', '0', '0.014', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'ice_crystal', 'frozen_water', 'wet_cold', 'coldness', 'wetness']
senticnet['flash_grenade'] = ['0', '0', '0.818', '-0.99', '#anger', '#disgust', 'negative', '-0.90', 'weapon', 'aerial_bomb', 'machine_gun', 'cannon', 'handgun']
senticnet['flash_light'] = ['-0.03', '-0.02', '-0.11', '-0.08', '#fear', '#disgust', 'negative', '-0.05', 'firebomb', 'fire_light', 'master_control', 'flare', 'torch']
senticnet['flash_mob'] = ['0', '-0.08', '0', '0', '#surprise', '#surprise', 'positive', '0.082', 'yo-yo', 'playroom', 'backgammon', 'action_figure', 'board_game']
senticnet['flash_sync'] = ['-0.82', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.84', 'block', 'obstruct', 'close', 'clogged', 'occluded']
senticnet['flash_synchronisation'] = ['-0.82', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.84', 'block', 'obstruct', 'close', 'clogged', 'occluded']
senticnet['flash_synchronization'] = ['-0.82', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.84', 'block', 'obstruct', 'close', 'clogged', 'occluded']
senticnet['flashback'] = ['-0.89', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.81', 'flashback', 'scene', 'recollection', 'episode', 'memory']
senticnet['flashback_episode'] = ['0.918', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.827', 'encounter', 'reveal', 'programme', 'lose', 'sequel']
senticnet['flashback_scene'] = ['-0.69', '0', '0.508', '-0.93', '#sadness', '#disgust', 'negative', '-0.71', 'dark', 'darkness', 'light', 'shadow', 'site']
senticnet['flashback_sequence'] = ['-0.69', '0', '0.508', '-0.93', '#sadness', '#disgust', 'negative', '-0.71', 'dark', 'darkness', 'light', 'shadow', 'site']
senticnet['flashblock'] = ['-0.89', '0', '0.672', '-0.69', '#sadness', '#disgust', 'negative', '-0.75', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['flashgun'] = ['0', '-0.02', '0.048', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'telescope', 'read_glass', 'eyeglass', 'binoculars', 'monocle']
senticnet['flashiest'] = ['-0.94', '0', '0.492', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'make_noise', 'disturb', 'confusion', 'hustle_and_bustle', 'bustle']
senticnet['flashing_light'] = ['0', '-0.03', '0.043', '-0.11', '#anger', '#disgust', 'negative', '-0.03', 'torch', 'safety_pin', 'laser', 'flare', 'firebomb']
senticnet['flashlight'] = ['0', '-0.03', '0.043', '-0.11', '#anger', '#disgust', 'negative', '-0.03', 'torch', 'safety_pin', 'laser', 'flare', 'firebomb']
senticnet['flashlight_beam'] = ['0.988', '0', '0', '0.789', '#joy', '#admiration', 'positive', '0.889', 'brightness', 'blaze', 'brilliance', 'glitter', 'glister']
senticnet['flashlight_bulb'] = ['0', '-0.03', '0.043', '-0.11', '#anger', '#disgust', 'negative', '-0.03', 'torch', 'safety_pin', 'laser', 'flare', 'firebomb']
senticnet['flashy'] = ['0', '0', '0.892', '-0.88', '#anger', '#disgust', 'negative', '-0.89', 'meretricious', 'showy', 'gaudy', 'tatty', 'brassy']
senticnet['flashy_jewelry'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'clothes_style', 'style', 'glamour', 'model', 'formal_clothes']
senticnet['flask'] = ['0.619', '-0.83', '0', '0.912', '#surprise', '#admiration', 'positive', '0.788', 'hipflask', 'pocket_flask', 'ampulla', 'canteen', 'vacuum_flask']
senticnet['flat'] = ['0', '0', '0.865', '-0.95', '#anger', '#disgust', 'negative', '-0.90', 'bucket_overflow', 'dirty_floor', 'spill_over', 'float', 'spoil_wood_floor']
senticnet['flat-brimmed'] = ['-0.75', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'large-brimmed', 'low-crowned', 'ear-flap', 'brimless', 'brimmed']
senticnet['flat_belly'] = ['-0.42', '-0.14', '-0.06', '-0.37', '#sadness', '#disgust', 'negative', '-0.18', 'brain_tumor', 'bad_hair_day', 'lyme_disease', 'diarrhoea', 'suffer_fool']
senticnet['flat_bread'] = ['0', '-0.11', '0', '0', '#surprise', '#surprise', 'positive', '0.113', 'cake', 'cookie', 'mousse', 'doughnut', 'brownie']
senticnet['flat_fee'] = ['0.687', '-0.98', '0', '0.666', '#joy', '#surprise', 'positive', '0.778', 'scale', 'scale_of_measurement', 'graduated_table', 'ordered_series', 'wage_scale']
senticnet['flat_fish'] = ['0.077', '0.097', '0', '0.116', '#interest', '#admiration', 'positive', '0.097', 'far_away', 'small_earth', 'location', 'fish', 'around_earth']
senticnet['flat_foot'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'swamp', 'boring', 'flat_tire', 'flatness', 'small_breast']
senticnet['flat_land'] = ['0.022', '0.069', '0.027', '0.059', '#interest', '#admiration', 'positive', '0.031', 'open_land', 'grassy', 'terrain', 'open_space', 'land']
senticnet['flat_leaf_parsley'] = ['0.636', '-0.79', '0', '0.552', '#joy', '#surprise', 'positive', '0.661', 'lettuce', 'endive', 'green', 'salad_green', 'food']
senticnet['flat_plate'] = ['0', '0.973', '0', '0.856', '#interest', '#admiration', 'positive', '0.914', 'flow', 'plate', 'laminar_boundary_layer', 'heat_transfer', 'transition']
senticnet['flat_rate'] = ['0.687', '-0.98', '0', '0.666', '#joy', '#surprise', 'positive', '0.778', 'scale', 'scale_of_measurement', 'graduated_table', 'ordered_series', 'wage_scale']
senticnet['flat_screen'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'floodlight', 'spotlight', 'headlight', 'light', 'teach_lesson']
senticnet['flat_screen_television'] = ['0.716', '0.96', '0', '0', '#joy', '#interest', 'positive', '0.838', 'airline_seat', 'small_seat', 'beanbag_chair', 'hide-a-bed_sofa', 'chair']
senticnet['flat_screen_tv'] = ['0.716', '0.96', '0', '0', '#joy', '#interest', 'positive', '0.838', 'airline_seat', 'small_seat', 'beanbag_chair', 'hide-a-bed_sofa', 'chair']
senticnet['flat_surface'] = ['0.022', '0.069', '0.027', '0.059', '#interest', '#admiration', 'positive', '0.031', 'perfect_surface', 'plane', 'flat_land', 'flat', 'floor']
senticnet['flat_terrain'] = ['-0.99', '0', '0.641', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'navigable', 'negotiable', 'surmountable', 'climbable', 'passable']
senticnet['flat_thin'] = ['0.053', '0', '0.025', '0.118', '#joy', '#admiration', 'positive', '0.049', 'paper_square', 'flat', 'part_of_book', 'floor', 'book_part']
senticnet['flat_tire'] = ['-0.10', '-0.07', '0.032', '-0.15', '#sadness', '#disgust', 'negative', '-0.05', 'crash_tree', 'press_accelerator', 'stop_traffic_light', 'accident', 'tire_puncture']
senticnet['flat_topography'] = ['0.614', '-0.95', '0', '0.584', '#joy', '#surprise', 'positive', '0.718', 'mother_lode', 'champion_lode', 'deposit', 'sedimentation', 'lode']
senticnet['flat_topped_hill'] = ['0', '0.093', '0', '0.078', '#interest', '#admiration', 'positive', '0.085', 'steep', 'landscape_feature', 'knoll', 'hill_mountain', 'incline']
senticnet['flat_trajectory'] = ['0.887', '-0.95', '0', '0.913', '#surprise', '#admiration', 'positive', '0.917', 'high_quality', 'superiority', 'level', 'grade', 'degree']
senticnet['flat_tyre'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'puncture', 'broken_car', 'broken_engine', 'broken_shelf', 'need_fix']
senticnet['flatbed_scanner'] = ['-0.08', '0', '0.011', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'deflate', 'read_cover', 'obtain_information', 'upgrade', 'spread_out']
senticnet['flatbed_trailer'] = ['-0.95', '0', '0.793', '0', '#sadness', '#anger', 'negative', '-0.87', 'block', 'obstacle', 'nodus', 'impediment', 'obstruction']
senticnet['flatbread'] = ['0', '-0.11', '0', '0', '#surprise', '#surprise', 'positive', '0.113', 'cake', 'cookie', 'mousse', 'doughnut', 'brownie']
senticnet['flatcar'] = ['-0.84', '0', '0.96', '-0.91', '#anger', '#disgust', 'negative', '-0.90', 'housewrecker', 'housebreaker', 'knacker', 'laborer', 'manual_laborer']
senticnet['flathead_catfish'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'divergent_strabismus', 'exotropia', 'squint', 'abnormality', 'abnormalcy']
senticnet['flathead_indian_reservation'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['flatiron_building'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'condominium', 'rent_apartment', 'high_rise', 'condo', 'row_house']
senticnet['flatmate'] = ['0.033', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.058', 'baby_dog', 'sweet', 'puppet', 'dog', 'love']
senticnet['flatness'] = ['-0.99', '0', '0.973', '0', '#sadness', '#anger', 'negative', '-0.98', 'swamp', 'boring', 'flat_tire', 'flat_foot', 'small_breast']
senticnet['flatscreen_tv'] = ['0.716', '0.96', '0', '0', '#joy', '#interest', 'positive', '0.838', 'airline_seat', 'small_seat', 'beanbag_chair', 'hide-a-bed_sofa', 'chair']
senticnet['flatten'] = ['-0.10', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.10', 'read_cover', 'spread_out', 'upgrade', 'look_behind', 'scan']
senticnet['flatten_out'] = ['-0.10', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.10', 'read_cover', 'spread_out', 'upgrade', 'look_behind', 'scan']
senticnet['flatter'] = ['0.17', '0', '0.081', '0.184', '#joy', '#admiration', 'positive', '0.091', 'food_holder', 'food_server', 'crockery', 'flattery', 'round_flat']
senticnet['flatter_trajectory'] = ['0.887', '-0.95', '0', '0.913', '#surprise', '#admiration', 'positive', '0.917', 'high_quality', 'superiority', 'level', 'grade', 'degree']
senticnet['flatterer'] = ['-0.63', '0', '0.683', '-0.96', '#anger', '#disgust', 'negative', '-0.76', 'slovenly_woman', 'slut', 'trollop', 'slattern', 'litterer']
senticnet['flattering'] = ['-0.83', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.91', 'unflattering', 'embarrassing', 'disparaging', 'unattractive', 'demeaning']
senticnet['flattery'] = ['0.134', '0.1', '-0.11', '0.246', '#joy', '#admiration', 'positive', '0.092', 'empathize', 'lovely', 'good_look', 'amiable', 'exchange_gift']
senticnet['flattish'] = ['0', '0', '0.865', '-0.95', '#anger', '#disgust', 'negative', '-0.90', 'bucket_overflow', 'dirty_floor', 'spill_over', 'float', 'spoil_wood_floor']
senticnet['flatulence'] = ['-0.03', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.07', 'inhale_air', 'dig_up', 'breaair', 'pour_water', 'breathe']
senticnet['flatulent'] = ['-0.77', '0', '0.671', '-0.80', '#sadness', '#disgust', 'negative', '-0.75', 'indigestible', 'nondigestible', 'undigested', 'stodgy', 'heavy']
senticnet['flatware'] = ['0.022', '0.069', '0.027', '0.059', '#interest', '#admiration', 'positive', '0.031', 'perfect_surface', 'flat_land', 'flat_surface', 'bowl', 'plate']
senticnet['flatworm'] = ['0.688', '-0.94', '0', '0.925', '#surprise', '#admiration', 'positive', '0.852', 'trematode', 'platyhelminth', 'liver_fluke', 'schistosome', 'blood_fluke']
senticnet['flaunt'] = ['-0.96', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.94', 'tout', 'display', 'flauntingly', 'flashy', 'flaunting']
senticnet['flaunting'] = ['-0.96', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.94', 'tout', 'display', 'flauntingly', 'flashy', 'flaunt']
senticnet['flauntingly'] = ['-0.96', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.94', 'tout', 'display', 'flaunt', 'flashy', 'flaunting']
senticnet['flavedo'] = ['0', '0.141', '0.01', '0.115', '#interest', '#admiration', 'positive', '0.082', 'sepal', 'achene', 'funiculus', 'seed', 'pip']
senticnet['flavone'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'capsaicin', 'chemical_irritant', 'piperine', 'pepper_spray']
senticnet['flavor'] = ['0.054', '0.068', '0', '0.074', '#interest', '#admiration', 'positive', '0.065', 'eat_food', 'sweetness', 'food_flavor', 'eatable', 'tasty']
senticnet['flavor_food'] = ['0', '0', '0', '0.869', '#admiration', '#admiration', 'positive', '0.869', 'cook', 'tasty', 'flavor', 'food', 'good_food']
senticnet['flavor_profile'] = ['0.054', '0.068', '0', '0.074', '#interest', '#admiration', 'positive', '0.065', 'eat_food', 'sweetness', 'food_flavor', 'eatable', 'tasty']
senticnet['flavorer'] = ['-0.86', '0', '0.982', '-0.86', '#anger', '#disgust', 'negative', '-0.90', 'seasoned_salt', 'flavourer', 'flavoring', 'flavouring', 'seasoner']
senticnet['flavorful'] = ['0.043', '0.091', '0', '0.113', '#interest', '#admiration', 'positive', '0.082', 'sweet_sour', 'tasty', 'yummy', 'popular_food', 'form_of_food']
senticnet['flavoring'] = ['-0.86', '0', '0.982', '-0.86', '#anger', '#disgust', 'negative', '-0.90', 'flavorer', 'flavourer', 'seasoned_salt', 'flavouring', 'seasoner']
senticnet['flavoring_agent'] = ['0', '0', '0.717', '-0.84', '#anger', '#disgust', 'negative', '-0.77', 'gymnast', 'bikini', 'underwear', 'basketball_hoop', 'dirty_sock']
senticnet['flavorless'] = ['-0.71', '0', '0.559', '-0.95', '#sadness', '#disgust', 'negative', '-0.74', 'unseasoned', 'unsalted', 'tasteless', 'flavourless', 'savorless']
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senticnet['flawless'] = ['0', '0.697', '0', '0.929', '#interest', '#admiration', 'positive', '0.813', 'value', 'good_value', 'ideal', 'perfection', 'ultimate']
senticnet['flawless_skin'] = ['0.636', '-0.95', '0', '0.664', '#surprise', '#admiration', 'positive', '0.751', 'voluptuous', 'statuesque', 'sculptured', 'buxom', 'sculptural']
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senticnet['flawlessness'] = ['-0.71', '0', '0.606', '-0.79', '#sadness', '#disgust', 'negative', '-0.70', 'finish', 'culture', 'dream', 'state', 'cultivation']
senticnet['flax_mill'] = ['-0.78', '0', '0.699', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'battery', 'stamp_battery', 'quartz_battery', 'quartz_mill', 'factory']
senticnet['flax_oil'] = ['0.026', '0.08', '0.043', '0.064', '#interest', '#admiration', 'positive', '0.032', 'eat_rice', 'white_brown', 'asian', 'eat_pasta', 'staple_food']
senticnet['flax_rust'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'rust_fungus', 'rust', 'blister_rust', 'apple_rust', 'fungus']
senticnet['flax_rust_fungus'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'rust_fungus', 'blister_rust', 'apple_rust', 'fungus']
senticnet['flaxen'] = ['0.209', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.118', 'old_dance', 'dance', 'slow_dance', 'dancer', 'enter_contest']
senticnet['flaxen_hair'] = ['0.636', '-0.95', '0', '0.664', '#surprise', '#admiration', 'positive', '0.751', 'voluptuous', 'statuesque', 'sculptured', 'buxom', 'sculptural']
senticnet['flea'] = ['0', '-0.09', '0.131', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'gnat', 'mosquito', 'mole_cricket', 'gypsy_moth', 'cucumber_beetle']
senticnet['flea-bitten'] = ['0', '0', '-0.96', '-0.81', '#fear', '#disgust', 'negative', '-0.89', 'clunker', 'creaky', 'decrepit', 'crazy', 'mangy']
senticnet['flea-ridden'] = ['-0.77', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.75', 'biscuit', 'infest', 'invade', 'inhabit', 'bot']
senticnet['flea_beetle'] = ['0', '-0.09', '0.123', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'gypsy_moth', 'japanese_beetle', 'cucumber_beetle', 'mosquito', 'earwig']
senticnet['flea_bite'] = ['-0.64', '0', '0.625', '-0.88', '#sadness', '#disgust', 'negative', '-0.71', 'difficulty', 'awkwardness', 'troublesomeness', 'difficultness', 'cumbersomeness']
senticnet['flea_infestation'] = ['-0.15', '0.162', '0.596', '-0.62', '#anger', '#disgust', 'negative', '-0.30', 'hackle', 'umbrage', 'offense', 'ire', 'see_red']
senticnet['flea_market'] = ['0.06', '0.067', '0', '-0.05', '#joy', '#interest', 'positive', '0.024', 'water_fountain', 'winery', 'brewery', 'load_dock', 'wish_well']
senticnet['fleck'] = ['-0.58', '0', '0.561', '-0.62', '#sadness', '#disgust', 'negative', '-0.58', 'spot', 'speckle', 'dapple', 'patch', 'mark']
senticnet['flection'] = ['-0.76', '0', '0.969', '-0.55', '#sadness', '#anger', 'negative', '-0.76', 'variant', 'deviation', 'flexion', 'discrepancy', 'variation']
senticnet['fledged'] = ['0.698', '-0.89', '0', '0', '#joy', '#surprise', 'positive', '0.794', 'full-fledged', 'full-on', 'full-scale', 'full-blown', 'fully-fledged']
senticnet['fledgeling'] = ['0.824', '-0.97', '0', '0.833', '#surprise', '#admiration', 'positive', '0.877', 'recruit', 'enlistee', 'novice', 'beginner', 'tyro']
senticnet['fledgling'] = ['0.776', '-0.55', '0', '0.883', '#joy', '#admiration', 'positive', '0.738', 'recruit', 'enlistee', 'novice', 'beginner', 'tyro']
senticnet['fledgling_label'] = ['0', '0.115', '0.036', '0.207', '#interest', '#admiration', 'positive', '0.095', 'total', 'amount', 'merit', 'award', 'excellence']
senticnet['fledgling_nation'] = ['0.887', '0.748', '0', '0.954', '#joy', '#admiration', 'positive', '0.863', 'political_action', 'political', 'mediocrity', 'discrimination', 'independent']
senticnet['flee'] = ['-0.05', '0.142', '-0.06', '0.261', '#interest', '#admiration', 'positive', '0.071', 'movement', 'pace', 'quick_motion', 'fast', 'go_forward']
senticnet['fleece'] = ['0.731', '0', '0', '0.974', '#joy', '#admiration', 'positive', '0.853', 'cotton', 'silk', 'wool', 'cotton_boll', 'plant']
senticnet['fleece_blanket'] = ['0', '0.092', '0.016', '0.074', '#interest', '#admiration', 'positive', '0.05', 'armoire', 'pillowcase', 'bed_sheet', 'duvet_cover', 'comforter']
senticnet['fleece_jacket'] = ['-0.02', '0.256', '0', '0.166', '#interest', '#admiration', 'positive', '0.132', 'coat', 'shirt', 'garment', 'dress', 'vest']
senticnet['fleeing_domestic_violence'] = ['-0.88', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.81', 'bum', 'red_light', 'petition', 'take', 'violent_action']
senticnet['fleeing_persecution'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'habitat', 'mom_house', 'windsor', 'pacific_northwest', 'wilderness']
senticnet['fleeing_suspect'] = ['0.964', '-0.57', '0', '0.827', '#joy', '#admiration', 'positive', '0.789', 'mortal', 'murder_suspect', 'suspect', 'rape_suspect', 'person']
senticnet['fleet'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'military', 'ship', 'war', 'cannon', 'pirate']
senticnet['fleet_admiral'] = ['0.688', '-0.90', '0', '0.877', '#surprise', '#admiration', 'positive', '0.824', 'flag_officer', 'rear_admiral', 'admiral', 'full_admiral', 'vice_admiral']
senticnet['fleet_flag_officer'] = ['0.681', '-0.91', '0', '0.873', '#surprise', '#admiration', 'positive', '0.824', 'admiral', 'rear_flag_officer', 'flag_officer', 'full_flag_officer', 'vice_flag_officer']
senticnet['fleeting'] = ['0', '0', '0.892', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'fugitive', 'lake', 'elusive', 'transient', 'flee']
senticnet['fleeting_glimpse'] = ['0', '0.095', '0', '0.112', '#interest', '#admiration', 'positive', '0.104', 'stare', 'look', 'observation', 'ship_part', 'see']
senticnet['fleeting_nature'] = ['-0.87', '0', '0.744', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['fleischman'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['fleisher_art'] = ['0.072', '0.126', '0', '0', '#joy', '#interest', 'positive', '0.099', 'subway_station', 'war_memorial', 'utopia', 'mortuary', 'hero']
senticnet['flemish_doping_tribunal'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'steer_wheel', 'gear_shift', 'chassis', 'brake_pedal', 'air_vent']
senticnet['flesh'] = ['0', '0', '-0.04', '-0.04', '#fear', '#disgust', 'negative', '-0.04', 'inside_body', 'dog_treat', 'body', 'live_meat', 'skeleton']
senticnet['flesh_wound'] = ['-0.18', '-0.12', '0.049', '-0.24', '#sadness', '#disgust', 'negative', '-0.09', 'tree_trunk', 'plank', 'artificial_leg', 'hardwood', 'tree_wood']
senticnet['fleshier'] = ['0.17', '0', '0.081', '0.184', '#joy', '#admiration', 'positive', '0.091', 'food_holder', 'food_server', 'crockery', 'flattery', 'round_flat']
senticnet['fleshiness'] = ['-0.84', '0', '0.707', '-0.99', '#sadness', '#disgust', 'negative', '-0.85', 'exogenous_obesity', 'blubber', 'fat', 'embonpoint', 'stoutness']
senticnet['fleshly'] = ['-0.72', '0', '0.737', '-0.60', '#sadness', '#anger', 'negative', '-0.68', 'call_on_carpet', 'take_to_task', 'chide', 'berate', 'bawl_out']
senticnet['fleshy'] = ['-0.82', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.86', 'wispy', 'feather-like', 'spiky', 'feathered', 'feathery']
senticnet['flett'] = ['-0.20', '-0.10', '0.07', '-0.24', '#sadness', '#disgust', 'negative', '-0.10', 'flipper', 'sea_life', 'seafood', 'swim_animal', 'fillet']
senticnet['flex'] = ['0', '0', '-0.76', '-0.81', '#fear', '#disgust', 'negative', '-0.78', 'twist', 'display', 'contortion', 'contortionist', 'bend']
senticnet['flexed'] = ['0.724', '0', '0', '0.887', '#joy', '#admiration', 'positive', '0.805', 'splayed', 'splay', 'splaying', 'toe', 'splay']
senticnet['flexi_disc'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['flexibility'] = ['0', '0.894', '0', '0.766', '#interest', '#admiration', 'positive', '0.83', 'elasticity', 'elastic', 'stretch', 'touch_toe', 'elongate']
senticnet['flexible'] = ['0', '0.066', '0.041', '0.109', '#interest', '#admiration', 'positive', '0.045', 'skin_cover', 'often_red', 'rigid', 'human_organ', 'different_shape']
senticnet['flexible_duct'] = ['0.628', '-0.96', '0', '0.682', '#surprise', '#admiration', 'positive', '0.759', 'electric_fan', 'blower', 'fan', 'punkah', 'device']
senticnet['flexible_enough'] = ['0.845', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.867', 'lose_weight', 'stay_fit', 'set', 'shape', 'weight_loss']
senticnet['flexible_fiberoptic'] = ['0.255', '0.182', '-0.06', '0.314', '#joy', '#admiration', 'positive', '0.173', 'doctor_office', 'urethroscope', 'sigmoidoscope', 'hospital', 'check_health']
senticnet['flexible_plastic'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'foil', 'tin_foil', 'gold_foil']
senticnet['flexible_schedule'] = ['0.907', '0', '0', '0.79', '#joy', '#admiration', 'positive', '0.849', 'work', 'task', 'hard_work', 'accomplish', 'find_accomplish']
senticnet['flexible_tube'] = ['0.255', '0.182', '-0.06', '0.314', '#joy', '#admiration', 'positive', '0.173', 'doctor_office', 'urethroscope', 'sigmoidoscope', 'hospital', 'check_health']
senticnet['flexibleness'] = ['0.64', '-0.95', '0', '0.639', '#joy', '#surprise', 'positive', '0.743', 'pliability', 'whip', 'bendability', 'malleability', 'plasticity']
senticnet['flexibly'] = ['-0.03', '0.086', '0', '0.114', '#interest', '#admiration', 'positive', '0.054', 'deduce', 'figure_out', 'arrive_conclusion', 'come_conclusion', 'come_up_new_idea']
senticnet['flexion'] = ['-0.76', '0', '0.969', '-0.55', '#sadness', '#anger', 'negative', '-0.76', 'variant', 'flection', 'deviation', 'discrepancy', 'variation']
senticnet['flexor_digitorum'] = ['-0.41', '0', '-0.28', '-0.05', '#sadness', '#fear', 'negative', '-0.25', 'numbness', 'carpal_tunnel_syndrome', 'weakness', 'pain', 'muscle_damage']
senticnet['flexor_retinaculum'] = ['-0.41', '0', '-0.28', '-0.05', '#sadness', '#fear', 'negative', '-0.25', 'numbness', 'carpal_tunnel_syndrome', 'weakness', 'pain', 'muscle_damage']
senticnet['flexor_tendon'] = ['-0.53', '0', '0.982', '-0.67', '#anger', '#disgust', 'negative', '-0.73', 'gristle', 'semilunar_cartilage', 'fibrocartilage', 'hyaline_cartilage', 'arytenoid']
senticnet['flexuosa'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['flexural'] = ['0.994', '0.815', '0', '0', '#joy', '#interest', 'positive', '0.904', 'shin_splint', 'get_out_breath', 'become_wind', 'get_exhausted', 'run_out_breath']
senticnet['flexural_strength'] = ['0.994', '0.815', '0', '0', '#joy', '#interest', 'positive', '0.904', 'shin_splint', 'get_out_breath', 'become_wind', 'get_exhausted', 'run_out_breath']
senticnet['flexure'] = ['0.601', '-0.87', '0', '0.5', '#joy', '#surprise', 'positive', '0.66', 'fold', 'crease', 'plication', 'crimp', 'angular_shape']
senticnet['flibbertigibbet'] = ['0.806', '-0.70', '0', '0.687', '#joy', '#surprise', 'positive', '0.734', 'saphead', 'muggins', 'tomfool', 'fool', 'sap']
senticnet['flicit'] = ['-0.81', '0', '0.885', '-0.92', '#anger', '#disgust', 'negative', '-0.87', 'miss', 'girl', 'damsel', 'maiden', 'damoiselle']
senticnet['flick'] = ['-0.85', '0', '0.514', '-0.73', '#sadness', '#disgust', 'negative', '-0.70', 'cinema_verite', 'movie', 'film', 'picture', 'moving_picture']
senticnet['flicker'] = ['0.075', '-0.18', '-0.06', '0.138', '#surprise', '#admiration', 'positive', '0.083', 'burn_heat', 'fire_burn', 'really_hot', 'char', 'hot']
senticnet['flickering'] = ['0', '0', '0.942', '-0.95', '#anger', '#disgust', 'negative', '-0.95', 'aflicker', 'quiver', 'flitter', 'flutter', 'waver']
senticnet['flickering_light'] = ['0.54', '-0.64', '0', '0.728', '#surprise', '#admiration', 'positive', '0.638', 'gleaming', 'luster', 'polish', 'glow', 'burnish']
senticnet['fliegerhorst'] = ['-0.97', '0', '0.737', '-0.97', '#sadness', '#disgust', 'negative', '-0.89', 'flare_path', 'airstrip', 'take_off', 'runway', 'grassland']
senticnet['flier'] = ['0.755', '0.729', '0', '0', '#joy', '#interest', 'positive', '0.742', 'handbill', 'fly', 'flyer', 'airman', 'aeronaut']
senticnet['flight'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'atmosphere', 'wing', 'nest', 'feather', 'bird_fly']
senticnet['flight_accident'] = ['-0.98', '0', '0.989', '-0.93', '#sadness', '#anger', 'negative', '-0.97', 'air_traffic', 'dangerous', 'danger', 'traffic', 'traffic_jam']
senticnet['flight_attendant'] = ['-0.08', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.08', 'turn_key', 'plan_route', 'open_car_door', 'book_flight', 'turn_car_off']
senticnet['flight_crew'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'atmosphere', 'wing', 'nest', 'feather', 'bird_fly']
senticnet['flight_deck'] = ['0.831', '-0.82', '0', '0.826', '#joy', '#admiration', 'positive', '0.826', 'deck', 'freeboard_deck', 'weather_deck', 'shelter_deck', 'boat_deck']
senticnet['flight_lieutenant'] = ['0.74', '-0.53', '0', '0.641', '#joy', '#admiration', 'positive', '0.64', 'marshal', 'sky_marshal', 'lawman', 'law_officer', 'peace_officer']
senticnet['flight_path'] = ['0', '0.905', '0', '0.965', '#interest', '#admiration', 'positive', '0.935', 'air_lane', 'air_duct', 'airway', 'skyway', 'airline']
senticnet['flight_sergeant'] = ['0.74', '-0.53', '0', '0.641', '#joy', '#admiration', 'positive', '0.64', 'marshal', 'sky_marshal', 'lawman', 'law_officer', 'peace_officer']
senticnet['flight_strip'] = ['-0.57', '0', '0.575', '-0.89', '#anger', '#disgust', 'negative', '-0.68', 'flare_path', 'airfield', 'ground', 'runway', 'grassland']
senticnet['flight_terminal'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'semi-truck', 'traffic_artery', 'aircraft_roadway', 'taxi', 'taxiway']
senticnet['flightless_bird'] = ['-0.68', '0', '0.956', '-0.90', '#anger', '#disgust', 'negative', '-0.84', 'giant_moa', 'anomalopteryx', 'ratite', 'ratite_bird', 'moa']
senticnet['flighty'] = ['0.963', '-0.98', '0', '0.514', '#joy', '#surprise', 'positive', '0.821', 'warm', 'excitable', 'nervous', 'skittish', 'spooky']
senticnet['flim_flam'] = ['-0.61', '0', '0.981', '-0.98', '#anger', '#disgust', 'negative', '-0.85', 'pious_platitude', 'nothingness', 'jazz', 'talk', 'malarky']
senticnet['flimsy'] = ['-0.08', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.09', 'fragile', 'brittle', 'frail', 'easy_to_break', 'breakable']
senticnet['flinch'] = ['-0.75', '0', '0.886', '0', '#sadness', '#anger', 'negative', '-0.82', 'wincing', 'steep', 'dye', 'wince', 'impatient']
senticnet['flint'] = ['0', '0', '-0.73', '-0.91', '#fear', '#disgust', 'negative', '-0.82', 'unrepentant', 'obstinate', 'obdurate', 'flinty', 'cussed']
senticnet['flint_flake'] = ['0.099', '-0.07', '-0.08', '0.195', '#joy', '#admiration', 'positive', '0.071', 'art', 'exhibit', 'craft', 'fine_art', 'artwork']
senticnet['flint_knife'] = ['0.063', '0.089', '0.036', '0.146', '#interest', '#admiration', 'positive', '0.066', 'horn', 'jew_harp', 'brass_instrument', 'trumpet', 'crash_cymbal']
senticnet['flinty'] = ['0', '0', '-0.74', '-0.91', '#fear', '#disgust', 'negative', '-0.82', 'unrepentant', 'obstinate', 'flint', 'obdurate', 'cussed']
senticnet['flip'] = ['-0.03', '0.11', '0', '0.128', '#interest', '#admiration', 'positive', '0.069', 'freestyle', 'take_leak', 'jump_air', 'backstroke', 'go_underwater']
senticnet['flip_chart'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'gift_wrap', 'kraft', 'kraft_paper', 'wrapping_paper', 'paper']
senticnet['flip_coin'] = ['-0.08', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.08', 'probability', 'hire_attorney', 'win', 'judge_decide', 'try_win']
senticnet['flip_dot'] = ['0.044', '0.08', '0', '-0.03', '#joy', '#interest', 'positive', '0.031', 'screen', 'pixel', 'visual_information', 'image', 'computer_monitor']
senticnet['flip_flop'] = ['-0.05', '0.068', '0', '0.102', '#interest', '#admiration', 'positive', '0.038', 'shoe', 'moccasin', 'footwear', 'shoe_type', 'slipper']
senticnet['flip_phone'] = ['0.035', '-0.01', '0.027', '0.085', '#joy', '#admiration', 'positive', '0.026', 'telephone', 'find_phone_number', 'telephone_room', 'phone', 'use_telephone']
senticnet['flip_side'] = ['-0.78', '0', '0.915', '-0.99', '#anger', '#disgust', 'negative', '-0.89', 'bodge', 'botch_up', 'flub', 'ball_up', 'go_wrong']
senticnet['flippancy'] = ['-0.76', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.81', 'intractability', 'unruliness', 'fractiousness', 'willfulness', 'against_rule']
senticnet['flippant'] = ['0.844', '0', '0', '0.712', '#joy', '#admiration', 'positive', '0.778', 'fluent', 'accurately', 'confidently', 'fluently', 'liquid']
senticnet['flipped_onto'] = ['-0.11', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.08', 'have_fun', 'spring_up', 'fall_well', 'shovel_snow', 'fly_around']
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senticnet['fluid_milk_product'] = ['0.057', '0', '0.018', '0.058', '#joy', '#admiration', 'positive', '0.032', 'porridge', 'food', 'milk', 'healthy', 'morning_food']
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senticnet['flush_toilet'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'commode', 'lavatory', 'potty_seat', 'potty_chair']
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senticnet['flushed'] = ['0.881', '0', '0', '0.866', '#joy', '#admiration', 'positive', '0.873', 'fortunate', 'rose_colored', 'rose_cheeked', 'blushful', 'rosy']
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senticnet['fly_around'] = ['-0.09', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.07', 'shovel_snow', 'smoke_joint', 'blow_away', 'roam', 'sit_floor']
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senticnet['fold_increase'] = ['-0.02', '0.095', '0', '0.117', '#interest', '#admiration', 'positive', '0.063', 'identification', 'paper_square', 'carry_money', 'money_hold', 'credit']
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senticnet['fold_up'] = ['0.283', '0.292', '-0.10', '0.272', '#joy', '#interest', 'positive', '0.184', 'fill_ink', 'hang_closet', 'make_out_metal', 'hold_garbage', 'display_item']
senticnet['foldable'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'carbon_fiber', 'aluminium', 'light', 'thin', 'performant']
senticnet['folded_neatly'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'lavatory', 'commode', 'potty_chair']
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senticnet['folderol'] = ['0.739', '-0.89', '0', '0.702', '#joy', '#surprise', 'positive', '0.78', 'pap', 'content', 'pablum', 'drivel', 'codswallop']
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senticnet['folding_knife'] = ['0.818', '-0.95', '0', '0.695', '#joy', '#surprise', 'positive', '0.823', 'jackknife', 'clasp_knife', 'penknife', 'switchblade', 'switchblade_knife']
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senticnet['folgers_coffee'] = ['0.348', '0.23', '-0.15', '0.294', '#joy', '#admiration', 'positive', '0.18', 'happy_emotion', 'niceness', 'contagious', 'good_emotion', 'savor']
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senticnet['foliage_color'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'tropical_fish', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['foliar_disease'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'rust_fungus', 'fungus']
senticnet['foliate'] = ['0.904', '0.747', '0', '0', '#joy', '#interest', 'positive', '0.826', 'pageboy', 'varlet', 'paginate', 'page', 'thomas_nelson_page']
senticnet['foliation'] = ['0', '-0.72', '0', '0.72', '#surprise', '#admiration', 'positive', '0.724', 'enriched', 'sugar', 'wine', 'addition', 'enrichment']
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senticnet['folie_deux'] = ['-0.61', '0', '0.9', '-0.56', '#sadness', '#anger', 'negative', '-0.69', 'mental_disorder', 'mental_disturbance', 'psychological_disorder', 'folie', 'disturbance']
senticnet['folio_volume'] = ['0.722', '-0.98', '0', '0.832', '#surprise', '#admiration', 'positive', '0.845', 'laudatory', 'praiseful', 'praising', 'encomiastic', 'complimentary']
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senticnet['folk_medicine'] = ['-0.60', '0', '0.544', '-0.62', '#sadness', '#disgust', 'negative', '-0.59', 'remedy', 'cure', 'therapeutic', 'antidote', 'atropine']
senticnet['folk_music'] = ['0.847', '-0.80', '0', '0', '#joy', '#surprise', 'positive', '0.828', 'play_jazz', 'lovely_music', 'play_in_band', 'produce_musical_sound', 'play_song']
senticnet['folk_musician'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
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senticnet['folk_remedy'] = ['-0.50', '0', '0.582', '-0.57', '#anger', '#disgust', 'negative', '-0.55', 'purgative', 'cathartic', 'physic', 'aperient', 'phenolphthalein']
senticnet['folk_rock_band'] = ['-0.04', '-0.04', '-0.04', '-0.04', '#fear', '#disgust', 'negative', '-0.02', 'music_shop', 'music_store', 'musical_instrument_store', 'string_quartet', 'high_school_band']
senticnet['folk_rock_singer_songwriter'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
senticnet['folk_singer'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
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senticnet['foot_fishing_boat'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'boat', 'yacht', 'big_boat', 'large_boat']
senticnet['foot_hurt'] = ['-0.71', '0', '0.794', '0', '#sadness', '#anger', 'negative', '-0.75', 'leg_tire', 'feel_tired', 'get_tired', 'get_tired_leg', 'sore_leg']
senticnet['foot_injury'] = ['-0.78', '0', '0.554', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'rubor', 'redness', 'inflammation', 'tennis_elbow', 'lateral_epicondylitis']
senticnet['foot_jumper'] = ['-0.81', '0', '0.747', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'vagrant', 'drifter', 'hobo', 'vagabond', 'dosser']
senticnet['foot_massage'] = ['0.53', '-0.64', '0', '0.76', '#surprise', '#admiration', 'positive', '0.646', 'heel', 'ankle', 'toe', 'foot', 'nail']
senticnet['foot_motion'] = ['0', '0.078', '0.027', '0.196', '#interest', '#admiration', 'positive', '0.082', 'foot_movement', 'foot_action', 'pace', 'stride', 'move_foot']
senticnet['foot_movement'] = ['0', '0.861', '0', '0.836', '#interest', '#admiration', 'positive', '0.849', 'foot_motion', 'move_foot', 'stride', 'foot_action', 'pace']
senticnet['foot_part'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'sole', 'type_of_shoes', 'foot', 'twelve_inch', 'heel']
senticnet['foot_pedal'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'peavey']
senticnet['foot_python'] = ['-0.85', '0', '0.685', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'allicrocodilian', 'crocodile', 'false_gavial', 'crocodilian', 'caiman']
senticnet['foot_rot'] = ['0.789', '-0.99', '0', '0.54', '#joy', '#surprise', 'positive', '0.776', 'imperfection', 'imperfectness', 'bugginess', 'defectiveness', 'sickness']
senticnet['foot_sailboat'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'boat', 'yacht', 'big_boat', 'large_boat']
senticnet['foot_soldier'] = ['-0.82', '0', '-0.84', '0', '#sadness', '#fear', 'negative', '-0.83', 'auxiliary', 'supplementary', 'subsidiary_company', 'subordinate', 'subsidiary']
senticnet['foot_tired'] = ['0', '0', '-0.86', '-0.85', '#fear', '#disgust', 'negative', '-0.85', 'much_exercise', 'wear_out_shoes', 'sweat_lot', 'dehydration', 'increase_body_temperature']
senticnet['foot_treadle'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'peavey']
senticnet['foot_yacht'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'boat', 'yacht', 'big_boat', 'large_boat']
senticnet['footage'] = ['-0.69', '0', '0.508', '-0.93', '#sadness', '#disgust', 'negative', '-0.71', 'dark', 'darkness', 'light', 'shadow', 'site']
senticnet['football'] = ['0.728', '0.83', '0', '0', '#joy', '#interest', 'positive', '0.779', 'ball', 'bounce', 'ball_bounce', 'soccer', 'play_football']
senticnet['football_bounce'] = ['0.986', '0.72', '0', '0', '#joy', '#interest', 'positive', '0.853', 'ball_bounce', 'bounce', 'ball', 'soccer', 'basketball_bounce']
senticnet['football_club'] = ['0.728', '0.83', '0', '0', '#joy', '#interest', 'positive', '0.779', 'ball', 'bounce', 'ball_bounce', 'soccer', 'play_football']
senticnet['football_coach'] = ['-0.03', '0', '0.044', '0', '#sadness', '#anger', 'negative', '-0.03', 'child', 'football', 'little_child', 'two_children', 'youth']
senticnet['football_field'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'sport_activity', 'grass', 'grassland', 'play_football', 'play_sport']
senticnet['football_game'] = ['0', '0.919', '0', '0.801', '#interest', '#admiration', 'positive', '0.86', 'game', 'beach_house', 'gym_locker', 'football', 'football_stadium']
senticnet['football_helmet'] = ['0.783', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.766', 'baseball_bat', 'water_gun', 'goalie', 'hockey_stick', 'bullseye']
senticnet['football_highlight'] = ['0.53', '-0.64', '0', '0.76', '#surprise', '#admiration', 'positive', '0.646', 'best_moment', 'sport_highlight', 'highlight', 'television_news', 'put_in_evidence']
senticnet['football_hooligan'] = ['-0.89', '0', '0.933', '-0.93', '#anger', '#disgust', 'negative', '-0.92', 'bullyboy', 'muscleman', 'muscle', 'skinhead', 'skin']
senticnet['football_hooligan_firm'] = ['-0.89', '0', '0.933', '-0.93', '#anger', '#disgust', 'negative', '-0.92', 'bullyboy', 'muscleman', 'muscle', 'skinhead', 'skin']
senticnet['football_hooliganism'] = ['-0.89', '0', '0.933', '-0.93', '#anger', '#disgust', 'negative', '-0.92', 'bullyboy', 'muscleman', 'muscle', 'skinhead', 'skin']
senticnet['football_league'] = ['0.728', '0.83', '0', '0', '#joy', '#interest', 'positive', '0.779', 'ball', 'bounce', 'ball_bounce', 'soccer', 'play_football']
senticnet['football_play'] = ['-0.07', '0.12', '-0.06', '0.235', '#interest', '#admiration', 'positive', '0.053', 'go_ahead', 'pace', 'foot_action', 'flee', 'stride']
senticnet['football_play_ground'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'stadium', 'sport_event', 'sport_arena', 'play_football', 'football_stadium']
senticnet['football_player'] = ['0.053', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.039', 'quarterback', 'manager', 'scrabble', 'card_type', 'contest']
senticnet['football_rule'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'obstruct_justice', 'obstruction', 'block_road', 'hinder', 'interfere']
senticnet['football_soccer'] = ['0.728', '0.83', '0', '0', '#joy', '#interest', 'positive', '0.779', 'ball', 'bounce', 'ball_bounce', 'soccer', 'play_football']
senticnet['football_stadium'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'stadium', 'sport_event', 'sport_arena', 'play_football', 'football_play_ground']
senticnet['football_team'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'play_football', 'football_stadium', 'football_play_ground', 'football_player', 'football']
senticnet['footballing_brain'] = ['-0.79', '0', '0.992', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['footbridge'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'suspension_bridge', 'under_bridge', 'roadway', 'county_highway', 'long_road']
senticnet['footcandle'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'incandescence', 'luminescence', 'brightness_level']
senticnet['footfall_pattern'] = ['-0.15', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.09', 'use_leg', 'walk_quickly', 'casual', 'take_step', 'strut']
senticnet['foothold'] = ['0.961', '-0.84', '0', '0.958', '#joy', '#admiration', 'positive', '0.923', 'pause', 'break', 'intermission', 'suspension', 'rest_period']
senticnet['footie'] = ['0.746', '-0.67', '0', '0.529', '#joy', '#surprise', 'positive', '0.651', 'labor', 'cinch', 'undertaking', 'project', 'task']
senticnet['footjoy'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'sports_equipment', 'sport_stuff', 'golf', 'sport_store', 'bag']
senticnet['footless'] = ['0', '0', '0.811', '-0.98', '#anger', '#disgust', 'negative', '-0.9', 'tights', 'legging', 'fishnet', 'hosiery', 'knee-high']
senticnet['footling'] = ['0', '0', '-0.76', '-0.81', '#fear', '#disgust', 'negative', '-0.78', 'picayune', 'lilliputian', 'little', 'fiddling', 'niggling']
senticnet['footman'] = ['-0.62', '0', '0.649', '-0.67', '#anger', '#disgust', 'negative', '-0.64', 'handmaid', 'handmaiden', 'chambermaid', 'maid', 'parlormaid']
senticnet['footpath'] = ['-0.84', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.81', 'grass_clipping', 'homeless_people', 'litter', 'streetlight', 'traffic_light']
senticnet['footprint'] = ['0', '-0.10', '-0.05', '0.076', '#surprise', '#admiration', 'positive', '0.041', 'dust', 'dirt', 'compost', 'track', 'soil']
senticnet['footshock'] = ['0.583', '-0.82', '0', '0.574', '#joy', '#surprise', 'positive', '0.661', 'stimulant', 'positive_reinforcing_stimulus', 'reinforcer', 'negative_reinforcing_stimulus', 'negative_reinforcer']
senticnet['footslog'] = ['0', '0', '-0.83', '-0.81', '#fear', '#disgust', 'negative', '-0.82', 'grind', 'plodding', 'donkeywork', 'slog', 'drudgery']
senticnet['footsore'] = ['-0.90', '0', '0.865', '-0.77', '#sadness', '#anger', 'negative', '-0.84', 'nosiness', 'snoopiness', 'prying', 'curiosity', 'wonder']
senticnet['footstep'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'walker', 'foot', 'wander', 'heel', 'sneaker']
senticnet['footstep_echo'] = ['0.788', '-0.60', '0', '0.884', '#joy', '#admiration', 'positive', '0.759', 'gap', 'rathole', 'aperture', 'bullet_hole', 'hole']
senticnet['footwear'] = ['-0.05', '0.068', '0', '0.102', '#interest', '#admiration', 'positive', '0.038', 'clothes', 'high_heel', 'leg_wear', 'shoe', 'shoe_type']
senticnet['footwear_brand'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['footwear_retailer'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['footwork'] = ['0', '-0.90', '0', '0.754', '#surprise', '#admiration', 'positive', '0.829', 'quickness', 'athleticism', 'explosiveness', 'drill', 'choreography']
senticnet['fop'] = ['0.944', '0', '0', '0.753', '#joy', '#admiration', 'positive', '0.849', 'dandy', 'clotheshorse', 'gallant', 'proud', 'chivalrous']
senticnet['foppery'] = ['-0.63', '0', '0.683', '-0.96', '#anger', '#disgust', 'negative', '-0.76', 'slovenly_woman', 'slut', 'trollop', 'slattern', 'litterer']
senticnet['foppish'] = ['0.828', '-0.88', '0', '0.551', '#joy', '#surprise', 'positive', '0.755', 'superiority', 'pride', 'superbia', 'imperiousness', 'domineeringness']
senticnet['for'] = ['0.295', '0.239', '0.049', '0.346', '#joy', '#admiration', 'positive', '0.208', 'absolute', 'complete', 'infinite', 'total', 'stuffed']
senticnet['for_all'] = ['0.138', '0.134', '-0.06', '0.151', '#joy', '#admiration', 'positive', '0.089', 'sip', 'gulp', 'better_cold', 'potable_liquid', 'liquid_beverage']
senticnet['for_free'] = ['0', '-0.83', '0', '0.913', '#surprise', '#admiration', 'positive', '0.875', 'free', 'costless', 'gratuitous', 'gratis', 'complimentary']
senticnet['for_sure'] = ['-0.71', '0', '0.785', '-0.55', '#sadness', '#anger', 'negative', '-0.68', 'unimpeachable', 'acknowledged', 'unquestionable', 'mathematical', 'beyond_doubt']
senticnet['forage'] = ['0', '0', '-0.76', '-0.94', '#fear', '#disgust', 'negative', '-0.85', 'foraging', 'pasture', 'grass', 'eatage', 'scrounge']
senticnet['forage_cap'] = ['-0.58', '0', '0.632', '-0.63', '#anger', '#disgust', 'negative', '-0.61', 'skimmer', 'trilby', 'chapeau', 'busby', 'shako']
senticnet['forage_crop'] = ['0.061', '0.093', '0.037', '0.114', '#interest', '#admiration', 'positive', '0.058', 'whole_grain', 'food_ingredient', 'staple_food', 'starchy_food', 'food_grain']
senticnet['foraging'] = ['0', '0', '-0.77', '-0.95', '#fear', '#disgust', 'negative', '-0.86', 'forage', 'pasture', 'grass', 'eatage', 'scrounge']
senticnet['foramen_lacerum'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['foray'] = ['0.954', '-0.87', '0', '0.583', '#joy', '#surprise', 'positive', '0.802', 'penetration', 'air_raid', 'air_attack', 'swoop', 'incursion']
senticnet['foray_into'] = ['-0.93', '0', '0.77', '-0.66', '#sadness', '#anger', 'negative', '-0.79', 'raid', 'come_in', 'encroach_upon', 'enter', 'get_into']
senticnet['forays_into'] = ['0.954', '-0.87', '0', '0.583', '#joy', '#surprise', 'positive', '0.802', 'penetration', 'air_raid', 'air_attack', 'swoop', 'incursion']
senticnet['forb'] = ['-0.94', '0', '0.608', '-0.95', '#sadness', '#disgust', 'negative', '-0.83', 'wild_licorice', 'wild_liquorice', 'alpine_milk_vetch', 'purple_milk_vetch', 'milk_vetch']
senticnet['forbear'] = ['0.731', '-0.59', '0', '0.634', '#joy', '#admiration', 'positive', '0.652', 'accommodate', 'obey', 'conform_to', 'toe_line', 'abide_by']
senticnet['forbearance'] = ['0.543', '-0.67', '0', '0.553', '#surprise', '#admiration', 'positive', '0.591', 'longanimity', 'patience', 'tolerance', 'good_nature', 'grace']
senticnet['forbid'] = ['-0.98', '0', '0.778', '0', '#sadness', '#anger', 'negative', '-0.88', 'bar', 'debar', 'criminalize', 'outlaw', 'ban']
senticnet['forbidden_city'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['forbidden_fruit'] = ['-0.93', '0', '0.77', '-0.66', '#sadness', '#anger', 'negative', '-0.79', 'enticement', 'allurement', 'hook', 'sweetener', 'influence']
senticnet['forbidding'] = ['0', '0', '0.811', '-0.84', '#anger', '#disgust', 'negative', '-0.82', 'formidable', 'redoubtable', 'daunting', 'alarming', 'frightening']
senticnet['forbin_project'] = ['-0.88', '0', '0.882', '-0.58', '#sadness', '#anger', 'negative', '-0.78', 'mortal', 'behemoth', 'unusual_person', 'aberrant', 'giant']
senticnet['forbore'] = ['-0.91', '0', '0.521', '-0.81', '#sadness', '#disgust', 'negative', '-0.74', 'bullying', 'domineering', 'blustery', 'roughshod', 'cavalier']
senticnet['forborne'] = ['0.546', '-0.74', '0', '0.581', '#surprise', '#admiration', 'positive', '0.624', 'hand', 'commend', 'reach', 'charge', 'give']
senticnet['force'] = ['0', '0', '-1', '-1', '#fear', '#disgust', 'negative', '-1.0', 'moon_gravity', 'coercion', 'volt', 'watt', 'power_source']
senticnet['force-feed'] = ['0', '0', '-0.93', '-0.79', '#fear', '#disgust', 'negative', '-0.86', 'forcefully', 'forcefulness', 'forceful', 'obligate', 'obligated']
senticnet['force_checkmate'] = ['0', '0', '0', '0.115', '#admiration', '#admiration', 'positive', '0.115', 'chess_knight', 'strategy', 'pawn', 'chess_queen', 'chess_rook']
senticnet['force_enter'] = ['-0.93', '0', '0.945', '-0.93', '#anger', '#disgust', 'negative', '-0.93', 'break_into_house', 'break_window_shop', 'accident', 'robbery', 'break_door']
senticnet['force_feed'] = ['0', '0', '-1', '-0.66', '#fear', '#disgust', 'negative', '-0.83', 'obligation', 'obligated', 'force', 'coercion', 'enslave']
senticnet['force_out'] = ['-0.83', '0', '0.965', '-0.84', '#anger', '#disgust', 'negative', '-0.88', 'out', 'sacrifice', 'force', 'putout', 'force_play']
senticnet['force_play'] = ['-0.83', '0', '0.965', '-0.84', '#anger', '#disgust', 'negative', '-0.88', 'out', 'sacrifice', 'force', 'force_out', 'putout']
senticnet['force_recon'] = ['-0.66', '0', '0.789', '-0.63', '#sadness', '#anger', 'negative', '-0.69', 'humbug', 'play_trick', 'cozen', 'pull_fast_one_on', 'play_trick_on']
senticnet['force_unit'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'newton', 'sthene', 'poundal']
senticnet['forced'] = ['-0.65', '0', '0.901', '-0.85', '#anger', '#disgust', 'negative', '-0.80', 'unwilling', 'unconscious', 'unwilled', 'senseless', 'unvoluntary']
senticnet['forced_assimilation'] = ['-0.96', '0', '0.766', '-0.96', '#sadness', '#disgust', 'negative', '-0.89', 'calumniation', 'traducement', 'hatchet_job', 'defamation', 'calumny']
senticnet['forced_convection'] = ['-0.98', '0', '0.516', '-0.90', '#sadness', '#disgust', 'negative', '-0.80', 'devastation', 'annihilation', 'pulverization', 'vaporization', 'destruction']
senticnet['forced_exhalation'] = ['0', '0.098', '-0.09', '0.115', '#interest', '#admiration', 'positive', '0.04', 'push_wind', 'breath', 'breaout', 'body_reaction', 'mouth_action']
senticnet['forced_labor'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'vassalage']
senticnet['forced_prostitution'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'vassalage']
senticnet['forced_relocation'] = ['-0.02', '0.119', '-0.06', '0.216', '#interest', '#admiration', 'positive', '0.062', 'go_forward', 'locomotion', 'movement', 'move', 'move_ahead']
senticnet['forced_removal'] = ['0', '0', '0.999', '-0.75', '#anger', '#disgust', 'negative', '-0.87', 'dismissal', 'discharge', 'elimination', 'destitution', 'removal']
senticnet['forceful'] = ['0', '0', '-0.93', '-0.79', '#fear', '#disgust', 'negative', '-0.86', 'forcefully', 'forcefulness', 'force-feed', 'obligate', 'obligated']
senticnet['forceful_action'] = ['0', '0', '0.808', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'idle', 'lazy', 'stay', 'take_easy', 'recline']
senticnet['forceful_personality'] = ['-0.71', '0', '0.697', '-0.54', '#sadness', '#anger', 'negative', '-0.65', 'vigor', 'strenuosity', 'athleticism', 'force', 'strength']
senticnet['forcefully'] = ['0', '0', '-0.93', '-0.79', '#fear', '#disgust', 'negative', '-0.86', 'forceful', 'forcefulness', 'force-feed', 'obligate', 'obligated']
senticnet['forcefulness'] = ['-0.71', '0', '0.697', '-0.54', '#sadness', '#anger', 'negative', '-0.65', 'vigor', 'strenuosity', 'athleticism', 'force', 'strength']
senticnet['forcemeat'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'dainty', 'delicacy', 'treat', 'gelatin_dessert', 'jelly']
senticnet['forceps'] = ['-0.03', '0', '-0.21', '-0.08', '#fear', '#disgust', 'negative', '-0.11', 'nurse', 'cure', 'doctor', 'medicine', 'physician']
senticnet['forcible'] = ['-0.91', '0', '0.521', '-0.81', '#sadness', '#disgust', 'negative', '-0.74', 'bullying', 'domineering', 'blustery', 'roughshod', 'cavalier']
senticnet['forcibly'] = ['0', '0.149', '0.719', '-0.66', '#anger', '#disgust', 'negative', '-0.41', 'belligerently', 'antagonism', 'class_feeling', 'aggressiveness', 'animus']
senticnet['forcibly_disarm'] = ['-0.98', '0', '0.654', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'bereave', 'expropriate', 'deprive', 'unclothe', 'take']
senticnet['ford'] = ['0.049', '0.139', '-0.09', '0.123', '#interest', '#admiration', 'positive', '0.055', 'guard', 'safety', 'insurance', 'policy', 'protect_home']
senticnet['ford_dealer'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'sell_car', 'truck_stop', 'vehicle', 'insurance', 'car']
senticnet['ford_dealership'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'sell_car', 'truck_stop', 'vehicle', 'insurance', 'car']
senticnet['ford_excursion'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'cable_car', 'baggage_compartment', 'subway', 'amusement_park', 'terminal']
senticnet['ford_expedition'] = ['-0.05', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'plane_land', 'floss', 'floss_tooth', 'ornament', 'wear_dress']
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senticnet['forget_else'] = ['-0.11', '0.085', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.04', 'solve_mystery', 'think_through', 'gain_insight', 'remember', 'find_answer']
senticnet['forget_everything'] = ['-0.72', '0', '0.745', '-0.91', '#anger', '#disgust', 'negative', '-0.79', 'gonorrhea', 'lose_mind', 'bad_feeling', 'schizophrenia', 'disrespect']
senticnet['forget_fact'] = ['-0.13', '0', '-0.07', '-0.03', '#sadness', '#fear', 'negative', '-0.08', 'creative_mind', 'question_teacher', 'innovative', 'question_authority', 'succeed_school']
senticnet['forget_problem'] = ['0.728', '0', '0', '0.998', '#joy', '#admiration', 'positive', '0.863', 'forget_trouble', 'get_happy', 'enjoy_person', 'stress_reduction', 'relaxation']
senticnet['forget_trouble'] = ['0.889', '0', '0', '0.931', '#joy', '#admiration', 'positive', '0.91', 'get_happy', 'forget_problem', 'find_solution', 'pleasure', 'spread_happiness']
senticnet['forgetful'] = ['-0.71', '0', '0.906', '-0.93', '#anger', '#disgust', 'negative', '-0.85', 'absent', 'inattentive', 'distracted', 'distrait', 'scatty']
senticnet['forgetfulness'] = ['-0.07', '-0.07', '-0.04', '-0.25', '#sadness', '#disgust', 'negative', '-0.07', 'tipsy', 'run_out_booze', 'loss_ofawareness', 'hangover', 'bang_head']
senticnet['forgetive'] = ['0.99', '0.897', '0', '0', '#joy', '#interest', 'positive', '0.944', 'inventive', 'inventively', 'inventory', 'promethean', 'invent']
senticnet['forgettable'] = ['-0.93', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.90', 'throw-away', 'throwaway', 'tongue-in-cheek', 'banal', 'insipid']
senticnet['forgivable'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'venial', 'excusable', 'mercifully', 'merciful', 'mercifulness']
senticnet['forgivably'] = ['0', '-0.58', '-1', '0', '#surprise', '#fear', 'negative', '-0.20', 'gratingly', 'ostentatiously', 'intrusively', 'objectionably', 'obtrusively']
senticnet['forgive'] = ['0.601', '0.647', '-0.26', '0.322', '#joy', '#interest', 'positive', '0.326', 'comfort_friend', 'propose_woman', 'thank', 'let_go', 'love_else']
senticnet['forgiveness'] = ['0.716', '0', '0', '0', '#joy', '#joy', 'positive', '0.716', 'mercy', 'compassion', 'forgivable', 'excusable', 'venial']
senticnet['forgiver'] = ['-0.76', '0', '0.684', '-0.86', '#sadness', '#disgust', 'negative', '-0.77', 'mortal', 'person', 'individual', 'human_being', 'pardon']
senticnet['forgiving'] = ['0.579', '-0.54', '0', '0.784', '#joy', '#admiration', 'positive', '0.635', 'exculpatory', 'vindicatory', 'absolvitory', 'exonerative', 'extenuating']
senticnet['forgo'] = ['0', '0', '-0.76', '-0.72', '#fear', '#disgust', 'negative', '-0.74', 'foreswear', 'relinquish', 'dispense_with', 'waive', 'throw_overboard']
senticnet['forgotten'] = ['0', '0', '0.935', '-0.76', '#anger', '#disgust', 'negative', '-0.84', 'hear_bad_news', 'death_love', 'disrespect', 'forget_everything', 'toothache']
senticnet['fork'] = ['-0.75', '0', '0.886', '-0.72', '#sadness', '#anger', 'negative', '-0.78', 'harass', 'move', 'check', 'aggress', 'pin']
senticnet['fork_river'] = ['0.941', '0.987', '0', '0', '#joy', '#interest', 'positive', '0.964', 'monkey', 'marmoset', 'lemur', 'fiance', 'koala']
senticnet['fork_tailed_swift'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'lovebird', 'parroket', 'paraquet', 'paroquet']
senticnet['forked_stick'] = ['-0.84', '0', '0.987', '-0.77', '#sadness', '#anger', 'negative', '-0.87', 'thorn', 'sticker', 'glochid', 'prickle', 'spikelet']
senticnet['forked_tail'] = ['0.041', '0', '0', '0.058', '#joy', '#admiration', 'positive', '0.05', 'bird_animal', 'wing', 'farm_bird', 'barnyard_animal', 'hen_baby']
senticnet['forking'] = ['0', '0', '-0.84', '-0.77', '#fear', '#disgust', 'negative', '-0.81', 'fork', 'branching', 'branch', 'ramification', 'leg']
senticnet['forking_out'] = ['0', '0.249', '0.012', '0.154', '#interest', '#admiration', 'positive', '0.13', 'utility', 'money_value', 'monetary_value', 'money_amount', 'sticker']
senticnet['forking_over'] = ['0', '0.249', '0.012', '0.154', '#interest', '#admiration', 'positive', '0.13', 'utility', 'money_value', 'monetary_value', 'money_amount', 'sticker']
senticnet['forklift'] = ['0.087', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.064', 'hoist', 'control_room', 'raise', 'platform_rail', 'torso']
senticnet['forklift_driver'] = ['0.969', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.936', 'attendant', 'manager', 'public_servant', 'specialist', 'accomplice']
senticnet['forklift_operator'] = ['0.969', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.936', 'attendant', 'manager', 'public_servant', 'specialist', 'accomplice']
senticnet['forlorn'] = ['-0.69', '0.32', '-0.38', '-0.08', '#sadness', '#fear', 'negative', '-0.21', 'forlornly', 'desolation', 'forlornness', 'weepiness', 'tearful']
senticnet['forlorn_hope'] = ['-0.83', '0', '0.965', '-0.84', '#anger', '#disgust', 'negative', '-0.88', 'labor', 'racket', 'endeavor', 'fraudulent_scheme', 'business_activity']
senticnet['forlornly'] = ['-0.69', '0.32', '-0.38', '-0.08', '#sadness', '#fear', 'negative', '-0.21', 'desolation', 'forlornness', 'forlorn', 'weepiness', 'tearful']
senticnet['forlornness'] = ['-0.69', '0.32', '-0.38', '-0.08', '#sadness', '#fear', 'negative', '-0.21', 'forlornly', 'desolation', 'forlorn', 'weepiness', 'tearful']
senticnet['form'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'figure', 'geometric_figure', 'octagon', 'polygon', 'hexagon']
senticnet['form_carbonic_acid'] = ['-0.78', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.83', 'break', 'end', 'die', 'fall_apart', 'go_to_piece']
senticnet['form_cherepovets_governorate'] = ['0.909', '0.986', '0', '0', '#joy', '#interest', 'positive', '0.948', 'separation', 'divide', 'division', 'slash', 'bagle']
senticnet['form_expression'] = ['0.081', '0', '0.051', '0.137', '#joy', '#admiration', 'positive', '0.056', 'instrumental', 'universal_language', 'melodic_noise', 'form_of_art', 'rock_music']
senticnet['form_government'] = ['0.061', '0.219', '-0.06', '0.192', '#interest', '#admiration', 'positive', '0.101', 'political_system', 'type_of_government', 'country', 'government_person', 'political_entity']
senticnet['form_of_art'] = ['0.093', '0', '0.017', '0.183', '#joy', '#admiration', 'positive', '0.086', 'art_creation', 'creative_activity', 'form_expression', 'universal_language', 'art_form']
senticnet['form_of_communication'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'communication_method', 'information_source', 'comment', 'written_language', 'give_info']
senticnet['form_of_energy'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'energy', 'kinetic', 'power', 'power_source', 'potential']
senticnet['form_of_entertainment'] = ['0.101', '-0.09', '0.016', '0', '#joy', '#surprise', 'positive', '0.058', 'fun_activity', 'form_of_social_interaction', 'stage_production', 'child_game', 'diversion']
senticnet['form_of_exercise'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'good_exercise', 'healthy_exercise', 'vigorous_activity', 'great_exercise', 'jogging']
senticnet['form_of_food'] = ['0.049', '0', '0.024', '0.056', '#joy', '#admiration', 'positive', '0.027', 'form', 'food', 'popular_food', 'raw_fish', 'japanese_food']
senticnet['form_of_gambling'] = ['-0.64', '0', '0.557', '-0.73', '#sadness', '#disgust', 'negative', '-0.64', 'waste_money', 'jail', 'ruin_family', 'bankrupt', 'loan_shark']
senticnet['form_of_money'] = ['0', '0.096', '0.082', '0.107', '#interest', '#admiration', 'positive', '0.04', 'monetary_unit', 'currency_unit', 'money_unit', 'american_coin', 'small_coin']
senticnet['form_of_music'] = ['0.136', '-0.09', '0.087', '-0.03', '#joy', '#surprise', 'positive', '0.027', 'style_music', 'pop_music', 'tune', 'rhythm', 'music']
senticnet['form_of_public_transportation'] = ['0', '-0.01', '0', '0.058', '#surprise', '#admiration', 'positive', '0.038', 'long_car', 'motor_vehicle', 'large_automobile', 'transportation', 'transportation_vehicle']
senticnet['form_of_punishment'] = ['-0.14', '-0.04', '0', '-0.10', '#sadness', '#disgust', 'negative', '-0.06', 'punish', 'inflict_pain', 'death_penalty', 'kill', 'punishment']
senticnet['form_of_punishmentment'] = ['-0.15', '-0.05', '0', '-0.10', '#sadness', '#disgust', 'negative', '-0.06', 'form_of_punishment', 'inflict_pain', 'death_penalty', 'kill', 'punish']
senticnet['form_of_social_interaction'] = ['0', '0.765', '0', '0.759', '#interest', '#admiration', 'positive', '0.762', 'child_game', 'kid_activity', 'have_fun', 'fun_activity', 'fun_game']
senticnet['form_of_transportation'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'mode_transportation', 'transportation', 'long_car', 'car_truck', 'means_of_transportation']
senticnet['form_opinion'] = ['-0.14', '-0.04', '0', '-0.10', '#sadness', '#disgust', 'negative', '-0.06', 'investigate_fact', 'interrogate', 'ask_question', 'epiphany', 'think_logically']
senticnet['form_sentence'] = ['0.921', '0.708', '0', '0', '#joy', '#interest', 'positive', '0.815', 'excrete', 'yolo', 'dream_with_eye_open', 'desire_live', 'eat_animal']
senticnet['form_team'] = ['-0.04', '-0.04', '0.085', '0', '#sadness', '#anger', 'negative', '-0.02', 'gather_team', 'buy_ball', 'suit_up', 'join_team', 'hoop']
senticnet['form_think'] = ['0', '0.069', '0', '0.108', '#interest', '#admiration', 'positive', '0.088', 'forget_fact', 'vocalization', 'creative_mind', 'human_sound', 'vocal']
senticnet['formability'] = ['0.8', '-0.90', '0', '0.675', '#joy', '#surprise', 'positive', '0.794', 'indestructibility', 'tensile_strength', 'continuity', 'persistence', 'changelessness']
senticnet['formal'] = ['0.859', '0.744', '0', '0', '#joy', '#interest', 'positive', '0.801', 'head_cover', 'frilly', 'fancy_clothes', 'feminine', 'formal_clothes']
senticnet['formal_axiomatic'] = ['-0.99', '0', '0.878', '-0.95', '#sadness', '#disgust', 'negative', '-0.94', 'adequacy', 'satisfactoriness', 'acceptability', 'acceptableness', 'quality']
senticnet['formal_cloth'] = ['0.53', '-0.64', '0', '0.76', '#surprise', '#admiration', 'positive', '0.646', 'woman_garment', 'woman_cloth', 'woman_suit', 'woman_pant', 'woman_shirt']
senticnet['formal_clothes'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'clothes_style', 'style', 'glamour', 'model', 'fancy_clothes']
senticnet['formal_definition'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'vocabulary', 'read_material', 'book_genre', 'many_words', 'book_library']
senticnet['formal_event'] = ['0.076', '0.139', '-0.06', '0.173', '#interest', '#admiration', 'positive', '0.082', 'lady', 'goddess', 'ceremony', 'priest', 'christian']
senticnet['formal_fallacy'] = ['0.633', '-0.67', '0', '0.776', '#surprise', '#admiration', 'positive', '0.693', 'double_entendre', 'loophole', 'ambiguity', 'amphiboly', 'parisology']
senticnet['formal_introduction'] = ['0.882', '0.76', '0', '0', '#joy', '#interest', 'positive', '0.821', 'socialize', 'business_card', 'database', 'professional', 'polite']
senticnet['formal_invitation'] = ['0', '0.852', '0', '0', '#interest', '#interest', 'positive', '0.852', 'streamer', 'tribute', 'salutation', 'gift', 'anniversary_gift']
senticnet['formal_logic'] = ['0.629', '-0.79', '0', '0.758', '#surprise', '#admiration', 'positive', '0.726', 'system_of_rules', 'symbolic_logic', 'mathematical_logic', 'logical_system', 'system']
senticnet['formal_notation'] = ['0.777', '-0.68', '0', '0.601', '#joy', '#surprise', 'positive', '0.686', 'lucidness', 'clarity', 'clearness', 'lucidity', 'pellucidity']
senticnet['formal_notification'] = ['0.031', '0.218', '-0.09', '0.299', '#interest', '#admiration', 'positive', '0.113', 'nominate', 'axiom', 'supreme', 'govern', 'tenet']
senticnet['formal_peace_treaty'] = ['0.397', '0.245', '-0.13', '0.224', '#joy', '#interest', 'positive', '0.183', 'agreement', 'provide', 'advice', 'contribution', 'offer']
senticnet['formal_recognition'] = ['0.258', '0.35', '-0.23', '0.318', '#interest', '#admiration', 'positive', '0.173', 'brownie_point', 'honor', 'reinforcement', 'positive_reinforcement', 'fame']
senticnet['formalise'] = ['-0.91', '0', '0.637', '-0.59', '#sadness', '#anger', 'negative', '-0.71', 'gauge', 'govern', 'regulate', 'standardize', 'order']
senticnet['formalism'] = ['0.136', '-0.11', '0.031', '0', '#joy', '#surprise', 'positive', '0.075', 'rap', 'popular_music', 'music', 'band_play', 'rock_roll']
senticnet['formalist'] = ['0.833', '0', '0', '0.912', '#joy', '#admiration', 'positive', '0.873', 'perfectionalist', 'traditionalist', 'purist', 'hard-core', 'pragmatist']
senticnet['formality'] = ['0.614', '-0.95', '0', '0.768', '#surprise', '#admiration', 'positive', '0.778', 'keeping', 'abidance', 'honoring', 'line', 'observance']
senticnet['formalize'] = ['-0.91', '0', '0.637', '-0.59', '#sadness', '#anger', 'negative', '-0.71', 'gauge', 'govern', 'regulate', 'standardize', 'order']
senticnet['formally'] = ['0', '0.719', '0', '0.87', '#interest', '#admiration', 'positive', '0.794', 'officially', 'project', 'student', 'research', 'plan']
senticnet['formalwear'] = ['0.053', '-0.01', '-0.05', '0.121', '#joy', '#admiration', 'positive', '0.034', 'coat', 'winter_clothes', 'stylish', 'adorn', 'protective_cover']
senticnet['formamide'] = ['0.766', '-0.87', '0', '0.666', '#joy', '#surprise', 'positive', '0.769', 'reactive', 'unstable', 'labile', 'activated', 'excited']
senticnet['format'] = ['0', '0.938', '0', '0.735', '#interest', '#admiration', 'positive', '0.837', 'data_format', 'initialise', 'formatting', 'data_formatting', 'initialize']
senticnet['format_specifier'] = ['-0.04', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.06', 'chemical_reaction', 'burn', 'chemical_process', 'get_burnt', 'set_on_fire']
senticnet['formation'] = ['0', '0.78', '0', '0.763', '#interest', '#admiration', 'positive', '0.772', 'slope', 'natural_depression', 'wall', 'collectivization', 'massif']
senticnet['formative'] = ['0.678', '-0.89', '0', '0.804', '#surprise', '#admiration', 'positive', '0.793', 'reconstructive', 'constructive', 'shaping', 'creative', 'inferential']
senticnet['formative_evaluation'] = ['-0.06', '0.18', '0', '0.143', '#interest', '#admiration', 'positive', '0.086', 'grade', 'multiple_choices', 'final', 'final_exam', 'school_grade']
senticnet['formative_role'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'baddie', 'theatrical_baddie', 'part', 'persona']
senticnet['formatted_disk'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'slot', 'box', 'load_dock', 'safe_deposit_box', 'emporium']
senticnet['formatting'] = ['0', '0.93', '0', '0.735', '#interest', '#admiration', 'positive', '0.833', 'data_format', 'initialise', 'format', 'data_formatting', 'initialize']
senticnet['former_aide'] = ['0', '0', '0', '0.876', '#admiration', '#admiration', 'positive', '0.876', 'good_manner', 'courteous', 'good_behavior', 'handle_carefully', 'politeness']
senticnet['former_altar_boy'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'acolyte', 'reverend', 'thurifer', 'clergyman', 'man_of_cloth']
senticnet['former_bodybuilder'] = ['-0.90', '0', '0.522', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'hooligan', 'ruffian', 'roughneck', 'rowdy', 'yob']
senticnet['former_bodyguard'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'yeoman', 'outrider', 'yeoman_of_guard', 'beefeater', 'defender']
senticnet['former_boyfriend'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'addicted', 'obsessed_love', 'addiction', 'obsession', 'weird']
senticnet['former_cfo'] = ['0.046', '0.116', '-0.05', '0.076', '#interest', '#admiration', 'positive', '0.045', 'head_guy', 'boss', 'officer', 'leader', 'supervisor']
senticnet['former_child_prodigy'] = ['0.592', '-0.93', '0', '0.935', '#surprise', '#admiration', 'positive', '0.819', 'child', 'small_fry', 'tyke', 'tike', 'kid']
senticnet['former_classmate'] = ['0.05', '0.141', '-0.07', '0.132', '#interest', '#admiration', 'positive', '0.063', 'public_servant', 'worker', 'manager', 'laborer', 'office_job']
senticnet['former_colleague'] = ['0.05', '0.141', '-0.07', '0.132', '#interest', '#admiration', 'positive', '0.063', 'public_servant', 'worker', 'manager', 'laborer', 'office_job']
senticnet['former_dictator'] = ['-0.73', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.72', 'royal_leader', 'despot', 'emperor', 'monarch', 'king']
senticnet['former_drug_addict'] = ['0', '0', '0.821', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'student', 'serious_student', 'addiction', 'graduate_student', 'hard_worker']
senticnet['former_fianc'] = ['0.033', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.058', 'baby_dog', 'sweet', 'puppet', 'dog', 'love']
senticnet['former_fiance'] = ['0.033', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.058', 'baby_dog', 'sweet', 'puppet', 'dog', 'love']
senticnet['former_girlfriend'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'addicted', 'obsessed_love', 'addiction', 'obsession', 'weird']
senticnet['former_heroin_addict'] = ['0', '0', '0.821', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'student', 'serious_student', 'addiction', 'graduate_student', 'hard_worker']
senticnet['former_kai_tak'] = ['0', '0.147', '0', '0.099', '#interest', '#admiration', 'positive', '0.123', 'airliner', 'airport', 'baggage_compartment', 'aircraft', 'cable_car']
senticnet['former_komusubi'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['former_lover'] = ['0.86', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.798', 'director_chair', 'alcohol', 'bride_groom', 'attendee', 'coworker']
senticnet['former_maegashira'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['former_managing_editor'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'adventurer', 'humankind']
senticnet['former_mistress_rielle'] = ['0', '0', '-0.11', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'hunting_season', 'hound', 'cougar', 'outlaw', 'dragon']
senticnet['former_sac_capital'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'busy_bee', 'gopher', 'actor', 'doer', 'worker']
senticnet['former_sekiwake'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['former_seminarian'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'acolyte', 'reverend', 'thurifer', 'clergyman', 'man_of_cloth']
senticnet['former_sponsor'] = ['0', '0.779', '0', '0.925', '#interest', '#admiration', 'positive', '0.852', 'university', 'different_application', 'mathematics', 'science', 'education']
senticnet['former_spouse'] = ['0.851', '0.708', '0', '0', '#joy', '#interest', 'positive', '0.779', 'devisee', 'legatee', 'recipient', 'receiver', 'pensioner']
senticnet['former_staffer'] = ['0', '0', '0', '0.876', '#admiration', '#admiration', 'positive', '0.876', 'good_manner', 'courteous', 'good_behavior', 'handle_carefully', 'politeness']
senticnet['former_stripper'] = ['0.82', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.862', 'pervert', 'healthy_person', 'philanthropist', 'arsonist', 'nazi']
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senticnet['fostering_economic_growth'] = ['0.913', '-0.63', '0', '0.961', '#joy', '#admiration', 'positive', '0.836', 'materialistic', 'mercenary', 'mundane', 'material', 'worldly']
senticnet['fostering_excellence'] = ['0.856', '0', '0', '0.963', '#joy', '#admiration', 'positive', '0.909', 'progress', 'get_better_job', 'further_career', 'graduate_school', 'get_diploma']
senticnet['fosters_collaboration'] = ['0', '0', '0', '0.881', '#admiration', '#admiration', 'positive', '0.881', 'educate_others', 'share_knowledge', 'share_opinion', 'discover_new', 'cooperation']
senticnet['foucauldian'] = ['0.654', '-0.71', '0', '0.624', '#joy', '#surprise', 'positive', '0.664', 'viewable', 'gross', 'telescopic', 'seeable', 'visual']
senticnet['foucault'] = ['0.654', '-0.71', '0', '0.624', '#joy', '#surprise', 'positive', '0.664', 'viewable', 'gross', 'telescopic', 'seeable', 'visual']
senticnet['foucaultian'] = ['0.654', '-0.71', '0', '0.624', '#joy', '#surprise', 'positive', '0.664', 'viewable', 'gross', 'telescopic', 'seeable', 'visual']
senticnet['foul'] = ['-0.97', '0', '0.922', '-0.53', '#sadness', '#anger', 'negative', '-0.80', 'muddy_up', 'muck_up', 'begrime', 'blemish', 'smear']
senticnet['foul-smelling'] = ['0', '-0.97', '0', '-0.82', '#surprise', '#disgust', 'positive', '0.074', 'bad_smell', 'odoriferous', 'bad-smelling', 'disgusting', 'strong-smelling']
senticnet['foul-up'] = ['-0.97', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.86', 'screwup', 'screw-up', 'fuck-up', 'mess-up', 'thingamabob']
senticnet['foul_language'] = ['-0.69', '0', '0.792', '-0.98', '#anger', '#disgust', 'negative', '-0.82', 'outcry', 'call', 'cry', 'yell', 'shout']
senticnet['foul_mood'] = ['-0.85', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.88', 'oppression', 'enslavement', 'slave', 'captivity', 'misery']
senticnet['foul_mouth'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['foul_mouthed_tirade'] = ['0', '0', '0.781', '-0.82', '#anger', '#disgust', 'negative', '-0.80', 'cry', 'acting_out', 'explosion', 'halloo', 'yell']
senticnet['foul_odor'] = ['-0.86', '0', '0.644', '-0.88', '#sadness', '#disgust', 'negative', '-0.79', 'frowsty', 'fetid', 'foetid', 'smelly', 'stinking']
senticnet['foul_play'] = ['-0.52', '0', '0.621', '-0.91', '#anger', '#disgust', 'negative', '-0.68', 'deviltry', 'devilry', 'irreverence', 'iniquity', 'sexual_immorality']
senticnet['foul_shot'] = ['-0.81', '0', '0.747', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'vagrant', 'drifter', 'hobo', 'vagabond', 'dosser']
senticnet['foul_smell'] = ['-0.86', '0', '0.644', '-0.88', '#sadness', '#disgust', 'negative', '-0.79', 'frowsty', 'fetid', 'foetid', 'smelly', 'stinking']
senticnet['foul_stench'] = ['-0.86', '0', '0.644', '-0.88', '#sadness', '#disgust', 'negative', '-0.79', 'frowsty', 'fetid', 'foetid', 'smelly', 'stinking']
senticnet['foul_taste'] = ['0.045', '0.141', '-0.05', '0.065', '#interest', '#admiration', 'positive', '0.049', 'kernel', 'starch', 'ginger', 'cereal_crop', 'pea']
senticnet['foulard'] = ['0.735', '-0.54', '0', '0.897', '#joy', '#admiration', 'positive', '0.727', 'net', 'network', 'mesh', 'meshing', 'meshwork']
senticnet['fouled_anchor'] = ['-0.94', '0', '0.569', '-0.72', '#sadness', '#disgust', 'negative', '-0.74', 'bitumastic', 'veneer', 'veneering', 'adhesive', 'coating']
senticnet['fouler'] = ['0.617', '-0.83', '0', '0.828', '#surprise', '#admiration', 'positive', '0.761', 'brag', 'embellish', 'overemphasize', 'overstress', 'vaunt']
senticnet['foulest'] = ['-0.86', '0', '0.891', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'baneful', 'pernicious', 'vesicatory', 'vesicant', 'noxious']
senticnet['foully'] = ['-0.52', '0', '0.542', '-0.93', '#anger', '#disgust', 'negative', '-0.67', 'intoxication', 'drunkenness', 'inebriation', 'inebriety', 'tipsiness']
senticnet['foulness'] = ['-0.53', '0', '0.733', '-0.61', '#anger', '#disgust', 'negative', '-0.62', 'distressfulness', 'intensiveness', 'intensity', 'severeness', 'seriousness']
senticnet['foundation'] = ['0', '0.075', '0', '0.109', '#interest', '#admiration', 'positive', '0.092', 'wall', 'horizontal_plane', 'house_front', 'roof', 'home_entrance']
senticnet['foundational'] = ['0', '0.148', '0', '0.148', '#interest', '#admiration', 'positive', '0.148', 'status', 'basic_need', 'mandatory', 'fundamental', 'classification']
senticnet['foundational_knowledge'] = ['0', '0.808', '0', '0.754', '#interest', '#admiration', 'positive', '0.781', 'know', 'understand', 'trivia', 'gain_insight', 'great_understand']
senticnet['founder'] = ['-0.77', '0', '0.511', '-0.53', '#sadness', '#disgust', 'negative', '-0.60', 'go_under', 'subside', 'fall', 'settle', 'submerge']
senticnet['founder_raj_rajaratnam'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'busy_bee', 'gopher', 'actor', 'doer', 'worker']
senticnet['founding_chair'] = ['0', '0', '0.88', '-0.92', '#anger', '#disgust', 'negative', '-0.90', 'sofa_bed', 'small_seat', 'beanbag_airline_seat', 'hide-a-bed_sofa', 'airline_seat']
senticnet['founding_chairman'] = ['-0.77', '0', '0.511', '-0.53', '#sadness', '#disgust', 'negative', '-0.60', 'go_under', 'subside', 'fall', 'settle', 'submerge']
senticnet['founding_director'] = ['-0.77', '0', '0.511', '-0.53', '#sadness', '#disgust', 'negative', '-0.60', 'go_under', 'subside', 'fall', 'settle', 'submerge']
senticnet['founding_editor'] = ['-0.77', '0', '0.511', '-0.53', '#sadness', '#disgust', 'negative', '-0.60', 'go_under', 'subside', 'fall', 'settle', 'submerge']
senticnet['founding_executive_director'] = ['-0.77', '0', '0.511', '-0.53', '#sadness', '#disgust', 'negative', '-0.60', 'go_under', 'subside', 'fall', 'settle', 'submerge']
senticnet['founding_father'] = ['0.812', '-0.56', '0', '0.748', '#joy', '#admiration', 'positive', '0.708', 'founder', 'beginner', 'mastermind', 'father', 'pioneer']
senticnet['founding_member'] = ['-0.77', '0', '0.511', '-0.53', '#sadness', '#disgust', 'negative', '-0.60', 'go_under', 'subside', 'fall', 'settle', 'submerge']
senticnet['founding_partner'] = ['0.046', '0.116', '-0.05', '0.076', '#interest', '#admiration', 'positive', '0.045', 'head_guy', 'boss', 'officer', 'leader', 'supervisor']
senticnet['founding_pastor'] = ['0.081', '0.146', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'cult', 'sect', 'conservative', 'worshiper', 'pastor']
senticnet['founding_trustee'] = ['-0.77', '0', '0.511', '-0.53', '#sadness', '#disgust', 'negative', '-0.60', 'go_under', 'subside', 'fall', 'settle', 'submerge']
senticnet['foundry_amf'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'biological_machine', 'social_creature', 'live_creature', 'powerful', 'alive']
senticnet['foundry_coke'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'glass_water', 'refreshed', 'smoothie', 'lemonade', 'cold_water']
senticnet['fount'] = ['-0.98', '0', '0.969', '-0.95', '#sadness', '#anger', 'negative', '-0.97', 'face', 'font', 'monospaced_font', 'typeface', 'occurrence']
senticnet['fountain'] = ['0', '0.949', '0', '0.869', '#interest', '#admiration', 'positive', '0.909', 'statue', 'statuary', 'sculpture', 'marble', 'urn']
senticnet['fountain_pen'] = ['0.025', '0.176', '-0.06', '0.114', '#interest', '#admiration', 'positive', '0.063', 'stamp_pad', 'pen', 'mechanical_pencil', 'ballpoint_pen', 'graphite_pencil']
senticnet['four-game'] = ['-0.85', '0', '-0.84', '0', '#sadness', '#fear', 'negative', '-0.84', 'unbeaten', 'five-game', 'six-game', 'three-game', 'winless']
senticnet['four-letter'] = ['0.923', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.903', 'unmentionable', 'expletive', 'unprintable', 'undecipherable', 'untranslatable']
senticnet['four-year-old'] = ['0.995', '-0.89', '0', '0', '#joy', '#surprise', 'positive', '0.947', 'five-year-old', 'three-year-old', 'six-year-old', 'two-year-old', 'seven-year-old']
senticnet['four_corners'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rectangular', 'rectangle', 'square', 'four_sides', 'rectangular_shape']
senticnet['four_cylinder_diesel'] = ['0.068', '0', '0.034', '0.076', '#joy', '#admiration', 'positive', '0.037', 'gallon', 'liquid_measurement', 'ounce', 'measurement', 'quart']
senticnet['four_disc_dvd'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['four_door_sedan'] = ['-0.05', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'plane_land', 'floss', 'floss_tooth', 'ornament', 'wear_dress']
senticnet['four_horsemen'] = ['-0.96', '0', '0.501', '-0.59', '#sadness', '#disgust', 'negative', '-0.68', 'calamity', 'catastrophe', 'tragedy', 'disaster', 'cataclysm']
senticnet['four_lane_expressway'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['four_lane_freeway'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['four_lane_highway'] = ['0', '0', '0', '0.085', '#admiration', '#admiration', 'positive', '0.085', 'use_farm', 'gate', 'door', 'doorway', 'entranceway']
senticnet['four_lane_thoroughfare'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'artery', 'skeletal_system', 'groin', 'neck', 'muscle']
senticnet['four_leaf_clover'] = ['0.343', '0.223', '-0.16', '0.314', '#joy', '#admiration', 'positive', '0.178', 'horseshoe', 'chance', 'sport_person', 'horse_shoes', 'deck_card']
senticnet['four_oclock'] = ['0.02', '0.067', '0', '0', '#joy', '#interest', 'positive', '0.044', 'sunrise', 'begin_day', 'bright_sky', 'sunny', 'part_of_day']
senticnet['four_quarters'] = ['0', '0.177', '0.078', '0.12', '#interest', '#admiration', 'positive', '0.073', 'monetary_value', 'money_value', 'monetary_amount', 'denomination', 'money_amount']
senticnet['four_seasons'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'weather', 'sunny', 'spring', 'snow', 'climate']
senticnet['four_sides'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'six_sides', 'polygon', 'square', 'parallelogram', 'rectangle']
senticnet['four_storey'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'top_floor', 'front_desk', 'concierge', 'exit', 'elevator']
senticnet['four_story_brick'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'condominium', 'rent_apartment', 'high_rise', 'condo', 'row_house']
senticnet['four_walls'] = ['0', '0', '0', '0.092', '#admiration', '#admiration', 'positive', '0.092', 'room', 'inside_house', 'house_part', 'square_shape', 'cube']
senticnet['four_wheeled_vehicle'] = ['-0.04', '-0.16', '0', '0.056', '#surprise', '#admiration', 'positive', '0.059', 'car_part', 'under_car', 'car_wheel', 'wheel', 'car']
senticnet['four_wheels'] = ['0', '-0.03', '0.013', '0.095', '#surprise', '#admiration', 'positive', '0.039', 'car', 'internal_combustion_engine', 'lorry', 'automobile', 'bus']
senticnet['fourfold'] = ['-0.02', '0.095', '0', '0.117', '#interest', '#admiration', 'positive', '0.063', 'identification', 'paper_square', 'carry_money', 'money_hold', 'credit']
senticnet['fourier_analysis'] = ['0', '0.981', '0', '0.857', '#interest', '#admiration', 'positive', '0.919', 'harmonic_analysis', 'analysis', 'mathematics', 'harmony', 'theory']
senticnet['fourier_domain'] = ['0.929', '-0.71', '0', '0.605', '#joy', '#surprise', 'positive', '0.748', 'incisiveness', 'trenchancy', 'efficacy', 'efficaciousness', 'power']
senticnet['fourier_space'] = ['0.807', '-0.88', '0', '0.718', '#joy', '#surprise', 'positive', '0.802', 'sex', 'gayness', 'sexual_gayness', 'lesbianism', 'sapphism']
senticnet['fourier_transform'] = ['0.506', '-0.56', '0', '0.788', '#surprise', '#admiration', 'positive', '0.62', 'backward', 'reverse', 'retrograde', 'reflexive', 'retral']
senticnet['fourteen'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'hundred_year', 'dozen', 'day_month', 'time_measurement']
senticnet['fourteen_year_old'] = ['0.575', '-0.62', '0', '0.596', '#surprise', '#admiration', 'positive', '0.598', 'altar_boy', 'cub', 'ball_boy', 'bat_boy', 'lad']
senticnet['fourteenth'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'home', 'position', 'time_limit', 'monthly', 'modern_home']
senticnet['fourteenth_century'] = ['0.978', '-0.60', '0', '0.895', '#joy', '#admiration', 'positive', '0.825', 'period', 'quattrocento', 'twentieth_century', 'time_period', 'period_of_time']
senticnet['fourteenth_episode'] = ['0.918', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.827', 'encounter', 'reveal', 'programme', 'lose', 'sequel']
senticnet['fourth'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'home', 'position', 'time_limit', 'monthly', 'modern_home']
senticnet['fourth-best'] = ['0.988', '-0.82', '0', '0', '#joy', '#surprise', 'positive', '0.906', 'third-best', 'second-best', 'fifth-best', 'second-worst', 'sixth-best']
senticnet['fourth_cataract'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['fourth_dimension'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'width', 'size', 'mass_measure', 'measure', 'gauge']
senticnet['fourth_floor'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'top_floor', 'front_desk', 'concierge', 'exit', 'elevator']
senticnet['fourth_grade'] = ['0.521', '0.677', '-0.27', '0.273', '#joy', '#interest', 'positive', '0.299', 'further_education', 'protect_child', 'look_closely', 'go_to_school', 'register']
senticnet['fourth_horseman'] = ['-0.96', '0', '0.501', '-0.59', '#sadness', '#disgust', 'negative', '-0.68', 'calamity', 'catastrophe', 'tragedy', 'disaster', 'cataclysm']
senticnet['fourth_installment'] = ['-0.06', '0.122', '-0.04', '0.113', '#interest', '#admiration', 'positive', '0.032', 'encounter', 'portray', 'reveal', 'episode', 'next_episode']
senticnet['fourth_metacarpal'] = ['0.542', '-0.57', '0', '0.929', '#surprise', '#admiration', 'positive', '0.682', 'touch', 'thumb', 'rub', 'handmaid', 'grasp']
senticnet['fourth_metatarsal'] = ['-0.60', '0', '0.701', '-0.72', '#anger', '#disgust', 'negative', '-0.67', 'lunate_bone', 'semilunar_bone', 'carpal_bone', 'triquetral', 'triquetral_bone']
senticnet['fourth_quarter'] = ['0.834', '0.932', '0', '0', '#joy', '#interest', 'positive', '0.883', 'dime', 'nickel', 'metal_money', 'coin', 'money']
senticnet['fourth_quarter_revenue'] = ['0.834', '0.932', '0', '0', '#joy', '#interest', 'positive', '0.883', 'dime', 'nickel', 'metal_money', 'coin', 'money']
senticnet['fourth_round'] = ['0.823', '0.982', '0', '0', '#joy', '#interest', 'positive', '0.902', 'spherical', 'three_dimensions', 'round_orb', 'coil', 'fill_with_sand']
senticnet['fourth_season_finale'] = ['0.918', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.827', 'encounter', 'reveal', 'programme', 'lose', 'sequel']
senticnet['fourth_stanza'] = ['0.075', '-0.01', '0.019', '0.067', '#joy', '#admiration', 'positive', '0.034', 'rhyme', 'paragraph', 'song_lyric', 'lyrics', 'stanza']
senticnet['fourth_trochanter'] = ['-0.60', '0', '0.701', '-0.72', '#anger', '#disgust', 'negative', '-0.67', 'lunate_bone', 'semilunar_bone', 'carpal_bone', 'triquetral', 'triquetral_bone']
senticnet['fourwing_saltbush'] = ['-0.86', '0', '0.665', '-0.61', '#sadness', '#anger', 'negative', '-0.71', 'wooded', 'braky', 'rushy', 'scrubby', 'forested']
senticnet['foutre'] = ['-0.93', '0', '0.861', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'knotty', 'contorted', 'writhed', 'crooked', 'anfractuous']
senticnet['foveal'] = ['0.685', '-0.91', '0', '0.566', '#joy', '#surprise', 'positive', '0.721', 'audibility', 'audibleness', 'visibleness', 'visibility', 'physical_property']
senticnet['fowk'] = ['0.947', '0.914', '0', '0', '#joy', '#interest', 'positive', '0.93', 'ball_bounce', 'bicycle_tire', 'tire', 'increase_price', 'gyroscope']
senticnet['fowl'] = ['-0.90', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.89', 'poultry', 'domestic_fowl', 'ban', 'fear', 'reuters']
senticnet['fowl_cholera'] = ['0.579', '-0.81', '0', '0.833', '#surprise', '#admiration', 'positive', '0.741', 'typhus', 'rat_typhus', 'urban_typhus', 'endemic_typhus', 'rickettsial_disease']
senticnet['fowling_piece'] = ['-0.18', '-0.12', '0.049', '-0.24', '#sadness', '#disgust', 'negative', '-0.09', 'tree_trunk', 'plank', 'artificial_leg', 'hardwood', 'tree_wood']
senticnet['fox'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'kit_fox', 'prairie_fox', 'white_fox']
senticnet['fox_broadcasting_company'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'kit_fox', 'prairie_fox', 'white_fox']
senticnet['fox_glee'] = ['0.761', '0.566', '-0.21', '0.293', '#joy', '#interest', 'positive', '0.352', 'gratify', 'jubilation', 'gloat', 'gloating', 'gloatingly']
senticnet['fox_musical_comedy'] = ['0.761', '0.566', '-0.21', '0.293', '#joy', '#interest', 'positive', '0.352', 'gratify', 'jubilation', 'gloat', 'gloating', 'gloatingly']
senticnet['fox_network'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'kit_fox', 'prairie_fox', 'white_fox']
senticnet['fox_news_channel'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'kit_fox', 'prairie_fox', 'white_fox']
senticnet['fox_squirrel'] = ['-0.54', '0', '0.768', '-0.50', '#sadness', '#anger', 'negative', '-0.60', 'polecat', 'hooded_skunk', 'hognosed_skunk', 'badger_skunk', 'rooter_skunk']
senticnet['foxiness'] = ['0.784', '-0.59', '0', '0.846', '#joy', '#admiration', 'positive', '0.742', 'astuteness', 'slyness', 'wiliness', 'craftiness', 'cunning']
senticnet['foxpro'] = ['0', '-0.99', '0', '0.862', '#surprise', '#admiration', 'positive', '0.928', 'helpful_tool', 'woodworking_tool', 'helpful', 'type_of_fruit', 'hand_tool']
senticnet['foxtail'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'meadow_grass', 'grass', 'manna_grass', 'sweet_grass', 'wood_meadowgrass']
senticnet['foxtail_grass'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'foxtail', 'meadow_grass', 'manna_grass', 'sweet_grass', 'wood_meadowgrass']
senticnet['foxy_brown'] = ['-0.19', '0', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.15', 'bad_child', 'misbehavior', 'bad_kid', 'bad_action', 'difficulty']
senticnet['foxydinosaur'] = ['-0.91', '0', '0.769', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'break', 'good_luck', 'chance_eventpy_chance', 'chance_event', 'coincidence']
senticnet['foyer'] = ['0.039', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'first_floor', 'basement', 'lower_floor', 'hallway', 'lobby']
senticnet['fozzie'] = ['-0.93', '0', '0.77', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'hothead', 'unpleasant_person', 'disagreeable_person', 'crabby_person', 'crab']
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senticnet['fratricide'] = ['-0.73', '0', '-0.76', '0', '#sadness', '#fear', 'negative', '-0.74', 'fraternal', 'fraternizing', 'fraternity', 'fraternize', 'brotherly']
senticnet['frau_totenkinder'] = ['-0.66', '0', '0.859', '-0.82', '#anger', '#disgust', 'negative', '-0.78', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['fraud'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'mail_fraud', 'swindle', 'rig']
senticnet['fraud_in_fact'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud', 'positive_fraud', 'mail_fraud', 'swindle', 'rig']
senticnet['fraud_prevention'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'mail_fraud', 'swindle', 'rig']
senticnet['fraud_scheme'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'mail_fraud', 'swindle', 'rig']
senticnet['fraude_fiscale'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'sexual_relationship', 'liaison', 'affair', 'involvement']
senticnet['fraudelent'] = ['0', '0', '-0.88', '-0.89', '#fear', '#disgust', 'negative', '-0.89', 'false', 'useless', 'fake', 'bogus', 'mislead']
senticnet['fraudster'] = ['-0.09', '0.156', '0.661', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'phony', 'deception', 'false', 'against_law', 'trick']
senticnet['fraudulence'] = ['-0.60', '0', '0.982', '-0.56', '#sadness', '#anger', 'negative', '-0.71', 'humbug', 'deceit', 'deception', 'chicanery', 'dissembling']
senticnet['fraudulent'] = ['0', '0', '-0.92', '-0.81', '#fear', '#disgust', 'negative', '-0.87', 'dishonesty', 'feigned', 'jitney', 'collusive', 'dishonest']
senticnet['fraudulent_activity'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'mail_fraud', 'swindle', 'rig']
senticnet['fraudulent_conduct'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'mail_fraud', 'swindle', 'rig']
senticnet['fraudulent_intent'] = ['-0.10', '0', '-0.05', '-0.24', '#sadness', '#disgust', 'negative', '-0.13', 'thieving', 'larceny', 'theft', 'thievery', 'stealing']
senticnet['fraudulent_scheme'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'labor', 'racket', 'forlorn_hope', 'endeavor', 'business_activity']
senticnet['fraudulently'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'mail_fraud', 'swindle', 'rig']
senticnet['fraudulently_obtain'] = ['-0.59', '0', '0.537', '-0.53', '#sadness', '#anger', 'negative', '-0.55', 'murder', 'mutilate', 'mangle', 'misrepresent', 'belie']
senticnet['fraught'] = ['-0.84', '0', '0.954', '0', '#sadness', '#anger', 'negative', '-0.89', 'transportation', 'charged', 'burden', 'cargo', 'hire']
senticnet['fraught_with'] = ['-0.13', '0.071', '0.694', '-0.67', '#anger', '#disgust', 'negative', '-0.35', 'crucify', 'dun', 'frustrating', 'aggravate', 'exasperation']
senticnet['fraxinus_americana'] = ['-0.79', '0', '0.906', '-0.67', '#sadness', '#anger', 'negative', '-0.79', 'water_dropwort', 'hemlock_water_dropwort', 'water_hemlock', 'plant_life', 'poisonous_plant']
senticnet['fraxinus_excelsior'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'bottlebrush_buckeye', 'red_buckeye', 'particolored_buckeye']
senticnet['fraxinus_pennsylvanica'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'bottlebrush_buckeye', 'red_buckeye', 'particolored_buckeye']
senticnet['frayed_relationship'] = ['-0.07', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.07', 'use_needle', 'doctor_tool', 'fix', 'embroidery', 'seam']
senticnet['frcpath'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'druggist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['frcpe'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'druggist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['frdric_chopin'] = ['0.591', '-0.91', '0', '0.605', '#surprise', '#admiration', 'positive', '0.704', 'farandole', 'folk_dance', 'polka', 'dance', 'typical_dance']
senticnet['freak'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'weird', 'abnormal', 'monster', 'lunatic', 'odd']
senticnet['freak_accident'] = ['-0.91', '0', '0.905', '-0.78', '#sadness', '#anger', 'negative', '-0.86', 'pity', 'shame', 'misfortune', 'mischance', 'misadventure']
senticnet['freak_out'] = ['0', '0', '0.771', '-0.82', '#anger', '#disgust', 'negative', '-0.79', 'amount_nothing', 'act_foolish', 'wet_pant', 'correct_mistake', 'cry_help']
senticnet['freak_show'] = ['0.954', '0', '0', '0', '#joy', '#joy', 'positive', '0.954', 'clown', 'halloween', 'entertainment', 'fun', 'happiness']
senticnet['freak_storm'] = ['-0.87', '0', '0.91', '-0.78', '#sadness', '#anger', 'negative', '-0.85', 'storm', 'thundershower', 'rain', 'line_storm', 'equinoctial_storm']
senticnet['freaked_out'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['freakin'] = ['-0.94', '0', '0.557', '-0.71', '#sadness', '#disgust', 'negative', '-0.74', 'sad', 'disappointing', 'unlucky', 'disengaged', 'cry']
senticnet['freaking'] = ['0', '0', '-0.85', '-0.87', '#fear', '#disgust', 'negative', '-0.86', 'bizarre', 'unusual', 'freakish', 'freak', 'unpredictable']
senticnet['freaking_out'] = ['-0.81', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.78', 'panic-struck', 'panic-stricken', 'panicked', 'terrified', 'frightened']
senticnet['freakish'] = ['0', '0', '-0.84', '-0.86', '#fear', '#disgust', 'negative', '-0.85', 'bizarre', 'unusual', 'freaking', 'freak', 'unpredictable']
senticnet['freakishly'] = ['0', '0', '-0.89', '-0.98', '#fear', '#disgust', 'negative', '-0.94', 'scarily', 'scared', 'frighten_away', 'scare_away', 'frighten_off']
senticnet['freakishness'] = ['-0.56', '0', '0.977', '-0.86', '#anger', '#disgust', 'negative', '-0.80', 'strangeness', 'quaintness', 'abnormality', 'queerness', 'crotchet']
senticnet['freakout'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amuck', 'murderousness', 'misocainea', 'misogyny']
senticnet['freakshow'] = ['0.086', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'carny', 'fortune_teller', 'midway', 'ferris_wheel', 'roller-coaster']
senticnet['freaky'] = ['0.877', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.938', 'flaky', 'outlandish', 'off-the-wall', 'bizarre', 'gonzo']
senticnet['freckle'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'helpless', 'unwanted', 'rabies', 'small_cat', 'big_rat']
senticnet['freckled_skin'] = ['-0.96', '0', '0.636', '-0.64', '#sadness', '#disgust', 'negative', '-0.75', 'burn', 'tan', 'hyperpigmentation', 'sunburn', 'physical_condition']
senticnet['freddy_krueger'] = ['-0.08', '0', '-0.04', '-0.15', '#sadness', '#disgust', 'negative', '-0.09', 'unsettle', 'sleepwalk', 'hallucinate', 'get_rest', 'reflect']
senticnet['frederic_rzewski'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'band_play', 'songwriter', 'rock_band']
senticnet['fredric_jameson'] = ['0.587', '-0.94', '0', '0.659', '#surprise', '#admiration', 'positive', '0.731', 'critique', 'review', 'criticism', 'textual_criticism', 'new_criticism']
senticnet['free'] = ['0.734', '0.772', '0', '0.841', '#interest', '#admiration', 'positive', '0.782', 'cost_nothing', 'inexpensive', 'good_buy', 'liberate', 'bargain']
senticnet['free-and-easy'] = ['-0.80', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.77', 'commitment-free', 'six-question', 'one-camera', 'three-camera', 'self-forgetful']
senticnet['free-flowering'] = ['0', '0', '0.89', '-0.74', '#anger', '#disgust', 'negative', '-0.81', 'strong-growing', 'frost-tender', 'quiet-running', 'slow-thinking', 'long-flowering']
senticnet['free-spoken'] = ['0', '0.765', '0', '0.891', '#interest', '#admiration', 'positive', '0.828', 'forthright', 'plainspoken', 'outspoken', 'blunt', 'candid']
senticnet['free-wheeling'] = ['-0.82', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.83', 'rough-and-tumble', 'hard-nosed', 'freewheeling', 'rambunctious', 'hard-drinking']
senticnet['free-will'] = ['0.739', '0', '0', '0.707', '#joy', '#admiration', 'positive', '0.723', 'freewill', 'predestination', 'atonement', 'salvation', 'determinism']
senticnet['free_agent_outfielder_torii'] = ['0', '0', '-0.11', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'hunting_season', 'hound', 'cougar', 'outlaw', 'dragon']
senticnet['free_bird'] = ['0', '0.886', '0', '0.883', '#interest', '#admiration', 'positive', '0.885', 'journey_far', 'near_destination', 'board_boat', 'bug_person', 'travel_by_airplane']
senticnet['free_continental_breakfast'] = ['0.866', '0.833', '0', '0', '#joy', '#interest', 'positive', '0.849', 'alcohol', 'porter', 'concierge', 'beer_mug', 'bar_stool']
senticnet['free_country'] = ['0.904', '0.881', '0', '0.774', '#joy', '#interest', 'positive', '0.853', 'country', 'democracy', 'democratic_country', 'unite', 'independent']
senticnet['free_fallin'] = ['0.086', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'carny', 'fortune_teller', 'midway', 'ferris_wheel', 'roller-coaster']
senticnet['free_food'] = ['0.93', '0.734', '0', '0.958', '#joy', '#admiration', 'positive', '0.874', 'art_lover', 'artist', 'oil_paint', 'oil_painting', 'artistic']
senticnet['free_house'] = ['-0.76', '0', '0.75', '-0.56', '#sadness', '#anger', 'negative', '-0.69', 'public_house', 'pub', 'saloon', 'pothouse', 'gin_mill']
senticnet['free_lance_journalist'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'adventurer', 'humankind']
senticnet['free_lance_reporter'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'adventurer', 'humankind']
senticnet['free_liver'] = ['0.493', '-0.83', '0', '0.776', '#surprise', '#admiration', 'positive', '0.701', 'mortal', 'person', 'individual', 'freewheeler', 'agent']
senticnet['free_love'] = ['-0.79', '0', '0.979', '-0.98', '#anger', '#disgust', 'negative', '-0.91', 'fornication', 'unlawful_carnal_knowledge', 'criminal_congress', 'adultery', 'criminal_conversation']
senticnet['free_mind'] = ['0', '0.969', '0', '0.986', '#interest', '#admiration', 'positive', '0.978', 'wave_hand', 'trade_stock', 'position_furniture', 'correct_mistake', 'lower_standard']
senticnet['free_night'] = ['0.53', '-0.64', '0', '0.76', '#surprise', '#admiration', 'positive', '0.646', 'take_care', 'baby', 'free_time', 'child_in_safe_hand', 'baby_sitter']
senticnet['free_online_encyclopedia'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'comic_book', 'child_book', 'child_story', 'comic', 'disney_cartoon']
senticnet['free_phagocyte'] = ['0.702', '-0.52', '0', '0.958', '#joy', '#admiration', 'positive', '0.728', 'somatic_cell', 'vegetative_cell', 'fixed_phagocyte', 'macrophage', 'phagocyte']
senticnet['free_prisoner'] = ['-0.15', '0.081', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.05', 'turn_key', 'open_lock', 'turn_car_off', 'open_door', 'start_engine']
senticnet['free_rein'] = ['-0.52', '0', '0.561', '-0.90', '#anger', '#disgust', 'negative', '-0.66', 'difference', 'tolerance', 'allowance', 'discrepancy', 'margin']
senticnet['free_seat'] = ['0.901', '0', '0', '0', '#joy', '#joy', 'positive', '0.901', 'seat', 'few_people', 'travel_comfortable', 'good_service', 'free']
senticnet['free_sex'] = ['0.045', '-0.66', '0', '0.363', '#surprise', '#admiration', 'positive', '0.357', 'enjoy_time_together', 'meet_girl', 'fall_in_love', 'kiss', 'infatuation']
senticnet['free_slave'] = ['0', '0.93', '0', '0.824', '#interest', '#admiration', 'positive', '0.877', 'helper', 'hire_help', 'laborer', 'occupation', 'career']
senticnet['free_spay_neuter'] = ['-0.78', '0', '0.91', '-0.91', '#anger', '#disgust', 'negative', '-0.87', 'castrate', 'neuter', 'alter', 'emasculate', 'desex']
senticnet['free_speech'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'indecency']
senticnet['free_spirit'] = ['0.776', '-0.57', '0', '0.582', '#joy', '#admiration', 'positive', '0.644', 'mortal', 'person', 'individual', 'soul', 'essence']
senticnet['free_stuff'] = ['0.889', '0.762', '0', '0.95', '#joy', '#admiration', 'positive', '0.867', 'taken_seriously', 'enough_food', 'simplicity', 'sate', 'complement']
senticnet['free_thinker'] = ['0.065', '0.17', '-0.05', '0.143', '#interest', '#admiration', 'positive', '0.081', 'supervisor', 'native_american', 'ambassador', 'master', 'general']
senticnet['free_ticket'] = ['0.901', '0', '0', '0', '#joy', '#joy', 'positive', '0.901', 'enjoy_concert', 'enjoy_club', 'ticket', 'free', 'enjoy_football_match']
senticnet['free_time'] = ['0.987', '0.882', '0', '0.905', '#joy', '#admiration', 'positive', '0.925', 'dress_nicely', 'buy_ticket', 'enjoy', 'plan_time', 'buy_popcorn']
senticnet['free_trade_pact'] = ['0.397', '0.245', '-0.13', '0.224', '#joy', '#interest', 'positive', '0.183', 'agreement', 'provide', 'advice', 'contribution', 'offer']
senticnet['free_weight'] = ['0.52', '-0.97', '0', '0.916', '#surprise', '#admiration', 'positive', '0.803', 'sporting_goods', 'weight', 'mat', 'sports_equipment', 'exercising_weight']
senticnet['free_wifi'] = ['-0.09', '-0.03', '0.061', '0', '#sadness', '#anger', 'negative', '-0.04', 'bad_reception', 'bad_connection', 'internet', 'phone_call', 'underground']
senticnet['freebie'] = ['-0.93', '0', '-0.76', '0', '#sadness', '#fear', 'negative', '-0.84', 'giveaway', 'give-away', 'sweepstakes', 'contest', 'giveaway']
senticnet['freeboard_deck'] = ['0.831', '-0.82', '0', '0.826', '#joy', '#admiration', 'positive', '0.826', 'deck', 'upper_deck', 'weather_deck', 'shelter_deck', 'boat_deck']
senticnet['freeboard_upper_deck'] = ['0.824', '-0.82', '0', '0.819', '#joy', '#admiration', 'positive', '0.824', 'upper_deck', 'deck', 'weather_upper_deck', 'shelter_upper_deck', 'boat_upper_deck']
senticnet['freebooter'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'correspondent', 'humankind']
senticnet['freecell'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'jewelry_store', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['freecycle'] = ['0.048', '0', '0', '0.092', '#joy', '#admiration', 'positive', '0.07', 'gym_locker', 'observatory', 'prison_cell', 'luggage_store', 'luggage_compartment']
senticnet['freed_slave'] = ['0', '0', '0.8', '-0.82', '#anger', '#disgust', 'negative', '-0.81', 'personnel', 'attendant', 'manager', 'elect_official', 'gentleman']
senticnet['freedive'] = ['0', '0.07', '0', '0', '#interest', '#interest', 'positive', '0.07', 'sunken_treasure', 'real_duck', 'urchin', 'mussel', 'gold_fish']
senticnet['freedom'] = ['0.965', '0.72', '0', '0.914', '#joy', '#admiration', 'positive', '0.866', 'praise', 'equality_everyone', 'honor', 'important', 'glory']
senticnet['freedom_fighter'] = ['-0.97', '0', '0.62', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'obstructor', 'obstructer', 'technophobe', 'obstructionist', 'naysayer']
senticnet['freedom_guaranteed_by'] = ['0.926', '0.937', '0', '0.975', '#interest', '#admiration', 'positive', '0.946', 'monument', 'statue', 'freedom', 'idol', 'sculpture']
senticnet['freedom_think'] = ['0.813', '0.916', '0', '0.841', '#interest', '#admiration', 'positive', '0.857', 'text_book', 'wiseman', 'librarian', 'professor', 'teacher']
senticnet['freefall'] = ['0', '0', '-0.86', '-0.86', '#fear', '#disgust', 'negative', '-0.86', 'amusement_park', 'have_fun', 'drag_race', 'pia_lot_of_seat', 'roller_coaster']
senticnet['freehand'] = ['0.741', '0.88', '0', '0', '#joy', '#interest', 'positive', '0.81', 'use_brush', 'hair', 'brush_hair', 'brush_tooth', 'brush']
senticnet['freehold'] = ['0', '0.255', '-0.23', '0.337', '#interest', '#admiration', 'positive', '0.12', 'own', 'ownership', 'possess', 'possession', 'possessive']
senticnet['freehold_land'] = ['0.716', '-0.51', '0', '0.647', '#joy', '#admiration', 'positive', '0.624', 'state', 'nation', 'country', 'ally', 'commonwealth']
senticnet['freehold_title'] = ['0.716', '-0.51', '0', '0.647', '#joy', '#admiration', 'positive', '0.624', 'state', 'nation', 'country', 'ally', 'commonwealth']
senticnet['freehouse'] = ['0.853', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.916', 'tavern', 'cafe', 'nightclub', 'bar', 'liquor_store']
senticnet['freelance_composer'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'band_play', 'classical', 'composer', 'songwriter', 'rock_band']
senticnet['freelance_correspondent'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'adventurer', 'humankind']
senticnet['freelance_interpreter'] = ['0', '0.202', '-0.06', '0.291', '#interest', '#admiration', 'positive', '0.142', 'vocabulary', 'translate', 'dictionary', 'translation', 'thesaurus']
senticnet['freelance_journalist'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'adventurer', 'humankind']
senticnet['freelance_reporter'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'adventurer', 'humankind']
senticnet['freeload'] = ['0.866', '-0.91', '0', '0.893', '#surprise', '#admiration', 'positive', '0.892', 'mooch', 'cadge', 'grub', 'sponge', 'bum']
senticnet['freely'] = ['0', '0.89', '0', '0.936', '#interest', '#admiration', 'positive', '0.913', 'project', 'study', 'development', 'result', 'well']
senticnet['freely_accessible'] = ['0.81', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.774', 'available', 'public', 'population', 'crowd', 'majority']
senticnet['freely_available'] = ['0.81', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.774', 'available', 'public', 'population', 'crowd', 'majority']
senticnet['freely_available_online'] = ['0.81', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.774', 'available', 'public', 'population', 'crowd', 'majority']
senticnet['freely_available_software'] = ['0.81', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.774', 'available', 'public', 'population', 'crowd', 'majority']
senticnet['freely_distributable'] = ['0.81', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.774', 'available', 'public', 'population', 'crowd', 'majority']
senticnet['freely_distribute'] = ['0.81', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.774', 'available', 'public', 'population', 'crowd', 'majority']
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senticnet['friendlier'] = ['0.777', '0.972', '0', '0.913', '#interest', '#admiration', 'positive', '0.887', 'serene', 'easy', 'adoration', 'togetherness', 'console']
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senticnet['from_ichneumonid_family'] = ['-0.77', '0', '0.576', '-0.92', '#sadness', '#disgust', 'negative', '-0.75', 'insect', 'firebug', 'water_bug', 'assassin_bug', 'heteropterous_insect']
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senticnet['front_rower'] = ['0', '0.996', '0', '0.85', '#interest', '#admiration', 'positive', '0.923', 'strength', 'strong', 'physical_strength', 'athlete', 'muscle']
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senticnet['front_zipper'] = ['0.775', '0.835', '0', '0', '#joy', '#interest', 'positive', '0.805', 'belt', 'leather_jacket', 'backpack', 'pouch', 'bag']
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senticnet['frontier_constabulary'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'webpage', 'store_record', 'ideal']
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senticnet['frontspiece'] = ['0.044', '0', '0', '0.206', '#joy', '#admiration', 'positive', '0.125', 'begin_end', 'book', 'title_page', 'book_end', 'book_content']
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senticnet['frost-tender'] = ['0', '0', '0.889', '-0.74', '#anger', '#disgust', 'negative', '-0.81', 'free-flowering', 'strong-growing', 'quiet-running', 'slow-thinking', 'long-flowering']
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senticnet['frosty'] = ['0', '0', '-0.85', '-0.97', '#fear', '#disgust', 'negative', '-0.91', 'wintry', 'glacial', 'icy', 'frigid', 'frozen']
senticnet['frosty_air'] = ['0.981', '0.891', '0', '0', '#joy', '#interest', 'positive', '0.936', 'bitterness', 'nip', 'bite', 'sting', 'sharpness']
senticnet['frosty_mug'] = ['0.161', '-0.14', '0.031', '-0.10', '#joy', '#surprise', 'positive', '0.043', 'barrel', 'reception', 'champagne', 'beer', 'wine']
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senticnet['frozen_carbon_dioxide'] = ['0.095', '0', '-0.04', '0.062', '#joy', '#admiration', 'positive', '0.037', 'photographer', 'water_spray', 'moisture', 'rise_water', 'droplet']
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senticnet['frozen_embryo'] = ['0.706', '0', '0', '0', '#joy', '#joy', 'positive', '0.706', 'birth', 'new_baby', 'born', 'birthday', 'thanksgiving']
senticnet['frozen_food'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'apple_pie', 'lasagna', 'jelly', 'a_lot_of_food', 'cottage_cheese']
senticnet['frozen_fruit'] = ['0.057', '0', '0.018', '0.058', '#joy', '#admiration', 'positive', '0.032', 'porridge', 'food', 'milk', 'healthy', 'morning_food']
senticnet['frozen_liquid'] = ['0.1', '0.076', '0', '0.156', '#joy', '#admiration', 'positive', '0.111', 'ice_crystal', 'little_dense_water', 'fragile', 'solid', 'white']
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senticnet['frozen_pizza'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'apple_pie', 'lasagna', 'jelly', 'a_lot_of_food', 'cottage_cheese']
senticnet['frozen_pond'] = ['0.05', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.046', 'skate', 'rollerblade', 'skateboard', 'ice_skate', 'skating']
senticnet['frozen_seafood'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'apple_pie', 'lasagna', 'jelly', 'a_lot_of_food', 'cottage_cheese']
senticnet['frozen_shoulder'] = ['-0.98', '0', '0.678', '-0.95', '#sadness', '#disgust', 'negative', '-0.87', 'rubor', 'redness', 'inflammation', 'epicondylitis', 'lateral_epicondylitis']
senticnet['frozen_solid'] = ['-0.64', '0', '0.612', '-0.84', '#sadness', '#disgust', 'negative', '-0.7', 'chilling', 'frost', 'lyophilization', 'icing', 'cooling']
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senticnet['frozen_water'] = ['0', '0', '0', '0.087', '#admiration', '#admiration', 'positive', '0.087', 'ice_crystal', 'frozen', 'ice_cube', 'coldness', 'water']
senticnet['frozen_waterfall'] = ['-0.99', '0', '0.641', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'navigable', 'negotiable', 'surmountable', 'passable', 'traversable']
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senticnet['frse'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'druggist', 'health_professional', 'primary_care_provider', 'health_care_provider']
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senticnet['fructify'] = ['0.969', '-0.86', '0', '0.631', '#joy', '#surprise', 'positive', '0.823', 'assuage', 'perfect', 'make_look_better', 'relieve', 'sublimate']
senticnet['fructose_malabsorption'] = ['-0.86', '0', '0.76', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['frugal'] = ['0.722', '-0.74', '0', '0.554', '#joy', '#surprise', 'positive', '0.673', 'saving', 'economical', 'scotch', 'thrifty', 'sparing']
senticnet['frugal_traveler'] = ['0', '0', '0.828', '-0.88', '#anger', '#disgust', 'negative', '-0.85', 'outing', 'junket', 'pleasure_trip', 'excursion', 'expedition']
senticnet['frugality'] = ['0.533', '-0.55', '0', '0.912', '#surprise', '#admiration', 'positive', '0.668', 'parsimony', 'parsimoniousness', 'thrift', 'economy', 'prudence']
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senticnet['fruit_basket'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['fruit_bowl'] = ['0.937', '0', '0', '0.828', '#joy', '#admiration', 'positive', '0.883', 'bakery', 'produce_section', 'thanksgiving_dinner', 'dinner_meal', 'fast_food']
senticnet['fruit_cake'] = ['0.644', '-0.94', '0', '0.729', '#surprise', '#admiration', 'positive', '0.773', 'gem', 'banana_bread', 'date_bread', 'nut_bread', 'quick_bread']
senticnet['fruit_cocktail'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'lemon_juice', 'chocolate_pudding', 'grape_juice', 'orange_juice', 'juice']
senticnet['fruit_drink'] = ['0.948', '0.992', '0', '0.974', '#interest', '#admiration', 'positive', '0.971', 'fermented_beverage', 'yogurt', 'healthy_drink', 'ferment_grape_juice', 'good_taste']
senticnet['fruit_grower'] = ['-0.08', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.11', 'serf', 'peasant', 'servant', 'poor', 'villein']
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senticnet['fruit_rot'] = ['-0.83', '0', '0.754', '-0.97', '#sadness', '#disgust', 'negative', '-0.85', 'brown_rot_gummosis', 'ring_rot', 'ring_disease', 'tobacco_wilt', 'gummosis']
senticnet['fruit_salad'] = ['0.937', '0', '0', '0.828', '#joy', '#admiration', 'positive', '0.883', 'bakery', 'produce_section', 'thanksgiving_dinner', 'dinner_meal', 'fast_food']
senticnet['fruit_seller'] = ['-0.80', '0', '0.718', '-0.79', '#sadness', '#disgust', 'negative', '-0.77', 'walk_exercise', 'lose_wallet', 'go_debt', 'pay_dinner', 'spend_much_money']
senticnet['fruit_smoothie'] = ['0.895', '0.946', '0', '0', '#joy', '#interest', 'positive', '0.92', 'need_food', 'a_little_hungry', 'dead_hungry', 'prepare_food', 'fill_stomach']
senticnet['fruit_stand'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'grocery_cart', 'salad_bar', 'super_market', 'candy_store', 'produce_store']
senticnet['fruit_syrup'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'dainty', 'delicacy', 'treat', 'jelly', 'aspic']
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senticnet['fruit_vegetable'] = ['0.102', '0', '0', '0', '#joy', '#joy', 'positive', '0.102', 'fresh_vegetable', 'cabbage', 'cut_flower', 'plant_matter', 'fruit']
senticnet['fruitage'] = ['0', '0.07', '0', '0.071', '#interest', '#admiration', 'positive', '0.071', 'tree_flower', 'bloom', 'perennial', 'poppy', 'greenery']
senticnet['fruitbody'] = ['-0.60', '0', '0.541', '-0.52', '#sadness', '#anger', 'negative', '-0.55', 'swoon', 'pass_out', 'faint', 'zonk_out', 'change_state']
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senticnet['fruitful'] = ['0.851', '-0.67', '0', '0.854', '#joy', '#admiration', 'positive', '0.792', 'fertile', 'breeding', 'prolific', 'bountiful', 'productive']
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senticnet['fruitful_discussion'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'discuss', 'communication', 'communicate_person', 'share', 'autograph']
senticnet['fruitful_exchange'] = ['0.851', '-0.67', '0', '0.854', '#joy', '#admiration', 'positive', '0.792', 'fertile', 'breeding', 'prolific', 'bountiful', 'productive']
senticnet['fruitful_interaction'] = ['0.851', '-0.67', '0', '0.854', '#joy', '#admiration', 'positive', '0.792', 'fertile', 'breeding', 'prolific', 'bountiful', 'productive']
senticnet['fruitfully'] = ['0.851', '-0.67', '0', '0.854', '#joy', '#admiration', 'positive', '0.792', 'fertile', 'breeding', 'prolific', 'bountiful', 'productive']
senticnet['fruitfulness'] = ['0.643', '-0.80', '0', '0.681', '#surprise', '#admiration', 'positive', '0.709', 'quality', 'richness', 'fertility', 'prolificacy', 'productiveness']
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senticnet['fruity_aroma'] = ['0.784', '-0.59', '0', '0.846', '#joy', '#admiration', 'positive', '0.742', 'astuteness', 'slyness', 'wiliness', 'craftiness', 'cunning']
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senticnet['frump'] = ['-0.59', '0', '0.573', '-0.56', '#sadness', '#anger', 'negative', '-0.57', 'termagant', 'old_bag', 'shrew', 'disagreeable_woman', 'hellcat']
senticnet['frumpy'] = ['-0.93', '0', '0', '-0.86', '#sadness', '#disgust', 'negative', '-0.89', 'scruffy', 'beat-up', 'rumpled', 'mangy', 'ratty']
senticnet['frustrate'] = ['-0.86', '0', '0.863', '0', '#sadness', '#anger', 'negative', '-0.86', 'aggravation', 'offend', 'nausea', 'disgust', 'irritate']
senticnet['frustrate_person'] = ['-0.79', '0', '0.91', '0', '#sadness', '#anger', 'negative', '-0.85', 'round_white', 'baby_chicken', 'oval_shape', 'baby_bird', 'inside_egg']
senticnet['frustrated'] = ['-0.84', '0', '0.898', '0', '#sadness', '#anger', 'negative', '-0.87', 'disappointed', 'thwarted', 'defeatism', 'foiled', 'discomfited']
senticnet['frustrating'] = ['-0.13', '0.071', '0.694', '-0.67', '#anger', '#disgust', 'negative', '-0.35', 'bedevil', 'crucify', 'dun', 'aggravate', 'exasperation']
senticnet['frustratingly'] = ['0.273', '0.226', '-0.13', '0.332', '#joy', '#admiration', 'positive', '0.173', 'morality', 'cagey', 'uncommon', 'evasive', 'capitalize']
senticnet['frustratingly_slow'] = ['-0.12', '-0.08', '0.051', '-0.17', '#sadness', '#disgust', 'negative', '-0.06', 'talk_neighbor', 'think_dog', 'boredom', 'act_bored', 'stop_bleed']
senticnet['frustration'] = ['-1', '0.33', '0', '0', '#sadness', '#interest', 'negative', '-0.33', 'frustrated', 'tired', 'stressed', 'disengaged', 'bored']
senticnet['frustration_tolerance'] = ['0.936', '0.757', '0', '0.927', '#joy', '#admiration', 'positive', '0.873', 'attitude', 'better', 'trait', 'blue_ribbon', 'exclamation']
senticnet['fry'] = ['-0.83', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.78', 'picaninny', 'kid', 'minor', 'tike', 'small_picaninny']
senticnet['fry_chicken'] = ['0.061', '0', '0', '0.078', '#joy', '#admiration', 'positive', '0.07', 'fry_fish', 'fry_egg', 'season_meal', 'season_dish', 'make_cake']
senticnet['fry_egg'] = ['0.061', '0', '0', '0.078', '#joy', '#admiration', 'positive', '0.07', 'fry_fish', 'fry_chicken', 'season_meal', 'season_dish', 'make_cake']
senticnet['fry_fish'] = ['-0.05', '0', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.05', 'salt_food', 'brown_hamburger', 'heat_meal', 'fry_chicken', 'bone_chicken']
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senticnet['funeral_celebration'] = ['-0.09', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.10', 'memento', 'memorial', 'funeral', 'funerary', 'cry']
senticnet['funeral_ceremony'] = ['-0.98', '0', '0.664', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'unfortunate', 'unfortunate_person', 'weeper', 'griever', 'bearer']
senticnet['funeral_chapel'] = ['-0.70', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.70', 'death_row', 'sad', 'loose_end', 'car_race', 'mortuary']
senticnet['funeral_cortege'] = ['-0.98', '0', '0.664', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'unfortunate', 'unfortunate_person', 'weeper', 'griever', 'bearer']
senticnet['funeral_director'] = ['-0.91', '0', '0.794', '-0.93', '#sadness', '#disgust', 'negative', '-0.88', 'grave', 'dead_person', 'funeral', 'tomb', 'coffin']
senticnet['funeral_director_association'] = ['-0.70', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.70', 'death_row', 'sad', 'loose_end', 'car_race', 'mortuary']
senticnet['funeral_home'] = ['-0.70', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.70', 'death_row', 'sad', 'loose_end', 'car_race', 'mortuary']
senticnet['funeral_motorcade'] = ['0', '0', '-0.04', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'good_band', 'band', 'kit', 'procession', 'oblivion']
senticnet['funeral_oration'] = ['0', '0', '-0.90', '-0.79', '#fear', '#disgust', 'negative', '-0.85', 'encomiastic', 'encomium', 'paean', 'eulogy', 'pean']
senticnet['funeral_parlor'] = ['-0.70', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.70', 'death_row', 'sad', 'loose_end', 'car_race', 'mortuary']
senticnet['funeral_procession'] = ['-0.98', '0', '0.664', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'unfortunate', 'unfortunate_person', 'weeper', 'griever', 'bearer']
senticnet['funeral_pyre'] = ['-0.77', '0', '0.588', '-0.80', '#sadness', '#disgust', 'negative', '-0.72', 'heap', 'agglomerate', 'piles', 'mound', 'cumulation']
senticnet['funeral_service'] = ['-0.70', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.70', 'death_row', 'sad', 'loose_end', 'car_race', 'mortuary']
senticnet['funerary'] = ['-0.09', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.10', 'funeral_celebration', 'memorial', 'funeral', 'memento', 'cry']
senticnet['funerary_art'] = ['-0.09', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.10', 'funeral_celebration', 'memorial', 'funeral', 'memento', 'cry']
senticnet['funerary_chapel'] = ['-0.92', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.91', 'bury_body', 'coffin', 'headstone', 'morgue', 'tombstone']
senticnet['funerary_complex'] = ['-0.09', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.10', 'funeral_celebration', 'memorial', 'funeral', 'memento', 'cry']
senticnet['funerary_cult'] = ['-0.09', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.10', 'funeral_celebration', 'memorial', 'funeral', 'memento', 'cry']
senticnet['funerary_monument'] = ['-0.92', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.91', 'bury_body', 'coffin', 'headstone', 'morgue', 'tombstone']
senticnet['funerary_temple'] = ['-0.92', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.91', 'bury_body', 'coffin', 'headstone', 'morgue', 'tombstone']
senticnet['funerary_urn'] = ['0', '0.082', '-0.09', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'coffin', 'headstone', 'morgue', 'bury_body', 'casket']
senticnet['funereal'] = ['-0.79', '0.262', '-0.45', '-0.04', '#sadness', '#fear', 'negative', '-0.25', 'downhearted', 'down_in_mouth', 'depressed', 'downcast', 'low-spirited']
senticnet['funfair'] = ['-0.72', '0', '0.978', '-0.86', '#anger', '#disgust', 'negative', '-0.85', 'amusement_park', 'common', 'pleasure_ground', 'village_green', 'tract']
senticnet['fungal_disease'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['fungal_hypha'] = ['0', '0.097', '0', '0.085', '#interest', '#admiration', 'positive', '0.091', 'lunula', 'arm_part', 'leave_hand', 'ten_fingers', 'five_fingers']
senticnet['fungal_infection'] = ['-0.96', '0', '0.694', '-0.62', '#sadness', '#anger', 'negative', '-0.76', 'ringworm', 'mycosis', 'kerion', 'tinea_pedis', 'athlete_foot']
senticnet['fungal_pathogen'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['fungal_plant_pathogen'] = ['-0.93', '0', '0.699', '-0.91', '#sadness', '#disgust', 'negative', '-0.84', 'onion_smut', 'head_smut', 'stinking_smut', 'flag_smut_fungus', 'covered_smut']
senticnet['fungemia'] = ['-0.72', '0', '0.778', '-0.98', '#anger', '#disgust', 'negative', '-0.82', 'actinic_dermatitis', 'hypericism', 'diaper_rash', 'dermatitis', 'seborrheic_dermatitis']
senticnet['fungi'] = ['-0.93', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.89', 'kingdom', 'gastroboletus_scabrosus', 'slime_mold', 'puffball', 'agaric']
senticnet['fungibility'] = ['0.928', '-0.65', '0', '0.856', '#joy', '#admiration', 'positive', '0.812', 'transferability', 'convertibility', 'changeableness', 'changeability', 'permutability']
senticnet['fungible'] = ['0.552', '-0.58', '0', '0.762', '#surprise', '#admiration', 'positive', '0.633', 'transposable', 'permutable', 'exchangeable', 'commutable', 'substitutable']
senticnet['fungicidal'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['fungicide'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'antifungal', 'antifungal_agent', 'lamisil', 'antimycotic', 'antimycotic_agent']
senticnet['fungistatic'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['fungizone'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['fungus'] = ['-0.87', '0', '0.723', '-0.94', '#sadness', '#disgust', 'negative', '-0.84', 'potato_wart_fungus', 'bottom_rot_fungus', 'potato_fungus', 'black_root_rot_fungus', 'sclerotinia']
senticnet['funhouse'] = ['0.086', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'carny', 'fortune_teller', 'midway', 'ferris_wheel', 'roller-coaster']
senticnet['funhouse_summer'] = ['0.957', '0.7', '0', '0', '#joy', '#interest', 'positive', '0.829', 'ballpark', 'coven', 'disneyland', 'hockey_game', 'symposium']
senticnet['funicular_cord'] = ['-0.03', '0', '0', '-0.11', '#sadness', '#disgust', 'negative', '-0.07', 'store_cloth', 'clothes_factory', 'stock', 'sword', 'gun_store']
senticnet['funiculus'] = ['0', '0.141', '0.01', '0.115', '#interest', '#admiration', 'positive', '0.082', 'fruit', 'rind', 'achene', 'seed', 'pip']
senticnet['funko'] = ['0.031', '-0.10', '0', '0', '#joy', '#surprise', 'positive', '0.07', 'teddy_bear', 'beanie_baby', 'yo-yo', 'board_game', 'toy']
senticnet['funky'] = ['0.65', '-0.61', '0', '0.711', '#joy', '#admiration', 'positive', '0.658', 'bouncing', 'scintillating', 'spirited', 'lively', 'zippy']
senticnet['funky_groove'] = ['0.584', '-0.62', '0', '0.815', '#surprise', '#admiration', 'positive', '0.675', 'squiggle', 'hand', 'cacography', 'handwriting', 'script']
senticnet['funky_guitar'] = ['0.857', '-0.77', '0', '0.878', '#joy', '#admiration', 'positive', '0.836', 'hand', 'round', 'approval', 'commendation', 'clapping']
senticnet['funnel_cloud'] = ['0', '-0.41', '-0.95', '0', '#surprise', '#fear', 'negative', '-0.26', 'blizzard', 'snow_storm', 'storm', 'danger', 'rainy']
senticnet['funnily_enough'] = ['0.746', '-0.67', '0', '0.529', '#joy', '#surprise', 'positive', '0.651', 'labor', 'cinch', 'undertaking', 'project', 'task']
senticnet['funny'] = ['0.881', '0', '0', '0.976', '#joy', '#admiration', 'positive', '0.928', 'fun', 'guffaw', 'make_laugh', 'amuse', 'droll']
senticnet['funny_farm'] = ['-0.80', '0', '0.798', '-0.96', '#sadness', '#disgust', 'negative', '-0.85', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['funny_friend'] = ['0.056', '0.157', '0', '0.154', '#interest', '#admiration', 'positive', '0.122', 'enjoy_outdoors', 'competition', 'learn_teamwork', 'exercise_entertainment', 'entertainment']
senticnet['funny_guy'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'male', 'bloke', 'boy', 'man_child', 'man']
senticnet['funny_house'] = ['-0.86', '0', '0.822', '-0.61', '#sadness', '#anger', 'negative', '-0.76', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['funny_joke'] = ['0.225', '-0.04', '0', '0.07', '#joy', '#admiration', 'positive', '0.115', 'funny', 'social_event', 'amuse', 'amusement', 'make_laugh']
senticnet['funny_look'] = ['0', '0', '0.879', '-0.73', '#anger', '#disgust', 'negative', '-0.80', 'fight_against', 'uprise', 'rival', 'disagreement', 'anti']
senticnet['funny_man'] = ['0.922', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.895', 'heroism', 'perpetrator', 'role_play', 'role', 'act']
senticnet['funny_moment'] = ['0.928', '0.778', '0', '0', '#joy', '#interest', 'positive', '0.853', 'child_book', 'disney_cartoon', 'child_story', 'child_movie', 'childhood']
senticnet['funny_stuff'] = ['-0.76', '0', '0.904', '-0.80', '#anger', '#disgust', 'negative', '-0.82', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['funnymen'] = ['-0.02', '-0.02', '0.062', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'jump_up_down', 'tie_shoelace', 'jump_rope', 'hike', 'skate']
senticnet['fur'] = ['-0.83', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.87', 'animal_hair', 'animal_fur', 'pelt', 'strand', 'make_wool']
senticnet['fur_cap'] = ['0.08', '0.106', '-0.04', '0.112', '#interest', '#admiration', 'positive', '0.063', 'helmet', 'head_gear', 'cap', 'head_wear', 'head_protection']
senticnet['fur_coat'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'clothes_style', 'style', 'glamour', 'model', 'formal_clothes']
senticnet['fur_collar'] = ['-0.11', '0.115', '0.67', '-0.65', '#anger', '#disgust', 'negative', '-0.33', 'lividity', 'livid', 'wrathfully', 'wrothful', 'wroth']
senticnet['fur_hat'] = ['0.08', '0.106', '-0.04', '0.112', '#interest', '#admiration', 'positive', '0.063', 'helmet', 'head_gear', 'cap', 'head_wear', 'head_protection']
senticnet['fur_keep_warm'] = ['-0.72', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.78', 'worm_intestine', 'bad_breath', 'bald', 'tic', 'claw']
senticnet['fur_seal'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'many_fishes', 'seahorse', 'starfish', 'plankton', 'sea_wee']
senticnet['fur_trapper'] = ['0.941', '0.987', '0', '0', '#joy', '#interest', 'positive', '0.964', 'monkey', 'marmoset', 'lemur', 'fiance', 'koala']
senticnet['fur_trim'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'animal_hair', 'dog_hair', 'animal_fur', 'dog_cat', 'bite_dog']
senticnet['furball'] = ['0.03', '0.068', '-0.04', '0.093', '#interest', '#admiration', 'positive', '0.037', 'beast', 'equine', 'live_creature', 'furry_creature', 'domesticate_animal']
senticnet['furbish_up'] = ['-0.56', '0', '0.917', '-0.88', '#anger', '#disgust', 'negative', '-0.79', 'troubleshoot', 'fiddle', 'fill', 'darn', 'sole']
senticnet['furby'] = ['0', '-0.10', '0', '0', '#surprise', '#surprise', 'positive', '0.105', 'bath_toy', 'stuff_animal', 'toy_soldier', 'action_figure', 'yo-yo']
senticnet['furcate'] = ['0', '0', '-0.78', '-0.89', '#fear', '#disgust', 'negative', '-0.84', 'disunite', 'divide', 'part', 'differentiate', 'separate']
senticnet['furcula'] = ['0.1', '-0.12', '0.05', '-0.03', '#joy', '#surprise', 'positive', '0.034', 'fancy_dress', 'blouse', 'fashion', 'dress', 'skirt']
senticnet['furfural'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'carboxylic_acid', 'succinic_acid', 'valeric_acid']
senticnet['furiant'] = ['0.591', '-0.91', '0', '0.605', '#surprise', '#admiration', 'positive', '0.704', 'farandole', 'folk_dance', 'polka', 'dance', 'typical_dance']
senticnet['furious'] = ['-0.13', '0.145', '0.624', '-0.63', '#anger', '#disgust', 'negative', '-0.31', 'enraged', 'infuriated', 'maddened', 'angered', 'indignantly']
senticnet['furious_backlash'] = ['-0.86', '0', '0.516', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'bronx_cheer', 'call', 'cry', 'yell', 'shout']
senticnet['furious_debate'] = ['0', '0', '0.94', '0', '#anger', '#anger', 'negative', '-0.94', 'feel_angry', 'hurt_feeling', 'feel_hurt', 'ridicule', 'vengeance']
senticnet['furious_reaction'] = ['-0.16', '0.179', '0.568', '-0.61', '#anger', '#disgust', 'negative', '-0.29', 'angrily', 'choler', 'see_red', 'dander', 'hackle']
senticnet['furiously'] = ['-0.12', '0.13', '0.647', '-0.64', '#anger', '#disgust', 'negative', '-0.32', 'infuriate', 'exasperating', 'infuriating', 'maddening', 'dudgeon']
senticnet['furiousness'] = ['-0.63', '0', '0.921', '-0.62', '#sadness', '#anger', 'negative', '-0.72', 'fury', 'wildness', 'ferocity', 'fierceness', 'savagery']
senticnet['furlike'] = ['-0.75', '0', '0.734', '-0.82', '#sadness', '#disgust', 'negative', '-0.77', 'puberulent', 'sericeous', 'haired', 'pappose', 'pilous']
senticnet['furlong_pole'] = ['0', '-0.04', '0.085', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'claw', 'fur_keep_warm', 'tic', 'bristle', 'muzzle']
senticnet['furnace'] = ['0.091', '0', '0.034', '0.049', '#joy', '#admiration', 'positive', '0.035', 'fireplace', 'flame', 'huge_fire', 'fire', 'heater']
senticnet['furnish'] = ['0', '0.128', '-0.12', '0.183', '#interest', '#admiration', 'positive', '0.063', 'support_roof', 'interior', 'ceiling', 'window_door', 'frame']
senticnet['furnished_apartment'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'home', 'furniture', 'kitchen', 'flat']
senticnet['furnishing'] = ['-0.74', '0', '0.722', '-0.56', '#sadness', '#anger', 'negative', '-0.67', 'trapping', 'accouterment', 'wear', 'wearable', 'belt']
senticnet['furniture'] = ['0.514', '-0.97', '0', '0.899', '#surprise', '#admiration', 'positive', '0.797', 'board', 'beaver_board', 'fiberboard', 'dry_wall', 'particle_board']
senticnet['furniture_factory'] = ['0.056', '0.157', '0', '0.154', '#interest', '#admiration', 'positive', '0.122', 'home_depot', 'lumberyard', 'carpet_store', 'front_porch', 'shoe_shop']
senticnet['furniture_maker'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'maker']
senticnet['furniture_mover'] = ['0.969', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.936', 'attendant', 'manager', 'public_servant', 'specialist', 'accomplice']
senticnet['furniture_restorer'] = ['-0.64', '0', '0.879', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'drone', 'poke', 'dawdler', 'laggard', 'straggler']
senticnet['furniture_retailer'] = ['0.056', '0.157', '0', '0.154', '#interest', '#admiration', 'positive', '0.122', 'home_depot', 'lumberyard', 'carpet_store', 'front_porch', 'shoe_shop']
senticnet['furniture_shop'] = ['0.056', '0.157', '0', '0.154', '#interest', '#admiration', 'positive', '0.122', 'hat_shop', 'chest', 'nervous_system', 'crotch', 'skeleton']
senticnet['furniture_showroom'] = ['0.056', '0.157', '0', '0.154', '#interest', '#admiration', 'positive', '0.122', 'home_depot', 'lumberyard', 'carpet_store', 'front_porch', 'shoe_shop']
senticnet['furniture_store'] = ['0.056', '0.157', '0', '0.154', '#interest', '#admiration', 'positive', '0.122', 'home_depot', 'lumberyard', 'carpet_store', 'front_porch', 'shoe_shop']
senticnet['furniture_warehouse'] = ['0.056', '0.157', '0', '0.154', '#interest', '#admiration', 'positive', '0.122', 'home_depot', 'lumberyard', 'carpet_store', 'front_porch', 'shoe_shop']
senticnet['furor'] = ['0', '0', '0.94', '0', '#anger', '#anger', 'negative', '-0.94', 'feel_angry', 'hurt_feeling', 'feel_hurt', 'ridicule', 'vengeance']
senticnet['furor_among'] = ['0', '0', '0.94', '0', '#anger', '#anger', 'negative', '-0.94', 'feel_angry', 'hurt_feeling', 'feel_hurt', 'ridicule', 'vengeance']
senticnet['furor_over'] = ['0', '0', '0.94', '0', '#anger', '#anger', 'negative', '-0.94', 'feel_angry', 'hurt_feeling', 'feel_hurt', 'ridicule', 'vengeance']
senticnet['furore_over'] = ['-0.87', '0', '0.762', '-0.92', '#sadness', '#disgust', 'negative', '-0.85', 'misreading', 'misconstrual', 'misconstruction', 'misinterpretation', 'interpretation']
senticnet['furrow'] = ['0.689', '-0.56', '0', '0.74', '#joy', '#admiration', 'positive', '0.665', 'crease', 'crinkle', 'wrinkle', 'heart_line', 'seam']
senticnet['furrow_slice'] = ['0.689', '-0.56', '0', '0.74', '#joy', '#admiration', 'positive', '0.665', 'crease', 'crinkle', 'wrinkle', 'heart_line', 'seam']
senticnet['furrowed_brow'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'simper', 'please', 'content', 'satisfy', 'gratify']
senticnet['furry'] = ['0', '0', '0.07', '0', '#anger', '#anger', 'negative', '-0.07', 'animal_hair', 'animal_skin', 'dog_fur', 'fluffy_animal', 'animal_fur']
senticnet['furry_creature'] = ['0.03', '0.068', '-0.04', '0.093', '#interest', '#admiration', 'positive', '0.037', 'critter', 'equine', 'live_creature', 'beast', 'domesticate_animal']
senticnet['further'] = ['0.846', '-0.76', '0', '0.688', '#joy', '#surprise', 'positive', '0.766', 'add_on', 'promote', 'encourage', 'boost', 'advance']
senticnet['further_afield'] = ['-0.95', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.83', 'outer', 'radiant', 'around_earth', 'nothingness', 'heavenly_body']
senticnet['further_alienate'] = ['-0.10', '0.176', '0.629', '-0.64', '#anger', '#disgust', 'negative', '-0.3', 'alienated', 'alienation', 'disaffection', 'estrange', 'disoriented']
senticnet['further_career'] = ['0.847', '0.643', '0', '0.973', '#joy', '#admiration', 'positive', '0.821', 'get_high_education', 'prepare_exam', 'demonstrate_knowledge', 'earn_money', 'get_better_job']
senticnet['further_clarification'] = ['-0.09', '0.18', '-0.04', '0.141', '#interest', '#admiration', 'positive', '0.046', 'acknowledge', 'explanation', 'solution', 'get_answer', 'make_sense']
senticnet['further_deterioration'] = ['0.963', '0', '0', '0.985', '#joy', '#admiration', 'positive', '0.974', 'settle', 'set', 'secure', 'affix', 'stable']
senticnet['further_education'] = ['0.847', '0.643', '0', '0.973', '#joy', '#admiration', 'positive', '0.821', 'further_knowledge', 'further_career', 'further', 'school_student', 'education']
senticnet['further_embolden'] = ['0.655', '0.635', '-0.23', '0.3', '#joy', '#interest', 'positive', '0.338', 'cheerfully', 'cheerful', 'jolly_up', 'hearten', 'chirk_up']
senticnet['further_entrench'] = ['0.655', '0.635', '-0.23', '0.3', '#joy', '#interest', 'positive', '0.338', 'cheerfully', 'cheerful', 'jolly_up', 'hearten', 'chirk_up']
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senticnet['fusilli'] = ['0.763', '0.9', '0', '0', '#joy', '#interest', 'positive', '0.832', 'breakfast', 'box_cereal', 'calcium', 'drink', 'carton_milk']
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senticnet['fusion_cuisine'] = ['0.882', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.822', 'ferment_grape_juice', 'chef_special', 'bottle_wine', 'fine_dining', 'decanter']
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senticnet['futilely'] = ['0.901', '0', '0', '0.805', '#joy', '#admiration', 'positive', '0.853', 'feebly', 'vainly', 'ineffectually', 'half-heartedly', 'timidly']
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senticnet['future'] = ['0', '0', '-0.92', '-0.96', '#fear', '#disgust', 'negative', '-0.94', 'side_by_side', 'succeeding', 'following', 'adjacent', 'next']
senticnet['future_contract'] = ['0.868', '-0.84', '0', '0.673', '#joy', '#surprise', 'positive', '0.796', 'instrument', 'option', 'derivative_instrument', 'legal_document', 'legal_instrument']
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senticnet['future_market'] = ['0.868', '-0.84', '0', '0.673', '#joy', '#surprise', 'positive', '0.796', 'instrument', 'option', 'derivative_instrument', 'legal_document', 'legal_instrument']
senticnet['future_robot'] = ['0.065', '0.209', '-0.11', '0.297', '#interest', '#admiration', 'positive', '0.113', 'drive_recklessly', 'type_of_dog', 'dog', 'dangerous', 'drive']
senticnet['future_scientific_development'] = ['0', '0.878', '0', '0.965', '#interest', '#admiration', 'positive', '0.921', 'fellowship', 'science', 'mathematics', 'award', 'permit_awardees']
senticnet['future_vitality'] = ['0.705', '0', '0', '0.707', '#joy', '#admiration', 'positive', '0.706', 'fellowship', 'scientific_effort', 'doctoral_degree', 'recent_recipient', 'academicyear_month']
senticnet['futurism'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'native_person', 'revolutionary', 'republic', 'revolution', 'look_ahead']
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senticnet['futuristic'] = ['0.843', '-0.99', '0', '0.706', '#joy', '#surprise', 'positive', '0.849', 'contrivance', 'gimbal', 'injector', 'mod_con', 'device']
senticnet['futz'] = ['0', '0', '0.764', '-0.77', '#anger', '#disgust', 'negative', '-0.77', 'censor', 'objectionable', 'arousing', 'unspeakable', 'offensive']
senticnet['fuuma'] = ['0.021', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'plastic_bottle', 'party', 'use_party', 'lighter_air', 'compress_air']
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senticnet['fuzz'] = ['0', '0', '0.064', '-0.04', '#anger', '#disgust', 'negative', '-0.05', 'calyx', 'funiculus', 'plant_product', 'bird_food', 'agricultural_product']
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senticnet['fy_budget'] = ['-0.05', '0.282', '-0.04', '0.107', '#interest', '#admiration', 'positive', '0.072', 'expense', 'dollar_cent', 'dollar_sign', 'amount_pay', 'payment_amount']
senticnet['fyodor_pavlovitch'] = ['-0.85', '0.21', '-0.56', '0', '#sadness', '#fear', 'negative', '-0.40', 'doleful', 'sadly', 'dolefully', 'ruefulness', 'wretched']
senticnet['g-spot'] = ['0', '-0.89', '0', '0.876', '#surprise', '#admiration', 'positive', '0.883', 'erogenous', 'clitoris', 'clitoral', 'perineum', 'genitals']
senticnet['gab'] = ['-0.51', '0', '0.545', '-0.91', '#anger', '#disgust', 'negative', '-0.65', 'gossip', 'chitchat', 'chit_chat', 'chat', 'small_talk']
senticnet['gaba_gamma_amino_butyric'] = ['0.071', '0', '-0.08', '-0.09', '#fear', '#disgust', 'negative', '-0.03', 'gunk', 'rotten_food', 'sour_cream', 'glass_of_milk', 'lemon_juice']
senticnet['gabaergic'] = ['0.698', '-0.61', '0', '0.619', '#joy', '#admiration', 'positive', '0.644', 'sensitivity', 'sensitiveness', 'irritability', 'sensibility', 'reactivity']
senticnet['gabardine'] = ['-0.54', '0', '0.612', '-0.98', '#anger', '#disgust', 'negative', '-0.71', 'scuff', 'mule', 'bootee', 'carpet_slipper', 'footwear']
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senticnet['gabby'] = ['0.949', '-0.69', '0', '0.992', '#joy', '#admiration', 'positive', '0.878', 'chatty', 'voluble', 'garrulous', 'loquacious', 'talky']
senticnet['gable'] = ['0.083', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.059', 'good_view', 'bird_droppings', 'slate', 'nature', 'homeowner']
senticnet['gaboon_viper'] = ['-0.96', '0', '0.865', '-0.75', '#sadness', '#anger', 'negative', '-0.85', 'viper', 'ophidian', 'serpent', 'snake', 'adder']
senticnet['gad'] = ['-0.66', '0', '0.949', '-0.75', '#anger', '#disgust', 'negative', '-0.78', 'roam', 'move', 'gallivant', 'jazz_around', 'travel']
senticnet['gadfly'] = ['-0.97', '0', '0.62', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'obstructor', 'obstructer', 'technophobe', 'obstructionist', 'resister']
senticnet['gadget'] = ['0.063', '0.131', '-0.07', '0.099', '#interest', '#admiration', 'positive', '0.055', 'machine', 'social_creature', 'live_creature', 'biological_machine', 'alive']
senticnet['gadgetry'] = ['0.843', '-0.99', '0', '0.706', '#joy', '#surprise', 'positive', '0.849', 'contrivance', 'gimbal', 'injector', 'mod_con', 'device']
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senticnet['gaelic_surname'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
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senticnet['gaffe'] = ['0', '0', '0.776', '-0.83', '#anger', '#disgust', 'negative', '-0.80', 'rusticity', 'slip', 'solecism', 'gaucherie', 'faux_pa']
senticnet['gaffer_tape'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'hanger', 'hang_up_clothes', 'hang_clothes', 'storage_space', 'laundry']
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senticnet['gagar'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'white_animal', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
senticnet['gaggle'] = ['-0.71', '0', '0.861', '0', '#sadness', '#anger', 'negative', '-0.78', 'hen', 'laying', 'cackly', 'cackler', 'cackle']
senticnet['gagosian_gallery'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'hip', 'joint', 'above_leg', 'knee']
senticnet['gagudju'] = ['-0.95', '0', '0.624', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['gaia_memory'] = ['0.8', '-0.97', '0', '0.512', '#joy', '#surprise', 'positive', '0.763', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['gaiety'] = ['0.918', '0.476', '-0.19', '0.417', '#joy', '#interest', 'positive', '0.404', 'merriment', 'cheery', 'festal', 'playfulness', 'pridefulness']
senticnet['gaiety_theatre'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'dress', 'pinny', 'dirndl', 'gown', 'muumuu']
senticnet['gaijin'] = ['-0.57', '0', '0.803', '-0.77', '#anger', '#disgust', 'negative', '-0.71', 'au_pair', 'exile', 'deportee', 'gringo', 'import']
senticnet['gaikokujin'] = ['-0.57', '0', '0.803', '-0.77', '#anger', '#disgust', 'negative', '-0.71', 'au_pair', 'exile', 'deportee', 'gringo', 'import']
senticnet['gaily'] = ['0.918', '0.476', '-0.19', '0.417', '#joy', '#interest', 'positive', '0.404', 'merriment', 'cheery', 'festal', 'playfulness', 'pridefulness']
senticnet['gain'] = ['0.733', '0.991', '0', '0.843', '#interest', '#admiration', 'positive', '0.856', 'improvement', 'advantage', 'return', 'receive', 'contribution']
senticnet['gain_admission'] = ['0.087', '0.248', '-0.19', '0.31', '#interest', '#admiration', 'positive', '0.113', 'enroll', 'enroll_school', 'enroll_class', 'pay_tuition', 'start_college']
senticnet['gain_energy'] = ['0.042', '-0.04', '-0.05', '-0.14', '#fear', '#disgust', 'negative', '-0.02', 'slow_heart_beat', 'feel_refreshed', 'late_work', 'tired', 'take_breath']
senticnet['gain_entry'] = ['0.829', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.908', 'cut_food', 'drink_beer', 'serve_drink', 'drink_much', 'drink_beverage']
senticnet['gain_experience'] = ['0.918', '0.476', '-0.19', '0.417', '#joy', '#interest', 'positive', '0.404', 'new_experience', 'experience', 'study', 'listen', 'gain']
senticnet['gain_familiarity_with'] = ['0', '0.948', '0', '0.948', '#interest', '#admiration', 'positive', '0.948', 'cope', 'farewell', 'incident', 'propose', 'welcome']
senticnet['gain_ground'] = ['0.506', '0.659', '-0.24', '0.242', '#joy', '#interest', 'positive', '0.29', 'climb_hill', 'reach_top', 'win_battle', 'score_goal', 'accomplish_mission']
senticnet['gain_information'] = ['0.941', '0.748', '0', '0.56', '#joy', '#interest', 'positive', '0.75', 'keep_up_current_event', 'gain_knowledge', 'expand_mind', 'pass_knowledge', 'get_information']
senticnet['gain_insight'] = ['0.892', '0.897', '0', '0.86', '#joy', '#interest', 'positive', '0.883', 'understand', 'trivia', 'great_understand', 'knowledge', 'know_fact']
senticnet['gain_insight_into'] = ['0.842', '0.829', '0', '0.888', '#joy', '#admiration', 'positive', '0.853', 'learn_new_subject', 'learn_information', 'learn_art', 'make_out_atom', 'educate']
senticnet['gain_insights_into'] = ['0.548', '-0.86', '0', '0.938', '#surprise', '#admiration', 'positive', '0.784', 'elaborate', 'clear', 'crystallize', 'clear_up', 'make_clear']
senticnet['gain_interest'] = ['0.896', '0', '0', '0', '#joy', '#joy', 'positive', '0.896', 'walk_exercise', 'pay_dinner', 'subscribe_magazine', 'buy_candy', 'buy_soda']
senticnet['gain_invaluable'] = ['0.995', '0.801', '0', '0.968', '#joy', '#admiration', 'positive', '0.921', 'wisdom', 'learn', 'know_world', 'find_out_truth', 'smart']
senticnet['gain_knowledge'] = ['0.932', '0.919', '0', '0.996', '#joy', '#admiration', 'positive', '0.949', 'increase_knowledge', 'become_smart', 'learn', 'become_intelligent', 'learn_information']
senticnet['gain_land'] = ['0.065', '0.209', '-0.11', '0.297', '#interest', '#admiration', 'positive', '0.113', 'buy_present_for_others', 'fight_inflation', 'buy_love', 'buy_present', 'buy_christmas_present']
senticnet['gain_momentum'] = ['0.819', '-0.87', '0', '0.902', '#surprise', '#admiration', 'positive', '0.865', 'tally', 'steal', 'score', 'hit', 'rack_up']
senticnet['gain_money'] = ['0.779', '0.978', '0', '0.975', '#interest', '#admiration', 'positive', '0.911', 'investment_return', 'monetary_gain', 'financial_gain', 'money_earn', 'extra_money']
senticnet['gain_new_knowledge'] = ['0.774', '0.728', '0', '0.932', '#joy', '#admiration', 'positive', '0.811', 'accumulate_knowledge', 'know', 'read_out_loud', 'gather_information', 'learn_history']
senticnet['gain_popularity'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'important', 'universal', 'vip', 'prestige', 'status']
senticnet['gain_power'] = ['0.852', '0', '0', '0', '#joy', '#joy', 'positive', '0.852', 'transmit_disease', 'control', 'end_war', 'protect_family', 'rape']
senticnet['gain_qualification'] = ['-0.14', '-0.05', '0', '-0.11', '#sadness', '#disgust', 'negative', '-0.06', 'finish_college', 'learn_evolution', 'complete_course', 'get_high_education', 'obtain_degree']
senticnet['gain_research_experience'] = ['-0.71', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.72', 'microbial_biology', 'undergraduate', 'research_experience', 'recent_doctoral_recipient', 'provide_opportunity']
senticnet['gain_respect'] = ['0.914', '0.836', '0', '0.799', '#joy', '#interest', 'positive', '0.85', 'experience_life', 'happiness', 'good_feeling', 'appreciate', 'say_love']
senticnet['gain_skill'] = ['0.716', '0.845', '0', '0.814', '#interest', '#admiration', 'positive', '0.792', 'entertain_others', 'develop_skill', 'learn_play', 'lovely_music', 'appreciate_music']
senticnet['gain_speed'] = ['-0.06', '0.163', '-0.12', '0.236', '#interest', '#admiration', 'positive', '0.052', 'jerk', 'pull_car', 'pull', 'movement', 'turn_around']
senticnet['gain_tax'] = ['-0.83', '0', '0.555', '-0.64', '#sadness', '#disgust', 'negative', '-0.67', 'single_tax', 'income_tax', 'taxation', 'capital_levy', 'departure_tax']
senticnet['gain_territory'] = ['0', '0', '-0.11', '-0.17', '#fear', '#disgust', 'negative', '-0.14', 'conquer_enemy', 'keep_peace', 'make_war', 'defeat_enemy', 'kill_soldier']
senticnet['gain_traction'] = ['0.819', '-0.87', '0', '0.902', '#surprise', '#admiration', 'positive', '0.865', 'tally', 'steal', 'score', 'hit', 'rack_up']
senticnet['gain_understand'] = ['0.802', '0.795', '0', '0.839', '#joy', '#admiration', 'positive', '0.812', 'learn_much', 'read_out_loud', 'discover_new', 'know_new', 'know_world']
senticnet['gain_valuable'] = ['0.733', '0.991', '0', '0.843', '#interest', '#admiration', 'positive', '0.856', 'improvement', 'advantage', 'return', 'receive', 'contribution']
senticnet['gain_wealth'] = ['0.839', '0.861', '0', '0.738', '#joy', '#interest', 'positive', '0.813', 'become_rich', 'increase_wealth', 'save_money', 'become_wealthy', 'find_money']
senticnet['gain_weight'] = ['-0.88', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.88', 'get_laugh', 'tooth_hurt', 'choke_death', 'sore_throat', 'feel_satisfied']
senticnet['gained_critical_acclaim'] = ['0.853', '0.828', '0', '0.98', '#joy', '#admiration', 'positive', '0.887', 'endorse', 'recommend', 'stimulate_mind', 'acculturation', 'support']
senticnet['gained_independence'] = ['0.887', '0.748', '0', '0.954', '#joy', '#admiration', 'positive', '0.863', 'political_action', 'political', 'mediocrity', 'discrimination', 'independent']
senticnet['gained_popularity'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'important', 'universal', 'vip', 'prestige', 'status']
senticnet['gained_popularity_among'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'important', 'universal', 'vip', 'prestige', 'status']
senticnet['gained_prominence'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'important', 'universal', 'vip', 'prestige', 'status']
senticnet['gainesville'] = ['-0.85', '0', '0.685', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'allicrocodilian', 'crocodile', 'false_gavial', 'crocodilian', 'caiman']
senticnet['gainful'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'lucrative', 'moneymaking', 'profitable', 'paying', 'paid']
senticnet['gainful_employment'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'lucrative', 'moneymaking', 'profitable', 'paying', 'paid']
senticnet['gainful_occupation'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'lucrative', 'moneymaking', 'profitable', 'paying', 'paid']
senticnet['gaining_admission'] = ['0.087', '0.248', '-0.19', '0.31', '#interest', '#admiration', 'positive', '0.113', 'enroll', 'enroll_school', 'enroll_class', 'pay_tuition', 'start_college']
senticnet['gaining_independence'] = ['0.887', '0.748', '0', '0.954', '#joy', '#admiration', 'positive', '0.863', 'political_action', 'political', 'mediocrity', 'discrimination', 'independent']
senticnet['gaining_popularity'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'important', 'universal', 'vip', 'prestige', 'status']
senticnet['gaining_valuable'] = ['0.733', '0.991', '0', '0.843', '#interest', '#admiration', 'positive', '0.856', 'improvement', 'advantage', 'return', 'receive', 'contribution']
senticnet['gaining_weight'] = ['-0.52', '0', '0.786', '-0.92', '#anger', '#disgust', 'negative', '-0.74', 'upset', 'bulimarexia', 'bulima_nervosa', 'eating_disorder', 'disorder']
senticnet['gainly'] = ['0.734', '-0.53', '0', '0.914', '#joy', '#admiration', 'positive', '0.726', 'graceful', 'gracile', 'willowy', 'elegant', 'smooth']
senticnet['gainsay'] = ['-0.77', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.79', 'gainsayer', 'disagreeable', 'deny', 'contradict', 'opposer']
senticnet['gainsayer'] = ['-0.77', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.78', 'gainsay', 'disagreeable', 'deny', 'contradict', 'opposer']
senticnet['gaint_buttercup'] = ['-0.51', '0', '0.767', '-0.80', '#anger', '#disgust', 'negative', '-0.69', 'common_buttercup', 'meadow_buttercup', 'tall_buttercup', 'tall_crowfoot', 'goldcup']
senticnet['gairden'] = ['0.914', '-0.56', '0', '0.869', '#joy', '#admiration', 'positive', '0.781', 'tabula_rasa', 'ego', 'subconscious', 'unconscious', 'psyche']
senticnet['gait'] = ['-0.15', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.09', 'use_leg', 'walk_quickly', 'casual', 'take_step', 'strut']
senticnet['gait_pattern'] = ['-0.15', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.09', 'use_leg', 'walk_quickly', 'casual', 'take_step', 'strut']
senticnet['gajender'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['gaji'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'white_animal', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
senticnet['gajya'] = ['0.086', '-0.10', '0.064', '0', '#joy', '#surprise', 'positive', '0.043', 'art_piece', 'lifetime', 'museum', 'vintage', 'collectible']
senticnet['gakowice'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'aphasia', 'anomic_aphasia']
senticnet['gal'] = ['-0.92', '0', '0.739', '0', '#sadness', '#anger', 'negative', '-0.83', 'lecherous', 'salacious', 'pornography', 'lascivious', 'lasciviously']
senticnet['gal_pal'] = ['-0.80', '0', '0.56', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'gossip', 'gossiper', 'blabbermouth', 'talkative', 'rumormonger']
senticnet['gala'] = ['0.138', '-0.11', '-0.06', '0.068', '#joy', '#surprise', 'positive', '0.063', 'reunion', 'special_moment', 'festivity', 'special_happen', 'celebration']
senticnet['gala_dinner'] = ['0.962', '0', '0', '0', '#joy', '#joy', 'positive', '0.962', 'interest_person', 'social_occasion', 'become_intoxicated', 'network', 'get_sex']
senticnet['galactic'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['galactic_center'] = ['0', '0.706', '0', '0.847', '#interest', '#admiration', 'positive', '0.776', 'dwarf_planet', 'universe', 'planetary', 'sun', 'moon']
senticnet['galactic_centre'] = ['0', '0.706', '0', '0.847', '#interest', '#admiration', 'positive', '0.776', 'dwarf_planet', 'universe', 'planetary', 'sun', 'moon']
senticnet['galactic_civil_war'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['galactic_core'] = ['0', '0.706', '0', '0.847', '#interest', '#admiration', 'positive', '0.776', 'dwarf_planet', 'universe', 'planetary', 'sun', 'moon']
senticnet['galactic_disk'] = ['0.737', '-0.96', '0', '0.552', '#joy', '#surprise', 'positive', '0.75', 'prominence', 'hump', 'bump', 'swelling', 'frontal_eminence']
senticnet['galactic_empire'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['galactic_halo'] = ['0', '0.706', '0', '0.847', '#interest', '#admiration', 'positive', '0.776', 'dwarf_planet', 'universe', 'planetary', 'sun', 'moon']
senticnet['galactic_neighborhood'] = ['0', '0.706', '0', '0.847', '#interest', '#admiration', 'positive', '0.776', 'dwarf_planet', 'universe', 'planetary', 'sun', 'moon']
senticnet['galactic_republic'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['galahad_threepwood'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'rascal', 'rapscallion', 'scalawag', 'holy_terror', 'monkey']
senticnet['galanin'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'gastrointestinal_hormone', 'secretin', 'ghrelin']
senticnet['galant_style'] = ['-0.5', '0', '0.97', '-0.52', '#anger', '#disgust', 'negative', '-0.66', 'simulated', 'apish', 'parrotlike', 'apelike', 'mimic']
senticnet['galapagos_marine'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['galapagos_rift'] = ['-0.02', '-0.01', '0.03', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'ocean_beach', 'beach', 'water_sea', 'shore', 'ground_ocean']
senticnet['galata'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'caravansary', 'caravanserai', 'khan', 'caravan_inn', 'hostel']
senticnet['galatasaray_cafe'] = ['-0.62', '0', '0.961', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'jeweled_headdress', 'diadem', 'tiara', 'headdress', 'headgear']
senticnet['galaxy'] = ['0', '0.706', '0', '0.847', '#interest', '#admiration', 'positive', '0.776', 'dwarf_planet', 'universe', 'planetary', 'sun', 'moon']
senticnet['galaxy_cluster'] = ['0', '0.706', '0', '0.847', '#interest', '#admiration', 'positive', '0.776', 'dwarf_planet', 'universe', 'planetary', 'sun', 'moon']
senticnet['galaxy_ngc'] = ['0', '0.706', '0', '0.847', '#interest', '#admiration', 'positive', '0.776', 'dwarf_planet', 'universe', 'planetary', 'sun', 'moon']
senticnet['galbanum'] = ['0.827', '0', '0', '0', '#joy', '#joy', 'positive', '0.827', 'wrathful', 'wrathfully', 'wroth', 'wrothful', 'lividity']
senticnet['galbulidae_family'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'saltine', 'soda_cracker', 'cracker', 'water_biscuit', 'graham_cracker']
senticnet['gale'] = ['-0.98', '0', '0.96', '-0.92', '#sadness', '#anger', 'negative', '-0.95', 'thunderstorm', 'storm', 'bad_weather', 'heavy_rain', 'hurricane']
senticnet['gale-force'] = ['-0.86', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.89', 'blustery', 'gusty', 'showery', 'squall', 'squally']
senticnet['gale_crater'] = ['0.052', '0.119', '0', '0.141', '#interest', '#admiration', 'positive', '0.104', 'neigh', 'domestic_animal', 'animal_type', 'animal', 'home_animal']
senticnet['galere'] = ['-0.69', '0', '0.829', '-0.69', '#anger', '#disgust', 'negative', '-0.73', 'camp', 'pack', 'clique', 'coterie', 'ingroup']
senticnet['galerie'] = ['0.07', '0', '0', '0', '#joy', '#joy', 'positive', '0.07', 'artist', 'artwork', 'sculpture', 'tapestry', 'drawing']
senticnet['galeries_lafayette'] = ['0', '0.081', '0', '0.071', '#interest', '#admiration', 'positive', '0.076', 'thrift_store', 'type_of_store', 'record_store', 'many_shops', 'clothes_store']
senticnet['gali_tibbon_afp_getty'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expansive', 'expansible', 'erectile', 'cavernous']
senticnet['galician_academy'] = ['0.059', '0.076', '-0.05', '0.079', '#interest', '#admiration', 'positive', '0.039', 'noble', 'king', 'earl', 'duchess', 'duke']
senticnet['galilean_relativity'] = ['-0.69', '0', '0.83', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'general_relativity', 'general_theory_of_relativity', 'general_relativity_theory', 'relativity', 'special_theory_of_relativity']
senticnet['galilee'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['galileo_atmospheric'] = ['0', '0.825', '0', '0', '#interest', '#interest', 'positive', '0.825', 'query', 'interrogation', 'inquire', 'response', 'solution']
senticnet['galiot'] = ['0', '0', '0.08', '0', '#anger', '#anger', 'negative', '-0.08', 'tree_animal', 'small_furry', 'rodent', 'big_mouse', 'small_rodent']
senticnet['galium_verum'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['gall'] = ['0.034', '0.126', '0.758', '-0.67', '#anger', '#disgust', 'negative', '-0.31', 'resentfully', 'rancor', 'seek_revenge', 'resentful', 'warpath']
senticnet['gall_bladder'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'bowel', 'scrotum', 'shoulder', 'anus', 'waist']
senticnet['gall_bladder_operation'] = ['0.934', '-0.88', '0', '0.797', '#joy', '#surprise', 'positive', '0.872', 'scan', 'checkup', 'scrutiny', 'medical_examination', 'medical_exam']
senticnet['gall_midge'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'apple_rust', 'fungus']
senticnet['gall_mite'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'apple_rust', 'fungus']
senticnet['gallagher'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
senticnet['gallaher_group'] = ['-0.99', '0', '0.056', '-0.98', '#sadness', '#disgust', 'negative', '-0.67', 'smoke', 'cigarette', 'bad_air', 'pollution', 'cigar']
senticnet['gallant'] = ['0.947', '0', '0', '0.762', '#joy', '#admiration', 'positive', '0.855', 'dandy', 'clotheshorse', 'fop', 'proud', 'chivalrous']
senticnet['gallant_conduct'] = ['0.827', '0.85', '0', '0.743', '#joy', '#interest', 'positive', '0.807', 'chivalry', 'honor', 'almighty', 'admire', 'respect']
senticnet['gallant_self_sacrifice'] = ['0.827', '0.85', '0', '0.743', '#joy', '#interest', 'positive', '0.807', 'chivalry', 'honor', 'almighty', 'admire', 'respect']
senticnet['gallantly'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'intrepidly', 'hardy', 'fearlessness', 'unafraid', 'unfearing']
senticnet['gallantry'] = ['0.603', '-0.98', '0', '0.648', '#surprise', '#admiration', 'positive', '0.746', 'urbanity', 'niceness', 'politeness', 'good_manner', 'politesse']
senticnet['gallaudet'] = ['-0.94', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.94', 'drive_recklessly', 'discord', 'mute', 'deny', 'helpless']
senticnet['gallaudet_college'] = ['-0.94', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.94', 'drive_recklessly', 'discord', 'mute', 'deny', 'helpless']
senticnet['gallaudet_university'] = ['-0.94', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.94', 'drive_recklessly', 'discord', 'mute', 'deny', 'helpless']
senticnet['galleon_group'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'busy_bee', 'gopher', 'actor', 'doer', 'worker']
senticnet['galleon_group_founder'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'busy_bee', 'gopher', 'actor', 'doer', 'worker']
senticnet['galleon_group_hedge_fund'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'busy_bee', 'gopher', 'actor', 'doer', 'worker']
senticnet['galleon_hedge_fund'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'busy_bee', 'gopher', 'actor', 'doer', 'worker']
senticnet['gallery'] = ['0.07', '0', '0', '0', '#joy', '#joy', 'positive', '0.07', 'artist', 'artwork', 'sculpture', 'tapestry', 'drawing']
senticnet['gallery_open'] = ['0', '0.074', '0', '0.055', '#interest', '#admiration', 'positive', '0.065', 'art_show', 'museum', 'antique_store', 'laundromat', 'friend_wedding']
senticnet['galley_kitchen'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'kitchen_cabinet', 'kitchen_cupboard', 'dishwasher', 'cupboard', 'own_kitchen']
senticnet['gallien_krueger'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'lever']
senticnet['gallinari'] = ['0.556', '-0.79', '0', '0.755', '#surprise', '#admiration', 'positive', '0.701', 'lush', 'alcoholic', 'alky', 'dipsomaniac', 'rummy']
senticnet['galling'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'annoying', 'vexatious', 'teasing']
senticnet['gallivant'] = ['0.779', '-0.55', '0', '0.678', '#joy', '#admiration', 'positive', '0.669', 'range', 'ramble', 'vagabond', 'tramp', 'roam']
senticnet['gallon'] = ['0.092', '0.07', '0.036', '0.068', '#joy', '#interest', 'positive', '0.049', 'pint', 'quart', 'lite', 'ounce', 'liquid_measurement']
senticnet['gallon_gasoline_tax'] = ['0.987', '0.902', '0', '0', '#joy', '#interest', 'positive', '0.945', 'franc', 'dime', 'dollar', 'shilling', 'banknote']
senticnet['gallon_jug'] = ['0', '-0.02', '0.027', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'shoulder_bag', 'satchel', 'satchel_bag', 'tote_bag', 'carry']
senticnet['gallon_plastic'] = ['0', '-0.02', '0.027', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'shoulder_bag', 'satchel', 'satchel_bag', 'tote_bag', 'carry']
senticnet['gallon_tariff'] = ['0.987', '0.902', '0', '0', '#joy', '#interest', 'positive', '0.945', 'franc', 'dime', 'dollar', 'shilling', 'banknote']
senticnet['gallop'] = ['0', '0', '-0.81', '-0.93', '#fear', '#disgust', 'negative', '-0.87', 'trot', 'canter', 'galloping', 'dart', 'trotting']
senticnet['galloped_away'] = ['0', '0', '0.752', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'get_hit_by_car', 'meander', 'full_moon', 'step_dog_poop', 'kick_ass']
senticnet['galloper'] = ['0.744', '-0.77', '0', '0.661', '#joy', '#surprise', 'positive', '0.725', 'sugarcoat', 'honey', 'glaze', 'change_taste', 'sugar']
senticnet['galloping'] = ['0', '0', '-0.81', '-0.93', '#fear', '#disgust', 'negative', '-0.87', 'trot', 'canter', 'gallop', 'dart', 'trotting']
senticnet['galloping_ghost'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'unofficial', 'informal', 'loose', 'drumhead']
senticnet['galloping_horse'] = ['0', '0', '0', '0.071', '#admiration', '#admiration', 'positive', '0.071', 'two_barbs', 'barb', 'mule', 'buddhist', 'ride_barb']
senticnet['gallows_humor'] = ['-0.77', '0.277', '-0.44', '-0.05', '#sadness', '#fear', 'negative', '-0.24', 'heavyheartedness', 'bored', 'world-weariness', 'heavyhearted', 'world-weary']
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senticnet['gambling_den'] = ['-0.80', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.79', 'whorehouse', 'brothel', 'massage_parlor', 'bagnio', 'house_of_prostitution']
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senticnet['gambusia'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'stone_crab', 'swimming_crab', 'rock_crab', 'crab', 'pea_crab']
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senticnet['game_card'] = ['0.028', '-0.00', '0', '0.096', '#joy', '#admiration', 'positive', '0.043', 'action_figure', 'toy_car', 'racquet', 'toy_marble', 'yo-yo']
senticnet['game_chance'] = ['0.035', '-0.05', '0', '0.101', '#surprise', '#admiration', 'positive', '0.063', 'form_of_gambling', 'game', 'consider_good_luck', 'game_skill', 'popular_game']
senticnet['game_changer'] = ['0.808', '-0.83', '0', '0.548', '#joy', '#surprise', 'positive', '0.73', 'successfulness', 'luckiness', 'prosperity', 'circumstance', 'blessing']
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senticnet['game_part'] = ['0.934', '0.798', '0', '0', '#joy', '#interest', 'positive', '0.866', 'build_block', 'hard_paper', 'fun', 'kid_game', 'with_person']
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senticnet['game_unreal_engine'] = ['0.089', '-0.01', '0.033', '0.082', '#joy', '#admiration', 'positive', '0.039', 'informative', 'informational', 'ephemeral', 'fun_read', 'ads']
senticnet['game_warden'] = ['0', '0', '-0.11', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'hunting_season', 'hound', 'cougar', 'outlaw', 'dragon']
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senticnet['gameboard'] = ['0', '0', '0', '0.115', '#admiration', '#admiration', 'positive', '0.115', 'chess_knight', 'strategy', 'pawn', 'chess_queen', 'chess_rook']
senticnet['gamecock'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'unofficial', 'informal', 'loose', 'drumhead']
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senticnet['gamelon'] = ['0.105', '-0.17', '0.057', '0.079', '#joy', '#surprise', 'positive', '0.076', 'cue', 'twig', 'long_stick', 'stick', 'adhere']
senticnet['gameness'] = ['-0.88', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.85', 'gimpiness', 'lameness', 'claudication', 'gimp', 'limping']
senticnet['games'] = ['0.738', '0.961', '0', '0', '#joy', '#interest', 'positive', '0.849', 'amusement', 'entertainment', 'fun', 'play', 'alleviate_boredom']
senticnet['gamesome'] = ['-0.44', '-0.15', '0', '-0.36', '#sadness', '#disgust', 'negative', '-0.21', 'baffle', 'stupefy', 'dumbfound', 'stupefaction', 'confuse']
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senticnet['gamey'] = ['0.827', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.792', 'spirited', 'mettlesome', 'game', 'gritty', 'gamy']
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senticnet['gaminess'] = ['-0.69', '0', '0.597', '-0.56', '#sadness', '#anger', 'negative', '-0.62', 'improperness', 'impropriety', 'raciness', 'spiciness', 'indelicacy']
senticnet['gamma_alpha'] = ['0.074', '0.114', '0', '0.126', '#interest', '#admiration', 'positive', '0.105', 'paragraph', 'newspaper_write', 'news_paper', 'newspaper_story', 'index']
senticnet['gamma_aminobutyric_acid_gaba'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'gastrointestinal_hormone', 'secretin', 'ghrelin']
senticnet['gamma_distribution'] = ['-0.59', '0', '0.672', '-0.83', '#anger', '#disgust', 'negative', '-0.7', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'asymmetry', 'irregularity']
senticnet['gamma_radiation'] = ['0.048', '0', '0', '0.092', '#joy', '#admiration', 'positive', '0.07', 'radio_wave', 'sun', 'solar_cell', 'x-ray', 'cosmic_ray']
senticnet['gamma_ray'] = ['0.048', '0', '0', '0.092', '#joy', '#admiration', 'positive', '0.07', 'radio_wave', 'sun', 'solar_cell', 'x-ray', 'cosmic_ray']
senticnet['gamma_ray_astronomy'] = ['0.048', '0', '0', '0.092', '#joy', '#admiration', 'positive', '0.07', 'radio_wave', 'sun', 'solar_cell', 'x-ray', 'cosmic_ray']
senticnet['gamma_ray_detector'] = ['0.048', '0', '0', '0.092', '#joy', '#admiration', 'positive', '0.07', 'radio_wave', 'sun', 'solar_cell', 'x-ray', 'cosmic_ray']
senticnet['gamma_ray_emission'] = ['0.048', '0', '0', '0.092', '#joy', '#admiration', 'positive', '0.07', 'radio_wave', 'sun', 'solar_cell', 'x-ray', 'cosmic_ray']
senticnet['gamma_ray_spectra'] = ['0.048', '0', '0', '0.092', '#joy', '#admiration', 'positive', '0.07', 'radio_wave', 'sun', 'solar_cell', 'x-ray', 'cosmic_ray']
senticnet['gamma_ray_spectrum'] = ['0.048', '0', '0', '0.092', '#joy', '#admiration', 'positive', '0.07', 'radio_wave', 'sun', 'solar_cell', 'x-ray', 'cosmic_ray']
senticnet['gammon'] = ['0.648', '-0.53', '0', '0.94', '#joy', '#admiration', 'positive', '0.709', 'bacon_strip', 'flitch', 'side_of_bacon', 'bacon', 'cut_of_pork']
senticnet['gamowteller'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'elementary_particle', 'neutron', 'electron', 'subatomic_particle', 'proton']
senticnet['gamut'] = ['0.624', '-0.65', '0', '0.939', '#surprise', '#admiration', 'positive', '0.739', 'reach', 'range', 'compass', 'scope', 'orbit']
senticnet['gamy'] = ['0', '0.844', '0', '0.811', '#interest', '#admiration', 'positive', '0.828', 'high-pitched', 'eminent', 'higher', 'high', 'in_high_spirits']
senticnet['ganache'] = ['0.059', '-0.02', '0.025', '0.109', '#joy', '#admiration', 'positive', '0.043', 'tasty', 'bake_treat', 'bake_goods', 'bake_good', 'loaf']
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senticnet['gang_activity'] = ['-0.47', '0.174', '-0.44', '0.105', '#sadness', '#fear', 'negative', '-0.16', 'wrong', 'wrongdoing', 'bad_choice', 'harmful_human', 'bad_behavior']
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senticnet['gang_banger'] = ['-0.52', '0', '0.981', '-0.61', '#anger', '#disgust', 'negative', '-0.70', 'criminal', 'bully', 'outlaw', 'malefactor', 'felon']
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senticnet['gang_starr'] = ['0.081', '-0.05', '0.052', '0.072', '#joy', '#admiration', 'positive', '0.039', 'rhythm', 'type_of_music', 'instrumental', 'lyric', 'melody']
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senticnet['ganglioside'] = ['0.772', '-0.56', '0', '0.824', '#joy', '#admiration', 'positive', '0.719', 'neutrophil', 'leukocyte', 'leucocyte', 'white_blood_cell', 'white_cell']
senticnet['gangrene'] = ['-0.72', '0', '-0.98', '0', '#sadness', '#fear', 'negative', '-0.85', 'amputation', 'amputated', 'limb', 'extremity', 'amputation']
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senticnet['gangsta_rapper'] = ['-0.19', '0', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.15', 'bad_child', 'misbehavior', 'bad_kid', 'bad_action', 'difficulty']
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senticnet['gangster_moll'] = ['0.794', '-0.50', '0', '0.531', '#joy', '#admiration', 'positive', '0.611', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
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senticnet['gangster_squizzy_taylor'] = ['-0.54', '0', '0.845', '-0.95', '#anger', '#disgust', 'negative', '-0.78', 'face', 'base', 'underside', 'sole', 'bilge']
senticnet['gangway'] = ['0.831', '-0.82', '0', '0.826', '#joy', '#admiration', 'positive', '0.826', 'deck', 'freeboard_deck', 'weather_deck', 'shelter_deck', 'boat_deck']
senticnet['ganjifa'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'jewelry_store', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['ganoderma'] = ['-0.60', '0', '0.541', '-0.52', '#sadness', '#anger', 'negative', '-0.55', 'swoon', 'pass_out', 'faint', 'zonk_out', 'change_state']
senticnet['ganoderma_applanatum'] = ['-0.60', '0', '0.541', '-0.52', '#sadness', '#anger', 'negative', '-0.55', 'swoon', 'pass_out', 'faint', 'zonk_out', 'change_state']
senticnet['ganser_syndrome'] = ['-0.55', '0', '0.736', '-0.71', '#anger', '#disgust', 'negative', '-0.66', 'neurosis', 'neuroticism', 'psychoneurosis', 'hysterocatalepsy', 'anxiety_hysteria']
senticnet['gantlet'] = ['0.955', '-0.93', '0', '0', '#joy', '#surprise', 'positive', '0.943', 'tack', 'foot_part', 'casual_shoes', 'spike', 'pin']
senticnet['gantry'] = ['0', '0.908', '0', '0.837', '#interest', '#admiration', 'positive', '0.873', 'war', 'gunner', 'cannon', 'turret', 'tower']
senticnet['ganzfeld_experiment'] = ['0.628', '-0.96', '0', '0.682', '#surprise', '#admiration', 'positive', '0.759', 'taboo', 'tabu', 'tendentiousness', 'irrational_hostility', 'bias']
senticnet['gaokao'] = ['-0.22', '-0.00', '-0.09', '-0.03', '#sadness', '#fear', 'negative', '-0.08', 'failure', 'bad_grade', 'a_lot_of_stress', 'fail', 'further_goal']
senticnet['gaol'] = ['0.977', '-0.55', '0', '0.679', '#joy', '#admiration', 'positive', '0.737', 'lockup', 'holding_cell', 'workhouse', 'bastille', 'hoosegow']
senticnet['gaol_fever'] = ['0.709', '-0.67', '0', '0.969', '#joy', '#admiration', 'positive', '0.784', 'murine_typhus', 'rat_typhus', 'urban_typhus', 'endemic_typhus', 'rickettsial_disease']
senticnet['gaoler'] = ['-0.78', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.87', 'jailbird', 'captive', 'imprisoned', 'convict', 'inmate']
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senticnet['gaping_loophole'] = ['0', '0', '0.76', '-0.92', '#anger', '#disgust', 'negative', '-0.84', 'ambiguity', 'prevarication', 'equivocation', 'unclearness', 'equivocalness']
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senticnet['gender_diversity'] = ['0.861', '0', '0', '0.879', '#joy', '#admiration', 'positive', '0.87', 'stem_educa', 'education', 'science', 'human_resource_development', 'award']
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senticnet['gender_makeup'] = ['-0.1', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.1', 'deflate', 'read_cover', 'look_behind', 'upgrade', 'scan']
senticnet['genderqueer'] = ['-0.97', '0', '0.971', '-0.84', '#sadness', '#anger', 'negative', '-0.93', 'fail', 'betray', 'disappoint', 'cross', 'disillusion']
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senticnet['gene_dosage'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'deflection', 'warp', 'aberrance']
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senticnet['gene_mutation'] = ['-0.87', '0', '0.873', '-0.62', '#sadness', '#anger', 'negative', '-0.78', 'pycnodysostosis', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'dwarfism']
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senticnet['general'] = ['0', '0.08', '0', '0', '#interest', '#interest', 'positive', '0.08', 'army_boss', 'sergeant', 'military_rank', 'lieutenant', 'high_rank']
senticnet['general-purpose'] = ['0', '0', '-0.93', '-0.84', '#fear', '#disgust', 'negative', '-0.88', 'special-purpose', 'widely-used', 'high-performance', 'microprocessor', 'programmable']
senticnet['general_anaesthesia'] = ['-0.65', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'put_to_sleep', 'operation', 'anesthesia', 'surgery', 'local_anesthesia']
senticnet['general_anaesthetic'] = ['-0.65', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'put_to_sleep', 'operation', 'anesthesia', 'surgery', 'local_anesthesia']
senticnet['general_anesthesia'] = ['-0.65', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'put_to_sleep', 'operation', 'anesthesia', 'surgery', 'local_anesthesia']
senticnet['general_anesthetic'] = ['-0.65', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'put_to_sleep', 'operation', 'anesthesia', 'surgery', 'local_anesthesia']
senticnet['general_assembly'] = ['0.055', '0.072', '-0.06', '0.094', '#interest', '#admiration', 'positive', '0.038', 'big_group', 'large_group', 'bunch_person', 'large_number', 'clique']
senticnet['general_aviation_airfield'] = ['-0.97', '0', '0.737', '-0.97', '#sadness', '#disgust', 'negative', '-0.89', 'flare_path', 'airstrip', 'take_off', 'runway', 'grassland']
senticnet['general_ban_ki'] = ['0.916', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.929', 'galaxy', 'universe', 'planetary', 'sun', 'dwarf_planet']
senticnet['general_counsel'] = ['0', '0.146', '-0.05', '0.077', '#interest', '#admiration', 'positive', '0.057', 'lawyer', 'negotiator', 'arbiter', 'judge_jury', 'decide_guilt']
senticnet['general_custer'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['general_debility'] = ['0.753', '-0.74', '0', '0.587', '#joy', '#surprise', 'positive', '0.695', 'draining', 'exhausting', 'debilitating', 'enervating', 'enfeebling']
senticnet['general_election'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'couch', 'stool', 'coach_seat']
senticnet['general_election_campaign'] = ['-0.81', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.81', 'take_charge', 'anti', 'go_against', 'death_wish', 'file_complaint']
senticnet['general_knowledge'] = ['0.745', '-0.77', '0', '0.655', '#joy', '#surprise', 'positive', '0.724', 'light', 'open', 'common_knowledge', 'surface', 'cognition']
senticnet['general_ledger'] = ['0', '0.119', '0', '0', '#interest', '#interest', 'positive', '0.119', 'ledger', 'computer_disk', 'memo_pad', 'intern', 'graphite_pencil']
senticnet['general_ledger_account'] = ['0', '0.119', '0', '0', '#interest', '#interest', 'positive', '0.119', 'ledger', 'computer_disk', 'memo_pad', 'intern', 'graphite_pencil']
senticnet['general_manager'] = ['0.046', '0.116', '-0.05', '0.076', '#interest', '#admiration', 'positive', '0.045', 'head_guy', 'boss', 'officer', 'leader', 'supervisor']
senticnet['general_nkunda'] = ['-0.98', '0', '0.654', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'bereave', 'expropriate', 'deprive', 'unclothe', 'take']
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senticnet['general_practitioner'] = ['0.738', '-0.53', '0', '0.856', '#joy', '#admiration', 'positive', '0.71', 'doctor', 'country_doctor', 'family_doctor', 'doctor_office', 'physician']
senticnet['general_practitioner_gp'] = ['0.738', '-0.53', '0', '0.856', '#joy', '#admiration', 'positive', '0.71', 'doctor', 'country_doctor', 'family_doctor', 'doctor_office', 'physician']
senticnet['general_relativistic'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'general_relativity', 'general_theory_of_relativity', 'relativity', 'special_relativity', 'special_theory_of_relativity']
senticnet['general_relativity'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'relativity', 'general_theory_of_relativity', 'relativity_theory', 'special_relativity', 'special_theory_of_relativity']
senticnet['general_relativity_theory'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'general_relativity', 'general_theory_of_relativity', 'relativity', 'special_relativity', 'special_theory_of_relativity']
senticnet['general_store'] = ['0.027', '0.083', '0.019', '0', '#joy', '#interest', 'positive', '0.03', 'large_supermarket', 'corner_shop', 'large_food_store', 'bazaar', 'dime_store']
senticnet['general_theory_of_relativity'] = ['-0.69', '0', '0.83', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'general_relativity', 'relativity', 'general_relativity_theory', 'special_relativity', 'special_theory_of_relativity']
senticnet['general_time'] = ['0.705', '-0.60', '0', '0.913', '#joy', '#admiration', 'positive', '0.741', 'eternal', 'usual', 'hot_weather', 'ell', 'common_practice']
senticnet['generaladmiral'] = ['0.688', '-0.90', '0', '0.877', '#surprise', '#admiration', 'positive', '0.824', 'flag_officer', 'rear_admiral', 'admiral', 'full_admiral', 'vice_admiral']
senticnet['generalisation'] = ['0', '0.859', '0', '0.84', '#interest', '#admiration', 'positive', '0.849', 'generalization', 'abstraction', 'generality', 'inductive_reasoning', 'stimulus_generalisation']
senticnet['generality'] = ['0', '0.864', '0', '0.841', '#interest', '#admiration', 'positive', '0.853', 'generalisation', 'abstraction', 'generalization', 'inductive_reasoning', 'stimulus_generalisation']
senticnet['generalizability'] = ['0.628', '-0.96', '0', '0.682', '#surprise', '#admiration', 'positive', '0.759', 'taboo', 'tabu', 'tendentiousness', 'irrational_hostility', 'bias']
senticnet['generalization'] = ['0', '0.865', '0', '0.844', '#interest', '#admiration', 'positive', '0.855', 'generalisation', 'abstraction', 'generality', 'inductive_reasoning', 'stimulus_generalisation']
senticnet['generalize'] = ['0', '0', '-0.72', '-0.87', '#fear', '#disgust', 'negative', '-0.8', 'infer', 'extrapolate', 'deduce', 'derive', 'guess']
senticnet['generalized_lymphadenopathy'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema', 'erythema_multiforme', 'windburn', 'hickey']
senticnet['generalized_seizure'] = ['-0.56', '0', '0.577', '-0.61', '#anger', '#disgust', 'negative', '-0.58', 'foggy', 'groggy', 'logy', 'lethargic', 'dazed']
senticnet['generalized_urticaria'] = ['-0.55', '0', '0.536', '-0.72', '#sadness', '#disgust', 'negative', '-0.60', 'hypersensitivity_reaction', 'rash', 'efflorescence', 'roseola', 'skin_rash']
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senticnet['generally_accepted_definition'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'vocabulary', 'read_material', 'book_genre', 'many_words', 'book_library']
senticnet['generalplan_ost'] = ['-0.08', '0', '-0.15', '-0.20', '#fear', '#disgust', 'negative', '-0.14', 'casualty', 'reaper', 'wound', 'cause_death', 'gunshot']
senticnet['generalship'] = ['0.646', '-0.9', '0', '0.684', '#surprise', '#admiration', 'positive', '0.743', 'acquirement', 'skill', 'accomplishment', 'acquisition', 'attainment']
senticnet['generate'] = ['0.811', '0', '0', '0.761', '#joy', '#admiration', 'positive', '0.786', 'sire', 'develop', 'engender', 'render', 'come_up']
senticnet['generate_electricity'] = ['0.986', '0.897', '0', '0', '#joy', '#interest', 'positive', '0.942', 'potential_energy', 'watt', 'electrical', 'power_source', 'volt']
senticnet['generate_energy'] = ['0.109', '0.129', '0', '0.098', '#joy', '#interest', 'positive', '0.112', 'potential', 'power_source', 'electrical', 'power', 'potential_energy']
senticnet['generate_heat'] = ['-0.06', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.08', 'turn_key', 'danger', 'open_car_door', 'shine_car', 'corrode']
senticnet['generate_revenue'] = ['0.886', '-0.77', '0', '0.815', '#joy', '#admiration', 'positive', '0.826', 'countenance', 'legitimate', 'permit', 'allow', 'let']
senticnet['generate_sound'] = ['0.109', '0.129', '0', '0.098', '#joy', '#interest', 'positive', '0.112', 'sand_paper', 'measure_angle', 'music_sheet', 'read_music', 'low_note']
senticnet['generated_electricity'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'breeze', 'windmill', 'wind', 'wind_turbine', 'windy']
senticnet['generates_electricity'] = ['0.986', '0.897', '0', '0', '#joy', '#interest', 'positive', '0.942', 'potential_energy', 'watt', 'electrical', 'power_source', 'volt']
senticnet['generates_revenue'] = ['0.886', '-0.77', '0', '0.815', '#joy', '#admiration', 'positive', '0.826', 'countenance', 'legitimate', 'permit', 'allow', 'let']
senticnet['generating_revenue'] = ['0.886', '-0.77', '0', '0.815', '#joy', '#admiration', 'positive', '0.826', 'countenance', 'legitimate', 'permit', 'allow', 'let']
senticnet['generation'] = ['0.916', '0.786', '0', '0', '#joy', '#interest', 'positive', '0.851', 'biogenesis', 'multiplication', 'people', 'propagation', 'posterity']
senticnet['generational_change'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'absolutism', 'autocracy', 'ascendancy', 'tyrant']
senticnet['generative'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'integrative', 'consolidative', 'unifying', 'combinatorial']
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senticnet['generator'] = ['0', '0.744', '0', '0.903', '#interest', '#admiration', 'positive', '0.824', 'source', 'writer', 'author', 'well', 'write']
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senticnet['generic-looking'] = ['0', '-0.86', '0', '0.819', '#surprise', '#admiration', 'positive', '0.843', 'well-thought-of', 'well-visited', 'tower-style', 'high-profiled', 'clever-clever']
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senticnet['genericized_trademark'] = ['0.917', '-0.98', '0', '0.613', '#joy', '#surprise', 'positive', '0.837', 'practice', 'activity', 'play', 'misuse', 'abuse']
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senticnet['generous'] = ['0.92', '0', '0', '0.752', '#joy', '#admiration', 'positive', '0.836', 'kind', 'help_out', 'benevolent', 'need', 'generosity']
senticnet['generous_bequest'] = ['0.608', '-0.55', '0', '0.726', '#joy', '#admiration', 'positive', '0.628', 'genius', 'raw_talent', 'flair', 'gift', 'knack']
senticnet['generous_contribution'] = ['0', '0.264', '-0.24', '0.34', '#interest', '#admiration', 'positive', '0.12', 'help_out', 'give', 'come_back', 'alms', 'lend']
senticnet['generous_donation'] = ['0', '0.264', '-0.24', '0.34', '#interest', '#admiration', 'positive', '0.12', 'help_out', 'give', 'come_back', 'alms', 'lend']
senticnet['generous_endowment'] = ['0.608', '-0.55', '0', '0.726', '#joy', '#admiration', 'positive', '0.628', 'genius', 'raw_talent', 'flair', 'gift', 'knack']
senticnet['generous_gift'] = ['0.608', '-0.55', '0', '0.726', '#joy', '#admiration', 'positive', '0.628', 'genius', 'raw_talent', 'flair', 'gift', 'knack']
senticnet['generous_hospitality'] = ['0.736', '-0.66', '0', '0.777', '#joy', '#admiration', 'positive', '0.725', 'lucky', 'propitious', 'prosperous', 'favorable', 'good_luck']
senticnet['generous_spirit'] = ['0.954', '0', '0', '0.964', '#joy', '#admiration', 'positive', '0.959', 'feel_for', 'sympathize_with', 'compassionateness', 'condole_with', 'commiserate']
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senticnet['genesect'] = ['0.028', '0', '0.027', '0.101', '#joy', '#admiration', 'positive', '0.034', 'animal_dwell', 'bee_home', 'live_place', 'dwell_place', 'place_to_live']
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senticnet['genesis_chapter'] = ['0', '0.106', '-0.05', '0.163', '#interest', '#admiration', 'positive', '0.072', 'band', 'kit', 'oblivion', 'rock_band', 'high_school_band']
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senticnet['genesis_rabbah'] = ['0', '0.106', '-0.05', '0.163', '#interest', '#admiration', 'positive', '0.072', 'band', 'kit', 'oblivion', 'rock_band', 'high_school_band']
senticnet['genetic_abnormality'] = ['-0.87', '0', '0.873', '-0.62', '#sadness', '#anger', 'negative', '-0.78', 'pycnodysostosis', 'genetic_disease', 'genetic_disorder', 'dwarfism', 'genetic_defect']
senticnet['genetic_analysis'] = ['0', '0.989', '0', '0.94', '#interest', '#admiration', 'positive', '0.964', 'determine', 'gene', 'study', 'protein', 'project']
senticnet['genetic_code'] = ['0.888', '-0.81', '0', '0.617', '#joy', '#surprise', 'positive', '0.773', 'bacteria_order', 'chromosome', 'ordering', 'genome', 'series']
senticnet['genetic_component'] = ['0.504', '-0.93', '0', '0.797', '#surprise', '#admiration', 'positive', '0.747', 'diabetes', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['genetic_defect'] = ['-0.87', '0', '0.873', '-0.62', '#sadness', '#anger', 'negative', '-0.78', 'pycnodysostosis', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'dwarfism']
senticnet['genetic_differentiation'] = ['-0.83', '0', '0.546', '-0.88', '#sadness', '#disgust', 'negative', '-0.75', 'difference', 'allomerism', 'deviation', 'variation', 'departure']
senticnet['genetic_disease'] = ['-0.87', '0', '0.873', '-0.62', '#sadness', '#anger', 'negative', '-0.78', 'pycnodysostosis', 'dwarfism', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['genetic_disorder'] = ['-0.98', '0', '0.182', '-0.91', '#sadness', '#disgust', 'negative', '-0.69', 'need_cure', 'ill', 'sickness', 'sick', 'disorder']
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senticnet['genetic_endowment'] = ['-0.95', '0', '0.684', '-0.76', '#sadness', '#disgust', 'negative', '-0.79', 'innate', 'nurtural', 'nonheritable', 'nontransmissible', 'nonhereditary']
senticnet['genetic_fallacy'] = ['-0.77', '0', '0.885', '-0.73', '#sadness', '#anger', 'negative', '-0.8', 'pathetic_fallacy', 'misconception', 'paralogism', 'logical_fallacy', 'pseudoscience']
senticnet['genetic_heterogeneity'] = ['0.504', '-0.93', '0', '0.797', '#surprise', '#admiration', 'positive', '0.747', 'diabetes', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['genetic_inheritance'] = ['-0.95', '0', '0.684', '-0.76', '#sadness', '#disgust', 'negative', '-0.79', 'innate', 'nurtural', 'nonheritable', 'nontransmissible', 'nonhereditary']
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senticnet['genetic_mutation_responsible'] = ['0.504', '-0.93', '0', '0.797', '#surprise', '#admiration', 'positive', '0.747', 'diabetes', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['genetic_similarity'] = ['-0.54', '0', '0.979', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'unlikeness', 'dissimilitude', 'difference', 'heterology', 'disparateness']
senticnet['genetic_susceptibility'] = ['-0.78', '0', '0.6', '-0.61', '#sadness', '#disgust', 'negative', '-0.66', 'suggestibility', 'sensitivity', 'capacity', 'reactivity', 'predisposition']
senticnet['genetic_variation'] = ['0.822', '0.976', '0', '0', '#joy', '#interest', 'positive', '0.899', 'population', 'specie', 'determine', 'animal', 'pattern']
senticnet['genetic_vulnerability'] = ['-0.78', '0', '0.6', '-0.61', '#sadness', '#disgust', 'negative', '-0.66', 'suggestibility', 'sensitivity', 'capacity', 'reactivity', 'predisposition']
senticnet['genetically_resistant'] = ['-0.69', '0', '0.516', '-0.94', '#sadness', '#disgust', 'negative', '-0.71', 'insubordinate', 'recalcitrant', 'obstreperous', 'defiant', 'resistive']
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senticnet['genial'] = ['0.656', '-0.73', '0', '0.736', '#surprise', '#admiration', 'positive', '0.708', 'strong', 'salubrious', 'healthy', 'solid', 'alimentary']
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senticnet['genially'] = ['-0.71', '0', '0.906', '-0.93', '#anger', '#disgust', 'negative', '-0.85', 'absent', 'forgetful', 'distracted', 'inattentive', 'scatty']
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senticnet['genital_pore'] = ['-0.93', '0', '0.607', '-0.67', '#sadness', '#disgust', 'negative', '-0.73', 'spiracle', 'stigma', 'orifice', 'opening', 'porta']
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senticnet['genital_stimulation'] = ['0.655', '-0.87', '0', '0.595', '#joy', '#surprise', 'positive', '0.709', 'erection', 'excitement', 'arousal', 'kill_brain_cell', 'feel_relieved']
senticnet['genital_ulcer_disease'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'gastroenteritis', 'stomach_flu', 'intestinal_flu', 'inflammatory_disease']
senticnet['genitalia'] = ['0', '0', '0', '-0.06', '#disgust', '#disgust', 'negative', '-0.06', 'body', 'nervous_system', 'normal_human_body', 'human_body', 'head_shoulder']
senticnet['genitals'] = ['0', '-0.89', '0', '0.876', '#surprise', '#admiration', 'positive', '0.885', 'g-spot', 'clitoris', 'clitoral', 'perineum', 'erogenous']
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senticnet['genius_iq'] = ['0', '0.943', '0', '0.789', '#interest', '#admiration', 'positive', '0.866', 'energy', 'genius', 'energy_use', 'gas_oil', 'invent']
senticnet['genius_locus'] = ['0.673', '-0.64', '0', '0.669', '#joy', '#admiration', 'positive', '0.661', 'eudemon', 'white_angel', 'good_spirit', 'guardian_angel', 'angel']
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senticnet['genly_ai'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'dryness', 'thirstiness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['gennosuke'] = ['0', '-0.01', '-0.08', '-0.16', '#fear', '#disgust', 'negative', '-0.07', 'siege', 'big_fight', 'battle', 'declare_war', 'conflict']
senticnet['genocidal'] = ['-0.08', '0', '-0.15', '-0.20', '#fear', '#disgust', 'negative', '-0.14', 'casualty', 'reaper', 'wound', 'cause_death', 'gunshot']
senticnet['genocidal_campaign'] = ['-0.08', '0', '-0.15', '-0.20', '#fear', '#disgust', 'negative', '-0.14', 'casualty', 'reaper', 'wound', 'cause_death', 'gunshot']
senticnet['genocidal_war'] = ['-0.67', '0', '0.938', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'massacre', 'mass_murder', 'carnage', 'slaughter', 'butchery']
senticnet['genocide'] = ['0', '0', '0.949', '-0.89', '#anger', '#disgust', 'negative', '-0.92', 'kill', 'violent_person', 'death_destruction', 'murder', 'death_penalty']
senticnet['genome'] = ['0.888', '-0.81', '0', '0.617', '#joy', '#surprise', 'positive', '0.773', 'bacteria_order', 'chromosome', 'genetic_code', 'ordering', 'series']
senticnet['genosha'] = ['0.02', '0.131', '0.749', '-0.67', '#anger', '#disgust', 'negative', '-0.31', 'belligerence', 'belligerency', 'belligerent', 'war', 'rancor']
senticnet['genotoxicity'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'deflection', 'warp', 'aberrance']
senticnet['genotype'] = ['-0.96', '0', '0.556', '-0.87', '#sadness', '#disgust', 'negative', '-0.79', 'animal_group', 'group', 'biological_group', 'clade', 'taxonomic_group']
senticnet['genre'] = ['0', '0.951', '0', '0', '#interest', '#interest', 'positive', '0.951', 'part_of_history', 'movie_action_movie', 'action_movie', 'fictional', 'horror_movie']
senticnet['genre-bending'] = ['0', '0.94', '0', '0.913', '#interest', '#admiration', 'positive', '0.926', 'mind-bending', 'mind-altering', 'mind-opening', 'mind-expanding', 'eye-opening']
senticnet['genre_fiction'] = ['0', '0.915', '0', '0.704', '#interest', '#admiration', 'positive', '0.809', 'low-brow', 'highbrow', 'high-brow', 'middle-brow', 'lowbrow']
senticnet['genre_music'] = ['0.946', '0.816', '0', '0.984', '#joy', '#admiration', 'positive', '0.915', 'music', 'good_music', 'hear_music', 'beautiful_music', 'enjoy_music']
senticnet['genteel'] = ['-0.65', '0', '0.858', '-0.83', '#anger', '#disgust', 'negative', '-0.78', 'turmoil', 'convulsion', 'tempest', 'disorder', 'earthquake']
senticnet['genteel_poverty'] = ['-0.91', '0', '0.598', '-0.52', '#sadness', '#anger', 'negative', '-0.68', 'parsimonious', 'stingy', 'scrimy', 'beggarly', 'chintzy']
senticnet['genteelness'] = ['0.639', '-0.70', '0', '0.507', '#joy', '#surprise', 'positive', '0.616', 'tastefulness', 'daintiness', 'eclat', 'breeding', 'elegance']
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senticnet['gentilhomme'] = ['0.947', '-0.99', '0', '0.936', '#joy', '#surprise', 'positive', '0.958', 'utterer', 'deipnosophist', 'interlocutor', 'conversational_partner', 'speaker']
senticnet['gentility'] = ['-0.88', '0', '0.666', '-0.63', '#sadness', '#anger', 'negative', '-0.73', 'clannishness', 'cliquishness', 'exclusiveness', 'arrogance', 'haughtiness']
senticnet['gentle'] = ['0', '0', '0', '0.961', '#admiration', '#admiration', 'positive', '0.961', 'kind', 'nice', 'precaution', 'proper', 'softly']
senticnet['gentle_boil'] = ['-0.66', '0', '0.992', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'cold', 'boil', 'temperature', 'low_temperature', 'frigidity']
senticnet['gentle_breeze'] = ['0.449', '0.305', '-0.19', '0.222', '#joy', '#interest', 'positive', '0.196', 'calm', 'peace', 'serenity', 'quiet', 'tranquility']
senticnet['gentle_curve'] = ['0.97', '0.758', '0', '0', '#joy', '#interest', 'positive', '0.864', 'trot', 'step_out', 'step_aside', 'progress', 'enter_place']
senticnet['gentle_rain'] = ['0.98', '-0.74', '0', '0.984', '#joy', '#admiration', 'positive', '0.903', 'prosperous', 'flourishing', 'successful', 'prospering', 'thriving']
senticnet['gentle_sigh'] = ['-0.93', '0', '0.791', '0', '#sadness', '#anger', 'negative', '-0.86', 'hopelessness', 'hopelessly', 'despairing', 'pessimistically', 'pessimistic']
senticnet['gentle_simmer'] = ['-0.66', '0', '0.992', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'cold', 'boil', 'temperature', 'low_temperature', 'frigidity']
senticnet['gentle_soul'] = ['-0.91', '0', '0.681', '-0.81', '#sadness', '#disgust', 'negative', '-0.80', 'chicken', 'doormat', 'softie', 'crybaby', 'wimp']
senticnet['gentle_yoga'] = ['0.84', '0.76', '0', '0.894', '#joy', '#admiration', 'positive', '0.831', 'keep_skin_healthy', 'stay_healthy', 'stay_fit', 'maintain_good_health', 'eat_healthily']
senticnet['gentlefolk'] = ['-0.91', '0', '0.598', '-0.52', '#sadness', '#anger', 'negative', '-0.68', 'parsimonious', 'stingy', 'scrimy', 'beggarly', 'chintzy']
senticnet['gentleman'] = ['0.093', '0.106', '-0.09', '0', '#joy', '#interest', 'positive', '0.036', 'educated', 'generous', 'princess', 'queen', 'professor']
senticnet['gentleman_farmer'] = ['-0.08', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.11', 'serf', 'peasant', 'servant', 'poor', 'villein']
senticnet['gentleman_magazine'] = ['0.722', '-0.98', '0', '0.832', '#surprise', '#admiration', 'positive', '0.845', 'laudatory', 'praiseful', 'praising', 'encomiastic', 'complimentary']
senticnet['gentleman_reg'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'cur', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['gentlemanlike'] = ['0.614', '-0.95', '0', '0.537', '#joy', '#surprise', 'positive', '0.703', 'mincing', 'twee', 'refined', 'gentlemanly', 'ladylike']
senticnet['gentlemanly'] = ['0.614', '-0.95', '0', '0.537', '#joy', '#surprise', 'positive', '0.703', 'mincing', 'twee', 'gentlemanlike', 'refined', 'ladylike']
senticnet['gentlemanly_manner'] = ['0.614', '-0.95', '0', '0.537', '#joy', '#surprise', 'positive', '0.703', 'mincing', 'twee', 'gentlemanlike', 'refined', 'ladylike']
senticnet['gentlemen'] = ['0.777', '0.913', '0', '0', '#joy', '#interest', 'positive', '0.845', 'ethical', 'honest', 'good', 'estimable', 'fair']
senticnet['gentleness'] = ['0', '0', '0', '0.98', '#admiration', '#admiration', 'positive', '0.98', 'kindness', 'demand', 'sympathy', 'type_of_bear', 'express_gratitude']
senticnet['gently'] = ['-0.85', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.79', 'long_distance', 'compass_direction', 'distance', 'afar', 'long_distance_relationship']
senticnet['gently_cook'] = ['-0.66', '0', '0.992', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'cold', 'boil', 'temperature', 'low_temperature', 'frigidity']
senticnet['gently_pull'] = ['0.988', '0.787', '0', '0', '#joy', '#interest', 'positive', '0.887', 'tug', 'carry', 'tow', 'extend', 'drag']
senticnet['gently_push'] = ['0.988', '0.787', '0', '0', '#joy', '#interest', 'positive', '0.887', 'tug', 'carry', 'tow', 'extend', 'drag']
senticnet['gently_rolling_terrain'] = ['0', '0', '0.865', '-0.95', '#anger', '#disgust', 'negative', '-0.90', 'bucket_overflow', 'dirty_floor', 'spill_over', 'float', 'spoil_wood_floor']
senticnet['gently_rub'] = ['-0.93', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.85', 'protect_from_sun', 'shade', 'meaning', 'corrector', 'sunglasses']
senticnet['gently_simmer'] = ['-0.66', '0', '0.992', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'cold', 'boil', 'temperature', 'low_temperature', 'frigidity']
senticnet['gently_sloping_hill'] = ['0', '0.093', '0', '0.078', '#interest', '#admiration', 'positive', '0.085', 'steep', 'landscape_feature', 'knoll', 'hill_mountain', 'incline']
senticnet['gently_stir'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'salad_shaker', 'store_shelf', 'kitchen_tool', 'pliers', 'ladle']
senticnet['gentry'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'bodyguard', 'outrider', 'bodyguard_of_guard', 'beefeater', 'defender']
senticnet['genu_valgum'] = ['0.582', '-0.66', '0', '0.661', '#surprise', '#admiration', 'positive', '0.637', 'talipes_equinus', 'deformity', 'malformation', 'talipes_valgus', 'talipes_calcaneus']
senticnet['genu_varum'] = ['0.582', '-0.66', '0', '0.661', '#surprise', '#admiration', 'positive', '0.637', 'talipes_equinus', 'deformity', 'malformation', 'talipes_valgus', 'talipes_calcaneus']
senticnet['genuflect'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'reverence', 'obeisance', 'genuflexion', 'kowtow', 'mafia']
senticnet['genuflection'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'reverence', 'obeisance', 'genuflexion', 'kowtow', 'mafia']
senticnet['genuflexion'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'reverence', 'genuflection', 'obeisance', 'kowtow', 'mafia']
senticnet['genuine'] = ['0.729', '0.872', '0', '0.877', '#interest', '#admiration', 'positive', '0.826', 'good_quality', 'original', 'good_character', 'mass_measure', 'description']
senticnet['genuine_affection'] = ['0.72', '-0.72', '0', '0.506', '#joy', '#surprise', 'positive', '0.651', 'standoffishness', 'withdrawnness', 'closeness', 'secretiveness', 'remoteness']
senticnet['genuine_compassion'] = ['0.954', '0', '0', '0.964', '#joy', '#admiration', 'positive', '0.959', 'feel_for', 'sympathize_with', 'compassionateness', 'condole_with', 'commiserate']
senticnet['genuine_emotion'] = ['0.741', '-0.74', '0', '0.666', '#joy', '#surprise', 'positive', '0.718', 'commonplaceness', 'mundanity', 'prosiness', 'prosaicness', 'ordinariness']
senticnet['genuine_enthusiasm'] = ['0.701', '0.874', '0', '0.752', '#interest', '#admiration', 'positive', '0.776', 'self_satisfaction', 'enjoy_life', 'good_feeling', 'good_attitude', 'enjoy']
senticnet['genuine_excitement'] = ['0.772', '0.742', '0', '0', '#joy', '#interest', 'positive', '0.757', 'enjoy', 'satisfaction', 'happiness', 'become_excited', 'joy']
senticnet['genuine_friendship'] = ['0.608', '0.647', '-0.26', '0.325', '#joy', '#interest', 'positive', '0.328', 'amicably', 'amicable', 'amicableness', 'brotherly', 'friendly']
senticnet['genuine_piety'] = ['0.838', '-0.55', '0', '0.622', '#joy', '#admiration', 'positive', '0.67', 'righteousness', 'godliness', 'dutifulness', 'devoutness', 'religiousness']
senticnet['genuine_reconciliation'] = ['0.497', '0.453', '-0.23', '0.189', '#joy', '#interest', 'positive', '0.226', 'calm', 'calmness', 'stillness', 'truce', 'love']
senticnet['genuine_remorse'] = ['-0.89', '0.168', '-0.60', '0.05', '#sadness', '#fear', 'negative', '-0.32', 'remorsefully', 'contritely', 'contriteness', 'ruefully', 'attrition']
senticnet['genuine_repentance'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitentially', 'penitent', 'repentantly', 'repentant']
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senticnet['genuine_warmth'] = ['0.58', '0.651', '-0.26', '0.308', '#joy', '#interest', 'positive', '0.319', 'well-disposed', 'approving', 'approve', 'sanctioned', 'approved']
senticnet['genuinely'] = ['0.724', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.851', 'truthfully', 'frankly', 'forsooth', 'veraciously', 'truly']
senticnet['genuinely_funny'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'rudeness', 'discourtesy', 'insolence', 'chutzpah', 'hutzpah']
senticnet['genuineness'] = ['0', '-0.98', '0', '0.962', '#surprise', '#admiration', 'positive', '0.974', 'legitimacy', 'authenticity', 'law', 'well', 'project']
senticnet['genus'] = ['0.802', '0.743', '0', '0', '#joy', '#interest', 'positive', '0.772', 'centerfold', 'fairy', 'honey_bee', 'infant', 'dove']
senticnet['genus_actia'] = ['-0.77', '0', '0.576', '-0.92', '#sadness', '#disgust', 'negative', '-0.75', 'insect', 'firebug', 'water_bug', 'assassin_bug', 'heteropterous_insect']
senticnet['genus_homo'] = ['0.537', '-0.51', '0', '0.807', '#joy', '#admiration', 'positive', '0.619', 'lesbian', 'fairy', 'fagot', 'faggot', 'fag']
senticnet['genus_leishmania'] = ['0.995', '-0.71', '0', '0.647', '#joy', '#surprise', 'positive', '0.784', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['genus_plasmodium'] = ['0.995', '-0.71', '0', '0.647', '#joy', '#surprise', 'positive', '0.784', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['genus_utricularia'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['genus_vespula'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'hornet', 'vespid', 'vespid_wasp']
senticnet['geo'] = ['0.873', '0.865', '0', '0', '#joy', '#interest', 'positive', '0.869', 'geographics', 'regional_science', 'regional_environmental', 'number', 'geography']
senticnet['geoboard'] = ['0', '-0.00', '0.034', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'celsius', 'grade', 'certificate', 'learn_tool', 'temperature_measure']
senticnet['geochemistry'] = ['0', '0.934', '0', '0.906', '#interest', '#admiration', 'positive', '0.92', 'different_application', 'petrology', 'geological_science', 'award', 'standard_grant']
senticnet['geodesic_flow'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'fragmentation', 'discharge', 'backfire', 'blast']
senticnet['geoff_toovey'] = ['0', '0.996', '0', '0.85', '#interest', '#admiration', 'positive', '0.923', 'strength', 'strong', 'physical_strength', 'athlete', 'muscle']
senticnet['geoffrey_keating_foras_feasa'] = ['0', '0.105', '0', '0.084', '#interest', '#admiration', 'positive', '0.095', 'archive', 'print_medium', 'quiet_place', 'register', 'media']
senticnet['geoffrey_winthrop_young'] = ['0', '0.076', '0', '0.063', '#interest', '#admiration', 'positive', '0.07', 'up_hill', 'elevation', 'up_mountain', 'ascend', 'upwards']
senticnet['geographic'] = ['0', '0.08', '0.032', '0.096', '#interest', '#admiration', 'positive', '0.048', 'school_subject', 'chemistry', 'geometric', 'physic', 'geology']
senticnet['geographic_coverage'] = ['0.828', '-0.89', '0', '0.693', '#joy', '#surprise', 'positive', '0.806', 'confines', 'expanse', 'view', 'internationality', 'purview']
senticnet['geographic_diversification'] = ['0', '0.312', '-0.10', '0.207', '#interest', '#admiration', 'positive', '0.137', 'infuse', 'loan', 'lend', 'increase_money', 'wealth']
senticnet['geographic_feature'] = ['0', '0.146', '0.02', '0.091', '#interest', '#admiration', 'positive', '0.072', 'landscape_feature', 'land_formation', 'land_feature', 'land_form', 'geographical_feature']
senticnet['geographic_mobility'] = ['0', '0', '0', '0.144', '#admiration', '#admiration', 'positive', '0.144', 'go_elsewhere', 'transport_person', 'get_around', 'locomotion', 'trucking']
senticnet['geographic_range'] = ['0.828', '-0.89', '0', '0.693', '#joy', '#surprise', 'positive', '0.806', 'confines', 'expanse', 'view', 'internationality', 'purview']
senticnet['geographic_representation'] = ['0', '0.157', '0', '0.147', '#interest', '#admiration', 'positive', '0.152', 'wall_hang', 'depiction', 'illustration', 'artwork', 'wall_decoration']
senticnet['geographical'] = ['-0.89', '0', '-1', '0', '#sadness', '#fear', 'negative', '-0.94', 'demographic', 'socioeconomic', 'socio-economic', 'population', 'demographic']
senticnet['geographical_dictionary'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'comic_book', 'child_book', 'child_story', 'comic', 'disney_cartoon']
senticnet['geographical_feature'] = ['0', '0.08', '0.032', '0.096', '#interest', '#admiration', 'positive', '0.048', 'land_form', 'land_formation', 'land_feature', 'geographic_feature', 'landscape_feature']
senticnet['geographical_mobility'] = ['0', '0', '0', '0.144', '#admiration', '#admiration', 'positive', '0.144', 'go_elsewhere', 'transport_person', 'get_around', 'locomotion', 'trucking']
senticnet['geographical_representation'] = ['0', '0.157', '0', '0.147', '#interest', '#admiration', 'positive', '0.152', 'wall_hang', 'depiction', 'illustration', 'artwork', 'wall_decoration']
senticnet['geographically_diverse'] = ['0.057', '0', '0', '0.153', '#joy', '#admiration', 'positive', '0.105', 'multiple', 'cute', 'variety', 'hairy', 'large_breadbox']
senticnet['geographics'] = ['0.877', '0.867', '0', '0', '#joy', '#interest', 'positive', '0.872', 'geography', 'regional_science', 'regional_environmental', 'number', 'geo']
senticnet['geography'] = ['0.878', '0.869', '0', '0', '#joy', '#interest', 'positive', '0.873', 'geographics', 'regional_science', 'regional_environmental', 'number', 'geo']
senticnet['geologic_feature'] = ['0', '0.08', '0.032', '0.096', '#interest', '#admiration', 'positive', '0.048', 'land_form', 'land_formation', 'land_feature', 'geographic_feature', 'landscape_feature']
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senticnet['getting_stuck'] = ['-0.63', '0', '0.829', '-0.79', '#anger', '#disgust', 'negative', '-0.75', 'stuffiness', 'blocked', 'problem', 'traffic', 'congestion']
senticnet['getting_worse'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'problem', 'serious_issue', 'overrate', 'prohibitionist_society', 'blade_weapon']
senticnet['getty_source_getty'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expansive', 'expansible', 'erectile', 'cavernous']
senticnet['geum_rivale'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'bulrush', 'nailrod', 'cattail', 'reedmace']
senticnet['gevaudan'] = ['-0.90', '0', '0.865', '-0.77', '#sadness', '#anger', 'negative', '-0.84', 'nosiness', 'snoopiness', 'prying', 'curiosity', 'wonder']
senticnet['geves_east_melbourne'] = ['0.552', '-0.61', '0', '0.588', '#surprise', '#admiration', 'positive', '0.583', 'barbershop_quartet', 'string_quartet', 'quartette', 'musical_organization', 'musical_group']
senticnet['gevme_corio_oval'] = ['0.552', '-0.61', '0', '0.588', '#surprise', '#admiration', 'positive', '0.583', 'barbershop_quartet', 'string_quartet', 'quartette', 'musical_organization', 'musical_group']
senticnet['gevsm_corio_oval'] = ['0.552', '-0.61', '0', '0.588', '#surprise', '#admiration', 'positive', '0.583', 'barbershop_quartet', 'string_quartet', 'quartette', 'musical_organization', 'musical_group']
senticnet['gewgaw'] = ['0', '0', '-0.86', '-0.86', '#fear', '#disgust', 'negative', '-0.86', 'gaud', 'novelty', 'trinket', 'bangle', 'bauble']
senticnet['gfci'] = ['-0.09', '-0.01', '-0.10', '-0.20', '#fear', '#disgust', 'negative', '-0.09', 'violence', 'siege', 'conflict', 'bloodshed', 'fight']
senticnet['gg_allin'] = ['0.542', '-0.83', '0', '0.927', '#surprise', '#admiration', 'positive', '0.769', 'incest', 'extramarital_sex', 'free_love', 'sexual_intercourse', 'intercourse']
senticnet['ghastly'] = ['-0.71', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.79', 'horrible', 'grim', 'bad', 'shocking', 'ghostly']
senticnet['ghastly_pallor'] = ['-0.66', '0', '0.782', '-0.52', '#sadness', '#anger', 'negative', '-0.65', 'lividness', 'pallidness', 'pallor', 'complexion', 'achromasia']
senticnet['ghent'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'feudal_lord', 'lord', 'master', 'overlord', 'king']
senticnet['gherkin'] = ['-0.81', '0', '0.543', '-0.63', '#sadness', '#disgust', 'negative', '-0.66', 'caper', 'pickle', 'relish', 'dill_pickle', 'sweet_pickle']
senticnet['gherkin_building'] = ['-0.81', '0', '0.543', '-0.63', '#sadness', '#disgust', 'negative', '-0.66', 'caper', 'pickle', 'relish', 'dill_pickle', 'sweet_pickle']
senticnet['gherkin_tower'] = ['-0.81', '0', '0.543', '-0.63', '#sadness', '#disgust', 'negative', '-0.66', 'caper', 'pickle', 'relish', 'dill_pickle', 'sweet_pickle']
senticnet['ghetto'] = ['-0.02', '-0.00', '-0.04', '0', '#sadness', '#fear', 'negative', '-0.02', 'medikit', 'manchester', 'medical_build', 'metropolitan_city', 'slum']
senticnet['ghibran'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_music', 'hear_music', 'artist', 'make_drawing', 'art']
senticnet['ghost'] = ['0', '0', '-0.79', '-0.73', '#fear', '#disgust', 'negative', '-0.76', 'spirit', 'soul', 'mind_control', 'angel', 'god']
senticnet['ghost_chili'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'high_temperature', 'hot', 'hot_weather', 'fire_product', 'heat']
senticnet['ghost_crab'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'stone_crab', 'swimming_crab', 'rock_crab', 'crab', 'pea_crab']
senticnet['ghost_rider'] = ['-0.84', '0', '0.96', '-0.91', '#anger', '#disgust', 'negative', '-0.90', 'housewrecker', 'housebreaker', 'knacker', 'laborer', 'manual_laborer']
senticnet['ghost_ship'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'succourer', 'recoverer', 'salvor', 'mortal']
senticnet['ghost_shrimp'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'stone_crab', 'swimming_crab', 'rock_crab', 'crab', 'pea_crab']
senticnet['ghost_town_dj'] = ['0', '0', '-0.87', '-0.88', '#fear', '#disgust', 'negative', '-0.88', 'exclamation', 'thumb_down', 'complaint', 'bad', 'dislike']
senticnet['ghost_train'] = ['0.089', '-0.18', '0.037', '-0.06', '#joy', '#surprise', 'positive', '0.043', 'balloon', 'ballon', 'fielder', 'kite', 'blimp']
senticnet['ghost_whisperer'] = ['0', '0', '0.785', '-0.72', '#anger', '#disgust', 'negative', '-0.75', 'sharp_nail', 'sharp', 'sharp_end', 'sharp_point', 'pointy']
senticnet['ghostlike'] = ['-0.82', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.80', 'evil', 'darkness', 'dark', 'sun_down', 'dark_side']
senticnet['ghostly'] = ['-0.71', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.8', 'horrible', 'grim', 'bad', 'shocking', 'ghastly']
senticnet['ghostly_white'] = ['-0.66', '0', '0.782', '-0.52', '#sadness', '#anger', 'negative', '-0.65', 'lividness', 'pallidness', 'pallor', 'complexion', 'achromasia']
senticnet['ghoul'] = ['-0.73', '0', '0.734', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'primitive', 'headhunter', 'barbarian', 'anthropophagus', 'anthropophagite']
senticnet['ghoulish'] = ['0', '0', '-0.79', '-0.99', '#fear', '#disgust', 'negative', '-0.89', 'represent', 'morbid', 'macabre', 'gothic', 'ghastly']
senticnet['ghrelin'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'cholecystokinin', 'secretin', 'gastrointestinal_hormone']
senticnet['ghz_band'] = ['-0.07', '0.078', '0', '0.078', '#interest', '#admiration', 'positive', '0.028', 'red_blue', 'red_orange', 'green_yellow', 'hue_tint', 'yellow_green']
senticnet['ghz_spectrum'] = ['-0.07', '0.078', '0', '0.078', '#interest', '#admiration', 'positive', '0.028', 'red_blue', 'red_orange', 'green_yellow', 'hue_tint', 'yellow_green']
senticnet['gi_bill'] = ['-0.07', '0', '0.073', '-0.26', '#sadness', '#disgust', 'negative', '-0.13', 'tuition', 'private_tuition', 'pay_tuition', 'enroll_class', 'enroll_school']
senticnet['gi_hormone'] = ['0.536', '-0.69', '0', '0.643', '#surprise', '#admiration', 'positive', '0.624', 'motilin', 'gastrin', 'cholecystokinin', 'secretin', 'ghrelin']
senticnet['gi_tract'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'anus', 'scrotum', 'shoulder', 'liver', 'waist']
senticnet['giacinto_scelsi'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'band_play', 'songwriter', 'rock_band']
senticnet['giacomo_balla'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'native_person', 'revolutionary', 'republic', 'revolution', 'look_ahead']
senticnet['giambattista_basile'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'narrative', 'bedtime', 'bed_time', 'long_story', 'saga']
senticnet['gianandrea_noseda'] = ['-0.91', '0', '0.931', '-0.72', '#sadness', '#anger', 'negative', '-0.85', 'hit_with_baton', 'get_hurt', 'whip', 'arrow', 'hurt']
senticnet['gianna_nannini'] = ['0.107', '0.11', '-0.08', '0.21', '#interest', '#admiration', 'positive', '0.086', 'live_healthy_life', 'forever_young', 'live_forever', 'live_long', 'live_good_life']
senticnet['giant'] = ['-0.79', '0', '0.7', '-0.51', '#sadness', '#anger', 'negative', '-0.66', 'animal', 'animate_being', 'beast', 'creature', 'brute']
senticnet['giant_amoeba'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['giant_anteater'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'kangaroo', 'giraffe', 'caribou', 'mule', 'horse']
senticnet['giant_beaver'] = ['0.941', '0.987', '0', '0', '#joy', '#interest', 'positive', '0.964', 'monkey', 'marmoset', 'lemur', 'fiance', 'koala']
senticnet['giant_black_hole'] = ['0', '0.706', '0', '0.847', '#interest', '#admiration', 'positive', '0.776', 'dwarf_planet', 'universe', 'planetary', 'sun', 'moon']
senticnet['giant_catcher'] = ['0', '0.075', '0', '0', '#interest', '#interest', 'positive', '0.075', 'cup', 'carafe', 'container_cup', 'mug', 'glasses']
senticnet['giant_christmas_tree'] = ['0.856', '0', '0', '0', '#joy', '#joy', 'positive', '0.856', 'tree', 'evergreen_tree', 'big_tree', 'santa_claus', 'christmas']
senticnet['giant_crab'] = ['0.96', '-0.77', '0', '0.929', '#joy', '#admiration', 'positive', '0.887', 'whale_louse', 'beach_flea', 'sand_hopper', 'sandhopper', 'amphipod']
senticnet['giant_crocodile'] = ['-0.85', '0', '0.685', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'alligator', 'crocodilian', 'false_gavial', 'gator', 'caiman']
senticnet['giant_diageo'] = ['0.951', '-0.97', '0', '0.639', '#joy', '#surprise', 'positive', '0.853', 'smoothie', 'drinking_water', 'nutrient', 'milk', 'refresher']
senticnet['giant_dipper_roller_coaster'] = ['0.089', '-0.18', '0.037', '-0.06', '#joy', '#surprise', 'positive', '0.043', 'balloon', 'ballon', 'fielder', 'kite', 'blimp']
senticnet['giant_eland'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'kangaroo', 'giraffe', 'caribou', 'mule', 'horse']
senticnet['giant_elliptical_galaxy'] = ['0', '0.706', '0', '0.847', '#interest', '#admiration', 'positive', '0.776', 'dwarf_planet', 'universe', 'planetary', 'sun', 'moon']
senticnet['giant_goliath'] = ['0.849', '-0.89', '0', '0.924', '#surprise', '#admiration', 'positive', '0.89', 'nonintellectual', 'lowbrow', 'lowbrowed', 'mindless', 'uncultivated']
senticnet['giant_ground_sloth'] = ['0.969', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.96', 'extinct', 'animal', 'ice', 'elephant', 'giant']
senticnet['giant_hive'] = ['-0.99', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.78', 'angioedema', 'atrophedema', 'dropsy', 'periodic_edema', 'lymphedema']
senticnet['giant_hornet'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'hornet', 'vespid', 'vespid_wasp']
senticnet['giant_inflatable'] = ['0', '-0.08', '0', '0', '#surprise', '#surprise', 'positive', '0.082', 'yo-yo', 'playroom', 'backgammon', 'action_figure', 'board_game']
senticnet['giant_kangaroo_rat'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'fox', 'prairie_fox', 'white_fox']
senticnet['giant_kraft'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'wrapping_paper', 'wrap_gift', 'butcher_paper', 'paper']
senticnet['giant_lego'] = ['0.036', '0.084', '0', '0', '#joy', '#interest', 'positive', '0.06', 'toy', 'bath_toy', 'yo-yo', 'toy_ball', 'duck']
senticnet['giant_lizard'] = ['-0.85', '0', '0.685', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'alligator', 'crocodilian', 'false_gavial', 'gator', 'caiman']
senticnet['giant_magnetoresistance_gmr'] = ['0.102', '0.078', '0', '0.087', '#joy', '#admiration', 'positive', '0.089', 'gold', 'platinum', 'iron_ore', 'silver', 'adjustable']
senticnet['giant_moa'] = ['-0.68', '0', '0.956', '-0.90', '#anger', '#disgust', 'negative', '-0.84', 'moa', 'anomalopteryx', 'ratite', 'ratite_bird', 'flightless_bird']
senticnet['giant_monster'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amuck', 'murderousness', 'misocainea', 'misogyny']
senticnet['giant_panda'] = ['0.02', '0.071', '0', '0.069', '#interest', '#admiration', 'positive', '0.053', 'many_animals', 'donkey', 'chimpanzee', 'cute_animal', 'creature']
senticnet['giant_panda_breeding'] = ['0.02', '0.071', '0', '0.069', '#interest', '#admiration', 'positive', '0.053', 'many_animals', 'donkey', 'chimpanzee', 'cute_animal', 'creature']
senticnet['giant_panda_cub'] = ['-0.95', '0', '0.735', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'novice', 'beginner', 'tyro', 'tiro', 'initiate']
senticnet['giant_pumpkin'] = ['0', '-0.01', '0.037', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'pear', 'green_apple', 'apricot', 'cherry', 'lime']
senticnet['giant_reptile'] = ['-0.85', '0', '0.685', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'alligator', 'crocodilian', 'false_gavial', 'gator', 'caiman']
senticnet['giant_retailer'] = ['0.912', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.917', 'carpet_store', 'lumberyard', 'furniture_store', 'front_porch', 'shoe_shop']
senticnet['giant_robot'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
senticnet['giant_schnauzer'] = ['0.039', '0', '0', '0.059', '#joy', '#admiration', 'positive', '0.049', 'big_dog', 'dog_cat', 'smart_dog', 'pet', 'domesticated_animal']
senticnet['giant_scorpion'] = ['0', '0', '-0.87', '0', '#fear', '#fear', 'negative', '-0.87', 'beetle', 'many_insects', 'grasshopper', 'firefly', 'mouse']
senticnet['giant_serpent'] = ['-0.75', '0', '0.708', '0', '#sadness', '#anger', 'negative', '-0.73', 'hound', 'cage_animal', 'kind_animal', 'hunter', 'animal']
senticnet['giant_sloth'] = ['0.969', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.96', 'extinct', 'animal', 'ice', 'elephant', 'giant']
senticnet['giant_snake'] = ['-0.68', '0', '0.611', '-0.59', '#sadness', '#anger', 'negative', '-0.63', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['giant_spider'] = ['0.03', '0.068', '-0.04', '0.093', '#interest', '#admiration', 'positive', '0.037', 'beast', 'equine', 'live_creature', 'furry_creature', 'domesticate_animal']
senticnet['giant_statue'] = ['0.085', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.079', 'sculpture', 'creative', 'yellow_ribbon', 'artwork', 'monument']
senticnet['giantism'] = ['-0.76', '0', '0.571', '-0.84', '#sadness', '#disgust', 'negative', '-0.72', 'giant', 'hypertrophy', 'disease', 'overgrowth', 'feel_pain']
senticnet['giardia_lamblia'] = ['0.995', '-0.71', '0', '0.647', '#joy', '#surprise', 'positive', '0.784', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['giardino'] = ['0.107', '0.11', '-0.08', '0.21', '#interest', '#admiration', 'positive', '0.086', 'live_healthy_life', 'forever_young', 'live_forever', 'live_long', 'live_good_life']
senticnet['gibb_river'] = ['-0.95', '0', '0.624', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['gibber'] = ['-0.61', '0', '0.738', '-0.80', '#anger', '#disgust', 'negative', '-0.71', 'babbling', 'double_talk', 'mumbo_jumbo', 'babble', 'lallation']
senticnet['gibbering'] = ['0', '0', '-0.76', '-0.90', '#fear', '#disgust', 'negative', '-0.83', 'prate', 'tittle-tattle', 'palaver', 'piffle', 'chatter']
senticnet['gibberish'] = ['-0.82', '0', '0.921', '-0.98', '#anger', '#disgust', 'negative', '-0.91', 'babbling', 'double_talk', 'mumbo_jumbo', 'babble', 'lallation']
senticnet['gibbethon'] = ['0.849', '-0.89', '0', '0.924', '#surprise', '#admiration', 'positive', '0.89', 'nonintellectual', 'lowbrow', 'lowbrowed', 'mindless', 'uncultivated']
senticnet['gibbous_moon'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['gibe'] = ['0.695', '-0.52', '0', '0.957', '#joy', '#admiration', 'positive', '0.726', 'jibe', 'slam', 'cheap_shot', 'shot', 'shaft']
senticnet['gibeah'] = ['0.849', '-0.89', '0', '0.924', '#surprise', '#admiration', 'positive', '0.89', 'nonintellectual', 'lowbrow', 'lowbrowed', 'mindless', 'uncultivated']
senticnet['gibraltar_regiment'] = ['0.059', '0.076', '-0.05', '0.079', '#interest', '#admiration', 'positive', '0.039', 'noble', 'king', 'earl', 'duchess', 'duke']
senticnet['gibson_sg'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'lever']
senticnet['giddily'] = ['0.512', '0.658', '-0.25', '0.256', '#joy', '#interest', 'positive', '0.293', 'riotously', 'expansively', 'ebulliently', 'zestfulness', 'gusto']
senticnet['giddiness'] = ['-0.91', '0', '0.808', '-0.72', '#sadness', '#anger', 'negative', '-0.81', 'levity', 'fun', 'frivolity', 'trait', 'silliness']
senticnet['giddy'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'unwell', 'sick', 'nausea', 'pregnant', 'low_blood_pressure']
senticnet['gideon_sundback'] = ['0.775', '0.835', '0', '0', '#joy', '#interest', 'positive', '0.805', 'belt', 'leather_jacket', 'backpack', 'pouch', 'bag']
senticnet['giemsa'] = ['0', '-0.01', '0.046', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'varnish', 'brush', 'dye', 'pigment', 'canvas']
senticnet['gif'] = ['-0.55', '0', '0.912', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'wrangle', 'dustup', 'quarrel', 'row', 'squabble']
senticnet['gif_animation'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'hardcore', 'declared', 'definitive', 'explicit']
senticnet['gif_format'] = ['-0.55', '0', '0.912', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'wrangle', 'dustup', 'quarrel', 'row', 'squabble']
senticnet['giffard_family'] = ['0.049', '0.16', '0', '0.155', '#interest', '#admiration', 'positive', '0.121', 'castle', 'palace', 'medieval', 'aristocrat', 'throne']
senticnet['gift'] = ['0.974', '0.788', '0', '0.964', '#joy', '#admiration', 'positive', '0.909', 'present', 'birthday_gift', 'win_lottery', 'holiday', 'birthday']
senticnet['gift_adult'] = ['-0.06', '0', '-0.09', '-0.10', '#fear', '#disgust', 'negative', '-0.08', 'human_truthful', 'nurse', 'doctor', 'elderly_person', 'doctor_surgery']
senticnet['gift_box'] = ['0.827', '-0.93', '0', '0.759', '#joy', '#surprise', 'positive', '0.84', 'party', 'use_party', 'birthday_party', 'gift_shop', 'talk_show']
senticnet['gift_shop'] = ['0.877', '-0.80', '0', '0', '#joy', '#surprise', 'positive', '0.843', 'convention', 'prisoner_war_camp', 'rave', 'box_ring', 'dark_alley']
senticnet['gift_wrap'] = ['0.942', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.965', 'generosity', 'oral_sex', 'win_lottery', 'consumerism', 'give_away']
senticnet['gifted_athlete'] = ['0', '0', '0', '0.97', '#admiration', '#admiration', 'positive', '0.97', 'increase_strength', 'keep_fit', 'fit_person', 'exercise', 'strengthen_heart']
senticnet['gifted_composer'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'band_play', 'songwriter', 'rock_band']
senticnet['gifted_pianist'] = ['0.592', '-0.93', '0', '0.935', '#surprise', '#admiration', 'positive', '0.819', 'child', 'small_fry', 'tyke', 'tike', 'kid']
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senticnet['gigantic'] = ['0', '-0.86', '0', '0.762', '#surprise', '#admiration', 'positive', '0.815', 'big', 'large', 'really_big', 'wide', 'tall']
senticnet['gigantic_statue'] = ['-0.88', '0', '0.882', '-0.58', '#sadness', '#anger', 'negative', '-0.78', 'mortal', 'behemoth', 'unusual_person', 'aberrant', 'giant']
senticnet['giggle'] = ['0.814', '0.705', '0', '0', '#joy', '#interest', 'positive', '0.759', 'make_fun', 'hear_joke', 'make_others_laugh', 'laugh', 'clown']
senticnet['giggly'] = ['0.936', '-0.81', '0', '0.83', '#joy', '#admiration', 'positive', '0.86', 'cherubic', 'lovely', 'lovable', 'endearing', 'angelic']
senticnet['gigi_gryce_except'] = ['0.045', '0', '0.033', '0.089', '#joy', '#admiration', 'positive', '0.034', 'spanish', 'vocalization', 'verbal_communication', 'vocal', 'mouth_noise']
senticnet['gigolo'] = ['-0.80', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.79', 'whorehouse', 'brothel', 'massage_parlor', 'bagnio', 'house_of_prostitution']
senticnet['gigot'] = ['0.783', '-0.63', '0', '0.829', '#joy', '#admiration', 'positive', '0.749', 'leg_of_lamb', 'lamb_roast', 'joint', 'roast', 'rack_of_lamb']
senticnet['gigue'] = ['0.591', '-0.91', '0', '0.605', '#surprise', '#admiration', 'positive', '0.704', 'farandole', 'folk_dance', 'polka', 'dance', 'typical_dance']
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senticnet['gil_kane'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'encyclopaedia', 'child_story', 'comic', 'disney_cartoon']
senticnet['gild'] = ['0.967', '-0.98', '0', '0.64', '#joy', '#surprise', 'positive', '0.863', 'club', 'social_club', 'society', 'guild', 'jockey_club']
senticnet['gild_lily'] = ['0.564', '-0.73', '0', '0.58', '#surprise', '#admiration', 'positive', '0.625', 'beautify', 'embellish', 'decorate', 'adorn', 'grace']
senticnet['gild_merchant'] = ['0.967', '-0.98', '0', '0.64', '#joy', '#surprise', 'positive', '0.863', 'club', 'social_club', 'society', 'guild', 'jockey_club']
senticnet['gild_refined_gold'] = ['0.967', '-0.98', '0', '0.64', '#joy', '#surprise', 'positive', '0.863', 'club', 'social_club', 'society', 'guild', 'jockey_club']
senticnet['gilded'] = ['0.83', '0.747', '0', '0', '#joy', '#interest', 'positive', '0.788', 'luxurious', 'princely', 'opulent', 'grand', 'deluxe']
senticnet['gilded_bronze'] = ['-0.62', '0', '0.961', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'crown', 'jeweled_headdress', 'tiara', 'headdress', 'headgear']
senticnet['gilded_silver'] = ['0.049', '0.145', '0', '0.171', '#interest', '#admiration', 'positive', '0.122', 'kingly', 'govern', 'head_wear', 'chivalry', 'blue_blood']
senticnet['gilded_statue'] = ['0.085', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.079', 'sculpture', 'creative', 'yellow_ribbon', 'artwork', 'monument']
senticnet['gilded_throne'] = ['0', '0', '-0.76', '-0.82', '#fear', '#disgust', 'negative', '-0.79', 'stage', 'thespian', 'actor', 'theatre', 'drama']
senticnet['gilded_wood'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'wallet', 'billfold', 'bank', 'piggy_bank', 'bank_vault']
senticnet['gilgamesh_usage'] = ['0.089', '-0.01', '0.033', '0.082', '#joy', '#admiration', 'positive', '0.039', 'informative', 'informational', 'ephemeral', 'fun_read', 'ads']
senticnet['gill'] = ['-0.20', '-0.10', '0.07', '-0.24', '#sadness', '#disgust', 'negative', '-0.10', 'flipper', 'sea_life', 'seafood', 'swim_animal', 'fillet']
senticnet['gill_hornby'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'lair', 'habitat', 'animal_home']
senticnet['gilles_le'] = ['-0.65', '0', '0.505', '-0.91', '#sadness', '#disgust', 'negative', '-0.69', 'hellhound', 'mythical_being', 'dragon', 'mythical_monster', 'amphisbaena']
senticnet['gilles_mendel'] = ['0', '-0.03', '0.022', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'shoulder_bag', 'satchel', 'water_bottle', 'tote_bag', 'carry']
senticnet['gillett_johnston'] = ['0.034', '0', '0.01', '0.087', '#joy', '#admiration', 'positive', '0.037', 'chime', 'bell_noise', 'alto', 'piano', 'ring_loudly']
senticnet['gillette_fusion'] = ['0', '-0.06', '0.071', '-0.17', '#anger', '#disgust', 'negative', '-0.06', 'double_edge_razor', 'clipper', 'pair_of_scissors', 'knife', 'razor_blade']
senticnet['gilt_metal'] = ['-0.53', '0', '0.682', '-0.74', '#anger', '#disgust', 'negative', '-0.65', 'girdle', 'cincture', 'cummerbund', 'waistband', 'waistcloth']
senticnet['gimbal'] = ['0.843', '-0.99', '0', '0.706', '#joy', '#surprise', 'positive', '0.849', 'gadgetry', 'contrivance', 'injector', 'mod_con', 'device']
senticnet['gimme_credit'] = ['0.711', '0', '0', '0', '#joy', '#joy', 'positive', '0.711', 'chemist', 'expert', 'scientist', 'esaminatore', 'physicist']
senticnet['gimmick'] = ['-0.87', '0', '-0.98', '0', '#sadness', '#fear', 'negative', '-0.92', 'gimmick', 'gimmicky', 'ploy', 'trick', 'fad']
senticnet['gimmickry'] = ['-0.54', '0', '0.7', '-0.64', '#anger', '#disgust', 'negative', '-0.62', 'flourish', 'rant', 'ornateness', 'fustian', 'claptrap']
senticnet['gimmicky'] = ['-0.87', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.93', 'gimmick', 'gimmick', 'ploy', 'trick', 'fad']
senticnet['gimp'] = ['-0.87', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.85', 'gimpiness', 'lameness', 'claudication', 'limping', 'gameness']
senticnet['gimpiness'] = ['-0.87', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.85', 'limping', 'lameness', 'claudication', 'gimp', 'gameness']
senticnet['gimpy'] = ['0', '0', '0.837', '-0.73', '#anger', '#disgust', 'negative', '-0.78', 'lame', 'feeble', 'crippled', 'square', 'halt']
senticnet['gin'] = ['0.843', '0.89', '0', '0', '#joy', '#interest', 'positive', '0.867', 'bourbon', 'vodka', 'scotch', 'brandy', 'wine']
senticnet['gin_lane'] = ['0.645', '0.637', '-0.23', '0.299', '#joy', '#interest', 'positive', '0.336', 'gladdened', 'stimulating', 'tickle_pink', 'exalt', 'beatify']
senticnet['gin_mill'] = ['-0.76', '0', '0.75', '-0.56', '#sadness', '#anger', 'negative', '-0.69', 'public_house', 'pub', 'saloon', 'pothouse', 'free_house']
senticnet['gin_palace'] = ['0.645', '0.637', '-0.23', '0.299', '#joy', '#interest', 'positive', '0.336', 'gladdened', 'stimulating', 'tickle_pink', 'exalt', 'beatify']
senticnet['gin_rummy'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'jewelry_store', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['gingell'] = ['-0.20', '-0.10', '0.07', '-0.24', '#sadness', '#disgust', 'negative', '-0.10', 'flipper', 'sea_life', 'seafood', 'swim_animal', 'fillet']
senticnet['ginger'] = ['0.077', '0', '0', '0', '#joy', '#joy', 'positive', '0.077', 'dip', 'garlic', 'oat', 'savory', 'rye']
senticnet['ginger_ale'] = ['0.877', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.919', 'score', 'economical', 'scotch_whiskey', 'spoil', 'scottish']
senticnet['ginger_beer'] = ['0.877', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.919', 'score', 'economical', 'scotch_whiskey', 'spoil', 'scottish']
senticnet['ginger_bread'] = ['0.027', '-0.09', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'cookie', 'pudding', 'custard', 'candy', 'muffin']
senticnet['ginger_root'] = ['0.077', '0', '0', '0', '#joy', '#joy', 'positive', '0.077', 'dip', 'garlic', 'oat', 'savory', 'rye']
senticnet['ginger_sauce'] = ['0.077', '0', '0', '0', '#joy', '#joy', 'positive', '0.077', 'dip', 'garlic', 'oat', 'savory', 'rye']
senticnet['ginger_syrup'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'lemon_juice', 'chocolate_pudding', 'grape_juice', 'orange_juice', 'juice']
senticnet['ginger_up'] = ['0.863', '-0.66', '0', '0.646', '#joy', '#surprise', 'positive', '0.725', 'enliven', 'liven', 'liven_up', 'invigorate', 'animate']
senticnet['gingerbread'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'lair', 'habitat', 'animal_home']
senticnet['gingerbread_house'] = ['0', '-0.01', '0.037', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'pear', 'green_apple', 'apricot', 'cherry', 'lime']
senticnet['gingerol'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'capsaicin', 'chemical_irritant', 'piperine', 'pepper_spray']
senticnet['gingham'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'facial_tissue', 'kleenex', 'hanky', 'handkerchief', 'tissue']
senticnet['gingiva'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'false_teeth', 'bridge', 'bridgework', 'dental_plate', 'dental_appliance']
senticnet['gingival_hypertrophy'] = ['-0.73', '0', '0.904', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'unshapely', 'shapeless', 'lumpy', 'deformed', 'malformed']
senticnet['gingival_inflammation'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'plaque']
senticnet['gingival_recession'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'plaque']
senticnet['gingival_tissue'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'plaque']
senticnet['ginmill'] = ['-0.81', '0', '0.553', '-0.92', '#sadness', '#disgust', 'negative', '-0.76', 'bar', 'barroom', 'saloon', 'sawdust_saloon', 'taproom']
senticnet['ginned_up'] = ['0', '0', '-0.93', '-0.88', '#fear', '#disgust', 'negative', '-0.91', 'rebuke', 'take_to_task', 'flog', 'call_on_carpet', 'cane']
senticnet['gino_paoli'] = ['0.107', '0.11', '-0.08', '0.21', '#interest', '#admiration', 'positive', '0.086', 'live_healthy_life', 'forever_young', 'live_forever', 'live_long', 'live_good_life']
senticnet['gion'] = ['0.973', '-0.50', '0', '0.612', '#joy', '#admiration', 'positive', '0.698', 'chic', 'chicness', 'jauntiness', 'modishness', 'stylishness']
senticnet['giorni'] = ['0.107', '0.11', '-0.08', '0.21', '#interest', '#admiration', 'positive', '0.086', 'live_healthy_life', 'forever_young', 'live_forever', 'live_long', 'live_good_life']
senticnet['giovanni_gabrieli'] = ['0.033', '0', '0.009', '0.057', '#joy', '#admiration', 'positive', '0.027', 'triangle', 'baton', 'bugle', 'type_of_bow', 'horn']
senticnet['giovanni_gioseffo_dal'] = ['0.964', '0.931', '0', '0', '#joy', '#interest', 'positive', '0.948', 'heel', 'foot_part', 'type_of_shoes', 'shoe_type', 'high_heel']
senticnet['gipsy_moth'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'cheapjack', 'pitchman']
senticnet['giraffe'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'cheetah', 'kangaroo', 'caribou', 'mule', 'horse']
senticnet['gird'] = ['0', '0', '0.945', '-1', '#anger', '#disgust', 'negative', '-0.97', 'loin', 'raiment', 'sackcloth', 'girded', 'clothed']
senticnet['girded'] = ['0', '0', '0.944', '-0.99', '#anger', '#disgust', 'negative', '-0.97', 'loin', 'raiment', 'gird', 'sackcloth', 'clothed']
senticnet['girder'] = ['0', '0.957', '0', '0.913', '#interest', '#admiration', 'positive', '0.935', 'truss', 'truss', 'joist', 'cantilever', 'screw']
senticnet['girder_bridge'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'suspension_bridge', 'under_bridge', 'roadway', 'county_highway', 'long_road']
senticnet['girdle'] = ['0.033', '-0.07', '0', '0', '#joy', '#surprise', 'positive', '0.053', 'stuff_pillow', 'write_suicide_note', 'write', 'write_letter', 'become_dull']
senticnet['girl'] = ['0.033', '-0.07', '0', '0', '#joy', '#surprise', 'positive', '0.053', 'female', 'house_wife', 'mother', 'woman', 'girlfriend']
senticnet['girl_basketball_team'] = ['0.035', '0.155', '0', '0', '#joy', '#interest', 'positive', '0.095', 'basketball_court', 'basketball_game', 'basketball', 'ball', 'sport']
senticnet['girl_clothes'] = ['0.053', '-0.06', '0.047', '0.087', '#surprise', '#admiration', 'positive', '0.04', 'formal_clothes', 'woman_clothes', 'frilly', 'woman_garment', 'dress']
senticnet['girl_disappearance'] = ['0', '0', '-0.88', '-0.99', '#fear', '#disgust', 'negative', '-0.93', 'murder_suspect', 'robbery_suspect', 'rape_suspect', 'murder', 'doubt']
senticnet['girl_fiona_fullerton'] = ['0.228', '0.123', '-0.09', '0', '#joy', '#interest', 'positive', '0.084', 'spend_time_with_child', 'social', 'friendship', 'companionship', 'get_close']
senticnet['girl_gemma_arterton'] = ['0.228', '0.123', '-0.09', '0', '#joy', '#interest', 'positive', '0.084', 'spend_time_with_child', 'social', 'friendship', 'companionship', 'get_close']
senticnet['girl_next_door'] = ['0.064', '0.1', '-0.07', '0.054', '#joy', '#interest', 'positive', '0.037', 'neighborhood', 'resident', 'neighbor', 'ceiling_fan', 'tenant']
senticnet['girl_room'] = ['0', '0.738', '0', '0.965', '#interest', '#admiration', 'positive', '0.851', 'fancy_dress', 'shoe_shop', 'kid_room', 'furniture_store', 'toy_box']
senticnet['girlfriend'] = ['0.033', '-0.07', '0', '0', '#joy', '#surprise', 'positive', '0.053', 'female', 'house_wife', 'mother', 'woman', 'girl']
senticnet['girlie'] = ['0.902', '0.995', '0', '0', '#joy', '#interest', 'positive', '0.949', 'beautiful_girl', 'hen_baby', 'pretty_woman', 'hen_child', 'bird']
senticnet['girlie_show_world'] = ['0.056', '0.139', '-0.07', '0.154', '#interest', '#admiration', 'positive', '0.07', 'belongings', 'material', 'fabric', 'guarantee', 'stuff']
senticnet['girlish'] = ['-0.88', '0', '0.864', '-0.72', '#sadness', '#anger', 'negative', '-0.82', 'malicious', 'venomous', 'bitchy', 'catty', 'cattish']
senticnet['girlishly'] = ['0.582', '-0.78', '0', '0.795', '#surprise', '#admiration', 'positive', '0.72', 'elfish', 'elvish', 'playful', 'frisky', 'sportive']
senticnet['girly'] = ['0.027', '-0.05', '0', '0.051', '#surprise', '#admiration', 'positive', '0.046', 'frill', 'fashion', 'puppet', 'glamour', 'fad']
senticnet['girolamo_frescobaldi'] = ['0.033', '0', '0.009', '0.057', '#joy', '#admiration', 'positive', '0.027', 'triangle', 'baton', 'bugle', 'type_of_bow', 'horn']
senticnet['girotti'] = ['0.901', '0', '0', '0.916', '#joy', '#admiration', 'positive', '0.909', 'get_away', 'bread', 'glory', 'counsel', 'vow']
senticnet['gisela_dulko_flavia_pennetta'] = ['-0.10', '0', '-0.05', '-0.24', '#sadness', '#disgust', 'negative', '-0.13', 'adulate', 'suck_up', 'toady', 'blandish', 'kowtow']
senticnet['gist'] = ['0', '0.087', '0.038', '0.106', '#interest', '#admiration', 'positive', '0.052', 'control_access', 'gate', 'door', 'use_farm', 'juice']
senticnet['give'] = ['0.743', '0', '0', '0.868', '#joy', '#admiration', 'positive', '0.805', 'receive', 'limit_time', 'offer', 'return', 'bid']
senticnet['give-away'] = ['-0.92', '0', '-0.76', '0', '#sadness', '#fear', 'negative', '-0.84', 'giveaway', 'giveaway', 'sweepstakes', 'contest', 'freebie']
senticnet['give_a_lot_of_flowers'] = ['0.025', '0.087', '0.022', '0.07', '#interest', '#admiration', 'positive', '0.04', 'a_lot_of_flowers', 'love', 'show_love', 'rose', 'flower']
senticnet['give_answer'] = ['-0.08', '0.109', '-0.07', '0.184', '#interest', '#admiration', 'positive', '0.034', 'write_back', 'elaborate', 'response', 'tell', 'explain_detail']
senticnet['give_assistance'] = ['0.643', '0.69', '0', '0.874', '#interest', '#admiration', 'positive', '0.736', 'comfort_friend', 'help', 'forgive', 'please_parent', 'make_better_world']
senticnet['give_attention'] = ['0.147', '0.167', '-0.12', '0.183', '#interest', '#admiration', 'positive', '0.093', 'give_food', 'feed', 'animal', 'domesticate_animal', 'feed_animal']
senticnet['give_away'] = ['-0.91', '0', '0.618', '-0.93', '#sadness', '#disgust', 'negative', '-0.82', 'get_stolen', 'second_hand', 'old_stuff', 'lose_wallet', 'away']
senticnet['give_back'] = ['0', '0.983', '0', '0', '#interest', '#interest', 'positive', '0.983', 'increase_money', 'money_transfer', 'give_take', 'transaction', 'financial_transaction']
senticnet['give_birth'] = ['0.761', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.838', 'father_child', 'mother_child', 'pregnancy', 'new_born', 'relationship']
senticnet['give_birth_child'] = ['0.069', '0.153', '-0.11', '0.112', '#interest', '#admiration', 'positive', '0.056', 'procreate', 'reproduce', 'copulate', 'make_love', 'sexual_intercourse']
senticnet['give_blood'] = ['0', '0', '0', '0.762', '#admiration', '#admiration', 'positive', '0.762', 'donate', 'raise_flag', 'battle_cancer', 'box_ear', 'lower_standard']
senticnet['give_cash'] = ['-0.08', '0.295', '-0.12', '0.189', '#interest', '#admiration', 'positive', '0.07', 'money', 'send_money', 'currency_exchange', 'money_exchange', 'bill_pay']
senticnet['give_change'] = ['0', '0.83', '0', '0.744', '#interest', '#admiration', 'positive', '0.787', 'carry_home', 'pay_money', 'settle_bill', 'make_change', 'count_change']
senticnet['give_comfort'] = ['0.072', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.046', 'stay_touch', 'communicate_friend', 'send_information', 'spread_meme', 'communicate_thought']
senticnet['give_credence'] = ['-0.78', '0', '0.704', '-0.91', '#sadness', '#disgust', 'negative', '-0.8', 'direct', 'take', 'lead', 'conduct', 'guide']
senticnet['give_direction'] = ['-0.04', '0.076', '0.033', '0.094', '#interest', '#admiration', 'positive', '0.023', 'elaborate', 'response', 'respond', 'explain', 'inquire']
senticnet['give_dog_exercise'] = ['0.114', '-0.11', '0.078', '0', '#joy', '#surprise', 'positive', '0.049', 'get_fresh_air', 'enjoy_outdoors', 'fun_exercise', 'get_physically_fit', 'learn_teamwork']
senticnet['give_flower'] = ['0.906', '0.992', '0', '0.947', '#interest', '#admiration', 'positive', '0.948', 'give_gift', 'buy_love', 'buy_present_for_others', 'propose_woman', 'kiss']
senticnet['give_food'] = ['0.076', '0.133', '0', '0.17', '#interest', '#admiration', 'positive', '0.126', 'sip', 'gulp', 'better_cold', 'potable_liquid', 'fluid']
senticnet['give_gift'] = ['0.874', '0', '0', '0.704', '#joy', '#admiration', 'positive', '0.789', 'give_flower', 'buy_love', 'buy_present_for_others', 'give_assistance', 'thank']
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senticnet['glass_front_cupboard'] = ['0.054', '-0.07', '0.025', '0', '#joy', '#surprise', 'positive', '0.035', 'table', 'dine_table', 'gate_leg_table', 'sideboard', 'dine_room_table']
senticnet['glass_front_display_cabinet'] = ['0.066', '0.071', '0', '0', '#joy', '#interest', 'positive', '0.069', 'old_photograph', 'picture_of_family', 'photo_album', 'family_picture', 'video_tape']
senticnet['glass_ionomer'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'false_teeth', 'bridge', 'dental_plate', 'partial_denture', 'dental_appliance']
senticnet['glass_jar'] = ['0.035', '-0.01', '0.009', '0.063', '#joy', '#admiration', 'positive', '0.026', 'container', 'carton', 'contain', 'hold_cupcake', 'store_dish']
senticnet['glass_jaw'] = ['0.969', '0.998', '0', '0', '#joy', '#interest', 'positive', '0.984', 'bell_sound', 'bell_noise', 'piece_of_jewelry', 'syllable', 'fastener']
senticnet['glass_lizard'] = ['-0.03', '-0.01', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'shit', 'wildflower', 'poop', 'wild_flower', 'dog_poop']
senticnet['glass_microscope_slide'] = ['0.129', '0', '0', '0', '#joy', '#joy', 'positive', '0.129', 'incline_plane', 'boulder', 'ramp', 'hill_mountain', 'rock']
senticnet['glass_of_milk'] = ['0.026', '0.08', '0.043', '0.064', '#interest', '#admiration', 'positive', '0.032', 'gunk', 'rotten_food', 'sour_cream', 'acid', 'lemon_juice']
senticnet['glass_of_water'] = ['0.093', '-0.02', '0', '0.056', '#joy', '#admiration', 'positive', '0.057', 'water', 'tap', 'rattle', 'run_water', 'vibration']
senticnet['glass_petri_dish'] = ['-0.86', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.80', 'riverbed', 'bottom_ocean', 'waterfall', 'down_drain', 'septic_tank']
senticnet['glass_plate'] = ['0.022', '0.069', '0.027', '0.059', '#interest', '#admiration', 'positive', '0.031', 'perfect_surface', 'plane', 'flat_land', 'flat', 'floor']
senticnet['glass_rod'] = ['-0.03', '-0.04', '0.079', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'string', 'bandage', 'suture', 'thread', 'needle_thread']
senticnet['glass_roof'] = ['0.046', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'light', 'bay_window', 'air_conditioner', 'sky', 'lamp']
senticnet['glass_skylight'] = ['0.046', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'light', 'bay_window', 'air_conditioner', 'sky', 'lamp']
senticnet['glass_slide'] = ['0.129', '0', '0', '0', '#joy', '#joy', 'positive', '0.129', 'incline_plane', 'boulder', 'ramp', 'hill_mountain', 'rock']
senticnet['glass_tube'] = ['-0.05', '0.068', '-0.10', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'tooth_decay', 'body_ache', 'symptom', 'pierce', 'tooth']
senticnet['glass_vase'] = ['0', '0.082', '-0.09', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'coffin', 'headstone', 'morgue', 'bury_body', 'casket']
senticnet['glass_vial'] = ['0.029', '-0.03', '0.066', '-0.08', '#anger', '#disgust', 'negative', '-0.02', 'sheet', 'drawer', 'file_box', 'desk_tray', 'letter_scale']
senticnet['glass_water'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'cold_water', 'refreshed', 'smoothie', 'lemonade', 'coke']
senticnet['glass_window'] = ['0', '0', '0', '0.077', '#admiration', '#admiration', 'positive', '0.077', 'glass_front', 'gate', 'doorway', 'use_farm', 'entranceway']
senticnet['glasses'] = ['0.214', '0.258', '-0.09', '0.2', '#joy', '#interest', 'positive', '0.146', 'cup', 'carafe', 'pitcher', 'mug', 'container_cup']
senticnet['glassful'] = ['0.102', '0.084', '0', '0.076', '#joy', '#interest', 'positive', '0.087', 'better_cold', 'sip', 'potable_liquid', 'ingest', 'liquid_beverage']
senticnet['glasshouse'] = ['-0.12', '0.066', '-0.08', '-0.06', '#sadness', '#fear', 'negative', '-0.05', 'garden_yard', 'see_sky', 'plant_tree', 'hair', 'seed_lawn']
senticnet['glassmaker'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'builder']
senticnet['glassware'] = ['0.093', '-0.02', '0', '0.056', '#joy', '#admiration', 'positive', '0.057', 'carafe', 'pitcher', 'container_cup', 'mug', 'glasses']
senticnet['glasswort'] = ['-0.89', '0', '0.556', '-0.72', '#sadness', '#disgust', 'negative', '-0.72', 'fucus', 'rockweed', 'black_rockweed', 'bladder_fucus', 'tang']
senticnet['glassy'] = ['0', '-0.87', '0', '0.862', '#surprise', '#admiration', 'positive', '0.869', 'crystalline', 'rubbery', 'glistening', 'silvery', 'limpid']
senticnet['glast'] = ['0.048', '0', '0', '0.092', '#joy', '#admiration', 'positive', '0.07', 'radio_wave', 'sun', 'solar_cell', 'x-ray', 'cosmic_ray']
senticnet['glaucous_below'] = ['-0.50', '0', '0.573', '-0.89', '#anger', '#disgust', 'negative', '-0.65', 'petallike', 'bowery', 'leafy', 'unifoliate', 'bifoliate']
senticnet['glaucous_green'] = ['-0.75', '0', '0.734', '-0.82', '#sadness', '#disgust', 'negative', '-0.77', 'puberulent', 'haired', 'furlike', 'pappose', 'pilous']
senticnet['glaucous_gull'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'white_bird', 'dove', 'egret', 'white_swan', 'eismowe']
senticnet['glaxosmithkline_consumer'] = ['-0.12', '0', '-0.12', '-0.08', '#sadness', '#fear', 'negative', '-0.11', 'hospital', 'check_up', 'check_doctor', 'check_pulse', 'check_blood_pressure']
senticnet['glaze'] = ['-0.06', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.04', 'polish', 'magnify', 'see_out', 'sill', 'pane']
senticnet['glazed_ceramic'] = ['-0.06', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.04', 'polish', 'magnify', 'see_out', 'sill', 'pane']
senticnet['glazed_pork'] = ['0.084', '-0.05', '0', '-0.04', '#joy', '#surprise', 'positive', '0.031', 'entree', 'feast', 'satiation', 'breakfast', 'supper']
senticnet['glazed_pottery'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'fabrication', 'carving', 'decoupage', 'artistic_creation']
senticnet['glcnac'] = ['-0.52', '0', '0.681', '-0.76', '#anger', '#disgust', 'negative', '-0.65', 'triose', 'tetrose', 'pentose', 'simple_sugar', 'aldose']
senticnet['gleam'] = ['-0.66', '0', '0.616', '-0.70', '#sadness', '#disgust', 'negative', '-0.66', 'flicker', 'coruscation', 'streak', 'glint', 'flash']
senticnet['gleaming'] = ['0.65', '-0.61', '0', '0.703', '#joy', '#admiration', 'positive', '0.657', 'effulgence', 'luster', 'polish', 'glow', 'burnish']
senticnet['gleaming_gold'] = ['0.049', '0.145', '0', '0.171', '#interest', '#admiration', 'positive', '0.122', 'kingly', 'govern', 'head_wear', 'chivalry', 'blue_blood']
senticnet['gleason_pictorial_drawing_room'] = ['0.756', '0', '0', '0', '#joy', '#joy', 'positive', '0.756', 'companionship', 'cherish', 'lover', 'bond', 'buddy']
senticnet['glee'] = ['0.761', '0.566', '-0.21', '0.293', '#joy', '#interest', 'positive', '0.352', 'gratify', 'jubilation', 'gloat', 'gloating', 'gloatingly']
senticnet['glee_cast'] = ['0.761', '0.566', '-0.21', '0.293', '#joy', '#interest', 'positive', '0.352', 'gratify', 'jubilation', 'gloat', 'gloating', 'gloatingly']
senticnet['glee_episode'] = ['0.761', '0.566', '-0.21', '0.293', '#joy', '#interest', 'positive', '0.352', 'gratify', 'jubilation', 'gloat', 'gloating', 'gloatingly']
senticnet['glee_recap'] = ['-0.73', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'unwed', 'unwedded', 'divorced', 'unmarried', 'widowed']
senticnet['gleeful'] = ['0.62', '0.643', '-0.23', '0.287', '#joy', '#interest', 'positive', '0.328', 'elated', 'bonheur', 'gladden', 'gladfulness', 'gladsome']
senticnet['gleefully'] = ['0.593', '0.647', '-0.23', '0.272', '#joy', '#interest', 'positive', '0.319', 'joyfully', 'joyously', 'joyousness', 'joyfulness', 'bonheur']
senticnet['gleefulness'] = ['0.695', '0.623', '-0.23', '0.319', '#joy', '#interest', 'positive', '0.351', 'uproarious', 'hilariously', 'hilarious', 'screaming', 'uproariously']
senticnet['gleet'] = ['-0.52', '0', '0.538', '-0.95', '#anger', '#disgust', 'negative', '-0.67', 'sanies', 'pus', 'purulence', 'suppuration', 'ichor']
senticnet['gleison_tibau'] = ['0.944', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.967', 'meeting', 'meet', 'match', 'encounter', 'round']
senticnet['glengoyne'] = ['0.877', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.919', 'score', 'economical', 'scotch_whiskey', 'spoil', 'scottish']
senticnet['glenlivet'] = ['0.877', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.919', 'score', 'economical', 'scotch_whiskey', 'spoil', 'scottish']
senticnet['glenoid'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'socket', 'tooth_socket', 'alveolus', 'acetabulum', 'cotyloid_cavity']
senticnet['glenoid_cavity'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'hip_socket', 'tooth_socket', 'alveolus', 'socket', 'cotyloid_cavity']
senticnet['glenoid_labrum'] = ['-0.74', '0', '0.598', '-0.75', '#sadness', '#disgust', 'negative', '-0.69', 'lunate_bone', 'semilunar_bone', 'carpal_bone', 'carpal', 'bone']
senticnet['glib'] = ['0', '0', '0.76', '-0.97', '#anger', '#disgust', 'negative', '-0.86', 'slippery', 'lib', 'patter', 'emasculate', 'geld']
senticnet['glibly'] = ['0.783', '-0.51', '0', '0.584', '#joy', '#admiration', 'positive', '0.627', 'appetent', 'avid', 'thirsty', 'devouring', 'envious']
senticnet['glide'] = ['-0.03', '-0.02', '0.059', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'bus_person', 'fly_high', 'go_down', 'buzz', 'drop_bomb']
senticnet['gliese'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['glimmer'] = ['0.789', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.772', 'light', 'mica', 'diamond', 'glow', 'twinkle']
senticnet['glimmering_light'] = ['0.54', '-0.64', '0', '0.728', '#surprise', '#admiration', 'positive', '0.638', 'gleaming', 'luster', 'polish', 'glow', 'burnish']
senticnet['glimpse'] = ['0', '0.095', '0', '0.112', '#interest', '#admiration', 'positive', '0.104', 'stare', 'look', 'observation', 'ship_part', 'see']
senticnet['glimpse_into'] = ['0', '0.095', '0', '0.112', '#interest', '#admiration', 'positive', '0.104', 'stare', 'look', 'observation', 'ship_part', 'see']
senticnet['glimpses_into'] = ['0', '0.095', '0', '0.112', '#interest', '#admiration', 'positive', '0.104', 'stare', 'look', 'observation', 'ship_part', 'see']
senticnet['glint'] = ['-0.66', '0', '0.616', '-0.70', '#sadness', '#disgust', 'negative', '-0.66', 'flicker', 'coruscation', 'streak', 'flash', 'gleam']
senticnet['glinting_off'] = ['-0.66', '0', '0.616', '-0.70', '#sadness', '#disgust', 'negative', '-0.66', 'flicker', 'coruscation', 'streak', 'flash', 'gleam']
senticnet['gliny'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'aphasia', 'anomic_aphasia']
senticnet['glipizide_glucotrol'] = ['-0.72', '0', '0.973', '-0.57', '#sadness', '#anger', 'negative', '-0.76', 'corpulency', 'blubber', 'fat', 'embonpoint', 'stoutness']
senticnet['glissando'] = ['-0.52', '0', '0.902', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'discordant', 'inharmonious', 'unresolved', 'false', 'disharmonious']
senticnet['glisten'] = ['0', '0.915', '0', '0.985', '#interest', '#admiration', 'positive', '0.95', 'flash', 'glistening', 'coruscate', 'glittering', 'glister']
senticnet['glistening'] = ['0', '0', '0.812', '-0.84', '#anger', '#disgust', 'negative', '-0.82', 'shining', 'sheeny', 'gleam', 'glossy', 'lustrous']
senticnet['glister'] = ['0.988', '0', '0', '0.784', '#joy', '#admiration', 'positive', '0.886', 'brightness', 'blaze', 'brilliance', 'glitter', 'glare']
senticnet['glitch'] = ['0', '0', '0.762', '-0.97', '#anger', '#disgust', 'negative', '-0.86', 'imperfect', 'malfunction', 'artifact', 'experimental', 'sonic']
senticnet['glitter'] = ['0.987', '0', '0', '0.786', '#joy', '#admiration', 'positive', '0.887', 'brightness', 'blaze', 'brilliance', 'glare', 'glister']
senticnet['glitter_glue'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'building', 'carving', 'artistic_creation', 'ceramic']
senticnet['glitterati'] = ['0.962', '0', '0', '0', '#joy', '#joy', 'positive', '0.962', 'interest_person', 'social_occasion', 'become_intoxicated', 'network', 'get_sex']
senticnet['glittered'] = ['0.898', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.873', 'garland', 'glittery', 'bauble', 'glitter', 'tinsel']
senticnet['glittering'] = ['0', '0', '0.798', '-0.81', '#anger', '#disgust', 'negative', '-0.80', 'glisten', 'scintillating', 'fulgid', 'coruscant', 'sparkly']
senticnet['glittering_gold'] = ['0.049', '0.145', '0', '0.171', '#interest', '#admiration', 'positive', '0.122', 'kingly', 'govern', 'head_wear', 'chivalry', 'blue_blood']
senticnet['glittery'] = ['0.893', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.869', 'garland', 'tinsel', 'bauble', 'glitter', 'glittered']
senticnet['glitz'] = ['0', '0', '0.98', '-0.70', '#anger', '#disgust', 'negative', '-0.84', 'foolhardiness', 'thoughtlessness', 'desperation', 'unthoughtfulness', 'brashness']
senticnet['glitzy'] = ['0', '-0.74', '0', '0.879', '#surprise', '#admiration', 'positive', '0.813', 'posh', 'upscale', 'snazzy', 'swanky', 'trendy']
senticnet['gloat'] = ['1', '0', '0', '-0.33', '#joy', '#disgust', 'positive', '0.335', 'relish', 'crow_over', 'rub_hands', 'rejoice', 'glad']
senticnet['gloat_over'] = ['0.689', '0.627', '-0.23', '0.315', '#joy', '#interest', 'positive', '0.35', 'jubilate', 'exuberant', 'carefree', 'lightsomeness', 'carefreeness']
senticnet['gloating'] = ['0.779', '0.595', '-0.21', '0.338', '#joy', '#interest', 'positive', '0.376', 'gloatingly', 'fulfillment', 'live_up_to', 'fulfill', 'self-satisfaction']
senticnet['gloating_over'] = ['1', '0', '0', '-0.33', '#joy', '#disgust', 'positive', '0.335', 'relish', 'crow_over', 'rub_hands', 'rejoice', 'glad']
senticnet['gloatingly'] = ['-0.87', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.86', 'gloat', 'gloating', 'cheer', 'dwell', 'schadenfreude']
senticnet['global'] = ['0.037', '0.177', '0', '0.147', '#interest', '#admiration', 'positive', '0.12', 'business_organization', 'company', 'economic_activity', 'large_business', 'partnership']
senticnet['global_analysis'] = ['0', '0.86', '0', '0.817', '#interest', '#admiration', 'positive', '0.839', 'space', 'study', 'differential_geometry', 'mathematics', 'award']
senticnet['global_aphasia'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'nominal_aphasia', 'anomic_aphasia']
senticnet['global_capitalism'] = ['0', '0', '-0.74', '-0.93', '#fear', '#disgust', 'negative', '-0.84', 'generosity', 'oral_sex', 'win_lottery', 'gift_wrap', 'give_away']
senticnet['global_change_research'] = ['-0.88', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.90', 'atmospheric_science', 'number', 'lower', 'ionosphere', 'effect']
senticnet['global_climate'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'weather', 'sunny', 'spring', 'snow', 'four_seasons']
senticnet['global_climate_change'] = ['0', '0', '-0.71', '-0.90', '#fear', '#disgust', 'negative', '-0.80', 'climate_change', 'end_world', 'apocalypse', 'kill_earth', 'dead_planet']
senticnet['global_climatic_change'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'weather', 'sunny', 'spring', 'snow', 'four_seasons']
senticnet['global_economic_meltdown'] = ['-0.99', '0', '0.691', '-0.65', '#sadness', '#anger', 'negative', '-0.78', 'depression', 'economic_crisis', 'exigency', 'slump', 'juncture']
senticnet['global_economy'] = ['0.838', '0.707', '0', '0.904', '#joy', '#admiration', 'positive', '0.816', 'natural_order', 'universe', 'existence', 'creation', 'cosmos']
senticnet['global_financial_meltdown'] = ['-0.99', '0', '0.691', '-0.65', '#sadness', '#anger', 'negative', '-0.78', 'depression', 'economic_crisis', 'exigency', 'slump', 'juncture']
senticnet['global_financial_turmoil'] = ['-0.99', '0', '0.691', '-0.65', '#sadness', '#anger', 'negative', '-0.78', 'depression', 'economic_crisis', 'exigency', 'slump', 'juncture']
senticnet['global_governance'] = ['0.952', '-0.84', '0', '0.875', '#joy', '#admiration', 'positive', '0.89', 'administration', 'judiciary', 'governing_body', 'establishment', 'brass']
senticnet['global_hegemony'] = ['0.828', '-0.67', '0', '0.811', '#joy', '#admiration', 'positive', '0.771', 'foolhardiness', 'brashness', 'thoughtlessness', 'desperation', 'unthoughtfulness']
senticnet['global_interdependence'] = ['0.838', '0.707', '0', '0.904', '#joy', '#admiration', 'positive', '0.816', 'natural_order', 'universe', 'existence', 'creation', 'cosmos']
senticnet['global_marketplace'] = ['0.837', '0', '0', '0', '#joy', '#joy', 'positive', '0.837', 'a_lot_of_sex', 'desirable', 'give_birth_child', 'sexual_intercourse', 'diversity']
senticnet['global_positioning_satellite'] = ['0', '0.071', '0', '0.143', '#interest', '#admiration', 'positive', '0.107', 'post_notice', 'indicator', 'way', 'one_way', 'arrow']
senticnet['global_positioning_system'] = ['0', '0.071', '0', '0.143', '#interest', '#admiration', 'positive', '0.107', 'post_notice', 'indicator', 'way', 'one_way', 'arrow']
senticnet['global_scale'] = ['0.838', '0.707', '0', '0.904', '#joy', '#admiration', 'positive', '0.816', 'natural_order', 'universe', 'existence', 'creation', 'cosmos']
senticnet['global_superpower'] = ['0.838', '0.707', '0', '0.904', '#joy', '#admiration', 'positive', '0.816', 'natural_order', 'universe', 'existence', 'creation', 'cosmos']
senticnet['global_thermonuclear_war'] = ['-0.71', '0', '-0.81', '-0.97', '#fear', '#disgust', 'negative', '-0.83', 'war', 'death', 'thermonuclear_war', 'bomb', 'get_killed']
senticnet['global_volcanism'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'institution', 'tundra', 'child_story', 'natural_history_museum', 'cartoon']
senticnet['global_warming'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'panda', 'hunting', 'air_pollution', 'endangered_species', 'sea_pollution']
senticnet['global_warming_treaty'] = ['0.397', '0.245', '-0.13', '0.224', '#joy', '#interest', 'positive', '0.183', 'agreement', 'provide', 'advice', 'contribution', 'offer']
senticnet['globalisation'] = ['-0.25', '0', '-0.17', '-0.08', '#sadness', '#fear', 'negative', '-0.16', 'exclusion', 'exclusion_zone', 'isolation', 'detach', 'except']
senticnet['globalism'] = ['0.946', '-0.54', '0', '0.774', '#joy', '#admiration', 'positive', '0.754', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['globalization'] = ['0.838', '0.707', '0', '0.904', '#joy', '#admiration', 'positive', '0.816', 'natural_order', 'universe', 'existence', 'creation', 'cosmos']
senticnet['globalized_economy'] = ['0.838', '0.707', '0', '0.904', '#joy', '#admiration', 'positive', '0.816', 'natural_order', 'universe', 'existence', 'creation', 'cosmos']
senticnet['globalized_world'] = ['0.838', '0.707', '0', '0.904', '#joy', '#admiration', 'positive', '0.816', 'natural_order', 'universe', 'existence', 'creation', 'cosmos']
senticnet['globally'] = ['0.838', '0.707', '0', '0.904', '#joy', '#admiration', 'positive', '0.816', 'natural_order', 'universe', 'existence', 'creation', 'cosmos']
senticnet['globally_competitive'] = ['0.837', '0', '0', '0', '#joy', '#joy', 'positive', '0.837', 'a_lot_of_sex', 'desirable', 'give_birth_child', 'sexual_intercourse', 'diversity']
senticnet['globe'] = ['-0.80', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.81', 'crystal_ball', 'ball', 'spherule', 'bolus', 'celestial_globe']
senticnet['globe_trotter'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'wanderlust', 'travel', 'visit_new_place', 'explore', 'discover']
senticnet['globec'] = ['-0.81', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.79', 'population_dynamic', 'effect', 'oceanography', 'predator', 'investigate']
senticnet['globose'] = ['0', '0', '0.064', '-0.04', '#anger', '#disgust', 'negative', '-0.05', 'bird_food', 'funiculus', 'plant_product', 'fuzz', 'agricultural_product']
senticnet['globose_conic_shape'] = ['0.969', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.772', 'aspheric', 'aspherical', 'prolate', 'rounded', 'crescent']
senticnet['globular'] = ['-0.55', '0', '0.869', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'air_bubble', 'foam', 'froth', 'soap_bubble', 'bubble']
senticnet['globular_cluster'] = ['0', '0.706', '0', '0.847', '#interest', '#admiration', 'positive', '0.776', 'dwarf_planet', 'universe', 'planetary', 'sun', 'moon']
senticnet['globule'] = ['-0.55', '0', '0.869', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'air_bubble', 'foam', 'froth', 'soap_bubble', 'bubble']
senticnet['globus_cruciger'] = ['-0.62', '0', '0.961', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'jeweled_headdress', 'diadem', 'tiara', 'headdress', 'headgear']
senticnet['globus_pallidus_interna'] = ['-0.56', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.63', 'aphasic', 'inarticulate', 'speechless', 'unarticulated', 'dumb']
senticnet['glochid'] = ['-0.84', '0', '0.987', '-0.77', '#sadness', '#anger', 'negative', '-0.87', 'thorn', 'sticker', 'pricker', 'prickle', 'spikelet']
senticnet['glochidium'] = ['-0.87', '0', '0.983', '-0.91', '#anger', '#disgust', 'negative', '-0.92', 'thorn', 'sticker', 'pricker', 'prickle', 'spikelet']
senticnet['glock_handgun'] = ['-0.75', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.75', 'hydrogen_bomb', 'rifle', 'revolver', 'pistol', 'gun']
senticnet['glock_pistol'] = ['-0.75', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.75', 'hydrogen_bomb', 'rifle', 'revolver', 'pistol', 'gun']
senticnet['glom'] = ['0.962', '0.791', '0', '0', '#joy', '#interest', 'positive', '0.877', 'nab', 'pick_up', 'thieve', 'cop', 'copper']
senticnet['glomerular_filtration'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'keep_alive', 'body_system', 'body_working', 'blood_pressure', 'heart']
senticnet['glomerular_filtration_rate'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'keep_alive', 'body_system', 'body_working', 'blood_pressure', 'heart']
senticnet['glomerular_filtration_rate_gfr'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'keep_alive', 'body_system', 'body_working', 'blood_pressure', 'heart']
senticnet['glomerulonephritis'] = ['-0.84', '0', '0.608', '-0.52', '#sadness', '#anger', 'negative', '-0.66', 'nephritis', 'nephrosis', 'kidney_disease', 'renal_disorder', 'nephropathy']
senticnet['gloom'] = ['-0.93', '0', '0', '-0.95', '#sadness', '#disgust', 'negative', '-0.94', 'dreary', 'melancholy', 'grim', 'drab', 'sorry']
senticnet['gloomful'] = ['-0.79', '0.257', '-0.47', '-0.03', '#sadness', '#fear', 'negative', '-0.26', 'glooming', 'darkness', 'somberness', 'gloominess', 'gloomily']
senticnet['gloomier'] = ['-0.79', '0.262', '-0.45', '-0.04', '#sadness', '#fear', 'negative', '-0.25', 'downhearted', 'down_in_mouth', 'depressed', 'downcast', 'low-spirited']
senticnet['gloomiest'] = ['-0.79', '0.262', '-0.45', '-0.04', '#sadness', '#fear', 'negative', '-0.25', 'downhearted', 'down_in_mouth', 'depressed', 'downcast', 'low-spirited']
senticnet['gloomily'] = ['-0.79', '0.257', '-0.47', '-0.03', '#sadness', '#fear', 'negative', '-0.26', 'gloomful', 'glooming', 'darkness', 'somberness', 'gloominess']
senticnet['gloominess'] = ['-0.79', '0.257', '-0.47', '-0.03', '#sadness', '#fear', 'negative', '-0.26', 'gloomful', 'glooming', 'darkness', 'somberness', 'gloomily']
senticnet['glooming'] = ['-0.79', '0.257', '-0.47', '-0.03', '#sadness', '#fear', 'negative', '-0.26', 'gloomful', 'darkness', 'somberness', 'gloominess', 'gloomily']
senticnet['gloomy'] = ['-0.79', '0.262', '-0.45', '-0.04', '#sadness', '#fear', 'negative', '-0.25', 'downhearted', 'down_in_mouth', 'depressed', 'downcast', 'low-spirited']
senticnet['gloomy_atmosphere'] = ['0', '1', '0', '0', '#interest', '#interest', 'positive', '1.0', 'interest', 'interesting', 'attention', 'strong_emotion', 'emotion']
senticnet['gloop'] = ['0', '0', '-0.72', '-0.92', '#fear', '#disgust', 'negative', '-0.82', 'bee_product', 'flammable', 'remove_hair', 'slick', 'really_hot']
senticnet['gloopy'] = ['0', '0', '-0.72', '-0.92', '#fear', '#disgust', 'negative', '-0.82', 'bee_product', 'flammable', 'remove_hair', 'slick', 'really_hot']
senticnet['glop'] = ['0.825', '-0.91', '0', '0.626', '#joy', '#surprise', 'positive', '0.789', 'treacle', 'sentimentalism', 'mush', 'slop', 'formulation']
senticnet['gloppy'] = ['0', '0', '-0.72', '-0.92', '#fear', '#disgust', 'negative', '-0.82', 'bee_product', 'flammable', 'remove_hair', 'slick', 'really_hot']
senticnet['glorification'] = ['0.997', '0', '0', '0.895', '#joy', '#admiration', 'positive', '0.946', 'glorifying', 'glorify', 'sanctification', 'glorifies', 'exaltation']
senticnet['glorified'] = ['0', '0.898', '0', '0.826', '#interest', '#admiration', 'positive', '0.862', 'blissful', 'blissfulness', 'elysium', 'bliss', 'elysian']
senticnet['glorifies'] = ['0.993', '0', '0', '0.891', '#joy', '#admiration', 'positive', '0.942', 'glorifying', 'glorify', 'sanctification', 'glorification', 'exaltation']
senticnet['glorify'] = ['0.54', '-0.97', '0', '0.569', '#surprise', '#admiration', 'positive', '0.695', 'overstate', 'hyperbolize', 'aggrandize', 'exaggerate', 'overdraw']
senticnet['glorify_god'] = ['0.082', '0.17', '-0.07', '0.291', '#interest', '#admiration', 'positive', '0.117', 'sanitary', 'sterile', 'pure', 'use_clean', 'refined']
senticnet['glorify_violence'] = ['0.54', '-0.97', '0', '0.569', '#surprise', '#admiration', 'positive', '0.695', 'overstate', 'hyperbolize', 'aggrandize', 'exaggerate', 'overdraw']
senticnet['glorifying'] = ['0.992', '0', '0', '0.889', '#joy', '#admiration', 'positive', '0.941', 'glorification', 'glorify', 'sanctification', 'glorifies', 'exaltation']
senticnet['glorious'] = ['0.891', '0', '0', '0.875', '#joy', '#admiration', 'positive', '0.883', 'storied', 'historied', 'divine', 'empyreal', 'glory']
senticnet['glorious_destiny'] = ['0', '0.862', '0', '0.774', '#interest', '#admiration', 'positive', '0.818', 'enter_contract', 'believe', 'give_help', 'karma', 'fate']
senticnet['glorious_sunshine'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'weather', 'four_seasons', 'fall_spring', 'rain_snow', 'climate']
senticnet['glorious_twelfth'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['glorious_victory'] = ['-0.94', '0', '0.745', '-0.97', '#sadness', '#disgust', 'negative', '-0.89', 'nerve', 'fearlessness', 'heart', 'dauntlessness', 'intrepidity']
senticnet['gloriously'] = ['0.512', '0.658', '-0.25', '0.256', '#joy', '#interest', 'positive', '0.293', 'riotously', 'expansively', 'ebulliently', 'zestfulness', 'gusto']
senticnet['glory'] = ['0.802', '0.798', '0', '0.773', '#joy', '#interest', 'positive', '0.791', 'honor', 'valor', 'mission_accomplished', 'respect', 'praise']
senticnet['gloss_black'] = ['0.02', '0.085', '-0.05', '0.085', '#interest', '#admiration', 'positive', '0.034', 'gold', 'silver', 'medal', 'carat', 'medallion']
senticnet['gloss_over'] = ['-0.13', '0.071', '0.694', '-0.67', '#anger', '#disgust', 'negative', '-0.35', 'crucify', 'dun', 'frustrating', 'aggravate', 'exasperation']
senticnet['glossary'] = ['-0.05', '0.084', '0', '0.062', '#interest', '#admiration', 'positive', '0.03', 'vocabulary', 'translation', 'book_library', 'book_poetry', 'book_genre']
senticnet['glossed_over'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'venial', 'excusable', 'mercifully', 'merciful', 'mercifulness']
senticnet['glosses_over'] = ['-0.81', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.80', 'overstate', 'hyperbolize', 'exaggerate', 'overdraw', 'magnify']
senticnet['glossier'] = ['0.17', '0', '0.081', '0.184', '#joy', '#admiration', 'positive', '0.091', 'food_holder', 'food_server', 'crockery', 'flattery', 'round_flat']
senticnet['glossiness'] = ['0', '0.953', '0', '0.881', '#interest', '#admiration', 'positive', '0.917', 'specularity', 'diffuse', 'reflectivity', 'reflectance', 'specular']
senticnet['glossing_over'] = ['-0.81', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.80', 'overstate', 'hyperbolize', 'exaggerate', 'overdraw', 'magnify']
senticnet['glossitis'] = ['-0.63', '0', '0.961', '-0.70', '#anger', '#disgust', 'negative', '-0.76', 'acute_glossitis', 'glossodynia_exfoliativa', 'rubor', 'redness', 'inflammation']
senticnet['glossodynia_exfoliativa'] = ['-0.63', '0', '0.961', '-0.70', '#anger', '#disgust', 'negative', '-0.76', 'acute_glossitis', 'glossitis', 'rubor', 'redness', 'inflammation']
senticnet['glossopharyngeal_nerve'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['glossy'] = ['0.042', '0', '0.012', '0.103', '#joy', '#admiration', 'positive', '0.044', 'book_write', 'media', 'document', 'readable', 'book_content']
senticnet['glossy_black'] = ['-0.60', '0', '0.895', '-0.54', '#sadness', '#anger', 'negative', '-0.68', 'coal_black', 'black', 'pitch_black', 'soot_black', 'achromatic_color']
senticnet['glossy_hair'] = ['0.352', '0.142', '0', '0.281', '#joy', '#admiration', 'positive', '0.258', 'knowledgeable', 'complement', 'much_need', 'see_ocean', 'good_sense_of_humor']
senticnet['glossy_magazine'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'library', 'old_testament', 'cook_book', 'a_lot_of_books', 'librarian']
senticnet['glossy_surface'] = ['-0.06', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.04', 'polish', 'magnify', 'see_out', 'sill', 'pane']
senticnet['gloster'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'cheapjack', 'pitchman']
senticnet['gloster_aircraft_company'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'cheapjack', 'pitchman']
senticnet['glottal'] = ['-0.71', '0', '0.998', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'tone', 'quality', 'twang', 'nasal_twang', 'timber']
senticnet['glottal_stop'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'letter', 'vowel', 'alphabet', 'basic_speech', 'kind_gesture']
senticnet['glottalization'] = ['-0.71', '0', '0.998', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'tone', 'quality', 'twang', 'nasal_twang', 'timber']
senticnet['glottic'] = ['0', '0', '0', '0.106', '#admiration', '#admiration', 'positive', '0.106', 'vocal_chord', 'mouth_noise', 'vocal_sound', 'human_sound', 'vocalization']
senticnet['glove'] = ['0.57', '-0.56', '0', '0.888', '#joy', '#admiration', 'positive', '0.675', 'wearable', 'wear', 'muff', 'hand_wear', 'clothing']
senticnet['glove_leather'] = ['0.087', '-0.03', '0', '0.063', '#joy', '#admiration', 'positive', '0.062', 'puppet', 'bouncy', 'curly', 'throw_object', 'testicle']
senticnet['gloved_finger'] = ['-0.63', '0', '0.559', '-0.82', '#sadness', '#disgust', 'negative', '-0.67', 'take_bull_by_horns', 'confront', 'front', 'encounter', 'oppose']
senticnet['gloved_hand'] = ['0.105', '-0.17', '0.057', '0.079', '#joy', '#surprise', 'positive', '0.076', 'cue', 'twig', 'long_stick', 'stick', 'adhere']
senticnet['glow'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'brightness_level', 'illumination', 'incandescence', 'luminescence', 'illuminance']
senticnet['glow-in-the-dark'] = ['0', '0', '0.764', '-0.72', '#anger', '#disgust', 'negative', '-0.74', 'self-adhesive', 'self-stick', 'adhesive', 'stick-on', 'adhesive-backed']
senticnet['glow_green'] = ['-0.76', '0', '0.573', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'gossip', 'gossiper', 'gossipmonger', 'talk_back', 'rumormonger']
senticnet['glow_stick'] = ['0.105', '-0.17', '0.057', '0.079', '#joy', '#surprise', 'positive', '0.076', 'cue', 'twig', 'long_stick', 'stick', 'adhere']
senticnet['glowed'] = ['0.889', '0.856', '0', '0', '#joy', '#interest', 'positive', '0.873', 'red-hot', 'white-hot', 'searing', 'blazing', 'smoldering']
senticnet['glower'] = ['0', '0', '0.839', '-0.87', '#anger', '#disgust', 'negative', '-0.85', 'fulgent', 'dazzling', 'egregious', 'blinding', 'glaring']
senticnet['glowering'] = ['0', '0', '-0.70', '-0.96', '#fear', '#disgust', 'negative', '-0.83', 'moody', 'dour', 'glum', 'dark', 'morose']
senticnet['glowing'] = ['0.785', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.87', 'ardor', 'ardent', 'concupiscence', 'sanguine', 'torrid']
senticnet['glowing_blue'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'naked_eye', 'eye', 'oculus_sinister']
senticnet['glowing_green'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'naked_eye', 'eye', 'oculus_sinister']
senticnet['glowing_red'] = ['-0.15', '-0.00', '0', '-0.10', '#sadness', '#disgust', 'negative', '-0.08', 'take_picture', 'try_understand', 'take_shit', 'point_out', 'check_mail']
senticnet['glowing_skin'] = ['-0.96', '0', '0.636', '-0.64', '#sadness', '#disgust', 'negative', '-0.75', 'burn', 'tan', 'hyperpigmentation', 'sunburn', 'physical_condition']
senticnet['glucagon_secretion'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'cholecystokinin', 'secretin', 'gastrointestinal_hormone']
senticnet['glucocorticoid_therapy'] = ['-0.05', '0', '-0.22', '-0.06', '#fear', '#disgust', 'negative', '-0.11', 'medicine', 'cure', 'medical_procedure', 'sleep_drug', 'nurse']
senticnet['glucophage_xr'] = ['-0.72', '0', '0.973', '-0.57', '#sadness', '#anger', 'negative', '-0.76', 'corpulency', 'blubber', 'fat', 'embonpoint', 'stoutness']
senticnet['glucosamine'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucose', 'corn_sugar', 'dextrose', 'dextroglucose', 'grape_sugar']
senticnet['glucosamine_chondroitin'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucose', 'corn_sugar', 'dextrose', 'dextroglucose', 'grape_sugar']
senticnet['glucosamine_hydrochloride'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucose', 'corn_sugar', 'dextrose', 'dextroglucose', 'grape_sugar']
senticnet['glucosamine_sulfate'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucose', 'corn_sugar', 'dextrose', 'dextroglucose', 'grape_sugar']
senticnet['glucose'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'dextrose', 'dextroglucose', 'grape_sugar']
senticnet['glucose_uptake'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'cholecystokinin', 'secretin', 'gastrointestinal_hormone']
senticnet['glucoside'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'capsaicin', 'chemical_irritant', 'piperine', 'pepper_spray']
senticnet['glucosinolate'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'capsaicin', 'chemical_irritant', 'piperine', 'pepper_spray']
senticnet['glucosyl'] = ['-0.52', '0', '0.681', '-0.76', '#anger', '#disgust', 'negative', '-0.65', 'triose', 'tetrose', 'pentose', 'simple_sugar', 'aldose']
senticnet['glucuronic_acid'] = ['-0.52', '0', '0.681', '-0.76', '#anger', '#disgust', 'negative', '-0.65', 'triose', 'tetrose', 'pentose', 'simple_sugar', 'aldose']
senticnet['glucuronide'] = ['0.992', '-0.80', '0', '0.584', '#joy', '#surprise', 'positive', '0.795', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'incontinence']
senticnet['glue'] = ['0.105', '-0.17', '0.057', '0.079', '#joy', '#surprise', 'positive', '0.076', 'stick', 'adhesive', 'follow', 'believe_in', 'adhere']
senticnet['glue_code'] = ['-0.95', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.84', 'hold_pencil', 'food_scrap', 'paper_product', 'waste_paper', 'cover']
senticnet['glue_ear'] = ['-0.87', '0', '0.773', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'rubor', 'redness', 'inflammation', 'otitis', 'otitis_interna']
senticnet['glue_gun'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'building', 'carving', 'artistic_creation', 'ceramic']
senticnet['glue_stick'] = ['0.038', '0', '0', '0.097', '#joy', '#admiration', 'positive', '0.068', 'blank_check', 'address_book', 'post-it_note', 'ball_point_pen', 'paper_clip']
senticnet['glued'] = ['0.99', '-0.89', '0', '0.885', '#joy', '#surprise', 'positive', '0.923', 'mounted', 'affixed', 'basifixed', 'appendant', 'pasted']
senticnet['glued_onto'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'aluminium_foil', 'foil', 'gold_foil']
senticnet['glued_together'] = ['-0.02', '0.103', '0.069', '0.151', '#interest', '#admiration', 'positive', '0.039', 'single', 'unit_of_measurement', 'do_it_yourself', 'enumeration', 'alone']
senticnet['gluey'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'wet', 'sticky', 'tacky', 'viscous', 'gooey']
senticnet['glug'] = ['0.102', '0.084', '0', '0.076', '#joy', '#interest', 'positive', '0.087', 'better_cold', 'sip', 'potable_liquid', 'ingest', 'liquid_beverage']
senticnet['gluino'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'elementary_particle', 'neutron', 'electron', 'subatomic_particle', 'proton']
senticnet['glulam'] = ['-0.69', '0', '0.725', '-0.89', '#anger', '#disgust', 'negative', '-0.77', 'ponderosa', 'ponderosa_pine', 'western_yellow_pine', 'bull_pine', 'longleaf_pine']
senticnet['glum'] = ['-0.55', '0', '0.557', '-0.70', '#sadness', '#anger', 'negative', '-0.60', 'downhearted', 'down_in_mouth', 'dispirited', 'grim', 'dejected']
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senticnet['go_get_haircut'] = ['0.848', '0.724', '0', '0', '#joy', '#interest', 'positive', '0.786', 'get_manicure', 'get_hair_colored', 'get_hair_style', 'stylish', 'get_haircut']
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senticnet['go_hike'] = ['0.777', '0.797', '0', '0', '#joy', '#interest', 'positive', '0.787', 'sail_around_world', 'wind_watch', 'examine', 'chair_committee', 'see_star_night']
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senticnet['go_kiss'] = ['0.854', '0.975', '0', '0.926', '#interest', '#admiration', 'positive', '0.918', 'look_through_binoculars', 'kiss', 'valentine_day', 'find_love', 'procreate']
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senticnet['go_movie_theater'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'watch_movie', 'go_to_theater', 'go_watch', 'find_out_play', 'go_cinema']
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senticnet['go_out_party'] = ['0.699', '0.62', '-0.23', '0.323', '#joy', '#interest', 'positive', '0.353', 'jolliness', 'joviality', 'jolity', 'josh', 'chaff']
senticnet['go_out_public'] = ['0.06', '0.076', '0', '0.071', '#interest', '#admiration', 'positive', '0.069', 'take_shower', 'take_shower_bath', 'take_bath', 'bath', 'wash_up']
senticnet['go_out_with_dad'] = ['0.902', '0.844', '0', '0', '#joy', '#interest', 'positive', '0.873', 'woman_child', 'relative', 'kin', 'family_relation', 'family_member']
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senticnet['go_outside'] = ['0.879', '0.83', '0', '0', '#joy', '#interest', 'positive', '0.855', 'go_park', 'put_shoes', 'wear_dress', 'wear_running_shoes', 'enjoy_nature']
senticnet['go_over'] = ['0.901', '0.862', '0', '0', '#joy', '#interest', 'positive', '0.881', 'acknowledge', 'organize_data', 'recognize_pattern', 'deduce', 'figure_out']
senticnet['go_overdrawn'] = ['0.54', '-0.97', '0', '0.569', '#surprise', '#admiration', 'positive', '0.695', 'overstate', 'hyperbolize', 'glorify', 'exaggerate', 'aggrandize']
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senticnet['go_potty'] = ['-0.04', '0', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.07', 'urinate', 'go_toilet', 'bladder_full', 'go_pee', 'pee']
senticnet['go_pub'] = ['0.92', '0.976', '0', '0.722', '#joy', '#interest', 'positive', '0.873', 'plan_vacation', 'leave_country', 'dance', 'have_fun', 'go_vacation']
senticnet['go_public'] = ['-0.03', '-0.04', '0', '0.06', '#surprise', '#admiration', 'positive', '0.026', 'make_better_world', 'change_society', 'take_oath', 'sex_same_gender', 'marry']
senticnet['go_read_book'] = ['0.028', '-0.01', '0', '0.087', '#joy', '#admiration', 'positive', '0.043', 'find_book', 'use_book', 'open_book', 'research', 'store_knowledge']
senticnet['go_restroom'] = ['-0.06', '0', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.07', 'drink_much_beer', 'use_fork', 'burp', 'acid_indigestion', 'eat_salad']
senticnet['go_rock_concert'] = ['0.967', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.96', 'able_hear', 'attend_concert', 'sensibility', 'buy_concert_ticket', 'turn_radio']
senticnet['go_room'] = ['-0.98', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.98', 'put_pyjamas', 'count_sheep', 'call_sick', 'sleepwalk', 'turn_out_light']
senticnet['go_scuba_dive'] = ['0.771', '0', '0', '0', '#joy', '#joy', 'positive', '0.771', 'die_young', 'cry_help', 'end_life', 'feel_stupid', 'feel_ashamed']
senticnet['go_sea'] = ['0', '-0.02', '0.06', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'get_boat', 'take_cruise', 'go_dive', 'take_leak', 'hold_fish']
senticnet['go_see_doctor'] = ['-0.97', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.95', 'blood_test', 'see_doctor', 'visit_doctor', 'go_doctor', 'find_doctor']
senticnet['go_shopping'] = ['0.771', '0', '0', '0', '#joy', '#joy', 'positive', '0.771', 'shop', 'store', 'shop_mall', 'shopping_center', 'buy_stuff']
senticnet['go_sleep'] = ['0.054', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.047', 'go_bed', 'sleep_night', 'relax', 'sleep', 'nap']
senticnet['go_slow'] = ['0.822', '0.86', '0', '0.751', '#joy', '#interest', 'positive', '0.811', 'drive_safe', 'take_nap', 'prevent_accident', 'feel_little_tired', 'rest_dream']
senticnet['go_sport_event'] = ['0', '0.071', '0', '0', '#interest', '#interest', 'positive', '0.071', 'go_baseball_game', 'go_performance', 'go_play', 'go_movie', 'go_opus']
senticnet['go_stadium'] = ['-0.05', '-0.02', '0.045', '0', '#sadness', '#anger', 'negative', '-0.02', 'find_out_play', 'drive_theater', 'turn_on_tv', 'near_tv', 'open_eye']
senticnet['go_stage'] = ['0.861', '0.928', '0', '0', '#joy', '#interest', 'positive', '0.895', 'perform', 'act_play', 'pretend', 'play_violin', 'play_guitar']
senticnet['go_stall'] = ['-0.05', '-0.01', '0.036', '-0.06', '#sadness', '#disgust', 'negative', '-0.03', 'bus_person', 'buzz', 'jump', 'glide', 'jump_high']
senticnet['go_store'] = ['0.771', '0', '0', '0', '#joy', '#joy', 'positive', '0.771', 'shop', 'store', 'shop_mall', 'shopping_center', 'buy_stuff']
senticnet['go_straight'] = ['0', '0.175', '0', '0.231', '#interest', '#admiration', 'positive', '0.203', 'compass_direction', 'direction', 'end_point', 'eight_sides', 'red_light']
senticnet['go_supermarket'] = ['-0.03', '-0.00', '0.058', '0', '#sadness', '#anger', 'negative', '-0.02', 'find_merchant', 'go_department_store', 'moolah', 'drive_to_store', 'go_to_shopping_mall']
senticnet['go_swim_pool'] = ['-0.03', '0', '0.044', '0', '#sadness', '#anger', 'negative', '-0.03', 'put_bath_suit', 'go_water', 'go_pool', 'jump_in_water', 'swim']
senticnet['go_swimming_pool'] = ['0.482', '0.669', '-0.27', '0.263', '#joy', '#interest', 'positive', '0.285', 'falling_water', 'put_bath_suit', 'water_fall', 'take_off_clothes', 'remove_clothes']
senticnet['go_symphony'] = ['0', '0.85', '0', '0.769', '#interest', '#admiration', 'positive', '0.809', 'enjoy_art', 'bring_friend', 'classical_music', 'understand_plot', 'insert_dvd_in_player']
senticnet['go_through'] = ['-0.02', '0.141', '-0.06', '0.285', '#interest', '#admiration', 'positive', '0.083', 'invade', 'go_build', 'hang_door', 'enter_room', 'separate_room']
senticnet['go_to_beach'] = ['0.755', '0.945', '0', '0', '#joy', '#interest', 'positive', '0.85', 'sunscreen', 'wear_sunscreen', 'sunblock', 'sunny_day', 'summertime']
senticnet['go_to_bed'] = ['0.907', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.879', 'fall_asleep', 'doze_off', 'turn_off_television', 'bed_time', 'drowsiness']
senticnet['go_to_cinema'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'theater_ticket', 'movie_ticket', 'watch_movie', 'enjoy_movie', 'have_fun']
senticnet['go_to_class'] = ['0.737', '0', '0', '0.94', '#joy', '#admiration', 'positive', '0.839', 'teach_lesson', 'professor', 'school_student', 'teacher', 'teach_class']
senticnet['go_to_court'] = ['-0.7', '0', '0.907', '0', '#sadness', '#anger', 'negative', '-0.80', 'lawyer', 'take_court', 'call_lawyer', 'determine_guilt', 'lawsuit']
senticnet['go_to_dog'] = ['-0.51', '0', '0.965', '-0.95', '#anger', '#disgust', 'negative', '-0.81', 'go_to_pot', 'deteriorate', 'dilapidate', 'decay', 'wear']
senticnet['go_to_gym'] = ['0.981', '0.955', '0', '0.709', '#joy', '#interest', 'positive', '0.882', 'shape_up', 'fit', 'healthy', 'keep_fit', 'better_shape']
senticnet['go_to_hairdresser'] = ['0', '-0.09', '0.035', '0.1', '#surprise', '#admiration', 'positive', '0.054', 'cut_hair', 'go_get_haircut', 'haircut', 'hairdressing_salon', 'hairdresser_chair']
senticnet['go_to_heaven'] = ['0.88', '0.909', '0', '0.855', '#joy', '#interest', 'positive', '0.881', 'human_emotion', 'emotion', 'donate', 'need_help', 'demand']
senticnet['go_to_hell'] = ['-0.79', '0', '-0.94', '-0.84', '#fear', '#disgust', 'negative', '-0.86', 'go_scuba_dive', 'cry_help', 'experience_pain', 'end_life', 'feel_stupid']
senticnet['go_to_jail'] = ['0', '0', '0', '-0.75', '#disgust', '#disgust', 'negative', '-0.75', 'put_in_jail', 'arrest', 'find_guilty', 'verdict', 'injustice']
senticnet['go_to_library'] = ['0', '0.074', '0.051', '0.105', '#interest', '#admiration', 'positive', '0.043', 'open_book', 'read_book', 'quiet_place', 'visit_library', 'buy_textbook']
senticnet['go_to_museum'] = ['0.114', '0.071', '-0.05', '0.074', '#joy', '#admiration', 'positive', '0.05', 'picture', 'look_art', 'look_picture', 'take_picture', 'see_picture']
senticnet['go_to_piece'] = ['-0.76', '0', '-0.89', '0', '#sadness', '#fear', 'negative', '-0.82', 'break', 'deteriorate', 'go_to_dog', 'dilapidate', 'decay']
senticnet['go_to_place'] = ['0.1', '-0.03', '0.034', '0.055', '#joy', '#admiration', 'positive', '0.04', 'get_place', 'travel', 'move', 'get_to_destination', 'visit_place']
senticnet['go_to_pot'] = ['-0.52', '0', '0.962', '-0.95', '#anger', '#disgust', 'negative', '-0.81', 'go_to_dog', 'deteriorate', 'dilapidate', 'decay', 'wear']
senticnet['go_to_prison'] = ['-0.83', '0', '-0.88', '-0.78', '#sadness', '#fear', 'negative', '-0.83', 'go_jail', 'jail_time', 'send_to_jail', 'injustice', 'feel_guilt']
senticnet['go_to_school'] = ['0.034', '0.252', '-0.13', '0.277', '#interest', '#admiration', 'positive', '0.107', 'book', 'read_book', 'hold_book', 'go_to_library', 'choose_book']
senticnet['go_to_shopping_mall'] = ['-0.02', '0', '0.042', '0', '#sadness', '#anger', 'negative', '-0.03', 'go_department_store', 'moolah', 'find_market', 'find_merchant', 'drive_to_store']
senticnet['go_to_sleep'] = ['0.101', '-0.23', '0.031', '0.105', '#surprise', '#admiration', 'positive', '0.103', 'finish_work', 'go_home', 'relax_time', 'end_day', 'meet_family']
senticnet['go_to_supermarket'] = ['0', '-0.03', '0.066', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'get_grocery', 'buy_goods', 'buy_bread', 'buy_gum', 'buy_milk']
senticnet['go_to_swimming_pool'] = ['-0.04', '0.069', '0', '0.075', '#interest', '#admiration', 'positive', '0.035', 'scuba_gear', 'scuba', 'learn_swimming', 'jump_into_water', 'enjoy_nature']
senticnet['go_to_theater'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'go_movie_theater', 'go_cinema', 'drive_theater', 'pay_ticket', 'watch_movie']
senticnet['go_to_university'] = ['0.061', '0.179', '0', '0.177', '#interest', '#admiration', 'positive', '0.139', 'study_lot', 'go_to_school', 'pass_exam', 'prepare_exam', 'study']
senticnet['go_to_work'] = ['-0.04', '-0.01', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.03', 'commute', 'transportation', 'drive_to_work', 'drive', 'drive_car']
senticnet['go_toilet'] = ['-0.79', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.74', 'go_pee', 'defecate', 'go_bathroom', 'brush_tooth', 'turn_heat']
senticnet['go_toward'] = ['0', '0.21', '-0.11', '0.345', '#interest', '#admiration', 'positive', '0.147', 'direction', 'unto', 'forehead', 'upon', 'toward']
senticnet['go_town'] = ['0', '0.185', '-0.06', '0.222', '#interest', '#admiration', 'positive', '0.114', 'book_flight', 'drive_car', 'visit_new_place', 'travel', 'explore']
senticnet['go_trial'] = ['-0.24', '0', '-0.19', '-0.05', '#sadness', '#fear', 'negative', '-0.16', 'send_to_prison', 'send_to_jail', 'arrest', 'find_guilty', 'commit_crime']
senticnet['go_trip'] = ['0', '0.185', '-0.06', '0.222', '#interest', '#admiration', 'positive', '0.114', 'book_flight', 'visit_new_place', 'trip', 'drive_car', 'travel']
senticnet['go_under'] = ['-0.77', '0', '0.511', '-0.53', '#sadness', '#disgust', 'negative', '-0.60', 'founder', 'subside', 'fall', 'settle', 'submerge']
senticnet['go_underwater'] = ['0', '0.743', '0', '0.938', '#interest', '#admiration', 'positive', '0.841', 'fall_overboard', 'swim', 'scuba_diving', 'dive', 'pool_party']
senticnet['go_university'] = ['-0.03', '-0.02', '0.029', '-0.05', '#sadness', '#disgust', 'negative', '-0.02', 'study', 'go_class', 'enroll_school', 'study_lot', 'a_lot_of_study']
senticnet['go_up'] = ['0', '0.185', '-0.06', '0.222', '#interest', '#admiration', 'positive', '0.114', 'ascend', 'move_upward', 'raise_up', 'upward_movement', 'move_up']
senticnet['go_up_down'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'walk_up', 'stroll', 'go_upstairs', 'climb_up', 'move_between_floor_building']
senticnet['go_uphill'] = ['0', '0.089', '0', '0.154', '#interest', '#admiration', 'positive', '0.121', 'move_upward', 'mountaineer', 'ascend', 'move_up', 'up_mountain']
senticnet['go_upstairs'] = ['0', '0.135', '-0.08', '0.123', '#interest', '#admiration', 'positive', '0.057', 'rise', 'lift', 'store_garage', 'arise', 'reflect_water']
senticnet['go_vacation'] = ['0.92', '0.976', '0', '0.722', '#joy', '#interest', 'positive', '0.873', 'plan_vacation', 'leave_country', 'have_fun', 'travel', 'go_pub']
senticnet['go_war'] = ['-0.73', '0', '-0.91', '-0.90', '#fear', '#disgust', 'negative', '-0.85', 'make_war', 'conquer_enemy', 'gain_territory', 'lose_war', 'ready_troop']
senticnet['go_watch'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'watch_movie', 'go_to_theater', 'go_movie_theater', 'find_out_play', 'go_cinema']
senticnet['go_water'] = ['-0.03', '0', '0.044', '0', '#sadness', '#anger', 'negative', '-0.03', 'put_bath_suit', 'go_swim_pool', 'go_pool', 'jump_in_water', 'swim']
senticnet['go_work'] = ['0.229', '0.127', '0', '0', '#joy', '#interest', 'positive', '0.178', 'return_work', 'start_project', 'work_box_office', 'write_program', 'begin_work']
senticnet['go_work_to_earn_money'] = ['0.05', '-0.02', '0', '0.111', '#joy', '#admiration', 'positive', '0.063', 'earn_live', 'get_paycheck', 'responsible', 'earn_money', 'earn_paycheck']
senticnet['go_wrong'] = ['-0.78', '0', '0.915', '-0.99', '#anger', '#disgust', 'negative', '-0.89', 'bodge', 'botch_up', 'flub', 'ball_up', 'bollix']
senticnet['goa_bean'] = ['-0.70', '0', '0.606', '-0.72', '#sadness', '#disgust', 'negative', '-0.67', 'common_bean', 'soya', 'bean', 'mung_bean', 'mung']
senticnet['goad'] = ['0', '0', '0.972', '-0.84', '#anger', '#disgust', 'negative', '-0.91', 'prod', 'spur', 'needle', 'urging', 'prick']
senticnet['goal'] = ['0.05', '0.223', '-0.04', '0.303', '#interest', '#admiration', 'positive', '0.132', 'option', 'conclusion', 'end_point', 'intend', 'conclude']
senticnet['goal_attainment'] = ['0.851', '0', '0', '0.957', '#joy', '#admiration', 'positive', '0.904', 'effort', 'go_meet', 'make_time', 'ambition', 'preparation']
senticnet['goal_cushion'] = ['0.724', '-0.92', '0', '0.854', '#surprise', '#admiration', 'positive', '0.833', 'tranquility', 'serenity', 'disposition', 'repose', 'placidity']
senticnet['goal_driven'] = ['0', '0.717', '0', '0.765', '#interest', '#admiration', 'positive', '0.741', 'intelligent_agent', 'artificial_intelligence', 'aim', 'proactive', 'intelligent']
senticnet['goal_tally'] = ['0.926', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.916', 'score_goal', 'accomplish', 'finalize', 'pot', 'make_point']
senticnet['goalie'] = ['0.899', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.878', 'bleacher', 'hockey_fan', 'competitor', 'spectator', 'hockey_stick']
senticnet['goalie_mask'] = ['0.783', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.766', 'baseball_bat', 'water_gun', 'goalie', 'hockey_stick', 'bullseye']
senticnet['goalless_draw'] = ['-0.09', '0.147', '-0.11', '0.205', '#interest', '#admiration', 'positive', '0.036', 'question_starter', 'resolution', 'available', 'explanation', 'request']
senticnet['goaltender_mask'] = ['0.783', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.766', 'baseball_bat', 'water_gun', 'goalie', 'hockey_stick', 'bullseye']
senticnet['goat'] = ['-0.51', '0', '0.838', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'pasang', 'ibex']
senticnet['goat_antelope'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'argali', 'argal', 'urial', 'white_sheep', 'wild_sheep']
senticnet['goat_curd'] = ['0.041', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.043', 'meatloaf', 'salad', 'delicious_meal', 'casserole', 'apple_pie']
senticnet['goat_farm'] = ['0.053', '0.094', '0', '0.136', '#interest', '#admiration', 'positive', '0.094', 'barn_animal', 'domesticate', 'grassland', 'sheep', 'cow']
senticnet['goat_hair'] = ['0.028', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.047', 'handkerchief', 'kerchief', 'hanky', 'facial_tissue', 'zipper']
senticnet['goat_herd'] = ['0.546', '-0.96', '0', '0.533', '#joy', '#surprise', 'positive', '0.683', 'dairy_cattle', 'dairy_cow', 'milch_cow', 'ayrshire', 'milcher']
senticnet['goat_milk'] = ['0.053', '0.085', '0.019', '0', '#joy', '#interest', 'positive', '0.04', 'farm', 'countryside', 'farm_animal', 'cow', 'healthy']
senticnet['goat_skin'] = ['0.028', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.047', 'handkerchief', 'kerchief', 'hanky', 'facial_tissue', 'zipper']
senticnet['goatee'] = ['0.852', '-0.93', '0', '0', '#joy', '#surprise', 'positive', '0.893', 'mullet', 'mackerel', 'mullet', 'catfish', 'flounder']
senticnet['goatherd'] = ['0.525', '-0.85', '0', '0.599', '#surprise', '#admiration', 'positive', '0.66', 'frolic', 'coquetry', 'romp', 'play', 'caper']
senticnet['goats_milk'] = ['0.053', '0.085', '0.019', '0', '#joy', '#interest', 'positive', '0.04', 'farm', 'countryside', 'farm_animal', 'cow', 'healthy']
senticnet['goatskin'] = ['0.028', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.047', 'handkerchief', 'kerchief', 'hanky', 'facial_tissue', 'zipper']
senticnet['gob'] = ['-0.92', '0', '0.783', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'clot', 'coagulum', 'lump', 'clew', 'agglomeration']
senticnet['gobble'] = ['0.175', '0.15', '0', '0.23', '#joy', '#admiration', 'positive', '0.185', 'ingest', 'sip', 'better_cold', 'gulp', 'consumable']
senticnet['gobbledegook'] = ['-0.61', '0', '0.73', '-0.85', '#anger', '#disgust', 'negative', '-0.73', 'diction', 'phrasing', 'verbalization', 'wording', 'phraseology']
senticnet['gobbledygook'] = ['0', '0', '0.857', '-0.74', '#anger', '#disgust', 'negative', '-0.79', 'legalese', 'verbiage', 'boiler-plate', 'formulaic', 'boilerplate']
senticnet['gobbling_up'] = ['-0.71', '0', '0.692', '-0.81', '#sadness', '#disgust', 'negative', '-0.74', 'famished', 'starved', 'hungry', 'esurient', 'peckish']
senticnet['goblin'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'gnome', 'elf', 'dwarf', 'undersized', 'small_person']
senticnet['goblin-like'] = ['0', '0', '-0.77', '-0.89', '#fear', '#disgust', 'negative', '-0.83', 'rat-like', 'gargoyle-like', 'squirrel-like', 'ratlike', 'scorpion-like']
senticnet['gobshite'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'rascal', 'rapscallion', 'scalawag', 'holy_terror', 'monkey']
senticnet['gochu'] = ['-0.63', '0', '0.791', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'little', 'pinch', 'modicum', 'small_indefinite_amount', 'indefinite_quantity']
senticnet['gochujang'] = ['0', '-0.03', '0.046', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'pasta', 'green_bean', 'mayonnaise', 'frozen_food', 'sour_milk']
senticnet['god'] = ['0.878', '0', '0', '0.85', '#joy', '#admiration', 'positive', '0.864', 'dogma', 'goddess', 'saint', 'holy_man', 'minister']
senticnet['god-fearing'] = ['0.857', '0', '0', '0.838', '#joy', '#admiration', 'positive', '0.847', 'devout', 'god-loving', 'pious', 'peace-loving', 'kind-hearted']
senticnet['god-like'] = ['0.705', '0', '0', '0.865', '#joy', '#admiration', 'positive', '0.785', 'super-human', 'superhuman', 'feat', 'ability', 'preternatural']
senticnet['god-loving'] = ['0', '0.836', '0', '0.87', '#interest', '#admiration', 'positive', '0.853', 'wasp-y', 'honest-hearted', 'serious-minded', 'clean-living', 'levelheaded']
senticnet['god_almighty'] = ['0.903', '0', '0', '0', '#joy', '#joy', 'positive', '0.903', 'gospel', 'religion', 'prayer', 'faith', 'world_religion']
senticnet['god_creation'] = ['0.052', '0', '0', '0.094', '#joy', '#admiration', 'positive', '0.073', 'orbit_around_sun', 'enormous', 'milky', 'milky_galaxy', 'nasa']
senticnet['god_creation_galaxy'] = ['0.047', '0', '0', '0.087', '#joy', '#admiration', 'positive', '0.067', 'orbit_around_sun', 'enormous', 'god_creation', 'milky', 'nasa']
senticnet['god_delusion'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'non-believer', 'doubter', 'agnostic', 'freethinker', 'sceptic']
senticnet['god_doth'] = ['-0.79', '0', '0.607', '-0.92', '#sadness', '#disgust', 'negative', '-0.77', 'stubbornness', 'obstinacy', 'obstinance', 'bullheadedness', 'pigheadedness']
senticnet['god_fearing_man'] = ['0.625', '-0.50', '0', '0.822', '#joy', '#admiration', 'positive', '0.649', 'docile', 'tame', 'yielding', 'sheeplike', 'sheepish']
senticnet['god_forbid'] = ['0', '0.074', '0.026', '0.105', '#interest', '#admiration', 'positive', '0.051', 'handwritten', 'scribble', 'music_part', 'written_message', 'calligraphy']
senticnet['god_giveth'] = ['0.887', '-0.84', '0', '0.819', '#joy', '#surprise', 'positive', '0.851', 'arouse', 'elicit', 'enkindle', 'kindle', 'evoke']
senticnet['god_glory'] = ['0.082', '0.17', '-0.07', '0.291', '#interest', '#admiration', 'positive', '0.117', 'sanitary', 'sterile', 'pure', 'use_clean', 'refined']
senticnet['god_grace'] = ['0.71', '0.882', '0', '0.9', '#interest', '#admiration', 'positive', '0.831', 'positive_reinforcement', 'brownie_point', 'benefit', 'give', 'gratification']
senticnet['god_hath'] = ['0.741', '-0.84', '0', '0.543', '#joy', '#surprise', 'positive', '0.711', 'integrity', 'righteousness', 'scrupulousness', 'incorruptness', 'honestness']
senticnet['god_mercy'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitentially', 'penitent', 'repentantly', 'repentant']
senticnet['god_omnipotence'] = ['0.071', '0.222', '-0.10', '0.234', '#interest', '#admiration', 'positive', '0.105', 'baron', 'almighty', 'deity', 'nobility', 'god']
senticnet['god_omniscience'] = ['0.779', '-0.75', '0', '0.526', '#joy', '#surprise', 'positive', '0.687', 'privateness', 'covertness', 'bosom', 'isolation', 'confidentiality']
senticnet['god_sake'] = ['-0.04', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.05', 'stop_talk', 'pay_attention', 'dry_mouth', 'hear_sound', 'able_hear']
senticnet['god_sovereignty'] = ['0.956', '0', '0', '0.864', '#joy', '#admiration', 'positive', '0.91', 'honesty', 'wear_around_neck', 'type_of_dog', 'future_robot', 'trust']
senticnet['god_wot'] = ['-0.44', '-0.15', '0', '-0.36', '#sadness', '#disgust', 'negative', '-0.21', 'baffle', 'stupefy', 'dumbfound', 'stupefaction', 'confuse']
senticnet['god_wrath'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitentially', 'penitent', 'repentantly', 'repentant']
senticnet['goddam'] = ['-0.89', '0', '0.739', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'rot', 'bunk', 'bunkum', 'buncombe', 'guff']
senticnet['goddamn'] = ['0', '0', '-0.13', '0', '#fear', '#fear', 'negative', '-0.13', 'womb', 'anus', 'sister_house', 'shit', 'land_moon']
senticnet['goddess'] = ['0.958', '0', '0', '0.887', '#joy', '#admiration', 'positive', '0.922', 'god', 'priest', 'church_head', 'monk', 'pastor']
senticnet['godhead'] = ['0.128', '0.152', '-0.09', '0.264', '#interest', '#admiration', 'positive', '0.113', 'environment', 'beautiful', 'live_environment', 'really_big', 'important_man']
senticnet['godhood'] = ['0', '0.862', '0', '0.774', '#interest', '#admiration', 'positive', '0.818', 'enter_contract', 'believe', 'give_help', 'karma', 'fate']
senticnet['godless_communism'] = ['0.045', '0.286', '-0.14', '0.229', '#interest', '#admiration', 'positive', '0.104', 'socialism', 'parliament', 'elect', 'government', 'elect_president']
senticnet['godlessness'] = ['0.905', '-0.80', '0', '0.583', '#joy', '#surprise', 'positive', '0.763', 'unrighteousness', 'undutifulness', 'impiety', 'ungodliness', 'irreligiousness']
senticnet['godlike'] = ['0.859', '0.772', '0', '0', '#joy', '#interest', 'positive', '0.816', 'godlikeness', 'divine', 'god', 'olympian', 'immortality']
senticnet['godlike_power'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'chairperson', 'commuter']
senticnet['godlikeness'] = ['0.853', '0.766', '0', '0', '#joy', '#interest', 'positive', '0.809', 'godlike', 'divine', 'god', 'olympian', 'immortality']
senticnet['godliness'] = ['0.082', '0.17', '-0.07', '0.291', '#interest', '#admiration', 'positive', '0.117', 'sanitary', 'sterile', 'pure', 'use_clean', 'refined']
senticnet['godly'] = ['0.656', '-0.73', '0', '0.736', '#surprise', '#admiration', 'positive', '0.708', 'strong', 'salubrious', 'healthy', 'solid', 'alimentary']
senticnet['godly_man'] = ['0.539', '-0.96', '0', '0.745', '#surprise', '#admiration', 'positive', '0.751', 'spouse', 'partner', 'married_person', 'mate', 'better_half']
senticnet['gods_grace'] = ['0.05', '0.363', '-0.09', '0.307', '#interest', '#admiration', 'positive', '0.157', 'faith', 'christ', 'prayer', 'spiritual', 'religious_purpose']
senticnet['gods_mercy'] = ['0.879', '0', '0', '0.986', '#joy', '#admiration', 'positive', '0.932', 'mercifully', 'merciful', 'ruth', 'commiseration', 'commiserate']
senticnet['godsend'] = ['0.808', '-0.83', '0', '0.548', '#joy', '#surprise', 'positive', '0.73', 'successfulness', 'luckiness', 'prosperity', 'circumstance', 'blessing']
senticnet['godsmejudge'] = ['-0.79', '0', '0.992', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['godson'] = ['0', '0.732', '0', '0.994', '#interest', '#admiration', 'positive', '0.863', 'foster_kid', 'daughter', 'relation', 'sister', 'adopt']
senticnet['goeckerman_treatment'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'radiation_therapy', 'radiation', 'actinotherapy', 'irradiation']
senticnet['goemon'] = ['0.021', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'plastic_bottle', 'party', 'use_party', 'lighter_air', 'compress_air']
senticnet['goemon_great'] = ['0.704', '-0.80', '0', '0', '#joy', '#surprise', 'positive', '0.755', 'quest', 'stunt', 'illusion', 'journey', 'activity']
senticnet['goetia'] = ['-0.82', '0', '0.72', '-0.53', '#sadness', '#anger', 'negative', '-0.69', 'invocation', 'charm', 'spell', 'magic_spell', 'magical_spell']
senticnet['goffer'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'busy_bee', 'gopher', 'actor', 'doer', 'worker']
senticnet['goffredo_petrassi'] = ['0.045', '0', '0.033', '0.089', '#joy', '#admiration', 'positive', '0.034', 'spanish', 'vocalization', 'verbal_communication', 'vocal', 'mouth_noise']
senticnet['gofraid'] = ['0', '0.105', '0', '0.084', '#interest', '#admiration', 'positive', '0.095', 'archive', 'print_medium', 'quiet_place', 'register', 'media']
senticnet['gog'] = ['0.065', '0.215', '0.011', '0.199', '#interest', '#admiration', 'positive', '0.117', 'domesticate_dog', 'domesticate_animal', 'pet_dog', 'cat', 'pet']
senticnet['gogebic_range'] = ['-0.98', '0', '0.521', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'battery', 'stamp_battery', 'quartz_battery', 'stamp_mill', 'factory']
senticnet['goggle-eyed'] = ['0', '-0.66', '0', '0', '#surprise', '#surprise', 'positive', '0.662', 'surprised', 'dazed', 'wide-eyed', 'surprise', 'open-mouthed']
senticnet['gogoomy'] = ['0', '0', '0.898', '-0.90', '#anger', '#disgust', 'negative', '-0.90', 'unconcern', 'unconcerned', 'apathy', 'indifference', 'carelessness']
senticnet['going'] = ['-0.66', '0', '0.585', '-0.91', '#sadness', '#disgust', 'negative', '-0.72', 'withdrawal', 'exit', 'dispatch', 'breaking_away', 'farewell']
senticnet['going_berserk'] = ['-0.85', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.88', 'oppression', 'enslavement', 'slave', 'captivity', 'misery']
senticnet['going_forward'] = ['0.844', '0', '0', '0', '#joy', '#joy', 'positive', '0.844', 'straight', 'late_time', 'preparedness', 'past_present', 'wait']
senticnet['going_nowhere'] = ['-0.63', '0', '0.829', '-0.79', '#anger', '#disgust', 'negative', '-0.75', 'stuffiness', 'blocked', 'problem', 'traffic', 'congestion']
senticnet['going_overdrawn'] = ['0.54', '-0.97', '0', '0.569', '#surprise', '#admiration', 'positive', '0.695', 'overstate', 'hyperbolize', 'glorify', 'exaggerate', 'aggrandize']
senticnet['goiter'] = ['0', '0.71', '0', '0.935', '#interest', '#admiration', 'positive', '0.823', 'hyperplasia', 'nodule', 'nodular', 'lesion', 'carcinoma']
senticnet['goitered_gazelle'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheep', 'argal', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['gojira'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amuck', 'murderousness', 'misocainea', 'misogyny']
senticnet['gokkun'] = ['0.725', '-0.66', '0', '0.906', '#joy', '#admiration', 'positive', '0.765', 'set', 'pose', 'lay', 'put', 'place']
senticnet['gold'] = ['0.866', '0.82', '0', '0.713', '#joy', '#interest', 'positive', '0.8', 'titanium', 'bronze', 'alloy', 'medal', 'copper']
senticnet['gold-digging'] = ['0', '0', '0.738', '-0.73', '#anger', '#disgust', 'negative', '-0.73', 'harridan', 'sharp-tongued', 'loutish', 'shrewish', 'oafish']
senticnet['gold_bracelet'] = ['0.048', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.071', 'ring', 'ring_loudly', 'diamond_ring', 'necklace', 'jewelry']
senticnet['gold_braid'] = ['-0.84', '0', '0.633', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'grizzle', 'postiche', 'wig', 'periwig', 'peruke']
senticnet['gold_bullion'] = ['-0.62', '0', '0.504', '-0.79', '#sadness', '#disgust', 'negative', '-0.64', 'green_gold', 'pure_gold', 'gold', 'guinea_gold', 'noble_metal']
senticnet['gold_chalice'] = ['0.037', '0.073', '0.027', '0.072', '#interest', '#admiration', 'positive', '0.039', 'sapphire', 'birth_stone', 'turquoise', 'gemstone', 'amethyst']
senticnet['gold_coin'] = ['-0.98', '0', '0.664', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'unfortunate', 'unfortunate_person', 'weeper', 'pallgriever', 'griever']
senticnet['gold_cup'] = ['-0.76', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.78', 'strangle', 'bloodshed', 'cripple', 'hear_bad_news', 'sexually_transmit_disease']
senticnet['gold_digger'] = ['-0.80', '0', '0.56', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'gossip', 'gossiper', 'blabbermouth', 'talkative', 'rumormonger']
senticnet['gold_dust'] = ['-0.62', '0', '0.504', '-0.79', '#sadness', '#disgust', 'negative', '-0.64', 'green_gold', 'pure_gold', 'gold', 'guinea_gold', 'noble_metal']
senticnet['gold_embroidery'] = ['-0.53', '0', '0.682', '-0.74', '#anger', '#disgust', 'negative', '-0.65', 'girdle', 'cincture', 'cummerbund', 'waistband', 'waistcloth']
senticnet['gold_escudo'] = ['-0.02', '0.264', '0', '0.095', '#interest', '#admiration', 'positive', '0.111', 'finance', 'franc', 'cent', 'banknote', 'mastercard']
senticnet['gold_fish'] = ['0', '0.07', '0', '0', '#interest', '#interest', 'positive', '0.07', 'sunken_treasure', 'scuba_diver', 'urchin', 'mussel', 'real_duck']
senticnet['gold_foil'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'aluminium_foil', 'tin_foil', 'foil']
senticnet['gold_glitter'] = ['0.023', '0.141', '-0.13', '0.174', '#interest', '#admiration', 'positive', '0.052', 'candy', 'push_wind', 'exhalation', 'breaout', 'mouth_action']
senticnet['gold_ingot'] = ['-0.62', '0', '0.504', '-0.79', '#sadness', '#disgust', 'negative', '-0.64', 'green_gold', 'pure_gold', 'gold', 'guinea_gold', 'noble_metal']
senticnet['gold_jewellery'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'anklet', 'ankle_bracelet', 'armilla']
senticnet['gold_jewelry'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'anklet', 'ankle_bracelet', 'armilla']
senticnet['gold_lace'] = ['-0.84', '0', '0.633', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'grizzle', 'postiche', 'wig', 'periwig', 'peruke']
senticnet['gold_leaf'] = ['-0.06', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.04', 'polish', 'magnify', 'see_out', 'sill', 'pane']
senticnet['gold_locket'] = ['0.048', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.071', 'ring', 'ring_loudly', 'diamond_ring', 'necklace', 'jewelry']
senticnet['gold_mining_camp'] = ['-0.98', '0', '0.521', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'battery', 'stamp_battery', 'quartz_battery', 'stamp_mill', 'factory']
senticnet['gold_mural_crown'] = ['0.03', '0', '0.017', '0.138', '#joy', '#admiration', 'positive', '0.05', 'up_down', 'curl', 'bump', 'hand_gesture', 'goodbye']
senticnet['gold_necklace'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'anklet', 'ankle_bracelet', 'armilla']
senticnet['gold_nugget'] = ['-0.62', '0', '0.504', '-0.79', '#sadness', '#disgust', 'negative', '-0.64', 'green_gold', 'pure_gold', 'gold', 'guinea_gold', 'noble_metal']
senticnet['gold_ore'] = ['-0.78', '0', '0.699', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'battery', 'stamp_battery', 'quartz_battery', 'quartz_mill', 'factory']
senticnet['gold_pendant'] = ['0.048', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.071', 'ring', 'ring_loudly', 'diamond_ring', 'necklace', 'jewelry']
senticnet['gold_plated_silver'] = ['0.052', '0', '0', '0.118', '#joy', '#admiration', 'positive', '0.085', 'diamond_ring', 'piece_of_jewelry', 'bell_sound', 'wedding_band', 'engagement']
senticnet['gold_prospector'] = ['-0.64', '0', '0.667', '-0.82', '#anger', '#disgust', 'negative', '-0.71', 'novice', 'beginner', 'tyro', 'tiro', 'initiate']
senticnet['gold_stripe'] = ['0.942', '-0.61', '0', '0.908', '#joy', '#admiration', 'positive', '0.82', 'adornment', 'cigar_band', 'hatband', 'neckband', 'pinstripe']
senticnet['gold_tassel'] = ['-0.84', '0', '0.633', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'grizzle', 'postiche', 'wig', 'periwig', 'peruke']
senticnet['gold_tooth'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'naked_eye', 'eye', 'oculus_sinister']
senticnet['gold_wedding_ring'] = ['0.048', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.071', 'ring', 'ring_loudly', 'diamond_ring', 'necklace', 'jewelry']
senticnet['gold_wreath'] = ['0.586', '-0.65', '0', '0.503', '#joy', '#surprise', 'positive', '0.58', 'decoration', 'distinguished_service_cross', 'medal', 'medallion', 'palm']
senticnet['goldbowl'] = ['-0.52', '0', '0.554', '-0.70', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'bowl', 'fish_bowl', 'jorum', 'mazer']
senticnet['goldbrick'] = ['-0.86', '0', '0.675', '-0.84', '#sadness', '#disgust', 'negative', '-0.79', 'shenanigan', 'chicanery', 'wile', 'dupery', 'trickery']
senticnet['goldbug'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
senticnet['goldcup'] = ['-0.51', '0', '0.773', '-0.80', '#anger', '#disgust', 'negative', '-0.69', 'common_buttercup', 'meadow_buttercup', 'tall_buttercup', 'tall_crowfoot', 'gaint_buttercup']
senticnet['golden'] = ['0.049', '0.145', '0', '0.171', '#interest', '#admiration', 'positive', '0.122', 'kingly', 'govern', 'head_wear', 'chivalry', 'blue_blood']
senticnet['golden-green'] = ['0', '0', '0.842', '-0.71', '#anger', '#disgust', 'negative', '-0.77', 'blueish-green', 'greenish-gold', 'ruby-brown', 'orange-copper', 'deep-brown']
senticnet['golden_age'] = ['-0.96', '0', '0.615', '-0.73', '#sadness', '#disgust', 'negative', '-0.77', 'flush', 'blossom', 'prime', 'peak', 'efflorescence']
senticnet['golden_ager'] = ['0.556', '-0.64', '0', '0.64', '#surprise', '#admiration', 'positive', '0.612', 'centenarian', 'nonagenarian', 'octogenarian', 'septuagenarian', 'sexagenarian']
senticnet['golden_ale'] = ['0', '0.091', '-0.05', '0.073', '#interest', '#admiration', 'positive', '0.037', 'privately', 'hold_liquid', 'make_wish', 'rinse_dish', 'find_toilet']
senticnet['golden_algae'] = ['-0.65', '0', '0.523', '-0.73', '#sadness', '#disgust', 'negative', '-0.64', 'confervoid_algae', 'seaweed', 'alga', 'diatom', 'brown_algae']
senticnet['golden_boot_award'] = ['0.232', '0.178', '-0.17', '0.223', '#joy', '#admiration', 'positive', '0.115', 'fame', 'alms', 'find_money', 'better_life', 'small_detail']
senticnet['golden_bracelet'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'bangle', 'ankle_bracelet', 'armilla']
senticnet['golden_buzzer'] = ['-0.08', '-0.03', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'graduate_student', 'student', 'serious_student', 'competition', 'hard_worker']
senticnet['golden_circlet'] = ['-0.62', '0', '0.961', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'jeweled_headdress', 'diadem', 'tiara', 'headdress', 'headgear']
senticnet['golden_crown'] = ['0.049', '0.145', '0', '0.171', '#interest', '#admiration', 'positive', '0.122', 'kingly', 'govern', 'head_wear', 'chivalry', 'blue_blood']
senticnet['golden_dragon'] = ['-0.75', '0', '0.708', '0', '#sadness', '#anger', 'negative', '-0.73', 'hound', 'cage_animal', 'kind_animal', 'hunter', 'animal']
senticnet['golden_eagle'] = ['-0.74', '0', '0.963', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['golden_era'] = ['0', '0.104', '0', '0.124', '#interest', '#admiration', 'positive', '0.114', 'good_trait', 'constant', 'time_increment', 'time_designation', 'time_length']
senticnet['golden_gate_bridge'] = ['0.864', '0.761', '0', '0', '#joy', '#interest', 'positive', '0.813', 'bridge', 'under_bridge', 'suspension_bridge', 'side_road', 'roadside']
senticnet['golden_glider'] = ['-0.58', '0', '0.661', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'explosive', 'cordite', 'dynamite', 'plastic_explosive', 'explosive_compound']
senticnet['golden_glow'] = ['-0.96', '0', '0.636', '-0.64', '#sadness', '#disgust', 'negative', '-0.75', 'burn', 'tan', 'hyperpigmentation', 'sunburn', 'physical_condition']
senticnet['golden_hair'] = ['0.049', '0.145', '0', '0.171', '#interest', '#admiration', 'positive', '0.122', 'kingly', 'govern', 'head_wear', 'chivalry', 'blue_blood']
senticnet['golden_haze'] = ['0.449', '0.305', '-0.19', '0.222', '#joy', '#interest', 'positive', '0.196', 'calm', 'peace', 'serenity', 'quiet', 'tranquility']
senticnet['golden_hind'] = ['0.582', '-0.97', '0', '0.802', '#surprise', '#admiration', 'positive', '0.788', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['golden_hue'] = ['0.577', '-0.69', '0', '0.645', '#surprise', '#admiration', 'positive', '0.639', 'shining', 'polished', 'shiny', 'bright', 'burnished']
senticnet['golden_jackal'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheep', 'argal', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['golden_joystick_award'] = ['0.232', '0.178', '-0.17', '0.223', '#joy', '#admiration', 'positive', '0.115', 'fame', 'alms', 'find_money', 'better_life', 'small_detail']
senticnet['golden_legend'] = ['0', '0.248', '-0.08', '0.173', '#interest', '#admiration', 'positive', '0.113', 'god', 'minister', 'evangelist', 'rector', 'goddess']
senticnet['golden_metallic_subcostal'] = ['-0.58', '0', '0.561', '-0.62', '#sadness', '#disgust', 'negative', '-0.58', 'spot', 'speckle', 'dapple', 'mark', 'fleck']
senticnet['golden_mural_crown'] = ['0.03', '0', '0.017', '0.138', '#joy', '#admiration', 'positive', '0.05', 'up_down', 'curl', 'bump', 'hand_gesture', 'goodbye']
senticnet['golden_nugget'] = ['0.057', '0.13', '0', '0.119', '#interest', '#admiration', 'positive', '0.102', 'friend_wedding', 'circular_route', 'spanish_restaurant', 'altar', 'monastery']
senticnet['golden_opportunity'] = ['0.961', '-0.84', '0', '0.958', '#joy', '#admiration', 'positive', '0.923', 'pause', 'break', 'intermission', 'suspension', 'rest_period']
senticnet['golden_oriole'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'amoeba', 'single_celled']
senticnet['golden_perch'] = ['0', '-0.02', '0.044', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'haddock', 'mermaid', 'fish_rod', 'tidal_wave', 'dryad']
senticnet['golden_plover'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['golden_retriever'] = ['-0.72', '0', '0.619', '-0.86', '#sadness', '#disgust', 'negative', '-0.73', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['golden_robe'] = ['-0.62', '0', '0.961', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'crown', 'jeweled_headdress', 'tiara', 'headdress', 'headgear']
senticnet['golden_rod'] = ['0', '0', '-0.14', '-0.03', '#fear', '#disgust', 'negative', '-0.09', 'pine_cone', 'fallen_leaf', 'poison_ivy', 'detritus', 'tress']
senticnet['golden_saxifrage'] = ['-0.83', '0', '0.768', '-0.69', '#sadness', '#anger', 'negative', '-0.76', 'water_carpet', 'water_mat', 'aquatic_plant', 'water_plant', 'hydrophyte']
senticnet['golden_shiner'] = ['0', '-0.02', '0.044', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'haddock', 'mermaid', 'fish_rod', 'tidal_wave', 'dryad']
senticnet['golden_snitch'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'chairperson', 'commuter']
senticnet['golden_spleen'] = ['-0.59', '0', '0.78', '-0.66', '#anger', '#disgust', 'negative', '-0.68', 'water_carpet', 'water_mat', 'aquatic_plant', 'water_plant', 'hydrophyte']
senticnet['golden_statue'] = ['0.085', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.079', 'sculpture', 'creative', 'yellow_ribbon', 'artwork', 'monument']
senticnet['golden_syrup'] = ['-0.08', '0.106', '0', '0.125', '#interest', '#admiration', 'positive', '0.047', 'sugar', 'treacle', 'black_treacle', 'add_sugar', 'sugar_substitute']
senticnet['golden_throne'] = ['0', '0', '-0.76', '-0.82', '#fear', '#disgust', 'negative', '-0.79', 'stage', 'thespian', 'actor', 'theatre', 'drama']
senticnet['golden_urn'] = ['0', '0.082', '-0.09', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'coffin', 'headstone', 'morgue', 'bury_body', 'casket']
senticnet['golden_vase'] = ['-0.93', '0', '0.861', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'knotty', 'contorted', 'writhed', 'crooked', 'anfractuous']
senticnet['golden_yellow'] = ['0', '0.19', '-0.09', '0.175', '#interest', '#admiration', 'positive', '0.089', 'red', 'green_black', 'green_red', 'peelable', 'precious_jewel']
senticnet['goldenrod'] = ['0.958', '-0.82', '0', '0.924', '#joy', '#admiration', 'positive', '0.901', 'bunchberry', 'dwarf_cornel', 'crackerberry', 'pudding_berry', 'common_white_dogwood']
senticnet['goldenrod_solidago'] = ['-0.92', '0', '0.912', '-0.94', '#sadness', '#disgust', 'negative', '-0.92', 'true_blackberry', 'sand_blackberry', 'blackberry_bush', 'blackberry', 'running_blackberry']
senticnet['goldfish'] = ['-0.83', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.78', 'picaninny', 'kid', 'minor', 'tike', 'small_picaninny']
senticnet['goldfish_bowl'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'bowl', 'jorum', 'mazer']
senticnet['goldilind'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['goldman_sachs_analyst'] = ['0.711', '0', '0', '0', '#joy', '#joy', 'positive', '0.711', 'chemist', 'expert', 'scientist', 'esaminatore', 'physicist']
senticnet['goldwork'] = ['0.876', '-0.76', '0', '0.963', '#joy', '#admiration', 'positive', '0.867', 'housecraft', 'tradecraft', 'watercraft', 'acquirement', 'skill']
senticnet['golenishtchev'] = ['-0.75', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.83', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['golf'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'table_tennis', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['golf_apparel'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'sports_equipment', 'sport_stuff', 'golf', 'sport_store', 'bag']
senticnet['golf_bag'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'sports_equipment', 'sport_stuff', 'golf', 'sport_store', 'bag']
senticnet['golf_ball'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'sports_equipment', 'sport_stuff', 'golf', 'sport_store', 'bag']
senticnet['golf_ball_sized_hail'] = ['0.898', '-0.70', '0', '0.712', '#joy', '#admiration', 'positive', '0.773', 'wish', 'reception', 'welcome', 'greeting', 'regard']
senticnet['golf_cart'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'sports_equipment', 'sport_stuff', 'golf', 'sport_store', 'bag']
senticnet['golf_club'] = ['0.137', '-0.11', '0.036', '-0.03', '#joy', '#surprise', 'positive', '0.045', 'wrench', 'metal_rod', 'nut_bolt', 'vise', 'spanner']
senticnet['golf_course'] = ['0.049', '0.145', '0', '0.171', '#interest', '#admiration', 'positive', '0.122', 'golf_playground', 'yard', 'golf', 'play_golf', 'play_game']
senticnet['golf_digest'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'table_tennis', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['golf_digest_list'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'table_tennis', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['golf_digest_magazine'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'table_tennis', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['golf_enthusiast'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'table_tennis', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['golf_place'] = ['0', '-0.01', '0.027', '-0.1', '#anger', '#disgust', 'negative', '-0.03', 'sand_paper', 'obstacle', 'chalk_board', 'paper', 'school_class']
senticnet['golf_playground'] = ['0.049', '0.145', '0', '0.171', '#interest', '#admiration', 'positive', '0.122', 'golf_course', 'yard', 'golf', 'play_golf', 'play_game']
senticnet['golf_pro'] = ['-0.71', '0', '0.845', '-0.69', '#sadness', '#anger', 'negative', '-0.75', 'lump', 'goon', 'mortal', 'clumsy_person', 'butterfingers']
senticnet['golf_shirt'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'sports_equipment', 'sport_stuff', 'golf', 'sport_store', 'bag']
senticnet['golf_tee'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'sports_equipment', 'sport_stuff', 'golf', 'sport_store', 'bag']
senticnet['golf_tournament'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'table_tennis', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['golfer'] = ['0', '0.764', '0', '0.809', '#interest', '#admiration', 'positive', '0.786', 'cultural_experience', 'athlete', 'film_critic', 'pastime', 'get_entertain']
senticnet['golfer_elbow'] = ['-0.98', '0', '0.678', '-0.95', '#sadness', '#disgust', 'negative', '-0.87', 'rubor', 'redness', 'inflammation', 'epicondylitis', 'lateral_epicondylitis']
senticnet['goliath_grouper'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'crab', 'swimming_crab', 'rock_crab', 'fiddler_crab', 'pea_crab']
senticnet['golly'] = ['-0.78', '0', '0.701', '-0.78', '#sadness', '#disgust', 'negative', '-0.75', 'express', 'utter', 'cry_out', 'ooh', 'gosh']
senticnet['golosh'] = ['-0.88', '0', '0.75', '-0.85', '#sadness', '#disgust', 'negative', '-0.82', 'rubber', 'galosh', 'overshoe', 'arctic', 'gumshoe']
senticnet['gomen'] = ['0.973', '-0.50', '0', '0.612', '#joy', '#admiration', 'positive', '0.698', 'chic', 'chicness', 'jauntiness', 'modishness', 'stylishness']
senticnet['gommateshwara'] = ['-0.88', '0', '0.882', '-0.58', '#sadness', '#anger', 'negative', '-0.78', 'mortal', 'behemoth', 'unusual_person', 'aberrant', 'giant']
senticnet['gommecourt'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'start_over', 'queue', 'find_line', 'take_turn', 'long_line']
senticnet['gomoku'] = ['0', '0', '0', '0.115', '#admiration', '#admiration', 'positive', '0.115', 'chess_knight', 'strategy', 'pawn', 'chess_queen', 'chess_rook']
senticnet['gomrath'] = ['0.916', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.929', 'galaxy', 'universe', 'planetary', 'sun', 'dwarf_planet']
senticnet['gonadal'] = ['0.672', '-0.55', '0', '0.992', '#joy', '#admiration', 'positive', '0.739', 'pressor', 'hormone', 'adrenalin', 'internal_secretion', 'catecholamine']
senticnet['gondokoro'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['gondola'] = ['0.215', '0.194', '-0.20', '0.279', '#joy', '#admiration', 'positive', '0.121', 'launch', 'mast', 'platform', 'launch_platform', 'ocean']
senticnet['gondola_ride'] = ['0.024', '0.152', '-0.06', '0.236', '#interest', '#admiration', 'positive', '0.087', 'vacation', 'exit', 'stance', 'take_break', 'holiday']
senticnet['gong_bao_chicken'] = ['0.084', '-0.05', '0', '-0.04', '#joy', '#surprise', 'positive', '0.031', 'entree', 'feast', 'satiation', 'breakfast', 'supper']
senticnet['goniatitida_order'] = ['0.065', '0.113', '0', '0.1', '#interest', '#admiration', 'positive', '0.093', 'big_group', 'clique', 'group_of_people', 'multitude', 'large_group']
senticnet['goniometer'] = ['0', '-0.00', '0.034', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'celsius', 'grade', 'certificate', 'learn_tool', 'temperature_measure']
senticnet['gonna'] = ['0.762', '-1', '0', '0', '#joy', '#surprise', 'positive', '0.881', 'okay', 'yeah', 'alright', 'gotta', 'hey']
senticnet['gonna_get'] = ['-0.94', '0', '0.557', '-0.71', '#sadness', '#disgust', 'negative', '-0.74', 'sad', 'disappointing', 'unlucky', 'disengaged', 'cry']
senticnet['gonochoristic'] = ['-0.87', '0', '0.983', '-0.91', '#anger', '#disgust', 'negative', '-0.92', 'thorn', 'sticker', 'pricker', 'prickle', 'spikelet']
senticnet['gonococcus'] = ['0.905', '-0.65', '0', '0.851', '#joy', '#admiration', 'positive', '0.805', 'bacteria', 'acidophil', 'acidophile', 'probiotic', 'probiotic_bacterium']
senticnet['gonorrhea'] = ['-0.89', '0', '-0.72', '-0.74', '#sadness', '#disgust', 'negative', '-0.78', 'psychoneurosis', 'schizophrenia', 'toothache', 'feel_pain', 'feel_bad']
senticnet['gonorrhoea'] = ['-0.61', '0', '0.899', '-0.62', '#anger', '#disgust', 'negative', '-0.71', 'venereal_disease', 'venereal_infection', 'social_disease', 'sexually_transmitted_disease', 'dose']
senticnet['gonzo'] = ['0.877', '-1', '0', '0', '#joy', '#surprise', 'positive', '0.939', 'freaky', 'outlandish', 'off-the-wall', 'bizarre', 'flaky']
senticnet['goo'] = ['0', '0', '-0.81', '-0.84', '#fear', '#disgust', 'negative', '-0.82', 'sticky_substance', 'flour_water', 'insert', 'adhesive', 'sticky']
senticnet['goober'] = ['0', '0', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.05', 'human', 'delusional', 'use_illegal_drug', 'busy_person', 'carpenter']
senticnet['good'] = ['0.922', '0.776', '0', '0', '#joy', '#interest', 'positive', '0.849', 'uncommon', 'niceness', 'great', 'hard_find', 'pleasant']
senticnet['good-hearted'] = ['0.879', '0', '0', '0', '#joy', '#joy', 'positive', '0.879', 'thankful', 'openhearted', 'enthusiasm', 'kindly', 'benevolent']
senticnet['good-looking'] = ['0.909', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.818', 'big', 'well-favored', 'handsome', 'better-looking', 'bighearted']
senticnet['good-quality'] = ['-0.75', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'week-old', 'month-old', 'cubed', 'day-old', 'wholemeal']
senticnet['good-time'] = ['0.827', '-0.83', '0', '0', '#joy', '#surprise', 'positive', '0.83', 'toe-tapping', 'foot-stomping', 'down-home', 'rollicking', 'hard-rocking']
senticnet['good_acoustic'] = ['-0.02', '-0.05', '0.078', '-0.12', '#anger', '#disgust', 'negative', '-0.04', 'play_in_band', 'annoy_mom', 'play_orchestra', 'blow_note', 'play_music']
senticnet['good_actor'] = ['0.47', '0.419', '-0.31', '0.367', '#joy', '#interest', 'positive', '0.234', 'wish_entertain', 'artist', 'see_actor', 'enjoyment', 'entertainment']
senticnet['good_appearance'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'hot_girl', 'good_looking', 'appear_beautiful', 'beauty', 'enjoy_view']
senticnet['good_approximation'] = ['0', '0.112', '0', '0.097', '#interest', '#admiration', 'positive', '0.105', 'math_relate', 'math_subject', 'school_study', 'median', 'method']
senticnet['good_attitude'] = ['0.281', '0.276', '-0.27', '0.315', '#joy', '#admiration', 'positive', '0.15', 'better_person', 'demand', 'attitude', 'please', 'courtesy']
senticnet['good_balance'] = ['0', '0.824', '0', '0.735', '#interest', '#admiration', 'positive', '0.779', 'put_helmet', 'buy_shoes', 'put_protective_gear', 'use_leg', 'slow']
senticnet['good_band'] = ['0.74', '0.981', '0', '0.73', '#joy', '#interest', 'positive', '0.817', 'good_music', 'string_quartet', 'music_shop', 'high_school_band', 'musical_instrument_store']
senticnet['good_behavior'] = ['0.926', '0.744', '0', '0.914', '#joy', '#admiration', 'positive', '0.861', 'politeness', 'courtesy', 'good_etiquette', 'courteous', 'obey']
senticnet['good_bet'] = ['0', '0', '0', '0.881', '#admiration', '#admiration', 'positive', '0.881', 'make_comparison', 'put_bid', 'ask_question', 'share_opinion', 'spread_out']
senticnet['good_body'] = ['0.837', '0.818', '0', '0.874', '#joy', '#admiration', 'positive', '0.843', 'healthy_activity', 'good_human', 'fitness', 'push_up', 'athletic_activity']
senticnet['good_book'] = ['0.044', '0', '0', '0.206', '#joy', '#admiration', 'positive', '0.125', 'cookbook', 'begin_end', 'book_end', 'novel', 'book']
senticnet['good_boy'] = ['0.877', '-0.92', '0', '0.795', '#joy', '#surprise', 'positive', '0.865', 'study_material', 'study_lot', 'study_language', 'go_class', 'diligent']
senticnet['good_break'] = ['0.039', '0.152', '0', '0.157', '#interest', '#admiration', 'positive', '0.116', 'keep_safe', 'drive_safely', 'avoid_problem', 'just_in_time', 'avoid_accident']
senticnet['good_buy'] = ['-0.02', '0.292', '-0.07', '0.296', '#interest', '#admiration', 'positive', '0.121', 'good_price', 'bargain', 'cheap_price', 'sale', 'good_deal']
senticnet['good_bye'] = ['0', '0.145', '0', '0.2', '#interest', '#admiration', 'positive', '0.172', 'hand_gesture', 'arm_movement', 'water_splash', 'hand_motion', 'water_form']
senticnet['good_catholic'] = ['0.021', '0.199', '0.035', '0.171', '#interest', '#admiration', 'positive', '0.089', 'church', 'confession', 'catholic_church', 'spirituality', 'christianity']
senticnet['good_character'] = ['0.093', '0.244', '-0.17', '0.333', '#interest', '#admiration', 'positive', '0.124', 'honest', 'character_trait', 'trait', 'good_quality', 'characteristic']
senticnet['good_cheer'] = ['0.918', '0.476', '-0.19', '0.417', '#joy', '#interest', 'positive', '0.404', 'merriment', 'cheery', 'festal', 'playfulness', 'pridefulness']
senticnet['good_child'] = ['0.935', '0', '0', '0.715', '#joy', '#admiration', 'positive', '0.825', 'feel_obligated', 'show_affection', 'express_love', 'show_love', 'pleasant_emotion']
senticnet['good_citizen'] = ['0.889', '0.98', '0', '0.982', '#interest', '#admiration', 'positive', '0.95', 'pay_off_debt', 'positive_reinforcement', 'good_value', 'good_deal', 'discount']
senticnet['good_condition'] = ['0.029', '0.21', '-0.08', '0.298', '#interest', '#admiration', 'positive', '0.113', 'characteristic', 'character_trait', 'healthy', 'forward_movement', 'result_injury']
senticnet['good_conversation'] = ['0.963', '0.974', '0', '0.721', '#joy', '#interest', 'positive', '0.886', 'good_listener', 'human_contact', 'friendship', 'prepare_snack', 'talk_much']
senticnet['good_credit'] = ['0.728', '0.922', '0', '0.996', '#interest', '#admiration', 'positive', '0.882', 'use_money', 'enough_money', 'money_credit', 'out_wallet', 'moolah']
senticnet['good_customer'] = ['0.799', '-0.92', '0', '0.896', '#surprise', '#admiration', 'positive', '0.874', 'save_money', 'buy_cheaper', 'better_price', 'sale', 'on_sale']
senticnet['good_day'] = ['0.799', '0.753', '0', '0.946', '#joy', '#admiration', 'positive', '0.833', 'remission', 'strong_relationship', 'sexual_pleasure', 'self_respect', 'happiness']
senticnet['good_deal'] = ['0.74', '0.777', '0', '0.706', '#joy', '#interest', 'positive', '0.741', 'cheap_price', 'tag', 'good_value', 'good_price', 'good_buy']
senticnet['good_deed'] = ['0.71', '0.882', '0', '0.9', '#interest', '#admiration', 'positive', '0.831', 'positive_reinforcement', 'brownie_point', 'benefit', 'give', 'gratification']
senticnet['good_diet'] = ['0.846', '0.845', '0', '0', '#joy', '#interest', 'positive', '0.845', 'healthy', 'healthy_lifestyle', 'eat_well', 'healthy_life', 'vitamin']
senticnet['good_dog'] = ['0', '0.078', '0', '0.069', '#interest', '#admiration', 'positive', '0.074', 'get_perm', 'dog_treat', 'in_person', 'inside_body', 'below_neck']
senticnet['good_drink'] = ['0.948', '0.992', '0', '0.974', '#interest', '#admiration', 'positive', '0.971', 'liquid_drink', 'hold_soda', 'popular_soda', 'healthy_drink', 'ferment_beverage']
senticnet['good_eat'] = ['0.061', '0', '0.039', '0.097', '#joy', '#admiration', 'positive', '0.04', 'edible', 'delicious', 'yummy', 'tasty', 'part_of_meal']
senticnet['good_education'] = ['0.188', '0.085', '0.035', '0.111', '#joy', '#admiration', 'positive', '0.087', 'financial_security', 'plenty_sleep', 'interest_job', 'good_job', 'keep_job']
senticnet['good_emotion'] = ['0.605', '0.329', '-0.27', '0.337', '#joy', '#admiration', 'positive', '0.249', 'wonderful_feeling', 'happy_emotion', 'ecstatic', 'happy_feeling', 'pleasant_emotion']
senticnet['good_environment'] = ['0.967', '0.982', '0', '0.777', '#joy', '#interest', 'positive', '0.909', 'maintain', 'make_strong', 'green', 'cultivate', 'usual_green']
senticnet['good_etiquette'] = ['0.105', '0.284', '-0.18', '0.418', '#interest', '#admiration', 'positive', '0.156', 'politeness', 'niceness', 'good_behavior', 'courtesy', 'reliable']
senticnet['good_everyone'] = ['0.799', '0.81', '0', '0.91', '#interest', '#admiration', 'positive', '0.84', 'learn_sport', 'acquire_knowledge', 'information_gathering', 'resolve_dispute', 'get_knowledge']
senticnet['good_example'] = ['0.84', '-0.86', '0', '0.525', '#joy', '#surprise', 'positive', '0.742', 'beaut', 'ideal', 'beauty', 'pattern', 'pacesetter']
senticnet['good_exercise'] = ['0.971', '0', '0', '0.867', '#joy', '#admiration', 'positive', '0.919', 'form_of_exercise', 'healthy_exercise', 'vigorous_activity', 'great_exercise', 'workout']
senticnet['good_eyesight'] = ['0.925', '0.962', '0', '0.848', '#joy', '#interest', 'positive', '0.912', 'able_read', 'good_sense_of_humor', 'taken_seriously', 'much_need', 'complement']
senticnet['good_faith'] = ['-0.08', '0', '-0.13', '0.085', '#sadness', '#fear', 'negative', '-0.04', 'job_offer', 'rejection', 'lucky', 'borrow_money', 'quit_work']
senticnet['good_faith_effort'] = ['-0.81', '0', '0.979', '-0.88', '#anger', '#disgust', 'negative', '-0.89', 'vexation', 'dead_weight', 'imposition', 'worry', 'concern']
senticnet['good_feeling'] = ['0.878', '0.861', '0', '0.908', '#joy', '#admiration', 'positive', '0.882', 'happiness', 'self_satisfaction', 'happy', 'feel_good', 'pleasant_emotion']
senticnet['good_food'] = ['0.076', '0', '0.045', '0.07', '#joy', '#admiration', 'positive', '0.034', 'good_meal', 'healthy', 'dairy_product', 'morning_food', 'flavor_food']
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senticnet['goofing_around'] = ['0', '-0.08', '0', '0', '#surprise', '#surprise', 'positive', '0.082', 'yo-yo', 'playroom', 'backgammon', 'action_figure', 'board_game']
senticnet['goofing_off'] = ['0', '0', '0.748', '-0.81', '#anger', '#disgust', 'negative', '-0.78', 'search_information', 'look_for_information', 'check_email', 'phone_line_busy', 'distract']
senticnet['goofy'] = ['0', '0.119', '-0.07', '0.189', '#interest', '#admiration', 'positive', '0.079', 'play_dead', 'pull_cart', 'woolly_animal', 'action', 'beatles_song']
senticnet['goofy_grin'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'hankie', 'kleenex', 'facial_tissue', 'handkerchief', 'tissue']
senticnet['google_adsense'] = ['0.886', '-0.77', '0', '0.815', '#joy', '#admiration', 'positive', '0.826', 'countenance', 'legitimate', 'permit', 'allow', 'let']
senticnet['google_chrome'] = ['-0.89', '0', '0.672', '-0.69', '#sadness', '#disgust', 'negative', '-0.75', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['google_doubleclick'] = ['0.886', '-0.77', '0', '0.815', '#joy', '#admiration', 'positive', '0.826', 'countenance', 'legitimate', 'permit', 'allow', 'let']
senticnet['google_search_bar'] = ['-0.09', '-0.04', '0.039', '-0.24', '#sadness', '#disgust', 'negative', '-0.08', 'learn_new_idea', 'learn_quickly', 'point_out', 'look_picture', 'jot_down_note']
senticnet['goolam'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['goon'] = ['-0.71', '0', '0.845', '-0.69', '#sadness', '#anger', 'negative', '-0.75', 'lump', 'clumsy_person', 'mortal', 'duffer', 'butterfingers']
senticnet['goose'] = ['-0.83', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.86', 'duck', 'goose', 'moose', 'pheasant', 'parka']
senticnet['goose_bump'] = ['0', '0.119', '-0.07', '0.189', '#interest', '#admiration', 'positive', '0.079', 'enjoy', 'good_feeling', 'excitement', 'emotion', 'good_emotion']
senticnet['goose_down'] = ['0.814', '-0.53', '0', '0.624', '#joy', '#admiration', 'positive', '0.659', 'feather', 'plume', 'duck_down', 'down_feather', 'swan_down']
senticnet['goose_pimple'] = ['-0.56', '0', '0.646', '-0.50', '#sadness', '#anger', 'negative', '-0.57', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['goose_skin'] = ['-0.92', '0', '0.66', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['goosebump'] = ['-0.89', '0', '0.722', '-0.94', '#sadness', '#disgust', 'negative', '-0.85', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['goosefish'] = ['0.96', '-0.77', '0', '0.929', '#joy', '#admiration', 'positive', '0.887', 'whale_louse', 'beach_flea', 'sand_hopper', 'sandhopper', 'amphipod']
senticnet['gooseflesh'] = ['-0.69', '0', '0.725', '-0.92', '#anger', '#disgust', 'negative', '-0.78', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['goosefoot'] = ['0.035', '0.104', '0', '0.12', '#interest', '#admiration', 'positive', '0.086', 'fruit_vegetable', 'vegetable', 'vegetable_fruit', 'fresh_vegetable', 'vegetarian_food']
senticnet['goosefoot_chenopodium'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'bulrush', 'nailrod', 'cattail', 'reedmace']
senticnet['gooseneck'] = ['-0.95', '0', '0.793', '0', '#sadness', '#anger', 'negative', '-0.87', 'block', 'obstacle', 'nodus', 'impediment', 'obstruction']
senticnet['goosy'] = ['0', '0', '0.912', '-0.77', '#anger', '#disgust', 'negative', '-0.84', 'anserine', 'dopy', 'foolish', 'dopey', 'jerky']
senticnet['gop_challenger'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['gop_congressional'] = ['0', '0.924', '0', '0.921', '#interest', '#admiration', 'positive', '0.923', 'settle_disagreement', 'hitler', 'communist', 'conservative', 'unchanging']
senticnet['gop_consultant'] = ['0.783', '-0.63', '0', '0.829', '#joy', '#admiration', 'positive', '0.749', 'leg_of_lamb', 'lamb_roast', 'joint', 'roast', 'rack_of_lamb']
senticnet['gop_incumbent'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['gopal_krishna_gokhale'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['gopher'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'busy_bee', 'actor', 'doer', 'worker']
senticnet['gopher_client'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'busy_bee', 'actor', 'doer', 'worker']
senticnet['gopher_menu'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'busy_bee', 'actor', 'doer', 'worker']
senticnet['gopher_protocol'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'busy_bee', 'actor', 'doer', 'worker']
senticnet['gopher_server'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'busy_bee', 'actor', 'doer', 'worker']
senticnet['gopherspace'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'busy_bee', 'actor', 'doer', 'worker']
senticnet['gopro_camera'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'naked_eye', 'eye', 'oculus_sinister']
senticnet['gorax'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'animal_home', 'habitat', 'beehive']
senticnet['gordo'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'common_bean', 'green_bean', 'wax_bean']
senticnet['gore'] = ['-0.71', '0', '-0.96', '-0.71', '#fear', '#disgust', 'negative', '-0.79', 'wind', 'heal_wind', 'welt', 'ache', 'cut']
senticnet['gore_tex'] = ['-0.81', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.85', 'bandage', 'painkiller', 'gauze', 'aspirin', 'contraceptive_device']
senticnet['gorge'] = ['-0.02', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'tunnel', 'dark_place', 'ski_resort', 'top_tree', 'compost_pile']
senticnet['gorgeous'] = ['0.749', '0.788', '0', '0.967', '#interest', '#admiration', 'positive', '0.835', 'nice_look', 'good_look', 'lovely', 'appearance', 'pretty']
senticnet['gorgeous_rendition'] = ['0.725', '-0.66', '0', '0.906', '#joy', '#admiration', 'positive', '0.765', 'set', 'pose', 'lay', 'put', 'place']
senticnet['gorgeously'] = ['0.512', '0.658', '-0.25', '0.256', '#joy', '#interest', 'positive', '0.293', 'riotously', 'expansively', 'ebulliently', 'zestfulness', 'gusto']
senticnet['gorget'] = ['-0.59', '0', '0.887', '-0.71', '#anger', '#disgust', 'negative', '-0.73', 'armet', 'basinet', 'helmet', 'heaume', 'morion']
senticnet['gorgon_head'] = ['0.026', '0.18', '-0.11', '0.18', '#interest', '#admiration', 'positive', '0.069', 'deity', 'god', 'angel', 'goddess', 'rabbi']
senticnet['gorilla'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'kangaroo', 'giraffe', 'caribou', 'mule', 'horse']
senticnet['gory'] = ['0', '0', '-0.93', '-0.94', '#fear', '#disgust', 'negative', '-0.94', 'bloodthirsty', 'slaughterous', 'sanguinary', 'butcherly', 'sanguineous']
senticnet['gosh'] = ['-0.79', '0', '0.697', '-0.78', '#sadness', '#disgust', 'negative', '-0.75', 'express', 'utter', 'cry_out', 'ooh', 'gee']
senticnet['gospel'] = ['0.028', '0.273', '0', '0.26', '#interest', '#admiration', 'positive', '0.187', 'religion', 'prayer', 'world_religion', 'philosophy', 'pastor']
senticnet['gospel_hymn'] = ['0', '0.176', '0', '0.216', '#interest', '#admiration', 'positive', '0.196', 'choir_stall', 'worshiper', 'pew', 'fundamentalist', 'pulpit']
senticnet['gospel_singer'] = ['0.882', '0.901', '0', '0', '#joy', '#interest', 'positive', '0.891', 'songwriter', 'vip', 'rock_group', 'guitar_player', 'violinist']
senticnet['gossamer'] = ['0', '0', '-0.73', '-0.86', '#fear', '#disgust', 'negative', '-0.79', 'gauzy', 'diaphanous', 'filmy', 'cobweb', 'ethereal']
senticnet['gossip'] = ['0', '0', '0.725', '-0.93', '#anger', '#disgust', 'negative', '-0.82', 'exchange_idea', 'exchange_information', 'share_information', 'give_information', 'express_opinion']
senticnet['gossip_columnist'] = ['-0.80', '0', '0.56', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'gossip', 'gossiper', 'blabbermouth', 'talkative', 'rumormonger']
senticnet['gossip_girl'] = ['-0.68', '0', '0.726', '-0.74', '#anger', '#disgust', 'negative', '-0.71', 'gossip', 'gossiper', 'gossipmonger', 'talebearer', 'rumormonger']
senticnet['gossip_rag'] = ['-0.80', '0', '0.56', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'gossip', 'gossiper', 'blabbermouth', 'talkative', 'rumormonger']
senticnet['gossip_website'] = ['0', '0', '-0.95', '-0.72', '#fear', '#disgust', 'negative', '-0.84', 'lubricious', 'lewd', 'lustful', 'obscene', 'prurient']
senticnet['gossiper'] = ['-0.80', '0', '0.56', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'gossip', 'blabbermouth', 'gossipmonger', 'talkative', 'rumormonger']
senticnet['gossipmonger'] = ['-0.80', '0', '0.56', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'gossip', 'gossiper', 'blabbermouth', 'talkative', 'rumormonger']
senticnet['gotama'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['goth'] = ['-0.86', '0', '0.946', '0', '#sadness', '#anger', 'negative', '-0.90', 'punk', 'thralldom', 'clothing', 'bondage', 'sexual']
senticnet['goth_girl'] = ['0.584', '-0.59', '0', '0.753', '#surprise', '#admiration', 'positive', '0.643', 'ungracious', 'discourteous', 'impolite', 'brattish', 'unparliamentary']
senticnet['goth_rock'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'explicit', 'declared', 'definitive', 'graphic']
senticnet['gotham'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'only_bird']
senticnet['gotham_city'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'only_bird']
senticnet['gotham_city_police'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'only_bird']
senticnet['gotham_underground'] = ['-0.86', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.86', 'demon', 'fool', 'cannibalism', 'jolly', 'treachery']
senticnet['gothic'] = ['0', '0', '-0.78', '-0.99', '#fear', '#disgust', 'negative', '-0.89', 'represent', 'morbid', 'ghoulish', 'macabre', 'ghastly']
senticnet['gothic_arch'] = ['0.029', '0.087', '0.065', '0.137', '#interest', '#admiration', 'positive', '0.047', 'archway', 'aisle', 'mat', 'passageway', 'right_angle']
senticnet['gothic_architecture'] = ['0.578', '-0.95', '0', '0.979', '#surprise', '#admiration', 'positive', '0.838', 'perpendicular', 'perpendicular_style', 'architectural_style', 'style_of_architecture', 'type_of_architecture']
senticnet['gothic_castle'] = ['0.021', '0.119', '0', '0.129', '#interest', '#admiration', 'positive', '0.09', 'manor', 'palace', 'medieval', 'tower', 'chess_game']
senticnet['gothic_cathedral'] = ['0.026', '0.219', '0', '0.16', '#interest', '#admiration', 'positive', '0.135', 'large_build', 'palace', 'high_rise', 'structure', 'fortification']
senticnet['gothic_chapel'] = ['0.026', '0.219', '0', '0.16', '#interest', '#admiration', 'positive', '0.135', 'large_build', 'palace', 'high_rise', 'structure', 'fortification']
senticnet['gothic_exterior'] = ['0', '0.128', '-0.12', '0.183', '#interest', '#admiration', 'positive', '0.063', 'support_roof', 'furnish', 'ceiling', 'window_door', 'frame']
senticnet['gothic_fiction'] = ['0.036', '0.219', '0', '0.19', '#interest', '#admiration', 'positive', '0.148', 'book_genre', 'literature', 'concoction', 'untruth', 'falsehood']
senticnet['gothic_horror'] = ['0.747', '0.85', '0', '0', '#joy', '#interest', 'positive', '0.798', 'night_vision', 'mystery', 'plot', 'imagine', 'myth']
senticnet['gothic_revival'] = ['0.578', '-0.95', '0', '0.979', '#surprise', '#admiration', 'positive', '0.838', 'perpendicular', 'perpendicular_style', 'gothic_architecture', 'style_of_architecture', 'type_of_architecture']
senticnet['gothic_revival_architecture'] = ['0.578', '-0.95', '0', '0.979', '#surprise', '#admiration', 'positive', '0.838', 'perpendicular', 'perpendicular_style', 'gothic_architecture', 'style_of_architecture', 'type_of_architecture']
senticnet['gothic_romance'] = ['0.747', '0.85', '0', '0', '#joy', '#interest', 'positive', '0.798', 'night_vision', 'mystery', 'plot', 'imagine', 'myth']
senticnet['gothic_script'] = ['-0.95', '0', '0.517', '-0.53', '#sadness', '#disgust', 'negative', '-0.67', 'copperplate', 'hand', 'minuscule', 'italic', 'round_hand']
senticnet['gothic_spire'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'castle', 'bell_tower', 'spire', 'steeple', 'religious_place']
senticnet['gothic_style'] = ['0.578', '-0.95', '0', '0.979', '#surprise', '#admiration', 'positive', '0.838', 'perpendicular', 'perpendicular_style', 'gothic_architecture', 'style_of_architecture', 'type_of_architecture']
senticnet['gothic_style_church'] = ['0.026', '0.219', '0', '0.16', '#interest', '#admiration', 'positive', '0.135', 'large_build', 'palace', 'high_rise', 'structure', 'fortification']
senticnet['gothic_tower'] = ['0.026', '0.219', '0', '0.16', '#interest', '#admiration', 'positive', '0.135', 'large_build', 'palace', 'high_rise', 'structure', 'fortification']
senticnet['gotta'] = ['0.762', '-1', '0', '0', '#joy', '#surprise', 'positive', '0.881', 'okay', 'yeah', 'gonna', 'alright', 'hey']
senticnet['gotta_go'] = ['-0.89', '0', '0.739', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'rot', 'bunk', 'bunkum', 'buncombe', 'guff']
senticnet['gotta_love'] = ['-0.61', '0', '0.981', '-0.98', '#anger', '#disgust', 'negative', '-0.85', 'pious_platitude', 'nothingness', 'jazz', 'talk', 'malarky']
senticnet['gottesman'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['gouache'] = ['0', '0.067', '0', '0.077', '#interest', '#admiration', 'positive', '0.072', 'pencil_mark', 'use_ink', 'script', 'pencil_paper', 'written_text']
senticnet['gouge'] = ['0', '0', '-0.92', '-0.87', '#fear', '#disgust', 'negative', '-0.90', 'ding', 'force_out', 'extort', 'nick', 'dent']
senticnet['gouger'] = ['0.599', '-0.93', '0', '0.583', '#joy', '#surprise', 'positive', '0.705', 'combatant', 'battler', 'belligerent', 'fighter', 'scrapper']
senticnet['goulash'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'main_course', 'cream_soup', 'soup', 'ragout', 'stew']
senticnet['gourd'] = ['-0.93', '0', '0.734', '0', '#sadness', '#anger', 'negative', '-0.83', 'gourd', 'melon', 'birdhouse', 'pumpkin', 'calabash']
senticnet['gourmand'] = ['0.897', '-0.96', '0', '0.785', '#joy', '#surprise', 'positive', '0.881', 'erotic', 'voluptuary', 'epicure', 'gourmet', 'sensualist']
senticnet['gourmet'] = ['0.897', '-0.96', '0', '0.785', '#joy', '#surprise', 'positive', '0.881', 'erotic', 'voluptuary', 'epicure', 'sensualist', 'gastronome']
senticnet['gourmet_breakfast'] = ['0.715', '0.785', '0', '0', '#joy', '#interest', 'positive', '0.75', 'scrumble_egg', 'heat_meal', 'pan', 'beat_egg', 'food']
senticnet['gourmet_coffee'] = ['0.807', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.859', 'bubble', 'steam', 'hot', 'boil_water', 'tea']
senticnet['gourmet_cuisine'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'artistic_creation', 'fabrication', 'carving', 'decoupage', 'ceramic']
senticnet['gourmet_dinner'] = ['0.128', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.085', 'banquet', 'rave', 'auction', 'limbo', 'loose_end']
senticnet['gourmet_kitchen'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'kitchen_cabinet', 'kitchen_cupboard', 'dishwasher', 'cupboard', 'own_kitchen']
senticnet['gourmet_meal'] = ['0.783', '-0.63', '0', '0.829', '#joy', '#admiration', 'positive', '0.749', 'leg_of_lamb', 'gigot', 'joint', 'roast', 'rack_of_lamb']
senticnet['gourmet_restaurant'] = ['0.06', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'eat_food', 'fast_food', 'pizza_parlor', 'pizza_store', 'kitchen_cupboard']
senticnet['gout'] = ['0', '-0.03', '-0.04', '-0.19', '#fear', '#disgust', 'negative', '-0.06', 'stomach_ache', 'become_full', 'full_stomach', 'a_little_hungry', 'heartburn']
senticnet['gouty'] = ['-0.44', '-0.15', '0', '-0.36', '#sadness', '#disgust', 'negative', '-0.21', 'baffle', 'stupefy', 'dumbfound', 'stupefaction', 'confuse']
senticnet['goveniana'] = ['-0.94', '0', '0.608', '-0.95', '#sadness', '#disgust', 'negative', '-0.83', 'wild_licorice', 'wild_liquorice', 'alpine_milk_vetch', 'purple_milk_vetch', 'milk_vetch']
senticnet['govern'] = ['0', '0.235', '-0.07', '0.241', '#interest', '#admiration', 'positive', '0.133', 'axiom', 'command', 'confirmation', 'tenet', 'government_person']
senticnet['govern_city'] = ['0', '0', '0', '0.104', '#admiration', '#admiration', 'positive', '0.104', 'downtown', 'city', 'large_town', 'govern', 'small_city']
senticnet['govern_country'] = ['-0.06', '0.205', '-0.10', '0.144', '#interest', '#admiration', 'positive', '0.045', 'govern_peasant', 'govern_city', 'govern_nation', 'lose_election', 'gain_money']
senticnet['govern_nation'] = ['0', '0.787', '0', '0.744', '#interest', '#admiration', 'positive', '0.766', 'date_man', 'date_woman', 'voice_concern', 'carry_briefcase', 'captain_ship']
senticnet['govern_peasant'] = ['-0.03', '0.248', '-0.05', '0.167', '#interest', '#admiration', 'positive', '0.082', 'govern_country', 'govern_person', 'peasant', 'preist', 'loathing']
senticnet['govern_person'] = ['-0.04', '0.239', '-0.06', '0.163', '#interest', '#admiration', 'positive', '0.075', 'govern_country', 'govern_peasant', 'peasant', 'preist', 'loathing']
senticnet['governable'] = ['0.5', '-0.9', '0', '0.525', '#surprise', '#admiration', 'positive', '0.642', 'administrable', 'controllable', 'manageable', 'steerable', 'dirigible']
senticnet['governance'] = ['0.952', '-0.84', '0', '0.875', '#joy', '#admiration', 'positive', '0.89', 'administration', 'judiciary', 'governing_body', 'establishment', 'brass']
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senticnet['graffiti_vandalism'] = ['-0.12', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.07', 'bum', 'homeless_people', 'homeless_person', 'panhandler', 'homeless']
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senticnet['graffitti'] = ['-0.12', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.07', 'bum', 'homeless_people', 'homeless_person', 'panhandler', 'homeless']
senticnet['grafitti'] = ['-0.12', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.07', 'bum', 'homeless_people', 'homeless_person', 'panhandler', 'homeless']
senticnet['graft'] = ['0', '0', '-0.77', '-0.86', '#fear', '#disgust', 'negative', '-0.82', 'abduction', 'punishment', 'emergency', 'illiteracy', 'heal']
senticnet['grafter'] = ['0.833', '-0.97', '0', '0.91', '#surprise', '#admiration', 'positive', '0.906', 'combatant', 'fencer', 'belligerent', 'fighter', 'scrapper']
senticnet['grah'] = ['-0.63', '0', '0.791', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'little', 'pinch', 'modicum', 'small_indefinite_amount', 'indefinite_quantity']
senticnet['graham_coxon'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'hazy', 'blurry', 'bleary', 'blurryred', 'foggy']
senticnet['graham_cracker'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'saltine', 'soda_cracker', 'oyster_cracker', 'water_biscuit', 'cracker']
senticnet['graham_cracker_crust'] = ['0.059', '-0.02', '0.025', '0.109', '#joy', '#admiration', 'positive', '0.043', 'tasty', 'bake_treat', 'bake_goods', 'bake_good', 'loaf']
senticnet['graham_farish'] = ['0', '0.721', '0', '0.731', '#interest', '#admiration', 'positive', '0.726', 'dimension', 'measure', 'tackle', 'length', 'measurement']
senticnet['graham_putnam_mahoney'] = ['-0.70', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.70', 'death_row', 'sad', 'loose_end', 'car_race', 'mortuary']
senticnet['graham_turner'] = ['-0.9', '0', '0.749', '-0.71', '#sadness', '#anger', 'negative', '-0.78', 'chute', 'slide', 'slideway', 'sloping_trough', 'gutter']
senticnet['grail'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'chairperson', 'commuter']
senticnet['grain'] = ['0', '0.065', '0.031', '0.052', '#interest', '#admiration', 'positive', '0.029', 'wheat', 'white_rice', 'rye', 'corn', 'oat']
senticnet['grain_alcohol'] = ['-0.53', '0', '0.598', '-0.73', '#anger', '#disgust', 'negative', '-0.62', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
senticnet['grain_bullet'] = ['0.887', '-0.95', '0', '0.913', '#surprise', '#admiration', 'positive', '0.917', 'high_quality', 'superiority', 'level', 'grade', 'degree']
senticnet['grain_drill'] = ['-0.99', '0', '0.827', '-0.54', '#sadness', '#anger', 'negative', '-0.78', 'treat', 'cover', 'handle', 'theologize', 'deal']
senticnet['grain_leather'] = ['0.706', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.806', 'shoe', 'flip_flop', 'slipper', 'casual_shoes', 'footwear']
senticnet['grain_legume'] = ['0.061', '0.093', '0.037', '0.114', '#interest', '#admiration', 'positive', '0.058', 'whole_grain', 'food_ingredient', 'staple_food', 'starchy_food', 'food_grain']
senticnet['grain_mill'] = ['-0.78', '0', '0.699', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'battery', 'stamp_battery', 'quartz_battery', 'quartz_mill', 'factory']
senticnet['grain_sand'] = ['-0.02', '-0.00', '0.045', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'sand', 'salt', 'quartz', 'sandy', 'silica']
senticnet['grain_size'] = ['-0.50', '0', '0.552', '-0.93', '#anger', '#disgust', 'negative', '-0.66', 'nubbiness', 'tweediness', 'harshness', 'graininess', 'granularity']
senticnet['grainier'] = ['-0.05', '0.07', '0', '0.062', '#interest', '#admiration', 'positive', '0.027', 'salty', 'fine_grain', 'powder', 'granulate', 'food_flavor']
senticnet['graininess'] = ['-0.9', '0', '0.841', '-0.73', '#sadness', '#anger', 'negative', '-0.82', 'nubbiness', 'tweediness', 'harshness', 'raggedness', 'granularity']
senticnet['grainy'] = ['-0.05', '0.07', '0', '0.062', '#interest', '#admiration', 'positive', '0.027', 'salty', 'fine_grain', 'powder', 'granulate', 'food_flavor']
senticnet['grainy_black'] = ['-0.05', '0.07', '0', '0.062', '#interest', '#admiration', 'positive', '0.027', 'salty', 'fine_grain', 'powder', 'granulate', 'food_flavor']
senticnet['grainy_footage'] = ['-0.05', '0.07', '0', '0.062', '#interest', '#admiration', 'positive', '0.027', 'salty', 'fine_grain', 'powder', 'granulate', 'food_flavor']
senticnet['grainy_texture'] = ['0', '0.141', '0.01', '0.115', '#interest', '#admiration', 'positive', '0.082', 'sepal', 'achene', 'funiculus', 'seed', 'pip']
senticnet['gram_negative'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'immobile', 'immotile']
senticnet['gram_negative_bacillus'] = ['-0.60', '0', '0.721', '-0.57', '#sadness', '#anger', 'negative', '-0.63', 'tubercle_bacillus', 'leprosy_bacillus', 'eubacteria', 'eubacterium', 'true_bacteria']
senticnet['gram_negative_bacterium'] = ['0.995', '-0.71', '0', '0.647', '#joy', '#surprise', 'positive', '0.784', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['gram_negative_coccobacilli'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'immobile', 'immotile']
senticnet['gram_negative_rod'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'immobile', 'immotile']
senticnet['gram_per_cubic_centimeter'] = ['0.728', '0.977', '0', '0.939', '#interest', '#admiration', 'positive', '0.881', 'air_pressure', 'altitude', 'air', 'heavy_air', 'air_density']
senticnet['gram_positive'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'immobile', 'immotile']
senticnet['gram_positive_bacterium'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'immobile', 'immotile']
senticnet['gram_positive_cocci'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'immobile', 'immotile']
senticnet['gramercy_park'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'condominium', 'rent_apartment', 'high_rise', 'condo', 'row_house']
senticnet['graminea'] = ['-0.56', '0', '0.892', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'borecole', 'colewort', 'kail', 'cole', 'kale']
senticnet['grammaire'] = ['-0.93', '0', '0.861', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'knotty', 'contorted', 'writhed', 'crooked', 'anfractuous']
senticnet['grammar'] = ['0', '0.118', '-0.08', '0.146', '#interest', '#admiration', 'positive', '0.058', 'typeface', 'jot', 'dissertation', 'unit_language', 'base']
senticnet['grammar_formalism'] = ['0.777', '-0.68', '0', '0.601', '#joy', '#surprise', 'positive', '0.686', 'lucidness', 'clarity', 'clearness', 'lucidity', 'pellucidity']
senticnet['grammar_mistake'] = ['-0.83', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.76', 'mistake', 'error', 'misspell', 'poor_english', 'bad_writing']
senticnet['grammar_part'] = ['0', '0.078', '-0.04', '0.205', '#interest', '#admiration', 'positive', '0.078', 'part_of_speech', 'speech_part', 'written_language', 'information_source', 'basic']
senticnet['grammatical'] = ['-0.02', '0.067', '-0.04', '0.192', '#interest', '#admiration', 'positive', '0.047', 'verbalize', 'orate', 'say', 'talkative', 'discourse']
senticnet['grammatical_category'] = ['-0.22', '0', '-0.09', '-0.16', '#sadness', '#disgust', 'negative', '-0.16', 'inequality', 'reject', 'discard', 'difference', 'gap']
senticnet['grammatical_construction'] = ['0', '0.156', '-0.06', '0.241', '#interest', '#admiration', 'positive', '0.11', 'mixture', 'noisy_place', 'make_go', 'move_vehicle', 'big_car']
senticnet['grammatical_form'] = ['0', '0.881', '0', '0.896', '#interest', '#admiration', 'positive', '0.889', 'single', 'unit_of_measurement', 'singular', 'measurement_unit', 'singular_form']
senticnet['grammatical_function'] = ['-0.57', '0', '0.713', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'assured', 'conclusion', 'certainty', 'determinateness', 'predictability']
senticnet['grammatical_mood'] = ['0', '0.156', '-0.06', '0.241', '#interest', '#admiration', 'positive', '0.11', 'mixture', 'noisy_place', 'make_go', 'move_vehicle', 'big_car']
senticnet['grammaticality'] = ['0.734', '-0.56', '0', '0.64', '#joy', '#admiration', 'positive', '0.645', 'inferential', 'suggestive', 'connotative', 'connotational', 'connotative_of']
senticnet['grammatically'] = ['0', '0.796', '0', '0.967', '#interest', '#admiration', 'positive', '0.881', 'semantically', 'syntactically', 'phonetically', 'morphologically', 'lexically']
senticnet['grammatically_incorrect'] = ['-0.02', '0.186', '0', '0.213', '#interest', '#admiration', 'positive', '0.125', 'description', 'mumble', 'bummer', 'numerical', 'proverb']
senticnet['grampians_national'] = ['-0.95', '0', '0.624', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['gran_akuma'] = ['-0.57', '0', '0.571', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['granary'] = ['0', '0', '-0.72', '-0.74', '#fear', '#disgust', 'negative', '-0.73', 'earn', 'pull_together', 'gather', 'collect', 'garner']
senticnet['grand'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'fantastic', 'amaze', 'pose', 'wondrous', 'wonderfully']
senticnet['grand_admiral'] = ['0.688', '-0.90', '0', '0.877', '#surprise', '#admiration', 'positive', '0.824', 'flag_officer', 'rear_admiral', 'admiral', 'full_admiral', 'vice_admiral']
senticnet['grand_avenue'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'boulevard', 'lane', 'thoroughfare', 'intersection', 'curb']
senticnet['grand_bargain'] = ['-0.04', '0.274', '-0.15', '0.278', '#interest', '#admiration', 'positive', '0.089', 'business_deal', 'trade', 'exchange', 'bargain', 'offer']
senticnet['grand_bazaar'] = ['0', '0.113', '0.041', '0.058', '#interest', '#admiration', 'positive', '0.043', 'corner_shop', 'general_store', 'many_shops', 'large_supermarket', 'goods']
senticnet['grand_canyon'] = ['0.106', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.062', 'western', 'sumo', 'rodeo', 'circus_act', 'have_fun']
senticnet['grand_canyon_national'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['grand_cayman_blue_iguana'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['grand_duke'] = ['0.051', '0.097', '0', '0.065', '#interest', '#admiration', 'positive', '0.071', 'king', 'queen', 'duchess', 'throne', 'lord']
senticnet['grand_finale'] = ['-0.95', '0', '0.992', '-0.63', '#sadness', '#anger', 'negative', '-0.86', 'interpretive_dance', 'interpretive_dancing', 'modern_dance', 'interpretative_dancing', 'stage_dancing']
senticnet['grand_fir'] = ['0.045', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.356', 'ponderosa', 'bull_pine', 'western_yellow_pine', 'yellow_pine', 'longleaf_pine']
senticnet['grand_guignol'] = ['-0.97', '0', '-0.81', '-0.95', '#sadness', '#disgust', 'negative', '-0.91', 'dislike', 'loathe', 'queasy', 'horrid', 'outrageous']
senticnet['grand_inquisitor'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquirer', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['grand_juror'] = ['0', '0.104', '0', '0.075', '#interest', '#admiration', 'positive', '0.089', 'judge', 'negotiator', 'jury', 'arbiter', 'member_jury']
senticnet['grand_jury'] = ['-0.21', '0.16', '-0.19', '0.057', '#sadness', '#fear', 'negative', '-0.04', 'murder', 'make_judgment', 'call_police', 'arrest_criminal', 'tell_truth']
senticnet['grand_mal_seizure'] = ['-0.56', '0', '0.577', '-0.61', '#anger', '#disgust', 'negative', '-0.58', 'foggy', 'groggy', 'logy', 'lethargic', 'dazed']
senticnet['grand_mosque'] = ['0', '0.224', '0', '0.197', '#interest', '#admiration', 'positive', '0.211', 'church', 'temple', 'cathedral', 'chapel', 'confession']
senticnet['grand_nephew'] = ['0.948', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.888', 'relation', 'mom', 'kin', 'cousin', 'granddaughter']
senticnet['grand_niece'] = ['0.948', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.888', 'relation', 'mom', 'kin', 'cousin', 'granddaughter']
senticnet['grand_palace'] = ['0', '0.105', '0', '0.076', '#interest', '#admiration', 'positive', '0.09', 'big_house', 'mansion', 'castle', 'block_flat', 'medieval']
senticnet['grand_piano'] = ['-0.98', '0', '0.614', '-0.90', '#sadness', '#disgust', 'negative', '-0.83', 'grand', 'upright', 'piano', 'mechanical_piano', 'player_piano']
senticnet['grand_prize'] = ['0.894', '0.881', '0', '0.803', '#joy', '#interest', 'positive', '0.859', 'remittance', 'afford', 'award', 'money_due', 'amount_owe']
senticnet['grand_slam'] = ['0.749', '0', '0', '0.952', '#joy', '#admiration', 'positive', '0.851', 'high_class', 'citation', 'suspicion', 'goodness', 'prosperity']
senticnet['grand_teton'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['grand_teton_national'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['grand_uncle'] = ['0.948', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.888', 'relation', 'mom', 'kin', 'cousin', 'granddaughter']
senticnet['grand_unified_bootloader'] = ['0.866', '-0.91', '0', '0.893', '#surprise', '#admiration', 'positive', '0.892', 'mooch', 'cadge', 'freeload', 'sponge', 'bum']
senticnet['grandad'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'mailman', 'package', 'mail', 'parcel', 'letter_slot']
senticnet['grandchild'] = ['0', '0.208', '-0.14', '0.162', '#interest', '#admiration', 'positive', '0.076', 'much_child', 'nowhere_live', 'offspring', 'descendant', 'human_offspring']
senticnet['grandcourt'] = ['0.525', '-0.85', '0', '0.599', '#surprise', '#admiration', 'positive', '0.66', 'frolic', 'gambol', 'romp', 'play', 'caper']
senticnet['granddad'] = ['0', '0.864', '0', '0.751', '#interest', '#admiration', 'positive', '0.807', 'family_member', 'sibling', 'parent', 'female_relative', 'relative']
senticnet['granddaughter'] = ['0.927', '0.877', '0', '0', '#joy', '#interest', 'positive', '0.902', 'relation', 'mom', 'kin', 'cousin', 'grandson']
senticnet['grande_dame'] = ['0.964', '-0.81', '0', '0.926', '#joy', '#admiration', 'positive', '0.9', 'girl', 'female', 'madam', 'dame', 'lady']
senticnet['grander'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'fantastic', 'amaze', 'pose', 'wondrous', 'wonderfully']
senticnet['grandest'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'fantastic', 'amaze', 'pose', 'wondrous', 'wonderfully']
senticnet['grandeur'] = ['0.131', '0.074', '0', '0.373', '#joy', '#admiration', 'positive', '0.193', 'superlative', 'excellence', 'ultimate', 'great', 'well_done']
senticnet['grandfather'] = ['0', '0.08', '0.016', '0.103', '#interest', '#admiration', 'positive', '0.056', 'father', 'mother', 'daughter', 'son', 'part_of_family']
senticnet['grandfatherly'] = ['0', '0.719', '0', '0.778', '#interest', '#admiration', 'positive', '0.748', 'avuncular', 'fatherly', 'affable', 'stolid', 'supercilious']
senticnet['grandier'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquirer', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['grandiflorum'] = ['-0.56', '0', '0.892', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'borecole', 'colewort', 'kail', 'cole', 'kale']
senticnet['grandifolia'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'carnauba_palm', 'carnauba', 'wax_palm', 'palmetto', 'palmyra']
senticnet['grandiloquence'] = ['-0.89', '0', '0.987', '-0.90', '#anger', '#disgust', 'negative', '-0.93', 'cacoethes', 'logomania', 'agromania', 'pyromania', 'necrophilia']
senticnet['grandiloquent'] = ['-0.89', '0', '0.987', '-0.90', '#anger', '#disgust', 'negative', '-0.93', 'cacoethes', 'logomania', 'agromania', 'pyromania', 'necrophilia']
senticnet['grandiose'] = ['0', '0', '-0.79', '-0.72', '#fear', '#disgust', 'negative', '-0.75', 'hifalutin', 'la-di-da', 'highfaluting', 'highfalutin', 'hoity-toity']
senticnet['grandiosity'] = ['-0.54', '0', '0.7', '-0.64', '#anger', '#disgust', 'negative', '-0.62', 'flourish', 'rant', 'ornateness', 'fustian', 'claptrap']
senticnet['grandly'] = ['0', '-0.83', '0', '0.991', '#surprise', '#admiration', 'positive', '0.915', 'quaintly', 'improbably', 'amusingly', 'incongruously', 'intriguingly']
senticnet['grandma'] = ['0.02', '0.209', '-0.14', '0.202', '#interest', '#admiration', 'positive', '0.071', 'familiar', 'mum', 'familiarity_uncle', 'care_giver', 'familiarity']
senticnet['grandma_house'] = ['0', '0', '-0.05', '-0.03', '#fear', '#disgust', 'negative', '-0.04', 'chess_tournament', 'game_room', 'old_folk_home', 'computer_lab', 'mortuary']
senticnet['grandmother'] = ['0.826', '0.862', '0', '0', '#joy', '#interest', 'positive', '0.844', 'aunt_uncle', 'picture_of_family', 'aunt', 'family_photo', 'relative']
senticnet['grandmotherly'] = ['0.585', '-0.88', '0', '0.615', '#surprise', '#admiration', 'positive', '0.695', 'parental_quality', 'maternal_quality', 'quality', 'maternalism', 'fatherliness']
senticnet['grandnephew'] = ['0.948', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.888', 'relation', 'mom', 'kin', 'cousin', 'granddaughter']
senticnet['grandness'] = ['0', '0.945', '0', '0.953', '#interest', '#admiration', 'positive', '0.949', 'stateliness', 'excellence', 'impressiveness', 'richness', 'expansivity']
senticnet['grandpa'] = ['0.934', '-0.57', '0', '0.767', '#joy', '#admiration', 'positive', '0.76', 'morosoph', 'simpleton', 'flibbertigibbet', 'foolish_woman', 'simple']
senticnet['grandparent'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'family', 'cousin', 'father', 'sister', 'parent']
senticnet['grandson'] = ['0.948', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.888', 'relation', 'mom', 'kin', 'cousin', 'granddaughter']
senticnet['grandstand'] = ['0.831', '-0.82', '0', '0.826', '#joy', '#admiration', 'positive', '0.826', 'deck', 'freeboard_deck', 'weather_deck', 'shelter_deck', 'boat_deck']
senticnet['granduncle'] = ['0.948', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.888', 'relation', 'mom', 'kin', 'cousin', 'granddaughter']
senticnet['granite_marker'] = ['0.072', '0.126', '0', '0', '#joy', '#interest', 'positive', '0.099', 'subway_station', 'war_memorial', 'utopia', 'mortuary', 'hero']
senticnet['granite_memorial'] = ['0.072', '0.126', '0', '0', '#joy', '#interest', 'positive', '0.099', 'subway_station', 'war_memorial', 'utopia', 'mortuary', 'hero']
senticnet['granite_monument'] = ['0.072', '0.126', '0', '0', '#joy', '#interest', 'positive', '0.099', 'subway_station', 'war_memorial', 'utopia', 'mortuary', 'hero']
senticnet['granite_pedestal'] = ['0.085', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.079', 'sculpture', 'creative', 'yellow_ribbon', 'artwork', 'monument']
senticnet['granite_plaque'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'dental_plaque']
senticnet['granite_quarry'] = ['0', '0', '-0.85', '-0.71', '#fear', '#disgust', 'negative', '-0.78', 'fair_game', 'pit', 'stone_pit', 'target', 'prey']
senticnet['granite_sculpture'] = ['0.085', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.079', 'sculpture', 'creative', 'yellow_ribbon', 'artwork', 'monument']
senticnet['granite_statue'] = ['0.085', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.079', 'sculpture', 'creative', 'yellow_ribbon', 'artwork', 'monument']
senticnet['granitic_magma'] = ['0', '0', '-0.92', '0', '#fear', '#fear', 'negative', '-0.92', 'water_flow', 'wind_water', 'water_stream', 'waterway', 'lava']
senticnet['granny'] = ['0.934', '-0.57', '0', '0.767', '#joy', '#admiration', 'positive', '0.76', 'morosoph', 'simpleton', 'flibbertigibbet', 'foolish_woman', 'simple']
senticnet['granny_flat'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'apartment', 'furniture', 'kitchen', 'flat']
senticnet['granny_knot'] = ['-0.06', '-0.01', '0.05', '-0.07', '#sadness', '#disgust', 'negative', '-0.04', 'embroidery', 'woven', 'cover_wind', 'torn', 'use_needle']
senticnet['granny_smith'] = ['0.02', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'many_fruits', 'buy_fruit', 'cherry', 'pineapple', 'tomato']
senticnet['granny_smith_apple'] = ['0.02', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'many_fruits', 'buy_fruit', 'cherry', 'pineapple', 'tomato']
senticnet['granny_weatherwax'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'jawless_fish', 'osteostracan', 'cephalaspid', 'ostracoderm', 'heterostracan']
senticnet['granola'] = ['-0.92', '0', '0.609', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'puffed_rice', 'cold_cereal', 'raisin_bran', 'corn_flake', 'bran_flake']
senticnet['granola_bar'] = ['0.895', '0.946', '0', '0', '#joy', '#interest', 'positive', '0.92', 'need_food', 'a_little_hungry', 'dead_hungry', 'prepare_food', 'fill_stomach']
senticnet['grant'] = ['0.865', '-0.84', '0', '0.915', '#joy', '#admiration', 'positive', '0.875', 'contract', 'sale_in_gross', 'contract_of_hazard', 'adhesion_contract', 'contract_of_adhesion']
senticnet['grant_amnesty'] = ['0.911', '-0.71', '0', '0.552', '#joy', '#surprise', 'positive', '0.726', 'deliver_goods', 'succeed', 'win', 'bring_home_bacon', 'come_through']
senticnet['grant_fund'] = ['0', '0.768', '0', '0.703', '#interest', '#admiration', 'positive', '0.736', 'fund', 'equipment', 'student', 'lab_improve', 'project']
senticnet['grant_hackett'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'chlorine', 'lifeguard', 'still_water', 'large_body', 'shallow_water']
senticnet['grant_summary_judgment'] = ['-0.86', '0', '0.897', '-0.81', '#sadness', '#anger', 'negative', '-0.85', 'disregard', 'ignore', 'brush_off', 'push_aside', 'flout']
senticnet['grant_totaling_million'] = ['0.865', '-0.84', '0', '0.915', '#joy', '#admiration', 'positive', '0.875', 'contract', 'sale_in_gross', 'contract_of_hazard', 'adhesion_contract', 'contract_of_adhesion']
senticnet['granted_bail'] = ['-0.08', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.052', 'bequest', 'excise', 'bribe', 'afford', 'owe']
senticnet['granted_citizenship'] = ['0', '-0.91', '0', '0.806', '#surprise', '#admiration', 'positive', '0.861', 'clean_shoes', 'black_stuff', 'paint_nail', 'ethnicity', 'black_goo']
senticnet['granted_clemency'] = ['0', '0', '0.869', '0', '#anger', '#anger', 'negative', '-0.86', 'convoke', 'loose_end', 'car_race', 'auction', 'mortuary']
senticnet['granted_immunity'] = ['0', '0', '0', '0.816', '#admiration', '#admiration', 'positive', '0.816', 'resistance', 'protection', 'untouchable', 'invulnerable', 'sacrosanct']
senticnet['granted_royal_assent'] = ['-0.57', '0', '0.576', '-0.78', '#anger', '#disgust', 'negative', '-0.64', 'reprieve', 'respite', 'suspend', 'call', 'delay']
senticnet['grantee'] = ['0', '0.885', '0', '0.869', '#interest', '#admiration', 'positive', '0.877', 'alienee', 'research', 'award', 'project', 'cost_sharing']
senticnet['grantee_institution'] = ['0.865', '-0.84', '0', '0.915', '#joy', '#admiration', 'positive', '0.875', 'contract', 'sale_in_gross', 'contract_of_hazard', 'adhesion_contract', 'contract_of_adhesion']
senticnet['granting'] = ['0', '0.787', '0', '0.908', '#interest', '#admiration', 'positive', '0.848', 'answerable', 'accordance', 'unseasonably', 'consensus', 'coincident']
senticnet['granting_bail'] = ['-0.08', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.052', 'bequest', 'excise', 'bribe', 'afford', 'owe']
senticnet['granting_citizenship'] = ['-0.91', '0', '0.637', '-0.59', '#sadness', '#anger', 'negative', '-0.71', 'gauge', 'govern', 'regulate', 'standardize', 'order']
senticnet['granting_clemency'] = ['0', '0', '0.869', '0', '#anger', '#anger', 'negative', '-0.86', 'convoke', 'loose_end', 'car_race', 'auction', 'mortuary']
senticnet['granting_immunity'] = ['0', '0', '0', '0.816', '#admiration', '#admiration', 'positive', '0.816', 'resistance', 'protection', 'untouchable', 'invulnerable', 'sacrosanct']
senticnet['grantor'] = ['0.621', '-0.60', '0', '0.545', '#joy', '#surprise', 'positive', '0.59', 'privilege', 'perquisite', 'public_easement', 'right_of_way', 'prerogative']
senticnet['granular'] = ['0', '0.065', '0.064', '0.079', '#interest', '#admiration', 'positive', '0.027', 'small_grain', 'white_grain', 'granule', 'food_type', 'staple_food']
senticnet['granular_activated_carbon'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
senticnet['granularity'] = ['-0.9', '0', '0.841', '-0.73', '#sadness', '#anger', 'negative', '-0.82', 'nubbiness', 'tweediness', 'harshness', 'graininess', 'raggedness']
senticnet['granulate'] = ['-0.05', '0.07', '0', '0.062', '#interest', '#admiration', 'positive', '0.027', 'salty', 'grainy', 'powder', 'fine_grain', 'food_flavor']
senticnet['granulate_cutworm'] = ['-0.02', '-0.09', '0.11', '-0.13', '#anger', '#disgust', 'negative', '-0.04', 'beetle', 'gypsy_moth', 'mole_cricket', 'grasshopper', 'many_insects']
senticnet['granulated_sugar'] = ['-0.08', '0.106', '0', '0.125', '#interest', '#admiration', 'positive', '0.047', 'sugar', 'treacle', 'black_treacle', 'add_sugar', 'sugar_substitute']
senticnet['granulation_tissue'] = ['-0.71', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.73', 'pain', 'gape', 'sunburn', 'ouch', 'cut_skin']
senticnet['granule'] = ['0', '0.065', '0.064', '0.079', '#interest', '#admiration', 'positive', '0.027', 'small_grain', 'white_grain', 'granular', 'food_type', 'staple_food']
senticnet['granulocyte'] = ['-0.55', '0', '0.922', '-0.88', '#anger', '#disgust', 'negative', '-0.78', 'leukocyte', 'leucocyte', 'white_blood_cell', 'white_cell', 'white_blood_corpuscle']
senticnet['granuloma_inguinale'] = ['-0.51', '0', '0.561', '-0.97', '#anger', '#disgust', 'negative', '-0.68', 'venereal_disease', 'venereal_infection', 'social_disease', 'sexually_transmitted_disease', 'dose']
senticnet['granuloma_venereum'] = ['-0.74', '0', '0.86', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'venereal_disease', 'venereal_infection', 'social_disease', 'sexually_transmitted_disease', 'dose']
senticnet['granulomatous_disease'] = ['-0.88', '0', '0.774', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fibroid_tumor', 'fibroid', 'benign_tumor', 'nonmalignant_tumor', 'nonmalignant_neoplasm']
senticnet['granulose'] = ['-0.86', '0', '0.511', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'separate', 'move', 'branch', 'diverge', 'fork']
senticnet['grape'] = ['-0.99', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.90', 'raisin', 'watermelon', 'apricot', 'plum', 'seedless']
senticnet['grape_jelly'] = ['0.95', '-0.86', '0', '0.947', '#joy', '#admiration', 'positive', '0.92', 'content', 'lore', 'folklore', 'cognitive_content', 'story']
senticnet['grape_juice'] = ['0.068', '0', '0', '0', '#joy', '#joy', 'positive', '0.068', 'soft_drink', 'apple_juice', 'cola', 'juice', 'ice_tea']
senticnet['grape_shot'] = ['0.957', '-0.93', '0', '0.795', '#joy', '#surprise', 'positive', '0.896', 'outdo', 'outstrip', 'outmatch', 'outgo', 'outperform']
senticnet['grape_sugar'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'dextrose', 'dextroglucose', 'glucose']
senticnet['grape_wine'] = ['0.161', '-0.14', '0.031', '-0.10', '#joy', '#surprise', 'positive', '0.043', 'alcohol', 'reception', 'champagne', 'beer', 'drink']
senticnet['grapefruit'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'oriental_medicine', 'plum', 'lemon_juice', 'green_apple', 'apricot']
senticnet['grapefruit_peel'] = ['-0.95', '0', '0.845', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'candied_citrus_peel', 'lemon_peel', 'orange_peel', 'candied_fruit', 'succade']
senticnet['grapeshot'] = ['0.957', '-0.93', '0', '0.795', '#joy', '#surprise', 'positive', '0.896', 'outdo', 'outstrip', 'outmatch', 'outgo', 'outperform']
senticnet['graph'] = ['0.027', '0.093', '0.025', '0.071', '#interest', '#admiration', 'positive', '0.041', 'octagon', 'angle', 'indication', 'geometry', 'facet']
senticnet['graph_paper'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'gift_wrap', 'kraft', 'kraft_paper', 'wrapping_paper', 'paper']
senticnet['graph_theoretic'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'combinatory', 'consolidative', 'unifying', 'integrative']
senticnet['graph_theory'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'combinatory', 'consolidative', 'unifying', 'integrative']
senticnet['grapheme'] = ['-0.61', '0', '0.721', '-0.68', '#anger', '#disgust', 'negative', '-0.67', 'character', 'logogram', 'logograph', 'graph', 'ideogram']
senticnet['graphic'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'hardcore', 'declared', 'definitive', 'explicit']
senticnet['graphic_art'] = ['-0.02', '0', '-0.04', '-0.03', '#fear', '#disgust', 'negative', '-0.03', 'studio', 'art_piece', 'art_gallery', 'artist', 'museum']
senticnet['graphic_card'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'computer_part', 'system', 'cpu', 'pencil_pen', 'monitor']
senticnet['graphic_design'] = ['-0.02', '0', '-0.04', '-0.03', '#fear', '#disgust', 'negative', '-0.03', 'studio', 'art_piece', 'art_gallery', 'artist', 'museum']
senticnet['graphic_display'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'hardcore', 'declared', 'definitive', 'explicit']
senticnet['graphic_equalizer'] = ['-0.95', '0', '-0.73', '-0.73', '#sadness', '#fear', 'negative', '-0.80', 'decibel', 'noise', 'loud_soft', 'sound', 'audio']
senticnet['graphic_imagery'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'hardcore', 'declared', 'definitive', 'explicit']
senticnet['graphic_layout'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'hardcore', 'declared', 'definitive', 'explicit']
senticnet['graphic_novel'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'encyclopaedia', 'child_story', 'comic', 'disney_cartoon']
senticnet['graphic_novel_adaptation'] = ['-0.06', '0.122', '-0.04', '0.113', '#interest', '#admiration', 'positive', '0.032', 'encounter', 'portray', 'reveal', 'episode', 'next_episode']
senticnet['graphic_presentation'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'hardcore', 'declared', 'definitive', 'explicit']
senticnet['graphic_representation'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'hardcore', 'declared', 'definitive', 'explicit']
senticnet['graphic_sexual'] = ['0', '0', '-0.95', '-0.72', '#fear', '#disgust', 'negative', '-0.84', 'lubricious', 'lewd', 'lustful', 'obscene', 'prurient']
senticnet['graphical'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'hardcore', 'declared', 'definitive', 'explicit']
senticnet['graphical_configuration'] = ['0.868', '0.717', '0', '0', '#joy', '#interest', 'positive', '0.792', 'tool_shed', 'tool', 'garage', 'tool_belt', 'hardware_store']
senticnet['graphical_debugger'] = ['0.868', '0.717', '0', '0', '#joy', '#interest', 'positive', '0.792', 'tool_shed', 'tool', 'garage', 'tool_belt', 'hardware_store']
senticnet['graphical_display'] = ['0.044', '0.08', '0', '-0.03', '#joy', '#interest', 'positive', '0.031', 'screen', 'pixel', 'visual_information', 'image', 'computer_monitor']
senticnet['graphical_editor'] = ['0.868', '0.717', '0', '0', '#joy', '#interest', 'positive', '0.792', 'tool_shed', 'tool', 'garage', 'tool_belt', 'hardware_store']
senticnet['graphical_interface'] = ['-0.84', '0', '0.689', '-0.90', '#sadness', '#disgust', 'negative', '-0.81', 'software', 'software_program', 'computer_software', 'software_system', 'authoring_language']
senticnet['graphical_notation'] = ['0.716', '-0.78', '0', '0.539', '#joy', '#surprise', 'positive', '0.678', 'depicted', 'pictured', 'portrayed', 'described', 'delineated']
senticnet['graphical_representation'] = ['0.031', '0.08', '0.023', '0.069', '#interest', '#admiration', 'positive', '0.039', 'pencil', 'marker', 'photo', 'icon', 'quill_brush']
senticnet['graphical_tool'] = ['0.868', '0.717', '0', '0', '#joy', '#interest', 'positive', '0.792', 'tool_shed', 'tool', 'garage', 'tool_belt', 'hardware_store']
senticnet['graphical_user_interface'] = ['-0.84', '0', '0.689', '-0.90', '#sadness', '#disgust', 'negative', '-0.81', 'software', 'software_program', 'computer_software', 'software_system', 'authoring_language']
senticnet['graphical_user_interface_gui'] = ['0.868', '0.717', '0', '0', '#joy', '#interest', 'positive', '0.792', 'tool_shed', 'tool', 'garage', 'tool_belt', 'hardware_store']
senticnet['graphically'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'hardcore', 'declared', 'definitive', 'explicit']
senticnet['graphically_represent'] = ['-0.09', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.11', 'write_down_idea', 'finish_book', 'write_down', 'take_note_class', 'take_note']
senticnet['graphicconverter'] = ['-0.55', '0', '0.912', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'wrangle', 'dustup', 'quarrel', 'row', 'squabble']
senticnet['graphite_epoxy'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'gold', 'bronze', 'alloy', 'medal', 'copper']
senticnet['graphite_pencil'] = ['0.022', '-0.06', '0.072', '-0.08', '#anger', '#disgust', 'negative', '-0.01', 'marker', 'pencil', 'pen', 'mechanical_pencil', 'stamp_pad']
senticnet['graphological'] = ['-0.84', '0', '0.585', '-0.85', '#sadness', '#disgust', 'negative', '-0.76', 'subject', 'discipline', 'study', 'concern', 'research']
senticnet['graphologist'] = ['-0.84', '0', '0.585', '-0.85', '#sadness', '#disgust', 'negative', '-0.76', 'subject', 'discipline', 'study', 'concern', 'research']
senticnet['graphology'] = ['-0.84', '0', '0.585', '-0.85', '#sadness', '#disgust', 'negative', '-0.76', 'subject', 'discipline', 'study', 'concern', 'research']
senticnet['grapnel'] = ['0', '0', '-0.83', '-0.8', '#fear', '#disgust', 'negative', '-0.81', 'armory', 'tank', 'hydrogen_bomb', 'rifle', 'use_hunt']
senticnet['grapple'] = ['-0.82', '0', '0.997', '0', '#sadness', '#anger', 'negative', '-0.90', 'grappling', 'device', 'iron', 'hold', 'grappler']
senticnet['grapple_with'] = ['-0.13', '0.071', '0.694', '-0.67', '#anger', '#disgust', 'negative', '-0.35', 'crucify', 'dun', 'frustrating', 'aggravate', 'exasperation']
senticnet['grappled_with'] = ['-0.13', '0.071', '0.694', '-0.67', '#anger', '#disgust', 'negative', '-0.35', 'crucify', 'dun', 'frustrating', 'aggravate', 'exasperation']
senticnet['grappler'] = ['-0.82', '0', '0.995', '0', '#sadness', '#anger', 'negative', '-0.91', 'grappling', 'device', 'iron', 'hold', 'grapple']
senticnet['grapples_with'] = ['-0.13', '0.071', '0.694', '-0.67', '#anger', '#disgust', 'negative', '-0.35', 'crucify', 'dun', 'frustrating', 'aggravate', 'exasperation']
senticnet['grappling'] = ['-0.82', '0', '0.993', '0', '#sadness', '#anger', 'negative', '-0.91', 'grapple', 'device', 'iron', 'hold', 'grappler']
senticnet['grappling_hook'] = ['0.026', '0.165', '-0.12', '0.292', '#interest', '#admiration', 'positive', '0.09', 'fetch', 'find', 'desire_win', 'take', 'obtain']
senticnet['grappling_with'] = ['-0.13', '0.071', '0.694', '-0.67', '#anger', '#disgust', 'negative', '-0.35', 'crucify', 'dun', 'frustrating', 'aggravate', 'exasperation']
senticnet['grasmoor'] = ['0', '0.093', '0', '0.078', '#interest', '#admiration', 'positive', '0.085', 'steep', 'landscape_feature', 'knoll', 'hill_mountain', 'incline']
senticnet['grasp'] = ['0', '0.137', '-0.09', '0.226', '#interest', '#admiration', 'positive', '0.088', 'grab', 'hold', 'grip', 'extend_arm', 'reach']
senticnet['grasp_hand'] = ['0', '0.126', '0', '0.062', '#interest', '#admiration', 'positive', '0.094', 'distribute', 'grab', 'walk_away', 'hand_over', 'extend_arm']
senticnet['grasp_knowledge'] = ['0.727', '0', '0', '0.842', '#joy', '#admiration', 'positive', '0.784', 'find_country', 'learn_much', 'know', 'columbus', 'become_knowledgeable']
senticnet['graspable'] = ['-0.63', '0', '0.694', '-0.66', '#anger', '#disgust', 'negative', '-0.66', 'corporality', 'tangibleness', 'palpability', 'reality', 'solidness']
senticnet['grasper'] = ['0', '0.088', '-0.08', '0.153', '#interest', '#admiration', 'positive', '0.053', 'grasp', 'hold', 'grab', 'reach', 'grip']
senticnet['grass'] = ['0.034', '0.111', '-0.09', '0.148', '#interest', '#admiration', 'positive', '0.05', 'up_mountain', 'hike_trail', 'tress', 'fallen_leaf', 'ant_hill']
senticnet['grass-roots'] = ['0', '0.854', '0', '0.799', '#interest', '#admiration', 'positive', '0.827', 'top-down', 'participatory', 'decentralized', 'bottom-up', 'participative']
senticnet['grass_airfield'] = ['-0.70', '0', '0.621', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'flare_path', 'airfield', 'grassland', 'runway', 'road']
senticnet['grass_airstrip'] = ['-0.70', '0', '0.621', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'flare_path', 'airfield', 'grassland', 'runway', 'road']
senticnet['grass_berm'] = ['0.953', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.929', 'hockey_fan', 'goalie', 'competitor', 'spectator', 'hockey_stick']
senticnet['grass_clipping'] = ['-0.83', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.80', 'footpath', 'homeless_people', 'litter', 'streetlight', 'traffic_light']
senticnet['grass_hay'] = ['0.061', '0.093', '0.037', '0.114', '#interest', '#admiration', 'positive', '0.058', 'whole_grain', 'food_ingredient', 'staple_food', 'starchy_food', 'food_grain']
senticnet['grass_landing_strip'] = ['-0.70', '0', '0.621', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'flare_path', 'airfield', 'grassland', 'runway', 'road']
senticnet['grass_leaved_goldenrod'] = ['0', '0.086', '0', '0', '#interest', '#interest', 'positive', '0.086', 'swim_ocean', 'weight_lift', 'sit_in_shade', 'cut_grass', 'trust_friend']
senticnet['grass_revival'] = ['-0.04', '-0.04', '-0.04', '-0.04', '#fear', '#disgust', 'negative', '-0.02', 'music_shop', 'music_store', 'musical_instrument_store', 'string_quartet', 'high_school_band']
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senticnet['grass_silage'] = ['0.061', '0.093', '0.037', '0.114', '#interest', '#admiration', 'positive', '0.058', 'whole_grain', 'food_ingredient', 'staple_food', 'starchy_food', 'food_grain']
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senticnet['grasshopper_escapement'] = ['0', '0', '0.95', '-0.86', '#anger', '#disgust', 'negative', '-0.90', 'watch', 'stopwatch', 'timepiece', 'wristwatch', 'find_pawnshop']
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senticnet['grassroots_campaign'] = ['-0.81', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.81', 'take_charge', 'anti', 'go_against', 'death_wish', 'file_complaint']
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senticnet['grassroots_outreach'] = ['-0.81', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.81', 'take_charge', 'anti', 'go_against', 'death_wish', 'file_complaint']
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senticnet['grassy_bank'] = ['0.071', '0.127', '0', '0.197', '#interest', '#admiration', 'positive', '0.132', 'place_work', 'workplace', 'office', 'work', 'hang_wall']
senticnet['grassy_hill'] = ['0.071', '0.127', '0', '0.197', '#interest', '#admiration', 'positive', '0.132', 'place_work', 'workplace', 'office', 'work', 'hang_wall']
senticnet['grassy_ridge'] = ['0', '0.093', '0', '0.078', '#interest', '#admiration', 'positive', '0.085', 'steep', 'landscape_feature', 'knoll', 'hill_mountain', 'incline']
senticnet['grassy_slope'] = ['0.071', '0.127', '0', '0.197', '#interest', '#admiration', 'positive', '0.132', 'place_work', 'workplace', 'office', 'work', 'hang_wall']
senticnet['grate'] = ['0', '0', '0.858', '-0.80', '#anger', '#disgust', 'negative', '-0.83', 'grating', 'fret', 'rankle', 'eat_into', 'grind']
senticnet['grated_carrot'] = ['0', '0', '0.066', '-0.07', '#anger', '#disgust', 'negative', '-0.07', 'container_cup', 'cup', 'grease_trap', 'dish', 'open_container']
senticnet['grated_cheese'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'horseradish', 'box_cereal', 'bottle_water']
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senticnet['graticule'] = ['0', '-0.00', '0.034', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'celsius', 'grade', 'certificate', 'learn_tool', 'temperature_measure']
senticnet['gratification'] = ['0.978', '0.81', '0', '0.848', '#joy', '#admiration', 'positive', '0.879', 'brownie_point', 'feel_proud', 'receive_thank', 'appreciate', 'positive_reinforcement']
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senticnet['gratifyingly'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly', 'cheering']
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senticnet['grating_sound'] = ['0.979', '-0.69', '0', '0.576', '#joy', '#surprise', 'positive', '0.749', 'big_cheese', 'supremo', 'important_person', 'influential_person', 'personage']
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senticnet['gratitude'] = ['0.798', '0.754', '0', '0.779', '#joy', '#admiration', 'positive', '0.777', 'affection', 'joy', 'glad', 'admire', 'sympathetic']
senticnet['gratuitous'] = ['0', '0', '0.765', '-0.84', '#anger', '#disgust', 'negative', '-0.80', 'costless', 'complimentary', 'needless', 'gratis', 'free']
senticnet['gratuitous_nudity'] = ['0', '0', '0.765', '-0.84', '#anger', '#disgust', 'negative', '-0.80', 'costless', 'complimentary', 'needless', 'gratis', 'free']
senticnet['gratuitous_violence'] = ['0', '0', '0.765', '-0.84', '#anger', '#disgust', 'negative', '-0.80', 'costless', 'complimentary', 'needless', 'gratis', 'free']
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senticnet['gratuity'] = ['0', '0.193', '-0.09', '0.181', '#interest', '#admiration', 'positive', '0.094', 'dollar', 'cash_bill', 'franc', 'money_earn', 'money_involve']
senticnet['grave'] = ['-0.88', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.90', 'coffin', 'casket', 'morgue', 'headstone', 'burial']
senticnet['grave_accent'] = ['-0.61', '0', '0.721', '-0.68', '#anger', '#disgust', 'negative', '-0.67', 'character', 'logogram', 'logograph', 'graph', 'ideogram']
senticnet['grave_marker'] = ['-0.88', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.90', 'coffin', 'casket', 'morgue', 'headstone', 'burial']
senticnet['grave_mound'] = ['0', '-0.02', '-0.08', '-0.05', '#fear', '#disgust', 'negative', '-0.03', 'mattress', 'sleep_furniture', 'master_bedroom', 'small_bed', 'pillow']
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senticnet['gravedigger_scene'] = ['-0.91', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.85', 'play', 'perpetrator', 'role_play', 'act_part', 'terrible_event']
senticnet['gravel'] = ['-0.74', '0', '0.709', '0', '#sadness', '#anger', 'negative', '-0.72', 'vex', 'stun', 'dazed', 'stunned', 'stupefied']
senticnet['gravel_airstrip'] = ['-0.70', '0', '0.621', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'flare_path', 'airfield', 'grassland', 'runway', 'road']
senticnet['gravel_bar'] = ['0.97', '0.758', '0', '0', '#joy', '#interest', 'positive', '0.864', 'trot', 'step_out', 'step_aside', 'progress', 'enter_place']
senticnet['gravel_parking_lot'] = ['0', '0', '0', '-0.09', '#disgust', '#disgust', 'negative', '-0.09', 'subway', 'bus', 'train_station', 'railway_station', 'bus_stop']
senticnet['gravel_pit'] = ['0', '0', '-0.85', '-0.71', '#fear', '#disgust', 'negative', '-0.78', 'fair_game', 'pit', 'stone_pit', 'target', 'prey']
senticnet['gravel_quarry'] = ['0', '0', '-0.85', '-0.71', '#fear', '#disgust', 'negative', '-0.78', 'fair_game', 'pit', 'stone_pit', 'target', 'prey']
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senticnet['gravel_trap'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['gravelly_voice'] = ['-0.93', '0', '0.576', '-0.54', '#sadness', '#anger', 'negative', '-0.68', 'grating', 'rasping', 'raspy', 'jarring', 'cacophonic']
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senticnet['graveness'] = ['0.912', '-0.64', '0', '0.889', '#joy', '#admiration', 'positive', '0.816', 'earnestness', 'sincerity', 'seriousness', 'stuffiness', 'stodginess']
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senticnet['graveside_service'] = ['-0.98', '0', '0.664', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'unfortunate', 'unfortunate_person', 'weeper', 'griever', 'bearer']
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senticnet['gravestone'] = ['-0.88', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.90', 'coffin', 'casket', 'morgue', 'headstone', 'burial']
senticnet['graveyard'] = ['-0.92', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.87', 'cemetery', 'funeral', 'funeral_home', 'tombstone', 'die']
senticnet['gravid'] = ['0.159', '0.068', '-0.07', '0.118', '#joy', '#admiration', 'positive', '0.067', 'stomach', 'uterus', 'pregnant', 'lap', 'womb']
senticnet['gravitas'] = ['0.841', '-0.56', '0', '0.654', '#joy', '#admiration', 'positive', '0.688', 'dignity', 'comportment', 'lordliness', 'personal_manner', 'manner']
senticnet['gravitate'] = ['0.708', '-0.80', '0', '0.747', '#surprise', '#admiration', 'positive', '0.754', 'move_around', 'tend', 'incline', 'lean', 'run']
senticnet['gravitate_toward'] = ['0', '0.37', '-0.27', '0.283', '#interest', '#admiration', 'positive', '0.126', 'thoughtful', 'attitude', 'swing', 'certainty', 'type_of_bear']
senticnet['gravitation'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'force_unit', 'sthene', 'poundal']
senticnet['gravitation_theory'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'general_relativity', 'general_theory_of_relativity', 'relativity', 'special_relativity', 'special_theory_of_relativity']
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senticnet['gravitational_anomaly'] = ['-0.68', '0', '0.946', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'abnormality', 'abnormalcy', 'birth_defect', 'congenital_anomaly', 'congenital_defect']
senticnet['gravitational_attraction'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'empty_room', 'a_lot_of_space', 'empty', 'empty_house', 'universe']
senticnet['gravitational_constant'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'newton', 'sthene', 'force_unit']
senticnet['gravitational_force'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotate_movement', 'rotate', 'go_around', 'manoeuvre', 'full_circle']
senticnet['gravitational_potential'] = ['-0.03', '0.178', '0', '0.168', '#interest', '#admiration', 'positive', '0.104', 'speed', 'fast', 'fast_and_furious', 'speedy', 'fleet']
senticnet['gravitational_theory'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'general_relativity', 'general_theory_of_relativity', 'relativity', 'special_relativity', 'special_theory_of_relativity']
senticnet['gravitational_wave'] = ['0', '0.698', '0', '0.954', '#interest', '#admiration', 'positive', '0.826', 'region', 'universe', 'star', 'study', 'black_hole']
senticnet['gravitationally_bound'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['gravitationally_unstable'] = ['-0.55', '0', '0.869', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'air_bubble', 'foam', 'froth', 'soap_bubble', 'bubble']
senticnet['gravity'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'wake', 'go_high', 'downward', 'elevation', 'attraction']
senticnet['gravity_duality'] = ['0', '0.721', '0', '0.731', '#interest', '#admiration', 'positive', '0.726', 'dimension', 'measure', 'tackle', 'length', 'measurement']
senticnet['gravlax'] = ['0.52', '-0.69', '0', '0.639', '#surprise', '#admiration', 'positive', '0.617', 'black_bread', 'pumpernickel', 'limpa', 'bread', 'breadstuff']
senticnet['grawemeyer_award'] = ['0.894', '0.881', '0', '0.803', '#joy', '#interest', 'positive', '0.859', 'remittance', 'afford', 'award', 'money_due', 'amount_owe']
senticnet['gray'] = ['-0.51', '0', '0.651', '-0.61', '#anger', '#disgust', 'negative', '-0.59', 'silver_gray', 'gray_hair', 'old', 'tattletale_gray', 'achromatic_color']
senticnet['gray-haired'] = ['0', '0', '-0.79', '-0.84', '#fear', '#disgust', 'negative', '-0.82', 'hoary', 'gray', 'rusty', 'canescent', 'grizzly']
senticnet['gray_ash'] = ['-0.1', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.1', 'deflate', 'read_cover', 'look_behind', 'upgrade', 'scan']
senticnet['gray_beard'] = ['0', '0', '0.841', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'ancient', 'many_years', 'year', 'old', 'teen']
senticnet['gray_fox'] = ['-0.03', '0', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.06', 'lion', 'leopard', 'mountain_lion', 'craven', 'wolf']
senticnet['gray_fox_urocyon'] = ['-0.54', '0', '0.768', '-0.50', '#sadness', '#anger', 'negative', '-0.60', 'polecat', 'hooded_skunk', 'hognosed_skunk', 'badger_skunk', 'rooter_skunk']
senticnet['gray_fur'] = ['-0.58', '0', '0.66', '-0.8', '#anger', '#disgust', 'negative', '-0.68', 'bald', 'depilous', 'glabrescent', 'glabrous', 'balding']
senticnet['gray_green_foliage'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'tropical_fish', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['gray_hair'] = ['0', '0', '0.841', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'ancient', 'many_years', 'year', 'old', 'teen']
senticnet['gray_inn'] = ['0.053', '0.122', '-0.07', '0.086', '#interest', '#admiration', 'positive', '0.046', 'attorney', 'lawyer', 'jury', 'decide_guilt', 'prosecutor']
senticnet['gray_mist'] = ['0', '0', '-0.94', '-0.91', '#fear', '#disgust', 'negative', '-0.92', 'night', 'blackout', 'darkness', 'dark', 'obscure']
senticnet['gray_mold'] = ['-0.83', '0', '0.754', '-0.97', '#sadness', '#disgust', 'negative', '-0.85', 'brown_rot_gummosis', 'ring_rot', 'ring_disease', 'tobacco_wilt', 'gummosis']
senticnet['gray_mustache'] = ['-0.64', '0', '0.632', '-0.61', '#sadness', '#anger', 'negative', '-0.62', 'bearded', 'barbate', 'bewhiskered', 'whiskered', 'whiskery']
senticnet['gray_scale'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'indigo', 'yellow', 'water_color', 'red', 'violet']
senticnet['gray_sweater'] = ['-0.02', '0.256', '0', '0.166', '#interest', '#admiration', 'positive', '0.132', 'coat', 'shirt', 'garment', 'dress', 'vest']
senticnet['gray_vireo'] = ['-0.66', '0', '0.982', '-0.93', '#anger', '#disgust', 'negative', '-0.86', 'bother', 'barge_in', 'come_in', 'break_in', 'crash']
senticnet['gray_whale'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'lordosis', 'kyphosis', 'spinal_curvature', 'hunchback', 'abnormality']
senticnet['gray_wolf'] = ['0', '-0.97', '0', '0', '#surprise', '#surprise', 'positive', '0.977', 'cougar', 'skunk', 'lion', 'tiger', 'lynx']
senticnet['graying_hair'] = ['0', '0', '0.841', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'ancient', 'many_years', 'year', 'old', 'teen']
senticnet['grayish'] = ['0.824', '0', '0', '0.867', '#joy', '#admiration', 'positive', '0.845', 'leathery', 'fleshy', 'papery', 'scaly', 'feathery']
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senticnet['grayscale_image'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'indigo', 'yellow', 'water_color', 'red', 'violet']
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senticnet['graze'] = ['0.032', '0.075', '0', '0', '#joy', '#interest', 'positive', '0.053', 'aperitif', 'fondue', 'crop', 'maple', 'kernel']
senticnet['graze_cattle'] = ['0.966', '0.881', '0', '0', '#joy', '#interest', 'positive', '0.923', 'harvest', 'crop', 'grow_crop', 'plant_crop', 'reap']
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senticnet['graze_sheep'] = ['0.966', '0.881', '0', '0', '#joy', '#interest', 'positive', '0.923', 'harvest', 'crop', 'grow_crop', 'plant_crop', 'reap']
senticnet['grazed_cattle'] = ['0.053', '0.094', '0', '0.136', '#interest', '#admiration', 'positive', '0.094', 'barn_animal', 'domesticate', 'grassland', 'sheep', 'cow']
senticnet['grazed_sheep'] = ['0.966', '0.881', '0', '0', '#joy', '#interest', 'positive', '0.923', 'harvest', 'crop', 'grow_crop', 'plant_crop', 'reap']
senticnet['grazier'] = ['-0.08', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.11', 'serf', 'peasant', 'servant', 'poor', 'villein']
senticnet['grazing_cattle'] = ['0.966', '0.881', '0', '0', '#joy', '#interest', 'positive', '0.923', 'harvest', 'crop', 'grow_crop', 'plant_crop', 'reap']
senticnet['grazing_land'] = ['-0.90', '0', '0.765', '-0.98', '#sadness', '#disgust', 'negative', '-0.88', 'pastureland', 'common_land', 'ley', 'pasture', 'lea']
senticnet['grazing_lease'] = ['0.966', '0.881', '0', '0', '#joy', '#interest', 'positive', '0.923', 'harvest', 'crop', 'grow_crop', 'plant_crop', 'reap']
senticnet['grazing_livestock'] = ['0.966', '0.881', '0', '0', '#joy', '#interest', 'positive', '0.923', 'harvest', 'crop', 'grow_crop', 'plant_crop', 'reap']
senticnet['grazing_sheep'] = ['0.966', '0.881', '0', '0', '#joy', '#interest', 'positive', '0.923', 'harvest', 'crop', 'grow_crop', 'plant_crop', 'reap']
senticnet['grease'] = ['0', '0', '-0.99', '-0.98', '#fear', '#disgust', 'negative', '-0.98', 'grime', 'pan', 'lube', 'lubricant', 'dirt']
senticnet['grease_pencil'] = ['0.029', '-0.03', '0.066', '-0.08', '#anger', '#disgust', 'negative', '-0.02', 'sheet', 'drawer', 'file_box', 'desk_tray', 'letter_scale']
senticnet['grease_trap'] = ['0', '0', '0.066', '-0.07', '#anger', '#disgust', 'negative', '-0.07', 'container_cup', 'cup', 'open_container', 'dish', 'salad_bowl']
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senticnet['greased_baking_sheet'] = ['0.03', '-0.05', '0', '0.059', '#surprise', '#admiration', 'positive', '0.046', 'platter', 'fork', 'spoon', 'tablespoon', 'bowl']
senticnet['greasemonkey'] = ['-0.89', '0', '0.672', '-0.69', '#sadness', '#disgust', 'negative', '-0.75', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['greasemonkey_script'] = ['-0.89', '0', '0.672', '-0.69', '#sadness', '#disgust', 'negative', '-0.75', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['greaseproof'] = ['-0.9', '0', '0.657', '-0.64', '#sadness', '#anger', 'negative', '-0.73', 'soundproof', 'mothproof', 'resistant', 'proof', 'imperviable']
senticnet['greaseproof_paper'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'foil', 'tin_foil', 'gold_foil']
senticnet['greasewood'] = ['-0.86', '0', '0.665', '-0.61', '#sadness', '#anger', 'negative', '-0.71', 'wooded', 'braky', 'rushy', 'scrubby', 'forested']
senticnet['greasy'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'crude', 'liquid_fuel', 'natural_resource', 'dark_liquid', 'fossil_fuel']
senticnet['greasy_food'] = ['-0.92', '0', '0.254', '-0.96', '#sadness', '#disgust', 'negative', '-0.71', 'fat', 'become_fat', 'fat_person', 'unhealthy_food', 'greasy']
senticnet['greasy_hair'] = ['-0.91', '0', '0.869', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'talk_hairdresser', 'hair_get_short', 'hair_become_short', 'keep_hair_short', 'hair_wash']
senticnet['great'] = ['0.905', '0.773', '0', '0.892', '#joy', '#admiration', 'positive', '0.857', 'best', 'excellent', 'well_done', 'goodness', 'good_quality']
senticnet['great-grandmother'] = ['0.706', '0', '0', '0.777', '#joy', '#admiration', 'positive', '0.742', 'patriarch', 'matriarch', 'grandmother', 'matriarchal', 'matriarch']
senticnet['great_acclaim'] = ['0.853', '0.828', '0', '0.98', '#joy', '#admiration', 'positive', '0.887', 'endorse', 'recommend', 'stimulate_mind', 'acculturation', 'support']
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senticnet['great_admirer'] = ['0.814', '-0.50', '0', '0.622', '#joy', '#admiration', 'positive', '0.648', 'friend', 'champion', 'supporter', 'protagonist', 'endorser']
senticnet['great_american_songbook'] = ['0', '0.176', '0', '0.216', '#interest', '#admiration', 'positive', '0.196', 'choir_stall', 'worshiper', 'pew', 'fundamentalist', 'pulpit']
senticnet['great_anger'] = ['0', '0', '0.766', '0', '#anger', '#anger', 'negative', '-0.76', 'choose_victim', 'target_practice', 'shoot_target', 'alibi', 'self_defense']
senticnet['great_archon'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
senticnet['great_aunt'] = ['0.02', '0.209', '-0.14', '0.202', '#interest', '#admiration', 'positive', '0.071', 'familiar', 'mum', 'familiarity_uncle', 'care_giver', 'familiarity']
senticnet['great_boon'] = ['0.808', '-0.83', '0', '0.548', '#joy', '#surprise', 'positive', '0.73', 'successfulness', 'luckiness', 'prosperity', 'circumstance', 'blessing']
senticnet['great_burdock'] = ['0.683', '-0.54', '0', '0.568', '#joy', '#admiration', 'positive', '0.599', 'subshrub', 'suffrutex', 'common_burdock', 'lesser_burdock', 'burdock']
senticnet['great_carrier'] = ['0.97', '0.729', '0', '0.946', '#joy', '#admiration', 'positive', '0.882', 'become_famous', 'become_rich', 'money', 'good_job', 'make_a_lot_of_money']
senticnet['great_communicator'] = ['0.749', '0.724', '0', '0', '#joy', '#interest', 'positive', '0.736', 'mouth_noise', 'vocal_cord', 'vocalization', 'vocal_sound', 'loud_soft']
senticnet['great_dane'] = ['0', '0', '0.737', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'annoy', 'skin_rash', 'bother', 'irritation', 'nag']
senticnet['great_dodecahedron'] = ['0.641', '-0.6', '0', '0.957', '#joy', '#admiration', 'positive', '0.733', 'ideal_solid', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'regular_octahedron']
senticnet['great_egret'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'amoeba', 'single_celled']
senticnet['great_exercise'] = ['0.905', '0.773', '0', '0.892', '#joy', '#admiration', 'positive', '0.857', 'good_exercise', 'great', 'exercise', 'healthy_lifestyle', 'physical_exercise']
senticnet['great_granddaughter'] = ['0.948', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.888', 'relation', 'mom', 'kin', 'cousin', 'granddaughter']
senticnet['great_grandfather'] = ['0.948', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.888', 'relation', 'mom', 'kin', 'cousin', 'granddaughter']
senticnet['great_grandmother'] = ['0.826', '0.862', '0', '0', '#joy', '#interest', 'positive', '0.844', 'aunt_uncle', 'picture_of_family', 'aunt', 'family_photo', 'relative']
senticnet['great_grandson'] = ['0.948', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.888', 'relation', 'mom', 'kin', 'cousin', 'granddaughter']
senticnet['great_granduncle'] = ['0.948', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.888', 'relation', 'mom', 'kin', 'cousin', 'granddaughter']
senticnet['great_great_grandfather'] = ['0.948', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.888', 'relation', 'mom', 'kin', 'cousin', 'granddaughter']
senticnet['great_great_grandson'] = ['0.948', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.888', 'relation', 'mom', 'kin', 'cousin', 'granddaughter']
senticnet['great_grey_owl'] = ['-0.03', '0', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.06', 'lion', 'leopard', 'mountain_lion', 'craven', 'wolf']
senticnet['great_helmsman'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['great_horned_owl_bubo'] = ['-0.53', '0', '0.648', '-0.70', '#anger', '#disgust', 'negative', '-0.63', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['great_horned_owls_bubo'] = ['-0.53', '0', '0.648', '-0.70', '#anger', '#disgust', 'negative', '-0.63', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['great_icosahedron'] = ['0.641', '-0.6', '0', '0.957', '#joy', '#admiration', 'positive', '0.733', 'ideal_solid', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'regular_octahedron']
senticnet['great_invention'] = ['-0.04', '0.077', '0.073', '0.118', '#interest', '#admiration', 'positive', '0.02', 'round_figure', 'orb', 'torus', 'circle_shape', 'ball_shape']
senticnet['great_joy'] = ['0.326', '0.149', '-0.07', '0', '#joy', '#interest', 'positive', '0.132', 'sense_pride', 'happiness', 'delight_sense', 'have_fun', 'feel_pleasure']
senticnet['great_keppel_island'] = ['0.84', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.798', 'big_breadbox', 'water_vehicle', 'military_ship', 'ship', 'small_boat']
senticnet['great_kiskadee'] = ['-0.74', '0', '0.963', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['great_lake'] = ['0', '0.747', '0', '0.817', '#interest', '#admiration', 'positive', '0.782', 'research', 'lake', 'impact', 'determine', 'focus']
senticnet['great_love'] = ['0', '0.811', '0', '0.933', '#interest', '#admiration', 'positive', '0.872', 'passion', 'lust', 'love', 'love_god', 'beloved']
senticnet['great_meteor_hotspot'] = ['-0.87', '0', '0.818', '0', '#sadness', '#anger', 'negative', '-0.84', 'slow_speed', 'wifi', 'computer_internet_connection', 'internet_connection', 'boot_up_computer']
senticnet['great_motivator'] = ['0', '0', '0', '0.869', '#admiration', '#admiration', 'positive', '0.869', 'prosper', 'succeed', 'realize', 'fly_high', 'thrive']
senticnet['great_nephew'] = ['0.948', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.888', 'relation', 'mom', 'kin', 'cousin', 'granddaughter']
senticnet['great_nicobar_biosphere'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['great_ouse'] = ['-0.77', '0', '0.583', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'rain', 'rain_down', 'sheet', 'rain_bucket', 'rain_buckets_down']
senticnet['great_performance'] = ['0.715', '0.717', '0', '0.813', '#interest', '#admiration', 'positive', '0.748', 'performance', 'show', 'skilled', 'play', 'interesting_show']
senticnet['great_pleasure'] = ['0.561', '-0.79', '0', '0.89', '#surprise', '#admiration', 'positive', '0.748', 'superlative', 'compliment', 'testimonial', 'praise', 'approval']
senticnet['great_pumpkin'] = ['0', '-0.01', '0.037', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'pear', 'green_apple', 'apricot', 'cherry', 'lime']
senticnet['great_relief'] = ['-0.22', '0', '-0.18', '-0.09', '#sadness', '#fear', 'negative', '-0.16', 'feel_depressed', 'clean_up_mess', 'sadness', 'rainy_day', 'erection']
senticnet['great_renown'] = ['0.852', '-0.75', '0', '0.763', '#joy', '#admiration', 'positive', '0.789', 'honor', 'laurel', 'fame', 'stock', 'character']
senticnet['great_repute'] = ['0.852', '-0.75', '0', '0.763', '#joy', '#admiration', 'positive', '0.789', 'honor', 'laurel', 'fame', 'stock', 'character']
senticnet['great_solemnity'] = ['0.701', '0.867', '0', '0', '#joy', '#interest', 'positive', '0.784', 'festivity', 'special_day', 'birthday', 'special_time', 'cake_candle']
senticnet['great_spotted_woodpecker'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['great_team'] = ['0.884', '-0.69', '0', '0.907', '#joy', '#admiration', 'positive', '0.828', 'sentic_team', 'fun', 'research', 'senticnet', 'family']
senticnet['great_time'] = ['0.758', '0.719', '0', '0.778', '#joy', '#admiration', 'positive', '0.752', 'set_mood', 'good_time', 'deafness', 'have_fun', 'enjoy_music']
senticnet['great_tribulation'] = ['-0.96', '0', '0.501', '-0.59', '#sadness', '#disgust', 'negative', '-0.68', 'calamity', 'catastrophe', 'tragedy', 'disaster', 'cataclysm']
senticnet['great_uncle'] = ['0.948', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.888', 'relation', 'mom', 'kin', 'cousin', 'granddaughter']
senticnet['great_understand'] = ['0.757', '0', '0', '0', '#joy', '#joy', 'positive', '0.757', 'add_knowledge', 'learn_much', 'learn', 'know_fact', 'understand']
senticnet['great_view'] = ['0.16', '0.067', '0', '0.083', '#joy', '#admiration', 'positive', '0.103', 'good_view', 'summit', 'gable', 'snowflake', 'antenna']
senticnet['great_whernside'] = ['0', '0.093', '0', '0.078', '#interest', '#admiration', 'positive', '0.085', 'steep', 'landscape_feature', 'knoll', 'hill_mountain', 'incline']
senticnet['great_yellowcress'] = ['0.758', '-0.97', '0', '0.851', '#surprise', '#admiration', 'positive', '0.861', 'marsh_cress', 'yellow_watercress', 'mountain_watercress', 'common_watercress', 'watercress']
senticnet['greatcoat'] = ['0.08', '0.106', '-0.04', '0.112', '#interest', '#admiration', 'positive', '0.063', 'helmet', 'head_gear', 'cap', 'head_wear', 'head_protection']
senticnet['greater'] = ['0.753', '0.813', '0', '0', '#joy', '#interest', 'positive', '0.783', 'increasing', 'increased', 'increase', 'higher', 'less']
senticnet['greater_anglia'] = ['0.861', '-0.52', '0', '0.598', '#joy', '#admiration', 'positive', '0.663', 'exit', 'get_off', 'deplane', 'go_out', 'get_out']
senticnet['greater_autonomy'] = ['0.887', '0.748', '0', '0.954', '#joy', '#admiration', 'positive', '0.863', 'political_action', 'political', 'mediocrity', 'discrimination', 'independent']
senticnet['greater_butterfly_orchid'] = ['-0.79', '0', '0.837', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'sunray', 'wilding', 'sagebrush_buttercup', 'wildflower', 'pasque_flower']
senticnet['greater_chickweed'] = ['-0.64', '0', '0.546', '-0.62', '#sadness', '#disgust', 'negative', '-0.60', 'herb', 'common_chickweed', 'chickweed', 'stitchwort', 'starwort']
senticnet['greater_coherence'] = ['0.869', '-0.88', '0', '0.598', '#joy', '#surprise', 'positive', '0.782', 'connectedness', 'connection', 'consistency', 'continuity', 'link']
senticnet['greater_consistency'] = ['0', '0', '0', '0.76', '#admiration', '#admiration', 'positive', '0.76', 'big_paycheck', 'self_confidence', 'balance_check_account', 'look_beautiful', 'much_money']
senticnet['greater_credence'] = ['0.751', '-0.98', '0', '0.989', '#surprise', '#admiration', 'positive', '0.909', 'okay', 'approval', 'commendation', 'okey', 'okeh']
senticnet['greater_depth'] = ['0', '0.136', '0', '0.196', '#interest', '#admiration', 'positive', '0.166', 'describe', 'explain', 'answer', 'description', 'example']
senticnet['greater_detail'] = ['0', '0.136', '0', '0.196', '#interest', '#admiration', 'positive', '0.166', 'describe', 'explain', 'answer', 'description', 'example']
senticnet['greater_flamingo'] = ['0.797', '-0.71', '0', '0.736', '#joy', '#admiration', 'positive', '0.751', 'wood_ibis', 'wood_stork', 'ibis', 'wading_bird', 'wader']
senticnet['greater_flexibility'] = ['-0.52', '0', '0.561', '-0.90', '#anger', '#disgust', 'negative', '-0.66', 'difference', 'tolerance', 'allowance', 'discrepancy', 'margin']
senticnet['greater_generality'] = ['-0.99', '0', '0.878', '-0.95', '#sadness', '#disgust', 'negative', '-0.94', 'adequacy', 'satisfactoriness', 'acceptability', 'acceptableness', 'quality']
senticnet['greater_insight_into'] = ['-0.06', '-0.02', '0.02', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'eye_strain', 'new_perspective', 'move_eye', 'gain_insight', 'great_understand']
senticnet['greater_job_satisfaction'] = ['0.961', '0.937', '0', '0', '#joy', '#interest', 'positive', '0.949', 'create_new_life', 'make_beautiful', 'stimulation', 'lower_stress', 'leisure_time']
senticnet['greater_leeway'] = ['-0.52', '0', '0.561', '-0.90', '#anger', '#disgust', 'negative', '-0.66', 'difference', 'tolerance', 'allowance', 'discrepancy', 'margin']
senticnet['greater_petrosal_nerve'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['greater_precision'] = ['0', '-0.90', '0', '0.791', '#surprise', '#admiration', 'positive', '0.849', 'precise', 'accuracy', 'correct', 'performance', 'measure']
senticnet['greater_scrutiny'] = ['-0.79', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.75', 'scan', 'checkup', 'medical_checkup', 'medical_examination', 'medical_exam']
senticnet['greater_spotted_eagle'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'hold_egg', 'baby_bird', 'hatch', 'lay_egg', 'bird_home']
senticnet['greater_stitchwort'] = ['-0.64', '0', '0.555', '-0.62', '#sadness', '#disgust', 'negative', '-0.60', 'herb', 'common_chickweed', 'stitchwort', 'chickweed', 'starwort']
senticnet['greater_susceptibility'] = ['-0.78', '0', '0.6', '-0.61', '#sadness', '#disgust', 'negative', '-0.66', 'suggestibility', 'sensitivity', 'capacity', 'reactivity', 'predisposition']
senticnet['greater_trochanter'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'hip_socket', 'tooth_socket', 'alveolus', 'socket', 'cotyloid_cavity']
senticnet['greater_tussock_sedge'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'bulrush', 'nailrod', 'cattail', 'reedmace']
senticnet['greater_uniformity'] = ['0', '0', '0', '0.76', '#admiration', '#admiration', 'positive', '0.76', 'big_paycheck', 'self_confidence', 'balance_check_account', 'look_beautiful', 'much_money']
senticnet['greater_vigilance'] = ['0', '1', '0', '0', '#interest', '#interest', 'positive', '1', 'confident', 'confidence', 'optimistic', 'assured', 'engrossed']
senticnet['greater_yellowstone'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['greater_yellowstone_ecosystem'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['greatest'] = ['0.973', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.873', 'highest', 'uttermost', 'utmost', 'farthest', 'extent']
senticnet['greatest_challenge'] = ['0', '0', '0', '-0.70', '#disgust', '#disgust', 'negative', '-0.70', 'uncertainty', 'uncertain_environment', 'insecure', 'blur', 'anxiety']
senticnet['greatest_natural_disaster'] = ['-0.87', '0', '0.889', '-0.62', '#sadness', '#anger', 'negative', '-0.79', 'ruination', 'ruin', 'deterioration', 'blight', 'impairment']
senticnet['greatest_obstacle'] = ['-0.88', '0', '0.944', '-0.91', '#anger', '#disgust', 'negative', '-0.91', 'difficulty', 'straitjacket', 'millstone', 'drag', 'obstruction']
senticnet['greatest_strength'] = ['0.989', '-0.80', '0', '0.569', '#joy', '#surprise', 'positive', '0.788', 'strength', 'tolerance', 'stamina', 'endurance', 'sufferance']
senticnet['greatest_threat'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'problem', 'serious_issue', 'overrate', 'prohibitionist_society', 'blade_weapon']
senticnet['greatest_treasure'] = ['0', '0.177', '0', '0.126', '#interest', '#admiration', 'positive', '0.151', 'treasure', 'storage_facility', 'shrine', 'lock_box', 'jewelry_box']
senticnet['greatly'] = ['0', '0.701', '0', '0.801', '#interest', '#admiration', 'positive', '0.751', 'project', 'research', 'study', 'development', 'different_application']
senticnet['greatly_appreciate'] = ['0.819', '0', '0', '0.816', '#joy', '#admiration', 'positive', '0.817', 'self_satisfaction', 'praise', 'good_feeling', 'happiness', 'applaud']
senticnet['greatly_assist'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'woodworking_tool']
senticnet['greatly_benefit'] = ['0.085', '0.365', '-0.20', '0.362', '#interest', '#admiration', 'positive', '0.153', 'bonus', 'monetary_gain', 'financial_gain', 'return', 'give']
senticnet['greatly_enhance'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'compound', 'intensify', 'deepen', 'potentiate', 'heighten']
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senticnet['greatly_expand'] = ['-0.04', '0.153', '-0.12', '0.232', '#interest', '#admiration', 'positive', '0.055', 'reach', 'elasticity', 'grow_long', 'extend_arm', 'tow']
senticnet['greatly_improve'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'compound', 'intensify', 'deepen', 'potentiate', 'heighten']
senticnet['greatly_reduce'] = ['0.676', '-0.70', '0', '0.8', '#surprise', '#admiration', 'positive', '0.727', 'better', 'soothe', 'improve', 'alleviate_pain', 'meliorate']
senticnet['greatly_superior'] = ['0', '0.886', '0', '0.772', '#interest', '#admiration', 'positive', '0.829', 'stalin', 'sail_sea', 'well_done', 'supervisor', 'small_ocean']
senticnet['greatly_underestimate'] = ['-0.12', '0.193', '0.597', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'upvalue', 'overrate', 'overprize', 'overstimate', 'overprice']
senticnet['greatness'] = ['0.131', '0.074', '0', '0.373', '#joy', '#admiration', 'positive', '0.193', 'superlative', 'excellence', 'ultimate', 'great', 'well_done']
senticnet['greatsword'] = ['0.93', '-0.78', '0', '0.873', '#joy', '#admiration', 'positive', '0.861', 'blade', 'brand', 'steel', 'claymore', 'sword']
senticnet['greave'] = ['-0.50', '0', '0.937', '-0.59', '#anger', '#disgust', 'negative', '-0.67', 'restorative', 'medicine', 'medication', 'medicament', 'medicinal_drug']
senticnet['grecian'] = ['-0.69', '0', '0.957', '-0.92', '#anger', '#disgust', 'negative', '-0.85', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['greco_roman_antiquity'] = ['0.136', '-0.11', '0.031', '0', '#joy', '#surprise', 'positive', '0.075', 'rap', 'popular_music', 'music', 'band_play', 'rock_roll']
senticnet['greed'] = ['-0.50', '0', '0.93', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'avarice', 'covetousness', 'possessiveness', 'desire', 'acquisitiveness']
senticnet['greedier'] = ['0', '0', '0.857', '-0.88', '#anger', '#disgust', 'negative', '-0.87', 'jealous', 'avaricious', 'prehensile', 'covetousness', 'grasp']
senticnet['greedily'] = ['0', '0', '0.99', '-0.82', '#anger', '#disgust', 'negative', '-0.90', 'bark', 'importunateness', 'importune', 'thoughtlessly', 'importunate']
senticnet['greediness'] = ['-0.81', '0', '0.886', '-0.65', '#sadness', '#anger', 'negative', '-0.78', 'opportunism', 'selfishness', 'rapaciousness', 'voraciousness', 'expedience']
senticnet['greedy'] = ['0', '0', '0.857', '-0.88', '#anger', '#disgust', 'negative', '-0.87', 'jealous', 'avaricious', 'prehensile', 'covetousness', 'grasp']
senticnet['greedy_person'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'blind_person', 'rubber_duck', 'kink', 'vegetarian', 'foreigner']
senticnet['greefe'] = ['-0.80', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'difficulty', 'awkwardness', 'flea_bite', 'difficultness', 'cumbersomeness']
senticnet['green'] = ['-0.02', '0.078', '0.042', '0.066', '#interest', '#admiration', 'positive', '0.019', 'sexual', 'purple_color', 'fragrant', 'colorful', 'purple']
senticnet['green-eyed'] = ['0.085', '0.084', '0.805', '-0.68', '#anger', '#disgust', 'negative', '-0.33', 'green-eyed_monster', 'overjealous', 'covetous', 'grasp', 'covet']
senticnet['green-eyed_monster'] = ['0.093', '0.086', '0.814', '-0.68', '#anger', '#disgust', 'negative', '-0.32', 'green-eyed', 'overjealous', 'covetous', 'grasp', 'covet']
senticnet['green_alga'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['green_anaconda'] = ['-0.68', '0', '0.611', '-0.59', '#sadness', '#anger', 'negative', '-0.63', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['green_apple'] = ['0', '-0.01', '0.037', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'pear', 'lime', 'apricot', 'cherry', 'pumpkin']
senticnet['green_arrow'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'bayonet', 'spear', 'shotgun', 'bullet', 'hydrogen_bomb']
senticnet['green_bean'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'frijole', 'common_bean', 'wax_bean']
senticnet['green_bean_casserole'] = ['0.937', '0', '0', '0.828', '#joy', '#admiration', 'positive', '0.883', 'bakery', 'produce_section', 'fruit_bowl', 'dinner_meal', 'fast_food']
senticnet['green_bell_pepper'] = ['-0.05', '-0.03', '0.023', '-0.09', '#sadness', '#disgust', 'negative', '-0.03', 'moldy_food', 'spam', 'paper_product', 'orange_juice', 'plant']
senticnet['green_black'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'purple', 'red', 'bright_red', 'red_green', 'peelable']
senticnet['green_blue_rgb'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'indigo', 'yellow', 'water_color', 'red', 'violet']
senticnet['green_bread'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'mouldy_bread', 'rotten', 'disgusting', 'taste_awful', 'mouldy']
senticnet['green_card'] = ['0', '0.76', '0', '0', '#interest', '#interest', 'positive', '0.76', 'overseas', 'live_abroad', 'study_abroad', 'elsewhere', 'abroad']
senticnet['green_chartreuse'] = ['-0.55', '0', '0.5', '-0.55', '#sadness', '#disgust', 'negative', '-0.53', 'remedy', 'cure', 'catholicon', 'therapeutic', 'curative']
senticnet['green_chili'] = ['0.859', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.888', 'green_apple', 'apricot', 'lime', 'peach', 'cherry']
senticnet['green_chilli'] = ['0.979', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.918', 'make_curry', 'cook_dinner', 'cook_meal', 'cook_curry', 'powder']
senticnet['green_color'] = ['0.034', '0.065', '0', '0.07', '#interest', '#admiration', 'positive', '0.056', 'leafy', 'renewable_resource', 'part_of_plant', 'leafy_green', 'root']
senticnet['green_eyeshade'] = ['-0.63', '0', '0.712', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'upset', 'disconcert', 'discompose', 'discomfit', 'untune']
senticnet['green_foliage'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'tropical_fish', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['green_fruit'] = ['0.053', '0.071', '0.058', '0.102', '#interest', '#admiration', 'positive', '0.042', 'round_fruit', 'red_fruit', 'small_fruit', 'crunchy', 'delicious_fruit']
senticnet['green_garden'] = ['0', '0.071', '0.017', '0.084', '#interest', '#admiration', 'positive', '0.046', 'usual_green', 'vegetation', 'green', 'green_growth', 'plant_big']
senticnet['green_glaze'] = ['-0.06', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.04', 'polish', 'magnify', 'see_out', 'sill', 'pane']
senticnet['green_glow'] = ['0', '-0.00', '-0.07', '-0.06', '#fear', '#disgust', 'negative', '-0.04', 'lighten', 'tornado', 'dynamite_explosion', 'forest_fire', 'lightning']
senticnet['green_goblin'] = ['-0.86', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.86', 'demon', 'fool', 'cannibalism', 'jolly', 'treachery']
senticnet['green_gold'] = ['-0.62', '0', '0.504', '-0.79', '#sadness', '#disgust', 'negative', '-0.64', 'gold', 'pure_gold', 'gold_dust', 'guinea_gold', 'noble_metal']
senticnet['green_goo'] = ['-0.09', '0', '-0.07', '-0.06', '#sadness', '#fear', 'negative', '-0.07', 'blow', 'gape', 'move_air', 'mud', 'gash']
senticnet['green_gram'] = ['0.026', '0.08', '0.043', '0.064', '#interest', '#admiration', 'positive', '0.032', 'eat_rice', 'white_brown', 'asian', 'eat_pasta', 'staple_food']
senticnet['green_grass'] = ['0.098', '0', '0', '0.055', '#joy', '#admiration', 'positive', '0.076', 'green', 'grass', 'vegetation', 'green_yard', 'grass_tree']
senticnet['green_growth'] = ['0', '0.071', '0.017', '0.084', '#interest', '#admiration', 'positive', '0.046', 'usual_green', 'vegetation', 'green', 'green_garden', 'plant_big']
senticnet['green_hue'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'tropical_fish', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['green_imperial_pigeon'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'lovebird', 'parroket', 'paraquet', 'paroquet']
senticnet['green_jacket'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'sports_equipment', 'sport_stuff', 'golf', 'sport_store', 'bag']
senticnet['green_leafy'] = ['0.904', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.907', 'eden', 'garden', 'usual_green', 'apple', 'leafy']
senticnet['green_leave'] = ['-0.05', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.06', 'vine', 'green_color', 'ivy', 'evergreen_tree', 'large_plant']
senticnet['green_oia_office'] = ['0', '0.967', '0', '0.957', '#interest', '#admiration', 'positive', '0.962', 'award', 'standard_grant', 'office', 'manager', 'title_renovation']
senticnet['green_orange'] = ['-0.06', '0.089', '0', '0.088', '#interest', '#admiration', 'positive', '0.039', 'yellow_green', 'red_orange', 'spectrum', 'red_green', 'black_green']
senticnet['green_papaya_salad'] = ['0.9', '0.963', '0', '0', '#joy', '#interest', 'positive', '0.931', 'form_of_food', 'japanese_food', 'uncooked', 'healthy', 'food']
senticnet['green_peppercorn_sauce'] = ['0.084', '-0.05', '0', '-0.04', '#joy', '#surprise', 'positive', '0.031', 'entree', 'feast', 'satiation', 'breakfast', 'supper']
senticnet['green_pigment'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['green_plant'] = ['0.427', '0.227', '0', '0.37', '#joy', '#admiration', 'positive', '0.341', 'forest', 'plant', 'jungle', 'grass', 'tree']
senticnet['green_red'] = ['0', '0.108', '0', '0.113', '#interest', '#admiration', 'positive', '0.111', 'color', 'visual', 'category', 'yellow_green', 'green_orange']
senticnet['green_roof'] = ['-0.12', '0.066', '-0.08', '-0.06', '#sadness', '#fear', 'negative', '-0.05', 'garden_yard', 'see_sky', 'plant_tree', 'hair', 'seed_lawn']
senticnet['green_salad'] = ['0.041', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.043', 'meatloaf', 'salad', 'delicious_meal', 'casserole', 'apple_pie']
senticnet['green_sea_turtle'] = ['0.96', '-0.77', '0', '0.929', '#joy', '#admiration', 'positive', '0.887', 'whale_louse', 'beach_flea', 'sand_hopper', 'sandhopper', 'amphipod']
senticnet['green_slime'] = ['-0.87', '0', '0.682', '-0.53', '#sadness', '#anger', 'negative', '-0.69', 'swash', 'spatter', 'slosh_around', 'plash', 'splash']
senticnet['green_stink_bug'] = ['-0.77', '0', '0.576', '-0.92', '#sadness', '#disgust', 'negative', '-0.75', 'insect', 'firebug', 'water_bug', 'assassin_bug', 'heteropterous_insect']
senticnet['green_stripe'] = ['0.942', '-0.61', '0', '0.908', '#joy', '#admiration', 'positive', '0.82', 'adornment', 'cigar_band', 'hatband', 'neckband', 'pinstripe']
senticnet['green_stuff'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'mow', 'grass', 'greenery', 'tree_flower', 'many_plants']
senticnet['green_sunfish'] = ['0', '-0.02', '0.044', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'haddock', 'mermaid', 'fish_rod', 'tidal_wave', 'dryad']
senticnet['green_sweater'] = ['0', '-0.08', '0.105', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'underwear', 'shirt', 'panties', 'robe', 'slack']
senticnet['green_tea'] = ['0.092', '0', '0', '0', '#joy', '#joy', 'positive', '0.092', 'coffee', 'tea', 'ice_tea', 'juice', 'part_of_dinner']
senticnet['green_tea_extract'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'wooden_spoon', 'cereal_bowl', 'teaspoon', 'container_cup', 'large_container']
senticnet['green_thumb'] = ['0.427', '0.227', '0', '0.37', '#joy', '#admiration', 'positive', '0.341', 'taken_seriously', 'complement', 'much_need', 'remission', 'knowledgeable']
senticnet['green_tree_frog'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'amphibian', 'aquatic']
senticnet['green_vegetable'] = ['0.175', '0.101', '0.035', '0.195', '#joy', '#admiration', 'positive', '0.109', 'leafy_green', 'rabbit_food', 'fruit_plant', 'vegetarian_food', 'lawn_green']
senticnet['green_winged_teal'] = ['-0.50', '0', '0.764', '-0.61', '#anger', '#disgust', 'negative', '-0.62', 'doctor', 'mountebank', 'charlatan', 'quack', 'physician']
senticnet['green_yard'] = ['0.098', '0', '0', '0.055', '#joy', '#admiration', 'positive', '0.076', 'green', 'grass', 'vegetation', 'green_grass', 'grass_tree']
senticnet['green_yellow'] = ['-0.06', '0.081', '0', '0.082', '#interest', '#admiration', 'positive', '0.034', 'yellow_green', 'red_orange', 'red_green', 'green_orange', 'red_blue']
senticnet['greenberg'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['greenbug'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'nuisance', 'annoyance', 'irritant', 'pain_in_ass', 'troublesome']
senticnet['greenery'] = ['0', '0.07', '0', '0.071', '#interest', '#admiration', 'positive', '0.071', 'tree_flower', 'bloom', 'perennial', 'poppy', 'blossom']
senticnet['greenford'] = ['0.623', '-0.56', '0', '0.974', '#joy', '#admiration', 'positive', '0.721', 'alterable', 'convertible', 'translatable', 'transformable', 'transmutable']
senticnet['greengrocer'] = ['0', '-0.76', '0', '0.705', '#surprise', '#admiration', 'positive', '0.734', 'fishmonger', 'grocer', 'shopkeeper', 'fruiterer', 'newsagent']
senticnet['greenhorn'] = ['-0.64', '0', '0.667', '-0.82', '#anger', '#disgust', 'negative', '-0.71', 'novice', 'beginner', 'tyro', 'tiro', 'initiate']
senticnet['greenhouse'] = ['-0.12', '0.066', '-0.08', '-0.06', '#sadness', '#fear', 'negative', '-0.05', 'garden_yard', 'see_sky', 'plant_tree', 'hair', 'seed_lawn']
senticnet['greenhouse_gas'] = ['-0.77', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.87', 'pollution', 'smog', 'unhealthy', 'bad_habit', 'lung_cancer']
senticnet['greenhouse_gas_carbon_dioxide'] = ['0.053', '0.264', '-0.11', '0.23', '#interest', '#admiration', 'positive', '0.107', 'natural_resource', 'petroleum', 'fossil_fuel', 'energy_source', 'potential_energy']
senticnet['greenhouse_gas_methane'] = ['0.053', '0.264', '-0.11', '0.23', '#interest', '#admiration', 'positive', '0.107', 'natural_resource', 'petroleum', 'fossil_fuel', 'energy_source', 'potential_energy']
senticnet['greenish-gold'] = ['0', '0', '0.834', '-0.71', '#anger', '#disgust', 'negative', '-0.77', 'blueish-green', 'golden-green', 'ruby-brown', 'orange-copper', 'deep-brown']
senticnet['greenish_blue'] = ['-0.02', '0.068', '0.04', '0.084', '#interest', '#admiration', 'positive', '0.022', 'peelable', 'green_black', 'purple', 'red', 'edible_human']
senticnet['greenland_shark'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'seal', 'otter', 'mussel', 'alligator', 'whale']
senticnet['greenness'] = ['-0.93', '0', '0.906', '-0.53', '#sadness', '#anger', 'negative', '-0.79', 'juvenility', 'state', 'immatureness', 'callowness', 'jejuneness']
senticnet['greenpeace'] = ['0', '0.734', '0', '0.893', '#interest', '#admiration', 'positive', '0.814', 'protection', 'offer_help', 'shield', 'protect_environment', 'activist']
senticnet['greensburg'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
senticnet['greenskeeper'] = ['-0.71', '0', '0.845', '-0.69', '#sadness', '#anger', 'negative', '-0.75', 'lump', 'goon', 'mortal', 'clumsy_person', 'butterfingers']
senticnet['greensward'] = ['-0.13', '0.146', '0.623', '-0.63', '#anger', '#disgust', 'negative', '-0.31', 'outraged', 'incensed', 'indignant', 'outrage', 'indignation']
senticnet['greenwich_village'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'condominium', 'rent_apartment', 'high_rise', 'condo', 'row_house']
senticnet['greenwood_avenue'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'boulevard', 'lane', 'thoroughfare', 'intersection', 'curb']
senticnet['greet'] = ['0.896', '0', '0', '0.755', '#joy', '#admiration', 'positive', '0.826', 'mutual_understand', 'greeting', 'communicate', 'hello', 'suggest']
senticnet['greet_card'] = ['0.93', '0', '0', '0', '#joy', '#joy', 'positive', '0.93', 'jingle', 'rhyme', 'song_lyric', 'refrain', 'musical_notation']
senticnet['greet_guest'] = ['-0.95', '0', '-0.73', '-0.73', '#sadness', '#fear', 'negative', '-0.80', 'alcoholism', 'hold_beer', 'become_intoxicated', 'intoxication', 'social_occasion']
senticnet['greet_person'] = ['-0.02', '0.093', '0.01', '0.084', '#interest', '#admiration', 'positive', '0.035', 'hail', 'diplomacy', 'salute', 'welcome', 'party']
senticnet['greeting'] = ['0.898', '-0.70', '0', '0.712', '#joy', '#admiration', 'positive', '0.773', 'wish', 'reception', 'welcome', 'hail', 'regard']
senticnet['gregariousness'] = ['-0.64', '0', '0.574', '-0.89', '#sadness', '#disgust', 'negative', '-0.70', 'sociability', 'ambiversion', 'openness', 'nakedness', 'extraversion']
senticnet['gregor_fisher'] = ['-0.89', '0', '0.58', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'resemblance', 'comparison', 'equivalence', 'reflection', 'alikeness']
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senticnet['grip_strength'] = ['-0.15', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.09', 'use_leg', 'walk_quickly', 'casual', 'take_step', 'strut']
senticnet['grip_tool'] = ['0', '0.103', '0', '0.111', '#interest', '#admiration', 'positive', '0.107', 'body_extremity', 'arm_part', 'extremity', 'human_appendage', 'appendage']
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senticnet['gripping_tale'] = ['-0.54', '0', '0.845', '-0.95', '#anger', '#disgust', 'negative', '-0.78', 'face', 'base', 'underside', 'sole', 'bilge']
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senticnet['gritted_teeth'] = ['-0.14', '0.152', '0.613', '-0.64', '#anger', '#disgust', 'negative', '-0.31', 'huffiness', 'huffily', 'offense', 'umbrage', 'umbrageous']
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senticnet['grittiest'] = ['0.881', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.937', 'granular', 'gamy', 'spirited', 'farinaceous', 'coarse-grained']
senticnet['grittily'] = ['0.881', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.937', 'granular', 'gamy', 'spirited', 'farinaceous', 'coarse-grained']
senticnet['gritty'] = ['0.881', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.937', 'granular', 'gamy', 'spirited', 'farinaceous', 'coarse-grained']
senticnet['gritty_drama'] = ['-0.54', '0', '0.845', '-0.95', '#anger', '#disgust', 'negative', '-0.78', 'face', 'base', 'underside', 'sole', 'bilge']
senticnet['gritty_realism'] = ['0.881', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.937', 'granular', 'gamy', 'spirited', 'farinaceous', 'coarse-grained']
senticnet['gritty_reality'] = ['-0.54', '0', '0.845', '-0.95', '#anger', '#disgust', 'negative', '-0.78', 'face', 'base', 'underside', 'sole', 'bilge']
senticnet['gritty_texture'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'wet', 'sticky', 'tacky', 'viscous', 'gooey']
senticnet['gritty_urban'] = ['-0.54', '0', '0.845', '-0.95', '#anger', '#disgust', 'negative', '-0.78', 'face', 'base', 'underside', 'sole', 'bilge']
senticnet['griz'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'unofficial', 'informal', 'loose', 'drumhead']
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senticnet['grizzled_hair'] = ['0.209', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.118', 'old_dance', 'dance', 'slow_dance', 'dancer', 'enter_contest']
senticnet['grizzled_old'] = ['-0.95', '0', '0.735', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'novice', 'beginner', 'cub', 'tiro', 'initiate']
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senticnet['grizzly_bear'] = ['0', '0', '-0.70', '-0.77', '#fear', '#disgust', 'negative', '-0.73', 'animal', 'brown_bear', 'forest', 'creature', 'polar_bear']
senticnet['grnes_gewlbe'] = ['0.02', '0.205', '0', '0', '#joy', '#interest', 'positive', '0.112', 'safe', 'bank_vault', 'bank', 'piggy_bank', 'bank_account']
senticnet['groan'] = ['0', '0', '-0.82', '-0.88', '#fear', '#disgust', 'negative', '-0.85', 'freak_out', 'loud_cry', 'loud_shout', 'squeak', 'clamor']
senticnet['grocer'] = ['0', '-0.76', '0', '0.697', '#surprise', '#admiration', 'positive', '0.73', 'fishmonger', 'greengrocer', 'shopkeeper', 'fruiterer', 'newsagent']
senticnet['grocer_shop'] = ['0.059', '0.212', '0', '0.143', '#interest', '#admiration', 'positive', '0.138', 'christmas_shop', 'go_grocery_shop', 'pay_credit_card', 'pay_cash', 'holiday_shop']
senticnet['groceries'] = ['0.03', '0.073', '0.027', '0.084', '#interest', '#admiration', 'positive', '0.04', 'grocery', 'store_vegetable', 'fresh_vegetable', 'vendor', 'fruit_vegetable']
senticnet['grocery'] = ['0.025', '-0.06', '0', '0.135', '#surprise', '#admiration', 'positive', '0.074', 'cold_drink', 'groceries', 'fresh_vegetable', 'vendor', 'goods']
senticnet['grocery_aisle'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'super_market', 'salad_bar', 'fruit_stand', 'candy_store', 'produce_store']
senticnet['grocery_bag'] = ['0', '0', '0', '0.93', '#admiration', '#admiration', 'positive', '0.93', 'yogurt', 'ferment_beverage', 'healthy_drink', 'ice_cream', 'healthy_snack']
senticnet['grocery_bill'] = ['0.03', '0.073', '0.027', '0.084', '#interest', '#admiration', 'positive', '0.04', 'grocery', 'store_vegetable', 'fresh_vegetable', 'vendor', 'fruit_vegetable']
senticnet['grocery_cart'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'super_market', 'salad_bar', 'fruit_stand', 'candy_store', 'produce_store']
senticnet['grocery_chain'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'apple_pie', 'lasagna', 'jelly', 'a_lot_of_food', 'cottage_cheese']
senticnet['grocery_list'] = ['0.03', '0.073', '0.027', '0.084', '#interest', '#admiration', 'positive', '0.04', 'grocery', 'store_vegetable', 'fresh_vegetable', 'vendor', 'fruit_vegetable']
senticnet['grocery_shop'] = ['0.059', '0.212', '0', '0.143', '#interest', '#admiration', 'positive', '0.138', 'christmas_shop', 'go_grocery_shop', 'pay_credit_card', 'pay_cash', 'holiday_shop']
senticnet['grocery_shopping'] = ['0.03', '0.073', '0.027', '0.084', '#interest', '#admiration', 'positive', '0.04', 'grocery', 'store_vegetable', 'fresh_vegetable', 'vendor', 'fruit_vegetable']
senticnet['grocery_store'] = ['0.02', '0', '0', '0.081', '#joy', '#admiration', 'positive', '0.051', 'city_country', 'supermarket', 'fast_food', 'restaurant', 'retail_store']
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senticnet['groin'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'skeletal_system', 'artery', 'scrotum', 'main_artery', 'diaphragm']
senticnet['groin_muscle'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'deltoid', 'muscle', 'abductor', 'skeletal_muscle']
senticnet['groin_problem'] = ['-0.69', '0', '0.768', '-0.67', '#sadness', '#anger', 'negative', '-0.71', 'dither', 'pother', 'worry', 'flap', 'scruple']
senticnet['grok'] = ['0.789', '-0.94', '0', '0.824', '#surprise', '#admiration', 'positive', '0.854', 'understand', 'twig', 'digest', 'tumble', 'intuit']
senticnet['grommet'] = ['0.775', '0.835', '0', '0', '#joy', '#interest', 'positive', '0.805', 'belt', 'leather_jacket', 'backpack', 'pouch', 'bag']
senticnet['groom'] = ['0.028', '-0.01', '0', '0.087', '#joy', '#admiration', 'positive', '0.043', 'improve_image', 'look_better', 'get_latest_hairstyle', 'change_appearance', 'dress_nice']
senticnet['groom_hair'] = ['-0.02', '0', '0.068', '0', '#sadness', '#anger', 'negative', '-0.04', 'make_hair_look_nice', 'comb_hair', 'fix_hair', 'arrange_hair', 'style_hair']
senticnet['groomed'] = ['0.888', '-0.68', '0', '0.914', '#joy', '#admiration', 'positive', '0.828', 'tidy', 'kempt', 'sleek', 'pomaded', 'brushed']
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senticnet['grooming'] = ['0.767', '-0.81', '0', '0.714', '#joy', '#surprise', 'positive', '0.764', 'practice', 'activity', 'discipline', 'athletic_training', 'drill']
senticnet['groove'] = ['0.082', '0', '0.073', '0.168', '#joy', '#admiration', 'positive', '0.059', 'instrumental', 'melodic_noise', 'piece_of_music', 'lyric', 'music_storage']
senticnet['groove_metal'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'explicit', 'declared', 'definitive', 'graphic']
senticnet['grooved_ware'] = ['0.099', '-0.07', '-0.08', '0.195', '#joy', '#admiration', 'positive', '0.071', 'art', 'exhibit', 'craft', 'fine_art', 'artwork']
senticnet['grope'] = ['0', '0', '0.888', '-0.71', '#anger', '#disgust', 'negative', '-0.80', 'groping', 'fondle', 'groped', 'scrabble', 'fondling']
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senticnet['groping'] = ['0', '0', '0.808', '-0.77', '#anger', '#disgust', 'negative', '-0.79', 'groped', 'fondling', 'grope', 'fumbling', 'caressing']
senticnet['grosgrain'] = ['0.936', '-0.91', '0', '0', '#joy', '#surprise', 'positive', '0.925', 'organza', 'tulle', 'voile', 'organdy', 'taffeta']
senticnet['grosgrain_ribbon'] = ['-0.06', '-0.01', '0.05', '-0.07', '#sadness', '#disgust', 'negative', '-0.04', 'embroidery', 'woven', 'cover_wind', 'torn', 'use_needle']
senticnet['gross'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'dirty', 'filthy', 'unclean', 'dingy', 'soiled']
senticnet['gross-out'] = ['0.819', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.839', 'slapstick', 'melodrama', 'comedy', 'farce', 'comedic']
senticnet['gross_dereliction'] = ['-0.93', '0', '0.624', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'carelessness', 'neglectfulness', 'laxness', 'laxity', 'remissness']
senticnet['gross_hematuria'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema', 'erythema_multiforme', 'windburn', 'hickey']
senticnet['gross_ignorance'] = ['0.983', '-0.63', '0', '0.697', '#joy', '#admiration', 'positive', '0.772', 'amateurishness', 'inability', 'ineptness', 'clumsiness', 'awkwardness']
senticnet['gross_immorality'] = ['-0.52', '0', '0.621', '-0.91', '#anger', '#disgust', 'negative', '-0.68', 'deviltry', 'devilry', 'irreverence', 'foul_play', 'iniquity']
senticnet['gross_incompetence'] = ['-0.61', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.69', 'irresponsibility', 'untrustiness', 'undependability', 'undependableness', 'unreliability']
senticnet['gross_indecency'] = ['0.542', '-0.83', '0', '0.927', '#surprise', '#admiration', 'positive', '0.769', 'incest', 'extramarital_sex', 'free_love', 'sexual_intercourse', 'intercourse']
senticnet['gross_injustice'] = ['0', '0', '-0.90', '-0.90', '#fear', '#disgust', 'negative', '-0.90', 'parody', 'farce', 'lampoon', 'farce_comedy', 'spoof']
senticnet['gross_misdemeanor'] = ['-0.84', '0', '0.745', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'crime', 'against_law', 'revenge', 'vengeance', 'theft']
senticnet['gross_monthly'] = ['0.687', '-0.98', '0', '0.666', '#joy', '#surprise', 'positive', '0.778', 'scale', 'scale_of_measurement', 'graduated_table', 'ordered_series', 'wage_scale']
senticnet['gross_neglect'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'negligence', 'evasion', 'dereliction', 'contributory_negligence']
senticnet['gross_negligence'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'negligence', 'evasion', 'dereliction', 'contributory_negligence']
senticnet['gross_out'] = ['-0.07', '0.139', '0.688', '-0.66', '#anger', '#disgust', 'negative', '-0.32', 'loathsome', 'churn_up', 'nauseate', 'sicken', 'repel']
senticnet['gross_output'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'indicator', 'retail_price_index', 'producer_price_index', 'wholesale_price_index', 'consumer_price_index']
senticnet['gross_salary'] = ['0.924', '0.968', '0', '0.876', '#joy', '#interest', 'positive', '0.923', 'live_simply', 'help_out', 'money', 'accolade', 'generous']
senticnet['gross_sensory'] = ['-0.66', '0', '0.789', '-0.63', '#sadness', '#anger', 'negative', '-0.69', 'humbug', 'play_trick', 'cozen', 'pull_fast_one_on', 'play_trick_on']
senticnet['grossest'] = ['0.768', '-0.86', '0', '0.67', '#joy', '#surprise', 'positive', '0.769', 'expiable', 'excusable', 'forgivable', 'venial', 'minor']
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senticnet['grossly_irresponsible'] = ['0', '0', '0.733', '-0.92', '#anger', '#disgust', 'negative', '-0.83', 'freewheeling', 'carefree', 'slaphappy', 'unaccountable', 'loose']
senticnet['grossly_negligent'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'negligence', 'evasion', 'dereliction', 'contributory_negligence']
senticnet['grossmith'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'dress', 'pinny', 'dirndl', 'gown', 'muumuu']
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senticnet['grotesque'] = ['-0.88', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.84', 'grotesqueness', 'form', 'style', 'troll', 'grotesquely']
senticnet['grotesquely'] = ['-0.89', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.84', 'grotesqueness', 'form', 'style', 'troll', 'grotesque']
senticnet['grotesqueness'] = ['-0.89', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.84', 'grotesque', 'form', 'style', 'troll', 'grotesquely']
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senticnet['ground-breaking'] = ['0.712', '0.913', '0', '0', '#joy', '#interest', 'positive', '0.813', 'innovativeness', 'trailblazing', 'groundbreaking', 'new-wave', 'innovative']
senticnet['ground_breaking_research'] = ['0.887', '0.877', '0', '0', '#joy', '#interest', 'positive', '0.882', 'ask_expert', 'absorb_information', 'analyze', 'learn_computer', 'search_information']
senticnet['ground_cinnamon'] = ['-0.95', '0', '0.845', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'grapefruit_peel', 'candied_citrus_peel', 'orange_peel', 'candied_fruit', 'succade']
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senticnet['ground_coriander'] = ['0.979', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.918', 'make_curry', 'cook_dinner', 'cook_meal', 'cook_curry', 'powder']
senticnet['ground_cover'] = ['0.082', '0', '0.073', '0.168', '#joy', '#admiration', 'positive', '0.059', 'soil', 'ground', 'vegetation', 'underground', 'ground_level']
senticnet['ground_cumin'] = ['0.1', '0', '0', '0', '#joy', '#joy', 'positive', '0.1', 'burn_material', 'ash', 'stay_warm', 'bonfire', 'campfire']
senticnet['ground_floor'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'top_floor', 'front_desk', 'concierge', 'exit', 'elevator']
senticnet['ground_floor_apartment'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'home', 'furniture', 'kitchen', 'flat']
senticnet['ground_floor_entrance'] = ['0.028', '0', '0', '0.051', '#joy', '#admiration', 'positive', '0.04', 'entranceway', 'doorway', 'entrance', 'entryway', 'corridor']
senticnet['ground_level'] = ['0.082', '0', '0.073', '0.168', '#joy', '#admiration', 'positive', '0.059', 'soil', 'ground', 'underground', 'floor', 'ground_cover']
senticnet['ground_material'] = ['-0.02', '-0.01', '0.071', '0', '#sadness', '#anger', 'negative', '-0.03', 'small_stone', 'small_rock', 'small_pebble', 'dirt', 'abrasive']
senticnet['ground_ocean'] = ['-0.02', '-0.01', '0.03', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'ocean_beach', 'beach', 'water_sea', 'shore', 'ocean_floor']
senticnet['ground_squirrel'] = ['0', '-0.97', '0', '0', '#surprise', '#surprise', 'positive', '0.977', 'cougar', 'skunk', 'lion', 'tiger', 'lynx']
senticnet['groundbreaking'] = ['0.735', '-0.83', '0', '0', '#joy', '#surprise', 'positive', '0.782', 'advanced', 'different', 'ceremony', 'innovative', 'new']
senticnet['groundbreaking_research'] = ['0.887', '0.877', '0', '0', '#joy', '#interest', 'positive', '0.882', 'ask_expert', 'absorb_information', 'analyze', 'learn_computer', 'search_information']
senticnet['groundcherry'] = ['-0.92', '0', '0.912', '-0.94', '#sadness', '#disgust', 'negative', '-0.92', 'true_blackberry', 'sand_blackberry', 'blackberry_bush', 'blackberry', 'running_blackberry']
senticnet['groundcover'] = ['-0.72', '0', '0.64', '-0.58', '#sadness', '#anger', 'negative', '-0.65', 'pearlwort', 'pearlweed', 'flora', 'rupturewort', 'whitlowwort']
senticnet['grounding_conductor'] = ['-0.09', '-0.01', '-0.10', '-0.20', '#fear', '#disgust', 'negative', '-0.09', 'violence', 'siege', 'conflict', 'bloodshed', 'fight']
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senticnet['groundwater'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'bottled_water', 'sugar_water']
senticnet['groundwood'] = ['0.024', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'stump', 'plank', 'timber', 'paper', 'mahogany']
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senticnet['group'] = ['0.065', '0.113', '0', '0.1', '#interest', '#admiration', 'positive', '0.093', 'big_group', 'clique', 'group_of_people', 'multitude', 'large_group']
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senticnet['group_discussion'] = ['-0.05', '0', '-0.10', '0', '#sadness', '#fear', 'negative', '-0.07', 'further_education', 'play_golf', 'study_group', 'teach_class', 'group_student']
senticnet['group_gathering'] = ['0', '0.811', '0', '0', '#interest', '#interest', 'positive', '0.811', 'meet_up', 'gather_person', 'public_gathering', 'group_of_people', 'gathering']
senticnet['group_lower_heterobranchia'] = ['0.096', '0.182', '-0.09', '0.198', '#interest', '#admiration', 'positive', '0.094', 'female_parent', 'relative', 'kin', 'relate_person', 'aunt_uncle']
senticnet['group_meet'] = ['0.045', '0.092', '-0.07', '0.14', '#interest', '#admiration', 'positive', '0.051', 'business_group', 'organization', 'organize_group', 'assembly', 'business_firm']
senticnet['group_of_people'] = ['0.124', '0', '0', '0.098', '#joy', '#admiration', 'positive', '0.111', 'team_up', 'group', 'social_group', 'mass_person', 'large_group']
senticnet['group_opisthobranchia'] = ['0.096', '0.182', '-0.09', '0.198', '#interest', '#admiration', 'positive', '0.094', 'female_parent', 'relative', 'kin', 'relate_person', 'aunt_uncle']
senticnet['group_sog'] = ['-0.07', '0.069', '-0.18', '0', '#sadness', '#fear', 'negative', '-0.06', 'medical_procedure', 'cure', 'doctor_surgery', 'treatment', 'doctor_work']
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senticnet['group_student'] = ['0', '0.98', '0', '0', '#interest', '#interest', 'positive', '0.98', 'team_work', 'student', 'college_course', 'university', 'class_lesson']
senticnet['group_swg'] = ['0.065', '0.113', '0', '0.1', '#interest', '#admiration', 'positive', '0.093', 'big_group', 'clique', 'group_of_people', 'multitude', 'large_group']
senticnet['group_together'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'needy', 'come_house', 'laugh_together', 'bridge_gap', 'journey_far']
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senticnet['grouped_together'] = ['-0.58', '0', '0.715', '-0.59', '#anger', '#disgust', 'negative', '-0.63', 'fly', 'flap', 'barndoor', 'coattail', 'codpiece']
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senticnet['groupsthe'] = ['0.065', '0.113', '0', '0.1', '#interest', '#admiration', 'positive', '0.093', 'big_group', 'clique', 'group_of_people', 'multitude', 'large_group']
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senticnet['grouse'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['grove'] = ['0', '0.093', '0', '0.078', '#interest', '#admiration', 'positive', '0.085', 'steep', 'landscape_feature', 'knoll', 'hill_mountain', 'incline']
senticnet['grovel'] = ['-0.79', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.89', 'shrink', 'cringe', 'creep', 'cower', 'hysterically']
senticnet['groveler'] = ['-0.55', '0', '0.588', '-0.65', '#anger', '#disgust', 'negative', '-0.59', 'apple_polisher', 'bootlicker', 'fawner', 'web', 'crawler']
senticnet['groveling'] = ['0.679', '-0.94', '0', '0.661', '#joy', '#surprise', 'positive', '0.762', 'bowing', 'cringing', 'submissive', 'sub', 'wormlike']
senticnet['grow'] = ['0', '0.229', '-0.09', '0.187', '#interest', '#admiration', 'positive', '0.106', 'increase', 'make_big', 'get_large', 'expansion', 'expand']
senticnet['grow_beard'] = ['0.05', '-0.02', '0', '0.111', '#joy', '#admiration', 'positive', '0.063', 'change_hair', 'makeover', 'transformation', 'dye_hair', 'get_haircut']
senticnet['grow_corn'] = ['-0.08', '-0.03', '0.048', '-0.02', '#sadness', '#anger', 'negative', '-0.03', 'flat_land', 'bumpy', 'farm_crop', 'stalk', 'cold_night']
senticnet['grow_crop'] = ['0', '0.066', '0.017', '0.057', '#interest', '#admiration', 'positive', '0.035', 'plant_crop', 'raise_animal', 'agriculture', 'grow_stuff', 'grow_food']
senticnet['grow_flower'] = ['0', '-0.09', '0.035', '0.1', '#surprise', '#admiration', 'positive', '0.054', 'dig_dirt', 'plant_flower', 'decorate_yard', 'plant_garden', 'cut_grass']
senticnet['grow_food'] = ['0', '0.078', '0', '0.069', '#interest', '#admiration', 'positive', '0.074', 'cultivation', 'grow_stuff', 'grow_crop', 'harvest_food', 'raise_crop']
senticnet['grow_garden'] = ['0', '0.078', '0', '0.069', '#interest', '#admiration', 'positive', '0.074', 'plant_tree', 'cut_grass', 'plant_flower', 'garden', 'flower']
senticnet['grow_healthy'] = ['0.755', '-0.71', '0', '0.972', '#joy', '#admiration', 'positive', '0.813', 'digest', 'digestion', 'grow_up', 'metabolism', 'healthy_lifestyle']
senticnet['grow_impatient'] = ['-0.12', '-0.08', '0.051', '-0.17', '#sadness', '#disgust', 'negative', '-0.06', 'talk_neighbor', 'think_dog', 'boredom', 'act_bored', 'stop_bleed']
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senticnet['grow_long'] = ['-0.04', '0.179', '-0.05', '0.287', '#interest', '#admiration', 'positive', '0.091', 'elasticity', 'elongate', 'widen', 'reach_out', 'extend']
senticnet['grow_old'] = ['0', '0.98', '0', '0', '#interest', '#interest', 'positive', '0.98', 'get_old', 'mature', 'man_spacecraft', 'blow_out_candle', 'grow_up']
senticnet['grow_older'] = ['0', '0', '0', '-0.84', '#disgust', '#disgust', 'negative', '-0.84', 'get_old', 'become_old', 'old', 'lose_energy', 'lose_health']
senticnet['grow_organic_garden'] = ['0', '-0.01', '0.065', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'stay_healthy', 'find_restaurant', 'bite_food', 'make_sandwich', 'obtain_food']
senticnet['grow_place'] = ['0', '0.089', '0.014', '0.086', '#interest', '#admiration', 'positive', '0.054', 'vegetable_grow', 'plant_ground', 'grow_pot', 'crop_place', 'grow_stuff']
senticnet['grow_plant'] = ['0', '0', '0', '0.074', '#admiration', '#admiration', 'positive', '0.074', 'grow_pot', 'soil', 'use_decoration', 'pot', 'make_tea']
senticnet['grow_pot'] = ['0', '0', '0', '0.074', '#admiration', '#admiration', 'positive', '0.074', 'grow_plant', 'soil', 'use_decoration', 'pot', 'make_tea']
senticnet['grow_richer'] = ['0.843', '0.977', '0', '0', '#joy', '#interest', 'positive', '0.91', 'business', 'finance', 'revenue', 'wealth', 'income']
senticnet['grow_stuff'] = ['0', '0.086', '0.025', '0.078', '#interest', '#admiration', 'positive', '0.046', 'harvest_food', 'cultivation', 'grow_food', 'grow_crop', 'agricultural']
senticnet['grow_tree'] = ['0.021', '0', '0', '0.081', '#joy', '#admiration', 'positive', '0.051', 'many_plants', 'tree_flower', 'greenery', 'flower_plant', 'vegetation']
senticnet['grow_up'] = ['0', '0.87', '0', '0', '#interest', '#interest', 'positive', '0.87', 'smartness', 'grow_old', 'good_news', 'learn_quickly', 'endless']
senticnet['grow_vegetable'] = ['-0.02', '0.073', '0.025', '0.054', '#interest', '#admiration', 'positive', '0.019', 'make_food', 'make_bread', 'follow_recipe', 'cook_curry', 'cook_meal']
senticnet['grow_weary'] = ['-0.12', '-0.08', '0.051', '-0.17', '#sadness', '#disgust', 'negative', '-0.06', 'talk_neighbor', 'think_dog', 'boredom', 'act_bored', 'stop_bleed']
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senticnet['groweth'] = ['0.969', '-0.86', '0', '0.631', '#joy', '#surprise', 'positive', '0.823', 'assuage', 'perfect', 'make_look_better', 'relieve', 'sublimate']
senticnet['growin'] = ['-0.53', '0', '0.508', '-0.74', '#sadness', '#disgust', 'negative', '-0.59', 'bedwetter', 'bed_wetter', 'mortal', 'wetter', 'person']
senticnet['growing'] = ['-0.59', '0', '0.983', '-0.67', '#anger', '#disgust', 'negative', '-0.75', 'growth', 'caenogenesis', 'maturation', 'development', 'ontogeny']
senticnet['growing_appetite'] = ['0', '0.285', '-0.07', '0.163', '#interest', '#admiration', 'positive', '0.124', 'plus', 'importance', 'a_lot', 'bonus', 'extra']
senticnet['growing_clout'] = ['-0.94', '0', '0.863', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'poke', 'knockout_punch', 'lick', 'punch', 'biff']
senticnet['growing_concern'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'problem', 'serious_issue', 'overrate', 'prohibitionist_society', 'blade_weapon']
senticnet['growing_demand'] = ['0.749', '0.764', '0', '0', '#joy', '#interest', 'positive', '0.756', 'obedience', 'order', 'requirement', 'permission', 'necessary']
senticnet['growing_discontent'] = ['-0.77', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.84', 'jumpiness', 'jitteriness', 'restive', 'nervy', 'jumpy']
senticnet['growing_disenchantment'] = ['-0.77', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.84', 'jumpiness', 'jitteriness', 'restive', 'nervy', 'jumpy']
senticnet['growing_impatience'] = ['0', '0', '0.941', '0', '#anger', '#anger', 'negative', '-0.94', 'embarrassment', 'nervous', 'shame', 'worry', 'angst']
senticnet['growing_impatient'] = ['-0.12', '-0.08', '0.051', '-0.17', '#sadness', '#disgust', 'negative', '-0.06', 'talk_neighbor', 'think_dog', 'boredom', 'act_bored', 'stop_bleed']
senticnet['growing_inequality'] = ['0.871', '-0.74', '0', '0.981', '#joy', '#admiration', 'positive', '0.867', 'unrighteousness', 'unjustness', 'unfairness', 'wrong', 'wrongfulness']
senticnet['growing_louder'] = ['0', '0', '0.802', '-0.75', '#anger', '#disgust', 'negative', '-0.78', 'cry', 'yell', 'scream', 'hollo', 'call']
senticnet['growing_menace'] = ['-0.94', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.88', 'combat', 'uprise', 'big_fight', 'opposition', 'violence']
senticnet['growing_popularity'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'important', 'universal', 'vip', 'prestige', 'status']
senticnet['growing_resentment'] = ['-0.77', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.84', 'jumpiness', 'jitteriness', 'restive', 'nervy', 'jumpy']
senticnet['growing_trend'] = ['0.05', '0.151', '0', '0.159', '#interest', '#admiration', 'positive', '0.12', 'glamour', 'stylish', 'style', 'clothes_style', 'fancy_clothes']
senticnet['growing_unrest'] = ['-0.77', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.84', 'jumpiness', 'jitteriness', 'restive', 'nervy', 'jumpy']
senticnet['growl'] = ['-0.08', '0', '-0.05', '-0.09', '#sadness', '#disgust', 'negative', '-0.07', 'hunt_game', 'cause_illness', 'crazy', 'bark', 'hunt_animal']
senticnet['growling'] = ['-0.81', '0', '0.737', '0', '#sadness', '#anger', 'negative', '-0.77', 'rumbling', 'croak', 'grudge', 'rumble', 'grumble']
senticnet['grown_up'] = ['0.086', '0.095', '-0.06', '0.066', '#joy', '#interest', 'positive', '0.045', 'married_couple', 'presidential_election', 'intelligent_person', 'use_binoculars', 'torpedo']
senticnet['grownup'] = ['0.724', '-0.70', '0', '0.571', '#joy', '#surprise', 'positive', '0.666', 'spinnable', 'susceptible', 'easy', 'pliant', 'plastic']
senticnet['grows_dim'] = ['0.54', '-0.64', '0', '0.728', '#surprise', '#admiration', 'positive', '0.638', 'gleaming', 'luster', 'polish', 'glow', 'burnish']
senticnet['growth'] = ['0.02', '0.192', '-0.14', '0.198', '#interest', '#admiration', 'positive', '0.068', 'get_big', 'get_large', 'grow', 'increase_size', 'flourish']
senticnet['growth_hormone_gh'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'gastrointestinal_hormone', 'secretin', 'ghrelin']
senticnet['growth_hormone_secretion'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'cholecystokinin', 'secretin', 'gastrointestinal_hormone']
senticnet['growth_retardation'] = ['0.874', '-0.73', '0', '0.608', '#joy', '#surprise', 'positive', '0.74', 'growth', 'growing', 'maturation', 'development', 'ontogeny']
senticnet['groyne'] = ['0.84', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.798', 'big_breadbox', 'water_vehicle', 'military_ship', 'ship', 'small_boat']
senticnet['grub'] = ['0.866', '-0.91', '0', '0.893', '#surprise', '#admiration', 'positive', '0.892', 'mooch', 'cadge', 'freeload', 'sponge', 'bum']
senticnet['grubber'] = ['0', '0', '-0.85', '-0.87', '#fear', '#disgust', 'negative', '-0.86', 'outsize', 'premium', 'unusual', 'unwontedly', 'unusually']
senticnet['grubbs_catalyst'] = ['0.918', '0.864', '0', '0', '#joy', '#interest', 'positive', '0.891', 'consequence', 'cause', 'bad_action', 'problem', 'political_statement']
senticnet['grubby'] = ['0', '0', '-0.76', '-0.75', '#fear', '#disgust', 'negative', '-0.76', 'raunchy', 'begrimed', 'dingy', 'grimy', 'dirty']
senticnet['grudge'] = ['0', '0', '0.901', '-0.92', '#anger', '#disgust', 'negative', '-0.91', 'animosity', 'jealous', 'bitterness', 'grudging', 'enviousness']
senticnet['grudge_against'] = ['0', '0', '0.901', '-0.92', '#anger', '#disgust', 'negative', '-0.91', 'animosity', 'jealous', 'bitterness', 'grudging', 'enviousness']
senticnet['grudges_against'] = ['0', '0', '0.901', '-0.92', '#anger', '#disgust', 'negative', '-0.91', 'animosity', 'jealous', 'bitterness', 'grudging', 'enviousness']
senticnet['grudging'] = ['0.067', '0.104', '0.791', '-0.67', '#anger', '#disgust', 'negative', '-0.32', 'enviousness', 'enviably', 'enviable', 'begrudge', 'sulkiness']
senticnet['grudging_respect'] = ['0.997', '0.796', '0', '0.89', '#joy', '#admiration', 'positive', '0.894', 'devotion', 'bravery', 'adoration', 'zeal', 'charm']
senticnet['grudgingly'] = ['-0.79', '0', '-0.70', '0', '#sadness', '#fear', 'negative', '-0.75', 'hesitance', 'hesitancy', 'hesitatingly', 'unassertive', 'unassertively']
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senticnet['gruenke'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['gruesome'] = ['0', '0', '0.766', '-0.99', '#anger', '#disgust', 'negative', '-0.88', 'grim', 'ghastly', 'macabre', 'sick', 'grisly']
senticnet['gruesome_death'] = ['-0.87', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.91', 'loss_oflife', 'chest_pain', 'die', 'tumor', 'end_life']
senticnet['gruesome_scene'] = ['-0.06', '0.09', '-0.14', '-0.15', '#fear', '#disgust', 'negative', '-0.06', 'casket', 'coffin', 'grave', 'headstone', 'bury_person']
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senticnet['gruff_exterior'] = ['-0.91', '0', '0.681', '-0.81', '#sadness', '#disgust', 'negative', '-0.80', 'chicken', 'doormat', 'softie', 'crybaby', 'wimp']
senticnet['gruff_voice'] = ['-0.93', '0', '0.576', '-0.54', '#sadness', '#anger', 'negative', '-0.68', 'grating', 'rasping', 'raspy', 'jarring', 'cacophonic']
senticnet['gruffly'] = ['-0.99', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.85', 'unenviable', 'abash', 'awkward', 'embarrassingly', 'embarrassing']
senticnet['grumble'] = ['-0.80', '0', '0.745', '0', '#sadness', '#anger', 'negative', '-0.77', 'rumbling', 'croak', 'grudge', 'rumble', 'growling']
senticnet['grump'] = ['-0.73', '0', '0.568', '-0.76', '#sadness', '#disgust', 'negative', '-0.69', 'hothead', 'unpleasant_person', 'disagreeable_person', 'crabby_person', 'crab']
senticnet['grumpily'] = ['-0.89', '0.168', '-0.60', '0.05', '#sadness', '#fear', 'negative', '-0.32', 'remorsefully', 'contritely', 'contrition', 'contriteness', 'attrition']
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senticnet['grumpy_old'] = ['-0.71', '0', '0.845', '-0.69', '#sadness', '#anger', 'negative', '-0.75', 'lump', 'goon', 'mortal', 'clumsy_person', 'butterfingers']
senticnet['grumpy_old_man'] = ['-0.95', '0', '0.673', '-0.95', '#sadness', '#disgust', 'negative', '-0.86', 'worrier', 'fusspot', 'worrywart', 'unwelcome_person', 'killjoy']
senticnet['grundgetta'] = ['-0.93', '0', '0.77', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'hothead', 'unpleasant_person', 'disagreeable_person', 'crabby_person', 'crab']
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senticnet['grungy'] = ['0', '0', '0.943', '-0.8', '#anger', '#disgust', 'negative', '-0.87', 'fight_fire', 'soapy', 'feel_dirty', 'smell_clean', 'filthy']
senticnet['grunt'] = ['-0.08', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.07', 'bicycle_store', 'enter_door', 'cost_a_lot_of_money', 'open', 'hang_door']
senticnet['grunt_work'] = ['0.76', '0.919', '0', '0', '#joy', '#interest', 'positive', '0.84', 'chore', 'make_happen', 'pick_up_trash', 'intuition', 'stop_procrastinate']
senticnet['gruntle'] = ['0.984', '-0.89', '0', '0.881', '#joy', '#surprise', 'positive', '0.919', 'tranquilize', 'tranquillize', 'calm_down', 'quieten', 'calm']
senticnet['gruumsh'] = ['0.826', '-0.65', '0', '0.587', '#joy', '#surprise', 'positive', '0.689', 'scoffer', 'vegetarian', 'eater', 'diner', 'dunker']
senticnet['gsce'] = ['-0.04', '0.097', '0.06', '0.146', '#interest', '#admiration', 'positive', '0.036', 'vector', 'ratio', 'length_measurement', 'measurement_unit', 'unit_of_measurement']
senticnet['gsla_saga'] = ['0.036', '0.219', '0', '0.19', '#interest', '#admiration', 'positive', '0.148', 'story', 'long_story', 'novel', 'fable', 'fiction']
senticnet['gu_bheil_iad'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['gu_mccrae'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'common_bean', 'green_bean', 'wax_bean']
senticnet['gua_boa'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'head_hurt', 'introspection', 'lifestyle_change', 'headache', 'move_eye']
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senticnet['guinea_gold'] = ['-0.62', '0', '0.504', '-0.79', '#sadness', '#disgust', 'negative', '-0.64', 'green_gold', 'pure_gold', 'gold_dust', 'gold', 'noble_metal']
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senticnet['guisarme'] = ['0.93', '-0.78', '0', '0.873', '#joy', '#admiration', 'positive', '0.861', 'blade', 'brand', 'steel', 'claymore', 'sword']
senticnet['guise'] = ['-0.98', '0', '0.537', '-0.72', '#sadness', '#disgust', 'negative', '-0.74', 'deceit', 'deception', 'dissembling', 'affectedness', 'pose']
senticnet['guitar'] = ['0.049', '0.167', '-0.05', '0.295', '#interest', '#admiration', 'positive', '0.115', 'four_sides', 'sound_system', 'dolby_surround', 'movie', 'surround']
senticnet['guitar_amp'] = ['0.036', '-0.04', '0.027', '0.063', '#surprise', '#admiration', 'positive', '0.028', 'radio', 'speaker', 'channel_selector', 'power_switch', 'female_television_connector']
senticnet['guitar_amplifier'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'lever']
senticnet['guitar_duo'] = ['0.899', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.813', 'concert', 'measure_angle', 'make_drawing', 'make_beautiful_music', 'listen_heart']
senticnet['guitar_lick'] = ['0.857', '-0.77', '0', '0.878', '#joy', '#admiration', 'positive', '0.836', 'hand', 'round', 'approval', 'commendation', 'clapping']
senticnet['guitar_player'] = ['0.032', '0.092', '0', '0.075', '#interest', '#admiration', 'positive', '0.066', 'rock_group', 'music_player', 'make_musicr', 'musical_group', 'eclectic']
senticnet['guitar_string'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'music_sound', 'musical_sound', 'piece_of_music', 'musical', 'music_note']
senticnet['guitarfish'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'plectognath', 'plectognathfish', 'blowfish', 'triggerfish', 'filefish']
senticnet['guitarist'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
senticnet['guitarist_bassist'] = ['0', '-0.02', '0.055', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'djembe', 'make_musicr', 'use_jazz_music', 'tune_instrument', 'percussion']
senticnet['guitarist_singer'] = ['0', '-0.02', '0.055', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'djembe', 'make_musicr', 'use_jazz_music', 'tune_instrument', 'percussion']
senticnet['guitarist_vocalist'] = ['0.049', '0.167', '-0.05', '0.295', '#interest', '#admiration', 'positive', '0.115', 'four_sides', 'sound_system', 'dolby_surround', 'movie', 'surround']
senticnet['gulch'] = ['0', '0.069', '0', '0.065', '#interest', '#admiration', 'positive', '0.067', 'alp', 'high_mountain', 'pass', 'ski_resort', 'high_place']
senticnet['gulf'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'harpoon', 'armory', 'lake', 'river', 'bay']
senticnet['gulf_coast'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'harpoon', 'armory', 'lake', 'river', 'bay']
senticnet['gulf_intracoastal_waterway'] = ['0.933', '0.795', '0', '0', '#joy', '#interest', 'positive', '0.864', 'water_passage', 'small_stream', 'water_flow', 'flowing_water', 'water_stream']
senticnet['gulf_region'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'harpoon', 'armory', 'lake', 'river', 'bay']
senticnet['gulf_shrimp'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'crab', 'swimming_crab', 'rock_crab', 'fiddler_crab', 'pea_crab']
senticnet['gulf_stream'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'pacific_ocean', 'small_lake', 'mere']
senticnet['gulfport'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
senticnet['gulika'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
senticnet['gull'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'amoeba', 'single_celled']
senticnet['gull_bst'] = ['0.028', '0', '0.027', '0.101', '#joy', '#admiration', 'positive', '0.034', 'animal_dwell', 'bee_home', 'live_place', 'dwell_place', 'place_to_live']
senticnet['gullet'] = ['-0.99', '0', '0.871', '-0.55', '#sadness', '#anger', 'negative', '-0.80', 'endure', 'brook', 'tolerate', 'bear_up', 'abide']
senticnet['gullibility'] = ['0', '0', '-0.73', '-0.80', '#fear', '#disgust', 'negative', '-0.77', 'self-delusion', 'delusion', 'deceit', 'irrationality', 'self-deception']
senticnet['gullible'] = ['0', '0', '0.957', '-0.85', '#anger', '#disgust', 'negative', '-0.90', 'ingenuous', 'naive', 'artless', 'childlike', 'guileless']
senticnet['gully_erosion'] = ['0.614', '-0.95', '0', '0.584', '#joy', '#surprise', 'positive', '0.718', 'mother_lode', 'champion_lode', 'deposit', 'sedimentation', 'lode']
senticnet['gulp'] = ['0.102', '0.084', '0', '0.076', '#joy', '#interest', 'positive', '0.087', 'better_cold', 'sip', 'potable_liquid', 'ingest', 'liquid_beverage']
senticnet['gulp_air'] = ['-0.93', '0', '0.607', '-0.67', '#sadness', '#disgust', 'negative', '-0.73', 'spiracle', 'stigma', 'orifice', 'opening', 'porta']
senticnet['gulp_down'] = ['-0.71', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.77', 'spill_milk', 'read_paper', 'drop_food', 'smell_food', 'choke']
senticnet['gulped_down'] = ['0.866', '-0.91', '0', '0.893', '#surprise', '#admiration', 'positive', '0.892', 'mooch', 'freeload', 'grub', 'sponge', 'bum']
senticnet['gulping'] = ['-0.72', '0', '0.561', '-0.68', '#sadness', '#disgust', 'negative', '-0.65', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['gulping_down'] = ['-0.71', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.77', 'spill_milk', 'read_paper', 'drop_food', 'smell_food', 'choke']
senticnet['gulps_down'] = ['0.102', '0.084', '0', '0.076', '#joy', '#interest', 'positive', '0.087', 'better_cold', 'sip', 'potable_liquid', 'ingest', 'liquid_beverage']
senticnet['gum'] = ['0.965', '0.775', '0', '0.968', '#joy', '#admiration', 'positive', '0.903', 'snot', 'bad_taste', 'mouth_part', 'beautiful_smile', 'near_mouth']
senticnet['gum_acacia'] = ['0.965', '0.775', '0', '0.968', '#joy', '#admiration', 'positive', '0.903', 'snot', 'bad_taste', 'mouth_part', 'beautiful_smile', 'near_mouth']
senticnet['gum_disease'] = ['0', '0', '0.813', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'ice_cream_melt', 'allergy', 'toothache', 'gonorrhea', 'tastebud']
senticnet['gum_inflammation'] = ['0', '0', '0.813', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'ice_cream_melt', 'allergy', 'toothache', 'gonorrhea', 'tastebud']
senticnet['gum_tissue'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'plaque']
senticnet['gumball_machine'] = ['0', '0', '0', '-0.06', '#disgust', '#disgust', 'negative', '-0.06', 'newsstand', 'bookshop', 'walmart', 'shoe_store', 'hooker']
senticnet['gumbo_limbo_bursera_simaruba'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'carnauba_palm', 'carnauba', 'wax_palm', 'palmetto', 'palmyra']
senticnet['gumdrop'] = ['0.027', '-0.09', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'cookie', 'pudding', 'custard', 'candy', 'muffin']
senticnet['gummosis'] = ['-0.83', '0', '0.754', '-0.97', '#sadness', '#disgust', 'negative', '-0.85', 'brown_rot_brown_rot', 'ring_rot', 'ring_disease', 'tobacco_wilt', 'brown_rot']
senticnet['gummy'] = ['0', '0', '-0.75', '-0.87', '#fear', '#disgust', 'negative', '-0.81', 'gluey', 'pasty', 'sticky', 'mucilaginous', 'glutinous']
senticnet['gummy_bear'] = ['0.023', '0.141', '-0.13', '0.174', '#interest', '#admiration', 'positive', '0.052', 'candy', 'push_wind', 'exhalation', 'breaout', 'mouth_action']
senticnet['gummy_food'] = ['0', '0', '0.753', '-0.73', '#anger', '#disgust', 'negative', '-0.74', 'chewy', 'bad_food', 'hard_to_chew', 'unpleasant', 'tough']
senticnet['gummy_stem_blight'] = ['-0.83', '0', '0.754', '-0.97', '#sadness', '#disgust', 'negative', '-0.85', 'brown_rot_gummosis', 'ring_rot', 'ring_disease', 'tobacco_wilt', 'gummosis']
senticnet['gummy_substance'] = ['0.025', '-0.00', '0', '0.125', '#joy', '#admiration', 'positive', '0.053', 'goo', 'adhesive', 'flour_water', 'insert', 'thick_liquid']
senticnet['gumption'] = ['0.605', '-0.93', '0', '0.742', '#surprise', '#admiration', 'positive', '0.761', 'road_sense', 'sagacity', 'sagaciousness', 'logic', 'nous']
senticnet['gumshoe'] = ['-0.88', '0', '0.75', '-0.85', '#sadness', '#disgust', 'negative', '-0.82', 'rubber', 'galosh', 'golosh', 'arctic', 'overshoe']
senticnet['gun'] = ['0', '0', '0.729', '-0.84', '#anger', '#disgust', 'negative', '-0.78', 'rifle', 'pistol', 'revolver', 'hydrogen_bomb', 'firearm']
senticnet['gun_barrel'] = ['-0.91', '0', '0.585', '-0.93', '#sadness', '#disgust', 'negative', '-0.81', 'bullet', 'gun', 'rifle', 'shoot', 'pistol']
senticnet['gun_brig'] = ['0', '0', '0.08', '0', '#anger', '#anger', 'negative', '-0.08', 'tree_animal', 'small_furry', 'rodent', 'big_mouse', 'small_rodent']
senticnet['gun_carriage'] = ['-0.86', '0', '0.83', '0', '#sadness', '#anger', 'negative', '-0.84', 'framework', 'clotheshorse', 'undercarriage', 'frame', 'wattle']
senticnet['gun_control'] = ['0', '0.847', '0', '0.851', '#interest', '#admiration', 'positive', '0.849', 'safety', 'prevent_massacre', 'solve_problem', 'safety_first', 'safe_city']
senticnet['gun_crime'] = ['-0.47', '0.174', '-0.44', '0.105', '#sadness', '#fear', 'negative', '-0.16', 'wrong', 'wrongdoing', 'bad_choice', 'harmful_human', 'bad_behavior']
senticnet['gun_holster'] = ['-0.53', '0', '0.682', '-0.74', '#anger', '#disgust', 'negative', '-0.65', 'girdle', 'cincture', 'sash', 'cummerbund', 'waistcloth']
senticnet['gun_knife'] = ['0', '0', '0.805', '-0.83', '#anger', '#disgust', 'negative', '-0.81', 'weapon', 'atomic_bomb', 'hydrogen_bomb', 'shotgun', 'dangerous_weapon']
senticnet['gun_moll'] = ['0.909', '-0.94', '0', '0.992', '#surprise', '#admiration', 'positive', '0.948', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['gun_nut'] = ['-0.64', '0', '0.781', '-0.74', '#anger', '#disgust', 'negative', '-0.72', 'weirdo', 'crazy', 'loony', 'lunatic', 'nutcase']
senticnet['gun_opened_fire'] = ['0.957', '-0.93', '0', '0.795', '#joy', '#surprise', 'positive', '0.896', 'outdo', 'outstrip', 'outmatch', 'outgo', 'outperform']
senticnet['gun_shop'] = ['-0.04', '-0.00', '-0.06', '-0.21', '#fear', '#disgust', 'negative', '-0.07', 'holster', 'armory', 'gun_store', 'arsenal', 'war_zone']
senticnet['gun_store'] = ['-0.04', '0', '-0.09', '-0.20', '#fear', '#disgust', 'negative', '-0.11', 'holster', 'arsenal', 'gun_shop', 'war', 'war_zone']
senticnet['gun_subject'] = ['-0.85', '0', '-0.98', '0', '#sadness', '#fear', 'negative', '-0.92', 'danger', 'gun', 'use_gun', 'kill', 'massacre']
senticnet['gun_tid'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['gun_totin'] = ['-0.64', '0', '0.781', '-0.74', '#anger', '#disgust', 'negative', '-0.72', 'weirdo', 'crazy', 'loony', 'lunatic', 'nutcase']
senticnet['gun_violence'] = ['-0.47', '0.174', '-0.44', '0.105', '#sadness', '#fear', 'negative', '-0.16', 'wrong', 'wrongdoing', 'bad_choice', 'harmful_human', 'bad_behavior']
senticnet['gunai_language'] = ['-0.95', '0', '0.624', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['gunboat'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'submarine', 'lifeboat', 'titanic', 'large_ship', 'canoe']
senticnet['gunboat_diplomacy'] = ['0', '0.098', '0', '0.08', '#interest', '#admiration', 'positive', '0.089', 'talk_person', 'wisdom', 'tact', 'statecraft', 'greet_person']
senticnet['guncotton'] = ['-0.88', '0', '0.643', '-0.61', '#sadness', '#anger', 'negative', '-0.71', 'cellulose_ester', 'pyroxylin', 'pyroxyline', 'pyrocellulose', 'nitrate']
senticnet['gundestrup_cauldron'] = ['0.099', '-0.07', '-0.08', '0.195', '#joy', '#admiration', 'positive', '0.071', 'art', 'exhibit', 'craft', 'fine_art', 'artwork']
senticnet['gunfight'] = ['0', '-0.01', '-0.08', '-0.16', '#fear', '#disgust', 'negative', '-0.07', 'siege', 'big_fight', 'battle', 'declare_war', 'conflict']
senticnet['gunfighter'] = ['-0.83', '0', '0.831', '-0.69', '#sadness', '#anger', 'negative', '-0.78', 'toughie', 'accessory', 'highbinder', 'hoodlum', 'thug']
senticnet['gunfire'] = ['0', '0', '0.97', '-0.70', '#anger', '#disgust', 'negative', '-0.83', 'automatic', 'atomic_bomb', 'hydrogen_bomb', 'shotgun', 'nuclear_weapon']
senticnet['gung_ho'] = ['-0.52', '0', '0.95', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'skeptic', 'sceptic', 'doubter', 'pessimist', 'negativist']
senticnet['gunge'] = ['0.036', '0.084', '0', '0', '#joy', '#interest', 'positive', '0.06', 'toy', 'bath_toy', 'yo-yo', 'toy_ball', 'duck']
senticnet['gunk'] = ['0.071', '0', '-0.08', '-0.09', '#fear', '#disgust', 'negative', '-0.03', 'acid', 'rotten_food', 'sour_cream', 'glass_of_milk', 'lemon_juice']
senticnet['gunman'] = ['-0.86', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.79', 'gun', 'firearm', 'handgun', 'rifle', 'pistol']
senticnet['gunman_fired'] = ['0', '0', '-0.04', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'good_band', 'band', 'kit', 'procession', 'oblivion']
senticnet['gunna'] = ['0.104', '0', '0', '0.088', '#joy', '#admiration', 'positive', '0.096', 'baby_flower', 'wee', 'bud', 'part_of_plant', 'leafy']
senticnet['gunner'] = ['0', '0.911', '0', '0.834', '#interest', '#admiration', 'positive', '0.873', 'war', 'turret', 'cannon', 'gantry', 'tower']
senticnet['gunner_fired'] = ['0.957', '-0.93', '0', '0.795', '#joy', '#surprise', 'positive', '0.896', 'outdo', 'outstrip', 'outmatch', 'outgo', 'outperform']
senticnet['gunnery_range'] = ['-0.97', '0', '0.737', '-0.97', '#sadness', '#disgust', 'negative', '-0.89', 'flare_path', 'airstrip', 'take_off', 'runway', 'grassland']
senticnet['gunny'] = ['0.572', '-0.71', '0', '0.895', '#surprise', '#admiration', 'positive', '0.728', 'material', 'burlap', 'sacking', 'fabric', 'cloth']
senticnet['gunpowder'] = ['0', '0', '0', '-0.10', '#disgust', '#disgust', 'negative', '-0.10', 'piece_of_metal', 'ammunition', 'badge', 'bullet', 'projectile']
senticnet['gunrunner'] = ['-0.87', '0', '0.574', '-0.73', '#sadness', '#disgust', 'negative', '-0.72', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['gunshot'] = ['-0.08', '-0.02', '0', '-0.19', '#sadness', '#disgust', 'negative', '-0.08', 'wound', 'lethal', 'vaccination', 'destruction', 'fire_bullet']
senticnet['gunshot_wound'] = ['-0.52', '0', '0.785', '-0.61', '#anger', '#disgust', 'negative', '-0.64', 'gap', 'rathole', 'aperture', 'hole', 'cranny']
senticnet['gunslinger'] = ['0', '0', '-0.06', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'elbow', 'arm', 'wrist', 'five_fingers', 'bendable']
senticnet['gunsmith'] = ['0', '0.115', '0', '0.105', '#interest', '#admiration', 'positive', '0.11', 'farrier', 'miner', 'horseshoer', 'blacksmith', 'smith']
senticnet['gunsynd'] = ['0.744', '-0.77', '0', '0.661', '#joy', '#surprise', 'positive', '0.725', 'sugarcoat', 'honey', 'glaze', 'change_taste', 'sugar']
senticnet['gunwale'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'propeller', 'hull', 'cleat', 'boat_part', 'stern']
senticnet['guosim'] = ['0', '0', '0.08', '0', '#anger', '#anger', 'negative', '-0.08', 'tree_animal', 'small_furry', 'rodent', 'big_mouse', 'small_rodent']
senticnet['gupta_dynasty'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
senticnet['gurgle'] = ['0.102', '0.084', '0', '0.076', '#joy', '#interest', 'positive', '0.087', 'better_cold', 'sip', 'potable_liquid', 'ingest', 'liquid_beverage']
senticnet['gurk'] = ['0', '0', '-0.86', '-0.78', '#fear', '#disgust', 'negative', '-0.82', 'acid_indigestion', 'burp', 'belch', 'hunger_go_away', 'choke']
senticnet['gurl'] = ['0.104', '0', '0', '0.088', '#joy', '#admiration', 'positive', '0.096', 'baby_flower', 'wee', 'bud', 'part_of_plant', 'leafy']
senticnet['guru'] = ['0.109', '0.129', '0', '0.098', '#joy', '#interest', 'positive', '0.112', 'good_view', 'summit', 'great_view', 'mountain_climber', 'powerful_man']
senticnet['guru_rinpoche'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['gush'] = ['0.86', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.922', 'doughnut', 'mousse', 'biscuit', 'custard', 'cookie']
senticnet['gushing'] = ['-0.99', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.86', 'violently', 'precipitate', 'shatter', 'spit', 'violent']
senticnet['gushy'] = ['0', '-0.83', '0', '0.918', '#surprise', '#admiration', 'positive', '0.875', 'emotional', 'excessive', 'unrestrained', 'effusive', 'maudlin']
senticnet['gusset'] = ['0.775', '0.835', '0', '0', '#joy', '#interest', 'positive', '0.805', 'belt', 'leather_jacket', 'backpack', 'pouch', 'bag']
senticnet['gussied_up'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['gust'] = ['0.957', '-0.57', '0', '0.857', '#joy', '#admiration', 'positive', '0.797', 'bluster', 'puff', 'sandblast', 'puff_of_air', 'whiff']
senticnet['gust_front'] = ['0', '-0.41', '-0.95', '0', '#surprise', '#fear', 'negative', '-0.26', 'blizzard', 'snow_storm', 'storm', 'danger', 'rainy']
senticnet['gustatory'] = ['0.732', '0.771', '0', '0', '#joy', '#interest', 'positive', '0.752', 'food', 'put_mouth', 'tastebud', 'sour_taste', 'tongue']
senticnet['gustatory_perception'] = ['-0.93', '0', '0.537', '-0.57', '#sadness', '#disgust', 'negative', '-0.68', 'taste', 'taste_sensation', 'gustatory_sensation', 'taste_perception', 'astringence']
senticnet['gustatory_sensation'] = ['-0.93', '0', '0.537', '-0.57', '#sadness', '#disgust', 'negative', '-0.68', 'taste', 'taste_sensation', 'astringence', 'taste_perception', 'gustatory_perception']
senticnet['gusto'] = ['0.508', '0.659', '-0.25', '0.253', '#joy', '#interest', 'positive', '0.291', 'zestfulness', 'exuberantly', 'riotously', 'expansively', 'ebulliently']
senticnet['gusty'] = ['-0.85', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.89', 'blustery', 'squally', 'showery', 'squall', 'gale-force']
senticnet['gusty_wind'] = ['-0.87', '0', '0.91', '-0.78', '#sadness', '#anger', 'negative', '-0.85', 'storm', 'rainstorm', 'rain', 'line_storm', 'equinoctial_storm']
senticnet['gut'] = ['0', '0.954', '0', '0.951', '#interest', '#admiration', 'positive', '0.952', 'bowel', 'intestine', 'catgut', 'examine', 'way']
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senticnet['hackney_coach'] = ['-0.71', '0', '0.906', '-0.93', '#anger', '#disgust', 'negative', '-0.85', 'absent', 'forgetful', 'distracted', 'inattentive', 'scatty']
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senticnet['hacksaw_blade'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'pair_of_scissors', 'c_clamp', 'tool_box', 'salad_shaker', 'ladle']
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senticnet['haddock'] = ['0', '-0.02', '0.026', '-0.08', '#anger', '#disgust', 'negative', '-0.02', 'dryad', 'pier', 'canoe', 'shallow', 'find_sea']
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senticnet['haematite'] = ['-0.03', '0', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.06', 'lithium', 'mineral', 'potassium', 'iodine', 'magnesium']
senticnet['haematologist'] = ['0.738', '-0.53', '0', '0.856', '#joy', '#admiration', 'positive', '0.71', 'doctor', 'country_doctor', 'family_doctor', 'doctor_office', 'physician']
senticnet['haematoma'] = ['-0.69', '0', '0.517', '-0.61', '#sadness', '#disgust', 'negative', '-0.61', 'hematoma', 'intumescence', 'puffiness', 'swelling', 'lump']
senticnet['haematoxylin'] = ['0', '-0.01', '0.046', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'varnish', 'brush', 'dye', 'pigment', 'canvas']
senticnet['haemoglobin_level'] = ['0.79', '-0.62', '0', '0.674', '#joy', '#admiration', 'positive', '0.695', 'add', 'contribute', 'lend', 'breathe', 'impart']
senticnet['haemophilia'] = ['-0.57', '0', '0.973', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['haemorrhagic'] = ['-0.69', '0', '0.852', '-0.69', '#sadness', '#anger', 'negative', '-0.74', 'conjunctivitis', 'ophthalmitis', 'rubor', 'redness', 'inflammation']
senticnet['haemorrhagic_fever'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'leptomeningitis', 'meningitis', 'epidemic_meningitis', 'brain_fever', 'cerebrospinal_fever']
senticnet['haemulidae'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'plectognath', 'plectognathfish', 'blowfish', 'triggerfish', 'filefish']
senticnet['haemulon'] = ['0.96', '-0.77', '0', '0.929', '#joy', '#admiration', 'positive', '0.887', 'whale_louse', 'beach_flea', 'sand_hopper', 'sandhopper', 'amphipod']
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senticnet['hag'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'jawless_fish', 'osteostracan', 'cephalaspid', 'ostracoderm', 'heterostracan']
senticnet['haggard'] = ['0', '0', '0.902', '-0.73', '#anger', '#disgust', 'negative', '-0.81', 'bird', 'emaciated', 'wild', 'wasted', 'haggardly']
senticnet['haggardly'] = ['0', '0', '0.896', '-0.73', '#anger', '#disgust', 'negative', '-0.81', 'bird', 'emaciated', 'wild', 'wasted', 'haggard']
senticnet['haggle'] = ['0', '0', '0.704', '-0.85', '#anger', '#disgust', 'negative', '-0.77', 'seller', 'haggler', 'hack', 'price', 'huckster']
senticnet['haggle_over'] = ['-0.67', '0', '0.838', '-0.73', '#anger', '#disgust', 'negative', '-0.75', 'altercation', 'fracas', 'difference', 'affray', 'bickering']
senticnet['haggler'] = ['0', '0', '0.694', '-0.85', '#anger', '#disgust', 'negative', '-0.77', 'seller', 'haggle', 'hack', 'price', 'huckster']
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senticnet['hagiographer'] = ['0', '0', '-0.90', '-0.79', '#fear', '#disgust', 'negative', '-0.85', 'encomiastic', 'encomium', 'paean', 'eulogy', 'pean']
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senticnet['hagiography'] = ['0.701', '0.939', '0', '0.718', '#interest', '#admiration', 'positive', '0.786', 'literature', 'bed_time_story', 'book_story', 'fiction', 'life_story']
senticnet['haha'] = ['-0.05', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.05', 'masturbate', 'jerk', 'jerk_off', 'watch_porn', 'porn']
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senticnet['hahahahaha'] = ['0.104', '0', '0', '0.088', '#joy', '#admiration', 'positive', '0.096', 'baby_flower', 'wee', 'bud', 'part_of_plant', 'leafy']
senticnet['hahnville'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
senticnet['haider_ackermann'] = ['0.735', '-0.54', '0', '0.897', '#joy', '#admiration', 'positive', '0.727', 'net', 'network', 'mesh', 'meshing', 'meshwork']
senticnet['haight_street'] = ['0.165', '-0.07', '0.033', '0', '#joy', '#surprise', 'positive', '0.068', 'metal_stick', 'drinking_place', 'bart', 'meet_place', 'water_hole']
senticnet['hail'] = ['0.898', '-0.70', '0', '0.712', '#joy', '#admiration', 'positive', '0.773', 'wish', 'reception', 'welcome', 'greeting', 'regard']
senticnet['hail_damage'] = ['-0.98', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.97', 'harm', 'hurt', 'cut_skin', 'bloody', 'ouch']
senticnet['hail_from'] = ['0.706', '0', '0', '0', '#joy', '#joy', 'positive', '0.706', 'birth', 'new_baby', 'mother_womb', 'birthday', 'thanksgiving']
senticnet['hail_storm'] = ['-0.87', '0', '0.91', '-0.78', '#sadness', '#anger', 'negative', '-0.85', 'storm', 'thundershower', 'rain', 'line_storm', 'equinoctial_storm']
senticnet['hailstone'] = ['-0.19', '-0.10', '0.041', '-0.23', '#sadness', '#disgust', 'negative', '-0.09', 'dandruff', 'cold_hot', 'snow', 'monsoon', 'rain']
senticnet['hailstorm'] = ['-0.78', '0', '0.906', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'rainstorm', 'atmospheric_phenomenon', 'northeaster', 'noreaster', 'storm']
senticnet['hainanese_chicken_rice'] = ['0.965', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.951', 'delicious_food', 'hot_meal', 'good_food', 'fresh_produce', 'tasty_food']
senticnet['hainaut'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'feudal_lord', 'lord', 'master', 'overlord', 'king']
senticnet['hair'] = ['0', '-0.01', '0.044', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'fur', 'two_eyes', 'fur_keep_warm', 'hold_hair', 'facial_hair']
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senticnet['hair_accessory'] = ['0.891', '0.897', '0', '0', '#joy', '#interest', 'positive', '0.894', 'cosmetic', 'hair_roller', 'hairdresser_chair', 'straighten_hair', 'sculpt_gel']
senticnet['hair_become_short'] = ['-0.05', '0', '-0.07', '-0.04', '#sadness', '#fear', 'negative', '-0.05', 'keep_hair_short', 'cold_head', 'short_hair', 'little_hair', 'hair_get_short']
senticnet['hair_brush'] = ['0.022', '-0.01', '0.062', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'electric_toothbrush', 'moistener', 'hairbrush', 'shampoo_conditioner', 'shave_kit']
senticnet['hair_care_article'] = ['-0.85', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'sculpt_gel', 'barrette', 'dandruff_shampoo', 'get_clean', 'mouthwash']
senticnet['hair_clip'] = ['0.925', '0.705', '0', '0', '#joy', '#interest', 'positive', '0.815', 'barrette', 'hairpin', 'hair_dryer', 'pin', 'curl_iron']
senticnet['hair_clipper'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'smart_technology', 'electric_shaver', 'hair_dryer', 'stay_healthy', 'wash_tooth']
senticnet['hair_color'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'blonde', 'purple', 'violet', 'dark_light', 'hue']
senticnet['hair_colored'] = ['0.029', '0', '0', '0.111', '#joy', '#admiration', 'positive', '0.07', 'maroon', 'color', 'bright_color', 'scarlet', 'cool_color']
senticnet['hair_conditioner'] = ['0.739', '0.742', '0', '0', '#joy', '#interest', 'positive', '0.74', 'dandruff_shampoo', 'hair_shape_gel', 'wax_dental_floss', 'fluoride_toothpaste', 'tartar_control_toothpaste']
senticnet['hair_dresser'] = ['0.99', '0', '0', '0.721', '#joy', '#admiration', 'positive', '0.855', 'hair', 'haircut', 'hairdresser_chair', 'scissor', 'hairstyle']
senticnet['hair_drier'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'barrette', 'hair_gel', 'blow_dryer', 'dry_hair', 'wet_hair']
senticnet['hair_dryer'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'barrette', 'hair_gel', 'blow_dryer', 'dry_hair', 'wet_hair']
senticnet['hair_dye'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'blonde', 'purple', 'violet', 'dark_light', 'hue']
senticnet['hair_gel'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'hair_dryer', 'barrette', 'hair_shape_gel', 'sculpt_gel', 'haircut_scissor']
senticnet['hair_get_short'] = ['-0.06', '0', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.05', 'talk_hairdresser', 'bad_haircut', 'hair_become_short', 'keep_hair_short', 'change_style']
senticnet['hair_long'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'get_new_hairstyle', 'new_hair_style', 'change_hair_style', 'get_haircut', 'look_smart']
senticnet['hair_lotion'] = ['0', '0.086', '-0.06', '0.12', '#interest', '#admiration', 'positive', '0.046', 'gel_toothpaste', 'tartar_control_toothpaste', 'fluoride_mouthwash', 'toothpaste', 'lotion']
senticnet['hair_ornament'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'bangle', 'ankle_bracelet', 'armilla']
senticnet['hair_ribbon'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'facial_tissue', 'kleenex', 'hanky', 'handkerchief', 'tissue']
senticnet['hair_roller'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'barber_comb', 'hairdresser_chair', 'hair_accessory', 'sculpt_gel', 'barrette']
senticnet['hair_salon'] = ['-0.02', '0.122', '0', '0.137', '#interest', '#admiration', 'positive', '0.078', 'barber_shop', 'barbershop', 'hairdressing_salon', 'beauty_salon', 'salon']
senticnet['hair_sculpt_gel'] = ['-0.04', '0.069', '0', '0.069', '#interest', '#admiration', 'positive', '0.031', 'sculpt_gel', 'hairstyle', 'hairdresser', 'cosmetic', 'gel']
senticnet['hair_setose'] = ['-0.75', '0', '0.734', '-0.82', '#sadness', '#disgust', 'negative', '-0.77', 'puberulent', 'haired', 'furlike', 'pappose', 'pilous']
senticnet['hair_shaft'] = ['0', '0.097', '0', '0.085', '#interest', '#admiration', 'positive', '0.091', 'lunula', 'arm_part', 'leave_hand', 'ten_fingers', 'five_fingers']
senticnet['hair_shape_gel'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'dandruff_shampoo', 'barrette', 'sculpt_gel', 'hair_gel', 'wax_dental_floss']
senticnet['hair_spray'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'sexual_excitement', 'sexy', 'wet_lip', 'kiss', 'clean_up']
senticnet['hair_straightener'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'barrette', 'hair_gel', 'blow_dryer', 'dry_hair', 'wet_hair']
senticnet['hair_style'] = ['0', '0.097', '0', '0.146', '#interest', '#admiration', 'positive', '0.121', 'hairstyle', 'hair', 'appearance', 'hold', 'look']
senticnet['hair_stylist'] = ['0.99', '0', '0', '0.721', '#joy', '#admiration', 'positive', '0.855', 'hair', 'haircut', 'hairdresser_chair', 'scissor', 'hairstyle']
senticnet['hair_texture'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'blonde', 'purple', 'violet', 'dark_light', 'hue']
senticnet['hair_trigger_temper'] = ['0', '0', '0.761', '-0.71', '#anger', '#disgust', 'negative', '-0.74', 'impulsive', 'tearaway', 'incautious', 'madcap', 'impetuous']
senticnet['hair_wash'] = ['-0.10', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.08', 'short_hair', 'talk_hairdresser', 'hair_become_short', 'pay_barber', 'keep_hair_short']
senticnet['hairball'] = ['0', '-0.01', '0.067', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'waste_water', 'clog', 'false_tooth', 'stench', 'still_water']
senticnet['hairbrush'] = ['0.022', '-0.01', '0.062', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'electric_toothbrush', 'moistener', 'hair_brush', 'shampoo_conditioner', 'shave_kit']
senticnet['haircloth'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'needle', 'sew_needle', 'sew_machine', 'needle_thread', 'oilcloth']
senticnet['haircut'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'visit_barber', 'new_hair_style', 'makeover', 'get_hair_colored', 'go_barber']
senticnet['haircut_scissor'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'barrette', 'sculpt_gel', 'barber_comb', 'hair_gel', 'hair_accessory']
senticnet['hairdo'] = ['-0.97', '0', '0.567', '-0.59', '#sadness', '#disgust', 'negative', '-0.71', 'coif', 'coiffure', 'marcel', 'hairstyle', 'hair_style']
senticnet['hairdresser'] = ['0.99', '0', '0', '0.721', '#joy', '#admiration', 'positive', '0.855', 'hair', 'haircut', 'hairdresser_chair', 'scissor', 'hairstyle']
senticnet['hairdresser_chair'] = ['0', '-0.05', '0.061', '-0.10', '#anger', '#disgust', 'negative', '-0.03', 'hair_accessory', 'barber_comb', 'hair_roller', 'hairdressing_salon', 'sculpt_gel']
senticnet['hairdressing_salon'] = ['0.844', '0.89', '0', '0', '#joy', '#interest', 'positive', '0.867', 'hairdresser', 'haircut', 'hairdresser_chair', 'cut_hair', 'hairstyle']
senticnet['hairdryer'] = ['0.866', '0.833', '0', '0', '#joy', '#interest', 'positive', '0.849', 'alcohol', 'porter', 'concierge', 'beer_mug', 'bar_stool']
senticnet['haired'] = ['-0.75', '0', '0.732', '-0.83', '#sadness', '#disgust', 'negative', '-0.77', 'puberulent', 'sericeous', 'furlike', 'pappose', 'pilous']
senticnet['hairless'] = ['-0.58', '0', '0.66', '-0.8', '#anger', '#disgust', 'negative', '-0.68', 'bald', 'depilous', 'glabrescent', 'glabrous', 'balding']
senticnet['hairlike'] = ['-0.02', '-0.05', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.02', 'motor_oil', 'make_toy', 'engine', 'emission', 'castor']
senticnet['hairline'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'lymph', 'exoskeleton', 'breast_pocket', 'toward', 'squint']
senticnet['hairline_fracture'] = ['-0.81', '0', '0.745', '-0.94', '#sadness', '#disgust', 'negative', '-0.83', 'injury', 'fracture', 'capillary_fracture', 'incomplete_fracture', 'simple_fracture']
senticnet['hairloss'] = ['0', '0', '0.797', '-0.97', '#anger', '#disgust', 'negative', '-0.88', 'remove_hair', 'bald', 'dirty_couch', 'dirty_sofa', 'animal_fur']
senticnet['hairpiece'] = ['-0.91', '0', '0.869', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'talk_hairdresser', 'hair_get_short', 'hair_become_short', 'keep_hair_short', 'hair_wash']
senticnet['hairpin'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['hairpin_bend'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['hairpin_loop'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['hairpin_structure'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['hairpin_turn'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['hairsplitter'] = ['-0.85', '0', '0.749', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'controversialist', 'fight', 'quarrel', 'obstructor', 'obstructer']
senticnet['hairsplitting'] = ['0', '-0.81', '0', '0.965', '#surprise', '#admiration', 'positive', '0.888', 'hair-splitting', 'quibbling', 'nicety', 'nit-picking', 'nitpicking']
senticnet['hairstyle'] = ['0', '0.097', '0', '0.146', '#interest', '#admiration', 'positive', '0.121', 'hair_style', 'hair', 'appearance', 'hold', 'look']
senticnet['hairstylist'] = ['0.99', '0', '0', '0.721', '#joy', '#admiration', 'positive', '0.855', 'hair', 'haircut', 'hairdresser_chair', 'scissor', 'hairstyle']
senticnet['hairy'] = ['-0.59', '0', '0.637', '-0.92', '#anger', '#disgust', 'negative', '-0.71', 'puberulent', 'sericeous', 'furlike', 'pappose', 'pilous']
senticnet['hairy_adaxially'] = ['-0.66', '0', '0.809', '-0.61', '#sadness', '#anger', 'negative', '-0.69', 'face', 'base', 'underbelly', 'sole', 'bilge']
senticnet['hairy_animal'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'brown_bear', 'woodland_creature', 'brown_fur', 'huge_animal', 'brown_hair']
senticnet['hajduk'] = ['0.954', '-0.90', '0', '0.867', '#joy', '#surprise', 'positive', '0.91', 'friend', 'sports_fan', 'rooter', 'fan', 'nut']
senticnet['hajduk_kula'] = ['0.954', '-0.90', '0', '0.867', '#joy', '#surprise', 'positive', '0.91', 'friend', 'sports_fan', 'rooter', 'fan', 'nut']
senticnet['hajduk_split'] = ['0.954', '-0.90', '0', '0.867', '#joy', '#surprise', 'positive', '0.91', 'friend', 'sports_fan', 'rooter', 'fan', 'nut']
senticnet['hakapik'] = ['0', '0', '-0.83', '-0.8', '#fear', '#disgust', 'negative', '-0.81', 'armory', 'tank', 'hydrogen_bomb', 'rifle', 'use_hunt']
senticnet['hakuho'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['hal_sutton'] = ['-0.71', '0', '0.845', '-0.69', '#sadness', '#anger', 'negative', '-0.75', 'lump', 'goon', 'mortal', 'clumsy_person', 'butterfingers']
senticnet['hala_droubi_from'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'dejectedness', 'low-spiritedness', 'lowness', 'downheartedness', 'cheerless']
senticnet['hala_torwar'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'head_hurt', 'introspection', 'lifestyle_change', 'headache', 'move_eye']
senticnet['halakah'] = ['-0.22', '0', '-0.09', '-0.16', '#sadness', '#disgust', 'negative', '-0.16', 'inequality', 'reject', 'discard', 'difference', 'gap']
senticnet['halal'] = ['0.726', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.862', 'kosher', 'gluten-free', 'vegetarian', 'vegan', 'gluten']
senticnet['halberd'] = ['0.93', '-0.78', '0', '0.873', '#joy', '#admiration', 'positive', '0.861', 'blade', 'brand', 'steel', 'claymore', 'sword']
senticnet['halcyon'] = ['0', '0.967', '0', '0.986', '#interest', '#admiration', 'positive', '0.976', 'mythical_creature', 'hero', 'mythical_being', 'calm', 'pleseant']
senticnet['hale'] = ['0.816', '0.76', '0', '0.956', '#joy', '#admiration', 'positive', '0.844', 'cold_air', 'tumble', 'frost', 'ice_cube', 'drop']
senticnet['haleness'] = ['0.939', '-0.74', '0', '0.57', '#joy', '#surprise', 'positive', '0.751', 'healthiness', 'wholeness', 'rosiness', 'health', 'vitality']
senticnet['half'] = ['-0.02', '0', '0.061', '0', '#sadness', '#anger', 'negative', '-0.04', 'cut_up', 'cut', 'cut_half', 'separate', 'bake_cookie']
senticnet['half-dark'] = ['-0.89', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.86', 'pitch-black', 'pitch-dark', 'semi-dark', 'rain-swept', 'half-lit']
senticnet['half-grin'] = ['-0.86', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.88', 'million-watt', 'derisive', 'rueful', 'sidesplitting', 'mirthless']
senticnet['half-heartedly'] = ['0', '-0.71', '0', '0.846', '#surprise', '#admiration', 'positive', '0.78', 'halfheartedly', 'feebly', 'futilely', 'ineffectually', 'lamely']
senticnet['half-lidded'] = ['0', '-0.93', '0', '0.904', '#surprise', '#admiration', 'positive', '0.921', 'horn-rimmed', 'owl-like', 'heavy-lidded', 'black-rimmed', 'owlish']
senticnet['half-lit'] = ['-0.89', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.86', 'pitch-black', 'half-dark', 'semi-dark', 'rain-swept', 'pitch-dark']
senticnet['half-sitting'] = ['0', '0.944', '0', '0.831', '#interest', '#admiration', 'positive', '0.887', 'spread-eagled', 'spreadeagled', 'belly-down', 'spread-eagle', 'cross-legged']
senticnet['half-size'] = ['-0.92', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.89', 'half-sized', 'full-sized', 'standard-sized', 'scaled-down', 'full-size']
senticnet['half-sized'] = ['-0.93', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.89', 'half-size', 'full-sized', 'standard-sized', 'scaled-down', 'full-size']
senticnet['half_asleep'] = ['0', '0.08', '0', '0.179', '#interest', '#admiration', 'positive', '0.13', 'wake_up_morning', 'up_early', 'get_up_early', 'gather_energy_tomorrow', 'sit_chair']
senticnet['half_brother'] = ['0.948', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.888', 'relation', 'mom', 'kin', 'cousin', 'granddaughter']
senticnet['half_crosswise'] = ['-0.06', '-0.05', '0.037', '-0.14', '#sadness', '#disgust', 'negative', '-0.05', 'cover_wound', 'cover_wind', 'welt', 'heal_wind', 'open_wind']
senticnet['half_dozen'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'hundred_year', 'twelve', 'day_month', 'time_measurement']
senticnet['half_heartedly'] = ['-0.79', '0', '-0.70', '0', '#sadness', '#fear', 'negative', '-0.75', 'hesitance', 'hesitancy', 'hesitatingly', 'unassertive', 'unassertively']
senticnet['half_hour'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'watch', 'clock', 'hour', 'hourglass', 'stopwatch']
senticnet['half_hour_documentary'] = ['0', '0.762', '0', '0', '#interest', '#interest', 'positive', '0.762', 'encyclopaedia', 'tropical_rainforest', 'learn_new_information', 'tropical_forest', 'child_story']
senticnet['half_hour_drive'] = ['-0.08', '0.179', '-0.06', '0.096', '#interest', '#admiration', 'positive', '0.034', 'ride_subway', 'buy_vending_machine', 'travel', 'buy_lottery_ticket', 'acquire']
senticnet['half_lengthwise'] = ['-0.02', '0.103', '0.069', '0.151', '#interest', '#admiration', 'positive', '0.039', 'single', 'unit_of_measurement', 'do_it_yourself', 'enumeration', 'alone']
senticnet['half_mast'] = ['0.053', '0.068', '-0.05', '0.06', '#interest', '#admiration', 'positive', '0.032', 'linen', 'star_stripe', 'velvet', 'nation', 'burlap']
senticnet['half_naked'] = ['0.951', '0.763', '0', '0', '#joy', '#interest', 'positive', '0.857', 'pull_muscle', 'heart_rate_increase', 'heavy_breathe', 'get_exhausted', 'tire']
senticnet['half_timbered_building'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'city_hall', 'courthouse', 'subway_station', 'public_place', 'bus_station']
senticnet['halfback_option'] = ['0.855', '0.741', '0', '0.862', '#joy', '#admiration', 'positive', '0.819', 'further_goal', 'pass_ball', 'pass_glass', 'help', 'pass_exam']
senticnet['halfhearted'] = ['0', '-0.78', '0', '0.846', '#surprise', '#admiration', 'positive', '0.814', 'well-intended', 'well-intentioned', 'well-meaning', 'intentioned', 'well-meant']
senticnet['halfheartedly'] = ['0', '-0.71', '0', '0.837', '#surprise', '#admiration', 'positive', '0.777', 'half-heartedly', 'feebly', 'futilely', 'ineffectually', 'lamely']
senticnet['halfpenny'] = ['0.054', '0.237', '-0.15', '0.33', '#interest', '#admiration', 'positive', '0.117', 'retrieve', 'bring', 'tote', 'take', 'go_get']
senticnet['halftime'] = ['0', '0.904', '0', '0', '#interest', '#interest', 'positive', '0.904', 'act', 'part_of_play', 'part_of_movie', 'emote', 'role']
senticnet['halftime_advantage'] = ['-0.76', '0', '0.794', '-0.91', '#anger', '#disgust', 'negative', '-0.82', 'discharge', 'spray', 'spritz', 'outpouring', 'run']
senticnet['halftime_buzzer'] = ['-0.81', '0', '0.747', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'vagrant', 'drifter', 'hobo', 'vagabond', 'dosser']
senticnet['halftime_support'] = ['0.814', '0', '0', '0.873', '#joy', '#admiration', 'positive', '0.843', 'fellowship', 'fulltime_support', 'support', 'threeyear_period', 'recipient']
senticnet['halftrack'] = ['-0.56', '0', '0.714', '-0.89', '#anger', '#disgust', 'negative', '-0.72', 'tank', 'tank_destroyer', 'armored_car', 'armoured_car', 'armored_personnel_carrier']
senticnet['halfway'] = ['-0.75', '0', '0.785', '-0.7', '#sadness', '#anger', 'negative', '-0.74', 'disappointing', 'dissatisfactory', 'unsatisfying', 'unsatisfactory', 'unacceptable']
senticnet['halfway_around'] = ['-0.95', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.83', 'outer', 'radiant', 'around_earth', 'nothingness', 'heavenly_body']
senticnet['halfway_down'] = ['-0.75', '0', '0.785', '-0.7', '#sadness', '#anger', 'negative', '-0.74', 'disappointing', 'dissatisfactory', 'unsatisfying', 'unsatisfactory', 'unacceptable']
senticnet['halfway_mark'] = ['0.844', '0', '0', '0', '#joy', '#joy', 'positive', '0.844', 'straight', 'late_time', 'preparedness', 'past_present', 'wait']
senticnet['halfway_point'] = ['0.844', '0', '0', '0', '#joy', '#joy', 'positive', '0.844', 'straight', 'late_time', 'preparedness', 'past_present', 'wait']
senticnet['halfway_through'] = ['0.033', '0.158', '-0.06', '0.145', '#interest', '#admiration', 'positive', '0.067', 'finish', 'end', 'finish_complete', 'end_point', 'finalize']
senticnet['halgan'] = ['-0.84', '0', '0.579', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'squiggle', 'hand', 'chicken_scratch', 'handwriting', 'script']
senticnet['halhul'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['halictid'] = ['0', '0', '0', '0.869', '#admiration', '#admiration', 'positive', '0.869', 'fly_animal', 'insect', 'mosquito', 'bug', 'fly_insect']
senticnet['halictidae'] = ['0', '0', '0', '0.869', '#admiration', '#admiration', 'positive', '0.869', 'fly_animal', 'insect', 'mosquito', 'bug', 'fly_insect']
senticnet['halide'] = ['0.032', '-0.02', '-0.04', '-0.08', '#fear', '#disgust', 'negative', '-0.01', 'liquid', 'carbonate_drink', 'chemical', 'soda', 'bromide']
senticnet['halidon_hill'] = ['-0.05', '0', '-0.08', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'siege', 'big_fight', 'war', 'declare_war', 'combat']
senticnet['halitosis'] = ['0', '0', '0.948', '-0.78', '#anger', '#disgust', 'negative', '-0.86', 'worm_intestine', 'fur_keep_warm', 'unpleasant-smelling', 'bad_breath', 'asthama']
senticnet['hall'] = ['0.805', '0.788', '0', '0', '#joy', '#interest', 'positive', '0.796', 'residence_hall', 'charles_francis_hall', 'marguerite_radclyffe_hall', 'anteroom', 'manse']
senticnet['hall_auditorium'] = ['0.872', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.811', 'lecture_theater', 'lecture', 'conference_auditorium', 'read_book', 'text_book']
senticnet['halloo'] = ['0', '0', '0.772', '-0.82', '#anger', '#disgust', 'negative', '-0.79', 'cry', 'acting_out', 'explosion', 'outburst', 'yell']
senticnet['hallowed'] = ['0.81', '0.853', '0', '0', '#joy', '#interest', 'positive', '0.832', 'purify', 'useful', 'sanctifier', 'unction', 'sanctify']
senticnet['halloween'] = ['0.754', '0', '0', '0.861', '#joy', '#admiration', 'positive', '0.807', 'clown', 'juggler', 'swing', 'acrobat', 'dare']
senticnet['halloween_celebration'] = ['0.957', '0.7', '0', '0', '#joy', '#interest', 'positive', '0.829', 'ballpark', 'coven', 'disneyland', 'hockey_game', 'symposium']
senticnet['halloween_costume'] = ['-0.76', '0', '0.672', '-0.88', '#sadness', '#disgust', 'negative', '-0.77', 'attire', 'garb', 'dress', 'fancy_dress', 'masquerade']
senticnet['halloween_episode'] = ['0.918', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.827', 'encounter', 'reveal', 'programme', 'lose', 'sequel']
senticnet['halloween_pumpkin'] = ['0', '-0.01', '0.037', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'pear', 'green_apple', 'apricot', 'cherry', 'lime']
senticnet['halloweentown'] = ['-0.63', '0', '0.674', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'baneful', 'evil', 'baleful', 'malevolent', 'malign']
senticnet['hallucinate'] = ['-0.76', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.78', 'change_mind', 'think_sex', 'invigorate', 'alter_state_consciousness', 'loss_of_consciousness']
senticnet['hallucinating'] = ['0', '0', '-0.73', '-0.85', '#fear', '#disgust', 'negative', '-0.79', 'delirious', 'excited', 'frantic', 'mad', 'unrestrained']
senticnet['hallucination'] = ['0', '0', '-0.84', '0', '#fear', '#fear', 'negative', '-0.84', 'blank_stare', 'unsettle', 'reflect', 'delusion', 'think_past']
senticnet['hallucinatory'] = ['-0.67', '0', '0.716', '-0.89', '#anger', '#disgust', 'negative', '-0.76', 'imaginary', 'notional', 'fabled', 'unreal', 'illusional']
senticnet['hallucinogen'] = ['-0.64', '0', '0.911', '-0.70', '#anger', '#disgust', 'negative', '-0.75', 'headache', 'see_dragon', 'illusion', 'distorted_vision', 'distorted_perception']
senticnet['hallucinogenic'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.77', 'mind-altering', 'psychoactive', 'mood-altering', 'psychotropic', 'psychedelics']
senticnet['hallucinogenic_drug'] = ['-0.69', '0', '0.991', '-0.50', '#sadness', '#anger', 'negative', '-0.73', 'headache', 'see_dragon', 'illusion', 'distorted_vision', 'distorted_perception']
senticnet['hallux'] = ['-0.60', '0', '0.701', '-0.72', '#anger', '#disgust', 'negative', '-0.67', 'lunate_bone', 'semilunar_bone', 'carpal_bone', 'triquetral', 'triquetral_bone']
senticnet['hallway'] = ['0.039', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'lower_floor', 'floor', 'ground_floor', 'first_floor', 'level_one']
senticnet['hallway_outside'] = ['0', '0.072', '0', '0', '#interest', '#interest', 'positive', '0.072', 'metal_box', 'cash_drawer', 'keyhole', 'clutch_bag', 'safe']
senticnet['halo'] = ['0.855', '0.856', '0', '0', '#joy', '#interest', 'positive', '0.855', 'gesticulation', 'feel', 'popularity', 'sentiment', 'sentimental']
senticnet['halo_around'] = ['0.906', '-0.60', '0', '0.653', '#joy', '#admiration', 'positive', '0.72', 'opaqueness', 'faintness', 'dimness', 'opacity', 'vagueness']
senticnet['halogen'] = ['0.921', '0.848', '0', '0', '#joy', '#interest', 'positive', '0.885', 'high-voltage', 'voltage', 'low-power', 'low-voltage', 'volt']
senticnet['halogen_lamp'] = ['-0.63', '0', '0.897', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'cd-rom', 'international_candle', 'candle', 'candlepower_unit', 'standard_candle']
senticnet['halomot'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'head_hurt', 'introspection', 'lifestyle_change', 'headache', 'move_eye']
senticnet['haloperidol'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['halt'] = ['-0.99', '0', '0.893', '-0.70', '#sadness', '#anger', 'negative', '-0.86', 'stop', 'red_light', 'eight_sides', 'come', 'pause']
senticnet['halter_top'] = ['0', '0', '0', '-0.06', '#disgust', '#disgust', 'negative', '-0.06', 'body', 'nervous_system', 'normal_human_body', 'human_body', 'head_shoulder']
senticnet['haltingly'] = ['-0.79', '0', '-0.70', '0', '#sadness', '#fear', 'negative', '-0.75', 'hesitance', 'hesitancy', 'hesitatingly', 'unassertive', 'unassertively']
senticnet['halvah'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'roll', 'custard', 'bread', 'biscuit']
senticnet['halve'] = ['-0.06', '0', '0.058', '0', '#sadness', '#anger', 'negative', '-0.06', 'divide', 'math_action', 'mathematical_operation', 'division', 'separate']
senticnet['halyard'] = ['0', '-0.05', '0.079', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'c_clamp', 'thumb_tack', 'pin', 'pliers', 'without_space']
senticnet['ham'] = ['0', '0.08', '0', '0', '#interest', '#interest', 'positive', '0.08', 'beef', 'pork', 'french_toast', 'steak', 'brisket']
senticnet['ham-fisted'] = ['-0.87', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.86', 'imbecilic', 'ill-informed', 'dimwitted', 'close-minded', 'tone-deaf']
senticnet['ham_actor'] = ['-0.71', '0', '0.794', '-0.89', '#anger', '#disgust', 'negative', '-0.80', 'role_player', 'thespian', 'actor', 'histrion', 'player']
senticnet['ham_hock'] = ['0.084', '-0.05', '0', '-0.04', '#joy', '#surprise', 'positive', '0.031', 'entree', 'feast', 'satiation', 'breakfast', 'supper']
senticnet['ham_soup'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'cabbage', 'lettuce', 'broccoli']
senticnet['hamam'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['hamartoma'] = ['-0.88', '0', '0.774', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fibroid_tumor', 'fibroid', 'benign_tumor', 'nonmalignant_tumor', 'nonmalignant_neoplasm']
senticnet['hamate'] = ['-0.60', '0', '0.701', '-0.72', '#anger', '#disgust', 'negative', '-0.67', 'lunate_bone', 'semilunar_bone', 'carpal_bone', 'triquetral', 'triquetral_bone']
senticnet['hamauzu'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_music', 'hear_music', 'artist', 'make_drawing', 'art']
senticnet['hamburger'] = ['0.062', '0', '0.027', '0.055', '#joy', '#admiration', 'positive', '0.03', 'porridge', 'meal', 'broth', 'appetizer', 'hearty']
senticnet['hamburger_bun'] = ['0', '-0.11', '0', '0', '#surprise', '#surprise', 'positive', '0.113', 'cake', 'cookie', 'mousse', 'doughnut', 'brownie']
senticnet['hamburger_dom'] = ['-0.72', '0', '0.978', '-0.86', '#anger', '#disgust', 'negative', '-0.85', 'amusement_park', 'common', 'pleasure_ground', 'village_green', 'tract']
senticnet['hamelia'] = ['0.938', '-0.77', '0', '0.944', '#joy', '#admiration', 'positive', '0.885', 'bush', 'scarlet_bush', 'scarlet_hamelia', 'coloradillo', 'shrub']
senticnet['hamish_rutherford'] = ['0.034', '-0.02', '0.054', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'compass', 'swiss_army_knife', 'bake_pan', 'coffee_mug', 'wine_opener']
senticnet['hamlet'] = ['-0.91', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.85', 'play', 'perpetrator', 'role_play', 'act_part', 'terrible_event']
senticnet['hammer'] = ['-0.03', '-0.02', '-0.05', '-0.15', '#fear', '#disgust', 'negative', '-0.05', 'knife', 'hatchet', 'sword', 'razor_blade', 'scalpel']
senticnet['hammer_nail'] = ['-0.07', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.07', 'market', 'refrigerate', 'buy_market', 'put_napkin_lap', 'defrost']
senticnet['hammerhead'] = ['-0.98', '0', '0.714', '-0.90', '#sadness', '#disgust', 'negative', '-0.86', 'stupid', 'stupid_person', 'stupe', 'dullard', 'dolt']
senticnet['hammerhead_shark'] = ['-0.20', '-0.10', '0.07', '-0.24', '#sadness', '#disgust', 'negative', '-0.10', 'gill', 'sea_life', 'seafood', 'swim_animal', 'fillet']
senticnet['hammerkop'] = ['-0.59', '0', '0.887', '-0.71', '#anger', '#disgust', 'negative', '-0.73', 'armet', 'basinet', 'helmet', 'heaume', 'morion']
senticnet['hammersmith_hospital'] = ['0.738', '-0.53', '0', '0.856', '#joy', '#admiration', 'positive', '0.71', 'doctor', 'country_doctor', 'family_doctor', 'doctor_office', 'physician']
senticnet['hammerstone'] = ['0.063', '0.089', '0.036', '0.146', '#interest', '#admiration', 'positive', '0.066', 'horn', 'jew_harp', 'brass_instrument', 'trumpet', 'crash_cymbal']
senticnet['hammondia'] = ['0.995', '-0.71', '0', '0.647', '#joy', '#surprise', 'positive', '0.784', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['hammy'] = ['-0.78', '0', '0.882', '0', '#sadness', '#anger', 'negative', '-0.83', 'overact', 'exaggerated', 'ham', 'act', 'overplay']
senticnet['hamper'] = ['-0.05', '-0.04', '0.017', '-0.05', '#sadness', '#disgust', 'negative', '-0.02', 'shoe_shop', 'shoe_store', 'attic', 'obstruct', 'closet']
senticnet['hampered_by'] = ['-0.05', '-0.04', '0.017', '-0.05', '#sadness', '#disgust', 'negative', '-0.02', 'shoe_shop', 'shoe_store', 'attic', 'obstruct', 'closet']
senticnet['hamsalekha'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_music', 'hear_music', 'artist', 'make_drawing', 'art']
senticnet['hamstring'] = ['-0.74', '0', '-0.98', '0', '#sadness', '#fear', 'negative', '-0.86', 'quadriceps', 'groin', 'sprained', 'sidelined', 'knee']
senticnet['hamstring_complaint'] = ['-0.69', '0', '0.768', '-0.67', '#sadness', '#anger', 'negative', '-0.71', 'dither', 'pother', 'worry', 'flap', 'scruple']
senticnet['hamstring_muscle'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'deltoid', 'muscle', 'abductor', 'skeletal_muscle']
senticnet['hamstring_problem'] = ['-0.69', '0', '0.768', '-0.67', '#sadness', '#anger', 'negative', '-0.71', 'dither', 'pother', 'worry', 'flap', 'scruple']
senticnet['hamstring_strain'] = ['-0.69', '0', '0.768', '-0.67', '#sadness', '#anger', 'negative', '-0.71', 'dither', 'pother', 'worry', 'flap', 'scruple']
senticnet['hamstring_tear'] = ['-0.69', '0', '0.768', '-0.67', '#sadness', '#anger', 'negative', '-0.71', 'dither', 'pother', 'worry', 'flap', 'scruple']
senticnet['hamstrung_by'] = ['-0.13', '0.071', '0.694', '-0.67', '#anger', '#disgust', 'negative', '-0.35', 'crucify', 'dun', 'frustrating', 'aggravate', 'exasperation']
senticnet['han_asperger'] = ['-0.91', '0', '0.508', '-0.89', '#sadness', '#disgust', 'negative', '-0.77', 'mental_disorder', 'mental_disturbance', 'psychological_disorder', 'folie', 'disturbance']
senticnet['han_hermann_hoppe'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'economic_libertarian', 'civil_libertarian', 'advocate', 'advocator', 'proponent']
senticnet['han_kng'] = ['0.751', '-0.98', '0', '0.989', '#surprise', '#admiration', 'positive', '0.909', 'okay', 'approval', 'commendation', 'okey', 'okeh']
senticnet['han_lippershey'] = ['-0.03', '0.201', '-0.13', '0.171', '#interest', '#admiration', 'positive', '0.049', 'scientific_statement', 'hypothetical', 'conjecture', 'theory', 'fact']
senticnet['han_unification'] = ['-0.86', '0', '0.619', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'character', 'logogram', 'logograph', 'graph', 'grapheme']
senticnet['hanc_igitur'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'reverence', 'genuflection', 'obeisance', 'kowtow', 'mafia']
senticnet['hand'] = ['0.883', '-0.59', '0', '0.533', '#joy', '#surprise', 'positive', '0.67', 'hook', 'meat_hook', 'mauler', 'right', 'left']
senticnet['hand-to-mouth'] = ['0', '0', '-0.74', '-0.86', '#fear', '#disgust', 'negative', '-0.80', 'paycheck-to-paycheck', 'moment-to-moment', 'hermit-like', 'eking', 'day-in-day-out']
senticnet['hand-waving'] = ['-0.75', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.75', 'platitudinous', 'trite', 'insipidity', 'cliched', 'platitude']
senticnet['hand-wringing'] = ['0', '0', '0.796', '-0.78', '#anger', '#disgust', 'negative', '-0.78', 'self-congratulation', 'self-abuse', 'self-delusion', 'self-flagellation', 'self-aggrandizement']
senticnet['hand_action'] = ['0.021', '0.146', '-0.12', '0.258', '#interest', '#admiration', 'positive', '0.074', 'hand_movement', 'hand_motion', 'feel_action', 'hand_gesture', 'back_forth']
senticnet['hand_baggage'] = ['0.022', '-0.03', '0.022', '0.104', '#surprise', '#admiration', 'positive', '0.036', 'luggage', 'baggage', 'passenger_ticket', 'ticket_counter', 'baggage_trunk']
senticnet['hand_carved_wooden'] = ['0', '0.773', '0', '0.895', '#interest', '#admiration', 'positive', '0.834', 'wooden_board', 'make_wood', 'wooden_box', 'wooden_floor', 'long_wood']
senticnet['hand_clap'] = ['0.857', '-0.77', '0', '0.878', '#joy', '#admiration', 'positive', '0.836', 'hand', 'round', 'approval', 'commendation', 'clapping']
senticnet['hand_clapping'] = ['0.502', '-0.84', '0', '0.769', '#surprise', '#admiration', 'positive', '0.705', 'hand', 'round', 'approval', 'commendation', 'handclap']
senticnet['hand_combatant'] = ['0.586', '-0.94', '0', '0.748', '#surprise', '#admiration', 'positive', '0.76', 'vanquish', 'beat', 'beat_out', 'trounce', 'crush']
senticnet['hand_crafted_jewelry'] = ['0.048', '0', '0', '0.092', '#joy', '#admiration', 'positive', '0.07', 'gym_locker', 'observatory', 'prison_cell', 'luggage_store', 'luggage_compartment']
senticnet['hand_cramp'] = ['0', '-0.75', '0', '0', '#surprise', '#surprise', 'positive', '0.755', 'writer_cramp', 'scratch_head', 'think_past', 'think_story', 'stare_off_space']
senticnet['hand_crank'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'biological_machine', 'social_creature', 'live_creature', 'powerful', 'alive']
senticnet['hand_dirty'] = ['0.76', '0.919', '0', '0', '#joy', '#interest', 'positive', '0.84', 'chore', 'make_happen', 'pick_up_trash', 'intuition', 'stop_procrastinate']
senticnet['hand_foot'] = ['0', '0.072', '0', '0.064', '#interest', '#admiration', 'positive', '0.068', 'physique', 'leg_arm', 'anatomy', 'human_part', 'below_neck']
senticnet['hand_gesture'] = ['0.034', '0.13', '-0.08', '0.258', '#interest', '#admiration', 'positive', '0.085', 'hand_motion', 'hand_movement', 'arm_movement', 'hand_action', 'goodbye']
senticnet['hand_glass'] = ['0.584', '-0.62', '0', '0.815', '#surprise', '#admiration', 'positive', '0.675', 'cheval_glass', 'car_mirror', 'mirror', 'hand_mirror', 'looking_glass']
senticnet['hand_grenade'] = ['0', '0', '0.818', '-0.99', '#anger', '#disgust', 'negative', '-0.90', 'weapon', 'aerial_bomb', 'machine_gun', 'cannon', 'handgun']
senticnet['hand_hewn'] = ['0', '0.773', '0', '0.895', '#interest', '#admiration', 'positive', '0.834', 'wooden_board', 'make_wood', 'wooden_box', 'wooden_floor', 'long_wood']
senticnet['hand_hold'] = ['0.84', '0.876', '0', '0', '#joy', '#interest', 'positive', '0.858', 'move_liquid', 'pour_liquid', 'fill_in_glass', 'pour', 'push']
senticnet['hand_in_hand'] = ['-0.04', '0.194', '-0.10', '0.124', '#interest', '#admiration', 'positive', '0.042', 'together', 'musical_note', 'music_note', 'just_in_time', 'same_time']
senticnet['hand_job'] = ['0.89', '-0.64', '0', '0.765', '#joy', '#admiration', 'positive', '0.767', 'wank', 'frottage', 'jacking_off', 'jerking_off', 'masturbation']
senticnet['hand_lotion'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'sexual_excitement', 'sexy', 'wet_lip', 'kiss', 'clean_up']
senticnet['hand_luggage'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'baggage', 'passenger_ticket', 'air_ticket', 'move_sidewalk', 'aircraft_roadway']
senticnet['hand_made'] = ['0', '0.925', '0', '0.969', '#interest', '#admiration', 'positive', '0.947', 'dead', 'hard', 'power_electricity', 'man-made', 'little_dense_water']
senticnet['hand_mirror'] = ['0.584', '-0.62', '0', '0.815', '#surprise', '#admiration', 'positive', '0.675', 'cheval_glass', 'car_mirror', 'hand_glass', 'mirror', 'looking_glass']
senticnet['hand_motion'] = ['0.034', '0.103', '-0.08', '0.239', '#interest', '#admiration', 'positive', '0.073', 'hand_gesture', 'hand_movement', 'hand_action', 'arm_movement', 'hand_signal']
senticnet['hand_movement'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'hand_action', 'hand_motion', 'hand_gesture', 'back_forth', 'feel_action']
senticnet['hand_out'] = ['-0.03', '0.275', '-0.17', '0.398', '#interest', '#admiration', 'positive', '0.116', 'donate', 'give_out', 'give', 'hand_over', 'receive']
senticnet['hand_over'] = ['0', '0.815', '0', '0.908', '#interest', '#admiration', 'positive', '0.861', 'donate', 'distribute', 'wedding_vow', 'hand_out', 'give']
senticnet['hand_print'] = ['0.026', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.049', 'coupon', 'bottle_water', 'plastic_container', 'carton_milk', 'loaf_bread']
senticnet['hand_signal'] = ['0', '0.105', '-0.06', '0.208', '#interest', '#admiration', 'positive', '0.082', 'hand_motion', 'hand_gesture', 'hand_movement', 'hand_action', 'arm_movement']
senticnet['hand_smoke'] = ['0.047', '0', '-0.10', '-0.06', '#fear', '#disgust', 'negative', '-0.03', 'smoke', 'tobacco_product', 'smoke_tobacco', 'lung_cancer', 'cigar']
senticnet['hand_tobacco_smoke'] = ['0.047', '0', '-0.10', '-0.06', '#fear', '#disgust', 'negative', '-0.03', 'smoke', 'tobacco_product', 'smoke_tobacco', 'lung_cancer', 'cigar']
senticnet['hand_tool'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'make_outline', 'press_button', 'put_bag', 'add_two_numbers_together', 'short_version']
senticnet['hand_towel'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'toilet_paper', 'beach_towel', 'face_towel', 'towel', 'tissue']
senticnet['hand_wash'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'clean_agent', 'tv_drama', 'cleanser', 'lather', 'hygiene']
senticnet['hand_wear'] = ['0.57', '-0.56', '0', '0.888', '#joy', '#admiration', 'positive', '0.675', 'wearable', 'wear', 'muff', 'glove', 'clothing']
senticnet['hand_woven'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_fabric']
senticnet['hand_woven_cloth'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_fabric']
senticnet['handakuten'] = ['-0.86', '0', '0.619', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'character', 'logogram', 'logograph', 'graph', 'grapheme']
senticnet['handbag'] = ['0', '-0.00', '0.045', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'shoulder_bag', 'pantyhose', 'holdall', 'carry', 'tote_bag']
senticnet['handball'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'golf', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['handbill'] = ['0.763', '0.725', '0', '0', '#joy', '#interest', 'positive', '0.744', 'flyer', 'fly', 'flier', 'airman', 'aeronaut']
senticnet['handbook'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'current_event', 'headline', 'paper_write', 'paper_page', 'many_pages']
senticnet['handclap'] = ['0.857', '-0.77', '0', '0.878', '#joy', '#admiration', 'positive', '0.836', 'hand', 'round', 'approval', 'commendation', 'clapping']
senticnet['handcuff'] = ['-0.13', '0.113', '-0.16', '0', '#sadness', '#fear', 'negative', '-0.06', 'question_witness', 'legal_process', 'find_guilty', 'convict_criminal', 'custody']
senticnet['handcuff_key'] = ['-0.13', '0.113', '-0.16', '0', '#sadness', '#fear', 'negative', '-0.06', 'question_witness', 'legal_process', 'find_guilty', 'convict_criminal', 'custody']
senticnet['handed_slam'] = ['-0.81', '0', '0.747', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'vagrant', 'drifter', 'hobo', 'vagabond', 'dosser']
senticnet['handedly'] = ['0', '0', '-0.97', '-0.70', '#fear', '#disgust', 'negative', '-0.84', 'singlehandedly', 'single-handedly', 'miraculously', 'succeeded', 'heroically']
senticnet['handful'] = ['0', '0', '0.992', '-0.90', '#anger', '#disgust', 'negative', '-0.94', 'smattering', 'smattering', 'plethora', 'bevy', 'sprinkling']
senticnet['handgrip'] = ['-0.81', '0', '0.844', '-0.76', '#sadness', '#anger', 'negative', '-0.80', 'hold', 'grip', 'basket_hilt', 'handle', 'hilt']
senticnet['handgun'] = ['0', '0', '0.742', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'firearm', 'rifle', 'machine_gun', 'gun', 'pistol']
senticnet['handheld'] = ['0', '-0.12', '0.011', '0.089', '#surprise', '#admiration', 'positive', '0.067', 'cellular_phone', 'telephone', 'wireless', 'connect', 'message']
senticnet['handheld_camera'] = ['0', '-0.02', '0.048', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'telescope', 'read_glass', 'eyeglass', 'binoculars', 'monocle']
senticnet['handheld_computer'] = ['0.113', '0.073', '0.028', '0.107', '#joy', '#admiration', 'positive', '0.066', 'pda', 'telephone_computer', 'browser', 'number_pad', 'modem']
senticnet['handheld_device'] = ['0.113', '0.073', '0.028', '0.107', '#joy', '#admiration', 'positive', '0.066', 'pda', 'telephone_computer', 'browser', 'number_pad', 'modem']
senticnet['handheld_pc'] = ['0.113', '0.073', '0.028', '0.107', '#joy', '#admiration', 'positive', '0.066', 'pda', 'telephone_computer', 'browser', 'number_pad', 'modem']
senticnet['handheld_scanner'] = ['-0.08', '0', '0.011', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'deflate', 'read_cover', 'obtain_information', 'upgrade', 'spread_out']
senticnet['handicap'] = ['-0.76', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.78', 'strangle', 'bloodshed', 'cripple', 'hear_bad_news', 'sexually_transmit_disease']
senticnet['handicap_chase'] = ['-0.76', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.78', 'strangle', 'bloodshed', 'cripple', 'hear_bad_news', 'sexually_transmit_disease']
senticnet['handicap_race'] = ['-0.76', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.78', 'strangle', 'bloodshed', 'cripple', 'hear_bad_news', 'sexually_transmit_disease']
senticnet['handicapped_license_plate'] = ['0', '0.137', '0', '0.133', '#interest', '#admiration', 'positive', '0.135', 'sign', 'indication', 'post_notice', 'notice', 'red_octagon']
senticnet['handicapper'] = ['-0.78', '0', '0.944', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'loser', 'speculator', 'mortal', 'better', 'gambler']
senticnet['handicraftsman'] = ['0.633', '-0.50', '0', '0.59', '#joy', '#admiration', 'positive', '0.575', 'craftsman', 'manicurist', 'artisan', 'journeyman', 'beautician']
senticnet['handily'] = ['0.949', '-0.71', '0', '0', '#joy', '#surprise', 'positive', '0.831', 'fitly', 'gain', 'conveniently', 'manner', 'convenient']
senticnet['handjob'] = ['0.305', '0.168', '-0.10', '0.299', '#joy', '#admiration', 'positive', '0.167', 'diversity', 'prosper', 'desirable', 'a_lot_of_sex', 'cherish']
senticnet['handkerchief'] = ['0.028', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.047', 'cowhide', 'kerchief', 'hanky', 'facial_tissue', 'zipper']
senticnet['handle'] = ['0', '0.072', '0', '0.117', '#interest', '#admiration', 'positive', '0.095', 'knob', 'door_knob', 'restrict_access', 'lock', 'latch']
senticnet['handle_carefully'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'upgrade', 'deflate', 'appraise', 'read_cover', 'look_behind']
senticnet['handlebar'] = ['0', '-0.09', '0.044', '0.055', '#surprise', '#admiration', 'positive', '0.036', 'two_wheels', 'gear', 'axle', 'engine', 'steer_wheel']
senticnet['handlebar_moustache'] = ['-0.64', '0', '0.647', '-0.55', '#sadness', '#anger', 'negative', '-0.61', 'puberulent', 'sericeous', 'furlike', 'pappose', 'pilous']
senticnet['handlebar_mustache'] = ['-0.90', '0', '0.522', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'hooligan', 'ruffian', 'roughneck', 'rowdy', 'yob']
senticnet['handler'] = ['0', '0', '-0.74', '-0.72', '#fear', '#disgust', 'negative', '-0.73', 'puppeteer', 'manipulator', 'move', 'arrange', 'marionette']
senticnet['handless'] = ['-0.61', '0', '0.533', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'bungling', 'bumbling', 'butterfingered', 'inept', 'maladroit']
senticnet['handley_page'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'cheapjack', 'pitchman']
senticnet['handmade'] = ['0', '0', '0', '0.122', '#admiration', '#admiration', 'positive', '0.122', 'pencil_mark', 'make_book', 'make_newspaper', 'script', 'written_text']
senticnet['handmade_paper'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'building', 'carving', 'artistic_creation', 'ceramic']
senticnet['handmade_premium_cigar'] = ['0.031', '0', '-0.08', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'smoke_cigarette', 'smoke', 'cinder', 'tobacco_product', 'smoke_pipe']
senticnet['handmaid'] = ['-0.62', '0', '0.645', '-0.67', '#anger', '#disgust', 'negative', '-0.65', 'lady_maid', 'handmaiden', 'chambermaid', 'maid', 'parlormaid']
senticnet['handmaiden'] = ['-0.62', '0', '0.649', '-0.67', '#anger', '#disgust', 'negative', '-0.64', 'handmaid', 'maid', 'chambermaid', 'lady_maid', 'parlormaid']
senticnet['handout'] = ['0', '0.844', '0', '0.726', '#interest', '#admiration', 'positive', '0.785', 'scotch_tape', 'fountain_pen', 'pen', 'stamp_pad', 'scrap_paper']
senticnet['handover'] = ['0', '0.789', '0', '0.933', '#interest', '#admiration', 'positive', '0.861', 'pass_ball', 'next_legislature', 'next', 'coming_next', 'successor']
senticnet['handrail'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'top_floor', 'front_desk', 'concierge', 'exit', 'ground_floor']
senticnet['hands-down'] = ['0', '-0.77', '0', '0.701', '#surprise', '#admiration', 'positive', '0.74', 'morning-line', 'odds-on', 'front-runner', 'shoe-in', 'shoo-in']
senticnet['handsaw'] = ['0.609', '-0.74', '0', '0.716', '#surprise', '#admiration', 'positive', '0.69', 'malacca', 'sword', 'swagger_stick', 'sword_cane', 'sword_stick']
senticnet['handset'] = ['0.04', '-0.04', '0.024', '0.094', '#surprise', '#admiration', 'positive', '0.039', 'receiver', 'speaker', 'phone', 'utterance', 'channel_selector']
senticnet['handshake'] = ['0.083', '0.129', '-0.09', '0.155', '#interest', '#admiration', 'positive', '0.068', 'shake_hand', 'say_hello', 'greet', 'breath_sigh_relief', 'hump_leg']
senticnet['handsome'] = ['0.917', '0.729', '0', '0', '#joy', '#interest', 'positive', '0.823', 'big', 'well-favored', 'good-looking', 'better-looking', 'bighearted']
senticnet['handsome_guy'] = ['0.902', '0.995', '0', '0', '#joy', '#interest', 'positive', '0.949', 'beautiful_girl', 'hen_baby', 'pretty_woman', 'hen_child', 'bird']
senticnet['handsome_prince'] = ['0.967', '0.768', '0', '0', '#joy', '#interest', 'positive', '0.867', 'centerfold', 'genus', 'honey_bee', 'infant', 'dove']
senticnet['handsome_profit'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'profitable', 'moneymaking', 'gainful', 'paying', 'paid']
senticnet['handsome_stranger'] = ['0.083', '0', '0', '0.075', '#joy', '#admiration', 'positive', '0.079', 'carny', 'carnival_ride', 'midway', 'ferris_wheel', 'roller-coaster']
senticnet['handsome_sum'] = ['0.685', '-0.58', '0', '0.56', '#joy', '#surprise', 'positive', '0.608', 'profit', 'net_income', 'net', 'net_profit', 'fast_buck']
senticnet['handsome_young'] = ['0.814', '-0.50', '0', '0.622', '#joy', '#admiration', 'positive', '0.648', 'friend', 'champion', 'supporter', 'protagonist', 'endorser']
senticnet['handsomest'] = ['0.978', '0', '0', '0.941', '#joy', '#admiration', 'positive', '0.96', 'splendiferous', 'glorious', 'excellent', 'resplendent', 'first-class']
senticnet['handspun'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_fabric']
senticnet['handstand'] = ['-0.68', '0', '0.545', '-0.68', '#sadness', '#disgust', 'negative', '-0.64', 'stunt', 'tumble', 'split', 'walkover', 'acrobatic_feat']
senticnet['handstand_position'] = ['-0.68', '0', '0.545', '-0.68', '#sadness', '#disgust', 'negative', '-0.64', 'stunt', 'tumble', 'split', 'walkover', 'acrobatic_feat']
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senticnet['handwear'] = ['0.891', '-0.59', '0', '0.502', '#joy', '#surprise', 'positive', '0.661', 'wearable', 'wear', 'muff', 'glove', 'clothing']
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senticnet['hardwood_swamp'] = ['0.85', '0.96', '0', '0', '#joy', '#interest', 'positive', '0.905', 'flatness', 'boring', 'flat_tire', 'flat_foot', 'small_breast']
senticnet['hardwood_tree'] = ['-0.69', '0', '0.725', '-0.89', '#anger', '#disgust', 'negative', '-0.77', 'ponderosa', 'ponderosa_pine', 'western_yellow_pine', 'bull_pine', 'longleaf_pine']
senticnet['hardwork'] = ['0.878', '-0.81', '0', '0', '#joy', '#surprise', 'positive', '0.847', 'find_friend_play', 'learn_rule', 'opponent', 'find_play', 'join_team']
senticnet['hardy'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'intrepidly', 'fearlessness', 'fearlessly', 'unafraid', 'unfearing']
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senticnet['hare_krishna_mantra'] = ['0.022', '0.123', '0.038', '0.129', '#interest', '#admiration', 'positive', '0.059', 'hymn', 'written_music', 'walk_beach', 'lyric', 'piece_of_music']
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senticnet['harima_province'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'overlord', 'lord']
senticnet['harjo'] = ['0.052', '0.099', '0', '0.067', '#interest', '#admiration', 'positive', '0.073', 'police_rank', 'head_guy', 'chief', 'leader', 'commander']
senticnet['hark_back'] = ['-0.96', '0', '0.615', '-0.73', '#sadness', '#disgust', 'negative', '-0.77', 'flush', 'blossom', 'prime', 'peak', 'efflorescence']
senticnet['harlequinade'] = ['0.628', '-0.94', '0', '0.731', '#surprise', '#admiration', 'positive', '0.766', 'folly', 'lunacy', 'craziness', 'indulgence', 'shtik']
senticnet['harley_davidson_motorbike'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'fuel', 'car', 'fossil_fuel', 'transport', 'natural_resource']
senticnet['harley_davidson_motorcycle'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'fuel', 'car', 'fossil_fuel', 'transport', 'natural_resource']
senticnet['harley_quinn'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'only_bird']
senticnet['harley_street'] = ['0.738', '-0.53', '0', '0.856', '#joy', '#admiration', 'positive', '0.71', 'doctor', 'country_doctor', 'family_doctor', 'doctor_office', 'physician']
senticnet['harlot'] = ['-0.64', '0', '0.665', '-0.78', '#anger', '#disgust', 'negative', '-0.69', 'slattern', 'camp_follower', 'call_girl', 'comfort_woman', 'ianfu']
senticnet['harlotry'] = ['-0.54', '0', '0.691', '-0.96', '#anger', '#disgust', 'negative', '-0.73', 'slut', 'trollop', 'libertine', 'adulteress', 'fornicatress']
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senticnet['harm_avoidance'] = ['0.995', '-0.73', '0', '0.83', '#joy', '#admiration', 'positive', '0.852', 'spiritism', 'otherworldliness', 'spirituality', 'spiritualism', 'internality']
senticnet['harm_reduction_approach'] = ['0', '0', '0.973', '-0.78', '#anger', '#disgust', 'negative', '-0.87', 'chastity', 'ignore_conception', 'restrain', 'abortion', 'stop']
senticnet['harm_reduction_strategy'] = ['-0.72', '0', '0.71', '-0.9', '#sadness', '#disgust', 'negative', '-0.77', 'perniciousness', 'unwholesomeness', 'toxicity', 'noisomeness', 'noxiousness']
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senticnet['harmful_human'] = ['-0.61', '0.113', '-0.57', '0.162', '#sadness', '#fear', 'negative', '-0.22', 'bad', 'inherit', 'hard_to_define', 'bad_health', 'treatable']
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senticnet['harmless_error'] = ['-0.84', '0', '0.627', '-0.76', '#sadness', '#disgust', 'negative', '-0.74', 'difficulty', 'inspiration', 'determinant', 'revelation', 'divine_guidance']
senticnet['harmless_rite'] = ['0', '0.089', '0', '0.129', '#interest', '#admiration', 'positive', '0.109', 'passageway', 'connect_room', 'entrance_room', 'corridor', 'walk_through']
senticnet['harmlessly'] = ['0', '0.753', '0', '0.842', '#interest', '#admiration', 'positive', '0.797', 'ricocheted', 'deflected', 'bounced', 'crossbar', 'uselessly']
senticnet['harmon_rabb'] = ['0', '0', '0.785', '-0.72', '#anger', '#disgust', 'negative', '-0.75', 'sharp_nail', 'sharp', 'sharp_end', 'sharp_point', 'pointy']
senticnet['harmonia_mundi'] = ['0.805', '-0.58', '0', '0.525', '#joy', '#surprise', 'positive', '0.637', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['harmonic'] = ['0.839', '0.732', '0', '0', '#joy', '#interest', 'positive', '0.785', 'tone', 'overtone', 'undertone', 'association', 'interference']
senticnet['harmonic_analysis'] = ['0', '0.982', '0', '0.867', '#interest', '#admiration', 'positive', '0.924', 'fourier_analysis', 'analysis', 'mathematics', 'harmony', 'theory']
senticnet['harmonic_distortion'] = ['-0.95', '0', '-0.73', '-0.73', '#sadness', '#fear', 'negative', '-0.80', 'decibel', 'noise', 'loud_soft', 'sound', 'audio']
senticnet['harmonic_language'] = ['-0.52', '0', '0.902', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'discordant', 'inharmonious', 'unresolved', 'false', 'disharmonious']
senticnet['harmonic_minor'] = ['-0.65', '0', '0.681', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'tone', 'whole_tone', 'step', 'whole_step', 'musical_interval']
senticnet['harmonic_progression'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['harmonic_structure'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['harmonica_solo'] = ['0.773', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.844', 'favorite', 'crowd', 'acclamation', 'bunch', 'large_crowd']
senticnet['harmonical'] = ['0.826', '-0.71', '0', '0.913', '#joy', '#admiration', 'positive', '0.819', 'true', 'pure', 'symphonious', 'harmonious', 'harmonized']
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senticnet['harmonicity'] = ['0.685', '-0.91', '0', '0.566', '#joy', '#surprise', 'positive', '0.721', 'audibility', 'audibleness', 'visibleness', 'visibility', 'physical_property']
senticnet['harmonious'] = ['0.826', '-0.71', '0', '0.913', '#joy', '#admiration', 'positive', '0.819', 'true', 'pure', 'symphonious', 'harmonical', 'harmonized']
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senticnet['harmonious_coexistence'] = ['0.826', '-0.71', '0', '0.913', '#joy', '#admiration', 'positive', '0.819', 'true', 'pure', 'symphonious', 'harmonical', 'harmonized']
senticnet['harmonious_interaction'] = ['0.826', '-0.71', '0', '0.913', '#joy', '#admiration', 'positive', '0.819', 'true', 'pure', 'symphonious', 'harmonical', 'harmonized']
senticnet['harmonious_relationship'] = ['0.826', '-0.71', '0', '0.913', '#joy', '#admiration', 'positive', '0.819', 'true', 'pure', 'symphonious', 'harmonical', 'harmonized']
senticnet['harmonious_society'] = ['0.826', '-0.71', '0', '0.913', '#joy', '#admiration', 'positive', '0.819', 'true', 'pure', 'symphonious', 'harmonical', 'harmonized']
senticnet['harmonious_whole'] = ['0.826', '-0.71', '0', '0.913', '#joy', '#admiration', 'positive', '0.819', 'true', 'pure', 'symphonious', 'harmonical', 'harmonized']
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senticnet['harmonise'] = ['0', '0.948', '0', '0.948', '#interest', '#admiration', 'positive', '0.948', 'cope', 'farewell', 'incident', 'propose', 'welcome']
senticnet['harmonium_player'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_music', 'hear_music', 'artist', 'make_drawing', 'art']
senticnet['harmonize'] = ['0.711', '-0.92', '0', '0.951', '#surprise', '#admiration', 'positive', '0.862', 'check', 'music', 'match', 'correspond', 'jibe']
senticnet['harmonized'] = ['0.82', '-0.72', '0', '0.913', '#joy', '#admiration', 'positive', '0.818', 'true', 'pure', 'symphonious', 'harmonical', 'harmonious']
senticnet['harmony'] = ['0.852', '0.907', '0', '0.805', '#joy', '#interest', 'positive', '0.855', 'tune', 'melody', 'lyric', 'written_music', 'music']
senticnet['harness'] = ['-0.76', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.78', 'avail', 'use', 'exploit', 'utilise', 'overexploit']
senticnet['harness_maker'] = ['-0.07', '0.228', '0.019', '0.089', '#interest', '#admiration', 'positive', '0.056', 'home_depot', 'lumberyard', 'furniture_store', 'front_porch', 'carpet_store']
senticnet['harness_shop'] = ['-0.07', '0.228', '0.019', '0.089', '#interest', '#admiration', 'positive', '0.056', 'home_depot', 'lumberyard', 'furniture_store', 'front_porch', 'carpet_store']
senticnet['harney_lake'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'habitat', 'mom_house', 'windsor', 'pacific_northwest', 'wilderness']
senticnet['harp_seal'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'seal', 'otter', 'mussel', 'alligator', 'whale']
senticnet['harper_bazaar'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'library', 'old_testament', 'cook_book', 'a_lot_of_books', 'librarian']
senticnet['harper_bazaar_magazine'] = ['-0.98', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.89', 'fairy', 'genus', 'honey_bee', 'infant', 'dove']
senticnet['harping'] = ['0.861', '0.849', '0', '0', '#joy', '#interest', 'positive', '0.855', 'drumbeat', 'incessant', 'insistent', 'unrelenting', 'drumbeat']
senticnet['harpoon'] = ['0', '0', '-0.83', '-0.8', '#fear', '#disgust', 'negative', '-0.81', 'armory', 'tank', 'hydrogen_bomb', 'rifle', 'use_hunt']
senticnet['harpoon_gun'] = ['0', '0', '-0.83', '-0.8', '#fear', '#disgust', 'negative', '-0.81', 'armory', 'tank', 'hydrogen_bomb', 'rifle', 'use_hunt']
senticnet['harpooner'] = ['0', '0', '-0.83', '-0.8', '#fear', '#disgust', 'negative', '-0.81', 'armory', 'tank', 'hydrogen_bomb', 'rifle', 'use_hunt']
senticnet['harpsichord'] = ['0.033', '0', '0.009', '0.057', '#joy', '#admiration', 'positive', '0.027', 'triangle', 'baton', 'bugle', 'type_of_bow', 'horn']
senticnet['harpsichord_maker'] = ['0.033', '0', '0.009', '0.057', '#joy', '#admiration', 'positive', '0.027', 'triangle', 'baton', 'bugle', 'type_of_bow', 'horn']
senticnet['harpy'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'jawless_fish', 'osteostracan', 'cephalaspid', 'ostracoderm', 'heterostracan']
senticnet['harpy_eagle'] = ['-0.74', '0', '0.963', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
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senticnet['harried'] = ['0', '0', '-0.81', '-0.99', '#fear', '#disgust', 'negative', '-0.90', 'preoccupation', 'preoccupied', 'bemused', 'preoccupy', 'prepossess']
senticnet['harriet_martineau'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['harriet_wistrich'] = ['0.053', '0.122', '-0.07', '0.086', '#interest', '#admiration', 'positive', '0.046', 'attorney', 'lawyer', 'jury', 'decide_guilt', 'prosecutor']
senticnet['harris_county_jail'] = ['-0.08', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.052', 'bequest', 'excise', 'bribe', 'afford', 'owe']
senticnet['harris_hawk'] = ['-0.74', '0', '0.963', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['harrison_avenue'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'boulevard', 'lane', 'thoroughfare', 'intersection', 'curb']
senticnet['harrison_stickle'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['harrow'] = ['-0.86', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.79', 'bush', 'drag', 'harrowing', 'obstacle', 'plow']
senticnet['harrow_road'] = ['0.623', '-0.56', '0', '0.974', '#joy', '#admiration', 'positive', '0.721', 'alterable', 'convertible', 'translatable', 'transformable', 'transmutable']
senticnet['harrowing'] = ['0.855', '0.919', '0', '0', '#joy', '#interest', 'positive', '0.887', 'life-and-death', 'life-altering', 'do-or-die', 'momentous', 'life-or-death']
senticnet['harrowing_journey'] = ['0.937', '0.8', '0', '0', '#joy', '#interest', 'positive', '0.869', 'quest', 'stunt', 'illusion', 'adventure', 'activity']
senticnet['harrumph'] = ['-0.14', '0.152', '0.613', '-0.64', '#anger', '#disgust', 'negative', '-0.31', 'huffiness', 'huffily', 'offense', 'umbrage', 'umbrageous']
senticnet['harsh'] = ['0.824', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.896', 'granulate', 'grainy', 'fine_grain', 'food_flavor', 'white_grain']
senticnet['harsh_arctic'] = ['-0.04', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.04', 'bad_luck', 'damn', 'artic', 'mammal_family', 'bad_mood']
senticnet['harsh_climate'] = ['0.855', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.848', 'stay_alive', 'get_oxygen', 'exist', 'alive', 'crave']
senticnet['harsh_criticism'] = ['-0.16', '0.179', '0.568', '-0.61', '#anger', '#disgust', 'negative', '-0.29', 'angrily', 'choler', 'see_red', 'dander', 'hackle']
senticnet['harsh_desert'] = ['-0.56', '0', '0.814', '-0.88', '#anger', '#disgust', 'negative', '-0.75', 'unfriendly', 'barren', 'bleak', 'desolate', 'wild']
senticnet['harsh_discipline'] = ['-0.83', '0', '0.924', '-0.79', '#sadness', '#anger', 'negative', '-0.85', 'kick_in_butt', 'licking', 'whacking', 'stoning', 'lapidation']
senticnet['harsh_environment'] = ['0.855', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.848', 'stay_alive', 'get_oxygen', 'exist', 'alive', 'crave']
senticnet['harsh_punishment'] = ['-0.83', '0', '0.924', '-0.79', '#sadness', '#anger', 'negative', '-0.85', 'kick_in_butt', 'licking', 'whacking', 'stoning', 'lapidation']
senticnet['harsh_realism'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'captivated', 'charmed', 'captivate', 'bewitch', 'enthrallment']
senticnet['harsh_reality'] = ['0', '0', '0', '-0.70', '#disgust', '#disgust', 'negative', '-0.70', 'uncertainty', 'uncertain_environment', 'insecure', 'blur', 'anxiety']
senticnet['harsh_repression'] = ['0', '0.793', '0', '0.847', '#interest', '#admiration', 'positive', '0.82', 'disagreement', 'fight_against', 'opposition', 'against', 'go_against']
senticnet['harsh_terrain'] = ['-0.56', '0', '0.814', '-0.88', '#anger', '#disgust', 'negative', '-0.75', 'unfriendly', 'barren', 'bleak', 'desolate', 'wild']
senticnet['harsh_treatment'] = ['-0.9', '0', '0.841', '-0.73', '#sadness', '#anger', 'negative', '-0.82', 'nubbiness', 'tweediness', 'raggedness', 'graininess', 'granularity']
senticnet['harshen'] = ['0', '0', '-0.73', '-0.83', '#fear', '#disgust', 'negative', '-0.78', 'whining', 'teeny', 'crotched', 'fretful', 'peevish']
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senticnet['harshest'] = ['-0.9', '0', '0.841', '-0.73', '#sadness', '#anger', 'negative', '-0.82', 'nubbiness', 'tweediness', 'raggedness', 'graininess', 'granularity']
senticnet['harshly'] = ['0', '0', '0.81', '-0.81', '#anger', '#disgust', 'negative', '-0.81', 'maul', 'slate', 'ill', 'trample', 'hardly']
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senticnet['hartebeest'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'argali', 'argal', 'urial', 'white_sheep', 'wild_sheep']
senticnet['harthacnut'] = ['-0.92', '0', '0.659', '-0.81', '#sadness', '#disgust', 'negative', '-0.8', 'unripe', 'napping', 'off_guard', 'off_one_guard', 'unprepared']
senticnet['harum-scarum'] = ['0', '0', '-0.92', '-0.98', '#fear', '#disgust', 'negative', '-0.95', 'carefree', 'freewheeling', 'slaphappy', 'happy-go-lucky', 'devil-may-care']
senticnet['harun_farocki'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'indirectness', 'mediateness', 'characteristic', 'allusiveness']
senticnet['harvest'] = ['0', '0.072', '0', '0.074', '#interest', '#admiration', 'positive', '0.073', 'food_production', 'grow_food', 'grow_crop', 'grow_stuff', 'cultivation']
senticnet['harvest_fall'] = ['0.91', '0.808', '0', '0', '#joy', '#interest', 'positive', '0.859', 'find_mall', 'mall', 'require_good_health', 'eat', 'arcade']
senticnet['harvest_food'] = ['0', '0.105', '0', '0.1', '#interest', '#admiration', 'positive', '0.103', 'agricultural', 'grow_food', 'cultivation', 'grow_stuff', 'grow_crop']
senticnet['harvest_mouse'] = ['-0.55', '0', '0.955', '-0.96', '#anger', '#disgust', 'negative', '-0.82', 'house_mouse', 'mouse', 'rat', 'fieldmouse', 'nude_mouse']
senticnet['harvested_commercially'] = ['0.046', '0', '0.041', '0.102', '#joy', '#admiration', 'positive', '0.036', 'good_eat', 'delicious', 'yummy', 'tasty', 'mouth_watering']
senticnet['harvested_mechanically'] = ['0.609', '-0.74', '0', '0.716', '#surprise', '#admiration', 'positive', '0.69', 'malacca', 'sword', 'swagger_stick', 'sword_cane', 'sword_stick']
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senticnet['harvie_ward'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'sciolist', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
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senticnet['hasbro_inc'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'builder']
senticnet['hash'] = ['0.022', '-0.13', '0.153', '-0.31', '#anger', '#disgust', 'negative', '-0.07', 'codeine', 'morphine', 'infection', 'cocaine', 'nuclear_war']
senticnet['hash_algorithm'] = ['0.022', '-0.13', '0.153', '-0.31', '#anger', '#disgust', 'negative', '-0.07', 'codeine', 'morphine', 'infection', 'cocaine', 'nuclear_war']
senticnet['hash_code'] = ['0.022', '-0.13', '0.153', '-0.31', '#anger', '#disgust', 'negative', '-0.07', 'codeine', 'morphine', 'infection', 'cocaine', 'nuclear_war']
senticnet['hash_function'] = ['0.022', '-0.13', '0.153', '-0.31', '#anger', '#disgust', 'negative', '-0.07', 'codeine', 'morphine', 'infection', 'cocaine', 'nuclear_war']
senticnet['hash_table'] = ['0.022', '-0.13', '0.153', '-0.31', '#anger', '#disgust', 'negative', '-0.07', 'codeine', 'morphine', 'infection', 'cocaine', 'nuclear_war']
senticnet['hash_value'] = ['0.022', '-0.13', '0.153', '-0.31', '#anger', '#disgust', 'negative', '-0.07', 'codeine', 'morphine', 'infection', 'cocaine', 'nuclear_war']
senticnet['hashed_password'] = ['0.022', '-0.13', '0.153', '-0.31', '#anger', '#disgust', 'negative', '-0.07', 'codeine', 'morphine', 'infection', 'cocaine', 'nuclear_war']
senticnet['hashing_algorithm'] = ['0.022', '-0.13', '0.153', '-0.31', '#anger', '#disgust', 'negative', '-0.07', 'codeine', 'morphine', 'infection', 'cocaine', 'nuclear_war']
senticnet['hashing_function'] = ['0.022', '-0.13', '0.153', '-0.31', '#anger', '#disgust', 'negative', '-0.07', 'codeine', 'morphine', 'infection', 'cocaine', 'nuclear_war']
senticnet['hashish'] = ['-0.63', '0', '0.819', '-0.54', '#sadness', '#anger', 'negative', '-0.66', 'smack', 'hell_dust', 'nose_drop', 'thunder', 'skag']
senticnet['hashmap'] = ['0.022', '-0.13', '0.153', '-0.31', '#anger', '#disgust', 'negative', '-0.07', 'codeine', 'morphine', 'infection', 'cocaine', 'nuclear_war']
senticnet['hasid'] = ['0', '-0.00', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'hot_chick', 'atheist', 'computer_user', 'fastener_binder', 'monitor']
senticnet['hasidic_jew'] = ['0', '-0.00', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'hot_chick', 'atheist', 'computer_user', 'fastener_binder', 'monitor']
senticnet['haskell_wexler'] = ['-0.85', '0', '0.514', '-0.73', '#sadness', '#disgust', 'negative', '-0.70', 'flick', 'movie', 'film', 'picture', 'moving_picture']
senticnet['hasmonean'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['hassall'] = ['-0.20', '-0.10', '0.07', '-0.24', '#sadness', '#disgust', 'negative', '-0.10', 'flipper', 'sea_life', 'seafood', 'swim_animal', 'fillet']
senticnet['hasselt'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'feudal_lord', 'lord', 'master', 'overlord', 'king']
senticnet['hassle'] = ['-0.59', '0.162', '0.221', '-0.50', '#sadness', '#disgust', 'negative', '-0.28', 'annoy', 'harass', 'provoke', 'beset', 'chivvy']
senticnet['hassle-free'] = ['-0.91', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.89', 'pain-free', 'uncomplicated', 'straightforward', 'procedure', 'painless']
senticnet['hassle_free'] = ['-0.04', '0.172', '-0.05', '0.057', '#interest', '#admiration', 'positive', '0.034', 'carry_money', 'put_money', 'require_signature', 'store_money', 'keep_money']
senticnet['hastate_leaf'] = ['-0.66', '0', '0.971', '-0.75', '#anger', '#disgust', 'negative', '-0.79', 'lyrate_leaf', 'needle', 'elliptic_leaf', 'simple_leaf', 'obovate_leaf']
senticnet['haste'] = ['0.03', '0.104', '0', '0.149', '#interest', '#admiration', 'positive', '0.094', 'swift', 'quick', 'rapid', 'fast', 'quickness']
senticnet['hasten'] = ['-0.97', '0', '0.504', '-0.52', '#sadness', '#disgust', 'negative', '-0.66', 'rush', 'race', 'hotfoot', 'push_forward', 'hie']
senticnet['hastert_rule'] = ['0.034', '-0.02', '0', '0.057', '#joy', '#admiration', 'positive', '0.039', 'female_television_connector', 'channel_selector', 'power_switch', 'tuner', 'radio']
senticnet['hastily'] = ['-0.76', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'disgraceful', 'shameful', 'dishonorably', 'inglorious', 'dishonored']
senticnet['hastily-written'] = ['0', '0', '0.837', '-0.97', '#anger', '#disgust', 'negative', '-0.90', 'benedictory', 'connectors-electric', 'checks-printing', 're-articulation', 'curtain-call']
senticnet['hastiness'] = ['-0.56', '0', '0.828', '-0.68', '#anger', '#disgust', 'negative', '-0.69', 'abruptness', 'precipitateness', 'precipitousness', 'precipitance', 'precipitancy']
senticnet['hasty'] = ['-0.78', '0', '0.727', '0', '#sadness', '#anger', 'negative', '-0.75', 'haste', 'hurried', 'rash', 'sputter', 'deliberate']
senticnet['hat'] = ['0.08', '0.106', '-0.04', '0.112', '#interest', '#admiration', 'positive', '0.063', 'helmet', 'head_gear', 'cap', 'head_wear', 'head_protection']
senticnet['hat_off'] = ['0', '0', '0', '0.869', '#admiration', '#admiration', 'positive', '0.869', 'prosper', 'succeed', 'realize', 'fly_high', 'thrive']
senticnet['hat_shop'] = ['0', '0', '0.936', '-0.74', '#anger', '#disgust', 'negative', '-0.84', 'furniture_shop', 'chest', 'nervous_system', 'crotch', 'skeleton']
senticnet['hat_trick'] = ['0.749', '0', '0', '0.952', '#joy', '#admiration', 'positive', '0.851', 'high_class', 'citation', 'suspicion', 'goodness', 'prosperity']
senticnet['hatband'] = ['0.942', '-0.61', '0', '0.908', '#joy', '#admiration', 'positive', '0.82', 'adornment', 'cigar_band', 'stripe', 'neckband', 'pinstripe']
senticnet['hatch'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'chick', 'wild_bird', 'baby_bird', 'lay_egg', 'bird_home']
senticnet['hatchback'] = ['-0.05', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'plane_land', 'floss', 'floss_tooth', 'ornament', 'wear_dress']
senticnet['hatched_larva'] = ['0.702', '-0.95', '0', '0.997', '#surprise', '#admiration', 'positive', '0.883', 'fertile', 'fecund', 'conceptive', 'impregnable', 'rank']
senticnet['hatchet'] = ['0', '0', '-0.78', '-0.87', '#fear', '#disgust', 'negative', '-0.82', 'knife', 'sword', 'sharp_weapon', 'angle_grinder', 'bayonet']
senticnet['hatchet_job'] = ['-0.62', '0', '0.698', '-0.81', '#anger', '#disgust', 'negative', '-0.71', 'malignment', 'vilification', 'smear', 'character_assassination', 'libel']
senticnet['hatchet_man'] = ['-0.85', '0', '0.938', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'menial', 'lackey', 'servant', 'flunkey', 'familiar']
senticnet['hatchway'] = ['0', '0', '-0.80', '-0.85', '#fear', '#disgust', 'negative', '-0.83', 'skitter', 'scuttle', 'scamper', 'opening', 'scurry']
senticnet['hate'] = ['-0.71', '0', '0.83', '-0.96', '#anger', '#disgust', 'negative', '-0.83', 'angry', 'mad', 'hatred', 'anger', 'resentment']
senticnet['hate_boss'] = ['-0.10', '-0.01', '0', '-0.17', '#sadness', '#disgust', 'negative', '-0.08', 'stress', 'quit_job', 'much_work', 'hate_colleague', 'need_holiday']
senticnet['hate_colleague'] = ['-0.10', '-0.01', '0', '-0.17', '#sadness', '#disgust', 'negative', '-0.08', 'stress', 'quit_job', 'hate_boss', 'much_work', 'need_holiday']
senticnet['hate_speech'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'indecency']
senticnet['hate_woman'] = ['-0.87', '0', '0.81', '-0.96', '#sadness', '#disgust', 'negative', '-0.88', 'mortal', 'person', 'individual', 'misogynist', 'misogamist']
senticnet['hated_enemy'] = ['-0.09', '0', '-0.05', '-0.24', '#sadness', '#disgust', 'negative', '-0.13', 'war', 'enemy', 'arsenal', 'big_fight', 'conflict']
senticnet['hateful'] = ['-0.09', '0.213', '0.596', '-0.61', '#anger', '#disgust', 'negative', '-0.27', 'hatefully', 'detest', 'misanthropy', 'misopedia', 'misology']
senticnet['hatefully'] = ['-0.09', '0.213', '0.596', '-0.61', '#anger', '#disgust', 'negative', '-0.27', 'hateful', 'detest', 'misanthropy', 'misopedia', 'misology']
senticnet['hater'] = ['-0.55', '0', '0.689', '-0.80', '#anger', '#disgust', 'negative', '-0.68', 'abominator', 'loather', 'mortal', 'anglophobe', 'person']
senticnet['hath_beene'] = ['-0.69', '0', '0.883', '-0.91', '#anger', '#disgust', 'negative', '-0.82', 'stick_in', 'enclose', 'insert', 'feed_in', 'put_in']
senticnet['hatha'] = ['0', '0', '0', '0.93', '#admiration', '#admiration', 'positive', '0.93', 'lift_weight', 'release_stress', 'move_body', 'take_hike', 'change_color']
senticnet['hatha_yoga'] = ['0', '0', '0', '0.93', '#admiration', '#admiration', 'positive', '0.93', 'lift_weight', 'release_stress', 'move_body', 'take_hike', 'change_color']
senticnet['hatred'] = ['0', '0', '0.995', '0.327', '#anger', '#admiration', 'negative', '-0.33', 'mockery', 'rivalry', 'foe', 'enemy', 'duel']
senticnet['hatred_toward'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['hattiesburg'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
senticnet['hattiesburg_american'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
senticnet['hauberk'] = ['-0.50', '0', '0.937', '-0.59', '#anger', '#disgust', 'negative', '-0.67', 'restorative', 'medicine', 'medication', 'medicament', 'medicinal_drug']
senticnet['haugesund_airport'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['haughtily'] = ['0.981', '-0.88', '0', '0.939', '#joy', '#admiration', 'positive', '0.936', 'slavelike', 'submissive', 'servile', 'subservient', 'slavish']
senticnet['haughtiness'] = ['0.968', '-0.88', '0', '0.831', '#joy', '#surprise', 'positive', '0.893', 'superiority', 'pride', 'superbia', 'imperiousness', 'domineeringness']
senticnet['haughty'] = ['0', '0', '-0.86', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'imperious', 'supercilious', 'disdainful', 'lordly', 'overbearing']
senticnet['hauksbk'] = ['-0.7', '0', '0.746', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'insufficiency', 'sparseness', 'sparsity', 'abstemiousness', 'lack_of_quantity']
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senticnet['haul_rope'] = ['0', '0.142', '0', '0.125', '#interest', '#admiration', 'positive', '0.134', 'weight_machine', 'power_drill', 'screw', 'screwdriver', 'air_pump']
senticnet['hauled_aboard'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'lifebelt', 'save_life', 'lifeguard', 'life_jacket', 'safety_equipment']
senticnet['hauler'] = ['0.665', '-0.64', '0', '0.871', '#joy', '#admiration', 'positive', '0.728', 'garbage_man', 'garbageman', 'garbage_collector', 'garbage_carter', 'garbage_hauler']
senticnet['haulier'] = ['0.865', '-0.80', '0', '0.871', '#joy', '#admiration', 'positive', '0.847', 'garbage_man', 'garbageman', 'garbage_collector', 'garbage_carter', 'garbage_hauler']
senticnet['haunch'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'hip', 'joint', 'above_leg', 'knee']
senticnet['haunt'] = ['-0.13', '0.071', '0.694', '-0.67', '#anger', '#disgust', 'negative', '-0.35', 'crucify', 'dun', 'frustrating', 'aggravate', 'exasperation']
senticnet['haunt_house'] = ['0.105', '0.073', '0', '0', '#joy', '#interest', 'positive', '0.089', 'tavern', 'nightclub', 'rum', 'brandy', 'scotch']
senticnet['haunted_hill'] = ['-0.08', '-0.01', '-0.16', '-0.13', '#fear', '#disgust', 'negative', '-0.09', 'hockey_game', 'movie', 'scary_movie', 'cinema', 'cctv']
senticnet['haunting'] = ['-0.81', '0', '0.913', '-0.85', '#anger', '#disgust', 'negative', '-0.86', 'ghost', 'stalk', 'obsess', 'frequent', 'persistent']
senticnet['haunting_ballad'] = ['0.98', '-0.74', '0', '0.984', '#joy', '#admiration', 'positive', '0.903', 'prosperous', 'flourishing', 'successful', 'prospering', 'thriving']
senticnet['haunting_beauty'] = ['-0.79', '0.257', '-0.47', '-0.03', '#sadness', '#fear', 'negative', '-0.26', 'gloomful', 'glooming', 'darkness', 'gloominess', 'gloomily']
senticnet['haunting_melody'] = ['-0.74', '0.292', '-0.42', '-0.06', '#sadness', '#fear', 'negative', '-0.23', 'lowness', 'dispiritedness', 'low-spiritedness', 'dejectedness', 'downheartedness']
senticnet['hauntingly'] = ['0.873', '0', '0', '0.745', '#joy', '#admiration', 'positive', '0.809', 'memorably', 'enchantingly', 'terrifically', 'indescribably', 'unforgettably']
senticnet['hauntingly_beautiful'] = ['-0.74', '0.292', '-0.42', '-0.06', '#sadness', '#fear', 'negative', '-0.23', 'lowness', 'dispiritedness', 'low-spiritedness', 'dejectedness', 'downheartedness']
senticnet['hauser_wirth'] = ['0.07', '0', '0', '0', '#joy', '#joy', 'positive', '0.07', 'artist', 'artwork', 'sculpture', 'tapestry', 'drawing']
senticnet['haute'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'elegant', 'stylish', 'smart', 'posh', 'sleek']
senticnet['haute_cuisine'] = ['0.882', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.822', 'ferment_grape_juice', 'chef_special', 'bottle_wine', 'fine_dining', 'decanter']
senticnet['haute_hippie'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'elegant', 'stylish', 'smart', 'posh', 'sleek']
senticnet['hauteur'] = ['0.828', '-0.88', '0', '0.551', '#joy', '#surprise', 'positive', '0.755', 'superiority', 'pride', 'superbia', 'imperiousness', 'domineeringness']
senticnet['hauz'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['havana_cigar'] = ['0.031', '0', '-0.08', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'smoke_cigarette', 'smoke', 'cinder', 'tobacco_product', 'smoke_pipe']
senticnet['havana_harbor'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'firework', 'bright_flash', 'nuclear_weapon', 'earthquake', 'nuclear_bomb']
senticnet['have'] = ['0.702', '-0.65', '0', '0.788', '#joy', '#admiration', 'positive', '0.713', 'bonk', 'pair', 'take', 'couple', 'fornicate']
senticnet['have_accident'] = ['-0.95', '0', '0.046', '-0.97', '#sadness', '#disgust', 'negative', '-0.65', 'fall_down', 'lose_consciousness', 'car_accident', 'motorbike_accident', 'accident']
senticnet['have_baby'] = ['0.1', '0.216', '-0.18', '0.22', '#interest', '#admiration', 'positive', '0.088', 'maternity', 'birth', 'become_mom', 'hospital', 'maternity_ward']
senticnet['have_best'] = ['-0.67', '0', '0.879', '-0.51', '#sadness', '#anger', 'negative', '-0.69', 'crush', 'trounce', 'beat', 'beat_out', 'vanquish']
senticnet['have_breakfast'] = ['0.773', '0.782', '0', '0', '#joy', '#interest', 'positive', '0.778', 'awake', 'get_up_early', 'drink_coffee', 'drink_milk', 'wake_up']
senticnet['have_epilepsy'] = ['-0.97', '0', '0.332', '-0.99', '#sadness', '#disgust', 'negative', '-0.76', 'epilepsy_attack', 'crowd', 'humanity', 'epilepsy', 'sickness']
senticnet['have_food'] = ['0.851', '0.656', '0', '0.542', '#joy', '#interest', 'positive', '0.683', 'find_restaurant', 'obtain_food', 'bite_food', 'order_lunch', 'decide_eat']
senticnet['have_fun'] = ['0.056', '-0.01', '0.036', '0.073', '#joy', '#admiration', 'positive', '0.028', 'thrill_ride', 'amusement_park', 'drag_race', 'enjoyment', 'roller_coaster']
senticnet['have_fun_together'] = ['0.755', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.728', 'explore_underwater', 'pool_party', 'pool', 'swim', 'go_underwater']
senticnet['have_good_carrier'] = ['0.936', '0.749', '0', '0.871', '#joy', '#admiration', 'positive', '0.852', 'get_married', 'finish_college', 'have_good_job', 'make_parent_happy', 'support_family']
senticnet['have_good_job'] = ['0.936', '0.749', '0', '0.871', '#joy', '#admiration', 'positive', '0.852', 'get_married', 'finish_college', 'make_parent_happy', 'have_good_carrier', 'support_family']
senticnet['have_intercourse'] = ['0.563', '-0.97', '0', '0.607', '#surprise', '#admiration', 'positive', '0.715', 'have', 'pair', 'take', 'couple', 'fornicate']
senticnet['have_lunch'] = ['-1.0', '0', '-0.73', '0', '#sadness', '#fear', 'negative', '-0.86', 'use_restroom', 'order_french_fries', 'get_front', 'eat_french_fries', 'become_full']
senticnet['have_much'] = ['-0.05', '0.102', '0', '0.167', '#interest', '#admiration', 'positive', '0.072', 'orphan', 'take_child', 'youth', 'become_parent', 'progeny']
senticnet['have_problem'] = ['-0.96', '0', '0.88', '-0.94', '#sadness', '#disgust', 'negative', '-0.93', 'problem', 'trouble', 'unwanted_outcome', 'exam_failed', 'job_interview_failed']
senticnet['have_sex'] = ['0.892', '-0.85', '0', '0.575', '#joy', '#surprise', 'positive', '0.774', 'have', 'pair', 'take', 'couple', 'fornicate']
senticnet['have_weapon'] = ['-0.92', '0', '0.162', '-0.93', '#sadness', '#disgust', 'negative', '-0.67', 'kill', 'kill_person', 'fight_war', 'war', 'weapon']
senticnet['have_word'] = ['-0.99', '0', '0.658', '-0.88', '#sadness', '#disgust', 'negative', '-0.84', 'castigate', 'tell_off', 'brush_down', 'chasten', 'objurgate']
senticnet['haven'] = ['0', '0.78', '0', '0.987', '#interest', '#admiration', 'positive', '0.883', 'harbor', 'oasis', 'harbour', 'seaport', 'refuge']
senticnet['haversack'] = ['0', '-0.02', '0.018', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'shoulder_bag', 'water_bottle', 'satchel_bag', 'tote_bag', 'carry']
senticnet['havin'] = ['-0.53', '0', '0.508', '-0.74', '#sadness', '#disgust', 'negative', '-0.59', 'bedwetter', 'bed_wetter', 'mortal', 'wetter', 'person']
senticnet['having_committed_adultery'] = ['-0.76', '0', '0.995', '-0.94', '#anger', '#disgust', 'negative', '-0.90', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'excretion']
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senticnet['hear_well'] = ['0.216', '-0.12', '0.085', '-0.11', '#joy', '#surprise', 'positive', '0.034', 'hear_song', 'sing_along', 'earring', 'listen_well', 'tap_foot']
senticnet['hearer'] = ['0.034', '-0.02', '0', '0.057', '#joy', '#admiration', 'positive', '0.039', 'female_television_connector', 'channel_selector', 'power_switch', 'tuner', 'radio']
senticnet['hearing_disorder'] = ['-0.52', '0', '0.832', '-0.58', '#anger', '#disgust', 'negative', '-0.64', 'disability', 'disablement', 'handicap', 'impairment', 'sensorineural_hearing_loss']
senticnet['hearing_impaired_child'] = ['-0.94', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.94', 'drive_recklessly', 'discord', 'mute', 'deny', 'helpless']
senticnet['hearing_impairment'] = ['-0.96', '0', '0.522', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'disability', 'disablement', 'handicap', 'impairment', 'sensorineural_hearing_loss']
senticnet['hearing_officer'] = ['0', '0.146', '0', '0.077', '#interest', '#admiration', 'positive', '0.111', 'negotiator', 'judge_jury', 'arbiter', 'decide_guilt', 'become_judge']
senticnet['hearing_panel'] = ['0', '0.146', '0', '0.077', '#interest', '#admiration', 'positive', '0.111', 'negotiator', 'judge_jury', 'arbiter', 'decide_guilt', 'become_judge']
senticnet['hearken'] = ['0.887', '-0.84', '0', '0.819', '#joy', '#surprise', 'positive', '0.851', 'arouse', 'elicit', 'enkindle', 'kindle', 'evoke']
senticnet['hearkens_back'] = ['0.807', '-0.83', '0', '0.688', '#joy', '#surprise', 'positive', '0.776', 'folly', 'lunacy', 'craziness', 'indulgence', 'foolery']
senticnet['hearsay'] = ['-0.56', '0', '0.593', '-0.59', '#anger', '#disgust', 'negative', '-0.58', 'antinomy', 'paradox', 'falsehood', 'contradiction_in_term', 'untruth']
senticnet['hearsay_exception'] = ['-0.63', '0', '0.967', '-0.67', '#anger', '#disgust', 'negative', '-0.76', 'condemnatory', 'condemning', 'accusatory', 'accusing', 'accusive']
senticnet['hearsay_rule'] = ['-0.63', '0', '0.967', '-0.67', '#anger', '#disgust', 'negative', '-0.76', 'condemnatory', 'condemning', 'accusatory', 'accusing', 'accusive']
senticnet['hearsay_testimony'] = ['-0.21', '0.16', '-0.19', '0.057', '#sadness', '#fear', 'negative', '-0.04', 'murder', 'make_judgment', 'call_police', 'arrest_criminal', 'tell_truth']
senticnet['hearse'] = ['0', '0', '-0.04', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'good_band', 'band', 'kit', 'procession', 'oblivion']
senticnet['heart'] = ['0.249', '0.33', '-0.19', '0.143', '#joy', '#interest', 'positive', '0.131', 'two_arms', 'nerve', 'liver', 'tummy', 'two_ears']
senticnet['heart-strings'] = ['0.995', '-0.95', '0', '0', '#joy', '#surprise', 'positive', '0.976', 'heartstrings', 'tugged', 'tugging', 'tug', 'cockle']
senticnet['heart-warming'] = ['0.929', '-0.74', '0', '0', '#joy', '#surprise', 'positive', '0.839', 'heartwarming', 'poignant', 'heart-wrenching', 'heartbreaking', 'endearing']
senticnet['heart-wrenching'] = ['0.932', '-0.74', '0', '0', '#joy', '#surprise', 'positive', '0.839', 'heart-warming', 'poignant', 'heartwarming', 'heartbreaking', 'endearing']
senticnet['heart_ailment'] = ['-0.98', '0', '0.827', '-0.94', '#sadness', '#disgust', 'negative', '-0.91', 'congestive_heart_failure', 'heart_attack', 'failure', 'heart_disease', 'cardiopathy']
senticnet['heart_and_soul'] = ['0.957', '-0.63', '0', '0.888', '#joy', '#admiration', 'positive', '0.826', 'content', 'quintessence', 'stuff', 'bare_bone', 'hypostasis']
senticnet['heart_arrhythmia'] = ['-0.98', '0', '0.827', '-0.94', '#sadness', '#disgust', 'negative', '-0.91', 'congestive_heart_failure', 'heart_attack', 'failure', 'heart_disease', 'cardiopathy']
senticnet['heart_attack'] = ['-0.82', '0', '-0.85', '-0.75', '#sadness', '#fear', 'negative', '-0.81', 'drug_addiction', 'die', 'old_age', 'apnea', 'call_help']
senticnet['heart_beat_fast'] = ['0.71', '0.772', '0', '0', '#joy', '#interest', 'positive', '0.741', 'health_improve', 'stare', 'extreme_elation', 'win_game', 'minor_injury']
senticnet['heart_block'] = ['-0.83', '0', '0.987', '-0.50', '#sadness', '#anger', 'negative', '-0.77', 'arrhythmia', 'atrioventricular_block', 'bradycardia', 'ventricular_fibrillation', 'tachycardia']
senticnet['heart_broken'] = ['-0.97', '0', '0.893', '-0.99', '#sadness', '#disgust', 'negative', '-0.95', 'bad_couple', 'wrong_person', 'fake_love', 'bad_person', 'bad_marriage']
senticnet['heart_bypass_surgery'] = ['0.934', '-0.88', '0', '0.797', '#joy', '#surprise', 'positive', '0.872', 'scan', 'checkup', 'scrutiny', 'medical_examination', 'medical_exam']
senticnet['heart_defect'] = ['-0.91', '0', '0.961', '-0.55', '#sadness', '#anger', 'negative', '-0.81', 'hypopigmentation', 'albinism', 'congenital_anomaly', 'congenital_defect', 'congenital_disorder']
senticnet['heart_disease'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'heart_problem', 'ill', 'illness', 'myocardiopathy', 'rheumatic_heart_disease']
senticnet['heart_failure'] = ['-0.98', '0', '0.827', '-0.94', '#sadness', '#disgust', 'negative', '-0.91', 'congestive_heart_failure', 'heart_attack', 'failure', 'heart_disease', 'cardiopathy']
senticnet['heart_line'] = ['0.689', '-0.56', '0', '0.74', '#joy', '#admiration', 'positive', '0.665', 'crease', 'crinkle', 'wrinkle', 'furrow', 'seam']
senticnet['heart_lung_transplant'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'displace', 'move', 'move_away', 'bump', 'dislocate']
senticnet['heart_muscle'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'chest', 'artery', 'vein', 'internal_organ', 'under_head']
senticnet['heart_problem'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'heart_disease', 'ill', 'illness', 'myocardiopathy', 'rheumatic_heart_disease']
senticnet['heart_rate'] = ['0.958', '-0.72', '0', '0.72', '#joy', '#surprise', 'positive', '0.801', 'pressor', 'hormone', 'endocrine', 'internal_secretion', 'catecholamine']
senticnet['heart_rate_increase'] = ['0', '0', '-0.90', '-0.84', '#fear', '#disgust', 'negative', '-0.87', 'breathlessness', 'breaheavily', 'get_exhausted', 'get_out_breath', 'heavy_breathe']
senticnet['heart_rhythm'] = ['-0.83', '0', '0.987', '-0.50', '#sadness', '#anger', 'negative', '-0.77', 'heart_block', 'atrioventricular_block', 'bradycardia', 'ventricular_fibrillation', 'tachycardia']
senticnet['heart_throb'] = ['-0.19', '0', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.15', 'bad_child', 'misbehavior', 'bad_kid', 'bad_action', 'difficulty']
senticnet['heart_transplant'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'displace', 'move', 'move_away', 'bump', 'dislocate']
senticnet['heart_transplantation'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'displace', 'move', 'move_away', 'bump', 'dislocate']
senticnet['heart_wrenching_tale'] = ['-0.90', '0.138', '-0.63', '0.071', '#sadness', '#fear', 'negative', '-0.33', 'woeful', 'execrable', 'deplorable', 'woebegone', 'plaintiveness']
senticnet['heartache'] = ['-0.88', '0.142', '-0.64', '0', '#sadness', '#fear', 'negative', '-0.46', 'heartbreak', 'sorrow', 'guilty_conscience', 'woe', 'repent']
senticnet['heartbeat'] = ['0.734', '0.828', '0', '0', '#joy', '#interest', 'positive', '0.781', 'form_of_music', 'popular_music', 'style_music', 'hear_well', 'dancer']
senticnet['heartbreak'] = ['-0.85', '0', '-0.72', '-0.86', '#sadness', '#disgust', 'negative', '-0.81', 'repent', 'grieve', 'guilt_trip', 'guilt_feeling', 'repentant']
senticnet['heartbreaker'] = ['-0.72', '0', '-0.73', '0', '#sadness', '#fear', 'negative', '-0.72', 'brokenhearted', 'disappointment', 'heartbreaking', 'grief', 'sorrow']
senticnet['heartbreaking'] = ['-0.89', '0.161', '-0.61', '0.053', '#sadness', '#fear', 'negative', '-0.32', 'heartrending', 'grievous', 'grievously', 'aggrieve', 'brokenheartedness']
senticnet['heartbroken'] = ['0', '0', '0.996', '-0.66', '#anger', '#disgust', 'negative', '-0.82', 'dissatisfied', 'displeased', 'disrespect', 'contempt', 'upset']
senticnet['heartburn'] = ['-0.98', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.91', 'become_full', 'indigestion', 'stomach_ache', 'get_full', 'a_little_hungry']
senticnet['heartburning'] = ['0.046', '0.117', '0.772', '-0.67', '#anger', '#disgust', 'negative', '-0.32', 'grievance', 'resentfully', 'gall', 'rancor', 'resentful']
senticnet['hearten'] = ['0.655', '0.635', '-0.23', '0.3', '#joy', '#interest', 'positive', '0.338', 'cheerfully', 'cheerful', 'jolly_up', 'chirk_up', 'embolden']
senticnet['heartfelt'] = ['0.904', '0.844', '0', '0', '#joy', '#interest', 'positive', '0.874', 'seriously', 'devout', 'earnestness', 'earnestly', 'seriousness']
senticnet['heartfelt_apology'] = ['-0.89', '0.168', '-0.60', '0.05', '#sadness', '#fear', 'negative', '-0.32', 'remorsefully', 'contritely', 'contriteness', 'ruefully', 'attrition']
senticnet['heartfelt_appreciation'] = ['0.798', '0.754', '0', '0.779', '#joy', '#admiration', 'positive', '0.777', 'affection', 'joy', 'glad', 'admire', 'sympathetic']
senticnet['heartfelt_emotion'] = ['-0.89', '0', '0.722', '-0.94', '#sadness', '#disgust', 'negative', '-0.85', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['heartfelt_gratitude'] = ['0.798', '0.754', '0', '0.779', '#joy', '#admiration', 'positive', '0.777', 'affection', 'joy', 'glad', 'admire', 'sympathetic']
senticnet['heartfelt_remorse'] = ['-0.89', '0.168', '-0.60', '0.05', '#sadness', '#fear', 'negative', '-0.32', 'remorsefully', 'contritely', 'contriteness', 'ruefully', 'attrition']
senticnet['heartfelt_tribute'] = ['0.904', '0.844', '0', '0', '#joy', '#interest', 'positive', '0.874', 'seriously', 'devout', 'earnestness', 'earnestly', 'seriousness']
senticnet['hearth'] = ['-0.88', '0', '-0.76', '0', '#sadness', '#fear', 'negative', '-0.82', 'residence', 'accomodation', 'domicile', 'home', 'dwell_place']
senticnet['hearthrug'] = ['0.071', '0.127', '0', '0.197', '#interest', '#admiration', 'positive', '0.132', 'place_work', 'workplace', 'office', 'work', 'hang_wall']
senticnet['heartily'] = ['0.488', '0.661', '-0.25', '0.244', '#joy', '#interest', 'positive', '0.284', 'appreciativeness', 'gratefulness', 'unafraid', 'intrepidly', 'audacious']
senticnet['heartland_athletic_conference'] = ['-0.02', '0.1', '-0.07', '0.089', '#interest', '#admiration', 'positive', '0.023', 'functionary', 'employment', 'labor', 'employ', 'toil']
senticnet['heartleaf'] = ['-0.52', '0', '0.527', '-0.78', '#anger', '#disgust', 'negative', '-0.61', 'herb', 'black_snakeroot', 'wild_ginger', 'asarabacca', 'herbaceous_plant']
senticnet['heartless'] = ['-0.94', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.90', 'hardheartedness', 'coldheartedness', 'heartlessly', 'heartlessness', 'hardhearted']
senticnet['heartlessly'] = ['-0.75', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.85', 'hardheartedness', 'coldheartedness', 'heartlessness', 'heartless', 'hardhearted']
senticnet['heartlessness'] = ['-0.82', '0', '-0.84', '0', '#sadness', '#fear', 'negative', '-0.83', 'hardheartedness', 'coldheartedness', 'heartlessly', 'heartless', 'hardhearted']
senticnet['heartrending'] = ['-0.89', '0.161', '-0.61', '0.053', '#sadness', '#fear', 'negative', '-0.32', 'heartbreaking', 'grievous', 'grievously', 'aggrieve', 'brokenheartedness']
senticnet['heartsease'] = ['0.621', '0.648', '-0.25', '0.296', '#joy', '#interest', 'positive', '0.328', 'pacifically', 'repose', 'peace-loving', 'peaceably', 'peaceable']
senticnet['heartsick'] = ['-0.82', '0.226', '-0.46', '-0.05', '#sadness', '#fear', 'negative', '-0.27', 'despondence', 'heartsickness', 'disconsolateness', 'despondent', 'despondency']
senticnet['heartsickness'] = ['-0.82', '0.226', '-0.46', '-0.05', '#sadness', '#fear', 'negative', '-0.27', 'despondence', 'disconsolateness', 'heartsick', 'despondent', 'despondency']
senticnet['heartstrings'] = ['0.997', '-0.95', '0', '0', '#joy', '#surprise', 'positive', '0.976', 'heart-strings', 'tugged', 'tugging', 'tug', 'cockle']
senticnet['heartthrob'] = ['-0.19', '0', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.15', 'bad_child', 'misbehavior', 'bad_kid', 'bad_action', 'difficulty']
senticnet['heartwarming'] = ['0.934', '-0.74', '0', '0', '#joy', '#surprise', 'positive', '0.84', 'heart-warming', 'poignant', 'heart-wrenching', 'heartbreaking', 'endearing']
senticnet['heartwater'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'leptomeningitis', 'meningitis', 'epidemic_meningitis', 'brain_fever', 'cerebrospinal_fever']
senticnet['hearty'] = ['0.062', '0', '0.027', '0.055', '#joy', '#admiration', 'positive', '0.03', 'porridge', 'meal', 'broth', 'hamburger', 'appetizer']
senticnet['hearty_breakfast'] = ['0.026', '0.069', '0', '0', '#joy', '#interest', 'positive', '0.048', 'eat_food', 'eat_beef', 'chew_food', 'espresso', 'eat_much']
senticnet['hearty_dinner'] = ['-0.85', '0', '0.691', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'jinks', 'high_jinks', 'hijinks', 'high_jinx', 'revel']
senticnet['hearty_fare'] = ['0.128', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.085', 'banquet', 'rave', 'auction', 'limbo', 'loose_end']
senticnet['hearty_laugh'] = ['0.739', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.747', 'giggle', 'make_fun', 'make_others_laugh', 'hear_joke', 'laughter']
senticnet['hearty_meal'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'green_bread', 'rotten', 'disgusting', 'taste_awful', 'mouldy']
senticnet['hearty_welcome'] = ['0.736', '-0.66', '0', '0.777', '#joy', '#admiration', 'positive', '0.725', 'lucky', 'propitious', 'prosperous', 'favorable', 'good_luck']
senticnet['heat'] = ['-0.90', '0', '0.251', '-0.96', '#sadness', '#disgust', 'negative', '-0.70', 'burn_inside', 'hot', 'fire', 'warmth', 'burn']
senticnet['heat-loving'] = ['0', '0', '0.751', '-0.89', '#anger', '#disgust', 'negative', '-0.82', 'moisture-loving', 'water-loving', 'sun-loving', 'shade-loving', 'hydrophilic']
senticnet['heat_buildup'] = ['0.942', '-0.79', '0', '0.901', '#joy', '#admiration', 'positive', '0.88', 'backup', 'accretion', 'deposition', 'deposit', 'increase']
senticnet['heat_conductivity'] = ['0.03', '0.068', '-0.06', '0.062', '#interest', '#admiration', 'positive', '0.025', 'pacific_northwest', 'american_forest', 'treetop', 'bath', 'hot_air']
senticnet['heat_equation'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'assumption', 'precondition', 'provision', 'constraint', 'premiss']
senticnet['heat_exhaustion'] = ['-0.72', '0', '0.762', '-0.79', '#anger', '#disgust', 'negative', '-0.75', 'heat_hyperpyrexia', 'heat_prostration', 'prostration', 'sunstroke', 'thermic_fever']
senticnet['heat_flux'] = ['0.03', '0.068', '-0.06', '0.062', '#interest', '#admiration', 'positive', '0.025', 'pacific_northwest', 'american_forest', 'treetop', 'bath', 'hot_air']
senticnet['heat_food'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'cook_rice', 'roast_meat', 'cook_food', 'make_food_taste_better', 'cook_hamburger']
senticnet['heat_haze'] = ['0.085', '0.071', '0.01', '0.085', '#joy', '#admiration', 'positive', '0.058', 'blue_color', 'munchkin', 'imaginary', 'crystal_clear', 'water_sky']
senticnet['heat_home'] = ['0.608', '0.647', '-0.26', '0.325', '#joy', '#interest', 'positive', '0.328', 'warm_house', 'produce_heat', 'heat_house', 'warm_room', 'warm_place']
senticnet['heat_house'] = ['-0.91', '0', '0.871', '-0.99', '#sadness', '#disgust', 'negative', '-0.92', 'fire', 'heat', 'burn', 'flame', 'hot']
senticnet['heat_hyperpyrexia'] = ['-0.72', '0', '0.762', '-0.79', '#anger', '#disgust', 'negative', '-0.75', 'heat_exhaustion', 'heat_prostration', 'prostration', 'sunstroke', 'thermic_fever']
senticnet['heat_intolerance'] = ['-0.58', '0', '0.773', '-0.79', '#anger', '#disgust', 'negative', '-0.71', 'perspiration', 'sweating', 'polyhidrosis', 'sudation', 'hidrosis']
senticnet['heat_light'] = ['-0.14', '0', '-0.21', '-0.07', '#sadness', '#fear', 'negative', '-0.14', 'arson', 'burn_down_house', 'set_on_fire', 'rapid_oxidation', 'flame']
senticnet['heat_meal'] = ['0.78', '0.811', '0', '0', '#joy', '#interest', 'positive', '0.796', 'scrumble_egg', 'beat_egg', 'pan', 'breakfast', 'food']
senticnet['heat_measure'] = ['0.034', '0.096', '0.061', '0.067', '#interest', '#admiration', 'positive', '0.034', 'temperature_measure', 'celsius', 'measure_heat', 'circle_part', 'temperature_measurement']
senticnet['heat_pasteurization'] = ['-0.08', '-0.04', '0.023', '-0.07', '#sadness', '#disgust', 'negative', '-0.03', 'tier', 'make_toast', 'house_smell_good', 'get_hot', 'bread_crumb']
senticnet['heat_pot_water'] = ['-0.04', '-0.02', '0', '-0.10', '#sadness', '#disgust', 'negative', '-0.04', 'stir_pot', 'chop_vegetable', 'open_package', 'cut_food', 'cut_vegetable']
senticnet['heat_prostration'] = ['-0.72', '0', '0.762', '-0.79', '#anger', '#disgust', 'negative', '-0.75', 'heat_exhaustion', 'heat_hyperpyrexia', 'prostration', 'sunstroke', 'thermic_fever']
senticnet['heat_pump'] = ['0.046', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'cold', 'electricity', 'circulate_air', 'blow_air', 'breeze']
senticnet['heat_rash'] = ['0.048', '-0.00', '0.011', '0.064', '#joy', '#admiration', 'positive', '0.027', 'become_wet', 'wash_away_dirt', 'run_out_soap', 'cleanliness', 'clean_skin']
senticnet['heat_resistant'] = ['-0.69', '0', '0.516', '-0.94', '#sadness', '#disgust', 'negative', '-0.71', 'insubordinate', 'recalcitrant', 'obstreperous', 'defiant', 'resistive']
senticnet['heat_shimmer'] = ['-0.94', '0', '0.866', '-0.91', '#sadness', '#disgust', 'negative', '-0.90', 'movement', 'apparent_motion', 'motion', 'apparent_movement', 'optical_illusion']
senticnet['heat_sink'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'computer_part', 'system', 'cpu', 'pencil_pen', 'monitor']
senticnet['heat_source'] = ['0.091', '0', '0.034', '0.049', '#joy', '#admiration', 'positive', '0.035', 'lantern', 'candle', 'heater', 'illuminate_room', 'furnace']
senticnet['heat_stroke'] = ['-0.72', '0', '0.762', '-0.79', '#anger', '#disgust', 'negative', '-0.75', 'heat_hyperpyrexia', 'heat_prostration', 'prostration', 'sunstroke', 'thermic_fever']
senticnet['heat_syncope'] = ['-0.72', '0', '0.762', '-0.79', '#anger', '#disgust', 'negative', '-0.75', 'heat_hyperpyrexia', 'heat_prostration', 'prostration', 'sunstroke', 'thermic_fever']
senticnet['heat_towel_rack'] = ['0.987', '0.866', '0', '0', '#joy', '#interest', 'positive', '0.926', 'electric_toothbrush', 'towel', 't_shirt', 'toilet_seat', 'hair_brush']
senticnet['heat_transfer'] = ['0', '0.832', '0', '0.782', '#interest', '#admiration', 'positive', '0.807', 'flow', 'surface', 'research', 'result', 'study']
senticnet['heat_up'] = ['0', '0', '-0.94', '-0.92', '#fear', '#disgust', 'negative', '-0.93', 'corrode', 'light_up', 'burn_heat', 'dig_up', 'burn_up']
senticnet['heated_argument'] = ['-0.60', '0', '0.777', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'batrachomyomachia', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['heated_confrontation'] = ['-0.60', '0', '0.777', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'batrachomyomachia', 'wrangle', 'dustup', 'quarrel', 'row']
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senticnet['heavenly_body'] = ['0.837', '0', '0', '0.908', '#joy', '#admiration', 'positive', '0.873', 'celestial_body', 'around_earth', 'celestial_object', 'small_earth', 'crescent']
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senticnet['heaviest_rainfall'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'condensation', 'water_fall', 'falling_water', 'frozen_water', 'water_drop']
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senticnet['heavy-lidded'] = ['0', '-0.93', '0', '0.899', '#surprise', '#admiration', 'positive', '0.918', 'horn-rimmed', 'owl-like', 'owlish', 'black-rimmed', 'half-lidded']
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senticnet['heavy_breathing'] = ['0', '-0.05', '0.027', '0.05', '#surprise', '#admiration', 'positive', '0.025', 'exercise', 'catch_breath', 'need_break', 'rest_up', 'regain_energy']
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senticnet['heavy_duty_steel'] = ['0', '0', '0.03', '-0.12', '#anger', '#disgust', 'negative', '-0.07', 'hard_material', 'hard_mineral', 'hard', 'hard_rock', 'construction_vehicle']
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senticnet['heavy_equipment'] = ['-0.02', '-0.05', '0', '0.069', '#surprise', '#admiration', 'positive', '0.032', 'fixture', 'endowment', 'material', 'stuff', 'kit']
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senticnet['heavy_machinery'] = ['0.966', '0.799', '0', '0', '#joy', '#interest', 'positive', '0.883', 'mechanical_device', 'move_part', 'machine', 'work_well', 'make_car']
senticnet['heavy_makeup'] = ['-0.76', '0', '0.672', '-0.88', '#sadness', '#disgust', 'negative', '-0.77', 'attire', 'garb', 'dress', 'fancy_dress', 'masquerade']
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senticnet['heavy_metal_music'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'explicit', 'declared', 'definitive', 'graphic']
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senticnet['heavy_object'] = ['-0.02', '0.083', '0.041', '0.184', '#interest', '#admiration', 'positive', '0.051', 'keep_out', 'metal_bar', 'control_access', 'door_handle', 'use_farm']
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senticnet['heavy_person'] = ['0', '0', '0.071', '0', '#anger', '#anger', 'negative', '-0.07', 'fat', 'heavy_weight', 'overindulge', 'heavy', 'fast_food']
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senticnet['heavy_rainfall'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'condensation', 'water_fall', 'falling_water', 'frozen_water', 'water_drop']
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senticnet['heavy_siltation'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'dirtiness', 'befoulment', 'defilement', 'dinginess', 'dinge']
senticnet['heavy_slumber'] = ['0.828', '0.832', '0', '0', '#joy', '#interest', 'positive', '0.83', 'daze', 'fulgurant', 'eye-popping', 'fulgurous', 'dazzling']
senticnet['heavy_snow'] = ['0', '0', '-0.93', '-0.82', '#fear', '#disgust', 'negative', '-0.88', 'thunderstorm', 'snow_storm', 'storm', 'weather', 'rainy']
senticnet['heavy_snowfall'] = ['0', '0', '-0.93', '-0.82', '#fear', '#disgust', 'negative', '-0.88', 'thunderstorm', 'snow_storm', 'storm', 'weather', 'rainy']
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senticnet['heavy_surf'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'driftwood', 'sea_shell', 'scenic_view', 'naked_woman', 'tidal_wave']
senticnet['heavy_thunderstorm'] = ['-0.87', '0', '0.91', '-0.78', '#sadness', '#anger', 'negative', '-0.85', 'storm', 'thundershower', 'rain', 'line_storm', 'equinoctial_storm']
senticnet['heavy_toll'] = ['-0.92', '0', '0.946', '-0.56', '#sadness', '#anger', 'negative', '-0.81', 'forcefulness', 'intensiveness', 'impulse', 'vigor', 'energy']
senticnet['heavy_traffic'] = ['-0.98', '0', '0.989', '-0.93', '#sadness', '#anger', 'negative', '-0.97', 'flight_accident', 'dangerous', 'danger', 'air_traffic', 'air_traffic_jam']
senticnet['heavy_tread'] = ['0.979', '-0.69', '0', '0.576', '#joy', '#surprise', 'positive', '0.749', 'big_cheese', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['heavy_truck'] = ['0.035', '0.112', '-0.07', '0.195', '#interest', '#admiration', 'positive', '0.067', 'car_action', 'car_travel', 'steer_car', 'make_go', 'transportation']
senticnet['heavy_weight'] = ['-0.13', '0.145', '0.624', '-0.63', '#anger', '#disgust', 'negative', '-0.31', 'heavy', 'heaviness', 'fat', 'obese', 'burden']
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senticnet['heavyheartedness'] = ['-0.77', '0.277', '-0.44', '-0.05', '#sadness', '#fear', 'negative', '-0.24', 'heavyhearted', 'bored', 'world-weariness', 'weltschmerz', 'world-weary']
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senticnet['heavyweight'] = ['-0.90', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.84', 'welterweight', 'middleweight', 'champion', 'champ', 'featherweight']
senticnet['heavyweight_bout'] = ['0.944', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.967', 'meeting', 'meet', 'match', 'encounter', 'round']
senticnet['heavyweight_champion'] = ['0', '0', '0', '0.834', '#admiration', '#admiration', 'positive', '0.834', 'royal_person', 'royal_title', 'top', 'master', 'rank']
senticnet['heavyweight_fight'] = ['-0.94', '0', '0.863', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'poke', 'clout', 'lick', 'punch', 'biff']
senticnet['heavyweight_title_fight'] = ['0.944', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.967', 'meeting', 'meet', 'match', 'encounter', 'round']
senticnet['heb'] = ['0.673', '-0.66', '0', '0.566', '#joy', '#surprise', 'positive', '0.634', 'wickedness', 'impiety', 'impiousness', 'unjustness', 'dishonor']
senticnet['hebdomad'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
senticnet['hebraic'] = ['-0.71', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.77', 'hebraical', 'israelite', 'hebrew', 'jew', 'nazism']
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senticnet['hebrew'] = ['-0.70', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.76', 'hebraical', 'israelite', 'hebraic', 'jew', 'nazism']
senticnet['hebrew_alphabet'] = ['0.049', '0.113', '0.107', '0.162', '#interest', '#admiration', 'positive', '0.054', 'list_number', 'ordinal', 'count', 'numerical', 'enumeration']
senticnet['hebrew_bible'] = ['0', '0.106', '-0.05', '0.163', '#interest', '#admiration', 'positive', '0.072', 'band', 'kit', 'oblivion', 'rock_band', 'high_school_band']
senticnet['hebrew_congregation'] = ['0', '0.209', '0.026', '0.184', '#interest', '#admiration', 'positive', '0.122', 'church', 'confession', 'temple', 'cathedral', 'monastery']
senticnet['hebrew_scholar'] = ['-0.85', '0', '0.749', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'hairsplitter', 'fight', 'quarrel', 'obstructor', 'obstructer']
senticnet['hebridean_whale'] = ['0.024', '0.102', '-0.06', '0.074', '#interest', '#admiration', 'positive', '0.033', 'marine_mammal', 'large_mammal', 'pack_animal', 'mammal', 'smart_creature']
senticnet['hebron'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['heck'] = ['-0.24', '-0.10', '-0.11', '-0.37', '#sadness', '#disgust', 'negative', '-0.15', 'conflict', 'bloodshed', 'siege', 'fight', 'war']
senticnet['heckle'] = ['0', '0', '0.877', '-0.97', '#anger', '#disgust', 'negative', '-0.92', 'barrack', 'heckler', 'harass', 'razz', 'badger']
senticnet['heckler'] = ['0', '0', '0.876', '-0.98', '#anger', '#disgust', 'negative', '-0.92', 'barrack', 'heckle', 'harass', 'razz', 'badger']
senticnet['hectic'] = ['0', '0', '0', '-0.76', '#disgust', '#disgust', 'negative', '-0.76', 'fevered', 'feverish', 'feverishly', 'buck_fever', 'expectantly']
senticnet['hectic_flush'] = ['-0.66', '0', '0.782', '-0.52', '#sadness', '#anger', 'negative', '-0.65', 'lividness', 'pallidness', 'complexion', 'wanness', 'achromasia']
senticnet['hectic_pace'] = ['0.681', '-0.93', '0', '0.78', '#surprise', '#admiration', 'positive', '0.799', 'behavior', 'state', 'play', 'agency', 'activeness']
senticnet['hectic_schedule'] = ['0', '0', '0', '-0.76', '#disgust', '#disgust', 'negative', '-0.76', 'fevered', 'feverish', 'feverishly', 'buck_fever', 'expectantly']
senticnet['hectoring'] = ['0', '0', '-0.78', '-0.86', '#fear', '#disgust', 'negative', '-0.82', 'domineering', 'imperious', 'bossy', 'domineer', 'lord']
senticnet['hederacea'] = ['-0.92', '0', '0.912', '-0.94', '#sadness', '#disgust', 'negative', '-0.92', 'true_blackberry', 'sand_blackberry', 'blackberry_bush', 'blackberry', 'running_blackberry']
senticnet['hedge'] = ['-0.77', '0', '0.864', '0', '#sadness', '#anger', 'negative', '-0.81', 'hem_in', 'surround', 'environ', 'ring', 'skirt']
senticnet['hedge_fund_galleon_group'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'busy_bee', 'gopher', 'actor', 'doer', 'worker']
senticnet['hedge_trimmer'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'smart_technology', 'electric_toothbrush', 'hair_dryer', 'stay_healthy', 'wash_tooth']
senticnet['hedgehog_anti_submarine_spigot'] = ['0', '0', '-0.95', '-0.72', '#fear', '#disgust', 'negative', '-0.83', 'cannon', 'explosive', 'firearm', 'pistol', 'machine_gun']
senticnet['hedonic'] = ['0.795', '0', '0', '0.787', '#joy', '#admiration', 'positive', '0.791', 'entree', 'soup', 'wine_glass', 'salt_pepper_shaker', 'plate_silverware']
senticnet['hedonic_adaptation'] = ['0.627', '-0.65', '0', '0.968', '#surprise', '#admiration', 'positive', '0.749', 'overwhelm', 'overpower', 'sweep_over', 'whelm', 'overcome']
senticnet['hedonic_regression'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'indicator', 'retail_price_index', 'producer_price_index', 'wholesale_price_index', 'consumer_price_index']
senticnet['hedonism'] = ['0.601', '-0.84', '0', '0.872', '#surprise', '#admiration', 'positive', '0.773', 'bioethics', 'moral_philosophy', 'philosophy', 'casuistry', 'eudemonism']
senticnet['hedonist'] = ['0.95', '-0.77', '0', '0.578', '#joy', '#surprise', 'positive', '0.767', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['hedonistic'] = ['0', '0.095', '0', '0.05', '#interest', '#admiration', 'positive', '0.073', 'need_earn_money', 'earn_money', 'responsible', 'perform_service', 'go_work_to_earn_money']
senticnet['hedonistic_lifestyle'] = ['0.929', '-0.79', '0', '0.915', '#joy', '#admiration', 'positive', '0.88', 'privacy', 'seclusion', 'privateness', 'solitariness', 'loneliness']
senticnet['hee_bee_gee'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'lair', 'habitat', 'animal_home']
senticnet['heedful'] = ['0.709', '-0.61', '0', '0.762', '#joy', '#admiration', 'positive', '0.695', 'careful', 'mindful', 'resonant', 'evocative', 'redolent']
senticnet['heedless'] = ['-0.92', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.95', 'mindless', 'thoughtlessness', 'noticing', 'unaware', 'inadvertence']
senticnet['heedlessness'] = ['0.991', '-0.75', '0', '0.815', '#joy', '#admiration', 'positive', '0.852', 'incaution', 'incautiousness', 'mindlessness', 'rashness', 'imprudence']
senticnet['heel'] = ['-0.03', '-0.02', '0.065', '-0.02', '#sadness', '#anger', 'negative', '-0.02', 'leg', 'toe', 'hold_canvas', 'foot', 'ankle']
senticnet['heel_bone'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'socket', 'tooth_socket', 'alveolus', 'acetabulum', 'cotyloid_cavity']
senticnet['heel_cord'] = ['0.84', '0.76', '0', '0.894', '#joy', '#admiration', 'positive', '0.831', 'keep_skin_healthy', 'stay_healthy', 'stay_fit', 'maintain_good_health', 'eat_healthily']
senticnet['heel_injury'] = ['-0.78', '0', '0.554', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'rubor', 'redness', 'inflammation', 'tennis_elbow', 'lateral_epicondylitis']
senticnet['heel_pain'] = ['-0.98', '0', '0.678', '-0.95', '#sadness', '#disgust', 'negative', '-0.87', 'rubor', 'redness', 'inflammation', 'epicondylitis', 'lateral_epicondylitis']
senticnet['heel_stable'] = ['-0.57', '0', '0.571', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['heel_stick'] = ['-0.74', '0', '0.766', '0', '#sadness', '#anger', 'negative', '-0.75', 'use_needle', 'clothes_repair', 'woven', 'embroidery', 'thread_needle']
senticnet['heel_strike'] = ['-0.15', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.09', 'use_leg', 'walk_quickly', 'casual', 'take_step', 'strut']
senticnet['heeled'] = ['0', '0', '-0.9', '-0.87', '#fear', '#disgust', 'negative', '-0.88', 'thigh-high', 'over-the-knee', 'mid-calf', 'knee-high', 'ankle-high']
senticnet['heelflip'] = ['-0.03', '0.11', '0', '0.128', '#interest', '#admiration', 'positive', '0.069', 'freestyle', 'take_leak', 'jump_air', 'backstroke', 'go_underwater']
senticnet['heem'] = ['-0.63', '0', '0.791', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'little', 'pinch', 'modicum', 'small_indefinite_amount', 'indefinite_quantity']
senticnet['hefeweizen'] = ['0', '0.091', '-0.05', '0.073', '#interest', '#admiration', 'positive', '0.037', 'privately', 'hold_liquid', 'make_wish', 'rinse_dish', 'find_toilet']
senticnet['heft'] = ['-0.94', '0', '0.863', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'poke', 'knockout_punch', 'lick', 'punch', 'biff']
senticnet['hefty'] = ['0.743', '0', '0', '0.798', '#joy', '#admiration', 'positive', '0.77', 'muscular', 'goodly', 'sinewy', 'powerful', 'brawny']
senticnet['hefty_bag'] = ['0', '0.11', '-0.05', '0.149', '#interest', '#admiration', 'positive', '0.067', 'pack_clothes', 'hold_stuff', 'carry_stuff', 'carry_book', 'carry_belongings']
senticnet['hefty_profit'] = ['-0.03', '0.139', '0', '0.109', '#interest', '#admiration', 'positive', '0.07', 'exchange_money', 'purchase', 'sell_goods', 'make_rich', 'incur_debt']
senticnet['hegeds_born'] = ['0.975', '-0.66', '0', '0.813', '#joy', '#admiration', 'positive', '0.816', 'cry', 'yell', 'scream', 'shrill', 'call']
senticnet['hegelian_dialectic'] = ['0.992', '-0.66', '0', '0.536', '#joy', '#surprise', 'positive', '0.729', 'nous', 'mind', 'brain', 'psyche', 'head']
senticnet['hegemon'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'ascendance', 'autocracy', 'absolutism', 'tyrant']
senticnet['hegemonic'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'absolutism', 'autocracy', 'ascendancy', 'tyrant']
senticnet['hegemonic_power'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'ascendance', 'autocracy', 'absolutism', 'tyrant']
senticnet['hegemonistic'] = ['0.846', '-0.53', '0', '0.919', '#joy', '#admiration', 'positive', '0.766', 'correct', 'compensate', 'overcompensate', 'make_up', 'equate']
senticnet['hegemony'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'absolutism', 'autocracy', 'ascendancy', 'tyrant']
senticnet['hegira'] = ['-0.69', '0', '0.998', '-0.84', '#anger', '#disgust', 'negative', '-0.84', 'evasion', 'lam', 'break', 'skedaddle', 'escape']
senticnet['hegvald'] = ['-0.52', '0', '0.506', '-0.94', '#sadness', '#disgust', 'negative', '-0.65', 'gouger', 'swindler', 'defrauder', 'fraud', 'welcher']
senticnet['heideggerian'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'indirectness', 'mediateness', 'characteristic', 'allusiveness']
senticnet['height'] = ['0.028', '0.123', '0.071', '0.162', '#interest', '#admiration', 'positive', '0.06', 'angle', 'altitude', 'pinnacle', 'downward', 'tall']
senticnet['height_adjustment'] = ['0', '0', '0', '0.109', '#admiration', '#admiration', 'positive', '0.109', 'knob', 'door_knob', 'door_handle', 'handle', 'hinge']
senticnet['heighten'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'compound', 'intensify', 'deepen', 'potentiate', 'enhance']
senticnet['heightened_arousal'] = ['-0.05', '0.122', '-0.12', '0.129', '#interest', '#admiration', 'positive', '0.02', 'bad_odor', 'smell_odor', 'inhale', 'whine', 'blow_nose']
senticnet['heightened_reactivity'] = ['-0.84', '0', '0.8', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'rigor_mortis', 'natural_process', 'natural_action', 'activity', 'action']
senticnet['heightened_scrutiny'] = ['-0.79', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.75', 'scan', 'checkup', 'medical_checkup', 'medical_examination', 'medical_exam']
senticnet['heightened_security'] = ['0', '1', '0', '0', '#interest', '#interest', 'positive', '1', 'confident', 'confidence', 'optimistic', 'assured', 'engrossed']
senticnet['heightened_vigilance'] = ['0', '1', '0', '0', '#interest', '#interest', 'positive', '1', 'confident', 'confidence', 'optimistic', 'assured', 'engrossed']
senticnet['heightening'] = ['0.652', '-0.64', '0', '0.953', '#joy', '#admiration', 'positive', '0.751', 'intensifying', 'thickening', 'aggravating', 'exacerbating', 'deepening']
senticnet['heinous_act'] = ['-0.06', '0', '-0.15', '0', '#sadness', '#fear', 'negative', '-0.10', 'heroism', 'role_play', 'repeat', 'act', 'part_of_movie']
senticnet['heinous_crime'] = ['-0.06', '0', '-0.15', '0', '#sadness', '#fear', 'negative', '-0.10', 'heroism', 'role_play', 'repeat', 'act', 'part_of_movie']
senticnet['heinous_sin'] = ['0', '0', '0.753', '-0.72', '#anger', '#disgust', 'negative', '-0.73', 'sinful', 'human_emotion', 'call', 'demand', 'yearn']
senticnet['heinousness'] = ['-0.99', '0', '0.947', '-0.88', '#sadness', '#anger', 'negative', '-0.94', 'atrociousness', 'barbarousness', 'inhumanity', 'atrocity', 'barbarity']
senticnet['heinrich_himmler'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'healthy_person', 'man', 'pervert', 'compatriot']
senticnet['heinrich_schiff'] = ['-0.91', '0', '0.931', '-0.72', '#sadness', '#anger', 'negative', '-0.85', 'hit_with_baton', 'get_hurt', 'whip', 'arrow', 'hurt']
senticnet['heinz_gnthardt_balzs_tarczy'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'saltine', 'soda_cracker', 'cracker', 'water_biscuit', 'graham_cracker']
senticnet['heinz_kohut'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'indirectness', 'mediateness', 'characteristic', 'allusiveness']
senticnet['heir'] = ['-0.83', '0', '0.938', '0', '#sadness', '#anger', 'negative', '-0.88', 'decedent', 'testate', 'intestacy', 'testator', 'intestate']
senticnet['heir_apparent_heir_apparent'] = ['-0.69', '0', '0.692', '-0.66', '#sadness', '#anger', 'negative', '-0.68', 'wrinkle', 'crinkle', 'crease', 'crisp', 'ruck_up']
senticnet['heir_male'] = ['0', '0.769', '0', '0', '#interest', '#interest', 'positive', '0.769', 'get_rich', 'get_money', 'inherit_property', 'benefit', 'enjoy_life']
senticnet['heir_presumptive_heir_apparent'] = ['-0.69', '0', '0.692', '-0.66', '#sadness', '#anger', 'negative', '-0.68', 'wrinkle', 'crinkle', 'crease', 'crisp', 'ruck_up']
senticnet['heirloom_tomato'] = ['0', '-0.01', '0.037', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'pear', 'green_apple', 'apricot', 'cherry', 'lime']
senticnet['heirloom_tomato_salad'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'saltine', 'soda_cracker', 'cracker', 'water_biscuit', 'graham_cracker']
senticnet['heishman'] = ['0.627', '-0.65', '0', '0.968', '#surprise', '#admiration', 'positive', '0.749', 'overwhelm', 'overpower', 'sweep_over', 'whelm', 'overcome']
senticnet['heist'] = ['0.024', '0.152', '-0.06', '0.236', '#interest', '#admiration', 'positive', '0.087', 'vacation', 'exit', 'stance', 'take_break', 'holiday']
senticnet['heist_film'] = ['-0.57', '0', '0.721', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'bush', 'native_pomegranate', 'caper_tree', 'native_orange', 'common_caper']
senticnet['heist_movie'] = ['-0.85', '0', '0.514', '-0.73', '#sadness', '#disgust', 'negative', '-0.70', 'cinema_verite', 'movie', 'film', 'picture', 'moving_picture']
senticnet['heistand'] = ['0.746', '-0.88', '0', '0.723', '#joy', '#surprise', 'positive', '0.783', 'strike', 'run_into', 'hit', 'impinge_on', 'collide_with']
senticnet['heka_nut'] = ['-0.55', '0', '0.5', '-0.55', '#sadness', '#disgust', 'negative', '-0.53', 'remedy', 'cure', 'catholicon', 'therapeutic', 'curative']
senticnet['hekou'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
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senticnet['hemianopsia'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['hemichordata'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'seed_fern', 'gymnosperm']
senticnet['hemiepiphytic'] = ['0', '0', '-0.14', '-0.03', '#fear', '#disgust', 'negative', '-0.09', 'pine_cone', 'fallen_leaf', 'poison_ivy', 'detritus', 'tress']
senticnet['hemiphractidae_family'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'amphibian', 'aquatic']
senticnet['hemiplegia'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'nominal_aphasia', 'anomic_aphasia']
senticnet['hemipteran'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'giant_hornet', 'hornet', 'hornet_wasp']
senticnet['hemispatial_neglect'] = ['-0.73', '0', '0.955', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'visual_agnosia', 'auditory_agnosia', 'brain_disorder', 'encephalopathy', 'brain_disease']
senticnet['hemisphere'] = ['0', '0', '0', '0.119', '#admiration', '#admiration', 'positive', '0.119', 'planet', 'earth', 'spherical', 'planet_solar_system', 'terrain']
senticnet['hemispheric'] = ['0', '0', '0', '0.119', '#admiration', '#admiration', 'positive', '0.119', 'planet', 'earth', 'spherical', 'planet_solar_system', 'terrain']
senticnet['hemispheric_free'] = ['0', '0', '0', '0.119', '#admiration', '#admiration', 'positive', '0.119', 'planet', 'earth', 'spherical', 'planet_solar_system', 'terrain']
senticnet['hemispheric_integration'] = ['0', '0', '0', '0.119', '#admiration', '#admiration', 'positive', '0.119', 'planet', 'earth', 'spherical', 'planet_solar_system', 'terrain']
senticnet['hemispheric_trade'] = ['0', '0', '0', '0.119', '#admiration', '#admiration', 'positive', '0.119', 'planet', 'earth', 'spherical', 'planet_solar_system', 'terrain']
senticnet['hemispherical'] = ['0.06', '-0.03', '0.037', '0.075', '#joy', '#admiration', 'positive', '0.034', 'semicircle', 'trapezoid', 'circle', 'triangular', 'facet']
senticnet['hemline'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'bowel', 'scrotum', 'shoulder', 'liver', 'anus']
senticnet['hemlock'] = ['-0.89', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.87', 'spruce', 'pine', 'fir', 'balsam', 'larch']
senticnet['hemlock_poisonous_plant'] = ['-0.80', '0', '0.897', '-0.68', '#sadness', '#anger', 'negative', '-0.79', 'poisonous_plant', 'water_dropwort', 'water_hemlock', 'plant_life', 'poison_sumac']
senticnet['hemlock_water_dropwort'] = ['-0.79', '0', '0.906', '-0.67', '#sadness', '#anger', 'negative', '-0.79', 'water_dropwort', 'poisonous_plant', 'water_hemlock', 'plant_life', 'poison_sumac']
senticnet['hemlock_woolly_adelgid'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['hemodilution'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'keep_alive', 'body_system', 'body_working', 'blood_pressure', 'heart']
senticnet['hemodynamics'] = ['0.76', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.856', 'bodily_property', 'biology', 'physiology', 'neurophysiology', 'myology']
senticnet['hemoglobinuria'] = ['-0.56', '0', '0.906', '-0.80', '#anger', '#disgust', 'negative', '-0.76', 'jaundice_of_newborn', 'disease', 'icterus_neonatorum', 'kernicterus', 'symptom']
senticnet['hemolysin'] = ['0.772', '-0.56', '0', '0.824', '#joy', '#admiration', 'positive', '0.719', 'neutrophil', 'leukocyte', 'leucocyte', 'white_blood_cell', 'white_cell']
senticnet['hemolytic_anemia'] = ['-0.57', '0', '0.973', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['hemolytic_uremic_syndrome'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'inflammatory_disease', 'stomach_flu', 'intestinal_flu', 'pelvic_inflammatory_disease']
senticnet['hemorrhagic_fever'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'leptomeningitis', 'meningitis', 'epidemic_meningitis', 'brain_fever', 'cerebrospinal_fever']
senticnet['hemostat'] = ['0', '0', '0.873', '-0.92', '#anger', '#disgust', 'negative', '-0.89', 'punch', 'sting', 'disable', 'ache', 'vex']
senticnet['hemothorax'] = ['0.759', '-0.72', '0', '0.893', '#joy', '#admiration', 'positive', '0.793', 'manifestation', 'cry', 'reflection', 'acting_out', 'explosion']
senticnet['hemp_fiber'] = ['0.024', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'stump', 'plank', 'timber', 'paper', 'mahogany']
senticnet['hen'] = ['0.039', '0.089', '-0.08', '0.143', '#interest', '#admiration', 'positive', '0.048', 'bird', 'nightingale', 'stork', 'eagle', 'rooster']
senticnet['hen_baby'] = ['0.053', '0.091', '0', '0.152', '#interest', '#admiration', 'positive', '0.099', 'inside_egg', 'baby_chicken', 'oval_shape', 'lay_egg', 'hatch']
senticnet['hen_child'] = ['0.902', '0.995', '0', '0', '#joy', '#interest', 'positive', '0.949', 'hot_babe', 'hen_baby', 'chick', 'beautiful_girl', 'bird']
senticnet['hen_night'] = ['0', '-0.08', '0', '0', '#surprise', '#surprise', 'positive', '0.082', 'yo-yo', 'playroom', 'backgammon', 'action_figure', 'board_game']
senticnet['hen_party'] = ['0.884', '0', '0', '0', '#joy', '#joy', 'positive', '0.884', 'egalitarian', 'enjoy', 'drink', 'have_fun', 'senator']
senticnet['henbane'] = ['0.95', '-0.86', '0', '0.947', '#joy', '#admiration', 'positive', '0.92', 'content', 'lore', 'folklore', 'cognitive_content', 'story']
senticnet['henceforth'] = ['-0.91', '0', '0.576', '-0.95', '#sadness', '#disgust', 'negative', '-0.81', 'stop', 'halt', 'lock_in', 'lock', 'block']
senticnet['henchman'] = ['0.053', '0.091', '0', '0.152', '#interest', '#admiration', 'positive', '0.099', 'neck', 'body_aprt', 'nape', 'shoulder', 'head']
senticnet['henchmen'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'yeoman', 'outrider', 'yeoman_of_guard', 'beefeater', 'defender']
senticnet['henchwoman'] = ['-0.95', '0', '0.774', '0', '#sadness', '#anger', 'negative', '-0.86', 'argue', 'rival', 'disagreement', 'opposition', 'anti']
senticnet['hendersonii'] = ['-0.92', '0', '0.912', '-0.94', '#sadness', '#disgust', 'negative', '-0.92', 'true_blackberry', 'sand_blackberry', 'blackberry_bush', 'blackberry', 'running_blackberry']
senticnet['hennessy_gold_cup'] = ['0.744', '-0.77', '0', '0.661', '#joy', '#surprise', 'positive', '0.725', 'sugarcoat', 'honey', 'glaze', 'change_taste', 'sugar']
senticnet['henninger_yeah'] = ['0.783', '-0.63', '0', '0.829', '#joy', '#admiration', 'positive', '0.749', 'leg_of_lamb', 'lamb_roast', 'joint', 'roast', 'rack_of_lamb']
senticnet['henpecked_husband'] = ['0.806', '-0.70', '0', '0.687', '#joy', '#surprise', 'positive', '0.734', 'saphead', 'muggins', 'tomfool', 'fool', 'sap']
senticnet['henri_broch'] = ['-0.84', '0', '0.585', '-0.85', '#sadness', '#disgust', 'negative', '-0.76', 'subject', 'discipline', 'study', 'concern', 'research']
senticnet['henri_tomasi'] = ['0.101', '-0.04', '0.036', '0', '#joy', '#surprise', 'positive', '0.038', 'song', 'rap', 'jingle', 'chord', 'lyric']
senticnet['henryk'] = ['0', '0', '0', '0.1', '#admiration', '#admiration', 'positive', '0.1', 'find_earth', 'wax', 'plutonium', 'shine', 'melt']
senticnet['henryk_grecki'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'band_play', 'songwriter', 'rock_band']
senticnet['henselae'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'leptomeningitis', 'cerebrospinal_meningitis', 'meningitis', 'brain_fever', 'cerebrospinal_fever']
senticnet['henslow'] = ['-0.04', '0.192', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.054', 'hypothetical', 'supposition', 'scientific_statement', 'psychology', 'educate_guess']
senticnet['hep'] = ['0.61', '-0.98', '0', '0.795', '#surprise', '#admiration', 'positive', '0.796', 'enlightened', 'conversant', 'informed', 'hip', 'hip_to']
senticnet['hepa_air'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'water_inside', 'water_spray', 'teapot', 'bodily_waste', 'different_type']
senticnet['hepa_filter'] = ['0.628', '-0.96', '0', '0.682', '#surprise', '#admiration', 'positive', '0.759', 'electric_fan', 'blower', 'fan', 'punkah', 'device']
senticnet['heparinized_saline'] = ['0.648', '0.618', '-0.26', '0.332', '#joy', '#interest', 'positive', '0.334', 'exhilarate', 'teased', 'titillating', 'titillate', 'titillation']
senticnet['hepatic'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'bowel', 'scrotum', 'shoulder', 'anus', 'waist']
senticnet['hepatic_disease'] = ['-0.60', '0', '0.544', '-0.53', '#sadness', '#anger', 'negative', '-0.56', 'acute_renal_failure', 'acute_kidney_failure', 'chronic_renal_failure', 'chronic_kidney_failure', 'kidney_failure']
senticnet['hepatic_dysfunction'] = ['-0.99', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.78', 'dropsy', 'atrophedema', 'giant_hive', 'periodic_edema', 'lymphedema']
senticnet['hepatic_failure'] = ['-0.60', '0', '0.544', '-0.53', '#sadness', '#anger', 'negative', '-0.56', 'acute_renal_failure', 'acute_kidney_failure', 'chronic_renal_failure', 'chronic_kidney_failure', 'kidney_failure']
senticnet['hepatic_impairment'] = ['-0.32', '0.133', '-0.38', '0', '#sadness', '#fear', 'negative', '-0.19', 'big_monster', 'unsafe', 'phobia', 'mock', 'feel_scared']
senticnet['hepatic_involvement'] = ['-0.85', '0', '0.657', '-0.86', '#sadness', '#disgust', 'negative', '-0.79', 'pulseless_disease', 'temporal_arteritis', 'rubor', 'redness', 'arteritis']
senticnet['hepatic_metabolism'] = ['0.992', '-0.80', '0', '0.584', '#joy', '#surprise', 'positive', '0.795', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'incontinence']
senticnet['hepatitis'] = ['-0.88', '0', '0.864', '-0.79', '#sadness', '#anger', 'negative', '-0.84', 'viral_hepatitis', 'disease', 'feel_sick', 'liver_disease', 'infectious_disease']
senticnet['hepatocellular'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'bowel', 'scrotum', 'shoulder', 'anus', 'waist']
senticnet['hepatomegaly'] = ['-0.58', '0', '0.773', '-0.79', '#anger', '#disgust', 'negative', '-0.71', 'perspiration', 'sweating', 'polyhidrosis', 'sudation', 'hidrosis']
senticnet['hepatosplenomegaly'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema', 'erythema_multiforme', 'windburn', 'hickey']
senticnet['hepatotoxic'] = ['-0.18', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.16', 'hazardous_health', 'dangerous_health', 'bad_health', 'gross', 'harmful']
senticnet['hepcat'] = ['0.797', '-0.95', '0', '0.921', '#surprise', '#admiration', 'positive', '0.891', 'lush', 'alcoholic', 'alky', 'dipsomaniac', 'boozer']
senticnet['heptagon'] = ['0.763', '-0.61', '0', '0.777', '#joy', '#admiration', 'positive', '0.717', 'acute_triangle', 'wedge_shape', 'cuneus', 'wedge', 'oblique_triangle']
senticnet['heptagonal'] = ['0.027', '0.139', '0', '0.203', '#interest', '#admiration', 'positive', '0.123', 'horse_shoes', 'four_leaf_clover', 'rotary_phone', 'ping_pong', 'billiards']
senticnet['heptane'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
senticnet['heracleum_sphondylium'] = ['0.861', '-0.87', '0', '0.597', '#joy', '#surprise', 'positive', '0.779', 'herb', 'cultivated_parsnip', 'wild_parsnip', 'madnep', 'herbaceous_plant']
senticnet['herald'] = ['0', '0', '-0.78', '-1', '#fear', '#disgust', 'negative', '-0.89', 'forerunner', 'announce', 'precursor', 'predecessor', 'harbinger']
senticnet['herald_newspaper'] = ['-0.05', '0.106', '0', '0.06', '#interest', '#admiration', 'positive', '0.036', 'buy', 'buy_newspaper', 'read_buy_newspaper', 'buy_book', 'read_news']
senticnet['heraldic'] = ['-0.60', '0', '0.523', '-0.95', '#sadness', '#disgust', 'negative', '-0.69', 'blabbermouthed', 'expansive', 'communicatory', 'talkative', 'leaky']
senticnet['heraldic_badge'] = ['-0.62', '0', '0.961', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'jeweled_headdress', 'diadem', 'tiara', 'headdress', 'headgear']
senticnet['heraldic_charge'] = ['0.042', '-0.00', '0.027', '0.099', '#joy', '#admiration', 'positive', '0.031', 'chess_knight', 'chess_queen', 'chess_pawn', 'pawn', 'chess_bishop']
senticnet['heraldic_crest'] = ['0.03', '0', '0.017', '0.138', '#joy', '#admiration', 'positive', '0.05', 'up_down', 'curl', 'bump', 'hand_gesture', 'goodbye']
senticnet['heraldic_device'] = ['0.03', '0', '0.017', '0.138', '#joy', '#admiration', 'positive', '0.05', 'up_down', 'curl', 'bump', 'hand_gesture', 'goodbye']
senticnet['heraldic_eagle'] = ['0', '0.105', '0', '0', '#interest', '#interest', 'positive', '0.105', 'beak', 'many_feathers', 'big_eye', 'bird', 'bird_mouth']
senticnet['heraldic_representation'] = ['0.03', '0', '0.017', '0.138', '#joy', '#admiration', 'positive', '0.05', 'up_down', 'curl', 'bump', 'hand_gesture', 'goodbye']
senticnet['heraldic_shield'] = ['0.586', '-0.65', '0', '0.503', '#joy', '#surprise', 'positive', '0.58', 'decoration', 'distinguished_service_cross', 'medal', 'medallion', 'palm']
senticnet['heraldic_symbol'] = ['-0.94', '0', '0.569', '-0.72', '#sadness', '#disgust', 'negative', '-0.74', 'bitumastic', 'veneer', 'veneering', 'adhesive', 'coating']
senticnet['heraldically'] = ['0.042', '-0.00', '0.027', '0.099', '#joy', '#admiration', 'positive', '0.031', 'chess_knight', 'chess_queen', 'chess_pawn', 'pawn', 'chess_bishop']
senticnet['herb'] = ['0.062', '0.115', '0', '0', '#joy', '#interest', 'positive', '0.088', 'lettuce', 'spinach', 'mint', 'baby_flower', 'cabbage']
senticnet['herb_garden'] = ['0.427', '0.227', '0', '0.37', '#joy', '#admiration', 'positive', '0.341', 'taken_seriously', 'complement', 'much_need', 'remission', 'knowledgeable']
senticnet['herbaceous_annual'] = ['-0.80', '0', '0.555', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'bush', 'wormwood', 'milkwort', 'germander', 'centaury']
senticnet['herbaceous_layer'] = ['-0.94', '0', '0.608', '-0.95', '#sadness', '#disgust', 'negative', '-0.83', 'wild_licorice', 'wild_liquorice', 'alpine_milk_vetch', 'purple_milk_vetch', 'milk_vetch']
senticnet['herbaceous_perennial'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'livelong', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['herbaceous_perennial_plant'] = ['-0.80', '0', '0.555', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'bush', 'wormwood', 'milkwort', 'germander', 'centaury']
senticnet['herbaceous_plant'] = ['-0.81', '0', '0.699', '-0.52', '#sadness', '#anger', 'negative', '-0.67', 'herb', 'spider_flower', 'spider_plant', 'stinking_clover', 'cleome']
senticnet['herbaceous_vegetation'] = ['-0.94', '0', '0.608', '-0.95', '#sadness', '#disgust', 'negative', '-0.83', 'wild_licorice', 'wild_liquorice', 'alpine_milk_vetch', 'purple_milk_vetch', 'milk_vetch']
senticnet['herbal'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'infective_agent', 'satisfier', 'quickener', 'agent']
senticnet['herbal_aroma'] = ['-0.73', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'unwed', 'unwedded', 'divorced', 'unmarried', 'widowed']
senticnet['herbal_ecstasy'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'use_drug', 'do_drug', 'drug_use', 'drug', 'addiction']
senticnet['herbal_medicine'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
senticnet['herbal_mixture'] = ['-0.55', '0', '0.5', '-0.55', '#sadness', '#disgust', 'negative', '-0.53', 'remedy', 'cure', 'catholicon', 'therapeutic', 'curative']
senticnet['herbal_remedy'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'infective_agent', 'satisfier', 'quickener', 'agent']
senticnet['herbal_treatment'] = ['-0.60', '0', '0.544', '-0.62', '#sadness', '#disgust', 'negative', '-0.59', 'remedy', 'cure', 'therapeutic', 'antidote', 'atropine']
senticnet['herbalism'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
senticnet['herbicide_glyphosate'] = ['-0.69', '0', '0.516', '-0.94', '#sadness', '#disgust', 'negative', '-0.71', 'insubordinate', 'recalcitrant', 'obstreperous', 'defiant', 'resistive']
senticnet['herbivore'] = ['0', '-0.77', '0', '0.861', '#surprise', '#admiration', 'positive', '0.82', 'animal', 'ecology', 'interaction', 'vegeterian', 'vegan']
senticnet['herbivory'] = ['-0.98', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.85', 'plant', 'stress', 'effect', 'tolerance', 'role']
senticnet['herbrand_universe'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly']
senticnet['hercule_poirot'] = ['-0.89', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.80', 'riddle', 'obituary', 'editorial', 'news_story', 'article']
senticnet['herculean'] = ['0', '0', '-0.99', '-0.94', '#fear', '#disgust', 'negative', '-0.97', 'arduous', 'gargantuan', 'laborious', 'thankless', 'near-impossible']
senticnet['herculean_strength'] = ['-0.94', '0', '0.745', '-0.97', '#sadness', '#disgust', 'negative', '-0.89', 'nerve', 'fearlessness', 'heart', 'dauntlessness', 'intrepidity']
senticnet['herculean_task'] = ['-0.03', '0.136', '-0.09', '0.105', '#interest', '#admiration', 'positive', '0.028', 'chore', 'hire_help', 'police_work', 'career', 'job']
senticnet['herd'] = ['0.032', '0.109', '-0.07', '0.085', '#interest', '#admiration', 'positive', '0.037', 'plural', 'kin', 'relate_person', 'younger', 'young_people']
senticnet['herd_sheep'] = ['0.109', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.087', 'social_animal', 'home_animal', 'household_animal', 'household_pet', 'domestic_animal']
senticnet['hereditary'] = ['0', '0.769', '0', '0', '#interest', '#interest', 'positive', '0.769', 'get_rich', 'get_money', 'inherit_property', 'benefit', 'enjoy_life']
senticnet['hereditary_angioedema'] = ['-0.57', '0', '0.973', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['hereditary_aristocracy'] = ['0.028', '0.12', '0.068', '0.176', '#interest', '#admiration', 'positive', '0.064', 'elite', 'number_person', 'count_person', 'small_group', 'enumeration']
senticnet['hereditary_chief'] = ['0', '0', '0', '0.79', '#admiration', '#admiration', 'positive', '0.79', 'greatness', 'absolute', 'sec', 'respectful', 'sentence_end']
senticnet['hereditary_condition'] = ['-0.50', '0', '0.542', '-0.51', '#anger', '#disgust', 'negative', '-0.51', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'hirschsprung_disease']
senticnet['hereditary_coproporphyria'] = ['-0.57', '0', '0.973', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['hereditary_disease'] = ['-0.57', '0', '0.973', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['hereditary_disorder'] = ['-0.50', '0', '0.542', '-0.51', '#anger', '#disgust', 'negative', '-0.51', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'hirschsprung_disease']
senticnet['hereditary_monarch'] = ['0.059', '0.092', '0', '0.107', '#interest', '#admiration', 'positive', '0.086', 'royalty', 'monarch', 'throne', 'emperor', 'king_queen']
senticnet['hereditary_monarchy'] = ['0.059', '0.092', '0', '0.107', '#interest', '#admiration', 'positive', '0.086', 'royalty', 'monarch', 'throne', 'emperor', 'king_queen']
senticnet['hereditary_peer'] = ['0.066', '0.163', '-0.06', '0.163', '#interest', '#admiration', 'positive', '0.083', 'lord', 'royal_title', 'almighty', 'duchess', 'omniscient']
senticnet['hereditary_prince'] = ['0.051', '0.097', '0', '0.065', '#interest', '#admiration', 'positive', '0.071', 'king', 'queen', 'duchess', 'throne', 'lord']
senticnet['heredity'] = ['-0.95', '0', '0.684', '-0.76', '#sadness', '#disgust', 'negative', '-0.79', 'innate', 'nurtural', 'nonheritable', 'nontransmissible', 'nonhereditary']
senticnet['hereford_cattle'] = ['0.546', '-0.96', '0', '0.533', '#joy', '#surprise', 'positive', '0.683', 'dairy_cattle', 'dairy_cow', 'milch_cow', 'ayrshire', 'milcher']
senticnet['heresy'] = ['-0.87', '0', '0', '-0.80', '#sadness', '#disgust', 'negative', '-0.84', 'heretical', 'heretic', 'religious', 'inquisition', 'controversial']
senticnet['heretic'] = ['-0.87', '0', '0', '-0.81', '#sadness', '#disgust', 'negative', '-0.84', 'heretical', 'heresy', 'religious', 'inquisition', 'controversial']
senticnet['heretical'] = ['-0.88', '0', '0', '-0.81', '#sadness', '#disgust', 'negative', '-0.84', 'heresy', 'heretic', 'religious', 'inquisition', 'controversial']
senticnet['heretofore'] = ['0.657', '-0.88', '0', '0.631', '#joy', '#surprise', 'positive', '0.723', 'cognisance', 'knowingness', 'awareness', 'feel', 'consciousness']
senticnet['hereupon'] = ['-0.91', '0', '0.521', '-0.81', '#sadness', '#disgust', 'negative', '-0.74', 'bullying', 'domineering', 'blustery', 'roughshod', 'cavalier']
senticnet['hergel'] = ['-0.80', '0', '0.815', '-0.98', '#anger', '#disgust', 'negative', '-0.86', 'inalienable', 'nontransferable', 'unassignable', 'untransferable', 'inviolable']
senticnet['herian'] = ['-0.75', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.83', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['hericium'] = ['-0.60', '0', '0.541', '-0.52', '#sadness', '#anger', 'negative', '-0.55', 'swoon', 'pass_out', 'faint', 'zonk_out', 'change_state']
senticnet['heritage'] = ['0.679', '-0.75', '0', '0.658', '#joy', '#surprise', 'positive', '0.698', 'moneymaking', 'acquisition', 'purchase', 'assumption', 'restitution']
senticnet['heritage_preservation'] = ['-0.97', '0', '0.885', '-0.97', '#sadness', '#disgust', 'negative', '-0.94', 'protection', 'conservation', 'environmentalism', 'reservation', 'immobilization']
senticnet['heritage_site'] = ['0.034', '0.13', '-0.08', '0.258', '#interest', '#admiration', 'positive', '0.085', 'tourism', 'nice_view', 'big_monument', 'temple', 'sightsee']
senticnet['herky_jerky'] = ['-0.89', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.85', 'high-strung', 'uptight', 'restive', 'overstrung', 'nervy']
senticnet['herm_scarf'] = ['0.824', '0.86', '0', '0.983', '#interest', '#admiration', 'positive', '0.889', 'look_smart', 'social_acceptance', 'improve_appearance', 'improve_look', 'look_good']
senticnet['hermann_rorschach'] = ['-0.84', '0', '0.585', '-0.85', '#sadness', '#disgust', 'negative', '-0.76', 'subject', 'discipline', 'study', 'concern', 'research']
senticnet['hermaphroditism'] = ['0.984', '-0.51', '0', '0.501', '#joy', '#surprise', 'positive', '0.667', 'birth_defect', 'congenital_anomaly', 'congenital_defect', 'congenital_disorder', 'congenital_abnormality']
senticnet['hermetic'] = ['0', '-0.95', '0', '0.742', '#surprise', '#admiration', 'positive', '0.849', 'air-tight', 'sealed', 'airtight', 'hermetically', 'water-tight']
senticnet['hermetically'] = ['0', '-0.95', '0', '0.748', '#surprise', '#admiration', 'positive', '0.849', 'air-tight', 'sealed', 'airtight', 'hermetic', 'water-tight']
senticnet['hermeticism'] = ['-0.51', '0', '0.979', '-0.89', '#anger', '#disgust', 'negative', '-0.79', 'invisible', 'concealed', 'nonvisual', 'hidden', 'out_of_sight']
senticnet['hermit'] = ['0', '0', '-0.10', '0', '#fear', '#fear', 'negative', '-0.10', 'lonely_person', 'vegetarian', 'raccoon', 'lab_rat', 'user']
senticnet['hermit-like'] = ['0', '0', '-0.75', '-0.86', '#fear', '#disgust', 'negative', '-0.80', 'paycheck-to-paycheck', 'moment-to-moment', 'hand-to-mouth', 'eking', 'day-in-day-out']
senticnet['hermitian'] = ['0.945', '0', '0', '0.75', '#joy', '#admiration', 'positive', '0.847', 'non-negative', 'eigenvalue', 'nonzero', 'nonnegative', 'real-valued']
senticnet['hermitic'] = ['-0.94', '0', '0.779', '-0.85', '#sadness', '#disgust', 'negative', '-0.86', 'eremitic', 'eremitical', 'cloistered', 'anchoritic', 'unworldly']
senticnet['hermsii'] = ['-0.75', '0', '0.69', '-0.69', '#sadness', '#disgust', 'negative', '-0.71', 'rubor', 'redness', 'sleeping_sickness', 'sleepy_sickness', 'cephalitis']
senticnet['hernia'] = ['0.087', '-0.03', '0', '0.063', '#joy', '#admiration', 'positive', '0.062', 'puppet', 'bouncy', 'curly', 'throw_object', 'glove_leather']
senticnet['hernia_operation'] = ['0.934', '-0.88', '0', '0.797', '#joy', '#surprise', 'positive', '0.872', 'scan', 'checkup', 'scrutiny', 'medical_examination', 'medical_exam']
senticnet['hernia_repair'] = ['-0.70', '0', '0.639', '-0.56', '#sadness', '#anger', 'negative', '-0.63', 'fibroma', 'adenoma', 'chondroma', 'enchondroma', 'benign_tumor']
senticnet['herniated_disc'] = ['-0.78', '0', '0.554', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'rubor', 'redness', 'inflammation', 'tennis_elbow', 'lateral_epicondylitis']
senticnet['herniation'] = ['0.777', '-0.89', '0', '0.508', '#joy', '#surprise', 'positive', '0.727', 'stretching', 'enlarge', 'enlargement', 'get_bigger', 'distension']
senticnet['hero'] = ['0', '0.991', '0', '0.804', '#interest', '#admiration', 'positive', '0.897', 'senator', 'save_life', 'egalitarian', 'pioneer', 'help_people']
senticnet['hero-worship'] = ['0', '0.747', '0', '0.727', '#interest', '#admiration', 'positive', '0.737', 'reverently', 'idolize', 'venerate', 'worship', 'veneration']
senticnet['hero_worship'] = ['0.591', '0.634', '-0.26', '0.328', '#joy', '#interest', 'positive', '0.321', 'laudably', 'praiseworthily', 'admirably', 'look_up_to', 'commendable']
senticnet['heroclix'] = ['0.031', '-0.10', '0', '0', '#joy', '#surprise', 'positive', '0.07', 'teddy_bear', 'beanie_baby', 'yo-yo', 'board_game', 'toy']
senticnet['heroic'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'intrepidly', 'hardy', 'fearlessness', 'fearlessly', 'unafraid']
senticnet['heroic_courage'] = ['0', '0', '0.764', '-0.77', '#anger', '#disgust', 'negative', '-0.77', 'restriction', 'tragedy', 'perpetrator', 'dominate', 'control']
senticnet['heroic_deed'] = ['-0.94', '0', '0.745', '-0.97', '#sadness', '#disgust', 'negative', '-0.89', 'nerve', 'fearlessness', 'heart', 'dauntlessness', 'intrepidity']
senticnet['heroic_devotion'] = ['0', '0', '0.764', '-0.77', '#anger', '#disgust', 'negative', '-0.77', 'restriction', 'tragedy', 'perpetrator', 'dominate', 'control']
senticnet['heroic_epic'] = ['0.089', '-0.01', '0.033', '0.082', '#joy', '#admiration', 'positive', '0.039', 'informative', 'informational', 'ephemeral', 'fun_read', 'ads']
senticnet['heroic_fantasy'] = ['0.747', '0.85', '0', '0', '#joy', '#interest', 'positive', '0.798', 'night_vision', 'mystery', 'plot', 'imagine', 'myth']
senticnet['heroic_poem'] = ['0.089', '-0.01', '0.033', '0.082', '#joy', '#admiration', 'positive', '0.039', 'informative', 'informational', 'ephemeral', 'fun_read', 'ads']
senticnet['heroic_sacrifice'] = ['0', '0', '0.764', '-0.77', '#anger', '#disgust', 'negative', '-0.77', 'restriction', 'tragedy', 'perpetrator', 'dominate', 'control']
senticnet['heroic_self_sacrifice'] = ['0', '0', '0.764', '-0.77', '#anger', '#disgust', 'negative', '-0.77', 'restriction', 'tragedy', 'perpetrator', 'dominate', 'control']
senticnet['heroic_struggle'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'patriotism', 'homeland']
senticnet['heroic_valor'] = ['-0.94', '0', '0.745', '-0.97', '#sadness', '#disgust', 'negative', '-0.89', 'nerve', 'fearlessness', 'heart', 'dauntlessness', 'intrepidity']
senticnet['heroically'] = ['0.743', '0', '0', '0.885', '#joy', '#admiration', 'positive', '0.814', 'unselfishly', 'sacrificially', 'courageously', 'selflessly', 'tirelessly']
senticnet['heroin'] = ['-0.63', '0', '0.819', '-0.54', '#sadness', '#anger', 'negative', '-0.66', 'smack', 'hell_dust', 'nose_drop', 'thunder', 'skag']
senticnet['heroin_abuse'] = ['-0.63', '0', '0.819', '-0.54', '#sadness', '#anger', 'negative', '-0.66', 'smack', 'hell_dust', 'nose_drop', 'thunder', 'skag']
senticnet['heroin_addict'] = ['0', '0', '0.821', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'student', 'serious_student', 'addiction', 'graduate_student', 'hard_worker']
senticnet['heroin_dealer'] = ['0', '0', '0.821', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'student', 'serious_student', 'addiction', 'graduate_student', 'hard_worker']
senticnet['heroin_habit'] = ['0', '0', '0.821', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'student', 'serious_student', 'addiction', 'graduate_student', 'hard_worker']
senticnet['heroin_purity'] = ['-0.63', '0', '0.819', '-0.54', '#sadness', '#anger', 'negative', '-0.66', 'smack', 'hell_dust', 'nose_drop', 'thunder', 'skag']
senticnet['heroine'] = ['0.806', '-0.57', '0', '0.744', '#joy', '#admiration', 'positive', '0.71', 'character', 'role', 'theatrical_role', 'part', 'persona']
senticnet['heroism'] = ['0', '0', '0.764', '-0.77', '#anger', '#disgust', 'negative', '-0.77', 'restriction', 'tragedy', 'perpetrator', 'dominate', 'control']
senticnet['heron'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'amoeba', 'single_celled']
senticnet['heron_tower'] = ['-0.81', '0', '0.543', '-0.63', '#sadness', '#disgust', 'negative', '-0.66', 'caper', 'pickle', 'relish', 'dill_pickle', 'sweet_pickle']
senticnet['herpes'] = ['-0.73', '-0.72', '-0.86', '-0.79', '#fear', '#disgust', 'negative', '-0.41', 'venereal_disease', 'overpopulation', 'std', 'loss_offreedom', 'lower_standard_live']
senticnet['herpes_genitalis'] = ['-0.98', '0', '0.703', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'venereal_disease', 'venereal_infection', 'social_disease', 'sexually_transmitted_disease', 'herpes_simplex']
senticnet['herpes_infection'] = ['-0.73', '-0.72', '-0.86', '-0.79', '#fear', '#disgust', 'negative', '-0.41', 'venereal_disease', 'overpopulation', 'std', 'loss_offreedom', 'lower_standard_live']
senticnet['herpes_outbreak'] = ['-0.73', '-0.72', '-0.86', '-0.79', '#fear', '#disgust', 'negative', '-0.41', 'venereal_disease', 'overpopulation', 'std', 'loss_offreedom', 'lower_standard_live']
senticnet['herpes_simplex'] = ['-0.98', '0', '0.703', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'venereal_disease', 'venereal_infection', 'social_disease', 'sexually_transmitted_disease', 'herpes_genitalis']
senticnet['herpes_simplex_infection'] = ['-0.72', '0', '0.778', '-0.98', '#anger', '#disgust', 'negative', '-0.82', 'actinic_dermatitis', 'hypericism', 'diaper_rash', 'dermatitis', 'seborrheic_dermatitis']
senticnet['herpes_simplex_type'] = ['-0.73', '-0.72', '-0.86', '-0.79', '#fear', '#disgust', 'negative', '-0.41', 'venereal_disease', 'overpopulation', 'std', 'loss_offreedom', 'lower_standard_live']
senticnet['herpes_simplex_virus'] = ['-0.96', '0', '0.726', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'herpes', 'labial_herpes', 'herpes_simplex', 'herpes_virus', 'virus']
senticnet['herpes_simplex_virus_hsv'] = ['-0.73', '-0.72', '-0.86', '-0.79', '#fear', '#disgust', 'negative', '-0.41', 'venereal_disease', 'overpopulation', 'std', 'loss_offreedom', 'lower_standard_live']
senticnet['herpes_virus'] = ['-0.96', '0', '0.726', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'herpes', 'labial_herpes', 'herpes_simplex', 'herpes_simplex_virus', 'virus']
senticnet['herpes_zoster'] = ['-0.73', '-0.72', '-0.86', '-0.79', '#fear', '#disgust', 'negative', '-0.41', 'venereal_disease', 'overpopulation', 'std', 'loss_offreedom', 'lower_standard_live']
senticnet['herpetic'] = ['-0.52', '0', '0.538', '-0.95', '#anger', '#disgust', 'negative', '-0.67', 'sanies', 'pus', 'purulence', 'gleet', 'ichor']
senticnet['herpetology'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'study_person', 'subject_study', 'zoo', 'zoologist', 'geology']
senticnet['herringbone'] = ['0', '0.068', '-0.06', '0.081', '#interest', '#admiration', 'positive', '0.028', 'pyramid', 'buckle', 'mexican_restaurant', 'anemone', 'japanese_person']
senticnet['hershey'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'wrapping_paper', 'wrap_gift', 'butcher_paper', 'paper']
senticnet['herzegovinian'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
senticnet['hesel'] = ['-0.11', '0', '-0.09', '0.082', '#sadness', '#fear', 'negative', '-0.04', 'stare', 'ear', 'facial_expression', 'mouth', 'dimple']
senticnet['hesitance'] = ['-0.93', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.94', 'hesitantly', 'hesitancy', 'hesitatingly', 'unassertive', 'unassertively']
senticnet['hesitancy'] = ['-0.82', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.79', 'hesitantly', 'hesitance', 'hesitatingly', 'unassertive', 'unassertively']
senticnet['hesitant'] = ['0.778', '-0.50', '0', '0.517', '#joy', '#admiration', 'positive', '0.599', 'on_fence', 'suspensive', 'undecided', 'indecisive', 'hesitating']
senticnet['hesitantly'] = ['-0.79', '0', '-0.70', '0', '#sadness', '#fear', 'negative', '-0.75', 'hesitance', 'hesitancy', 'hesitatingly', 'unassertive', 'unassertively']
senticnet['hesitate'] = ['-0.88', '0', '0.925', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'falter', 'waver', 'linger', 'boggle', 'dwell_on']
senticnet['hesitating'] = ['0.778', '-0.50', '0', '0.517', '#joy', '#admiration', 'positive', '0.599', 'on_fence', 'suspensive', 'undecided', 'hesitant', 'indecisive']
senticnet['hesitatingly'] = ['-0.85', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.82', 'hesitantly', 'hesitance', 'hesitancy', 'unassertive', 'unassertively']
senticnet['hesitation'] = ['-0.89', '0', '0.565', '-0.76', '#sadness', '#disgust', 'negative', '-0.73', 'irresolution', 'uncertainty', 'doubt', 'incertitude', 'dubiety']
senticnet['heston_aerodrome'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'cheapjack', 'pitchman']
senticnet['heston_aircraft'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'cheapjack', 'pitchman']
senticnet['hesychasm'] = ['0.853', '-0.95', '0', '0.605', '#joy', '#surprise', 'positive', '0.805', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['hesychast'] = ['0.853', '-0.95', '0', '0.605', '#joy', '#surprise', 'positive', '0.805', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['hetero'] = ['0.537', '-0.51', '0', '0.807', '#joy', '#admiration', 'positive', '0.619', 'lesbian', 'fairy', 'fagot', 'faggot', 'fag']
senticnet['hetero_diels_alder_reaction'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
senticnet['heterobasidion_annosum'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['heterochromia'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'blonde', 'purple', 'violet', 'dark_light', 'hue']
senticnet['heterocyclic_compound'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'enhance', 'intensify', 'deepen', 'potentiate', 'heighten']
senticnet['heterodera'] = ['-0.93', '0', '0.699', '-0.91', '#sadness', '#disgust', 'negative', '-0.84', 'onion_smut', 'head_smut', 'stinking_smut', 'flag_smut_fungus', 'covered_smut']
senticnet['heterodoxy'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'nonconformity', 'iconoclasm', 'nonconformism', 'nonconformance', 'orientation']
senticnet['heterogeneity'] = ['-0.64', '0', '0.773', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'diverseness', 'multifariousness', 'diversity', 'variety', 'nonuniformity']
senticnet['heterogeneous'] = ['-0.82', '0', '0.756', '-0.51', '#sadness', '#anger', 'negative', '-0.69', 'inhomogeneous', 'nonuniform', 'assorted', 'sundry', 'mixed']
senticnet['heterogeneous_storage'] = ['0.781', '-0.79', '0', '0.816', '#surprise', '#admiration', 'positive', '0.798', 'deliver_goods', 'succeed', 'win', 'bring_home_bacon', 'come_through']
senticnet['heterogeneousness'] = ['-0.91', '0', '0.905', '-0.92', '#sadness', '#disgust', 'negative', '-0.91', 'diverseness', 'multifariousness', 'diversity', 'variety', 'nonuniformity']
senticnet['heterogenous'] = ['-0.54', '0', '0.737', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'inhomogeneous', 'nonuniform', 'assorted', 'sundry', 'mixed']
senticnet['heterokont'] = ['0.807', '-0.86', '0', '0.611', '#joy', '#surprise', 'positive', '0.762', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['heterology'] = ['-0.54', '0', '0.979', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'unlikeness', 'dissimilitude', 'difference', 'dissimilarity', 'disparateness']
senticnet['heteronormative'] = ['-0.97', '0', '0.971', '-0.84', '#sadness', '#anger', 'negative', '-0.93', 'fail', 'betray', 'disappoint', 'cross', 'disillusion']
senticnet['heterophony'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['heterophylla'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'carnauba_palm', 'carnauba', 'wax_palm', 'palmetto', 'palmyra']
senticnet['heteropterous_insect'] = ['-0.77', '0', '0.576', '-0.92', '#sadness', '#disgust', 'negative', '-0.75', 'insect', 'firebug', 'water_bug', 'assassin_bug', 'reduviid']
senticnet['heteroscedasticity'] = ['-0.59', '0', '0.672', '-0.83', '#anger', '#disgust', 'negative', '-0.7', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'asymmetry', 'irregularity']
senticnet['heterosexual'] = ['0.833', '0', '0', '0.807', '#joy', '#admiration', 'positive', '0.82', 'lust', 'amiable', 'give_hug', 'giggle', 'smile']
senticnet['heterosexual_intercourse'] = ['0', '0.095', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'fragrant', 'use_decoration', 'colorful', 'green', 'cultivate_yard']
senticnet['heterosexual_marriage'] = ['0.839', '-0.70', '0', '0.93', '#joy', '#admiration', 'positive', '0.824', 'virtuous', 'chaste', 'pure', 'continent', 'virginal']
senticnet['heterosexual_relationship'] = ['0.839', '-0.70', '0', '0.93', '#joy', '#admiration', 'positive', '0.824', 'virtuous', 'chaste', 'pure', 'continent', 'virginal']
senticnet['heterosexual_sex'] = ['-0.84', '0', '0.882', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'perversion', 'fetishism', 'paraphilia', 'sexual_perversion', 'buggery']
senticnet['heterosexually_married'] = ['0.839', '-0.70', '0', '0.93', '#joy', '#admiration', 'positive', '0.824', 'virtuous', 'chaste', 'pure', 'continent', 'virginal']
senticnet['heterostracan'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'hag', 'osteostracan', 'cephalaspid', 'ostracoderm', 'jawless_fish']
senticnet['heterotopic_ossification'] = ['-0.59', '0', '0.911', '-0.87', '#anger', '#disgust', 'negative', '-0.79', 'induration', 'sclerosis', 'disseminated_sclerosis', 'multiple_sclerosis', 'pathology']
senticnet['heterozygosity_loh'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'deflection', 'warp', 'aberrance']
senticnet['hethe'] = ['0.049', '0.16', '0', '0.155', '#interest', '#admiration', 'positive', '0.121', 'castle', 'palace', 'medieval', 'aristocrat', 'throne']
senticnet['hetty_green'] = ['0.976', '0.811', '0', '0', '#joy', '#interest', 'positive', '0.893', 'deposit', 'bank_loan', 'wealth', 'finance', 'account']
senticnet['heuchera'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'livelong', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['heuristic'] = ['-0.91', '0', '0.598', '-0.52', '#sadness', '#anger', 'negative', '-0.68', 'stingy', 'penurious', 'scrimy', 'beggarly', 'chintzy']
senticnet['heusden'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'feudal_lord', 'lord', 'master', 'overlord', 'king']
senticnet['heut'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['hevajra'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['hevea'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'wall_cover', 'two-dimensional', 'cover_wall', 'pigment', 'dye']
senticnet['heverlee'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'feudal_lord', 'lord', 'master', 'overlord', 'king']
senticnet['hewn_out'] = ['-0.96', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.96', 'tunnel', 'international_space_station', 'stick_wall', 'car_race', 'auction']
senticnet['hewn_stone'] = ['0.026', '0.219', '0', '0.16', '#interest', '#admiration', 'positive', '0.135', 'large_build', 'palace', 'high_rise', 'structure', 'fortification']
senticnet['hex'] = ['0.521', '-0.55', '0', '0.657', '#surprise', '#admiration', 'positive', '0.577', 'bewitch', 'enchant', 'jinx', 'becharm', 'witch']
senticnet['hexachlorophene'] = ['0.701', '-0.76', '0', '0.904', '#surprise', '#admiration', 'positive', '0.791', 'curative', 'heal', 'medicine', 'get_cured', 'take_care']
senticnet['hexafluorophosphate'] = ['0.032', '-0.02', '-0.04', '-0.08', '#fear', '#disgust', 'negative', '-0.01', 'liquid', 'carbonate_drink', 'chemical', 'soda', 'bromide']
senticnet['hexafluorosilicate'] = ['-0.03', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'lithium', 'mineral', 'potassium', 'iodine', 'manganese']
senticnet['hexagon'] = ['0', '0.093', '0', '0.205', '#interest', '#admiration', 'positive', '0.149', 'form', 'body_type', 'figure', 'entity', 'shape']
senticnet['hexagonal'] = ['0.022', '0', '0.015', '0.096', '#joy', '#admiration', 'positive', '0.034', 'write_tool', 'write_implement', 'cylindric', 'write_device', 'write_paper']
senticnet['hexagonal_lattice'] = ['0.641', '-0.6', '0', '0.957', '#joy', '#admiration', 'positive', '0.733', 'ideal_solid', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'regular_octahedron']
senticnet['hexagonal_prism'] = ['0.06', '-0.03', '0.037', '0.075', '#joy', '#admiration', 'positive', '0.034', 'semicircle', 'trapezoid', 'circle', 'triangular', 'facet']
senticnet['hexamer'] = ['-0.02', '0.095', '0', '0.117', '#interest', '#admiration', 'positive', '0.063', 'identification', 'paper_square', 'carry_money', 'money_hold', 'credit']
senticnet['hexameter'] = ['0.075', '-0.01', '0.019', '0.067', '#joy', '#admiration', 'positive', '0.034', 'rhyme', 'paragraph', 'song_lyric', 'lyrics', 'stanza']
senticnet['hexamethylenediamine'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'carboxylic_acid', 'succinic_acid', 'valeric_acid']
senticnet['hexanedioic_acid'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'carboxylic_acid', 'adipic_acid', 'succinic_acid', 'valeric_acid']
senticnet['hexose'] = ['-0.52', '0', '0.681', '-0.76', '#anger', '#disgust', 'negative', '-0.65', 'triose', 'tetrose', 'pentose', 'simple_sugar', 'aldose']
senticnet['hey'] = ['0.756', '-1', '0', '0', '#joy', '#surprise', 'positive', '0.878', 'okay', 'yeah', 'gonna', 'gotta', 'alright']
senticnet['hey_baby'] = ['0.946', '-0.91', '0', '0.664', '#joy', '#surprise', 'positive', '0.841', 'pretender', 'phony', 'phoney', 'hypocrite', 'dissembler']
senticnet['hey_presto'] = ['0.746', '-0.67', '0', '0.529', '#joy', '#surprise', 'positive', '0.651', 'labor', 'cinch', 'undertaking', 'project', 'task']
senticnet['heyday'] = ['0.906', '-0.62', '0', '0.941', '#joy', '#admiration', 'positive', '0.825', 'honeymoon', 'period', 'time', 'efflorescence', 'millennium']
senticnet['hezrou'] = ['0.526', '-0.99', '0', '0.673', '#surprise', '#admiration', 'positive', '0.732', 'faery', 'faerie', 'fairy', 'leprechaun', 'sandman']
senticnet['hf_spectrum'] = ['-0.07', '0.078', '0', '0.078', '#interest', '#admiration', 'positive', '0.028', 'red_blue', 'red_orange', 'green_yellow', 'hue_tint', 'yellow_green']
senticnet['hhs_oig'] = ['0.801', '0.86', '0', '0', '#joy', '#interest', 'positive', '0.831', 'credit_card', 'profit', 'mastercard', 'american_express', 'back_to_black']
senticnet['hi-res'] = ['0', '0.796', '0', '0.797', '#interest', '#admiration', 'positive', '0.796', 'low-resolution', 'high-definition', 'full-color', 'high-resolution', 'imaging']
senticnet['hi-tech'] = ['0.892', '-0.95', '0', '0', '#joy', '#surprise', 'positive', '0.922', 'high-tech', 'low-tech', 'no-tech', 'environmentally-friendly', 'low-cost']
senticnet['hi_tech'] = ['0.843', '-0.99', '0', '0.706', '#joy', '#surprise', 'positive', '0.849', 'contrivance', 'gimbal', 'injector', 'mod_con', 'device']
senticnet['hiatal_hernia'] = ['-0.99', '0', '0.964', '-0.70', '#sadness', '#anger', 'negative', '-0.88', 'blepharitis', 'inflammation', 'epiglottitis', 'orchitis', 'prostatitis']
senticnet['hiatus'] = ['-0.77', '0', '0.905', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'defervescence', 'subsidence', 'break', 'remission', 'remittal']
senticnet['hib_vaccine'] = ['-0.07', '0', '-0.05', '-0.18', '#sadness', '#disgust', 'negative', '-0.10', 'medical_therapy', 'vaccination', 'doctor', 'medicine', 'hospital']
senticnet['hibernating'] = ['-0.85', '0', '-0.76', '0', '#sadness', '#fear', 'negative', '-0.80', 'soggy', 'dormant', 'inert', 'sluggish', 'torpid']
senticnet['hibernation'] = ['-0.75', '0', '0.512', '-0.75', '#sadness', '#disgust', 'negative', '-0.67', 'inactivity', 'inactiveness', 'slumber', 'inaction', 'dormancy']
senticnet['hiberniae'] = ['0', '0.105', '0', '0.084', '#interest', '#admiration', 'positive', '0.095', 'archive', 'print_medium', 'quiet_place', 'register', 'media']
senticnet['hibiki'] = ['-0.04', '0.138', '-0.08', '0.111', '#interest', '#admiration', 'positive', '0.031', 'geometry', 'craft', 'blade_geometry', 'blade', 'collect_item']
senticnet['hibiscus'] = ['0.951', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.9', 'excessive', 'temporize', 'clematis', 'temperate', 'sedate']
senticnet['hic_iacit'] = ['-0.7', '0', '0.746', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'insufficiency', 'sparseness', 'sparsity', 'abstemiousness', 'lack_of_quantity']
senticnet['hiccup'] = ['-0.23', '-0.13', '0.055', '-0.29', '#sadness', '#disgust', 'negative', '-0.11', 'back_pain', 'a_lot_of_noise', 'step_dog_shit', 'dog_bark', 'backache']
senticnet['hick'] = ['-0.96', '0', '0.924', '-0.86', '#sadness', '#anger', 'negative', '-0.91', 'redneck', 'yokel', 'rube', 'rustic', 'hayseed']
senticnet['hickey'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
senticnet['hicklin_test'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'indecency']
senticnet['hickok_belt'] = ['0', '0', '0', '0.97', '#admiration', '#admiration', 'positive', '0.97', 'increase_strength', 'keep_fit', 'fit_person', 'exercise', 'strengthen_heart']
senticnet['hickory_tree'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'black_locust']
senticnet['hicp'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'indicator', 'retail_price_index', 'producer_price_index', 'wholesale_price_index', 'consumer_price_index']
senticnet['hidden'] = ['-0.13', '0.07', '-0.10', '0.104', '#sadness', '#fear', 'negative', '-0.01', 'unseen', 'hide', 'conceal', 'hide_evidence', 'out_of_sight']
senticnet['hidden_behind'] = ['-0.13', '0.07', '-0.10', '0.104', '#sadness', '#fear', 'negative', '-0.01', 'conceal', 'trick', 'concealment', 'hide_body', 'hide_truth']
senticnet['hidden_beneath'] = ['-0.13', '0.07', '-0.10', '0.104', '#sadness', '#fear', 'negative', '-0.01', 'conceal', 'trick', 'concealment', 'hide_body', 'hide_truth']
senticnet['hidden_inside'] = ['-0.13', '0.07', '-0.10', '0.104', '#sadness', '#fear', 'negative', '-0.01', 'unseen', 'hide', 'conceal', 'hide_evidence', 'out_of_sight']
senticnet['hidden_treasure'] = ['0', '0.177', '0', '0.126', '#interest', '#admiration', 'positive', '0.151', 'treasure', 'storage_facility', 'shrine', 'lock_box', 'jewelry_box']
senticnet['hidden_underneath'] = ['-0.13', '0.07', '-0.10', '0.104', '#sadness', '#fear', 'negative', '-0.01', 'unseen', 'hide', 'conceal', 'hide_evidence', 'out_of_sight']
senticnet['hiddenness'] = ['0.779', '-0.75', '0', '0.526', '#joy', '#surprise', 'positive', '0.687', 'privateness', 'covertness', 'bosom', 'isolation', 'confidentiality']
senticnet['hide'] = ['-0.13', '0.07', '-0.10', '0.104', '#sadness', '#fear', 'negative', '-0.01', 'conceal', 'trick', 'concealment', 'hide_body', 'hide_truth']
senticnet['hide-a-bed'] = ['0', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.09', 'elderly_person', 'old_lady', 'homosexual', 'linen_chest', 'dust_mite']
senticnet['hide-a-bed_sofa'] = ['0.922', '-0.91', '0', '0', '#joy', '#surprise', 'positive', '0.92', 'sofa_bed', 'small_seat', 'beanbag_chair', 'airline_seat', 'chair']
senticnet['hide_behind'] = ['-0.02', '-0.02', '0.062', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'jump_up_down', 'tie_shoelace', 'jump_rope', 'hike', 'skate']
senticnet['hide_body'] = ['0', '0', '0.738', '-0.91', '#anger', '#disgust', 'negative', '-0.82', 'run_away', 'jump_over', 'murder', 'wilt', 'run_president']
senticnet['hide_evidence'] = ['0', '0', '0.877', '-0.97', '#anger', '#disgust', 'negative', '-0.92', 'choose_victim', 'stab_knife', 'take_life', 'use_weapon', 'homicide']
senticnet['hide_face'] = ['-0.91', '0', '0.429', '-0.96', '#sadness', '#disgust', 'negative', '-0.76', 'hide_identity', 'wear_mask', 'bad_person', 'thief', 'robbery']
senticnet['hide_from_view'] = ['-0.95', '0', '0.08', '-0.98', '#sadness', '#disgust', 'negative', '-0.67', 'lie', 'hide', 'fraud', 'camouflage', 'dishonesty']
senticnet['hide_identity'] = ['-0.38', '0.108', '-0.38', '0.072', '#sadness', '#fear', 'negative', '-0.14', 'undercover', 'hide', 'fake', 'hacker', 'internet']
senticnet['hide_mistake'] = ['-0.76', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.76', 'dissimulation', 'cover_up', 'camouflage', 'deception', 'error']
senticnet['hide_shell'] = ['0', '0.073', '0', '0.069', '#interest', '#admiration', 'positive', '0.071', 'race_hare', 'live_old', 'swim', 'sea_shell', 'hold_fish']
senticnet['hide_truth'] = ['-0.16', '0.117', '-0.22', '0.075', '#sadness', '#fear', 'negative', '-0.04', 'liar', 'mistake', 'deceive', 'phony', 'stay_out_jail']
senticnet['hideaway'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'lurking_place', 'spot', 'drop', 'hiding_place', 'den']
senticnet['hidebound'] = ['0.633', '-0.92', '0', '0.869', '#surprise', '#admiration', 'positive', '0.808', 'advocate', 'bigot', 'dogmatist', 'partisan', 'advocator']
senticnet['hideous'] = ['-0.16', '0.19', '0.572', '-0.62', '#anger', '#disgust', 'negative', '-0.29', 'dislikeable', 'antipathy', 'distaste', 'aversion', 'disapproval']
senticnet['hideously'] = ['-0.99', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.87', 'horrifying', 'atrocious', 'dreadfully', 'horrifyingly', 'horrified']
senticnet['hideousness'] = ['-0.87', '0', '0.584', '-0.71', '#sadness', '#disgust', 'negative', '-0.72', 'unsightliness', 'eyesore', 'ugliness', 'unattractiveness', 'garishness']
senticnet['hideout'] = ['0.828', '0.961', '0', '0', '#joy', '#interest', 'positive', '0.894', 'home_plate', 'mew', 'take_rest', 'live_place', 'bee_home']
senticnet['hides_behind'] = ['-0.13', '0.07', '-0.10', '0.104', '#sadness', '#fear', 'negative', '-0.01', 'conceal', 'trick', 'concealment', 'hide_body', 'hide_truth']
senticnet['hiding'] = ['0', '-0.78', '-0.84', '0', '#surprise', '#fear', 'negative', '-0.03', 'disguise', 'disguised', 'conceal', 'disguising', 'disguise']
senticnet['hiding_behind'] = ['-0.13', '0.07', '-0.10', '0.104', '#sadness', '#fear', 'negative', '-0.01', 'conceal', 'trick', 'concealment', 'hide_body', 'hide_truth']
senticnet['hiding_place'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'lurking_place', 'spot', 'drop', 'hideaway', 'den']
senticnet['hidrosis'] = ['-0.58', '0', '0.773', '-0.79', '#anger', '#disgust', 'negative', '-0.71', 'perspiration', 'sweating', 'diaphoresis', 'sudation', 'polyhidrosis']
senticnet['hie'] = ['-0.97', '0', '0.504', '-0.52', '#sadness', '#disgust', 'negative', '-0.66', 'rush', 'race', 'hotfoot', 'hasten', 'push_forward']
senticnet['hierarchical'] = ['0', '0.869', '0', '0.981', '#interest', '#admiration', 'positive', '0.925', 'bottom-up', 'top-down', 'command-and-control', 'decentralized', 'bureaucratic']
senticnet['hierarchical_menu'] = ['0.146', '0.095', '-0.04', '0', '#joy', '#interest', 'positive', '0.065', 'paper_menu', 'diner', 'cafe', 'restaurant', 'dine_room']
senticnet['hierarchically'] = ['0', '0.783', '0', '0.802', '#interest', '#admiration', 'positive', '0.792', 'hierarchical', 'grouped', 'thematically', 'chronologically', 'semantically']
senticnet['hierarchization'] = ['0.654', '-0.71', '0', '0.624', '#joy', '#surprise', 'positive', '0.664', 'viewable', 'gross', 'telescopic', 'seeable', 'visual']
senticnet['hierarchy'] = ['0.037', '0.143', '0', '0.09', '#interest', '#admiration', 'positive', '0.09', 'rank', 'military_order', 'supervisor', 'army_position', 'leader']
senticnet['hieratic'] = ['0', '0.887', '0', '0.868', '#interest', '#admiration', 'positive', '0.877', 'demotic', 'sacred', 'hieroglyph', 'priest', 'cuneiform']
senticnet['hieroglyph'] = ['0', '0.885', '0', '0.864', '#interest', '#admiration', 'positive', '0.875', 'demotic', 'sacred', 'hieratic', 'priest', 'cuneiform']
senticnet['hieroglyphic'] = ['0.719', '0.757', '0', '0', '#joy', '#interest', 'positive', '0.738', 'matriculate', 'enroll_class', 'enroll_school', 'enroll', 'register']
senticnet['hierusalem'] = ['-0.52', '0', '0.701', '-0.50', '#sadness', '#anger', 'negative', '-0.57', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['hifalutin'] = ['0.596', '-0.81', '0', '0.767', '#surprise', '#admiration', 'positive', '0.726', 'inflated', 'pretentious', 'snooty', 'highfalutin', 'portentous']
senticnet['higgledy_piggledy'] = ['-0.44', '-0.13', '-0.08', '-0.40', '#sadness', '#disgust', 'negative', '-0.19', 'disorder', 'untidy', 'mess', 'heap', 'chaos']
senticnet['higgs_boson'] = ['0.022', '0.073', '0', '0.14', '#interest', '#admiration', 'positive', '0.078', 'elementary_particle', 'electron', 'proton_neutron', 'neutron', 'proton']
senticnet['higgs_mechanism'] = ['-0.69', '0', '0.83', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'general_relativity', 'general_theory_of_relativity', 'general_relativity_theory', 'relativity', 'special_theory_of_relativity']
senticnet['higgs_particle'] = ['0.022', '0.073', '0', '0.14', '#interest', '#admiration', 'positive', '0.078', 'elementary_particle', 'electron', 'proton_neutron', 'neutron', 'proton']
senticnet['higgsino'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'elementary_particle', 'neutron', 'electron', 'subatomic_particle', 'proton']
senticnet['high'] = ['0', '0.14', '-0.04', '0.165', '#interest', '#admiration', 'positive', '0.085', 'upwards', 'high_land', 'altitude', 'top', 'upward']
senticnet['high-and-mighty'] = ['0', '0', '0.74', '-0.75', '#anger', '#disgust', 'negative', '-0.74', 'autocratic', 'dominating', 'bossy', 'magisterial', 'peremptory']
senticnet['high-brow'] = ['0', '0.912', '0', '0.697', '#interest', '#admiration', 'positive', '0.804', 'low-brow', 'highbrow', 'middlebrow', 'middle-brow', 'lowbrow']
senticnet['high-definition'] = ['0', '0.795', '0', '0.797', '#interest', '#admiration', 'positive', '0.796', 'low-resolution', 'high-resolution', 'full-color', 'hi-res', 'imaging']
senticnet['high-end'] = ['0.737', '0.732', '0', '0', '#joy', '#interest', 'positive', '0.734', 'next-generation', 'high-performance', 'scalable', 'state-of-the-art', 'innovative']
senticnet['high-five'] = ['0.749', '0', '0', '0.733', '#joy', '#admiration', 'positive', '0.741', 'self-stimulation', 'harasser', 'same-age', 'self-pleasure', 'self-pleasuring']
senticnet['high-flown'] = ['0.975', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.851', 'sublime', 'exalted', 'elevated', 'lofty', 'grand']
senticnet['high-hat'] = ['0', '0', '-0.93', '-0.94', '#fear', '#disgust', 'negative', '-0.94', 'contumelious', 'disdainfulness', 'scorn', 'disdainfully', 'disdainful']
senticnet['high-interest'] = ['0', '0', '0.906', '-0.74', '#anger', '#disgust', 'negative', '-0.82', 'higher-interest', 'high-rate', 'lower-interest', 'credit-card', 'low-interest']
senticnet['high-level'] = ['0', '0.887', '0', '0.88', '#interest', '#admiration', 'positive', '0.883', 'low-level', 'top-level', 'higher-level', 'lower-level', 'implementation']
senticnet['high-margin'] = ['0', '0.903', '0', '0.86', '#interest', '#admiration', 'positive', '0.881', 'big-ticket', 'high-ticket', 'high-price', 'bigger-ticket', 'low-ticket']
senticnet['high-necked'] = ['0', '0', '-0.90', '-0.80', '#fear', '#disgust', 'negative', '-0.85', 'double-breasted', 'button-front', 'collarless', 'button-down', 'long-sleeved']
senticnet['high-nutrition'] = ['0.753', '0', '0', '0.922', '#joy', '#admiration', 'positive', '0.838', 'nectar-producing', 'frost-tender', 'seed-producing', 'five-petaled', 'nectar-rich']
senticnet['high-output'] = ['0.933', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.832', 'replaceable', 'commercial-grade', 'rechargeable', 'long-lasting', 'long-life']
senticnet['high-paid'] = ['-0.77', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.77', 'sinecure', 'well-paid', 'cushy', 'low-paying', 'sinecure']
senticnet['high-performance'] = ['0.739', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.739', 'next-generation', 'high-end', 'scalable', 'state-of-the-art', 'innovative']
senticnet['high-pitched'] = ['0', '0.841', '0', '0.807', '#interest', '#admiration', 'positive', '0.824', 'higher', 'eminent', 'gamy', 'high', 'in_high_spirits']
senticnet['high-powered'] = ['0', '0.859', '0', '0.734', '#interest', '#admiration', 'positive', '0.796', 'bouncing', 'vigorous', 'munch', 'spicy', 'strident']
senticnet['high-pressure'] = ['0', '0', '-0.84', '-0.79', '#fear', '#disgust', 'negative', '-0.81', 'low-pressure', 'pressurized', 'high-temperature', 'low-temperature', 'superheated']
senticnet['high-price'] = ['0', '0.904', '0', '0.854', '#interest', '#admiration', 'positive', '0.879', 'big-ticket', 'high-margin', 'high-ticket', 'bigger-ticket', 'low-ticket']
senticnet['high-priced'] = ['0', '0', '-0.74', '-0.72', '#fear', '#disgust', 'negative', '-0.73', 'bargain-basement', 'low-priced', 'dirt-cheap', 'cut-price', 'cut-rate']
senticnet['high-profiled'] = ['0', '-0.86', '0', '0.815', '#surprise', '#admiration', 'positive', '0.839', 'generic-looking', 'well-visited', 'tower-style', 'well-thought-of', 'clever-clever']
senticnet['high-quality'] = ['0', '0.999', '0', '0.842', '#interest', '#admiration', 'positive', '0.92', 'top-quality', 'top-notch', 'highest-quality', 'first-rate', 'top-of-the-line']
senticnet['high-rate'] = ['0', '0', '0.904', '-0.75', '#anger', '#disgust', 'negative', '-0.82', 'higher-interest', 'high-interest', 'lower-interest', 'credit-card', 'low-interest']
senticnet['high-resolution'] = ['0', '0.803', '0', '0.798', '#interest', '#admiration', 'positive', '0.8', 'low-resolution', 'high-definition', 'full-color', 'hi-res', 'imaging']
senticnet['high-spirited'] = ['0.957', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.911', 'boil', 'enthusiastic', 'zestful', 'ebullient', 'ebulliently']
senticnet['high-strength'] = ['0', '0', '0.836', '-0.76', '#anger', '#disgust', 'negative', '-0.80', 'corrosion-resistant', 'non-corrosive', 'rustproof', 'wear-resistant', 'powder-coated']
senticnet['high-strung'] = ['-0.97', '0', '-0.98', '0', '#sadness', '#fear', 'negative', '-0.98', 'uptight', 'restive', 'overstrung', 'nervy', 'jumpy']
senticnet['high-tech'] = ['0', '0.815', '0', '0.713', '#interest', '#admiration', 'positive', '0.764', 'hi-tech', 'cutting-edge', 'high-technology', 'technology', 'state-of-the-art']
senticnet['high-technology'] = ['0', '0.806', '0', '0.71', '#interest', '#admiration', 'positive', '0.758', 'hi-tech', 'cutting-edge', 'high-tech', 'technology', 'state-of-the-art']
senticnet['high-temperature'] = ['0', '0', '-0.84', '-0.79', '#fear', '#disgust', 'negative', '-0.81', 'high-pressure', 'pressurized', 'low-pressure', 'low-temperature', 'superheated']
senticnet['high-ticket'] = ['0', '0.911', '0', '0.864', '#interest', '#admiration', 'positive', '0.887', 'big-ticket', 'high-margin', 'high-price', 'bigger-ticket', 'low-ticket']
senticnet['high-voltage'] = ['0', '0', '-0.98', '-0.90', '#fear', '#disgust', 'negative', '-0.94', 'danger', 'die', 'electrocute', 'kill', 'die_young']
senticnet['high-yield'] = ['0.981', '0.976', '0', '0', '#joy', '#interest', 'positive', '0.978', 'high-yielding', 'yield', 'investment-grade', 'fixed-income', 't-bill']
senticnet['high-yielding'] = ['0.974', '0.976', '0', '0', '#joy', '#interest', 'positive', '0.975', 'high-yield', 'yield', 'investment-grade', 'fixed-income', 't-bill']
senticnet['high_acidity'] = ['-0.82', '0', '0.901', '-0.65', '#sadness', '#anger', 'negative', '-0.79', 'taste', 'taste_sensation', 'gustatory_sensation', 'taste_perception', 'gustatory_perception']
senticnet['high_altar'] = ['0.023', '0.218', '0.01', '0.235', '#interest', '#admiration', 'positive', '0.116', 'confessional', 'altar', 'chapel', 'temple', 'christianity']
senticnet['high_altitude'] = ['0.023', '0.113', '0.053', '0.153', '#interest', '#admiration', 'positive', '0.059', 'high', 'high_land', 'incline_plane', 'top', 'diagonal']
senticnet['high_altitude_balloon'] = ['-0.04', '0.092', '0', '0.062', '#interest', '#admiration', 'positive', '0.036', 'hangar', 'aircraft_carrier', 'hot_air_balloon', 'war_museum', 'spaceship']
senticnet['high_altitude_flight'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'atmosphere', 'wing', 'nest', 'feather', 'bird_fly']
senticnet['high_animal'] = ['0', '0.072', '0', '0.09', '#interest', '#admiration', 'positive', '0.081', 'equine', 'wild_animal', 'rodent', 'primate', 'mustang']
senticnet['high_arched_palate'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'lordosis', 'spinal_curvature', 'humpback', 'hunchback', 'abnormality']
senticnet['high_attrition_rate'] = ['-0.89', '0.168', '-0.60', '0.05', '#sadness', '#fear', 'negative', '-0.32', 'remorsefully', 'contritely', 'contrition', 'contriteness', 'ruefully']
senticnet['high_avalanche_danger'] = ['0', '-0.03', '-0.10', '-0.07', '#fear', '#disgust', 'negative', '-0.04', 'autumn', 'winter', 'skier', 'snow_fall', 'volcano']
senticnet['high_blood_cholesterol'] = ['-0.85', '0', '-0.71', '-0.86', '#sadness', '#disgust', 'negative', '-0.81', 'get_exercise', 'train_marathon', 'run_marathon', 'run_long_distance', 'go_gym']
senticnet['high_blood_pressure'] = ['-0.85', '0', '-0.71', '-0.86', '#sadness', '#disgust', 'negative', '-0.81', 'get_exercise', 'train_marathon', 'run_marathon', 'run_long_distance', 'go_gym']
senticnet['high_blood_sugar'] = ['-0.85', '0', '-0.71', '-0.86', '#sadness', '#disgust', 'negative', '-0.81', 'get_exercise', 'train_marathon', 'run_marathon', 'run_long_distance', 'go_gym']
senticnet['high_brow'] = ['-0.88', '0', '0.666', '-0.63', '#sadness', '#anger', 'negative', '-0.73', 'clannishness', 'cliquishness', 'exclusiveness', 'arrogance', 'haughtiness']
senticnet['high_caliber'] = ['0.887', '-0.95', '0', '0.913', '#surprise', '#admiration', 'positive', '0.917', 'high_quality', 'superiority', 'level', 'grade', 'degree']
senticnet['high_calorie'] = ['0.105', '0.095', '0', '0.169', '#joy', '#admiration', 'positive', '0.123', 'delicious', 'yummy', 'sweet', 'creamy', 'tasty']
senticnet['high_chair'] = ['0.76', '0.843', '0', '0', '#joy', '#interest', 'positive', '0.801', 'alcohol', 'bar', 'bar_stool', 'beer_mug', 'minibar']
senticnet['high_cholesterol'] = ['-0.95', '0', '-0.94', '-0.76', '#sadness', '#fear', 'negative', '-0.88', 'smoke_cigarette', 'cholesterol', 'get_lung_cancer', 'unhealthy', 'die']
senticnet['high_choral'] = ['0.02', '0.296', '-0.09', '0.258', '#interest', '#admiration', 'positive', '0.121', 'school_band', 'drum_set', 'band', 'string_quartet', 'choral']
senticnet['high_churchman'] = ['-0.85', '0', '0.749', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'hairsplitter', 'fight', 'quarrel', 'obstructor', 'obstructer']
senticnet['high_class'] = ['0', '0', '0', '0.96', '#admiration', '#admiration', 'positive', '0.96', 'status', 'social_class', 'best', 'chivalry', 'kingly']
senticnet['high_coercivity'] = ['0.102', '0.078', '0', '0.087', '#joy', '#admiration', 'positive', '0.089', 'gold', 'platinum', 'iron_ore', 'silver', 'adjustable']
senticnet['high_cost'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'costly', 'high_price', 'debit', 'expense', 'expensive']
senticnet['high_decibel'] = ['-0.65', '0', '0.578', '-0.76', '#sadness', '#disgust', 'negative', '-0.66', 'grating', 'rasping', 'raspy', 'jarring', 'gruff']
senticnet['high_density'] = ['0.728', '0.977', '0', '0.939', '#interest', '#admiration', 'positive', '0.881', 'air_pressure', 'altitude', 'air', 'heavy_air', 'air_density']
senticnet['high_dose_methylprednisolone'] = ['0.627', '-0.65', '0', '0.968', '#surprise', '#admiration', 'positive', '0.749', 'overwhelm', 'overpower', 'sweep_over', 'whelm', 'overcome']
senticnet['high_dudgeon'] = ['-0.12', '0.13', '0.647', '-0.64', '#anger', '#disgust', 'negative', '-0.32', 'dudgeon', 'infuriating', 'exasperating', 'maddening', 'infuriate']
senticnet['high_education'] = ['0.383', '0.246', '-0.10', '0.256', '#joy', '#admiration', 'positive', '0.194', 'finish_school', 'finish_college', 'get_high_education', 'obtain_degree', 'prepare_exam']
senticnet['high_esteem'] = ['0', '0', '0', '0.716', '#admiration', '#admiration', 'positive', '0.716', 'certainty', 'dignity', 'attitude', 'strong_feeling', 'prefer']
senticnet['high_explosive'] = ['-0.84', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.85', 'inferno', 'forest_fire', 'fire_light', 'dismissal', 'wash_mouth_soap']
senticnet['high_falutin'] = ['0.596', '-0.81', '0', '0.767', '#surprise', '#admiration', 'positive', '0.726', 'inflated', 'hifalutin', 'snooty', 'pretentious', 'portentous']
senticnet['high_fat_dairy'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'grill', 'cook_grill', 'barbecue', 'cook', 'grilled_vegetable']
senticnet['high_fat_diet'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'cholecystokinin', 'secretin', 'gastrointestinal_hormone']
senticnet['high_fever'] = ['-0.93', '0', '0.52', '-0.99', '#sadness', '#disgust', 'negative', '-0.81', 'disease', 'illness', 'sickness', 'sick', 'sick_person']
senticnet['high_fidelity_audio'] = ['0.055', '0', '0.03', '0.136', '#joy', '#admiration', 'positive', '0.054', 'loud_soft', 'decibel', 'loudness', 'sound', 'melodic_noise']
senticnet['high_functioning_autism'] = ['0.89', '-0.70', '0', '0.643', '#joy', '#surprise', 'positive', '0.745', 'dyslexia', 'dyscalculia', 'learning_disability', 'disorder', 'upset']
senticnet['high_functioning_autistic'] = ['-0.91', '0', '0.508', '-0.89', '#sadness', '#disgust', 'negative', '-0.77', 'mental_disorder', 'mental_disturbance', 'psychological_disorder', 'folie', 'disturbance']
senticnet['high_heat_flux'] = ['0.03', '0.068', '-0.06', '0.062', '#interest', '#admiration', 'positive', '0.025', 'pacific_northwest', 'american_forest', 'treetop', 'bath', 'hot_air']
senticnet['high_heel'] = ['-0.05', '0.068', '0', '0.102', '#interest', '#admiration', 'positive', '0.038', 'footwear', 'foot', 'leg_wear', 'shoe', 'shoe_type']
senticnet['high_heeled_shoe'] = ['-0.99', '0', '0.806', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'wear', 'biohazard_suit', 'coverall', 'protective_garment', 'wearable']
senticnet['high_humidity'] = ['0.718', '0', '0', '0', '#joy', '#joy', 'positive', '0.718', 'water_container', 'still_water', 'clean_water', 'water', 'cool_water']
senticnet['high_input_impedance'] = ['0.056', '-0.01', '0.036', '0.073', '#joy', '#admiration', 'positive', '0.028', 'loud_sound', 'stereo', 'tuner', 'speaker', 'steam_engine']
senticnet['high_iq'] = ['0', '0.943', '0', '0.789', '#interest', '#admiration', 'positive', '0.866', 'energy', 'genius', 'energy_use', 'gas_oil', 'invent']
senticnet['high_jinks'] = ['-0.85', '0', '0.691', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'jinks', 'jollification', 'hijinks', 'high_jinx', 'revel']
senticnet['high_jinx'] = ['-0.85', '0', '0.691', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'jinks', 'high_jinks', 'hijinks', 'jollification', 'revel']
senticnet['high_jollification'] = ['-0.85', '0', '0.682', '-0.82', '#sadness', '#disgust', 'negative', '-0.79', 'jollification', 'jinks', 'hijollification', 'high_jinx', 'revel']
senticnet['high_land'] = ['0', '0.093', '0.025', '0.122', '#interest', '#admiration', 'positive', '0.063', 'cover_snow', 'landscape_feature', 'beautiful_look', 'upwards', 'steep']
senticnet['high_level'] = ['0.051', '0.214', '0', '0.142', '#interest', '#admiration', 'positive', '0.136', 'tennis_match', 'middle_road', 'stick_wall', 'odd', 'auction']
senticnet['high_magnification'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'eye', 'peeper', 'oculus_sinister']
senticnet['high_moisture_content'] = ['0', '-0.03', '0.05', '-0.14', '#anger', '#disgust', 'negative', '-0.05', 'humidity', 'wetness', 'damp', 'humid', 'dank']
senticnet['high_motivational_intensity'] = ['0.43', '0.657', '-0.28', '0.244', '#joy', '#interest', 'positive', '0.262', 'expectation', 'look_for', 'amour_propre', 'self-love', 'conceit']
senticnet['high_mountain'] = ['0', '0.069', '0', '0.065', '#interest', '#admiration', 'positive', '0.067', 'canyon', 'alp', 'great_view', 'ski_resort', 'high_place']
senticnet['high_music'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'hear_loss', 'damaged_eardrum', 'deaf', 'loud_music', 'damage_ear']
senticnet['high_music_note'] = ['-0.17', '-0.04', '0.035', '-0.08', '#sadness', '#disgust', 'negative', '-0.06', 'bad_music', 'squeak', 'annoy_sound', 'bang', 'loud']
senticnet['high_octane'] = ['-0.03', '0.09', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.021', 'bright_color', 'primary_color', 'color', 'color_rainbow', 'light_red']
senticnet['high_octane_gasoline'] = ['0.855', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.86', 'ticket_counter', 'newspaper_stand', 'luggage_carrier', 'aircraft_roadway', 'timetable']
senticnet['high_oleic_acid'] = ['0.878', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.883', 'wheat', 'grain', 'rye', 'white_rice', 'rice']
senticnet['high_osmolarity'] = ['0.772', '-0.56', '0', '0.824', '#joy', '#admiration', 'positive', '0.719', 'neutrophil', 'leukocyte', 'leucocyte', 'white_blood_cell', 'white_cell']
senticnet['high_pay_job'] = ['0.109', '0.104', '0', '0', '#joy', '#interest', 'positive', '0.106', 'go_job', 'find_woman', 'get_hired', 'happiness', 'say_welcome']
senticnet['high_penetrance'] = ['-0.95', '0', '0.684', '-0.76', '#sadness', '#disgust', 'negative', '-0.79', 'innate', 'nurtural', 'inborn', 'nontransmissible', 'nonheritable']
senticnet['high_performance_connection'] = ['0', '0.907', '0', '0.993', '#interest', '#admiration', 'positive', '0.95', 'award', 'telecommunication', 'network_infrastructure', 'internet_announcement', 'provide_partial_support']
senticnet['high_permeability'] = ['0.89', '-0.62', '0', '0.548', '#joy', '#surprise', 'positive', '0.686', 'breakthrough', 'interpenetration', 'foray', 'maraud', 'raid']
senticnet['high_phone_bill'] = ['-0.25', '-0.15', '0.059', '-0.36', '#sadness', '#disgust', 'negative', '-0.12', 'owe_money', 'interrupt', 'boredom', 'phone_line_busy', 'lose_track_time']
senticnet['high_pitch'] = ['0', '-0.03', '0', '0.054', '#surprise', '#admiration', 'positive', '0.043', 'mouth_sound', 'animal_sound', 'sound', 'noise', 'loud_shout']
senticnet['high_pitched_scream'] = ['0', '0', '-0.80', '0', '#fear', '#fear', 'negative', '-0.80', 'shout', 'yell', 'annoy', 'groan', 'cry_help']
senticnet['high_pitched_whine'] = ['-0.08', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.07', 'bicycle_store', 'enter_door', 'cost_a_lot_of_money', 'open', 'hang_door']
senticnet['high_place'] = ['0', '0.069', '0', '0.065', '#interest', '#admiration', 'positive', '0.067', 'alp', 'high_mountain', 'altitude', 'ski_resort', 'canyon']
senticnet['high_point'] = ['0.052', '0.09', '0.056', '0.124', '#interest', '#admiration', 'positive', '0.052', 'top', 'pinnacle', 'peak', 'high_land', 'landscape_feature']
senticnet['high_post'] = ['0.023', '0.07', '0', '0.085', '#interest', '#admiration', 'positive', '0.059', 'president', 'use_school', 'wood_plank', 'chairperson', 'chairman']
senticnet['high_powered_rifle'] = ['-0.75', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.75', 'hydrogen_bomb', 'rifle', 'revolver', 'pistol', 'gun']
senticnet['high_precision'] = ['0', '-0.90', '0', '0.791', '#surprise', '#admiration', 'positive', '0.849', 'precise', 'accuracy', 'correct', 'performance', 'measure']
senticnet['high_price'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'expensive', 'spend_lot', 'unaffordable', 'overprice', 'immoderate']
senticnet['high_priced_prostitution'] = ['0.052', '0', '0', '0.118', '#joy', '#admiration', 'positive', '0.085', 'diamond_ring', 'piece_of_jewelry', 'bell_sound', 'wedding_band', 'engagement']
senticnet['high_protein'] = ['0.052', '0.09', '0.056', '0.124', '#interest', '#admiration', 'positive', '0.052', 'dairy_product', 'healthy', 'type_of_food', 'build_muscel', 'tree_fruit']
senticnet['high_purity_heroin'] = ['-0.63', '0', '0.819', '-0.54', '#sadness', '#anger', 'negative', '-0.66', 'smack', 'hell_dust', 'nose_drop', 'thunder', 'skag']
senticnet['high_quality'] = ['0.131', '-0.10', '-0.05', '0.469', '#joy', '#admiration', 'positive', '0.164', 'really_good', 'well_done', 'great', 'better_good', 'high_standard']
senticnet['high_rank'] = ['0.023', '0.079', '0', '0', '#joy', '#interest', 'positive', '0.051', 'military_rank', 'army_rank', 'army_leader', 'military_position', 'army_boss']
senticnet['high_ranking_member'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'yeoman', 'outrider', 'yeoman_of_guard', 'beefeater', 'defender']
senticnet['high_ranking_military'] = ['0.786', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.744', 'attacker', 'army', 'samurai', 'infantry', 'military_force']
senticnet['high_ranking_nazi'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'healthy_person', 'man', 'pervert', 'compatriot']
senticnet['high_relative_humidity'] = ['0.718', '0', '0', '0', '#joy', '#joy', 'positive', '0.718', 'water_container', 'still_water', 'clean_water', 'water', 'cool_water']
senticnet['high_repute'] = ['0.594', '-0.68', '0', '0.957', '#surprise', '#admiration', 'positive', '0.746', 'prehigh_status', 'high_status', 'senior_status', 'higher_status', 'higher_rank']
senticnet['high_resolution'] = ['-0.08', '0', '0.011', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'deflate', 'read_cover', 'obtain_information', 'upgrade', 'spread_out']
senticnet['high_rise'] = ['0.046', '0.111', '0', '0', '#joy', '#interest', 'positive', '0.079', 'duplex', 'rent_apartment', 'brownstone', 'condominium', 'skyscraper']
senticnet['high_rise_building'] = ['0', '0.105', '0', '0.076', '#interest', '#admiration', 'positive', '0.09', 'house', 'mansion', 'block_flat', 'home', 'modern_home']
senticnet['high_salary'] = ['0', '0.133', '-0.09', '0.211', '#interest', '#admiration', 'positive', '0.082', 'much_money', 'extra_money', 'need_money', 'earn_wage', 'money_earn']
senticnet['high_school'] = ['0.065', '-0.02', '0.009', '0', '#joy', '#surprise', 'positive', '0.026', 'college_class', 'school_level', 'class', 'session', 'lecture']
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senticnet['highly_commended_award'] = ['0.232', '0.178', '-0.17', '0.223', '#joy', '#admiration', 'positive', '0.115', 'fame', 'alms', 'find_money', 'better_life', 'small_detail']
senticnet['highly_communicable'] = ['-0.11', '0', '-0.15', '-0.24', '#fear', '#disgust', 'negative', '-0.17', 'disease', 'infectious_disease', 'polio', 'malady', 'sickness']
senticnet['highly_competitive'] = ['0.887', '0', '0', '0', '#joy', '#joy', 'positive', '0.887', 'fun', 'difficult_play', 'enjoy_sport', 'accept_challenge', 'competition']
senticnet['highly_competitive_market'] = ['0.838', '0.75', '0', '0.708', '#joy', '#interest', 'positive', '0.765', 'festival', 'party', 'banquet', 'big_event', 'rock_concert']
senticnet['highly_competitive_marketplace'] = ['0.838', '0.75', '0', '0.708', '#joy', '#interest', 'positive', '0.765', 'festival', 'party', 'banquet', 'big_event', 'rock_concert']
senticnet['highly_contagious_disease'] = ['-0.11', '0', '-0.15', '-0.24', '#fear', '#disgust', 'negative', '-0.17', 'disease', 'infectious_disease', 'polio', 'malady', 'sickness']
senticnet['highly_controversial'] = ['0', '0', '0.94', '0', '#anger', '#anger', 'negative', '-0.94', 'feel_angry', 'hurt_feeling', 'feel_hurt', 'ridicule', 'vengeance']
senticnet['highly_customizable'] = ['0.085', '0.218', '-0.07', '0.308', '#interest', '#admiration', 'positive', '0.135', 'delicate', 'useful', 'good', 'light_colored', 'beautiful']
senticnet['highly_debatable'] = ['0.544', '-0.69', '0', '0.658', '#surprise', '#admiration', 'positive', '0.632', 'shakable', 'shakeable', 'challengeable', 'disputable', 'contestable']
senticnet['highly_decorative'] = ['-0.54', '0', '0.7', '-0.64', '#anger', '#disgust', 'negative', '-0.62', 'flourish', 'rant', 'bombast', 'fustian', 'claptrap']
senticnet['highly_dependable'] = ['0.512', '-0.52', '0', '0.874', '#surprise', '#admiration', 'positive', '0.635', 'responsibleness', 'reproducibility', 'duplicability', 'solidness', 'responsibility']
senticnet['highly_destructive'] = ['0', '0', '-0.71', '0', '#fear', '#fear', 'negative', '-0.71', 'hazardous', 'violent', 'take_life', 'threat', 'lethal']
senticnet['highly_diverse'] = ['0.057', '0', '0', '0.153', '#joy', '#admiration', 'positive', '0.105', 'multiple', 'cute', 'variety', 'hairy', 'large_breadbox']
senticnet['highly_durable'] = ['0', '0', '0.03', '-0.12', '#anger', '#disgust', 'negative', '-0.07', 'hard_material', 'hard_mineral', 'hard', 'hard_rock', 'construction_vehicle']
senticnet['highly_dynamic'] = ['-0.03', '0.128', '-0.09', '0.255', '#interest', '#admiration', 'positive', '0.065', 'live_simply', 'transition', 'change_life', 'backup', 'go_forward']
senticnet['highly_effective'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'woodworking_tool']
senticnet['highly_efficient'] = ['0.028', '0.138', '-0.04', '0.215', '#interest', '#admiration', 'positive', '0.083', 'skilled', 'expert', 'tin', 'dense_water', 'metal_container']
senticnet['highly_elliptical'] = ['0.989', '-0.90', '0', '0.876', '#joy', '#surprise', 'positive', '0.923', 'reach', 'range', 'compass', 'scope', 'confines']
senticnet['highly_elliptical_orbit'] = ['0.989', '-0.90', '0', '0.876', '#joy', '#surprise', 'positive', '0.923', 'reach', 'range', 'compass', 'scope', 'confines']
senticnet['highly_emotional'] = ['-0.17', '0.154', '-0.28', '0.073', '#sadness', '#fear', 'negative', '-0.05', 'doctor_visit', 'bad_feeling', 'consequence', 'symptom', 'physical_reaction']
senticnet['highly_enjoyable'] = ['0.65', '-0.61', '0', '0.711', '#joy', '#admiration', 'positive', '0.658', 'bouncing', 'scintillating', 'spirited', 'peppy', 'zippy']
senticnet['highly_enriched_uranium'] = ['-0.03', '-0.03', '-0.05', '-0.20', '#fear', '#disgust', 'negative', '-0.06', 'nuclear_bomb', 'bomb', 'explosive', 'atomic_bomb', 'grenade']
senticnet['highly_evocative'] = ['0.709', '-0.61', '0', '0.762', '#joy', '#admiration', 'positive', '0.695', 'careful', 'heedful', 'resonant', 'mindful', 'redolent']
senticnet['highly_exothermic'] = ['-0.90', '0', '0.825', '-0.61', '#sadness', '#anger', 'negative', '-0.78', 'flaming', 'combustion', 'fire', 'flame', 'internal_combustion']
senticnet['highly_fertile'] = ['0.702', '-0.95', '0', '0.997', '#surprise', '#admiration', 'positive', '0.883', 'fertilizable', 'fecund', 'conceptive', 'impregnable', 'rank']
senticnet['highly_flammable'] = ['-0.90', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.85', 'flame', 'combustible', 'dangerous', 'incendiary', 'heat']
senticnet['highly_flexible'] = ['0.085', '0.218', '-0.07', '0.308', '#interest', '#admiration', 'positive', '0.135', 'delicate', 'useful', 'good', 'light_colored', 'beautiful']
senticnet['highly_impressionable'] = ['0.724', '-0.70', '0', '0.571', '#joy', '#surprise', 'positive', '0.666', 'spinnable', 'susceptible', 'easy', 'pliant', 'plastic']
senticnet['highly_inefficient'] = ['-0.12', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.09', 'painful', 'uncomfortable', 'violent', 'fun_learn_play', 'expensive']
senticnet['highly_infectious_disease'] = ['-0.11', '0', '-0.15', '-0.24', '#fear', '#disgust', 'negative', '-0.17', 'disease', 'infectious_disease', 'polio', 'malady', 'sickness']
senticnet['highly_informative'] = ['0.026', '0', '0', '0.084', '#joy', '#admiration', 'positive', '0.055', 'book_write', 'make_book', 'written_text', 'paragraph', 'book_paper']
senticnet['highly_innovative'] = ['0.98', '-0.74', '0', '0.984', '#joy', '#admiration', 'positive', '0.903', 'prosperous', 'flourishing', 'palmy', 'prospering', 'thriving']
senticnet['highly_insecure'] = ['-0.81', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.89', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecurity']
senticnet['highly_intelligent'] = ['0.995', '0.801', '0', '0.968', '#joy', '#admiration', 'positive', '0.921', 'smart', 'smartness', 'curious', 'become_wise', 'comprehend']
senticnet['highly_intolerant'] = ['0', '0.985', '0', '0.794', '#interest', '#admiration', 'positive', '0.889', 'patient', 'bear_up', 'tolerate', 'endurable', 'swallow']
senticnet['highly_irresponsible'] = ['0', '0', '0.733', '-0.92', '#anger', '#disgust', 'negative', '-0.83', 'freewheeling', 'carefree', 'slaphappy', 'unaccountable', 'loose']
senticnet['highly_knowledgeable'] = ['0.891', '0.721', '0', '0.921', '#joy', '#admiration', 'positive', '0.844', 'inventor', 'connoisseur', 'medical_doctor', 'teacher', 'judge']
senticnet['highly_literate'] = ['-0.90', '0', '0.76', '-0.87', '#sadness', '#disgust', 'negative', '-0.84', 'analphabet', 'analphabetic', 'functional_illiterate', 'ignoramus', 'know_nothing']
senticnet['highly_lucrative'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'profitable', 'moneymaking', 'gainful', 'paying', 'paid']
senticnet['highly_maneuverable'] = ['0.028', '0.138', '-0.04', '0.215', '#interest', '#admiration', 'positive', '0.083', 'skilled', 'expert', 'tin', 'dense_water', 'metal_container']
senticnet['highly_mobile'] = ['0', '0', '0', '0.144', '#admiration', '#admiration', 'positive', '0.144', 'go_elsewhere', 'transport_person', 'get_around', 'locomotion', 'trucking']
senticnet['highly_motile'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'nonmotile', 'immobile']
senticnet['highly_nonlinear'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'constancy', 'monotony', 'invariance', 'changelessness', 'unchangeability']
senticnet['highly_pathogenic_bird_flu'] = ['0', '0', '-0.98', '-0.85', '#fear', '#disgust', 'negative', '-0.92', 'contagious_disease', 'infectious_disease', 'sexually_transmit_disease', 'disease', 'malady']
senticnet['highly_perishable'] = ['-0.08', '-0.04', '0.023', '-0.07', '#sadness', '#disgust', 'negative', '-0.03', 'tier', 'make_toast', 'house_smell_good', 'get_hot', 'bread_crumb']
senticnet['highly_permeable'] = ['-0.52', '0', '0.596', '-0.76', '#anger', '#disgust', 'negative', '-0.62', 'retention', 'retentiveness', 'retentivity', 'solidness', 'nonabsorbency']
senticnet['highly_polished'] = ['0.577', '-0.69', '0', '0.645', '#surprise', '#admiration', 'positive', '0.639', 'shining', 'polished', 'shiny', 'bright', 'burnished']
senticnet['highly_portable'] = ['0.932', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.959', 'operational', 'functional', 'operable', 'available', 'useable']
senticnet['highly_potent'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'imperium', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['highly_probable'] = ['0.509', '-0.62', '0', '0.786', '#surprise', '#admiration', 'positive', '0.64', 'agreeable', 'accordant', 'consentient', 'unanimous', 'according']
senticnet['highly_probative'] = ['-0.63', '0', '0.967', '-0.67', '#anger', '#disgust', 'negative', '-0.76', 'condemnatory', 'condemning', 'accusatory', 'accusing', 'accusive']
senticnet['highly_problematic'] = ['-0.88', '0', '0.907', '-0.84', '#sadness', '#anger', 'negative', '-0.88', 'intolerable', 'unacceptable', 'bitter', 'impossible', 'insufferable']
senticnet['highly_productive'] = ['0.702', '-0.95', '0', '0.997', '#surprise', '#admiration', 'positive', '0.883', 'fertilizable', 'fecund', 'conceptive', 'impregnable', 'rank']
senticnet['highly_profitable'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'profitable', 'moneymaking', 'gainful', 'paying', 'paid']
senticnet['highly_questionable'] = ['-0.88', '0', '0.907', '-0.84', '#sadness', '#anger', 'negative', '-0.88', 'intolerable', 'unacceptable', 'bitter', 'impossible', 'insufferable']
senticnet['highly_radioactive'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'dust', 'particulate', 'particulate_matter']
senticnet['highly_radioactive_material'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'dust', 'particulate', 'particulate_matter']
senticnet['highly_readable'] = ['0.026', '0', '0', '0.084', '#joy', '#admiration', 'positive', '0.055', 'book_write', 'make_book', 'written_text', 'paragraph', 'book_paper']
senticnet['highly_recommend'] = ['0.781', '0', '0', '0.706', '#joy', '#admiration', 'positive', '0.744', 'goodness', 'best', 'quality', 'great', 'well_done']
senticnet['highly_redundant'] = ['0.63', '-0.55', '0', '0.946', '#joy', '#admiration', 'positive', '0.711', 'complect', 'connect', 'link', 'link_up', 'join']
senticnet['highly_reliable'] = ['0.512', '-0.52', '0', '0.874', '#surprise', '#admiration', 'positive', '0.635', 'responsibleness', 'reproducibility', 'duplicability', 'solidness', 'responsibility']
senticnet['highly_resistant'] = ['-0.69', '0', '0.516', '-0.94', '#sadness', '#disgust', 'negative', '-0.71', 'insubordinate', 'recalcitrant', 'obstreperous', 'defiant', 'resistive']
senticnet['highly_respectable'] = ['0.666', '-0.83', '0', '0.896', '#surprise', '#admiration', 'positive', '0.798', 'nice', 'decent', 'solid', 'presentable', 'upstanding']
senticnet['highly_respected'] = ['0.865', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.887', 'manager', 'colleague', 'office_worker', 'coworker', 'secretary']
senticnet['highly_responsive'] = ['0.92', '-0.70', '0', '0.674', '#joy', '#surprise', 'positive', '0.765', 'sensitivity', 'sensitiveness', 'irritability', 'sensibility', 'excitability']
senticnet['highly_satisfactory'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly']
senticnet['highly_scalable'] = ['0.637', '-0.91', '0', '0.757', '#surprise', '#admiration', 'positive', '0.768', 'tractability', 'manageableness', 'docility', 'obedience', 'amenability']
senticnet['highly_selective_vagotomy'] = ['0', '0', '0', '0.106', '#admiration', '#admiration', 'positive', '0.106', 'vocal_chord', 'mouth_noise', 'vocal_sound', 'human_sound', 'vocalization']
senticnet['highly_sensitive'] = ['0', '0', '-0.66', '0', '#fear', '#fear', 'negative', '-0.66', 'anger', 'rage', 'annoyance', 'apprehension', 'fear']
senticnet['highly_skeptical'] = ['0', '0', '-0.99', '-0.99', '#fear', '#disgust', 'negative', '-0.99', 'unbelieving', 'sceptical', 'doubting', 'disbelieving', 'questioning']
senticnet['highly_skilled'] = ['0.988', '0.715', '0', '0.764', '#joy', '#admiration', 'positive', '0.822', 'coordination', 'coordinated', 'find_ball', 'cooperation', 'teamwork']
senticnet['highly_skilled_workforce'] = ['0.837', '0', '0', '0', '#joy', '#joy', 'positive', '0.837', 'a_lot_of_sex', 'desirable', 'give_birth_child', 'sexual_intercourse', 'diversity']
senticnet['highly_sociable'] = ['0.992', '0', '0', '0.801', '#joy', '#admiration', 'positive', '0.897', 'feel_content', 'make_feel_better', 'celebrate_special_occasion', 'survival', 'express_appreciation']
senticnet['highly_strung'] = ['-0.71', '0', '-0.98', '0', '#sadness', '#fear', 'negative', '-0.84', 'high-strung', 'uptight', 'restive', 'overstrung', 'nervy']
senticnet['highly_subjective'] = ['0.054', '0.221', '-0.12', '0.236', '#interest', '#admiration', 'positive', '0.098', 'abstract', 'exclamation', 'versatile', 'nice', 'subjective']
senticnet['highly_successful'] = ['0.889', '0', '0', '0', '#joy', '#joy', 'positive', '0.889', 'succeed', 'increase_self_esteem', 'increase_self-esteem', 'help_others', 'achievement']
senticnet['highly_suggestive'] = ['-0.58', '0', '0.814', '-0.54', '#sadness', '#anger', 'negative', '-0.64', 'abnormal', 'irregular', 'vicarious', 'supernormal', 'subnormal']
senticnet['highly_susceptible'] = ['-0.78', '0', '0.6', '-0.61', '#sadness', '#disgust', 'negative', '-0.66', 'suggestibility', 'sensitivity', 'capacity', 'reactivity', 'predisposition']
senticnet['highly_tolerant'] = ['0', '0.985', '0', '0.794', '#interest', '#admiration', 'positive', '0.889', 'patient', 'bear_up', 'tolerate', 'endurable', 'swallow']
senticnet['highly_toxic'] = ['-0.53', '0', '0.86', '-0.65', '#anger', '#disgust', 'negative', '-0.68', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['highly_toxic_chemical'] = ['-0.18', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.16', 'hazardous_health', 'dangerous_health', 'bad_health', 'gross', 'harmful']
senticnet['highly_turbid'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'dirtiness', 'befoulment', 'defilement', 'dinginess', 'dinge']
senticnet['highly_uncertain'] = ['0', '0', '0', '-0.70', '#disgust', '#disgust', 'negative', '-0.70', 'uncertain_future', 'uncertain_environment', 'insecure', 'blur', 'anxiety']
senticnet['highly_versatile'] = ['0.085', '0.218', '-0.07', '0.308', '#interest', '#admiration', 'positive', '0.135', 'delicate', 'useful', 'good', 'light_colored', 'beautiful']
senticnet['highly_visible'] = ['0.53', '-0.64', '0', '0.76', '#surprise', '#admiration', 'positive', '0.646', 'focus', 'get_focused', 'focusing', 'centering', 'particularism']
senticnet['highly_vulnerable'] = ['-0.81', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.89', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecurity']
senticnet['highwater'] = ['0.738', '-0.71', '0', '0.854', '#joy', '#admiration', 'positive', '0.768', 'neap_tide', 'neap', 'springtide', 'direct_tide', 'tide']
senticnet['highway'] = ['0', '0.072', '0', '0.092', '#interest', '#admiration', 'positive', '0.082', 'street', 'wide_street', 'freeway', 'country_road', 'road']
senticnet['highway_congestion'] = ['0', '0', '0.735', '-0.70', '#anger', '#disgust', 'negative', '-0.72', 'swing_set', 'roadway_square', 'fresh_air', 'dog_poop', 'wildflower']
senticnet['highway_corridor'] = ['0', '0', '0', '0.085', '#admiration', '#admiration', 'positive', '0.085', 'use_farm', 'gate', 'door', 'doorway', 'entranceway']
senticnet['highway_interchange'] = ['0', '0', '0', '0.085', '#admiration', '#admiration', 'positive', '0.085', 'use_farm', 'gate', 'door', 'doorway', 'entranceway']
senticnet['highway_road'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'highway', 'road', 'long_road', 'asphalt', 'avenue']
senticnet['highway_robber'] = ['-0.83', '0', '0.831', '-0.69', '#sadness', '#anger', 'negative', '-0.78', 'toughie', 'accessory', 'highbinder', 'hoodlum', 'thug']
senticnet['highway_run_concurrently'] = ['0.032', '0.116', '0.016', '0', '#joy', '#interest', 'positive', '0.044', 'curb', 'crossroad', 'ring_road', 'lane', 'interstate']
senticnet['highway_traffic'] = ['-0.98', '0', '0.989', '-0.93', '#sadness', '#anger', 'negative', '-0.97', 'flight_accident', 'dangerous', 'danger', 'air_traffic', 'air_traffic_jam']
senticnet['highwayman'] = ['-0.83', '0', '0.831', '-0.69', '#sadness', '#anger', 'negative', '-0.78', 'toughie', 'accessory', 'highbinder', 'hoodlum', 'thug']
senticnet['hiiumaa_islet_landscape'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['hijet'] = ['0.035', '0.112', '-0.07', '0.195', '#interest', '#admiration', 'positive', '0.067', 'car_action', 'car_travel', 'steer_car', 'make_go', 'transportation']
senticnet['hijinks'] = ['-0.85', '0', '0.691', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'jinks', 'high_jinks', 'jollification', 'high_jinx', 'revel']
senticnet['hijollification'] = ['-0.85', '0', '0.684', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'jollification', 'high_jollification', 'jinks', 'high_jinx', 'revel']
senticnet['hike'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'hike_trail', 'trail', 'picnic', 'enjoy_nature', 'nice_walk']
senticnet['hike_trail'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'hike', 'trail', 'picnic', 'enjoy_nature', 'nice_walk']
senticnet['hikmat'] = ['0.798', '-0.51', '0', '0.973', '#joy', '#admiration', 'positive', '0.763', 'inexperienced_person', 'boy_scout', 'dear', 'virgin', 'mortal']
senticnet['hilarious'] = ['0.695', '0.623', '-0.23', '0.319', '#joy', '#interest', 'positive', '0.351', 'uproarious', 'screaming', 'hilariously', 'uproariously', 'hilarity']
senticnet['hilariously'] = ['0.695', '0.623', '-0.23', '0.319', '#joy', '#interest', 'positive', '0.351', 'uproarious', 'screaming', 'uproariously', 'hilarious', 'hilarity']
senticnet['hilarity'] = ['0.695', '0.623', '-0.23', '0.319', '#joy', '#interest', 'positive', '0.351', 'uproarious', 'hilariously', 'hilarious', 'screaming', 'uproariously']
senticnet['hilbert_scheme'] = ['0.875', '-0.65', '0', '0.861', '#joy', '#admiration', 'positive', '0.797', 'completeness', 'work', 'whole_shebang', 'essential', 'whole']
senticnet['hilbert_space_completion'] = ['-0.53', '0', '0.993', '-0.88', '#anger', '#disgust', 'negative', '-0.80', 'expansiveness', 'impressiveness', 'stateliness', 'loftiness', 'excellence']
senticnet['hilbert_symbol'] = ['0.875', '-0.65', '0', '0.861', '#joy', '#admiration', 'positive', '0.797', 'completeness', 'work', 'whole_shebang', 'essential', 'whole']
senticnet['hilbert_transform'] = ['0.506', '-0.56', '0', '0.788', '#surprise', '#admiration', 'positive', '0.62', 'backward', 'reverse', 'retrograde', 'reflexive', 'retral']
senticnet['hildr'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'jawless_fish', 'osteostracan', 'cephalaspid', 'ostracoderm', 'heterostracan']
senticnet['hilight'] = ['0.777', '-0.85', '0', '0.615', '#joy', '#surprise', 'positive', '0.748', 'think', 'play', 'trifle', 'abstract', 'warm_to']
senticnet['hill'] = ['0', '0.093', '0', '0.078', '#interest', '#admiration', 'positive', '0.085', 'steep', 'landscape_feature', 'knoll', 'hill_mountain', 'incline']
senticnet['hill_cemetery'] = ['-0.50', '0', '0.762', '-0.89', '#anger', '#disgust', 'negative', '-0.71', 'cemetery', 'graveyard', 'burial_site', 'grassland', 'burying_ground']
senticnet['hill_mountain'] = ['0', '0.093', '0', '0.078', '#interest', '#admiration', 'positive', '0.085', 'steep', 'landscape_feature', 'knoll', 'hill', 'incline']
senticnet['hill_myna'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'death_adder', 'ophidian', 'serpent', 'elapid', 'taipan']
senticnet['hillbilly'] = ['0', '0.816', '0', '0.915', '#interest', '#admiration', 'positive', '0.865', 'cultivation', 'civilization', 'refinement', 'refining', 'style']
senticnet['hillbilly_heroin'] = ['0.138', '0.103', '0', '0.095', '#joy', '#interest', 'positive', '0.112', 'masturbation', 'sexual_intercourse', 'sex', 'warm_fuzzy_feeling', 'fuck']
senticnet['hillcrest_cemetery'] = ['-0.98', '0', '0.664', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'unfortunate', 'unfortunate_person', 'weeper', 'griever', 'bearer']
senticnet['hillock'] = ['0', '0.093', '0', '0.078', '#interest', '#admiration', 'positive', '0.085', 'steep', 'landscape_feature', 'knoll', 'hill_mountain', 'incline']
senticnet['hillside'] = ['0', '0.093', '0', '0.078', '#interest', '#admiration', 'positive', '0.085', 'steep', 'landscape_feature', 'knoll', 'hill_mountain', 'incline']
senticnet['hillside_strangler'] = ['0.864', '-0.54', '0', '0.613', '#joy', '#admiration', 'positive', '0.675', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['hilltop'] = ['0', '0.093', '0', '0.078', '#interest', '#admiration', 'positive', '0.085', 'steep', 'landscape_feature', 'knoll', 'hill_mountain', 'incline']
senticnet['hilly_landscape'] = ['0.97', '0.758', '0', '0', '#joy', '#interest', 'positive', '0.864', 'trot', 'step_out', 'step_aside', 'progress', 'enter_place']
senticnet['hilt'] = ['-0.81', '0', '0.844', '-0.76', '#sadness', '#anger', 'negative', '-0.80', 'hold', 'grip', 'basket_hilt', 'handle', 'handgrip']
senticnet['hilton_kramer'] = ['0.811', '-0.63', '0', '0.959', '#joy', '#admiration', 'positive', '0.8', 'drama_critic', 'theater_critic', 'professional', 'art_critic', 'literary_critic']
senticnet['himalayan_goral'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheep', 'argal', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['himalayan_rescue'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['himalayan_snowcock'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'argali', 'argal', 'urial', 'white_sheep', 'wild_sheep']
senticnet['himalayan_tahr'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'argali', 'argal', 'urial', 'white_sheep', 'wild_sheep']
senticnet['himsel'] = ['-0.83', '0', '0.87', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'precipitate', 'rain_down', 'shower_down', 'spit', 'patter']
senticnet['hind'] = ['-0.04', '0.226', '-0.20', '0.209', '#interest', '#admiration', 'positive', '0.045', 'rear', 'elongation', 'go_backwards', 'rear_end', 'buttock']
senticnet['hind_feet'] = ['0.098', '0.084', '0.819', '-0.68', '#anger', '#disgust', 'negative', '-0.33', 'grasp', 'covet', 'covetousness', 'avaricious', 'grabby']
senticnet['hind_foot'] = ['-0.60', '0', '0.701', '-0.72', '#anger', '#disgust', 'negative', '-0.67', 'lunate_bone', 'semilunar_bone', 'carpal_bone', 'triquetral', 'triquetral_bone']
senticnet['hind_leg'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'hip', 'joint', 'above_leg', 'knee']
senticnet['hind_limb'] = ['-0.60', '0', '0.701', '-0.72', '#anger', '#disgust', 'negative', '-0.67', 'lunate_bone', 'semilunar_bone', 'carpal_bone', 'triquetral', 'triquetral_bone']
senticnet['hind_neck'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'neck', 'body_part', 'henchman', 'shoulder', 'head']
senticnet['hind_quarter'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'hip', 'joint', 'above_leg', 'knee']
senticnet['hind_toe'] = ['0.098', '0.084', '0.819', '-0.68', '#anger', '#disgust', 'negative', '-0.33', 'grasp', 'covet', 'covetousness', 'avaricious', 'grabby']
senticnet['hindenburg_line'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'start_over', 'queue', 'find_line', 'take_turn', 'long_line']
senticnet['hinder'] = ['-0.99', '0', '0.581', '-0.97', '#sadness', '#disgust', 'negative', '-0.84', 'obstruct_justice', 'football_rule', 'block_road', 'obstruction', 'interfere']
senticnet['hindfeet'] = ['0.098', '0.084', '0.819', '-0.68', '#anger', '#disgust', 'negative', '-0.33', 'grasp', 'covet', 'covetousness', 'avaricious', 'grabby']
senticnet['hindfoot'] = ['-0.60', '0', '0.701', '-0.72', '#anger', '#disgust', 'negative', '-0.67', 'lunate_bone', 'semilunar_bone', 'carpal_bone', 'triquetral', 'triquetral_bone']
senticnet['hindgut'] = ['0.04', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.055', 'kidney', 'pancreas', 'liver', 'bone', 'neck']
senticnet['hindley_milner'] = ['0.777', '-0.68', '0', '0.601', '#joy', '#surprise', 'positive', '0.686', 'lucidness', 'clarity', 'clearness', 'lucidity', 'pellucidity']
senticnet['hindneck'] = ['0.041', '0', '0', '0.058', '#joy', '#admiration', 'positive', '0.05', 'bird_animal', 'wing', 'farm_bird', 'barnyard_animal', 'hen_baby']
senticnet['hindrance'] = ['0', '0', '0', '-0.72', '#disgust', '#disgust', 'negative', '-0.72', 'impediment', 'obstacle', 'obstruction', 'drag', 'annoyance']
senticnet['hindsight'] = ['0', '0.117', '-0.06', '0.177', '#interest', '#admiration', 'positive', '0.076', 'start', 'thumb', 'knuckle', 'extend_arm', 'reach']
senticnet['hindu'] = ['0', '0.196', '0', '0.19', '#interest', '#admiration', 'positive', '0.193', 'sanctuary', 'bible', 'philosophy', 'capital_build', 'monastery']
senticnet['hindu_calendar'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['hindu_temple'] = ['0', '0.224', '0', '0.197', '#interest', '#admiration', 'positive', '0.211', 'church', 'temple', 'cathedral', 'chapel', 'confession']
senticnet['hindu_wedding'] = ['0', '0.819', '0', '0.987', '#interest', '#admiration', 'positive', '0.903', 'celebrate', 'wedding_ceremony', 'propose_marriage', 'engage', 'engagement']
senticnet['hindustani_classical_music'] = ['0.99', '0', '0', '0.719', '#joy', '#admiration', 'positive', '0.855', 'instrumental', 'lyric', 'lyrics', 'sing', 'melody']
senticnet['hindwing_shining_greyish_brown'] = ['-0.95', '0', '0.892', '-0.81', '#sadness', '#anger', 'negative', '-0.88', 'blurry', 'foggy', 'hazy', 'muzzy', 'blurred']
senticnet['hinge'] = ['0', '0', '0', '0.112', '#admiration', '#admiration', 'positive', '0.112', 'build_open', 'home_entrance', 'door_knob', 'knob', 'entry']
senticnet['hinge_joint'] = ['0', '0', '0.061', '-0.03', '#anger', '#disgust', 'negative', '-0.04', 'garden_tool', 'straight_line', 'sharp', 'razor_blade', 'chock']
senticnet['hinge_pin'] = ['0', '0.069', '0', '0.083', '#interest', '#admiration', 'positive', '0.076', 'latch', 'restrict_access', 'knob', 'door_knob', 'gate']
senticnet['hinged_door'] = ['0', '0.069', '0', '0.083', '#interest', '#admiration', 'positive', '0.076', 'latch', 'restrict_access', 'knob', 'door_knob', 'gate']
senticnet['hinged_lid'] = ['0', '-0.02', '0', '0.051', '#surprise', '#admiration', 'positive', '0.037', 'cardboard_box', 'bag', 'package', 'box_cereal', 'bin']
senticnet['hint'] = ['-0.03', '0.245', '-0.18', '0.232', '#interest', '#admiration', 'positive', '0.064', 'dare', 'jeopardy', 'suggestion', 'resolution', 'etiquette']
senticnet['hip'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'haunch', 'joint', 'above_leg', 'knee']
senticnet['hip_bone'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'socket', 'tooth_socket', 'alveolus', 'acetabulum', 'cotyloid_cavity']
senticnet['hip_dysplasia'] = ['0.789', '-0.99', '0', '0.54', '#joy', '#surprise', 'positive', '0.776', 'imperfection', 'imperfectness', 'bugginess', 'defectiveness', 'sickness']
senticnet['hip_flask'] = ['0.214', '0.258', '-0.09', '0.2', '#joy', '#interest', 'positive', '0.146', 'cup', 'carafe', 'pitcher', 'container_cup', 'glasses']
senticnet['hip_flexion'] = ['-0.15', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.09', 'use_leg', 'walk_quickly', 'casual', 'take_step', 'strut']
senticnet['hip_hop'] = ['0.081', '-0.05', '0.052', '0.072', '#joy', '#admiration', 'positive', '0.039', 'rhythm', 'type_of_music', 'instrumental', 'lyric', 'melody']
senticnet['hip_hop_artist'] = ['0', '0.712', '0', '0.844', '#interest', '#admiration', 'positive', '0.778', 'dollar_change', 'small_currency', 'currency_unit', 'metal_money', 'american_money']
senticnet['hip_hop_culture'] = ['0.081', '-0.05', '0.052', '0.072', '#joy', '#admiration', 'positive', '0.039', 'rhythm', 'type_of_music', 'instrumental', 'lyric', 'melody']
senticnet['hip_hop_dance'] = ['-0.06', '-0.00', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.05', 'madman', 'trouble', 'agony', 'cause_problem', 'river_stream']
senticnet['hip_hop_dj'] = ['0.081', '-0.05', '0.052', '0.072', '#joy', '#admiration', 'positive', '0.039', 'rhythm', 'type_of_music', 'instrumental', 'lyric', 'melody']
senticnet['hip_hop_duo'] = ['0', '0.712', '0', '0.844', '#interest', '#admiration', 'positive', '0.778', 'dollar_change', 'small_currency', 'currency_unit', 'metal_money', 'american_money']
senticnet['hip_hop_genre'] = ['0.081', '-0.05', '0.052', '0.072', '#joy', '#admiration', 'positive', '0.039', 'rhythm', 'type_of_music', 'instrumental', 'lyric', 'melody']
senticnet['hip_hop_icon'] = ['0', '0.712', '0', '0.844', '#interest', '#admiration', 'positive', '0.778', 'dollar_change', 'small_currency', 'currency_unit', 'metal_money', 'american_money']
senticnet['hip_hop_mogul'] = ['-0.19', '0', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.15', 'bad_child', 'misbehavior', 'bad_kid', 'bad_action', 'difficulty']
senticnet['hip_hop_music'] = ['0.081', '-0.05', '0.052', '0.072', '#joy', '#admiration', 'positive', '0.039', 'rhythm', 'type_of_music', 'instrumental', 'lyric', 'melody']
senticnet['hip_hop_musician'] = ['0', '0.712', '0', '0.844', '#interest', '#admiration', 'positive', '0.778', 'dollar_change', 'small_currency', 'currency_unit', 'metal_money', 'american_money']
senticnet['hip_hop_producer'] = ['0', '0.712', '0', '0.844', '#interest', '#admiration', 'positive', '0.778', 'dollar_change', 'small_currency', 'currency_unit', 'metal_money', 'american_money']
senticnet['hip_hop_rap'] = ['0.081', '-0.05', '0.052', '0.072', '#joy', '#admiration', 'positive', '0.039', 'rhythm', 'type_of_music', 'instrumental', 'lyric', 'melody']
senticnet['hip_hop_rapper'] = ['0', '0.712', '0', '0.844', '#interest', '#admiration', 'positive', '0.778', 'dollar_change', 'small_currency', 'currency_unit', 'metal_money', 'american_money']
senticnet['hip_hop_scene'] = ['0.081', '-0.05', '0.052', '0.072', '#joy', '#admiration', 'positive', '0.039', 'rhythm', 'type_of_music', 'instrumental', 'lyric', 'melody']
senticnet['hip_implant'] = ['-0.03', '0', '-0.09', '-0.14', '#fear', '#disgust', 'negative', '-0.09', 'x-ray_machine', 'artificial_arm', 'ambulance', 'syringe', 'latex_glove']
senticnet['hip_injury'] = ['-0.78', '0', '0.554', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'rubor', 'redness', 'inflammation', 'tennis_elbow', 'lateral_epicondylitis']
senticnet['hip_joint'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'socket', 'tooth_socket', 'alveolus', 'acetabulum', 'cotyloid_cavity']
senticnet['hip_pain'] = ['-0.98', '0', '0.678', '-0.95', '#sadness', '#disgust', 'negative', '-0.87', 'rubor', 'redness', 'inflammation', 'epicondylitis', 'lateral_epicondylitis']
senticnet['hip_shaking'] = ['0.26', '0', '0', '0.089', '#joy', '#admiration', 'positive', '0.174', 'balcony_rail', 'movie_theater_seat', 'row_seat', 'usher', 'sticky_floor']
senticnet['hip_socket'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'socket', 'tooth_socket', 'alveolus', 'acetabulum', 'cotyloid_cavity']
senticnet['hip_to'] = ['0.61', '-0.98', '0', '0.795', '#surprise', '#admiration', 'positive', '0.796', 'enlightened', 'conversant', 'hep', 'hip', 'informed']
senticnet['hipflask'] = ['0.619', '-0.83', '0', '0.912', '#surprise', '#admiration', 'positive', '0.788', 'flask', 'pocket_flask', 'ampulla', 'canteen', 'vacuum_flask']
senticnet['hiphop'] = ['0.081', '-0.05', '0.052', '0.072', '#joy', '#admiration', 'positive', '0.039', 'rhythm', 'type_of_music', 'instrumental', 'lyric', 'melody']
senticnet['hippest'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'elegant', 'stylish', 'smart', 'posh', 'sleek']
senticnet['hippie'] = ['-0.84', '0', '-0.73', '0', '#sadness', '#fear', 'negative', '-0.79', 'free_spirit', 'army_man', 'opposition', 'army_person', 'warrior']
senticnet['hippie_chic'] = ['-0.54', '0', '0.612', '-0.98', '#anger', '#disgust', 'negative', '-0.71', 'scuff', 'mule', 'bootee', 'carpet_slipper', 'footwear']
senticnet['hippoboscidae'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'annelid', 'annelid_worm', 'leech']
senticnet['hippocampal_function'] = ['0.998', '-0.63', '0', '0.574', '#joy', '#surprise', 'positive', '0.736', 'cleavable', 'divisible', 'separable', 'severable', 'dissociative']
senticnet['hippocastanaceae'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'horse_chestnut', 'red_buckeye', 'particolored_buckeye']
senticnet['hipponax'] = ['-0.81', '0', '0.52', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'affray', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['hippopotamus'] = ['-0.85', '0', '0.685', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'alligator', 'crocodilian', 'false_gavial', 'gator', 'caiman']
senticnet['hippy'] = ['0.979', '0.982', '0', '0.752', '#joy', '#interest', 'positive', '0.904', 'rock_star', 'philanthropist', 'male_human', 'singer', 'nazi']
senticnet['hipster'] = ['0.52', '-0.91', '0', '0.65', '#surprise', '#admiration', 'positive', '0.694', 'flower_people', 'hippy', 'youth_subculture', 'beat_generation', 'beat']
senticnet['hiraab'] = ['-0.84', '0', '0.579', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'squiggle', 'hand', 'chicken_scratch', 'handwriting', 'script']
senticnet['hire'] = ['-0.03', '0.098', '0', '0.086', '#interest', '#admiration', 'positive', '0.05', 'overprice', 'job', 'ask_person', 'find_job', 'rent']
senticnet['hire_attorney'] = ['-0.25', '0.068', '-0.15', '-0.05', '#sadness', '#fear', 'negative', '-0.09', 'hire_lawyer', 'see_lawyer', 'helper', 'contact_lawyer', 'call_lawyer']
senticnet['hire_help'] = ['-0.03', '0.106', '-0.05', '0.08', '#interest', '#admiration', 'positive', '0.025', 'job', 'task', 'ask_person', 'career', 'chore']
senticnet['hire_lawyer'] = ['-0.18', '0.112', '-0.13', '0', '#sadness', '#fear', 'negative', '-0.07', 'hire_attorney', 'see_lawyer', 'contact_lawyer', 'call_lawyer', 'go_court']
senticnet['hired_armed_lugger'] = ['0', '0', '0.08', '0', '#anger', '#anger', 'negative', '-0.08', 'tree_animal', 'small_furry', 'rodent', 'big_mouse', 'small_rodent']
senticnet['hired_assassin'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'yeoman', 'outrider', 'yeoman_of_guard', 'beefeater', 'defender']
senticnet['hiring_bilingual'] = ['-0.93', '0', '-0.99', '-0.92', '#sadness', '#fear', 'negative', '-0.95', 'barrier', 'shut', 'shut_down', 'close_down', 'obstacle']
senticnet['hiring_manager'] = ['-0.06', '0.149', '-0.09', '0.181', '#interest', '#admiration', 'positive', '0.044', 'ask_nicely', 'job_offer', 'question_ask', 'make_happen', 'make_effort']
senticnet['hirka_mountain'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'head_hurt', 'introspection', 'lifestyle_change', 'headache', 'move_eye']
senticnet['hiro_mashima'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['hiroshima'] = ['0', '0', '-0.72', '-0.94', '#fear', '#disgust', 'negative', '-0.83', 'pistol', 'gun', 'hydrogen_bomb', 'handgun', 'revolver']
senticnet['hiroshima_bomb'] = ['0', '0', '-0.72', '-0.94', '#fear', '#disgust', 'negative', '-0.83', 'pistol', 'gun', 'hydrogen_bomb', 'handgun', 'revolver']
senticnet['hiroshima_nagasaki'] = ['0', '0', '-0.72', '-0.94', '#fear', '#disgust', 'negative', '-0.83', 'pistol', 'gun', 'hydrogen_bomb', 'handgun', 'revolver']
senticnet['hirschsprung_disease'] = ['-0.50', '0', '0.542', '-0.51', '#anger', '#disgust', 'negative', '-0.51', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['hirst'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'hip', 'joint', 'above_leg', 'knee']
senticnet['hirsute'] = ['-0.64', '0', '0.647', '-0.55', '#sadness', '#anger', 'negative', '-0.61', 'puberulent', 'sericeous', 'furlike', 'pappose', 'pilous']
senticnet['hirundinidae'] = ['-0.92', '0', '0.64', '-0.52', '#sadness', '#anger', 'negative', '-0.69', 'animal_order', 'percher', 'thyreophoran', 'marginocephalian', 'euronithopod']
senticnet['hiry'] = ['0.084', '-0.05', '0.013', '0', '#joy', '#surprise', 'positive', '0.043', 'carnival_ride', 'carny', 'fortune_teller', 'horseback', 'ferris_wheel']
senticnet['hisaishi'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_music', 'hear_music', 'artist', 'make_drawing', 'art']
senticnet['hish_nahiyah'] = ['-0.84', '0', '0.579', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'squiggle', 'hand', 'chicken_scratch', 'handwriting', 'script']
senticnet['hispanicized_spelling_jachcha'] = ['-0.73', '0', '0.55', '-0.75', '#sadness', '#disgust', 'negative', '-0.68', 'mortal', 'person', 'individual', 'mangler', 'mutilator']
senticnet['hispid'] = ['-0.75', '0', '0.734', '-0.82', '#sadness', '#disgust', 'negative', '-0.77', 'puberulent', 'haired', 'furlike', 'pappose', 'pilous']
senticnet['hispid_hare'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheep', 'argal', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['hispida'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['hisself'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'impaired', 'dickey', 'lessened', 'diminished']
senticnet['hissing_sound'] = ['0', '0.098', '-0.09', '0.115', '#interest', '#admiration', 'positive', '0.04', 'push_wind', 'breath', 'breaout', 'body_reaction', 'mouth_action']
senticnet['hissy_fit'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'fragmentation', 'discharge', 'backfire', 'blast']
senticnet['histamine_blocker'] = ['-0.53', '0', '0.954', '-0.54', '#anger', '#disgust', 'negative', '-0.67', 'antacid', 'ranitidine', 'gastric_antacid', 'alkalizer', 'antiacid']
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senticnet['historic'] = ['0', '0.952', '0', '0.894', '#interest', '#admiration', 'positive', '0.923', 'historical', 'landmark', 'heritage', 'memorial', 'downtown']
senticnet['historic_carousel'] = ['0.089', '-0.18', '0.037', '-0.06', '#joy', '#surprise', 'positive', '0.043', 'balloon', 'ballon', 'fielder', 'kite', 'blimp']
senticnet['historic_farmhouse'] = ['0', '0.113', '0', '0.111', '#interest', '#admiration', 'positive', '0.112', 'animal_house', 'cow_home', 'animal_place', 'crop_place', 'acreage']
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senticnet['historic_preservation'] = ['-0.97', '0', '0.885', '-0.97', '#sadness', '#disgust', 'negative', '-0.94', 'protection', 'conservation', 'environmentalism', 'reservation', 'immobilization']
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senticnet['historical_context'] = ['0', '0.139', '0', '0.18', '#interest', '#admiration', 'positive', '0.16', 'aspect', 'vista', 'scenic', 'spectacular', 'view']
senticnet['historical_epic'] = ['0.089', '-0.01', '0.033', '0.082', '#joy', '#admiration', 'positive', '0.039', 'informative', 'informational', 'ephemeral', 'fun_read', 'ads']
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senticnet['historical_musicology'] = ['0.045', '0', '0.033', '0.089', '#joy', '#admiration', 'positive', '0.034', 'spanish', 'vocalization', 'verbal_communication', 'vocal', 'mouth_noise']
senticnet['historical_overview'] = ['0', '0.154', '-0.05', '0.17', '#interest', '#admiration', 'positive', '0.089', 'court_action', 'accessory', 'legal_document', 'short', 'protective_cover']
senticnet['historical_perspective'] = ['0', '0.139', '0', '0.18', '#interest', '#admiration', 'positive', '0.16', 'aspect', 'vista', 'scenic', 'spectacular', 'view']
senticnet['historical_revisionism'] = ['-0.72', '0', '0.755', '0', '#sadness', '#anger', 'negative', '-0.73', 'disgrace', 'shameful', 'dishonorably', 'inglorious', 'dishonored']
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senticnet['historicist_style'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'adobe', 'adobe_brick', 'clinker', 'brick', 'firebrick']
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senticnet['historiographical'] = ['0.587', '-0.94', '0', '0.659', '#surprise', '#admiration', 'positive', '0.731', 'critique', 'review', 'criticism', 'literary_criticism', 'new_criticism']
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senticnet['history'] = ['-0.03', '0.106', '-0.05', '0.08', '#interest', '#admiration', 'positive', '0.025', 'long_ago', 'past_event', 'social_science', 'school_subject', 'subject']
senticnet['history_book'] = ['0.046', '0', '0', '0.085', '#joy', '#admiration', 'positive', '0.066', 'action_movie', 'history', 'medieval_time', 'human_history', 'part_of_history']
senticnet['histrion'] = ['-0.71', '0', '0.794', '-0.89', '#anger', '#disgust', 'negative', '-0.80', 'role_player', 'thespian', 'actor', 'ham_actor', 'player']
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senticnet['hit'] = ['-0.95', '0', '0.762', '0', '#sadness', '#anger', 'negative', '-0.85', 'dribble', 'break_window', 'best_opponent', 'hit_hard', 'skip']
senticnet['hit_ball'] = ['0.932', '0.931', '0', '0.78', '#joy', '#interest', 'positive', '0.881', 'strike_out', 'throw_ball', 'catch_baseball', 'play_football', 'run_first_base']
senticnet['hit_ball_hard'] = ['0', '-0.75', '0', '0.766', '#surprise', '#admiration', 'positive', '0.762', 'play_rugby', 'break_record', 'play_volleyball', 'run_basis', 'play_basketball']
senticnet['hit_ball_with_bat'] = ['0', '-0.05', '0.066', '-0.13', '#anger', '#disgust', 'negative', '-0.05', 'strike_out', 'throw_ball', 'hit_ball', 'catch_baseball', 'run_first_base']
senticnet['hit_baseball_bat'] = ['-0.31', '-0.07', '0.014', '-0.23', '#sadness', '#disgust', 'negative', '-0.12', 'hit_face', 'bloody_nose', 'lose_a_lot_of_money', 'car_wreck', 'go_insane']
senticnet['hit_bottle'] = ['0.081', '-0.05', '0', '-0.03', '#joy', '#surprise', 'positive', '0.036', 'drink_alcohol', 'consume_alcoholic_beverage', 'drink_much', 'drink_liquor', 'meet_woman']
senticnet['hit_brother'] = ['-0.06', '0', '0.077', '-0.10', '#anger', '#disgust', 'negative', '-0.08', 'backup', 'make_easy', 'compose_music', 'make_drawing', 'use_musical_instrument']
senticnet['hit_car'] = ['-0.19', '-0.05', '0', '-0.20', '#sadness', '#disgust', 'negative', '-0.11', 'automobile_accident', 'accident', 'use_brake', 'stagger', 'hurt_back']
senticnet['hit_corner'] = ['-0.17', '-0.01', '0', '-0.18', '#sadness', '#disgust', 'negative', '-0.11', 'pass_out', 'bump_head', 'loss_ofawareness', 'bang_head', 'serious_accident']
senticnet['hit_face'] = ['-0.93', '0', '0.847', '-1.0', '#sadness', '#disgust', 'negative', '-0.92', 'fight', 'punch', 'beat', 'physical_pain', 'hit']
senticnet['hit_fist'] = ['-0.19', '-0.00', '-0.09', '-0.08', '#sadness', '#fear', 'negative', '-0.09', 'hit_hard', 'violent_action', 'demolish', 'mock', 'terminate']
senticnet['hit_golf_ball'] = ['0.121', '-0.19', '0', '0', '#joy', '#surprise', 'positive', '0.157', 'dance_well', 'enter_contest', 'hook_up', 'old_dance', 'dance']
senticnet['hit_ground'] = ['-0.09', '0', '-0.08', '-0.08', '#sadness', '#fear', 'negative', '-0.09', 'fall_ground', 'loss_ofbreath', 'loss_ofawareness', 'broken_limb', 'hope_win']
senticnet['hit_hard'] = ['0', '0', '0.821', '-0.93', '#anger', '#disgust', 'negative', '-0.87', 'violent_action', 'break_apart', 'demolish', 'break_glass', 'pulverize']
senticnet['hit_hard_wall'] = ['0.738', '-0.91', '0', '0', '#joy', '#surprise', 'positive', '0.827', 'bounce_up_down', 'hit', 'ball_game', 'hit_hard', 'play_catch']
senticnet['hit_hardest'] = ['-0.92', '0', '0.946', '-0.56', '#sadness', '#anger', 'negative', '-0.81', 'forcefulness', 'intensiveness', 'impulse', 'vigor', 'energy']
senticnet['hit_head'] = ['-0.97', '0', '0.532', '-0.98', '#sadness', '#disgust', 'negative', '-0.83', 'head', 'jump_out_of_window', 'fall_down', 'injury', 'physical_pain']
senticnet['hit_headlines'] = ['-0.08', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.09', 'crossword', 'grade_paper', 'type_of_paper', 'breaking_news', 'narrative']
senticnet['hit_home_run'] = ['0.506', '0.659', '-0.24', '0.242', '#joy', '#interest', 'positive', '0.29', 'score_point', 'run_first_base', 'win_game', 'score_goal', 'strike_out']
senticnet['hit_jackpot'] = ['0.528', '-0.84', '0', '0.65', '#surprise', '#admiration', 'positive', '0.674', 'deliver_goods', 'succeed', 'win', 'bring_home_bacon', 'come_through']
senticnet['hit_target'] = ['0', '0', '-0.94', '-0.88', '#fear', '#disgust', 'negative', '-0.91', 'shoot_cannon', 'shoot', 'shoot_person', 'weapon_war', 'ammunition']
senticnet['hit_tree'] = ['0', '0', '-0.71', '-0.80', '#fear', '#disgust', 'negative', '-0.76', 'see_dog', 'go_downhill', 'fall_butt', 'break_bone', 'crash_tree']
senticnet['hit_wall'] = ['-0.87', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.92', 'little_pollution', 'physical_exertion', 'dehydration', 'sweat_lot', 'strain_muscle']
senticnet['hit_with_baton'] = ['-0.91', '0', '0.931', '-0.72', '#sadness', '#anger', 'negative', '-0.85', 'baton', 'get_hurt', 'whip', 'arrow', 'hurt']
senticnet['hitch'] = ['-0.95', '0', '0.793', '0', '#sadness', '#anger', 'negative', '-0.87', 'block', 'obstacle', 'nodus', 'impediment', 'obstruction']
senticnet['hitchcockian'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'anxiety', 'apprehension', 'insecurity', 'tension', 'horror_movie']
senticnet['hitchhike'] = ['0.991', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.893', 'ask_for_lift', 'take_walk', 'take_bus', 'travel', 'take_metro']
senticnet['hither'] = ['0.947', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.895', 'over', 'heaven', 'beyond', 'yonder', 'away']
senticnet['hitherto'] = ['-0.94', '0', '0.991', '-0.65', '#sadness', '#anger', 'negative', '-0.86', 'birthday_suit', 'raw', 'nakedness', 'nude', 'undress']
senticnet['hitherto_unknown'] = ['0.774', '-0.63', '0', '0.567', '#joy', '#surprise', 'positive', '0.658', 'catching', 'breakthrough', 'spying', 'determination', 'tracing']
senticnet['hitler'] = ['-0.11', '0', '0.033', '-0.32', '#sadness', '#disgust', 'negative', '-0.15', 'conquer_enemy', 'lose_war', 'lose_territory', 'go_war', 'enslave_person']
senticnet['hitler_regime'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'healthy_person', 'man', 'pervert', 'compatriot']
senticnet['hitler_third_reich'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'healthy_person', 'man', 'pervert', 'compatriot']
senticnet['hitler_youth'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'healthy_person', 'man', 'pervert', 'compatriot']
senticnet['hitlerism'] = ['-0.91', '0', '0.688', '-0.57', '#sadness', '#anger', 'negative', '-0.72', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
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senticnet['hitless'] = ['0', '0', '0.702', '-0.73', '#anger', '#disgust', 'negative', '-0.71', 'scoreless', 'shutout', 'inning', 'no-hit', 'strikeout']
senticnet['hitmaker'] = ['0', '0', '0.876', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'untune', 'discompose', 'discomfit', 'disconcert', 'upset']
senticnet['hitman'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'addicted', 'obsessed_love', 'addiction', 'obsession', 'weird']
senticnet['hitmen'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'yeoman', 'outrider', 'yeoman_of_guard', 'beefeater', 'defender']
senticnet['hitoshi_sakimoto'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_music', 'hear_music', 'artist', 'make_drawing', 'art']
senticnet['hitro_okesene'] = ['0.997', '-0.84', '0', '0.927', '#joy', '#admiration', 'positive', '0.923', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['hitter'] = ['0', '0', '-0.72', '-0.89', '#fear', '#disgust', 'negative', '-0.80', 'hit', 'batter', 'slugger', 'pain', 'cause_pain']
senticnet['hittest'] = ['0.777', '-0.85', '0', '0.615', '#joy', '#surprise', 'positive', '0.748', 'think', 'play', 'trifle', 'abstract', 'warm_to']
senticnet['hitting_mr_tomlinson'] = ['-0.91', '0', '0.931', '-0.72', '#sadness', '#anger', 'negative', '-0.85', 'hit_with_baton', 'get_hurt', 'whip', 'arrow', 'hurt']
senticnet['hiv'] = ['-0.98', '0', '0.942', '-0.9', '#sadness', '#anger', 'negative', '-0.94', 'disease', 'aid', 'immunological_disorder', 'die', 'immunodeficiency']
senticnet['hiv_infected_blood'] = ['-0.74', '0', '0.766', '0', '#sadness', '#anger', 'negative', '-0.75', 'use_needle', 'clothes_repair', 'woven', 'embroidery', 'thread_needle']
senticnet['hiv_stigma'] = ['-0.77', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'mark', 'emblem', 'religious_sign', 'stain', 'brand']
senticnet['hiv_therapy'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'indinavir', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['hiv_vaccine'] = ['-0.07', '0', '-0.05', '-0.18', '#sadness', '#disgust', 'negative', '-0.10', 'medical_therapy', 'vaccination', 'doctor', 'medicine', 'hospital']
senticnet['hive'] = ['-0.06', '-0.01', '-0.04', '0', '#sadness', '#fear', 'negative', '-0.03', 'hut', 'animal_home', 'lair', 'habitat', 'beehive']
senticnet['hive_entrance'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'lair', 'habitat', 'animal_home']
senticnet['hive_up'] = ['0.915', '0.943', '0', '0', '#joy', '#interest', 'positive', '0.929', 'lay_away', 'stash', 'squirrel_away', 'cache', 'memory_cache']
senticnet['hix_oyster'] = ['0.084', '-0.05', '0', '-0.04', '#joy', '#surprise', 'positive', '0.031', 'entree', 'feast', 'satiation', 'breakfast', 'supper']
senticnet['hj_clan'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'overlord', 'lord']
senticnet['hk_quote'] = ['0', '0.189', '0.04', '0.091', '#interest', '#admiration', 'positive', '0.08', 'combination', 'margin', 'teller', 'merge', 'finite']
senticnet['hk_zrinjski_mostar'] = ['0.954', '-0.90', '0', '0.867', '#joy', '#surprise', 'positive', '0.91', 'friend', 'sports_fan', 'rooter', 'fan', 'nut']
senticnet['hm_chief_inspector'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'law_enforcement_agency', 'law', 'agency']
senticnet['hm_fowey'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'succourer', 'recoverer', 'salvor', 'mortal']
senticnet['hm_hood'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'submarine', 'lifeboat', 'titanic', 'large_ship', 'canoe']
senticnet['hm_inspector'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'law_enforcement_agency', 'law', 'agency']
senticnet['hm_iron'] = ['0.033', '0.088', '-0.06', '0.068', '#interest', '#admiration', 'positive', '0.032', 'earl', 'noble', 'king', 'sultan', 'royal_title']
senticnet['hm_pinafore'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'dress', 'pinny', 'dirndl', 'gown', 'muumuu']
senticnet['hm_royal'] = ['0.582', '-0.97', '0', '0.802', '#surprise', '#admiration', 'positive', '0.788', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['hm_worcester'] = ['0.764', '-0.96', '0', '0.883', '#surprise', '#admiration', 'positive', '0.87', 'boatmanship', 'acquirement', 'skill', 'accomplishment', 'acquisition']
senticnet['hmg_coa_reductase_inhibitor'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'spironolactone', 'spironolactone_drug']
senticnet['hmic'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'law_enforcement_agency', 'law', 'agency']
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senticnet['holy_relic'] = ['0', '0.82', '0', '0.847', '#interest', '#admiration', 'positive', '0.833', 'reverently', 'hero-worship', 'idolize', 'veneration', 'reverentially']
senticnet['holy_sacrament'] = ['0.824', '0', '0', '0.754', '#joy', '#admiration', 'positive', '0.789', 'holy', 'eucharist', 'holy_eucharist', 'liturgy', 'sacrament_of_eucharist']
senticnet['holy_sacrifice'] = ['0.693', '-0.69', '0', '0.714', '#surprise', '#admiration', 'positive', '0.702', 'vow', 'dedicate', 'apply', 'rededicate', 'use']
senticnet['holy_see'] = ['0.013', '0.199', '0.027', '0.167', '#interest', '#admiration', 'positive', '0.088', 'church', 'confession', 'good_catholic', 'spirituality', 'christianity']
senticnet['holy_shit'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amok', 'murderousness', 'misocainea', 'misogyny']
senticnet['holy_spirit'] = ['0.128', '0.152', '-0.09', '0.264', '#interest', '#admiration', 'positive', '0.113', 'environment', 'beautiful', 'live_environment', 'really_big', 'important_man']
senticnet['holy_terror'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'rascal', 'rapscallion', 'scalawag', 'scallywag', 'monkey']
senticnet['holy_trinity_parish'] = ['0.081', '0.146', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'cult', 'sect', 'conservative', 'worshiper', 'pastor']
senticnet['holy_whore'] = ['0', '0', '-0.81', '-0.75', '#fear', '#disgust', 'negative', '-0.78', 'watch_out', 'keep_one_eye_peeled', 'keep_one_eye_skinned', 'keep_one_eye_open', 'aware']
senticnet['holy_zeal'] = ['0', '0.908', '0', '0.99', '#interest', '#admiration', 'positive', '0.949', 'beneficence', 'beneficially', 'beneficent', 'beneficed', 'benefic']
senticnet['holyfield'] = ['0.969', '0.998', '0', '0', '#joy', '#interest', 'positive', '0.984', 'bell_sound', 'bell_noise', 'piece_of_jewelry', 'syllable', 'fastener']
senticnet['holzman'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['homage'] = ['0.781', '-0.73', '0', '0.786', '#joy', '#admiration', 'positive', '0.766', 'prop', 'politeness', 'deference', 'civility', 'court']
senticnet['homalocephale'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'hag', 'osteostracan', 'cephalaspid', 'ostracoderm', 'jawless_fish']
senticnet['home'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'apartment', 'furniture', 'kitchen', 'flat']
senticnet['home-bound'] = ['-0.71', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.84', 'shut-in', 'house-bound', 'shut-in', 'housebound', 'wellhead']
senticnet['home_animal'] = ['0.727', '0', '0', '0.705', '#joy', '#admiration', 'positive', '0.716', 'loyal', 'household_animal', 'house_pet', 'good_pet', 'best_friend']
senticnet['home_architectural_structure'] = ['0.898', '0.817', '0', '0', '#joy', '#interest', 'positive', '0.857', 'large_architectural_structure', 'architectural_structure', 'build', 'architect', 'structure_support']
senticnet['home_build'] = ['0', '0.07', '0', '0.125', '#interest', '#admiration', 'positive', '0.098', 'live_quarter', 'house', 'make_building', 'domestic_dwell', 'small_building']
senticnet['home_city'] = ['0', '0.065', '0', '0.088', '#interest', '#admiration', 'positive', '0.076', 'country_live', 'big_land', 'cow_home', 'acreage', 'large_land']
senticnet['home_cooked_meal'] = ['0.026', '0.069', '0', '0', '#joy', '#interest', 'positive', '0.048', 'eat_food', 'eat_beef', 'chew_food', 'espresso', 'eat_much']
senticnet['home_decoration'] = ['-0.02', '0.087', '0', '0.112', '#interest', '#admiration', 'positive', '0.059', 'social_convention', 'cover_floor', 'decoration', 'floor_cover', 'cover_wall']
senticnet['home_depot'] = ['0.912', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.917', 'carpet_store', 'lumberyard', 'furniture_store', 'front_porch', 'shoe_shop']
senticnet['home_depot_hd'] = ['0.912', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.917', 'carpet_store', 'lumberyard', 'furniture_store', 'front_porch', 'shoe_shop']
senticnet['home_depot_inc'] = ['0.912', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.917', 'carpet_store', 'lumberyard', 'furniture_store', 'front_porch', 'shoe_shop']
senticnet['home_depot_store'] = ['0.912', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.917', 'carpet_store', 'lumberyard', 'furniture_store', 'front_porch', 'shoe_shop']
senticnet['home_economics'] = ['0', '0.848', '0', '0.711', '#interest', '#admiration', 'positive', '0.779', 'sociology', 'social_science', 'proxemics', 'economics', 'anthropology']
senticnet['home_entrance'] = ['0.989', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.939', 'entry', 'build_open', 'house_front', 'hinge', 'entrance_room']
senticnet['home_ground'] = ['0.092', '-0.09', '0', '0', '#joy', '#surprise', 'positive', '0.091', 'sport', 'pitch', 'baseball_bat', 'match', 'collectible']
senticnet['home_improvement'] = ['0.116', '0', '-0.04', '0.091', '#joy', '#admiration', 'positive', '0.053', 'cover_wall', 'wall_cover', 'latex', 'please_eye', 'create_image']
senticnet['home_improvement_store'] = ['0.949', '-0.83', '0', '0', '#joy', '#surprise', 'positive', '0.889', 'under_carpet', 'shoe_shop', 'carpet_store', 'attic', 'closet']
senticnet['home_institution'] = ['0.734', '0', '0', '0.939', '#joy', '#admiration', 'positive', '0.837', 'participant', 'student', 'workshop', 'faculty', 'summer']
senticnet['home_made'] = ['0.967', '-0.82', '0', '0', '#joy', '#surprise', 'positive', '0.897', 'big_breadbox', 'bread', 'container', 'baked_goods', 'eat_bread']
senticnet['home_plate'] = ['0', '0.175', '-0.06', '0.064', '#interest', '#admiration', 'positive', '0.059', 'hideout', 'stronghold', 'safe_place', 'base', 'fort']
senticnet['home_price_index'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'indicator', 'retail_price_index', 'producer_price_index', 'wholesale_price_index', 'consumer_price_index']
senticnet['home_renovation'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'buy_bedroom', 'buy_pillow', 'buy_home_appliance', 'buy_bed_sheet', 'buy_bed']
senticnet['home_room'] = ['0.866', '0.808', '0', '0', '#joy', '#interest', 'positive', '0.837', 'room', 'classroom', 'study_hall', 'lecture_room', 'schoolroom']
senticnet['home_run'] = ['0.742', '0.995', '0', '0.747', '#interest', '#admiration', 'positive', '0.828', 'score_home_run', 'win_game', 'reach_advantage', 'jump_chance', 'compete_against']
senticnet['home_sweet_home'] = ['-0.10', '0.235', '-0.12', '0.246', '#interest', '#admiration', 'positive', '0.063', 'love', 'settle_down', 'family_nest', 'family', 'happy_family']
senticnet['home_turf'] = ['0.954', '-0.87', '0', '0.583', '#joy', '#surprise', 'positive', '0.802', 'penetration', 'air_raid', 'air_attack', 'swoop', 'incursion']
senticnet['homebound'] = ['0.956', '-0.70', '0', '0.653', '#joy', '#surprise', 'positive', '0.771', 'centenarian', 'nonagenarian', 'octogenarian', 'septuagenarian', 'sexagenarian']
senticnet['homebound_elderly'] = ['0.956', '-0.70', '0', '0.653', '#joy', '#surprise', 'positive', '0.771', 'centenarian', 'nonagenarian', 'octogenarian', 'septuagenarian', 'sexagenarian']
senticnet['homebuilder'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'maker']
senticnet['homecare'] = ['-0.12', '0', '-0.12', '-0.08', '#sadness', '#fear', 'negative', '-0.11', 'hospital', 'check_up', 'check_doctor', 'check_pulse', 'check_blood_pressure']
senticnet['homecoming_celebration'] = ['0.119', '-0.13', '-0.06', '0', '#joy', '#surprise', 'positive', '0.063', 'gala', 'celebration', 'special_event', 'special_happen', 'special_moment']
senticnet['homecoming_weekend'] = ['0.119', '-0.13', '-0.06', '0', '#joy', '#surprise', 'positive', '0.063', 'gala', 'celebration', 'special_event', 'special_happen', 'special_moment']
senticnet['homegrown_talent'] = ['0.608', '-0.55', '0', '0.726', '#joy', '#admiration', 'positive', '0.628', 'genius', 'endowment', 'flair', 'gift', 'knack']
senticnet['homeland'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'motherland', 'patriotism']
senticnet['homeland_defense'] = ['-0.04', '0', '-0.06', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'make_war', 'conquer_enemy', 'use_military', 'gain_territory', 'keep_peace']
senticnet['homeland_security'] = ['-0.04', '0', '-0.06', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'make_war', 'conquer_enemy', 'use_military', 'gain_territory', 'keep_peace']
senticnet['homeless'] = ['-0.88', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.81', 'bum', 'red_light', 'petition', 'take', 'violent_action']
senticnet['homeless_mentally_ill'] = ['-0.88', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.81', 'bum', 'red_light', 'petition', 'take', 'violent_action']
senticnet['homeless_people'] = ['-0.12', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.07', 'graffiti', 'bum', 'homeless_person', 'panhandler', 'homeless']
senticnet['homeless_person'] = ['-0.88', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.81', 'homeless_people', 'homeless', 'need_money', 'need_food', 'poor_person']
senticnet['homeless_shelter'] = ['-0.76', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.76', 'bowl_alley', 'gutter', 'crack_house', 'junction', 'tavern']
senticnet['homeless_youth'] = ['-0.88', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.81', 'bum', 'red_light', 'petition', 'take', 'violent_action']
senticnet['homelike'] = ['0.54', '0.398', '-0.20', '0.251', '#joy', '#interest', 'positive', '0.246', 'comfortable', 'calm', 'calmness', 'cozy', 'soothe']
senticnet['homely'] = ['0.656', '-0.73', '0', '0.736', '#surprise', '#admiration', 'positive', '0.708', 'strong', 'salubrious', 'healthy', 'solid', 'alimentary']
senticnet['homemade'] = ['-0.97', '0', '0.84', '-0.52', '#sadness', '#anger', 'negative', '-0.78', 'pis_aller', 'last_resort', 'temporary_expedient', 'expedient', 'stopgap']
senticnet['homemade_bomb'] = ['0', '-0.03', '-0.04', '-0.19', '#fear', '#disgust', 'negative', '-0.06', 'nuclear_bomb', 'explosive', 'grenade', 'nuclear_weapon', 'aerial_bomb']
senticnet['homemade_cake'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'relax', 'eat_food', 'eat_snack', 'eat_sweet', 'childhood']
senticnet['homemade_chocolate'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'relax', 'eat_food', 'eat_snack', 'eat_sweet', 'childhood']
senticnet['homemade_granola'] = ['-0.92', '0', '0.609', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'puffed_rice', 'granola', 'raisin_bran', 'corn_flake', 'bran_flake']
senticnet['homemade_liquor'] = ['0.081', '-0.05', '0', '-0.03', '#joy', '#surprise', 'positive', '0.036', 'drink_alcohol', 'consume_alcoholic_beverage', 'drink_much', 'hit_bottle', 'meet_woman']
senticnet['homemade_soup'] = ['-0.92', '0', '0.609', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'puffed_rice', 'granola', 'raisin_bran', 'corn_flake', 'bran_flake']
senticnet['homeopath'] = ['0.738', '-0.53', '0', '0.856', '#joy', '#admiration', 'positive', '0.71', 'doctor', 'country_doctor', 'family_doctor', 'doctor_office', 'physician']
senticnet['homeopathic_doctor'] = ['0.738', '-0.53', '0', '0.856', '#joy', '#admiration', 'positive', '0.71', 'doctor', 'country_doctor', 'family_doctor', 'doctor_office', 'physician']
senticnet['homeopathic_medicine'] = ['-0.60', '0', '0.544', '-0.62', '#sadness', '#disgust', 'negative', '-0.59', 'remedy', 'cure', 'therapeutic', 'antidote', 'atropine']
senticnet['homeopathic_physician'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'druggist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['homeostasis'] = ['0', '0', '-0.92', '-0.95', '#fear', '#disgust', 'negative', '-0.94', 'homeostatic', 'physiologic', 'circadian', 'plasticity', 'physiological']
senticnet['homeostatic'] = ['0', '0', '-0.92', '-0.95', '#fear', '#disgust', 'negative', '-0.93', 'homeostasis', 'physiologic', 'circadian', 'plasticity', 'physiological']
senticnet['homeotherm'] = ['-0.98', '0', '0.705', '-0.93', '#sadness', '#disgust', 'negative', '-0.87', 'animal', 'animate_being', 'beast', 'creature', 'brute']
senticnet['homeowner'] = ['0.066', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.04', 'workman', 'man', 'stalactite', 'pauper', 'nazi']
senticnet['homepage'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'swat', 'store_record', 'ideal']
senticnet['homer_iliad'] = ['-0.81', '0', '0.52', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'affray', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['homer_odyssey'] = ['-0.81', '0', '0.52', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'affray', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['homeroom_teacher'] = ['0.521', '0.677', '-0.27', '0.273', '#joy', '#interest', 'positive', '0.299', 'further_education', 'protect_child', 'look_closely', 'go_to_school', 'register']
senticnet['homesick'] = ['-0.80', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.87', 'lonely', 'antsy', 'despondent', 'disheartened', 'hungover']
senticnet['homespun'] = ['0', '-0.72', '0', '0.878', '#surprise', '#admiration', 'positive', '0.802', 'unpretending', 'unpretentious', 'understated', 'pretentious', 'humble']
senticnet['homestake_mining_company'] = ['-0.98', '0', '0.521', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'battery', 'stamp_battery', 'quartz_battery', 'stamp_mill', 'factory']
senticnet['homestay'] = ['0.875', '0', '0', '0.802', '#joy', '#admiration', 'positive', '0.839', 'foreigner', 'exile', 'deportee', 'gringo', 'import']
senticnet['homestay_accommodation'] = ['0.875', '0', '0', '0.802', '#joy', '#admiration', 'positive', '0.839', 'foreigner', 'exile', 'deportee', 'gringo', 'import']
senticnet['homestead'] = ['0', '0.113', '0', '0.111', '#interest', '#admiration', 'positive', '0.112', 'animal_house', 'cow_home', 'animal_place', 'crop_place', 'acreage']
senticnet['homesteader'] = ['0', '0', '0.735', '-1', '#anger', '#disgust', 'negative', '-0.86', 'squatter', 'slum', 'squatter', 'squatting', 'settler']
senticnet['hometown'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'apartment', 'furniture', 'kitchen', 'flat']
senticnet['hometown_newspaper'] = ['-0.05', '0.106', '0', '0.06', '#interest', '#admiration', 'positive', '0.036', 'buy', 'buy_newspaper', 'read_buy_newspaper', 'buy_book', 'read_news']
senticnet['homeward'] = ['0', '0.152', '-0.08', '0.208', '#interest', '#admiration', 'positive', '0.091', 'departure', 'leave', 'colored_green', 'green_color', 'make_big']
senticnet['homework'] = ['-0.12', '0', '0', '-0.15', '#sadness', '#disgust', 'negative', '-0.13', 'go_college', 'study_test', 'sharpen_pencil', 'revise', 'take_test']
senticnet['homework_assignment'] = ['-0.12', '0', '0', '-0.15', '#sadness', '#disgust', 'negative', '-0.13', 'go_college', 'study_test', 'sharpen_pencil', 'revise', 'take_test']
senticnet['homeworld'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['homicidal'] = ['-0.98', '0', '0.992', '0', '#sadness', '#anger', 'negative', '-0.98', 'difficult', 'murder', 'criminal', 'murderous', 'murderously']
senticnet['homicidal_maniac'] = ['-0.74', '0', '0.604', '-0.88', '#sadness', '#disgust', 'negative', '-0.74', 'weirdo', 'crazy', 'loony', 'looney', 'nutcase']
senticnet['homicidal_violence'] = ['0', '0', '-0.95', '-0.85', '#fear', '#disgust', 'negative', '-0.90', 'choose_victim', 'murder_weapon', 'hide_evidence', 'murder', 'shoot_gun']
senticnet['homicide'] = ['0', '0', '-0.95', '-0.85', '#fear', '#disgust', 'negative', '-0.90', 'choose_victim', 'murder_weapon', 'hide_evidence', 'murder', 'shoot_gun']
senticnet['homicide_detective'] = ['0', '0', '-0.95', '-0.85', '#fear', '#disgust', 'negative', '-0.90', 'choose_victim', 'murder_weapon', 'hide_evidence', 'murder', 'shoot_gun']
senticnet['homicide_investigation'] = ['0', '0', '-0.95', '-0.85', '#fear', '#disgust', 'negative', '-0.90', 'choose_victim', 'murder_weapon', 'hide_evidence', 'murder', 'shoot_gun']
senticnet['homicide_per'] = ['-0.47', '0.174', '-0.44', '0.105', '#sadness', '#fear', 'negative', '-0.16', 'wrong', 'wrongdoing', 'bad_choice', 'harmful_human', 'bad_behavior']
senticnet['homicide_rate'] = ['-0.47', '0.174', '-0.44', '0.105', '#sadness', '#fear', 'negative', '-0.16', 'wrong', 'wrongdoing', 'bad_choice', 'harmful_human', 'bad_behavior']
senticnet['homicide_suicide'] = ['0', '0', '-0.95', '-0.85', '#fear', '#disgust', 'negative', '-0.90', 'choose_victim', 'murder_weapon', 'hide_evidence', 'murder', 'shoot_gun']
senticnet['homicide_unit'] = ['0', '0', '-0.95', '-0.85', '#fear', '#disgust', 'negative', '-0.90', 'choose_victim', 'murder_weapon', 'hide_evidence', 'murder', 'shoot_gun']
senticnet['homie'] = ['0.797', '-0.95', '0', '0.921', '#surprise', '#admiration', 'positive', '0.891', 'lush', 'alcoholic', 'alky', 'dipsomaniac', 'boozer']
senticnet['homiletical'] = ['-0.67', '0', '0.936', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'unprepared', 'extemporary', 'extempore', 'impromptu', 'unrehearsed']
senticnet['homiletics'] = ['0.607', '-0.99', '0', '0.847', '#surprise', '#admiration', 'positive', '0.817', 'eristic', 'puppetry', 'artistry', 'aviation', 'airmanship']
senticnet['hominid'] = ['0.914', '-0.57', '0', '0.908', '#joy', '#admiration', 'positive', '0.798', 'primate', 'homo', 'man', 'human_being', 'human']
senticnet['hominin'] = ['0.537', '-0.51', '0', '0.807', '#joy', '#admiration', 'positive', '0.619', 'lesbian', 'fairy', 'fagot', 'faggot', 'fag']
senticnet['hominoid'] = ['-0.93', '0', '0.508', '-0.58', '#sadness', '#disgust', 'negative', '-0.67', 'inhuman', 'bloodless', 'apelike', 'nonhuman', 'nonhumanal']
senticnet['hominum'] = ['0.654', '-0.75', '0', '0.78', '#surprise', '#admiration', 'positive', '0.729', 'suit', 'cause', 'lawsuit', 'occurrence', 'countersuit']
senticnet['hommes_ont'] = ['0', '0.919', '0', '0.888', '#interest', '#admiration', 'positive', '0.903', 'surprise', 'eye-popping', 'fulgurous', 'dazzling', 'bedaze']
senticnet['homo'] = ['0.537', '-0.51', '0', '0.807', '#joy', '#admiration', 'positive', '0.619', 'lesbian', 'fairy', 'fagot', 'faggot', 'fag']
senticnet['homo_ergaster'] = ['0.537', '-0.51', '0', '0.807', '#joy', '#admiration', 'positive', '0.619', 'lesbian', 'fairy', 'fagot', 'faggot', 'fag']
senticnet['homo_erotic'] = ['-0.9', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.80', 'filth', 'smut', 'vulgarism', 'dirt', 'obscenity']
senticnet['homo_superior'] = ['0.651', '-0.73', '0', '0.975', '#surprise', '#admiration', 'positive', '0.788', 'profoundness', 'profundity', 'sagacity', 'sagaciousness', 'discernment']
senticnet['homoallylic_alcohol'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
senticnet['homocystinuria'] = ['-0.57', '0', '0.973', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['homodont_dentition'] = ['-0.58', '0', '0.561', '-0.62', '#sadness', '#disgust', 'negative', '-0.58', 'spot', 'speckle', 'dapple', 'mark', 'fleck']
senticnet['homoerotic'] = ['-0.9', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.80', 'filth', 'smut', 'vulgarism', 'dirt', 'obscenity']
senticnet['homoeroticism'] = ['0.886', '-0.79', '0', '0.584', '#joy', '#surprise', 'positive', '0.753', 'sex', 'inversion', 'sexual_inversion', 'lesbianism', 'sapphism']
senticnet['homogeneity'] = ['-0.63', '0', '0.822', '-0.95', '#anger', '#disgust', 'negative', '-0.80', 'uniformness', 'homogeneousness', 'consistency', 'consistence', 'similarity']
senticnet['homogeneous_mixture'] = ['-0.98', '0', '0.516', '-0.90', '#sadness', '#disgust', 'negative', '-0.80', 'devastation', 'annihilation', 'pulverization', 'vaporization', 'destruction']
senticnet['homogeneousness'] = ['-0.63', '0', '0.822', '-0.95', '#anger', '#disgust', 'negative', '-0.80', 'homogeneity', 'uniformness', 'consistency', 'consistence', 'similarity']
senticnet['homogenisation'] = ['-0.63', '0', '0.822', '-0.95', '#anger', '#disgust', 'negative', '-0.80', 'uniformness', 'homogeneousness', 'consistency', 'consistence', 'similarity']
senticnet['homogenization'] = ['-0.77', '0', '0.914', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'confluence', 'conflux', 'merging', 'blending', 'combination']
senticnet['homogenize'] = ['-0.77', '0', '0.914', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'confluence', 'conflux', 'merging', 'blending', 'combination']
senticnet['homogot'] = ['0.531', '-0.52', '0', '0.801', '#joy', '#admiration', 'positive', '0.618', 'lesbian', 'fairy', 'homoot', 'fag', 'homo']
senticnet['homograph'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'foot_motion', 'foot_action', 'foot_movement', 'stride', 'pace']
senticnet['homographic'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicado', 'incommunicative', 'impassive', 'inexpressive', 'silent']
senticnet['homoioteleuton'] = ['-0.22', '0', '-0.09', '-0.16', '#sadness', '#disgust', 'negative', '-0.16', 'inequality', 'reject', 'discard', 'difference', 'gap']
senticnet['homoiotherm'] = ['-0.91', '0', '0.958', '-0.5', '#sadness', '#anger', 'negative', '-0.78', 'animal', 'animate_being', 'beast', 'creature', 'brute']
senticnet['homonym'] = ['-0.79', '0', '0', '-0.91', '#sadness', '#disgust', 'negative', '-0.85', 'ambiguity', 'ambiguous', 'confusing', 'interpretation', 'ambiguously']
senticnet['homoot'] = ['0.528', '-0.52', '0', '0.8', '#joy', '#admiration', 'positive', '0.617', 'lesbian', 'fairy', 'fag', 'homogot', 'homo']
senticnet['homophile'] = ['0.662', '-0.74', '0', '0.836', '#surprise', '#admiration', 'positive', '0.748', 'lesbian', 'fairy', 'fagot', 'faggot', 'fag']
senticnet['homophily'] = ['-0.93', '0', '0.586', '-0.66', '#sadness', '#disgust', 'negative', '-0.73', 'camp', 'pack', 'clique', 'coterie', 'brain_trust']
senticnet['homophobe'] = ['0.778', '-0.73', '0', '0.559', '#joy', '#surprise', 'positive', '0.689', 'sex', 'inversion', 'sexual_inversion', 'lesbianism', 'sapphism']
senticnet['homophobia'] = ['0', '0.264', '0', '0.246', '#interest', '#admiration', 'positive', '0.255', 'christianity', 'judaism', 'religion', 'world_religion', 'catholic_church']
senticnet['homophobic'] = ['0', '0', '0', '-0.99', '#disgust', '#disgust', 'negative', '-0.99', 'uneducated', 'guilty_crime', 'precise', 'strange', 'ignorant']
senticnet['homophone'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'foot_motion', 'foot_action', 'foot_movement', 'stride', 'pace']
senticnet['homophonic'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['homophonic_texture'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['homophony'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['homoplasy'] = ['-0.83', '0', '0.546', '-0.88', '#sadness', '#disgust', 'negative', '-0.75', 'difference', 'allomerism', 'deviation', 'variation', 'departure']
senticnet['homosexual'] = ['0.547', '-0.85', '0', '0.924', '#surprise', '#admiration', 'positive', '0.776', 'lesbian', 'fairy', 'fagot', 'faggot', 'fag']
senticnet['homosexual_activity'] = ['-0.84', '0', '0.882', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'perversion', 'fetishism', 'paraphilia', 'sexual_perversion', 'buggery']
senticnet['homosexual_agenda'] = ['0.778', '-0.73', '0', '0.559', '#joy', '#surprise', 'positive', '0.689', 'sex', 'inversion', 'sexual_inversion', 'lesbianism', 'sapphism']
senticnet['homosexual_behavior'] = ['-0.84', '0', '0.882', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'perversion', 'fetishism', 'paraphilia', 'sexual_perversion', 'buggery']
senticnet['homosexual_conduct'] = ['-0.84', '0', '0.882', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'perversion', 'fetishism', 'paraphilia', 'sexual_perversion', 'buggery']
senticnet['homosexual_intercourse'] = ['0.542', '-0.83', '0', '0.927', '#surprise', '#admiration', 'positive', '0.769', 'incest', 'extramarital_sex', 'free_love', 'sexual_intercourse', 'intercourse']
senticnet['homosexual_lifestyle'] = ['0.778', '-0.73', '0', '0.559', '#joy', '#surprise', 'positive', '0.689', 'sex', 'inversion', 'sexual_inversion', 'lesbianism', 'sapphism']
senticnet['homosexual_relationship'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'liaison', 'affair', 'involvement']
senticnet['homosexual_sex'] = ['-0.84', '0', '0.882', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'perversion', 'fetishism', 'paraphilia', 'sexual_perversion', 'buggery']
senticnet['homosexual_sodomy'] = ['-0.84', '0', '0.882', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'perversion', 'fetishism', 'paraphilia', 'sexual_perversion', 'buggery']
senticnet['homosexualism'] = ['0.778', '-0.73', '0', '0.559', '#joy', '#surprise', 'positive', '0.689', 'sex', 'inversion', 'sexual_inversion', 'lesbianism', 'sapphism']
senticnet['homosexuality'] = ['0.591', '-0.96', '0', '0.861', '#surprise', '#admiration', 'positive', '0.805', 'sex', 'inversion', 'sexual_inversion', 'lesbianism', 'sapphism']
senticnet['homosocial'] = ['0.915', '0.755', '0', '0', '#joy', '#interest', 'positive', '0.835', 'sympathetic', 'confidence', 'adoration', 'closeness', 'grateful']
senticnet['homotherm'] = ['-0.59', '0', '0.79', '-0.63', '#anger', '#disgust', 'negative', '-0.67', 'animal', 'animate_being', 'beast', 'creature', 'brute']
senticnet['homozygous_mutation'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'deflection', 'warp', 'aberrance']
senticnet['honcho'] = ['-0.85', '0', '0.938', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'menial', 'lackey', 'servant', 'flunkey', 'familiar']
senticnet['honda_civic'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'cable_car', 'baggage_compartment', 'subway', 'amusement_park', 'terminal']
senticnet['honda_dealer'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'sell_car', 'truck_stop', 'vehicle', 'insurance', 'car']
senticnet['honda_dealership'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'sell_car', 'truck_stop', 'vehicle', 'insurance', 'car']
senticnet['honda_fireblade'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'fuel', 'car', 'fossil_fuel', 'transport', 'natural_resource']
senticnet['honda_motorbike'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'fuel', 'car', 'fossil_fuel', 'transport', 'natural_resource']
senticnet['honda_odyssey'] = ['-0.05', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'plane_land', 'floss', 'floss_tooth', 'ornament', 'wear_dress']
senticnet['honda_pilot'] = ['-0.05', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'plane_land', 'floss', 'floss_tooth', 'ornament', 'wear_dress']
senticnet['hone'] = ['0', '0.784', '0', '0.701', '#interest', '#admiration', 'positive', '0.742', 'sharpen', 'whet', 'file', 'grind', 'skill']
senticnet['honest'] = ['0.773', '0.708', '0', '0.961', '#joy', '#admiration', 'positive', '0.814', 'good_character', 'virtue', 'truthful', 'good_quality', 'goodness']
senticnet['honest-hearted'] = ['0', '0.842', '0', '0.874', '#interest', '#admiration', 'positive', '0.858', 'wasp-y', 'clean-living', 'serious-minded', 'god-loving', 'levelheaded']
senticnet['honest_broker'] = ['0.763', '0', '0', '0.719', '#joy', '#admiration', 'positive', '0.741', 'mist', 'haze', 'fog', 'misty', 'drizzle']
senticnet['honest_livelihood'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'lucrative', 'moneymaking', 'profitable', 'paying', 'paid']
senticnet['honestie'] = ['-0.67', '0', '0.936', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'extemporaneous', 'unprepared', 'extempore', 'impromptu', 'unrehearsed']
senticnet['honestly'] = ['0.76', '0.998', '0', '0.75', '#joy', '#interest', 'positive', '0.836', 'honesty', 'honest', 'integrity', 'virtue', 'good_character']
senticnet['honestness'] = ['0.741', '-0.84', '0', '0.543', '#joy', '#surprise', 'positive', '0.711', 'integrity', 'righteousness', 'scrupulousness', 'incorruptness', 'incorruption']
senticnet['honesty'] = ['0.867', '0', '0', '0.852', '#joy', '#admiration', 'positive', '0.859', 'truthful', 'honest', 'integrity', 'goodness', 'virtue']
senticnet['honesty_humility'] = ['-0.93', '0', '0.586', '-0.66', '#sadness', '#disgust', 'negative', '-0.73', 'camp', 'pack', 'clique', 'coterie', 'brain_trust']
senticnet['honewort'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['honey'] = ['0.744', '-0.77', '0', '0.661', '#joy', '#surprise', 'positive', '0.725', 'sugarcoat', 'dulcify', 'glaze', 'change_taste', 'sugar']
senticnet['honey_bee'] = ['0.916', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.881', 'centerfold', 'genus', 'fairy', 'infant', 'dove']
senticnet['honey_drown'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'lair', 'habitat', 'animal_home']
senticnet['honey_insect'] = ['0.03', '0.078', '0', '0.101', '#interest', '#admiration', 'positive', '0.07', 'flora', 'bee', 'species', 'fauna', 'live_organism']
senticnet['honey_locust'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'locust_tree', 'black_locust']
senticnet['honey_nut'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'veal', 'beef', 'food_group', 'animal_flesh', 'curry']
senticnet['honey_sweet'] = ['0.933', '-0.87', '0', '0.766', '#joy', '#surprise', 'positive', '0.858', 'scent', 'aroma', 'olfactory_property', 'smell', 'odor']
senticnet['honeybee'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'lair', 'habitat', 'animal_home']
senticnet['honeycomb'] = ['0.039', '0', '0', '0.075', '#joy', '#admiration', 'positive', '0.057', 'hive', 'ration', 'nutrient', 'result_cook', 'noodle']
senticnet['honeycomb_pattern'] = ['0.067', '0.088', '-0.07', '0.246', '#interest', '#admiration', 'positive', '0.081', 'multiple_purposes', 'revolve', 'start_run', 'wiggle', 'twist']
senticnet['honeydew'] = ['0.025', '-0.00', '0', '0.125', '#joy', '#admiration', 'positive', '0.053', 'goo', 'adhesive', 'flour_water', 'insert', 'thick_liquid']
senticnet['honeyeater'] = ['-0.84', '0', '0.655', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'piping_crow', 'butcherbird', 'currawong', 'bell_magpie', 'oscine']
senticnet['honeyed'] = ['0.701', '-0.50', '0', '0.922', '#joy', '#admiration', 'positive', '0.711', 'mellifluous', 'melodic', 'dulcet', 'lyrical', 'mellisonant']
senticnet['honeylocust'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'locust_tree']
senticnet['honeymoon'] = ['0.878', '0.84', '0', '0.91', '#joy', '#admiration', 'positive', '0.876', 'stumble', 'slip', 'fall', 'trip', 'tumble']
senticnet['honeymoon_cruise'] = ['0.878', '0.84', '0', '0.91', '#joy', '#admiration', 'positive', '0.876', 'stumble', 'slip', 'fall', 'trip', 'tumble']
senticnet['honeymoon_destination'] = ['0.878', '0.84', '0', '0.91', '#joy', '#admiration', 'positive', '0.876', 'stumble', 'slip', 'fall', 'trip', 'tumble']
senticnet['honeymoon_suite'] = ['0.878', '0.84', '0', '0.91', '#joy', '#admiration', 'positive', '0.876', 'stumble', 'slip', 'fall', 'trip', 'tumble']
senticnet['honeymoon_trip'] = ['0.878', '0.84', '0', '0.91', '#joy', '#admiration', 'positive', '0.876', 'stumble', 'slip', 'fall', 'trip', 'tumble']
senticnet['honeymooner'] = ['0.869', '-0.53', '0', '0.772', '#joy', '#admiration', 'positive', '0.725', 'bride', 'groom', 'bridegroom', 'spouse', 'partner']
senticnet['honeysuckle'] = ['0.958', '-0.82', '0', '0.924', '#joy', '#admiration', 'positive', '0.901', 'bunchberry', 'dwarf_cornel', 'crackerberry', 'pudding_berry', 'common_white_dogwood']
senticnet['hong_kong_action_thriller'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amuck', 'murderousness', 'misocainea', 'misogyny']
senticnet['hong_kong_phooey'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'child_movie', 'child_story', 'funny_moment', 'childhood']
senticnet['honiton_lace'] = ['-0.62', '0', '0.961', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'crown', 'diadem', 'jeweled_headdress', 'headdress', 'headgear']
senticnet['honk'] = ['0.691', '-0.97', '0', '0.771', '#surprise', '#admiration', 'positive', '0.812', 'squeal', 'blate', 'churr', 'let_loose', 'sibilate']
senticnet['honk_horn'] = ['-0.08', '-0.07', '0.045', '-0.12', '#sadness', '#disgust', 'negative', '-0.04', 'shift_gear', 'turn_steer_wheel', 'stop_stop_sign', 'stop_red_light', 'discover_new_place']
senticnet['honkey'] = ['-0.93', '0', '0.77', '-0.79', '#sadness', '#disgust', 'negative', '-0.83', 'whitey', 'honky', 'caucasian', 'honkie', 'mortal']
senticnet['honkie'] = ['-0.93', '0', '0.77', '-0.79', '#sadness', '#disgust', 'negative', '-0.83', 'whitey', 'honky', 'honkey', 'caucasian', 'mortal']
senticnet['honky'] = ['-0.93', '0', '0.77', '-0.79', '#sadness', '#disgust', 'negative', '-0.83', 'whitey', 'caucasian', 'honkey', 'honkie', 'mortal']
senticnet['honky_tonk'] = ['-0.81', '0', '0.553', '-0.92', '#sadness', '#disgust', 'negative', '-0.76', 'bar', 'sawdust_saloon', 'saloon', 'ginmill', 'taproom']
senticnet['honky_tonk_piano'] = ['0.584', '-0.62', '0', '0.815', '#surprise', '#admiration', 'positive', '0.675', 'squiggle', 'hand', 'cacography', 'handwriting', 'script']
senticnet['honkytonk'] = ['-0.80', '0', '0.508', '-0.80', '#sadness', '#disgust', 'negative', '-0.70', 'cabaret', 'nightclub', 'night_club', 'club', 'nightspot']
senticnet['honor'] = ['0.857', '0.824', '0', '0.708', '#joy', '#interest', 'positive', '0.796', 'mandatory', 'gentleman', 'obey', 'virtue', 'dignity']
senticnet['honor_bestowed_upon'] = ['0.857', '0.824', '0', '0.708', '#joy', '#interest', 'positive', '0.796', 'mandatory', 'gentleman', 'obey', 'virtue', 'dignity']
senticnet['honor_college'] = ['0.963', '0.858', '0', '0', '#joy', '#interest', 'positive', '0.91', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'collegian']
senticnet['honor_parent'] = ['-0.14', '0', '-0.05', '-0.03', '#sadness', '#fear', 'negative', '-0.08', 'laugh', 'throw_fit', 'smile', 'battle_illness', 'laugh_clown']
senticnet['honor_person'] = ['0', '0.763', '0', '0.972', '#interest', '#admiration', 'positive', '0.867', 'remember', 'contemplation', 'remember_past', 'use_memory', 'commemorate_event']
senticnet['honor_program'] = ['0.963', '0.858', '0', '0', '#joy', '#interest', 'positive', '0.91', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'collegian']
senticnet['honor_quiktrip'] = ['-0.02', '0.095', '0', '0.117', '#interest', '#admiration', 'positive', '0.063', 'identification', 'paper_square', 'carry_money', 'money_hold', 'credit']
senticnet['honorable'] = ['0.777', '0.913', '0', '0', '#joy', '#interest', 'positive', '0.845', 'ethical', 'honest', 'good', 'estimable', 'fair']
senticnet['honorable_gentleman'] = ['-0.55', '0', '0.965', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'interjection', 'delay', 'insert', 'disruption', 'interpolation']
senticnet['honorable_mention'] = ['0.682', '-0.70', '0', '0.98', '#surprise', '#admiration', 'positive', '0.789', 'prize', 'seal_of_approval', 'laurel', 'aliyah', 'cachet']
senticnet['honorable_mention_award'] = ['0.894', '0.881', '0', '0.803', '#joy', '#interest', 'positive', '0.859', 'remittance', 'afford', 'award', 'money_due', 'amount_owe']
senticnet['honorableness'] = ['0.909', '-0.85', '0', '0.846', '#joy', '#surprise', 'positive', '0.87', 'respectability', 'reputability', 'venerability', 'venerableness', 'righteousness']
senticnet['honorably'] = ['-0.93', '0', '0.936', '-0.88', '#sadness', '#anger', 'negative', '-0.91', 'lose_team', 'cause_suffer', 'leave_out', 'loathing', 'lyme_disease']
senticnet['honorarium'] = ['0', '0.814', '0', '0.796', '#interest', '#admiration', 'positive', '0.805', 'stipend', 'allowance', 'fee', 'bursary', 'payment']
senticnet['honorary'] = ['0.102', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.076', 'adjust_volume', 'tap_toe', 'enter_contest', 'tap_foot', 'dance_well']
senticnet['honorary_award'] = ['0.232', '0.178', '-0.17', '0.223', '#joy', '#admiration', 'positive', '0.115', 'fame', 'alms', 'find_money', 'better_life', 'small_detail']
senticnet['honorary_canon'] = ['0.031', '0.272', '0', '0.231', '#interest', '#admiration', 'positive', '0.178', 'minister', 'church_head', 'pastor', 'protestant', 'evangelist']
senticnet['honorary_chaplain'] = ['0.031', '0.272', '0', '0.231', '#interest', '#admiration', 'positive', '0.178', 'minister', 'church_head', 'pastor', 'protestant', 'evangelist']
senticnet['honored'] = ['0.988', '-0.99', '0', '0.585', '#joy', '#surprise', 'positive', '0.857', 'estimable', 'respected', 'esteemed', 'reputable', 'prestigious']
senticnet['honored_guest'] = ['0', '0.852', '0', '0', '#interest', '#interest', 'positive', '0.852', 'streamer', 'tribute', 'salutation', 'gift', 'anniversary_gift']
senticnet['honored_posthumously'] = ['0.857', '0.824', '0', '0.708', '#joy', '#interest', 'positive', '0.796', 'mandatory', 'gentleman', 'obey', 'virtue', 'dignity']
senticnet['honoree'] = ['0.857', '0.824', '0', '0.708', '#joy', '#interest', 'positive', '0.796', 'mandatory', 'gentleman', 'obey', 'virtue', 'dignity']
senticnet['honorific'] = ['0', '0.837', '0', '0.746', '#interest', '#admiration', 'positive', '0.791', 'epithet', 'honorific', 'pejorative', 'bestowed', 'euphemistic']
senticnet['honoring'] = ['0.559', '-0.83', '0', '0.593', '#surprise', '#admiration', 'positive', '0.664', 'abidance', 'compliance', 'punctilio', 'conformity', 'conformation']
senticnet['honour_bachelor_degree'] = ['-0.75', '0', '0.589', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'lowness', 'grind', 'immoderation', 'immoderateness', 'amplitude_level']
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senticnet['hook_up'] = ['0.192', '-0.16', '0.055', '-0.03', '#joy', '#surprise', 'positive', '0.067', 'serve_drink', 'go_club', 'drink_beer', 'get_wasted', 'socialize']
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senticnet['hookworm_infection'] = ['-0.71', '0', '0.74', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'kwashiorkor', 'marasmus', 'malnutrition', 'hypovitaminosis', 'deficiency_disease']
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senticnet['hooper'] = ['-0.20', '-0.10', '0.07', '-0.24', '#sadness', '#disgust', 'negative', '-0.10', 'flipper', 'sea_life', 'seafood', 'swim_animal', 'fillet']
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senticnet['hoopla'] = ['0.882', '-0.95', '0', '0', '#joy', '#surprise', 'positive', '0.916', 'hyped', 'buzz', 'bullshit', 'hype', 'rumor']
senticnet['hoopoe'] = ['-0.02', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.04', 'wren', 'bird', 'quail', 'sparrow', 'chick']
senticnet['hoor'] = ['-0.63', '0', '0.791', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'little', 'pinch', 'modicum', 'small_indefinite_amount', 'indefinite_quantity']
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senticnet['hooshang'] = ['-0.71', '0', '0.836', '0', '#sadness', '#anger', 'negative', '-0.77', 'awkward', 'unenviable', 'embarrassingly', 'embarrassing', 'sheepish']
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senticnet['hop'] = ['0.023', '0', '0', '0.102', '#joy', '#admiration', 'positive', '0.063', 'jump', 'move_slowly', 'leap', 'revolve', 'sway']
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senticnet['hope'] = ['0', '0.892', '0', '0', '#interest', '#interest', 'positive', '0.892', 'worship', 'trust', 'optimism', 'devotion', 'believe']
senticnet['hope_peace'] = ['0.701', '0.764', '0', '0.828', '#interest', '#admiration', 'positive', '0.764', 'gather_berry', 'hang_picture_on_wall', 'see_star_night', 'need_go_bathroom', 'wave_flag']
senticnet['hope_win'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'expectation', 'optimism', 'win_game', 'dream_victory', 'wish_success']
senticnet['hoped-for'] = ['0.719', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.798', 'longed-for', 'long-promised', 'much-touted', 'much-hyped', 'much-awaited']
senticnet['hopeful'] = ['0.315', '0.687', '-0.29', '0.224', '#joy', '#interest', 'positive', '0.232', 'hopefully', 'anticipate', 'affectionate', 'fond', 'lovesome']
senticnet['hopefully'] = ['0.37', '0.686', '-0.28', '0.233', '#joy', '#interest', 'positive', '0.251', 'hopeful', 'anticipate', 'hopefulness', 'affectionateness', 'affectional']
senticnet['hopefulness'] = ['0.475', '0.684', '-0.27', '0.263', '#joy', '#interest', 'positive', '0.288', 'optimistically', 'optimistic', 'encouragingly', 'encouraging', 'bucked_up']
senticnet['hopeless'] = ['-0.87', '0', '0.943', '-0.86', '#sadness', '#anger', 'negative', '-0.89', 'fearful', 'dreadful', 'humiliate', 'hysteria', 'dumb']
senticnet['hopelessly'] = ['-0.95', '0', '-0.70', '0', '#sadness', '#fear', 'negative', '-0.83', 'dispiritedly', 'hopelessness', 'despairing', 'pessimistically', 'pessimistic']
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senticnet['hopf_link'] = ['-0.06', '-0.01', '0.05', '-0.07', '#sadness', '#disgust', 'negative', '-0.04', 'embroidery', 'woven', 'cover_wind', 'torn', 'use_needle']
senticnet['hopping_around'] = ['0.03', '0.068', '-0.04', '0.093', '#interest', '#admiration', 'positive', '0.037', 'beast', 'equine', 'live_creature', 'furry_creature', 'domesticate_animal']
senticnet['hopscotch'] = ['0.155', '-0.11', '0.009', '-0.05', '#joy', '#surprise', 'positive', '0.05', 'dance_well', 'relate_music', 'enter_contest', 'jump_around', 'cheer_up']
senticnet['horatio_alger_award'] = ['0.857', '0.824', '0', '0.708', '#joy', '#interest', 'positive', '0.796', 'mandatory', 'gentleman', 'obey', 'virtue', 'dignity']
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senticnet['horizontal'] = ['0.033', '0', '0', '0.081', '#joy', '#admiration', 'positive', '0.057', 'door', 'door_lock', 'window_door', 'wall', 'doorway']
senticnet['horizontal_component'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'aberrance', 'warp', 'chromosomal_aberration']
senticnet['horizontal_convective'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'poppy_seed', 'custard', 'bread', 'biscuit']
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senticnet['horizontal_direction'] = ['0', '0.175', '0', '0.231', '#interest', '#admiration', 'positive', '0.203', 'compass_direction', 'go_straight', 'end_point', 'eight_sides', 'red_light']
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senticnet['horizontal_line'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'bayonet', 'spear', 'shotgun', 'bullet', 'hydrogen_bomb']
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senticnet['horizontal_surface'] = ['0.058', '0', '0.045', '0.078', '#joy', '#admiration', 'positive', '0.03', 'relax', 'sleep_place', 'comfy', 'rest_place', 'occur']
senticnet['horizontal_tricolor'] = ['0.053', '0.068', '-0.05', '0.06', '#interest', '#admiration', 'positive', '0.032', 'linen', 'star_stripe', 'velvet', 'nation', 'burlap']
senticnet['horizontal_velocity'] = ['-0.03', '0.178', '0', '0.168', '#interest', '#admiration', 'positive', '0.104', 'speed', 'fast', 'fast_and_furious', 'speedy', 'fleet']
senticnet['horizontally'] = ['0.022', '0.069', '0.027', '0.059', '#interest', '#admiration', 'positive', '0.031', 'perfect_surface', 'plane', 'flat_land', 'flat', 'floor']
senticnet['hormonal'] = ['0.672', '-0.55', '0', '0.992', '#joy', '#admiration', 'positive', '0.739', 'pressor', 'hormone', 'adrenalin', 'internal_secretion', 'catecholamine']
senticnet['hormonal_contraception'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'safe_sex', 'sex', 'prevent_pregnancy', 'avoid_pregnancy', 'become_aroused']
senticnet['hormonal_control'] = ['0.672', '-0.55', '0', '0.992', '#joy', '#admiration', 'positive', '0.739', 'pressor', 'hormone', 'adrenalin', 'internal_secretion', 'catecholamine']
senticnet['hormonal_iud'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'birth_control_pill', 'condom', 'contraceptive_pill', 'sex_toy', 'prevent_pregnancy']
senticnet['hormonal_regulation'] = ['0.672', '-0.55', '0', '0.992', '#joy', '#admiration', 'positive', '0.739', 'pressor', 'hormone', 'adrenalin', 'internal_secretion', 'catecholamine']
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senticnet['hormone_ghrelin'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'cholecystokinin', 'secretin', 'gastrointestinal_hormone']
senticnet['hormone_insulin'] = ['0.079', '-0.10', '0.046', '0', '#joy', '#surprise', 'positive', '0.047', 'nerve', 'liver', 'groin', 'scrotum', 'diaphragm']
senticnet['hormone_leptin'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'cholecystokinin', 'secretin', 'gastrointestinal_hormone']
senticnet['horn'] = ['0.063', '0.089', '0.036', '0.146', '#interest', '#admiration', 'positive', '0.066', 'antler', 'jew_harp', 'brass_instrument', 'trumpet', 'crash_cymbal']
senticnet['horn-rimmed'] = ['0', '-0.93', '0', '0.905', '#surprise', '#admiration', 'positive', '0.921', 'owlish', 'owl-like', 'heavy-lidded', 'black-rimmed', 'half-lidded']
senticnet['horn_clause'] = ['0.777', '-0.68', '0', '0.601', '#joy', '#surprise', 'positive', '0.686', 'lucidness', 'clarity', 'clearness', 'lucidity', 'pellucidity']
senticnet['horned_frog'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'amphibian', 'aquatic']
senticnet['horned_lark'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['horned_snake'] = ['-0.68', '0', '0.611', '-0.59', '#sadness', '#anger', 'negative', '-0.63', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['horned_toad'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'amphibian', 'aquatic']
senticnet['horner_syndrome'] = ['0.722', '-0.87', '0', '0.696', '#joy', '#surprise', 'positive', '0.763', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['hornet'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'giant_hornet', 'vespid', 'vespid_wasp']
senticnet['hornet_stadium'] = ['0', '-0.84', '0', '0.723', '#surprise', '#admiration', 'positive', '0.785', 'circular_route', 'stadium', 'memorial', 'station', 'row_house']
senticnet['hornet_wasp'] = ['-0.67', '0', '0.938', '-0.56', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'giant_hornet', 'hornet', 'vespid']
senticnet['hornworm'] = ['0', '-0.09', '0.123', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'gypsy_moth', 'japanese_beetle', 'cucumber_beetle', 'mosquito', 'earwig']
senticnet['horny'] = ['0.132', '0.115', '-0.06', '0.121', '#joy', '#admiration', 'positive', '0.076', 'make_person', 'survival_species', 'make_baby', 'lover', 'man_wife']
senticnet['horologist'] = ['0.853', '0', '0', '0.954', '#joy', '#admiration', 'positive', '0.903', 'portraitist', 'watercolorist', 'printmaker', 'lutenist', 'miniaturist']
senticnet['horoscope'] = ['0.083', '0', '0', '0.075', '#joy', '#admiration', 'positive', '0.079', 'carny', 'carnival_ride', 'midway', 'ferris_wheel', 'roller-coaster']
senticnet['horovitz'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
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senticnet['horrendous_crime'] = ['-0.06', '0', '-0.15', '0', '#sadness', '#fear', 'negative', '-0.10', 'heroism', 'role_play', 'repeat', 'act', 'part_of_movie']
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senticnet['horrible'] = ['-0.93', '0', '-0.93', '-0.94', '#sadness', '#disgust', 'negative', '-0.93', 'dread', 'distressed', 'terrible', 'dismay', 'uneasiness']
senticnet['horrible_accident'] = ['-0.91', '0', '0.905', '-0.78', '#sadness', '#anger', 'negative', '-0.86', 'pity', 'shame', 'misfortune', 'mischance', 'misadventure']
senticnet['horrible_feeling'] = ['-0.80', '0', '-0.8', '0', '#sadness', '#fear', 'negative', '-0.80', 'sinking', 'misgiving', 'qualm', 'scruple', 'insecurely']
senticnet['horrible_smell'] = ['0', '0.073', '0', '0.092', '#interest', '#admiration', 'positive', '0.082', 'false_tooth', 'scent', 'waste_water', 'sniff', 'odor']
senticnet['horribly'] = ['-0.78', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.84', 'horrifying', 'atrocious', 'dreadfully', 'horrifyingly', 'horrified']
senticnet['horrid'] = ['-0.23', '0.235', '0.456', '-0.57', '#anger', '#disgust', 'negative', '-0.25', 'outrageous', 'queasy', 'misanthropical', 'misanthropic', 'sulky']
senticnet['horridly'] = ['-0.96', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.87', 'horrifying', 'atrocious', 'dreadfully', 'horrifyingly', 'horrified']
senticnet['horrific'] = ['-0.34', '0.317', '0.205', '-0.43', '#sadness', '#disgust', 'negative', '-0.16', 'discouraging', 'misanthropic', 'misanthropical', 'discouraged', 'outrageous']
senticnet['horrific_accident'] = ['-0.91', '0', '0.905', '-0.78', '#sadness', '#anger', 'negative', '-0.86', 'pity', 'shame', 'misfortune', 'mischance', 'misadventure']
senticnet['horrific_car_accident'] = ['-0.73', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.79', 'die_painfully', 'arrest_police', 'get_accident', 'fall_head', 'skin_knee']
senticnet['horrific_car_crash'] = ['-0.73', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.79', 'die_painfully', 'arrest_police', 'get_accident', 'fall_head', 'skin_knee']
senticnet['horrific_crash'] = ['-0.90', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.86', 'trauma', 'car_wreck', 'broken_bone', 'bad_knee', 'wreck']
senticnet['horrific_violence'] = ['-0.67', '0', '0.938', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'massacre', 'mass_murder', 'carnage', 'slaughter', 'butchery']
senticnet['horrifically'] = ['-0.93', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.96', 'dire', 'fearsome', 'dreaded', 'direful', 'frightening']
senticnet['horrified'] = ['-0.71', '0', '0.988', '0', '#sadness', '#anger', 'negative', '-0.85', 'horrifying', 'atrocious', 'dreadfully', 'horrifyingly', 'monstrously']
senticnet['horrify'] = ['-0.87', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.93', 'appall', 'horror', 'horrifying', 'atrocious', 'horrifyingly']
senticnet['horrifying'] = ['-0.86', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.93', 'atrocious', 'dreadfully', 'horrifyingly', 'horrified', 'horribly']
senticnet['horrifyingly'] = ['-0.99', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.96', 'horrifying', 'atrocious', 'dreadfully', 'horrified', 'horribly']
senticnet['horripilation'] = ['-0.68', '0', '0.601', '-0.80', '#sadness', '#disgust', 'negative', '-0.69', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['horror'] = ['-0.97', '0', '-0.81', '-0.95', '#sadness', '#disgust', 'negative', '-0.91', 'dislike', 'loathe', 'queasy', 'horrid', 'outrageous']
senticnet['horror-stricken'] = ['-0.72', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.73', 'horrifying', 'atrocious', 'dreadfully', 'horrifyingly', 'horrified']
senticnet['horror-struck'] = ['-0.95', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.97', 'horrifying', 'atrocious', 'dreadfully', 'horrifyingly', 'horrified']
senticnet['horror_anthology_film'] = ['-0.08', '-0.01', '-0.16', '-0.13', '#fear', '#disgust', 'negative', '-0.09', 'hockey_game', 'movie', 'scary_movie', 'cinema', 'cctv']
senticnet['horror_classic'] = ['-0.08', '-0.01', '-0.16', '-0.13', '#fear', '#disgust', 'negative', '-0.09', 'hockey_game', 'movie', 'scary_movie', 'cinema', 'cctv']
senticnet['horror_comedy'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amuck', 'murderousness', 'misocainea', 'misogyny']
senticnet['horror_comedy_film'] = ['-0.79', '0', '0.885', '-0.76', '#sadness', '#anger', 'negative', '-0.81', 'infernal', 'mortal', 'dead_soul', 'dead_soule', 'living_dead']
senticnet['horror_film'] = ['-0.97', '0', '-0.81', '-0.95', '#sadness', '#disgust', 'negative', '-0.91', 'dislike', 'loathe', 'queasy', 'horrid', 'outrageous']
senticnet['horror_flick'] = ['-0.97', '0', '-0.81', '-0.95', '#sadness', '#disgust', 'negative', '-0.91', 'dislike', 'loathe', 'queasy', 'horrid', 'outrageous']
senticnet['horror_genre'] = ['-0.97', '0', '-0.81', '-0.95', '#sadness', '#disgust', 'negative', '-0.91', 'dislike', 'loathe', 'queasy', 'horrid', 'outrageous']
senticnet['horror_movie'] = ['-0.08', '-0.01', '-0.16', '-0.13', '#fear', '#disgust', 'negative', '-0.09', 'hockey_game', 'movie', 'scary_movie', 'cinema', 'cctv']
senticnet['horror_punk'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'explicit', 'declared', 'definitive', 'graphic']
senticnet['horror_stricken'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['horror_tale'] = ['-0.97', '0', '-0.81', '-0.95', '#sadness', '#disgust', 'negative', '-0.91', 'dislike', 'loathe', 'queasy', 'horrid', 'outrageous']
senticnet['horror_thriller'] = ['-0.08', '-0.01', '-0.16', '-0.13', '#fear', '#disgust', 'negative', '-0.09', 'hockey_game', 'movie', 'scary_movie', 'cinema', 'cctv']
senticnet['horse'] = ['0', '0', '0', '0.071', '#admiration', '#admiration', 'positive', '0.071', 'two_barbs', 'barb', 'mule', 'buddhist', 'ride_barb']
senticnet['horse_animal_fancier'] = ['0.69', '0', '0', '0.977', '#joy', '#admiration', 'positive', '0.833', 'enthusiast', 'horseman', 'fancier', 'animal_fancier', 'lover']
senticnet['horse_artillery'] = ['-0.85', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.84', 'atomic_bomb', 'weapon_war', 'gun', 'pistol', 'hydrogen_bomb']
senticnet['horse_barn'] = ['0.053', '0.094', '0', '0.136', '#interest', '#admiration', 'positive', '0.094', 'barn_animal', 'domesticate', 'grassland', 'sheep', 'cow']
senticnet['horse_chestnut'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'bottlebrush_buckeye', 'red_buckeye', 'particolored_buckeye']
senticnet['horse_fancier'] = ['-0.61', '0', '0.606', '-0.53', '#sadness', '#anger', 'negative', '-0.58', 'enthusiast', 'cat_fancier', 'horseman', 'animal_fancier', 'fancier']
senticnet['horse_farm'] = ['0.046', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'dairy', 'milk_product', 'salad_dress', 'food_production', 'farm']
senticnet['horse_mackerel'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'many_fishes', 'seahorse', 'starfish', 'plankton', 'sea_wee']
senticnet['horse_mane'] = ['0', '0', '0', '0.071', '#admiration', '#admiration', 'positive', '0.071', 'two_barbs', 'barb', 'mule', 'buddhist', 'ride_barb']
senticnet['horse_movie'] = ['0.707', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.848', 'saloon', 'metal_detector', 'box_ring', 'minibar', 'war_zone']
senticnet['horse_riding_accident'] = ['-0.73', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.79', 'die_painfully', 'arrest_police', 'get_accident', 'fall_head', 'skin_knee']
senticnet['horse_sense'] = ['0.525', '-0.70', '0', '0.848', '#surprise', '#admiration', 'positive', '0.692', 'road_sense', 'sagacity', 'sagaciousness', 'logic', 'nous']
senticnet['horse_shoe'] = ['0.027', '0.139', '0', '0.203', '#interest', '#admiration', 'positive', '0.123', 'horse_shoes', 'four_leaf_clover', 'rotary_phone', 'ping_pong', 'billiards']
senticnet['horse_shoes'] = ['0.037', '0.148', '0', '0.212', '#interest', '#admiration', 'positive', '0.132', 'horseshoe', 'four_leaf_clover', 'rotary_phone', 'ping_pong', 'billiards']
senticnet['horse_slaughter'] = ['-0.67', '0', '0.938', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'massacre', 'mass_murder', 'carnage', 'bloodbath', 'butchery']
senticnet['horse_thief'] = ['-0.83', '0', '0.831', '-0.69', '#sadness', '#anger', 'negative', '-0.78', 'toughie', 'accessory', 'highbinder', 'hoodlum', 'thug']
senticnet['horse_trader'] = ['-0.78', '0', '0.944', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'loser', 'gambler', 'mortal', 'better', 'bettor']
senticnet['horse_trailer'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'trailer', 'trail_car', 'camion', 'wagon', 'towboat']
senticnet['horse_trainer'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'coach', 'instructor', 'personal_trainer', 'fit_person', 'train']
senticnet['horseback'] = ['0.064', '0', '0', '0.123', '#joy', '#admiration', 'positive', '0.093', 'horse', 'passenger_car', 'car_action', 'conveyance', 'car_travel']
senticnet['horseback_ride'] = ['0.046', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.041', 'anarchy', 'play_marble', 'dinner_party', 'spray_paint_grafitti', 'family_activity']
senticnet['horseback_rider'] = ['0', '0.768', '0', '0.758', '#interest', '#admiration', 'positive', '0.763', 'business_man', 'artwork', 'peace_sign', 'logo', 'captive']
senticnet['horsechestnut'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'horse_chestnut', 'red_buckeye', 'particolored_buckeye']
senticnet['horsehair'] = ['0.028', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.047', 'handkerchief', 'kerchief', 'hanky', 'facial_tissue', 'zipper']
senticnet['horsehair_wig'] = ['-0.84', '0', '0.633', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'grizzle', 'postiche', 'wig', 'periwig', 'peruke']
senticnet['horsehide'] = ['0.028', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.047', 'handkerchief', 'kerchief', 'hanky', 'facial_tissue', 'zipper']
senticnet['horseleech'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'leech', 'annelid', 'annelid_worm', 'segmented_worm']
senticnet['horseless_carriage'] = ['-0.04', '-0.16', '0', '0.056', '#surprise', '#admiration', 'positive', '0.059', 'car_part', 'under_car', 'car_wheel', 'wheel', 'car']
senticnet['horseman'] = ['-0.61', '0', '0.606', '-0.53', '#sadness', '#anger', 'negative', '-0.58', 'enthusiast', 'cat_fancier', 'animal_fancier', 'horse_fancier', 'fancier']
senticnet['horsemanship'] = ['0.798', '-0.54', '0', '0.552', '#joy', '#admiration', 'positive', '0.631', 'acquirement', 'skill', 'accomplishment', 'acquisition', 'attainment']
senticnet['horsemen'] = ['0', '0', '0', '0.071', '#admiration', '#admiration', 'positive', '0.071', 'two_barbs', 'barb', 'mule', 'buddhist', 'ride_barb']
senticnet['horseradish'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'mayonnaise', 'box_cereal', 'bottle_water']
senticnet['horseradish_sauce'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'mayonnaise', 'box_cereal', 'bottle_water']
senticnet['horseshit'] = ['-0.70', '0', '0.668', '-0.6', '#sadness', '#anger', 'negative', '-0.65', 'rot', 'bunk', 'bunkum', 'buncombe', 'guff']
senticnet['horseshoe'] = ['0.027', '0.139', '0', '0.203', '#interest', '#admiration', 'positive', '0.123', 'horse_shoes', 'four_leaf_clover', 'rotary_phone', 'ping_pong', 'billiards']
senticnet['horseshoe_bend'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['horseshoe_mesa'] = ['0', '0.069', '0', '0.065', '#interest', '#admiration', 'positive', '0.067', 'alp', 'high_mountain', 'pass', 'ski_resort', 'high_place']
senticnet['horseshoe_shape'] = ['0.027', '0.139', '0', '0.203', '#interest', '#admiration', 'positive', '0.123', 'horse_shoes', 'four_leaf_clover', 'rotary_phone', 'ping_pong', 'billiards']
senticnet['horseshoe_worm'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'annelid', 'annelid_worm', 'leech']
senticnet['horseshoer'] = ['0', '0.115', '0', '0.105', '#interest', '#admiration', 'positive', '0.11', 'farrier', 'miner', 'blacksmith', 'locksmith', 'smith']
senticnet['horsetail_equisetum'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'seed_fern', 'pteridosperm']
senticnet['horsethief'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'common_bean', 'green_bean', 'wax_bean']
senticnet['hortative'] = ['0.904', '-0.58', '0', '0.736', '#joy', '#admiration', 'positive', '0.743', 'naming', 'appellative', 'denotative', 'extensional', 'referent']
senticnet['horticultural'] = ['0.427', '0.227', '0', '0.37', '#joy', '#admiration', 'positive', '0.341', 'taken_seriously', 'complement', 'much_need', 'remission', 'knowledgeable']
senticnet['horticultural_therapy'] = ['0.427', '0.227', '0', '0.37', '#joy', '#admiration', 'positive', '0.341', 'taken_seriously', 'complement', 'much_need', 'remission', 'knowledgeable']
senticnet['horticulture'] = ['0.427', '0.227', '0', '0.37', '#joy', '#admiration', 'positive', '0.341', 'taken_seriously', 'complement', 'much_need', 'remission', 'knowledgeable']
senticnet['horticulturist'] = ['0.427', '0.227', '0', '0.37', '#joy', '#admiration', 'positive', '0.341', 'taken_seriously', 'complement', 'much_need', 'remission', 'knowledgeable']
senticnet['horus_heresy'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['horwood'] = ['-0.20', '-0.10', '0.07', '-0.24', '#sadness', '#disgust', 'negative', '-0.10', 'flipper', 'sea_life', 'seafood', 'swim_animal', 'fillet']
senticnet['hosanna'] = ['0.6', '-0.92', '0', '0.601', '#surprise', '#admiration', 'positive', '0.71', 'cry', 'yell', 'call', 'outcry', 'shout']
senticnet['hose'] = ['0', '-0.07', '0', '0', '#surprise', '#surprise', 'positive', '0.075', 'gyroscope', 'air_pump', 'ball_bounce', 'motor_oil', 'tire']
senticnet['hosed_down'] = ['0.72', '-0.76', '0', '0.695', '#joy', '#surprise', 'positive', '0.726', 'suck_up', 'blot', 'wipe_up', 'mop', 'sponge_up']
senticnet['hosiery'] = ['0', '0', '0.801', '-0.99', '#anger', '#disgust', 'negative', '-0.89', 'tights', 'legging', 'fishnet', 'footless', 'knee-high']
senticnet['hosing_down'] = ['0.72', '-0.76', '0', '0.695', '#joy', '#surprise', 'positive', '0.726', 'suck_up', 'blot', 'wipe_up', 'mop', 'sponge_up']
senticnet['hosohedra'] = ['0.042', '-0.00', '0.027', '0.099', '#joy', '#admiration', 'positive', '0.031', 'chess_knight', 'chess_queen', 'chess_pawn', 'pawn', 'chess_bishop']
senticnet['hosokawa_clan'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'overlord', 'lord']
senticnet['hospice'] = ['-0.62', '0', '0.987', '-0.74', '#anger', '#disgust', 'negative', '-0.78', 'disinfection', 'treatment', 'medical_aid', 'digitalization', 'bloodletting']
senticnet['hospice_care'] = ['-0.03', '-0.02', '-0.08', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'ibuprofen', 'phenobarbital', 'analgesic', 'sleep_drug', 'pill']
senticnet['hospice_movement'] = ['-0.03', '-0.02', '-0.08', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'ibuprofen', 'phenobarbital', 'analgesic', 'sleep_drug', 'pill']
senticnet['hospitable'] = ['0.957', '0.907', '0', '0.844', '#joy', '#interest', 'positive', '0.903', 'celebrate_special_occasion', 'express_appreciation', 'celebrate_holiday', 'make_smile', 'celebrate_birthday']
senticnet['hospital'] = ['-0.02', '0', '-0.15', '-0.14', '#fear', '#disgust', 'negative', '-0.11', 'doctor_office', 'clinic', 'medical_office', 'emergency_room', 'doctor']
senticnet['hospital_emergency_room'] = ['0', '0', '-0.73', '0', '#fear', '#fear', 'negative', '-0.73', 'clinic', 'emergency_ward', 'hospital', 'dentist_office', 'medical_office']
senticnet['hospital_gown'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'duvet_cover', 'pillow', 'comforter', 'pillowcase', 'blanket']
senticnet['hospital_initiative_bfhi'] = ['0.777', '0.972', '0', '0.913', '#interest', '#admiration', 'positive', '0.887', 'serene', 'easy', 'adoration', 'togetherness', 'console']
senticnet['hospital_morgue'] = ['-0.06', '0.09', '-0.14', '-0.15', '#fear', '#disgust', 'negative', '-0.06', 'casket', 'coffin', 'grave', 'headstone', 'bury_person']
senticnet['hospital_mortuary'] = ['-0.06', '0.09', '-0.14', '-0.15', '#fear', '#disgust', 'negative', '-0.06', 'casket', 'coffin', 'grave', 'headstone', 'bury_person']
senticnet['hospital_neonatal_intensive'] = ['-0.51', '0', '0.747', '-0.94', '#anger', '#disgust', 'negative', '-0.73', 'stillborn_infant', 'neonate', 'preterm_baby', 'premature_infant', 'preterm_infant']
senticnet['hospital_room'] = ['0.839', '-0.89', '0', '0.687', '#joy', '#surprise', 'positive', '0.805', 'emergency_room', 'room', 'operating_room', 'operating_theater', 'surgery']
senticnet['hospitalisation'] = ['-0.09', '0', '-0.16', '-0.21', '#fear', '#disgust', 'negative', '-0.15', 'sickness', 'malady', 'polio', 'disease', 'ailment']
senticnet['hospitality'] = ['0.58', '0.651', '-0.26', '0.308', '#joy', '#interest', 'positive', '0.319', 'well-disposed', 'approving', 'approve', 'sanctioned', 'approved']
senticnet['hospitality_suite'] = ['0.841', '0', '0', '0.71', '#joy', '#admiration', 'positive', '0.775', 'waiter', 'bar_service', 'beverage_service', 'concierge', 'minibar']
senticnet['hospitalization'] = ['-0.89', '0', '-0.89', '0', '#sadness', '#fear', 'negative', '-0.89', 'non-fatal', 'all-cause', 'infarction', 'nonfatal', 'nonlethal']
senticnet['hospitalize'] = ['-0.87', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.81', 'influenza', 'cripple', 'overpay', 'ill_health', 'diabetes']
senticnet['hospitalized_overnight'] = ['0.934', '-0.88', '0', '0.797', '#joy', '#surprise', 'positive', '0.872', 'scan', 'checkup', 'scrutiny', 'medical_examination', 'medical_exam']
senticnet['hosptial'] = ['0', '0', '-0.73', '0', '#fear', '#fear', 'negative', '-0.73', 'clinic', 'emergency_ward', 'hospital', 'dentist_office', 'medical_office']
senticnet['host'] = ['0', '0.084', '0', '0.083', '#interest', '#admiration', 'positive', '0.084', 'home_sweet_home', 'guest', 'take_care', 'offer_accommodation', 'show_around']
senticnet['host_cat_deeley'] = ['-0.95', '0', '0.992', '-0.63', '#sadness', '#anger', 'negative', '-0.86', 'interpretive_dance', 'interpretive_dancing', 'modern_dance', 'interpretative_dancing', 'stage_dancing']
senticnet['host_galaxy'] = ['0', '0.706', '0', '0.847', '#interest', '#admiration', 'positive', '0.776', 'dwarf_planet', 'universe', 'planetary', 'sun', 'moon']
senticnet['host_kristie_lu'] = ['0.209', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.118', 'old_dance', 'dance', 'slow_dance', 'dancer', 'enter_contest']
senticnet['host_party'] = ['0.725', '0.742', '0', '0', '#joy', '#interest', 'positive', '0.734', 'make_friend', 'relax_friend', 'skilled', 'social_event', 'breed']
senticnet['host_susceptibility'] = ['-0.78', '0', '0.6', '-0.61', '#sadness', '#disgust', 'negative', '-0.66', 'suggestibility', 'sensitivity', 'capacity', 'reactivity', 'predisposition']
senticnet['hostage'] = ['-0.95', '0', '-0.89', '0', '#sadness', '#fear', 'negative', '-0.92', 'specialist', 'prisoner', 'connoisseur', 'accomplice', 'egalitarian']
senticnet['hostage_negotiator'] = ['0', '0.133', '0', '0.088', '#interest', '#admiration', 'positive', '0.111', 'juror', 'decide_guilt', 'become_judge', 'arbiter', 'jury']
senticnet['hostel'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'caravansary', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['hostess'] = ['0', '-0.01', '0.025', '-0.06', '#anger', '#disgust', 'negative', '-0.02', 'chef_special', 'bottle_soda', 'dispense', 'bottle_wine', 'waiter']
senticnet['hostile'] = ['0', '0.161', '0.7', '-0.65', '#anger', '#disgust', 'negative', '-0.39', 'acrimony', 'enmity', 'vindictive', 'despiteful', 'vindictively']
senticnet['hostile_alien'] = ['-0.03', '0.118', '0.472', '-0.46', '#anger', '#disgust', 'negative', '-0.21', 'detached', 'set-apart', 'separated', 'isolated', 'sickening']
senticnet['hostile_attitude'] = ['0', '0.149', '0.719', '-0.66', '#anger', '#disgust', 'negative', '-0.41', 'belligerently', 'antagonism', 'class_feeling', 'aggressiveness', 'animus']
senticnet['hostile_fire'] = ['-0.16', '0', '-0.18', '-0.23', '#fear', '#disgust', 'negative', '-0.19', 'attack_enemy', 'cause_death', 'die', 'lose_war', 'imprisonment']
senticnet['hostile_reaction'] = ['-0.16', '0.179', '0.568', '-0.61', '#anger', '#disgust', 'negative', '-0.29', 'angrily', 'choler', 'see_red', 'dander', 'hackle']
senticnet['hostile_sexism'] = ['-0.93', '0', '0.586', '-0.66', '#sadness', '#disgust', 'negative', '-0.73', 'camp', 'pack', 'clique', 'coterie', 'brain_trust']
senticnet['hostile_terrain'] = ['-0.66', '0', '0.709', '-0.88', '#anger', '#disgust', 'negative', '-0.75', 'untrue', 'apostate', 'unfaithful', 'perfidious', 'punic']
senticnet['hostile_territory'] = ['-0.03', '0', '-0.08', '-0.20', '#fear', '#disgust', 'negative', '-0.10', 'gun_shop', 'arsenal', 'holster', 'armory', 'gun_store']
senticnet['hostilely'] = ['0', '0.149', '0.719', '-0.66', '#anger', '#disgust', 'negative', '-0.41', 'belligerently', 'antagonism', 'class_feeling', 'aggressiveness', 'animus']
senticnet['hostility'] = ['0', '0', '-0.85', '-0.93', '#fear', '#disgust', 'negative', '-0.89', 'aggression', 'animosity', 'aggressive', 'malice', 'grudge']
senticnet['hostility_toward'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['hot'] = ['0', '0', '-0.93', '-0.77', '#fear', '#disgust', 'negative', '-0.85', 'heat', 'hot_weather', 'hotness', 'hot_temperature', 'high_temperature']
senticnet['hot-air_balloon'] = ['0', '0', '0', '0.869', '#admiration', '#admiration', 'positive', '0.869', 'balloon', 'float_through_air', 'air', 'float', 'luna_park']
senticnet['hot_air'] = ['-0.95', '0', '0.218', '-0.93', '#sadness', '#disgust', 'negative', '-0.70', 'steam', 'hot', 'fire', 'heat', 'sweat']
senticnet['hot_air_balloon'] = ['-0.04', '0.092', '0', '0.062', '#interest', '#admiration', 'positive', '0.036', 'hangar', 'blimp', 'aircraft_carrier', 'war_museum', 'spaceship']
senticnet['hot_airplay_chart'] = ['0.997', '0.892', '0', '0', '#joy', '#interest', 'positive', '0.945', 'advertisement', 'large_outdoor_board', 'motorway', 'expressway', 'train_track']
senticnet['hot_babe'] = ['0.902', '0.995', '0', '0', '#joy', '#interest', 'positive', '0.949', 'beautiful_girl', 'hen_baby', 'pretty_woman', 'hen_child', 'bird']
senticnet['hot_bath'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
senticnet['hot_beverage'] = ['0.951', '-0.97', '0', '0.639', '#joy', '#surprise', 'positive', '0.853', 'smoothie', 'drinking_water', 'nutrient', 'milk', 'refresher']
senticnet['hot_broth'] = ['0.07', '0.084', '0.023', '0', '#joy', '#interest', 'positive', '0.044', 'hearty', 'porridge', 'hot_liquid', 'liquid_food', 'meat_vegetable']
senticnet['hot_button'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'pervert', 'horny', 'uncomfortable', 'uneasy', 'short_tempered']
senticnet['hot_cereal'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'veal', 'beef', 'food_group', 'animal_flesh', 'curry']
senticnet['hot_chart'] = ['0.094', '0.091', '0', '0.125', '#joy', '#admiration', 'positive', '0.103', 'diagram', 'graph', 'medium', 'fragment', 'representation']
senticnet['hot_chick'] = ['0', '-0.00', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'jew', 'atheist', 'computer_user', 'fastener_binder', 'monitor']
senticnet['hot_chocolate'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'coffee', 'table', 'coffee_shop', 'coffee_house', 'coffee_table']
senticnet['hot_cider'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'relax', 'eat_food', 'eat_snack', 'eat_sweet', 'childhood']
senticnet['hot_cocoa'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'relax', 'eat_food', 'eat_snack', 'eat_sweet', 'childhood']
senticnet['hot_coffee_roll'] = ['-0.89', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.93', 'coffee_roll', 'cross_bun', 'bear_paw', 'bear_claw', 'bun']
senticnet['hot_corona'] = ['0.931', '-0.82', '0', '0.709', '#joy', '#surprise', 'positive', '0.822', 'light', 'aureole', 'radiance', 'visible_light', 'visible_radiation']
senticnet['hot_cross_bun'] = ['-0.84', '0', '0.713', '-0.63', '#sadness', '#anger', 'negative', '-0.73', 'cross_bun', 'coffee_roll', 'bear_paw', 'bear_claw', 'bun']
senticnet['hot_day'] = ['0.085', '0.107', '-0.08', '0', '#joy', '#interest', 'positive', '0.037', 'hot_sun', 'hot_weather', 'hot', 'bright_light', 'light']
senticnet['hot_dirty_dish'] = ['0', '0', '0.856', '-0.96', '#anger', '#disgust', 'negative', '-0.90', 'dirty_dish_overflow', 'faucet', 'hot_tap', 'dirty_dish', 'washcloth']
senticnet['hot_dog'] = ['0.028', '-0.07', '0', '0', '#joy', '#surprise', 'positive', '0.05', 'casserole', 'meatloaf', 'delicious_meal_bun', 'hamburger', 'delicious_meal']
senticnet['hot_dog_bun'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'meatloaf', 'hot_dog', 'delicious_meal', 'eat_hamburger', 'old_lady_house']
senticnet['hot_dog_cart'] = ['-0.80', '0', '0.718', '-0.79', '#sadness', '#disgust', 'negative', '-0.77', 'walk_exercise', 'lose_wallet', 'go_debt', 'pay_dinner', 'spend_much_money']
senticnet['hot_dog_stand'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'candy', 'sweet', 'sugar', 'sweet_tooth', 'chocolate']
senticnet['hot_dog_vendor'] = ['-0.80', '0', '0.718', '-0.79', '#sadness', '#disgust', 'negative', '-0.77', 'walk_exercise', 'lose_wallet', 'go_debt', 'pay_dinner', 'spend_much_money']
senticnet['hot_faucet'] = ['0', '0', '0.865', '-0.95', '#anger', '#disgust', 'negative', '-0.90', 'faucet_overflow', 'dirty_dish', 'hot_tap', 'faucet', 'washcloth']
senticnet['hot_filament'] = ['-0.03', '-0.04', '0.079', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'string', 'bandage', 'suture', 'thread', 'needle_thread']
senticnet['hot_flash_sudden'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'aridness', 'dryness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['hot_fudge'] = ['0.023', '0.141', '-0.13', '0.174', '#interest', '#admiration', 'positive', '0.052', 'candy', 'push_wind', 'exhalation', 'breaout', 'mouth_action']
senticnet['hot_girl'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'appear_beautiful', 'good_looking', 'good_appearance', 'beauty', 'enjoy_view']
senticnet['hot_glue'] = ['0', '0.068', '0', '0', '#interest', '#interest', 'positive', '0.068', 'artist', 'oil_paint', 'painter', 'creative_person', 'make_paint']
senticnet['hot_grill'] = ['0.03', '-0.05', '0', '0.059', '#surprise', '#admiration', 'positive', '0.046', 'platter', 'fork', 'spoon', 'tablespoon', 'bowl']
senticnet['hot_lava'] = ['0', '0', '-0.92', '0', '#fear', '#fear', 'negative', '-0.92', 'water_flow', 'wind_water', 'water_stream', 'waterway', 'magma']
senticnet['hot_liquid'] = ['0.079', '0.071', '0', '0', '#joy', '#interest', 'positive', '0.075', 'broth', 'hearty', 'serve_hot', 'liquid_food', 'porridge']
senticnet['hot_lotto'] = ['0.858', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.813', 'box_office', 'movie', 'cctv', 'cinema', 'movie_theater']
senticnet['hot_magma'] = ['0', '0', '-0.92', '0', '#fear', '#fear', 'negative', '-0.92', 'water_flow', 'wind_water', 'water_stream', 'waterway', 'lava']
senticnet['hot_meal'] = ['0.206', '0.093', '0.049', '0.143', '#joy', '#admiration', 'positive', '0.098', 'good_meal', 'delicious_food', 'broth', 'meal', 'liquid_food']
senticnet['hot_mix_asphalt'] = ['0', '0.127', '-0.04', '0.194', '#interest', '#admiration', 'positive', '0.092', 'concrete', 'tar', 'pavement', 'big_city_life', 'popular_building_material']
senticnet['hot_peaking_at'] = ['0.094', '0.091', '0', '0.125', '#joy', '#admiration', 'positive', '0.103', 'diagram', 'graph', 'medium', 'fragment', 'representation']
senticnet['hot_pepper'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'chemical_irritant', 'gingerol', 'piperine', 'pepper_spray']
senticnet['hot_plasma'] = ['0.931', '-0.82', '0', '0.709', '#joy', '#surprise', 'positive', '0.822', 'light', 'aureole', 'radiance', 'visible_light', 'visible_radiation']
senticnet['hot_poker'] = ['-0.84', '0', '0.987', '-0.77', '#sadness', '#anger', 'negative', '-0.87', 'thorn', 'sticker', 'glochid', 'prickle', 'spikelet']
senticnet['hot_rod'] = ['-0.73', '0', '0.72', '-0.93', '#sadness', '#disgust', 'negative', '-0.79', 'imp', 'elf', 'hob', 'leprechaun', 'pixie']
senticnet['hot_shower'] = ['0.786', '0', '0', '0', '#joy', '#joy', 'positive', '0.786', 'camping', 'outdoor', 'tent', 'bonfire', 'camp_out']
senticnet['hot_single_chart'] = ['0.094', '0.091', '0', '0.125', '#joy', '#admiration', 'positive', '0.103', 'diagram', 'graph', 'medium', 'fragment', 'representation']
senticnet['hot_smoked_mackerel'] = ['0.084', '-0.05', '0', '-0.04', '#joy', '#surprise', 'positive', '0.031', 'entree', 'feast', 'satiation', 'breakfast', 'supper']
senticnet['hot_smoked_salmon'] = ['0.644', '-0.94', '0', '0.729', '#surprise', '#admiration', 'positive', '0.773', 'gem', 'banana_bread', 'date_bread', 'nut_bread', 'quick_bread']
senticnet['hot_soapy_water'] = ['-0.60', '0', '0.992', '-0.94', '#anger', '#disgust', 'negative', '-0.84', 'cleanse', 'lather', 'floss', 'decontaminate', 'bathe']
senticnet['hot_spot'] = ['-0.87', '0', '0.818', '0', '#sadness', '#anger', 'negative', '-0.84', 'slow_speed', 'wifi', 'computer_internet_connection', 'internet_connection', 'boot_up_computer']
senticnet['hot_standby'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_musical_sound', 'play_orchestra', 'make_noise', 'play_music', 'make_drawing']
senticnet['hot_summer'] = ['0', '0', '-0.88', '-0.84', '#fear', '#disgust', 'negative', '-0.86', 'appear_beautiful', 'stand_alone', 'rush_job', 'forward_email', 'deal_card_player']
senticnet['hot_sun'] = ['0', '0', '-0.95', '-0.99', '#fear', '#disgust', 'negative', '-0.97', 'illumination', 'hot_day', 'hot', 'yellow_orange', 'hot_weather']
senticnet['hot_table'] = ['0.788', '0.937', '0', '0', '#joy', '#interest', 'positive', '0.863', 'good_meal', 'chef_special', 'special', 'plate_silverware', 'chopstick']
senticnet['hot_tap'] = ['0', '0', '0.865', '-0.95', '#anger', '#disgust', 'negative', '-0.90', 'faucet_overflow', 'hot_faucet', 'dirty_dish', 'faucet', 'washcloth']
senticnet['hot_temper'] = ['-0.04', '0.164', '0.685', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maliciousness', 'maliciously', 'maleficence', 'malefic', 'malign']
senticnet['hot_temperature'] = ['-0.84', '0', '-0.72', '-0.88', '#sadness', '#disgust', 'negative', '-0.82', 'high_temperature', 'heat', 'feel_sick', 'fever', 'char']
senticnet['hot_tub'] = ['0', '0', '0', '0.964', '#admiration', '#admiration', 'positive', '0.964', 'swimming_pool', 'swim', 'swimsuit', 'lake', 'have_fun']
senticnet['hot_water'] = ['-0.87', '0', '0.673', '-0.55', '#sadness', '#anger', 'negative', '-0.7', 'plight', 'difficulty', 'care', 'corner', 'box']
senticnet['hot_water_heater'] = ['0.02', '0.076', '0', '0', '#joy', '#interest', 'positive', '0.048', 'land_earth', 'tidal_wave', 'puddle', 'brine', 'spurt']
senticnet['hot_weather'] = ['0', '0', '-0.77', '-0.93', '#fear', '#disgust', 'negative', '-0.85', 'hot', 'hotness', 'high_temperature', 'heat', 'hot_temperature']
senticnet['hot_woman'] = ['0.053', '0.091', '0', '0.152', '#interest', '#admiration', 'positive', '0.099', 'pretty_woman', 'hen_baby', 'baby_chicken', 'hot_babe', 'cute']
senticnet['hotbed'] = ['-0.93', '0', '0.791', '0', '#sadness', '#anger', 'negative', '-0.86', 'greenhouse', 'conservatory', 'bathe', 'dry', 'hothouse']
senticnet['hotchpotch'] = ['-0.85', '0', '0.583', '-0.71', '#sadness', '#disgust', 'negative', '-0.71', 'muddle', 'fuddle', 'mare_nest', 'rummage', 'clutter']
senticnet['hotdog'] = ['0.662', '-0.63', '0', '0.628', '#joy', '#surprise', 'positive', '0.642', 'knackwurst', 'knockwurst', 'salami', 'frank', 'sausage']
senticnet['hotel'] = ['-0.07', '0', '0.009', '-0.12', '#sadness', '#disgust', 'negative', '-0.07', 'check_out', 'travel', 'holiday', 'go_home', 'staycation']
senticnet['hotel_accommodation'] = ['0', '0', '0', '0.097', '#admiration', '#admiration', 'positive', '0.097', 'transport_person', 'travel_long_distance', 'get_plane', 'get_bus', 'move']
senticnet['hotel_ballroom'] = ['-0.79', '0', '0.948', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['hotel_bathroom'] = ['0', '-0.10', '0', '0', '#surprise', '#surprise', 'positive', '0.106', 'washroom', 'bath_robe', 'toilet', 'bathroom', 'bathroom_drawer']
senticnet['hotel_bathroom_drawerroom'] = ['0.035', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.05', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'bath', 'bathroom_drawer']
senticnet['hotel_casino'] = ['0.057', '0.13', '0', '0.119', '#interest', '#admiration', 'positive', '0.102', 'friend_wedding', 'circular_route', 'spanish_restaurant', 'altar', 'monastery']
senticnet['hotel_receptionist'] = ['0.141', '-0.12', '-0.06', '-0.06', '#joy', '#surprise', 'positive', '0.036', 'emergency_ward', 'dentist', 'receptionist', 'reception', 'receptionist_call']
senticnet['hotel_restaurant'] = ['0.06', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'eat_food', 'fast_food', 'pizza_parlor', 'pizza_store', 'kitchen_cupboard']
senticnet['hotel_room'] = ['0.964', '0.88', '0', '0', '#joy', '#interest', 'positive', '0.922', 'room', 'living_room', 'hotel', 'staycation', 'vacation']
senticnet['hotel_room_door'] = ['0', '0', '0', '0.077', '#admiration', '#admiration', 'positive', '0.077', 'glass_front', 'gate', 'doorway', 'use_farm', 'entranceway']
senticnet['hotel_suite'] = ['0.962', '0', '0', '0', '#joy', '#joy', 'positive', '0.962', 'interest_person', 'social_occasion', 'become_intoxicated', 'network', 'get_sex']
senticnet['hotfix'] = ['-0.58', '0', '0.561', '-0.62', '#sadness', '#disgust', 'negative', '-0.58', 'spot', 'speckle', 'dapple', 'mark', 'fleck']
senticnet['hotfoot'] = ['-0.97', '0', '0.504', '-0.52', '#sadness', '#disgust', 'negative', '-0.66', 'rush', 'race', 'push_forward', 'hasten', 'hie']
senticnet['hothead'] = ['-0.57', '0', '0.711', '-0.99', '#anger', '#disgust', 'negative', '-0.76', 'grouch', 'grump', 'crosspatch', 'churl', 'crank']
senticnet['hotheaded'] = ['0', '0', '0.761', '-0.71', '#anger', '#disgust', 'negative', '-0.74', 'impulsive', 'tearaway', 'incautious', 'madcap', 'impetuous']
senticnet['hothouse'] = ['-0.92', '0', '0.791', '0', '#sadness', '#anger', 'negative', '-0.85', 'greenhouse', 'conservatory', 'bathe', 'dry', 'hotbed']
senticnet['hotkey'] = ['0.146', '0.095', '-0.04', '0', '#joy', '#interest', 'positive', '0.065', 'paper_menu', 'diner', 'cafe', 'restaurant', 'dine_room']
senticnet['hotlink'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'swat', 'store_record', 'ideal']
senticnet['hotly_contested_election'] = ['0', '0.156', '-0.15', '0.179', '#interest', '#admiration', 'positive', '0.061', 'candidate', 'presidential', 'vote', 'elect', 'appoint']
senticnet['hotness'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'high_temperature', 'hot', 'hot_weather', 'fire_product', 'heat']
senticnet['hotspot'] = ['-0.87', '0', '0.818', '0', '#sadness', '#anger', 'negative', '-0.84', 'slow_speed', 'wifi', 'computer_internet_connection', 'internet_connection', 'boot_up_computer']
senticnet['hotsync_button'] = ['0', '-0.02', '-0.08', '-0.05', '#fear', '#disgust', 'negative', '-0.03', 'mattress', 'sleep_furniture', 'master_bedroom', 'small_bed', 'pillow']
senticnet['hotter'] = ['0', '0.094', '0', '0', '#interest', '#interest', 'positive', '0.094', 'cold', 'cavern', 'under_umbrella', 'parasol', 'stick_wall']
senticnet['hottie'] = ['-0.19', '0', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.15', 'bad_child', 'misbehavior', 'bad_kid', 'bad_action', 'difficulty']
senticnet['houdini'] = ['0.9', '-0.88', '0', '0.687', '#joy', '#surprise', 'positive', '0.823', 'conjurer', 'escape_expert', 'mind_reader', 'telepathist', 'performer']
senticnet['houla_massacre'] = ['-0.67', '0', '0.938', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'massacre', 'mass_murder', 'carnage', 'slaughter', 'butchery']
senticnet['hound'] = ['0', '0', '0.022', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'kind_animal', 'wild_animal', 'predator', 'feline', 'fang']
senticnet['houndshark'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'puffer', 'pufferfish', 'blowfish', 'triggerfish', 'plectognath']
senticnet['houndstooth'] = ['0', '-0.00', '0.047', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'cover_body', 'make_drape', 'weave', 'weave_cloth', 'make_cloth']
senticnet['hour'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'watch', 'clock', 'hourglass', 'minute', 'stopwatch']
senticnet['hour_bus_ride'] = ['-0.08', '0.179', '-0.06', '0.096', '#interest', '#admiration', 'positive', '0.034', 'ride_subway', 'buy_vending_machine', 'travel', 'buy_lottery_ticket', 'acquire']
senticnet['hour_drive'] = ['-0.08', '0.179', '-0.06', '0.096', '#interest', '#admiration', 'positive', '0.034', 'ride_subway', 'buy_vending_machine', 'travel', 'buy_lottery_ticket', 'acquire']
senticnet['hour_lunch_break'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'watch', 'clock', 'hourglass', 'minute', 'stopwatch']
senticnet['hour_nap'] = ['-0.56', '0', '0.577', '-0.61', '#anger', '#disgust', 'negative', '-0.58', 'foggy', 'lethargic', 'logy', 'stuporous', 'dazed']
senticnet['hour_oral_examination'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'organize_thought', 'scratch_head', 'know_answer', 'speak_out', 'know_talk']
senticnet['hour_per'] = ['0.295', '0.239', '0.049', '0.346', '#joy', '#admiration', 'positive', '0.208', 'absolute', 'complete', 'infinite', 'total', 'stuffed']
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senticnet['housecleaner'] = ['0.875', '0', '0', '0.802', '#joy', '#admiration', 'positive', '0.839', 'foreigner', 'exile', 'deportee', 'gringo', 'import']
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senticnet['household_disposable_income'] = ['0.881', '0', '0', '0.875', '#joy', '#admiration', 'positive', '0.878', 'minor', 'meek', 'low', 'humble', 'small']
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senticnet['household_size'] = ['0.051', '0.112', '0', '0', '#joy', '#interest', 'positive', '0.082', 'neighbor', 'family_reunion', 'clan', 'desire', 'little_specific']
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senticnet['household_vacuum_cleaner'] = ['-0.04', '-0.02', '0.089', '-0.14', '#anger', '#disgust', 'negative', '-0.06', 'steam_locomotive', 'loudspeaker', 'collector', 'siren', 'activist']
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senticnet['housing_allowance'] = ['0.924', '0.968', '0', '0.876', '#joy', '#interest', 'positive', '0.923', 'live_simply', 'help_out', 'money', 'accolade', 'generous']
senticnet['housing_bubble'] = ['-0.10', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.10', 'read_cover', 'spread_out', 'upgrade', 'look_behind', 'scan']
senticnet['housing_shortage'] = ['-0.83', '0', '-0.88', '-0.78', '#sadness', '#fear', 'negative', '-0.83', 'little', 'sadness', 'small_amount', 'lack', 'absence']
senticnet['housing_slowdown'] = ['0', '0.285', '-0.07', '0.163', '#interest', '#admiration', 'positive', '0.124', 'plus', 'importance', 'a_lot', 'bonus', 'extra']
senticnet['hovel'] = ['0.846', '-0.66', '0', '0.941', '#joy', '#admiration', 'positive', '0.817', 'hut', 'mudhif', 'hutch', 'shack', 'shanty']
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senticnet['howell'] = ['-0.64', '0', '0.781', '-0.74', '#anger', '#disgust', 'negative', '-0.72', 'weirdo', 'crazy', 'loony', 'lunatic', 'nutcase']
senticnet['howitzer'] = ['0', '0', '0.965', '-0.79', '#anger', '#disgust', 'negative', '-0.87', 'anti-tank', 'antiaircraft', 'anti-aircraft', 'anti-armor', 'antitank']
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senticnet['howl_laughter'] = ['0.828', '-0.88', '0', '0', '#joy', '#surprise', 'positive', '0.857', 'crack_joke', 'break_silence', 'laugh_joke', 'laugh', 'thank']
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senticnet['howler'] = ['-0.71', '0', '0.83', '-0.68', '#sadness', '#anger', 'negative', '-0.74', 'gag', 'jest', 'jape', 'joke', 'laugh']
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senticnet['hp_bladesystem'] = ['0.637', '-0.91', '0', '0.757', '#surprise', '#admiration', 'positive', '0.768', 'tractability', 'manageableness', 'docility', 'obedience', 'amenability']
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senticnet['hpv_vaccine'] = ['-0.07', '0', '-0.05', '-0.18', '#sadness', '#disgust', 'negative', '-0.10', 'medical_therapy', 'vaccination', 'doctor', 'medicine', 'hospital']
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senticnet['htc_hero'] = ['0.027', '-0.09', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'cookie', 'pudding', 'custard', 'candy', 'muffin']
senticnet['htc_sense'] = ['0.027', '-0.09', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'cookie', 'pudding', 'custard', 'candy', 'muffin']
senticnet['html_code'] = ['0', '-0.12', '0', '0', '#surprise', '#surprise', 'positive', '0.127', 'chop', 'glue', 'carve', 'bandage', 'split']
senticnet['html_document_header'] = ['0.055', '0.117', '0', '0.133', '#interest', '#admiration', 'positive', '0.102', 'graphical_representation', 'pencil_pen', 'note_pad', 'marker', 'stationery']
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senticnet['huachinango'] = ['0.084', '-0.05', '0', '-0.04', '#joy', '#surprise', 'positive', '0.031', 'entree', 'feast', 'satiation', 'breakfast', 'supper']
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senticnet['hubba_bubba'] = ['0.023', '0.141', '-0.13', '0.174', '#interest', '#admiration', 'positive', '0.052', 'candy', 'push_wind', 'exhalation', 'breaout', 'mouth_action']
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senticnet['hubble_space_telescope'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'binoculars', 'read_glass', 'eyeglass', 'camera', 'monocle']
senticnet['hubbub'] = ['-0.65', '0', '0.858', '-0.83', '#anger', '#disgust', 'negative', '-0.78', 'turmoil', 'convulsion', 'tempest', 'disorder', 'earthquake']
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senticnet['huffy'] = ['-0.14', '0.152', '0.613', '-0.64', '#anger', '#disgust', 'negative', '-0.31', 'huffiness', 'huffily', 'offense', 'umbrage', 'umbrageous']
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senticnet['huge_animal'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'brown_bear', 'woodland_creature', 'hairy_animal', 'brown_fur', 'brown_hair']
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senticnet['hugely_profitable'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'profitable', 'moneymaking', 'gainful', 'paying', 'paid']
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senticnet['huggable'] = ['0.936', '-0.81', '0', '0.83', '#joy', '#admiration', 'positive', '0.86', 'cherubic', 'lovely', 'lovable', 'endearing', 'angelic']
senticnet['hugger-mugger'] = ['0', '0', '-0.75', '-0.97', '#fear', '#disgust', 'negative', '-0.86', 'secret', 'hush-hush', 'clandestine', 'cloak-and-dagger', 'hole-and-corner']
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senticnet['hugo_strange'] = ['-0.86', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.86', 'demon', 'fool', 'cannibalism', 'jolly', 'treachery']
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senticnet['huiyuan_brand'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'glass_water', 'refreshed', 'smoothie', 'lemonade', 'cold_water']
senticnet['hula_hoop'] = ['0.05', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.046', 'skate', 'rollerblade', 'skateboard', 'ice_skate', 'skating']
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senticnet['human-to-human'] = ['0.837', '0.879', '0', '0', '#joy', '#interest', 'positive', '0.858', 'peer-to-peer', 'person-to-person', 'skin-to-skin', 'cell-to-cell', 'fecal-oral']
senticnet['human_action'] = ['0.022', '0', '0', '0.186', '#joy', '#admiration', 'positive', '0.104', 'vigorous_activity', 'human_movement', 'body_movement', 'fast_movement', 'activity']
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senticnet['human_body'] = ['0.026', '0.069', '0', '0', '#joy', '#interest', 'positive', '0.048', 'atom', 'fuse', 'nucleus', 'cell', 'rash']
senticnet['human_body_louse'] = ['-0.73', '0', '0.599', '-0.96', '#sadness', '#disgust', 'negative', '-0.76', 'spotted_fever', 'rickettsial_disease', 'rickettsialpox', 'typhus', 'typhus_fever']
senticnet['human_brain'] = ['0', '0.786', '0', '0.728', '#interest', '#admiration', 'positive', '0.757', 'hold_brain', 'inside_head', 'cranium', 'skull', 'mind']
senticnet['human_cannonball'] = ['0', '0.786', '0', '0.917', '#interest', '#admiration', 'positive', '0.852', 'act', 'portray', 'play_ping_pong', 'school_play', 'dangerous']
senticnet['human_cell'] = ['0.88', '0.88', '0', '0', '#joy', '#interest', 'positive', '0.88', 'mother_father', 'brother_sister', 'aunt_uncle', 'mom_dad', 'family']
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senticnet['human_communication'] = ['0', '0', '0', '0.164', '#admiration', '#admiration', 'positive', '0.164', 'verbal_communication', 'orate', 'mouth_move', 'chat', 'speech']
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senticnet['human_digestive_system'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'belly', 'nervous_system', 'human_body', 'stomach', 'tummy']
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senticnet['human_flesh'] = ['-0.73', '0', '0.734', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'primitive', 'headhunter', 'barbarian', 'anthropophagus', 'anthropophagite']
senticnet['human_flourishing'] = ['0.971', '-0.71', '0', '0.656', '#joy', '#surprise', 'positive', '0.781', 'wellbeing', 'successfulness', 'eudaimonia', 'upbeat', 'strength']
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senticnet['human_group'] = ['0.084', '0.129', '-0.04', '0.105', '#interest', '#admiration', 'positive', '0.068', 'many_people', 'happy_together', 'group_of_people', 'team_up', 'meet_up']
senticnet['human_hair'] = ['0.05', '0.081', '0.012', '-0.04', '#joy', '#interest', 'positive', '0.02', 'semi-truck', 'fire_hydrant', 'row_house', 'flight_terminal', 'taxi']
senticnet['human_hand'] = ['0.064', '0', '0', '0.057', '#joy', '#admiration', 'positive', '0.06', 'keep_hand_warm', 'hand', 'tong', 'finger', 'grasper']
senticnet['human_history'] = ['0.046', '0', '0', '0.085', '#joy', '#admiration', 'positive', '0.066', 'action_movie', 'history', 'medieval_time', 'part_of_history', 'history_book']
senticnet['human_infirmity'] = ['0.702', '-0.94', '0', '0.582', '#joy', '#surprise', 'positive', '0.743', 'beatable', 'vanquishable', 'vincible', 'subduable', 'subjugable']
senticnet['human_ingenuity'] = ['0', '0.858', '0', '0.79', '#interest', '#admiration', 'positive', '0.824', 'heavenly_body', 'star_planet', 'celestial_body', 'around_earth', 'unlimited']
senticnet['human_intellect'] = ['0', '0.706', '0', '0.934', '#interest', '#admiration', 'positive', '0.82', 'value', 'good_value', 'ideal', 'flawless', 'ultimate']
senticnet['human_intelligence'] = ['0.081', '0', '0', '0', '#joy', '#joy', 'positive', '0.081', 'bottle_wine', 'table_chair', 'coconut', 'chef_special', 'oatmeal']
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senticnet['human_language'] = ['0', '0', '0', '0.113', '#admiration', '#admiration', 'positive', '0.113', 'language', 'speech', 'oration', 'phone_conversation', 'say']
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senticnet['human_lifespan'] = ['0.097', '0.099', '-0.09', '0.137', '#interest', '#admiration', 'positive', '0.059', 'live_long', 'long_life', 'live_healthy_lifestyle', 'increase_muscle_mass', 'feel_healthy']
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senticnet['human_misery'] = ['-0.87', '0', '0.889', '-0.62', '#sadness', '#anger', 'negative', '-0.79', 'ruination', 'ruin', 'deterioration', 'blight', 'impairment']
senticnet['human_motion'] = ['0', '0.069', '0.019', '0.193', '#interest', '#admiration', 'positive', '0.081', 'human_movement', 'activity', 'vigorous_activity', 'jogging', 'fast_movement']
senticnet['human_movement'] = ['-0.02', '0.078', '0', '0.204', '#interest', '#admiration', 'positive', '0.085', 'human_motion', 'vigorous_activity', 'work', 'fast_movement', 'human_action']
senticnet['human_nature'] = ['0.083', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.059', 'good_view', 'bird_droppings', 'slate', 'gable', 'homeowner']
senticnet['human_nervous_system'] = ['0', '0.786', '0', '0.728', '#interest', '#admiration', 'positive', '0.757', 'hold_brain', 'inside_head', 'cranium', 'skull', 'mind']
senticnet['human_noise'] = ['0.078', '0', '0', '0.176', '#joy', '#admiration', 'positive', '0.127', 'vocalization', 'mouth_noise', 'human_sound', 'audible', 'vocal_sound']
senticnet['human_offspring'] = ['0', '0.09', '-0.06', '0.065', '#interest', '#admiration', 'positive', '0.032', 'descendant', 'female_child', 'small_adult', 'young', 'young_people']
senticnet['human_organ'] = ['0', '0.078', '0', '0.096', '#interest', '#admiration', 'positive', '0.087', 'human_part', 'part_of_body', 'body_organ', 'human', 'anatomy']
senticnet['human_papillomavirus_infection'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'gastroenteritis', 'stomach_flu', 'intestinal_flu', 'inflammatory_disease']
senticnet['human_part'] = ['0', '0.076', '0', '0.086', '#interest', '#admiration', 'positive', '0.081', 'part_of_body', 'body_organ', 'human', 'human_organ', 'anatomy']
senticnet['human_pathogen'] = ['-0.60', '0', '0.721', '-0.57', '#sadness', '#anger', 'negative', '-0.63', 'tubercle_bacillus', 'leprosy_bacillus', 'eubacteria', 'eubacterium', 'true_bacteria']
senticnet['human_physiology'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'college_major', 'geology', 'physic', 'chemistry', 'subject_school']
senticnet['human_psyche'] = ['-0.96', '0', '0.108', '-0.90', '#sadness', '#disgust', 'negative', '-0.66', 'evil', 'darkness', 'dark', 'sun_down', 'shadow']
senticnet['human_race_survive'] = ['0.224', '0.155', '-0.05', '0.217', '#joy', '#admiration', 'positive', '0.135', 'financial_security', 'take_vacation', 'acquire_wealth', 'accomplish', 'prepare_event']
senticnet['human_readable'] = ['0.026', '0', '0', '0.084', '#joy', '#admiration', 'positive', '0.055', 'book_write', 'make_book', 'written_text', 'paragraph', 'book_paper']
senticnet['human_relationship'] = ['0.988', '-0.60', '0', '0.7', '#joy', '#admiration', 'positive', '0.766', 'indebtedness', 'bonding', 'obligation', 'relationship', 'personal_relation']
senticnet['human_resource'] = ['0.831', '0', '0', '0.92', '#joy', '#admiration', 'positive', '0.875', 'education', 'directorate', 'technical_support', 'collectively_span', 'collectively']
senticnet['human_resource_development'] = ['0.997', '0', '0', '0.742', '#joy', '#admiration', 'positive', '0.869', 'education', 'award', 'standard_grant', 'different_application', 'continue_grant']
senticnet['human_respiratory_syncytial'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchitis', 'chronic_bronchitis', 'respiratory_disease', 'respiratory_illness', 'respiratory_disorder']
senticnet['human_sacrifice'] = ['-0.17', '0', '-0.16', '-0.09', '#sadness', '#fear', 'negative', '-0.14', 'murder', 'hurt_others', 'death_penalty', 'death_destruction', 'end_live']
senticnet['human_sense'] = ['0.027', '0.144', '-0.07', '0.265', '#interest', '#admiration', 'positive', '0.089', 'five_senses', 'sensory_input', 'sense', 'ear_activity', 'human']
senticnet['human_shape'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'dog_treat', 'structural_support', 'human_part', 'dog_toy', 'inside_body']
senticnet['human_sound'] = ['0.089', '0', '0', '0.13', '#joy', '#admiration', 'positive', '0.11', 'vocalization', 'loud_soft', 'vocal_cord', 'audible', 'mouth_noise']
senticnet['human_subject'] = ['0', '0.965', '0', '0.901', '#interest', '#admiration', 'positive', '0.933', 'sponsor', 'cultural_anthropology', 'linguistics', 'geography', 'law']
senticnet['human_subjectivity'] = ['0.654', '-0.71', '0', '0.624', '#joy', '#surprise', 'positive', '0.664', 'viewable', 'gross', 'telescopic', 'seeable', 'visual']
senticnet['human_trafficker'] = ['-0.87', '0', '0.574', '-0.73', '#sadness', '#disgust', 'negative', '-0.72', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['human_trait'] = ['0.155', '0.381', '-0.24', '0.308', '#interest', '#admiration', 'positive', '0.151', 'charge', 'physical_quantity', 'human', 'excise', 'prize']
senticnet['human_truthful'] = ['-0.92', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.88', 'doctor', 'nurse', 'medical_person', 'surgeon', 'doctor_surgery']
senticnet['human_visual_perception'] = ['0.685', '-0.91', '0', '0.566', '#joy', '#surprise', 'positive', '0.721', 'audibility', 'audibleness', 'visibleness', 'visibility', 'physical_property']
senticnet['humane'] = ['0.832', '-0.89', '0', '0', '#joy', '#surprise', 'positive', '0.863', 'humanity', 'compassionate', 'humaneness', 'humanitarian', 'human']
senticnet['humane_slaughter'] = ['-0.67', '0', '0.938', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'massacre', 'mass_murder', 'carnage', 'bloodbath', 'butchery']
senticnet['humane_slaughter_act'] = ['-0.67', '0', '0.938', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'massacre', 'mass_murder', 'carnage', 'bloodbath', 'butchery']
senticnet['humaneness'] = ['0.83', '-0.89', '0', '0', '#joy', '#surprise', 'positive', '0.863', 'humanity', 'compassionate', 'humane', 'humanitarian', 'human']
senticnet['humanism'] = ['-0.99', '0', '0.835', '-0.58', '#sadness', '#anger', 'negative', '-0.80', 'beginning', 'origin', 'root', 'wellspring', 'source']
senticnet['humanist'] = ['0.886', '0.864', '0', '0', '#joy', '#interest', 'positive', '0.875', 'monumental', 'manner', 'huge', 'monumentality', 'monument']
senticnet['humanist_minuscule'] = ['-0.95', '0', '0.517', '-0.53', '#sadness', '#disgust', 'negative', '-0.67', 'copperplate', 'hand', 'minuscule', 'italic', 'round_hand']
senticnet['humanist_tradition'] = ['0', '0.915', '0', '0.821', '#interest', '#admiration', 'positive', '0.868', 'culture', 'special_day', 'holiday', 'party_time', 'yearly_celebration']
senticnet['humanistic_tradition'] = ['0', '0.915', '0', '0.821', '#interest', '#admiration', 'positive', '0.868', 'culture', 'special_day', 'holiday', 'party_time', 'yearly_celebration']
senticnet['humanitarian'] = ['0.829', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.866', 'humanity', 'compassionate', 'humaneness', 'humane', 'human']
senticnet['humanitarian_aid'] = ['0.732', '0.729', '0', '0', '#joy', '#interest', 'positive', '0.73', 'feel_relief', 'good_feeling', 'feel_ok', 'feel_relieved', 'feel_better']
senticnet['humanitarian_assistance'] = ['0.732', '0.729', '0', '0', '#joy', '#interest', 'positive', '0.73', 'feel_relief', 'good_feeling', 'feel_ok', 'feel_relieved', 'feel_better']
senticnet['humanitarian_catastrophe'] = ['-0.67', '0', '0.938', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'massacre', 'mass_murder', 'carnage', 'slaughter', 'butchery']
senticnet['humanitarian_disaster'] = ['-0.87', '0', '0.889', '-0.62', '#sadness', '#anger', 'negative', '-0.79', 'ruination', 'ruin', 'deterioration', 'blight', 'impairment']
senticnet['humanitarian_emergency'] = ['-0.99', '0', '0.691', '-0.65', '#sadness', '#anger', 'negative', '-0.78', 'depression', 'economic_crisis', 'exigency', 'slump', 'juncture']
senticnet['humanitarian_relief'] = ['0.732', '0.729', '0', '0', '#joy', '#interest', 'positive', '0.73', 'feel_relief', 'good_feeling', 'feel_ok', 'feel_relieved', 'feel_better']
senticnet['humanitarianism'] = ['0.946', '-0.54', '0', '0.774', '#joy', '#admiration', 'positive', '0.754', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['humanity'] = ['0.028', '0.139', '-0.07', '0.222', '#interest', '#admiration', 'positive', '0.079', 'human_sense', 'sense', 'five_senses', 'bad_odor', 'sensory_input']
senticnet['humankind'] = ['0.972', '0.966', '0', '0', '#joy', '#interest', 'positive', '0.969', 'gender', 'mankind', 'person', 'pedophile', 'guardian']
senticnet['humanlike'] = ['0.651', '-0.73', '0', '0.975', '#surprise', '#admiration', 'positive', '0.788', 'profoundness', 'profundity', 'sagacity', 'sagaciousness', 'discernment']
senticnet['humanoid'] = ['-0.73', '0', '0.72', '-0.93', '#sadness', '#disgust', 'negative', '-0.79', 'imp', 'elf', 'hob', 'leprechaun', 'pixie']
senticnet['humanoid_alien'] = ['-0.03', '0.118', '0.472', '-0.46', '#anger', '#disgust', 'negative', '-0.21', 'detached', 'set-apart', 'separated', 'isolated', 'sickening']
senticnet['humanoid_form'] = ['0', '0.839', '0', '0.963', '#interest', '#admiration', 'positive', '0.901', 'imputrescible', 'undestroyable', 'imperishable', 'durable', 'perdurable']
senticnet['humanoid_race'] = ['0.651', '-0.73', '0', '0.975', '#surprise', '#admiration', 'positive', '0.788', 'profoundness', 'profundity', 'sagacity', 'sagaciousness', 'discernment']
senticnet['humar'] = ['0.518', '-0.51', '0', '0.65', '#surprise', '#admiration', 'positive', '0.562', 'clematis', 'legume', 'leguminous_plant', 'climbing_fern', 'root_climber']
senticnet['humate'] = ['0.599', '-0.87', '0', '0.673', '#surprise', '#admiration', 'positive', '0.717', 'humic_shale', 'material', 'stuff', 'peat', 'lignite']
senticnet['humble'] = ['0.958', '0', '0', '0.968', '#joy', '#admiration', 'positive', '0.963', 'kind', 'nice', 'respectful', 'meek', 'red_carpet']
senticnet['humble_gratitude'] = ['0.488', '0.661', '-0.25', '0.244', '#joy', '#interest', 'positive', '0.284', 'appreciativeness', 'gratefulness', 'unafraid', 'intrepidly', 'audacious']
senticnet['humble_servant'] = ['0.958', '0', '0', '0.968', '#joy', '#admiration', 'positive', '0.963', 'kind', 'nice', 'respectful', 'meek', 'red_carpet']
senticnet['humbleness'] = ['0.884', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.899', 'humbling', 'demeaning', 'niceness', 'demeaningly', 'kindness']
senticnet['humblest'] = ['0.958', '0', '0', '0.968', '#joy', '#admiration', 'positive', '0.963', 'kind', 'nice', 'respectful', 'meek', 'red_carpet']
senticnet['humbling'] = ['0.861', '0', '0', '0.976', '#joy', '#admiration', 'positive', '0.918', 'kind', 'humbleness', 'nice', 'meek', 'kindness']
senticnet['humbly'] = ['0.736', '-0.66', '0', '0.777', '#joy', '#admiration', 'positive', '0.725', 'lucky', 'propitious', 'prosperous', 'favorable', 'good_luck']
senticnet['humbly_beg'] = ['-0.96', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.89', 'assassinate', 'charge', 'accuse', 'malign', 'libel']
senticnet['humbly_pray'] = ['-0.89', '0.161', '-0.61', '0.053', '#sadness', '#fear', 'negative', '-0.32', 'heartrending', 'heartbreaking', 'grievous', 'grievously', 'brokenheartedness']
senticnet['humboldt_bay'] = ['-0.06', '0.21', '0', '0.076', '#interest', '#admiration', 'positive', '0.074', 'new_think', 'figure_out', 'brain_activity', 'idea', 'come_up_idea']
senticnet['humboldt_county'] = ['-0.06', '0.21', '0', '0.076', '#interest', '#admiration', 'positive', '0.074', 'new_think', 'figure_out', 'brain_activity', 'idea', 'come_up_idea']
senticnet['humbucker'] = ['0.748', '-0.90', '0', '0.99', '#surprise', '#admiration', 'positive', '0.882', 'shakiness', 'quivering', 'shaking', 'vibration', 'quiver']
senticnet['humbucking_pickup'] = ['0.748', '-0.90', '0', '0.99', '#surprise', '#admiration', 'positive', '0.882', 'shakiness', 'quivering', 'shaking', 'vibration', 'quiver']
senticnet['humbug'] = ['-0.60', '0', '0.982', '-0.56', '#sadness', '#anger', 'negative', '-0.71', 'chicanery', 'deceit', 'deception', 'fraudulence', 'dissembling']
senticnet['humdinger'] = ['0.581', '-0.59', '0', '0.906', '#surprise', '#admiration', 'positive', '0.695', 'nonesuch', 'nonsuch', 'ideal', 'nonpareil', 'apotheosis']
senticnet['humdrum'] = ['-0.96', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.96', 'cliche', 'banality', 'prosaic', 'unexciting', 'commonplace']
senticnet['humean'] = ['-0.55', '0', '0.58', '-0.98', '#anger', '#disgust', 'negative', '-0.70', 'fatalist', 'determinist', 'predestinarian', 'predestinationist', 'philosopher']
senticnet['humeral_head'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'socket', 'tooth_socket', 'alveolus', 'acetabulum', 'cotyloid_cavity']
senticnet['humeral_veil'] = ['0.849', '-0.50', '0', '0.845', '#joy', '#admiration', 'positive', '0.732', 'robe', 'gown', 'veil', 'vest', 'cassock']
senticnet['humerus_upper_arm'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'above_leg', 'joint', 'hip', 'knee']
senticnet['humic_shale'] = ['0.599', '-0.87', '0', '0.673', '#surprise', '#admiration', 'positive', '0.717', 'humate', 'material', 'stuff', 'peat', 'lignite']
senticnet['humid'] = ['0', '-0.03', '0.05', '-0.14', '#anger', '#disgust', 'negative', '-0.05', 'moistureity', 'wetness', 'damp', 'moisture', 'dank']
senticnet['humid_atmosphere'] = ['-0.02', '-0.02', '0.076', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'wear_raincoat', 'carry_umbrella', 'feel_cold', 'camp_out', 'splash_water']
senticnet['humid_environment'] = ['-0.02', '-0.02', '0.076', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'wear_raincoat', 'carry_umbrella', 'feel_cold', 'camp_out', 'splash_water']
senticnet['humid_tropical'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'woodland', 'timberland']
senticnet['humid_tropical_forest'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'woodland', 'timberland']
senticnet['humid_weather'] = ['0.15', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.087', 'winter', 'winter_time', 'snow_fall', 'snowy', 'put_warm_clothes']
senticnet['humidifier'] = ['0.628', '-0.96', '0', '0.682', '#surprise', '#admiration', 'positive', '0.759', 'electric_fan', 'blower', 'fan', 'punkah', 'device']
senticnet['humidity'] = ['0', '-0.03', '0.05', '-0.14', '#anger', '#disgust', 'negative', '-0.05', 'moisture', 'wetness', 'damp', 'humid', 'dank']
senticnet['humidor'] = ['0.031', '0', '-0.08', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'smoke_cigarette', 'smoke', 'cinder', 'tobacco_product', 'smoke_pipe']
senticnet['humiliate'] = ['-0.75', '0', '0.962', '-0.92', '#anger', '#disgust', 'negative', '-0.88', 'hopeless', 'fearful', 'scary', 'dreadful', 'intimidated']
senticnet['humiliated'] = ['-0.84', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.91', 'mortify', 'embarrassed', 'self-hatred', 'self-disgust', 'abase']
senticnet['humiliating'] = ['-0.76', '0', '0.759', '-0.84', '#sadness', '#disgust', 'negative', '-0.79', 'humbling', 'demeaning', 'humbleness', 'demeaningly', 'humiliatingly']
senticnet['humiliating_defeat'] = ['-0.85', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.89', 'humbling', 'demeaning', 'humbleness', 'humiliating', 'demeaningly']
senticnet['humiliatingly'] = ['-0.85', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.89', 'humbling', 'demeaning', 'humbleness', 'humiliating', 'demeaningly']
senticnet['humiliation'] = ['-0.58', '0.282', '-0.41', '-0.16', '#sadness', '#fear', 'negative', '-0.21', 'shame', 'upset', 'despair', 'fright', 'unhappiness']
senticnet['humility'] = ['0', '0', '0', '0.99', '#admiration', '#admiration', 'positive', '0.99', 'concerned', 'anxious', 'consternation', 'hysteria', 'fidgetiness']
senticnet['hummable'] = ['0.65', '-0.61', '0', '0.711', '#joy', '#admiration', 'positive', '0.658', 'bouncing', 'scintillating', 'spirited', 'lively', 'zippy']
senticnet['humming_along'] = ['0.815', '-0.75', '0', '0.744', '#joy', '#surprise', 'positive', '0.772', 'perk', 'convalesce', 'improve', 'look_better', 'meliorate']
senticnet['hummingbird'] = ['0.788', '0.803', '0', '0', '#joy', '#interest', 'positive', '0.796', 'bird', 'colorful', 'pup', 'seagull', 'minnow']
senticnet['hummingbird_hawk_moth'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'hornet', 'vespid', 'vespid_wasp']
senticnet['humor'] = ['0.706', '0', '0', '0.792', '#joy', '#admiration', 'positive', '0.749', 'entertain_others', 'express_emotion', 'sing_song', 'listen', 'sound_good']
senticnet['humor_magazine'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['humorous'] = ['0', '0.792', '0', '0.941', '#interest', '#admiration', 'positive', '0.867', 'humor', 'bawdily', 'bawdy', 'laughter', 'humorously']
senticnet['humorously'] = ['0', '0.789', '0', '0.934', '#interest', '#admiration', 'positive', '0.862', 'humor', 'bawdily', 'bawdy', 'laughter', 'humorous']
senticnet['hump'] = ['-0.74', '0', '0.677', '-0.77', '#sadness', '#disgust', 'negative', '-0.73', 'whore', 'jazz', 'fornicate', 'bonk', 'screw']
senticnet['hump_leg'] = ['0.032', '0', '0', '0.057', '#joy', '#admiration', 'positive', '0.044', 'communicate', 'chat', 'talk_person', 'start_conversation', 'busy_signal']
senticnet['humpback'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'lordosis', 'kyphosis', 'spinal_curvature', 'hunchback', 'abnormality']
senticnet['humpback_whale'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'many_fishes', 'seahorse', 'starfish', 'plankton', 'sea_wee']
senticnet['humpty'] = ['0.934', '-0.57', '0', '0.767', '#joy', '#admiration', 'positive', '0.76', 'morosoph', 'simpleton', 'flibbertigibbet', 'foolish_woman', 'simple']
senticnet['humpty_doo'] = ['-0.95', '0', '0.624', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['humulene'] = ['-0.67', '0', '0.503', '-0.67', '#sadness', '#disgust', 'negative', '-0.61', 'stink_up', 'smell_up', 'stink_out', 'incense', 'perfume']
senticnet['humvee'] = ['-0.56', '0', '0.714', '-0.89', '#anger', '#disgust', 'negative', '-0.72', 'tank', 'tank_destroyer', 'armored_car', 'armoured_car', 'armored_personnel_carrier']
senticnet['huna'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
senticnet['hunc'] = ['0', '0.079', '0', '0', '#interest', '#interest', 'positive', '0.079', 'change_diaper', 'lower_standard_live', 'become_pregnant', 'find_mate', 'make_baby']
senticnet['hunchback'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'lordosis', 'kyphosis', 'humpback', 'spinal_curvature', 'abnormality']
senticnet['hunchback_quasimodo'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'lordosis', 'kyphosis', 'humpback', 'spinal_curvature', 'abnormality']
senticnet['hunchun'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['hundred-percenter'] = ['0', '0', '-0.76', '-0.79', '#fear', '#disgust', 'negative', '-0.78', 'jingo', 'chauvinist', 'flag-waver', 'patrioteer', 'jingoist']
senticnet['hundred_fifty'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'hundred_year', 'twelve', 'day_month', 'time_measurement']
senticnet['hundred_mile_away'] = ['-0.95', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.83', 'outer', 'radiant', 'around_earth', 'nothingness', 'heavenly_body']
senticnet['hundred_thousand'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'hundred_year', 'twelve', 'day_month', 'time_measurement']
senticnet['hundred_thousand_men'] = ['-0.84', '0', '0.553', '-0.89', '#sadness', '#disgust', 'negative', '-0.76', 'encumbrance', 'incumbrance', 'block', 'bar', 'blockade']
senticnet['hundred_year'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'dozen', 'twelve', 'day_month', 'time_measurement']
senticnet['hung-over'] = ['0.769', '-0.89', '0', '0', '#joy', '#surprise', 'positive', '0.831', 'groggy', 'jet-lagged', 'clear-headed', 'bleary-eyed', 'wide-awake']
senticnet['hunger'] = ['-0.71', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.72', 'hungry', 'crave', 'need_food', 'smell_food', 'need_grocery']
senticnet['hunger_go_away'] = ['0.771', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.875', 'get_full', 'a_little_hungry', 'become_little_hungry', 'feel_little_hungry', 'satisfied']
senticnet['hunger_hormone'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'cholecystokinin', 'secretin', 'gastrointestinal_hormone']
senticnet['hunger_relief'] = ['-0.10', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.08', 'come_house', 'desperate', 'welcome_friend', 'captain_boat', 'receive_email']
senticnet['hungover'] = ['-0.80', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.87', 'lonely', 'antsy', 'despondent', 'disheartened', 'homesick']
senticnet['hungrily'] = ['-0.71', '0', '0.692', '-0.81', '#sadness', '#disgust', 'negative', '-0.74', 'famished', 'starved', 'hungry', 'esurient', 'peckish']
senticnet['hungry'] = ['-0.71', '0', '0.692', '-0.81', '#sadness', '#disgust', 'negative', '-0.74', 'famished', 'starved', 'ravenous', 'esurient', 'peckish']
senticnet['hunk'] = ['-0.88', '0', '0', '-1', '#sadness', '#disgust', 'negative', '-0.94', 'misery', 'miser', 'hoard', 'miserly', 'money']
senticnet['hunt'] = ['-0.02', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.07', 'shoot', 'archery', 'fire_gun', 'shoot_cannon', 'shoot_person']
senticnet['hunt_animal'] = ['-0.08', '0', '-0.05', '-0.09', '#sadness', '#disgust', 'negative', '-0.07', 'hunt_game', 'cause_illness', 'crazy', 'kill', 'growl']
senticnet['hunt_caribou'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'cheetah', 'giraffe', 'kangaroo', 'mule', 'horse']
senticnet['hunt_duck'] = ['0.714', '-0.93', '0', '0', '#joy', '#surprise', 'positive', '0.822', 'call_mom', 'answer_telephone', 'talk_telephone', 'receive_letter', 'learn_listen']
senticnet['hunt_fish'] = ['0', '0.072', '0', '0', '#interest', '#interest', 'positive', '0.072', 'counselor', 'rest_place', 'temporary_house', 'summer_vacation', 'military_installation']
senticnet['hunt_food'] = ['-0.04', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.05', 'corner_prey', 'cut_knife', 'circle_prey', 'eat', 'hunt_game']
senticnet['hunt_fox'] = ['-0.75', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.87', 'cause_accident', 'mark_territory', 'sense_fear', 'bladder_full', 'crap']
senticnet['hunt_game'] = ['-0.11', '-0.03', '-0.06', '-0.12', '#sadness', '#disgust', 'negative', '-0.06', 'corner_prey', 'shoot', 'kill', 'arm_bomb', 'commit_genocide']
senticnet['hunt_human'] = ['-0.22', '-0.07', '-0.14', '0.08', '#sadness', '#fear', 'negative', '-0.05', 'stagger', 'gnaw', 'monitor_heart_rate', 'prevent_conception', 'choke']
senticnet['hunter'] = ['0', '0', '-0.11', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'hunting_season', 'hound', 'cougar', 'outlaw', 'dragon']
senticnet['hunter_gatherer'] = ['0.735', '-0.71', '0', '0.566', '#joy', '#surprise', 'positive', '0.672', 'improvement', 'betterment', 'habituation', 'adjustment', 'shakedown']
senticnet['huntin'] = ['-0.97', '0', '0.677', '-0.95', '#sadness', '#disgust', 'negative', '-0.86', 'stunt', 'game', 'mutant', 'invertebrate', 'animate_being']
senticnet['hunting'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'panda', 'endangered_species', 'air_pollution', 'global_warming', 'sea_pollution']
senticnet['hunting_accident'] = ['-0.92', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.93', 'serious_accident', 'accident', 'hit_car', 'loss_ofawareness', 'bang_head']
senticnet['hunting_caribou'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'cheetah', 'giraffe', 'kangaroo', 'mule', 'horse']
senticnet['hunting_cartridge'] = ['0.887', '-0.95', '0', '0.913', '#surprise', '#admiration', 'positive', '0.917', 'high_quality', 'superiority', 'level', 'grade', 'degree']
senticnet['hunting_deer'] = ['0', '0', '-0.11', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'hunting_season', 'hound', 'cougar', 'outlaw', 'dragon']
senticnet['hunting_expedition'] = ['0', '0', '-0.11', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'hunting_season', 'hound', 'cougar', 'outlaw', 'dragon']
senticnet['hunting_horn'] = ['-0.54', '0', '0.811', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'bird_of_prey', 'raptor', 'raptorial_bird', 'parrot', 'bird']
senticnet['hunting_knife'] = ['-0.71', '0', '0.954', '-0.56', '#sadness', '#anger', 'negative', '-0.74', 'jackknife', 'pocket_knife', 'penknife', 'switchblade', 'switchblade_knife']
senticnet['hunting_lodge'] = ['0.021', '0.119', '0', '0.129', '#interest', '#admiration', 'positive', '0.09', 'manor', 'palace', 'medieval', 'tower', 'chess_game']
senticnet['hunting_problem'] = ['-0.96', '0', '0.704', '-0.97', '#sadness', '#disgust', 'negative', '-0.87', 'break', 'crash', 'smash', 'damage', 'break_arrow']
senticnet['hunting_rifle'] = ['-0.75', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.75', 'hydrogen_bomb', 'rifle', 'revolver', 'pistol', 'gun']
senticnet['hunting_season'] = ['0', '0', '-0.11', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'hunter', 'hound', 'cougar', 'outlaw', 'dragon']
senticnet['hunting_trip'] = ['0', '0', '-0.11', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'hunting_season', 'hound', 'cougar', 'outlaw', 'dragon']
senticnet['huntington_chorea'] = ['-0.94', '0', '0.821', '-0.75', '#sadness', '#anger', 'negative', '-0.84', 'predementedness', 'insanity', 'dementedness', 'senile_psychosis', 'alcoholic_dementia']
senticnet['huntington_disease'] = ['-0.95', '0', '0.621', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'disease', 'brain_disorder', 'brain_disease', 'ill', 'epilepsy']
senticnet['huntington_disease_hd'] = ['-0.95', '0', '0.621', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'disease', 'brain_disorder', 'brain_disease', 'ill', 'epilepsy']
senticnet['huntingtons_disease'] = ['-0.95', '0', '0.621', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'disease', 'brain_disorder', 'brain_disease', 'ill', 'epilepsy']
senticnet['huntsman_spider'] = ['0.96', '-0.77', '0', '0.929', '#joy', '#admiration', 'positive', '0.887', 'whale_louse', 'beach_flea', 'sand_hopper', 'sandhopper', 'amphipod']
senticnet['hurdle'] = ['-0.72', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.76', 'obstruction', 'obstacle', 'hindrance', 'insurmountable', 'novice']
senticnet['hurl'] = ['-0.83', '0', '0.87', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'precipitate', 'mizzle', 'shower_down', 'rain_down', 'patter']
senticnet['hurler'] = ['-0.92', '0', '0.767', '-0.85', '#sadness', '#disgust', 'negative', '-0.84', 'closer', 'finisher', 'pitcher', 'relief_pitcher', 'twirler']
senticnet['hurler_syndrome'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'dystrophy', 'myotonic_dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['hurly_burly'] = ['-0.65', '0', '0.858', '-0.83', '#anger', '#disgust', 'negative', '-0.78', 'turmoil', 'convulsion', 'tempest', 'disorder', 'earthquake']
senticnet['hurricane'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'atmospheric_phenomenon', 'natural_disaster', 'flood', 'destruction', 'tropical_storm']
senticnet['hurricane_camille'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'atmospheric_phenomenon', 'natural_disaster', 'flood', 'destruction', 'tropical_storm']
senticnet['hurricane_charley'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'atmospheric_phenomenon', 'natural_disaster', 'flood', 'destruction', 'tropical_storm']
senticnet['hurricane_force'] = ['-0.78', '0', '0.906', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'rainstorm', 'atmospheric_phenomenon', 'northeaster', 'noreaster', 'hailstorm']
senticnet['hurricane_ike'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'atmospheric_phenomenon', 'natural_disaster', 'flood', 'destruction', 'tropical_storm']
senticnet['hurricane_isabel'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'atmospheric_phenomenon', 'natural_disaster', 'flood', 'destruction', 'tropical_storm']
senticnet['hurricane_isidore'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'atmospheric_phenomenon', 'natural_disaster', 'flood', 'destruction', 'tropical_storm']
senticnet['hurricane_landfall'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'atmospheric_phenomenon', 'natural_disaster', 'flood', 'destruction', 'tropical_storm']
senticnet['hurricane_lili'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'atmospheric_phenomenon', 'natural_disaster', 'flood', 'destruction', 'tropical_storm']
senticnet['hurricane_made_landfall'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'atmospheric_phenomenon', 'natural_disaster', 'flood', 'destruction', 'tropical_storm']
senticnet['hurricane_paloma'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'atmospheric_phenomenon', 'natural_disaster', 'flood', 'destruction', 'tropical_storm']
senticnet['hurricane_season'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'atmospheric_phenomenon', 'natural_disaster', 'flood', 'destruction', 'tropical_storm']
senticnet['hurricane_strength'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'atmospheric_phenomenon', 'natural_disaster', 'flood', 'destruction', 'tropical_storm']
senticnet['hurried'] = ['-0.78', '0', '0.725', '0', '#sadness', '#anger', 'negative', '-0.75', 'haste', 'hasty', 'rash', 'sputter', 'deliberate']
senticnet['hurried_away'] = ['-0.85', '0.21', '-0.56', '0', '#sadness', '#fear', 'negative', '-0.40', 'doleful', 'sadly', 'dolefully', 'ruefulness', 'wretched']
senticnet['hurriedly'] = ['-0.79', '0', '-0.70', '0', '#sadness', '#fear', 'negative', '-0.75', 'hesitance', 'hesitancy', 'hesitatingly', 'unassertive', 'unassertively']
senticnet['hurriedness'] = ['-0.87', '0', '0.837', '-0.89', '#sadness', '#disgust', 'negative', '-0.86', 'abruptness', 'precipitateness', 'precipitousness', 'precipitance', 'precipitancy']
senticnet['hurriquake'] = ['0', '0.078', '0.027', '0.196', '#interest', '#admiration', 'positive', '0.082', 'heel', 'ankle', 'toe', 'foot_massage', 'foot']
senticnet['hurry'] = ['-0.86', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'downfall', 'hurriedness', 'hastiness', 'haste', 'precipitation']
senticnet['hurry_along'] = ['-0.08', '0.124', '-0.09', '0.176', '#interest', '#admiration', 'positive', '0.031', 'jump_over', 'jump_up', 'mark_territory', 'jump', 'hurry_up']
senticnet['hurry_up'] = ['-0.08', '0.124', '-0.09', '0.176', '#interest', '#admiration', 'positive', '0.031', 'jump_over', 'hurry_along', 'mark_territory', 'jump', 'jump_up']
senticnet['hurry_work'] = ['-0.73', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.73', 'carry_briefcase', 'run', 'bicycle_across_town', 'sail_around_world', 'voice_concern']
senticnet['hurt'] = ['-0.79', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.75', 'harm', 'ouch', 'pain', 'damage', 'ache']
senticnet['hurt_animal'] = ['-0.25', '0', '-0.18', '-0.08', '#sadness', '#fear', 'negative', '-0.17', 'dig_grave', 'eat_animal', 'say_prayer', 'dig_hole', 'wear_skirt']
senticnet['hurt_back'] = ['-0.99', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.86', 'break_leg', 'loss_ofbreath', 'hit_car', 'get_injured', 'sprain_ankle']
senticnet['hurt_badly'] = ['-0.79', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.75', 'harm', 'ouch', 'pain', 'damage', 'ache']
senticnet['hurt_ear'] = ['0.817', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.792', 'make_noise', 'hear_song', 'tap_foot', 'earring', 'change_mood']
senticnet['hurt_else'] = ['-0.72', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.74', 'obstruct_justice', 'get_away_crime', 'punish', 'commit_perjury', 'commit_murder']
senticnet['hurt_feeling'] = ['-0.99', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.87', 'feel_hurt', 'dislike', 'feel_bad', 'become_angry', 'get_fired']
senticnet['hurt_foot'] = ['-0.99', '0', '0.521', '-0.92', '#sadness', '#disgust', 'negative', '-0.81', 'pain', 'hurt', 'injury', 'painful', 'knee_injury']
senticnet['hurt_others'] = ['-0.76', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.87', 'get_rid_of_enemy', 'destroy_live', 'terrorize', 'death_destruction', 'murder']
senticnet['hurt_person'] = ['-0.7', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.74', 'stab', 'cause_injury', 'destroy', 'maim', 'die_painfully']
senticnet['hurt_skin'] = ['-0.90', '0', '0.209', '-0.92', '#sadness', '#disgust', 'negative', '-0.68', 'sun_burn', 'erythema', 'fire', 'lighting', 'burn_skin']
senticnet['hurt_toe'] = ['-0.12', '-0.02', '0.028', '0', '#sadness', '#anger', 'negative', '-0.04', 'move_leg', 'trip_fall_down', 'swing_arm', 'tie_shoes', 'take_photo']
senticnet['hurt_wrongly'] = ['-0.99', '0', '0.434', '-0.90', '#sadness', '#disgust', 'negative', '-0.77', 'friendly_fire', 'fight_war', 'battle', 'attack', 'kill_friend']
senticnet['hurtful'] = ['-0.18', '0', '-0.11', '-0.11', '#sadness', '#fear', 'negative', '-0.13', 'destructive', 'bloody', 'severe', 'gash', 'ouch']
senticnet['hurtgen_forest'] = ['0.737', '-0.96', '0', '0.552', '#joy', '#surprise', 'positive', '0.75', 'prominence', 'hump', 'bump', 'swelling', 'frontal_eminence']
senticnet['hurtin'] = ['-0.10', '0', '-0.05', '-0.24', '#sadness', '#disgust', 'negative', '-0.13', 'adulate', 'suck_up', 'toady', 'blandish', 'kowtow']
senticnet['hurting'] = ['-0.73', '0', '0.555', '-0.90', '#sadness', '#disgust', 'negative', '-0.73', 'aching', 'dysmenorrhea', 'labor_pain', 'neuralgia', 'neuralgy']
senticnet['hurtled_down'] = ['-0.11', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.08', 'have_fun', 'spring_up', 'fall_well', 'shovel_snow', 'fly_around']
senticnet['hurtling_down'] = ['-0.11', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.08', 'have_fun', 'spring_up', 'fall_well', 'shovel_snow', 'fly_around']
senticnet['hurva_synagogue'] = ['0', '0.209', '0.026', '0.184', '#interest', '#admiration', 'positive', '0.122', 'church', 'confession', 'temple', 'cathedral', 'monastery']
senticnet['husband'] = ['0.716', '0.79', '0', '0', '#joy', '#interest', 'positive', '0.753', 'wife', 'boy', 'mum', 'male_parent', 'male_relative']
senticnet['husband_wife'] = ['0.898', '0', '0', '0.709', '#joy', '#admiration', 'positive', '0.803', 'holy_matrimony', 'good_teacher', 'female_person', 'existence', 'marriage']
senticnet['husbandman'] = ['0.633', '-0.50', '0', '0.59', '#joy', '#admiration', 'positive', '0.575', 'craftsman', 'manicurist', 'artisan', 'journeyman', 'beautician']
senticnet['hush'] = ['0.206', '0.329', '-0.07', '0.22', '#interest', '#admiration', 'positive', '0.171', 'listen_sermon', 'religious_purpose', 'belief', 'spirituality', 'faith']
senticnet['hush-hush'] = ['0', '0', '-0.75', '-0.96', '#fear', '#disgust', 'negative', '-0.86', 'secret', 'clandestine', 'hugger-mugger', 'cloak-and-dagger', 'hole-and-corner']
senticnet['hush_money'] = ['-0.95', '0', '-0.70', '-0.96', '#sadness', '#disgust', 'negative', '-0.87', 'corrupt', 'bribe_police', 'debt', 'tax', 'persuade']
senticnet['hush_up'] = ['-0.69', '0', '0.89', '-0.70', '#anger', '#disgust', 'negative', '-0.76', 'still', 'burke', 'moderate', 'suppress', 'quell']
senticnet['husky'] = ['-0.53', '0', '0.798', '-0.73', '#anger', '#disgust', 'negative', '-0.68', 'robust', 'hardy', 'sturdy', 'chesty', 'stalwart']
senticnet['husky_voice'] = ['-0.53', '0', '0.798', '-0.73', '#anger', '#disgust', 'negative', '-0.68', 'husky', 'hardy', 'sturdy', 'robust', 'stalwart']
senticnet['hussainia'] = ['0', '0.224', '0', '0.197', '#interest', '#admiration', 'positive', '0.211', 'church', 'temple', 'cathedral', 'chapel', 'confession']
senticnet['hussy'] = ['0.947', '0.914', '0', '0', '#joy', '#interest', 'positive', '0.93', 'ball_bounce', 'bicycle_tire', 'tire', 'increase_price', 'gyroscope']
senticnet['hustle'] = ['-0.94', '0', '0.492', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'make_noise', 'disturb', 'confusion', 'hustle_and_bustle', 'bustle']
senticnet['hustle_and_bustle'] = ['-0.94', '0', '0.492', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'make_noise', 'disturb', 'confusion', 'hustle', 'bustle']
senticnet['hustle_bustle'] = ['-0.94', '0', '0.492', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'make_noise', 'disturb', 'confusion', 'hustle_and_bustle', 'bustle']
senticnet['hustler'] = ['-0.94', '0', '0.742', '0', '#sadness', '#anger', 'negative', '-0.84', 'pretend', 'prostitute', 'hustle', 'amateur', 'male']
senticnet['hut'] = ['-0.06', '-0.01', '-0.04', '0', '#sadness', '#fear', 'negative', '-0.03', 'hive', 'animal_home', 'lair', 'habitat', 'beehive']
senticnet['hutch'] = ['0.846', '-0.66', '0', '0.941', '#joy', '#admiration', 'positive', '0.817', 'hut', 'hovel', 'mudhif', 'shack', 'shanty']
senticnet['hutzpah'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'rudeness', 'discourtesy', 'insolence', 'ccheekiness', 'cheekiness']
senticnet['huxford'] = ['-0.9', '0', '0.749', '-0.71', '#sadness', '#anger', 'negative', '-0.78', 'chute', 'slide', 'slideway', 'sloping_trough', 'gutter']
senticnet['huxley'] = ['-0.04', '0.192', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.054', 'hypothetical', 'supposition', 'scientific_statement', 'psychology', 'educate_guess']
senticnet['hvac_system'] = ['0.628', '-0.96', '0', '0.682', '#surprise', '#admiration', 'positive', '0.759', 'electric_fan', 'blower', 'fan', 'punkah', 'device']
senticnet['hyacinth_macaw'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet']
senticnet['hyaena'] = ['0', '0', '-0.95', '-0.81', '#fear', '#disgust', 'negative', '-0.88', 'herb', 'plant', 'poisonous_plant', 'yellow_flower', 'lichen']
senticnet['hyaku'] = ['0.973', '-0.50', '0', '0.612', '#joy', '#admiration', 'positive', '0.698', 'chic', 'chicness', 'jauntiness', 'modishness', 'stylishness']
senticnet['hyaline_cartilage'] = ['-0.53', '0', '0.982', '-0.67', '#anger', '#disgust', 'negative', '-0.73', 'meniscus', 'semilunar_cartilage', 'fibrocartilage', 'gristle', 'arytenoid']
senticnet['hyalinization'] = ['0.905', '-0.65', '0', '0.851', '#joy', '#admiration', 'positive', '0.805', 'gonococcus', 'bacteria', 'bacteriae', 'probiotic', 'probiotic_bacterium']
senticnet['hyaluronic_acid'] = ['0', '0', '0.025', '-0.09', '#anger', '#disgust', 'negative', '-0.06', 'body_fluid', 'artery', 'skeletal_system', 'bodily_fluid', 'near_mouth']
senticnet['hybrid'] = ['0.042', '-0.03', '0', '0.112', '#joy', '#admiration', 'positive', '0.063', 'double_nature', 'clean', 'highway_road', 'go_green', 'car_drive']
senticnet['hybridity'] = ['0.122', '0.159', '-0.05', '0.217', '#interest', '#admiration', 'positive', '0.111', 'profile_picture', 'unity', 'country_person', 'unite', 'signature']
senticnet['hydatidiform_mole'] = ['-0.75', '0', '0.715', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'abdominal_pregnancy', 'ectopic_gestation', 'tubal_pregnancy', 'pregnancy', 'gestation']
senticnet['hydra'] = ['0', '0', '-0.87', '0', '#fear', '#fear', 'negative', '-0.87', 'beetle', 'many_insects', 'grasshopper', 'firefly', 'mouse']
senticnet['hydra-headed'] = ['0', '0', '0.967', '-0.98', '#anger', '#disgust', 'negative', '-0.97', 'all-devouring', 'all-destroying', 'supineness', 'incontrollable', 'always-evolving']
senticnet['hydration_pack'] = ['0', '-0.02', '0.027', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'shoulder_bag', 'satchel', 'satchel_bag', 'tote_bag', 'carry']
senticnet['hydraulic_cement'] = ['0', '0', '-0.95', '-0.72', '#fear', '#disgust', 'negative', '-0.83', 'cannon', 'explosive', 'firearm', 'pistol', 'machine_gun']
senticnet['hydraulic_cylinder'] = ['0', '0.142', '0', '0.125', '#interest', '#admiration', 'positive', '0.134', 'weight_machine', 'power_drill', 'screw', 'screwdriver', 'air_pump']
senticnet['hydraulic_elevator'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'top_floor', 'front_desk', 'concierge', 'exit', 'ground_floor']
senticnet['hydraulic_lift'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'top_floor', 'front_desk', 'concierge', 'exit', 'ground_floor']
senticnet['hydraulic_lime'] = ['0', '0', '-0.95', '-0.72', '#fear', '#disgust', 'negative', '-0.83', 'cannon', 'explosive', 'firearm', 'pistol', 'machine_gun']
senticnet['hydraulic_pressure'] = ['-0.12', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.09', 'breeze', 'move_air', 'air_movement', 'gape', 'force']
senticnet['hydraulic_ram'] = ['0', '0.142', '0', '0.125', '#interest', '#admiration', 'positive', '0.134', 'weight_machine', 'power_drill', 'screw', 'screwdriver', 'air_pump']
senticnet['hydraulic_winch'] = ['0', '0.142', '-0.09', '0.183', '#interest', '#admiration', 'positive', '0.078', 'arise', 'rise', 'raise_up', 'go_up', 'go_upstairs']
senticnet['hydraulically'] = ['0.882', '0', '0', '0.854', '#joy', '#admiration', 'positive', '0.868', 'pneumatically', 'actuated', 'mechanically', 'electrically', 'pneumatic']
senticnet['hydride'] = ['0.964', '0.898', '0', '0', '#joy', '#interest', 'positive', '0.931', 'pertinent', 'relevant', 'germane', 'rider', 'applicable']
senticnet['hydride_shift'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
senticnet['hydride_transfer'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
senticnet['hydriodic_acid'] = ['-0.81', '0', '0.774', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'acid', 'hypophosphoric_acid', 'periodic_acid', 'oxyacid', 'phosphorous_acid']
senticnet['hydro'] = ['0', '0.859', '0', '0.802', '#interest', '#admiration', 'positive', '0.831', 'article', 'energy', 'east', 'sun', 'power']
senticnet['hydrobromic_acid'] = ['0.032', '-0.02', '-0.04', '-0.08', '#fear', '#disgust', 'negative', '-0.01', 'liquid', 'carbonate_drink', 'chemical', 'soda', 'bromide']
senticnet['hydrocarbon_fuel'] = ['0', '0', '-0.06', '0', '#fear', '#fear', 'negative', '-0.06', 'greasy', 'car_gas', 'fossil_fuel', 'crude', 'lubrication']
senticnet['hydrocele'] = ['0.087', '-0.03', '0', '0.063', '#joy', '#admiration', 'positive', '0.062', 'puppet', 'bouncy', 'curly', 'throw_object', 'glove_leather']
senticnet['hydrochloric_acid'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
senticnet['hydrochloric_acid_hcl'] = ['0.032', '-0.02', '-0.04', '-0.08', '#fear', '#disgust', 'negative', '-0.01', 'liquid', 'carbonate_drink', 'chemical', 'soda', 'bromide']
senticnet['hydrochloride'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
senticnet['hydrochloride_salt'] = ['-0.81', '0', '0.774', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'acid', 'hypophosphoric_acid', 'periodic_acid', 'oxyacid', 'phosphorous_acid']
senticnet['hydrocodone'] = ['0.138', '0.103', '0', '0.095', '#joy', '#interest', 'positive', '0.112', 'masturbation', 'sexual_intercourse', 'sex', 'warm_fuzzy_feeling', 'fuck']
senticnet['hydrocortisone'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'radiation_therapy', 'radiation', 'actinotherapy', 'irradiation']
senticnet['hydrocortisone_cream'] = ['-0.81', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.82', 'peanut', 'leave_over', 'floppy', 'circular_motion', 'store_vegetable']
senticnet['hydrodynamic'] = ['0.676', '0.63', '-0.23', '0.31', '#joy', '#interest', 'positive', '0.345', 'perkiness', 'walk_on_air', 'exultingly', 'exultantly', 'jubilance']
senticnet['hydrodynamic_drag'] = ['0.676', '0.63', '-0.23', '0.31', '#joy', '#interest', 'positive', '0.345', 'perkiness', 'walk_on_air', 'exultingly', 'exultantly', 'jubilance']
senticnet['hydroelectric_energy'] = ['0.986', '0.897', '0', '0', '#joy', '#interest', 'positive', '0.942', 'potential_energy', 'watt', 'electrical', 'power_source', 'volt']
senticnet['hydroelectric_turbine'] = ['-0.97', '0', '0.658', '-0.60', '#sadness', '#anger', 'negative', '-0.74', 'gas_turbine', 'turbine', 'impulse_turbine', 'reaction_turbine', 'steam_turbine']
senticnet['hydrofluoric_acid'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
senticnet['hydrogel'] = ['-0.04', '0.069', '0', '0.075', '#interest', '#admiration', 'positive', '0.035', 'sculpt_gel', 'hairstyle', 'hairdresser', 'cosmetic', 'hair_sculpt_gel']
senticnet['hydrogen'] = ['0.909', '0.983', '0', '0', '#joy', '#interest', 'positive', '0.946', 'oxygen', 'breathable', 'atmosphere', 'air', 'inhale_air']
senticnet['hydrogen_bomb'] = ['0', '0', '-0.77', '-0.92', '#fear', '#disgust', 'negative', '-0.85', 'revolver', 'shotgun', 'pistol', 'rifle', 'gun']
senticnet['hydrogen_bromide'] = ['0.032', '-0.02', '-0.04', '-0.08', '#fear', '#disgust', 'negative', '-0.01', 'liquid', 'carbonate_drink', 'chemical', 'soda', 'bromide']
senticnet['hydrogen_chloride_hcl'] = ['-0.67', '0', '0.503', '-0.67', '#sadness', '#disgust', 'negative', '-0.61', 'stink_up', 'smell_up', 'stink_out', 'incense', 'perfume']
senticnet['hydrogen_cyanide'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'rodent', 'tree_animal']
senticnet['hydrogen_cyanide_hcn'] = ['-0.67', '0', '0.503', '-0.67', '#sadness', '#disgust', 'negative', '-0.61', 'stink_up', 'smell_up', 'stink_out', 'incense', 'perfume']
senticnet['hydrogen_fluoride'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
senticnet['hydrogen_fluoride_hf'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
senticnet['hydrogen_fuel'] = ['0', '0', '-0.06', '0', '#fear', '#fear', 'negative', '-0.06', 'greasy', 'car_gas', 'fossil_fuel', 'crude', 'lubrication']
senticnet['hydrogen_molecule'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'elementary_particle', 'neutron', 'electron', 'subatomic_particle', 'proton']
senticnet['hydrogen_oxygen'] = ['0', '0', '-0.06', '0', '#fear', '#fear', 'negative', '-0.06', 'greasy', 'car_gas', 'fossil_fuel', 'crude', 'lubrication']
senticnet['hydrogen_peroxide'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
senticnet['hydrogen_selenide'] = ['-0.67', '0', '0.503', '-0.67', '#sadness', '#disgust', 'negative', '-0.61', 'stink_up', 'smell_up', 'stink_out', 'incense', 'perfume']
senticnet['hydrogen_sulfide'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'rodent', 'tree_animal']
senticnet['hydrogen_sulfide_rotten_egg'] = ['-0.67', '0', '0.503', '-0.67', '#sadness', '#disgust', 'negative', '-0.61', 'stink_up', 'smell_up', 'stink_out', 'incense', 'perfume']
senticnet['hydrogen_sulphide'] = ['-0.67', '0', '0.503', '-0.67', '#sadness', '#disgust', 'negative', '-0.61', 'stink_up', 'smell_up', 'stink_out', 'incense', 'perfume']
senticnet['hydrogenation'] = ['-0.95', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.90', 'oxidation', 'catalyst', 'converter', 'catalytic', 'exhaust']
senticnet['hydroid'] = ['-0.79', '0', '0.709', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'animal', 'animate_being', 'ctenophore', 'invertebrate', 'beast']
senticnet['hydroiodic_acid'] = ['0.032', '-0.02', '-0.04', '-0.08', '#fear', '#disgust', 'negative', '-0.01', 'liquid', 'carbonate_drink', 'chemical', 'soda', 'bromide']
senticnet['hydrologic_science'] = ['0.882', '0.733', '0', '0', '#joy', '#interest', 'positive', '0.807', 'different_application', 'sponsor', 'sediment', 'transport', 'study']
senticnet['hydrological'] = ['0', '0', '-0.71', '-0.95', '#fear', '#disgust', 'negative', '-0.83', 'meteorological', 'oceanographic', 'meteorology', 'climatic', 'climatological']
senticnet['hydrolytic'] = ['-0.80', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.84', 'popularization', 'profanation', 'demoralization', 'corruption', 'humiliation']
senticnet['hydrolytic_stability'] = ['0.8', '-0.90', '0', '0.675', '#joy', '#surprise', 'positive', '0.794', 'indestructibility', 'tensile_strength', 'continuity', 'persistence', 'changelessness']
senticnet['hydromorphone'] = ['-0.98', '0', '0.601', '-0.69', '#sadness', '#disgust', 'negative', '-0.76', 'pain_pill_hydrochloride', 'pain_pill', 'acetaminophen', 'codeine', 'medicine']
senticnet['hydromorphone_hydrochloride'] = ['-0.98', '0', '0.601', '-0.69', '#sadness', '#disgust', 'negative', '-0.76', 'pain_pill', 'hydromorphone', 'acetaminophen', 'codeine', 'medicine']
senticnet['hydronym'] = ['0', '0.08', '0.032', '0.096', '#interest', '#admiration', 'positive', '0.048', 'land_form', 'land_formation', 'land_feature', 'geographic_feature', 'landscape_feature']
senticnet['hydrophila'] = ['-0.99', '0', '0.859', '-0.62', '#sadness', '#anger', 'negative', '-0.82', 'lax', 'loose', 'diarrheal', 'diarrhoeal', 'unconstipated']
senticnet['hydrophilic'] = ['0', '0', '0.755', '-0.89', '#anger', '#disgust', 'negative', '-0.82', 'moisture-loving', 'heat-loving', 'sun-loving', 'shade-loving', 'water-loving']
senticnet['hydrophilicity'] = ['-0.52', '0', '0.596', '-0.76', '#anger', '#disgust', 'negative', '-0.62', 'retention', 'retentiveness', 'retentivity', 'solidness', 'nonabsorbency']
senticnet['hydrophobic'] = ['0', '0', '0.019', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'worm_intestine', 'bristle', 'fur_keep_warm', 'animal', 'whisker']
senticnet['hydrophobic_core'] = ['0', '0', '0.019', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'worm_intestine', 'bristle', 'fur_keep_warm', 'animal', 'whisker']
senticnet['hydrophobic_hydrocarbon'] = ['0.096', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.057', 'platform_rail', 'rail', 'catwalk', 'column', 'token_turnstile']
senticnet['hydrophyte'] = ['-0.83', '0', '0.768', '-0.69', '#sadness', '#anger', 'negative', '-0.76', 'water_carpet', 'water_mat', 'aquatic_plant', 'water_plant', 'golden_saxifrage']
senticnet['hydropiper'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'bulrush', 'nailrod', 'cattail', 'reedmace']
senticnet['hydroponic'] = ['-0.12', '0.066', '-0.08', '-0.06', '#sadness', '#fear', 'negative', '-0.05', 'garden_yard', 'see_sky', 'plant_tree', 'hair', 'seed_lawn']
senticnet['hydroponic_greenhouse'] = ['-0.12', '0.066', '-0.08', '-0.06', '#sadness', '#fear', 'negative', '-0.05', 'garden_yard', 'see_sky', 'plant_tree', 'hair', 'seed_lawn']
senticnet['hydroponic_system'] = ['-0.12', '0.066', '-0.08', '-0.06', '#sadness', '#fear', 'negative', '-0.05', 'garden_yard', 'see_sky', 'plant_tree', 'hair', 'seed_lawn']
senticnet['hydroponically'] = ['-0.12', '0.066', '-0.08', '-0.06', '#sadness', '#fear', 'negative', '-0.05', 'garden_yard', 'see_sky', 'plant_tree', 'hair', 'seed_lawn']
senticnet['hydrops'] = ['-0.63', '0', '0.982', '-0.64', '#anger', '#disgust', 'negative', '-0.75', 'angioedema', 'atrophedema', 'giant_hive', 'periodic_edema', 'lymphedema']
senticnet['hydrosalpinx'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'gastroenteritis', 'stomach_flu', 'intestinal_flu', 'inflammatory_disease']
senticnet['hydrostatic'] = ['0.676', '0.63', '-0.23', '0.31', '#joy', '#interest', 'positive', '0.345', 'perkiness', 'walk_on_air', 'exultingly', 'exultantly', 'jubilance']
senticnet['hydrostatic_equilibrium'] = ['0.034', '-0.07', '0.068', '0.103', '#surprise', '#admiration', 'positive', '0.035', 'use_sport', 'bouncy', 'globe', 'orb', 'disc_shaped']
senticnet['hydrostatic_head'] = ['-0.12', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.09', 'breeze', 'move_air', 'air_movement', 'gape', 'force']
senticnet['hydrostatic_pressure'] = ['0.676', '0.63', '-0.23', '0.31', '#joy', '#interest', 'positive', '0.345', 'perkiness', 'walk_on_air', 'exultingly', 'exultantly', 'jubilance']
senticnet['hydrotherapy'] = ['-0.60', '0', '0.544', '-0.62', '#sadness', '#disgust', 'negative', '-0.59', 'remedy', 'cure', 'therapeutic', 'antidote', 'atropine']
senticnet['hydrothermal_system'] = ['0', '0', '-0.92', '0', '#fear', '#fear', 'negative', '-0.92', 'water_flow', 'wind_water', 'water_stream', 'waterway', 'lava']
senticnet['hydrothermal_vent'] = ['-0.02', '-0.01', '0.03', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'ocean_beach', 'beach', 'water_sea', 'shore', 'ground_ocean']
senticnet['hydroxide'] = ['0.032', '-0.02', '-0.04', '-0.08', '#fear', '#disgust', 'negative', '-0.01', 'liquid', 'carbonate_drink', 'chemical', 'soda', 'bromide']
senticnet['hydroxy_group'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'enhance', 'intensify', 'deepen', 'potentiate', 'heighten']
senticnet['hydroxychloroquine'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'radiation_therapy', 'radiation', 'actinotherapy', 'irradiation']
senticnet['hyena'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'kangaroo', 'giraffe', 'caribou', 'mule', 'horse']
senticnet['hygeine'] = ['0.076', '-0.00', '0', '0', '#joy', '#surprise', 'positive', '0.042', 'clean_body', 'feel_fresh', 'remove_dirt_body', 'shower', 'feel_refreshed']
senticnet['hygiene'] = ['0.736', '0.832', '0', '0', '#joy', '#interest', 'positive', '0.784', 'hand_wash', 'body_care', 'godliness', 'clean_agent', 'tv_drama']
senticnet['hygienic'] = ['0.939', '-0.74', '0', '0.57', '#joy', '#surprise', 'positive', '0.751', 'haleness', 'wholeness', 'rosiness', 'health', 'vitality']
senticnet['hygroscope'] = ['0', '0.184', '-0.05', '0.179', '#interest', '#admiration', 'positive', '0.104', 'absorb_water', 'hold_water', 'antiquate', 'molecule', 'tangent']
senticnet['hygroscopic'] = ['0', '-0.03', '0.05', '-0.14', '#anger', '#disgust', 'negative', '-0.05', 'humidity', 'wetness', 'damp', 'humid', 'dank']
senticnet['hygroscopic_nature'] = ['0', '-0.03', '0.05', '-0.14', '#anger', '#disgust', 'negative', '-0.05', 'humidity', 'wetness', 'damp', 'humid', 'dank']
senticnet['hygroscopicity'] = ['-0.52', '0', '0.596', '-0.76', '#anger', '#disgust', 'negative', '-0.62', 'retention', 'retentiveness', 'retentivity', 'solidness', 'nonabsorbency']
senticnet['hyland_memorial'] = ['0.093', '-0.02', '0', '0.056', '#joy', '#admiration', 'positive', '0.057', 'pitcher', 'carafe', 'container_pitcher', 'mug', 'glass_of_water']
senticnet['hylarana'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'amphibian', 'aquatic']
senticnet['hylodidae_family'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'amphibian', 'aquatic']
senticnet['hylozoism'] = ['-0.55', '0', '0.58', '-0.98', '#anger', '#disgust', 'negative', '-0.70', 'fatalist', 'determinist', 'predestinarian', 'predestinationist', 'philosopher']
senticnet['hymenolepis_diminuta'] = ['-0.74', '0', '0.94', '-0.87', '#anger', '#disgust', 'negative', '-0.85', 'fungal_infection', 'mycosis', 'kerion', 'tinea_pedis', 'athlete_foot']
senticnet['hymenophyllaceae_family'] = ['-0.50', '0', '0.573', '-0.89', '#anger', '#disgust', 'negative', '-0.65', 'petallike', 'bowery', 'leafy', 'unifoliate', 'bifoliate']
senticnet['hymenophyllum'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['hymenopteran'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'giant_hornet', 'hornet', 'hornet_wasp']
senticnet['hymn'] = ['0.073', '0.109', '0.031', '0.165', '#interest', '#admiration', 'positive', '0.079', 'song', 'song_lyric', 'lyrics', 'melody', 'lyric']
senticnet['hymn_writer'] = ['-0.85', '0', '0.749', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'hairsplitter', 'fight', 'quarrel', 'obstructor', 'obstructer']
senticnet['hymnal'] = ['0', '0.176', '0', '0.216', '#interest', '#admiration', 'positive', '0.196', 'choir_stall', 'worshiper', 'pew', 'fundamentalist', 'pulpit']
senticnet['hymnbook'] = ['0', '0.176', '0', '0.216', '#interest', '#admiration', 'positive', '0.196', 'choir_stall', 'worshiper', 'pew', 'fundamentalist', 'pulpit']
senticnet['hymnlike'] = ['0.898', '0.84', '0', '0', '#joy', '#interest', 'positive', '0.869', 'pondering', 'broody', 'contemplative', 'meditative', 'think']
senticnet['hymnwriter'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
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senticnet['hypnotherapy'] = ['0.542', '-0.63', '0', '0.912', '#surprise', '#admiration', 'positive', '0.695', 'implosion_therapy', 'flooding', 'reciprocal_inhibition', 'behavior_modification', 'desensitization_technique']
senticnet['hypnotic'] = ['0', '0.817', '0', '0.96', '#interest', '#admiration', 'positive', '0.888', 'important', 'take_over', 'essential', 'high_class', 'movie_picture']
senticnet['hypnotic_effect'] = ['0.354', '-0.23', '-0.05', '0', '#joy', '#surprise', 'positive', '0.178', 'happiness', 'enjoy_show', 'deafness', 'listen_classical_music', 'applaud']
senticnet['hypnotically'] = ['0', '0', '0', '-0.80', '#disgust', '#disgust', 'negative', '-0.80', 'uneducated', 'homophobic', 'guilty_crime', 'weird', 'strange']
senticnet['hypnotism'] = ['0', '0.871', '0', '0.861', '#interest', '#admiration', 'positive', '0.866', 'predict_future', 'psychic_phenomenon', 'parapsychology', 'clairvoyance', 'foreknowledge']
senticnet['hypnotizability'] = ['0.854', '-0.79', '0', '0.977', '#joy', '#admiration', 'positive', '0.875', 'profitableness', 'auspiciousness', 'luck', 'advantageousness', 'positivity']
senticnet['hypoalbuminemia'] = ['-0.54', '0', '0.92', '-0.84', '#anger', '#disgust', 'negative', '-0.76', 'dearth', 'demand', 'mineral_deficiency', 'famine', 'need']
senticnet['hypoblast'] = ['0', '0.067', '0', '0.07', '#interest', '#admiration', 'positive', '0.069', 'peck', 'bushel', 'chicken_egg', 'ovum', 'egg']
senticnet['hypobromous_acid'] = ['0.032', '-0.02', '-0.04', '-0.08', '#fear', '#disgust', 'negative', '-0.01', 'liquid', 'carbonate_drink', 'chemical', 'soda', 'bromide']
senticnet['hypocellular'] = ['0.905', '-0.65', '0', '0.851', '#joy', '#admiration', 'positive', '0.805', 'gonococcus', 'bacteria', 'bacteriae', 'probiotic', 'probiotic_bacterium']
senticnet['hypochlorite'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
senticnet['hypochlorous_acid'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
senticnet['hypochondria'] = ['-0.98', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.95', 'neurotic', 'workaholic', 'egomaniac', 'hypochondriac', 'worrier']
senticnet['hypochondriac'] = ['-0.97', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.95', 'neurotic', 'workaholic', 'egomaniac', 'hypochondria', 'worrier']
senticnet['hypocomplementemia'] = ['-0.85', '0', '0.657', '-0.86', '#sadness', '#disgust', 'negative', '-0.79', 'pulseless_disease', 'temporal_arteritis', 'rubor', 'redness', 'arteritis']
senticnet['hypocrisy'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'pretense', 'feigning', 'dissembling', 'pretence', 'crocodile_tear']
senticnet['hypocrite'] = ['-0.71', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.79', 'plaster_saint', 'humbug', 'pretender', 'phoney', 'imposter']
senticnet['hypocritical'] = ['-0.52', '0', '0.651', '-0.54', '#anger', '#disgust', 'negative', '-0.57', 'feigned', 'dissimulative', 'specious', 'insincere', 'fulsome']
senticnet['hypocritically'] = ['0.796', '0', '0', '0.912', '#joy', '#admiration', 'positive', '0.854', 'self-righteously', 'arrogantly', 'sanctimoniously', 'pompously', 'piously']
senticnet['hypodermic'] = ['-0.02', '0', '-0.12', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'x-ray_machine', 'medical_record', 'latex_glove', 'medicine', 'doctor_examination_room']
senticnet['hypodermic_needle'] = ['0', '-0.01', '-0.07', '-0.18', '#fear', '#disgust', 'negative', '-0.08', 'syringe', 'latex_glove', 'medical_record', 'x-ray_machine', 'doctor_examination_room']
senticnet['hypodermic_syringe'] = ['-0.02', '0', '-0.12', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'x-ray_machine', 'medical_record', 'latex_glove', 'medicine', 'doctor_examination_room']
senticnet['hypoesthesia'] = ['-0.58', '0', '0.773', '-0.79', '#anger', '#disgust', 'negative', '-0.71', 'perspiration', 'sweating', 'polyhidrosis', 'sudation', 'hidrosis']
senticnet['hypoestrogenism'] = ['-0.73', '0', '0.555', '-0.90', '#sadness', '#disgust', 'negative', '-0.73', 'aching', 'hurting', 'labor_pain', 'neuralgia', 'neuralgy']
senticnet['hypoglossal_nerve'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['hypogonadotropic_hypogonadism'] = ['0.905', '-0.65', '0', '0.851', '#joy', '#admiration', 'positive', '0.805', 'gonococcus', 'bacteria', 'bacteriae', 'probiotic', 'probiotic_bacterium']
senticnet['hypokinesia'] = ['0.722', '-0.87', '0', '0.696', '#joy', '#surprise', 'positive', '0.763', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['hypomania'] = ['-0.78', '0', '0.918', '-0.96', '#anger', '#disgust', 'negative', '-0.88', 'major_depressive_episode', 'depression', 'depressive_disorder', 'clinical_depression', 'involutional_depression']
senticnet['hypomanic'] = ['-0.78', '0', '0.918', '-0.96', '#anger', '#disgust', 'negative', '-0.88', 'major_depressive_episode', 'depression', 'depressive_disorder', 'clinical_depression', 'involutional_depression']
senticnet['hyponym'] = ['0.904', '-0.58', '0', '0.736', '#joy', '#admiration', 'positive', '0.743', 'naming', 'appellative', 'denotative', 'extensional', 'referent']
senticnet['hypooxyacid'] = ['-0.81', '0', '0.773', '-0.78', '#sadness', '#disgust', 'negative', '-0.79', 'acid', 'hypophosphoric_acid', 'periodic_acid', 'phosphorous_acid', 'oxyacid']
senticnet['hypophosphatasia'] = ['-0.87', '0', '0.531', '-0.68', '#sadness', '#disgust', 'negative', '-0.69', 'rubor', 'redness', 'dermatomyositis', 'myositis', 'inflammation']
senticnet['hypophosphatemia'] = ['-0.54', '0', '0.92', '-0.84', '#anger', '#disgust', 'negative', '-0.76', 'dearth', 'demand', 'mineral_deficiency', 'famine', 'need']
senticnet['hypophosphoric_acid'] = ['-0.81', '0', '0.774', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'acid', 'oxyacid', 'periodic_acid', 'hypophosphorous_acid', 'phosphorous_acid']
senticnet['hypophosphorous_acid'] = ['-0.81', '0', '0.774', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'acid', 'hypophosphoric_acid', 'periodic_acid', 'oxyacid', 'phosphorous_acid']
senticnet['hypopigmentation'] = ['-0.91', '0', '0.961', '-0.55', '#sadness', '#anger', 'negative', '-0.81', 'albinism', 'birth_defect', 'congenital_anomaly', 'congenital_defect', 'congenital_disorder']
senticnet['hypoplastic'] = ['0.905', '-0.65', '0', '0.851', '#joy', '#admiration', 'positive', '0.805', 'gonococcus', 'bacteria', 'bacteriae', 'probiotic', 'probiotic_bacterium']
senticnet['hypopnea'] = ['-0.03', '-0.03', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'eat_poison', 'commit_suicide', 'drug_addiction', 'drunk_drive', 'jump_off_cliff']
senticnet['hypopyon'] = ['-0.78', '0', '0.855', '-0.99', '#anger', '#disgust', 'negative', '-0.87', 'blood_blister', 'pustule', 'water_blister', 'vesicle', 'cyst']
senticnet['hyporeflexia'] = ['-0.78', '0', '0.764', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'withering', 'downfall', 'weakening', 'fading', 'attenuation']
senticnet['hypostasis'] = ['0.645', '-0.76', '0', '0.961', '#surprise', '#admiration', 'positive', '0.791', 'content', 'quintessence', 'stuff', 'bare_bone', 'essence']
senticnet['hypotensive'] = ['-0.56', '0', '0.577', '-0.61', '#anger', '#disgust', 'negative', '-0.58', 'foggy', 'groggy', 'logy', 'lethargic', 'dazed']
senticnet['hypotenuse'] = ['0.763', '-0.61', '0', '0.777', '#joy', '#admiration', 'positive', '0.717', 'acute_triangle', 'wedge_shape', 'cuneus', 'wedge', 'oblique_triangle']
senticnet['hypothalamic'] = ['0.672', '-0.55', '0', '0.992', '#joy', '#admiration', 'positive', '0.739', 'pressor', 'hormone', 'adrenalin', 'internal_secretion', 'catecholamine']
senticnet['hypothalamic_pituitary'] = ['0.672', '-0.55', '0', '0.992', '#joy', '#admiration', 'positive', '0.739', 'pressor', 'hormone', 'adrenalin', 'internal_secretion', 'catecholamine']
senticnet['hypothecate'] = ['-0.95', '0', '0.822', '-0.88', '#sadness', '#disgust', 'negative', '-0.88', 'formulate', 'speculate', 'conjecture', 'mature', 'suppose']
senticnet['hypothermia'] = ['-0.96', '0', '0.172', '-0.99', '#sadness', '#disgust', 'negative', '-0.71', 'severe_cold', 'cold', 'cold_winter', 'possibility_to_die', 'freeze']
senticnet['hypothesis'] = ['-0.04', '0.127', '-0.06', '0.137', '#interest', '#admiration', 'positive', '0.04', 'experiment', 'theory', 'supposition', 'prove_theory', 'test_hypothesis']
senticnet['hypothesize'] = ['0.615', '-0.89', '0', '0.714', '#surprise', '#admiration', 'positive', '0.743', 'reconstruct', 'retrace', 'explicate', 'expect', 'formulate']
senticnet['hypothetical'] = ['-0.06', '0.247', '-0.11', '0.217', '#interest', '#admiration', 'positive', '0.072', 'theory', 'supposition', 'hypothesis', 'scientific_statement', 'conjecture']
senticnet['hypothetical_creature'] = ['-0.65', '0', '0.859', '-0.63', '#sadness', '#anger', 'negative', '-0.71', 'alien', 'extraterrestrial_being', 'extraterrestrial', 'imaginary_being', 'imaginary_creature']
senticnet['hypothetical_creature_being'] = ['-0.66', '0', '0.851', '-0.64', '#sadness', '#anger', 'negative', '-0.71', 'alien', 'extraterrestrial', 'hypothetical_creature', 'imaginary_being', 'imaginary_creature']
senticnet['hypothetical_imperative'] = ['0.841', '-0.65', '0', '0.645', '#joy', '#surprise', 'positive', '0.714', 'moral_principle', 'rule', 'caveat_emptor', 'higher_law', 'precept']
senticnet['hypothetical_question'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquirer', 'enquirer', 'inquisitor', 'querier', 'asker']
senticnet['hypothyroidism'] = ['-0.98', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.91', 'thyroid', 'adrenal', 'hormone', 'gland', 'hyperthyroidism']
senticnet['hypotonic'] = ['-0.57', '0', '0.767', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'verboseness', 'verbosity', 'prolixity', 'turgidness', 'flatulence']
senticnet['hypotonic_solution'] = ['-0.57', '0', '0.767', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'verboseness', 'verbosity', 'prolixity', 'turgidness', 'flatulence']
senticnet['hypotonicity'] = ['-0.62', '0', '0.901', '-0.71', '#anger', '#disgust', 'negative', '-0.74', 'pressure', 'hypertonicity', 'osmotic_pressure', 'pressure_level', 'force']
senticnet['hypoventilation'] = ['-0.03', '-0.03', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'eat_poison', 'commit_suicide', 'drug_addiction', 'drunk_drive', 'jump_off_cliff']
senticnet['hypovitaminosis'] = ['-0.71', '0', '0.74', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'kwashiorkor', 'marasmus', 'avitaminosis', 'malnutrition', 'deficiency_disease']
senticnet['hypoxaemia'] = ['-0.03', '-0.03', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'eat_poison', 'commit_suicide', 'drug_addiction', 'drunk_drive', 'jump_off_cliff']
senticnet['hypoxemia'] = ['-0.03', '-0.03', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'eat_poison', 'commit_suicide', 'drug_addiction', 'drunk_drive', 'jump_off_cliff']
senticnet['hypoxemic'] = ['-0.03', '-0.03', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'eat_poison', 'commit_suicide', 'drug_addiction', 'drunk_drive', 'jump_off_cliff']
senticnet['hypoxic_zone'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'harpoon', 'armory', 'lake', 'river', 'bay']
senticnet['hypoxylon_canker'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['hyrule_castle'] = ['0.086', '-0.10', '0.064', '0', '#joy', '#surprise', 'positive', '0.043', 'art_piece', 'lifetime', 'museum', 'vintage', 'collectible']
senticnet['hysterectomy'] = ['0.97', '0.729', '0', '0.946', '#joy', '#admiration', 'positive', '0.882', 'make_baby', 'give_birth_child', 'sexual_intercourse', 'find_partner', 'make_love']
senticnet['hysteresis'] = ['0.727', '0.733', '0', '0', '#joy', '#interest', 'positive', '0.73', 'undershoot', 'overshooting', 'overshoot', 'deceleration', 'overshoot']
senticnet['hysteresis_loop'] = ['-0.97', '0', '0.831', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'skewness', 'irregularity']
senticnet['hysteretic'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'constancy', 'monotony', 'invariance', 'changelessness', 'unchangeability']
senticnet['hysteria'] = ['-0.85', '0', '-0.89', '0', '#sadness', '#fear', 'negative', '-0.87', 'anxious', 'fearful', 'panicked', 'panic-stricken', 'panic-struck']
senticnet['hysteric'] = ['-0.70', '0', '0.624', '-0.65', '#sadness', '#disgust', 'negative', '-0.66', 'sick_person', 'diseased_person', 'sufferer', 'claustrophobe', 'wildness']
senticnet['hysterical'] = ['-0.91', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.81', 'hysterically', 'dire', 'direful', 'dreaded', 'fearsome']
senticnet['hysterical_laughter'] = ['0', '0', '-0.80', '0', '#fear', '#fear', 'negative', '-0.80', 'shout', 'yell', 'annoy', 'groan', 'cry_help']
senticnet['hysterical_neurosis'] = ['-0.55', '0', '0.736', '-0.71', '#anger', '#disgust', 'negative', '-0.66', 'neurosis', 'neuroticism', 'psychoneurosis', 'hysterocatalepsy', 'anxiety_hysteria']
senticnet['hysterically'] = ['-0.90', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.84', 'hysterical', 'dire', 'direful', 'dreaded', 'fearsome']
senticnet['hystero_epilepsy'] = ['-0.55', '0', '0.736', '-0.71', '#anger', '#disgust', 'negative', '-0.66', 'neurosis', 'neuroticism', 'psychoneurosis', 'hysterocatalepsy', 'anxiety_hysteria']
senticnet['hysterocatalepsy'] = ['-0.55', '0', '0.736', '-0.71', '#anger', '#disgust', 'negative', '-0.66', 'neurosis', 'neuroticism', 'psychoneurosis', 'hysterical_neurosis', 'anxiety_hysteria']
senticnet['hysteron_proteron'] = ['0.623', '-0.53', '0', '0.873', '#joy', '#admiration', 'positive', '0.676', 'petitio_principii', 'petitio', 'logical_fallacy', 'fallacy', 'false_belief']
senticnet['hysteroscope'] = ['0.255', '0.182', '-0.06', '0.314', '#joy', '#admiration', 'positive', '0.173', 'doctor_office', 'urethroscope', 'sigmoidoscope', 'hospital', 'check_health']
senticnet['hysteroscopic'] = ['-0.70', '0', '0.639', '-0.56', '#sadness', '#anger', 'negative', '-0.63', 'fibroma', 'adenoma', 'chondroma', 'enchondroma', 'benign_tumor']
senticnet['hysteroscopy'] = ['0.255', '0.182', '-0.06', '0.314', '#joy', '#admiration', 'positive', '0.173', 'doctor_office', 'urethroscope', 'sigmoidoscope', 'hospital', 'check_health']
senticnet['hytelnet'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'busy_bee', 'actor', 'doer', 'worker']
senticnet['i-beam'] = ['0.779', '0.809', '0', '0', '#joy', '#interest', 'positive', '0.794', 'cork', 'rod', 'nest_table', 'wand', 'beam']
senticnet['iaasb'] = ['-0.12', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.09', 'understand', 'teach_others', 'trivia', 'notify', 'seek_understanding']
senticnet['iac_share_rose'] = ['-0.73', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'unwed', 'unwedded', 'divorced', 'unmarried', 'widowed']
senticnet['iad_ag'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['iad_air'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['iaido'] = ['-0.13', '-0.07', '0.05', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'try_best', 'loose', 'try_hard', 'strategize', 'sore_knee']
senticnet['iambic_verse'] = ['-0.81', '0', '0.52', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'affray', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['ianfu'] = ['-0.91', '0', '0.974', '-0.61', '#sadness', '#anger', 'negative', '-0.83', 'slattern', 'camp_follower', 'call_girl', 'comfort_woman', 'bawd']
senticnet['iatse'] = ['0.967', '-0.98', '0', '0.64', '#joy', '#surprise', 'positive', '0.863', 'club', 'social_club', 'society', 'jockey_club', 'gild']
senticnet['iatse_local'] = ['0.967', '-0.98', '0', '0.64', '#joy', '#surprise', 'positive', '0.863', 'club', 'social_club', 'society', 'jockey_club', 'gild']
senticnet['ib_learner'] = ['0', '0.189', '0.04', '0.091', '#interest', '#admiration', 'positive', '0.08', 'combination', 'margin', 'teller', 'merge', 'finite']
senticnet['ibdv'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'leptomeningitis', 'cerebrospinal_meningitis', 'meningitis', 'brain_fever', 'cerebrospinal_fever']
senticnet['ibex'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'pasang', 'markhor', 'goat', 'wild_goat']
senticnet['ibf_champion'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['ibiraci'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'head_hurt', 'introspection', 'lifestyle_change', 'headache', 'move_eye']
senticnet['ibis'] = ['0.797', '-0.71', '0', '0.736', '#joy', '#admiration', 'positive', '0.751', 'wood_ibis', 'wood_stork', 'sacred_ibis', 'wading_bird', 'wader']
senticnet['ibm_mainframe'] = ['0', '-0.99', '0', '0.862', '#surprise', '#admiration', 'positive', '0.928', 'helpful_tool', 'woodworking_tool', 'helpful', 'type_of_fruit', 'hand_tool']
senticnet['ibm_selectric'] = ['0', '0', '0.128', '0', '#anger', '#anger', 'negative', '-0.12', 'grid', 'efficient', 'calculator', 'computer', 'personal_computer']
senticnet['ibm_selectric_typewriter'] = ['0', '0', '0.128', '0', '#anger', '#anger', 'negative', '-0.12', 'grid', 'efficient', 'calculator', 'computer', 'personal_computer']
senticnet['ibm_thinkpad'] = ['0.113', '0.073', '0.028', '0.107', '#joy', '#admiration', 'positive', '0.066', 'pda', 'telephone_computer', 'browser', 'number_pad', 'modem']
senticnet['ibm_typewriter'] = ['0', '0', '0.128', '0', '#anger', '#anger', 'negative', '-0.12', 'grid', 'efficient', 'calculator', 'computer', 'personal_computer']
senticnet['ibn_awf'] = ['-0.84', '0', '0.579', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'squiggle', 'hand', 'chicken_scratch', 'handwriting', 'script']
senticnet['ibn_hawqal'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['ibn_khuwaylid'] = ['-0.84', '0', '0.579', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'squiggle', 'hand', 'chicken_scratch', 'handwriting', 'script']
senticnet['ibogaine'] = ['-0.73', '0', '0.944', '-0.94', '#anger', '#disgust', 'negative', '-0.87', 'lysergic_acid_diethylamide', 'mescaline', 'peyote', 'phencyclidine', 'drug']
senticnet['ibsf'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'wildcat', 'informal', 'loose', 'drumhead']
senticnet['ibuprofen'] = ['-0.04', '-0.01', '-0.09', '-0.20', '#fear', '#disgust', 'negative', '-0.08', 'analgesic', 'palliative', 'sleep_drug', 'phenobarbital', 'medicine']
senticnet['icc_jurisdiction'] = ['0', '0', '0', '0.816', '#admiration', '#admiration', 'positive', '0.816', 'resistance', 'protection', 'untouchable', 'invulnerable', 'sacrosanct']
senticnet['ice'] = ['-0.93', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.88', 'cold_weather', 'snow', 'arctic', 'cold', 'without_human_habitation']
senticnet['ice-cold'] = ['-0.77', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.85', 'icy-cold', 'chilled', 'phosphate-buffered', 'margarita', 'sipping']
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senticnet['ice_floe'] = ['0.912', '0', '0', '0.723', '#joy', '#admiration', 'positive', '0.818', 'floe', 'measurement', 'researcher', 'climate', 'instrument']
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senticnet['ice_hockey_rink'] = ['0.02', '0.244', '0', '0.225', '#interest', '#admiration', 'positive', '0.163', 'game', 'disneyland', 'ballpark', 'hockey_game', 'sport_event']
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senticnet['ice_skating'] = ['0.05', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.046', 'skate', 'rollerblade', 'skateboard', 'ice_skate', 'skating']
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senticnet['ichor'] = ['-0.52', '0', '0.538', '-0.95', '#anger', '#disgust', 'negative', '-0.67', 'sanies', 'pus', 'purulence', 'suppuration', 'gleet']
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senticnet['iciness'] = ['-0.65', '0', '0.814', '-0.79', '#anger', '#disgust', 'negative', '-0.75', 'cold', 'low_temperature', 'frigidity', 'frigidness', 'coldness']
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senticnet['ickleton'] = ['0.049', '0.16', '0', '0.155', '#interest', '#admiration', 'positive', '0.121', 'castle', 'palace', 'medieval', 'aristocrat', 'throne']
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senticnet['icmp_error_message'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'identifiable', 'specifiable', 'recognizable', 'diagnosable', 'classifiable']
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senticnet['icy_surface'] = ['0.884', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.919', 'ufo', 'milky', 'space_junk', 'galaxy', 'solar_system']
senticnet['icy_water'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'lifebelt', 'save_life', 'lifeguard', 'life_jacket', 'safety_equipment']
senticnet['icy_wind'] = ['0.981', '0.891', '0', '0', '#joy', '#interest', 'positive', '0.936', 'bitterness', 'nip', 'bite', 'sting', 'sharpness']
senticnet['icy_winter'] = ['-0.56', '0', '0.578', '-0.74', '#anger', '#disgust', 'negative', '-0.62', 'cold', 'unloving', 'loveless', 'detached', 'unaffectionate']
senticnet['id_badge'] = ['0.739', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.814', 'receipt', 'bet', 'electric_bill', 'letter_slot', 'deal']
senticnet['id_card'] = ['0.739', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.814', 'receipt', 'bet', 'electric_bill', 'letter_slot', 'deal']
senticnet['ide_integration'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'carbon_fiber']
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senticnet['ideacarbon'] = ['0.053', '0.264', '-0.11', '0.23', '#interest', '#admiration', 'positive', '0.107', 'natural_resource', 'petroleum', 'fossil_fuel', 'energy_source', 'potential_energy']
senticnet['ideal'] = ['0.069', '0.21', '-0.06', '0.285', '#interest', '#admiration', 'positive', '0.124', 'record', 'protect_house', 'strive', 'store_record', 'perfect']
senticnet['ideal_candidate'] = ['0.749', '0.724', '0', '0', '#joy', '#interest', 'positive', '0.736', 'mouth_noise', 'vocal_cord', 'vocalization', 'vocal_sound', 'loud_soft']
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senticnet['ideal_venue'] = ['0.53', '-0.64', '0', '0.76', '#surprise', '#admiration', 'positive', '0.646', 'focus', 'get_focused', 'focusing', 'centering', 'particularism']
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senticnet['ideate'] = ['-0.65', '0', '0.792', '-0.61', '#sadness', '#anger', 'negative', '-0.68', 'fancy', 'foresee', 'fantasy', 'daydream', 'woolgather']
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senticnet['ilex_aquifolium'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'bottlebrush_buckeye', 'red_buckeye', 'particolored_buckeye']
senticnet['ilex_opaca'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'horse_chestnut', 'red_buckeye', 'particolored_buckeye']
senticnet['iliad'] = ['-0.71', '0', '0.885', '0', '#sadness', '#anger', 'negative', '-0.79', 'tragic', 'tragically', 'tragedienne', 'tragedy', 'sadness']
senticnet['ilikai_hotel'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'drifter', 'bum', 'sundowner']
senticnet['ilk'] = ['0', '0', '0', '-0.71', '#disgust', '#disgust', 'negative', '-0.71', 'despise', 'disgust', 'animosity', 'brood', 'maleficence']
senticnet['ill'] = ['-0.89', '0', '-0.99', '-0.75', '#sadness', '#fear', 'negative', '-0.87', 'sickness', 'polio', 'malady', 'illness', 'ailment']
senticnet['ill-fated'] = ['0', '0', '-0.90', '-0.94', '#fear', '#disgust', 'negative', '-0.92', 'luckless', 'ill-omened', 'ill-starred', 'doomed', 'unlucky']
senticnet['ill-fitting'] = ['0', '-0.98', '0', '-0.83', '#surprise', '#disgust', 'positive', '0.078', 'too-tight', 'worn-out', 'too-small', 'well-fitting', 'ratty']
senticnet['ill-gotten'] = ['-0.95', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.91', 'improperly', 'properly', 'improper', 'awry', 'abuse']
senticnet['ill-informed'] = ['-0.87', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.86', 'imbecilic', 'tone-deaf', 'dimwitted', 'close-minded', 'ham-fisted']
senticnet['ill-mannered'] = ['0', '0', '0.865', '-0.78', '#anger', '#disgust', 'negative', '-0.82', 'ungracious', 'ungentlemanly', 'unmannerly', 'loutish', 'discourteous']
senticnet['ill-omened'] = ['0', '0', '-0.90', '-0.93', '#fear', '#disgust', 'negative', '-0.92', 'luckless', 'unlucky', 'ill-starred', 'doomed', 'ill-fated']
senticnet['ill-starred'] = ['0', '0', '-0.91', '-0.94', '#fear', '#disgust', 'negative', '-0.92', 'luckless', 'ill-omened', 'unlucky', 'doomed', 'ill-fated']
senticnet['ill-timed'] = ['0', '0', '-0.70', '-0.91', '#fear', '#disgust', 'negative', '-0.81', 'wrong', 'prematurely', 'unseasonable', 'premature', 'untimely']
senticnet['ill_feeling'] = ['-0.12', '0.193', '0.593', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'distaste', 'aversion', 'disapproval', 'disapprove', 'disapproving']
senticnet['ill_fortune'] = ['-0.43', '-0.14', '-0.06', '-0.33', '#sadness', '#disgust', 'negative', '-0.17', 'brain_tumor', 'lyme_disease', 'cause_suffer', 'lose_team', 'bad_hair_day']
senticnet['ill_health'] = ['-0.85', '0', '-0.82', '-0.86', '#sadness', '#disgust', 'negative', '-0.85', 'terminal_illness', 'physical_pain', 'schizophrenia', 'influenza', 'poor_health']
senticnet['ill_humor'] = ['-0.58', '0', '0.944', '-0.58', '#sadness', '#anger', 'negative', '-0.70', 'crossness', 'fretfulness', 'peevishness', 'petulance', 'moodiness']
senticnet['ill_luck'] = ['-0.69', '0', '0.957', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'hard_cheese', 'weakness', 'gutter', 'adversity', 'hardship']
senticnet['ill_nature'] = ['-0.95', '0', '0.694', '-0.94', '#sadness', '#disgust', 'negative', '-0.86', 'sullenness', 'moroseness', 'crabbiness', 'crabbedness', 'crankiness']
senticnet['ill_person'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'ill', 'avoid_food', 'sickness', 'sick', 'fast_day']
senticnet['ill_repute'] = ['0.754', '-0.82', '0', '0.947', '#surprise', '#admiration', 'positive', '0.841', 'whorehouse', 'brothel', 'bordello', 'massage_parlor', 'house_of_prostitution']
senticnet['ill_temper'] = ['-0.13', '0.151', '0.613', '-0.62', '#anger', '#disgust', 'negative', '-0.30', 'bad_temper', 'umbrage', 'offense', 'outraged', 'indignant']
senticnet['ill_treatment'] = ['-0.83', '0', '0.924', '-0.79', '#sadness', '#anger', 'negative', '-0.85', 'kick_in_butt', 'licking', 'whacking', 'stoning', 'lapidation']
senticnet['ill_wisher'] = ['-0.89', '0.161', '-0.61', '0.053', '#sadness', '#fear', 'negative', '-0.32', 'heartrending', 'heartbreaking', 'grievous', 'grievously', 'brokenheartedness']
senticnet['illegal'] = ['-0.19', '0.14', '-0.28', '0.141', '#sadness', '#fear', 'negative', '-0.04', 'violent', 'inherit', 'dangerous', 'bad_health', 'tricky']
senticnet['illegal_activity'] = ['0', '0', '0.968', '-0.73', '#anger', '#disgust', 'negative', '-0.84', 'blackmail', 'sell_out', 'go_bankrupt', 'deserve', 'penalty']
senticnet['illegal_deforestation'] = ['0', '-0.02', '0.085', '-0.09', '#anger', '#disgust', 'negative', '-0.05', 'tree_wood', 'forest', 'sideways', 'dead_tree', 'wood']
senticnet['illegal_detention'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicative', 'unpronounceable', 'impassive', 'inexpressive', 'silent']
senticnet['illegal_drug'] = ['0', '-0.00', '-0.08', '-0.10', '#fear', '#disgust', 'negative', '-0.06', 'drug', 'medication', 'narcotic', 'illegal', 'cook_grill']
senticnet['illegal_firearm'] = ['-0.84', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.86', 'handgun', 'rifle', 'weapon', 'pistol', 'gun']
senticnet['illegal_occupation'] = ['-0.72', '0', '0.742', '-0.80', '#anger', '#disgust', 'negative', '-0.75', 'illicitness', 'unlawfulness', 'illegitimacy', 'shadiness', 'quality']
senticnet['illegal_per_se'] = ['0.658', '-0.52', '0', '0.626', '#joy', '#admiration', 'positive', '0.602', 'point_in_time', 'threshold', 'birth', 'commencement', 'incipience']
senticnet['illegality'] = ['-0.72', '0', '0.742', '-0.80', '#anger', '#disgust', 'negative', '-0.75', 'illicitness', 'unlawfulness', 'illegitimacy', 'shadiness', 'quality']
senticnet['illegitimacy'] = ['-0.72', '0', '0.742', '-0.80', '#anger', '#disgust', 'negative', '-0.75', 'illicitness', 'illegality', 'unlawfulness', 'shadiness', 'quality']
senticnet['illegitimate'] = ['0', '0', '-0.76', '-0.94', '#fear', '#disgust', 'negative', '-0.85', 'sinister', 'inauspicious', 'perverse', 'shield', 'grim']
senticnet['illegitimate_birth'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'spousal_relationship', 'bigamy']
senticnet['illegitimate_child'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'spousal_relationship', 'bigamy']
senticnet['illeness'] = ['-0.98', '0', '-0.73', '0', '#sadness', '#fear', 'negative', '-0.86', 'medicine', 'get_cured', 'kill_specific_group', 'cure', 'avoid_pain']
senticnet['illesteva'] = ['0', '-0.03', '0.022', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'shoulder_bag', 'satchel', 'water_bottle', 'tote_bag', 'carry']
senticnet['illiberal'] = ['-0.12', '0.193', '0.597', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'unfriendly', 'unfriendliness', 'disapproval', 'disapprove', 'disapproving']
senticnet['illiberality'] = ['0', '0', '0.914', '-0.75', '#anger', '#disgust', 'negative', '-0.83', 'garrulousness', 'avariciousness', 're-localization', 'over-specialization', 'over-zealousness']
senticnet['illicit'] = ['0', '0', '-0.84', '-0.97', '#fear', '#disgust', 'negative', '-0.91', 'illegitimate', 'unconventional', 'unlawful', 'wrongful', 'improper']
senticnet['illicit_activity'] = ['-0.72', '0', '0.742', '-0.80', '#anger', '#disgust', 'negative', '-0.75', 'illicitness', 'unlawfulness', 'illegitimacy', 'shadiness', 'quality']
senticnet['illicit_drug'] = ['-0.63', '0', '0.819', '-0.54', '#sadness', '#anger', 'negative', '-0.66', 'smack', 'hell_dust', 'nose_drop', 'thunder', 'skag']
senticnet['illicit_heroin'] = ['-0.63', '0', '0.819', '-0.54', '#sadness', '#anger', 'negative', '-0.66', 'smack', 'hell_dust', 'nose_drop', 'thunder', 'skag']
senticnet['illicit_intercourse'] = ['-0.81', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.79', 'stifle', 'forgotten', 'old', 'touch_ground', 'seldom']
senticnet['illicit_opium'] = ['-0.63', '0', '0.819', '-0.54', '#sadness', '#anger', 'negative', '-0.66', 'smack', 'hell_dust', 'nose_drop', 'thunder', 'skag']
senticnet['illicit_sex'] = ['-0.74', '0', '0.677', '-0.77', '#sadness', '#disgust', 'negative', '-0.73', 'whore', 'jazz', 'hump', 'bonk', 'screw']
senticnet['illicit_substance'] = ['-0.72', '0', '0.71', '-0.9', '#sadness', '#disgust', 'negative', '-0.77', 'perniciousness', 'unwholesomeness', 'toxicity', 'noisomeness', 'noxiousness']
senticnet['illicitly'] = ['0', '0', '0.966', '-0.82', '#anger', '#disgust', 'negative', '-0.89', 'inappropriately', 'jackass', 'political', 'unfitly', 'inappropriate']
senticnet['illicitness'] = ['-0.72', '0', '0.742', '-0.80', '#anger', '#disgust', 'negative', '-0.75', 'unlawfulness', 'illegality', 'illegitimacy', 'shadiness', 'quality']
senticnet['illidan'] = ['-0.67', '0', '0.546', '-0.63', '#sadness', '#disgust', 'negative', '-0.61', 'fink', 'snitch', 'snitcher', 'sneak', 'stoolpigeon']
senticnet['illimitable'] = ['-0.80', '0', '0.761', '-0.70', '#sadness', '#anger', 'negative', '-0.75', 'immeasurable', 'limitless', 'measureless', 'unmeasured', 'abysmal']
senticnet['illiquidity'] = ['0', '0', '0', '-0.70', '#disgust', '#disgust', 'negative', '-0.70', 'uncertain_future', 'uncertain_environment', 'insecure', 'blur', 'anxiety']
senticnet['illiteracy'] = ['-0.89', '0', '-0.82', '-0.73', '#sadness', '#fear', 'negative', '-0.81', 'bad_karma', 'bad_food', 'abduction', 'ignorance', 'loathing']
senticnet['illiteracy_rate'] = ['-0.90', '0', '0.76', '-0.87', '#sadness', '#disgust', 'negative', '-0.84', 'analphabet', 'analphabetic', 'functional_illiterate', 'ignoramus', 'know_nothing']
senticnet['illiterate'] = ['-0.90', '0', '0.76', '-0.87', '#sadness', '#disgust', 'negative', '-0.84', 'analphabet', 'analphabetic', 'functional_illiterate', 'ignoramus', 'know_nothing']
senticnet['illiterate_person'] = ['-0.56', '0', '0.969', '-0.65', '#anger', '#disgust', 'negative', '-0.72', 'analphabet', 'analphabetic', 'functional_illiterate', 'ignoramus', 'know_nothing']
senticnet['illiterateic'] = ['-0.90', '0', '0.759', '-0.87', '#sadness', '#disgust', 'negative', '-0.84', 'illiterate', 'analphabet', 'functional_illiterate', 'ignoramus', 'know_nothing']
senticnet['illness'] = ['-0.09', '0', '-0.16', '-0.21', '#fear', '#disgust', 'negative', '-0.15', 'sickness', 'malady', 'polio', 'disease', 'ailment']
senticnet['illocution'] = ['0.904', '-0.58', '0', '0.736', '#joy', '#admiration', 'positive', '0.743', 'naming', 'appellative', 'denotative', 'extensional', 'referent']
senticnet['illocutionary'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'mediacy', 'mediateness', 'characteristic', 'allusiveness']
senticnet['illocutionary_act'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'mediacy', 'mediateness', 'characteristic', 'allusiveness']
senticnet['illocutionary_force'] = ['0.904', '-0.58', '0', '0.736', '#joy', '#admiration', 'positive', '0.743', 'naming', 'appellative', 'denotative', 'extensional', 'referent']
senticnet['illocutionary_intention'] = ['0.633', '-0.67', '0', '0.776', '#surprise', '#admiration', 'positive', '0.693', 'double_entendre', 'loophole', 'ambiguity', 'amphiboly', 'parisology']
senticnet['illogic'] = ['-0.55', '0', '0.741', '-0.76', '#anger', '#disgust', 'negative', '-0.68', 'invalidityality', 'invalidityalness', 'invalidity', 'inconsequence', 'fallaciousness']
senticnet['illogical'] = ['-0.84', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'absurd', 'inconsequential', 'visceral', 'nonrational', 'groundless']
senticnet['illogicality'] = ['-0.55', '0', '0.741', '-0.76', '#anger', '#disgust', 'negative', '-0.68', 'invalidity', 'illogicalness', 'illogic', 'inconsequence', 'fallaciousness']
senticnet['illogicalness'] = ['-0.55', '0', '0.741', '-0.76', '#anger', '#disgust', 'negative', '-0.68', 'illogicality', 'invalidity', 'illogic', 'inconsequence', 'fallaciousness']
senticnet['illud'] = ['0', '0.079', '0', '0', '#interest', '#interest', 'positive', '0.079', 'change_diaper', 'lower_standard_live', 'become_pregnant', 'find_mate', 'make_baby']
senticnet['illuminance'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'incandescence', 'luminescence', 'brightness_level']
senticnet['illuminant'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'incandescence', 'luminescence', 'brightness_level']
senticnet['illuminate'] = ['0', '0.83', '0', '0.847', '#interest', '#admiration', 'positive', '0.839', 'enhance', 'shed_light_on', 'light_up', 'elucidate', 'clear']
senticnet['illuminate_room'] = ['0', '0', '-0.11', '0', '#fear', '#fear', 'negative', '-0.11', 'light', 'illumination', 'hot_sun', 'ultraviolet', 'provide_light']
senticnet['illuminated'] = ['0.945', '0', '0', '0.703', '#joy', '#admiration', 'positive', '0.824', 'bright', 'luminous', 'luminously', 'deneb', 'lambent']
senticnet['illumination'] = ['0', '0', '-0.11', '0', '#fear', '#fear', 'negative', '-0.11', 'light', 'illuminate_room', 'hot_sun', 'ultraviolet', 'provide_light']
senticnet['illumination_intensity'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'incandescence', 'luminescence', 'brightness_level']
senticnet['illuminative'] = ['0', '0.918', '0', '0.958', '#interest', '#admiration', 'positive', '0.938', 'marry', 'get_married', 'love', 'join', 'hook_up_with']
senticnet['illumine'] = ['0.853', '-0.71', '0', '0.92', '#joy', '#admiration', 'positive', '0.831', 'find_desk', 'learn_information', 'become_educated', 'learn', 'listen_teacher']
senticnet['illusion'] = ['-0.88', '0', '0.589', '-0.85', '#sadness', '#disgust', 'negative', '-0.77', 'phantom', 'shadow', 'disbelief', 'dark_magic', 'fool']
senticnet['illusional'] = ['-0.67', '0', '0.716', '-0.89', '#anger', '#disgust', 'negative', '-0.76', 'imaginary', 'notional', 'fabled', 'hallucinatory', 'unreal']
senticnet['illusionary'] = ['0', '0', '-0.72', '-0.97', '#fear', '#disgust', 'negative', '-0.84', 'illusion', 'fallacious', 'illusory', 'imaginary', 'veridical']
senticnet['illusionism'] = ['-0.88', '0', '0.589', '-0.85', '#sadness', '#disgust', 'negative', '-0.77', 'phantom', 'shadow', 'disbelief', 'dark_magic', 'fool']
senticnet['illusionist'] = ['-0.98', '0', '0.599', '-0.93', '#sadness', '#disgust', 'negative', '-0.83', 'diviner', 'intellect', 'anticipant', 'intellectual', 'anticipator']
senticnet['illusionist_penn'] = ['0', '0.186', '0', '0.075', '#interest', '#admiration', 'positive', '0.131', 'money_inside', 'check_account', 'savings', 'money_storage', 'store_money']
senticnet['illusionistic'] = ['0.921', '-0.90', '0', '0.608', '#joy', '#surprise', 'positive', '0.811', 'overstate', 'hyperbolize', 'glorify', 'exaggerate', 'overdraw']
senticnet['illusive'] = ['0.273', '0.226', '-0.13', '0.332', '#joy', '#admiration', 'positive', '0.173', 'morality', 'cagey', 'uncommon', 'evasive', 'capitalize']
senticnet['illusory'] = ['0', '0', '-0.71', '-0.97', '#fear', '#disgust', 'negative', '-0.84', 'illusion', 'fallacious', 'illusionary', 'imaginary', 'veridical']
senticnet['illusory_nature'] = ['-0.87', '0', '0.744', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['illustrate'] = ['0.777', '-0.66', '0', '0.839', '#joy', '#admiration', 'positive', '0.76', 'elaborate', 'lucubrate', 'expatiate', 'exposit', 'enlarge']
senticnet['illustrate_point'] = ['0.966', '0.886', '0', '0', '#joy', '#interest', 'positive', '0.926', 'tell_story', 'disseminate', 'reach_tentative_agreement', 'express_information', 'explain']
senticnet['illustrated_encyclopedia'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'comic_book', 'child_book', 'child_story', 'comic', 'disney_cartoon']
senticnet['illustrated_schematically'] = ['0.854', '0.76', '0', '0', '#joy', '#interest', 'positive', '0.807', 'geometric_figure', 'polygon', 'form', 'parallelogram', 'hexagon']
senticnet['illustration'] = ['-0.02', '0.11', '0', '0.131', '#interest', '#admiration', 'positive', '0.072', 'wall_decoration', 'image', 'representation', 'artwork', 'wall_hang']
senticnet['illustrative'] = ['0', '0.135', '-0.09', '0.257', '#interest', '#admiration', 'positive', '0.101', 'description', 'example', 'show_sample', 'sample', 'teach']
senticnet['illustrious'] = ['0.793', '0.859', '0', '0', '#joy', '#interest', 'positive', '0.826', 'notable', 'far-famed', 'famous', 'famed', 'celebrated']
senticnet['illyana'] = ['-0.04', '-0.02', '0.027', '-0.03', '#sadness', '#disgust', 'negative', '-0.01', 'precious_metal', 'olympic_medal', 'shiny_metal', 'many_different_kinds', 'valuable_metal']
senticnet['ilvala'] = ['0', '0', '0.737', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'annoy', 'skin_rash', 'bother', 'irritation', 'great_dane']
senticnet['imagawa_clan'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'overlord', 'lord']
senticnet['image'] = ['0', '0.119', '-0.05', '0.151', '#interest', '#admiration', 'positive', '0.072', 'wall_decoration', 'illustration', 'wall_hang', 'artwork', 'representation']
senticnet['image_makeover'] = ['0.65', '-0.61', '0', '0.703', '#joy', '#admiration', 'positive', '0.657', 'gleaming', 'luster', 'polish', 'glow', 'effulgence']
senticnet['image_map'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'illuminate_room', 'give_light', 'light', 'light_bulb', 'lantern']
senticnet['image_processing_software'] = ['-0.84', '0', '0.689', '-0.90', '#sadness', '#disgust', 'negative', '-0.81', 'software', 'software_program', 'computer_software', 'software_system', 'authoring_language']
senticnet['image_source_getty'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expansive', 'expansible', 'erectile', 'cavernous']
senticnet['image_stabilization'] = ['0', '-0.02', '0.048', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'telescope', 'read_glass', 'eyeglass', 'binoculars', 'monocle']
senticnet['image_stabilizer'] = ['-0.82', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.84', 'block', 'obstruct', 'close', 'clogged', 'occluded']
senticnet['imagery'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'hardcore', 'declared', 'definitive', 'explicit']
senticnet['imaginary'] = ['0.075', '0.207', '-0.06', '0.12', '#interest', '#admiration', 'positive', '0.085', 'false', 'unreal', 'pretend', 'fictional', 'fictitious']
senticnet['imaginary_being'] = ['-0.65', '0', '0.859', '-0.63', '#sadness', '#anger', 'negative', '-0.71', 'alien', 'extraterrestrial_being', 'extraterrestrial', 'hypothetical_creature', 'imaginary_creature']
senticnet['imaginary_creature'] = ['-0.65', '0', '0.859', '-0.63', '#sadness', '#anger', 'negative', '-0.71', 'alien', 'extraterrestrial_being', 'extraterrestrial', 'imaginary_being', 'hypothetical_creature']
senticnet['imaginary_place'] = ['-0.92', '0', '0.687', '-0.75', '#sadness', '#disgust', 'negative', '-0.78', 'hellfire', 'red_region', 'infernal_region', 'mythical_place', 'fictitious_place']
senticnet['imagination'] = ['0', '0.264', '-0.09', '0.135', '#interest', '#admiration', 'positive', '0.103', 'imaginative', 'idea', 'mental_activity', 'new_think', 'honor_person']
senticnet['imaginative'] = ['0', '0.287', '-0.10', '0.171', '#interest', '#admiration', 'positive', '0.119', 'mental_activity', 'brain_function', 'polarize', 'new_think', 'idea']
senticnet['imaginative_literature'] = ['0.587', '-0.94', '0', '0.659', '#surprise', '#admiration', 'positive', '0.731', 'critique', 'review', 'criticism', 'textual_criticism', 'new_criticism']
senticnet['imaginative_sympathy'] = ['-0.90', '0.138', '-0.63', '0.071', '#sadness', '#fear', 'negative', '-0.33', 'woeful', 'execrable', 'deplorable', 'woebegone', 'plaintiveness']
senticnet['imaginatively'] = ['0', '0.764', '0', '0.845', '#interest', '#admiration', 'positive', '0.804', 'empathetically', 'empathic', 'empathetic', 'kind-heartedness', 'kindheartedness']
senticnet['imaginativeness'] = ['-0.95', '0', '0.872', '-0.65', '#sadness', '#anger', 'negative', '-0.82', 'fancy', 'fantasy', 'dream', 'creativity', 'creativeness']
senticnet['imagine'] = ['-0.81', '0', '0.568', '-0.83', '#sadness', '#disgust', 'negative', '-0.74', 'fancy', 'foresee', 'fantasy', 'daydream', 'woolgather']
senticnet['imaging'] = ['0', '0.802', '0', '0.794', '#interest', '#admiration', 'positive', '0.798', 'low-resolution', 'high-definition', 'full-color', 'hi-res', 'high-resolution']
senticnet['imaging_infrared_iir'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'chairperson', 'commuter']
senticnet['imaging_infrared_seeker'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'chairperson', 'commuter']
senticnet['imaging_modality'] = ['0.785', '-0.99', '0', '0.981', '#surprise', '#admiration', 'positive', '0.92', 'ear', 'absolute_pitch', 'perfect_pitch', 'audition', 'sense_audition']
senticnet['imaging_spectrometer'] = ['0', '0.825', '0', '0', '#interest', '#interest', 'positive', '0.825', 'query', 'interrogation', 'inquire', 'response', 'solution']
senticnet['imagistic'] = ['0.76', '-0.59', '0', '0.713', '#joy', '#admiration', 'positive', '0.689', 'spare_enemy', 'magnanimous', 'generous', 'forgiving', 'deal_gently']
senticnet['imago'] = ['0.642', '-0.58', '0', '0.751', '#joy', '#admiration', 'positive', '0.659', 'change', 'become_better', 'change_aspect', 'look_better', 'become_bigger']
senticnet['imago_dei'] = ['0.779', '-0.75', '0', '0.526', '#joy', '#surprise', 'positive', '0.687', 'privateness', 'covertness', 'bosom', 'isolation', 'confidentiality']
senticnet['imambara'] = ['0', '0.224', '0', '0.197', '#interest', '#admiration', 'positive', '0.211', 'church', 'temple', 'cathedral', 'chapel', 'confession']
senticnet['imamzadeh'] = ['0', '0.224', '0', '0.197', '#interest', '#admiration', 'positive', '0.211', 'church', 'temple', 'cathedral', 'chapel', 'confession']
senticnet['imap_account'] = ['0.06', '0', '0', '0.059', '#joy', '#admiration', 'positive', '0.059', 'traffic_signal', 'electrical_circuit', 'parking_lot', 'hand_luggage', 'message_board']
senticnet['imaret'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'caravansary', 'caravanserai', 'khan', 'caravan_inn', 'hostel']
senticnet['imax_digital'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'arm_movement', 'hand_gesture', 'water_motion', 'hand_signal', 'sea_movement']
senticnet['imax_theater'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'theater', 'drive-in_movie', 'watch_movie', 'cinema', 'movie']
senticnet['imbalance'] = ['-0.59', '0', '0.6', '-0.51', '#sadness', '#anger', 'negative', '-0.57', 'asymmetry', 'dissymmetry', 'radial_asymmetry', 'directivity', 'directionality']
senticnet['imbalance_between'] = ['0.871', '-0.74', '0', '0.981', '#joy', '#admiration', 'positive', '0.867', 'unrighteousness', 'unjustness', 'unfairness', 'wrong', 'wrongfulness']
senticnet['imbecile'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'simple', 'stupid', 'idiotic', 'moron', 'daft']
senticnet['imbecilic'] = ['-0.86', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.86', 'tone-deaf', 'ill-informed', 'dimwitted', 'close-minded', 'ham-fisted']
senticnet['imbecility'] = ['-0.90', '0', '0.596', '-0.95', '#sadness', '#disgust', 'negative', '-0.82', 'slowness', 'mental_retardation', 'backwardness', 'subnormality', 'retardation']
senticnet['imbibe'] = ['0.869', '-0.59', '0', '0.66', '#joy', '#admiration', 'positive', '0.709', 'assimilate', 'take_up', 'reabsorb', 'resorb', 'absorb']
senticnet['imbroglio'] = ['-0.87', '0', '0.762', '-0.92', '#sadness', '#disgust', 'negative', '-0.85', 'misreading', 'misconstrual', 'misconstruction', 'misinterpretation', 'interpretation']
senticnet['imbue'] = ['0.858', '-0.88', '0', '0.508', '#joy', '#surprise', 'positive', '0.751', 'encourage', 'stir', 'shake', 'excite', 'shake_up']
senticnet['imbued_with'] = ['0.693', '-0.52', '0', '0.82', '#joy', '#admiration', 'positive', '0.681', 'concrete_representation', 'shape', 'representation', 'mental_representation', 'internal_representation']
senticnet['imcapableness'] = ['0.94', '-0.65', '0', '0.598', '#joy', '#surprise', 'positive', '0.729', 'grasp', 'capableness', 'perfectibility', 'reach', 'aptitude']
senticnet['imeem'] = ['0.886', '-0.77', '0', '0.815', '#joy', '#admiration', 'positive', '0.826', 'countenance', 'legitimate', 'permit', 'allow', 'let']
senticnet['imf_bailout'] = ['-0.12', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'escape', 'escape_situation', 'cancel_meeting', 'absent', 'cancel_appointment']
senticnet['imho'] = ['0', '0', '-0.86', '-0.99', '#fear', '#disgust', 'negative', '-0.93', 'niggle', 'quiddity', 'bicker', 'pettifog', 'cavil']
senticnet['imitate'] = ['-0.04', '0.127', '-0.12', '0.223', '#interest', '#admiration', 'positive', '0.046', 'act', 'emote', 'portray', 'shorten', 'bold']
senticnet['imitation'] = ['0.099', '0.242', '-0.15', '0.291', '#interest', '#admiration', 'positive', '0.119', 'replicate', 'similarity', 'make_copy', 'make_right', 'band_together']
senticnet['imitative'] = ['-0.5', '0', '0.97', '-0.52', '#anger', '#disgust', 'negative', '-0.66', 'simulated', 'apish', 'parrotlike', 'apelike', 'mimic']
senticnet['imitative_counterpoint'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['imitator'] = ['-0.96', '0', '0.81', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'mimic', 'beguiler', 'slicker', 'mimicker', 'deceiver']
senticnet['imitrex'] = ['0.969', '0.817', '0', '0.828', '#joy', '#admiration', 'positive', '0.871', 'ibuprofen', 'painkiller', 'medicine', 'analgesic', 'phenobarbital']
senticnet['immaculate'] = ['0.717', '-0.91', '0', '0', '#joy', '#surprise', 'positive', '0.816', 'pure', 'stain', 'virgin', 'immaculateness', 'impeccable']
senticnet['immaculate_collection'] = ['0.056', '0.139', '-0.07', '0.154', '#interest', '#admiration', 'positive', '0.07', 'belongings', 'material', 'fabric', 'guarantee', 'stuff']
senticnet['immaculate_white'] = ['0', '0', '0', '0.963', '#admiration', '#admiration', 'positive', '0.963', 'wear', 'furnishing', 'wearable_technology', 'wearable', 'belt']
senticnet['immaculately'] = ['0', '0.768', '0', '0.881', '#interest', '#admiration', 'positive', '0.825', 'blemish', 'stain', 'defiled', 'flawless', 'perfect']
senticnet['immaculateness'] = ['0.711', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.816', 'pure', 'stain', 'virgin', 'immaculate', 'impeccable']
senticnet['immagination'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'dejectedness', 'low-spiritedness', 'lowness', 'downheartedness', 'cheerless']
senticnet['immanence'] = ['0.779', '-0.75', '0', '0.526', '#joy', '#surprise', 'positive', '0.687', 'privateness', 'covertness', 'bosom', 'isolation', 'confidentiality']
senticnet['immanent'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'metastability', 'monotony', 'invariance', 'constancy', 'unchangeability']
senticnet['immanuel_lutheran_church'] = ['0.081', '0.146', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'cult', 'sect', 'conservative', 'worshiper', 'pastor']
senticnet['immaterial'] = ['-0.86', '0', '0.894', '-0.72', '#sadness', '#anger', 'negative', '-0.82', 'inapplicable', 'unsuitable', 'irrelevant', 'extraneous', 'orthogonal']
senticnet['immaterial_substance'] = ['0.509', '-0.62', '0', '0.786', '#surprise', '#admiration', 'positive', '0.64', 'agreeable', 'accordant', 'consentient', 'unanimous', 'according']
senticnet['immateriality'] = ['-0.63', '0', '0.694', '-0.66', '#anger', '#disgust', 'negative', '-0.66', 'corporality', 'tangibleness', 'palpability', 'reality', 'solidness']
senticnet['immature'] = ['-0.77', '0', '0.772', '0', '#sadness', '#anger', 'negative', '-0.77', 'small_boy', 'young_kid', 'fussy', 'helpless', 'little_person']
senticnet['immature_fruit'] = ['0', '0.141', '0.01', '0.115', '#interest', '#admiration', 'positive', '0.082', 'sepal', 'achene', 'funiculus', 'seed', 'pip']
senticnet['immatureness'] = ['-0.93', '0', '0.906', '-0.53', '#sadness', '#anger', 'negative', '-0.79', 'juvenility', 'state', 'greenness', 'callowness', 'jejuneness']
senticnet['immaturity'] = ['-0.72', '0', '0.713', '-0.94', '#sadness', '#disgust', 'negative', '-0.79', 'juvenility', 'state', 'greenness', 'callowness', 'jejuneness']
senticnet['immeasurable'] = ['-0.80', '0', '0.761', '-0.70', '#sadness', '#anger', 'negative', '-0.75', 'illimitable', 'limitless', 'measureless', 'unmeasured', 'abysmal']
senticnet['immediacy'] = ['0.772', '-0.95', '0', '0.89', '#surprise', '#admiration', 'positive', '0.872', 'downrightness', 'straightforwardness', 'characteristic', 'directness', 'immediateness']
senticnet['immediate'] = ['0', '0', '0', '-0.78', '#disgust', '#disgust', 'negative', '-0.78', 'suddenly', 'precipitous', 'sudden', 'unexpected', 'sudden_asthma_attack']
senticnet['immediate-release'] = ['0', '0', '-0.83', '-0.84', '#fear', '#disgust', 'negative', '-0.83', 'time-released', 'timed-release', 'slow-release', 'controlled-release', 'time-release']
senticnet['immediate_aftermath'] = ['0', '0.909', '0', '0.948', '#interest', '#admiration', 'positive', '0.928', 'set_off', 'shake_up', 'scandalmongering', 'sensationalistic', 'sensational']
senticnet['immediate_cease_fire'] = ['0.911', '-0.71', '0', '0.552', '#joy', '#surprise', 'positive', '0.726', 'deliver_goods', 'succeed', 'win', 'bring_home_bacon', 'come_through']
senticnet['immediate_cessation'] = ['-0.91', '0', '0.576', '-0.95', '#sadness', '#disgust', 'negative', '-0.81', 'stop', 'halt', 'lock_in', 'lock', 'block']
senticnet['immediate_danger'] = ['0.565', '-0.76', '0', '0.982', '#surprise', '#admiration', 'positive', '0.771', 'risk', 'hazard', 'jeopardy', 'health_hazard', 'peril']
senticnet['immediate_feedback'] = ['0.347', '0.348', '-0.13', '0.394', '#interest', '#admiration', 'positive', '0.239', 'good_deed', 'benefit', 'gratification', 'self_respect', 'bonus']
senticnet['immediate_gratification'] = ['0.978', '0.81', '0', '0.848', '#joy', '#admiration', 'positive', '0.879', 'brownie_point', 'feel_proud', 'receive_thank', 'appreciate', 'positive_reinforcement']
senticnet['immediate_halt'] = ['-0.91', '0', '0.576', '-0.95', '#sadness', '#disgust', 'negative', '-0.81', 'stop', 'halt', 'lock_in', 'lock', 'block']
senticnet['immediate_outcry'] = ['-0.86', '0', '0.516', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'bronx_cheer', 'call', 'cry', 'yell', 'shout']
senticnet['immediate_relief'] = ['0.615', '0.643', '-0.24', '0.29', '#joy', '#interest', 'positive', '0.325', 'allay', 'easiness', 'pacifically', 'peaceable', 'repose']
senticnet['immediate_repayment'] = ['-0.82', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.78', 'go_broke', 'receive_change', 'run_out_money', 'compare_price', 'bankruptcy']
senticnet['immediate_resignation'] = ['-0.72', '0', '0.98', '0', '#sadness', '#anger', 'negative', '-0.85', 'resignedly', 'abject', 'unhopeful', 'abjectly', 'reconcile']
senticnet['immediate_revocation'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'steer_wheel', 'gear_shift', 'chassis', 'brake_pedal', 'air_vent']
senticnet['immediate_suspension'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'steer_wheel', 'gear_shift', 'chassis', 'brake_pedal', 'air_vent']
senticnet['immediately'] = ['0.944', '0.754', '0', '0', '#joy', '#interest', 'positive', '0.849', 'now_time', 'right_now', 'contemporary', 'timely', 'time']
senticnet['immediately_apparent'] = ['0.958', '-0.83', '0', '0.81', '#joy', '#surprise', 'positive', '0.869', 'discernible', 'perceptible', 'noticeable', 'detectable', 'observable']
senticnet['immediately_cease'] = ['-0.91', '0', '0.576', '-0.95', '#sadness', '#disgust', 'negative', '-0.81', 'stop', 'halt', 'lock_in', 'lock', 'block']
senticnet['immediately_upon_receipt'] = ['0.862', '0.709', '0', '0', '#joy', '#interest', 'positive', '0.785', 'invoice', 'bill', 'check', 'mastercard', 'payment_counter']
senticnet['immediateness'] = ['0.772', '-0.95', '0', '0.89', '#surprise', '#admiration', 'positive', '0.872', 'downrightness', 'straightforwardness', 'characteristic', 'immediacy', 'directness']
senticnet['immediatism'] = ['0.909', '-0.85', '0', '0.846', '#joy', '#surprise', 'positive', '0.87', 'respectability', 'reputability', 'venerability', 'venerableness', 'righteousness']
senticnet['immemorial'] = ['-0.99', '0', '0.947', '-0.88', '#sadness', '#anger', 'negative', '-0.94', 'atrociousness', 'inhumanity', 'heinousness', 'atrocity', 'barbarity']
senticnet['immense'] = ['0', '0.069', '0.017', '0.068', '#interest', '#admiration', 'positive', '0.04', 'large_land', 'vast_land', 'big_land', 'large', 'land']
senticnet['immense_fortune'] = ['0.976', '0.811', '0', '0', '#joy', '#interest', 'positive', '0.893', 'deposit', 'bank_loan', 'wealth', 'finance', 'account']
senticnet['immense_popularity'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'important', 'universal', 'vip', 'prestige', 'status']
senticnet['immense_pressure'] = ['-0.12', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.09', 'breeze', 'move_air', 'air_movement', 'gape', 'force']
senticnet['immense_talent'] = ['0.608', '-0.55', '0', '0.726', '#joy', '#admiration', 'positive', '0.628', 'genius', 'endowment', 'flair', 'gift', 'knack']
senticnet['immense_wealth'] = ['0.843', '0.977', '0', '0', '#joy', '#interest', 'positive', '0.91', 'business', 'finance', 'revenue', 'wealth', 'income']
senticnet['immensely_popular'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'important', 'universal', 'vip', 'prestige', 'status']
senticnet['immensely_rich'] = ['0.843', '0.977', '0', '0', '#joy', '#interest', 'positive', '0.91', 'business', 'finance', 'revenue', 'wealth', 'income']
senticnet['immensely_successful'] = ['0.98', '-0.74', '0', '0.984', '#joy', '#admiration', 'positive', '0.903', 'prosperous', 'flourishing', 'palmy', 'prospering', 'thriving']
senticnet['immensely_wealthy'] = ['0.976', '0.811', '0', '0', '#joy', '#interest', 'positive', '0.893', 'deposit', 'bank_loan', 'wealth', 'finance', 'account']
senticnet['immensity'] = ['0', '0.858', '0', '0.79', '#interest', '#admiration', 'positive', '0.824', 'heavenly_body', 'star_planet', 'celestial_body', 'around_earth', 'unlimited']
senticnet['immensurable'] = ['-0.62', '0', '0.865', '-0.65', '#anger', '#disgust', 'negative', '-0.71', 'illimitable', 'limitless', 'measureless', 'unmeasured', 'abysmal']
senticnet['immerse'] = ['0.633', '-0.96', '0', '0.612', '#joy', '#surprise', 'positive', '0.738', 'particularism', 'absorption', 'concentration', 'engrossment', 'centering']
senticnet['immersion'] = ['0.633', '-0.96', '0', '0.612', '#joy', '#surprise', 'positive', '0.738', 'particularism', 'absorption', 'concentration', 'engrossment', 'centering']
senticnet['immersion_foot'] = ['-0.50', '0', '0.737', '-0.74', '#anger', '#disgust', 'negative', '-0.66', 'injury', 'harm', 'hurt', 'trench_foot', 'cryopathy']
senticnet['immersion_heater'] = ['0.02', '0.076', '0', '0', '#joy', '#interest', 'positive', '0.048', 'land_earth', 'tidal_wave', 'puddle', 'brine', 'spurt']
senticnet['immersive'] = ['0.633', '-0.96', '0', '0.612', '#joy', '#surprise', 'positive', '0.738', 'particularism', 'absorption', 'concentration', 'engrossment', 'centering']
senticnet['immersive_entertainment'] = ['0.951', '0', '0', '0.731', '#joy', '#admiration', 'positive', '0.841', 'pastime', 'enjoyment', 'pleasure', 'enjoy', 'fun']
senticnet['immersive_vr'] = ['0.026', '0.14', '-0.13', '0.31', '#interest', '#admiration', 'positive', '0.085', 'teach_new', 'share_idea', 'gesture', 'start_talk', 'exchange_idea']
senticnet['immigrant'] = ['0', '0', '0', '-0.95', '#disgust', '#disgust', 'negative', '-0.95', 'far_from_home', 'expat', 'live_abroad', 'connoisseur', 'work_abroad']
senticnet['immigration'] = ['0.946', '0.943', '0', '0', '#joy', '#interest', 'positive', '0.944', 'people', 'periodic_event', 'migration', 'motion', 'emigration']
senticnet['imminence'] = ['0', '0.66', '0', '0', '#interest', '#interest', 'positive', '0.66', 'hope', 'hopeful', 'ambitious', 'desire', 'faithful']
senticnet['imminent'] = ['0.891', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.827', 'upcoming', 'anticipated', 'debut', 'release', 'forthcoming']
senticnet['imminent_arrival'] = ['0', '0.66', '0', '0', '#interest', '#interest', 'positive', '0.66', 'hope', 'hopeful', 'ambitious', 'desire', 'faithful']
senticnet['imminent_collapse'] = ['-0.13', '-0.05', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'monitor_heart_rate', 'feel_tired', 'bump_head', 'exhaustion', 'become_fatigued']
senticnet['imminent_danger'] = ['0.565', '-0.76', '0', '0.982', '#surprise', '#admiration', 'positive', '0.771', 'risk', 'hazard', 'jeopardy', 'health_hazard', 'peril']
senticnet['imminent_death'] = ['-0.87', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.91', 'loss_oflife', 'chest_pain', 'die', 'tumor', 'end_life']
senticnet['imminent_harm'] = ['0.565', '-0.76', '0', '0.982', '#surprise', '#admiration', 'positive', '0.771', 'risk', 'hazard', 'jeopardy', 'health_hazard', 'peril']
senticnet['imminent_peril'] = ['-0.99', '0', '0.691', '-0.65', '#sadness', '#anger', 'negative', '-0.78', 'depression', 'economic_crisis', 'crisis', 'slump', 'juncture']
senticnet['immiscible'] = ['0.976', '0.786', '0', '0', '#joy', '#interest', 'positive', '0.881', 'combustible', 'state_matter', 'natural_resource', 'liquid_fuel', 'fossil_fuel']
senticnet['immiseration'] = ['-0.65', '0', '0.691', '-0.54', '#sadness', '#anger', 'negative', '-0.63', 'moronity', 'mental_deficiency', 'imbecility', 'stupidity', 'mental_defectiveness']
senticnet['immix'] = ['0', '0', '0.962', '-0.91', '#anger', '#disgust', 'negative', '-0.94', 'flux', 'mix', 'blend', 'conflate', 'commingle']
senticnet['immobile'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'nonmotile', 'immotile']
senticnet['immobilise'] = ['-0.59', '0', '0.704', '-0.5', '#sadness', '#anger', 'negative', '-0.59', 'lay_up', 'modify', 'nobble', 'alter', 'change']
senticnet['immobility'] = ['0', '0', '0.768', '-0.97', '#anger', '#disgust', 'negative', '-0.86', 'stall', 'halt', 'standstill', 'arrest', 'complete']
senticnet['immobilization'] = ['-0.97', '0', '0.885', '-0.97', '#sadness', '#disgust', 'negative', '-0.94', 'protection', 'conservation', 'environmentalism', 'reservation', 'preservation']
senticnet['immobilize'] = ['0.63', '-0.69', '0', '0.865', '#surprise', '#admiration', 'positive', '0.73', 'curtail', 'limit', 'abridge', 'ration', 'restrict']
senticnet['immoderate'] = ['-0.89', '0', '0.6', '-0.92', '#sadness', '#disgust', 'negative', '-0.80', 'outrageous', 'exorbitant', 'extortionate', 'usurious', 'abnormal']
senticnet['immoderately'] = ['-0.74', '0', '0.585', '-0.58', '#sadness', '#anger', 'negative', '-0.63', 'lack_of_quality', 'meagreness', 'poorness', 'scantiness', 'scantness']
senticnet['immoderateness'] = ['-0.50', '0', '0.89', '-0.78', '#anger', '#disgust', 'negative', '-0.72', 'excess', 'degree', 'grade', 'level', 'excessiveness']
senticnet['immoderatenessiveness'] = ['-0.51', '0', '0.89', '-0.79', '#anger', '#disgust', 'negative', '-0.73', 'immoderateness', 'degree', 'grade', 'level', 'excess']
senticnet['immoderation'] = ['-0.76', '0', '0.79', '-0.98', '#anger', '#disgust', 'negative', '-0.84', 'excess', 'degree', 'grade', 'level', 'excessiveness']
senticnet['immodest'] = ['-0.89', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.87', 'indecent', 'immodesty', 'modesty', 'shameless', 'immodestly']
senticnet['immodestly'] = ['0', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.913', 'skimpily', 'scantly', 'scantily', 'nattily', 'shabbily']
senticnet['immodesty'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'indecency', 'obscenity']
senticnet['immoral'] = ['0', '0', '0', '-0.95', '#disgust', '#disgust', 'negative', '-0.95', 'unethical', 'bad', 'obscene', 'indecent', 'shameless']
senticnet['immoral_behavior'] = ['-0.52', '0', '0.621', '-0.91', '#anger', '#disgust', 'negative', '-0.68', 'deviltry', 'devilry', 'irreverence', 'foul_play', 'iniquity']
senticnet['immoral_conduct'] = ['-0.72', '0', '0.742', '-0.80', '#anger', '#disgust', 'negative', '-0.75', 'illicitness', 'unlawfulness', 'illegitimacy', 'shadiness', 'quality']
senticnet['immorality'] = ['-0.69', '0', '0.89', '-0.98', '#anger', '#disgust', 'negative', '-0.85', 'evil', 'wrongness', 'putrefaction', 'wantonness', 'corruptibility']
senticnet['immorally'] = ['-0.85', '0', '0.749', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'cold_foot', 'eat_worm', 'farm_bird', 'yellow_animal', 'biped']
senticnet['immortal'] = ['0', '0', '0', '0.816', '#admiration', '#admiration', 'positive', '0.816', 'immortality', 'live_forever', 'deathless', 'eternal_life', 'everlasting_life']
senticnet['immortal_glory'] = ['-0.73', '0', '0.732', '-0.53', '#sadness', '#anger', 'negative', '-0.66', 'insurmountable', 'insuperable', 'unconquerable', 'indomitable', 'unsubduable']
senticnet['immortal_soul'] = ['0', '0', '-0.75', '-0.74', '#fear', '#disgust', 'negative', '-0.75', 'reaper', 'dead', 'cadaver', 'dead_person', 'end_life']
senticnet['immortality'] = ['0', '0', '0', '0.825', '#admiration', '#admiration', 'positive', '0.825', 'live_forever', 'immortal', 'eternal_life', 'everlasting_life', 'deathless']
senticnet['immortalize'] = ['0.54', '-0.97', '0', '0.569', '#surprise', '#admiration', 'positive', '0.695', 'overstate', 'hyperbolize', 'aggrandize', 'exaggerate', 'overdraw']
senticnet['immotile'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'nonmotile', 'immobile']
senticnet['immovability'] = ['-0.70', '0', '0.947', '-0.95', '#anger', '#disgust', 'negative', '-0.87', 'tautness', 'fixity', 'secureness', 'steadiness', 'firmness']
senticnet['immovable'] = ['0.975', '-0.99', '0', '0.644', '#joy', '#surprise', 'positive', '0.871', 'entail', 'barony', 'seigneury', 'seigniory', 'demesne']
senticnet['immovableness'] = ['-0.80', '0', '0.675', '-0.64', '#sadness', '#anger', 'negative', '-0.70', 'tautness', 'fixity', 'secureness', 'steadiness', 'firmness']
senticnet['immovale'] = ['0', '0', '-0.81', '-0.71', '#fear', '#disgust', 'negative', '-0.76', 'breathless', 'tight', 'narrow', 'without_space', 'small']
senticnet['immoveable'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immobile', 'unmovable', 'nonmotile', 'immotile']
senticnet['immune'] = ['-0.92', '0', '0.731', '-0.85', '#sadness', '#disgust', 'negative', '-0.83', 'unimpressed', 'untouched', 'uninfluenced', 'unswayed', 'unaffected']
senticnet['immune-mediated'] = ['-0.79', '0', '0', '-0.91', '#sadness', '#disgust', 'negative', '-0.85', 'non-infectious', 'non-infective', 'noninfectious', 'infective', 'nonmalignant']
senticnet['immune_cell'] = ['0.772', '-0.56', '0', '0.824', '#joy', '#admiration', 'positive', '0.719', 'neutrophil', 'leukocyte', 'leucocyte', 'white_blood_cell', 'white_cell']
senticnet['immune_disorder'] = ['-0.75', '0', '0.515', '-0.93', '#sadness', '#disgust', 'negative', '-0.73', 'sclerosis', 'induration', 'degenerative_disorder', 'autoimmune_disease', 'disseminated_sclerosis']
senticnet['immune_dysfunction'] = ['-0.78', '0', '0.6', '-0.61', '#sadness', '#disgust', 'negative', '-0.66', 'suggestibility', 'sensitivity', 'capacity', 'reactivity', 'predisposition']
senticnet['immune_dysregulation'] = ['-0.75', '0', '0.515', '-0.93', '#sadness', '#disgust', 'negative', '-0.73', 'sclerosis', 'induration', 'degenerative_disorder', 'autoimmune_disease', 'disseminated_sclerosis']
senticnet['immune_reaction'] = ['-0.99', '0', '0.651', '-0.51', '#sadness', '#anger', 'negative', '-0.72', 'sending', 'compulsion', 'influence', 'inducement', 'causation']
senticnet['immune_surveillance'] = ['0.702', '-0.52', '0', '0.958', '#joy', '#admiration', 'positive', '0.728', 'somatic_cell', 'vegetative_cell', 'fixed_phagocyte', 'macrophage', 'free_phagocyte']
senticnet['immunisation'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'medical_therapy', 'vaccine', 'doctor', 'medicine', 'hospital']
senticnet['immunity'] = ['0', '0', '0', '0.816', '#admiration', '#admiration', 'positive', '0.816', 'resistance', 'protection', 'untouchable', 'invulnerable', 'sacrosanct']
senticnet['immunization'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'medical_therapy', 'vaccine', 'doctor', 'medicine', 'hospital']
senticnet['immunocompetence'] = ['-0.78', '0', '0.6', '-0.61', '#sadness', '#disgust', 'negative', '-0.66', 'suggestibility', 'sensitivity', 'capacity', 'reactivity', 'predisposition']
senticnet['immunocompromise'] = ['-0.71', '0', '0.74', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'kwashiorkor', 'marasmus', 'malnutrition', 'hypovitaminosis', 'deficiency_disease']
senticnet['immunodeficiency'] = ['-0.98', '0', '0.942', '-0.9', '#sadness', '#anger', 'negative', '-0.94', 'disease', 'aid', 'immunological_disorder', 'die', 'hiv']
senticnet['immunoglobulin_light'] = ['0.096', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.057', 'platform_rail', 'rail', 'catwalk', 'column', 'token_turnstile']
senticnet['immunological'] = ['0.672', '-0.55', '0', '0.992', '#joy', '#admiration', 'positive', '0.739', 'pressor', 'hormone', 'adrenalin', 'internal_secretion', 'catecholamine']
senticnet['immunological_disorder'] = ['-0.65', '0', '0.744', '-0.80', '#anger', '#disgust', 'negative', '-0.73', 'upset', 'disorder', 'immunodeficiency', 'anergy', 'immunosuppression']
senticnet['immunologist'] = ['0.904', '-0.93', '0', '0.639', '#joy', '#surprise', 'positive', '0.824', 'toxicologist', 'epidemiologist', 'medical_scientist', 'serologist', 'scientist']
senticnet['immunopathology'] = ['-0.59', '0', '0.911', '-0.87', '#anger', '#disgust', 'negative', '-0.79', 'osteosclerosis', 'sclerosis', 'disseminated_sclerosis', 'multiple_sclerosis', 'induration']
senticnet['immunophenotype'] = ['-0.58', '0', '0.814', '-0.54', '#sadness', '#anger', 'negative', '-0.64', 'abnormal', 'irregular', 'vicarious', 'supernormal', 'subnormal']
senticnet['immunosuppression'] = ['-0.65', '0', '0.744', '-0.80', '#anger', '#disgust', 'negative', '-0.73', 'upset', 'disorder', 'immunodeficiency', 'anergy', 'immunological_disorder']
senticnet['immutability'] = ['-0.83', '0', '0.696', '-0.98', '#sadness', '#disgust', 'negative', '-0.84', 'unalterability', 'agelessness', 'changelessness', 'unchangeability', 'unchangeableness']
senticnet['immutable'] = ['0.295', '0.239', '0.049', '0.346', '#joy', '#admiration', 'positive', '0.208', 'full', 'replicate', 'infinite', 'total', 'unending']
senticnet['immutable_divine'] = ['-0.81', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.79', 'stifle', 'forgotten', 'old', 'touch_ground', 'seldom']
senticnet['immutableness'] = ['-0.72', '0', '0.81', '-0.78', '#anger', '#disgust', 'negative', '-0.77', 'unalterability', 'agelessness', 'changelessness', 'unchangeability', 'unchangeableness']
senticnet['immutably'] = ['-0.90', '0', '0.653', '-0.99', '#sadness', '#disgust', 'negative', '-0.85', 'unalterable', 'static', 'changeless', 'confirmed', 'fixed']
senticnet['imodium'] = ['0', '0', '-0.70', '-0.76', '#fear', '#disgust', 'negative', '-0.73', 'choke', 'become_full', 'get_full', 'burp', 'sickness']
senticnet['imp'] = ['0.526', '-0.99', '0', '0.673', '#surprise', '#admiration', 'positive', '0.732', 'faery', 'faerie', 'fairy', 'leprechaun', 'sandman']
senticnet['imp_spiral'] = ['0.526', '-0.99', '0', '0.673', '#surprise', '#admiration', 'positive', '0.732', 'faery', 'faerie', 'fairy', 'leprechaun', 'sandman']
senticnet['impact'] = ['-0.08', '0.23', '-0.18', '0.196', '#interest', '#admiration', 'positive', '0.039', 'tell_why', 'inquire', 'explanation', 'question_start', 'request']
senticnet['impact_negatively'] = ['-0.53', '0', '0.57', '-0.73', '#anger', '#disgust', 'negative', '-0.61', 'run_into', 'strike', 'hit', 'bump_into', 'collide_with']
senticnet['impair'] = ['-0.62', '0', '0.756', '-0.62', '#anger', '#disgust', 'negative', '-0.66', 'vitiate', 'ruin', 'maul', 'mangle', 'deface']
senticnet['impaired'] = ['-0.97', '0', '0.622', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'dicky', 'dickey', 'lessened', 'diminished']
senticnet['impaired_cognitive'] = ['0.874', '-0.73', '0', '0.608', '#joy', '#surprise', 'positive', '0.74', 'growth', 'growing', 'maturation', 'development', 'ontogeny']
senticnet['impaired_concentration'] = ['0', '0', '0.958', '-0.82', '#anger', '#disgust', 'negative', '-0.89', 'become_drunk', 'tipsy', 'act_stupid', 'forgetfulness', 'hangover']
senticnet['impaired_coordination'] = ['0', '0', '0.958', '-0.82', '#anger', '#disgust', 'negative', '-0.89', 'become_drunk', 'tipsy', 'act_stupid', 'forgetfulness', 'hangover']
senticnet['impaired_driver'] = ['-0.10', '-0.04', '0', '-0.14', '#sadness', '#disgust', 'negative', '-0.06', 'adjust_seat', 'angry_behavior', 'fasten_seatbelt', 'traffic_jam', 'skid_mark']
senticnet['impaired_immune_system'] = ['0.753', '-0.74', '0', '0.587', '#joy', '#surprise', 'positive', '0.695', 'draining', 'exhausting', 'debilitating', 'enervating', 'enfeebling']
senticnet['impaired_immunity'] = ['-0.10', '0', '-0.16', '-0.23', '#fear', '#disgust', 'negative', '-0.16', 'contagious_disease', 'infectious_disease', 'polio', 'sickness', 'malady']
senticnet['impaired_judgment'] = ['0', '0', '0.958', '-0.82', '#anger', '#disgust', 'negative', '-0.89', 'become_drunk', 'tipsy', 'act_stupid', 'forgetfulness', 'hangover']
senticnet['impaired_liver_function'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'bowel', 'scrotum', 'shoulder', 'anus', 'waist']
senticnet['impaired_lung_function'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchiolitis', 'chronic_bronchitis', 'respiratory_disease', 'bronchitis', 'respiratory_disorder']
senticnet['impaired_memory'] = ['0', '0', '0.958', '-0.82', '#anger', '#disgust', 'negative', '-0.89', 'become_drunk', 'tipsy', 'act_stupid', 'forgetfulness', 'hangover']
senticnet['impaired_vision'] = ['-0.22', '-0.03', '0.026', '-0.13', '#sadness', '#disgust', 'negative', '-0.08', 'blind_man', 'blind_person', 'shade', 'need_help', 'shadow']
senticnet['impairment'] = ['-0.64', '0', '0.731', '-0.52', '#sadness', '#anger', 'negative', '-0.63', 'hurt', 'detriment', 'deformation', 'distortion', 'modification']
senticnet['impairment_person'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'nymph', 'mole', 'nightly_ritual', 'rabbit', 'squirrel']
senticnet['impale'] = ['-0.16', '-0.07', '-0.07', '-0.21', '#sadness', '#disgust', 'negative', '-0.09', 'cause_injury', 'hurt_person', 'maim', 'elephant_sit_chest', 'parry']
senticnet['impaled_upon'] = ['-0.50', '0', '0.937', '-0.59', '#anger', '#disgust', 'negative', '-0.67', 'restorative', 'medicine', 'medication', 'medicament', 'medicinal_drug']
senticnet['impalement'] = ['-0.87', '0', '0.932', '-0.6', '#sadness', '#anger', 'negative', '-0.80', 'atrocity', 'maltreatment', 'abuse', 'inhumanity', 'inhuman_treatment']
senticnet['impalpable'] = ['0.03', '0', '0', '0.119', '#joy', '#admiration', 'positive', '0.074', 'notional', 'abstract', 'wow', 'made_out_of_plastic', 'make_paper']
senticnet['impart'] = ['0.99', '-0.76', '0', '0.571', '#joy', '#surprise', 'positive', '0.777', 'add', 'contribute', 'lend', 'breathe', 'instill']
senticnet['impart_knowledge'] = ['0.818', '0.85', '0', '0.753', '#joy', '#interest', 'positive', '0.807', 'offer_opinion', 'teach_student', 'education', 'learn_new_information', 'teach_child']
senticnet['impartial'] = ['0.837', '-0.99', '0', '0.559', '#joy', '#surprise', 'positive', '0.797', 'disinterested', 'dispassionate', 'indifferent', 'unbiased', 'fair']
senticnet['impartial_inquiry'] = ['0.845', '0.916', '0', '0', '#joy', '#interest', 'positive', '0.881', 'question', 'answer', 'query', 'question_start', 'question_answering']
senticnet['impartial_investigation'] = ['0.837', '-0.99', '0', '0.559', '#joy', '#surprise', 'positive', '0.797', 'disinterested', 'dispassionate', 'indifferent', 'unbiased', 'fair']
senticnet['impartial_judge'] = ['0.837', '-0.99', '0', '0.559', '#joy', '#surprise', 'positive', '0.797', 'disinterested', 'dispassionate', 'indifferent', 'unbiased', 'fair']
senticnet['impartial_judgment'] = ['0.837', '-0.99', '0', '0.559', '#joy', '#surprise', 'positive', '0.797', 'disinterested', 'dispassionate', 'indifferent', 'unbiased', 'fair']
senticnet['impartial_judicial'] = ['0.837', '-0.99', '0', '0.559', '#joy', '#surprise', 'positive', '0.797', 'disinterested', 'dispassionate', 'indifferent', 'unbiased', 'fair']
senticnet['impartial_judiciary'] = ['0.837', '-0.99', '0', '0.559', '#joy', '#surprise', 'positive', '0.797', 'disinterested', 'dispassionate', 'indifferent', 'unbiased', 'fair']
senticnet['impartial_jury'] = ['0.713', '0', '0', '0.746', '#joy', '#admiration', 'positive', '0.73', 'carnival', 'circus', 'fairground', 'rodeo', 'disneyland']
senticnet['impartial_justice'] = ['0.594', '-0.81', '0', '0.93', '#surprise', '#admiration', 'positive', '0.78', 'hold', 'hold_on', 'persist', 'hang_on', 'maintain']
senticnet['impartial_manner'] = ['0.837', '-0.99', '0', '0.559', '#joy', '#surprise', 'positive', '0.797', 'disinterested', 'dispassionate', 'indifferent', 'unbiased', 'fair']
senticnet['impartial_spectator'] = ['-0.97', '0', '0.781', '-0.91', '#sadness', '#disgust', 'negative', '-0.88', 'weakness', 'fault', 'flaw', 'defect', 'wart']
senticnet['impartial_trial'] = ['0.837', '-0.99', '0', '0.559', '#joy', '#surprise', 'positive', '0.797', 'disinterested', 'dispassionate', 'indifferent', 'unbiased', 'fair']
senticnet['impartial_tribunal'] = ['0.837', '-0.99', '0', '0.559', '#joy', '#surprise', 'positive', '0.797', 'disinterested', 'dispassionate', 'indifferent', 'unbiased', 'fair']
senticnet['impartiality'] = ['0.738', '0.753', '0', '0', '#joy', '#interest', 'positive', '0.746', 'candor', 'disinterest', 'justice', 'unbiasedness', 'fairness']
senticnet['impartially'] = ['0.837', '-0.99', '0', '0.559', '#joy', '#surprise', 'positive', '0.797', 'disinterested', 'dispassionate', 'indifferent', 'unbiased', 'fair']
senticnet['impassable'] = ['-0.99', '0', '0.641', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'navigable', 'negotiable', 'surmountable', 'climbable', 'passable']
senticnet['impasse'] = ['0', '0', '-0.76', '-0.78', '#fear', '#disgust', 'negative', '-0.77', 'gridlocked', 'stalemate', 'rush-hour', 'partisanship', 'gridlock']
senticnet['impassibility'] = ['0.779', '-0.75', '0', '0.526', '#joy', '#surprise', 'positive', '0.687', 'privateness', 'covertness', 'bosom', 'isolation', 'confidentiality']
senticnet['impassioned'] = ['0', '0', '0.944', '-0.76', '#anger', '#disgust', 'negative', '-0.85', 'fervid', 'ardent', 'torrid', 'fervent', 'fiery']
senticnet['impassive'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicado', 'unpronounceable', 'incommunicative', 'inexpressive', 'silent']
senticnet['impassively'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicado', 'unpronounceable', 'incommunicative', 'inexpressive', 'silent']
senticnet['impassiveness'] = ['0', '0', '-0.79', '-0.81', '#fear', '#disgust', 'negative', '-0.80', 'apathetically', 'impassivity', 'emotionlessness', 'unemotionality', 'stolidity']
senticnet['impassivity'] = ['0', '0', '-0.86', '-0.80', '#fear', '#disgust', 'negative', '-0.83', 'impassiveness', 'apathetically', 'emotionlessness', 'unemotionality', 'stolidity']
senticnet['impasto'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'wall_decoration', 'renovation', 'painting', 'artwork', 'new_house']
senticnet['impatience'] = ['0', '0', '0', '-0.97', '#disgust', '#disgust', 'negative', '-0.97', 'fidget', 'impatient', 'nervous', 'restlessness', 'nervousness']
senticnet['impatient'] = ['-0.75', '0', '0', '-0.91', '#sadness', '#disgust', 'negative', '-0.83', 'fidget', 'impatience', 'restlessness', 'impatiently', 'raring']
senticnet['impatiently'] = ['-0.96', '0', '0', '-0.86', '#sadness', '#disgust', 'negative', '-0.91', 'raring', 'fretful', 'fidgetiness', 'antsy', 'fidgety']
senticnet['impeach'] = ['-0.76', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.82', 'deposition', 'unimpeachable', 'impeachable', 'hinder', 'discredit']
senticnet['impeachability'] = ['-0.98', '0', '0.657', '-0.95', '#sadness', '#disgust', 'negative', '-0.86', 'guiltiness', 'indictability', 'blameworthiness', 'culpability', 'culpableness']
senticnet['impeachable'] = ['-0.76', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.82', 'deposition', 'unimpeachable', 'impeach', 'hinder', 'discredit']
senticnet['impeccable'] = ['0', '0', '-0.99', '-0.91', '#fear', '#disgust', 'negative', '-0.95', 'exceptional', 'superb', 'craftsmanship', 'unsurpassed', 'unparalleled']
senticnet['impeccable_character'] = ['0.865', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.887', 'manager', 'colleague', 'office_worker', 'coworker', 'secretary']
senticnet['impeccable_integrity'] = ['0.865', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.887', 'manager', 'colleague', 'office_worker', 'coworker', 'secretary']
senticnet['impeccable_taste'] = ['0.902', '-0.72', '0', '0.919', '#joy', '#admiration', 'positive', '0.849', 'preciousness', 'costliness', 'dearness', 'cost', 'expensiveness']
senticnet['impeccable_technique'] = ['0.608', '-0.55', '0', '0.726', '#joy', '#admiration', 'positive', '0.628', 'genius', 'raw_talent', 'endowment', 'knack', 'talent']
senticnet['impeccably'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'terrific', 'wondrously', 'tremendous', 'marvelous', 'wondrous']
senticnet['impecunious'] = ['0', '0', '-0.85', '-0.75', '#fear', '#disgust', 'negative', '-0.80', 'pinched', 'in_straitened_circumstance', 'hard_up', 'penniless', 'penurious']
senticnet['impede'] = ['-0.39', '0.149', '-0.16', '0.062', '#sadness', '#fear', 'negative', '-0.08', 'block', 'confuse', 'confound', 'frustrate', 'baffle']
senticnet['impediment'] = ['-0.88', '0', '0.944', '-0.91', '#anger', '#disgust', 'negative', '-0.91', 'difficulty', 'straitjacket', 'millstone', 'drag', 'obstruction']
senticnet['impedimenta'] = ['-0.84', '0', '0.553', '-0.89', '#sadness', '#disgust', 'negative', '-0.76', 'encumbrance', 'incumbrance', 'block', 'bar', 'blockade']
senticnet['impeding'] = ['-0.77', '0', '0.999', '0', '#sadness', '#anger', 'negative', '-0.88', 'obturate', 'occlude', 'hinder', 'impede', 'obstruct']
senticnet['impel'] = ['0.717', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.779', 'impulse', 'impulsion', 'impeller', 'ago', 'action']
senticnet['impeller'] = ['-0.86', '0', '-0.84', '0', '#sadness', '#fear', 'negative', '-0.85', 'centrifugal', 'centripetal', 'self-priming', 'spiral', 'pump']
senticnet['impels'] = ['0.734', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.746', 'energy', 'activity', 'impetuous', 'force', 'impetus']
senticnet['impend_danger'] = ['-0.27', '0.17', '-0.30', '0', '#sadness', '#fear', 'negative', '-0.13', 'caution', 'retribution', 'wrong', 'kidnap', 'mental_disorder']
senticnet['impending_collapse'] = ['-0.13', '-0.05', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'monitor_heart_rate', 'feel_tired', 'bump_head', 'exhaustion', 'become_fatigued']
senticnet['impending_death'] = ['-0.87', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.91', 'loss_oflife', 'chest_pain', 'die', 'tumor', 'end_life']
senticnet['impending_shortage'] = ['-0.83', '0', '-0.88', '-0.78', '#sadness', '#fear', 'negative', '-0.83', 'little', 'sadness', 'small_amount', 'lack', 'absence']
senticnet['impenetrability'] = ['-0.63', '0', '0.694', '-0.66', '#anger', '#disgust', 'negative', '-0.66', 'corporality', 'tangibleness', 'palpability', 'reality', 'solidness']
senticnet['impenetrable'] = ['-0.99', '0', '0.797', '-0.56', '#sadness', '#anger', 'negative', '-0.78', 'dark', 'uncomprehensible', 'lost', 'missed', 'unfathomable']
senticnet['impenetrable_jargon'] = ['-0.04', '-0.02', '0.041', '-0.03', '#sadness', '#anger', 'negative', '-0.02', 'slang', 'lingo', 'microtext', 'chatspeak', 'unusual_language']
senticnet['impenitence'] = ['-0.63', '0', '0.942', '-0.52', '#sadness', '#anger', 'negative', '-0.70', 'stubbornness', 'obstinacy', 'obstinance', 'bullheadedness', 'pigheadedness']
senticnet['impenitency'] = ['-0.79', '0', '0.607', '-0.92', '#sadness', '#disgust', 'negative', '-0.77', 'stubbornness', 'obstinacy', 'obstinance', 'bullheadedness', 'pigheadedness']
senticnet['impenitent'] = ['-0.67', '0', '0.715', '-0.89', '#anger', '#disgust', 'negative', '-0.76', 'obstinate', 'unrepentant', 'unregenerated', 'unconverted', 'unpersuaded']
senticnet['imperative'] = ['0', '0.314', '-0.20', '0.404', '#interest', '#admiration', 'positive', '0.172', 'compel', 'permission', 'take', 'take_over', 'computer_key']
senticnet['imperative_mood'] = ['0', '0.156', '-0.06', '0.241', '#interest', '#admiration', 'positive', '0.11', 'mixture', 'noisy_place', 'make_go', 'move_vehicle', 'big_car']
senticnet['imperatively'] = ['-0.91', '0', '0.521', '-0.81', '#sadness', '#disgust', 'negative', '-0.74', 'bullying', 'domineering', 'blustery', 'roughshod', 'cavalier']
senticnet['imperator'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['imperceptible'] = ['-0.78', '0', '0.792', '-0.61', '#sadness', '#anger', 'negative', '-0.73', 'incognizable', 'incognoscible', 'insensible', 'unobservable', 'indiscernible']
senticnet['imperceptibly'] = ['0', '0', '0.926', '-0.92', '#anger', '#disgust', 'negative', '-0.92', 'insinuation', 'insinuative', 'imply', 'insinuate', 'allude']
senticnet['imperf'] = ['-0.87', '0', '0.894', '0', '#sadness', '#anger', 'negative', '-0.88', 'glitch', 'imperfect', 'incomplete', 'continuous', 'describing']
senticnet['imperfect'] = ['-0.87', '0', '0.901', '0', '#sadness', '#anger', 'negative', '-0.88', 'glitch', 'imperf', 'incomplete', 'continuous', 'describing']
senticnet['imperfect_tense'] = ['0', '0.156', '-0.06', '0.241', '#interest', '#admiration', 'positive', '0.11', 'mixture', 'noisy_place', 'make_go', 'move_vehicle', 'big_car']
senticnet['imperfectibility'] = ['0.946', '-0.64', '0', '0.602', '#joy', '#surprise', 'positive', '0.73', 'grasp', 'perfectibility', 'capableness', 'reach', 'aptitude']
senticnet['imperfection'] = ['-0.84', '0', '0.613', '-0.60', '#sadness', '#anger', 'negative', '-0.68', 'weakness', 'fault', 'flaw', 'defect', 'wart']
senticnet['imperfectly'] = ['-0.84', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.89', 'abortive', 'ill', 'degree', 'completely', 'fully']
senticnet['imperfectness'] = ['-0.97', '0', '0.781', '-0.91', '#sadness', '#disgust', 'negative', '-0.88', 'weakness', 'fault', 'flaw', 'defect', 'wart']
senticnet['imperforate_hymen'] = ['-0.75', '0', '0.715', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'abdominal_pregnancy', 'ectopic_gestation', 'tubal_pregnancy', 'pregnancy', 'gestation']
senticnet['imperforate_shell'] = ['0.969', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.772', 'aspheric', 'aspherical', 'prolate', 'rounded', 'crescent']
senticnet['imperial'] = ['0.039', '0', '0.038', '0.099', '#joy', '#admiration', 'positive', '0.033', 'ruler', 'kingly', 'nobleman', 'blue_blood', 'nobility']
senticnet['imperial_crown'] = ['-0.62', '0', '0.961', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'jeweled_headdress', 'diadem', 'tiara', 'headdress', 'headgear']
senticnet['imperial_domination'] = ['0.54', '-0.97', '0', '0.569', '#surprise', '#admiration', 'positive', '0.695', 'overstate', 'hyperbolize', 'glorify', 'exaggerate', 'overdraw']
senticnet['imperial_dynasty'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
senticnet['imperial_eagle'] = ['-0.98', '0', '0.664', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'unfortunate', 'unfortunate_person', 'weeper', 'pallgriever', 'griever']
senticnet['imperial_gallon'] = ['0.068', '0', '0.034', '0.076', '#joy', '#admiration', 'positive', '0.037', 'gallon', 'liquid_measurement', 'ounce', 'measurement', 'quart']
senticnet['imperial_insignia'] = ['-0.98', '0', '0.664', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'unfortunate', 'unfortunate_person', 'weeper', 'pallgriever', 'griever']
senticnet['imperial_measurement'] = ['0', '0.082', '0.076', '0.111', '#interest', '#admiration', 'positive', '0.039', 'measurement_unit', 'measure', 'centimetre', 'length', 'measurement']
senticnet['imperial_palace'] = ['0', '0.105', '0', '0.076', '#interest', '#admiration', 'positive', '0.09', 'big_house', 'mansion', 'castle', 'block_flat', 'medieval']
senticnet['imperial_roman'] = ['-0.03', '0.092', '0.07', '0.1', '#interest', '#admiration', 'positive', '0.021', 'gladiator', 'civilization', 'church_build', 'coliseum', 'every_town']
senticnet['imperial_rome'] = ['-0.03', '0.092', '0.07', '0.1', '#interest', '#admiration', 'positive', '0.021', 'gladiator', 'civilization', 'church_build', 'coliseum', 'every_town']
senticnet['imperial_stout'] = ['0', '0.091', '-0.05', '0.073', '#interest', '#admiration', 'positive', '0.037', 'privately', 'hold_liquid', 'make_wish', 'rinse_dish', 'find_toilet']
senticnet['imperialism'] = ['-0.65', '0', '0.691', '-0.54', '#sadness', '#anger', 'negative', '-0.63', 'moronity', 'mental_deficiency', 'imbecility', 'stupidity', 'mental_defectiveness']
senticnet['imperialist'] = ['0.828', '-0.67', '0', '0.811', '#joy', '#admiration', 'positive', '0.771', 'foolhardiness', 'brashness', 'thoughtlessness', 'desperation', 'unthoughtfulness']
senticnet['imperialist_domination'] = ['-0.65', '0', '0.691', '-0.54', '#sadness', '#anger', 'negative', '-0.63', 'moronity', 'mental_deficiency', 'imbecility', 'stupidity', 'mental_defectiveness']
senticnet['imperialist_war'] = ['-0.67', '0', '0.85', '-0.59', '#sadness', '#anger', 'negative', '-0.70', 'rack', 'boot', 'whip', 'iron_boot', 'instrument_of_torture']
senticnet['imperialistic'] = ['0.585', '-0.7', '0', '0.586', '#surprise', '#admiration', 'positive', '0.624', 'jingoistic', 'ultranationalistic', 'superpatriotic', 'nationalistic', 'patriotic']
senticnet['imperil'] = ['-0.16', '0', '-0.18', '-0.23', '#fear', '#disgust', 'negative', '-0.19', 'eat_off', 'remove', 'diminish', 'decrease', 'reduce']
senticnet['imperiled_wildlife'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'panda', 'hunting', 'air_pollution', 'global_warming', 'sea_pollution']
senticnet['imperilment'] = ['0.565', '-0.76', '0', '0.982', '#surprise', '#admiration', 'positive', '0.771', 'risk', 'hazard', 'jeopardy', 'health_hazard', 'peril']
senticnet['imperio'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['imperious'] = ['0', '0', '-0.86', '-0.95', '#fear', '#disgust', 'negative', '-0.90', 'supercilious', 'haughty', 'disdainful', 'lordly', 'overbearing']
senticnet['imperious_necessity'] = ['-0.99', '0', '0.691', '-0.65', '#sadness', '#anger', 'negative', '-0.78', 'depression', 'economic_crisis', 'crisis', 'slump', 'juncture']
senticnet['imperiously'] = ['0.759', '0', '0', '0.706', '#joy', '#admiration', 'positive', '0.732', 'shrieked', 'indignantly', 'plaintively', 'mockingly', 'shrilly']
senticnet['imperiousness'] = ['0.968', '-0.88', '0', '0.831', '#joy', '#surprise', 'positive', '0.893', 'superiority', 'pride', 'superbia', 'haughtiness', 'domineeringness']
senticnet['imperishable'] = ['-0.95', '0', '0.799', '-0.67', '#sadness', '#anger', 'negative', '-0.81', 'imputrescible', 'undestroyable', 'indestructible', 'durable', 'perdurable']
senticnet['imperium'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['impermanence'] = ['-0.87', '0', '0.744', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['impermanency'] = ['-0.85', '0', '0.968', '-0.75', '#sadness', '#anger', 'negative', '-0.86', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['impermanent'] = ['0.053', '0.264', '-0.11', '0.23', '#interest', '#admiration', 'positive', '0.107', 'totality', 'temporary', 'character_trait', 'interim', 'end_soon']
senticnet['impermanent_nature'] = ['-0.87', '0', '0.744', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['impermeability'] = ['-0.52', '0', '0.596', '-0.76', '#anger', '#disgust', 'negative', '-0.62', 'retention', 'retentiveness', 'retentivity', 'solidness', 'nonabsorbency']
senticnet['impermeable'] = ['0', '0', '-0.99', '-0.89', '#fear', '#disgust', 'negative', '-0.94', 'non-porous', 'non-absorbent', 'porous', 'nonporous', 'non-abrasive']
senticnet['impermeableness'] = ['-0.64', '0', '0.868', '-0.95', '#anger', '#disgust', 'negative', '-0.82', 'retention', 'retentiveness', 'retentivity', 'solidness', 'nonabsorbency']
senticnet['impermissible'] = ['-0.75', '0', '0.614', '-0.74', '#sadness', '#disgust', 'negative', '-0.70', 'out', 'unmentionable', 'untouchable', 'taboo', 'tabu']
senticnet['impersonal'] = ['-0.61', '0', '0.572', '-0.83', '#sadness', '#disgust', 'negative', '-0.67', 'numb', 'benumbed', 'anesthetic', 'insensible', 'asleep']
senticnet['impersonal_nature'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'nonsense', 'meaninglessness', 'bunk', 'absurdity', 'hokum']
senticnet['impersonality'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'nonsense', 'meaninglessness', 'bunk', 'absurdity', 'hokum']
senticnet['impersonate'] = ['-0.09', '0.156', '0.661', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'phony', 'deception', 'false', 'against_law', 'trick']
senticnet['impersonation'] = ['0', '-0.98', '0', '0.93', '#surprise', '#admiration', 'positive', '0.959', 'spot-on', 'pitch-perfect', 'bang-on', 'uncannily', 'dead-on']
senticnet['impersonator'] = ['-0.58', '0', '0.768', '-0.89', '#anger', '#disgust', 'negative', '-0.75', 'mimic', 'beguiler', 'slicker', 'mimicker', 'deceiver']
senticnet['impertinence'] = ['0', '0', '-0.79', '-0.80', '#fear', '#disgust', 'negative', '-0.8', 'impertinent', 'impudent', 'cheeky', 'pert', 'pertinent']
senticnet['impertinent'] = ['0', '0', '-0.79', '-0.79', '#fear', '#disgust', 'negative', '-0.79', 'impertinence', 'impudent', 'cheeky', 'pert', 'pertinent']
senticnet['imperturbability'] = ['0.527', '0.659', '-0.24', '0.258', '#joy', '#interest', 'positive', '0.299', 'imperturbableness', 'tranquil', 'unagitated', 'placidly', 'placidity']
senticnet['imperturbable'] = ['0.828', '-0.88', '0', '0.551', '#joy', '#surprise', 'positive', '0.755', 'superiority', 'pride', 'superbia', 'imperiousness', 'domineeringness']
senticnet['imperturbableness'] = ['0.527', '0.659', '-0.24', '0.258', '#joy', '#interest', 'positive', '0.299', 'imperturbability', 'tranquil', 'unagitated', 'placidly', 'placidity']
senticnet['imperviable'] = ['-0.9', '0', '0.657', '-0.64', '#sadness', '#anger', 'negative', '-0.73', 'soundproof', 'mothproof', 'resistant', 'proof', 'greaseproof']
senticnet['impervious'] = ['0', '0.705', '0', '0.789', '#interest', '#admiration', 'positive', '0.747', 'soundproof', 'impermeable', 'impenetrable', 'impregnable', 'waterproof']
senticnet['impetigo'] = ['0', '0', '0.946', '-0.75', '#anger', '#disgust', 'negative', '-0.85', 'ringworm', 'louse', 'eczema', 'scabies', 'mite']
senticnet['impetuosity'] = ['-0.82', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.80', 'impetuously', 'impetuous', 'rank', 'sudden', 'force']
senticnet['impetuous'] = ['0', '0', '0.979', '-0.87', '#anger', '#disgust', 'negative', '-0.92', 'impulsive', 'tearaway', 'hotheaded', 'madcap', 'incautious']
senticnet['impetuously'] = ['-0.81', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.80', 'impetuosity', 'impetuous', 'rank', 'sudden', 'force']
senticnet['impetus'] = ['0.742', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.748', 'energy', 'activity', 'impetuous', 'force', 'impels']
senticnet['impfinity'] = ['0.526', '-0.99', '0', '0.673', '#surprise', '#admiration', 'positive', '0.732', 'faery', 'faerie', 'fairy', 'leprechaun', 'sandman']
senticnet['impiety'] = ['-0.79', '0', '0', '-0.80', '#sadness', '#disgust', 'negative', '-0.80', 'irreverence', 'disrespect', 'apostasy', 'atheism', 'sacrilege']
senticnet['impinge'] = ['-0.53', '0', '0.57', '-0.73', '#anger', '#disgust', 'negative', '-0.61', 'run_into', 'strike', 'hit', 'bump_into', 'collide_with']
senticnet['impinge_on'] = ['-0.53', '0', '0.57', '-0.73', '#anger', '#disgust', 'negative', '-0.61', 'run_into', 'strike', 'hit', 'bump_into', 'collide_with']
senticnet['impinge_upon'] = ['-0.53', '0', '0.57', '-0.73', '#anger', '#disgust', 'negative', '-0.61', 'run_into', 'strike', 'hit', 'bump_into', 'collide_with']
senticnet['impinged_upon'] = ['-0.53', '0', '0.57', '-0.73', '#anger', '#disgust', 'negative', '-0.61', 'run_into', 'strike', 'hit', 'bump_into', 'collide_with']
senticnet['impinges_on'] = ['-0.53', '0', '0.57', '-0.73', '#anger', '#disgust', 'negative', '-0.61', 'run_into', 'strike', 'hit', 'bump_into', 'collide_with']
senticnet['impious'] = ['-0.90', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.82', 'pious', 'impiousness', 'impiously', 'reverence', 'lacking']
senticnet['impiously'] = ['-0.74', '0', '0.817', '0', '#sadness', '#anger', 'negative', '-0.78', 'very', 'wicked', 'ill', 'wickedly', 'male']
senticnet['impiousness'] = ['0.619', '-0.97', '0', '0.728', '#surprise', '#admiration', 'positive', '0.774', 'unrighteousness', 'undutifulness', 'godlessness', 'ungodliness', 'irreligiousness']
senticnet['impish'] = ['0', '0', '-0.92', '-0.84', '#fear', '#disgust', 'negative', '-0.88', 'mischievous', 'prankish', 'implike', 'arch', 'pixilated']
senticnet['implacable'] = ['-0.89', '0', '0.844', '0', '#sadness', '#anger', 'negative', '-0.86', 'stern', 'relentless', 'unrelenting', 'grim', 'merciless']
senticnet['implacably'] = ['-0.69', '0', '0.516', '-0.94', '#sadness', '#disgust', 'negative', '-0.71', 'insubordinate', 'defiant', 'obstreperous', 'resistant', 'resistive']
senticnet['implanon'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'birth_control_pill', 'condom', 'contraceptive_pill', 'sex_toy', 'prevent_pregnancy']
senticnet['implant'] = ['0.918', '0', '0', '0.994', '#joy', '#admiration', 'positive', '0.956', 'steady', 'ingrain', 'cement', 'link', 'stick']
senticnet['implant_placement'] = ['-0.03', '0', '-0.09', '-0.14', '#fear', '#disgust', 'negative', '-0.09', 'x-ray_machine', 'artificial_arm', 'ambulance', 'syringe', 'latex_glove']
senticnet['implant_surgery'] = ['0.745', '-0.55', '0', '0.597', '#joy', '#admiration', 'positive', '0.633', 'lift', 'phalloplasty', 'abdominoplasty', 'tummy_tuck', 'autoplasty']
senticnet['implantable'] = ['-0.62', '0', '0.996', '-0.68', '#anger', '#disgust', 'negative', '-0.76', 'pack', 'face_pack', 'pickup', 'prosthesis', 'corrective']
senticnet['implausibility'] = ['0.536', '-0.71', '0', '0.533', '#joy', '#surprise', 'positive', '0.594', 'incredulity', 'mental_rejection', 'skepticism', 'mistrust', 'distrust']
senticnet['implausible'] = ['0', '0', '-0.92', '-0.91', '#fear', '#disgust', 'negative', '-0.91', 'farfetched', 'unbelievable', 'cockamamie', 'cockamamy', 'unlikely']
senticnet['implausibly'] = ['-0.73', '0', '-0.84', '0', '#sadness', '#fear', 'negative', '-0.79', 'fabulously', 'fantastically', 'improbably', 'incredibly', 'unbelievably']
senticnet['implement'] = ['0', '0', '0', '0.14', '#admiration', '#admiration', 'positive', '0.14', 'type_of_game', 'monopoly', 'game_part', 'expensive_hobby', 'kid_game']
senticnet['implementability'] = ['0.99', '-0.88', '0', '0.831', '#joy', '#surprise', 'positive', '0.9', 'functionality', 'sensibleness', 'viability', 'usefulness', 'realism']
senticnet['implementation'] = ['0.025', '0.107', '0', '0.097', '#interest', '#admiration', 'positive', '0.076', 'work', 'place_work', 'workplace', 'disorganize', 'employment']
senticnet['implicate'] = ['-0.70', '0', '0.874', '-0.75', '#anger', '#disgust', 'negative', '-0.77', 'blame', 'accusation', 'inculpation', 'complaint', 'imprecation']
senticnet['implication'] = ['0.793', '0.859', '0', '0', '#joy', '#interest', 'positive', '0.826', 'entailment', 'conditional_relation', 'deduction', 'logical_implication', 'significance']
senticnet['implicative'] = ['0.734', '-0.56', '0', '0.64', '#joy', '#admiration', 'positive', '0.645', 'inferential', 'suggestive', 'connotative', 'connotational', 'connotative_of']
senticnet['implicature'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'mediacy', 'mediateness', 'characteristic', 'allusiveness']
senticnet['implicit'] = ['0.531', '-0.76', '0', '0.603', '#surprise', '#admiration', 'positive', '0.634', 'tacit', 'understand', 'unvoiced', 'silent', 'unspoken']
senticnet['implicit_price_deflator'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'indicator', 'retail_price_index', 'producer_price_index', 'wholesale_price_index', 'consumer_price_index']
senticnet['implicit_promise'] = ['0', '0.286', '-0.22', '0.383', '#interest', '#admiration', 'positive', '0.147', 'oath', 'propose', 'suggest', 'ask', 'trust']
senticnet['implicit_threat'] = ['-0.88', '0', '0.733', '-0.60', '#sadness', '#anger', 'negative', '-0.74', 'vengeance', 'payback', 'retribution', 'reprisal', 'return']
senticnet['implied_volatility'] = ['0.868', '-0.84', '0', '0.673', '#joy', '#surprise', 'positive', '0.796', 'instrument', 'option', 'derivative_instrument', 'legal_document', 'legal_instrument']
senticnet['implike'] = ['0', '0', '-0.92', '-0.84', '#fear', '#disgust', 'negative', '-0.88', 'mischievous', 'prankish', 'impish', 'arch', 'pixilated']
senticnet['implode'] = ['0', '0', '-0.89', '-0.75', '#fear', '#disgust', 'negative', '-0.82', 'burst', 'imploded', 'inward', 'implosion', 'collapse']
senticnet['imploded'] = ['0', '0', '-0.89', '-0.75', '#fear', '#disgust', 'negative', '-0.82', 'burst', 'implode', 'inward', 'implosion', 'collapse']
senticnet['implore'] = ['-0.85', '0', '0.518', '-0.57', '#sadness', '#disgust', 'negative', '-0.64', 'conjure', 'appeal', 'beg', 'plead', 'pray']
senticnet['imploringly'] = ['0', '0.991', '0', '0.922', '#interest', '#admiration', 'positive', '0.957', 'solicitude', 'concern', 'solicitous', 'care', 'attentive']
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senticnet['implosion_therapy'] = ['0.542', '-0.63', '0', '0.912', '#surprise', '#admiration', 'positive', '0.695', 'behavior_modification', 'flooding', 'reciprocal_inhibition', 'aversion_therapy', 'desensitization_technique']
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senticnet['imponderable'] = ['0.92', '-1', '0', '0', '#joy', '#surprise', 'positive', '0.96', 'considerable', 'estimate', 'appreciable', 'perceptible', 'appreciate']
senticnet['import'] = ['-0.03', '0.223', '-0.15', '0.35', '#interest', '#admiration', 'positive', '0.097', 'return', 'gain', 'cheap_buy', 'receive', 'bequest']
senticnet['import_duty'] = ['-0.03', '0.223', '-0.15', '0.35', '#interest', '#admiration', 'positive', '0.097', 'return', 'gain', 'cheap_buy', 'receive', 'bequest']
senticnet['import_export'] = ['-0.03', '0.223', '-0.15', '0.35', '#interest', '#admiration', 'positive', '0.097', 'return', 'gain', 'cheap_buy', 'receive', 'bequest']
senticnet['import_licence'] = ['-0.03', '0.223', '-0.15', '0.35', '#interest', '#admiration', 'positive', '0.097', 'return', 'gain', 'cheap_buy', 'receive', 'bequest']
senticnet['import_quota'] = ['-0.03', '0.223', '-0.15', '0.35', '#interest', '#admiration', 'positive', '0.097', 'return', 'gain', 'cheap_buy', 'receive', 'bequest']
senticnet['importance'] = ['0', '0.737', '0', '0.914', '#interest', '#admiration', 'positive', '0.826', 'assurance', 'quality', 'total', 'worth', 'amount']
senticnet['important'] = ['0', '0.712', '0', '0.929', '#interest', '#admiration', 'positive', '0.821', 'capitalize', 'good_quality', 'niceness', 'good', 'hard_find']
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senticnet['important_good_health'] = ['0.044', '0.092', '-0.05', '0.165', '#interest', '#admiration', 'positive', '0.062', 'good_human', 'good_body', 'healthy', 'push_up', 'good_health']
senticnet['important_human'] = ['0', '0.924', '0', '0.885', '#interest', '#admiration', 'positive', '0.905', 'priceless', 'universal', 'visual_representation', 'bourgeois', 'artistic_expression']
senticnet['important_job'] = ['0', '0.112', '-0.04', '0.099', '#interest', '#admiration', 'positive', '0.054', 'profession', 'career', 'vocation', 'occupation', 'laborer']
senticnet['important_man'] = ['0.186', '-0.02', '0', '0.166', '#joy', '#admiration', 'positive', '0.125', 'please_person', 'beautiful', 'divine', 'necessary', 'universal']
senticnet['important_message'] = ['0', '0.913', '0', '0', '#interest', '#interest', 'positive', '0.913', 'loud_shout', 'loud_cry', 'loud_yell', 'freak_out', 'human_noise']
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senticnet['important_person'] = ['0', '0.755', '0', '0.951', '#interest', '#admiration', 'positive', '0.853', 'priceless', 'universal', 'good', 'subjective', 'easy_use']
senticnet['important_role'] = ['0', '0.906', '0', '0.712', '#interest', '#admiration', 'positive', '0.809', 'play', 'study', 'different_application', 'research', 'project']
senticnet['important_stay_healthy'] = ['0.819', '0', '0', '0.874', '#joy', '#admiration', 'positive', '0.847', 'godliness', 'remove_stain', 'physical_reaction', 'neurotic', 'shrinkage']
senticnet['important_step_forward'] = ['0', '0', '0.078', '0', '#anger', '#anger', 'negative', '-0.07', 'street_city', 'small_street', 'walkway', 'black_pavement', 'dark_road']
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senticnet['imported_cabbageworm'] = ['-0.02', '-0.09', '0.11', '-0.13', '#anger', '#disgust', 'negative', '-0.04', 'beetle', 'gypsy_moth', 'mole_cricket', 'grasshopper', 'many_insects']
senticnet['imported_crude'] = ['0', '0', '-0.06', '0', '#fear', '#fear', 'negative', '-0.06', 'greasy', 'car_gas', 'fossil_fuel', 'liquid_fuel', 'lubrication']
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senticnet['importing_country'] = ['-0.03', '0.223', '-0.15', '0.35', '#interest', '#admiration', 'positive', '0.097', 'return', 'gain', 'cheap_buy', 'receive', 'bequest']
senticnet['importunate'] = ['0', '0', '0.998', '-0.82', '#anger', '#disgust', 'negative', '-0.90', 'bark', 'importunateness', 'importune', 'thoughtlessly', 'greedily']
senticnet['importunateness'] = ['0', '0', '0.994', '-0.82', '#anger', '#disgust', 'negative', '-0.90', 'bark', 'importunate', 'importune', 'thoughtlessly', 'greedily']
senticnet['importune'] = ['0', '0', '0.997', '-0.82', '#anger', '#disgust', 'negative', '-0.91', 'bark', 'importunateness', 'importunate', 'thoughtlessly', 'greedily']
senticnet['importunity'] = ['-0.98', '0', '0.537', '-0.72', '#sadness', '#disgust', 'negative', '-0.75', 'reprimand', 'wigging', 'rebuke', 'reproof', 'reproval']
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senticnet['impose_capital_punishment'] = ['-0.19', '0', '-0.17', '-0.14', '#sadness', '#fear', 'negative', '-0.17', 'death_penalty', 'die', 'death_destruction', 'kill', 'murder']
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senticnet['imposing_edifice'] = ['0.026', '0.219', '0', '0.16', '#interest', '#admiration', 'positive', '0.135', 'large_build', 'palace', 'high_rise', 'structure', 'fortification']
senticnet['imposition'] = ['-0.70', '0', '0.552', '-0.68', '#sadness', '#disgust', 'negative', '-0.64', 'protection', 'regimentation', 'taxation', 'trade_protection', 'reimposition']
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senticnet['impossible_task'] = ['-0.80', '0', '-0.80', '-0.96', '#sadness', '#disgust', 'negative', '-0.85', 'unable', 'absurd', 'difficult', 'impractical', 'unimaginable']
senticnet['impossibleness'] = ['-0.51', '0', '0.789', '-0.6', '#anger', '#disgust', 'negative', '-0.63', 'inconceivability', 'inconceivableness', 'nonexistence', 'nonentity', 'unattainableness']
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senticnet['imposter'] = ['-0.74', '0', '0.93', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'beguiler', 'slicker', 'pretender', 'ringer', 'deceiver']
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senticnet['impotence'] = ['-0.96', '0', '0.531', '-0.51', '#sadness', '#anger', 'negative', '-0.67', 'impuissance', 'weakness', 'unpersuasiveness', 'uninterestingness', 'helplessness']
senticnet['impotency'] = ['-0.79', '0', '0.792', '-0.86', '#sadness', '#disgust', 'negative', '-0.81', 'impuissance', 'weakness', 'unpersuasiveness', 'uninterestingness', 'helplessness']
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senticnet['impound'] = ['0', '0', '-0.87', '-0.85', '#fear', '#disgust', 'negative', '-0.86', 'seize', 'seclude', 'attach', 'sequester', 'confiscate']
senticnet['impound_lot'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'trailer', 'trail_car', 'camion', 'wagon', 'towboat']
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senticnet['impracticable'] = ['0.783', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.79', 'practicable', 'functional', 'employable', 'practical', 'feasible']
senticnet['impracticableness'] = ['-0.61', '0', '0.985', '-0.66', '#anger', '#disgust', 'negative', '-0.75', 'infeasibility', 'unfeasibility', 'inutility', 'uselessness', 'unusefulness']
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senticnet['impreciseness'] = ['0', '0', '0.915', '-0.79', '#anger', '#disgust', 'negative', '-0.85', 'error', 'exact', 'imprecise', 'approximation', 'uncertainty']
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senticnet['impregnable'] = ['0.702', '-0.95', '0', '0.997', '#surprise', '#admiration', 'positive', '0.883', 'fertilizable', 'fecund', 'conceptive', 'fertile', 'rank']
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senticnet['impregnate_female'] = ['0.081', '0.134', '-0.07', '0', '#joy', '#interest', 'positive', '0.046', 'sexual_intercourse', 'find_mate', 'fuck', 'make_love', 'pregnant']
senticnet['impregnation'] = ['0.789', '-0.92', '0', '0.964', '#surprise', '#admiration', 'positive', '0.893', 'ammonification', 'carbonation', 'permeation', 'saturation', 'diffusion']
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senticnet['impress_boss'] = ['0.748', '0.844', '0', '0.51', '#joy', '#interest', 'positive', '0.701', 'share_knowledge', 'find_solution', 'share_information', 'tell_information', 'work_hard']
senticnet['impress_date'] = ['0.463', '0.561', '-0.29', '0.281', '#joy', '#interest', 'positive', '0.253', 'write_date', 'digitate_date', 'use_computer', 'use_pen', 'date']
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senticnet['impress_girlfriend'] = ['0.964', '0', '0', '0', '#joy', '#joy', 'positive', '0.964', 'weight_gain', 'become_little_hungry', 'eat_pizza', 'eat', 'satisfy_crave']
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senticnet['impress_person'] = ['0', '0.827', '0', '0.84', '#interest', '#admiration', 'positive', '0.833', 'tell_day', 'get_know', 'look_sexy', 'find_new_friend', 'visit_friend']
senticnet['impress_upon'] = ['0.649', '-0.61', '0', '0.723', '#joy', '#admiration', 'positive', '0.664', 'bring_out', 'present', 'reintroduce', 'introduce', 'inform']
senticnet['impressible'] = ['0.808', '-0.69', '0', '0.938', '#joy', '#admiration', 'positive', '0.815', 'spinnable', 'susceptible', 'easy', 'pliant', 'plastic']
senticnet['impression'] = ['0.852', '0.705', '0', '0', '#joy', '#interest', 'positive', '0.778', 'presentation', 'bold', 'movie_star', 'emote', 'tv_show']
senticnet['impressionable'] = ['0.724', '-0.70', '0', '0.571', '#joy', '#surprise', 'positive', '0.666', 'spinnable', 'susceptible', 'easy', 'pliant', 'plastic']
senticnet['impressionable_age'] = ['0.724', '-0.70', '0', '0.571', '#joy', '#surprise', 'positive', '0.666', 'spinnable', 'susceptible', 'easy', 'pliant', 'plastic']
senticnet['impressionable_child'] = ['0.724', '-0.70', '0', '0.571', '#joy', '#surprise', 'positive', '0.666', 'spinnable', 'susceptible', 'easy', 'pliant', 'plastic']
senticnet['impressionable_young'] = ['0.724', '-0.70', '0', '0.571', '#joy', '#surprise', 'positive', '0.666', 'spinnable', 'susceptible', 'easy', 'pliant', 'plastic']
senticnet['impressionable_youth'] = ['0.724', '-0.70', '0', '0.571', '#joy', '#surprise', 'positive', '0.666', 'spinnable', 'susceptible', 'easy', 'pliant', 'plastic']
senticnet['impressionism'] = ['0', '0.956', '0', '0.881', '#interest', '#admiration', 'positive', '0.918', 'expressionism', 'cubism', 'fauvism', 'post-impressionism', 'surrealism']
senticnet['impressionistic'] = ['0.709', '-0.61', '0', '0.762', '#joy', '#admiration', 'positive', '0.695', 'careful', 'heedful', 'resonant', 'mindful', 'redolent']
senticnet['impressive'] = ['0.562', '-0.69', '0', '0.883', '#surprise', '#admiration', 'positive', '0.714', 'majestic', 'formidable', 'awesome', 'awing', 'brilliant']
senticnet['impressive_achievement'] = ['0.562', '-0.69', '0', '0.883', '#surprise', '#admiration', 'positive', '0.714', 'majestic', 'formidable', 'awesome', 'awing', 'brilliant']
senticnet['impressive_feat'] = ['0.749', '0', '0', '0.952', '#joy', '#admiration', 'positive', '0.851', 'high_class', 'citation', 'suspicion', 'goodness', 'prosperity']
senticnet['impressive_physique'] = ['-0.90', '0', '0.522', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'hooligan', 'ruffian', 'roughneck', 'rowdy', 'yob']
senticnet['impressively'] = ['0.813', '0.9', '0', '0', '#joy', '#interest', 'positive', '0.857', 'prodigiously', 'astonishingly', 'wonderfully', 'enormously', 'prodigious']
senticnet['impressiveness'] = ['0', '0.954', '0', '0.962', '#interest', '#admiration', 'positive', '0.958', 'stateliness', 'excellence', 'grandness', 'richness', 'expansivity']
senticnet['imprimatur'] = ['0.751', '-0.98', '0', '0.989', '#surprise', '#admiration', 'positive', '0.909', 'okay', 'approval', 'commendation', 'okey', 'okeh']
senticnet['imprint_label'] = ['0', '0.115', '0.036', '0.207', '#interest', '#admiration', 'positive', '0.095', 'total', 'amount', 'merit', 'award', 'excellence']
senticnet['imprison'] = ['-0.86', '0', '-0.99', '-0.93', '#fear', '#disgust', 'negative', '-0.93', 'feel_guilt', 'die', 'send_to_prison', 'feel_remorse', 'go_to_prison']
senticnet['imprisoned'] = ['-0.99', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.91', 'jailbird', 'prisoner', 'captive', 'convict', 'inmate']
senticnet['imprisoned_indefinitely'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicative', 'unpronounceable', 'impassive', 'inexpressive', 'silent']
senticnet['imprisonment'] = ['-0.80', '0', '-0.80', '-0.96', '#sadness', '#disgust', 'negative', '-0.85', 'stab_knife', 'die', 'feel_guilt', 'seek_revenge', 'feel_remorse']
senticnet['improbability'] = ['0.602', '-0.72', '0', '0.892', '#surprise', '#admiration', 'positive', '0.741', 'unlikelihood', 'unlikeliness', 'uncertainty', 'precariousness', 'uncertainness']
senticnet['improbable'] = ['-0.54', '0', '0.646', '-0.62', '#anger', '#disgust', 'negative', '-0.60', 'unbelievable', 'undreamed', 'undreamed_of', 'undreamt', 'undreamt_of']
senticnet['improbableness'] = ['0.76', '-0.86', '0', '0.819', '#surprise', '#admiration', 'positive', '0.814', 'unlikelihood', 'unlikeliness', 'uncertainty', 'precariousness', 'uncertainness']
senticnet['improbably'] = ['0', '-0.83', '0', '0.995', '#surprise', '#admiration', 'positive', '0.916', 'quaintly', 'incongruously', 'amusingly', 'grandly', 'intriguingly']
senticnet['impromptu'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'play_by_ear', 'improvise', 'natural', 'easy_going', 'spontaneous']
senticnet['impromptu_speech'] = ['-0.67', '0', '0.936', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'unprepared', 'extemporary', 'extempore', 'impromptu', 'unrehearsed']
senticnet['improper'] = ['-0.64', '0', '0.52', '-0.53', '#sadness', '#disgust', 'negative', '-0.56', 'wrong', 'incorrect', 'unbecoming', 'uncomely', 'unseemly']
senticnet['improper_disclosure'] = ['-0.02', '0.175', '-0.11', '0.148', '#interest', '#admiration', 'positive', '0.045', 'tolerant', 'phrase', 'inquiry', 'explain', 'question']
senticnet['improper_footwear'] = ['-0.50', '0', '0.681', '-0.89', '#anger', '#disgust', 'negative', '-0.69', 'sweat', 'effort', 'elbow_grease', 'exertion', 'travail']
senticnet['improper_governmental'] = ['-0.88', '0', '0.733', '-0.60', '#sadness', '#anger', 'negative', '-0.74', 'vengeance', 'payback', 'retribution', 'retaliation', 'return']
senticnet['improper_installation'] = ['-0.92', '0', '0.256', '-0.98', '#sadness', '#disgust', 'negative', '-0.72', 'lousy', 'bad_quality', 'failure', 'poor_quality', 'low_quality']
senticnet['improper_motive'] = ['-0.50', '0', '0.89', '-0.78', '#anger', '#disgust', 'negative', '-0.72', 'excess', 'degree', 'grade', 'level', 'immoderateness']
senticnet['improper_storage'] = ['-0.55', '0', '0.843', '-0.58', '#anger', '#disgust', 'negative', '-0.66', 'thieving', 'larceny', 'theft', 'thievery', 'stealing']
senticnet['improperly'] = ['-0.94', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.90', 'ill-gotten', 'properly', 'improper', 'awry', 'abuse']
senticnet['improperness'] = ['-0.88', '0', '0.705', '-0.86', '#sadness', '#disgust', 'negative', '-0.82', 'wrongness', 'inappropriateness', 'indecency', 'incorrectness', 'indelicacy']
senticnet['impropriety'] = ['-0.53', '0', '0.924', '-0.59', '#anger', '#disgust', 'negative', '-0.68', 'wrongness', 'inappropriateness', 'indecency', 'incorrectness', 'indelicacy']
senticnet['improvable'] = ['0.779', '-0.88', '0', '0.521', '#joy', '#surprise', 'positive', '0.727', 'amendable', 'correctable', 'corrigible', 'reformable', 'redeemable']
senticnet['improve'] = ['0.977', '0.805', '0', '0.908', '#joy', '#admiration', 'positive', '0.897', 'set_example', 'make_better', 'get_better', 'proposition', 'make_right']
senticnet['improve_appearance'] = ['0.89', '0.796', '0', '0.74', '#joy', '#interest', 'positive', '0.809', 'look_smart', 'look_neat', 'look_good', 'make_good_impression', 'look_presentable']
senticnet['improve_company'] = ['0', '0.312', '-0.13', '0.328', '#interest', '#admiration', 'positive', '0.168', 'increase', 'efficiency', 'increase_energy', 'work_hard', 'increase_efficiency']
senticnet['improve_glycemic_control'] = ['-0.72', '0', '0.973', '-0.57', '#sadness', '#anger', 'negative', '-0.76', 'corpulency', 'blubber', 'fat', 'embonpoint', 'stoutness']
senticnet['improve_health'] = ['0.071', '0', '0', '0', '#joy', '#joy', 'positive', '0.071', 'build_muscle', 'become_fit', 'stay_shape', 'exercise', 'good_health']
senticnet['improve_image'] = ['0', '-0.04', '0', '0.069', '#surprise', '#admiration', 'positive', '0.057', 'look_better', 'dress_nice', 'change_appearance', 'get_latest_hairstyle', 'groom']
senticnet['improve_knowledge'] = ['0.741', '0.898', '0', '0.734', '#joy', '#interest', 'positive', '0.791', 'plan_trip', 'desire_social_interaction', 'see_different', 'proselytize', 'buy_airline_ticket']
senticnet['improve_life'] = ['0.853', '0.936', '0', '0.949', '#interest', '#admiration', 'positive', '0.913', 'educate_others', 'add_knowledge', 'become_smart', 'increase_knowledge', 'expand_mind']
senticnet['improve_look'] = ['0.985', '0.931', '0', '0.789', '#joy', '#interest', 'positive', '0.902', 'look_good', 'look_smart', 'improve_appearance', 'look_presentable', 'look_neat']
senticnet['improve_manageability'] = ['0.637', '-0.91', '0', '0.757', '#surprise', '#admiration', 'positive', '0.768', 'tractability', 'manageableness', 'docility', 'obedience', 'amenability']
senticnet['improve_muscle_strength'] = ['0.84', '0.76', '0', '0.894', '#joy', '#admiration', 'positive', '0.831', 'keep_skin_healthy', 'stay_healthy', 'stay_fit', 'maintain_good_health', 'eat_healthily']
senticnet['improve_patient_care'] = ['-0.12', '0', '-0.12', '-0.08', '#sadness', '#fear', 'negative', '-0.11', 'hospital', 'check_up', 'check_doctor', 'check_pulse', 'check_blood_pressure']
senticnet['improve_performance'] = ['0.906', '0.719', '0', '0.962', '#joy', '#admiration', 'positive', '0.862', 'new_idea', 'appreciation', 'insight', 'increase_intelligence', 'inspiration']
senticnet['improve_self-image'] = ['0.178', '0.089', '-0.08', '0.119', '#joy', '#admiration', 'positive', '0.076', 'own_benefit', 'healthy_live', 'keep_body_healthy', 'prolong_life', 'think_clearly']
senticnet['improve_skill'] = ['0.896', '0.732', '0', '0.817', '#joy', '#admiration', 'positive', '0.815', 'please_teacher', 'find_new', 'pass_exam', 'raise_self_esteem', 'get_degree']
senticnet['improve_vision'] = ['-0.07', '-0.06', '0.01', '-0.11', '#sadness', '#disgust', 'negative', '-0.03', 'pupil', 'love_art', 'learn_evolution', 'use_eye', 'correct_vision']
senticnet['improve_world'] = ['0.836', '0.836', '0', '0.72', '#joy', '#interest', 'positive', '0.797', 'make_world_better_place', 'help_others', 'fame', 'personal_growth', 'personal_improvement']
senticnet['improved_durability'] = ['0.8', '-0.90', '0', '0.675', '#joy', '#surprise', 'positive', '0.794', 'indestructibility', 'tensile_strength', 'continuity', 'persistence', 'changelessness']
senticnet['improvement'] = ['0.925', '0.949', '0', '0.899', '#joy', '#interest', 'positive', '0.924', 'let', 'betterment', 'return', 'permission', 'imperative']
senticnet['improvement_retailer'] = ['0.912', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.917', 'carpet_store', 'lumberyard', 'furniture_store', 'front_porch', 'shoe_shop']
senticnet['improvement_thereon'] = ['0.621', '-0.60', '0', '0.545', '#joy', '#surprise', 'positive', '0.59', 'privilege', 'perquisite', 'public_easement', 'right_of_way', 'prerogative']
senticnet['improvidence'] = ['-0.91', '0', '0.598', '-0.52', '#sadness', '#anger', 'negative', '-0.68', 'parsimonious', 'stingy', 'scrimy', 'beggarly', 'chintzy']
senticnet['improvident'] = ['-0.63', '0', '0.867', '-0.57', '#sadness', '#anger', 'negative', '-0.69', 'myopic', 'short', 'shortsighted', 'unforesightful', 'thriftless']
senticnet['improving_patient_care'] = ['-0.12', '0', '-0.12', '-0.08', '#sadness', '#fear', 'negative', '-0.11', 'hospital', 'check_up', 'check_doctor', 'check_pulse', 'check_blood_pressure']
senticnet['improving_workplace_safety'] = ['0.032', '0.11', '0', '0.105', '#interest', '#admiration', 'positive', '0.082', 'work', 'place_work', 'implementation', 'disorganize', 'employment']
senticnet['improvisation'] = ['0.022', '0', '0', '0.109', '#joy', '#admiration', 'positive', '0.066', 'instrumental', 'singer', 'hip_hop', 'melodic_noise', 'music_player']
senticnet['improvisational'] = ['0.022', '0', '0', '0.109', '#joy', '#admiration', 'positive', '0.066', 'instrumental', 'singer', 'hip_hop', 'melodic_noise', 'music_player']
senticnet['improvisational_dance'] = ['-0.95', '0', '0.992', '-0.63', '#sadness', '#anger', 'negative', '-0.86', 'modern_dance', 'interpretive_dancing', 'interpretative_dance', 'interpretative_dancing', 'stage_dancing']
senticnet['improvisational_music'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'band_play', 'classical', 'composer', 'songwriter', 'rock_band']
senticnet['improvisatory'] = ['-0.67', '0', '0.936', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'unprepared', 'extemporary', 'extempore', 'impromptu', 'unrehearsed']
senticnet['improvise'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'impromptu', 'play_by_ear', 'natural', 'easy_going', 'spontaneous']
senticnet['imprudence'] = ['0', '0', '0.795', '-0.78', '#anger', '#disgust', 'negative', '-0.78', 'incaution', 'neglect', 'mindlessness', 'rashness', 'heedlessness']
senticnet['imprudent'] = ['-0.92', '0', '0.876', '-0.96', '#sadness', '#disgust', 'negative', '-0.92', 'bigmouthed', 'blabby', 'blabbermouthed', 'talkative', 'indiscreet']
senticnet['imprudently'] = ['-0.92', '0', '0.876', '-0.96', '#sadness', '#disgust', 'negative', '-0.92', 'bigmouthed', 'blabby', 'blabbermouthed', 'talkative', 'indiscreet']
senticnet['impudence'] = ['0.768', '-0.86', '0', '0.67', '#joy', '#surprise', 'positive', '0.769', 'expiable', 'excusable', 'forgivable', 'venial', 'minor']
senticnet['impudent'] = ['-0.79', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.82', 'brash', 'deposit', 'rock', 'insensitive', 'alluvial']
senticnet['impudently'] = ['0.881', '0.94', '0', '0', '#joy', '#interest', 'positive', '0.91', 'daringly', 'daring', 'intrepidly', 'venturously', 'boldly']
senticnet['impudicitia'] = ['-0.64', '0', '0.625', '-0.88', '#sadness', '#disgust', 'negative', '-0.71', 'difficulty', 'awkwardness', 'flea_bite', 'difficultness', 'cumbersomeness']
senticnet['impugn'] = ['0', '0', '-0.78', '-0.88', '#fear', '#disgust', 'negative', '-0.83', 'attack', 'impugnable', 'repugnant', 'pugilism', 'pugnacious']
senticnet['impugnable'] = ['0', '0', '-0.78', '-0.88', '#fear', '#disgust', 'negative', '-0.83', 'attack', 'impugn', 'repugnant', 'pugilism', 'pugnacious']
senticnet['impuissance'] = ['-0.96', '0', '0.531', '-0.51', '#sadness', '#anger', 'negative', '-0.67', 'impotence', 'weakness', 'unpersuasiveness', 'uninterestingness', 'helplessness']
senticnet['impuissant'] = ['-0.94', '0', '0.987', '-0.64', '#sadness', '#anger', 'negative', '-0.85', 'unable', 'ineffectual', 'ineffective', 'impotent', 'effete']
senticnet['impulse'] = ['-0.63', '0', '0.677', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'motive', 'need', 'motivation', 'abience', 'adience']
senticnet['impulse_shop'] = ['-0.03', '-0.04', '0.064', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'buy_clothes', 'spend_money', 'get_grocery', 'window_shop', 'buy_goods']
senticnet['impulse_toward'] = ['-0.80', '0', '0.507', '-0.76', '#sadness', '#disgust', 'negative', '-0.69', 'adamant', 'inflexible', 'intransigent', 'ironclad', 'brassbound']
senticnet['impulse_turbine'] = ['-0.97', '0', '0.658', '-0.60', '#sadness', '#anger', 'negative', '-0.74', 'gas_turbine', 'hydroelectric_turbine', 'turbine', 'reaction_turbine', 'steam_turbine']
senticnet['impulsion'] = ['0.714', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.78', 'impulse', 'impel', 'impeller', 'ago', 'action']
senticnet['impulsive'] = ['0.631', '-0.87', '0', '0.887', '#surprise', '#admiration', 'positive', '0.798', 'incautious', 'tearaway', 'hotheaded', 'madcap', 'impetuous']
senticnet['impulsive_behavior'] = ['0.995', '-0.73', '0', '0.83', '#joy', '#admiration', 'positive', '0.852', 'spiritism', 'otherworldliness', 'spirituality', 'spiritualism', 'internality']
senticnet['impulsively'] = ['-0.75', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'impulse', 'impulsive', 'force', 'impulsion', 'impulsiveness']
senticnet['impulsiveness'] = ['-0.93', '0', '0.672', '-0.94', '#sadness', '#disgust', 'negative', '-0.85', 'recklessness', 'foolhardiness', 'rashness', 'trait', 'unthoughtfulness']
senticnet['impulsivity'] = ['0.995', '-0.73', '0', '0.83', '#joy', '#admiration', 'positive', '0.852', 'spiritism', 'otherworldliness', 'spirituality', 'spiritualism', 'internality']
senticnet['impunity'] = ['0.73', '-0.56', '0', '0.621', '#joy', '#admiration', 'positive', '0.64', 'amnesty', 'unsusceptibility', 'diplomatic_immunity', 'indemnity', 'exemption']
senticnet['impure'] = ['-0.81', '0', '0.668', '-0.87', '#sadness', '#disgust', 'negative', '-0.78', 'dirty', 'dingy', 'muddied', 'alloyed', 'adulterate']
senticnet['impureness'] = ['0.693', '-0.64', '0', '0.895', '#joy', '#admiration', 'positive', '0.743', 'debasement', 'dirtiness', 'pollution', 'taint', 'adulteration']
senticnet['impurity'] = ['0', '0', '0.867', '-0.88', '#anger', '#disgust', 'negative', '-0.87', 'waste_material', 'dross', 'exhaust', 'garbage', 'corrupt']
senticnet['impurity_concentration'] = ['0.965', '-0.84', '0', '0.599', '#joy', '#surprise', 'positive', '0.802', 'mental_faculty', 'faculty', 'module', 'remembrance', 'anamnesis']
senticnet['imputability'] = ['0.511', '-0.59', '0', '0.576', '#surprise', '#admiration', 'positive', '0.561', 'due', 'traceable', 'ascribable', 'attributable', 'referable']
senticnet['imputable'] = ['0.511', '-0.59', '0', '0.576', '#surprise', '#admiration', 'positive', '0.561', 'due', 'traceable', 'ascribable', 'attributable', 'referable']
senticnet['imputrescible'] = ['-0.95', '0', '0.799', '-0.67', '#sadness', '#anger', 'negative', '-0.81', 'imperishable', 'undestroyable', 'indestructible', 'durable', 'perdurable']
senticnet['imran_tahir'] = ['0.034', '-0.02', '0.054', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'compass', 'swiss_army_knife', 'bake_pan', 'coffee_mug', 'wine_opener']
senticnet['imself'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'impaired', 'dickey', 'lessened', 'diminished']
senticnet['imsl'] = ['0', '0', '0.737', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'annoy', 'skin_rash', 'bother', 'irritation', 'great_dane']
senticnet['in'] = ['0.082', '0.191', '0', '0.198', '#interest', '#admiration', 'positive', '0.157', 'regal', 'imperial', 'greatness', 'nobleman', 'magnitude']
senticnet['in-demand'] = ['0', '-0.85', '0', '0.756', '#surprise', '#admiration', 'positive', '0.803', 'sought-after', 'coveted', 'renowned', 'prized', 'prestigious']
senticnet['in-depth'] = ['0.788', '0', '0', '0.961', '#joy', '#admiration', 'positive', '0.875', 'thorough', 'detailed', 'absolute', 'utter', 'comprehension']
senticnet['in-fighting'] = ['0', '0.967', '0', '0.734', '#interest', '#admiration', 'positive', '0.851', 'infighting', 'factionalism', 'squabbling', 'bickering', 'factional']
senticnet['in-patient'] = ['-0.96', '0', '0.745', '0', '#sadness', '#anger', 'negative', '-0.85', 'inpatient', 'out-patient', 'outpatient', 'ambulatory', 'psychiatric']
senticnet['in-person'] = ['0', '0', '-0.78', '-0.79', '#fear', '#disgust', 'negative', '-0.79', 'face-to-face', 'one-on-one', 'one-to-one', 'conversation', 'meeting']
senticnet['in-room'] = ['-0.87', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.87', 'surcharge', 'complimentary', 'fee', 'chargeable', 'surcharge']
senticnet['in-your-face'] = ['0', '-0.83', '-0.98', '0', '#surprise', '#fear', 'negative', '-0.07', 'confrontational', 'over-the-top', 'brash', 'hard-hitting', 'take-no-prisoners']
senticnet['in_between'] = ['0.034', '0.139', '0.005', '0.24', '#interest', '#admiration', 'positive', '0.102', 'center', 'middle', 'in_middle', 'downtown', 'placement']
senticnet['in_brief'] = ['-0.78', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.77', 'shortly', 'briefly', 'short', 'curtly', 'soon']
senticnet['in_charge'] = ['0.053', '0.248', '-0.18', '0.258', '#interest', '#admiration', 'positive', '0.093', 'first', 'dominant_role', 'alpha_male', 'master', 'alpha']
senticnet['in_dearth'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'shortage', 'scarceness', 'insufficient', 'short_fall', 'poverty']
senticnet['in_earnest'] = ['-0.70', '0', '-0.73', '0', '#sadness', '#fear', 'negative', '-0.71', 'devout', 'earnestness', 'earnestly', 'seriousness', 'seriously']
senticnet['in_effect'] = ['0', '0.872', '0', '0.819', '#interest', '#admiration', 'positive', '0.845', 'effectively', 'efficaciously', 'student', 'project', 'research']
senticnet['in_full'] = ['0.705', '0', '0', '0.965', '#joy', '#admiration', 'positive', '0.835', 'fully', 'to_the_full', 'full', 'amply', 'project']
senticnet['in_general'] = ['-0.87', '0', '0', '-1', '#sadness', '#disgust', 'negative', '-0.93', 'generally', 'broadly', 'mostly', 'loosely', 'in_the_main']
senticnet['in_high_spirits'] = ['0', '0.848', '0', '0.807', '#interest', '#admiration', 'positive', '0.828', 'high-pitched', 'eminent', 'gamy', 'high', 'higher']
senticnet['in_middle'] = ['0.034', '0.137', '0.008', '0.242', '#interest', '#admiration', 'positive', '0.101', 'in_between', 'middle', 'center', 'downtown', 'placement']
senticnet['in_person'] = ['0', '0', '-0.75', '-0.75', '#fear', '#disgust', 'negative', '-0.75', 'dead_person', 'corpse', 'cadaver', 'human_chest', 'below_neck']
senticnet['in_straitened_circumstance'] = ['0', '0', '-0.86', '-0.76', '#fear', '#disgust', 'negative', '-0.81', 'pinched', 'impecunious', 'hard_up', 'penniless', 'penurious']
senticnet['in_the_first_place'] = ['-0.97', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.97', 'chiefly', 'mainly', 'primarily', 'in_the_main', 'principally']
senticnet['in_the_main'] = ['-0.97', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.98', 'chiefly', 'mainly', 'in_the_first_place', 'primarily', 'principally']
senticnet['inability'] = ['-0.78', '0', '0.707', '-0.86', '#sadness', '#disgust', 'negative', '-0.78', 'mental_block', 'uncreativeness', 'stupidity', 'illiteracy', 'analphabetism']
senticnet['inability_host'] = ['0', '0', '0.933', '-0.86', '#anger', '#disgust', 'negative', '-0.89', 'mess', 'disgusting', 'dirty_house', 'dirty_kitchen', 'dirty_stuff']
senticnet['inaccessable'] = ['0', '0', '0.921', '-0.87', '#anger', '#disgust', 'negative', '-0.9', 'outback', 'trackless', 'pathless', 'roadless', 'untracked']
senticnet['inaccessibility'] = ['0', '0', '-0.94', '-0.74', '#fear', '#disgust', 'negative', '-0.84', 'non-availability', 'unavailability', 'interruption', 'discontinuance', 'absence']
senticnet['inaccessible'] = ['0', '0', '0.921', '-0.87', '#anger', '#disgust', 'negative', '-0.9', 'outback', 'trackless', 'pathless', 'roadless', 'untracked']
senticnet['inaccessible_terrain'] = ['0', '0', '0.921', '-0.87', '#anger', '#disgust', 'negative', '-0.9', 'outback', 'trackless', 'pathless', 'roadless', 'untracked']
senticnet['inaccuracy'] = ['-0.84', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.91', 'inexactly', 'inaccurately', 'inaccurate', 'incorrectly', 'off-key']
senticnet['inaccurate'] = ['-0.51', '0', '0.502', '-0.58', '#sadness', '#disgust', 'negative', '-0.53', 'wrong', 'incorrect', 'wide_of_mark', 'unfaithful', 'faulty']
senticnet['inaccurately'] = ['-0.83', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.90', 'inexactly', 'inaccuracy', 'inaccurate', 'incorrectly', 'off-key']
senticnet['inaction'] = ['0', '0', '0.808', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'idle', 'lazy', 'stay', 'take_easy', 'recline']
senticnet['inactivate'] = ['0', '0', '0.707', '-0.70', '#anger', '#disgust', 'negative', '-0.70', 'inactivating', 'inactivates', 'deactivate', 'neutralize', 'inhibit']
senticnet['inactivated_vaccine'] = ['-0.07', '0', '-0.05', '-0.18', '#sadness', '#disgust', 'negative', '-0.10', 'medical_therapy', 'vaccination', 'doctor', 'medicine', 'hospital']
senticnet['inactivates'] = ['0', '0', '0.704', '-0.70', '#anger', '#disgust', 'negative', '-0.70', 'inactivating', 'inactivate', 'deactivate', 'neutralize', 'inhibit']
senticnet['inactivating'] = ['0', '0', '0.707', '-0.71', '#anger', '#disgust', 'negative', '-0.71', 'inactivate', 'inactivates', 'deactivate', 'neutralize', 'inhibit']
senticnet['inactivation_gate'] = ['0', '0', '0.019', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'worm_intestine', 'bristle', 'fur_keep_warm', 'animal', 'whisker']
senticnet['inactive'] = ['0', '0', '0.808', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'inaction', 'lazy', 'stay', 'idle', 'recline']
senticnet['inactiveness'] = ['-0.65', '0', '0.793', '-0.81', '#anger', '#disgust', 'negative', '-0.75', 'halt', 'extinction', 'stagnancy', 'stagnation', 'stasis']
senticnet['inactivity'] = ['0', '0', '0', '-0.76', '#disgust', '#disgust', 'negative', '-0.76', 'coffee_break', 'rest_night', 'count_sheep', 'work_break', 'good_night']
senticnet['inadequacy'] = ['-0.51', '0', '0.998', '-0.76', '#anger', '#disgust', 'negative', '-0.75', 'lack_of_quality', 'meagreness', 'poorness', 'scantiness', 'scantness']
senticnet['inadequate'] = ['-0.58', '0', '0.996', '-0.84', '#anger', '#disgust', 'negative', '-0.81', 'undermanned', 'understaffed', 'deficient', 'lacking', 'wanting']
senticnet['inadequate_fluid_intake'] = ['-0.72', '0', '0.762', '-0.79', '#anger', '#disgust', 'negative', '-0.75', 'heat_hyperpyrexia', 'heat_prostration', 'prostration', 'sunstroke', 'thermic_fever']
senticnet['inadequate_intake'] = ['-0.71', '0', '0.74', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'malnutrition', 'marasmus', 'avitaminosis', 'hypovitaminosis', 'deficiency_disease']
senticnet['inadequate_nutrition'] = ['-0.56', '0', '-0.49', '-0.09', '#sadness', '#fear', 'negative', '-0.38', 'lack', 'absence', 'poverty', 'emptiness', 'hunger']
senticnet['inadequate_ventilation'] = ['0.718', '0', '0', '0', '#joy', '#joy', 'positive', '0.718', 'water_container', 'still_water', 'clean_water', 'water', 'cool_water']
senticnet['inadequateness'] = ['0.774', '-0.82', '0', '0.63', '#joy', '#surprise', 'positive', '0.744', 'quality', 'perishability', 'perishableness', 'unsatisfactoriness', 'inadequacy']
senticnet['inadmissible'] = ['-0.72', '0', '0.742', '-0.80', '#anger', '#disgust', 'negative', '-0.75', 'illicitness', 'illegality', 'illegitimacy', 'shadiness', 'quality']
senticnet['inadmissible_hearsay'] = ['0.817', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.824', 'witness', 'prosecutor', 'legal_process', 'lawyer', 'jury']
senticnet['inadvertence'] = ['-0.92', '0', '0.705', '0', '#sadness', '#anger', 'negative', '-0.81', 'inadvertently', 'miss', 'oversight', 'fault', 'supervision']
senticnet['inadvertency'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'negligence', 'evasion', 'dereliction', 'contributory_negligence']
senticnet['inadvertent'] = ['-0.83', '0', '0.508', '-0.79', '#sadness', '#disgust', 'negative', '-0.71', 'unwitting', 'unplanned', 'unintentional', 'accidental', 'unintended']
senticnet['inadvertent_disclosure'] = ['-0.02', '0.175', '-0.11', '0.148', '#interest', '#admiration', 'positive', '0.045', 'tolerant', 'phrase', 'inquiry', 'explain', 'question']
senticnet['inadvertently'] = ['-0.92', '0', '0.699', '0', '#sadness', '#anger', 'negative', '-0.81', 'oversight', 'miss', 'inadvertence', 'fault', 'supervision']
senticnet['inadvisable'] = ['-0.92', '0', '0.876', '-0.96', '#sadness', '#disgust', 'negative', '-0.92', 'bigmouthed', 'blabby', 'blabbermouthed', 'talkative', 'indiscreet']
senticnet['inalienable'] = ['0.867', '0.774', '0', '0', '#joy', '#interest', 'positive', '0.821', 'infrangible', 'nontransferable', 'unassignable', 'untransferable', 'inviolable']
senticnet['inane'] = ['-0.92', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.9', 'pointless', 'lack', 'meaning', 'void', 'vacuity']
senticnet['inanimate'] = ['0.03', '0', '0', '0.119', '#joy', '#admiration', 'positive', '0.074', 'notional', 'abstract', 'wow', 'made_out_of_plastic', 'make_paper']
senticnet['inanimate_matter'] = ['0.03', '0', '0', '0.119', '#joy', '#admiration', 'positive', '0.074', 'notional', 'abstract', 'wow', 'made_out_of_plastic', 'make_paper']
senticnet['inanimate_nature'] = ['0.03', '0', '0', '0.119', '#joy', '#admiration', 'positive', '0.074', 'notional', 'abstract', 'wow', 'made_out_of_plastic', 'make_paper']
senticnet['inanition'] = ['0.243', '0.632', '-0.23', '0.145', '#joy', '#interest', 'positive', '0.196', 'lassitude', 'emotionless', 'unemotionality', 'stolidity', 'phlegm']
senticnet['inanity'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['inapplicability'] = ['-0.91', '0', '0.795', '-0.94', '#sadness', '#disgust', 'negative', '-0.88', 'ineluctability', 'assured', 'predictability', 'slam_dunk', 'inevitability']
senticnet['inapplicable'] = ['-0.86', '0', '0.894', '-0.72', '#sadness', '#anger', 'negative', '-0.82', 'irrelevant', 'unsuitable', 'immaterial', 'extraneous', 'orthogonal']
senticnet['inapposite'] = ['-0.84', '0', '0.627', '-0.76', '#sadness', '#disgust', 'negative', '-0.74', 'difficulty', 'inspiration', 'determinant', 'revelation', 'divine_guidance']
senticnet['inappropriate'] = ['-0.85', '0', '0.959', '0', '#sadness', '#anger', 'negative', '-0.90', 'frivolous', 'bleep', 'facetiously', 'familiar', 'off-the-wall']
senticnet['inappropriate_behavior'] = ['0.723', '0.72', '0', '0', '#joy', '#interest', 'positive', '0.722', 'late', 'get_dark', 'rainy_day', 'split_end', 'house_look_better']
senticnet['inappropriately'] = ['0', '0', '0.966', '-0.81', '#anger', '#disgust', 'negative', '-0.89', 'illicitly', 'jackass', 'political', 'unfitly', 'inappropriate']
senticnet['inappropriateness'] = ['-0.88', '0', '0.705', '-0.86', '#sadness', '#disgust', 'negative', '-0.82', 'wrongness', 'improperness', 'indecency', 'incorrectness', 'indelicacy']
senticnet['inapt'] = ['-0.76', '0', '0.994', '-0.60', '#sadness', '#anger', 'negative', '-0.78', 'unfortunate', 'infelicitous', 'inept', 'awkward', 'clumsy']
senticnet['inaptitude'] = ['0.939', '-0.50', '0', '0.733', '#joy', '#admiration', 'positive', '0.724', 'smooth_out', 'clear', 'relieve', 'smooth', 'disembody']
senticnet['inarticulate'] = ['-0.56', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.63', 'aphasic', 'aphonic', 'speechless', 'unarticulated', 'dumb']
senticnet['inartistic'] = ['0.036', '0', '0.094', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'facial_hair', 'need_shave', 'need_beauty_center', 'long_beard', 'carelessness']
senticnet['inattention'] = ['-0.55', '0', '0.88', '-0.75', '#anger', '#disgust', 'negative', '-0.73', 'disregard', 'distraction', 'inattentiveness', 'neglect', 'heedlessness']
senticnet['inattentive'] = ['-0.71', '0', '0.906', '-0.93', '#anger', '#disgust', 'negative', '-0.85', 'absent', 'forgetful', 'distracted', 'distrait', 'scatty']
senticnet['inattentiveness'] = ['-0.93', '0', '0.689', '-0.85', '#sadness', '#disgust', 'negative', '-0.82', 'neglectfulness', 'carelessness', 'negligence', 'neglect', 'unconscientiousness']
senticnet['inaudible'] = ['-0.99', '0', '0.798', '-0.71', '#sadness', '#anger', 'negative', '-0.83', 'silent', 'voiceless', 'unsounded', 'breathed', 'supersonic']
senticnet['inaudibly'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'reverence', 'genuflection', 'obeisance', 'kowtow', 'mafia']
senticnet['inaugural'] = ['0.884', '0.855', '0', '0', '#joy', '#interest', 'positive', '0.869', 'maiden', 'opening', 'opening_move', 'enterprisingness', 'initiative']
senticnet['inaugural_ceremony'] = ['0.82', '0', '0', '0.885', '#joy', '#admiration', 'positive', '0.853', 'religious_event', 'sunday', 'happy_event', 'tradition', 'wedding']
senticnet['inaugural_exhibition'] = ['0.07', '0', '0', '0', '#joy', '#joy', 'positive', '0.07', 'artist', 'artwork', 'sculpture', 'tapestry', 'drawing']
senticnet['inaugural_flight'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'atmosphere', 'wing', 'nest', 'feather', 'bird_fly']
senticnet['inaugural_holder'] = ['0', '0', '0.88', '-0.92', '#anger', '#disgust', 'negative', '-0.90', 'sofa_bed', 'small_seat', 'beanbag_airline_seat', 'hide-a-bed_sofa', 'airline_seat']
senticnet['inaugural_meeting'] = ['0', '0.214', '0.012', '0.208', '#interest', '#admiration', 'positive', '0.137', 'lecture_hall', 'lecture_theater', 'lesson', 'lecture', 'school_session']
senticnet['inaugural_winner'] = ['0.232', '0.178', '-0.17', '0.223', '#joy', '#admiration', 'positive', '0.115', 'fame', 'alms', 'find_money', 'better_life', 'small_detail']
senticnet['inaugurate'] = ['0.872', '0', '0', '0.717', '#joy', '#admiration', 'positive', '0.794', 'generate', 'beget', 'engender', 'procreate', 'spawn']
senticnet['inauguration'] = ['0.82', '0', '0', '0.885', '#joy', '#admiration', 'positive', '0.853', 'religious_event', 'sunday', 'happy_event', 'tradition', 'wedding']
senticnet['inauguration_ceremony'] = ['0.82', '0', '0', '0.885', '#joy', '#admiration', 'positive', '0.853', 'religious_event', 'sunday', 'happy_event', 'tradition', 'wedding']
senticnet['inauspicious'] = ['-0.74', '0', '0.982', '-0.81', '#anger', '#disgust', 'negative', '-0.84', 'unfavorable', 'admonishing', 'reproachful', 'reproving', 'untoward']
senticnet['inauthentic'] = ['-0.86', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.80', 'ersatz', 'me-too', 'second-rate', 'overproduced', 'anachronistic']
senticnet['inborn'] = ['-0.95', '0', '0.684', '-0.76', '#sadness', '#disgust', 'negative', '-0.79', 'innate', 'nurtural', 'nonheritable', 'nontransmissible', 'nonhereditary']
senticnet['inborn_error'] = ['-0.57', '0', '0.973', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['inbound'] = ['-0.98', '0', '0.989', '-0.93', '#sadness', '#anger', 'negative', '-0.97', 'flight_accident', 'dangerous', 'danger', 'air_traffic', 'air_traffic_jam']
senticnet['inbound_traffic'] = ['-0.98', '0', '0.989', '-0.93', '#sadness', '#anger', 'negative', '-0.97', 'flight_accident', 'dangerous', 'danger', 'air_traffic', 'air_traffic_jam']
senticnet['inbox'] = ['0.968', '0.902', '0', '0', '#joy', '#interest', 'positive', '0.935', 'file_drawer', 'drawer', 'folder', 'organiser', 'blotter']
senticnet['inbox_folder'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'car_trunk', 'walmart', 'warehouse', 'store', 'storage_place']
senticnet['inbuilt'] = ['0.716', '-0.62', '0', '0.896', '#joy', '#admiration', 'positive', '0.746', 'constitutional', 'integral', 'intimate', 'inner', 'intrinsic']
senticnet['inc_yum'] = ['0.965', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.951', 'delicious_food', 'hot_meal', 'good_food', 'fresh_produce', 'tasty_food']
senticnet['incalculable'] = ['-0.61', '0', '0.784', '-0.84', '#anger', '#disgust', 'negative', '-0.74', 'indeterminable', 'infinite', 'myriad', 'immeasurable', 'countless']
senticnet['incalculable_damage'] = ['-0.87', '0', '0.889', '-0.62', '#sadness', '#anger', 'negative', '-0.79', 'devastation', 'ruin', 'deterioration', 'blight', 'impairment']
senticnet['incalculably'] = ['-0.12', '0.193', '0.597', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'upvalue', 'overvalue', 'overprize', 'overstimate', 'overprice']
senticnet['incandescence'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'brightness_level', 'luminescence', 'illuminance']
senticnet['incandescent'] = ['-0.83', '0', '0.896', '-0.95', '#anger', '#disgust', 'negative', '-0.89', 'storied', 'glorious', 'historied', 'divine', 'empyreal']
senticnet['incandescent_filament'] = ['-0.63', '0', '0.897', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'cd-rom', 'international_candle', 'candle', 'candlepower_unit', 'standard_candle']
senticnet['incandescent_lamp'] = ['-0.63', '0', '0.897', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'cd-rom', 'international_candle', 'candle', 'candlepower_unit', 'standard_candle']
senticnet['incandescent_light'] = ['-0.63', '0', '0.897', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'cd-rom', 'international_candle', 'candle', 'candlepower_unit', 'standard_candle']
senticnet['incandescent_lighting'] = ['-0.90', '0', '0.209', '-0.92', '#sadness', '#disgust', 'negative', '-0.68', 'sun_burn', 'erythema', 'fire', 'burn_skin', 'hurt_skin']
senticnet['incantation'] = ['0.022', '0.123', '0.038', '0.129', '#interest', '#admiration', 'positive', '0.059', 'hymn', 'written_music', 'walk_beach', 'lyric', 'piece_of_music']
senticnet['incantatory'] = ['0.898', '0.84', '0', '0', '#joy', '#interest', 'positive', '0.869', 'pondering', 'broody', 'contemplative', 'meditative', 'think']
senticnet['incapability'] = ['-0.50', '0', '0.807', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'powerlessness', 'impotence', 'impotency', 'ineffectiveness', 'inefficaciousness']
senticnet['incapable'] = ['-0.77', '0', '0.639', '-0.79', '#sadness', '#disgust', 'negative', '-0.73', 'unpersuadable', 'unsuasible', 'unresponsive', 'resistant', 'insusceptible']
senticnet['incapacitate'] = ['-0.59', '0', '0.704', '-0.5', '#sadness', '#anger', 'negative', '-0.59', 'lay_up', 'modify', 'nobble', 'alter', 'change']
senticnet['incapacitated'] = ['-0.95', '0', '0.701', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'helpless', 'powerless', 'feeble', 'weak', 'nerveless']
senticnet['incapacitating'] = ['0', '0', '0.909', '-0.93', '#anger', '#disgust', 'negative', '-0.92', 'non-lethal', 'lethal', 'less-lethal', 'less-than-lethal', 'nonlethal']
senticnet['incapacitating_agent'] = ['-0.90', '0', '0.529', '-0.84', '#sadness', '#disgust', 'negative', '-0.75', 'poison_gas', 'nerve_agent', 'mustard_gas', 'mustard_agent', 'blistering_agent']
senticnet['incapacitation'] = ['-0.56', '0', '-0.49', '-0.09', '#sadness', '#fear', 'negative', '-0.38', 'lack', 'poor', 'poverty', 'emptiness', 'hunger']
senticnet['incapacity'] = ['-0.94', '0', '0.923', '-0.98', '#sadness', '#disgust', 'negative', '-0.95', 'heap', 'push', 'accept', 'bring', 'lunge']
senticnet['incarcerate'] = ['-0.43', '0.107', '-0.42', '-0.03', '#sadness', '#fear', 'negative', '-0.19', 'send_to_jail', 'go_jail', 'jail_time', 'go_to_prison', 'injustice']
senticnet['incarceration'] = ['-0.43', '0.107', '-0.42', '-0.03', '#sadness', '#fear', 'negative', '-0.19', 'send_to_jail', 'go_jail', 'jail_time', 'go_to_prison', 'injustice']
senticnet['incarceration_rate'] = ['-0.47', '0.174', '-0.44', '0.105', '#sadness', '#fear', 'negative', '-0.16', 'wrong', 'wrongdoing', 'bad_choice', 'harmful_human', 'bad_behavior']
senticnet['incarnate'] = ['0.128', '0.152', '-0.09', '0.264', '#interest', '#admiration', 'positive', '0.113', 'environment', 'beautiful', 'live_environment', 'really_big', 'important_man']
senticnet['incarnational'] = ['-0.99', '0', '0.835', '-0.58', '#sadness', '#anger', 'negative', '-0.80', 'beginning', 'origin', 'root', 'wellspring', 'source']
senticnet['incaution'] = ['0.991', '-0.75', '0', '0.815', '#joy', '#admiration', 'positive', '0.852', 'imprudence', 'incautiousness', 'mindlessness', 'rashness', 'heedlessness']
senticnet['incautious'] = ['-0.70', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.71', 'impulsive', 'tearaway', 'hotheaded', 'madcap', 'impetuous']
senticnet['incautiousness'] = ['0.991', '-0.75', '0', '0.815', '#joy', '#admiration', 'positive', '0.852', 'incaution', 'imprudence', 'mindlessness', 'rashness', 'heedlessness']
senticnet['incendiarism'] = ['-0.83', '0', '0.711', '-0.63', '#sadness', '#anger', 'negative', '-0.72', 'inflammation', 'lighting', 'arson', 'burning', 'ignition']
senticnet['incendiary'] = ['0', '0', '-0.99', '-0.97', '#fear', '#disgust', 'negative', '-0.98', 'flame', 'combustible', 'dangerous', 'flammable', 'heat']
senticnet['incense'] = ['0.827', '0', '0', '0', '#joy', '#joy', 'positive', '0.827', 'wrathful', 'wrathfully', 'wroth', 'wrothful', 'lividity']
senticnet['incense_burner'] = ['0.827', '0', '0', '0', '#joy', '#joy', 'positive', '0.827', 'wrathful', 'wrathfully', 'wroth', 'wrothful', 'lividity']
senticnet['incense_cedar'] = ['0.045', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.356', 'ponderosa', 'bull_pine', 'western_yellow_pine', 'yellow_pine', 'longleaf_pine']
senticnet['incense_smoke'] = ['0.827', '0', '0', '0', '#joy', '#joy', 'positive', '0.827', 'wrathful', 'wrathfully', 'wroth', 'wrothful', 'lividity']
senticnet['incense_stick'] = ['0.827', '0', '0', '0', '#joy', '#joy', 'positive', '0.827', 'wrathful', 'wrathfully', 'wroth', 'wrothful', 'lividity']
senticnet['incensed'] = ['-0.14', '0.137', '0.623', '-0.63', '#anger', '#disgust', 'negative', '-0.31', 'outraged', 'umbrageous', 'indignant', 'outrage', 'indignation']
senticnet['incent'] = ['0.716', '0.813', '0', '0', '#joy', '#interest', 'positive', '0.764', 'bonus', 'reinforce', 'promotion', 'meritocracy', 'gratification']
senticnet['incenter'] = ['0.763', '-0.61', '0', '0.777', '#joy', '#admiration', 'positive', '0.717', 'acute_triangle', 'wedge_shape', 'cuneus', 'wedge', 'oblique_triangle']
senticnet['incentive'] = ['0.716', '0.813', '0', '0', '#joy', '#interest', 'positive', '0.764', 'bonus', 'reinforce', 'promotion', 'meritocracy', 'gratification']
senticnet['incentive_compatibility'] = ['-0.96', '0', '0.603', '-0.91', '#sadness', '#disgust', 'negative', '-0.82', 'potential', 'potency', 'conceivableness', 'conceivability', 'possibleness']
senticnet['incentive_laden'] = ['-0.04', '0.274', '-0.15', '0.278', '#interest', '#admiration', 'positive', '0.089', 'business_deal', 'trade', 'exchange', 'bargain', 'offer']
senticnet['incentive_payment'] = ['0.716', '0.813', '0', '0', '#joy', '#interest', 'positive', '0.764', 'bonus', 'reinforce', 'promotion', 'meritocracy', 'gratification']
senticnet['incentive_salience'] = ['0.795', '0', '0', '0.787', '#joy', '#admiration', 'positive', '0.791', 'entree', 'soup', 'wine_glass', 'salt_pepper_shaker', 'plate_silverware']
senticnet['incentivise'] = ['0.716', '0.813', '0', '0', '#joy', '#interest', 'positive', '0.764', 'bonus', 'reinforce', 'promotion', 'meritocracy', 'gratification']
senticnet['incentivize'] = ['0.716', '0.813', '0', '0', '#joy', '#interest', 'positive', '0.764', 'bonus', 'reinforce', 'promotion', 'meritocracy', 'gratification']
senticnet['incertitude'] = ['0.595', '-0.88', '0', '0.521', '#joy', '#surprise', 'positive', '0.666', 'incredulity', 'mental_rejection', 'skepticism', 'mistrust', 'distrust']
senticnet['incessant'] = ['0', '0', '-0.70', '-0.95', '#fear', '#disgust', 'negative', '-0.82', 'unceasing', 'never-ending', 'constant', 'ceaseless', 'perpetual']
senticnet['incessantly'] = ['-0.82', '0', '0.778', '0', '#sadness', '#anger', 'negative', '-0.80', 'incessant', 'cease', 'ramble', 'motormouth', 'pause']
senticnet['incest'] = ['0.752', '-0.57', '0', '0.967', '#joy', '#admiration', 'positive', '0.765', 'criminal_congress', 'extramarital_sex', 'free_love', 'sexual_intercourse', 'intercourse']
senticnet['incestuous_marriage'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'spousal_relationship', 'bigamy']
senticnet['inch'] = ['0', '0', '0.122', '0', '#anger', '#anger', 'negative', '-0.12', 'mile', 'ohm', 'meter', 'ounce', 'length']
senticnet['inch_barrel'] = ['-0.97', '0', '0.677', '-0.95', '#sadness', '#disgust', 'negative', '-0.86', 'stunt', 'game', 'mutant', 'invertebrate', 'animate_being']
senticnet['inch_blade'] = ['0.818', '-0.95', '0', '0.695', '#joy', '#surprise', 'positive', '0.823', 'jackknife', 'clasp_knife', 'penknife', 'switchblade', 'switchblade_knife']
senticnet['inch_gash'] = ['-0.12', '-0.02', '0', '-0.15', '#sadness', '#disgust', 'negative', '-0.08', 'incision', 'laceration', 'abrasion', 'cut_skin', 'painful_injury']
senticnet['inch_knife'] = ['0.818', '-0.95', '0', '0.695', '#joy', '#surprise', 'positive', '0.823', 'jackknife', 'clasp_knife', 'penknife', 'switchblade', 'switchblade_knife']
senticnet['inch_mm'] = ['0.734', '0.884', '0', '0', '#joy', '#interest', 'positive', '0.809', 'dimension', 'size', 'mass_measure', 'length_dimension', 'gauge']
senticnet['inch_telescope'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'binoculars', 'read_glass', 'eyeglass', 'camera', 'monocle']
senticnet['inch_vinyl'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
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senticnet['increasingly_skeptical'] = ['0', '0', '-0.99', '-0.99', '#fear', '#disgust', 'negative', '-0.99', 'unbelieving', 'sceptical', 'doubting', 'disbelieving', 'questioning']
senticnet['increasingly_tense'] = ['-0.17', '0.154', '-0.28', '0.073', '#sadness', '#fear', 'negative', '-0.05', 'doctor_visit', 'bad_feeling', 'consequence', 'symptom', 'physical_reaction']
senticnet['increasingly_vital_role'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'baddie', 'theatrical_baddie', 'part', 'persona']
senticnet['incredibility'] = ['0.536', '-0.71', '0', '0.533', '#joy', '#surprise', 'positive', '0.594', 'incredulity', 'mental_rejection', 'skepticism', 'mistrust', 'distrust']
senticnet['incredible'] = ['0', '0.735', '0', '0.727', '#interest', '#admiration', 'positive', '0.731', 'credible', 'eye-popping', 'awe', 'amazing', 'profoundly']
senticnet['incredible_courage'] = ['-0.94', '0', '0.745', '-0.97', '#sadness', '#disgust', 'negative', '-0.89', 'nerve', 'fearlessness', 'heart', 'dauntlessness', 'intrepidity']
senticnet['incredible_feat'] = ['0.749', '0', '0', '0.952', '#joy', '#admiration', 'positive', '0.851', 'high_class', 'citation', 'suspicion', 'goodness', 'prosperity']
senticnet['incredible_talent'] = ['0.608', '-0.55', '0', '0.726', '#joy', '#admiration', 'positive', '0.628', 'genius', 'endowment', 'flair', 'gift', 'knack']
senticnet['incredibly'] = ['-0.73', '0', '-0.84', '0', '#sadness', '#fear', 'negative', '-0.78', 'fabulously', 'fantastically', 'improbably', 'implausibly', 'unbelievably']
senticnet['incredibly_annoying'] = ['-0.97', '0', '-0.97', '-0.84', '#sadness', '#fear', 'negative', '-0.93', 'school', 'school_system', 'class', 'college', 'college_class']
senticnet['incredibly_catchy'] = ['-0.89', '0', '0.722', '-0.94', '#sadness', '#disgust', 'negative', '-0.85', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['incredibly_cheap'] = ['-0.04', '0.201', '-0.06', '0.312', '#interest', '#admiration', 'positive', '0.101', 'save_money', 'good_price', 'inexpensive', 'bargain', 'discount']
senticnet['incredibly_difficult'] = ['-0.38', '0.165', '-0.32', '0.243', '#sadness', '#fear', 'negative', '-0.07', 'hard_to_define', 'inherit', 'harmful_human', 'bad_choice', 'badness']
senticnet['incredibly_diverse'] = ['0.057', '0', '0', '0.153', '#joy', '#admiration', 'positive', '0.105', 'multiple', 'cute', 'variety', 'hairy', 'large_breadbox']
senticnet['incredibly_expensive'] = ['-0.78', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.86', 'high_cost', 'spend_money', 'waste_money', 'high_price', 'unaffordable']
senticnet['incredibly_frustrating'] = ['-0.13', '0.071', '0.694', '-0.67', '#anger', '#disgust', 'negative', '-0.35', 'bedevil', 'crucify', 'dun', 'aggravate', 'exasperation']
senticnet['incredibly_funny'] = ['-0.90', '0.138', '-0.63', '0.071', '#sadness', '#fear', 'negative', '-0.33', 'woeful', 'execrable', 'deplorable', 'woebegone', 'plaintiveness']
senticnet['incredibly_rich'] = ['0.843', '0.977', '0', '0', '#joy', '#interest', 'positive', '0.91', 'business', 'finance', 'revenue', 'wealth', 'income']
senticnet['incredibly_smart'] = ['0.995', '0.801', '0', '0.968', '#joy', '#admiration', 'positive', '0.921', 'acquire_knowledge', 'comprehend', 'great', 'accumulate_knowledge', 'increase_knowledge']
senticnet['incredibly_useful'] = ['0.868', '0.717', '0', '0', '#joy', '#interest', 'positive', '0.792', 'tool_shed', 'tool', 'garage', 'tool_belt', 'hardware_store']
senticnet['incredibly_versatile'] = ['0.085', '0.218', '-0.07', '0.308', '#interest', '#admiration', 'positive', '0.135', 'delicate', 'useful', 'good', 'light_colored', 'beautiful']
senticnet['incredulity'] = ['0.911', '-0.76', '0', '0.802', '#joy', '#admiration', 'positive', '0.825', 'doubt', 'incertitude', 'dubiety', 'doubtfulness', 'dubiousness']
senticnet['incredulous'] = ['-0.71', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.75', 'quizzical', 'rueful', 'bemused', 'puzzled', 'sidelong']
senticnet['incredulously'] = ['0', '-0.90', '0', '0.925', '#surprise', '#admiration', 'positive', '0.913', 'questioningly', 'worriedly', 'quizzically', 'inquisitively', 'blankly']
senticnet['increment'] = ['0.037', '0', '0.026', '0.129', '#joy', '#admiration', 'positive', '0.047', 'measurement_unit', 'many_items', 'long', 'circle_part', 'narrow']
senticnet['incremental'] = ['0.037', '0', '0.026', '0.129', '#joy', '#admiration', 'positive', '0.047', 'measurement_unit', 'many_items', 'long', 'circle_part', 'narrow']
senticnet['incremental_cost'] = ['-0.02', '0.249', '0', '0.116', '#interest', '#admiration', 'positive', '0.112', 'price', 'expense', 'bill', 'value', 'worth']
senticnet['incrementalism'] = ['0.545', '-0.59', '0', '0.865', '#surprise', '#admiration', 'positive', '0.67', 'farseeing', 'longsighted', 'telescopic', 'hyperopic', 'hypermetropic']
senticnet['incrementality'] = ['0.929', '-0.71', '0', '0.605', '#joy', '#surprise', 'positive', '0.748', 'incisiveness', 'trenchancy', 'efficacy', 'efficaciousness', 'power']
senticnet['incrementally'] = ['0.893', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.915', 'dramatically', 'steadily', 'considerably', 'exponentially', 'rapidly']
senticnet['incriminate'] = ['-0.06', '0', '-0.09', '-0.18', '#fear', '#disgust', 'negative', '-0.11', 'accuse', 'become_lawyer', 'liar', 'accuse_person', 'blame']
senticnet['incriminating_evidence'] = ['0', '0', '-0.88', '-0.99', '#fear', '#disgust', 'negative', '-0.93', 'murder_suspect', 'robbery_suspect', 'rape_suspect', 'murder', 'doubt']
senticnet['incriminating_testimony'] = ['-0.73', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'obstruct_justice', 'get_away_crime', 'punish', 'hurt_else', 'commit_murder']
senticnet['incrimination'] = ['-0.70', '0', '0.874', '-0.75', '#anger', '#disgust', 'negative', '-0.77', 'blame', 'accusation', 'inculpation', 'complaint', 'imprecation']
senticnet['incubated_overnight'] = ['-0.86', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.80', 'riverbed', 'bottom_ocean', 'waterfall', 'down_drain', 'septic_tank']
senticnet['incubus'] = ['0.888', '-0.63', '0', '0.892', '#joy', '#admiration', 'positive', '0.805', 'daemon', 'daimon', 'devil', 'demon', 'fiend']
senticnet['inculcate'] = ['-0.94', '0', '0.834', '0', '#sadness', '#anger', 'negative', '-0.88', 'inculcation', 'induce', 'repeat', 'sermonize', 'teach']
senticnet['inculcation'] = ['-0.94', '0', '0.83', '0', '#sadness', '#anger', 'negative', '-0.88', 'inculcate', 'induce', 'repeat', 'sermonize', 'teach']
senticnet['inculpate'] = ['0.789', '-0.98', '0', '0.765', '#joy', '#surprise', 'positive', '0.846', 'show', 'imply', 'smack', 'incriminate', 'paint_picture']
senticnet['inculpation'] = ['-0.70', '0', '0.874', '-0.75', '#anger', '#disgust', 'negative', '-0.77', 'blame', 'incrimination', 'accusation', 'complaint', 'imprecation']
senticnet['inculpative'] = ['-0.95', '0', '0.771', '-0.67', '#sadness', '#anger', 'negative', '-0.8', 'condemnatory', 'condemning', 'accusatory', 'accusing', 'accusive']
senticnet['inculpatory'] = ['-0.63', '0', '0.967', '-0.67', '#anger', '#disgust', 'negative', '-0.76', 'condemnatory', 'condemning', 'accusatory', 'accusing', 'accusive']
senticnet['incumbent'] = ['0', '0.8', '0', '0.865', '#interest', '#admiration', 'positive', '0.833', 'incumbent', 'republican', 'democratic', 'candidate', 'elected']
senticnet['incumbent_congressman'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['incumbent_lec'] = ['0', '0.089', '0.01', '0.118', '#interest', '#admiration', 'positive', '0.066', 'crate', 'mom_house', 'sack', 'poker_game', 'safekeep']
senticnet['incumbent_on'] = ['0.919', '-0.56', '0', '0.747', '#joy', '#admiration', 'positive', '0.742', 'bounden', 'compulsory', 'obligate', 'obligatory', 'mandatory']
senticnet['incumbent_president'] = ['0', '0.156', '-0.15', '0.179', '#interest', '#admiration', 'positive', '0.061', 'candidate', 'presidential', 'vote', 'elect', 'appoint']
senticnet['incumbent_senator'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['incumbent_upon'] = ['-0.81', '0', '0.979', '-0.88', '#anger', '#disgust', 'negative', '-0.89', 'vexation', 'dead_weight', 'imposition', 'worry', 'concern']
senticnet['incumbrance'] = ['-0.93', '0', '0.922', '-0.79', '#sadness', '#anger', 'negative', '-0.88', 'onus', 'burden', 'encumbrance', 'dead_weight', 'load']
senticnet['incur'] = ['-0.03', '0.269', '-0.06', '0.136', '#interest', '#admiration', 'positive', '0.078', 'cost', 'money_payment', 'debit', 'price', 'money_charge']
senticnet['incur_debt'] = ['-0.82', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.78', 'go_broke', 'receive_change', 'run_out_money', 'compare_price', 'bankruptcy']
senticnet['incur_significant'] = ['-0.03', '0.269', '-0.06', '0.136', '#interest', '#admiration', 'positive', '0.078', 'cost', 'money_payment', 'debit', 'price', 'money_charge']
senticnet['incurable'] = ['-0.92', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.93', 'irrecoverable', 'irretrievable', 'shipwreck', 'retrievable', 'irreparable']
senticnet['incurable_cancer'] = ['-0.09', '0', '-0.16', '-0.21', '#fear', '#disgust', 'negative', '-0.15', 'sickness', 'malady', 'polio', 'disease', 'ailment']
senticnet['incurable_disease'] = ['0.713', '0', '0', '0.766', '#joy', '#admiration', 'positive', '0.74', 'doctor', 'nurse', 'medical_procedure', 'medicine', 'doctor_surgery']
senticnet['incurably'] = ['-0.63', '0', '0.591', '-0.73', '#sadness', '#disgust', 'negative', '-0.65', 'irreality', 'void', 'impossibleness', 'unreality', 'impossibility']
senticnet['incurrent'] = ['-0.93', '0', '0.607', '-0.67', '#sadness', '#disgust', 'negative', '-0.73', 'spiracle', 'stigma', 'orifice', 'opening', 'porta']
senticnet['incurring_debt'] = ['-0.82', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.78', 'go_broke', 'receive_change', 'run_out_money', 'compare_price', 'bankruptcy']
senticnet['incursion'] = ['0.954', '-0.87', '0', '0.583', '#joy', '#surprise', 'positive', '0.802', 'penetration', 'air_raid', 'air_attack', 'swoop', 'foray']
senticnet['incursion_into'] = ['0.954', '-0.87', '0', '0.583', '#joy', '#surprise', 'positive', '0.802', 'penetration', 'air_raid', 'air_attack', 'swoop', 'incursion']
senticnet['indebtedness'] = ['0.628', '-0.60', '0', '0.532', '#joy', '#surprise', 'positive', '0.589', 'account_payable', 'payable', 'limited_liability', 'debt', 'arrears']
senticnet['indecency'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'obscenity']
senticnet['indecent'] = ['0', '0', '0', '-0.95', '#disgust', '#disgust', 'negative', '-0.95', 'unethical', 'bad', 'obscene', 'immoral', 'shameless']
senticnet['indecent_act'] = ['0.542', '-0.83', '0', '0.927', '#surprise', '#admiration', 'positive', '0.769', 'incest', 'extramarital_sex', 'free_love', 'sexual_intercourse', 'intercourse']
senticnet['indecent_content'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'indecency']
senticnet['indecent_language'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['indecent_material'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'indecency']
senticnet['indecent_proposal'] = ['0.542', '-0.83', '0', '0.927', '#surprise', '#admiration', 'positive', '0.769', 'incest', 'extramarital_sex', 'free_love', 'sexual_intercourse', 'intercourse']
senticnet['indecipherable'] = ['-0.82', '0', '0.758', '-0.70', '#sadness', '#anger', 'negative', '-0.76', 'unclassifiable', 'unidentifiable', 'unrecognisable', 'intangible', 'undiagnosable']
senticnet['indecision'] = ['-0.74', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.82', 'look_buy', 'window_shop', 'spend_money', 'pay_check', 'impulse_shop']
senticnet['indecisive'] = ['0.778', '-0.50', '0', '0.517', '#joy', '#admiration', 'positive', '0.599', 'on_fence', 'suspensive', 'undecided', 'hesitant', 'hesitating']
senticnet['indecisive_battle'] = ['-0.05', '0', '-0.08', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'siege', 'big_fight', 'war', 'declare_war', 'combat']
senticnet['indecisiveness'] = ['0.928', '-0.83', '0', '0.656', '#joy', '#surprise', 'positive', '0.806', 'wavering', 'doubt', 'incertitude', 'dubiety', 'doubtfulness']
senticnet['indecorous'] = ['0', '0', '-0.84', '-0.72', '#fear', '#disgust', 'negative', '-0.78', 'uncomely', 'unseemly', 'indecent', 'unbecoming', 'indelicate']
senticnet['indeed'] = ['0', '0.73', '0', '0.813', '#interest', '#admiration', 'positive', '0.771', 'truly', 'frank', 'amen', 'faithfully', 'accurately']
senticnet['indef_blocked_user'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['indefatigability'] = ['-0.94', '0', '0.745', '-0.97', '#sadness', '#disgust', 'negative', '-0.89', 'nerve', 'fearlessness', 'heart', 'dauntlessness', 'intrepidity']
senticnet['indefatigable'] = ['0', '0', '0.8', '-0.82', '#anger', '#disgust', 'negative', '-0.81', 'tireless', 'untiring', 'unflagging', 'unstinting', 'unremitting']
senticnet['indefatigably'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'hardy', 'fearlessness', 'fearlessly', 'unafraid', 'unfearing']
senticnet['indefensible'] = ['-0.73', '0', '0.846', '-0.68', '#sadness', '#anger', 'negative', '-0.75', 'inexcusable', 'unwarranted', 'insupportable', 'unjustifiable', 'unwarrantable']
senticnet['indefinable'] = ['0', '0', '-0.86', '-0.77', '#fear', '#disgust', 'negative', '-0.82', 'untellable', 'unspeakable', 'indescribable', 'ineffable', 'unutterable']
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senticnet['indefinite_article'] = ['0', '0.156', '-0.06', '0.241', '#interest', '#admiration', 'positive', '0.11', 'mixture', 'noisy_place', 'make_go', 'move_vehicle', 'big_car']
senticnet['indefinite_future'] = ['-0.96', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.87', 'hesitation', 'stay', 'rest_foot', 'pause', 'rest_leg']
senticnet['indefinite_pronoun'] = ['0.633', '-0.67', '0', '0.776', '#surprise', '#admiration', 'positive', '0.693', 'double_entendre', 'loophole', 'ambiguity', 'amphiboly', 'parisology']
senticnet['indefinite_quantity'] = ['-0.69', '0', '0.74', '-0.66', '#sadness', '#anger', 'negative', '-0.70', 'large_indefinite_quantity', 'large_indefinite_amount', 'small_indefinite_quantity', 'small_indefinite_amount', 'breakage']
senticnet['indefinite_strike'] = ['-0.77', '0', '0.505', '-0.50', '#sadness', '#anger', 'negative', '-0.59', 'wildcat_strike', 'sympathy_strike', 'sympathetic_strike', 'walkout', 'job_action']
senticnet['indefinite_suspension'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'steer_wheel', 'gear_shift', 'chassis', 'brake_pedal', 'air_vent']
senticnet['indefinitely'] = ['-0.70', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.78', 'forever', 'persistent', 'recurring', 'numerous', 'permanent']
senticnet['indefinitely_blocked_user'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['indefiniteness'] = ['0.558', '-0.76', '0', '0.568', '#surprise', '#admiration', 'positive', '0.63', 'inconclusiveness', 'indeterminateness', 'indefinity', 'indetermination', 'indeterminacy']
senticnet['indefinity'] = ['0.558', '-0.76', '0', '0.568', '#surprise', '#admiration', 'positive', '0.63', 'indefiniteness', 'indeterminateness', 'inconclusiveness', 'indetermination', 'indeterminacy']
senticnet['indehiscent'] = ['0', '0', '0.064', '-0.04', '#anger', '#disgust', 'negative', '-0.05', 'bird_food', 'funiculus', 'plant_product', 'fuzz', 'agricultural_product']
senticnet['indelible'] = ['0', '0.827', '0', '0.846', '#interest', '#admiration', 'positive', '0.837', 'inexterminable', 'inextirpable', 'inexpungible', 'inexpungeable', 'ineradicable']
senticnet['indelible_mark'] = ['0', '0.827', '0', '0.846', '#interest', '#admiration', 'positive', '0.837', 'inexterminable', 'inextirpable', 'inexpungible', 'inexpungeable', 'ineradicable']
senticnet['indelibly'] = ['0', '0.827', '0', '0.846', '#interest', '#admiration', 'positive', '0.837', 'inexterminable', 'inextirpable', 'inexpungible', 'inexpungeable', 'ineradicable']
senticnet['indelicacy'] = ['-0.69', '0', '0.597', '-0.56', '#sadness', '#anger', 'negative', '-0.62', 'improperness', 'impropriety', 'raciness', 'spiciness', 'gaminess']
senticnet['indelicate'] = ['0', '0', '-0.85', '-0.89', '#fear', '#disgust', 'negative', '-0.87', 'improper', 'indelicately', 'coarse', 'indelicateness', 'immodest']
senticnet['indelicately'] = ['0', '0', '-0.86', '-0.89', '#fear', '#disgust', 'negative', '-0.88', 'improper', 'indelicate', 'coarse', 'indelicateness', 'immodest']
senticnet['indelicateness'] = ['0', '0', '-0.86', '-0.90', '#fear', '#disgust', 'negative', '-0.88', 'improper', 'indelicately', 'coarse', 'indelicate', 'immodest']
senticnet['indemnification'] = ['-0.88', '0', '0.51', '-0.91', '#sadness', '#disgust', 'negative', '-0.77', 'atonement', 'expiation', 'punitive_damage', 'exemplary_damage', 'compensation']
senticnet['indemnify'] = ['-0.87', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.82', 'innocuous', 'harmful', 'benign', 'harmless', 'liable']
senticnet['indemnity'] = ['0.73', '-0.56', '0', '0.621', '#joy', '#admiration', 'positive', '0.64', 'amnesty', 'unsusceptibility', 'diplomatic_immunity', 'exemption', 'impunity']
senticnet['indemnity_agreement'] = ['0.73', '-0.56', '0', '0.621', '#joy', '#admiration', 'positive', '0.64', 'amnesty', 'unsusceptibility', 'diplomatic_immunity', 'exemption', 'impunity']
senticnet['indemnity_payment'] = ['0.73', '-0.56', '0', '0.621', '#joy', '#admiration', 'positive', '0.64', 'amnesty', 'unsusceptibility', 'diplomatic_immunity', 'exemption', 'impunity']
senticnet['indemonstrable'] = ['-0.92', '0', '0.547', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'assumption', 'boundary_condition', 'provision', 'proviso', 'precondition']
senticnet['indent'] = ['-0.98', '0', '0.959', '-0.68', '#sadness', '#anger', 'negative', '-0.87', 'expanse', 'margin', 'blank_spaceation', 'blank_spaceion', 'blank_space']
senticnet['indentation'] = ['-0.98', '0', '0.959', '-0.68', '#sadness', '#anger', 'negative', '-0.87', 'expanse', 'margin', 'blank_space', 'indention', 'indent']
senticnet['indentation_depth'] = ['-0.54', '0', '0.909', '-0.97', '#anger', '#disgust', 'negative', '-0.81', 'suppleness', 'pliancy', 'pliantness', 'flexibility', 'flexibleness']
senticnet['indention'] = ['-0.98', '0', '0.959', '-0.68', '#sadness', '#anger', 'negative', '-0.87', 'expanse', 'margin', 'indentation', 'blank_space', 'indent']
senticnet['indentured_labor'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'vassalage']
senticnet['indentured_servant'] = ['0', '0', '0.8', '-0.82', '#anger', '#disgust', 'negative', '-0.81', 'personnel', 'attendant', 'manager', 'elect_official', 'gentleman']
senticnet['indentured_servitude'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'vassalage']
senticnet['independence'] = ['0.887', '0.748', '0', '0.954', '#joy', '#admiration', 'positive', '0.863', 'political_action', 'political', 'mediocrity', 'discrimination', 'independent']
senticnet['independence_day'] = ['0.898', '0.877', '0', '0.767', '#joy', '#interest', 'positive', '0.847', 'independent', 'democracy', 'pledge_allegiance', 'unite', 'free_country']
senticnet['independence_movement'] = ['0.887', '0.748', '0', '0.954', '#joy', '#admiration', 'positive', '0.863', 'political_action', 'political', 'mediocrity', 'discrimination', 'independent']
senticnet['independent'] = ['0.904', '0.881', '0', '0.774', '#joy', '#interest', 'positive', '0.853', 'independence_day', 'democracy', 'pledge_allegiance', 'unite', 'free_country']
senticnet['independent_auditor'] = ['0.801', '0.86', '0', '0', '#joy', '#interest', 'positive', '0.831', 'credit_card', 'profit', 'mastercard', 'american_express', 'back_to_black']
senticnet['independent_consultant'] = ['0.891', '0.721', '0', '0.921', '#joy', '#admiration', 'positive', '0.844', 'inventor', 'connoisseur', 'medical_doctor', 'teacher', 'judge']
senticnet['independent_inquiry'] = ['0.845', '0.916', '0', '0', '#joy', '#interest', 'positive', '0.881', 'question', 'answer', 'query', 'question_start', 'question_answering']
senticnet['independent_judicial_inquiry'] = ['0.845', '0.916', '0', '0', '#joy', '#interest', 'positive', '0.881', 'question', 'answer', 'query', 'question_start', 'question_answering']
senticnet['independent_judiciary'] = ['0.837', '-0.99', '0', '0.559', '#joy', '#surprise', 'positive', '0.797', 'disinterested', 'dispassionate', 'indifferent', 'unbiased', 'fair']
senticnet['independent_label'] = ['0', '0.115', '0.036', '0.207', '#interest', '#admiration', 'positive', '0.095', 'total', 'amount', 'merit', 'award', 'excellence']
senticnet['independent_mlc'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'couch', 'stool', 'coach_seat']
senticnet['independent_record_label'] = ['0', '0.115', '0.036', '0.207', '#interest', '#admiration', 'positive', '0.095', 'total', 'amount', 'merit', 'award', 'excellence']
senticnet['independent_research_career'] = ['0', '0.799', '0', '0.867', '#interest', '#admiration', 'positive', '0.833', 'promising_student', 'award', 'project', 'research', 'study']
senticnet['independent_statehood'] = ['0.887', '0.748', '0', '0.954', '#joy', '#admiration', 'positive', '0.863', 'political_action', 'political', 'mediocrity', 'discrimination', 'independent']
senticnet['independent_verification'] = ['0.84', '-0.97', '0', '0.713', '#joy', '#surprise', 'positive', '0.844', 'proof', 'bed_check', 'crosscheck', 'parity_check', 'redundancy_check']
senticnet['independently_wealthy'] = ['0.973', '0.707', '0', '0', '#joy', '#interest', 'positive', '0.84', 'money', 'down_payment', 'much_money', 'slogan', 'take_out_loan']
senticnet['indescribable'] = ['0', '0', '-0.86', '-0.78', '#fear', '#disgust', 'negative', '-0.82', 'untellable', 'unspeakable', 'ineffable', 'indefinable', 'unutterable']
senticnet['indescribably'] = ['0.87', '0', '0', '0.742', '#joy', '#admiration', 'positive', '0.806', 'memorably', 'enchantingly', 'terrifically', 'unforgettably', 'hauntingly']
senticnet['indestructable'] = ['0', '0.839', '0', '0.963', '#interest', '#admiration', 'positive', '0.901', 'imputrescible', 'undestroyable', 'imperishable', 'durable', 'perdurable']
senticnet['indestructibility'] = ['0.8', '-0.90', '0', '0.675', '#joy', '#surprise', 'positive', '0.794', 'durability', 'tensile_strength', 'continuity', 'persistence', 'changelessness']
senticnet['indestructible'] = ['0', '0.839', '0', '0.963', '#interest', '#admiration', 'positive', '0.901', 'imputrescible', 'undestroyable', 'imperishable', 'durable', 'perdurable']
senticnet['indeterminable'] = ['-0.61', '0', '0.784', '-0.84', '#anger', '#disgust', 'negative', '-0.74', 'incalculable', 'infinite', 'myriad', 'immeasurable', 'countless']
senticnet['indeterminacy'] = ['0.558', '-0.76', '0', '0.568', '#surprise', '#admiration', 'positive', '0.63', 'indefiniteness', 'indeterminateness', 'indefinity', 'indetermination', 'inconclusiveness']
senticnet['indeterminancy'] = ['0.558', '-0.76', '0', '0.568', '#surprise', '#admiration', 'positive', '0.63', 'indefiniteness', 'indeterminateness', 'indefinity', 'indetermination', 'inconclusiveness']
senticnet['indeterminate'] = ['-0.64', '0', '0.576', '-0.83', '#sadness', '#disgust', 'negative', '-0.68', 'nisi', 'inconclusive', 'equivocal', 'ambiguous', 'uncertain']
senticnet['indeterminateness'] = ['0.558', '-0.76', '0', '0.568', '#surprise', '#admiration', 'positive', '0.63', 'indefiniteness', 'inconclusiveness', 'indefinity', 'indetermination', 'indeterminacy']
senticnet['indetermination'] = ['0.558', '-0.76', '0', '0.568', '#surprise', '#admiration', 'positive', '0.63', 'indefiniteness', 'indeterminateness', 'indefinity', 'inconclusiveness', 'indeterminacy']
senticnet['indeterminism'] = ['0.558', '-0.76', '0', '0.568', '#surprise', '#admiration', 'positive', '0.63', 'indefiniteness', 'indeterminateness', 'indefinity', 'indetermination', 'inconclusiveness']
senticnet['indeterministic'] = ['-0.55', '0', '0.58', '-0.98', '#anger', '#disgust', 'negative', '-0.70', 'fatalist', 'determinist', 'predestinarian', 'predestinationist', 'philosopher']
senticnet['index'] = ['0.074', '0.114', '0', '0.126', '#interest', '#admiration', 'positive', '0.105', 'paragraph', 'newspaper_write', 'news_paper', 'newspaper_story', 'chapter']
senticnet['index_card'] = ['0', '0.094', '0', '0', '#interest', '#interest', 'positive', '0.094', 'appointment', 'pocket_notebook', 'appointment_book', 'list_appointment', 'ring_binder']
senticnet['index_card_drawer'] = ['0.878', '0.79', '0', '0', '#joy', '#interest', 'positive', '0.834', 'cook_book', 'a_lot_of_books', 'bookmark', 'religious_tract', 'old_testament']
senticnet['index_finger'] = ['0', '0', '-0.06', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'bandit', 'arm', 'wrist', 'five_fingers', 'bendable']
senticnet['index_lai'] = ['-0.93', '0', '0.906', '-0.53', '#sadness', '#anger', 'negative', '-0.79', 'juvenility', 'state', 'immatureness', 'callowness', 'jejuneness']
senticnet['index_ndvi'] = ['-0.93', '0', '0.906', '-0.53', '#sadness', '#anger', 'negative', '-0.79', 'juvenility', 'state', 'immatureness', 'callowness', 'jejuneness']
senticnet['indexation_relief'] = ['-0.83', '0', '0.555', '-0.64', '#sadness', '#disgust', 'negative', '-0.67', 'single_tax', 'income_tax', 'taxation', 'capital_levy', 'departure_tax']
senticnet['indexicality'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'mediacy', 'mediateness', 'characteristic', 'allusiveness']
senticnet['indian_classical_music'] = ['0.99', '0', '0', '0.719', '#joy', '#admiration', 'positive', '0.855', 'instrumental', 'lyric', 'lyrics', 'sing', 'melody']
senticnet['indian_cuisine'] = ['0.882', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.822', 'ferment_grape_juice', 'chef_special', 'bottle_wine', 'fine_dining', 'decanter']
senticnet['indian_empire_cie'] = ['0.756', '0', '0', '0', '#joy', '#joy', 'positive', '0.756', 'companionship', 'cherish', 'lover', 'bond', 'buddy']
senticnet['indian_freedom_fighter'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['indian_independence'] = ['0.887', '0.748', '0', '0.954', '#joy', '#admiration', 'positive', '0.863', 'political_action', 'political', 'mediocrity', 'discrimination', 'independent']
senticnet['indian_ocean'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'pacific_ocean', 'small_lake', 'mere']
senticnet['indian_ocean_tsunami'] = ['-0.17', '0', '-0.25', '-0.13', '#sadness', '#fear', 'negative', '-0.18', 'destruction', 'bigot', 'kidnap', 'torture', 'censorship']
senticnet['indian_paintbrush'] = ['-0.86', '0', '0.665', '-0.61', '#sadness', '#anger', 'negative', '-0.71', 'brambly', 'braky', 'wooded', 'scrubby', 'forested']
senticnet['indian_pond_heron'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'amoeba', 'single_celled']
senticnet['indian_rope_trick'] = ['0.818', '0.913', '0', '0.825', '#interest', '#admiration', 'positive', '0.852', 'greeting', 'blackjack', 'pass_card', 'credit', 'play_blackjack']
senticnet['indian_social_reformer'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['indian_squaw'] = ['-0.18', '-0.12', '0.049', '-0.24', '#sadness', '#disgust', 'negative', '-0.09', 'tree_trunk', 'plank', 'artificial_leg', 'hardwood', 'tree_wood']
senticnet['indian_takeaway'] = ['0.059', '0.212', '0', '0.143', '#interest', '#admiration', 'positive', '0.138', 'christmas_shop', 'go_grocery_shop', 'pay_credit_card', 'pay_cash', 'holiday_shop']
senticnet['indian_tribe'] = ['0.989', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.97', 'committee', 'council', 'caste', 'association', 'organization']
senticnet['indicate'] = ['0', '0.149', '0.015', '0.194', '#interest', '#admiration', 'positive', '0.109', 'reason_why', 'punctuation', 'specify', 'finger_action', 'sport_score']
senticnet['indicates_contraction'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'mathematical', 'muscle', 'math_function', 'feel_sick', 'separation']
senticnet['indication'] = ['0', '0.151', '-0.05', '0.189', '#interest', '#admiration', 'positive', '0.097', 'placard', 'red_octagon', 'indicate', 'sign', 'direction']
senticnet['indicative'] = ['0.958', '-0.83', '0', '0.81', '#joy', '#surprise', 'positive', '0.869', 'discernible', 'perceptible', 'noticeable', 'detectable', 'observable']
senticnet['indicator'] = ['0', '0.071', '0', '0.143', '#interest', '#admiration', 'positive', '0.107', 'post_notice', 'gps', 'way', 'one_way', 'arrow']
senticnet['indictability'] = ['-0.98', '0', '0.657', '-0.95', '#sadness', '#disgust', 'negative', '-0.86', 'impeachability', 'guiltiness', 'blameworthiness', 'culpability', 'culpableness']
senticnet['indictment'] = ['0.654', '-0.59', '0', '0.774', '#joy', '#admiration', 'positive', '0.673', 'instrument', 'murder_charge', 'murder_indictment', 'true_bill', 'legal_document']
senticnet['indie'] = ['0.916', '-0.93', '0', '0.674', '#joy', '#surprise', 'positive', '0.841', 'smash', 'bang', 'smasher', 'strike', 'hit']
senticnet['indie_artist'] = ['0.916', '-0.93', '0', '0.674', '#joy', '#surprise', 'positive', '0.841', 'smash', 'bang', 'smasher', 'strike', 'hit']
senticnet['indie_comedy'] = ['0.206', '-0.13', '0', '0', '#joy', '#surprise', 'positive', '0.172', 'dancer', 'old_dance', 'dance', 'slow_dance', 'ballroom_dance']
senticnet['indie_flick'] = ['-0.85', '0', '0.514', '-0.73', '#sadness', '#disgust', 'negative', '-0.70', 'cinema_verite', 'movie', 'film', 'picture', 'moving_picture']
senticnet['indie_label'] = ['0', '0.115', '0.036', '0.207', '#interest', '#admiration', 'positive', '0.095', 'total', 'amount', 'merit', 'award', 'excellence']
senticnet['indie_record_label'] = ['0', '0.115', '0.036', '0.207', '#interest', '#admiration', 'positive', '0.095', 'total', 'amount', 'merit', 'award', 'excellence']
senticnet['indie_rock'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'explicit', 'declared', 'definitive', 'graphic']
senticnet['indie_rocker'] = ['0.797', '-0.95', '0', '0.921', '#surprise', '#admiration', 'positive', '0.891', 'lush', 'alcoholic', 'alky', 'dipsomaniac', 'boozer']
senticnet['indifference'] = ['0', '0', '0.709', '-0.92', '#anger', '#disgust', 'negative', '-0.81', 'apathetic', 'unemotionality', 'passionless', 'stolidity', 'phlegm']
senticnet['indifference_toward'] = ['0', '0', '0.709', '-0.92', '#anger', '#disgust', 'negative', '-0.81', 'apathetic', 'unemotionality', 'passionless', 'stolidity', 'phlegm']
senticnet['indifferent'] = ['0', '0', '0.952', '-0.96', '#anger', '#disgust', 'negative', '-0.96', 'withdrawal', 'detachment', 'indifferently', 'aloofness', 'apathetic']
senticnet['indifferentism'] = ['-0.94', '0', '0.687', '-0.75', '#sadness', '#disgust', 'negative', '-0.79', 'clannishness', 'cliquishness', 'snobbery', 'arrogance', 'haughtiness']
senticnet['indifferently'] = ['-0.74', '0', '-0.9', '0', '#sadness', '#fear', 'negative', '-0.82', 'withdrawal', 'detachment', 'apathetic', 'unconcerned', 'unconcern']
senticnet['indigence'] = ['-0.73', '0', '0.872', '-0.97', '#anger', '#disgust', 'negative', '-0.86', 'poverty', 'impoverishment', 'poorness', 'beggary', 'mendicancy']
senticnet['indigenous'] = ['0', '-0.91', '0', '0.829', '#surprise', '#admiration', 'positive', '0.871', 'red_indian', 'far_west', 'live_place', 'family_live', 'original']
senticnet['indigenous_tribe'] = ['0.989', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.97', 'committee', 'council', 'caste', 'association', 'organization']
senticnet['indigent'] = ['-0.95', '0', '0.977', '0', '#sadness', '#anger', 'negative', '-0.96', 'impoverished', 'needy', 'necessitous', 'poverty-stricken', 'destitute']
senticnet['indigestible'] = ['-0.77', '0', '0.671', '-0.80', '#sadness', '#disgust', 'negative', '-0.75', 'flatulent', 'nondigestible', 'undigested', 'stodgy', 'heavy']
senticnet['indigestion'] = ['0', '-0.03', '-0.04', '-0.19', '#fear', '#disgust', 'negative', '-0.06', 'stomach_ache', 'become_full', 'full_stomach', 'a_little_hungry', 'heartburn']
senticnet['indignant'] = ['-0.13', '0.146', '0.623', '-0.63', '#anger', '#disgust', 'negative', '-0.31', 'umbrageous', 'outraged', 'incensed', 'outrage', 'indignation']
senticnet['indignantly'] = ['-0.13', '0.145', '0.625', '-0.63', '#anger', '#disgust', 'negative', '-0.31', 'outrage', 'indignation', 'umbrageous', 'outraged', 'incensed']
senticnet['indignation'] = ['-0.13', '0.145', '0.625', '-0.63', '#anger', '#disgust', 'negative', '-0.31', 'outrage', 'indignantly', 'umbrageous', 'outraged', 'incensed']
senticnet['indignity'] = ['-0.92', '0', '0.699', '-0.93', '#sadness', '#disgust', 'negative', '-0.85', 'discourtesy', 'offense', 'outrage', 'scandalization', 'affront']
senticnet['indigo'] = ['-0.10', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.10', 'cyan', 'aqua', 'water_color', 'yellow', 'violet']
senticnet['indinavir'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'protease_inhibitor', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['indinavir_ritonavir'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'protease_inhibitor', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['indirect'] = ['0.05', '0.166', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.074', 'halt', 'red_light', 'director', 'finish', 'direction']
senticnet['indirect_discourse'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'mediacy', 'mediateness', 'characteristic', 'allusiveness']
senticnet['indirect_discrimination'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
senticnet['indirect_effect'] = ['0.05', '0.166', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.074', 'halt', 'red_light', 'director', 'finish', 'direction']
senticnet['indirect_evidence'] = ['-0.64', '0', '0.576', '-0.83', '#sadness', '#disgust', 'negative', '-0.68', 'nisi', 'indeterminate', 'equivocal', 'ambiguous', 'indecisive']
senticnet['indirect_expression'] = ['-0.89', '0', '0.669', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'deceit', 'deception', 'misrepresentation', 'equivocation', 'circumlocution']
senticnet['indirect_inguinal_hernia'] = ['0', '0', '0.021', '-0.06', '#anger', '#disgust', 'negative', '-0.04', 'skin', 'skeletal_system', 'shoulder', 'cuticle', 'groin']
senticnet['indirect_lighting'] = ['-0.90', '0', '0.209', '-0.92', '#sadness', '#disgust', 'negative', '-0.68', 'sun_burn', 'erythema', 'fire', 'burn_skin', 'hurt_skin']
senticnet['indirect_ownership'] = ['0.069', '0.07', '0', '0.116', '#interest', '#admiration', 'positive', '0.085', 'entrepreneur', 'invest_money', 'move_around', 'traveling_person', 'guilty_person']
senticnet['indirect_sequel'] = ['-0.06', '0.122', '-0.04', '0.113', '#interest', '#admiration', 'positive', '0.032', 'encounter', 'portray', 'reveal', 'episode', 'next_episode']
senticnet['indirect_subsidiary'] = ['0.08', '0.195', '0', '0.137', '#interest', '#admiration', 'positive', '0.137', 'company', 'enterprise', 'business', 'partnership', 'large_business']
senticnet['indirect_taxation'] = ['-0.08', '0.358', '-0.20', '0.237', '#interest', '#admiration', 'positive', '0.077', 'extortion', 'money_due', 'afford', 'bribe', 'owe']
senticnet['indirectly'] = ['0.05', '0.166', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.074', 'halt', 'red_light', 'director', 'finish', 'direction']
senticnet['indirectly_affect'] = ['-0.53', '0', '0.57', '-0.73', '#anger', '#disgust', 'negative', '-0.61', 'run_into', 'strike', 'hit', 'bump_into', 'collide_with']
senticnet['indirectness'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'mediacy', 'mediateness', 'characteristic', 'allusiveness']
senticnet['indiscernibility'] = ['0.534', '-0.86', '0', '0.676', '#surprise', '#admiration', 'positive', '0.692', 'suitability', 'suitableness', 'habitability', 'habitableness', 'qualification']
senticnet['indiscernible'] = ['-0.78', '0', '0.792', '-0.61', '#sadness', '#anger', 'negative', '-0.73', 'incognizable', 'incognoscible', 'insensible', 'unobservable', 'imperceptible']
senticnet['indiscipline'] = ['-0.83', '0', '0.531', '-0.59', '#sadness', '#disgust', 'negative', '-0.65', 'indulgence', 'incontinence', 'trait', 'dissoluteness', 'unrestraint']
senticnet['indiscreet'] = ['-0.92', '0', '0.876', '-0.96', '#sadness', '#disgust', 'negative', '-0.92', 'bigmouthed', 'blabby', 'blabbermouthed', 'talkative', 'imprudent']
senticnet['indiscreetness'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'sagacity', 'sagaciousness', 'discernment', 'judgment', 'gossip']
senticnet['indiscretion'] = ['-0.71', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.81', 'behavior', 'imprudence', 'brief', 'sexual', 'indiscreet']
senticnet['indiscriminate'] = ['-0.73', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.82', 'indiscriminately', 'equal-opportunity', 'catch-all', 'indistinguishably', 'wholesale']
senticnet['indiscriminate_destruction'] = ['-0.14', '-0.00', '-0.12', '-0.21', '#sadness', '#disgust', 'negative', '-0.11', 'torture', 'demolish', 'maim', 'terminate', 'hurt_person']
senticnet['indiscriminately'] = ['-0.73', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.82', 'indiscriminate', 'equal-opportunity', 'catch-all', 'indistinguishably', 'wholesale']
senticnet['indispensable'] = ['-0.86', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.82', 'dispensed', 'dispensation', 'dispensable', 'essential', 'dispense']
senticnet['indispensable_strait'] = ['0.084', '-0.05', '0.013', '0', '#joy', '#surprise', 'positive', '0.043', 'carnival_ride', 'carny', 'fortune_teller', 'horseback', 'ferris_wheel']
senticnet['indispensably_necessary'] = ['0.967', '-0.69', '0', '0.52', '#joy', '#surprise', 'positive', '0.729', 'marksmanship', 'oarsmanship', 'seamanship', 'swordsmanship', 'numeracy']
senticnet['indispose'] = ['-0.11', '0.19', '0.604', '-0.63', '#anger', '#disgust', 'negative', '-0.29', 'disinclination', 'disinclined', 'bad_student', 'unfriendly', 'inimical']
senticnet['indisposition'] = ['-0.63', '0', '0.584', '-0.54', '#sadness', '#anger', 'negative', '-0.58', 'resistance', 'disinclination', 'involuntariness', 'temperament', 'hesitancy']
senticnet['indisputability'] = ['0.776', '-0.66', '0', '0.778', '#joy', '#admiration', 'positive', '0.74', 'demonstrability', 'provability', 'positivity', 'positiveness', 'certainty']
senticnet['indisputable'] = ['0.971', '0', '0', '0.758', '#joy', '#admiration', 'positive', '0.865', 'irrefutable', 'incontestable', 'incontrovertible', 'undeniable', 'final']
senticnet['indisputably'] = ['0.544', '-0.69', '0', '0.658', '#surprise', '#admiration', 'positive', '0.632', 'shakable', 'shakeable', 'challengeable', 'disputable', 'contestable']
senticnet['indissoluble'] = ['-0.98', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.91', 'inseparable', 'interdependent', 'inalienable', 'indivisible', 'inviolable']
senticnet['indissoluble_marriage'] = ['-0.81', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.79', 'stifle', 'forgotten', 'old', 'touch_ground', 'seldom']
senticnet['indistinct'] = ['-0.95', '0', '0.892', '-0.81', '#sadness', '#anger', 'negative', '-0.88', 'blurry', 'foggy', 'hazy', 'muzzy', 'blurred']
senticnet['indistinct_vision'] = ['-0.94', '0', '0.855', '-1.0', '#sadness', '#disgust', 'negative', '-0.93', 'fear', 'danger', 'trouble', 'scare', 'alien']
senticnet['indistinctly'] = ['0.695', '-0.83', '0', '0.959', '#surprise', '#admiration', 'positive', '0.829', 'opaqueness', 'faintness', 'dimness', 'opacity', 'vagueness']
senticnet['indistinctness'] = ['0.695', '-0.83', '0', '0.959', '#surprise', '#admiration', 'positive', '0.829', 'opaqueness', 'faintness', 'dimness', 'opacity', 'vagueness']
senticnet['indistinguishable_from'] = ['-0.82', '0', '0.758', '-0.70', '#sadness', '#anger', 'negative', '-0.76', 'unclassifiable', 'unidentifiable', 'unrecognisable', 'intangible', 'undiagnosable']
senticnet['indistinguishably'] = ['-0.73', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.82', 'indiscriminate', 'equal-opportunity', 'catch-all', 'indiscriminately', 'wholesale']
senticnet['indium'] = ['-0.05', '-0.00', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'pigment', 'many_shades', 'cyan', 'green_yellow', 'material']
senticnet['individual'] = ['0.093', '0.137', '-0.07', '0.137', '#interest', '#admiration', 'positive', '0.072', 'person', 'alone', 'user', 'independent', 'separate']
senticnet['individual_liberty'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'economic_libertarian', 'libertarian', 'advocate', 'advocator', 'proponent']
senticnet['individualism'] = ['0.618', '-0.75', '0', '0.628', '#surprise', '#admiration', 'positive', '0.665', 'uniqueness', 'singularity', 'specialty', 'special', 'distinctiveness']
senticnet['individualist'] = ['0.95', '-0.77', '0', '0.578', '#joy', '#surprise', 'positive', '0.767', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['individualistic'] = ['0', '0', '-0.73', '-0.71', '#fear', '#disgust', 'negative', '-0.72', 'selfish', 'egotistical', 'egocentrically', 'egocentricity', 'egocentric']
senticnet['individuality'] = ['0.959', '-0.53', '0', '0.772', '#joy', '#admiration', 'positive', '0.756', 'uniqueness', 'singularity', 'specialty', 'special', 'distinctiveness']
senticnet['individuate'] = ['0', '0.918', '0', '0.958', '#interest', '#admiration', 'positive', '0.938', 'marry', 'get_married', 'love', 'join', 'hook_up_with']
senticnet['individuation'] = ['0.664', '-0.90', '0', '0.788', '#surprise', '#admiration', 'positive', '0.785', 'individualism', 'individuality', 'distinctiveness', 'idiosyncrasy', 'foible']
senticnet['indivisibility'] = ['0.196', '0.129', '-0.06', '0.184', '#joy', '#admiration', 'positive', '0.111', 'pleasant', 'pleasurable', 'bourgeois', 'important_human', 'good']
senticnet['indivisible'] = ['-0.97', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.90', 'inseparable', 'interdependent', 'inalienable', 'indissoluble', 'inviolable']
senticnet['indoctrinate'] = ['0', '0', '0.801', '-0.75', '#anger', '#disgust', 'negative', '-0.78', 'indoctrination', 'rudiment', 'science', 'principle', 'brain-washing']
senticnet['indoctrination'] = ['0', '0', '0.805', '-0.74', '#anger', '#disgust', 'negative', '-0.77', 'indoctrinate', 'rudiment', 'science', 'principle', 'brain-washing']
senticnet['indolence'] = ['-0.66', '0', '0.749', '-0.54', '#sadness', '#anger', 'negative', '-0.65', 'inertia', 'inactiveness', 'inactivity', 'faineance', 'idleness']
senticnet['indolent'] = ['0', '0', '-0.96', '-0.77', '#fear', '#disgust', 'negative', '-0.86', 'otiose', 'slothful', 'faineant', 'lazy', 'work-shy']
senticnet['indolently'] = ['-0.71', '0', '0.906', '-0.93', '#anger', '#disgust', 'negative', '-0.85', 'absent', 'forgetful', 'distracted', 'inattentive', 'scatty']
senticnet['indomitable'] = ['0', '0.739', '0', '0.847', '#interest', '#admiration', 'positive', '0.793', 'insurmountable', 'insuperable', 'inexpugnable', 'unconquerable', 'unsubduable']
senticnet['indomitable_courage'] = ['0', '0', '0.764', '-0.77', '#anger', '#disgust', 'negative', '-0.77', 'restriction', 'tragedy', 'perpetrator', 'dominate', 'control']
senticnet['indomitable_fighting_spirit'] = ['0', '0', '0.764', '-0.77', '#anger', '#disgust', 'negative', '-0.77', 'restriction', 'tragedy', 'perpetrator', 'dominate', 'control']
senticnet['indomitable_spirit'] = ['0.812', '0.937', '0', '0', '#joy', '#interest', 'positive', '0.875', 'retentive', 'long', 'dogged', 'dour', 'recollective']
senticnet['indonesia'] = ['-0.92', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.85', 'brex', 'indonesian_government', 'busang', 'barrick', 'work']
senticnet['indonesian_government'] = ['-0.92', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.85', 'brex', 'busang', 'indonesia', 'barrick', 'work']
senticnet['indoor'] = ['0.034', '0.09', '0.039', '0.061', '#interest', '#admiration', 'positive', '0.036', 'border', 'limit', 'edge', 'boundary', 'side']
senticnet['indoor_amusement_park'] = ['0.522', '-0.88', '0', '0.965', '#surprise', '#admiration', 'positive', '0.789', 'overage', 'asset', 'security', 'business', 'hole_card']
senticnet['indoor_arena'] = ['0', '-0.84', '0', '0.723', '#surprise', '#admiration', 'positive', '0.785', 'circular_route', 'stadium', 'memorial', 'station', 'row_house']
senticnet['indoor_athletic'] = ['0.02', '0.244', '0', '0.225', '#interest', '#admiration', 'positive', '0.163', 'game', 'disneyland', 'ballpark', 'hockey_game', 'sport_event']
senticnet['indoor_basketball'] = ['0.041', '0.086', '0.016', '0', '#joy', '#interest', 'positive', '0.037', 'team', 'play_sport', 'game', 'exercise', 'match']
senticnet['indoor_ice_hockey'] = ['0.026', '-0.03', '0', '0.091', '#surprise', '#admiration', 'positive', '0.051', 'stadium', 'auditorium', 'television_station', 'music_hall', 'movie_theater']
senticnet['indoor_ice_rink'] = ['0.02', '0.244', '0', '0.225', '#interest', '#admiration', 'positive', '0.163', 'game', 'disneyland', 'ballpark', 'hockey_game', 'sport_event']
senticnet['indoor_ice_skating'] = ['0.02', '0.244', '0', '0.225', '#interest', '#admiration', 'positive', '0.163', 'game', 'disneyland', 'ballpark', 'hockey_game', 'sport_event']
senticnet['indoor_lighting'] = ['-0.90', '0', '0.209', '-0.92', '#sadness', '#disgust', 'negative', '-0.68', 'sun_burn', 'erythema', 'fire', 'burn_skin', 'hurt_skin']
senticnet['indoor_outdoor'] = ['0.076', '0.079', '0.027', '0.118', '#interest', '#admiration', 'positive', '0.061', 'outside', 'plant_place', 'plant_ground', 'ground', 'cultivate_yard']
senticnet['indoor_recreation'] = ['0', '0.962', '0', '0.911', '#interest', '#admiration', 'positive', '0.936', 'play_friend', 'competition', 'break', 'watch_baseball', 'fun_exercise']
senticnet['indoor_rink'] = ['0.02', '0.244', '0', '0.225', '#interest', '#admiration', 'positive', '0.163', 'game', 'disneyland', 'ballpark', 'hockey_game', 'sport_event']
senticnet['indoor_shopping_mall'] = ['0.041', '0.098', '0', '0', '#joy', '#interest', 'positive', '0.07', 'utopia', 'java', 'memorial', 'nowhere', 'coffee']
senticnet['indoor_skating_rink'] = ['0', '0', '0', '-0.1', '#disgust', '#disgust', 'negative', '-0.1', 'ice_rink', 'skate', 'room_temperature', 'loose_end', 'smooth']
senticnet['indoor_soccer'] = ['0.02', '0.244', '0', '0.225', '#interest', '#admiration', 'positive', '0.163', 'game', 'disneyland', 'ballpark', 'hockey_game', 'sport_event']
senticnet['indoor_speed_skating'] = ['0.02', '0.244', '0', '0.225', '#interest', '#admiration', 'positive', '0.163', 'game', 'disneyland', 'ballpark', 'hockey_game', 'sport_event']
senticnet['indoor_venue'] = ['0.026', '-0.03', '0', '0.091', '#surprise', '#admiration', 'positive', '0.051', 'stadium', 'auditorium', 'television_station', 'music_hall', 'movie_theater']
senticnet['indorse'] = ['0.969', '-0.97', '0', '0.946', '#joy', '#surprise', 'positive', '0.964', 'okay', 'warrant', 'sanction', 'approve', 'champion']
senticnet['indorser'] = ['0.822', '-0.92', '0', '0.575', '#joy', '#surprise', 'positive', '0.775', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['indubitability'] = ['0.783', '-0.67', '0', '0.771', '#joy', '#admiration', 'positive', '0.742', 'demonstrability', 'provability', 'positivity', 'positiveness', 'certainty']
senticnet['indubitable'] = ['-0.96', '0', '0.91', '-0.61', '#sadness', '#anger', 'negative', '-0.82', 'unpersuadable', 'unsuasible', 'unresponsive', 'resistant', 'incapable']
senticnet['induce'] = ['-0.94', '0', '0.826', '0', '#sadness', '#anger', 'negative', '-0.88', 'inculcation', 'inculcate', 'repeat', 'sermonize', 'teach']
senticnet['induce_sickness'] = ['-0.90', '0', '0.117', '-0.93', '#sadness', '#disgust', 'negative', '-0.65', 'disease', 'illness', 'sickness', 'doctor', 'hospital']
senticnet['induced'] = ['0.947', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.911', 'induction', 'induction', 'suppression', 'induce', 'hob']
senticnet['induced_drag'] = ['-0.97', '0', '0.831', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'skewness', 'irregularity']
senticnet['inducement'] = ['-0.99', '0', '0.651', '-0.51', '#sadness', '#anger', 'negative', '-0.72', 'sending', 'compulsion', 'influence', 'causation', 'trigger']
senticnet['inducingly'] = ['-0.73', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.72', 'shrink', 'grovel', 'creep', 'cower', 'hysterically']
senticnet['inducive'] = ['0.59', '-0.59', '0', '0.527', '#joy', '#surprise', 'positive', '0.572', 'responsible', 'causative', 'activating', 'tributary', 'motivative']
senticnet['induction'] = ['0.955', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.915', 'induced', 'induction', 'suppression', 'induce', 'hob']
senticnet['induction_ceremony'] = ['0.82', '0', '0', '0.885', '#joy', '#admiration', 'positive', '0.853', 'religious_event', 'sunday', 'happy_event', 'tradition', 'wedding']
senticnet['induction_coil'] = ['-0.86', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.85', 'static_electricity', 'shock', 'medical_condition', 'painful', 'sudden']
senticnet['inductive_approach'] = ['-0.99', '0', '0.878', '-0.95', '#sadness', '#disgust', 'negative', '-0.94', 'adequacy', 'satisfactoriness', 'acceptability', 'acceptableness', 'quality']
senticnet['inductive_argument'] = ['0.715', '-0.82', '0', '0.659', '#joy', '#surprise', 'positive', '0.734', 'reasoning', 'logical_thinking', 'abstract_thought', 'casuistry', 'line_of_inquiry']
senticnet['inductive_logic'] = ['-0.99', '0', '0.878', '-0.95', '#sadness', '#disgust', 'negative', '-0.94', 'adequacy', 'satisfactoriness', 'acceptability', 'acceptableness', 'quality']
senticnet['inductive_proof'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly']
senticnet['inductive_reasoning'] = ['0', '0.862', '0', '0.837', '#interest', '#admiration', 'positive', '0.849', 'generalisation', 'abstraction', 'generality', 'generalization', 'stimulus_generalisation']
senticnet['inductive_step'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly']
senticnet['inductor_current'] = ['0', '0.098', '-0.06', '0.15', '#interest', '#admiration', 'positive', '0.061', 'make_book', 'use_ink', 'make_newspaper', 'pencil_mark', 'computer_command']
senticnet['indulge'] = ['0.846', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.916', 'pander', 'gratify', 'humor', 'luxuriate', 'spend']
senticnet['indulgence'] = ['0.931', '-0.84', '0', '0.829', '#joy', '#surprise', 'positive', '0.869', 'folly', 'lunacy', 'craziness', 'buffoonery', 'shtik']
senticnet['indulgent'] = ['0.558', '-0.90', '0', '0.736', '#surprise', '#admiration', 'positive', '0.733', 'luxurious', 'luxuriant', 'sybaritic', 'voluptuary', 'gay']
senticnet['indult'] = ['0.901', '0', '0', '0.916', '#joy', '#admiration', 'positive', '0.909', 'get_away', 'bread', 'glory', 'counsel', 'vow']
senticnet['induration'] = ['-0.59', '0', '0.911', '-0.87', '#anger', '#disgust', 'negative', '-0.79', 'osteosclerosis', 'sclerosis', 'disseminated_sclerosis', 'multiple_sclerosis', 'pathology']
senticnet['indusium'] = ['0', '0', '0.064', '-0.04', '#anger', '#disgust', 'negative', '-0.05', 'bird_food', 'funiculus', 'plant_product', 'fuzz', 'agricultural_product']
senticnet['industrial'] = ['0', '0.164', '-0.08', '0.215', '#interest', '#admiration', 'positive', '0.098', 'manufacture', 'factory_output', 'creation', 'assembly_line', 'make']
senticnet['industrial-grade'] = ['0', '0', '0.911', '-0.82', '#anger', '#disgust', 'negative', '-0.86', 'professional-grade', 'commercial-grade', 'heavy-duty', 'commercial-quality', 'weather-resistant']
senticnet['industrial_capitalism'] = ['0.836', '-0.83', '0', '0.511', '#joy', '#surprise', 'positive', '0.726', 'appearance', 'start', 'casus_belli', 'beginning', 'egress']
senticnet['industrial_chemist'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'druggist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['industrial_conglomerate'] = ['0.111', '0.18', '0', '0.128', '#interest', '#admiration', 'positive', '0.14', 'corporation', 'partnership', 'large_business', 'business_organization', 'enterprise']
senticnet['industrial_effluent'] = ['-0.58', '0', '0.714', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'diversionist', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['industrial_machinery'] = ['0.966', '0.799', '0', '0', '#joy', '#interest', 'positive', '0.883', 'mechanical_device', 'move_part', 'machine', 'work_well', 'make_car']
senticnet['industrial_pollution'] = ['0', '0', '0.735', '-0.70', '#anger', '#disgust', 'negative', '-0.72', 'swing_set', 'roadway_square', 'fresh_air', 'dog_poop', 'wildflower']
senticnet['industrial_production'] = ['0', '0.164', '-0.08', '0.215', '#interest', '#admiration', 'positive', '0.098', 'manufacture', 'factory_output', 'creation', 'assembly_line', 'make']
senticnet['industrial_revolution'] = ['0', '0.104', '0', '0.124', '#interest', '#admiration', 'positive', '0.114', 'good_trait', 'constant', 'time_increment', 'time_designation', 'time_length']
senticnet['industrial_sector'] = ['0.067', '0.172', '0', '0.121', '#interest', '#admiration', 'positive', '0.12', 'economic_activity', 'business', 'support_family', 'work_done', 'earn_money']
senticnet['industrial_technology'] = ['-0.81', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.82', 'material_research', 'material', 'effect', 'number', 'microstructure']
senticnet['industrial_waste'] = ['-0.58', '0', '0.714', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'diversionist', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['industrial_wasteland'] = ['-0.70', '0', '0.611', '-0.69', '#sadness', '#disgust', 'negative', '-0.67', 'dump', 'garbage_dump', 'trash_dump', 'garbage_heap', 'wasteyard']
senticnet['industrialism'] = ['0.563', '-0.72', '0', '0.644', '#surprise', '#admiration', 'positive', '0.645', 'idiom', 'idiomatic_expression', 'phrasal_idiom', 'set_phrase', 'phrase']
senticnet['industrialist'] = ['0', '0.784', '0', '0.87', '#interest', '#admiration', 'positive', '0.827', 'philanthropist', 'magnate', 'billionaire', 'businessman', 'manufacturer']
senticnet['industrialization'] = ['-0.65', '0', '0.691', '-0.54', '#sadness', '#anger', 'negative', '-0.63', 'moronity', 'mental_deficiency', 'imbecility', 'stupidity', 'mental_defectiveness']
senticnet['industrialized_country'] = ['0.716', '-0.51', '0', '0.647', '#joy', '#admiration', 'positive', '0.624', 'state', 'nation', 'ally', 'land', 'commonwealth']
senticnet['industrialized_nation'] = ['0.716', '-0.51', '0', '0.647', '#joy', '#admiration', 'positive', '0.624', 'state', 'nation', 'ally', 'land', 'commonwealth']
senticnet['industrialized_world'] = ['0.838', '0.707', '0', '0.904', '#joy', '#admiration', 'positive', '0.816', 'natural_order', 'universe', 'existence', 'creation', 'cosmos']
senticnet['industrie_und_glck'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'jewelry_store', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['industriously'] = ['0', '0', '0.808', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'inaction', 'lazy', 'stay', 'inactive', 'recline']
senticnet['industriousness'] = ['0.799', '-0.61', '0', '0.789', '#joy', '#admiration', 'positive', '0.735', 'sedulity', 'sedulousness', 'assiduity', 'assiduousness', 'studiousness']
senticnet['industry'] = ['0.05', '0.145', '0', '0.174', '#interest', '#admiration', 'positive', '0.123', 'manufacture', 'make_product', 'business', 'manufacturer', 'enterprise']
senticnet['industry_source'] = ['-0.71', '0', '0.889', '0', '#sadness', '#anger', 'negative', '-0.80', 'tonne', 'reuters', 'trader', 'kong', 'week']
senticnet['industryuniv_coop_re_center'] = ['0', '0.888', '0', '0.85', '#interest', '#admiration', 'positive', '0.869', 'award', 'standard_grant', 'university', 'technology_transfer', 'project']
senticnet['indweller'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
senticnet['indwelling_sin'] = ['0.673', '-0.66', '0', '0.566', '#joy', '#surprise', 'positive', '0.634', 'wickedness', 'impiety', 'impiousness', 'unjustness', 'dishonor']
senticnet['indwelling_spirit'] = ['0', '0.703', '0', '0.81', '#interest', '#admiration', 'positive', '0.756', 'ghost', 'soul', 'mind_control', 'angel', 'god']
senticnet['inebriant'] = ['-0.91', '0', '0.79', '-0.68', '#sadness', '#anger', 'negative', '-0.79', 'intoxicant', 'alcohol', 'alcoholic_drink', 'alcoholic_beverage', 'rotgut']
senticnet['inebriate'] = ['0.645', '0.637', '-0.23', '0.299', '#joy', '#interest', 'positive', '0.336', 'gladdened', 'stimulating', 'tickle_pink', 'exalt', 'beatify']
senticnet['inebriation'] = ['-0.52', '0', '0.542', '-0.93', '#anger', '#disgust', 'negative', '-0.67', 'intoxication', 'drunkenness', 'sottishness', 'inebriety', 'tipsiness']
senticnet['inebriety'] = ['-0.52', '0', '0.542', '-0.93', '#anger', '#disgust', 'negative', '-0.67', 'intoxication', 'drunkenness', 'inebriation', 'sottishness', 'tipsiness']
senticnet['inedible'] = ['0.046', '0', '0.041', '0.102', '#joy', '#admiration', 'positive', '0.036', 'good_eat', 'delicious', 'yummy', 'tasty', 'mouth_watering']
senticnet['ineffability'] = ['0.779', '-0.75', '0', '0.526', '#joy', '#surprise', 'positive', '0.687', 'privateness', 'covertness', 'bosom', 'isolation', 'confidentiality']
senticnet['ineffable'] = ['0', '0', '-0.86', '-0.77', '#fear', '#disgust', 'negative', '-0.81', 'untellable', 'unspeakable', 'indescribable', 'indefinable', 'unutterable']
senticnet['ineffaceable'] = ['0', '0.827', '0', '0.846', '#interest', '#admiration', 'positive', '0.837', 'inexterminable', 'inextirpable', 'inexpungible', 'inexpungeable', 'ineradicable']
senticnet['ineffective'] = ['-0.94', '0', '0.987', '-0.64', '#sadness', '#anger', 'negative', '-0.85', 'unable', 'ineffectual', 'impotent', 'impuissant', 'effete']
senticnet['ineffectiveness'] = ['-0.50', '0', '0.807', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'powerlessness', 'impotence', 'impotency', 'inefficacy', 'inefficaciousness']
senticnet['ineffectual'] = ['-0.94', '0', '0.987', '-0.64', '#sadness', '#anger', 'negative', '-0.85', 'unable', 'impotent', 'ineffective', 'impuissant', 'effete']
senticnet['ineffectuality'] = ['-0.88', '0', '0.663', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'powerlessness', 'impotence', 'impotency', 'inefficacy', 'inefficaciousness']
senticnet['ineffectually'] = ['0.905', '0', '0', '0.809', '#joy', '#admiration', 'positive', '0.857', 'futilely', 'vainly', 'feebly', 'half-heartedly', 'timidly']
senticnet['ineffectualness'] = ['-0.65', '0', '0.855', '-0.78', '#anger', '#disgust', 'negative', '-0.76', 'powerlessness', 'impotence', 'impotency', 'inefficacy', 'inefficaciousness']
senticnet['inefficaciousness'] = ['-0.50', '0', '0.807', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'powerlessness', 'impotence', 'impotency', 'inefficacy', 'ineffectiveness']
senticnet['inefficacy'] = ['-0.50', '0', '0.807', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'powerlessness', 'impotence', 'impotency', 'ineffectiveness', 'inefficaciousness']
senticnet['inefficiency'] = ['-0.14', '-0.05', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.05', 'increase_price', 'gyroscope', 'ball_bounce', 'loss', 'lose']
senticnet['inefficient'] = ['-0.70', '0', '0.914', '-0.72', '#anger', '#disgust', 'negative', '-0.78', 'unadaptability', 'rigidity', 'rigidness', 'unfitness', 'inability']
senticnet['inefficient_bureaucracy'] = ['0', '0', '0', '-0.94', '#disgust', '#disgust', 'negative', '-0.94', 'boring', 'fill_form', 'annoying', 'form', 'fill_out_form']
senticnet['inegalitarian'] = ['0.704', '0.798', '0', '0', '#joy', '#interest', 'positive', '0.751', 'sub', 'consent', 'share', 'sign', 'subscribe']
senticnet['inelastic'] = ['0.808', '0', '0', '0.943', '#joy', '#admiration', 'positive', '0.875', 'a_lot_of_practice', 'flexibility', 'take_lesson', 'coordination', 'air_tank']
senticnet['inelastic_collision'] = ['-0.12', '0', '-0.09', '-0.04', '#sadness', '#fear', 'negative', '-0.08', 'hold_two_together', 'install_window', 'float_through_air', 'spring_up', 'collision']
senticnet['inelasticity'] = ['-0.81', '0', '0.83', '-0.97', '#anger', '#disgust', 'negative', '-0.87', 'deadness', 'rigidity', 'rigidness', 'stiffness', 'physical_property']
senticnet['inelegance'] = ['-0.99', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.86', 'tastelessness', 'sleaziness', 'coarseness', 'commonness', 'drabness']
senticnet['inelegant'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicado', 'unpronounceable', 'impassive', 'incommunicative', 'silent']
senticnet['ineligibility'] = ['-0.60', '0', '0.832', '-0.53', '#sadness', '#anger', 'negative', '-0.65', 'unsuitable', 'undesirable', 'unqualified', 'disqualified', 'unentitled']
senticnet['ineligible'] = ['-0.60', '0', '0.832', '-0.53', '#sadness', '#anger', 'negative', '-0.65', 'unsuitable', 'undesirable', 'unqualified', 'disqualified', 'unentitled']
senticnet['ineligible_player'] = ['-0.60', '0', '0.832', '-0.53', '#sadness', '#anger', 'negative', '-0.65', 'unsuitable', 'undesirable', 'unqualified', 'disqualified', 'unentitled']
senticnet['ineluctability'] = ['-0.91', '0', '0.795', '-0.94', '#sadness', '#disgust', 'negative', '-0.88', 'assured', 'unavoidability', 'predictability', 'slam_dunk', 'inevitability']
senticnet['ineluctable'] = ['0', '0', '0.926', '-0.85', '#anger', '#disgust', 'negative', '-0.88', 'inevitable', 'inescapably', 'escapable', 'inescapable', 'avoided']
senticnet['ineluctably'] = ['-0.67', '0', '0.97', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'joint', 'node', 'articulation', 'join', 'connexion']
senticnet['inept'] = ['-0.76', '0', '0.994', '-0.60', '#sadness', '#anger', 'negative', '-0.78', 'unfortunate', 'inapt', 'infelicitous', 'awkward', 'clumsy']
senticnet['ineptitude'] = ['0', '0', '-0.97', '-0.89', '#fear', '#disgust', 'negative', '-0.93', 'inability', 'competence', 'inept', 'incompetence', 'perform']
senticnet['ineptness'] = ['0.983', '-0.63', '0', '0.697', '#joy', '#admiration', 'positive', '0.772', 'amateurishness', 'inability', 'unskillfulness', 'clumsiness', 'awkwardness']
senticnet['inequality'] = ['-0.22', '0', '-0.09', '-0.16', '#sadness', '#disgust', 'negative', '-0.16', 'difference', 'reject', 'discard', 'divergency', 'gap']
senticnet['inequitable'] = ['-0.74', '0', '0.934', '-0.85', '#anger', '#disgust', 'negative', '-0.84', 'dirty', 'below_belt', 'foul', 'cheating', 'raw']
senticnet['inequitable_conduct'] = ['-0.55', '0', '0.741', '-0.76', '#anger', '#disgust', 'negative', '-0.68', 'illogicality', 'illogicalness', 'illogic', 'inconsequence', 'fallaciousness']
senticnet['inequitable_distribution'] = ['-0.56', '0', '-0.49', '-0.09', '#sadness', '#fear', 'negative', '-0.38', 'lack', 'absence', 'poverty', 'emptiness', 'hunger']
senticnet['inequitable_treatment'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
senticnet['inequity'] = ['0.871', '-0.74', '0', '0.981', '#joy', '#admiration', 'positive', '0.867', 'unrighteousness', 'unjustness', 'unfairness', 'wrong', 'wrongfulness']
senticnet['ineradicable'] = ['-0.65', '0', '0.61', '-0.83', '#sadness', '#disgust', 'negative', '-0.70', 'inexterminable', 'inextirpable', 'inexpungible', 'inexpungeable', 'indelible']
senticnet['inerrable'] = ['-0.69', '0', '0.679', '-0.89', '#sadness', '#disgust', 'negative', '-0.75', 'infallible', 'inerrant', 'unerring', 'unfailing', 'foolproof']
senticnet['inerrancy'] = ['-0.83', '0', '0.648', '-0.60', '#sadness', '#anger', 'negative', '-0.69', 'infallibility', 'dependability', 'dependableness', 'reliability', 'reliableness']
senticnet['inerrant'] = ['-0.69', '0', '0.679', '-0.89', '#sadness', '#disgust', 'negative', '-0.75', 'inerrable', 'infallible', 'unerring', 'unfailing', 'foolproof']
senticnet['inerrantist'] = ['-0.83', '0', '0.648', '-0.60', '#sadness', '#anger', 'negative', '-0.69', 'infallibility', 'dependability', 'dependableness', 'reliability', 'reliableness']
senticnet['inert'] = ['-0.54', '0', '0.868', '-0.96', '#anger', '#disgust', 'negative', '-0.79', 'inactive', 'unreactive', 'indifferent', 'stable', 'neutral']
senticnet['inert_coal'] = ['0', '0.94', '0', '0', '#interest', '#interest', 'positive', '0.94', 'diesel', 'fuel', 'kerosene', 'coal', 'gas']
senticnet['inert_gas'] = ['-0.87', '0', '0.949', '-0.80', '#sadness', '#anger', 'negative', '-0.87', 'neutral', 'motionless', 'noble_gas', 'gas', 'argonon']
senticnet['inert_matter'] = ['0.03', '0', '0', '0.119', '#joy', '#admiration', 'positive', '0.074', 'notional', 'abstract', 'wow', 'made_out_of_plastic', 'make_paper']
senticnet['inertia'] = ['-0.70', '0', '0.569', '-0.88', '#sadness', '#disgust', 'negative', '-0.72', 'languor', 'passiveness', 'phlegm', 'passivity', 'indolence']
senticnet['inertial_frame'] = ['-0.69', '0', '0.83', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'general_relativity', 'general_theory_of_relativity', 'general_relativity_theory', 'relativity', 'special_theory_of_relativity']
senticnet['inertial_navigation'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'chairperson', 'commuter']
senticnet['inescapable'] = ['0', '0', '0.931', '-0.84', '#anger', '#disgust', 'negative', '-0.88', 'inevitable', 'inescapably', 'escapable', 'ineluctable', 'avoided']
senticnet['inescapably'] = ['0', '0', '0.929', '-0.84', '#anger', '#disgust', 'negative', '-0.88', 'inevitable', 'inescapable', 'escapable', 'ineluctable', 'avoided']
senticnet['inescutcheon'] = ['0.586', '-0.65', '0', '0.503', '#joy', '#surprise', 'positive', '0.58', 'decoration', 'distinguished_service_cross', 'medal', 'medallion', 'palm']
senticnet['inessential'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'minor', 'unimportant', 'petty', 'negligible', 'trivial']
senticnet['inestimable'] = ['0', '0.995', '0', '0.807', '#interest', '#admiration', 'positive', '0.901', 'calculated', 'degree', 'magnitude', 'estimated', 'inestimably']
senticnet['inestimably'] = ['0', '0.992', '0', '0.801', '#interest', '#admiration', 'positive', '0.897', 'calculated', 'degree', 'magnitude', 'estimated', 'inestimable']
senticnet['inevitability'] = ['-0.91', '0', '0.795', '-0.94', '#sadness', '#disgust', 'negative', '-0.88', 'ineluctability', 'unavoidability', 'predictability', 'slam_dunk', 'assured']
senticnet['inevitable'] = ['-0.78', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.85', 'time_up', 'expire', 'desirable', 'good_quality', 'life_end']
senticnet['inevitable_consequence'] = ['-0.80', '0', '0.507', '-0.76', '#sadness', '#disgust', 'negative', '-0.69', 'adamant', 'inflexible', 'intransigent', 'ironclad', 'brassbound']
senticnet['inevitable_outcome'] = ['-0.80', '0', '0.507', '-0.76', '#sadness', '#disgust', 'negative', '-0.69', 'adamant', 'inflexible', 'intransigent', 'ironclad', 'brassbound']
senticnet['inevitably'] = ['0', '0', '0.837', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'inevitable', 'predictably', 'avoid', 'usual', 'impossible']
senticnet['inevitably_lead'] = ['0', '0', '-0.75', '-0.76', '#fear', '#disgust', 'negative', '-0.76', 'breed', 'beget', 'get', 'father', 'spawn']
senticnet['inexact'] = ['-0.71', '0', '0.8', '-0.69', '#sadness', '#anger', 'negative', '-0.73', 'approximative', 'rough', 'approximate', 'loose', 'odd']
senticnet['inexactitude'] = ['0.789', '-0.99', '0', '0.54', '#joy', '#surprise', 'positive', '0.776', 'imperfection', 'imperfectness', 'bugginess', 'lameness', 'sickness']
senticnet['inexactly'] = ['-0.80', '0', '-0.76', '0', '#sadness', '#fear', 'negative', '-0.78', 'approximately', 'delicacy', 'grossly', 'roughly', 'sketchily']
senticnet['inexcusable'] = ['-0.73', '0', '0.846', '-0.68', '#sadness', '#anger', 'negative', '-0.75', 'indefensible', 'unwarranted', 'insupportable', 'unjustifiable', 'unwarrantable']
senticnet['inexcusably'] = ['0', '-0.85', '0', '-0.84', '#surprise', '#disgust', 'positive', '0.008', 'deservedly', 'unjustifiably', 'undeservedly', 'unaccountably', 'deservingly']
senticnet['inexhaustible'] = ['-0.81', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.87', 'super-abundant', 'superabundance', 'superabundant', 'measureless', 'ever-dwindling']
senticnet['inexorability'] = ['-0.80', '0', '0.507', '-0.76', '#sadness', '#disgust', 'negative', '-0.69', 'adamant', 'inflexible', 'intransigent', 'ironclad', 'brassbound']
senticnet['inexorable'] = ['-0.80', '0', '0.507', '-0.76', '#sadness', '#disgust', 'negative', '-0.69', 'adamant', 'inflexible', 'intransigent', 'ironclad', 'brassbound']
senticnet['inexorably'] = ['0', '0.21', '-0.11', '0.345', '#interest', '#admiration', 'positive', '0.147', 'direction', 'unto', 'forehead', 'upon', 'go_toward']
senticnet['inexpediency'] = ['-0.97', '0', '0.675', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'infeasibility', 'unfeasibility', 'inutility', 'uselessness', 'unusefulness']
senticnet['inexpedient'] = ['-0.71', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.82', 'impolitic', 'policy', 'unwise', 'impoliticly', 'impoliticness']
senticnet['inexpensive'] = ['-0.10', '0.246', '-0.17', '0.261', '#interest', '#admiration', 'positive', '0.057', 'important', 'cost_nothing', 'capitalize', 'cheap', 'good_quality']
senticnet['inexpensiveness'] = ['0.554', '-0.72', '0', '0.651', '#surprise', '#admiration', 'positive', '0.644', 'reasonableness', 'modestness', 'cost', 'cut_rate', 'cut_price']
senticnet['inexperience'] = ['-0.93', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.90', 'greenness', 'experience', 'unwary', 'unfamiliarity', 'naive']
senticnet['inexperienced'] = ['-0.85', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.82', 'unskilled', 'amateur', 'uninitiated', 'uninformed', 'inexpert']
senticnet['inexperienced_person'] = ['0.798', '-0.51', '0', '0.973', '#joy', '#admiration', 'positive', '0.763', 'naif', 'boy_scout', 'dear', 'virgin', 'mortal']
senticnet['inexperient'] = ['0.697', '-0.87', '0', '0.748', '#surprise', '#admiration', 'positive', '0.773', 'naive', 'new', 'fledgling', 'young', 'raw']
senticnet['inexpert'] = ['-0.85', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.82', 'unskilled', 'amateur', 'uninitiated', 'uninformed', 'inexperienced']
senticnet['inexpiable'] = ['-0.53', '0', '0.901', '-0.51', '#sadness', '#anger', 'negative', '-0.64', 'unpardonable', 'inexcusable', 'deadly', 'unforgivable', 'mortal']
senticnet['inexplicable'] = ['-0.87', '0', '0.681', '-0.82', '#sadness', '#disgust', 'negative', '-0.79', 'unexplainable', 'unaccountable', 'unexplained', 'inscrutable', 'mystifying']
senticnet['inexplicit'] = ['0.668', '-0.57', '0', '0.937', '#joy', '#admiration', 'positive', '0.727', 'tacit', 'understand', 'unvoiced', 'silent', 'unspoken']
senticnet['inexplicitness'] = ['-0.58', '0', '0.871', '-0.70', '#anger', '#disgust', 'negative', '-0.72', 'elusiveness', 'unclearness', 'ambiguity', 'equivocalness', 'vagueness']
senticnet['inexpressible'] = ['0', '0', '-0.86', '-0.77', '#fear', '#disgust', 'negative', '-0.81', 'untellable', 'unspeakable', 'indescribable', 'indefinable', 'unutterable']
senticnet['inexpressibly'] = ['-0.86', '0', '0.632', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'airy', 'shadowy', 'stringy', 'wraithlike', 'ethereal']
senticnet['inexpressive'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicado', 'unpronounceable', 'impassive', 'incommunicative', 'silent']
senticnet['inexpugnable'] = ['-0.73', '0', '0.732', '-0.53', '#sadness', '#anger', 'negative', '-0.66', 'insurmountable', 'insuperable', 'unconquerable', 'indomitable', 'unsubduable']
senticnet['inexpungeable'] = ['-0.65', '0', '0.61', '-0.83', '#sadness', '#disgust', 'negative', '-0.70', 'inexterminable', 'inextirpable', 'inexpungible', 'ineradicable', 'indelible']
senticnet['inexpungible'] = ['-0.65', '0', '0.61', '-0.83', '#sadness', '#disgust', 'negative', '-0.70', 'inexterminable', 'inextirpable', 'ineradicable', 'inexpungeable', 'indelible']
senticnet['inexterminable'] = ['-0.65', '0', '0.61', '-0.83', '#sadness', '#disgust', 'negative', '-0.70', 'ineradicable', 'inextirpable', 'inexpungible', 'inexpungeable', 'indelible']
senticnet['inextinguishable'] = ['0', '0', '-0.75', '-0.98', '#fear', '#disgust', 'negative', '-0.86', 'unquenchable', 'ever-lasting', 'quenchless', 'extinguishes', 'ever-burning']
senticnet['inextirpable'] = ['-0.65', '0', '0.61', '-0.83', '#sadness', '#disgust', 'negative', '-0.70', 'inexterminable', 'ineradicable', 'inexpungible', 'inexpungeable', 'indelible']
senticnet['inextricable'] = ['0', '0', '-0.92', '-0.87', '#fear', '#disgust', 'negative', '-0.90', 'inseparable', 'inescapable', 'indissoluble', 'inextricably', 'intermingling']
senticnet['inextricably'] = ['0.742', '0.866', '0', '0', '#joy', '#interest', 'positive', '0.804', 'intertwined', 'inseparably', 'intimately', 'entwined', 'inescapably']
senticnet['inextricably_bound_up'] = ['0.968', '-0.76', '0', '0.583', '#joy', '#surprise', 'positive', '0.773', 'riddle', 'diffuse', 'permeate', 'spiritize', 'penetrate']
senticnet['inezia'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'lovebird', 'parroket', 'paraquet', 'paroquet']
senticnet['infallibility'] = ['0.969', '0', '0', '0.73', '#joy', '#admiration', 'positive', '0.849', 'inerrancy', 'reliability', 'reliableness', 'trustworthy', 'sure']
senticnet['infallible'] = ['0', '0.784', '0', '0.995', '#interest', '#admiration', 'positive', '0.889', 'inerrable', 'inerrant', 'unfailing', 'foolproof', 'proof']
senticnet['infallibly'] = ['-0.69', '0', '0.806', '-0.74', '#anger', '#disgust', 'negative', '-0.74', 'hypothesis', 'theory', 'base', 'possibility', 'presumption']
senticnet['infamous'] = ['-0.98', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.98', 'notorious', 'nefarious', 'notoriety', 'disreputable', 'notoriously']
senticnet['infamous_nazi'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'healthy_person', 'man', 'pervert', 'compatriot']
senticnet['infamous_pirate'] = ['0', '0', '0.034', '-0.08', '#anger', '#disgust', 'negative', '-0.06', 'lifeboat', 'mast', 'bad_man', 'keel', 'yacht']
senticnet['infamy'] = ['-0.93', '0', '0.936', '-0.88', '#sadness', '#anger', 'negative', '-0.91', 'lose_team', 'cause_suffer', 'leave_out', 'loathing', 'lyme_disease']
senticnet['infancy'] = ['0', '0.075', '0', '0.159', '#interest', '#admiration', 'positive', '0.117', 'many_plants', 'blossom', 'development', 'poppy', 'adult']
senticnet['infant'] = ['-0.04', '0', '-0.04', '0', '#sadness', '#fear', 'negative', '-0.04', 'new_life', 'small_child', 'baby', 'family', 'kid']
senticnet['infant_daughter'] = ['0.707', '0', '0', '0.734', '#joy', '#admiration', 'positive', '0.72', 'husband', 'mum', 'parent', 'mother', 'woman_child']
senticnet['infant_feeding'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'nursing', 'schedule_feeding', 'demand_feeding', 'breast_feeding', 'feeding']
senticnet['infant_prodigy'] = ['0.529', '-0.75', '0', '0.859', '#surprise', '#admiration', 'positive', '0.714', 'child', 'small_fry', 'tyke', 'tike', 'kid']
senticnet['infant_son'] = ['0.108', '-0.04', '0.043', '0.065', '#joy', '#admiration', 'positive', '0.043', 'family', 'love_back', 'mother', 'carry_baby', 'change_diaper']
senticnet['infant_toddler'] = ['0.063', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.065', 'look_catalog', 'make_selection', 'good_credit', 'menu', 'save_money']
senticnet['infanticide'] = ['-0.17', '0', '-0.16', '-0.09', '#sadness', '#fear', 'negative', '-0.14', 'murder', 'hurt_others', 'death_penalty', 'death_destruction', 'end_live']
senticnet['infantile'] = ['0', '0', '0.893', '-0.73', '#anger', '#disgust', 'negative', '-0.81', 'puerile', 'childish', 'immature', 'spastic', 'narcissistic']
senticnet['infantile_esotropia'] = ['-0.88', '0', '0.762', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'divergent_strabismus', 'exotropia', 'walleye', 'abnormality', 'abnormalcy']
senticnet['infantine'] = ['0.525', '-0.85', '0', '0.599', '#surprise', '#admiration', 'positive', '0.66', 'frolic', 'gambol', 'romp', 'play', 'caper']
senticnet['infantry'] = ['-0.03', '0', '-0.06', '-0.17', '#fear', '#disgust', 'negative', '-0.09', 'army', 'attacker', 'soldier', 'military', 'samurai']
senticnet['infantry_assault'] = ['-0.85', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.84', 'atomic_bomb', 'weapon_war', 'gun', 'pistol', 'hydrogen_bomb']
senticnet['infantry_battalion'] = ['0', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.09', 'army', 'attacker', 'military', 'soldier', 'infantry']
senticnet['infantry_soldier'] = ['0.833', '-0.97', '0', '0.91', '#surprise', '#admiration', 'positive', '0.906', 'fencer', 'battler', 'belligerent', 'fighter', 'scrapper']
senticnet['infantry_unit'] = ['0', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.09', 'army', 'attacker', 'military', 'soldier', 'infantry']
senticnet['infantryman'] = ['0.833', '-0.97', '0', '0.91', '#surprise', '#admiration', 'positive', '0.906', 'fencer', 'battler', 'belligerent', 'fighter', 'scrapper']
senticnet['infarct'] = ['-0.81', '0', '0.684', '-0.81', '#sadness', '#disgust', 'negative', '-0.77', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['infarction'] = ['-0.90', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.89', 'non-fatal', 'all-cause', 'nonfatal', 'hospitalization', 'nonlethal']
senticnet['infatuate'] = ['0.887', '-0.84', '0', '0.819', '#joy', '#surprise', 'positive', '0.851', 'arouse', 'elicit', 'enkindle', 'kindle', 'evoke']
senticnet['infatuation'] = ['0.924', '0.928', '0', '0', '#joy', '#interest', 'positive', '0.926', 'puppy_love', 'calf_love', 'amatory', 'amorousness', 'amorous']
senticnet['infeasibility'] = ['-0.97', '0', '0.675', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'impracticability', 'unfeasibility', 'inutility', 'uselessness', 'unusefulness']
senticnet['infeasible'] = ['-0.80', '0', '-0.80', '-0.96', '#sadness', '#disgust', 'negative', '-0.85', 'unable', 'absurd', 'difficult', 'impractical', 'unimaginable']
senticnet['infect'] = ['-0.83', '0', '-0.82', '-0.86', '#sadness', '#disgust', 'negative', '-0.84', 'cause_cold', 'tourism', 'reproduce', 'gland', 'procreate']
senticnet['infect_cattle'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'agent', 'satisfier', 'quickener', 'invigorator']
senticnet['infect_healthy'] = ['-0.83', '0', '-0.82', '-0.86', '#sadness', '#disgust', 'negative', '-0.84', 'cause_cold', 'tourism', 'reproduce', 'gland', 'procreate']
senticnet['infected'] = ['-0.66', '0', '0.988', '-0.63', '#sadness', '#anger', 'negative', '-0.76', 'dirty', 'putrefactive', 'putrefacient', 'abscessed', 'contaminative']
senticnet['infected_cattle'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'agent', 'satisfier', 'quickener', 'invigorator']
senticnet['infected_meat'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'agent', 'satisfier', 'quickener', 'invigorator']
senticnet['infection'] = ['-0.19', '-0.03', '-0.15', '-0.25', '#sadness', '#disgust', 'negative', '-0.14', 'cancer', 'ill_health', 'cause_sickness', 'terminal_illness', 'threaten']
senticnet['infectious'] = ['-0.71', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.72', 'contagious', 'communicable', 'epidemic', 'pandemic', 'epizootic']
senticnet['infectious_agent'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'agent', 'infective_agent', 'satisfier', 'quickener', 'invigorator']
senticnet['infectious_bacteria'] = ['-0.83', '0', '-0.82', '-0.86', '#sadness', '#disgust', 'negative', '-0.84', 'cause_cold', 'tourism', 'reproduce', 'gland', 'procreate']
senticnet['infectious_diarrhea'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'inflammatory_disease', 'stomach_flu', 'intestinal_flu', 'pelvic_inflammatory_disease']
senticnet['infectious_disease'] = ['0', '0', '-0.76', '-0.95', '#fear', '#disgust', 'negative', '-0.86', 'contagious_disease', 'disease', 'polio', 'sickness', 'malady']
senticnet['infectious_disease_niaid'] = ['-0.96', '0', '0.848', '-0.82', '#sadness', '#anger', 'negative', '-0.87', 'sneeze', 'sign_sickness', 'runny_nose', 'flu', 'common_cold']
senticnet['infectious_enthusiasm'] = ['0.505', '-0.85', '0', '0.94', '#surprise', '#admiration', 'positive', '0.767', 'infatuation', 'ardor', 'fervor', 'fervency', 'fervidness']
senticnet['infectious_laugh'] = ['0.695', '0.623', '-0.23', '0.319', '#joy', '#interest', 'positive', '0.351', 'uproarious', 'hilariously', 'hilarious', 'screaming', 'uproariously']
senticnet['infectious_material'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'agent', 'satisfier', 'quickener', 'invigorator']
senticnet['infectiously'] = ['0.512', '0.658', '-0.25', '0.256', '#joy', '#interest', 'positive', '0.293', 'riotously', 'expansively', 'ebulliently', 'zestfulness', 'gusto']
senticnet['infective'] = ['-0.80', '0', '0', '-0.91', '#sadness', '#disgust', 'negative', '-0.86', 'non-infectious', 'non-infective', 'immune-mediated', 'noninfectious', 'nonmalignant']
senticnet['infective_agent'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'agent', 'satisfier', 'quickener', 'invigorator']
senticnet['infective_larva'] = ['-0.83', '0', '-0.82', '-0.86', '#sadness', '#disgust', 'negative', '-0.84', 'cause_cold', 'tourism', 'reproduce', 'gland', 'procreate']
senticnet['infectivity'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'agent', 'satisfier', 'quickener', 'invigorator']
senticnet['infelicitous'] = ['-0.76', '0', '0.994', '-0.60', '#sadness', '#anger', 'negative', '-0.78', 'unfortunate', 'inapt', 'inept', 'awkward', 'clumsy']
senticnet['infer'] = ['0', '0', '-0.72', '-0.87', '#fear', '#disgust', 'negative', '-0.79', 'generalize', 'extrapolate', 'deduce', 'derive', 'guess']
senticnet['inference'] = ['-0.83', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.78', 'causality', 'causation', 'causal', 'cause-and-effect', 'causative']
senticnet['inference_engine'] = ['0', '0.717', '0', '0.765', '#interest', '#admiration', 'positive', '0.741', 'helper', 'artificial_intelligence', 'goal_driven', 'proactive', 'intelligent']
senticnet['inferential'] = ['0.734', '-0.56', '0', '0.64', '#joy', '#admiration', 'positive', '0.645', 'connotative', 'suggestive', 'implicative', 'connotational', 'connotative_of']
senticnet['inferior'] = ['0', '0', '0', '-0.74', '#disgust', '#disgust', 'negative', '-0.74', 'least', 'submission', 'reduction', 'slave', 'envy']
senticnet['inferior_colliculi'] = ['0', '0.886', '0', '0.772', '#interest', '#admiration', 'positive', '0.829', 'stalin', 'sail_sea', 'well_done', 'supervisor', 'small_ocean']
senticnet['inferior_mirage'] = ['-0.94', '0', '0.866', '-0.91', '#sadness', '#disgust', 'negative', '-0.90', 'movement', 'apparent_motion', 'motion', 'apparent_movement', 'optical_illusion']
senticnet['inferior_vena_cavae'] = ['0', '0.886', '0', '0.772', '#interest', '#admiration', 'positive', '0.829', 'stalin', 'sail_sea', 'well_done', 'supervisor', 'small_ocean']
senticnet['inferior_venae_cavae'] = ['0', '0.886', '0', '0.772', '#interest', '#admiration', 'positive', '0.829', 'stalin', 'sail_sea', 'well_done', 'supervisor', 'small_ocean']
senticnet['inferiority'] = ['-0.57', '0', '0.53', '-0.63', '#sadness', '#disgust', 'negative', '-0.58', 'low_status', 'lowness', 'backseat', 'lowliness', 'subordinateness']
senticnet['inferiority_complex'] = ['-0.57', '0', '0.53', '-0.63', '#sadness', '#disgust', 'negative', '-0.58', 'low_status', 'lowness', 'backseat', 'lowliness', 'subordinateness']
senticnet['infernal'] = ['-0.79', '0', '0.885', '-0.76', '#sadness', '#anger', 'negative', '-0.81', 'dead_soul', 'mortal', 'zombi', 'zombie', 'living_dead']
senticnet['infernal_machine'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amok', 'murderousness', 'misocainea', 'misogyny']
senticnet['infernal_region'] = ['-0.92', '0', '0.687', '-0.75', '#sadness', '#disgust', 'negative', '-0.78', 'hellfire', 'red_region', 'imaginary_place', 'mythical_place', 'fictitious_place']
senticnet['inferno'] = ['0', '0', '-0.12', '-0.09', '#fear', '#disgust', 'negative', '-0.11', 'flame', 'rapid_oxidation', 'huge_fire', 'cinder', 'fire']
senticnet['inferre'] = ['-0.81', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.79', 'stifle', 'forgotten', 'old', 'touch_ground', 'seldom']
senticnet['infertile'] = ['-0.98', '0', '0.441', '-0.96', '#sadness', '#disgust', 'negative', '-0.79', 'desert_land', 'barren_land', 'sand', 'barren', 'desert']
senticnet['infertility'] = ['-0.73', '0', '0.634', '-0.85', '#sadness', '#disgust', 'negative', '-0.74', 'cacogenesis', 'impotence', 'impotency', 'barrenness', 'dysgenesis']
senticnet['infest'] = ['-0.76', '0', '-0.73', '0', '#sadness', '#fear', 'negative', '-0.75', 'biscuit', 'flea-ridden', 'invade', 'inhabit', 'bot']
senticnet['infestation'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'nuisance', 'annoyance', 'irritant', 'pain_in_ass', 'troublesome']
senticnet['infidel'] = ['-0.57', '0', '0.584', '-0.75', '#anger', '#disgust', 'negative', '-0.63', 'paynim', 'nonreligious_person', 'idolizer', 'idolater', 'idol_worshiper']
senticnet['infidelity'] = ['-0.94', '0', '0.944', '-0.90', '#sadness', '#anger', 'negative', '-0.93', 'lust', 'pregnant', 'desire', 'care_deeply', 'love']
senticnet['infielder'] = ['0', '0.075', '0', '0', '#interest', '#interest', 'positive', '0.075', 'cup', 'carafe', 'container_cup', 'mug', 'glasses']
senticnet['infighting'] = ['0', '0', '0.726', '-0.83', '#anger', '#disgust', 'negative', '-0.77', 'divisiveness', 'disharmony', 'dissension', 'discord', 'disunity']
senticnet['infiltrate'] = ['-0.69', '0', '0.756', '-0.99', '#anger', '#disgust', 'negative', '-0.81', 'waster', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['infiltration'] = ['0.89', '-0.62', '0', '0.548', '#joy', '#surprise', 'positive', '0.686', 'breakthrough', 'interpenetration', 'foray', 'maraud', 'raid']
senticnet['infiltrative'] = ['-0.88', '0', '0.774', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fibroid_tumor', 'fibroid', 'benign_tumor', 'nonmalignant_tumor', 'nonmalignant_neoplasm']
senticnet['infiltrator'] = ['-0.69', '0', '0.756', '-0.99', '#anger', '#disgust', 'negative', '-0.81', 'waster', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['infinita'] = ['-0.93', '0', '0.861', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'knotty', 'contorted', 'writhed', 'crooked', 'anfractuous']
senticnet['infinitary'] = ['0.734', '-0.56', '0', '0.64', '#joy', '#admiration', 'positive', '0.645', 'inferential', 'suggestive', 'connotative', 'connotational', 'connotative_of']
senticnet['infinite'] = ['0', '0.141', '0', '0.185', '#interest', '#admiration', 'positive', '0.163', 'infinity', 'interval', 'boundless', 'endless', 'dimension']
senticnet['infinite_density'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'empty_room', 'a_lot_of_space', 'empty', 'empty_house', 'universe']
senticnet['infinite_divisibility'] = ['-0.64', '0', '0.87', '-0.74', '#anger', '#disgust', 'negative', '-0.75', 'large_indefinite_amount', 'piles', 'mess', 'large_number', 'tons']
senticnet['infinite_expanse'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'empty_room', 'a_lot_of_space', 'empty', 'empty_house', 'universe']
senticnet['infinite_number'] = ['-0.61', '0', '0.856', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'unfixed', 'indefinite', 'nebulous', 'indecisive', 'coy']
senticnet['infinite_patience'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'fishing_rod', 'fishing', 'catch_fish', 'fish_lake', 'bait']
senticnet['infinite_perfection'] = ['-0.64', '0', '0.87', '-0.74', '#anger', '#disgust', 'negative', '-0.75', 'large_indefinite_amount', 'piles', 'mess', 'large_number', 'tons']
senticnet['infinite_pity'] = ['-0.66', '0', '0.782', '-0.52', '#sadness', '#anger', 'negative', '-0.65', 'lividness', 'pallidness', 'pallor', 'complexion', 'achromasia']
senticnet['infinite_variety'] = ['0.876', '0.854', '0', '0.79', '#joy', '#interest', 'positive', '0.84', 'new', 'timely', 'know_time', 'sec', 'current']
senticnet['infinite_void'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'empty_room', 'a_lot_of_space', 'empty', 'empty_house', 'universe']
senticnet['infinitely'] = ['-0.64', '0', '0.87', '-0.74', '#anger', '#disgust', 'negative', '-0.75', 'large_indefinite_amount', 'piles', 'mess', 'large_number', 'tons']
senticnet['infinitely_greater'] = ['-0.12', '0.193', '0.597', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'upvalue', 'overvalue', 'overprize', 'overstimate', 'overprice']
senticnet['infinitely_wise'] = ['-0.69', '0', '0.806', '-0.74', '#anger', '#disgust', 'negative', '-0.74', 'hypothesis', 'theory', 'base', 'possibility', 'presumption']
senticnet['infinitesimal_generator'] = ['-0.53', '0', '0.993', '-0.88', '#anger', '#disgust', 'negative', '-0.80', 'expansiveness', 'impressiveness', 'stateliness', 'loftiness', 'excellence']
senticnet['infinitive'] = ['0.732', '0.8', '0', '0', '#joy', '#interest', 'positive', '0.766', 'fast', 'place', 'substance', 'substantive', 'person']
senticnet['infinitude'] = ['-0.64', '0', '0.87', '-0.74', '#anger', '#disgust', 'negative', '-0.75', 'large_indefinite_amount', 'piles', 'mess', 'large_number', 'tons']
senticnet['infinitum'] = ['-0.69', '0', '0.806', '-0.74', '#anger', '#disgust', 'negative', '-0.74', 'hypothesis', 'theory', 'base', 'possibility', 'presumption']
senticnet['infinity'] = ['0', '0.111', '0', '0.127', '#interest', '#admiration', 'positive', '0.119', 'infinite', 'none', 'void', 'emptiness', 'nothing']
senticnet['infinity_edge'] = ['0', '0', '0', '0.964', '#admiration', '#admiration', 'positive', '0.964', 'swimming_pool', 'swim', 'swimsuit', 'lake', 'have_fun']
senticnet['infinity_symbol'] = ['0.042', '-0.00', '0.027', '0.099', '#joy', '#admiration', 'positive', '0.031', 'chess_knight', 'chess_queen', 'chess_pawn', 'pawn', 'chess_bishop']
senticnet['infirm'] = ['0', '0', '0.852', '-0.97', '#anger', '#disgust', 'negative', '-0.91', 'frail', 'ill', 'valetudinarian', 'doddering', 'irresolute']
senticnet['infirmary'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'lazaret', 'lazarette', 'lazar_house', 'pesthouse', 'lazaretto']
senticnet['infirmaryte'] = ['-0.96', '0', '0.252', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'infirmary', 'lazaret', 'lazar_house', 'pesthouse', 'infirmaryto']
senticnet['infirmaryto'] = ['-0.96', '0', '0.252', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'infirmary', 'infirmaryte', 'lazar_house', 'pesthouse', 'lazaret']
senticnet['infirmity'] = ['0.528', '-0.71', '0', '0.848', '#surprise', '#admiration', 'positive', '0.698', 'cachexia', 'cachexy', 'wasting', 'softness', 'unfitness']
senticnet['infix'] = ['0.903', '0', '0', '0.759', '#joy', '#admiration', 'positive', '0.831', 'inculcate', 'connect', 'glue', 'instill', 'graft']
senticnet['inflamatory'] = ['-0.95', '0', '0.771', '-0.67', '#sadness', '#anger', 'negative', '-0.8', 'inculpative', 'condemning', 'accusatory', 'accusing', 'accusive']
senticnet['inflame'] = ['-0.88', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.81', 'instigate', 'fire_up', 'incite', 'stir_up', 'sensational']
senticnet['inflamed_skin'] = ['0.048', '-0.00', '0.011', '0.064', '#joy', '#admiration', 'positive', '0.027', 'become_wet', 'wash_away_dirt', 'run_out_soap', 'cleanliness', 'clean_skin']
senticnet['inflammable'] = ['0', '0', '0.784', '-0.99', '#anger', '#disgust', 'negative', '-0.88', 'odorous', 'malodorous', 'odoriferous', 'sweet-smelling', 'pungent']
senticnet['inflammation'] = ['-0.99', '0', '0.964', '-0.70', '#sadness', '#anger', 'negative', '-0.88', 'blepharitis', 'diverticulitis', 'epiglottitis', 'orchitis', 'prostatitis']
senticnet['inflammatory_bowel'] = ['-0.75', '0', '0.868', '-0.99', '#anger', '#disgust', 'negative', '-0.87', 'regional_enteritis', 'regional_ileitis', 'ulcerative_colitis', 'rubor', 'redness']
senticnet['inflammatory_bowel_disease'] = ['-0.85', '0', '0.945', '-0.95', '#anger', '#disgust', 'negative', '-0.91', 'regional_enteritis', 'regional_ileitis', 'ulcerative_colitis', 'rubor', 'redness']
senticnet['inflammatory_bowel_disease_ibd'] = ['-0.75', '0', '0.868', '-0.99', '#anger', '#disgust', 'negative', '-0.87', 'regional_enteritis', 'regional_ileitis', 'ulcerative_colitis', 'rubor', 'redness']
senticnet['inflammatory_cell'] = ['-0.66', '0', '0.877', '-0.64', '#sadness', '#anger', 'negative', '-0.72', 'somatic_cell', 'mastocyte', 'labrocyte', 'target_cell', 'phagocyte']
senticnet['inflammatory_condition'] = ['-0.75', '0', '0.515', '-0.93', '#sadness', '#disgust', 'negative', '-0.73', 'sclerosis', 'induration', 'degenerative_disorder', 'autoimmune_disease', 'disseminated_sclerosis']
senticnet['inflammatory_cytokine'] = ['-0.66', '0', '0.877', '-0.64', '#sadness', '#anger', 'negative', '-0.72', 'somatic_cell', 'mastocyte', 'labrocyte', 'target_cell', 'phagocyte']
senticnet['inflammatory_disease'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'gastroenteritis', 'stomach_flu', 'intestinal_flu', 'pelvic_inflammatory_disease']
senticnet['inflammatory_disorder'] = ['-0.75', '0', '0.515', '-0.93', '#sadness', '#disgust', 'negative', '-0.73', 'sclerosis', 'induration', 'degenerative_disorder', 'autoimmune_disease', 'disseminated_sclerosis']
senticnet['inflammatory_language'] = ['-0.95', '0', '0.771', '-0.67', '#sadness', '#anger', 'negative', '-0.8', 'inculpative', 'condemning', 'accusatory', 'accusing', 'accusive']
senticnet['inflammatory_muscle'] = ['-0.87', '0', '0.531', '-0.68', '#sadness', '#disgust', 'negative', '-0.69', 'rubor', 'redness', 'dermatomyositis', 'myositis', 'inflammation']
senticnet['inflammatory_myopathy'] = ['-0.87', '0', '0.531', '-0.68', '#sadness', '#disgust', 'negative', '-0.69', 'rubor', 'redness', 'dermatomyositis', 'myositis', 'inflammation']
senticnet['inflammatory_reaction'] = ['-0.52', '0', '0.538', '-0.95', '#anger', '#disgust', 'negative', '-0.67', 'sanies', 'pus', 'purulence', 'gleet', 'ichor']
senticnet['inflammatory_skin'] = ['-0.72', '0', '0.778', '-0.98', '#anger', '#disgust', 'negative', '-0.82', 'actinic_dermatitis', 'hypericism', 'diaper_rash', 'diaper_dermatitis', 'dermatitis']
senticnet['inflatable'] = ['-0.04', '0.092', '0', '0.062', '#interest', '#admiration', 'positive', '0.036', 'hangar', 'aircraft_carrier', 'hot_air_balloon', 'war_museum', 'spaceship']
senticnet['inflatable_boat'] = ['0', '-0.02', '0.046', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'sail_ship', 'canoe', 'ship', 'submarine', 'yacht']
senticnet['inflatable_raft'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'lifebelt', 'save_life', 'lifeguard', 'life_jacket', 'safety_equipment']
senticnet['inflatable_rubber'] = ['0', '-0.07', '0', '0', '#surprise', '#surprise', 'positive', '0.075', 'gyroscope', 'air_pump', 'ball_bounce', 'motor_oil', 'tire']
senticnet['inflate'] = ['0.051', '-0.07', '0.043', '0.055', '#surprise', '#admiration', 'positive', '0.035', 'bounce', 'throw', 'play_catch', 'bounce_up_down', 'play_dog']
senticnet['inflate_tire'] = ['-0.08', '0', '0.116', '0', '#sadness', '#anger', 'negative', '-0.10', 'circulate_blood', 'scrub_floor', 'grind_grain', 'beat_egg', 'bind_book']
senticnet['inflated'] = ['0.596', '-0.81', '0', '0.763', '#surprise', '#admiration', 'positive', '0.725', 'highfalutin', 'hifalutin', 'snooty', 'pretentious', 'portentous']
senticnet['inflated_price'] = ['-0.03', '0.139', '0', '0.109', '#interest', '#admiration', 'positive', '0.07', 'exchange_money', 'purchase', 'sell_goods', 'make_rich', 'incur_debt']
senticnet['inflation'] = ['0.982', '0.831', '0', '0.837', '#joy', '#admiration', 'positive', '0.883', 'find_job', 'get_job', 'eat_fruit', 'calm_mind', 'get_hired']
senticnet['inflation_gauge'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'indicator', 'retail_price_index', 'producer_price_index', 'wholesale_price_index', 'consumer_price_index']
senticnet['inflation_rate'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'indicator', 'retail_price_index', 'producer_price_index', 'wholesale_price_index', 'consumer_price_index']
senticnet['inflationary'] = ['0', '0', '-0.71', '-0.8', '#fear', '#disgust', 'negative', '-0.75', 'deflationary', 'inflation', 'deflation', 'recessionary', 'hyperinflation']
senticnet['inflator'] = ['-0.10', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.10', 'read_cover', 'spread_out', 'upgrade', 'look_behind', 'scan']
senticnet['inflected_verb'] = ['0.633', '-0.67', '0', '0.776', '#surprise', '#admiration', 'positive', '0.693', 'double_entendre', 'loophole', 'ambiguity', 'amphiboly', 'parisology']
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senticnet['injector'] = ['0.843', '-0.99', '0', '0.706', '#joy', '#surprise', 'positive', '0.849', 'gadgetry', 'gimbal', 'contrivance', 'mod_con', 'device']
senticnet['injudicious'] = ['-0.91', '0', '0.745', '0', '#sadness', '#anger', 'negative', '-0.83', 'injudiciously', 'imprudent', 'unwise', 'injudiciousness', 'judge']
senticnet['injudiciously'] = ['-0.91', '0', '0.744', '0', '#sadness', '#anger', 'negative', '-0.83', 'injudicious', 'imprudent', 'unwise', 'injudiciousness', 'judge']
senticnet['injudiciousness'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'lack_of_judgment', 'lack_of_discreetness', 'lack_of_discernment', 'reckless', 'rentless']
senticnet['injun'] = ['-0.18', '-0.12', '0.049', '-0.24', '#sadness', '#disgust', 'negative', '-0.09', 'tree_trunk', 'plank', 'artificial_leg', 'hardwood', 'tree_wood']
senticnet['injunctive'] = ['0.931', '0.947', '0', '0', '#joy', '#interest', 'positive', '0.939', 'antidote', 'cure', 'therapeutic', 'curative', 'repair']
senticnet['injunctive_relief'] = ['0.931', '0.947', '0', '0', '#joy', '#interest', 'positive', '0.939', 'antidote', 'cure', 'therapeutic', 'curative', 'repair']
senticnet['injure'] = ['0', '0', '-0.88', '-0.91', '#fear', '#disgust', 'negative', '-0.89', 'cause_injury', 'slander', 'skin_knee', 'serious_injury', 'maim']
senticnet['injure_person'] = ['-0.99', '0', '0.749', '-0.98', '#sadness', '#disgust', 'negative', '-0.90', 'cut', 'hurt', 'pain', 'injury', 'blood']
senticnet['injured'] = ['-0.63', '0', '0.503', '-0.73', '#sadness', '#disgust', 'negative', '-0.62', 'wounded', 'mangled', 'torn', 'hurt', 'eviscerate']
senticnet['injured_knee'] = ['0.94', '0.78', '0', '0', '#joy', '#interest', 'positive', '0.86', 'breaheavily', 'heavy_breathe', 'get_exhausted', 'exhaustion', 'see_neighbor']
senticnet['injurious'] = ['0', '0', '-0.96', '-0.70', '#fear', '#disgust', 'negative', '-0.83', 'damaging', 'harmful', 'invidious', 'injury', 'slanderous']
senticnet['injuriously'] = ['-0.65', '0', '0.716', '-0.94', '#anger', '#disgust', 'negative', '-0.77', 'bad_hat', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['injury'] = ['-0.91', '-0.85', '-0.93', '-0.99', '#fear', '#disgust', 'negative', '-0.49', 'strengthful', 'bruise', 'broken_bone', 'head_injury', 'get_hurt']
senticnet['injury_prevention'] = ['0.082', '0.319', '-0.10', '0.147', '#interest', '#admiration', 'positive', '0.11', 'protect', 'defend', 'secure', 'guard', 'watch_over']
senticnet['injury_scare'] = ['-0.69', '0', '0.768', '-0.67', '#sadness', '#anger', 'negative', '-0.71', 'dither', 'pother', 'worry', 'flap', 'scruple']
senticnet['injury_setback'] = ['-0.69', '0', '0.768', '-0.67', '#sadness', '#anger', 'negative', '-0.71', 'dither', 'pother', 'worry', 'flap', 'scruple']
senticnet['injustice'] = ['-0.94', '0', '0.886', '-0.84', '#sadness', '#anger', 'negative', '-0.89', 'put_in_jail', 'go_jail', 'go_to_prison', 'send_to_jail', 'jail_time']
senticnet['injustice_suffered_by'] = ['-0.87', '0', '0.673', '-0.55', '#sadness', '#anger', 'negative', '-0.7', 'hot_water', 'difficulty', 'care', 'corner', 'box']
senticnet['ink'] = ['0.022', '-0.05', '0.079', '-0.11', '#anger', '#disgust', 'negative', '-0.02', 'stationery', 'ball_point_pen', 'paper', 'refill', 'note']
senticnet['ink_cartridge'] = ['0.023', '0', '0', '0.066', '#joy', '#admiration', 'positive', '0.044', 'ink_paper', 'pencil_paper', 'use_ink', 'pencil_mark', 'type_of_document']
senticnet['ink_paper'] = ['0.023', '0', '0', '0.066', '#joy', '#admiration', 'positive', '0.044', 'ink_cartridge', 'pencil_paper', 'use_ink', 'pencil_mark', 'type_of_document']
senticnet['ink_pen'] = ['0.031', '0.08', '0.023', '0.069', '#interest', '#admiration', 'positive', '0.039', 'marker', 'graphite_pencil', 'pen', 'mechanical_graphite_pencil', 'stamp_pad']
senticnet['ink_ribbon'] = ['0', '0', '0.128', '0', '#anger', '#anger', 'negative', '-0.12', 'grid', 'efficient', 'calculator', 'computer', 'personal_computer']
senticnet['ink_sketch'] = ['0', '0.067', '0', '0.077', '#interest', '#admiration', 'positive', '0.072', 'pencil_mark', 'use_ink', 'script', 'pencil_paper', 'written_text']
senticnet['inka_wasi'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'head_hurt', 'introspection', 'lifestyle_change', 'headache', 'move_eye']
senticnet['inkiness'] = ['-0.78', '0', '0.693', '-0.69', '#sadness', '#anger', 'negative', '-0.72', 'coal_black', 'jet_black', 'pitch_black', 'soot_black', 'achromatic_color']
senticnet['inkstand'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'wallet', 'billfold', 'bank', 'piggy_bank', 'bank_vault']
senticnet['inky'] = ['0', '0.111', '0', '0.073', '#interest', '#admiration', 'positive', '0.092', 'dark_light', 'black', 'purple', 'dark', 'hue_tint']
senticnet['inky_black'] = ['0', '0', '-0.94', '-0.91', '#fear', '#disgust', 'negative', '-0.92', 'night', 'blackout', 'darkness', 'dark', 'obscure']
senticnet['inland_navigation'] = ['0.933', '0.795', '0', '0', '#joy', '#interest', 'positive', '0.864', 'water_passage', 'small_stream', 'water_flow', 'flowing_water', 'water_stream']
senticnet['inland_port'] = ['-0.02', '-0.02', '0.064', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'ocean_water', 'fillet_fish', 'chowder', 'fillet', 'underwater']
senticnet['inland_revenue'] = ['-0.08', '0.358', '-0.20', '0.237', '#interest', '#admiration', 'positive', '0.077', 'extortion', 'money_due', 'afford', 'bribe', 'owe']
senticnet['inland_waterway'] = ['0.933', '0.795', '0', '0', '#joy', '#interest', 'positive', '0.864', 'water_passage', 'small_stream', 'water_flow', 'flowing_water', 'water_stream']
senticnet['inlay_technique'] = ['-0.52', '0', '0.506', '-0.94', '#sadness', '#disgust', 'negative', '-0.65', 'gouger', 'swindler', 'defrauder', 'fraud', 'welcher']
senticnet['inlet_manifold'] = ['0.042', '-0.02', '0.035', '0.065', '#joy', '#admiration', 'positive', '0.023', 'food_container', 'metal_cylinder', 'metal_container', 'small_ball', 'storage_container']
senticnet['inlet_valve'] = ['0.843', '-0.99', '0', '0.706', '#joy', '#surprise', 'positive', '0.849', 'gadgetry', 'gimbal', 'contrivance', 'mod_con', 'device']
senticnet['inline_skate'] = ['0.05', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.046', 'skate', 'rollerblade', 'skateboard', 'ice_skate', 'skating']
senticnet['inline_skating'] = ['0.05', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.046', 'skate', 'rollerblade', 'skateboard', 'ice_skate', 'skating']
senticnet['inmate'] = ['0', '0', '-0.83', '-0.85', '#fear', '#disgust', 'negative', '-0.84', 'jailbird', 'prisoner', 'imprisoned', 'convict', 'captive']
senticnet['inmost'] = ['0.833', '0.698', '0', '0', '#joy', '#interest', 'positive', '0.765', 'inner-most', 'outermost', 'deepest', 'inner', 'innermost']
senticnet['inn'] = ['0.904', '0.768', '0', '0', '#joy', '#interest', 'positive', '0.836', 'hostel', 'hangout', 'bar', 'tavern', 'pub']
senticnet['inn_pub'] = ['0.853', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.916', 'tavern', 'cafe', 'nightclub', 'bar', 'liquor_store']
senticnet['innate'] = ['-0.95', '0', '0.684', '-0.76', '#sadness', '#disgust', 'negative', '-0.79', 'nonheritable', 'nurtural', 'inborn', 'nontransmissible', 'nonhereditary']
senticnet['innate_ability'] = ['-0.95', '0', '0.684', '-0.76', '#sadness', '#disgust', 'negative', '-0.79', 'innate', 'nurtural', 'nonheritable', 'nontransmissible', 'nonhereditary']
senticnet['innate_biological'] = ['-0.95', '0', '0.684', '-0.76', '#sadness', '#disgust', 'negative', '-0.79', 'innate', 'nurtural', 'nonheritable', 'nontransmissible', 'nonhereditary']
senticnet['innate_capacity'] = ['0.668', '-0.87', '0', '0.606', '#joy', '#surprise', 'positive', '0.715', 'adeptness', 'adroitness', 'deftness', 'quickness', 'expertness']
senticnet['innate_immune_response'] = ['0.702', '-0.52', '0', '0.958', '#joy', '#admiration', 'positive', '0.728', 'somatic_cell', 'vegetative_cell', 'fixed_phagocyte', 'macrophage', 'free_phagocyte']
senticnet['innate_reflex'] = ['0.89', '-0.82', '0', '0.776', '#joy', '#surprise', 'positive', '0.829', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'babinski']
senticnet['innate_sense'] = ['0.772', '-0.95', '0', '0.89', '#surprise', '#admiration', 'positive', '0.872', 'downrightness', 'straightforwardness', 'characteristic', 'immediacy', 'directness']
senticnet['innate_talent'] = ['0.794', '-0.61', '0', '0.675', '#joy', '#admiration', 'positive', '0.694', 'fruitfulness', 'genius', 'fecundity', 'ability', 'wizardry']
senticnet['innately'] = ['0.995', '0.801', '0', '0.968', '#joy', '#admiration', 'positive', '0.921', 'smart', 'smartness', 'curious', 'become_wise', 'comprehend']
senticnet['innately_superior'] = ['-0.53', '0', '0.977', '-0.54', '#anger', '#disgust', 'negative', '-0.68', 'rank', 'surpass', 'shine_at', 'excel_at', 'exceed']
senticnet['inner'] = ['0.716', '-0.62', '0', '0.896', '#joy', '#admiration', 'positive', '0.746', 'constitutional', 'integral', 'intimate', 'intrinsic', 'inbuilt']
senticnet['inner-most'] = ['0.83', '0.698', '0', '0', '#joy', '#interest', 'positive', '0.764', 'innermost', 'outermost', 'deepest', 'inner', 'inmost']
senticnet['inner_calm'] = ['0.595', '-0.65', '0', '0.89', '#surprise', '#admiration', 'positive', '0.713', 'tranquility', 'serenity', 'disposition', 'repose', 'placidity']
senticnet['inner_chamber'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'vestibule', 'door', 'central_passage', 'gate', 'door_handle']
senticnet['inner_city_youth'] = ['0.025', '0.073', '-0.05', '0.1', '#interest', '#admiration', 'positive', '0.037', 'young_age', 'energy', 'hope', 'young_life', 'healthy']
senticnet['inner_contentment'] = ['0.733', '0', '0', '0.801', '#joy', '#admiration', 'positive', '0.767', 'serenity', 'recreate', 'peace_of_mind', 'ease', 'serene']
senticnet['inner_corona'] = ['0.931', '-0.82', '0', '0.709', '#joy', '#surprise', 'positive', '0.822', 'light', 'aureole', 'radiance', 'visible_light', 'visible_radiation']
senticnet['inner_courtyard'] = ['0', '0.105', '0', '0.076', '#interest', '#admiration', 'positive', '0.09', 'big_house', 'mansion', 'castle', 'block_flat', 'medieval']
senticnet['inner_cylinder'] = ['-0.05', '0.068', '-0.10', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'tooth_decay', 'body_ache', 'symptom', 'pierce', 'tooth']
senticnet['inner_ear_balance'] = ['-0.56', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.63', 'aphasic', 'inarticulate', 'speechless', 'unarticulated', 'dumb']
senticnet['inner_ear_infection'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'stiff', 'rigid', 'inflexible', 'harsh', 'immovable']
senticnet['inner_edge'] = ['0.737', '-0.96', '0', '0.552', '#joy', '#surprise', 'positive', '0.75', 'prominence', 'hump', 'bump', 'swelling', 'frontal_eminence']
senticnet['inner_gorge'] = ['0', '0.069', '0', '0.065', '#interest', '#admiration', 'positive', '0.067', 'alp', 'high_mountain', 'pass', 'ski_resort', 'high_place']
senticnet['inner_harbor'] = ['0', '0', '0.027', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'port', 'sea', 'boat', 'ocean', 'ship']
senticnet['inner_layer'] = ['-0.03', '0', '0', '-0.11', '#sadness', '#disgust', 'negative', '-0.07', 'store_cloth', 'clothes_factory', 'stock', 'sword', 'gun_store']
senticnet['inner_margin'] = ['-0.7', '0', '0.617', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'silvery', 'achromatic', 'bluish_black', 'hueless', 'pitchy']
senticnet['inner_membrane'] = ['0', '0', '0.019', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'worm_intestine', 'bristle', 'fur_keep_warm', 'animal', 'whisker']
senticnet['inner_peace'] = ['0.829', '0.74', '0', '0.94', '#joy', '#admiration', 'positive', '0.836', 'strong_relationship', 'run_business', 'good_sense_of_humor', 'world_peace', 'wear_necklace']
senticnet['inner_planet'] = ['-0.07', '0', '0.014', '0', '#sadness', '#anger', 'negative', '-0.04', 'ground_material', 'abrasive', 'soil_earth', 'cold_night', 'small_rock']
senticnet['inner_ring'] = ['0.052', '0', '0', '0.118', '#joy', '#admiration', 'positive', '0.085', 'diamond_ring', 'piece_of_jewelry', 'bell_sound', 'wedding_band', 'engagement']
senticnet['inner_shell'] = ['-0.05', '0.068', '-0.10', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'tooth_decay', 'body_ache', 'symptom', 'pierce', 'tooth']
senticnet['inner_surface'] = ['-0.05', '0.068', '-0.10', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'tooth_decay', 'body_ache', 'symptom', 'pierce', 'tooth']
senticnet['inner_thigh'] = ['0', '0', '0', '-0.06', '#disgust', '#disgust', 'negative', '-0.06', 'body', 'nervous_system', 'normal_human_body', 'human_body', 'head_shoulder']
senticnet['inner_torment'] = ['-0.69', '0.32', '-0.38', '-0.08', '#sadness', '#fear', 'negative', '-0.21', 'forlornly', 'desolation', 'forlorn', 'weepiness', 'tearful']
senticnet['inner_tube'] = ['0', '-0.07', '0', '0', '#surprise', '#surprise', 'positive', '0.075', 'gyroscope', 'air_pump', 'ball_bounce', 'motor_oil', 'tire']
senticnet['inner_turmoil'] = ['-0.83', '0', '0.703', '-0.85', '#sadness', '#disgust', 'negative', '-0.79', 'hurly_burly', 'kerfuffle', 'commotion', 'disruption', 'disturbance']
senticnet['innermost'] = ['0.836', '0.701', '0', '0', '#joy', '#interest', 'positive', '0.768', 'inner-most', 'outermost', 'deepest', 'inner', 'inmost']
senticnet['innermost_ring'] = ['0.052', '0', '0', '0.118', '#joy', '#admiration', 'positive', '0.085', 'diamond_ring', 'piece_of_jewelry', 'bell_sound', 'wedding_band', 'engagement']
senticnet['innervate'] = ['-0.86', '0', '0.511', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'separate', 'move', 'branch', 'diverge', 'fork']
senticnet['inning'] = ['0', '0', '0.787', '-0.75', '#anger', '#disgust', 'negative', '-0.77', 'shutout', 'scoreless', 'hitless', 'strikeout', 'one-run']
senticnet['innisfallen'] = ['0', '0.105', '0', '0.084', '#interest', '#admiration', 'positive', '0.095', 'archive', 'print_medium', 'quiet_place', 'register', 'media']
senticnet['innocence'] = ['0.758', '0.818', '0', '0', '#joy', '#interest', 'positive', '0.788', 'little_boy', 'youngster', 'youth', 'brat', 'lad']
senticnet['innocency'] = ['-0.84', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.90', 'mortify', 'embarrassed', 'self-hatred', 'self-disgust', 'humiliated']
senticnet['innocent'] = ['0.039', '0', '0', '0.066', '#joy', '#admiration', 'positive', '0.053', 'small_human', 'small_person', 'young_kid', 'immature', 'small_boy']
senticnet['innocent_iv'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquirer', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['innocently'] = ['-0.04', '0.164', '0.685', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maliciousness', 'spitefulness', 'maleficence', 'malefic', 'malign']
senticnet['innoculation'] = ['-0.99', '0', '0.859', '-0.62', '#sadness', '#anger', 'negative', '-0.82', 'lax', 'loose', 'diarrheal', 'diarrhoeal', 'unconstipated']
senticnet['innocuous'] = ['-0.76', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.74', 'inoffensive', 'inconsequential', 'banal', 'harmless', 'unremarkable']
senticnet['innovant'] = ['0.911', '-0.94', '0', '0.953', '#surprise', '#admiration', 'positive', '0.935', 'common_sense', 'good_sense', 'horse_sense', 'mother_wit', 'gumption']
senticnet['innovate'] = ['0.76', '0.88', '0', '0', '#joy', '#interest', 'positive', '0.82', 'graduate_education', 'training', 'collaborative_research', 'establish_new_model', 'fertile_environment']
senticnet['innovation'] = ['0', '0.864', '0', '0.917', '#interest', '#admiration', 'positive', '0.891', 'realization', 'figure_out', 'come_conclusion', 'arrive_conclusion', 'resolve_problem']
senticnet['innovationpfi'] = ['-0.97', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.93', 'partnrships', 'month', 'activity_described', 'hurt', 'number']
senticnet['innovative'] = ['0.714', '0.918', '0', '0', '#joy', '#interest', 'positive', '0.816', 'innovativeness', 'trailblazing', 'groundbreaking', 'new-wave', 'ground-breaking']
senticnet['innovativeness'] = ['-0.50', '0', '0.733', '-0.55', '#anger', '#disgust', 'negative', '-0.59', 'unconventionality', 'novelty', 'originality', 'freshness', 'ability']
senticnet['innuendo'] = ['-0.95', '0', '0.716', '0', '#sadness', '#anger', 'negative', '-0.83', 'derogatory', 'implication', 'insinuation', 'hint', 'allusion']
senticnet['innumerable'] = ['0', '0.093', '0', '0.053', '#interest', '#admiration', 'positive', '0.073', 'underground', 'under_ground', 'environment', 'outpost', 'dark_place']
senticnet['innumerableness'] = ['-0.85', '0', '0.605', '-0.73', '#sadness', '#disgust', 'negative', '-0.73', 'fewness', 'numerousness', 'numerosity', 'number', 'countlessness']
senticnet['innumerate'] = ['-0.90', '0', '0.76', '-0.87', '#sadness', '#disgust', 'negative', '-0.84', 'analphabet', 'analphabetic', 'functional_illiterate', 'ignoramus', 'know_nothing']
senticnet['inoffensive'] = ['-0.77', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.74', 'innocuous', 'inconsequential', 'banal', 'harmless', 'unremarkable']
senticnet['inoperable'] = ['-0.85', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.85', 'inoperative', 'unusable', 'malfunctioning', 'non-functioning', 'deactivated']
senticnet['inoperable_cancer'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['inoperable_lung_cancer'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['inoperative'] = ['-0.85', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'inoperable', 'unusable', 'malfunctioning', 'non-functioning', 'deactivated']
senticnet['inopportune'] = ['0', '0', '0.749', '-0.80', '#anger', '#disgust', 'negative', '-0.77', 'unsuitable', 'time', 'contretemps', 'importune', 'inopportunely']
senticnet['inopportunely'] = ['0', '0', '0.744', '-0.81', '#anger', '#disgust', 'negative', '-0.77', 'unsuitable', 'time', 'contretemps', 'importune', 'inopportune']
senticnet['inordinate'] = ['-0.78', '0', '0.854', '-0.9', '#anger', '#disgust', 'negative', '-0.84', 'muss', 'untidiness', 'mess', 'muddle', 'messiness']
senticnet['inordinate_desire'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'lecherousness', 'lustfulness', 'amorousness']
senticnet['inordinately'] = ['-0.88', '0', '0.766', '0', '#sadness', '#anger', 'negative', '-0.82', 'absurdly', 'unnaturally', 'intolerably', 'unjustifiably', 'monstrously']
senticnet['inorganic'] = ['0', '0', '-0.80', '-0.83', '#fear', '#disgust', 'negative', '-0.82', 'flux', 'number', 'variability', 'better_understanding', 'polar_programsrelated']
senticnet['inorganic_chemical'] = ['0.035', '0.088', '0.031', '0.113', '#interest', '#admiration', 'positive', '0.051', 'cultivate_yard', 'same_answer', 'poisonous_human', 'juicy', 'natural_material']
senticnet['inorganic_fertiliser'] = ['0.071', '0', '0.031', '0.064', '#joy', '#admiration', 'positive', '0.035', 'organic_matter', 'decompose', 'recycle', 'soil', 'compost_pile']
senticnet['inorganic_fertilizer'] = ['0.071', '0', '0.031', '0.064', '#joy', '#admiration', 'positive', '0.035', 'organic_matter', 'decompose', 'recycle', 'soil', 'compost_pile']
senticnet['inorganic_material'] = ['0.035', '0.088', '0.031', '0.113', '#interest', '#admiration', 'positive', '0.051', 'cultivate_yard', 'same_answer', 'poisonous_human', 'juicy', 'natural_material']
senticnet['inorganic_matter'] = ['-0.7', '0', '0.77', '-0.75', '#anger', '#disgust', 'negative', '-0.74', 'fake', 'unnatural', 'man_made', 'artificial_sea', 'artificial_taste']
senticnet['inorganic_mercury'] = ['-0.18', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.16', 'hazardous_health', 'dangerous_health', 'bad_health', 'gross', 'harmful']
senticnet['inorganic_substance'] = ['0.035', '0.088', '0.031', '0.113', '#interest', '#admiration', 'positive', '0.051', 'cultivate_yard', 'same_answer', 'poisonous_human', 'juicy', 'natural_material']
senticnet['inosine_monophosphate_imp'] = ['0.526', '-0.99', '0', '0.673', '#surprise', '#admiration', 'positive', '0.732', 'faery', 'faerie', 'fairy', 'leprechaun', 'sandman']
senticnet['inositol'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucose', 'corn_sugar', 'dextrose', 'dextroglucose', 'grape_sugar']
senticnet['inostrancevia'] = ['-0.02', '0', '0.044', '0', '#sadness', '#anger', 'negative', '-0.03', 'large_cat', 'fast_person', 'household_pet', 'big_cat', 'good_pet']
senticnet['inotropic'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'keep_alive', 'body_system', 'body_working', 'blood_pressure', 'heart']
senticnet['inova_fairfax_hospital'] = ['0', '0', '-0.73', '0', '#fear', '#fear', 'negative', '-0.73', 'clinic', 'emergency_ward', 'hospital', 'dentist_office', 'medical_office']
senticnet['inpatient'] = ['-0.96', '0', '0.742', '0', '#sadness', '#anger', 'negative', '-0.85', 'outpatient', 'out-patient', 'in-patient', 'ambulatory', 'psychiatric']
senticnet['inpatient_care'] = ['0', '0', '-0.73', '0', '#fear', '#fear', 'negative', '-0.73', 'clinic', 'emergency_ward', 'hospital', 'dentist_office', 'medical_office']
senticnet['inpatient_rehabilitation'] = ['0.641', '-0.78', '0', '0.721', '#surprise', '#admiration', 'positive', '0.715', 'correctional_rehabilitation', 'physical_rehabilitation', 'physical_restoration', 'therapeutic_rehabilitation', 'vocational_rehabilitation']
senticnet['input'] = ['0', '0.706', '0', '0.84', '#interest', '#admiration', 'positive', '0.773', 'comment', 'remark', 'input_signal', 'stimulus', 'stimulation']
senticnet['input_device'] = ['0', '0', '0.076', '0', '#anger', '#anger', 'negative', '-0.07', 'device', 'computer', 'browser', 'number_pad', 'telephone_computer']
senticnet['input_file'] = ['0', '0.098', '-0.06', '0.15', '#interest', '#admiration', 'positive', '0.061', 'make_book', 'use_ink', 'make_newspaper', 'pencil_mark', 'computer_command']
senticnet['input_output'] = ['0', '0.098', '-0.06', '0.15', '#interest', '#admiration', 'positive', '0.061', 'make_book', 'use_ink', 'make_newspaper', 'pencil_mark', 'computer_command']
senticnet['input_price_index'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'indicator', 'retail_price_index', 'producer_price_index', 'wholesale_price_index', 'consumer_price_index']
senticnet['input_queue'] = ['-0.15', '0', '0.029', '-0.11', '#sadness', '#disgust', 'negative', '-0.1', 'start_over', 'front_line', 'find_line', 'take_turn', 'long_line']
senticnet['input_shaft'] = ['0', '0.142', '0', '0.125', '#interest', '#admiration', 'positive', '0.134', 'weight_machine', 'power_drill', 'screw', 'screwdriver', 'air_pump']
senticnet['input_signal'] = ['0', '0.705', '0', '0.831', '#interest', '#admiration', 'positive', '0.768', 'comment', 'remark', 'input', 'stimulus', 'stimulation']
senticnet['input_tray'] = ['0.129', '0', '0', '0', '#joy', '#joy', 'positive', '0.129', 'incline_plane', 'boulder', 'ramp', 'hill_mountain', 'rock']
senticnet['input_voltage'] = ['0', '0.098', '-0.06', '0.15', '#interest', '#admiration', 'positive', '0.061', 'make_book', 'use_ink', 'make_newspaper', 'pencil_mark', 'computer_command']
senticnet['inquietude'] = ['-0.95', '0', '-0.76', '0', '#sadness', '#fear', 'negative', '-0.85', 'disquietude', 'edginess', 'edgy', 'uptight', 'restive']
senticnet['inquiline'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'annelid', 'annelid_worm', 'leech']
senticnet['inquire'] = ['-0.10', '0.127', '-0.11', '0.174', '#interest', '#admiration', 'positive', '0.022', 'query', 'response', 'respond', 'interrogation', 'inquiry']
senticnet['inquire_into'] = ['-0.10', '0.127', '-0.11', '0.174', '#interest', '#admiration', 'positive', '0.022', 'query', 'response', 'respond', 'interrogation', 'inquiry']
senticnet['inquirer'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquisitor', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['inquirer_reporter'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquisitor', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['inquiring_into'] = ['-0.10', '0.127', '-0.11', '0.174', '#interest', '#admiration', 'positive', '0.022', 'query', 'response', 'respond', 'interrogation', 'inquiry']
senticnet['inquiring_judge'] = ['-0.88', '0', '0.727', '-0.82', '#sadness', '#disgust', 'negative', '-0.81', 'defervescence', 'subsidence', 'break', 'remission', 'abatement']
senticnet['inquiringly'] = ['-0.71', '0', '0.906', '-0.93', '#anger', '#disgust', 'negative', '-0.85', 'absent', 'forgetful', 'distracted', 'inattentive', 'scatty']
senticnet['inquiry'] = ['0.845', '0.916', '0', '0', '#joy', '#interest', 'positive', '0.881', 'question', 'answer', 'query', 'question_start', 'question_answering']
senticnet['inquiry_into'] = ['0.845', '0.916', '0', '0', '#joy', '#interest', 'positive', '0.881', 'question', 'answer', 'query', 'question_start', 'question_answering']
senticnet['inquisition'] = ['-0.58', '0', '0.701', '-0.97', '#anger', '#disgust', 'negative', '-0.75', 'interrogatory', 'interview', 'catechism', 'deposition', 'third_degree']
senticnet['inquisitive'] = ['0.847', '0.855', '0', '0', '#joy', '#interest', 'positive', '0.851', 'curious', 'seek_information', 'ask_question', 'question', 'question_knowledge']
senticnet['inquisitively'] = ['0', '-0.90', '0', '0.923', '#surprise', '#admiration', 'positive', '0.912', 'questioningly', 'worriedly', 'incredulously', 'quizzically', 'blankly']
senticnet['inquisitiveness'] = ['0', '0', '0.93', '-0.98', '#anger', '#disgust', 'negative', '-0.95', 'precociousness', 'precocity', 'inventiveness', 'adventurousness', 'mischievousness']
senticnet['inquisitor'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquirer', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['inquisitor_general'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquirer', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['inquisitorial'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquirer', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['inroad'] = ['-0.69', '0', '0.752', '-0.82', '#anger', '#disgust', 'negative', '-0.75', 'usurpation', 'trespass', 'encroachment', 'intrusion', 'violation']
senticnet['inrush'] = ['-0.97', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.92', 'voltage', 'quiescent', 'sinusoidal', 'proliferating', 'steady-state']
senticnet['inrush_current'] = ['-0.09', '-0.01', '-0.10', '-0.20', '#fear', '#disgust', 'negative', '-0.09', 'violence', 'siege', 'conflict', 'bloodshed', 'fight']
senticnet['insalubriousness'] = ['-0.75', '0', '0.893', '-0.61', '#sadness', '#anger', 'negative', '-0.75', 'unwholesomeness', 'morbidness', 'morbidity', 'insalubrity', 'unhealthfulness']
senticnet['insalubrity'] = ['-0.75', '0', '0.893', '-0.61', '#sadness', '#anger', 'negative', '-0.75', 'unwholesomeness', 'morbidness', 'morbidity', 'unhealthfulness', 'insalubriousness']
senticnet['insane'] = ['0', '0', '0.729', '-0.93', '#anger', '#disgust', 'negative', '-0.83', 'seek_revenge', 'die', 'take_life', 'threaten', 'cause_death']
senticnet['insane_asylum'] = ['-0.75', '0', '0.547', '-0.57', '#sadness', '#disgust', 'negative', '-0.62', 'snake_pit', 'booby_hatch', 'crazy_house', 'cuckoo_nest', 'funny_farm']
senticnet['insanely'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['insanely_catchy'] = ['0.75', '0.71', '0', '0', '#joy', '#interest', 'positive', '0.73', 'spherical_shape', 'use_sport', 'flexible', 'ball', 'fill_air']
senticnet['insanely_expensive'] = ['-0.04', '0.201', '-0.06', '0.312', '#interest', '#admiration', 'positive', '0.101', 'save_money', 'good_price', 'inexpensive', 'bargain', 'discount']
senticnet['insanitary'] = ['-0.93', '0', '0.75', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'invalidism', 'injury', 'affliction', 'biliousness', 'harm']
senticnet['insanity'] = ['0', '0', '0.808', '-0.75', '#anger', '#disgust', 'negative', '-0.78', 'prejudice', 'angry_emotion', 'fight', 'mental_disorder', 'dislike']
senticnet['insatiable'] = ['-0.72', '0', '0.933', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'unsatisfied', 'quenchless', 'unquenchable', 'unsated', 'unsatiated']
senticnet['insatiable_appetite'] = ['-0.71', '0', '0.692', '-0.81', '#sadness', '#disgust', 'negative', '-0.74', 'famished', 'starved', 'hungry', 'esurient', 'peckish']
senticnet['insatiable_curiosity'] = ['0.505', '-0.85', '0', '0.94', '#surprise', '#admiration', 'positive', '0.767', 'infatuation', 'ardor', 'fervor', 'fervency', 'fervidness']
senticnet['insatiable_demand'] = ['0.749', '0.764', '0', '0', '#joy', '#interest', 'positive', '0.756', 'obedience', 'order', 'requirement', 'permission', 'necessary']
senticnet['insatiable_desire'] = ['-0.63', '0', '0.608', '-0.74', '#sadness', '#disgust', 'negative', '-0.66', 'pall', 'take_in', 'take', 'ingest', 'have']
senticnet['insatiable_hunger'] = ['-0.87', '0', '-0.71', '-0.96', '#sadness', '#disgust', 'negative', '-0.85', 'avid', 'eagerly', 'eagerness', 'avidity', 'avidness']
senticnet['insatiable_sexual'] = ['0.769', '0', '0', '0', '#joy', '#joy', 'positive', '0.769', 'make_breakfast', 'enjoy_meal', 'put_food_into_mouth', 'prepare_meal', 'prepare_breakfast']
senticnet['insatiable_thirst'] = ['-0.87', '0', '-0.71', '-0.96', '#sadness', '#disgust', 'negative', '-0.85', 'avid', 'eagerly', 'eagerness', 'avidity', 'avidness']
senticnet['insatiate'] = ['-0.91', '0', '0.803', '-0.53', '#sadness', '#anger', 'negative', '-0.75', 'unsatisfied', 'quenchless', 'unquenchable', 'unsated', 'unsatiated']
senticnet['inscribe'] = ['0.719', '0.757', '0', '0', '#joy', '#interest', 'positive', '0.738', 'matriculate', 'enroll_class', 'enroll_school', 'enroll', 'register']
senticnet['inscription'] = ['0.719', '0.757', '0', '0', '#joy', '#interest', 'positive', '0.738', 'matriculate', 'enroll_class', 'enroll_school', 'enroll', 'register']
senticnet['inscrutability'] = ['0.973', '-0.77', '0', '0.983', '#joy', '#admiration', 'positive', '0.911', 'opaqueness', 'faintness', 'dimness', 'opacity', 'vagueness']
senticnet['inscrutable'] = ['-0.62', '0', '0.825', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'unexplainable', 'unaccountable', 'unexplained', 'incomprehensible', 'mystifying']
senticnet['inscrutably'] = ['-0.62', '0', '0.825', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'unexplainable', 'unaccountable', 'unexplained', 'incomprehensible', 'mystifying']
senticnet['insect'] = ['0', '-0.04', '0.089', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'bug', 'annoy_bug', 'nuisance_insect', 'fly_insect', 'social_insect']
senticnet['insect_bite'] = ['-0.93', '0', '0.631', '-0.9', '#sadness', '#disgust', 'negative', '-0.82', 'injury', 'harm', 'hurt', 'mosquito_bite', 'trauma']
senticnet['insect_collection'] = ['-0.05', '0.21', '-0.07', '0.102', '#interest', '#admiration', 'positive', '0.047', 'coin', 'collection', 'butterfly_collection', 'coin_collection', 'album_collection']
senticnet['insect_damage'] = ['-0.08', '-0.04', '0.023', '-0.07', '#sadness', '#disgust', 'negative', '-0.03', 'tier', 'make_toast', 'house_smell_good', 'get_hot', 'bread_crumb']
senticnet['insect_infestation'] = ['-0.08', '-0.04', '0.023', '-0.07', '#sadness', '#disgust', 'negative', '-0.03', 'tier', 'make_toast', 'house_smell_good', 'get_hot', 'bread_crumb']
senticnet['insect_pest'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'nuisance', 'annoyance', 'irritant', 'pain_in_ass', 'troublesome']
senticnet['insect_prey'] = ['0.812', '0.872', '0', '0', '#joy', '#interest', 'positive', '0.842', 'insect', 'fly_away', 'small_insect', 'maggot', 'chihuahua']
senticnet['insect_repellant'] = ['0.03', '0.117', '0', '0.084', '#interest', '#admiration', 'positive', '0.077', 'prevent_germ', 'kill_germ', 'thin_wood', 'tree_product', 'book_page']
senticnet['insect_repellent'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['insect_resistant'] = ['-0.69', '0', '0.516', '-0.94', '#sadness', '#disgust', 'negative', '-0.71', 'insubordinate', 'recalcitrant', 'obstreperous', 'defiant', 'resistive']
senticnet['insect_sting'] = ['-0.74', '0', '0.766', '0', '#sadness', '#anger', 'negative', '-0.75', 'use_needle', 'clothes_repair', 'woven', 'embroidery', 'thread_needle']
senticnet['insect_venom'] = ['0', '0', '-0.73', '-0.81', '#fear', '#disgust', 'negative', '-0.77', 'flab', 'bad_hair_day', 'lose_friend', 'alcoholism', 'brain_tumor']
senticnet['insectary'] = ['-0.12', '0.066', '-0.08', '-0.06', '#sadness', '#fear', 'negative', '-0.05', 'garden_yard', 'see_sky', 'plant_tree', 'hair', 'seed_lawn']
senticnet['insecticidal'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['insecticidal_soap'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['insecticide'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['insectivore'] = ['-0.02', '0', '0.044', '0', '#sadness', '#anger', 'negative', '-0.03', 'large_cat', 'fast_person', 'household_pet', 'big_cat', 'good_pet']
senticnet['insectoid'] = ['-0.73', '0', '0.72', '-0.93', '#sadness', '#disgust', 'negative', '-0.79', 'imp', 'elf', 'hob', 'leprechaun', 'pixie']
senticnet['insecure'] = ['-0.81', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.89', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecurity']
senticnet['insecurely'] = ['-0.79', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.79', 'misgiving', 'qualm', 'scruple', 'insecurity', 'insecure']
senticnet['insecurity'] = ['-0.84', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.82', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecure']
senticnet['insee_thong'] = ['-0.73', '0', '0.55', '-0.75', '#sadness', '#disgust', 'negative', '-0.68', 'mortal', 'person', 'individual', 'mangler', 'mutilator']
senticnet['insein_prison'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicative', 'unpronounceable', 'impassive', 'inexpressive', 'silent']
senticnet['inseminate'] = ['0.049', '0.098', '-0.07', '0.121', '#interest', '#admiration', 'positive', '0.047', 'procreate', 'reproduce', 'start_family', 'cause_cold', 'unprotect_sex']
senticnet['insensate'] = ['-0.99', '0', '0.658', '-0.73', '#sadness', '#disgust', 'negative', '-0.79', 'adrift', 'afloat', 'aimless', 'directionless', 'purposeless']
senticnet['insensibility'] = ['-0.79', '0', '0.636', '-0.75', '#sadness', '#disgust', 'negative', '-0.72', 'insensitiveness', 'tin_ear', 'hardness', 'unfitness', 'unfeelingness']
senticnet['insensible'] = ['-0.61', '0', '0.572', '-0.83', '#sadness', '#disgust', 'negative', '-0.67', 'numb', 'benumbed', 'anesthetic', 'emotionless', 'asleep']
senticnet['insensibly'] = ['-0.90', '0', '0.596', '-0.95', '#sadness', '#disgust', 'negative', '-0.82', 'slowness', 'mental_retardation', 'backwardness', 'subnormality', 'retardation']
senticnet['insensitive'] = ['-0.04', '-0.04', '-0.04', '-0.04', '#fear', '#disgust', 'negative', '-0.02', 'blunted', 'dull', 'unsharpened', 'blunt', 'edgeless']
senticnet['insensitiveness'] = ['-0.79', '0', '0.636', '-0.75', '#sadness', '#disgust', 'negative', '-0.72', 'insensibility', 'tin_ear', 'hardness', 'unfitness', 'unfeelingness']
senticnet['insensitivity'] = ['-0.84', '0', '0.547', '-0.65', '#sadness', '#disgust', 'negative', '-0.68', 'insensibility', 'tin_ear', 'hardness', 'unfitness', 'unfeelingness']
senticnet['insentient'] = ['0.03', '0', '0', '0.119', '#joy', '#admiration', 'positive', '0.074', 'notional', 'abstract', 'wow', 'made_out_of_plastic', 'make_paper']
senticnet['inseparable'] = ['-0.98', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.90', 'indivisible', 'interdependent', 'inalienable', 'indissoluble', 'inviolable']
senticnet['inseparably'] = ['0.735', '0.86', '0', '0', '#joy', '#interest', 'positive', '0.797', 'intertwined', 'inextricably', 'intimately', 'entwined', 'inescapably']
senticnet['insert'] = ['0.043', '-0.05', '0.043', '0.204', '#surprise', '#admiration', 'positive', '0.064', 'flour_water', 'sticky_substance', 'adhesive', 'computer_command', 'tooth_brush']
senticnet['insert_dvd_in_player'] = ['-0.08', '-0.06', '0.043', '-0.18', '#sadness', '#disgust', 'negative', '-0.06', 'press_play', 'turn_on_dvd_player', 'turn_channel', 'buy_popcorn', 'turn_television']
senticnet['insert_menu'] = ['0.055', '0.117', '0', '0.133', '#interest', '#admiration', 'positive', '0.102', 'graphical_representation', 'pencil_pen', 'note_pad', 'marker', 'stationery']
senticnet['inservice'] = ['-0.69', '0', '0.659', '-0.69', '#sadness', '#disgust', 'negative', '-0.68', 'help', 'paralegal', 'legal_assistant', 'paramedic', 'paramedical']
senticnet['inshore_lifeboat'] = ['0', '-0.02', '0.046', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'sail_ship', 'canoe', 'ship', 'submarine', 'yacht']
senticnet['inside'] = ['0', '0.096', '0', '0.115', '#interest', '#admiration', 'positive', '0.106', 'part_of_building', 'architectural_structure', 'place_house', 'live_quarter', 'small_building']
senticnet['inside_body'] = ['-0.04', '0.077', '-0.09', '-0.08', '#fear', '#disgust', 'negative', '-0.03', 'body', 'corpse', 'normal_human_body', 'dead_person', 'nervous_system']
senticnet['inside_brain'] = ['-0.97', '0', '-0.73', '0', '#sadness', '#fear', 'negative', '-0.85', 'anus', 'stub', 'nervous_system', 'nape', 'under_head']
senticnet['inside_build'] = ['0', '0.086', '0.046', '0.133', '#interest', '#admiration', 'positive', '0.058', 'house_place', 'empty', 'house_location', 'empty_place', 'land_measurement']
senticnet['inside_egg'] = ['0.053', '0.091', '0', '0.152', '#interest', '#admiration', 'positive', '0.099', 'baby_chicken', 'hen_baby', 'oval_shape', 'lay_egg', 'hatch']
senticnet['inside_head'] = ['0.936', '0.764', '0', '0', '#joy', '#interest', 'positive', '0.85', 'cranium', 'head', 'skull', 'hold_brain', 'face']
senticnet['inside_house'] = ['0', '0', '0', '0.092', '#admiration', '#admiration', 'positive', '0.092', 'room', 'four_walls', 'house_part', 'square_shape', 'interior']
senticnet['inside_scoop'] = ['0', '0.096', '0', '0.075', '#interest', '#admiration', 'positive', '0.085', 'stern', 'quick_look', 'glimpse', 'peep', 'stare']
senticnet['insider_misuse'] = ['-0.69', '0', '0.752', '-0.82', '#anger', '#disgust', 'negative', '-0.75', 'usurpation', 'trespass', 'encroachment', 'inroad', 'violation']
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senticnet['insulin'] = ['0.718', '0.849', '0', '0.704', '#joy', '#interest', 'positive', '0.757', 'good_tooth', 'self_confidence', 'big_paycheck', 'good_credit', 'look_beautiful']
senticnet['insulin_resistant'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'fatty', 'worse', 'health_issue', 'clinical_problem']
senticnet['insulin_secretion'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'cholecystokinin', 'secretin', 'gastrointestinal_hormone']
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senticnet['insurance_coverage'] = ['-0.96', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.95', 'reporting', 'reportage', 'coverage', 'abortion', 'murder']
senticnet['insurance_fraud'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'mail_fraud', 'swindle', 'rig']
senticnet['insurance_policy'] = ['0.73', '-0.56', '0', '0.621', '#joy', '#admiration', 'positive', '0.64', 'amnesty', 'unsusceptibility', 'diplomatic_immunity', 'exemption', 'impunity']
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senticnet['intake'] = ['0', '0.839', '0', '0.7', '#interest', '#admiration', 'positive', '0.769', 'uptake', 'ingestion', 'consumption', 'research', 'determine']
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senticnet['intangible_asset'] = ['0', '0.238', '0.021', '0.152', '#interest', '#admiration', 'positive', '0.123', 'money_value', 'money_amount', 'worth', 'monetary_amount', 'importance']
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senticnet['integer_arithmetic'] = ['0.076', '0.096', '-0.08', '0.167', '#interest', '#admiration', 'positive', '0.063', 'statistic', 'mathematical_function', 'calculation', 'list_number', 'calculate']
senticnet['integer_value'] = ['-0.04', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.06', 'chemical_reaction', 'burn', 'chemical_process', 'get_burnt', 'set_on_fire']
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senticnet['integrative_animal_biology'] = ['0', '0.812', '0', '0.753', '#interest', '#admiration', 'positive', '0.782', 'integrative_biology', 'neuroscie', 'award', 'different_application', 'continue_grant']
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senticnet['integrative_medicine'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
senticnet['integrative_plant_biology'] = ['0', '0.888', '0', '0.852', '#interest', '#admiration', 'positive', '0.87', 'integrative_biology', 'neuroscie', 'award', 'continue_grant', 'sponsor']
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senticnet['integro_differential_equation'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'assumption', 'precondition', 'provision', 'constraint', 'premiss']
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senticnet['intellectual_acuity'] = ['0.968', '-0.87', '0', '0.992', '#joy', '#admiration', 'positive', '0.946', 'sagacity', 'sagaciousness', 'care', 'caution', 'discernment']
senticnet['intellectual_arrogance'] = ['-0.88', '0', '0.663', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'powerlessness', 'impotence', 'impotency', 'inefficacy', 'inefficaciousness']
senticnet['intellectual_brilliance'] = ['0.968', '-0.87', '0', '0.992', '#joy', '#admiration', 'positive', '0.946', 'sagacity', 'sagaciousness', 'care', 'caution', 'discernment']
senticnet['intellectual_curiosity'] = ['0.505', '-0.85', '0', '0.94', '#surprise', '#admiration', 'positive', '0.767', 'infatuation', 'ardor', 'fervor', 'fervency', 'fervidness']
senticnet['intellectual_disability'] = ['-0.77', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.78', 'chicken_pox', 'diabetes', 'lyme_disease', 'brain_tumor', 'suffer_fool']
senticnet['intellectual_dishonesty'] = ['-0.56', '0', '0.909', '-0.62', '#anger', '#disgust', 'negative', '-0.69', 'unkemptness', 'disorder', 'slovenliness', 'untidiness', 'disorderliness']
senticnet['intellectual_excitement'] = ['0.772', '0.742', '0', '0', '#joy', '#interest', 'positive', '0.757', 'enjoy', 'satisfaction', 'happiness', 'become_excited', 'joy']
senticnet['intellectual_focus'] = ['0.992', '0', '0', '0.944', '#joy', '#admiration', 'positive', '0.968', 'graduate_education', 'human_resource', 'specific_award_reside', 'directorate', 'education']
senticnet['intellectual_honesty'] = ['0.968', '-0.87', '0', '0.992', '#joy', '#admiration', 'positive', '0.946', 'sagacity', 'sagaciousness', 'care', 'caution', 'discernment']
senticnet['intellectual_impairment'] = ['0.874', '-0.73', '0', '0.608', '#joy', '#surprise', 'positive', '0.74', 'growth', 'growing', 'maturation', 'development', 'ontogeny']
senticnet['intellectual_inferiority'] = ['-0.57', '0', '0.53', '-0.63', '#sadness', '#disgust', 'negative', '-0.58', 'low_status', 'lowness', 'backseat', 'lowliness', 'subordinateness']
senticnet['intellectual_inquiry'] = ['0.103', '-0.06', '0.061', '-0.03', '#joy', '#surprise', 'positive', '0.017', 'artistic_expression', 'creativity', 'self_expression', 'creative_soul', 'make_beautiful']
senticnet['intellectual_integrity'] = ['0.968', '-0.87', '0', '0.992', '#joy', '#admiration', 'positive', '0.946', 'sagacity', 'sagaciousness', 'care', 'caution', 'discernment']
senticnet['intellectual_intuition'] = ['-0.57', '0', '0.713', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'assured', 'conclusion', 'certainty', 'predictability', 'definiteness']
senticnet['intellectual_merit'] = ['0', '0.982', '0', '0.709', '#interest', '#admiration', 'positive', '0.845', 'broader', 'project', 'development', 'broader_impact', 'research']
senticnet['intellectual_rigor'] = ['0', '0.803', '0', '0.71', '#interest', '#admiration', 'positive', '0.756', 'novelty', 'innovativeness', 'freshness', 'ability', 'power']
senticnet['intellectual_sophistication'] = ['0.99', '0', '0', '0.716', '#joy', '#admiration', 'positive', '0.853', 'encyclopedism', 'learning', 'scholarship', 'learnedness', 'eruditeness']
senticnet['intellectual_stimulation'] = ['0', '0.142', '0', '0.125', '#interest', '#admiration', 'positive', '0.134', 'exercise_mind', 'exercise', 'mind', 'exercise_muscle', 'occupy_mind']
senticnet['intellectual_superiority'] = ['0.626', '-0.96', '0', '0.788', '#surprise', '#admiration', 'positive', '0.794', 'foxiness', 'slyness', 'wiliness', 'craftiness', 'cunning']
senticnet['intellectual_tradition'] = ['0', '0.915', '0', '0.821', '#interest', '#admiration', 'positive', '0.868', 'culture', 'special_day', 'holiday', 'party_time', 'yearly_celebration']
senticnet['intellectualism'] = ['-0.94', '0', '0.687', '-0.75', '#sadness', '#disgust', 'negative', '-0.79', 'clannishness', 'cliquishness', 'snobbery', 'arrogance', 'haughtiness']
senticnet['intellectuality'] = ['0.995', '-0.69', '0', '0.528', '#joy', '#surprise', 'positive', '0.74', 'grasp', 'hug', 'nestle', 'snuggle', 'grip']
senticnet['intellectually_dishonest'] = ['-0.88', '0', '0.907', '-0.84', '#sadness', '#anger', 'negative', '-0.88', 'intolerable', 'unacceptable', 'bitter', 'impossible', 'insufferable']
senticnet['intellectually_honest'] = ['-0.84', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.79', 'piss_pant', 'rejection', 'argue', 'make_statement', 'become_politician']
senticnet['intellectually_vibrant'] = ['-0.05', '0.096', '0', '0.066', '#interest', '#admiration', 'positive', '0.037', 'hue_tint', 'spectrum', 'red_blue', 'green_yellow', 'tint']
senticnet['intellegent'] = ['0.995', '0.801', '0', '0.968', '#joy', '#admiration', 'positive', '0.921', 'smart', 'smartness', 'curious', 'become_wise', 'comprehend']
senticnet['intelligence'] = ['0', '0', '0', '0.881', '#admiration', '#admiration', 'positive', '0.881', 'knowledge', 'trivia', 'understand_problem', 'gain_insight', 'solution_to_problem']
senticnet['intelligence_quotient'] = ['0', '0.943', '0', '0.789', '#interest', '#admiration', 'positive', '0.866', 'energy', 'genius', 'energy_use', 'gas_oil', 'invent']
senticnet['intelligence_quotient_iq'] = ['0', '0.943', '0', '0.789', '#interest', '#admiration', 'positive', '0.866', 'energy', 'genius', 'energy_use', 'gas_oil', 'invent']
senticnet['intelligent'] = ['0.995', '0.801', '0', '0.968', '#joy', '#admiration', 'positive', '0.921', 'smart', 'smartness', 'curious', 'become_wise', 'comprehend']
senticnet['intelligent_agent'] = ['0', '0.717', '0', '0.765', '#interest', '#admiration', 'positive', '0.741', 'helper', 'artificial_intelligence', 'goal_driven', 'proactive', 'intelligent']
senticnet['intelligent_person'] = ['0.086', '0.095', '-0.06', '0.066', '#joy', '#interest', 'positive', '0.045', 'married_couple', 'presidential_election', 'grown_up', 'use_binoculars', 'torpedo']
senticnet['intelligently'] = ['0.775', '0.715', '0', '0', '#joy', '#interest', 'positive', '0.745', 'smartly', 'elegant', 'smart', 'quickly', 'fillip']
senticnet['intelligentsia'] = ['0.594', '-0.73', '0', '0.909', '#surprise', '#admiration', 'positive', '0.746', 'elite', 'clerisy', 'nobility', 'elect', 'aristocracy']
senticnet['intelligenzaktion'] = ['-0.08', '0', '-0.15', '-0.20', '#fear', '#disgust', 'negative', '-0.14', 'casualty', 'reaper', 'wound', 'cause_death', 'gunshot']
senticnet['intelligibility'] = ['0.869', '-0.88', '0', '0.598', '#joy', '#surprise', 'positive', '0.782', 'connectedness', 'connection', 'consistency', 'continuity', 'link']
senticnet['intelligible'] = ['0.744', '-0.76', '0', '0.669', '#joy', '#surprise', 'positive', '0.726', 'accessible', 'fathomable', 'approachable', 'perceivable', 'comprehendible']
senticnet['intelligibly'] = ['0.515', '-0.80', '0', '0.791', '#surprise', '#admiration', 'positive', '0.703', 'accessible', 'fathomable', 'approachable', 'perceivable', 'intelligible']
senticnet['intemperance'] = ['-0.66', '0', '0.749', '-0.54', '#sadness', '#anger', 'negative', '-0.65', 'inertia', 'inactiveness', 'inactivity', 'faineance', 'indolence']
senticnet['intemperate'] = ['-0.57', '0', '0.79', '-0.89', '#anger', '#disgust', 'negative', '-0.75', 'abuse', 'vilification', 'insult', 'revilement', 'contumely']
senticnet['intemperate_language'] = ['-0.95', '0', '0.771', '-0.67', '#sadness', '#anger', 'negative', '-0.8', 'inculpative', 'condemning', 'accusatory', 'accusing', 'accusive']
senticnet['intemperateness'] = ['0.987', '-0.74', '0', '0.695', '#joy', '#surprise', 'positive', '0.809', 'vice', 'drink', 'drinking', 'boozing', 'drunkenness']
senticnet['intend'] = ['0', '0.338', '-0.13', '0.298', '#interest', '#admiration', 'positive', '0.166', 'permission', 'conclusion', 'purpose', 'ask', 'conclude']
senticnet['intended'] = ['0.752', '0.978', '0', '0', '#joy', '#interest', 'positive', '0.865', 'willful', 'vandalism', 'willfully', 'intentional', 'willfulness']
senticnet['intendment'] = ['-0.86', '0', '0.632', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'airy', 'shadowy', 'stringy', 'wraithlike', 'ethereal']
senticnet['intense'] = ['-0.36', '-0.17', '-0.20', '0.062', '#sadness', '#fear', 'negative', '-0.08', 'excessive', 'extreme', 'nervous', 'tense', 'intense_heat']
senticnet['intense_anxiety'] = ['-0.80', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.89', 'worried', 'solicitous', 'solicitously', 'solicitude', 'solicitousness']
senticnet['intense_aroma'] = ['0.784', '-0.59', '0', '0.846', '#joy', '#admiration', 'positive', '0.742', 'astuteness', 'slyness', 'wiliness', 'craftiness', 'cunning']
senticnet['intense_barrage'] = ['0', '0', '-0.85', '-0.99', '#fear', '#disgust', 'negative', '-0.92', 'onslaught', 'battery', 'shelling', 'bombardment', 'outpouring']
senticnet['intense_bombardment'] = ['0', '0', '-0.85', '-0.99', '#fear', '#disgust', 'negative', '-0.92', 'onslaught', 'battery', 'shelling', 'bombardment', 'outpouring']
senticnet['intense_competition'] = ['0.887', '0', '0', '0', '#joy', '#joy', 'positive', '0.887', 'fun', 'difficult_play', 'enjoy_sport', 'accept_challenge', 'competition']
senticnet['intense_criticism'] = ['-0.16', '0.179', '0.568', '-0.61', '#anger', '#disgust', 'negative', '-0.29', 'angrily', 'choler', 'see_red', 'dander', 'hackle']
senticnet['intense_curiosity'] = ['0.601', '0.647', '-0.26', '0.322', '#joy', '#interest', 'positive', '0.326', 'captivated', 'charmed', 'captivate', 'bewitch', 'enthrallment']
senticnet['intense_debate'] = ['0', '0', '0.94', '0', '#anger', '#anger', 'negative', '-0.94', 'feel_angry', 'hurt_feeling', 'feel_hurt', 'ridicule', 'vengeance']
senticnet['intense_deisre'] = ['0.06', '0.377', '-0.30', '0.339', '#interest', '#admiration', 'positive', '0.119', 'yearn', 'strong_desire', 'desire', 'demand', 'ask_for']
senticnet['intense_emotion'] = ['0.708', '-0.60', '0', '0.661', '#joy', '#admiration', 'positive', '0.659', 'love', 'conditioned_emotional_response', 'conditioned_emotion', 'fright', 'reverence']
senticnet['intense_flavor'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'mayonnaise', 'box_cereal', 'bottle_water']
senticnet['intense_glare'] = ['0.988', '0', '0', '0.789', '#joy', '#admiration', 'positive', '0.889', 'brightness', 'blaze', 'brilliance', 'glitter', 'glister']
senticnet['intense_heat'] = ['-0.94', '0', '0.112', '-0.99', '#sadness', '#disgust', 'negative', '-0.68', 'pain', 'hurt', 'injury', 'heat', 'fire']
senticnet['intense_hurricane'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'atmospheric_phenomenon', 'natural_disaster', 'flood', 'destruction', 'tropical_storm']
senticnet['intense_interrogation'] = ['-0.65', '0', '0.718', '-0.73', '#anger', '#disgust', 'negative', '-0.70', 'inquirer', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['intense_jealousy'] = ['0', '0', '0.794', '-0.99', '#anger', '#disgust', 'negative', '-0.89', 'envy', 'resentment', 'grudge', 'greedy', 'jealous']
senticnet['intense_pressure'] = ['-0.12', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.09', 'breeze', 'move_air', 'air_movement', 'gape', 'force']
senticnet['intense_pulsed_light'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'radiation_therapy', 'radiation', 'actinotherapy', 'irradiation']
senticnet['intense_rainfall'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'condensation', 'water_fall', 'falling_water', 'frozen_water', 'water_drop']
senticnet['intense_scrutiny'] = ['-0.79', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.75', 'scan', 'checkup', 'medical_checkup', 'medical_examination', 'medical_exam']
senticnet['intense_sunlight'] = ['0', '-0.03', '0.05', '-0.14', '#anger', '#disgust', 'negative', '-0.05', 'humidity', 'wetness', 'damp', 'humid', 'dank']
senticnet['intensely'] = ['-0.86', '0', '-0.76', '0', '#sadness', '#fear', 'negative', '-0.81', 'extremely', 'ferociously', 'savage', 'viciously', 'aggressive']
senticnet['intensely_emotional'] = ['0.655', '0.635', '-0.23', '0.3', '#joy', '#interest', 'positive', '0.338', 'emotional_response', 'emotional_reaction', 'sentimental', 'sensitive', 'emotional']
senticnet['intensely_personal'] = ['0.722', '-0.60', '0', '0.596', '#joy', '#surprise', 'positive', '0.641', 'medicament', 'medicinal_drug', 'medication', 'aloe', 'bitter_aloe']
senticnet['intensely_private'] = ['0.929', '-0.79', '0', '0.915', '#joy', '#admiration', 'positive', '0.88', 'privacy', 'seclusion', 'privateness', 'solitariness', 'loneliness']
senticnet['intensely_radioactive'] = ['-0.02', '0', '0.047', '-0.11', '#anger', '#disgust', 'negative', '-0.06', 'hold_trash', 'put_garbage', 'rubbish', 'put_trash', 'store_trash']
senticnet['intensely_sweet'] = ['0.16', '0', '0.039', '0.121', '#joy', '#admiration', 'positive', '0.081', 'taste_bitter', 'sweetness', 'sweet', 'sweet_food', 'after_taste']
senticnet['intensest'] = ['-0.96', '0', '0.787', '0', '#sadness', '#anger', 'negative', '-0.87', 'disconcertion', 'untune', 'disconcertment', 'discomfiture', 'discomfit']
senticnet['intensification'] = ['0.836', '-0.83', '0', '0.511', '#joy', '#surprise', 'positive', '0.726', 'appearance', 'start', 'casus_belli', 'beginning', 'egress']
senticnet['intensify'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'compound', 'enhance', 'deepen', 'potentiate', 'heighten']
senticnet['intensify_cooperation'] = ['0', '0', '0', '0.881', '#admiration', '#admiration', 'positive', '0.881', 'educate_others', 'share_knowledge', 'share_opinion', 'discover_new', 'collaboration']
senticnet['intensifying'] = ['0.652', '-0.64', '0', '0.953', '#joy', '#admiration', 'positive', '0.751', 'heightening', 'thickening', 'aggravating', 'exacerbating', 'deepening']
senticnet['intensifying_competition'] = ['0.749', '0.764', '0', '0', '#joy', '#interest', 'positive', '0.756', 'obedience', 'order', 'requirement', 'permission', 'necessary']
senticnet['intensionality'] = ['0.734', '-0.56', '0', '0.64', '#joy', '#admiration', 'positive', '0.645', 'inferential', 'suggestive', 'connotative', 'connotational', 'connotative_of']
senticnet['intensity'] = ['-0.93', '0', '0.698', '-0.85', '#sadness', '#disgust', 'negative', '-0.83', 'candlepower', 'light_intensity', 'acoustic_power', 'sound_pressure_level', 'threshold_level']
senticnet['intensive'] = ['0.633', '-0.96', '0', '0.612', '#joy', '#surprise', 'positive', '0.738', 'particularism', 'absorption', 'concentration', 'engrossment', 'centering']
senticnet['intensive_care'] = ['0', '0', '-0.73', '0', '#fear', '#fear', 'negative', '-0.73', 'clinic', 'emergency_ward', 'hospital', 'dentist_office', 'medical_office']
senticnet['intensive_care_unit'] = ['0', '0', '-0.73', '0', '#fear', '#fear', 'negative', '-0.73', 'clinic', 'emergency_ward', 'hospital', 'dentist_office', 'medical_office']
senticnet['intensive_cultivation'] = ['0.702', '-0.95', '0', '0.997', '#surprise', '#admiration', 'positive', '0.883', 'fertilizable', 'fecund', 'conceptive', 'impregnable', 'rank']
senticnet['intensive_immersion'] = ['0.633', '-0.96', '0', '0.612', '#joy', '#surprise', 'positive', '0.738', 'particularism', 'absorption', 'concentration', 'engrossment', 'centering']
senticnet['intensive_instruction'] = ['0.633', '-0.96', '0', '0.612', '#joy', '#surprise', 'positive', '0.738', 'particularism', 'absorption', 'concentration', 'engrossment', 'centering']
senticnet['intensive_rehabilitation'] = ['0.641', '-0.78', '0', '0.721', '#surprise', '#admiration', 'positive', '0.715', 'correctional_rehabilitation', 'physical_rehabilitation', 'physical_restoration', 'therapeutic_rehabilitation', 'vocational_rehabilitation']
senticnet['intensive_spanish_language'] = ['0.633', '-0.96', '0', '0.612', '#joy', '#surprise', 'positive', '0.738', 'particularism', 'absorption', 'concentration', 'engrossment', 'centering']
senticnet['intensiveness'] = ['-0.60', '0', '0.614', '-0.94', '#anger', '#disgust', 'negative', '-0.72', 'badness', 'severity', 'severeness', 'fury', 'wildness']
senticnet['intent'] = ['0', '0.338', '-0.13', '0.298', '#interest', '#admiration', 'positive', '0.166', 'permission', 'conclusion', 'purpose', 'ask', 'conclude']
senticnet['intention'] = ['-0.91', '0', '0', '-0.96', '#sadness', '#disgust', 'negative', '-0.94', 'unbearable', 'rude', 'short_line', 'time_out', 'consequence']
senticnet['intentional'] = ['0', '-0.91', '0', '0.97', '#surprise', '#admiration', 'positive', '0.943', 'purposeful', 'purposive', 'determined', 'purposefully', 'purpose']
senticnet['intentional_adulteration'] = ['0', '0', '0.709', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'debasement', 'dirtiness', 'pollution', 'taint', 'impureness']
senticnet['intentional_destruction'] = ['-0.14', '-0.00', '-0.12', '-0.21', '#sadness', '#disgust', 'negative', '-0.11', 'torture', 'demolish', 'maim', 'terminate', 'hurt_person']
senticnet['intentional_discrimination'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
senticnet['intentional_disregard'] = ['-0.92', '0', '0.682', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'contrariness', 'wildness', 'intractability', 'intractableness', 'perversity']
senticnet['intentional_infliction'] = ['-0.71', '0', '-0.75', '-0.90', '#fear', '#disgust', 'negative', '-0.79', 'convent', 'monastery', 'capital_build', 'confession', 'synagogue']
senticnet['intentional_misconduct'] = ['-0.92', '0', '0.682', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'contrariness', 'wildness', 'intractability', 'intractableness', 'perversity']
senticnet['intentional_murder'] = ['-0.70', '0', '0.874', '-0.75', '#anger', '#disgust', 'negative', '-0.77', 'blame', 'accusation', 'inculpation', 'complaint', 'imprecation']
senticnet['intentional_tort'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'culpable_negligence', 'evasion', 'dereliction', 'negligence']
senticnet['intentional_violation'] = ['-0.92', '0', '0.682', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'contrariness', 'wildness', 'intractability', 'intractableness', 'perversity']
senticnet['intentionality'] = ['0.052', '0', '0', '0.131', '#joy', '#admiration', 'positive', '0.091', 'run_treadmill', 'dangerous_drive', 'helpful_tool', 'profitable', 'creepy']
senticnet['intentionally'] = ['-0.04', '0.164', '0.685', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maliciousness', 'spitefulness', 'maleficence', 'malefic', 'malign']
senticnet['intentionally_mislead'] = ['-0.74', '0', '0.741', '-0.92', '#sadness', '#disgust', 'negative', '-0.80', 'loathing', 'unattractive', 'bad_luck', 'feel_stressed', 'screw_up']
senticnet['intentioned'] = ['0', '-0.78', '0', '0.844', '#surprise', '#admiration', 'positive', '0.815', 'well-intended', 'well-intentioned', 'well-meaning', 'well-meant', 'halfhearted']
senticnet['intently'] = ['-0.95', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.86', 'uneasily', 'apprehensively', 'apprehensive', 'worried', 'solicitousness']
senticnet['intentness'] = ['0.927', '-0.92', '0', '0.76', '#joy', '#surprise', 'positive', '0.87', 'diligence', 'industriousness', 'singleness', 'assiduity', 'engrossment']
senticnet['inter'] = ['-0.02', '-0.02', '0.097', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'boat', 'big_boat', 'rowboat', 'ship', 'military_ship']
senticnet['inter-denominational'] = ['-0.75', '0', '0.931', '0', '#sadness', '#anger', 'negative', '-0.84', 'non-denominational', 'nondenominational', 'interdenominational', 'non-religious', 'pentecostal']
senticnet['inter_disciplinary_research'] = ['0.887', '0.877', '0', '0', '#joy', '#interest', 'positive', '0.882', 'ask_expert', 'absorb_information', 'analyze', 'learn_computer', 'search_information']
senticnet['inter_lata'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'long_distance_relationship', 'distant', 'kilometer', 'length_measurement', 'distance_measure']
senticnet['inter_processor'] = ['0.63', '-0.55', '0', '0.946', '#joy', '#admiration', 'positive', '0.711', 'complect', 'connect', 'link', 'link_up', 'join']
senticnet['inter_se'] = ['0.509', '-0.62', '0', '0.786', '#surprise', '#admiration', 'positive', '0.64', 'agreeable', 'accordant', 'consentient', 'unanimous', 'according']
senticnet['inter_tidal'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'frog', 'amphibian']
senticnet['inter_vivos_trust'] = ['0.851', '0.708', '0', '0', '#joy', '#interest', 'positive', '0.779', 'devisee', 'legatee', 'recipient', 'receiver', 'pensioner']
senticnet['inter_war'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'dictatorship', 'dictator', 'colony', 'world_domination', 'oppression']
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senticnet['into_adulthood'] = ['0', '0.075', '0', '0.159', '#interest', '#admiration', 'positive', '0.117', 'many_plants', 'blossom', 'development', 'poppy', 'adult']
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senticnet['intra_abdominal_pressure'] = ['0.777', '-0.89', '0', '0.508', '#joy', '#surprise', 'positive', '0.727', 'stretching', 'enlarge', 'dilatation', 'get_bigger', 'enlargement']
senticnet['intra_household'] = ['0.051', '0.112', '0', '0', '#joy', '#interest', 'positive', '0.082', 'neighbor', 'family_reunion', 'clan', 'desire', 'little_specific']
senticnet['intra_lata'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'long_distance_relationship', 'distant', 'kilometer', 'length_measurement', 'distance_measure']
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senticnet['intraluminal_pressure'] = ['0.777', '-0.89', '0', '0.508', '#joy', '#surprise', 'positive', '0.727', 'stretching', 'enlarge', 'dilatation', 'get_bigger', 'enlargement']
senticnet['intramolecular'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
senticnet['intramural_football'] = ['0.728', '0.83', '0', '0', '#joy', '#interest', 'positive', '0.779', 'ball', 'bounce', 'ball_bounce', 'soccer', 'play_football']
senticnet['intramuscular'] = ['0', '0', '-0.95', '-0.87', '#fear', '#disgust', 'negative', '-0.91', 'sharp_weapon', 'bayonet', 'ammunition', 'spear', 'arrow']
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senticnet['intranet_web_site'] = ['0', '0.08', '0', '0', '#interest', '#interest', 'positive', '0.08', 'internet', 'go_online', 'visit_web_site', 'find_information', 'choose_book']
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senticnet['intranuclear'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'nonmotile', 'immobile']
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senticnet['intraoperatively'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'blatancy', 'obviousness']
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senticnet['intrapleural_pressure'] = ['0', '0.098', '-0.09', '0.115', '#interest', '#admiration', 'positive', '0.04', 'push_wind', 'breath', 'breaout', 'body_reaction', 'mouth_action']
senticnet['intrapsychic'] = ['0.461', '0.674', '-0.26', '0.247', '#joy', '#interest', 'positive', '0.28', 'affectionateness', 'warmness', 'philia', 'affectional', 'emotive']
senticnet['intrastate_toll'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'long_distance_relationship', 'distant', 'kilometer', 'length_measurement', 'distance_measure']
senticnet['intrathoracic'] = ['0.759', '-0.72', '0', '0.893', '#joy', '#admiration', 'positive', '0.793', 'manifestation', 'cry', 'reflection', 'acting_out', 'explosion']
senticnet['intrathoracic_pressure'] = ['0', '0.098', '-0.09', '0.115', '#interest', '#admiration', 'positive', '0.04', 'push_wind', 'breath', 'breaout', 'body_reaction', 'mouth_action']
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senticnet['intrauterine_cannibalism'] = ['-0.17', '0', '-0.16', '-0.09', '#sadness', '#fear', 'negative', '-0.14', 'murder', 'hurt_others', 'death_penalty', 'death_destruction', 'end_live']
senticnet['intrauterine_device'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'birth_control_pill', 'condom', 'contraceptive_pill', 'sex_toy', 'prevent_pregnancy']
senticnet['intrauterine_device_iud'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'birth_control_pill', 'condom', 'contraceptive_pill', 'sex_toy', 'prevent_pregnancy']
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senticnet['intravascular_fluid'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'keep_alive', 'body_system', 'body_working', 'blood_pressure', 'heart']
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senticnet['invading_pathogen'] = ['0.702', '-0.52', '0', '0.958', '#joy', '#admiration', 'positive', '0.728', 'somatic_cell', 'vegetative_cell', 'fixed_phagocyte', 'macrophage', 'free_phagocyte']
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senticnet['invariableness'] = ['-0.93', '0', '0.749', '-0.68', '#sadness', '#anger', 'negative', '-0.79', 'unvariedness', 'changelessness', 'unchangeability', 'unchangeableness', 'unchangingness']
senticnet['invariably'] = ['-0.97', '0', '0', '-0.86', '#sadness', '#disgust', 'negative', '-0.91', 'worst', 'optional', 'rare', 'lass', 'temporary']
senticnet['invariance'] = ['-0.75', '0', '0.679', '-0.77', '#sadness', '#disgust', 'negative', '-0.73', 'unvariedness', 'changelessness', 'unchangeability', 'unchangeableness', 'unchangingness']
senticnet['invariant'] = ['-0.90', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.83', 'constant', 'unvarying', 'changeless', 'work', 'number']
senticnet['invariant_under'] = ['-0.75', '0', '0.679', '-0.77', '#sadness', '#disgust', 'negative', '-0.73', 'unvariedness', 'changelessness', 'unchangeability', 'unchangeableness', 'unchangingness']
senticnet['invasion'] = ['0.849', '-0.54', '0', '0.543', '#joy', '#surprise', 'positive', '0.646', 'diffusion', 'dispersion', 'scattering', 'spread', 'irradiation']
senticnet['invasion_fleet'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['invasion_robbery'] = ['-0.75', '0', '0.94', '0', '#sadness', '#anger', 'negative', '-0.84', 'steal_money', 'theft', 'stick_up', 'robbery', 'rob']
senticnet['invasive'] = ['-0.70', '0', '0.978', '0', '#sadness', '#anger', 'negative', '-0.84', 'carcinoma', 'invade', 'country', 'military', 'privacy']
senticnet['invasive_disease'] = ['-0.99', '0', '0.964', '-0.70', '#sadness', '#anger', 'negative', '-0.88', 'blepharitis', 'diverticulitis', 'inflammation', 'orchitis', 'prostatitis']
senticnet['invective'] = ['-0.89', '0', '0.955', '-0.94', '#anger', '#disgust', 'negative', '-0.93', 'abuse', 'vilification', 'insult', 'revilement', 'contumely']
senticnet['invective_against'] = ['-0.57', '0', '0.79', '-0.89', '#anger', '#disgust', 'negative', '-0.75', 'abuse', 'vilification', 'insult', 'revilement', 'contumely']
senticnet['invectives_against'] = ['-0.57', '0', '0.79', '-0.89', '#anger', '#disgust', 'negative', '-0.75', 'abuse', 'vilification', 'insult', 'revilement', 'contumely']
senticnet['inveigh'] = ['-0.64', '0', '0.663', '-0.54', '#sadness', '#anger', 'negative', '-0.61', 'quetch', 'kvetch', 'complain', 'sound_off', 'plain']
senticnet['inveighing_against'] = ['-0.57', '0', '0.79', '-0.89', '#anger', '#disgust', 'negative', '-0.75', 'abuse', 'vilification', 'insult', 'revilement', 'contumely']
senticnet['inveighs_against'] = ['0.922', '-0.94', '0', '0.965', '#surprise', '#admiration', 'positive', '0.945', 'utilitarian', 'egalitarian', 'equalitarian', 'elitist', 'philosopher']
senticnet['inveigle'] = ['0', '0', '-0.83', '-0.73', '#fear', '#disgust', 'negative', '-0.78', 'wheedle', 'palaver', 'cajole', 'coax', 'blarney']
senticnet['invent'] = ['0.542', '-0.60', '0', '0.757', '#surprise', '#admiration', 'positive', '0.636', 'fabricate', 'manufacture', 'cook_up', 'confabulate', 'make_up']
senticnet['invention'] = ['-0.03', '0.201', '-0.13', '0.171', '#interest', '#admiration', 'positive', '0.049', 'scientific_statement', 'hypothetical', 'conjecture', 'theory', 'fact']
senticnet['inventive'] = ['0.995', '0.898', '0', '0', '#joy', '#interest', 'positive', '0.947', 'forgetive', 'inventively', 'inventory', 'promethean', 'invent']
senticnet['inventively'] = ['0.986', '0.89', '0', '0', '#joy', '#interest', 'positive', '0.938', 'forgetive', 'inventive', 'inventory', 'promethean', 'invent']
senticnet['inventiveness'] = ['-0.84', '0', '0.741', '-0.54', '#sadness', '#anger', 'negative', '-0.70', 'resourcefulness', 'imagination', 'creativity', 'creativeness', 'creative_thinking']
senticnet['inventor'] = ['0.891', '0.721', '0', '0.921', '#joy', '#admiration', 'positive', '0.844', 'expert', 'connoisseur', 'medical_doctor', 'teacher', 'judge']
senticnet['inventory'] = ['0.92', '0.983', '0', '0', '#joy', '#interest', 'positive', '0.952', 'inventorying', 'stock-taking', 'take_stock', 'stocktaking', 'stock_list']
senticnet['inventory_accumulation'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'mathematical', 'muscle', 'math_function', 'feel_sick', 'separation']
senticnet['inventory_overhang'] = ['0', '0.285', '-0.07', '0.163', '#interest', '#admiration', 'positive', '0.124', 'plus', 'importance', 'a_lot', 'bonus', 'extra']
senticnet['inventory_shrinkage'] = ['-0.55', '0', '0.843', '-0.58', '#anger', '#disgust', 'negative', '-0.66', 'thieving', 'larceny', 'theft', 'thievery', 'stealing']
senticnet['inventorying'] = ['0.912', '0.982', '0', '0', '#joy', '#interest', 'positive', '0.947', 'inventory', 'stock-taking', 'take_stock', 'stocktaking', 'stock_list']
senticnet['inverse'] = ['0.506', '-0.56', '0', '0.788', '#surprise', '#admiration', 'positive', '0.62', 'backward', 'reverse', 'retrograde', 'reflexive', 'retral']
senticnet['inverse_agonist'] = ['-0.95', '0', '0.774', '0', '#sadness', '#anger', 'negative', '-0.86', 'argue', 'rival', 'disagreement', 'opposition', 'anti']
senticnet['inverse_condemnation'] = ['-0.62', '0', '0.7', '-0.83', '#anger', '#disgust', 'negative', '-0.72', 'take', 'pull', 'return', 'payback', 'proceeds']
senticnet['inverse_hyperbolic_cosine'] = ['-0.53', '0', '0.993', '-0.88', '#anger', '#disgust', 'negative', '-0.80', 'expansiveness', 'impressiveness', 'stateliness', 'loftiness', 'excellence']
senticnet['inverse_transform'] = ['0.506', '-0.56', '0', '0.788', '#surprise', '#admiration', 'positive', '0.62', 'backward', 'reverse', 'retrograde', 'reflexive', 'retral']
senticnet['inversely'] = ['0.971', '0.803', '0', '0', '#joy', '#interest', 'positive', '0.887', 'linearly', 'non-linearly', 'logarithmically', 'quadratically', 'monotonically']
senticnet['inversion'] = ['0.807', '-0.88', '0', '0.718', '#joy', '#surprise', 'positive', '0.802', 'sex', 'gayness', 'sexual_gayness', 'lesbianism', 'sapphism']
senticnet['invert'] = ['0.807', '-0.88', '0', '0.718', '#joy', '#surprise', 'positive', '0.802', 'sex', 'gayness', 'sexual_gayness', 'lesbianism', 'sapphism']
senticnet['invert_sugar'] = ['-0.08', '0.106', '0', '0.125', '#interest', '#admiration', 'positive', '0.047', 'sugar', 'treacle', 'black_treacle', 'add_sugar', 'sugar_substitute']
senticnet['invertebrate'] = ['-0.79', '0', '0.709', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'animal', 'animate_being', 'ctenophore', 'comb_jelly', 'beast']
senticnet['invertebrate_abundance'] = ['0', '0.184', '-0.05', '0.179', '#interest', '#admiration', 'positive', '0.104', 'rich', 'a_lot', 'abundant', 'wealth', 'plentiful']
senticnet['invertebrate_prey'] = ['0.812', '0.872', '0', '0', '#joy', '#interest', 'positive', '0.842', 'insect', 'fly_away', 'small_insect', 'maggot', 'chihuahua']
senticnet['invertebrate_zoology'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'study_person', 'subject_study', 'zoo', 'zoologist', 'geology']
senticnet['inverted_comma'] = ['0.85', '0', '0', '0.947', '#joy', '#admiration', 'positive', '0.898', 'citation', 'quote', 'quotation_mark', 'cite', 'quotation']
senticnet['inverted_nipple'] = ['-0.63', '0', '0.559', '-0.82', '#sadness', '#disgust', 'negative', '-0.67', 'take_bull_by_horns', 'confront', 'front', 'encounter', 'oppose']
senticnet['inverted_tooth'] = ['0.096', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.057', 'platform_rail', 'rail', 'catwalk', 'column', 'token_turnstile']
senticnet['inverter'] = ['0', '-0.03', '0', '0.093', '#surprise', '#admiration', 'positive', '0.063', 'work_well', 'internal_combustion_engine', 'broken_car', 'mode_transportation', 'machine']
senticnet['invertible'] = ['0.506', '-0.56', '0', '0.788', '#surprise', '#admiration', 'positive', '0.62', 'backward', 'reverse', 'retrograde', 'reflexive', 'retral']
senticnet['invest'] = ['-0.07', '0.202', '-0.06', '0.164', '#interest', '#admiration', 'positive', '0.056', 'transaction', 'give_back', 'exchange_money', 'exchange_goods', 'cash_check']
senticnet['invest_money'] = ['0', '0.789', '0', '0.848', '#interest', '#admiration', 'positive', '0.819', 'bank_money', 'hold_coin', 'lend_money', 'vault', 'bank_loan']
senticnet['investigate'] = ['-0.14', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.09', 'look_fact', 'investigate_fact', 'interrogate', 'think_through', 'ask_question']
senticnet['investigate_fact'] = ['-0.24', '-0.05', '-0.11', '-0.09', '#sadness', '#fear', 'negative', '-0.1', 'say_eureka', 'think_logically', 'look_fact', 'ask_question', 'research_subject']
senticnet['investigating'] = ['0.809', '-0.71', '0', '0.708', '#joy', '#surprise', 'positive', '0.745', 'tally', 'numeration', 'tap', 'scrutiny', 'testing']
senticnet['investigation'] = ['-0.20', '0.129', '-0.17', '0.065', '#sadness', '#fear', 'negative', '-0.04', 'look_fact', 'deduce', 'say_eureka', 'investigate_fact', 'rational']
senticnet['investigation_into'] = ['0.845', '0.916', '0', '0', '#joy', '#interest', 'positive', '0.881', 'question', 'answer', 'query', 'question_start', 'question_answering']
senticnet['investigative'] = ['-0.85', '0', '-0.89', '0', '#sadness', '#fear', 'negative', '-0.87', 'forensics', 'investigator', 'judiciary', 'forensic', 'investigation']
senticnet['investigative_journalist'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'adventurer', 'humankind']
senticnet['investigative_reporter'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'adventurer', 'humankind']
senticnet['investigator'] = ['0.846', '-0.75', '0', '0.69', '#joy', '#surprise', 'positive', '0.764', 'experimenter', 'boffin', 'fieldworker', 'postdoc', 'post_doc']
senticnet['investigator_institution'] = ['0', '0.835', '0', '0.828', '#interest', '#admiration', 'positive', '0.831', 'award', 'equal_amount', 'fund', 'student', 'university']
senticnet['investiture_ceremony'] = ['0.82', '0', '0', '0.885', '#joy', '#admiration', 'positive', '0.853', 'religious_event', 'sunday', 'happy_event', 'tradition', 'wedding']
senticnet['investment'] = ['0', '0.312', '-0.10', '0.207', '#interest', '#admiration', 'positive', '0.137', 'infuse', 'loan', 'lend', 'increase_money', 'wealth']
senticnet['investment-grade'] = ['0.979', '0.971', '0', '0', '#joy', '#interest', 'positive', '0.975', 'high-yielding', 'yield', 'high-yield', 'fixed-income', 't-bill']
senticnet['investment_corporation_opic'] = ['0', '0.766', '0', '0', '#interest', '#interest', 'positive', '0.766', 'abroad', 'foreign', 'study_abroad', 'elsewhere', 'live_abroad']
senticnet['investment_dresser'] = ['-0.54', '0', '0.612', '-0.98', '#anger', '#disgust', 'negative', '-0.71', 'scuff', 'mule', 'bootee', 'carpet_slipper', 'footwear']
senticnet['investment_portfolio'] = ['0', '0.312', '-0.10', '0.207', '#interest', '#admiration', 'positive', '0.137', 'infuse', 'loan', 'lend', 'increase_money', 'wealth']
senticnet['investment_return'] = ['0', '0.371', '-0.13', '0.226', '#interest', '#admiration', 'positive', '0.155', 'monetary_gain', 'money_earn', 'financial_gain', 'gain_money', 'extra_money']
senticnet['investor'] = ['0', '0.076', '0', '0', '#interest', '#interest', 'positive', '0.076', 'person', 'alive', 'human_being', 'young_man', 'gardener']
senticnet['investor_appetite'] = ['0.769', '0', '0', '0', '#joy', '#joy', 'positive', '0.769', 'make_breakfast', 'enjoy_meal', 'put_food_into_mouth', 'prepare_meal', 'prepare_breakfast']
senticnet['investor_confidence'] = ['0.997', '0', '0', '0.972', '#joy', '#admiration', 'positive', '0.984', 'sympathetic', 'adoration', 'confident', 'enthusiasm', 'grateful']
senticnet['inveterate'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'lack_of_judgment', 'lack_of_discreetness', 'lack_of_discernment', 'reckless', 'rentless']
senticnet['invidious'] = ['-0.71', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.82', 'invidiousness', 'invidiously', 'injurious', 'unfair', 'envious']
senticnet['invidious_discrimination'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
senticnet['invidiously'] = ['-0.72', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.82', 'invidiousness', 'invidious', 'injurious', 'unfair', 'envious']
senticnet['invidiousness'] = ['-0.72', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.82', 'invidious', 'invidiously', 'injurious', 'unfair', 'envious']
senticnet['invigilate'] = ['0.585', '-0.64', '0', '0.51', '#joy', '#surprise', 'positive', '0.58', 'watch_over', 'keep_an_eye_on', 'watch', 'observe', 'follow']
senticnet['invigilation'] = ['0', '0.899', '0', '0.769', '#interest', '#admiration', 'positive', '0.834', 'management', 'supervising', 'supervision', 'oversight', 'superintendence']
senticnet['invigorate'] = ['-0.72', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.72', 'fall_asleep', 'doze_off', 'drowsiness', 'alter_state_consciousness', 'feel_little_tired']
senticnet['invigorating'] = ['0.843', '-0.81', '0', '0.624', '#joy', '#surprise', 'positive', '0.762', 'exhilarating', 'animating', 'enlivening', 'refreshing', 'restorative']
senticnet['invigoration'] = ['0.868', '-0.91', '0', '0.862', '#joy', '#surprise', 'positive', '0.88', 'chirpiness', 'spirit', 'sprightliness', 'liveliness', 'activity']
senticnet['invigorator'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'infective_agent', 'satisfier', 'quickener', 'agent']
senticnet['invillager'] = ['0.896', '-0.52', '0', '0.833', '#joy', '#admiration', 'positive', '0.75', 'indweller', 'habitant', 'villager', 'dweller', 'denizen']
senticnet['invincible'] = ['-0.91', '0', '-0.89', '0', '#sadness', '#fear', 'negative', '-0.90', 'juggernaut', 'relentless', 'unstoppable', 'unleashing', 'unrelenting']
senticnet['invincible_ignorance'] = ['0.511', '-0.59', '0', '0.576', '#surprise', '#admiration', 'positive', '0.561', 'due', 'traceable', 'ascribable', 'attributable', 'referable']
senticnet['inviolability'] = ['0.926', '0.937', '0', '0.975', '#interest', '#admiration', 'positive', '0.946', 'monument', 'statue', 'freedom', 'idol', 'sculpture']
senticnet['inviolable'] = ['0.993', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.925', 'infrangible', 'nontransferable', 'unassignable', 'untransferable', 'inalienable']
senticnet['inviolate'] = ['0', '0.727', '0', '0.915', '#interest', '#admiration', 'positive', '0.821', 'inviolable', 'sacrosanct', 'inalienable', 'sanctity', 'self-evident']
senticnet['invisalign'] = ['-0.05', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'plane_land', 'station_wagon', 'floss_tooth', 'ornament', 'wear_dress']
senticnet['inviscid'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'assumption', 'precondition', 'provision', 'constraint', 'premiss']
senticnet['invisible'] = ['-0.51', '0', '0.979', '-0.89', '#anger', '#disgust', 'negative', '-0.79', 'occult', 'concealed', 'nonvisual', 'hidden', 'out_of_sight']
senticnet['invisible_barrier'] = ['-0.93', '0', '-0.99', '-0.92', '#sadness', '#fear', 'negative', '-0.95', 'language_barrier', 'shut', 'shut_down', 'close_down', 'obstacle']
senticnet['invisibly'] = ['0', '-0.75', '0', '0.816', '#surprise', '#admiration', 'positive', '0.787', 'inconspicuously', 'discretely', 'discreetly', 'unobtrusively', 'stealthily']
senticnet['invitation'] = ['0', '0.852', '0', '0', '#interest', '#interest', 'positive', '0.852', 'streamer', 'tribute', 'salutation', 'gift', 'anniversary_gift']
senticnet['invitation_response'] = ['-0.47', '0.169', '-0.55', '0.399', '#sadness', '#fear', 'negative', '-0.11', 'invitation', 'request', 'tell_why', 'circus_performer', 'summons']
senticnet['invitational_handicap'] = ['-0.76', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.78', 'strangle', 'bloodshed', 'cripple', 'hear_bad_news', 'sexually_transmit_disease']
senticnet['invite'] = ['0.06', '0', '0.014', '0.089', '#joy', '#admiration', 'positive', '0.045', 'social_skill', 'socialize', 'greet', 'charisma', 'communicate']
senticnet['invite_dinner'] = ['0.878', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.899', 'get_know', 'chat', 'mental_health', 'converse', 'stay_touch']
senticnet['invite_friend'] = ['0', '0.709', '0', '0', '#interest', '#interest', 'positive', '0.709', 'buy_drink', 'drink_beer', 'serve_beer', 'pay_tab', 'throw_away_trash']
senticnet['invite_friend_over'] = ['0', '0.74', '0', '0', '#interest', '#interest', 'positive', '0.74', 'invite_guest', 'clean_apartment', 'make_joke', 'call_up', 'invite_friend']
senticnet['invite_guest'] = ['0', '0', '0', '0.713', '#admiration', '#admiration', 'positive', '0.713', 'invite_person', 'invite_friend_over', 'buy_dinner', 'put_napkin_lap', 'wrap_up']
senticnet['invite_person'] = ['0', '0.71', '0', '0.799', '#interest', '#admiration', 'positive', '0.754', 'invite_guest', 'wage_slave', 'make_profit', 'meet_need', 'buy_cheese']
senticnet['inviting_atmosphere'] = ['0.777', '0.972', '0', '0.913', '#interest', '#admiration', 'positive', '0.887', 'serene', 'easy', 'adoration', 'togetherness', 'console']
senticnet['invocation'] = ['-0.82', '0', '0.72', '-0.53', '#sadness', '#anger', 'negative', '-0.69', 'conjuration', 'charm', 'spell', 'magic_spell', 'magical_spell']
senticnet['invocation_return_null'] = ['-0.91', '0', '-0.77', '-0.75', '#sadness', '#fear', 'negative', '-0.81', 'emptiness', 'star_planet', 'nothingness', 'outer', 'around_earth']
senticnet['invoice'] = ['0', '0.248', '-0.07', '0.121', '#interest', '#admiration', 'positive', '0.099', 'bill', 'mastercard', 'yellowstone', 'expense', 'owe']
senticnet['invoke'] = ['0', '0', '-0.77', '-0.70', '#fear', '#disgust', 'negative', '-0.74', 'invoking', 'invoked', 'invokes', 'invocation', 'assert']
senticnet['invoked'] = ['0', '0', '-0.77', '-0.71', '#fear', '#disgust', 'negative', '-0.74', 'invoking', 'invoke', 'invokes', 'invocation', 'assert']
senticnet['invokes'] = ['0', '0', '-0.77', '-0.71', '#fear', '#disgust', 'negative', '-0.74', 'invoking', 'invoked', 'invoke', 'invocation', 'assert']
senticnet['invoking'] = ['0', '0', '-0.78', '-0.71', '#fear', '#disgust', 'negative', '-0.74', 'invoke', 'invoked', 'invokes', 'invocation', 'assert']
senticnet['invoking_cloture'] = ['-0.81', '0', '0.513', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'ballot', 'vote', 'voting', 'balloting', 'veto']
senticnet['involatile'] = ['-0.67', '0', '0.503', '-0.67', '#sadness', '#disgust', 'negative', '-0.61', 'stink_up', 'smell_up', 'stink_out', 'incense', 'perfume']
senticnet['involucre'] = ['0', '0', '0.064', '-0.04', '#anger', '#disgust', 'negative', '-0.05', 'bird_food', 'funiculus', 'plant_product', 'fuzz', 'agricultural_product']
senticnet['involuntarily'] = ['-0.05', '0.122', '-0.12', '0.129', '#interest', '#admiration', 'positive', '0.02', 'bad_odor', 'smell_odor', 'inhale', 'whine', 'blow_nose']
senticnet['involuntariness'] = ['-0.63', '0', '0.584', '-0.54', '#sadness', '#anger', 'negative', '-0.58', 'resistance', 'disinclination', 'indisposition', 'temperament', 'hesitancy']
senticnet['involuntary'] = ['-0.05', '0.122', '-0.12', '0.129', '#interest', '#admiration', 'positive', '0.02', 'bad_odor', 'smell_odor', 'inhale', 'whine', 'blow_nose']
senticnet['involuntary_domestic_servitude'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'vassalage']
senticnet['involuntary_euthanasia'] = ['-0.05', '0.122', '-0.12', '0.129', '#interest', '#admiration', 'positive', '0.02', 'bad_odor', 'smell_odor', 'inhale', 'whine', 'blow_nose']
senticnet['involuntary_movement'] = ['-0.05', '0.122', '-0.12', '0.129', '#interest', '#admiration', 'positive', '0.02', 'bad_odor', 'smell_odor', 'inhale', 'whine', 'blow_nose']
senticnet['involuntary_muscular'] = ['-0.05', '0.122', '-0.12', '0.129', '#interest', '#admiration', 'positive', '0.02', 'bad_odor', 'smell_odor', 'inhale', 'whine', 'blow_nose']
senticnet['involuntary_servitude'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'vassalage']
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senticnet['iohn'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'bodyguard', 'outrider', 'bodyguard_of_guard', 'beefeater', 'defender']
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senticnet['ion'] = ['0.995', '0.908', '0', '0', '#joy', '#interest', 'positive', '0.952', 'study', 'research', 'reaction', 'membrane', 'ring']
senticnet['ion_beam'] = ['-0.93', '0', '0.739', '-0.96', '#sadness', '#disgust', 'negative', '-0.88', 'prevarication', 'fairytale', 'fairy_tale', 'fairy_story', 'play']
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senticnet['ion_channel'] = ['0', '0', '0.019', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'worm_intestine', 'bristle', 'fur_keep_warm', 'animal', 'whisker']
senticnet['ion_chromatography'] = ['0.838', '-0.61', '0', '0.556', '#joy', '#surprise', 'positive', '0.671', 'activity', 'natural_process', 'natural_action', 'column_chromatography', 'paper_chromatography']
senticnet['ion_exchange_chromatography'] = ['0.838', '-0.61', '0', '0.556', '#joy', '#surprise', 'positive', '0.671', 'activity', 'natural_process', 'natural_action', 'column_chromatography', 'paper_chromatography']
senticnet['ion_implantation'] = ['-0.93', '0', '0.739', '-0.96', '#sadness', '#disgust', 'negative', '-0.88', 'prevarication', 'fairytale', 'fairy_tale', 'fairy_story', 'play']
senticnet['ion_permeability'] = ['-0.84', '0', '0.8', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'rigor_mortis', 'natural_process', 'natural_action', 'activity', 'action']
senticnet['ion_pump'] = ['0.843', '-0.99', '0', '0.706', '#joy', '#surprise', 'positive', '0.849', 'gadgetry', 'gimbal', 'contrivance', 'mod_con', 'device']
senticnet['ion_source'] = ['0.843', '-0.99', '0', '0.706', '#joy', '#surprise', 'positive', '0.849', 'gadgetry', 'gimbal', 'contrivance', 'mod_con', 'device']
senticnet['ionamin'] = ['-0.72', '0', '0.973', '-0.57', '#sadness', '#anger', 'negative', '-0.76', 'corpulency', 'blubber', 'fat', 'embonpoint', 'stoutness']
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senticnet['ionization'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'emotional_arousal', 'excitement', 'arousal', 'anger']
senticnet['ionization_energy'] = ['0.695', '-0.85', '0', '0.501', '#joy', '#surprise', 'positive', '0.684', 'alternative_energy', 'energy', 'energy_of_activation', 'atomic_energy', 'nuclear_energy']
senticnet['ionization_potential'] = ['0.695', '-0.85', '0', '0.501', '#joy', '#surprise', 'positive', '0.684', 'alternative_energy', 'energy', 'energy_of_activation', 'atomic_energy', 'nuclear_energy']
senticnet['ionize'] = ['0', '0.073', '-0.05', '0.093', '#interest', '#admiration', 'positive', '0.038', 'thicken', 'condense_water', 'condensed_milk', 'weather_condition', 'condensation']
senticnet['ionizing_radiation'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'alpha_radiation', 'alpha_ray', 'beta_radiation', 'beta_ray', 'electron_radiation']
senticnet['ionosphere'] = ['-0.88', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.90', 'atmospheric_science', 'number', 'lower', 'global_change_research', 'effect']
senticnet['ip_sock'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['ip_traffic'] = ['-0.98', '0', '0.989', '-0.93', '#sadness', '#anger', 'negative', '-0.97', 'flight_accident', 'dangerous', 'danger', 'air_traffic', 'air_traffic_jam']
senticnet['ipa_symbol'] = ['-0.61', '0', '0.721', '-0.68', '#anger', '#disgust', 'negative', '-0.67', 'character', 'logogram', 'logograph', 'graph', 'ideogram']
senticnet['ipad_air'] = ['0.027', '-0.09', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'cookie', 'pudding', 'custard', 'candy', 'muffin']
senticnet['ipaq'] = ['0.113', '0.073', '0.028', '0.107', '#joy', '#admiration', 'positive', '0.066', 'pda', 'telephone_computer', 'browser', 'number_pad', 'modem']
senticnet['ipc_mechanism'] = ['0.749', '0.724', '0', '0', '#joy', '#interest', 'positive', '0.736', 'mouth_noise', 'vocal_cord', 'vocalization', 'vocal_sound', 'loud_soft']
senticnet['ipcc_deputy'] = ['0', '0', '0.88', '-0.92', '#anger', '#disgust', 'negative', '-0.90', 'sofa_bed', 'small_seat', 'beanbag_airline_seat', 'hide-a-bed_sofa', 'airline_seat']
senticnet['ipcc_investigation'] = ['0.845', '0.916', '0', '0', '#joy', '#interest', 'positive', '0.881', 'question', 'answer', 'query', 'question_start', 'question_answering']
senticnet['ipecac'] = ['-0.62', '0', '0.983', '-0.89', '#anger', '#disgust', 'negative', '-0.83', 'remedy', 'cure', 'therapeutic', 'curative', 'emetic']
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senticnet['ipod'] = ['0.159', '-0.00', '0.011', '0.079', '#joy', '#admiration', 'positive', '0.058', 'ceiling_fan', 'kitchenette', 'technologic', 'formula', 'music']
senticnet['ipod_dock'] = ['0.036', '-0.04', '0.027', '0.063', '#surprise', '#admiration', 'positive', '0.028', 'radio', 'speaker', 'channel_selector', 'power_switch', 'female_television_connector']
senticnet['ipomoea_aquatica'] = ['0.035', '0.104', '0', '0.12', '#interest', '#admiration', 'positive', '0.086', 'fruit_vegetable', 'vegetable', 'vegetable_fruit', 'fresh_vegetable', 'vegetarian_food']
senticnet['ipse'] = ['0', '0.079', '0', '0', '#interest', '#interest', 'positive', '0.079', 'change_diaper', 'lower_standard_live', 'become_pregnant', 'find_mate', 'make_baby']
senticnet['iq'] = ['0', '0.943', '0', '0.789', '#interest', '#admiration', 'positive', '0.866', 'energy', 'genius', 'energy_use', 'gas_oil', 'invent']
senticnet['iq_score'] = ['0', '0.943', '0', '0.789', '#interest', '#admiration', 'positive', '0.866', 'energy', 'genius', 'energy_use', 'gas_oil', 'invent']
senticnet['iq_test'] = ['0', '0.943', '0', '0.789', '#interest', '#admiration', 'positive', '0.866', 'energy', 'genius', 'energy_use', 'gas_oil', 'invent']
senticnet['iq_test_measure'] = ['0', '0.943', '0', '0.789', '#interest', '#admiration', 'positive', '0.866', 'energy', 'genius', 'energy_use', 'gas_oil', 'invent']
senticnet['iqbal_masih'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'vassalage']
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senticnet['ira_bloom'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['ira_contribution'] = ['0.763', '-0.67', '0', '0.945', '#joy', '#admiration', 'positive', '0.795', 'set', 'income_bracket', 'bracket', 'income', 'price_bracket']
senticnet['iran_notorious_evin'] = ['0', '0', '0.995', '-0.89', '#anger', '#disgust', 'negative', '-0.94', 'shoe_store', 'chess_tournament', 'dress_room', 'front_desk', 'locker_room']
senticnet['iran_nuclear'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'dynamite', 'gunshot', 'missile', 'nuclear_bomb', 'fall_sky']
senticnet['iranian_nuclear'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'dynamite', 'gunshot', 'missile', 'nuclear_bomb', 'fall_sky']
senticnet['irans_nuclear'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'dynamite', 'gunshot', 'missile', 'nuclear_bomb', 'fall_sky']
senticnet['irascibility'] = ['-0.12', '0.13', '0.646', '-0.64', '#anger', '#disgust', 'negative', '-0.32', 'quick_temper', 'short_temper', 'irascible', 'choleric', 'ill_temper']
senticnet['irascible'] = ['-0.12', '0.13', '0.646', '-0.64', '#anger', '#disgust', 'negative', '-0.32', 'irascibility', 'quick_temper', 'short_temper', 'choleric', 'ill_temper']
senticnet['irate'] = ['-0.91', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.83', 'angry', 'irately', 'ire', 'irateness', 'gazer']
senticnet['irately'] = ['-0.92', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.83', 'angry', 'irate', 'ire', 'irateness', 'gazer']
senticnet['irateness'] = ['-0.92', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.84', 'angry', 'irately', 'ire', 'irate', 'gazer']
senticnet['irbesartan'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'spironolactone', 'spironolactone_drug']
senticnet['irc_chat'] = ['-0.51', '0', '0.545', '-0.91', '#anger', '#disgust', 'negative', '-0.65', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['irda'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'run_treadmill', 'complete_thesis', 'dwarf_planet', 'insure', 'ridgeback']
senticnet['ire'] = ['-0.16', '0.179', '0.568', '-0.61', '#anger', '#disgust', 'negative', '-0.29', 'angrily', 'choler', 'see_red', 'dander', 'hackle']
senticnet['irekssaga'] = ['0.036', '0.219', '0', '0.19', '#interest', '#admiration', 'positive', '0.148', 'story', 'long_story', 'novel', 'fable', 'fiction']
senticnet['irell_manella'] = ['0', '0.146', '-0.05', '0.077', '#interest', '#admiration', 'positive', '0.057', 'lawyer', 'negotiator', 'arbiter', 'judge_jury', 'decide_guilt']
senticnet['irenic'] = ['0.925', '0', '0', '0.96', '#joy', '#admiration', 'positive', '0.943', 'concession', 'conciliate', 'dovish', 'conciliative', 'conciliatory']
senticnet['irfani'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['irfb'] = ['-0.68', '0', '0.923', '-0.68', '#sadness', '#anger', 'negative', '-0.76', 'negative', 'veto', 'refute', 'rebut', 'controvert']
senticnet['iridescent'] = ['0.577', '-0.69', '0', '0.645', '#surprise', '#admiration', 'positive', '0.639', 'shining', 'polished', 'shiny', 'bright', 'burnished']
senticnet['iridium_ir'] = ['-0.05', '-0.00', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'pigment', 'many_shades', 'cyan', 'green_yellow', 'material']
senticnet['iridology'] = ['-0.84', '0', '0.585', '-0.85', '#sadness', '#disgust', 'negative', '-0.76', 'subject', 'discipline', 'study', 'concern', 'research']
senticnet['iris_atrophy'] = ['-0.82', '0', '0.996', '-0.61', '#sadness', '#anger', 'negative', '-0.81', 'visual_impairment', 'visual_defect', 'vision_defect', 'visual_disorder', 'avitaminosis']
senticnet['iris_diaphragm'] = ['0.057', '0', '0.012', '0.08', '#joy', '#admiration', 'positive', '0.042', 'see_better', 'glasses', 'become_gigantic', 'magnify', 'contact_lens']
senticnet['iris_wildthyme'] = ['0.756', '0', '0', '0', '#joy', '#joy', 'positive', '0.756', 'companionship', 'cherish', 'lover', 'bond', 'buddy']
senticnet['irish_bull'] = ['-0.96', '0', '0.812', '-0.69', '#sadness', '#anger', 'negative', '-0.82', 'rot', 'bunk', 'bunkum', 'buncombe', 'guff']
senticnet['irish_descent'] = ['0.706', '0', '0', '0', '#joy', '#joy', 'positive', '0.706', 'birth', 'new_baby', 'mother_womb', 'birthday', 'thanksgiving']
senticnet['irish_drugmaker'] = ['0.559', '0.656', '-0.26', '0.285', '#joy', '#interest', 'positive', '0.31', 'zealous', 'avidity', 'eagerness', 'avidness', 'thirstily']
senticnet['irish_peerage'] = ['0.066', '0.163', '-0.06', '0.163', '#interest', '#admiration', 'positive', '0.083', 'lord', 'royal_title', 'almighty', 'duchess', 'omniscient']
senticnet['irish_politician'] = ['0', '0.868', '0', '0.853', '#interest', '#admiration', 'positive', '0.861', 'country_leader', 'prime_minister', 'president', 'elect_official', 'leader']
senticnet['irish_pub'] = ['0.853', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.916', 'tavern', 'cafe', 'nightclub', 'bar', 'liquor_store']
senticnet['irish_single_chart'] = ['0.094', '0.091', '0', '0.125', '#joy', '#admiration', 'positive', '0.103', 'diagram', 'graph', 'medium', 'fragment', 'representation']
senticnet['irish_soda_bread'] = ['0.644', '-0.94', '0', '0.729', '#surprise', '#admiration', 'positive', '0.773', 'gem', 'banana_bread', 'date_bread', 'nut_bread', 'quick_bread']
senticnet['irish_whiskey'] = ['0.877', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.919', 'score', 'economical', 'scotch_whiskey', 'spoil', 'scottish']
senticnet['irk'] = ['0', '0', '-0.82', '-0.74', '#fear', '#disgust', 'negative', '-0.78', 'bother', 'irritate', 'annoy', 'irksome', 'irritation']
senticnet['irksome'] = ['-0.66', '0', '0.594', '-0.67', '#sadness', '#disgust', 'negative', '-0.64', 'boring', 'deadening', 'uninteresting', 'tiresome', 'wearisome']
senticnet['irksomely'] = ['-0.89', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.89', 'proser', 'spout', 'tedious', 'tediously', 'wearisomely']
senticnet['irlen_syndrome'] = ['0.89', '-0.70', '0', '0.643', '#joy', '#surprise', 'positive', '0.745', 'dyslexia', 'dyscalculia', 'learning_disability', 'disorder', 'upset']
senticnet['irnham'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'bodyguard', 'outrider', 'bodyguard_of_guard', 'beefeater', 'defender']
senticnet['irobot_roomba'] = ['-0.04', '-0.02', '0.089', '-0.14', '#anger', '#disgust', 'negative', '-0.06', 'steam_locomotive', 'loudspeaker', 'collector', 'siren', 'activist']
senticnet['iron'] = ['0.706', '-0.94', '0', '0.622', '#joy', '#surprise', 'positive', '0.756', 'alloy_cast_iron', 'alloy_cast_cast_iron', 'pot_metal', 'cast_iron', 'metal']
senticnet['iron-fisted'] = ['-0.77', '0', '-0.84', '0', '#sadness', '#fear', 'negative', '-0.81', 'brutal', 'ruthless', 'cruel', 'ruthlessly', 'machiavellian']
senticnet['iron_balustrade'] = ['0', '0', '0.095', '0', '#anger', '#anger', 'negative', '-0.09', 'build_part', 'flat', 'step', 'large_rock', 'hard_surface']
senticnet['iron_bangle'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'anklet', 'ankle_bracelet', 'armilla']
senticnet['iron_boot'] = ['-0.67', '0', '0.85', '-0.59', '#sadness', '#anger', 'negative', '-0.70', 'rack', 'boot', 'whip', 'instrument_of_torture', 'iron_heel']
senticnet['iron_cage'] = ['0', '0', '0.995', '-0.89', '#anger', '#disgust', 'negative', '-0.94', 'shoe_store', 'chess_tournament', 'dress_room', 'front_desk', 'locker_room']
senticnet['iron_chelation_therapy'] = ['0.79', '-0.62', '0', '0.674', '#joy', '#admiration', 'positive', '0.695', 'add', 'contribute', 'lend', 'breathe', 'impart']
senticnet['iron_clothes'] = ['0', '0.799', '0', '0', '#interest', '#interest', 'positive', '0.799', 'absorb_moisture', 'sit_on_beach', 'clothes', 'shine_shoes', 'iron_shirt']
senticnet['iron_curtain'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'ideological_barrier', 'bamboo_curtain', 'color_bar', 'color_line', 'barrier']
senticnet['iron_deficiency'] = ['-0.54', '0', '0.92', '-0.84', '#anger', '#disgust', 'negative', '-0.76', 'dearth', 'demand', 'mineral_deficiency', 'famine', 'need']
senticnet['iron_deficiency_anaemia'] = ['-0.71', '0', '0.74', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'malnutrition', 'marasmus', 'avitaminosis', 'hypovitaminosis', 'deficiency_disease']
senticnet['iron_deficiency_anemia'] = ['-0.86', '0', '0.76', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['iron_deficient_anemia'] = ['-0.71', '0', '0.74', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'kwashiorkor', 'marasmus', 'malnutrition', 'hypovitaminosis', 'deficiency_disease']
senticnet['iron_dome_rocket'] = ['-0.04', '0', '-0.06', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'make_war', 'conquer_enemy', 'use_military', 'gain_territory', 'keep_peace']
senticnet['iron_fence'] = ['0.952', '-0.52', '0', '0.835', '#joy', '#admiration', 'positive', '0.771', 'billboard', 'hoarding', 'sandwich_board', 'scoreboard', 'shingle']
senticnet['iron_forge'] = ['-0.78', '0', '0.699', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'battery', 'stamp_battery', 'quartz_battery', 'quartz_mill', 'factory']
senticnet['iron_fortified_cereal'] = ['-0.03', '0.086', '0', '0.076', '#interest', '#admiration', 'positive', '0.042', 'drink_water', 'drink_espresso', 'drink', 'drink_coffee', 'drink_hot_chocolate']
senticnet['iron_gray_hair'] = ['0.209', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.118', 'old_dance', 'dance', 'slow_dance', 'dancer', 'enter_contest']
senticnet['iron_heel'] = ['-0.67', '0', '0.85', '-0.59', '#sadness', '#anger', 'negative', '-0.70', 'rack', 'boot', 'whip', 'iron_boot', 'instrument_of_torture']
senticnet['iron_ironing_board'] = ['0.866', '0.833', '0', '0', '#joy', '#interest', 'positive', '0.849', 'alcohol', 'porter', 'concierge', 'beer_mug', 'bar_stool']
senticnet['iron_knocker'] = ['0.979', '-0.69', '0', '0.576', '#joy', '#surprise', 'positive', '0.749', 'big_cheese', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['iron_mold'] = ['-0.51', '0', '0.937', '-0.92', '#anger', '#disgust', 'negative', '-0.79', 'bloodstain', 'mud_stain', 'discoloration', 'iron_mould', 'oil_stain']
senticnet['iron_mould'] = ['-0.51', '0', '0.937', '-0.92', '#anger', '#disgust', 'negative', '-0.79', 'bloodstain', 'mud_stain', 'iron_mold', 'discoloration', 'oil_stain']
senticnet['iron_ore'] = ['0.102', '0.078', '0', '0.087', '#joy', '#admiration', 'positive', '0.089', 'gold', 'platinum', 'adjustable', 'silver', 'magnetic']
senticnet['iron_oxide'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['iron_pot'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'address_book', 'get_rich', 'blank_check', 'swiss_army_knife', 'whisk']
senticnet['iron_shirt'] = ['-0.08', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.08', 'absorb_moisture', 'sit_on_beach', 'clothes', 'iron_clothes', 'shine_shoes']
senticnet['iron_steak'] = ['0', '0', '0.865', '-0.95', '#anger', '#disgust', 'negative', '-0.90', 'bucket_overflow', 'dirty_floor', 'spill_over', 'float', 'spoil_wood_floor']
senticnet['iron_sulfate'] = ['-0.03', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'lithium', 'mineral', 'potassium', 'iodine', 'manganese']
senticnet['iron_sulfur'] = ['-0.84', '0', '0.8', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'rigor_mortis', 'natural_process', 'natural_action', 'activity', 'action']
senticnet['ironbottom_sound'] = ['0.084', '-0.05', '0.013', '0', '#joy', '#surprise', 'positive', '0.043', 'carnival_ride', 'carny', 'fortune_teller', 'horseback', 'ferris_wheel']
senticnet['ironclad'] = ['-0.80', '0', '0.507', '-0.76', '#sadness', '#disgust', 'negative', '-0.69', 'adamant', 'inexorable', 'intransigent', 'inflexible', 'brassbound']
senticnet['ironic'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'nonsense', 'meaninglessness', 'bunk', 'absurdity', 'hokum']
senticnet['ironic_detachment'] = ['0.741', '-0.74', '0', '0.666', '#joy', '#surprise', 'positive', '0.718', 'commonplaceness', 'mundanity', 'prosiness', 'prosaicness', 'ordinariness']
senticnet['ironic_humor'] = ['-0.77', '0', '0.537', '-0.64', '#sadness', '#disgust', 'negative', '-0.65', 'grim', 'saturnine', 'sardonic', 'sarcastic', 'black']
senticnet['ironical'] = ['-0.52', '0', '0.71', '-0.94', '#anger', '#disgust', 'negative', '-0.72', 'atrocious', 'horrific', 'menacing', 'minacious', 'alarming']
senticnet['ironically'] = ['-0.11', '0.188', '0.608', '-0.63', '#anger', '#disgust', 'negative', '-0.29', 'contempt', 'disincline', 'disinclination', 'disinclined', 'indispose']
senticnet['ironing_board'] = ['0.866', '0.833', '0', '0', '#joy', '#interest', 'positive', '0.849', 'alcohol', 'porter', 'concierge', 'beer_mug', 'bar_stool']
senticnet['ironist'] = ['0.922', '-0.94', '0', '0.965', '#surprise', '#admiration', 'positive', '0.945', 'utilitarian', 'egalitarian', 'equalitarian', 'elitist', 'philosopher']
senticnet['ironman_triathlon'] = ['-0.04', '-0.03', '0', '-0.16', '#sadness', '#disgust', 'negative', '-0.05', 'crazy', 'damage_body', 'crazy_person', 'threat', 'max']
senticnet['ironmonger'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'pharmaceutical_chemist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['ironstone'] = ['0', '0', '-0.85', '-0.71', '#fear', '#disgust', 'negative', '-0.78', 'fair_game', 'pit', 'stone_pit', 'target', 'prey']
senticnet['irony'] = ['0', '0.963', '0', '0.785', '#interest', '#admiration', 'positive', '0.874', 'oat', 'white_rice', 'grain', 'wheat', 'granule']
senticnet['irradiance'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'incandescence', 'luminescence', 'brightness_level']
senticnet['irradiate'] = ['0.927', '-0.89', '0', '0.604', '#joy', '#surprise', 'positive', '0.809', 'vaticinate', 'call', 'enlighten', 'prophesy', 'predict']
senticnet['irradiation'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'radiation_therapy', 'radiation', 'actinotherapy', 'phototherapy']
senticnet['irrational'] = ['0', '0', '0.712', '-0.92', '#anger', '#disgust', 'negative', '-0.81', 'nice', 'good', 'lovely', 'niceness', 'hard_find']
senticnet['irrational_exuberance'] = ['-0.55', '0', '0.869', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'air_bubble', 'foam', 'froth', 'soap_bubble', 'globule']
senticnet['irrational_fear'] = ['-0.79', '-0.71', '-0.96', '-0.95', '#fear', '#disgust', 'negative', '-0.49', 'fear', 'caution', 'deep_sadness', 'bad_feeling', 'psychoneurosis']
senticnet['irrational_hostility'] = ['-0.79', '0', '0.627', '-0.81', '#sadness', '#disgust', 'negative', '-0.74', 'taboo', 'tabu', 'tendentiousness', 'bias', 'experimenter_bias']
senticnet['irrational_impulse'] = ['0.864', '-0.74', '0', '0.978', '#joy', '#admiration', 'positive', '0.862', 'passion', 'irrational_motive', 'compulsion', 'motivation', 'need']
senticnet['irrational_motive'] = ['0.864', '-0.74', '0', '0.978', '#joy', '#admiration', 'positive', '0.862', 'passion', 'irrational_impulse', 'compulsion', 'motivation', 'need']
senticnet['irrationalism'] = ['0.95', '-0.77', '0', '0.578', '#joy', '#surprise', 'positive', '0.767', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['irrationalist'] = ['0.95', '-0.77', '0', '0.578', '#joy', '#surprise', 'positive', '0.767', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['irrationality'] = ['0', '0', '-0.94', '-0.71', '#fear', '#disgust', 'negative', '-0.82', 'unreason', 'irrational', 'faculty', 'fatuity', 'dada']
senticnet['irrationally'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'irrational', 'asininely', 'absurdly', 'reason', 'freak']
senticnet['irreality'] = ['-0.94', '0', '0.925', '-0.97', '#sadness', '#disgust', 'negative', '-0.95', 'nonexistence', 'nonentity', 'cloud', 'falseness', 'falsity']
senticnet['irreclaimable'] = ['0.645', '0.637', '-0.23', '0.299', '#joy', '#interest', 'positive', '0.336', 'gladdened', 'stimulating', 'tickle_pink', 'exalt', 'beatify']
senticnet['irreconcilability'] = ['-0.95', '0', '0.715', '0', '#sadness', '#anger', 'negative', '-0.83', 'incompatible', 'irreconcilable', 'reconcile', 'irreconcilably', 'intransigent']
senticnet['irreconcilable'] = ['-0.95', '0', '0.722', '0', '#sadness', '#anger', 'negative', '-0.83', 'incompatible', 'irreconcilability', 'reconcile', 'irreconcilably', 'intransigent']
senticnet['irreconcilably'] = ['-0.95', '0', '0.717', '0', '#sadness', '#anger', 'negative', '-0.83', 'incompatible', 'irreconcilability', 'reconcile', 'irreconcilable', 'intransigent']
senticnet['irrecoverable'] = ['-0.92', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.93', 'irretrievable', 'incurable', 'shipwreck', 'retrievable', 'irreparable']
senticnet['irredeemable'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'venial', 'excusable', 'mercifully', 'merciful', 'mercifulness']
senticnet['irredeemably'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'lamentably', 'deplorably', 'bereaved', 'bereft', 'grief-stricken']
senticnet['irreducibility'] = ['0.534', '-0.86', '0', '0.676', '#surprise', '#admiration', 'positive', '0.692', 'suitability', 'suitableness', 'habitability', 'habitableness', 'qualification']
senticnet['irreducible'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'width', 'size', 'mass_measure', 'measure', 'gauge']
senticnet['irreducibly'] = ['0.558', '-0.76', '0', '0.568', '#surprise', '#admiration', 'positive', '0.63', 'indefiniteness', 'indeterminateness', 'indefinity', 'indetermination', 'inconclusiveness']
senticnet['irrefragable'] = ['-0.99', '0', '0.658', '-0.73', '#sadness', '#disgust', 'negative', '-0.79', 'adrift', 'afloat', 'aimless', 'directionless', 'purposeless']
senticnet['irrefutable'] = ['0.735', '0.709', '0', '0', '#joy', '#interest', 'positive', '0.722', 'undeniable', 'incontestable', 'incontrovertible', 'indisputable', 'questionable']
senticnet['irregular'] = ['-0.58', '0', '0.814', '-0.54', '#sadness', '#anger', 'negative', '-0.64', 'atypical', 'abnormal', 'vicarious', 'supernormal', 'subnormal']
senticnet['irregular_menstruation'] = ['-0.73', '0', '0.555', '-0.90', '#sadness', '#disgust', 'negative', '-0.73', 'aching', 'hurting', 'labor_pain', 'neuralgia', 'neuralgy']
senticnet['irregular_nucleus'] = ['0.905', '-0.65', '0', '0.851', '#joy', '#admiration', 'positive', '0.805', 'gonococcus', 'bacteria', 'bacteriae', 'probiotic', 'probiotic_bacterium']
senticnet['irregular_rhythm'] = ['-0.56', '0', '0.849', '-0.61', '#anger', '#disgust', 'negative', '-0.67', 'convulsive', 'unsteady', 'jerky', 'spasmodic', 'spastic']
senticnet['irregular_shape'] = ['-0.05', '0', '-0.12', '-0.16', '#fear', '#disgust', 'negative', '-0.11', 'reduced', 'tiny', 'small_size', 'narrow', 'poor']
senticnet['irregular_terrain'] = ['-0.99', '0', '0.641', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'navigable', 'negotiable', 'surmountable', 'climbable', 'passable']
senticnet['irregular_warfare'] = ['0.833', '-0.97', '0', '0.91', '#surprise', '#admiration', 'positive', '0.906', 'fencer', 'battler', 'belligerent', 'fighter', 'scrapper']
senticnet['irregularity'] = ['-0.59', '0', '0.672', '-0.83', '#anger', '#disgust', 'negative', '-0.7', 'geometrical_asymmetry', 'radial_asymmetry', 'lopsidedness', 'skewness', 'asymmetry']
senticnet['irregularly'] = ['0', '0.152', '0', '0.119', '#interest', '#admiration', 'positive', '0.136', 'replicate', 'start_end', 'everyday', 'description', 'precise']
senticnet['irrelevance'] = ['0.75', '-0.87', '0', '0.603', '#joy', '#surprise', 'positive', '0.743', 'quality', 'futility', 'impracticality', 'worthlessness', 'impracticability']
senticnet['irrelevancy'] = ['0.864', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.924', 'trash', 'trash_container', 'junk_store', 'coffee_cup', 'trash_receptacle']
senticnet['irrelevant'] = ['-0.86', '0', '0.894', '-0.72', '#sadness', '#anger', 'negative', '-0.82', 'inapplicable', 'unsuitable', 'immaterial', 'extraneous', 'orthogonal']
senticnet['irreligion'] = ['-0.94', '0', '0.687', '-0.75', '#sadness', '#disgust', 'negative', '-0.79', 'clannishness', 'cliquishness', 'snobbery', 'arrogance', 'haughtiness']
senticnet['irreligious'] = ['-0.93', '0', '0.75', '-0.64', '#sadness', '#anger', 'negative', '-0.77', 'heathen', 'heathenish', 'pagan', 'lapsed', 'nonchurchgoing']
senticnet['irreligiousness'] = ['0.905', '-0.80', '0', '0.583', '#joy', '#surprise', 'positive', '0.763', 'unrighteousness', 'undutifulness', 'godlessness', 'ungodliness', 'impiety']
senticnet['irremediable'] = ['0', '0', '0.753', '-0.78', '#anger', '#disgust', 'negative', '-0.77', 'irreparable', 'irreversible', 'beyond_repair', 'unrectifiable', 'irrevocable']
senticnet['irreparable'] = ['-0.94', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.83', 'amended', 'cured', 'irretrievable', 'repaired', 'rectified']
senticnet['irreparable_damage'] = ['-0.98', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.97', 'harm', 'hurt', 'cut_skin', 'bloody', 'ouch']
senticnet['irreparably'] = ['0', '0.954', '0', '0.943', '#interest', '#admiration', 'positive', '0.948', 'irreversibly', 'irrevocably', 'reversibly', 'irretrievably', 'destabilized']
senticnet['irreplaceable'] = ['0.896', '0', '0', '0.978', '#joy', '#admiration', 'positive', '0.937', 'unique', 'amazing', 'rare', 'love', 'absolute']
senticnet['irreplaceableness'] = ['-0.56', '0', '0.574', '-0.69', '#anger', '#disgust', 'negative', '-0.61', 'incommutability', 'unexchangeability', 'changelessness', 'unchangeability', 'unchangeableness']
senticnet['irrepressibility'] = ['0.546', '-0.73', '0', '0.583', '#surprise', '#admiration', 'positive', '0.62', 'sprightliness', 'spiritedness', 'brio', 'bright', 'delicacy']
senticnet['irrepressible'] = ['-0.89', '0', '0.678', '-0.62', '#sadness', '#anger', 'negative', '-0.73', 'undisciplined', 'anarchic', 'anarchical', 'uncontrolled', 'uncontrollable']
senticnet['irrepressible_spirit'] = ['0.772', '-0.95', '0', '0.89', '#surprise', '#admiration', 'positive', '0.872', 'downrightness', 'straightforwardness', 'characteristic', 'immediacy', 'directness']
senticnet['irrepressibly'] = ['0.512', '0.658', '-0.25', '0.256', '#joy', '#interest', 'positive', '0.293', 'riotously', 'expansively', 'ebulliently', 'zestfulness', 'gusto']
senticnet['irreproachable'] = ['0.666', '-0.83', '0', '0.896', '#surprise', '#admiration', 'positive', '0.798', 'nice', 'decent', 'solid', 'presentable', 'upstanding']
senticnet['irreproducibility'] = ['-0.92', '0', '0.546', '-0.52', '#sadness', '#anger', 'negative', '-0.66', 'irresponsibility', 'irresponsibleness', 'instability', 'undependability', 'fallibility']
senticnet['irresistible'] = ['0.962', '0', '0', '0.91', '#joy', '#admiration', 'positive', '0.936', 'irresistibleness', 'resistless', 'enchantress', 'ineluctable', 'resist']
senticnet['irresistible_charm'] = ['0', '0', '0', '0.751', '#admiration', '#admiration', 'positive', '0.751', 'enchant', 'favorable', 'favorably', 'charmy', 'comfortableness']
senticnet['irresistible_force'] = ['-0.80', '0', '0.507', '-0.76', '#sadness', '#disgust', 'negative', '-0.69', 'adamant', 'inflexible', 'intransigent', 'ironclad', 'brassbound']
senticnet['irresistibleness'] = ['0.961', '0', '0', '0.905', '#joy', '#admiration', 'positive', '0.933', 'irresistible', 'resistless', 'enchantress', 'ineluctable', 'resist']
senticnet['irresistibly'] = ['-0.80', '0', '0.507', '-0.76', '#sadness', '#disgust', 'negative', '-0.69', 'adamant', 'inflexible', 'intransigent', 'ironclad', 'brassbound']
senticnet['irresolute'] = ['-0.96', '0', '0.797', '0', '#sadness', '#anger', 'negative', '-0.88', 'undecided', 'fainthearted', 'infirm', 'lacking', 'unsure']
senticnet['irresoluteness'] = ['-0.52', '0', '0.959', '-0.70', '#anger', '#disgust', 'negative', '-0.72', 'unpredictability', 'indecisiveness', 'indecision', 'trait', 'volatility']
senticnet['irresolution'] = ['-0.89', '0', '0.565', '-0.76', '#sadness', '#disgust', 'negative', '-0.73', 'hesitation', 'uncertainty', 'doubt', 'incertitude', 'dubiety']
senticnet['irresolvable'] = ['0', '0', '0.877', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'uncrossable', 'chasm', 'irreconcilable', 'unbridgeable', 'gulf']
senticnet['irrespective'] = ['0.324', '0.461', '-0.34', '0.442', '#interest', '#admiration', 'positive', '0.22', 'obedience', 'regard', 'valor', 'affection', 'honor']
senticnet['irresponsibility'] = ['-0.61', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.69', 'untrustworthiness', 'untrustiness', 'undependability', 'undependableness', 'unreliability']
senticnet['irresponsible'] = ['0', '0', '0.733', '-0.92', '#anger', '#disgust', 'negative', '-0.83', 'freewheeling', 'carefree', 'slaphappy', 'unaccountable', 'loose']
senticnet['irresponsible_behavior'] = ['0', '0', '0.853', '-0.84', '#anger', '#disgust', 'negative', '-0.84', 'flagrant', 'cry', 'glaring', 'gross', 'rank']
senticnet['irresponsibleness'] = ['-0.77', '0', '0.688', '-0.52', '#sadness', '#anger', 'negative', '-0.66', 'untrustworthiness', 'untrustiness', 'undependability', 'undependableness', 'unreliability']
senticnet['irresponsibly'] = ['-0.92', '0', '0.876', '-0.96', '#sadness', '#disgust', 'negative', '-0.92', 'bigmouthed', 'blabby', 'blabbermouthed', 'talkative', 'indiscreet']
senticnet['irretrievable'] = ['-0.91', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.92', 'irrecoverable', 'incurable', 'shipwreck', 'retrievable', 'irreparable']
senticnet['irretrievably'] = ['0', '0.948', '0', '0.941', '#interest', '#admiration', 'positive', '0.944', 'irreparably', 'irrevocably', 'reversibly', 'irreversibly', 'destabilized']
senticnet['irreverence'] = ['-0.52', '0', '0.621', '-0.91', '#anger', '#disgust', 'negative', '-0.68', 'deviltry', 'devilry', 'iniquity', 'foul_play', 'sexual_immorality']
senticnet['irreverent'] = ['0.615', '-0.57', '0', '0.502', '#joy', '#surprise', 'positive', '0.563', 'aweless', 'awless', 'disrespectful', 'sacrilegious', 'blasphemous']
senticnet['irreversible'] = ['0', '0', '0.761', '-0.78', '#anger', '#disgust', 'negative', '-0.77', 'irreparable', 'irremediable', 'beyond_repair', 'unrectifiable', 'irrevocable']
senticnet['irreversible_damage'] = ['0.565', '-0.76', '0', '0.982', '#surprise', '#admiration', 'positive', '0.771', 'risk', 'hazard', 'jeopardy', 'health_hazard', 'peril']
senticnet['irreversible_degradation'] = ['-0.80', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.84', 'popularization', 'profanation', 'demoralization', 'corruption', 'humiliation']
senticnet['irreversible_dementia'] = ['-0.94', '0', '0.821', '-0.75', '#sadness', '#anger', 'negative', '-0.84', 'predementedness', 'insanity', 'dementedness', 'senile_psychosis', 'alcoholic_dementia']
senticnet['irreversible_harm'] = ['0.565', '-0.76', '0', '0.982', '#surprise', '#admiration', 'positive', '0.771', 'risk', 'hazard', 'jeopardy', 'health_hazard', 'peril']
senticnet['irreversibly'] = ['0', '0.957', '0', '0.944', '#interest', '#admiration', 'positive', '0.95', 'irreparably', 'irrevocably', 'reversibly', 'irretrievably', 'destabilized']
senticnet['irrevocable'] = ['0', '0', '0.751', '-0.78', '#anger', '#disgust', 'negative', '-0.76', 'irreparable', 'irremediable', 'beyond_repair', 'unrectifiable', 'irreversible']
senticnet['irrevocable_resignation'] = ['-0.72', '0', '0.98', '0', '#sadness', '#anger', 'negative', '-0.85', 'resignedly', 'abject', 'unhopeful', 'abjectly', 'reconcile']
senticnet['irrevocable_trust'] = ['0.851', '0.708', '0', '0', '#joy', '#interest', 'positive', '0.779', 'devisee', 'legatee', 'recipient', 'receiver', 'pensioner']
senticnet['irrevocably'] = ['0', '0.948', '0', '0.936', '#interest', '#admiration', 'positive', '0.942', 'irreparably', 'irreversibly', 'reversibly', 'irretrievably', 'destabilized']
senticnet['irrigated_acreage'] = ['0', '0.122', '0', '0.123', '#interest', '#admiration', 'positive', '0.122', 'acre', 'cow_home', 'crop_place', 'big_land', 'animal_house']
senticnet['irrigated_cropland'] = ['0', '0.122', '0', '0.123', '#interest', '#admiration', 'positive', '0.122', 'acre', 'cow_home', 'crop_place', 'big_land', 'animal_house']
senticnet['irritability'] = ['-0.90', '0', '0.647', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'ill_humor', 'distemper', 'pet', 'testiness', 'touchiness']
senticnet['irritable'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'bad_tempered', 'nervous', 'neurasthenic', 'cranky', 'testy']
senticnet['irritable_bowel_syndrome'] = ['-0.73', '0', '-0.85', '-0.72', '#sadness', '#fear', 'negative', '-0.77', 'lay_off', 'hurt', 'pain_suffer', 'lose_mind', 'forget_everything']
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senticnet['irritant'] = ['-0.53', '0', '0.86', '-0.65', '#anger', '#disgust', 'negative', '-0.68', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['irritate'] = ['-0.12', '0', '0.688', '-0.68', '#anger', '#disgust', 'negative', '-0.49', 'offend', 'bothersome', 'pique', 'disgust', 'frustrating']
senticnet['irritated'] = ['0', '0.324', '0', '0', '#interest', '#interest', 'negative', '0.324', 'annoyed', 'annoyance', 'vexed', 'upset', 'crabby']
senticnet['irritated_skin'] = ['-0.81', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.82', 'peanut', 'leave_over', 'floppy', 'circular_motion', 'store_vegetable']
senticnet['irritating'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['irritatingly'] = ['-0.80', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.8', 'scared', 'frighten_away', 'scare_away', 'frighten_off', 'scare_off']
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senticnet['irrorated_with'] = ['-0.7', '0', '0.617', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'silvery', 'achromatic', 'bluish_black', 'hueless', 'pitchy']
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senticnet['irrupt'] = ['-0.66', '0', '0.982', '-0.93', '#anger', '#disgust', 'negative', '-0.86', 'bother', 'barge_in', 'come_in', 'break_in', 'crash']
senticnet['irruption'] = ['-0.50', '0', '0.975', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'entrance', 'entering', 'entry', 'ingress', 'incoming']
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senticnet['isa_slot'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'computer_part', 'system', 'cpu', 'pencil_pen', 'monitor']
senticnet['isabel_marant'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'elegant', 'stylish', 'smart', 'posh', 'sleek']
senticnet['isabella_sankey'] = ['0.926', '0.937', '0', '0.975', '#interest', '#admiration', 'positive', '0.946', 'monument', 'statue', 'freedom', 'idol', 'sculpture']
senticnet['isaiah'] = ['0.075', '-0.01', '0.019', '0.067', '#joy', '#admiration', 'positive', '0.034', 'rhyme', 'paragraph', 'song_lyric', 'lyrics', 'stanza']
senticnet['iscariot'] = ['-0.67', '0', '0.546', '-0.63', '#sadness', '#disgust', 'negative', '-0.61', 'fink', 'snitch', 'snitcher', 'sneak', 'stoolpigeon']
senticnet['ischemic_heart_disease'] = ['-0.98', '0', '0.827', '-0.94', '#sadness', '#disgust', 'negative', '-0.91', 'congestive_heart_failure', 'heart_attack', 'failure', 'heart_disease', 'cardiopathy']
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senticnet['ischial'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'socket', 'tooth_socket', 'alveolus', 'acetabulum', 'cotyloid_cavity']
senticnet['ischinger'] = ['0.911', '-0.71', '0', '0.552', '#joy', '#surprise', 'positive', '0.726', 'deliver_goods', 'succeed', 'win', 'bring_home_bacon', 'come_through']
senticnet['ischium'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'above_leg', 'joint', 'hip', 'knee']
senticnet['ischnocnema'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'amphibian', 'aquatic']
senticnet['isdn'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'long_distance_relationship', 'distant', 'kilometer', 'length_measurement', 'distance_measure']
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senticnet['ishant'] = ['-0.62', '0', '0.713', '-0.98', '#anger', '#disgust', 'negative', '-0.77', 'muddy_up', 'muck_up', 'foul', 'blemish', 'smear']
senticnet['ishant_sharma'] = ['0.034', '-0.02', '0.054', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'compass', 'swiss_army_knife', 'bake_pan', 'coffee_mug', 'wine_opener']
senticnet['ishfaq_ahmad'] = ['-0.90', '0', '0.748', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'alternative_energy', 'activation_energy', 'energy_of_activation', 'energy', 'nuclear_energy']
senticnet['ishwar_chandra_vidyasagar'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['isknder_vigoa_prez'] = ['0.792', '-0.86', '0', '0.888', '#surprise', '#admiration', 'positive', '0.849', 'absurd', 'inconsequential', 'intuitive', 'visceral', 'nonrational']
senticnet['islam'] = ['0.076', '0.316', '-0.18', '0.262', '#interest', '#admiration', 'positive', '0.118', 'obey_god', 'dogma', 'evangelist', 'faith', 'believe']
senticnet['islamic'] = ['0.783', '0.804', '0', '0.826', '#interest', '#admiration', 'positive', '0.804', 'religious_purpose', 'spiritual', 'faith', 'religion', 'judaism']
senticnet['islamic_cooperation_oic'] = ['0.851', '-0.65', '0', '0.66', '#joy', '#admiration', 'positive', '0.723', 'omen', 'portent', 'presage', 'prognostic', 'prognostication']
senticnet['islamic_golden_age'] = ['-0.96', '0', '0.615', '-0.73', '#sadness', '#disgust', 'negative', '-0.77', 'flush', 'blossom', 'prime', 'peak', 'efflorescence']
senticnet['islamism'] = ['0', '0', '0', '0.986', '#admiration', '#admiration', 'positive', '0.986', 'idea', 'decision', 'consider', 'polarize', 'make_right_decision']
senticnet['islamist'] = ['0.648', '0.627', '-0.26', '0.329', '#joy', '#interest', 'positive', '0.334', 'brotherly', 'friendly', 'fraternal', 'amicability', 'amicable']
senticnet['islamist_dominated_parliament'] = ['-0.78', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.83', 'break', 'end', 'die', 'fall_apart', 'go_to_piece']
senticnet['islamist_group'] = ['0.648', '0.627', '-0.26', '0.329', '#joy', '#interest', 'positive', '0.334', 'brotherly', 'friendly', 'fraternal', 'amicability', 'amicable']
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senticnet['islamist_muslim_brotherhood'] = ['0.648', '0.627', '-0.26', '0.329', '#joy', '#interest', 'positive', '0.334', 'brotherly', 'friendly', 'fraternal', 'amicability', 'amicable']
senticnet['islamophobe'] = ['-0.21', '0', '-0.19', '-0.18', '#sadness', '#fear', 'negative', '-0.2', 'censorship', 'bad_word', 'kidnap', 'intolerance', 'sectarian']
senticnet['islamophobia'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'racism', 'intolerant', 'xenophobic', 'racist', 'nazism']
senticnet['island'] = ['0', '0.112', '0', '0.137', '#interest', '#admiration', 'positive', '0.124', 'near_ocean', 'popular_vacation_destination', 'land_feature', 'ocean_shore', 'oceanside']
senticnet['island-dweller'] = ['0.725', '0.949', '0', '0', '#joy', '#interest', 'positive', '0.837', 'islander', 'island', 'adirondack', 'isle', 'think']
senticnet['islander'] = ['0.728', '0.958', '0', '0', '#joy', '#interest', 'positive', '0.843', 'island-dweller', 'island', 'adirondack', 'isle', 'think']
senticnet['isle'] = ['0.724', '0.951', '0', '0', '#joy', '#interest', 'positive', '0.837', 'island-dweller', 'island', 'adirondack', 'islander', 'think']
senticnet['islet_cell'] = ['0.079', '-0.10', '0.046', '0', '#joy', '#surprise', 'positive', '0.047', 'nerve', 'liver', 'groin', 'scrotum', 'diaphragm']
senticnet['islet_cell_tumor'] = ['0.079', '-0.10', '0.046', '0', '#joy', '#surprise', 'positive', '0.047', 'nerve', 'liver', 'groin', 'scrotum', 'diaphragm']
senticnet['ism'] = ['0.95', '-0.77', '0', '0.578', '#joy', '#surprise', 'positive', '0.767', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'amoralism']
senticnet['iso_osmotic'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
senticnet['isoamyl_acetate'] = ['0', '0', '0.717', '-0.84', '#anger', '#disgust', 'negative', '-0.77', 'gymnast', 'bikini', 'underwear', 'basketball_hoop', 'dirty_sock']
senticnet['isobutyl'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'carboxylic_acid', 'succinic_acid', 'valeric_acid']
senticnet['isocratic'] = ['0.838', '-0.61', '0', '0.556', '#joy', '#surprise', 'positive', '0.671', 'activity', 'natural_process', 'natural_action', 'column_chromatography', 'paper_chromatography']
senticnet['isoflurane'] = ['-0.65', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'put_to_sleep', 'operation', 'anesthesia', 'surgery', 'local_anesthesia']
senticnet['isolate'] = ['-0.81', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.79', 'bad_choice', 'treatable', 'wrong', 'anguish', 'mock']
senticnet['isolate_iran'] = ['0.655', '0.635', '-0.23', '0.3', '#joy', '#interest', 'positive', '0.338', 'cheerfully', 'cheerful', 'jolly_up', 'hearten', 'chirk_up']
senticnet['isolated'] = ['-0.96', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.91', 'insular', 'peninsula', 'situate', 'surroundings', 'standoffish']
senticnet['isolating_iran'] = ['0', '0.098', '0', '0.08', '#interest', '#admiration', 'positive', '0.089', 'talk_person', 'wisdom', 'tact', 'statecraft', 'greet_person']
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senticnet['isolationism'] = ['-0.83', '0', '0.904', '0', '#sadness', '#anger', 'negative', '-0.87', 'disappointed', 'thwarted', 'frustrated', 'foiled', 'discomfited']
senticnet['isolationist'] = ['0.828', '-0.67', '0', '0.811', '#joy', '#admiration', 'positive', '0.771', 'foolhardiness', 'brashness', 'thoughtlessness', 'desperation', 'unthoughtfulness']
senticnet['isomerization'] = ['0.695', '-0.85', '0', '0.501', '#joy', '#surprise', 'positive', '0.684', 'alternative_energy', 'energy', 'energy_of_activation', 'atomic_energy', 'nuclear_energy']
senticnet['isometric'] = ['0', '0.997', '0', '0.872', '#interest', '#admiration', 'positive', '0.934', 'isometric', 'maximal', 'exercise', 'cut-away', 'cross-sectional']
senticnet['isomorphic'] = ['-0.72', '0', '0.715', '0', '#sadness', '#anger', 'negative', '-0.71', 'subset', 'nonempty', 'disjoint', 'non-empty', 'vertex']
senticnet['isomorphism_between'] = ['-0.75', '0', '0.679', '-0.77', '#sadness', '#disgust', 'negative', '-0.73', 'unvariedness', 'changelessness', 'unchangeability', 'unchangeableness', 'unchangingness']
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senticnet['isopod_crustacean'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'annelid', 'annelid_worm', 'leech']
senticnet['isopropanol'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
senticnet['isopropyl'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
senticnet['isopropyl_alcohol'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
senticnet['isoproterenol'] = ['0.95', '-0.95', '0', '0.769', '#joy', '#surprise', 'positive', '0.89', 'adrenocorticotropic', 'adrenocorticotrophic', 'stimulant', 'stimulating', 'stimulative']
senticnet['isorhythmic'] = ['-0.5', '0', '0.97', '-0.52', '#anger', '#disgust', 'negative', '-0.66', 'simulated', 'apish', 'parrotlike', 'apelike', 'mimic']
senticnet['isoroku_yamamoto'] = ['0.688', '-0.90', '0', '0.877', '#surprise', '#admiration', 'positive', '0.824', 'flag_officer', 'rear_admiral', 'admiral', 'full_admiral', 'vice_admiral']
senticnet['isoscalar'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'elementary_particle', 'neutron', 'electron', 'subatomic_particle', 'proton']
senticnet['isosceles_right'] = ['0.763', '-0.61', '0', '0.777', '#joy', '#admiration', 'positive', '0.717', 'acute_triangle', 'wedge_shape', 'cuneus', 'wedge', 'oblique_triangle']
senticnet['isosceles_triangle'] = ['0.932', '0.753', '0', '0', '#joy', '#interest', 'positive', '0.843', 'circle', 'loop', 'arc', 'coil', 'trapezoid']
senticnet['isospin'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'elementary_particle', 'neutron', 'electron', 'subatomic_particle', 'proton']
senticnet['isospora'] = ['0.995', '-0.71', '0', '0.647', '#joy', '#surprise', 'positive', '0.784', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['isothiocyanate'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'capsaicin', 'chemical_irritant', 'piperine', 'pepper_spray']
senticnet['isotonic'] = ['-0.74', '0', '0.723', '-0.59', '#sadness', '#anger', 'negative', '-0.68', 'tone', 'tautness', 'tonus', 'condition', 'status']
senticnet['isotonic_saline'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
senticnet['isotope'] = ['-0.76', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.85', 'number', 'oceanography', 'geophysics', 'marine_geology', 'seawater']
senticnet['isotopic_composition'] = ['0.883', '0.918', '0', '0', '#joy', '#interest', 'positive', '0.901', 'concentration', 'study', 'determine', 'relationship', 'project']
senticnet['isotropic'] = ['0', '0', '0.78', '-0.9', '#anger', '#disgust', 'negative', '-0.84', 'anisotropic', 'inhomogeneous', 'dielectric', 'nonlinear', 'anisotropy']
senticnet['isotropy'] = ['0', '0.929', '0', '0.74', '#interest', '#admiration', 'positive', '0.835', 'symmetricalness', 'correspondence', 'proportion', 'symmetry', 'balance']
senticnet['isovector'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'elementary_particle', 'neutron', 'electron', 'subatomic_particle', 'proton']
senticnet['isp'] = ['0.687', '-0.50', '0', '0.8', '#joy', '#admiration', 'positive', '0.663', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['israelite'] = ['-0.71', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.77', 'hebraical', 'hebrew', 'hebraic', 'jew', 'nazism']
senticnet['issachar'] = ['0.849', '-0.89', '0', '0.924', '#surprise', '#admiration', 'positive', '0.89', 'nonintellectual', 'lowbrow', 'lowbrowed', 'mindless', 'uncultivated']
senticnet['issuance'] = ['0.862', '0.709', '0', '0', '#joy', '#interest', 'positive', '0.785', 'invoice', 'bill', 'check', 'mastercard', 'payment_counter']
senticnet['issued_photo_id'] = ['0.931', '-0.52', '0', '0.67', '#joy', '#admiration', 'positive', '0.709', 'document', 'certificate_of_incorporation', 'credential', 'diploma', 'sheepskin']
senticnet['issued_photo_identification'] = ['0.739', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.814', 'receipt', 'bet', 'electric_bill', 'letter_slot', 'deal']
senticnet['issuer'] = ['-0.02', '0.175', '-0.11', '0.148', '#interest', '#admiration', 'positive', '0.045', 'tolerant', 'phrase', 'inquiry', 'explain', 'question']
senticnet['issuing_tax_exempt'] = ['-0.82', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.78', 'go_broke', 'receive_change', 'run_out_money', 'compare_price', 'bankruptcy']
senticnet['ist_der'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['ist_die'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['ist_ein'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['istvn'] = ['0.975', '-0.66', '0', '0.813', '#joy', '#admiration', 'positive', '0.816', 'cry', 'yell', 'scream', 'shrill', 'call']
senticnet['isu_foundation'] = ['0.608', '-0.55', '0', '0.726', '#joy', '#admiration', 'positive', '0.628', 'genius', 'raw_talent', 'flair', 'gift', 'knack']
senticnet['isvara'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
senticnet['iswar_chandra_vidyasagar'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['itachi_uchiha'] = ['0.021', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'plastic_bottle', 'party', 'use_party', 'lighter_air', 'compress_air']
senticnet['italeli'] = ['-0.57', '0', '0.576', '-0.78', '#anger', '#disgust', 'negative', '-0.64', 'reprieve', 'respite', 'suspend', 'call', 'delay']
senticnet['italian_antifascist'] = ['-0.97', '0', '0.62', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'obstructor', 'obstructer', 'technophobe', 'obstructionist', 'naysayer']
senticnet['italian_architect_renzo'] = ['-0.75', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.80', 'high-strung', 'uptight', 'restive', 'nervy', 'jumpy']
senticnet['italian_climber'] = ['0.518', '-0.51', '0', '0.65', '#surprise', '#admiration', 'positive', '0.562', 'clematis', 'legume', 'leguminous_plant', 'climbing_fern', 'root_climber']
senticnet['italian_cuisine'] = ['0.882', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.822', 'ferment_grape_juice', 'chef_special', 'bottle_wine', 'fine_dining', 'decanter']
senticnet['italian_dinner'] = ['0.974', '0.789', '0', '0.828', '#joy', '#admiration', 'positive', '0.864', 'eat_hamburger', 'eat_quickly', 'eat_well', 'eat_apple', 'eat_breakfast']
senticnet['italian_eatery'] = ['0.06', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'eat_food', 'fast_food', 'pizza_parlor', 'pizza_store', 'kitchen_cupboard']
senticnet['italian_fascism'] = ['-0.81', '-0.71', '0.843', '-0.72', '#sadness', '#anger', 'negative', '-0.41', 'lack_of_rules', 'fascism', 'despot', 'tyrant', 'anarchism']
senticnet['italian_food'] = ['0.061', '0.07', '-0.05', '0.073', '#interest', '#admiration', 'positive', '0.037', 'expensive_food', 'eat_food', 'fine_dining', 'caviar', 'lobster']
senticnet['italian_futurism'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'native_person', 'revolutionary', 'republic', 'revolution', 'look_ahead']
senticnet['italian_futurist'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'native_person', 'revolutionary', 'republic', 'revolution', 'look_ahead']
senticnet['italian_horror_film'] = ['-0.79', '0', '0.885', '-0.76', '#sadness', '#anger', 'negative', '-0.81', 'infernal', 'mortal', 'dead_soul', 'dead_soule', 'living_dead']
senticnet['italian_parsley'] = ['0.636', '-0.79', '0', '0.552', '#joy', '#surprise', 'positive', '0.661', 'lettuce', 'endive', 'green', 'salad_green', 'food']
senticnet['italian_pasta'] = ['0.763', '0.9', '0', '0', '#joy', '#interest', 'positive', '0.832', 'breakfast', 'box_cereal', 'calcium', 'drink', 'carton_milk']
senticnet['italian_restaurant'] = ['0.068', '0.066', '0.011', '-0.03', '#joy', '#interest', 'positive', '0.022', 'trattoria', 'eat_pizza', 'pizzeria', 'restaurant_diner', 'eatery']
senticnet['italian_vogue'] = ['-0.98', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.89', 'fairy', 'genus', 'honey_bee', 'infant', 'dove']
senticnet['italianate_structure'] = ['0.026', '0.219', '0', '0.16', '#interest', '#admiration', 'positive', '0.135', 'large_build', 'palace', 'high_rise', 'structure', 'fortification']
senticnet['italianate_villa'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'condominium', 'rent_apartment', 'high_rise', 'condo', 'row_house']
senticnet['italic'] = ['-0.95', '0', '0.517', '-0.53', '#sadness', '#disgust', 'negative', '-0.67', 'copperplate', 'hand', 'minuscule', 'cursive', 'round_hand']
senticnet['itar_scheme'] = ['0', '0', '-0.9', '-0.79', '#fear', '#disgust', 'negative', '-0.84', 'forbid', 'prohibit', 'veto', 'disallow', 'interdict']
senticnet['itch'] = ['0', '0', '0.891', '-0.83', '#anger', '#disgust', 'negative', '-0.86', 'rash', 'scratch', 'measles', 'skin_irritation', 'nip']
senticnet['itchiness'] = ['0', '-0.04', '0.063', '-0.12', '#anger', '#disgust', 'negative', '-0.04', 'animal_hair', 'scratchy', 'softness', 'fuzzy', 'fur']
senticnet['itching'] = ['-0.77', '0', '0.895', '-0.73', '#sadness', '#anger', 'negative', '-0.8', 'itch', 'urtication', 'itchiness', 'cutaneous_sensation', 'tickle']
senticnet['itchy'] = ['0', '0', '0.955', '-0.86', '#anger', '#disgust', 'negative', '-0.90', 'pall', 'dreadful', 'afraid', 'disquiet', 'fright']
senticnet['itchy_nose'] = ['-0.06', '0.183', '0.651', '-0.64', '#anger', '#disgust', 'negative', '-0.29', 'allergic_reaction', 'chew', 'pierce', 'mastication', 'put_mouth']
senticnet['itchy_rash'] = ['-0.10', '0', '-0.05', '-0.11', '#sadness', '#disgust', 'negative', '-0.08', 'jaw', 'cheek', 'dimple', 'forehead', 'beard']
senticnet['itchy_red'] = ['-0.10', '0', '-0.05', '-0.11', '#sadness', '#disgust', 'negative', '-0.08', 'jaw', 'cheek', 'dimple', 'forehead', 'beard']
senticnet['itchy_skin'] = ['-0.96', '0', '0.108', '-0.90', '#sadness', '#disgust', 'negative', '-0.66', 'poisonous_fly', 'dangerous_insect', 'dangerous_fly', 'dangerous_animal', 'poisonous']
senticnet['item_folder'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'car_trunk', 'walmart', 'warehouse', 'store', 'storage_place']
senticnet['iteration'] = ['0.037', '0', '0.026', '0.129', '#joy', '#admiration', 'positive', '0.047', 'measurement_unit', 'many_items', 'long', 'circle_part', 'narrow']
senticnet['iterative'] = ['-0.03', '0.2', '-0.10', '0.298', '#interest', '#admiration', 'positive', '0.09', 'procedure', 'action', 'create_form', 'transform', 'posture']
senticnet['iterator'] = ['-0.55', '0', '0.882', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'bugger_all', 'zero', 'naught', 'zip', 'nothing']
senticnet['ither'] = ['0.027', '0', '0.012', '0.063', '#joy', '#admiration', 'positive', '0.026', 'vegetation', 'tree_flower', 'green_growth', 'small', 'greenery']
senticnet['ithiel_town'] = ['0.793', '0.83', '0', '0', '#joy', '#interest', 'positive', '0.811', 'townspeople', 'township', 'town', 'townsfolk', 'people']
senticnet['itinerary'] = ['0', '0', '0', '0.097', '#admiration', '#admiration', 'positive', '0.097', 'transport_person', 'travel_long_distance', 'get_plane', 'get_bus', 'move']
senticnet['itochu'] = ['0.59', '-0.75', '0', '0.543', '#joy', '#surprise', 'positive', '0.63', 'venture', 'sally', 'labor', 'cause', 'risk']
senticnet['itochu_corporation'] = ['0.59', '-0.75', '0', '0.543', '#joy', '#surprise', 'positive', '0.63', 'venture', 'sally', 'labor', 'cause', 'risk']
senticnet['itty_bitty'] = ['-0.78', '0', '0.915', '-0.99', '#anger', '#disgust', 'negative', '-0.89', 'bodge', 'botch_up', 'flub', 'ball_up', 'go_wrong']
senticnet['itunes_music'] = ['0.101', '-0.04', '0.036', '0', '#joy', '#surprise', 'positive', '0.038', 'song', 'rap', 'jingle', 'chord', 'lyric']
senticnet['itv_comedy_benidorm'] = ['-0.97', '0', '0.728', '-0.78', '#sadness', '#disgust', 'negative', '-0.82', 'beguiler', 'slicker', 'phony', 'faker', 'deceiver']
senticnet['itv_gameshow'] = ['0.757', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.831', 'baseball_bat', 'water_gun', 'goalie', 'hockey_puck', 'hockey_stick']
senticnet['iucn'] = ['0', '0', '0', '0.857', '#admiration', '#admiration', 'positive', '0.857', 'say', 'beckon', 'call', 'conserve', 'retort']
senticnet['iucr_newsletter'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'current_event', 'headline', 'paper_write', 'paper_page', 'many_pages']
senticnet['iud'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'birth_control_pill', 'condom', 'contraceptive_pill', 'sex_toy', 'prevent_pregnancy']
senticnet['iud_intrauterine_device'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'birth_control_pill', 'condom', 'contraceptive_pill', 'sex_toy', 'prevent_pregnancy']
senticnet['iui'] = ['0', '0.871', '0', '0.941', '#interest', '#admiration', 'positive', '0.906', 'user_interface', 'port', 'interface', 'material_research', 'work']
senticnet['iupui_university'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'a_lot_of_books', 'old_testament', 'cook_book', 'magazine', 'librarian']
senticnet['iuz'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'feudal_lord', 'lord']
senticnet['iv_associational_significance'] = ['-0.80', '0', '0.815', '-0.98', '#anger', '#disgust', 'negative', '-0.86', 'inalienable', 'nontransferable', 'unassignable', 'untransferable', 'inviolable']
senticnet['iv_catheter'] = ['-0.74', '0', '0.766', '0', '#sadness', '#anger', 'negative', '-0.75', 'use_needle', 'clothes_repair', 'woven', 'embroidery', 'thread_needle']
senticnet['iv_infusion'] = ['0', '0', '-0.95', '-0.87', '#fear', '#disgust', 'negative', '-0.91', 'sharp_weapon', 'bayonet', 'ammunition', 'spear', 'arrow']
senticnet['iv_injection'] = ['0', '0', '-0.95', '-0.87', '#fear', '#disgust', 'negative', '-0.91', 'sharp_weapon', 'bayonet', 'ammunition', 'spear', 'arrow']
senticnet['iv_sedation'] = ['-0.65', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'put_to_sleep', 'operation', 'anesthesia', 'surgery', 'local_anesthesia']
senticnet['iva_ropati'] = ['0.997', '-0.84', '0', '0.927', '#joy', '#admiration', 'positive', '0.923', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['ives_goddard'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'institution', 'tundra', 'child_story', 'natural_history_museum', 'cartoon']
senticnet['ivolvement'] = ['0.967', '0', '0', '0.963', '#joy', '#admiration', 'positive', '0.965', 'solve', 'participate', 'find_direction', 'fantasize', 'enjoy_art']
senticnet['ivor_novello_award'] = ['0.232', '0.178', '-0.17', '0.223', '#joy', '#admiration', 'positive', '0.115', 'fame', 'alms', 'find_money', 'better_life', 'small_detail']
senticnet['ivory'] = ['0.085', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.074', 'soap', 'drizzle', 'bubbly', 'solid_water', 'foam']
senticnet['ivory_silk'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'pinafore', 'pinny', 'dirndl', 'dress', 'muumuu']
senticnet['ivver'] = ['0.947', '0.914', '0', '0', '#joy', '#interest', 'positive', '0.93', 'ball_bounce', 'bicycle_tire', 'tire', 'increase_price', 'gyroscope']
senticnet['ivy'] = ['-0.05', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.06', 'vine', 'green_color', 'green_leave', 'evergreen_tree', 'large_plant']
senticnet['ivy_asset_management'] = ['0.025', '0.27', '-0.11', '0.243', '#interest', '#admiration', 'positive', '0.105', 'keep_safe', 'store_dry_goods', 'store_stuff', 'secure', 'save']
senticnet['iwi'] = ['-0.91', '0', '0.769', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'break', 'good_luck', 'chance_eventpy_chance', 'chance_event', 'coincidence']
senticnet['iwi_te_ti_haunui'] = ['-0.91', '0', '0.769', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'break', 'good_luck', 'chance_eventpy_chance', 'chance_event', 'coincidence']
senticnet['iwo_jima'] = ['0.084', '-0.05', '0.013', '0', '#joy', '#surprise', 'positive', '0.043', 'carnival_ride', 'carny', 'fortune_teller', 'horseback', 'ferris_wheel']
senticnet['iwrg_rey_del'] = ['0.052', '0', '0', '0.118', '#joy', '#admiration', 'positive', '0.085', 'diamond_ring', 'piece_of_jewelry', 'bell_sound', 'wedding_band', 'engagement']
senticnet['ixc'] = ['0', '0.089', '0.01', '0.118', '#interest', '#admiration', 'positive', '0.066', 'crate', 'mom_house', 'sack', 'poker_game', 'safekeep']
senticnet['ixic_quote'] = ['0', '0.189', '0.04', '0.091', '#interest', '#admiration', 'positive', '0.08', 'combination', 'margin', 'teller', 'merge', 'finite']
senticnet['iyengar_yoga'] = ['0', '0', '0', '0.93', '#admiration', '#admiration', 'positive', '0.93', 'lift_weight', 'release_stress', 'move_body', 'take_hike', 'change_color']
senticnet['iyo_province'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'overlord', 'lord']
senticnet['izakaya'] = ['0.973', '-0.50', '0', '0.612', '#joy', '#admiration', 'positive', '0.698', 'chic', 'chicness', 'jauntiness', 'modishness', 'stylishness']
senticnet['izembek'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'habitat', 'mom_house', 'windsor', 'pacific_northwest', 'wilderness']
senticnet['j_bach'] = ['0.022', '0', '0', '0.109', '#joy', '#admiration', 'positive', '0.066', 'instrumental', 'singer', 'hip_hop', 'melodic_noise', 'music_player']
senticnet['jab'] = ['0.969', '0.998', '0', '0', '#joy', '#interest', 'positive', '0.984', 'bell_sound', 'bell_noise', 'piece_of_jewelry', 'syllable', 'fastener']
senticnet['jabaster'] = ['-0.71', '0', '0.836', '0', '#sadness', '#anger', 'negative', '-0.77', 'awkward', 'unenviable', 'embarrassingly', 'embarrassing', 'sheepish']
senticnet['jabber'] = ['-0.88', '0', '0.859', '0', '#sadness', '#anger', 'negative', '-0.87', 'gibberish', 'jabberer', 'jaw-jaw', 'blithering', 'gabble']
senticnet['jabberer'] = ['-0.89', '0', '0.852', '0', '#sadness', '#anger', 'negative', '-0.87', 'gibberish', 'jabber', 'jaw-jaw', 'blithering', 'gabble']
senticnet['jabberwock_section'] = ['0', '0', '-0.81', '-0.75', '#fear', '#disgust', 'negative', '-0.78', 'watch_out', 'keep_one_eye_peeled', 'keep_one_eye_skinned', 'keep_one_eye_open', 'aware']
senticnet['jabir_ibn_hayyan'] = ['0', '0.887', '0', '0.973', '#interest', '#admiration', 'positive', '0.93', 'servant', 'art_of_transmuting_metal', 'stone', 'alchemy', 'philosopher']
senticnet['jabot'] = ['0.522', '-0.82', '0', '0.568', '#surprise', '#admiration', 'positive', '0.638', 'adornment', 'goffer', 'gauffer', 'ruffle', 'peplum']
senticnet['jacamar_galbula'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'lovebird', 'parroket', 'paraquet', 'paroquet']
senticnet['jackass'] = ['0', '0', '0.958', '-0.82', '#anger', '#disgust', 'negative', '-0.88', 'illicitly', 'inappropriately', 'political', 'unfitly', 'inappropriate']
senticnet['jacket'] = ['-0.02', '0.256', '0', '0.166', '#interest', '#admiration', 'positive', '0.132', 'coat', 'shirt', 'garment', 'dress', 'vest']
senticnet['jacket_pocket'] = ['0', '-0.02', '0.027', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'shoulder_bag', 'satchel', 'satchel_bag', 'tote_bag', 'carry']
senticnet['jacket_potato'] = ['0.644', '-0.94', '0', '0.729', '#surprise', '#admiration', 'positive', '0.773', 'gem', 'banana_bread', 'date_bread', 'nut_bread', 'quick_bread']
senticnet['jackfruit'] = ['0.859', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.888', 'green_apple', 'apricot', 'lime', 'peach', 'cherry']
senticnet['jacking_off'] = ['0.89', '-0.64', '0', '0.765', '#joy', '#admiration', 'positive', '0.767', 'wank', 'frottage', 'masturbation', 'jerking_off', 'hand_job']
senticnet['jackknife'] = ['0.818', '-0.95', '0', '0.695', '#joy', '#surprise', 'positive', '0.823', 'pocket_knife', 'clasp_knife', 'penknife', 'switchblade', 'switchblade_knife']
senticnet['jackpot'] = ['0.833', '-0.93', '0', '0', '#joy', '#surprise', 'positive', '0.885', 'jackpot', 'payout', 'winning', 'lotto', 'lottery']
senticnet['jackpot_prize'] = ['0.858', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.813', 'box_office', 'movie', 'cctv', 'cinema', 'movie_theater']
senticnet['jackpot_winner'] = ['0.858', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.813', 'box_office', 'movie', 'cctv', 'cinema', 'movie_theater']
senticnet['jackrabbit'] = ['-0.03', '0', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.06', 'lion', 'leopard', 'mountain_lion', 'craven', 'wolf']
senticnet['jacksonian_democracy'] = ['-0.54', '0', '0.979', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'unlikeness', 'dissimilitude', 'difference', 'heterology', 'dissimilarity']
senticnet['jacksonvilleall'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'aphasia', 'anomic_aphasia']
senticnet['jacobean'] = ['0.827', '0.775', '0', '0', '#joy', '#interest', 'positive', '0.801', 'restore', 'power', 'restoration', 'recuperation', 'monarchy']
senticnet['jacobean_pulpit'] = ['0.023', '0.218', '0.01', '0.235', '#interest', '#admiration', 'positive', '0.116', 'confessional', 'altar', 'chapel', 'temple', 'christianity']
senticnet['jacobian_determinant'] = ['0.506', '-0.56', '0', '0.788', '#surprise', '#admiration', 'positive', '0.62', 'backward', 'reverse', 'retrograde', 'reflexive', 'retral']
senticnet['jacobsite'] = ['-0.03', '0', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.06', 'lithium', 'mineral', 'potassium', 'iodine', 'magnesium']
senticnet['jacques_ppin'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'blue_ribbon', 'badge', 'allegory', 'stripe', 'merit_badge']
senticnet['jacques_rudolph'] = ['-0.62', '0', '0.713', '-0.98', '#anger', '#disgust', 'negative', '-0.77', 'muddy_up', 'muck_up', 'foul', 'blemish', 'smear']
senticnet['jactitation'] = ['-0.88', '0', '0.666', '-0.90', '#sadness', '#disgust', 'negative', '-0.81', 'crow', 'crowing', 'bluster', 'bragging', 'vaporing']
senticnet['jacuzzi'] = ['0', '0', '0', '0.964', '#admiration', '#admiration', 'positive', '0.964', 'swimming_pool', 'swim', 'swimsuit', 'lake', 'have_fun']
senticnet['jacuzzi_tub'] = ['0.716', '0.96', '0', '0', '#joy', '#interest', 'positive', '0.838', 'airline_seat', 'small_seat', 'beanbag_chair', 'hide-a-bed_sofa', 'chair']
senticnet['jacuzzi_whirlpool'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
senticnet['jade'] = ['-0.91', '0', '0.594', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'fatigue', 'wear', 'fag', 'tire', 'overfatigue']
senticnet['jaffa'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['jaffa_gate'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['jaffa_road'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['jag'] = ['0', '0', '0.785', '-0.72', '#anger', '#disgust', 'negative', '-0.75', 'sharp_nail', 'sharp', 'sharp_end', 'sharp_point', 'pointy']
senticnet['jagera'] = ['-0.95', '0', '0.624', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['jaggedness'] = ['-0.53', '0', '0.765', '-0.94', '#anger', '#disgust', 'negative', '-0.74', 'unevenness', 'unregularity', 'ruggedness', 'irregularity', 'patchiness']
senticnet['jaguar_dealership'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'sell_car', 'truck_stop', 'vehicle', 'insurance', 'car']
senticnet['jaguarundi'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'fox', 'prairie_fox', 'white_fox']
senticnet['jahaz_mahal'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['jahwist'] = ['0', '0.106', '-0.05', '0.163', '#interest', '#admiration', 'positive', '0.072', 'band', 'kit', 'oblivion', 'rock_band', 'high_school_band']
senticnet['jai_waetford'] = ['0', '0', '0.876', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'untune', 'discompose', 'discomfit', 'disconcert', 'upset']
senticnet['jail'] = ['-0.02', '0', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.06', 'crack_house', 'prison', 'memorial', 'bowl_alley', 'homeless_shelter']
senticnet['jail_break'] = ['-0.93', '0', '0.941', '-0.94', '#anger', '#disgust', 'negative', '-0.93', 'break_rule', 'escape_prison', 'illegal', 'escape_justice', 'prison_breaker']
senticnet['jail_cell'] = ['0.816', '-0.91', '0', '0.944', '#surprise', '#admiration', 'positive', '0.891', 'jail', 'jailhouse', 'gaol', 'poky', 'clink']
senticnet['jail_time'] = ['-0.73', '0', '-0.77', '-0.77', '#fear', '#disgust', 'negative', '-0.76', 'go_to_prison', 'go_jail', 'injustice', 'incarceration', 'send_to_jail']
senticnet['jailbait'] = ['0.081', '0.095', '0', '0', '#joy', '#interest', 'positive', '0.088', 'build_tower_out_toy_block', 'a_lot_of_sex', 'play_hockey', 'play_baseball', 'play_football']
senticnet['jailbird'] = ['0', '0', '-0.94', '-0.97', '#fear', '#disgust', 'negative', '-0.95', 'captive', 'prisoner', 'imprisoned', 'convict', 'inmate']
senticnet['jailer'] = ['-0.78', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.87', 'jailbird', 'captive', 'imprisoned', 'convict', 'inmate']
senticnet['jailhouse'] = ['0.92', '-0.98', '0', '0.776', '#joy', '#surprise', 'positive', '0.895', 'lockup', 'holding_cell', 'workhouse', 'bastille', 'hoosegow']
senticnet['jailor'] = ['-0.78', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.87', 'jailbird', 'captive', 'imprisoned', 'convict', 'inmate']
senticnet['jain_monk'] = ['0.065', '0.17', '-0.05', '0.143', '#interest', '#admiration', 'positive', '0.081', 'supervisor', 'native_american', 'ambassador', 'master', 'general']
senticnet['jake'] = ['-0.74', '0', '0.877', '-0.97', '#anger', '#disgust', 'negative', '-0.86', 'miss', 'girl', 'chachka', 'young_lady', 'young_woman']
senticnet['jake_ellenberger'] = ['0.944', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.967', 'meeting', 'meet', 'match', 'encounter', 'round']
senticnet['jake_shimabukuro'] = ['0.98', '-0.74', '0', '0.984', '#joy', '#admiration', 'positive', '0.903', 'prosperous', 'flourishing', 'successful', 'prospering', 'thriving']
senticnet['jakks_pacific'] = ['0', '-0.10', '0', '0', '#surprise', '#surprise', 'positive', '0.105', 'bath_toy', 'stuff_animal', 'toy_soldier', 'action_figure', 'yo-yo']
senticnet['jakob_bhme'] = ['0.065', '0.17', '-0.05', '0.143', '#interest', '#admiration', 'positive', '0.081', 'supervisor', 'native_american', 'ambassador', 'master', 'general']
senticnet['jakob_disease'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'agent', 'satisfier', 'quickener', 'invigorator']
senticnet['jakob_hammermeier'] = ['-0.57', '0', '0.571', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['jalan_tun_tan_cheng'] = ['0', '0.069', '0', '0.083', '#interest', '#admiration', 'positive', '0.076', 'latch', 'restrict_access', 'knob', 'door_knob', 'gate']
senticnet['jalopy'] = ['-0.99', '0', '0.597', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'dysphemism', 'car', 'auto', 'automobile', 'machine']
senticnet['jam'] = ['-0.61', '0', '0.513', '-0.97', '#sadness', '#disgust', 'negative', '-0.7', 'conserve', 'preserve', 'strawberry_jam', 'strawberry_preserves', 'dessert']
senticnet['jama_coaque_ecological'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['jama_masjid'] = ['0', '0.224', '0', '0.197', '#interest', '#admiration', 'positive', '0.211', 'church', 'temple', 'cathedral', 'chapel', 'confession']
senticnet['jambi_malay'] = ['-0.56', '0', '0.617', '-0.80', '#anger', '#disgust', 'negative', '-0.66', 'prolixity', 'prolixness', 'wordiness', 'repetitiveness', 'repetitiousness']
senticnet['jame_mosque'] = ['0', '0.224', '0', '0.197', '#interest', '#admiration', 'positive', '0.211', 'church', 'temple', 'cathedral', 'chapel', 'confession']
senticnet['jameh_mosque'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['jamesian'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'mediacy', 'mediateness', 'characteristic', 'indirectness']
senticnet['jami_mosque'] = ['0', '0.224', '0', '0.197', '#interest', '#admiration', 'positive', '0.211', 'church', 'temple', 'cathedral', 'chapel', 'confession']
senticnet['jamia_masjid'] = ['0', '0.224', '0', '0.197', '#interest', '#admiration', 'positive', '0.211', 'church', 'temple', 'cathedral', 'chapel', 'confession']
senticnet['jamia_mosque'] = ['0', '0.224', '0', '0.197', '#interest', '#admiration', 'positive', '0.211', 'church', 'temple', 'cathedral', 'chapel', 'confession']
senticnet['jammed_shut'] = ['0.829', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.908', 'cut_food', 'drink_beer', 'serve_drink', 'drink_much', 'drink_beverage']
senticnet['jammy'] = ['-0.78', '0', '0.542', '-0.94', '#sadness', '#disgust', 'negative', '-0.75', 'taste', 'taste_sensation', 'gustatory_sensation', 'taste_perception', 'gustatory_perception']
senticnet['jamun'] = ['0.859', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.888', 'green_apple', 'apricot', 'lime', 'peach', 'cherry']
senticnet['jamyang'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['janelle_monae'] = ['0.916', '-0.93', '0', '0.674', '#joy', '#surprise', 'positive', '0.841', 'smash', 'bang', 'smasher', 'strike', 'hit']
senticnet['janes_strategic'] = ['0.795', '-0.97', '0', '0.679', '#joy', '#surprise', 'positive', '0.817', 'informatory', 'consultative', 'consultatory', 'consultive', 'revealing']
senticnet['janette_beverley'] = ['0.909', '0.986', '0', '0', '#joy', '#interest', 'positive', '0.948', 'separation', 'divide', 'division', 'slash', 'bagle']
senticnet['jangle'] = ['-0.89', '0', '0.722', '-0.94', '#sadness', '#disgust', 'negative', '-0.85', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['jangly'] = ['0.65', '-0.61', '0', '0.711', '#joy', '#admiration', 'positive', '0.658', 'bouncing', 'scintillating', 'spirited', 'lively', 'zippy']
senticnet['janitor'] = ['0', '0', '-0.09', '0', '#fear', '#fear', 'negative', '-0.09', 'specialist', 'truck_driver', 'egalitarian', 'public_servant', 'pioneer']
senticnet['janitor_closet'] = ['0', '0.072', '0', '0', '#interest', '#interest', 'positive', '0.072', 'metal_box', 'cash_drawer', 'keyhole', 'clutch_bag', 'safe']
senticnet['janitor_room'] = ['-0.05', '-0.01', '0.039', '0', '#sadness', '#anger', 'negative', '-0.02', 'pantry', 'buy_gum', 'shop_grocery', 'buy_clothes', 'find_pantry']
senticnet['jann_arden'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'cur', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['janowicz'] = ['0', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.91', 'umpire', 'referee', 'legal_proceed', 'clang', 'clamor']
senticnet['janus-faced'] = ['0', '0', '-0.75', '-0.93', '#fear', '#disgust', 'negative', '-0.84', 'duplicitous', 'deceitful', 'double-dealing', 'ambidextrous', 'two-handed']
senticnet['janusz'] = ['0', '0', '0', '0.1', '#admiration', '#admiration', 'positive', '0.1', 'find_earth', 'wax', 'plutonium', 'shine', 'melt']
senticnet['japan'] = ['0', '0.766', '0', '0', '#interest', '#interest', 'positive', '0.766', 'abroad', 'foreign', 'study_abroad', 'elsewhere', 'live_abroad']
senticnet['japan_atomic_energy'] = ['-0.90', '0', '0.748', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'alternative_energy', 'activation_energy', 'energy_of_activation', 'energy', 'nuclear_energy']
senticnet['japanese_aircraft_carrier'] = ['0.084', '-0.05', '0.013', '0', '#joy', '#surprise', 'positive', '0.043', 'carnival_ride', 'carny', 'fortune_teller', 'horseback', 'ferris_wheel']
senticnet['japanese_animation'] = ['0.75', '-0.71', '0', '0', '#joy', '#surprise', 'positive', '0.73', 'manga', 'cartoon', 'anime', 'movie', 'film']
senticnet['japanese_anime'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['japanese_beetle'] = ['0', '-0.09', '0.119', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'cucumber_beetle', 'mole_cricket', 'gypsy_moth', 'earwig', 'mosquito']
senticnet['japanese_bobtail'] = ['0', '0', '0.022', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'kind_animal', 'wild_animal', 'predator', 'hound', 'fang']
senticnet['japanese_counterattack'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['japanese_cuisine'] = ['0.882', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.822', 'ferment_grape_juice', 'chef_special', 'bottle_wine', 'fine_dining', 'decanter']
senticnet['japanese_encephalitis_je'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'leptomeningitis', 'cerebrospinal_meningitis', 'meningitis', 'brain_fever', 'cerebrospinal_fever']
senticnet['japanese_fluently'] = ['-0.93', '0', '-0.99', '-0.92', '#sadness', '#fear', 'negative', '-0.95', 'barrier', 'shut', 'shut_down', 'close_down', 'obstacle']
senticnet['japanese_food'] = ['0.736', '0.724', '0', '0', '#joy', '#interest', 'positive', '0.73', 'form_of_food', 'healthy', 'bland', 'raw_fish', 'food']
senticnet['japanese_giant_hornet'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'hornet', 'vespid', 'vespid_wasp']
senticnet['japanese_kanji'] = ['-0.86', '0', '0.619', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'character', 'logogram', 'logograph', 'graph', 'grapheme']
senticnet['japanese_larch'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'locust_tree']
senticnet['japanese_manga'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['japanese_martial_art'] = ['-0.06', '-0.00', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.05', 'madman', 'trouble', 'agony', 'cause_problem', 'river_stream']
senticnet['japanese_occupation'] = ['0', '0.112', '-0.04', '0.099', '#interest', '#admiration', 'positive', '0.054', 'profession', 'career', 'vocation', 'important_job', 'laborer']
senticnet['japanese_person'] = ['0', '0.068', '-0.06', '0.081', '#interest', '#admiration', 'positive', '0.028', 'pyramid', 'buckle', 'mexican_restaurant', 'anemone', 'herringbone']
senticnet['japanese_professional_wrestler'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['japanese_star_anise'] = ['0', '0', '0.709', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'debasement', 'dirtiness', 'pollution', 'taint', 'impureness']
senticnet['japanese_sumo_wrestler'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['japanese_sushi'] = ['0.9', '0.963', '0', '0', '#joy', '#interest', 'positive', '0.931', 'form_of_food', 'japanese_food', 'uncooked', 'healthy', 'food']
senticnet['japanese_tosa'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'animal_hair', 'dog_hair', 'animal_fur', 'dog_cat', 'bite_dog']
senticnet['japanese_wrestler'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['jape'] = ['0.771', '-0.92', '0', '0.696', '#joy', '#surprise', 'positive', '0.797', 'humor', 'wow', 'funny', 'dirty_joke', 'dirty_story']
senticnet['japery'] = ['0.742', '-0.61', '0', '0.924', '#joy', '#admiration', 'positive', '0.76', 'folly', 'lunacy', 'craziness', 'indulgence', 'shtik']
senticnet['jar'] = ['0.035', '-0.01', '0.009', '0.063', '#joy', '#admiration', 'positive', '0.026', 'container', 'carton', 'contain', 'hold_cupcake', 'store_dish']
senticnet['jar_against'] = ['-0.54', '0', '0.746', '-0.54', '#sadness', '#anger', 'negative', '-0.61', 'run_into', 'strike', 'hit', 'impinge_on', 'collide_with']
senticnet['jar_lid'] = ['0.035', '-0.01', '0.009', '0.063', '#joy', '#admiration', 'positive', '0.026', 'container', 'carton', 'contain', 'hold_cupcake', 'store_dish']
senticnet['jardwadjali'] = ['-0.95', '0', '0.624', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['jargon'] = ['-0.04', '-0.02', '0.041', '-0.03', '#sadness', '#anger', 'negative', '-0.02', 'slang', 'lingo', 'microtext', 'chatspeak', 'unusual_language']
senticnet['jargon_free'] = ['0.515', '-0.80', '0', '0.791', '#surprise', '#admiration', 'positive', '0.703', 'accessible', 'fathomable', 'approachable', 'perceivable', 'intelligible']
senticnet['jargon_laden'] = ['-0.04', '-0.02', '0.041', '-0.03', '#sadness', '#anger', 'negative', '-0.02', 'slang', 'lingo', 'microtext', 'chatspeak', 'unusual_language']
senticnet['jargonistic'] = ['-0.04', '-0.02', '0.041', '-0.03', '#sadness', '#anger', 'negative', '-0.02', 'slang', 'lingo', 'microtext', 'chatspeak', 'unusual_language']
senticnet['jarosaw'] = ['0', '0', '0', '0.1', '#admiration', '#admiration', 'positive', '0.1', 'find_earth', 'wax', 'plutonium', 'shine', 'melt']
senticnet['jarring'] = ['-0.93', '0', '0.576', '-0.54', '#sadness', '#anger', 'negative', '-0.68', 'grating', 'rasping', 'raspy', 'cacophonic', 'gruff']
senticnet['jasmine'] = ['0.077', '0', '0', '0', '#joy', '#joy', 'positive', '0.077', 'dip', 'garlic', 'oat', 'savory', 'rye']
senticnet['jasper_conran'] = ['0', '-0.03', '0.022', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'shoulder_bag', 'satchel', 'water_bottle', 'tote_bag', 'carry']
senticnet['jasper_maskelyne'] = ['0.9', '-0.88', '0', '0.687', '#joy', '#surprise', 'positive', '0.823', 'conjurer', 'escape_expert', 'mind_reader', 'telepathist', 'performer']
senticnet['jataka'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['jatropha_oil'] = ['0.855', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.86', 'ticket_counter', 'newspaper_stand', 'luggage_carrier', 'aircraft_roadway', 'timetable']
senticnet['jaundice'] = ['-0.56', '0', '0.906', '-0.80', '#anger', '#disgust', 'negative', '-0.76', 'jaundice_of_newborn', 'disease', 'icterus_neonatorum', 'kernicterus', 'symptom']
senticnet['jaundice_of_newborn'] = ['-0.64', '0', '0.903', '-0.63', '#sadness', '#anger', 'negative', '-0.72', 'icterus', 'disease', 'icterus_neonatorum', 'kernicterus', 'symptom']
senticnet['jaundiced'] = ['-0.87', '0', '0', '-0.95', '#sadness', '#disgust', 'negative', '-0.91', 'homophobic', 'racist', 'discriminatory', 'antiblack', 'invidious']
senticnet['jaune'] = ['-0.58', '0', '0.661', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'explosive', 'cordite', 'dynamite', 'plastic_explosive', 'explosive_compound']
senticnet['jaunt'] = ['0', '0', '0.828', '-0.88', '#anger', '#disgust', 'negative', '-0.85', 'outing', 'junket', 'pleasure_trip', 'excursion', 'expedition']
senticnet['jauntiness'] = ['0.973', '-0.50', '0', '0.612', '#joy', '#admiration', 'positive', '0.698', 'chic', 'chicness', 'chichi', 'modishness', 'stylishness']
senticnet['jaunty'] = ['0', '0', '-0.97', '-0.73', '#fear', '#disgust', 'negative', '-0.85', 'dashing', 'raffish', 'rakish', 'natty', 'dapper']
senticnet['java'] = ['0.041', '0.098', '0', '0', '#joy', '#interest', 'positive', '0.07', 'utopia', 'emporium', 'memorial', 'nowhere', 'coffee']
senticnet['java_byte_code'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'carbon_fiber']
senticnet['java_code'] = ['-0.95', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.84', 'hold_pencil', 'food_scrap', 'paper_product', 'waste_paper', 'cover']
senticnet['java_ee_web'] = ['0', '0', '0', '0.075', '#admiration', '#admiration', 'positive', '0.075', 'job_application', 'resume', 'respond', 'questionnaire', 'help_understand']
senticnet['java_interpreter'] = ['0', '0.202', '-0.06', '0.291', '#interest', '#admiration', 'positive', '0.142', 'vocabulary', 'translate', 'dictionary', 'translation', 'thesaurus']
senticnet['java_serialization'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'carbon_fiber']
senticnet['java_sparrow'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet']
senticnet['java_tm'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'carbon_fiber']
senticnet['java_toolkit'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'carbon_fiber']
senticnet['javacard'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'carbon_fiber']
senticnet['javaone'] = ['-0.79', '0', '0.948', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['javaone_conference'] = ['-0.79', '0', '0.948', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['javascript'] = ['-0.89', '0', '0.672', '-0.69', '#sadness', '#disgust', 'negative', '-0.75', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['javascript_api'] = ['-0.89', '0', '0.672', '-0.69', '#sadness', '#disgust', 'negative', '-0.75', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['javascript_console'] = ['-0.89', '0', '0.672', '-0.69', '#sadness', '#disgust', 'negative', '-0.75', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['javascript_debugger'] = ['-0.89', '0', '0.672', '-0.69', '#sadness', '#disgust', 'negative', '-0.75', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['javascript_engine'] = ['-0.89', '0', '0.672', '-0.69', '#sadness', '#disgust', 'negative', '-0.75', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['javelin_thrower'] = ['-0.04', '-0.03', '0', '-0.16', '#sadness', '#disgust', 'negative', '-0.05', 'crazy', 'damage_body', 'crazy_person', 'threat', 'max']
senticnet['javelina'] = ['0', '-0.97', '0', '0', '#surprise', '#surprise', 'positive', '0.977', 'cougar', 'skunk', 'lion', 'tiger', 'lynx']
senticnet['javor_ivanjica'] = ['0.954', '-0.90', '0', '0.867', '#joy', '#surprise', 'positive', '0.91', 'friend', 'sports_fan', 'rooter', 'fan', 'nut']
senticnet['javy_guerra'] = ['0.663', '-0.68', '0', '0.892', '#surprise', '#admiration', 'positive', '0.747', 'saphead', 'muggins', 'tomfool', 'fool', 'sap']
senticnet['jaw'] = ['0', '-0.02', '0.033', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'tooth', 'teeth', 'dimple', 'mouth', 'cliche']
senticnet['jaw-jaw'] = ['-0.89', '0', '0.854', '0', '#sadness', '#anger', 'negative', '-0.87', 'gibberish', 'jabberer', 'jabber', 'blithering', 'gabble']
senticnet['jaw_bone'] = ['0', '-0.02', '0.033', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'tooth', 'teeth', 'dimple', 'mouth', 'cliche']
senticnet['jaw_drop'] = ['-0.90', '0.138', '-0.63', '0.071', '#sadness', '#fear', 'negative', '-0.33', 'woeful', 'execrable', 'deplorable', 'woebegone', 'plaintiveness']
senticnet['jaw_droppingly'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['jaw_harp'] = ['0.821', '-0.53', '0', '0.785', '#joy', '#admiration', 'positive', '0.712', 'fife', 'nose_flute', 'piccolo', 'woodwind', 'flute']
senticnet['jaw_joint'] = ['0', '-0.02', '0.033', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'tooth', 'teeth', 'dimple', 'mouth', 'cliche']
senticnet['jaw_surgery'] = ['0.745', '-0.55', '0', '0.597', '#joy', '#admiration', 'positive', '0.633', 'lift', 'phalloplasty', 'abdominoplasty', 'tummy_tuck', 'autoplasty']
senticnet['jaw_thrust'] = ['0', '0.142', '-0.09', '0.183', '#interest', '#admiration', 'positive', '0.078', 'arise', 'rise', 'raise_up', 'go_up', 'go_upstairs']
senticnet['jawbone'] = ['0.651', '-0.92', '0', '0.657', '#surprise', '#admiration', 'positive', '0.746', 'schmooze', 'schmoose', 'shmooze', 'shmoose', 'chew_fat']
senticnet['jawless_fish'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'hag', 'osteostracan', 'cephalaspid', 'ostracoderm', 'heterostracan']
senticnet['jawless_vertebrate'] = ['-0.78', '0', '0.745', '-0.51', '#sadness', '#anger', 'negative', '-0.68', 'hag', 'osteostracan', 'cephalaspid', 'ostracoderm', 'heterostracan']
senticnet['jawline'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'lymph', 'exoskeleton', 'breast_pocket', 'toward', 'squint']
senticnet['jayhawk'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'unofficial', 'informal', 'loose', 'drumhead']
senticnet['jayson_brunsdon'] = ['0', '-0.03', '0.022', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'shoulder_bag', 'satchel', 'water_bottle', 'tote_bag', 'carry']
senticnet['jazz'] = ['0.901', '0', '0', '0', '#joy', '#joy', 'positive', '0.901', 'music', 'vapor', 'blues', 'megrim', 'demoralization']
senticnet['jazz_around'] = ['0.697', '-0.83', '0', '0.911', '#surprise', '#admiration', 'positive', '0.814', 'range', 'ramble', 'vagabond', 'tramp', 'roam']
senticnet['jazz_band'] = ['0.901', '0', '0', '0', '#joy', '#joy', 'positive', '0.901', 'music', 'vapor', 'blues', 'megrim', 'demoralization']
senticnet['jazz_bassist'] = ['0.901', '0', '0', '0', '#joy', '#joy', 'positive', '0.901', 'music', 'vapor', 'blues', 'megrim', 'demoralization']
senticnet['jazz_caf'] = ['0.98', '-0.74', '0', '0.984', '#joy', '#admiration', 'positive', '0.903', 'prosperous', 'flourishing', 'successful', 'prospering', 'thriving']
senticnet['jazz_club'] = ['-0.04', '-0.03', '-0.04', '-0.04', '#sadness', '#fear', 'negative', '-0.02', 'country_band', 'bluegrass_band', 'music_class', 'string_quartet', 'music_store']
senticnet['jazz_composer'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'band_play', 'songwriter', 'rock_band']
senticnet['jazz_guitar'] = ['0.901', '0', '0', '0', '#joy', '#joy', 'positive', '0.901', 'music', 'vapor', 'blues', 'megrim', 'demoralization']
senticnet['jazz_guitarist'] = ['0.901', '0', '0', '0', '#joy', '#joy', 'positive', '0.901', 'music', 'vapor', 'blues', 'megrim', 'demoralization']
senticnet['jazz_improvisation'] = ['0.536', '-0.56', '0', '0.567', '#surprise', '#admiration', 'positive', '0.557', 'recreation', 'diversion', 'move', 'motility', 'movement']
senticnet['jazz_legend'] = ['0.901', '0', '0', '0', '#joy', '#joy', 'positive', '0.901', 'music', 'vapor', 'blues', 'megrim', 'demoralization']
senticnet['jazz_musician'] = ['0.901', '0', '0', '0', '#joy', '#joy', 'positive', '0.901', 'music', 'vapor', 'blues', 'megrim', 'demoralization']
senticnet['jazz_orchestra'] = ['0.901', '0', '0', '0', '#joy', '#joy', 'positive', '0.901', 'music', 'vapor', 'blues', 'megrim', 'demoralization']
senticnet['jazz_pianist'] = ['0.901', '0', '0', '0', '#joy', '#joy', 'positive', '0.901', 'music', 'vapor', 'blues', 'megrim', 'demoralization']
senticnet['jazz_piano'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'band_play', 'classical', 'composer', 'songwriter', 'rock_band']
senticnet['jazz_quartet'] = ['0.901', '0', '0', '0', '#joy', '#joy', 'positive', '0.901', 'music', 'vapor', 'blues', 'megrim', 'demoralization']
senticnet['jazz_quintet'] = ['0.901', '0', '0', '0', '#joy', '#joy', 'positive', '0.901', 'music', 'vapor', 'blues', 'megrim', 'demoralization']
senticnet['jazz_record_label'] = ['0', '0.115', '0.036', '0.207', '#interest', '#admiration', 'positive', '0.095', 'total', 'amount', 'merit', 'award', 'excellence']
senticnet['jazz_scene'] = ['0.901', '0', '0', '0', '#joy', '#joy', 'positive', '0.901', 'music', 'vapor', 'blues', 'megrim', 'demoralization']
senticnet['jazz_singer'] = ['0.882', '0.901', '0', '0', '#joy', '#interest', 'positive', '0.891', 'songwriter', 'vip', 'rock_group', 'guitar_player', 'violinist']
senticnet['jazz_trio'] = ['0.901', '0', '0', '0', '#joy', '#joy', 'positive', '0.901', 'music', 'vapor', 'blues', 'megrim', 'demoralization']
senticnet['jazz_up'] = ['0.513', '-0.64', '0', '0.776', '#surprise', '#admiration', 'positive', '0.645', 'enliven', 'liven', 'liven_up', 'invigorate', 'animate']
senticnet['jazzed_up'] = ['0.827', '-0.55', '0', '0.679', '#joy', '#admiration', 'positive', '0.687', 'spirit', 'spirit_up', 'inspirit', 'energize', 'jazz_up']
senticnet['jazzman'] = ['0.901', '0', '0', '0', '#joy', '#joy', 'positive', '0.901', 'music', 'vapor', 'blues', 'megrim', 'demoralization']
senticnet['jazzy'] = ['0', '0', '-0.88', '-0.97', '#fear', '#disgust', 'negative', '-0.92', 'showy', 'flashy', 'splashy', 'flamboyant', 'gaudy']
senticnet['jazzy_piano'] = ['0.584', '-0.62', '0', '0.815', '#surprise', '#admiration', 'positive', '0.675', 'squiggle', 'hand', 'cacography', 'handwriting', 'script']
senticnet['jc_penney'] = ['0.912', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.917', 'carpet_store', 'lumberyard', 'furniture_store', 'front_porch', 'shoe_shop']
senticnet['jd_degree'] = ['-0.75', '0', '0.589', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'lowness', 'grind', 'immoderation', 'immoderateness', 'amplitude_level']
senticnet['je_pense'] = ['0.911', '-0.94', '0', '0.953', '#surprise', '#admiration', 'positive', '0.935', 'common_sense', 'good_sense', 'horse_sense', 'mother_wit', 'gumption']
senticnet['jealous'] = ['0', '0', '0.911', '-0.73', '#anger', '#disgust', 'negative', '-0.82', 'green-eyed', 'green-eyed_monster', 'overjealous', 'avaricious', 'grabby']
senticnet['jealously'] = ['0.089', '0.09', '0.81', '-0.68', '#anger', '#disgust', 'negative', '-0.32', 'covetous', 'envious', 'enviously', 'covetously', 'green-eyed']
senticnet['jealousy'] = ['0', '0', '0.794', '-0.99', '#anger', '#disgust', 'negative', '-0.89', 'envy', 'resentment', 'grudge', 'greedy', 'jealous']
senticnet['jeans'] = ['0', '-0.05', '0.088', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'clothes', 'pair_of_pants', 'shirt', 'slack', 'jacket']
senticnet['jeantel'] = ['0.716', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.733', 'call', 'use_phone', 'orate', 'use_telephone', 'speech']
senticnet['jedi'] = ['-0.96', '0', '0.108', '-0.90', '#sadness', '#disgust', 'negative', '-0.66', 'evil', 'darkness', 'dark', 'sun_down', 'shadow']
senticnet['jeep'] = ['0.75', '-0.59', '0', '0.633', '#joy', '#admiration', 'positive', '0.66', 'car', 'auto', 'automobile', 'machine', 'motorcar']
senticnet['jeep_cherokee'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'cable_car', 'baggage_compartment', 'subway', 'amusement_park', 'terminal']
senticnet['jeep_commander'] = ['0.909', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.952', 'displace', 'swing', 'snowshoe', 'shuttle', 'bang']
senticnet['jeep_wrangler'] = ['-0.05', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'plane_land', 'floss', 'floss_tooth', 'ornament', 'wear_dress']
senticnet['jeer'] = ['-0.73', '0', '0.865', '-0.87', '#anger', '#disgust', 'negative', '-0.82', 'bad_surprise', 'shout', 'abrupt', 'rude', 'mocking']
senticnet['jeez'] = ['0', '0', '-0.13', '0', '#fear', '#fear', 'negative', '-0.13', 'womb', 'anus', 'sister_house', 'shit', 'land_moon']
senticnet['jehovah'] = ['0.673', '-0.66', '0', '0.566', '#joy', '#surprise', 'positive', '0.634', 'wickedness', 'impiety', 'impiousness', 'unjustness', 'dishonor']
senticnet['jejunal'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'anus', 'scrotum', 'shoulder', 'liver', 'waist']
senticnet['jejune'] = ['0', '0', '0.975', '-0.97', '#anger', '#disgust', 'negative', '-0.97', 'arid', 'empty', 'devoid', 'van', 'desolate']
senticnet['jejuneness'] = ['-0.93', '0', '0.906', '-0.53', '#sadness', '#anger', 'negative', '-0.79', 'juvenility', 'state', 'greenness', 'callowness', 'immatureness']
senticnet['jejunoileal_atresia'] = ['-0.75', '0', '0.715', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'abdominal_pregnancy', 'ectopic_gestation', 'tubal_pregnancy', 'pregnancy', 'gestation']
senticnet['jejunum'] = ['0.04', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.055', 'kidney', 'pancreas', 'liver', 'bone', 'neck']
senticnet['jelle_klaasen'] = ['0.823', '0.982', '0', '0', '#joy', '#interest', 'positive', '0.902', 'spherical', 'three_dimensions', 'round_orb', 'coil', 'fill_with_sand']
senticnet['jello'] = ['0', '0', '-0.1', '-0.02', '#fear', '#disgust', 'negative', '-0.06', 'icebox', 'bottle', 'toothbrush_bristle', 'tan_lotion', 'refrigerator']
senticnet['jelly'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'dainty', 'delicacy', 'treat', 'gelatin_dessert', 'aspic']
senticnet['jelly_bean'] = ['0.027', '-0.09', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'cookie', 'pudding', 'custard', 'candy', 'muffin']
senticnet['jelly_belly'] = ['0.027', '-0.09', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'cookie', 'pudding', 'custard', 'candy', 'muffin']
senticnet['jelly_fish'] = ['0', '0', '0.021', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'driftwood', 'tidal_wave', 'seashell', 'titanic', 'dead_fish']
senticnet['jelly_sandwich'] = ['0.056', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.053', 'leftover', 'dusty', 'mayonnaise', 'leave_over', 'savory']
senticnet['jellybean'] = ['0.027', '-0.09', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'cookie', 'pudding', 'custard', 'candy', 'muffin']
senticnet['jellyfish'] = ['-0.79', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.8', 'frog', 'tadpole', 'tadpole', 'duckling', 'larva']
senticnet['jellylike'] = ['0.025', '-0.00', '0', '0.125', '#joy', '#admiration', 'positive', '0.053', 'goo', 'adhesive', 'flour_water', 'insert', 'thick_liquid']
senticnet['jemmy'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'impaired', 'dickey', 'lessened', 'diminished']
senticnet['jenga'] = ['0.801', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.796', 'verbosity', 'sport_play', 'excite_game', 'contest', 'fun_game']
senticnet['jenn_grant'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'cur', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['jenna'] = ['-0.74', '0', '0.877', '-0.97', '#anger', '#disgust', 'negative', '-0.86', 'miss', 'girl', 'chachka', 'young_lady', 'young_woman']
senticnet['jenna_ushkowitz'] = ['0.761', '0.566', '-0.21', '0.293', '#joy', '#interest', 'positive', '0.352', 'gratify', 'jubilation', 'gloat', 'gloating', 'gloatingly']
senticnet['jenoff'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'gouger', 'swindler', 'confidence_man', 'conman', 'fraud']
senticnet['jenolan'] = ['-0.95', '0', '0.624', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['jeopardise'] = ['-0.93', '0', '-0.91', '-0.84', '#sadness', '#fear', 'negative', '-0.89', 'destroy_live', 'take_life', 'death_destruction', 'get_rid_of_enemy', 'intimidate']
senticnet['jeopardize'] = ['-0.51', '0', '0.92', '-0.66', '#anger', '#disgust', 'negative', '-0.70', 'hazard', 'stake', 'lay_on_line', 'essay', 'assay']
senticnet['jeopardy'] = ['-0.99', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.97', 'question', 'answer', 'question_answering', 'inquiry', 'outcome']
senticnet['jephthah'] = ['0.849', '-0.89', '0', '0.924', '#surprise', '#admiration', 'positive', '0.89', 'nonintellectual', 'lowbrow', 'lowbrowed', 'mindless', 'uncultivated']
senticnet['jer'] = ['0.055', '0.189', '-0.09', '0.113', '#interest', '#admiration', 'positive', '0.065', 'tame', 'crack', 'sense_fear', 'friend_person', 'good_manner']
senticnet['jere_osgood'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['jeremiad'] = ['-0.79', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.78', 'jeremiad', 'polemic', 'diatribe', 'tirade', 'chest-thumping']
senticnet['jerk'] = ['-0.78', '0', '0.726', '-0.93', '#sadness', '#disgust', 'negative', '-0.81', 'smile_child', 'child', 'pass_gene', 'young_child', 'dolt']
senticnet['jerk_off'] = ['-0.05', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.05', 'masturbate', 'jerk', 'wank', 'porn', 'watch_porn']
senticnet['jerkily'] = ['0.035', '0.115', '0', '0.209', '#interest', '#admiration', 'positive', '0.12', 'dance_move', 'anagram', 'pace', 'tread', 'foot_action']
senticnet['jerking_off'] = ['0.89', '-0.64', '0', '0.765', '#joy', '#admiration', 'positive', '0.767', 'wank', 'frottage', 'jacking_off', 'masturbation', 'hand_job']
senticnet['jerky'] = ['-0.56', '0', '0.849', '-0.61', '#anger', '#disgust', 'negative', '-0.67', 'convulsive', 'arrhythmic', 'unsteady', 'spasmodic', 'spastic']
senticnet['jerry-built'] = ['0', '0', '0.741', '-0.92', '#anger', '#disgust', 'negative', '-0.83', 'shoddy', 'tawdry', 'misleading', 'deceptive', 'cheapjack']
senticnet['jerusalem'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['jerusalem_artichoke'] = ['0.035', '0.104', '0', '0.12', '#interest', '#admiration', 'positive', '0.086', 'fruit_vegetable', 'vegetable', 'vegetable_fruit', 'fresh_vegetable', 'vegetarian_food']
senticnet['jerusalemite'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['jerwin_agudong'] = ['0.069', '0.07', '0', '0.116', '#interest', '#admiration', 'positive', '0.085', 'traveller', 'tourist', 'travel', 'explorer', 'adventurer']
senticnet['jerzy'] = ['0', '0', '0', '0.1', '#admiration', '#admiration', 'positive', '0.1', 'find_earth', 'wax', 'plutonium', 'shine', 'melt']
senticnet['jerzy_popieuszko'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'acolyte', 'reverend', 'thurifer', 'clergyman', 'man_of_cloth']
senticnet['jess_franco'] = ['-0.79', '0', '0.885', '-0.76', '#sadness', '#anger', 'negative', '-0.81', 'infernal', 'mortal', 'dead_soul', 'dead_soule', 'living_dead']
senticnet['jest'] = ['0.891', '-0.6', '0', '0.854', '#joy', '#admiration', 'positive', '0.782', 'humor', 'wow', 'funny', 'dirty_joke', 'dirty_story']
senticnet['jester'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'witty_remark', 'cross_talking', 'swordplay', 'wordplay', 'banter']
senticnet['jestingly'] = ['0.666', '-0.87', '0', '0.736', '#surprise', '#admiration', 'positive', '0.759', 'spread', 'diffuse', 'circulate', 'circularize', 'distribute']
senticnet['jesu'] = ['-0.93', '0', '0.861', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'knotty', 'contorted', 'writhed', 'crooked', 'anfractuous']
senticnet['jesuit_priest'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'acolyte', 'reverend', 'thurifer', 'clergyman', 'man_of_cloth']
senticnet['jesuitism'] = ['0.95', '-0.77', '0', '0.578', '#joy', '#surprise', 'positive', '0.767', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['jet'] = ['0.877', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.814', 'airplane', 'aeroplane', 'plane', 'aircraft', 'pilot']
senticnet['jet-lagged'] = ['0.768', '-0.89', '0', '0', '#joy', '#surprise', 'positive', '0.833', 'groggy', 'wide-awake', 'clear-headed', 'bleary-eyed', 'hung-over']
senticnet['jet_black'] = ['-0.60', '0', '0.895', '-0.54', '#sadness', '#anger', 'negative', '-0.68', 'coal_black', 'black', 'pitch_black', 'soot_black', 'achromatic_color']
senticnet['jet_fighter'] = ['-0.94', '0', '0.866', '-0.91', '#sadness', '#disgust', 'negative', '-0.90', 'movement', 'apparent_motion', 'motion', 'apparent_movement', 'optical_illusion']
senticnet['jet_fuel'] = ['0.855', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.86', 'ticket_counter', 'newspaper_stand', 'luggage_carrier', 'aircraft_roadway', 'timetable']
senticnet['jet_injector'] = ['-0.02', '0', '-0.12', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'x-ray_machine', 'medical_record', 'latex_glove', 'medicine', 'doctor_examination_room']
senticnet['jet_lag'] = ['-0.99', '0', '0.986', '-0.94', '#sadness', '#anger', 'negative', '-0.97', 'travel', 'bad_rest', 'feel_sleepy', 'tired', 'long_trip']
senticnet['jet_nightclub'] = ['-0.94', '0', '0.866', '-0.91', '#sadness', '#disgust', 'negative', '-0.90', 'movement', 'apparent_motion', 'motion', 'apparent_movement', 'optical_illusion']
senticnet['jet_pack'] = ['-0.04', '0.092', '0', '0.062', '#interest', '#admiration', 'positive', '0.036', 'hangar', 'aircraft_carrier', 'hot_air_balloon', 'war_museum', 'spaceship']
senticnet['jet_propellant'] = ['0.855', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.86', 'ticket_counter', 'newspaper_stand', 'luggage_carrier', 'aircraft_roadway', 'timetable']
senticnet['jet_ski'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'boat', 'yacht', 'big_boat', 'large_boat']
senticnet['jetblue_flight'] = ['-0.08', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.08', 'turn_key', 'plan_route', 'open_car_door', 'book_flight', 'turn_car_off']
senticnet['jetblue_plane'] = ['-0.08', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.08', 'turn_key', 'plan_route', 'open_car_door', 'book_flight', 'turn_car_off']
senticnet['jeter'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'dicky', 'impaired', 'lessened', 'diminished']
senticnet['jetfile'] = ['0.792', '-0.83', '0', '0.575', '#joy', '#surprise', 'positive', '0.734', 'connectedness', 'connection', 'consistency', 'continuity', 'link']
senticnet['jetliner'] = ['-0.08', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.08', 'turn_key', 'plan_route', 'open_car_door', 'book_flight', 'turn_car_off']
senticnet['jetpack'] = ['-0.04', '0.092', '0', '0.062', '#interest', '#admiration', 'positive', '0.036', 'hangar', 'aircraft_carrier', 'hot_air_balloon', 'war_museum', 'spaceship']
senticnet['jetsam'] = ['-0.12', '0', '-0.10', '-0.03', '#sadness', '#fear', 'negative', '-0.08', 'dog_poop', 'picnic', 'hike_trail', 'compost', 'wildlife']
senticnet['jetted_off'] = ['0.878', '0.84', '0', '0.91', '#joy', '#admiration', 'positive', '0.876', 'stumble', 'slip', 'fall', 'trip', 'tumble']
senticnet['jetting_off'] = ['0.071', '0', '0.031', '0.064', '#joy', '#admiration', 'positive', '0.035', 'far_from_home', 'immigrant', 'live_abroad', 'connoisseur', 'work_abroad']
senticnet['jettisonable'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'train_station', 'government_office', 'cable_car', 'sport_stadium', 'terminal']
senticnet['jetty'] = ['0.84', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.798', 'big_breadbox', 'water_vehicle', 'military_ship', 'ship', 'small_boat']
senticnet['jetway'] = ['-0.08', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.08', 'turn_key', 'plan_route', 'open_car_door', 'book_flight', 'turn_car_off']
senticnet['jetzt'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['jeudi_matin'] = ['0.667', '-0.98', '0', '0.61', '#joy', '#surprise', 'positive', '0.753', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['jew'] = ['0', '-0.00', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'hot_chick', 'atheist', 'computer_user', 'fastener_binder', 'monitor']
senticnet['jew_harp'] = ['0.063', '0.089', '0.036', '0.146', '#interest', '#admiration', 'positive', '0.066', 'horn', 'antler', 'brass_instrument', 'trumpet', 'crash_cymbal']
senticnet['jew_hater'] = ['0', '-0.00', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'hot_chick', 'atheist', 'computer_user', 'fastener_binder', 'monitor']
senticnet['jew_ish'] = ['0', '-0.00', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'hot_chick', 'atheist', 'computer_user', 'fastener_binder', 'monitor']
senticnet['jewel'] = ['0', '-0.81', '0', '0.97', '#surprise', '#admiration', 'positive', '0.893', 'gem', 'expensive_stone', 'precious_stone', 'precious_gem', 'jewelry']
senticnet['jewel_thief'] = ['0.794', '-0.50', '0', '0.531', '#joy', '#admiration', 'positive', '0.611', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['jeweled_headdress'] = ['-0.62', '0', '0.961', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'crown', 'diadem', 'tiara', 'headdress', 'headgear']
senticnet['jeweler'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'card_deck', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['jewelery'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'anklet', 'ankle_bracelet', 'armilla']
senticnet['jewelled_headdress'] = ['-0.73', '0', '0.764', '-0.53', '#sadness', '#anger', 'negative', '-0.68', 'crown', 'diadem', 'tiara', 'headdress', 'headgear']
senticnet['jeweller'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'card_deck', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['jeweller_shop'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'card_deck', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['jewellery'] = ['0.532', '-0.60', '0', '0.875', '#surprise', '#admiration', 'positive', '0.67', 'gem', 'adornment', 'jewel', 'bead', 'bangle']
senticnet['jewellery_box'] = ['0', '0', '-0.86', '-0.85', '#fear', '#disgust', 'negative', '-0.85', 'gaud', 'novelty', 'gewgaw', 'bangle', 'bauble']
senticnet['jewellery_brand'] = ['0.532', '-0.60', '0', '0.875', '#surprise', '#admiration', 'positive', '0.67', 'gem', 'adornment', 'jewel', 'bead', 'bangle']
senticnet['jewellery_designer'] = ['0.532', '-0.60', '0', '0.875', '#surprise', '#admiration', 'positive', '0.67', 'gem', 'adornment', 'jewel', 'bead', 'bangle']
senticnet['jewellery_shop'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'card_deck', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['jewellery_store'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'card_deck', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['jewellry'] = ['0.532', '-0.60', '0', '0.875', '#surprise', '#admiration', 'positive', '0.67', 'gem', 'adornment', 'jewel', 'bead', 'bangle']
senticnet['jewelry'] = ['0.656', '-0.54', '0', '0.879', '#joy', '#admiration', 'positive', '0.694', 'gem', 'adornment', 'jewel', 'bead', 'bangle']
senticnet['jewelry_box'] = ['0', '0.177', '0', '0.126', '#interest', '#admiration', 'positive', '0.151', 'treasure', 'storage_facility', 'shrine', 'lock_box', 'treasure_chest']
senticnet['jewelry_shop'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'card_deck', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['jewelry_store'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'card_deck', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['jewelry_store_owner'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'card_deck', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['jewfish'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'many_fishes', 'seahorse', 'starfish', 'plankton', 'sea_wee']
senticnet['jewish'] = ['0.033', '0.335', '-0.06', '0.27', '#interest', '#admiration', 'positive', '0.144', 'protestant', 'sect', 'christian', 'religious', 'prayer']
senticnet['jewish_ancestry'] = ['0', '-0.00', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'hot_chick', 'atheist', 'computer_user', 'fastener_binder', 'monitor']
senticnet['jewish_bolshevik'] = ['-0.08', '0', '-0.15', '-0.20', '#fear', '#disgust', 'negative', '-0.14', 'casualty', 'reaper', 'wound', 'cause_death', 'gunshot']
senticnet['jewish_cemetery'] = ['0', '0.209', '0.026', '0.184', '#interest', '#admiration', 'positive', '0.122', 'church', 'confession', 'temple', 'cathedral', 'monastery']
senticnet['jewish_community'] = ['0.701', '0.939', '0', '0.718', '#interest', '#admiration', 'positive', '0.786', 'book_story', 'bed', 'seduce', 'fond_memory', 'suburb']
senticnet['jewish_congregation'] = ['0', '0.209', '0.026', '0.184', '#interest', '#admiration', 'positive', '0.122', 'church', 'confession', 'temple', 'cathedral', 'monastery']
senticnet['jewish_conspiracy'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'racism', 'intolerant', 'xenophobic', 'racist', 'nazism']
senticnet['jewish_descent'] = ['0', '-0.00', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'hot_chick', 'atheist', 'computer_user', 'fastener_binder', 'monitor']
senticnet['jewish_heritage'] = ['0', '-0.00', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'hot_chick', 'atheist', 'computer_user', 'fastener_binder', 'monitor']
senticnet['jewish_identity'] = ['0.122', '0.159', '-0.05', '0.217', '#interest', '#admiration', 'positive', '0.111', 'profile_picture', 'unity', 'country_person', 'unite', 'signature']
senticnet['jewish_philosopher'] = ['0', '-0.00', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'hot_chick', 'atheist', 'computer_user', 'fastener_binder', 'monitor']
senticnet['jewish_rabbi'] = ['0', '-0.00', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'hot_chick', 'atheist', 'computer_user', 'fastener_binder', 'monitor']
senticnet['jewish_synagogue'] = ['0', '0.209', '0.026', '0.184', '#interest', '#admiration', 'positive', '0.122', 'church', 'confession', 'temple', 'cathedral', 'monastery']
senticnet['jewish_tradition'] = ['0', '0.915', '0', '0.821', '#interest', '#admiration', 'positive', '0.868', 'culture', 'special_day', 'holiday', 'party_time', 'yearly_celebration']
senticnet['jewish_zionist'] = ['0', '-0.00', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'hot_chick', 'atheist', 'computer_user', 'fastener_binder', 'monitor']
senticnet['jewry'] = ['0', '-0.00', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'hot_chick', 'atheist', 'computer_user', 'fastener_binder', 'monitor']
senticnet['jex_blake'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'druggist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['jfehrt'] = ['0.975', '-0.66', '0', '0.813', '#joy', '#admiration', 'positive', '0.816', 'cry', 'yell', 'scream', 'shrill', 'call']
senticnet['jfif'] = ['-0.55', '0', '0.912', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'wrangle', 'dustup', 'quarrel', 'row', 'squabble']
senticnet['jfk_international_airport'] = ['0', '0.147', '0', '0.099', '#interest', '#admiration', 'positive', '0.123', 'airliner', 'airport', 'baggage_compartment', 'aircraft', 'cable_car']
senticnet['jgermeister'] = ['0.964', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.964', 'carbonated_drink', 'sugar_free', 'coke', 'diet_coke', 'beverage']
senticnet['jhalak_dikhhla_jaa'] = ['-0.08', '-0.03', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'graduate_student', 'student', 'serious_student', 'competition', 'hard_worker']
senticnet['jiao_liuyang'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'nazi', 'man', 'pervert', 'healthy_person']
senticnet['jiaohe'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['jiayuguan'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['jibe'] = ['0.993', '-0.93', '0', '0', '#joy', '#surprise', 'positive', '0.965', 'ask', 'tell_why', 'permission', 'consent', 'say_yes']
senticnet['jicama'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'cabbage', 'lettuce', 'broccoli']
senticnet['jiffy_lube'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'clean', 'polish', 'shiny', 'water', 'sexy_girl']
senticnet['jig_tipped_with'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'fishing_rod', 'fishing', 'patience', 'fish_lake', 'catch_fish']
senticnet['jigga'] = ['-0.86', '0', '0.675', '-0.84', '#sadness', '#disgust', 'negative', '-0.79', 'shenanigan', 'chicanery', 'wile', 'dupery', 'trickery']
senticnet['jiggle'] = ['-0.83', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.85', 'vibrate', 'convulse', 'tremble', 'back_forth', 'turn_around']
senticnet['jigonhsasee'] = ['0.763', '0', '0', '0.719', '#joy', '#admiration', 'positive', '0.741', 'mist', 'haze', 'fog', 'misty', 'drizzle']
senticnet['jigsaw_puzzle'] = ['0', '0.211', '-0.12', '0.286', '#interest', '#admiration', 'positive', '0.125', 'bring_together', 'fuse', 'redo', 'fuse_together', 'request']
senticnet['jiji_press_afp_getty'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expansive', 'expansible', 'erectile', 'cavernous']
senticnet['jill'] = ['0.733', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.806', 'sweet', 'lass', 'rat', 'sweetheart', 'sugar-plum']
senticnet['jillette'] = ['0', '0.186', '0', '0.075', '#interest', '#admiration', 'positive', '0.131', 'money_inside', 'check_account', 'savings', 'money_storage', 'store_money']
senticnet['jimdandy'] = ['0.839', '-0.63', '0', '0.83', '#joy', '#admiration', 'positive', '0.768', 'nonesuch', 'nonsuch', 'ideal', 'nonpareil', 'apotheosis']
senticnet['jimhickey'] = ['0.658', '-0.50', '0', '0.667', '#joy', '#admiration', 'positive', '0.611', 'nonesuch', 'nonsuch', 'ideal', 'nonpareil', 'apotheosis']
senticnet['jingle'] = ['0.163', '0', '0.013', '0.166', '#joy', '#admiration', 'positive', '0.105', 'clang', 'chord', 'song', 'rhapsody', 'hymn']
senticnet['jingo'] = ['0', '0', '-0.76', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'chauvinist', 'hundred-percenter', 'flag-waver', 'patrioteer', 'jingoist']
senticnet['jingoism'] = ['0.68', '-0.96', '0', '0.597', '#joy', '#surprise', 'positive', '0.746', 'flag_waving', 'demagoguery', 'appeal', 'prayer', 'entreaty']
senticnet['jingoist'] = ['0', '0', '-0.76', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'jingo', 'hundred-percenter', 'flag-waver', 'patrioteer', 'chauvinist']
senticnet['jingoistic'] = ['0.585', '-0.7', '0', '0.586', '#surprise', '#admiration', 'positive', '0.624', 'patriotic', 'ultranationalistic', 'superpatriotic', 'nationalistic', 'chauvinistic']
senticnet['jinks'] = ['-0.85', '0', '0.691', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'jollification', 'high_jollification', 'hijollification', 'high_jinx', 'revel']
senticnet['jinnicky'] = ['0.526', '-0.99', '0', '0.673', '#surprise', '#admiration', 'positive', '0.732', 'faery', 'faerie', 'fairy', 'leprechaun', 'sandman']
senticnet['jinx'] = ['0.521', '-0.55', '0', '0.657', '#surprise', '#admiration', 'positive', '0.577', 'bewitch', 'enchant', 'becharm', 'hex', 'witch']
senticnet['jinyoung'] = ['-0.93', '0', '0.791', '0', '#sadness', '#anger', 'negative', '-0.86', 'hopelessness', 'hopelessly', 'despairing', 'pessimistically', 'pessimistic']
senticnet['jiri_belohlavek'] = ['-0.91', '0', '0.931', '-0.72', '#sadness', '#anger', 'negative', '-0.85', 'hit_with_baton', 'get_hurt', 'whip', 'arrow', 'hurt']
senticnet['jitney'] = ['0', '0', '-0.93', '-0.82', '#fear', '#disgust', 'negative', '-0.87', 'dishonesty', 'feigned', 'fraudulent', 'collusive', 'dishonest']
senticnet['jitter'] = ['-0.08', '0.097', '-0.10', '-0.10', '#fear', '#disgust', 'negative', '-0.05', 'rewind', 'hear_funny', 'view_movie', 'see_film', 'use_remote_control']
senticnet['jitterbug'] = ['0.816', '-0.89', '0', '0', '#joy', '#surprise', 'positive', '0.857', 'polka', 'trapeze', 'twist', 'flea', 'mosquito']
senticnet['jitteriness'] = ['-0.81', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.78', 'restiveness', 'jumpiness', 'restive', 'nervy', 'jumpy']
senticnet['jittery'] = ['-0.89', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.85', 'high-strung', 'uptight', 'restive', 'overstrung', 'nervy']
senticnet['jiu_jitsu'] = ['-0.06', '-0.00', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.05', 'madman', 'trouble', 'agony', 'cause_problem', 'river_stream']
senticnet['jiujitsuguy'] = ['0.533', '0.818', '0', '0.683', '#interest', '#admiration', 'positive', '0.678', 'gain_information', 'report_news', 'solve', 'seek_answer', 'gather_information']
senticnet['jivamukti_yoga'] = ['0', '0', '0', '0.93', '#admiration', '#admiration', 'positive', '0.93', 'lift_weight', 'release_stress', 'move_body', 'take_hike', 'change_color']
senticnet['jivaro'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'sedum', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['jiwire'] = ['-0.87', '0', '0.818', '0', '#sadness', '#anger', 'negative', '-0.84', 'slow_speed', 'wifi', 'computer_internet_connection', 'internet_connection', 'boot_up_computer']
senticnet['jiyt'] = ['-0.50', '0', '0.989', '-0.91', '#anger', '#disgust', 'negative', '-0.80', 'camp', 'pack', 'clique', 'coterie', 'ingroup']
senticnet['jizz'] = ['-0.05', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.05', 'masturbate', 'jerk', 'jerk_off', 'watch_porn', 'porn']
senticnet['jlabel'] = ['0.055', '0.117', '0', '0.133', '#interest', '#admiration', 'positive', '0.102', 'graphical_representation', 'pencil_pen', 'note_pad', 'marker', 'stationery']
senticnet['jmme'] = ['-0.11', '0', '-0.09', '0.082', '#sadness', '#fear', 'negative', '-0.04', 'stare', 'ear', 'facial_expression', 'mouth', 'dimple']
senticnet['jmonji'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['joachim_von_braun'] = ['-0.56', '0', '0.655', '-0.85', '#anger', '#disgust', 'negative', '-0.69', 'slovenly_woman', 'slut', 'trollop', 'slattern', 'slob']
senticnet['job'] = ['0.907', '0', '0', '0.79', '#joy', '#admiration', 'positive', '0.849', 'work', 'task', 'hard_work', 'accomplish', 'find_accomplish']
senticnet['job_action'] = ['-0.77', '0', '0.505', '-0.50', '#sadness', '#anger', 'negative', '-0.59', 'wildcat_strike', 'sympathy_strike', 'sympathetic_strike', 'walkout', 'work_stoppage']
senticnet['job_application'] = ['0', '0', '0', '0.075', '#admiration', '#admiration', 'positive', '0.075', 'application', 'resume', 'respond', 'questionnaire', 'help_understand']
senticnet['job_creation'] = ['0.067', '0.172', '0', '0.121', '#interest', '#admiration', 'positive', '0.12', 'economic_activity', 'business', 'support_family', 'work_done', 'earn_money']
senticnet['job_description'] = ['0.907', '0', '0', '0.79', '#joy', '#admiration', 'positive', '0.849', 'work', 'task', 'hard_work', 'accomplish', 'find_accomplish']
senticnet['job_insecurity'] = ['-0.84', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.82', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecure']
senticnet['job_interview_failed'] = ['-0.94', '0', '0.808', '-0.96', '#sadness', '#disgust', 'negative', '-0.90', 'problem', 'trouble', 'punishment', 'exam_failed', 'unwanted_outcome']
senticnet['job_offer'] = ['-0.02', '0.107', '0', '0.159', '#interest', '#admiration', 'positive', '0.081', 'ask_person', 'overprice', 'hire', 'hire_help', 'job']
senticnet['job_pay'] = ['-0.05', '0.242', '-0.08', '0.113', '#interest', '#admiration', 'positive', '0.053', 'money_earn', 'work_for_money', 'monthly_due', 'earnings', 'money_pay']
senticnet['job_placement'] = ['0', '0.098', '0.012', '0.276', '#interest', '#admiration', 'positive', '0.121', 'position', 'job', 'find_own_place_in_world', 'character_trait', 'place']
senticnet['job_satisfaction'] = ['0.032', '0.11', '0', '0.105', '#interest', '#admiration', 'positive', '0.082', 'work', 'place_work', 'implementation', 'disorganize', 'employment']
senticnet['job_search'] = ['-0.06', '0.149', '-0.09', '0.181', '#interest', '#admiration', 'positive', '0.044', 'ask_nicely', 'job_offer', 'question_ask', 'make_happen', 'make_effort']
senticnet['job_seeker'] = ['-0.06', '0.149', '-0.09', '0.181', '#interest', '#admiration', 'positive', '0.044', 'ask_nicely', 'job_offer', 'question_ask', 'make_happen', 'make_effort']
senticnet['jobless'] = ['-0.95', '0', '0.156', '-0.98', '#sadness', '#disgust', 'negative', '-0.69', 'fired', 'unemployed', 'become_poor', 'need_money', 'need_new_job']
senticnet['jobless_figure'] = ['0', '0', '0.948', '-0.96', '#anger', '#disgust', 'negative', '-0.95', 'work_much', 'lack_money', 'wage_slavery', 'buy_tool', 'get_paycheck']
senticnet['jobless_rate'] = ['0', '0', '0.948', '-0.96', '#anger', '#disgust', 'negative', '-0.95', 'work_much', 'lack_money', 'wage_slavery', 'buy_tool', 'get_paycheck']
senticnet['jobless_total'] = ['0', '0', '0.948', '-0.96', '#anger', '#disgust', 'negative', '-0.95', 'work_much', 'lack_money', 'wage_slavery', 'buy_tool', 'get_paycheck']
senticnet['jobseeker'] = ['0.907', '0', '0', '0.79', '#joy', '#admiration', 'positive', '0.849', 'work', 'task', 'hard_work', 'accomplish', 'find_accomplish']
senticnet['jobweb'] = ['0', '0.08', '0', '0', '#interest', '#interest', 'positive', '0.08', 'internet', 'go_online', 'visit_web_site', 'find_information', 'choose_book']
senticnet['jock_itch'] = ['-0.10', '0', '-0.05', '-0.11', '#sadness', '#disgust', 'negative', '-0.08', 'jaw', 'cheek', 'dimple', 'forehead', 'beard']
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senticnet['jockey_club_cup'] = ['-0.76', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.78', 'strangle', 'bloodshed', 'cripple', 'hear_bad_news', 'sexually_transmit_disease']
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senticnet['jockstrap'] = ['0', '0', '0', '-0.06', '#disgust', '#disgust', 'negative', '-0.06', 'body', 'nervous_system', 'normal_human_body', 'human_body', 'head_shoulder']
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senticnet['jocular'] = ['0', '-0.90', '0', '0.872', '#surprise', '#admiration', 'positive', '0.887', 'waggish', 'puckish', 'pusillanimous', 'straight-shooting', 'waspish']
senticnet['jocularity'] = ['0.699', '0.622', '-0.23', '0.322', '#joy', '#interest', 'positive', '0.353', 'jocundity', 'hilariously', 'screaming', 'hilarious', 'gleefulness']
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senticnet['joey'] = ['-0.74', '0', '0.877', '-0.97', '#anger', '#disgust', 'negative', '-0.86', 'miss', 'girl', 'chachka', 'young_lady', 'young_woman']
senticnet['joffrey_ballet'] = ['0.12', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.09', 'ballroom', 'perform_art', 'nightclub', 'ballerina', 'venue']
senticnet['jog'] = ['0', '0', '-0.92', '-0.77', '#fear', '#disgust', 'negative', '-0.85', 'meander', 'ramble', 'swan', 'roll', 'wander']
senticnet['jog_trot'] = ['0', '0', '0.752', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'get_hit_by_car', 'meander', 'full_moon', 'step_dog_poop', 'kick_ass']
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senticnet['jogged_along'] = ['-0.93', '0', '0.791', '0', '#sadness', '#anger', 'negative', '-0.86', 'hopelessness', 'hopelessly', 'despairing', 'pessimistically', 'pessimistic']
senticnet['jogger'] = ['0.1', '0', '0', '0', '#joy', '#joy', 'positive', '0.1', 'bench', 'park_bench', 'stool', 'bar', 'oak']
senticnet['jogging'] = ['0', '0.144', '-0.07', '0.222', '#interest', '#admiration', 'positive', '0.096', 'shoe', 'wear_shoes', 'put_shoes', 'put_on_shoes', 'sport']
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senticnet['jogging_track'] = ['0.041', '0.086', '0.016', '0', '#joy', '#interest', 'positive', '0.037', 'team', 'play_sport', 'game', 'exercise', 'match']
senticnet['johann_gutenberg'] = ['-0.03', '0.201', '-0.13', '0.171', '#interest', '#admiration', 'positive', '0.049', 'scientific_statement', 'hypothetical', 'conjecture', 'theory', 'fact']
senticnet['johannes_gutenberg'] = ['-0.03', '0.201', '-0.13', '0.171', '#interest', '#admiration', 'positive', '0.049', 'scientific_statement', 'hypothetical', 'conjecture', 'theory', 'fact']
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senticnet['johnstone_paint_trophy'] = ['-0.09', '0.147', '-0.11', '0.205', '#interest', '#admiration', 'positive', '0.036', 'question_starter', 'resolution', 'available', 'explanation', 'request']
senticnet['johnstown'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
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senticnet['join_army'] = ['0', '-0.86', '0', '0.775', '#surprise', '#admiration', 'positive', '0.818', 'advance_battle', 'fight_war', 'fight_enemy', 'wage_war', 'storm_fortress']
senticnet['join_band'] = ['0.867', '0.949', '0', '0', '#joy', '#interest', 'positive', '0.908', 'join', 'play_in_band', 'play_tune', 'band', 'play_music']
senticnet['join_club'] = ['0', '0.148', '0', '0.084', '#interest', '#admiration', 'positive', '0.116', 'reply_question', 'unite', 'walk_school', 'needy', 'receive_email']
senticnet['join_gym'] = ['-0.06', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.06', 'go_gym', 'check_doctor', 'workout', 'lift_weight', 'eat_balance_diet']
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senticnet['join_party'] = ['0', '0.719', '0', '0.75', '#interest', '#admiration', 'positive', '0.734', 'laugh', 'enjoy_show', 'join', 'become_excited', 'laughter']
senticnet['join_team'] = ['0.785', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.855', 'buy_ball', 'form_team', 'gather_team', 'learn_rule', 'work_hard']
senticnet['join_together'] = ['0.136', '0', '-0.12', '0.231', '#joy', '#admiration', 'positive', '0.08', 'special', 'fuse_together', 'merge', 'event', 'occurrence']
senticnet['joined_itn'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'adventurer', 'humankind']
senticnet['joined_onstage'] = ['0.773', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.844', 'favorite', 'crowd', 'acclamation', 'bunch', 'large_crowd']
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senticnet['joint_base_balad'] = ['-0.66', '0', '0.789', '-0.63', '#sadness', '#anger', 'negative', '-0.69', 'humbug', 'play_trick', 'cozen', 'pull_fast_one_on', 'play_trick_on']
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senticnet['joint_caretaker_manager'] = ['-0.9', '0', '0.749', '-0.71', '#sadness', '#anger', 'negative', '-0.78', 'chute', 'slide', 'slideway', 'sloping_trough', 'gutter']
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senticnet['joint_checking_account'] = ['0', '0.192', '0', '0.052', '#interest', '#admiration', 'positive', '0.122', 'bank_vault', 'wallet', 'billfold', 'hold_change', 'bank']
senticnet['joint_declaration'] = ['0.397', '0.245', '-0.13', '0.224', '#joy', '#interest', 'positive', '0.183', 'agreement', 'provide', 'advice', 'contribution', 'offer']
senticnet['joint_effort'] = ['0', '0', '0', '0.881', '#admiration', '#admiration', 'positive', '0.881', 'educate_others', 'share_knowledge', 'share_opinion', 'discover_new', 'cooperation']
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senticnet['joint_resolution'] = ['-0.81', '0', '0.513', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'ballot', 'vote', 'voting', 'balloting', 'veto']
senticnet['joint_sponsorship'] = ['0.851', '-0.65', '0', '0.66', '#joy', '#admiration', 'positive', '0.723', 'omen', 'portent', 'presage', 'prognostic', 'prognostication']
senticnet['joint_venture'] = ['0.59', '-0.75', '0', '0.543', '#joy', '#surprise', 'positive', '0.63', 'venture', 'sally', 'labor', 'cause', 'risk']
senticnet['joint_venture_agreement'] = ['0.59', '-0.75', '0', '0.543', '#joy', '#surprise', 'positive', '0.63', 'venture', 'sally', 'labor', 'cause', 'risk']
senticnet['joint_venture_between'] = ['0.59', '-0.75', '0', '0.543', '#joy', '#surprise', 'positive', '0.63', 'venture', 'sally', 'labor', 'cause', 'risk']
senticnet['joint_venture_jv'] = ['0.59', '-0.75', '0', '0.543', '#joy', '#surprise', 'positive', '0.63', 'venture', 'sally', 'labor', 'cause', 'risk']
senticnet['joint_venture_partner'] = ['0.59', '-0.75', '0', '0.543', '#joy', '#surprise', 'positive', '0.63', 'venture', 'sally', 'labor', 'cause', 'risk']
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senticnet['jointly_owned_by'] = ['0.59', '-0.75', '0', '0.543', '#joy', '#surprise', 'positive', '0.63', 'venture', 'sally', 'labor', 'cause', 'risk']
senticnet['joist'] = ['0', '0.963', '0', '0.916', '#interest', '#admiration', 'positive', '0.94', 'truss', 'girder', 'truss', 'cantilever', 'screw']
senticnet['joji_yuasa'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_music', 'hear_music', 'artist', 'make_drawing', 'art']
senticnet['jojo_bizarre_adventure'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['joke'] = ['0.867', '0.949', '0', '0', '#joy', '#interest', 'positive', '0.908', 'tell_funny_joke', 'tell_joke', 'funny_joke', 'jest', 'witticism']
senticnet['joke_around'] = ['0.706', '0.791', '0', '0', '#joy', '#interest', 'positive', '0.748', 'get_laid', 'see_friend', 'social_occasion', 'get_close', 'great_time']
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senticnet['jolity'] = ['0.699', '0.62', '-0.23', '0.323', '#joy', '#interest', 'positive', '0.353', 'jolliness', 'joviality', 'josh', 'chaff', 'banter']
senticnet['jollification'] = ['-0.85', '0', '0.691', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'jinks', 'high_jinks', 'hijinks', 'high_jinx', 'revel']
senticnet['jollify'] = ['0.988', '0.986', '0', '0.73', '#joy', '#interest', 'positive', '0.901', 'express_love', 'show_affection', 'show_love', 'feel_obligated', 'make_happy']
senticnet['jolliness'] = ['0.699', '0.62', '-0.23', '0.323', '#joy', '#interest', 'positive', '0.353', 'joviality', 'jolity', 'josh', 'chaff', 'banter']
senticnet['jollity'] = ['0.918', '0.476', '-0.19', '0.417', '#joy', '#interest', 'positive', '0.404', 'merriment', 'cheery', 'festal', 'playfulness', 'pridefulness']
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senticnet['jolly_along'] = ['0.655', '0.635', '-0.23', '0.3', '#joy', '#interest', 'positive', '0.338', 'cheerfully', 'cheerful', 'jolly_up', 'hearten', 'chirk_up']
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senticnet['jolly_good'] = ['0.71', '0.618', '-0.23', '0.327', '#joy', '#interest', 'positive', '0.356', 'jovial', 'jocund', 'mirthful', 'joviality', 'jolity']
senticnet['jolly_old'] = ['0.71', '0.618', '-0.23', '0.327', '#joy', '#interest', 'positive', '0.356', 'jovial', 'jocund', 'mirthful', 'joviality', 'jolity']
senticnet['jolly_rancher'] = ['0.023', '0.141', '-0.13', '0.174', '#interest', '#admiration', 'positive', '0.052', 'candy', 'push_wind', 'exhalation', 'breaout', 'mouth_action']
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senticnet['jolt'] = ['-0.73', '0', '0.865', '-0.87', '#anger', '#disgust', 'negative', '-0.82', 'shock', 'shake', 'abrupt', 'electric_shock', 'damage']
senticnet['jonah'] = ['-0.67', '0', '0.838', '-0.86', '#anger', '#disgust', 'negative', '-0.79', 'unsuccessful_person', 'loser', 'abandoned_person', 'desperate', 'unfortunate']
senticnet['jonah_goldberg'] = ['0', '0', '-0.93', '-0.88', '#fear', '#disgust', 'negative', '-0.91', 'rebuke', 'take_to_task', 'flog', 'call_on_carpet', 'cane']
senticnet['jonas_bjerre'] = ['0.028', '0', '0.027', '0.101', '#joy', '#admiration', 'positive', '0.034', 'animal_dwell', 'bee_home', 'live_place', 'dwell_place', 'place_to_live']
senticnet['jonjo'] = ['-0.79', '0', '0.992', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['jonne_valtonen'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_music', 'hear_music', 'artist', 'make_drawing', 'art']
senticnet['joo_grande'] = ['-0.06', '-0.00', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.05', 'madman', 'trouble', 'agony', 'cause_problem', 'river_stream']
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senticnet['jos_ferreira'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'dryness', 'thirstiness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['josephte'] = ['-0.71', '0', '0.917', '-0.51', '#sadness', '#anger', 'negative', '-0.71', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['josh'] = ['0.699', '0.62', '-0.23', '0.323', '#joy', '#interest', 'positive', '0.353', 'jolliness', 'joviality', 'jolity', 'chaff', 'banter']
senticnet['josh_collmenter'] = ['0.663', '-0.68', '0', '0.892', '#surprise', '#admiration', 'positive', '0.747', 'saphead', 'muggins', 'tomfool', 'fool', 'sap']
senticnet['josh_pyke'] = ['0.98', '-0.74', '0', '0.984', '#joy', '#admiration', 'positive', '0.903', 'prosperous', 'flourishing', 'successful', 'prospering', 'thriving']
senticnet['josh_ritter'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'cur', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['josh_rouse'] = ['0.946', '-0.91', '0', '0.664', '#joy', '#surprise', 'positive', '0.841', 'pretender', 'phony', 'phoney', 'hypocrite', 'dissembler']
senticnet['josh_turner'] = ['0.086', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'carny', 'fortune_teller', 'midway', 'ferris_wheel', 'roller-coaster']
senticnet['josh_tyrangiel'] = ['0.7', '-0.62', '0', '0.58', '#joy', '#surprise', 'positive', '0.636', 'white_magic', 'thaumaturgy', 'juju', 'conjuration', 'supernaturalism']
senticnet['josie_carroll'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'coach', 'instructor', 'personal_trainer', 'fit_person', 'train']
senticnet['joss_paper'] = ['0.827', '0', '0', '0', '#joy', '#joy', 'positive', '0.827', 'wrathful', 'wrathfully', 'wroth', 'wrothful', 'lividity']
senticnet['joss_stick'] = ['0.827', '0', '0', '0', '#joy', '#joy', 'positive', '0.827', 'wrathful', 'wrathfully', 'wroth', 'wrothful', 'lividity']
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senticnet['jot_down_idea'] = ['0', '0.074', '0.026', '0.105', '#interest', '#admiration', 'positive', '0.051', 'make_note', 'write_note', 'write_down', 'jot_down_note', 'leave_message']
senticnet['jot_down_note'] = ['-0.11', '-0.04', '0', '-0.19', '#sadness', '#disgust', 'negative', '-0.08', 'learn_new_idea', 'study_library', 'pop_up', 'listen_lecture', 'remember_information']
senticnet['jotted_down'] = ['0', '-0.04', '0.061', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'index_card', 'ring_binder', 'highlighter_pen', 'memo_pad', 'notepad']
senticnet['jotting_down'] = ['0', '-0.04', '0.061', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'index_card', 'ring_binder', 'highlighter_pen', 'memo_pad', 'notepad']
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senticnet['kaloyan_nunatak'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'head_hurt', 'introspection', 'lifestyle_change', 'headache', 'move_eye']
senticnet['kalpakkam'] = ['-0.90', '0', '0.748', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'alternative_energy', 'activation_energy', 'energy_of_activation', 'energy', 'nuclear_energy']
senticnet['kalvos_damian_new'] = ['0', '0.113', '0.041', '0.058', '#interest', '#admiration', 'positive', '0.043', 'corner_shop', 'general_store', 'many_shops', 'large_supermarket', 'goods']
senticnet['kama_pleasure'] = ['-0.73', '0', '0.55', '-0.75', '#sadness', '#disgust', 'negative', '-0.68', 'mortal', 'person', 'individual', 'mangler', 'mutilator']
senticnet['kamaiya'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'vassalage']
senticnet['kamaiya_system'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'vassalage']
senticnet['kamaloka'] = ['-0.85', '0', '0.968', '-0.75', '#sadness', '#anger', 'negative', '-0.86', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['kamarupa_kingdom'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
senticnet['kamen_rider_wizard'] = ['-0.63', '0', '0.674', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'baneful', 'evil', 'baleful', 'malevolent', 'malign']
senticnet['kamet'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['kamikaze'] = ['0', '0', '0', '-0.94', '#disgust', '#disgust', 'negative', '-0.94', 'terrorism', 'brainwash', 'sect', 'military_camp', 'faction']
senticnet['kamilaroi'] = ['-0.95', '0', '0.624', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['kaminokawa'] = ['0.914', '-0.56', '0', '0.869', '#joy', '#admiration', 'positive', '0.781', 'tabula_rasa', 'ego', 'subconscious', 'unconscious', 'psyche']
senticnet['kamlari'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'vassalage']
senticnet['kamouraska'] = ['-0.71', '0', '0.917', '-0.51', '#sadness', '#anger', 'negative', '-0.71', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['kampfgruppe_peiper'] = ['0.737', '-0.96', '0', '0.552', '#joy', '#surprise', 'positive', '0.75', 'prominence', 'hump', 'bump', 'swelling', 'frontal_eminence']
senticnet['kandahar_air'] = ['-0.66', '0', '0.789', '-0.63', '#sadness', '#anger', 'negative', '-0.69', 'humbug', 'play_trick', 'cozen', 'pull_fast_one_on', 'play_trick_on']
senticnet['kandahar_airfield'] = ['-0.66', '0', '0.789', '-0.63', '#sadness', '#anger', 'negative', '-0.69', 'humbug', 'play_trick', 'cozen', 'pull_fast_one_on', 'play_trick_on']
senticnet['kandahar_airport'] = ['-0.66', '0', '0.789', '-0.63', '#sadness', '#anger', 'negative', '-0.69', 'humbug', 'play_trick', 'cozen', 'pull_fast_one_on', 'play_trick_on']
senticnet['kandahar_international_airport'] = ['-0.66', '0', '0.789', '-0.63', '#sadness', '#anger', 'negative', '-0.69', 'humbug', 'play_trick', 'cozen', 'pull_fast_one_on', 'play_trick_on']
senticnet['kandamby'] = ['-0.62', '0', '0.713', '-0.98', '#anger', '#disgust', 'negative', '-0.77', 'muddy_up', 'muck_up', 'foul', 'blemish', 'smear']
senticnet['kandukuri_veeresalingam'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['kane_williamson'] = ['-0.13', '0', '-0.11', '-0.04', '#sadness', '#fear', 'negative', '-0.1', 'concern_health', 'medical_problem', 'strengthful', 'surgery', 'injury']
senticnet['kangaroo'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'cheetah', 'giraffe', 'caribou', 'mule', 'horse']
senticnet['kangaroo_armoured_personnel'] = ['0', '0.089', '0.01', '0.118', '#interest', '#admiration', 'positive', '0.066', 'crate', 'mom_house', 'sack', 'poker_game', 'safekeep']
senticnet['kangaroo_rat'] = ['-0.55', '0', '0.955', '-0.96', '#anger', '#disgust', 'negative', '-0.82', 'house_mouse', 'mouse', 'rat', 'fieldmouse', 'nude_mouse']
senticnet['kanji'] = ['-0.86', '0', '0.619', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'character', 'logogram', 'logograph', 'graph', 'grapheme']
senticnet['kanji_character'] = ['-0.86', '0', '0.619', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'character', 'logogram', 'logograph', 'graph', 'grapheme']
senticnet['kanji_chinese'] = ['-0.86', '0', '0.619', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'character', 'logogram', 'logograph', 'graph', 'grapheme']
senticnet['kantian'] = ['0.863', '-0.94', '0', '0.986', '#surprise', '#admiration', 'positive', '0.932', 'postulate', 'posit', 'assume', 'presume', 'take_for_granted']
senticnet['kantian_view'] = ['0.992', '-0.66', '0', '0.536', '#joy', '#surprise', 'positive', '0.729', 'nous', 'mind', 'brain', 'psyche', 'head']
senticnet['kanzenban_edition'] = ['0.025', '0.115', '0.06', '0.183', '#interest', '#admiration', 'positive', '0.066', 'volume_measurement', 'liquid_measurement', 'mass_measure', 'geometry', 'size']
senticnet['kaogong_yuanwailang'] = ['-0.12', '-0.05', '0', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'diminish', 'reduce', 'lower', 'lower_music', 'decrease']
senticnet['kaopectate'] = ['-0.53', '0', '0.954', '-0.54', '#anger', '#disgust', 'negative', '-0.67', 'ranitidine', 'histamine_blocker', 'gastric_ranitidine', 'alkalizer', 'antiacid']
senticnet['kapitn_zur_see'] = ['0.688', '-0.90', '0', '0.877', '#surprise', '#admiration', 'positive', '0.824', 'flag_officer', 'rear_admiral', 'admiral', 'full_admiral', 'vice_admiral']
senticnet['kapler'] = ['0.663', '-0.68', '0', '0.892', '#surprise', '#admiration', 'positive', '0.747', 'saphead', 'muggins', 'tomfool', 'fool', 'sap']
senticnet['kara_goucher'] = ['-0.04', '-0.03', '0', '-0.16', '#sadness', '#disgust', 'negative', '-0.05', 'crazy', 'damage_body', 'crazy_person', 'threat', 'max']
senticnet['karakul_sheep'] = ['-0.53', '0', '0.751', '-0.97', '#anger', '#disgust', 'negative', '-0.75', 'guanaco', 'domestic_llama', 'llama', 'artiodactyl', 'artiodactyl_mammal']
senticnet['karana_sarira'] = ['-0.71', '0', '0.509', '-0.92', '#sadness', '#disgust', 'negative', '-0.71', 'natural_process', 'natural_action', 'activity', 'curing', 'congelation']
senticnet['karaoke'] = ['0.925', '0.993', '0', '0.832', '#joy', '#interest', 'positive', '0.917', 'dance_samba', 'dance', 'skate', 'jump_rope', 'play_frisbee']
senticnet['karaoke_bar'] = ['0.763', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.75', 'pub', 'disco', 'dance_club', 'cafe', 'tavern']
senticnet['karaoke_contest'] = ['-0.95', '0', '0.992', '-0.63', '#sadness', '#anger', 'negative', '-0.86', 'interpretive_dance', 'interpretive_dancing', 'modern_dance', 'interpretative_dancing', 'stage_dancing']
senticnet['karaoke_lounge'] = ['0.925', '0.993', '0', '0.832', '#joy', '#interest', 'positive', '0.917', 'dance_samba', 'dance', 'skate', 'jump_rope', 'play_frisbee']
senticnet['karaoke_machine'] = ['0.925', '0.993', '0', '0.832', '#joy', '#interest', 'positive', '0.917', 'dance_samba', 'dance', 'skate', 'jump_rope', 'play_frisbee']
senticnet['karaoke_night'] = ['0', '-0.08', '0', '0', '#surprise', '#surprise', 'positive', '0.082', 'yo-yo', 'playroom', 'backgammon', 'action_figure', 'board_game']
senticnet['karaoke_style'] = ['0.925', '0.993', '0', '0.832', '#joy', '#interest', 'positive', '0.917', 'dance_samba', 'dance', 'skate', 'jump_rope', 'play_frisbee']
senticnet['karat_gold'] = ['0', '0.931', '0', '0.947', '#interest', '#admiration', 'positive', '0.939', 'gold', 'metal', 'ash_gray', 'fluent', 'eloquent']
senticnet['karate'] = ['0.532', '0.657', '-0.25', '0.268', '#joy', '#interest', 'positive', '0.3', 'sport', 'martial_art', 'gymnastics', 'athletics', 'discipline']
senticnet['karezza'] = ['0.732', '0', '0', '0.706', '#joy', '#admiration', 'positive', '0.719', 'pleasant_emotion', 'happy_emotion', 'happy_feeling', 'show_affection', 'good_emotion']
senticnet['karla_faye_tucker'] = ['0', '0', '0.869', '0', '#anger', '#anger', 'negative', '-0.86', 'convoke', 'loose_end', 'car_race', 'auction', 'mortuary']
senticnet['karlheinz_stockhausen'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_music', 'hear_music', 'artist', 'make_drawing', 'art']
senticnet['karma'] = ['0', '-0.91', '0', '0.781', '#surprise', '#admiration', 'positive', '0.847', 'enter_contract', 'believe', 'give_help', 'destiny', 'fate']
senticnet['karmanau'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'dejectedness', 'low-spiritedness', 'lowness', 'downheartedness', 'cheerless']
senticnet['karst_cave'] = ['-0.02', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'tunnel', 'dark_place', 'ski_resort', 'top_tree', 'compost_pile']
senticnet['karst_topography'] = ['0.614', '-0.95', '0', '0.584', '#joy', '#surprise', 'positive', '0.718', 'mother_lode', 'champion_lode', 'deposit', 'sedimentation', 'lode']
senticnet['kart_racing_track'] = ['0', '0', '0', '-0.1', '#disgust', '#disgust', 'negative', '-0.1', 'ice_rink', 'skate', 'room_temperature', 'loose_end', 'smooth']
senticnet['karting_track'] = ['0', '0', '0', '-0.1', '#disgust', '#disgust', 'negative', '-0.1', 'ice_rink', 'skate', 'room_temperature', 'loose_end', 'smooth']
senticnet['karuppu_sami'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['karuvatta'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['kaschei'] = ['0.894', '0', '0', '0.924', '#joy', '#admiration', 'positive', '0.909', 'captivate', 'belonging', 'comfortably', 'solace', 'comfortableness']
senticnet['kashkak'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'head_hurt', 'introspection', 'lifestyle_change', 'headache', 'move_eye']
senticnet['kashubia'] = ['0', '0', '0', '0.1', '#admiration', '#admiration', 'positive', '0.1', 'find_earth', 'wax', 'plutonium', 'shine', 'melt']
senticnet['kaspar_hauser'] = ['-0.05', '-0.19', '-0.17', '0.219', '#surprise', '#admiration', 'positive', '0.046', 'riddle', 'mystery', 'obituary', 'editorial', 'book_genre']
senticnet['kassapa'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['kastchei'] = ['-0.75', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.83', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['katamon_neighborhood'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['katana'] = ['-0.04', '0.138', '-0.08', '0.111', '#interest', '#admiration', 'positive', '0.031', 'geometry', 'craft', 'blade_geometry', 'blade', 'collect_item']
senticnet['kataomoi'] = ['0.028', '0', '0.027', '0.101', '#joy', '#admiration', 'positive', '0.034', 'animal_dwell', 'bee_home', 'live_place', 'dwell_place', 'place_to_live']
senticnet['kate_hudson'] = ['0.54', '-0.64', '0', '0.566', '#surprise', '#admiration', 'positive', '0.582', 'pleaser', 'entertainer', 'mortal', 'busker', 'drawing_card']
senticnet['kate_mulgrew'] = ['0', '0', '0', '0.068', '#admiration', '#admiration', 'positive', '0.068', 'dark_corner', 'child_game', 'play_person', 'role_play', 'part_of_play']
senticnet['kate_spade'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['katerina_ivanovna'] = ['-0.85', '0.21', '-0.56', '0', '#sadness', '#fear', 'negative', '-0.40', 'doleful', 'sadly', 'dolefully', 'ruefulness', 'wretched']
senticnet['katich'] = ['-0.13', '0', '-0.11', '-0.04', '#sadness', '#fear', 'negative', '-0.1', 'concern_health', 'medical_problem', 'strengthful', 'surgery', 'injury']
senticnet['katisha'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'dress', 'pinny', 'dirndl', 'gown', 'muumuu']
senticnet['katkar'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['katsuobushi'] = ['0.9', '0.963', '0', '0', '#joy', '#interest', 'positive', '0.931', 'form_of_food', 'japanese_food', 'uncooked', 'healthy', 'food']
senticnet['katz'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['katzman'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['kayak'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'boat', 'ship', 'large_boat', 'sailboat', 'big_boat']
senticnet['kayaker'] = ['0', '0.07', '0', '0', '#interest', '#interest', 'positive', '0.07', 'sunken_treasure', 'real_duck', 'urchin', 'mussel', 'gold_fish']
senticnet['kayne_west'] = ['0', '0', '0.876', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'untune', 'discompose', 'discomfit', 'disconcert', 'upset']
senticnet['kazaa_peer'] = ['0.848', '0.735', '0', '0', '#joy', '#interest', 'positive', '0.791', 'observer', 'peek', 'appearance', 'seer', 'glimpse']
senticnet['kazimierz'] = ['0', '0', '0', '0.1', '#admiration', '#admiration', 'positive', '0.1', 'find_earth', 'wax', 'plutonium', 'shine', 'melt']
senticnet['kazimierz_dolny'] = ['0', '0', '0', '0.1', '#admiration', '#admiration', 'positive', '0.1', 'find_earth', 'wax', 'plutonium', 'shine', 'melt']
senticnet['kazuki_tokudome'] = ['-0.56', '0', '0.617', '-0.80', '#anger', '#disgust', 'negative', '-0.66', 'prolixity', 'prolixness', 'wordiness', 'repetitiveness', 'repetitiousness']
senticnet['kcal_mol'] = ['0.695', '-0.85', '0', '0.501', '#joy', '#surprise', 'positive', '0.684', 'alternative_energy', 'energy', 'energy_of_activation', 'atomic_energy', 'nuclear_energy']
senticnet['kcb_cbe'] = ['0.74', '-0.53', '0', '0.641', '#joy', '#admiration', 'positive', '0.64', 'marshal', 'sky_marshal', 'lawman', 'law_officer', 'peace_officer']
senticnet['kcpa'] = ['0.546', '-0.74', '0', '0.581', '#surprise', '#admiration', 'positive', '0.624', 'hand', 'commend', 'reach', 'charge', 'give']
senticnet['kearns_sayre_syndrome'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'myotonic_muscular_dystrophy', 'dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['kebab_shop'] = ['0.059', '0.212', '0', '0.143', '#interest', '#admiration', 'positive', '0.138', 'christmas_shop', 'go_grocery_shop', 'pay_credit_card', 'pay_cash', 'holiday_shop']
senticnet['keel'] = ['0', '0', '0.034', '-0.08', '#anger', '#disgust', 'negative', '-0.06', 'lifeboat', 'mast', 'bad_man', 'pirate', 'yacht']
senticnet['keel_arch'] = ['0.641', '-0.56', '0', '0.748', '#joy', '#admiration', 'positive', '0.653', 'trefoil_arch', 'ogee_arch', 'pointed_arch', 'drop_arch', 'arch']
senticnet['keel_over'] = ['-0.53', '0', '0.752', '-0.89', '#anger', '#disgust', 'negative', '-0.72', 'fall', 'topple', 'descend', 'go_down', 'come_down']
senticnet['keelback'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'death_adder', 'ophidian', 'serpent', 'elapid', 'taipan']
senticnet['keen'] = ['0', '0', '0.779', '-0.94', '#anger', '#disgust', 'negative', '-0.86', 'discriminating', 'acute', 'lament', 'knifelike', 'incisive']
senticnet['keen_amateur'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'sciolist', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
senticnet['keen_eye'] = ['0.682', '-0.52', '0', '0.613', '#joy', '#admiration', 'positive', '0.607', 'nice', 'fastidious', 'dainty', 'prissy', 'pernickety']
senticnet['keen_fisherman'] = ['-0.79', '0', '0.992', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['keen_golfer'] = ['0.758', '0.719', '0', '0', '#joy', '#interest', 'positive', '0.738', 'plan_party', 'dance_music', 'become_adult', 'stay_fit', 'drive_crazy']
senticnet['keen_grasp'] = ['-0.91', '0', '0.874', '-0.86', '#sadness', '#anger', 'negative', '-0.88', 'resourcefulness', 'imagination', 'creativity', 'creativeness', 'creative_thinking']
senticnet['keen_insight'] = ['0.968', '-0.87', '0', '0.992', '#joy', '#admiration', 'positive', '0.946', 'sagacity', 'sagaciousness', 'care', 'caution', 'discernment']
senticnet['keen_intellect'] = ['0.812', '0.937', '0', '0', '#joy', '#interest', 'positive', '0.875', 'retentive', 'long', 'dogged', 'dour', 'recollective']
senticnet['keen_interest'] = ['0', '0.33', '0', '0', '#interest', '#interest', 'positive', '0.33', 'interested', 'focused', 'attentive', 'curious', 'fascinated']
senticnet['keen_observation'] = ['0.682', '-0.52', '0', '0.613', '#joy', '#admiration', 'positive', '0.607', 'nice', 'fastidious', 'dainty', 'prissy', 'pernickety']
senticnet['keenan'] = ['0.532', '-0.68', '0', '0.984', '#surprise', '#admiration', 'positive', '0.733', 'potato', 'white_potato', 'chips', 'spud', 'tater']
senticnet['keenly'] = ['0.979', '0.878', '0', '0', '#joy', '#interest', 'positive', '0.928', 'eager', 'eagerness', 'thirstily', 'impatient', 'hot']
senticnet['keenness'] = ['0.532', '0.657', '-0.25', '0.268', '#joy', '#interest', 'positive', '0.3', 'eagerly', 'eagerness', 'avidity', 'thirstily', 'avidness']
senticnet['keep'] = ['0.02', '0.228', '-0.14', '0.306', '#interest', '#admiration', 'positive', '0.102', 'hold_onto', 'retain', 'upkeep', 'have', 'hold']
senticnet['keep_afloat'] = ['0', '0.263', '-0.14', '0.295', '#interest', '#admiration', 'positive', '0.139', 'keep', 'hold', 'retain', 'hold_stuff', 'upkeep']
senticnet['keep_alive'] = ['0.712', '0.935', '0', '0.968', '#interest', '#admiration', 'positive', '0.872', 'survival', 'stay_alive', 'sustain_life', 'enjoy_taste', 'make_feel_better']
senticnet['keep_aloof'] = ['0.666', '-0.87', '0', '0.736', '#surprise', '#admiration', 'positive', '0.759', 'spread', 'diffuse', 'circulate', 'circularize', 'distribute']
senticnet['keep_an_eye_on'] = ['0.939', '-0.57', '0', '0.765', '#joy', '#admiration', 'positive', '0.759', 'check_up_on', 'suss_out', 'check_over', 'check_into', 'check']
senticnet['keep_balance'] = ['0', '-0.71', '0', '0.885', '#surprise', '#admiration', 'positive', '0.798', 'knee_bend', 'bend_over', 'find_chair', 'bend_knee', 'tie_shoes']
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senticnet['kenward'] = ['-0.20', '-0.10', '0.07', '-0.24', '#sadness', '#disgust', 'negative', '-0.10', 'flipper', 'sea_life', 'seafood', 'swim_animal', 'fillet']
senticnet['kenzo_tribouillard_afp_getty'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expansive', 'expansible', 'erectile', 'cavernous']
senticnet['kepler'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'force_unit', 'sthene', 'poundal']
senticnet['keratin'] = ['0', '0.097', '0', '0.085', '#interest', '#admiration', 'positive', '0.091', 'lunula', 'arm_part', 'leave_hand', 'ten_fingers', 'five_fingers']
senticnet['keratoconjunctivitis_sicca'] = ['-0.72', '0', '0.778', '-0.98', '#anger', '#disgust', 'negative', '-0.82', 'actinic_dermatitis', 'hypericism', 'diaper_rash', 'dermatitis', 'seborrheic_dermatitis']
senticnet['keratoderma_blennorrhagica'] = ['-0.84', '0', '0.581', '-0.93', '#sadness', '#disgust', 'negative', '-0.78', 'keratosis_blennorrhagica', 'keratosis', 'keratosis_pilaris', 'seborrheic_keratosis', 'verruca']
senticnet['keratomalacia'] = ['-0.82', '0', '0.996', '-0.61', '#sadness', '#anger', 'negative', '-0.81', 'visual_impairment', 'visual_defect', 'vision_defect', 'visual_disorder', 'avitaminosis']
senticnet['keratosis'] = ['-0.84', '0', '0.581', '-0.93', '#sadness', '#disgust', 'negative', '-0.78', 'keratosis_blennorrhagica', 'keratoderma_blennorrhagica', 'keratosis_pilaris', 'seborrheic_keratosis', 'verruca']
senticnet['keratosis_blennorrhagica'] = ['-0.84', '0', '0.581', '-0.93', '#sadness', '#disgust', 'negative', '-0.78', 'keratosis', 'keratoderma_blennorrhagica', 'keratosis_pilaris', 'seborrheic_keratosis', 'verruca']
senticnet['keratosis_pilaris'] = ['-0.84', '0', '0.581', '-0.93', '#sadness', '#disgust', 'negative', '-0.78', 'keratosis_blennorrhagica', 'keratoderma_blennorrhagica', 'keratosis', 'seborrheic_keratosis', 'verruca']
senticnet['keratotic'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema', 'erythema_multiforme', 'windburn', 'hickey']
senticnet['kerbside'] = ['-0.04', '0', '0.041', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'back_sofa', 'home_improvement_store', 'closet', 'under_carpet', 'shoe_shop']
senticnet['kerbside_collection'] = ['-0.04', '0', '0.041', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'back_sofa', 'home_improvement_store', 'closet', 'under_carpet', 'shoe_shop']
senticnet['kerchief'] = ['0.028', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.047', 'cowhide', 'handkerchief', 'hanky', 'facial_tissue', 'zipper']
senticnet['kerf'] = ['0', '0', '0.061', '-0.03', '#anger', '#disgust', 'negative', '-0.04', 'garden_tool', 'straight_line', 'sharp', 'razor_blade', 'chock']
senticnet['kerfuffle'] = ['-0.79', '0', '0.974', '-0.99', '#anger', '#disgust', 'negative', '-0.91', 'turmoil', 'convulsion', 'tempest', 'disorder', 'earthquake']
senticnet['kerion'] = ['-0.96', '0', '0.694', '-0.62', '#sadness', '#anger', 'negative', '-0.76', 'fungal_infection', 'mycosis', 'ringworm', 'tinea_pedis', 'athlete_foot']
senticnet['kernberg'] = ['0.995', '-0.73', '0', '0.83', '#joy', '#admiration', 'positive', '0.852', 'spiritism', 'otherworldliness', 'spirituality', 'spiritualism', 'internality']
senticnet['kernel'] = ['0', '0.07', '0.041', '0.058', '#interest', '#admiration', 'positive', '0.029', 'maize', 'wheat_rice', 'grain', 'farm_plant', 'cereal_crop']
senticnet['kernel_patch'] = ['-0.58', '0', '0.561', '-0.62', '#sadness', '#disgust', 'negative', '-0.58', 'spot', 'speckle', 'dapple', 'mark', 'fleck']
senticnet['kernicterus'] = ['-0.64', '0', '0.903', '-0.63', '#sadness', '#anger', 'negative', '-0.72', 'jaundice_of_newborn', 'disease', 'icterus_neonatorum', 'icterus', 'symptom']
senticnet['kernville'] = ['-0.06', '0.21', '0', '0.076', '#interest', '#admiration', 'positive', '0.074', 'new_think', 'figure_out', 'brain_activity', 'idea', 'come_up_idea']
senticnet['kerosene'] = ['0', '0', '-0.08', '0', '#fear', '#fear', 'negative', '-0.08', 'petrol', 'diesel', 'butane', 'fuel', 'gasoline']
senticnet['kerosene_fuel'] = ['0.855', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.86', 'ticket_counter', 'newspaper_stand', 'luggage_carrier', 'aircraft_roadway', 'timetable']
senticnet['kerosene_heater'] = ['0.02', '0.076', '0', '0', '#joy', '#interest', 'positive', '0.048', 'land_earth', 'tidal_wave', 'puddle', 'brine', 'spurt']
senticnet['kerosene_lamp'] = ['0.088', '0.076', '0.016', '0.094', '#joy', '#admiration', 'positive', '0.06', 'copper_wire', 'stove', 'several_types', 'oven', 'suspect']
senticnet['kerr_barlow'] = ['0.609', '-0.74', '0', '0.716', '#surprise', '#admiration', 'positive', '0.69', 'malacca', 'sword', 'swagger_stick', 'sword_cane', 'sword_stick']
senticnet['kerri_mason'] = ['0.997', '0.892', '0', '0', '#joy', '#interest', 'positive', '0.945', 'advertisement', 'large_outdoor_board', 'motorway', 'expressway', 'train_track']
senticnet['kerser'] = ['-0.73', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'unwed', 'unwedded', 'divorced', 'unmarried', 'widowed']
senticnet['kes_wingfield'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'yeoman', 'outrider', 'yeoman_of_guard', 'beefeater', 'defender']
senticnet['keshab_chandra_sen'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['keshri'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['keshub_chunder_sen'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['kessler_syndrome'] = ['0.899', '0.803', '0', '0', '#joy', '#interest', 'positive', '0.851', 'ufo', 'milky', 'martian', 'galaxy', 'solar_system']
senticnet['kestin'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['kestrel'] = ['-0.74', '0', '0.963', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['ketchup'] = ['0.824', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.871', 'cottage_cheese', 'blackberry', 'dairy_product', 'drinkable', 'savory']
senticnet['ketchup_bottle'] = ['0.937', '0', '0', '0.828', '#joy', '#admiration', 'positive', '0.883', 'bakery', 'produce_section', 'thanksgiving_dinner', 'dinner_meal', 'fast_food']
senticnet['ketoconazole_nizoral'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['ketoprofen'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucose', 'corn_sugar', 'dextrose', 'dextroglucose', 'grape_sugar']
senticnet['ketoxime'] = ['-0.55', '0', '0.736', '-0.71', '#anger', '#disgust', 'negative', '-0.66', 'neurosis', 'neuroticism', 'psychoneurosis', 'hysterocatalepsy', 'anxiety_hysteria']
senticnet['kettle'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'address_book', 'get_rich', 'blank_check', 'swiss_army_knife', 'whisk']
senticnet['keurig_coffee'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'builder']
senticnet['kevadiya'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['kevlar'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'gold', 'bronze', 'alloy', 'medal', 'copper']
senticnet['kevlar_body_armor'] = ['0.939', '-0.63', '0', '0.653', '#joy', '#admiration', 'positive', '0.741', 'vest', 'waistcoat', 'bulletproof_vest', 'suit_of_armor', 'coat_of_mail']
senticnet['kevlar_helmet'] = ['0.939', '-0.63', '0', '0.653', '#joy', '#admiration', 'positive', '0.741', 'vest', 'waistcoat', 'bulletproof_vest', 'suit_of_armor', 'coat_of_mail']
senticnet['kevlar_vest'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'wear', 'protective_garment', 'coverall', 'shoulder_pad', 'wearable']
senticnet['kewpie'] = ['0.037', '-0.00', '0', '0.12', '#joy', '#admiration', 'positive', '0.055', 'bread_crumb', 'candy', 'bun', 'sweet', 'gush']
senticnet['key'] = ['0.943', '-0.78', '0', '0.571', '#joy', '#surprise', 'positive', '0.767', 'master', 'ignition_key', 'latchkey', 'passkey', 'master_key']
senticnet['key_chain'] = ['0.814', '0.977', '0', '0', '#joy', '#interest', 'positive', '0.895', 'key_ring', 'tote_bag', 'checkbook_cover', 'key_string', 'trail']
senticnet['key_component'] = ['0.918', '0.864', '0', '0', '#joy', '#interest', 'positive', '0.891', 'consequence', 'cause', 'bad_action', 'problem', 'political_statement']
senticnet['key_encryption'] = ['0.584', '-0.72', '0', '0.742', '#surprise', '#admiration', 'positive', '0.684', 'decompression', 'cryptography', 'coding', 'secret_writing', 'steganography']
senticnet['key_fob'] = ['0', '0.069', '0', '0.083', '#interest', '#admiration', 'positive', '0.076', 'latch', 'restrict_access', 'knob', 'door_knob', 'gate']
senticnet['key_indicator'] = ['0', '0.721', '0', '0.731', '#interest', '#admiration', 'positive', '0.726', 'dimension', 'measure', 'tackle', 'length', 'measurement']
senticnet['key_lime_pie'] = ['0.092', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.071', 'bake_treat', 'round_pastry', 'bake_good', 'bake_goods', 'sweet_food']
senticnet['key_resolve_foal'] = ['0', '0.105', '0', '0', '#interest', '#interest', 'positive', '0.105', 'beak', 'many_feathers', 'big_eye', 'bird', 'bird_mouth']
senticnet['key_ring'] = ['0.806', '0.969', '0', '0', '#joy', '#interest', 'positive', '0.887', 'key_chain', 'tote_bag', 'checkbook_cover', 'key_string', 'trail']
senticnet['key_role'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'baddie', 'theatrical_baddie', 'part', 'persona']
senticnet['key_string'] = ['0.809', '0.974', '0', '0', '#joy', '#interest', 'positive', '0.891', 'key_ring', 'tote_bag', 'checkbook_cover', 'key_chain', 'trail']
senticnet['keyboard'] = ['0.893', '0.979', '0', '0', '#joy', '#interest', 'positive', '0.936', 'find_phone_number', 'dial_phone_number', 'pick_up_telephone_receiver', 'dial', 'dial_telephone']
senticnet['keyboard_instrument'] = ['0.033', '0', '0.009', '0.057', '#joy', '#admiration', 'positive', '0.027', 'triangle', 'baton', 'bugle', 'type_of_bow', 'horn']
senticnet['keyboard_player'] = ['0', '-0.02', '0.055', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'djembe', 'make_musicr', 'use_jazz_music', 'tune_instrument', 'percussion']
senticnet['keyboardist'] = ['0.049', '0.167', '-0.05', '0.295', '#interest', '#admiration', 'positive', '0.115', 'four_sides', 'sound_system', 'dolby_surround', 'movie', 'surround']
senticnet['keycard'] = ['0', '0.069', '0', '0.083', '#interest', '#admiration', 'positive', '0.076', 'latch', 'restrict_access', 'knob', 'door_knob', 'gate']
senticnet['keychain'] = ['0', '0.084', '0', '0', '#interest', '#interest', 'positive', '0.084', 'keyhole', 'clothes_washer', 'briefcase', 'purse', 'clutch_bag']
senticnet['keyed_lock'] = ['0', '0.069', '0', '0.083', '#interest', '#admiration', 'positive', '0.076', 'latch', 'restrict_access', 'knob', 'door_knob', 'gate']
senticnet['keyhole'] = ['0', '0.084', '0', '0', '#interest', '#interest', 'positive', '0.084', 'keychain', 'clothes_washer', 'briefcase', 'purse', 'clutch_bag']
senticnet['keyid'] = ['0.022', '-0.13', '0.153', '-0.31', '#anger', '#disgust', 'negative', '-0.07', 'codeine', 'morphine', 'infection', 'cocaine', 'nuclear_war']
senticnet['keyless_entry'] = ['0', '0', '0', '0.109', '#admiration', '#admiration', 'positive', '0.109', 'knob', 'door_knob', 'door_handle', 'handle', 'hinge']
senticnet['keyless_entry_system'] = ['0', '0', '0', '0.109', '#admiration', '#admiration', 'positive', '0.109', 'knob', 'door_knob', 'door_handle', 'handle', 'hinge']
senticnet['keyless_ignition'] = ['0', '0', '0', '0.109', '#admiration', '#admiration', 'positive', '0.109', 'knob', 'door_knob', 'door_handle', 'handle', 'hinge']
senticnet['keynote'] = ['0.928', '0.946', '0', '0', '#joy', '#interest', 'positive', '0.937', 'plenary', 'convened', 'session', 'lecture', 'meeting']
senticnet['keynote_speech'] = ['-0.79', '0', '0.948', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['keypad'] = ['0.893', '0.979', '0', '0', '#joy', '#interest', 'positive', '0.936', 'find_phone_number', 'dial_phone_number', 'pick_up_telephone_receiver', 'dial', 'dial_telephone']
senticnet['keystream'] = ['-0.52', '0', '0.821', '-0.65', '#anger', '#disgust', 'negative', '-0.66', 'work_out', 'reckon', 'calculate', 'average_out', 'cypher']
senticnet['keyway'] = ['0', '0.069', '0', '0.083', '#interest', '#admiration', 'positive', '0.076', 'latch', 'restrict_access', 'knob', 'door_knob', 'gate']
senticnet['keyword'] = ['-0.05', '0.084', '0', '0.062', '#interest', '#admiration', 'positive', '0.03', 'vocabulary', 'translation', 'book_library', 'book_poetry', 'book_genre']
senticnet['keyword_search'] = ['-0.05', '0.084', '0', '0.062', '#interest', '#admiration', 'positive', '0.03', 'vocabulary', 'translation', 'book_library', 'book_poetry', 'book_genre']
senticnet['kfc'] = ['0.965', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.951', 'delicious_food', 'hot_meal', 'good_food', 'fresh_produce', 'tasty_food']
senticnet['kfc_chicken'] = ['0.965', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.951', 'delicious_food', 'hot_meal', 'good_food', 'fresh_produce', 'tasty_food']
senticnet['kfc_parent_company'] = ['0.965', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.951', 'delicious_food', 'hot_meal', 'good_food', 'fresh_produce', 'tasty_food']
senticnet['kgb_agent'] = ['-0.69', '0', '0.756', '-0.99', '#anger', '#disgust', 'negative', '-0.81', 'waster', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['khadgar'] = ['-0.04', '-0.02', '0.027', '-0.03', '#sadness', '#disgust', 'negative', '-0.01', 'precious_metal', 'olympic_medal', 'shiny_metal', 'many_different_kinds', 'valuable_metal']
senticnet['khajipet'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['khakhua'] = ['-0.73', '0', '0.734', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'primitive', 'headhunter', 'barbarian', 'anthropophagus', 'anthropophagite']
senticnet['khaki'] = ['-0.79', '0', '0.599', '-0.93', '#sadness', '#disgust', 'negative', '-0.77', 'uniform', 'battle_dress', 'dress_uniform', 'fatigue', 'military_uniform']
senticnet['khaki_campbell'] = ['-0.50', '0', '0.764', '-0.61', '#anger', '#disgust', 'negative', '-0.62', 'doctor', 'mountebank', 'charlatan', 'quack', 'physician']
senticnet['khaled_desouki_afp_getty'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expansive', 'expansible', 'erectile', 'cavernous']
senticnet['khalid_khannouchi'] = ['-0.04', '-0.03', '0', '-0.16', '#sadness', '#disgust', 'negative', '-0.05', 'crazy', 'damage_body', 'crazy_person', 'threat', 'max']
senticnet['khalq_faction'] = ['-0.50', '0', '0.989', '-0.91', '#anger', '#disgust', 'negative', '-0.80', 'camp', 'pack', 'clique', 'coterie', 'ingroup']
senticnet['khan'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'caravansary', 'caravanserai', 'hostel', 'caravan_inn', 'imaret']
senticnet['khan_el_khalili'] = ['0', '0.113', '0.041', '0.058', '#interest', '#admiration', 'positive', '0.043', 'corner_shop', 'general_store', 'many_shops', 'large_supermarket', 'goods']
senticnet['khandha'] = ['-0.85', '0', '0.968', '-0.75', '#sadness', '#anger', 'negative', '-0.86', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['khanik'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'white_animal', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
senticnet['khanqah'] = ['0', '0.224', '0', '0.197', '#interest', '#admiration', 'positive', '0.211', 'church', 'temple', 'cathedral', 'chapel', 'confession']
senticnet['kharagpur_junction'] = ['0.861', '-0.52', '0', '0.598', '#joy', '#admiration', 'positive', '0.663', 'exit', 'get_off', 'deplane', 'go_out', 'get_out']
senticnet['kharaj'] = ['-0.08', '0', '-0.15', '-0.20', '#fear', '#disgust', 'negative', '-0.14', 'ask_money', 'extortion', 'wringing', 'intimidate', 'psychological_pressure']
senticnet['kharga'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['kheir'] = ['0.798', '-0.51', '0', '0.973', '#joy', '#admiration', 'positive', '0.763', 'inexperienced_person', 'boy_scout', 'dear', 'virgin', 'mortal']
senticnet['kherubim'] = ['-0.88', '0', '0.882', '-0.58', '#sadness', '#anger', 'negative', '-0.78', 'mortal', 'behemoth', 'unusual_person', 'aberrant', 'giant']
senticnet['khety'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
senticnet['khmer_empire'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
senticnet['khorasan_wheat'] = ['0.061', '0.093', '0.037', '0.114', '#interest', '#admiration', 'positive', '0.058', 'whole_grain', 'food_ingredient', 'staple_food', 'starchy_food', 'food_grain']
senticnet['khorne'] = ['0.826', '-0.65', '0', '0.587', '#joy', '#surprise', 'positive', '0.689', 'scoffer', 'vegetarian', 'eater', 'diner', 'dunker']
senticnet['khowst'] = ['-0.66', '0', '0.789', '-0.63', '#sadness', '#anger', 'negative', '-0.69', 'humbug', 'play_trick', 'cozen', 'pull_fast_one_on', 'play_trick_on']
senticnet['khudiram'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['khumbu'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['khumbu_region'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['ki_suck_han'] = ['0.706', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.768', 'train_station', 'subway_stop', 'parking_lot', 'subway_station', 'park_structure']
senticnet['ki_suk_han'] = ['0.706', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.768', 'train_station', 'subway_stop', 'parking_lot', 'subway_station', 'park_structure']
senticnet['ki_te'] = ['-0.91', '0', '0.769', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'break', 'good_luck', 'chance_eventpy_chance', 'chance_event', 'coincidence']
senticnet['kia_rio'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'cable_car', 'baggage_compartment', 'subway', 'amusement_park', 'terminal']
senticnet['kiama_blowhole'] = ['-0.93', '0', '0.607', '-0.67', '#sadness', '#disgust', 'negative', '-0.73', 'spiracle', 'stigma', 'orifice', 'opening', 'porta']
senticnet['kiaran_mclaughlin'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'coach', 'instructor', 'personal_trainer', 'fit_person', 'train']
senticnet['kiba'] = ['0.021', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'plastic_bottle', 'party', 'use_party', 'lighter_air', 'compress_air']
senticnet['kichi'] = ['0.973', '-0.50', '0', '0.612', '#joy', '#admiration', 'positive', '0.698', 'chic', 'chicness', 'jauntiness', 'modishness', 'stylishness']
senticnet['kick'] = ['0', '0', '0.752', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'get_hit_by_car', 'meander', 'full_moon', 'step_dog_poop', 'trot']
senticnet['kick-backs'] = ['0.882', '0.782', '0', '0', '#joy', '#interest', 'positive', '0.832', 'off-the-books', 'back-room', 'bribe', 'under-the-table', 'underhanded']
senticnet['kick_ass'] = ['0', '0', '0.752', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'get_hit_by_car', 'meander', 'full_moon', 'step_dog_poop', 'trot']
senticnet['kick_ball'] = ['0', '0.702', '0', '0.925', '#interest', '#admiration', 'positive', '0.814', 'rush', 'catch', 'kick', 'flush', 'boot']
senticnet['kick_downstairs'] = ['-0.77', '0', '0.72', '-0.98', '#sadness', '#disgust', 'negative', '-0.82', 'designate', 'depute', 'reduce', 'sideline', 'delegate']
senticnet['kick_in_butt'] = ['-0.83', '0', '0.924', '-0.79', '#sadness', '#anger', 'negative', '-0.85', 'corporal_punishment', 'licking', 'whacking', 'stoning', 'lapidation']
senticnet['kick_out'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'debar']
senticnet['kick_start'] = ['0.66', '-0.85', '0', '0.744', '#surprise', '#admiration', 'positive', '0.752', 'further', 'promote', 'encourage', 'conduce', 'advance']
senticnet['kick_upstairs'] = ['0.586', '-0.96', '0', '0.9', '#surprise', '#admiration', 'positive', '0.818', 'ennoble', 'gentle', 'prefer', 'spot_promote', 'favor']
senticnet['kickapoo_tribe'] = ['0.989', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.97', 'committee', 'council', 'caste', 'association', 'organization']
senticnet['kickback'] = ['-0.91', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.88', 'cringe', 'kick', 'recoil', 'recoilless', 'shrink']
senticnet['kickback_scheme'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'fraud', 'swindle', 'rig']
senticnet['kickball'] = ['0', '-0.08', '0', '0', '#surprise', '#surprise', 'positive', '0.082', 'yo-yo', 'playroom', 'backgammon', 'action_figure', 'board_game']
senticnet['kicked_out'] = ['-0.84', '0', '0.506', '-0.62', '#sadness', '#disgust', 'negative', '-0.66', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['kickflip'] = ['-0.18', '0', '-0.14', '-0.11', '#sadness', '#fear', 'negative', '-0.14', 'fly_kite', 'walk_dog', 'ride_roller_coaster', 'rake_leave', 'jump_up_down']
senticnet['kickstand'] = ['-0.03', '0.11', '0', '0.128', '#interest', '#admiration', 'positive', '0.069', 'freestyle', 'take_leak', 'jump_air', 'backstroke', 'go_underwater']
senticnet['kickstart'] = ['0.66', '-0.85', '0', '0.744', '#surprise', '#admiration', 'positive', '0.752', 'further', 'promote', 'encourage', 'conduce', 'advance']
senticnet['kicktail'] = ['-0.18', '0', '-0.14', '-0.11', '#sadness', '#fear', 'negative', '-0.14', 'fly_kite', 'walk_dog', 'ride_roller_coaster', 'rake_leave', 'jump_up_down']
senticnet['kid'] = ['0.121', '0.099', '0', '0.063', '#joy', '#interest', 'positive', '0.094', 'little_child', 'baby', 'child', 'youngster', 'young_child']
senticnet['kid_activity'] = ['0.042', '0', '0', '0.109', '#joy', '#admiration', 'positive', '0.075', 'game_action', 'form_of_social_interaction', 'fun_activity', 'stage_production', 'child_game']
senticnet['kid_game'] = ['0', '-0.02', '0', '0.099', '#surprise', '#admiration', 'positive', '0.064', 'fun_game', 'child_game', 'trinket', 'play_device', 'plaything']
senticnet['kid_play'] = ['0.955', '-0.78', '0', '0', '#joy', '#surprise', 'positive', '0.869', 'child_plaything', 'child_play', 'trinket', 'plaything', 'play_device']
senticnet['kid_room'] = ['0.801', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.778', 'fancy_dress', 'shoe_shop', 'girl_room', 'furniture_store', 'toy_box']
senticnet['kid_toy'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'fun', 'tiny_person', 'mannequin', 'build_block', 'plaything']
senticnet['kiddie'] = ['0', '0', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.05', 'human', 'delusional', 'use_illegal_drug', 'busy_person', 'carpenter']
senticnet['kiddie_pool'] = ['0', '0', '0', '0.964', '#admiration', '#admiration', 'positive', '0.964', 'swimming_pool', 'swim', 'swimsuit', 'lake', 'have_fun']
senticnet['kiddieland'] = ['0.056', '-0.01', '0.036', '0.073', '#joy', '#admiration', 'positive', '0.028', 'thrill_ride', 'have_fun', 'drag_race', 'enjoyment', 'roller_coaster']
senticnet['kiddin'] = ['0.104', '0', '0', '0.088', '#joy', '#admiration', 'positive', '0.096', 'baby_flower', 'wee', 'bud', 'part_of_plant', 'leafy']
senticnet['kidnap'] = ['-0.89', '-0.71', '-0.76', '-0.93', '#sadness', '#disgust', 'negative', '-0.47', 'bigot', 'censorship', 'jail_time', 'reprimand', 'intolerance']
senticnet['kidnapper'] = ['0.864', '-0.54', '0', '0.613', '#joy', '#admiration', 'positive', '0.675', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['kidney'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'scrotum', 'liver', 'bowel', 'pancreas', 'body_part']
senticnet['kidney_bean'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'common_bean', 'frijol', 'frijole', 'green_bean', 'wax_bean']
senticnet['kidney_cancer'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['kidney_damage'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'bowel', 'scrotum', 'shoulder', 'anus', 'waist']
senticnet['kidney_disease'] = ['0.576', '-0.57', '0', '0.768', '#joy', '#admiration', 'positive', '0.639', 'uropathy', 'polyuria', 'polycystic_kidney_disease', 'renal_failure', 'kidney_failure']
senticnet['kidney_donation'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'displace', 'move', 'move_away', 'bump', 'dislocate']
senticnet['kidney_donor'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'displace', 'move', 'move_away', 'bump', 'dislocate']
senticnet['kidney_failure'] = ['-0.60', '0', '0.544', '-0.53', '#sadness', '#anger', 'negative', '-0.56', 'acute_renal_failure', 'acute_kidney_failure', 'chronic_renal_failure', 'chronic_kidney_failure', 'nephrosis']
senticnet['kidney_stone'] = ['0.978', '0.812', '0', '0', '#joy', '#interest', 'positive', '0.895', 'nephrolith', 'urinary_calculus', 'renal_calculus', 'help', 'stone']
senticnet['kidney_tissue'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'bowel', 'scrotum', 'shoulder', 'anus', 'waist']
senticnet['kidney_transplant'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'displace', 'move', 'move_away', 'bump', 'dislocate']
senticnet['kidney_transplantation'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'displace', 'move', 'move_away', 'bump', 'dislocate']
senticnet['kidney_vetch'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'livelong', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['kidron_valley'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['kielce'] = ['0', '0', '0', '0.1', '#admiration', '#admiration', 'positive', '0.1', 'find_earth', 'wax', 'plutonium', 'shine', 'melt']
senticnet['kiem_tv'] = ['-0.06', '0.21', '0', '0.076', '#interest', '#admiration', 'positive', '0.074', 'new_think', 'figure_out', 'brain_activity', 'idea', 'come_up_idea']
senticnet['kieran_powell'] = ['0.034', '-0.02', '0.054', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'compass', 'swiss_army_knife', 'bake_pan', 'coffee_mug', 'wine_opener']
senticnet['kieron_pollard'] = ['-0.62', '0', '0.713', '-0.98', '#anger', '#disgust', 'negative', '-0.77', 'muddy_up', 'muck_up', 'foul', 'blemish', 'smear']
senticnet['kieserite'] = ['-0.03', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'lithium', 'mineral', 'potassium', 'iodine', 'manganese']
senticnet['kiesle'] = ['-0.95', '0', '0.673', '-0.85', '#sadness', '#disgust', 'negative', '-0.82', 'discharge', 'free', 'dethrone', 'disinvest', 'unfrock']
senticnet['kievo'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
senticnet['kilauea_volcano'] = ['0', '0', '-0.92', '0', '#fear', '#fear', 'negative', '-0.92', 'water_flow', 'wind_water', 'water_stream', 'waterway', 'magma']
senticnet['kilesa'] = ['-0.85', '0', '0.968', '-0.75', '#sadness', '#anger', 'negative', '-0.86', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['kill'] = ['0', '0', '-0.93', '-0.98', '#fear', '#disgust', 'negative', '-0.95', 'death_destruction', 'murder', 'death', 'kill_enemy', 'get_rid_of_enemy']
senticnet['kill_adolf_hitler'] = ['-0.69', '0', '0.756', '-0.99', '#anger', '#disgust', 'negative', '-0.81', 'waster', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['kill_animal'] = ['-0.08', '-0.04', '0', '-0.13', '#sadness', '#disgust', 'negative', '-0.05', 'target_practice', 'shoot_target', 'shoot_bullet', 'self_defense', 'corner_prey']
senticnet['kill_brain_cell'] = ['0.875', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.935', 'eat_popcorn', 'cry', 'enjoy_show', 'change_channel', 'entertain']
senticnet['kill_bug'] = ['-0.05', '-0.04', '0.01', '-0.11', '#sadness', '#disgust', 'negative', '-0.03', 'potato', 'break', 'toxic_waste', 'tear_apart', 'destroy']
senticnet['kill_cat'] = ['-0.96', '0', '-0.78', '-0.72', '#sadness', '#fear', 'negative', '-0.82', 'corner_prey', 'execute', 'hunt_game', 'elephant_sit_chest', 'suffocate']
senticnet['kill_chicken'] = ['-0.05', '0', '-0.14', '-0.05', '#sadness', '#fear', 'negative', '-0.08', 'question_authority', 'smoke_cigarette', 'battle_disease', 'cook_vegetable', 'prepare_ingredient']
senticnet['kill_consciousness'] = ['-0.91', '0', '0.513', '-0.93', '#sadness', '#disgust', 'negative', '-0.78', 'die', 'cause_injury', 'kill', 'end_life', 'feel_pain']
senticnet['kill_deer'] = ['-0.08', '0', '-0.07', '0', '#sadness', '#fear', 'negative', '-0.08', 'hunt', 'hunt_game', 'use_gun', 'slay', 'corner_prey']
senticnet['kill_earth'] = ['0', '0', '-0.72', '-0.89', '#fear', '#disgust', 'negative', '-0.80', 'global_climate_change', 'end_world', 'apocalypse', 'climate_change', 'dead_planet']
senticnet['kill_endangered_spieces'] = ['-0.94', '-0.80', '-0.82', '-0.75', '#sadness', '#fear', 'negative', '-0.42', 'kind_animal', 'cover_fur', 'big_cat', 'bad_eyesight', 'type_of_animal']
senticnet['kill_enemy'] = ['0', '0.827', '0', '0.84', '#interest', '#admiration', 'positive', '0.833', 'win_war', 'win_battle', 'beat_enemy', 'become_strong', 'become_best']
senticnet['kill_forest'] = ['-0.23', '-0.09', '0', '-0.24', '#sadness', '#disgust', 'negative', '-0.12', 'fallen_tree', 'dangerous', 'woodman', 'wood', 'accident']
senticnet['kill_friend'] = ['-0.99', '0', '0.434', '-0.90', '#sadness', '#disgust', 'negative', '-0.77', 'friendly_fire', 'fight_war', 'battle', 'attack', 'hurt_wrongly']
senticnet['kill_germ'] = ['0.768', '-0.63', '0', '0.717', '#joy', '#admiration', 'positive', '0.705', 'antibacterial', 'medication', 'antibiotic_drug', 'antibiotic', 'bacitracin']
senticnet['kill_human'] = ['0', '0', '-0.89', '-0.88', '#fear', '#disgust', 'negative', '-0.89', 'drop_airplane', 'windsurf', 'kill_life', 'destroy_city', 'violence']
senticnet['kill_human_being'] = ['-0.17', '-0.15', '-0.21', '-0.08', '#sadness', '#fear', 'negative', '-0.08', 'find_subway_stop', 'find_bank', 'find_farm', 'slander', 'die']
senticnet['kill_insect'] = ['0.73', '0', '0', '0', '#joy', '#joy', 'positive', '0.73', 'house_look_better', 'tidiness', 'solve_problem', 'remove_spot', 'kill_spider']
senticnet['kill_life'] = ['-0.78', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.86', 'cut_down', 'destroy_home', 'destroy_city', 'destroy_target', 'cut_down_tree']
senticnet['kill_live_creature'] = ['-0.92', '0', '0.996', '-0.96', '#anger', '#disgust', 'negative', '-0.95', 'kill', 'kill_animal', 'bad_person', 'murder', 'kill_person']
senticnet['kill_man'] = ['-0.72', '0', '0.623', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'eliminate', 'kill_person', 'kill', 'hit', 'commit_murder']
senticnet['kill_mouse'] = ['0.73', '0', '0', '0', '#joy', '#joy', 'positive', '0.73', 'catch_mouse', 'eat_cat_food', 'kill_insect', 'dream_with_eye_open', 'rain_parade']
senticnet['kill_people'] = ['-0.07', '-0.05', '-0.04', '-0.15', '#sadness', '#disgust', 'negative', '-0.05', 'shoot_bullet', 'destroy_home', 'shoot_target', 'target_practice', 'destroy_live']
senticnet['kill_person'] = ['-0.91', '0', '0.513', '-0.93', '#sadness', '#disgust', 'negative', '-0.78', 'cut_down', 'cause_bleeding', 'cut_finger', 'beat_person', 'cause_strong_pain']
senticnet['kill_pest'] = ['0', '0', '-0.87', '-0.92', '#fear', '#disgust', 'negative', '-0.89', 'destroy_target', 'kill', 'eliminate', 'destroy_city', 'target_practice']
senticnet['kill_self'] = ['-0.29', '-0.04', '-0.23', '-0.11', '#sadness', '#fear', 'negative', '-0.15', 'end_life', 'kill', 'take_life', 'call_help', 'capital_punishment']
senticnet['kill_soldier'] = ['-0.05', '-0.04', '0', '-0.19', '#sadness', '#disgust', 'negative', '-0.06', 'make_war', 'conquer_enemy', 'keep_peace', 'settle_disagreement', 'gain_territory']
senticnet['kill_specific_group'] = ['-0.12', '0.082', '-0.10', '0', '#sadness', '#fear', 'negative', '-0.05', 'wave_flag', 'correct_mistake', 'age_gracefully', 'hurt_animal', 'bicycle_across_town']
senticnet['kill_spider'] = ['0.73', '0', '0', '0', '#joy', '#joy', 'positive', '0.73', 'house_look_better', 'tidiness', 'solve_problem', 'remove_spot', 'kill_insect']
senticnet['kill_time'] = ['-0.96', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.83', 'pastime', 'cultural_experience', 'stimulate_mind', 'imagine', 'escape_reality']
senticnet['killed'] = ['-0.96', '0', '0.159', '-0.98', '#sadness', '#disgust', 'negative', '-0.70', 'fired', 'unemployed', 'become_poor', 'need_money', 'need_new_job']
senticnet['killed_instantly'] = ['-0.95', '0', '0.712', '-0.52', '#sadness', '#anger', 'negative', '-0.73', 'uncut', 'unharmed', 'unscathed', 'unwounded', 'uninjured']
senticnet['killer'] = ['-0.06', '-0.02', '0', '-0.15', '#sadness', '#disgust', 'negative', '-0.06', 'gunshot', 'wound', 'fall_sky', 'lethal', 'fire_bullet']
senticnet['killer_bee'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'hornet', 'vespid', 'vespid_wasp']
senticnet['killer_blow'] = ['-0.94', '0', '0.863', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'poke', 'clout', 'lick', 'punch', 'biff']
senticnet['killer_croc'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'only_bird']
senticnet['killer_frost'] = ['-0.58', '0', '0.661', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'explosive', 'cordite', 'dynamite', 'plastic_explosive', 'explosive_compound']
senticnet['killer_instinct'] = ['-0.83', '0', '0.802', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'dishonesty', 'deceitfulness', 'craftiness', 'disingenuousness', 'artfulness']
senticnet['killer_moth'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'only_bird']
senticnet['killer_shark'] = ['-0.20', '-0.10', '0.07', '-0.24', '#sadness', '#disgust', 'negative', '-0.10', 'gill', 'sea_life', 'seafood', 'swim_animal', 'fillet']
senticnet['killer_shrike'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
senticnet['killer_whale'] = ['-0.20', '-0.10', '0.07', '-0.24', '#sadness', '#disgust', 'negative', '-0.10', 'gill', 'sea_life', 'seafood', 'swim_animal', 'fillet']
senticnet['killerchihuahua'] = ['0.533', '0.818', '0', '0.683', '#interest', '#admiration', 'positive', '0.678', 'gain_information', 'report_news', 'solve', 'seek_answer', 'gather_information']
senticnet['killifish'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'stone_crab', 'swimming_crab', 'rock_crab', 'crab', 'pea_crab']
senticnet['killing'] = ['-0.71', '0', '0.748', '-0.55', '#sadness', '#anger', 'negative', '-0.67', 'casualty', 'fatal_accident', 'fatality', 'violent_death', 'death']
senticnet['killing_innocent'] = ['-0.57', '0', '0.56', '-0.87', '#sadness', '#disgust', 'negative', '-0.67', 'reprimand', 'rebuke', 'reproof', 'reproval', 'reprehension']
senticnet['killing_spree'] = ['-0.67', '0', '0.938', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'massacre', 'mass_murder', 'carnage', 'slaughter', 'butchery']
senticnet['killjoy'] = ['-0.82', '0', '0.556', '-0.86', '#sadness', '#disgust', 'negative', '-0.75', 'worrier', 'fusspot', 'worrywart', 'unwelcome_person', 'party_pooper']
senticnet['kiln'] = ['-0.06', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.04', 'polish', 'magnify', 'see_out', 'sill', 'pane']
senticnet['kilodistance_measurement'] = ['0.941', '0.74', '0', '0', '#joy', '#interest', 'positive', '0.841', 'distance_measure', 'length_measurement', 'distance_measurement', 'meter', 'imperial_measurement']
senticnet['kilogram_per'] = ['-0.64', '0', '0.87', '-0.74', '#anger', '#disgust', 'negative', '-0.75', 'infinitude', 'piles', 'mess', 'large_number', 'large_indefinite_amount']
senticnet['kilometer'] = ['0.868', '0.819', '0', '0', '#joy', '#interest', 'positive', '0.843', 'distance_measure', 'measure', 'length_measurement', 'meter', 'distance_measurement']
senticnet['kilometer_away'] = ['-0.95', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.83', 'outer', 'radiant', 'around_earth', 'nothingness', 'heavenly_body']
senticnet['kilometer_long'] = ['0.868', '0.819', '0', '0', '#joy', '#interest', 'positive', '0.843', 'distance_measure', 'measure', 'length_measurement', 'meter', 'distance_measurement']
senticnet['kilometer_mile'] = ['0.868', '0.819', '0', '0', '#joy', '#interest', 'positive', '0.843', 'distance_measure', 'measure', 'length_measurement', 'meter', 'distance_measurement']
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senticnet['kilometre'] = ['0.868', '0.819', '0', '0', '#joy', '#interest', 'positive', '0.843', 'distance_measure', 'measure', 'length_measurement', 'meter', 'distance_measurement']
senticnet['kilometre_offshore'] = ['-0.04', '-0.01', '0.103', '0', '#sadness', '#anger', 'negative', '-0.04', 'near_beach', 'along_beach', 'full_water', 'ocean_ground', 'salty']
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senticnet['kilovolt'] = ['0', '0', '-1', '-1', '#fear', '#disgust', 'negative', '-1.0', 'moon_gravity', 'force', 'coercion', 'watt', 'power_source']
senticnet['kimono'] = ['0', '-0.04', '0.074', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'nightgown', 'garment', 'judo', 'stocking', 'apparel']
senticnet['kimya_dawson'] = ['0.946', '-0.91', '0', '0.664', '#joy', '#surprise', 'positive', '0.841', 'pretender', 'phony', 'phoney', 'hypocrite', 'dissembler']
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senticnet['kind'] = ['0.933', '0', '0', '0.946', '#joy', '#admiration', 'positive', '0.94', 'kindness', 'love_affection', 'please', 'show_love', 'beloved']
senticnet['kind-hearted'] = ['0.583', '0.694', '-0.53', '0.517', '#joy', '#interest', 'positive', '0.316', 'compatibility', 'kindheartedness', 'compatibly', 'kindhearted', 'kind-heartedness']
senticnet['kind-heartedness'] = ['0.56', '0.708', '-0.53', '0.514', '#joy', '#interest', 'positive', '0.312', 'kind', 'kindhearted', 'kindheartedness', 'fellow_feeling', 'compatibly']
senticnet['kind_animal'] = ['-0.89', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.82', 'hound', 'zoo', 'cage_animal', 'hunter', 'animal']
senticnet['kind_food'] = ['0.071', '0', '0.031', '0.064', '#joy', '#admiration', 'positive', '0.035', 'root_vegetable', 'legume', 'japanese_food', 'popular_food', 'energy']
senticnet['kind_gesture'] = ['0.8', '0.802', '0', '0', '#joy', '#interest', 'positive', '0.801', 'card', 'character', 'letter', 'good_movie', 'chance']
senticnet['kind_pet'] = ['0', '0', '0', '0.865', '#admiration', '#admiration', 'positive', '0.865', 'animal', 'specific', 'pet', 'disarray', 'tamed']
senticnet['kind_request'] = ['0.601', '0.647', '-0.26', '0.322', '#joy', '#interest', 'positive', '0.326', 'ask_permission', 'cup_hand', 'tear_paper', 'laugh_clown', 'love_pet']
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senticnet['kindergarten'] = ['0.084', '-0.08', '0.025', '0', '#joy', '#surprise', 'positive', '0.049', 'fun_pastime', 'stage_production', 'have_fun', 'fun_activity', 'diversion']
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senticnet['kindergarten_teacher'] = ['-0.04', '0.088', '0.013', '0.069', '#interest', '#admiration', 'positive', '0.024', 'kindergarden', 'daycare', 'nursery', 'child', 'asylum']
senticnet['kindergartener'] = ['-0.06', '0', '0.01', '0', '#sadness', '#anger', 'negative', '-0.03', 'youth', 'young_child', 'small_child', 'lad', 'tiny']
senticnet['kindergartner'] = ['-0.06', '0', '0.01', '0', '#sadness', '#anger', 'negative', '-0.03', 'youth', 'young_child', 'small_child', 'lad', 'tiny']
senticnet['kindest'] = ['0.736', '-0.66', '0', '0.777', '#joy', '#admiration', 'positive', '0.725', 'lucky', 'propitious', 'prosperous', 'favorable', 'good_luck']
senticnet['kindhearted'] = ['0.581', '0.696', '-0.53', '0.519', '#joy', '#interest', 'positive', '0.315', 'good_match', 'compatible', 'kind', 'compatibility', 'kindheartedness']
senticnet['kindheartedness'] = ['0.56', '0.708', '-0.53', '0.514', '#joy', '#interest', 'positive', '0.312', 'kind-hearted', 'kindhearted', 'kind-heartedness', 'fellow_feeling', 'compatibly']
senticnet['kindle'] = ['0.722', '-0.76', '0', '0.946', '#surprise', '#admiration', 'positive', '0.811', 'arouse', 'elicit', 'enask_for', 'ask_for', 'evoke']
senticnet['kindlier'] = ['0.808', '-0.69', '0', '0.938', '#joy', '#admiration', 'positive', '0.815', 'spinnable', 'susceptible', 'easy', 'pliant', 'plastic']
senticnet['kindliest'] = ['-0.64', '0', '0.52', '-0.53', '#sadness', '#disgust', 'negative', '-0.56', 'wrong', 'incorrect', 'unbecoming', 'improper', 'unseemly']
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senticnet['kindly_feeling'] = ['0.643', '0.64', '-0.24', '0.3', '#joy', '#interest', 'positive', '0.336', 'high_spirits', 'elating', 'exhilarating', 'elated', 'cheerful']
senticnet['kindness'] = ['0', '0', '0', '0.98', '#admiration', '#admiration', 'positive', '0.98', 'gentleness', 'demand', 'sympathy', 'type_of_bear', 'express_gratitude']
senticnet['kindred_spirit'] = ['0.814', '-0.50', '0', '0.622', '#joy', '#admiration', 'positive', '0.648', 'friend', 'champion', 'supporter', 'protagonist', 'endorser']
senticnet['kinesthetic'] = ['0.859', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.863', 'auditory', 'visual', 'sensory', 'tactile', 'aural']
senticnet['kinet'] = ['0.226', '0.207', '-0.20', '0.258', '#joy', '#admiration', 'positive', '0.122', 'create_destroy', 'power_source', 'potential', 'hyper', 'form_of_energy']
senticnet['kinetic'] = ['0.535', '-0.58', '0', '0.748', '#surprise', '#admiration', 'positive', '0.623', 'moving', 'restless', 'afoot', 'ahorse', 'ahorseback']
senticnet['kinetic_energy'] = ['0.983', '0.733', '0', '0', '#joy', '#interest', 'positive', '0.858', 'forceful', 'power_source', 'electrical', 'gas_oil', 'electric_power']
senticnet['kinetic_friction'] = ['0', '0', '-0.11', '0', '#fear', '#fear', 'negative', '-0.11', 'chemical_reaction', 'set_fire', 'grow_yard', 'heat_light', 'burn_down_house']
senticnet['kinetic_isotope_effect'] = ['0.695', '-0.85', '0', '0.501', '#joy', '#surprise', 'positive', '0.684', 'alternative_energy', 'energy', 'energy_of_activation', 'atomic_energy', 'nuclear_energy']
senticnet['kinetica_staff'] = ['0', '0.084', '0', '0', '#interest', '#interest', 'positive', '0.084', 'call_restaurant', 'reservation', 'reserve', 'dinner', 'reserved']
senticnet['kinetically'] = ['0', '-0.96', '0', '0.882', '#surprise', '#admiration', 'positive', '0.924', 'thermodynamically', 'biochemically', 'climatically', 'dimensionally', 'metastable']
senticnet['kinetics'] = ['-0.96', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.90', 'dynamic', 'number', 'effect', 'material_research', 'work']
senticnet['kinfolk'] = ['0', '0.833', '0', '0.697', '#interest', '#admiration', 'positive', '0.765', 'tribe', 'folk', 'family', 'ethnic_music', 'family_line']
senticnet['king'] = ['0.044', '0.095', '0', '0.083', '#interest', '#admiration', 'positive', '0.074', 'monarch', 'duchess', 'noble', 'duke', 'royal_leader']
senticnet['king_ahab'] = ['0.849', '-0.89', '0', '0.924', '#surprise', '#admiration', 'positive', '0.89', 'nonintellectual', 'lowbrow', 'lowbrowed', 'mindless', 'uncultivated']
senticnet['king_canute'] = ['-0.92', '0', '0.659', '-0.81', '#sadness', '#disgust', 'negative', '-0.8', 'unripe', 'napping', 'off_guard', 'off_one_guard', 'unprepared']
senticnet['king_cnut'] = ['-0.92', '0', '0.659', '-0.81', '#sadness', '#disgust', 'negative', '-0.8', 'unripe', 'napping', 'off_guard', 'off_one_guard', 'unprepared']
senticnet['king_cobra'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'death_adder', 'ophidian', 'serpent', 'elapid', 'taipan']
senticnet['king_crab'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'crab', 'swimming_crab', 'rock_crab', 'fiddler_crab', 'pea_crab']
senticnet['king_dominion'] = ['0.056', '-0.01', '0.036', '0.073', '#joy', '#admiration', 'positive', '0.028', 'thrill_ride', 'have_fun', 'drag_race', 'enjoyment', 'roller_coaster']
senticnet['king_fulk'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['king_gesar'] = ['0.089', '-0.01', '0.033', '0.082', '#joy', '#admiration', 'positive', '0.039', 'informative', 'informational', 'ephemeral', 'fun_read', 'ads']
senticnet['king_joffrey_baratheon'] = ['-0.73', '0', '0.55', '-0.75', '#sadness', '#disgust', 'negative', '-0.68', 'mortal', 'person', 'individual', 'mangler', 'mutilator']
senticnet['king_lear'] = ['-0.91', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.85', 'play', 'perpetrator', 'role_play', 'act_part', 'terrible_event']
senticnet['king_musick'] = ['0.036', '0.111', '-0.05', '0.054', '#interest', '#admiration', 'positive', '0.038', 'despot', 'supervisor', 'chief', 'commander', 'earl']
senticnet['king_oberon'] = ['0.967', '0.768', '0', '0', '#joy', '#interest', 'positive', '0.867', 'centerfold', 'genus', 'honey_bee', 'infant', 'dove']
senticnet['king_queen'] = ['0.057', '0.094', '-0.04', '0.136', '#interest', '#admiration', 'positive', '0.06', 'blue_blood', 'royalty', 'king', 'monarch', 'regal']
senticnet['king_richerd'] = ['-0.98', '0', '0.654', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'deprive', 'expropriate', 'disarm', 'unclothe', 'take']
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senticnet['king_sedgemoor_drain'] = ['-0.77', '0', '0.583', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'rain', 'rain_down', 'sheet', 'rain_bucket', 'rain_buckets_down']
senticnet['king_zvonimir'] = ['-0.62', '0', '0.961', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'jeweled_headdress', 'diadem', 'tiara', 'headdress', 'headgear']
senticnet['kingdok'] = ['-0.66', '0', '0.859', '-0.82', '#anger', '#disgust', 'negative', '-0.78', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['kingdom'] = ['0.052', '0.113', '-0.07', '0.103', '#interest', '#admiration', 'positive', '0.049', 'monarchy', 'reign', 'aboriginal', 'royal', 'president']
senticnet['kingdom_heart_birth'] = ['-0.59', '0', '0.799', '-0.69', '#anger', '#disgust', 'negative', '-0.69', 'untroubled', 'undisturbed', 'unmolested', 'unworried', 'unconcerned']
senticnet['kingly'] = ['0.067', '0.134', '-0.05', '0.182', '#interest', '#admiration', 'positive', '0.083', 'blue_blood', 'regal', 'king_queen', 'royalty', 'royal_person']
senticnet['kingpin'] = ['-0.98', '0', '0.714', '-0.90', '#sadness', '#disgust', 'negative', '-0.86', 'stupid', 'stupid_person', 'stupe', 'dullard', 'dolt']
senticnet['kingship'] = ['0.059', '0.092', '0', '0.107', '#interest', '#admiration', 'positive', '0.086', 'royalty', 'monarch', 'throne', 'emperor', 'king_queen']
senticnet['kingsley_napley'] = ['0.053', '0.122', '-0.07', '0.086', '#interest', '#admiration', 'positive', '0.046', 'attorney', 'lawyer', 'jury', 'decide_guilt', 'prosecutor']
senticnet['kingston_penitentiary'] = ['0.816', '-0.91', '0', '0.944', '#surprise', '#admiration', 'positive', '0.891', 'jail', 'jailhouse', 'gaol', 'poky', 'clink']
senticnet['kinji_fukasaku'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amuck', 'murderousness', 'misocainea', 'misogyny']
senticnet['kink'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'blind_person', 'rubber_duck', 'greedy_person', 'vegetarian', 'foreigner']
senticnet['kinky_hair'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'blonde', 'purple', 'violet', 'dark_light', 'hue']
senticnet['kinky_sex'] = ['0.081', '0.095', '0', '0', '#joy', '#interest', 'positive', '0.088', 'build_tower_out_toy_block', 'a_lot_of_sex', 'play_hockey', 'play_baseball', 'play_football']
senticnet['kinmen'] = ['-0.65', '0', '0.505', '-0.91', '#sadness', '#disgust', 'negative', '-0.69', 'hellhound', 'mythical_being', 'dragon', 'mythical_monster', 'amphisbaena']
senticnet['kinmen_county'] = ['-0.65', '0', '0.505', '-0.91', '#sadness', '#disgust', 'negative', '-0.69', 'hellhound', 'mythical_being', 'dragon', 'mythical_monster', 'amphisbaena']
senticnet['kino_lorber'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['kinorhyncha'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'annelid', 'annelid_worm', 'leech']
senticnet['kinraid'] = ['-0.95', '0', '-0.76', '0', '#sadness', '#fear', 'negative', '-0.85', 'disquietude', 'edginess', 'edgy', 'uptight', 'restive']
senticnet['kinship'] = ['0.774', '0.928', '0', '0', '#joy', '#interest', 'positive', '0.851', 'relate_person', 'kin', 'mom_dad', 'brother_sister', 'aunt_uncle']
senticnet['kinsman'] = ['-0.11', '0.112', '0.674', '-0.66', '#anger', '#disgust', 'negative', '-0.33', 'wrathfully', 'wrathful', 'wrothful', 'lividly', 'lividity']
senticnet['kinswoman'] = ['0.539', '-0.96', '0', '0.745', '#surprise', '#admiration', 'positive', '0.751', 'spouse', 'partner', 'married_person', 'mate', 'better_half']
senticnet['kintaro'] = ['0.021', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'plastic_bottle', 'party', 'use_party', 'lighter_air', 'compress_air']
senticnet['kinyon'] = ['-0.69', '0', '0.692', '-0.66', '#sadness', '#anger', 'negative', '-0.68', 'wrinkle', 'crinkle', 'crease', 'crisp', 'ruck_up']
senticnet['kiosk'] = ['0.813', '0.832', '0', '0.874', '#interest', '#admiration', 'positive', '0.84', 'ferris_wheel', 'concession_stand', 'food', 'carnival_ride', 'theme_park']
senticnet['kip_keino'] = ['-0.04', '-0.03', '0', '-0.16', '#sadness', '#disgust', 'negative', '-0.05', 'crazy', 'damage_body', 'crazy_person', 'threat', 'max']
senticnet['kip_thorne'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'general_relativity', 'general_theory_of_relativity', 'relativity', 'special_relativity', 'special_theory_of_relativity']
senticnet['kir_navarrin'] = ['-0.81', '0', '0.945', '-0.51', '#sadness', '#anger', 'negative', '-0.75', 'occult', 'concealed', 'nonvisual', 'hidden', 'out_of_sight']
senticnet['kiriam'] = ['-0.75', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.83', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['kirill_kudryavtsev_afp_getty'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expansive', 'expansible', 'erectile', 'cavernous']
senticnet['kirke_vester'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'concupiscence', 'lustfulness', 'amorousness']
senticnet['kirkeby'] = ['0.746', '-0.88', '0', '0.723', '#joy', '#surprise', 'positive', '0.783', 'strike', 'run_into', 'hit', 'impinge_on', 'collide_with']
senticnet['kirkyard'] = ['-0.88', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.90', 'coffin', 'casket', 'morgue', 'headstone', 'burial']
senticnet['kirlampudi'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['kirschenmann'] = ['-0.56', '0', '0.655', '-0.85', '#anger', '#disgust', 'negative', '-0.69', 'slovenly_woman', 'slut', 'trollop', 'slattern', 'slob']
senticnet['kiruthigai'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['kiryat_moshe'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['kiss'] = ['0.973', '0.804', '0', '0', '#joy', '#interest', 'positive', '0.889', 'touch', 'love_else', 'propose_woman', 'start_family', 'procreate']
senticnet['kiss_girl'] = ['-0.12', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.09', 'wedge_door', 'fancy_girl', 'steal_store', 'visit_grandmother', 'part_of_hair']
senticnet['kiss_of_death'] = ['-0.59', '0', '0.505', '-0.87', '#sadness', '#disgust', 'negative', '-0.65', 'calamity', 'catastrophe', 'tragedy', 'disaster', 'cataclysm']
senticnet['kiss_partner'] = ['0.655', '-0.87', '0', '0.595', '#joy', '#surprise', 'positive', '0.709', 'orgasm', 'sexual_excitement', 'call_dog', 'erection', 'wet_lip']
senticnet['kissable'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'high_temperature', 'hot', 'hot_weather', 'fire_product', 'heat']
senticnet['kisser'] = ['0.305', '0.168', '-0.10', '0.299', '#joy', '#admiration', 'positive', '0.167', 'diversity', 'prosper', 'desirable', 'a_lot_of_sex', 'cherish']
senticnet['kissing_bug'] = ['-0.77', '0', '0.576', '-0.92', '#sadness', '#disgust', 'negative', '-0.75', 'insect', 'firebug', 'water_bug', 'assassin_bug', 'heteropterous_insect']
senticnet['kisspeptin'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'cholecystokinin', 'secretin', 'gastrointestinal_hormone']
senticnet['kit'] = ['-0.04', '-0.03', '-0.05', '-0.02', '#sadness', '#fear', 'negative', '-0.02', 'high_school_band', 'drum_set', 'string_quartet', 'music_shop', 'set']
senticnet['kit_bag'] = ['0', '0.11', '-0.05', '0.149', '#interest', '#admiration', 'positive', '0.067', 'pack_clothes', 'hold_stuff', 'carry_stuff', 'carry_book', 'carry_belongings']
senticnet['kit_fox'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'fox', 'prairie_fox', 'white_fox']
senticnet['kit_kat'] = ['0.023', '0.141', '-0.13', '0.174', '#interest', '#admiration', 'positive', '0.052', 'candy', 'push_wind', 'exhalation', 'breaout', 'mouth_action']
senticnet['kitchen'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'kitchen_cabinet', 'kitchen_cupboard', 'dishwasher', 'cupboard', 'own_kitchen']
senticnet['kitchen_cabinet'] = ['0.03', '-0.07', '0', '0', '#joy', '#surprise', 'positive', '0.052', 'kitchen_cupboard', 'kitchen', 'cupboard', 'dishwasher', 'own_kitchen']
senticnet['kitchen_counter'] = ['0', '-0.02', '0.041', '-0.10', '#anger', '#disgust', 'negative', '-0.03', 'separate_shower', 'shampoo_conditioner', 'towel_bar', 'shower_head', 'toilet_brush']
senticnet['kitchen_cupboard'] = ['0.021', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.041', 'own_kitchen', 'cupboard', 'kitchen_cabinet', 'pantry', 'kitchen']
senticnet['kitchen_dining_room'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'kitchen_cabinet', 'kitchen_cupboard', 'dishwasher', 'cupboard', 'own_kitchen']
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senticnet['kk_crvena_zvezda'] = ['0.954', '-0.90', '0', '0.867', '#joy', '#surprise', 'positive', '0.91', 'friend', 'sports_fan', 'rooter', 'fan', 'nut']
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senticnet['klliye'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
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senticnet['kmail'] = ['0.031', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.062', 'letter_slot', 'postal_counter', 'mailbox', 'letter_scale', 'envelope']
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senticnet['knee-high'] = ['0', '0', '-0.89', '-0.86', '#fear', '#disgust', 'negative', '-0.88', 'thigh-high', 'over-the-knee', 'mid-calf', 'heeled', 'ankle-high']
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senticnet['knife_blade'] = ['-0.71', '0', '0.954', '-0.56', '#sadness', '#anger', 'negative', '-0.74', 'jackknife', 'pocket_knife', 'penknife', 'switchblade', 'switchblade_knife']
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senticnet['knife_thrower'] = ['0.9', '-0.88', '0', '0.687', '#joy', '#surprise', 'positive', '0.823', 'conjurer', 'escape_expert', 'mind_reader', 'telepathist', 'performer']
senticnet['knife_wielding_man'] = ['0.964', '-0.57', '0', '0.827', '#joy', '#admiration', 'positive', '0.789', 'mortal', 'murder_suspect', 'suspect', 'rape_suspect', 'person']
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senticnet['knight_banneret'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'bodyguard', 'outrider', 'bodyguard_of_guard', 'beefeater', 'defender']
senticnet['knight_dame'] = ['-0.53', '0', '0.977', '-0.54', '#anger', '#disgust', 'negative', '-0.68', 'rank', 'surpass', 'shine_at', 'excel_at', 'exceed']
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senticnet['knight_errant'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'correspondent', 'humankind']
senticnet['knight_protection'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'protective_gear', 'metal_ore', 'lightweight_metal', 'metal', 'knight']
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senticnet['know_story'] = ['-0.08', '-0.04', '0.046', '-0.13', '#sadness', '#disgust', 'negative', '-0.05', 'receive_information', 'share_knowledge', 'pass_information', 'express_opinion', 'give_information']
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senticnet['knowledge_base'] = ['0.749', '0.902', '0', '0', '#joy', '#interest', 'positive', '0.826', 'discipline', 'scientific_knowledge', 'region', 'dataset', 'content']
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senticnet['koinonia'] = ['0', '0.898', '0', '0.781', '#interest', '#admiration', 'positive', '0.84', 'companionship', 'family', 'society', 'company', 'fellowship']
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senticnet['kokum'] = ['0.52', '-0.69', '0', '0.639', '#surprise', '#admiration', 'positive', '0.617', 'black_bread', 'pumpernickel', 'limpa', 'bread', 'rye_bread']
senticnet['kom_ombo'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['komatsuna'] = ['0.035', '0.104', '0', '0.12', '#interest', '#admiration', 'positive', '0.086', 'fruit_vegetable', 'vegetable', 'vegetable_fruit', 'fresh_vegetable', 'vegetarian_food']
senticnet['komitee'] = ['0.851', '-0.65', '0', '0.66', '#joy', '#admiration', 'positive', '0.723', 'omen', 'portent', 'presage', 'prognostic', 'prognostication']
senticnet['kommt'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['komodo_dragon'] = ['-0.85', '0', '0.685', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'alligator', 'crocodilian', 'false_gavial', 'gator', 'caiman']
senticnet['komuter_station'] = ['0', '0.147', '0', '0.099', '#interest', '#admiration', 'positive', '0.123', 'airport', 'subway', 'bus_stop', 'bus', 'control_tower']
senticnet['konak'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'caravansary', 'caravanserai', 'khan', 'caravan_inn', 'hostel']
senticnet['kong'] = ['-0.71', '0', '0.884', '0', '#sadness', '#anger', 'negative', '-0.79', 'tonne', 'reuters', 'trader', 'industry_source', 'week']
senticnet['kongur_tagh'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['koniah'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['konishiki'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['konjac'] = ['0.026', '0.08', '0.043', '0.064', '#interest', '#admiration', 'positive', '0.032', 'eat_rice', 'white_brown', 'asian', 'eat_pasta', 'staple_food']
senticnet['konjevii'] = ['0.939', '-0.71', '0', '0.775', '#joy', '#admiration', 'positive', '0.81', 'vox_angelica', 'voix_celeste', 'organ_stop', 'pipe', 'diapason']
senticnet['konqueror'] = ['-0.89', '0', '0.672', '-0.69', '#sadness', '#disgust', 'negative', '-0.75', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['konteradmiral'] = ['0.688', '-0.90', '0', '0.877', '#surprise', '#admiration', 'positive', '0.824', 'flag_officer', 'rear_admiral', 'admiral', 'full_admiral', 'vice_admiral']
senticnet['kook'] = ['-0.71', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.77', 'weirdo', 'crazy', 'strange', 'bug', 'kooky']
senticnet['kooky'] = ['0.904', '0.754', '0', '0', '#joy', '#interest', 'positive', '0.829', 'daft', 'balmy', 'barmy', 'bonkers', 'loony']
senticnet['kool_aide'] = ['0.081', '-0.05', '0', '-0.03', '#joy', '#surprise', 'positive', '0.036', 'drink_alcohol', 'consume_alcoholic_beverage', 'drink_much', 'hit_bottle', 'meet_woman']
senticnet['koolaid'] = ['0.11', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'alcoholic_beverage', 'adult_drink', 'alcoholic_drink', 'drink_alcohol', 'alcohol']
senticnet['koosh_ball'] = ['0', '-0.03', '0.042', '-0.07', '#anger', '#disgust', 'negative', '-0.02', 'ball', 'ping_pong_show', 'dandruff', 'solid_water', 'empty_bottle']
senticnet['kop_tiatie_cac'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
senticnet['korangy'] = ['-0.96', '0', '0.885', '-0.75', '#sadness', '#anger', 'negative', '-0.86', 'controvert', 'answer', 'disown', 'renounce', 'repudiate']
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senticnet['korengal_valley'] = ['-0.03', '0', '-0.08', '-0.20', '#fear', '#disgust', 'negative', '-0.10', 'gun_shop', 'arsenal', 'holster', 'armory', 'gun_store']
senticnet['korfball'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'golf', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['kori_bustard'] = ['-0.53', '0', '0.648', '-0.70', '#anger', '#disgust', 'negative', '-0.63', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
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senticnet['kornl'] = ['0.975', '-0.66', '0', '0.813', '#joy', '#admiration', 'positive', '0.816', 'cry', 'yell', 'scream', 'shrill', 'call']
senticnet['korsakoff_syndrome'] = ['-0.94', '0', '0.821', '-0.75', '#sadness', '#anger', 'negative', '-0.84', 'predementedness', 'insanity', 'dementedness', 'senile_psychosis', 'alcoholic_dementia']
senticnet['kortrijk'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'feudal_lord', 'lord', 'master', 'overlord', 'king']
senticnet['kosher'] = ['0.73', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.861', 'halal', 'gluten-free', 'vegetarian', 'vegan', 'gluten']
senticnet['kosher_butcher'] = ['0', '-0.00', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'hot_chick', 'atheist', 'computer_user', 'fastener_binder', 'monitor']
senticnet['koshi_tappu_wildlife'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['kostchtchie'] = ['0.826', '-0.65', '0', '0.587', '#joy', '#surprise', 'positive', '0.689', 'scoffer', 'vegetarian', 'eater', 'diner', 'dunker']
senticnet['kostya_tszyu'] = ['0.833', '-0.97', '0', '0.91', '#surprise', '#admiration', 'positive', '0.906', 'combatant', 'fencer', 'belligerent', 'fighter', 'scrapper']
senticnet['kotelnik'] = ['0.969', '0.998', '0', '0', '#joy', '#interest', 'positive', '0.984', 'bell_sound', 'bell_noise', 'piece_of_jewelry', 'syllable', 'fastener']
senticnet['kotobukiya'] = ['0.031', '-0.10', '0', '0', '#joy', '#surprise', 'positive', '0.07', 'teddy_bear', 'beanie_baby', 'yo-yo', 'board_game', 'toy']
senticnet['kotomitsuki'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['kotooshu'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['koukoulion'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'reverence', 'obeisance', 'genuflexion', 'kowtow', 'mafia']
senticnet['kovarik'] = ['-0.56', '0', '0.655', '-0.85', '#anger', '#disgust', 'negative', '-0.69', 'slovenly_woman', 'slut', 'trollop', 'slattern', 'slob']
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senticnet['kpa_psi'] = ['-0.12', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.09', 'breeze', 'move_air', 'air_movement', 'gape', 'force']
senticnet['kpmg_audit'] = ['0.801', '0.86', '0', '0', '#joy', '#interest', 'positive', '0.831', 'credit_card', 'profit', 'mastercard', 'american_express', 'back_to_black']
senticnet['kr_one'] = ['0.081', '-0.05', '0.052', '0.072', '#joy', '#admiration', 'positive', '0.039', 'rhythm', 'type_of_music', 'instrumental', 'lyric', 'melody']
senticnet['kraepelin'] = ['-0.55', '0', '0.736', '-0.71', '#anger', '#disgust', 'negative', '-0.66', 'neurosis', 'neuroticism', 'psychoneurosis', 'hysterocatalepsy', 'anxiety_hysteria']
senticnet['kraft'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'wrapping_paper', 'wrap_gift', 'butcher_paper', 'paper']
senticnet['kraft_bid'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'wrapping_paper', 'wrap_gift', 'butcher_paper', 'paper']
senticnet['kraft_cheese'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'wrapping_paper', 'wrap_gift', 'butcher_paper', 'paper']
senticnet['kraft_chief_executive'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'wrapping_paper', 'wrap_gift', 'butcher_paper', 'paper']
senticnet['kraft_food_inc'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'wrapping_paper', 'wrap_gift', 'butcher_paper', 'paper']
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senticnet['kraft_pulp'] = ['0.024', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'stump', 'plank', 'timber', 'paper', 'mahogany']
senticnet['kraft_takeover'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'wrapping_paper', 'wrap_gift', 'butcher_paper', 'paper']
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senticnet['kraurosis'] = ['-0.86', '0', '0.738', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'amyotrophia', 'amyotrophy', 'tabes', 'wasting_away', 'symptom']
senticnet['krauthammer'] = ['0.783', '-0.63', '0', '0.829', '#joy', '#admiration', 'positive', '0.749', 'leg_of_lamb', 'lamb_roast', 'joint', 'roast', 'rack_of_lamb']
senticnet['kraven'] = ['0', '0', '-0.11', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'hunting_season', 'hound', 'cougar', 'outlaw', 'dragon']
senticnet['krazy_glue'] = ['0.852', '0.818', '0', '0', '#joy', '#interest', 'positive', '0.835', 'handout', 'fountain_pen', 'pen', 'stamp_pad', 'scrap_paper']
senticnet['kree_empire'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'feudal_lord', 'lord']
senticnet['kripalu'] = ['0', '0', '0', '0.93', '#admiration', '#admiration', 'positive', '0.93', 'lift_weight', 'release_stress', 'move_body', 'take_hike', 'change_color']
senticnet['kripke_structure'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'integrative', 'consolidative', 'unifying', 'combinatorial']
senticnet['krishna_paksha'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['kristevan'] = ['0.519', '-0.55', '0', '0.746', '#surprise', '#admiration', 'positive', '0.606', 'narrow_down', 'peg_down', 'nail_down', 'narrow', 'pin_down']
senticnet['kristiina'] = ['0.975', '-0.66', '0', '0.813', '#joy', '#admiration', 'positive', '0.816', 'cry', 'yell', 'scream', 'shrill', 'call']
senticnet['kroenen'] = ['0.8', '-0.97', '0', '0.512', '#joy', '#surprise', 'positive', '0.763', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['kroly'] = ['0.975', '-0.66', '0', '0.813', '#joy', '#admiration', 'positive', '0.816', 'cry', 'yell', 'scream', 'shrill', 'call']
senticnet['kromdraai'] = ['-0.59', '0', '0.506', '-0.52', '#sadness', '#disgust', 'negative', '-0.54', 'dump', 'garbage_dump', 'trash_dump', 'rubbish_dump', 'wasteyard']
senticnet['kronos_quartet'] = ['0.805', '-0.58', '0', '0.525', '#joy', '#surprise', 'positive', '0.637', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['kropotkine'] = ['-0.64', '0', '0.625', '-0.88', '#sadness', '#disgust', 'negative', '-0.71', 'difficulty', 'awkwardness', 'flea_bite', 'difficultness', 'cumbersomeness']
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senticnet['krupuk'] = ['0.52', '-0.69', '0', '0.639', '#surprise', '#admiration', 'positive', '0.617', 'black_bread', 'pumpernickel', 'limpa', 'bread', 'rye_bread']
senticnet['krynica'] = ['0', '0', '0', '0.1', '#admiration', '#admiration', 'positive', '0.1', 'find_earth', 'wax', 'plutonium', 'shine', 'melt']
senticnet['kryssan'] = ['-0.80', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'difficulty', 'awkwardness', 'flea_bite', 'difficultness', 'cumbersomeness']
senticnet['krzysztof_penderecki'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'band_play', 'songwriter', 'rock_band']
senticnet['kss_mich'] = ['-0.05', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.05', 'masturbate', 'jerk', 'jerk_off', 'watch_porn', 'porn']
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senticnet['kuai'] = ['0.965', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.951', 'delicious_food', 'hot_meal', 'good_food', 'fresh_produce', 'tasty_food']
senticnet['kudos'] = ['0.996', '-0.82', '0', '0.569', '#joy', '#surprise', 'positive', '0.796', 'superlative', 'compliment', 'testimonial', 'praise', 'approval']
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senticnet['kuiper_airborne_observatory'] = ['-0.64', '0', '0.524', '-0.59', '#sadness', '#disgust', 'negative', '-0.58', 'bum', 'dreamer', 'daydreamer', 'escapist', 'wishful_thinker']
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senticnet['kujtim'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'white_animal', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
senticnet['kukere'] = ['0', '0', '0.876', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'untune', 'discompose', 'discomfit', 'disconcert', 'upset']
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senticnet['kuli'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'white_animal', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
senticnet['kumanovo_region'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
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senticnet['kumquat'] = ['0.859', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.888', 'green_apple', 'apricot', 'lime', 'peach', 'cherry']
senticnet['kun_yomi'] = ['-0.86', '0', '0.619', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'character', 'logogram', 'logograph', 'graph', 'grapheme']
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senticnet['kundalini'] = ['0.088', '0.135', '0', '0', '#joy', '#interest', 'positive', '0.112', 'watch_others', 'meditate', 'imagine', 'mantra', 'dance_music']
senticnet['kundalini_yoga'] = ['0', '0', '0', '0.93', '#admiration', '#admiration', 'positive', '0.93', 'lift_weight', 'release_stress', 'move_body', 'take_hike', 'change_color']
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senticnet['kung_fu_master'] = ['0.021', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'plastic_bottle', 'party', 'use_party', 'lighter_air', 'compress_air']
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senticnet['kungo_island'] = ['0.793', '0.847', '0', '0', '#joy', '#interest', 'positive', '0.82', 'cross_bun', 'hot_cross_bun', 'bear_paw', 'coffee_roll', 'bun']
senticnet['kunnakudi_vaidyanathan'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_music', 'hear_music', 'artist', 'make_drawing', 'art']
senticnet['kunoichi'] = ['0.021', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'plastic_bottle', 'party', 'use_party', 'lighter_air', 'compress_air']
senticnet['kunyah'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
senticnet['kuomintang'] = ['-0.65', '0', '0.505', '-0.91', '#sadness', '#disgust', 'negative', '-0.69', 'hellhound', 'mythical_being', 'dragon', 'mythical_monster', 'amphisbaena']
senticnet['kuomintang_kmt'] = ['-0.65', '0', '0.505', '-0.91', '#sadness', '#disgust', 'negative', '-0.69', 'hellhound', 'mythical_being', 'dragon', 'mythical_monster', 'amphisbaena']
senticnet['kuomintang_party'] = ['-0.65', '0', '0.505', '-0.91', '#sadness', '#disgust', 'negative', '-0.69', 'hellhound', 'mythical_being', 'dragon', 'mythical_monster', 'amphisbaena']
senticnet['kurchatov_institute'] = ['-0.90', '0', '0.748', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'alternative_energy', 'activation_energy', 'energy_of_activation', 'energy', 'nuclear_energy']
senticnet['kuril_island'] = ['0.096', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.057', 'platform_rail', 'rail', 'catwalk', 'column', 'token_turnstile']
senticnet['kursk_salient'] = ['0.737', '-0.96', '0', '0.552', '#joy', '#surprise', 'positive', '0.75', 'prominence', 'hump', 'bump', 'swelling', 'frontal_eminence']
senticnet['kurthnaga'] = ['-0.73', '0', '0.55', '-0.75', '#sadness', '#disgust', 'negative', '-0.68', 'mortal', 'person', 'individual', 'mangler', 'mutilator']
senticnet['kusanagi'] = ['-0.04', '0.138', '-0.08', '0.111', '#interest', '#admiration', 'positive', '0.031', 'geometry', 'craft', 'blade_geometry', 'blade', 'collect_item']
senticnet['kusarigama'] = ['-0.04', '0.138', '-0.08', '0.111', '#interest', '#admiration', 'positive', '0.031', 'geometry', 'craft', 'blade_geometry', 'blade', 'collect_item']
senticnet['kv_ac'] = ['0.547', '-0.69', '0', '0.52', '#joy', '#surprise', 'positive', '0.586', 'vivification', 'invigoration', 'activity', 'animation', 'activating']
senticnet['kv_ac_overhead'] = ['0.547', '-0.69', '0', '0.52', '#joy', '#surprise', 'positive', '0.586', 'vivification', 'invigoration', 'activity', 'animation', 'activating']
senticnet['kva'] = ['0', '-0.03', '0', '0.093', '#surprise', '#admiration', 'positive', '0.063', 'work_well', 'internal_combustion_engine', 'broken_car', 'mode_transportation', 'machine']
senticnet['kvetch'] = ['-0.74', '0', '0.68', '-0.58', '#sadness', '#anger', 'negative', '-0.67', 'whiner', 'complainer', 'moaner', 'sniveller', 'crybaby']
senticnet['kwaku_alston'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'kit_fox', 'prairie_fox', 'white_fox']
senticnet['kwaque'] = ['0', '0', '0.898', '-0.90', '#anger', '#disgust', 'negative', '-0.90', 'unconcern', 'unconcerned', 'apathy', 'indifference', 'carelessness']
senticnet['kwashiorkor'] = ['-0.71', '0', '0.74', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'malnutrition', 'marasmus', 'avitaminosis', 'hypovitaminosis', 'deficiency_disease']
senticnet['kwh_battery'] = ['0', '0', '-1', '-1', '#fear', '#disgust', 'negative', '-1.0', 'moon_gravity', 'force', 'coercion', 'watt', 'power_source']
senticnet['kwh_lithium_ion_battery'] = ['0', '0.11', '-0.05', '0.149', '#interest', '#admiration', 'positive', '0.067', 'pack_clothes', 'hold_stuff', 'carry_stuff', 'carry_book', 'carry_belongings']
senticnet['kyirong'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['kylie'] = ['0', '0.864', '0', '0.751', '#interest', '#admiration', 'positive', '0.807', 'family_member', 'sibling', 'parent', 'female_relative', 'relative']
senticnet['kyoto_pact'] = ['0.397', '0.245', '-0.13', '0.224', '#joy', '#interest', 'positive', '0.183', 'agreement', 'provide', 'advice', 'contribution', 'offer']
senticnet['kyphosis'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'lordosis', 'spinal_curvature', 'humpback', 'hunchback', 'abnormality']
senticnet['kyrie_eleison'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'reverence', 'genuflection', 'obeisance', 'kowtow', 'mafia']
senticnet['kyudo'] = ['0', '-0.07', '0.091', '-0.19', '#anger', '#disgust', 'negative', '-0.07', 'projectile', 'use_hunt', 'weapon', 'ammunition', 'firearm']
senticnet['kzinti'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['kzuke_province'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'overlord', 'lord']
senticnet['la-di-da'] = ['0', '0', '-0.8', '-0.73', '#fear', '#disgust', 'negative', '-0.76', 'hifalutin', 'grandiose', 'highfaluting', 'highfalutin', 'hoity-toity']
senticnet['lab'] = ['0.03', '0.117', '0', '0.084', '#interest', '#admiration', 'positive', '0.077', 'laboratory', 'elementary_school', 'science_class', 'college_class', 'doctor_office']
senticnet['lab_coat'] = ['0', '0', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.08', 'scientist', 'geology', 'doctor_office', 'protractor', 'coat']
senticnet['lab_improve'] = ['0.765', '0', '0', '0.948', '#joy', '#admiration', 'positive', '0.857', 'undergraduate_education', 'education', 'different_application', 'student', 'chemistry']
senticnet['lab_rat'] = ['0', '0', '-0.10', '0', '#fear', '#fear', 'negative', '-0.10', 'lonely_person', 'vegetarian', 'raccoon', 'hermit', 'user']
senticnet['labarum'] = ['-0.98', '0', '0.664', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'unfortunate', 'unfortunate_person', 'weeper', 'pallgriever', 'griever']
senticnet['label'] = ['0', '0.115', '0.036', '0.207', '#interest', '#admiration', 'positive', '0.095', 'total', 'amount', 'merit', 'award', 'excellence']
senticnet['label_imprint'] = ['0', '0.115', '0.036', '0.207', '#interest', '#admiration', 'positive', '0.095', 'total', 'amount', 'merit', 'award', 'excellence']
senticnet['label_mate'] = ['0', '0.712', '0', '0.844', '#interest', '#admiration', 'positive', '0.778', 'dollar_change', 'small_currency', 'currency_unit', 'metal_money', 'american_money']
senticnet['labeled_packet'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'identifiable', 'specifiable', 'recognizable', 'diagnosable', 'classifiable']
senticnet['labeler'] = ['0.044', '0.08', '0', '-0.03', '#joy', '#interest', 'positive', '0.031', 'birthday_card', 'restaurant_bill', 'bake_pan', 'account_book', 'computer_monitor']
senticnet['labial_herpes'] = ['-0.96', '0', '0.726', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'herpes', 'herpes_virus', 'herpes_simplex', 'herpes_simplex_virus', 'virus']
senticnet['labiaplasty'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
senticnet['labile'] = ['0.766', '-0.87', '0', '0.666', '#joy', '#surprise', 'positive', '0.769', 'oxidizable', 'unstable', 'reactive', 'activated', 'excited']
senticnet['labiodental'] = ['-0.71', '0', '0.998', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'tone', 'quality', 'twang', 'nasal_twang', 'timber']
senticnet['labiovelar'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'pronunciation', 'sibilation', 'mispronunciation', 'homophony']
senticnet['labium'] = ['0', '0', '0.021', '-0.06', '#anger', '#disgust', 'negative', '-0.04', 'skin', 'skeletal_system', 'shoulder', 'cuticle', 'groin']
senticnet['labium_majora'] = ['0.087', '-0.03', '0', '0.063', '#joy', '#admiration', 'positive', '0.062', 'puppet', 'bouncy', 'curly', 'throw_object', 'glove_leather']
senticnet['labium_minora'] = ['-0.63', '0', '0.559', '-0.82', '#sadness', '#disgust', 'negative', '-0.67', 'take_bull_by_horns', 'confront', 'front', 'encounter', 'oppose']
senticnet['labor'] = ['-0.04', '0.136', '-0.11', '0.119', '#interest', '#admiration', 'positive', '0.025', 'task', 'job', 'hire', 'employment', 'hire_help']
senticnet['labor-intensive'] = ['0', '-0.85', '-0.82', '0', '#surprise', '#fear', 'positive', '0.013', 'error-prone', 'time-consuming', 'tedious', 'time-intensive', 'resource-intensive']
senticnet['labor_dispute'] = ['-0.77', '0', '0.505', '-0.50', '#sadness', '#anger', 'negative', '-0.59', 'wildcat_strike', 'sympathy_strike', 'sympathetic_strike', 'walkout', 'job_action']
senticnet['labor_force'] = ['0', '0', '0.948', '-0.96', '#anger', '#disgust', 'negative', '-0.95', 'work_much', 'lack_money', 'wage_slavery', 'buy_tool', 'get_paycheck']
senticnet['labor_intensive'] = ['-0.12', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.09', 'painful', 'uncomfortable', 'violent', 'fun_learn_play', 'expensive']
senticnet['labor_market'] = ['0', '0.923', '0', '0', '#interest', '#interest', 'positive', '0.923', 'salary', 'remittance', 'owe', 'payment', 'severance']
senticnet['labor_negotiator'] = ['0', '0.133', '0', '0.088', '#interest', '#admiration', 'positive', '0.111', 'juror', 'decide_guilt', 'become_judge', 'arbiter', 'jury']
senticnet['labor_pain'] = ['-0.73', '0', '0.555', '-0.90', '#sadness', '#disgust', 'negative', '-0.73', 'aching', 'dysmenorrhea', 'hurting', 'neuralgia', 'neuralgy']
senticnet['labor_party_dfl'] = ['-0.08', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.11', 'serf', 'peasant', 'servant', 'poor', 'villein']
senticnet['labor_preselection'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'couch', 'stool', 'coach_seat']
senticnet['labor_productivity'] = ['0', '0.098', '-0.06', '0.15', '#interest', '#admiration', 'positive', '0.061', 'make_book', 'use_ink', 'make_newspaper', 'pencil_mark', 'computer_command']
senticnet['labor_shortage'] = ['-0.83', '0', '-0.88', '-0.78', '#sadness', '#fear', 'negative', '-0.83', 'little', 'sadness', 'small_amount', 'lack', 'absence']
senticnet['labor_strife'] = ['-0.77', '0', '0.505', '-0.50', '#sadness', '#anger', 'negative', '-0.59', 'wildcat_strike', 'sympathy_strike', 'sympathetic_strike', 'walkout', 'job_action']
senticnet['labor_unrest'] = ['-0.77', '0', '0.505', '-0.50', '#sadness', '#anger', 'negative', '-0.59', 'wildcat_strike', 'sympathy_strike', 'sympathetic_strike', 'walkout', 'job_action']
senticnet['laboratory'] = ['-0.04', '0.136', '-0.11', '0.119', '#interest', '#admiration', 'positive', '0.025', 'computer_lab', 'use_computer', 'lab', 'scientist', 'laborer']
senticnet['laboratory_course'] = ['0', '0.917', '0', '0.982', '#interest', '#admiration', 'positive', '0.95', 'student', 'different_application', 'lab_improve', 'education', 'project']
senticnet['laboratory_exercise'] = ['0.938', '0', '0', '0.998', '#joy', '#admiration', 'positive', '0.968', 'student', 'project', 'different_application', 'lab_improve', 'award']
senticnet['laboratory_experience'] = ['0', '0.975', '0', '0.826', '#interest', '#admiration', 'positive', '0.9', 'student', 'different_application', 'project', 'lab_improve', 'education']
senticnet['laboratory_glassware'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'drink_container', 'liquid_container', 'drink_cup', 'drink_holder', 'type_of_container']
senticnet['laboratory_notebook'] = ['0.022', '-0.05', '0.079', '-0.11', '#anger', '#disgust', 'negative', '-0.02', 'stationery', 'ink', 'ball_point_pen', 'refill', 'note']
senticnet['laborer'] = ['0', '0.126', '-0.07', '0.104', '#interest', '#admiration', 'positive', '0.052', 'worker', 'occupation', 'blue_collar', 'profession', 'helper']
senticnet['laborious'] = ['0', '0.874', '0', '0.893', '#interest', '#admiration', 'positive', '0.883', 'painstaking', 'trial-and-error', 'guesswork', 'time-consuming', 'experimentation']
senticnet['laborious_task'] = ['-0.03', '0.136', '-0.09', '0.105', '#interest', '#admiration', 'positive', '0.028', 'chore', 'hire_help', 'police_work', 'career', 'job']
senticnet['laboriously'] = ['0', '0.901', '0', '0.989', '#interest', '#admiration', 'positive', '0.945', 'meticulously', 'painstakingly', 'methodically', 'lovingly', 'skillfully']
senticnet['laboriousness'] = ['-0.64', '0', '0.928', '-0.70', '#anger', '#disgust', 'negative', '-0.75', 'difficulty', 'arduousness', 'strenuousness', 'effortfulness', 'operoseness']
senticnet['labour_force'] = ['0.85', '0', '0', '0', '#joy', '#joy', 'positive', '0.85', 'task', 'labor', 'police_work', 'employ', 'career']
senticnet['labour_market'] = ['-0.25', '0', '-0.17', '-0.08', '#sadness', '#fear', 'negative', '-0.16', 'exclusion', 'exclusion_zone', 'isolation', 'detach', 'except']
senticnet['labour_mobility'] = ['0', '0', '0', '0.144', '#admiration', '#admiration', 'positive', '0.144', 'go_elsewhere', 'transport_person', 'get_around', 'locomotion', 'trucking']
senticnet['labour_mp_sir_stuart'] = ['0.034', '0', '0.01', '0.087', '#joy', '#admiration', 'positive', '0.037', 'chime', 'bell_noise', 'alto', 'piano', 'ring_loudly']
senticnet['labour_party_politician'] = ['0', '0.868', '0', '0.853', '#interest', '#admiration', 'positive', '0.861', 'country_leader', 'prime_minister', 'president', 'elect_official', 'leader']
senticnet['labour_politician'] = ['0', '0.868', '0', '0.853', '#interest', '#admiration', 'positive', '0.861', 'country_leader', 'prime_minister', 'president', 'elect_official', 'leader']
senticnet['labour_shortage'] = ['-0.83', '0', '-0.88', '-0.78', '#sadness', '#fear', 'negative', '-0.83', 'little', 'sadness', 'small_amount', 'lack', 'absence']
senticnet['labourer'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'lucrative', 'moneymaking', 'profitable', 'paying', 'paid']
senticnet['labouring_poor'] = ['-0.91', '0', '0.598', '-0.52', '#sadness', '#anger', 'negative', '-0.68', 'parsimonious', 'stingy', 'scrimy', 'beggarly', 'chintzy']
senticnet['labouring_under'] = ['-0.74', '0', '0.622', '-0.86', '#sadness', '#disgust', 'negative', '-0.74', 'tribulate', 'upset', 'grieve', 'disconcert', 'aggrieve']
senticnet['labrador_retriever'] = ['-0.72', '0', '0.619', '-0.86', '#sadness', '#disgust', 'negative', '-0.73', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['labrador_tea'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'black_locust']
senticnet['labrocyte'] = ['-0.66', '0', '0.877', '-0.64', '#sadness', '#anger', 'negative', '-0.72', 'mast_cell', 'mastocyte', 'somatic_cell', 'target_cell', 'phagocyte']
senticnet['labrum'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'socket', 'tooth_socket', 'alveolus', 'acetabulum', 'cotyloid_cavity']
senticnet['labrusca'] = ['0.784', '-0.59', '0', '0.846', '#joy', '#admiration', 'positive', '0.742', 'astuteness', 'slyness', 'wiliness', 'craftiness', 'cunning']
senticnet['labyrinthine'] = ['-0.44', '-0.13', '-0.08', '-0.40', '#sadness', '#disgust', 'negative', '-0.19', 'disorder', 'untidy', 'mess', 'heap', 'chaos']
senticnet['labyrinthitis'] = ['-0.87', '0', '0.773', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'rubor', 'redness', 'inflammation', 'otitis', 'otitis_interna']
senticnet['lace'] = ['-0.71', '0', '0.539', '-0.88', '#sadness', '#disgust', 'negative', '-0.71', 'shoe_string', 'shoestring', 'cord', 'bootlace', 'shoelace']
senticnet['lace_bra'] = ['0.923', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.939', 'under_garment', 'undergarment', 'underwear', 'woman_clothes', 'lady_underwear']
senticnet['lace_collar'] = ['-0.84', '0', '0.633', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'grizzle', 'postiche', 'horsehair_wig', 'wig', 'peruke']
senticnet['lace_gown'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'pinafore', 'pinny', 'dirndl', 'dress', 'muumuu']
senticnet['lace_skirt'] = ['0.923', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.939', 'under_garment', 'undergarment', 'underwear', 'woman_clothes', 'lady_underwear']
senticnet['lacedaemon'] = ['0.8', '-0.79', '0', '0.751', '#joy', '#surprise', 'positive', '0.782', 'elder_statesman', 'stateswoman', 'politician', 'politico', 'pol']
senticnet['lacedaemonian'] = ['0.8', '-0.79', '0', '0.751', '#joy', '#surprise', 'positive', '0.782', 'elder_statesman', 'stateswoman', 'politician', 'politico', 'pol']
senticnet['lacerate'] = ['-0.10', '-0.06', '0.02', '-0.18', '#sadness', '#disgust', 'negative', '-0.06', 'slash', 'sever', 'chop_wood', 'spread_butter', 'slit']
senticnet['laceration'] = ['-0.12', '-0.04', '0', '-0.17', '#sadness', '#disgust', 'negative', '-0.08', 'cause_bleed', 'open_wind', 'gash', 'lacerate', 'painful_injury']
senticnet['lachrymator'] = ['-0.53', '0', '0.86', '-0.65', '#anger', '#disgust', 'negative', '-0.68', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['lachrymose'] = ['-0.71', '0.312', '-0.4', '-0.08', '#sadness', '#fear', 'negative', '-0.22', 'tearful', 'weeping', 'dolorous', 'tearfulness', 'weepiness']
senticnet['lacing'] = ['-0.59', '0', '0.939', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'liquor', 'spirits', 'booze', 'hard_drink', 'hard_liquor']
senticnet['laciniata'] = ['0.861', '-0.87', '0', '0.597', '#joy', '#surprise', 'positive', '0.779', 'herb', 'cultivated_parsnip', 'wild_parsnip', 'madnep', 'herbaceous_plant']
senticnet['lack'] = ['-0.71', '0.312', '-0.4', '-0.08', '#sadness', '#fear', 'negative', '-0.22', 'lack_energy', 'lack_money', 'lack_of_sleep', 'lack_time', 'lack_light']
senticnet['lack_brightness'] = ['-0.07', '0', '-0.04', '0', '#sadness', '#fear', 'negative', '-0.06', 'lack_interest', 'become_dull', 'boring', 'tedious', 'dark']
senticnet['lack_energy'] = ['-0.77', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.83', 'exhaustion', 'get_out_breath', 'tiredness', 'become_wind', 'shin_splint']
senticnet['lack_interest'] = ['-0.07', '0', '-0.04', '0', '#sadness', '#fear', 'negative', '-0.06', 'become_dull', 'lack_brightness', 'boring', 'tedious', 'dark']
senticnet['lack_light'] = ['-0.36', '-0.06', '-0.09', '-0.12', '#sadness', '#disgust', 'negative', '-0.12', 'catch_rain', 'cold_weather', 'coldness', 'frozen', 'rainy_day']
senticnet['lack_merit'] = ['-0.86', '0', '0.897', '-0.81', '#sadness', '#anger', 'negative', '-0.85', 'disregard', 'ignore', 'brush_off', 'push_aside', 'flout']
senticnet['lack_money'] = ['-0.13', '0.068', '-0.10', '-0.06', '#sadness', '#fear', 'negative', '-0.05', 'sell_stock', 'count_money', 'need_money', 'unemployment', 'earn_money']
senticnet['lack_of_attraction'] = ['-0.90', '0', '0.877', '-0.91', '#sadness', '#disgust', 'negative', '-0.89', 'boring_activity', 'feel_bored', 'feel_tired', 'boring_movie', 'boring_show']
senticnet['lack_of_confidence'] = ['-0.91', '0', '0.653', '-0.95', '#sadness', '#disgust', 'negative', '-0.84', 'feel_down', 'feel_inferior', 'depress', 'unhappy', 'weak']
senticnet['lack_of_confident'] = ['-0.92', '0', '0.644', '-0.96', '#sadness', '#disgust', 'negative', '-0.84', 'feel_down', 'feel_inferior', 'depress', 'unhappy', 'weak']
senticnet['lack_of_discernment'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'lack_of_judgment', 'lack_of_discreetness', 'injudiciousness', 'reckless', 'rentless']
senticnet['lack_of_discreetness'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'lack_of_judgment', 'injudiciousness', 'lack_of_discernment', 'reckless', 'rentless']
senticnet['lack_of_energy'] = ['-0.96', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.86', 'diet', 'fast_day', 'sacrifice', 'religious', 'avoid_food']
senticnet['lack_of_exercise'] = ['-0.06', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'high_cholesterol', 'fat', 'lazy', 'fat_guy', 'round']
senticnet['lack_of_food'] = ['-0.74', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'run_out_of_time', 'run_out_of_money', 'starving', 'run_out', 'low_battery']
senticnet['lack_of_inspiration'] = ['-0.71', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.85', 'ordinariness', 'commonplaceness', 'mediocrity', 'passableness', 'adequacy']
senticnet['lack_of_judgment'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'injudiciousness', 'lack_of_discreetness', 'lack_of_discernment', 'reckless', 'rentless']
senticnet['lack_of_oxygen'] = ['-0.83', '0', '0.665', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'gasp', 'get_scared', 'scary', 'cry', 'whiff']
senticnet['lack_of_quality'] = ['-0.76', '0', '0.783', '-0.93', '#anger', '#disgust', 'negative', '-0.82', 'insufficiency', 'sparseness', 'sparsity', 'abstemiousness', 'lack_of_quantity']
senticnet['lack_of_quantity'] = ['-0.76', '0', '0.783', '-0.93', '#anger', '#disgust', 'negative', '-0.82', 'insufficiency', 'sparseness', 'sparsity', 'abstemiousness', 'lack_of_quality']
senticnet['lack_of_rest'] = ['0', '0', '0.933', '-0.86', '#anger', '#disgust', 'negative', '-0.89', 'tired', 'energy_exhaustion', 'need_rest', 'exhausted', 'dead_tired']
senticnet['lack_of_rules'] = ['-0.81', '-0.71', '0.843', '-0.72', '#sadness', '#anger', 'negative', '-0.41', 'anarchism', 'fascism', 'despot', 'tyrant', 'nazism']
senticnet['lack_of_sleep'] = ['-0.93', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.88', 'tiredness', 'sore_knee', 'tired', 'sleepiness', 'fatigue']
senticnet['lack_of_water'] = ['-0.06', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.04', 'friction', 'abrasive', 'dirt', 'sand', 'ground_material']
senticnet['lack_originality'] = ['-0.86', '0', '0', '-0.96', '#sadness', '#disgust', 'negative', '-0.91', 'dull', 'ordinary', 'unoriginal', 'murky', 'banal']
senticnet['lack_time'] = ['-0.39', '0', '0.029', '-0.44', '#sadness', '#disgust', 'negative', '-0.29', 'psychosis', 'discomfort', 'anxiety', 'hurt_feeling', 'frustration']
senticnet['lackadaisical'] = ['-0.88', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.92', 'languorous', 'languid', 'languor', 'languorously', 'careless']
senticnet['lacked_composure'] = ['0.724', '-0.92', '0', '0.854', '#surprise', '#admiration', 'positive', '0.833', 'tranquility', 'serenity', 'disposition', 'repose', 'placidity']
senticnet['lackey'] = ['-0.12', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.12', 'get_done', 'free_slave', 'earn_paycheck', 'work_hard', 'finish_task']
senticnet['lacking'] = ['-0.58', '0', '0.996', '-0.84', '#anger', '#disgust', 'negative', '-0.81', 'undermanned', 'understaffed', 'deficient', 'inadequate', 'wanting']
senticnet['lackluster'] = ['-0.62', '0', '0.988', '-0.59', '#sadness', '#anger', 'negative', '-0.73', 'motley', 'mixture', 'variety', 'assortment', 'alphabet_soup']
senticnet['lacklustre'] = ['0', '0', '0.954', '-0.76', '#anger', '#disgust', 'negative', '-0.85', 'fervid', 'ardent', 'impassioned', 'fervent', 'fiery']
senticnet['laconian'] = ['-0.69', '0', '0.957', '-0.92', '#anger', '#disgust', 'negative', '-0.85', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['laconic'] = ['0', '0', '-0.88', '-0.90', '#fear', '#disgust', 'negative', '-0.89', 'aphorism', 'concise', 'laconically', 'spartan', 'few']
senticnet['laconic_style'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'mediacy', 'mediateness', 'characteristic', 'indirectness']
senticnet['laconically'] = ['0', '0', '-0.89', '-0.91', '#fear', '#disgust', 'negative', '-0.90', 'aphorism', 'concise', 'laconic', 'spartan', 'few']
senticnet['laconicism'] = ['-0.66', '0', '0.812', '-0.88', '#anger', '#disgust', 'negative', '-0.78', 'laconism', 'terseness', 'conciseness', 'concision', 'pithiness']
senticnet['laconism'] = ['-0.66', '0', '0.812', '-0.88', '#anger', '#disgust', 'negative', '-0.78', 'terseness', 'laconicism', 'conciseness', 'concision', 'pithiness']
senticnet['lacquered_wood'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'building', 'carving', 'artistic_creation', 'ceramic']
senticnet['lacrimal_bone'] = ['0.886', '-0.99', '0', '0.542', '#joy', '#surprise', 'positive', '0.808', 'jowl', 'lower_jaw', 'mandible', 'lantern_jaw', 'lantern_jawr_bone']
senticnet['lacrimal_duct'] = ['-0.78', '0', '0.855', '-0.99', '#anger', '#disgust', 'negative', '-0.87', 'blood_blister', 'pustule', 'water_blister', 'vesicle', 'cyst']
senticnet['lacrimal_sac'] = ['-0.78', '0', '0.855', '-0.99', '#anger', '#disgust', 'negative', '-0.87', 'blood_blister', 'pustule', 'water_blister', 'vesicle', 'cyst']
senticnet['lacrimation'] = ['-0.58', '0', '0.773', '-0.79', '#anger', '#disgust', 'negative', '-0.71', 'perspiration', 'sweating', 'polyhidrosis', 'sudation', 'hidrosis']
senticnet['lacrosse_federation'] = ['-0.68', '0', '0.923', '-0.68', '#sadness', '#anger', 'negative', '-0.76', 'negative', 'veto', 'refute', 'rebut', 'controvert']
senticnet['lacrosse_stick'] = ['0.783', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.766', 'baseball_bat', 'water_gun', 'goalie', 'hockey_stick', 'bullseye']
senticnet['lactaid'] = ['-0.86', '0', '0.76', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['lactamase'] = ['-0.69', '0', '0.516', '-0.94', '#sadness', '#disgust', 'negative', '-0.71', 'insubordinate', 'recalcitrant', 'obstreperous', 'defiant', 'resistive']
senticnet['lactase'] = ['-0.86', '0', '0.76', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['lactase_deficiency'] = ['-0.79', '0', '0.949', '-0.72', '#sadness', '#anger', 'negative', '-0.82', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['lactase_enzyme'] = ['-0.86', '0', '0.76', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['lactated_ringer_solution'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
senticnet['lactic_acid_bacteria'] = ['0', '0.104', '-0.09', '0.146', '#interest', '#admiration', 'positive', '0.052', 'thaw', 'slush', 'water_inside', 'give_water', 'obtain_water']
senticnet['lactic_acid_buildup'] = ['0.84', '0.76', '0', '0.894', '#joy', '#admiration', 'positive', '0.831', 'keep_skin_healthy', 'stay_healthy', 'stay_fit', 'maintain_good_health', 'eat_healthily']
senticnet['lactitol'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
senticnet['lactobacillus'] = ['-0.99', '0', '0.987', '-0.96', '#sadness', '#anger', 'negative', '-0.98', 'acidophilus', 'probiotic', 'probiotic_bacterium', 'probiotic_microflora', 'probiotic_flora']
senticnet['lactobacillus_acidophilus'] = ['-0.50', '0', '0.656', '-0.93', '#anger', '#disgust', 'negative', '-0.69', 'lactobacillus', 'probiotic', 'probiotic_bacterium', 'probiotic_microflora', 'probiotic_flora']
senticnet['lactose'] = ['0.04', '0', '0', '0.144', '#joy', '#admiration', 'positive', '0.092', 'liquid', 'fluid', 'potable_liquid', 'white_color', 'juice']
senticnet['lactose_free'] = ['-0.86', '0', '0.76', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['lactose_intolerance'] = ['-0.86', '0', '0.76', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['lactose_intolerant'] = ['-0.86', '0', '0.76', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['lactulose'] = ['-0.50', '0', '0.582', '-0.57', '#anger', '#disgust', 'negative', '-0.55', 'purgative', 'cathartic', 'physic', 'aperient', 'phenolphthalein']
senticnet['lacuna'] = ['0', '0', '-0.92', '-0.98', '#fear', '#disgust', 'negative', '-0.95', 'space', 'blank', 'dummy', 'clean', 'vacuous']
senticnet['lad'] = ['0.029', '0.076', '0', '0', '#joy', '#interest', 'positive', '0.052', 'youngster', 'teenager', 'junior', 'adolescent', 'maturity']
senticnet['ladder'] = ['-0.83', '0', '0.943', '0', '#sadness', '#anger', 'negative', '-0.88', 'messy', 'swine', 'debauched', 'pen', 'sty']
senticnet['laddie'] = ['0.947', '0.914', '0', '0', '#joy', '#interest', 'positive', '0.93', 'ball_bounce', 'bicycle_tire', 'tire', 'increase_price', 'gyroscope']
senticnet['laddish'] = ['-0.69', '0', '0.597', '-0.56', '#sadness', '#anger', 'negative', '-0.62', 'improperness', 'impropriety', 'indelicacy', 'spiciness', 'gaminess']
senticnet['laden'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'oppressiveness', 'tyrannical', 'downtrodden', 'oppressive', 'tyrannous']
senticnet['ladie'] = ['-0.11', '0.112', '0.674', '-0.66', '#anger', '#disgust', 'negative', '-0.33', 'wrathfully', 'wrathful', 'wrothful', 'lividly', 'lividity']
senticnet['ladies_slipper'] = ['0.277', '0.161', '0', '0.337', '#joy', '#admiration', 'positive', '0.258', 'moccasin_flower', 'nerveroot', 'showy_lady_slipper', 'yellow_lady_slipper', 'clustered_lady_slipper']
senticnet['ladle'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'address_book', 'get_rich', 'blank_check', 'swiss_army_knife', 'whisk']
senticnet['ladleful'] = ['0.07', '0.084', '0.023', '0', '#joy', '#interest', 'positive', '0.044', 'hearty', 'porridge', 'hot_liquid', 'liquid_food', 'meat_vegetable']
senticnet['lady'] = ['0', '0.844', '0', '0.769', '#interest', '#admiration', 'positive', '0.806', 'wench', 'female', 'lesbian', 'princess', 'girl']
senticnet['lady_amateur'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'sciolist', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
senticnet['lady_bedstraw'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
senticnet['lady_bracknell'] = ['0', '0', '0', '0.994', '#admiration', '#admiration', 'positive', '0.994', 'seriousness', 'in_earnest', 'heartfelt', 'devout', 'earnestly']
senticnet['lady_bridget'] = ['-0.71', '0', '0.906', '-0.93', '#anger', '#disgust', 'negative', '-0.85', 'absent', 'forgetful', 'distracted', 'inattentive', 'scatty']
senticnet['lady_chatterley_lover'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'indecency']
senticnet['lady_deathstrike'] = ['0.02', '0.131', '0.749', '-0.67', '#anger', '#disgust', 'negative', '-0.31', 'belligerence', 'belligerency', 'belligerent', 'war', 'rancor']
senticnet['lady_fern'] = ['-0.52', '0', '0.527', '-0.78', '#anger', '#disgust', 'negative', '-0.61', 'herb', 'black_snakeroot', 'heartleaf', 'asarabacca', 'herbaceous_plant']
senticnet['lady_gaga'] = ['0.056', '0.139', '-0.07', '0.154', '#interest', '#admiration', 'positive', '0.07', 'belongings', 'material', 'fabric', 'guarantee', 'stuff']
senticnet['lady_grantham'] = ['-0.62', '0', '0.649', '-0.67', '#anger', '#disgust', 'negative', '-0.64', 'handmaid', 'handmaiden', 'chambermaid', 'maid', 'parlormaid']
senticnet['lady_hair'] = ['0.773', '0.995', '0', '0', '#joy', '#interest', 'positive', '0.884', 'beauty_shop', 'beauty_parlor', 'barber_shop', 'hair_salon', 'beauty_salon']
senticnet['lady_harriet'] = ['-0.62', '0', '0.649', '-0.67', '#anger', '#disgust', 'negative', '-0.64', 'handmaid', 'handmaiden', 'chambermaid', 'maid', 'parlormaid']
senticnet['lady_isabella'] = ['-0.76', '0', '0.995', '-0.94', '#anger', '#disgust', 'negative', '-0.90', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'excretion']
senticnet['lady_maid'] = ['-0.62', '0', '0.649', '-0.67', '#anger', '#disgust', 'negative', '-0.64', 'handmaid', 'handmaiden', 'chambermaid', 'maid', 'parlormaid']
senticnet['lady_mastermind'] = ['0.02', '0.131', '0.749', '-0.67', '#anger', '#disgust', 'negative', '-0.31', 'belligerence', 'belligerency', 'belligerent', 'war', 'rancor']
senticnet['lady_molly'] = ['-0.62', '0', '0.649', '-0.67', '#anger', '#disgust', 'negative', '-0.64', 'handmaid', 'handmaiden', 'chambermaid', 'maid', 'parlormaid']
senticnet['lady_of_pleasure'] = ['-0.74', '0', '0.69', '-0.89', '#sadness', '#disgust', 'negative', '-0.77', 'slattern', 'camp_follower', 'call_girl', 'comfort_woman', 'ianfu']
senticnet['lady_portmore'] = ['-0.70', '0', '0.502', '-0.51', '#sadness', '#disgust', 'negative', '-0.57', 'distress', 'straiten', 'impact', 'bear_on', 'touch']
senticnet['lady_slipper'] = ['-0.62', '0', '0.96', '-0.85', '#anger', '#disgust', 'negative', '-0.81', 'moccasin_flower', 'nerveroot', 'showy_lady_slipper', 'yellow_lady_slipper', 'clustered_lady_slipper']
senticnet['lady_styx'] = ['0', '0.081', '0', '0', '#interest', '#interest', 'positive', '0.081', 'favorite', 'guard', 'crowd', 'several_people', 'clique']
senticnet['lady_toilet'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'commode', 'potty_seat', 'potty_chair']
senticnet['lady_tremaine'] = ['-0.63', '0', '0.674', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'baneful', 'evil', 'baleful', 'malevolent', 'malign']
senticnet['lady_underwear'] = ['0.776', '0.996', '0', '0', '#joy', '#interest', 'positive', '0.886', 'under_garment', 'undergarment', 'underwear', 'woman_clothes', 'pantie']
senticnet['ladybug'] = ['-0.02', '-0.07', '0.12', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'ant', 'gnat', 'gypsy_moth', 'mole_cricket', 'cucumber_beetle']
senticnet['ladyfish'] = ['0.96', '-0.77', '0', '0.929', '#joy', '#admiration', 'positive', '0.887', 'whale_louse', 'beach_flea', 'sand_hopper', 'sandhopper', 'amphipod']
senticnet['ladylike'] = ['0.614', '-0.95', '0', '0.537', '#joy', '#surprise', 'positive', '0.703', 'mincing', 'twee', 'gentlemanlike', 'gentlemanly', 'refined']
senticnet['lael'] = ['0.746', '-0.88', '0', '0.723', '#joy', '#surprise', 'positive', '0.783', 'strike', 'run_into', 'hit', 'impinge_on', 'collide_with']
senticnet['laetare'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'reverence', 'genuflection', 'obeisance', 'kowtow', 'mafia']
senticnet['laevigatum'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'seed_fern', 'pteridosperm']
senticnet['laffy_taffy'] = ['0.023', '0.141', '-0.13', '0.174', '#interest', '#admiration', 'positive', '0.052', 'candy', 'push_wind', 'exhalation', 'breaout', 'mouth_action']
senticnet['lafite'] = ['0.331', '0.119', '0.022', '0.229', '#joy', '#admiration', 'positive', '0.164', 'spice_life', 'red_light_district', 'fresh_produce', 'enough_food', 'knowledgeable']
senticnet['lag'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'put_off', 'delay', 'retard', 'postpone', 'procrastination']
senticnet['lag_behind'] = ['0', '0.994', '0', '0.88', '#interest', '#admiration', 'positive', '0.937', 'exceed', 'outshout', 'outstrip', 'outshine', 'outsail']
senticnet['lagardere'] = ['-0.90', '0', '0.924', '0', '#sadness', '#anger', 'negative', '-0.91', 'privatization', 'denationalization', 'denationalisation', 'privatisation', 'thomson']
senticnet['lagavulin'] = ['0.877', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.919', 'score', 'economical', 'scotch_whiskey', 'spoil', 'scottish']
senticnet['lager'] = ['0', '0.091', '-0.05', '0.073', '#interest', '#admiration', 'positive', '0.037', 'privately', 'hold_liquid', 'make_wish', 'rinse_dish', 'find_toilet']
senticnet['lagerstroemia'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'carnauba_palm', 'carnauba', 'wax_palm', 'palmetto', 'palmyra']
senticnet['lagerstroemia_indica'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'horse_chestnut', 'red_buckeye', 'particolored_buckeye']
senticnet['laggard'] = ['-0.64', '0', '0.879', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'drone', 'poke', 'dawdler', 'straggler', 'lagger']
senticnet['lagger'] = ['-0.64', '0', '0.879', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'drone', 'poke', 'dawdler', 'laggard', 'straggler']
senticnet['lagging_strand'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['lagrange_point'] = ['0.989', '-0.90', '0', '0.876', '#joy', '#surprise', 'positive', '0.923', 'reach', 'range', 'compass', 'scope', 'confines']
senticnet['lagrangian'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'assumption', 'precondition', 'provision', 'constraint', 'premiss']
senticnet['laguna_madre'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'harpoon', 'armory', 'lake', 'river', 'bay']
senticnet['laguna_pueblo'] = ['0.052', '0.099', '0', '0.067', '#interest', '#admiration', 'positive', '0.073', 'police_rank', 'head_guy', 'chief', 'leader', 'commander']
senticnet['laid-back'] = ['0', '0.705', '0', '0.79', '#interest', '#admiration', 'positive', '0.748', 'adventurous', 'fun-loving', 'adventuresome', 'outdoorsy', 'easygoing']
senticnet['laidback'] = ['-0.94', '0', '0.492', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'make_noise', 'disturb', 'confusion', 'hustle_and_bustle', 'bustle']
senticnet['lair'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'animal_home', 'habitat', 'beehive']
senticnet['laissez_faire'] = ['-0.88', '0', '0.62', '-0.61', '#sadness', '#anger', 'negative', '-0.70', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['lake'] = ['0.876', '0.839', '0', '0', '#joy', '#interest', 'positive', '0.857', 'harpoon', 'gulf', 'armory', 'river', 'bay']
senticnet['lake_campground'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'common_bean', 'green_bean', 'wax_bean']
senticnet['lake_lesjaskogsvatnet'] = ['-0.03', '0', '0.014', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'ice_crystal', 'frozen_water', 'wet_cold', 'coldness', 'wetness']
senticnet['lake_nasser'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['lake_ontario_lake_trout'] = ['-0.83', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.78', 'picaninny', 'kid', 'minor', 'tike', 'small_picaninny']
senticnet['lake_river'] = ['0', '-0.03', '0.053', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'puddle', 'beach', 'waterfall', 'sandy_beach', 'bay']
senticnet['lake_tana'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['lake_trout'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'divergent_strabismus', 'exotropia', 'squint', 'abnormality', 'abnormalcy']
senticnet['lakers_metta_world'] = ['0.497', '0.453', '-0.23', '0.189', '#joy', '#interest', 'positive', '0.226', 'calm', 'calmness', 'stillness', 'truce', 'love']
senticnet['lakeside_shopping_centre'] = ['0.623', '-0.56', '0', '0.974', '#joy', '#admiration', 'positive', '0.721', 'alterable', 'convertible', 'translatable', 'transformable', 'transmutable']
senticnet['lakland'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'lever']
senticnet['lakota'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['lakota_culture'] = ['0.052', '0.099', '0', '0.067', '#interest', '#admiration', 'positive', '0.073', 'police_rank', 'head_guy', 'chief', 'leader', 'commander']
senticnet['lakota_language'] = ['0.052', '0.099', '0', '0.067', '#interest', '#admiration', 'positive', '0.073', 'police_rank', 'head_guy', 'chief', 'leader', 'commander']
senticnet['lakpa'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['lallation'] = ['-0.84', '0', '0.829', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'upset', 'dysarthria', 'dysphonia', 'disorder', 'defect_of_speech']
senticnet['lam'] = ['-0.69', '0', '0.998', '-0.84', '#anger', '#disgust', 'negative', '-0.84', 'evasion', 'escape', 'break', 'skedaddle', 'hegira']
senticnet['lama_guanicoe'] = ['-0.53', '0', '0.751', '-0.97', '#anger', '#disgust', 'negative', '-0.75', 'guanaco', 'domestic_llama', 'llama', 'artiodactyl', 'artiodactyl_mammal']
senticnet['lamar_valley'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['lamarck'] = ['-0.04', '0.192', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.054', 'hypothetical', 'supposition', 'scientific_statement', 'psychology', 'educate_guess']
senticnet['lamarckian'] = ['-0.04', '0.192', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.054', 'hypothetical', 'supposition', 'scientific_statement', 'psychology', 'educate_guess']
senticnet['lamarckism'] = ['-0.04', '0.192', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.054', 'hypothetical', 'supposition', 'scientific_statement', 'psychology', 'educate_guess']
senticnet['lamb'] = ['0.715', '0.969', '0', '0', '#joy', '#interest', 'positive', '0.842', 'servant', 'goat', 'hog', 'peasant', 'shepherd']
senticnet['lamb_burger'] = ['0.783', '-0.63', '0', '0.829', '#joy', '#admiration', 'positive', '0.749', 'leg_of_lamb', 'gigot', 'joint', 'roast', 'rack_of_lamb']
senticnet['lamb_lettuce'] = ['0.598', '-0.72', '0', '0.941', '#surprise', '#admiration', 'positive', '0.753', 'sicklepod', 'tower_rock_cress', 'tower_mustard', 'rock_cress', 'rock_cress_plant']
senticnet['lamb_meat'] = ['0', '-0.03', '0.057', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'shear', 'bandage', 'ewe', 'mutton', 'seam']
senticnet['lamb_roast'] = ['0.783', '-0.63', '0', '0.829', '#joy', '#admiration', 'positive', '0.749', 'leg_of_lamb', 'gigot', 'joint', 'roast', 'rack_of_lamb']
senticnet['lamb_shank'] = ['-0.02', '-0.02', '0.064', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'ocean_water', 'fillet_fish', 'chowder', 'port', 'underwater']
senticnet['lamb_shoulder'] = ['-0.02', '-0.02', '0.064', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'ocean_water', 'fillet_fish', 'chowder', 'port', 'underwater']
senticnet['lamb_stew'] = ['0.783', '-0.63', '0', '0.829', '#joy', '#admiration', 'positive', '0.749', 'leg_of_lamb', 'gigot', 'joint', 'roast', 'rack_of_lamb']
senticnet['lambast'] = ['-0.12', '0.13', '0.647', '-0.64', '#anger', '#disgust', 'negative', '-0.32', 'exasperating', 'infuriating', 'maddening', 'furiously', 'dudgeon']
senticnet['lambaste'] = ['0', '0', '-0.93', '-0.88', '#fear', '#disgust', 'negative', '-0.91', 'rebuke', 'take_to_task', 'flog', 'call_on_carpet', 'cane']
senticnet['lambda_chapter'] = ['0.074', '0.114', '0', '0.126', '#interest', '#admiration', 'positive', '0.105', 'paragraph', 'newspaper_write', 'news_paper', 'newspaper_story', 'index']
senticnet['lambent'] = ['0.938', '0', '0', '0.696', '#joy', '#admiration', 'positive', '0.817', 'bright', 'illuminated', 'luminously', 'deneb', 'luminous']
senticnet['lambert_azimuthal_equal'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'arm_movement', 'hand_gesture', 'water_motion', 'hand_signal', 'sea_movement']
senticnet['lambertian'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'incandescence', 'luminescence', 'brightness_level']
senticnet['lamberto_bava'] = ['-0.79', '0', '0.885', '-0.76', '#sadness', '#anger', 'negative', '-0.81', 'infernal', 'mortal', 'dead_soul', 'dead_soule', 'living_dead']
senticnet['lamborghini'] = ['0.755', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.728', 'high_class', 'expensive_car', 'ferrari', 'fancy_car', 'sports_car']
senticnet['lamborghini_aventador'] = ['0.755', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.728', 'high_class', 'expensive_car', 'ferrari', 'fancy_car', 'sports_car']
senticnet['lamborghini_countach'] = ['-0.73', '0', '0.72', '-0.93', '#sadness', '#disgust', 'negative', '-0.79', 'imp', 'elf', 'hob', 'leprechaun', 'pixie']
senticnet['lamborghini_diablo'] = ['0.755', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.728', 'high_class', 'expensive_car', 'ferrari', 'fancy_car', 'sports_car']
senticnet['lamborghini_gallardo'] = ['0.755', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.728', 'high_class', 'expensive_car', 'ferrari', 'fancy_car', 'sports_car']
senticnet['lame'] = ['0', '0', '0.846', '-0.72', '#anger', '#disgust', 'negative', '-0.78', 'gimpy', 'feeble', 'crippled', 'square', 'halt']
senticnet['lame-duck'] = ['-0.94', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.86', 'obstructionist', 'do-nothing', 'know-nothing', 'spineless', 'feckless']
senticnet['lame_attempt'] = ['-0.61', '0', '0.981', '-0.98', '#anger', '#disgust', 'negative', '-0.85', 'pious_platitude', 'nothingness', 'jazz', 'talk', 'malarky']
senticnet['lame_beaver'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'common_bean', 'green_bean', 'wax_bean']
senticnet['lame_deer'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['lame_duck'] = ['0.984', '-0.93', '0', '0.927', '#joy', '#surprise', 'positive', '0.948', 'elected_official', 'lieutenant_governor', 'selectman', 'selectwoman', 'official']
senticnet['lamellar_bone'] = ['-0.53', '0', '0.982', '-0.67', '#anger', '#disgust', 'negative', '-0.73', 'meniscus', 'semilunar_cartilage', 'gristle', 'hyaline_cartilage', 'arytenoid']
senticnet['lamely'] = ['0', '-0.63', '-1', '0', '#surprise', '#fear', 'negative', '-0.18', 'disingenuously', 'unconvincingly', 'coyly', 'apologetically', 'cheekily']
senticnet['lameness'] = ['0.789', '-0.99', '0', '0.54', '#joy', '#surprise', 'positive', '0.776', 'imperfection', 'imperfectness', 'bugginess', 'defectiveness', 'sickness']
senticnet['lament'] = ['-0.85', '0', '0.752', '-0.73', '#sadness', '#anger', 'negative', '-0.78', 'feel_sorry', 'sadness', 'depression', 'deep_sadness', 'negative_feeling']
senticnet['lamentable'] = ['-0.68', '0', '0.82', '-0.96', '#anger', '#disgust', 'negative', '-0.82', 'deliberateness', 'leisureliness', 'procrastination', 'rate', 'pace']
senticnet['lamentably'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'deplorably', 'woefully', 'bereaved', 'bereft', 'grief-stricken']
senticnet['lamentation'] = ['-0.8', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.8', 'heartache', 'grief', 'heartbreak', 'woe', 'guilty_conscience']
senticnet['lamenter'] = ['-0.69', '0', '0.88', '-0.92', '#anger', '#disgust', 'negative', '-0.83', 'unfortunate', 'unfortunate_person', 'weeper', 'pallbearer', 'bearer']
senticnet['lamina'] = ['-0.03', '0', '0', '-0.11', '#sadness', '#disgust', 'negative', '-0.07', 'store_cloth', 'clothes_factory', 'stock', 'sword', 'gun_store']
senticnet['lamina_pinnate'] = ['-0.86', '0', '0.511', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'separate', 'move', 'branch', 'diverge', 'fork']
senticnet['laminar_boundary_layer'] = ['0', '0.972', '0', '0.856', '#interest', '#admiration', 'positive', '0.914', 'flow', 'plate', 'flat_plate', 'heat_transfer', 'transition']
senticnet['laminar_flame'] = ['-0.90', '0', '0.825', '-0.61', '#sadness', '#anger', 'negative', '-0.78', 'flaming', 'combustion', 'fire', 'flame', 'internal_combustion']
senticnet['laminaria_hyperborea'] = ['-0.89', '0', '0.556', '-0.72', '#sadness', '#disgust', 'negative', '-0.72', 'fucus', 'rockweed', 'black_rockweed', 'bladder_fucus', 'tang']
senticnet['laminated_poster'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'gift_wrap', 'kraft', 'kraft_paper', 'wrapping_paper', 'paper']
senticnet['laminated_wood'] = ['0', '0.773', '0', '0.895', '#interest', '#admiration', 'positive', '0.834', 'wooden_board', 'make_wood', 'wooden_box', 'wooden_floor', 'long_wood']
senticnet['lamisil'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'antifungal', 'antifungal_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['lamisil_agent'] = ['0.925', '-0.56', '0', '0.61', '#joy', '#admiration', 'positive', '0.699', 'lamisil', 'antifungal', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['lamivudine'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'indinavir', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['lammergeier'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'argali', 'argal', 'urial', 'white_sheep', 'wild_sheep']
senticnet['lamp'] = ['0.046', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'light', 'bay_window', 'air_conditioner', 'sky', 'skylight']
senticnet['lamp_emit'] = ['-0.63', '0', '0.897', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'cd-rom', 'international_candle', 'candle', 'candlepower_unit', 'standard_candle']
senticnet['lamp_post'] = ['0.952', '-0.52', '0', '0.835', '#joy', '#admiration', 'positive', '0.771', 'billboard', 'hoarding', 'sandwich_board', 'scoreboard', 'shingle']
senticnet['lampblack'] = ['0.037', '0.084', '0', '0', '#joy', '#interest', 'positive', '0.06', 'leave_over', 'leftover', 'cellulose', 'cane', 'remainder']
senticnet['lamping'] = ['0.838', '0.961', '0', '0', '#joy', '#interest', 'positive', '0.899', 'resplendent', 'brilliant', 'splendor', 'effulgent', 'resplendence']
senticnet['lamplight'] = ['-0.66', '0', '0.616', '-0.70', '#sadness', '#disgust', 'negative', '-0.66', 'flicker', 'coruscation', 'streak', 'glint', 'flash']
senticnet['lampoon'] = ['-0.91', '0', '-0.76', '0', '#sadness', '#fear', 'negative', '-0.83', 'mockery', 'spoof', 'sendup', 'takeoff', 'parody']
senticnet['lamppost'] = ['0.952', '-0.52', '0', '0.835', '#joy', '#admiration', 'positive', '0.771', 'billboard', 'hoarding', 'sandwich_board', 'scoreboard', 'shingle']
senticnet['lampshade'] = ['0.022', '-0.01', '0.062', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'electric_toothbrush', 'moistener', 'hairbrush', 'shampoo_conditioner', 'shave_kit']
senticnet['lampwork'] = ['-0.52', '0', '0.506', '-0.94', '#sadness', '#disgust', 'negative', '-0.65', 'gouger', 'swindler', 'defrauder', 'fraud', 'welcher']
senticnet['lancaster_regiment'] = ['0.033', '0.088', '-0.06', '0.068', '#interest', '#admiration', 'positive', '0.032', 'earl', 'noble', 'king', 'sultan', 'royal_title']
senticnet['lance'] = ['0.928', '-0.53', '0', '0.527', '#joy', '#admiration', 'positive', '0.662', 'lancet', 'surgical_knife', 'catling', 'scalpel', 'knife']
senticnet['lanceolata'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['lancet'] = ['0.932', '-0.52', '0', '0.534', '#joy', '#admiration', 'positive', '0.662', 'surgical_knife', 'catling', 'cut', 'scalpel', 'knife']
senticnet['lancme'] = ['0.863', '0.861', '0', '0', '#joy', '#interest', 'positive', '0.862', 'aroma', 'scent', 'smell', 'odor', 'sniff']
senticnet['lancre'] = ['0.967', '0.768', '0', '0', '#joy', '#interest', 'positive', '0.867', 'centerfold', 'genus', 'honey_bee', 'infant', 'dove']
senticnet['land'] = ['0.716', '-0.51', '0', '0.647', '#joy', '#admiration', 'positive', '0.624', 'state', 'nation', 'country', 'ally', 'commonwealth']
senticnet['land_acquisition'] = ['-0.02', '0.119', '-0.06', '0.216', '#interest', '#admiration', 'positive', '0.062', 'go_forward', 'locomotion', 'movement', 'move', 'move_ahead']
senticnet['land_degradation'] = ['0', '-0.02', '0.085', '-0.09', '#anger', '#disgust', 'negative', '-0.05', 'tree_wood', 'forest', 'sideways', 'dead_tree', 'wood']
senticnet['land_earth'] = ['-0.06', '0', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.05', 'septic_tank', 'spurt', 'crush_ice', 'down_drain', 'brine']
senticnet['land_feature'] = ['0', '0.094', '0.03', '0.097', '#interest', '#admiration', 'positive', '0.054', 'geographical_feature', 'land_formation', 'land_form', 'geographic_feature', 'landscape_feature']
senticnet['land_fish'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'communication_system', 'fish', 'catch_fish', 'triathlon', 'bait']
senticnet['land_form'] = ['0', '0.082', '0.042', '0.058', '#interest', '#admiration', 'positive', '0.033', 'land_formation', 'geographical_feature', 'land_feature', 'geographic_feature', 'large_land_mass']
senticnet['land_formation'] = ['0', '0.088', '0.045', '0.074', '#interest', '#admiration', 'positive', '0.039', 'land_form', 'geographical_feature', 'geographic_feature', 'land_feature', 'landscape_feature']
senticnet['land_ground'] = ['0', '-0.00', '0.03', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'full_bladder', 'throw_away_trash', 'hold_liquid', 'drink', 'hold_beverage']
senticnet['land_mass'] = ['0.042', '0.082', '0.026', '0', '#joy', '#interest', 'positive', '0.033', 'boundary', 'border', 'territory', 'region', 'large_border']
senticnet['land_measurement'] = ['0', '0.119', '0.06', '0.158', '#interest', '#admiration', 'positive', '0.072', 'examine', 'plenty', 'measurement', 'scope', 'meter']
senticnet['land_moon'] = ['0', '0', '-0.14', '0', '#fear', '#fear', 'negative', '-0.14', 'womb', 'anus', 'sister_house', 'shit', 'asshole']
senticnet['land_owner'] = ['0.02', '0.188', '-0.07', '0.196', '#interest', '#admiration', 'positive', '0.083', 'laborer', 'blue_collar', 'own_land', 'own_person', 'helper']
senticnet['land_ownership'] = ['0.716', '-0.51', '0', '0.647', '#joy', '#admiration', 'positive', '0.624', 'state', 'nation', 'country', 'ally', 'commonwealth']
senticnet['land_plane'] = ['0', '0.119', '0.06', '0.158', '#interest', '#admiration', 'positive', '0.072', 'travel_by_airplane', 'travel_abroad', 'pilot', 'plane', 'transport']
senticnet['land_runway'] = ['-0.02', '0', '0.042', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'bus_person', 'travel_across_world', 'arrive_late', 'drop_bomb', 'cool_engine']
senticnet['land_sea'] = ['0.745', '0.976', '0', '0', '#joy', '#interest', 'positive', '0.861', 'vast', 'along_beach', 'near_beach', 'big_blue', 'full_water']
senticnet['land_speculator'] = ['-0.78', '0', '0.944', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'loser', 'gambler', 'mortal', 'better', 'bettor']
senticnet['land_subsidence'] = ['-0.88', '0', '0.907', '-0.52', '#sadness', '#anger', 'negative', '-0.77', 'wearing', 'corrasion', 'eroding', 'eating_away', 'wearing_away']
senticnet['landed_aristocracy'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'ascendance', 'autocracy', 'absolutism', 'tyrant']
senticnet['landed_estate'] = ['0.975', '-0.78', '0', '0.969', '#joy', '#admiration', 'positive', '0.911', 'demesne', 'estate', 'land', 'barony', 'acre']
senticnet['landed_gentry'] = ['0.973', '0.707', '0', '0', '#joy', '#interest', 'positive', '0.84', 'money', 'down_payment', 'much_money', 'slogan', 'take_out_loan']
senticnet['landed_safely'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'atmosphere', 'wing', 'nest', 'feather', 'bird_fly']
senticnet['landed_upside_down'] = ['-0.11', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.08', 'have_fun', 'spring_up', 'fall_well', 'shovel_snow', 'fly_around']
senticnet['lander'] = ['0.052', '0.119', '0', '0.141', '#interest', '#admiration', 'positive', '0.104', 'neigh', 'domestic_animal', 'animal_type', 'animal', 'home_animal']
senticnet['landespolizei'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'law_enforcement_agency', 'law', 'agency']
senticnet['landfall'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'atmospheric_phenomenon', 'natural_disaster', 'flood', 'destruction', 'tropical_storm']
senticnet['landfill'] = ['0.916', '0.947', '0', '0', '#joy', '#interest', 'positive', '0.931', 'dump', 'utility_room', 'trash_bin', 'tool_shed', 'antique_store']
senticnet['landfill_operator'] = ['-0.81', '0', '0.987', '-0.64', '#sadness', '#anger', 'negative', '-0.81', 'burn_garbage', 'eliminate_garbage', 'incinerate_waste', 'dump', 'garbage_dump']
senticnet['landfill_site'] = ['-0.81', '0', '0.987', '-0.64', '#sadness', '#anger', 'negative', '-0.81', 'burn_garbage', 'eliminate_garbage', 'incinerate_waste', 'dump', 'garbage_dump']
senticnet['landholder'] = ['0', '0.179', '-0.05', '0.162', '#interest', '#admiration', 'positive', '0.095', 'house_land', 'real_estate', 'acreage', 'house_place', 'good_investment']
senticnet['landing_safely'] = ['-0.05', '0.066', '0', '0.079', '#interest', '#admiration', 'positive', '0.029', 'skip', 'jump_high', 'float_through_air', 'jump_over', 'bail_water']
senticnet['landing_site'] = ['0.052', '0.119', '0', '0.141', '#interest', '#admiration', 'positive', '0.104', 'neigh', 'domestic_animal', 'animal_type', 'animal', 'home_animal']
senticnet['landing_strip'] = ['-0.70', '0', '0.621', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'flare_path', 'airfield', 'grassland', 'runway', 'road']
senticnet['landing_upside_down'] = ['-0.11', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.08', 'have_fun', 'spring_up', 'fall_well', 'shovel_snow', 'fly_around']
senticnet['landlady'] = ['-0.62', '0', '0.649', '-0.67', '#anger', '#disgust', 'negative', '-0.64', 'handmaid', 'handmaiden', 'chambermaid', 'maid', 'parlormaid']
senticnet['landlady_daughter'] = ['-0.88', '0', '0.864', '-0.72', '#sadness', '#anger', 'negative', '-0.82', 'vixenish', 'venomous', 'bitchy', 'catty', 'malicious']
senticnet['landline'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'long_distance_relationship', 'distant', 'kilometer', 'length_measurement', 'distance_measure']
senticnet['landline_phone'] = ['0.035', '-0.01', '0.027', '0.085', '#joy', '#admiration', 'positive', '0.026', 'telephone', 'find_phone_number', 'telephone_room', 'phone', 'use_telephone']
senticnet['landlocked_country'] = ['0.716', '-0.51', '0', '0.647', '#joy', '#admiration', 'positive', '0.624', 'state', 'nation', 'ally', 'land', 'commonwealth']
senticnet['landmark'] = ['0', '0.812', '0', '0.898', '#interest', '#admiration', 'positive', '0.855', 'historic', 'memorial', 'iconic', 'architectural', 'heritage']
senticnet['landmark_legislation'] = ['0', '0.089', '0', '0.129', '#interest', '#admiration', 'positive', '0.109', 'passageway', 'connect_room', 'entrance_room', 'corridor', 'walk_through']
senticnet['landmass'] = ['0', '0.076', '0', '0', '#interest', '#interest', 'positive', '0.076', 'region', 'land', 'land_mass', 'political_unit', 'territory']
senticnet['landnmabk'] = ['-0.7', '0', '0.746', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'insufficiency', 'sparseness', 'sparsity', 'abstemiousness', 'lack_of_quantity']
senticnet['landowner'] = ['0.621', '-0.60', '0', '0.545', '#joy', '#surprise', 'positive', '0.59', 'privilege', 'perquisite', 'public_easement', 'right_of_way', 'prerogative']
senticnet['landownership'] = ['0.716', '-0.51', '0', '0.647', '#joy', '#admiration', 'positive', '0.624', 'state', 'nation', 'country', 'ally', 'commonwealth']
senticnet['landrith'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'dicky', 'impaired', 'lessened', 'diminished']
senticnet['landrover'] = ['0.905', '-0.99', '0', '0.704', '#joy', '#surprise', 'positive', '0.868', 'car', 'auto', 'automobile', 'machine', 'motorcar']
senticnet['landscape'] = ['-0.89', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.94', 'landscape', 'landscaping', 'painting', 'scenery', 'architectural']
senticnet['landscape_feature'] = ['0', '0.099', '0.035', '0.129', '#interest', '#admiration', 'positive', '0.064', 'geographic_feature', 'knoll', 'land_formation', 'land_feature', 'high_land']
senticnet['landscaped'] = ['0', '-0.81', '0', '0.849', '#surprise', '#admiration', 'positive', '0.832', 'well-maintained', 'well-tended', 'manicured', 'spotlessly', 'well-kept']
senticnet['landscaping'] = ['0', '0.833', '0', '0.726', '#interest', '#admiration', 'positive', '0.779', 'lawn', 'landscape', 'green', 'paving', 'garden']
senticnet['landside_terminal'] = ['0', '0.147', '0', '0.099', '#interest', '#admiration', 'positive', '0.123', 'airliner', 'airport', 'baggage_compartment', 'aircraft', 'cable_car']
senticnet['landslide'] = ['0', '-0.03', '-0.10', '-0.07', '#fear', '#disgust', 'negative', '-0.04', 'autumn', 'winter', 'skier', 'snow_fall', 'volcano']
senticnet['landslide_victory'] = ['0', '0.156', '-0.15', '0.179', '#interest', '#admiration', 'positive', '0.061', 'candidate', 'presidential', 'vote', 'elect', 'appoint']
senticnet['landsman'] = ['0.764', '-0.96', '0', '0.883', '#surprise', '#admiration', 'positive', '0.87', 'boatmanship', 'acquirement', 'skill', 'accomplishment', 'acquisition']
senticnet['landsraad'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['landtag'] = ['0.055', '0.072', '-0.06', '0.094', '#interest', '#admiration', 'positive', '0.038', 'big_group', 'large_group', 'bunch_person', 'large_number', 'clique']
senticnet['lane'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'boulevard', 'curb', 'thoroughfare', 'intersection', 'avenue']
senticnet['lane_bowling_alley'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'theater', 'drive-in_movie', 'watch_movie', 'cinema', 'movie']
senticnet['lane_bryant'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['lane_dual_carriageway'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['lane_expressway'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['lane_freeway'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['lane_swimming_pool'] = ['0.041', '0.086', '0.016', '0', '#joy', '#interest', 'positive', '0.037', 'team', 'play_sport', 'game', 'exercise', 'match']
senticnet['lange_syndrome'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'dystrophy', 'myotonic_dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['langoustine'] = ['-0.02', '-0.02', '0.064', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'ocean_water', 'fillet_fish', 'chowder', 'port', 'underwater']
senticnet['langtang'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['langtang_lirung'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['langthwaite'] = ['-0.91', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.85', 'play', 'perpetrator', 'role_play', 'act_part', 'terrible_event']
senticnet['language'] = ['0', '0', '0', '0.115', '#admiration', '#admiration', 'positive', '0.115', 'human_language', 'speech', 'enunciation', 'medium', 'say']
senticnet['language-like'] = ['-0.84', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.78', 'non-linguistic', 'paralinguistic', 'extra-linguistic', 'nonlinguistic', 'socio-environmental']
senticnet['language_barrier'] = ['-0.93', '0', '-0.99', '-0.92', '#sadness', '#fear', 'negative', '-0.95', 'barrier', 'shut', 'shut_down', 'close_down', 'obstacle']
senticnet['language_comprehension'] = ['-0.02', '0.067', '-0.04', '0.192', '#interest', '#admiration', 'positive', '0.047', 'verbalize', 'orate', 'say', 'talkative', 'discourse']
senticnet['language_fluency'] = ['0.633', '-0.96', '0', '0.612', '#joy', '#surprise', 'positive', '0.738', 'particularism', 'absorption', 'concentration', 'engrossment', 'centering']
senticnet['language_fluently'] = ['-0.93', '0', '-0.99', '-0.92', '#sadness', '#fear', 'negative', '-0.95', 'barrier', 'shut', 'shut_down', 'close_down', 'obstacle']
senticnet['language_immersion'] = ['0.633', '-0.96', '0', '0.612', '#joy', '#surprise', 'positive', '0.738', 'particularism', 'absorption', 'concentration', 'engrossment', 'centering']
senticnet['language_impairment'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'nominal_aphasia', 'anomic_aphasia']
senticnet['language_interpreter'] = ['0', '0.202', '-0.06', '0.291', '#interest', '#admiration', 'positive', '0.142', 'vocabulary', 'translate', 'dictionary', 'translation', 'thesaurus']
senticnet['language_part'] = ['0.049', '0.082', '-0.04', '0.119', '#interest', '#admiration', 'positive', '0.051', 'readable', 'written_text', 'language', 'make_book', 'part_of_speech']
senticnet['language_skill'] = ['0.864', '0.773', '0', '0', '#joy', '#interest', 'positive', '0.819', 'learn_write', 'find_paper', 'fluent', 'think_write', 'learn_spell']
senticnet['languid'] = ['-0.90', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.91', 'languorous', 'lackadaisical', 'dreamy', 'languor', 'languorously']
senticnet['languidly'] = ['-0.77', '0', '0.855', '-0.57', '#sadness', '#anger', 'negative', '-0.73', 'unhurried', 'easygoing', 'deliberate', 'easy', 'careful']
senticnet['languish'] = ['-0.99', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.86', 'waste', 'ache', 'yearn', 'yen', 'pine_away']
senticnet['languor'] = ['0.509', '0.65', '-0.25', '0.255', '#joy', '#interest', 'positive', '0.291', 'languorous', 'languid', 'lackadaisical', 'dreamy', 'languorously']
senticnet['languorous'] = ['0.509', '0.65', '-0.25', '0.255', '#joy', '#interest', 'positive', '0.291', 'languid', 'lackadaisical', 'dreamy', 'languor', 'languorously']
senticnet['languorously'] = ['0.509', '0.65', '-0.25', '0.255', '#joy', '#interest', 'positive', '0.291', 'languorous', 'languid', 'lackadaisical', 'dreamy', 'languor']
senticnet['lanie_lane'] = ['-0.10', '0', '-0.05', '-0.24', '#sadness', '#disgust', 'negative', '-0.13', 'adulate', 'suck_up', 'toady', 'blandish', 'kowtow']
senticnet['lanikai'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'drifter', 'bum', 'sundowner']
senticnet['lanius_collurio'] = ['-0.92', '0', '0.64', '-0.52', '#sadness', '#anger', 'negative', '-0.69', 'animal_order', 'percher', 'thyreophoran', 'marginocephalian', 'euronithopod']
senticnet['lank'] = ['-0.88', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.91', 'rangy', 'tall', 'lanky', 'ungraceful', 'awkward']
senticnet['lanky'] = ['-0.88', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.91', 'rangy', 'tall', 'lank', 'ungraceful', 'awkward']
senticnet['lansdown_hill'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
senticnet['lantana_camara'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'clematis', 'legume', 'leguminous_plant', 'climber', 'root_climber']
senticnet['lantern'] = ['0.091', '0', '0.034', '0.049', '#joy', '#admiration', 'positive', '0.035', 'heat_source', 'candle', 'heater', 'illuminate_room', 'furnace']
senticnet['lantern_jaw'] = ['0.886', '-0.99', '0', '0.542', '#joy', '#surprise', 'positive', '0.808', 'jowl', 'lower_jaw', 'mandible', 'mandibula', 'mandibular_bone']
senticnet['lantern_jawr_bone'] = ['0.881', '-1', '0', '0.535', '#joy', '#surprise', 'positive', '0.805', 'jowl', 'lower_jaw', 'mandible', 'lantern_jaw', 'mandibula']
senticnet['lanugo'] = ['0', '0', '0.883', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'get_old', 'dry_skin', 'become_wet', 'clothes_shrink', 'wrinkle']
senticnet['lanyard'] = ['0', '0.072', '0', '0.064', '#interest', '#admiration', 'positive', '0.068', 'zipper', 'pant', 'trousers', 'cowboy_boot', 'accessory']
senticnet['lanzerath'] = ['0.737', '-0.96', '0', '0.552', '#joy', '#surprise', 'positive', '0.75', 'prominence', 'hump', 'bump', 'swelling', 'frontal_eminence']
senticnet['laotian_rock'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'carbon_monoxide', 'tree_animal']
senticnet['laowai'] = ['-0.57', '0', '0.803', '-0.77', '#anger', '#disgust', 'negative', '-0.71', 'au_pair', 'exile', 'deportee', 'gringo', 'import']
senticnet['lap'] = ['0.159', '0.068', '-0.07', '0.118', '#joy', '#admiration', 'positive', '0.067', 'stomach', 'uterus', 'pregnant', 'womb', 'gravid']
senticnet['lap_dance'] = ['0.82', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.862', 'pervert', 'healthy_person', 'philanthropist', 'arsonist', 'nazi']
senticnet['lap_dancer'] = ['0.82', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.862', 'pervert', 'healthy_person', 'philanthropist', 'arsonist', 'nazi']
senticnet['lap_pool'] = ['0', '0', '0', '0.964', '#admiration', '#admiration', 'positive', '0.964', 'swimming_pool', 'swim', 'swimsuit', 'lake', 'have_fun']
senticnet['lapack'] = ['0', '0', '0.737', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'annoy', 'skin_rash', 'bother', 'irritation', 'great_dane']
senticnet['lapadula'] = ['0.034', '0', '0.01', '0.087', '#joy', '#admiration', 'positive', '0.037', 'chime', 'bell_noise', 'alto', 'piano', 'ring_loudly']
senticnet['laparoscope'] = ['0.255', '0.182', '-0.06', '0.314', '#joy', '#admiration', 'positive', '0.173', 'doctor_office', 'urethroscope', 'sigmoidoscope', 'hospital', 'check_health']
senticnet['laparoscopic_myomectomy'] = ['-0.70', '0', '0.639', '-0.56', '#sadness', '#anger', 'negative', '-0.63', 'fibroma', 'adenoma', 'chondroma', 'enchondroma', 'benign_tumor']
senticnet['laparoscopic_resection'] = ['-0.70', '0', '0.639', '-0.56', '#sadness', '#anger', 'negative', '-0.63', 'fibroma', 'adenoma', 'chondroma', 'enchondroma', 'benign_tumor']
senticnet['lapd_robbery'] = ['0', '0', '-0.95', '-0.85', '#fear', '#disgust', 'negative', '-0.90', 'choose_victim', 'murder_weapon', 'hide_evidence', 'murder', 'shoot_gun']
senticnet['lapdog'] = ['-0.87', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.93', 'sycophant', 'suck-up', 'yes-man', 'war-monger', 'flunky']
senticnet['lapel'] = ['0.857', '-0.56', '0', '0.721', '#joy', '#admiration', 'positive', '0.716', 'ask_for', 'lobby', 'quest', 'canvass', 'solicit']
senticnet['laphroaig'] = ['0.877', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.919', 'score', 'economical', 'scotch_whiskey', 'spoil', 'scottish']
senticnet['lapidary'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'art', 'art_piece', 'museum', 'artist', 'jewel']
senticnet['lapidation'] = ['-0.83', '0', '0.924', '-0.79', '#sadness', '#anger', 'negative', '-0.85', 'kick_in_butt', 'licking', 'whacking', 'stoning', 'corporal_punishment']
senticnet['lapis_lazuli'] = ['0.037', '0.073', '0.027', '0.072', '#interest', '#admiration', 'positive', '0.039', 'sapphire', 'birth_stone', 'turquoise', 'gemstone', 'amethyst']
senticnet['laplace'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'force_unit', 'sthene', 'poundal']
senticnet['laplace_distribution'] = ['-0.59', '0', '0.672', '-0.83', '#anger', '#disgust', 'negative', '-0.7', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'asymmetry', 'irregularity']
senticnet['laplace_equation'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'assumption', 'precondition', 'provision', 'constraint', 'premiss']
senticnet['laplace_operator'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'assumption', 'precondition', 'provision', 'constraint', 'premiss']
senticnet['lapse'] = ['-0.97', '0', '-0.98', '0', '#sadness', '#fear', 'negative', '-0.97', 'standard', 'legatee', 'right', 'void', 'lap']
senticnet['lapsed'] = ['-0.94', '0', '0.742', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'heathen', 'heathenish', 'pagan', 'nonchurchgoing', 'irreligious']
senticnet['lapsing_into'] = ['-0.89', '0', '0.987', '-0.90', '#anger', '#disgust', 'negative', '-0.93', 'cacoethes', 'logomania', 'agromania', 'pyromania', 'necrophilia']
senticnet['laptop'] = ['0.715', '0.842', '0', '0', '#joy', '#interest', 'positive', '0.778', 'processor', 'browse_web', 'computer_type', 'desktop', 'language']
senticnet['laptop_bag'] = ['0.041', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.043', 'fit_room', 'clothes_store_change_room', 'dress_room', 'clothes_shelf', 'changing_room']
senticnet['laptop_computer'] = ['0.113', '0.073', '0.028', '0.107', '#joy', '#admiration', 'positive', '0.066', 'pda', 'telephone_computer', 'browser', 'number_pad', 'modem']
senticnet['laptop_pc'] = ['0.113', '0.073', '0.028', '0.107', '#joy', '#admiration', 'positive', '0.066', 'pda', 'telephone_computer', 'browser', 'number_pad', 'modem']
senticnet['laptop_screen'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'floodlight', 'spotlight', 'headlight', 'light', 'teach_lesson']
senticnet['lapua_magnum'] = ['0.887', '-0.95', '0', '0.913', '#surprise', '#admiration', 'positive', '0.917', 'high_quality', 'superiority', 'level', 'grade', 'degree']
senticnet['larceny'] = ['-0.51', '0', '0.717', '-0.75', '#anger', '#disgust', 'negative', '-0.66', 'raid', 'thieving', 'plunderage', 'embezzlement', 'theft']
senticnet['larch'] = ['-0.89', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.87', 'spruce', 'pine', 'hemlock', 'balsam', 'fir']
senticnet['lard'] = ['0.037', '0', '0', '0.063', '#joy', '#admiration', 'positive', '0.05', 'food_type', 'food_group', 'carbohydrate', 'starch', 'staple_food']
senticnet['larder'] = ['0', '0', '-0.96', '-0.88', '#fear', '#disgust', 'negative', '-0.92', 'pantry', 'buttery', 'fulsome', 'oily', 'oleaginous']
senticnet['large'] = ['0.032', '0.085', '0.049', '0.222', '#interest', '#admiration', 'positive', '0.073', 'big', 'wide', 'huge', 'really_big', 'tall']
senticnet['large-boned'] = ['0', '-0.80', '0', '0.71', '#surprise', '#admiration', 'positive', '0.758', 'heavyset', 'heavy-set', 'broad-shouldered', 'rail-thin', 'tallish']
senticnet['large-brimmed'] = ['-0.75', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'brimless', 'low-crowned', 'ear-flap', 'flat-brimmed', 'brimmed']
senticnet['large-hearted'] = ['0.547', '0.674', '-0.30', '0.287', '#joy', '#interest', 'positive', '0.301', 'charitable', 'good-hearted', 'kindly', 'openhearted', 'benevolent']
senticnet['large-scale'] = ['0', '0', '0.826', '-0.73', '#anger', '#disgust', 'negative', '-0.78', 'full-scale', 'full-blown', 'full-fledged', 'all-out', 'full-on']
senticnet['large_accommodation'] = ['0', '0.11', '0', '0.086', '#interest', '#admiration', 'positive', '0.098', 'accommodation', 'government', 'govern_city', 'government_accommodation', 'building']
senticnet['large_amount'] = ['0', '0.208', '0', '0.291', '#interest', '#admiration', 'positive', '0.249', 'a_lot', 'abundance', 'large_mass', 'human_mass', 'number_person']
senticnet['large_architectural_structure'] = ['0.892', '0.808', '0', '0', '#joy', '#interest', 'positive', '0.85', 'build', 'architectural_structure', 'home_architectural_structure', 'architect', 'structure_support']
senticnet['large_automobile'] = ['0.027', '0', '0.026', '0.073', '#joy', '#admiration', 'positive', '0.025', 'automatic_car', 'expensive_car', 'limousine', 'means_of_transport', 'roulotte']
senticnet['large_baking_sheet'] = ['0.03', '-0.05', '0', '0.059', '#surprise', '#admiration', 'positive', '0.046', 'platter', 'fork', 'spoon', 'tablespoon', 'bowl']
senticnet['large_boat'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'big_boat', 'boat', 'small_boat', 'water_vehicle', 'ship']
senticnet['large_body'] = ['0', '0.705', '0', '0.937', '#interest', '#admiration', 'positive', '0.821', 'water', 'make_up_water', 'cover_water', 'water_spill', 'adenozine']
senticnet['large_border'] = ['0.033', '0.082', '0.022', '0', '#joy', '#interest', 'positive', '0.031', 'boundary', 'border', 'territory', 'region', 'land_mass']
senticnet['large_bowel'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'anus', 'scrotum', 'shoulder', 'liver', 'waist']
senticnet['large_breadbox'] = ['0', '0.073', '0.009', '0.122', '#interest', '#admiration', 'positive', '0.062', 'smart_animal', 'person', 'domestic_animal', 'human', 'big_person']
senticnet['large_build'] = ['0', '0.884', '0', '0.779', '#interest', '#admiration', 'positive', '0.832', 'architectural_structure', 'build', 'home_build', 'architect', 'build_support']
senticnet['large_building'] = ['0', '0.119', '0', '0.095', '#interest', '#admiration', 'positive', '0.107', 'building', 'government', 'govern_city', 'government_building', 'accommodation']
senticnet['large_business'] = ['0.091', '0.186', '0', '0.211', '#interest', '#admiration', 'positive', '0.163', 'business_firm', 'partnership', 'business_organization', 'business_group', 'company']
senticnet['large_car'] = ['0.027', '0', '0', '0.146', '#joy', '#admiration', 'positive', '0.086', 'long_car', 'big_car', 'transportation', 'car_type', 'work_well']
senticnet['large_cardboard_box'] = ['0', '-0.02', '0', '0.051', '#surprise', '#admiration', 'positive', '0.037', 'cardboard_box', 'bag', 'package', 'box_cereal', 'bin']
senticnet['large_cat'] = ['-0.02', '0', '0.044', '0', '#sadness', '#anger', 'negative', '-0.03', 'fast_person', 'carnivore', 'household_pet', 'big_cat', 'good_pet']
senticnet['large_city'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'jerusalem', 'jewish_community', 'vatican', 'big_city', 'major_city']
senticnet['large_city_shop_mall'] = ['0', '0', '0.051', '-0.03', '#anger', '#disgust', 'negative', '-0.04', 'suburban_shop_mall', 'mini_mall', 'corner_shop', 'corner_grocery', 'large_food_store']
senticnet['large_company'] = ['0.069', '0.159', '0', '0.167', '#interest', '#admiration', 'positive', '0.132', 'business_group', 'group_company', 'business_firm', 'large_business', 'partnership']
senticnet['large_container'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'wooden_spoon', 'caffeine', 'teaspoon', 'container_cup', 'cereal_bowl']
senticnet['large_corporation'] = ['0.028', '0.087', '0', '0.085', '#interest', '#admiration', 'positive', '0.067', 'corporation', 'expensive_car', 'business', 'large_group', 'motor_vehicle']
senticnet['large_country'] = ['0.043', '0.109', '0', '0.061', '#interest', '#admiration', 'positive', '0.071', 'nation_state', 'democratic_country', 'free_country', 'international', 'republic']
senticnet['large_crowd'] = ['0', '0.985', '0', '0.856', '#interest', '#admiration', 'positive', '0.92', 'public', 'crowd', 'entertain_crowd', 'committee', 'audience']
senticnet['large_cup'] = ['0.03', '0', '0.022', '0.062', '#joy', '#admiration', 'positive', '0.023', 'beer_container', 'hold_drink', 'cool_drink', 'store_ice_cream', 'drink_holder']
senticnet['large_dog'] = ['0.956', '0.963', '0', '0.764', '#joy', '#interest', 'positive', '0.894', 'good_weather', 'giant', 'type_of_dog', 'future_robot', 'sunny_day']
senticnet['large_dutch_oven'] = ['-0.83', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.78', 'picaninny', 'kid', 'minor', 'tike', 'small_picaninny']
senticnet['large_family'] = ['0', '0', '0', '0.939', '#admiration', '#admiration', 'positive', '0.939', 'baby', 'small_child', 'youth', 'quiet_baby', 'deliver_baby']
senticnet['large_food_store'] = ['0.093', '-0.02', '0', '0.056', '#joy', '#admiration', 'positive', '0.057', 'large_supermarket', 'food_store', 'convenience_food_store', 'corner_shop', 'corner_grocery']
senticnet['large_fraction'] = ['0', '0.119', '0', '0.106', '#interest', '#admiration', 'positive', '0.112', 'divide', 'numerator', 'subtraction', 'math_action', 'division']
senticnet['large_frying_pan'] = ['-0.83', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.78', 'picaninny', 'kid', 'minor', 'tike', 'small_picaninny']
senticnet['large_gash'] = ['-0.12', '-0.02', '0', '-0.15', '#sadness', '#disgust', 'negative', '-0.08', 'incision', 'laceration', 'abrasion', 'cut_skin', 'painful_injury']
senticnet['large_group'] = ['0.068', '0.129', '0', '0.143', '#interest', '#admiration', 'positive', '0.113', 'big_group', 'large_number', 'bunch_person', 'multitude', 'group']
senticnet['large_hat'] = ['0.033', '0.072', '0.02', '0.079', '#interest', '#admiration', 'positive', '0.041', 'hat', 'country_side', 'big_hat', 'rural_area', 'big_cap']
senticnet['large_house'] = ['-0.05', '0.116', '-0.04', '0.102', '#interest', '#admiration', 'positive', '0.029', 'house', 'dwelling', 'multi-story_build', 'rent_out', 'lease']
senticnet['large_indefinite_amount'] = ['-0.64', '0', '0.87', '-0.74', '#anger', '#disgust', 'negative', '-0.75', 'infinitude', 'piles', 'mess', 'large_number', 'tons']
senticnet['large_indefinite_quantity'] = ['-0.63', '0', '0.708', '-0.96', '#anger', '#disgust', 'negative', '-0.77', 'infinitude', 'piles', 'mess', 'large_number', 'tons']
senticnet['large_integer'] = ['0.787', '-0.52', '0', '0.806', '#joy', '#admiration', 'positive', '0.707', 'gross', 'century', 'grand', 'million', 'billion']
senticnet['large_intestine'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'anus', 'scrotum', 'shoulder', 'liver', 'waist']
senticnet['large_kitchen_knife'] = ['0.818', '-0.95', '0', '0.695', '#joy', '#surprise', 'positive', '0.823', 'jackknife', 'clasp_knife', 'penknife', 'switchblade', 'switchblade_knife']
senticnet['large_land'] = ['0', '0.067', '0.04', '0.091', '#interest', '#admiration', 'positive', '0.039', 'big_land', 'county', 'grassland', 'land', 'large_state']
senticnet['large_land_mass'] = ['0.042', '0.082', '0.026', '0', '#joy', '#interest', 'positive', '0.033', 'boundary', 'land_mass', 'territory', 'zone', 'border']
senticnet['large_leaved_aster'] = ['-0.52', '0', '0.527', '-0.78', '#anger', '#disgust', 'negative', '-0.61', 'herb', 'black_snakeroot', 'heartleaf', 'asarabacca', 'herbaceous_plant']
senticnet['large_leaved_lime'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'bottlebrush_buckeye', 'red_buckeye', 'particolored_buckeye']
senticnet['large_lecture_hall'] = ['0.872', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.811', 'lecture_theater', 'lecture', 'conference_auditorium', 'read_book', 'text_book']
senticnet['large_magellanic_cloud'] = ['0', '0.706', '0', '0.847', '#interest', '#admiration', 'positive', '0.776', 'dwarf_planet', 'universe', 'planetary', 'sun', 'moon']
senticnet['large_mammal'] = ['0.029', '0', '0.018', '0.059', '#joy', '#admiration', 'positive', '0.023', 'domestic_animal', 'wild_animal', 'hoof_mammal', 'big_animal', 'animal']
senticnet['large_mass'] = ['0.027', '0.095', '0.011', '0.178', '#interest', '#admiration', 'positive', '0.072', 'large_amount', 'human_mass', 'mass_person', 'crowd', 'group_of_people']
senticnet['large_mixing_bowl'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'salad_shaker', 'store_shelf', 'kitchen_tool', 'pliers', 'ladle']
senticnet['large_mouse'] = ['-0.19', '-0.01', '-0.09', '-0.03', '#sadness', '#fear', 'negative', '-0.07', 'easter_bunny', 'cute_animal', 'domestic_pet', 'household_pet', 'red_eye']
senticnet['large_number'] = ['0.03', '0.128', '0', '0.124', '#interest', '#admiration', 'positive', '0.094', 'large_group', 'big_group', 'push_together', 'mass_person', 'bunch_person']
senticnet['large_outdoor_board'] = ['0.997', '0.887', '0', '0', '#joy', '#interest', 'positive', '0.942', 'advertisement', 'billboard', 'motorway', 'expressway', 'train_track']
senticnet['large_person'] = ['0.06', '0', '0.037', '0.149', '#joy', '#admiration', 'positive', '0.057', 'multiple_people', 'beautiful_well_useful', 'group', 'big', 'large']
senticnet['large_plant'] = ['0.02', '0', '0', '0.057', '#joy', '#admiration', 'positive', '0.038', 'plant_part', 'small_tree', 'tall_person', 'green_leave', 'leafy']
senticnet['large_portion'] = ['0.956', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.935', 'aggregate', 'total', 'whole', 'complete', 'integral']
senticnet['large_rock'] = ['0', '0.715', '0', '0.861', '#interest', '#admiration', 'positive', '0.788', 'rock_formation', 'big_rock', 'landscape_feature', 'geographic_feature', 'land_formation']
senticnet['large_saucepan'] = ['-0.66', '0', '0.992', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'cold', 'boil', 'temperature', 'low_temperature', 'frigidity']
senticnet['large_scale_deforestation'] = ['0', '-0.02', '0.085', '-0.09', '#anger', '#disgust', 'negative', '-0.05', 'tree_wood', 'forest', 'sideways', 'dead_tree', 'wood']
senticnet['large_scale_redevelopment'] = ['0', '0.229', '-0.09', '0.187', '#interest', '#admiration', 'positive', '0.106', 'increase', 'make_big', 'get_large', 'grow', 'expand']
senticnet['large_ship'] = ['0.02', '0', '0', '0.057', '#joy', '#admiration', 'positive', '0.038', 'ship', 'boat', 'yacht', 'sail_ship', 'cruiser']
senticnet['large_shrub'] = ['-0.80', '0', '0.555', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'bush', 'wormwood', 'milkwort', 'germander', 'centaury']
senticnet['large_skillet'] = ['-0.66', '0', '0.992', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'cold', 'boil', 'temperature', 'low_temperature', 'frigidity']
senticnet['large_skillet_over'] = ['-0.83', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.78', 'picaninny', 'kid', 'minor', 'tike', 'small_picaninny']
senticnet['large_state'] = ['0.043', '0', '0', '0.09', '#joy', '#admiration', 'positive', '0.067', 'political_division', 'political_unit', 'political_state', 'grassland', 'territory']
senticnet['large_store'] = ['0', '0.752', '0', '0.796', '#interest', '#admiration', 'positive', '0.774', 'group_company', 'section', 'business_group', 'industry', 'type_of_store']
senticnet['large_structure'] = ['0.049', '0.076', '0.021', '0.158', '#interest', '#admiration', 'positive', '0.066', 'structure', 'construction', 'build_structure', 'creation', 'jetty']
senticnet['large_sum'] = ['0.685', '-0.58', '0', '0.56', '#joy', '#surprise', 'positive', '0.608', 'profit', 'net_income', 'net', 'net_profit', 'fast_buck']
senticnet['large_supermarket'] = ['0', '0.113', '0.041', '0.058', '#interest', '#admiration', 'positive', '0.043', 'corner_shop', 'general_store', 'many_shops', 'bazaar', 'goods']
senticnet['large_town'] = ['0', '0.838', '0', '0.851', '#interest', '#admiration', 'positive', '0.845', 'govern_city', 'beautiful', 'city_life', 'large_building', 'downtown']
senticnet['large_tract'] = ['0.716', '-0.51', '0', '0.647', '#joy', '#admiration', 'positive', '0.624', 'state', 'nation', 'country', 'ally', 'commonwealth']
senticnet['large_wading_bird'] = ['-0.02', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.04', 'wren', 'bird', 'quail', 'sparrow', 'chick']
senticnet['largely_anecdotal'] = ['-0.64', '0', '0.576', '-0.83', '#sadness', '#disgust', 'negative', '-0.68', 'nisi', 'indeterminate', 'equivocal', 'ambiguous', 'indecisive']
senticnet['largely_arboreal'] = ['0', '0', '-0.81', '-0.92', '#fear', '#disgust', 'negative', '-0.87', 'tree-living', 'dendroidal', 'arborous', 'arboriform', 'arborary']
senticnet['largely_circumstantial'] = ['-0.64', '0', '0.576', '-0.83', '#sadness', '#disgust', 'negative', '-0.68', 'nisi', 'indeterminate', 'equivocal', 'ambiguous', 'indecisive']
senticnet['largely_discredited'] = ['-0.72', '0', '0.755', '0', '#sadness', '#anger', 'negative', '-0.73', 'disgrace', 'shameful', 'dishonorably', 'inglorious', 'dishonored']
senticnet['largely_due'] = ['-0.02', '0.125', '0.672', '-0.66', '#anger', '#disgust', 'negative', '-0.31', 'venom', 'malice', 'offensive', 'animosity', 'despise']
senticnet['largely_forgotten'] = ['0', '0', '0.935', '-0.76', '#anger', '#disgust', 'negative', '-0.84', 'hear_bad_news', 'death_love', 'disrespect', 'forget_everything', 'toothache']
senticnet['largely_illiterate'] = ['-0.90', '0', '0.76', '-0.87', '#sadness', '#disgust', 'negative', '-0.84', 'analphabet', 'analphabetic', 'functional_illiterate', 'ignoramus', 'know_nothing']
senticnet['largely_inaccessible'] = ['0', '0', '0.921', '-0.87', '#anger', '#disgust', 'negative', '-0.9', 'outback', 'trackless', 'pathless', 'roadless', 'untracked']
senticnet['largely_ineffective'] = ['-0.50', '0', '0.807', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'powerlessness', 'impotence', 'impotency', 'inefficacy', 'inefficaciousness']
senticnet['largely_irrelevant'] = ['0.544', '-0.69', '0', '0.658', '#surprise', '#admiration', 'positive', '0.632', 'shakable', 'shakeable', 'challengeable', 'disputable', 'contestable']
senticnet['largely_nocturnal'] = ['0', '0', '-0.81', '-0.92', '#fear', '#disgust', 'negative', '-0.87', 'tree-living', 'dendroidal', 'arborous', 'arboriform', 'arborary']
senticnet['largely_obsolete'] = ['-0.90', '0', '0.517', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'outdated', 'noncurrent', 'disused', 'obsolete', 'superannuated']
senticnet['largely_responsible'] = ['0.955', '0.945', '0', '0.835', '#joy', '#interest', 'positive', '0.912', 'support_family', 'provide_service', 'accomplish', 'find_job', 'pay_well']
senticnet['largely_unavailable'] = ['-0.77', '0', '0.644', '-0.68', '#sadness', '#disgust', 'negative', '-0.70', 'unobtainable', 'unprocurable', 'out_of_stock', 'bunk', 'untouchable']
senticnet['largely_unknown'] = ['-0.09', '0', '0', '-0.19', '#sadness', '#disgust', 'negative', '-0.14', 'add_knowledge', 'learn', 'become_smart', 'discover', 'make_out_atom']
senticnet['largemouth'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'divergent_strabismus', 'exotropia', 'squint', 'abnormality', 'abnormalcy']
senticnet['largemouth_bass_fishing'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'divergent_strabismus', 'exotropia', 'squint', 'abnormality', 'abnormalcy']
senticnet['largeness'] = ['-0.68', '0', '0.863', '-0.69', '#anger', '#disgust', 'negative', '-0.74', 'pretension', 'artificiality', 'affectedness', 'supernaturalness', 'unnaturalness']
senticnet['larger'] = ['0.82', '0', '0', '0.838', '#joy', '#admiration', 'positive', '0.829', 'tumid', 'heavy', 'great', 'bombastic', 'orotund']
senticnet['larger-scale'] = ['-0.83', '0', '-0.84', '0', '#sadness', '#fear', 'negative', '-0.83', 'large-scale', 'smaller-scale', 'farming', 'small-scale', 'medium-scale']
senticnet['largest'] = ['0.845', '0', '0', '0.789', '#joy', '#admiration', 'positive', '0.817', 'tumid', 'heavy', 'great', 'bombastic', 'orotund']
senticnet['largest_bottler'] = ['0.951', '-0.97', '0', '0.639', '#joy', '#surprise', 'positive', '0.853', 'smoothie', 'drinking_water', 'nutrient', 'milk', 'refresher']
senticnet['largest_daily_newspaper'] = ['-0.05', '0.106', '0', '0.06', '#interest', '#admiration', 'positive', '0.036', 'buy', 'buy_newspaper', 'read_buy_newspaper', 'buy_book', 'read_news']
senticnet['largest_freshwater_fish'] = ['0', '-0.02', '0.044', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'haddock', 'mermaid', 'fish_rod', 'tidal_wave', 'dryad']
senticnet['largest_indoor_arena'] = ['0.026', '-0.03', '0', '0.091', '#surprise', '#admiration', 'positive', '0.051', 'stadium', 'auditorium', 'television_station', 'music_hall', 'movie_theater']
senticnet['largest_jackpot'] = ['0.858', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.813', 'box_office', 'movie', 'cctv', 'cinema', 'movie_theater']
senticnet['largest_maker'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'builder']
senticnet['largest_manufacturer'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'builder']
senticnet['largest_outdoor_swimming'] = ['0', '0', '0', '0.964', '#admiration', '#admiration', 'positive', '0.964', 'swimming_pool', 'swim', 'swimsuit', 'lake', 'have_fun']
senticnet['largest_retail_pharmacy'] = ['0.096', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.057', 'platform_rail', 'rail', 'catwalk', 'column', 'token_turnstile']
senticnet['largest_seaport'] = ['-0.02', '-0.02', '0.064', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'ocean_water', 'fillet_fish', 'chowder', 'fillet', 'underwater']
senticnet['largest_stockholder'] = ['0.069', '0.07', '0', '0.116', '#interest', '#admiration', 'positive', '0.085', 'entrepreneur', 'invest_money', 'move_around', 'traveling_person', 'guilty_person']
senticnet['larix_decidua'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'clematis', 'legume', 'leguminous_plant', 'climber', 'root_climber']
senticnet['lark'] = ['-0.85', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.82', 'sparrow', 'lark', 'meadow', 'wren', 'heron']
senticnet['larrikin'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'rascal', 'rapscallion', 'scalawag', 'holy_terror', 'monkey']
senticnet['larva'] = ['-0.79', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.8', 'frog', 'tadpole', 'jellyfish', 'duckling', 'tadpole']
senticnet['larva_feed_on'] = ['0.861', '-0.87', '0', '0.597', '#joy', '#surprise', 'positive', '0.779', 'herb', 'cultivated_parsnip', 'wild_parsnip', 'madnep', 'herbaceous_plant']
senticnet['larva_hatch'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'chick', 'wild_bird', 'baby_bird', 'lay_egg', 'bird_home']
senticnet['larva_undergo'] = ['0.642', '-0.58', '0', '0.751', '#joy', '#admiration', 'positive', '0.659', 'change', 'become_better', 'change_aspect', 'look_better', 'become_bigger']
senticnet['larval_development'] = ['0.642', '-0.58', '0', '0.751', '#joy', '#admiration', 'positive', '0.659', 'change', 'become_better', 'change_aspect', 'look_better', 'become_bigger']
senticnet['larval_form'] = ['-0.87', '0', '0.983', '-0.91', '#anger', '#disgust', 'negative', '-0.92', 'thorn', 'sticker', 'pricker', 'prickle', 'spikelet']
senticnet['larval_stage'] = ['-0.87', '0', '0.983', '-0.91', '#anger', '#disgust', 'negative', '-0.92', 'thorn', 'sticker', 'pricker', 'prickle', 'spikelet']
senticnet['larval_survival'] = ['0.988', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.98', 'sustain_life', 'sustenance', 'nutrition', 'keep_alive', 'satisfy_hunger']
senticnet['laryngeal'] = ['-0.84', '0', '0.829', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'upset', 'dysarthria', 'defect_of_speech', 'disorder', 'lallation']
senticnet['laryngeal_edema'] = ['-0.99', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.78', 'dropsy', 'atrophedema', 'giant_hive', 'periodic_edema', 'lymphedema']
senticnet['laryngectomy'] = ['0', '0', '0', '0.106', '#admiration', '#admiration', 'positive', '0.106', 'vocal_chord', 'mouth_noise', 'vocal_sound', 'human_sound', 'vocalization']
senticnet['laryngitis'] = ['0', '0', '0.766', '-0.99', '#anger', '#disgust', 'negative', '-0.88', 'tonsillitis', 'bronchitis', 'hoarseness', 'pharyngitis', 'sinusitis']
senticnet['laryngomalacia'] = ['-0.84', '0', '0.829', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'upset', 'dysarthria', 'defect_of_speech', 'disorder', 'lallation']
senticnet['laryngoscope'] = ['0.255', '0.182', '-0.06', '0.314', '#joy', '#admiration', 'positive', '0.173', 'doctor_office', 'urethroscope', 'sigmoidoscope', 'hospital', 'check_health']
senticnet['laryngostenosis'] = ['-0.81', '0', '0.684', '-0.81', '#sadness', '#disgust', 'negative', '-0.77', 'rhinostenosis', 'enterostenosis', 'stenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['larynx'] = ['0', '0', '0', '0.106', '#admiration', '#admiration', 'positive', '0.106', 'vocal_chord', 'mouth_noise', 'vocal_sound', 'human_sound', 'vocalization']
senticnet['lasagna'] = ['0.043', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'apple_pie', 'a_lot_of_food', 'jelly', 'frozen_food', 'cottage_cheese']
senticnet['lascivious'] = ['-0.92', '0', '0.744', '0', '#sadness', '#anger', 'negative', '-0.83', 'lecherous', 'salacious', 'pornography', 'gal', 'lasciviously']
senticnet['lasciviously'] = ['-0.93', '0', '0.739', '0', '#sadness', '#anger', 'negative', '-0.83', 'lecherous', 'salacious', 'pornography', 'gal', 'lascivious']
senticnet['lasciviousness'] = ['0.626', '-0.72', '0', '0.864', '#surprise', '#admiration', 'positive', '0.74', 'amorousness', 'sexiness', 'amativeness', 'eroticism', 'erotism']
senticnet['laser'] = ['0', '-0.03', '0.043', '-0.11', '#anger', '#disgust', 'negative', '-0.03', 'torch', 'safety_pin', 'flashlight', 'flare', 'firebomb']
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senticnet['laser_cutter'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'biological_machine', 'social_creature', 'live_creature', 'powerful', 'alive']
senticnet['laser_designator'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'chairperson', 'commuter']
senticnet['laser_disc'] = ['0', '0', '0', '-0.17', '#disgust', '#disgust', 'negative', '-0.17', 'medicine', 'aspirin', 'blank_check', 'painkiller', 'gauze']
senticnet['laser_enrichment'] = ['-0.90', '0', '0.748', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'alternative_energy', 'activation_energy', 'energy_of_activation', 'energy', 'nuclear_energy']
senticnet['laser_excitation'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'emotional_arousal', 'excitement', 'arousal', 'anger']
senticnet['laser_gun'] = ['0.757', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.831', 'baseball_bat', 'water_gun', 'goalie', 'hockey_puck', 'hockey_stick']
senticnet['laser_hawk'] = ['0.744', '-0.77', '0', '0.661', '#joy', '#surprise', 'positive', '0.725', 'sugarcoat', 'honey', 'glaze', 'change_taste', 'sugar']
senticnet['laser_intensity'] = ['-0.93', '0', '0.698', '-0.85', '#sadness', '#disgust', 'negative', '-0.83', 'candlepower', 'light_intensity', 'acoustic_power', 'sound_pressure_level', 'threshold_level']
senticnet['laser_lance'] = ['0.757', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.831', 'baseball_bat', 'water_gun', 'goalie', 'hockey_puck', 'hockey_stick']
senticnet['laser_light'] = ['-0.50', '0', '0.75', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'beam', 'beam_of_light', 'sunbeam', 'ray', 'ray_of_light']
senticnet['laser_pistol'] = ['0.757', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.831', 'baseball_bat', 'water_gun', 'goalie', 'hockey_puck', 'hockey_stick']
senticnet['laser_pointer'] = ['0', '-0.03', '0.043', '-0.11', '#anger', '#disgust', 'negative', '-0.03', 'torch', 'safety_pin', 'laser', 'flare', 'firebomb']
senticnet['laser_pulse'] = ['-0.50', '0', '0.75', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'beam', 'beam_of_light', 'sunbeam', 'ray', 'ray_of_light']
senticnet['laser_raman'] = ['0', '0.825', '0', '0', '#interest', '#interest', 'positive', '0.825', 'query', 'interrogation', 'inquire', 'response', 'solution']
senticnet['laser_scanner'] = ['-0.08', '0', '0.011', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'deflate', 'read_cover', 'obtain_information', 'upgrade', 'spread_out']
senticnet['laser_seeker'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'chairperson', 'commuter']
senticnet['laser_sight'] = ['-0.86', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.79', 'gun', 'firearm', 'handgun', 'rifle', 'pistol']
senticnet['laser_therapy'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'radiation_therapy', 'radiation', 'actinotherapy', 'irradiation']
senticnet['laserdisc_release'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['laserdisc_version'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['lash'] = ['0.766', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.768', 'eyeliner', 'mascara', 'lipstick', 'eyeshadow', 'eyelash']
senticnet['lash_out'] = ['0.494', '0.45', '-0.11', '0.056', '#joy', '#interest', 'positive', '0.222', 'recreate', 'peacefulness', 'euphoria', 'serenity', 'closeness']
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senticnet['lashing_out'] = ['0', '0', '1', '0', '#anger', '#anger', 'negative', '-1', 'furious', 'raging', 'stormy', 'roaring', 'wrathful']
senticnet['laspeyres_index'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'indicator', 'retail_price_index', 'producer_price_index', 'wholesale_price_index', 'consumer_price_index']
senticnet['lass'] = ['0.947', '0.914', '0', '0', '#joy', '#interest', 'positive', '0.93', 'ball_bounce', 'bicycle_tire', 'tire', 'increase_price', 'gyroscope']
senticnet['lassa_fever'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'leptomeningitis', 'meningitis', 'epidemic_meningitis', 'brain_fever', 'cerebrospinal_fever']
senticnet['lassitude'] = ['0.243', '0.632', '-0.23', '0.145', '#joy', '#interest', 'positive', '0.196', 'listlessness', 'emotionless', 'unemotionality', 'stolidity', 'phlegm']
senticnet['lasso'] = ['0', '0', '0.846', '-0.91', '#anger', '#disgust', 'negative', '-0.88', 'dissolute', 'corinthian', 'dissipation', 'dissolutely', 'dissoluteness']
senticnet['last'] = ['0.033', '0.158', '-0.06', '0.145', '#interest', '#admiration', 'positive', '0.067', 'finish', 'end', 'finish_complete', 'end_point', 'finalize']
senticnet['last-ditch'] = ['0.948', '-0.78', '0', '0', '#joy', '#surprise', 'positive', '0.865', 'face-saving', 'quick-and-dirty', 'middle-ground', 'stopgap', 'confidence-building']
senticnet['last_call'] = ['-0.03', '0.081', '0.0', '0.084', '#interest', '#admiration', 'positive', '0.044', 'take_final', 'take_exam', 'flash_card', 'take_examination', 'take_midterm']
senticnet['last_chance'] = ['-0.92', '0', '0.44', '-0.94', '#sadness', '#disgust', 'negative', '-0.77', 'chance', 'risk', 'risk_to_lose', 'dangerous', 'danger']
senticnet['last_clue'] = ['-0.44', '-0.15', '0', '-0.36', '#sadness', '#disgust', 'negative', '-0.21', 'loud', 'uncontrollable', 'obsolete', 'light_colored', 'noisy']
senticnet['last_decade'] = ['0', '0.078', '0', '0.096', '#interest', '#admiration', 'positive', '0.087', 'ancient_rome', 'ancient', 'age', 'ancient_artifact', 'century']
senticnet['last_ditch'] = ['0.961', '-0.84', '0', '0.958', '#joy', '#admiration', 'positive', '0.923', 'pause', 'break', 'intermission', 'suspension', 'rest_period']
senticnet['last_forever'] = ['0', '0', '0', '-0.88', '#disgust', '#disgust', 'negative', '-0.88', 'hunt_fox', 'software_company', 'waste', 'crap', 'useless']
senticnet['last_lifetime'] = ['-0.19', '0', '-0.13', '-0.10', '#sadness', '#fear', 'negative', '-0.14', 'hurt', 'trauma', 'maim', 'bleed', 'bloody']
senticnet['last_little'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'bad_quality', 'cheap', 'fragile', 'short', 'feeble']
senticnet['last_long'] = ['0.132', '0.212', '0', '0.242', '#interest', '#admiration', 'positive', '0.195', 'good_quality', 'premium', 'unbreakable', 'steel', 'strong']
senticnet['last_long_time'] = ['0.276', '0.207', '-0.14', '0.123', '#joy', '#interest', 'positive', '0.116', 'generate_heat', 'cool_house', 'cool_room', 'warm_house', 'power_engine']
senticnet['last_minute'] = ['-0.88', '0.178', '-0.6', '0', '#sadness', '#fear', 'negative', '-0.43', 'rush_job', 'rush_work', 'late', 'urgency', 'emergency']
senticnet['last_month'] = ['0.968', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.859', 'day_of_week', 'monday', 'fortnight', 'weekend', 'midweek']
senticnet['last_remaining_repechage'] = ['0.763', '-0.67', '0', '0.945', '#joy', '#admiration', 'positive', '0.795', 'set', 'income_bracket', 'tax_bracket', 'income_tax_bracket', 'price_bracket']
senticnet['last_resort'] = ['-0.97', '0', '0.84', '-0.52', '#sadness', '#anger', 'negative', '-0.78', 'pis_aller', 'expedient', 'temporary_expedient', 'makeshift', 'stopgap']
senticnet['last_resting_place'] = ['-0.88', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.90', 'coffin', 'casket', 'morgue', 'headstone', 'burial']
senticnet['last_week'] = ['0.967', '0.996', '0', '0.862', '#joy', '#interest', 'positive', '0.942', 'party_time', 'new_baby', 'nativity', 'holiday', 'occasion']
senticnet['last_weekend'] = ['0', '0.734', '0', '0', '#interest', '#interest', 'positive', '0.734', 'sleep', 'go_sleep', 'relax', 'go_out', 'sleep_night']
senticnet['last_year'] = ['-0.85', '0', '0.584', '-0.79', '#sadness', '#disgust', 'negative', '-0.74', 'age', 'old_age', 'second_childhood', 'eld', 'geezerhood']
senticnet['lasting'] = ['-0.95', '0', '0.808', '-0.61', '#sadness', '#anger', 'negative', '-0.79', 'everlasting', 'standing', 'imperishable', 'enduring', 'aeonian']
senticnet['lasting_damage'] = ['-0.98', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.97', 'harm', 'hurt', 'cut_skin', 'bloody', 'ouch']
senticnet['lasting_fame'] = ['0.889', '0', '0', '0', '#joy', '#joy', 'positive', '0.889', 'succeed', 'increase_self_esteem', 'increase_self-esteem', 'help_others', 'achievement']
senticnet['lasting_impression'] = ['0', '0.827', '0', '0.846', '#interest', '#admiration', 'positive', '0.837', 'inexterminable', 'inextirpable', 'inexpungible', 'inexpungeable', 'ineradicable']
senticnet['lasting_peace'] = ['0.497', '0.453', '-0.23', '0.189', '#joy', '#interest', 'positive', '0.226', 'calm', 'calmness', 'stillness', 'truce', 'love']
senticnet['lasting_prosperity'] = ['0.837', '0', '0', '0', '#joy', '#joy', 'positive', '0.837', 'a_lot_of_sex', 'desirable', 'give_birth_child', 'sexual_intercourse', 'diversity']
senticnet['lasting_stability'] = ['0.497', '0.453', '-0.23', '0.189', '#joy', '#interest', 'positive', '0.226', 'calm', 'calmness', 'stillness', 'truce', 'love']
senticnet['lasting_tribute'] = ['0.857', '0.824', '0', '0.708', '#joy', '#interest', 'positive', '0.796', 'mandatory', 'gentleman', 'obey', 'virtue', 'dignity']
senticnet['lastingly'] = ['0', '-0.75', '0', '0.844', '#surprise', '#admiration', 'positive', '0.798', 'transiently', 'diffusely', 'potently', 'reproducibly', 'focally']
senticnet['lastingness'] = ['0.919', '-0.65', '0', '0.541', '#joy', '#surprise', 'positive', '0.704', 'indestructibility', 'tensile_strength', 'continuity', 'persistence', 'changelessness']
senticnet['latch'] = ['0', '0.065', '0', '0.071', '#interest', '#admiration', 'positive', '0.068', 'lock', 'restrict_access', 'knob', 'door_knob', 'gate']
senticnet['latch_key'] = ['0.943', '-0.78', '0', '0.571', '#joy', '#surprise', 'positive', '0.767', 'master', 'ignition_key', 'key', 'passkey', 'master_key']
senticnet['latch_on'] = ['0.51', '-0.56', '0', '0.768', '#surprise', '#admiration', 'positive', '0.613', 'hold', 'clasp', 'cling', 'hold_on', 'hang']
senticnet['latching_mechanism'] = ['0', '0.069', '0', '0.083', '#interest', '#admiration', 'positive', '0.076', 'latch', 'restrict_access', 'knob', 'door_knob', 'gate']
senticnet['latchkey'] = ['0.943', '-0.78', '0', '0.571', '#joy', '#surprise', 'positive', '0.767', 'master', 'ignition_key', 'key', 'passkey', 'master_key']
senticnet['latchkey_child'] = ['0.943', '-0.78', '0', '0.571', '#joy', '#surprise', 'positive', '0.767', 'master', 'ignition_key', 'key', 'passkey', 'master_key']
senticnet['late'] = ['-0.08', '0.077', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.03', 'get_dark', 'part_of_day', 'day_part', 'end_day', 'time_day']
senticnet['late-flowering'] = ['0.759', '0', '0', '0.964', '#joy', '#admiration', 'positive', '0.861', 'early-ripening', 'crapshooter', 'quick-growing', 'non-blooming', 'early-blooming']
senticnet['late_adolescence'] = ['0', '0.075', '0', '0.159', '#interest', '#admiration', 'positive', '0.117', 'many_plants', 'blossom', 'development', 'poppy', 'adult']
senticnet['late_afternoon'] = ['-0.70', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.72', 'blackness', 'blackout', 'darkness', 'dark', 'obscure']
senticnet['late_anaphase'] = ['-0.03', '0.169', '-0.07', '0.218', '#interest', '#admiration', 'positive', '0.071', 'elongate', 'elasticity', 'extension', 'grow_long', 'widen']
senticnet['late_antiquity'] = ['-0.03', '0.092', '0.07', '0.1', '#interest', '#admiration', 'positive', '0.021', 'gladiator', 'civilization', 'church_build', 'coliseum', 'every_town']
senticnet['late_appointment'] = ['-0.26', '-0.12', '0.026', '-0.35', '#sadness', '#disgust', 'negative', '-0.13', 'pollution', 'lose_temper', 'speeding_ticket', 'blackout', 'crash_car']
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senticnet['late_century'] = ['0.978', '-0.60', '0', '0.895', '#joy', '#admiration', 'positive', '0.825', 'period', 'quattrocento', 'twentieth_century', 'time_period', 'period_of_time']
senticnet['late_eighteenth_century'] = ['0.978', '-0.60', '0', '0.895', '#joy', '#admiration', 'positive', '0.825', 'period', 'quattrocento', 'twentieth_century', 'time_period', 'period_of_time']
senticnet['late_fifteenth_century'] = ['0.978', '-0.60', '0', '0.895', '#joy', '#admiration', 'positive', '0.825', 'period', 'quattrocento', 'twentieth_century', 'time_period', 'period_of_time']
senticnet['late_fightback'] = ['0.724', '-0.92', '0', '0.854', '#surprise', '#admiration', 'positive', '0.833', 'tranquility', 'serenity', 'disposition', 'repose', 'placidity']
senticnet['late_instar'] = ['0', '-0.09', '0.123', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'gypsy_moth', 'japanese_beetle', 'cucumber_beetle', 'mosquito', 'earwig']
senticnet['late_last_month'] = ['0.968', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.859', 'day_of_week', 'monday', 'fortnight', 'weekend', 'midweek']
senticnet['late_medieval_period'] = ['0.978', '-0.60', '0', '0.895', '#joy', '#admiration', 'positive', '0.825', 'period', 'quattrocento', 'twentieth_century', 'time_period', 'period_of_time']
senticnet['late_mesolithic'] = ['-0.68', '0', '0.517', '-0.59', '#sadness', '#disgust', 'negative', '-0.59', 'dump', 'garbage_dump', 'trash_dump', 'rubbish_dump', 'wasteyard']
senticnet['late_neolithic'] = ['-0.68', '0', '0.517', '-0.59', '#sadness', '#disgust', 'negative', '-0.59', 'dump', 'garbage_dump', 'trash_dump', 'rubbish_dump', 'wasteyard']
senticnet['late_nineteenth_century'] = ['0.978', '-0.60', '0', '0.895', '#joy', '#admiration', 'positive', '0.825', 'period', 'quattrocento', 'twentieth_century', 'time_period', 'period_of_time']
senticnet['late_nite'] = ['0.925', '0.993', '0', '0.832', '#joy', '#interest', 'positive', '0.917', 'dance_samba', 'dance', 'skate', 'jump_rope', 'play_frisbee']
senticnet['late_ol_dirty'] = ['-0.35', '-0.13', '-0.05', '-0.38', '#sadness', '#disgust', 'negative', '-0.16', 'dick', 'idiot', 'asshole', 'fuck', 'motherfucker']
senticnet['late_onset_alzheimer'] = ['0.504', '-0.93', '0', '0.797', '#surprise', '#admiration', 'positive', '0.747', 'diabetes', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
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senticnet['late_time'] = ['-0.04', '0.088', '0.013', '0.069', '#interest', '#admiration', 'positive', '0.024', 'end_day', 'part_of_day', 'time_day', 'day_end', 'day_part']
senticnet['late_twentieth_century'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'absolutism', 'autocracy', 'ascendancy', 'tyrant']
senticnet['late_winter_storm'] = ['0', '0', '-0.93', '-0.82', '#fear', '#disgust', 'negative', '-0.88', 'thunderstorm', 'snow_storm', 'storm', 'weather', 'rainy']
senticnet['late_work'] = ['0.042', '-0.04', '-0.05', '-0.14', '#fear', '#disgust', 'negative', '-0.02', 'slow_heart_beat', 'feel_refreshed', 'gain_energy', 'tired', 'take_breath']
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senticnet['latency'] = ['0.554', '-0.78', '0', '0.792', '#surprise', '#admiration', 'positive', '0.709', 'prospect', 'chance', 'possibility', 'possibleness', 'potentiality']
senticnet['latent'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'potential', 'residual', 'inherent', 'intrinsic', 'innate']
senticnet['latent_defect'] = ['-0.92', '0', '0.256', '-0.98', '#sadness', '#disgust', 'negative', '-0.72', 'lousy', 'bad_quality', 'failure', 'poor_quality', 'low_quality']
senticnet['latent_period'] = ['-0.53', '0', '0.608', '-0.93', '#anger', '#disgust', 'negative', '-0.69', 'reprieve', 'respite', 'hiatus', 'abatement', 'suspension']
senticnet['lateral'] = ['-0.92', '0', '0.835', '-0.68', '#sadness', '#anger', 'negative', '-0.81', 'aerial', 'passing', 'forward_pass', 'passing_pass', 'spot_pass']
senticnet['lateral_aspect'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'skeletal_muscle', 'pterygoid_muscle', 'abductor', 'abductor_muscle']
senticnet['lateral_blast'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'firework', 'bright_flash', 'nuclear_weapon', 'earthquake', 'nuclear_bomb']
senticnet['lateral_collateral_ligament'] = ['-0.53', '0', '0.982', '-0.67', '#anger', '#disgust', 'negative', '-0.73', 'gristle', 'semilunar_cartilage', 'fibrocartilage', 'hyaline_cartilage', 'arytenoid']
senticnet['lateral_condyle'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'above_leg', 'joint', 'hip', 'knee']
senticnet['lateral_epicondylitis'] = ['-0.98', '0', '0.678', '-0.95', '#sadness', '#disgust', 'negative', '-0.87', 'rubor', 'redness', 'inflammation', 'tennis_elbow', 'epicondylitis']
senticnet['lateral_geniculate_body'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['lateral_inhibition'] = ['0', '0.131', '-0.08', '0.239', '#interest', '#admiration', 'positive', '0.096', 'human_sense', 'ear_activity', 'vibration', 'five_senses', 'sensor']
senticnet['lateral_load'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'aberrance', 'warp', 'chromosomal_aberration']
senticnet['lateral_motion'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'aberrance', 'warp', 'chromosomal_aberration']
senticnet['lateral_pass'] = ['-0.92', '0', '0.835', '-0.68', '#sadness', '#anger', 'negative', '-0.81', 'aerial', 'lateral', 'forward_pass', 'passing', 'spot_pass']
senticnet['lateral_prefrontal_cortex'] = ['0.998', '-0.63', '0', '0.574', '#joy', '#surprise', 'positive', '0.736', 'cleavable', 'divisible', 'separable', 'severable', 'dissociative']
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senticnet['laurea_magistrale'] = ['-0.75', '0', '0.589', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'lowness', 'grind', 'immoderation', 'immoderateness', 'amplitude_level']
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senticnet['laurieann_gibson'] = ['-0.95', '0', '0.992', '-0.63', '#sadness', '#anger', 'negative', '-0.86', 'interpretive_dance', 'interpretive_dancing', 'modern_dance', 'interpretative_dancing', 'stage_dancing']
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senticnet['lavatory_seat'] = ['0.822', '0.802', '0', '0', '#joy', '#interest', 'positive', '0.812', 'electric_toothbrush', 'towel', 't_shirt', 'heat_towel_rack', 'hair_brush']
senticnet['lavender'] = ['0.591', '-0.55', '0', '0.514', '#joy', '#surprise', 'positive', '0.553', 'violet', 'mauve', 'purple', 'reddish_purple', 'royal_purple']
senticnet['lavender_lavandula'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
senticnet['lavish'] = ['0', '-0.83', '0', '0.775', '#surprise', '#admiration', 'positive', '0.803', 'rich', 'sumptuous', 'luxurious', 'luxuriant', 'lush']
senticnet['lavish_ceremony'] = ['0.057', '0.13', '0', '0.119', '#interest', '#admiration', 'positive', '0.102', 'friend_wedding', 'circular_route', 'spanish_restaurant', 'altar', 'monastery']
senticnet['lavish_decoration'] = ['-0.54', '0', '0.7', '-0.64', '#anger', '#disgust', 'negative', '-0.62', 'flourish', 'rant', 'bombast', 'fustian', 'claptrap']
senticnet['lavish_dinner'] = ['0.962', '0', '0', '0', '#joy', '#joy', 'positive', '0.962', 'interest_person', 'social_occasion', 'become_intoxicated', 'network', 'get_sex']
senticnet['lavish_wedding'] = ['0.878', '0.84', '0', '0.91', '#joy', '#admiration', 'positive', '0.876', 'stumble', 'slip', 'fall', 'trip', 'tumble']
senticnet['lavished_upon'] = ['0.902', '-0.72', '0', '0.919', '#joy', '#admiration', 'positive', '0.849', 'preciousness', 'costliness', 'dearness', 'cost', 'expensiveness']
senticnet['lavishly'] = ['0.902', '-0.72', '0', '0.919', '#joy', '#admiration', 'positive', '0.849', 'preciousness', 'costliness', 'dearness', 'cost', 'expensiveness']
senticnet['lavishness'] = ['0.902', '-0.72', '0', '0.919', '#joy', '#admiration', 'positive', '0.849', 'preciousness', 'costliness', 'dearness', 'cost', 'expensiveness']
senticnet['law'] = ['0', '0.164', '0', '0.129', '#interest', '#admiration', 'positive', '0.147', 'legislation', 'edict', 'prosecutor', 'regulation', 'judge_jury']
senticnet['law-abiding'] = ['0', '0', '0.696', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'tax-paying', 'taxpaying', 'tax-payers', 'hard-working', 'citizenry']
senticnet['law_enforcement'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'constabulary', 'law', 'agency']
senticnet['law_enforcement_agency'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'constabulary', 'law', 'agency']
senticnet['law_enforcement_officer'] = ['0.865', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.887', 'manager', 'colleague', 'office_worker', 'coworker', 'secretary']
senticnet['law_enforcement_personnel'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'constabulary', 'law', 'agency']
senticnet['law_firm'] = ['0', '0.146', '-0.05', '0.077', '#interest', '#admiration', 'positive', '0.057', 'lawyer', 'negotiator', 'arbiter', 'judge_jury', 'decide_guilt']
senticnet['law_librarianship'] = ['0', '0.097', '0', '0.058', '#interest', '#admiration', 'positive', '0.077', 'occupation', 'career', 'tradesman', 'vocation', 'important_job']
senticnet['law_llm'] = ['0.036', '0.111', '-0.05', '0.054', '#interest', '#admiration', 'positive', '0.038', 'despot', 'supervisor', 'chief', 'commander', 'earl']
senticnet['law_moot_court'] = ['0', '0', '0', '0.879', '#admiration', '#admiration', 'positive', '0.879', 'sport_competition', 'win_competition', 'race', 'match', 'championship']
senticnet['law_of_diminishing_return'] = ['0.778', '-0.81', '0', '0.587', '#joy', '#surprise', 'positive', '0.728', 'law_of_effect', 'principle', 'rule', 'law_of_nature', 'distribution_law']
senticnet['law_of_effect'] = ['0.778', '-0.81', '0', '0.587', '#joy', '#surprise', 'positive', '0.728', 'law_of_nature', 'principle', 'rule', 'law_of_diminishing_return', 'distribution_law']
senticnet['law_of_nature'] = ['0.778', '-0.81', '0', '0.587', '#joy', '#surprise', 'positive', '0.728', 'law_of_effect', 'principle', 'rule', 'law_of_diminishing_return', 'distribution_law']
senticnet['law_officer'] = ['0.74', '-0.53', '0', '0.641', '#joy', '#admiration', 'positive', '0.64', 'air_marshal', 'sky_marshal', 'lawman', 'marshal', 'peace_officer']
senticnet['lawbreaker'] = ['-0.53', '0', '0.825', '-0.87', '#anger', '#disgust', 'negative', '-0.74', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['lawbreaking'] = ['0', '0', '0.948', '-0.81', '#anger', '#disgust', 'negative', '-0.88', 'unlawful', 'criminal', 'law', 'illegal', 'break']
senticnet['lawful'] = ['0', '0.824', '0', '0.788', '#interest', '#admiration', 'positive', '0.806', 'true', 'rule-governed', 'legitimate', 'licit', 'rightful']
senticnet['lawful_arrest'] = ['0.817', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.824', 'witness', 'prosecutor', 'legal_process', 'lawyer', 'jury']
senticnet['lawful_authority'] = ['-0.72', '0', '0.742', '-0.80', '#anger', '#disgust', 'negative', '-0.75', 'illicitness', 'illegality', 'illegitimacy', 'shadiness', 'quality']
senticnet['lawful_combatant'] = ['0.833', '-0.97', '0', '0.91', '#surprise', '#admiration', 'positive', '0.906', 'fencer', 'battler', 'belligerent', 'fighter', 'scrapper']
senticnet['lawful_excuse'] = ['-0.70', '0', '0.874', '-0.75', '#anger', '#disgust', 'negative', '-0.77', 'blame', 'accusation', 'inculpation', 'complaint', 'imprecation']
senticnet['lawful_manner'] = ['0', '0.824', '0', '0.788', '#interest', '#admiration', 'positive', '0.806', 'true', 'rule-governed', 'legitimate', 'licit', 'rightful']
senticnet['lawful_possession'] = ['0', '0.824', '0', '0.788', '#interest', '#admiration', 'positive', '0.806', 'true', 'rule-governed', 'legitimate', 'licit', 'rightful']
senticnet['lawful_purpose'] = ['0', '0.824', '0', '0.788', '#interest', '#admiration', 'positive', '0.806', 'true', 'rule-governed', 'legitimate', 'licit', 'rightful']
senticnet['lawfully'] = ['0.814', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.897', 'law', 'lawful', 'rightfully', 'captain', 'legally']
senticnet['lawfully_married'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'bigamy', 'wedlock']
senticnet['lawfully_present'] = ['-0.60', '0', '0.832', '-0.53', '#sadness', '#anger', 'negative', '-0.65', 'unsuitable', 'undesirable', 'unqualified', 'disqualified', 'unentitled']
senticnet['lawless'] = ['-0.82', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.86', 'restrained', 'disorderly', 'illegal', 'governed', 'lawlessly']
senticnet['lawlessly'] = ['-0.82', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.86', 'restrained', 'disorderly', 'illegal', 'governed', 'lawless']
senticnet['lawlessness'] = ['-0.71', '0', '0.701', '0', '#sadness', '#anger', 'negative', '-0.70', 'anarchist', 'defiance', 'law', 'anarchy', 'lack']
senticnet['lawlike'] = ['0.863', '-0.94', '0', '0.986', '#surprise', '#admiration', 'positive', '0.932', 'postulate', 'posit', 'assume', 'presume', 'take_for_granted']
senticnet['lawlor'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
senticnet['lawmaker'] = ['0', '0', '0', '0.876', '#admiration', '#admiration', 'positive', '0.876', 'good_manner', 'courteous', 'good_behavior', 'handle_carefully', 'politeness']
senticnet['lawman'] = ['0.74', '-0.53', '0', '0.641', '#joy', '#admiration', 'positive', '0.64', 'air_marshal', 'sky_marshal', 'marshal', 'law_officer', 'peace_officer']
senticnet['lawmen'] = ['0', '0', '-0.11', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'hunting_season', 'hound', 'cougar', 'hunter', 'dragon']
senticnet['lawn'] = ['0', '0.164', '0', '0.129', '#interest', '#admiration', 'positive', '0.147', 'garden_shed', 'yard', 'grass', 'lawn_green', 'golf_course']
senticnet['lawn_chair'] = ['0.074', '0', '0', '0', '#joy', '#joy', 'positive', '0.074', 'hike_trail', 'ant_hill', 'compost', 'picnic', 'soccer_field']
senticnet['lawn_furniture'] = ['-0.05', '0.157', '0', '0.168', '#interest', '#admiration', 'positive', '0.09', 'totting', 'make_selection', 'buy_diamond_ring', 'compute', 'go_shopping']
senticnet['lawn_green'] = ['0.175', '0.101', '0.035', '0.195', '#joy', '#admiration', 'positive', '0.109', 'leafy_green', 'rabbit_food', 'fruit_plant', 'vegetarian_food', 'green_vegetable']
senticnet['lawn_maintenance'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'mop_floor', 'absorb_moisture', 'sit_on_beach', 'scrub_floor', 'cut_grass']
senticnet['lawn_mower'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'mop_floor', 'absorb_moisture', 'sit_on_beach', 'scrub_floor', 'cut_grass']
senticnet['lawnmower'] = ['-0.04', '-0.02', '0.089', '-0.14', '#anger', '#disgust', 'negative', '-0.06', 'steam_locomotive', 'loudspeaker', 'collector', 'siren', 'activist']
senticnet['lawrie_mcmenemy'] = ['-0.9', '0', '0.749', '-0.71', '#sadness', '#anger', 'negative', '-0.78', 'chute', 'slide', 'slideway', 'sloping_trough', 'gutter']
senticnet['lawsuit'] = ['-0.91', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.85', 'lawyer', 'take_court', 'call_lawyer', 'determine_guilt', 'go_to_court']
senticnet['lawsuit_against'] = ['-0.91', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.85', 'lawyer', 'take_court', 'call_lawyer', 'determine_guilt', 'go_to_court']
senticnet['lawsuit_allege'] = ['0.025', '0.27', '-0.11', '0.243', '#interest', '#admiration', 'positive', '0.105', 'keep_safe', 'store_dry_goods', 'store_stuff', 'secure', 'save']
senticnet['lawsuit_contend'] = ['-0.92', '0', '0.256', '-0.98', '#sadness', '#disgust', 'negative', '-0.72', 'lousy', 'bad_quality', 'failure', 'poor_quality', 'low_quality']
senticnet['lawsuit_filed_against'] = ['-0.91', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.85', 'lawyer', 'take_court', 'call_lawyer', 'determine_guilt', 'go_to_court']
senticnet['lawsuit_filed_last_week'] = ['-0.91', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.85', 'lawyer', 'take_court', 'call_lawyer', 'determine_guilt', 'go_to_court']
senticnet['lawsuits_against'] = ['-0.91', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.85', 'lawyer', 'take_court', 'call_lawyer', 'determine_guilt', 'go_to_court']
senticnet['lawyer'] = ['0', '0.124', '0', '0.06', '#interest', '#admiration', 'positive', '0.092', 'arbiter', 'negotiator', 'judge_jury', 'gavel', 'prosecutor']
senticnet['lawyer_office'] = ['0.054', '0.07', '0', '0.09', '#interest', '#admiration', 'positive', '0.071', 'embassy', 'office', 'government_agency', 'workplace', 'desk']
senticnet['lax'] = ['-0.56', '0', '0.743', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'neglectful', 'derelict', 'inattentive', 'delinquent', 'negligent']
senticnet['lax_security'] = ['-0.55', '0', '0.843', '-0.58', '#anger', '#disgust', 'negative', '-0.66', 'thieving', 'larceny', 'theft', 'thievery', 'stealing']
senticnet['laxation'] = ['-0.89', '0', '0.925', '-0.58', '#sadness', '#anger', 'negative', '-0.80', 'shit', 'bowel_movement', 'movement', 'dump', 'asshole']
senticnet['laxative'] = ['-0.50', '0', '0.582', '-0.57', '#anger', '#disgust', 'negative', '-0.55', 'purgative', 'cathartic', 'physic', 'aperient', 'phenolphthalein']
senticnet['laxative_abuse'] = ['-0.52', '0', '0.786', '-0.92', '#anger', '#disgust', 'negative', '-0.74', 'upset', 'bulimarexia', 'bulima_nervosa', 'eating_disorder', 'disorder']
senticnet['laxdla_saga'] = ['0.036', '0.219', '0', '0.19', '#interest', '#admiration', 'positive', '0.148', 'story', 'long_story', 'novel', 'fable', 'fiction']
senticnet['laxiflora'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['laxity'] = ['-0.93', '0', '0.624', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'carelessness', 'willful_neglect', 'laxness', 'neglectfulness', 'remissness']
senticnet['laxness'] = ['-0.93', '0', '0.624', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'carelessness', 'willful_neglect', 'neglectfulness', 'laxity', 'remissness']
senticnet['lay'] = ['0.075', '0.091', '-0.06', '0.059', '#joy', '#interest', 'positive', '0.04', 'good_night', 'go_room', 'turn_off_computer', 'take_afternoon_nap', 'wind_down']
senticnet['lay_asleep'] = ['0.051', '0.074', '-0.05', '0.084', '#interest', '#admiration', 'positive', '0.04', 'rest_night', 'lie_down', 'shut_eye', 'lay_down', 'count_sheep']
senticnet['lay_awake'] = ['0', '0.08', '0', '0.179', '#interest', '#admiration', 'positive', '0.13', 'wake_up_morning', 'up_early', 'get_up_early', 'gather_energy_tomorrow', 'sit_chair']
senticnet['lay_away'] = ['0.916', '0.935', '0', '0', '#joy', '#interest', 'positive', '0.925', 'cache', 'stash', 'squirrel_away', 'hive_up', 'memory_cache']
senticnet['lay_bare'] = ['-0.13', '0.071', '0.694', '-0.67', '#anger', '#disgust', 'negative', '-0.35', 'crucify', 'dun', 'frustrating', 'aggravate', 'exasperation']
senticnet['lay_beside'] = ['0.071', '0.127', '0', '0.197', '#interest', '#admiration', 'positive', '0.132', 'place_work', 'workplace', 'office', 'work', 'hang_wall']
senticnet['lay_claim'] = ['0.838', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.887', 'proud', 'glory', 'brag', 'vaunt', 'pride']
senticnet['lay_down'] = ['0.299', '0.126', '0', '0.207', '#joy', '#admiration', 'positive', '0.211', 'inactivity', 'count_sheep', 'shut_eye', 'rest_night', 'bed_sleep']
senticnet['lay_egg'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'chick', 'wild_bird', 'hatch', 'baby_bird', 'bird_home']
senticnet['lay_golden'] = ['0.039', '0.089', '-0.08', '0.143', '#interest', '#admiration', 'positive', '0.048', 'bird', 'nightingale', 'stork', 'eagle', 'rooster']
senticnet['lay_off'] = ['-0.38', '-0.17', '0.051', '-0.51', '#sadness', '#disgust', 'negative', '-0.19', 'migraine', 'ignore', 'lose_mind', 'forget_everything', 'varicose_vein']
senticnet['lay_on_line'] = ['-0.51', '0', '0.92', '-0.66', '#anger', '#disgust', 'negative', '-0.70', 'hazard', 'stake', 'jeopardize', 'essay', 'assay']
senticnet['lay_prostrate'] = ['-0.60', '0', '0.761', '-0.84', '#anger', '#disgust', 'negative', '-0.73', 'vain', 'unproductive', 'fruitless', 'futile', 'sleeveless']
senticnet['lay_up'] = ['-0.95', '0', '0.647', '-0.52', '#sadness', '#anger', 'negative', '-0.70', 'disable', 'modify', 'nobble', 'alter', 'change']
senticnet['layabout'] = ['0.556', '-0.79', '0', '0.755', '#surprise', '#admiration', 'positive', '0.701', 'lush', 'alcoholic', 'alky', 'rummy', 'boozer']
senticnet['layer'] = ['0.043', '0', '0', '0.143', '#joy', '#admiration', 'positive', '0.093', 'cover_ice', 'soft_sweet', 'sheet', 'bake_treat', 'bake_good']
senticnet['layer_encapsulation'] = ['-0.78', '0', '0.915', '-0.99', '#anger', '#disgust', 'negative', '-0.89', 'bodge', 'botch_up', 'flub', 'ball_up', 'go_wrong']
senticnet['layered_defense'] = ['-0.04', '0', '-0.06', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'make_war', 'conquer_enemy', 'use_military', 'gain_territory', 'keep_peace']
senticnet['laying'] = ['-0.70', '0', '0.86', '0', '#sadness', '#anger', 'negative', '-0.78', 'hen', 'cackle', 'cackly', 'cackler', 'gaggle']
senticnet['laying_aside'] = ['-0.77', '0', '0.748', '-0.60', '#sadness', '#anger', 'negative', '-0.70', 'ignobleness', 'dishonorableness', 'sleaziness', 'unrespectability', 'disreputability']
senticnet['laying_waste'] = ['-0.93', '0', '0.722', '-0.66', '#sadness', '#anger', 'negative', '-0.77', 'penetration', 'air_raid', 'air_attack', 'swoop', 'incursion']
senticnet['layover'] = ['-0.08', '0.179', '-0.06', '0.096', '#interest', '#admiration', 'positive', '0.034', 'ride_subway', 'buy_vending_machine', 'travel', 'buy_lottery_ticket', 'acquire']
senticnet['layperson'] = ['0.515', '-0.80', '0', '0.791', '#surprise', '#admiration', 'positive', '0.703', 'accessible', 'fathomable', 'approachable', 'perceivable', 'intelligible']
senticnet['laytonville'] = ['-0.06', '0.21', '0', '0.076', '#interest', '#admiration', 'positive', '0.074', 'new_think', 'figure_out', 'brain_activity', 'idea', 'come_up_idea']
senticnet['layup'] = ['-0.81', '0', '0.747', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'vagrant', 'drifter', 'hobo', 'vagabond', 'dosser']
senticnet['laz_barrera'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'coach', 'instructor', 'personal_trainer', 'fit_person', 'train']
senticnet['lazar_house'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'lazaret', 'lazarette', 'infirmary', 'pesthouse', 'lazaretto']
senticnet['lazaret'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'infirmary', 'infirmaryte', 'lazar_house', 'pesthouse', 'infirmaryto']
senticnet['lazarette'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'lazaret', 'infirmary', 'lazar_house', 'pesthouse', 'lazaretto']
senticnet['lazaretto'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'lazaret', 'lazarette', 'lazar_house', 'pesthouse', 'infirmary']
senticnet['lazarus'] = ['0', '0', '0.966', '-0.99', '#anger', '#disgust', 'negative', '-0.98', 'poverty', 'exhaust', 'impoverish', 'beggar', 'shack']
senticnet['lazarus_pit'] = ['0.8', '-0.97', '0', '0.512', '#joy', '#surprise', 'positive', '0.763', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['laze'] = ['0.08', '0', '0', '0', '#joy', '#joy', 'positive', '0.08', 'nap', 'take_nap', 'lie_down', 'lie_back', 'lay_down']
senticnet['laze_around'] = ['0.026', '0.069', '0', '0', '#joy', '#interest', 'positive', '0.048', 'eat_food', 'eat_beef', 'chew_food', 'espresso', 'eat_much']
senticnet['lazily'] = ['-0.77', '0', '0.855', '-0.57', '#sadness', '#anger', 'negative', '-0.73', 'unhurried', 'easygoing', 'deliberate', 'easy', 'careful']
senticnet['laziness'] = ['0', '0', '0.859', '-0.95', '#anger', '#disgust', 'negative', '-0.90', 'fall_asleep', 'bed_time', 'drowsiness', 'take_rest', 'need_relax']
senticnet['lazy'] = ['0', '0', '0.9', '-0.75', '#anger', '#disgust', 'negative', '-0.82', 'take_easy', 'lie_back', 'sleepy', 'avoid_commitment', 'recuperate']
senticnet['lazy_afternoon'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'drink', 'party', 'friend_gathering', 'rest_area', 'relax_time']
senticnet['lazy_attitude'] = ['-0.06', '-0.04', '0.052', '-0.08', '#sadness', '#disgust', 'negative', '-0.03', 'turn_off_alarm_clock', 'take_sleep_pill', 'lie_down_close_eye', 'lie_down_bed', 'count_sheep']
senticnet['lazy_eye'] = ['-0.13', '0', '0.013', '-0.08', '#sadness', '#disgust', 'negative', '-0.07', 'blind', 'dig_tunnel', 'underground_animal', 'big_mouse', 'small_rodent']
senticnet['lazy_person'] = ['0', '0', '0.933', '-0.86', '#anger', '#disgust', 'negative', '-0.89', 'dog', 'type_of_dog', 'cat', 'lazy', 'future_robot']
senticnet['lazy_river'] = ['0', '0', '0', '0.964', '#admiration', '#admiration', 'positive', '0.964', 'swimming_pool', 'swim', 'swimsuit', 'lake', 'have_fun']
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senticnet['lcd_monitor'] = ['0.021', '0.081', '0.017', '0.113', '#interest', '#admiration', 'positive', '0.05', 'aspect', 'perspective', 'look_round', 'scenic', 'look_through']
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senticnet['lcd_touchscreen'] = ['0.044', '0.08', '0', '-0.03', '#joy', '#interest', 'positive', '0.031', 'screen', 'pixel', 'visual_information', 'image', 'computer_monitor']
senticnet['ldap'] = ['0', '0.175', '0', '0.231', '#interest', '#admiration', 'positive', '0.203', 'list', 'schedule', 'index', 'agenda', 'register']
senticnet['le_bhith'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['le_blank'] = ['0', '0.762', '0', '0', '#interest', '#interest', 'positive', '0.762', 'encyclopaedia', 'tropical_rainforest', 'learn_new_information', 'tropical_forest', 'child_story']
senticnet['le_blute_fin'] = ['-0.52', '0', '0.506', '-0.94', '#sadness', '#disgust', 'negative', '-0.65', 'gouger', 'swindler', 'defrauder', 'fraud', 'welcher']
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senticnet['le_chevalier'] = ['-0.81', '0', '0.885', '-0.92', '#anger', '#disgust', 'negative', '-0.87', 'miss', 'girl', 'damsel', 'maiden', 'damoiselle']
senticnet['le_cordon_bleu'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'blue_ribbon', 'badge', 'allegory', 'stripe', 'merit_badge']
senticnet['le_cur'] = ['-0.85', '0', '0.968', '-0.89', '#anger', '#disgust', 'negative', '-0.90', 'family', 'household', 'house', 'home', 'broken_home']
senticnet['le_darcy'] = ['0.833', '-0.97', '0', '0.91', '#surprise', '#admiration', 'positive', '0.906', 'combatant', 'fencer', 'belligerent', 'fighter', 'scrapper']
senticnet['le_dawson'] = ['-0.89', '0', '0.58', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'resemblance', 'comparison', 'equivalence', 'reflection', 'alikeness']
senticnet['le_despencer'] = ['0.975', '-0.78', '0', '0.969', '#joy', '#admiration', 'positive', '0.911', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['le_diable'] = ['-0.85', '0', '0.968', '-0.89', '#anger', '#disgust', 'negative', '-0.90', 'family', 'household', 'house', 'home', 'broken_home']
senticnet['le_disques_du_crpuscule'] = ['0', '0.115', '0.036', '0.207', '#interest', '#admiration', 'positive', '0.095', 'total', 'amount', 'merit', 'award', 'excellence']
senticnet['le_drame'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'sexual_relationship', 'liaison', 'affair', 'involvement']
senticnet['le_dsarmement'] = ['0.667', '-0.98', '0', '0.61', '#joy', '#surprise', 'positive', '0.753', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['le_eaux'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'indweller', 'invillager', 'villager', 'dweller', 'denizen']
senticnet['le_fay'] = ['-0.04', '-0.02', '0.027', '-0.03', '#sadness', '#disgust', 'negative', '-0.01', 'precious_metal', 'olympic_medal', 'shiny_metal', 'many_different_kinds', 'valuable_metal']
senticnet['le_gavroche'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'blue_ribbon', 'badge', 'allegory', 'stripe', 'merit_badge']
senticnet['le_juge'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'sexual_relationship', 'liaison', 'affair', 'involvement']
senticnet['le_labo'] = ['0.863', '0.861', '0', '0', '#joy', '#interest', 'positive', '0.862', 'aroma', 'scent', 'smell', 'odor', 'sniff']
senticnet['le_locataire'] = ['-0.85', '0', '0.968', '-0.89', '#anger', '#disgust', 'negative', '-0.90', 'family', 'household', 'house', 'home', 'broken_home']
senticnet['le_manteau'] = ['-0.58', '0', '0.661', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'explosive', 'cordite', 'dynamite', 'plastic_explosive', 'explosive_compound']
senticnet['le_mariage'] = ['-0.85', '0', '0.968', '-0.89', '#anger', '#disgust', 'negative', '-0.90', 'family', 'household', 'house', 'home', 'broken_home']
senticnet['le_mcontentement'] = ['0', '0.919', '0', '0.888', '#interest', '#admiration', 'positive', '0.903', 'surprise', 'eye-popping', 'fulgurous', 'dazzling', 'bedaze']
senticnet['le_oiseaux'] = ['-0.85', '0', '0.968', '-0.89', '#anger', '#disgust', 'negative', '-0.90', 'family', 'household', 'house', 'home', 'broken_home']
senticnet['le_prfet'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'indweller', 'invillager', 'villager', 'dweller', 'denizen']
senticnet['le_procureur'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'sexual_relationship', 'liaison', 'affair', 'involvement']
senticnet['le_prsident_russe'] = ['0.667', '-0.98', '0', '0.61', '#joy', '#surprise', 'positive', '0.753', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['le_ragazze_di'] = ['0.107', '0.11', '-0.08', '0.21', '#interest', '#admiration', 'positive', '0.086', 'live_healthy_life', 'forever_young', 'live_forever', 'live_long', 'live_good_life']
senticnet['le_roi'] = ['-0.52', '0', '0.701', '-0.50', '#sadness', '#anger', 'negative', '-0.57', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['le_sarde'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'saltine', 'soda_cracker', 'cracker', 'water_biscuit', 'graham_cracker']
senticnet['le_scandale'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'sexual_relationship', 'liaison', 'affair', 'involvement']
senticnet['le_transloy'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'start_over', 'queue', 'find_line', 'take_turn', 'long_line']
senticnet['le_tribunal'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'sexual_relationship', 'liaison', 'affair', 'involvement']
senticnet['lea'] = ['-0.90', '0', '0.765', '-0.98', '#sadness', '#disgust', 'negative', '-0.88', 'pastureland', 'grazing_land', 'ley', 'pasture', 'common_land']
senticnet['lea_sandeman'] = ['0.331', '0.119', '0.022', '0.229', '#joy', '#admiration', 'positive', '0.164', 'spice_life', 'red_light_district', 'fresh_produce', 'enough_food', 'knowledgeable']
senticnet['lead'] = ['0.524', '-0.75', '0', '0.828', '#surprise', '#admiration', 'positive', '0.704', 'go_onwards', 'go_deep', 'run', 'pass', 'extend']
senticnet['lead_astray'] = ['-0.82', '0', '0.55', '-0.91', '#sadness', '#disgust', 'negative', '-0.76', 'misdirect', 'deprave', 'vitiate', 'profane', 'demoralize']
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senticnet['lead_guitar'] = ['0.049', '0.167', '-0.05', '0.295', '#interest', '#admiration', 'positive', '0.115', 'four_sides', 'sound_system', 'dolby_surround', 'movie', 'surround']
senticnet['lead_guitarist'] = ['0', '-0.02', '0.055', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'djembe', 'make_musicr', 'use_jazz_music', 'tune_instrument', 'percussion']
senticnet['lead_negotiator'] = ['0', '0.133', '0', '0.088', '#interest', '#admiration', 'positive', '0.111', 'juror', 'decide_guilt', 'become_judge', 'arbiter', 'jury']
senticnet['lead_off'] = ['0.977', '-0.69', '0', '0.851', '#joy', '#admiration', 'positive', '0.842', 'pervert', 'debauch', 'corrupt', 'subvert', 'debase']
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senticnet['lead_pencil'] = ['0.922', '0.839', '0', '0', '#joy', '#interest', 'positive', '0.881', 'eraser_holder', 'stick_eraser', 'mechanical_pencil', 'electric_pencil_sharpener', 'classroom_chair']
senticnet['lead_rope'] = ['0.093', '-0.02', '0', '0.056', '#joy', '#admiration', 'positive', '0.057', 'jockey', 'control_horse', 'horse', 'sprinter', 'saddle']
senticnet['lead_singer'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
senticnet['lead_team'] = ['-0.06', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.04', 'direct_person', 'teacher', 'hard_worker', 'leadership', 'principal']
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senticnet['leadenhall_building'] = ['-0.81', '0', '0.543', '-0.63', '#sadness', '#disgust', 'negative', '-0.66', 'caper', 'pickle', 'relish', 'dill_pickle', 'sweet_pickle']
senticnet['leader'] = ['0.035', '0.074', '0', '0', '#joy', '#interest', 'positive', '0.054', 'commander', 'head_guy', 'police_rank', 'chief', 'navy_officer']
senticnet['leader_mao_zedong'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'patriotism', 'homeland']
senticnet['leader_sun_myung'] = ['0.916', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.929', 'galaxy', 'universe', 'planetary', 'sun', 'dwarf_planet']
senticnet['leadership'] = ['0.053', '0.136', '0', '0.057', '#interest', '#admiration', 'positive', '0.082', 'boss', 'supervisor', 'leader', 'head_person', 'head_guy']
senticnet['leadership_position'] = ['0.033', '0.082', '0', '0', '#joy', '#interest', 'positive', '0.058', 'military_officer', 'head_guy', 'navy_officer', 'navy_leader', 'military_position']
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senticnet['leadfoot'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
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senticnet['leading_manufacturer'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'builder']
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senticnet['leaf_vegetable'] = ['0.035', '0.104', '0', '0.12', '#interest', '#admiration', 'positive', '0.086', 'fruit_vegetable', 'vegetable', 'vegetable_fruit', 'fresh_vegetable', 'vegetarian_food']
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senticnet['leased_space'] = ['-0.02', '0.119', '-0.06', '0.216', '#interest', '#admiration', 'positive', '0.062', 'go_forward', 'locomotion', 'movement', 'move', 'move_ahead']
senticnet['leasehold_interest'] = ['0.621', '-0.60', '0', '0.545', '#joy', '#surprise', 'positive', '0.59', 'privilege', 'perquisite', 'public_easement', 'right_of_way', 'prerogative']
senticnet['leash'] = ['-0.05', '0.072', '0.012', '0.066', '#interest', '#admiration', 'positive', '0.018', 'pet', 'bell_cat', 'animal', 'dog_tag', 'fur']
senticnet['least'] = ['-0.99', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.84', 'a_little', 'little', 'small_amount', 'shortage', 'scarce']
senticnet['least_bell_vireo'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'panda', 'hunting', 'air_pollution', 'global_warming', 'sea_pollution']
senticnet['least_burdensome'] = ['0.5', '-0.9', '0', '0.525', '#surprise', '#admiration', 'positive', '0.642', 'manageable', 'controllable', 'governable', 'steerable', 'dirigible']
senticnet['least_effort'] = ['-0.68', '0', '0.635', '-0.54', '#sadness', '#anger', 'negative', '-0.62', 'sweat', 'effort', 'elbow_grease', 'exertion', 'travail']
senticnet['least_resistance'] = ['-0.75', '0', '0.955', '-0.77', '#anger', '#disgust', 'negative', '-0.82', 'sweat', 'effort', 'elbow_grease', 'exertion', 'travail']
senticnet['least_restrictive_alternative'] = ['0.5', '-0.9', '0', '0.525', '#surprise', '#admiration', 'positive', '0.642', 'manageable', 'controllable', 'governable', 'steerable', 'dirigible']
senticnet['least_square_solution'] = ['0.506', '-0.56', '0', '0.788', '#surprise', '#admiration', 'positive', '0.62', 'backward', 'reverse', 'retrograde', 'reflexive', 'retral']
senticnet['leather'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'coat', 'suit', 'armor', 'sweater', 'belt']
senticnet['leather_armor'] = ['-0.50', '0', '0.937', '-0.59', '#anger', '#disgust', 'negative', '-0.67', 'restorative', 'medicine', 'medication', 'medicament', 'medicinal_drug']
senticnet['leather_bag'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'sports_equipment', 'sport_stuff', 'golf', 'sport_store', 'bag']
senticnet['leather_belt'] = ['0.028', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.047', 'handkerchief', 'kerchief', 'hanky', 'facial_tissue', 'zipper']
senticnet['leather_bound'] = ['0', '-0.04', '0.061', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'index_card', 'ring_binder', 'highlighter_pen', 'memo_pad', 'notepad']
senticnet['leather_briefcase'] = ['0.998', '0', '0', '0', '#joy', '#joy', 'positive', '0.998', 'panties', 'lingerie', 'pantyhose', 'slack', 'prescription_medication']
senticnet['leather_chair'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'coach_seat', 'stool', 'seat']
senticnet['leather_clad'] = ['-0.90', '0', '0.522', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'hooligan', 'ruffian', 'roughneck', 'rowdy', 'yob']
senticnet['leather_cord'] = ['0', '0.072', '0', '0.064', '#interest', '#admiration', 'positive', '0.068', 'zipper', 'pant', 'trousers', 'cowboy_boot', 'accessory']
senticnet['leather_couch'] = ['0', '-0.02', '0.041', '-0.10', '#anger', '#disgust', 'negative', '-0.03', 'separate_shower', 'shampoo_conditioner', 'towel_bar', 'shower_head', 'toilet_brush']
senticnet['leather_glove'] = ['0.022', '-0.01', '0.062', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'electric_toothbrush', 'moistener', 'hairbrush', 'shampoo_conditioner', 'shave_kit']
senticnet['leather_interior'] = ['0.02', '0.085', '-0.05', '0.085', '#interest', '#admiration', 'positive', '0.034', 'gold', 'silver', 'medal', 'carat', 'medallion']
senticnet['leather_jacket'] = ['0.817', '0.862', '0', '0', '#joy', '#interest', 'positive', '0.839', 'belt', 'zipper', 'backpack', 'pouch', 'bag']
senticnet['leather_pouch'] = ['0', '0.177', '0', '0.126', '#interest', '#admiration', 'positive', '0.151', 'treasure', 'storage_facility', 'shrine', 'lock_box', 'jewelry_box']
senticnet['leather_purse'] = ['0', '-0.02', '0.018', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'shoulder_bag', 'water_bottle', 'satchel_bag', 'tote_bag', 'carry']
senticnet['leather_rot'] = ['0', '0', '-0.94', '-0.96', '#fear', '#disgust', 'negative', '-0.95', 'say_prayer', 'fall_off_chair', 'put_box', 'feel_terrible', 'lift_leg']
senticnet['leather_satchel'] = ['0', '-0.02', '0.018', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'shoulder_bag', 'water_bottle', 'satchel_bag', 'tote_bag', 'carry']
senticnet['leather_sheath'] = ['-0.71', '0', '0.954', '-0.56', '#sadness', '#anger', 'negative', '-0.74', 'jackknife', 'pocket_knife', 'penknife', 'switchblade', 'switchblade_knife']
senticnet['leather_shoe'] = ['0.028', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.047', 'handkerchief', 'kerchief', 'hanky', 'facial_tissue', 'zipper']
senticnet['leather_skirt'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'facial_tissue', 'kleenex', 'hanky', 'handkerchief', 'tissue']
senticnet['leather_sofa'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'coach_seat', 'stool', 'seat']
senticnet['leather_steering_wheel'] = ['0.725', '0.796', '0', '0', '#joy', '#interest', 'positive', '0.76', 'car_stereo', 'battery', 'seatbelt', 'spare_tire', 'brake_pedal']
senticnet['leather_strap'] = ['0.028', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.047', 'handkerchief', 'kerchief', 'hanky', 'facial_tissue', 'zipper']
senticnet['leather_thong'] = ['-0.71', '0', '0.954', '-0.56', '#sadness', '#anger', 'negative', '-0.74', 'jackknife', 'pocket_knife', 'penknife', 'switchblade', 'switchblade_knife']
senticnet['leather_tote'] = ['0', '0.092', '0.016', '0.074', '#interest', '#admiration', 'positive', '0.05', 'armoire', 'pillowcase', 'bed_sheet', 'duvet_cover', 'comforter']
senticnet['leatherface'] = ['-0.03', '-0.01', '-0.16', '-0.06', '#fear', '#disgust', 'negative', '-0.06', 'sticky_floor', 'theater_box', 'projector', 'row_seat', 'theater_seat']
senticnet['leatherjacket'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'puffer', 'pufferfish', 'blowfish', 'triggerfish', 'plectognath']
senticnet['leathery'] = ['-0.98', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.91', 'hoary', 'scabby', 'scaly', 'slimy', 'warty']
senticnet['leathery_skin'] = ['0.352', '0.142', '0', '0.281', '#joy', '#admiration', 'positive', '0.258', 'knowledgeable', 'complement', 'much_need', 'see_ocean', 'good_sense_of_humor']
senticnet['leave'] = ['0', '0', '0', '0.094', '#admiration', '#admiration', 'positive', '0.094', 'green_leave', 'large_plant', 'leaf', 'plant_part', 'stem']
senticnet['leave_alone'] = ['-0.14', '-0.11', '0.105', '-0.22', '#sadness', '#disgust', 'negative', '-0.09', 'abandon', 'think', 'confine', 'smelly_foot', 'lyme_disease']
senticnet['leave_behind'] = ['-0.18', '0.093', '-0.17', '0', '#sadness', '#fear', 'negative', '-0.08', 'time_out', 'rest_time', 'speeding_ticket', 'put_down', 'mock']
senticnet['leave_build'] = ['-0.07', '0.146', '-0.07', '0.197', '#interest', '#admiration', 'positive', '0.048', 'invade', 'go_through', 'hang_door', 'go_build', 'enter_build']
senticnet['leave_country'] = ['0.92', '0.976', '0', '0.722', '#joy', '#interest', 'positive', '0.873', 'plan_vacation', 'go_vacation', 'holiday', 'travel', 'go_pub']
senticnet['leave_document'] = ['0.164', '0.13', '0.031', '0.209', '#joy', '#admiration', 'positive', '0.118', 'voicemail_message', 'write_note', 'leave_message', 'send_email', 'message']
senticnet['leave_hand'] = ['0', '0.097', '0', '0.085', '#interest', '#admiration', 'positive', '0.091', 'lunula', 'arm_part', 'cuticle', 'ten_fingers', 'five_fingers']
senticnet['leave_home'] = ['-0.16', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.10', 'leave_house', 'look_map', 'open_front_door', 'decide_to_go', 'book_flight']
senticnet['leave_house'] = ['-0.05', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.04', 'decide_to_go', 'obey_rule', 'open_front_door', 'look_map', 'dress_appropriately']
senticnet['leave_house_first'] = ['-0.07', '0', '0.012', '0', '#sadness', '#anger', 'negative', '-0.04', 'know_go', 'decide_to_go', 'look_map', 'travel', 'leave_house']
senticnet['leave_job'] = ['0', '0', '0.87', '-0.87', '#anger', '#disgust', 'negative', '-0.87', 'go_away', 'ask_to_leave', 'drive_out', 'chase_away', 'drive_away']
senticnet['leave_leg'] = ['0', '-0.01', '0.046', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'bone', 'nipple', 'backbone', 'thigh', 'scrotum']
senticnet['leave_line'] = ['-0.04', '-0.01', '0.011', '-0.06', '#sadness', '#disgust', 'negative', '-0.02', 'stand_in_line', 'wait_line', 'get_line', 'wait', 'stand_queue']
senticnet['leave_message'] = ['0.164', '0.13', '0.031', '0.209', '#joy', '#admiration', 'positive', '0.118', 'voicemail_message', 'write_note', 'leave_document', 'send_email', 'message']
senticnet['leave_museum'] = ['-0.13', '-0.06', '0.043', '-0.21', '#sadness', '#disgust', 'negative', '-0.08', 'learn_much', 'make_out_atom', 'add_knowledge', 'learn', 'inform']
senticnet['leave_office'] = ['-0.78', '0', '0.801', '-0.87', '#anger', '#disgust', 'negative', '-0.81', 'resign', 'renounce', 'step_down', 'abdicate', 'vacate']
senticnet['leave_out'] = ['-0.44', '-0.15', '0', '-0.36', '#sadness', '#disgust', 'negative', '-0.21', 'cause_suffer', 'lose_team', 'loathing', 'disrespect', 'lyme_disease']
senticnet['leave_over'] = ['0.056', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.053', 'leftover', 'dusty', 'mayonnaise', 'peanut_butter', 'savory']
senticnet['leave_place'] = ['-0.08', '0.092', '-0.07', '0.15', '#interest', '#admiration', 'positive', '0.021', 'drive_highway', 'push_limit', 'steer_car', 'turn_leave', 'break_down']
senticnet['leave_room'] = ['-0.09', '-0.00', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.05', 'go_new_place', 'leave_home', 'travel_by_airplane', 'travel_abroad', 'board_airplane']
senticnet['leave_table'] = ['-0.07', '0', '-0.05', '-0.08', '#sadness', '#disgust', 'negative', '-0.06', 'head_class', 'crowd_room', 'seat_chair', 'conduct_experiment', 'page_through_book']
senticnet['leave_theater'] = ['0.854', '0.94', '0', '0', '#joy', '#interest', 'positive', '0.897', 'read_credit', 'channel_surf', 'turn_channel', 'eat_pizza', 'change_channel']
senticnet['leave_tip'] = ['0.164', '0.13', '0.031', '0.209', '#joy', '#admiration', 'positive', '0.118', 'spill_food_clothes', 'drop_food', 'order_food', 'go_restroom', 'order_drink']
senticnet['leave_without_pay'] = ['-0.18', '0.138', '-0.21', '0.086', '#sadness', '#fear', 'negative', '-0.04', 'steal', 'rob', 'shoplift', 'bad_citizen', 'robbery']
senticnet['leave_work'] = ['-0.09', '0.072', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.03', 'quit_job', 'floss_tooth', 'set_clock', 'out_bed', 'sell_vegetable']
senticnet['leaveth'] = ['-0.75', '0.282', '-0.42', '-0.06', '#sadness', '#fear', 'negative', '-0.24', 'depressing', 'cheerless', 'low-spiritedness', 'lowness', 'dispiritedness']
senticnet['leaving_aside'] = ['0', '0', '-0.86', '-0.99', '#fear', '#disgust', 'negative', '-0.93', 'niggle', 'quiddity', 'bicker', 'pettifog', 'cavil']
senticnet['lebesguestieltjes_integral'] = ['-0.53', '0', '0.993', '-0.88', '#anger', '#disgust', 'negative', '-0.80', 'expansiveness', 'impressiveness', 'stateliness', 'loftiness', 'excellence']
senticnet['lebewohl'] = ['0.914', '-0.56', '0', '0.869', '#joy', '#admiration', 'positive', '0.781', 'tabula_rasa', 'ego', 'subconscious', 'unconscious', 'psyche']
senticnet['lebor_gabla'] = ['0', '0.105', '0', '0.084', '#interest', '#admiration', 'positive', '0.095', 'archive', 'print_medium', 'quiet_place', 'register', 'media']
senticnet['lebowitz'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['lech'] = ['0.789', '-0.73', '0', '0.857', '#joy', '#admiration', 'positive', '0.793', 'fetishist', 'degenerateer', 'degenerate', 'letch', 'satyr']
senticnet['lecher'] = ['0', '0', '0.733', '-0.98', '#anger', '#disgust', 'negative', '-0.85', 'fetishist', 'degenerate', 'lech', 'letch', 'pervert']
senticnet['lecherous'] = ['-0.76', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.76', 'lascivious', 'leer', 'liquorish', 'satyr', 'lecher']
senticnet['lecherousness'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'concupiscence', 'lustfulness', 'amorousness']
senticnet['lechery'] = ['-0.71', '0', '0.899', '0', '#sadness', '#anger', 'negative', '-0.80', 'indulgence', 'lecherous', 'lubricity', 'lecher', 'libidinousness']
senticnet['lectern'] = ['0', '0.214', '0.012', '0.208', '#interest', '#admiration', 'positive', '0.137', 'altar', 'confessional', 'church', 'chapel', 'christianity']
senticnet['lector'] = ['0.773', '0', '0', '0.848', '#joy', '#admiration', 'positive', '0.81', 'educator', 'academician', 'schoolmaster', 'pedagogue', 'teacher']
senticnet['lecture'] = ['0', '0.214', '0.012', '0.208', '#interest', '#admiration', 'positive', '0.137', 'lecture_hall', 'lecture_theater', 'lesson', 'conference', 'school_session']
senticnet['lecture_hall'] = ['0.872', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.811', 'lecture_theater', 'lecture', 'conference_auditorium', 'read_book', 'text_book']
senticnet['lecture_room'] = ['0.862', '0.812', '0', '0', '#joy', '#interest', 'positive', '0.837', 'room', 'home_room', 'study_hall', 'classroom', 'schoolroom']
senticnet['lecture_theater'] = ['0', '0.074', '0.051', '0.105', '#interest', '#admiration', 'positive', '0.043', 'lecture_hall', 'lecture', 'textbook', 'text_book', 'lesson']
senticnet['lecture_theatre'] = ['0.872', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.811', 'lecture_theater', 'lecture', 'conference_auditorium', 'read_book', 'text_book']
senticnet['lecturer'] = ['-0.03', '-0.00', '-0.05', '-0.03', '#fear', '#disgust', 'negative', '-0.02', 'professor', 'teacher', 'instructor', 'student', 'college_student']
senticnet['lecythidaceae'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'seed_fern', 'pteridosperm']
senticnet['led'] = ['0', '0', '0.962', '-0.96', '#anger', '#disgust', 'negative', '-0.96', 'abominable', 'nature', 'bestial', 'beastly', 'beastliness']
senticnet['led_astray'] = ['-0.78', '0', '0.704', '-0.91', '#sadness', '#disgust', 'negative', '-0.8', 'direct', 'take', 'lead', 'conduct', 'guide']
senticnet['ledeb'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'sedum', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['ledge'] = ['0', '0.115', '0', '0', '#interest', '#interest', 'positive', '0.115', 'coordinate', 'middle_road', 'mountain_top', 'coordinator', 'tennis_match']
senticnet['ledger'] = ['0.97', '0.791', '0', '0', '#joy', '#interest', 'positive', '0.881', 'pocket_notebook', 'file', 'general_ledger', 'ring_binder', 'address_label']
senticnet['ledum_groenlandicum'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
senticnet['leech'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'annelid', 'annelid_worm', 'segmented_worm']
senticnet['leech_worm'] = ['-0.72', '0', '0.602', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'leech', 'annelid', 'segmented_worm']
senticnet['leeds_town_hall'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'city_hall', 'courthouse', 'subway_station', 'public_place', 'bus_station']
senticnet['leekie_soup'] = ['-0.54', '0', '0.811', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'bird_of_prey', 'raptor', 'raptorial_bird', 'parrot', 'bird']
senticnet['leer'] = ['-0.11', '0', '-0.09', '0.082', '#sadness', '#fear', 'negative', '-0.04', 'stare', 'ear', 'facial_expression', 'mouth', 'dimple']
senticnet['leeriness'] = ['-0.70', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.74', 'cautious', 'concern', 'nervous', 'reservation', 'leery']
senticnet['leering'] = ['0.768', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.881', 'grin', 'smile', 'ear-to-ear', 'smirk', 'toothy']
senticnet['leery'] = ['-0.70', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.73', 'cautious', 'concern', 'nervous', 'reservation', 'leeriness']
senticnet['leetle'] = ['-0.85', '0', '0.583', '-0.71', '#sadness', '#disgust', 'negative', '-0.71', 'muddle', 'rummage', 'mare_nest', 'welter', 'clutter']
senticnet['leeway'] = ['-0.52', '0', '0.561', '-0.90', '#anger', '#disgust', 'negative', '-0.66', 'difference', 'tolerance', 'allowance', 'discrepancy', 'margin']
senticnet['left'] = ['0.883', '-0.59', '0', '0.533', '#joy', '#surprise', 'positive', '0.67', 'hook', 'meat_hook', 'mauler', 'right', 'hand']
senticnet['left-hand'] = ['0', '0.811', '0', '0.937', '#interest', '#admiration', 'positive', '0.874', 'leftmost', 'left-most', 'right-most', 'right-hand', 'rightmost']
senticnet['left-handed'] = ['0.803', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.882', 'smoke', 'right-footed', 'skillfully', 'skillful', 'handily']
senticnet['left-most'] = ['0', '0.811', '0', '0.941', '#interest', '#admiration', 'positive', '0.876', 'leftmost', 'rightmost', 'right-most', 'right-hand', 'left-hand']
senticnet['left_achilles_tendon'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'deltoid', 'muscle', 'abductor', 'skeletal_muscle']
senticnet['left_ajar'] = ['0.085', '0', '-0.05', '0.116', '#joy', '#admiration', 'positive', '0.05', 'dance_well', 'water_faucet', 'enter_contest', 'dance_type', 'dance_music']
senticnet['left_ankle'] = ['-0.83', '0', '0.509', '-0.84', '#sadness', '#disgust', 'negative', '-0.72', 'wrench', 'injury', 'harm', 'hurt', 'trauma']
senticnet['left_ankle_injury'] = ['-0.78', '0', '0.554', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'rubor', 'redness', 'inflammation', 'tennis_elbow', 'lateral_epicondylitis']
senticnet['left_arm'] = ['0', '0', '-0.06', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'bandit', 'arm', 'wrist', 'five_fingers', 'bendable']
senticnet['left_arm_seamer'] = ['0.034', '-0.02', '0.054', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'compass', 'swiss_army_knife', 'bake_pan', 'coffee_mug', 'wine_opener']
senticnet['left_atrial'] = ['0.777', '-0.89', '0', '0.508', '#joy', '#surprise', 'positive', '0.727', 'stretching', 'enlarge', 'enlargement', 'get_bigger', 'distension']
senticnet['left_big_toe'] = ['-0.83', '0', '0.509', '-0.84', '#sadness', '#disgust', 'negative', '-0.72', 'wrench', 'injury', 'harm', 'hurt', 'trauma']
senticnet['left_calf_muscle'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'deltoid', 'muscle', 'abductor', 'skeletal_muscle']
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senticnet['legging'] = ['0', '0', '0.808', '-0.99', '#anger', '#disgust', 'negative', '-0.90', 'tights', 'footless', 'fishnet', 'hosiery', 'knee-high']
senticnet['leggy'] = ['0.636', '-0.95', '0', '0.664', '#surprise', '#admiration', 'positive', '0.751', 'voluptuous', 'statuesque', 'sculptured', 'buxom', 'sculptural']
senticnet['legibility'] = ['0.805', '-0.62', '0', '0.86', '#joy', '#admiration', 'positive', '0.762', 'clearness', 'clarity', 'discernability', 'uncloudedness', 'distinctness']
senticnet['legible'] = ['0.026', '0', '0', '0.084', '#joy', '#admiration', 'positive', '0.055', 'book_write', 'make_book', 'written_text', 'paragraph', 'book_paper']
senticnet['legionnaire_disease'] = ['-0.11', '0', '-0.15', '-0.24', '#fear', '#disgust', 'negative', '-0.17', 'disease', 'infectious_disease', 'polio', 'malady', 'sickness']
senticnet['legislate'] = ['-0.88', '0', '0.977', '-0.81', '#sadness', '#anger', 'negative', '-0.89', 'execute', 'run', 'compel', 'oblige', 'obligate']
senticnet['legislation'] = ['0', '0.178', '-0.04', '0.118', '#interest', '#admiration', 'positive', '0.084', 'law', 'edict', 'prosecutor', 'regulation', 'judge_jury']
senticnet['legislative'] = ['0.028', '0.153', '-0.05', '0.18', '#interest', '#admiration', 'positive', '0.076', 'nominate', 'administration', 'reign', 'order', 'regulation']
senticnet['legislative_aide'] = ['0', '0', '0', '0.876', '#admiration', '#admiration', 'positive', '0.876', 'good_manner', 'courteous', 'good_behavior', 'handle_carefully', 'politeness']
senticnet['legislative_assembly'] = ['0.055', '0.072', '-0.06', '0.094', '#interest', '#admiration', 'positive', '0.038', 'big_group', 'large_group', 'bunch_person', 'large_number', 'clique']
senticnet['legislative_assembly_seat'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'couch', 'stool', 'coach_seat']
senticnet['legislative_assistant'] = ['0', '0', '0', '0.876', '#admiration', '#admiration', 'positive', '0.876', 'good_manner', 'courteous', 'good_behavior', 'handle_carefully', 'politeness']
senticnet['legislative_body'] = ['0.055', '0.072', '-0.06', '0.094', '#interest', '#admiration', 'positive', '0.038', 'big_group', 'large_group', 'bunch_person', 'large_number', 'clique']
senticnet['legislative_council'] = ['0.055', '0.072', '-0.06', '0.094', '#interest', '#admiration', 'positive', '0.038', 'big_group', 'large_group', 'bunch_person', 'large_number', 'clique']
senticnet['legislative_council_seat'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'couch', 'stool', 'coach_seat']
senticnet['legislative_election'] = ['0', '0.156', '-0.15', '0.179', '#interest', '#admiration', 'positive', '0.061', 'candidate', 'presidential', 'vote', 'elect', 'appoint']
senticnet['legislative_enactment'] = ['0.509', '-0.62', '0', '0.786', '#surprise', '#admiration', 'positive', '0.64', 'agreeable', 'accordant', 'consentient', 'unanimous', 'according']
senticnet['legislative_liaison'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'sexual_relationship', 'affair', 'involvement']
senticnet['legislative_maneuver'] = ['-0.81', '0', '0.513', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'ballot', 'vote', 'voting', 'balloting', 'veto']
senticnet['legislative_veto'] = ['-0.81', '0', '0.513', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'ballot', 'vote', 'voting', 'balloting', 'veto']
senticnet['legislator'] = ['0', '0.868', '0', '0.853', '#interest', '#admiration', 'positive', '0.861', 'country_leader', 'prime_minister', 'president', 'elect_official', 'leader']
senticnet['legislature'] = ['0.03', '0.124', '0', '0.099', '#interest', '#admiration', 'positive', '0.084', 'government', 'rule_government', 'reign', 'legal', 'state_head']
senticnet['legit'] = ['-0.09', '0.156', '0.661', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'phony', 'deception', 'false', 'against_law', 'trick']
senticnet['legitimacy'] = ['0', '-0.98', '0', '0.966', '#surprise', '#admiration', 'positive', '0.975', 'genuineness', 'authenticity', 'law', 'well', 'project']
senticnet['legitimate'] = ['0.886', '-0.77', '0', '0.815', '#joy', '#admiration', 'positive', '0.826', 'countenance', 'monetize', 'permit', 'allow', 'let']
senticnet['legitimate_expectation'] = ['-0.72', '0', '0.742', '-0.80', '#anger', '#disgust', 'negative', '-0.75', 'illicitness', 'illegality', 'illegitimacy', 'shadiness', 'quality']
senticnet['legitimately'] = ['-0.72', '0', '0.742', '-0.80', '#anger', '#disgust', 'negative', '-0.75', 'illicitness', 'unlawfulness', 'illegitimacy', 'shadiness', 'quality']
senticnet['legitimation'] = ['0.751', '-0.98', '0', '0.989', '#surprise', '#admiration', 'positive', '0.909', 'okay', 'approval', 'commendation', 'okey', 'okeh']
senticnet['legitimise'] = ['0.54', '-0.97', '0', '0.569', '#surprise', '#admiration', 'positive', '0.695', 'overstate', 'hyperbolize', 'glorify', 'exaggerate', 'overdraw']
senticnet['legitimist'] = ['0.059', '0.092', '0', '0.107', '#interest', '#admiration', 'positive', '0.086', 'royalty', 'monarch', 'throne', 'emperor', 'king_queen']
senticnet['legitimization'] = ['0.751', '-0.98', '0', '0.989', '#surprise', '#admiration', 'positive', '0.909', 'okay', 'approval', 'commendation', 'okey', 'okeh']
senticnet['legitimize'] = ['0.751', '-0.98', '0', '0.989', '#surprise', '#admiration', 'positive', '0.909', 'okay', 'approval', 'commendation', 'okey', 'okeh']
senticnet['legless'] = ['0', '0', '-0.80', '-0.85', '#fear', '#disgust', 'negative', '-0.83', 'limbless', 'armless', 'white-haired', 'lanky', 'lizard']
senticnet['legside'] = ['-0.13', '0', '-0.11', '-0.04', '#sadness', '#fear', 'negative', '-0.1', 'concern_health', 'medical_problem', 'strengthful', 'surgery', 'injury']
senticnet['legside_wide'] = ['-0.62', '0', '0.713', '-0.98', '#anger', '#disgust', 'negative', '-0.77', 'muddy_up', 'muck_up', 'foul', 'blemish', 'smear']
senticnet['legume'] = ['0.518', '-0.51', '0', '0.65', '#surprise', '#admiration', 'positive', '0.562', 'clematis', 'climber', 'leguminous_plant', 'climbing_fern', 'root_climber']
senticnet['legume_pod'] = ['0', '0.141', '0.01', '0.115', '#interest', '#admiration', 'positive', '0.082', 'fruit', 'rind', 'funiculus', 'seed', 'pip']
senticnet['leguminous_plant'] = ['0.518', '-0.51', '0', '0.65', '#surprise', '#admiration', 'positive', '0.562', 'clematis', 'legume', 'climber', 'climbing_fern', 'root_climber']
senticnet['legwear'] = ['0', '-0.07', '0.099', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'prescription_medication', 'lingerie', 'sachet', 'shave_kit', 'panties']
senticnet['legwork'] = ['0', '0.735', '0', '0.808', '#interest', '#admiration', 'positive', '0.772', 'dexterous', 'skill', 'skillful', 'sly', 'subtle']
senticnet['lehigh_valley'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
senticnet['lehman_brother_bankruptcy'] = ['-0.13', '-0.05', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'monitor_heart_rate', 'feel_tired', 'bump_head', 'exhaustion', 'become_fatigued']
senticnet['lehman_engel'] = ['0.022', '0', '0', '0.109', '#joy', '#admiration', 'positive', '0.066', 'instrumental', 'singer', 'hip_hop', 'melodic_noise', 'music_player']
senticnet['leibman'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['leibnitz'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'force_unit', 'sthene', 'poundal']
senticnet['leibniz'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'force_unit', 'sthene', 'poundal']
senticnet['leibnizian'] = ['0.992', '-0.66', '0', '0.536', '#joy', '#surprise', 'positive', '0.729', 'nous', 'mind', 'brain', 'psyche', 'head']
senticnet['leicestershire_police'] = ['0', '0.084', '0', '0', '#interest', '#interest', 'positive', '0.084', 'call_restaurant', 'reservation', 'reserve', 'dinner', 'reserved']
senticnet['leigh_syndrome'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'dystrophy', 'myotonic_dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['leila_cobo'] = ['0.997', '0.892', '0', '0', '#joy', '#interest', 'positive', '0.945', 'advertisement', 'large_outdoor_board', 'motorway', 'expressway', 'train_track']
senticnet['leisure'] = ['0.793', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.766', 'recreation', 'pastime', 'date_activity', 'leisure_time', 'enjoy_game']
senticnet['leisure_activity'] = ['0.117', '-0.00', '0', '0', '#joy', '#surprise', 'positive', '0.062', 'explore', 'web_surf', 'hobby', 'pastime', 'exploration']
senticnet['leisure_time'] = ['0.867', '0.714', '0', '0', '#joy', '#interest', 'positive', '0.79', 'leisure', 'pastime', 'enjoy_beauty', 'mentally_challenged', 'intellectual_stimulation']
senticnet['leisure_travel'] = ['0', '0', '0', '0.097', '#admiration', '#admiration', 'positive', '0.097', 'transport_person', 'travel_long_distance', 'get_plane', 'get_bus', 'move']
senticnet['leisure_wear'] = ['-0.92', '0', '0.595', '-0.67', '#sadness', '#disgust', 'negative', '-0.73', 'wear', 'wearable', 'clothing', 'article_of_clothing', 'vesture']
senticnet['leisureliness'] = ['-0.68', '0', '0.82', '-0.96', '#anger', '#disgust', 'negative', '-0.82', 'dilatoriness', 'deliberateness', 'procrastination', 'rate', 'pace']
senticnet['leisurely'] = ['-0.77', '0', '0.855', '-0.57', '#sadness', '#anger', 'negative', '-0.73', 'unhurried', 'easygoing', 'deliberate', 'easy', 'careful']
senticnet['leisurely_breakfast'] = ['0.026', '0.069', '0', '0', '#joy', '#interest', 'positive', '0.048', 'eat_food', 'eat_beef', 'chew_food', 'espresso', 'eat_much']
senticnet['leisurely_lunch'] = ['0.026', '0.069', '0', '0', '#joy', '#interest', 'positive', '0.048', 'eat_food', 'eat_beef', 'chew_food', 'espresso', 'eat_much']
senticnet['leisurely_pace'] = ['-0.77', '0', '0.855', '-0.57', '#sadness', '#anger', 'negative', '-0.73', 'unhurried', 'easygoing', 'deliberate', 'easy', 'careful']
senticnet['leisurely_stroll'] = ['-0.77', '0', '0.855', '-0.57', '#sadness', '#anger', 'negative', '-0.73', 'unhurried', 'easygoing', 'deliberate', 'easy', 'careful']
senticnet['leisurely_trip'] = ['0', '0', '0.828', '-0.88', '#anger', '#disgust', 'negative', '-0.85', 'outing', 'junket', 'pleasure_trip', 'excursion', 'expedition']
senticnet['leisurely_walk'] = ['-0.77', '0', '0.855', '-0.57', '#sadness', '#anger', 'negative', '-0.73', 'unhurried', 'easygoing', 'deliberate', 'easy', 'careful']
senticnet['leitersburg'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['lela_rose'] = ['0', '-0.03', '0.022', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'shoulder_bag', 'satchel', 'water_bottle', 'tote_bag', 'carry']
senticnet['lemelson_center'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'institution', 'tundra', 'child_story', 'natural_history_museum', 'cartoon']
senticnet['lemhi'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['lemma'] = ['0.756', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.819', 'theorem', 'equation', 'approximation', 'theorem', 'algebraic']
senticnet['lemon'] = ['-0.62', '0', '0.512', '-0.80', '#sadness', '#disgust', 'negative', '-0.64', 'vanilla', 'taste', 'sapidity', 'taste_sensation', 'gustatory_sensation']
senticnet['lemon_basil'] = ['0.1', '0', '0', '0', '#joy', '#joy', 'positive', '0.1', 'burn_material', 'ash', 'stay_warm', 'bonfire', 'campfire']
senticnet['lemon_curd'] = ['0.059', '-0.02', '0.025', '0.109', '#joy', '#admiration', 'positive', '0.043', 'tasty', 'bake_treat', 'bake_goods', 'bake_good', 'loaf']
senticnet['lemon_flavor'] = ['0.112', '0', '0', '0.141', '#joy', '#admiration', 'positive', '0.127', 'sweet', 'sweet_food', 'bake_good', 'round_pastry', 'eatable']
senticnet['lemon_juice'] = ['0.071', '0', '-0.08', '-0.09', '#fear', '#disgust', 'negative', '-0.03', 'gunk', 'rotten_food', 'sour_cream', 'glass_of_milk', 'acid']
senticnet['lemon_lime'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'lemon_juice', 'chocolate_pudding', 'grape_juice', 'orange_juice', 'juice']
senticnet['lemon_mascarpone'] = ['-0.95', '0', '0.845', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'grapefruit_peel', 'lemon_peel', 'orange_peel', 'candied_fruit', 'succade']
senticnet['lemon_meringue_pie'] = ['0.037', '-0.00', '0', '0.12', '#joy', '#admiration', 'positive', '0.055', 'bread_crumb', 'candy', 'bun', 'sweet', 'gush']
senticnet['lemon_peel'] = ['-0.95', '0', '0.845', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'grapefruit_peel', 'candied_citrus_peel', 'orange_peel', 'candied_fruit', 'succade']
senticnet['lemon_posset'] = ['0.52', '-0.69', '0', '0.639', '#surprise', '#admiration', 'positive', '0.617', 'black_bread', 'pumpernickel', 'limpa', 'bread', 'rye_bread']
senticnet['lemon_rind'] = ['-0.95', '0', '0.845', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'grapefruit_peel', 'candied_citrus_peel', 'orange_peel', 'candied_fruit', 'succade']
senticnet['lemon_sole'] = ['0.709', '0.964', '0', '0', '#joy', '#interest', 'positive', '0.837', 'fish_trout', 'catch_butterfly', 'fish_lake', 'fish_compliment', 'fish_stream']
senticnet['lemon_tart'] = ['0.092', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.071', 'bake_treat', 'round_pastry', 'bake_good', 'bake_goods', 'sweet_food']
senticnet['lemon_yellow'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'tropical_fish', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['lemon_zest'] = ['-0.95', '0', '0.845', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'grapefruit_peel', 'candied_citrus_peel', 'orange_peel', 'candied_fruit', 'succade']
senticnet['lemonade'] = ['0.111', '0', '0', '0', '#joy', '#joy', 'positive', '0.111', 'coke', 'cider', 'soda', 'juice', 'cola']
senticnet['lemonade_stand'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'candy', 'sweet', 'sugar', 'sweet_tooth', 'chocolate']
senticnet['lemur'] = ['0.859', '0.768', '0', '0', '#joy', '#interest', 'positive', '0.814', 'monkey', 'marmoset', 'fiance', 'beaver', 'koala']
senticnet['lemurian'] = ['0.086', '-0.10', '0.064', '0', '#joy', '#surprise', 'positive', '0.043', 'art_piece', 'lifetime', 'museum', 'vintage', 'collectible']
senticnet['lemuridae'] = ['-0.93', '0', '0.508', '-0.58', '#sadness', '#disgust', 'negative', '-0.67', 'inhuman', 'bloodless', 'apelike', 'nonhuman', 'nonhumanal']
senticnet['lend'] = ['-0.05', '0.253', '-0.16', '0.209', '#interest', '#admiration', 'positive', '0.06', 'donation', 'give_back', 'loan', 'investment', 'give']
senticnet['lend_credence'] = ['-0.86', '0', '0.897', '-0.81', '#sadness', '#anger', 'negative', '-0.85', 'disregard', 'ignore', 'brush_off', 'push_aside', 'flout']
senticnet['lend_hand'] = ['0', '-0.88', '0', '0.825', '#surprise', '#admiration', 'positive', '0.853', 'work_team', 'help', 'move_furniture', 'bus_table', 'win_award']
senticnet['lend_money'] = ['0.882', '0.89', '0', '0', '#joy', '#interest', 'positive', '0.886', 'bank_money', 'hold_coin', 'invest_money', 'vault', 'bank_loan']
senticnet['lending'] = ['-1', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.87', 'borrowing', 'borrowed', 'debt', 'spending', 'loan']
senticnet['lendingtree'] = ['-0.63', '0', '0.862', '-0.52', '#sadness', '#anger', 'negative', '-0.67', 'knot', 'bunch', 'tuft', 'tussock', 'agglomeration']
senticnet['lends_money'] = ['0.882', '0.89', '0', '0', '#joy', '#interest', 'positive', '0.886', 'bank_money', 'hold_coin', 'invest_money', 'vault', 'bank_loan']
senticnet['length'] = ['0.707', '0.959', '0', '0', '#joy', '#interest', 'positive', '0.833', 'length_measurement', 'measurement', 'imperial_measurement', 'measure', 'spatial_dimension']
senticnet['length_contraction'] = ['-0.69', '0', '0.83', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'general_relativity', 'general_theory_of_relativity', 'general_relativity_theory', 'relativity', 'special_theory_of_relativity']
senticnet['length_dimension'] = ['0.942', '0.818', '0', '0', '#joy', '#interest', 'positive', '0.88', 'dimension', 'size', 'mass_measure', 'width', 'gauge']
senticnet['length_measurement'] = ['0.938', '0.733', '0', '0', '#joy', '#interest', 'positive', '0.835', 'meter', 'imperial_measurement', 'length', 'distance_measure', 'measurement_unit']
senticnet['lengthen'] = ['0.71', '0', '0', '0.713', '#joy', '#admiration', 'positive', '0.712', 'inflate', 'proliferate', 'boost', 'thicken', 'multiply']
senticnet['lengthwise'] = ['0', '0', '0.061', '-0.03', '#anger', '#disgust', 'negative', '-0.04', 'garden_tool', 'straight_line', 'sharp', 'razor_blade', 'chock']
senticnet['lengthy_conversation'] = ['0.716', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.733', 'call', 'use_phone', 'orate', 'use_telephone', 'speech']
senticnet['lengthy_debate'] = ['0.779', '-0.88', '0', '0.521', '#joy', '#surprise', 'positive', '0.727', 'corrigible', 'correctable', 'improvable', 'reformable', 'redeemable']
senticnet['lengthy_discussion'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'discuss', 'communication', 'communicate_person', 'share', 'autograph']
senticnet['lenience'] = ['0.787', '-0.88', '0', '0.858', '#surprise', '#admiration', 'positive', '0.843', 'permissiveness', 'leniency', 'toleration', 'disposition', 'temperament']
senticnet['leniency'] = ['0.787', '-0.88', '0', '0.858', '#surprise', '#admiration', 'positive', '0.843', 'lenience', 'permissiveness', 'toleration', 'disposition', 'temperament']
senticnet['lenient'] = ['0.961', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.941', 'lax', 'leniency', 'strict', 'lenience', 'indulgent']
senticnet['leniently'] = ['0', '0.149', '0.719', '-0.66', '#anger', '#disgust', 'negative', '-0.41', 'belligerently', 'antagonism', 'class_feeling', 'aggressiveness', 'animus']
senticnet['lenify'] = ['0.644', '-0.91', '0', '0.992', '#surprise', '#admiration', 'positive', '0.851', 'tranquilize', 'tranquillize', 'calm_down', 'quieten', 'calm']
senticnet['lenition'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'pronunciation', 'sibilation', 'mispronunciation', 'homophony']
senticnet['lenity'] = ['-0.91', '0', '0.521', '-0.81', '#sadness', '#disgust', 'negative', '-0.74', 'bullying', 'domineering', 'blustery', 'roughshod', 'cavalier']
senticnet['lenny_castro'] = ['0.049', '0.167', '-0.05', '0.295', '#interest', '#admiration', 'positive', '0.115', 'four_sides', 'sound_system', 'dolby_surround', 'movie', 'surround']
senticnet['lens'] = ['0.026', '-0.16', '-0.05', '0.076', '#surprise', '#admiration', 'positive', '0.055', 'monocle', 'eyeglass', 'spectacle', 'contact_lens', 'sheer']
senticnet['lens_aperture'] = ['-0.82', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.84', 'block', 'obstruct', 'close', 'clogged', 'occluded']
senticnet['lens_barrel'] = ['-0.82', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.84', 'block', 'obstruct', 'close', 'clogged', 'occluded']
senticnet['lens_cap'] = ['-0.82', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.84', 'block', 'obstruct', 'close', 'clogged', 'occluded']
senticnet['lens_distortion'] = ['0.906', '-0.60', '0', '0.653', '#joy', '#admiration', 'positive', '0.72', 'opaqueness', 'faintness', 'dimness', 'opacity', 'vagueness']
senticnet['lens_flare'] = ['0.988', '0', '0', '0.789', '#joy', '#admiration', 'positive', '0.889', 'brightness', 'blaze', 'brilliance', 'glitter', 'glister']
senticnet['lens_mount'] = ['-0.82', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.84', 'block', 'obstruct', 'close', 'clogged', 'occluded']
senticnet['lentic'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'frog', 'amphibian']
senticnet['lentigo'] = ['-0.66', '0', '0.911', '-0.99', '#anger', '#disgust', 'negative', '-0.85', 'liver_spot', 'spot', 'freckle', 'macule', 'plague_spot']
senticnet['lentil_soup'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'cook_meat', 'cook_hamburger', 'cook_food', 'cook_rice', 'brown_meat']
senticnet['leopard'] = ['-0.03', '0', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.06', 'lion', 'craven', 'mountain_lion', 'bobcat', 'wolf']
senticnet['leopard_cat'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheep', 'argal', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['leopard_frog'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'amphibian', 'aquatic']
senticnet['leopard_print'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'elegant', 'stylish', 'smart', 'posh', 'sleek']
senticnet['leopard_seal'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'lordosis', 'kyphosis', 'spinal_curvature', 'hunchback', 'abnormality']
senticnet['leopard_shark'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'many_fishes', 'seahorse', 'starfish', 'plankton', 'sea_wee']
senticnet['leopard_skin'] = ['0.833', '0', '0', '0.859', '#joy', '#admiration', 'positive', '0.846', 'trapping', 'furnishing', 'wear', 'wearable', 'gear']
senticnet['leopard_syndrome'] = ['-0.87', '0', '0.873', '-0.62', '#sadness', '#anger', 'negative', '-0.78', 'dwarfism', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['leopold_auer'] = ['0.592', '-0.93', '0', '0.935', '#surprise', '#admiration', 'positive', '0.819', 'child', 'small_fry', 'tyke', 'tike', 'kid']
senticnet['leopoldw'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'aphasia', 'anomic_aphasia']
senticnet['leper'] = ['0', '0', '-0.82', '-0.97', '#fear', '#disgust', 'negative', '-0.89', 'tuberculosis', 'leprosy', 'cholera', 'syphilis', 'smallpox']
senticnet['leper_queen'] = ['0.02', '0.131', '0.749', '-0.67', '#anger', '#disgust', 'negative', '-0.31', 'belligerence', 'belligerency', 'belligerent', 'war', 'rancor']
senticnet['lepidium'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
senticnet['lepidodendron'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'seed_fern', 'pteridosperm']
senticnet['lepidote'] = ['-0.77', '0', '0.626', '-0.78', '#sadness', '#disgust', 'negative', '-0.72', 'bumpy', 'scabrous', 'unsmooth', 'leprose', 'roughish']
senticnet['leprechaun'] = ['-0.73', '0', '0.72', '-0.93', '#sadness', '#disgust', 'negative', '-0.79', 'imp', 'elf', 'hob', 'gremlin', 'pixie']
senticnet['lepromatous_leprosy'] = ['-0.87', '0', '0.531', '-0.68', '#sadness', '#disgust', 'negative', '-0.69', 'rubor', 'redness', 'dermatomyositis', 'inclusion_body_myositis', 'inflammation']
senticnet['leprose'] = ['-0.77', '0', '0.626', '-0.78', '#sadness', '#disgust', 'negative', '-0.72', 'bumpy', 'scabrous', 'lepidote', 'unsmooth', 'roughish']
senticnet['leprosy'] = ['0', '0', '-0.81', '-0.96', '#fear', '#disgust', 'negative', '-0.89', 'tuberculosis', 'leper', 'cholera', 'syphilis', 'smallpox']
senticnet['leprosy_bacillus'] = ['-0.90', '0', '0.908', '-0.53', '#sadness', '#anger', 'negative', '-0.78', 'tubercle_bacillus', 'mycobacteria', 'eubacteria', 'eubacterium', 'true_bacteria']
senticnet['leptin'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'cholecystokinin', 'secretin', 'gastrointestinal_hormone']
senticnet['leptin_resistance'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'cholecystokinin', 'secretin', 'gastrointestinal_hormone']
senticnet['leptodactylodon'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'amphibian', 'aquatic']
senticnet['leptomeningitis'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'meningitis', 'cerebrospinal_meningitis', 'epidemic_meningitis', 'brain_fever', 'cerebrospinal_fever']
senticnet['lepton'] = ['0.02', '0', '0', '0.087', '#joy', '#admiration', 'positive', '0.053', 'elementary_particle', 'neutron', 'electron', 'proton', 'subatomic_particle']
senticnet['leptospermum'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['leptospira_biflexa'] = ['0.995', '-0.71', '0', '0.647', '#joy', '#surprise', 'positive', '0.784', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['leptotene'] = ['0.792', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.81', 'dispersing_phase', 'generation', 'phase', 'safe_period', 'diplotene']
senticnet['leresis'] = ['0.916', '-0.87', '0', '0.79', '#joy', '#surprise', 'positive', '0.861', 'garrulity', 'garrulousness', 'loquaciousness', 'loquacity', 'talkativeness']
senticnet['lerne'] = ['-0.85', '0', '0.583', '-0.71', '#sadness', '#disgust', 'negative', '-0.71', 'muddle', 'rummage', 'mare_nest', 'welter', 'clutter']
senticnet['lesbian'] = ['0.191', '0.168', '-0.16', '0.131', '#joy', '#interest', 'positive', '0.082', 'female', 'lady', 'woman', 'wench', 'bride']
senticnet['lesbian_affair'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'liaison', 'affair', 'involvement']
senticnet['lesbian_feminist'] = ['-0.97', '0', '0.971', '-0.84', '#sadness', '#anger', 'negative', '-0.93', 'fail', 'betray', 'disappoint', 'cross', 'disillusion']
senticnet['lesbian_gay'] = ['-0.97', '0', '0.971', '-0.84', '#sadness', '#anger', 'negative', '-0.93', 'fail', 'betray', 'disappoint', 'cross', 'disillusion']
senticnet['lesbian_relationship'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'liaison', 'affair', 'involvement']
senticnet['lesbian_sex'] = ['0.081', '0.095', '0', '0', '#joy', '#interest', 'positive', '0.088', 'build_tower_out_toy_block', 'a_lot_of_sex', 'play_hockey', 'play_baseball', 'play_football']
senticnet['lesbianism'] = ['0.807', '-0.88', '0', '0.718', '#joy', '#surprise', 'positive', '0.802', 'sex', 'inversion', 'sexual_inversion', 'gayness', 'sapphism']
senticnet['lesch_nyhan_syndrome'] = ['-0.57', '0', '0.973', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['leschenaultii'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'death_adder', 'ophidian', 'serpent', 'elapid', 'taipan']
senticnet['lescol'] = ['-0.72', '0', '0.973', '-0.57', '#sadness', '#anger', 'negative', '-0.76', 'corpulency', 'blubber', 'fat', 'embonpoint', 'stoutness']
senticnet['lesion'] = ['0', '0.712', '0', '0.931', '#interest', '#admiration', 'positive', '0.822', 'hyperplasia', 'nodule', 'goiter', 'nodular', 'carcinoma']
senticnet['lesney'] = ['0.087', '0.113', '-0.08', '0', '#joy', '#interest', 'positive', '0.038', 'find_match', 'strike_against', 'cigarette_lighter', 'match', 'use_lighter']
senticnet['lesperance'] = ['0.746', '-0.88', '0', '0.723', '#joy', '#surprise', 'positive', '0.783', 'strike', 'run_into', 'hit', 'impinge_on', 'collide_with']
senticnet['less'] = ['-0.90', '0', '0.278', '-0.99', '#sadness', '#disgust', 'negative', '-0.72', 'few', 'little', 'minus_sign', 'negative', 'minus']
senticnet['less-lethal'] = ['0', '0', '0.916', '-0.93', '#anger', '#disgust', 'negative', '-0.92', 'non-lethal', 'lethal', 'nonlethal', 'less-than-lethal', 'incapacitating']
senticnet['less-than-lethal'] = ['0', '0', '0.91', '-0.93', '#anger', '#disgust', 'negative', '-0.92', 'non-lethal', 'lethal', 'less-lethal', 'nonlethal', 'incapacitating']
senticnet['less_affluent'] = ['0.973', '0.707', '0', '0', '#joy', '#interest', 'positive', '0.84', 'money', 'down_payment', 'much_money', 'slogan', 'take_out_loan']
senticnet['less_apt'] = ['0.869', '0.807', '0', '0', '#joy', '#interest', 'positive', '0.838', 'appropriate', 'suitable', 'proper', 'liable', 'likely']
senticnet['less_attractive'] = ['0.817', '-0.62', '0', '0.62', '#joy', '#surprise', 'positive', '0.688', 'quality', 'fascination', 'allurement', 'allure', 'temptingness']
senticnet['less_bulky'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'carbon_fiber', 'aluminium', 'light', 'thin', 'performant']
senticnet['less_costly'] = ['-0.12', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.09', 'painful', 'uncomfortable', 'violent', 'fun_learn_play', 'expensive']
senticnet['less_dogmatic'] = ['0.633', '-0.92', '0', '0.869', '#surprise', '#admiration', 'positive', '0.808', 'advocate', 'bigot', 'dogmatist', 'partisan', 'advocator']
senticnet['less_enthusiastic'] = ['-0.62', '0', '0.988', '-0.59', '#sadness', '#anger', 'negative', '-0.73', 'motley', 'mixture', 'variety', 'assortment', 'alphabet_soup']
senticnet['less_excusable'] = ['-0.56', '0', '0.828', '-0.68', '#anger', '#disgust', 'negative', '-0.69', 'abruptness', 'precipitateness', 'precipitousness', 'hastiness', 'precipitancy']
senticnet['less_expensive'] = ['-0.04', '0.172', '-0.05', '0.057', '#interest', '#admiration', 'positive', '0.034', 'carry_money', 'put_money', 'require_signature', 'store_money', 'keep_money']
senticnet['less_fortunate'] = ['0.71', '0.882', '0', '0.9', '#interest', '#admiration', 'positive', '0.831', 'positive_reinforcement', 'brownie_point', 'benefit', 'give', 'gratification']
senticnet['less_frequent'] = ['0', '0.188', '-0.04', '0.191', '#interest', '#admiration', 'positive', '0.111', 'usual_ordinary', 'normal', 'typical', 'usual', 'daily_routine']
senticnet['less_frequently'] = ['0', '0.188', '-0.04', '0.191', '#interest', '#admiration', 'positive', '0.111', 'usual_ordinary', 'normal', 'typical', 'usual', 'daily_routine']
senticnet['less_hassle'] = ['-0.59', '0.162', '0.221', '-0.50', '#sadness', '#disgust', 'negative', '-0.28', 'annoy', 'harass', 'provoke', 'beset', 'chivvy']
senticnet['less_hectic'] = ['0', '0', '0', '-0.76', '#disgust', '#disgust', 'negative', '-0.76', 'fevered', 'feverish', 'feverishly', 'buck_fever', 'expectantly']
senticnet['less_hospitable'] = ['-0.56', '0', '0.814', '-0.88', '#anger', '#disgust', 'negative', '-0.75', 'unfriendly', 'barren', 'bleak', 'desolate', 'wild']
senticnet['less_item'] = ['-0.76', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'small_shop', 'carry_goods', 'hold_flower', 'buy_goods', 'hold_grocery']
senticnet['less_judgmental'] = ['0', '0.985', '0', '0.794', '#interest', '#admiration', 'positive', '0.889', 'patient', 'bear_up', 'tolerate', 'endurable', 'swallow']
senticnet['less_likely'] = ['0', '0.37', '-0.27', '0.283', '#interest', '#admiration', 'positive', '0.126', 'thoughtful', 'attitude', 'swing', 'certainty', 'type_of_bear']
senticnet['less_marine_opisthobranch'] = ['0.969', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.772', 'aspheric', 'aspherical', 'prolate', 'rounded', 'crescent']
senticnet['less_optimistic'] = ['-0.70', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.75', 'pessimism', 'pessimistically', 'dispiritedly', 'hopelessness', 'hopelessly']
senticnet['less_permeable'] = ['-0.52', '0', '0.596', '-0.76', '#anger', '#disgust', 'negative', '-0.62', 'retention', 'retentiveness', 'retentivity', 'solidness', 'nonabsorbency']
senticnet['less_potent'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'imperium', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['less_profitable'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'profitable', 'moneymaking', 'gainful', 'paying', 'paid']
senticnet['less_prone'] = ['-0.69', '0', '0.516', '-0.94', '#sadness', '#disgust', 'negative', '-0.71', 'insubordinate', 'recalcitrant', 'obstreperous', 'defiant', 'resistive']
senticnet['less_pulmonate_gastropod'] = ['0.969', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.772', 'aspheric', 'aspherical', 'prolate', 'rounded', 'crescent']
senticnet['less_quality'] = ['0.057', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'cheap_stuff', 'save_money', 'discount_store', 'occasion', 'poor_people']
senticnet['less_remunerative'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'lucrative', 'moneymaking', 'profitable', 'paying', 'paid']
senticnet['less_saturated_fat'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'grill', 'cook_grill', 'barbecue', 'cook', 'grilled_vegetable']
senticnet['less_stressful'] = ['0', '0', '0', '-0.76', '#disgust', '#disgust', 'negative', '-0.76', 'fevered', 'feverish', 'feverishly', 'buck_fever', 'expectantly']
senticnet['less_susceptible'] = ['-0.69', '0', '0.516', '-0.94', '#sadness', '#disgust', 'negative', '-0.71', 'insubordinate', 'recalcitrant', 'obstreperous', 'defiant', 'resistive']
senticnet['less_tolerant'] = ['0', '0.985', '0', '0.794', '#interest', '#admiration', 'positive', '0.889', 'patient', 'bear_up', 'tolerate', 'endurable', 'swallow']
senticnet['less_virulent'] = ['-0.08', '-0.03', '-0.07', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'injury', 'soreness', 'pain', 'hurt', 'dislocation']
senticnet['lessee'] = ['0.621', '-0.60', '0', '0.545', '#joy', '#surprise', 'positive', '0.59', 'privilege', 'perquisite', 'public_easement', 'right_of_way', 'prerogative']
senticnet['lessen'] = ['0', '0', '0.957', '-0.72', '#anger', '#disgust', 'negative', '-0.83', 'damage', 'sever', 'ruin', 'demolish', 'tear']
senticnet['lessen_competition'] = ['0.658', '-0.52', '0', '0.626', '#joy', '#admiration', 'positive', '0.602', 'point_in_time', 'threshold', 'birth', 'commencement', 'incipience']
senticnet['lessened'] = ['-0.97', '0', '0.618', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'dicky', 'impaired', 'dickey', 'diminished']
senticnet['lesser'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'less', 'small', 'smaller', 'unimportant', 'poor']
senticnet['lesser-known'] = ['-0.81', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.78', 'little-known', 'obscure', 'undiscovered', 'little-used', 'unheralded']
senticnet['lesser_amount'] = ['-0.04', '0.268', '0', '0.159', '#interest', '#admiration', 'positive', '0.127', 'total', 'amount', 'a_lot', 'aggregate', 'bill']
senticnet['lesser_burdock'] = ['0.677', '-0.52', '0', '0.77', '#joy', '#admiration', 'positive', '0.659', 'subshrub', 'suffrutex', 'common_burdock', 'clotbur', 'great_burdock']
senticnet['lesser_butterfly_orchid'] = ['-0.79', '0', '0.837', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'sunray', 'wilding', 'sagebrush_buttercup', 'wildflower', 'pasque_flower']
senticnet['lesser_celandine'] = ['-0.52', '0', '0.527', '-0.78', '#anger', '#disgust', 'negative', '-0.61', 'herb', 'black_snakeroot', 'heartleaf', 'asarabacca', 'herbaceous_plant']
senticnet['lesser_centaury'] = ['-0.90', '0', '0.72', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'broad_leaved_centaury', 'rosita', 'tufted_centaury', 'centaury', 'seaside_centaury']
senticnet['lesser_charge'] = ['0.654', '-0.59', '0', '0.774', '#joy', '#admiration', 'positive', '0.673', 'instrument', 'indictment', 'murder_indictment', 'true_bill', 'legal_document']
senticnet['lesser_occipital_nerve'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['lesser_spearwort'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'cattail', 'nailrod', 'reed_mace', 'reedmace']
senticnet['lesser_trochanter'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'above_leg', 'joint', 'hip', 'knee']
senticnet['lesson'] = ['0.191', '0.168', '-0.16', '0.131', '#joy', '#interest', 'positive', '0.082', 'school_session', 'school_class', 'school_course', 'academy', 'college_class']
senticnet['let'] = ['-0.07', '0.345', '-0.23', '0.395', '#interest', '#admiration', 'positive', '0.108', 'permission', 'allow', 'suggest', 'take', 'limit_time']
senticnet['let_alone'] = ['-0.70', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.71', 'devout', 'earnestness', 'earnestly', 'seriousness', 'in_earnest']
senticnet['let_alone_thrive'] = ['0.855', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.848', 'stay_alive', 'get_oxygen', 'exist', 'alive', 'crave']
senticnet['let_body_recover'] = ['0.854', '0', '0', '0.853', '#joy', '#admiration', 'positive', '0.853', 'feel_refreshed', 'get_rest', 'rejuvenation', 'heal_injury', 'recuperate']
senticnet['let_down'] = ['-0.06', '0', '-0.16', '0', '#sadness', '#fear', 'negative', '-0.11', 'form_sentence', 'go_scuba_dive', 'cry_help', 'smoke_cigar', 'embarass']
senticnet['let_em'] = ['-0.04', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.05', 'stop_talk', 'pay_attention', 'dry_mouth', 'hear_sound', 'able_hear']
senticnet['let_er_rip'] = ['-0.78', '0', '0.915', '-0.99', '#anger', '#disgust', 'negative', '-0.89', 'bodge', 'botch_up', 'flub', 'ball_up', 'go_wrong']
senticnet['let_go'] = ['-0.03', '0.119', '-0.04', '0.156', '#interest', '#admiration', 'positive', '0.047', 'water_drip', 'fall', 'little_water', 'forgive', 'rain_water']
senticnet['let_go_of'] = ['0.693', '-0.52', '0', '0.992', '#joy', '#admiration', 'positive', '0.737', 'release', 'unlock', 'let_go', 'relinquish', 'disengage']
senticnet['let_in'] = ['0', '0.175', '-0.13', '0.204', '#interest', '#admiration', 'positive', '0.083', 'door', 'control_access', 'gate', 'corridor', 'admission']
senticnet['let_know'] = ['0', '0', '0.084', '-0.06', '#anger', '#disgust', 'negative', '-0.07', 'make_peace', 'measure_angle', 'make_easy', 'listen_heart', 'solo']
senticnet['let_loose'] = ['0.691', '-0.97', '0', '0.771', '#surprise', '#admiration', 'positive', '0.812', 'squeal', 'blate', 'churr', 'honk', 'sibilate']
senticnet['let_off_steam'] = ['0.18', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.097', 'reduce_stress', 'recover_illness', 'lower_blood_pressure', 'make_feel_good', 'serenity']
senticnet['let_out'] = ['0.602', '-0.96', '0', '0.756', '#surprise', '#admiration', 'positive', '0.775', 'squeal', 'blate', 'churr', 'honk', 'sibilate']
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senticnet['lethal_strain'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'leptomeningitis', 'cerebrospinal_meningitis', 'meningitis', 'brain_fever', 'cerebrospinal_fever']
senticnet['lethally'] = ['0', '0', '-0.71', '0', '#fear', '#fear', 'negative', '-0.71', 'hazardous', 'violent', 'take_life', 'threat', 'lethal']
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senticnet['letter_opener'] = ['0.888', '0.947', '0', '0', '#joy', '#interest', 'positive', '0.917', 'thumb_tack', 'pin_up_girl', 'glue_stick', 'push_pin', 'pair_of_scissors']
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senticnet['letterbox'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'mailman', 'package', 'mail', 'parcel', 'letter_slot']
senticnet['letterhead'] = ['0.927', '0.742', '0', '0.879', '#joy', '#admiration', 'positive', '0.849', 'letter', 'postman', 'stamp', 'arrive_mail', 'envelope']
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senticnet['letting_go'] = ['0', '0.263', '-0.14', '0.295', '#interest', '#admiration', 'positive', '0.139', 'keep', 'hold', 'retain', 'hold_stuff', 'upkeep']
senticnet['letting_loose'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amok', 'murderousness', 'misocainea', 'misogyny']
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senticnet['lettuce_leaf'] = ['0.937', '0', '0', '0.828', '#joy', '#admiration', 'positive', '0.883', 'bakery', 'produce_section', 'thanksgiving_dinner', 'dinner_meal', 'fast_food']
senticnet['leucaena'] = ['0.061', '0.093', '0.037', '0.114', '#interest', '#admiration', 'positive', '0.058', 'whole_grain', 'food_ingredient', 'staple_food', 'starchy_food', 'food_grain']
senticnet['leucaena_leucocephala'] = ['0.826', '0.787', '0', '0', '#joy', '#interest', 'positive', '0.806', 'fig', 'baby_flower', 'carpel', 'strawberry', 'star_anise']
senticnet['leucippe'] = ['-0.87', '0', '0.599', '-0.71', '#sadness', '#disgust', 'negative', '-0.73', 'hysteria', 'fury', 'delirium', 'frenzy', 'craze']
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senticnet['leucopogon'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['leucorrhoea'] = ['-0.73', '0', '0.555', '-0.90', '#sadness', '#disgust', 'negative', '-0.73', 'aching', 'hurting', 'labor_pain', 'neuralgia', 'neuralgy']
senticnet['leukaemia'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['leukemia'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['leukocyte'] = ['0.602', '-0.73', '0', '0.942', '#surprise', '#admiration', 'positive', '0.76', 'eosinophil', 'leucocyte', 'white_blood_cell', 'white_cell', 'white_blood_corpuscle']
senticnet['leukodystrophy'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'dystrophy', 'myotonic_dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['leukoencephalopathy'] = ['-0.75', '0', '0.515', '-0.93', '#sadness', '#disgust', 'negative', '-0.73', 'sclerosis', 'induration', 'degenerative_disorder', 'autoimmune_disease', 'disseminated_sclerosis']
senticnet['leukotriene'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'chemical_irritant', 'gingerol', 'piperine', 'pepper_spray']
senticnet['leur_demande'] = ['0.667', '-0.98', '0', '0.61', '#joy', '#surprise', 'positive', '0.753', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['leuven'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'feudal_lord', 'lord', 'master', 'overlord', 'king']
senticnet['lev_landau'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'general_relativity', 'general_theory_of_relativity', 'relativity', 'special_relativity', 'special_theory_of_relativity']
senticnet['lev_manovich'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'indirectness', 'mediateness', 'characteristic', 'allusiveness']
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senticnet['level'] = ['0.763', '0.921', '0', '0', '#joy', '#interest', 'positive', '0.842', 'yardstick', 'next_level', 'scale', 'measure_tool', 'same_level']
senticnet['level_one'] = ['0.039', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'lower_floor', 'floor', 'ground_floor', 'hallway', 'first_floor']
senticnet['level_trough'] = ['-0.12', '-0.05', '0', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'diminish', 'reduce', 'lower', 'lower_music', 'decrease']
senticnet['levelheaded'] = ['0.834', '-0.60', '0', '0.688', '#joy', '#admiration', 'positive', '0.71', 'commonsense', 'sound', 'healthy', 'sensible', 'commonsensible']
senticnet['leveling_off'] = ['0.05', '0.151', '0', '0.159', '#interest', '#admiration', 'positive', '0.12', 'glamour', 'stylish', 'style', 'clothes_style', 'fancy_clothes']
senticnet['levelling_off'] = ['0.05', '0.151', '0', '0.159', '#interest', '#admiration', 'positive', '0.12', 'glamour', 'stylish', 'style', 'clothes_style', 'fancy_clothes']
senticnet['lever'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'peavey']
senticnet['lever_arm'] = ['0', '0.142', '0', '0.125', '#interest', '#admiration', 'positive', '0.134', 'weight_machine', 'power_drill', 'screw', 'screwdriver', 'air_pump']
senticnet['leverage'] = ['0.758', '0', '0', '0.936', '#joy', '#admiration', 'positive', '0.847', 'influence', 'pry', 'pivot', 'rigger', 'advantage']
senticnet['leverpostej'] = ['0.52', '-0.69', '0', '0.639', '#surprise', '#admiration', 'positive', '0.617', 'black_bread', 'pumpernickel', 'limpa', 'bread', 'breadstuff']
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senticnet['levett'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'bodyguard', 'outrider', 'bodyguard_of_guard', 'beefeater', 'defender']
senticnet['levett_family'] = ['0.049', '0.16', '0', '0.155', '#interest', '#admiration', 'positive', '0.121', 'castle', 'palace', 'medieval', 'aristocrat', 'throne']
senticnet['leviable'] = ['-0.08', '0.358', '-0.20', '0.237', '#interest', '#admiration', 'positive', '0.077', 'extortion', 'money_due', 'afford', 'bribe', 'owe']
senticnet['leviathan'] = ['-0.93', '0', '0.544', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'mutant', 'variation', 'sport', 'lusus_naturae', 'mutation']
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senticnet['levirate'] = ['0.903', '0.684', '0', '0.791', '#joy', '#admiration', 'positive', '0.793', 'relative', 'family_member', 'relationship', 'family', 'mother']
senticnet['levitate'] = ['0.037', '0.214', '-0.12', '0.237', '#interest', '#admiration', 'positive', '0.09', 'raise_up', 'upward_movement', 'move_upward', 'rise_up', 'come_up']
senticnet['levitating'] = ['-0.83', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.79', 'friction-free', 'stress-free', 'frictionless', 'noiseless', 'effortless']
senticnet['leviticus'] = ['0', '0.854', '0', '0.962', '#interest', '#admiration', 'positive', '0.908', 'book_of_leviticus', 'law', 'christian', 'god', 'think']
senticnet['levity'] = ['0.897', '0', '0', '0', '#joy', '#joy', 'positive', '0.897', 'gratefully', 'appreciatively', 'self-pride', 'conceit', 'self-love']
senticnet['levodopa_carbidopa'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['levothyroxine'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'spironolactone', 'adrenal_cortical_steroid', 'antihypertensive', 'antihypertensive_drug']
senticnet['levothyroxine_sodium'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'spironolactone', 'adrenal_cortical_steroid', 'antihypertensive', 'antihypertensive_drug']
senticnet['lew_rockwell'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'economic_libertarian', 'civil_libertarian', 'advocate', 'advocator', 'proponent']
senticnet['lewd'] = ['0', '0', '-0.98', '-0.91', '#fear', '#disgust', 'negative', '-0.95', 'common', 'harlot', 'lay', 'profanity', 'salacious']
senticnet['lewdly'] = ['-0.74', '0', '0.677', '-0.77', '#sadness', '#disgust', 'negative', '-0.73', 'whore', 'jazz', 'hump', 'bonk', 'screw']
senticnet['lewdness'] = ['0', '0', '0.746', '-0.88', '#anger', '#disgust', 'negative', '-0.81', 'scape', 'libidinous', 'salacity', 'unchastity', 'lewd']
senticnet['lewy_body_dementia'] = ['-0.94', '0', '0.821', '-0.75', '#sadness', '#anger', 'negative', '-0.84', 'predementedness', 'insanity', 'dementedness', 'senile_psychosis', 'alcoholic_dementia']
senticnet['lex_luthor'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'only_bird']
senticnet['lex_regia'] = ['-0.81', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.79', 'stifle', 'forgotten', 'old', 'touch_ground', 'seldom']
senticnet['lexica'] = ['-0.05', '0.084', '0', '0.062', '#interest', '#admiration', 'positive', '0.03', 'vocabulary', 'translation', 'book_library', 'book_poetry', 'book_genre']
senticnet['lexical_database'] = ['-0.05', '0.084', '0', '0.062', '#interest', '#admiration', 'positive', '0.03', 'vocabulary', 'translation', 'book_library', 'book_poetry', 'book_genre']
senticnet['lexical_phonology'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'integrative', 'consolidative', 'unifying', 'combinatorial']
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senticnet['lexically'] = ['0', '0.8', '0', '0.964', '#interest', '#admiration', 'positive', '0.882', 'semantically', 'grammatically', 'phonetically', 'morphologically', 'syntactically']
senticnet['lexicographer'] = ['0.7', '0.821', '0', '0', '#joy', '#interest', 'positive', '0.76', 'encyclopedia', 'compiler', 'writer', 'lisp_compiler', 'fortran_compiler']
senticnet['lexicon'] = ['-0.05', '0.084', '0', '0.062', '#interest', '#admiration', 'positive', '0.03', 'vocabulary', 'translation', 'book_library', 'book_poetry', 'book_genre']
senticnet['lexus_dealership'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'sell_car', 'truck_stop', 'vehicle', 'insurance', 'car']
senticnet['lexus_suv'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'cable_car', 'baggage_compartment', 'subway', 'amusement_park', 'terminal']
senticnet['lexx'] = ['-0.74', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.80', 'casualty', 'extermination', 'cause_death', 'abduction', 'die']
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senticnet['leyte'] = ['0.084', '-0.05', '0.013', '0', '#joy', '#surprise', 'positive', '0.043', 'carnival_ride', 'carny', 'fortune_teller', 'horseback', 'ferris_wheel']
senticnet['leyte_gulf'] = ['0.084', '-0.05', '0.013', '0', '#joy', '#surprise', 'positive', '0.043', 'carnival_ride', 'carny', 'fortune_teller', 'horseback', 'ferris_wheel']
senticnet['lezak'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'chlorine', 'lifeguard', 'still_water', 'large_body', 'shallow_water']
senticnet['lfgar'] = ['-0.92', '0', '0.659', '-0.81', '#sadness', '#disgust', 'negative', '-0.8', 'unripe', 'napping', 'off_guard', 'off_one_guard', 'unprepared']
senticnet['lfgifu'] = ['-0.92', '0', '0.659', '-0.81', '#sadness', '#disgust', 'negative', '-0.8', 'unripe', 'napping', 'off_guard', 'off_one_guard', 'unprepared']
senticnet['lgv'] = ['-0.54', '0', '0.54', '-0.54', '#sadness', '#disgust', 'negative', '-0.54', 'venereal_disease', 'venereal_infection', 'social_disease', 'sexually_transmitted_disease', 'dose']
senticnet['lhakpa'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['lhotse'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['li_fraumeni_syndrome'] = ['0.504', '-0.93', '0', '0.797', '#surprise', '#admiration', 'positive', '0.747', 'diabetes', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['li_pendens'] = ['0.804', '-0.99', '0', '0.507', '#joy', '#surprise', 'positive', '0.768', 'pending', 'civil_action', 'counterclaim', 'custody', 'action_at_law']
senticnet['liability'] = ['0', '0', '0.824', '-0.72', '#anger', '#disgust', 'negative', '-0.77', 'ratability', 'susceptibility', 'susceptibleness', 'taxability', 'rateability']
senticnet['liability_insurance_policy'] = ['0.73', '-0.56', '0', '0.621', '#joy', '#admiration', 'positive', '0.64', 'amnesty', 'unsusceptibility', 'diplomatic_immunity', 'exemption', 'impunity']
senticnet['liable'] = ['0.861', '0.801', '0', '0', '#joy', '#interest', 'positive', '0.831', 'appropriate', 'suitable', 'proper', 'apt', 'likely']
senticnet['liaise'] = ['0.918', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.914', 'medium', 'intermediate', 'intercede', 'average', 'arbitrate']
senticnet['liaise_with'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'sexual_relationship', 'affair', 'involvement']
senticnet['liaises_with'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'sexual_relationship', 'affair', 'involvement']
senticnet['liaising_with'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'sexual_relationship', 'affair', 'involvement']
senticnet['liaison'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'sexual_relationship', 'affair', 'involvement']
senticnet['liaison_between'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'sexual_relationship', 'affair', 'involvement']
senticnet['liaison_office'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'sexual_relationship', 'affair', 'involvement']
senticnet['liaison_officer'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'sexual_relationship', 'affair', 'involvement']
senticnet['liaisons_between'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'sexual_relationship', 'affair', 'involvement']
senticnet['liam_walsh'] = ['0.833', '-0.97', '0', '0.91', '#surprise', '#admiration', 'positive', '0.906', 'combatant', 'fencer', 'belligerent', 'fighter', 'scrapper']
senticnet['liar'] = ['-0.77', '0', '0.742', '-0.80', '#sadness', '#disgust', 'negative', '-0.77', 'lose_election', 'politician', 'government', 'prosecutor', 'elect_person']
senticnet['liasing_with'] = ['0', '0.084', '0', '0', '#interest', '#interest', 'positive', '0.084', 'call_restaurant', 'reservation', 'reserve', 'dinner', 'reserved']
senticnet['liason'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'sexual_relationship', 'affair', 'involvement']
senticnet['liason_between'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'sexual_relationship', 'affair', 'involvement']
senticnet['lib'] = ['0.83', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.91', 'liberal', 'grit', 'liberally', 'tolerant', 'liberation']
senticnet['libation'] = ['0.807', '-0.92', '0', '0.796', '#joy', '#surprise', 'positive', '0.841', 'agape', 'confirmation', 'religious_ritual', 'rite', 'service']
senticnet['libby_prison'] = ['-0.78', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.87', 'jailbird', 'captive', 'imprisoned', 'convict', 'inmate']
senticnet['libel'] = ['0.907', '-0.56', '0', '0.721', '#joy', '#admiration', 'positive', '0.73', 'assassinate', 'charge', 'accuse', 'malign', 'asperse']
senticnet['libel_action'] = ['0.682', '-0.67', '0', '0.588', '#joy', '#surprise', 'positive', '0.648', 'lawful', 'civil_action', 'counterclaim', 'occurrence', 'li_pendens']
senticnet['libel_suit'] = ['0.682', '-0.67', '0', '0.588', '#joy', '#surprise', 'positive', '0.648', 'lawful', 'civil_action', 'counterclaim', 'occurrence', 'li_pendens']
senticnet['libeller'] = ['-0.63', '0', '0.581', '-0.84', '#sadness', '#disgust', 'negative', '-0.68', 'malignment', 'vilification', 'smear', 'character_assassination', 'libel']
senticnet['liberal'] = ['0.83', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.905', 'lib', 'grit', 'liberally', 'tolerant', 'liberation']
senticnet['liberal_activist'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'economic_libertarian', 'libertarian', 'advocate', 'advocator', 'proponent']
senticnet['liberal_art_education'] = ['0', '0.097', '0', '0.058', '#interest', '#admiration', 'positive', '0.077', 'occupation', 'career', 'tradesman', 'vocation', 'important_job']
senticnet['liberal_candidate'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'couch', 'stool', 'coach_seat']
senticnet['liberal_capitalism'] = ['-0.89', '0', '0.678', '-0.62', '#sadness', '#anger', 'negative', '-0.73', 'undisciplined', 'anarchic', 'uncontrolled', 'irrepressible', 'uncontrollable']
senticnet['liberal_constitutionalism'] = ['-0.54', '0', '0.979', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'unlikeness', 'dissimilitude', 'difference', 'heterology', 'dissimilarity']
senticnet['liberal_democracy'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'ascendance', 'absolutism', 'ascendancy', 'tyrant']
senticnet['liberal_democratic'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'adventurer', 'pioneer', 'correspondent', 'humankind']
senticnet['liberal_faction'] = ['-0.50', '0', '0.989', '-0.91', '#anger', '#disgust', 'negative', '-0.80', 'camp', 'pack', 'clique', 'coterie', 'ingroup']
senticnet['liberal_humanism'] = ['0.995', '-0.69', '0', '0.528', '#joy', '#surprise', 'positive', '0.74', 'grasp', 'hug', 'nestle', 'snuggle', 'grip']
senticnet['liberal_ideology'] = ['0', '0', '0', '0.986', '#admiration', '#admiration', 'positive', '0.986', 'idea', 'decision', 'consider', 'polarize', 'make_right_decision']
senticnet['liberal_individualism'] = ['0', '0', '0', '0.986', '#admiration', '#admiration', 'positive', '0.986', 'idea', 'decision', 'consider', 'polarize', 'make_right_decision']
senticnet['liberal_internationalism'] = ['0.946', '-0.54', '0', '0.774', '#joy', '#admiration', 'positive', '0.754', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['liberal_internationalist'] = ['0.946', '-0.54', '0', '0.774', '#joy', '#admiration', 'positive', '0.754', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['liberal_judicial_activism'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'economic_libertarian', 'libertarian', 'advocate', 'advocator', 'proponent']
senticnet['liberal_orthodoxy'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'rudeness', 'discourtesy', 'insolence', 'ccheekiness', 'cheekiness']
senticnet['liberal_party_candidate'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'couch', 'stool', 'coach_seat']
senticnet['liberal_party_politician'] = ['0', '0.868', '0', '0.853', '#interest', '#admiration', 'positive', '0.861', 'country_leader', 'prime_minister', 'president', 'elect_official', 'leader']
senticnet['liberal_politician'] = ['0', '0.868', '0', '0.853', '#interest', '#admiration', 'positive', '0.861', 'country_leader', 'prime_minister', 'president', 'elect_official', 'leader']
senticnet['liberal_preselection'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'couch', 'stool', 'coach_seat']
senticnet['liberal_protestantism'] = ['-0.57', '0', '0.791', '-0.74', '#anger', '#disgust', 'negative', '-0.70', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['liberal_statesman'] = ['0', '0.868', '0', '0.853', '#interest', '#admiration', 'positive', '0.861', 'country_leader', 'prime_minister', 'president', 'elect_official', 'leader']
senticnet['liberal_theology'] = ['-0.83', '0', '0.648', '-0.60', '#sadness', '#anger', 'negative', '-0.69', 'infallibility', 'dependability', 'dependableness', 'reliability', 'reliableness']
senticnet['liberalism'] = ['0.862', '0.733', '0', '0', '#joy', '#interest', 'positive', '0.797', 'economic', 'law', 'liberty', 'communism', 'movement']
senticnet['liberality'] = ['-0.91', '0', '0.598', '-0.52', '#sadness', '#anger', 'negative', '-0.68', 'parsimonious', 'stingy', 'scrimy', 'beggarly', 'chintzy']
senticnet['liberally'] = ['0.795', '0.774', '0', '0', '#joy', '#interest', 'positive', '0.784', 'liberal', 'large', 'broadly', 'bounty', 'generously']
senticnet['liberate'] = ['0.601', '0.647', '-0.26', '0.322', '#joy', '#interest', 'positive', '0.326', 'break_free', 'set_free', 'relief', 'freedom', 'liberty']
senticnet['liberation'] = ['0.85', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.88', 'release_energy', 'release_stress', 'release_tension', 'release', 'relief']
senticnet['liberation_struggle'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'patriotism', 'homeland']
senticnet['libertarian'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'economic_libertarian', 'civil_libertarian', 'advocate', 'advocator', 'proponent']
senticnet['libertarian_ideology'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'economic_libertarian', 'civil_libertarian', 'advocate', 'advocator', 'proponent']
senticnet['libertarian_movement'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'economic_libertarian', 'civil_libertarian', 'advocate', 'advocator', 'proponent']
senticnet['libertarian_party'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'economic_libertarian', 'civil_libertarian', 'advocate', 'advocator', 'proponent']
senticnet['libertarian_perspective'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'economic_libertarian', 'civil_libertarian', 'advocate', 'advocator', 'proponent']
senticnet['libertarian_philosophy'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'economic_libertarian', 'civil_libertarian', 'advocate', 'advocator', 'proponent']
senticnet['libertarian_socialism'] = ['0.563', '-0.72', '0', '0.644', '#surprise', '#admiration', 'positive', '0.645', 'idiom', 'idiomatic_expression', 'phrasal_idiom', 'set_phrase', 'phrase']
senticnet['libertarian_socialist'] = ['0.563', '-0.72', '0', '0.644', '#surprise', '#admiration', 'positive', '0.645', 'idiom', 'idiomatic_expression', 'phrasal_idiom', 'set_phrase', 'phrase']
senticnet['libertarianism'] = ['0', '0.878', '0', '0.967', '#interest', '#admiration', 'positive', '0.922', 'paternalistic', 'authoritarianism', 'collectivism', 'individualism', 'paternalism']
senticnet['libertine'] = ['-0.54', '0', '0.691', '-0.96', '#anger', '#disgust', 'negative', '-0.73', 'slut', 'trollop', 'adulterer', 'adulteress', 'fornicatress']
senticnet['libertinism'] = ['0.995', '-0.69', '0', '0.528', '#joy', '#surprise', 'positive', '0.74', 'grasp', 'hug', 'nestle', 'snuggle', 'grip']
senticnet['liberty'] = ['0.926', '0.937', '0', '0.975', '#interest', '#admiration', 'positive', '0.946', 'monument', 'statue', 'freedom', 'idol', 'sculpture']
senticnet['libidinal'] = ['-0.78', '0', '0.542', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'hysterical_neurosis', 'hysteria', 'mental_disorder', 'mental_disturbance', 'psychological_disorder']
senticnet['libidinous'] = ['0', '0', '0.738', '-0.88', '#anger', '#disgust', 'negative', '-0.81', 'scape', 'lewdness', 'salacity', 'unchastity', 'lewd']
senticnet['libidinousness'] = ['-0.71', '0', '0.898', '0', '#sadness', '#anger', 'negative', '-0.80', 'indulgence', 'lecherous', 'lubricity', 'lecher', 'lechery']
senticnet['libido'] = ['0.655', '-0.87', '0', '0.595', '#joy', '#surprise', 'positive', '0.709', 'erection', 'excitement', 'arousal', 'kill_brain_cell', 'feel_relieved']
senticnet['librarian'] = ['0.762', '0', '0', '0.979', '#joy', '#admiration', 'positive', '0.871', 'contestant', 'cook_book', 'bookmark', 'a_lot_of_books', 'graduate_student']
senticnet['librarianship'] = ['0', '0.097', '0', '0.058', '#interest', '#admiration', 'positive', '0.077', 'occupation', 'career', 'tradesman', 'vocation', 'important_job']
senticnet['library'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'a_lot_of_books', 'old_testament', 'cook_book', 'magazine', 'librarian']
senticnet['library_collection'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'a_lot_of_books', 'old_testament', 'cook_book', 'magazine', 'librarian']
senticnet['library_project_imslp'] = ['0.926', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.916', 'score_goal', 'accomplish', 'finalize', 'pot', 'make_point']
senticnet['librarythe'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'a_lot_of_books', 'old_testament', 'cook_book', 'magazine', 'librarian']
senticnet['libtiff'] = ['-0.55', '0', '0.912', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'wrangle', 'dustup', 'quarrel', 'row', 'squabble']
senticnet['libwrap'] = ['-0.95', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.84', 'hold_pencil', 'food_scrap', 'paper_product', 'waste_paper', 'cover']
senticnet['licence'] = ['0', '0.757', '0', '0.811', '#interest', '#admiration', 'positive', '0.784', 'license', 'drive_license', 'find_car_key', 'start_car_engine', 'put_key_ignition']
senticnet['license'] = ['0.673', '-0.66', '0', '0.921', '#joy', '#admiration', 'positive', '0.753', 'instrument', 'building_permit', 'driver_license', 'driver_licence', 'driving_license']
senticnet['license_expire'] = ['0.857', '-0.68', '0', '0.971', '#joy', '#admiration', 'positive', '0.839', 'revitalize', 'refurbish', 'reconstruct', 'renovate', 'freshen_up']
senticnet['license_plate'] = ['0', '0.137', '0', '0.133', '#interest', '#admiration', 'positive', '0.135', 'sign', 'indication', 'post_notice', 'notice', 'red_octagon']
senticnet['license_suspension'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'steer_wheel', 'gear_shift', 'chassis', 'brake_pedal', 'air_vent']
senticnet['licensed_acupuncturist'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
senticnet['licensed_conveyancer'] = ['0.053', '0.122', '-0.07', '0.086', '#interest', '#admiration', 'positive', '0.046', 'attorney', 'lawyer', 'jury', 'decide_guilt', 'prosecutor']
senticnet['licensed_electrician'] = ['0', '0.115', '0', '0.105', '#interest', '#admiration', 'positive', '0.11', 'farrier', 'miner', 'horseshoer', 'blacksmith', 'smith']
senticnet['licensed_mortician'] = ['-0.70', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.70', 'death_row', 'sad', 'loose_end', 'car_race', 'mortuary']
senticnet['licensed_practical'] = ['-0.69', '0', '0.659', '-0.69', '#sadness', '#disgust', 'negative', '-0.68', 'help', 'paralegal', 'legal_assistant', 'paramedic', 'paramedical']
senticnet['licensed_psychologist'] = ['-0.69', '0', '0.659', '-0.69', '#sadness', '#disgust', 'negative', '-0.68', 'help', 'paralegal', 'legal_assistant', 'paramedic', 'paramedical']
senticnet['licensing_agreement'] = ['0.865', '-0.84', '0', '0.915', '#joy', '#admiration', 'positive', '0.875', 'grant', 'sale_in_gross', 'contract_of_hazard', 'adhesion_contract', 'contract_of_adhesion']
senticnet['licentious'] = ['-0.84', '0', '0.852', '0', '#sadness', '#anger', 'negative', '-0.84', 'liberal', 'libertine', 'wild', 'licentiously', 'licentiousness']
senticnet['licentiously'] = ['-0.85', '0', '0.845', '0', '#sadness', '#anger', 'negative', '-0.84', 'liberal', 'libertine', 'wild', 'licentious', 'licentiousness']
senticnet['licentiousness'] = ['-0.85', '0', '0.852', '0', '#sadness', '#anger', 'negative', '-0.85', 'liberal', 'libertine', 'wild', 'licentiously', 'licentious']
senticnet['licet'] = ['0.654', '-0.75', '0', '0.78', '#surprise', '#admiration', 'positive', '0.729', 'suit', 'cause', 'lawsuit', 'occurrence', 'countersuit']
senticnet['lichen'] = ['-0.89', '0', '0.619', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'lichen_planus', 'lichen_ruber_planus', 'skin_disease', 'disease_of_skin', 'skin_disorder']
senticnet['lichen_planus'] = ['-0.89', '0', '0.619', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'lichen', 'lichen_ruber_planus', 'skin_disease', 'disease_of_skin', 'skin_disorder']
senticnet['lichen_ruber_planus'] = ['-0.89', '0', '0.619', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'lichen_planus', 'lichen', 'skin_disease', 'disease_of_skin', 'skin_disorder']
senticnet['licit'] = ['0', '0.817', '0', '0.782', '#interest', '#admiration', 'positive', '0.799', 'true', 'rule-governed', 'legitimate', 'lawful', 'rightful']
senticnet['lick'] = ['0.074', '0', '-0.06', '0.136', '#joy', '#admiration', 'positive', '0.05', 'mastication', 'use_mouth', 'enjoy_food', 'masticate', 'savor']
senticnet['lick_creek'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['lick_finger'] = ['-0.04', '-0.00', '0.022', '-0.07', '#sadness', '#disgust', 'negative', '-0.03', 'walk_exercise', 'lick', 'impulse_shop', 'buy_clothes', 'purchase_food']
senticnet['licker'] = ['0.074', '0', '-0.06', '0.136', '#joy', '#admiration', 'positive', '0.05', 'mastication', 'use_mouth', 'enjoy_food', 'masticate', 'savor']
senticnet['licking'] = ['-0.64', '0', '0.717', '-0.98', '#anger', '#disgust', 'negative', '-0.78', 'debacle', 'lurch', 'whitewash', 'finish', 'failure']
senticnet['licking_ice_cream'] = ['0.06', '-0.03', '0.037', '0.075', '#joy', '#admiration', 'positive', '0.034', 'semicircle', 'trapezoid', 'circle', 'triangular', 'facet']
senticnet['licorice'] = ['0.965', '0.775', '0', '0.968', '#joy', '#admiration', 'positive', '0.903', 'snot', 'bad_taste', 'mouth_part', 'beautiful_smile', 'near_mouth']
senticnet['licorice_root'] = ['0.701', '-0.76', '0', '0.904', '#surprise', '#admiration', 'positive', '0.791', 'curative', 'heal', 'medicine', 'get_cured', 'take_care']
senticnet['lictor'] = ['-0.98', '0', '0.664', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'unfortunate', 'unfortunate_person', 'weeper', 'pallgriever', 'griever']
senticnet['lid'] = ['0.034', '0.07', '0.057', '0.123', '#interest', '#admiration', 'positive', '0.043', 'socket', 'cover', 'breakable', 'easy_break', 'easy_tear']
senticnet['liddle_syndrome'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'illness', 'ill', 'tension', 'bad_health', 'malignant_hypertension']
senticnet['lidless'] = ['0.805', '-0.67', '0', '0.901', '#joy', '#admiration', 'positive', '0.792', 'vigilant', 'wakeful', 'watchful', 'fly', 'sleepless']
senticnet['lie'] = ['0', '0', '-0.75', '-0.78', '#fear', '#disgust', 'negative', '-0.77', 'fallacy', 'falsehood', 'lie_court', 'cover_face', 'dishonesty']
senticnet['lie_ahead'] = ['0.844', '0', '0', '0', '#joy', '#joy', 'positive', '0.844', 'straight', 'late_time', 'preparedness', 'past_present', 'wait']
senticnet['lie_around'] = ['0.027', '0.104', '0', '0', '#joy', '#interest', 'positive', '0.066', 'turn_aircon_on', 'sit_in_shade', 'take_off_shoes', 'hold_breath', 'sit_still']
senticnet['lie_back'] = ['0.985', '0', '0', '0.713', '#joy', '#admiration', 'positive', '0.849', 'feel_little_tired', 'take_nap', 'sleepy', 'asleep', 'get_sleep']
senticnet['lie_brick'] = ['-0.04', '0.201', '-0.11', '0.17', '#interest', '#admiration', 'positive', '0.055', 'draw_blueprint', 'help_person', 'fix_car', 'make_better', 'build_house']
senticnet['lie_court'] = ['-0.21', '0.16', '-0.19', '0.057', '#sadness', '#fear', 'negative', '-0.04', 'tell_truth', 'lier', 'find_guilty', 'call_police', 'lie']
senticnet['lie_detection'] = ['-0.84', '0', '0.585', '-0.85', '#sadness', '#disgust', 'negative', '-0.76', 'subject', 'discipline', 'study', 'concern', 'research']
senticnet['lie_detector'] = ['-0.65', '0', '0.718', '-0.73', '#anger', '#disgust', 'negative', '-0.70', 'inquirer', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['lie_down'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'sleepwalk', 'wear_pyjamas', 'lay_down', 'wind_down', 'close_eye']
senticnet['lie_down_bed'] = ['0.165', '0', '0.032', '0.114', '#joy', '#admiration', 'positive', '0.082', 'good_sleep', 'shut_eye', 'place_sleep', 'use_bed', 'comfortable_place_sleep']
senticnet['lie_down_close_eye'] = ['-0.05', '-0.04', '0.054', '-0.07', '#sadness', '#disgust', 'negative', '-0.03', 'sleep', 'take_sleep_pill', 'turn_off_alarm_clock', 'lie_down_bed', 'count_sheep']
senticnet['lie_floor'] = ['-0.10', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.11', 'driving', 'fix_flat_tire', 'change_tire', 'workshop', 'car']
senticnet['lie_forrit'] = ['-0.79', '0', '0.992', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['lie_group'] = ['0.858', '0.941', '0', '0', '#joy', '#interest', 'positive', '0.899', 'mathematics', 'theory', 'analysis', 'sphere', 'example']
senticnet['lie_idle'] = ['0', '0', '0.808', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'inaction', 'lazy', 'stay', 'inactive', 'recline']
senticnet['lie_on_bed'] = ['0.877', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.924', 'sit_down', 'take_break', 'stay_bed', 'sit_chair', 'go_sleep']
senticnet['lie_on_grass'] = ['0.737', '0.787', '0', '0.915', '#interest', '#admiration', 'positive', '0.813', 'plant_tree', 'pick_flower', 'flower', 'find_flower', 'enjoy_nature']
senticnet['lie_under_oath'] = ['-0.73', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'obstruct_justice', 'get_away_crime', 'punish', 'hurt_else', 'commit_murder']
senticnet['lie_with'] = ['0.533', '-0.58', '0', '0.814', '#surprise', '#admiration', 'positive', '0.644', 'have', 'pair', 'take', 'couple', 'fornicate']
senticnet['lieben'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['lied_under_oath'] = ['-0.73', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'obstruct_justice', 'get_away_crime', 'punish', 'hurt_else', 'commit_murder']
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senticnet['life_cycle_cost'] = ['-0.02', '0.249', '0', '0.116', '#interest', '#admiration', 'positive', '0.112', 'price', 'expense', 'bill', 'value', 'worth']
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senticnet['lifeforce'] = ['0.8', '-0.97', '0', '0.512', '#joy', '#surprise', 'positive', '0.763', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
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senticnet['lifelike'] = ['0.81', '0', '0', '0.95', '#joy', '#admiration', 'positive', '0.88', 'vivid', 'graphic', 'pictorial', 'bright', 'brilliant']
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senticnet['lifelong_bachelor'] = ['0.929', '-0.79', '0', '0.915', '#joy', '#admiration', 'positive', '0.88', 'privacy', 'seclusion', 'privateness', 'solitariness', 'loneliness']
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senticnet['lifelong_immunity'] = ['0', '0', '0', '0.816', '#admiration', '#admiration', 'positive', '0.816', 'resistance', 'protection', 'untouchable', 'invulnerable', 'sacrosanct']
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senticnet['lige'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'feudal_lord', 'lord', 'master', 'overlord', 'king']
senticnet['liggett_group'] = ['-0.99', '0', '0.056', '-0.98', '#sadness', '#disgust', 'negative', '-0.67', 'smoke', 'cigarette', 'bad_air', 'pollution', 'cigar']
senticnet['light'] = ['0.085', '0.107', '-0.08', '0', '#joy', '#interest', 'positive', '0.037', 'hot_sun', 'sunbright_light', 'hot', 'hot_day', 'bright_light']
senticnet['light-duty'] = ['0', '0', '-0.86', '-0.84', '#fear', '#disgust', 'negative', '-0.85', 'medium-duty', 'heavy-duty', 'diesel-powered', 'on-highway', 'off-highway']
senticnet['light-hearted'] = ['0.993', '-0.66', '0', '0', '#joy', '#surprise', 'positive', '0.831', 'frivolity', 'serenity', 'serene', 'joyful', 'birthday_surprise']
senticnet['light_alloy'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'performant']
senticnet['light_armoured_vehicle'] = ['-0.56', '0', '0.714', '-0.89', '#anger', '#disgust', 'negative', '-0.72', 'tank', 'tank_destroyer', 'armored_car', 'armoured_car', 'armored_personnel_carrier']
senticnet['light_beam'] = ['-0.50', '0', '0.75', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'beam', 'beam_of_light', 'sunbeam', 'ray', 'ray_of_light']
senticnet['light_breeze'] = ['0', '-0.01', '0.048', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'mild', 'breeze', 'tropical_storm', 'wind', 'air_movement']
senticnet['light_brown_hair'] = ['0', '0', '0.841', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'ancient', 'many_years', 'year', 'old', 'teen']
senticnet['light_bulb'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'illuminate_room', 'give_light', 'light', 'light_source', 'lantern']
senticnet['light_candle'] = ['-0.08', '0', '-0.08', '-0.12', '#fear', '#disgust', 'negative', '-0.09', 'touch_fire', 'excessive_heat', 'blow_out_match', 'burn', 'create_light']
senticnet['light_cigar'] = ['-0.95', '0', '0.924', '-0.93', '#sadness', '#disgust', 'negative', '-0.93', 'cause_mental_illness', 'catch_fire', 'smoke_cigarette', 'age_prematurely', 'light_pipe']
senticnet['light_cigarette'] = ['-0.97', '0', '0.621', '-0.91', '#sadness', '#disgust', 'negative', '-0.83', 'smoke_cigarette', 'cancer', 'lung_cancer', 'sickness', 'cigarette']
senticnet['light_colored'] = ['0.085', '0.214', '-0.08', '0.305', '#interest', '#admiration', 'positive', '0.131', 'delicate', 'useful', 'good', 'versatile', 'beautiful']
senticnet['light_colored_clothing'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'change_tire', 'fix_flat_tire', 'play_rugby', 'wear_silly_hat', 'become_adult']
senticnet['light_complexion'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'plato', 'natural_world', 'hair', 'feature', 'stick_out']
senticnet['light_cruiser'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'submarine', 'lifeboat', 'titanic', 'large_ship', 'canoe']
senticnet['light_cyan'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'indigo', 'yellow', 'water_color', 'red', 'violet']
senticnet['light_dark'] = ['0', '0', '-0.08', '0', '#fear', '#fear', 'negative', '-0.08', 'burn_material', 'heat_house', 'heat_home', 'make_light', 'burn_fuel']
senticnet['light_diet'] = ['-0.63', '0', '0.819', '-0.99', '#anger', '#disgust', 'negative', '-0.81', 'diet', 'soft_diet', 'spoon_food', 'bland_diet', 'ulcer_diet']
senticnet['light_dim'] = ['0.067', '-0.2', '0.064', '0', '#joy', '#surprise', 'positive', '0.068', 'wand', 'long_stick', 'branch', 'twig', 'stick']
senticnet['light_drizzle'] = ['-0.87', '0', '0.91', '-0.78', '#sadness', '#anger', 'negative', '-0.85', 'storm', 'rainstorm', 'rain', 'line_storm', 'equinoctial_storm']
senticnet['light_emanating_from'] = ['-0.82', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.80', 'evil', 'darkness', 'dark', 'sun_down', 'dark_side']
senticnet['light_fire'] = ['0', '0', '-0.84', '-0.85', '#fear', '#disgust', 'negative', '-0.85', 'start_flame_fire', 'make_campfire', 'play_fire', 'start_fire', 'strike_match']
senticnet['light_fixture'] = ['0.09', '-0.03', '0.015', '0', '#joy', '#surprise', 'positive', '0.036', 'tape_player', 'dvd_player', 'tv_set', 'sit_at_theater', 'technician']
senticnet['light_flicker'] = ['-0.03', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.04', 'rabbit', 'squirrel', 'mole', 'chipmunk', 'nymph']
senticnet['light_heavyweight_bout'] = ['0.944', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.967', 'meeting', 'meet', 'match', 'encounter', 'round']
senticnet['light_heavyweight_champion'] = ['0', '0', '0', '0.834', '#admiration', '#admiration', 'positive', '0.834', 'royal_person', 'royal_title', 'top', 'master', 'rank']
senticnet['light_in_sky'] = ['0.02', '0.067', '0', '0', '#joy', '#interest', 'positive', '0.044', 'sunrise', 'begin_day', 'afternoon', 'soon', 'part_of_day']
senticnet['light_infantry_battalion'] = ['0', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.09', 'army', 'attacker', 'military', 'soldier', 'infantry']
senticnet['light_instrument'] = ['-0.03', '-0.00', '-0.04', '-0.14', '#fear', '#disgust', 'negative', '-0.05', 'medical_instrument', 'cystoscope', 'gastroscope', 'endoscope', 'sigmoidoscope']
senticnet['light_intensity'] = ['-0.93', '0', '0.698', '-0.85', '#sadness', '#disgust', 'negative', '-0.83', 'candlepower', 'intensity', 'acoustic_power', 'sound_pressure_level', 'threshold_level']
senticnet['light_machine_gun'] = ['0', '0', '0.954', '-0.74', '#anger', '#disgust', 'negative', '-0.85', 'gun', 'pistol', 'revolver', 'firearm', 'handgun']
senticnet['light_machinegun'] = ['0.957', '-0.93', '0', '0.795', '#joy', '#surprise', 'positive', '0.896', 'outdo', 'outstrip', 'outmatch', 'outgo', 'outperform']
senticnet['light_match'] = ['-0.97', '0', '0.707', '-0.9', '#sadness', '#disgust', 'negative', '-0.86', 'fire', 'burn', 'flame', 'heat', 'ash']
senticnet['light_metal'] = ['0.023', '0', '0', '0.066', '#joy', '#admiration', 'positive', '0.044', 'soft_metal', 'light_weight', 'metal_type', 'magnetic', 'lightweight_metal']
senticnet['light_microscope'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'eye', 'peeper', 'oculus_sinister']
senticnet['light_middleweight_champion'] = ['0', '0', '0', '0.834', '#admiration', '#admiration', 'positive', '0.834', 'royal_person', 'royal_title', 'top', 'master', 'rank']
senticnet['light_photophobia'] = ['0', '0', '-0.66', '0', '#fear', '#fear', 'negative', '-0.66', 'anger', 'rage', 'annoyance', 'apprehension', 'fear']
senticnet['light_pia_lot_of_light'] = ['0.789', '0.774', '0', '0', '#joy', '#interest', 'positive', '0.782', 'blow_out', 'create_light', 'burn_wood', 'burn_paper', 'heat_light']
senticnet['light_pipe'] = ['-0.95', '0', '0.924', '-0.93', '#sadness', '#disgust', 'negative', '-0.93', 'cause_mental_illness', 'catch_fire', 'smoke_cigarette', 'light_cigar', 'age_prematurely']
senticnet['light_pollution'] = ['0', '0', '0.735', '-0.70', '#anger', '#disgust', 'negative', '-0.72', 'swing_set', 'roadway_square', 'fresh_air', 'dog_poop', 'wildflower']
senticnet['light_rail_line'] = ['0', '0', '0', '0.085', '#admiration', '#admiration', 'positive', '0.085', 'use_farm', 'gate', 'door', 'doorway', 'entranceway']
senticnet['light_red'] = ['-0.05', '0', '0.037', '0', '#sadness', '#anger', 'negative', '-0.04', 'color', 'bright_color', 'secondary_color', 'red_apple', 'primary_color']
senticnet['light_reflex'] = ['-0.63', '0', '0.891', '-0.87', '#anger', '#disgust', 'negative', '-0.79', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['light_source'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'illuminate_room', 'give_light', 'light', 'light_bulb', 'lantern']
senticnet['light_soy_sauce'] = ['0.979', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.918', 'make_curry', 'cook_dinner', 'cook_meal', 'cook_curry', 'powder']
senticnet['light_up'] = ['0.821', '0.772', '0', '0', '#joy', '#interest', 'positive', '0.796', 'corrode', 'burn_up', 'burn_heat', 'dig_up', 'heat_up']
senticnet['light_up_room'] = ['0.613', '0.678', '-0.27', '0.391', '#joy', '#interest', 'positive', '0.353', 'illuminate_room', 'light', 'switch_on_light', 'create_light', 'light_in_sky']
senticnet['light_weight'] = ['0.023', '0', '0', '0.066', '#joy', '#admiration', 'positive', '0.044', 'soft_metal', 'light_metal', 'metal_type', 'magnetic', 'lightweight_metal']
senticnet['lighted'] = ['0.639', '-0.73', '0', '0.789', '#surprise', '#admiration', 'positive', '0.722', 'ablaze', 'aflame', 'afire', 'aflare', 'alight']
senticnet['lighted_candle'] = ['0', '0.232', '-0.10', '0.156', '#interest', '#admiration', 'positive', '0.094', 'light', 'eat_cake', 'birthday_party', 'candle_light', 'romantic_atmosphere']
senticnet['lighted_runway'] = ['-0.70', '0', '0.621', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'flare_path', 'airfield', 'grassland', 'runway', 'road']
senticnet['lighted_tube'] = ['0.255', '0.182', '-0.06', '0.314', '#joy', '#admiration', 'positive', '0.173', 'doctor_office', 'urethroscope', 'sigmoidoscope', 'hospital', 'check_health']
senticnet['lighten'] = ['0', '-0.00', '-0.07', '-0.06', '#fear', '#disgust', 'negative', '-0.04', 'bright_flash', 'tornado', 'dynamite_explosion', 'forest_fire', 'lightning']
senticnet['lightening'] = ['0.995', '-0.53', '0', '0.977', '#joy', '#admiration', 'positive', '0.835', 'change_of_state', 'discoloration', 'faded', 'whitening', 'change_of_color']
senticnet['lighter'] = ['0.215', '0.194', '-0.20', '0.279', '#joy', '#admiration', 'positive', '0.121', 'use_match', 'gather_kindle', 'combustible_material', 'rub_two_stick_together', 'diligence']
senticnet['lighter_air'] = ['0.02', '-0.07', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'inert_gas', 'gas', 'non-renewable_resource', 'fuel', 'noble_gas']
senticnet['lighter_fluid'] = ['0.886', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.881', 'fire_hydrant', 'stair_rail', 'christian_fundamentalist', 'extinguisher', 'reception_desk']
senticnet['lighter_skin'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'blonde', 'purple', 'violet', 'dark_light', 'hue']
senticnet['lighter_tone'] = ['0.941', '0.822', '0', '0', '#joy', '#interest', 'positive', '0.881', 'melodic_noise', 'universal_language', 'instrumental', 'musical_note', 'audio']
senticnet['lighter_water'] = ['0', '0', '-0.10', '0', '#fear', '#fear', 'negative', '-0.10', 'oxygen', 'breathable', 'inhale_air', 'hydrogen', 'air']
senticnet['lighter_weight'] = ['-0.53', '0', '0.765', '-0.94', '#anger', '#disgust', 'negative', '-0.74', 'jaggedness', 'unregularity', 'unevenness', 'irregularity', 'patchiness']
senticnet['lighthearted'] = ['0', '-0.70', '0', '0.737', '#surprise', '#admiration', 'positive', '0.72', 'light-hearted', 'tongue-in-cheek', 'irreverent', 'humorous', 'satirical']
senticnet['lightheartedness'] = ['0.69', '0.626', '-0.23', '0.317', '#joy', '#interest', 'positive', '0.35', 'insouciance', 'carefree', 'lightsomeness', 'unworried', 'carefreeness']
senticnet['lighthouse_tower'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'castle', 'bell_tower', 'spire', 'steeple', 'religious_place']
senticnet['lighting'] = ['-0.90', '0', '0.209', '-0.92', '#sadness', '#disgust', 'negative', '-0.68', 'sun_burn', 'erythema', 'fire', 'burn_skin', 'hurt_skin']
senticnet['lighting_design'] = ['-0.90', '0', '0.209', '-0.92', '#sadness', '#disgust', 'negative', '-0.68', 'sun_burn', 'erythema', 'fire', 'burn_skin', 'hurt_skin']
senticnet['lighting_fixture'] = ['-0.90', '0', '0.209', '-0.92', '#sadness', '#disgust', 'negative', '-0.68', 'sun_burn', 'erythema', 'fire', 'burn_skin', 'hurt_skin']
senticnet['lightly_armored'] = ['-0.89', '0', '0.943', '-0.9', '#sadness', '#anger', 'negative', '-0.91', 'armored', 'mailed', 'bulletproof', 'lightly_armoured', 'scaled']
senticnet['lightly_armoured'] = ['-0.89', '0', '0.94', '-0.89', '#sadness', '#anger', 'negative', '-0.91', 'armored', 'mailed', 'lightly_armored', 'bulletproof', 'scaled']
senticnet['lightly_butter'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'neat', 'kitchen_drawer', 'cutlery_drawer', 'dine_table', 'table']
senticnet['lightly_coat'] = ['-0.83', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.78', 'picaninny', 'kid', 'minor', 'tike', 'small_picaninny']
senticnet['lightly_floured_surface'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'poppy_seed', 'custard', 'bread', 'biscuit']
senticnet['lightness'] = ['0.672', '0.639', '-0.24', '0.314', '#joy', '#interest', 'positive', '0.346', 'enchant', 'euphoriant', 'euphoria', 'comfortably', 'comfortableness']
senticnet['lightning'] = ['0', '-0.00', '-0.07', '-0.06', '#fear', '#disgust', 'negative', '-0.04', 'lighten', 'tornado', 'dynamite_explosion', 'forest_fire', 'bright_flash']
senticnet['lightning_arrester'] = ['0.13', '0.097', '-0.07', '0.198', '#joy', '#admiration', 'positive', '0.087', 'jiggle', 'back_forth', 'vibrate', 'blend', 'mixture']
senticnet['lightning_flash'] = ['0', '-0.00', '-0.07', '-0.06', '#fear', '#disgust', 'negative', '-0.04', 'lighten', 'tornado', 'dynamite_explosion', 'forest_fire', 'lightning']
senticnet['lightning_storm'] = ['0', '-0.41', '-0.95', '0', '#surprise', '#fear', 'negative', '-0.26', 'blizzard', 'snow_storm', 'storm', 'danger', 'rainy']
senticnet['lightning_strike'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'firework', 'bright_flash', 'nuclear_weapon', 'earthquake', 'nuclear_bomb']
senticnet['lightoller'] = ['0', '-0.02', '0.046', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'sail_ship', 'canoe', 'ship', 'submarine', 'yacht']
senticnet['lightsaber'] = ['-0.04', '0.138', '-0.08', '0.111', '#interest', '#admiration', 'positive', '0.031', 'geometry', 'craft', 'blade_geometry', 'blade', 'collect_item']
senticnet['lightsome'] = ['0.918', '0.476', '-0.19', '0.417', '#joy', '#interest', 'positive', '0.404', 'merriment', 'cheery', 'festal', 'playfulness', 'pridefulness']
senticnet['lightsomeness'] = ['0.69', '0.626', '-0.23', '0.317', '#joy', '#interest', 'positive', '0.35', 'lightheartedness', 'insouciance', 'carefree', 'unworried', 'carefreeness']
senticnet['lightweight'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'carbon_fiber', 'aluminium', 'light', 'thin', 'performant']
senticnet['lightweight_aggregate'] = ['0', '0.127', '-0.04', '0.194', '#interest', '#admiration', 'positive', '0.092', 'concrete', 'tar', 'pavement', 'big_city_life', 'popular_building_material']
senticnet['lightweight_aluminium'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'performant']
senticnet['lightweight_aluminum'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'gold', 'bronze', 'alloy', 'medal', 'copper']
senticnet['lightweight_carbon'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'performant']
senticnet['lightweight_carbon_fiber'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'performant']
senticnet['lightweight_carbon_fibre'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'performant']
senticnet['lightweight_champion'] = ['0', '0', '0', '0.834', '#admiration', '#admiration', 'positive', '0.834', 'royal_person', 'royal_title', 'top', 'master', 'rank']
senticnet['lightweight_concrete'] = ['0', '0.127', '-0.04', '0.194', '#interest', '#admiration', 'positive', '0.092', 'concrete', 'tar', 'pavement', 'big_city_life', 'popular_building_material']
senticnet['lightweight_fabric'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'white_cotton', 'sheepskin_material', 'woven_sheepskin', 'sheepskin', 'clothes_type']
senticnet['lightweight_knight_protection'] = ['0.035', '-0.67', '0', '0.354', '#surprise', '#admiration', 'positive', '0.353', 'protective_gear', 'knight_protection_ore', 'metal', 'knight_protection', 'knight']
senticnet['lightweight_metal'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'protective_gear', 'metal_ore', 'knight_protection', 'metal', 'knight']
senticnet['lightweight_plastic'] = ['-0.02', '0.078', '0', '0.08', '#interest', '#admiration', 'positive', '0.045', 'rigid', 'fragile', 'opaque', 'delicate', 'versatile']
senticnet['lightwell'] = ['0.029', '0.087', '0.065', '0.137', '#interest', '#admiration', 'positive', '0.047', 'archway', 'arch', 'mat', 'aisle', 'walkway']
senticnet['lignite'] = ['0.599', '-0.87', '0', '0.673', '#surprise', '#admiration', 'positive', '0.717', 'humic_shale', 'material', 'stuff', 'peat', 'humate']
senticnet['ligue_nationale_du'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'sciolist', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
senticnet['likability'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'high_temperature', 'hot', 'hot_weather', 'fire_product', 'heat']
senticnet['likable'] = ['0', '-0.73', '0', '0.88', '#surprise', '#admiration', 'positive', '0.806', 'liked', 'friendly', 'personable', 'amiable', 'unlikable']
senticnet['like-thinking'] = ['0', '0', '-0.86', '-0.98', '#fear', '#disgust', 'negative', '-0.92', 'middle-school-aged', 'bird-loving', 'limited-resource', 'rock-loving', 'newly-trained']
senticnet['likeability'] = ['0.817', '-0.62', '0', '0.62', '#joy', '#surprise', 'positive', '0.688', 'quality', 'fascination', 'allurement', 'allure', 'temptingness']
senticnet['likeable'] = ['0.532', '0.655', '-0.25', '0.277', '#joy', '#interest', 'positive', '0.301', 'good_looking', 'lovable', 'reassuring', 'reassuringly', 'assure']
senticnet['liked'] = ['0', '-0.73', '0', '0.871', '#surprise', '#admiration', 'positive', '0.802', 'likable', 'friendly', 'personable', 'amiable', 'unlikable']
senticnet['likelihood'] = ['0', '0', '0', '-0.70', '#disgust', '#disgust', 'negative', '-0.70', 'uncertain_future', 'uncertain_environment', 'insecure', 'blur', 'anxiety']
senticnet['likely'] = ['0.062', '0.293', '0.182', '0.316', '#interest', '#admiration', 'positive', '0.122', 'different', 'particular', 'outlier', 'strange', 'exclusion']
senticnet['likely_discover_truth'] = ['0', '0.964', '0', '0.725', '#interest', '#admiration', 'positive', '0.845', 'newbie', 'matrimony', 'contrary', 'inquisitive', 'new_person']
senticnet['likeness'] = ['-0.8', '0', '0.758', '-0.79', '#sadness', '#disgust', 'negative', '-0.78', 'resemblance', 'comparison', 'equivalence', 'reflection', 'spitting_image']
senticnet['likening'] = ['-0.94', '0', '0.904', '-0.75', '#sadness', '#anger', 'negative', '-0.86', 'confrontation', 'contrast', 'scrutiny', 'collation', 'comparing']
senticnet['likert'] = ['0.854', '-0.79', '0', '0.977', '#joy', '#admiration', 'positive', '0.875', 'profitableness', 'auspiciousness', 'luck', 'advantageousness', 'positivity']
senticnet['likert_scale'] = ['0.854', '-0.79', '0', '0.977', '#joy', '#admiration', 'positive', '0.875', 'profitableness', 'auspiciousness', 'luck', 'advantageousness', 'positivity']
senticnet['likert_type'] = ['0.854', '-0.79', '0', '0.977', '#joy', '#admiration', 'positive', '0.875', 'profitableness', 'auspiciousness', 'luck', 'advantageousness', 'positivity']
senticnet['likert_type_scale'] = ['0.854', '-0.79', '0', '0.977', '#joy', '#admiration', 'positive', '0.875', 'profitableness', 'auspiciousness', 'luck', 'advantageousness', 'positivity']
senticnet['liking'] = ['0.554', '0.655', '-0.27', '0.301', '#joy', '#interest', 'positive', '0.309', 'predilection', 'well-disposed', 'friendliness', 'likeable', 'penchant']
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senticnet['lil_buck'] = ['-0.95', '0', '0.992', '-0.63', '#sadness', '#anger', 'negative', '-0.86', 'modern_dance', 'interpretive_dancing', 'interpretative_dance', 'interpretative_dancing', 'stage_dancing']
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senticnet['limb_girdle_muscular_dystrophy'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'dystrophy', 'myotonic_dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
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senticnet['limbo_anti_submarine'] = ['0', '0', '-0.95', '-0.72', '#fear', '#disgust', 'negative', '-0.83', 'cannon', 'explosive', 'firearm', 'pistol', 'machine_gun']
senticnet['limburger_cheese'] = ['0', '0', '0.943', '-0.8', '#anger', '#disgust', 'negative', '-0.87', 'fight_fire', 'filthy', 'dirty', 'stink', 'fall_mud']
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senticnet['limerence'] = ['0.833', '0', '0', '0.807', '#joy', '#admiration', 'positive', '0.82', 'lust', 'amiable', 'give_hug', 'giggle', 'smile']
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senticnet['limestone_facade'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'condominium', 'rent_apartment', 'high_rise', 'condo', 'row_house']
senticnet['limestone_karst'] = ['0', '0.08', '0.032', '0.096', '#interest', '#admiration', 'positive', '0.048', 'land_form', 'land_formation', 'land_feature', 'geographic_feature', 'landscape_feature']
senticnet['limestone_plateau'] = ['0', '0.08', '0.032', '0.096', '#interest', '#admiration', 'positive', '0.048', 'land_form', 'land_formation', 'land_feature', 'geographic_feature', 'landscape_feature']
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senticnet['limestone_trim'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'condominium', 'rent_apartment', 'high_rise', 'condo', 'row_house']
senticnet['limicoline_bird'] = ['0.786', '-0.82', '0', '0.776', '#joy', '#surprise', 'positive', '0.794', 'red_phalarope', 'northern_phalarope', 'shorebird', 'shore_bird', 'phalarope']
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senticnet['limit_time'] = ['-0.03', '0.255', '-0.20', '0.359', '#interest', '#admiration', 'positive', '0.093', 'distribute', 'hand_out', 'give', 'suggest', 'donate']
senticnet['limit_value_tlv'] = ['0', '0.098', '0', '0.089', '#interest', '#admiration', 'positive', '0.093', 'wood_panel', 'wooden_structure', 'hinge', 'entrance_room', 'corridor']
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senticnet['limited-resource'] = ['0', '0', '-0.86', '-0.97', '#fear', '#disgust', 'negative', '-0.92', 'middle-school-aged', 'bird-loving', 'rock-loving', 'like-thinking', 'newly-trained']
senticnet['limited_edition'] = ['0.031', '-0.10', '0', '0', '#joy', '#surprise', 'positive', '0.07', 'teddy_bear', 'beanie_baby', 'yo-yo', 'board_game', 'toy']
senticnet['limited_edition_cd'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['limited_edition_dvd'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['limited_liability'] = ['0.628', '-0.60', '0', '0.532', '#joy', '#surprise', 'positive', '0.589', 'account_payable', 'payable', 'indebtedness', 'debt', 'arrears']
senticnet['limited_liability_company'] = ['0.08', '0.195', '0', '0.137', '#interest', '#admiration', 'positive', '0.137', 'company', 'enterprise', 'business', 'partnership', 'large_business']
senticnet['limited_liability_corporation'] = ['0.08', '0.195', '0', '0.137', '#interest', '#admiration', 'positive', '0.137', 'company', 'enterprise', 'business', 'partnership', 'large_business']
senticnet['limitless'] = ['0', '0.858', '0', '0.79', '#interest', '#admiration', 'positive', '0.824', 'heavenly_body', 'star_planet', 'celestial_body', 'around_earth', 'unlimited']
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senticnet['limnological'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'frog', 'amphibian']
senticnet['limo'] = ['0', '0', '0.832', '-0.97', '#anger', '#disgust', 'negative', '-0.90', 'viscous', 'ore', 'sliming', 'tailing', 'slime']
senticnet['limonene'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'capsaicin', 'chemical_irritant', 'piperine', 'pepper_spray']
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senticnet['limousine_service'] = ['0.04', '0.144', '-0.07', '0.122', '#interest', '#admiration', 'positive', '0.058', 'travel', 'abroad', 'foreign_country', 'transit', 'elsewhere']
senticnet['limp'] = ['0', '0.069', '0.031', '0.149', '#interest', '#admiration', 'positive', '0.062', 'walk_part', 'extension', 'foot_action', 'walk_device', 'stand_upon']
senticnet['limp_wrist'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
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senticnet['limpidity'] = ['0.824', '-0.95', '0', '0.513', '#joy', '#surprise', 'positive', '0.765', 'clearcutness', 'explicitness', 'preciseness', 'perspicuity', 'perspicuousness']
senticnet['limping'] = ['-0.87', '0', '0', '-0.81', '#sadness', '#disgust', 'negative', '-0.84', 'gimpiness', 'lameness', 'claudication', 'gimp', 'gameness']
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senticnet['linalool'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'capsaicin', 'chemical_irritant', 'piperine', 'pepper_spray']
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senticnet['linden_dollar'] = ['-0.05', '0.215', '-0.08', '0.293', '#interest', '#admiration', 'positive', '0.092', 'swap', 'give_take', 'exchange_goods', 'transaction', 'increase_money']
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senticnet['line-shooter'] = ['0', '0', '0.99', '-0.71', '#anger', '#disgust', 'negative', '-0.85', 'boaster', 'braggart', 'vaunter', 'blowhard', 'bragger']
senticnet['line_intersect'] = ['-0.04', '0.074', '0.019', '0.071', '#interest', '#admiration', 'positive', '0.021', 'sharpness', 'sharp_end', 'sharp_corner', 'near_end', 'edge']
senticnet['line_of_destiny'] = ['0.781', '-0.66', '0', '0.666', '#joy', '#admiration', 'positive', '0.703', 'crease', 'crinkle', 'wrinkle', 'furrow', 'seam']
senticnet['line_of_fate'] = ['0.658', '-0.88', '0', '0.932', '#surprise', '#admiration', 'positive', '0.826', 'crease', 'crinkle', 'wrinkle', 'furrow', 'seam']
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senticnet['line_storm'] = ['-0.87', '0', '0.91', '-0.78', '#sadness', '#anger', 'negative', '-0.85', 'storm', 'thundershower', 'rain', 'rainstorm', 'equinoctial_storm']
senticnet['line_up'] = ['0.701', '0.786', '0', '0', '#joy', '#interest', 'positive', '0.744', 'find_end_line', 'come_early', 'find_line', 'take_number', 'wait_patiently']
senticnet['linea_alba'] = ['0.719', '-0.57', '0', '0.651', '#joy', '#admiration', 'positive', '0.647', 'chap', 'break', 'slit', 'gap', 'shift']
senticnet['lineal_world'] = ['0', '0', '0', '0.834', '#admiration', '#admiration', 'positive', '0.834', 'royal_person', 'royal_title', 'top', 'master', 'rank']
senticnet['lineament'] = ['0.725', '0', '0', '0.879', '#joy', '#admiration', 'positive', '0.802', 'distinguish', 'distinct', 'flair', 'distinctive', 'mold']
senticnet['linear'] = ['0', '0', '0.924', '-0.93', '#anger', '#disgust', 'negative', '-0.92', 'analog', 'one-dimensional', 'elongate', 'additive', 'analogue']
senticnet['linear_elastic'] = ['0.808', '0', '0', '0.943', '#joy', '#admiration', 'positive', '0.875', 'a_lot_of_practice', 'flexibility', 'take_lesson', 'coordination', 'air_tank']
senticnet['linear_interpolation'] = ['-0.55', '0', '0.965', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'interjection', 'delay', 'insert', 'interposition', 'disruption']
senticnet['linear_lanceolate'] = ['-0.75', '0', '0.734', '-0.82', '#sadness', '#disgust', 'negative', '-0.77', 'puberulent', 'haired', 'furlike', 'pappose', 'pilous']
senticnet['linear_momentum'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'newton', 'sthene', 'force_unit']
senticnet['linear_motion'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotate_movement', 'rotate', 'go_around', 'manoeuvre', 'full_circle']
senticnet['linear_programming'] = ['0', '0.869', '0', '0.869', '#interest', '#admiration', 'positive', '0.869', 'statistic', 'applied_math', 'mathematics', 'applied_mathematics', 'probability_theory']
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senticnet['linear_transformation'] = ['0.506', '-0.56', '0', '0.788', '#surprise', '#admiration', 'positive', '0.62', 'backward', 'reverse', 'retrograde', 'reflexive', 'retral']
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senticnet['linearly_elastic'] = ['0.808', '0', '0', '0.943', '#joy', '#admiration', 'positive', '0.875', 'a_lot_of_practice', 'flexibility', 'take_lesson', 'coordination', 'air_tank']
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senticnet['linen_chest'] = ['0', '0', '-0.06', '-0.11', '#fear', '#disgust', 'negative', '-0.09', 'elderly_person', 'hide-a-bed', 'homosexual', 'old_lady', 'dust_mite']
senticnet['linen_closet'] = ['0', '0.082', '0.009', '0.135', '#interest', '#admiration', 'positive', '0.069', 'duvet_cover', 'pillow', 'mattress', 'bed_sheet', 'duvet']
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senticnet['linguistic_context'] = ['0.791', '0', '0', '0.854', '#joy', '#admiration', 'positive', '0.823', 'circumstance', 'setting', 'context', 'context_of_use', 'project']
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senticnet['link_up'] = ['0.63', '-0.55', '0', '0.946', '#joy', '#admiration', 'positive', '0.711', 'intercommunicate', 'connect', 'link', 'complect', 'join']
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senticnet['linux_infringes_upon'] = ['0.98', '0.857', '0', '0.966', '#joy', '#admiration', 'positive', '0.934', 'sincere', 'brownie_point', 'positive_reinforcement', 'acceptance', 'mutual_understand']
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senticnet['lip_pencil'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'sexual_excitement', 'sexy', 'wet_lip', 'kiss', 'clean_up']
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senticnet['lip_synch'] = ['-0.95', '0', '0.992', '-0.63', '#sadness', '#anger', 'negative', '-0.86', 'interpretive_dance', 'interpretive_dancing', 'modern_dance', 'interpretative_dancing', 'stage_dancing']
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senticnet['lipid_metabolism'] = ['0.672', '-0.55', '0', '0.992', '#joy', '#admiration', 'positive', '0.739', 'pressor', 'hormone', 'adrenalin', 'internal_secretion', 'catecholamine']
senticnet['lipid_oxidation'] = ['-0.08', '-0.04', '0.023', '-0.07', '#sadness', '#disgust', 'negative', '-0.03', 'tier', 'make_toast', 'house_smell_good', 'get_hot', 'bread_crumb']
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senticnet['lipophilicity'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'imperium', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['lipoplasty'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
senticnet['lipopolysaccharide'] = ['0.702', '-0.52', '0', '0.958', '#joy', '#admiration', 'positive', '0.728', 'somatic_cell', 'vegetative_cell', 'fixed_phagocyte', 'macrophage', 'free_phagocyte']
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senticnet['liposuction'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
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senticnet['lipscombe_cup'] = ['0.997', '-0.84', '0', '0.927', '#joy', '#admiration', 'positive', '0.923', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
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senticnet['liquid_chromatographic'] = ['0.838', '-0.61', '0', '0.556', '#joy', '#surprise', 'positive', '0.671', 'activity', 'natural_process', 'natural_action', 'column_chromatography', 'paper_chromatography']
senticnet['liquid_chromatography'] = ['0.838', '-0.61', '0', '0.556', '#joy', '#surprise', 'positive', '0.671', 'activity', 'natural_process', 'natural_action', 'column_chromatography', 'paper_chromatography']
senticnet['liquid_chromatography_lc'] = ['0.838', '-0.61', '0', '0.556', '#joy', '#surprise', 'positive', '0.671', 'activity', 'natural_process', 'natural_action', 'column_chromatography', 'paper_chromatography']
senticnet['liquid_container'] = ['0', '0.071', '0', '0.128', '#interest', '#admiration', 'positive', '0.1', 'drink_holder', 'recyclable', 'type_of_container', 'drink_container', 'storage_container']
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senticnet['liquid_deuterium'] = ['-0.90', '0', '0.825', '-0.61', '#sadness', '#anger', 'negative', '-0.78', 'flaming', 'combustion', 'fire', 'flame', 'internal_combustion']
senticnet['liquid_drink'] = ['0.948', '0.992', '0', '0.974', '#interest', '#admiration', 'positive', '0.971', 'ferment_beverage', 'yogurt', 'fermented_juice', 'healthy_drink', 'alcoholic_drink']
senticnet['liquid_food'] = ['0.079', '0.065', '0.013', '0.062', '#joy', '#interest', 'positive', '0.048', 'broth', 'ingest', 'potable_liquid', 'serve_hot', 'porridge']
senticnet['liquid_formulation'] = ['0.701', '-0.76', '0', '0.904', '#surprise', '#admiration', 'positive', '0.791', 'curative', 'heal', 'medicine', 'get_cured', 'take_care']
senticnet['liquid_fuel'] = ['0', '0', '-0.06', '0', '#fear', '#fear', 'negative', '-0.06', 'greasy', 'car_gas', 'fossil_fuel', 'crude', 'lubrication']
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senticnet['liquid_measure'] = ['0.828', '0.721', '0', '0', '#joy', '#interest', 'positive', '0.774', 'liquid_unit', 'amount_water', 'small_size', 'amount_waterment', 'small_amount']
senticnet['liquid_measurement'] = ['0.83', '0.855', '0', '0', '#joy', '#interest', 'positive', '0.843', 'liquid_measure', 'imperial_measurement', 'measure_unit', 'volume_measurement', 'liquid_unit']
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senticnet['liquid_mixture'] = ['0', '0.073', '-0.05', '0.093', '#interest', '#admiration', 'positive', '0.038', 'thicken', 'condense_water', 'condensed_milk', 'weather_condition', 'condensation']
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senticnet['lithely'] = ['0', '-0.89', '0', '0.814', '#surprise', '#admiration', 'positive', '0.855', 'adroitly', 'deftly', 'adeptly', 'nimbly', 'dexterously']
senticnet['litheness'] = ['0.992', '-0.82', '0', '0.901', '#joy', '#admiration', 'positive', '0.906', 'gracility', 'grace', 'lissomeness', 'gracefulness', 'suppleness']
senticnet['lithesome'] = ['0', '0', '-1', '-0.86', '#fear', '#disgust', 'negative', '-0.93', 'urbane', 'refined', 'slim', 'svelte', 'polished']
senticnet['lithic_assemblage'] = ['0.099', '-0.07', '-0.08', '0.195', '#joy', '#admiration', 'positive', '0.071', 'art', 'exhibit', 'craft', 'fine_art', 'artwork']
senticnet['lithic_material'] = ['0.099', '-0.07', '-0.08', '0.195', '#joy', '#admiration', 'positive', '0.071', 'art', 'exhibit', 'craft', 'fine_art', 'artwork']
senticnet['lithium'] = ['-0.04', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.06', 'magnesium', 'mineral', 'potassium', 'iodine', 'manganese']
senticnet['lithium_deuteride'] = ['-0.77', '0', '0.764', '-0.54', '#sadness', '#anger', 'negative', '-0.69', 'pullulation', 'apomixis', 'blastogenesis', 'budding', 'asexual_reproduction']
senticnet['lithium_eskalith'] = ['-0.72', '0', '0.973', '-0.57', '#sadness', '#anger', 'negative', '-0.76', 'corpulency', 'blubber', 'fat', 'embonpoint', 'stoutness']
senticnet['lithium_ion_battery'] = ['0', '0', '-1', '-1', '#fear', '#disgust', 'negative', '-1.0', 'moon_gravity', 'force', 'coercion', 'watt', 'power_source']
senticnet['lithium_ion_battery_pack'] = ['0', '0', '-1', '-1', '#fear', '#disgust', 'negative', '-1.0', 'moon_gravity', 'force', 'coercion', 'watt', 'power_source']
senticnet['lithobid'] = ['-0.72', '0', '0.973', '-0.57', '#sadness', '#anger', 'negative', '-0.76', 'corpulency', 'blubber', 'fat', 'embonpoint', 'stoutness']
senticnet['lithology'] = ['0', '0.823', '0', '0.95', '#interest', '#admiration', 'positive', '0.886', 'petrology', 'geochemistry', 'different_application', 'geological_science', 'award']
senticnet['lithophyte'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['lithophytic'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['lithophytic_plant'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'lithophyte', 'rock_plant']
senticnet['lithosphere'] = ['0.894', '0.839', '0', '0', '#joy', '#interest', 'positive', '0.867', 'geosphere', 'layer', 'crust', 'evolution', 'continental_dynamic']
senticnet['litigant'] = ['0.071', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.057', 'carpenter', 'vexatious_litigant', 'tape_player', 'copper_wire', 'several_types']
senticnet['litigation'] = ['0', '0.255', '-0.10', '0.15', '#interest', '#admiration', 'positive', '0.099', 'legal_document', 'lawyer', 'court_action', 'see_lawyer', 'lawsuit']
senticnet['litigation_associate'] = ['0.102', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.076', 'adjust_volume', 'tap_toe', 'enter_contest', 'tap_foot', 'dance_well']
senticnet['litigation_counsel'] = ['0', '0.146', '-0.05', '0.077', '#interest', '#admiration', 'positive', '0.057', 'lawyer', 'negotiator', 'arbiter', 'judge_jury', 'decide_guilt']
senticnet['litigator'] = ['0', '0.146', '-0.05', '0.077', '#interest', '#admiration', 'positive', '0.057', 'lawyer', 'negotiator', 'arbiter', 'judge_jury', 'decide_guilt']
senticnet['litigious'] = ['-0.85', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.78', 'combative', 'litigiously', 'litigiousness', 'litigation', 'argumentative']
senticnet['litigiously'] = ['-0.86', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.79', 'combative', 'litigious', 'litigiousness', 'litigation', 'argumentative']
senticnet['litigiousness'] = ['-0.86', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.79', 'combative', 'litigiously', 'litigious', 'litigation', 'argumentative']
senticnet['litoria'] = ['-0.03', '-0.01', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'shit', 'wildflower', 'poop', 'wild_flower', 'dog_poop']
senticnet['litotes'] = ['-0.87', '0', '0.753', '0', '#sadness', '#anger', 'negative', '-0.81', 'nondisjunction', 'miosis', 'cell_division', 'meiosis', 'understatement']
senticnet['litre'] = ['0.068', '0', '0.034', '0.076', '#joy', '#admiration', 'positive', '0.037', 'gallon', 'liquid_measurement', 'ounce', 'measurement', 'quart']
senticnet['litre_engine'] = ['0.068', '0', '0.034', '0.076', '#joy', '#admiration', 'positive', '0.037', 'gallon', 'liquid_measurement', 'ounce', 'measurement', 'quart']
senticnet['litre_fuel_tank'] = ['0.068', '0', '0.034', '0.076', '#joy', '#admiration', 'positive', '0.037', 'gallon', 'liquid_measurement', 'ounce', 'measurement', 'quart']
senticnet['litre_per'] = ['0.068', '0', '0.034', '0.076', '#joy', '#admiration', 'positive', '0.037', 'gallon', 'liquid_measurement', 'ounce', 'measurement', 'quart']
senticnet['litre_petrol'] = ['0.068', '0', '0.034', '0.076', '#joy', '#admiration', 'positive', '0.037', 'gallon', 'liquid_measurement', 'ounce', 'measurement', 'quart']
senticnet['litre_petrol_engine'] = ['0.068', '0', '0.034', '0.076', '#joy', '#admiration', 'positive', '0.037', 'gallon', 'liquid_measurement', 'ounce', 'measurement', 'quart']
senticnet['litre_tank'] = ['0.068', '0', '0.034', '0.076', '#joy', '#admiration', 'positive', '0.037', 'gallon', 'liquid_measurement', 'ounce', 'measurement', 'quart']
senticnet['litter'] = ['-0.85', '0', '0.683', '-0.84', '#sadness', '#disgust', 'negative', '-0.79', 'pack', 'strain', 'biological_group', 'brood', 'animal_group']
senticnet['litter_bin'] = ['-0.04', '0', '0.041', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'back_sofa', 'home_improvement_store', 'closet', 'under_carpet', 'shoe_shop']
senticnet['litter_box'] = ['-0.04', '0', '-0.07', '-0.09', '#fear', '#disgust', 'negative', '-0.07', 'go_toilet', 'go_bathroom', 'go_pee', 'read_bedtime_story', 'quiet_cry_baby']
senticnet['litter_removal'] = ['0.935', '0', '0', '0', '#joy', '#joy', 'positive', '0.935', 'clean_up_kitchen', 'feel_dirty', 'remove_stain', 'stink', 'stink_mess']
senticnet['litter_strewn'] = ['-0.70', '0', '0.611', '-0.69', '#sadness', '#disgust', 'negative', '-0.67', 'dump', 'garbage_dump', 'trash_dump', 'garbage_heap', 'wasteyard']
senticnet['littered_with'] = ['0', '0', '-0.91', '-0.73', '#fear', '#disgust', 'negative', '-0.82', 'overabundant', 'prevailing', 'plethoric', 'predominant', 'dominant']
senticnet['litterer'] = ['-0.56', '0', '0.655', '-0.85', '#anger', '#disgust', 'negative', '-0.69', 'slovenly_woman', 'slut', 'trollop', 'slattern', 'slob']
senticnet['little'] = ['-0.99', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.84', 'least', 'a_little', 'small_amount', 'shortage', 'scarce']
senticnet['little-known'] = ['-0.81', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.77', 'lesser-known', 'obscure', 'undiscovered', 'little-used', 'unheralded']
senticnet['little-used'] = ['-0.82', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.78', 'lesser-known', 'obscure', 'undiscovered', 'little-known', 'unheralded']
senticnet['little_animal'] = ['0.911', '0', '0', '0', '#joy', '#joy', 'positive', '0.911', 'small_cat', 'curiosity', 'curious_animal', 'small_animal', 'baby_cat']
senticnet['little_baby'] = ['-0.04', '0', '0.029', '0', '#sadness', '#anger', 'negative', '-0.03', 'newborn_baby', 'nostalgic', 'baby', 'blind_person', 'youth']
senticnet['little_bighorn'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['little_bit'] = ['0.057', '0.141', '-0.06', '0.162', '#interest', '#admiration', 'positive', '0.074', 'belongings', 'material', 'fabric', 'guarantee', 'madonna']
senticnet['little_bit_cocky'] = ['0.084', '-0.05', '0', '-0.04', '#joy', '#surprise', 'positive', '0.031', 'entree', 'feast', 'satiation', 'breakfast', 'supper']
senticnet['little_bit_sore'] = ['-0.69', '0', '0.768', '-0.67', '#sadness', '#anger', 'negative', '-0.71', 'dither', 'pother', 'worry', 'flap', 'scruple']
senticnet['little_bite'] = ['0.807', '0.763', '0', '0', '#joy', '#interest', 'positive', '0.785', 'broken', 'find_road', 'need_eat_meal', 'small_bite', 'repair']
senticnet['little_bittern'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'amoeba', 'single_celled']
senticnet['little_bluestem'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'bulrush', 'nailrod', 'reed_mace', 'reedmace']
senticnet['little_bo_peep'] = ['0.525', '-0.85', '0', '0.599', '#surprise', '#admiration', 'positive', '0.66', 'frolic', 'coquetry', 'romp', 'play', 'caper']
senticnet['little_boy'] = ['-0.12', '0', '-0.08', '-0.10', '#sadness', '#disgust', 'negative', '-0.10', 'brat', 'youngster', 'youth', 'innocence', 'lad']
senticnet['little_brat'] = ['-0.14', '0.152', '0.613', '-0.64', '#anger', '#disgust', 'negative', '-0.31', 'huffiness', 'huffily', 'offense', 'umbrage', 'umbrageous']
senticnet['little_bugger'] = ['-0.53', '0', '0.508', '-0.74', '#sadness', '#disgust', 'negative', '-0.59', 'bedwetter', 'bed_wetter', 'mortal', 'wetter', 'person']
senticnet['little_buttercup'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'dress', 'pinny', 'dirndl', 'gown', 'muumuu']
senticnet['little_child'] = ['0', '0.255', '-0.10', '0.15', '#interest', '#admiration', 'positive', '0.099', 'young_child', 'kid', 'baby', 'child', 'youngster']
senticnet['little_critter'] = ['0.03', '0.068', '-0.04', '0.093', '#interest', '#admiration', 'positive', '0.037', 'beast', 'equine', 'live_creature', 'furry_creature', 'domesticate_animal']
senticnet['little_dense_water'] = ['0.1', '0.076', '0', '0.156', '#joy', '#admiration', 'positive', '0.111', 'ice_crystal', 'frozen_liquid', 'fragile', 'solid', 'white']
senticnet['little_effort'] = ['-0.05', '0', '0.014', '-0.03', '#sadness', '#disgust', 'negative', '-0.03', 'take_dump', 'plan_time', 'go_office', 'put_nice_clothes', 'out_bed']
senticnet['little_egret'] = ['0.797', '-0.71', '0', '0.736', '#joy', '#admiration', 'positive', '0.751', 'wood_ibis', 'wood_stork', 'ibis', 'wading_bird', 'wader']
senticnet['little_egret_egretta_garzetta'] = ['0.797', '-0.71', '0', '0.736', '#joy', '#admiration', 'positive', '0.751', 'wood_ibis', 'wood_stork', 'ibis', 'wading_bird', 'wader']
senticnet['little_elbow_grease'] = ['0.76', '0.919', '0', '0', '#joy', '#interest', 'positive', '0.84', 'chore', 'make_happen', 'pick_up_trash', 'intuition', 'stop_procrastinate']
senticnet['little_englander'] = ['0.585', '-0.7', '0', '0.586', '#surprise', '#admiration', 'positive', '0.624', 'jingoistic', 'ultranationalistic', 'superpatriotic', 'nationalistic', 'patriotic']
senticnet['little_gem_arborvita'] = ['0', '-0.00', '0.032', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'weed', 'shamrock', 'lichen', 'moss', 'herb']
senticnet['little_girl'] = ['0.738', '-0.53', '0', '0.856', '#joy', '#admiration', 'positive', '0.71', 'farm_girl', 'flower_girl', 'moppet', 'schoolgirl', 'female']
senticnet['little_grain'] = ['0', '0.121', '0.041', '0.12', '#interest', '#admiration', 'positive', '0.067', 'yellow_color', 'nutritious', 'cereal_crop', 'small_grain', 'food_grain']
senticnet['little_grebe'] = ['0.797', '-0.71', '0', '0.736', '#joy', '#admiration', 'positive', '0.751', 'wood_ibis', 'wood_stork', 'ibis', 'wading_bird', 'wader']
senticnet['little_hair'] = ['-0.06', '0', '-0.08', '-0.06', '#fear', '#disgust', 'negative', '-0.06', 'cold_head', 'short_hair', 'keep_hair_short', 'hair_become_short', 'new_look']
senticnet['little_horse'] = ['0', '-0.11', '0.022', '0.06', '#surprise', '#admiration', 'positive', '0.051', 'donkey', 'small_horse', 'male_cow', 'hoof_animal', 'pony']
senticnet['little_hunger'] = ['0.198', '0.15', '-0.09', '0', '#joy', '#interest', 'positive', '0.086', 'make_feel_better', 'feel_good', 'good_feeling', 'feel_better', 'peace_mind']
senticnet['little_incentive'] = ['0.716', '0.813', '0', '0', '#joy', '#interest', 'positive', '0.764', 'bonus', 'reinforce', 'promotion', 'meritocracy', 'gratification']
senticnet['little_kid'] = ['0.068', '0.069', '-0.05', '0.056', '#joy', '#interest', 'positive', '0.034', 'human_offspring', 'young', 'small_adult', 'young_kid', 'small_boy']
senticnet['little_leaven_leaveneth'] = ['-0.92', '0', '0.783', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'clot', 'coagulum', 'gob', 'clew', 'agglomeration']
senticnet['little_leeway'] = ['-0.52', '0', '0.561', '-0.90', '#anger', '#disgust', 'negative', '-0.66', 'difference', 'tolerance', 'allowance', 'discrepancy', 'margin']
senticnet['little_mermaid'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'lordosis', 'kyphosis', 'humpback', 'spinal_curvature', 'abnormality']
senticnet['little_minded'] = ['-0.79', '0', '0', '-0.96', '#sadness', '#disgust', 'negative', '-0.88', 'idiot', 'douchebag', 'dumb', 'dolt', 'stupid']
senticnet['little_money'] = ['-0.81', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.81', 'run_out_money', 'spend_much_money', 'bankruptcy', 'comparison_shop', 'spend_a_lot_of_money']
senticnet['little_niangua_river'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'common_bean', 'green_bean', 'wax_bean']
senticnet['little_person'] = ['0.025', '0.073', '-0.05', '0.1', '#interest', '#admiration', 'positive', '0.037', 'boy_girl', 'small_kid', 'small_boy', 'young_kid', 'young_people']
senticnet['little_pollution'] = ['-0.74', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.75', 'tired_leg', 'traveling_destination', 'get_sweaty', 'sweat_lot', 'polluted']
senticnet['little_potato'] = ['-0.69', '0', '0.776', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'rhizoctinia_disease', 'russet_scab', 'stem_canker', 'rosette', 'plant_disease']
senticnet['little_rascal'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'holy_terror', 'rapscallion', 'scalawag', 'scallywag', 'monkey']
senticnet['little_resemblance'] = ['-0.88', '0', '0.813', '-0.57', '#sadness', '#anger', 'negative', '-0.75', 'affinity', 'mutual_resemblance', 'alikeness', 'likeness', 'similitude']
senticnet['little_resemblence'] = ['-0.92', '0', '0.946', '-0.56', '#sadness', '#anger', 'negative', '-0.81', 'forcefulness', 'intensiveness', 'impulse', 'vigor', 'energy']
senticnet['little_specific'] = ['0.07', '0.151', '-0.10', '0.112', '#interest', '#admiration', 'positive', '0.056', 'clan', 'happy_together', 'aunt_uncle', 'relative', 'kin']
senticnet['little_tern'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'hold_egg', 'baby_bird', 'hatch', 'lay_egg', 'bird_home']
senticnet['little_terror'] = ['-0.96', '0', '0.805', '-0.88', '#sadness', '#disgust', 'negative', '-0.88', 'rascal', 'rapscallion', 'scalawag', 'scallywag', 'monkey']
senticnet['little_tidbit'] = ['0.73', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.851', 'sting', 'nip', 'bite', 'flea', 'least']
senticnet['little_time'] = ['-0.98', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.85', 'late', 'debt', 'deadline', 'stress', 'take_up_time']
senticnet['little_water'] = ['-0.77', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'water_drip', 'fall', 'thirst', 'lack_of_water', 'thirsty']
senticnet['littlewood'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'intrepidly', 'fearlessness', 'fearlessly', 'unafraid', 'unfearing']
senticnet['liturgical_music'] = ['0.99', '0', '0', '0.719', '#joy', '#admiration', 'positive', '0.855', 'instrumental', 'lyric', 'lyrics', 'sing', 'melody']
senticnet['liturgical_vestment'] = ['0.849', '-0.50', '0', '0.845', '#joy', '#admiration', 'positive', '0.732', 'robe', 'gown', 'veil', 'vest', 'cassock']
senticnet['liturgy'] = ['0.825', '0', '0', '0.76', '#joy', '#admiration', 'positive', '0.792', 'holy', 'eucharist', 'holy_eucharist', 'holy_sacrament', 'sacrament_of_eucharist']
senticnet['liv_ex'] = ['0.331', '0.119', '0.022', '0.229', '#joy', '#admiration', 'positive', '0.164', 'spice_life', 'red_light_district', 'fresh_produce', 'enough_food', 'knowledgeable']
senticnet['livable_wage'] = ['0', '0.893', '0', '0', '#interest', '#interest', 'positive', '0.893', 'salary', 'wage', 'send_money', 'earnings', 'pay']
senticnet['live'] = ['0', '0.942', '0', '0.916', '#interest', '#admiration', 'positive', '0.929', 'live_city', 'get_oxygen', 'inhabit', 'survive', 'tourism']
senticnet['live_abroad'] = ['0', '0.766', '0', '0', '#interest', '#interest', 'positive', '0.766', 'overseas', 'green_card', 'study_abroad', 'elsewhere', 'abroad']
senticnet['live_alone'] = ['-0.79', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'cause_suffer', 'lose_team', 'leave_out', 'disrespect', 'alone']
senticnet['live_animal'] = ['0.047', '0.087', '0', '0.08', '#interest', '#admiration', 'positive', '0.071', 'social_animal', 'smart_animal', 'omnivore', 'social_creature', 'large_mouse']
senticnet['live_apartment'] = ['0.036', '0.138', '0.058', '0.223', '#interest', '#admiration', 'positive', '0.085', 'home_sweet_home', 'house', 'rent_room', 'rent_house', 'need_companionship']
senticnet['live_bait'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'fishing_rod', 'fishing', 'patience', 'fish_lake', 'catch_fish']
senticnet['live_band'] = ['0.845', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.802', 'bluegrass_band', 'string_quartet', 'music_shop', 'country_band', 'high_school_band']
senticnet['live_castle'] = ['-0.07', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.06', 'read_book', 'read_magazine', 'read_newspaper', 'read', 'borrow_book']
senticnet['live_chat'] = ['-0.51', '0', '0.545', '-0.91', '#anger', '#disgust', 'negative', '-0.65', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['live_city'] = ['0.045', '0.078', '-0.07', '0.125', '#interest', '#admiration', 'positive', '0.044', 'live', 'get_oxygen', 'live_inside', 'reside', 'live_life']
senticnet['live_country'] = ['0.172', '0.138', '0', '0.097', '#joy', '#interest', 'positive', '0.136', 'live_good_life', 'healthy_environment', 'native_person', 'live_healthy_life', 'exercise_often']
senticnet['live_creature'] = ['0.027', '0.132', '-0.06', '0.182', '#interest', '#admiration', 'positive', '0.07', 'life_form', 'breathe', 'poisonous_human', 'live_organism', 'alive']
senticnet['live_dangerously'] = ['0.054', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.055', 'crash_tree', 'accident', 'fall_butt', 'get_accident', 'hit_tree']
senticnet['live_entity'] = ['0', '0.071', '0', '0', '#interest', '#interest', 'positive', '0.071', 'eukaryote', 'human_chest', 'organism', 'system', 'secret_agent']
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senticnet['lock_front_door'] = ['-0.04', '-0.03', '0.045', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'dress_occasion', 'put_clothes', 'make_secure', 'try_new_clothes', 'change_clothes']
senticnet['lock_in'] = ['-0.91', '0', '0.576', '-0.95', '#sadness', '#disgust', 'negative', '-0.81', 'stop', 'halt', 'cease', 'lock', 'block']
senticnet['lock_unlock'] = ['0', '0.069', '0', '0.083', '#interest', '#admiration', 'positive', '0.076', 'latch', 'restrict_access', 'knob', 'door_knob', 'gate']
senticnet['lock_up'] = ['-0.03', '0', '-0.06', '-0.05', '#fear', '#disgust', 'negative', '-0.04', 'drive_car', 'move_car', 'fly_plane', 'take_car_drive', 'go_drive']
senticnet['lockable'] = ['0', '0.069', '0', '0.083', '#interest', '#admiration', 'positive', '0.076', 'latch', 'restrict_access', 'knob', 'door_knob', 'gate']
senticnet['lockbox'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'slot', 'box', 'load_dock', 'diskette', 'emporium']
senticnet['locked_cupboard'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'slot', 'box', 'load_dock', 'diskette', 'emporium']
senticnet['locked_door'] = ['0', '0', '0', '0.077', '#admiration', '#admiration', 'positive', '0.077', 'glass_front', 'gate', 'doorway', 'use_farm', 'entranceway']
senticnet['locked_drawer'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'slot', 'box', 'load_dock', 'diskette', 'emporium']
senticnet['locked_up'] = ['0.816', '-0.91', '0', '0.944', '#surprise', '#admiration', 'positive', '0.891', 'jail', 'jailhouse', 'gaol', 'poky', 'clink']
senticnet['locker'] = ['0', '0.072', '0', '0', '#interest', '#interest', 'positive', '0.072', 'metal_box', 'cash_drawer', 'keyhole', 'clutch_bag', 'safe']
senticnet['locker_room'] = ['0.898', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.865', 'shoe_store', 'chess_tournament', 'dress_room', 'front_desk', 'prison_cell']
senticnet['locket'] = ['0', '0.177', '0', '0.126', '#interest', '#admiration', 'positive', '0.151', 'treasure', 'storage_facility', 'shrine', 'lock_box', 'jewelry_box']
senticnet['lockheed_constellation'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'atmosphere', 'wing', 'nest', 'feather', 'bird_fly']
senticnet['lockheed_vega'] = ['-0.04', '0.092', '0', '0.062', '#interest', '#admiration', 'positive', '0.036', 'hangar', 'aircraft_carrier', 'hot_air_balloon', 'war_museum', 'spaceship']
senticnet['locking_device'] = ['0', '0', '0', '0.109', '#admiration', '#admiration', 'positive', '0.109', 'knob', 'door_knob', 'door_handle', 'handle', 'hinge']
senticnet['locking_lever'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'peavey']
senticnet['locking_mechanism'] = ['0', '0.069', '0', '0.083', '#interest', '#admiration', 'positive', '0.076', 'latch', 'restrict_access', 'knob', 'door_knob', 'gate']
senticnet['locking_pin'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'peavey']
senticnet['locking_ring'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'peavey']
senticnet['lockjaw'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'naked_eye', 'eye', 'oculus_sinister']
senticnet['lockout'] = ['-0.77', '0', '0.505', '-0.50', '#sadness', '#anger', 'negative', '-0.59', 'wildcat_strike', 'sympathy_strike', 'sympathetic_strike', 'walkout', 'job_action']
senticnet['lockset'] = ['0', '0.069', '0', '0.083', '#interest', '#admiration', 'positive', '0.076', 'latch', 'restrict_access', 'knob', 'door_knob', 'gate']
senticnet['locksmith'] = ['0', '0.115', '0', '0.105', '#interest', '#admiration', 'positive', '0.11', 'farrier', 'miner', 'horseshoer', 'blacksmith', 'smith']
senticnet['lockstep'] = ['0', '0.804', '0', '0.795', '#interest', '#admiration', 'positive', '0.8', 'lock-step', 'corporatist', 'two-party', 'blindly', 'arm-in-arm']
senticnet['lockup'] = ['0.816', '-0.91', '0', '0.944', '#surprise', '#admiration', 'positive', '0.891', 'jail', 'jailhouse', 'gaol', 'poky', 'clink']
senticnet['lockwood_smith'] = ['0.034', '-0.02', '0', '0.057', '#joy', '#admiration', 'positive', '0.039', 'female_television_connector', 'channel_selector', 'power_switch', 'tuner', 'radio']
senticnet['loco_moco'] = ['0.028', '-0.07', '0', '0', '#joy', '#surprise', 'positive', '0.05', 'casserole', 'delicious_meal', 'hot_dog_bun', 'hamburger', 'hot_dog']
senticnet['locomobile'] = ['-0.04', '-0.16', '0', '0.056', '#surprise', '#admiration', 'positive', '0.059', 'car_part', 'under_car', 'car_wheel', 'wheel', 'car']
senticnet['locomotion'] = ['0', '0', '0', '0.19', '#admiration', '#admiration', 'positive', '0.19', 'personal_transport', 'get_around', 'travel', 'change_location', 'movement']
senticnet['locomotive'] = ['0.069', '0.097', '0', '0.172', '#interest', '#admiration', 'positive', '0.113', 'type_of_engine', 'liquid_gas', 'warm_air', 'hot_air', 'steamy']
senticnet['locomotive_engineer_ble'] = ['0.648', '0.627', '-0.26', '0.329', '#joy', '#interest', 'positive', '0.334', 'brotherly', 'friendly', 'fraternal', 'amicability', 'amicable']
senticnet['locomotor'] = ['-0.15', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.09', 'use_leg', 'walk_quickly', 'casual', 'take_step', 'strut']
senticnet['locomotor_activity'] = ['0.795', '0', '0', '0.787', '#joy', '#admiration', 'positive', '0.791', 'entree', 'soup', 'wine_glass', 'salt_pepper_shaker', 'plate_silverware']
senticnet['locum'] = ['0.738', '-0.53', '0', '0.856', '#joy', '#admiration', 'positive', '0.71', 'doctor', 'country_doctor', 'family_doctor', 'doctor_office', 'physician']
senticnet['locum_consultant'] = ['0.738', '-0.53', '0', '0.856', '#joy', '#admiration', 'positive', '0.71', 'doctor', 'country_doctor', 'family_doctor', 'doctor_office', 'physician']
senticnet['locus'] = ['0.896', '0.962', '0', '0', '#joy', '#interest', 'positive', '0.929', 'locale', 'venue', 'gene', 'determine', 'project']
senticnet['locust_infestation'] = ['-0.98', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.89', 'dry', 'lose_team', 'leave_out', 'disrespect', 'cause_suffer']
senticnet['locust_tree'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'black_locust']
senticnet['locust_walk'] = ['0.053', '-0.12', '0.015', '-0.03', '#joy', '#surprise', 'positive', '0.03', 'student_place', 'learning_place', 'school_session', 'after_school', 'professor']
senticnet['locution'] = ['-0.89', '0', '0.669', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'deceit', 'deception', 'misrepresentation', 'indirect_expression', 'equivocation']
senticnet['locutionary_act'] = ['0.904', '-0.58', '0', '0.736', '#joy', '#admiration', 'positive', '0.743', 'naming', 'appellative', 'denotative', 'extensional', 'referent']
senticnet['lode'] = ['0.614', '-0.95', '0', '0.584', '#joy', '#surprise', 'positive', '0.718', 'mother_lode', 'champion_lode', 'deposit', 'sedimentation', 'alluviation']
senticnet['lode_gold'] = ['-0.98', '0', '0.521', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'battery', 'stamp_battery', 'quartz_battery', 'stamp_mill', 'factory']
senticnet['lodestone'] = ['0.538', '-0.72', '0', '0.606', '#surprise', '#admiration', 'positive', '0.622', 'attraction', 'captivate', 'complementary', 'attract', 'magnetism']
senticnet['lodge'] = ['0.058', '0.075', '0', '0.09', '#interest', '#admiration', 'positive', '0.074', 'refuge', 'temporary_home', 'shelter', 'mom_house', 'hut']
senticnet['lodge_totem'] = ['0', '0.105', '0', '0', '#interest', '#interest', 'positive', '0.105', 'beak', 'many_feathers', 'big_eye', 'bird', 'bird_mouth']
senticnet['lodgepole_pine'] = ['0.045', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.356', 'ponderosa', 'bull_pine', 'western_yellow_pine', 'yellow_pine', 'longleaf_pine']
senticnet['lodgepole_pine_pinus_contorta'] = ['-0.94', '0', '0.608', '-0.95', '#sadness', '#disgust', 'negative', '-0.83', 'wild_licorice', 'wild_liquorice', 'alpine_milk_vetch', 'purple_milk_vetch', 'milk_vetch']
senticnet['lodging'] = ['-0.81', '0', '0.921', '-0.80', '#sadness', '#anger', 'negative', '-0.84', 'immovability', 'unchanging', 'lodgment', 'permanent', 'fixity']
senticnet['lodgings'] = ['0.053', '0.1', '0', '0', '#joy', '#interest', 'positive', '0.076', 'registry_office', 'board_room', 'telephone_booth', 'cubicle', 'reception_area']
senticnet['lodgment'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'grievance', 'accusation', 'plaint', 'allegation', 'accuse']
senticnet['loehmann'] = ['-0.05', '-0.04', '0.017', '-0.05', '#sadness', '#disgust', 'negative', '-0.02', 'shoe_shop', 'hamper', 'attic', 'obstruct', 'closet']
senticnet['loess_soil'] = ['0.614', '-0.95', '0', '0.584', '#joy', '#surprise', 'positive', '0.718', 'mother_lode', 'champion_lode', 'deposit', 'sedimentation', 'lode']
senticnet['lofepramine'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'infective_agent', 'satisfier', 'quickener', 'agent']
senticnet['loft'] = ['0.591', '-0.74', '0', '0.672', '#surprise', '#admiration', 'positive', '0.669', 'room', 'chillax', 'hotel_room', 'furniture_store', 'living_room']
senticnet['loft_apartment'] = ['0.591', '-0.74', '0', '0.672', '#surprise', '#admiration', 'positive', '0.669', 'room', 'chillax', 'hotel_room', 'furniture_store', 'living_room']
senticnet['loft_bed'] = ['0.591', '-0.74', '0', '0.672', '#surprise', '#admiration', 'positive', '0.669', 'room', 'chillax', 'hotel_room', 'furniture_store', 'living_room']
senticnet['loft_crag'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['loft_space'] = ['0.591', '-0.74', '0', '0.672', '#surprise', '#admiration', 'positive', '0.669', 'room', 'chillax', 'hotel_room', 'furniture_store', 'living_room']
senticnet['loft_style'] = ['0.591', '-0.74', '0', '0.672', '#surprise', '#admiration', 'positive', '0.669', 'room', 'chillax', 'hotel_room', 'furniture_store', 'living_room']
senticnet['loftier'] = ['0.98', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.854', 'sublime', 'exalted', 'elevated', 'high-flown', 'grand']
senticnet['loftiest'] = ['0.131', '0.074', '0', '0.373', '#joy', '#admiration', 'positive', '0.193', 'superlative', 'excellence', 'ultimate', 'great', 'well_done']
senticnet['loftiness'] = ['-0.53', '0', '0.993', '-0.88', '#anger', '#disgust', 'negative', '-0.80', 'expansiveness', 'expansivity', 'stateliness', 'impressiveness', 'excellence']
senticnet['loftlike'] = ['0.591', '-0.74', '0', '0.672', '#surprise', '#admiration', 'positive', '0.669', 'room', 'chillax', 'hotel_room', 'furniture_store', 'living_room']
senticnet['lofty'] = ['0.98', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.854', 'sublime', 'exalted', 'elevated', 'high-flown', 'grand']
senticnet['lofty_morality'] = ['0.909', '-0.85', '0', '0.846', '#joy', '#surprise', 'positive', '0.87', 'respectability', 'reputability', 'venerability', 'venerableness', 'righteousness']
senticnet['log'] = ['0.026', '0.165', '-0.12', '0.292', '#interest', '#admiration', 'positive', '0.09', 'fetch', 'find', 'desire_win', 'take', 'obtain']
senticnet['log_cabin'] = ['0.019', '0', '-0.05', '-0.06', '#fear', '#disgust', 'negative', '-0.03', 'igloo', 'chess_tournament', 'frozen', 'death_row', 'loose_end']
senticnet['log_flume'] = ['0.089', '-0.18', '0.037', '-0.06', '#joy', '#surprise', 'positive', '0.043', 'balloon', 'ballon', 'fielder', 'kite', 'blimp']
senticnet['log_flume_ride'] = ['0.089', '-0.18', '0.037', '-0.06', '#joy', '#surprise', 'positive', '0.043', 'balloon', 'ballon', 'fielder', 'kite', 'blimp']
senticnet['log_off'] = ['0', '0', '0', '0.911', '#admiration', '#admiration', 'positive', '0.911', 'find_computer', 'visit_zoo', 'start_computer', 'switch_computer', 'build_computer']
senticnet['log_onto'] = ['0.934', '0.983', '0', '0', '#joy', '#interest', 'positive', '0.959', 'email', 'use_computer', 'access_internet', 'send_message', 'send_letter']
senticnet['loganberry'] = ['-0.92', '0', '0.912', '-0.94', '#sadness', '#disgust', 'negative', '-0.92', 'true_blackberry', 'sand_blackberry', 'blackberry_bush', 'blackberry', 'running_blackberry']
senticnet['logarithm'] = ['0.506', '-0.56', '0', '0.788', '#surprise', '#admiration', 'positive', '0.62', 'backward', 'reverse', 'retrograde', 'reflexive', 'retral']
senticnet['logarithmic'] = ['0', '0.203', '0.036', '0.204', '#interest', '#admiration', 'positive', '0.124', 'amount', 'count', 'total', 'quantity', 'total_amount']
senticnet['logarithmically'] = ['0.979', '0.809', '0', '0', '#joy', '#interest', 'positive', '0.894', 'linearly', 'non-linearly', 'inversely', 'quadratically', 'monotonically']
senticnet['loggerhead'] = ['-0.95', '0', '0.698', '-0.68', '#sadness', '#anger', 'negative', '-0.77', 'stupid', 'stupid_person', 'stupe', 'dullard', 'dolt']
senticnet['logginess'] = ['-0.73', '0', '0.596', '-0.77', '#sadness', '#disgust', 'negative', '-0.7', 'exhaustion', 'loss_of_strength', 'weariness', 'eyestrain', 'asthenopia']
senticnet['logging_railroad'] = ['-0.98', '0', '0.521', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'battery', 'stamp_battery', 'quartz_battery', 'stamp_mill', 'factory']
senticnet['logic'] = ['-0.08', '0.25', '-0.05', '0.141', '#interest', '#admiration', 'positive', '0.063', 'put_two_together', 'think_process', 'analysis', 'brain_function', 'make_right_decision']
senticnet['logic_puzzle'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'foot_motion', 'foot_action', 'foot_movement', 'stride', 'pace']
senticnet['logical'] = ['-0.02', '0.276', '-0.06', '0.169', '#interest', '#admiration', 'positive', '0.089', 'mathematic', 'test_hypothesis', 'conjecture', 'subject', 'hypothetical']
senticnet['logical_argument'] = ['0.715', '-0.82', '0', '0.659', '#joy', '#surprise', 'positive', '0.734', 'reasoning', 'logical_thinking', 'abstract_thought', 'casuistry', 'line_of_inquiry']
senticnet['logical_coherence'] = ['0.534', '-0.86', '0', '0.676', '#surprise', '#admiration', 'positive', '0.692', 'suitability', 'suitableness', 'habitability', 'habitableness', 'qualification']
senticnet['logical_conclusion'] = ['-0.04', '0.26', '-0.15', '0.239', '#interest', '#admiration', 'positive', '0.075', 'conclude', 'result', 'end_point', 'end', 'finish']
senticnet['logical_contradiction'] = ['-0.55', '0', '0.58', '-0.98', '#anger', '#disgust', 'negative', '-0.70', 'fatalist', 'determinist', 'predestinarian', 'predestinationist', 'philosopher']
senticnet['logical_deduction'] = ['0.715', '-0.82', '0', '0.659', '#joy', '#surprise', 'positive', '0.734', 'reasoning', 'logical_thinking', 'abstract_thought', 'casuistry', 'line_of_inquiry']
senticnet['logical_equivalence'] = ['0.734', '-0.56', '0', '0.64', '#joy', '#admiration', 'positive', '0.645', 'inferential', 'suggestive', 'connotative', 'connotational', 'connotative_of']
senticnet['logical_fallacy'] = ['0.623', '-0.53', '0', '0.873', '#joy', '#admiration', 'positive', '0.676', 'petitio_principii', 'petitio', 'hysteron_proteron', 'fallacy', 'false_belief']
senticnet['logical_fallacy_principii'] = ['0.622', '-0.54', '0', '0.867', '#joy', '#admiration', 'positive', '0.676', 'petitio', 'logical_fallacy', 'hysteron_proteron', 'fallacy', 'false_belief']
senticnet['logical_implication'] = ['0.787', '0.856', '0', '0', '#joy', '#interest', 'positive', '0.822', 'entailment', 'conditional_relation', 'deduction', 'implication', 'significance']
senticnet['logical_inconsistency'] = ['-0.91', '0', '0.795', '-0.94', '#sadness', '#disgust', 'negative', '-0.88', 'ineluctability', 'assured', 'predictability', 'slam_dunk', 'inevitability']
senticnet['logical_necessity'] = ['-0.55', '0', '0.58', '-0.98', '#anger', '#disgust', 'negative', '-0.70', 'fatalist', 'determinist', 'predestinarian', 'predestinationist', 'philosopher']
senticnet['logical_structure'] = ['0.515', '-0.80', '0', '0.791', '#surprise', '#admiration', 'positive', '0.703', 'accessible', 'fathomable', 'approachable', 'perceivable', 'intelligible']
senticnet['logical_system'] = ['0.629', '-0.79', '0', '0.758', '#surprise', '#admiration', 'positive', '0.726', 'system_of_rules', 'symbolic_logic', 'mathematical_logic', 'formal_logic', 'system']
senticnet['logical_thinking'] = ['0.651', '-0.63', '0', '0.589', '#joy', '#surprise', 'positive', '0.624', 'ratiocination', 'logical_argument', 'line_of_reasoning', 'thinking', 'thought']
senticnet['logicality'] = ['0.539', '-0.77', '0', '0.657', '#surprise', '#admiration', 'positive', '0.656', 'rationality', 'quality', 'rationalness', 'completeness', 'consistency']
senticnet['logically'] = ['0', '0.722', '0', '0.893', '#interest', '#admiration', 'positive', '0.807', 'thematically', 'chronological', 'alphabetically', 'grouped', 'chronologically']
senticnet['logically_consistent'] = ['-0.55', '0', '0.58', '-0.98', '#anger', '#disgust', 'negative', '-0.70', 'fatalist', 'determinist', 'predestinarian', 'predestinationist', 'philosopher']
senticnet['logically_impossible'] = ['-0.92', '0', '0.547', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'assumption', 'boundary_condition', 'provision', 'proviso', 'precondition']
senticnet['logically_incompatible'] = ['-0.91', '0', '0.795', '-0.94', '#sadness', '#disgust', 'negative', '-0.88', 'ineluctability', 'assured', 'predictability', 'slam_dunk', 'inevitability']
senticnet['logically_possible'] = ['0.863', '-0.94', '0', '0.986', '#surprise', '#admiration', 'positive', '0.932', 'postulate', 'posit', 'assume', 'presume', 'take_for_granted']
senticnet['logicalness'] = ['0.771', '-0.6', '0', '0.596', '#joy', '#surprise', 'positive', '0.656', 'rationality', 'quality', 'rationalness', 'completeness', 'consistency']
senticnet['logier'] = ['-0.89', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.82', 'sluggish', 'languid', 'sedentary', 'slothful', 'idle']
senticnet['login'] = ['0.949', '0.866', '0', '0', '#joy', '#interest', 'positive', '0.907', 'snail_mail', 'postage', 'send_package', 'buy_stamp', 'send_letter']
senticnet['logique'] = ['0.911', '-0.94', '0', '0.953', '#surprise', '#admiration', 'positive', '0.935', 'common_sense', 'good_sense', 'horse_sense', 'mother_wit', 'gumption']
senticnet['logistic'] = ['0.814', '-0.87', '0', '0.918', '#surprise', '#admiration', 'positive', '0.87', 'ship_company', 'company', 'work_force', 'workforce', 'full_complement']
senticnet['logistic_support'] = ['-0.07', '0.069', '-0.18', '0', '#sadness', '#fear', 'negative', '-0.06', 'medical_procedure', 'cure', 'doctor_surgery', 'treatment', 'doctor_work']
senticnet['logistical'] = ['0.814', '-0.87', '0', '0.918', '#surprise', '#admiration', 'positive', '0.87', 'ship_company', 'company', 'work_force', 'workforce', 'full_complement']
senticnet['logistical_support'] = ['0.814', '-0.87', '0', '0.918', '#surprise', '#admiration', 'positive', '0.87', 'ship_company', 'company', 'work_force', 'workforce', 'full_complement']
senticnet['logistically_difficult'] = ['-0.77', '0', '0.644', '-0.68', '#sadness', '#disgust', 'negative', '-0.70', 'unobtainable', 'unprocurable', 'out_of_stock', 'bunk', 'untouchable']
senticnet['logit'] = ['-0.59', '0', '0.672', '-0.83', '#anger', '#disgust', 'negative', '-0.7', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'asymmetry', 'irregularity']
senticnet['logjam'] = ['0.829', '-0.95', '0', '0.948', '#surprise', '#admiration', 'positive', '0.91', 'check', 'arrest', 'hitch', 'halt', 'stay']
senticnet['lognormal'] = ['-0.59', '0', '0.672', '-0.83', '#anger', '#disgust', 'negative', '-0.7', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'asymmetry', 'irregularity']
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senticnet['lone_star_tick'] = ['0.579', '-0.81', '0', '0.833', '#surprise', '#admiration', 'positive', '0.741', 'typhus', 'rat_typhus', 'urban_typhus', 'endemic_typhus', 'rickettsial_disease']
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senticnet['long-flowering'] = ['0', '0', '0.893', '-0.75', '#anger', '#disgust', 'negative', '-0.82', 'free-flowering', 'frost-tender', 'quiet-running', 'slow-thinking', 'strong-growing']
senticnet['long-lasting'] = ['0.931', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.835', 'replaceable', 'commercial-grade', 'rechargeable', 'long-life', 'high-output']
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senticnet['long-promised'] = ['0.712', '-0.88', '0', '0', '#joy', '#surprise', 'positive', '0.796', 'longed-for', 'hoped-for', 'much-touted', 'much-hyped', 'much-awaited']
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senticnet['long-sleeved'] = ['0.812', '-0.74', '0', '0', '#joy', '#surprise', 'positive', '0.776', 'sweater', 'cardigan', 'pullover', 'turtleneck', 'sleeveless']
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senticnet['long_bad_camera'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'slow', 'long_focus', 'bad_camera', 'focus_time', 'low_quality_photo']
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senticnet['long_bus_ride'] = ['-0.08', '0.179', '-0.06', '0.096', '#interest', '#admiration', 'positive', '0.034', 'ride_subway', 'buy_vending_machine', 'travel', 'buy_lottery_ticket', 'acquire']
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senticnet['long_dark_hair'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'plato', 'natural_world', 'hair', 'feature', 'stick_out']
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senticnet['long_for_relax'] = ['0', '0.915', '0', '0', '#interest', '#interest', 'positive', '0.915', 'desire_social_interaction', 'lose_track_time', 'eye_strain', 'extreme_boredom', 'boredom']
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senticnet['long_haul_truck_driver'] = ['0.969', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.936', 'attendant', 'manager', 'public_servant', 'specialist', 'accomplice']
senticnet['long_island'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'sailboat', 'boat', 'rowboat', 'big_boat', 'ship']
senticnet['long_last'] = ['0', '0.992', '0', '0.873', '#interest', '#admiration', 'positive', '0.932', 'stand_still', 'roll_over', 'stretch_out', 'weather_storm', 'near_destination']
senticnet['long_life'] = ['0.96', '0.788', '0', '0', '#joy', '#interest', 'positive', '0.874', 'live_long', 'live_long_life', 'live_healthy_lifestyle', 'longevity', 'healthy']
senticnet['long_lifespan'] = ['0.947', '0.919', '0', '0.927', '#joy', '#admiration', 'positive', '0.931', 'live_long_healthy_life', 'live_ripe_old_age', 'live_well', 'live_long_happy_life', 'nice_place_to_live']
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senticnet['long_sandy_beach'] = ['0.203', '0.077', '0', '0.189', '#joy', '#admiration', 'positive', '0.156', 'good_life', 'coast', 'relax', 'sediment', 'shell']
senticnet['long_snout'] = ['0.098', '0.084', '0.819', '-0.68', '#anger', '#disgust', 'negative', '-0.33', 'grasp', 'covet', 'covetousness', 'avaricious', 'grabby']
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senticnet['look_out'] = ['-0.07', '0.079', '0', '0.129', '#interest', '#admiration', 'positive', '0.045', 'look_outside', 'see_out', 'look_through', 'see_through', 'open_close']
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senticnet['look_outside'] = ['-0.07', '0.079', '0', '0.129', '#interest', '#admiration', 'positive', '0.045', 'look_out', 'see_out', 'look_through', 'see_through', 'open_close']
senticnet['look_over'] = ['0.703', '0.84', '0', '0', '#joy', '#interest', 'positive', '0.771', 'skim_through', 'read_cover', 'take_look', 'read_through', 'read_fast']
senticnet['look_picture'] = ['0.875', '0.932', '0', '0', '#joy', '#interest', 'positive', '0.903', 'see_ad', 'turn_page', 'take_shit', 'eye_strain', 'move_eye']
senticnet['look_presentable'] = ['0.824', '0.86', '0', '0.983', '#interest', '#admiration', 'positive', '0.889', 'look_smart', 'social_acceptance', 'improve_appearance', 'improve_look', 'look_good']
senticnet['look_pretty'] = ['0.926', '0', '0', '0.702', '#joy', '#admiration', 'positive', '0.814', 'look_neat', 'look_smart', 'look_good', 'improve_look', 'look_different']
senticnet['look_price'] = ['0.036', '0.232', '-0.05', '0.192', '#interest', '#admiration', 'positive', '0.101', 'money_spend', 'achievable', 'purchase', 'establish_credit', 'pay_debt']
senticnet['look_round'] = ['0.021', '0.081', '0.017', '0.113', '#interest', '#admiration', 'positive', '0.05', 'aspect', 'perspective', 'viewfinder', 'scenic', 'look_through']
senticnet['look_scary'] = ['0.103', '-0.06', '0.013', '0.13', '#joy', '#admiration', 'positive', '0.071', 'make_assumption', 'type_of_credit_card', 'elusive', 'tangible', 'morality']
senticnet['look_scenery'] = ['0.898', '0.983', '0', '0', '#joy', '#interest', 'positive', '0.941', 'get_tired_leg', 'shift_gear', 'become_tired', 'tired_leg', 'see_neighbor']
senticnet['look_sexy'] = ['0', '0.804', '0', '0.749', '#interest', '#admiration', 'positive', '0.776', 'become_friend', 'start_conversation', 'say_hello', 'eye_contact', 'attractive']
senticnet['look_sky'] = ['0.043', '0', '0', '0.057', '#joy', '#admiration', 'positive', '0.05', 'blow_horn', 'cross_bridge', 'skip', 'leave_place', 'shoulder_burden']
senticnet['look_slimmer'] = ['-0.42', '-0.14', '-0.06', '-0.37', '#sadness', '#disgust', 'negative', '-0.18', 'brain_tumor', 'bad_hair_day', 'lyme_disease', 'diarrhoea', 'suffer_fool']
senticnet['look_smart'] = ['0.941', '0', '0', '0.856', '#joy', '#admiration', 'positive', '0.898', 'look_neat', 'improve_appearance', 'look_presentable', 'improve_look', 'look_good']
senticnet['look_stylish'] = ['-0.06', '-0.04', '0.094', '0', '#sadness', '#anger', 'negative', '-0.03', 'keep_foot_warm', 'protect_foot', 'high_heel', 'walk_upon', 'stand_upon']
senticnet['look_through'] = ['0', '0.87', '0', '0.811', '#interest', '#admiration', 'positive', '0.841', 'look_out', 'see_out', 'look_outside', 'see_through', 'window_cover']
senticnet['look_through_binoculars'] = ['0.854', '0.975', '0', '0.926', '#interest', '#admiration', 'positive', '0.918', 'go_kiss', 'kiss', 'valentine_day', 'find_love', 'procreate']
senticnet['look_to'] = ['-0.86', '0', '0.609', '-0.5', '#sadness', '#anger', 'negative', '-0.65', 'look_forward', 'await', 'look_for', 'hang_on', 'expect']
senticnet['look_truth'] = ['0', '0.985', '0', '0.809', '#interest', '#admiration', 'positive', '0.897', 'resolve_conflict', 'look_through', 'serve_jury', 'register_vote', 'look_evidence']
senticnet['look_unattractive'] = ['-0.03', '0.157', '0.698', '-0.66', '#anger', '#disgust', 'negative', '-0.30', 'fat', 'overweight', 'fatty', 'unhealthy', 'unlovely']
senticnet['look_up'] = ['0', '0.073', '0', '0', '#interest', '#interest', 'positive', '0.073', 'open_mind', 'absorb_information', 'remember_fact', 'use_dictionary', 'turn_page']
senticnet['look_up_information'] = ['0.088', '-0.00', '0', '0', '#joy', '#surprise', 'positive', '0.045', 'find_newspaper', 'news', 'headlines', 'enter_contest', 'latest_news']
senticnet['look_up_phone_number'] = ['0', '-0.03', '0.135', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'telephone_number', 'read_email', 'find_phone_number', 'pass_information', 'give_information']
senticnet['look_up_to'] = ['0.575', '0.636', '-0.26', '0.318', '#joy', '#interest', 'positive', '0.315', 'laudably', 'admirably', 'praiseworthily', 'commendable', 'admirable']
senticnet['looked_forward'] = ['0', '0.66', '0', '0', '#interest', '#interest', 'positive', '0.66', 'hope', 'hopeful', 'ambitious', 'desire', 'faithful']
senticnet['looked_rusty'] = ['0.034', '-0.02', '0.054', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'compass', 'swiss_army_knife', 'bake_pan', 'coffee_mug', 'wine_opener']
senticnet['looked_sluggish'] = ['0.034', '-0.02', '0.054', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'compass', 'swiss_army_knife', 'bake_pan', 'coffee_mug', 'wine_opener']
senticnet['looker'] = ['0.723', '-0.76', '0', '0.674', '#joy', '#surprise', 'positive', '0.72', 'cheerer', 'gawker', 'percipient', 'rubberneck', 'starer']
senticnet['looking'] = ['0.754', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.752', 'view', 'peek', 'stare', 'lookout', 'sight']
senticnet['looking_ahead'] = ['0', '0', '-0.91', '-0.95', '#fear', '#disgust', 'negative', '-0.93', 'side_by_side', 'succeeding', 'following', 'adjacent', 'future']
senticnet['looking_at'] = ['0.684', '-0.58', '0', '0.647', '#joy', '#admiration', 'positive', '0.638', 'view', 'peek', 'stare', 'lookout', 'sight']
senticnet['looking_glass'] = ['0.584', '-0.62', '0', '0.815', '#surprise', '#admiration', 'positive', '0.675', 'cheval_glass', 'car_mirror', 'hand_glass', 'hand_mirror', 'mirror']
senticnet['looking_good'] = ['0', '-0.86', '0', '0.918', '#surprise', '#admiration', 'positive', '0.892', 'looking_old', 'aging', 'age_gracefully', 'accepting_change', 'cosmetic_surgery']
senticnet['looking_intently'] = ['0.553', '0.658', '-0.25', '0.271', '#joy', '#interest', 'positive', '0.308', 'placid', 'unruffled', 'placidity', 'placidness', 'tranquil']
senticnet['looking_old'] = ['0', '-0.86', '0', '0.92', '#surprise', '#admiration', 'positive', '0.895', 'age_gracefully', 'aging', 'looking_good', 'accepting_change', 'cosmetic_surgery']
senticnet['lookout'] = ['0.521', '-0.54', '0', '0.691', '#surprise', '#admiration', 'positive', '0.586', 'view', 'peek', 'stare', 'looking', 'sight']
senticnet['lookout_man'] = ['0', '0', '-0.75', '-0.97', '#fear', '#disgust', 'negative', '-0.86', 'watch', 'lookout', 'sentry', 'sentinel', 'spotter']
senticnet['lookout_tower'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'castle', 'bell_tower', 'spire', 'steeple', 'religious_place']
senticnet['lookup'] = ['0.026', '0.165', '-0.12', '0.292', '#interest', '#admiration', 'positive', '0.09', 'fetch', 'find', 'desire_win', 'take', 'obtain']
senticnet['loom'] = ['0.056', '0.105', '0.786', '-0.68', '#anger', '#disgust', 'negative', '-0.32', 'huffishness', 'sulkiness', 'sulk', 'hover', 'bulk_large']
senticnet['loom_large'] = ['-0.13', '0.071', '0.694', '-0.67', '#anger', '#disgust', 'negative', '-0.35', 'crucify', 'dun', 'frustrating', 'aggravate', 'exasperation']
senticnet['looming_sequester'] = ['0', '0', '-0.87', '-0.84', '#fear', '#disgust', 'negative', '-0.85', 'seize', 'seclude', 'attach', 'impound', 'confiscate']
senticnet['looming_shortage'] = ['-0.83', '0', '-0.88', '-0.78', '#sadness', '#fear', 'negative', '-0.83', 'little', 'sadness', 'small_amount', 'lack', 'absence']
senticnet['looming_threat'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'dust', 'chalk_dust', 'radioactive_dust', 'particulate', 'particulate_matter']
senticnet['loon'] = ['-0.99', '0', '0.804', '-0.52', '#sadness', '#anger', 'negative', '-0.77', 'addlehead', 'birdbrain', 'ape', 'misfit', 'mortal']
senticnet['looney'] = ['-0.64', '0', '0.781', '-0.74', '#anger', '#disgust', 'negative', '-0.72', 'weirdo', 'crazy', 'loony', 'lunatic', 'nutcase']
senticnet['looney_tune_short'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'child_movie', 'child_story', 'funny_moment', 'childhood']
senticnet['loony'] = ['-0.64', '0', '0.781', '-0.74', '#anger', '#disgust', 'negative', '-0.72', 'weirdo', 'crazy', 'lunatic', 'looney', 'nutcase']
senticnet['loony_bin'] = ['-0.53', '0', '0.874', '-0.63', '#anger', '#disgust', 'negative', '-0.68', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['loop'] = ['0.881', '0.757', '0', '0', '#joy', '#interest', 'positive', '0.819', 'hockey_puck', 'hockey_stick', 'disk', 'puck', 'bullseye']
senticnet['loop_diuretic'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'spironolactone', 'adrenal_cortical_steroid', 'antihypertensive', 'antihypertensive_drug']
senticnet['loop_hole'] = ['0', '0', '0.76', '-0.92', '#anger', '#disgust', 'negative', '-0.84', 'ambiguity', 'prevarication', 'equivocation', 'unclearness', 'equivocalness']
senticnet['loop_quantum_gravity'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'general_relativity', 'general_theory_of_relativity', 'relativity', 'special_relativity', 'special_theory_of_relativity']
senticnet['looped_around'] = ['0.067', '0.088', '-0.07', '0.246', '#interest', '#admiration', 'positive', '0.081', 'multiple_purposes', 'revolve', 'start_run', 'wiggle', 'twist']
senticnet['loophole'] = ['0', '0', '0.76', '-0.92', '#anger', '#disgust', 'negative', '-0.84', 'ambiguity', 'prevarication', 'equivocation', 'unclearness', 'equivocalness']
senticnet['loose'] = ['-0.09', '-0.07', '0.052', '-0.08', '#sadness', '#disgust', 'negative', '-0.04', 'try_win', 'roll_dice', 'win', 'try_best', 'sore_knee']
senticnet['loose_alliance'] = ['-0.50', '0', '0.989', '-0.91', '#anger', '#disgust', 'negative', '-0.80', 'camp', 'pack', 'clique', 'coterie', 'ingroup']
senticnet['loose_cannon'] = ['0', '0', '0.761', '-0.71', '#anger', '#disgust', 'negative', '-0.74', 'impulsive', 'tearaway', 'incautious', 'madcap', 'impetuous']
senticnet['loose_change'] = ['0.857', '0.786', '0', '0', '#joy', '#interest', 'positive', '0.822', 'spare_change', 'coin_slot', 'checkbook_cover', 'paper_bill', 'banknote']
senticnet['loose_clothing'] = ['-0.97', '0', '0.54', '-0.55', '#sadness', '#disgust', 'negative', '-0.69', 'ablate', 'ablate_off', 'ablate_out', 'ablate_down', 'ablate_thin']
senticnet['loose_coalition'] = ['0.065', '0.113', '0', '0.1', '#interest', '#admiration', 'positive', '0.093', 'big_group', 'clique', 'group_of_people', 'multitude', 'large_group']
senticnet['loose_dirt'] = ['-0.06', '-0.01', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'low', 'ground', 'look_down', 'ground_level', 'under_house']
senticnet['loose_end'] = ['0', '0', '-0.06', '-0.03', '#fear', '#disgust', 'negative', '-0.04', 'morgue', 'death_row', 'car_race', 'auction', 'mortuary']
senticnet['loose_fitting_clothing'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'change_tire', 'fix_flat_tire', 'play_rugby', 'wear_silly_hat', 'become_adult']
senticnet['loose_gravel'] = ['-0.06', '-0.01', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'low', 'ground', 'look_down', 'ground_level', 'under_house']
senticnet['loose_leaf_notebook'] = ['0', '-0.04', '0.061', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'index_card', 'ring_binder', 'highlighter_pen', 'memo_pad', 'notepad']
senticnet['loose_leaf_paper'] = ['0', '-0.04', '0.061', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'index_card', 'ring_binder', 'highlighter_pen', 'memo_pad', 'notepad']
senticnet['loose_money'] = ['-0.04', '-0.07', '0.117', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'gamble_money', 'win_money', 'play_rule', 'winner_loser', 'play_fun']
senticnet['loose_sand'] = ['-0.06', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.04', 'friction', 'lack_of_water', 'dirt', 'abrasive', 'ground_material']
senticnet['loose_sequel'] = ['-0.06', '0.122', '-0.04', '0.113', '#interest', '#admiration', 'positive', '0.032', 'encounter', 'portray', 'reveal', 'episode', 'next_episode']
senticnet['loose_stool'] = ['-0.89', '0', '0.925', '-0.58', '#sadness', '#anger', 'negative', '-0.80', 'shit', 'laxation', 'movement', 'dump', 'asshole']
senticnet['loose_tooth'] = ['-0.92', '0', '0.794', '-0.93', '#sadness', '#disgust', 'negative', '-0.88', 'tooth_pain', 'pain', 'feel_terrible', 'scared', 'painful']
senticnet['loose_track_time'] = ['-0.03', '-0.04', '0.146', '-0.18', '#anger', '#disgust', 'negative', '-0.07', 'move_finger', 'miss_call', 'miss_out', 'part_of_band', 'make_noise']
senticnet['loosely'] = ['-0.78', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.76', 'generally', 'broadly_speaking', 'broadly', 'number', 'work']
senticnet['loosen_muscle'] = ['0.072', '0', '0.013', '0.124', '#joy', '#admiration', 'positive', '0.061', 'shrinkage', 'remove_spot', 'relieve_tension', 'remove_stain', 'tidiness']
senticnet['loosen_up'] = ['-0.51', '0', '0.738', '-0.83', '#anger', '#disgust', 'negative', '-0.69', 'relax', 'sit_back', 'unbend', 'unwind', 'decompress']
senticnet['loosener'] = ['-0.62', '0', '0.713', '-0.98', '#anger', '#disgust', 'negative', '-0.77', 'muddy_up', 'muck_up', 'foul', 'blemish', 'smear']
senticnet['looseness'] = ['0.664', '-0.88', '0', '0.732', '#surprise', '#admiration', 'positive', '0.76', 'mobility', 'portability', 'movability', 'play', 'unsteadiness']
senticnet['loosening'] = ['-0.97', '0', '0.856', '-0.63', '#sadness', '#anger', 'negative', '-0.82', 'laxation', 'change', 'tune', 'accommodation', 'modification']
senticnet['looser'] = ['0.896', '0.849', '0', '0', '#joy', '#interest', 'positive', '0.873', 'meet_new_person', 'pick_up_woman', 'get_wasted', 'drink_beer', 'get_drink']
senticnet['loot'] = ['0', '0', '0.934', '-0.79', '#anger', '#disgust', 'negative', '-0.86', 'peso', 'bribe', 'mastercard', 'profit', 'finance']
senticnet['lophodermium'] = ['-0.93', '0', '0.699', '-0.91', '#sadness', '#disgust', 'negative', '-0.84', 'onion_smut', 'head_smut', 'stinking_smut', 'flag_smut_fungus', 'covered_smut']
senticnet['lophophore'] = ['-0.93', '0', '0.607', '-0.67', '#sadness', '#disgust', 'negative', '-0.73', 'spiracle', 'stigma', 'orifice', 'opening', 'porta']
senticnet['lopinavir_ritonavir'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'indinavir', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['lopped_off'] = ['-0.06', '-0.05', '0.037', '-0.14', '#sadness', '#disgust', 'negative', '-0.05', 'cover_wound', 'cover_wind', 'welt', 'heal_wind', 'open_wind']
senticnet['lopsidedness'] = ['-0.59', '0', '0.672', '-0.83', '#anger', '#disgust', 'negative', '-0.7', 'geometrical_irregularity', 'radial_asymmetry', 'asymmetry', 'skewness', 'irregularity']
senticnet['loquacious'] = ['0.949', '-0.69', '0', '0.992', '#joy', '#admiration', 'positive', '0.878', 'chatty', 'gabby', 'garrulous', 'voluble', 'talky']
senticnet['loquaciousness'] = ['0.664', '-0.94', '0', '0.613', '#joy', '#surprise', 'positive', '0.739', 'outspokenness', 'frankness', 'garrulity', 'garrulousness', 'communicativeness']
senticnet['loquacity'] = ['0.916', '-0.87', '0', '0.79', '#joy', '#surprise', 'positive', '0.861', 'garrulity', 'garrulousness', 'loquaciousness', 'leresis', 'talkativeness']
senticnet['loquat'] = ['0.02', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'many_fruits', 'buy_fruit', 'cherry', 'pineapple', 'tomato']
senticnet['loracarbef'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['lord'] = ['0.059', '0.153', '-0.05', '0.144', '#interest', '#admiration', 'positive', '0.076', 'duchess', 'baron', 'king', 'duke', 'noble']
senticnet['lord_alphonso'] = ['-0.87', '0', '0.838', '-0.69', '#sadness', '#anger', 'negative', '-0.80', 'blither', 'prattle', 'gibber', 'blabber', 'gabble']
senticnet['lord_bacon'] = ['-0.99', '0', '0.947', '-0.88', '#sadness', '#anger', 'negative', '-0.94', 'atrociousness', 'inhumanity', 'heinousness', 'atrocity', 'barbarity']
senticnet['lord_boyce'] = ['-0.85', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.86', 'attack', 'raid', 'fight_against', 'conflict', 'opposition']
senticnet['lord_bramall'] = ['-0.85', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.86', 'attack', 'raid', 'fight_against', 'conflict', 'opposition']
senticnet['lord_buddha'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['lord_dartmouth'] = ['0.582', '-0.97', '0', '0.802', '#surprise', '#admiration', 'positive', '0.788', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['lord_emsworth'] = ['0.934', '-0.57', '0', '0.767', '#joy', '#admiration', 'positive', '0.76', 'morosoph', 'simpleton', 'flibbertigibbet', 'foolish_woman', 'simple']
senticnet['lord_farquaad'] = ['-0.63', '0', '0.674', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'baneful', 'evil', 'baleful', 'malevolent', 'malign']
senticnet['lord_goff'] = ['-0.72', '0', '0.742', '-0.80', '#anger', '#disgust', 'negative', '-0.75', 'illicitness', 'illegality', 'illegitimacy', 'shadiness', 'quality']
senticnet['lord_guthrie'] = ['-0.85', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.86', 'attack', 'raid', 'fight_against', 'conflict', 'opposition']
senticnet['lord_hath'] = ['0.741', '-0.84', '0', '0.543', '#joy', '#surprise', 'positive', '0.711', 'integrity', 'righteousness', 'scrupulousness', 'incorruptness', 'honestness']
senticnet['lord_hertford'] = ['0.582', '-0.97', '0', '0.802', '#surprise', '#admiration', 'positive', '0.788', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['lord_keeper'] = ['-0.94', '0', '0.569', '-0.72', '#sadness', '#disgust', 'negative', '-0.74', 'bitumastic', 'veneer', 'veneering', 'adhesive', 'coating']
senticnet['lord_moynihan'] = ['-0.66', '0', '0.721', '-0.53', '#sadness', '#anger', 'negative', '-0.64', 'unlimited', 'outright', 'unconditional', 'unqualified', 'unqualifiedal']
senticnet['lord_orville'] = ['-0.56', '0', '0.828', '-0.68', '#anger', '#disgust', 'negative', '-0.69', 'abruptness', 'precipitateness', 'precipitousness', 'hastiness', 'precipitancy']
senticnet['lord_prayer'] = ['0.023', '0.295', '-0.05', '0.283', '#interest', '#admiration', 'positive', '0.136', 'religion', 'religious_purpose', 'world_religion', 'gospel', 'judaism']
senticnet['lord_sandwich'] = ['0.582', '-0.97', '0', '0.802', '#surprise', '#admiration', 'positive', '0.788', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['lord_spake'] = ['-0.52', '0', '0.701', '-0.50', '#sadness', '#anger', 'negative', '-0.57', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['lord_tedder'] = ['0.74', '-0.53', '0', '0.641', '#joy', '#admiration', 'positive', '0.64', 'marshal', 'sky_marshal', 'lawman', 'law_officer', 'peace_officer']
senticnet['lord_vader'] = ['-0.96', '0', '0.108', '-0.90', '#sadness', '#disgust', 'negative', '-0.66', 'evil', 'darkness', 'dark', 'sun_down', 'shadow']
senticnet['lord_willoughby'] = ['0.582', '-0.97', '0', '0.802', '#surprise', '#admiration', 'positive', '0.788', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['lordliness'] = ['0.841', '-0.56', '0', '0.654', '#joy', '#admiration', 'positive', '0.688', 'dignity', 'gravitas', 'comportment', 'personal_manner', 'manner']
senticnet['lordly'] = ['0', '0', '-0.86', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'imperious', 'haughty', 'disdainful', 'supercilious', 'overbearing']
senticnet['lordosis'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'spinal_curvature', 'kyphosis', 'humpback', 'hunchback', 'abnormality']
senticnet['lords_prayer'] = ['0.023', '0.295', '-0.05', '0.283', '#interest', '#admiration', 'positive', '0.136', 'religion', 'religious_purpose', 'world_religion', 'gospel', 'judaism']
senticnet['lordship'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'imperium', 'potency', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['lore'] = ['0.95', '-0.86', '0', '0.947', '#joy', '#admiration', 'positive', '0.92', 'content', 'old_wives_tale', 'folklore', 'cognitive_content', 'mythology']
senticnet['lorentz_symmetry'] = ['-0.75', '0', '0.679', '-0.77', '#sadness', '#disgust', 'negative', '-0.73', 'unvariedness', 'changelessness', 'unchangeability', 'unchangeableness', 'unchangingness']
senticnet['lorentz_transformation'] = ['-0.69', '0', '0.83', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'general_relativity', 'general_theory_of_relativity', 'general_relativity_theory', 'relativity', 'special_theory_of_relativity']
senticnet['lorenz_machine'] = ['-0.05', '-0.19', '-0.17', '0.219', '#surprise', '#admiration', 'positive', '0.046', 'riddle', 'mystery', 'obituary', 'editorial', 'book_genre']
senticnet['lorn'] = ['-0.92', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.82', 'forlorn', 'lovelorn', 'doomed', 'abandoned', 'lonely']
senticnet['lorry'] = ['0.028', '0', '0.013', '0.061', '#joy', '#admiration', 'positive', '0.025', 'truck', 'vehicle', 'limousine', 'car', 'big_car']
senticnet['lorry_driver'] = ['0.969', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.936', 'attendant', 'manager', 'public_servant', 'specialist', 'accomplice']
senticnet['lortab'] = ['0.138', '0.103', '0', '0.095', '#joy', '#interest', 'positive', '0.112', 'masturbation', 'sexual_intercourse', 'sex', 'warm_fuzzy_feeling', 'fuck']
senticnet['losartan'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'spironolactone', 'spironolactone_drug']
senticnet['lose'] = ['-0.87', '-0.66', '0.98', '-0.76', '#sadness', '#anger', 'negative', '-0.48', 'tired', 'gyroscope', 'increase_price', 'ball_bounce', 'sore_knee']
senticnet['lose_a_lot_of_money'] = ['-0.88', '0', '-0.96', '-0.79', '#sadness', '#fear', 'negative', '-0.88', 'team_lose', 'foolish', 'hit_baseball_bat', 'winner_loser', 'lose_game']
senticnet['lose_article'] = ['-0.92', '0', '0.228', '-0.91', '#sadness', '#disgust', 'negative', '-0.68', 'lose_file', 'mistake', 'lose_document', 'error', 'fallacy']
senticnet['lose_balance'] = ['0', '0', '-0.74', '-0.82', '#fear', '#disgust', 'negative', '-0.78', 'fall_down', 'fall_over', 'trip_fall', 'fall', 'take_photo']
senticnet['lose_ball'] = ['0', '-0.06', '0.077', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'punch_bag', 'increase_price', 'ball_bounce', 'tired_iron', 'buy_ball']
senticnet['lose_battle'] = ['-0.12', '-0.03', '-0.10', '-0.18', '#sadness', '#disgust', 'negative', '-0.09', 'military_action', 'beaten', 'victory_defeat', 'defeat', 'win_battle']
senticnet['lose_blood'] = ['0.046', '0.117', '0.772', '-0.67', '#anger', '#disgust', 'negative', '-0.32', 'die', 'die_in_war', 'lose_war', 'get_hit', 'kamikaze']
senticnet['lose_consciousness'] = ['-0.95', '0', '0.549', '-0.99', '#sadness', '#disgust', 'negative', '-0.83', 'lose_track_of_time', 'memory_loss', 'bad_memory', 'sickness', 'become_old']
senticnet['lose_control'] = ['-0.20', '-0.11', '-0.04', '-0.20', '#sadness', '#disgust', 'negative', '-0.08', 'hangover', 'alcoholism', 'throw_up', 'forgetfulness', 'bee_sting']
senticnet['lose_credibility'] = ['0', '0', '0', '-0.94', '#disgust', '#disgust', 'negative', '-0.94', 'screw_up', 'feel_stressed', 'bad_feeling', 'complication', 'without_money']
senticnet['lose_document'] = ['-0.92', '0', '0.228', '-0.91', '#sadness', '#disgust', 'negative', '-0.68', 'lose_file', 'mistake', 'lose_article', 'error', 'fallacy']
senticnet['lose_election'] = ['-0.04', '0.168', '-0.07', '0.09', '#interest', '#admiration', 'positive', '0.033', 'elect_official', 'elect_person', 'prime_minister', 'politician', 'country_leader']
senticnet['lose_energy'] = ['0', '0', '0', '-0.84', '#disgust', '#disgust', 'negative', '-0.84', 'get_old', 'grow_older', 'old', 'become_old', 'lose_health']
senticnet['lose_file'] = ['-0.92', '0', '0.228', '-0.91', '#sadness', '#disgust', 'negative', '-0.68', 'lose_article', 'mistake', 'lose_document', 'error', 'misplace']
senticnet['lose_focus'] = ['-0.08', '-0.03', '0.043', '-0.12', '#sadness', '#disgust', 'negative', '-0.05', 'poor_strategy', 'bad_luck', 'lose', 'lose_game', 'miss_point']
senticnet['lose_foot'] = ['0', '0', '-0.90', '0', '#fear', '#fear', 'negative', '-0.90', 'slip', 'skid', 'stumble', 'reach_high_shelf', 'trip']
senticnet['lose_friend'] = ['-0.73', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.73', 'black_eye', 'censorship', 'complication', 'get_hurt', 'schizophrenia']
senticnet['lose_game'] = ['-0.08', '-0.03', '0.043', '-0.12', '#sadness', '#disgust', 'negative', '-0.05', 'poor_strategy', 'bad_luck', 'lose', 'lose_focus', 'miss_point']
senticnet['lose_hair'] = ['-0.96', '0', '0.278', '-0.98', '#sadness', '#disgust', 'negative', '-0.74', 'need_cure', 'take_medicine', 'spend_money', 'stress', 'anxiety']
senticnet['lose_health'] = ['0', '0', '0', '-0.84', '#disgust', '#disgust', 'negative', '-0.84', 'get_old', 'grow_older', 'old', 'lose_energy', 'become_old']
senticnet['lose_job'] = ['-0.12', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.18', 'die_young', 'go_scuba_dive', 'go_insane', 'feel_stupid', 'feel_ashamed']
senticnet['lose_key'] = ['-0.16', '0', '-0.06', '-0.07', '#sadness', '#disgust', 'negative', '-0.09', 'master_new_skill', 'master_skill', 'chair_committee', 'lose_virginity', 'honor_parent']
senticnet['lose_life'] = ['-0.97', '0', '0.061', '-0.90', '#sadness', '#disgust', 'negative', '-0.64', 'end_of_life', 'pass_away', 'death', 'stop_living', 'die_person']
senticnet['lose_memory'] = ['-0.15', '-0.05', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.05', 'bad_grade', 'loss', 'loose', 'sore_knee', 'get_tired']
senticnet['lose_mind'] = ['-0.97', '0', '0.7', '-0.96', '#sadness', '#disgust', 'negative', '-0.87', 'go_insane', 'punch_face', 'feel_bad', 'feel_stupid', 'become_angry']
senticnet['lose_money'] = ['-0.85', '0', '0.814', '-0.91', '#sadness', '#disgust', 'negative', '-0.86', 'buy_chip', 'shuffle_card', 'go_broke', 'lose', 'go_debt']
senticnet['lose_patience'] = ['0', '0', '0.75', '-0.88', '#anger', '#disgust', 'negative', '-0.81', 'upset', 'faze', 'discompose', 'discomfit', 'disconcert']
senticnet['lose_place'] = ['-0.15', '0', '0', '-0.14', '#sadness', '#disgust', 'negative', '-0.15', 'start_over', 'wait_patiently', 'find_line_stand', 'act_bored', 'find_line']
senticnet['lose_qualification'] = ['0', '0', '0', '-0.77', '#disgust', '#disgust', 'negative', '-0.77', 'stress', 'tension', 'fail', 'complete_course', 'become_stressed']
senticnet['lose_sight'] = ['-0.05', '0.178', '-0.12', '0.142', '#interest', '#admiration', 'positive', '0.033', 'recognize', 'investigation', 'deduce', 'aware', 'cognition']
senticnet['lose_sleep'] = ['-0.91', '0', '0.849', '-0.92', '#sadness', '#disgust', 'negative', '-0.89', 'feel_stupid', 'buy_stock', 'finish_task', 'lose_temper', 'stress']
senticnet['lose_soul'] = ['-0.24', '0', '-0.24', '-0.04', '#sadness', '#fear', 'negative', '-0.17', 'porpoise', 'titanic', 'seahorse', 'many_fishes', 'sea_wee']
senticnet['lose_team'] = ['-0.94', '0', '0.984', '0', '#sadness', '#anger', 'negative', '-0.96', 'cause_suffer', 'leave_out', 'sadness', 'disrespect', 'lyme_disease']
senticnet['lose_temper'] = ['-0.92', '0', '0.845', '-0.88', '#sadness', '#disgust', 'negative', '-0.88', 'lose_game', 'lose_sleep', 'break_leg', 'blackout', 'become_angry']
senticnet['lose_territory'] = ['-0.11', '0', '0.033', '-0.32', '#sadness', '#disgust', 'negative', '-0.15', 'conquer_enemy', 'lose_war', 'hitler', 'go_war', 'enslave_person']
senticnet['lose_time'] = ['-0.70', '0', '0.824', '-0.93', '#anger', '#disgust', 'negative', '-0.82', 'monotony', 'bad_karma', 'coworker', 'kill_time', 'lay_off']
senticnet['lose_track_of_time'] = ['-0.95', '0', '0.825', '-0.98', '#sadness', '#disgust', 'negative', '-0.91', 'car_accident', 'accident', 'motorbike_accident', 'lose_consciousness', 'sport_accident']
senticnet['lose_track_time'] = ['-0.75', '0', '0.876', '-0.94', '#anger', '#disgust', 'negative', '-0.85', 'boredom', 'get_excited', 'extreme_boredom', 'bored', 'eye_strain']
senticnet['lose_traction'] = ['-0.11', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.08', 'have_fun', 'spring_up', 'fall_well', 'shovel_snow', 'fly_around']
senticnet['lose_virginity'] = ['-0.16', '0', '-0.06', '-0.07', '#sadness', '#disgust', 'negative', '-0.10', 'master_new_skill', 'master_skill', 'chair_committee', 'lose_key', 'honor_parent']
senticnet['lose_wallet'] = ['-0.80', '0', '0.718', '-0.79', '#sadness', '#disgust', 'negative', '-0.77', 'walk_exercise', 'push_cart', 'go_debt', 'pay_dinner', 'spend_much_money']
senticnet['lose_war'] = ['-0.98', '0', '0.782', '-0.87', '#sadness', '#disgust', 'negative', '-0.88', 'beaten', 'loss', 'lose_battle', 'military_action', 'terrify']
senticnet['lose_weight'] = ['0', '0', '0', '0.071', '#admiration', '#admiration', 'positive', '0.071', 'get_shape', 'stay_fit', 'weight_loss', 'maintain_health', 'get_healthy']
senticnet['loser'] = ['-0.87', '-0.66', '0.98', '-0.76', '#sadness', '#anger', 'negative', '-0.48', 'boring', 'dumb', 'dull', 'get_bored', 'get_drink']
senticnet['loser_bracket'] = ['0.763', '-0.67', '0', '0.945', '#joy', '#admiration', 'positive', '0.795', 'set', 'income_bracket', 'tax_bracket', 'income_tax_bracket', 'price_bracket']
senticnet['loses_credibility'] = ['-0.63', '0', '0.712', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'upset', 'disconcert', 'discompose', 'discomfit', 'untune']
senticnet['losheim_gap'] = ['0.737', '-0.96', '0', '0.552', '#joy', '#surprise', 'positive', '0.75', 'prominence', 'hump', 'bump', 'swelling', 'frontal_eminence']
senticnet['losing_momentum'] = ['0.844', '0', '0', '0', '#joy', '#joy', 'positive', '0.844', 'straight', 'late_time', 'preparedness', 'past_present', 'wait']
senticnet['losing_patience'] = ['-0.12', '-0.08', '0.051', '-0.17', '#sadness', '#disgust', 'negative', '-0.06', 'talk_neighbor', 'think_dog', 'boredom', 'act_bored', 'stop_bleed']
senticnet['losing_weight'] = ['-0.06', '-0.02', '0.021', '-0.06', '#sadness', '#disgust', 'negative', '-0.03', 'clean_window', 'vacuum_carpet', 'repetitive_task', 'run_long_time', 'exercise_daily']
senticnet['loss'] = ['-0.81', '0', '0.884', '-0.87', '#anger', '#disgust', 'negative', '-0.85', 'lose_war', 'beaten', 'cost_fortune', 'bad_grade', 'military_action']
senticnet['loss_attributable'] = ['-0.50', '0', '0.89', '-0.78', '#anger', '#disgust', 'negative', '-0.72', 'immoderateness', 'degree', 'grade', 'level', 'immoderatenessiveness']
senticnet['loss_aversion'] = ['-0.12', '0.193', '0.597', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'upvalue', 'overrate', 'overprize', 'overstimate', 'overprice']
senticnet['loss_equation_usle'] = ['0.196', '0.129', '-0.06', '0.184', '#joy', '#admiration', 'positive', '0.111', 'pleasant', 'pleasurable', 'bourgeois', 'important_human', 'good']
senticnet['loss_of_consciousness'] = ['-0.03', '0', '-0.19', '-0.11', '#fear', '#disgust', 'negative', '-0.11', 'become_unconscious', 'insensible', 'stop_move', 'unconsciousness', 'stop_think']
senticnet['loss_of_strength'] = ['-0.73', '0', '0.596', '-0.77', '#sadness', '#disgust', 'negative', '-0.7', 'exhaustion', 'weariness', 'logginess', 'eyestrain', 'asthenopia']
senticnet['loss_ofawareness'] = ['-0.17', '-0.00', '-0.15', '-0.16', '#sadness', '#disgust', 'negative', '-0.12', 'bang_head', 'fall_ground', 'concussion', 'serious_accident', 'loss_ofbreath']
senticnet['loss_ofbreath'] = ['-0.04', '0', '-0.12', '-0.07', '#fear', '#disgust', 'negative', '-0.08', 'break_leg', 'fall_ground', 'loss_ofawareness', 'fall_down', 'breafast']
senticnet['loss_offreedom'] = ['-0.35', '0', '-0.27', '0', '#sadness', '#fear', 'negative', '-0.31', 'lower_standard_live', 'overpopulation', 'marry_kid', 'quiet_baby', 'cum']
senticnet['loss_oflife'] = ['-0.81', '0', '-0.71', '-0.8', '#sadness', '#disgust', 'negative', '-0.77', 'death', 'kill', 'serious_injury', 'die', 'fall_dead']
senticnet['loss_ofmemory'] = ['-0.17', '-0.02', '0', '-0.26', '#sadness', '#disgust', 'negative', '-0.13', 'paralysis', 'head_hurt', 'headache', 'relieve_pain', 'make_sad']
senticnet['loss_ofmoney'] = ['-0.08', '-0.03', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.05', 'salivation', 'world_hunger', 'salivate', 'weight_gain', 'buy_steak']
senticnet['lossberg'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'start_over', 'queue', 'find_line', 'take_turn', 'long_line']
senticnet['lossless_compression'] = ['0', '0', '-0.1', '0', '#fear', '#fear', 'negative', '-0.1', 'grow_yard', 'heat', 'burn', 'corrode', 'stain_clothes']
senticnet['lossless_compression_algorithm'] = ['0', '0', '-0.1', '0', '#fear', '#fear', 'negative', '-0.1', 'grow_yard', 'heat', 'burn', 'corrode', 'stain_clothes']
senticnet['lossy_compression'] = ['0', '0', '-0.1', '0', '#fear', '#fear', 'negative', '-0.1', 'grow_yard', 'heat', 'burn', 'corrode', 'stain_clothes']
senticnet['lost'] = ['0', '0', '-0.94', '-0.77', '#fear', '#disgust', 'negative', '-0.85', 'need_information', 'need_help', 'need_know', 'ignorant', 'needy_person']
senticnet['lost_and_found'] = ['0', '0.086', '-0.06', '0.12', '#interest', '#admiration', 'positive', '0.046', 'odds', 'misplace', 'sea_level', 'load_dock', 'room_temperature']
senticnet['lost_cause'] = ['-0.60', '0', '0.694', '-0.96', '#anger', '#disgust', 'negative', '-0.75', 'movement', 'cause', 'campaign', 'crusade', 'drive']
senticnet['lost_forever'] = ['0', '0', '0.935', '-0.76', '#anger', '#disgust', 'negative', '-0.84', 'hear_bad_news', 'death_love', 'disrespect', 'forget_everything', 'toothache']
senticnet['lost_in_nature'] = ['0', '0.085', '0', '0.059', '#interest', '#admiration', 'positive', '0.072', 'find', 'lake', 'trekking', 'find_lake', 'wear_shoes']
senticnet['lost_luggage'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'hand_baggage', 'baggage', 'passenger_ticket', 'ticket_counter', 'baggage_trunk']
senticnet['lost_money'] = ['-0.87', '-0.79', '0.729', '-0.97', '#sadness', '#disgust', 'negative', '-0.44', 'bankruptcy', 'empty_pocket', 'cashless', 'hobo', 'debt']
senticnet['lost_patience'] = ['-0.12', '-0.08', '0.051', '-0.17', '#sadness', '#disgust', 'negative', '-0.06', 'talk_neighbor', 'think_dog', 'boredom', 'act_bored', 'stop_bleed']
senticnet['lost_pet'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'find_animal', 'wild_horse', 'animal', 'doghouse', 'find_dog']
senticnet['lost_preselection'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'couch', 'stool', 'coach_seat']
senticnet['lost_via_tko'] = ['0.823', '0.982', '0', '0', '#joy', '#interest', 'positive', '0.902', 'spherical', 'three_dimensions', 'round_orb', 'coil', 'fill_with_sand']
senticnet['lot'] = ['0', '0.108', '0.029', '0.09', '#interest', '#admiration', 'positive', '0.056', 'quantity', 'amount', 'rate', 'allotment', 'pound']
senticnet['lot_easier'] = ['-0.38', '0.165', '-0.32', '0.243', '#sadness', '#fear', 'negative', '-0.07', 'hard_to_define', 'inherit', 'harmful_human', 'bad_choice', 'badness']
senticnet['lot_of_space'] = ['0', '0.088', '0', '0.061', '#interest', '#admiration', 'positive', '0.074', 'empty_room', 'above_earth', 'without_light', 'limitless', 'outer']
senticnet['lotbinire'] = ['-0.71', '0', '0.917', '-0.51', '#sadness', '#anger', 'negative', '-0.71', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['lothario'] = ['-0.19', '0', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.15', 'bad_child', 'misbehavior', 'bad_kid', 'bad_action', 'difficulty']
senticnet['lotic'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'frog', 'amphibian']
senticnet['lotion'] = ['0', '0.086', '-0.06', '0.12', '#interest', '#admiration', 'positive', '0.046', 'gel_toothpaste', 'tartar_control_toothpaste', 'fluoride_mouthwash', 'toothpaste', 'hair_lotion']
senticnet['lottery'] = ['0.858', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.813', 'box_office', 'movie', 'cctv', 'cinema', 'movie_theater']
senticnet['lottery_jackpot'] = ['0.858', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.813', 'box_office', 'movie', 'cctv', 'cinema', 'movie_theater']
senticnet['lottery_prize'] = ['0.858', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.813', 'box_office', 'movie', 'cctv', 'cinema', 'movie_theater']
senticnet['lottery_retailer'] = ['0.858', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.813', 'box_office', 'movie', 'cctv', 'cinema', 'movie_theater']
senticnet['lottery_ticket'] = ['-0.78', '0', '0.944', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'loser', 'speculator', 'mortal', 'better', 'gambler']
senticnet['lotto'] = ['0.828', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.887', 'jackpot', 'payout', 'winning', 'jackpot', 'lottery']
senticnet['lotto_draw'] = ['0.858', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.813', 'box_office', 'movie', 'cctv', 'cinema', 'movie_theater']
senticnet['lotty'] = ['-0.75', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.83', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['lotus_elise'] = ['0.755', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.728', 'high_class', 'expensive_car', 'ferrari', 'fancy_car', 'sports_car']
senticnet['lotus_pedestal'] = ['0', '0', '-0.76', '-0.82', '#fear', '#disgust', 'negative', '-0.79', 'stage', 'thespian', 'actor', 'theatre', 'drama']
senticnet['lou_ferrigno'] = ['-0.90', '0', '0.522', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'hooligan', 'ruffian', 'roughneck', 'rowdy', 'yob']
senticnet['lou_gehrig_disease'] = ['-0.95', '0', '0.621', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'disease', 'brain_disorder', 'brain_disease', 'ill', 'epilepsy']
senticnet['lou_gehrigs_disease'] = ['-0.95', '0', '0.621', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'disease', 'brain_disorder', 'brain_disease', 'ill', 'epilepsy']
senticnet['lou_macari'] = ['-0.9', '0', '0.749', '-0.71', '#sadness', '#anger', 'negative', '-0.78', 'chute', 'slide', 'slideway', 'sloping_trough', 'gutter']
senticnet['louche'] = ['0.725', '-0.88', '0', '0.508', '#joy', '#surprise', 'positive', '0.706', 'disreputable', 'seedy', 'discredited', 'sleazy', 'discreditable']
senticnet['loud'] = ['-0.95', '0', '-0.73', '-0.73', '#sadness', '#fear', 'negative', '-0.80', 'loud_sound', 'loudness', 'loud_soft', 'loud_noise', 'audio']
senticnet['loud_bang'] = ['0', '-0.00', '-0.07', '-0.06', '#fear', '#disgust', 'negative', '-0.04', 'lighten', 'tornado', 'dynamite_explosion', 'forest_fire', 'lightning']
senticnet['loud_boom'] = ['0', '-0.00', '-0.07', '-0.06', '#fear', '#disgust', 'negative', '-0.04', 'lighten', 'tornado', 'dynamite_explosion', 'forest_fire', 'lightning']
senticnet['loud_crack'] = ['0', '-0.00', '-0.07', '-0.06', '#fear', '#disgust', 'negative', '-0.04', 'lighten', 'tornado', 'dynamite_explosion', 'forest_fire', 'lightning']
senticnet['loud_cry'] = ['-0.06', '-0.01', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.05', 'loud_shout', 'freak_out', 'loud_yell', 'groan', 'clamor']
senticnet['loud_explosion'] = ['0', '-0.00', '-0.07', '-0.06', '#fear', '#disgust', 'negative', '-0.04', 'lighten', 'tornado', 'dynamite_explosion', 'forest_fire', 'lightning']
senticnet['loud_laugh'] = ['-0.85', '0', '0.583', '-0.71', '#sadness', '#disgust', 'negative', '-0.71', 'muddle', 'rummage', 'mare_nest', 'welter', 'clutter']
senticnet['loud_mouth'] = ['-0.95', '0', '0.673', '-0.95', '#sadness', '#disgust', 'negative', '-0.86', 'worrier', 'fusspot', 'worrywart', 'unwelcome_person', 'killjoy']
senticnet['loud_music'] = ['-0.95', '0', '-0.73', '-0.73', '#sadness', '#fear', 'negative', '-0.80', 'make_noise', 'high_volume', 'loudly', 'noise', 'loud']
senticnet['loud_noise'] = ['0', '0', '-0.72', '-0.81', '#fear', '#disgust', 'negative', '-0.77', 'loud_sound', 'noise', 'decibel', 'clamor', 'loud']
senticnet['loud_ovation'] = ['-0.86', '0', '0.516', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'outcry', 'call', 'cry', 'yell', 'shout']
senticnet['loud_roar'] = ['0', '-0.00', '-0.07', '-0.06', '#fear', '#disgust', 'negative', '-0.04', 'lighten', 'tornado', 'dynamite_explosion', 'forest_fire', 'lightning']
senticnet['loud_scream'] = ['0', '0', '-0.91', '-0.72', '#fear', '#disgust', 'negative', '-0.81', 'loudness', 'vibration', 'echo', 'detect', 'decibel']
senticnet['loud_shout'] = ['-0.05', '-0.00', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.03', 'loud_yell', 'loud_cry', 'freak_out', 'human_noise', 'noise']
senticnet['loud_soft'] = ['-0.95', '0', '-0.74', '-0.74', '#sadness', '#fear', 'negative', '-0.81', 'decibel', 'noise', 'loudness', 'sound', 'audio']
senticnet['loud_sound'] = ['-0.95', '0', '-0.73', '-0.73', '#sadness', '#fear', 'negative', '-0.80', 'amplifier', 'stereo', 'tuner', 'speaker', 'steam_engine']
senticnet['loud_thud'] = ['0', '-0.00', '-0.07', '-0.06', '#fear', '#disgust', 'negative', '-0.04', 'lighten', 'tornado', 'dynamite_explosion', 'forest_fire', 'lightning']
senticnet['loud_whir'] = ['0.952', '-0.52', '0', '0.835', '#joy', '#admiration', 'positive', '0.771', 'billboard', 'hoarding', 'sandwich_board', 'scoreboard', 'shingle']
senticnet['loud_yell'] = ['0', '0', '-0.91', '-0.72', '#fear', '#disgust', 'negative', '-0.81', 'loudness', 'vibration', 'echo', 'detect', 'decibel']
senticnet['louder'] = ['0', '0', '0.802', '-0.75', '#anger', '#disgust', 'negative', '-0.78', 'cry', 'yell', 'scream', 'hollo', 'call']
senticnet['loudly'] = ['-0.95', '0', '-0.73', '-0.73', '#sadness', '#fear', 'negative', '-0.80', 'make_noise', 'high_volume', 'loud_music', 'noise', 'loud']
senticnet['loudmouth'] = ['-0.95', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.86', 'brag', 'cocky', 'boastful', 'cock', 'self-aggrandizing']
senticnet['loudness'] = ['-0.95', '0', '-0.73', '-0.73', '#sadness', '#fear', 'negative', '-0.80', 'decibel', 'noise', 'loud_soft', 'sound', 'audio']
senticnet['loudspeaker'] = ['0.903', '0.835', '0', '0', '#joy', '#interest', 'positive', '0.869', 'speaker', 'ear_piece', 'listen_to_music', 'music', 'jazz']
senticnet['lough_ce'] = ['0', '0.105', '0', '0.084', '#interest', '#admiration', 'positive', '0.095', 'archive', 'print_medium', 'quiet_place', 'register', 'media']
senticnet['loughinsholin'] = ['0.975', '-0.78', '0', '0.969', '#joy', '#admiration', 'positive', '0.911', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['loughlin'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
senticnet['louisa_gouliamaki_afp_getty'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expansive', 'expansible', 'erectile', 'cavernous']
senticnet['louisiana'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
senticnet['louisiana_coast'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'harpoon', 'armory', 'lake', 'river', 'bay']
senticnet['louisville_courier_journal'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquisitor', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['lounge'] = ['0.841', '0', '0', '0.71', '#joy', '#admiration', 'positive', '0.775', 'waiter', 'bar_service', 'beverage_service', 'concierge', 'minibar']
senticnet['lounge_bar'] = ['0.841', '0', '0', '0.71', '#joy', '#admiration', 'positive', '0.775', 'waiter', 'bar_service', 'beverage_service', 'concierge', 'minibar']
senticnet['lounge_chair'] = ['0.052', '0.119', '0', '0.141', '#interest', '#admiration', 'positive', '0.104', 'cinema', 'theater', 'seat', 'chair', 'row_seat']
senticnet['lounge_lizard'] = ['0.807', '-0.83', '0', '0.688', '#joy', '#surprise', 'positive', '0.776', 'folly', 'lunacy', 'craziness', 'indulgence', 'foolery']
senticnet['lounge_singer'] = ['-0.80', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.79', 'whorehouse', 'brothel', 'massage_parlor', 'bagnio', 'house_of_prostitution']
senticnet['lounger'] = ['-0.76', '0', '0.941', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'wear', 'lounging_pajama', 'lounging_pyjama', 'wearable', 'loungewear']
senticnet['loungewear'] = ['-0.76', '0', '0.941', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'wear', 'lounging_pajama', 'lounging_pyjama', 'wearable', 'lounger']
senticnet['lounging_around'] = ['0', '0.075', '0.028', '0.173', '#interest', '#admiration', 'positive', '0.073', 'friend', 'spend_time_with_friend', 'party', 'fun', 'hang_out_with_friend']
senticnet['lounging_pajama'] = ['-0.76', '0', '0.941', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'wear', 'loungewear', 'lounging_pyjama', 'wearable', 'lounger']
senticnet['lounging_pyjama'] = ['-0.76', '0', '0.941', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'wear', 'lounging_pajama', 'loungewear', 'wearable', 'lounger']
senticnet['loup_garou'] = ['-0.79', '0', '0.885', '-0.76', '#sadness', '#anger', 'negative', '-0.81', 'infernal', 'mortal', 'dead_soul', 'dead_soule', 'living_dead']
senticnet['loupe'] = ['0.057', '0', '0.012', '0.08', '#joy', '#admiration', 'positive', '0.042', 'see_better', 'glasses', 'become_gigantic', 'magnify', 'contact_lens']
senticnet['lourde_chute'] = ['0.502', '-0.82', '0', '0.779', '#surprise', '#admiration', 'positive', '0.703', 'mortal', 'person', 'individual', 'free_liver', 'agent']
senticnet['lourie'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['louse'] = ['0', '0', '0.941', '-0.83', '#anger', '#disgust', 'negative', '-0.88', 'gnat', 'flea', 'mosquito', 'muzzle', 'mite']
senticnet['louse_fly'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'annelid', 'annelid_worm', 'leech']
senticnet['lousiness'] = ['-0.84', '0', '0.767', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'pediculosis_capitis', 'head_louse', 'pediculosis_corporis', 'pediculosis_pubis', 'crab']
senticnet['lousy'] = ['-0.92', '0', '0.256', '-0.98', '#sadness', '#disgust', 'negative', '-0.72', 'low_quality', 'bad_quality', 'failure', 'poor_quality', 'defective']
senticnet['lout'] = ['0', '0', '-0.87', '-0.99', '#fear', '#disgust', 'negative', '-0.93', 'oaf', 'clod', 'goon', 'stumblebum', 'lummox']
senticnet['loutish'] = ['0', '0', '0.872', '-0.78', '#anger', '#disgust', 'negative', '-0.82', 'ungracious', 'ungentlemanly', 'ill-mannered', 'unmannerly', 'discourteous']
senticnet['louvain'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'feudal_lord', 'lord', 'master', 'overlord', 'king']
senticnet['loux'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'dicky', 'impaired', 'lessened', 'diminished']
senticnet['lovable'] = ['0.936', '-0.81', '0', '0.83', '#joy', '#admiration', 'positive', '0.86', 'cherubic', 'lovely', 'adorable', 'endearing', 'angelic']
senticnet['lovable_character'] = ['0.936', '-0.81', '0', '0.83', '#joy', '#admiration', 'positive', '0.86', 'cherubic', 'lovely', 'adorable', 'endearing', 'angelic']
senticnet['lovable_rogue'] = ['0.556', '-0.79', '0', '0.755', '#surprise', '#admiration', 'positive', '0.701', 'lush', 'alcoholic', 'alky', 'rummy', 'boozer']
senticnet['lovably'] = ['-0.95', '0', '0.772', '-0.89', '#sadness', '#disgust', 'negative', '-0.87', 'smart_aleck', 'upstart', 'snob', 'snooty', 'unpleasant_person']
senticnet['love'] = ['1', '0', '0', '0.66', '#joy', '#admiration', 'positive', '0.83', 'tenderness', 'affection', 'kindness', 'care', 'passion']
senticnet['love_affair'] = ['-0.88', '0', '0.982', '0', '#sadness', '#anger', 'negative', '-0.93', 'intrigue', 'affair', 'divorce', 'betrayal', 'adultery']
senticnet['love_affection'] = ['0.94', '0', '0', '0.84', '#joy', '#admiration', 'positive', '0.89', 'kind', 'kindness', 'humble', 'show_love', 'express_gratitude']
senticnet['love_animal'] = ['-0.08', '-0.00', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'fix_breakfast', 'salt_food', 'prepare_breakfast', 'bone_chicken', 'set_table']
senticnet['love_art'] = ['0.706', '0.791', '0', '0.746', '#interest', '#admiration', 'positive', '0.748', 'go_museum', 'obtain_information', 'sightsee', 'learn_culture', 'bring_friend']
senticnet['love_back'] = ['0.932', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.921', 'show_empathy', 'warm_fuzzy_feeling', 'trust', 'become_aroused', 'say_welcome']
senticnet['love_child'] = ['0', '0', '-0.06', '-0.06', '#fear', '#disgust', 'negative', '-0.06', 'baby', 'quiet_baby', 'marry_kid', 'loss_offreedom', 'cum']
senticnet['love_city'] = ['0.713', '0.825', '0', '0.907', '#interest', '#admiration', 'positive', '0.815', 'romance', 'love', 'love_story', 'get_rid_of_enemy', 'glamour']
senticnet['love_else'] = ['-0.02', '0.118', '-0.09', '0.207', '#interest', '#admiration', 'positive', '0.05', 'kiss', 'trust', 'end_happily', 'commit', 'flirt']
senticnet['love_family'] = ['0.802', '0', '0', '0.739', '#joy', '#admiration', 'positive', '0.77', 'cherish', 'support', 'brotherhood', 'run_business', 'wear_necklace']
senticnet['love_feast'] = ['0.544', '-0.67', '0', '0.653', '#surprise', '#admiration', 'positive', '0.625', 'get_together', 'bee', 'social_affair', 'company', 'function']
senticnet['love_god'] = ['0.836', '0.867', '0', '0', '#joy', '#interest', 'positive', '0.851', 'religion', 'great_love', 'get_help', 'religious_belief', 'hope']
senticnet['love_line'] = ['0.924', '-0.76', '0', '0.728', '#joy', '#surprise', 'positive', '0.805', 'crease', 'crinkle', 'wrinkle', 'furrow', 'seam']
senticnet['love_music'] = ['0.52', '0.666', '-0.26', '0.269', '#joy', '#interest', 'positive', '0.298', 'have_fun', 'hear_beautiful_music', 'sound_beautiful', 'beautiful_music', 'record_music']
senticnet['love_one'] = ['0.817', '0', '0', '0.95', '#joy', '#admiration', 'positive', '0.883', 'orphan', 'become_parent', 'take_child', 'have_much', 'progeny']
senticnet['love_others'] = ['0.117', '-0.08', '-0.05', '0', '#joy', '#surprise', 'positive', '0.048', 'make_love_spouse', 'dance_together', 'kiss', 'give_flower', 'feel_joy']
senticnet['love_parent'] = ['0.945', '0', '0', '0.831', '#joy', '#admiration', 'positive', '0.888', 'world_peace', 'run_business', 'strong_relationship', 'cherish', 'express_gratitude']
senticnet['love_person'] = ['0.731', '0', '0', '0.974', '#joy', '#admiration', 'positive', '0.853', 'cotton', 'silk', 'wool', 'fleece', 'cotton_boll']
senticnet['love_pet'] = ['0.867', '0.802', '0', '0.793', '#joy', '#interest', 'positive', '0.821', 'master_ability', 'bicycle_school', 'succeed_school', 'solve_equation', 'master_skill']
senticnet['love_potion'] = ['-0.55', '0', '0.5', '-0.55', '#sadness', '#disgust', 'negative', '-0.53', 'remedy', 'cure', 'catholicon', 'therapeutic', 'curative']
senticnet['love_sick'] = ['-0.09', '-0.02', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'lose_mind', 'drive_crazy', 'annoy', 'sick', 'stupid']
senticnet['love_story'] = ['0.713', '0.825', '0', '0.907', '#interest', '#admiration', 'positive', '0.815', 'romance', 'love', 'glamour', 'get_rid_of_enemy', 'love_city']
senticnet['love_triangle'] = ['-0.91', '0', '0.221', '-0.91', '#sadness', '#disgust', 'negative', '-0.68', 'cheat', 'cheat_on', 'intruder', 'have_problem', 'heart_broken']
senticnet['loveable'] = ['0.721', '-0.54', '0', '0.824', '#joy', '#admiration', 'positive', '0.697', 'cherubic', 'lovely', 'adorable', 'endearing', 'angelic']
senticnet['lovebird'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet']
senticnet['lovechild'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'rascal', 'rapscallion', 'scalawag', 'holy_terror', 'monkey']
senticnet['lovecraftian'] = ['0.747', '0.85', '0', '0', '#joy', '#interest', 'positive', '0.798', 'night_vision', 'mystery', 'plot', 'imagine', 'myth']
senticnet['loved'] = ['0.589', '-0.81', '0', '0.561', '#joy', '#surprise', 'positive', '0.655', 'preferred', 'wanted', 'admired', 'darling', 'precious']
senticnet['lovefest'] = ['-0.79', '0', '0.948', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['loveless'] = ['-0.56', '0', '0.578', '-0.74', '#anger', '#disgust', 'negative', '-0.62', 'cold', 'frigid', 'unloving', 'detached', 'unaffectionate']
senticnet['loveliest'] = ['0.725', '0.775', '0', '0.725', '#joy', '#interest', 'positive', '0.742', 'nice_look', 'uneducated', 'nice', 'gorgeous', 'contrary']
senticnet['loveliness'] = ['0.772', '0.772', '0', '0', '#joy', '#interest', 'positive', '0.772', 'lovable', 'lovely', 'attractiveness', 'beauty', 'beautiful']
senticnet['lovell_telescope'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'binoculars', 'read_glass', 'eyeglass', 'camera', 'monocle']
senticnet['lovelorn'] = ['-0.85', '0', '0.655', '-0.67', '#sadness', '#disgust', 'negative', '-0.72', 'unloved', 'unbeloved', 'bereft', 'estranged', 'disinherited']
senticnet['lovely'] = ['0.725', '0.775', '0', '0.725', '#joy', '#interest', 'positive', '0.742', 'nice_look', 'uneducated', 'nice', 'gorgeous', 'contrary']
senticnet['lovely_girl'] = ['0.947', '0.914', '0', '0', '#joy', '#interest', 'positive', '0.93', 'ball_bounce', 'bicycle_tire', 'tire', 'increase_price', 'gyroscope']
senticnet['lovely_jubbly'] = ['-0.79', '0', '0.992', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['lovely_lady'] = ['0.591', '-0.76', '0', '0.66', '#surprise', '#admiration', 'positive', '0.673', 'darling', 'pet', 'favorite', 'first_choice', 'macushla']
senticnet['lovely_music'] = ['0.758', '-0.75', '0', '0.683', '#joy', '#surprise', 'positive', '0.731', 'produce_musical_sound', 'play_tune', 'play_jazz', 'play_in_band', 'play_song']
senticnet['lover'] = ['0.767', '0', '0', '0.893', '#joy', '#admiration', 'positive', '0.83', 'man_wife', 'foreplay', 'companion', 'find_mate', 'cherish']
senticnet['lover_leap'] = ['0.057', '0.075', '0.039', '0.059', '#interest', '#admiration', 'positive', '0.038', 'summit', 'big_rock', 'high_land', 'peak', 'mountain_side']
senticnet['lovesick'] = ['0', '0', '0.858', '-0.82', '#anger', '#disgust', 'negative', '-0.84', 'oddly', 'quaintly', 'odd', 'queerly', 'estrangement']
senticnet['lovesome'] = ['0', '0.919', '0', '0.834', '#interest', '#admiration', 'positive', '0.877', 'fond', 'affectionate', 'anticipate', 'apathetically', 'listlessness']
senticnet['loveth'] = ['0.887', '-0.84', '0', '0.819', '#joy', '#surprise', 'positive', '0.851', 'arouse', 'elicit', 'enkindle', 'kindle', 'evoke']
senticnet['loving'] = ['0.494', '0.673', '-0.26', '0.25', '#joy', '#interest', 'positive', '0.287', 'adorably', 'lovingly', 'fondly', 'endearingly', 'devotedness']
senticnet['loving_father'] = ['0.954', '0', '0', '0.964', '#joy', '#admiration', 'positive', '0.959', 'feel_for', 'sympathize_with', 'compassionateness', 'condole_with', 'commiserate']
senticnet['loving_god'] = ['0.05', '0.363', '-0.09', '0.307', '#interest', '#admiration', 'positive', '0.157', 'faith', 'christ', 'prayer', 'spiritual', 'religious_purpose']
senticnet['loving_husband'] = ['0.539', '-0.96', '0', '0.745', '#surprise', '#admiration', 'positive', '0.751', 'spouse', 'partner', 'married_person', 'mate', 'better_half']
senticnet['loving_kindness_meditation'] = ['0.668', '-0.87', '0', '0.606', '#joy', '#surprise', 'positive', '0.715', 'adeptness', 'adroitness', 'deftness', 'quickness', 'expertness']
senticnet['loving_relationship'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'liaison', 'affair', 'involvement']
senticnet['lovingly'] = ['0.494', '0.673', '-0.26', '0.25', '#joy', '#interest', 'positive', '0.287', 'adorably', 'loving', 'fondly', 'endearingly', 'devotedness']
senticnet['lovingness'] = ['0.528', '0.673', '-0.27', '0.272', '#joy', '#interest', 'positive', '0.299', 'caring', 'warmheartedness', 'amorousness', 'amative', 'amorous']
senticnet['low'] = ['-0.77', '0.248', '-0.45', '0', '#sadness', '#fear', 'negative', '-0.32', 'down', 'grim', 'gloomful', 'darkness', 'somberness']
senticnet['low-brow'] = ['0', '0.907', '0', '0.697', '#interest', '#admiration', 'positive', '0.802', 'middlebrow', 'highbrow', 'high-brow', 'middle-brow', 'lowbrow']
senticnet['low-cost'] = ['0', '0', '0.936', '-0.76', '#anger', '#disgust', 'negative', '-0.85', 'bare-bones', 'unpretentious', 'stripped-down', 'no-frills', 'no-nonsense']
senticnet['low-crowned'] = ['-0.76', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'large-brimmed', 'brimless', 'ear-flap', 'flat-brimmed', 'brimmed']
senticnet['low-interest'] = ['0', '0', '0.898', '-0.74', '#anger', '#disgust', 'negative', '-0.82', 'higher-interest', 'high-rate', 'lower-interest', 'credit-card', 'high-interest']
senticnet['low-key'] = ['0.919', '-0.95', '0', '0', '#joy', '#surprise', 'positive', '0.934', 'diplomacy', 'subtle', 'nice', 'pernicious', 'sly']
senticnet['low-level'] = ['0', '0.882', '0', '0.879', '#interest', '#admiration', 'positive', '0.881', 'high-level', 'top-level', 'higher-level', 'lower-level', 'implementation']
senticnet['low-paying'] = ['-0.77', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.77', 'sinecure', 'well-paid', 'cushy', 'sinecure', 'high-paid']
senticnet['low-power'] = ['0.927', '0.844', '0', '0', '#joy', '#interest', 'positive', '0.885', 'high-voltage', 'voltage', 'low-voltage', 'halogen', 'volt']
senticnet['low-pressure'] = ['0', '0', '-0.83', '-0.79', '#fear', '#disgust', 'negative', '-0.81', 'high-pressure', 'pressurized', 'high-temperature', 'low-temperature', 'superheated']
senticnet['low-priced'] = ['0', '0', '-0.74', '-0.72', '#fear', '#disgust', 'negative', '-0.73', 'bargain-basement', 'cut-rate', 'dirt-cheap', 'cut-price', 'high-priced']
senticnet['low-resolution'] = ['0', '0.794', '0', '0.794', '#interest', '#admiration', 'positive', '0.794', 'high-resolution', 'high-definition', 'full-color', 'hi-res', 'imaging']
senticnet['low-scoring'] = ['0', '-0.84', '0', '0.977', '#surprise', '#admiration', 'positive', '0.912', 'three-set', 'come-from-behind', 'victory', 'hard-fought', 'nail-biting']
senticnet['low-slung'] = ['-0.76', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.82', 'slouchy', 'squarish', 'boxy', 'blocky', 'cropped']
senticnet['low-spirited'] = ['-0.79', '0.253', '-0.46', '-0.05', '#sadness', '#fear', 'negative', '-0.26', 'downhearted', 'down_in_mouth', 'depressed', 'downcast', 'gloomy']
senticnet['low-spiritedness'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'dejectedness', 'dispiritedness', 'lowness', 'downheartedness', 'cheerless']
senticnet['low-tech'] = ['0.899', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.923', 'high-tech', 'hi-tech', 'no-tech', 'environmentally-friendly', 'low-cost']
senticnet['low-temperature'] = ['0', '0', '-0.83', '-0.79', '#fear', '#disgust', 'negative', '-0.81', 'high-pressure', 'pressurized', 'high-temperature', 'low-pressure', 'superheated']
senticnet['low-ticket'] = ['0', '0.908', '0', '0.855', '#interest', '#admiration', 'positive', '0.881', 'big-ticket', 'high-margin', 'high-price', 'bigger-ticket', 'high-ticket']
senticnet['low-voltage'] = ['0.931', '0.852', '0', '0', '#joy', '#interest', 'positive', '0.891', 'high-voltage', 'voltage', 'low-power', 'halogen', 'volt']
senticnet['low_agricultural_productivity'] = ['-0.56', '0', '-0.49', '-0.09', '#sadness', '#fear', 'negative', '-0.38', 'lack', 'absence', 'poverty', 'emptiness', 'hunger']
senticnet['low_altitude'] = ['0.023', '0.113', '0.053', '0.153', '#interest', '#admiration', 'positive', '0.059', 'high', 'high_land', 'incline_plane', 'top', 'diagonal']
senticnet['low_altitude_air'] = ['-0.04', '0', '-0.06', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'make_war', 'conquer_enemy', 'use_military', 'gain_territory', 'keep_peace']
senticnet['low_amplitude'] = ['-0.93', '0', '0.698', '-0.85', '#sadness', '#disgust', 'negative', '-0.83', 'candlepower', 'light_intensity', 'acoustic_power', 'sound_pressure_level', 'threshold_level']
senticnet['low_aspect_ratio'] = ['-0.97', '0', '0.831', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'skewness', 'irregularity']
senticnet['low_battery'] = ['-0.98', '0', '0.989', '-0.93', '#sadness', '#anger', 'negative', '-0.97', 'run_out_of_time', 'run_out_of_money', 'lack_of_food', 'run_out', 'low_charge']
senticnet['low_bitrate'] = ['0.055', '0', '0.03', '0.136', '#joy', '#admiration', 'positive', '0.054', 'loud_soft', 'decibel', 'loudness', 'sound', 'melodic_noise']
senticnet['low_blood_pressure'] = ['-0.85', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.82', 'faint', 'blood_pressure', 'feel_sick', 'feel_dizzy', 'fear']
senticnet['low_blow'] = ['-0.58', '0', '0.831', '-0.54', '#sadness', '#anger', 'negative', '-0.65', 'abuse', 'vilification', 'insult', 'revilement', 'contumely']
senticnet['low_brow'] = ['0', '0.915', '0', '0.704', '#interest', '#admiration', 'positive', '0.809', 'low-brow', 'highbrow', 'high-brow', 'middle-brow', 'lowbrow']
senticnet['low_calorie_diet'] = ['0.076', '0', '0', '0', '#joy', '#joy', 'positive', '0.076', 'increase_metabolism', 'big_muscle', 'feel_fit', 'diet', 'become_healthy']
senticnet['low_carb_diet'] = ['0.076', '0', '0', '0', '#joy', '#joy', 'positive', '0.076', 'increase_metabolism', 'big_muscle', 'feel_fit', 'diet', 'become_healthy']
senticnet['low_carbohydrate'] = ['0.076', '0', '0', '0', '#joy', '#joy', 'positive', '0.076', 'increase_metabolism', 'big_muscle', 'feel_fit', 'diet', 'become_healthy']
senticnet['low_carbohydrate_diet'] = ['0.076', '0', '0', '0', '#joy', '#joy', 'positive', '0.076', 'increase_metabolism', 'big_muscle', 'feel_fit', 'diet', 'become_healthy']
senticnet['low_caste'] = ['-0.90', '0', '0.76', '-0.87', '#sadness', '#disgust', 'negative', '-0.84', 'analphabet', 'analphabetic', 'functional_illiterate', 'ignoramus', 'know_nothing']
senticnet['low_charge'] = ['-0.99', '0', '0.989', '-0.93', '#sadness', '#anger', 'negative', '-0.97', 'run_out_of_time', 'run_out_of_money', 'lack_of_food', 'run_out', 'low_battery']
senticnet['low_conductivity'] = ['0.89', '-0.62', '0', '0.548', '#joy', '#surprise', 'positive', '0.686', 'breakthrough', 'interpenetration', 'foray', 'maraud', 'raid']
senticnet['low_cost'] = ['0.554', '-0.72', '0', '0.651', '#surprise', '#admiration', 'positive', '0.644', 'reasonableness', 'modestness', 'cost', 'cut_rate', 'cut_price']
senticnet['low_cost_alternative'] = ['0.02', '0.277', '-0.11', '0.336', '#interest', '#admiration', 'positive', '0.129', 'different', 'similar', 'substitute', 'option', 'exception']
senticnet['low_cost_spay'] = ['-0.78', '0', '0.91', '-0.91', '#anger', '#disgust', 'negative', '-0.87', 'castrate', 'neuter', 'alter', 'emasculate', 'desex']
senticnet['low_cost_spay_neuter'] = ['-0.78', '0', '0.91', '-0.91', '#anger', '#disgust', 'negative', '-0.87', 'castrate', 'neuter', 'alter', 'emasculate', 'desex']
senticnet['low_density'] = ['0.728', '0.977', '0', '0.939', '#interest', '#admiration', 'positive', '0.881', 'air_pressure', 'altitude', 'air', 'heavy_air', 'air_density']
senticnet['low_down'] = ['-0.29', '0.076', '-0.11', '0.104', '#sadness', '#fear', 'negative', '-0.05', 'lower_down', 'look_down', 'turn_down', 'cold_night', 'go_down']
senticnet['low_down_volume'] = ['-0.29', '0.076', '-0.11', '0.104', '#sadness', '#fear', 'negative', '-0.05', 'low_down', 'lower_down', 'noise', 'cold_night', 'diminish']
senticnet['low_earth_orbit'] = ['0.989', '-0.90', '0', '0.876', '#joy', '#surprise', 'positive', '0.923', 'reach', 'range', 'compass', 'scope', 'confines']
senticnet['low_experience'] = ['0', '0', '0.937', '0', '#anger', '#anger', 'negative', '-0.93', 'dumb', 'idiot', 'newbie', 'stupid', 'asshole']
senticnet['low_fat_dairy'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'grill', 'cook_grill', 'barbecue', 'cook', 'grilled_vegetable']
senticnet['low_fat_diet'] = ['0.076', '0', '0', '0', '#joy', '#joy', 'positive', '0.076', 'increase_metabolism', 'big_muscle', 'feel_fit', 'diet', 'become_healthy']
senticnet['low_fat_milk'] = ['-0.03', '0.086', '0', '0.076', '#interest', '#admiration', 'positive', '0.042', 'drink_water', 'drink_espresso', 'drink', 'drink_coffee', 'drink_hot_chocolate']
senticnet['low_fat_protein'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'grill', 'cook_grill', 'barbecue', 'cook', 'grilled_vegetable']
senticnet['low_fat_yogurt'] = ['-0.92', '0', '0.609', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'puffed_rice', 'granola', 'raisin_bran', 'corn_flake', 'bran_flake']
senticnet['low_frequency'] = ['0.048', '0.084', '0', '0.209', '#interest', '#admiration', 'positive', '0.114', 'move', 'motion', 'motility', 'movement', 'flicker']
senticnet['low_growl'] = ['0.041', '0', '0', '0.064', '#joy', '#admiration', 'positive', '0.053', 'squeak', 'clang', 'musical_note', 'bad_music', 'roar']
senticnet['low_humidity'] = ['0', '-0.03', '0.05', '-0.14', '#anger', '#disgust', 'negative', '-0.05', 'humidity', 'wetness', 'damp', 'humid', 'dank']
senticnet['low_impedance'] = ['0.056', '-0.01', '0.036', '0.073', '#joy', '#admiration', 'positive', '0.028', 'loud_sound', 'stereo', 'tuner', 'speaker', 'steam_engine']
senticnet['low_income'] = ['0.063', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.065', 'look_catalog', 'make_selection', 'good_credit', 'menu', 'save_money']
senticnet['low_income_elderly'] = ['0.956', '-0.70', '0', '0.653', '#joy', '#surprise', 'positive', '0.771', 'centenarian', 'nonagenarian', 'octogenarian', 'septuagenarian', 'sexagenarian']
senticnet['low_iq'] = ['-0.98', '0', '0.674', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'slow', 'slow_pace', 'stupid', 'slow_car', 'slow_vehicle']
senticnet['low_level'] = ['-0.12', '-0.05', '0', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'diminish', 'reduce', 'lower', 'lower_music', 'decrease']
senticnet['low_magnification'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'eye', 'peeper', 'oculus_sinister']
senticnet['low_moan'] = ['-0.96', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.96', 'weep', 'bawl', 'shed_tear', 'cry_hard', 'wail']
senticnet['low_moisture_content'] = ['0', '-0.03', '0.05', '-0.14', '#anger', '#disgust', 'negative', '-0.05', 'humidity', 'wetness', 'damp', 'humid', 'dank']
senticnet['low_morale'] = ['-0.89', '0.168', '-0.60', '0.05', '#sadness', '#fear', 'negative', '-0.32', 'remorsefully', 'contritely', 'contrition', 'contriteness', 'ruefully']
senticnet['low_muscle_tone'] = ['-0.75', '0', '0.515', '-0.93', '#sadness', '#disgust', 'negative', '-0.73', 'sclerosis', 'induration', 'disseminated_sclerosis', 'autoimmune_disease', 'autoimmune_disorder']
senticnet['low_note'] = ['-0.03', '-0.05', '0.071', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'chamber_music', 'stressful_situation', 'make_sound', 'blare', 'annoy_neighbor']
senticnet['low_pathogenic'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'hold_egg', 'baby_bird', 'hatch', 'lay_egg', 'bird_home']
senticnet['low_pay_work'] = ['0', '0', '-0.06', '-0.03', '#fear', '#disgust', 'negative', '-0.05', 'day_job', 'much_work', 'coworker', 'wage_slavery', 'pay_child_support']
senticnet['low_penetrance'] = ['-0.78', '0', '0.6', '-0.61', '#sadness', '#disgust', 'negative', '-0.66', 'suggestibility', 'sensitivity', 'capacity', 'reactivity', 'predisposition']
senticnet['low_permeability'] = ['-0.52', '0', '0.596', '-0.76', '#anger', '#disgust', 'negative', '-0.62', 'retention', 'retentiveness', 'retentivity', 'solidness', 'nonabsorbency']
senticnet['low_porosity'] = ['-0.52', '0', '0.596', '-0.76', '#anger', '#disgust', 'negative', '-0.62', 'retention', 'retentiveness', 'retentivity', 'solidness', 'nonabsorbency']
senticnet['low_quality'] = ['-0.92', '0', '0.256', '-0.98', '#sadness', '#disgust', 'negative', '-0.72', 'lousy', 'bad_quality', 'failure', 'poor_quality', 'defective']
senticnet['low_quality_photo'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'slow', 'long_focus', 'focus_time', 'long_focus_time', 'bad_camera']
senticnet['low_rainfall'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'condensation', 'water_fall', 'falling_water', 'frozen_water', 'water_drop']
senticnet['low_relative_humidity'] = ['0', '-0.03', '0.05', '-0.14', '#anger', '#disgust', 'negative', '-0.05', 'humidity', 'wetness', 'damp', 'humid', 'dank']
senticnet['low_self-esteem'] = ['-0.75', '0', '0.733', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'ice_cream_melt', 'pimple', 'worm_intestine', 'chicken_pox', 'groin']
senticnet['low_self_efficacy'] = ['0.851', '0', '0', '0.957', '#joy', '#admiration', 'positive', '0.904', 'effort', 'go_meet', 'make_time', 'ambition', 'preparation']
senticnet['low_self_esteem'] = ['-0.77', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'mark', 'emblem', 'religious_sign', 'stain', 'brand']
senticnet['low_sperm_count'] = ['0.97', '0.729', '0', '0.946', '#joy', '#admiration', 'positive', '0.882', 'make_baby', 'give_birth_child', 'sexual_intercourse', 'find_partner', 'make_love']
senticnet['low_spirited'] = ['-1', '0', '0', '0', '#sadness', '#sadness', 'negative', '-1.0', 'desperate', 'grieving', 'sorrowful', 'grief', 'discouraged']
senticnet['low_status'] = ['-0.87', '0', '0.818', '-0.55', '#sadness', '#anger', 'negative', '-0.75', 'inferiority', 'lower_status', 'lower_rank', 'status', 'position']
senticnet['low_sulphur_content'] = ['0', '0', '-0.06', '0', '#fear', '#fear', 'negative', '-0.06', 'greasy', 'car_gas', 'fossil_fuel', 'liquid_fuel', 'lubrication']
senticnet['low_tech'] = ['0.843', '-0.99', '0', '0.706', '#joy', '#surprise', 'positive', '0.849', 'contrivance', 'gimbal', 'injector', 'mod_con', 'device']
senticnet['low_temperature'] = ['-0.90', '0', '0.512', '-0.8', '#sadness', '#disgust', 'negative', '-0.74', 'pressor', 'chill', 'frostiness', 'cool', 'gelidity']
senticnet['low_thermal_conductivity'] = ['0.03', '0.068', '-0.06', '0.062', '#interest', '#admiration', 'positive', '0.025', 'pacific_northwest', 'american_forest', 'treetop', 'bath', 'hot_air']
senticnet['low_tide_mark'] = ['-0.04', '-0.01', '0.103', '0', '#sadness', '#anger', 'negative', '-0.04', 'near_beach', 'along_beach', 'full_water', 'ocean_ground', 'salty']
senticnet['low_vapor_pressure'] = ['0', '0.073', '-0.05', '0.093', '#interest', '#admiration', 'positive', '0.038', 'vapor', 'water_vapor', 'evaporate', 'condense_water', 'bubble']
senticnet['low_viscosity'] = ['-0.52', '0', '0.596', '-0.76', '#anger', '#disgust', 'negative', '-0.62', 'retention', 'retentiveness', 'retentivity', 'solidness', 'nonabsorbency']
senticnet['low_voltage'] = ['0', '-0.03', '0', '0.093', '#surprise', '#admiration', 'positive', '0.063', 'work_well', 'internal_combustion_engine', 'broken_car', 'mode_transportation', 'machine']
senticnet['low_voltage_dc'] = ['0', '-0.03', '0', '0.093', '#surprise', '#admiration', 'positive', '0.063', 'work_well', 'internal_combustion_engine', 'broken_car', 'mode_transportation', 'machine']
senticnet['low_voltage_power'] = ['0', '-0.03', '0', '0.093', '#surprise', '#admiration', 'positive', '0.063', 'work_well', 'internal_combustion_engine', 'broken_car', 'mode_transportation', 'machine']
senticnet['low_wage'] = ['0', '0.923', '0', '0', '#interest', '#interest', 'positive', '0.923', 'salary', 'remittance', 'owe', 'payment', 'severance']
senticnet['lowball'] = ['0.998', '-0.96', '0', '0.91', '#joy', '#surprise', 'positive', '0.956', 'gauge', 'guess', 'approximate', 'estimate', 'judge']
senticnet['lowbrow'] = ['0.849', '-0.89', '0', '0.924', '#surprise', '#admiration', 'positive', '0.89', 'philistine', 'nonintellectual', 'nonintellectualed', 'mindless', 'uncultivated']
senticnet['lowbrowed'] = ['0.845', '-0.90', '0', '0.924', '#surprise', '#admiration', 'positive', '0.89', 'nonintellectual', 'lowbrow', 'philistine', 'mindless', 'uncultivated']
senticnet['lower'] = ['-0.12', '-0.05', '0', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'diminish', 'reduce', 'decrease', 'decrease_music', 'low_level']
senticnet['lower-interest'] = ['0', '0', '0.905', '-0.74', '#anger', '#disgust', 'negative', '-0.82', 'higher-interest', 'high-rate', 'high-interest', 'credit-card', 'low-interest']
senticnet['lower-level'] = ['0', '0.824', '0', '0.83', '#interest', '#admiration', 'positive', '0.827', 'upper-level', 'upper-division', 'college-level', 'graduate-level', 'mid-level']
senticnet['lower-ranking'] = ['0', '0', '0.723', '-0.90', '#anger', '#disgust', 'negative', '-0.81', 'lowly', 'petty', 'junior-grade', 'subaltern', 'secondary']
senticnet['lower_abdomen'] = ['0.087', '-0.03', '0', '0.063', '#joy', '#admiration', 'positive', '0.062', 'puppet', 'bouncy', 'curly', 'throw_object', 'glove_leather']
senticnet['lower_abdominal_pain'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'gastroenteritis', 'stomach_flu', 'intestinal_flu', 'inflammatory_disease']
senticnet['lower_blood_pressure'] = ['0.921', '0.975', '0', '0', '#joy', '#interest', 'positive', '0.948', 'feel_better', 'renew_energy', 'feel_energized', 'feel_refreshed', 'feel_energetic']
senticnet['lower_body'] = ['-0.04', '0', '0.059', '0', '#sadness', '#anger', 'negative', '-0.05', 'human_limb', 'two_feet', 'thigh', 'ten_toes', 'body_limb']
senticnet['lower_body_temperature'] = ['-0.72', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.81', 'faint', 'get_cold', 'cold', 'get_sick', 'sick']
senticnet['lower_crust'] = ['0', '0', '-0.92', '0', '#fear', '#fear', 'negative', '-0.92', 'water_flow', 'wind_water', 'water_stream', 'waterway', 'lava']
senticnet['lower_deck'] = ['0.831', '-0.82', '0', '0.826', '#joy', '#admiration', 'positive', '0.826', 'deck', 'freeboard_deck', 'weather_deck', 'shelter_deck', 'boat_deck']
senticnet['lower_dimensional_subspace'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'arm_movement', 'hand_gesture', 'water_motion', 'hand_signal', 'sea_movement']
senticnet['lower_dir'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'webpage', 'store_record', 'ideal']
senticnet['lower_down'] = ['-0.29', '0.076', '-0.11', '0.104', '#sadness', '#fear', 'negative', '-0.05', 'low_down', 'low_down_volume', 'noise', 'cold_night', 'diminish']
senticnet['lower_expectation'] = ['-0.14', '0', '-0.05', '-0.04', '#sadness', '#fear', 'negative', '-0.07', 'state_fact', 'guess_answer', 'erase_blackboard', 'voice_opinion', 'communicate']
senticnet['lower_extremity'] = ['0.582', '-0.66', '0', '0.661', '#surprise', '#admiration', 'positive', '0.637', 'talipes_equinus', 'deformity', 'malformation', 'talipes_valgus', 'talipes_calcaneus']
senticnet['lower_eyelid'] = ['-0.58', '0', '0.715', '-0.59', '#anger', '#disgust', 'negative', '-0.63', 'fly', 'overlap', 'barndoor', 'coattail', 'codpiece']
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senticnet['lucrative'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'profitable', 'moneymaking', 'gainful', 'paying', 'paid']
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senticnet['ludicrously'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'lamentably', 'deplorably', 'bereaved', 'bereft', 'grief-stricken']
senticnet['ludlow_street'] = ['0.591', '-0.74', '0', '0.672', '#surprise', '#admiration', 'positive', '0.669', 'room', 'chillax', 'hotel_room', 'furniture_store', 'living_room']
senticnet['ludo'] = ['0.796', '0.735', '0', '0', '#joy', '#interest', 'positive', '0.766', 'paddle', 'playfully', 'playful', 'frolic', 'disport']
senticnet['ludovico_technique'] = ['0.542', '-0.63', '0', '0.912', '#surprise', '#admiration', 'positive', '0.695', 'implosion_therapy', 'flooding', 'reciprocal_inhibition', 'behavior_modification', 'desensitization_technique']
senticnet['ludvig_holberg'] = ['-0.81', '0', '0.52', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'affray', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['ludwik'] = ['0', '0', '0', '0.1', '#admiration', '#admiration', 'positive', '0.1', 'find_earth', 'wax', 'plutonium', 'shine', 'melt']
senticnet['lufilian_arc'] = ['0', '0.072', '0', '0.064', '#interest', '#admiration', 'positive', '0.068', 'zipper', 'pant', 'trousers', 'cowboy_boot', 'accessory']
senticnet['lufttransport'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['lug_around'] = ['-0.17', '0.131', '-0.20', '0.097', '#sadness', '#fear', 'negative', '-0.03', 'lost_and_found', 'tv_series', 'bad_situation', 'odds', 'risk']
senticnet['luggable'] = ['0.113', '0.073', '0.028', '0.107', '#joy', '#admiration', 'positive', '0.066', 'pda', 'telephone_computer', 'browser', 'number_pad', 'modem']
senticnet['luggage'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'hand_baggage', 'baggage', 'passenger_ticket', 'ticket_counter', 'baggage_trunk']
senticnet['luggage_carousel'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'hand_baggage', 'baggage', 'passenger_ticket', 'ticket_counter', 'baggage_trunk']
senticnet['luggage_carrier'] = ['0.073', '0.097', '-0.05', '-0.03', '#joy', '#interest', 'positive', '0.02', 'newspaper_stand', 'jet_fuel', 'ticket_counter', 'aircraft_roadway', 'firetruck']
senticnet['luggage_compartment'] = ['0', '0.248', '-0.07', '0.121', '#interest', '#admiration', 'positive', '0.099', 'gym_locker', 'observatory', 'prison_cell', 'luggage_store', 'garage_sale']
senticnet['luggage_cum'] = ['-0.58', '0', '0.714', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'waster', 'diversionist', 'destroyer', 'ruiner', 'undoer']
senticnet['luggage_rack'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'train_station', 'government_office', 'cable_car', 'sport_stadium', 'terminal']
senticnet['luggage_store'] = ['0', '0.248', '-0.07', '0.121', '#interest', '#admiration', 'positive', '0.099', 'gym_locker', 'observatory', 'prison_cell', 'luggage_compartment', 'garage_sale']
senticnet['luggage_tag'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'sports_equipment', 'sport_stuff', 'golf', 'sport_store', 'bag']
senticnet['luggage_trolley'] = ['0.961', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.901', 'porter', 'beverage_service', 'bar_service', 'concierge', 'minibar']
senticnet['lugger'] = ['0', '0', '0.08', '0', '#anger', '#anger', 'negative', '-0.08', 'tree_animal', 'small_furry', 'rodent', 'big_mouse', 'small_rodent']
senticnet['lugging_around'] = ['-0.17', '0.131', '-0.20', '0.097', '#sadness', '#fear', 'negative', '-0.03', 'lost_and_found', 'tv_series', 'bad_situation', 'odds', 'risk']
senticnet['lugu'] = ['-0.63', '0', '0.791', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'little', 'pinch', 'modicum', 'small_indefinite_amount', 'indefinite_quantity']
senticnet['lugubrious'] = ['-0.98', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.98', 'degree', 'lugubriousness', 'exaggerated', 'dismal', 'lugubriously']
senticnet['lugubriously'] = ['-0.99', '0', '-0.98', '0', '#sadness', '#fear', 'negative', '-0.99', 'degree', 'lugubriousness', 'exaggerated', 'dismal', 'lugubrious']
senticnet['lugubriousness'] = ['-0.99', '0', '-0.98', '0', '#sadness', '#fear', 'negative', '-0.99', 'degree', 'lugubrious', 'exaggerated', 'dismal', 'lugubriously']
senticnet['luhring_augustine'] = ['0.07', '0', '0', '0', '#joy', '#joy', 'positive', '0.07', 'artist', 'artwork', 'sculpture', 'tapestry', 'drawing']
senticnet['lui_demandant'] = ['0.667', '-0.98', '0', '0.61', '#joy', '#surprise', 'positive', '0.753', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['lui_ont'] = ['0', '0.919', '0', '0.888', '#interest', '#admiration', 'positive', '0.903', 'surprise', 'eye-popping', 'fulgurous', 'dazzling', 'bedaze']
senticnet['luigi_dallapiccola'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'band_play', 'songwriter', 'rock_band']
senticnet['luigi_mansion_dark'] = ['0.916', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.929', 'galaxy', 'universe', 'planetary', 'sun', 'dwarf_planet']
senticnet['luigi_russolo'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'native_person', 'revolutionary', 'republic', 'revolution', 'look_ahead']
senticnet['luino'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'expressway', 'turnpike', 'toll_road', 'highway']
senticnet['luke_laird'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
senticnet['luke_ricketson'] = ['0.997', '-0.84', '0', '0.927', '#joy', '#admiration', 'positive', '0.923', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['luke_vibert'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['lukewarm_coffee'] = ['0.214', '0.258', '-0.09', '0.2', '#joy', '#interest', 'positive', '0.146', 'cup', 'carafe', 'pitcher', 'container_cup', 'glasses']
senticnet['lukewarm_water'] = ['0', '0', '0.865', '-0.95', '#anger', '#disgust', 'negative', '-0.90', 'dirty_dish_overflow', 'hot_dirty_dish', 'hot_tap', 'dirty_dish', 'washcloth']
senticnet['lull'] = ['0.506', '0.659', '-0.24', '0.242', '#joy', '#interest', 'positive', '0.29', 'calml', 'quiet', 'soothing', 'assuasive', 'tranquillize']
senticnet['lullaby'] = ['0.845', '0.789', '0', '0.806', '#joy', '#admiration', 'positive', '0.813', 'vocal_music', 'melody', 'sound_make', 'tune', 'lyric']
senticnet['lulled_into'] = ['-0.99', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.93', 'complacency', 'self-complacency', 'complacence', 'self_satisfaction', 'self_complacent']
senticnet['lulled_into_complacency'] = ['-0.99', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.93', 'complacency', 'self-complacency', 'complacence', 'self_satisfaction', 'self_complacent']
senticnet['lululemon_athletica'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['lumbago'] = ['0', '0', '0.815', '-0.94', '#anger', '#disgust', 'negative', '-0.88', 'rheumatic', 'arthritis', 'backache', 'neuralgia', 'rheumatism']
senticnet['lumbar_region'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'lordosis', 'spinal_curvature', 'humpback', 'hunchback', 'abnormality']
senticnet['lumber'] = ['0.024', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'stump', 'plank', 'timber', 'wood_pulp', 'mahogany']
senticnet['lumber_yard'] = ['0.056', '0.157', '0', '0.154', '#interest', '#admiration', 'positive', '0.122', 'home_depot', 'lumberyard', 'carpet_store', 'front_porch', 'shoe_shop']
senticnet['lumberyard'] = ['-0.07', '0.228', '0.019', '0.089', '#interest', '#admiration', 'positive', '0.056', 'home_depot', 'carpet_store', 'furniture_store', 'front_porch', 'shoe_shop']
senticnet['lumen_output'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'incandescence', 'luminescence', 'brightness_level']
senticnet['lumen_per_square'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'incandescence', 'luminescence', 'brightness_level']
senticnet['lumen_per_watt'] = ['-0.63', '0', '0.897', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'cd-rom', 'international_candle', 'candle', 'candlepower_unit', 'standard_candle']
senticnet['luminaire'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'incandescence', 'luminescence', 'brightness_level']
senticnet['luminal_side'] = ['-0.84', '0', '0.8', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'rigor_mortis', 'natural_process', 'natural_action', 'activity', 'action']
senticnet['luminance'] = ['0.969', '-0.87', '0', '0.687', '#joy', '#surprise', 'positive', '0.843', 'glow', 'illumination', 'incandescence', 'luminescence', 'illuminance']
senticnet['luminescence'] = ['-0.92', '0', '0.734', '-0.51', '#sadness', '#anger', 'negative', '-0.72', 'chemiluminescence', 'light', 'bioluminescence', 'visible_light', 'visible_radiation']
senticnet['luminosity'] = ['0.897', '-0.74', '0', '0.833', '#joy', '#admiration', 'positive', '0.825', 'glow', 'illumination', 'incandescence', 'luminescence', 'illuminance']
senticnet['luminous'] = ['0.946', '0', '0', '0.705', '#joy', '#admiration', 'positive', '0.825', 'bright', 'illuminated', 'luminously', 'deneb', 'lambent']
senticnet['luminous_efficacy'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'incandescence', 'luminescence', 'brightness_level']
senticnet['luminous_efficiency'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'incandescence', 'luminescence', 'brightness_level']
senticnet['luminous_flux'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'incandescence', 'luminescence', 'brightness_level']
senticnet['luminous_intensity'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'incandescence', 'luminescence', 'brightness_level']
senticnet['luminously'] = ['0.936', '0', '0', '0.697', '#joy', '#admiration', 'positive', '0.817', 'bright', 'illuminated', 'luminous', 'deneb', 'lambent']
senticnet['luminousness'] = ['0.693', '-0.67', '0', '0.508', '#joy', '#surprise', 'positive', '0.626', 'glow', 'illumination', 'incandescence', 'luminescence', 'illuminance']
senticnet['lummox'] = ['0', '0', '-0.87', '-0.98', '#fear', '#disgust', 'negative', '-0.92', 'oaf', 'clod', 'goon', 'stumblebum', 'lout']
senticnet['lumo'] = ['0.537', '-0.51', '0', '0.807', '#joy', '#admiration', 'positive', '0.619', 'lesbian', 'fairy', 'fagot', 'faggot', 'fag']
senticnet['lump'] = ['-0.92', '0', '0.783', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'clot', 'coagulum', 'gob', 'clew', 'agglomeration']
senticnet['lump_sum'] = ['0.924', '0.968', '0', '0.876', '#joy', '#interest', 'positive', '0.923', 'live_simply', 'help_out', 'money', 'accolade', 'generous']
senticnet['lump_sum_payment'] = ['-0.04', '0.277', '-0.10', '0.11', '#interest', '#admiration', 'positive', '0.059', 'owe', 'tax', 'debit', 'afford', 'wage']
senticnet['lumpen'] = ['0', '0', '0.872', '-0.78', '#anger', '#disgust', 'negative', '-0.82', 'abject', 'banished', 'castaway', 'pariah', 'outcast']
senticnet['lumper'] = ['0', '0', '-0.73', '-0.82', '#fear', '#disgust', 'negative', '-0.77', 'docker', 'stevedore', 'loader', 'longshoreman', 'dock_worker']
senticnet['lumpsucker'] = ['0.96', '-0.77', '0', '0.929', '#joy', '#admiration', 'positive', '0.887', 'whale_louse', 'beach_flea', 'sand_hopper', 'sandhopper', 'amphipod']
senticnet['lumpy'] = ['-0.73', '0', '0.904', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'acromegalic', 'shapeless', 'unshapely', 'deformed', 'malformed']
senticnet['lumpy_skin_disease'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'leptomeningitis', 'meningitis', 'epidemic_meningitis', 'brain_fever', 'cerebrospinal_fever']
senticnet['luna_park'] = ['0.089', '-0.18', '0.037', '-0.06', '#joy', '#surprise', 'positive', '0.043', 'balloon', 'ballon', 'fielder', 'kite', 'blimp']
senticnet['luna_park_sydney'] = ['0.089', '-0.18', '0.037', '-0.06', '#joy', '#surprise', 'positive', '0.043', 'balloon', 'ballon', 'fielder', 'kite', 'blimp']
senticnet['lunacy'] = ['0.931', '-0.84', '0', '0.829', '#joy', '#surprise', 'positive', '0.869', 'folly', 'buffoonery', 'craziness', 'indulgence', 'shtik']
senticnet['lunar'] = ['0.916', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.929', 'galaxy', 'universe', 'planetary', 'sun', 'dwarf_planet']
senticnet['lunar_cycle'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['lunar_disk'] = ['0.916', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.929', 'galaxy', 'universe', 'planetary', 'sun', 'dwarf_planet']
senticnet['lunar_eclipse'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['lunar_month'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['lunar_orbit'] = ['0.989', '-0.90', '0', '0.876', '#joy', '#surprise', 'positive', '0.923', 'reach', 'range', 'compass', 'scope', 'confines']
senticnet['lunar_rover'] = ['0.052', '0.119', '0', '0.141', '#interest', '#admiration', 'positive', '0.104', 'neigh', 'domestic_animal', 'animal_type', 'animal', 'home_animal']
senticnet['lunar_surface'] = ['0.052', '0.119', '0', '0.141', '#interest', '#admiration', 'positive', '0.104', 'neigh', 'domestic_animal', 'animal_type', 'animal', 'home_animal']
senticnet['lunate_bone'] = ['-0.74', '0', '0.598', '-0.75', '#sadness', '#disgust', 'negative', '-0.69', 'carpal', 'semilunar_bone', 'carpal_bone', 'triquetral', 'triquetral_bone']
senticnet['lunatic'] = ['-0.64', '0', '0.781', '-0.74', '#anger', '#disgust', 'negative', '-0.72', 'weirdo', 'crazy', 'loony', 'looney', 'nutcase']
senticnet['lunatic_asylum'] = ['-0.67', '0', '0.931', '-0.77', '#anger', '#disgust', 'negative', '-0.79', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['lunatic_fringe'] = ['0.203', '0.077', '0', '0.189', '#joy', '#admiration', 'positive', '0.156', 'sea_shell', 'coast', 'seaside', 'sediment', 'shell']
senticnet['lunch'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'cafe', 'buy_sandwich', 'eat_food', 'buy_food', 'finish_job']
senticnet['lunch_box'] = ['0', '0', '0', '0.93', '#admiration', '#admiration', 'positive', '0.93', 'yogurt', 'ferment_beverage', 'healthy_drink', 'ice_cream', 'healthy_snack']
senticnet['lunch_break'] = ['0.84', '0.769', '0', '0', '#joy', '#interest', 'positive', '0.804', 'employment', 'prepare_event', 'dig_ground', 'work', 'become_wage-slave']
senticnet['lunch_buffet'] = ['0.128', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.085', 'banquet', 'rave', 'auction', 'limbo', 'loose_end']
senticnet['lunch_hour'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'cafe', 'buy_sandwich', 'eat_food', 'buy_food', 'finish_job']
senticnet['lunch_menu'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'grocery_cart', 'super_market', 'fruit_stand', 'candy_store', 'produce_store']
senticnet['lunch_wagon'] = ['0', '0', '0.044', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'large_supermarket', 'food_store', 'large_food_store', 'corner_shop', 'convenience_food_store']
senticnet['lunchbox'] = ['0', '0', '0', '0.93', '#admiration', '#admiration', 'positive', '0.93', 'yogurt', 'ferment_beverage', 'healthy_drink', 'ice_cream', 'healthy_snack']
senticnet['luncheon'] = ['0', '0.852', '0', '0', '#interest', '#interest', 'positive', '0.852', 'streamer', 'tribute', 'salutation', 'gift', 'anniversary_gift']
senticnet['luncheon_voucher'] = ['0.921', '-0.57', '0', '0.962', '#joy', '#admiration', 'positive', '0.818', 'book_token', 'meal_ticket', 'coupon', 'twofer', 'commercial_document']
senticnet['luncheonette'] = ['0', '0', '0.044', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'large_supermarket', 'food_store', 'large_food_store', 'corner_shop', 'convenience_food_store']
senticnet['lunchtime'] = ['0.715', '0.785', '0', '0', '#joy', '#interest', 'positive', '0.75', 'scrumble_egg', 'heat_meal', 'pan', 'beat_egg', 'food']
senticnet['lung'] = ['0.811', '0.851', '0', '0', '#joy', '#interest', 'positive', '0.831', 'atmosphere', 'bird_fly', 'thunder_storm', 'air_breathe', 'breathe']
senticnet['lung_ailment'] = ['-0.09', '0', '-0.16', '-0.21', '#fear', '#disgust', 'negative', '-0.15', 'sickness', 'malady', 'polio', 'disease', 'ailment']
senticnet['lung_cancer'] = ['0.048', '-0.03', '-0.05', '-0.14', '#fear', '#disgust', 'negative', '-0.03', 'smoke', 'smoke_tobacco', 'cigar', 'cigarette_smoke', 'tobacco_product']
senticnet['lung_disease'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchiolitis', 'chronic_bronchitis', 'respiratory_disease', 'bronchitis', 'respiratory_disorder']
senticnet['lung_disorder'] = ['-0.92', '0', '0.824', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'bronchopneumonia', 'bronchial_pneumonia', 'pneumonia', 'pleuropneumonia', 'pneumococcal_pneumonia']
senticnet['lung_fluke'] = ['0.509', '-0.9', '0', '0.507', '#joy', '#surprise', 'positive', '0.639', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['lung_inflammation'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchiolitis', 'chronic_bronchitis', 'respiratory_disease', 'bronchitis', 'respiratory_disorder']
senticnet['lung_irritant'] = ['-0.53', '0', '0.86', '-0.65', '#anger', '#disgust', 'negative', '-0.68', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['lung_irritation'] = ['-0.70', '0', '-0.83', '-0.98', '#fear', '#disgust', 'negative', '-0.84', 'body_hurt', 'ache', 'ouch', 'cut_skin', 'scar']
senticnet['lung_transplant'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'displace', 'move', 'move_away', 'bump', 'dislocate']
senticnet['lunga_perimeter'] = ['0.084', '-0.05', '0.013', '0', '#joy', '#surprise', 'positive', '0.043', 'carnival_ride', 'carny', 'fortune_teller', 'horseback', 'ferris_wheel']
senticnet['lunge'] = ['-0.05', '0.119', '-0.10', '0.159', '#interest', '#admiration', 'positive', '0.03', 'shrug', 'push', 'progress', 'advance', 'thrust']
senticnet['lungful'] = ['0.757', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.754', 'inhale', 'cough', 'respiration', 'involuntary', 'smell_odor']
senticnet['lungworm'] = ['0.688', '-0.94', '0', '0.925', '#surprise', '#admiration', 'positive', '0.852', 'flatworm', 'platyhelminth', 'trematode', 'schistosome', 'blood_fluke']
senticnet['lunitidal_interval'] = ['0.861', '-0.78', '0', '0.911', '#joy', '#admiration', 'positive', '0.853', 'round', 'pause', 'space', 'time_interval', 'break']
senticnet['lunkhead'] = ['-0.73', '0', '0.624', '-0.65', '#sadness', '#disgust', 'negative', '-0.67', 'stupid', 'stupid_person', 'stupe', 'dullard', 'dolt']
senticnet['lunula'] = ['0', '0.097', '0', '0.085', '#interest', '#admiration', 'positive', '0.091', 'leave_hand', 'arm_part', 'cuticle', 'ten_fingers', 'five_fingers']
senticnet['lupus_pernio'] = ['-0.85', '0', '0.657', '-0.86', '#sadness', '#disgust', 'negative', '-0.79', 'pulseless_disease', 'temporal_arteritis', 'rubor', 'redness', 'arteritis']
senticnet['lurch'] = ['-0.64', '0', '0.717', '-0.98', '#anger', '#disgust', 'negative', '-0.78', 'debacle', 'licking', 'whitewash', 'finish', 'failure']
senticnet['lurch_forward'] = ['-0.11', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.08', 'have_fun', 'spring_up', 'fall_well', 'shovel_snow', 'fly_around']
senticnet['lurched_forward'] = ['-0.11', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.08', 'have_fun', 'spring_up', 'fall_well', 'shovel_snow', 'fly_around']
senticnet['lure'] = ['0.02', '0.162', '0', '0.062', '#interest', '#admiration', 'positive', '0.081', 'catch', 'trap', 'ambush', 'bait_trap', 'decoy']
senticnet['lurid'] = ['0', '-0.92', '0', '0.944', '#surprise', '#admiration', 'positive', '0.934', 'scandalmongering', 'sensationalistic', 'sensationally', 'inflame', 'fire_up']
senticnet['luridly'] = ['0', '-0.92', '0', '0.944', '#surprise', '#admiration', 'positive', '0.934', 'scandalmongering', 'sensationalistic', 'sensationally', 'inflame', 'fire_up']
senticnet['lurk'] = ['-0.80', '0', '0.715', '0', '#sadness', '#anger', 'negative', '-0.75', 'ambuscade', 'ambush', 'forum', 'lurker', 'waylay']
senticnet['lurker'] = ['-0.80', '0', '0.714', '0', '#sadness', '#anger', 'negative', '-0.76', 'ambuscade', 'ambush', 'forum', 'lurk', 'waylay']
senticnet['lurking_around'] = ['0.788', '-0.60', '0', '0.884', '#joy', '#admiration', 'positive', '0.759', 'gap', 'rathole', 'aperture', 'bullet_hole', 'hole']
senticnet['lurking_place'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'hiding_place', 'spot', 'drop', 'hideaway', 'den']
senticnet['lurks_behind'] = ['-0.62', '0', '0.825', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'unexplainable', 'unaccountable', 'unexplained', 'incomprehensible', 'mystifying']
senticnet['luscious'] = ['0.834', '-0.72', '0', '0', '#joy', '#surprise', 'positive', '0.779', 'toothsome', 'delectable', 'juicy', 'red-hot', 'voluptuous']
senticnet['luscious_fruit'] = ['0.05', '0', '0.047', '0.068', '#joy', '#admiration', 'positive', '0.024', 'tree_fruit', 'yellow_fruit', 'small_fruit', 'vine_fruit', 'red_fruit']
senticnet['lusciously'] = ['0.112', '0', '0', '0.141', '#joy', '#admiration', 'positive', '0.127', 'aftertaste', 'sweet_food', 'bake_good', 'round_pastry', 'eatable']
senticnet['lush'] = ['0.556', '-0.79', '0', '0.755', '#surprise', '#admiration', 'positive', '0.701', 'rummy', 'alcoholic', 'alky', 'dipsomaniac', 'boozer']
senticnet['lush_greenery'] = ['0.873', '0', '0', '0.876', '#joy', '#admiration', 'positive', '0.875', 'tranquility', 'peace-loving', 'peace_of_mind', 'repose', 'heartsease']
senticnet['lushly'] = ['0', '0.923', '0', '0.694', '#interest', '#admiration', 'positive', '0.808', 'tropically', 'nautically', 'landscaped', 'exotically', 'park-like']
senticnet['lust'] = ['0', '0', '0.909', '-0.77', '#anger', '#disgust', 'negative', '-0.84', 'love', 'desire', 'beloved', 'passion', 'sexuality']
senticnet['luster'] = ['0.65', '-0.61', '0', '0.703', '#joy', '#admiration', 'positive', '0.657', 'gleaming', 'effulgence', 'polish', 'glow', 'burnish']
senticnet['lustful'] = ['0', '0', '-0.96', '-0.73', '#fear', '#disgust', 'negative', '-0.84', 'lubricious', 'lewd', 'salacious', 'obscene', 'prurient']
senticnet['lustfulness'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'lecherousness', 'concupiscence', 'amorousness']
senticnet['lustily'] = ['-0.54', '0', '0.811', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'bird_of_prey', 'raptor', 'raptorial_bird', 'parrot', 'bird']
senticnet['lustiness'] = ['0.81', '0.874', '0', '0', '#joy', '#interest', 'positive', '0.842', 'hardiness', 'validity', 'robustness', 'research', 'project']
senticnet['lustmord'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amuck', 'murderousness', 'misocainea', 'misogyny']
senticnet['lustre'] = ['0.577', '-0.69', '0', '0.645', '#surprise', '#admiration', 'positive', '0.639', 'shining', 'polished', 'shiny', 'bright', 'burnished']
senticnet['lustrous'] = ['0.577', '-0.69', '0', '0.645', '#surprise', '#admiration', 'positive', '0.639', 'shining', 'polished', 'shiny', 'bright', 'burnished']
senticnet['lusty'] = ['0.908', '-0.91', '0', '0', '#joy', '#surprise', 'positive', '0.911', 'lustful', 'horny', 'slutty', 'redhead', 'naughty']
senticnet['lusus_naturae'] = ['-0.93', '0', '0.544', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'mutant', 'variation', 'sport', 'leviathan', 'mutation']
senticnet['luta_livre'] = ['-0.06', '-0.00', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.05', 'madman', 'trouble', 'agony', 'cause_problem', 'river_stream']
senticnet['lute'] = ['0.033', '0', '0.009', '0.057', '#joy', '#admiration', 'positive', '0.027', 'triangle', 'baton', 'bugle', 'type_of_bow', 'horn']
senticnet['lute_music'] = ['0.033', '0', '0.009', '0.057', '#joy', '#admiration', 'positive', '0.027', 'triangle', 'baton', 'bugle', 'type_of_bow', 'horn']
senticnet['lutenist'] = ['0.851', '0', '0', '0.961', '#joy', '#admiration', 'positive', '0.906', 'portraitist', 'watercolorist', 'printmaker', 'miniaturist', 'horologist']
senticnet['luteolin'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'capsaicin', 'chemical_irritant', 'piperine', 'pepper_spray']
senticnet['lutheran_church'] = ['0.081', '0.146', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'cult', 'sect', 'conservative', 'worshiper', 'pastor']
senticnet['lutheran_congregation'] = ['0.081', '0.146', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'cult', 'sect', 'conservative', 'worshiper', 'pastor']
senticnet['lutin'] = ['0.526', '-0.99', '0', '0.673', '#surprise', '#admiration', 'positive', '0.732', 'faery', 'faerie', 'fairy', 'leprechaun', 'sandman']
senticnet['luvvie'] = ['0.71', '-0.81', '0', '0.937', '#surprise', '#admiration', 'positive', '0.819', 'erudite', 'scholarly', 'pedantic', 'learned', 'bookish']
senticnet['lux'] = ['-0.63', '0', '0.897', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'cd-rom', 'international_candle', 'candle', 'candlepower_unit', 'standard_candle']
senticnet['lux_cafe'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'fantastic', 'amaze', 'pose', 'wondrous', 'wonderfully']
senticnet['luxate'] = ['0', '0', '0.842', '-0.90', '#anger', '#disgust', 'negative', '-0.87', 'dislocate', 'detach', 'disjoin', 'incapacitate', 'disintegrate']
senticnet['luxe'] = ['0.827', '0', '0', '0.928', '#joy', '#admiration', 'positive', '0.877', 'chic', 'luxurious', 'luxury', 'stylish', 'boutique']
senticnet['luxor_hotel'] = ['0.057', '0.13', '0', '0.119', '#interest', '#admiration', 'positive', '0.102', 'friend_wedding', 'circular_route', 'spanish_restaurant', 'altar', 'monastery']
senticnet['luxuria'] = ['0.69', '-0.64', '0', '0.825', '#joy', '#admiration', 'positive', '0.72', 'lust', 'mortal_sin', 'sinning', 'pride', 'superbia']
senticnet['luxuriance'] = ['-0.13', '0.146', '0.623', '-0.63', '#anger', '#disgust', 'negative', '-0.31', 'outraged', 'incensed', 'indignant', 'outrage', 'indignation']
senticnet['luxuriant'] = ['0.558', '-0.90', '0', '0.736', '#surprise', '#admiration', 'positive', '0.733', 'luxurious', 'indulgent', 'sybaritic', 'voluptuary', 'gay']
senticnet['luxuriant_vegetation'] = ['-0.93', '0', '0.906', '-0.53', '#sadness', '#anger', 'negative', '-0.79', 'juvenility', 'state', 'immatureness', 'callowness', 'jejuneness']
senticnet['luxuriate'] = ['0.547', '-0.94', '0', '0.74', '#surprise', '#admiration', 'positive', '0.745', 'sap', 'expend', 'indulge', 'use_up', 'spend']
senticnet['luxurious'] = ['0.558', '-0.90', '0', '0.736', '#surprise', '#admiration', 'positive', '0.733', 'indulgent', 'luxuriant', 'sybaritic', 'voluptuary', 'gay']
senticnet['luxurious_apartment'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'home', 'furniture', 'kitchen', 'flat']
senticnet['luxurious_bath'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
senticnet['luxurious_lifestyle'] = ['0.973', '0.707', '0', '0', '#joy', '#interest', 'positive', '0.84', 'money', 'down_payment', 'much_money', 'slogan', 'take_out_loan']
senticnet['luxurious_palace'] = ['0', '0.105', '0', '0.076', '#interest', '#admiration', 'positive', '0.09', 'big_house', 'mansion', 'castle', 'block_flat', 'medieval']
senticnet['luxurious_resort'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'lurking_place', 'spot', 'drop', 'hiding_place', 'den']
senticnet['luxuriously'] = ['-0.77', '0', '0.855', '-0.57', '#sadness', '#anger', 'negative', '-0.73', 'unhurried', 'easygoing', 'deliberate', 'easy', 'careful']
senticnet['luxury'] = ['0.757', '0.882', '0', '0.78', '#interest', '#admiration', 'positive', '0.806', 'riches', 'immoral', 'richness', 'fame', 'wealthy']
senticnet['luxury_apartment'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'home', 'furniture', 'kitchen', 'flat']
senticnet['luxury_boutique_hotel'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'lurking_place', 'spot', 'drop', 'hiding_place', 'den']
senticnet['luxury_car'] = ['0.755', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.728', 'high_class', 'expensive_car', 'ferrari', 'fancy_car', 'sports_car']
senticnet['luxury_resort'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'lurking_place', 'spot', 'drop', 'hiding_place', 'den']
senticnet['luxury_yacht'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'boat', 'yacht', 'big_boat', 'large_boat']
senticnet['lxvi'] = ['0.055', '0.189', '-0.09', '0.113', '#interest', '#admiration', 'positive', '0.065', 'tame', 'crack', 'sense_fear', 'friend_person', 'good_manner']
senticnet['lxvii'] = ['0.055', '0.189', '-0.09', '0.113', '#interest', '#admiration', 'positive', '0.065', 'tame', 'crack', 'sense_fear', 'friend_person', 'good_manner']
senticnet['lxxiv'] = ['0.055', '0.189', '-0.09', '0.113', '#interest', '#admiration', 'positive', '0.065', 'tame', 'crack', 'sense_fear', 'friend_person', 'good_manner']
senticnet['lyce_senior'] = ['-0.04', '0.088', '0.013', '0.069', '#interest', '#admiration', 'positive', '0.024', 'kindergarden', 'daycare', 'nursery', 'child', 'asylum']
senticnet['lyceum_theatre'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'dress', 'pinny', 'dirndl', 'gown', 'muumuu']
senticnet['lychee'] = ['0.859', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.888', 'green_apple', 'apricot', 'lime', 'peach', 'cherry']
senticnet['lycodon'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'death_adder', 'ophidian', 'serpent', 'elapid', 'taipan']
senticnet['lycopersicon_esculentum'] = ['-0.56', '0', '0.892', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'borecole', 'colewort', 'kail', 'cole', 'kale']
senticnet['lycopod'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'seed_fern', 'pteridosperm']
senticnet['lycopodiophyta'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'gymnosperm', 'pteridosperm']
senticnet['lycopodium'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'clematis', 'legume', 'leguminous_plant', 'climber', 'root_climber']
senticnet['lydda'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['lydekkerina'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'hag', 'osteostracan', 'cephalaspid', 'ostracoderm', 'jawless_fish']
senticnet['lydgate'] = ['0.525', '-0.85', '0', '0.599', '#surprise', '#admiration', 'positive', '0.66', 'frolic', 'gambol', 'romp', 'play', 'caper']
senticnet['lye'] = ['0.096', '0', '0', '0', '#joy', '#joy', 'positive', '0.096', 'bubbly', 'clean_agent', 'lather', 'cleanser', 'foam']
senticnet['lyell'] = ['-0.04', '0.192', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.054', 'hypothetical', 'supposition', 'scientific_statement', 'psychology', 'educate_guess']
senticnet['lyin'] = ['-0.53', '0', '0.508', '-0.74', '#sadness', '#disgust', 'negative', '-0.59', 'bedwetter', 'bed_wetter', 'mortal', 'wetter', 'person']
senticnet['lying_asleep'] = ['0.071', '0.127', '0', '0.197', '#interest', '#admiration', 'positive', '0.132', 'place_work', 'workplace', 'office', 'work', 'hang_wall']
senticnet['lying_beside'] = ['0.071', '0.127', '0', '0.197', '#interest', '#admiration', 'positive', '0.132', 'place_work', 'workplace', 'office', 'work', 'hang_wall']
senticnet['lying_naked'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'duvet_cover', 'pillow', 'comforter', 'pillowcase', 'blanket']
senticnet['lying_under_oath'] = ['-0.73', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'obstruct_justice', 'get_away_crime', 'punish', 'hurt_else', 'commit_murder']
senticnet['lykke_li'] = ['0.028', '0', '0.027', '0.101', '#joy', '#admiration', 'positive', '0.034', 'animal_dwell', 'bee_home', 'live_place', 'dwell_place', 'place_to_live']
senticnet['lyme_disease'] = ['-0.43', '-0.14', '-0.06', '-0.36', '#sadness', '#disgust', 'negative', '-0.18', 'brain_tumor', 'suffer_fool', 'lose_team', 'cause_suffer', 'disrespect']
senticnet['lyme_disease_bacterium'] = ['-0.73', '0', '0.599', '-0.96', '#sadness', '#disgust', 'negative', '-0.76', 'spotted_fever', 'trench_fever', 'rickettsial_disease', 'typhus', 'typhus_fever']
senticnet['lymph'] = ['0.851', '0.706', '0', '0', '#joy', '#interest', 'positive', '0.778', 'breast_pocket', 'exoskeleton', 'forehead', 'toward', 'squint']
senticnet['lymphadenopathy'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema', 'erythema_multiforme', 'windburn', 'hickey']
senticnet['lymphatic_drainage'] = ['-0.99', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.78', 'angioedema', 'atrophedema', 'giant_hive', 'periodic_edema', 'dropsy']
senticnet['lymphatic_flow'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'keep_alive', 'body_system', 'body_working', 'blood_pressure', 'heart']
senticnet['lymphedema'] = ['-0.99', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.78', 'angioedema', 'atrophedema', 'giant_hive', 'periodic_edema', 'dropsy']
senticnet['lymphocytic_infiltration'] = ['0.905', '-0.65', '0', '0.851', '#joy', '#admiration', 'positive', '0.805', 'gonococcus', 'bacteria', 'bacteriae', 'probiotic', 'probiotic_bacterium']
senticnet['lymphocytic_leukemia'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['lymphoedema'] = ['-0.99', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.78', 'angioedema', 'atrophedema', 'giant_hive', 'periodic_edema', 'dropsy']
senticnet['lymphoid_hyperplasia'] = ['-0.88', '0', '0.774', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fibroid_tumor', 'fibroid', 'benign_tumor', 'nonmalignant_tumor', 'nonmalignant_neoplasm']
senticnet['lymphoma'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['lymphopenia'] = ['-0.53', '0', '0.608', '-0.93', '#anger', '#disgust', 'negative', '-0.69', 'reprieve', 'respite', 'hiatus', 'abatement', 'suspension']
senticnet['lymphoproliferative_disorder'] = ['-0.75', '0', '0.515', '-0.93', '#sadness', '#disgust', 'negative', '-0.73', 'sclerosis', 'induration', 'degenerative_disorder', 'autoimmune_disease', 'disseminated_sclerosis']
senticnet['lynx'] = ['0', '-0.97', '0', '0', '#surprise', '#surprise', 'positive', '0.977', 'mountain_lion', 'skunk', 'lion', 'tiger', 'cougar']
senticnet['lyonshall_castle'] = ['0.049', '0.16', '0', '0.155', '#interest', '#admiration', 'positive', '0.121', 'castle', 'palace', 'medieval', 'aristocrat', 'throne']
senticnet['lyophilization'] = ['-0.64', '0', '0.612', '-0.84', '#sadness', '#disgust', 'negative', '-0.7', 'chilling', 'frost', 'freeze', 'icing', 'cooling']
senticnet['lyra_belacqua'] = ['0.756', '0', '0', '0', '#joy', '#joy', 'positive', '0.756', 'companionship', 'cherish', 'lover', 'bond', 'buddy']
senticnet['lyrae'] = ['0', '-0.72', '0', '0.712', '#surprise', '#admiration', 'positive', '0.72', 'heavenly_body', 'around_earth', 'source_light', 'sol', 'celestial_body']
senticnet['lyrate_leaf'] = ['-0.66', '0', '0.971', '-0.75', '#anger', '#disgust', 'negative', '-0.79', 'simple_leaf', 'needle', 'elliptic_leaf', 'hastate_leaf', 'obovate_leaf']
senticnet['lyric'] = ['0.091', '-0.03', '0.058', '0.077', '#joy', '#admiration', 'positive', '0.036', 'lyrics', 'melody', 'piece_of_music', 'song_lyric', 'rhythm']
senticnet['lyric_booklet'] = ['-0.73', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'unwed', 'unwedded', 'divorced', 'unmarried', 'widowed']
senticnet['lyric_poetry'] = ['0.236', '0.094', '-0.08', '0.118', '#joy', '#admiration', 'positive', '0.091', 'verse', 'song_lyric', 'ode', 'rhyme', 'song']
senticnet['lyric_suite'] = ['0.591', '-0.91', '0', '0.605', '#surprise', '#admiration', 'positive', '0.704', 'farandole', 'folk_dance', 'polka', 'dance', 'typical_dance']
senticnet['lyrical'] = ['0.704', '-0.50', '0', '0.923', '#joy', '#admiration', 'positive', '0.71', 'mellifluous', 'mellisonant', 'dulcet', 'melodic', 'honeyed']
senticnet['lyrical_imagery'] = ['-0.77', '0.277', '-0.44', '-0.05', '#sadness', '#fear', 'negative', '-0.24', 'heavyheartedness', 'bored', 'world-weariness', 'heavyhearted', 'world-weary']
senticnet['lyrical_interpretation'] = ['0.045', '0', '0.033', '0.089', '#joy', '#admiration', 'positive', '0.034', 'spanish', 'vocalization', 'verbal_communication', 'vocal', 'mouth_noise']
senticnet['lyrical_prose'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'mediacy', 'mediateness', 'characteristic', 'indirectness']
senticnet['lyrical_style'] = ['-0.74', '0.292', '-0.42', '-0.06', '#sadness', '#fear', 'negative', '-0.23', 'lowness', 'dispiritedness', 'low-spiritedness', 'dejectedness', 'downheartedness']
senticnet['lyrical_wit'] = ['-0.89', '0', '0.722', '-0.94', '#sadness', '#disgust', 'negative', '-0.85', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['lyrically'] = ['-0.74', '0.292', '-0.42', '-0.06', '#sadness', '#fear', 'negative', '-0.23', 'lowness', 'dispiritedness', 'low-spiritedness', 'dejectedness', 'downheartedness']
senticnet['lyrichord'] = ['0.805', '-0.58', '0', '0.525', '#joy', '#surprise', 'positive', '0.637', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['lyricism'] = ['0.609', '-0.61', '0', '0.592', '#joy', '#surprise', 'positive', '0.605', 'spirit', 'rabidity', 'rabidness', 'sprightliness', 'ebullience']
senticnet['lyricist'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
senticnet['lyrics'] = ['0.071', '-0.01', '0.048', '0.078', '#joy', '#admiration', 'positive', '0.028', 'song_lyric', 'melody', 'lyric', 'piece_of_music', 'song']
senticnet['lysergic_acid_diethylamide'] = ['-0.73', '0', '0.944', '-0.94', '#anger', '#disgust', 'negative', '-0.87', 'psychedelic_drug', 'mescaline', 'peyote', 'phencyclidine', 'drug']
senticnet['lysergic_acid_diethylamide_lsd'] = ['-0.73', '0', '0.944', '-0.94', '#anger', '#disgust', 'negative', '-0.87', 'lysergic_acid_diethylamide', 'mescaline', 'peyote', 'phencyclidine', 'drug']
senticnet['lysis_buffer'] = ['-0.04', '0.069', '0', '0.075', '#interest', '#admiration', 'positive', '0.035', 'sculpt_gel', 'hairstyle', 'hairdresser', 'cosmetic', 'hair_sculpt_gel']
senticnet['lysol'] = ['0', '0.095', '0', '0.074', '#interest', '#admiration', 'positive', '0.084', 'buy_wash_powder', 'water_soap', 'turn_faucet', 'take_off', 'fill_bathtub_water']
senticnet['lysosomal_storage_disease'] = ['-0.57', '0', '0.973', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['lyve'] = ['0.887', '-0.84', '0', '0.819', '#joy', '#surprise', 'positive', '0.851', 'arouse', 'elicit', 'enkindle', 'kindle', 'evoke']
senticnet['maachah'] = ['-0.52', '0', '0.701', '-0.50', '#sadness', '#anger', 'negative', '-0.57', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['maagha'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['maalox'] = ['0', '0', '-0.70', '-0.76', '#fear', '#disgust', 'negative', '-0.73', 'choke', 'become_full', 'get_full', 'burp', 'sickness']
senticnet['maamoun_youssef'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'dejectedness', 'low-spiritedness', 'lowness', 'downheartedness', 'cheerless']
senticnet['maasai_mara_game'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['maasai_mara_national'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['maaseh'] = ['0', '0.106', '-0.05', '0.163', '#interest', '#admiration', 'positive', '0.072', 'band', 'kit', 'oblivion', 'rock_band', 'high_school_band']
senticnet['mabbutt'] = ['0.941', '-0.83', '0', '0.973', '#joy', '#admiration', 'positive', '0.917', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['mabee_library'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'a_lot_of_books', 'old_testament', 'cook_book', 'magazine', 'librarian']
senticnet['mac_lir'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'sedum', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['mac_mohan'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['mac_wiseman'] = ['-0.04', '-0.04', '-0.04', '-0.04', '#fear', '#disgust', 'negative', '-0.02', 'music_shop', 'music_store', 'musical_instrument_store', 'string_quartet', 'high_school_band']
senticnet['macabre'] = ['0', '0', '-0.78', '-0.99', '#fear', '#disgust', 'negative', '-0.88', 'represent', 'morbid', 'ghoulish', 'gothic', 'ghastly']
senticnet['macaca_mulatta'] = ['0.921', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.825', 'monkey', 'fiance', 'lemur', 'beaver', 'koala']
senticnet['macadamia'] = ['0.859', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.888', 'green_apple', 'apricot', 'lime', 'peach', 'cherry']
senticnet['macanudo'] = ['0.031', '0', '-0.08', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'smoke_cigarette', 'smoke', 'cinder', 'tobacco_product', 'smoke_pipe']
senticnet['macaque'] = ['0.921', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.825', 'monkey', 'fiance', 'lemur', 'beaver', 'koala']
senticnet['macaque_monkey'] = ['0.921', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.825', 'monkey', 'fiance', 'lemur', 'beaver', 'koala']
senticnet['macaranga'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'seed_fern', 'pteridosperm']
senticnet['macaroni_cheese'] = ['0.644', '-0.94', '0', '0.729', '#surprise', '#admiration', 'positive', '0.773', 'gem', 'banana_bread', 'date_bread', 'nut_bread', 'quick_bread']
senticnet['macaronic_verse'] = ['-0.81', '0', '0.52', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'affray', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['macaroon'] = ['0.731', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.837', 'make_bread', 'cook_dinner', 'cook_meal', 'cook_curry', 'follow_recipe']
senticnet['macbeth'] = ['-0.91', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.85', 'play', 'perpetrator', 'role_play', 'act_part', 'terrible_event']
senticnet['mace'] = ['-0.91', '0', '0.931', '-0.72', '#sadness', '#anger', 'negative', '-0.85', 'hit_with_baton', 'get_hurt', 'whip', 'arrow', 'hurt']
senticnet['maceration'] = ['0', '0.104', '-0.09', '0.146', '#interest', '#admiration', 'positive', '0.052', 'thaw', 'slush', 'water_inside', 'give_water', 'obtain_water']
senticnet['macguffin'] = ['-0.57', '0', '0.721', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'bush', 'native_pomegranate', 'caper_tree', 'native_orange', 'common_caper']
senticnet['mach_ay_cave'] = ['-0.02', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'tunnel', 'dark_place', 'ski_resort', 'top_tree', 'compost_pile']
senticnet['mach_number'] = ['-0.03', '0.178', '0', '0.168', '#interest', '#admiration', 'positive', '0.104', 'speed', 'fast', 'fast_and_furious', 'speedy', 'fleet']
senticnet['mach_principle'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'general_relativity', 'general_theory_of_relativity', 'relativity', 'special_relativity', 'special_theory_of_relativity']
senticnet['machete'] = ['0.939', '0.785', '0', '0', '#joy', '#interest', 'positive', '0.862', 'dagger', 'cut_tool', 'blade', 'vaccination', 'knife']
senticnet['machiavellian'] = ['-0.77', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.80', 'brutal', 'iron-fisted', 'cruel', 'ruthlessly', 'ruthless']
senticnet['machilid'] = ['-0.70', '0', '0.994', '-0.99', '#anger', '#disgust', 'negative', '-0.89', 'insect', 'bristletail', 'silverfish', 'jumping_bristletail', 'thysanuran_insect']
senticnet['machinability'] = ['0.8', '-0.90', '0', '0.675', '#joy', '#surprise', 'positive', '0.794', 'indestructibility', 'tensile_strength', 'continuity', 'persistence', 'changelessness']
senticnet['machine'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'biological_machine', 'social_creature', 'live_creature', 'powerful', 'alive']
senticnet['machine-gun'] = ['0', '0', '0.753', '-0.97', '#anger', '#disgust', 'negative', '-0.86', 'barrage', 'well-aimed', 'withering', 'musketry', 'fusillade']
senticnet['machine_embroidery'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'building', 'carving', 'artistic_creation', 'ceramic']
senticnet['machine_gun'] = ['0.82', '0.948', '0', '0', '#joy', '#interest', 'positive', '0.884', 'handgun', 'weapon', 'firearm', 'cannon', 'rifle']
senticnet['machine_gun_ammunition'] = ['0', '0', '-0.92', '-0.91', '#fear', '#disgust', 'negative', '-0.92', 'automatic', 'firearm', 'gun', 'weapon', 'projectile']
senticnet['machine_gun_fire'] = ['0', '0', '-0.85', '-0.99', '#fear', '#disgust', 'negative', '-0.92', 'onslaught', 'battery', 'shelling', 'bombardment', 'outpouring']
senticnet['machine_gun_nest'] = ['0', '0', '0.818', '-0.99', '#anger', '#disgust', 'negative', '-0.90', 'weapon', 'aerial_bomb', 'machine_gun', 'cannon', 'handgun']
senticnet['machine_part'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'market_product', 'eat_pasta', 'use_drug', 'speak_english', 'forward_email']
senticnet['machine_pistol'] = ['0', '0', '-0.87', '-0.82', '#fear', '#disgust', 'negative', '-0.84', 'pistol', 'gun', 'hydrogen_bomb', 'rifle', 'shotgun']
senticnet['machine_washable'] = ['-0.81', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.85', 'bandage', 'painkiller', 'gauze', 'aspirin', 'contraceptive_device']
senticnet['machined_aluminum'] = ['0.72', '0', '0', '0.733', '#joy', '#admiration', 'positive', '0.726', 'spanner', 'gold', 'strong_material', 'platinum', 'durable']
senticnet['machinegun_fire'] = ['0', '0', '-0.85', '-0.99', '#fear', '#disgust', 'negative', '-0.92', 'onslaught', 'battery', 'shelling', 'bombardment', 'outpouring']
senticnet['machinery'] = ['0.966', '0.799', '0', '0', '#joy', '#interest', 'positive', '0.883', 'mechanical_device', 'move_part', 'machine', 'work_well', 'make_car']
senticnet['machinic'] = ['0.654', '-0.71', '0', '0.624', '#joy', '#surprise', 'positive', '0.664', 'viewable', 'gross', 'telescopic', 'seeable', 'visual']
senticnet['machinist'] = ['0', '0.105', '-0.05', '0.068', '#interest', '#admiration', 'positive', '0.04', 'mechanic', 'manufacturer', 'factory', 'machinery', 'make_car']
senticnet['machinist_union'] = ['-0.77', '0', '0.505', '-0.50', '#sadness', '#anger', 'negative', '-0.59', 'wildcat_strike', 'sympathy_strike', 'sympathetic_strike', 'walkout', 'job_action']
senticnet['machismo'] = ['0.585', '-0.7', '0', '0.586', '#surprise', '#admiration', 'positive', '0.624', 'jingoistic', 'ultranationalistic', 'superpatriotic', 'nationalistic', 'patriotic']
senticnet['macho'] = ['0.585', '-0.7', '0', '0.586', '#surprise', '#admiration', 'positive', '0.624', 'jingoistic', 'ultranationalistic', 'superpatriotic', 'nationalistic', 'patriotic']
senticnet['macho_man'] = ['-0.19', '0', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.15', 'bad_child', 'misbehavior', 'bad_kid', 'bad_action', 'difficulty']
senticnet['mack_daddy'] = ['0.542', '-0.83', '0', '0.927', '#surprise', '#admiration', 'positive', '0.769', 'incest', 'extramarital_sex', 'free_love', 'sexual_intercourse', 'intercourse']
senticnet['mackerel'] = ['0.847', '-0.93', '0', '0', '#joy', '#surprise', 'positive', '0.891', 'mullet', 'mullet', 'goatee', 'catfish', 'flounder']
senticnet['mackerel_shark'] = ['-0.54', '0', '0.983', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'elasmobranch', 'selachian', 'hammerhead', 'cow_shark', 'shark']
senticnet['mackey'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
senticnet['maclura_pomifera'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'black_locust']
senticnet['macoma'] = ['-0.89', '0', '0.556', '-0.72', '#sadness', '#disgust', 'negative', '-0.72', 'fucus', 'rockweed', 'black_rockweed', 'bladder_fucus', 'tang']
senticnet['macpherson_inquiry'] = ['0.845', '0.916', '0', '0', '#joy', '#interest', 'positive', '0.881', 'question', 'answer', 'query', 'question_start', 'question_answering']
senticnet['macphisto'] = ['-0.86', '0', '0.675', '-0.84', '#sadness', '#disgust', 'negative', '-0.79', 'shenanigan', 'chicanery', 'wile', 'dupery', 'trickery']
senticnet['macram'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'building', 'carving', 'artistic_creation', 'ceramic']
senticnet['macrame'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'building', 'carving', 'artistic_creation', 'ceramic']
senticnet['macrauchenia'] = ['-0.93', '0', '0.508', '-0.58', '#sadness', '#disgust', 'negative', '-0.67', 'inhuman', 'bloodless', 'apelike', 'nonhuman', 'nonhumanal']
senticnet['macro_definition'] = ['-0.98', '0', '0.869', '-0.80', '#sadness', '#anger', 'negative', '-0.88', 'error', 'programming_error', 'semantic_error', 'runtime_error', 'computer_error']
senticnet['macro_economic'] = ['0.913', '-0.63', '0', '0.961', '#joy', '#admiration', 'positive', '0.836', 'materialistic', 'mercenary', 'mundane', 'material', 'worldly']
senticnet['macro_mode'] = ['0.021', '0.081', '0.017', '0.113', '#interest', '#admiration', 'positive', '0.05', 'aspect', 'perspective', 'look_round', 'scenic', 'look_through']
senticnet['macro_package'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'wall_cover', 'two-dimensional', 'cover_wall', 'pigment', 'dye']
senticnet['macroalgal'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'dirtiness', 'befoulment', 'defilement', 'dinginess', 'dinge']
senticnet['macroblock'] = ['0.033', '0', '0', '0.081', '#joy', '#admiration', 'positive', '0.057', 'door', 'door_lock', 'window_door', 'wall', 'doorway']
senticnet['macrocarpa'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'carnauba_palm', 'carnauba', 'wax_palm', 'palmetto', 'palmyra']
senticnet['macrocephaly'] = ['-0.50', '0', '0.542', '-0.51', '#anger', '#disgust', 'negative', '-0.51', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'hirschsprung_disease']
senticnet['macrocosmic'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'metastability', 'monotony', 'invariance', 'constancy', 'unchangeability']
senticnet['macrocytic_anemia'] = ['0.905', '-0.65', '0', '0.851', '#joy', '#admiration', 'positive', '0.805', 'gonococcus', 'bacteria', 'bacteriae', 'probiotic', 'probiotic_bacterium']
senticnet['macrodantin'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['macroeconomic'] = ['0.913', '-0.63', '0', '0.961', '#joy', '#admiration', 'positive', '0.836', 'materialistic', 'mercenary', 'mundane', 'material', 'worldly']
senticnet['macroeconomy'] = ['0.913', '-0.63', '0', '0.961', '#joy', '#admiration', 'positive', '0.836', 'materialistic', 'mercenary', 'mundane', 'material', 'worldly']
senticnet['macrophage'] = ['0.702', '-0.52', '0', '0.958', '#joy', '#admiration', 'positive', '0.728', 'somatic_cell', 'vegetative_cell', 'fixed_phagocyte', 'phagocyte', 'free_phagocyte']
senticnet['macrophage_activation'] = ['-0.66', '0', '0.877', '-0.64', '#sadness', '#anger', 'negative', '-0.72', 'somatic_cell', 'mastocyte', 'labrocyte', 'target_cell', 'phagocyte']
senticnet['macrophage_infiltration'] = ['-0.84', '0', '0.8', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'rigor_mortis', 'natural_process', 'natural_action', 'activity', 'action']
senticnet['macroscopic'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'amoeba', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['macroscopic_level'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'amoeba', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['macroscopic_scale'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'amoeba', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['macrospora'] = ['-0.93', '0', '0.699', '-0.91', '#sadness', '#disgust', 'negative', '-0.84', 'onion_smut', 'head_smut', 'stinking_smut', 'flag_smut_fungus', 'covered_smut']
senticnet['macrotexture'] = ['0', '-0.01', '0.046', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'cement', 'tar', 'concrete', 'big_city_life', 'popular_building_material']
senticnet['macular'] = ['-0.82', '0', '0.996', '-0.61', '#sadness', '#anger', 'negative', '-0.81', 'visual_impairment', 'visual_defect', 'vision_defect', 'visual_disorder', 'avitaminosis']
senticnet['macular_degeneration'] = ['0.671', '-0.56', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'degeneration', 'attack', 'obsolescence', 'process', 'physical_process']
senticnet['macular_dystrophy'] = ['-0.82', '0', '0.996', '-0.61', '#sadness', '#anger', 'negative', '-0.81', 'visual_impairment', 'visual_defect', 'vision_defect', 'visual_disorder', 'avitaminosis']
senticnet['macular_edema'] = ['-0.86', '0', '0.858', '-0.91', '#sadness', '#disgust', 'negative', '-0.87', 'edema', 'oedema', 'hydrops', 'dropsy', 'eye_disease']
senticnet['macular_hole'] = ['-0.86', '0', '0.858', '-0.91', '#sadness', '#disgust', 'negative', '-0.87', 'edema', 'oedema', 'hydrops', 'dropsy', 'eye_disease']
senticnet['macule'] = ['-0.66', '0', '0.911', '-0.99', '#anger', '#disgust', 'negative', '-0.85', 'liver_spot', 'spot', 'freckle', 'lentigo', 'plague_spot']
senticnet['maculopapular'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema', 'erythema_multiforme', 'windburn', 'hickey']
senticnet['maculopapular_rash'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema', 'erythema_multiforme', 'windburn', 'hickey']
senticnet['maculopathy'] = ['-0.86', '0', '0.858', '-0.91', '#sadness', '#disgust', 'negative', '-0.87', 'edema', 'oedema', 'hydrops', 'dropsy', 'eye_disease']
senticnet['macushla'] = ['0.59', '-0.77', '0', '0.656', '#surprise', '#admiration', 'positive', '0.673', 'darling', 'pet', 'favorite', 'first_choice', 'dearie']
senticnet['macvector'] = ['0.868', '0.717', '0', '0', '#joy', '#interest', 'positive', '0.792', 'tool_shed', 'tool', 'garage', 'tool_belt', 'hardware_store']
senticnet['macworld_conference'] = ['-0.79', '0', '0.948', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['macy'] = ['0', '0.081', '0', '0.071', '#interest', '#admiration', 'positive', '0.076', 'thrift_store', 'type_of_store', 'record_store', 'many_shops', 'clothes_store']
senticnet['macy_store'] = ['0', '0.081', '0', '0.071', '#interest', '#admiration', 'positive', '0.076', 'thrift_store', 'type_of_store', 'record_store', 'many_shops', 'clothes_store']
senticnet['mad'] = ['0', '0', '0.839', '-0.87', '#anger', '#disgust', 'negative', '-0.85', 'rage', 'resentment', 'temper', 'malice', 'animosity']
senticnet['mad_bomber'] = ['-0.69', '0', '0.756', '-0.99', '#anger', '#disgust', 'negative', '-0.81', 'waster', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['mad_cow_disease'] = ['0', '0', '-0.77', '-0.7', '#fear', '#disgust', 'negative', '-0.74', 'toothache', 'diarrhoea', 'ice_cream_melt', 'flab', 'bad_hair_day']
senticnet['mad_dash'] = ['-0.88', '0.178', '-0.6', '0', '#sadness', '#fear', 'negative', '-0.43', 'rush_job', 'rush_work', 'late', 'urgency', 'emergency']
senticnet['mad_person'] = ['-0.92', '0', '0.553', '-0.91', '#sadness', '#disgust', 'negative', '-0.79', 'mad_scientist', 'fool', 'mad', 'crazy', 'mental_disease']
senticnet['mad_rush'] = ['-0.88', '0.178', '-0.6', '0', '#sadness', '#fear', 'negative', '-0.43', 'rush_job', 'rush_work', 'late', 'urgency', 'emergency']
senticnet['mad_scientist'] = ['-0.98', '0', '0.349', '-0.90', '#sadness', '#disgust', 'negative', '-0.74', 'fool', 'mad', 'crazy', 'mental_disease', 'mental_problem']
senticnet['mad_scramble'] = ['-0.88', '0.178', '-0.6', '0', '#sadness', '#fear', 'negative', '-0.43', 'rush_job', 'rush_work', 'late', 'urgency', 'emergency']
senticnet['madam'] = ['0.964', '-0.81', '0', '0.926', '#joy', '#admiration', 'positive', '0.9', 'girl', 'female', 'grande_dame', 'dame', 'lady']
senticnet['madame_blavatsky'] = ['-0.51', '0', '0.979', '-0.89', '#anger', '#disgust', 'negative', '-0.79', 'invisible', 'concealed', 'nonvisual', 'hidden', 'out_of_sight']
senticnet['madame_duval'] = ['-0.85', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.92', 'chagrined', 'abashed', 'chagrin', 'abash', 'embarrassingly']
senticnet['madame_haupt'] = ['-0.83', '0', '0.87', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'precipitate', 'rain_down', 'shower_down', 'spit', 'patter']
senticnet['madcap'] = ['0.631', '-0.87', '0', '0.887', '#surprise', '#admiration', 'positive', '0.798', 'impulsive', 'tearaway', 'hotheaded', 'incautious', 'impetuous']
senticnet['madden'] = ['-0.89', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.92', 'mad', 'insane', 'maddening', 'inflame', 'furious']
senticnet['maddened'] = ['-0.13', '0.14', '0.624', '-0.64', '#anger', '#disgust', 'negative', '-0.31', 'enraged', 'infuriated', 'furious', 'angered', 'indignantly']
senticnet['maddening'] = ['-0.12', '0.13', '0.647', '-0.64', '#anger', '#disgust', 'negative', '-0.32', 'infuriate', 'exasperating', 'infuriating', 'furiously', 'dudgeon']
senticnet['maddeningly'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicado', 'unpronounceable', 'impassive', 'incommunicative', 'silent']
senticnet['maddinson'] = ['0.034', '-0.02', '0.054', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'compass', 'swiss_army_knife', 'bake_pan', 'coffee_mug', 'wine_opener']
senticnet['made-up'] = ['0', '-0.79', '0', '0.799', '#surprise', '#admiration', 'positive', '0.797', 'nonsensical', 'fictitious', 'gibberish', 'fictional', 'nonsense']
senticnet['made_available'] = ['0', '0.901', '0', '0.992', '#interest', '#admiration', 'positive', '0.947', 'project', 'research', 'student', 'result', 'well']
senticnet['made_little_headway'] = ['0.819', '-0.87', '0', '0.902', '#surprise', '#admiration', 'positive', '0.865', 'tally', 'steal', 'score', 'hit', 'rack_up']
senticnet['made_of_glass'] = ['0.801', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.768', 'drink_wine', 'wine_opener', 'good_wine', 'wine_glass', 'wine_bottle']
senticnet['made_out_of_plastic'] = ['-0.05', '0.092', '0', '0.132', '#interest', '#admiration', 'positive', '0.057', 'recycle', 'reuse', 'find_car', 'make_out_cloth', 'find_kitchen']
senticnet['made_publicly_available'] = ['0.81', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.774', 'available', 'public', 'population', 'crowd', 'majority']
senticnet['made_up'] = ['-0.79', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'fabled', 'fictitious', 'pretend', 'make_believe', 'unreal']
senticnet['madea_family'] = ['0.119', '-0.13', '-0.06', '0', '#joy', '#surprise', 'positive', '0.063', 'gala', 'celebration', 'special_event', 'special_happen', 'special_moment']
senticnet['mademoiselle'] = ['-0.81', '0', '0.885', '-0.92', '#anger', '#disgust', 'negative', '-0.87', 'miss', 'girl', 'damsel', 'maiden', 'damoiselle']
senticnet['mademoiselle_cormon'] = ['-0.85', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.92', 'chagrined', 'abashed', 'chagrin', 'abash', 'embarrassingly']
senticnet['madhouse'] = ['-0.67', '0', '0.931', '-0.77', '#anger', '#disgust', 'negative', '-0.79', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['madly'] = ['-0.72', '0', '0.721', '0', '#sadness', '#anger', 'negative', '-0.72', 'reason', 'wildly', 'manner', 'understanding', 'mad']
senticnet['madman'] = ['-0.06', '-0.00', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.05', 'capoeira', 'trouble', 'agony', 'cause_problem', 'river_stream']
senticnet['madman_paradise'] = ['0.952', '0.918', '0', '0', '#joy', '#interest', 'positive', '0.935', 'tack', 'foot_part', 'casual_shoes', 'gauntlet', 'pin']
senticnet['madmen'] = ['0', '0', '0.729', '-0.93', '#anger', '#disgust', 'negative', '-0.83', 'seek_revenge', 'die', 'take_life', 'threaten', 'cause_death']
senticnet['madnep'] = ['0.861', '-0.87', '0', '0.597', '#joy', '#surprise', 'positive', '0.779', 'herb', 'cultivated_parsnip', 'wild_parsnip', 'pastinaca_sativa', 'herbaceous_plant']
senticnet['madness'] = ['0', '0', '-0.92', '-0.88', '#fear', '#disgust', 'negative', '-0.90', 'temper', 'disgust', 'offend', 'despise', 'animosity']
senticnet['madoff'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'gouger', 'swindler', 'confidence_man', 'conman', 'fraud']
senticnet['madoff_accountant'] = ['0.025', '0.27', '-0.11', '0.243', '#interest', '#admiration', 'positive', '0.105', 'keep_safe', 'store_dry_goods', 'store_stuff', 'secure', 'save']
senticnet['madoff_firm'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'busy_bee', 'gopher', 'actor', 'doer', 'worker']
senticnet['madoff_longtime_secretary'] = ['0.025', '0.27', '-0.11', '0.243', '#interest', '#admiration', 'positive', '0.105', 'keep_safe', 'store_dry_goods', 'store_stuff', 'secure', 'save']
senticnet['madonna'] = ['0.056', '0.139', '-0.07', '0.154', '#interest', '#admiration', 'positive', '0.07', 'belongings', 'material', 'fabric', 'guarantee', 'stuff']
senticnet['madrasa'] = ['0', '0.224', '0', '0.197', '#interest', '#admiration', 'positive', '0.211', 'church', 'temple', 'cathedral', 'chapel', 'confession']
senticnet['madripoor'] = ['0.02', '0.131', '0.749', '-0.67', '#anger', '#disgust', 'negative', '-0.31', 'belligerence', 'belligerency', 'belligerent', 'war', 'rancor']
senticnet['madrissah'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitentially', 'penitent', 'penitence', 'repentant']
senticnet['maeglin'] = ['-0.67', '0', '0.546', '-0.63', '#sadness', '#disgust', 'negative', '-0.61', 'fink', 'snitch', 'snitcher', 'sneak', 'stoolpigeon']
senticnet['maelstrom'] = ['-0.85', '0', '0.583', '-0.71', '#sadness', '#disgust', 'negative', '-0.71', 'muddle', 'fuddle', 'mare_nest', 'rummage', 'clutter']
senticnet['maersk_air'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['maestoso'] = ['0.591', '-0.91', '0', '0.605', '#surprise', '#admiration', 'positive', '0.704', 'farandole', 'folk_dance', 'polka', 'dance', 'typical_dance']
senticnet['mafia'] = ['-0.85', '0', '0.749', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'suck_up', 'toady', 'bootlick', 'kowtow', 'court_favor']
senticnet['mafic_magma'] = ['0', '0', '-0.92', '0', '#fear', '#fear', 'negative', '-0.92', 'water_flow', 'wind_water', 'water_stream', 'waterway', 'lava']
senticnet['magazine'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'library', 'old_testament', 'cook_book', 'a_lot_of_books', 'librarian']
senticnet['magazine_islamic_turkistan'] = ['0.056', '0.157', '0', '0.154', '#interest', '#admiration', 'positive', '0.122', 'exercise', 'exercise_regularly', 'live_healthy_life', 'exercise_often', 'exercise_much']
senticnet['magazine_newspaper'] = ['0.021', '0', '0', '0.076', '#joy', '#admiration', 'positive', '0.049', 'newspaper_story', 'newspaper_write', 'newspaper_article', 'article', 'editorial']
senticnet['magazine_photo_shoot'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'fragmentation', 'discharge', 'backfire', 'blast']
senticnet['magazine_publisher'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'library', 'old_testament', 'cook_book', 'a_lot_of_books', 'librarian']
senticnet['magazine_rack'] = ['0.974', '0.813', '0', '0', '#joy', '#interest', 'positive', '0.893', 'dentist_chair', 'sofa', 'couch', 'sofa_bed', 'mattress']
senticnet['magdalena_bay'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'harpoon', 'armory', 'lake', 'river', 'bay']
senticnet['mage'] = ['0', '0.081', '0', '0', '#interest', '#interest', 'positive', '0.081', 'favorite', 'guard', 'crowd', 'several_people', 'clique']
senticnet['magenta'] = ['-0.45', '0.081', '-0.25', '0', '#sadness', '#fear', 'negative', '-0.20', 'green_yellow', 'color', 'blue_green', 'cyan', 'purple']
senticnet['magenta_dye'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'indigo', 'yellow', 'water_color', 'red', 'violet']
senticnet['maggi_soup'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'builder']
senticnet['maggia'] = ['-0.98', '0', '0.714', '-0.90', '#sadness', '#disgust', 'negative', '-0.86', 'stupid', 'stupid_person', 'stupe', 'dullard', 'dolt']
senticnet['maggie'] = ['-0.03', '-0.00', '0.087', '-0.05', '#anger', '#disgust', 'negative', '-0.04', 'dog_fur', 'dog_cat', 'dog_hair', 'pet', 'animal_hair']
senticnet['maggie_tulliver'] = ['-0.05', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'plane_land', 'station_wagon', 'floss_tooth', 'ornament', 'wear_dress']
senticnet['maggot'] = ['0', '0', '0.109', '-0.03', '#anger', '#disgust', 'negative', '-0.07', 'small_fly', 'insect', 'annoy_bug', 'parasite', 'tiny_insect']
senticnet['maghrebi_arabic'] = ['-0.84', '0', '0.579', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'squiggle', 'hand', 'chicken_scratch', 'handwriting', 'script']
senticnet['magian'] = ['-0.71', '0', '0.836', '0', '#sadness', '#anger', 'negative', '-0.77', 'awkward', 'unenviable', 'embarrassingly', 'embarrassing', 'sheepish']
senticnet['magic'] = ['0.793', '-0.95', '0', '0', '#joy', '#surprise', 'positive', '0.873', 'play_ping_pong', 'musical_device', 'school_play', 'stunt', 'family_activity']
senticnet['magic_bullet'] = ['0.713', '0', '0', '0.766', '#joy', '#admiration', 'positive', '0.74', 'doctor', 'nurse', 'medical_procedure', 'medicine', 'doctor_surgery']
senticnet['magic_cube'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'block', 'dice', 'ping_pong_paddle', 'ball_player', 'trapezoid']
senticnet['magic_elixir'] = ['0.95', '-0.86', '0', '0.947', '#joy', '#admiration', 'positive', '0.92', 'content', 'lore', 'folklore', 'cognitive_content', 'story']
senticnet['magic_marker'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'gift_wrap', 'kraft', 'kraft_paper', 'wrapping_paper', 'paper']
senticnet['magic_mirror'] = ['0.967', '0.768', '0', '0', '#joy', '#interest', 'positive', '0.867', 'centerfold', 'genus', 'honey_bee', 'infant', 'dove']
senticnet['magic_potion'] = ['-0.55', '0', '0.5', '-0.55', '#sadness', '#disgust', 'negative', '-0.53', 'remedy', 'cure', 'catholicon', 'therapeutic', 'curative']
senticnet['magic_realism'] = ['0.036', '0.219', '0', '0.19', '#interest', '#admiration', 'positive', '0.148', 'book_genre', 'literature', 'concoction', 'untruth', 'falsehood']
senticnet['magic_realist'] = ['0.036', '0.219', '0', '0.19', '#interest', '#admiration', 'positive', '0.148', 'book_genre', 'literature', 'concoction', 'untruth', 'falsehood']
senticnet['magic_spell'] = ['-0.82', '0', '0.72', '-0.53', '#sadness', '#anger', 'negative', '-0.69', 'invocation', 'charm', 'spell', 'conjuration', 'magical_spell']
senticnet['magic_sword'] = ['0.105', '-0.17', '0.057', '0.079', '#joy', '#surprise', 'positive', '0.076', 'cue', 'twig', 'long_stick', 'stick', 'adhere']
senticnet['magic_trick'] = ['0.818', '0.913', '0', '0.825', '#interest', '#admiration', 'positive', '0.852', 'greeting', 'blackjack', 'pass_card', 'credit', 'play_blackjack']
senticnet['magic_wand'] = ['0.105', '-0.17', '0.057', '0.079', '#joy', '#surprise', 'positive', '0.076', 'cue', 'twig', 'long_stick', 'stick', 'adhere']
senticnet['magical'] = ['0', '0.903', '0', '0.911', '#interest', '#admiration', 'positive', '0.907', 'character', 'play_person', 'personality_trait', 'illusion', 'fool']
senticnet['magical_artifact'] = ['0.086', '-0.10', '0.064', '0', '#joy', '#surprise', 'positive', '0.043', 'art_piece', 'lifetime', 'museum', 'vintage', 'collectible']
senticnet['magical_girl'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['magical_moment'] = ['0.504', '0.156', '-0.17', '0.193', '#joy', '#admiration', 'positive', '0.169', 'good_emotion', 'happy', 'happy_emotion', 'happy_feeling', 'pleasant_emotion']
senticnet['magical_potion'] = ['-0.55', '0', '0.5', '-0.55', '#sadness', '#disgust', 'negative', '-0.53', 'remedy', 'cure', 'catholicon', 'therapeutic', 'curative']
senticnet['magical_realism'] = ['0.747', '0.85', '0', '0', '#joy', '#interest', 'positive', '0.798', 'night_vision', 'mystery', 'plot', 'imagine', 'myth']
senticnet['magical_ritual'] = ['-0.82', '0', '0.72', '-0.53', '#sadness', '#anger', 'negative', '-0.69', 'invocation', 'charm', 'spell', 'magic_spell', 'magical_spell']
senticnet['magical_spell'] = ['-0.82', '0', '0.72', '-0.53', '#sadness', '#anger', 'negative', '-0.69', 'invocation', 'charm', 'spell', 'magic_spell', 'conjuration']
senticnet['magical_sword'] = ['0.086', '-0.10', '0.064', '0', '#joy', '#surprise', 'positive', '0.043', 'art_piece', 'lifetime', 'museum', 'vintage', 'collectible']
senticnet['magical_tale'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'mythical', 'tale', 'dictionary', 'magic', 'perfect_world']
senticnet['magical_wand'] = ['0.105', '-0.17', '0.057', '0.079', '#joy', '#surprise', 'positive', '0.076', 'cue', 'twig', 'long_stick', 'stick', 'adhere']
senticnet['magically'] = ['0.761', '-0.83', '0', '0', '#joy', '#surprise', 'positive', '0.798', 'miraculously', 'mysteriously', 'healed', 'thankfully', 'somehow']
senticnet['magician'] = ['0', '0.918', '0', '0', '#interest', '#interest', 'positive', '0.918', 'partygoer', 'invitation', 'halloween', 'clown', 'alcohol']
senticnet['magician_assistant'] = ['0.083', '0', '0', '0.075', '#joy', '#admiration', 'positive', '0.079', 'carny', 'carnival_ride', 'midway', 'ferris_wheel', 'roller-coaster']
senticnet['magician_penn'] = ['0', '0.186', '0', '0.075', '#interest', '#admiration', 'positive', '0.131', 'money_inside', 'check_account', 'savings', 'money_storage', 'store_money']
senticnet['magicienne'] = ['0', '0', '0.08', '0', '#anger', '#anger', 'negative', '-0.08', 'tree_animal', 'small_furry', 'rodent', 'big_mouse', 'small_rodent']
senticnet['magique'] = ['-0.58', '0', '0.661', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'explosive', 'cordite', 'dynamite', 'plastic_explosive', 'explosive_compound']
senticnet['magister'] = ['0.036', '0.111', '-0.05', '0.054', '#interest', '#admiration', 'positive', '0.038', 'despot', 'supervisor', 'chief', 'commander', 'earl']
senticnet['magisterial'] = ['0', '0', '0.74', '-0.75', '#anger', '#disgust', 'negative', '-0.74', 'autocratic', 'dominating', 'high-and-mighty', 'bossy', 'peremptory']
senticnet['magisterial_authority'] = ['0.751', '-0.98', '0', '0.989', '#surprise', '#admiration', 'positive', '0.909', 'okay', 'approval', 'commendation', 'okey', 'okeh']
senticnet['maglite'] = ['0', '-0.03', '0.043', '-0.11', '#anger', '#disgust', 'negative', '-0.03', 'torch', 'safety_pin', 'laser', 'flare', 'firebomb']
senticnet['maglubiyet'] = ['0', '0.081', '0', '0', '#interest', '#interest', 'positive', '0.081', 'favorite', 'guard', 'crowd', 'several_people', 'clique']
senticnet['magma'] = ['0', '0', '-0.92', '0', '#fear', '#fear', 'negative', '-0.92', 'water_flow', 'wind_water', 'water_stream', 'waterway', 'lava']
senticnet['magma_beneath'] = ['0', '0', '-0.92', '0', '#fear', '#fear', 'negative', '-0.92', 'water_flow', 'wind_water', 'water_stream', 'waterway', 'lava']
senticnet['magma_chamber'] = ['0', '0', '-0.92', '0', '#fear', '#fear', 'negative', '-0.92', 'water_flow', 'wind_water', 'water_stream', 'waterway', 'lava']
senticnet['magma_molten_rock'] = ['0', '0', '-0.92', '0', '#fear', '#fear', 'negative', '-0.92', 'water_flow', 'wind_water', 'water_stream', 'waterway', 'lava']
senticnet['magma_ocean'] = ['0', '0', '-0.92', '0', '#fear', '#fear', 'negative', '-0.92', 'water_flow', 'wind_water', 'water_stream', 'waterway', 'lava']
senticnet['magma_reservoir'] = ['0', '0', '-0.92', '0', '#fear', '#fear', 'negative', '-0.92', 'water_flow', 'wind_water', 'water_stream', 'waterway', 'lava']
senticnet['magmatic'] = ['0', '0', '-0.92', '0', '#fear', '#fear', 'negative', '-0.92', 'water_flow', 'wind_water', 'water_stream', 'waterway', 'lava']
senticnet['magnanimity'] = ['0.757', '0', '0', '0.74', '#joy', '#admiration', 'positive', '0.748', 'selflessness', 'self-sacrifice', 'unselfishness', 'humbleness', 'fearlessness']
senticnet['magnanimous'] = ['0.075', '0.252', '-0.14', '0.186', '#interest', '#admiration', 'positive', '0.091', 'tall_play_basketball', 'niceness', 'altruistic', 'kindness', 'type_of_bear']
senticnet['magnate'] = ['0', '0', '-0.99', '-0.73', '#fear', '#disgust', 'negative', '-0.86', 'king', 'baron', 'business_leader', 'mogul', 'big_businessman']
senticnet['magners_irish'] = ['0.079', '-0.02', '0.023', '0', '#joy', '#surprise', 'positive', '0.027', 'wine', 'lemonade', 'juice', 'whisky', 'soft_drink']
senticnet['magnesil'] = ['0.581', '-0.88', '0', '0.534', '#joy', '#surprise', 'positive', '0.667', 'better', 'improve', 'pure', 'meliorate', 'amend']
senticnet['magnesium'] = ['-0.03', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'lithium', 'mineral', 'potassium', 'iodine', 'manganese']
senticnet['magnesium_alloy'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'gold', 'bronze', 'alloy', 'medal', 'copper']
senticnet['magnesium_carbonate'] = ['-0.03', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'lithium', 'mineral', 'potassium', 'iodine', 'manganese']
senticnet['magnesium_chloride'] = ['-0.03', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'lithium', 'mineral', 'potassium', 'iodine', 'manganese']
senticnet['magnesium_citrate'] = ['-0.50', '0', '0.582', '-0.57', '#anger', '#disgust', 'negative', '-0.55', 'purgative', 'cathartic', 'physic', 'aperient', 'phenolphthalein']
senticnet['magnesium_deficiency'] = ['-0.03', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'lithium', 'mineral', 'potassium', 'iodine', 'manganese']
senticnet['magnesium_hydroxide'] = ['-0.03', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'lithium', 'mineral', 'potassium', 'iodine', 'manganese']
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senticnet['major_leaguer'] = ['0', '0.075', '0', '0', '#interest', '#interest', 'positive', '0.075', 'cup', 'carafe', 'container_cup', 'mug', 'glasses']
senticnet['major_obstacle'] = ['-0.93', '0', '-0.99', '-0.92', '#sadness', '#fear', 'negative', '-0.95', 'language_barrier', 'shut', 'shut_down', 'close_down', 'obstacle']
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senticnet['major_step_toward'] = ['0', '0', '0.078', '0', '#anger', '#anger', 'negative', '-0.07', 'street_city', 'small_street', 'walkway', 'black_pavement', 'dark_road']
senticnet['major_stockholder'] = ['0.08', '0.195', '0', '0.137', '#interest', '#admiration', 'positive', '0.137', 'company', 'enterprise', 'business', 'partnership', 'large_business']
senticnet['major_structural_damage'] = ['-0.98', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.97', 'harm', 'hurt', 'cut_skin', 'bloody', 'ouch']
senticnet['major_stumbling_block'] = ['-0.04', '0', '0.023', '0', '#sadness', '#anger', 'negative', '-0.03', 'golf_place', 'college_class', 'school_study', 'school_class', 'school_course']
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senticnet['major_tourist_attraction'] = ['0.034', '0.13', '-0.08', '0.258', '#interest', '#admiration', 'positive', '0.085', 'tourism', 'nice_view', 'big_monument', 'temple', 'sightsee']
senticnet['major_tourist_destination'] = ['0.034', '0.13', '-0.08', '0.258', '#interest', '#admiration', 'positive', '0.085', 'tourism', 'nice_view', 'big_monument', 'temple', 'sightsee']
senticnet['major_transportation_hub'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'artery', 'skeletal_system', 'groin', 'neck', 'muscle']
senticnet['major_winter_storm'] = ['0', '0', '-0.93', '-0.82', '#fear', '#disgust', 'negative', '-0.88', 'thunderstorm', 'snow_storm', 'storm', 'weather', 'rainy']
senticnet['majordomo'] = ['0.031', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.062', 'letter_slot', 'postal_counter', 'mailbox', 'letter_scale', 'envelope']
senticnet['majordomo_mailing_list'] = ['0.031', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.062', 'letter_slot', 'postal_counter', 'mailbox', 'letter_scale', 'envelope']
senticnet['majoritarian'] = ['-0.55', '0', '0.931', '-0.74', '#anger', '#disgust', 'negative', '-0.74', 'totalitarian', 'authoritarian', 'dictatorial', 'despotic', 'monarchal']
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senticnet['majority_leader'] = ['0.034', '-0.02', '0', '0.057', '#joy', '#admiration', 'positive', '0.039', 'female_television_connector', 'channel_selector', 'power_switch', 'tuner', 'radio']
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senticnet['majority_owned_subsidiary'] = ['0.08', '0.195', '0', '0.137', '#interest', '#admiration', 'positive', '0.137', 'company', 'enterprise', 'business', 'partnership', 'large_business']
senticnet['majority_stake'] = ['0.59', '-0.75', '0', '0.543', '#joy', '#surprise', 'positive', '0.63', 'venture', 'sally', 'labor', 'cause', 'risk']
senticnet['majority_stockholder'] = ['0.111', '0.18', '0', '0.128', '#interest', '#admiration', 'positive', '0.14', 'corporation', 'partnership', 'large_business', 'business_organization', 'enterprise']
senticnet['majority_vote'] = ['0.779', '-0.88', '0', '0.521', '#joy', '#surprise', 'positive', '0.727', 'corrigible', 'correctable', 'improvable', 'reformable', 'redeemable']
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senticnet['makara_sankranti'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
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senticnet['make_apple'] = ['0.105', '0.226', '-0.08', '0.176', '#interest', '#admiration', 'positive', '0.106', 'business', 'hold_onto', 'glass', 'drink_cup', 'retention']
senticnet['make_appointment'] = ['-0.08', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.08', 'schedule_appointment', 'put_nice_clothes', 'schedule_appointment_doctor', 'go_hair_salon', 'make_doctor_appointment']
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senticnet['make_arrangement'] = ['-0.06', '0.176', '-0.07', '0.125', '#interest', '#admiration', 'positive', '0.041', 'schedule_time', 'organize', 'pack_luggage', 'talk_to_travel_agent', 'decide_to_go']
senticnet['make_assumption'] = ['0.085', '0.101', '0', '0.235', '#interest', '#admiration', 'positive', '0.14', 'look_scary', 'elusive', 'morality', 'type_of_credit_card', 'free_stuff']
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senticnet['make_ball'] = ['0.743', '0.868', '0', '0', '#joy', '#interest', 'positive', '0.805', 'make_coat', 'tie', 'overcoat', 'make_out_leather', 't-shirt']
senticnet['make_beautiful'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'creative_expression', 'pastime', 'self_expression', 'date_activity', 'artistic_expression']
senticnet['make_beautiful_music'] = ['0.821', '0.98', '0', '0.869', '#interest', '#admiration', 'positive', '0.89', 'make_music', 'produce_musical_sound', 'create_music', 'play_stringed_instrument', 'play_music']
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senticnet['make_big'] = ['0.952', '0.842', '0', '0.878', '#joy', '#admiration', 'positive', '0.891', 'enlarge', 'flourish', 'get_big', 'get_large', 'expand']
senticnet['make_book'] = ['0', '0.077', '0', '0.068', '#interest', '#admiration', 'positive', '0.073', 'document', 'file', 'paper_write', 'readable', 'newspaper_write']
senticnet['make_boundary'] = ['0.84', '0.974', '0', '0', '#joy', '#interest', 'positive', '0.907', 'make_outline', 'run_out_ink', 'proofread', 'learn_spell', 'think_creatively']
senticnet['make_bow'] = ['0.05', '0.114', '-0.07', '0.131', '#interest', '#admiration', 'positive', '0.054', 'towel_off', 'dry_off', 'make_love', 'find_woman', 'sex']
senticnet['make_bread'] = ['0.883', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.938', 'bake_cake', 'cook_dinner', 'cook_meal', 'cook_curry', 'follow_recipe']
senticnet['make_breakfast'] = ['0.769', '0', '0', '0', '#joy', '#joy', 'positive', '0.769', 'appetite', 'enjoy_meal', 'put_food_into_mouth', 'prepare_meal', 'prepare_breakfast']
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senticnet['make_cake'] = ['0.061', '0', '0', '0.078', '#joy', '#admiration', 'positive', '0.07', 'fry_fish', 'fry_egg', 'season_meal', 'season_dish', 'fry_chicken']
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senticnet['make_campfire'] = ['0', '0', '-0.84', '-0.85', '#fear', '#disgust', 'negative', '-0.85', 'start_flame_fire', 'play_fire', 'light_fire', 'start_fire', 'strike_match']
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senticnet['make_card'] = ['-0.10', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.10', 'write_computer_program', 'write_note', 'book_reservation', 'mind_store', 'come_house']
senticnet['make_change'] = ['-0.1', '0.272', '-0.14', '0.15', '#interest', '#admiration', 'positive', '0.044', 'give_change', 'settle_bill', 'pay_back', 'owe_money', 'collect_money']
senticnet['make_cheese'] = ['-0.03', '-0.04', '0.102', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'unlimited', 'limitless', 'star_planet', 'orbit_earth', 'void']
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senticnet['make_choice'] = ['-0.08', '0.283', '-0.09', '0.17', '#interest', '#admiration', 'positive', '0.069', 'select', 'make_decision', 'decision', 'decide', 'consider']
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senticnet['make_clear'] = ['0.548', '-0.86', '0', '0.938', '#surprise', '#admiration', 'positive', '0.784', 'elaborate', 'clear', 'crystallize', 'clear_up', 'elucidate']
senticnet['make_cloth'] = ['0', '-0.04', '0.126', '-0.05', '#anger', '#disgust', 'negative', '-0.04', 'make_shirt', 'make_clothes', 'cloth', 'article_clothes', 'apparel']
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senticnet['make_coffee'] = ['0', '0.874', '0', '0', '#interest', '#interest', 'positive', '0.874', 'piss', 'relieve', 'make_ice', 'wash_dish', 'water_plant']
senticnet['make_comfortable'] = ['0.97', '0.753', '0', '0.832', '#joy', '#admiration', 'positive', '0.852', 'sleep_back', 'sit_rock_chair', 'relax', 'share_secret', 'lift_spirit']
senticnet['make_commitment'] = ['0', '0.874', '0', '0', '#interest', '#interest', 'positive', '0.874', 'make_promise', 'honest', 'keep_relationship', 'keep_promise', 'trust']
senticnet['make_comparison'] = ['0.771', '0.783', '0', '0', '#joy', '#interest', 'positive', '0.777', 'put_bid', 'come_warranty', 'customer_service', 'define', 'comparison']
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senticnet['make_contact'] = ['0.037', '0.122', '-0.09', '0.215', '#interest', '#admiration', 'positive', '0.069', 'sensual', 'feel_action', 'hand_action', 'hand_movement', 'extend_arm']
senticnet['make_copy'] = ['0', '0.161', '0', '0.188', '#interest', '#admiration', 'positive', '0.174', 'replicate', 'computer_command', 'imitation', 'output', 'type_of_paper']
senticnet['make_crowd_wave'] = ['0.924', '0.968', '0', '0.876', '#joy', '#interest', 'positive', '0.923', 'win_game', 'score', 'score_touchdown', 'pride', 'score_goal']
senticnet['make_cry'] = ['-0.26', '-0.05', '-0.16', '-0.16', '#sadness', '#fear', 'negative', '-0.13', 'play_hard', 'bring_joy', 'drive_crazy', 'end_relationship', 'learn_lesson']
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senticnet['make_decision'] = ['0', '0.737', '0', '0.997', '#interest', '#admiration', 'positive', '0.867', 'rational', 'make_choice', 'consider_option', 'arrive_conclusion', 'come_decision']
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senticnet['make_do'] = ['-0.79', '0', '0.974', '-0.83', '#anger', '#disgust', 'negative', '-0.86', 'manage', 'make_out', 'cope', 'get_by', 'fend']
senticnet['make_doctor_appointment'] = ['-0.08', '0', '-0.12', '-0.13', '#fear', '#disgust', 'negative', '-0.11', 'schedule_appointment_doctor', 'wear_clean_underwear', 'make_appointment_with_doctor', 'doctor_appointment', 'call_doctor']
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senticnet['make_easy'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'backup', 'dork', 'perform_of_music', 'remember_childhood', 'make_sound']
senticnet['make_eat'] = ['-0.02', '0.224', '-0.07', '0.116', '#interest', '#admiration', 'positive', '0.061', 'provide_food', 'money_charge', 'satiate_hunger', 'fee', 'payment']
senticnet['make_effort'] = ['-0.07', '0.18', '-0.08', '0.188', '#interest', '#admiration', 'positive', '0.052', 'make_happen', 'resume', 'attempt', 'question_ask', 'obligation']
senticnet['make_egg'] = ['0.03', '0', '0', '0.091', '#joy', '#admiration', 'positive', '0.06', 'part_of_meal', 'produce_egg', 'good_eat', 'nugget', 'yum']
senticnet['make_end_meet'] = ['0', '0.893', '0', '0', '#interest', '#interest', 'positive', '0.893', 'salary', 'wage', 'send_money', 'earnings', 'pay']
senticnet['make_enemy'] = ['-0.09', '0', '-0.09', '-0.20', '#fear', '#disgust', 'negative', '-0.12', 'conflict', 'declare_war', 'find_enemy', 'violence', 'big_fight']
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senticnet['make_fabric'] = ['-0.02', '0', '0.068', '0', '#sadness', '#anger', 'negative', '-0.04', 'make_cloth', 'make_clothes', 'clothes_fabric', 'make_shirt', 'cloth_material']
senticnet['make_face'] = ['-0.08', '-0.04', '-0.10', '-0.10', '#fear', '#disgust', 'negative', '-0.06', 'wave_hand', 'act_silly', 'offend', 'annoy', 'wet_pant']
senticnet['make_factory'] = ['0.039', '0.188', '-0.09', '0.226', '#interest', '#admiration', 'positive', '0.089', 'build_process', 'factory_output', 'make_goods', 'make', 'make_stuff']
senticnet['make_false_accusation'] = ['-0.9', '0', '0.009', '-0.93', '#sadness', '#disgust', 'negative', '-0.61', 'tell_lie', 'lie', 'false_accusation', 'tell_untruth', 'dishonesty']
senticnet['make_fashion_statement'] = ['0.742', '0', '0', '0.905', '#joy', '#admiration', 'positive', '0.824', 'stocking', 'intimate', 'robe', 'cover_leg', 'leg']
senticnet['make_feel_angry'] = ['0', '0', '0.937', '0', '#anger', '#anger', 'negative', '-0.93', 'disappointment', 'become_angry', 'disappoint', 'sit_dark', 'silliness']
senticnet['make_feel_bad'] = ['-0.14', '-0.05', '0', '-0.15', '#sadness', '#disgust', 'negative', '-0.08', 'make_noise', 'make_sound', 'annoy_neighbor', 'part_of_band', 'remember_childhood']
senticnet['make_feel_better'] = ['0.99', '0.823', '0', '0.735', '#joy', '#interest', 'positive', '0.849', 'feel_good', 'feel_satisfaction', 'good_feeling', 'happiness', 'little_hunger']
senticnet['make_feel_good'] = ['0.995', '0.801', '0', '0.968', '#joy', '#admiration', 'positive', '0.921', 'laugh', 'long_life', 'healthy', 'support_cause', 'stay_happy']
senticnet['make_feel_happy'] = ['0.211', '-0.12', '0', '-0.04', '#joy', '#surprise', 'positive', '0.099', 'give_pleasure', 'comedian', 'fun', 'enjoyable', 'get_happy']
senticnet['make_fire'] = ['0.025', '0', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.02', 'create_flame', 'create_fire', 'burn_finger', 'gather_wood', 'light_cigarette']
senticnet['make_fist'] = ['-0.10', '-0.04', '0.095', '-0.33', '#sadness', '#disgust', 'negative', '-0.12', 'angry_emotion', 'black_eye', 'whack', 'taunt', 'arrogance']
senticnet['make_flour'] = ['0.036', '-0.06', '0.037', '0.05', '#surprise', '#admiration', 'positive', '0.027', 'bake', 'make_grain', 'bake_food', 'pot_roast', 'eat']
senticnet['make_food'] = ['-0.02', '0.073', '0.025', '0.054', '#interest', '#admiration', 'positive', '0.019', 'cook_meal', 'make_bread', 'follow_recipe', 'cook_curry', 'cook_meat']
senticnet['make_food_taste_better'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'heat_food', 'roast_meat', 'cook_food', 'cook_rice', 'cook_hamburger']
senticnet['make_friend'] = ['0.048', '-0.08', '0.036', '0', '#joy', '#surprise', 'positive', '0.031', 'meet_person', 'meet_friend', 'chit_chat', 'meet_girl', 'socialize']
senticnet['make_friend_happy'] = ['0.968', '0.706', '0', '0', '#joy', '#interest', 'positive', '0.837', 'jollify', 'get_present', 'oral_sex', 'make_happy', 'gift']
senticnet['make_fruit'] = ['0.101', '0.174', '0', '0.271', '#interest', '#admiration', 'positive', '0.182', 'serve_cold', 'tasty_good', 'delicious', 'high_calorie', 'soft_sweet']
senticnet['make_fun'] = ['-0.16', '-0.01', '-0.17', '-0.17', '#fear', '#disgust', 'negative', '-0.12', 'make_others_laugh', 'laugh', 'mean_person', 'tell_others', 'satirist']
senticnet['make_furniture'] = ['0.074', '0.138', '0', '0.104', '#interest', '#admiration', 'positive', '0.105', 'aesthetic', 'please_eye', 'reflect_light', 'yellow_orange', 'decorative']
senticnet['make_gesture'] = ['-0.08', '0', '0.015', '0', '#sadness', '#anger', 'negative', '-0.05', 'use_hand_gesture', 'unusual_face', 'move_tongue', 'make_unusual_face', 'share_idea']
senticnet['make_gift'] = ['0.17', '-0.08', '0', '-0.05', '#joy', '#surprise', 'positive', '0.066', 'surprise', 'give_pleasure', 'celebrate_holiday', 'make_feel_happy', 'get_reaction']
senticnet['make_go'] = ['0.021', '0.098', '-0.09', '0.247', '#interest', '#admiration', 'positive', '0.068', 'run_car', 'car_steer', 'passenger_car', 'car_action', 'car_movement']
senticnet['make_goal'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'score', 'victorious', 'lift_car', 'silly', 'win']
senticnet['make_gold'] = ['0', '0.08', '0', '0.246', '#interest', '#admiration', 'positive', '0.163', 'jewel', 'piece_of_jewelry', 'bell_sound', 'get_rich', 'jewelry']
senticnet['make_good_decision'] = ['0.256', '0.174', '0', '0.296', '#joy', '#admiration', 'positive', '0.242', 'feel_important', 'feel_smart', 'others_understand', 'good_memory', 'mentally_challenged']
senticnet['make_good_impression'] = ['0.082', '0', '-0.06', '0.129', '#joy', '#admiration', 'positive', '0.048', 'look_nice', 'improve_appearance', 'hygiene', 'stay_confident', 'social_acceptance']
senticnet['make_goods'] = ['0', '0.125', '0', '0.18', '#interest', '#admiration', 'positive', '0.152', 'make_product', 'make_stuff', 'assembly_line', 'build_process', 'manufacture']
senticnet['make_grain'] = ['0.801', '-0.74', '0', '0', '#joy', '#surprise', 'positive', '0.774', 'bake_food', 'eat', 'make_flour', 'bake', 'require_good_health']
senticnet['make_hair_look_nice'] = ['-0.02', '0', '0.068', '0', '#sadness', '#anger', 'negative', '-0.04', 'arrange_hair', 'comb_hair', 'fix_hair', 'groom_hair', 'style_hair']
senticnet['make_happen'] = ['-0.06', '0.223', '-0.09', '0.187', '#interest', '#admiration', 'positive', '0.063', 'obligation', 'make_effort', 'task', 'accomplish', 'job_offer']
senticnet['make_happy'] = ['0.997', '0.99', '0', '0.735', '#joy', '#interest', 'positive', '0.907', 'express_love', 'show_affection', 'show_love', 'feel_obligated', 'jollify']
senticnet['make_haste'] = ['0.038', '0', '0', '0.097', '#joy', '#admiration', 'positive', '0.068', 'rule', 'decision', 'follow', 'discipline', 'acceptance']
senticnet['make_headway'] = ['0.819', '-0.87', '0', '0.902', '#surprise', '#admiration', 'positive', '0.865', 'tally', 'steal', 'score', 'hit', 'rack_up']
senticnet['make_headway_against'] = ['0.819', '-0.87', '0', '0.902', '#surprise', '#admiration', 'positive', '0.865', 'tally', 'steal', 'score', 'hit', 'rack_up']
senticnet['make_healthy'] = ['0.124', '0.072', '-0.07', '0.152', '#joy', '#admiration', 'positive', '0.067', 'keep_body_healthy', 'healthy', 'own_benefit', 'keep_skin_healthy', 'stay_physically_fit']
senticnet['make_hole'] = ['-0.86', '0', '0.963', '0', '#sadness', '#anger', 'negative', '-0.91', 'kill_people', 'violent_person', 'destroy_home', 'stab', 'elephant_sit_chest']
senticnet['make_home'] = ['-0.10', '0.235', '-0.12', '0.246', '#interest', '#admiration', 'positive', '0.063', 'love', 'settle_down', 'build_house', 'family_nest', 'buy_furniture']
senticnet['make_hot'] = ['0.043', '0.13', '-0.08', '0.105', '#interest', '#admiration', 'positive', '0.049', 'necessary_live', 'human_fuel', 'flatulence', 'often_red', 'plasma']
senticnet['make_hotel_reservation'] = ['0', '0.926', '0', '0.824', '#interest', '#admiration', 'positive', '0.875', 'see_know', 'talk_person', 'conduct_business', 'call_mom', 'send_information']
senticnet['make_house_look_better'] = ['-0.06', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.07', 'stop_bleed', 'make_look_better', 'change_color', 'sit_bench', 'fee_animal']
senticnet['make_ice'] = ['0.029', '0', '0.042', '-0.07', '#anger', '#disgust', 'negative', '-0.02', 'fry_fish', 'make_coffee', 'clean_fish', 'piss', 'relieve']
senticnet['make_inroad_into'] = ['0.819', '-0.87', '0', '0.902', '#surprise', '#admiration', 'positive', '0.865', 'tally', 'steal', 'score', 'hit', 'rack_up']
senticnet['make_joke'] = ['0.184', '-0.10', '-0.06', '0.104', '#joy', '#surprise', 'positive', '0.082', 'sense_of_humor', 'good_joke', 'plan_party', 'good_mood', 'tell_joke']
senticnet['make_judgment'] = ['-0.03', '0.158', '-0.05', '0.075', '#interest', '#admiration', 'positive', '0.037', 'go_court', 'make_decision', 'examine_evidence', 'question_witness', 'serve_jury']
senticnet['make_laugh'] = ['0.123', '-0.11', '0.011', '-0.09', '#joy', '#surprise', 'positive', '0.033', 'give_pleasure', 'spread_happiness', 'good_time', 'amuse', 'comedian']
senticnet['make_law'] = ['0.853', '0.936', '0', '0.949', '#interest', '#admiration', 'positive', '0.913', 'form_government', 'type_of_government', 'star_wars', 'constitution', 'govern']
senticnet['make_life_better'] = ['0.853', '0.936', '0', '0.949', '#interest', '#admiration', 'positive', '0.913', 'improve_life', 'good_life', 'finish_college', 'serenity', 'make_life_easy']
senticnet['make_life_easy'] = ['0.034', '0.067', '0', '0.098', '#interest', '#admiration', 'positive', '0.066', 'perform_task', 'job_offer', 'hire_help', 'present_bill', 'job']
senticnet['make_light'] = ['0', '0', '-0.08', '0', '#fear', '#fear', 'negative', '-0.08', 'burn_material', 'heat_house', 'heat_home', 'burn_fuel', 'light_dark']
senticnet['make_list'] = ['0.034', '0.067', '0', '0.098', '#interest', '#admiration', 'positive', '0.066', 'drive_to_store', 'list', 'plan', 'go_shopping', 'organize']
senticnet['make_little_hungry'] = ['0.824', '0.84', '0', '0', '#joy', '#interest', 'positive', '0.832', 'become_fat', 'stomach_ache', 'get_fat', 'feel_rejuvenated', 'feel_full']
senticnet['make_live'] = ['0', '0.1', '0', '0', '#interest', '#interest', 'positive', '0.1', 'support_family', 'work_hard', 'earn_money', 'make_money', 'need_earn_money']
senticnet['make_look_better'] = ['0.969', '-0.86', '0', '0.631', '#joy', '#surprise', 'positive', '0.823', 'assuage', 'perfect', 'fructify', 'relieve', 'sublimate']
senticnet['make_look_good'] = ['0', '0.863', '0', '0.989', '#interest', '#admiration', 'positive', '0.926', 'get_rid_of_dirt', 'expect_guest', 'disinfect', 'cleanliness', 'hygiene']
senticnet['make_loud_noise'] = ['-0.93', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.88', 'strum', 'make_noise', 'low_note', 'make_sound', 'keep_beat']
senticnet['make_love'] = ['0.97', '0.729', '0', '0.946', '#joy', '#admiration', 'positive', '0.882', 'fuck', 'give_birth_child', 'sexual_intercourse', 'find_partner', 'become_pregnant']
senticnet['make_love_spouse'] = ['0.085', '-0.19', '0', '0', '#joy', '#surprise', 'positive', '0.142', 'sex', 'entertain_person', 'enjoy_together', 'love_others', 'laugh_joke']
senticnet['make_mad'] = ['-0.09', '-0.02', '-0.05', '-0.18', '#sadness', '#disgust', 'negative', '-0.07', 'taunt', 'violent_person', 'get_hurt', 'warmonger', 'death_penalty']
senticnet['make_make_clothes'] = ['0', '-0.04', '0.123', '-0.06', '#anger', '#disgust', 'negative', '-0.04', 'make_shirt', 'cloth', 'make_cloth', 'article_make_clothes', 'apparel']
senticnet['make_meal'] = ['0', '0.105', '0.015', '0.068', '#interest', '#admiration', 'positive', '0.053', 'animal_eat', 'able_eat', 'bait_trap', 'eat_rice', 'provide_food']
senticnet['make_memory'] = ['0.116', '0.091', '0.011', '0.162', '#joy', '#admiration', 'positive', '0.089', 'stay_touch_friend', 'stay_happy', 'sleep_late', 'say_love', 'make_progress']
senticnet['make_merry'] = ['0.592', '-0.92', '0', '0.703', '#surprise', '#admiration', 'positive', '0.741', 'racket', 'revel', 'make_whoopie', 'roister', 'make_happy']
senticnet['make_mess'] = ['-0.78', '0', '0.805', '-0.94', '#anger', '#disgust', 'negative', '-0.84', 'prepare_snack', 'make_pot_coffee', 'play_pool', 'gather_friend', 'have_fun']
senticnet['make_metal'] = ['0.736', '0.776', '0', '0', '#joy', '#interest', 'positive', '0.756', 'magnetic', 'iron_ore', 'solid_room_temperature', 'ore', 'tin']
senticnet['make_milk'] = ['0.89', '0.733', '0', '0', '#joy', '#interest', 'positive', '0.811', 'bovine', 'good_friend', 'boil_milk', 'barn_animal', 'produce_milk']
senticnet['make_mistake'] = ['-0.81', '0', '0.809', '-0.86', '#sadness', '#disgust', 'negative', '-0.83', 'make_error', 'count_finger', 'fault', 'become_frustrated', 'guess_answer']
senticnet['make_model'] = ['-0.11', '0', '0.022', '-0.09', '#sadness', '#disgust', 'negative', '-0.07', 'pick_out', 'hard_work', 'cut_food', 'produce_product', 'circulate_blood']
senticnet['make_money'] = ['0.024', '0.097', '0', '0', '#joy', '#interest', 'positive', '0.06', 'earn_money', 'make_live', 'entrepreneur', 'work_hard', 'need_earn_money']
senticnet['make_move'] = ['0.792', '0.986', '0', '0', '#joy', '#interest', 'positive', '0.889', 'bounce_up_down', 'throw', 'play_catch', 'play', 'toss']
senticnet['make_movie'] = ['0.137', '0.105', '-0.08', '0.214', '#joy', '#admiration', 'positive', '0.092', 'art', 'ideal', 'good_memory', 'strive', 'own_space']
senticnet['make_music'] = ['0.793', '0.811', '0', '0.908', '#interest', '#admiration', 'positive', '0.837', 'create_music', 'play_music', 'produce_musical_sound', 'play_orchestra', 'play_tune']
senticnet['make_musical_sound'] = ['0.793', '0.811', '0', '0.908', '#interest', '#admiration', 'positive', '0.837', 'play_orchestra', 'play_music', 'play_tune', 'create_music', 'make_music']
senticnet['make_musicr'] = ['0.032', '0.092', '0', '0.075', '#interest', '#admiration', 'positive', '0.066', 'rock_group', 'music_player', 'eclectic', 'musical_group', 'guitar_player']
senticnet['make_new'] = ['0.793', '0.811', '0', '0.908', '#interest', '#admiration', 'positive', '0.837', 'make_new_friend', 'embellish', 'make_beautiful', 'become_beautiful', 'adorn']
senticnet['make_new_friend'] = ['0.746', '0', '0', '0.907', '#joy', '#admiration', 'positive', '0.827', 'foreplay', 'enjoy_company', 'new_friendship', 'friendship', 'companionship']
senticnet['make_newspaper'] = ['0', '0', '0', '0.122', '#admiration', '#admiration', 'positive', '0.122', 'pencil_mark', 'make_book', 'handmade', 'script', 'written_text']
senticnet['make_noise'] = ['-0.03', '-0.06', '0.079', '-0.11', '#anger', '#disgust', 'negative', '-0.03', 'make_sound', 'part_of_band', 'make_music', 'keep_beat', 'play_music']
senticnet['make_note'] = ['-0.07', '0', '0.026', '-0.05', '#sadness', '#disgust', 'negative', '-0.05', 'write_note', 'jot_down_idea', 'take_note_class', 'write', 'note']
senticnet['make_offer'] = ['0.809', '0.804', '0', '0', '#joy', '#interest', 'positive', '0.806', 'price_goods', 'charge_credit_card', 'deal_card', 'sell_car', 'sign_agreement']
senticnet['make_oppose'] = ['-0.06', '0.067', '-0.09', '-0.07', '#fear', '#disgust', 'negative', '-0.04', 'oppose', 'whack', 'rudeness', 'fist', 'hand']
senticnet['make_others_happy'] = ['0.748', '0.786', '0', '0.715', '#joy', '#interest', 'positive', '0.75', 'bounty', 'time_relax', 'feel_obligated', 'make_gift', 'generosity']
senticnet['make_others_laugh'] = ['0.748', '0.786', '0', '0.715', '#joy', '#interest', 'positive', '0.75', 'make_fun', 'entertain', 'clown', 'make_others_happy', 'laugh']
senticnet['make_out'] = ['0.748', '0.786', '0', '0.715', '#joy', '#interest', 'positive', '0.75', 'sex', 'one_night_stand', 'enjoy', 'meet_new_friend', 'party']
senticnet['make_out_atom'] = ['-0.09', '-0.08', '0.048', '-0.25', '#sadness', '#disgust', 'negative', '-0.07', 'atom', 'learn', 'learn_new_subject', 'know_happen_world', 'learn_information']
senticnet['make_out_cloth'] = ['-0.05', '0.092', '0', '0.132', '#interest', '#admiration', 'positive', '0.057', 'recycle', 'reuse', 'find_car', 'made_out_of_plastic', 'find_kitchen']
senticnet['make_out_date'] = ['0.817', '0.78', '0', '0.971', '#joy', '#admiration', 'positive', '0.856', 'enjoy_performance', 'laugh_lot', 'go_date', 'feel_happy', 'see_show']
senticnet['make_out_fabric'] = ['0', '0', '0', '0.078', '#admiration', '#admiration', 'positive', '0.078', 'star_stripe', 'wave_wind', 'make_fabric', 'red_white_blue', 'emblem']
senticnet['make_out_leather'] = ['0', '-0.91', '0', '0.74', '#surprise', '#admiration', 'positive', '0.827', 'make_coat', 'tie', 'overcoat', 'make_ball', 't-shirt']
senticnet['make_out_metal'] = ['0.02', '0.103', '0', '0.138', '#interest', '#admiration', 'positive', '0.087', 'mix_metal', 'door_opener', 'alloy', 'hardware', 'ore']
senticnet['make_out_paper'] = ['-0.20', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.20', 'print_paper', 'written_paper', 'reuse', 'further_knowledge', 'recycle']
senticnet['make_out_rubber'] = ['0', '0.966', '0', '0.779', '#interest', '#admiration', 'positive', '0.873', 'spherical_shape', 'use_sport', 'bouncy', 'three_dimensional', 'fill_air']
senticnet['make_outline'] = ['0.846', '0.981', '0', '0', '#joy', '#interest', 'positive', '0.913', 'make_boundary', 'run_out_ink', 'proofread', 'learn_spell', 'think_creatively']
senticnet['make_paint'] = ['0.165', '0.069', '0.028', '0.057', '#joy', '#interest', 'positive', '0.066', 'buy_paint', 'stir_paint_brush', 'wall_art', 'stir_paint', 'pick_up_brush']
senticnet['make_paper'] = ['-0.02', '0.109', '-0.05', '0.111', '#interest', '#admiration', 'positive', '0.035', 'find_library', 'read_human', 'find_car', 'find_hotel', 'find_kitchen']
senticnet['make_paper_airplane'] = ['0', '0.964', '0', '0.877', '#interest', '#admiration', 'positive', '0.92', 'paper_airplane', 'play', 'write_note', 'have_fun', 'make_note']
senticnet['make_parent_happy'] = ['0.936', '0.749', '0', '0.871', '#joy', '#admiration', 'positive', '0.852', 'get_married', 'finish_college', 'have_good_job', 'have_good_carrier', 'support_family']
senticnet['make_patchwork_quilt'] = ['0.936', '0.749', '0', '0.871', '#joy', '#admiration', 'positive', '0.852', 'paint_picture', 'tie-dye', 'paint_house', 'create_art', 'letter_sign']
senticnet['make_payment'] = ['0', '0.831', '0', '0.904', '#interest', '#admiration', 'positive', '0.867', 'buy_stuff', 'buy', 'check_price', 'comparison_shop', 'return_goods']
senticnet['make_peace'] = ['-0.05', '-0.00', '0.083', '-0.13', '#anger', '#disgust', 'negative', '-0.06', 'make_a_lot_of_noise', 'listen_orchestra', 'compose_music', 'play_piano', 'make_noise']
senticnet['make_person'] = ['0.132', '0.115', '-0.06', '0.121', '#joy', '#admiration', 'positive', '0.076', 'man_wife', 'survival_species', 'make_baby', 'lover', 'horny']
senticnet['make_person_upset'] = ['-0.92', '0', '0.917', '-0.95', '#sadness', '#disgust', 'negative', '-0.93', 'fight', 'war', 'battle', 'attack', 'conflict']
senticnet['make_pie'] = ['0.728', '0.713', '0', '0.715', '#joy', '#admiration', 'positive', '0.719', 'bake_cake', 'birthday_gift', 'love', 'make_food', 'eat_pie']
senticnet['make_plan'] = ['0.728', '0.713', '0', '0.715', '#joy', '#admiration', 'positive', '0.719', 'good_plan', 'organize', 'plan', 'make_list', 'write_note']
senticnet['make_point'] = ['0.686', '0.526', '0', '0.812', '#joy', '#admiration', 'positive', '0.675', 'learn_game', 'spread_information', 'convey_idea', 'surf_internet', 'fantasize']
senticnet['make_pollution'] = ['-0.92', '0', '0.917', '-0.95', '#sadness', '#disgust', 'negative', '-0.93', 'ozone_hole', 'polluted_air', 'pollution', 'create_pollution', 'pollution_factory']
senticnet['make_popcorn'] = ['0.686', '0.526', '0', '0.812', '#joy', '#admiration', 'positive', '0.675', 'watch_movie', 'eat_snack', 'popcorn', 'have_fun', 'prepare_food']
senticnet['make_porn'] = ['0', '0', '0.937', '-0.87', '#anger', '#disgust', 'negative', '-0.90', 'deal_drug', 'sell_new_book', 'go_off_strike', 'go_public', 'open_business']
senticnet['make_pot_coffee'] = ['-0.10', '-0.05', '0.032', '-0.09', '#sadness', '#disgust', 'negative', '-0.04', 'prepare_snack', 'learn_listen', 'drink_coffee', 'prepare_drink', 'drink_tea']
senticnet['make_potato'] = ['0', '0', '-0.93', '-0.74', '#fear', '#disgust', 'negative', '-0.84', 'drink_a_lot_of_alcohol', 'drink_a_lot_of_booze', 'get_drunk', 'drink_much', 'drunk']
senticnet['make_presentation'] = ['0.686', '0.526', '0', '0.812', '#joy', '#admiration', 'positive', '0.675', 'teach_student', 'prepare_lesson', 'presentation', 'explain_subject', 'use_computer']
senticnet['make_pretty'] = ['0.024', '0', '0.017', '0.151', '#joy', '#admiration', 'positive', '0.053', 'cover_table', 'pretty', 'make_up', 'remove_paint', 'paint_nail']
senticnet['make_product'] = ['0', '0.15', '0', '0.181', '#interest', '#admiration', 'positive', '0.165', 'make_goods', 'make_stuff', 'produce_goods', 'manufacture', 'factory_output']
senticnet['make_profit'] = ['-0.06', '0.166', '0', '0.095', '#interest', '#admiration', 'positive', '0.065', 'transact_business', 'exchange_money', 'provide_employment', 'pay_service', 'make_rich']
senticnet['make_progress'] = ['0.783', '0.744', '0', '0.791', '#joy', '#admiration', 'positive', '0.773', 'advance', 'stay_touch_friend', 'grow', 'spread_word', 'spread']
senticnet['make_promise'] = ['0', '0.868', '0', '0.935', '#interest', '#admiration', 'positive', '0.901', 'trust', 'make_commitment', 'rely', 'offer_solace', 'post_letter']
senticnet['make_purchase'] = ['0', '0.868', '0', '0.935', '#interest', '#admiration', 'positive', '0.901', 'buy', 'check_price', 'buy_stuff', 'buy_goods', 'buy_happiness']
senticnet['make_quilt'] = ['0.054', '-0.05', '0.101', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'make_clothes', 'under_garment', 'make_suit', 'piece_of_clothing', 'make_dress']
senticnet['make_reservation'] = ['0', '0.869', '0', '0.919', '#interest', '#admiration', 'positive', '0.894', 'pay_fee', 'bring_money', 'take_friend_out', 'make_sure_money', 'bring_cash']
senticnet['make_rich'] = ['0', '0.078', '0', '0.099', '#interest', '#admiration', 'positive', '0.088', 'exchange_money', 'buy_sell', 'sell', 'acquire_goods', 'sell_goods']
senticnet['make_right'] = ['0', '0.263', '-0.21', '0.319', '#interest', '#admiration', 'positive', '0.122', 'make_better', 'improve', 'consent', 'neediness', 'tell_everyone']
senticnet['make_right_decision'] = ['0', '0.744', '0', '0.771', '#interest', '#admiration', 'positive', '0.758', 'put_two_together', 'reach_conclusion', 'think', 'analysis', 'come_up_new_idea']
senticnet['make_rule'] = ['-0.04', '0.113', '0', '0.174', '#interest', '#admiration', 'positive', '0.081', 'decision_making', 'talkative', 'speak', 'call_person', 'verbal']
senticnet['make_sad'] = ['-0.91', '0', '0.886', '-0.95', '#sadness', '#disgust', 'negative', '-0.91', 'pain', 'hurt', 'sadness', 'ache', 'sore']
senticnet['make_sale'] = ['0.079', '0.132', '-0.10', '0.216', '#interest', '#admiration', 'positive', '0.08', 'draw_diagram', 'form_of_communication', 'tell_love', 'explain_detail', 'tell']
senticnet['make_sandwich'] = ['0.119', '0', '0', '0', '#joy', '#joy', 'positive', '0.119', 'create_food', 'stuff_face', 'make_toast', 'snack', 'fill_stomach']
senticnet['make_scarf'] = ['0.101', '-0.11', '0.157', '-0.14', '#anger', '#disgust', 'negative', '-0.02', 'make_sweater', 'make_clothes', 'sweater', 'make_cloth', 'make_shirt']
senticnet['make_secure'] = ['-0.04', '-0.04', '0.044', '-0.07', '#anger', '#disgust', 'negative', '-0.02', 'dress_occasion', 'put_clothes', 'lock_front_door', 'try_new_clothes', 'change_clothes']
senticnet['make_selection'] = ['0', '0.125', '0', '0.105', '#interest', '#admiration', 'positive', '0.115', 'go_shopping', 'go_department_store', 'go_to_shopping_mall', 'moolah', 'drive_to_store']
senticnet['make_sense'] = ['-0.12', '0.239', '-0.07', '0.203', '#interest', '#admiration', 'positive', '0.061', 'acknowledge', 'clarify', 'reason', 'give_hint', 'speak_up']
senticnet['make_sex'] = ['0.705', '0.967', '0', '0', '#joy', '#interest', 'positive', '0.836', 'have_fun', 'enjoy_sex', 'need_companionship', 'sit_rock_chair', 'watch_sunset']
senticnet['make_shirt'] = ['0', '-0.04', '0.126', '-0.05', '#anger', '#disgust', 'negative', '-0.04', 'make_cloth', 'make_clothes', 'cloth', 'article_clothes', 'apparel']
senticnet['make_shop_list'] = ['0.705', '0.967', '0', '0', '#joy', '#interest', 'positive', '0.836', 'shop_list', 'write_note', 'make_list', 'go_shopping', 'go_to_supermarket']
senticnet['make_sick'] = ['-0.90', '0', '0.875', '0', '#sadness', '#anger', 'negative', '-0.89', 'result_death', 'unwell', 'ill', 'eliminate', 'socialist']
senticnet['make_significant_headway'] = ['0.819', '-0.87', '0', '0.902', '#surprise', '#admiration', 'positive', '0.865', 'tally', 'steal', 'score', 'hit', 'rack_up']
senticnet['make_sketch'] = ['-0.02', '0.111', '0', '0.05', '#interest', '#admiration', 'positive', '0.044', 'sketch', 'use_crayon', 'draw', 'doodle', 'pencil_art']
senticnet['make_small'] = ['0.994', '0.795', '0', '0', '#joy', '#interest', 'positive', '0.895', 'hide', 'shy', 'embarrassed', 'small', 'psychological_terror']
senticnet['make_smile'] = ['0.143', '-0.12', '-0.08', '-0.08', '#joy', '#surprise', 'positive', '0.024', 'hospitable', 'surprise', 'get_reaction', 'lift_spirit', 'celebrate_special_occasion']
senticnet['make_sound'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'noisy', 'noise_pollution', 'blaring', 'shouting', 'loud']
senticnet['make_speech'] = ['-0.03', '0.087', '-0.08', '0.11', '#interest', '#admiration', 'positive', '0.019', 'verbal', 'say', 'discourse', 'verbalize', 'speech']
senticnet['make_sport'] = ['0', '0', '0', '0.077', '#admiration', '#admiration', 'positive', '0.077', 'eat_right', 'eat_healthy_food', 'drink_plenty_water', 'take_vitamin', 'exercise']
senticnet['make_statement'] = ['0', '0.991', '0', '0.859', '#interest', '#admiration', 'positive', '0.925', 'tell', 'breath_sigh_relief', 'compromise', 'speak_out', 'discuss']
senticnet['make_steel'] = ['0.066', '0', '0.027', '0.054', '#joy', '#admiration', 'positive', '0.031', 'power_engine', 'dense', 'make_metal', 'make_out_metal', 'magnetism']
senticnet['make_strong'] = ['0.072', '0.094', '0', '0.149', '#interest', '#admiration', 'positive', '0.105', 'go_to_gym', 'bodybuilding', 'do_exercise', 'aerobic_exercise', 'eat_healthy_food']
senticnet['make_stuff'] = ['-0.03', '0.168', '0', '0.228', '#interest', '#admiration', 'positive', '0.119', 'make', 'make_product', 'make_goods', 'factory_output', 'creation']
senticnet['make_suit'] = ['-0.03', '0.168', '0', '0.228', '#interest', '#admiration', 'positive', '0.119', 'clothes', 'woman_dress', 'piece_of_clothing', 'make_clothes', 'robe']
senticnet['make_sure'] = ['0.072', '0.094', '0', '0.149', '#interest', '#admiration', 'positive', '0.105', 'ask_person', 'make_happen', 'accomplish', 'job_offer', 'present_bill']
senticnet['make_sure_enough_money'] = ['0.799', '-0.92', '0', '0.896', '#surprise', '#admiration', 'positive', '0.874', 'bring_cash', 'make_sure_money', 'find_wallet', 'go_supermarket', 'find_shop']
senticnet['make_sure_money'] = ['0.799', '-0.92', '0', '0.896', '#surprise', '#admiration', 'positive', '0.874', 'bring_cash', 'find_shop', 'find_wallet', 'go_supermarket', 'make_sure_enough_money']
senticnet['make_sweater'] = ['0.101', '-0.11', '0.157', '-0.14', '#anger', '#disgust', 'negative', '-0.02', 'make_scarf', 'make_clothes', 'sweater', 'make_cloth', 'make_shirt']
senticnet['make_tea'] = ['-0.08', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.05', 'salt_food', 'chicken', 'brown_hamburger', 'fry_chicken', 'bone_chicken']
senticnet['make_telephone_call'] = ['0.934', '0.786', '0', '0', '#joy', '#interest', 'positive', '0.86', 'call_phone', 'meet_at_coffee_shop', 'call_person', 'use_telephone', 'call_telephone']
senticnet['make_terrorist_attack'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'plan_terrorist_attack', 'terrorist', 'bomb', 'attack', 'terrorist_attack']
senticnet['make_thing_work'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'optimism', 'triumph', 'achievement', 'success', 'victory']
senticnet['make_throw_up'] = ['0', '0.099', '-0.04', '0.111', '#interest', '#admiration', 'positive', '0.055', 'cause_headache', 'blind', 'big_rat', 'chipmunk', 'grizzly']
senticnet['make_time'] = ['-0.04', '0.132', '0', '0.057', '#interest', '#admiration', 'positive', '0.047', 'patience', 'motivation', 'open_eye', 'free_time', 'leave_home']
senticnet['make_tire'] = ['-0.11', '-0.09', '0.038', '-0.15', '#sadness', '#disgust', 'negative', '-0.05', 'injury', 'physical_injury', 'strengthful', 'strike_ball', 'leg_hurt']
senticnet['make_toast'] = ['-0.08', '-0.04', '0.023', '-0.07', '#sadness', '#disgust', 'negative', '-0.03', 'toast', 'bread_crumb', 'messy', 'bread', 'spoilage']
senticnet['make_toy'] = ['-0.02', '-0.05', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.02', 'motor_oil', 'castor', 'engine', 'emission', 'bristle']
senticnet['make_tree'] = ['0', '-0.03', '0', '0.094', '#surprise', '#admiration', 'positive', '0.065', 'cut_lumber', 'cut_tree', 'chop_tree', 'cut_down', 'make_wood']
senticnet['make_unusual_face'] = ['-0.08', '0', '0.015', '0', '#sadness', '#anger', 'negative', '-0.05', 'use_hand_gesture', 'unusual_face', 'move_tongue', 'make_gesture', 'share_idea']
senticnet['make_up'] = ['0', '0.202', '-0.10', '0.241', '#interest', '#admiration', 'positive', '0.112', 'abstract', 'tell_everyone', 'always_read', 'choice', 'explanation']
senticnet['make_up_idea'] = ['0', '0.202', '-0.10', '0.241', '#interest', '#admiration', 'positive', '0.112', 'make_out_atom', 'learn', 'learn_new_subject', 'know_happen_world', 'learn_information']
senticnet['make_up_mind'] = ['-0.03', '0.149', '0', '0.071', '#interest', '#admiration', 'positive', '0.061', 'become_lawyer', 'examine_evidence', 'determined', 'consider_evidence', 'judgment']
senticnet['make_up_person'] = ['-0.03', '0.141', '-0.08', '0.108', '#interest', '#admiration', 'positive', '0.032', 'prohibitionist_society', 'overrate', 'use_game_chess', 'find_airplane', 'use_garden']
senticnet['make_up_powder'] = ['0', '0.944', '0', '0.826', '#interest', '#admiration', 'positive', '0.885', 'white_substance', 'food_ingredient', 'white_grain', 'white_powder', 'granulate']
senticnet['make_up_story'] = ['0', '0.153', '-0.08', '0.131', '#interest', '#admiration', 'positive', '0.068', 'use_imagination', 'mental_activity', 'make_up_idea', 'imagine', 'remember_event']
senticnet['make_up_water'] = ['0', '0', '0', '0.186', '#admiration', '#admiration', 'positive', '0.186', 'go_green', 'water', 'beautiful', 'big', 'cover_water']
senticnet['make_use'] = ['0.815', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.893', 'use', 'operate', 'handle', 'do_it_yourself', 'activate']
senticnet['make_war'] = ['-0.07', '-0.04', '-0.06', '-0.20', '#sadness', '#disgust', 'negative', '-0.07', 'conquer_enemy', 'protect_country', 'go_war', 'use_military', 'kill_soldier']
senticnet['make_well'] = ['0', '0.225', '-0.09', '0.142', '#interest', '#admiration', 'positive', '0.091', 'trustworthy', 'assurance', 'importance', 'impermanent', 'derivation']
senticnet['make_whoopie'] = ['0.592', '-0.92', '0', '0.703', '#surprise', '#admiration', 'positive', '0.741', 'racket', 'revel', 'roister', 'make_merry', 'make_happy']
senticnet['make_wine'] = ['-0.07', '-0.02', '0', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'drink_wine', 'food_poison', 'spill_food_clothes', 'order_food', 'eat_salad']
senticnet['make_wish'] = ['0', '0.091', '-0.05', '0.073', '#interest', '#admiration', 'positive', '0.037', 'privately', 'hold_liquid', 'brew', 'rinse_dish', 'find_toilet']
senticnet['make_wood'] = ['0', '-0.03', '0', '0.094', '#surprise', '#admiration', 'positive', '0.065', 'cut_lumber', 'cut_tree', 'chop_tree', 'cut_down', 'make_tree']
senticnet['make_wool'] = ['0', '-0.01', '0.051', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'animal_hair', 'animal_fur', 'pelt', 'strand', 'fur']
senticnet['make_work'] = ['0', '0.745', '0', '0.701', '#interest', '#admiration', 'positive', '0.723', 'collect_debt', 'buy_tool', 'negotiate_contract', 'buy_gas', 'increase_net_worth']
senticnet['make_world_better_place'] = ['0.79', '0.745', '0', '0.927', '#joy', '#admiration', 'positive', '0.821', 'help_others', 'improve_world', 'ego_boost', 'make_money', 'help_person']
senticnet['makeen'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'webpage', 'store_record', 'ideal']
senticnet['makeover'] = ['0.944', '-0.60', '0', '0.756', '#joy', '#admiration', 'positive', '0.768', 'beauty_treatment', 'beautification', 'manicure', 'pedicure', 'facial']
senticnet['maker'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'builder']
senticnet['makes_sense'] = ['0.845', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.867', 'lose_weight', 'stay_fit', 'set', 'shape', 'weight_loss']
senticnet['makeshift'] = ['-0.97', '0', '0.84', '-0.52', '#sadness', '#anger', 'negative', '-0.78', 'pis_aller', 'last_resort', 'temporary_expedient', 'expedient', 'stopgap']
senticnet['makeshift_bomb'] = ['-0.56', '0', '0.714', '-0.89', '#anger', '#disgust', 'negative', '-0.72', 'tank', 'tank_destroyer', 'armored_car', 'armoured_car', 'armored_personnel_carrier']
senticnet['makeshift_dance_floor'] = ['0.075', '0', '0', '0.158', '#joy', '#admiration', 'positive', '0.116', 'sway', 'swirl', 'body_movement', 'move_around', 'dance_move']
senticnet['makeshift_morgue'] = ['-0.06', '0.09', '-0.14', '-0.15', '#fear', '#disgust', 'negative', '-0.06', 'casket', 'coffin', 'grave', 'headstone', 'bury_person']
senticnet['makeshift_prison'] = ['0', '0', '0.995', '-0.89', '#anger', '#disgust', 'negative', '-0.94', 'shoe_store', 'chess_tournament', 'dress_room', 'front_desk', 'locker_room']
senticnet['makeshift_raft'] = ['-0.99', '0', '0.658', '-0.73', '#sadness', '#disgust', 'negative', '-0.79', 'purposeless', 'afloat', 'aimless', 'directionless', 'planless']
senticnet['makeshift_shelter'] = ['-0.97', '0', '0.84', '-0.52', '#sadness', '#anger', 'negative', '-0.78', 'pis_aller', 'last_resort', 'temporary_expedient', 'expedient', 'stopgap']
senticnet['makeshift_stage'] = ['-0.71', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.84', 'traffic_jam', 'accumulate_stress', 'stress', 'accumulate_stress_city', 'bad_service']
senticnet['makeshift_tent'] = ['-0.97', '0', '0.84', '-0.52', '#sadness', '#anger', 'negative', '-0.78', 'pis_aller', 'last_resort', 'temporary_expedient', 'expedient', 'stopgap']
senticnet['makeshift_wooden'] = ['-0.97', '0', '0.84', '-0.52', '#sadness', '#anger', 'negative', '-0.78', 'pis_aller', 'last_resort', 'temporary_expedient', 'expedient', 'stopgap']
senticnet['maketh'] = ['0.887', '-0.84', '0', '0.819', '#joy', '#surprise', 'positive', '0.851', 'arouse', 'elicit', 'enkindle', 'kindle', 'evoke']
senticnet['maketh_mention'] = ['0.655', '-0.82', '0', '0.956', '#surprise', '#admiration', 'positive', '0.813', 'superlative', 'compliment', 'testimonial', 'praise', 'approval']
senticnet['makeup'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'blonde', 'purple', 'violet', 'dark_light', 'hue']
senticnet['makeup_artist'] = ['0.99', '0', '0', '0.721', '#joy', '#admiration', 'positive', '0.855', 'hair', 'haircut', 'hairdresser_chair', 'scissor', 'hairstyle']
senticnet['makeup_kit'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'naked_eye', 'eye', 'oculus_sinister']
senticnet['makeup_remover'] = ['-0.75', '0', '-0.75', '-0.99', '#sadness', '#disgust', 'negative', '-0.83', 'tease', 'upset', 'lubricant', 'upset_person', 'piston']
senticnet['makin_atoll'] = ['0.084', '-0.05', '0.013', '0', '#joy', '#surprise', 'positive', '0.043', 'carnival_ride', 'carny', 'fortune_teller', 'horseback', 'ferris_wheel']
senticnet['making_a_lot_of_noise'] = ['0', '0', '0.945', '-0.81', '#anger', '#disgust', 'negative', '-0.88', 'sound_level', 'speaker', 'noisy', 'adjust_volume', 'hear_song']
senticnet['making_headway'] = ['0.819', '-0.87', '0', '0.902', '#surprise', '#admiration', 'positive', '0.865', 'tally', 'steal', 'score', 'hit', 'rack_up']
senticnet['making_sure'] = ['0.688', '0.825', '0', '0.623', '#joy', '#interest', 'positive', '0.712', 'crazy', 'play_dead', 'feel_ashamed', 'better_person', 'need_help']
senticnet['makka_pakka'] = ['0.036', '0.084', '0', '0', '#joy', '#interest', 'positive', '0.06', 'toy', 'bath_toy', 'yo-yo', 'toy_ball', 'duck']
senticnet['mako_shark'] = ['-0.20', '-0.10', '0.07', '-0.24', '#sadness', '#disgust', 'negative', '-0.10', 'gill', 'sea_life', 'seafood', 'swim_animal', 'fillet']
senticnet['maksymw'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'aphasia', 'anomic_aphasia']
senticnet['maktampur'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['makura'] = ['0.973', '-0.50', '0', '0.612', '#joy', '#admiration', 'positive', '0.698', 'chic', 'chicness', 'jauntiness', 'modishness', 'stylishness']
senticnet['makybe_diva'] = ['0.744', '-0.77', '0', '0.661', '#joy', '#surprise', 'positive', '0.725', 'sugarcoat', 'honey', 'glaze', 'change_taste', 'sugar']
senticnet['mal_meninga'] = ['0', '0.996', '0', '0.85', '#interest', '#admiration', 'positive', '0.923', 'strength', 'strong', 'physical_strength', 'athlete', 'muscle']
senticnet['malabsorption'] = ['-0.86', '0', '0.76', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['malabsorption_syndrome'] = ['-0.60', '0', '0.544', '-0.53', '#sadness', '#anger', 'negative', '-0.56', 'acute_renal_failure', 'acute_kidney_failure', 'chronic_renal_failure', 'chronic_kidney_failure', 'kidney_failure']
senticnet['malacca'] = ['0.609', '-0.74', '0', '0.716', '#surprise', '#admiration', 'positive', '0.69', 'city', 'malaysian_city', 'asia', 'old_city', 'colony']
senticnet['malacca_cane'] = ['0.609', '-0.74', '0', '0.716', '#surprise', '#admiration', 'positive', '0.69', 'malacca', 'cane', 'swagger_stick', 'sword_cane', 'sword_stick']
senticnet['malachite'] = ['0.037', '0.073', '0.027', '0.072', '#interest', '#admiration', 'positive', '0.039', 'sapphire', 'birth_stone', 'turquoise', 'gemstone', 'amethyst']
senticnet['malacology'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'study_person', 'subject_study', 'zoo', 'zoologist', 'geology']
senticnet['malaconotidae'] = ['-0.84', '0', '0.655', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'piping_crow', 'butcherbird', 'currawong', 'bell_magpie', 'oscine']
senticnet['malaconotidae_family'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'lovebird', 'parroket', 'paraquet', 'paroquet']
senticnet['maladaptive'] = ['-0.98', '0', '0.582', '-0.56', '#sadness', '#anger', 'negative', '-0.70', 'unsocial', 'antisocial', 'lonely', 'alone', 'solitary']
senticnet['maladroit'] = ['-0.61', '0', '0.533', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'bungling', 'bumbling', 'butterfingered', 'inept', 'handless']
senticnet['malady'] = ['-0.85', '0', '-0.99', '-0.74', '#sadness', '#fear', 'negative', '-0.86', 'polio', 'ailment', 'sickness', 'illness', 'disease']
senticnet['malairt'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['malaise'] = ['-0.97', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.86', 'apathy', 'squall', 'doldrums', 'boredom', 'ennui']
senticnet['malakand'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'webpage', 'store_record', 'ideal']
senticnet['malakand_division'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'webpage', 'store_record', 'ideal']
senticnet['malakand_region'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'webpage', 'store_record', 'ideal']
senticnet['malalcahuello'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['malapropism'] = ['-0.77', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.80', 'absurdly', 'absurd', 'irrationally', 'clownishly', 'ridicule']
senticnet['malar_hypoplasia'] = ['0.582', '-0.66', '0', '0.661', '#surprise', '#admiration', 'positive', '0.637', 'talipes_equinus', 'deformity', 'malformation', 'talipes_valgus', 'talipes_calcaneus']
senticnet['malar_rash'] = ['-0.64', '0', '0.903', '-0.63', '#sadness', '#anger', 'negative', '-0.72', 'jaundice_of_newborn', 'disease', 'icterus_neonatorum', 'kernicterus', 'symptom']
senticnet['malaria'] = ['-0.88', '0', '-0.84', '-0.95', '#sadness', '#disgust', 'negative', '-0.89', 'polio', 'infectious_disease', 'malady', 'sickness', 'ailment']
senticnet['malaria_control'] = ['-0.88', '0', '-0.84', '-0.95', '#sadness', '#disgust', 'negative', '-0.89', 'polio', 'infectious_disease', 'malady', 'sickness', 'ailment']
senticnet['malaria_epidemic'] = ['-0.55', '0', '0.757', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'rubor', 'redness', 'cephalitis', 'sleepy_sickness', 'epidemic_encephalitis']
senticnet['malaria_eradication'] = ['-0.88', '0', '-0.84', '-0.95', '#sadness', '#disgust', 'negative', '-0.89', 'polio', 'infectious_disease', 'malady', 'sickness', 'ailment']
senticnet['malaria_infection'] = ['-0.55', '0', '0.757', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'rubor', 'redness', 'cephalitis', 'sleepy_sickness', 'epidemic_encephalitis']
senticnet['malaria_parasite'] = ['-0.55', '0', '0.757', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'rubor', 'redness', 'cephalitis', 'sleepy_sickness', 'epidemic_encephalitis']
senticnet['malaria_prevention'] = ['-0.88', '0', '-0.84', '-0.95', '#sadness', '#disgust', 'negative', '-0.89', 'polio', 'infectious_disease', 'malady', 'sickness', 'ailment']
senticnet['malaria_transmission'] = ['-0.55', '0', '0.757', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'rubor', 'redness', 'cephalitis', 'sleepy_sickness', 'epidemic_encephalitis']
senticnet['malaria_vaccine'] = ['-0.07', '0', '-0.05', '-0.18', '#sadness', '#disgust', 'negative', '-0.10', 'medical_therapy', 'vaccination', 'doctor', 'medicine', 'hospital']
senticnet['malarial_fever'] = ['0.579', '-0.81', '0', '0.833', '#surprise', '#admiration', 'positive', '0.741', 'murine_typhus', 'rat_typhus', 'urban_typhus', 'endemic_typhus', 'rickettsial_disease']
senticnet['malarkey'] = ['-0.61', '0', '0.981', '-0.98', '#anger', '#disgust', 'negative', '-0.85', 'pious_platitude', 'nothingness', 'jazz', 'talk', 'malarky']
senticnet['malarky'] = ['-0.61', '0', '0.981', '-0.98', '#anger', '#disgust', 'negative', '-0.85', 'pious_platitude', 'nothingness', 'jazz', 'malarkey', 'talk']
senticnet['malassezia'] = ['-0.57', '0', '0.873', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'fungal_infection', 'mycosis', 'kerion', 'tinea_pedis', 'athlete_foot']
senticnet['malathion_poisoning'] = ['-0.98', '0', '0.717', '-0.51', '#sadness', '#anger', 'negative', '-0.73', 'parathion_poisoning', 'pesticide_poisoning', 'toxic_condition', 'poisoning', 'intoxication']
senticnet['malaynon'] = ['-0.56', '0', '0.617', '-0.80', '#anger', '#disgust', 'negative', '-0.66', 'prolixity', 'prolixness', 'wordiness', 'repetitiveness', 'repetitiousness']
senticnet['malaysian_city'] = ['0.606', '-0.74', '0', '0.707', '#surprise', '#admiration', 'positive', '0.686', 'city', 'malacca', 'asia', 'old_city', 'colony']
senticnet['malcolm_mcdowell'] = ['0.794', '-0.50', '0', '0.531', '#joy', '#admiration', 'positive', '0.611', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['malcontent'] = ['-0.88', '0', '0.718', '-0.92', '#sadness', '#disgust', 'negative', '-0.84', 'ungratified', 'disaffected', 'discontented', 'unsatisfied', 'restless']
senticnet['maldi'] = ['0.838', '-0.61', '0', '0.556', '#joy', '#surprise', 'positive', '0.671', 'activity', 'natural_process', 'natural_action', 'column_chromatography', 'paper_chromatography']
senticnet['maldistribution'] = ['0.871', '-0.74', '0', '0.981', '#joy', '#admiration', 'positive', '0.867', 'unrighteousness', 'unjustness', 'unfairness', 'wrong', 'wrongfulness']
senticnet['male'] = ['0.97', '0.73', '0', '0', '#joy', '#interest', 'positive', '0.85', 'man_child', 'bloke', 'member_of_family', 'male_person', 'guy']
senticnet['male_admirer'] = ['0.818', '0', '0', '0.755', '#joy', '#admiration', 'positive', '0.786', 'dandy', 'beau', 'boyfriend', 'rich', 'young_man']
senticnet['male_attire'] = ['-0.76', '0', '0.672', '-0.88', '#sadness', '#disgust', 'negative', '-0.77', 'attire', 'garb', 'dress', 'fancy_dress', 'masquerade']
senticnet['male_breadwinner'] = ['0.051', '0.112', '0', '0', '#joy', '#interest', 'positive', '0.082', 'neighbor', 'family_reunion', 'clan', 'desire', 'little_specific']
senticnet['male_chauvinism'] = ['-0.68', '0', '0.949', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'chauvinism', 'sexism', 'antifeminism', 'sexual_discrimination', 'discrimination']
senticnet['male_chauvinist'] = ['0.585', '-0.7', '0', '0.586', '#surprise', '#admiration', 'positive', '0.624', 'jingoistic', 'ultranationalistic', 'superpatriotic', 'nationalistic', 'patriotic']
senticnet['male_chicken'] = ['0.026', '0.069', '0', '0', '#joy', '#interest', 'positive', '0.048', 'chicken_egg', 'chicken_soup', 'chick', 'chicken', 'quail']
senticnet['male_child'] = ['0.631', '-0.52', '0', '0.942', '#joy', '#admiration', 'positive', '0.7', 'altar_boy', 'cub', 'ball_boy', 'bat_boy', 'lad']
senticnet['male_continence'] = ['0.531', '0.673', '-0.27', '0.275', '#joy', '#interest', 'positive', '0.301', 'amorous', 'amorousness', 'amatory', 'enamoredness', 'puppy_love']
senticnet['male_counterpart'] = ['0.779', '0', '0', '0.892', '#joy', '#admiration', 'positive', '0.836', 'damsel_human', 'damsel_person', 'niece', 'damsel', 'woman']
senticnet['male_cow'] = ['0', '-0.02', '0', '0.06', '#surprise', '#admiration', 'positive', '0.041', 'baby_cow', 'donkey', 'little_horse', 'hoof_animal', 'animal']
senticnet['male_cub'] = ['0.02', '0.071', '0', '0.069', '#interest', '#admiration', 'positive', '0.053', 'many_animals', 'donkey', 'chimpanzee', 'cute_animal', 'creature']
senticnet['male_dancer'] = ['-0.68', '0', '0.545', '-0.68', '#sadness', '#disgust', 'negative', '-0.64', 'stunt', 'tumble', 'split', 'walkover', 'acrobatic_feat']
senticnet['male_dominance'] = ['-0.68', '0', '0.949', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'chauvinism', 'sexism', 'antifeminism', 'sexual_discrimination', 'discrimination']
senticnet['male_dominated_culture'] = ['-0.68', '0', '0.949', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'chauvinism', 'sexism', 'antifeminism', 'sexual_discrimination', 'discrimination']
senticnet['male_dominated_society'] = ['-0.68', '0', '0.949', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'chauvinism', 'sexism', 'antifeminism', 'sexual_discrimination', 'discrimination']
senticnet['male_domination'] = ['-0.68', '0', '0.949', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'chauvinism', 'sexism', 'antifeminism', 'sexual_discrimination', 'discrimination']
senticnet['male_ejaculation'] = ['0.655', '-0.87', '0', '0.595', '#joy', '#surprise', 'positive', '0.709', 'erection', 'excitement', 'arousal', 'kill_brain_cell', 'feel_relieved']
senticnet['male_female'] = ['0.779', '0', '0', '0.892', '#joy', '#admiration', 'positive', '0.836', 'damsel_human', 'damsel_person', 'niece', 'damsel', 'woman']
senticnet['male_fertilize'] = ['0.702', '-0.95', '0', '0.997', '#surprise', '#admiration', 'positive', '0.883', 'fertile', 'fecund', 'conceptive', 'impregnable', 'rank']
senticnet['male_gaze'] = ['0.911', '0', '0', '0.85', '#joy', '#admiration', 'positive', '0.881', 'sophisticated', 'lovely', 'elegant', 'contrary', 'common_man']
senticnet['male_heir'] = ['0', '0.769', '0', '0', '#interest', '#interest', 'positive', '0.769', 'get_rich', 'get_money', 'inherit_property', 'benefit', 'enjoy_life']
senticnet['male_homosexual'] = ['-0.84', '0', '0.882', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'perversion', 'fetishism', 'paraphilia', 'sexual_perversion', 'buggery']
senticnet['male_homosexuality'] = ['-0.84', '0', '0.882', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'perversion', 'fetishism', 'paraphilia', 'sexual_perversion', 'buggery']
senticnet['male_human'] = ['0.884', '0.895', '0', '0', '#joy', '#interest', 'positive', '0.889', 'kind', 'couple', 'friend', 'sing_microphone', 'male_person']
senticnet['male_japanese_quail'] = ['0.702', '-0.95', '0', '0.997', '#surprise', '#admiration', 'positive', '0.883', 'fertile', 'fecund', 'conceptive', 'impregnable', 'rank']
senticnet['male_nude'] = ['-0.94', '0', '0.991', '-0.65', '#sadness', '#anger', 'negative', '-0.86', 'birthday_suit', 'raw', 'altogether', 'nakedness', 'undress']
senticnet['male_orgasm'] = ['0.655', '-0.87', '0', '0.595', '#joy', '#surprise', 'positive', '0.709', 'erection', 'excitement', 'arousal', 'kill_brain_cell', 'feel_relieved']
senticnet['male_panda'] = ['0.02', '0.071', '0', '0.069', '#interest', '#admiration', 'positive', '0.053', 'many_animals', 'donkey', 'chimpanzee', 'cute_animal', 'creature']
senticnet['male_parent'] = ['0.887', '0.925', '0', '0', '#joy', '#interest', 'positive', '0.906', 'male_relative', 'special_person', 'family_member', 'mother_brother', 'father']
senticnet['male_person'] = ['0.973', '0.885', '0', '0', '#joy', '#interest', 'positive', '0.929', 'male', 'man_child', 'bloke', 'male_human', 'masculine']
senticnet['male_polar_bear'] = ['0', '0', '-0.74', '-0.90', '#fear', '#disgust', 'negative', '-0.82', 'animal', 'grizzly_bear', 'forest', 'creature', 'brown_bear']
senticnet['male_primogeniture'] = ['0', '0.769', '0', '0', '#interest', '#interest', 'positive', '0.769', 'get_rich', 'get_money', 'inherit_property', 'benefit', 'enjoy_life']
senticnet['male_prostitute'] = ['0', '0.942', '0', '0', '#interest', '#interest', 'positive', '0.942', 'guy', 'special_person', 'guardian', 'boy', 'male']
senticnet['male_relative'] = ['0.899', '0.778', '0', '0', '#joy', '#interest', 'positive', '0.839', 'male_parent', 'family_member', 'mother_brother', 'respective', 'familial_relationship']
senticnet['male_reproductive'] = ['0.672', '-0.55', '0', '0.992', '#joy', '#admiration', 'positive', '0.739', 'pressor', 'hormone', 'adrenalin', 'internal_secretion', 'catecholamine']
senticnet['male_sexuality'] = ['0.074', '0.151', '-0.09', '0.119', '#interest', '#admiration', 'positive', '0.062', 'small_man', 'male_person', 'male_child', 'gender', 'man_child']
senticnet['male_sperm'] = ['0.049', '0.098', '-0.07', '0.121', '#interest', '#admiration', 'positive', '0.047', 'procreate', 'reproduce', 'start_family', 'cause_cold', 'unprotect_sex']
senticnet['male_sterilization'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'birth_control_pill', 'condom', 'contraceptive_pill', 'sex_toy', 'prevent_pregnancy']
senticnet['male_stripper'] = ['0.82', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.862', 'pervert', 'healthy_person', 'philanthropist', 'arsonist', 'nazi']
senticnet['male_supremacist'] = ['-0.68', '0', '0.949', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'chauvinism', 'sexism', 'antifeminism', 'sexual_discrimination', 'discrimination']
senticnet['male_supremacy'] = ['-0.68', '0', '0.949', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'chauvinism', 'sexism', 'antifeminism', 'sexual_discrimination', 'discrimination']
senticnet['male_swimmer'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'chlorine', 'lifeguard', 'still_water', 'large_body', 'shallow_water']
senticnet['male_transsexual'] = ['0.779', '0', '0', '0.892', '#joy', '#admiration', 'positive', '0.836', 'damsel_human', 'damsel_person', 'niece', 'damsel', 'woman']
senticnet['maledetto'] = ['-0.64', '0', '0.625', '-0.88', '#sadness', '#disgust', 'negative', '-0.71', 'difficulty', 'awkwardness', 'flea_bite', 'difficultness', 'cumbersomeness']
senticnet['maledict'] = ['-0.69', '0', '0.917', '-0.98', '#anger', '#disgust', 'negative', '-0.86', 'call_down', 'stir', 'call_forth', 'conjure', 'bring_up']
senticnet['malediction'] = ['0', '0', '0.753', '-0.72', '#anger', '#disgust', 'negative', '-0.73', 'sinful', 'human_emotion', 'call', 'demand', 'yearn']
senticnet['malefactor'] = ['-0.71', '0', '0.669', '-0.94', '#sadness', '#disgust', 'negative', '-0.77', 'toughie', 'accessory', 'highbinder', 'hoodlum', 'thug']
senticnet['malefic'] = ['-0.04', '0.166', '0.684', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maleficence', 'malign', 'maliciously', 'spitefulness', 'maliciousness']
senticnet['maleficence'] = ['-0.04', '0.166', '0.684', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'malign', 'malefic', 'maliciously', 'spitefulness', 'maliciousness']
senticnet['maleficent'] = ['-0.63', '0', '0.674', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'baneful', 'evil', 'baleful', 'malevolent', 'malign']
senticnet['maleficium'] = ['-0.79', '0', '0.506', '-0.93', '#sadness', '#disgust', 'negative', '-0.74', 'magic', 'thaumaturgy', 'enchantment', 'witchcraft', 'witchery']
senticnet['maleic_anhydride'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'carboxylic_acid', 'succinic_acid', 'valeric_acid']
senticnet['malenko'] = ['0.052', '0', '0', '0.118', '#joy', '#admiration', 'positive', '0.085', 'diamond_ring', 'piece_of_jewelry', 'bell_sound', 'wedding_band', 'engagement']
senticnet['malevolence'] = ['-0.05', '0.181', '0.656', '-0.64', '#anger', '#disgust', 'negative', '-0.29', 'malignity', 'malevolently', 'loathing', 'execration', 'abhor']
senticnet['malevolency'] = ['-0.94', '0', '0.897', '-0.93', '#sadness', '#disgust', 'negative', '-0.92', 'cattiness', 'bitchiness', 'spitefulness', 'cruelness', 'evil']
senticnet['malevolent'] = ['-0.04', '0.17', '0.676', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maliciously', 'spitefulness', 'maliciousness', 'maleficence', 'malefic']
senticnet['malevolent_program'] = ['-0.86', '0', '0.769', '-0.88', '#sadness', '#disgust', 'negative', '-0.84', 'trojan', 'trojan_horse', 'virus', 'worm', 'computer_virus']
senticnet['malevolent_program_horse'] = ['-0.87', '0', '0.76', '-0.89', '#sadness', '#disgust', 'negative', '-0.84', 'malevolent_program', 'trojan', 'virus', 'worm', 'computer_virus']
senticnet['malevolent_spirit'] = ['-0.04', '0.17', '0.676', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maliciously', 'spitefulness', 'maliciousness', 'maleficence', 'malefic']
senticnet['malevolently'] = ['-0.05', '0.181', '0.656', '-0.64', '#anger', '#disgust', 'negative', '-0.29', 'malignity', 'malevolence', 'loathing', 'execration', 'abhor']
senticnet['malfeasance'] = ['-0.61', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.69', 'irresponsibility', 'untrustiness', 'undependability', 'undependableness', 'unreliability']
senticnet['malformation'] = ['-0.76', '0', '0.754', '-0.56', '#sadness', '#anger', 'negative', '-0.69', 'affliction', 'pigeon_breast', 'chicken_breast', 'clubfoot', 'talipes']
senticnet['malformed'] = ['-0.74', '0', '0.896', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'unshapely', 'shapeless', 'lumpy', 'deformed', 'acromegalic']
senticnet['malfunction'] = ['-0.12', '0.079', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.05', 'pour_water', 'collide', 'wear_bath_suit', 'sit_in_shade', 'feel_heat']
senticnet['malfunctioning'] = ['-0.73', '0', '0.787', '-0.66', '#sadness', '#anger', 'negative', '-0.72', 'wrong', 'haywire', 'bad', 'out_of_whack', 'amiss']
senticnet['malic_acid'] = ['0', '0.104', '-0.09', '0.146', '#interest', '#admiration', 'positive', '0.052', 'thaw', 'slush', 'water_inside', 'give_water', 'obtain_water']
senticnet['malice'] = ['-0.93', '0', '0.7', '-0.76', '#sadness', '#disgust', 'negative', '-0.79', 'despise', 'aggression', 'disgust', 'scorn', 'hostility']
senticnet['malice_aforethought'] = ['-0.93', '0', '0.7', '-0.76', '#sadness', '#disgust', 'negative', '-0.79', 'despise', 'aggression', 'disgust', 'scorn', 'hostility']
senticnet['malicious'] = ['-0.88', '0', '0.864', '-0.72', '#sadness', '#anger', 'negative', '-0.82', 'vixenish', 'venomous', 'bitchy', 'catty', 'cattish']
senticnet['malicious_activity'] = ['-0.69', '0', '0.752', '-0.82', '#anger', '#disgust', 'negative', '-0.75', 'usurpation', 'trespass', 'encroachment', 'inroad', 'violation']
senticnet['malicious_attacker'] = ['-0.02', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.07', 'army', 'military', 'infantry', 'samurai', 'soldier']
senticnet['malicious_behavior'] = ['-0.69', '0', '0.752', '-0.82', '#anger', '#disgust', 'negative', '-0.75', 'usurpation', 'trespass', 'encroachment', 'inroad', 'violation']
senticnet['malicious_code'] = ['-0.02', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.07', 'army', 'military', 'infantry', 'samurai', 'soldier']
senticnet['malicious_destruction'] = ['-0.14', '0', '-0.21', '-0.07', '#sadness', '#fear', 'negative', '-0.14', 'heat_light', 'burn_down_house', 'set_on_fire', 'rapid_oxidation', 'flame']
senticnet['malicious_gossip'] = ['-0.70', '0', '0.965', '-0.53', '#sadness', '#anger', 'negative', '-0.73', 'earful', 'scuttlebutt', 'talk_of_town', 'talk', 'scandal']
senticnet['malicious_hacker'] = ['-0.02', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.07', 'army', 'military', 'infantry', 'samurai', 'soldier']
senticnet['malicious_intent'] = ['-0.04', '0.164', '0.685', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maliciousness', 'spitefulness', 'maleficence', 'malefic', 'malign']
senticnet['malicious_javascript'] = ['-0.02', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.07', 'army', 'military', 'infantry', 'samurai', 'soldier']
senticnet['malicious_mischief'] = ['-0.73', '0', '0.547', '-0.69', '#sadness', '#disgust', 'negative', '-0.65', 'devilment', 'roguery', 'mischief', 'shenanigan', 'deviltry']
senticnet['malicious_script'] = ['-0.02', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.07', 'army', 'military', 'infantry', 'samurai', 'soldier']
senticnet['malicious_user'] = ['-0.02', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.07', 'army', 'military', 'infantry', 'samurai', 'soldier']
senticnet['maliciously'] = ['-0.04', '0.164', '0.685', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maliciousness', 'spitefulness', 'maleficence', 'malefic', 'malign']
senticnet['maliciousness'] = ['-0.04', '0.164', '0.685', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maliciously', 'spitefulness', 'maleficence', 'malefic', 'malign']
senticnet['malign'] = ['-0.04', '0.166', '0.684', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maleficence', 'malefic', 'maliciously', 'spitefulness', 'maliciousness']
senticnet['malignaggi'] = ['0.969', '0.998', '0', '0', '#joy', '#interest', 'positive', '0.984', 'bell_sound', 'bell_noise', 'piece_of_jewelry', 'syllable', 'fastener']
senticnet['malignant'] = ['0', '0', '-0.96', '-0.77', '#fear', '#disgust', 'negative', '-0.86', 'otiose', 'slothful', 'faineant', 'lazy', 'work-shy']
senticnet['malignant_cancer'] = ['-0.88', '0', '0.774', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fibroma', 'fibroid', 'benign_tumor', 'nonmalignant_tumor', 'nonmalignant_neoplasm']
senticnet['malignant_disease'] = ['-0.03', '-0.02', '-0.08', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'ibuprofen', 'phenobarbital', 'analgesic', 'sleep_drug', 'pill']
senticnet['malignant_fever'] = ['0.579', '-0.81', '0', '0.833', '#surprise', '#admiration', 'positive', '0.741', 'murine_typhus', 'rat_typhus', 'urban_typhus', 'endemic_typhus', 'rickettsial_disease']
senticnet['malignant_hypertension'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'illness', 'ill', 'tension', 'bad_health', 'hypertension']
senticnet['malignity'] = ['-0.05', '0.181', '0.656', '-0.64', '#anger', '#disgust', 'negative', '-0.29', 'malevolence', 'malevolently', 'loathing', 'execration', 'abhor']
senticnet['malignment'] = ['-0.96', '0', '0.766', '-0.96', '#sadness', '#disgust', 'negative', '-0.89', 'calumniation', 'traducement', 'hatchet_job', 'defamation', 'calumny']
senticnet['malikipuram'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['malinga'] = ['-0.62', '0', '0.713', '-0.98', '#anger', '#disgust', 'negative', '-0.77', 'muddy_up', 'muck_up', 'foul', 'blemish', 'smear']
senticnet['mall'] = ['0.907', '0.803', '0', '0', '#joy', '#interest', 'positive', '0.855', 'harvest_fall', 'find_mall', 'require_good_health', 'eat', 'arcade']
senticnet['mall_parking_lot'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'clean', 'polish', 'shiny', 'water', 'sexy_girl']
senticnet['mallard'] = ['-0.50', '0', '0.764', '-0.61', '#anger', '#disgust', 'negative', '-0.62', 'doctor', 'mountebank', 'charlatan', 'quack', 'physician']
senticnet['mallard_duck'] = ['-0.50', '0', '0.764', '-0.61', '#anger', '#disgust', 'negative', '-0.62', 'doctor', 'mountebank', 'charlatan', 'quack', 'physician']
senticnet['malleability'] = ['0.64', '-0.95', '0', '0.639', '#joy', '#surprise', 'positive', '0.743', 'pliability', 'whip', 'bendability', 'flexibleness', 'plasticity']
senticnet['malleable'] = ['0.118', '-0.06', '0.075', '0.086', '#joy', '#admiration', 'positive', '0.047', 'soft_hard', 'dance_type', 'attract', 'adjust', 'pedal']
senticnet['mallet'] = ['0.056', '0.107', '0', '0.09', '#interest', '#admiration', 'positive', '0.084', 'religious_fanatic', 'maul', 'door_knob', 'mailman', 'court_trial']
senticnet['mallet_locomotive'] = ['0.069', '0.097', '0', '0.172', '#interest', '#admiration', 'positive', '0.113', 'type_of_engine', 'liquid_gas', 'warm_air', 'hot_air', 'steamy']
senticnet['mallow'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'musk_mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
senticnet['malmedy_massacre'] = ['0.737', '-0.96', '0', '0.552', '#joy', '#surprise', 'positive', '0.75', 'prominence', 'hump', 'bump', 'swelling', 'frontal_eminence']
senticnet['malnourished'] = ['-0.60', '0', '0.757', '-0.75', '#anger', '#disgust', 'negative', '-0.70', 'unfed', 'underfed', 'undernourished', 'starved', 'starving']
senticnet['malnourished_child'] = ['-0.71', '0', '0.74', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'malnutrition', 'marasmus', 'avitaminosis', 'hypovitaminosis', 'deficiency_disease']
senticnet['malnourishment'] = ['-0.94', '0', '0.923', '-0.98', '#sadness', '#disgust', 'negative', '-0.95', 'heap', 'push', 'accept', 'bring', 'lunge']
senticnet['malnutrition'] = ['-0.71', '0', '0.74', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'kwashiorkor', 'marasmus', 'avitaminosis', 'hypovitaminosis', 'deficiency_disease']
senticnet['malodor'] = ['-0.63', '0', '0.523', '-0.98', '#sadness', '#disgust', 'negative', '-0.71', 'niff', 'pong', 'smell', 'odor', 'olfactory_sensation']
senticnet['malodorous'] = ['-0.86', '0', '0.644', '-0.88', '#sadness', '#disgust', 'negative', '-0.79', 'frowsty', 'fetid', 'foetid', 'smelly', 'stinking']
senticnet['malodorousness'] = ['-0.75', '0', '0.803', '-0.54', '#sadness', '#anger', 'negative', '-0.7', 'body_odor', 'aroma', 'scent', 'olfactory_property', 'smell']
senticnet['malodourous'] = ['-0.52', '0', '0.952', '-0.96', '#anger', '#disgust', 'negative', '-0.81', 'frowsty', 'fetid', 'foetid', 'smelly', 'stinking']
senticnet['malolactic_fermentation'] = ['0', '0.104', '-0.09', '0.146', '#interest', '#admiration', 'positive', '0.052', 'thaw', 'slush', 'water_inside', 'give_water', 'obtain_water']
senticnet['malone'] = ['-0.69', '0', '0.89', '-0.70', '#anger', '#disgust', 'negative', '-0.76', 'still', 'suppress', 'moderate', 'hush_up', 'quell']
senticnet['malonic_acid'] = ['-0.81', '0', '0.774', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'acid', 'hypophosphoric_acid', 'periodic_acid', 'hypooxyacid', 'oxyacid']
senticnet['malpensa_airport'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'expressway', 'turnpike', 'toll_road', 'highway']
senticnet['malt'] = ['-0.08', '0.106', '0', '0.125', '#interest', '#admiration', 'positive', '0.047', 'sugar', 'treacle', 'black_treacle', 'add_sugar', 'sugar_substitute']
senticnet['malt_beverage'] = ['0.951', '-0.97', '0', '0.639', '#joy', '#surprise', 'positive', '0.853', 'smoothie', 'drinking_water', 'nutrient', 'milk', 'refresher']
senticnet['malt_shovel'] = ['0.853', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.916', 'tavern', 'cafe', 'nightclub', 'bar', 'liquor_store']
senticnet['malt_vinegar'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'horseradish', 'box_cereal', 'bottle_water']
senticnet['malt_whiskey'] = ['0.877', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.919', 'score', 'economical', 'scotch_whiskey', 'spoil', 'scottish']
senticnet['malt_whisky'] = ['0.877', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.919', 'score', 'economical', 'scotch_whiskey', 'spoil', 'scottish']
senticnet['maltese_lira'] = ['0.771', '0.846', '0', '0', '#joy', '#interest', 'positive', '0.808', 'dollar_bill', 'franc', 'banknote', 'dollar', 'cent']
senticnet['malting_quality'] = ['0.878', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.883', 'wheat', 'grain', 'rye', 'white_rice', 'rice']
senticnet['maltodextrin'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
senticnet['maltreat'] = ['0', '0', '0.817', '-0.91', '#anger', '#disgust', 'negative', '-0.86', 'damage', 'tear', 'lambaste', 'pinch', 'spoil']
senticnet['maltreated_child'] = ['-0.8', '0', '0.875', '-0.74', '#sadness', '#anger', 'negative', '-0.80', 'inhuman_treatment', 'cruelty', 'child_neglect', 'mistreatment', 'child_abuse']
senticnet['maltreatment'] = ['-0.8', '0', '0.875', '-0.74', '#sadness', '#anger', 'negative', '-0.80', 'inhuman_treatment', 'cruelty', 'child_neglect', 'mistreatment', 'child_abuse']
senticnet['maltreatment_recurrence'] = ['-0.8', '0', '0.875', '-0.74', '#sadness', '#anger', 'negative', '-0.80', 'inhuman_treatment', 'cruelty', 'child_neglect', 'mistreatment', 'child_abuse']
senticnet['malty'] = ['0', '0.091', '-0.05', '0.073', '#interest', '#admiration', 'positive', '0.037', 'privately', 'hold_liquid', 'make_wish', 'rinse_dish', 'find_toilet']
senticnet['malus_domestica'] = ['-0.56', '0', '0.892', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'borecole', 'colewort', 'kail', 'cole', 'kale']
senticnet['malversation'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'negligence', 'evasion', 'dereliction', 'contributory_negligence']
senticnet['malware'] = ['0', '0', '-0.98', '-0.85', '#fear', '#disgust', 'negative', '-0.92', 'contagious_disease', 'infectious_disease', 'sexually_transmit_disease', 'disease', 'malady']
senticnet['mama'] = ['0.961', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.97', 'mummy', 'mum', 'mommy', 'mummy', 'tomb']
senticnet['mama_boy'] = ['0.584', '-0.59', '0', '0.753', '#surprise', '#admiration', 'positive', '0.643', 'ungracious', 'discourteous', 'impolite', 'brattish', 'unparliamentary']
senticnet['mambo'] = ['0.116', '-0.02', '0.025', '0.156', '#joy', '#admiration', 'positive', '0.069', 'dance_move', 'latin_dance', 'body_movement', 'waltz', 'dance']
senticnet['mamda'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['mamey'] = ['0.859', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.888', 'green_apple', 'apricot', 'lime', 'peach', 'cherry']
senticnet['mamilla'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['mamluk_era'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['mamma'] = ['0.773', '0.727', '0', '0', '#joy', '#interest', 'positive', '0.75', 'respect', 'expect', 'kind', 'affection', 'regard']
senticnet['mammal'] = ['0', '-0.10', '0', '0', '#surprise', '#surprise', 'positive', '0.101', 'animal', 'cage', 'reptile', 'wildlife', 'zoo']
senticnet['mammal_family'] = ['-0.04', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.04', 'bad_luck', 'tundra', 'artic', 'damn', 'bad_mood']
senticnet['mammalian_carnivore'] = ['-0.02', '0', '0.044', '0', '#sadness', '#anger', 'negative', '-0.03', 'large_cat', 'fast_person', 'household_pet', 'big_cat', 'good_pet']
senticnet['mammalian_cell'] = ['0.772', '-0.56', '0', '0.824', '#joy', '#admiration', 'positive', '0.719', 'neutrophil', 'leukocyte', 'leucocyte', 'white_blood_cell', 'white_cell']
senticnet['mammalian_host'] = ['-0.83', '0', '-0.82', '-0.86', '#sadness', '#disgust', 'negative', '-0.84', 'cause_cold', 'tourism', 'reproduce', 'gland', 'procreate']
senticnet['mammalian_physiology'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'college_major', 'geology', 'physic', 'chemistry', 'subject_school']
senticnet['mammalogy'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'study_person', 'subject_study', 'zoo', 'zoologist', 'geology']
senticnet['mammary'] = ['-0.63', '0', '0.559', '-0.82', '#sadness', '#disgust', 'negative', '-0.67', 'take_bull_by_horns', 'confront', 'front', 'encounter', 'oppose']
senticnet['mammary_gland'] = ['0.059', '0', '0.042', '0.055', '#joy', '#admiration', 'positive', '0.024', 'warm_blood', 'hairy', 'hairy_animal', 'nipple', 'woodland_creature']
senticnet['mammoplasty'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
senticnet['mammoth'] = ['0.969', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.96', 'extinct', 'animal', 'ice', 'elephant', 'giant']
senticnet['mammoth_ice'] = ['0', '-0.00', '0.038', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'earthworm', 'compost', 'fallen_leaf', 'detritus', 'worm']
senticnet['mammoth_ivory'] = ['0.063', '0.089', '0.036', '0.146', '#interest', '#admiration', 'positive', '0.066', 'horn', 'jew_harp', 'brass_instrument', 'trumpet', 'crash_cymbal']
senticnet['mammoth_steppe'] = ['-0.04', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.04', 'bad_luck', 'damn', 'artic', 'mammal_family', 'bad_mood']
senticnet['mammoth_tusk'] = ['0.063', '0.089', '0.036', '0.146', '#interest', '#admiration', 'positive', '0.066', 'horn', 'jew_harp', 'brass_instrument', 'trumpet', 'crash_cymbal']
senticnet['mamzer'] = ['0', '-0.00', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'hot_chick', 'atheist', 'computer_user', 'fastener_binder', 'monitor']
senticnet['man'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'army_fighter', 'army_person', 'sailor', 'fighter', 'workfighter']
senticnet['man-made'] = ['0', '0.967', '0', '0.719', '#interest', '#admiration', 'positive', '0.843', 'dead', 'hard', 'power_electricity', 'hand_made', 'little_dense_water']
senticnet['man_back_pocket'] = ['0', '0.092', '0', '0', '#interest', '#interest', 'positive', '0.092', 'pocket', 'pant_back_pocket', 'back_pocket', 'purse', 'clothes_washer']
senticnet['man_best_friend'] = ['0', '0', '-0.71', '-0.91', '#fear', '#disgust', 'negative', '-0.81', 'shell_animal', 'caged_lion', 'home_animal', 'captivity', 'keep_in_cage']
senticnet['man_child'] = ['0.84', '0.994', '0', '0', '#joy', '#interest', 'positive', '0.917', 'special_person', 'male_parent', 'member_of_family', 'boy', 'male']
senticnet['man_clothes'] = ['0.74', '0.786', '0', '0', '#joy', '#interest', 'positive', '0.763', 'slack', 'shirt', 'article_clothes', 'leg_wear', 'clothes']
senticnet['man_eater'] = ['-0.73', '0', '0.734', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'primitive', 'headhunter', 'barbarian', 'anthropophagus', 'anthropophagite']
senticnet['man_gagement'] = ['0.052', '0', '0', '0.118', '#joy', '#admiration', 'positive', '0.085', 'diamond_ring', 'piece_of_jewelry', 'bell_sound', 'wedding_band', 'engagement']
senticnet['man_inhumanity'] = ['-0.99', '0', '0.947', '-0.88', '#sadness', '#anger', 'negative', '-0.94', 'atrociousness', 'barbarousness', 'heinousness', 'atrocity', 'barbarity']
senticnet['man_made'] = ['-0.7', '0', '0.77', '-0.75', '#anger', '#disgust', 'negative', '-0.74', 'fake', 'unnatural', 'artificial', 'artificial_sea', 'artificial_taste']
senticnet['man_make'] = ['0', '0.1', '0.018', '0.217', '#interest', '#admiration', 'positive', '0.1', 'water_passage', 'artificial', 'waterway', 'wide', 'manmade']
senticnet['man_of_cloth'] = ['-0.67', '0', '0.994', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'altar_boy', 'reverend', 'thurifer', 'clergyman', 'acolyte']
senticnet['man_pocket'] = ['0.917', '0.916', '0', '0', '#joy', '#interest', 'positive', '0.917', 'back_pocket', 'pant_pocket', 'coin_purse', 'checkbook', 'pocket']
senticnet['man_post'] = ['0.972', '0.852', '0', '0', '#joy', '#interest', 'positive', '0.912', 'marry_man', 'service_car', 'date_woman', 'oil_hinge', 'dig_grave']
senticnet['man_purse'] = ['0', '0.08', '0', '0', '#interest', '#interest', 'positive', '0.08', 'purse', 'pocket', 'wallet', 'woman_purse', 'bag']
senticnet['man_ship'] = ['-0.02', '0.203', '-0.09', '0.171', '#interest', '#admiration', 'positive', '0.063', 'take_charge', 'file_complaint', 'courageous', 'oversee', 'obey_law']
senticnet['man_soweth'] = ['0.81', '0', '0', '0.703', '#joy', '#admiration', 'positive', '0.756', 'harvest', 'crop', 'grow_crop', 'plant_crop', 'graze_cattle']
senticnet['man_spacecraft'] = ['0', '0.783', '0', '0.864', '#interest', '#admiration', 'positive', '0.824', 'gather_berry', 'hang_picture_on_wall', 'think_future', 'wave_flag', 'chair_committee']
senticnet['man_wife'] = ['0.132', '0.115', '-0.06', '0.121', '#joy', '#admiration', 'positive', '0.076', 'make_person', 'survival_species', 'make_baby', 'lover', 'horny']
senticnet['manage'] = ['0.781', '-0.79', '0', '0.816', '#surprise', '#admiration', 'positive', '0.798', 'deliver_goods', 'succeed', 'win', 'bring_home_bacon', 'come_through']
senticnet['manage_money'] = ['0', '0.787', '0', '0.71', '#interest', '#admiration', 'positive', '0.748', 'cashless', 'bill_pay', 'pay_purchase', 'charge_money', 'organize']
senticnet['manageability'] = ['0.637', '-0.91', '0', '0.757', '#surprise', '#admiration', 'positive', '0.768', 'tractability', 'manageableness', 'docility', 'obedience', 'amenability']
senticnet['manageable'] = ['0.5', '-0.9', '0', '0.525', '#surprise', '#admiration', 'positive', '0.642', 'administrable', 'controllable', 'governable', 'steerable', 'dirigible']
senticnet['manageableness'] = ['0.637', '-0.91', '0', '0.757', '#surprise', '#admiration', 'positive', '0.768', 'manageability', 'tractability', 'docility', 'obedience', 'amenability']
senticnet['managed_care'] = ['-0.12', '0', '-0.12', '-0.08', '#sadness', '#fear', 'negative', '-0.11', 'hospital', 'check_up', 'check_doctor', 'check_pulse', 'check_blood_pressure']
senticnet['management'] = ['0', '0.76', '0', '0.788', '#interest', '#admiration', 'positive', '0.774', 'ambassador', 'supervisor', 'boss', 'master', 'ceo']
senticnet['management-level'] = ['0', '0', '0.908', '-0.95', '#anger', '#disgust', 'negative', '-0.93', 'middle-level', 'mid-level', 'lower-level', 'mid-management', 'secondary-school']
senticnet['management_pim'] = ['0.946', '0.738', '0', '0', '#joy', '#interest', 'positive', '0.842', 'owner', 'belong', 'own', 'close', 'asset']
senticnet['management_shakeup'] = ['-0.98', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.92', 'thrilling', 'electrifying', 'sensational', 'sensationally', 'scandalmongering']
senticnet['management_unit_pmu'] = ['0.851', '-0.65', '0', '0.66', '#joy', '#admiration', 'positive', '0.723', 'omen', 'portent', 'presage', 'prognostic', 'prognostication']
senticnet['manager'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'chairperson', 'investigator', 'slut', 'seeker', 'commuter']
senticnet['manager_guy_oseary'] = ['0.056', '0.139', '-0.07', '0.154', '#interest', '#admiration', 'positive', '0.07', 'belongings', 'material', 'fabric', 'guarantee', 'stuff']
senticnet['manager_leopold_caudillo'] = ['0', '-0.84', '0', '0.723', '#surprise', '#admiration', 'positive', '0.785', 'circular_route', 'stadium', 'memorial', 'station', 'row_house']
senticnet['manager_pimco'] = ['0.228', '0.123', '-0.09', '0', '#joy', '#interest', 'positive', '0.084', 'spend_time_with_child', 'social', 'friendship', 'companionship', 'get_close']
senticnet['manager_rafa_benitez'] = ['-0.80', '0', '0.507', '-0.76', '#sadness', '#disgust', 'negative', '-0.69', 'inflexible', 'inexorable', 'intransigent', 'ironclad', 'brassbound']
senticnet['manager_raj_rajaratnam'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'busy_bee', 'gopher', 'actor', 'doer', 'worker']
senticnet['manager_susan'] = ['0', '0.701', '0', '0.747', '#interest', '#admiration', 'positive', '0.724', 'award', 'graduate_education', 'education', 'fellowship', 'research_fellowship']
senticnet['managerial'] = ['0.971', '0.921', '0', '0', '#joy', '#interest', 'positive', '0.946', 'supervision', 'administrative', 'supervisory', 'oversight', 'responsibility']
senticnet['managerial_expertise'] = ['0', '0.983', '0', '0', '#interest', '#interest', 'positive', '0.983', 'find_problem', 'analyze', 'expert_report', 'examination', 'check']
senticnet['managerial_merry_go'] = ['0.823', '0.982', '0', '0', '#joy', '#interest', 'positive', '0.902', 'spherical', 'three_dimensions', 'round_orb', 'coil', 'fill_with_sand']
senticnet['managerially'] = ['0', '0.834', '0', '0.814', '#interest', '#admiration', 'positive', '0.824', 'organizationally', 'taxonomically', 'theologically', 'doctrinally', 'ecclesiastically']
senticnet['managership'] = ['-0.9', '0', '0.749', '-0.71', '#sadness', '#anger', 'negative', '-0.78', 'chute', 'slide', 'slideway', 'sloping_trough', 'gutter']
senticnet['managing_director'] = ['0.046', '0.116', '-0.05', '0.076', '#interest', '#admiration', 'positive', '0.045', 'head_guy', 'boss', 'officer', 'leader', 'supervisor']
senticnet['managing_editor'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['managing_partner'] = ['0.046', '0.116', '-0.05', '0.076', '#interest', '#admiration', 'positive', '0.045', 'head_guy', 'boss', 'officer', 'leader', 'supervisor']
senticnet['manakondur'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['manang'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['manas_mind'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
senticnet['manaslu'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['manastir'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
senticnet['manatee'] = ['-0.85', '0', '0.685', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'allicrocodilian', 'crocodile', 'false_gavial', 'crocodilian', 'caiman']
senticnet['mancation'] = ['0.024', '0.152', '-0.06', '0.236', '#interest', '#admiration', 'positive', '0.087', 'vacation', 'exit', 'stance', 'take_break', 'holiday']
senticnet['manchester'] = ['-0.02', '-0.01', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.02', 'medikit', 'ghetto', 'medical_build', 'metropolitan_city', 'slum']
senticnet['mandakini'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['mandala-like'] = ['0.752', '-0.95', '0', '0', '#joy', '#surprise', 'positive', '0.855', 'wheel-like', 'turbine-like', 'cross-like', 'donut-like', 'propeller-like']
senticnet['mandalay_bay'] = ['0.057', '0.13', '0', '0.119', '#interest', '#admiration', 'positive', '0.102', 'friend_wedding', 'circular_route', 'spanish_restaurant', 'altar', 'monastery']
senticnet['mandalay_bay_hotel'] = ['0.057', '0.13', '0', '0.119', '#interest', '#admiration', 'positive', '0.102', 'friend_wedding', 'circular_route', 'spanish_restaurant', 'altar', 'monastery']
senticnet['mandarin_collar'] = ['0', '-0.05', '0.092', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'piece_of_clothing', 'clothes', 'robe', 'slack', 'shirt']
senticnet['mandarin_orange'] = ['0.859', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.888', 'green_apple', 'apricot', 'lime', 'peach', 'cherry']
senticnet['mandarina_duck'] = ['0', '-0.03', '0.022', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'shoulder_bag', 'satchel', 'water_bottle', 'tote_bag', 'carry']
senticnet['mandasa'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['mandate'] = ['0.028', '0.153', '-0.05', '0.18', '#interest', '#admiration', 'positive', '0.076', 'nominate', 'administration', 'reign', 'order', 'regulation']
senticnet['mandatory'] = ['0', '0.298', '-0.21', '0.312', '#interest', '#admiration', 'positive', '0.13', 'compulsory', 'necessary', 'requirement', 'needful', 'compel']
senticnet['mandatory_conscription'] = ['0.924', '-0.55', '0', '0.756', '#joy', '#admiration', 'positive', '0.744', 'bounden', 'incumbent_on', 'obligate', 'obligatory', 'mandatory']
senticnet['mandatory_death_penalty'] = ['-0.19', '0', '-0.17', '-0.14', '#sadness', '#fear', 'negative', '-0.17', 'death_penalty', 'die', 'death_destruction', 'kill', 'murder']
senticnet['mandatory_disclosure'] = ['-0.02', '0.175', '-0.11', '0.148', '#interest', '#admiration', 'positive', '0.045', 'tolerant', 'phrase', 'inquiry', 'explain', 'question']
senticnet['mandatory_requirement'] = ['0.924', '-0.55', '0', '0.756', '#joy', '#admiration', 'positive', '0.744', 'bounden', 'incumbent_on', 'obligate', 'obligatory', 'mandatory']
senticnet['mandatory_vaccination'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'medical_therapy', 'vaccine', 'doctor', 'medicine', 'hospital']
senticnet['mandatum'] = ['0.751', '-0.98', '0', '0.989', '#surprise', '#admiration', 'positive', '0.909', 'okay', 'approval', 'commendation', 'okey', 'okeh']
senticnet['mandible'] = ['0.886', '-0.99', '0', '0.542', '#joy', '#surprise', 'positive', '0.808', 'jowl', 'lower_jaw', 'lantern_jaw', 'mandibula', 'mandibular_bone']
senticnet['mandibula'] = ['0.886', '-0.99', '0', '0.542', '#joy', '#surprise', 'positive', '0.808', 'jowl', 'lower_jaw', 'mandible', 'lantern_jaw', 'lantern_jawr_bone']
senticnet['mandibular_bone'] = ['0.886', '-0.99', '0', '0.542', '#joy', '#surprise', 'positive', '0.808', 'jowl', 'lower_jaw', 'mandible', 'mandibula', 'lantern_jaw']
senticnet['mandibular_nerve'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['mandolin_player'] = ['-0.04', '-0.04', '-0.04', '-0.04', '#fear', '#disgust', 'negative', '-0.02', 'music_shop', 'music_store', 'musical_instrument_store', 'string_quartet', 'high_school_band']
senticnet['mandoline'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'neat', 'kitchen_drawer', 'cutlery_drawer', 'dine_table', 'table']
senticnet['mandorla'] = ['0.026', '0.18', '-0.11', '0.18', '#interest', '#admiration', 'positive', '0.069', 'deity', 'god', 'angel', 'goddess', 'rabbi']
senticnet['mandragora'] = ['-0.87', '0', '0.599', '-0.71', '#sadness', '#disgust', 'negative', '-0.73', 'hysteria', 'fury', 'delirium', 'frenzy', 'craze']
senticnet['mandy'] = ['-0.74', '0', '0.877', '-0.97', '#anger', '#disgust', 'negative', '-0.86', 'miss', 'girl', 'chachka', 'young_lady', 'young_woman']
senticnet['mane'] = ['0.039', '0.086', '0', '0', '#joy', '#interest', 'positive', '0.063', 'leopard', 'hair', 'lynx', 'only_mammal', 'hare']
senticnet['manelick'] = ['0.502', '-0.82', '0', '0.779', '#surprise', '#admiration', 'positive', '0.703', 'mortal', 'person', 'individual', 'free_liver', 'agent']
senticnet['maneuver'] = ['-0.82', '0', '0.739', '-0.50', '#sadness', '#anger', 'negative', '-0.69', 'fake', 'juke', 'feint', 'manoeuvre', 'tactical_feint']
senticnet['maneuver_warfare'] = ['-0.82', '0', '0.739', '-0.50', '#sadness', '#anger', 'negative', '-0.69', 'fake', 'juke', 'maneuver', 'manoeuvre', 'feint']
senticnet['maneuverability'] = ['0', '0', '0', '0.144', '#admiration', '#admiration', 'positive', '0.144', 'go_elsewhere', 'transport_person', 'get_around', 'locomotion', 'trucking']
senticnet['maneuverable'] = ['0.934', '0', '0', '0.7', '#joy', '#admiration', 'positive', '0.817', 'agile', 'dexterous', 'responsive', 'lithe', 'nimble']
senticnet['maneuvering_room'] = ['0.961', '-0.84', '0', '0.958', '#joy', '#admiration', 'positive', '0.923', 'pause', 'break', 'intermission', 'suspension', 'rest_period']
senticnet['manfully'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'intrepidly', 'hardy', 'fearlessness', 'unafraid', 'unfearing']
senticnet['manga'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['manga_adaptation'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['manga_anthology'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['manga_artist'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['manga_magazine'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['manganese'] = ['-0.03', '0', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.06', 'lithium', 'mineral', 'potassium', 'iodine', 'magnesium']
senticnet['manganese_dioxide'] = ['-0.03', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'lithium', 'mineral', 'potassium', 'iodine', 'manganese']
senticnet['manganese_metal'] = ['-0.03', '0', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.06', 'lithium', 'mineral', 'potassium', 'iodine', 'magnesium']
senticnet['manganese_oxide'] = ['-0.03', '0', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.06', 'lithium', 'mineral', 'potassium', 'iodine', 'magnesium']
senticnet['mangaon'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['mangle'] = ['-0.62', '0', '0.756', '-0.62', '#anger', '#disgust', 'negative', '-0.66', 'vitiate', 'ruin', 'maul', 'deface', 'impair']
senticnet['mangled'] = ['-0.64', '0', '0.494', '-0.74', '#sadness', '#disgust', 'negative', '-0.62', 'wounded', 'eviscerate', 'torn', 'hurt', 'injured']
senticnet['mangled_body'] = ['-0.06', '0.09', '-0.14', '-0.15', '#fear', '#disgust', 'negative', '-0.06', 'casket', 'coffin', 'grave', 'headstone', 'bury_person']
senticnet['mangled_metal'] = ['-0.87', '0', '0.682', '-0.91', '#sadness', '#disgust', 'negative', '-0.82', 'dump', 'garbage_dump', 'trash_dump', 'rubbish_dump', 'wasteyard']
senticnet['mangler'] = ['-0.74', '0', '0.541', '-0.75', '#sadness', '#disgust', 'negative', '-0.68', 'mortal', 'person', 'individual', 'maimer', 'mutilator']
senticnet['mango'] = ['0.859', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.888', 'green_apple', 'apricot', 'lime', 'peach', 'cherry']
senticnet['mango_chutney'] = ['-0.81', '0', '0.543', '-0.63', '#sadness', '#disgust', 'negative', '-0.66', 'caper', 'gherkin', 'relish', 'dill_pickle', 'sweet_pickle']
senticnet['mango_cultivar'] = ['0.859', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.888', 'green_apple', 'apricot', 'lime', 'peach', 'cherry']
senticnet['mango_juice'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'lemon_juice', 'chocolate_pudding', 'grape_juice', 'orange_juice', 'juice']
senticnet['mango_mangifera_indica'] = ['0.826', '0.787', '0', '0', '#joy', '#interest', 'positive', '0.806', 'fig', 'baby_flower', 'carpel', 'strawberry', 'star_anise']
senticnet['mango_salsa'] = ['0', '0', '0.066', '-0.07', '#anger', '#disgust', 'negative', '-0.07', 'container_cup', 'cup', 'grease_trap', 'dish', 'open_container']
senticnet['mango_sticky_rice'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'saltine', 'soda_cracker', 'cracker', 'water_biscuit', 'graham_cracker']
senticnet['mangosteen'] = ['0.859', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.888', 'green_apple', 'apricot', 'lime', 'peach', 'cherry']
senticnet['mangrove_snapper'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['mangrove_swamp'] = ['0.85', '0.96', '0', '0', '#joy', '#interest', 'positive', '0.905', 'flatness', 'boring', 'flat_tire', 'flat_foot', 'small_breast']
senticnet['mangue'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'dryness', 'thirstiness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['mangy'] = ['-0.93', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.90', 'scruffy', 'beat-up', 'rumpled', 'ratty', 'frumpy']
senticnet['mangy_dog'] = ['-0.81', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.86', 'impassiveness', 'impassivity', 'emotionlessness', 'unemotionality', 'stolidity']
senticnet['manhandle'] = ['-0.18', '-0.00', '-0.18', '0', '#sadness', '#fear', 'negative', '-0.11', 'pulverize', 'violent_action', 'smash', 'hit_hard', 'melt_away']
senticnet['manhattan'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'condominium', 'rent_apartment', 'high_rise', 'condo', 'row_house']
senticnet['manhattan_apartment'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'home', 'furniture', 'kitchen', 'flat']
senticnet['manhattan_nightclub'] = ['0.763', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.75', 'pub', 'disco', 'dance_club', 'cafe', 'tavern']
senticnet['manhattan_project'] = ['0', '0', '-0.72', '-0.94', '#fear', '#disgust', 'negative', '-0.83', 'pistol', 'gun', 'hydrogen_bomb', 'handgun', 'revolver']
senticnet['manhole_cover'] = ['0.104', '-0.11', '0', '-0.04', '#joy', '#surprise', 'positive', '0.057', 'reception', 'disco', 'confetti', 'dance_club', 'balloon']
senticnet['manhood'] = ['0.131', '0.074', '0', '0.373', '#joy', '#admiration', 'positive', '0.193', 'superlative', 'excellence', 'ultimate', 'great', 'well_done']
senticnet['manhood_suffrage'] = ['-0.98', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.93', 'foment', 'agitated', 'tumult', 'stir_up', 'earnestness']
senticnet['mani_sharma'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_music', 'hear_music', 'artist', 'make_drawing', 'art']
senticnet['mania'] = ['-0.99', '0', '0.693', '-0.63', '#sadness', '#anger', 'negative', '-0.77', 'logorrhea', 'logomania', 'agromania', 'pyromania', 'necrophilia']
senticnet['maniac'] = ['-0.74', '0', '0.604', '-0.88', '#sadness', '#disgust', 'negative', '-0.74', 'weirdo', 'crazy', 'loony', 'looney', 'nutcase']
senticnet['maniacal'] = ['-0.04', '0.17', '0.676', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maliciously', 'spitefulness', 'maliciousness', 'maleficence', 'malefic']
senticnet['maniacally'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['manic'] = ['-0.89', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.85', 'high-strung', 'uptight', 'restive', 'overstrung', 'nervy']
senticnet['manic_depression'] = ['-0.61', '0', '0.527', '-0.57', '#sadness', '#disgust', 'negative', '-0.57', 'affective_disorder', 'major_affective_disorder', 'emotional_disorder', 'emotional_disturbance', 'cyclothymia']
senticnet['manic_depressive'] = ['-0.61', '0', '0.527', '-0.57', '#sadness', '#disgust', 'negative', '-0.57', 'affective_disorder', 'major_affective_disorder', 'emotional_disorder', 'emotional_disturbance', 'cyclothymia']
senticnet['manic_depressive_disorder'] = ['-0.99', '0', '0.982', '-0.62', '#sadness', '#anger', 'negative', '-0.86', 'dysthymia', 'dysthymic_depression', 'affective_disorder', 'major_affective_disorder', 'emotional_disorder']
senticnet['manic_depressive_illness'] = ['-0.51', '0', '0.9', '-0.68', '#anger', '#disgust', 'negative', '-0.69', 'affective_disorder', 'major_affective_disorder', 'emotional_disorder', 'emotional_disturbance', 'cyclothymia']
senticnet['manic_disorder'] = ['-0.90', '0', '0.543', '-0.91', '#sadness', '#disgust', 'negative', '-0.78', 'affective_disorder', 'major_affective_disorder', 'emotional_disorder', 'emotional_disturbance', 'hysteria']
senticnet['manic_energy'] = ['0.847', '-0.65', '0', '0.955', '#joy', '#admiration', 'positive', '0.82', 'bright', 'sparkle', 'twinkle', 'spark', 'energy']
senticnet['manic_episode'] = ['-0.99', '0', '0.982', '-0.62', '#sadness', '#anger', 'negative', '-0.86', 'dysthymia', 'dysthymic_depression', 'affective_disorder', 'major_affective_disorder', 'emotional_disorder']
senticnet['manically'] = ['-0.12', '0.13', '0.647', '-0.64', '#anger', '#disgust', 'negative', '-0.32', 'infuriate', 'exasperating', 'infuriating', 'maddening', 'dudgeon']
senticnet['manicheism'] = ['-0.99', '0', '0.835', '-0.58', '#sadness', '#anger', 'negative', '-0.80', 'beginning', 'origin', 'root', 'wellspring', 'source']
senticnet['manickam'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['manicure'] = ['0.944', '-0.60', '0', '0.756', '#joy', '#admiration', 'positive', '0.768', 'makeover', 'beautification', 'beauty_treatment', 'pedicure', 'facial']
senticnet['manicured'] = ['0', '-0.81', '0', '0.847', '#surprise', '#admiration', 'positive', '0.83', 'well-maintained', 'well-tended', 'well-kept', 'spotlessly', 'landscaped']
senticnet['manicurist'] = ['0.633', '-0.50', '0', '0.59', '#joy', '#admiration', 'positive', '0.575', 'craftsman', 'beautician', 'artisan', 'journeyman', 'artificer']
senticnet['manifest'] = ['0.805', '0.923', '0', '0', '#joy', '#interest', 'positive', '0.864', 'obviously', 'clearly', 'distinctly', 'evidently', 'pregnantly']
senticnet['manifest_contradiction'] = ['0.509', '-0.62', '0', '0.786', '#surprise', '#admiration', 'positive', '0.64', 'agreeable', 'accordant', 'consentient', 'unanimous', 'according']
senticnet['manifest_injustice'] = ['-0.72', '0', '0.742', '-0.80', '#anger', '#disgust', 'negative', '-0.75', 'illicitness', 'illegality', 'illegitimacy', 'shadiness', 'quality']
senticnet['manifestation'] = ['0.565', '-0.79', '0', '0.959', '#surprise', '#admiration', 'positive', '0.772', 'ebullition', 'cry', 'reflection', 'acting_out', 'explosion']
senticnet['manifestly'] = ['0', '0.847', '0', '0.719', '#interest', '#admiration', 'positive', '0.783', 'patently', 'plainly', 'plain', 'obviously', 'apparently']
senticnet['manifesto_pledge'] = ['0', '0.286', '-0.22', '0.383', '#interest', '#admiration', 'positive', '0.147', 'oath', 'propose', 'suggest', 'ask', 'trust']
senticnet['manifold'] = ['0.957', '0.755', '0', '0', '#joy', '#interest', 'positive', '0.856', 'multiply', 'manifold_paper', 'multiplex', 'study', 'topology']
senticnet['manifold_paper'] = ['0.947', '0.747', '0', '0', '#joy', '#interest', 'positive', '0.847', 'multiply', 'manifold', 'multiplex', 'study', 'topology']
senticnet['manighar'] = ['0.744', '-0.77', '0', '0.661', '#joy', '#surprise', 'positive', '0.725', 'sugarcoat', 'honey', 'glaze', 'change_taste', 'sugar']
senticnet['manikato'] = ['0.744', '-0.77', '0', '0.661', '#joy', '#surprise', 'positive', '0.725', 'sugarcoat', 'honey', 'glaze', 'change_taste', 'sugar']
senticnet['manikkam'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['manila_envelope'] = ['0.029', '-0.03', '0.066', '-0.08', '#anger', '#disgust', 'negative', '-0.02', 'sheet', 'drawer', 'file_box', 'desk_tray', 'letter_scale']
senticnet['manilkara'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['maniple'] = ['0.849', '-0.50', '0', '0.845', '#joy', '#admiration', 'positive', '0.732', 'robe', 'gown', 'veil', 'vest', 'cassock']
senticnet['manipulability'] = ['0.637', '-0.91', '0', '0.757', '#surprise', '#admiration', 'positive', '0.768', 'manageability', 'manageableness', 'docility', 'obedience', 'tractability']
senticnet['manipulable'] = ['0.841', '-0.57', '0', '0.891', '#joy', '#admiration', 'positive', '0.769', 'tamable', 'tameable', 'ductile', 'malleable', 'docile']
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senticnet['manipulation'] = ['-0.87', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.86', 'manipulate', 'chiropractic', 'manipulatory', 'psychopath', 'taxi']
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senticnet['mankind'] = ['0.954', '0.711', '0', '0', '#joy', '#interest', 'positive', '0.833', 'gender', 'humankind', 'person', 'pedophile', 'guardian']
senticnet['mankowski'] = ['0.746', '-0.88', '0', '0.723', '#joy', '#surprise', 'positive', '0.783', 'strike', 'run_into', 'hit', 'impinge_on', 'collide_with']
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senticnet['manly_courage'] = ['0.646', '-0.9', '0', '0.684', '#surprise', '#admiration', 'positive', '0.743', 'acquirement', 'skill', 'accomplishment', 'acquisition', 'attainment']
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senticnet['manmade'] = ['0', '0.09', '0.009', '0.216', '#interest', '#admiration', 'positive', '0.099', 'water_passage', 'artificial', 'waterway', 'wide', 'man_make']
senticnet['mann_library'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'a_lot_of_books', 'old_testament', 'cook_book', 'magazine', 'librarian']
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senticnet['manna_grass'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'foxtail', 'foxtail_grass', 'meadow_grass', 'sweet_grass', 'wood_meadowgrass']
senticnet['mannar_biosphere_reserve'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'harpoon', 'armory', 'lake', 'river', 'bay']
senticnet['mannequin'] = ['0', '-0.08', '0', '0', '#surprise', '#surprise', 'positive', '0.087', 'toy', 'stuff_animal', 'dummy', 'doll', 'action_figure']
senticnet['manner'] = ['0.101', '0.237', '-0.23', '0.236', '#interest', '#admiration', 'positive', '0.086', 'etiquette', 'politeness', 'suspicion', 'obedience', 'please']
senticnet['manner_of_speaking'] = ['-0.89', '0', '0.697', '-0.51', '#sadness', '#anger', 'negative', '-0.70', 'tone_of_voice', 'tone', 'catch', 'address', 'tongue']
senticnet['mannerism'] = ['-0.5', '0', '0.97', '-0.52', '#anger', '#disgust', 'negative', '-0.66', 'simulated', 'apish', 'parrotlike', 'apelike', 'mimic']
senticnet['mannerly'] = ['0.614', '-0.95', '0', '0.537', '#joy', '#surprise', 'positive', '0.703', 'mincing', 'twee', 'gentlemanlike', 'refined', 'ladylike']
senticnet['mannersmith'] = ['0', '0', '0', '0.99', '#admiration', '#admiration', 'positive', '0.99', 'politeness', 'good_behavior', 'good_etiquette', 'manner', 'hint']
senticnet['mannich_reaction'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
senticnet['mannie_fresh'] = ['0', '0.712', '0', '0.844', '#interest', '#admiration', 'positive', '0.778', 'dollar_change', 'small_currency', 'currency_unit', 'metal_money', 'american_money']
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senticnet['mannitol'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
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senticnet['manoeuvrability'] = ['-0.53', '0', '0.765', '-0.94', '#anger', '#disgust', 'negative', '-0.74', 'jaggedness', 'unregularity', 'unevenness', 'irregularity', 'patchiness']
senticnet['manoeuvre'] = ['0.043', '0.127', '-0.06', '0.268', '#interest', '#admiration', 'positive', '0.092', 'rotate_movement', 'drive_car', 'movement', 'car_action', 'motion']
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senticnet['manor'] = ['0.049', '0.16', '0', '0.155', '#interest', '#admiration', 'positive', '0.121', 'castle', 'palace', 'medieval', 'aristocrat', 'throne']
senticnet['manor_estate'] = ['0.049', '0.16', '0', '0.155', '#interest', '#admiration', 'positive', '0.121', 'castle', 'palace', 'medieval', 'aristocrat', 'throne']
senticnet['manor_house'] = ['0.049', '0.16', '0', '0.155', '#interest', '#admiration', 'positive', '0.121', 'castle', 'palace', 'medieval', 'aristocrat', 'throne']
senticnet['manorial'] = ['0', '0.928', '0', '0.955', '#interest', '#admiration', 'positive', '0.942', 'fiefdom', 'fief', 'oligarchy', 'fiefdom', 'city-state']
senticnet['manorial_estate'] = ['0.049', '0.16', '0', '0.155', '#interest', '#admiration', 'positive', '0.121', 'castle', 'palace', 'medieval', 'aristocrat', 'throne']
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senticnet['manpower_training'] = ['0', '0.754', '0', '0.913', '#interest', '#admiration', 'positive', '0.834', 'award', 'chemistry', 'continue_grant', 'young_scholar', 'university']
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senticnet['manriki_gusari'] = ['0.955', '-0.93', '0', '0', '#joy', '#surprise', 'positive', '0.943', 'tack', 'foot_part', 'casual_shoes', 'spike', 'pin']
senticnet['mansard_roof'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'condominium', 'rent_apartment', 'high_rise', 'condo', 'row_house']
senticnet['manse'] = ['0.798', '0.785', '0', '0', '#joy', '#interest', 'positive', '0.792', 'residence_hall', 'charles_francis_hall', 'marguerite_radclyffe_hall', 'anteroom', 'hall']
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senticnet['manslaughter'] = ['0', '0', '-0.95', '-0.85', '#fear', '#disgust', 'negative', '-0.90', 'choose_victim', 'murder_weapon', 'hide_evidence', 'murder', 'shoot_gun']
senticnet['manslaughter_charge'] = ['0.654', '-0.59', '0', '0.774', '#joy', '#admiration', 'positive', '0.673', 'instrument', 'indictment', 'murder_indictment', 'true_bill', 'legal_document']
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senticnet['mantel_piece'] = ['0.979', '-0.69', '0', '0.576', '#joy', '#surprise', 'positive', '0.749', 'big_cheese', 'supremo', 'important_person', 'influential_person', 'personage']
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senticnet['mantid'] = ['0.748', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.873', 'butterfly', 'moth', 'parrot', 'beetle', 'falcon']
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senticnet['mantle_cavity'] = ['-0.93', '0', '0.607', '-0.67', '#sadness', '#disgust', 'negative', '-0.73', 'spiracle', 'stigma', 'orifice', 'opening', 'porta']
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senticnet['mantoux_test'] = ['-0.74', '0', '0.766', '0', '#sadness', '#anger', 'negative', '-0.75', 'use_needle', 'clothes_repair', 'woven', 'embroidery', 'thread_needle']
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senticnet['manual_alphabet'] = ['-0.94', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.94', 'drive_recklessly', 'discord', 'mute', 'deny', 'helpless']
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senticnet['manual_toothbrush'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'smart_technology', 'electric_shaver', 'hair_dryer', 'stay_healthy', 'wash_tooth']
senticnet['manual_typewriter'] = ['0', '0', '0.128', '0', '#anger', '#anger', 'negative', '-0.12', 'grid', 'efficient', 'calculator', 'computer', 'personal_computer']
senticnet['manual_wheelchair'] = ['0', '0', '0', '0.144', '#admiration', '#admiration', 'positive', '0.144', 'go_elsewhere', 'transport_person', 'get_around', 'locomotion', 'trucking']
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senticnet['manubrium'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'hip_socket', 'tooth_socket', 'alveolus', 'socket', 'cotyloid_cavity']
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senticnet['manufacturability'] = ['0.512', '-0.52', '0', '0.874', '#surprise', '#admiration', 'positive', '0.635', 'responsibleness', 'reproducibility', 'duplicability', 'solidness', 'responsibility']
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senticnet['manufacture_custom'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'builder']
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senticnet['manufacturer'] = ['0.059', '0.152', '0', '0.15', '#interest', '#admiration', 'positive', '0.12', 'industry', 'enterprise', 'corporation', 'business', 'factory']
senticnet['manufacturing'] = ['0', '0.921', '0', '0.993', '#interest', '#admiration', 'positive', '0.957', 'manufacture', 'fabricate', 'make_up', 'cook_up', 'construct']
senticnet['manufacturing_defect'] = ['-0.91', '0', '0.905', '-0.78', '#sadness', '#anger', 'negative', '-0.86', 'pity', 'shame', 'misfortune', 'mischance', 'misadventure']
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senticnet['manufacturing_output'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'mathematical', 'muscle', 'math_function', 'feel_sick', 'separation']
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senticnet['many_chemicals'] = ['0', '0', '-0.77', '-0.77', '#fear', '#disgust', 'negative', '-0.77', 'dangerous_item', 'many_cultures', 'venomous', 'many_countries', 'dangerous']
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senticnet['many_countries'] = ['0', '0', '-0.77', '-0.77', '#fear', '#disgust', 'negative', '-0.77', 'many_chemicals', 'many_cultures', 'venomous', 'dangerous_item', 'dangerous']
senticnet['many_cultures'] = ['0', '0', '-0.77', '-0.77', '#fear', '#disgust', 'negative', '-0.77', 'many_chemicals', 'dangerous_item', 'venomous', 'many_countries', 'dangerous']
senticnet['many_different_colors'] = ['0', '0', '0', '-0.76', '#disgust', '#disgust', 'negative', '-0.76', 'live_underground', 'jump_over_fence', 'tame', 'eat_rat', 'growl']
senticnet['many_different_kinds'] = ['-0.04', '-0.02', '0.021', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'precious_metal', 'olympic_medal', 'shiny_metal', 'bloodstone', 'valuable_metal']
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senticnet['many_feathers_beak'] = ['0.02', '0.076', '0', '0', '#joy', '#interest', 'positive', '0.048', 'beak', 'bird_mouth', 'wing', 'many_feathers', 'water_bird']
senticnet['many_fishes'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'porpoise', 'seahorse', 'starfish', 'plankton', 'sea_wee']
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senticnet['many_fruits'] = ['0.014', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.04', 'buy_fruit', 'peach', 'cherry', 'pineapple', 'tomato']
senticnet['many_games'] = ['0', '0.11', '0.013', '0.159', '#interest', '#admiration', 'positive', '0.085', 'game_chess', 'play_marble', 'billiards', 'chess', 'chess_game']
senticnet['many_homes'] = ['-0.03', '-0.00', '0.02', '-0.04', '#sadness', '#disgust', 'negative', '-0.02', 'fancy_house', 'atrium', 'front_house', 'abide', 'outside_house']
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senticnet['many_pages'] = ['0', '0.934', '0', '0', '#interest', '#interest', 'positive', '0.934', 'current_event', 'headline', 'paper_write', 'paper_page', 'bulletin']
senticnet['many_people'] = ['0.09', '0.13', '-0.09', '0.122', '#interest', '#admiration', 'positive', '0.061', 'a_little_specific', 'happy_together', 'clan', 'aunt_uncle', 'human_group']
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senticnet['many_shades'] = ['0', '0', '0', '0.097', '#admiration', '#admiration', 'positive', '0.097', 'cobalt', 'pigment', 'cyan', 'green_yellow', 'tint']
senticnet['many_shops'] = ['0', '0.072', '0', '0.064', '#interest', '#admiration', 'positive', '0.068', 'thrift_store', 'type_of_store', 'record_store', 'department_store', 'clothes_store']
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senticnet['many_types'] = ['0.868', '0.778', '0', '0', '#joy', '#interest', 'positive', '0.823', 'several_types', 'copper_wire', 'gas_stove', 'tape_player', 'control_room']
senticnet['many_windows'] = ['0', '0', '0', '0.078', '#admiration', '#admiration', 'positive', '0.078', 'unix', 'window', 'train_window', 'solaris', 'doorway']
senticnet['many_words'] = ['0', '0.94', '0', '0', '#interest', '#interest', 'positive', '0.94', 'vocabulary', 'read_material', 'book_genre', 'definition', 'book_library']
senticnet['many_yards'] = ['0', '0.064', '0.038', '0.071', '#interest', '#admiration', 'positive', '0.032', 'ecosystem', 'measure_distance', 'distance_measurement', 'mow', 'length']
senticnet['many_years'] = ['0', '0', '0.832', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'ancient', 'gray_hair', 'year', 'old', 'teen']
senticnet['mao_tse_tung'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
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senticnet['maoist'] = ['0', '0', '-0.70', '-0.83', '#fear', '#disgust', 'negative', '-0.76', 'settle_disagreement', 'hitler', 'republican', 'conservative', 'unchanging']
senticnet['maoist_china'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['map'] = ['0.087', '0.08', '0.013', '0', '#joy', '#interest', 'positive', '0.051', 'neighborhood_map', 'billboard', 'underground_neighborhood_map', 'advertise_panel', 'restaurant_entrance']
senticnet['map_projection'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'arm_movement', 'hand_gesture', 'water_motion', 'hand_signal', 'sea_movement']
senticnet['maple'] = ['0.032', '0.075', '0', '0', '#joy', '#interest', 'positive', '0.053', 'aperitif', 'fondue', 'crop', 'graze', 'kernel']
senticnet['maple_acer'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'black_locust']
senticnet['maple_avenue'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'boulevard', 'lane', 'thoroughfare', 'intersection', 'curb']
senticnet['maple_bacon'] = ['0.274', '-0.01', '0.012', '0.132', '#joy', '#admiration', 'positive', '0.102', 'dance_type', 'swirl', 'throw_football', 'jump_around', 'dance_move']
senticnet['maple_street'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'boulevard', 'lane', 'thoroughfare', 'intersection', 'curb']
senticnet['maple_sugar'] = ['-0.08', '0.106', '0', '0.125', '#interest', '#admiration', 'positive', '0.047', 'sugar', 'treacle', 'black_treacle', 'add_sugar', 'sugar_substitute']
senticnet['mapping'] = ['0.867', '0.996', '0', '0', '#joy', '#interest', 'positive', '0.931', 'function', 'map', 'mathematical_function', 'represent', 'threshold_function']
senticnet['maqellar'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
senticnet['maqil'] = ['0.141', '0', '-0.06', '0.086', '#joy', '#admiration', 'positive', '0.055', 'dancer', 'stout', 'old_dance', 'climb_rock', 'jump_around']
senticnet['mara_liasson'] = ['0.783', '-0.63', '0', '0.829', '#joy', '#admiration', 'positive', '0.749', 'leg_of_lamb', 'lamb_roast', 'joint', 'roast', 'rack_of_lamb']
senticnet['marabou_stork'] = ['-0.02', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.04', 'wren', 'bird', 'quail', 'sparrow', 'chick']
senticnet['maracatu'] = ['-0.06', '-0.00', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.05', 'madman', 'trouble', 'agony', 'cause_problem', 'river_stream']
senticnet['maraschino'] = ['0.693', '-0.94', '0', '0.713', '#surprise', '#admiration', 'positive', '0.784', 'anisette', 'cordial', 'ratafia', 'pastis', 'ratafee']
senticnet['marasmus'] = ['-0.71', '0', '0.74', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'kwashiorkor', 'malnutrition', 'avitaminosis', 'hypovitaminosis', 'deficiency_disease']
senticnet['maratha_sardar'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['marathon'] = ['0', '0', '0', '0.108', '#admiration', '#admiration', 'positive', '0.108', 'fast', 'flee', 'sprint', 'race', 'good_form_exercise']
senticnet['marathon_runner'] = ['-0.04', '-0.03', '0', '-0.16', '#sadness', '#disgust', 'negative', '-0.05', 'crazy', 'damage_body', 'crazy_person', 'threat', 'max']
senticnet['marathon_swimmer'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'chlorine', 'lifeguard', 'still_water', 'large_body', 'shallow_water']
senticnet['marathon_swimming'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'chlorine', 'lifeguard', 'still_water', 'large_body', 'shallow_water']
senticnet['marathoner'] = ['-0.04', '-0.03', '0', '-0.16', '#sadness', '#disgust', 'negative', '-0.05', 'crazy', 'damage_body', 'crazy_person', 'threat', 'max']
senticnet['maraud'] = ['-0.93', '0', '0.722', '-0.66', '#sadness', '#anger', 'negative', '-0.77', 'penetration', 'air_raid', 'air_attack', 'swoop', 'incursion']
senticnet['marauder'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'unofficial', 'informal', 'loose', 'drumhead']
senticnet['marble'] = ['0', '0.077', '0.039', '0.154', '#interest', '#admiration', 'positive', '0.064', 'dome', 'round_figure', 'almond', 'round_ball', 'sphere']
senticnet['marble_bar'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'beachcomber', 'bum', 'drifter']
senticnet['marble_bathroom'] = ['0.866', '0.833', '0', '0', '#joy', '#interest', 'positive', '0.849', 'alcohol', 'porter', 'concierge', 'beer_mug', 'bar_stool']
senticnet['marble_bone_disease'] = ['-0.70', '0', '0.64', '-0.60', '#sadness', '#anger', 'negative', '-0.65', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['marble_bust'] = ['0.085', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.079', 'sculpture', 'creative', 'yellow_ribbon', 'artwork', 'monument']
senticnet['marble_canyon'] = ['0', '0.069', '0', '0.065', '#interest', '#admiration', 'positive', '0.067', 'alp', 'high_mountain', 'pass', 'ski_resort', 'high_place']
senticnet['marble_clad'] = ['-0.54', '0', '0.7', '-0.64', '#anger', '#disgust', 'negative', '-0.62', 'flourish', 'rant', 'bombast', 'fustian', 'claptrap']
senticnet['marble_fireplace'] = ['0', '-0.02', '-0.08', '-0.05', '#fear', '#disgust', 'negative', '-0.03', 'mattress', 'sleep_furniture', 'cradle', 'small_bed', 'pillow']
senticnet['marble_floor'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'stage_border', 'row_seat', 'stage_entrance', 'theater_seat', 'theater_box']
senticnet['marble_headstone'] = ['-0.88', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.90', 'coffin', 'casket', 'morgue', 'headstone', 'burial']
senticnet['marble_mausoleum'] = ['-0.88', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.90', 'coffin', 'casket', 'morgue', 'headstone', 'burial']
senticnet['marble_palace'] = ['0', '0.105', '0', '0.076', '#interest', '#admiration', 'positive', '0.09', 'big_house', 'mansion', 'castle', 'block_flat', 'medieval']
senticnet['marble_pedestal'] = ['0.085', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.079', 'sculpture', 'creative', 'yellow_ribbon', 'artwork', 'monument']
senticnet['marble_plaque'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'dental_plaque']
senticnet['marble_quarry'] = ['0', '0', '-0.85', '-0.71', '#fear', '#disgust', 'negative', '-0.78', 'fair_game', 'pit', 'stone_pit', 'target', 'prey']
senticnet['marble_sculpture'] = ['0.085', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.079', 'sculpture', 'creative', 'yellow_ribbon', 'artwork', 'monument']
senticnet['marble_slab'] = ['0', '0.082', '-0.09', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'coffin', 'headstone', 'morgue', 'bury_body', 'casket']
senticnet['marble_slab_creamery'] = ['0.274', '-0.01', '0.012', '0.132', '#joy', '#admiration', 'positive', '0.102', 'dance_type', 'swirl', 'throw_football', 'jump_around', 'dance_move']
senticnet['marble_staircase'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'stage_border', 'row_seat', 'stage_entrance', 'theater_seat', 'theater_box']
senticnet['marble_statue'] = ['0.085', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.079', 'sculpture', 'creative', 'yellow_ribbon', 'artwork', 'monument']
senticnet['marble_tablet'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'dental_plaque']
senticnet['marble_tomb'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'wallet', 'billfold', 'bank', 'piggy_bank', 'bank_vault']
senticnet['marble_tombstone'] = ['-0.88', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.90', 'coffin', 'casket', 'morgue', 'headstone', 'burial']
senticnet['marble_urn'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'wallet', 'billfold', 'bank', 'piggy_bank', 'bank_vault']
senticnet['marburg_hemorrhagic_fever'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'leptomeningitis', 'meningitis', 'epidemic_meningitis', 'brain_fever', 'cerebrospinal_fever']
senticnet['marburg_virus_disease'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'leptomeningitis', 'meningitis', 'epidemic_meningitis', 'brain_fever', 'cerebrospinal_fever']
senticnet['marcade'] = ['-0.75', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.83', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['marcel'] = ['-0.97', '0', '0.567', '-0.59', '#sadness', '#disgust', 'negative', '-0.71', 'coif', 'coiffure', 'hairdo', 'hairstyle', 'hair_style']
senticnet['marcelfure'] = ['-0.97', '0', '0.564', '-0.6', '#sadness', '#disgust', 'negative', '-0.71', 'marcel', 'coif', 'hairdo', 'hairstyle', 'hair_style']
senticnet['marcellus'] = ['0', '0', '-0.92', '-0.78', '#fear', '#disgust', 'negative', '-0.85', 'rome', 'plebe', 'patrician', 'unwashed', 'plebeian']
senticnet['marchamont_nedham'] = ['0.058', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.054', 'letter_slot', 'envelope', 'address_label', 'mailman', 'postage_stamp']
senticnet['marchausse'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'law_enforcement_agency', 'law', 'agency']
senticnet['marcher_lord'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'feudal_lord', 'lord']
senticnet['marco_longari_afp_getty'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expansive', 'expansible', 'erectile', 'cavernous']
senticnet['marco_polo_sheep'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheep', 'argal', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['marcum'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'dicky', 'impaired', 'lessened', 'diminished']
senticnet['mardi_gras_parade'] = ['0.957', '0.7', '0', '0', '#joy', '#interest', 'positive', '0.829', 'ballpark', 'coven', 'disneyland', 'hockey_game', 'symposium']
senticnet['mardi_une'] = ['0.667', '-0.98', '0', '0.61', '#joy', '#surprise', 'positive', '0.753', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['mare'] = ['0.734', '0.756', '0', '0', '#joy', '#interest', 'positive', '0.745', 'almighty', 'power', 'puissant', 'excellent', 'mighty']
senticnet['mare_nest'] = ['-0.85', '0', '0.583', '-0.71', '#sadness', '#disgust', 'negative', '-0.71', 'muddle', 'fuddle', 'rummage', 'welter', 'clutter']
senticnet['marg_ret'] = ['-0.80', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'difficulty', 'awkwardness', 'flea_bite', 'difficultness', 'cumbersomeness']
senticnet['margarita'] = ['-0.78', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.86', 'icy-cold', 'chilled', 'phosphate-buffered', 'ice-cold', 'sipping']
senticnet['margin'] = ['0.022', '0.243', '0', '0.176', '#interest', '#admiration', 'positive', '0.147', 'tip', 'bottom_line', 'extra_money', 'profile', 'sharpness']
senticnet['margin_coarsely'] = ['-0.50', '0', '0.573', '-0.89', '#anger', '#disgust', 'negative', '-0.65', 'petallike', 'bowery', 'leafy', 'unifoliate', 'bifoliate']
senticnet['marginal'] = ['-0.54', '0', '0.739', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'unremunerative', 'idle', 'lean', 'dead', 'unprofitable']
senticnet['marginal_cost'] = ['-0.02', '0.249', '0', '0.116', '#interest', '#admiration', 'positive', '0.112', 'price', 'expense', 'bill', 'value', 'worth']
senticnet['marginal_income_tax'] = ['0.763', '-0.67', '0', '0.945', '#joy', '#admiration', 'positive', '0.795', 'set', 'income_bracket', 'bracket', 'income', 'price_bracket']
senticnet['marginal_productivity'] = ['-0.54', '0', '0.739', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'unremunerative', 'idle', 'lean', 'dead', 'unprofitable']
senticnet['marginal_rate'] = ['0.763', '-0.67', '0', '0.945', '#joy', '#admiration', 'positive', '0.795', 'set', 'income_bracket', 'bracket', 'income', 'price_bracket']
senticnet['marginal_seat'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'couch', 'stool', 'coach_seat']
senticnet['marginal_tax_bracket'] = ['0.763', '-0.67', '0', '0.945', '#joy', '#admiration', 'positive', '0.795', 'set', 'income_bracket', 'bracket', 'income', 'price_bracket']
senticnet['marginal_tax_rate'] = ['0.763', '-0.67', '0', '0.945', '#joy', '#admiration', 'positive', '0.795', 'set', 'income_bracket', 'bracket', 'income', 'price_bracket']
senticnet['marginal_utility'] = ['-0.54', '0', '0.739', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'unremunerative', 'idle', 'lean', 'dead', 'unprofitable']
senticnet['marginalisation'] = ['-0.84', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.82', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecure']
senticnet['marginalise'] = ['0.033', '-0.19', '0.203', '-0.22', '#anger', '#disgust', 'negative', '-0.05', 'antiquate', 'optional', 'kleenex', 'key_chain', 'sachet']
senticnet['marginality'] = ['-0.54', '0', '0.739', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'unremunerative', 'idle', 'lean', 'dead', 'unprofitable']
senticnet['marginalization'] = ['-0.84', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.82', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecure']
senticnet['marginalize'] = ['0.033', '-0.19', '0.203', '-0.22', '#anger', '#disgust', 'negative', '-0.05', 'antiquate', 'optional', 'kleenex', 'key_chain', 'sachet']
senticnet['marginally'] = ['0', '0', '-0.76', '-0.99', '#fear', '#disgust', 'negative', '-0.88', 'marginal', 'sufficiently', 'margin', 'barely', 'slightly']
senticnet['marginocephalian'] = ['-0.92', '0', '0.64', '-0.52', '#sadness', '#anger', 'negative', '-0.69', 'perching_bird', 'percher', 'thyreophoran', 'animal_order', 'euronithopod']
senticnet['margolin'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['marguerite_radclyffe_hall'] = ['0.795', '0.778', '0', '0', '#joy', '#interest', 'positive', '0.786', 'residence_hall', 'charles_francis_hall', 'hall', 'anteroom', 'manse']
senticnet['mariachi'] = ['-0.50', '0', '0.902', '-0.54', '#anger', '#disgust', 'negative', '-0.65', 'gamelan', 'jug_band', 'pop_group', 'ensemble', 'skiffle_group']
senticnet['mariachi_music'] = ['0.116', '-0.02', '0.025', '0.156', '#joy', '#admiration', 'positive', '0.069', 'dance_move', 'latin_dance', 'body_movement', 'waltz', 'dance']
senticnet['mariae'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'sedum', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['mariah_carey'] = ['0.056', '0.139', '-0.07', '0.154', '#interest', '#admiration', 'positive', '0.07', 'belongings', 'material', 'fabric', 'guarantee', 'stuff']
senticnet['mariana_trench'] = ['-0.02', '-0.01', '0.03', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'ocean_beach', 'beach', 'water_sea', 'shore', 'ground_ocean']
senticnet['marianne_cahill'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'concupiscence', 'lustfulness', 'amorousness']
senticnet['maricn'] = ['0.537', '-0.51', '0', '0.807', '#joy', '#admiration', 'positive', '0.619', 'lesbian', 'fairy', 'fagot', 'homo', 'fag']
senticnet['marigold'] = ['0.077', '0', '0', '0', '#joy', '#joy', 'positive', '0.077', 'dip', 'garlic', 'oat', 'savory', 'rye']
senticnet['marihuana'] = ['-0.94', '0', '0.517', '-0.75', '#sadness', '#disgust', 'negative', '-0.74', 'narcotic', 'bhang', 'cannabis', 'marijuana', 'soft_drug']
senticnet['mariinsky_ballet'] = ['0.12', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.09', 'ballroom', 'perform_art', 'nightclub', 'ballerina', 'venue']
senticnet['marijuana'] = ['0.962', '0', '0', '0', '#joy', '#joy', 'positive', '0.962', 'cannabis', 'pleasure_garden', 'trellis', 'mushroom', 'cabbage']
senticnet['marijuana_cigarette'] = ['0', '0.931', '0', '0', '#interest', '#interest', 'positive', '0.931', 'football', 'rake_leave', 'spray_paint_grafitti', 'juggle', 'play_hide_seek']
senticnet['marijuana_prohibition'] = ['-0.72', '0', '0.71', '-0.9', '#sadness', '#disgust', 'negative', '-0.77', 'perniciousness', 'unwholesomeness', 'toxicity', 'noisomeness', 'noxiousness']
senticnet['marijuana_smoke'] = ['0.047', '0', '-0.10', '-0.06', '#fear', '#disgust', 'negative', '-0.03', 'smoke', 'tobacco_product', 'smoke_tobacco', 'lung_cancer', 'cigar']
senticnet['marinade'] = ['0.07', '0.084', '0.023', '0', '#joy', '#interest', 'positive', '0.044', 'hearty', 'porridge', 'hot_liquid', 'liquid_food', 'meat_vegetable']
senticnet['marinara'] = ['0.043', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'apple_pie', 'a_lot_of_food', 'jelly', 'frozen_food', 'cottage_cheese']
senticnet['marinara_sauce'] = ['0.043', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'apple_pie', 'a_lot_of_food', 'jelly', 'frozen_food', 'cottage_cheese']
senticnet['marinate'] = ['-0.66', '0', '0.992', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'cold', 'boil', 'temperature', 'low_temperature', 'frigidity']
senticnet['marinated'] = ['-0.73', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.78', 'skinless', 'bone-in', 'sirloin', 'boneless', 'loin']
senticnet['marinated_flank_steak'] = ['0', '0', '0.066', '-0.07', '#anger', '#disgust', 'negative', '-0.07', 'container_cup', 'cup', 'grease_trap', 'dish', 'open_container']
senticnet['marine'] = ['0', '0', '-0.06', '-0.08', '#fear', '#disgust', 'negative', '-0.07', 'sergent', 'military', 'warrior', 'army_person', 'military_force']
senticnet['marine_angelfish'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'puffer', 'pufferfish', 'blowfish', 'triggerfish', 'plectognath']
senticnet['marine_animal'] = ['0.052', '0.066', '0', '0.106', '#interest', '#admiration', 'positive', '0.075', 'shell_fish', 'seafood', 'ocean', 'ocean_animal', 'crustacean']
senticnet['marine_aquaria'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'autumn_leaf', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['marine_aquarium'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'autumn_leaf', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['marine_archaeologist'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'succourer', 'recoverer', 'salvor', 'mortal']
senticnet['marine_battalion'] = ['0', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.09', 'army', 'attacker', 'military', 'soldier', 'infantry']
senticnet['marine_biologist'] = ['0', '0.07', '0', '0', '#interest', '#interest', 'positive', '0.07', 'sunken_treasure', 'real_duck', 'urchin', 'mussel', 'gold_fish']
senticnet['marine_biology'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'college_major', 'geology', 'physic', 'chemistry', 'subject_school']
senticnet['marine_bivalve_mollusc'] = ['-0.79', '0', '0.709', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'animal', 'animate_being', 'ctenophore', 'invertebrate', 'beast']
senticnet['marine_bivalve_mollusk'] = ['-0.79', '0', '0.709', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'animal', 'animate_being', 'ctenophore', 'invertebrate', 'beast']
senticnet['marine_brigade'] = ['-0.9', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.9', 'withdrawal', 'indifferently', 'apathetic', 'indifferent', 'concerned']
senticnet['marine_casualty'] = ['-0.16', '0', '-0.18', '-0.23', '#fear', '#disgust', 'negative', '-0.19', 'attack_enemy', 'cause_death', 'die', 'lose_war', 'imprisonment']
senticnet['marine_expeditionary_brigade'] = ['-0.66', '0', '0.789', '-0.63', '#sadness', '#anger', 'negative', '-0.69', 'humbug', 'play_trick', 'cozen', 'pull_fast_one_on', 'play_trick_on']
senticnet['marine_expeditionary_force_mef'] = ['-0.66', '0', '0.789', '-0.63', '#sadness', '#anger', 'negative', '-0.69', 'humbug', 'play_trick', 'cozen', 'pull_fast_one_on', 'play_trick_on']
senticnet['marine_geology'] = ['-0.77', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.85', 'number', 'oceanography', 'geophysics', 'isotope', 'seawater']
senticnet['marine_heterobranch'] = ['0.969', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.772', 'aspheric', 'aspherical', 'prolate', 'rounded', 'crescent']
senticnet['marine_infantry_battalion'] = ['-0.9', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.9', 'withdrawal', 'indifferently', 'apathetic', 'indifferent', 'concerned']
senticnet['marine_light_infantry'] = ['0.059', '0.076', '-0.05', '0.079', '#interest', '#admiration', 'positive', '0.039', 'noble', 'king', 'earl', 'duchess', 'duke']
senticnet['marine_mammal'] = ['0.024', '0.102', '-0.06', '0.074', '#interest', '#admiration', 'positive', '0.033', 'dolphin', 'large_mammal', 'pack_animal', 'mammal', 'smart_creature']
senticnet['marine_polychaete'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'annelid', 'annelid_worm', 'leech']
senticnet['marine_polychaete_worm'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'plectognath', 'plectognathfish', 'blowfish', 'triggerfish', 'filefish']
senticnet['marine_salvage'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'succourer', 'recoverer', 'salvor', 'mortal']
senticnet['marine_snail'] = ['-0.79', '0', '0.709', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'animal', 'animate_being', 'ctenophore', 'invertebrate', 'beast']
senticnet['marine_zoology'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'study_person', 'subject_study', 'zoo', 'zoologist', 'geology']
senticnet['marineland'] = ['-0.20', '-0.10', '0.07', '-0.24', '#sadness', '#disgust', 'negative', '-0.10', 'gill', 'sea_life', 'seafood', 'swim_animal', 'fillet']
senticnet['mariner'] = ['0.587', '-0.67', '0', '0.729', '#surprise', '#admiration', 'positive', '0.662', 'sea_lawyer', 'seaman', 'tar', 'old_salt', 'seafarer']
senticnet['marinetti'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'native_person', 'revolutionary', 'republic', 'revolution', 'look_ahead']
senticnet['marinum'] = ['-0.60', '0', '0.721', '-0.57', '#sadness', '#anger', 'negative', '-0.63', 'tubercle_bacillus', 'leprosy_bacillus', 'eubacteria', 'eubacterium', 'true_bacteria']
senticnet['marionette'] = ['0', '0', '-0.74', '-0.73', '#fear', '#disgust', 'negative', '-0.73', 'puppeteer', 'handler', 'move', 'arrange', 'manipulator']
senticnet['marisa_berenson'] = ['-0.88', '0', '0.864', '-0.72', '#sadness', '#anger', 'negative', '-0.82', 'malicious', 'venomous', 'bitchy', 'catty', 'cattish']
senticnet['marital'] = ['0.903', '0.684', '0', '0.791', '#joy', '#admiration', 'positive', '0.793', 'relative', 'family_member', 'relationship', 'family', 'mother']
senticnet['marital_bond'] = ['0.903', '0.684', '0', '0.791', '#joy', '#admiration', 'positive', '0.793', 'relative', 'family_member', 'relationship', 'family', 'mother']
senticnet['marital_coercion'] = ['-0.70', '0', '0.874', '-0.75', '#anger', '#disgust', 'negative', '-0.77', 'blame', 'accusation', 'inculpation', 'complaint', 'imprecation']
senticnet['marital_dissolution'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'bigamy', 'wedlock']
senticnet['marital_infidelity'] = ['-0.94', '0', '0.944', '-0.90', '#sadness', '#anger', 'negative', '-0.93', 'lust', 'pregnant', 'desire', 'care_deeply', 'love']
senticnet['marital_property'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'bigamy', 'wedlock']
senticnet['marital_relationship'] = ['0.903', '0.684', '0', '0.791', '#joy', '#admiration', 'positive', '0.793', 'relative', 'family_member', 'relationship', 'family', 'mother']
senticnet['marital_sex'] = ['0.531', '0.673', '-0.27', '0.275', '#joy', '#interest', 'positive', '0.301', 'amorous', 'amorousness', 'amatory', 'enamoredness', 'puppy_love']
senticnet['marital_status'] = ['-0.64', '0', '0.752', '-0.68', '#anger', '#disgust', 'negative', '-0.69', 'widowhood', 'marriage', 'matrimony', 'union', 'spousal_relationship']
senticnet['marital_strife'] = ['-0.94', '0', '0.944', '-0.90', '#sadness', '#anger', 'negative', '-0.93', 'lust', 'pregnant', 'desire', 'care_deeply', 'love']
senticnet['maritime_boundary'] = ['0.034', '0.09', '0.039', '0.061', '#interest', '#admiration', 'positive', '0.036', 'border', 'limit', 'edge', 'indoor', 'side']
senticnet['maritime_counter_terrorism'] = ['-0.07', '0.069', '-0.18', '0', '#sadness', '#fear', 'negative', '-0.06', 'medical_procedure', 'cure', 'doctor_surgery', 'treatment', 'doctor_work']
senticnet['maritime_jurisdiction'] = ['0.657', '-0.88', '0', '0.631', '#joy', '#surprise', 'positive', '0.723', 'cognisance', 'knowingness', 'awareness', 'feel', 'consciousness']
senticnet['maritime_warfare'] = ['0.764', '-0.96', '0', '0.883', '#surprise', '#admiration', 'positive', '0.87', 'boatmanship', 'acquirement', 'skill', 'accomplishment', 'acquisition']
senticnet['marjoram'] = ['0.632', '-0.93', '0', '0.928', '#surprise', '#admiration', 'positive', '0.831', 'herb', 'oregano', 'origanum', 'pot_origanum', 'wild_origanum']
senticnet['mark'] = ['-0.02', '0.155', '-0.07', '0.123', '#interest', '#admiration', 'positive', '0.045', 'blemish', 'symbol', 'punctuation', 'put_ink_paper', 'font']
senticnet['mark_dery'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'indirectness', 'mediateness', 'characteristic', 'allusiveness']
senticnet['mark_exam'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'review', 'double_check', 'spelling', 'correct_homework', 'check_spelling']
senticnet['mark_hix'] = ['0.897', '-0.96', '0', '0.785', '#joy', '#surprise', 'positive', '0.881', 'erotic', 'voluptuary', 'epicure', 'gourmet', 'sensualist']
senticnet['mark_horo'] = ['0.997', '-0.84', '0', '0.927', '#joy', '#admiration', 'positive', '0.923', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['mark_leckey'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'hip', 'joint', 'above_leg', 'knee']
senticnet['mark_linn_baker'] = ['0', '0', '0', '0.068', '#admiration', '#admiration', 'positive', '0.068', 'dark_corner', 'child_game', 'play_person', 'role_play', 'part_of_play']
senticnet['mark_melancon'] = ['0.663', '-0.68', '0', '0.892', '#surprise', '#admiration', 'positive', '0.747', 'saphead', 'muggins', 'tomfool', 'fool', 'sap']
senticnet['mark_paper'] = ['-0.08', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.08', 'make_note', 'doodle', 'write_note', 'mark_exam', 'write_book']
senticnet['mark_salter'] = ['0', '0', '0', '0.876', '#admiration', '#admiration', 'positive', '0.876', 'good_manner', 'courteous', 'good_behavior', 'handle_carefully', 'politeness']
senticnet['mark_selby'] = ['-0.66', '0', '0.771', '-0.95', '#anger', '#disgust', 'negative', '-0.79', 'delude', 'deceive', 'cozen', 'play_trick_on', 'lead_on']
senticnet['mark_shenton'] = ['0.811', '-0.63', '0', '0.959', '#joy', '#admiration', 'positive', '0.8', 'drama_critic', 'critic', 'professional', 'art_critic', 'literary_critic']
senticnet['mark_taper_forum'] = ['0', '0.066', '-0.05', '0.089', '#interest', '#admiration', 'positive', '0.032', 'pineapple', 'kale', 'banana', 'pickle', 'tomato']
senticnet['mark_territory'] = ['-0.12', '0', '-0.06', '-0.03', '#sadness', '#fear', 'negative', '-0.07', 'follow_person', 'stay_in_house', 'cause_accident', 'sense_fear', 'guide_blind']
senticnet['mark_tey'] = ['0.623', '-0.56', '0', '0.974', '#joy', '#admiration', 'positive', '0.721', 'alterable', 'convertible', 'translatable', 'transformable', 'transmutable']
senticnet['mark_twain_humor'] = ['0.947', '-0.99', '0', '0.936', '#joy', '#surprise', 'positive', '0.958', 'utterer', 'deipnosophist', 'interlocutor', 'conversational_partner', 'speaker']
senticnet['mark_waid'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'encyclopaedia', 'child_story', 'comic', 'disney_cartoon']
senticnet['markair'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['marked'] = ['0.836', '-0.89', '0', '0.865', '#surprise', '#admiration', 'positive', '0.867', 'conspicuous', 'bold', 'outstanding', 'prominent', 'striking']
senticnet['marked_price'] = ['0', '0.262', '0', '0.135', '#interest', '#admiration', 'positive', '0.199', 'fee', 'price', 'amount_charge', 'sticker', 'bill']
senticnet['marked_slowdown'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'mathematical', 'muscle', 'math_function', 'feel_sick', 'separation']
senticnet['markedly'] = ['0', '0.837', '0', '0.75', '#interest', '#admiration', 'positive', '0.793', 'dramatically', 'significantly', 'considerably', 'radically', 'drastically']
senticnet['markedly_different'] = ['0.14', '0.345', '-0.20', '0.378', '#interest', '#admiration', 'positive', '0.165', 'good_etiquette', 'same', 'similarity', 'band_together', 'niceness']
senticnet['marker'] = ['0.031', '0.08', '0.023', '0.069', '#interest', '#admiration', 'positive', '0.039', 'pencil', 'graphical_representation', 'photo', 'icon', 'quill_brush']
senticnet['marker_pen'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'gift_wrap', 'kraft', 'kraft_paper', 'wrapping_paper', 'paper']
senticnet['markert'] = ['0.031', '0.08', '0.023', '0.069', '#interest', '#admiration', 'positive', '0.039', 'pencil', 'pencil_art', 'pencil_sharpener', 'paper_clip', 'electric_pencil_sharpener']
senticnet['market'] = ['0', '0.071', '0.009', '0.104', '#interest', '#admiration', 'positive', '0.055', 'retail_store', 'supermarket', 'grocery_store', 'farmer_market', 'own_freezer']
senticnet['market_basket'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'indicator', 'retail_price_index', 'producer_price_index', 'wholesale_price_index', 'consumer_price_index']
senticnet['market_meltdown'] = ['-0.13', '-0.05', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'monitor_heart_rate', 'feel_tired', 'bump_head', 'exhaustion', 'become_fatigued']
senticnet['market_penetration'] = ['0.89', '-0.62', '0', '0.548', '#joy', '#surprise', 'positive', '0.686', 'breakthrough', 'interpenetration', 'foray', 'maraud', 'raid']
senticnet['market_product'] = ['0.796', '0.821', '0', '0', '#joy', '#interest', 'positive', '0.808', 'bridge_difference', 'speak_english', 'stand_alone', 'deal_card_player', 'forward_email']
senticnet['market_strategist'] = ['0.711', '0', '0', '0', '#joy', '#joy', 'positive', '0.711', 'chemist', 'expert', 'scientist', 'esaminatore', 'physicist']
senticnet['market_value_fmv'] = ['0.713', '0', '0', '0.746', '#joy', '#admiration', 'positive', '0.73', 'carnival', 'circus', 'fairground', 'rodeo', 'disneyland']
senticnet['market_volatility'] = ['0', '0', '0', '-0.70', '#disgust', '#disgust', 'negative', '-0.70', 'uncertain_future', 'uncertain_environment', 'insecure', 'blur', 'anxiety']
senticnet['marketability'] = ['0.817', '-0.62', '0', '0.62', '#joy', '#surprise', 'positive', '0.688', 'quality', 'fascination', 'allurement', 'allure', 'temptingness']
senticnet['marketable'] = ['-0.95', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.84', 'unsellable', 'unsaleable', 'unmarketable', 'salable', 'unsalable']
senticnet['marketer'] = ['0.814', '-0.58', '0', '0.646', '#joy', '#admiration', 'positive', '0.681', 'seller', 'cosmetician', 'vender', 'vendor', 'trafficker']
senticnet['marketing'] = ['0.8', '0.995', '0', '0', '#joy', '#interest', 'positive', '0.897', 'advertisement', 'sale', 'announcement', 'billboard', 'promotion']
senticnet['marketing_campaign'] = ['-0.81', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.81', 'take_charge', 'anti', 'go_against', 'death_wish', 'file_complaint']
senticnet['marketing_consulting_firm'] = ['0.046', '0.116', '-0.05', '0.076', '#interest', '#admiration', 'positive', '0.045', 'head_guy', 'boss', 'officer', 'leader', 'supervisor']
senticnet['marketing_guru'] = ['0.534', '-0.69', '0', '0.724', '#surprise', '#admiration', 'positive', '0.65', 'knocker', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['marketing_manager'] = ['0.046', '0.116', '-0.05', '0.076', '#interest', '#admiration', 'positive', '0.045', 'head_guy', 'boss', 'officer', 'leader', 'supervisor']
senticnet['marketing_ploy'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'intend', 'mimic', 'imitation', 'copy', 'heap']
senticnet['marketing_savvy'] = ['0.989', '-0.80', '0', '0.569', '#joy', '#surprise', 'positive', '0.788', 'strength', 'tolerance', 'stamina', 'endurance', 'sufferance']
senticnet['marketing_whiz'] = ['0.534', '-0.69', '0', '0.724', '#surprise', '#admiration', 'positive', '0.65', 'knocker', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['marketplace'] = ['0.838', '0.75', '0', '0.708', '#joy', '#interest', 'positive', '0.765', 'festival', 'party', 'banquet', 'big_event', 'rock_concert']
senticnet['markhor'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'pasang', 'wild_goat', 'goat', 'ibex']
senticnet['marking'] = ['-0.91', '0', '0.796', '-0.87', '#sadness', '#disgust', 'negative', '-0.86', 'hatch', 'pattern', 'crosshatch', 'hachure', 'shading']
senticnet['marking_pen'] = ['0.022', '0', '0.015', '0.096', '#joy', '#admiration', 'positive', '0.034', 'pencil', 'pen', 'highlighter', 'pencil_sharpener', 'write_instrument']
senticnet['markov_property'] = ['-0.75', '0', '0.679', '-0.77', '#sadness', '#disgust', 'negative', '-0.73', 'unvariedness', 'changelessness', 'unchangeability', 'unchangeableness', 'unchangingness']
senticnet['markovian'] = ['-0.89', '0', '0.972', '-0.50', '#sadness', '#anger', 'negative', '-0.79', 'unsteadiness', 'haphazardness', 'unregularity', 'variability', 'fitfulness']
senticnet['marksman'] = ['0.757', '-0.50', '0', '0.861', '#joy', '#admiration', 'positive', '0.708', 'rifleman', 'deadeye', 'sniper', 'shot', 'shooter']
senticnet['marksmanship'] = ['0.93', '-0.95', '0', '0.909', '#joy', '#surprise', 'positive', '0.93', 'acquirement', 'skill', 'accomplishment', 'acquisition', 'attainment']
senticnet['marksmanship_unit'] = ['0.93', '-0.95', '0', '0.909', '#joy', '#surprise', 'positive', '0.93', 'acquirement', 'skill', 'accomplishment', 'acquisition', 'attainment']
senticnet['marksmen'] = ['0.93', '-0.95', '0', '0.909', '#joy', '#surprise', 'positive', '0.93', 'acquirement', 'skill', 'accomplishment', 'acquisition', 'attainment']
senticnet['marktplatz'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'city_hall', 'courthouse', 'subway_station', 'public_place', 'bus_station']
senticnet['marky_mark'] = ['-0.19', '0', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.15', 'bad_child', 'misbehavior', 'bad_kid', 'bad_action', 'difficulty']
senticnet['marlin_equity'] = ['0.841', '0.721', '0', '0.74', '#joy', '#admiration', 'positive', '0.767', 'good_manner', 'two_people', 'love', 'guardian', 'guy']
senticnet['marlinspike_seamanship'] = ['0.764', '-0.96', '0', '0.883', '#surprise', '#admiration', 'positive', '0.87', 'boatmanship', 'acquirement', 'skill', 'accomplishment', 'acquisition']
senticnet['marmite'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'cookie', 'bagel', 'biscuit', 'custard', 'muffin']
senticnet['marmoset'] = ['0.921', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.825', 'monkey', 'fiance', 'lemur', 'beaver', 'koala']
senticnet['maronite_catholic_eparchy'] = ['0.8', '-0.84', '0', '0.868', '#surprise', '#admiration', 'positive', '0.837', 'commonwealth', 'province', 'administrative_district', 'administrative_division', 'territorial_division']
senticnet['maroon'] = ['-0.02', '0', '0.035', '-0.07', '#anger', '#disgust', 'negative', '-0.04', 'color', 'dark_color', 'warm_color', 'secondary_color', 'color_rainbow']
senticnet['marque_notamment'] = ['0', '0.112', '-0.04', '0.099', '#interest', '#admiration', 'positive', '0.054', 'profession', 'career', 'vocation', 'important_job', 'laborer']
senticnet['marquessate'] = ['0.975', '-0.78', '0', '0.969', '#joy', '#admiration', 'positive', '0.911', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['marquetry'] = ['0.876', '-0.76', '0', '0.963', '#joy', '#admiration', 'positive', '0.867', 'housecraft', 'tradecraft', 'watercraft', 'acquirement', 'skill']
senticnet['marquette_tribune'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['marquisate'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'overlord', 'lord']
senticnet['marrano'] = ['0', '-0.00', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'hot_chick', 'atheist', 'computer_user', 'fastener_binder', 'monitor']
senticnet['marred'] = ['-0.85', '0', '0.801', '-0.86', '#sadness', '#disgust', 'negative', '-0.84', 'scarred', 'blemished', 'flyblown', 'acned', 'pimpled']
senticnet['marred_by'] = ['0', '0', '-0.91', '-0.73', '#fear', '#disgust', 'negative', '-0.82', 'overabundant', 'prevailing', 'plethoric', 'predominant', 'dominant']
senticnet['marriage'] = ['0.812', '0.982', '0', '0.777', '#joy', '#interest', 'positive', '0.857', 'marry', 'marry_person', 'affair', 'infidelity', 'vow']
senticnet['marriage_ceremony'] = ['0', '0.819', '0', '0.987', '#interest', '#admiration', 'positive', '0.903', 'celebrate', 'wedding_ceremony', 'propose_marriage', 'engage', 'engagement']
senticnet['marriage_certificate'] = ['0.931', '-0.52', '0', '0.67', '#joy', '#admiration', 'positive', '0.709', 'document', 'certificate_of_incorporation', 'credential', 'diploma', 'sheepskin']
senticnet['marriage_license'] = ['0.931', '-0.52', '0', '0.67', '#joy', '#admiration', 'positive', '0.709', 'document', 'certificate_of_incorporation', 'credential', 'diploma', 'sheepskin']
senticnet['marriage_of_convenience'] = ['0.612', '-0.88', '0', '0.526', '#joy', '#surprise', 'positive', '0.674', 'marriage', 'matrimony', 'union', 'spousal_relationship', 'wedlock']
senticnet['marriage_vow'] = ['0.088', '0.317', '-0.29', '0.379', '#interest', '#admiration', 'positive', '0.122', 'oath', 'commitment', 'marriage', 'obedience', 'enter_contract']
senticnet['married'] = ['0.94', '0', '0', '0.949', '#joy', '#admiration', 'positive', '0.944', 'marry', 'hook_up_with', 'love', 'conjoin', 'get_hitched_with']
senticnet['married_couple'] = ['0', '0.713', '0', '0.991', '#interest', '#admiration', 'positive', '0.852', 'intelligent_person', 'grown_up', 'love', 'couple', 'offer_solace']
senticnet['married_couple_filing_jointly'] = ['0.763', '-0.67', '0', '0.945', '#joy', '#admiration', 'positive', '0.795', 'set', 'income_bracket', 'bracket', 'income', 'price_bracket']
senticnet['married_person'] = ['0.905', '-0.51', '0', '0.913', '#joy', '#admiration', 'positive', '0.778', 'spouse', 'partner', 'helpmate', 'mate', 'better_half']
senticnet['marriott_wardman_park'] = ['-0.79', '0', '0.948', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['marrow'] = ['0', '0', '0.99', '-0.74', '#anger', '#disgust', 'negative', '-0.86', 'pith', 'kernel', 'centre', 'core', 'substance']
senticnet['marrow_transplantation'] = ['0.79', '-0.62', '0', '0.674', '#joy', '#admiration', 'positive', '0.695', 'add', 'contribute', 'lend', 'breathe', 'impart']
senticnet['marry'] = ['0.916', '0.9', '0', '0.933', '#joy', '#admiration', 'positive', '0.916', 'marriage', 'infidelity', 'love', 'get_married', 'trust']
senticnet['marry_kid'] = ['0', '0', '-0.09', '-0.11', '#fear', '#disgust', 'negative', '-0.10', 'get_trouble', 'produce_child', 'arousal', 'get_married', 'become_pregnant']
senticnet['marry_man'] = ['0.881', '0.715', '0', '0', '#joy', '#interest', 'positive', '0.798', 'service_car', 'date_woman', 'oil_hinge', 'man_post', 'date_man']
senticnet['marry_person'] = ['0.916', '0.9', '0', '0.933', '#joy', '#admiration', 'positive', '0.916', 'marry', 'marriage', 'marriage_ceremony', 'wedding_ceremony', 'propose_marriage']
senticnet['mars_atmosphere'] = ['0.095', '0', '-0.04', '0.062', '#joy', '#admiration', 'positive', '0.037', 'photographer', 'water_spray', 'moisture', 'rise_water', 'droplet']
senticnet['mars_exploration_rover'] = ['0.052', '0.119', '0', '0.141', '#interest', '#admiration', 'positive', '0.104', 'neigh', 'domestic_animal', 'animal_type', 'animal', 'home_animal']
senticnet['mars_north_pole'] = ['0.884', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.919', 'ufo', 'milky', 'space_junk', 'galaxy', 'solar_system']
senticnet['mars_odyssey_spacecraft'] = ['0', '0.825', '0', '0', '#interest', '#interest', 'positive', '0.825', 'query', 'interrogation', 'inquire', 'response', 'solution']
senticnet['mars_orbit'] = ['0.989', '-0.90', '0', '0.876', '#joy', '#surprise', 'positive', '0.923', 'reach', 'range', 'compass', 'scope', 'confines']
senticnet['mars_pathfinder'] = ['0.052', '0.119', '0', '0.141', '#interest', '#admiration', 'positive', '0.104', 'neigh', 'domestic_animal', 'animal_type', 'animal', 'home_animal']
senticnet['mars_probe'] = ['0.899', '0.803', '0', '0', '#joy', '#interest', 'positive', '0.851', 'ufo', 'milky', 'martian', 'galaxy', 'solar_system']
senticnet['mars_reconnaissance_orbiter'] = ['0.052', '0.119', '0', '0.141', '#interest', '#admiration', 'positive', '0.104', 'neigh', 'domestic_animal', 'animal_type', 'animal', 'home_animal']
senticnet['mars_rover'] = ['0.052', '0.119', '0', '0.141', '#interest', '#admiration', 'positive', '0.104', 'neigh', 'domestic_animal', 'animal_type', 'animal', 'home_animal']
senticnet['mars_rover_opportunity'] = ['0.052', '0.119', '0', '0.141', '#interest', '#admiration', 'positive', '0.104', 'neigh', 'domestic_animal', 'animal_type', 'animal', 'home_animal']
senticnet['marsh'] = ['0.85', '0.96', '0', '0', '#joy', '#interest', 'positive', '0.905', 'flatness', 'boring', 'flat_tire', 'flat_foot', 'small_breast']
senticnet['marsh_cinquefoil'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['marsh_creek'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['marsh_cress'] = ['0.758', '-0.97', '0', '0.851', '#surprise', '#admiration', 'positive', '0.861', 'watercress', 'yellow_watercress', 'mountain_watercress', 'common_watercress', 'great_yellowcress']
senticnet['marsh_harrier'] = ['0.797', '-0.71', '0', '0.736', '#joy', '#admiration', 'positive', '0.751', 'wood_ibis', 'wood_stork', 'ibis', 'wading_bird', 'wader']
senticnet['marsh_marigold'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['marshal'] = ['0.74', '-0.53', '0', '0.641', '#joy', '#admiration', 'positive', '0.64', 'air_marshal', 'sky_marshal', 'lawman', 'law_officer', 'peace_officer']
senticnet['marshal_admiral'] = ['0.688', '-0.90', '0', '0.877', '#surprise', '#admiration', 'positive', '0.824', 'flag_officer', 'rear_admiral', 'admiral', 'full_admiral', 'vice_admiral']
senticnet['marshalship'] = ['0.664', '-0.86', '0', '0.975', '#surprise', '#admiration', 'positive', '0.835', 'spot', 'berth', 'position', 'post', 'office']
senticnet['marshland'] = ['0.85', '0.96', '0', '0', '#joy', '#interest', 'positive', '0.905', 'flatness', 'boring', 'flat_tire', 'flat_foot', 'small_breast']
senticnet['marshmallow'] = ['0.023', '0.141', '-0.13', '0.174', '#interest', '#admiration', 'positive', '0.052', 'candy', 'push_wind', 'exhalation', 'breaout', 'mouth_action']
senticnet['marshy'] = ['0', '0', '-0.97', '-0.78', '#fear', '#disgust', 'negative', '-0.88', 'soggy', 'mucky', 'muddy', 'boggy', 'miry']
senticnet['marshy_grassland'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'biome', 'timberland']
senticnet['marshy_land'] = ['0.85', '0.96', '0', '0', '#joy', '#interest', 'positive', '0.905', 'flatness', 'boring', 'flat_tire', 'flat_foot', 'small_breast']
senticnet['marshy_terrain'] = ['0.85', '0.96', '0', '0', '#joy', '#interest', 'positive', '0.905', 'flatness', 'boring', 'flat_tire', 'flat_foot', 'small_breast']
senticnet['marsupialization'] = ['-0.56', '0', '0.806', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['marsupium'] = ['-0.87', '0', '0.983', '-0.91', '#anger', '#disgust', 'negative', '-0.92', 'thorn', 'sticker', 'pricker', 'prickle', 'spikelet']
senticnet['mart'] = ['0.041', '0.098', '0', '0', '#joy', '#interest', 'positive', '0.07', 'utopia', 'java', 'memorial', 'nowhere', 'coffee']
senticnet['martell_webster'] = ['0.556', '-0.79', '0', '0.755', '#surprise', '#admiration', 'positive', '0.701', 'lush', 'alcoholic', 'alky', 'dipsomaniac', 'rummy']
senticnet['martensitic_transformation'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'constancy', 'monotony', 'invariance', 'changelessness', 'unchangeability']
senticnet['martes_pennanti'] = ['0.818', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.895', 'black_cat', 'fisher', 'fisherman', 'fisher_cat', 'pekan']
senticnet['martial'] = ['-0.77', '0', '0.963', '0', '#sadness', '#anger', 'negative', '-0.86', 'amazonian', 'hawkish', 'warlike', 'bellicose', 'belligerent']
senticnet['martial_art'] = ['0.789', '0.843', '0', '0', '#joy', '#interest', 'positive', '0.816', 'gymnastics', 'judo', 'jump_around', 'go_underwater', 'athletics']
senticnet['martial_art_expert'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'yeoman', 'outrider', 'yeoman_of_guard', 'beefeater', 'defender']
senticnet['martial_artist'] = ['0.021', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'plastic_bottle', 'party', 'use_party', 'lighter_air', 'compress_air']
senticnet['martial_eagle'] = ['-0.53', '0', '0.648', '-0.70', '#anger', '#disgust', 'negative', '-0.63', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['martial_spirit'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'motherland', 'homeland']
senticnet['martian'] = ['0.884', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.919', 'ufo', 'milky', 'space_junk', 'galaxy', 'solar_system']
senticnet['martian_atmosphere'] = ['0.095', '0', '-0.04', '0.062', '#joy', '#admiration', 'positive', '0.037', 'photographer', 'water_spray', 'moisture', 'rise_water', 'droplet']
senticnet['martian_civilization'] = ['0.884', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.919', 'ufo', 'milky', 'space_junk', 'galaxy', 'solar_system']
senticnet['martian_climate'] = ['0.884', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.919', 'ufo', 'milky', 'space_junk', 'galaxy', 'solar_system']
senticnet['martian_equator'] = ['0.884', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.919', 'ufo', 'milky', 'space_junk', 'galaxy', 'solar_system']
senticnet['martian_landscape'] = ['0.884', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.919', 'ufo', 'milky', 'space_junk', 'galaxy', 'solar_system']
senticnet['martian_moon'] = ['0.884', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.919', 'ufo', 'milky', 'space_junk', 'galaxy', 'solar_system']
senticnet['martian_north_pole'] = ['0.884', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.919', 'ufo', 'milky', 'space_junk', 'galaxy', 'solar_system']
senticnet['martian_rock'] = ['0.884', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.919', 'ufo', 'milky', 'space_junk', 'galaxy', 'solar_system']
senticnet['martian_soil'] = ['0.884', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.919', 'ufo', 'milky', 'space_junk', 'galaxy', 'solar_system']
senticnet['martian_surface'] = ['0.884', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.919', 'ufo', 'milky', 'space_junk', 'galaxy', 'solar_system']
senticnet['martian_terrain'] = ['0.884', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.919', 'ufo', 'milky', 'space_junk', 'galaxy', 'solar_system']
senticnet['martian_winter'] = ['0.052', '0.119', '0', '0.141', '#interest', '#admiration', 'positive', '0.104', 'neigh', 'domestic_animal', 'animal_type', 'animal', 'home_animal']
senticnet['martingale'] = ['-0.78', '0', '0.944', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'loser', 'speculator', 'mortal', 'better', 'gambler']
senticnet['martini'] = ['0.736', '0.957', '0', '0', '#joy', '#interest', 'positive', '0.847', 'liquor_store', 'tavern', 'drinking_place', 'mix_drink', 'bar']
senticnet['martini_bar'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'buy_sandwich', 'lunch', 'eat_food', 'buy_food', 'finish_job']
senticnet['martinsyde'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'cheapjack', 'pitchman']
senticnet['martoma'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'busy_bee', 'gopher', 'actor', 'doer', 'worker']
senticnet['marty_crequer'] = ['0.997', '-0.84', '0', '0.927', '#joy', '#admiration', 'positive', '0.923', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['marty_stuart'] = ['-0.04', '-0.04', '-0.04', '-0.04', '#fear', '#disgust', 'negative', '-0.02', 'music_shop', 'music_store', 'musical_instrument_store', 'string_quartet', 'high_school_band']
senticnet['martyr'] = ['-0.79', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.82', 'martyrdom', 'martyrize', 'crucifixion', 'martyred', 'persecution']
senticnet['martyr_saint'] = ['0', '0.248', '-0.08', '0.173', '#interest', '#admiration', 'positive', '0.113', 'god', 'minister', 'evangelist', 'rector', 'goddess']
senticnet['martyrdom'] = ['-0.78', '0', '-0.84', '0', '#sadness', '#fear', 'negative', '-0.81', 'martyr', 'martyrize', 'crucifixion', 'martyred', 'persecution']
senticnet['martyred'] = ['-0.79', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.82', 'martyr', 'martyrize', 'crucifixion', 'martyrdom', 'persecution']
senticnet['martyrize'] = ['-0.78', '0', '-0.84', '0', '#sadness', '#fear', 'negative', '-0.81', 'martyr', 'martyrdom', 'crucifixion', 'martyred', 'persecution']
senticnet['martyrology'] = ['0', '0.105', '0', '0.084', '#interest', '#admiration', 'positive', '0.095', 'archive', 'print_medium', 'quiet_place', 'register', 'media']
senticnet['marubeni_corporation'] = ['0.59', '-0.75', '0', '0.543', '#joy', '#surprise', 'positive', '0.63', 'venture', 'sally', 'labor', 'cause', 'risk']
senticnet['marumaro'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'hornet', 'vespid', 'vespid_wasp']
senticnet['marvel'] = ['0.836', '0.728', '0', '0.911', '#joy', '#admiration', 'positive', '0.825', 'amazingly', 'surprisedly', 'astonishingly', 'surprised', 'surprising']
senticnet['marvel_comic'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'encyclopaedia', 'child_story', 'comic', 'disney_cartoon']
senticnet['marvel_zombie_universe'] = ['-0.03', '-0.01', '-0.16', '-0.06', '#fear', '#disgust', 'negative', '-0.06', 'sticky_floor', 'theater_box', 'projector', 'row_seat', 'theater_seat']
senticnet['marveled_at'] = ['0.044', '-0.65', '0', '0.364', '#surprise', '#admiration', 'positive', '0.356', 'awestricken', 'awed', 'wondrous', 'wondrously', 'terrific']
senticnet['marveling_at'] = ['0.044', '-0.65', '0', '0.364', '#surprise', '#admiration', 'positive', '0.356', 'awestricken', 'awed', 'wondrous', 'wondrously', 'terrific']
senticnet['marvelled_at'] = ['0', '0', '0', '0.705', '#admiration', '#admiration', 'positive', '0.705', 'positive', 'confidence', 'sympathetic', 'regard', 'fellow_feeling']
senticnet['marvelled_greatly'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitentially', 'penitent', 'penitence', 'repentant']
senticnet['marvelling_at'] = ['0.044', '-0.65', '0', '0.364', '#surprise', '#admiration', 'positive', '0.356', 'awestricken', 'awed', 'wondrous', 'wondrously', 'terrific']
senticnet['marvellously'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'terrific', 'wondrously', 'tremendous', 'marvelous', 'wondrous']
senticnet['marvelous'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'terrific', 'wondrously', 'tremendous', 'awesome', 'wondrous']
senticnet['marvelously'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'terrific', 'wondrously', 'tremendous', 'marvelous', 'wondrous']
senticnet['marxian'] = ['0.127', '0.069', '0.029', '0.16', '#joy', '#admiration', 'positive', '0.082', 'basic_human_need', 'construct', 'set_down', 'make', 'build_process']
senticnet['marxism'] = ['0', '0', '0', '0.986', '#admiration', '#admiration', 'positive', '0.986', 'idea', 'decision', 'consider', 'polarize', 'make_right_decision']
senticnet['marxist'] = ['0', '0', '0', '0.986', '#admiration', '#admiration', 'positive', '0.986', 'idea', 'decision', 'consider', 'polarize', 'make_right_decision']
senticnet['marxist_doctrine'] = ['0.127', '0.069', '0.029', '0.16', '#joy', '#admiration', 'positive', '0.082', 'basic_human_need', 'construct', 'set_down', 'make', 'build_process']
senticnet['marxist_ideology'] = ['0', '0', '0', '0.986', '#admiration', '#admiration', 'positive', '0.986', 'idea', 'decision', 'consider', 'polarize', 'make_right_decision']
senticnet['marxist_leninist'] = ['0', '0', '-0.70', '-0.83', '#fear', '#disgust', 'negative', '-0.76', 'settle_disagreement', 'hitler', 'republican', 'conservative', 'unchanging']
senticnet['marysville'] = ['-0.06', '0.21', '0', '0.076', '#interest', '#admiration', 'positive', '0.074', 'new_think', 'figure_out', 'brain_activity', 'idea', 'come_up_idea']
senticnet['marzipan'] = ['0.059', '-0.02', '0.025', '0.109', '#joy', '#admiration', 'positive', '0.043', 'tasty', 'bake_treat', 'bake_goods', 'bake_good', 'loaf']
senticnet['masaaki_mochizuki'] = ['-0.57', '0', '0.571', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['masai_mara_game'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['masai_mara_national'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['masamune'] = ['-0.04', '0.138', '-0.08', '0.111', '#interest', '#admiration', 'positive', '0.031', 'geometry', 'craft', 'blade_geometry', 'blade', 'collect_item']
senticnet['masao_maruyama'] = ['-0.67', '0', '0.931', '-0.77', '#anger', '#disgust', 'negative', '-0.79', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['masato_tanaka'] = ['-0.57', '0', '0.571', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['masayuki_kojima'] = ['-0.67', '0', '0.931', '-0.77', '#anger', '#disgust', 'negative', '-0.79', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['mascara'] = ['0.772', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.77', 'eyeliner', 'lash', 'lipstick', 'eyeshadow', 'eyelash']
senticnet['mascarpone'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'grapefruit_peel', 'lemon_peel', 'orange_peel', 'candied_citrus_peel', 'succade']
senticnet['mascarpone_cheese'] = ['0.028', '0.138', '-0.04', '0.215', '#interest', '#admiration', 'positive', '0.083', 'water', 'dense', 'thick', 'milk', 'dense_water']
senticnet['mascot'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'unofficial', 'informal', 'loose', 'drumhead']
senticnet['masculine'] = ['0.074', '0.151', '-0.09', '0.119', '#interest', '#admiration', 'positive', '0.062', 'small_man', 'male_person', 'male_child', 'gender', 'man_child']
senticnet['masculine_feminine'] = ['0.911', '0', '0', '0.85', '#joy', '#admiration', 'positive', '0.881', 'sophisticated', 'lovely', 'elegant', 'contrary', 'common_man']
senticnet['masculine_gender'] = ['0.074', '0.151', '-0.09', '0.119', '#interest', '#admiration', 'positive', '0.062', 'small_man', 'male_person', 'male_child', 'gender', 'man_child']
senticnet['masculine_identity'] = ['0.074', '0.151', '-0.09', '0.119', '#interest', '#admiration', 'positive', '0.062', 'small_man', 'male_person', 'male_child', 'gender', 'man_child']
senticnet['masculine_noun'] = ['0.732', '0.8', '0', '0', '#joy', '#interest', 'positive', '0.766', 'fast', 'place', 'substance', 'substantive', 'person']
senticnet['masculinist'] = ['0.585', '-0.7', '0', '0.586', '#surprise', '#admiration', 'positive', '0.624', 'jingoistic', 'ultranationalistic', 'superpatriotic', 'nationalistic', 'patriotic']
senticnet['masculinity'] = ['0.911', '0', '0', '0.85', '#joy', '#admiration', 'positive', '0.881', 'sophisticated', 'lovely', 'elegant', 'contrary', 'common_man']
senticnet['maserati'] = ['0', '0.886', '0', '0.899', '#interest', '#admiration', 'positive', '0.893', 'rich', 'well_off', 'wealthy', 'financially_stable', 'ferrari']
senticnet['maserati_quattroporte'] = ['0.755', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.728', 'high_class', 'expensive_car', 'ferrari', 'fancy_car', 'sports_car']
senticnet['mash'] = ['-0.95', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.96', 'mashed', 'mashing', 'potato', 'masher', 'mix']
senticnet['mash_potato'] = ['0.084', '-0.05', '0', '-0.04', '#joy', '#surprise', 'positive', '0.031', 'entree', 'feast', 'satiation', 'breakfast', 'supper']
senticnet['mash_tun'] = ['0.161', '-0.14', '0.031', '-0.10', '#joy', '#surprise', 'positive', '0.043', 'barrel', 'reception', 'champagne', 'beer', 'wine']
senticnet['mashed'] = ['-0.95', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.96', 'mash', 'mashing', 'potato', 'masher', 'mix']
senticnet['mashed_potato'] = ['0', '-0.11', '0', '0', '#surprise', '#surprise', 'positive', '0.113', 'cake', 'cookie', 'mousse', 'doughnut', 'brownie']
senticnet['masher'] = ['-0.95', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.96', 'mashed', 'mashing', 'potato', 'mash', 'mix']
senticnet['mashing'] = ['-0.96', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.97', 'mashed', 'mash', 'potato', 'masher', 'mix']
senticnet['masjid'] = ['0', '0.224', '0', '0.197', '#interest', '#admiration', 'positive', '0.211', 'church', 'temple', 'cathedral', 'chapel', 'confession']
senticnet['masjid_mosque'] = ['0', '0.224', '0', '0.197', '#interest', '#admiration', 'positive', '0.211', 'church', 'temple', 'cathedral', 'chapel', 'confession']
senticnet['mask'] = ['0.108', '-0.04', '0.043', '0.065', '#joy', '#admiration', 'positive', '0.043', 'sunglasses', 'glass', 'carnival_mask', 'drink_cup', 'focus_light']
senticnet['mask_thumbnail'] = ['0.055', '0.117', '0', '0.133', '#interest', '#admiration', 'positive', '0.102', 'graphical_representation', 'pencil_pen', 'note_pad', 'marker', 'stationery']
senticnet['mask_ventilation'] = ['-0.57', '0', '0.737', '-0.85', '#anger', '#disgust', 'negative', '-0.72', 'emergency_procedure', 'emergency', 'external_respiration', 'respiration', 'ventilation']
senticnet['maskaev'] = ['-0.94', '0', '0.863', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'poke', 'clout', 'lick', 'punch', 'biff']
senticnet['masked_hero'] = ['-0.69', '0', '0.756', '-0.99', '#anger', '#disgust', 'negative', '-0.81', 'waster', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['masked_hunter'] = ['-0.77', '0', '0.576', '-0.92', '#sadness', '#disgust', 'negative', '-0.75', 'insect', 'firebug', 'water_bug', 'assassin_bug', 'heteropterous_insect']
senticnet['masked_killer'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'addicted', 'obsessed_love', 'addiction', 'obsession', 'weird']
senticnet['masked_magician'] = ['0.818', '0.913', '0', '0.825', '#interest', '#admiration', 'positive', '0.852', 'greeting', 'blackjack', 'pass_card', 'credit', 'play_blackjack']
senticnet['masked_wrestler'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'find_out', 'dig_out', 'unearth', 'become_aware', 'dish_out']
senticnet['maskell'] = ['-0.20', '-0.10', '0.07', '-0.24', '#sadness', '#disgust', 'negative', '-0.10', 'flipper', 'sea_life', 'seafood', 'swim_animal', 'fillet']
senticnet['maskilim'] = ['0', '-0.00', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'hot_chick', 'atheist', 'computer_user', 'fastener_binder', 'monitor']
senticnet['masking'] = ['-0.58', '0', '0.568', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'aesthesis', 'taste', 'threshold', 'perception', 'limen']
senticnet['masking_tape'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'gift_wrap', 'kraft', 'kraft_paper', 'wrapping_paper', 'paper']
senticnet['maskinong'] = ['-0.71', '0', '0.917', '-0.51', '#sadness', '#anger', 'negative', '-0.71', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['masochism'] = ['-0.62', '0', '0.515', '-0.63', '#sadness', '#disgust', 'negative', '-0.59', 'immodesty', 'perversion', 'paraphilia', 'sexual_perversion', 'fetishism']
senticnet['masochistic'] = ['-0.62', '0', '0.515', '-0.63', '#sadness', '#disgust', 'negative', '-0.59', 'immodesty', 'perversion', 'paraphilia', 'sexual_perversion', 'fetishism']
senticnet['mason_bee'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'lair', 'habitat', 'animal_home']
senticnet['mason_hamlin'] = ['-0.75', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.80', 'high-strung', 'uptight', 'restive', 'nervy', 'jumpy']
senticnet['mason_jar'] = ['0.035', '-0.01', '0.009', '0.063', '#joy', '#admiration', 'positive', '0.026', 'container', 'carton', 'contain', 'hold_cupcake', 'store_dish']
senticnet['masonic_funeral'] = ['-0.98', '0', '0.664', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'unfortunate', 'unfortunate_person', 'weeper', 'griever', 'bearer']
senticnet['masonic_hall'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'city_hall', 'courthouse', 'subway_station', 'public_place', 'bus_station']
senticnet['masonic_order'] = ['0.605', '0.647', '-0.26', '0.323', '#joy', '#interest', 'positive', '0.327', 'friendly', 'brotherly', 'amicableness', 'amicably', 'amicable']
senticnet['masonry'] = ['0', '0.936', '0', '0.868', '#interest', '#admiration', 'positive', '0.902', 'carpentry', 'plastering', 'bricklayer', 'bricklaying', 'tiling']
senticnet['masonry_building'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'adobe', 'adobe_brick', 'clinker', 'brick', 'firebrick']
senticnet['masonry_structure'] = ['0.026', '0.219', '0', '0.16', '#interest', '#admiration', 'positive', '0.135', 'large_build', 'palace', 'high_rise', 'structure', 'fortification']
senticnet['masonry_tower'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'castle', 'bell_tower', 'spire', 'steeple', 'religious_place']
senticnet['masoretic'] = ['-0.22', '0', '-0.09', '-0.16', '#sadness', '#disgust', 'negative', '-0.16', 'inequality', 'reject', 'discard', 'difference', 'gap']
senticnet['masquerade'] = ['-0.76', '0', '0.672', '-0.88', '#sadness', '#disgust', 'negative', '-0.77', 'attire', 'garb', 'dress', 'fancy_dress', 'costume']
senticnet['mass'] = ['0.029', '0.178', '0', '0.204', '#interest', '#admiration', 'positive', '0.137', 'volume_measurement', 'tons', 'large_amount', 'size', 'heavy_weight']
senticnet['mass_brawl'] = ['-0.89', '0', '0.933', '-0.93', '#anger', '#disgust', 'negative', '-0.92', 'bullyboy', 'muscleman', 'muscle', 'skinhead', 'skin']
senticnet['mass_casualty_incident'] = ['0', '0', '-0.82', '0', '#fear', '#fear', 'negative', '-0.82', 'sickness', 'polio', 'malady', 'ailment', 'treatment']
senticnet['mass_civil_disobedience'] = ['-0.98', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.93', 'foment', 'agitated', 'tumult', 'stir_up', 'earnestness']
senticnet['mass_communication'] = ['-0.02', '0', '-0.04', '-0.03', '#fear', '#disgust', 'negative', '-0.03', 'studio', 'art_piece', 'art_gallery', 'artist', 'museum']
senticnet['mass_consumerism'] = ['0', '0', '-0.74', '-0.93', '#fear', '#disgust', 'negative', '-0.84', 'generosity', 'oral_sex', 'win_lottery', 'gift_wrap', 'give_away']
senticnet['mass_deacidification'] = ['-0.97', '0', '0.885', '-0.97', '#sadness', '#disgust', 'negative', '-0.94', 'protection', 'conservation', 'environmentalism', 'reservation', 'immobilization']
senticnet['mass_density'] = ['-0.03', '0.178', '0', '0.168', '#interest', '#admiration', 'positive', '0.104', 'speed', 'fast', 'fast_and_furious', 'speedy', 'fleet']
senticnet['mass_deportation'] = ['-0.08', '0', '-0.15', '-0.20', '#fear', '#disgust', 'negative', '-0.14', 'casualty', 'reaper', 'wound', 'cause_death', 'gunshot']
senticnet['mass_evacuation'] = ['-0.17', '0', '-0.25', '-0.13', '#sadness', '#fear', 'negative', '-0.18', 'destruction', 'bigot', 'kidnap', 'torture', 'censorship']
senticnet['mass_extermination'] = ['-0.08', '0', '-0.15', '-0.20', '#fear', '#disgust', 'negative', '-0.14', 'casualty', 'reaper', 'wound', 'cause_death', 'gunshot']
senticnet['mass_flux'] = ['-0.03', '0.178', '0', '0.168', '#interest', '#admiration', 'positive', '0.104', 'speed', 'fast', 'fast_and_furious', 'speedy', 'fleet']
senticnet['mass_fraction'] = ['0.728', '0.977', '0', '0.939', '#interest', '#admiration', 'positive', '0.881', 'air_pressure', 'altitude', 'air', 'heavy_air', 'air_density']
senticnet['mass_hysteria'] = ['-0.35', '0', '-0.23', '-0.11', '#sadness', '#fear', 'negative', '-0.23', 'get_erection', 'wet_clothes', 'pain_suffer', 'obsessive_compulsive_disorder', 'catch_cold']
senticnet['mass_immunization'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'medical_therapy', 'vaccine', 'doctor', 'medicine', 'hospital']
senticnet['mass_incarceration'] = ['-0.43', '0.107', '-0.42', '-0.03', '#sadness', '#fear', 'negative', '-0.19', 'send_to_jail', 'go_jail', 'jail_time', 'go_to_prison', 'injustice']
senticnet['mass_measure'] = ['0', '0.152', '0.074', '0.207', '#interest', '#admiration', 'positive', '0.095', 'measure', 'size', 'measure_unit', 'measure_pound', 'heaviness']
senticnet['mass_media'] = ['0', '0', '-0.74', '-0.93', '#fear', '#disgust', 'negative', '-0.84', 'generosity', 'oral_sex', 'win_lottery', 'gift_wrap', 'give_away']
senticnet['mass_murder'] = ['-0.58', '0', '0.687', '-0.77', '#anger', '#disgust', 'negative', '-0.68', 'murder', 'slaying', 'bloodbath', 'bloodshed', 'bloodletting']
senticnet['mass_person'] = ['0.086', '0.077', '0.026', '0.117', '#joy', '#admiration', 'positive', '0.064', 'big_group', 'group_of_people', 'large_number', 'bunch_person', 'large_group']
senticnet['mass_production'] = ['0.086', '0.077', '0.026', '0.117', '#joy', '#admiration', 'positive', '0.064', 'production', 'business', 'company', 'employment', 'occupation']
senticnet['mass_rally'] = ['0.669', '-0.58', '0', '0.803', '#joy', '#admiration', 'positive', '0.685', 'pep_rally', 'revival', 'revival_meeting', 'gathering', 'assemblage']
senticnet['mass_slaughter'] = ['-0.67', '0', '0.938', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'massacre', 'mass_murder', 'carnage', 'slaughter', 'butchery']
senticnet['mass_spectrometry'] = ['0.953', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.897', 'chemistry', 'frontier_research', 'frontier', 'chemist', 'compound']
senticnet['mass_transit'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'seeker', 'chairperson']
senticnet['mass_unemployment'] = ['0', '0', '0.948', '-0.96', '#anger', '#disgust', 'negative', '-0.95', 'work_much', 'lack_money', 'wage_slavery', 'buy_tool', 'get_paycheck']
senticnet['mass_vaccination'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'medical_therapy', 'vaccine', 'doctor', 'medicine', 'hospital']
senticnet['massacre'] = ['-0.97', '0', '0.914', '-0.99', '#sadness', '#disgust', 'negative', '-0.96', 'murder', 'slaying', 'bloodbath', 'bloodshed', 'bloodletting']
senticnet['massage'] = ['0.862', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.899', 'sensual', 'hand_action', 'feel_action', 'touch', 'hand_movement']
senticnet['massage_parlor'] = ['0.754', '-0.82', '0', '0.947', '#surprise', '#admiration', 'positive', '0.841', 'whorehouse', 'brothel', 'bordello', 'bagnio', 'house_of_prostitution']
senticnet['massage_table'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'coach_seat', 'stool', 'seat']
senticnet['masseuse'] = ['0.99', '0', '0', '0.721', '#joy', '#admiration', 'positive', '0.855', 'hair', 'haircut', 'hairdresser_chair', 'scissor', 'hairstyle']
senticnet['massif'] = ['0', '0.772', '0', '0.753', '#interest', '#admiration', 'positive', '0.762', 'slope', 'natural_depression', 'wall', 'collectivization', 'formation']
senticnet['massimo_ranieri'] = ['0.107', '0.11', '-0.08', '0.21', '#interest', '#admiration', 'positive', '0.086', 'live_healthy_life', 'forever_young', 'live_forever', 'live_long', 'live_good_life']
senticnet['massive'] = ['0.039', '0', '0.012', '0.298', '#joy', '#admiration', 'positive', '0.108', 'wide', 'really_big', 'large', 'huge', 'big']
senticnet['massive_bailout'] = ['-0.12', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'escape', 'escape_situation', 'cancel_meeting', 'absent', 'cancel_appointment']
senticnet['massive_brawl'] = ['-0.99', '0', '0.603', '-0.82', '#sadness', '#disgust', 'negative', '-0.80', 'batrachomyomachia', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['massive_buildup'] = ['0.942', '-0.79', '0', '0.901', '#joy', '#admiration', 'positive', '0.88', 'backup', 'accretion', 'deposition', 'deposit', 'increase']
senticnet['massive_cleanup'] = ['0.935', '0', '0', '0', '#joy', '#joy', 'positive', '0.935', 'clean_up_kitchen', 'feel_dirty', 'remove_stain', 'stink', 'stink_mess']
senticnet['massive_deforestation'] = ['0', '-0.02', '0.085', '-0.09', '#anger', '#disgust', 'negative', '-0.05', 'tree_wood', 'forest', 'sideways', 'dead_tree', 'wood']
senticnet['massive_destruction'] = ['-0.87', '0', '0.889', '-0.62', '#sadness', '#anger', 'negative', '-0.79', 'ruination', 'ruin', 'deterioration', 'blight', 'impairment']
senticnet['massive_devastation'] = ['-0.87', '0', '0.889', '-0.62', '#sadness', '#anger', 'negative', '-0.79', 'ruination', 'ruin', 'deterioration', 'blight', 'impairment']
senticnet['massive_explosion'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'firework', 'bright_flash', 'nuclear_weapon', 'earthquake', 'nuclear_bomb']
senticnet['massive_fraud'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'mail_fraud', 'swindle', 'rig']
senticnet['massive_landslide'] = ['0', '-0.03', '-0.10', '-0.07', '#fear', '#disgust', 'negative', '-0.04', 'autumn', 'winter', 'skier', 'snow_fall', 'volcano']
senticnet['massive_overcapacity'] = ['0', '0.285', '-0.07', '0.163', '#interest', '#admiration', 'positive', '0.124', 'plus', 'importance', 'a_lot', 'bonus', 'extra']
senticnet['massive_overhaul'] = ['-0.98', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.92', 'thrilling', 'electrifying', 'sensational', 'sensationally', 'scandalmongering']
senticnet['massive_retaliation'] = ['-0.82', '0', '0.739', '-0.50', '#sadness', '#anger', 'negative', '-0.69', 'fake', 'juke', 'maneuver', 'manoeuvre', 'feint']
senticnet['massive_stroke'] = ['-0.73', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.79', 'die_painfully', 'arrest_police', 'get_accident', 'fall_head', 'skin_knee']
senticnet['massively'] = ['0', '-0.86', '0', '0.762', '#surprise', '#admiration', 'positive', '0.815', 'big', 'large', 'really_big', 'wide', 'tall']
senticnet['massoud_hossaini_afp_getty'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expansive', 'expansible', 'erectile', 'cavernous']
senticnet['massowah'] = ['-0.71', '0', '0.836', '0', '#sadness', '#anger', 'negative', '-0.77', 'awkward', 'unenviable', 'embarrassingly', 'embarrassing', 'sheepish']
senticnet['mast'] = ['0.857', '0.86', '0', '0', '#joy', '#interest', 'positive', '0.859', 'yacht', 'tree', 'dock', 'find_sea', 'pirate']
senticnet['mast_cell'] = ['-0.66', '0', '0.877', '-0.64', '#sadness', '#anger', 'negative', '-0.72', 'somatic_cell', 'mastocyte', 'labrocyte', 'target_cell', 'phagocyte']
senticnet['mast_cell_activation'] = ['-0.66', '0', '0.877', '-0.64', '#sadness', '#anger', 'negative', '-0.72', 'somatic_cell', 'mastocyte', 'labrocyte', 'target_cell', 'phagocyte']
senticnet['mast_cell_degranulation'] = ['-0.66', '0', '0.877', '-0.64', '#sadness', '#anger', 'negative', '-0.72', 'somatic_cell', 'mastocyte', 'labrocyte', 'target_cell', 'phagocyte']
senticnet['mastaba'] = ['-0.92', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.91', 'bury_body', 'coffin', 'headstone', 'morgue', 'tombstone']
senticnet['mastaba_tomb'] = ['-0.92', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.91', 'bury_body', 'coffin', 'headstone', 'morgue', 'tombstone']
senticnet['mastema'] = ['-0.52', '0', '0.701', '-0.50', '#sadness', '#anger', 'negative', '-0.57', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['master'] = ['0.036', '0.111', '-0.05', '0.054', '#interest', '#admiration', 'positive', '0.038', 'despot', 'supervisor', 'chief', 'commander', 'earl']
senticnet['master_ability'] = ['0.858', '0.793', '0', '0.785', '#joy', '#interest', 'positive', '0.812', 'love_pet', 'bicycle_school', 'succeed_school', 'solve_equation', 'master_skill']
senticnet['master_and_commander'] = ['0', '0.871', '0', '0.861', '#interest', '#admiration', 'positive', '0.866', 'role', 'alpha_male', 'dominant', 'dominant_role', 'sergeant']
senticnet['master_athletics_triple_jump'] = ['0.113', '0.126', '0', '0.11', '#joy', '#interest', 'positive', '0.116', 'file', 'pencil_pen', 'document', 'note_pad', 'scrap_paper']
senticnet['master_bathroom'] = ['0.716', '0.96', '0', '0', '#joy', '#interest', 'positive', '0.838', 'airline_seat', 'small_seat', 'beanbag_chair', 'hide-a-bed_sofa', 'chair']
senticnet['master_bedroom'] = ['0', '-0.02', '-0.08', '-0.05', '#fear', '#disgust', 'negative', '-0.03', 'mattress', 'sleep_furniture', 'cradle', 'small_bed', 'pillow']
senticnet['master_bedroom_suite'] = ['0', '-0.02', '-0.08', '-0.05', '#fear', '#disgust', 'negative', '-0.03', 'mattress', 'sleep_furniture', 'cradle', 'small_bed', 'pillow']
senticnet['master_blifil'] = ['-0.65', '0', '0.716', '-0.94', '#anger', '#disgust', 'negative', '-0.77', 'bad_hat', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['master_builder'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'maker']
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senticnet['maximum_altitude'] = ['0.023', '0.113', '0.053', '0.153', '#interest', '#admiration', 'positive', '0.059', 'high', 'high_land', 'incline_plane', 'top', 'diagonal']
senticnet['maximum_amount'] = ['-0.04', '0.268', '0', '0.159', '#interest', '#admiration', 'positive', '0.127', 'total', 'amount', 'a_lot', 'aggregate', 'bill']
senticnet['maximum_amplitude'] = ['-0.93', '0', '0.698', '-0.85', '#sadness', '#disgust', 'negative', '-0.83', 'candlepower', 'light_intensity', 'acoustic_power', 'sound_pressure_level', 'threshold_level']
senticnet['maximum_depth'] = ['0.707', '0.959', '0', '0', '#joy', '#interest', 'positive', '0.833', 'length_measurement', 'measurement', 'imperial_measurement', 'measure', 'spatial_dimension']
senticnet['maximum_deviation'] = ['-0.76', '0', '0.969', '-0.55', '#sadness', '#anger', 'negative', '-0.76', 'variant', 'flection', 'flexion', 'discrepancy', 'variation']
senticnet['maximum_entropy'] = ['-0.98', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.87', 'become_fat', 'flatulence', 'feel_rejuvenated', 'tan_skin', 'make_angry']
senticnet['maximum_flexibility'] = ['-0.52', '0', '0.561', '-0.90', '#anger', '#disgust', 'negative', '-0.66', 'difference', 'tolerance', 'allowance', 'discrepancy', 'margin']
senticnet['maximum_height'] = ['0.734', '0.884', '0', '0', '#joy', '#interest', 'positive', '0.809', 'dimension', 'size', 'mass_measure', 'length_dimension', 'gauge']
senticnet['maximum_intensity'] = ['-0.93', '0', '0.698', '-0.85', '#sadness', '#disgust', 'negative', '-0.83', 'candlepower', 'light_intensity', 'acoustic_power', 'sound_pressure_level', 'threshold_level']
senticnet['maximum_length'] = ['0.707', '0.959', '0', '0', '#joy', '#interest', 'positive', '0.833', 'length_measurement', 'measurement', 'imperial_measurement', 'measure', 'spatial_dimension']
senticnet['maximum_life_span'] = ['0.097', '0.099', '-0.09', '0.137', '#interest', '#admiration', 'positive', '0.059', 'live_long', 'long_life', 'live_healthy_lifestyle', 'increase_muscle_mass', 'feel_healthy']
senticnet['maximum_lifespan'] = ['0.097', '0.099', '-0.09', '0.137', '#interest', '#admiration', 'positive', '0.059', 'live_long', 'long_life', 'live_healthy_lifestyle', 'increase_muscle_mass', 'feel_healthy']
senticnet['maximum_likelihood'] = ['0.83', '-0.83', '0', '0.872', '#surprise', '#admiration', 'positive', '0.846', 'overappraisal', 'overvaluation', 'overestimate', 'overestimation', 'commercial_document']
senticnet['maximum_likelihood_estimation'] = ['0.83', '-0.83', '0', '0.872', '#surprise', '#admiration', 'positive', '0.846', 'overappraisal', 'overvaluation', 'overestimate', 'overestimation', 'commercial_document']
senticnet['maximum_likelihood_estimator'] = ['-0.59', '0', '0.672', '-0.83', '#anger', '#disgust', 'negative', '-0.7', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'asymmetry', 'irregularity']
senticnet['maximum_pell_grant'] = ['-0.07', '0', '0.073', '-0.26', '#sadness', '#disgust', 'negative', '-0.13', 'tuition', 'private_tuition', 'pay_tuition', 'enroll_class', 'enroll_school']
senticnet['maximum_permissible'] = ['0.052', '0', '0', '0.322', '#joy', '#admiration', 'positive', '0.187', 'a_lot', 'abundance', 'plenty', 'superlative', 'comparative']
senticnet['maximum_restraint'] = ['-0.02', '0.201', '-0.06', '0.246', '#interest', '#admiration', 'positive', '0.09', 'end_point', 'commence', 'restriction', 'direction', 'omega']
senticnet['maximum_velocity'] = ['0.052', '0', '0', '0.322', '#joy', '#admiration', 'positive', '0.187', 'a_lot', 'abundance', 'plenty', 'superlative', 'comparative']
senticnet['maximum_width'] = ['0.707', '0.959', '0', '0', '#joy', '#interest', 'positive', '0.833', 'length_measurement', 'measurement', 'imperial_measurement', 'measure', 'spatial_dimension']
senticnet['maxmara'] = ['0.053', '-0.01', '-0.05', '0.121', '#joy', '#admiration', 'positive', '0.034', 'coat', 'winter_clothes', 'stylish', 'adorn', 'protective_cover']
senticnet['maxmo_park'] = ['0.304', '0.339', '-0.19', '0.333', '#interest', '#admiration', 'positive', '0.195', 'careful', 'reinforce', 'load_bear', 'go_outdoors', 'allowance']
senticnet['maxwell'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'force_unit', 'sthene', 'poundal']
senticnet['maxwell_demon'] = ['-0.98', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.87', 'become_fat', 'flatulence', 'feel_rejuvenated', 'tan_skin', 'make_angry']
senticnet['maxzide'] = ['0.502', '-0.82', '0', '0.779', '#surprise', '#admiration', 'positive', '0.703', 'mortal', 'person', 'individual', 'free_liver', 'agent']
senticnet['maya_deren'] = ['-0.85', '0', '0.514', '-0.73', '#sadness', '#disgust', 'negative', '-0.70', 'flick', 'movie', 'film', 'picture', 'moving_picture']
senticnet['mayan_prophecy'] = ['-0.96', '0', '0.501', '-0.59', '#sadness', '#disgust', 'negative', '-0.68', 'calamity', 'catastrophe', 'tragedy', 'disaster', 'cataclysm']
senticnet['maybe'] = ['-0.72', '0', '0.752', '-0.81', '#anger', '#disgust', 'negative', '-0.76', 'wrongdoing', 'jail_time', 'bad_behavior', 'kidnap', 'reprimand']
senticnet['mayhap'] = ['-0.71', '0', '0.836', '0', '#sadness', '#anger', 'negative', '-0.77', 'awkward', 'unenviable', 'embarrassingly', 'embarrassing', 'sheepish']
senticnet['mayhem'] = ['0', '0', '-0.70', '-0.90', '#fear', '#disgust', 'negative', '-0.80', 'madness', 'chaos', 'carnage', 'havoc', 'rage']
senticnet['mayonaise'] = ['0.041', '-0.07', '0.034', '0', '#joy', '#surprise', 'positive', '0.026', 'butter', 'bagel', 'pie', 'noodle', 'muffin']
senticnet['mayonnaise'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'horseradish', 'box_cereal', 'bottle_water']
senticnet['mayonnaise_jar'] = ['0.035', '-0.01', '0.009', '0.063', '#joy', '#admiration', 'positive', '0.026', 'container', 'carton', 'contain', 'hold_cupcake', 'store_dish']
senticnet['mayor'] = ['0.079', '0.152', '-0.06', '0', '#joy', '#interest', 'positive', '0.057', 'president', 'rogue', 'supervisor', 'ambassador', 'leader']
senticnet['mayoral_campaign'] = ['-0.81', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.81', 'take_charge', 'anti', 'go_against', 'death_wish', 'file_complaint']
senticnet['mayoral_election'] = ['0', '0.156', '-0.15', '0.179', '#interest', '#admiration', 'positive', '0.061', 'candidate', 'presidential', 'vote', 'elect', 'appoint']
senticnet['mayordomo_mayor'] = ['0.059', '0.076', '-0.05', '0.079', '#interest', '#admiration', 'positive', '0.039', 'noble', 'king', 'earl', 'duchess', 'duke']
senticnet['mazdayasnian_religion'] = ['0.633', '-0.50', '0', '0.59', '#joy', '#admiration', 'positive', '0.575', 'craftsman', 'manicurist', 'artisan', 'journeyman', 'beautician']
senticnet['maze_prison'] = ['0', '0', '0.995', '-0.89', '#anger', '#disgust', 'negative', '-0.94', 'shoe_store', 'chess_tournament', 'dress_room', 'front_desk', 'locker_room']
senticnet['maze_runner'] = ['-0.06', '0.122', '-0.04', '0.113', '#interest', '#admiration', 'positive', '0.032', 'encounter', 'portray', 'reveal', 'episode', 'next_episode']
senticnet['mazelike'] = ['-0.44', '-0.13', '-0.08', '-0.40', '#sadness', '#disgust', 'negative', '-0.19', 'disorder', 'untidy', 'mess', 'heap', 'chaos']
senticnet['mazer'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['mazovian'] = ['0', '0', '0', '0.1', '#admiration', '#admiration', 'positive', '0.1', 'find_earth', 'wax', 'plutonium', 'shine', 'melt']
senticnet['mazowsze'] = ['0', '0', '0', '0.1', '#admiration', '#admiration', 'positive', '0.1', 'find_earth', 'wax', 'plutonium', 'shine', 'melt']
senticnet['mazraeh_ye_tall'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'identifiable', 'specifiable', 'recognizable', 'diagnosable', 'classifiable']
senticnet['mazurka'] = ['0.591', '-0.91', '0', '0.605', '#surprise', '#admiration', 'positive', '0.704', 'farandole', 'folk_dance', 'polka', 'dance', 'typical_dance']
senticnet['mba_degree'] = ['-0.75', '0', '0.589', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'lowness', 'grind', 'immoderation', 'immoderateness', 'amplitude_level']
senticnet['mbangala'] = ['-0.84', '0', '0.579', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'squiggle', 'hand', 'chicken_scratch', 'handwriting', 'script']
senticnet['mbanza_kongo'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'dryness', 'thirstiness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['mbox'] = ['0.028', '0', '0.027', '0.101', '#joy', '#admiration', 'positive', '0.034', 'animal_dwell', 'bee_home', 'live_place', 'dwell_place', 'place_to_live']
senticnet['mbti'] = ['-0.84', '0', '0.585', '-0.85', '#sadness', '#disgust', 'negative', '-0.76', 'subject', 'discipline', 'study', 'concern', 'research']
senticnet['mc_donald'] = ['0.056', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.037', 'eat_food', 'restaurant', 'meal', 'fast', 'fast_food']
senticnet['mc_quote'] = ['0', '0.189', '0.04', '0.091', '#interest', '#admiration', 'positive', '0.08', 'combination', 'margin', 'teller', 'merge', 'finite']
senticnet['mcardle_disease'] = ['-0.68', '0', '0.898', '-0.97', '#anger', '#disgust', 'negative', '-0.85', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['mcas_test'] = ['-0.22', '-0.00', '-0.09', '-0.03', '#sadness', '#fear', 'negative', '-0.08', 'failure', 'bad_grade', 'a_lot_of_stress', 'fail', 'further_goal']
senticnet['mccain_maverick'] = ['0.65', '-0.61', '0', '0.703', '#joy', '#admiration', 'positive', '0.657', 'gleaming', 'luster', 'polish', 'glow', 'effulgence']
senticnet['mccarthy'] = ['0.532', '-0.68', '0', '0.984', '#surprise', '#admiration', 'positive', '0.733', 'potato', 'white_potato', 'chips', 'spud', 'tater']
senticnet['mccormack'] = ['0.532', '-0.68', '0', '0.984', '#surprise', '#admiration', 'positive', '0.733', 'potato', 'white_potato', 'chips', 'spud', 'tater']
senticnet['mccormick_tribune_plaza'] = ['0', '0', '0', '-0.1', '#disgust', '#disgust', 'negative', '-0.1', 'ice_rink', 'skate', 'room_temperature', 'loose_end', 'smooth']
senticnet['mcdonald_fast_food'] = ['0.096', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.057', 'platform_rail', 'rail', 'catwalk', 'column', 'token_turnstile']
senticnet['mcdonald_nyse_mcd'] = ['0.965', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.951', 'delicious_food', 'hot_meal', 'good_food', 'fresh_produce', 'tasty_food']
senticnet['mcdonaldization'] = ['0', '0', '-0.74', '-0.93', '#fear', '#disgust', 'negative', '-0.84', 'generosity', 'oral_sex', 'win_lottery', 'gift_wrap', 'give_away']
senticnet['mcgaugh'] = ['0.627', '-0.65', '0', '0.968', '#surprise', '#admiration', 'positive', '0.749', 'overwhelm', 'overpower', 'sweep_over', 'whelm', 'overcome']
senticnet['mcgee'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
senticnet['mcgill_queen'] = ['0.889', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.807', 'compression', 'push_hard', 'print_newspaper', 'journalist', 'make_newspaper']
senticnet['mcgrady'] = ['0.556', '-0.79', '0', '0.755', '#surprise', '#admiration', 'positive', '0.701', 'lush', 'alcoholic', 'alky', 'dipsomaniac', 'rummy']
senticnet['mcgrath'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
senticnet['mcguire'] = ['0.532', '-0.68', '0', '0.984', '#surprise', '#admiration', 'positive', '0.733', 'potato', 'white_potato', 'chips', 'spud', 'tater']
senticnet['mchale_navy'] = ['0', '0', '0.785', '-0.72', '#anger', '#disgust', 'negative', '-0.75', 'sharp_nail', 'sharp', 'sharp_end', 'sharp_point', 'pointy']
senticnet['mchugh'] = ['0.532', '-0.68', '0', '0.984', '#surprise', '#admiration', 'positive', '0.733', 'potato', 'white_potato', 'chips', 'spud', 'tater']
senticnet['mckeesport'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
senticnet['mckenna'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
senticnet['mckenny_union'] = ['0.053', '-0.12', '0.015', '-0.03', '#joy', '#surprise', 'positive', '0.03', 'student_place', 'learning_place', 'school_session', 'after_school', 'professor']
senticnet['mckinleyville'] = ['-0.06', '0.21', '0', '0.076', '#interest', '#admiration', 'positive', '0.074', 'new_think', 'figure_out', 'brain_activity', 'idea', 'come_up_idea']
senticnet['mclaggan'] = ['-0.64', '0', '0.879', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'drone', 'poke', 'dawdler', 'laggard', 'straggler']
senticnet['mclamore'] = ['0.914', '-0.56', '0', '0.869', '#joy', '#admiration', 'positive', '0.781', 'tabula_rasa', 'ego', 'subconscious', 'unconscious', 'psyche']
senticnet['mclaughlin'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
senticnet['mcmenemy'] = ['-0.9', '0', '0.749', '-0.71', '#sadness', '#anger', 'negative', '-0.78', 'chute', 'slide', 'slideway', 'sloping_trough', 'gutter']
senticnet['mcneilly'] = ['-0.64', '0', '0.781', '-0.74', '#anger', '#disgust', 'negative', '-0.72', 'weirdo', 'crazy', 'loony', 'lunatic', 'nutcase']
senticnet['mcp_joint'] = ['-0.81', '0', '0.789', '-0.51', '#sadness', '#anger', 'negative', '-0.70', 'rubor', 'redness', 'inflammation', 'synovitis', 'tendosynovitis']
senticnet['mcpherson_ridge'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['mcstravick'] = ['-0.64', '0', '0.879', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'drone', 'poke', 'dawdler', 'laggard', 'straggler']
senticnet['mct_oil'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
senticnet['mctwist'] = ['-0.05', '0.066', '0', '0.079', '#interest', '#admiration', 'positive', '0.029', 'skip', 'jump_high', 'float_through_air', 'jump_over', 'bail_water']
senticnet['mcworld'] = ['0', '0', '-0.74', '-0.93', '#fear', '#disgust', 'negative', '-0.84', 'generosity', 'oral_sex', 'win_lottery', 'gift_wrap', 'give_away']
senticnet['mdewakanton'] = ['0.989', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.97', 'committee', 'council', 'caste', 'association', 'organization']
senticnet['mdna'] = ['0.056', '0.139', '-0.07', '0.154', '#interest', '#admiration', 'positive', '0.07', 'belongings', 'material', 'fabric', 'guarantee', 'stuff']
senticnet['mdna_album'] = ['0.056', '0.139', '-0.07', '0.154', '#interest', '#admiration', 'positive', '0.07', 'belongings', 'material', 'fabric', 'guarantee', 'stuff']
senticnet['me-too'] = ['-0.87', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.80', 'inauthentic', 'ersatz', 'second-rate', 'overproduced', 'anachronistic']
senticnet['mea_culpa'] = ['-0.69', '0', '0.941', '-0.75', '#anger', '#disgust', 'negative', '-0.8', 'flub', 'blunder', 'blooper', 'bungle', 'botch']
senticnet['mea_shearim'] = ['0', '0.209', '0.026', '0.184', '#interest', '#admiration', 'positive', '0.122', 'church', 'confession', 'temple', 'cathedral', 'monastery']
senticnet['meadow'] = ['0.026', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.049', 'state_park', 'arboretum', 'park', 'grass', 'backyard']
senticnet['meadow_buttercup'] = ['-0.74', '0', '0.593', '-0.77', '#sadness', '#disgust', 'negative', '-0.70', 'herb', 'crowfoot', 'tall_buttercup', 'tall_crowfoot', 'common_buttercup']
senticnet['meadow_foxtail'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'meadow_grass', 'grass', 'manna_grass', 'sweet_grass', 'wood_meadowgrass']
senticnet['meadow_grass'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'foxtail', 'foxtail_grass', 'manna_grass', 'sweet_grass', 'wood_meadowgrass']
senticnet['meadow_pipit'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['meadow_rue'] = ['-0.52', '0', '0.527', '-0.78', '#anger', '#disgust', 'negative', '-0.61', 'herb', 'black_snakeroot', 'heartleaf', 'asarabacca', 'herbaceous_plant']
senticnet['meadow_saxifrage'] = ['-0.56', '0', '0.848', '-0.80', '#anger', '#disgust', 'negative', '-0.73', 'herb', 'yellow_mountain_saxifrage', 'breakstone', 'mossy_saxifrage', 'western_saxifrage']
senticnet['meadow_sweet'] = ['-0.86', '0', '0.665', '-0.61', '#sadness', '#anger', 'negative', '-0.71', 'brambly', 'braky', 'wooded', 'scrubby', 'forested']
senticnet['meadow_vole'] = ['-0.55', '0', '0.955', '-0.96', '#anger', '#disgust', 'negative', '-0.82', 'house_mouse', 'mouse', 'rat', 'fieldmouse', 'nude_mouse']
senticnet['meadowgrass'] = ['-0.86', '0', '0.737', '-0.55', '#sadness', '#anger', 'negative', '-0.71', 'foxtail', 'foxtail_grass', 'manna_grass', 'sweet_grass', 'wood_meadowgrass']
senticnet['meadowhall_interchange'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'train_station', 'bus', 'subway_station', 'public_place', 'pier']
senticnet['meadowsweet'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['meadville'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
senticnet['meager'] = ['-0.94', '0', '0.952', '-0.83', '#sadness', '#anger', 'negative', '-0.91', 'miserable', 'scanty', 'exiguous', 'spare', 'hardscrabble']
senticnet['meager_diet'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'green_bread', 'rotten', 'disgusting', 'taste_awful', 'mouldy']
senticnet['meager_existence'] = ['-0.55', '0', '0.912', '-0.65', '#anger', '#disgust', 'negative', '-0.70', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['meager_income'] = ['0.057', '0.181', '-0.04', '0.18', '#interest', '#admiration', 'positive', '0.092', 'small_money', 'alter', 'money_change', 'money_coin', 'small_coin']
senticnet['meagerly'] = ['-0.92', '0', '0.503', '-0.62', '#sadness', '#disgust', 'negative', '-0.68', 'miserable', 'scanty', 'exiguous', 'spare', 'hardscrabble']
senticnet['meagher'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
senticnet['meagher_county'] = ['-0.98', '0', '0.521', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'battery', 'stamp_battery', 'quartz_battery', 'stamp_mill', 'factory']
senticnet['meagre'] = ['-0.93', '0', '0.863', '-0.51', '#sadness', '#anger', 'negative', '-0.77', 'miserable', 'scanty', 'exiguous', 'spare', 'hardscrabble']
senticnet['meagre_income'] = ['0.057', '0.181', '-0.04', '0.18', '#interest', '#admiration', 'positive', '0.092', 'small_money', 'alter', 'money_change', 'money_coin', 'small_coin']
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senticnet['meal'] = ['0.062', '0', '0.027', '0.055', '#joy', '#admiration', 'positive', '0.03', 'porridge', 'appetizer', 'broth', 'hamburger', 'hearty']
senticnet['meal_allowance'] = ['0.924', '0.968', '0', '0.876', '#joy', '#interest', 'positive', '0.923', 'live_simply', 'help_out', 'money', 'accolade', 'generous']
senticnet['meal_preparation'] = ['0.076', '-0.00', '0', '0', '#joy', '#surprise', 'positive', '0.042', 'clean_body', 'feel_fresh', 'remove_dirt_body', 'shower', 'feel_refreshed']
senticnet['meal_served'] = ['0.748', '0.978', '0', '0', '#joy', '#interest', 'positive', '0.863', 'meet_friend', 'eat_out', 'enjoy_food', 'give_party', 'satisfy_hunger']
senticnet['meal_ticket'] = ['0.921', '-0.57', '0', '0.962', '#joy', '#admiration', 'positive', '0.818', 'book_token', 'coupon', 'luncheon_voucher', 'twofer', 'commercial_document']
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senticnet['mealy'] = ['0', '0', '-0.75', '-0.77', '#fear', '#disgust', 'negative', '-0.76', 'coarse-grained', 'gritty', 'grainy', 'farinaceous', 'granular']
senticnet['mealybug'] = ['0', '-0.09', '0.123', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'gypsy_moth', 'japanese_beetle', 'cucumber_beetle', 'mosquito', 'earwig']
senticnet['mean'] = ['0.766', '0.747', '0', '0', '#joy', '#interest', 'positive', '0.756', 'cruel', 'petition', 'anti', 'exclamation', 'bad']
senticnet['mean-spirited'] = ['-0.82', '0', '0.823', '0', '#sadness', '#anger', 'negative', '-0.82', 'angry', 'bitter', 'language', 'acrimonious', 'acrimoniously']
senticnet['mean_annual_rainfall'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'condensation', 'water_fall', 'falling_water', 'frozen_water', 'water_drop']
senticnet['mean_flow_velocity'] = ['-0.03', '0.178', '0', '0.168', '#interest', '#admiration', 'positive', '0.104', 'speed', 'fast', 'fast_and_furious', 'speedy', 'fleet']
senticnet['mean_life'] = ['0.219', '0.158', '-0.09', '0.164', '#joy', '#admiration', 'positive', '0.112', 'nice_place_to_live', 'complement', 'reliability', 'calm_mind', 'best_life']
senticnet['mean_person'] = ['0', '0', '0.974', '-0.91', '#anger', '#disgust', 'negative', '-0.94', 'conquer_enemy', 'go_war', 'gain_territory', 'selfish', 'make_war']
senticnet['mean_streak'] = ['-0.91', '0', '0.681', '-0.81', '#sadness', '#disgust', 'negative', '-0.80', 'chicken', 'doormat', 'softie', 'crybaby', 'wimp']
senticnet['meander'] = ['0.97', '0.758', '0', '0', '#joy', '#interest', 'positive', '0.864', 'trot', 'step_out', 'step_aside', 'progress', 'enter_place']
senticnet['meander_along'] = ['0.97', '0.758', '0', '0', '#joy', '#interest', 'positive', '0.864', 'trot', 'step_out', 'step_aside', 'progress', 'enter_place']
senticnet['meander_through'] = ['0.876', '0.883', '0', '0', '#joy', '#interest', 'positive', '0.879', 'walk_along', 'gait', 'take_step', 'run_slowly', 'locomotion']
senticnet['meandering_channel'] = ['0.97', '0.758', '0', '0', '#joy', '#interest', 'positive', '0.864', 'trot', 'step_out', 'step_aside', 'progress', 'enter_place']
senticnet['meandering_course'] = ['0.97', '0.758', '0', '0', '#joy', '#interest', 'positive', '0.864', 'trot', 'step_out', 'step_aside', 'progress', 'enter_place']
senticnet['meandering_river'] = ['0.97', '0.758', '0', '0', '#joy', '#interest', 'positive', '0.864', 'trot', 'step_out', 'step_aside', 'progress', 'enter_place']
senticnet['meandering_stream'] = ['0.97', '0.758', '0', '0', '#joy', '#interest', 'positive', '0.864', 'trot', 'step_out', 'step_aside', 'progress', 'enter_place']
senticnet['meaner'] = ['0.17', '0', '0.081', '0.184', '#joy', '#admiration', 'positive', '0.091', 'food_holder', 'food_server', 'crockery', 'flattery', 'round_flat']
senticnet['meanest'] = ['0.838', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.887', 'proud', 'glory', 'brag', 'vaunt', 'pride']
senticnet['meaneth'] = ['-0.79', '0', '0.607', '-0.92', '#sadness', '#disgust', 'negative', '-0.77', 'stubbornness', 'obstinacy', 'obstinance', 'bullheadedness', 'pigheadedness']
senticnet['meaning'] = ['0.903', '0.949', '0', '0', '#joy', '#interest', 'positive', '0.926', 'protect_from_sun', 'shade', 'concealer', 'corrector', 'sunglasses']
senticnet['meaningful'] = ['0.738', '-0.52', '0', '0.775', '#joy', '#admiration', 'positive', '0.679', 'significant', 'meaning', 'purposeful', 'substantive', 'pregnant']
senticnet['meaningfully'] = ['0.765', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.813', 'deeply', 'importantly', 'incredible', 'profoundly', 'very']
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senticnet['meaninglessness'] = ['-0.72', '0', '0', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'balderdash', 'piffle', 'content', 'empty_word', 'empty_talk']
senticnet['meanly'] = ['-0.63', '0', '0.867', '-0.57', '#sadness', '#anger', 'negative', '-0.69', 'myopic', 'short', 'shortsighted', 'unforesightful', 'improvident']
senticnet['meanness'] = ['0', '0', '0.906', '-0.97', '#anger', '#disgust', 'negative', '-0.94', 'anger', 'screw_up', 'disrespect', 'hear_bad_news', 'car_wreck']
senticnet['means'] = ['-0.87', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.82', 'medium', 'container', 'instrumentation', 'ceramic', 'system']
senticnet['means_of_transport'] = ['0.027', '0', '0.026', '0.073', '#joy', '#admiration', 'positive', '0.025', 'large_automobile', 'expensive_car', 'limousine', 'automatic_car', 'roulotte']
senticnet['means_of_transportation'] = ['0', '-0.01', '0', '0.067', '#surprise', '#admiration', 'positive', '0.038', 'form_of_transportation', 'conveyance', 'find_car_key', 'long_car', 'transportation']
senticnet['meantime'] = ['0', '0', '-0.91', '-0.95', '#fear', '#disgust', 'negative', '-0.93', 'side_by_side', 'succeeding', 'following', 'adjacent', 'future']
senticnet['measles'] = ['-0.81', '0', '0.9', '-0.82', '#anger', '#disgust', 'negative', '-0.84', 'infectious_disease', 'polio', 'malady', 'sickness', 'itch']
senticnet['measles_epidemic'] = ['-0.07', '0', '-0.22', '-0.15', '#fear', '#disgust', 'negative', '-0.15', 'gonorrhea', 'infectious_disease', 'psychoneurosis', 'sickness', 'polio']
senticnet['measles_immunization'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'medical_therapy', 'vaccine', 'doctor', 'medicine', 'hospital']
senticnet['measles_vaccination'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'medical_therapy', 'vaccine', 'doctor', 'medicine', 'hospital']
senticnet['measles_vaccine'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'medical_therapy', 'vaccine', 'doctor', 'medicine', 'hospital']
senticnet['measly'] = ['0', '0.249', '0.012', '0.154', '#interest', '#admiration', 'positive', '0.13', 'utility', 'money_value', 'monetary_value', 'money_amount', 'sticker']
senticnet['measurability'] = ['0.929', '-0.71', '0', '0.605', '#joy', '#surprise', 'positive', '0.748', 'incisiveness', 'trenchancy', 'efficacy', 'efficaciousness', 'power']
senticnet['measurable'] = ['0.899', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.922', 'answerable', 'commensurable', 'commensurate', 'standard', 'adjust']
senticnet['measurable_criterion'] = ['-0.96', '0', '0.603', '-0.91', '#sadness', '#disgust', 'negative', '-0.82', 'potential', 'potency', 'conceivableness', 'conceivability', 'possibleness']
senticnet['measure'] = ['0', '0.1', '0.097', '0.155', '#interest', '#admiration', 'positive', '0.053', 'spatial_dimension', 'imperial_measurement', 'length', 'measurement', 'measurement_unit']
senticnet['measure_angle'] = ['0', '-0.04', '0.094', '-0.10', '#anger', '#disgust', 'negative', '-0.05', 'make_musical_sound', 'make_music', 'play_orchestra', 'play_music', 'annoy_mom']
senticnet['measure_degree'] = ['0.062', '0.082', '0.047', '0', '#joy', '#interest', 'positive', '0.032', 'celsius', 'temperature_measure', 'measure_heat', 'temperature_measurement', 'heat_measure']
senticnet['measure_device'] = ['0', '0.084', '0.076', '0.144', '#interest', '#admiration', 'positive', '0.051', 'measurement_tool', 'measure_tool', 'measure_inch', 'measure', 'length']
senticnet['measure_distance'] = ['0.952', '0.984', '0', '0', '#joy', '#interest', 'positive', '0.968', 'distance_measurement', 'distance_measure', 'length_measurement', 'kilometer', 'meter']
senticnet['measure_heat'] = ['0.062', '0.082', '0.047', '0', '#joy', '#interest', 'positive', '0.032', 'celsius', 'temperature_measure', 'measure_degree', 'temperature_measurement', 'heat_measure']
senticnet['measure_inch'] = ['0', '0.084', '0.076', '0.144', '#interest', '#admiration', 'positive', '0.051', 'measurement_tool', 'measure_tool', 'measure_device', 'measure', 'length']
senticnet['measure_ingredient'] = ['0', '-0.01', '0.014', '-0.10', '#anger', '#disgust', 'negative', '-0.03', 'burp', 'prepare_ingredient', 'say_grace', 'add_spice', 'swallow_food']
senticnet['measure_length'] = ['0.853', '0.79', '0', '0', '#joy', '#interest', 'positive', '0.822', 'length_measurement', 'measure', 'meter', 'size_up', 'measurement_unit']
senticnet['measure_liquid'] = ['0.025', '0', '0.016', '0.067', '#joy', '#admiration', 'positive', '0.025', 'liquid', 'ferment_beverage', 'carbonate_drink', 'serve_wine', 'good_drink']
senticnet['measure_passage_time'] = ['0.154', '0.086', '0.03', '0.153', '#joy', '#admiration', 'positive', '0.091', 'keep_track_time', 'know_time', 'time_event', 'time_runner', 'time']
senticnet['measure_person'] = ['0.755', '0.994', '0', '0', '#joy', '#interest', 'positive', '0.875', 'stay_young', 'cycle', 'era', 'many_years', 'know_time']
senticnet['measure_pound'] = ['0', '0.138', '0.077', '0.186', '#interest', '#admiration', 'positive', '0.082', 'weight_measurement', 'heavy_weight', 'mass_measure', 'measurement_device', 'heaviness']
senticnet['measure_theoretic'] = ['-0.99', '0', '0.878', '-0.95', '#sadness', '#disgust', 'negative', '-0.94', 'adequacy', 'satisfactoriness', 'acceptability', 'acceptableness', 'quality']
senticnet['measure_time'] = ['0', '0.138', '0.077', '0.186', '#interest', '#admiration', 'positive', '0.082', 'show_time', 'timekeeper', 'measure', 'keep_time', 'time_measure']
senticnet['measure_tool'] = ['0', '0.121', '0.054', '0.158', '#interest', '#admiration', 'positive', '0.075', 'measurement_tool', 'measure_inch', 'measure_device', 'yardstick', 'measurement_device']
senticnet['measure_unit'] = ['0', '0.122', '0.111', '0.162', '#interest', '#admiration', 'positive', '0.058', 'count', 'total_number', 'imperial_measurement', 'mass_measure', 'list_number']
senticnet['measureless'] = ['-0.80', '0', '0.761', '-0.70', '#sadness', '#anger', 'negative', '-0.75', 'illimitable', 'limitless', 'immeasurable', 'unmeasured', 'abysmal']
senticnet['measurement'] = ['0', '0.097', '0.093', '0.142', '#interest', '#admiration', 'positive', '0.049', 'spatial_dimension', 'length', 'measure', 'imperial_measurement', 'measurement_unit']
senticnet['measurement_accuracy'] = ['0', '0', '-0.66', '0', '#fear', '#fear', 'negative', '-0.66', 'anger', 'rage', 'annoyance', 'apprehension', 'fear']
senticnet['measurement_device'] = ['0', '0.125', '0.074', '0.097', '#interest', '#admiration', 'positive', '0.049', 'weigh', 'balance', 'proportion', 'measure_pound', 'weight_measurement']
senticnet['measurement_interval'] = ['0.04', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.055', 'measurement', 'time_interval', 'length', 'time', 'spatial_dimension']
senticnet['measurement_technique'] = ['0', '0.721', '0', '0.731', '#interest', '#admiration', 'positive', '0.726', 'dimension', 'measure', 'tackle', 'length', 'measurement']
senticnet['measurement_tool'] = ['0', '0.093', '0.061', '0.147', '#interest', '#admiration', 'positive', '0.06', 'measure_tool', 'measure_device', 'measure_inch', 'yardstick', 'measure_pound']
senticnet['measurement_unit'] = ['0', '0.097', '0.088', '0.142', '#interest', '#admiration', 'positive', '0.05', 'imperial_measurement', 'length_measurement', 'measure', 'unit_of_measurement', 'measurement']
senticnet['measuring_device'] = ['0', '-0.00', '0.034', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'celsius', 'grade', 'certificate', 'learn_tool', 'temperature_measure']
senticnet['measuring_instrument'] = ['0', '0.721', '0', '0.731', '#interest', '#admiration', 'positive', '0.726', 'dimension', 'measure', 'tackle', 'length', 'measurement']
senticnet['measuring_progress_toward'] = ['-0.96', '0', '0.603', '-0.91', '#sadness', '#disgust', 'negative', '-0.82', 'potential', 'potency', 'conceivableness', 'conceivability', 'possibleness']
senticnet['measuring_tape'] = ['0', '-0.00', '0.034', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'celsius', 'grade', 'certificate', 'learn_tool', 'temperature_measure']
senticnet['meat'] = ['0.811', '0.844', '0', '0', '#joy', '#interest', 'positive', '0.828', 'slice_meat', 'toast', 'make_flour', 'bake_bread', 'bread']
senticnet['meat-free'] = ['-0.92', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.83', 'meatless', 'vegetarian', 'veggie', 'lasagna', 'vegan']
senticnet['meat_broth'] = ['0.07', '0.084', '0.023', '0', '#joy', '#interest', 'positive', '0.044', 'hearty', 'porridge', 'hot_liquid', 'liquid_food', 'meat_vegetable']
senticnet['meat_cleaver'] = ['0.818', '-0.95', '0', '0.695', '#joy', '#surprise', 'positive', '0.823', 'jackknife', 'clasp_knife', 'penknife', 'switchblade', 'switchblade_knife']
senticnet['meat_consumption'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'grill', 'cook_grill', 'barbecue', 'cook', 'grilled_vegetable']
senticnet['meat_cut'] = ['0.039', '-0.00', '0.018', '0.075', '#joy', '#admiration', 'positive', '0.026', 'high_protein', 'red_meat', 'pig_meat', 'piece', 'dairy_product']
senticnet['meat_dish'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'cook_meat', 'cook_hamburger', 'cook_food', 'cook_rice', 'brown_meat']
senticnet['meat_eater'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'reptile', 'animal', 'predator', 'alligator', 'river']
senticnet['meat_hook'] = ['0.883', '-0.59', '0', '0.533', '#joy', '#surprise', 'positive', '0.67', 'hook', 'hand', 'mauler', 'right', 'left']
senticnet['meat_inspection_act'] = ['0', '0', '0.709', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'debasement', 'dirtiness', 'pollution', 'taint', 'impureness']
senticnet['meat_intake'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'grill', 'cook_grill', 'barbecue', 'cook', 'grilled_vegetable']
senticnet['meat_pie'] = ['0.644', '-0.94', '0', '0.729', '#surprise', '#admiration', 'positive', '0.773', 'gem', 'banana_bread', 'date_bread', 'nut_bread', 'quick_bread']
senticnet['meat_sauce'] = ['0.043', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'apple_pie', 'a_lot_of_food', 'jelly', 'frozen_food', 'cottage_cheese']
senticnet['meat_vegetable'] = ['0.07', '0.084', '0.023', '0', '#joy', '#interest', 'positive', '0.044', 'hearty', 'porridge', 'hot_liquid', 'liquid_food', 'broth']
senticnet['meatball_sub'] = ['-0.98', '0', '0.594', '-0.98', '#sadness', '#disgust', 'negative', '-0.85', 'fast_food', 'deep_fried', 'unhealthy_food', 'reuse_oil', 'junk_food']
senticnet['meathead'] = ['-0.76', '0', '0.904', '-0.80', '#anger', '#disgust', 'negative', '-0.82', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['meatless'] = ['-0.91', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.83', 'meat-free', 'vegetarian', 'veggie', 'lasagna', 'vegan']
senticnet['meatloaf'] = ['0.028', '-0.07', '0', '0', '#joy', '#surprise', 'positive', '0.05', 'casserole', 'delicious_meal', 'hot_dog_bun', 'hamburger', 'hot_dog']
senticnet['meatpacking_district'] = ['0.591', '-0.74', '0', '0.672', '#surprise', '#admiration', 'positive', '0.669', 'room', 'chillax', 'hotel_room', 'furniture_store', 'living_room']
senticnet['meatpuppet'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['meb_keflezighi'] = ['-0.04', '-0.03', '0', '-0.16', '#sadness', '#disgust', 'negative', '-0.05', 'crazy', 'damage_body', 'crazy_person', 'threat', 'max']
senticnet['mebbe'] = ['-0.85', '0', '0.583', '-0.71', '#sadness', '#disgust', 'negative', '-0.71', 'muddle', 'rummage', 'mare_nest', 'welter', 'clutter']
senticnet['mecamylamine'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['mechanic'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'mechanical', 'mechanical_device', 'mechanical_pencil', 'mechanic_engineer', 'technician']
senticnet['mechanic_engineer'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'mechanical', 'mechanical_device', 'mechanical_pencil', 'mechanic', 'technician']
senticnet['mechanic_structure'] = ['0', '0.988', '0', '0.862', '#interest', '#admiration', 'positive', '0.925', 'mater', 'material_research', 'research', 'standard_grant', 'study']
senticnet['mechanical'] = ['0.964', '0.995', '0', '0', '#joy', '#interest', 'positive', '0.98', 'mechanical_device', 'move_part', 'device', 'make_metal', 'ore']
senticnet['mechanical_abrasion'] = ['0.89', '-0.62', '0', '0.548', '#joy', '#surprise', 'positive', '0.686', 'breakthrough', 'interpenetration', 'foray', 'maraud', 'raid']
senticnet['mechanical_advantage'] = ['0', '0.142', '0', '0.125', '#interest', '#admiration', 'positive', '0.134', 'weight_machine', 'power_drill', 'screw', 'screwdriver', 'air_pump']
senticnet['mechanical_arm'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'biological_machine', 'social_creature', 'live_creature', 'powerful', 'alive']
senticnet['mechanical_breakdown'] = ['-0.91', '0', '0.905', '-0.78', '#sadness', '#anger', 'negative', '-0.86', 'pity', 'shame', 'misfortune', 'mischance', 'misadventure']
senticnet['mechanical_bull'] = ['0.086', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'carny', 'fortune_teller', 'midway', 'ferris_wheel', 'roller-coaster']
senticnet['mechanical_calculator'] = ['-0.03', '0.201', '-0.13', '0.171', '#interest', '#admiration', 'positive', '0.049', 'scientific_statement', 'hypothetical', 'conjecture', 'theory', 'fact']
senticnet['mechanical_clock'] = ['0', '0', '0.95', '-0.86', '#anger', '#disgust', 'negative', '-0.90', 'watch', 'stopwatch', 'timepiece', 'wristwatch', 'find_pawnshop']
senticnet['mechanical_complex_machine'] = ['0.025', '0', '0', '0.128', '#joy', '#admiration', 'positive', '0.076', 'device', 'mechanism', 'human_invention', 'machine', 'complex_machine']
senticnet['mechanical_device'] = ['0', '-0.00', '0', '0.097', '#surprise', '#admiration', 'positive', '0.051', 'mechanism', 'mechanical', 'move_part', 'device', 'machinery']
senticnet['mechanical_failure'] = ['-0.91', '0', '0.905', '-0.78', '#sadness', '#anger', 'negative', '-0.86', 'pity', 'shame', 'misfortune', 'mischance', 'misadventure']
senticnet['mechanical_graphite_pencil'] = ['0.031', '0.08', '0.023', '0.069', '#interest', '#admiration', 'positive', '0.039', 'marker', 'graphite_pencil', 'pen', 'pencil', 'stamp_pad']
senticnet['mechanical_linkage'] = ['0', '0.142', '0', '0.125', '#interest', '#admiration', 'positive', '0.134', 'weight_machine', 'power_drill', 'screw', 'screwdriver', 'air_pump']
senticnet['mechanical_malfunction'] = ['-0.91', '0', '0.905', '-0.78', '#sadness', '#anger', 'negative', '-0.86', 'pity', 'shame', 'misfortune', 'mischance', 'misadventure']
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senticnet['median_wage'] = ['0', '0.923', '0', '0', '#interest', '#interest', 'positive', '0.923', 'salary', 'remittance', 'owe', 'payment', 'severance']
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senticnet['medicare_fee_schedule'] = ['0.687', '-0.98', '0', '0.666', '#joy', '#surprise', 'positive', '0.778', 'scale', 'scale_of_measurement', 'graduated_table', 'ordered_series', 'wage_scale']
senticnet['medicare_fraud'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'mail_fraud', 'swindle', 'rig']
senticnet['medicare_payroll_tax'] = ['0.687', '-0.98', '0', '0.666', '#joy', '#surprise', 'positive', '0.778', 'scale', 'scale_of_measurement', 'graduated_table', 'ordered_series', 'wage_scale']
senticnet['medicate'] = ['0.804', '0', '0', '0.996', '#joy', '#admiration', 'positive', '0.9', 'remedy', 'harmonize', 'heal', 'cure', 'soothe']
senticnet['medication'] = ['0.707', '0.849', '0', '0', '#joy', '#interest', 'positive', '0.778', 'antibiotic', 'cure', 'medicine', 'ibuprofen', 'analgesic']
senticnet['medication_practice_ismp'] = ['0.033', '0.186', '0', '0', '#joy', '#interest', 'positive', '0.11', 'bank', 'bank_vault', 'wallet', 'government_agreement', 'coffer']
senticnet['medicinal'] = ['0.716', '0.775', '0', '0', '#joy', '#interest', 'positive', '0.746', 'meliorative', 'remedial', 'wholesome', 'salute', 'salutary']
senticnet['medicinal_drug'] = ['0.564', '-0.77', '0', '0.862', '#surprise', '#admiration', 'positive', '0.733', 'curative', 'heal', 'medicine', 'get_cured', 'take_care']
senticnet['medicinal_herb'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'infective_agent', 'satisfier', 'quickener', 'agent']
senticnet['medicinal_leech'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'leech', 'horseleech', 'annelid', 'annelid_worm', 'segmented_worm']
senticnet['medicinal_marijuana'] = ['0', '0.931', '0', '0', '#interest', '#interest', 'positive', '0.931', 'football', 'rake_leave', 'spray_paint_grafitti', 'juggle', 'play_hide_seek']
senticnet['medicinally'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'infective_agent', 'satisfier', 'quickener', 'agent']
senticnet['medicine'] = ['0', '0.772', '0', '0.91', '#interest', '#admiration', 'positive', '0.841', 'sleep_drug', 'analgesic', 'ibuprofen', 'antibiotic', 'cure']
senticnet['medicine_acam'] = ['0.856', '0', '0', '0.963', '#joy', '#admiration', 'positive', '0.909', 'progress', 'get_better_job', 'further_career', 'graduate_school', 'get_diploma']
senticnet['medicine_cabinet'] = ['-0.02', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'drugstore', 'bathroom_cabinet', 'pharmacy', 'medicine_chest', 'own_bathroom']
senticnet['medicine_chest'] = ['-0.03', '-0.01', '-0.08', '-0.16', '#fear', '#disgust', 'negative', '-0.06', 'medicine_cabinet', 'drugstore', 'pharmacy', 'bathroom_cabinet', 'first-aid_kit']
senticnet['medicine_dropper'] = ['-0.02', '0', '-0.12', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'x-ray_machine', 'medical_record', 'latex_glove', 'medicine', 'doctor_examination_room']
senticnet['medicine_tcm'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
senticnet['medieval'] = ['0.021', '0.119', '0', '0.129', '#interest', '#admiration', 'positive', '0.09', 'manor', 'palace', 'castle', 'tower', 'chess_game']
senticnet['medieval_castle'] = ['0.021', '0.119', '0', '0.129', '#interest', '#admiration', 'positive', '0.09', 'manor', 'palace', 'medieval', 'tower', 'chess_game']
senticnet['medieval_era'] = ['0.978', '-0.60', '0', '0.895', '#joy', '#admiration', 'positive', '0.825', 'period', 'quattrocento', 'twentieth_century', 'time_period', 'period_of_time']
senticnet['medieval_europe'] = ['-0.03', '0.092', '0.07', '0.1', '#interest', '#admiration', 'positive', '0.021', 'gladiator', 'civilization', 'church_build', 'coliseum', 'every_town']
senticnet['medieval_fantasy'] = ['0.747', '0.85', '0', '0', '#joy', '#interest', 'positive', '0.798', 'night_vision', 'mystery', 'plot', 'imagine', 'myth']
senticnet['medieval_guild'] = ['0.967', '-0.98', '0', '0.64', '#joy', '#surprise', 'positive', '0.863', 'club', 'social_club', 'society', 'jockey_club', 'gild']
senticnet['medieval_inquisition'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquirer', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['medieval_period'] = ['0.978', '-0.60', '0', '0.895', '#joy', '#admiration', 'positive', '0.825', 'period', 'quattrocento', 'twentieth_century', 'time_period', 'period_of_time']
senticnet['medieval_time'] = ['-0.03', '0', '-0.13', '-0.08', '#fear', '#disgust', 'negative', '-0.08', 'underworld', 'war', 'arsenal', 'samurai', 'war_zone']
senticnet['medievalism'] = ['0.563', '-0.72', '0', '0.644', '#surprise', '#admiration', 'positive', '0.645', 'idiom', 'idiomatic_expression', 'phrasal_idiom', 'set_phrase', 'phrase']
senticnet['medikit'] = ['-0.02', '-0.00', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.02', 'ghetto', 'manchester', 'medical_build', 'metropolitan_city', 'slum']
senticnet['mediocre'] = ['-0.92', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.88', 'medium', 'mediocrity', 'bad', 'mean', 'median']
senticnet['mediocrity'] = ['-0.70', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.84', 'ordinariness', 'commonplaceness', 'lack_of_inspiration', 'passableness', 'adequacy']
senticnet['meditate'] = ['0', '0', '0', '0.729', '#admiration', '#admiration', 'positive', '0.729', 'drink_cup_coffee', 'read_good_book', 'feel_little_tired', 'toss_turn', 'get_sleep']
senticnet['meditation'] = ['0.088', '0.135', '0', '0', '#joy', '#interest', 'positive', '0.112', 'watch_others', 'meditate', 'imagine', 'mantra', 'dance_music']
senticnet['meditation_retreat'] = ['0.088', '0.135', '0', '0', '#joy', '#interest', 'positive', '0.112', 'watch_others', 'meditate', 'imagine', 'mantra', 'dance_music']
senticnet['meditation_technique'] = ['0.088', '0.135', '0', '0', '#joy', '#interest', 'positive', '0.112', 'watch_others', 'meditate', 'imagine', 'mantra', 'dance_music']
senticnet['meditational'] = ['0.088', '0.135', '0', '0', '#joy', '#interest', 'positive', '0.112', 'watch_others', 'meditate', 'imagine', 'mantra', 'dance_music']
senticnet['meditative'] = ['0', '0.89', '0', '0.788', '#interest', '#admiration', 'positive', '0.839', 'pondering', 'ruminative', 'broody', 'contemplative', 'think']
senticnet['meditative_concentration'] = ['0.668', '-0.87', '0', '0.606', '#joy', '#surprise', 'positive', '0.715', 'adeptness', 'adroitness', 'deftness', 'quickness', 'expertness']
senticnet['meditative_practice'] = ['0.668', '-0.87', '0', '0.606', '#joy', '#surprise', 'positive', '0.715', 'adeptness', 'adroitness', 'deftness', 'quickness', 'expertness']
senticnet['meditatively'] = ['-0.71', '0', '0.906', '-0.93', '#anger', '#disgust', 'negative', '-0.85', 'absent', 'forgetful', 'distracted', 'inattentive', 'scatty']
senticnet['meditator'] = ['0', '0', '-0.71', '-0.84', '#fear', '#disgust', 'negative', '-0.77', 'remember', 'commemorate_event', 'remember_past', 'use_memory', 'honor_person']
senticnet['mediterranean_cruise'] = ['0.878', '0.84', '0', '0.91', '#joy', '#admiration', 'positive', '0.876', 'stumble', 'slip', 'fall', 'trip', 'tumble']
senticnet['mediterranean_cuisine'] = ['0.882', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.822', 'ferment_grape_juice', 'chef_special', 'bottle_wine', 'fine_dining', 'decanter']
senticnet['mediterranean_fruit_fly'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'nuisance', 'annoyance', 'irritant', 'pain_in_ass', 'troublesome']
senticnet['mediterranean_sea'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'pacific_ocean', 'small_lake', 'mere']
senticnet['medium'] = ['0.039', '0.083', '0', '0.186', '#interest', '#admiration', 'positive', '0.103', 'media', 'grammar_part', 'output', 'description', 'ads']
senticnet['medium-duty'] = ['0', '0', '-0.86', '-0.85', '#fear', '#disgust', 'negative', '-0.85', 'light-duty', 'heavy-duty', 'diesel-powered', 'on-highway', 'off-highway']
senticnet['medium-range'] = ['-0.77', '0', '0.772', '0', '#sadness', '#anger', 'negative', '-0.77', 'long-range', 'missile', 'short-range', 'surface-to-surface', 'air-to-air']
senticnet['medium-scale'] = ['-0.83', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.84', 'large-scale', 'smaller-scale', 'farming', 'larger-scale', 'small-scale']
senticnet['medium_bowl'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'salad_shaker', 'store_shelf', 'kitchen_tool', 'pliers', 'ladle']
senticnet['medium_brown_hair'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'plato', 'natural_world', 'hair', 'feature', 'stick_out']
senticnet['medium_chain_triglyceride'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
senticnet['medium_heat'] = ['-0.66', '0', '0.992', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'cold', 'boil', 'temperature', 'low_temperature', 'frigidity']
senticnet['medium_pacer'] = ['-0.13', '0', '-0.11', '-0.04', '#sadness', '#fear', 'negative', '-0.1', 'concern_health', 'medical_problem', 'strengthful', 'surgery', 'injury']
senticnet['medium_red_onion'] = ['-0.95', '0', '0.845', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'grapefruit_peel', 'lemon_peel', 'orange_peel', 'candied_fruit', 'succade']
senticnet['medium_saucepan'] = ['-0.66', '0', '0.992', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'cold', 'boil', 'temperature', 'low_temperature', 'frigidity']
senticnet['medium_scale'] = ['-0.05', '0', '-0.12', '-0.16', '#fear', '#disgust', 'negative', '-0.11', 'reduced', 'tiny', 'small_size', 'narrow', 'poor']
senticnet['medium_size'] = ['-0.05', '0', '-0.12', '-0.16', '#fear', '#disgust', 'negative', '-0.11', 'reduced', 'tiny', 'small_size', 'narrow', 'poor']
senticnet['medium_sized_bird'] = ['-0.59', '0', '0.887', '-0.71', '#anger', '#disgust', 'negative', '-0.73', 'armet', 'basinet', 'helmet', 'heaume', 'morion']
senticnet['medium_sized_passerine_bird'] = ['-0.92', '0', '0.64', '-0.52', '#sadness', '#anger', 'negative', '-0.69', 'animal_order', 'percher', 'thyreophoran', 'marginocephalian', 'euronithopod']
senticnet['medium_sized_tree'] = ['-0.80', '0', '0.555', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'bush', 'wormwood', 'milkwort', 'germander', 'centaury']
senticnet['medium_tank'] = ['-0.90', '0', '0.517', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'outdated', 'noncurrent', 'disused', 'obsolete', 'superannuated']
senticnet['medium_tempo'] = ['0.155', '-0.11', '0.009', '-0.05', '#joy', '#surprise', 'positive', '0.05', 'dance_well', 'relate_music', 'enter_contest', 'jump_around', 'cheer_up']
senticnet['mediumistic'] = ['0.728', '-0.72', '0', '0.881', '#joy', '#admiration', 'positive', '0.779', 'predict_future', 'psychic_phenomenon', 'parapsychology', 'precognition', 'foreknowledge']
senticnet['mediumship'] = ['0', '0.871', '0', '0.861', '#interest', '#admiration', 'positive', '0.866', 'predict_future', 'psychic_phenomenon', 'parapsychology', 'clairvoyance', 'foreknowledge']
senticnet['medival'] = ['-0.99', '0', '0.947', '-0.88', '#sadness', '#anger', 'negative', '-0.94', 'atrociousness', 'inhumanity', 'heinousness', 'atrocity', 'barbarity']
senticnet['medley'] = ['0.773', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.844', 'favorite', 'crowd', 'acclamation', 'bunch', 'large_crowd']
senticnet['medley_swimmer'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'chlorine', 'lifeguard', 'still_water', 'large_body', 'shallow_water']
senticnet['medora'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['medrese'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['medroxyprogesterone_acetate'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'spironolactone', 'adrenal_cortical_steroid', 'antihypertensive', 'antihypertensive_drug']
senticnet['medullary_cavity'] = ['-0.74', '0', '0.598', '-0.75', '#sadness', '#disgust', 'negative', '-0.69', 'lunate_bone', 'semilunar_bone', 'carpal_bone', 'carpal', 'bone']
senticnet['meek'] = ['0.625', '-0.50', '0', '0.822', '#joy', '#admiration', 'positive', '0.649', 'docile', 'tame', 'yielding', 'sheeplike', 'sheepish']
senticnet['meekly'] = ['0.923', '0', '0', '0.87', '#joy', '#admiration', 'positive', '0.897', 'stoically', 'mutely', 'glumly', 'obediently', 'sullenly']
senticnet['meekness'] = ['-0.83', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.88', 'submission', 'self-deprecating', 'self-depreciation', 'humbleness', 'demeaning']
senticnet['meena_bazar'] = ['0', '0.113', '0.041', '0.058', '#interest', '#admiration', 'positive', '0.043', 'corner_shop', 'general_store', 'many_shops', 'large_supermarket', 'goods']
senticnet['meenakari'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'anklet', 'ankle_bracelet', 'armilla']
senticnet['meese_commission'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'indecency']
senticnet['meet'] = ['0.62', '-0.91', '0', '0.766', '#surprise', '#admiration', 'positive', '0.768', 'accommodate', 'satisfy', 'fulfill', 'conform_to', 'please']
senticnet['meet_at_coffee_shop'] = ['0', '0', '0', '0.077', '#admiration', '#admiration', 'positive', '0.077', 'find_person', 'contact_friend', 'arrange_meet', 'chat', 'call_phone']
senticnet['meet_diagnostic_criterion'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'acknowledgeable', 'specifiable', 'recognizable', 'identifiable', 'classifiable']
senticnet['meet_family'] = ['0.101', '-0.23', '0.031', '0.105', '#surprise', '#admiration', 'positive', '0.103', 'finish_work', 'go_home', 'relax_time', 'go_to_sleep', 'end_day']
senticnet['meet_famous_person'] = ['0.878', '0', '0', '0.782', '#joy', '#admiration', 'positive', '0.83', 'eat_together', 'feel_pleasure', 'show_love', 'gain_respect', 'hug']
senticnet['meet_friend'] = ['0.048', '-0.08', '0.036', '0', '#joy', '#surprise', 'positive', '0.031', 'meet_person', 'chit_chat', 'make_friend', 'meet_girl', 'socialize']
senticnet['meet_friend_for_lunch'] = ['0.751', '0.987', '0', '0', '#joy', '#interest', 'positive', '0.869', 'eat_pudding', 'eat_food', 'serve_meal', 'eat_out', 'consume_nourishment']
senticnet['meet_friends'] = ['0.751', '0.987', '0', '0', '#joy', '#interest', 'positive', '0.869', 'meet_new_friend', 'friend', 'go_outdoors', 'have_fun', 'meet_friend_for_lunch']
senticnet['meet_girl'] = ['0.05', '-0.05', '0', '0.054', '#surprise', '#admiration', 'positive', '0.054', 'flirt', 'make_friend', 'meet_friend', 'enjoy_company_friend', 'meet_interesting_person']
senticnet['meet_goal'] = ['0.792', '0.874', '0', '0', '#joy', '#interest', 'positive', '0.833', 'make_money', 'receive_money', 'work_hard', 'make_live', 'support_family']
senticnet['meet_greet'] = ['0', '0', '0', '0.768', '#admiration', '#admiration', 'positive', '0.768', 'offer_help', 'desire_help', 'reply_question', 'lower_expectation', 'erase_blackboard']
senticnet['meet_informally'] = ['0.06', '0', '0.014', '0.089', '#joy', '#admiration', 'positive', '0.045', 'social_skill', 'socialize', 'greet', 'charisma', 'communicate']
senticnet['meet_interesting_person'] = ['0.05', '-0.05', '0', '0.054', '#surprise', '#admiration', 'positive', '0.054', 'flirt', 'make_friend', 'meet_friend', 'enjoy_company_friend', 'meet_girl']
senticnet['meet_member_opposite_sex'] = ['0.885', '0.868', '0', '0', '#joy', '#interest', 'positive', '0.877', 'social_interaction', 'get_drunk', 'get_hammered', 'good_time', 'get_to_know_person']
senticnet['meet_monthly'] = ['0.065', '0.113', '0', '0.1', '#interest', '#admiration', 'positive', '0.093', 'big_group', 'clique', 'group_of_people', 'multitude', 'large_group']
senticnet['meet_need'] = ['0.842', '0.787', '0', '0', '#joy', '#interest', 'positive', '0.815', 'responsibility', 'obligation', 'ask_nicely', 'help_person', 'animal_person']
senticnet['meet_neighbor'] = ['0', '0.097', '0', '0.059', '#interest', '#admiration', 'positive', '0.078', 'say_hi', 'neighbor', 'talk_neighbor', 'see_neighbor', 'help_neighbor']
senticnet['meet_new_friend'] = ['0.771', '0.792', '0', '0', '#joy', '#interest', 'positive', '0.782', 'win_loose', 'restore_energy', 'blow_off_steam', 'meet_others', 'feel_rejuvenated']
senticnet['meet_new_person'] = ['0.763', '0.805', '0', '0', '#joy', '#interest', 'positive', '0.784', 'become_drunk', 'get_wasted', 'act_stupid', 'pick_up_woman', 'drunkenness']
senticnet['meet_old_friend'] = ['-0.11', '-0.04', '0', '-0.10', '#sadness', '#disgust', 'negative', '-0.05', 'get_lose', 'arrive_late', 'get_stick_traffic', 'honk_horn', 'plan_day']
senticnet['meet_others'] = ['0.741', '0.898', '0', '0.734', '#joy', '#interest', 'positive', '0.791', 'good_time', 'get_know', 'make_new_friend', 'get_to_know_person', 'social_interaction']
senticnet['meet_people'] = ['0.985', '0', '0', '0.931', '#joy', '#admiration', 'positive', '0.958', 'get_social_contact', 'go_party', 'go_out', 'expand_horizon', 'cultural_experience']
senticnet['meet_person'] = ['0.048', '-0.08', '0.036', '0', '#joy', '#surprise', 'positive', '0.031', 'chit_chat', 'meet_friend', 'make_friend', 'meet_girl', 'socialize']
senticnet['meet_place'] = ['0.092', '0', '0', '0', '#joy', '#joy', 'positive', '0.092', 'open_night', 'metal_stick', 'hangout', 'water_hole', 'place_to_drink']
senticnet['meet_regularly'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'sexual_relationship', 'affair', 'involvement']
senticnet['meet_up'] = ['0.12', '-0.19', '0', '-0.09', '#joy', '#surprise', 'positive', '0.072', 'social_gathering', 'bash', 'radical', 'gathering', 'get_together']
senticnet['meet_weekly'] = ['0.065', '0.113', '0', '0.1', '#interest', '#admiration', 'positive', '0.093', 'big_group', 'clique', 'group_of_people', 'multitude', 'large_group']
senticnet['meet_woman'] = ['0.081', '-0.05', '0', '-0.03', '#joy', '#surprise', 'positive', '0.036', 'drink_alcohol', 'consume_alcoholic_beverage', 'drink_much', 'hit_bottle', 'drink_liquor']
senticnet['meeting'] = ['0.044', '0.08', '-0.07', '0.095', '#interest', '#admiration', 'positive', '0.035', 'group_meet', 'diplomacy', 'head_committee', 'proverb', 'gathering']
senticnet['meetly'] = ['0.885', '0', '0', '0.835', '#joy', '#admiration', 'positive', '0.86', 'aright', 'aptly', 'gradely', 'properly', 'proper']
senticnet['mefanoxam'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'rust_fungus', 'fungus']
senticnet['meg'] = ['-0.03', '-0.00', '0.087', '-0.05', '#anger', '#disgust', 'negative', '-0.04', 'dog_fur', 'dog_cat', 'dog_hair', 'pet', 'animal_hair']
senticnet['mega_hit'] = ['0.625', '-0.90', '0', '0.54', '#joy', '#surprise', 'positive', '0.69', 'smash', 'bang', 'smasher', 'strike', 'hit']
senticnet['mega_million_jackpot'] = ['0.858', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.813', 'box_office', 'movie', 'cctv', 'cinema', 'movie_theater']
senticnet['mega_million_lottery'] = ['0.858', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.813', 'box_office', 'movie', 'cctv', 'cinema', 'movie_theater']
senticnet['mega_ramp'] = ['-0.18', '0', '-0.14', '-0.11', '#sadness', '#fear', 'negative', '-0.14', 'fly_kite', 'walk_dog', 'ride_roller_coaster', 'rake_leave', 'jump_up_down']
senticnet['mega_star'] = ['0', '0.793', '0', '0.91', '#interest', '#admiration', 'positive', '0.852', 'superstar', 'vip', 'accomplishment', 'performance', 'concert']
senticnet['mega_stardom'] = ['0', '0.793', '0', '0.91', '#interest', '#admiration', 'positive', '0.852', 'superstar', 'vip', 'accomplishment', 'performance', 'concert']
senticnet['megachilidae_family'] = ['0', '0', '0', '0.869', '#admiration', '#admiration', 'positive', '0.869', 'fly_animal', 'insect', 'mosquito', 'bug', 'fly_insect']
senticnet['megacolon'] = ['-0.99', '0', '0.964', '-0.70', '#sadness', '#anger', 'negative', '-0.88', 'blepharitis', 'inflammation', 'epiglottitis', 'orchitis', 'prostatitis']
senticnet['megadeal'] = ['-0.04', '0.274', '-0.15', '0.278', '#interest', '#admiration', 'positive', '0.089', 'business_deal', 'trade', 'exchange', 'bargain', 'offer']
senticnet['megahertz_band'] = ['-0.07', '0.078', '0', '0.078', '#interest', '#admiration', 'positive', '0.028', 'red_blue', 'red_orange', 'green_yellow', 'hue_tint', 'yellow_green']
senticnet['megahit'] = ['0.916', '-0.93', '0', '0.674', '#joy', '#surprise', 'positive', '0.841', 'smash', 'bang', 'smasher', 'strike', 'hit']
senticnet['megalania'] = ['-0.02', '0', '0.044', '0', '#sadness', '#anger', 'negative', '-0.03', 'large_cat', 'fast_person', 'household_pet', 'big_cat', 'good_pet']
senticnet['megaloblastic_anemia'] = ['-0.54', '0', '0.92', '-0.84', '#anger', '#disgust', 'negative', '-0.76', 'dearth', 'demand', 'mineral_deficiency', 'famine', 'need']
senticnet['megalomania'] = ['0.618', '-0.59', '0', '0.773', '#joy', '#admiration', 'positive', '0.662', 'insanity', 'psychosis', 'psychopathy', 'psychological_state', 'psychological_condition']
senticnet['megalomaniacal'] = ['-0.04', '0.17', '0.676', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maliciously', 'spitefulness', 'maliciousness', 'maleficence', 'malefic']
senticnet['megalon'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amuck', 'murderousness', 'misocainea', 'misogyny']
senticnet['megaman'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
senticnet['megaplex'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'theater', 'drive-in_movie', 'watch_movie', 'cinema', 'movie']
senticnet['megaplier'] = ['0.858', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.813', 'box_office', 'movie', 'cctv', 'cinema', 'movie_theater']
senticnet['megapodiidae_family'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'lovebird', 'parroket', 'paraquet', 'paroquet']
senticnet['megastar'] = ['0', '0.793', '0', '0.91', '#interest', '#admiration', 'positive', '0.852', 'superstar', 'vip', 'accomplishment', 'performance', 'concert']
senticnet['megastore'] = ['0.041', '0.098', '0', '0', '#joy', '#interest', 'positive', '0.07', 'utopia', 'java', 'memorial', 'nowhere', 'coffee']
senticnet['megaton'] = ['0', '0', '0.859', '-0.81', '#anger', '#disgust', 'negative', '-0.83', 'kiloton', 'warhead', 'megaton', 'thermonuclear', 'nuke']
senticnet['megaton_bomb'] = ['0', '0', '-0.72', '-0.94', '#fear', '#disgust', 'negative', '-0.83', 'pistol', 'gun', 'hydrogen_bomb', 'handgun', 'revolver']
senticnet['megawatt_wind_power'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'breeze', 'windmill', 'wind', 'wind_turbine', 'windy']
senticnet['megestrol'] = ['-0.72', '0', '0.973', '-0.57', '#sadness', '#anger', 'negative', '-0.76', 'corpulency', 'blubber', 'fat', 'embonpoint', 'stoutness']
senticnet['megpoid'] = ['0.028', '0', '0.027', '0.101', '#joy', '#admiration', 'positive', '0.034', 'animal_dwell', 'bee_home', 'live_place', 'dwell_place', 'place_to_live']
senticnet['megrez'] = ['0.977', '-0.93', '0', '0.583', '#joy', '#surprise', 'positive', '0.831', 'giant', 'shinny', 'supernova', 'supergiant', 'binary_star']
senticnet['megrim'] = ['-0.84', '0.205', '-0.48', '-0.03', '#sadness', '#fear', 'negative', '-0.29', 'depressed', 'sadness', 'blues', 'feel_blue', 'demoralization']
senticnet['mehinku'] = ['-0.84', '0', '0.579', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'squiggle', 'hand', 'chicken_scratch', 'handwriting', 'script']
senticnet['mehmet_okur'] = ['0.556', '-0.79', '0', '0.755', '#surprise', '#admiration', 'positive', '0.701', 'lush', 'alcoholic', 'alky', 'dipsomaniac', 'rummy']
senticnet['mei_lan'] = ['0.02', '0.071', '0', '0.069', '#interest', '#admiration', 'positive', '0.053', 'many_animals', 'donkey', 'chimpanzee', 'cute_animal', 'creature']
senticnet['mei_sheng'] = ['0.02', '0.071', '0', '0.069', '#interest', '#admiration', 'positive', '0.053', 'many_animals', 'donkey', 'chimpanzee', 'cute_animal', 'creature']
senticnet['mei_xiang'] = ['-0.95', '0', '0.735', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'novice', 'beginner', 'tyro', 'tiro', 'initiate']
senticnet['mein_herz'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['meinhardt_raabe'] = ['0.931', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.855', 'crystal_clear', 'blue_sky', 'imaginary', 'water_sky', 'blue_color']
senticnet['meiosis'] = ['-0.87', '0', '0.76', '0', '#sadness', '#anger', 'negative', '-0.81', 'nondisjunction', 'miosis', 'cell_division', 'litotes', 'understatement']
senticnet['meiotic_nondisjunction'] = ['0.504', '-0.93', '0', '0.797', '#surprise', '#admiration', 'positive', '0.747', 'diabetes', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['meiotically'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'nonmotile', 'immobile']
senticnet['meison'] = ['-0.50', '0', '0.989', '-0.91', '#anger', '#disgust', 'negative', '-0.80', 'camp', 'pack', 'clique', 'coterie', 'ingroup']
senticnet['mek_quake'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amuck', 'murderousness', 'misocainea', 'misogyny']
senticnet['mekteb'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'caravansary', 'caravanserai', 'khan', 'caravan_inn', 'hostel']
senticnet['mektebi'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'caravansary', 'caravanserai', 'khan', 'caravan_inn', 'hostel']
senticnet['mel_din'] = ['0', '0.105', '0', '0.084', '#interest', '#admiration', 'positive', '0.095', 'archive', 'print_medium', 'quiet_place', 'register', 'media']
senticnet['mel_fisher'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'succourer', 'recoverer', 'salvor', 'mortal']
senticnet['mel_gussow'] = ['0.811', '-0.63', '0', '0.959', '#joy', '#admiration', 'positive', '0.8', 'drama_critic', 'critic', 'professional', 'art_critic', 'literary_critic']
senticnet['mel_smith'] = ['-0.89', '0', '0.58', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'resemblance', 'comparison', 'equivalence', 'reflection', 'alikeness']
senticnet['melancholia'] = ['-0.74', '0.292', '-0.42', '-0.06', '#sadness', '#fear', 'negative', '-0.23', 'lowness', 'dispiritedness', 'low-spiritedness', 'dejectedness', 'downheartedness']
senticnet['melancholiac'] = ['-0.64', '0', '0.854', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'melancholic', 'sick_person', 'diseased_person', 'sufferer', 'depressive']
senticnet['melancholic'] = ['-0.74', '0.292', '-0.42', '-0.06', '#sadness', '#fear', 'negative', '-0.23', 'lowness', 'dispiritedness', 'low-spiritedness', 'dejectedness', 'downheartedness']
senticnet['melancholic_tone'] = ['-0.74', '0.292', '-0.42', '-0.06', '#sadness', '#fear', 'negative', '-0.23', 'lowness', 'dispiritedness', 'low-spiritedness', 'dejectedness', 'downheartedness']
senticnet['melancholy'] = ['-0.95', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.96', 'unhappy', 'sorrow', 'guilty', 'grim', 'heartache']
senticnet['melancholy_mood'] = ['0.593', '0.647', '-0.23', '0.272', '#joy', '#interest', 'positive', '0.319', 'joyfully', 'joyously', 'gleefully', 'joyfulness', 'bonheur']
senticnet['melancholy_tone'] = ['-0.74', '0.292', '-0.42', '-0.06', '#sadness', '#fear', 'negative', '-0.23', 'lowness', 'dispiritedness', 'low-spiritedness', 'dejectedness', 'downheartedness']
senticnet['melanin'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['melanin_pigment'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['melanocytic'] = ['-0.88', '0', '0.774', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fibroid_tumor', 'fibroid', 'benign_tumor', 'nonmalignant_tumor', 'nonmalignant_neoplasm']
senticnet['melanocytic_nevus'] = ['-0.66', '0', '0.911', '-0.99', '#anger', '#disgust', 'negative', '-0.85', 'liver_spot', 'spot', 'freckle', 'macule', 'plague_spot']
senticnet['melasma'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'radiation_therapy', 'radiation', 'actinotherapy', 'irradiation']
senticnet['melbourne_cup_winner'] = ['0.744', '-0.77', '0', '0.661', '#joy', '#surprise', 'positive', '0.725', 'sugarcoat', 'honey', 'glaze', 'change_taste', 'sugar']
senticnet['melbourne_rod_laver'] = ['0.026', '-0.03', '0', '0.091', '#surprise', '#admiration', 'positive', '0.051', 'stadium', 'auditorium', 'television_station', 'music_hall', 'movie_theater']
senticnet['melbourne_storm'] = ['0', '0.996', '0', '0.85', '#interest', '#admiration', 'positive', '0.923', 'strength', 'strong', 'physical_strength', 'athlete', 'muscle']
senticnet['melbourne_town_hall'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'city_hall', 'courthouse', 'subway_station', 'public_place', 'bus_station']
senticnet['melee'] = ['0', '-0.01', '-0.08', '-0.16', '#fear', '#disgust', 'negative', '-0.07', 'siege', 'big_fight', 'battle', 'declare_war', 'conflict']
senticnet['melee_fighter'] = ['0.586', '-0.94', '0', '0.748', '#surprise', '#admiration', 'positive', '0.76', 'vanquish', 'beat', 'beat_out', 'trounce', 'crush']
senticnet['melee_weapon'] = ['0.757', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.831', 'baseball_bat', 'water_gun', 'goalie', 'hockey_puck', 'hockey_stick']
senticnet['meliaceae'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'seed_fern', 'pteridosperm']
senticnet['melilot'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['melilotus_alba'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
senticnet['meliorate'] = ['0.526', '-0.86', '0', '0.932', '#surprise', '#admiration', 'positive', '0.773', 'assuage', 'perfect', 'fructify', 'relieve', 'sublimate']
senticnet['meliorative'] = ['0.72', '0.779', '0', '0', '#joy', '#interest', 'positive', '0.75', 'salutary', 'remedial', 'wholesome', 'salute', 'medicinal']
senticnet['meliorist'] = ['-0.71', '0', '0.967', '-0.68', '#sadness', '#anger', 'negative', '-0.78', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['meliosma'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'bulrush', 'nailrod', 'cattail', 'reedmace']
senticnet['melipona'] = ['0', '0', '0', '0.869', '#admiration', '#admiration', 'positive', '0.869', 'fly_animal', 'insect', 'mosquito', 'bug', 'fly_insect']
senticnet['melismatic'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['melkersson_rosenthal_syndrome'] = ['-0.87', '0', '0.531', '-0.68', '#sadness', '#disgust', 'negative', '-0.69', 'rubor', 'redness', 'dermatomyositis', 'inclusion_body_myositis', 'inflammation']
senticnet['melle_mel'] = ['-0.86', '0', '0.675', '-0.84', '#sadness', '#disgust', 'negative', '-0.79', 'shenanigan', 'chicanery', 'wile', 'dupery', 'trickery']
senticnet['mellifera'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'hornet', 'vespid', 'vespid_wasp']
senticnet['mellifluous'] = ['0.704', '-0.50', '0', '0.923', '#joy', '#admiration', 'positive', '0.71', 'melodic', 'mellisonant', 'dulcet', 'lyrical', 'honeyed']
senticnet['mellifluous_voice'] = ['-0.71', '0', '0.998', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'tone', 'quality', 'twang', 'nasality', 'timber']
senticnet['mellisonant'] = ['0.704', '-0.50', '0', '0.923', '#joy', '#admiration', 'positive', '0.71', 'mellifluous', 'melodic', 'dulcet', 'lyrical', 'honeyed']
senticnet['mello_yello'] = ['0.964', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.964', 'carbonated_drink', 'sugar_free', 'coke', 'diet_coke', 'beverage']
senticnet['mellow'] = ['0.75', '0.71', '0', '0', '#joy', '#interest', 'positive', '0.73', 'spherical_shape', 'use_sport', 'flexible', 'ball', 'fill_air']
senticnet['mellowness'] = ['0.593', '-0.65', '0', '0.711', '#surprise', '#admiration', 'positive', '0.654', 'naturalness', 'condescension', 'condescendingness', 'affability', 'friendliness']
senticnet['melmond'] = ['0.643', '0.64', '-0.24', '0.3', '#joy', '#interest', 'positive', '0.336', 'high_spirits', 'elating', 'exhilarating', 'elated', 'cheerful']
senticnet['melodic'] = ['0.704', '-0.50', '0', '0.923', '#joy', '#admiration', 'positive', '0.71', 'mellifluous', 'mellisonant', 'dulcet', 'lyrical', 'honeyed']
senticnet['melodic_death_metal'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'explicit', 'declared', 'definitive', 'graphic']
senticnet['melodic_invention'] = ['0.824', '-0.95', '0', '0.513', '#joy', '#surprise', 'positive', '0.765', 'clearcutness', 'explicitness', 'preciseness', 'perspicuity', 'perspicuousness']
senticnet['melodic_line'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['melodic_material'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['melodic_noise'] = ['0.104', '-0.01', '0.012', '0.141', '#joy', '#admiration', 'positive', '0.061', 'instrumental', 'audio', 'vocal_music', 'lyric', 'universal_language']
senticnet['melodic_phrase'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['melodic_style'] = ['0.609', '-0.61', '0', '0.592', '#joy', '#surprise', 'positive', '0.605', 'spirit', 'rabidity', 'rabidness', 'sprightliness', 'ebullience']
senticnet['melodicism'] = ['0.75', '0.71', '0', '0', '#joy', '#interest', 'positive', '0.73', 'spherical_shape', 'use_sport', 'flexible', 'ball', 'fill_air']
senticnet['melodious'] = ['0.024', '0.188', '-0.10', '0.304', '#interest', '#admiration', 'positive', '0.103', 'woolly_animal', 'goofy', 'big_person', 'beatles_song', 'song']
senticnet['melodrama'] = ['-0.90', '0', '0.816', '0', '#sadness', '#anger', 'negative', '-0.86', 'cloak-and-dagger', 'melodramatically', 'opera', 'cheese', 'melodramatic']
senticnet['melodramatic'] = ['-0.89', '0', '0.824', '0', '#sadness', '#anger', 'negative', '-0.85', 'cloak-and-dagger', 'melodramatically', 'opera', 'cheese', 'melodrama']
senticnet['melodramatically'] = ['-0.89', '0', '0.815', '0', '#sadness', '#anger', 'negative', '-0.85', 'cloak-and-dagger', 'melodramatic', 'opera', 'cheese', 'melodrama']
senticnet['melody'] = ['0.879', '-0.96', '0', '0.7', '#joy', '#surprise', 'positive', '0.848', 'music', 'flourish', 'tune', 'musical', 'signature']
senticnet['melody_maker'] = ['0.7', '-0.62', '0', '0.58', '#joy', '#surprise', 'positive', '0.636', 'white_magic', 'thaumaturgy', 'juju', 'conjuration', 'supernaturalism']
senticnet['melon'] = ['-0.94', '0', '0.728', '0', '#sadness', '#anger', 'negative', '-0.83', 'gourd', 'gourd', 'birdhouse', 'pumpkin', 'calabash']
senticnet['melt'] = ['0.919', '0.88', '0', '0', '#joy', '#interest', 'positive', '0.899', 'carve', 'char', 'last_long_time', 'heat', 'fire_product']
senticnet['melt-down'] = ['0', '0', '0.932', '-0.91', '#anger', '#disgust', 'negative', '-0.92', 'nosedive', 'disarray', 'tail-spin', 'tizzy', 'tailspin']
senticnet['melt_away'] = ['-0.18', '-0.00', '-0.18', '0', '#sadness', '#fear', 'negative', '-0.11', 'pulverise', 'violent_action', 'smash', 'hit_hard', 'pulverize']
senticnet['melt_butter'] = ['-0.83', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.78', 'picaninny', 'kid', 'minor', 'tike', 'small_picaninny']
senticnet['melt_off'] = ['0', '0', '0.926', '-0.94', '#anger', '#disgust', 'negative', '-0.93', 'dilute', 'reduce', 'thin', 'cut', 'lose_weight']
senticnet['meltdown'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'fragmentation', 'discharge', 'backfire', 'blast']
senticnet['melted_brie'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'aridness', 'dryness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['melted_cheese'] = ['0', '0', '-0.72', '-0.92', '#fear', '#disgust', 'negative', '-0.82', 'bee_product', 'flammable', 'remove_hair', 'slick', 'really_hot']
senticnet['melted_ice'] = ['-0.03', '0', '0.014', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'ice_crystal', 'frozen_water', 'wet_cold', 'coldness', 'wetness']
senticnet['melted_snow'] = ['-0.03', '0', '0.014', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'ice_crystal', 'frozen_water', 'wet_cold', 'coldness', 'wetness']
senticnet['melted_wax'] = ['0', '0.232', '-0.10', '0.156', '#interest', '#admiration', 'positive', '0.094', 'light', 'eat_cake', 'birthday_party', 'candle_light', 'romantic_atmosphere']
senticnet['melting_point'] = ['0.695', '-0.85', '0', '0.501', '#joy', '#surprise', 'positive', '0.684', 'alternative_energy', 'energy', 'energy_of_activation', 'atomic_energy', 'nuclear_energy']
senticnet['melting_temperature'] = ['0.695', '-0.85', '0', '0.501', '#joy', '#surprise', 'positive', '0.684', 'alternative_energy', 'energy', 'energy_of_activation', 'atomic_energy', 'nuclear_energy']
senticnet['meltingly'] = ['0.112', '0', '0', '0.141', '#joy', '#admiration', 'positive', '0.127', 'aftertaste', 'sweet_food', 'bake_good', 'round_pastry', 'eatable']
senticnet['member'] = ['0.112', '0.117', '-0.07', '0.114', '#interest', '#admiration', 'positive', '0.068', 'social_group', 'association', 'team_up', 'group_of_people', 'human_group']
senticnet['member_jury'] = ['0.031', '0.107', '0', '0.065', '#interest', '#admiration', 'positive', '0.068', 'negotiator', 'juror', 'jury', 'judge', 'justice']
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senticnet['memory_subsystem'] = ['0.792', '-0.83', '0', '0.575', '#joy', '#surprise', 'positive', '0.734', 'connectedness', 'connection', 'consistency', 'continuity', 'link']
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senticnet['men_clothing_store'] = ['0.026', '0.082', '0', '0', '#joy', '#interest', 'positive', '0.054', 'account_department', 'office', 'accountant_office', 'shirt_pocket', 'desk_drawer']
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senticnet['mensal_line'] = ['0.603', '-0.98', '0', '0.807', '#surprise', '#admiration', 'positive', '0.797', 'crease', 'crinkle', 'wrinkle', 'furrow', 'seam']
senticnet['menstrual_cycle'] = ['0.995', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.918', 'tell_everyone', 'reason', 'clarify', 'forethought', 'opinion']
senticnet['menstrual_flow'] = ['-0.89', '0', '0.925', '-0.58', '#sadness', '#anger', 'negative', '-0.80', 'shit', 'laxation', 'movement', 'dump', 'asshole']
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senticnet['menstrual_period'] = ['0.995', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.918', 'tell_everyone', 'reason', 'clarify', 'forethought', 'opinion']
senticnet['menstrual_regulation'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'birth_control_pill', 'condom', 'contraceptive_pill', 'sex_toy', 'prevent_pregnancy']
senticnet['menstruate'] = ['0.995', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.918', 'tell_everyone', 'reason', 'clarify', 'forethought', 'opinion']
senticnet['menstruating_woman'] = ['-0.69', '0', '0.858', '-0.83', '#anger', '#disgust', 'negative', '-0.79', 'draggled', 'befouled', 'filthy', 'nasty', 'scummy']
senticnet['menstruation'] = ['0', '-0.04', '0.018', '0.165', '#surprise', '#admiration', 'positive', '0.063', 'know_time', 'timekeeper', 'taste_sweet', 'tell_time', 'comfort_friend']
senticnet['menstruum'] = ['-0.67', '0', '0.863', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'incompressibility', 'substance', 'firmness', 'consistency', 'hardness']
senticnet['menswear'] = ['0.053', '-0.01', '-0.05', '0.121', '#joy', '#admiration', 'positive', '0.034', 'coat', 'winter_clothes', 'stylish', 'adorn', 'protective_cover']
senticnet['menswear_collection'] = ['0.053', '-0.01', '-0.05', '0.121', '#joy', '#admiration', 'positive', '0.034', 'coat', 'winter_clothes', 'stylish', 'adorn', 'protective_cover']
senticnet['menswear_line'] = ['0', '-0.03', '0.022', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'shoulder_bag', 'satchel', 'water_bottle', 'tote_bag', 'carry']
senticnet['menswear_shop'] = ['-0.07', '0.228', '0.019', '0.089', '#interest', '#admiration', 'positive', '0.056', 'home_depot', 'lumberyard', 'furniture_store', 'front_porch', 'carpet_store']
senticnet['menswear_store'] = ['-0.05', '-0.04', '0.017', '-0.05', '#sadness', '#disgust', 'negative', '-0.02', 'shoe_shop', 'hamper', 'attic', 'obstruct', 'closet']
senticnet['mental'] = ['-0.03', '0.21', '-0.05', '0.093', '#interest', '#admiration', 'positive', '0.052', 'intellect', 'new_think', 'eureka', 'idea', 'cognition']
senticnet['mental_abnormality'] = ['0', '0', '0.896', '-0.96', '#anger', '#disgust', 'negative', '-0.92', 'antisocial', 'sociopathic', 'psychopathic_personality', 'disturbance', 'schizoid']
senticnet['mental_activity'] = ['0.993', '0.848', '0', '0', '#joy', '#interest', 'positive', '0.92', 'imaginative', 'use_imagination', 'use_brain', 'arrive_conclusion', 'eureka']
senticnet['mental_agony'] = ['-0.61', '0.262', '-0.38', '-0.22', '#sadness', '#fear', 'negative', '-0.23', 'humiliation', 'shame', 'misery', 'fright', 'upset']
senticnet['mental_amentia'] = ['-0.61', '0', '0.818', '-0.76', '#anger', '#disgust', 'negative', '-0.73', 'slowness', 'retardation', 'backwardness', 'subnormality', 'amentia']
senticnet['mental_anguish'] = ['-0.97', '0', '0.969', '-0.50', '#sadness', '#anger', 'negative', '-0.81', 'nervousness', 'tenseness', 'tension', 'nerve', 'stress']
senticnet['mental_arithmetic'] = ['0.076', '0.096', '-0.08', '0.167', '#interest', '#admiration', 'positive', '0.063', 'statistic', 'mathematical_function', 'calculation', 'list_number', 'calculate']
senticnet['mental_asylum'] = ['-0.67', '0', '0.931', '-0.77', '#anger', '#disgust', 'negative', '-0.79', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['mental_attitude'] = ['0.701', '0.874', '0', '0.752', '#interest', '#admiration', 'positive', '0.776', 'self_satisfaction', 'enjoy_life', 'good_feeling', 'good_attitude', 'enjoy']
senticnet['mental_block'] = ['-0.78', '0', '0.707', '-0.86', '#sadness', '#disgust', 'negative', '-0.78', 'inability', 'uncreativeness', 'stupidity', 'illiteracy', 'analphabetism']
senticnet['mental_clarity'] = ['0.668', '-0.87', '0', '0.606', '#joy', '#surprise', 'positive', '0.715', 'adeptness', 'adroitness', 'deftness', 'quickness', 'expertness']
senticnet['mental_condition'] = ['0.775', '-0.63', '0', '0.68', '#joy', '#admiration', 'positive', '0.697', 'birth_trauma', 'psychological_state', 'psychological_condition', 'mental_state', 'psychic_trauma']
senticnet['mental_confusion'] = ['-0.50', '0', '0.85', '-0.82', '#anger', '#disgust', 'negative', '-0.72', 'confusion', 'disarray', 'bafflement', 'confusedness', 'muddiness']
senticnet['mental_defect'] = ['-0.74', '0', '0.893', '-0.58', '#sadness', '#anger', 'negative', '-0.73', 'encopresis', 'upset', 'psychosomatic_disorder', 'conversion_disorder', 'conversion_reaction']
senticnet['mental_defectiveness'] = ['-0.50', '0', '0.912', '-0.65', '#anger', '#disgust', 'negative', '-0.68', 'feeblemindedness', 'slowness', 'mental_retardation', 'backwardness', 'subnormality']
senticnet['mental_deficiency'] = ['-0.55', '0', '0.522', '-0.53', '#sadness', '#disgust', 'negative', '-0.53', 'slowness', 'mental_retardation', 'backwardness', 'subnormality', 'retardation']
senticnet['mental_derangement'] = ['-0.74', '0', '0.893', '-0.58', '#sadness', '#anger', 'negative', '-0.73', 'encopresis', 'upset', 'psychosomatic_disorder', 'conversion_disorder', 'conversion_reaction']
senticnet['mental_deterioration'] = ['-0.94', '0', '0.821', '-0.75', '#sadness', '#anger', 'negative', '-0.84', 'predementedness', 'insanity', 'dementedness', 'senile_psychosis', 'alcoholic_dementia']
senticnet['mental_disability'] = ['-0.77', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.78', 'chicken_pox', 'diabetes', 'lyme_disease', 'brain_tumor', 'suffer_fool']
senticnet['mental_disease'] = ['-0.95', '0', '0.902', '-0.91', '#sadness', '#disgust', 'negative', '-0.92', 'sadness', 'crazy_mind', 'mental_problem', 'depression', 'disease']
senticnet['mental_disorder'] = ['-0.72', '0', '-0.86', '-0.83', '#fear', '#disgust', 'negative', '-0.81', 'intolerance', 'extreme_fear', 'psychosis', 'insanity', 'impend_danger']
senticnet['mental_disorder_dsm'] = ['-0.55', '0', '0.736', '-0.71', '#anger', '#disgust', 'negative', '-0.66', 'neurosis', 'neuroticism', 'psychoneurosis', 'hysterocatalepsy', 'anxiety_hysteria']
senticnet['mental_disturbance'] = ['-0.74', '0', '0.893', '-0.58', '#sadness', '#anger', 'negative', '-0.73', 'encopresis', 'upset', 'psychosomatic_disorder', 'conversion_disorder', 'conversion_reaction']
senticnet['mental_exercise'] = ['0.795', '0', '0', '0.811', '#joy', '#admiration', 'positive', '0.803', 'hobby', 'use_mind', 'contemplation', 'use_brain', 'meditation']
senticnet['mental_exhaustion'] = ['-0.97', '0', '0.969', '-0.50', '#sadness', '#anger', 'negative', '-0.81', 'nervousness', 'tenseness', 'tension', 'nerve', 'stress']
senticnet['mental_faculty'] = ['0.526', '-0.74', '0', '0.862', '#surprise', '#admiration', 'positive', '0.71', 'understanding', 'intellect', 'ability', 'retentiveness', 'retentivity']
senticnet['mental_fatigue'] = ['-0.97', '0', '0.969', '-0.50', '#sadness', '#anger', 'negative', '-0.81', 'nervousness', 'tenseness', 'tension', 'nerve', 'stress']
senticnet['mental_fortitude'] = ['0.724', '-0.92', '0', '0.854', '#surprise', '#admiration', 'positive', '0.833', 'tranquility', 'serenity', 'disposition', 'repose', 'placidity']
senticnet['mental_health'] = ['0.178', '-0.02', '0.072', '0.138', '#joy', '#admiration', 'positive', '0.066', 'get_know', 'stay_touch', 'human_contact', 'invite_dinner', 'communicate_family']
senticnet['mental_health_care'] = ['-0.12', '0', '-0.12', '-0.08', '#sadness', '#fear', 'negative', '-0.11', 'hospital', 'check_up', 'check_doctor', 'check_pulse', 'check_blood_pressure']
senticnet['mental_health_disorder'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'acknowledgeable', 'specifiable', 'recognizable', 'identifiable', 'classifiable']
senticnet['mental_health_nursing'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'infant_feeding', 'schedule_feeding', 'demand_feeding', 'breast_feeding', 'feeding']
senticnet['mental_home'] = ['-0.60', '0', '0.685', '-0.66', '#anger', '#disgust', 'negative', '-0.65', 'snake_pit', 'booby_hatch', 'crazy_house', 'cuckoo_nest', 'funny_farm']
senticnet['mental_hospital'] = ['-0.57', '0', '0.548', '-0.52', '#sadness', '#anger', 'negative', '-0.55', 'snake_pit', 'booby_hatch', 'crazy_house', 'cuckoo_nest', 'funny_farm']
senticnet['mental_illness'] = ['-0.33', '0.159', '-0.32', '-0.04', '#sadness', '#fear', 'negative', '-0.13', 'go_to_jail', 'put_in_jail', 'conceal_evidence', 'retribution', 'incarceration']
senticnet['mental_image'] = ['-0.76', '0', '0.629', '-0.71', '#sadness', '#disgust', 'negative', '-0.70', 'memory_picture', 'afterimage', 'aftersensation', 'image', 'memory_image']
senticnet['mental_imagery'] = ['0.693', '-0.52', '0', '0.82', '#joy', '#admiration', 'positive', '0.681', 'embodiment', 'shape', 'representation', 'concrete_representation', 'internal_representation']
senticnet['mental_imbalance'] = ['-0.74', '0', '0.893', '-0.58', '#sadness', '#anger', 'negative', '-0.73', 'encopresis', 'upset', 'psychosomatic_disorder', 'conversion_disorder', 'conversion_reaction']
senticnet['mental_impairment'] = ['-0.74', '0', '0.893', '-0.58', '#sadness', '#anger', 'negative', '-0.73', 'encopresis', 'upset', 'psychosomatic_disorder', 'conversion_disorder', 'conversion_reaction']
senticnet['mental_incapacity'] = ['0.528', '-0.71', '0', '0.848', '#surprise', '#admiration', 'positive', '0.698', 'cachexia', 'cachexy', 'wasting', 'softness', 'unfitness']
senticnet['mental_instability'] = ['-0.61', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.69', 'irresponsibility', 'untrustiness', 'undependability', 'undependableness', 'unreliability']
senticnet['mental_institution'] = ['-0.96', '0', '0.656', '-0.51', '#sadness', '#anger', 'negative', '-0.71', 'snake_pit', 'booby_hatch', 'crazy_house', 'cuckoo_nest', 'funny_farm']
senticnet['mental_phenomenon'] = ['-0.58', '0', '0.535', '-0.72', '#sadness', '#disgust', 'negative', '-0.61', 'byproduct', 'effect', 'result', 'consequence', 'outcome']
senticnet['mental_problem'] = ['-0.95', '0', '0.902', '-0.91', '#sadness', '#disgust', 'negative', '-0.92', 'sadness', 'mental_disease', 'crazy_mind', 'depression', 'disease']
senticnet['mental_rejection'] = ['0.738', '-0.83', '0', '0.981', '#surprise', '#admiration', 'positive', '0.851', 'doubt', 'incertitude', 'dubiety', 'doubtfulness', 'dubiousness']
senticnet['mental_representation'] = ['0.693', '-0.52', '0', '0.82', '#joy', '#admiration', 'positive', '0.681', 'embodiment', 'shape', 'representation', 'concrete_representation', 'internal_representation']
senticnet['mental_retardation'] = ['-0.74', '0', '0.893', '-0.79', '#anger', '#disgust', 'negative', '-0.81', 'moronity', 'mental_deficiency', 'imbecility', 'stupidity', 'mental_defectiveness']
senticnet['mental_stamina'] = ['-0.97', '0', '0.969', '-0.50', '#sadness', '#anger', 'negative', '-0.81', 'nervousness', 'tenseness', 'tension', 'nerve', 'stress']
senticnet['mental_state'] = ['0.964', '0.987', '0', '0', '#joy', '#interest', 'positive', '0.976', 'thought', 'mind', 'inside_head', 'thoughtfulness', 'brain_function']
senticnet['mental_strain'] = ['-0.97', '0', '0.969', '-0.50', '#sadness', '#anger', 'negative', '-0.81', 'nervousness', 'tenseness', 'tension', 'nerve', 'stress']
senticnet['mental_strength'] = ['0.608', '-0.55', '0', '0.726', '#joy', '#admiration', 'positive', '0.628', 'genius', 'endowment', 'flair', 'gift', 'knack']
senticnet['mental_telepathy'] = ['0', '0.871', '0', '0.861', '#interest', '#admiration', 'positive', '0.866', 'predict_future', 'psychic_phenomenon', 'parapsychology', 'clairvoyance', 'foreknowledge']
senticnet['mental_torture'] = ['-0.58', '0.282', '-0.41', '-0.16', '#sadness', '#fear', 'negative', '-0.21', 'shame', 'upset', 'despair', 'fright', 'unhappiness']
senticnet['mental_trauma'] = ['-0.88', '0', '-0.79', '-0.85', '#sadness', '#disgust', 'negative', '-0.84', 'pain', 'severe_pain', 'agony', 'physical_pain', 'suffer']
senticnet['mentalese'] = ['-0.99', '0', '0.878', '-0.95', '#sadness', '#disgust', 'negative', '-0.94', 'adequacy', 'satisfactoriness', 'acceptability', 'acceptableness', 'quality']
senticnet['mentality'] = ['0.909', '0.983', '0', '0', '#joy', '#interest', 'positive', '0.946', 'old', 'elderly', 'ripped', 'matured', 'age_well']
senticnet['mentally-challenged'] = ['0', '0', '0.95', '-0.73', '#anger', '#disgust', 'negative', '-0.84', 'deaf-mute', 'twelve-year-old', 'blond-haired', 'eleven-year-old', 'thirteen-year-old']
senticnet['mentally_challenged'] = ['0.816', '0.921', '0', '0.93', '#interest', '#admiration', 'positive', '0.889', 'happy', 'pleasant_experience', 'sit_sun', 'time_think', 'enjoy_beauty']
senticnet['mentally_defective'] = ['-0.98', '0', '0.674', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'slow', 'slow_pace', 'stupid', 'slow_car', 'slow_vehicle']
senticnet['mentally_deficient'] = ['-0.98', '0', '0.674', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'slow', 'slow_pace', 'stupid', 'slow_car', 'slow_vehicle']
senticnet['mentally_healthy'] = ['-0.98', '0', '0.582', '-0.56', '#sadness', '#anger', 'negative', '-0.70', 'unsocial', 'antisocial', 'lonely', 'alone', 'solitary']
senticnet['mentally_ill'] = ['0.203', '0.077', '0', '0.189', '#joy', '#admiration', 'positive', '0.156', 'sea_shell', 'coast', 'seaside', 'sediment', 'shell']
senticnet['mentally_retarded'] = ['-0.98', '0', '0.674', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'slow', 'slow_pace', 'stupid', 'slow_car', 'slow_vehicle']
senticnet['mentally_unstable'] = ['0', '0', '0.821', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'student', 'serious_student', 'addiction', 'graduate_student', 'hard_worker']
senticnet['mentation'] = ['0.634', '-0.84', '0', '0.719', '#surprise', '#admiration', 'positive', '0.731', 'reasoning', 'consideration', 'logical_thinking', 'abstract_thought', 'excogitation']
senticnet['menteri_besar'] = ['-0.57', '0', '0.576', '-0.78', '#anger', '#disgust', 'negative', '-0.64', 'reprieve', 'respite', 'suspend', 'call', 'delay']
senticnet['mentha_aquatica'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
senticnet['mention'] = ['0', '0.203', '-0.11', '0.28', '#interest', '#admiration', 'positive', '0.123', 'remark', 'reply', 'comment', 'statement', 'idiom']
senticnet['mentioned_above'] = ['-0.58', '0', '0.715', '-0.59', '#anger', '#disgust', 'negative', '-0.63', 'fly', 'flap', 'barndoor', 'coattail', 'codpiece']
senticnet['mentor'] = ['-0.10', '0.068', '-0.12', '-0.11', '#fear', '#disgust', 'negative', '-0.07', 'change_life', 'housewife', 'know_well', 'finish_college', 'get_high_education']
senticnet['menu'] = ['0.146', '0.095', '-0.04', '0', '#joy', '#interest', 'positive', '0.065', 'paper_menu', 'diner', 'cafe', 'restaurant', 'dine_room']
senticnet['menu_bar'] = ['0.055', '0.117', '0', '0.133', '#interest', '#admiration', 'positive', '0.102', 'graphical_representation', 'pencil_pen', 'note_pad', 'marker', 'stationery']
senticnet['menu_option'] = ['0.146', '0.095', '-0.04', '0', '#joy', '#interest', 'positive', '0.065', 'paper_menu', 'diner', 'cafe', 'restaurant', 'dine_room']
senticnet['menuet'] = ['0.591', '-0.91', '0', '0.605', '#surprise', '#admiration', 'positive', '0.704', 'farandole', 'folk_dance', 'polka', 'dance', 'typical_dance']
senticnet['menyanthes_trifoliata'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
senticnet['menziesii'] = ['-0.94', '0', '0.608', '-0.95', '#sadness', '#disgust', 'negative', '-0.83', 'wild_licorice', 'wild_liquorice', 'alpine_milk_vetch', 'purple_milk_vetch', 'milk_vetch']
senticnet['meow'] = ['-0.85', '0', '0', '-0.91', '#sadness', '#disgust', 'negative', '-0.88', 'purring', 'purr', 'purr', 'cuddle', 'growl']
senticnet['meowth'] = ['0.028', '0', '0.027', '0.101', '#joy', '#admiration', 'positive', '0.034', 'animal_dwell', 'bee_home', 'live_place', 'dwell_place', 'place_to_live']
senticnet['mephitic'] = ['-0.86', '0', '0.644', '-0.88', '#sadness', '#disgust', 'negative', '-0.79', 'frowsty', 'fetid', 'foetid', 'smelly', 'stinking']
senticnet['mercantile_marine'] = ['0.764', '-0.96', '0', '0.883', '#surprise', '#admiration', 'positive', '0.87', 'boatmanship', 'acquirement', 'skill', 'accomplishment', 'acquisition']
senticnet['mercantile_store'] = ['0.026', '0.082', '0', '0', '#joy', '#interest', 'positive', '0.054', 'account_department', 'office', 'accountant_office', 'shirt_pocket', 'desk_drawer']
senticnet['mercanzia'] = ['0.967', '-0.98', '0', '0.64', '#joy', '#surprise', 'positive', '0.863', 'club', 'social_club', 'society', 'jockey_club', 'gild']
senticnet['mercaptan'] = ['-0.67', '0', '0.503', '-0.67', '#sadness', '#disgust', 'negative', '-0.61', 'stink_up', 'smell_up', 'stink_out', 'incense', 'perfume']
senticnet['merced_river'] = ['0', '0.069', '0', '0.065', '#interest', '#admiration', 'positive', '0.067', 'alp', 'high_mountain', 'pass', 'ski_resort', 'high_place']
senticnet['mercedes_convertible'] = ['0.028', '0', '0.013', '0.061', '#joy', '#admiration', 'positive', '0.025', 'truck', 'vehicle', 'big_car', 'car', 'lorry']
senticnet['mercedes_limousine'] = ['0', '0', '-0.04', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'good_band', 'band', 'kit', 'procession', 'oblivion']
senticnet['mercedes_sl'] = ['0.755', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.728', 'high_class', 'expensive_car', 'ferrari', 'fancy_car', 'sports_car']
senticnet['mercedes_suv'] = ['-0.10', '-0.07', '0.032', '-0.15', '#sadness', '#disgust', 'negative', '-0.05', 'crash_tree', 'press_accelerator', 'stop_traffic_light', 'accident', 'tire_puncture']
senticnet['mercenary'] = ['0.913', '-0.63', '0', '0.961', '#joy', '#admiration', 'positive', '0.836', 'materialistic', 'worldly', 'mundane', 'material', 'economic']
senticnet['mercenary_soldier'] = ['-0.62', '0', '0.98', '-0.81', '#anger', '#disgust', 'negative', '-0.80', 'tart', 'prostitute', 'harlot', 'bawd', 'sporting_lady']
senticnet['merchandise'] = ['0', '0.076', '0', '0', '#interest', '#interest', 'positive', '0.076', 'goods', 'vendor', 'trolley', 'shopper', 'grocery']
senticnet['merchandize'] = ['0.902', '-0.72', '0', '0.919', '#joy', '#admiration', 'positive', '0.849', 'preciousness', 'costliness', 'expensiveness', 'cost', 'lavishness']
senticnet['merchant'] = ['0', '0.17', '0', '0.101', '#interest', '#admiration', 'positive', '0.136', 'seller', 'trade', 'bazaar', 'stock_market', 'wall_street']
senticnet['merchant_brig'] = ['0', '0', '0.08', '0', '#anger', '#anger', 'negative', '-0.08', 'tree_animal', 'small_furry', 'rodent', 'big_mouse', 'small_rodent']
senticnet['merchant_guild'] = ['0.967', '-0.98', '0', '0.64', '#joy', '#surprise', 'positive', '0.863', 'club', 'social_club', 'society', 'jockey_club', 'gild']
senticnet['merchant_marine'] = ['-0.78', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.79', 'shipping', 'transportation', 'merchant_vessel', 'cargo_ship', 'embark']
senticnet['merchant_vessel'] = ['-0.78', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.78', 'merchant_marine', 'transportation', 'shipping', 'cargo_ship', 'embark']
senticnet['merchants_guild'] = ['0.967', '-0.98', '0', '0.64', '#joy', '#surprise', 'positive', '0.863', 'club', 'social_club', 'society', 'jockey_club', 'gild']
senticnet['merciful'] = ['0.803', '0', '0', '0.789', '#joy', '#admiration', 'positive', '0.796', 'mercifully', 'mercifulness', 'ruth', 'commiseration', 'commiserate']
senticnet['mercifully'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'merciful', 'mercifulness', 'ruth', 'commiseration', 'commiserate']
senticnet['mercifulness'] = ['0.879', '0', '0', '0.986', '#joy', '#admiration', 'positive', '0.932', 'mercifully', 'merciful', 'ruth', 'commiseration', 'commiserate']
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senticnet['methanation'] = ['0.918', '0.864', '0', '0', '#joy', '#interest', 'positive', '0.891', 'consequence', 'cause', 'bad_action', 'problem', 'political_statement']
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senticnet['methane_ch'] = ['0.053', '0.264', '-0.11', '0.23', '#interest', '#admiration', 'positive', '0.107', 'natural_resource', 'petroleum', 'fossil_fuel', 'energy_source', 'potential_energy']
senticnet['methane_ice'] = ['0.095', '0', '-0.04', '0.062', '#joy', '#admiration', 'positive', '0.037', 'photographer', 'water_spray', 'moisture', 'rise_water', 'droplet']
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senticnet['methanol_extract'] = ['0.932', '-0.56', '0', '0.617', '#joy', '#admiration', 'positive', '0.703', 'lamisil', 'lamisil_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
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senticnet['methodist_cemetery'] = ['-0.50', '0', '0.762', '-0.89', '#anger', '#disgust', 'negative', '-0.71', 'cemetery', 'graveyard', 'burial_site', 'grassland', 'burying_ground']
senticnet['methodist_church'] = ['0.081', '0.146', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'cult', 'sect', 'conservative', 'worshiper', 'pastor']
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senticnet['methodist_episcopal_church'] = ['0.081', '0.146', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'cult', 'sect', 'conservative', 'worshiper', 'pastor']
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senticnet['methodological_expertise'] = ['0', '0.983', '0', '0', '#interest', '#interest', 'positive', '0.983', 'find_problem', 'analyze', 'expert_report', 'examination', 'check']
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senticnet['methoxsalen'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'chemical_irritant', 'gingerol', 'piperine', 'pepper_spray']
senticnet['methoxy_group'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
senticnet['methylcellulose'] = ['-0.04', '0.069', '0', '0.075', '#interest', '#admiration', 'positive', '0.035', 'sculpt_gel', 'hairstyle', 'hairdresser', 'cosmetic', 'hair_sculpt_gel']
senticnet['methylergonovine'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['methylmalonic_acidemia'] = ['-0.50', '0', '0.542', '-0.51', '#anger', '#disgust', 'negative', '-0.51', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'hirschsprung_disease']
senticnet['methylparaben'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
senticnet['methylsulfonylmethane'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucose', 'corn_sugar', 'dextrose', 'dextroglucose', 'grape_sugar']
senticnet['methysergide'] = ['0.95', '-0.95', '0', '0.769', '#joy', '#surprise', 'positive', '0.89', 'adrenocorticotropic', 'adrenocorticotrophic', 'stimulant', 'stimulating', 'stimulative']
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senticnet['meticulous'] = ['0.682', '-0.52', '0', '0.613', '#joy', '#admiration', 'positive', '0.607', 'nice', 'fastidious', 'dainty', 'prissy', 'pernickety']
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senticnet['meticulous_detail'] = ['0', '0.136', '0', '0.196', '#interest', '#admiration', 'positive', '0.166', 'describe', 'explain', 'answer', 'description', 'example']
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senticnet['metoclopramide'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['metopic_suture'] = ['0.719', '-0.57', '0', '0.651', '#joy', '#admiration', 'positive', '0.647', 'chap', 'break', 'slit', 'gap', 'shift']
senticnet['metrazol_shock'] = ['-0.72', '0', '0.732', '-0.59', '#sadness', '#anger', 'negative', '-0.68', 'insulin_shock', 'insulin_shock_therapy', 'insulin_shock_treatment', 'shock_therapy', 'shock']
senticnet['metrazol_shock_therapy'] = ['-0.72', '0', '0.732', '-0.59', '#sadness', '#anger', 'negative', '-0.68', 'insulin_shock', 'insulin_shock_therapy', 'insulin_shock_treatment', 'metrazol_shock', 'shock_therapy']
senticnet['metre_freestyle'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'chlorine', 'lifeguard', 'still_water', 'large_body', 'shallow_water']
senticnet['metre_runner'] = ['-0.04', '-0.03', '0', '-0.16', '#sadness', '#disgust', 'negative', '-0.05', 'crazy', 'damage_body', 'crazy_person', 'threat', 'max']
senticnet['metre_tall_tower'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'castle', 'bell_tower', 'spire', 'steeple', 'religious_place']
senticnet['metre_telescope'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'binoculars', 'read_glass', 'eyeglass', 'camera', 'monocle']
senticnet['metric'] = ['0', '0.203', '0.036', '0.204', '#interest', '#admiration', 'positive', '0.124', 'amount', 'count', 'total', 'quantity', 'total_amount']
senticnet['metric_system'] = ['0', '0.203', '0.036', '0.204', '#interest', '#admiration', 'positive', '0.124', 'amount', 'count', 'total', 'quantity', 'total_amount']
senticnet['metric_tons_per'] = ['-0.64', '0', '0.87', '-0.74', '#anger', '#disgust', 'negative', '-0.75', 'infinitude', 'piles', 'mess', 'large_number', 'large_indefinite_amount']
senticnet['metrical'] = ['-0.84', '0', '0.574', '-0.66', '#sadness', '#disgust', 'negative', '-0.69', 'cadenced', 'cadent', 'rhythmical', 'metric', 'lilting']
senticnet['metrical_form'] = ['-0.61', '0', '0.73', '-0.85', '#anger', '#disgust', 'negative', '-0.73', 'verbalization', 'phrasing', 'verbiage', 'wording', 'phraseology']
senticnet['metrical_structure'] = ['-0.22', '0', '-0.09', '-0.16', '#sadness', '#disgust', 'negative', '-0.16', 'inequality', 'reject', 'discard', 'difference', 'gap']
senticnet['metrizability'] = ['0.637', '-0.91', '0', '0.757', '#surprise', '#admiration', 'positive', '0.768', 'manageability', 'manageableness', 'docility', 'obedience', 'tractability']
senticnet['metro'] = ['0.87', '0.778', '0', '0', '#joy', '#interest', 'positive', '0.824', 'bus', 'tram', 'train', 'transport', 'transportation']
senticnet['metro_station'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'train_station', 'bus', 'subway_station', 'public_place', 'pier']
senticnet['metrocard'] = ['-0.03', '0.29', '-0.12', '0.203', '#interest', '#admiration', 'positive', '0.083', 'excise', 'severance', 'wage', 'afford', 'revenue']
senticnet['metrolink_station'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'train_station', 'bus', 'subway_station', 'public_place', 'pier']
senticnet['metrolink_ventura_county'] = ['0.861', '-0.52', '0', '0.598', '#joy', '#admiration', 'positive', '0.663', 'exit', 'get_off', 'deplane', 'go_out', 'get_out']
senticnet['metronidazole_flagyl'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['metropolia'] = ['0.8', '-0.84', '0', '0.868', '#surprise', '#admiration', 'positive', '0.837', 'commonwealth', 'province', 'administrative_district', 'administrative_division', 'territorial_division']
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senticnet['metropolitan_archbishop'] = ['0.8', '-0.84', '0', '0.868', '#surprise', '#admiration', 'positive', '0.837', 'commonwealth', 'province', 'administrative_district', 'administrative_division', 'territorial_division']
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senticnet['metropolitan_archeparchy'] = ['0.8', '-0.84', '0', '0.868', '#surprise', '#admiration', 'positive', '0.837', 'commonwealth', 'province', 'administrative_district', 'administrative_division', 'territorial_division']
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senticnet['metropolitan_police'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'law_enforcement_agency', 'law', 'agency']
senticnet['metropolitan_police_department'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'constabulary', 'law', 'agency']
senticnet['metropolitanate'] = ['0.8', '-0.84', '0', '0.868', '#surprise', '#admiration', 'positive', '0.837', 'commonwealth', 'province', 'administrative_district', 'administrative_division', 'territorial_division']
senticnet['metrorrhagia'] = ['-0.73', '0', '0.555', '-0.90', '#sadness', '#disgust', 'negative', '-0.73', 'aching', 'hurting', 'labor_pain', 'neuralgia', 'neuralgy']
senticnet['metta_bhavana'] = ['0.668', '-0.87', '0', '0.606', '#joy', '#surprise', 'positive', '0.715', 'adeptness', 'adroitness', 'deftness', 'quickness', 'expertness']
senticnet['mettle'] = ['0.605', '-0.93', '0', '0.742', '#surprise', '#admiration', 'positive', '0.761', 'road_sense', 'sagacity', 'sagaciousness', 'logic', 'nous']
senticnet['mettlesome'] = ['0.831', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.791', 'spirited', 'gamey', 'game', 'gritty', 'gamy']
senticnet['metzora'] = ['-0.69', '0', '0.858', '-0.83', '#anger', '#disgust', 'negative', '-0.79', 'draggled', 'befouled', 'filthy', 'nasty', 'scummy']
senticnet['meum'] = ['0', '0.079', '0', '0', '#interest', '#interest', 'positive', '0.079', 'change_diaper', 'lower_standard_live', 'become_pregnant', 'find_mate', 'make_baby']
senticnet['meurtre'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'sexual_relationship', 'liaison', 'affair', 'involvement']
senticnet['meuse_argonne_offensive'] = ['0.737', '-0.96', '0', '0.552', '#joy', '#surprise', 'positive', '0.75', 'prominence', 'hump', 'bump', 'swelling', 'frontal_eminence']
senticnet['mevalonic_acid'] = ['-0.52', '0', '0.681', '-0.76', '#anger', '#disgust', 'negative', '-0.65', 'triose', 'tetrose', 'pentose', 'simple_sugar', 'aldose']
senticnet['mew'] = ['0.028', '0', '0.027', '0.101', '#joy', '#admiration', 'positive', '0.034', 'animal_dwell', 'bee_home', 'live_place', 'dwell_place', 'place_to_live']
senticnet['mewl'] = ['-0.85', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.84', 'whimper', 'pule', 'sniffle', 'cry', 'intermittent']
senticnet['mewtwo'] = ['0.028', '0', '0.027', '0.101', '#joy', '#admiration', 'positive', '0.034', 'animal_dwell', 'bee_home', 'live_place', 'dwell_place', 'place_to_live']
senticnet['mexican_restaurant'] = ['0', '0.067', '-0.06', '0.071', '#interest', '#admiration', 'positive', '0.024', 'pyramid', 'buckle', 'japanese_person', 'anemone', 'herringbone']
senticnet['meyer_lemon'] = ['0.05', '0', '0.047', '0.068', '#joy', '#admiration', 'positive', '0.024', 'tree_fruit', 'yellow_fruit', 'small_fruit', 'vine_fruit', 'red_fruit']
senticnet['meyer_lutz'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'dress', 'pinny', 'dirndl', 'gown', 'muumuu']
senticnet['mezza_voce'] = ['0.824', '-0.95', '0', '0.513', '#joy', '#surprise', 'positive', '0.765', 'clearcutness', 'explicitness', 'preciseness', 'perspicuity', 'perspicuousness']
senticnet['mezzanine'] = ['0.077', '0.073', '0.013', '-0.02', '#joy', '#interest', 'positive', '0.027', 'registry_office', 'radio_studio', 'book', 'conference_auditorium', 'book_office']
senticnet['mezzanine_above'] = ['0.077', '0.073', '0.013', '-0.02', '#joy', '#interest', 'positive', '0.027', 'registry_office', 'radio_studio', 'book', 'conference_auditorium', 'book_office']
senticnet['mezzanine_floor'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'top_floor', 'front_desk', 'concierge', 'exit', 'elevator']
senticnet['mezzanine_gallery'] = ['0.07', '0', '0', '0', '#joy', '#joy', 'positive', '0.07', 'artist', 'artwork', 'sculpture', 'tapestry', 'drawing']
senticnet['mezzanine_level'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'top_floor', 'front_desk', 'concierge', 'exit', 'elevator']
senticnet['mfa_degree'] = ['-0.02', '0', '-0.04', '-0.03', '#fear', '#disgust', 'negative', '-0.03', 'studio', 'art_piece', 'art_gallery', 'artist', 'museum']
senticnet['mfn_treatment'] = ['-0.81', '0', '0.931', '-0.77', '#sadness', '#anger', 'negative', '-0.84', 'mutualness', 'mutuality', 'complementarity', 'correlation', 'correlativity']
senticnet['mg_kg_min'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
senticnet['mhz_band'] = ['-0.07', '0.078', '0', '0.078', '#interest', '#admiration', 'positive', '0.028', 'red_blue', 'red_orange', 'green_yellow', 'hue_tint', 'yellow_green']
senticnet['mhz_spectrum'] = ['-0.07', '0.078', '0', '0.078', '#interest', '#admiration', 'positive', '0.028', 'red_blue', 'red_orange', 'green_yellow', 'hue_tint', 'yellow_green']
senticnet['mi_configuration'] = ['-0.08', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.09', 'mediocrity', 'effect', 'suspicion', 'exclusion', 'pugilist']
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senticnet['mia_kirshner'] = ['0.794', '-0.50', '0', '0.531', '#joy', '#admiration', 'positive', '0.611', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['miasma'] = ['0', '0', '-0.90', '-0.79', '#fear', '#disgust', 'negative', '-0.84', 'stench', 'morass', 'fetid', 'putrid', 'pestilent']
senticnet['miasmatic'] = ['-0.93', '0', '0.75', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'invalidism', 'injury', 'affliction', 'biliousness', 'harm']
senticnet['mica'] = ['0.786', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.776', 'light', 'glimmer', 'diamond', 'glow', 'twinkle']
senticnet['micaceous_clay'] = ['-0.06', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.04', 'polish', 'magnify', 'see_out', 'sill', 'pane']
senticnet['micafungin'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['micarta'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'useful_tool']
senticnet['micellar'] = ['-0.98', '0', '0.516', '-0.90', '#sadness', '#disgust', 'negative', '-0.80', 'devastation', 'annihilation', 'pulverization', 'vaporization', 'destruction']
senticnet['michauxii'] = ['-0.92', '0', '0.912', '-0.94', '#sadness', '#disgust', 'negative', '-0.92', 'true_blackberry', 'sand_blackberry', 'blackberry_bush', 'blackberry', 'running_blackberry']
senticnet['michel_fokine'] = ['0.12', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.09', 'ballroom', 'perform_art', 'nightclub', 'ballerina', 'venue']
senticnet['michel_gurard'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'blue_ribbon', 'badge', 'allegory', 'stripe', 'merit_badge']
senticnet['michelangelo_antonioni_film'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'fragmentation', 'discharge', 'backfire', 'blast']
senticnet['michelin_star_chef'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'blue_ribbon', 'badge', 'allegory', 'stripe', 'merit_badge']
senticnet['michelin_starred_chef'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'blue_ribbon', 'badge', 'allegory', 'stripe', 'merit_badge']
senticnet['michelin_starred_restaurant'] = ['0.06', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'eat_food', 'fast_food', 'pizza_parlor', 'pizza_store', 'kitchen_cupboard']
senticnet['michelob_ultra'] = ['0.964', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.964', 'carbonated_drink', 'sugar_free', 'coke', 'diet_coke', 'beverage']
senticnet['michelson-morley_experiment'] = ['0', '0.754', '0', '0.814', '#interest', '#admiration', 'positive', '0.784', 'experimentation', 'control_experiment', 'test', 'pilot_experiment', 'trial']
senticnet['michiko_kakutani'] = ['0.811', '-0.63', '0', '0.959', '#joy', '#admiration', 'positive', '0.8', 'drama_critic', 'theater_critic', 'professional', 'art_critic', 'literary_critic']
senticnet['micht'] = ['0.027', '0', '0.012', '0.063', '#joy', '#admiration', 'positive', '0.026', 'vegetation', 'tree_flower', 'green_growth', 'small', 'greenery']
senticnet['michx'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'horse_chestnut', 'red_buckeye', 'particolored_buckeye']
senticnet['mickel'] = ['0.746', '-0.88', '0', '0.723', '#joy', '#surprise', 'positive', '0.783', 'strike', 'run_into', 'hit', 'impinge_on', 'collide_with']
senticnet['mickey'] = ['0.17', '0.125', '-0.04', '0.196', '#joy', '#admiration', 'positive', '0.111', 'pal', 'single_person', 'comrade', 'good', 'companion']
senticnet['mickey_carroll'] = ['0.931', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.855', 'crystal_clear', 'blue_sky', 'imaginary', 'water_sky', 'blue_color']
senticnet['mickey_katz'] = ['-0.74', '0', '0.68', '-0.58', '#sadness', '#anger', 'negative', '-0.67', 'whiner', 'complainer', 'moaner', 'sniveller', 'crybaby']
senticnet['mickey_mouse_cartoon'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'child_movie', 'child_story', 'funny_moment', 'childhood']
senticnet['micky_quinn'] = ['-0.9', '0', '0.749', '-0.71', '#sadness', '#anger', 'negative', '-0.78', 'chute', 'slide', 'slideway', 'sloping_trough', 'gutter']
senticnet['miconazole'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['miconazole_nitrate'] = ['-0.81', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.82', 'peanut', 'leave_over', 'floppy', 'circular_motion', 'store_vegetable']
senticnet['micrantha'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['micro_lpga_classic'] = ['0.034', '0', '0.01', '0.087', '#joy', '#admiration', 'positive', '0.037', 'chime', 'bell_noise', 'alto', 'piano', 'ring_loudly']
senticnet['micro_usb'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'run_treadmill', 'complete_thesis', 'dwarf_planet', 'insure', 'ridgeback']
senticnet['micro_usb_port'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'run_treadmill', 'complete_thesis', 'dwarf_planet', 'insure', 'ridgeback']
senticnet['microaerophilic'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'immobile', 'immotile']
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senticnet['microbial_biology'] = ['-0.96', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.84', 'model_organism', 'basic_biology', 'undergraduate', 'research_experience', 'recent_doctoral_recipient']
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senticnet['microbial_degradation'] = ['-0.80', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.84', 'popularization', 'profanation', 'demoralization', 'corruption', 'humiliation']
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senticnet['microbiologist'] = ['0', '0.909', '0', '0.799', '#interest', '#admiration', 'positive', '0.854', 'research', 'research_topic', 'research_subject', 'search', 'student']
senticnet['microbiology'] = ['0.751', '0.947', '0', '0', '#joy', '#interest', 'positive', '0.849', 'pharmacology', 'toxicology', 'biochemistry', 'toxicological', 'pathology']
senticnet['microbrew'] = ['0', '0.091', '-0.05', '0.073', '#interest', '#admiration', 'positive', '0.037', 'privately', 'hold_liquid', 'make_wish', 'rinse_dish', 'find_toilet']
senticnet['microburst'] = ['0', '-0.41', '-0.95', '0', '#surprise', '#fear', 'negative', '-0.26', 'blizzard', 'snow_storm', 'storm', 'danger', 'rainy']
senticnet['microcarpa'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['microchip'] = ['-0.83', '0', '0.521', '-0.71', '#sadness', '#disgust', 'negative', '-0.69', 'stool', 'dejection', 'droppings', 'cow_droppings', 'cow_dung']
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senticnet['microclimate'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'weather', 'sunny', 'spring', 'snow', 'four_seasons']
senticnet['microcontroller'] = ['-0.83', '0', '0.521', '-0.71', '#sadness', '#disgust', 'negative', '-0.69', 'stool', 'dejection', 'droppings', 'cow_droppings', 'cow_dung']
senticnet['microcosmic'] = ['0.654', '-0.71', '0', '0.624', '#joy', '#surprise', 'positive', '0.664', 'viewable', 'gross', 'telescopic', 'seeable', 'visual']
senticnet['microdeletion'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'myotonic_muscular_dystrophy', 'dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
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senticnet['microfauna'] = ['0', '0.184', '-0.05', '0.179', '#interest', '#admiration', 'positive', '0.104', 'rich', 'a_lot', 'abundant', 'wealth', 'plentiful']
senticnet['microfiber_cloth'] = ['0.986', '0', '0', '0', '#joy', '#joy', 'positive', '0.986', 'personal_cleanliness', 'cleanness', 'wipe_up_spill', 'smell_good', 'clean_carpet']
senticnet['microfibre'] = ['-0.81', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.85', 'bandage', 'painkiller', 'gauze', 'aspirin', 'contraceptive_device']
senticnet['microfilming_project'] = ['-0.97', '0', '0.885', '-0.97', '#sadness', '#disgust', 'negative', '-0.94', 'protection', 'conservation', 'environmentalism', 'reservation', 'immobilization']
senticnet['microfilter'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'water_inside', 'water_spray', 'teapot', 'bodily_waste', 'different_type']
senticnet['microform_collection'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'a_lot_of_books', 'old_testament', 'cook_book', 'magazine', 'librarian']
senticnet['micrognathia'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'part_of_face', 'facial_organ', 'face_feature', 'facial_part', 'near_nose']
senticnet['microhylidae_family'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'amphibian', 'aquatic']
senticnet['microloma'] = ['0', '0', '-0.14', '-0.03', '#fear', '#disgust', 'negative', '-0.09', 'pine_cone', 'fallen_leaf', 'poison_ivy', 'detritus', 'tress']
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senticnet['micrometeorite'] = ['0.899', '0.803', '0', '0', '#joy', '#interest', 'positive', '0.851', 'ufo', 'milky', 'martian', 'galaxy', 'solar_system']
senticnet['micrometeoroid'] = ['0.899', '0.803', '0', '0', '#joy', '#interest', 'positive', '0.851', 'ufo', 'milky', 'martian', 'galaxy', 'solar_system']
senticnet['micrometer_scale'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'amoeba', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['micrometer_size'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'amoeba', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['micron_filter'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'water_inside', 'water_spray', 'teapot', 'bodily_waste', 'different_type']
senticnet['micron_scale'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'amoeba', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['micronase'] = ['-0.72', '0', '0.973', '-0.57', '#sadness', '#anger', 'negative', '-0.76', 'corpulency', 'blubber', 'fat', 'embonpoint', 'stoutness']
senticnet['micronucleus_test'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'deflection', 'warp', 'aberrance']
senticnet['micronutrient_deficiency'] = ['-0.54', '0', '0.92', '-0.84', '#anger', '#disgust', 'negative', '-0.76', 'dearth', 'demand', 'mineral_deficiency', 'famine', 'need']
senticnet['microorganism'] = ['0.802', '0.783', '0', '0', '#joy', '#interest', 'positive', '0.792', 'earthworm', 'bacterium', 'germ', 'bug', 'cutworm']
senticnet['microphage'] = ['0.772', '-0.56', '0', '0.824', '#joy', '#admiration', 'positive', '0.719', 'neutrophil', 'leukocyte', 'leucocyte', 'white_blood_cell', 'white_cell']
senticnet['microphone_boom'] = ['0.813', '0.822', '0', '0', '#joy', '#interest', 'positive', '0.817', 'sound_control_room', 'beat_box', 'ticket_taker', 'speaker', 'contralto']
senticnet['microphone_input'] = ['0.055', '0', '0.03', '0.136', '#joy', '#admiration', 'positive', '0.054', 'loud_soft', 'decibel', 'loudness', 'sound', 'melodic_noise']
senticnet['microphone_stand'] = ['0.26', '0', '0', '0.089', '#joy', '#admiration', 'positive', '0.174', 'balcony_rail', 'movie_theater_seat', 'row_seat', 'usher', 'sticky_floor']
senticnet['microphthalmia'] = ['-0.88', '0', '0.762', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'divergent_strabismus', 'exotropia', 'walleye', 'abnormality', 'abnormalcy']
senticnet['microphylla'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'carnauba_palm', 'carnauba', 'wax_palm', 'palmetto', 'palmyra']
senticnet['microphysical'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'hurricane', 'natural_disaster', 'flood', 'atmospheric_pressure', 'tropical_storm']
senticnet['micropipette'] = ['-0.86', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.80', 'riverbed', 'bottom_ocean', 'waterfall', 'down_drain', 'septic_tank']
senticnet['microplane'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'useful_tool']
senticnet['microporosity'] = ['-0.52', '0', '0.596', '-0.76', '#anger', '#disgust', 'negative', '-0.62', 'retention', 'retentiveness', 'retentivity', 'solidness', 'nonabsorbency']
senticnet['microprocessor'] = ['0', '0', '-0.93', '-0.84', '#fear', '#disgust', 'negative', '-0.88', 'special-purpose', 'widely-used', 'high-performance', 'general-purpose', 'programmable']
senticnet['microprocessor_chip'] = ['-0.83', '0', '0.521', '-0.71', '#sadness', '#disgust', 'negative', '-0.69', 'stool', 'dejection', 'droppings', 'cow_droppings', 'cow_dung']
senticnet['micropsia'] = ['0.906', '-0.60', '0', '0.653', '#joy', '#admiration', 'positive', '0.72', 'opaqueness', 'faintness', 'dimness', 'opacity', 'vagueness']
senticnet['microreactor'] = ['-0.98', '0', '0.516', '-0.90', '#sadness', '#disgust', 'negative', '-0.80', 'devastation', 'annihilation', 'pulverization', 'vaporization', 'destruction']
senticnet['microscope'] = ['0', '0.07', '0', '0.063', '#interest', '#admiration', 'positive', '0.067', 'peep', 'glimpse', 'see', 'stare', 'observation']
senticnet['microscope_slide'] = ['0.129', '0', '0', '0', '#joy', '#joy', 'positive', '0.129', 'incline_plane', 'boulder', 'ramp', 'hill_mountain', 'rock']
senticnet['microscopic'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'amoeba', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['microscopic_anatomy'] = ['-0.59', '0', '0.911', '-0.87', '#anger', '#disgust', 'negative', '-0.79', 'osteosclerosis', 'sclerosis', 'disseminated_sclerosis', 'multiple_sclerosis', 'induration']
senticnet['microscopic_imager'] = ['0.052', '0.119', '0', '0.141', '#interest', '#admiration', 'positive', '0.104', 'neigh', 'domestic_animal', 'animal_type', 'animal', 'home_animal']
senticnet['microscopic_level'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'amoeba', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['microscopic_organism'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['microscopic_scale'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'amoeba', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['microscopic_structure'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'amoeba', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['microscopically'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'amoeba', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['microscopy'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'amoeba', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['microsd_card_slot'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'run_treadmill', 'complete_thesis', 'dwarf_planet', 'insure', 'ridgeback']
senticnet['microsegregation'] = ['-0.77', '0', '0.914', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'confluence', 'conflux', 'merging', 'blending', 'combination']
senticnet['microsoft_excel'] = ['-0.84', '0', '0.689', '-0.90', '#sadness', '#disgust', 'negative', '-0.81', 'software', 'software_program', 'computer_software', 'software_system', 'authoring_language']
senticnet['microsoft_office'] = ['-0.03', '0.112', '-0.08', '0.164', '#interest', '#admiration', 'positive', '0.04', 'run', 'jump_over', 'quick', 'sprint', 'flee']
senticnet['microsoft_outlook'] = ['-0.03', '0.112', '-0.08', '0.164', '#interest', '#admiration', 'positive', '0.04', 'run', 'jump_over', 'quick', 'sprint', 'flee']
senticnet['microsoft_window_update'] = ['-0.58', '0', '0.561', '-0.62', '#sadness', '#disgust', 'negative', '-0.58', 'spot', 'speckle', 'dapple', 'mark', 'fleck']
senticnet['microsomal'] = ['-0.52', '0', '0.681', '-0.76', '#anger', '#disgust', 'negative', '-0.65', 'triose', 'tetrose', 'pentose', 'simple_sugar', 'aldose']
senticnet['microsporum'] = ['-0.57', '0', '0.873', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'fungal_infection', 'mycosis', 'kerion', 'tinea_pedis', 'athlete_foot']
senticnet['microsporum_gypseum'] = ['-0.57', '0', '0.873', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'fungal_infection', 'mycosis', 'kerion', 'tinea_pedis', 'athlete_foot']
senticnet['microstructural_evolution'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'constancy', 'monotony', 'invariance', 'changelessness', 'unchangeability']
senticnet['microstructure'] = ['-0.81', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.82', 'material_research', 'material', 'effect', 'number', 'industrial_technology']
senticnet['microsurgery'] = ['0.745', '-0.55', '0', '0.597', '#joy', '#admiration', 'positive', '0.633', 'lift', 'phalloplasty', 'abdominoplasty', 'tummy_tuck', 'autoplasty']
senticnet['microtext'] = ['-0.04', '-0.02', '0.04', '-0.04', '#sadness', '#anger', 'negative', '-0.02', 'slang', 'lingo', 'jargon', 'chatspeak', 'unusual_language']
senticnet['microtonal'] = ['-0.52', '0', '0.902', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'discordant', 'inharmonious', 'unresolved', 'false', 'disharmonious']
senticnet['microtonality'] = ['0.045', '0', '0.033', '0.089', '#joy', '#admiration', 'positive', '0.034', 'spanish', 'vocalization', 'verbal_communication', 'vocal', 'mouth_noise']
senticnet['microvascular_surgery'] = ['0.745', '-0.55', '0', '0.597', '#joy', '#admiration', 'positive', '0.633', 'lift', 'phalloplasty', 'abdominoplasty', 'tummy_tuck', 'autoplasty']
senticnet['microverse'] = ['0.086', '-0.10', '0.064', '0', '#joy', '#surprise', 'positive', '0.043', 'art_piece', 'lifetime', 'museum', 'vintage', 'collectible']
senticnet['microvilli'] = ['0', '0.097', '0', '0.085', '#interest', '#admiration', 'positive', '0.091', 'lunula', 'arm_part', 'leave_hand', 'ten_fingers', 'five_fingers']
senticnet['microwavable'] = ['0.348', '0.23', '-0.15', '0.294', '#joy', '#admiration', 'positive', '0.18', 'happy_emotion', 'niceness', 'contagious', 'good_emotion', 'savor']
senticnet['microwave_oven'] = ['0.088', '0.076', '0.016', '0.094', '#joy', '#admiration', 'positive', '0.06', 'copper_wire', 'stove', 'several_types', 'oven', 'suspect']
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senticnet['mid_eighteenth_century'] = ['0.978', '-0.60', '0', '0.895', '#joy', '#admiration', 'positive', '0.825', 'period', 'quattrocento', 'twentieth_century', 'time_period', 'period_of_time']
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senticnet['mid_thigh'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'bowel', 'scrotum', 'shoulder', 'liver', 'anus']
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senticnet['midair'] = ['0.969', '0.843', '0', '0', '#joy', '#interest', 'positive', '0.906', 'tree_house', 'same_again', 'reverse', 'spot', 'clothes_accessory']
senticnet['midair_collision'] = ['-0.91', '0', '0.905', '-0.78', '#sadness', '#anger', 'negative', '-0.86', 'pity', 'shame', 'misfortune', 'mischance', 'misadventure']
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senticnet['middle-brow'] = ['0', '0.906', '0', '0.694', '#interest', '#admiration', 'positive', '0.8', 'low-brow', 'highbrow', 'high-brow', 'middlebrow', 'lowbrow']
senticnet['middle-class'] = ['0', '0.899', '0', '0.926', '#interest', '#admiration', 'positive', '0.913', 'well-to-do', 'affluent', 'wealthier', 'better-off', 'well-off']
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senticnet['middle_east_diplomacy'] = ['0', '0.098', '0', '0.08', '#interest', '#admiration', 'positive', '0.089', 'talk_person', 'wisdom', 'tact', 'statecraft', 'greet_person']
senticnet['middle_eastern_cuisine'] = ['0.882', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.822', 'ferment_grape_juice', 'chef_special', 'bottle_wine', 'fine_dining', 'decanter']
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senticnet['middle_schooler'] = ['0.521', '0.677', '-0.27', '0.273', '#joy', '#interest', 'positive', '0.299', 'further_education', 'protect_child', 'look_closely', 'go_to_school', 'register']
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senticnet['middlebrow'] = ['0', '0.915', '0', '0.704', '#interest', '#admiration', 'positive', '0.809', 'low-brow', 'highbrow', 'high-brow', 'middle-brow', 'lowbrow']
senticnet['middleman'] = ['-0.05', '0.215', '-0.08', '0.293', '#interest', '#admiration', 'positive', '0.092', 'swap', 'give_take', 'exchange_goods', 'transaction', 'increase_money']
senticnet['middlesex_hospital'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'druggist', 'health_professional', 'primary_care_provider', 'health_care_provider']
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senticnet['middleweight'] = ['-0.79', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.77', 'bantamweight', 'welterweight', 'featherweight', 'flyweight', 'heavyweight']
senticnet['middleweight_bout'] = ['0.944', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.967', 'meeting', 'meet', 'match', 'encounter', 'round']
senticnet['middleweight_champion'] = ['0', '0', '0', '0.834', '#admiration', '#admiration', 'positive', '0.834', 'royal_person', 'royal_title', 'top', 'master', 'rank']
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senticnet['mideasttalk'] = ['0', '0', '0.833', '-0.71', '#anger', '#disgust', 'negative', '-0.77', 'blood_serum', 'zuma', 'anatolia', 'uunet', 'serum']
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senticnet['midflight'] = ['-0.08', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.08', 'turn_key', 'plan_route', 'open_car_door', 'book_flight', 'turn_car_off']
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senticnet['midkemia'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'cur', 'fice', 'pariah_dog', 'domestic_dog']
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senticnet['midnattsolscupen_midnight_sun'] = ['0.093', '-0.02', '0', '0.056', '#joy', '#admiration', 'positive', '0.057', 'pitcher', 'carafe', 'container_pitcher', 'mug', 'glass_of_water']
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senticnet['midp_mobile'] = ['0', '0.189', '0.04', '0.091', '#interest', '#admiration', 'positive', '0.08', 'combination', 'margin', 'teller', 'merge', 'finite']
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senticnet['midriff'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'bowel', 'scrotum', 'shoulder', 'liver', 'anus']
senticnet['midsection'] = ['0.893', '0.769', '0', '0', '#joy', '#interest', 'positive', '0.831', 'body', 'two_ears', 'dog_pet', 'pain', 'abdomen']
senticnet['midship'] = ['0.831', '-0.82', '0', '0.826', '#joy', '#admiration', 'positive', '0.826', 'deck', 'freeboard_deck', 'weather_deck', 'shelter_deck', 'boat_deck']
senticnet['midship_section'] = ['0.937', '0.847', '0', '0', '#joy', '#interest', 'positive', '0.892', 'arrow', 'spear', 'buy_wood', 'baton', 'archery']
senticnet['midshipman'] = ['0.764', '0', '0', '0.869', '#joy', '#admiration', 'positive', '0.817', 'cadet', 'lieutenant', 'lieutenant-colonel', 'navy', 'brigadier']
senticnet['midsize_suv'] = ['0.035', '0.112', '-0.07', '0.195', '#interest', '#admiration', 'positive', '0.067', 'car_action', 'car_travel', 'steer_car', 'make_go', 'transportation']
senticnet['midst'] = ['-0.76', '0', '0.869', '0', '#sadness', '#anger', 'negative', '-0.81', 'agony', 'throe', 'agonizing', 'brink', 'protracted']
senticnet['midtempo'] = ['0.65', '-0.61', '0', '0.711', '#joy', '#admiration', 'positive', '0.658', 'bouncing', 'scintillating', 'spirited', 'lively', 'zippy']
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senticnet['midway_through'] = ['0', '0.904', '0', '0', '#interest', '#interest', 'positive', '0.904', 'act', 'part_of_play', 'part_of_movie', 'emote', 'role']
senticnet['midweek'] = ['0.733', '0.935', '0', '0', '#joy', '#interest', 'positive', '0.834', 'friday', 'monday', 'fortnight', 'weekend', 'day_of_week']
senticnet['midwestern_town'] = ['-0.55', '0', '0.912', '-0.65', '#anger', '#disgust', 'negative', '-0.70', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
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senticnet['midwifery_care'] = ['0.1', '0.216', '-0.18', '0.22', '#interest', '#admiration', 'positive', '0.088', 'female', 'maternity_ward', 'motherhood', 'woman', 'mother']
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senticnet['mien'] = ['0.82', '-0.85', '0', '0.939', '#surprise', '#admiration', 'positive', '0.87', 'dignity', 'gravitas', 'lordliness', 'personal_manner', 'manner']
senticnet['miff'] = ['-0.85', '0', '0.564', '-0.92', '#sadness', '#disgust', 'negative', '-0.77', 'botheration', 'impatience', 'snit', 'bummer', 'pinprick']
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senticnet['mightier'] = ['-0.73', '0', '0.732', '-0.53', '#sadness', '#anger', 'negative', '-0.66', 'insurmountable', 'insuperable', 'inexpugnable', 'indomitable', 'unsubduable']
senticnet['mightiest'] = ['-0.73', '0', '0.732', '-0.53', '#sadness', '#anger', 'negative', '-0.66', 'insurmountable', 'insuperable', 'inexpugnable', 'indomitable', 'unsubduable']
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senticnet['mighty'] = ['0.739', '0.759', '0', '0', '#joy', '#interest', 'positive', '0.749', 'almighty', 'power', 'puissant', 'excellent', 'mare']
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senticnet['migraine_attack'] = ['-0.73', '0', '-0.85', '-0.72', '#sadness', '#fear', 'negative', '-0.77', 'lay_off', 'hurt', 'pain_suffer', 'lose_mind', 'forget_everything']
senticnet['migraine_aura'] = ['-0.73', '0', '-0.85', '-0.72', '#sadness', '#fear', 'negative', '-0.77', 'lay_off', 'hurt', 'pain_suffer', 'lose_mind', 'forget_everything']
senticnet['migraine_headache'] = ['-0.73', '0', '-0.85', '-0.72', '#sadness', '#fear', 'negative', '-0.77', 'lay_off', 'hurt', 'pain_suffer', 'lose_mind', 'forget_everything']
senticnet['migraine_pain'] = ['-0.73', '0', '-0.85', '-0.72', '#sadness', '#fear', 'negative', '-0.77', 'lay_off', 'hurt', 'pain_suffer', 'lose_mind', 'forget_everything']
senticnet['migraine_prevention'] = ['-0.73', '0', '-0.85', '-0.72', '#sadness', '#fear', 'negative', '-0.77', 'lay_off', 'hurt', 'pain_suffer', 'lose_mind', 'forget_everything']
senticnet['migraine_sufferer'] = ['-0.73', '0', '-0.85', '-0.72', '#sadness', '#fear', 'negative', '-0.77', 'lay_off', 'hurt', 'pain_suffer', 'lose_mind', 'forget_everything']
senticnet['migrant_domestic'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'vassalage']
senticnet['migrate'] = ['0.071', '0.115', '-0.08', '0.167', '#interest', '#admiration', 'positive', '0.066', 'live_inside', 'inhabit', 'live_life', 'protection_weather', 'raise_family']
senticnet['migrating_caribou'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'cheetah', 'giraffe', 'kangaroo', 'mule', 'horse']
senticnet['migrating_waterfowl'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'habitat', 'mom_house', 'windsor', 'pacific_northwest', 'wilderness']
senticnet['migration'] = ['0.952', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.948', 'people', 'periodic_event', 'immigration', 'motion', 'emigration']
senticnet['migratory_bird'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'hold_egg', 'baby_bird', 'hatch', 'lay_egg', 'bird_home']
senticnet['migratory_waterfowl'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'habitat', 'mom_house', 'windsor', 'pacific_northwest', 'wilderness']
senticnet['mihi'] = ['0', '0.079', '0', '0', '#interest', '#interest', 'positive', '0.079', 'change_diaper', 'lower_standard_live', 'become_pregnant', 'find_mate', 'make_baby']
senticnet['mihly'] = ['0.975', '-0.66', '0', '0.813', '#joy', '#admiration', 'positive', '0.816', 'cry', 'yell', 'scream', 'shrill', 'call']
senticnet['mihly_vrsmarty'] = ['0.975', '-0.66', '0', '0.813', '#joy', '#admiration', 'positive', '0.816', 'cry', 'yell', 'scream', 'shrill', 'call']
senticnet['miji'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'white_animal', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
senticnet['mikado'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'dress', 'pinny', 'dirndl', 'gown', 'muumuu']
senticnet['mikawa_province'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'overlord', 'lord']
senticnet['mikko_franck'] = ['-0.91', '0', '0.931', '-0.72', '#sadness', '#anger', 'negative', '-0.85', 'hit_with_baton', 'get_hurt', 'whip', 'arrow', 'hurt']
senticnet['mikls_radnti'] = ['0.975', '-0.66', '0', '0.813', '#joy', '#admiration', 'positive', '0.816', 'cry', 'yell', 'scream', 'shrill', 'call']
senticnet['milacidae'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'saltine', 'soda_cracker', 'cracker', 'water_biscuit', 'graham_cracker']
senticnet['milan_furniture'] = ['0.713', '0', '0', '0.746', '#joy', '#admiration', 'positive', '0.73', 'carnival', 'circus', 'fairground', 'rodeo', 'disneyland']
senticnet['milan_metro'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'expressway', 'turnpike', 'toll_road', 'highway']
senticnet['milan_suburban_railway'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'expressway', 'turnpike', 'toll_road', 'highway']
senticnet['milano_centrale'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'expressway', 'turnpike', 'toll_road', 'highway']
senticnet['milanvenice_railway'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'expressway', 'turnpike', 'toll_road', 'highway']
senticnet['milch_cow'] = ['0.546', '-0.96', '0', '0.533', '#joy', '#surprise', 'positive', '0.683', 'dairy_cattle', 'dairy_cow', 'ayrshire', 'milk_cow', 'milcher']
senticnet['milcher'] = ['0.546', '-0.96', '0', '0.533', '#joy', '#surprise', 'positive', '0.683', 'dairy_cattle', 'dairy_cow', 'milch_cow', 'milk_cow', 'ayrshire']
senticnet['mild'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'hurricane', 'tropical_storm', 'breeze', 'tender', 'atmospheric_phenomenon']
senticnet['mild-mannered'] = ['-0.97', '0', '0.859', '0', '#sadness', '#anger', 'negative', '-0.91', 'unassuming', 'affable', 'self-effacing', 'taciturn', 'soft-spoken']
senticnet['mild_acid'] = ['0.071', '0', '-0.08', '-0.09', '#fear', '#disgust', 'negative', '-0.03', 'gunk', 'rotten_food', 'sour_cream', 'glass_of_milk', 'lemon_juice']
senticnet['mild_autism'] = ['-0.91', '0', '0.508', '-0.89', '#sadness', '#disgust', 'negative', '-0.77', 'mental_disorder', 'mental_disturbance', 'psychological_disorder', 'folie', 'disturbance']
senticnet['mild_concussion'] = ['0.94', '0.78', '0', '0', '#joy', '#interest', 'positive', '0.86', 'breaheavily', 'heavy_breathe', 'get_exhausted', 'exhaustion', 'see_neighbor']
senticnet['mild_dehydration'] = ['-0.72', '0', '0.762', '-0.79', '#anger', '#disgust', 'negative', '-0.75', 'heat_hyperpyrexia', 'heat_prostration', 'prostration', 'sunstroke', 'thermic_fever']
senticnet['mild_dementia'] = ['-0.94', '0', '0.821', '-0.75', '#sadness', '#anger', 'negative', '-0.84', 'predementedness', 'insanity', 'dementedness', 'senile_psychosis', 'alcoholic_dementia']
senticnet['mild_depression'] = ['-0.99', '0', '0.982', '-0.62', '#sadness', '#anger', 'negative', '-0.86', 'dysthymia', 'dysthymic_depression', 'affective_disorder', 'major_affective_disorder', 'emotional_disorder']
senticnet['mild_detergent'] = ['0.986', '0', '0', '0', '#joy', '#joy', 'positive', '0.986', 'personal_cleanliness', 'cleanness', 'wipe_up_spill', 'smell_good', 'clean_carpet']
senticnet['mild_diarrhea'] = ['-0.75', '0', '0.868', '-0.99', '#anger', '#disgust', 'negative', '-0.87', 'regional_enteritis', 'regional_ileitis', 'ulcerative_colitis', 'rubor', 'redness']
senticnet['mild_dyslexia'] = ['0.89', '-0.70', '0', '0.643', '#joy', '#surprise', 'positive', '0.745', 'dyslexia', 'dyscalculia', 'learning_disability', 'disorder', 'upset']
senticnet['mild_fever'] = ['-0.06', '0.183', '0.651', '-0.64', '#anger', '#disgust', 'negative', '-0.29', 'allergic_reaction', 'chew', 'pierce', 'mastication', 'put_mouth']
senticnet['mild_flavor'] = ['0.043', '0.091', '0', '0.113', '#interest', '#admiration', 'positive', '0.082', 'sweet_sour', 'tasty', 'yummy', 'popular_food', 'form_of_food']
senticnet['mild_headache'] = ['-0.06', '0', '-0.15', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'severe_pain', 'discomfort', 'physical_reaction', 'choke_death', 'embarrassment']
senticnet['mild_hypertension'] = ['-0.85', '0', '-0.71', '-0.86', '#sadness', '#disgust', 'negative', '-0.81', 'get_exercise', 'train_marathon', 'run_marathon', 'run_long_distance', 'go_gym']
senticnet['mild_hypothermia'] = ['-0.72', '0', '0.762', '-0.79', '#anger', '#disgust', 'negative', '-0.75', 'heat_hyperpyrexia', 'heat_prostration', 'prostration', 'sunstroke', 'thermic_fever']
senticnet['mild_jaundice'] = ['0.753', '-0.74', '0', '0.587', '#joy', '#surprise', 'positive', '0.695', 'draining', 'exhausting', 'debilitating', 'enervating', 'enfeebling']
senticnet['mild_laxative'] = ['-0.50', '0', '0.582', '-0.57', '#anger', '#disgust', 'negative', '-0.55', 'purgative', 'cathartic', 'physic', 'aperient', 'phenolphthalein']
senticnet['mild_mental_retardation'] = ['0.89', '-0.70', '0', '0.643', '#joy', '#surprise', 'positive', '0.745', 'dyslexia', 'dyscalculia', 'learning_disability', 'disorder', 'upset']
senticnet['mild_nausea'] = ['-0.06', '0', '-0.15', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'severe_pain', 'discomfort', 'physical_reaction', 'choke_death', 'embarrassment']
senticnet['mild_recession'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'mathematical', 'muscle', 'math_function', 'feel_sick', 'separation']
senticnet['mild_sedation'] = ['-0.65', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'put_to_sleep', 'operation', 'anesthesia', 'surgery', 'local_anesthesia']
senticnet['mild_skin_irritation'] = ['-0.70', '0', '-0.83', '-0.98', '#fear', '#disgust', 'negative', '-0.84', 'body_hurt', 'ache', 'ouch', 'cut_skin', 'scar']
senticnet['mild_soap'] = ['0', '0.086', '-0.06', '0.12', '#interest', '#admiration', 'positive', '0.046', 'gel_toothpaste', 'tartar_control_toothpaste', 'fluoride_mouthwash', 'toothpaste', 'hair_lotion']
senticnet['mild_sprain'] = ['-0.83', '0', '0.509', '-0.84', '#sadness', '#disgust', 'negative', '-0.72', 'wrench', 'injury', 'harm', 'hurt', 'trauma']
senticnet['mild_steel'] = ['0.72', '0', '0', '0.733', '#joy', '#admiration', 'positive', '0.726', 'spanner', 'gold', 'strong_material', 'platinum', 'durable']
senticnet['mild_taste'] = ['0', '0.141', '0.01', '0.115', '#interest', '#admiration', 'positive', '0.082', 'sepal', 'achene', 'funiculus', 'seed', 'pip']
senticnet['mild_weather'] = ['0.15', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.087', 'winter', 'winter_time', 'snow_fall', 'snowy', 'put_warm_clothes']
senticnet['mild_winter'] = ['0.15', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.087', 'winter', 'winter_time', 'snow_fall', 'snowy', 'put_warm_clothes']
senticnet['milder_weather'] = ['0.15', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.087', 'winter', 'winter_time', 'snow_fall', 'snowy', 'put_warm_clothes']
senticnet['mildew'] = ['-0.93', '0', '0.997', '-0.50', '#sadness', '#anger', 'negative', '-0.81', 'decay', 'spoiling', 'mold', 'mould', 'souring']
senticnet['mildew_growth'] = ['0.718', '0', '0', '0', '#joy', '#joy', 'positive', '0.718', 'water_container', 'still_water', 'clean_water', 'water', 'cool_water']
senticnet['mildly'] = ['0', '0', '-0.74', '-0.87', '#fear', '#disgust', 'negative', '-0.80', 'absolute', 'out-and-out', 'rank', 'right-down', 'sheer']
senticnet['mildly_addictive'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'wooden_spoon', 'cereal_bowl', 'teaspoon', 'container_cup', 'large_container']
senticnet['mildly_obese'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'fatty', 'worse', 'health_issue', 'clinical_problem']
senticnet['mildly_positive'] = ['-0.62', '0', '0.988', '-0.59', '#sadness', '#anger', 'negative', '-0.73', 'motley', 'mixture', 'variety', 'assortment', 'alphabet_soup']
senticnet['mildly_radioactive'] = ['-0.02', '0', '0.047', '-0.11', '#anger', '#disgust', 'negative', '-0.06', 'hold_trash', 'put_garbage', 'rubbish', 'put_trash', 'store_trash']
senticnet['mildly_retarded'] = ['-0.98', '0', '0.674', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'slow', 'slow_pace', 'stupid', 'slow_car', 'slow_vehicle']
senticnet['mildly_spicy'] = ['0.076', '0', '0.037', '0.065', '#joy', '#admiration', 'positive', '0.035', 'serve_cold', 'tasty_good', 'tasty', 'sour', 'high_calorie']
senticnet['mildly_sweet'] = ['0.112', '0', '0', '0.141', '#joy', '#admiration', 'positive', '0.127', 'sweet', 'sweet_food', 'bake_good', 'round_pastry', 'eatable']
senticnet['mildly_toxic'] = ['-0.53', '0', '0.86', '-0.65', '#anger', '#disgust', 'negative', '-0.68', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['mildness'] = ['0.646', '-0.69', '0', '0.609', '#joy', '#surprise', 'positive', '0.651', 'good_manner', 'personal_manner', 'courtesy', 'comportment', 'mien']
senticnet['mile'] = ['-0.70', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.79', 'far', 'distant', 'distance', 'distance_measure', 'kilometer']
senticnet['mile_apart'] = ['0.609', '0.597', '-0.23', '0.398', '#joy', '#interest', 'positive', '0.341', 'near', 'pick_up', 'togetherness', 'gay', 'capture']
senticnet['mile_away'] = ['-0.95', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.83', 'outer', 'radiant', 'around_earth', 'nothingness', 'heavenly_body']
senticnet['mile_downwind'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'dust', 'particulate', 'particulate_matter']
senticnet['mile_handicap'] = ['-0.76', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.78', 'strangle', 'bloodshed', 'cripple', 'hear_bad_news', 'sexually_transmit_disease']
senticnet['mile_jaques_dalcroze'] = ['0.536', '-0.56', '0', '0.567', '#surprise', '#admiration', 'positive', '0.557', 'recreation', 'diversion', 'move', 'motility', 'movement']
senticnet['mile_journey'] = ['0.937', '0.8', '0', '0', '#joy', '#interest', 'positive', '0.869', 'quest', 'stunt', 'illusion', 'adventure', 'activity']
senticnet['mile_kane'] = ['0.946', '-0.91', '0', '0.664', '#joy', '#surprise', 'positive', '0.841', 'pretender', 'phony', 'phoney', 'hypocrite', 'dissembler']
senticnet['mile_km'] = ['0.868', '0.819', '0', '0', '#joy', '#interest', 'positive', '0.843', 'distance_measure', 'measure', 'length_measurement', 'meter', 'distance_measurement']
senticnet['mile_round_trip'] = ['0.825', '0', '0', '0.703', '#joy', '#admiration', 'positive', '0.764', 'pay_toll', 'get_fast', 'access', 'fast_travel', 'access_quickly']
senticnet['miliaria'] = ['-0.64', '0', '0.903', '-0.63', '#sadness', '#anger', 'negative', '-0.72', 'jaundice_of_newborn', 'disease', 'icterus_neonatorum', 'kernicterus', 'symptom']
senticnet['milieu'] = ['-0.50', '0', '0.733', '-0.55', '#anger', '#disgust', 'negative', '-0.59', 'originality', 'novelty', 'innovativeness', 'freshness', 'ability']
senticnet['militancy'] = ['-0.71', '0', '0.928', '-0.74', '#anger', '#disgust', 'negative', '-0.79', 'set_off', 'shake_up', 'stir_up', 'instigate', 'inflame']
senticnet['militant'] = ['-0.93', '0', '0.86', '-0.90', '#sadness', '#disgust', 'negative', '-0.9', 'dry', 'insurgent', 'crusader', 'passive_resister', 'demonstrator']
senticnet['militant_atheist'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'non-believer', 'doubter', 'agnostic', 'freethinker', 'sceptic']
senticnet['militant_faction'] = ['-0.50', '0', '0.989', '-0.91', '#anger', '#disgust', 'negative', '-0.80', 'camp', 'pack', 'clique', 'coterie', 'ingroup']
senticnet['militant_secularism'] = ['0.929', '0.507', '-0.17', '0.396', '#joy', '#interest', 'positive', '0.415', 'plume', 'congratulate', 'smugness', 'smug', 'proudly']
senticnet['militant_stronghold'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'webpage', 'store_record', 'ideal']
senticnet['militantly_anti'] = ['0.778', '-0.73', '0', '0.559', '#joy', '#surprise', 'positive', '0.689', 'sex', 'inversion', 'sexual_inversion', 'lesbianism', 'sapphism']
senticnet['militarism'] = ['0.729', '-0.56', '0', '0.785', '#joy', '#admiration', 'positive', '0.692', 'ideology', 'hawkishness', 'warmongering', 'war_advocacy', 'political_orientation']
senticnet['militarist'] = ['0.585', '-0.7', '0', '0.586', '#surprise', '#admiration', 'positive', '0.624', 'jingoistic', 'ultranationalistic', 'superpatriotic', 'nationalistic', 'patriotic']
senticnet['militaristic'] = ['0.585', '-0.7', '0', '0.586', '#surprise', '#admiration', 'positive', '0.624', 'jingoistic', 'ultranationalistic', 'superpatriotic', 'nationalistic', 'patriotic']
senticnet['military'] = ['0.786', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.744', 'attacker', 'army', 'samurai', 'infantry', 'military_force']
senticnet['military_action'] = ['-0.19', '0', '-0.11', '-0.05', '#sadness', '#fear', 'negative', '-0.12', 'lose_battle', 'give_up', 'beaten', 'lose_war', 'defeat']
senticnet['military_adventurism'] = ['0.828', '-0.67', '0', '0.811', '#joy', '#admiration', 'positive', '0.771', 'foolhardiness', 'brashness', 'thoughtlessness', 'desperation', 'unthoughtfulness']
senticnet['military_airbase'] = ['-0.70', '0', '0.621', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'flare_path', 'airfield', 'grassland', 'runway', 'road']
senticnet['military_airfield'] = ['-0.70', '0', '0.621', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'flare_path', 'airfield', 'grassland', 'runway', 'road']
senticnet['military_base'] = ['0', '0', '-0.07', '-0.17', '#fear', '#disgust', 'negative', '-0.12', 'crime_scene', 'police_station', 'battlefield', 'war_museum', 'roadblock']
senticnet['military_buildup'] = ['0.942', '-0.79', '0', '0.901', '#joy', '#admiration', 'positive', '0.88', 'backup', 'accretion', 'deposition', 'deposit', 'increase']
senticnet['military_camp'] = ['0', '0', '0', '-0.94', '#disgust', '#disgust', 'negative', '-0.94', 'terrorism', 'kamikaze', 'sect', 'brainwash', 'faction']
senticnet['military_capability'] = ['-0.84', '0', '0.86', '-0.73', '#sadness', '#anger', 'negative', '-0.81', 'strength', 'sea_power', 'military_strength', 'military_posture', 'posture']
senticnet['military_checkpoint'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'corner_two_streets', 'street_corner', 'roadside', 'city_street', 'schoolyard']
senticnet['military_commander'] = ['0.786', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.744', 'attacker', 'army', 'samurai', 'infantry', 'military_force']
senticnet['military_confrontation'] = ['0.828', '-0.67', '0', '0.811', '#joy', '#admiration', 'positive', '0.771', 'foolhardiness', 'brashness', 'thoughtlessness', 'desperation', 'unthoughtfulness']
senticnet['military_conscription'] = ['0.924', '-0.55', '0', '0.756', '#joy', '#admiration', 'positive', '0.744', 'bounden', 'incumbent_on', 'obligate', 'obligatory', 'mandatory']
senticnet['military_coup'] = ['-0.84', '0', '0.744', '-0.58', '#sadness', '#anger', 'negative', '-0.72', 'subversion', 'subversive_activity', 'termination', 'ending', 'conclusion']
senticnet['military_despotism'] = ['-0.65', '0', '0.716', '-0.94', '#anger', '#disgust', 'negative', '-0.77', 'bad_hat', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['military_dictator'] = ['-0.73', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.72', 'royal_leader', 'despot', 'emperor', 'monarch', 'king']
senticnet['military_dictatorship'] = ['-0.73', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.72', 'royal_leader', 'despot', 'emperor', 'monarch', 'king']
senticnet['military_division'] = ['0.919', '0.781', '0', '0', '#joy', '#interest', 'positive', '0.85', 'army', 'soldier_group', 'military_force', 'military', 'army_position']
senticnet['military_force'] = ['0.031', '0.079', '0', '0', '#joy', '#interest', 'positive', '0.055', 'army_unit', 'attacker', 'soldier_group', 'army', 'military']
senticnet['military_group'] = ['-0.03', '0.092', '0.051', '0.077', '#interest', '#admiration', 'positive', '0.021', 'classification', 'military_division', 'placement', 'nobleman', 'ordinal']
senticnet['military_installation'] = ['0', '0.072', '0', '0', '#interest', '#interest', 'positive', '0.072', 'counselor', 'rest_place', 'temporary_house', 'summer_vacation', 'hunt_fish']
senticnet['military_intelligence'] = ['0.786', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.744', 'attacker', 'army', 'samurai', 'infantry', 'military_force']
senticnet['military_machine'] = ['-0.59', '0', '0.665', '-0.52', '#sadness', '#anger', 'negative', '-0.59', 'military', 'armed_forces', 'armed_service', 'military_reserve', 'war_machine']
senticnet['military_occupation'] = ['0', '0.112', '-0.04', '0.099', '#interest', '#admiration', 'positive', '0.054', 'profession', 'career', 'vocation', 'important_job', 'laborer']
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senticnet['military_order'] = ['0', '0.121', '0', '0.068', '#interest', '#admiration', 'positive', '0.095', 'rank', 'army_position', 'army_boss', 'general', 'hierarchy']
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senticnet['milling_wheat'] = ['0.878', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.883', 'wheat', 'grain', 'rye', 'white_rice', 'rice']
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senticnet['million_dollar_endowment'] = ['0.608', '-0.55', '0', '0.726', '#joy', '#admiration', 'positive', '0.628', 'genius', 'raw_talent', 'flair', 'gift', 'knack']
senticnet['million_dollar_gift'] = ['0.608', '-0.55', '0', '0.726', '#joy', '#admiration', 'positive', '0.628', 'genius', 'raw_talent', 'flair', 'gift', 'knack']
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senticnet['million_tonne_per'] = ['0', '0.164', '-0.08', '0.215', '#interest', '#admiration', 'positive', '0.098', 'manufacture', 'factory_output', 'creation', 'assembly_line', 'make']
senticnet['million_tons_annually'] = ['-0.64', '0', '0.87', '-0.74', '#anger', '#disgust', 'negative', '-0.75', 'infinitude', 'piles', 'mess', 'large_number', 'large_indefinite_amount']
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senticnet['millionaire_businessman'] = ['0', '0', '-0.99', '-0.73', '#fear', '#disgust', 'negative', '-0.86', 'king', 'baron', 'business_leader', 'mogul', 'big_businessman']
senticnet['millionaire_raffle'] = ['0.858', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.813', 'box_office', 'movie', 'cctv', 'cinema', 'movie_theater']
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senticnet['mimar_sinan'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'caravansary', 'caravanserai', 'khan', 'caravan_inn', 'hostel']
senticnet['mime_artist'] = ['0.9', '-0.88', '0', '0.687', '#joy', '#surprise', 'positive', '0.823', 'conjurer', 'escape_expert', 'mind_reader', 'telepathist', 'performer']
senticnet['mimeche'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'druggist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['mimeograph'] = ['0', '0', '0.128', '0', '#anger', '#anger', 'negative', '-0.12', 'grid', 'efficient', 'calculator', 'computer', 'personal_computer']
senticnet['mimetic'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'mediacy', 'mediateness', 'characteristic', 'allusiveness']
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senticnet['mimic_natural'] = ['-0.7', '0', '0.77', '-0.75', '#anger', '#disgust', 'negative', '-0.74', 'fake', 'unnatural', 'man_made', 'artificial_sea', 'artificial_taste']
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senticnet['minarchist'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'economic_libertarian', 'civil_libertarian', 'advocate', 'advocator', 'proponent']
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senticnet['mink_coat'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'clothes_style', 'style', 'glamour', 'model', 'formal_clothes']
senticnet['minke_whale'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'lordosis', 'kyphosis', 'spinal_curvature', 'hunchback', 'abnormality']
senticnet['minkowski_space'] = ['-0.75', '0', '0.679', '-0.77', '#sadness', '#disgust', 'negative', '-0.73', 'unvariedness', 'changelessness', 'unchangeability', 'unchangeableness', 'unchangingness']
senticnet['minnow'] = ['0', '-0.02', '0.044', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'haddock', 'mermaid', 'fish_rod', 'tidal_wave', 'dryad']
senticnet['minor'] = ['-0.99', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.87', 'expiable', 'excusable', 'forgivable', 'venial', 'pardonable']
senticnet['minor_arcanum'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'jewelry_store', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['minor_damage'] = ['-0.98', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.97', 'harm', 'hurt', 'cut_skin', 'bloody', 'ouch']
senticnet['minor_frostbite'] = ['-0.72', '0', '0.762', '-0.79', '#anger', '#disgust', 'negative', '-0.75', 'heat_hyperpyrexia', 'heat_prostration', 'prostration', 'sunstroke', 'thermic_fever']
senticnet['minor_groove'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['minor_hockey'] = ['0', '0', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.05', 'human', 'delusional', 'use_illegal_drug', 'busy_person', 'carpenter']
senticnet['minor_inconvenience'] = ['-0.59', '0.162', '0.221', '-0.50', '#sadness', '#disgust', 'negative', '-0.28', 'annoy', 'harass', 'provoke', 'beset', 'chivvy']
senticnet['minor_injury'] = ['-0.79', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.84', 'knee_injury', 'injure', 'accident', 'twist_ankle', 'get_injured']
senticnet['minor_league_pitcher'] = ['0', '0.075', '0', '0', '#interest', '#interest', 'positive', '0.075', 'cup', 'carafe', 'container_cup', 'mug', 'glasses']
senticnet['minor_league_toledo_mud'] = ['0.039', '0.089', '-0.08', '0.143', '#interest', '#admiration', 'positive', '0.048', 'bird', 'nightingale', 'stork', 'eagle', 'rooster']
senticnet['minor_planet'] = ['0.73', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.851', 'stuff_use_calculation', 'ridgeback', 'slight', 'low_blood_pressure', 'competitive_activity']
senticnet['minor_quibble'] = ['0', '0', '-0.86', '-0.99', '#fear', '#disgust', 'negative', '-0.93', 'niggle', 'quiddity', 'bicker', 'pettifog', 'cavil']
senticnet['minor_scuffle'] = ['-0.99', '0', '0.603', '-0.82', '#sadness', '#disgust', 'negative', '-0.80', 'batrachomyomachia', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['minor_skirmish'] = ['-0.05', '0', '-0.08', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'siege', 'big_fight', 'war', 'declare_war', 'combat']
senticnet['minor_surgery'] = ['-0.52', '0', '0.906', '-0.67', '#anger', '#disgust', 'negative', '-0.70', 'operation', 'surgery', 'surgical_operation', 'surgical_procedure', 'surgical_process']
senticnet['minor_surgical_procedure'] = ['-0.88', '0', '0.774', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fibroma', 'fibroid', 'benign_tumor', 'nonmalignant_tumor', 'nonmalignant_neoplasm']
senticnet['minor_tranquilizer'] = ['-0.86', '0', '0.646', '-0.88', '#sadness', '#disgust', 'negative', '-0.8', 'tranquilizer', 'put_to_sleep', 'antianxiety_drug', 'anxiolytic', 'anxiolytic_drug']
senticnet['minor_triad'] = ['-0.65', '0', '0.681', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'tone', 'whole_tone', 'step', 'whole_step', 'musical_interval']
senticnet['minoris_umi'] = ['0.977', '-0.93', '0', '0.583', '#joy', '#surprise', 'positive', '0.831', 'giant', 'shinny', 'supernova', 'supergiant', 'binary_star']
senticnet['minority'] = ['-0.94', '0', '0.716', '0', '#sadness', '#anger', 'negative', '-0.82', 'underrepresented_group', 'unfair', 'illegal', 'bad_management', 'discrimination']
senticnet['minority_ethnic'] = ['-0.25', '0', '-0.17', '-0.08', '#sadness', '#fear', 'negative', '-0.16', 'exclusion', 'exclusion_zone', 'isolation', 'detach', 'except']
senticnet['minority_faction'] = ['-0.50', '0', '0.989', '-0.91', '#anger', '#disgust', 'negative', '-0.80', 'camp', 'pack', 'clique', 'coterie', 'ingroup']
senticnet['minority_leader'] = ['0.034', '-0.02', '0', '0.057', '#joy', '#admiration', 'positive', '0.039', 'female_television_connector', 'channel_selector', 'power_switch', 'tuner', 'radio']
senticnet['minority_recruitment'] = ['0.105', '0.226', '-0.08', '0.176', '#interest', '#admiration', 'positive', '0.106', 'control', 'hold_onto', 'glass', 'drink_cup', 'make_apple']
senticnet['minority_representation'] = ['0', '0.157', '0', '0.147', '#interest', '#admiration', 'positive', '0.152', 'wall_hang', 'depiction', 'illustration', 'artwork', 'wall_decoration']
senticnet['minority_stake'] = ['0.59', '-0.75', '0', '0.543', '#joy', '#surprise', 'positive', '0.63', 'venture', 'sally', 'labor', 'cause', 'risk']
senticnet['minotaur'] = ['-0.93', '0', '0.544', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'mutant', 'variation', 'sport', 'lusus_naturae', 'mutation']
senticnet['minpack'] = ['0', '0', '0.737', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'annoy', 'skin_rash', 'bother', 'irritation', 'great_dane']
senticnet['mint'] = ['0.062', '0.115', '0', '0', '#joy', '#interest', 'positive', '0.088', 'lettuce', 'spinach', 'herb', 'baby_flower', 'cabbage']
senticnet['mint_condition'] = ['0.031', '-0.10', '0', '0', '#joy', '#surprise', 'positive', '0.07', 'teddy_bear', 'beanie_baby', 'yo-yo', 'board_game', 'toy']
senticnet['mint_green'] = ['-0.45', '0.081', '-0.25', '0', '#sadness', '#fear', 'negative', '-0.20', 'green_yellow', 'color', 'blue_green', 'cyan', 'purple']
senticnet['mint_ice_cream'] = ['-0.73', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'unwed', 'unwedded', 'divorced', 'unmarried', 'widowed']
senticnet['mint_mentha'] = ['0.632', '-0.93', '0', '0.928', '#surprise', '#admiration', 'positive', '0.831', 'herb', 'oregano', 'marjoram', 'pot_marjoram', 'origanum']
senticnet['mint_sauce'] = ['-0.81', '0', '0.543', '-0.63', '#sadness', '#disgust', 'negative', '-0.66', 'caper', 'gherkin', 'relish', 'dill_pickle', 'sweet_pickle']
senticnet['mint_yoghurt'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'cabbage', 'lettuce', 'broccoli']
senticnet['mintage'] = ['0.729', '0.849', '0', '0', '#joy', '#interest', 'positive', '0.789', 'metal_money', 'coinage', 'specie', 'study', 'project']
senticnet['minuartia'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['minuet'] = ['0.591', '-0.91', '0', '0.605', '#surprise', '#admiration', 'positive', '0.704', 'farandole', 'folk_dance', 'polka', 'dance', 'typical_dance']
senticnet['minus'] = ['-0.04', '0.171', '0', '0.194', '#interest', '#admiration', 'positive', '0.106', 'mass_measure', 'numerical', 'liquid_unit', 'lack', 'total_number']
senticnet['minus_degree_centigrade'] = ['-0.03', '0', '0.014', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'ice_crystal', 'frozen_water', 'wet_cold', 'coldness', 'wetness']
senticnet['minus_depreciation'] = ['0', '0.238', '0.021', '0.152', '#interest', '#admiration', 'positive', '0.123', 'money_value', 'money_amount', 'worth', 'monetary_amount', 'importance']
senticnet['minus_sign'] = ['-0.90', '0', '0.278', '-0.99', '#sadness', '#disgust', 'negative', '-0.72', 'few', 'little', 'less', 'negative', 'minus']
senticnet['minuscule'] = ['-0.95', '0', '0.517', '-0.53', '#sadness', '#disgust', 'negative', '-0.67', 'copperplate', 'hand', 'cursive', 'italic', 'round_hand']
senticnet['minute'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'watch', 'clock', 'hour', 'hourglass', 'stopwatch']
senticnet['minute-by-minute'] = ['0.748', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.826', 'blow-by-blow', 'eye-witness', 'play-by-play', 'round-by-round', 'recounting']
senticnet['minute_bus_ride'] = ['-0.08', '0.179', '-0.06', '0.096', '#interest', '#admiration', 'positive', '0.034', 'ride_subway', 'buy_vending_machine', 'travel', 'buy_lottery_ticket', 'acquire']
senticnet['minute_clip'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'paperclip', 'clasp', 'glue_stick', 'clothespin', 'clamp']
senticnet['minute_documentary'] = ['0', '0.762', '0', '0', '#interest', '#interest', 'positive', '0.762', 'encyclopaedia', 'tropical_rainforest', 'learn_new_information', 'tropical_forest', 'child_story']
senticnet['minute_free_kick'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'watch', 'clock', 'hour', 'hourglass', 'stopwatch']
senticnet['minute_intermission'] = ['0', '0.904', '0', '0', '#interest', '#interest', 'positive', '0.904', 'act', 'part_of_play', 'part_of_movie', 'emote', 'role']
senticnet['minute_opener'] = ['0.034', '-0.02', '0.054', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'compass', 'swiss_army_knife', 'bake_pan', 'coffee_mug', 'wine_opener']
senticnet['minute_pendulum'] = ['0.036', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'minute', 'second', 'hour', 'stopwatch', 'pendulum']
senticnet['minute_second'] = ['0.036', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'minute', 'pendulum', 'hour', 'stopwatch', 'second']
senticnet['minute_second_left'] = ['0', '0.904', '0', '0', '#interest', '#interest', 'positive', '0.904', 'act', 'part_of_play', 'part_of_movie', 'emote', 'role']
senticnet['minute_span'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'watch', 'clock', 'hour', 'hourglass', 'stopwatch']
senticnet['minute_walk'] = ['-0.08', '0.179', '-0.06', '0.096', '#interest', '#admiration', 'positive', '0.034', 'ride_subway', 'buy_vending_machine', 'travel', 'buy_lottery_ticket', 'acquire']
senticnet['minute_walk_away'] = ['0.623', '-0.56', '0', '0.974', '#joy', '#admiration', 'positive', '0.721', 'alterable', 'convertible', 'translatable', 'transformable', 'transmutable']
senticnet['minutely'] = ['0.567', '-0.56', '0', '0.724', '#joy', '#admiration', 'positive', '0.617', 'truth', 'accuracy', 'preciseness', 'trueness', 'precision']
senticnet['minutest'] = ['0.567', '-0.56', '0', '0.724', '#joy', '#admiration', 'positive', '0.617', 'truth', 'accuracy', 'preciseness', 'trueness', 'precision']
senticnet['minutia'] = ['0', '0.136', '0', '0.196', '#interest', '#admiration', 'positive', '0.166', 'describe', 'explain', 'answer', 'description', 'example']
senticnet['miosha'] = ['0.082', '0.319', '-0.10', '0.147', '#interest', '#admiration', 'positive', '0.11', 'protect', 'defend', 'secure', 'guard', 'watch_over']
senticnet['miosis'] = ['-0.88', '0', '0.755', '0', '#sadness', '#anger', 'negative', '-0.81', 'nondisjunction', 'meiosis', 'cell_division', 'litotes', 'understatement']
senticnet['miracidium'] = ['-0.87', '0', '0.983', '-0.91', '#anger', '#disgust', 'negative', '-0.92', 'thorn', 'sticker', 'pricker', 'prickle', 'spikelet']
senticnet['miracle'] = ['0.816', '-0.83', '0', '0.7', '#joy', '#surprise', 'positive', '0.784', 'zeus', 'gasp', 'pardon', 'invisible', 'ceremony']
senticnet['miracle_baby'] = ['-0.51', '0', '0.747', '-0.94', '#anger', '#disgust', 'negative', '-0.73', 'stillborn_infant', 'neonate', 'preterm_baby', 'premature_infant', 'preterm_infant']
senticnet['miracle_cure'] = ['0.713', '0', '0', '0.766', '#joy', '#admiration', 'positive', '0.74', 'doctor', 'nurse', 'medical_procedure', 'medicine', 'doctor_surgery']
senticnet['miracle_play'] = ['0.734', '-0.60', '0', '0.612', '#joy', '#admiration', 'positive', '0.65', 'theater_of_absurd', 'playlet', 'drama', 'morality_play', 'mystery_play']
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senticnet['miraculous'] = ['0.929', '0.885', '0', '0', '#joy', '#interest', 'positive', '0.907', 'miracle', 'god', 'manna', 'miraculousness', 'miraculously']
senticnet['miraculous_cure'] = ['0.713', '0', '0', '0.766', '#joy', '#admiration', 'positive', '0.74', 'doctor', 'nurse', 'medical_procedure', 'medicine', 'doctor_surgery']
senticnet['miraculous_escape'] = ['-0.95', '0', '0.712', '-0.52', '#sadness', '#anger', 'negative', '-0.73', 'uncut', 'unharmed', 'unscathed', 'unwounded', 'uninjured']
senticnet['miraculously'] = ['0.771', '-0.83', '0', '0', '#joy', '#surprise', 'positive', '0.803', 'magically', 'mysteriously', 'healed', 'thankfully', 'somehow']
senticnet['miraculousness'] = ['0.92', '0.879', '0', '0', '#joy', '#interest', 'positive', '0.899', 'miracle', 'god', 'manna', 'miraculous', 'miraculously']
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senticnet['mirage_aircraft'] = ['-0.94', '0', '0.866', '-0.91', '#sadness', '#disgust', 'negative', '-0.90', 'movement', 'apparent_motion', 'motion', 'apparent_movement', 'optical_illusion']
senticnet['mirage_hotel'] = ['0.057', '0.13', '0', '0.119', '#interest', '#admiration', 'positive', '0.102', 'friend_wedding', 'circular_route', 'spanish_restaurant', 'altar', 'monastery']
senticnet['mirdita'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
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senticnet['mirror'] = ['0.584', '-0.62', '0', '0.815', '#surprise', '#admiration', 'positive', '0.675', 'cheval_glass', 'car_mirror', 'hand_glass', 'hand_mirror', 'looking_glass']
senticnet['mirror_image'] = ['-0.80', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.77', 'contrary', 'contradiction', 'lie', 'liar', 'bad_person']
senticnet['mirrored_wall'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'stage_border', 'row_seat', 'stage_entrance', 'theater_seat', 'theater_box']
senticnet['mirth'] = ['0.807', '0.76', '0', '0.704', '#joy', '#interest', 'positive', '0.757', 'rejoice', 'joyful', 'jovial', 'jocund', 'mirthful']
senticnet['mirthful'] = ['0.715', '0.617', '-0.23', '0.329', '#joy', '#interest', 'positive', '0.358', 'jocund', 'jovial', 'jolly', 'joviality', 'banter']
senticnet['mirthfully'] = ['0.653', '0.635', '-0.24', '0.294', '#joy', '#interest', 'positive', '0.335', 'merrily', 'jubilantly', 'happily', 'gayly', 'blithely']
senticnet['mirthfulness'] = ['0.695', '0.623', '-0.23', '0.319', '#joy', '#interest', 'positive', '0.351', 'uproarious', 'hilariously', 'hilarious', 'screaming', 'uproariously']
senticnet['mirthless'] = ['-0.85', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.87', 'million-watt', 'derisive', 'rueful', 'sidesplitting', 'half-grin']
senticnet['miry'] = ['0', '0', '-0.97', '-0.78', '#fear', '#disgust', 'negative', '-0.88', 'marshy', 'mucky', 'muddy', 'boggy', 'soggy']
senticnet['mis-alignment'] = ['0', '0', '-0.72', '-0.72', '#fear', '#disgust', 'negative', '-0.72', 'misalignment', 'misaligned', 'alignment', 'misalignment', 'slippage']
senticnet['mis-cast'] = ['-0.93', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.86', 'woefully', 'miscast', 'overacting', 'well-cast', 'snubbed']
senticnet['mis-interpretation'] = ['0', '0', '0.839', '-0.74', '#anger', '#disgust', 'negative', '-0.79', 'misinterpretation', 'mishandling', 'misuse', 'misappropriation', 'misapplication']
senticnet['mis-represent'] = ['0', '0', '0.754', '-0.81', '#anger', '#disgust', 'negative', '-0.78', 'overdevelop', 'misaligned', 'misalign', 'over-stuff', 'over-extend']
senticnet['misadventure'] = ['-0.62', '0', '0.507', '-0.72', '#sadness', '#disgust', 'negative', '-0.62', 'accident', 'misfortune', 'bad_luck', 'near_miss', 'derailment']
senticnet['misalign'] = ['0', '0', '0.754', '-0.81', '#anger', '#disgust', 'negative', '-0.78', 'overdevelop', 'misaligned', 'mis-represent', 'over-stuff', 'over-extend']
senticnet['misaligned'] = ['0', '0', '-0.73', '-0.72', '#fear', '#disgust', 'negative', '-0.73', 'misalignment', 'misalignment', 'alignment', 'mis-alignment', 'slippage']
senticnet['misalignment'] = ['0', '0', '-0.72', '-0.72', '#fear', '#disgust', 'negative', '-0.72', 'misalignment', 'misaligned', 'alignment', 'mis-alignment', 'slippage']
senticnet['misalliance'] = ['-0.61', '0', '0.668', '-0.81', '#anger', '#disgust', 'negative', '-0.7', 'mesalliance', 'marriage', 'matrimony', 'union', 'spousal_relationship']
senticnet['misandrist'] = ['-0.07', '0.195', '0.629', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'misogynic', 'misogyny', 'murderousness', 'amuck', 'amok']
senticnet['misandry'] = ['0.982', '0.764', '0', '0', '#joy', '#interest', 'positive', '0.873', 'misogyny', 'altruistically', 'altruist', 'altruistic', 'philanthropic']
senticnet['misanthropic'] = ['-0.25', '0.301', '0.351', '-0.51', '#anger', '#disgust', 'negative', '-0.20', 'misanthropical', 'discouraging', 'outrageous', 'horrid', 'queasy']
senticnet['misanthropical'] = ['-0.25', '0.301', '0.351', '-0.51', '#anger', '#disgust', 'negative', '-0.20', 'misanthropic', 'discouraging', 'outrageous', 'horrid', 'queasy']
senticnet['misanthropy'] = ['-0.09', '0.201', '0.613', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'misopedia', 'misology', 'misogamy', 'misoneism', 'hatefully']
senticnet['misapplication'] = ['0', '0', '0.846', '-0.73', '#anger', '#disgust', 'negative', '-0.78', 'misinterpretation', 'mishandling', 'misuse', 'misappropriation', 'mis-interpretation']
senticnet['misapply'] = ['-0.78', '0', '0.704', '-0.91', '#sadness', '#disgust', 'negative', '-0.8', 'direct', 'take', 'lead', 'conduct', 'guide']
senticnet['misapprehend'] = ['0', '0', '-0.73', '-0.88', '#fear', '#disgust', 'negative', '-0.80', 'misconstrue', 'amiss', 'misinterpret', 'misunderstand', 'misconceive']
senticnet['misappropriation'] = ['-0.75', '0', '0.982', '-0.89', '#anger', '#disgust', 'negative', '-0.87', 'raid', 'thieving', 'plunderage', 'larceny', 'theft']
senticnet['misattribute'] = ['-0.77', '0', '0.981', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'trip_up', 'stumble', 'misjudge', 'err', 'fall_for']
senticnet['misattribution'] = ['-0.77', '0', '0.981', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'trip_up', 'stumble', 'misjudge', 'err', 'fall_for']
senticnet['misbegot'] = ['0', '0', '-0.82', '-0.90', '#fear', '#disgust', 'negative', '-0.86', 'misbegotten', 'inauthentic', 'spurious', 'bastardly', 'specious']
senticnet['misbegotten'] = ['0', '0', '-0.82', '-0.91', '#fear', '#disgust', 'negative', '-0.86', 'spurious', 'inauthentic', 'misbegot', 'bastardly', 'specious']
senticnet['misbehave'] = ['0', '0', '0.933', '-0.88', '#anger', '#disgust', 'negative', '-0.91', 'jump_chance', 'reach_advantage', 'conquer_opponent', 'debate_politics', 'fool_around']
senticnet['misbehaving_child'] = ['-0.24', '0', '-0.19', '0', '#sadness', '#fear', 'negative', '-0.22', 'mock', 'prison_sentence', 'wrongdoing', 'jail_time', 'violent_action']
senticnet['misbehavior'] = ['-0.50', '0.112', '-0.38', '0.066', '#sadness', '#fear', 'negative', '-0.17', 'bad_behavior', 'retribution', 'mischief', 'correct_behavior', 'bad_child']
senticnet['misc_subject'] = ['0.737', '0', '0', '0.837', '#joy', '#admiration', 'positive', '0.787', 'religion', 'messageid', 'date', 'politics', 'year']
senticnet['miscalculate'] = ['-0.73', '0', '0.527', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'underestimate', 'err', 'overrate', 'mistake', 'misjudge']
senticnet['miscalculation'] = ['-0.50', '0', '0.762', '-0.89', '#anger', '#disgust', 'negative', '-0.72', 'error', 'fault', 'backfire', 'mistake', 'boomerang']
senticnet['miscarriage'] = ['-0.91', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.86', 'death', 'feticide', 'sickness', 'abortion', 'spontaneous_abortion']
senticnet['miscarry'] = ['-0.62', '0', '0.784', '-0.86', '#anger', '#disgust', 'negative', '-0.76', 'bodge', 'botch_up', 'flub', 'ball_up', 'bollix']
senticnet['miscast'] = ['-0.93', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.85', 'woefully', 'mis-cast', 'overacting', 'well-cast', 'snubbed']
senticnet['miscegenation_tate_cod'] = ['0.082', '0.191', '0', '0.198', '#interest', '#admiration', 'positive', '0.157', 'regal', 'imperial', 'greatness', 'nobleman', 'magnitude']
senticnet['miscegenation_tatut'] = ['0.082', '0.191', '0', '0.198', '#interest', '#admiration', 'positive', '0.157', 'regal', 'imperial', 'greatness', 'nobleman', 'magnitude']
senticnet['miscellanea'] = ['0', '0.703', '0', '0.823', '#interest', '#admiration', 'positive', '0.763', 'miscellany', 'assortment', 'mixture', 'smorgasbord', 'mixed_bag']
senticnet['miscellaneous_correspondence'] = ['0', '0', '0', '0.12', '#admiration', '#admiration', 'positive', '0.12', 'written_communication', 'written_message', 'handwritten', 'message', 'correlation']
senticnet['miscellany'] = ['0', '0.702', '0', '0.821', '#interest', '#admiration', 'positive', '0.761', 'smorgasbord', 'assortment', 'mixture', 'miscellanea', 'mixed_bag']
senticnet['mischance'] = ['-0.91', '0', '0.905', '-0.78', '#sadness', '#anger', 'negative', '-0.86', 'pity', 'shame', 'mishap', 'misfortune', 'misadventure']
senticnet['mischaracterize'] = ['0', '0', '0', '-0.87', '#disgust', '#disgust', 'negative', '-0.87', 'move_eye', 'learn_current_event', 'become_informed', 'time_waste', 'eye_strain']
senticnet['mischief'] = ['-0.39', '0.11', '-0.37', '0.152', '#sadness', '#fear', 'negative', '-0.12', 'misbehavior', 'cause_problem', 'trouble', 'bad_behavior', 'problem']
senticnet['mischief_maker'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'rascal', 'rapscallion', 'scalawag', 'holy_terror', 'monkey']
senticnet['mischievous'] = ['0', '0', '-0.94', '-0.75', '#fear', '#disgust', 'negative', '-0.84', 'elfish', 'gremlin', 'monkey', 'imp', 'impish']
senticnet['mischievous_grin'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'simper', 'please', 'content', 'satisfy', 'gratify']
senticnet['mischievous_sense'] = ['0.897', '0', '0', '0', '#joy', '#joy', 'positive', '0.897', 'gratefully', 'appreciatively', 'self-pride', 'conceit', 'self-love']
senticnet['mischievously'] = ['-0.89', '0.168', '-0.60', '0.05', '#sadness', '#fear', 'negative', '-0.32', 'remorsefully', 'contritely', 'contrition', 'contriteness', 'attrition']
senticnet['mischievousness'] = ['0.738', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.756', 'rascality', 'monkey_business', 'roguishness', 'deviltry', 'devilry']
senticnet['miscibility'] = ['-0.98', '0', '0.516', '-0.90', '#sadness', '#disgust', 'negative', '-0.80', 'devastation', 'annihilation', 'pulverization', 'vaporization', 'destruction']
senticnet['miscommunication'] = ['-0.93', '0', '-0.99', '-0.92', '#sadness', '#fear', 'negative', '-0.95', 'barrier', 'shut', 'shut_down', 'close_down', 'obstacle']
senticnet['misconceive'] = ['0', '0', '-0.74', '-0.87', '#fear', '#disgust', 'negative', '-0.80', 'misapprehend', 'amiss', 'misinterpret', 'misunderstand', 'misconstrue']
senticnet['misconception'] = ['-0.70', '0', '0.533', '-0.62', '#sadness', '#disgust', 'negative', '-0.62', 'erroneous_belief', 'error', 'misunderstanding', 'hallucination', 'fancy']
senticnet['misconduct'] = ['-0.63', '0', '0.728', '-0.58', '#sadness', '#anger', 'negative', '-0.65', 'usurpation', 'inroad', 'wrongdoing', 'wrongful_conduct', 'actus_reus']
senticnet['misconstrual'] = ['-0.87', '0', '0.762', '-0.92', '#sadness', '#disgust', 'negative', '-0.85', 'misreading', 'misinterpretation', 'misconstruction', 'imbroglio', 'interpretation']
senticnet['misconstruction'] = ['-0.87', '0', '0.762', '-0.92', '#sadness', '#disgust', 'negative', '-0.85', 'misreading', 'misconstrual', 'misinterpretation', 'imbroglio', 'interpretation']
senticnet['misconstrue'] = ['0', '0', '-0.73', '-0.87', '#fear', '#disgust', 'negative', '-0.80', 'misapprehend', 'amiss', 'misinterpret', 'misunderstand', 'misconceive']
senticnet['miscount'] = ['-0.60', '0', '0.673', '-0.91', '#anger', '#disgust', 'negative', '-0.73', 'count', 'numeration', 'tally', 'counting', 'enumeration']
senticnet['miscreant'] = ['-0.51', '0', '0.571', '-0.59', '#anger', '#disgust', 'negative', '-0.56', 'scapegrace', 'black_sheep', 'degenerate', 'wretch', 'pervert']
senticnet['miscue'] = ['0', '0', '0.895', '-0.96', '#anger', '#disgust', 'negative', '-0.92', 'misplayed', 'misplay', 'misplay', 'misjudgment', 'bobbled']
senticnet['misdeed'] = ['-0.61', '0', '0.856', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'misbehavior', 'shenanigan', 'hell', 'vandalism', 'hooliganism']
senticnet['misdelivery'] = ['-0.55', '0', '0.843', '-0.58', '#anger', '#disgust', 'negative', '-0.66', 'thieving', 'larceny', 'theft', 'thievery', 'stealing']
senticnet['misdemean'] = ['-0.81', '0', '0.964', '-0.62', '#sadness', '#anger', 'negative', '-0.8', 'act_up', 'move', 'fall_from_grace', 'carry_on', 'act']
senticnet['misdemeanor'] = ['-0.89', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.93', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['misdemeanor_assault'] = ['-0.60', '0', '0.777', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'batrachomyomachia', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['misdemeanor_battery_charge'] = ['0.654', '-0.59', '0', '0.774', '#joy', '#admiration', 'positive', '0.673', 'instrument', 'indictment', 'murder_indictment', 'true_bill', 'legal_document']
senticnet['misdemeanor_conviction'] = ['-0.84', '0', '0.745', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'crime', 'against_law', 'revenge', 'vengeance', 'theft']
senticnet['misdemeanor_count'] = ['-0.84', '0', '0.745', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'crime', 'against_law', 'revenge', 'vengeance', 'theft']
senticnet['misdemeanor_crime'] = ['-0.84', '0', '0.745', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'crime', 'against_law', 'revenge', 'vengeance', 'theft']
senticnet['misdemeanor_offense'] = ['-0.84', '0', '0.745', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'crime', 'against_law', 'revenge', 'vengeance', 'theft']
senticnet['misdemeanor_punishable_by'] = ['-0.89', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.93', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['misdemeanor_violation'] = ['-0.84', '0', '0.745', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'crime', 'against_law', 'revenge', 'vengeance', 'theft']
senticnet['misdiagnose'] = ['-0.77', '0', '0.981', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'trip_up', 'stumble', 'misjudge', 'err', 'fall_for']
senticnet['misdirect'] = ['-0.90', '0', '0.888', '-0.51', '#sadness', '#anger', 'negative', '-0.76', 'suborn', 'modify', 'poison', 'lead_astray', 'alter']
senticnet['misdirection'] = ['0', '0', '0.879', '-0.94', '#anger', '#disgust', 'negative', '-0.91', 'diversion', 'tactic', 'diversionary', 'propagandistic', 'ploy']
senticnet['mise_en_abyme'] = ['-0.77', '0', '0.885', '-0.73', '#sadness', '#anger', 'negative', '-0.8', 'fallacy', 'misconception', 'paralogism', 'logical_fallacy', 'pseudoscience']
senticnet['mise_en_scne'] = ['0.76', '-0.59', '0', '0.713', '#joy', '#admiration', 'positive', '0.689', 'spare_enemy', 'magnanimous', 'generous', 'forgiving', 'deal_gently']
senticnet['miser'] = ['-0.87', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.93', 'misery', 'hunk', 'hoard', 'miserly', 'money']
senticnet['miserable'] = ['-0.80', '0', '0.987', '-0.85', '#anger', '#disgust', 'negative', '-0.88', 'sorrowfully', 'dolefully', 'ruefulness', 'doleful', 'wretched']
senticnet['miserable_condition'] = ['-0.93', '0', '0.75', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'invalidism', 'injury', 'affliction', 'biliousness', 'harm']
senticnet['miserable_existence'] = ['-0.98', '0', '0.802', '-0.88', '#sadness', '#disgust', 'negative', '-0.88', 'poverty', 'impoverishment', 'poorness', 'beggary', 'mendicancy']
senticnet['miserablist'] = ['-0.61', '0', '0.738', '-0.80', '#anger', '#disgust', 'negative', '-0.71', 'dissident', 'beat', 'bohemian', 'maverick', 'recusant']
senticnet['miserably'] = ['-0.72', '0.304', '-0.41', '-0.07', '#sadness', '#fear', 'negative', '-0.22', 'suffering', 'pitiable', 'piteous', 'hapless', 'pitiful']
senticnet['miserably_poor'] = ['-0.63', '0', '0.867', '-0.57', '#sadness', '#anger', 'negative', '-0.69', 'myopic', 'short', 'shortsighted', 'unforesightful', 'improvident']
senticnet['miserliness'] = ['0.858', '-0.63', '0', '0.961', '#joy', '#admiration', 'positive', '0.818', 'closeness', 'parsimony', 'parsimoniousness', 'meanness', 'minginess']
senticnet['miserly'] = ['0', '0', '0.824', '-0.8', '#anger', '#disgust', 'negative', '-0.81', 'mean', 'miserliness', 'miser', 'stingy', 'niggard']
senticnet['misery'] = ['-0.77', '0', '0.797', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'unhappiness', 'sorrow', 'remorse', 'melancholy', 'guilt']
senticnet['misery_memoir'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'nonsense', 'meaninglessness', 'bunk', 'absurdity', 'hokum']
senticnet['misestimation'] = ['-0.51', '0', '0.674', '-0.85', '#anger', '#disgust', 'negative', '-0.68', 'error', 'fault', 'backfire', 'mistake', 'boomerang']
senticnet['misfeasance'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'negligence', 'evasion', 'dereliction', 'contributory_negligence']
senticnet['misfield'] = ['-0.62', '0', '0.713', '-0.98', '#anger', '#disgust', 'negative', '-0.77', 'muddy_up', 'muck_up', 'foul', 'blemish', 'smear']
senticnet['misfire'] = ['-0.69', '0', '0.941', '-0.75', '#anger', '#disgust', 'negative', '-0.8', 'flub', 'blunder', 'blooper', 'bungle', 'botch']
senticnet['misfit'] = ['-0.99', '0', '0.804', '-0.52', '#sadness', '#anger', 'negative', '-0.77', 'addlehead', 'birdbrain', 'ape', 'loon', 'mortal']
senticnet['misfortunate'] = ['-0.72', '0.304', '-0.41', '-0.07', '#sadness', '#fear', 'negative', '-0.22', 'suffering', 'miserably', 'pitiable', 'piteous', 'hapless']
senticnet['misfortune'] = ['-0.91', '0', '0.905', '-0.78', '#sadness', '#anger', 'negative', '-0.86', 'pity', 'shame', 'mishap', 'mischance', 'misadventure']
senticnet['misgauge'] = ['-0.57', '0', '0.666', '-0.66', '#anger', '#disgust', 'negative', '-0.63', 'approximate', 'guess', 'gauge', 'estimate', 'judge']
senticnet['misgiving'] = ['-0.75', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.81', 'qualm', 'scruple', 'insecurely', 'insecurity', 'insecure']
senticnet['misgovernment'] = ['-0.82', '0', '0.575', '-0.92', '#sadness', '#disgust', 'negative', '-0.77', 'governing', 'administration', 'government', 'governance', 'government_activity']
senticnet['misguide'] = ['-0.78', '0', '0.704', '-0.91', '#sadness', '#disgust', 'negative', '-0.8', 'direct', 'take', 'lead', 'conduct', 'guide']
senticnet['misguided'] = ['-0.79', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.89', 'well-intentioned', 'intentioned', 'good-hearted', 'well-intended', 'well-meaning']
senticnet['mish_mash'] = ['-0.62', '0', '0.988', '-0.59', '#sadness', '#anger', 'negative', '-0.73', 'motley', 'mixture', 'variety', 'assortment', 'alphabet_soup']
senticnet['mishandling'] = ['0', '0', '0.841', '-0.74', '#anger', '#disgust', 'negative', '-0.79', 'misinterpretation', 'misapplication', 'misuse', 'misappropriation', 'mis-interpretation']
senticnet['mishap'] = ['-0.91', '0', '0.905', '-0.78', '#sadness', '#anger', 'negative', '-0.86', 'pity', 'shame', 'misfortune', 'mischance', 'misadventure']
senticnet['misheard'] = ['-0.77', '0', '0.981', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'trip_up', 'stumble', 'misjudge', 'err', 'fall_for']
senticnet['mishegaas'] = ['0.579', '-0.58', '0', '0.99', '#surprise', '#admiration', 'positive', '0.717', 'folly', 'lunacy', 'craziness', 'indulgence', 'foolery']
senticnet['mishegoss'] = ['0.807', '-0.83', '0', '0.688', '#joy', '#surprise', 'positive', '0.776', 'folly', 'lunacy', 'craziness', 'indulgence', 'foolery']
senticnet['mishmash'] = ['-0.62', '0', '0.988', '-0.59', '#sadness', '#anger', 'negative', '-0.73', 'motley', 'mixture', 'variety', 'assortment', 'alphabet_soup']
senticnet['misidentify'] = ['-0.77', '0', '0.981', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'trip_up', 'stumble', 'misjudge', 'err', 'fall_for']
senticnet['misinform'] = ['-0.97', '0', '0.737', '-0.55', '#sadness', '#anger', 'negative', '-0.75', 'deceive', 'sandbag', 'betray', 'lead_astray', 'tergiversate']
senticnet['misinterpret'] = ['-0.82', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.81', 'misunderstand', 'interpretation', 'misinterpretation', 'misinterpreter', 'misread']
senticnet['misinterpretation'] = ['-0.87', '0', '0.762', '-0.92', '#sadness', '#disgust', 'negative', '-0.85', 'misreading', 'misconstrual', 'misconstruction', 'imbroglio', 'interpretation']
senticnet['misinterpreter'] = ['-0.83', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.82', 'misunderstand', 'interpretation', 'misinterpretation', 'misinterpret', 'misread']
senticnet['misjudge'] = ['-0.73', '0', '0.527', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'underestimate', 'err', 'overrate', 'mistake', 'miscalculate']
senticnet['misjudgement'] = ['-0.84', '0', '0.828', '-0.57', '#sadness', '#anger', 'negative', '-0.75', 'bobble', 'error', 'misstep', 'fumble', 'stumble']
senticnet['misjudgment'] = ['0', '0', '0.898', '-0.96', '#anger', '#disgust', 'negative', '-0.93', 'misplayed', 'miscue', 'misplay', 'misplay', 'bobbled']
senticnet['mislay'] = ['-0.17', '0.131', '-0.20', '0.097', '#sadness', '#fear', 'negative', '-0.03', 'lost_and_found', 'tv_series', 'bad_situation', 'odds', 'risk']
senticnet['mislead'] = ['-0.74', '0', '0.741', '-0.92', '#sadness', '#disgust', 'negative', '-0.80', 'loathing', 'unattractive', 'bad_luck', 'feel_stressed', 'screw_up']
senticnet['misleader'] = ['-0.64', '0', '0.962', '-0.68', '#anger', '#disgust', 'negative', '-0.76', 'beguiler', 'slicker', 'leader', 'deceiver', 'cheat']
senticnet['misleading'] = ['0', '0', '-0.94', '-0.96', '#fear', '#disgust', 'negative', '-0.95', 'fallacious', 'fallacy', 'false', 'falsify', 'illusory']
senticnet['mismanage'] = ['-0.78', '0', '0.757', '0', '#sadness', '#anger', 'negative', '-0.77', 'management', 'responsibility', 'misconduct', 'incompetent', 'dishonest']
senticnet['mismanagement'] = ['-0.94', '0', '0.923', '-0.98', '#sadness', '#disgust', 'negative', '-0.95', 'heap', 'push', 'accept', 'bring', 'lunge']
senticnet['mismarry'] = ['0.756', '-0.53', '0', '0.859', '#joy', '#admiration', 'positive', '0.717', 'marry', 'get_married', 'love', 'conjoin', 'hook_up_with']
senticnet['mismatch_between_supply'] = ['0.749', '0.764', '0', '0', '#joy', '#interest', 'positive', '0.756', 'obedience', 'order', 'requirement', 'permission', 'necessary']
senticnet['mismatched'] = ['-0.87', '0', '0.627', '-0.70', '#sadness', '#disgust', 'negative', '-0.73', 'unequalized', 'unequal', 'nonequivalent', 'uneven', 'unbalanced']
senticnet['mismate'] = ['-0.94', '0', '0.805', '-0.93', '#sadness', '#disgust', 'negative', '-0.89', 'couple', 'pair', 'twin', 'match', 'mate']
senticnet['mismated'] = ['-0.84', '0', '0.717', '-0.77', '#sadness', '#disgust', 'negative', '-0.77', 'incompatible', 'unsuited', 'unmatched', 'odd', 'mismatched']
senticnet['misnomer'] = ['-0.98', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.84', 'oxymoron', 'overstatement', 'misconception', 'pejorative', 'oversimplification']
senticnet['miso_paste'] = ['0.829', '0.857', '0', '0', '#joy', '#interest', 'positive', '0.843', 'fruit_tree', 'baby_flower', 'carpel', 'strawberry', 'fig']
senticnet['miso_soup'] = ['0', '-0.03', '0.046', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'pasta', 'green_bean', 'mayonnaise', 'frozen_food', 'sour_milk']
senticnet['misocainea'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amuck', 'murderousness', 'amok', 'misogyny']
senticnet['misogamist'] = ['-0.86', '0', '0.818', '-0.96', '#sadness', '#disgust', 'negative', '-0.88', 'mortal', 'person', 'individual', 'misogynist', 'hate_woman']
senticnet['misogamy'] = ['-0.08', '0.202', '0.615', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'misopedia', 'misology', 'misoneism', 'misanthropy', 'misogynism']
senticnet['misogynic'] = ['-0.07', '0.195', '0.629', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'misogyny', 'misogynism', 'murderousness', 'amuck', 'amok']
senticnet['misogynism'] = ['-0.07', '0.195', '0.629', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'misogynic', 'misogyny', 'murderousness', 'amuck', 'amok']
senticnet['misogynist'] = ['-0.87', '0', '0.81', '-0.97', '#sadness', '#disgust', 'negative', '-0.88', 'mortal', 'person', 'individual', 'misogamist', 'hate_woman']
senticnet['misogynistic'] = ['0.585', '-0.7', '0', '0.586', '#surprise', '#admiration', 'positive', '0.624', 'jingoistic', 'ultranationalistic', 'superpatriotic', 'nationalistic', 'patriotic']
senticnet['misogyny'] = ['-0.07', '0.195', '0.629', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'misogynic', 'misogynism', 'murderousness', 'amuck', 'amok']
senticnet['misology'] = ['-0.08', '0.202', '0.615', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'misopedia', 'misogamy', 'misoneism', 'misanthropy', 'misogynism']
senticnet['misoneism'] = ['-0.08', '0.2', '0.619', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'misopedia', 'misogamy', 'misology', 'misanthropy', 'misogyny']
senticnet['misopedia'] = ['-0.08', '0.202', '0.615', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'misology', 'misogamy', 'misoneism', 'misanthropy', 'misogynism']
senticnet['misperceive'] = ['-0.77', '0', '0.981', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'trip_up', 'stumble', 'misjudge', 'err', 'fall_for']
senticnet['misplace'] = ['-0.17', '0.131', '-0.20', '0.097', '#sadness', '#fear', 'negative', '-0.03', 'lost_and_found', 'tv_series', 'bad_situation', 'odds', 'risk']
senticnet['misplacement'] = ['-0.55', '0', '0.843', '-0.58', '#anger', '#disgust', 'negative', '-0.66', 'thieving', 'larceny', 'theft', 'thievery', 'stealing']
senticnet['misplay'] = ['0', '0', '0.901', '-0.96', '#anger', '#disgust', 'negative', '-0.93', 'misplayed', 'miscue', 'misplay', 'misjudgment', 'bobbled']
senticnet['misplayed'] = ['0', '0', '0.891', '-0.96', '#anger', '#disgust', 'negative', '-0.92', 'misplay', 'miscue', 'misplay', 'misjudgment', 'bobbled']
senticnet['misprice'] = ['-0.12', '0.193', '0.597', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'upvalue', 'overrate', 'overprize', 'overstimate', 'overprice']
senticnet['misprint'] = ['-0.84', '0', '0', '-0.80', '#sadness', '#disgust', 'negative', '-0.82', 'typo', 'misspelling', 'inadvertence', 'miscalculation', 'misprinted']
senticnet['misprinted'] = ['-0.84', '0', '0', '-0.81', '#sadness', '#disgust', 'negative', '-0.82', 'typo', 'misspelling', 'inadvertence', 'miscalculation', 'misprint']
senticnet['mispronounce'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicado', 'incommunicative', 'impassive', 'inexpressive', 'silent']
senticnet['mispronunciation'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'pronunciation', 'homophony']
senticnet['misquote'] = ['-0.80', '0', '0.91', '0', '#sadness', '#anger', 'negative', '-0.85', 'incorrect', 'incorrectly', 'inaccurately', 'misunderstand', 'wrong']
senticnet['misread'] = ['-0.83', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.82', 'misunderstand', 'interpretation', 'misinterpretation', 'misinterpreter', 'misinterpret']
senticnet['misreading'] = ['-0.87', '0', '0.762', '-0.92', '#sadness', '#disgust', 'negative', '-0.85', 'misinterpretation', 'misconstrual', 'misconstruction', 'imbroglio', 'interpretation']
senticnet['misreckoning'] = ['-0.91', '0', '0.943', '-0.81', '#sadness', '#anger', 'negative', '-0.89', 'error', 'fault', 'backfire', 'mistake', 'boomerang']
senticnet['misremember'] = ['-0.77', '0', '0.981', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'trip_up', 'stumble', 'misjudge', 'err', 'fall_for']
senticnet['misrepresent'] = ['-0.60', '0', '0.813', '-0.52', '#sadness', '#anger', 'negative', '-0.65', 'warp', 'dissemble', 'feign', 'sham', 'pretend']
senticnet['misrepresentation'] = ['-0.58', '0', '0.516', '-0.79', '#sadness', '#disgust', 'negative', '-0.63', 'humbug', 'pretence', 'evasion', 'feigning', 'dissembling']
senticnet['misrule'] = ['-0.68', '0', '0.809', '-0.83', '#anger', '#disgust', 'negative', '-0.77', 'governing', 'administration', 'government', 'governance', 'government_activity']
senticnet['miss'] = ['-0.95', '0', '0.733', '-0.98', '#sadness', '#disgust', 'negative', '-0.89', 'miss_bus', 'miss_flight', 'miss_coincidence', 'miss_out', 'miss_train']
senticnet['miss_appointment'] = ['-0.91', '0', '0.71', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'quiet_mind', 'lower_standard', 'sleep_bed', 'think_future', 'sit_rock_chair']
senticnet['miss_asphyxia'] = ['-0.64', '0', '0.625', '-0.88', '#sadness', '#disgust', 'negative', '-0.71', 'difficulty', 'awkwardness', 'flea_bite', 'difficultness', 'cumbersomeness']
senticnet['miss_ball'] = ['-0.07', '-0.06', '0.068', '-0.08', '#sadness', '#disgust', 'negative', '-0.04', 'droped_the_catch', 'try_best', 'loose', 'sore_knee', 'lose_game']
senticnet['miss_bridget'] = ['-0.96', '0', '0.787', '0', '#sadness', '#anger', 'negative', '-0.87', 'disconcertion', 'untune', 'disconcertment', 'discomfiture', 'discomfit']
senticnet['miss_bus'] = ['-0.18', '0', '-0.05', '-0.08', '#sadness', '#disgust', 'negative', '-0.10', 'gain_knowledge', 'study_library', 'solve_equation', 'guess_answer', 'love_pet']
senticnet['miss_call'] = ['-0.03', '-0.04', '0.146', '-0.18', '#anger', '#disgust', 'negative', '-0.07', 'move_finger', 'loose_track_time', 'miss_out', 'part_of_band', 'make_noise']
senticnet['miss_canfield'] = ['0.941', '0.987', '0', '0', '#joy', '#interest', 'positive', '0.964', 'monkey', 'marmoset', 'lemur', 'fiance', 'koala']
senticnet['miss_celia'] = ['0', '0', '-0.06', '-0.11', '#fear', '#disgust', 'negative', '-0.09', 'elderly_person', 'hide-a-bed', 'homosexual', 'linen_chest', 'dust_mite']
senticnet['miss_chance'] = ['-1', '0', '0', '-0.66', '#sadness', '#disgust', 'negative', '-0.83', 'regret', 'powerless', 'resentful', 'remorse', 'sad']
senticnet['miss_coincidence'] = ['-0.95', '0', '0.733', '-0.98', '#sadness', '#disgust', 'negative', '-0.89', 'miss_bus', 'miss_flight', 'miss', 'miss_out', 'miss_train']
senticnet['miss_cornelia'] = ['-0.85', '0.21', '-0.56', '0', '#sadness', '#fear', 'negative', '-0.40', 'doleful', 'sadly', 'dolefully', 'ruefulness', 'wretched']
senticnet['miss_crawford'] = ['0.918', '0.476', '-0.19', '0.417', '#joy', '#interest', 'positive', '0.404', 'merriment', 'cheery', 'festal', 'playfulness', 'pridefulness']
senticnet['miss_dead_friend'] = ['-0.96', '0', '0.093', '-0.99', '#sadness', '#disgust', 'negative', '-0.68', 'miss_one_person', 'nostalgia', 'miss_dead_person', 'miss_out', 'miss_dead_parent']
senticnet['miss_dead_parent'] = ['-0.96', '0', '0.093', '-0.99', '#sadness', '#disgust', 'negative', '-0.68', 'miss_one_person', 'nostalgia', 'miss_dead_person', 'miss_dead_friend', 'miss_out']
senticnet['miss_dead_person'] = ['-0.96', '0', '0.093', '-0.99', '#sadness', '#disgust', 'negative', '-0.68', 'miss_one_person', 'nostalgia', 'miss_out', 'miss_dead_friend', 'miss_dead_parent']
senticnet['miss_flight'] = ['-0.95', '0', '0.733', '-0.98', '#sadness', '#disgust', 'negative', '-0.89', 'miss_bus', 'miss', 'miss_coincidence', 'miss_out', 'miss_train']
senticnet['miss_flite'] = ['-0.56', '0', '0.738', '-0.61', '#anger', '#disgust', 'negative', '-0.64', 'mix_up', 'muddle', 'puddle', 'assemble', 'piece']
senticnet['miss_harleth'] = ['-0.80', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'difficulty', 'awkwardness', 'flea_bite', 'difficultness', 'cumbersomeness']
senticnet['miss_margland'] = ['-0.56', '0', '0.828', '-0.68', '#anger', '#disgust', 'negative', '-0.69', 'abruptness', 'precipitateness', 'precipitousness', 'hastiness', 'precipitancy']
senticnet['miss_one_person'] = ['-0.96', '0', '0.093', '-0.99', '#sadness', '#disgust', 'negative', '-0.68', 'miss_out', 'nostalgia', 'miss_dead_person', 'miss_dead_friend', 'miss_dead_parent']
senticnet['miss_out'] = ['-0.96', '0', '0.093', '-0.99', '#sadness', '#disgust', 'negative', '-0.68', 'miss_one_person', 'nostalgia', 'miss_dead_person', 'miss_dead_friend', 'miss_dead_parent']
senticnet['miss_parent'] = ['0', '0', '0', '-0.95', '#disgust', '#disgust', 'negative', '-0.95', 'immigrant', 'nostalgia', 'long_distance', 'long_distance_relationship', 'live_far_from_home']
senticnet['miss_piggy'] = ['-0.93', '0', '0.77', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'hothead', 'unpleasant_person', 'disagreeable_person', 'crabby_person', 'crab']
senticnet['miss_point'] = ['-0.08', '-0.03', '0.043', '-0.12', '#sadness', '#disgust', 'negative', '-0.05', 'poor_strategy', 'bad_luck', 'lose', 'lose_focus', 'lose_game']
senticnet['miss_pole'] = ['0', '0', '-0.06', '-0.11', '#fear', '#disgust', 'negative', '-0.09', 'elderly_person', 'hide-a-bed', 'homosexual', 'linen_chest', 'dust_mite']
senticnet['miss_red_light'] = ['-0.92', '0', '0.833', '-0.97', '#sadness', '#disgust', 'negative', '-0.90', 'accident', 'drive_fast', 'car_accident', 'big_car_accident', 'break_rule']
senticnet['miss_rosetta'] = ['0.591', '-0.76', '0', '0.66', '#surprise', '#admiration', 'positive', '0.673', 'darling', 'pet', 'favorite', 'first_choice', 'macushla']
senticnet['miss_shepperson'] = ['-0.54', '0', '0.739', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'unprofitable', 'idle', 'lean', 'dead', 'marginal']
senticnet['miss_silvester'] = ['-0.10', '0.127', '-0.11', '0.174', '#interest', '#admiration', 'positive', '0.022', 'query', 'response', 'respond', 'interrogation', 'inquiry']
senticnet['miss_tilney'] = ['-0.95', '0', '-0.76', '0', '#sadness', '#fear', 'negative', '-0.85', 'disquietude', 'edginess', 'edgy', 'uptight', 'restive']
senticnet['miss_tox'] = ['-0.44', '-0.15', '0', '-0.36', '#sadness', '#disgust', 'negative', '-0.21', 'baffle', 'stupefy', 'dumbfound', 'stupefaction', 'confuse']
senticnet['miss_train'] = ['-0.95', '0', '0.733', '-0.98', '#sadness', '#disgust', 'negative', '-0.89', 'miss_bus', 'miss_flight', 'miss_coincidence', 'miss_out', 'miss']
senticnet['miss_tuttle'] = ['-0.64', '0', '0.625', '-0.88', '#sadness', '#disgust', 'negative', '-0.71', 'difficulty', 'awkwardness', 'flea_bite', 'difficultness', 'cumbersomeness']
senticnet['miss_work'] = ['0', '-0.01', '-0.06', '-0.06', '#fear', '#disgust', 'negative', '-0.03', 'sleep', 'sleep_upon', 'miss_bus', 'avoid_work', 'get_sleep']
senticnet['missale'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'reverence', 'genuflection', 'obeisance', 'kowtow', 'mafia']
senticnet['missed'] = ['-0.99', '0', '0.796', '-0.57', '#sadness', '#anger', 'negative', '-0.78', 'dark', 'uncomprehensible', 'lost', 'impenetrable', 'unfathomable']
senticnet['missed_free_throw'] = ['-0.81', '0', '0.747', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'vagrant', 'drifter', 'hobo', 'vagabond', 'dosser']
senticnet['missed_jumper'] = ['-0.81', '0', '0.747', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'vagrant', 'drifter', 'hobo', 'vagabond', 'dosser']
senticnet['missense_mutation'] = ['-0.87', '0', '0.873', '-0.62', '#sadness', '#anger', 'negative', '-0.78', 'pycnodysostosis', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'dwarfism']
senticnet['misshape'] = ['0', '0', '0.827', '-0.73', '#anger', '#disgust', 'negative', '-0.78', 'disfigure', 'fake', 'deviate', 'garble', 'alter']
senticnet['misshapen'] = ['0', '0', '0.759', '-0.87', '#anger', '#disgust', 'negative', '-0.81', 'awry', 'distortable', 'doublespeak', 'embellish', 'distort']
senticnet['missile'] = ['0', '0', '-0.89', '-0.75', '#fear', '#disgust', 'negative', '-0.82', 'explosive', 'projectile', 'nuclear_bomb', 'ammunition', 'dynamite']
senticnet['missile_defense'] = ['-0.04', '0', '-0.06', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'make_war', 'conquer_enemy', 'use_military', 'gain_territory', 'keep_peace']
senticnet['missile_defense_agency'] = ['-0.04', '0', '-0.06', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'make_war', 'conquer_enemy', 'use_military', 'gain_territory', 'keep_peace']
senticnet['missile_seeker'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'chairperson', 'commuter']
senticnet['missile_warhead'] = ['-0.84', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.85', 'inferno', 'forest_fire', 'fire_light', 'dismissal', 'wash_mouth_soap']
senticnet['missing'] = ['-0.98', '0', '0.801', '-0.66', '#sadness', '#anger', 'negative', '-0.81', 'lacking', 'wanting', 'nonextant', 'absent', 'nonexistent']
senticnet['missing_limb'] = ['-0.62', '0', '0.996', '-0.68', '#anger', '#disgust', 'negative', '-0.76', 'pack', 'face_pack', 'pickup', 'prosthesis', 'corrective']
senticnet['missing_teeth'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'false_teeth', 'bridge', 'dental_plate', 'partial_denture', 'dental_appliance']
senticnet['mission'] = ['0', '0', '0', '0.75', '#admiration', '#admiration', 'positive', '0.75', 'make_happen', 'military_service', 'objective', 'hardship', 'set_instruction']
senticnet['mission_abroad'] = ['0', '0.766', '0', '0', '#interest', '#interest', 'positive', '0.766', 'abroad', 'foreign', 'study_abroad', 'elsewhere', 'live_abroad']
senticnet['mission_accomplished'] = ['0.805', '0', '0', '0.725', '#joy', '#admiration', 'positive', '0.765', 'reach_destination', 'get_room', 'arrive', 'get_to_destination', 'get_place']
senticnet['mission_revival_style'] = ['0.578', '-0.95', '0', '0.979', '#surprise', '#admiration', 'positive', '0.838', 'perpendicular', 'perpendicular_style', 'gothic_architecture', 'style_of_architecture', 'type_of_architecture']
senticnet['mission_statement'] = ['-0.03', '0.081', '0', '0.095', '#interest', '#admiration', 'positive', '0.047', 'sight', 'eye_function', 'eye_sense', 'see_ability', 'aim']
senticnet['missionary_post'] = ['-0.54', '0', '0.863', '-0.76', '#anger', '#disgust', 'negative', '-0.72', 'denomination', 'committee', 'citizen_committee', 'mission', 'ngo']
senticnet['missionary_zeal'] = ['0', '0.908', '0', '0.99', '#interest', '#admiration', 'positive', '0.949', 'beneficence', 'beneficially', 'beneficent', 'beneficed', 'benefic']
senticnet['missive'] = ['-0.84', '0', '0.572', '-0.68', '#sadness', '#disgust', 'negative', '-0.69', 'reply', 'response', 'sass', 'sassing', 'backtalk']
senticnet['misspeaking'] = ['-0.80', '0', '0.963', '0', '#sadness', '#anger', 'negative', '-0.88', 'fable', 'forgery', 'oleaginous', 'falsely', 'untruthfully']
senticnet['misspecification'] = ['0.83', '-0.83', '0', '0.872', '#surprise', '#admiration', 'positive', '0.846', 'overappraisal', 'overvaluation', 'overestimate', 'overestimation', 'commercial_document']
senticnet['misspell'] = ['-0.77', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.76', 'mistake', 'error', 'grammar_mistake', 'poor_english', 'bad_writing']
senticnet['misspelling'] = ['0', '0', '-0.71', '-0.97', '#fear', '#disgust', 'negative', '-0.84', 'misinterpretation', 'mispronunciation', 'misreading', 'mistranslated', 'mistranslation']
senticnet['misspent'] = ['-0.78', '0', '0', '-0.86', '#sadness', '#disgust', 'negative', '-0.82', 'profuse', 'squandering', 'squanderer', 'waster', 'squander']
senticnet['misstate'] = ['-0.59', '0', '0.537', '-0.53', '#sadness', '#anger', 'negative', '-0.55', 'murder', 'mutilate', 'mangle', 'misrepresent', 'belie']
senticnet['misstep'] = ['-0.69', '0', '0.941', '-0.75', '#anger', '#disgust', 'negative', '-0.8', 'flub', 'blunder', 'blooper', 'bungle', 'botch']
senticnet['missy'] = ['-0.74', '0', '0.877', '-0.97', '#anger', '#disgust', 'negative', '-0.86', 'miss', 'girl', 'chachka', 'young_lady', 'young_woman']
senticnet['missy_peregrym'] = ['-0.97', '0', '0.728', '-0.78', '#sadness', '#disgust', 'negative', '-0.82', 'beguiler', 'slicker', 'phony', 'faker', 'deceiver']
senticnet['mist'] = ['0', '0', '-0.73', '-0.79', '#fear', '#disgust', 'negative', '-0.76', 'hard_see', 'rain_fall', 'dew', 'rain', 'dizzy']
senticnet['mistake'] = ['-0.74', '0', '0.876', '-0.86', '#anger', '#disgust', 'negative', '-0.82', 'problem', 'grammar_mistake', 'error', 'trouble', 'misspell']
senticnet['mistakenly'] = ['0', '0', '0.879', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'amiss', 'mistakingly', 'mistake', 'error', 'wrongly']
senticnet['mistaking'] = ['-0.52', '0', '0.502', '-0.89', '#sadness', '#disgust', 'negative', '-0.64', 'misreading', 'misconstrual', 'misconstruction', 'imbroglio', 'interpretation']
senticnet['mistakingly'] = ['0', '0', '0.872', '-0.83', '#anger', '#disgust', 'negative', '-0.85', 'amiss', 'mistakenly', 'mistake', 'error', 'wrongly']
senticnet['mister'] = ['0.072', '0.098', '0', '0', '#joy', '#interest', 'positive', '0.085', 'title', 'leader', 'head', 'chief', 'commander']
senticnet['mister_hyde'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
senticnet['mister_sinister'] = ['0.02', '0.131', '0.749', '-0.67', '#anger', '#disgust', 'negative', '-0.31', 'belligerence', 'belligerency', 'belligerent', 'war', 'rancor']
senticnet['mistral_class_helicopter'] = ['0', '0.089', '0.01', '0.118', '#interest', '#admiration', 'positive', '0.066', 'crate', 'mom_house', 'sack', 'poker_game', 'safekeep']
senticnet['mistralian_norm'] = ['0.136', '-0.11', '0.031', '0', '#joy', '#surprise', 'positive', '0.075', 'rap', 'popular_music', 'music', 'band_play', 'rock_roll']
senticnet['mistranslated'] = ['0', '0', '-0.71', '-0.97', '#fear', '#disgust', 'negative', '-0.84', 'misinterpretation', 'mispronunciation', 'misreading', 'mistranslation', 'misspelling']
senticnet['mistranslation'] = ['0', '0', '-0.71', '-0.97', '#fear', '#disgust', 'negative', '-0.84', 'misinterpretation', 'mispronunciation', 'misreading', 'mistranslated', 'misspelling']
senticnet['mistreat'] = ['-0.85', '0', '0.749', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'cold_foot', 'eat_worm', 'farm_bird', 'yellow_animal', 'biped']
senticnet['mistreatment'] = ['-0.89', '0', '0.643', '-0.88', '#sadness', '#disgust', 'negative', '-0.80', 'maltreatment', 'abuse', 'vexation', 'harassment', 'disregard']
senticnet['mistress'] = ['-0.98', '0', '0.791', '-0.85', '#sadness', '#disgust', 'negative', '-0.87', 'hooker', 'prostitute', 'bad_girl', 'working_girl', 'pay_for_sex']
senticnet['mistrust'] = ['0', '0', '0', '-0.90', '#disgust', '#disgust', 'negative', '-0.90', 'cause_suffer', 'disrespect', 'lose_team', 'bad_luck', 'leave_out']
senticnet['mistrust_between'] = ['0', '0', '0', '-0.90', '#disgust', '#disgust', 'negative', '-0.90', 'cause_suffer', 'disrespect', 'lose_team', 'bad_luck', 'leave_out']
senticnet['mistrustful'] = ['0', '0', '-0.81', '-0.75', '#fear', '#disgust', 'negative', '-0.78', 'suspicious', 'untrusting', 'leery', 'fishy', 'wary']
senticnet['mistwraith'] = ['-0.04', '0.166', '0.684', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'malign', 'malefic', 'maliciously', 'spitefulness', 'maliciousness']
senticnet['misty'] = ['0.085', '0', '0', '0.089', '#joy', '#admiration', 'positive', '0.087', 'steam', 'haze', 'mist', 'initiation', 'summer_weather']
senticnet['misty_knight'] = ['0.02', '0.131', '0.749', '-0.67', '#anger', '#disgust', 'negative', '-0.31', 'belligerence', 'belligerency', 'belligerent', 'war', 'rancor']
senticnet['misunderstand'] = ['-0.87', '0', '0.867', '-0.77', '#sadness', '#anger', 'negative', '-0.83', 'persuade', 'misunderstanding', 'interrupt', 'look_eye', 'exchange_idea']
senticnet['misunderstanding'] = ['-0.98', '0', '0.983', '-0.70', '#sadness', '#anger', 'negative', '-0.88', 'exchange_idea', 'persuade', 'dry_mouth', 'increase_understand', 'share_idea']
senticnet['misuse'] = ['-0.99', '0', '0.827', '-0.63', '#sadness', '#anger', 'negative', '-0.81', 'substance_abuse', 'drug_abuse', 'habit', 'usage', 'use']
senticnet['mit'] = ['0.823', '0.801', '0', '0', '#joy', '#interest', 'positive', '0.812', 'sun', 'use', 'server', 'note', 'openwindows']
senticnet['mitar'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'white_animal', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
senticnet['mitch'] = ['0.699', '0.62', '-0.23', '0.323', '#joy', '#interest', 'positive', '0.353', 'jolliness', 'joviality', 'jolity', 'chaff', 'banter']
senticnet['mitch_pileggi'] = ['0.918', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.827', 'encounter', 'reveal', 'programme', 'lose', 'sequel']
senticnet['mite'] = ['-0.03', '-0.07', '0.093', '-0.12', '#anger', '#disgust', 'negative', '-0.04', 'gnat', 'ladybug', 'bed_bug', 'ant', 'mosquito']
senticnet['mitigate'] = ['0.676', '-0.70', '0', '0.8', '#surprise', '#admiration', 'positive', '0.727', 'better', 'soothe', 'improve', 'alleviate_pain', 'meliorate']
senticnet['mitigating_circumstance'] = ['-0.61', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.69', 'irresponsibility', 'untrustiness', 'undependability', 'undependableness', 'unreliability']
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senticnet['mitochondrial_disease'] = ['-0.87', '0', '0.873', '-0.62', '#sadness', '#anger', 'negative', '-0.78', 'pycnodysostosis', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'dwarfism']
senticnet['mitochondrial_dysfunction'] = ['-0.78', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.80', 'deterioration', 'decline', 'decay', 'degradation', 'slide']
senticnet['mitochondrion'] = ['0.729', '0.962', '0', '0', '#joy', '#interest', 'positive', '0.845', 'sarcosome', 'chondriosome', 'organelle', 'protein', 'chloroplast']
senticnet['mitotic'] = ['-0.87', '0', '0.915', '-0.95', '#anger', '#disgust', 'negative', '-0.91', 'inactivity', 'inactiveness', 'slumber', 'inaction', 'hibernation']
senticnet['mitotically'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'nonmotile', 'immobile']
senticnet['mitral'] = ['0.777', '-0.89', '0', '0.508', '#joy', '#surprise', 'positive', '0.727', 'stretching', 'enlarge', 'enlargement', 'get_bigger', 'distension']
senticnet['mitral_insufficiency'] = ['-0.81', '0', '0.684', '-0.81', '#sadness', '#disgust', 'negative', '-0.77', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['mitral_regurgitation'] = ['0.777', '-0.89', '0', '0.508', '#joy', '#surprise', 'positive', '0.727', 'stretching', 'enlarge', 'enlargement', 'get_bigger', 'distension']
senticnet['mitral_stenosis'] = ['-0.81', '0', '0.684', '-0.81', '#sadness', '#disgust', 'negative', '-0.77', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'stenosis']
senticnet['mitsubishi_rayon'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'builder']
senticnet['mitsubishi_shogun'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'fuel', 'car', 'fossil_fuel', 'transport', 'natural_resource']
senticnet['mitt'] = ['0.526', '-0.70', '0', '0.607', '#surprise', '#admiration', 'positive', '0.613', 'hook', 'meat_hook', 'mauler', 'right', 'left']
senticnet['mitten'] = ['0.938', '0.727', '0', '0', '#joy', '#interest', 'positive', '0.833', 'strap', 'man_clothes', 'bra', 'gauntlet', 'tie']
senticnet['mitzvah'] = ['0.71', '0.882', '0', '0.9', '#interest', '#admiration', 'positive', '0.831', 'positive_reinforcement', 'brownie_point', 'benefit', 'give', 'gratification']
senticnet['mivart'] = ['-0.04', '0.192', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.054', 'hypothetical', 'supposition', 'scientific_statement', 'psychology', 'educate_guess']
senticnet['mix'] = ['0.773', '-0.88', '0', '0', '#joy', '#surprise', 'positive', '0.829', 'jiggle', 'vibrate', 'prop_up', 'blend', 'ferment']
senticnet['mix_drink'] = ['0.931', '0.799', '0', '0', '#joy', '#interest', 'positive', '0.865', 'liquor_store', 'tavern', 'drinking_place', 'martini', 'bar']
senticnet['mix_metal'] = ['0.024', '0.108', '0', '0.144', '#interest', '#admiration', 'positive', '0.092', 'make_out_metal', 'door_opener', 'alloy', 'hardware', 'ore']
senticnet['mix_track'] = ['0.067', '0.193', '0', '0.294', '#interest', '#admiration', 'positive', '0.185', 'compose_music', 'placement', 'small_group', 'combine_sound', 'location']
senticnet['mix_up'] = ['-0.56', '0', '0.738', '-0.61', '#anger', '#disgust', 'negative', '-0.64', 'addle', 'muddle', 'puddle', 'assemble', 'piece']
senticnet['mixed'] = ['-0.83', '0', '0.751', '-0.52', '#sadness', '#anger', 'negative', '-0.70', 'inhomogeneous', 'nonuniform', 'assorted', 'heterogeneous', 'sundry']
senticnet['mixed-up'] = ['-0.89', '0', '-0.98', '0', '#sadness', '#fear', 'negative', '-0.94', 'messed-up', 'fucked-up', 'screwed-up', 'jumbled', 'kooky']
senticnet['mixed_agonist_antagonist'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'imperium', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['mixed_bag'] = ['-0.62', '0', '0.988', '-0.59', '#sadness', '#anger', 'negative', '-0.73', 'motley', 'mixture', 'variety', 'assortment', 'alphabet_soup']
senticnet['mixed_breed_dog'] = ['0.039', '0', '0', '0.059', '#joy', '#admiration', 'positive', '0.049', 'big_dog', 'dog_cat', 'smart_dog', 'pet', 'domesticated_animal']
senticnet['mixed_conifer'] = ['0.045', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.356', 'ponderosa', 'bull_pine', 'western_yellow_pine', 'yellow_pine', 'longleaf_pine']
senticnet['mixed_conifer_forest'] = ['0.045', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.356', 'ponderosa', 'bull_pine', 'western_yellow_pine', 'yellow_pine', 'longleaf_pine']
senticnet['mixed_incontinence'] = ['-0.77', '0', '0.966', '-0.51', '#sadness', '#anger', 'negative', '-0.75', 'enuresis', 'incontinency', 'incontinence', 'stress_incontinence', 'urge_incontinence']
senticnet['mixed_metal'] = ['0.038', '0', '0.008', '0.049', '#joy', '#admiration', 'positive', '0.026', 'platinum', 'silver', 'alloy', 'titanium', 'durable']
senticnet['mixed_reaction'] = ['-0.16', '0.179', '0.568', '-0.61', '#anger', '#disgust', 'negative', '-0.29', 'angrily', 'choler', 'see_red', 'dander', 'hackle']
senticnet['mixed_salad'] = ['0', '0', '0.066', '-0.07', '#anger', '#disgust', 'negative', '-0.07', 'container_cup', 'cup', 'grease_trap', 'dish', 'open_container']
senticnet['mixed_woodland'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'biome', 'timberland']
senticnet['mixer'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'salad_shaker', 'store_shelf', 'kitchen_tool', 'pliers', 'ladle']
senticnet['mixing_bowl'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'salad_shaker', 'store_shelf', 'kitchen_tool', 'pliers', 'ladle']
senticnet['mixmag'] = ['0.7', '-0.62', '0', '0.58', '#joy', '#surprise', 'positive', '0.636', 'white_magic', 'thaumaturgy', 'juju', 'conjuration', 'supernaturalism']
senticnet['mixolydian'] = ['-0.65', '0', '0.681', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'tone', 'whole_tone', 'step', 'whole_step', 'musical_interval']
senticnet['mixolydian_mode'] = ['-0.65', '0', '0.681', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'tone', 'whole_tone', 'step', 'whole_step', 'musical_interval']
senticnet['mixtape'] = ['0', '0.712', '0', '0.844', '#interest', '#admiration', 'positive', '0.778', 'dollar_change', 'small_currency', 'currency_unit', 'metal_money', 'american_money']
senticnet['mixture'] = ['0.027', '0.15', '0', '0.22', '#interest', '#admiration', 'positive', '0.132', 'interjection', 'large_corporation', 'move_vehicle', 'blend', 'ford']
senticnet['mizrabel'] = ['-0.63', '0', '0.674', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'baneful', 'evil', 'baleful', 'malevolent', 'malign']
senticnet['mizu'] = ['0.973', '-0.50', '0', '0.612', '#joy', '#admiration', 'positive', '0.698', 'chic', 'chicness', 'jauntiness', 'modishness', 'stylishness']
senticnet['mizzen_mast'] = ['0.937', '0.847', '0', '0', '#joy', '#interest', 'positive', '0.892', 'arrow', 'spear', 'buy_wood', 'baton', 'archery']
senticnet['mizzle'] = ['-0.83', '0', '0.87', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'precipitate', 'rain_down', 'shower_down', 'spit', 'patter']
senticnet['ml_syringe'] = ['-0.02', '0', '-0.12', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'x-ray_machine', 'medical_record', 'latex_glove', 'medicine', 'doctor_examination_room']
senticnet['mladost'] = ['0.954', '-0.90', '0', '0.867', '#joy', '#surprise', 'positive', '0.91', 'friend', 'sports_fan', 'rooter', 'fan', 'nut']
senticnet['mladost_luani'] = ['0.954', '-0.90', '0', '0.867', '#joy', '#surprise', 'positive', '0.91', 'friend', 'sports_fan', 'rooter', 'fan', 'nut']
senticnet['mllerian'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'nonmotile', 'immobile']
senticnet['mllerian_mimicry'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'blatancy', 'patency', 'obviousness']
senticnet['mm_ammunition'] = ['0', '0', '-0.92', '-0.91', '#fear', '#disgust', 'negative', '-0.92', 'automatic', 'firearm', 'gun', 'weapon', 'projectile']
senticnet['mm_handgun'] = ['-0.75', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.75', 'hydrogen_bomb', 'rifle', 'revolver', 'pistol', 'gun']
senticnet['mm_mortar'] = ['0', '0', '-0.95', '-0.72', '#fear', '#disgust', 'negative', '-0.83', 'cannon', 'explosive', 'firearm', 'pistol', 'machine_gun']
senticnet['mm_pistol'] = ['0', '0', '-0.87', '-0.82', '#fear', '#disgust', 'negative', '-0.84', 'pistol', 'gun', 'hydrogen_bomb', 'rifle', 'shotgun']
senticnet['mma_fighter'] = ['0.833', '-0.97', '0', '0.91', '#surprise', '#admiration', 'positive', '0.906', 'combatant', 'fencer', 'belligerent', 'fighter', 'scrapper']
senticnet['mmpi'] = ['-0.84', '0', '0.585', '-0.85', '#sadness', '#disgust', 'negative', '-0.76', 'subject', 'discipline', 'study', 'concern', 'research']
senticnet['mmr_vaccination'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'medical_therapy', 'vaccine', 'doctor', 'medicine', 'hospital']
senticnet['mmr_vaccine'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'medical_therapy', 'vaccine', 'doctor', 'medicine', 'hospital']
senticnet['mnage'] = ['-0.85', '0', '0.968', '-0.89', '#anger', '#disgust', 'negative', '-0.90', 'family', 'household', 'house', 'home', 'broken_home']
senticnet['mnemiopsis_leidyi'] = ['-0.79', '0', '0.709', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'animal', 'animate_being', 'ctenophore', 'invertebrate', 'beast']
senticnet['mnire_disease'] = ['-0.87', '0', '0.773', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'rubor', 'redness', 'inflammation', 'otitis', 'otitis_interna']
senticnet['mo'] = ['0.042', '0.225', '-0.10', '0.191', '#interest', '#admiration', 'positive', '0.088', 'social_creature', 'sect', 'hypocrite', 'sentient', 'jewish']
senticnet['moa'] = ['-0.68', '0', '0.956', '-0.90', '#anger', '#disgust', 'negative', '-0.84', 'giant_moa', 'anomalopteryx', 'ratite', 'ratite_bird', 'flightless_bird']
senticnet['moa_bird'] = ['-0.68', '0', '0.95', '-0.91', '#anger', '#disgust', 'negative', '-0.84', 'giant_moa', 'anomalopteryx', 'moa', 'ratite', 'flightless_bird']
senticnet['moan'] = ['-0.11', '-0.02', '-0.11', '-0.11', '#fear', '#disgust', 'negative', '-0.07', 'scream', 'gasp', 'groan', 'hurt_feeling', 'make_anger']
senticnet['moana_hotel'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'drifter', 'bum', 'sundowner']
senticnet['moander'] = ['0.826', '-0.65', '0', '0.587', '#joy', '#surprise', 'positive', '0.689', 'scoffer', 'vegetarian', 'eater', 'diner', 'dunker']
senticnet['moaner'] = ['-0.74', '0', '0.68', '-0.58', '#sadness', '#anger', 'negative', '-0.67', 'whiner', 'complainer', 'kvetch', 'sniveller', 'crybaby']
senticnet['moat'] = ['0.048', '0', '0.009', '0.06', '#joy', '#admiration', 'positive', '0.033', 'pier', 'rich_person', 'squirt', 'puddle', 'dryad']
senticnet['moated_castle'] = ['0.021', '0.119', '0', '0.129', '#interest', '#admiration', 'positive', '0.09', 'manor', 'palace', 'medieval', 'tower', 'chess_game']
senticnet['moated_manor_house'] = ['0.049', '0.16', '0', '0.155', '#interest', '#admiration', 'positive', '0.121', 'castle', 'palace', 'medieval', 'aristocrat', 'throne']
senticnet['mob'] = ['-0.11', '-0.02', '-0.11', '-0.11', '#fear', '#disgust', 'negative', '-0.07', 'crowd', 'mass', 'crush', 'throng', 'group']
senticnet['mob_enforcer'] = ['0.794', '-0.50', '0', '0.531', '#joy', '#admiration', 'positive', '0.611', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['mob_rule'] = ['-0.86', '0', '0.859', '-0.91', '#sadness', '#disgust', 'negative', '-0.88', 'mess', 'family_activity', 'work_together', 'roller_coaster', 'watch_comedy']
senticnet['mobbing'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'boss_around', 'violence', 'physical_pain', 'psychological_terror', 'bully']
senticnet['mobile'] = ['0.11', '0.073', '0', '0.205', '#joy', '#admiration', 'positive', '0.129', 'basic_unit', 'microscopic', 'carry_around', 'hold_place', 'tiny_organism']
senticnet['mobile_hotspot'] = ['-0.87', '0', '0.818', '0', '#sadness', '#anger', 'negative', '-0.84', 'slow_speed', 'wifi', 'computer_internet_connection', 'internet_connection', 'boot_up_computer']
senticnet['mobile_phone'] = ['0.054', '0', '0.035', '0.066', '#joy', '#admiration', 'positive', '0.028', 'phone', 'telephone', 'cellphone', 'telephone_room', 'pay_phone']
senticnet['mobile_riverine_force'] = ['-0.07', '0.069', '-0.18', '0', '#sadness', '#fear', 'negative', '-0.06', 'medical_procedure', 'cure', 'doctor_surgery', 'treatment', 'doctor_work']
senticnet['mobile_robot'] = ['0', '0.717', '0', '0.765', '#interest', '#admiration', 'positive', '0.741', 'helper', 'artificial_intelligence', 'goal_driven', 'proactive', 'intelligent']
senticnet['mobile_telephone'] = ['0.035', '-0.01', '0.027', '0.085', '#joy', '#admiration', 'positive', '0.026', 'telephone', 'find_phone_number', 'telephone_room', 'phone', 'use_telephone']
senticnet['mobility'] = ['0', '0', '0', '0.144', '#admiration', '#admiration', 'positive', '0.144', 'go_elsewhere', 'transport_person', 'get_around', 'locomotion', 'trucking']
senticnet['mobility_scooter'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'fuel', 'car', 'fossil_fuel', 'transport', 'natural_resource']
senticnet['mobilization'] = ['0.814', '-0.87', '0', '0.918', '#surprise', '#admiration', 'positive', '0.87', 'ship_company', 'company', 'work_force', 'workforce', 'full_complement']
senticnet['mobilizational'] = ['-0.54', '0', '0.979', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'unlikeness', 'dissimilitude', 'difference', 'heterology', 'dissimilarity']
senticnet['mobilize'] = ['-0.94', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.88', 'combat', 'uprise', 'big_fight', 'opposition', 'violence']
senticnet['mobitz_type'] = ['-0.56', '0', '0.849', '-0.61', '#anger', '#disgust', 'negative', '-0.67', 'convulsive', 'unsteady', 'jerky', 'spasmodic', 'spastic']
senticnet['mobridge'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['mobster'] = ['0.867', '-0.84', '0', '0.876', '#joy', '#admiration', 'positive', '0.863', 'meet', 'team', 'team_up', 'embed', 'aggroup']
senticnet['mobutu_sese_seko'] = ['-0.73', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.72', 'royal_leader', 'despot', 'emperor', 'monarch', 'king']
senticnet['moccasin'] = ['0.706', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.806', 'shoe', 'flip_flop', 'slipper', 'casual_shoes', 'footwear']
senticnet['moccasin_creek'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'common_bean', 'green_bean', 'wax_bean']
senticnet['moccasin_flower'] = ['0.277', '0.161', '0', '0.337', '#joy', '#admiration', 'positive', '0.258', 'lady_slipper', 'nerveroot', 'showy_lady_slipper', 'yellow_lady_slipper', 'clustered_lady_slipper']
senticnet['moceanu'] = ['0.964', '0.713', '0', '0', '#joy', '#interest', 'positive', '0.839', 'martial_art', 'judo', 'jump_around', 'go_underwater', 'athletics']
senticnet['mocha'] = ['-0.71', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.78', 'latte', 'cappuccino', 'espresso', 'caramel', 'vanilla']
senticnet['mock'] = ['-0.43', '0.065', '-0.32', '0', '#sadness', '#fear', 'negative', '-0.23', 'wrong', 'bad_choice', 'unsafe', 'reprimand', 'caution']
senticnet['mock_execution'] = ['-0.65', '0', '0.718', '-0.73', '#anger', '#disgust', 'negative', '-0.70', 'inquirer', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['mock_heroic'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'narrative', 'bedtime', 'bed_time', 'long_story', 'saga']
senticnet['mock_horror'] = ['-0.86', '0', '0.516', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'outcry', 'call', 'cry', 'yell', 'shout']
senticnet['mockernut'] = ['-0.69', '0', '0.725', '-0.89', '#anger', '#disgust', 'negative', '-0.77', 'ponderosa', 'ponderosa_pine', 'western_yellow_pine', 'bull_pine', 'longleaf_pine']
senticnet['mockery'] = ['0', '0', '0.907', '-0.97', '#anger', '#disgust', 'negative', '-0.94', 'scoff', 'duh', 'taunt', 'mock', 'imitation']
senticnet['mocking'] = ['-0.73', '0', '0.865', '-0.87', '#anger', '#disgust', 'negative', '-0.82', 'jeer', 'shout', 'abrupt', 'rude', 'bad_surprise']
senticnet['mocking_laugh'] = ['-0.83', '0', '0.87', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'precipitate', 'rain_down', 'shower_down', 'spit', 'patter']
senticnet['mocking_smile'] = ['-0.11', '0', '-0.09', '0.082', '#sadness', '#fear', 'negative', '-0.04', 'stare', 'ear', 'facial_expression', 'mouth', 'dimple']
senticnet['mockingly'] = ['0', '0', '-0.73', '-0.78', '#fear', '#disgust', 'negative', '-0.76', 'deridingly', 'mock', 'sardonically', 'ridicule', 'derisively']
senticnet['mod_con'] = ['0.843', '-0.99', '0', '0.706', '#joy', '#surprise', 'positive', '0.849', 'gadgetry', 'gimbal', 'injector', 'contrivance', 'device']
senticnet['modal'] = ['0.785', '-0.99', '0', '0.981', '#surprise', '#admiration', 'positive', '0.92', 'ear', 'absolute_pitch', 'perfect_pitch', 'audition', 'sense_audition']
senticnet['modal_operator'] = ['0.929', '-0.71', '0', '0.605', '#joy', '#surprise', 'positive', '0.748', 'incisiveness', 'trenchancy', 'efficacy', 'efficaciousness', 'power']
senticnet['modal_value'] = ['0.802', '0.988', '0', '0', '#joy', '#interest', 'positive', '0.895', 'style', 'fashion', 'mood', 'mode', 'manner']
senticnet['modality'] = ['0.785', '-0.99', '0', '0.981', '#surprise', '#admiration', 'positive', '0.92', 'ear', 'absolute_pitch', 'perfect_pitch', 'audition', 'sense_audition']
senticnet['modality_specific'] = ['0.785', '-0.99', '0', '0.981', '#surprise', '#admiration', 'positive', '0.92', 'ear', 'absolute_pitch', 'perfect_pitch', 'audition', 'sense_audition']
senticnet['modane'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'expressway', 'turnpike', 'toll_road', 'highway']
senticnet['mode'] = ['0.809', '0.992', '0', '0', '#joy', '#interest', 'positive', '0.901', 'style', 'fashion', 'mood', 'modal_value', 'manner']
senticnet['mode_dial'] = ['0.021', '0.081', '0.017', '0.113', '#interest', '#admiration', 'positive', '0.05', 'aspect', 'perspective', 'look_round', 'scenic', 'look_through']
senticnet['mode_of_transport'] = ['0.704', '0.781', '0', '0', '#joy', '#interest', 'positive', '0.742', 'form_of_transportation', 'vehicle_type', 'means_of_transportation', 'mode_transportation', 'transportation']
senticnet['mode_transportation'] = ['0.181', '0', '0.034', '0.203', '#joy', '#admiration', 'positive', '0.117', 'fast_car', 'form_of_transportation', 'means_of_transportation', 'transportation', 'car_model']
senticnet['model'] = ['0', '0.809', '0', '0.76', '#interest', '#admiration', 'positive', '0.784', 'clothes_style', 'fancy_clothes', 'frilly', 'glamour', 'style']
senticnet['model_organism'] = ['-0.96', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.83', 'microbial_biology', 'basic_biology', 'undergraduate', 'research_experience', 'recent_doctoral_recipient']
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senticnet['modelview_matrix'] = ['0.777', '-0.85', '0', '0.615', '#joy', '#surprise', 'positive', '0.748', 'think', 'play', 'trifle', 'abstract', 'warm_to']
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senticnet['modem_connection'] = ['0.078', '0.103', '-0.09', '0.191', '#interest', '#admiration', 'positive', '0.069', 'connect', 'aunt_uncle', 'short_form', 'relationship', 'kinship']
senticnet['modem_dial_up'] = ['0.078', '0.103', '-0.09', '0.191', '#interest', '#admiration', 'positive', '0.069', 'connect', 'aunt_uncle', 'short_form', 'relationship', 'kinship']
senticnet['moderate'] = ['0.74', '0.727', '0', '0', '#joy', '#interest', 'positive', '0.734', 'check', 'hold', 'hold_in', 'abnegate', 'control']
senticnet['moderate_democrat'] = ['0', '0.924', '0', '0.921', '#interest', '#admiration', 'positive', '0.923', 'settle_disagreement', 'hitler', 'communist', 'conservative', 'unchanging']
senticnet['moderate_exercise'] = ['-0.06', '-0.02', '0.021', '-0.06', '#sadness', '#disgust', 'negative', '-0.03', 'clean_window', 'vacuum_carpet', 'repetitive_task', 'run_long_time', 'exercise_daily']
senticnet['moderate_faction'] = ['-0.50', '0', '0.989', '-0.91', '#anger', '#disgust', 'negative', '-0.80', 'camp', 'pack', 'clique', 'coterie', 'ingroup']
senticnet['moderate_intensity_exercise'] = ['-0.06', '-0.02', '0.021', '-0.06', '#sadness', '#disgust', 'negative', '-0.03', 'clean_window', 'vacuum_carpet', 'repetitive_task', 'run_long_time', 'exercise_daily']
senticnet['moderate_malnutrition'] = ['-0.71', '0', '0.74', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'malnutrition', 'marasmus', 'avitaminosis', 'hypovitaminosis', 'deficiency_disease']
senticnet['moderate_pace'] = ['-0.04', '0.123', '0', '0.237', '#interest', '#admiration', 'positive', '0.107', 'foot_motion', 'foot_movement', 'foot_action', 'move_ahead', 'stride']
senticnet['moderate_physical_activity'] = ['-0.06', '-0.02', '0.021', '-0.06', '#sadness', '#disgust', 'negative', '-0.03', 'clean_window', 'vacuum_carpet', 'repetitive_task', 'run_long_time', 'exercise_daily']
senticnet['moderate_rainfall'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'condensation', 'water_fall', 'falling_water', 'frozen_water', 'water_drop']
senticnet['moderate_republican'] = ['0', '0.924', '0', '0.921', '#interest', '#admiration', 'positive', '0.923', 'settle_disagreement', 'hitler', 'communist', 'conservative', 'unchanging']
senticnet['moderate_wind_shear'] = ['-0.12', '-0.05', '0', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'diminish', 'reduce', 'lower', 'lower_music', 'decrease']
senticnet['moderated_discussion'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'discuss', 'communication', 'communicate_person', 'share', 'autograph']
senticnet['moderately_evolute'] = ['0.969', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.772', 'aspheric', 'aspherical', 'prolate', 'rounded', 'crescent']
senticnet['moderately_heavy'] = ['-0.82', '0.226', '-0.46', '-0.05', '#sadness', '#fear', 'negative', '-0.27', 'heaviness', 'heavy_weight', 'heavy_person', 'heavy_breathe', 'heavy_air']
senticnet['moderately_large'] = ['-0.05', '0', '-0.12', '-0.16', '#fear', '#disgust', 'negative', '-0.11', 'reduced', 'tiny', 'small_size', 'narrow', 'poor']
senticnet['moderately_obese'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'fatty', 'worse', 'health_issue', 'clinical_problem']
senticnet['moderately_overweight'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'fatty', 'worse', 'health_issue', 'clinical_problem']
senticnet['moderately_resistant'] = ['-0.69', '0', '0.516', '-0.94', '#sadness', '#disgust', 'negative', '-0.71', 'insubordinate', 'recalcitrant', 'obstreperous', 'defiant', 'resistive']
senticnet['moderately_successful'] = ['0.98', '-0.74', '0', '0.984', '#joy', '#admiration', 'positive', '0.903', 'prosperous', 'flourishing', 'palmy', 'prospering', 'thriving']
senticnet['moderately_thick'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'harsh', 'common', 'rough-cut', 'uncouth', 'vulgar']
senticnet['moderately_tolerant'] = ['-0.69', '0', '0.516', '-0.94', '#sadness', '#disgust', 'negative', '-0.71', 'insubordinate', 'recalcitrant', 'obstreperous', 'defiant', 'resistive']
senticnet['moderately_toxic'] = ['-0.53', '0', '0.86', '-0.65', '#anger', '#disgust', 'negative', '-0.68', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['moderately_warm'] = ['0', '0.094', '0', '0', '#interest', '#interest', 'positive', '0.094', 'cold', 'cavern', 'under_umbrella', 'parasol', 'stick_wall']
senticnet['moderately_wealthy'] = ['0.973', '0.707', '0', '0', '#joy', '#interest', 'positive', '0.84', 'money', 'down_payment', 'much_money', 'slogan', 'take_out_loan']
senticnet['moderating'] = ['0.761', '-0.75', '0', '0.77', '#joy', '#admiration', 'positive', '0.761', 'analgesic', 'analgetic', 'anodyne', 'weakening', 'alleviative']
senticnet['moderation'] = ['0.056', '0.157', '0', '0.154', '#interest', '#admiration', 'positive', '0.122', 'exercise', 'exercise_regularly', 'live_healthy_life', 'exercise_often', 'exercise_much']
senticnet['modern'] = ['0.828', '0', '0', '0.945', '#joy', '#admiration', 'positive', '0.886', 'timely', 'right_now', 'up_date', 'art', 'contemporary']
senticnet['modern-day'] = ['0', '0.959', '0', '0.932', '#interest', '#admiration', 'positive', '0.946', 'present-day', 'inhabitant', 'historical', 'eastern', 'inhabited']
senticnet['modern_biblical_scholarship'] = ['0.587', '-0.94', '0', '0.659', '#surprise', '#admiration', 'positive', '0.731', 'critique', 'review', 'criticism', 'literary_criticism', 'new_criticism']
senticnet['modern_civilisation'] = ['0', '0.08', '0', '0.117', '#interest', '#admiration', 'positive', '0.099', 'stand_still', 'go_home', 'pull_out', 'stretch_out', 'advance_civilization']
senticnet['modern_civilization'] = ['-0.65', '0', '0.691', '-0.54', '#sadness', '#anger', 'negative', '-0.63', 'moronity', 'mental_deficiency', 'imbecility', 'stupidity', 'mental_defectiveness']
senticnet['modern_conservatism'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'economic_libertarian', 'libertarian', 'advocate', 'advocator', 'proponent']
senticnet['modern_dance'] = ['-0.95', '0', '0.992', '-0.63', '#sadness', '#anger', 'negative', '-0.86', 'interpretive_dance', 'interpretive_dancing', 'interpretative_dance', 'interpretative_dancing', 'stage_dancing']
senticnet['modern_era'] = ['0', '0.104', '0', '0.124', '#interest', '#admiration', 'positive', '0.114', 'good_trait', 'constant', 'time_increment', 'time_designation', 'time_length']
senticnet['modern_home'] = ['0', '0.105', '0', '0.076', '#interest', '#admiration', 'positive', '0.09', 'house', 'mansion', 'high_rise_building', 'home', 'block_flat']
senticnet['modern_times'] = ['0.86', '-0.79', '0', '0.93', '#joy', '#admiration', 'positive', '0.861', 'time', 'present_times', 'modern_world', 'contemporary_world', 'period']
senticnet['modern_warfare'] = ['0.833', '-0.97', '0', '0.91', '#surprise', '#admiration', 'positive', '0.906', 'fencer', 'battler', 'belligerent', 'fighter', 'scrapper']
senticnet['modern_weaponry'] = ['0.833', '-0.97', '0', '0.91', '#surprise', '#admiration', 'positive', '0.906', 'fencer', 'battler', 'belligerent', 'fighter', 'scrapper']
senticnet['modern_western_civilization'] = ['0', '0.08', '0', '0.117', '#interest', '#admiration', 'positive', '0.099', 'stand_still', 'go_home', 'pull_out', 'stretch_out', 'advance_civilization']
senticnet['modern_world'] = ['0.86', '-0.79', '0', '0.93', '#joy', '#admiration', 'positive', '0.861', 'modern_times', 'present_times', 'time', 'contemporary_world', 'period']
senticnet['modernisation'] = ['0', '0.88', '0', '0.807', '#interest', '#admiration', 'positive', '0.844', 'adaptation', 'modernization', 'improvement', 'renovation', 'department']
senticnet['modernise'] = ['0.845', '-0.59', '0', '0.926', '#joy', '#admiration', 'positive', '0.789', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['modernism'] = ['0.563', '-0.72', '0', '0.644', '#surprise', '#admiration', 'positive', '0.645', 'idiom', 'idiomatic_expression', 'phrasal_idiom', 'set_phrase', 'phrase']
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senticnet['modernist_abstraction'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'design', 'carving', 'decoupage', 'ceramic']
senticnet['modernist_aesthetic'] = ['0.563', '-0.72', '0', '0.644', '#surprise', '#admiration', 'positive', '0.645', 'idiom', 'idiomatic_expression', 'phrasal_idiom', 'set_phrase', 'phrase']
senticnet['modernist_architecture'] = ['0.578', '-0.95', '0', '0.979', '#surprise', '#admiration', 'positive', '0.838', 'perpendicular', 'perpendicular_style', 'gothic_architecture', 'style_of_architecture', 'type_of_architecture']
senticnet['modernist_art'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'design', 'carving', 'decoupage', 'ceramic']
senticnet['modernist_composer'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'band_play', 'songwriter', 'rock_band']
senticnet['modernist_literature'] = ['0.587', '-0.94', '0', '0.659', '#surprise', '#admiration', 'positive', '0.731', 'critique', 'review', 'criticism', 'textual_criticism', 'new_criticism']
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senticnet['modernist_poetry'] = ['0.587', '-0.94', '0', '0.659', '#surprise', '#admiration', 'positive', '0.731', 'critique', 'review', 'criticism', 'textual_criticism', 'new_criticism']
senticnet['modernist_style'] = ['0.578', '-0.95', '0', '0.979', '#surprise', '#admiration', 'positive', '0.838', 'perpendicular', 'perpendicular_style', 'gothic_architecture', 'style_of_architecture', 'type_of_architecture']
senticnet['modernist_tradition'] = ['0.563', '-0.72', '0', '0.644', '#surprise', '#admiration', 'positive', '0.645', 'idiom', 'idiomatic_expression', 'phrasal_idiom', 'set_phrase', 'phrase']
senticnet['modernity'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'absolutism', 'autocracy', 'ascendancy', 'tyrant']
senticnet['modernization'] = ['0', '0.883', '0', '0.811', '#interest', '#admiration', 'positive', '0.847', 'adaptation', 'modernisation', 'improvement', 'renovation', 'department']
senticnet['modernization_fram'] = ['0.641', '-0.78', '0', '0.721', '#surprise', '#admiration', 'positive', '0.715', 'correctional_rehabilitation', 'physical_rehabilitation', 'physical_restoration', 'therapeutic_rehabilitation', 'vocational_rehabilitation']
senticnet['modernization_fram_overhaul'] = ['0.641', '-0.78', '0', '0.721', '#surprise', '#admiration', 'positive', '0.715', 'correctional_rehabilitation', 'physical_rehabilitation', 'physical_restoration', 'therapeutic_rehabilitation', 'vocational_rehabilitation']
senticnet['modernization_fram_program'] = ['0.641', '-0.78', '0', '0.721', '#surprise', '#admiration', 'positive', '0.715', 'correctional_rehabilitation', 'physical_rehabilitation', 'physical_restoration', 'therapeutic_rehabilitation', 'vocational_rehabilitation']
senticnet['modernize'] = ['0.845', '-0.59', '0', '0.926', '#joy', '#admiration', 'positive', '0.789', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['modest'] = ['0.881', '0', '0', '0.875', '#joy', '#admiration', 'positive', '0.878', 'minor', 'meek', 'low', 'humble', 'small']
senticnet['modest_mouse'] = ['0.304', '0.339', '-0.19', '0.333', '#interest', '#admiration', 'positive', '0.195', 'careful', 'reinforce', 'load_bear', 'go_outdoors', 'allowance']
senticnet['modest_sum'] = ['0.057', '0.181', '-0.04', '0.18', '#interest', '#admiration', 'positive', '0.092', 'small_money', 'alter', 'money_change', 'money_coin', 'small_coin']
senticnet['modestly'] = ['0.881', '0', '0', '0.875', '#joy', '#admiration', 'positive', '0.878', 'minor', 'meek', 'low', 'humble', 'small']
senticnet['modestness'] = ['0.686', '-0.63', '0', '0.959', '#joy', '#admiration', 'positive', '0.759', 'propriety', 'properness', 'correctitude', 'modesty', 'decency']
senticnet['modesty'] = ['0.027', '0', '0.102', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'protect_rain', 'make_dress', 'cover_table', 'secure', 'protect_body']
senticnet['modicum'] = ['-0.63', '0', '0.791', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'little', 'pinch', 'small_indefinite_amount', 'dreg', 'indefinite_quantity']
senticnet['modifiability'] = ['0.512', '-0.52', '0', '0.874', '#surprise', '#admiration', 'positive', '0.635', 'responsibleness', 'reproducibility', 'duplicability', 'solidness', 'responsibility']
senticnet['modification'] = ['-0.97', '0', '0.856', '-0.63', '#sadness', '#anger', 'negative', '-0.82', 'laxation', 'change', 'tune', 'accommodation', 'loosening']
senticnet['modification_thereof'] = ['0.509', '-0.62', '0', '0.786', '#surprise', '#admiration', 'positive', '0.64', 'agreeable', 'accordant', 'consentient', 'unanimous', 'according']
senticnet['modifier'] = ['0.033', '0.234', '-0.08', '0.274', '#interest', '#admiration', 'positive', '0.114', 'change', 'transform', 'adjust', 'precise', 'versatile']
senticnet['modify'] = ['-0.5', '0', '0.598', '-0.80', '#anger', '#disgust', 'negative', '-0.63', 'temper', 'corrupt', 'cushion', 'syncopate', 'spoil']
senticnet['modish'] = ['0.952', '0.844', '0', '0.96', '#joy', '#admiration', 'positive', '0.919', 'fashion', 'demand', 'kindness', 'better_person', 'warm_feeling']
senticnet['modishness'] = ['0.547', '-0.92', '0', '0.667', '#surprise', '#admiration', 'positive', '0.714', 'elegance', 'jauntiness', 'rakishness', 'nattiness', 'dapperness']
senticnet['moduco'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquirer', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['modular'] = ['-0.74', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.76', 'prefabricated', 'module', 'flexible', 'integrated', 'storage']
senticnet['modular_arithmetic'] = ['0.076', '0.096', '-0.08', '0.167', '#interest', '#admiration', 'positive', '0.063', 'statistic', 'mathematical_function', 'calculation', 'list_number', 'calculate']
senticnet['modularity'] = ['0.869', '-0.88', '0', '0.598', '#joy', '#surprise', 'positive', '0.782', 'connectedness', 'connection', 'consistency', 'continuity', 'link']
senticnet['modulated'] = ['0', '0.941', '0', '0.839', '#interest', '#admiration', 'positive', '0.89', 'discontinuously', 'sinusoidally', 'oscillates', 'non-linearly', 'oscillated']
senticnet['modulation'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'emotional_arousal', 'excitement', 'arousal', 'anger']
senticnet['modulator'] = ['0.056', '-0.01', '0.036', '0.073', '#joy', '#admiration', 'positive', '0.028', 'loud_sound', 'stereo', 'tuner', 'speaker', 'steam_engine']
senticnet['module'] = ['0.939', '-0.51', '0', '0.537', '#joy', '#admiration', 'positive', '0.664', 'understanding', 'intellect', 'ability', 'retentiveness', 'retentivity']
senticnet['modus_operandi'] = ['0.544', '-0.91', '0', '0.941', '#surprise', '#admiration', 'positive', '0.8', 'rut', 'groove', 'rat_race', 'procedure', 'process']
senticnet['moe_southwest'] = ['0.061', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.055', 'cook', 'stick_butter', 'find_recipe', 'bake_oven', 'food_bake']
senticnet['moessbauer_spectrometer'] = ['0.052', '0.119', '0', '0.141', '#interest', '#admiration', 'positive', '0.104', 'neigh', 'domestic_animal', 'animal_type', 'animal', 'home_animal']
senticnet['mofongo'] = ['0.116', '-0.02', '0.025', '0.156', '#joy', '#admiration', 'positive', '0.069', 'dance_move', 'latin_dance', 'body_movement', 'waltz', 'dance']
senticnet['moganshan'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['moggy'] = ['-0.72', '0', '0.619', '-0.86', '#sadness', '#disgust', 'negative', '-0.73', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['mogollon_rim'] = ['0', '0.069', '0', '0.065', '#interest', '#admiration', 'positive', '0.067', 'alp', 'high_mountain', 'pass', 'ski_resort', 'high_place']
senticnet['mogul'] = ['0', '0', '-0.99', '-0.73', '#fear', '#disgust', 'negative', '-0.86', 'king', 'baron', 'business_leader', 'magnate', 'big_businessman']
senticnet['mohagher_iqbal'] = ['0', '0.133', '0', '0.088', '#interest', '#admiration', 'positive', '0.111', 'juror', 'decide_guilt', 'become_judge', 'arbiter', 'jury']
senticnet['mohair'] = ['-0.53', '0', '0.751', '-0.97', '#anger', '#disgust', 'negative', '-0.75', 'guanaco', 'domestic_llama', 'llama', 'artiodactyl', 'artiodactyl_mammal']
senticnet['mohammed_el_beltagy'] = ['0.648', '0.627', '-0.26', '0.329', '#joy', '#interest', 'positive', '0.334', 'brotherly', 'friendly', 'fraternal', 'amicability', 'amicable']
senticnet['mohammed_yousry'] = ['0', '0.202', '-0.06', '0.291', '#interest', '#admiration', 'positive', '0.142', 'vocabulary', 'translate', 'dictionary', 'translation', 'thesaurus']
senticnet['mohawk_hairstyle'] = ['0.072', '0.244', '-0.20', '0.252', '#interest', '#admiration', 'positive', '0.091', 'doe', 'sweet', 'live_organism', 'love', 'alive']
senticnet['mohican'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['mohist'] = ['0.909', '-0.85', '0', '0.846', '#joy', '#surprise', 'positive', '0.87', 'respectability', 'reputability', 'venerability', 'venerableness', 'righteousness']
senticnet['mohmand'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'webpage', 'store_record', 'ideal']
senticnet['moil'] = ['-0.72', '0', '0.88', '0', '#sadness', '#anger', 'negative', '-0.80', 'stewing', 'simmering', 'churn', 'boiling', 'seethe']
senticnet['moir_pattern'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'hazy', 'blurry', 'bleary', 'blurryred', 'foggy']
senticnet['moist'] = ['-0.02', '-0.02', '0.076', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'wear_raincoat', 'carry_umbrella', 'feel_cold', 'camp_out', 'splash_water']
senticnet['moist_air'] = ['0', '-0.03', '0.05', '-0.14', '#anger', '#disgust', 'negative', '-0.05', 'humidity', 'wetness', 'damp', 'humid', 'dank']
senticnet['moist_convection'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'hurricane', 'natural_disaster', 'flood', 'atmospheric_pressure', 'tropical_storm']
senticnet['moist_weather'] = ['-0.75', '0', '0.512', '-0.75', '#sadness', '#disgust', 'negative', '-0.67', 'inactivity', 'inactiveness', 'slumber', 'inaction', 'hibernation']
senticnet['moistener'] = ['0', '-0.03', '0.05', '-0.14', '#anger', '#disgust', 'negative', '-0.05', 'electric_razor', 'shampoo_conditioner', 'hair_brush', 'tartar_control_toothpaste', 'gel_toothpaste']
senticnet['moisture'] = ['0', '-0.03', '0.05', '-0.14', '#anger', '#disgust', 'negative', '-0.05', 'humidity', 'wetness', 'damp', 'humid', 'dank']
senticnet['moisture-loving'] = ['0', '0', '0.756', '-0.88', '#anger', '#disgust', 'negative', '-0.82', 'water-loving', 'heat-loving', 'sun-loving', 'shade-loving', 'hydrophilic']
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senticnet['moisturiser'] = ['0.048', '-0.00', '0.011', '0.064', '#joy', '#admiration', 'positive', '0.027', 'become_wet', 'wash_away_dirt', 'run_out_soap', 'cleanliness', 'clean_skin']
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senticnet['mojama'] = ['0.084', '-0.05', '0', '-0.04', '#joy', '#surprise', 'positive', '0.031', 'entree', 'feast', 'satiation', 'breakfast', 'supper']
senticnet['mojave_air'] = ['-0.02', '-0.02', '0.064', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'ocean_water', 'fillet_fish', 'chowder', 'fillet', 'underwater']
senticnet['mojave_river'] = ['0', '0.069', '0', '0.065', '#interest', '#admiration', 'positive', '0.067', 'alp', 'high_mountain', 'pass', 'ski_resort', 'high_place']
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senticnet['molar_pregnancy'] = ['-0.75', '0', '0.715', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'abdominal_pregnancy', 'ectopic_gestation', 'tubal_pregnancy', 'pregnancy', 'gestation']
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senticnet['mold_contamination'] = ['0.718', '0', '0', '0', '#joy', '#joy', 'positive', '0.718', 'water_container', 'still_water', 'clean_water', 'water', 'cool_water']
senticnet['mold_growth'] = ['0.718', '0', '0', '0', '#joy', '#joy', 'positive', '0.718', 'water_container', 'still_water', 'clean_water', 'water', 'cool_water']
senticnet['mold_mildew'] = ['0.718', '0', '0', '0', '#joy', '#joy', 'positive', '0.718', 'water_container', 'still_water', 'clean_water', 'water', 'cool_water']
senticnet['moldable'] = ['0', '0', '-0.72', '-0.93', '#fear', '#disgust', 'negative', '-0.82', 'charge_plate', 'fictile', 'charge_card', 'credit_card', 'plastic']
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senticnet['moldy'] = ['0', '0', '-0.80', '-0.91', '#fear', '#disgust', 'negative', '-0.86', 'old-fashioned', 'fustiness', 'fusty', 'musty', 'stuffy']
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senticnet['molecular_basis'] = ['0', '0.706', '0', '0.783', '#interest', '#admiration', 'positive', '0.744', 'study', 'function', 'determine', 'protein', 'interaction']
senticnet['molecular_biochemistry'] = ['0.776', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.843', 'molecular', 'cellular_bioscience', 'life_science_biological', 'award', 'latest']
senticnet['molecular_biologist'] = ['0', '0.909', '0', '0.799', '#interest', '#admiration', 'positive', '0.854', 'research', 'research_topic', 'research_subject', 'search', 'student']
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senticnet['molecular_evolution'] = ['0', '0.754', '0', '0.994', '#interest', '#admiration', 'positive', '0.874', 'fellowship', 'award', 'different_application', 'research', 'study']
senticnet['molecular_formula_chno'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'enhance', 'intensify', 'deepen', 'potentiate', 'heighten']
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senticnet['molecular_hydrogen'] = ['0.095', '0', '-0.04', '0.062', '#joy', '#admiration', 'positive', '0.037', 'photographer', 'water_spray', 'moisture', 'rise_water', 'droplet']
senticnet['molecular_level'] = ['0', '0.969', '0', '0.778', '#interest', '#admiration', 'positive', '0.873', 'study', 'research', 'project', 'development', 'process']
senticnet['molecular_mechanism'] = ['0', '0.942', '0', '0.733', '#interest', '#admiration', 'positive', '0.837', 'regulation', 'cell', 'study', 'protein', 'determine']
senticnet['molecular_orbital'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'elementary_particle', 'neutron', 'electron', 'subatomic_particle', 'proton']
senticnet['molecular_pathology'] = ['-0.59', '0', '0.911', '-0.87', '#anger', '#disgust', 'negative', '-0.79', 'osteosclerosis', 'sclerosis', 'disseminated_sclerosis', 'multiple_sclerosis', 'induration']
senticnet['molecular_scale'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'amoeba', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['molecule'] = ['0', '0.184', '-0.05', '0.179', '#interest', '#admiration', 'positive', '0.104', 'hygroscope', 'hold_water', 'antiquate', 'absorb_water', 'tangent']
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senticnet['molest_child'] = ['0', '0.942', '0', '0', '#interest', '#interest', 'positive', '0.942', 'guy', 'special_person', 'guardian', 'boy', 'male']
senticnet['molestation'] = ['0', '0.942', '0', '0', '#interest', '#interest', 'positive', '0.942', 'guy', 'special_person', 'guardian', 'boy', 'male']
senticnet['molester'] = ['0.864', '-0.54', '0', '0.613', '#joy', '#admiration', 'positive', '0.675', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['molesting_child'] = ['0', '0.942', '0', '0', '#interest', '#interest', 'positive', '0.942', 'guy', 'special_person', 'guardian', 'boy', 'male']
senticnet['molinist'] = ['-0.72', '0', '0.552', '-0.76', '#sadness', '#disgust', 'negative', '-0.68', 'break', 'good_luck', 'happy_chance', 'hap', 'coincidence']
senticnet['moll'] = ['0.834', '-0.80', '0', '0.889', '#joy', '#admiration', 'positive', '0.843', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['mollify'] = ['0.942', '-0.97', '0', '0.554', '#joy', '#surprise', 'positive', '0.824', 'tranquilize', 'tranquillize', 'calm_down', 'quieten', 'calm']
senticnet['molloy'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
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senticnet['mollusk'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'seal', 'walrus', 'otter', 'alligator', 'whale']
senticnet['molly_coddle'] = ['-0.63', '0', '0.683', '-0.96', '#anger', '#disgust', 'negative', '-0.76', 'slovenly_woman', 'slut', 'trollop', 'slattern', 'litterer']
senticnet['molly_parker'] = ['0.794', '-0.50', '0', '0.531', '#joy', '#admiration', 'positive', '0.611', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
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senticnet['molotovribbentrop_pact'] = ['0.397', '0.245', '-0.13', '0.224', '#joy', '#interest', 'positive', '0.183', 'agreement', 'provide', 'advice', 'contribution', 'offer']
senticnet['molten_lava'] = ['0', '0', '-0.92', '0', '#fear', '#fear', 'negative', '-0.92', 'water_flow', 'wind_water', 'water_stream', 'waterway', 'lava']
senticnet['molten_lead'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'address_book', 'get_rich', 'blank_check', 'swiss_army_knife', 'whisk']
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senticnet['molten_material'] = ['0', '0', '-0.92', '0', '#fear', '#fear', 'negative', '-0.92', 'water_flow', 'wind_water', 'water_stream', 'waterway', 'lava']
senticnet['molten_rock'] = ['0', '0', '-0.92', '0', '#fear', '#fear', 'negative', '-0.92', 'water_flow', 'wind_water', 'water_stream', 'waterway', 'lava']
senticnet['molten_wax'] = ['-0.92', '0', '0.783', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'clot', 'coagulum', 'gob', 'clew', 'agglomeration']
senticnet['molton_brown'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'beauty_parlor', 'lady_hair', 'beauty_shop', 'beauty_salon', 'make_up']
senticnet['molybdenite'] = ['0.555', '-0.99', '0', '0.851', '#surprise', '#admiration', 'positive', '0.8', 'quartz', 'cerussite', 'white_lead_ore', 'cobaltite', 'mineral']
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senticnet['mom'] = ['0.855', '0', '0', '0.899', '#joy', '#admiration', 'positive', '0.877', 'bride', 'girl', 'flower_girl', 'daughter', 'grandmother']
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senticnet['mom_house'] = ['0.032', '0.08', '0', '0.068', '#interest', '#admiration', 'positive', '0.06', 'way', 'menagerie', 'accomodation', 'habitat', 'home']
senticnet['moment'] = ['0.74', '0', '0', '0.978', '#joy', '#admiration', 'positive', '0.859', 'instant', 'mo', 'second', 'present_moment', 'consequence']
senticnet['moment-to-moment'] = ['0', '0', '-0.74', '-0.86', '#fear', '#disgust', 'negative', '-0.80', 'paycheck-to-paycheck', 'hand-to-mouth', 'hermit-like', 'eking', 'day-in-day-out']
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senticnet['momentary_distraction'] = ['0', '-0.33', '0', '0', '#surprise', '#surprise', 'positive', '0.33', 'hopeless', 'distracted', 'diverted', 'inattentive', 'confused']
senticnet['momentary_glimpse'] = ['0', '0.095', '0', '0.112', '#interest', '#admiration', 'positive', '0.104', 'stare', 'look', 'observation', 'ship_part', 'see']
senticnet['momentous'] = ['0.871', '0', '0', '0.929', '#joy', '#admiration', 'positive', '0.9', 'important', 'outstanding', 'consequence', 'momentousness', 'momentously']
senticnet['momentous_discovery'] = ['0.774', '-0.63', '0', '0.567', '#joy', '#surprise', 'positive', '0.658', 'catching', 'breakthrough', 'spying', 'determination', 'tracing']
senticnet['momentously'] = ['0.87', '0', '0', '0.92', '#joy', '#admiration', 'positive', '0.895', 'important', 'outstanding', 'consequence', 'momentousness', 'momentous']
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senticnet['momentum'] = ['0.997', '0', '0', '0.972', '#joy', '#admiration', 'positive', '0.984', 'sympathetic', 'adoration', 'confident', 'enthusiasm', 'grateful']
senticnet['momentum_equation'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'assumption', 'precondition', 'provision', 'constraint', 'premiss']
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senticnet['mommy'] = ['0.793', '0', '0', '0.845', '#joy', '#admiration', 'positive', '0.819', 'adopt_child', 'father_child', 'mother_child', 'kinship', 'father_son']
senticnet['momordica'] = ['0.035', '0.104', '0', '0.12', '#interest', '#admiration', 'positive', '0.086', 'fruit_vegetable', 'vegetable', 'vegetable_fruit', 'fresh_vegetable', 'vegetarian_food']
senticnet['momotaro'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'hornet', 'vespid', 'vespid_wasp']
senticnet['mon_valley'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
senticnet['mona_charen'] = ['0.783', '-0.63', '0', '0.829', '#joy', '#admiration', 'positive', '0.749', 'leg_of_lamb', 'lamb_roast', 'joint', 'roast', 'rack_of_lamb']
senticnet['mona_hieroglyphica'] = ['0', '0.887', '0', '0.973', '#interest', '#admiration', 'positive', '0.93', 'servant', 'art_of_transmuting_metal', 'stone', 'alchemy', 'philosopher']
senticnet['mona_passage'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'pacific_ocean', 'small_lake', 'mere']
senticnet['monachal'] = ['-0.81', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.79', 'stifle', 'forgotten', 'old', 'touch_ground', 'seldom']
senticnet['monadic'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'integrative', 'consolidative', 'unifying', 'combinatorial']
senticnet['monarch'] = ['0.051', '0.077', '0', '0.091', '#interest', '#admiration', 'positive', '0.073', 'king', 'royal_leader', 'emperor', 'duchess', 'royalty']
senticnet['monarchal'] = ['-0.55', '0', '0.931', '-0.74', '#anger', '#disgust', 'negative', '-0.74', 'totalitarian', 'authoritarian', 'dictatorial', 'despotic', 'undemocratic']
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senticnet['monarchical_form'] = ['0.028', '0.12', '0.068', '0.176', '#interest', '#admiration', 'positive', '0.064', 'elite', 'number_person', 'count_person', 'small_group', 'enumeration']
senticnet['monarchical_government'] = ['0.028', '0.12', '0.068', '0.176', '#interest', '#admiration', 'positive', '0.064', 'elite', 'number_person', 'count_person', 'small_group', 'enumeration']
senticnet['monarchical_power'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'ascendance', 'autocracy', 'absolutism', 'tyrant']
senticnet['monarchical_rule'] = ['0.059', '0.092', '0', '0.107', '#interest', '#admiration', 'positive', '0.086', 'royalty', 'monarch', 'throne', 'emperor', 'king_queen']
senticnet['monarchism'] = ['0.059', '0.092', '0', '0.107', '#interest', '#admiration', 'positive', '0.086', 'royalty', 'monarch', 'throne', 'emperor', 'king_queen']
senticnet['monarchist'] = ['0.059', '0.092', '0', '0.107', '#interest', '#admiration', 'positive', '0.086', 'royalty', 'monarch', 'throne', 'emperor', 'king_queen']
senticnet['monarchy'] = ['0.059', '0.092', '0', '0.107', '#interest', '#admiration', 'positive', '0.086', 'royalty', 'monarch', 'throne', 'emperor', 'king_queen']
senticnet['monare'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['monastery'] = ['0.026', '0.244', '0', '0.205', '#interest', '#admiration', 'positive', '0.158', 'capital_build', 'temple', 'confession', 'chapel', 'church']
senticnet['monastic'] = ['0.853', '-0.95', '0', '0.605', '#joy', '#surprise', 'positive', '0.805', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['monastic_discipline'] = ['0.853', '-0.95', '0', '0.605', '#joy', '#surprise', 'positive', '0.805', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['monastic_grange'] = ['0.049', '0.16', '0', '0.155', '#interest', '#admiration', 'positive', '0.121', 'castle', 'palace', 'medieval', 'aristocrat', 'throne']
senticnet['monastic_life'] = ['0.853', '-0.95', '0', '0.605', '#joy', '#surprise', 'positive', '0.805', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['monastic_tradition'] = ['0', '0.915', '0', '0.821', '#interest', '#admiration', 'positive', '0.868', 'culture', 'special_day', 'holiday', 'party_time', 'yearly_celebration']
senticnet['monasticism'] = ['0.853', '-0.95', '0', '0.605', '#joy', '#surprise', 'positive', '0.805', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['monatomic'] = ['0', '0.073', '-0.05', '0.093', '#interest', '#admiration', 'positive', '0.038', 'vapor', 'water_vapor', 'evaporate', 'condense_water', 'bubble']
senticnet['monchhichi'] = ['0.71', '0.92', '0', '0', '#joy', '#interest', 'positive', '0.815', 'toy_car', 'toy_ball', 'action_figure', 'toy_marble', 'beanie_baby']
senticnet['moncler'] = ['0.053', '-0.01', '-0.05', '0.121', '#joy', '#admiration', 'positive', '0.034', 'coat', 'winter_clothes', 'stylish', 'adorn', 'protective_cover']
senticnet['monday'] = ['-0.88', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.83', 'friday', 'day_of_week', 'decade', 'sadness', 'slack']
senticnet['monday_afternoon'] = ['0.967', '0.996', '0', '0.862', '#joy', '#interest', 'positive', '0.942', 'party_time', 'new_baby', 'nativity', 'holiday', 'occasion']
senticnet['monday_morning'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'monday', 'feel_tired', 'set_alarm', 'go_to_work', 'traffic_jam']
senticnet['monday_night'] = ['0', '0.734', '0', '0', '#interest', '#interest', 'positive', '0.734', 'sleep', 'go_sleep', 'relax', 'go_out', 'sleep_night']
senticnet['mondelez'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'wrapping_paper', 'wrap_gift', 'butcher_paper', 'paper']
senticnet['mondelez_international'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'wrapping_paper', 'wrap_gift', 'butcher_paper', 'paper']
senticnet['mondo_cane'] = ['-0.79', '0', '0.885', '-0.76', '#sadness', '#anger', 'negative', '-0.81', 'infernal', 'mortal', 'dead_soul', 'dead_soule', 'living_dead']
senticnet['monetary'] = ['0', '0.285', '0', '0.191', '#interest', '#admiration', 'positive', '0.238', 'financial', 'fiscal', 'payment_amount', 'sticker', 'money_amount']
senticnet['monetary_amount'] = ['-0.03', '0.251', '0', '0.164', '#interest', '#admiration', 'positive', '0.128', 'money_value', 'money_amount', 'fee', 'monetary', 'monetary_value']
senticnet['monetary_compensation'] = ['0.73', '-0.56', '0', '0.621', '#joy', '#admiration', 'positive', '0.64', 'amnesty', 'unsusceptibility', 'diplomatic_immunity', 'exemption', 'impunity']
senticnet['monetary_donation'] = ['0', '0.264', '-0.24', '0.34', '#interest', '#admiration', 'positive', '0.12', 'help_out', 'give', 'come_back', 'alms', 'lend']
senticnet['monetary_gain'] = ['0.702', '0.972', '0', '0.751', '#interest', '#admiration', 'positive', '0.808', 'financial_gain', 'earnings', 'investment_return', 'money_earn', 'income']
senticnet['monetary_incentive'] = ['0.716', '0.813', '0', '0', '#joy', '#interest', 'positive', '0.764', 'bonus', 'reinforce', 'promotion', 'meritocracy', 'gratification']
senticnet['monetary_penalty'] = ['-0.89', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.93', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['monetary_prize'] = ['0.894', '0.881', '0', '0.803', '#joy', '#interest', 'positive', '0.859', 'remittance', 'afford', 'award', 'money_due', 'amount_owe']
senticnet['monetary_resource'] = ['0.534', '-0.86', '0', '0.736', '#surprise', '#admiration', 'positive', '0.713', 'fund', 'finance', 'escrow_fund', 'cash_in_hand', 'pecuniary_resource']
senticnet['monetary_union'] = ['0.771', '0.846', '0', '0', '#joy', '#interest', 'positive', '0.808', 'dollar_bill', 'franc', 'banknote', 'dollar', 'cent']
senticnet['monetary_unit'] = ['0', '0.096', '0.072', '0.088', '#interest', '#admiration', 'positive', '0.037', 'form_of_money', 'money_unit', 'currency_unit', 'american_coin', 'small_coin']
senticnet['monetary_value'] = ['0', '0.238', '0.021', '0.152', '#interest', '#admiration', 'positive', '0.123', 'money_value', 'money_amount', 'worth', 'monetary_amount', 'importance']
senticnet['moneth'] = ['-0.11', '0.112', '0.674', '-0.66', '#anger', '#disgust', 'negative', '-0.33', 'wrathfully', 'wrathful', 'wrothful', 'lividly', 'lividity']
senticnet['monetisation'] = ['0.886', '-0.77', '0', '0.815', '#joy', '#admiration', 'positive', '0.826', 'countenance', 'legitimate', 'permit', 'allow', 'let']
senticnet['monetise'] = ['0.886', '-0.77', '0', '0.815', '#joy', '#admiration', 'positive', '0.826', 'countenance', 'legitimate', 'permit', 'allow', 'let']
senticnet['monetization'] = ['0.886', '-0.77', '0', '0.815', '#joy', '#admiration', 'positive', '0.826', 'countenance', 'legitimate', 'permit', 'allow', 'let']
senticnet['monetize'] = ['0.886', '-0.77', '0', '0.815', '#joy', '#admiration', 'positive', '0.826', 'countenance', 'legitimate', 'permit', 'allow', 'let']
senticnet['money'] = ['0', '0.871', '0', '0.861', '#interest', '#admiration', 'positive', '0.866', 'down_payment', 'take_out_loan', 'cash', 'wealthy', 'bring_wallet']
senticnet['money_amount'] = ['-0.02', '0.245', '0.012', '0.153', '#interest', '#admiration', 'positive', '0.09', 'money_value', 'sticker', 'dollar_sign', 'value_amount', 'monetary_amount']
senticnet['money_bank'] = ['0.993', '0.938', '0', '0', '#joy', '#interest', 'positive', '0.966', 'bring_wallet', 'money_credit', 'moolah', 'good_credit', 'enough_money']
senticnet['money_buy'] = ['0.046', '0.071', '0', '0.066', '#interest', '#admiration', 'positive', '0.061', 'go_grocery_store', 'go_supermarket', 'provide_family', 'sell_sandwich', 'plan_meal']
senticnet['money_change'] = ['0', '0.119', '0.07', '0.104', '#interest', '#admiration', 'positive', '0.051', 'small_coin', 'money_coin', 'small_money', 'small_change', 'small_currency']
senticnet['money_charge'] = ['-0.06', '0.295', '-0.08', '0.17', '#interest', '#admiration', 'positive', '0.078', 'money_payment', 'amount_due', 'payment_amount', 'amount_owe', 'currency']
senticnet['money_clip'] = ['0.024', '0.146', '0.015', '0', '#joy', '#interest', 'positive', '0.052', 'bank_vault', 'hold_change', 'wallet', 'bank_account', 'billfold']
senticnet['money_coin'] = ['0', '0.134', '0.065', '0.135', '#interest', '#admiration', 'positive', '0.068', 'small_coin', 'small_money', 'small_currency', 'money_change', 'metal_money']
senticnet['money_collector'] = ['0.03', '0.187', '0.018', '0.115', '#interest', '#admiration', 'positive', '0.079', 'money_keeper', 'hold_coin', 'money_hold', 'money_place', 'money_holder']
senticnet['money_credit'] = ['-0.82', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.82', 'moolah', 'plan_advance', 'bind_agreement', 'ask_money', 'bring_wallet']
senticnet['money_due'] = ['-0.97', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.84', 'debt', 'money_owe', 'amount_owe', 'money', 'money_loss']
senticnet['money_earn'] = ['0.806', '0.776', '0', '0.761', '#joy', '#interest', 'positive', '0.781', 'job_pay', 'extra_money', 'investment_return', 'financial_gain', 'monetary_gain']
senticnet['money_exchange'] = ['-0.07', '0.314', '-0.11', '0.229', '#interest', '#admiration', 'positive', '0.087', 'send_money', 'give_cash', 'financial_transaction', 'currency_exchange', 'money']
senticnet['money_hold'] = ['0.023', '0.215', '0', '0.128', '#interest', '#admiration', 'positive', '0.122', 'money_place', 'money_storage', 'money_holder', 'wallet', 'put_money']
senticnet['money_holder'] = ['0.026', '0.194', '0', '0.097', '#interest', '#admiration', 'positive', '0.106', 'money_storage', 'keep_money', 'put_money', 'money_hold', 'money_place']
senticnet['money_inside'] = ['0', '0.136', '0.017', '0.065', '#interest', '#admiration', 'positive', '0.061', 'hold_money', 'check_account', 'teller', 'money_storage', 'savings']
senticnet['money_involve'] = ['-0.03', '0.238', '0', '0.137', '#interest', '#admiration', 'positive', '0.113', 'price_high', 'money_pay', 'monetary_amount', 'money_payment', 'monetary_value']
senticnet['money_keeper'] = ['0.034', '0.196', '0', '0.119', '#interest', '#admiration', 'positive', '0.116', 'money_place', 'money_collector', 'money_holder', 'money_hold', 'hold_coin']
senticnet['money_launderer'] = ['-0.87', '0', '0.574', '-0.73', '#sadness', '#disgust', 'negative', '-0.72', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['money_laundering_conspiracy'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'fraud', 'swindle', 'rig']
senticnet['money_loss'] = ['-0.87', '0', '0.803', '0', '#sadness', '#anger', 'negative', '-0.83', 'money_pay', 'money_payment', 'amount_owe', 'amount_pay', 'amount_due']
senticnet['money_make'] = ['0.96', '0.937', '0', '0.747', '#joy', '#interest', 'positive', '0.881', 'monetary_gain', 'financial_gain', 'profit', 'earnings', 'earn_income']
senticnet['money_maker'] = ['0.024', '0.297', '-0.05', '0.186', '#interest', '#admiration', 'positive', '0.112', 'savings_account', 'enterprise', 'business', 'corporation', 'bank']
senticnet['money_owe'] = ['0.702', '0.772', '0', '0.963', '#interest', '#admiration', 'positive', '0.812', 'money_due', 'amount_owe', 'debt', 'money_payment', 'money_loss']
senticnet['money_pay'] = ['0.914', '0.816', '0', '0.732', '#joy', '#interest', 'positive', '0.821', 'money', 'earnings', 'repay', 'money_owe', 'income']
senticnet['money_payment'] = ['0.858', '0.706', '0', '0.709', '#joy', '#admiration', 'positive', '0.758', 'payment_amount', 'amount_owe', 'bill', 'money_charge', 'amount_due']
senticnet['money_place'] = ['0.021', '0.212', '0', '0.11', '#interest', '#admiration', 'positive', '0.114', 'money_hold', 'money_holder', 'money_storage', 'money_keeper', 'bank']
senticnet['money_relate'] = ['-0.09', '0.333', '-0.11', '0.201', '#interest', '#admiration', 'positive', '0.083', 'percentage', 'payment_amount', 'money_loss', 'money_charge', 'money_payment']
senticnet['money_save'] = ['0', '0.316', '0', '0.232', '#interest', '#admiration', 'positive', '0.274', 'lower_price', 'good_deal', 'money_transfer', 'good_price', 'money_relate']
senticnet['money_spend'] = ['-0.02', '0.218', '-0.06', '0.074', '#interest', '#admiration', 'positive', '0.051', 'spend', 'pay_money', 'purchase', 'owe_money', 'pay_goods']
senticnet['money_spinner'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'profitable', 'moneymaking', 'gainful', 'paying', 'paid']
senticnet['money_storage'] = ['0.026', '0.202', '0.017', '0.104', '#interest', '#admiration', 'positive', '0.079', 'money_holder', 'money_hold', 'money_place', 'keep_money', 'hold_money']
senticnet['money_ticket'] = ['-0.06', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.06', 'purchase_ticket', 'buy_ticket_theater', 'buy_ticket', 'use_ticket', 'book_ticket']
senticnet['money_transfer'] = ['-0.08', '0.313', '-0.09', '0.199', '#interest', '#admiration', 'positive', '0.083', 'repay', 'money', 'money_pay', 'transfer', 'earnings']
senticnet['money_unit'] = ['0', '0.145', '0.061', '0.112', '#interest', '#admiration', 'positive', '0.065', 'currency_unit', 'monetary_unit', 'form_of_money', 'american_coin', 'american_money']
senticnet['money_value'] = ['0', '0.245', '0.016', '0.156', '#interest', '#admiration', 'positive', '0.128', 'money_amount', 'monetary_value', 'monetary_amount', 'worth', 'sticker']
senticnet['money_wallet'] = ['0', '0.146', '0', '0', '#interest', '#interest', 'positive', '0.146', 'life-long_mate', 'volume_control', 'belly_button', 'work_to_earn_money', 'two_ears']
senticnet['moneybelt'] = ['0.998', '0', '0', '0', '#joy', '#joy', 'positive', '0.998', 'panties', 'lingerie', 'pantyhose', 'slack', 'prescription_medication']
senticnet['moneydance'] = ['-0.03', '0.112', '-0.08', '0.164', '#interest', '#admiration', 'positive', '0.04', 'run', 'jump_over', 'quick', 'sprint', 'flee']
senticnet['moneymaking'] = ['0.679', '-0.75', '0', '0.658', '#joy', '#surprise', 'positive', '0.698', 'acquisition', 'heritage', 'purchase', 'assumption', 'restitution']
senticnet['monger'] = ['-0.71', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.79', 'trader', 'principal', 'bargainer', 'dealer', 'problem']
senticnet['mongol_tatar'] = ['0.937', '0.773', '0', '0', '#joy', '#interest', 'positive', '0.855', 'calculus', 'potassium_bitartrate', 'tartar', 'tatar', 'tophus']
senticnet['mongolism'] = ['-0.67', '0', '0.973', '-0.50', '#sadness', '#anger', 'negative', '-0.71', 'trisomy', 'birth_defect', 'congenital_anomaly', 'congenital_defect', 'congenital_disorder']
senticnet['monika_scislowska'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'dejectedness', 'low-spiritedness', 'lowness', 'downheartedness', 'cheerless']
senticnet['moniker'] = ['0.072', '0.244', '-0.20', '0.252', '#interest', '#admiration', 'positive', '0.091', 'doe', 'sweet', 'live_organism', 'love', 'alive']
senticnet['monimia'] = ['-0.95', '0', '-0.76', '0', '#sadness', '#fear', 'negative', '-0.85', 'disquietude', 'edginess', 'edgy', 'uptight', 'restive']
senticnet['monique_wittig'] = ['-0.07', '0.195', '0.629', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'misogynic', 'misogyny', 'murderousness', 'amuck', 'amok']
senticnet['monist'] = ['-0.55', '0', '0.58', '-0.98', '#anger', '#disgust', 'negative', '-0.70', 'fatalist', 'determinist', 'predestinarian', 'predestinationist', 'philosopher']
senticnet['monistat'] = ['-0.53', '0', '0.954', '-0.54', '#anger', '#disgust', 'negative', '-0.67', 'ranitidine', 'histamine_blocker', 'gastric_ranitidine', 'alkalizer', 'antiacid']
senticnet['monistic'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'metastability', 'monotony', 'invariance', 'constancy', 'unchangeability']
senticnet['monition'] = ['0.543', '-0.98', '0', '0.554', '#surprise', '#admiration', 'positive', '0.694', 'rebuke', 'reproof', 'reproval', 'reprehension', 'reprimand']
senticnet['monitor'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'computer_part', 'system', 'motherboard', 'pencil_pen', 'cpu']
senticnet['monitor_compliance'] = ['-0.88', '0', '0.977', '-0.81', '#sadness', '#anger', 'negative', '-0.89', 'execute', 'run', 'compel', 'oblige', 'obligate']
senticnet['monitor_heart_rate'] = ['-0.10', '-0.02', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.04', 'collapse', 'stand_around', 'perspire', 'hit_car', 'strike_out']
senticnet['monitor_lizard'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'death_adder', 'ophidian', 'serpent', 'elapid', 'taipan']
senticnet['monitored_continuously'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'computer_part', 'system', 'motherboard', 'pencil_pen', 'cpu']
senticnet['monitored_remotely'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'computer_part', 'system', 'motherboard', 'pencil_pen', 'cpu']
senticnet['monitoring'] = ['0', '0.878', '0', '0.722', '#interest', '#admiration', 'positive', '0.8', 'monitor', 'supervise', 'project', 'research', 'award']
senticnet['monitoring_bracelet'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'bangle', 'ankle_bracelet', 'armilla']
senticnet['monk'] = ['0.025', '0.246', '0', '0.226', '#interest', '#admiration', 'positive', '0.166', 'pastor', 'church_head', 'protestant', 'prayer', 'religion']
senticnet['monkey'] = ['0.786', '0.749', '0', '0', '#joy', '#interest', 'positive', '0.768', 'fiance', 'marmoset', 'lemur', 'beaver', 'koala']
senticnet['monkey_business'] = ['0.738', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.756', 'rascality', 'mischievousness', 'roguishness', 'deviltry', 'devilry']
senticnet['monkey_flower'] = ['0.598', '-0.72', '0', '0.941', '#surprise', '#admiration', 'positive', '0.753', 'sicklepod', 'tower_rock_cress', 'tower_mustard', 'rock_cress', 'rock_cress_plant']
senticnet['monkey_wrench'] = ['-0.12', '-0.09', '0.015', '-0.09', '#sadness', '#disgust', 'negative', '-0.03', 'broken_car', 'work_well', 'damage_car', 'need_fix', 'car_wheel']
senticnet['monkey_wrench_into'] = ['0.955', '-0.93', '0', '0', '#joy', '#surprise', 'positive', '0.943', 'tack', 'foot_part', 'casual_shoes', 'spike', 'pin']
senticnet['monkfish'] = ['-0.02', '-0.02', '0.064', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'ocean_water', 'fillet_fish', 'chowder', 'port', 'underwater']
senticnet['monkish'] = ['-0.94', '0', '0.779', '-0.85', '#sadness', '#disgust', 'negative', '-0.86', 'eremitic', 'eremitical', 'cloistered', 'anchoritic', 'unworldly']
senticnet['monkton_combe_school'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
senticnet['mono_lp'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['monoamine_oxidase_inhibitor'] = ['0.672', '0.632', '-0.23', '0.312', '#joy', '#interest', 'positive', '0.344', 'euphoria', 'buoyancy', 'perkiness', 'jubilance', 'ecstasy']
senticnet['monoarticular'] = ['-0.81', '0', '0.789', '-0.51', '#sadness', '#anger', 'negative', '-0.70', 'rubor', 'redness', 'inflammation', 'synovitis', 'tendosynovitis']
senticnet['monobook'] = ['-0.89', '0', '0.672', '-0.69', '#sadness', '#disgust', 'negative', '-0.75', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['monobook_skin'] = ['-0.89', '0', '0.672', '-0.69', '#sadness', '#disgust', 'negative', '-0.75', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['monocalcium_phosphate'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
senticnet['monochromacy'] = ['-0.74', '0', '0.793', '-0.62', '#sadness', '#anger', 'negative', '-0.72', 'visual_impairment', 'visual_defect', 'vision_defect', 'visual_disorder', 'color_blindness']
senticnet['monochromatic'] = ['0', '0.738', '0', '0.799', '#interest', '#admiration', 'positive', '0.768', 'apprehensive', 'eagle-eyed', 'knowledgeable', 'locality', 'perceptive']
senticnet['monochromatic_light'] = ['-0.50', '0', '0.75', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'beam', 'beam_of_light', 'sunbeam', 'ray', 'ray_of_light']
senticnet['monochromator'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'water_inside', 'water_spray', 'teapot', 'bodily_waste', 'different_type']
senticnet['monochrome'] = ['-0.05', '0.07', '0', '0.062', '#interest', '#admiration', 'positive', '0.027', 'salty', 'fine_grain', 'powder', 'granulate', 'food_flavor']
senticnet['monochrome_display'] = ['0.044', '0.08', '0', '-0.03', '#joy', '#interest', 'positive', '0.031', 'screen', 'pixel', 'visual_information', 'image', 'computer_monitor']
senticnet['monocle'] = ['0', '-0.02', '0.048', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'telescope', 'read_glass', 'eyeglass', 'camera', 'binoculars']
senticnet['monocled_cobra'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'death_adder', 'ophidian', 'serpent', 'elapid', 'taipan']
senticnet['monocoque_body'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'carbon_fiber', 'aluminium', 'light', 'thin', 'performant']
senticnet['monoculture'] = ['-0.77', '0', '0.914', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'confluence', 'conflux', 'merging', 'blending', 'combination']
senticnet['monody'] = ['-0.5', '0', '0.97', '-0.52', '#anger', '#disgust', 'negative', '-0.66', 'simulated', 'apish', 'parrotlike', 'apelike', 'mimic']
senticnet['monofilament_line'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'catch_fish', 'fishing', 'patience', 'fish_lake', 'bait']
senticnet['monogamous_marriage'] = ['0.839', '-0.70', '0', '0.93', '#joy', '#admiration', 'positive', '0.824', 'virtuous', 'chaste', 'pure', 'continent', 'virginal']
senticnet['monogamous_relationship'] = ['0.839', '-0.70', '0', '0.93', '#joy', '#admiration', 'positive', '0.824', 'virtuous', 'chaste', 'pure', 'continent', 'virginal']
senticnet['monogamy'] = ['0', '0', '-0.73', '-0.94', '#fear', '#disgust', 'negative', '-0.83', 'celibate', 'vow', 'celibacy', 'priesthood', 'ordination']
senticnet['monogenean'] = ['-0.87', '0', '0.983', '-0.91', '#anger', '#disgust', 'negative', '-0.92', 'thorn', 'sticker', 'pricker', 'prickle', 'spikelet']
senticnet['monograph'] = ['-0.04', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.08', 'data', 'awareness', 'information', 'saga', 'knowledge']
senticnet['monogyne'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'giant_hornet', 'hornet', 'hornet_wasp']
senticnet['monolith'] = ['-0.88', '0', '0.882', '-0.58', '#sadness', '#anger', 'negative', '-0.78', 'mortal', 'behemoth', 'unusual_person', 'aberrant', 'giant']
senticnet['monolithic'] = ['-0.92', '0', '0.859', '-0.64', '#sadness', '#anger', 'negative', '-0.80', 'undistributed', 'undiversified', 'unanimous', 'whole', 'solid']
senticnet['monologic'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'mediacy', 'mediateness', 'characteristic', 'allusiveness']
senticnet['monological'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'mediacy', 'mediateness', 'characteristic', 'allusiveness']
senticnet['monologist'] = ['0.563', '-0.81', '0', '0.577', '#surprise', '#admiration', 'positive', '0.65', 'mortal', 'person', 'individual', 'performer', 'artist']
senticnet['monologue'] = ['-0.67', '0', '0.936', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'unprepared', 'extemporary', 'extempore', 'impromptu', 'unrehearsed']
senticnet['monomania'] = ['0', '0', '-0.92', '-0.88', '#fear', '#disgust', 'negative', '-0.90', 'temper', 'disgust', 'offend', 'despise', 'animosity']
senticnet['monomaniacal'] = ['-0.91', '0', '0.794', '-0.93', '#sadness', '#disgust', 'negative', '-0.88', 'fundamentalist', 'extremist', 'obsession', 'stubborn', 'violent']
senticnet['mononeuritis_multiplex'] = ['-0.87', '0', '0.531', '-0.68', '#sadness', '#disgust', 'negative', '-0.69', 'rubor', 'redness', 'dermatomyositis', 'inclusion_body_myositis', 'inflammation']
senticnet['monophony'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['monopolist'] = ['0', '0.745', '0', '0.973', '#interest', '#admiration', 'positive', '0.859', 'profit-seeking', 'profit-driven', 'profit-maximizing', 'profit-making', 'profit-oriented']
senticnet['monopolize'] = ['0.54', '-0.97', '0', '0.569', '#surprise', '#admiration', 'positive', '0.695', 'overstate', 'hyperbolize', 'glorify', 'exaggerate', 'overdraw']
senticnet['monopolize_power'] = ['-0.53', '0', '0.752', '-0.89', '#anger', '#disgust', 'negative', '-0.72', 'fall', 'keel_over', 'descend', 'go_down', 'come_down']
senticnet['monopoly'] = ['-0.89', '0', '0.855', '0', '#sadness', '#anger', 'negative', '-0.87', 'tyranny', 'despotism', 'absolutism', 'absolute_power', 'autocracy']
senticnet['monoprint'] = ['0', '0.067', '0', '0.077', '#interest', '#admiration', 'positive', '0.072', 'pencil_mark', 'use_ink', 'script', 'pencil_paper', 'written_text']
senticnet['monopropellant'] = ['0', '0', '0', '0.073', '#admiration', '#admiration', 'positive', '0.073', 'fire_starter', 'organic_substance', 'renewable_resource', 'use_fuel', 'tree_product']
senticnet['monopulse'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'chairperson', 'commuter']
senticnet['monorail_station'] = ['0', '0.147', '0', '0.099', '#interest', '#admiration', 'positive', '0.123', 'airport', 'subway', 'bus_stop', 'bus', 'control_tower']
senticnet['monorail_train'] = ['0.069', '0.07', '0', '0.116', '#interest', '#admiration', 'positive', '0.085', 'traveller', 'tourist', 'travel', 'explorer', 'adventurer']
senticnet['monosaccharide'] = ['-0.52', '0', '0.681', '-0.76', '#anger', '#disgust', 'negative', '-0.65', 'triose', 'tetrose', 'pentose', 'simple_sugar', 'aldose']
senticnet['monosemy'] = ['0.981', '-0.76', '0', '0.733', '#joy', '#surprise', 'positive', '0.827', 'lucidness', 'clarity', 'clearness', 'lucidity', 'pellucidity']
senticnet['monosomy'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'deflection', 'warp', 'aberrance']
senticnet['monospaced_font'] = ['-0.98', '0', '0.969', '-0.95', '#sadness', '#anger', 'negative', '-0.97', 'face', 'font', 'fount', 'typeface', 'writing']
senticnet['monosyllabic'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicado', 'unpronounceable', 'impassive', 'incommunicative', 'silent']
senticnet['monosyllable'] = ['0', '0.156', '-0.06', '0.241', '#interest', '#admiration', 'positive', '0.11', 'mixture', 'noisy_place', 'make_go', 'move_vehicle', 'big_car']
senticnet['monosymptomatic'] = ['-0.75', '0', '0.504', '-0.77', '#sadness', '#disgust', 'negative', '-0.67', 'disablement', 'anorgasmia', 'hearing_impairment', 'hearing_disorder', 'bow_leg']
senticnet['monoterpenoid'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'enhance', 'intensify', 'deepen', 'potentiate', 'heighten']
senticnet['monotherapy'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'protease_inhibitor', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['monotone'] = ['0.941', '0.822', '0', '0', '#joy', '#interest', 'positive', '0.881', 'melodic_noise', 'universal_language', 'instrumental', 'musical_note', 'audio']
senticnet['monotonic'] = ['-0.87', '0', '0.771', '0', '#sadness', '#anger', 'negative', '-0.82', 'monotonous', 'variety', 'day-to-day', 'humdrum', 'tedious']
senticnet['monotonically'] = ['0.973', '0.809', '0', '0', '#joy', '#interest', 'positive', '0.891', 'linearly', 'non-linearly', 'inversely', 'quadratically', 'logarithmically']
senticnet['monotonicity'] = ['-0.75', '0', '0.679', '-0.77', '#sadness', '#disgust', 'negative', '-0.73', 'unvariedness', 'changelessness', 'unchangeability', 'unchangeableness', 'unchangingness']
senticnet['monotonous'] = ['-0.87', '0', '0.775', '0', '#sadness', '#anger', 'negative', '-0.82', 'monotonic', 'variety', 'day-to-day', 'humdrum', 'tedious']
senticnet['monotony'] = ['-0.16', '-0.07', '-0.05', '-0.20', '#sadness', '#disgust', 'negative', '-0.08', 'repetitive_task', 'lose_time', 'much_work', 'work_much', 'advertise_company']
senticnet['monovalent'] = ['0', '0', '0.933', '-0.75', '#anger', '#disgust', 'negative', '-0.84', 'bivalent', 'multivalent', 'divalent', 'meromorphic', 'univalent']
senticnet['monoxide'] = ['-0.99', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.88', 'arsenic', 'poisoning', 'strychnine', 'sulfide', 'cyanide']
senticnet['monroe_street'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'boulevard', 'lane', 'thoroughfare', 'intersection', 'curb']
senticnet['monroeville'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
senticnet['monsieur'] = ['-0.81', '0', '0.885', '-0.92', '#anger', '#disgust', 'negative', '-0.87', 'miss', 'girl', 'damsel', 'maiden', 'damoiselle']
senticnet['monsieur_ibrahim'] = ['0.798', '-0.51', '0', '0.973', '#joy', '#admiration', 'positive', '0.763', 'inexperienced_person', 'boy_scout', 'dear', 'virgin', 'mortal']
senticnet['monsieur_le'] = ['-0.81', '0', '0.885', '-0.92', '#anger', '#disgust', 'negative', '-0.87', 'miss', 'girl', 'damsel', 'maiden', 'damoiselle']
senticnet['monsignor'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'acolyte', 'reverend', 'thurifer', 'clergyman', 'man_of_cloth']
senticnet['monsignor_guido_marini'] = ['0.036', '0.111', '-0.05', '0.054', '#interest', '#admiration', 'positive', '0.038', 'despot', 'supervisor', 'chief', 'commander', 'earl']
senticnet['monsoon'] = ['-0.19', '-0.10', '0.041', '-0.23', '#sadness', '#disgust', 'negative', '-0.09', 'hailstone', 'cold_hot', 'snow', 'dandruff', 'rain']
senticnet['monsoon_rainfall'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'condensation', 'water_fall', 'falling_water', 'frozen_water', 'water_drop']
senticnet['monster'] = ['0', '0', '0.806', '-0.81', '#anger', '#disgust', 'negative', '-0.81', 'savage', 'ogre', 'demon', 'devil', 'demoniac']
senticnet['monster_beverage'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'use_drug', 'do_drug', 'drug_use', 'drug', 'addiction']
senticnet['monster_munch'] = ['0.895', '0.946', '0', '0', '#joy', '#interest', 'positive', '0.92', 'need_food', 'a_little_hungry', 'dead_hungry', 'prepare_food', 'fill_stomach']
senticnet['monster_truck'] = ['0', '0', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.05', 'human', 'delusional', 'use_illegal_drug', 'busy_person', 'carpenter']
senticnet['monstertrak'] = ['-0.06', '0.149', '-0.09', '0.181', '#interest', '#admiration', 'positive', '0.044', 'ask_nicely', 'job_offer', 'question_ask', 'make_happen', 'make_effort']
senticnet['monstrosity'] = ['-0.65', '0', '0.645', '-0.53', '#sadness', '#anger', 'negative', '-0.60', 'mutant', 'variation', 'sport', 'leviathan', 'mutation']
senticnet['monstrous'] = ['-0.80', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.78', 'frightful', 'monster', 'atrocious', 'enormously', 'hideous']
senticnet['monstrous_creature'] = ['0.8', '-0.97', '0', '0.512', '#joy', '#surprise', 'positive', '0.763', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['monstrous_injustice'] = ['0', '0.149', '0.719', '-0.66', '#anger', '#disgust', 'negative', '-0.41', 'belligerently', 'antagonism', 'class_feeling', 'aggressiveness', 'animus']
senticnet['monstrously'] = ['-0.71', '0', '0.995', '0', '#sadness', '#anger', 'negative', '-0.85', 'horrifying', 'atrocious', 'dreadfully', 'horrifyingly', 'horrified']
senticnet['mont_blanc_range'] = ['0', '-0.03', '-0.10', '-0.07', '#fear', '#disgust', 'negative', '-0.04', 'autumn', 'winter', 'skier', 'snow_fall', 'volcano']
senticnet['mont_maudit'] = ['0', '-0.03', '-0.10', '-0.07', '#fear', '#disgust', 'negative', '-0.04', 'autumn', 'winter', 'skier', 'snow_fall', 'volcano']
senticnet['montage'] = ['-0.69', '0', '0.508', '-0.93', '#sadness', '#disgust', 'negative', '-0.71', 'dark', 'darkness', 'light', 'shadow', 'site']
senticnet['montage_sequence'] = ['-0.69', '0', '0.508', '-0.93', '#sadness', '#disgust', 'negative', '-0.71', 'dark', 'darkness', 'light', 'shadow', 'site']
senticnet['montane_forest'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'woodland', 'timberland']
senticnet['monteith_death'] = ['0.761', '0.566', '-0.21', '0.293', '#joy', '#interest', 'positive', '0.352', 'gratify', 'jubilation', 'gloat', 'gloating', 'gloatingly']
senticnet['montenegrin'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
senticnet['monterey_canyon'] = ['-0.02', '-0.01', '0.03', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'ocean_beach', 'beach', 'water_sea', 'shore', 'ground_ocean']
senticnet['montesquiou'] = ['-0.81', '0', '0.885', '-0.92', '#anger', '#disgust', 'negative', '-0.87', 'miss', 'girl', 'damsel', 'maiden', 'damoiselle']
senticnet['montessori'] = ['-0.04', '0.088', '0.013', '0.069', '#interest', '#admiration', 'positive', '0.024', 'kindergarden', 'daycare', 'nursery', 'child', 'asylum']
senticnet['montessori_education'] = ['-0.04', '0.088', '0.013', '0.069', '#interest', '#admiration', 'positive', '0.024', 'kindergarden', 'daycare', 'nursery', 'child', 'asylum']
senticnet['montessori_preschool'] = ['-0.04', '0.088', '0.013', '0.069', '#interest', '#admiration', 'positive', '0.024', 'kindergarden', 'daycare', 'nursery', 'child', 'asylum']
senticnet['montessori_school'] = ['-0.04', '0.088', '0.013', '0.069', '#interest', '#admiration', 'positive', '0.024', 'kindergarden', 'daycare', 'nursery', 'child', 'asylum']
senticnet['month'] = ['-0.87', '0', '0', '-0.86', '#sadness', '#disgust', 'negative', '-0.87', 'severally', 'number', 'effect', 'respectively', 'material_research']
senticnet['month-old'] = ['-0.75', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'week-old', 'day-old', 'cubed', 'good-quality', 'wholemeal']
senticnet['month_old_boy'] = ['-0.06', '0', '0.01', '0', '#sadness', '#anger', 'negative', '-0.03', 'youth', 'young_child', 'small_child', 'lad', 'tiny']
senticnet['monthly'] = ['-0.08', '0.244', '-0.09', '0.141', '#interest', '#admiration', 'positive', '0.052', 'monthly_pay', 'monthly_payment', 'money_owe', 'money_pay', 'repay']
senticnet['monthly_allowance'] = ['0.924', '0.968', '0', '0.876', '#joy', '#interest', 'positive', '0.923', 'live_simply', 'help_out', 'money', 'accolade', 'generous']
senticnet['monthly_average_rainfall'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'condensation', 'water_fall', 'falling_water', 'frozen_water', 'water_drop']
senticnet['monthly_bulletin'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'current_event', 'headline', 'paper_write', 'paper_page', 'many_pages']
senticnet['monthly_column'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'library', 'old_testament', 'cook_book', 'a_lot_of_books', 'librarian']
senticnet['monthly_comic_book'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'encyclopaedia', 'child_story', 'comic', 'disney_cartoon']
senticnet['monthly_digest'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'current_event', 'headline', 'paper_write', 'paper_page', 'many_pages']
senticnet['monthly_due'] = ['-0.05', '0.249', '-0.06', '0.133', '#interest', '#admiration', 'positive', '0.067', 'earnings', 'money_pay', 'rent', 'income', 'give_money']
senticnet['monthly_electronic_newsletter'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'current_event', 'headline', 'paper_write', 'paper_page', 'many_pages']
senticnet['monthly_fee'] = ['0.687', '-0.98', '0', '0.666', '#joy', '#surprise', 'positive', '0.778', 'scale', 'scale_of_measurement', 'graduated_table', 'ordered_series', 'wage_scale']
senticnet['monthly_magazine'] = ['-0.05', '0.106', '0', '0.06', '#interest', '#admiration', 'positive', '0.036', 'buy', 'buy_newspaper', 'read_buy_newspaper', 'buy_book', 'read_news']
senticnet['monthly_minimum_wage'] = ['0', '0.923', '0', '0', '#interest', '#interest', 'positive', '0.923', 'salary', 'remittance', 'owe', 'payment', 'severance']
senticnet['monthly_newsletter'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'current_event', 'headline', 'paper_write', 'paper_page', 'many_pages']
senticnet['monthly_pay'] = ['-0.08', '0.298', '-0.14', '0.185', '#interest', '#admiration', 'positive', '0.063', 'money_due', 'rent', 'repay', 'money_owe', 'revenue']
senticnet['monthly_payment'] = ['0', '0.289', '-0.11', '0.17', '#interest', '#admiration', 'positive', '0.115', 'monthly_pay', 'money_owe', 'money_pay', 'monthly', 'monthly_due']
senticnet['monthly_precipitation'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'condensation', 'water_fall', 'falling_water', 'frozen_water', 'water_drop']
senticnet['monthly_premium'] = ['0.687', '-0.98', '0', '0.666', '#joy', '#surprise', 'positive', '0.778', 'scale', 'scale_of_measurement', 'graduated_table', 'ordered_series', 'wage_scale']
senticnet['monthly_publication'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'current_event', 'headline', 'paper_write', 'paper_page', 'many_pages']
senticnet['monthly_rainfall'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'condensation', 'water_fall', 'falling_water', 'frozen_water', 'water_drop']
senticnet['monthly_subscription'] = ['0.687', '-0.50', '0', '0.8', '#joy', '#admiration', 'positive', '0.663', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['monthly_wage'] = ['0', '0.923', '0', '0', '#interest', '#interest', 'positive', '0.923', 'salary', 'remittance', 'owe', 'payment', 'severance']
senticnet['montmagny'] = ['-0.71', '0', '0.917', '-0.51', '#sadness', '#anger', 'negative', '-0.71', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['montmorillonite_clay'] = ['-0.52', '0', '0.596', '-0.76', '#anger', '#disgust', 'negative', '-0.62', 'retention', 'retentiveness', 'retentivity', 'solidness', 'nonabsorbency']
senticnet['monty_bodkin'] = ['-0.88', '0', '0.864', '-0.72', '#sadness', '#anger', 'negative', '-0.82', 'vixenish', 'venomous', 'bitchy', 'catty', 'malicious']
senticnet['monument'] = ['0', '-0.77', '0', '0.855', '#surprise', '#admiration', 'positive', '0.816', 'liberty', 'statue', 'memorial', 'city_hall', 'sculpture']
senticnet['monumental'] = ['0.891', '0.87', '0', '0', '#joy', '#interest', 'positive', '0.881', 'humanist', 'manner', 'huge', 'monumentality', 'monument']
senticnet['monumental_entrance'] = ['0.029', '0.087', '0.065', '0.137', '#interest', '#admiration', 'positive', '0.047', 'archway', 'aisle', 'mat', 'passageway', 'right_angle']
senticnet['monumental_statue'] = ['0.085', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.079', 'sculpture', 'creative', 'yellow_ribbon', 'artwork', 'monument']
senticnet['monumental_task'] = ['-0.03', '0.136', '-0.09', '0.105', '#interest', '#admiration', 'positive', '0.028', 'chore', 'hire_help', 'police_work', 'career', 'job']
senticnet['monumental_tomb'] = ['-0.92', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.91', 'bury_body', 'coffin', 'headstone', 'morgue', 'tombstone']
senticnet['monumentalism'] = ['-0.69', '0', '0.957', '-0.92', '#anger', '#disgust', 'negative', '-0.85', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['monumentality'] = ['0.889', '0.864', '0', '0', '#joy', '#interest', 'positive', '0.877', 'humanist', 'manner', 'huge', 'monumental', 'monument']
senticnet['monumentally'] = ['-0.99', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.96', 'horrifying', 'atrocious', 'dreadfully', 'horrified', 'horribly']
senticnet['moo'] = ['0', '0.115', '0', '0.105', '#interest', '#admiration', 'positive', '0.11', 'yellow_green', 'pink', 'vintage', 'black_white', 'beautiful_well_useful']
senticnet['mooch'] = ['0.866', '-0.91', '0', '0.893', '#surprise', '#admiration', 'positive', '0.892', 'freeload', 'cadge', 'grub', 'sponge', 'bum']
senticnet['mood'] = ['0', '1', '0', '0', '#interest', '#interest', 'positive', '1.0', 'interest', 'interesting', 'attention', 'strong_emotion', 'emotion']
senticnet['mood-altering'] = ['0', '0', '-0.75', '-0.8', '#fear', '#disgust', 'negative', '-0.77', 'hallucinogenic', 'psychoactive', 'mind-altering', 'psychotropic', 'psychedelics']
senticnet['mood_disorder'] = ['-0.78', '0', '0.918', '-0.96', '#anger', '#disgust', 'negative', '-0.88', 'major_depressive_episode', 'depression', 'depressive_disorder', 'clinical_depression', 'involutional_depression']
senticnet['mood_enhancer'] = ['0.95', '-0.95', '0', '0.769', '#joy', '#surprise', 'positive', '0.89', 'adrenocorticotropic', 'adrenocorticotrophic', 'stimulant', 'stimulating', 'stimulative']
senticnet['mood_lighting'] = ['0.157', '0.126', '0', '0.166', '#joy', '#admiration', 'positive', '0.15', 'comfortable', 'sleep_place', 'rest_place', 'restful', 'occur']
senticnet['moodily'] = ['0.553', '0.658', '-0.25', '0.271', '#joy', '#interest', 'positive', '0.308', 'placid', 'unruffled', 'placidity', 'placidness', 'tranquil']
senticnet['moodiness'] = ['-0.97', '0', '0.848', '-0.58', '#sadness', '#anger', 'negative', '-0.80', 'moroseness', 'sullenness', 'ill_humor', 'glumness', 'distemper']
senticnet['moody'] = ['0', '0', '-0.70', '-0.96', '#fear', '#disgust', 'negative', '-0.83', 'morose', 'dour', 'glum', 'dark', 'glowering']
senticnet['moohan'] = ['-0.64', '0', '0.879', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'drone', 'poke', 'dawdler', 'laggard', 'straggler']
senticnet['moolah'] = ['-0.82', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.82', 'ask_money', 'plan_advance', 'bind_agreement', 'money_credit', 'bring_wallet']
senticnet['moon'] = ['0.916', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.929', 'galaxy', 'universe', 'planetary', 'sun', 'dwarf_planet']
senticnet['moon_blindness'] = ['-0.68', '0', '0.517', '-0.70', '#sadness', '#disgust', 'negative', '-0.63', 'visual_impairment', 'visual_defect', 'vision_defect', 'visual_disorder', 'avitaminosis']
senticnet['moon_curser'] = ['-0.87', '0', '0.6', '-0.76', '#sadness', '#disgust', 'negative', '-0.74', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['moon_gravity'] = ['0', '0', '-1', '-1', '#fear', '#disgust', 'negative', '-1.0', 'coercion', 'force', 'volt', 'watt', 'power_source']
senticnet['moon_orbit'] = ['0.989', '-0.90', '0', '0.876', '#joy', '#surprise', 'positive', '0.923', 'reach', 'range', 'compass', 'scope', 'confines']
senticnet['moon_phase'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['moon_south_pole'] = ['0.899', '0.803', '0', '0', '#joy', '#interest', 'positive', '0.851', 'ufo', 'milky', 'martian', 'galaxy', 'solar_system']
senticnet['moon_star'] = ['-0.09', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.08', 'night', 'darkness', 'sun_down', 'dusk', 'day_end']
senticnet['moonbat'] = ['0', '0', '0.743', '-0.94', '#anger', '#disgust', 'negative', '-0.84', 'stupid', 'increase_body_temperature', 'idiot', 'get_excited', 'sense_accomplishment']
senticnet['moonbeam'] = ['0.03', '0.085', '0', '0', '#joy', '#interest', 'positive', '0.058', 'favorite_color', 'colorful', 'different_color', 'pink', 'happy']
senticnet['moondragon'] = ['-0.88', '0', '0.882', '-0.58', '#sadness', '#anger', 'negative', '-0.78', 'mortal', 'behemoth', 'unusual_person', 'aberrant', 'giant']
senticnet['moondust'] = ['0.916', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.929', 'galaxy', 'universe', 'planetary', 'sun', 'dwarf_planet']
senticnet['mooney'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
senticnet['moong_dal'] = ['0.026', '0.08', '0.043', '0.064', '#interest', '#admiration', 'positive', '0.032', 'eat_rice', 'white_brown', 'asian', 'eat_pasta', 'staple_food']
senticnet['moonless_night'] = ['-0.11', '0.074', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.05', 'sun_down', 'night', 'night_time', 'moon_star', 'without_light']
senticnet['moonlight'] = ['0.042', '0.126', '-0.06', '0.165', '#interest', '#admiration', 'positive', '0.067', 'sparkle', 'shiny', 'shinny', 'gray', 'shine']
senticnet['moonlit'] = ['0.525', '-0.85', '0', '0.599', '#surprise', '#admiration', 'positive', '0.66', 'frolic', 'coquetry', 'romp', 'play', 'caper']
senticnet['moonlit_night'] = ['0.042', '0.126', '-0.06', '0.165', '#interest', '#admiration', 'positive', '0.067', 'sparkle', 'shiny', 'shinny', 'gray', 'shine']
senticnet['moonlit_sky'] = ['0.042', '0.126', '-0.06', '0.165', '#interest', '#admiration', 'positive', '0.067', 'sparkle', 'shiny', 'shinny', 'gray', 'shine']
senticnet['moonrise'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['moonshadow'] = ['-0.73', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'unwed', 'unwedded', 'divorced', 'unmarried', 'widowed']
senticnet['moonshine'] = ['0.899', '0.991', '0', '0', '#joy', '#interest', 'positive', '0.945', 'bourbon', 'scotch', 'vodka', 'brandy', 'wine']
senticnet['moonshiner'] = ['0.899', '0.991', '0', '0', '#joy', '#interest', 'positive', '0.945', 'bourbon', 'scotch', 'vodka', 'brandy', 'wine']
senticnet['moonstar'] = ['0.02', '0.131', '0.749', '-0.67', '#anger', '#disgust', 'negative', '-0.31', 'belligerence', 'belligerency', 'belligerent', 'war', 'rancor']
senticnet['moorage'] = ['0', '0', '0.027', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'port', 'sea', 'boat', 'ocean', 'ship']
senticnet['moore'] = ['-0.69', '0', '0.89', '-0.70', '#anger', '#disgust', 'negative', '-0.76', 'still', 'suppress', 'moderate', 'hush_up', 'quell']
senticnet['moorhen'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'amoeba', 'single_celled']
senticnet['mooring_mast'] = ['-0.04', '0.092', '0', '0.062', '#interest', '#admiration', 'positive', '0.036', 'hangar', 'aircraft_carrier', 'hot_air_balloon', 'war_museum', 'spaceship']
senticnet['moorish_castle'] = ['0.021', '0.119', '0', '0.129', '#interest', '#admiration', 'positive', '0.09', 'manor', 'palace', 'medieval', 'tower', 'chess_game']
senticnet['moorish_revival'] = ['0.578', '-0.95', '0', '0.979', '#surprise', '#admiration', 'positive', '0.838', 'perpendicular', 'perpendicular_style', 'gothic_architecture', 'style_of_architecture', 'type_of_architecture']
senticnet['moorish_revival_style'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'adobe', 'adobe_brick', 'clinker', 'brick', 'firebrick']
senticnet['moorland'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'biome', 'timberland']
senticnet['moose'] = ['-0.83', '0', '-0.89', '0', '#sadness', '#fear', 'negative', '-0.86', 'duck', 'goose', 'goose', 'pheasant', 'parka']
senticnet['moose_hide'] = ['0.706', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.806', 'shoe', 'flip_flop', 'slipper', 'casual_shoes', 'footwear']
senticnet['moot'] = ['-0.78', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.74', 'irrelevant', 'debatable', 'dismissed', 'arguable', 'appellate']
senticnet['moot_corp'] = ['0', '0', '0', '0.879', '#admiration', '#admiration', 'positive', '0.879', 'sport_competition', 'win_competition', 'race', 'match', 'championship']
senticnet['moot_courtroom'] = ['0.872', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.811', 'lecture_theater', 'lecture', 'conference_auditorium', 'read_book', 'text_book']
senticnet['moot_hall'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'city_hall', 'courthouse', 'subway_station', 'public_place', 'bus_station']
senticnet['moot_point'] = ['0.544', '-0.69', '0', '0.658', '#surprise', '#admiration', 'positive', '0.632', 'shakable', 'shakeable', 'challengeable', 'disputable', 'contestable']
senticnet['mop'] = ['0.72', '-0.76', '0', '0.695', '#joy', '#surprise', 'positive', '0.726', 'clean_up', 'blot', 'wipe_up', 'suck_up', 'sponge_up']
senticnet['mop_floor'] = ['0.874', '0', '0', '0.918', '#joy', '#admiration', 'positive', '0.896', 'mow_grass', 'meet_need', 'clean_sink', 'help_person', 'bring_food']
senticnet['mop_handle'] = ['0.022', '-0.01', '0.062', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'electric_toothbrush', 'moistener', 'hair_brush', 'shampoo_conditioner', 'shave_kit']
senticnet['mop_up'] = ['0.72', '-0.76', '0', '0.695', '#joy', '#surprise', 'positive', '0.726', 'suck_up', 'blot', 'wipe_up', 'mop', 'sponge_up']
senticnet['mope'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'unicycle', 'truck', 'lorry', 'vehicle', 'vehicle_type']
senticnet['moping_around'] = ['-0.85', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.88', 'oppression', 'enslavement', 'slave', 'captivity', 'misery']
senticnet['mopped_up'] = ['0.72', '-0.76', '0', '0.695', '#joy', '#surprise', 'positive', '0.726', 'suck_up', 'blot', 'wipe_up', 'mop', 'sponge_up']
senticnet['moppet'] = ['0.738', '-0.53', '0', '0.856', '#joy', '#admiration', 'positive', '0.71', 'farm_girl', 'flower_girl', 'little_girl', 'schoolgirl', 'female']
senticnet['mopping_up'] = ['0.72', '-0.76', '0', '0.695', '#joy', '#surprise', 'positive', '0.726', 'suck_up', 'blot', 'wipe_up', 'mop', 'sponge_up']
senticnet['mopub'] = ['0.886', '-0.77', '0', '0.815', '#joy', '#admiration', 'positive', '0.826', 'countenance', 'legitimate', 'permit', 'allow', 'let']
senticnet['moraceae'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'seed_fern', 'pteridosperm']
senticnet['moral'] = ['-0.65', '0', '0.683', '-0.72', '#anger', '#disgust', 'negative', '-0.68', 'motive', 'need', 'motivation', 'conscience', 'scruple']
senticnet['moral_absolutism'] = ['-0.57', '0', '0.791', '-0.74', '#anger', '#disgust', 'negative', '-0.70', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['moral_ambiguity'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'nonsense', 'meaninglessness', 'bunk', 'absurdity', 'hokum']
senticnet['moral_ambivalence'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'nonsense', 'meaninglessness', 'bunk', 'absurdity', 'hokum']
senticnet['moral_approbation'] = ['-0.97', '0', '0.781', '-0.91', '#sadness', '#disgust', 'negative', '-0.88', 'weakness', 'fault', 'flaw', 'defect', 'wart']
senticnet['moral_authority'] = ['0.751', '-0.98', '0', '0.989', '#surprise', '#admiration', 'positive', '0.909', 'okay', 'approval', 'commendation', 'okey', 'okeh']
senticnet['moral_certainty'] = ['-0.86', '0', '0.675', '-0.84', '#sadness', '#disgust', 'negative', '-0.79', 'shenanigan', 'chicanery', 'wile', 'goldbrick', 'trickery']
senticnet['moral_condemnation'] = ['0.658', '-0.52', '0', '0.626', '#joy', '#admiration', 'positive', '0.602', 'point_in_time', 'threshold', 'birth', 'commencement', 'incipience']
senticnet['moral_conscience'] = ['0.841', '-0.65', '0', '0.645', '#joy', '#surprise', 'positive', '0.714', 'precept', 'rule', 'caveat_emptor', 'higher_law', 'hypothetical_imperative']
senticnet['moral_courage'] = ['0', '0', '0.764', '-0.77', '#anger', '#disgust', 'negative', '-0.77', 'restriction', 'tragedy', 'perpetrator', 'dominate', 'control']
senticnet['moral_cowardice'] = ['-0.61', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.69', 'untrustworthiness', 'untrustiness', 'undependability', 'undependableness', 'unreliability']
senticnet['moral_culpability'] = ['-0.61', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.69', 'irresponsibility', 'untrustiness', 'undependability', 'undependableness', 'unreliability']
senticnet['moral_decay'] = ['0', '0', '-0.96', '-0.71', '#fear', '#disgust', 'negative', '-0.84', 'turpitude', 'putrefaction', 'corruption', 'depravation', 'degeneracy']
senticnet['moral_degradation'] = ['0', '0', '-0.96', '-0.71', '#fear', '#disgust', 'negative', '-0.84', 'turpitude', 'putrefaction', 'corruption', 'depravation', 'degeneracy']
senticnet['moral_depravity'] = ['-0.61', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.69', 'irresponsibility', 'untrustiness', 'undependability', 'undependableness', 'unreliability']
senticnet['moral_dimension'] = ['0.841', '-0.65', '0', '0.645', '#joy', '#surprise', 'positive', '0.714', 'precept', 'rule', 'caveat_emptor', 'higher_law', 'hypothetical_imperative']
senticnet['moral_discourse'] = ['0.632', '-0.80', '0', '0.537', '#joy', '#surprise', 'positive', '0.658', 'incisiveness', 'trenchancy', 'efficacy', 'effectiveness', 'power']
senticnet['moral_disengagement'] = ['-0.75', '0', '0.886', '-0.72', '#sadness', '#anger', 'negative', '-0.78', 'harass', 'move', 'check', 'fork', 'pin']
senticnet['moral_duty'] = ['0.841', '-0.65', '0', '0.645', '#joy', '#surprise', 'positive', '0.714', 'precept', 'rule', 'caveat_emptor', 'higher_law', 'hypothetical_imperative']
senticnet['moral_equivalence'] = ['-0.89', '0', '0.669', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'deceit', 'deception', 'misrepresentation', 'indirect_expression', 'circumlocution']
senticnet['moral_evil'] = ['-0.87', '0', '0.858', '-0.77', '#sadness', '#anger', 'negative', '-0.83', 'pass_off', 'fall_out', 'occur', 'happen', 'hap']
senticnet['moral_fortitude'] = ['0.605', '-0.93', '0', '0.742', '#surprise', '#admiration', 'positive', '0.761', 'road_sense', 'sagacity', 'sagaciousness', 'logic', 'nous']
senticnet['moral_imagination'] = ['0.734', '-0.64', '0', '0.933', '#joy', '#admiration', 'positive', '0.771', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['moral_imperative'] = ['0.841', '-0.65', '0', '0.645', '#joy', '#surprise', 'positive', '0.714', 'precept', 'rule', 'caveat_emptor', 'higher_law', 'hypothetical_imperative']
senticnet['moral_indignation'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['moral_integrity'] = ['0', '0', '0', '0.817', '#admiration', '#admiration', 'positive', '0.817', 'truthful', 'honesty', 'honest', 'unity', 'good_character']
senticnet['moral_intuition'] = ['0.841', '-0.65', '0', '0.645', '#joy', '#surprise', 'positive', '0.714', 'precept', 'rule', 'caveat_emptor', 'higher_law', 'hypothetical_imperative']
senticnet['moral_judgement'] = ['0.841', '-0.65', '0', '0.645', '#joy', '#surprise', 'positive', '0.714', 'precept', 'rule', 'caveat_emptor', 'higher_law', 'hypothetical_imperative']
senticnet['moral_judgment'] = ['0.841', '-0.65', '0', '0.645', '#joy', '#surprise', 'positive', '0.714', 'precept', 'rule', 'caveat_emptor', 'higher_law', 'hypothetical_imperative']
senticnet['moral_justification'] = ['0.841', '-0.65', '0', '0.645', '#joy', '#surprise', 'positive', '0.714', 'precept', 'rule', 'caveat_emptor', 'higher_law', 'hypothetical_imperative']
senticnet['moral_laxity'] = ['0.889', '-0.69', '0', '0.908', '#joy', '#admiration', 'positive', '0.83', 'pharisaic', 'pious', 'sanctimonious', 'reverent', 'worshipful']
senticnet['moral_legitimacy'] = ['0.751', '-0.98', '0', '0.989', '#surprise', '#admiration', 'positive', '0.909', 'okay', 'approval', 'commendation', 'okey', 'okeh']
senticnet['moral_nihilism'] = ['0.95', '-0.77', '0', '0.578', '#joy', '#surprise', 'positive', '0.767', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['moral_obligation'] = ['0.841', '-0.65', '0', '0.645', '#joy', '#surprise', 'positive', '0.714', 'precept', 'rule', 'caveat_emptor', 'higher_law', 'hypothetical_imperative']
senticnet['moral_outrage'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['moral_perfection'] = ['0.971', '-0.71', '0', '0.656', '#joy', '#surprise', 'positive', '0.781', 'wellbeing', 'human_flourishing', 'successfulness', 'upbeat', 'strength']
senticnet['moral_persuasion'] = ['0', '0.325', '-0.18', '0.335', '#interest', '#admiration', 'positive', '0.16', 'get_rid', 'cause_change', 'interaction', 'suggest', 'movement_towards']
senticnet['moral_philosopher'] = ['0.922', '-0.94', '0', '0.965', '#surprise', '#admiration', 'positive', '0.945', 'utilitarian', 'egalitarian', 'equalitarian', 'elitist', 'philosopher']
senticnet['moral_philosophy'] = ['0.601', '-0.84', '0', '0.872', '#surprise', '#admiration', 'positive', '0.773', 'bioethics', 'hedonism', 'philosophy', 'casuistry', 'eudemonism']
senticnet['moral_principle'] = ['0.841', '-0.65', '0', '0.645', '#joy', '#surprise', 'positive', '0.714', 'precept', 'rule', 'caveat_emptor', 'higher_law', 'hypothetical_imperative']
senticnet['moral_purity'] = ['0.909', '-0.85', '0', '0.846', '#joy', '#surprise', 'positive', '0.87', 'respectability', 'reputability', 'venerability', 'venerableness', 'righteousness']
senticnet['moral_rectitude'] = ['0.922', '-0.94', '0', '0.965', '#surprise', '#admiration', 'positive', '0.945', 'utilitarian', 'egalitarian', 'equalitarian', 'elitist', 'philosopher']
senticnet['moral_relativism'] = ['0', '0.189', '0', '0.177', '#interest', '#admiration', 'positive', '0.183', 'good', 'value', 'elusive', 'uncommon', 'versatile']
senticnet['moral_responsibility'] = ['-0.81', '0', '0.979', '-0.88', '#anger', '#disgust', 'negative', '-0.89', 'vexation', 'dead_weight', 'imposition', 'worry', 'concern']
senticnet['moral_sanction'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'forgivable', 'excusable', 'mercifully', 'merciful', 'mercifulness']
senticnet['moral_sense'] = ['0.714', '-0.58', '0', '0.667', '#joy', '#admiration', 'positive', '0.655', 'morality', 'wee_small_voice', 'small_voice', 'voice_of_conscience', 'sense_of_shame']
senticnet['moral_sensibility'] = ['0.909', '-0.85', '0', '0.846', '#joy', '#surprise', 'positive', '0.87', 'respectability', 'reputability', 'venerability', 'venerableness', 'righteousness']
senticnet['moral_sentiment'] = ['-0.57', '0', '0.53', '-0.63', '#sadness', '#disgust', 'negative', '-0.58', 'low_status', 'lowness', 'backseat', 'lowliness', 'subordinateness']
senticnet['moral_suasion'] = ['0', '0.325', '-0.18', '0.335', '#interest', '#admiration', 'positive', '0.16', 'get_rid', 'cause_change', 'interaction', 'suggest', 'movement_towards']
senticnet['moral_superiority'] = ['0.757', '-0.82', '0', '0.982', '#surprise', '#admiration', 'positive', '0.855', 'first_class', 'choiceness', 'first_water', 'fineness', 'quality']
senticnet['moral_theology'] = ['0.664', '-0.57', '0', '0.981', '#joy', '#admiration', 'positive', '0.741', 'theodicy', 'homiletics', 'discipline', 'study', 'subject']
senticnet['moral_turpitude'] = ['-0.61', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.69', 'irresponsibility', 'untrustiness', 'undependability', 'undependableness', 'unreliability']
senticnet['moral_virtue'] = ['0.971', '-0.71', '0', '0.656', '#joy', '#surprise', 'positive', '0.781', 'wellbeing', 'human_flourishing', 'successfulness', 'upbeat', 'strength']
senticnet['moralism'] = ['0.95', '-0.77', '0', '0.578', '#joy', '#surprise', 'positive', '0.767', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['moralist'] = ['0.922', '-0.94', '0', '0.965', '#surprise', '#admiration', 'positive', '0.945', 'utilitarian', 'egalitarian', 'equalitarian', 'elitist', 'philosopher']
senticnet['moralistic'] = ['0.633', '-0.92', '0', '0.869', '#surprise', '#admiration', 'positive', '0.808', 'advocate', 'bigot', 'dogmatist', 'partisan', 'advocator']
senticnet['morality'] = ['0', '0.189', '0', '0.177', '#interest', '#admiration', 'positive', '0.183', 'good', 'value', 'elusive', 'uncommon', 'versatile']
senticnet['morality_play'] = ['0.734', '-0.60', '0', '0.612', '#joy', '#admiration', 'positive', '0.65', 'theater_of_absurd', 'playlet', 'miracle_play', 'drama', 'mystery_play']
senticnet['morality_tale'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'mythical', 'tale', 'dictionary', 'magic', 'perfect_world']
senticnet['moralize'] = ['0', '0.792', '0', '0.735', '#interest', '#admiration', 'positive', '0.764', 'moralizer', 'didactic', 'moralizing', 'moral', 'sententious']
senticnet['moralizer'] = ['0', '0.786', '0', '0.726', '#interest', '#admiration', 'positive', '0.756', 'moralize', 'didactic', 'moralizing', 'moral', 'sententious']
senticnet['moralizing'] = ['0', '0.786', '0', '0.734', '#interest', '#admiration', 'positive', '0.76', 'moralizer', 'didactic', 'moralize', 'moral', 'sententious']
senticnet['morally'] = ['-0.80', '0', '0.905', '0', '#sadness', '#anger', 'negative', '-0.85', 'iniquitous', 'wicked', 'iniquitousness', 'objectionable', 'sinful']
senticnet['morally_bankrupt'] = ['0', '0', '-0.90', '-0.90', '#fear', '#disgust', 'negative', '-0.90', 'parody', 'farce', 'lampoon', 'farce_comedy', 'spoof']
senticnet['morally_corrupt'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['morally_impossible'] = ['0.509', '-0.62', '0', '0.786', '#surprise', '#admiration', 'positive', '0.64', 'agreeable', 'accordant', 'consentient', 'unanimous', 'according']
senticnet['morally_legitimate'] = ['0.841', '-0.65', '0', '0.645', '#joy', '#surprise', 'positive', '0.714', 'precept', 'rule', 'caveat_emptor', 'higher_law', 'hypothetical_imperative']
senticnet['morally_reprehensible'] = ['-0.79', '0', '0.691', '-0.88', '#sadness', '#disgust', 'negative', '-0.79', 'cold', 'pitiless', 'cruel', 'unkind', 'vicious']
senticnet['morally_repugnant'] = ['-0.79', '0', '0.691', '-0.88', '#sadness', '#disgust', 'negative', '-0.79', 'cold', 'pitiless', 'cruel', 'unkind', 'vicious']
senticnet['morally_right'] = ['0.841', '-0.65', '0', '0.645', '#joy', '#surprise', 'positive', '0.714', 'precept', 'rule', 'caveat_emptor', 'higher_law', 'hypothetical_imperative']
senticnet['morally_upright'] = ['0.777', '0.913', '0', '0', '#joy', '#interest', 'positive', '0.845', 'ethical', 'honest', 'good', 'estimable', 'fair']
senticnet['moran'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
senticnet['morass'] = ['-0.99', '0', '0.932', '0', '#sadness', '#anger', 'negative', '-0.96', 'trap', 'insidious', 'mantrap', 'entrap', 'ensnare']
senticnet['moratorium'] = ['0.976', '-0.71', '0', '0.863', '#joy', '#admiration', 'positive', '0.853', 'hold', 'delay', 'wait', 'time_lag', 'postponement']
senticnet['moray_eel'] = ['0.96', '-0.77', '0', '0.929', '#joy', '#admiration', 'positive', '0.887', 'whale_louse', 'beach_flea', 'sand_hopper', 'sandhopper', 'amphipod']
senticnet['moray_gymnothorax'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'plectognath', 'plectognathfish', 'blowfish', 'triggerfish', 'filefish']
senticnet['morbid'] = ['-0.75', '0', '0.909', '-0.96', '#anger', '#disgust', 'negative', '-0.87', 'noisome', 'unhealthy', 'unwholesome', 'nauseating', 'nauseous']
senticnet['morbid_anatomy'] = ['-0.59', '0', '0.911', '-0.87', '#anger', '#disgust', 'negative', '-0.79', 'osteosclerosis', 'sclerosis', 'disseminated_sclerosis', 'multiple_sclerosis', 'induration']
senticnet['morbid_fascination'] = ['-0.93', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.94', 'horrify', 'horror', 'horrifying', 'atrocious', 'horrifyingly']
senticnet['morbid_fear'] = ['-0.79', '-0.71', '-0.96', '-0.95', '#fear', '#disgust', 'negative', '-0.49', 'fear', 'caution', 'deep_sadness', 'bad_feeling', 'psychoneurosis']
senticnet['morbid_obesity'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'fatty', 'worse', 'health_issue', 'clinical_problem']
senticnet['morbidity'] = ['-0.75', '0', '0.893', '-0.61', '#sadness', '#anger', 'negative', '-0.75', 'unwholesomeness', 'morbidness', 'unhealthfulness', 'insalubrity', 'insalubriousness']
senticnet['morbidly'] = ['0', '0', '-0.70', '-0.95', '#fear', '#disgust', 'negative', '-0.83', 'moody', 'dour', 'glum', 'dark', 'glowering']
senticnet['morbidly_obese'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'fatty', 'worse', 'health_issue', 'clinical_problem']
senticnet['morbidness'] = ['-0.75', '0', '0.893', '-0.61', '#sadness', '#anger', 'negative', '-0.75', 'unwholesomeness', 'unhealthfulness', 'morbidity', 'insalubrity', 'insalubriousness']
senticnet['morcar'] = ['-0.92', '0', '0.659', '-0.81', '#sadness', '#disgust', 'negative', '-0.8', 'unripe', 'napping', 'off_guard', 'off_one_guard', 'unprepared']
senticnet['mordacious'] = ['0', '0', '-0.90', '-1', '#fear', '#disgust', 'negative', '-0.95', 'barbed', 'biting', 'acrid', 'pungent', 'nipping']
senticnet['mordancy'] = ['0.947', '-0.99', '0', '0.936', '#joy', '#surprise', 'positive', '0.958', 'utterer', 'deipnosophist', 'interlocutor', 'conversational_partner', 'speaker']
senticnet['mordant'] = ['-0.77', '0', '0.537', '-0.64', '#sadness', '#disgust', 'negative', '-0.65', 'grim', 'saturnine', 'sardonic', 'sarcastic', 'black']
senticnet['mordant_humor'] = ['-0.77', '0', '0.626', '-0.78', '#sadness', '#disgust', 'negative', '-0.72', 'bumpy', 'unsmooth', 'lepidote', 'leprose', 'roughish']
senticnet['mordant_wit'] = ['-0.77', '0', '0.537', '-0.64', '#sadness', '#disgust', 'negative', '-0.65', 'grim', 'saturnine', 'sardonic', 'sarcastic', 'black']
senticnet['mordenkainen'] = ['0.594', '-0.54', '0', '0.518', '#joy', '#surprise', 'positive', '0.554', 'exorcist', 'necromancer', 'magus', 'sorceress', 'witch_doctor']
senticnet['mordru'] = ['0.086', '-0.10', '0.064', '0', '#joy', '#surprise', 'positive', '0.043', 'art_piece', 'lifetime', 'museum', 'vintage', 'collectible']
senticnet['moredhel'] = ['-0.04', '-0.02', '0.027', '-0.03', '#sadness', '#disgust', 'negative', '-0.01', 'precious_metal', 'olympic_medal', 'shiny_metal', 'many_different_kinds', 'valuable_metal']
senticnet['moreish'] = ['0.041', '0.069', '0.032', '0.13', '#interest', '#admiration', 'positive', '0.052', 'edible_human', 'round_fruit', 'peelable', 'green_fruit', 'nutritious']
senticnet['morenci'] = ['-0.98', '0', '0.521', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'battery', 'stamp_battery', 'quartz_battery', 'stamp_mill', 'factory']
senticnet['moreton_island'] = ['0.84', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.798', 'big_breadbox', 'water_vehicle', 'military_ship', 'ship', 'small_boat']
senticnet['morfran'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'jawless_fish', 'osteostracan', 'cephalaspid', 'ostracoderm', 'heterostracan']
senticnet['morgan_le_fay'] = ['-0.75', '0', '0.559', '-0.88', '#sadness', '#disgust', 'negative', '-0.73', 'fairy', 'faery', 'faerie', 'fay', 'sprite']
senticnet['morgana'] = ['-0.63', '0', '0.674', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'baneful', 'evil', 'baleful', 'malevolent', 'malign']
senticnet['morganatic_marriage'] = ['-0.70', '0', '0.919', '-0.73', '#anger', '#disgust', 'negative', '-0.78', 'unfruitful', 'unsuccessful', 'acarpous', 'abortive', 'stillborn']
senticnet['morgue'] = ['-0.06', '0.09', '-0.14', '-0.15', '#fear', '#disgust', 'negative', '-0.06', 'casket', 'coffin', 'grave', 'headstone', 'bury_person']
senticnet['morgue_attendant'] = ['-0.06', '0.09', '-0.14', '-0.15', '#fear', '#disgust', 'negative', '-0.06', 'casket', 'coffin', 'grave', 'headstone', 'bury_person']
senticnet['mori_iwi'] = ['-0.91', '0', '0.769', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'break', 'good_luck', 'chance_eventpy_chance', 'chance_event', 'coincidence']
senticnet['moribund'] = ['0', '0', '0.915', '-0.73', '#anger', '#disgust', 'negative', '-0.82', 'die', 'approaching', 'expiring', 'moribundly', 'dying']
senticnet['moribundly'] = ['0', '0', '0.911', '-0.73', '#anger', '#disgust', 'negative', '-0.82', 'die', 'approaching', 'expiring', 'moribund', 'dying']
senticnet['morinda_citrifolia'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'palm_nut', 'nut', 'edible_nut', 'cembra_nut', 'cedar_nut']
senticnet['moringa'] = ['0.035', '0.104', '0', '0.12', '#interest', '#admiration', 'positive', '0.086', 'fruit_vegetable', 'vegetable', 'vegetable_fruit', 'fresh_vegetable', 'vegetarian_food']
senticnet['moringa_oleifera'] = ['0.826', '0.787', '0', '0', '#joy', '#interest', 'positive', '0.806', 'fig', 'baby_flower', 'carpel', 'strawberry', 'star_anise']
senticnet['morio_asaka'] = ['-0.67', '0', '0.931', '-0.77', '#anger', '#disgust', 'negative', '-0.79', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['morion'] = ['-0.59', '0', '0.887', '-0.71', '#anger', '#disgust', 'negative', '-0.73', 'armet', 'basinet', 'casque', 'heaume', 'helmet']
senticnet['morkel'] = ['-0.13', '0', '-0.11', '-0.04', '#sadness', '#fear', 'negative', '-0.1', 'concern_health', 'medical_problem', 'strengthful', 'surgery', 'injury']
senticnet['mormon'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'apple_tree', 'rosebush', 'snake', 'fox', 'ferret']
senticnet['mornin'] = ['-0.83', '0', '0.87', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'precipitate', 'rain_down', 'shower_down', 'spit', 'patter']
senticnet['morning'] = ['0.919', '-0.83', '0', '0', '#joy', '#surprise', 'positive', '0.877', 'breakfast', 'dry_fruit', 'carton_milk', 'wheat', 'box_cereal']
senticnet['morning-line'] = ['0', '-0.77', '0', '0.698', '#surprise', '#admiration', 'positive', '0.738', 'odds-on', 'hands-down', 'front-runner', 'shoe-in', 'shoo-in']
senticnet['morning_coffee'] = ['0.715', '0.785', '0', '0', '#joy', '#interest', 'positive', '0.75', 'scrumble_egg', 'heat_meal', 'pan', 'beat_egg', 'food']
senticnet['morning_fog'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'hazy', 'blur', 'bleary', 'blurred', 'blurry']
senticnet['morning_food'] = ['0.899', '-0.83', '0', '0', '#joy', '#surprise', 'positive', '0.865', 'breakfast', 'bake_product', 'eatable', 'sweet_treat', 'bake_good']
senticnet['morning_glory_ipomoea'] = ['0', '0', '-0.14', '-0.03', '#fear', '#disgust', 'negative', '-0.09', 'pine_cone', 'fallen_leaf', 'poison_ivy', 'detritus', 'tress']
senticnet['morning_mist'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'hazy', 'blur', 'bleary', 'blurred', 'blurry']
senticnet['morning_snack'] = ['0.895', '0.946', '0', '0', '#joy', '#interest', 'positive', '0.92', 'need_food', 'a_little_hungry', 'dead_hungry', 'prepare_food', 'fill_stomach']
senticnet['morning_twilight'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['morningglory'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'meadow_grass', 'grass', 'manna_grass', 'sweet_grass', 'wood_meadowgrass']
senticnet['moron'] = ['0', '0', '0.743', '-0.94', '#anger', '#disgust', 'negative', '-0.84', 'stupid', 'increase_body_temperature', 'idiot', 'get_excited', 'sense_accomplishment']
senticnet['moronga'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'saltine', 'soda_cracker', 'cracker', 'water_biscuit', 'graham_cracker']
senticnet['moronic'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['moronity'] = ['-0.56', '0', '0.735', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'slowness', 'mental_retardation', 'backwardness', 'subnormality', 'retardation']
senticnet['morose'] = ['0', '0', '-0.70', '-0.95', '#fear', '#disgust', 'negative', '-0.83', 'moody', 'dour', 'glum', 'dark', 'glowering']
senticnet['morosely'] = ['-0.81', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.86', 'impassiveness', 'impassivity', 'emotionlessness', 'unemotionality', 'stolidity']
senticnet['moroseness'] = ['-0.95', '0', '0.694', '-0.94', '#sadness', '#disgust', 'negative', '-0.86', 'sullenness', 'ill_nature', 'crabbiness', 'crabbedness', 'crankiness']
senticnet['morosoph'] = ['0.794', '-0.78', '0', '0.822', '#joy', '#admiration', 'positive', '0.799', 'saphead', 'muggins', 'tomfool', 'fool', 'sap']
senticnet['morphea'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema', 'erythema_multiforme', 'windburn', 'hickey']
senticnet['morpheme'] = ['-0.61', '0', '0.721', '-0.68', '#anger', '#disgust', 'negative', '-0.67', 'character', 'logogram', 'logograph', 'graph', 'ideogram']
senticnet['morphia'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'apomorphine', 'anodyne', 'painkiller', 'pain_pill']
senticnet['morphine'] = ['0', '0', '-0.11', '-0.20', '#fear', '#disgust', 'negative', '-0.16', 'codeine', 'drug', 'phenobarbital', 'analgesic', 'sleep_drug']
senticnet['morphine_overdose'] = ['-0.87', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.91', 'loss_oflife', 'chest_pain', 'die', 'tumor', 'end_life']
senticnet['morphine_sulfate'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['morpho_syntactic'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'integrative', 'consolidative', 'unifying', 'combinatorial']
senticnet['morphogenetic'] = ['-0.84', '0', '0.8', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'rigor_mortis', 'natural_process', 'natural_action', 'activity', 'action']
senticnet['morphologically'] = ['0', '0.794', '0', '0.961', '#interest', '#admiration', 'positive', '0.877', 'semantically', 'grammatically', 'phonetically', 'syntactically', 'lexically']
senticnet['morphology'] = ['0.823', '0.815', '0', '0', '#joy', '#interest', 'positive', '0.819', 'plate_tectonics', 'science', 'architectonics', 'plate_tectonic_theory', 'tectonics']
senticnet['morquio_syndrome'] = ['-0.50', '0', '0.542', '-0.51', '#anger', '#disgust', 'negative', '-0.51', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'hirschsprung_disease']
senticnet['morrigan'] = ['-0.79', '0', '0.92', '-0.75', '#sadness', '#anger', 'negative', '-0.82', 'evil_spirit', 'incubus', 'daemon', 'succuba', 'dybbuk']
senticnet['morrison_foerster_llp'] = ['0.841', '0.721', '0', '0.74', '#joy', '#admiration', 'positive', '0.767', 'good_manner', 'two_people', 'love', 'guardian', 'guy']
senticnet['morrow'] = ['0.622', '0.641', '-0.24', '0.285', '#joy', '#interest', 'positive', '0.327', 'gleeful', 'gladsome', 'happiness', 'gladness', 'gladfulness']
senticnet['morrow_morning'] = ['0', '0.152', '-0.08', '0.208', '#interest', '#admiration', 'positive', '0.091', 'departure', 'leave', 'colored_green', 'green_color', 'make_big']
senticnet['morse_code'] = ['0.144', '-0.11', '0.013', '-0.03', '#joy', '#surprise', 'positive', '0.052', 'dance', 'old_dance', 'slow_dance', 'enter_contest', 'stout']
senticnet['morsel'] = ['-0.82', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.87', 'morsel', 'nibble', 'mouthful', 'deliciousness', 'delectable']
senticnet['morsi'] = ['0.648', '0.627', '-0.26', '0.329', '#joy', '#interest', 'positive', '0.334', 'brotherly', 'friendly', 'fraternal', 'amicability', 'amicable']
senticnet['morsi_muslim_brotherhood'] = ['0.648', '0.627', '-0.26', '0.329', '#joy', '#interest', 'positive', '0.334', 'brotherly', 'friendly', 'fraternal', 'amicability', 'amicable']
senticnet['morsi_supporter'] = ['-0.93', '0', '0.688', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'dissident', 'dissenter', 'nimby', 'objector', 'contestant']
senticnet['mortal'] = ['0', '0', '-0.75', '-0.74', '#fear', '#disgust', 'negative', '-0.75', 'reaper', 'dead', 'cadaver', 'dead_person', 'end_life']
senticnet['mortal_body'] = ['0', '0', '0', '0.816', '#admiration', '#admiration', 'positive', '0.816', 'immortality', 'live_forever', 'deathless', 'eternal_life', 'everlasting_life']
senticnet['mortal_combat'] = ['0', '-0.01', '-0.08', '-0.16', '#fear', '#disgust', 'negative', '-0.07', 'siege', 'big_fight', 'battle', 'declare_war', 'conflict']
senticnet['mortal_enemy'] = ['0', '0', '0.85', '-0.88', '#anger', '#disgust', 'negative', '-0.86', 'rogue', 'seaman', 'lieutenant', 'officer', 'captain']
senticnet['mortal_flesh'] = ['0.668', '-0.88', '0', '0.893', '#surprise', '#admiration', 'positive', '0.815', 'chirpiness', 'spirit', 'sprightliness', 'liveliness', 'activity']
senticnet['mortal_man'] = ['0', '0', '0', '0.816', '#admiration', '#admiration', 'positive', '0.816', 'immortality', 'live_forever', 'deathless', 'eternal_life', 'everlasting_life']
senticnet['mortal_men'] = ['0', '0', '0', '0.816', '#admiration', '#admiration', 'positive', '0.816', 'immortality', 'live_forever', 'deathless', 'eternal_life', 'everlasting_life']
senticnet['mortal_realm'] = ['-0.78', '0', '0.74', '-0.94', '#sadness', '#disgust', 'negative', '-0.82', 'eject', 'chuck_out', 'exclude', 'turf_out', 'boot_out']
senticnet['mortal_sin'] = ['0.69', '-0.64', '0', '0.825', '#joy', '#admiration', 'positive', '0.72', 'lust', 'luxuria', 'sinning', 'pride', 'superbia']
senticnet['mortality'] = ['-0.87', '0', '0.744', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'temporariness', 'transience', 'transiency', 'transitoriness', 'impermanence']
senticnet['mortally'] = ['-1', '0', '0.948', '0', '#sadness', '#anger', 'negative', '-0.97', 'fatal', 'finality', 'doom', 'fatally', 'deadly']
senticnet['mortar'] = ['0', '0', '-0.95', '-0.72', '#fear', '#disgust', 'negative', '-0.83', 'cannon', 'explosive', 'firearm', 'pistol', 'machine_gun']
senticnet['mortar_ammunition'] = ['0', '0', '-0.92', '-0.91', '#fear', '#disgust', 'negative', '-0.92', 'automatic', 'firearm', 'gun', 'weapon', 'projectile']
senticnet['mortar_battery'] = ['-0.85', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.84', 'atomic_bomb', 'weapon_war', 'gun', 'pistol', 'hydrogen_bomb']
senticnet['mortar_bookseller'] = ['0', '0', '-0.95', '-0.72', '#fear', '#disgust', 'negative', '-0.83', 'cannon', 'explosive', 'firearm', 'pistol', 'machine_gun']
senticnet['mortar_bookstore'] = ['0', '0', '-0.95', '-0.72', '#fear', '#disgust', 'negative', '-0.83', 'cannon', 'explosive', 'firearm', 'pistol', 'machine_gun']
senticnet['mortar_fire'] = ['0', '0', '-0.85', '-0.99', '#fear', '#disgust', 'negative', '-0.92', 'onslaught', 'battery', 'shelling', 'bombardment', 'outpouring']
senticnet['mortar_platoon'] = ['0', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.09', 'army', 'attacker', 'military', 'soldier', 'infantry']
senticnet['mortar_retail'] = ['0', '0', '-0.95', '-0.72', '#fear', '#disgust', 'negative', '-0.83', 'cannon', 'explosive', 'firearm', 'pistol', 'machine_gun']
senticnet['mortar_retail_store'] = ['0', '0', '-0.95', '-0.72', '#fear', '#disgust', 'negative', '-0.83', 'cannon', 'explosive', 'firearm', 'pistol', 'machine_gun']
senticnet['mortar_retailer'] = ['0', '0', '-0.95', '-0.72', '#fear', '#disgust', 'negative', '-0.83', 'cannon', 'explosive', 'firearm', 'pistol', 'machine_gun']
senticnet['mortar_round'] = ['0', '0', '0.818', '-0.99', '#anger', '#disgust', 'negative', '-0.90', 'weapon', 'aerial_bomb', 'machine_gun', 'cannon', 'handgun']
senticnet['mortar_shell'] = ['0', '0', '0.818', '-0.99', '#anger', '#disgust', 'negative', '-0.90', 'weapon', 'aerial_bomb', 'machine_gun', 'cannon', 'handgun']
senticnet['mortar_store'] = ['0', '0', '-0.95', '-0.72', '#fear', '#disgust', 'negative', '-0.83', 'cannon', 'explosive', 'firearm', 'pistol', 'machine_gun']
senticnet['mortar_tube'] = ['0', '0', '-0.95', '-0.72', '#fear', '#disgust', 'negative', '-0.83', 'cannon', 'explosive', 'firearm', 'pistol', 'machine_gun']
senticnet['mortarboard'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'pinafore', 'pinny', 'dirndl', 'dress', 'muumuu']
senticnet['mortgage'] = ['-0.06', '0.287', '-0.08', '0.116', '#interest', '#admiration', 'positive', '0.065', 'money_transfer', 'loan', 'cash_pay', 'wealth', 'income']
senticnet['mortgage_fraud'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'mail_fraud', 'swindle', 'rig']
senticnet['mortice'] = ['-0.71', '0', '0.932', '-0.50', '#sadness', '#anger', 'negative', '-0.71', 'refuse', 'subvert', 'decline', 'balk', 'disobey']
senticnet['mortician'] = ['-0.70', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.70', 'death_row', 'sad', 'loose_end', 'car_race', 'mortuary']
senticnet['mortification'] = ['-0.91', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.87', 'uncomfortableness', 'self-consciousness', 'self-conscious', 'confusedly', 'discombobulation']
senticnet['mortified'] = ['-0.79', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.75', 'humiliated', 'mortifying', 'self-hatred', 'self-disgust', 'shameful']
senticnet['mortify'] = ['-0.77', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.74', 'abase', 'embarrassed', 'self-hatred', 'self-disgust', 'humiliated']
senticnet['mortifying'] = ['-0.78', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.75', 'humiliated', 'mortified', 'self-hatred', 'self-disgust', 'shameful']
senticnet['morton_neuroma'] = ['0.789', '-0.99', '0', '0.54', '#joy', '#surprise', 'positive', '0.776', 'imperfection', 'imperfectness', 'bugginess', 'defectiveness', 'sickness']
senticnet['mortuary'] = ['0', '0', '-0.05', '-0.03', '#fear', '#disgust', 'negative', '-0.04', 'grandma_house', 'game_room', 'old_folk_home', 'computer_lab', 'chess_tournament']
senticnet['mortuary_chapel'] = ['-0.50', '0', '0.762', '-0.89', '#anger', '#disgust', 'negative', '-0.71', 'cemetery', 'graveyard', 'burial_site', 'grassland', 'burying_ground']
senticnet['mortuary_cult'] = ['-0.09', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.10', 'funeral_celebration', 'memorial', 'funeral', 'memento', 'cry']
senticnet['mortuary_temple'] = ['-0.92', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.91', 'bury_body', 'coffin', 'headstone', 'morgue', 'tombstone']
senticnet['morus_alba'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'black_locust']
senticnet['mosaic'] = ['0.838', '0.828', '0', '0', '#joy', '#interest', 'positive', '0.833', 'vista', 'spectacular', 'perspective', 'image', 'scenic']
senticnet['mosaic_authorship'] = ['-0.69', '0', '0.806', '-0.74', '#anger', '#disgust', 'negative', '-0.74', 'hypothesis', 'theory', 'base', 'possibility', 'presumption']
senticnet['moscone_center'] = ['-0.79', '0', '0.948', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['mosconi_cup'] = ['-0.68', '0', '0.923', '-0.68', '#sadness', '#anger', 'negative', '-0.76', 'negative', 'veto', 'refute', 'rebut', 'controvert']
senticnet['moscow_correspondent'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'adventurer', 'humankind']
senticnet['moses_pendleton'] = ['-0.95', '0', '0.992', '-0.63', '#sadness', '#anger', 'negative', '-0.86', 'modern_dance', 'interpretive_dancing', 'interpretative_dance', 'interpretative_dancing', 'stage_dancing']
senticnet['moses_taft'] = ['0.914', '-0.56', '0', '0.869', '#joy', '#admiration', 'positive', '0.781', 'tabula_rasa', 'ego', 'subconscious', 'unconscious', 'psyche']
senticnet['mosey'] = ['-0.83', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.77', 'saunter', 'amble', 'traipse', 'moseyed', 'ambled']
senticnet['moseyed'] = ['-0.83', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.77', 'saunter', 'amble', 'traipse', 'mosey', 'ambled']
senticnet['mosh_pit'] = ['0.26', '0', '0', '0.089', '#joy', '#admiration', 'positive', '0.174', 'balcony_rail', 'movie_theater_seat', 'row_seat', 'usher', 'sticky_floor']
senticnet['moshpit'] = ['0.26', '0', '0', '0.089', '#joy', '#admiration', 'positive', '0.174', 'balcony_rail', 'movie_theater_seat', 'row_seat', 'usher', 'sticky_floor']
senticnet['mosieh'] = ['-0.93', '0', '0.791', '0', '#sadness', '#anger', 'negative', '-0.86', 'hopelessness', 'hopelessly', 'despairing', 'pessimistically', 'pessimistic']
senticnet['mosque'] = ['0', '0.224', '0', '0.197', '#interest', '#admiration', 'positive', '0.211', 'church', 'temple', 'cathedral', 'chapel', 'confession']
senticnet['mosque_compound'] = ['0', '0.224', '0', '0.197', '#interest', '#admiration', 'positive', '0.211', 'church', 'temple', 'cathedral', 'chapel', 'confession']
senticnet['mosque_minaret'] = ['0', '0.224', '0', '0.197', '#interest', '#admiration', 'positive', '0.211', 'church', 'temple', 'cathedral', 'chapel', 'confession']
senticnet['mosquito'] = ['0', '-0.09', '0.128', '-0.12', '#anger', '#disgust', 'negative', '-0.05', 'gnat', 'mole_cricket', 'gypsy_moth', 'flea', 'cucumber_beetle']
senticnet['mosquito_bite'] = ['0', '0', '0.904', '-0.88', '#anger', '#disgust', 'negative', '-0.89', 'enjoy_fresh_air', 'look_scenery', 'become_tired', 'dog_bark', 'leg_hurt']
senticnet['mosquito_repellent'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['moss'] = ['0.757', '-0.91', '0', '0', '#joy', '#surprise', 'positive', '0.838', 'stem', 'evergreen', 'leaf', 'branch', 'evergreen_tree']
senticnet['moss_hollow'] = ['0', '0.156', '-0.06', '0.198', '#interest', '#admiration', 'positive', '0.098', 'grassland', 'camping', 'camp_out', 'campsite', 'campground']
senticnet['mossflower'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'elapid', 'ophidian', 'serpent', 'krait', 'taipan']
senticnet['mossy_saxifrage'] = ['-0.56', '0', '0.848', '-0.80', '#anger', '#disgust', 'negative', '-0.73', 'herb', 'yellow_mountain_saxifrage', 'meadow_saxifrage', 'breakstone', 'western_saxifrage']
senticnet['mostaert'] = ['-0.52', '0', '0.506', '-0.94', '#sadness', '#disgust', 'negative', '-0.65', 'gouger', 'swindler', 'defrauder', 'fraud', 'welcher']
senticnet['mostarda'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'grapefruit_peel', 'lemon_peel', 'orange_peel', 'candied_citrus_peel', 'succade']
senticnet['mostly'] = ['-0.88', '0', '0', '-1', '#sadness', '#disgust', 'negative', '-0.94', 'in_general', 'broadly', 'generally', 'loosely', 'in_the_main']
senticnet['mostly_cloudy'] = ['0.025', '-0.06', '-0.05', '0.161', '#surprise', '#admiration', 'positive', '0.048', 'carry_water', 'up_high', 'weather_condition', 'dew', 'hazy']
senticnet['mostly_diurnal'] = ['0.967', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.91', 'furry_creature', 'hairy', 'furry', 'woodland_creature', 'animal_furry']
senticnet['mostly_glabrous_except'] = ['-0.50', '0', '0.573', '-0.89', '#anger', '#disgust', 'negative', '-0.65', 'petallike', 'bowery', 'leafy', 'unifoliate', 'bifoliate']
senticnet['mostly_illiterate'] = ['-0.90', '0', '0.76', '-0.87', '#sadness', '#disgust', 'negative', '-0.84', 'analphabet', 'analphabetic', 'functional_illiterate', 'ignoramus', 'know_nothing']
senticnet['mostly_nocturnal'] = ['0', '0', '-0.81', '-0.92', '#fear', '#disgust', 'negative', '-0.87', 'tree-living', 'dendroidal', 'arborous', 'arboriform', 'arborary']
senticnet['mostly_sunny'] = ['0.025', '-0.06', '-0.05', '0.161', '#surprise', '#admiration', 'positive', '0.048', 'carry_water', 'up_high', 'weather_condition', 'dew', 'hazy']
senticnet['mote'] = ['0', '-0.02', '0.091', '-0.07', '#anger', '#disgust', 'negative', '-0.04', 'ladybug', 'mite', 'pinhead', 'gnat', 'flea']
senticnet['motel_clerk'] = ['0', '0.186', '0', '0.075', '#interest', '#admiration', 'positive', '0.131', 'money_inside', 'check_account', 'savings', 'money_storage', 'store_money']
senticnet['motel_room'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'downstairs', 'appliance_store', 'apartment', 'shack', 'neighbor_house']
senticnet['moth'] = ['0', '-0.07', '0.098', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'japanese_beetle', 'cucumber_beetle', 'mole_cricket', 'gypsy_moth', 'caterpillar']
senticnet['moth_larva'] = ['0.866', '-0.91', '0', '0.893', '#surprise', '#admiration', 'positive', '0.892', 'mooch', 'cadge', 'freeload', 'sponge', 'bum']
senticnet['mother'] = ['0.108', '-0.04', '0.043', '0.065', '#joy', '#admiration', 'positive', '0.043', 'family', 'love_back', 'carry_baby', 'father', 'change_diaper']
senticnet['mother_brother'] = ['0', '0.986', '0', '0.745', '#interest', '#admiration', 'positive', '0.865', 'familial_relationship', 'family_relation', 'sibling', 'family_member', 'male_parent']
senticnet['mother_child'] = ['0', '0.763', '0', '0.918', '#interest', '#admiration', 'positive', '0.841', 'father_child', 'father_son', 'father_offspring', 'human_offspring', 'descendant']
senticnet['mother_daddy'] = ['0', '0.723', '0', '0.856', '#interest', '#admiration', 'positive', '0.789', 'family_member', 'dad', 'daddy', 'brother', 'uncle']
senticnet['mother_drovski'] = ['-0.93', '0', '0.791', '0', '#sadness', '#anger', 'negative', '-0.86', 'hopelessness', 'hopelessly', 'despairing', 'pessimistically', 'pessimistic']
senticnet['mother_father'] = ['0', '0.935', '0', '0.798', '#interest', '#admiration', 'positive', '0.867', 'father_mother', 'mom_dad', 'parent_child', 'brother_sister', 'granddaughter']
senticnet['mother_fucker'] = ['-0.66', '0', '0.595', '-0.83', '#sadness', '#disgust', 'negative', '-0.7', 'bastard', 'dickhead', 'disagreeable_person', 'motherfucker', 'son_of_bitch']
senticnet['mother_goose'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'mythical', 'tale', 'dictionary', 'magic', 'perfect_world']
senticnet['mother_hen'] = ['0.568', '-0.79', '0', '0.804', '#surprise', '#admiration', 'positive', '0.723', 'age', 'get_on', 'senesce', 'mature', 'maturate']
senticnet['mother_lode'] = ['0.614', '-0.95', '0', '0.584', '#joy', '#surprise', 'positive', '0.718', 'lode', 'champion_lode', 'deposit', 'sedimentation', 'alluviation']
senticnet['mother_wit'] = ['0.639', '-0.56', '0', '0.622', '#joy', '#admiration', 'positive', '0.608', 'road_sense', 'sagacity', 'sagaciousness', 'logic', 'nous']
senticnet['mother_womb'] = ['0.706', '0', '0', '0', '#joy', '#joy', 'positive', '0.706', 'birth', 'new_baby', 'born', 'birthday', 'thanksgiving']
senticnet['motherboard'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'computer_part', 'system', 'cpu', 'pencil_pen', 'monitor']
senticnet['motherfucker'] = ['-0.35', '-0.13', '-0.05', '-0.38', '#sadness', '#disgust', 'negative', '-0.16', 'bastard', 'idiot', 'asshole', 'fuck', 'dick']
senticnet['motherfuckin'] = ['-0.86', '0', '0.675', '-0.84', '#sadness', '#disgust', 'negative', '-0.79', 'shenanigan', 'chicanery', 'wile', 'dupery', 'trickery']
senticnet['motherhood'] = ['0.1', '0.216', '-0.18', '0.22', '#interest', '#admiration', 'positive', '0.088', 'female', 'maternity_ward', 'maternity', 'woman', 'mother']
senticnet['motherland'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'patriotism', 'homeland']
senticnet['motherless'] = ['0', '0', '0.831', '-0.78', '#anger', '#disgust', 'negative', '-0.80', 'fatherless', 'destitute', 'widow', 'single-parent', 'friendless']
senticnet['motherliness'] = ['0.585', '-0.88', '0', '0.615', '#surprise', '#admiration', 'positive', '0.695', 'parental_quality', 'maternal_quality', 'quality', 'maternalism', 'fatherliness']
senticnet['motherly'] = ['0.801', '0', '0', '0.705', '#joy', '#admiration', 'positive', '0.753', 'caring', 'benevolent', 'benign', 'fatherly', 'warmer']
senticnet['motherly_love'] = ['0.585', '-0.88', '0', '0.615', '#surprise', '#admiration', 'positive', '0.695', 'parental_quality', 'maternal_quality', 'quality', 'maternalism', 'fatherliness']
senticnet['mothers_womb'] = ['0.159', '0.068', '-0.07', '0.118', '#joy', '#admiration', 'positive', '0.067', 'stomach', 'uterus', 'pregnant', 'lap', 'gravid']
senticnet['mothproof'] = ['-0.9', '0', '0.657', '-0.64', '#sadness', '#anger', 'negative', '-0.73', 'soundproof', 'imperviable', 'resistant', 'proof', 'greaseproof']
senticnet['motile'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'immobile', 'immotile']
senticnet['motilin'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'gastrointestinal_hormone', 'gastrin', 'cholecystokinin', 'secretin', 'ghrelin']
senticnet['motility'] = ['0.536', '-0.56', '0', '0.567', '#surprise', '#admiration', 'positive', '0.557', 'recreation', 'diversion', 'move', 'eurhythmics', 'movement']
senticnet['motion'] = ['0', '0.095', '-0.07', '0.237', '#interest', '#admiration', 'positive', '0.087', 'movement', 'dance_move', 'move', 'manoeuvre', 'push']
senticnet['motion_blackberry'] = ['0.807', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.881', 'fruit', 'mobile_phone', 'drinkable', 'savory', 'eat_fruit']
senticnet['motion_blur'] = ['0.906', '-0.60', '0', '0.653', '#joy', '#admiration', 'positive', '0.72', 'opaqueness', 'faintness', 'dimness', 'opacity', 'vagueness']
senticnet['motion_detector'] = ['0', '-0.03', '0.043', '-0.11', '#anger', '#disgust', 'negative', '-0.03', 'torch', 'safety_pin', 'laser', 'flare', 'firebomb']
senticnet['motion_ltd'] = ['0.807', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.881', 'fruit', 'mobile_phone', 'drinkable', 'savory', 'eat_fruit']
senticnet['motion_picture'] = ['0', '-0.03', '-0.09', '-0.05', '#fear', '#disgust', 'negative', '-0.03', 'play_in_theater', 'film', 'fake_tooth', 'tooth', 'movie']
senticnet['motion_picture_adaptation'] = ['-0.06', '0.122', '-0.04', '0.113', '#interest', '#admiration', 'positive', '0.032', 'encounter', 'portray', 'reveal', 'episode', 'next_episode']
senticnet['motion_rim'] = ['0.807', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.881', 'fruit', 'mobile_phone', 'drinkable', 'savory', 'eat_fruit']
senticnet['motion_sensor'] = ['0', '-0.03', '0.043', '-0.11', '#anger', '#disgust', 'negative', '-0.03', 'torch', 'safety_pin', 'laser', 'flare', 'firebomb']
senticnet['motion_sickness'] = ['0', '0', '-0.94', '-0.76', '#fear', '#disgust', 'negative', '-0.85', 'get_heard', 'ignore_distraction', 'get_fight', 'loss_ofmemory', 'break_leg']
senticnet['motionless'] = ['-0.82', '0', '0.99', '-0.87', '#anger', '#disgust', 'negative', '-0.89', 'nonmoving', 'rigid', 'inactive', 'still', 'static']
senticnet['motivate'] = ['0.997', '0.748', '0', '0.969', '#joy', '#admiration', 'positive', '0.905', 'car_action', 'steer_car', 'passenger_car', 'cheer_up', 'control_car']
senticnet['motivate_others'] = ['0.108', '0.176', '-0.06', '0.133', '#interest', '#admiration', 'positive', '0.089', 'win_marathon', 'support_cause', 'prove_fitness', 'enjoy_competition', 'earn_a_lot_of_money']
senticnet['motivating'] = ['0.795', '0.774', '0', '0', '#joy', '#interest', 'positive', '0.784', 'child_book', 'interesting_story', 'good_story', 'scholar', 'nice_ending']
senticnet['motivation'] = ['0.851', '0', '0', '0.957', '#joy', '#admiration', 'positive', '0.904', 'effort', 'go_meet', 'make_time', 'ambition', 'preparation']
senticnet['motivational'] = ['0.347', '0.348', '-0.13', '0.394', '#interest', '#admiration', 'positive', '0.239', 'good_deed', 'benefit', 'gratification', 'self_respect', 'bonus']
senticnet['motivational_enhancement'] = ['0.542', '-0.63', '0', '0.912', '#surprise', '#admiration', 'positive', '0.695', 'implosion_therapy', 'flooding', 'reciprocal_inhibition', 'aversion_therapy', 'desensitization_technique']
senticnet['motivative'] = ['0.59', '-0.59', '0', '0.527', '#joy', '#surprise', 'positive', '0.572', 'responsible', 'inducive', 'activating', 'tributary', 'causative']
senticnet['motivator'] = ['0.674', '-0.78', '0', '0.793', '#surprise', '#admiration', 'positive', '0.751', 'rational_motive', 'incentive', 'inducement', 'motivation', 'need']
senticnet['motive'] = ['-0.05', '0.27', '-0.16', '0.245', '#interest', '#admiration', 'positive', '0.075', 'purpose', 'reason', 'explanation', 'forethought', 'question_start']
senticnet['motive_power'] = ['0.069', '0.097', '0', '0.172', '#interest', '#admiration', 'positive', '0.113', 'type_of_engine', 'liquid_gas', 'warm_air', 'hot_air', 'steamy']
senticnet['motivic'] = ['0.045', '0', '0.033', '0.089', '#joy', '#admiration', 'positive', '0.034', 'spanish', 'vocalization', 'verbal_communication', 'vocal', 'mouth_noise']
senticnet['motley'] = ['-0.62', '0', '0.988', '-0.59', '#sadness', '#anger', 'negative', '-0.73', 'alphabet_soup', 'mixture', 'variety', 'assortment', 'mixed_bag']
senticnet['motley_assortment'] = ['-0.87', '0', '0.682', '-0.91', '#sadness', '#disgust', 'negative', '-0.82', 'dump', 'garbage_dump', 'trash_dump', 'rubbish_dump', 'wasteyard']
senticnet['motley_crew'] = ['-0.72', '0.304', '-0.41', '-0.07', '#sadness', '#fear', 'negative', '-0.22', 'suffering', 'miserably', 'pitiable', 'piteous', 'pitiful']
senticnet['motocyele_race'] = ['0.856', '0.928', '0', '0.828', '#joy', '#interest', 'positive', '0.871', 'champion', 'competition', 'win_race', 'professional', 'award']
senticnet['motor'] = ['0.67', '-0.91', '0', '0.805', '#surprise', '#admiration', 'positive', '0.796', 'electric_motor', 'engine', 'stepper', 'stepping_motor', 'machine']
senticnet['motor_bike'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'fuel', 'car', 'fossil_fuel', 'transport', 'natural_resource']
senticnet['motor_boat'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'boat', 'yacht', 'big_boat', 'large_boat']
senticnet['motor_car'] = ['-0.04', '-0.16', '0', '0.056', '#surprise', '#admiration', 'positive', '0.059', 'car_part', 'under_car', 'car_wheel', 'wheel', 'car']
senticnet['motor_carrier'] = ['0', '-0.01', '0', '0.064', '#surprise', '#admiration', 'positive', '0.042', 'car_gas', 'car_power', 'fossil_fuel', 'run_car', 'car_engine']
senticnet['motor_coordination'] = ['0.874', '-0.73', '0', '0.608', '#joy', '#surprise', 'positive', '0.74', 'growth', 'growing', 'maturation', 'development', 'ontogeny']
senticnet['motor_corporation'] = ['-0.04', '-0.16', '0', '0.056', '#surprise', '#admiration', 'positive', '0.059', 'car_part', 'under_car', 'car_wheel', 'wheel', 'car']
senticnet['motor_cortex'] = ['0', '0.131', '-0.08', '0.239', '#interest', '#admiration', 'positive', '0.096', 'human_sense', 'ear_activity', 'vibration', 'five_senses', 'sensor']
senticnet['motor_dysfunction'] = ['-0.78', '0', '0.764', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'withering', 'downfall', 'weakening', 'fading', 'attenuation']
senticnet['motor_function'] = ['-0.78', '0', '0.669', '-0.89', '#sadness', '#disgust', 'negative', '-0.78', 'pain_sensation', 'painful_sensation', 'prickling', 'tingling', 'pain']
senticnet['motor_generator'] = ['0', '0', '-1', '-1', '#fear', '#disgust', 'negative', '-1.0', 'moon_gravity', 'force', 'coercion', 'watt', 'power_source']
senticnet['motor_impairment'] = ['0.874', '-0.73', '0', '0.608', '#joy', '#surprise', 'positive', '0.74', 'growth', 'growing', 'maturation', 'development', 'ontogeny']
senticnet['motor_inn'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'beachcomber', 'bum', 'drifter']
senticnet['motor_innervation'] = ['-0.41', '0', '-0.28', '-0.05', '#sadness', '#fear', 'negative', '-0.25', 'numbness', 'carpal_tunnel_syndrome', 'weakness', 'pain', 'muscle_damage']
senticnet['motor_lifeboat'] = ['0', '-0.02', '0.046', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'sail_ship', 'canoe', 'ship', 'submarine', 'yacht']
senticnet['motor_neuron_disease'] = ['-0.95', '0', '0.621', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'disease', 'brain_disorder', 'brain_disease', 'ill', 'epilepsy']
senticnet['motor_neurone_disease'] = ['-0.95', '0', '0.621', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'disease', 'brain_disorder', 'brain_disease', 'ill', 'epilepsy']
senticnet['motor_neurone_disease_mnd'] = ['-0.95', '0', '0.621', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'disease', 'brain_disorder', 'brain_disease', 'ill', 'epilepsy']
senticnet['motor_oil'] = ['0', '-0.07', '0', '0', '#surprise', '#surprise', 'positive', '0.075', 'paint', 'internal_combustion_engine', 'trunk', 'machine', 'engine']
senticnet['motor_scooter'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'fuel', 'car', 'fossil_fuel', 'transport', 'natural_resource']
senticnet['motor_vehicle'] = ['0', '-0.01', '0', '0.064', '#surprise', '#admiration', 'positive', '0.042', 'car_gas', 'car_power', 'fossil_fuel', 'run_car', 'car_engine']
senticnet['motor_vehicle_accident'] = ['-0.92', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.93', 'serious_accident', 'accident', 'hit_car', 'loss_ofawareness', 'bang_head']
senticnet['motor_vehicle_crash'] = ['-0.71', '0', '0.748', '-0.55', '#sadness', '#anger', 'negative', '-0.67', 'casualty', 'fatal_accident', 'killing', 'violent_death', 'death']
senticnet['motor_vehicle_dealer'] = ['0', '-0.01', '0', '0.064', '#surprise', '#admiration', 'positive', '0.042', 'car_gas', 'car_power', 'fossil_fuel', 'run_car', 'car_engine']
senticnet['motor_vehicle_dmv'] = ['0', '-0.01', '0', '0.064', '#surprise', '#admiration', 'positive', '0.042', 'car_gas', 'car_power', 'fossil_fuel', 'run_car', 'car_engine']
senticnet['motor_vehicle_exhaust'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'rodent', 'tree_animal']
senticnet['motor_vehicle_operator'] = ['0', '-0.01', '0', '0.064', '#surprise', '#admiration', 'positive', '0.042', 'car_gas', 'car_power', 'fossil_fuel', 'run_car', 'car_engine']
senticnet['motor_vehicle_registration'] = ['0', '-0.01', '0', '0.064', '#surprise', '#admiration', 'positive', '0.042', 'car_gas', 'car_power', 'fossil_fuel', 'run_car', 'car_engine']
senticnet['motor_vehicle_traffic'] = ['-0.98', '0', '0.989', '-0.93', '#sadness', '#anger', 'negative', '-0.97', 'flight_accident', 'dangerous', 'danger', 'air_traffic', 'air_traffic_jam']
senticnet['motor_yacht'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'boat', 'yacht', 'big_boat', 'large_boat']
senticnet['motorbike'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'fuel', 'car', 'fossil_fuel', 'transport', 'natural_resource']
senticnet['motorbike_accident'] = ['-0.95', '0', '0.046', '-0.97', '#sadness', '#disgust', 'negative', '-0.65', 'fall_down', 'lose_consciousness', 'car_accident', 'have_accident', 'accident']
senticnet['motorbike_crash'] = ['-0.73', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.79', 'die_painfully', 'arrest_police', 'get_accident', 'fall_head', 'skin_knee']
senticnet['motorbike_rider'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'fuel', 'car', 'fossil_fuel', 'transport', 'natural_resource']
senticnet['motorboat'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'boat', 'yacht', 'big_boat', 'large_boat']
senticnet['motorcade'] = ['0', '0', '-0.04', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'good_band', 'band', 'kit', 'procession', 'oblivion']
senticnet['motorcade_route'] = ['0', '0', '-0.04', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'good_band', 'band', 'kit', 'procession', 'oblivion']
senticnet['motorcar'] = ['-0.51', '0', '0.552', '-0.65', '#anger', '#disgust', 'negative', '-0.57', 'car', 'auto', 'automobile', 'machine', 'gas_guzzler']
senticnet['motorcoach'] = ['0.069', '0.07', '0', '0.116', '#interest', '#admiration', 'positive', '0.085', 'traveller', 'tourist', 'travel', 'explorer', 'adventurer']
senticnet['motorcycle'] = ['-0.06', '0.122', '-0.04', '0.113', '#interest', '#admiration', 'positive', '0.032', 'taxi', 'bus', 'tram', 'airplane', 'plane']
senticnet['motorcycle_accident'] = ['-0.92', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.93', 'serious_accident', 'accident', 'hit_car', 'loss_ofawareness', 'bang_head']
senticnet['motorcycle_crash'] = ['-0.92', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.93', 'serious_accident', 'accident', 'hit_car', 'loss_ofawareness', 'bang_head']
senticnet['motorcycle_escort'] = ['0', '0', '-0.04', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'good_band', 'band', 'kit', 'procession', 'oblivion']
senticnet['motorcycle_gang'] = ['0.867', '-0.84', '0', '0.876', '#joy', '#admiration', 'positive', '0.863', 'meet', 'team', 'team_up', 'embed', 'aggroup']
senticnet['motorcycle_jacket'] = ['-0.54', '0', '0.612', '-0.98', '#anger', '#disgust', 'negative', '-0.71', 'scuff', 'mule', 'bootee', 'carpet_slipper', 'footwear']
senticnet['motorcycle_rider'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'fuel', 'car', 'fossil_fuel', 'transport', 'natural_resource']
senticnet['motorcycle_shop'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'sell_car', 'truck_stop', 'vehicle', 'insurance', 'car']
senticnet['motorcycle_stunt'] = ['0.086', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'carny', 'fortune_teller', 'midway', 'ferris_wheel', 'roller-coaster']
senticnet['motorcyclist'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'fuel', 'car', 'fossil_fuel', 'transport', 'natural_resource']
senticnet['motorcyle'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'fuel', 'car', 'fossil_fuel', 'transport', 'natural_resource']
senticnet['motorist'] = ['-0.10', '-0.04', '0', '-0.14', '#sadness', '#disgust', 'negative', '-0.06', 'adjust_seat', 'angry_behavior', 'fasten_seatbelt', 'traffic_jam', 'skid_mark']
senticnet['motorized_transport'] = ['-0.04', '-0.16', '0', '0.056', '#surprise', '#admiration', 'positive', '0.059', 'car_part', 'under_car', 'car_wheel', 'wheel', 'car']
senticnet['motorized_treadmill'] = ['0.094', '0', '0', '0', '#joy', '#joy', 'positive', '0.094', 'hooker', 'registry_office', 'restaurant_entrance', 'elevator', 'exit']
senticnet['motorized_vehicle'] = ['0', '-0.01', '0', '0.064', '#surprise', '#admiration', 'positive', '0.042', 'car_gas', 'car_power', 'fossil_fuel', 'run_car', 'car_engine']
senticnet['motormouth'] = ['-0.83', '0', '0.776', '0', '#sadness', '#anger', 'negative', '-0.80', 'incessant', 'cease', 'ramble', 'incessantly', 'pause']
senticnet['motorola_droid'] = ['0.027', '-0.09', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'cookie', 'pudding', 'custard', 'candy', 'muffin']
senticnet['motortruck'] = ['0.65', '-0.63', '0', '0.629', '#joy', '#surprise', 'positive', '0.638', 'camion', 'rig', 'wrecker', 'dump_truck', 'dumper']
senticnet['motorwagen'] = ['-0.04', '-0.16', '0', '0.056', '#surprise', '#admiration', 'positive', '0.059', 'car_part', 'under_car', 'car_wheel', 'wheel', 'car']
senticnet['motorway'] = ['0.559', '-0.98', '0', '0.501', '#joy', '#surprise', 'positive', '0.681', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['motorway_exit'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'expressway', 'turnpike', 'toll_road', 'highway']
senticnet['motorway_interchange'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'artery', 'skeletal_system', 'groin', 'neck', 'muscle']
senticnet['mottled_appearance'] = ['0.352', '0.142', '0', '0.281', '#joy', '#admiration', 'positive', '0.258', 'knowledgeable', 'complement', 'much_need', 'see_ocean', 'good_sense_of_humor']
senticnet['mottled_gray'] = ['-0.60', '0', '0.895', '-0.54', '#sadness', '#anger', 'negative', '-0.68', 'coal_black', 'black', 'pitch_black', 'soot_black', 'achromatic_color']
senticnet['mottled_pattern'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'helpless', 'unwanted', 'rabies', 'small_cat', 'big_rat']
senticnet['motto'] = ['0.881', '0.974', '0', '0', '#joy', '#interest', 'positive', '0.927', 'catchword', 'slogan', 'shibboleth', 'think', 'people']
senticnet['moue'] = ['-0.83', '0', '0.87', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'precipitate', 'rain_down', 'shower_down', 'spit', 'patter']
senticnet['mouflon'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheep', 'argal', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['mould'] = ['-0.07', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.07', 'paint', 'use_tool', 'cartoon_character', 'use_oil_paint', 'buy_paint']
senticnet['mouldboard'] = ['-0.99', '0', '0.827', '-0.54', '#sadness', '#anger', 'negative', '-0.78', 'treat', 'cover', 'handle', 'theologize', 'deal']
senticnet['moulded_plastic'] = ['0.087', '0.113', '-0.08', '0', '#joy', '#interest', 'positive', '0.038', 'find_match', 'strike_against', 'cigarette_lighter', 'match', 'use_lighter']
senticnet['mouldy'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'poison', 'rotten', 'disgusting', 'taste_awful', 'green_bread']
senticnet['mouldy_bread'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'green_bread', 'rotten', 'disgusting', 'taste_awful', 'mouldy']
senticnet['mouldy_food'] = ['0', '0', '0.862', '-0.79', '#anger', '#disgust', 'negative', '-0.82', 'social_insect', 'big_mouse', 'rodent', 'small_animal', 'dig_tunnel']
senticnet['mound'] = ['-0.77', '0', '0.588', '-0.80', '#sadness', '#disgust', 'negative', '-0.72', 'heap', 'agglomerate', 'piles', 'funeral_pyre', 'cumulation']
senticnet['mounir'] = ['0.798', '-0.51', '0', '0.973', '#joy', '#admiration', 'positive', '0.763', 'inexperienced_person', 'boy_scout', 'dear', 'virgin', 'mortal']
senticnet['mount'] = ['0.799', '0', '0', '0.747', '#joy', '#admiration', 'positive', '0.773', 'strengthen', 'enlarge', 'aggrandize', 'heighten', 'increase']
senticnet['mount_edgcumbe'] = ['0.582', '-0.97', '0', '0.802', '#surprise', '#admiration', 'positive', '0.788', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['mount_ever'] = ['0.032', '0.198', '0', '0.104', '#interest', '#admiration', 'positive', '0.111', 'nowhere', 'utopia', 'international_space_station', 'limbo', 'emporium']
senticnet['mount_everest'] = ['0', '0.076', '0', '0.063', '#interest', '#admiration', 'positive', '0.07', 'up_hill', 'elevation', 'up_mountain', 'ascend', 'upwards']
senticnet['mount_everest_expedition'] = ['0', '0.076', '0', '0.063', '#interest', '#admiration', 'positive', '0.07', 'up_hill', 'elevation', 'up_mountain', 'ascend', 'upwards']
senticnet['mount_howitt'] = ['-0.95', '0', '0.624', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['mount_lofty'] = ['-0.95', '0', '0.624', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['mount_sharp'] = ['0.052', '0.119', '0', '0.141', '#interest', '#admiration', 'positive', '0.104', 'neigh', 'domestic_animal', 'animal_type', 'animal', 'home_animal']
senticnet['mount_zion_cemetery'] = ['-0.50', '0', '0.762', '-0.89', '#anger', '#disgust', 'negative', '-0.71', 'cemetery', 'graveyard', 'burial_site', 'grassland', 'burying_ground']
senticnet['mountain'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'find_mountain', 'find', 'enjoy_nature', 'beautiful_view', 'enjoy_view']
senticnet['mountain_caribou'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'cheetah', 'giraffe', 'kangaroo', 'mule', 'horse']
senticnet['mountain_climb'] = ['0', '0.166', '-0.13', '0.275', '#interest', '#admiration', 'positive', '0.102', 'climb_mountain', 'climb', 'chop_down_tree', 'reflect_water', 'store_garage']
senticnet['mountain_climb_mountain'] = ['0', '0.157', '-0.14', '0.271', '#interest', '#admiration', 'positive', '0.095', 'climb', 'climb_mountain', 'chop_down_tree', 'reflect_water', 'store_garage']
senticnet['mountain_climber'] = ['0.109', '0.129', '0', '0.098', '#joy', '#interest', 'positive', '0.112', 'good_view', 'summit', 'great_view', 'guru', 'powerful_man']
senticnet['mountain_cougar'] = ['0', '-0.97', '0', '0', '#surprise', '#surprise', 'positive', '0.979', 'lion', 'skunk', 'cougar', 'tiger', 'lynx']
senticnet['mountain_dew'] = ['0.714', '0.763', '0', '0', '#joy', '#interest', 'positive', '0.738', 'lemonade', 'soda', 'sprite', 'coke', 'cola']
senticnet['mountain_goat'] = ['0', '0.102', '0', '0', '#interest', '#interest', 'positive', '0.102', 'frostbite', 'eskimo', 'weather_event', 'glacier', 'coldness']
senticnet['mountain_hare'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'argali', 'argal', 'urial', 'white_sheep', 'wild_sheep']
senticnet['mountain_hemlock'] = ['0.045', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.356', 'ponderosa', 'bull_pine', 'western_yellow_pine', 'yellow_pine', 'longleaf_pine']
senticnet['mountain_hideaway'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'lurking_place', 'spot', 'drop', 'hiding_place', 'den']
senticnet['mountain_laurel'] = ['0.958', '-0.82', '0', '0.924', '#joy', '#admiration', 'positive', '0.901', 'bunchberry', 'dwarf_cornel', 'crackerberry', 'pudding_berry', 'common_white_dogwood']
senticnet['mountain_lion'] = ['0', '-0.97', '0', '0', '#surprise', '#surprise', 'positive', '0.977', 'cougar', 'skunk', 'lion', 'tiger', 'lynx']
senticnet['mountain_mahogany'] = ['0.773', '-0.55', '0', '0.949', '#joy', '#admiration', 'positive', '0.757', 'cornel', 'dwarf_cornel', 'crackerberry', 'pudding_berry', 'common_white_dogwood']
senticnet['mountain_massif'] = ['0', '0.08', '0.032', '0.096', '#interest', '#admiration', 'positive', '0.048', 'land_form', 'land_formation', 'land_feature', 'geographic_feature', 'landscape_feature']
senticnet['mountain_pine_beetle'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['mountain_plover'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'fox', 'prairie_fox', 'white_fox']
senticnet['mountain_quail'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['mountain_range'] = ['0', '0.08', '0.032', '0.096', '#interest', '#admiration', 'positive', '0.048', 'land_form', 'land_formation', 'land_feature', 'geographic_feature', 'landscape_feature']
senticnet['mountain_scenery'] = ['0.896', '0', '0', '0.709', '#joy', '#admiration', 'positive', '0.802', 'perspective', 'vista', 'view', 'aspect', 'see_view']
senticnet['mountain_sheep'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'argali', 'argal', 'urial', 'white_sheep', 'wild_sheep']
senticnet['mountain_side'] = ['0.958', '0.964', '0', '0', '#joy', '#interest', 'positive', '0.961', 'incline_plane', 'angle', 'facet', 'apex', 'height']
senticnet['mountain_top'] = ['0', '0.077', '0', '0', '#interest', '#interest', 'positive', '0.077', 'middle_road', 'expedition', 'mountain', 'limbo', 'death_row']
senticnet['mountain_watercress'] = ['0.758', '-0.97', '0', '0.851', '#surprise', '#admiration', 'positive', '0.861', 'marsh_cress', 'yellow_watercress', 'watercress', 'common_watercress', 'great_yellowcress']
senticnet['mountaineer'] = ['0', '0.076', '0', '0.063', '#interest', '#admiration', 'positive', '0.07', 'up_hill', 'elevation', 'up_mountain', 'ascend', 'upwards']
senticnet['mountaineer_card'] = ['0.739', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.814', 'receipt', 'bet', 'electric_bill', 'letter_slot', 'deal']
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senticnet['mouth_part'] = ['0.037', '0.076', '0', '0.091', '#interest', '#admiration', 'positive', '0.068', 'face_part', 'facial_feature', 'facial_part', 'facial_organ', 'near_mouth']
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senticnet['mouth_watering'] = ['0.036', '0', '0.036', '0.102', '#joy', '#admiration', 'positive', '0.034', 'good_eat', 'delicious', 'yummy', 'tasty', 'edible']
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senticnet['movable_bridge'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'suspension_bridge', 'under_bridge', 'roadway', 'county_highway', 'long_road']
senticnet['movable_pulley'] = ['-0.73', '0', '0.55', '-0.75', '#sadness', '#disgust', 'negative', '-0.68', 'mortal', 'person', 'individual', 'mangler', 'mutilator']
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senticnet['move_between_floor_building'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'walk_up', 'stroll', 'go_upstairs', 'go_up_down', 'climb_up']
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senticnet['move_car'] = ['0.709', '0', '0', '0.989', '#joy', '#admiration', 'positive', '0.849', 'out_car', 'avoid_fall', 'drive_car', 'take_car_drive', 'go_drive']
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senticnet['move_up'] = ['0.032', '0.159', '-0.05', '0.188', '#interest', '#admiration', 'positive', '0.082', 'ascend', 'move_upward', 'go_up', 'move_upwards', 'go_high']
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senticnet['much_need'] = ['0.345', '0.138', '0', '0.273', '#joy', '#admiration', 'positive', '0.252', 'knowledgeable', 'complement', 'smooth_skin', 'see_ocean', 'good_sense_of_humor']
senticnet['much_nicer'] = ['0.147', '0.126', '-0.09', '0.314', '#joy', '#admiration', 'positive', '0.123', 'lovely', 'niceness', 'good', 'pleasant', 'capitalize']
senticnet['much_pleasanter'] = ['-0.44', '-0.15', '0', '-0.36', '#sadness', '#disgust', 'negative', '-0.21', 'baffle', 'stupefy', 'dumbfound', 'stupefaction', 'confuse']
senticnet['much_prettier'] = ['0.824', '0.86', '0', '0.983', '#interest', '#admiration', 'positive', '0.889', 'look_smart', 'social_acceptance', 'improve_appearance', 'improve_look', 'look_good']
senticnet['much_quicker'] = ['0.729', '0.974', '0', '0', '#joy', '#interest', 'positive', '0.851', 'travel', 'fast', 'travel_fast', 'take_airplane', 'drive_fast']
senticnet['much_richer'] = ['0.843', '0.977', '0', '0', '#joy', '#interest', 'positive', '0.91', 'business', 'finance', 'revenue', 'wealth', 'income']
senticnet['much_shorter'] = ['0', '0', '-0.78', '-0.71', '#fear', '#disgust', 'negative', '-0.74', 'unretentive', 'poor', 'inadequate', 'little', 'forgetful']
senticnet['much_simpler'] = ['-0.04', '0.172', '-0.05', '0.057', '#interest', '#admiration', 'positive', '0.034', 'carry_money', 'put_money', 'require_signature', 'store_money', 'keep_money']
senticnet['much_slower'] = ['0.729', '0.974', '0', '0', '#joy', '#interest', 'positive', '0.851', 'travel', 'fast', 'travel_fast', 'take_airplane', 'drive_fast']
senticnet['much_smaller'] = ['-0.05', '0', '-0.12', '-0.16', '#fear', '#disgust', 'negative', '-0.11', 'reduced', 'tiny', 'small_size', 'narrow', 'poor']
senticnet['much_sun'] = ['-0.98', '0', '0.685', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'burn_skin', 'ultraviolet', 'erythema', 'skin_cancer', 'pain']
senticnet['much_thinner'] = ['0.17', '0', '0.081', '0.184', '#joy', '#admiration', 'positive', '0.091', 'food_holder', 'food_server', 'crockery', 'flattery', 'round_flat']
senticnet['much_water'] = ['-0.05', '0.068', '0', '0.102', '#interest', '#admiration', 'positive', '0.038', 'small_ocean', 'water_mass', 'water_molecule', 'water_body', 'water_feature']
senticnet['much_work'] = ['-0.10', '-0.01', '0', '-0.17', '#sadness', '#disgust', 'negative', '-0.08', 'stress', 'quit_job', 'hate_boss', 'hate_colleague', 'need_holiday']
senticnet['much_worse'] = ['-0.93', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.96', 'dire', 'fearsome', 'dreaded', 'direful', 'frightening']
senticnet['muchamore'] = ['0.026', '0.18', '-0.11', '0.18', '#interest', '#admiration', 'positive', '0.069', 'deity', 'god', 'angel', 'goddess', 'rabbi']
senticnet['mucilage'] = ['-0.57', '0', '0.767', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'verboseness', 'verbosity', 'prolixity', 'turgidness', 'flatulence']
senticnet['mucilaginous'] = ['0', '0', '-0.76', '-0.88', '#fear', '#disgust', 'negative', '-0.82', 'gluey', 'pasty', 'sticky', 'gummy', 'glutinous']
senticnet['mucin'] = ['0', '0', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.05', 'jaw', 'mouth', 'dimple', 'nose', 'cheek']
senticnet['muck'] = ['-0.02', '-0.01', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.03', 'dung', 'manure', 'shit', 'dog_poop', 'crap']
senticnet['muck_up'] = ['-0.97', '0', '0.922', '-0.53', '#sadness', '#anger', 'negative', '-0.80', 'muddy_up', 'begrime', 'foul', 'blemish', 'smear']
senticnet['muckheap'] = ['-0.51', '0', '0.883', '-0.85', '#anger', '#disgust', 'negative', '-0.75', 'heap', 'agglomerate', 'piles', 'mound', 'cumulation']
senticnet['muckhill'] = ['-0.54', '0', '0.502', '-0.66', '#sadness', '#disgust', 'negative', '-0.57', 'heap', 'agglomerate', 'piles', 'mound', 'cumulation']
senticnet['mucking_up'] = ['-0.78', '0', '0.915', '-0.99', '#anger', '#disgust', 'negative', '-0.89', 'bodge', 'botch_up', 'flub', 'ball_up', 'go_wrong']
senticnet['muckle'] = ['0.027', '0', '0.012', '0.063', '#joy', '#admiration', 'positive', '0.026', 'vegetation', 'tree_flower', 'green_growth', 'small', 'greenery']
senticnet['mucky'] = ['0', '0', '-0.97', '-0.78', '#fear', '#disgust', 'negative', '-0.88', 'marshy', 'soggy', 'muddy', 'boggy', 'miry']
senticnet['mucocele'] = ['-0.52', '0', '0.538', '-0.95', '#anger', '#disgust', 'negative', '-0.67', 'sanies', 'pus', 'purulence', 'gleet', 'ichor']
senticnet['mucoid'] = ['0', '0', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.05', 'jaw', 'mouth', 'dimple', 'nose', 'cheek']
senticnet['mucopurulent'] = ['-0.52', '0', '0.538', '-0.95', '#anger', '#disgust', 'negative', '-0.67', 'sanies', 'pus', 'purulence', 'gleet', 'ichor']
senticnet['mucosa'] = ['0', '0', '0.867', '-0.99', '#anger', '#disgust', 'negative', '-0.93', 'mucus', 'secretion', 'mucous', 'membrane', 'irritation']
senticnet['mucosal_irritation'] = ['-0.95', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.89', 'chronic_gastritis', 'rubor', 'redness', 'gastritis', 'inflammation']
senticnet['mucosal_surface'] = ['-0.52', '0', '0.538', '-0.95', '#anger', '#disgust', 'negative', '-0.67', 'sanies', 'pus', 'purulence', 'gleet', 'ichor']
senticnet['mucosectomy'] = ['-0.70', '0', '0.639', '-0.56', '#sadness', '#anger', 'negative', '-0.63', 'fibroma', 'adenoma', 'chondroma', 'enchondroma', 'benign_tumor']
senticnet['mucous'] = ['0', '0', '0.874', '-0.98', '#anger', '#disgust', 'negative', '-0.93', 'mucus', 'secretion', 'mucosa', 'membrane', 'irritation']
senticnet['mucous_membrane_exposure'] = ['-0.74', '0', '0.766', '0', '#sadness', '#anger', 'negative', '-0.75', 'use_needle', 'clothes_repair', 'woven', 'embroidery', 'thread_needle']
senticnet['mucous_substance'] = ['0', '0', '0', '-0.96', '#disgust', '#disgust', 'negative', '-0.96', 'thick_substance', 'viscous_substance', 'phlegm', 'disgusting', 'disgust']
senticnet['mucronate'] = ['-0.75', '0', '0.734', '-0.82', '#sadness', '#disgust', 'negative', '-0.77', 'puberulent', 'haired', 'furlike', 'pappose', 'pilous']
senticnet['mucus'] = ['0', '0', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.05', 'jaw', 'mouth', 'dimple', 'nose', 'cheek']
senticnet['mucus_layer'] = ['0', '0', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.05', 'jaw', 'mouth', 'dimple', 'nose', 'cheek']
senticnet['mucus_production'] = ['0', '0', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.05', 'jaw', 'mouth', 'dimple', 'nose', 'cheek']
senticnet['mucus_secreted_by'] = ['0', '0', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.05', 'jaw', 'mouth', 'dimple', 'nose', 'cheek']
senticnet['mud'] = ['-0.71', '0', '0.704', '-0.89', '#sadness', '#disgust', 'negative', '-0.76', 'soil', 'mire', 'dirt', 'bleaching_clay', 'bleaching_earth']
senticnet['mud-brick'] = ['-0.80', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.81', 'windowless', 'dilapidated', 'roofless', 'multi-story', 'ramshackle']
senticnet['mud_flat'] = ['-0.87', '0', '0.875', '-0.92', '#anger', '#disgust', 'negative', '-0.88', 'tract', 'piece_of_land', 'piece_of_ground', 'parcel_of_land', 'parcel']
senticnet['mud_hut'] = ['-0.03', '-0.00', '0.066', '0', '#sadness', '#anger', 'negative', '-0.03', 'dirt', 'allergy', 'dust', 'wet_floor', 'insect']
senticnet['mud_mortar'] = ['0', '0', '-0.95', '-0.72', '#fear', '#disgust', 'negative', '-0.83', 'cannon', 'explosive', 'firearm', 'pistol', 'machine_gun']
senticnet['mud_pit'] = ['-0.71', '0', '0.704', '-0.89', '#sadness', '#disgust', 'negative', '-0.76', 'soil', 'mire', 'dirt', 'bleaching_clay', 'bleaching_earth']
senticnet['mud_stain'] = ['-0.51', '0', '0.937', '-0.92', '#anger', '#disgust', 'negative', '-0.79', 'bloodstain', 'discoloration', 'iron_mold', 'iron_mould', 'oil_stain']
senticnet['mudd_club'] = ['0.763', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.75', 'pub', 'disco', 'dance_club', 'cafe', 'tavern']
senticnet['mudd_library'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'a_lot_of_books', 'old_testament', 'cook_book', 'magazine', 'librarian']
senticnet['muddied'] = ['-0.82', '0', '0.664', '-0.87', '#sadness', '#disgust', 'negative', '-0.78', 'dirty', 'dingy', 'impure', 'alloyed', 'adulterate']
senticnet['muddiness'] = ['-0.50', '0', '0.85', '-0.82', '#anger', '#disgust', 'negative', '-0.72', 'confusion', 'disarray', 'mental_confusion', 'confusedness', 'bafflement']
senticnet['muddle'] = ['-0.78', '0', '0.854', '-0.9', '#anger', '#disgust', 'negative', '-0.84', 'muss', 'untidiness', 'mess', 'disorderliness', 'messiness']
senticnet['muddy'] = ['-0.93', '0', '0.897', '-0.92', '#sadness', '#disgust', 'negative', '-0.91', 'dirty', 'soil', 'begrime', 'grime', 'colly']
senticnet['muddy_bottom'] = ['-0.02', '-0.01', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.03', 'dung', 'manure', 'shit', 'dog_poop', 'crap']
senticnet['muddy_sand'] = ['-0.06', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.04', 'friction', 'lack_of_water', 'dirt', 'abrasive', 'ground_material']
senticnet['muddy_up'] = ['-0.96', '0', '0.646', '-0.62', '#sadness', '#anger', 'negative', '-0.74', 'dirty', 'soil', 'begrime', 'grime', 'colly']
senticnet['muddy_water'] = ['-0.02', '-0.01', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.03', 'dung', 'manure', 'shit', 'dog_poop', 'crap']
senticnet['mudflap'] = ['-0.84', '0', '0.96', '-0.91', '#anger', '#disgust', 'negative', '-0.90', 'housewrecker', 'housebreaker', 'knacker', 'laborer', 'manual_laborer']
senticnet['mudhif'] = ['0.846', '-0.66', '0', '0.941', '#joy', '#admiration', 'positive', '0.817', 'hut', 'hovel', 'hutch', 'shack', 'shanty']
senticnet['mudita'] = ['0.668', '-0.87', '0', '0.606', '#joy', '#surprise', 'positive', '0.715', 'adeptness', 'adroitness', 'deftness', 'quickness', 'expertness']
senticnet['mudkip'] = ['0.028', '0', '0.027', '0.101', '#joy', '#admiration', 'positive', '0.034', 'animal_dwell', 'bee_home', 'live_place', 'dwell_place', 'place_to_live']
senticnet['mudroom'] = ['0', '0.171', '-0.05', '0.102', '#interest', '#admiration', 'positive', '0.072', 'bed', 'mattress', 'bead', 'bed_cover', 'seduce']
senticnet['mudslide'] = ['0', '-0.03', '-0.10', '-0.07', '#fear', '#disgust', 'negative', '-0.04', 'autumn', 'winter', 'skier', 'snow_fall', 'volcano']
senticnet['mudslinger'] = ['-0.74', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.76', 'spatter', 'vilification', 'defamation', 'sprinkling', 'aspersion']
senticnet['muesli'] = ['0.547', '-0.96', '0', '0.89', '#surprise', '#admiration', 'positive', '0.799', 'pudding', 'ice_cream', 'chocolate', 'chocolate_pudding', 'chocolate_dessert']
senticnet['muezzin_call'] = ['0.023', '0.295', '-0.05', '0.283', '#interest', '#admiration', 'positive', '0.136', 'religion', 'religious_purpose', 'world_religion', 'gospel', 'judaism']
senticnet['muff'] = ['0.57', '-0.56', '0', '0.888', '#joy', '#admiration', 'positive', '0.675', 'wearable', 'wear', 'hand_wear', 'glove', 'clothing']
senticnet['muffin'] = ['0.048', '-0.08', '0', '0', '#joy', '#surprise', 'positive', '0.066', 'cookie', 'pudding', 'pie', 'candy', 'bagel']
senticnet['muffin_pan'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'neat', 'kitchen_drawer', 'cutlery_drawer', 'dine_table', 'table']
senticnet['muffin_top'] = ['-0.42', '-0.14', '-0.06', '-0.37', '#sadness', '#disgust', 'negative', '-0.18', 'brain_tumor', 'bad_hair_day', 'lyme_disease', 'diarrhoea', 'suffer_fool']
senticnet['muffle'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'surprise', 'suspense', 'bandage', 'blindfolded', 'blindfold']
senticnet['muffled'] = ['-0.72', '0', '0', '-0.96', '#sadness', '#disgust', 'negative', '-0.84', 'tinny', 'crackly', 'sounding', 'screechy', 'shrill']
senticnet['mug'] = ['0.214', '0.258', '-0.09', '0.2', '#joy', '#interest', 'positive', '0.146', 'cup', 'carafe', 'pitcher', 'container_cup', 'glasses']
senticnet['mugar_memorial_library'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'a_lot_of_books', 'old_testament', 'cook_book', 'magazine', 'librarian']
senticnet['mugaritz'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'artistic_creation', 'fabrication', 'carving', 'decoupage', 'ceramic']
senticnet['muggins'] = ['0.934', '-0.57', '0', '0.767', '#joy', '#admiration', 'positive', '0.76', 'morosoph', 'simpleton', 'flibbertigibbet', 'foolish_woman', 'simple']
senticnet['muggy'] = ['-0.92', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.83', 'drizzly', 'showery', 'blustery', 'squally', 'spring-like']
senticnet['mughal_architecture'] = ['-0.54', '0', '0.7', '-0.64', '#anger', '#disgust', 'negative', '-0.62', 'flourish', 'rant', 'bombast', 'fustian', 'claptrap']
senticnet['mughal_army'] = ['-0.93', '0', '0.722', '-0.66', '#sadness', '#anger', 'negative', '-0.77', 'penetration', 'air_raid', 'air_attack', 'swoop', 'incursion']
senticnet['mughlai'] = ['0.882', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.822', 'ferment_grape_juice', 'chef_special', 'bottle_wine', 'fine_dining', 'decanter']
senticnet['mugison'] = ['0.028', '0', '0.027', '0.101', '#joy', '#admiration', 'positive', '0.034', 'animal_dwell', 'bee_home', 'live_place', 'dwell_place', 'place_to_live']
senticnet['mugwort'] = ['-0.66', '0', '0.811', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'subshrub', 'suffrutex', 'old_woman', 'wormwood', 'southernwood']
senticnet['muhamet'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'white_animal', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
senticnet['muhlenbergia'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
senticnet['mukluk'] = ['0.706', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.806', 'shoe', 'flip_flop', 'slipper', 'casual_shoes', 'footwear']
senticnet['mukoko'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicative', 'unpronounceable', 'impassive', 'inexpressive', 'silent']
senticnet['muktinath'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['mulan'] = ['-0.63', '0', '0.674', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'baneful', 'evil', 'baleful', 'malevolent', 'malign']
senticnet['mulatto'] = ['0', '0', '0.8', '-0.82', '#anger', '#disgust', 'negative', '-0.81', 'personnel', 'attendant', 'manager', 'elect_official', 'gentleman']
senticnet['mulatto_slave'] = ['0', '0', '0.8', '-0.82', '#anger', '#disgust', 'negative', '-0.81', 'personnel', 'attendant', 'manager', 'elect_official', 'gentleman']
senticnet['mulberry_tree'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'black_locust']
senticnet['mulcahy'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
senticnet['mulch'] = ['-0.12', '0', '-0.10', '-0.03', '#sadness', '#fear', 'negative', '-0.08', 'dog_poop', 'picnic', 'hike_trail', 'detritus', 'wildlife']
senticnet['mulct'] = ['-0.08', '0', '-0.15', '-0.20', '#fear', '#disgust', 'negative', '-0.14', 'ask_money', 'extortion', 'wringing', 'intimidate', 'psychological_pressure']
senticnet['mule'] = ['0', '0', '0', '0.071', '#admiration', '#admiration', 'positive', '0.071', 'two_horses', 'horse', 'barb', 'buddhist', 'ride_horse']
senticnet['mule_train'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'common_bean', 'green_bean', 'wax_bean']
senticnet['muleback'] = ['0.064', '0', '0', '0.123', '#joy', '#admiration', 'positive', '0.093', 'horse', 'passenger_car', 'car_action', 'conveyance', 'car_travel']
senticnet['muleteer'] = ['0', '0', '0', '0.071', '#admiration', '#admiration', 'positive', '0.071', 'two_horses', 'horse', 'barb', 'buddhist', 'ride_horse']
senticnet['mulish'] = ['0', '0', '-0.90', '0', '#fear', '#fear', 'negative', '-0.90', 'obstinate', 'persistent', 'pigheaded', 'stubborn', 'headstrong']
senticnet['mulishness'] = ['-0.76', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.81', 'intractability', 'unruliness', 'fractiousness', 'willfulness', 'against_rule']
senticnet['mull_over'] = ['-0.94', '0', '0.866', '-0.92', '#sadness', '#disgust', 'negative', '-0.91', 'worry', 'anxiety', 'hate', 'bad_feeling', 'bad_memory']
senticnet['mullein'] = ['0.632', '-0.93', '0', '0.928', '#surprise', '#admiration', 'positive', '0.831', 'herb', 'oregano', 'marjoram', 'pot_marjoram', 'origanum']
senticnet['mullet'] = ['0.856', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.891', 'mullet', 'mackerel', 'goatee', 'catfish', 'flounder']
senticnet['mullet_roe'] = ['0.9', '0.963', '0', '0', '#joy', '#interest', 'positive', '0.931', 'form_of_food', 'japanese_food', 'uncooked', 'healthy', 'food']
senticnet['multi-national'] = ['-0.71', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.74', 'conglomerate', 'subsidiary', 'multinational', 'conglomerate', 'diversified']
senticnet['multi-story'] = ['-0.79', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.80', 'windowless', 'dilapidated', 'mud-brick', 'roofless', 'ramshackle']
senticnet['multi-story_build'] = ['-0.05', '0.116', '-0.04', '0.102', '#interest', '#admiration', 'positive', '0.029', 'house', 'dwelling', 'lease', 'rent_out', 'large_house']
senticnet['multi_agent'] = ['0', '0.717', '0', '0.765', '#interest', '#admiration', 'positive', '0.741', 'helper', 'artificial_intelligence', 'goal_driven', 'proactive', 'intelligent']
senticnet['multi_billion_dollar'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'profitable', 'moneymaking', 'gainful', 'paying', 'paid']
senticnet['multi_billionaire'] = ['0', '0', '-0.99', '-0.73', '#fear', '#disgust', 'negative', '-0.86', 'king', 'baron', 'business_leader', 'mogul', 'big_businessman']
senticnet['multi_car_crash'] = ['-0.90', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.86', 'trauma', 'car_wreck', 'broken_bone', 'bad_knee', 'wreck']
senticnet['multi_chip'] = ['-0.83', '0', '0.521', '-0.71', '#sadness', '#disgust', 'negative', '-0.69', 'stool', 'dejection', 'droppings', 'cow_droppings', 'cow_dung']
senticnet['multi_cultural'] = ['0.057', '0', '0', '0.153', '#joy', '#admiration', 'positive', '0.105', 'multiple', 'cute', 'variety', 'hairy', 'large_breadbox']
senticnet['multi_culturalism'] = ['0.946', '-0.54', '0', '0.774', '#joy', '#admiration', 'positive', '0.754', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['multi_cultus'] = ['0.807', '-0.83', '0', '0.688', '#joy', '#surprise', 'positive', '0.776', 'folly', 'lunacy', 'craziness', 'indulgence', 'foolery']
senticnet['multi_decadal'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'weather', 'sunny', 'spring', 'snow', 'four_seasons']
senticnet['multi_dimensional'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'width', 'size', 'mass_measure', 'measure', 'gauge']
senticnet['multi_disciplinary'] = ['0.887', '0.877', '0', '0', '#joy', '#interest', 'positive', '0.882', 'ask_expert', 'absorb_information', 'analyze', 'learn_computer', 'search_information']
senticnet['multi_disciplinary_research'] = ['0.887', '0.877', '0', '0', '#joy', '#interest', 'positive', '0.882', 'ask_expert', 'absorb_information', 'analyze', 'learn_computer', 'search_information']
senticnet['multi_functional'] = ['0.085', '0.218', '-0.07', '0.308', '#interest', '#admiration', 'positive', '0.135', 'delicate', 'useful', 'good', 'light_colored', 'beautiful']
senticnet['multi_infarct_dementia'] = ['-0.94', '0', '0.821', '-0.75', '#sadness', '#anger', 'negative', '-0.84', 'predementedness', 'insanity', 'dementedness', 'senile_psychosis', 'alcoholic_dementia']
senticnet['multi_instrumentalist'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
senticnet['multi_lateral'] = ['0.043', '0.109', '0', '0.061', '#interest', '#admiration', 'positive', '0.071', 'nation_state', 'democratic_country', 'free_country', 'large_country', 'republic']
senticnet['multi_media'] = ['-0.02', '0', '-0.04', '-0.03', '#fear', '#disgust', 'negative', '-0.03', 'studio', 'art_piece', 'art_gallery', 'artist', 'museum']
senticnet['multi_megaton'] = ['-0.84', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.85', 'inferno', 'forest_fire', 'fire_light', 'dismissal', 'wash_mouth_soap']
senticnet['multi_million_dollar'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'profitable', 'moneymaking', 'gainful', 'paying', 'paid']
senticnet['multi_millionaire'] = ['0.976', '0.811', '0', '0', '#joy', '#interest', 'positive', '0.893', 'deposit', 'bank_loan', 'wealth', 'finance', 'account']
senticnet['multi_organ_failure'] = ['-0.92', '0', '0.824', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'bronchopneumonia', 'bronchial_pneumonia', 'pneumonia', 'pleuropneumonia', 'pneumococcal_pneumonia']
senticnet['multi_platform'] = ['0.951', '0', '0', '0.731', '#joy', '#admiration', 'positive', '0.841', 'pastime', 'enjoyment', 'pleasure', 'enjoy', 'fun']
senticnet['multi_plex'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'theater', 'drive-in_movie', 'watch_movie', 'cinema', 'movie']
senticnet['multi_polar'] = ['0.838', '0.707', '0', '0.904', '#joy', '#admiration', 'positive', '0.816', 'natural_order', 'universe', 'existence', 'creation', 'cosmos']
senticnet['multi_projector'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'arm_movement', 'hand_gesture', 'water_motion', 'hand_signal', 'sea_movement']
senticnet['multi_purpose_arena'] = ['0', '-0.84', '0', '0.723', '#surprise', '#admiration', 'positive', '0.785', 'circular_route', 'stadium', 'memorial', 'station', 'row_house']
senticnet['multi_purpose_athletic'] = ['0.041', '0.086', '0.016', '0', '#joy', '#interest', 'positive', '0.037', 'team', 'play_sport', 'game', 'exercise', 'match']
senticnet['multi_purpose_hall'] = ['0.872', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.811', 'lecture_theater', 'lecture', 'conference_auditorium', 'read_book', 'text_book']
senticnet['multi_purpose_indoor'] = ['0.02', '0.244', '0', '0.225', '#interest', '#admiration', 'positive', '0.163', 'game', 'disneyland', 'ballpark', 'hockey_game', 'sport_event']
senticnet['multi_purpose_indoor_arena'] = ['0.026', '-0.03', '0', '0.091', '#surprise', '#admiration', 'positive', '0.051', 'stadium', 'auditorium', 'television_station', 'music_hall', 'movie_theater']
senticnet['multi_purpose_room'] = ['0.872', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.811', 'lecture_theater', 'lecture', 'conference_auditorium', 'read_book', 'text_book']
senticnet['multi_purpose_venue'] = ['0', '0', '0', '-0.1', '#disgust', '#disgust', 'negative', '-0.1', 'ice_rink', 'skate', 'room_temperature', 'loose_end', 'smooth']
senticnet['multi_sensory'] = ['0.633', '-0.96', '0', '0.612', '#joy', '#surprise', 'positive', '0.738', 'particularism', 'absorption', 'concentration', 'engrossment', 'centering']
senticnet['multi_syllabic'] = ['0.584', '-0.62', '0', '0.815', '#surprise', '#admiration', 'positive', '0.675', 'squiggle', 'hand', 'cacography', 'handwriting', 'script']
senticnet['multi_vendor'] = ['0.637', '-0.91', '0', '0.757', '#surprise', '#admiration', 'positive', '0.768', 'tractability', 'manageableness', 'docility', 'obedience', 'amenability']
senticnet['multi_volume'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'comic_book', 'child_book', 'child_story', 'comic', 'disney_cartoon']
senticnet['multiagent'] = ['0', '0.717', '0', '0.765', '#interest', '#admiration', 'positive', '0.741', 'helper', 'artificial_intelligence', 'goal_driven', 'proactive', 'intelligent']
senticnet['multiagent_system'] = ['0', '0.717', '0', '0.765', '#interest', '#admiration', 'positive', '0.741', 'helper', 'artificial_intelligence', 'goal_driven', 'proactive', 'intelligent']
senticnet['multibillion_dollar'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'profitable', 'moneymaking', 'gainful', 'paying', 'paid']
senticnet['multibillion_euro'] = ['-0.04', '0.274', '-0.15', '0.278', '#interest', '#admiration', 'positive', '0.089', 'business_deal', 'trade', 'exchange', 'bargain', 'offer']
senticnet['multiblock'] = ['0.865', '-0.80', '0', '0.52', '#joy', '#surprise', 'positive', '0.73', 'set', 'pose', 'lay', 'intersperse', 'put']
senticnet['multibyte'] = ['-0.04', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.06', 'chemical_reaction', 'burn', 'chemical_process', 'get_burnt', 'set_on_fire']
senticnet['multibyte_character'] = ['-0.04', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.06', 'chemical_reaction', 'burn', 'chemical_process', 'get_burnt', 'set_on_fire']
senticnet['multicellular_organism'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['multichannel_audio'] = ['0.055', '0', '0.03', '0.136', '#joy', '#admiration', 'positive', '0.054', 'loud_soft', 'decibel', 'loudness', 'sound', 'melodic_noise']
senticnet['multicourse'] = ['0.128', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.085', 'banquet', 'rave', 'auction', 'limbo', 'loose_end']
senticnet['multicultural'] = ['0.057', '0', '0', '0.153', '#joy', '#admiration', 'positive', '0.105', 'multiple', 'cute', 'variety', 'hairy', 'large_breadbox']
senticnet['multidecadal'] = ['-0.69', '0', '0.835', '-0.56', '#sadness', '#anger', 'negative', '-0.69', 'roughness', 'inclemency', 'bad_weather', 'inclementness', 'windiness']
senticnet['multidimensional'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'width', 'size', 'mass_measure', 'measure', 'gauge']
senticnet['multidimensionality'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'width', 'size', 'mass_measure', 'measure', 'gauge']
senticnet['multidisciplinary'] = ['0', '0.981', '0', '0.884', '#interest', '#admiration', 'positive', '0.932', 'teacher_enhancement', 'education', 'teacher', 'science', 'different_application']
senticnet['multidisciplinary_activity'] = ['0', '0.968', '0', '1.0', '#interest', '#admiration', 'positive', '0.984', 'division', 'directorate', 'office', 'mp_office', 'material_research']
senticnet['multidisciplinary_background'] = ['0.877', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.91', 'graduate_education', 'igert', 'engineer', 'challenge', 'training']
senticnet['multidisciplinary_nature'] = ['0', '0.33', '0', '0', '#interest', '#interest', 'positive', '0.33', 'interested', 'focused', 'attentive', 'curious', 'fascinated']
senticnet['multidisciplinary_perspective'] = ['0', '0.139', '0', '0.18', '#interest', '#admiration', 'positive', '0.16', 'aspect', 'vista', 'scenic', 'spectacular', 'view']
senticnet['multidisciplinary_research'] = ['0.887', '0.877', '0', '0', '#joy', '#interest', 'positive', '0.882', 'ask_expert', 'absorb_information', 'analyze', 'learn_computer', 'search_information']
senticnet['multifaceted'] = ['-0.56', '0', '0.925', '-0.66', '#anger', '#disgust', 'negative', '-0.71', 'varied', 'various', 'shifting', 'versatile', 'varying']
senticnet['multifactor_productivity'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'indicator', 'retail_price_index', 'producer_price_index', 'wholesale_price_index', 'consumer_price_index']
senticnet['multifactorial'] = ['0.504', '-0.93', '0', '0.797', '#surprise', '#admiration', 'positive', '0.747', 'diabetes', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['multifarious'] = ['0', '0.869', '0', '0.728', '#interest', '#admiration', 'positive', '0.798', 'multiform', 'numberless', 'scad', 'umpteen', 'some-more']
senticnet['multifariousness'] = ['-0.64', '0', '0.773', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'diverseness', 'heterogeneity', 'diversity', 'variety', 'nonuniformity']
senticnet['multiflora'] = ['0', '0', '-0.14', '-0.03', '#fear', '#disgust', 'negative', '-0.09', 'pine_cone', 'fallen_leaf', 'poison_ivy', 'detritus', 'tress']
senticnet['multiflora_rose'] = ['-0.92', '0', '0.912', '-0.94', '#sadness', '#disgust', 'negative', '-0.92', 'true_blackberry', 'sand_blackberry', 'blackberry_bush', 'blackberry', 'running_blackberry']
senticnet['multiflorum'] = ['0.861', '-0.87', '0', '0.597', '#joy', '#surprise', 'positive', '0.779', 'herb', 'cultivated_parsnip', 'wild_parsnip', 'madnep', 'herbaceous_plant']
senticnet['multiform'] = ['0', '0.87', '0', '0.733', '#interest', '#admiration', 'positive', '0.801', 'multifarious', 'numberless', 'scad', 'umpteen', 'some-more']
senticnet['multifunctional'] = ['0.085', '0.218', '-0.07', '0.308', '#interest', '#admiration', 'positive', '0.135', 'delicate', 'useful', 'good', 'light_colored', 'beautiful']
senticnet['multigrain'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'veal', 'beef', 'food_group', 'animal_flesh', 'curry']
senticnet['multihull'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'boat', 'yacht', 'big_boat', 'large_boat']
senticnet['multilateral'] = ['0', '0', '0', '0.881', '#admiration', '#admiration', 'positive', '0.881', 'educate_others', 'share_knowledge', 'share_opinion', 'discover_new', 'collaboration']
senticnet['multilateral_approach'] = ['0', '0.098', '0', '0.08', '#interest', '#admiration', 'positive', '0.089', 'talk_person', 'wisdom', 'tact', 'statecraft', 'greet_person']
senticnet['multilateral_cooperation'] = ['0', '0', '0', '0.881', '#admiration', '#admiration', 'positive', '0.881', 'educate_others', 'share_knowledge', 'share_opinion', 'discover_new', 'collaboration']
senticnet['multilateral_diplomacy'] = ['0', '0.098', '0', '0.08', '#interest', '#admiration', 'positive', '0.089', 'talk_person', 'wisdom', 'tact', 'statecraft', 'greet_person']
senticnet['multilateralism'] = ['0', '0.098', '0', '0.08', '#interest', '#admiration', 'positive', '0.089', 'talk_person', 'wisdom', 'tact', 'statecraft', 'greet_person']
senticnet['multilateralist'] = ['0', '0.098', '0', '0.08', '#interest', '#admiration', 'positive', '0.089', 'talk_person', 'wisdom', 'tact', 'statecraft', 'greet_person']
senticnet['multimammate_mouse'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'carbon_monoxide', 'tree_animal']
senticnet['multimedia'] = ['-0.02', '0', '-0.04', '-0.03', '#fear', '#disgust', 'negative', '-0.03', 'studio', 'art_piece', 'art_gallery', 'artist', 'museum']
senticnet['multimedia_artist'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'indirectness', 'mediateness', 'characteristic', 'allusiveness']
senticnet['multimillion_dollar'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'profitable', 'moneymaking', 'gainful', 'paying', 'paid']
senticnet['multimillion_dollar_contract'] = ['0.865', '-0.84', '0', '0.915', '#joy', '#admiration', 'positive', '0.875', 'grant', 'sale_in_gross', 'contract_of_hazard', 'adhesion_contract', 'contract_of_adhesion']
senticnet['multimillion_dollar_deal'] = ['-0.04', '0.274', '-0.15', '0.278', '#interest', '#admiration', 'positive', '0.089', 'business_deal', 'trade', 'exchange', 'bargain', 'offer']
senticnet['multimillionaire'] = ['0', '-0.97', '0', '0.917', '#surprise', '#admiration', 'positive', '0.944', 'out-of-touch', 'plutocrat', 'plutocrat', 'super-rich', 'corporatist']
senticnet['multimodal'] = ['0.785', '-0.99', '0', '0.981', '#surprise', '#admiration', 'positive', '0.92', 'ear', 'absolute_pitch', 'perfect_pitch', 'audition', 'sense_audition']
senticnet['multimodality'] = ['0.785', '-0.99', '0', '0.981', '#surprise', '#admiration', 'positive', '0.92', 'ear', 'absolute_pitch', 'perfect_pitch', 'audition', 'sense_audition']
senticnet['multinational'] = ['-0.72', '0', '-0.76', '0', '#sadness', '#fear', 'negative', '-0.74', 'conglomerate', 'subsidiary', 'conglomerate', 'multi-national', 'diversified']
senticnet['multinational_corporate'] = ['0.037', '0.177', '0', '0.147', '#interest', '#admiration', 'positive', '0.12', 'business_organization', 'company', 'economic_activity', 'large_business', 'partnership']
senticnet['multinational_corporation'] = ['0.037', '0.177', '0', '0.147', '#interest', '#admiration', 'positive', '0.12', 'business_organization', 'company', 'economic_activity', 'large_business', 'partnership']
senticnet['multinucleate'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'nonmotile', 'immobile']
senticnet['multipath'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'short_circuit', 'error', 'mistake', 'undesired', 'trouble']
senticnet['multipath_propagation'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'short_circuit', 'error', 'mistake', 'undesired', 'trouble']
senticnet['multiphase_flow'] = ['0.976', '0.786', '0', '0', '#joy', '#interest', 'positive', '0.881', 'combustible', 'state_matter', 'natural_resource', 'liquid_fuel', 'fossil_fuel']
senticnet['multiplatinum'] = ['0.916', '-0.93', '0', '0.674', '#joy', '#surprise', 'positive', '0.841', 'smash', 'bang', 'smasher', 'strike', 'hit']
senticnet['multiplayer_online_battle'] = ['0.026', '-0.03', '0', '0.091', '#surprise', '#admiration', 'positive', '0.051', 'stadium', 'auditorium', 'television_station', 'music_hall', 'movie_theater']
senticnet['multiple'] = ['0.054', '0', '0', '0.152', '#joy', '#admiration', 'positive', '0.103', 'diverse', 'cute', 'variety', 'hairy', 'large_breadbox']
senticnet['multiple_choices'] = ['-0.06', '0.18', '0', '0.143', '#interest', '#admiration', 'positive', '0.086', 'grade', 'evaluation', 'final', 'final_exam', 'school_grade']
senticnet['multiple_entry'] = ['0.709', '0', '0', '0.989', '#joy', '#admiration', 'positive', '0.849', 'theme_park', 'short_cut', 'save_time', 'combo_pass', 'ticket']
senticnet['multiple_myeloma'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['multiple_neuritis'] = ['-0.65', '0', '0.873', '-0.63', '#sadness', '#anger', 'negative', '-0.72', 'polyneuritis', 'neuritis', 'rubor', 'redness', 'inflammation']
senticnet['multiple_organ_failure'] = ['-0.98', '0', '0.827', '-0.94', '#sadness', '#disgust', 'negative', '-0.91', 'congestive_heart_failure', 'heart_attack', 'failure', 'heart_disease', 'cardiopathy']
senticnet['multiple_people'] = ['0.053', '0.106', '0', '0.092', '#interest', '#admiration', 'positive', '0.084', 'mass_person', 'big_group', 'bunch_person', 'push_together', 'team_up']
senticnet['multiple_personality_disorder'] = ['-0.61', '0', '0.527', '-0.57', '#sadness', '#disgust', 'negative', '-0.57', 'affective_disorder', 'major_affective_disorder', 'emotional_disorder', 'emotional_disturbance', 'cyclothymia']
senticnet['multiple_purposes'] = ['0', '0', '0', '0.861', '#admiration', '#admiration', 'positive', '0.861', 'wrap_around', 'different_motive', 'carry', 'haul', 'spring_leak']
senticnet['multiple_sclerosis'] = ['-0.71', '0', '0.85', '-0.82', '#anger', '#disgust', 'negative', '-0.79', 'sclerosis', 'induration', 'degenerative_disorder', 'autoimmune_disease', 'autoimmune_disorder']
senticnet['multiple_sleep_latency'] = ['0.928', '-0.87', '0', '0.679', '#joy', '#surprise', 'positive', '0.829', 'examine', 'experiment', 'alpha_test', 'beta_test', 'road_test']
senticnet['multiple_voting'] = ['0.611', '-0.82', '0', '0.737', '#surprise', '#admiration', 'positive', '0.726', 'choice', 'option', 'veto', 'balloting', 'block_vote']
senticnet['multiplex'] = ['0.949', '0.755', '0', '0', '#joy', '#interest', 'positive', '0.852', 'multiply', 'manifold_paper', 'manifold', 'study', 'topology']
senticnet['multiplex_cinema'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'theater', 'drive-in_movie', 'watch_movie', 'cinema', 'movie']
senticnet['multiplex_theater'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'theater', 'drive-in_movie', 'watch_movie', 'cinema', 'movie']
senticnet['multiplexing_protocol'] = ['0.078', '0.103', '-0.09', '0.191', '#interest', '#admiration', 'positive', '0.069', 'connect', 'aunt_uncle', 'short_form', 'relationship', 'kinship']
senticnet['multiplication'] = ['0', '0.784', '0', '0.848', '#interest', '#admiration', 'positive', '0.816', 'subtraction', 'add', 'mathematical_operation', 'equal', 'arithmetic']
senticnet['multiplication_table'] = ['0.076', '0.096', '-0.08', '0.167', '#interest', '#admiration', 'positive', '0.063', 'statistic', 'mathematical_function', 'calculation', 'list_number', 'calculate']
senticnet['multiplicative'] = ['0.506', '-0.56', '0', '0.788', '#surprise', '#admiration', 'positive', '0.62', 'backward', 'reverse', 'retrograde', 'reflexive', 'retral']
senticnet['multiplicity'] = ['0.083', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.059', 'good_view', 'bird_droppings', 'slate', 'gable', 'homeowner']
senticnet['multiplier'] = ['0.037', '0', '0.026', '0.129', '#joy', '#admiration', 'positive', '0.047', 'measurement_unit', 'many_items', 'long', 'circle_part', 'narrow']
senticnet['multiplist'] = ['0.534', '-0.86', '0', '0.676', '#surprise', '#admiration', 'positive', '0.692', 'suitability', 'suitableness', 'habitability', 'habitableness', 'qualification']
senticnet['multiply'] = ['-0.05', '0.131', '0.038', '0.129', '#interest', '#admiration', 'positive', '0.041', 'add', 'addition', 'separate', 'navigation', 'halve']
senticnet['multiply_rapidly'] = ['-0.83', '0', '-0.82', '-0.86', '#sadness', '#disgust', 'negative', '-0.84', 'cause_cold', 'tourism', 'reproduce', 'gland', 'procreate']
senticnet['multipolar_world'] = ['0.846', '-0.53', '0', '0.919', '#joy', '#admiration', 'positive', '0.766', 'correct', 'compensate', 'overcompensate', 'make_up', 'equate']
senticnet['multiprobe'] = ['0', '0.825', '0', '0', '#interest', '#interest', 'positive', '0.825', 'query', 'interrogation', 'inquire', 'response', 'solution']
senticnet['multiprotein_complex'] = ['-0.84', '0', '0.8', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'rigor_mortis', 'natural_process', 'natural_action', 'activity', 'action']
senticnet['multipurpose_arena'] = ['0', '-0.84', '0', '0.723', '#surprise', '#admiration', 'positive', '0.785', 'circular_route', 'stadium', 'memorial', 'station', 'row_house']
senticnet['multipurpose_room'] = ['0.872', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.811', 'lecture_theater', 'lecture', 'conference_auditorium', 'read_book', 'text_book']
senticnet['multisensory_integration'] = ['0.785', '-0.99', '0', '0.981', '#surprise', '#admiration', 'positive', '0.92', 'ear', 'absolute_pitch', 'perfect_pitch', 'audition', 'sense_audition']
senticnet['multispiral'] = ['0.969', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.772', 'aspheric', 'aspherical', 'prolate', 'rounded', 'crescent']
senticnet['multistate_lottery'] = ['0.858', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.813', 'box_office', 'movie', 'cctv', 'cinema', 'movie_theater']
senticnet['multisystem_disease'] = ['-0.85', '0', '0.657', '-0.86', '#sadness', '#disgust', 'negative', '-0.79', 'pulseless_disease', 'temporal_arteritis', 'rubor', 'redness', 'arteritis']
senticnet['multisystem_disorder'] = ['-0.50', '0', '0.542', '-0.51', '#anger', '#disgust', 'negative', '-0.51', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'hirschsprung_disease']
senticnet['multithread'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'carbon_fiber']
senticnet['multitude'] = ['0.044', '0.075', '0.019', '0.081', '#interest', '#admiration', 'positive', '0.045', 'big_group', 'clique', 'several_people', 'throng', 'group']
senticnet['multivalent'] = ['0', '0', '0.932', '-0.75', '#anger', '#disgust', 'negative', '-0.84', 'bivalent', 'univalent', 'divalent', 'meromorphic', 'monovalent']
senticnet['multivariate'] = ['0.83', '-0.83', '0', '0.872', '#surprise', '#admiration', 'positive', '0.846', 'overappraisal', 'overvaluation', 'overestimate', 'overestimation', 'commercial_document']
senticnet['multivocality'] = ['0.519', '-0.55', '0', '0.746', '#surprise', '#admiration', 'positive', '0.606', 'narrow_down', 'peg_down', 'nail_down', 'narrow', 'pin_down']
senticnet['multivolume'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'comic_book', 'child_book', 'child_story', 'comic', 'disney_cartoon']
senticnet['multiyear_contract'] = ['0.865', '-0.84', '0', '0.915', '#joy', '#admiration', 'positive', '0.875', 'grant', 'sale_in_gross', 'contract_of_hazard', 'adhesion_contract', 'contract_of_adhesion']
senticnet['multiyear_deal'] = ['-0.04', '0.274', '-0.15', '0.278', '#interest', '#admiration', 'positive', '0.089', 'business_deal', 'trade', 'exchange', 'bargain', 'offer']
senticnet['multiyear_project'] = ['0', '0.097', '0', '0.092', '#interest', '#admiration', 'positive', '0.095', 'urgent', 'classify_ad', 'important', 'review', 'repetition']
senticnet['mum'] = ['0', '0.864', '0', '0.751', '#interest', '#admiration', 'positive', '0.807', 'family_member', 'sibling', 'parent', 'female_relative', 'relative']
senticnet['mumble'] = ['0', '0.129', '0.017', '0.183', '#interest', '#admiration', 'positive', '0.098', 'pronounce', 'redo', 'description', 'think', 'ponder']
senticnet['mumblecore'] = ['-0.85', '0', '0.514', '-0.73', '#sadness', '#disgust', 'negative', '-0.70', 'flick', 'movie', 'film', 'picture', 'moving_picture']
senticnet['mumbo_jumbo'] = ['-0.61', '0', '0.738', '-0.80', '#anger', '#disgust', 'negative', '-0.71', 'babbling', 'double_talk', 'gibber', 'babble', 'lallation']
senticnet['mumford_son_babel'] = ['0.916', '-0.93', '0', '0.674', '#joy', '#surprise', 'positive', '0.841', 'smash', 'bang', 'smasher', 'strike', 'hit']
senticnet['mumification_necrosis'] = ['-0.68', '0', '0.916', '-0.91', '#anger', '#disgust', 'negative', '-0.83', 'dry_gangrene', 'cold_gangrene', 'slough', 'mummification', 'pathology']
senticnet['mummery'] = ['-0.81', '0', '0.65', '-0.65', '#sadness', '#disgust', 'negative', '-0.70', 'meaninglessness', 'nonsense', 'bunk', 'nonsensicality', 'hokum']
senticnet['mummification'] = ['-0.68', '0', '0.916', '-0.91', '#anger', '#disgust', 'negative', '-0.83', 'dry_gangrene', 'cold_gangrene', 'mumification_necrosis', 'slough', 'pathology']
senticnet['mummified_corpse'] = ['-0.06', '0.09', '-0.14', '-0.15', '#fear', '#disgust', 'negative', '-0.06', 'casket', 'coffin', 'grave', 'headstone', 'bury_person']
senticnet['mummy'] = ['0.962', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.966', 'mummy', 'mum', 'mommy', 'mama', 'tomb']
senticnet['mumps'] = ['0', '0', '-0.96', '-0.75', '#fear', '#disgust', 'negative', '-0.86', 'ridiculous', 'ill_health', 'terminal_illness', 'flu', 'chicken_pox']
senticnet['munch'] = ['-0.03', '-0.00', '0.04', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'eat_rice', 'eat_food_off', 'consume', 'eat', 'full_stomach']
senticnet['munchkin'] = ['0.931', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.855', 'crystal_clear', 'blue_sky', 'imaginary', 'water_sky', 'blue_color']
senticnet['munchkinland'] = ['0.931', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.855', 'crystal_clear', 'blue_sky', 'imaginary', 'water_sky', 'blue_color']
senticnet['mundane'] = ['-0.77', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.83', 'dull', 'boring', 'tedious', 'monotonous', 'tiresome']
senticnet['mundane_reality'] = ['0.075', '0.207', '-0.06', '0.12', '#interest', '#admiration', 'positive', '0.085', 'false', 'unreal', 'pretend', 'fictional', 'fictitious']
senticnet['mundaneness'] = ['0.741', '-0.74', '0', '0.666', '#joy', '#surprise', 'positive', '0.718', 'commonplaceness', 'mundanity', 'prosiness', 'prosaicness', 'ordinariness']
senticnet['mundanity'] = ['0.741', '-0.74', '0', '0.666', '#joy', '#surprise', 'positive', '0.718', 'mundaneness', 'commonplaceness', 'prosiness', 'prosaicness', 'ordinariness']
senticnet['mung'] = ['-0.70', '0', '0.606', '-0.72', '#sadness', '#disgust', 'negative', '-0.67', 'common_bean', 'soya', 'goa_bean', 'soya_bean', 'bean']
senticnet['mung_bean'] = ['-0.70', '0', '0.606', '-0.72', '#sadness', '#disgust', 'negative', '-0.67', 'common_bean', 'soya', 'goa_bean', 'bean', 'mung']
senticnet['mungbean'] = ['0.061', '0.093', '0.037', '0.114', '#interest', '#admiration', 'positive', '0.058', 'whole_grain', 'food_ingredient', 'staple_food', 'starchy_food', 'food_grain']
senticnet['mungo_park'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'correspondent', 'humankind']
senticnet['munhall'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
senticnet['munich_archdiocese'] = ['-0.95', '0', '0.673', '-0.85', '#sadness', '#disgust', 'negative', '-0.82', 'discharge', 'free', 'dethrone', 'disinvest', 'unfrock']
senticnet['municipal'] = ['0.123', '0.19', '-0.11', '0.183', '#interest', '#admiration', 'positive', '0.096', 'local_food', 'zone', 'headquarters', 'regional', 'urban']
senticnet['municipal_airport'] = ['-0.70', '0', '0.621', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'flare_path', 'airfield', 'grassland', 'runway', 'road']
senticnet['municipal_art_gallery'] = ['0.07', '0', '0', '0', '#joy', '#joy', 'positive', '0.07', 'artist', 'artwork', 'sculpture', 'tapestry', 'drawing']
senticnet['municipal_auditorium'] = ['0', '-0.84', '0', '0.723', '#surprise', '#admiration', 'positive', '0.785', 'circular_route', 'stadium', 'memorial', 'station', 'row_house']
senticnet['municipal_boundary'] = ['0.034', '0.09', '0.039', '0.061', '#interest', '#admiration', 'positive', '0.036', 'border', 'limit', 'edge', 'indoor', 'side']
senticnet['municipal_cemetery'] = ['-0.50', '0', '0.762', '-0.89', '#anger', '#disgust', 'negative', '-0.71', 'cemetery', 'graveyard', 'burial_site', 'grassland', 'burying_ground']
senticnet['municipal_drinking_water'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'bottled_water', 'sugar_water']
senticnet['municipal_landfill'] = ['-0.81', '0', '0.987', '-0.64', '#sadness', '#anger', 'negative', '-0.81', 'burn_garbage', 'eliminate_garbage', 'incinerate_waste', 'dump', 'garbage_dump']
senticnet['municipal_sewage'] = ['-0.58', '0', '0.714', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'diversionist', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['municipal_stadium'] = ['0', '-0.84', '0', '0.723', '#surprise', '#admiration', 'positive', '0.785', 'circular_route', 'stadium', 'memorial', 'station', 'row_house']
senticnet['municipal_tap_water'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'drinking_water', 'sugar_water']
senticnet['municipal_waste'] = ['-0.81', '0', '0.987', '-0.64', '#sadness', '#anger', 'negative', '-0.81', 'burn_garbage', 'eliminate_garbage', 'incinerate_waste', 'dump', 'garbage_dump']
senticnet['municipal_water_supply'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'bottled_water', 'sugar_water']
senticnet['municipia'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['munificence'] = ['0.85', '-0.72', '0', '0.989', '#joy', '#admiration', 'positive', '0.854', 'excellence', 'stateliness', 'loftiness', 'expansivity', 'expansiveness']
senticnet['munificent'] = ['0', '0', '-0.89', '-0.74', '#fear', '#disgust', 'negative', '-0.82', 'unsparing', 'overgenerous', 'lavish', 'too-generous', 'unstinted']
senticnet['munjal'] = ['0', '0', '0.737', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'annoy', 'skin_rash', 'bother', 'irritation', 'great_dane']
senticnet['munro_bagger'] = ['-0.79', '0', '0.992', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['muon'] = ['0.048', '0', '0', '0.092', '#joy', '#admiration', 'positive', '0.07', 'radio_wave', 'sun', 'solar_cell', 'x-ray', 'cosmic_ray']
senticnet['muon_neutrino'] = ['0.022', '0.073', '0', '0.14', '#interest', '#admiration', 'positive', '0.078', 'elementary_particle', 'electron', 'proton_neutron', 'neutron', 'proton']
senticnet['muon_storage'] = ['0.052', '0', '0', '0.118', '#joy', '#admiration', 'positive', '0.085', 'diamond_ring', 'piece_of_jewelry', 'bell_sound', 'wedding_band', 'engagement']
senticnet['muppet'] = ['-0.93', '0', '0.77', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'hothead', 'unpleasant_person', 'disagreeable_person', 'crabby_person', 'crab']
senticnet['muppet_character'] = ['-0.93', '0', '0.77', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'hothead', 'unpleasant_person', 'disagreeable_person', 'crabby_person', 'crab']
senticnet['muppet_movie'] = ['-0.93', '0', '0.77', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'hothead', 'unpleasant_person', 'disagreeable_person', 'crabby_person', 'crab']
senticnet['muppeteer'] = ['-0.93', '0', '0.77', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'hothead', 'unpleasant_person', 'disagreeable_person', 'crabby_person', 'crab']
senticnet['muqu'] = ['-0.63', '0', '0.791', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'little', 'pinch', 'modicum', 'small_indefinite_amount', 'indefinite_quantity']
senticnet['muradiye'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'caravansary', 'caravanserai', 'khan', 'caravan_inn', 'hostel']
senticnet['muraena'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'plectognath', 'plectognathfish', 'blowfish', 'triggerfish', 'filefish']
senticnet['mural'] = ['0.047', '0', '0', '0.055', '#joy', '#admiration', 'positive', '0.051', 'painting', 'sculpture', 'oil_painting', 'tapestry', 'artwork']
senticnet['mural_crown'] = ['0.586', '-0.65', '0', '0.503', '#joy', '#surprise', 'positive', '0.58', 'decoration', 'distinguished_service_cross', 'medal', 'medallion', 'palm']
senticnet['muramasa'] = ['-0.04', '0.138', '-0.08', '0.111', '#interest', '#admiration', 'positive', '0.031', 'geometry', 'craft', 'blade_geometry', 'blade', 'collect_item']
senticnet['murder'] = ['0', '0', '-0.96', '-0.82', '#fear', '#disgust', 'negative', '-0.89', 'death_destruction', 'kill', 'get_rid_of_enemy', 'destroy_live', 'dead']
senticnet['murder_charge'] = ['0.654', '-0.59', '0', '0.774', '#joy', '#admiration', 'positive', '0.673', 'instrument', 'indictment', 'murder_indictment', 'true_bill', 'legal_document']
senticnet['murder_charge_against'] = ['0.654', '-0.59', '0', '0.774', '#joy', '#admiration', 'positive', '0.673', 'instrument', 'indictment', 'murder_indictment', 'true_bill', 'legal_document']
senticnet['murder_conviction'] = ['0', '0', '0.869', '0', '#anger', '#anger', 'negative', '-0.86', 'convoke', 'loose_end', 'car_race', 'auction', 'mortuary']
senticnet['murder_indictment'] = ['0.654', '-0.59', '0', '0.774', '#joy', '#admiration', 'positive', '0.673', 'instrument', 'murder_charge', 'indictment', 'true_bill', 'legal_document']
senticnet['murder_mystery'] = ['-0.89', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.80', 'riddle', 'obituary', 'editorial', 'news_story', 'article']
senticnet['murder_suspect'] = ['0.964', '-0.57', '0', '0.827', '#joy', '#admiration', 'positive', '0.789', 'mortal', 'suspect', 'robbery_suspect', 'rape_suspect', 'person']
senticnet['murder_trial'] = ['0.654', '-0.59', '0', '0.774', '#joy', '#admiration', 'positive', '0.673', 'instrument', 'indictment', 'murder_indictment', 'true_bill', 'legal_document']
senticnet['murder_victim'] = ['0.864', '-0.54', '0', '0.613', '#joy', '#admiration', 'positive', '0.675', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['murder_weapon'] = ['-0.96', '0', '-0.77', '-0.70', '#sadness', '#fear', 'negative', '-0.81', 'choose_victim', 'alibi', 'use_military', 'shoot_person', 'homicide']
senticnet['murdered_uatu'] = ['0', '0.081', '0', '0', '#interest', '#interest', 'positive', '0.081', 'favorite', 'guard', 'crowd', 'several_people', 'clique']
senticnet['murderer'] = ['-0.06', '0', '-0.12', '-0.19', '#fear', '#disgust', 'negative', '-0.12', 'kill', 'get_rid_of_enemy', 'enslave_person', 'hitler', 'go_war']
senticnet['murderous'] = ['-0.97', '0', '0.994', '0', '#sadness', '#anger', 'negative', '-0.98', 'difficult', 'murder', 'criminal', 'homicidal', 'murderously']
senticnet['murderous_rage'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['murderous_rampage'] = ['-0.67', '0', '0.938', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'massacre', 'mass_murder', 'carnage', 'slaughter', 'butchery']
senticnet['murderous_regime'] = ['-0.73', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.72', 'royal_leader', 'despot', 'emperor', 'monarch', 'king']
senticnet['murderously'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'amuck', 'amok', 'murderousness', 'misocainea', 'misogyny']
senticnet['murderousness'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amuck', 'amok', 'misocainea', 'misogyny']
senticnet['muridae'] = ['-0.68', '0', '0.956', '-0.90', '#anger', '#disgust', 'negative', '-0.84', 'giant_moa', 'anomalopteryx', 'moa', 'moa_bird', 'flightless_bird']
senticnet['murine_typhus'] = ['0.579', '-0.81', '0', '0.833', '#surprise', '#admiration', 'positive', '0.741', 'typhus', 'rat_typhus', 'urban_typhus', 'endemic_typhus', 'rickettsial_disease']
senticnet['muriwhenua'] = ['-0.91', '0', '0.769', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'break', 'good_luck', 'chance_eventpy_chance', 'chance_event', 'coincidence']
senticnet['murk'] = ['0', '0', '-0.94', '-0.91', '#fear', '#disgust', 'negative', '-0.92', 'night', 'blackout', 'darkness', 'dark', 'obscure']
senticnet['murky'] = ['-0.72', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.81', 'water', 'atmosphere', 'banal', 'bland', 'go_lake']
senticnet['murky_water'] = ['-0.71', '0', '0.704', '-0.89', '#sadness', '#disgust', 'negative', '-0.76', 'soil', 'mire', 'dirt', 'bleaching_clay', 'bleaching_earth']
senticnet['murmur'] = ['-0.94', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.94', 'drive_recklessly', 'discord', 'deaf', 'deny', 'silent']
senticnet['murmuring_sound'] = ['0.449', '0.305', '-0.19', '0.222', '#joy', '#interest', 'positive', '0.196', 'calm', 'peace', 'serenity', 'quiet', 'tranquility']
senticnet['murphy'] = ['0.532', '-0.68', '0', '0.984', '#surprise', '#admiration', 'positive', '0.733', 'potato', 'white_potato', 'chips', 'spud', 'tater']
senticnet['murray_bookchin'] = ['0.563', '-0.72', '0', '0.644', '#surprise', '#admiration', 'positive', '0.645', 'idiom', 'idiomatic_expression', 'phrasal_idiom', 'set_phrase', 'phrase']
senticnet['murray_davydenko'] = ['-0.10', '0', '-0.05', '-0.24', '#sadness', '#disgust', 'negative', '-0.13', 'adulate', 'suck_up', 'toady', 'blandish', 'kowtow']
senticnet['murray_janowicz'] = ['-0.10', '0', '-0.05', '-0.24', '#sadness', '#disgust', 'negative', '-0.13', 'adulate', 'suck_up', 'toady', 'blandish', 'kowtow']
senticnet['murray_mclauchlan'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'cur', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['murray_rothbard'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'economic_libertarian', 'civil_libertarian', 'advocate', 'advocator', 'proponent']
senticnet['murrine'] = ['0.609', '-0.74', '0', '0.716', '#surprise', '#admiration', 'positive', '0.69', 'malacca', 'sword', 'swagger_stick', 'sword_cane', 'sword_stick']
senticnet['mursay'] = ['-0.84', '0', '0.579', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'squiggle', 'hand', 'chicken_scratch', 'handwriting', 'script']
senticnet['mursi_muslim_brotherhood'] = ['0.648', '0.627', '-0.26', '0.329', '#joy', '#interest', 'positive', '0.334', 'brotherly', 'friendly', 'fraternal', 'amicability', 'amicable']
senticnet['murther'] = ['-0.76', '0', '0.995', '-0.94', '#anger', '#disgust', 'negative', '-0.90', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'excretion']
senticnet['musalla'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['muscadine'] = ['0.02', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'many_fruits', 'buy_fruit', 'cherry', 'pineapple', 'tomato']
senticnet['muscial'] = ['0.022', '0', '0', '0.109', '#joy', '#admiration', 'positive', '0.066', 'instrumental', 'singer', 'hip_hop', 'melodic_noise', 'music_player']
senticnet['muscicapidae'] = ['-0.92', '0', '0.64', '-0.52', '#sadness', '#anger', 'negative', '-0.69', 'animal_order', 'percher', 'thyreophoran', 'marginocephalian', 'euronithopod']
senticnet['muscle'] = ['0.228', '0.123', '-0.09', '0', '#joy', '#interest', 'positive', '0.084', 'bone', 'neck', 'shoulder', 'skeletal_system', 'bone_human']
senticnet['muscle_ache'] = ['-0.12', '-0.01', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.06', 'become_fatigued', 'fatigue', 'lack_energy', 'tiredness', 'exhaustion']
senticnet['muscle_activation'] = ['-0.15', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.09', 'use_leg', 'walk_quickly', 'casual', 'take_step', 'strut']
senticnet['muscle_atrophy'] = ['-0.78', '0', '0.764', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'withering', 'downfall', 'weakening', 'fading', 'attenuation']
senticnet['muscle_attachment'] = ['0.1', '-0.12', '0.05', '-0.03', '#joy', '#surprise', 'positive', '0.034', 'fancy_dress', 'blouse', 'fashion', 'dress', 'skirt']
senticnet['muscle_beach'] = ['-0.90', '0', '0.522', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'hooligan', 'ruffian', 'roughneck', 'rowdy', 'yob']
senticnet['muscle_bound'] = ['-0.90', '0', '0.522', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'hooligan', 'ruffian', 'roughneck', 'rowdy', 'yob']
senticnet['muscle_carnitine'] = ['-0.54', '0', '0.92', '-0.84', '#anger', '#disgust', 'negative', '-0.76', 'dearth', 'demand', 'mineral_deficiency', 'famine', 'need']
senticnet['muscle_coordination'] = ['0.983', '-0.63', '0', '0.697', '#joy', '#admiration', 'positive', '0.772', 'amateurishness', 'inability', 'ineptness', 'unskillfulness', 'awkwardness']
senticnet['muscle_cramp'] = ['-0.07', '-0.04', '0', '-0.1', '#sadness', '#disgust', 'negative', '-0.04', 'shortness_breath', 'breathlessness', 'big_muscle', 'increase_metabolism', 'cramp']
senticnet['muscle_damage'] = ['-0.41', '0', '-0.28', '-0.05', '#sadness', '#fear', 'negative', '-0.25', 'numbness', 'median_nerve', 'weakness', 'pain', 'carpal_tunnel_syndrome']
senticnet['muscle_dysmorphia'] = ['-0.52', '0', '0.786', '-0.92', '#anger', '#disgust', 'negative', '-0.74', 'upset', 'bulimarexia', 'bulima_nervosa', 'eating_disorder', 'disorder']
senticnet['muscle_fatigue'] = ['-0.50', '0', '0.681', '-0.89', '#anger', '#disgust', 'negative', '-0.69', 'sweat', 'effort', 'elbow_grease', 'exertion', 'travail']
senticnet['muscle_imbalance'] = ['0.84', '0.76', '0', '0.894', '#joy', '#admiration', 'positive', '0.831', 'keep_skin_healthy', 'stay_healthy', 'stay_fit', 'maintain_good_health', 'eat_healthily']
senticnet['muscle_injury'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'deltoid', 'muscle', 'abductor', 'skeletal_muscle']
senticnet['muscle_pain'] = ['-0.11', '-0.08', '0.028', '-0.18', '#sadness', '#disgust', 'negative', '-0.06', 'injury', 'contusion', 'broken_bone', 'leg_hurt', 'blister']
senticnet['muscle_relax'] = ['0', '0.098', '-0.09', '0.115', '#interest', '#admiration', 'positive', '0.04', 'push_wind', 'breath', 'breaout', 'body_reaction', 'mouth_action']
senticnet['muscle_rigidity'] = ['-0.29', '0.095', '-0.11', '-0.13', '#sadness', '#disgust', 'negative', '-0.11', 'say_aha', 'think_hard', 'think_past', 'scratch_head', 'head_hurt']
senticnet['muscle_spasm'] = ['-0.56', '0', '0.981', '-0.67', '#anger', '#disgust', 'negative', '-0.73', 'tenesmus', 'blepharospasm', 'twitch', 'twitching', 'vellication']
senticnet['muscle_strain'] = ['-0.50', '0', '0.681', '-0.89', '#anger', '#disgust', 'negative', '-0.69', 'sweat', 'effort', 'elbow_grease', 'exertion', 'travail']
senticnet['muscle_strength'] = ['0.84', '0.76', '0', '0.894', '#joy', '#admiration', 'positive', '0.831', 'keep_skin_healthy', 'stay_healthy', 'stay_fit', 'maintain_good_health', 'eat_healthily']
senticnet['muscle_tear'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'deltoid', 'muscle', 'abductor', 'skeletal_muscle']
senticnet['muscle_tense'] = ['0.757', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.754', 'inhale', 'cough', 'respiration', 'involuntary', 'smell_odor']
senticnet['muscle_tone'] = ['0.84', '0.76', '0', '0.894', '#joy', '#admiration', 'positive', '0.831', 'keep_skin_healthy', 'stay_healthy', 'stay_fit', 'maintain_good_health', 'eat_healthily']
senticnet['musclebound'] = ['-0.90', '0', '0.522', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'hooligan', 'ruffian', 'roughneck', 'rowdy', 'yob']
senticnet['musclehead'] = ['0.902', '0.995', '0', '0', '#joy', '#interest', 'positive', '0.949', 'beautiful_girl', 'hen_baby', 'pretty_woman', 'hen_child', 'bird']
senticnet['muscleman'] = ['-0.90', '0', '0.522', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'hooligan', 'ruffian', 'roughneck', 'rowdy', 'yob']
senticnet['muscogee_creek'] = ['0.052', '0.099', '0', '0.067', '#interest', '#admiration', 'positive', '0.073', 'police_rank', 'head_guy', 'chief', 'leader', 'commander']
senticnet['muscogee_creek_nation'] = ['0.989', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.97', 'committee', 'council', 'caste', 'association', 'organization']
senticnet['muscovado'] = ['-0.08', '0.106', '0', '0.125', '#interest', '#admiration', 'positive', '0.047', 'sugar', 'treacle', 'black_treacle', 'add_sugar', 'sugar_substitute']
senticnet['muscular'] = ['0.055', '0', '0.033', '0.088', '#joy', '#admiration', 'positive', '0.037', 'good_shape', 'train_hard', 'fit', 'out_shape', 'shape']
senticnet['muscular_atrophy'] = ['-0.78', '0', '0.764', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'withering', 'downfall', 'weakening', 'fading', 'attenuation']
senticnet['muscular_contraction'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'mathematical', 'muscle', 'math_function', 'feel_sick', 'separation']
senticnet['muscular_coordination'] = ['-0.89', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.82', 'fretful', 'antsy', 'fidgety', 'stewing', 'impatiently']
senticnet['muscular_dystrophy'] = ['-0.68', '0', '0.908', '-0.68', '#sadness', '#anger', 'negative', '-0.75', 'myotonic_muscular_dystrophy', 'myotonic_dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['muscular_endurance'] = ['0.603', '-0.77', '0', '0.641', '#surprise', '#admiration', 'positive', '0.674', 'suitability', 'suitableness', 'habitability', 'habitableness', 'qualification']
senticnet['muscular_foot'] = ['-0.93', '0', '0.607', '-0.67', '#sadness', '#disgust', 'negative', '-0.73', 'spiracle', 'stigma', 'orifice', 'opening', 'porta']
senticnet['muscular_hypertrophy'] = ['-0.78', '0', '0.764', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'withering', 'downfall', 'weakening', 'fading', 'attenuation']
senticnet['muscular_hypotonia'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'aphasia', 'anomic_aphasia']
senticnet['muscular_neck'] = ['0.209', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.118', 'old_dance', 'dance', 'slow_dance', 'dancer', 'enter_contest']
senticnet['muscular_pain'] = ['-0.98', '0', '0.678', '-0.95', '#sadness', '#disgust', 'negative', '-0.87', 'rubor', 'redness', 'inflammation', 'tennis_elbow', 'lateral_epicondylitis']
senticnet['muscular_physique'] = ['-0.90', '0', '0.522', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'hooligan', 'ruffian', 'roughneck', 'rowdy', 'yob']
senticnet['muscular_rigidity'] = ['-0.58', '0', '0.773', '-0.79', '#anger', '#disgust', 'negative', '-0.71', 'perspiration', 'sweating', 'polyhidrosis', 'sudation', 'hidrosis']
senticnet['muscular_tension'] = ['-0.89', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.82', 'fretful', 'antsy', 'fidgety', 'stewing', 'impatiently']
senticnet['muscular_tissue'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expandible', 'expansive', 'erectile', 'cavernous']
senticnet['muscularity'] = ['-0.54', '0', '0.909', '-0.97', '#anger', '#disgust', 'negative', '-0.81', 'bendability', 'pliancy', 'pliantness', 'flexibility', 'flexibleness']
senticnet['musculoskeletal_injury'] = ['-0.50', '0', '0.681', '-0.89', '#anger', '#disgust', 'negative', '-0.69', 'sweat', 'effort', 'elbow_grease', 'exertion', 'travail']
senticnet['musculoskeletal_pain'] = ['-0.98', '0', '0.678', '-0.95', '#sadness', '#disgust', 'negative', '-0.87', 'rubor', 'redness', 'inflammation', 'tennis_elbow', 'lateral_epicondylitis']
senticnet['muse'] = ['0', '0', '-0.95', '-0.96', '#fear', '#disgust', 'negative', '-0.95', 'goddess', 'think_over', 'chew_over', 'ponder', 'meditate']
senticnet['musetta_waltz'] = ['0.807', '-0.83', '0', '0.688', '#joy', '#surprise', 'positive', '0.776', 'folly', 'lunacy', 'craziness', 'indulgence', 'foolery']
senticnet['museum'] = ['0.086', '-0.10', '0.064', '0', '#joy', '#surprise', 'positive', '0.043', 'art_piece', 'lifetime', 'ancient_artifact', 'vintage', 'collectible']
senticnet['museum_gift_shop'] = ['0.877', '-0.80', '0', '0', '#joy', '#surprise', 'positive', '0.843', 'convention', 'prisoner_war_camp', 'rave', 'box_ring', 'dark_alley']
senticnet['museum_servicesthe'] = ['0.052', '0.099', '0', '0.067', '#interest', '#admiration', 'positive', '0.073', 'police_rank', 'head_guy', 'chief', 'leader', 'commander']
senticnet['museum_ticket'] = ['0.035', '0.078', '0.025', '0', '#joy', '#interest', 'positive', '0.029', 'coin_slot', 'transit_ticket', 'passenger_ticket', 'train_ticket', 'token_slot']
senticnet['mush'] = ['0.825', '-0.91', '0', '0.626', '#joy', '#surprise', 'positive', '0.789', 'treacle', 'glop', 'sentimentalism', 'slop', 'formulation']
senticnet['mushroom'] = ['0.962', '0', '0', '0', '#joy', '#joy', 'positive', '0.962', 'marijuana', 'pleasure_garden', 'trellis', 'cannabis', 'cabbage']
senticnet['mushroom_cloud'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'fragmentation', 'discharge', 'backfire', 'blast']
senticnet['mushroom_poisoning'] = ['-0.53', '0', '0.678', '-0.73', '#anger', '#disgust', 'negative', '-0.64', 'malady', 'food_poisoning', 'botulism', 'ptomaine', 'ptomaine_poisoning']
senticnet['mushroom_risotto'] = ['0.721', '-0.78', '0', '0', '#joy', '#surprise', 'positive', '0.754', 'delicious', 'luscious', 'pleasant-tasting', 'scrumptious', 'toothsome']
senticnet['mushroom_sauce'] = ['0.041', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.043', 'meatloaf', 'salad', 'delicious_meal', 'casserole', 'apple_pie']
senticnet['mushroom_soup'] = ['0.028', '0.138', '-0.04', '0.215', '#interest', '#admiration', 'positive', '0.083', 'water', 'dense', 'thick', 'milk', 'dense_water']
senticnet['mushy'] = ['0', '0', '-0.75', '-0.70', '#fear', '#disgust', 'negative', '-0.73', 'mawkish', 'drippy', 'hokey', 'bathetic', 'maudlin']
senticnet['music'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'band_play', 'classical', 'composer', 'songwriter', 'rock_band']
senticnet['music_album'] = ['0.946', '0.964', '0', '0', '#joy', '#interest', 'positive', '0.955', 'listen_cd', 'turn_on_radio', 'play_music', 'play_tune', 'play_cd']
senticnet['music_band'] = ['-0.03', '0.095', '0', '0.103', '#interest', '#admiration', 'positive', '0.053', 'play_in_band', 'play_music', 'play_tune', 'make_music', 'play_orchestra']
senticnet['music_class'] = ['-0.04', '-0.03', '-0.04', '-0.04', '#sadness', '#fear', 'negative', '-0.02', 'country_band', 'bluegrass_band', 'jazz_club', 'string_quartet', 'music_store']
senticnet['music_concert'] = ['-0.04', '-0.04', '-0.04', '-0.05', '#sadness', '#fear', 'negative', '-0.02', 'high_school_band', 'music_shop', 'drum_set', 'string_quartet', 'music_store']
senticnet['music_critic'] = ['0.811', '-0.63', '0', '0.959', '#joy', '#admiration', 'positive', '0.8', 'drama_critic', 'critic', 'professional', 'art_critic', 'literary_critic']
senticnet['music_dance'] = ['0.184', '-0.13', '0.037', '0', '#joy', '#surprise', 'positive', '0.095', 'form_of_music', 'style_music', 'lyric', 'popular_music', 'universal_language']
senticnet['music_ensemble'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'classical_music', 'make_beautiful_music', 'create_music', 'part_of_band', 'make_music']
senticnet['music_genre'] = ['0.835', '0.79', '0', '0', '#joy', '#interest', 'positive', '0.813', 'music_type', 'type_of_music', 'small_boulder', 'valuable_gem', 'rock_music']
senticnet['music_group'] = ['0.241', '0.185', '-0.11', '0.16', '#joy', '#interest', 'positive', '0.118', 'eclectic', 'rock_group', 'musical_group', 'sound_nice', 'rolling_stone']
senticnet['music_hall'] = ['0.91', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.875', 'record_studio', 'auditorium', 'concert_hall', 'studio', 'tv_station']
senticnet['music_lover'] = ['0.806', '0.776', '0', '0.761', '#joy', '#interest', 'positive', '0.781', 'appreciate_music', 'entertainment', 'see_performance', 'listen_pleasure', 'relieve_boredom']
senticnet['music_note'] = ['0.056', '-0.01', '0.036', '0.073', '#joy', '#admiration', 'positive', '0.028', 'song', 'musical_note', 'amplifier', 'chord', 'lyrics']
senticnet['music_part'] = ['0', '0.074', '0.026', '0.105', '#interest', '#admiration', 'positive', '0.051', 'handwritten', 'scribble', 'jot', 'written_message', 'calligraphy']
senticnet['music_play'] = ['0', '0', '0.048', '-0.03', '#anger', '#disgust', 'negative', '-0.04', 'electric_guitar', 'use_party', 'orchestra_play', 'party', 'howler']
senticnet['music_player'] = ['0.032', '0.092', '0', '0.075', '#interest', '#admiration', 'positive', '0.066', 'rock_group', 'eclectic', 'make_musicr', 'musical_group', 'guitar_player']
senticnet['music_room'] = ['0.917', '0.998', '0', '0', '#joy', '#interest', 'positive', '0.958', 'music_store', 'music_shop', 'band_room', 'string_quartet', 'musical_instrument_store']
senticnet['music_school'] = ['-0.04', '-0.00', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.02', 'old_movie', 'civic_center', 'opus_house', 'choir', 'concert']
senticnet['music_sheet'] = ['0.109', '0.129', '0', '0.098', '#joy', '#interest', 'positive', '0.112', 'sand_paper', 'measure_angle', 'generate_sound', 'read_music', 'low_note']
senticnet['music_shop'] = ['0.768', '0', '0', '0', '#joy', '#joy', 'positive', '0.768', 'music_store', 'music_lover', 'music', 'buy_music_album', 'buy_guitar']
senticnet['music_sound'] = ['0.93', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.952', 'guitar_string', 'musical_sound', 'piece_of_music', 'melody', 'music_note']
senticnet['music_storage'] = ['0.768', '0', '0', '0', '#joy', '#joy', 'positive', '0.768', 'music_room', 'store_music', 'music_lover', 'music', 'save_information']
senticnet['music_store'] = ['0.768', '0', '0', '0', '#joy', '#joy', 'positive', '0.768', 'music_shop', 'string_quartet', 'musical_instrument_store', 'music_room', 'high_school_band']
senticnet['music_studio'] = ['0.711', '0.851', '0', '0', '#joy', '#interest', 'positive', '0.781', 'music_room', 'band_room', 'music_store', 'musical_instrument_store', 'music_shop']
senticnet['music_tone'] = ['0.03', '0.07', '0.018', '0.124', '#interest', '#admiration', 'positive', '0.052', 'melody', 'musical_notation', 'music_sound', 'musical_note', 'piece_of_music']
senticnet['music_type'] = ['0.714', '0.877', '0', '0', '#joy', '#interest', 'positive', '0.795', 'type_of_music', 'pop_music', 'written_music', 'lyric', 'piece_of_music']
senticnet['music_video'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'paperclip', 'clasp', 'glue_stick', 'clothespin', 'clamp']
senticnet['musical'] = ['0.022', '0', '0', '0.109', '#joy', '#admiration', 'positive', '0.066', 'instrumental', 'singer', 'hip_hop', 'melodic_noise', 'music_player']
senticnet['musical_accompaniment'] = ['-0.95', '0', '0.992', '-0.63', '#sadness', '#anger', 'negative', '-0.86', 'modern_dance', 'interpretive_dancing', 'interpretative_dance', 'interpretative_dancing', 'stage_dancing']
senticnet['musical_arranger'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
senticnet['musical_comedy'] = ['0.206', '-0.13', '0', '0', '#joy', '#surprise', 'positive', '0.172', 'dancer', 'old_dance', 'dance', 'slow_dance', 'ballroom_dance']
senticnet['musical_composition'] = ['0.053', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'science_fiction', 'stereo', 'contralto', 'radio', 'music']
senticnet['musical_device'] = ['0.793', '-0.95', '0', '0', '#joy', '#surprise', 'positive', '0.873', 'play_ping_pong', 'magic', 'school_play', 'stunt', 'family_activity']
senticnet['musical_group'] = ['0.089', '0', '0', '0.13', '#joy', '#admiration', 'positive', '0.11', 'jazz', 'rock_group', 'guitar_player', 'pluck', 'beautiful_music']
senticnet['musical_instrument'] = ['-0.03', '-0.00', '0.018', '-0.08', '#sadness', '#disgust', 'negative', '-0.03', 'reed_instrument', 'stringed_use_jazz_music', 'instrument_music', 'use_jazz_music', 'listen_music']
senticnet['musical_instrument_store'] = ['0.969', '0.711', '0', '0', '#joy', '#interest', 'positive', '0.84', 'string_quartet', 'music_store', 'band_room', 'music_shop', 'high_school_band']
senticnet['musical_interval'] = ['-0.65', '0', '0.681', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'tone', 'whole_tone', 'step', 'whole_step', 'semitone']
senticnet['musical_notation'] = ['0.732', '0.877', '0', '0', '#joy', '#interest', 'positive', '0.804', 'musical_symbol', 'melody', 'music_sound', 'music_part', 'musical_note']
senticnet['musical_note'] = ['0.056', '0', '0.046', '0.094', '#joy', '#admiration', 'positive', '0.035', 'music_note', 'lyrics', 'word', 'song', 'melody']
senticnet['musical_organization'] = ['0.552', '-0.61', '0', '0.588', '#surprise', '#admiration', 'positive', '0.583', 'barbershop_quartet', 'string_quartet', 'string_quartette', 'quartette', 'musical_group']
senticnet['musical_performance'] = ['0.584', '-0.78', '0', '0.546', '#joy', '#surprise', 'positive', '0.638', 'acting', 'performing_art', 'dancing', 'dance', 'terpsichore']
senticnet['musical_prodigy'] = ['0.592', '-0.93', '0', '0.935', '#surprise', '#admiration', 'positive', '0.819', 'child', 'small_fry', 'tyke', 'tike', 'kid']
senticnet['musical_repertoire'] = ['0.022', '0', '0', '0.109', '#joy', '#admiration', 'positive', '0.066', 'instrumental', 'singer', 'hip_hop', 'melodic_noise', 'music_player']
senticnet['musical_sound'] = ['0.942', '0.707', '0', '0', '#joy', '#interest', 'positive', '0.825', 'music_sound', 'guitar_string', 'piece_of_music', 'melody', 'music_note']
senticnet['musical_symbol'] = ['0.79', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.816', 'music_part', 'musical_notation', 'music_sound', 'piece_of_music', 'melody']
senticnet['musical_talent'] = ['0.592', '-0.93', '0', '0.935', '#surprise', '#admiration', 'positive', '0.819', 'child', 'small_fry', 'tyke', 'tike', 'kid']
senticnet['musical_theater'] = ['0.022', '0', '0', '0.109', '#joy', '#admiration', 'positive', '0.066', 'instrumental', 'singer', 'hip_hop', 'melodic_noise', 'music_player']
senticnet['musical_theatre'] = ['0.022', '0', '0', '0.109', '#joy', '#admiration', 'positive', '0.066', 'instrumental', 'singer', 'hip_hop', 'melodic_noise', 'music_player']
senticnet['musicality'] = ['0', '0.803', '0', '0.71', '#interest', '#admiration', 'positive', '0.756', 'novelty', 'innovativeness', 'freshness', 'ability', 'power']
senticnet['musician'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'band_play', 'classical', 'composer', 'songwriter', 'rock_band']
senticnet['musicianship'] = ['0.608', '-0.55', '0', '0.726', '#joy', '#admiration', 'positive', '0.628', 'genius', 'endowment', 'flair', 'gift', 'knack']
senticnet['musicmatch'] = ['0.101', '-0.04', '0.036', '0', '#joy', '#surprise', 'positive', '0.038', 'song', 'rap', 'jingle', 'chord', 'lyric']
senticnet['musicmatch_jukebox'] = ['0.101', '-0.04', '0.036', '0', '#joy', '#surprise', 'positive', '0.038', 'song', 'rap', 'jingle', 'chord', 'lyric']
senticnet['musicnet'] = ['0.101', '-0.04', '0.036', '0', '#joy', '#surprise', 'positive', '0.038', 'song', 'rap', 'jingle', 'chord', 'lyric']
senticnet['musicological'] = ['0.136', '-0.11', '0.031', '0', '#joy', '#surprise', 'positive', '0.075', 'rap', 'popular_music', 'music', 'band_play', 'rock_roll']
senticnet['musicologist'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'band_play', 'songwriter', 'rock_band']
senticnet['musing'] = ['-0.80', '0', '-0.89', '0', '#sadness', '#fear', 'negative', '-0.85', 'pondering', 'think', 'broody', 'contemplative', 'meditative']
senticnet['musk_deer'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheep', 'argal', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['musk_mallow'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
senticnet['musk_ox'] = ['-0.04', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.04', 'bad_luck', 'damn', 'artic', 'mammal_family', 'bad_mood']
senticnet['muskeg'] = ['-0.04', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.04', 'bad_luck', 'damn', 'artic', 'mammal_family', 'bad_mood']
senticnet['muskellunge'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'divergent_strabismus', 'exotropia', 'squint', 'abnormality', 'abnormalcy']
senticnet['musket'] = ['0', '0', '-0.87', '-0.82', '#fear', '#disgust', 'negative', '-0.84', 'pistol', 'gun', 'hydrogen_bomb', 'rifle', 'shotgun']
senticnet['musket_ball'] = ['-0.18', '-0.12', '0.049', '-0.24', '#sadness', '#disgust', 'negative', '-0.09', 'tree_trunk', 'plank', 'artificial_leg', 'hardwood', 'tree_wood']
senticnet['musket_fire'] = ['0.957', '-0.93', '0', '0.795', '#joy', '#surprise', 'positive', '0.896', 'outdo', 'outstrip', 'outmatch', 'outgo', 'outperform']
senticnet['musketball'] = ['-0.18', '-0.12', '0.049', '-0.24', '#sadness', '#disgust', 'negative', '-0.09', 'tree_trunk', 'plank', 'artificial_leg', 'hardwood', 'tree_wood']
senticnet['musketry'] = ['0', '0', '0.753', '-0.97', '#anger', '#disgust', 'negative', '-0.86', 'barrage', 'well-aimed', 'withering', 'fusillade', 'machine-gun']
senticnet['muskie'] = ['0', '0', '-0.95', '-0.90', '#fear', '#disgust', 'negative', '-0.92', 'musky', 'walleye', 'lure', 'scent', 'woodsy']
senticnet['muskox'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'seal', 'otter', 'mussel', 'alligator', 'whale']
senticnet['muskoxen'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'cheetah', 'giraffe', 'kangaroo', 'mule', 'horse']
senticnet['muskrat'] = ['0.941', '0.987', '0', '0', '#joy', '#interest', 'positive', '0.964', 'monkey', 'marmoset', 'lemur', 'fiance', 'koala']
senticnet['musky'] = ['0', '0', '-0.94', '-0.89', '#fear', '#disgust', 'negative', '-0.91', 'muskie', 'walleye', 'lure', 'scent', 'woodsy']
senticnet['musky_odor'] = ['0.843', '0', '0', '0.971', '#joy', '#admiration', 'positive', '0.907', 'aroma', 'fragrance', 'smell', 'smell_good', 'odor']
senticnet['musky_scent'] = ['0.843', '0', '0', '0.971', '#joy', '#admiration', 'positive', '0.907', 'aroma', 'fragrance', 'smell', 'smell_good', 'odor']
senticnet['musky_smell'] = ['0.843', '0', '0', '0.971', '#joy', '#admiration', 'positive', '0.907', 'aroma', 'fragrance', 'smell', 'smell_good', 'odor']
senticnet['musl'] = ['0.858', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.813', 'box_office', 'movie', 'cctv', 'cinema', 'movie_theater']
senticnet['muslim'] = ['0.783', '0.804', '0', '0.826', '#interest', '#admiration', 'positive', '0.804', 'religious_purpose', 'spiritual', 'faith', 'religion', 'judaism']
senticnet['muslim_brotherhood'] = ['0.648', '0.627', '-0.26', '0.329', '#joy', '#interest', 'positive', '0.334', 'brotherly', 'friendly', 'fraternal', 'amicability', 'amicable']
senticnet['muslim_brotherhood_movement'] = ['0.648', '0.627', '-0.26', '0.329', '#joy', '#interest', 'positive', '0.334', 'brotherly', 'friendly', 'fraternal', 'amicability', 'amicable']
senticnet['muslim_fundamentalism'] = ['-0.30', '0.083', '-0.21', '-0.17', '#sadness', '#fear', 'negative', '-0.15', 'end_life', 'death', 'seek_revenge', 'kill_self', 'kill']
senticnet['muslim_prayer'] = ['0.023', '0.295', '-0.05', '0.283', '#interest', '#admiration', 'positive', '0.136', 'religion', 'religious_purpose', 'world_religion', 'gospel', 'judaism']
senticnet['muslim_shrine'] = ['0', '0.224', '0', '0.197', '#interest', '#admiration', 'positive', '0.211', 'church', 'temple', 'cathedral', 'chapel', 'confession']
senticnet['muslim_youth_wamy'] = ['0.055', '0.072', '-0.06', '0.094', '#interest', '#admiration', 'positive', '0.038', 'big_group', 'large_group', 'bunch_person', 'large_number', 'clique']
senticnet['muslin'] = ['-0.54', '0', '0.7', '-0.64', '#anger', '#disgust', 'negative', '-0.62', 'flourish', 'rant', 'bombast', 'ornateness', 'claptrap']
senticnet['muslin_bag'] = ['0.035', '-0.01', '0.009', '0.063', '#joy', '#admiration', 'positive', '0.026', 'container', 'carton', 'contain', 'hold_cupcake', 'store_dish']
senticnet['musophagidae'] = ['-0.68', '0', '0.956', '-0.90', '#anger', '#disgust', 'negative', '-0.84', 'giant_moa', 'anomalopteryx', 'moa', 'moa_bird', 'flightless_bird']
senticnet['musoyama'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['musquito'] = ['0', '0', '0.08', '0', '#anger', '#anger', 'negative', '-0.08', 'tree_animal', 'small_furry', 'rodent', 'big_mouse', 'small_rodent']
senticnet['muss'] = ['-0.78', '0', '0.854', '-0.9', '#anger', '#disgust', 'negative', '-0.84', 'disorderliness', 'untidiness', 'mess', 'muddle', 'messiness']
senticnet['muss-be'] = ['-0.71', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.73', 'grunt', 'disgruntle', 'disgruntled', 'discontent', 'temper']
senticnet['mussel'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'seal', 'walrus', 'otter', 'alligator', 'whale']
senticnet['mussitate'] = ['-0.62', '0', '0.885', '-0.66', '#anger', '#disgust', 'negative', '-0.72', 'talk', 'utter', 'speak', 'mouth', 'verbalize']
senticnet['mussulman'] = ['-0.71', '0', '0.836', '0', '#sadness', '#anger', 'negative', '-0.77', 'awkward', 'unenviable', 'embarrassingly', 'embarrassing', 'sheepish']
senticnet['mussy'] = ['-0.85', '0', '0.985', '-0.71', '#sadness', '#anger', 'negative', '-0.84', 'messy', 'untidy', 'disheveled', 'dirty', 'frowzled']
senticnet['must-win'] = ['-0.84', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.87', 'do-or-die', 'make-or-break', 'win-or-go-home', 'nail-biting', 'hard-fought']
senticnet['mustache'] = ['0', '0', '0.841', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'ancient', 'many_years', 'year', 'old', 'teen']
senticnet['mustang'] = ['0', '0.072', '0', '0.09', '#interest', '#admiration', 'positive', '0.081', 'equine', 'wild_animal', 'rodent', 'primate', 'high_animal']
senticnet['mustang_ranch'] = ['-0.80', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.79', 'whorehouse', 'brothel', 'massage_parlor', 'bagnio', 'house_of_prostitution']
senticnet['mustard_agent'] = ['-0.90', '0', '0.529', '-0.84', '#sadness', '#disgust', 'negative', '-0.75', 'nerve_gas', 'nerve_agent', 'mustard_gas', 'poison_gas', 'blistering_agent']
senticnet['mustard_gas'] = ['-0.90', '0', '0.529', '-0.84', '#sadness', '#disgust', 'negative', '-0.75', 'nerve_gas', 'nerve_agent', 'poison_gas', 'mustard_agent', 'blistering_agent']
senticnet['mustard_sauce'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'mayonnaise', 'box_cereal', 'bottle_water']
senticnet['mustard_yellow'] = ['-0.02', '0.068', '0.04', '0.084', '#interest', '#admiration', 'positive', '0.022', 'peelable', 'green_black', 'purple', 'red', 'edible_human']
senticnet['mustela_frenata'] = ['-0.54', '0', '0.768', '-0.50', '#sadness', '#anger', 'negative', '-0.60', 'polecat', 'hooded_skunk', 'hognosed_skunk', 'badger_skunk', 'rooter_skunk']
senticnet['muster_enough'] = ['0.819', '-0.87', '0', '0.902', '#surprise', '#admiration', 'positive', '0.865', 'tally', 'steal', 'score', 'hit', 'rack_up']
senticnet['musty'] = ['0', '0', '-0.80', '-0.90', '#fear', '#disgust', 'negative', '-0.85', 'old-fashioned', 'fustiness', 'moldy', 'fusty', 'stuffy']
senticnet['musty_smell'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dirtiness', 'dinge']
senticnet['mutability'] = ['0.772', '-0.73', '0', '0.958', '#joy', '#admiration', 'positive', '0.82', 'quality', 'changeability', 'mutableness', 'changefulness', 'exchangeability']
senticnet['mutable'] = ['0.896', '-0.66', '0', '0.667', '#joy', '#admiration', 'positive', '0.742', 'changeful', 'distortable', 'reversible', 'adjustable', 'unstable']
senticnet['mutableness'] = ['0.772', '-0.73', '0', '0.958', '#joy', '#admiration', 'positive', '0.82', 'quality', 'mutability', 'changeability', 'changefulness', 'exchangeability']
senticnet['mutant'] = ['-0.97', '0', '0.677', '-0.95', '#sadness', '#disgust', 'negative', '-0.86', 'stunt', 'game', 'animate_being', 'invertebrate', 'varmint']
senticnet['mutant_gene'] = ['-0.87', '0', '0.873', '-0.62', '#sadness', '#anger', 'negative', '-0.78', 'pycnodysostosis', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'dwarfism']
senticnet['mutant_strain'] = ['-0.08', '-0.03', '-0.07', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'injury', 'soreness', 'pain', 'hurt', 'dislocation']
senticnet['mutate'] = ['-0.83', '0', '-0.82', '-0.86', '#sadness', '#disgust', 'negative', '-0.84', 'cause_cold', 'tourism', 'reproduce', 'gland', 'procreate']
senticnet['mutated_gene'] = ['-0.87', '0', '0.873', '-0.62', '#sadness', '#anger', 'negative', '-0.78', 'pycnodysostosis', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'dwarfism']
senticnet['mutation'] = ['-0.93', '0', '0.544', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'mutant', 'variation', 'sport', 'leviathan', 'lusus_naturae']
senticnet['mutational'] = ['-0.83', '0', '0.546', '-0.88', '#sadness', '#disgust', 'negative', '-0.75', 'difference', 'allomerism', 'deviation', 'variation', 'departure']
senticnet['mute'] = ['-0.94', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.94', 'drive_recklessly', 'discord', 'deaf', 'deny', 'silent']
senticnet['mute_button'] = ['-0.97', '0', '0.728', '-0.78', '#sadness', '#disgust', 'negative', '-0.82', 'beguiler', 'slicker', 'phony', 'faker', 'deceiver']
senticnet['mute_testimony'] = ['0', '-0.02', '-0.08', '-0.05', '#fear', '#disgust', 'negative', '-0.03', 'mattress', 'sleep_furniture', 'master_bedroom', 'small_bed', 'pillow']
senticnet['mutely'] = ['0.918', '0', '0', '0.873', '#joy', '#admiration', 'positive', '0.895', 'meekly', 'stoically', 'glumly', 'obediently', 'sullenly']
senticnet['mutex_lock'] = ['0', '0.069', '0', '0.083', '#interest', '#admiration', 'positive', '0.076', 'latch', 'restrict_access', 'knob', 'door_knob', 'gate']
senticnet['mutilate'] = ['-0.59', '0', '0.537', '-0.53', '#sadness', '#anger', 'negative', '-0.55', 'murder', 'falsify', 'mangle', 'misrepresent', 'belie']
senticnet['mutilated_body'] = ['-0.06', '0.09', '-0.14', '-0.15', '#fear', '#disgust', 'negative', '-0.06', 'casket', 'coffin', 'grave', 'headstone', 'bury_person']
senticnet['mutilation'] = ['-0.17', '0', '-0.16', '-0.09', '#sadness', '#fear', 'negative', '-0.14', 'murder', 'hurt_others', 'death_penalty', 'death_destruction', 'end_live']
senticnet['mutilator'] = ['-0.74', '0', '0.548', '-0.76', '#sadness', '#disgust', 'negative', '-0.68', 'mortal', 'person', 'individual', 'mangler', 'maimer']
senticnet['mutillidae'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'giant_hornet', 'hornet', 'hornet_wasp']
senticnet['mutinous'] = ['-0.65', '0', '0.795', '-0.96', '#anger', '#disgust', 'negative', '-0.80', 'contumacious', 'disobedient', 'unruly', 'rebellious', 'insubordinate']
senticnet['mutism'] = ['-0.56', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.63', 'inarticulate', 'aphonic', 'speechless', 'unarticulated', 'dumb']
senticnet['mutt'] = ['-0.72', '0', '0.619', '-0.86', '#sadness', '#disgust', 'negative', '-0.73', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['mutter'] = ['-0.52', '0', '0.607', '-0.54', '#anger', '#disgust', 'negative', '-0.56', 'bewail', 'bemoan', 'quetch', 'scold', 'gripe']
senticnet['mutton'] = ['0', '-0.03', '0.057', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'lamb_meat', 'bandage', 'ewe', 'shear', 'seam']
senticnet['mutton_chop'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'cook_meat', 'cook_hamburger', 'cook_food', 'cook_rice', 'brown_meat']
senticnet['muttonhead'] = ['-0.81', '0', '0.871', '-0.67', '#sadness', '#anger', 'negative', '-0.78', 'stupid', 'stupid_person', 'stupe', 'dullard', 'dolt']
senticnet['muttrc'] = ['-0.72', '0', '0.619', '-0.86', '#sadness', '#disgust', 'negative', '-0.73', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['muttrc_file'] = ['-0.72', '0', '0.619', '-0.86', '#sadness', '#disgust', 'negative', '-0.73', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['mutual'] = ['0.986', '0.737', '0', '0', '#joy', '#interest', 'positive', '0.861', 'chemistry', 'rapport', 'relationship', 'trust', 'respect']
senticnet['mutual_acceptance'] = ['-0.81', '0', '0.931', '-0.77', '#sadness', '#anger', 'negative', '-0.84', 'mutualness', 'mutuality', 'complementarity', 'correlation', 'correlativity']
senticnet['mutual_acquaintance'] = ['0.05', '0.141', '-0.07', '0.132', '#interest', '#admiration', 'positive', '0.063', 'public_servant', 'worker', 'manager', 'laborer', 'office_job']
senticnet['mutual_admiration'] = ['0.608', '0.647', '-0.26', '0.325', '#joy', '#interest', 'positive', '0.328', 'amicably', 'amicable', 'amicableness', 'brotherly', 'friendly']
senticnet['mutual_advantage'] = ['0.073', '0.256', '-0.19', '0.371', '#interest', '#admiration', 'positive', '0.127', 'good_etiquette', 'okay', 'niceness', 'needful', 'need']
senticnet['mutual_affection'] = ['0.608', '0.647', '-0.26', '0.325', '#joy', '#interest', 'positive', '0.328', 'amicably', 'amicable', 'amicableness', 'brotherly', 'friendly']
senticnet['mutual_agreement'] = ['0.865', '-0.84', '0', '0.915', '#joy', '#admiration', 'positive', '0.875', 'grant', 'sale_in_gross', 'contract_of_hazard', 'adhesion_contract', 'contract_of_adhesion']
senticnet['mutual_aid_society'] = ['0.605', '0.647', '-0.26', '0.323', '#joy', '#interest', 'positive', '0.327', 'friendly', 'brotherly', 'amicableness', 'amicably', 'amicable']
senticnet['mutual_animosity'] = ['0', '0.161', '0.7', '-0.65', '#anger', '#disgust', 'negative', '-0.39', 'friction', 'hostile', 'vindictive', 'despiteful', 'vindictively']
senticnet['mutual_assistance'] = ['-0.81', '0', '0.931', '-0.77', '#sadness', '#anger', 'negative', '-0.84', 'mutualness', 'mutuality', 'complementarity', 'correlation', 'correlativity']
senticnet['mutual_benefit'] = ['0.847', '0.806', '0', '0', '#joy', '#interest', 'positive', '0.827', 'win_win_situation', 'agreement', 'win_game', 'convenience', 'win']
senticnet['mutual_confidence'] = ['0.997', '0', '0', '0.972', '#joy', '#admiration', 'positive', '0.984', 'sympathetic', 'adoration', 'confident', 'enthusiasm', 'grateful']
senticnet['mutual_cooperation'] = ['-0.81', '0', '0.931', '-0.77', '#sadness', '#anger', 'negative', '-0.84', 'mutualness', 'mutuality', 'complementarity', 'correlation', 'correlativity']
senticnet['mutual_dependence'] = ['0.826', '-0.71', '0', '0.913', '#joy', '#admiration', 'positive', '0.819', 'true', 'pure', 'symphonious', 'harmonical', 'harmonized']
senticnet['mutual_dislike'] = ['0.72', '-0.72', '0', '0.506', '#joy', '#surprise', 'positive', '0.651', 'standoffishness', 'withdrawnness', 'closeness', 'secretiveness', 'remoteness']
senticnet['mutual_distrust'] = ['0', '0.151', '0.717', '-0.66', '#anger', '#disgust', 'negative', '-0.41', 'class_feeling', 'belligerently', 'hostilely', 'aggressiveness', 'bad_blood']
senticnet['mutual_enrichment'] = ['0.826', '-0.71', '0', '0.913', '#joy', '#admiration', 'positive', '0.819', 'true', 'pure', 'symphonious', 'harmonical', 'harmonized']
senticnet['mutual_esteem'] = ['0.605', '0.647', '-0.26', '0.323', '#joy', '#interest', 'positive', '0.327', 'friendly', 'brotherly', 'amicableness', 'amicably', 'amicable']
senticnet['mutual_exclusion'] = ['0.792', '-0.83', '0', '0.575', '#joy', '#surprise', 'positive', '0.734', 'connectedness', 'connection', 'consistency', 'continuity', 'link']
senticnet['mutual_forbearance'] = ['0.621', '0.648', '-0.25', '0.296', '#joy', '#interest', 'positive', '0.328', 'pacifically', 'repose', 'peace-loving', 'peaceably', 'peace_of_mind']
senticnet['mutual_friend'] = ['0.05', '0.141', '-0.07', '0.132', '#interest', '#admiration', 'positive', '0.063', 'public_servant', 'worker', 'manager', 'laborer', 'office_job']
senticnet['mutual_friendship'] = ['0.605', '0.647', '-0.26', '0.323', '#joy', '#interest', 'positive', '0.327', 'friendly', 'brotherly', 'amicableness', 'amicably', 'amicable']
senticnet['mutual_hostility'] = ['0', '0.151', '0.717', '-0.66', '#anger', '#disgust', 'negative', '-0.41', 'class_feeling', 'belligerently', 'hostilely', 'aggressiveness', 'bad_blood']
senticnet['mutual_interdependence'] = ['-0.81', '0', '0.931', '-0.77', '#sadness', '#anger', 'negative', '-0.84', 'mutualness', 'mutuality', 'reciprocity', 'correlation', 'correlativity']
senticnet['mutual_interest'] = ['0', '0', '0', '0.881', '#admiration', '#admiration', 'positive', '0.881', 'educate_others', 'share_knowledge', 'share_opinion', 'discover_new', 'collaboration']
senticnet['mutual_masturbation'] = ['0.947', '-0.81', '0', '0.656', '#joy', '#surprise', 'positive', '0.805', 'sexual_intercourse', 'intercourse', 'sex_act', 'copulation', 'hank_panky']
senticnet['mutual_mistrust'] = ['0', '0.151', '0.717', '-0.66', '#anger', '#disgust', 'negative', '-0.41', 'class_feeling', 'belligerently', 'hostilely', 'aggressiveness', 'bad_blood']
senticnet['mutual_obligation'] = ['-0.81', '0', '0.931', '-0.77', '#sadness', '#anger', 'negative', '-0.84', 'mutualness', 'mutuality', 'complementarity', 'correlation', 'correlativity']
senticnet['mutual_recognition'] = ['-0.81', '0', '0.931', '-0.77', '#sadness', '#anger', 'negative', '-0.84', 'mutualness', 'mutuality', 'complementarity', 'correlation', 'correlativity']
senticnet['mutual_recrimination'] = ['-0.97', '0', '0.712', '-0.78', '#sadness', '#disgust', 'negative', '-0.82', 'discrepant', 'inconsistent', 'scratchy', 'uneven', 'contradictory']
senticnet['mutual_reinforcement'] = ['-0.81', '0', '0.931', '-0.77', '#sadness', '#anger', 'negative', '-0.84', 'mutualness', 'mutuality', 'reciprocity', 'correlation', 'correlativity']
senticnet['mutual_repulsion'] = ['0.538', '-0.72', '0', '0.606', '#surprise', '#admiration', 'positive', '0.622', 'attraction', 'captivate', 'complementary', 'attract', 'magnetism']
senticnet['mutual_resemblance'] = ['-0.88', '0', '0.813', '-0.57', '#sadness', '#anger', 'negative', '-0.75', 'affinity', 'resemblance', 'alikeness', 'likeness', 'similitude']
senticnet['mutual_respect'] = ['0.826', '-0.71', '0', '0.913', '#joy', '#admiration', 'positive', '0.819', 'true', 'pure', 'symphonious', 'harmonical', 'harmonized']
senticnet['mutual_suspicion'] = ['0', '0.151', '0.717', '-0.66', '#anger', '#disgust', 'negative', '-0.41', 'class_feeling', 'belligerently', 'hostilely', 'aggressiveness', 'bad_blood']
senticnet['mutual_sympathy'] = ['0.605', '0.647', '-0.26', '0.323', '#joy', '#interest', 'positive', '0.327', 'friendly', 'brotherly', 'amicableness', 'amicably', 'amicable']
senticnet['mutual_trust'] = ['-0.81', '0', '0.931', '-0.77', '#sadness', '#anger', 'negative', '-0.84', 'mutualness', 'mutuality', 'complementarity', 'correlation', 'correlativity']
senticnet['mutual_understand'] = ['0.147', '0.241', '-0.22', '0.455', '#interest', '#admiration', 'positive', '0.155', 'request', 'consent', 'give_answer', 'tell', 'proclamation']
senticnet['mutualism'] = ['-0.81', '0', '0.931', '-0.77', '#sadness', '#anger', 'negative', '-0.84', 'mutualness', 'mutuality', 'complementarity', 'correlation', 'correlativity']
senticnet['mutuality'] = ['-0.81', '0', '0.931', '-0.77', '#sadness', '#anger', 'negative', '-0.84', 'mutualness', 'reciprocity', 'complementarity', 'correlation', 'correlativity']
senticnet['mutually'] = ['0.826', '-0.71', '0', '0.913', '#joy', '#admiration', 'positive', '0.819', 'true', 'pure', 'symphonious', 'harmonical', 'harmonized']
senticnet['mutually_antagonistic'] = ['-0.97', '0', '0.712', '-0.78', '#sadness', '#disgust', 'negative', '-0.82', 'discrepant', 'inconsistent', 'scratchy', 'uneven', 'contradictory']
senticnet['mutually_beneficial'] = ['0', '0', '0', '0.881', '#admiration', '#admiration', 'positive', '0.881', 'educate_others', 'share_knowledge', 'share_opinion', 'discover_new', 'collaboration']
senticnet['mutually_complementary'] = ['0.826', '-0.71', '0', '0.913', '#joy', '#admiration', 'positive', '0.819', 'true', 'pure', 'symphonious', 'harmonical', 'harmonized']
senticnet['mutually_consistent'] = ['-0.96', '0', '0.603', '-0.91', '#sadness', '#disgust', 'negative', '-0.82', 'potential', 'potency', 'conceivableness', 'conceivability', 'possibleness']
senticnet['mutually_convenient_time'] = ['0.065', '0', '-0.05', '0.085', '#joy', '#admiration', 'positive', '0.032', 'join_club', 'social_skill', 'go_abroad', 'drive_around', 'ride_train']
senticnet['mutually_dependent'] = ['0.826', '-0.71', '0', '0.913', '#joy', '#admiration', 'positive', '0.819', 'true', 'pure', 'symphonious', 'harmonical', 'harmonized']
senticnet['mutually_exclusive'] = ['-0.81', '0', '0.931', '-0.77', '#sadness', '#anger', 'negative', '-0.84', 'mutualness', 'mutuality', 'reciprocity', 'correlation', 'correlativity']
senticnet['mutually_perpendicular'] = ['0', '0.175', '0', '0.231', '#interest', '#admiration', 'positive', '0.203', 'compass_direction', 'go_straight', 'end_point', 'eight_sides', 'red_light']
senticnet['mutually_supportive'] = ['0.826', '-0.71', '0', '0.913', '#joy', '#admiration', 'positive', '0.819', 'true', 'pure', 'symphonious', 'harmonical', 'harmonized']
senticnet['mutualness'] = ['-0.81', '0', '0.931', '-0.77', '#sadness', '#anger', 'negative', '-0.84', 'reciprocity', 'mutuality', 'complementarity', 'correlation', 'correlativity']
senticnet['muumuu'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'pinafore', 'pinny', 'dirndl', 'gown', 'dress']
senticnet['muy'] = ['0.973', '-0.50', '0', '0.612', '#joy', '#admiration', 'positive', '0.698', 'chic', 'chicness', 'jauntiness', 'modishness', 'stylishness']
senticnet['muzz'] = ['-0.10', '0', '-0.05', '-0.24', '#sadness', '#disgust', 'negative', '-0.13', 'adulate', 'suck_up', 'toady', 'blandish', 'kowtow']
senticnet['muzzle'] = ['0', '-0.10', '0.126', '-0.16', '#anger', '#disgust', 'negative', '-0.06', 'flea', 'louse', 'gnat', 'mosquito', 'whisker']
senticnet['muzzle_energy'] = ['0.887', '-0.95', '0', '0.913', '#surprise', '#admiration', 'positive', '0.917', 'high_quality', 'superiority', 'level', 'grade', 'degree']
senticnet['muzzle_flash'] = ['0', '-0.00', '-0.07', '-0.06', '#fear', '#disgust', 'negative', '-0.04', 'lighten', 'tornado', 'dynamite_explosion', 'forest_fire', 'lightning']
senticnet['muzzle_velocity'] = ['0.887', '-0.95', '0', '0.913', '#surprise', '#admiration', 'positive', '0.917', 'high_quality', 'superiority', 'level', 'grade', 'degree']
senticnet['muzzy'] = ['-0.95', '0', '0.892', '-0.81', '#sadness', '#anger', 'negative', '-0.88', 'blurry', 'foggy', 'hazy', 'indistinct', 'blurred']
senticnet['mya_arenaria'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'seal', 'walrus', 'otter', 'alligator', 'whale']
senticnet['myalgia'] = ['-0.55', '0', '0.536', '-0.72', '#sadness', '#disgust', 'negative', '-0.60', 'hypersensitivity_reaction', 'rash', 'efflorescence', 'roseola', 'skin_rash']
senticnet['myasthenia'] = ['-0.65', '0', '0.873', '-0.63', '#sadness', '#anger', 'negative', '-0.72', 'polyneuritis', 'multiple_neuritis', 'rubor', 'redness', 'inflammation']
senticnet['mycah'] = ['-0.73', '0', '0.55', '-0.75', '#sadness', '#disgust', 'negative', '-0.68', 'mortal', 'person', 'individual', 'mangler', 'mutilator']
senticnet['mycenaean_pottery'] = ['0.099', '-0.07', '-0.08', '0.195', '#joy', '#admiration', 'positive', '0.071', 'art', 'exhibit', 'craft', 'fine_art', 'artwork']
senticnet['mycobacteria'] = ['-0.90', '0', '0.908', '-0.53', '#sadness', '#anger', 'negative', '-0.78', 'tubercle_bacillus', 'leprosy_bacillus', 'eubacteria', 'eubacterium', 'true_bacteria']
senticnet['mycobacterium'] = ['-0.60', '0', '0.721', '-0.57', '#sadness', '#anger', 'negative', '-0.63', 'tubercle_bacillus', 'leprosy_bacillus', 'eubacteria', 'eubacterium', 'true_bacteria']
senticnet['mycobacterium_avium'] = ['-0.60', '0', '0.721', '-0.57', '#sadness', '#anger', 'negative', '-0.63', 'tubercle_bacillus', 'leprosy_bacillus', 'eubacteria', 'eubacterium', 'true_bacteria']
senticnet['mycobacterium_chelonae'] = ['-0.60', '0', '0.721', '-0.57', '#sadness', '#anger', 'negative', '-0.63', 'tubercle_bacillus', 'leprosy_bacillus', 'eubacteria', 'eubacterium', 'true_bacteria']
senticnet['mycobacterium_fortuitum'] = ['-0.60', '0', '0.721', '-0.57', '#sadness', '#anger', 'negative', '-0.63', 'tubercle_bacillus', 'leprosy_bacillus', 'eubacteria', 'eubacterium', 'true_bacteria']
senticnet['mycobacterium_phlei'] = ['-0.60', '0', '0.721', '-0.57', '#sadness', '#anger', 'negative', '-0.63', 'tubercle_bacillus', 'leprosy_bacillus', 'eubacteria', 'eubacterium', 'true_bacteria']
senticnet['mycology'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'study_person', 'subject_study', 'zoo', 'zoologist', 'geology']
senticnet['mycoplasma_pneumonia'] = ['-0.85', '0', '0.549', '-0.93', '#sadness', '#disgust', 'negative', '-0.78', 'coccidioidomycosis', 'coccidiomycosis', 'mycosis', 'desert_rheumatism', 'cryptococcosis']
senticnet['mycoplasma_pneumoniae'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchitis', 'chronic_bronchitis', 'respiratory_disease', 'respiratory_illness', 'respiratory_disorder']
senticnet['mycosis'] = ['-0.85', '0', '0.549', '-0.93', '#sadness', '#disgust', 'negative', '-0.78', 'coccidioidomycosis', 'coccidiomycosis', 'valley_fever', 'desert_rheumatism', 'cryptococcosis']
senticnet['mycosphaerella'] = ['-0.93', '0', '0.699', '-0.91', '#sadness', '#disgust', 'negative', '-0.84', 'onion_smut', 'head_smut', 'stinking_smut', 'flag_smut_fungus', 'covered_smut']
senticnet['mycteria'] = ['-0.02', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.04', 'wren', 'bird', 'quail', 'sparrow', 'chick']
senticnet['myel'] = ['-0.86', '0', '0.511', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'separate', 'move', 'branch', 'diverge', 'fork']
senticnet['myelatelia'] = ['-0.53', '0', '0.626', '-0.86', '#anger', '#disgust', 'negative', '-0.67', 'imperfection', 'imperfectness', 'defect', 'birth_defect', 'congenital_anomaly']
senticnet['myelodysplastic_syndrome'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['myelomeningocele'] = ['-0.91', '0', '0.961', '-0.55', '#sadness', '#anger', 'negative', '-0.81', 'hypopigmentation', 'albinism', 'congenital_anomaly', 'congenital_defect', 'congenital_disorder']
senticnet['myelopathy'] = ['0.722', '-0.87', '0', '0.696', '#joy', '#surprise', 'positive', '0.763', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['myford_touch'] = ['0.725', '0.796', '0', '0', '#joy', '#interest', 'positive', '0.76', 'car_stereo', 'battery', 'seatbelt', 'spare_tire', 'brake_pedal']
senticnet['mylanta'] = ['-0.53', '0', '0.954', '-0.54', '#anger', '#disgust', 'negative', '-0.67', 'ranitidine', 'histamine_blocker', 'gastric_ranitidine', 'alkalizer', 'antiacid']
senticnet['mylar'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'aluminium_foil', 'tin_foil', 'gold_foil']
senticnet['mynde'] = ['-0.11', '0.112', '0.674', '-0.66', '#anger', '#disgust', 'negative', '-0.33', 'wrathfully', 'wrathful', 'wrothful', 'lividly', 'lividity']
senticnet['myob'] = ['-0.03', '0.112', '-0.08', '0.164', '#interest', '#admiration', 'positive', '0.04', 'run', 'jump_over', 'quick', 'sprint', 'flee']
senticnet['myobatrachidae_family'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'amphibian', 'aquatic']
senticnet['myocardial_infarction'] = ['-0.98', '0', '0.827', '-0.94', '#sadness', '#disgust', 'negative', '-0.91', 'congestive_heart_failure', 'heart_attack', 'failure', 'heart_disease', 'cardiopathy']
senticnet['myocardial_ischemia'] = ['-0.20', '0', '-0.22', '-0.19', '#sadness', '#fear', 'negative', '-0.20', 'go_hospital', 'death', 'die', 'take_aspirin', 'heart_attack']
senticnet['myocardial_perfusion'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'blatancy', 'obviousness']
senticnet['myocardiopathy'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'heart_problem', 'ill', 'illness', 'heart_disease', 'rheumatic_heart_disease']
senticnet['myoclonic'] = ['0.722', '-0.87', '0', '0.696', '#joy', '#surprise', 'positive', '0.763', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['myoelectric'] = ['-0.62', '0', '0.996', '-0.68', '#anger', '#disgust', 'negative', '-0.76', 'pack', 'face_pack', 'pickup', 'prosthesis', 'corrective']
senticnet['myofibrillar'] = ['-0.03', '0.169', '-0.07', '0.218', '#interest', '#admiration', 'positive', '0.071', 'elongate', 'elasticity', 'extension', 'grow_long', 'widen']
senticnet['myology'] = ['0.766', '0.956', '0', '0', '#joy', '#interest', 'positive', '0.861', 'bodily_property', 'biology', 'hemodynamics', 'neurophysiology', 'physiology']
senticnet['myoma'] = ['-0.70', '0', '0.639', '-0.56', '#sadness', '#anger', 'negative', '-0.63', 'fibroma', 'adenoma', 'chondroma', 'enchondroma', 'benign_tumor']
senticnet['myopathic'] = ['0.722', '-0.87', '0', '0.696', '#joy', '#surprise', 'positive', '0.763', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['myopathy'] = ['-0.87', '0', '0.531', '-0.68', '#sadness', '#disgust', 'negative', '-0.69', 'rubor', 'redness', 'dermatomyositis', 'inclusion_body_myositis', 'inflammation']
senticnet['myope'] = ['-0.63', '0', '0.625', '-0.98', '#sadness', '#disgust', 'negative', '-0.75', 'aphakic', 'blind_person', 'visually_impaired_person', 'mortal', 'hyperope']
senticnet['myopic'] = ['-0.63', '0', '0.867', '-0.57', '#sadness', '#anger', 'negative', '-0.69', 'improvident', 'short', 'shortsighted', 'unforesightful', 'thriftless']
senticnet['myosin_regulatory'] = ['0.096', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.057', 'platform_rail', 'rail', 'catwalk', 'column', 'token_turnstile']
senticnet['myositis'] = ['-0.87', '0', '0.531', '-0.68', '#sadness', '#disgust', 'negative', '-0.69', 'rubor', 'redness', 'dermatomyositis', 'inclusion_body_myositis', 'inflammation']
senticnet['myotomy'] = ['-0.56', '0', '0.806', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['myotonia'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'myotonic_muscular_dystrophy', 'dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['myotonia_atrophica'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'myotonic_muscular_dystrophy', 'myotonic_dystrophy', 'dystrophy', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['myotonic_dystrophy'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'myotonic_muscular_dystrophy', 'dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['myotonic_muscular_dystrophy'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'dystrophy', 'myotonic_dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['myrcene'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'capsaicin', 'chemical_irritant', 'piperine', 'pepper_spray']
senticnet['myriad'] = ['0', '0.89', '0', '0.756', '#interest', '#admiration', 'positive', '0.823', 'indeterminable', 'infinite', 'incalculable', 'immeasurable', 'countless']
senticnet['myriophyllum'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'bulrush', 'nailrod', 'reed_mace', 'reedmace']
senticnet['myristica'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'seed_fern', 'pteridosperm']
senticnet['myristicin'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'capsaicin', 'chemical_irritant', 'piperine', 'pepper_spray']
senticnet['myrmica_schencki'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'hornet', 'vespid', 'vespid_wasp']
senticnet['myrmicaria'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'giant_hornet', 'hornet', 'hornet_wasp']
senticnet['mysimon'] = ['0.94', '0.804', '0', '0', '#joy', '#interest', 'positive', '0.872', 'spend_a_lot_of_money', 'window_shop', 'spend_much_money', 'check_price', 'compare_price']
senticnet['mystere'] = ['-0.94', '0', '0.866', '-0.91', '#sadness', '#disgust', 'negative', '-0.90', 'movement', 'apparent_motion', 'motion', 'apparent_movement', 'optical_illusion']
senticnet['mysteria'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'sedum', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['mysterious'] = ['0', '0.066', '-0.06', '0.088', '#interest', '#admiration', 'positive', '0.031', 'foggy', 'hazy', 'cloudy', 'wet_air', 'evaporate']
senticnet['mysterious_ailment'] = ['0.713', '0', '0', '0.766', '#joy', '#admiration', 'positive', '0.74', 'doctor', 'nurse', 'medical_procedure', 'medicine', 'doctor_surgery']
senticnet['mysterious_alien'] = ['0.086', '-0.10', '0.064', '0', '#joy', '#surprise', 'positive', '0.043', 'art_piece', 'lifetime', 'museum', 'vintage', 'collectible']
senticnet['mysterious_artifact'] = ['0.086', '-0.10', '0.064', '0', '#joy', '#surprise', 'positive', '0.043', 'art_piece', 'lifetime', 'museum', 'vintage', 'collectible']
senticnet['mysterious_disappearance'] = ['-0.89', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.80', 'riddle', 'obituary', 'editorial', 'news_story', 'article']
senticnet['mysterious_figure'] = ['0.086', '-0.10', '0.064', '0', '#joy', '#surprise', 'positive', '0.043', 'art_piece', 'lifetime', 'museum', 'vintage', 'collectible']
senticnet['mysterious_stranger'] = ['0.083', '0', '0', '0.075', '#joy', '#admiration', 'positive', '0.079', 'carny', 'carnival_ride', 'midway', 'ferris_wheel', 'roller-coaster']
senticnet['mysterious_villain'] = ['-0.69', '0', '0.756', '-0.99', '#anger', '#disgust', 'negative', '-0.81', 'waster', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['mysterious_woman'] = ['0.083', '0', '0', '0.075', '#joy', '#admiration', 'positive', '0.079', 'carny', 'carnival_ride', 'midway', 'ferris_wheel', 'roller-coaster']
senticnet['mysteriously'] = ['0.766', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.804', 'magically', 'miraculously', 'healed', 'thankfully', 'somehow']
senticnet['mystery'] = ['-0.89', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.80', 'riddle', 'obituary', 'editorial', 'news_story', 'article']
senticnet['mystery_novel'] = ['-0.89', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.80', 'riddle', 'obituary', 'editorial', 'news_story', 'article']
senticnet['mystery_play'] = ['0.734', '-0.60', '0', '0.612', '#joy', '#admiration', 'positive', '0.65', 'theater_of_absurd', 'playlet', 'miracle_play', 'morality_play', 'drama']
senticnet['mystic'] = ['0.291', '0.27', '-0.12', '0', '#joy', '#interest', 'positive', '0.147', 'unknown', 'seeker', 'add_knowledge', 'deep_thinker', 'sense_of_fulfillment']
senticnet['mystical'] = ['0.128', '0.152', '-0.09', '0.264', '#interest', '#admiration', 'positive', '0.113', 'environment', 'beautiful', 'live_environment', 'really_big', 'important_man']
senticnet['mystical_experience'] = ['0.08', '0.153', '-0.12', '0.224', '#interest', '#admiration', 'positive', '0.083', 'meditation', 'self_contemplation', 'soul_searching', 'self_examination', 'self_observation']
senticnet['mystical_tradition'] = ['0', '0.915', '0', '0.821', '#interest', '#admiration', 'positive', '0.868', 'culture', 'special_day', 'holiday', 'party_time', 'yearly_celebration']
senticnet['mystical_union'] = ['0.971', '-0.71', '0', '0.656', '#joy', '#surprise', 'positive', '0.781', 'wellbeing', 'human_flourishing', 'successfulness', 'upbeat', 'strength']
senticnet['mystically'] = ['0', '0', '0', '0.816', '#admiration', '#admiration', 'positive', '0.816', 'immortality', 'live_forever', 'deathless', 'eternal_life', 'everlasting_life']
senticnet['mysticism'] = ['0.037', '0.293', '0', '0.269', '#interest', '#admiration', 'positive', '0.2', 'religious_purpose', 'prayer', 'judaism', 'christianity', 'religion']
senticnet['mysticker'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
senticnet['mystification'] = ['-0.80', '0', '0.795', '-0.81', '#sadness', '#disgust', 'negative', '-0.80', 'confusion', 'disarray', 'mental_confusion', 'confusedness', 'muddiness']
senticnet['mystify'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'surprise', 'baffle', 'stupefy', 'dumbfound', 'stupefaction']
senticnet['mystifying'] = ['-0.62', '0', '0.825', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'unexplainable', 'unaccountable', 'unexplained', 'inscrutable', 'incomprehensible']
senticnet['mystique'] = ['0.817', '-0.62', '0', '0.62', '#joy', '#surprise', 'positive', '0.688', 'quality', 'fascination', 'attractivenessment', 'attractiveness', 'temptingness']
senticnet['mystre'] = ['-0.94', '0', '0.866', '-0.91', '#sadness', '#disgust', 'negative', '-0.90', 'movement', 'apparent_motion', 'motion', 'apparent_movement', 'optical_illusion']
senticnet['mysupermarket'] = ['0.875', '0.706', '0', '0', '#joy', '#interest', 'positive', '0.79', 'customer', 'clerk', 'patron', 'vintner', 'hairdresser']
senticnet['myth'] = ['0', '0', '-0.81', '-0.82', '#fear', '#disgust', 'negative', '-0.82', 'fable', 'story', 'saga', 'long_story', 'classic']
senticnet['mythic'] = ['0.075', '0.207', '-0.06', '0.12', '#interest', '#admiration', 'positive', '0.085', 'false', 'unreal', 'pretend', 'fictional', 'fictitious']
senticnet['mythical'] = ['0', '0', '-0.08', '0', '#fear', '#fear', 'negative', '-0.08', 'fable', 'story', 'dictionary', 'saga', 'fairy_story']
senticnet['mythical_beast'] = ['-0.93', '0', '0.544', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'mutant', 'variation', 'sport', 'lusus_naturae', 'mutation']
senticnet['mythical_being'] = ['-0.80', '0', '0.747', '-0.74', '#sadness', '#disgust', 'negative', '-0.76', 'mythical_monster', 'mythical_creature', 'hero', 'houri', 'halcyon']
senticnet['mythical_bird'] = ['0', '0.105', '0', '0', '#interest', '#interest', 'positive', '0.105', 'beak', 'many_feathers', 'big_eye', 'bird', 'bird_mouth']
senticnet['mythical_creature'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'creature', 'vertebrate', 'beast', 'arthropod', 'arachnid']
senticnet['mythical_monster'] = ['-0.65', '0', '0.505', '-0.91', '#sadness', '#disgust', 'negative', '-0.69', 'hellhound', 'mythical_being', 'dragon', 'roc', 'amphisbaena']
senticnet['mythical_place'] = ['-0.92', '0', '0.687', '-0.75', '#sadness', '#disgust', 'negative', '-0.78', 'hellfire', 'red_region', 'imaginary_place', 'infernal_region', 'fictitious_place']
senticnet['mythological'] = ['-0.72', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.79', 'mythical', 'mythology', 'allegorical', 'deity', 'archetypal']
senticnet['mythologize'] = ['0.54', '-0.97', '0', '0.569', '#surprise', '#admiration', 'positive', '0.695', 'overstate', 'hyperbolize', 'aggrandize', 'exaggerate', 'overdraw']
senticnet['mythology'] = ['0.942', '-0.86', '0', '0.939', '#joy', '#admiration', 'positive', '0.916', 'content', 'old_wives_tale', 'folklore', 'cognitive_content', 'lore']
senticnet['myxedema'] = ['-0.65', '0', '0.873', '-0.63', '#sadness', '#anger', 'negative', '-0.72', 'neuritis', 'multiple_neuritis', 'rubor', 'redness', 'inflammation']
senticnet['myxoid'] = ['-0.88', '0', '0.774', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fibroid_tumor', 'fibroid', 'benign_tumor', 'nonmalignant_tumor', 'nonmalignant_neoplasm']
senticnet['myxosporean'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'annelid', 'annelid_worm', 'leech']
senticnet['myzus_persicae'] = ['-0.93', '0', '0.699', '-0.91', '#sadness', '#disgust', 'negative', '-0.84', 'onion_smut', 'head_smut', 'stinking_smut', 'flag_smut_fungus', 'covered_smut']
senticnet['naamah'] = ['0.539', '-0.96', '0', '0.745', '#surprise', '#admiration', 'positive', '0.751', 'spouse', 'partner', 'married_person', 'mate', 'better_half']
senticnet['nab'] = ['0.922', '0.905', '0', '0', '#joy', '#interest', 'positive', '0.913', 'impressive', 'nail', 'arresting', 'pick_up', 'apprehend']
senticnet['nabba'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'sciolist', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
senticnet['nabbp'] = ['-0.58', '0', '0.757', '-0.67', '#anger', '#disgust', 'negative', '-0.67', 'breech', 'knee_breech', 'knee_pants', 'trunk_hose', 'knickers']
senticnet['nabisco'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'wrapping_paper', 'wrap_gift', 'butcher_paper', 'paper']
senticnet['nabuchodonosor'] = ['-0.52', '0', '0.701', '-0.50', '#sadness', '#anger', 'negative', '-0.57', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['nach_deach'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['nach_dem'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['nach_der'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['nacho'] = ['-0.87', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.88', 'person', 'stockholder', 'commit_crime', 'guilty_person', 'criminal']
senticnet['naczelna'] = ['0', '0.793', '0', '0.847', '#interest', '#admiration', 'positive', '0.82', 'disagreement', 'fight_against', 'opposition', 'against', 'go_against']
senticnet['nada_surf'] = ['0.304', '0.339', '-0.19', '0.333', '#interest', '#admiration', 'positive', '0.195', 'careful', 'reinforce', 'load_bear', 'go_outdoors', 'allowance']
senticnet['nadia_comneci'] = ['0.964', '0.713', '0', '0', '#joy', '#interest', 'positive', '0.839', 'martial_art', 'judo', 'jump_around', 'go_underwater', 'athletics']
senticnet['nadine_strossen'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'economic_libertarian', 'libertarian', 'advocate', 'advocator', 'proponent']
senticnet['nadolol'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'spironolactone', 'adrenal_cortical_steroid', 'antihypertensive', 'antihypertensive_drug']
senticnet['nadph_oxidase'] = ['0.702', '-0.52', '0', '0.958', '#joy', '#admiration', 'positive', '0.728', 'somatic_cell', 'vegetative_cell', 'fixed_phagocyte', 'macrophage', 'free_phagocyte']
senticnet['naebody'] = ['0.027', '0', '0.012', '0.063', '#joy', '#admiration', 'positive', '0.026', 'vegetation', 'tree_flower', 'green_growth', 'small', 'greenery']
senticnet['naegleria_fowleri'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['naff'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'wet', 'sticky', 'gluey', 'viscous', 'gooey']
senticnet['nag'] = ['0', '0', '0.737', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'annoy', 'skin_rash', 'bother', 'irritation', 'great_dane']
senticnet['nag_fortran_library'] = ['0', '0', '0.737', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'annoy', 'skin_rash', 'bother', 'irritation', 'great_dane']
senticnet['nag_library'] = ['0', '0', '0.737', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'annoy', 'skin_rash', 'bother', 'irritation', 'great_dane']
senticnet['nag_smp_library'] = ['0', '0', '0.737', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'annoy', 'skin_rash', 'bother', 'irritation', 'great_dane']
senticnet['nag_software'] = ['0', '0', '0.737', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'annoy', 'skin_rash', 'bother', 'irritation', 'great_dane']
senticnet['nagaina'] = ['0', '0', '0.737', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'annoy', 'skin_rash', 'bother', 'irritation', 'great_dane']
senticnet['nagamasa'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'overlord', 'lord']
senticnet['nagana'] = ['-0.55', '0', '0.757', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'rubor', 'redness', 'cephalitis', 'sleepy_sickness', 'epidemic_encephalitis']
senticnet['nagarjunsagar_srisailam_tiger'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['nagasaki'] = ['0', '0', '-0.72', '-0.94', '#fear', '#disgust', 'negative', '-0.83', 'pistol', 'gun', 'hydrogen_bomb', 'handgun', 'revolver']
senticnet['nagasaki_bomb'] = ['0', '0', '-0.72', '-0.94', '#fear', '#disgust', 'negative', '-0.83', 'pistol', 'gun', 'hydrogen_bomb', 'handgun', 'revolver']
senticnet['nagger'] = ['-0.71', '0', '0.972', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'scold', 'nag', 'scolder', 'harridan', 'common_scold']
senticnet['nagging_feeling'] = ['-0.80', '0', '-0.8', '0', '#sadness', '#fear', 'negative', '-0.80', 'sinking', 'misgiving', 'qualm', 'scruple', 'insecurely']
senticnet['nagging_pain'] = ['-0.93', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.92', 'pull_muscle', 'become_exhausted', 'increased_heart_rate', 'breathlessness', 'tired_leg']
senticnet['nagging_sense'] = ['-0.74', '0.292', '-0.42', '-0.06', '#sadness', '#fear', 'negative', '-0.23', 'joyless', 'joylessly', 'dolefulness', 'uncheerful', 'depressing']
senticnet['nagging_wife'] = ['-0.72', '0.304', '-0.41', '-0.07', '#sadness', '#fear', 'negative', '-0.22', 'suffering', 'miserably', 'pitiable', 'piteous', 'hapless']
senticnet['naginata'] = ['0.93', '-0.78', '0', '0.873', '#joy', '#admiration', 'positive', '0.861', 'blade', 'brand', 'steel', 'claymore', 'sword']
senticnet['nagumo'] = ['0.084', '-0.05', '0.013', '0', '#joy', '#surprise', 'positive', '0.043', 'carnival_ride', 'carny', 'fortune_teller', 'horseback', 'ferris_wheel']
senticnet['nah'] = ['0.104', '0', '0', '0.088', '#joy', '#admiration', 'positive', '0.096', 'baby_flower', 'wee', 'bud', 'part_of_plant', 'leafy']
senticnet['naif'] = ['0.798', '-0.51', '0', '0.973', '#joy', '#admiration', 'positive', '0.763', 'inexperienced_person', 'boy_scout', 'dear', 'virgin', 'mortal']
senticnet['nail'] = ['0', '0.078', '0.027', '0.196', '#interest', '#admiration', 'positive', '0.082', 'heel', 'ankle', 'toe', 'foot_massage', 'foot']
senticnet['nail-biting'] = ['0', '0', '0', '-0.96', '#disgust', '#disgust', 'negative', '-0.96', 'suspenseful', 'suspensive', 'cliff-hanging', 'apathetically', 'concern']
senticnet['nail_biter'] = ['-0.83', '0', '0.912', '-0.67', '#sadness', '#anger', 'negative', '-0.80', 'dissatisfaction', 'displeasure', 'disappointment', 'boredom', 'ennui']
senticnet['nail_bomb'] = ['0', '0', '0.818', '-0.99', '#anger', '#disgust', 'negative', '-0.90', 'weapon', 'aerial_bomb', 'machine_gun', 'cannon', 'handgun']
senticnet['nail_clipper'] = ['-0.08', '0.232', '0', '0.099', '#interest', '#admiration', 'positive', '0.084', 'wax_dental_floss', 'fluoride_toothpaste', 'toilet_paper', 'dental_floss', 'tartar_control_toothpaste']
senticnet['nail_down'] = ['0.747', '-0.63', '0', '0.755', '#joy', '#admiration', 'positive', '0.712', 'deliver_goods', 'succeed', 'win', 'bring_home_bacon', 'come_through']
senticnet['nail_dystrophy'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema', 'erythema_multiforme', 'windburn', 'hickey']
senticnet['nail_polish'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'sexual_excitement', 'sexy', 'wet_lip', 'kiss', 'clean_up']
senticnet['nail_salon'] = ['0.99', '0', '0', '0.721', '#joy', '#admiration', 'positive', '0.855', 'hair', 'haircut', 'hairdresser_chair', 'scissor', 'hairstyle']
senticnet['nail_technician'] = ['0.99', '0', '0', '0.721', '#joy', '#admiration', 'positive', '0.855', 'hair', 'haircut', 'hairdresser_chair', 'scissor', 'hairstyle']
senticnet['nail_varnish'] = ['0.022', '-0.01', '0.062', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'electric_toothbrush', 'moistener', 'hairbrush', 'shampoo_conditioner', 'shave_kit']
senticnet['nailed_shut'] = ['0.829', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.908', 'cut_food', 'drink_beer', 'serve_drink', 'drink_much', 'drink_beverage']
senticnet['nailrod'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'bulrush', 'cattail', 'reed_mace', 'reedmace']
senticnet['naisha'] = ['-0.96', '0', '0.766', '-0.96', '#sadness', '#disgust', 'negative', '-0.89', 'calumniation', 'traducement', 'hatchet_job', 'defamation', 'calumny']
senticnet['naive'] = ['0', '0', '0.957', '-0.85', '#anger', '#disgust', 'negative', '-0.90', 'ingenuous', 'gullible', 'artless', 'childlike', 'guileless']
senticnet['naive_person'] = ['0', '0', '0.894', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'young_child', 'child', 'youth', 'small_child', 'pass_gene']
senticnet['naive_realism'] = ['-0.97', '0', '0.781', '-0.91', '#sadness', '#disgust', 'negative', '-0.88', 'weakness', 'fault', 'flaw', 'defect', 'wart']
senticnet['naively'] = ['0.653', '0.635', '-0.24', '0.294', '#joy', '#interest', 'positive', '0.335', 'merrily', 'jubilantly', 'happily', 'mirthfully', 'gayly']
senticnet['naiveness'] = ['-0.61', '0', '0.599', '-0.57', '#sadness', '#anger', 'negative', '-0.59', 'simple_mindedness', 'simpleness', 'ingenuousness', 'artlessness', 'simplicity']
senticnet['naivete'] = ['-0.59', '0', '0.834', '-0.72', '#anger', '#disgust', 'negative', '-0.71', 'simple_mindedness', 'simpleness', 'ingenuousness', 'artlessness', 'simplicity']
senticnet['naivety'] = ['-0.70', '0', '0.64', '-0.82', '#sadness', '#disgust', 'negative', '-0.72', 'simple_mindedness', 'simpleness', 'ingenuousness', 'artlessness', 'simplicity']
senticnet['naked'] = ['-0.03', '0', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.03', 'take_clothes_off', 'remove_clothes', 'naked_woman', 'turn_water', 'take_off_clothes']
senticnet['naked_body'] = ['0.024', '0', '0', '0.074', '#joy', '#admiration', 'positive', '0.049', 'sheet', 'cover', 'quilt', 'comforter', 'modesty']
senticnet['naked_eye'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'eye', 'peeper', 'oculus_sinister']
senticnet['naked_flame'] = ['0.886', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.881', 'fire_hydrant', 'stair_rail', 'christian_fundamentalist', 'extinguisher', 'reception_desk']
senticnet['naked_mole_rat'] = ['0.921', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.825', 'monkey', 'fiance', 'lemur', 'beaver', 'koala']
senticnet['naked_woman'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'driftwood', 'sea_shell', 'scenic_view', 'shore', 'tidal_wave']
senticnet['nakedly'] = ['-0.71', '0', '0.995', '0', '#sadness', '#anger', 'negative', '-0.85', 'horrifying', 'atrocious', 'dreadfully', 'horrifyingly', 'horrified']
senticnet['nakedness'] = ['-0.94', '0', '0.991', '-0.65', '#sadness', '#anger', 'negative', '-0.86', 'birthday_suit', 'raw', 'altogether', 'nude', 'undress']
senticnet['nalaikh'] = ['-0.63', '0', '0.791', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'little', 'pinch', 'modicum', 'small_indefinite_amount', 'indefinite_quantity']
senticnet['nam_myoho_renge_kyo'] = ['0.022', '0.123', '0.038', '0.129', '#interest', '#admiration', 'positive', '0.059', 'hymn', 'written_music', 'walk_beach', 'lyric', 'piece_of_music']
senticnet['nama_rupa'] = ['-0.95', '0', '0.909', '-0.97', '#sadness', '#disgust', 'negative', '-0.94', 'unreality', 'incorporeality', 'quality', 'intangibility', 'intangibleness']
senticnet['namajague'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'find_out', 'dig_out', 'unearth', 'become_aware', 'dish_out']
senticnet['nambla'] = ['0', '0.942', '0', '0', '#interest', '#interest', 'positive', '0.942', 'guy', 'special_person', 'guardian', 'boy', 'male']
senticnet['namche_bazaar'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['namdalen_region'] = ['0.929', '0.507', '-0.17', '0.396', '#joy', '#interest', 'positive', '0.415', 'plume', 'congratulate', 'smugness', 'smug', 'proudly']
senticnet['nameable'] = ['0.78', '0.993', '0', '0', '#joy', '#interest', 'positive', '0.887', 'memorability', 'notable', 'catchy', 'memory', 'memorable']
senticnet['nameboard'] = ['0.952', '-0.52', '0', '0.835', '#joy', '#admiration', 'positive', '0.771', 'billboard', 'hoarding', 'sandwich_board', 'scoreboard', 'shingle']
senticnet['named_mango_cultivar'] = ['0.859', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.888', 'green_apple', 'apricot', 'lime', 'peach', 'cherry']
senticnet['namelessness'] = ['-0.98', '0', '0.75', '0', '#sadness', '#anger', 'negative', '-0.86', 'anonymity', 'internet', 'tonne', 'future', 'hide']
senticnet['namely'] = ['0.929', '-0.71', '0', '0.605', '#joy', '#surprise', 'positive', '0.748', 'incisiveness', 'trenchancy', 'efficacy', 'efficaciousness', 'power']
senticnet['namesake'] = ['0.072', '0.244', '-0.20', '0.252', '#interest', '#admiration', 'positive', '0.091', 'doe', 'sweet', 'live_organism', 'love', 'alive']
senticnet['nametag'] = ['0', '0.137', '0', '0.133', '#interest', '#admiration', 'positive', '0.135', 'sign', 'indication', 'post_notice', 'notice', 'red_octagon']
senticnet['namgyal'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['naming'] = ['0.904', '-0.58', '0', '0.736', '#joy', '#admiration', 'positive', '0.743', 'denotative', 'appellative', 'designative', 'extensional', 'referent']
senticnet['naming_ceremony'] = ['0.82', '0', '0', '0.885', '#joy', '#admiration', 'positive', '0.853', 'religious_event', 'sunday', 'happy_event', 'tradition', 'wedding']
senticnet['namor'] = ['-0.98', '0', '0.714', '-0.90', '#sadness', '#disgust', 'negative', '-0.86', 'stupid', 'stupid_person', 'stupe', 'dullard', 'dolt']
senticnet['namur'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'feudal_lord', 'lord', 'master', 'overlord', 'king']
senticnet['nanda_devi'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['nang'] = ['0.973', '-0.50', '0', '0.612', '#joy', '#admiration', 'positive', '0.698', 'chic', 'chicness', 'jauntiness', 'modishness', 'stylishness']
senticnet['nanism'] = ['-0.81', '0', '0.96', '-0.96', '#anger', '#disgust', 'negative', '-0.91', 'pycnodysostosis', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['nanite'] = ['-0.88', '0', '0.882', '-0.58', '#sadness', '#anger', 'negative', '-0.78', 'mortal', 'behemoth', 'unusual_person', 'aberrant', 'giant']
senticnet['nankeen'] = ['-0.84', '0', '0.633', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'grizzle', 'postiche', 'horsehair_wig', 'wig', 'peruke']
senticnet['nanki_poo'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'dress', 'pinny', 'dirndl', 'gown', 'muumuu']
senticnet['nanny'] = ['0', '0.864', '0', '0.751', '#interest', '#admiration', 'positive', '0.807', 'family_member', 'sibling', 'parent', 'female_relative', 'relative']
senticnet['nanny_ogg'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'jawless_fish', 'osteostracan', 'cephalaspid', 'ostracoderm', 'heterostracan']
senticnet['nano_scale'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'amoeba', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['nanoindentation'] = ['-0.54', '0', '0.909', '-0.97', '#anger', '#disgust', 'negative', '-0.81', 'suppleness', 'pliancy', 'pliantness', 'flexibility', 'flexibleness']
senticnet['nanometer_resolution'] = ['-0.54', '0', '0.909', '-0.97', '#anger', '#disgust', 'negative', '-0.81', 'suppleness', 'pliancy', 'pliantness', 'flexibility', 'flexibleness']
senticnet['nanometer_scale'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'amoeba', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['nanometer_size'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'amoeba', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['nanoparticles'] = ['0', '0.783', '0', '0.995', '#interest', '#admiration', 'positive', '0.889', 'research', 'study', 'project', 'well', 'polymer']
senticnet['nanoscale'] = ['0.812', '0.749', '0', '0', '#joy', '#interest', 'positive', '0.78', 'nanoscience', 'technology', 'nanoscale_science', 'develop', 'control']
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senticnet['nasalize'] = ['0.63', '-0.97', '0', '0.626', '#joy', '#surprise', 'positive', '0.742', 'pronounce', 'enounce', 'say', 'sound_out', 'enunciate']
senticnet['nascent'] = ['0.836', '-0.83', '0', '0.511', '#joy', '#surprise', 'positive', '0.726', 'appearance', 'start', 'casus_belli', 'beginning', 'egress']
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senticnet['nasig_membership'] = ['0.723', '0', '0', '0.762', '#joy', '#admiration', 'positive', '0.742', 'presence', 'feel_important', 'existence', 'feel_satisfied', 'feel_presence']
senticnet['nasir_khusraw'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['nasociliary_nerve'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['nasolabial_flap'] = ['-0.58', '0', '0.715', '-0.59', '#anger', '#disgust', 'negative', '-0.63', 'fly', 'overlap', 'barndoor', 'coattail', 'codpiece']
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senticnet['nastassja_kinski'] = ['0.794', '-0.50', '0', '0.531', '#joy', '#admiration', 'positive', '0.611', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
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senticnet['nasty_bite'] = ['0.876', '0.948', '0', '0', '#joy', '#interest', 'positive', '0.912', 'sting', 'tidbit', 'nip', 'tiny_amount', 'piece']
senticnet['nasty_gash'] = ['0.94', '0.78', '0', '0', '#joy', '#interest', 'positive', '0.86', 'breaheavily', 'heavy_breathe', 'get_exhausted', 'exhaustion', 'see_neighbor']
senticnet['nasty_habit'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'vapor', 'gaseous', 'haze', 'smoke', 'smog']
senticnet['nasty_piece'] = ['0.739', '-0.89', '0', '0.702', '#joy', '#surprise', 'positive', '0.78', 'pap', 'content', 'pablum', 'folderol', 'drivel']
senticnet['nasty_stuff'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['nasty_taste'] = ['0.045', '0.141', '-0.05', '0.065', '#interest', '#admiration', 'positive', '0.049', 'kernel', 'starch', 'ginger', 'cereal_crop', 'pea']
senticnet['nat_holman'] = ['0.035', '0.155', '0', '0', '#joy', '#interest', 'positive', '0.095', 'basketball_court', 'basketball_game', 'basketball', 'ball', 'sport']
senticnet['natasha'] = ['-0.03', '-0.00', '0.087', '-0.05', '#anger', '#disgust', 'negative', '-0.04', 'dog_fur', 'dog_cat', 'dog_hair', 'pet', 'animal_hair']
senticnet['natatorium'] = ['0', '0', '0', '0.964', '#admiration', '#admiration', 'positive', '0.964', 'swimming_pool', 'swim', 'swimsuit', 'lake', 'have_fun']
senticnet['natavalasa'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
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senticnet['natchez'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
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senticnet['nate_campbell'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
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senticnet['nation-wide'] = ['0.041', '0.122', '-0.08', '0.171', '#interest', '#admiration', 'positive', '0.063', 'nation', 'govern_city', 'national', 'large_town', 'big_city_life']
senticnet['nation_afn'] = ['0.055', '0.072', '-0.06', '0.094', '#interest', '#admiration', 'positive', '0.038', 'big_group', 'large_group', 'bunch_person', 'large_number', 'clique']
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senticnet['nation_largest_retailer'] = ['0.912', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.917', 'carpet_store', 'lumberyard', 'furniture_store', 'front_porch', 'shoe_shop']
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senticnet['nation_unies_ban_ki'] = ['0.916', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.929', 'galaxy', 'universe', 'planetary', 'sun', 'dwarf_planet']
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senticnet['national_biography_dnb'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'comic_book', 'child_book', 'child_story', 'comic', 'disney_cartoon']
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senticnet['national_constituent_assembly'] = ['0.055', '0.072', '-0.06', '0.094', '#interest', '#admiration', 'positive', '0.038', 'big_group', 'large_group', 'bunch_person', 'large_number', 'clique']
senticnet['national_electricity_grid'] = ['0.986', '0.897', '0', '0', '#joy', '#interest', 'positive', '0.942', 'potential_energy', 'watt', 'electrical', 'power_source', 'volt']
senticnet['national_elk_refuge'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['national_event'] = ['-0.18', '0.081', '-0.18', '0', '#sadness', '#fear', 'negative', '-0.09', 'world_event', 'type_of_paper', 'give_info', 'cnn', 'journalist']
senticnet['national_final_rodeo'] = ['0.938', '0.779', '0', '0', '#joy', '#interest', 'positive', '0.859', 'carnival', 'circus', 'fairground', 'fair', 'disneyland']
senticnet['national_gendarmerie'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'law_enforcement_agency', 'law', 'agency']
senticnet['national_geographic_society'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'institution', 'tundra', 'child_story', 'natural_history_museum', 'cartoon']
senticnet['national_geographic_traveler'] = ['-0.65', '0', '0.943', '-0.75', '#anger', '#disgust', 'negative', '-0.78', 'rubberneck', 'sightseer', 'excursionist', 'tripper', 'holidaymaker']
senticnet['national_geographics'] = ['0.936', '0', '0', '0.734', '#joy', '#admiration', 'positive', '0.835', 'geography', 'regional_environmental', 'number', 'regional_science', 'manager']
senticnet['national_highway'] = ['0', '0', '0.024', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'toll_road', 'toll_highway', 'superhighway', 'intertoll_highway', 'motorway']
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senticnet['national_leader'] = ['0.838', '-0.58', '0', '0.945', '#joy', '#admiration', 'positive', '0.791', 'elder_statesman', 'stateswoman', 'politician', 'politico', 'pol']
senticnet['national_liberation'] = ['0.887', '0.748', '0', '0.954', '#joy', '#admiration', 'positive', '0.863', 'political_action', 'political', 'mediocrity', 'discrimination', 'independent']
senticnet['national_liberation_struggle'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'patriotism', 'homeland']
senticnet['national_lottery'] = ['0.858', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.813', 'box_office', 'movie', 'cctv', 'cinema', 'movie_theater']
senticnet['national_memorial_arboretum'] = ['0.072', '0.126', '0', '0', '#joy', '#interest', 'positive', '0.099', 'subway_station', 'war_memorial', 'utopia', 'mortuary', 'hero']
senticnet['national_park'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['national_pastime'] = ['0.891', '0.835', '0', '0.721', '#joy', '#interest', 'positive', '0.816', 'hobby', 'diversion', 'challenge', 'enjoy_game', 'leisure']
senticnet['national_prominence'] = ['0', '0.793', '0', '0.91', '#interest', '#admiration', 'positive', '0.852', 'superstar', 'vip', 'accomplishment', 'performance', 'concert']
senticnet['national_reconciliation'] = ['0.497', '0.453', '-0.23', '0.189', '#joy', '#interest', 'positive', '0.226', 'calm', 'calmness', 'stillness', 'truce', 'love']
senticnet['national_science_foundation'] = ['0', '0.902', '0', '0.882', '#interest', '#admiration', 'positive', '0.892', 'award', 'different_application', 'continue_grant', 'mathematics', 'sponsor']
senticnet['national_security'] = ['-0.04', '0', '-0.06', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'make_war', 'conquer_enemy', 'use_military', 'gain_territory', 'keep_peace']
senticnet['national_smete_digital_library'] = ['0.928', '0.975', '0', '0', '#joy', '#interest', 'positive', '0.952', 'different_application', 'award', 'project', 'standard_grant', 'digital_library']
senticnet['national_socialism'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'healthy_person', 'man', 'pervert', 'compatriot']
senticnet['national_socialist_ideology'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'healthy_person', 'man', 'pervert', 'compatriot']
senticnet['national_symphony_orchestra'] = ['0.506', '0.659', '-0.24', '0.242', '#joy', '#interest', 'positive', '0.29', 'sing', 'music_studio', 'music_room', 'choir', 'symphony']
senticnet['national_treasure'] = ['0.522', '-0.88', '0', '0.965', '#surprise', '#admiration', 'positive', '0.789', 'overage', 'asset', 'security', 'business', 'hole_card']
senticnet['national_unity'] = ['0.887', '0.748', '0', '0.954', '#joy', '#admiration', 'positive', '0.863', 'political_action', 'political', 'mediocrity', 'discrimination', 'independent']
senticnet['national_weather'] = ['-0.03', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.04', 'crystal_clear', 'water_sky', 'weather_condition', 'weather_phenomenon', 'sun_cover']
senticnet['national_wildlife_refuge'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'habitat', 'mom_house', 'windsor', 'pacific_northwest', 'wilderness']
senticnet['national_zoological_park'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'institution', 'tundra', 'child_story', 'natural_history_museum', 'cartoon']
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senticnet['nationalist'] = ['0', '0.904', '0', '0.768', '#interest', '#admiration', 'positive', '0.836', 'become_politician', 'help_understand', 'disagree', 'know_better', 'investigation']
senticnet['nationalist_discourse'] = ['0.122', '0.159', '-0.05', '0.217', '#interest', '#admiration', 'positive', '0.111', 'profile_picture', 'unity', 'country_person', 'unite', 'signature']
senticnet['nationalist_faction'] = ['-0.50', '0', '0.989', '-0.91', '#anger', '#disgust', 'negative', '-0.80', 'camp', 'pack', 'clique', 'coterie', 'ingroup']
senticnet['nationalist_ideology'] = ['0', '0', '0', '0.986', '#admiration', '#admiration', 'positive', '0.986', 'idea', 'decision', 'consider', 'polarize', 'make_right_decision']
senticnet['nationalist_movement'] = ['0.887', '0.748', '0', '0.954', '#joy', '#admiration', 'positive', '0.863', 'political_action', 'political', 'mediocrity', 'discrimination', 'independent']
senticnet['nationalist_politician'] = ['0', '0.868', '0', '0.853', '#interest', '#admiration', 'positive', '0.861', 'country_leader', 'prime_minister', 'president', 'elect_official', 'leader']
senticnet['nationalist_sentiment'] = ['-0.77', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.84', 'jumpiness', 'jitteriness', 'restive', 'nervy', 'jumpy']
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senticnet['nationality'] = ['0', '-0.91', '0', '0.806', '#surprise', '#admiration', 'positive', '0.861', 'clean_shoes', 'black_stuff', 'paint_nail', 'ethnicity', 'black_goo']
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senticnet['nationally_recognized_expert'] = ['0.891', '0.721', '0', '0.921', '#joy', '#admiration', 'positive', '0.844', 'inventor', 'connoisseur', 'medical_doctor', 'teacher', 'judge']
senticnet['nationally_recognized_scholar'] = ['0', '0', '0', '0.97', '#admiration', '#admiration', 'positive', '0.97', 'lecturer', 'teacher', 'instructor', 'college_teacher', 'teach_lesson']
senticnet['nationally_representative'] = ['-0.04', '0.1', '0.051', '0.137', '#interest', '#admiration', 'positive', '0.035', 'find_number', 'regularly', 'list_number', 'calculator', 'navigation']
senticnet['nationhood'] = ['0.122', '0.159', '-0.05', '0.217', '#interest', '#admiration', 'positive', '0.111', 'profile_picture', 'unity', 'country_person', 'unite', 'signature']
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senticnet['nationwide_strike'] = ['-0.77', '0', '0.505', '-0.50', '#sadness', '#anger', 'negative', '-0.59', 'wildcat_strike', 'sympathy_strike', 'sympathetic_strike', 'walkout', 'job_action']
senticnet['native'] = ['0', '0.885', '0', '0.781', '#interest', '#admiration', 'positive', '0.833', 'domestic', 'local', 'royal', 'title', 'alias']
senticnet['native_alaskan'] = ['0.052', '0.099', '0', '0.067', '#interest', '#admiration', 'positive', '0.073', 'police_rank', 'head_guy', 'chief', 'leader', 'commander']
senticnet['native_american'] = ['0.052', '0.099', '0', '0.067', '#interest', '#admiration', 'positive', '0.073', 'police_rank', 'head_guy', 'chief', 'leader', 'commander']
senticnet['native_american_culture'] = ['0.052', '0.099', '0', '0.067', '#interest', '#admiration', 'positive', '0.073', 'police_rank', 'head_guy', 'chief', 'leader', 'commander']
senticnet['native_american_heritage'] = ['0.052', '0.099', '0', '0.067', '#interest', '#admiration', 'positive', '0.073', 'police_rank', 'head_guy', 'chief', 'leader', 'commander']
senticnet['native_american_tribal'] = ['0.052', '0.099', '0', '0.067', '#interest', '#admiration', 'positive', '0.073', 'police_rank', 'head_guy', 'chief', 'leader', 'commander']
senticnet['native_american_tribe'] = ['0.989', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.97', 'committee', 'council', 'caste', 'association', 'organization']
senticnet['native_language'] = ['-0.93', '0', '-0.99', '-0.92', '#sadness', '#fear', 'negative', '-0.95', 'barrier', 'shut', 'shut_down', 'close_down', 'obstacle']
senticnet['native_ojibwe_language'] = ['-0.93', '0', '0.861', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'knotty', 'contorted', 'writhed', 'crooked', 'anfractuous']
senticnet['native_orange'] = ['-0.57', '0', '0.721', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'bush', 'native_pomegranate', 'caper_tree', 'caper', 'common_caper']
senticnet['native_person'] = ['0.172', '0.138', '0', '0.097', '#joy', '#interest', 'positive', '0.136', 'live_good_life', 'healthy_environment', 'live_country', 'live_healthy_life', 'exercise_often']
senticnet['native_plant'] = ['-0.79', '0', '0.837', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'sunray', 'wilding', 'sagebrush_buttercup', 'pasqueflower', 'pasque_flower']
senticnet['native_pomegranate'] = ['-0.57', '0', '0.721', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'bush', 'caper', 'caper_tree', 'native_orange', 'common_caper']
senticnet['native_speaker'] = ['-0.93', '0', '-0.99', '-0.92', '#sadness', '#fear', 'negative', '-0.95', 'barrier', 'shut', 'shut_down', 'close_down', 'obstacle']
senticnet['native_tongue'] = ['-0.93', '0', '-0.99', '-0.92', '#sadness', '#fear', 'negative', '-0.95', 'barrier', 'shut', 'shut_down', 'close_down', 'obstacle']
senticnet['native_wildflower'] = ['-0.79', '0', '0.837', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'sunray', 'wilding', 'sagebrush_buttercup', 'pasqueflower', 'pasque_flower']
senticnet['nativity'] = ['0.132', '-0.08', '-0.07', '0.067', '#joy', '#surprise', 'positive', '0.051', 'fun_time', 'party_time', 'saturday', 'holiday', 'annual_celebration']
senticnet['nativity_scene'] = ['0.856', '0', '0', '0', '#joy', '#joy', 'positive', '0.856', 'tree', 'evergreen_tree', 'big_tree', 'santa_claus', 'christmas']
senticnet['natixis_bleichroeder'] = ['0.711', '0', '0', '0', '#joy', '#joy', 'positive', '0.711', 'chemist', 'expert', 'scientist', 'esaminatore', 'physicist']
senticnet['natter'] = ['0.981', '-0.90', '0', '0.756', '#joy', '#surprise', 'positive', '0.88', 'schmooze', 'schmoose', 'shmooze', 'shmoose', 'jawbone']
senticnet['nattily'] = ['0', '-0.93', '0', '0.886', '#surprise', '#admiration', 'positive', '0.911', 'immodestly', 'scantly', 'scantily', 'skimpily', 'shabbily']
senticnet['nattiness'] = ['0.853', '-0.76', '0', '0.966', '#joy', '#admiration', 'positive', '0.861', 'elegance', 'jauntiness', 'rakishness', 'chicness', 'dapperness']
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senticnet['natural_attrition'] = ['-0.89', '0.168', '-0.60', '0.05', '#sadness', '#fear', 'negative', '-0.32', 'remorsefully', 'contritely', 'contrition', 'contriteness', 'ruefully']
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senticnet['natural_depression'] = ['0', '0.772', '0', '0.754', '#interest', '#admiration', 'positive', '0.763', 'slope', 'formation', 'wall', 'collectivization', 'massif']
senticnet['natural_disaster'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'hurricane', 'atmospheric_phenomenon', 'flood', 'dangerous', 'tropical_storm']
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senticnet['natural_environment'] = ['0', '0.243', '-0.11', '0.201', '#interest', '#admiration', 'positive', '0.108', 'upkeep', 'contain', 'keep_safe', 'keep', 'prevent']
senticnet['natural_event'] = ['-0.73', '0', '0.591', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'whammy', 'happening', 'occurrence', 'occurrent', 'setback']
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senticnet['natural_gas'] = ['0.764', '0.717', '0', '0', '#joy', '#interest', 'positive', '0.74', 'soft_wood', 'wood', 'fiberglass', 'glass', 'franchise']
senticnet['natural_gas_condensate'] = ['-0.67', '0', '0.503', '-0.67', '#sadness', '#disgust', 'negative', '-0.61', 'stink_up', 'smell_up', 'stink_out', 'incense', 'perfume']
senticnet['natural_heritage'] = ['-0.97', '0', '0.885', '-0.97', '#sadness', '#disgust', 'negative', '-0.94', 'protection', 'conservation', 'environmentalism', 'reservation', 'immobilization']
senticnet['natural_history'] = ['0.701', '-0.77', '0', '0.659', '#joy', '#surprise', 'positive', '0.713', 'discipline', 'study', 'subject', 'science', 'natural_science']
senticnet['natural_history_amnh'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'institution', 'tundra', 'child_story', 'natural_history_museum', 'cartoon']
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senticnet['natural_language_processing'] = ['0.844', '0.916', '0', '0', '#joy', '#interest', 'positive', '0.88', 'research_subject', 'linguistics', 'know_language', 'speak_write', 'natural_language_understanding']
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senticnet['natural_resource_conservation'] = ['0', '0', '0', '0.857', '#admiration', '#admiration', 'positive', '0.857', 'say', 'beckon', 'call', 'conserve', 'retort']
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senticnet['natural_resource_protection'] = ['0', '0', '0', '0.857', '#admiration', '#admiration', 'positive', '0.857', 'say', 'beckon', 'call', 'conserve', 'retort']
senticnet['natural_rubber_latex'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'wall_cover', 'two-dimensional', 'cover_wall', 'pigment', 'dye']
senticnet['natural_scenery'] = ['0.896', '0', '0', '0.709', '#joy', '#admiration', 'positive', '0.802', 'perspective', 'vista', 'view', 'aspect', 'see_view']
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senticnet['natural_selection'] = ['-0.04', '0.192', '-0.07', '0.152', '#interest', '#admiration', 'positive', '0.054', 'hypothetical', 'supposition', 'scientific_statement', 'psychology', 'educate_guess']
senticnet['natural_skin_tone'] = ['-0.93', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.85', 'protect_from_sun', 'shade', 'meaning', 'corrector', 'sunglasses']
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senticnet['natural_sweetener'] = ['0.026', '0.08', '0.043', '0.064', '#interest', '#admiration', 'positive', '0.032', 'eat_rice', 'white_brown', 'asian', 'eat_pasta', 'staple_food']
senticnet['natural_tendency'] = ['0', '0.37', '-0.27', '0.283', '#interest', '#admiration', 'positive', '0.126', 'thoughtful', 'attitude', 'swing', 'certainty', 'type_of_bear']
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senticnet['natural_world'] = ['0', '0.88', '0', '0.954', '#interest', '#admiration', 'positive', '0.917', 'plato', 'brown_hair', 'wild_nature', 'feature', 'stick_out']
senticnet['naturalisation'] = ['0', '-0.91', '0', '0.806', '#surprise', '#admiration', 'positive', '0.861', 'clean_shoes', 'black_stuff', 'paint_nail', 'ethnicity', 'black_goo']
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senticnet['naturalization_certificate'] = ['0.931', '-0.52', '0', '0.67', '#joy', '#admiration', 'positive', '0.709', 'document', 'certificate_of_incorporation', 'credential', 'diploma', 'sheepskin']
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senticnet['naturally_occurring_arsenic'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'bottled_water', 'sugar_water']
senticnet['naturally_occurring_smallpox'] = ['-0.07', '0', '-0.22', '-0.15', '#fear', '#disgust', 'negative', '-0.15', 'gonorrhea', 'infectious_disease', 'psychoneurosis', 'sickness', 'polio']
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senticnet['nature_conservancy'] = ['0', '0', '0', '0.857', '#admiration', '#admiration', 'positive', '0.857', 'say', 'beckon', 'call', 'conserve', 'retort']
senticnet['nature_conservancy_tnc'] = ['0', '0', '0', '0.857', '#admiration', '#admiration', 'positive', '0.857', 'say', 'beckon', 'call', 'conserve', 'retort']
senticnet['nature_conservation'] = ['0', '0', '0', '0.857', '#admiration', '#admiration', 'positive', '0.857', 'say', 'beckon', 'call', 'conserve', 'retort']
senticnet['naturopathic'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
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senticnet['naturopathic_physician'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
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senticnet['natwest_tower'] = ['-0.81', '0', '0.543', '-0.63', '#sadness', '#disgust', 'negative', '-0.66', 'caper', 'pickle', 'relish', 'dill_pickle', 'sweet_pickle']
senticnet['naugahyde'] = ['0.028', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.047', 'handkerchief', 'kerchief', 'hanky', 'facial_tissue', 'zipper']
senticnet['naught'] = ['-0.55', '0', '0.882', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'null', 'zero', 'bugger_all', 'zip', 'nothing']
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senticnet['nauseous'] = ['-0.06', '0.128', '0.707', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'sickish', 'turn_stomach', 'vile', 'sickening', 'noisome']
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senticnet['nautically'] = ['0', '0.923', '0', '0.698', '#interest', '#admiration', 'positive', '0.81', 'lushly', 'tropically', 'landscaped', 'exotically', 'park-like']
senticnet['nautiloid'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'hag', 'osteostracan', 'cephalaspid', 'ostracoderm', 'jawless_fish']
senticnet['nautilus'] = ['-0.52', '0', '0.728', '-0.92', '#anger', '#disgust', 'negative', '-0.72', 'octopod', 'cephalopod', 'cephalopod_mollusk', 'paper_octopod', 'octopus']
senticnet['navajo'] = ['0.052', '0.099', '0', '0.067', '#interest', '#admiration', 'positive', '0.073', 'police_rank', 'head_guy', 'chief', 'leader', 'commander']
senticnet['naval'] = ['0.786', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.744', 'attacker', 'army', 'samurai', 'infantry', 'military_force']
senticnet['naval_air_base'] = ['-0.97', '0', '0.737', '-0.97', '#sadness', '#disgust', 'negative', '-0.89', 'flare_path', 'airstrip', 'take_off', 'runway', 'grassland']
senticnet['naval_architect'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'maker']
senticnet['naval_architecture'] = ['0.764', '-0.96', '0', '0.883', '#surprise', '#admiration', 'positive', '0.87', 'boatmanship', 'acquirement', 'skill', 'accomplishment', 'acquisition']
senticnet['naval_battle'] = ['-0.05', '0', '-0.08', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'siege', 'big_fight', 'war', 'declare_war', 'combat']
senticnet['naval_bombardment'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['naval_commander'] = ['0.688', '-0.90', '0', '0.877', '#surprise', '#admiration', 'positive', '0.824', 'fleet_admiral', 'rear_admiral', 'admiral', 'full_admiral', 'flag_officer']
senticnet['naval_engagement'] = ['-0.05', '0', '-0.08', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'siege', 'big_fight', 'war', 'declare_war', 'combat']
senticnet['naval_enigma'] = ['-0.52', '0', '0.821', '-0.65', '#anger', '#disgust', 'negative', '-0.66', 'work_out', 'reckon', 'calculate', 'average_out', 'cypher']
senticnet['naval_ensign'] = ['0.053', '0.068', '-0.05', '0.06', '#interest', '#admiration', 'positive', '0.032', 'linen', 'star_stripe', 'velvet', 'nation', 'burlap']
senticnet['naval_officer'] = ['0.688', '-0.90', '0', '0.877', '#surprise', '#admiration', 'positive', '0.824', 'fleet_admiral', 'rear_admiral', 'admiral', 'full_admiral', 'flag_officer']
senticnet['nave'] = ['0.029', '0.11', '0', '0.092', '#interest', '#admiration', 'positive', '0.077', 'party', 'use_party', 'special_happen', 'happy_occasion', 'special_moment']
senticnet['navety'] = ['-0.70', '0', '0.64', '-0.82', '#sadness', '#disgust', 'negative', '-0.72', 'simple_mindedness', 'simpleness', 'ingenuousness', 'artlessness', 'simplicity']
senticnet['navicular'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'socket', 'tooth_socket', 'alveolus', 'acetabulum', 'cotyloid_cavity']
senticnet['navicular_bone'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'socket', 'tooth_socket', 'alveolus', 'acetabulum', 'cotyloid_cavity']
senticnet['navicular_disease'] = ['0.789', '-0.99', '0', '0.54', '#joy', '#surprise', 'positive', '0.776', 'imperfection', 'imperfectness', 'bugginess', 'defectiveness', 'sickness']
senticnet['navicular_syndrome'] = ['0.789', '-0.99', '0', '0.54', '#joy', '#surprise', 'positive', '0.776', 'imperfection', 'imperfectness', 'bugginess', 'defectiveness', 'sickness']
senticnet['navier_stokes_equation'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'assumption', 'precondition', 'provision', 'constraint', 'premiss']
senticnet['navigable'] = ['0', '0', '0.978', '-0.86', '#anger', '#disgust', 'negative', '-0.92', 'passable', 'negotiable', 'surmountable', 'climbable', 'traversable']
senticnet['navigable_canal'] = ['0.933', '0.795', '0', '0', '#joy', '#interest', 'positive', '0.864', 'water_passage', 'small_stream', 'water_flow', 'flowing_water', 'water_stream']
senticnet['navigable_channel'] = ['0.933', '0.795', '0', '0', '#joy', '#interest', 'positive', '0.864', 'water_passage', 'small_stream', 'water_flow', 'flowing_water', 'water_stream']
senticnet['navigable_river'] = ['0.933', '0.795', '0', '0', '#joy', '#interest', 'positive', '0.864', 'water_passage', 'small_stream', 'water_flow', 'flowing_water', 'water_stream']
senticnet['navigable_water'] = ['0.933', '0.795', '0', '0', '#joy', '#interest', 'positive', '0.864', 'water_passage', 'small_stream', 'water_flow', 'flowing_water', 'water_stream']
senticnet['navigable_waterway'] = ['0.933', '0.795', '0', '0', '#joy', '#interest', 'positive', '0.864', 'water_passage', 'small_stream', 'water_flow', 'flowing_water', 'water_stream']
senticnet['navigate'] = ['0.716', '0.713', '0', '0', '#joy', '#interest', 'positive', '0.714', 'journey', 'adventure', 'quest', 'find', 'travel']
senticnet['navigate_through'] = ['-0.09', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.11', 'write_down_idea', 'finish_book', 'write_down', 'take_note_class', 'take_note']
senticnet['navigating_through'] = ['-0.50', '0', '0.737', '-0.55', '#anger', '#disgust', 'negative', '-0.59', 'unacquainted_with', 'unfamiliar_with', 'unknown', 'unacquainted', 'strange']
senticnet['navigation'] = ['-0.03', '0.127', '0.041', '0.15', '#interest', '#admiration', 'positive', '0.051', 'mathematical_operation', 'math_process', 'math_action', 'location', 'math_operation']
senticnet['navigation_bar'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'swat', 'store_record', 'ideal']
senticnet['navigational_channel'] = ['0.933', '0.795', '0', '0', '#joy', '#interest', 'positive', '0.864', 'water_passage', 'small_stream', 'water_flow', 'flowing_water', 'water_stream']
senticnet['navisite'] = ['-0.63', '0', '0.862', '-0.52', '#sadness', '#anger', 'negative', '-0.67', 'knot', 'bunch', 'tuft', 'tussock', 'agglomeration']
senticnet['navisoft'] = ['-0.63', '0', '0.862', '-0.52', '#sadness', '#anger', 'negative', '-0.67', 'knot', 'bunch', 'tuft', 'tussock', 'agglomeration']
senticnet['navman'] = ['0', '0.071', '0', '0.143', '#interest', '#admiration', 'positive', '0.107', 'post_notice', 'indicator', 'way', 'one_way', 'arrow']
senticnet['navpix'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expansive', 'expansible', 'erectile', 'cavernous']
senticnet['navy'] = ['-0.03', '0.127', '0.041', '0.15', '#interest', '#admiration', 'positive', '0.051', 'army_rank', 'army_leader', 'navy_rank', 'army_navy', 'navy_leader']
senticnet['navy_brig'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicative', 'unpronounceable', 'impassive', 'inexpressive', 'silent']
senticnet['navy_cross'] = ['0.899', '-0.79', '0', '0.691', '#joy', '#surprise', 'positive', '0.796', 'decoration', 'laurel_wreath', 'medal', 'medallion', 'palm']
senticnet['navy_leader'] = ['0.041', '0.095', '0', '0', '#joy', '#interest', 'positive', '0.068', 'leadership_position', 'navy_officer', 'head_person', 'police_rank', 'head_guy']
senticnet['navy_lieutenant'] = ['0.045', '0', '0', '0.088', '#joy', '#admiration', 'positive', '0.067', 'leader', 'seaman', 'captain', 'officer', 'traitor']
senticnet['navy_officer'] = ['0.041', '0.095', '0', '0', '#joy', '#interest', 'positive', '0.068', 'leadership_position', 'head_guy', 'head_person', 'police_rank', 'navy_leader']
senticnet['navy_rank'] = ['0.028', '0.067', '0', '0', '#joy', '#interest', 'positive', '0.048', 'military_officer', 'navy_leader', 'navy_officer', 'leadership_position', 'military_position']
senticnet['navy_rank_insignia'] = ['0.688', '-0.90', '0', '0.877', '#surprise', '#admiration', 'positive', '0.824', 'flag_officer', 'rear_admiral', 'admiral', 'full_admiral', 'vice_admiral']
senticnet['nawang'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['naxos_label'] = ['0.805', '-0.58', '0', '0.525', '#joy', '#surprise', 'positive', '0.637', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['nay'] = ['-0.04', '0.268', '-0.06', '0.227', '#interest', '#admiration', 'positive', '0.097', 'proposition', 'abstract', 'get_rid', 'theoretical', 'business_transaction']
senticnet['naya_rivera'] = ['0.761', '0.566', '-0.21', '0.293', '#joy', '#interest', 'positive', '0.352', 'gratify', 'jubilation', 'gloat', 'gloating', 'gloatingly']
senticnet['naysayer'] = ['-0.97', '0', '0.62', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'obstructor', 'obstructer', 'technophobe', 'obstructionist', 'resister']
senticnet['nazarite'] = ['0.088', '0.317', '-0.29', '0.379', '#interest', '#admiration', 'positive', '0.122', 'oath', 'commitment', 'marriage', 'obedience', 'enter_contract']
senticnet['nazi'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'healthy_person', 'man', 'pervert', 'compatriot']
senticnet['nazi_anti_semitism'] = ['-0.81', '-0.71', '0.843', '-0.72', '#sadness', '#anger', 'negative', '-0.41', 'lack_of_rules', 'fascism', 'despot', 'tyrant', 'anarchism']
senticnet['nazi_collaborator'] = ['-0.97', '0', '0.62', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'obstructor', 'obstructer', 'technophobe', 'obstructionist', 'naysayer']
senticnet['nazi_dictatorship'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'healthy_person', 'man', 'pervert', 'compatriot']
senticnet['nazi_era'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'healthy_person', 'man', 'pervert', 'compatriot']
senticnet['nazi_extermination'] = ['-0.08', '0', '-0.15', '-0.20', '#fear', '#disgust', 'negative', '-0.14', 'casualty', 'reaper', 'wound', 'cause_death', 'gunshot']
senticnet['nazi_fascism'] = ['-0.81', '-0.71', '0.843', '-0.72', '#sadness', '#anger', 'negative', '-0.41', 'lack_of_rules', 'fascism', 'despot', 'tyrant', 'anarchism']
senticnet['nazi_flag'] = ['0.053', '0.068', '-0.05', '0.06', '#interest', '#admiration', 'positive', '0.032', 'linen', 'star_stripe', 'velvet', 'nation', 'burlap']
senticnet['nazi_german'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'healthy_person', 'man', 'pervert', 'compatriot']
senticnet['nazi_germany'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'healthy_person', 'man', 'pervert', 'compatriot']
senticnet['nazi_ideology'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'healthy_person', 'man', 'pervert', 'compatriot']
senticnet['nazi_occultism'] = ['-0.51', '0', '0.979', '-0.89', '#anger', '#disgust', 'negative', '-0.79', 'invisible', 'concealed', 'nonvisual', 'hidden', 'out_of_sight']
senticnet['nazi_party'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'healthy_person', 'man', 'pervert', 'compatriot']
senticnet['nazi_propaganda'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'healthy_person', 'man', 'pervert', 'compatriot']
senticnet['nazi_racial'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'healthy_person', 'man', 'pervert', 'compatriot']
senticnet['nazi_regime'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'healthy_person', 'man', 'pervert', 'compatriot']
senticnet['nazi_reich'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'healthy_person', 'man', 'pervert', 'compatriot']
senticnet['nazi_rgime'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'healthy_person', 'man', 'pervert', 'compatriot']
senticnet['nazi_spy'] = ['-0.69', '0', '0.756', '-0.99', '#anger', '#disgust', 'negative', '-0.81', 'waster', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['nazi_sympathizer'] = ['-0.97', '0', '0.62', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'obstructor', 'obstructer', 'technophobe', 'obstructionist', 'naysayer']
senticnet['nazi_zombie'] = ['-0.03', '-0.01', '-0.16', '-0.06', '#fear', '#disgust', 'negative', '-0.06', 'sticky_floor', 'theater_box', 'projector', 'row_seat', 'theater_seat']
senticnet['naziism'] = ['-0.81', '-0.71', '0.843', '-0.72', '#sadness', '#anger', 'negative', '-0.41', 'lack_of_rules', 'fascism', 'despot', 'tyrant', 'anarchism']
senticnet['nazirite_vow'] = ['0.088', '0.317', '-0.29', '0.379', '#interest', '#admiration', 'positive', '0.122', 'oath', 'commitment', 'marriage', 'obedience', 'enter_contract']
senticnet['nazism'] = ['-0.81', '-0.71', '0.843', '-0.72', '#sadness', '#anger', 'negative', '-0.41', 'lack_of_rules', 'fascism', 'despot', 'tyrant', 'anarchism']
senticnet['nazko_cone'] = ['-0.87', '0', '0.818', '0', '#sadness', '#anger', 'negative', '-0.84', 'slow_speed', 'wifi', 'computer_internet_connection', 'internet_connection', 'boot_up_computer']
senticnet['nba_leading_scorer'] = ['0.556', '-0.79', '0', '0.755', '#surprise', '#admiration', 'positive', '0.701', 'lush', 'alcoholic', 'alky', 'dipsomaniac', 'rummy']
senticnet['nba_player'] = ['0', '0', '0', '0.97', '#admiration', '#admiration', 'positive', '0.97', 'increase_strength', 'keep_fit', 'fit_person', 'exercise', 'strengthen_heart']
senticnet['nbc_news_correspondent'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'adventurer', 'humankind']
senticnet['nbc_reality_tv'] = ['-0.08', '-0.03', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'graduate_student', 'student', 'serious_student', 'competition', 'hard_worker']
senticnet['nbc_television'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'theater_seat', 'theater_box', 'sticky_floor', 'stage_entrance', 'balcony']
senticnet['nbcs_meet'] = ['0.889', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.807', 'compression', 'push_hard', 'print_newspaper', 'journalist', 'make_newspaper']
senticnet['nbl_membership'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'wildcat', 'informal', 'loose', 'drumhead']
senticnet['nbrega'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'dryness', 'thirstiness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['ncaa_championship_team'] = ['0.035', '0.155', '0', '0', '#joy', '#interest', 'positive', '0.095', 'basketball_court', 'basketball_game', 'basketball', 'ball', 'sport']
senticnet['ncic'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'constabulary', 'law', 'agency']
senticnet['ncis_franchise'] = ['0', '0', '0.785', '-0.72', '#anger', '#disgust', 'negative', '-0.75', 'sharp_nail', 'sharp', 'sharp_end', 'sharp_point', 'pointy']
senticnet['ncsa_mosaic'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'busy_bee', 'actor', 'doer', 'worker']
senticnet['ndvi'] = ['-0.93', '0', '0.906', '-0.53', '#sadness', '#anger', 'negative', '-0.79', 'juvenility', 'state', 'immatureness', 'callowness', 'jejuneness']
senticnet['neac'] = ['0.851', '-0.65', '0', '0.66', '#joy', '#admiration', 'positive', '0.723', 'omen', 'portent', 'presage', 'prognostic', 'prognostication']
senticnet['neandertal'] = ['-0.78', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.83', 'boorish', 'loutish', 'swinish', 'oafish', 'neanderthal']
senticnet['neanderthal'] = ['-0.78', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.82', 'neandertal', 'loutish', 'swinish', 'oafish', 'boorish']
senticnet['neap'] = ['0.632', '-0.61', '0', '0.525', '#joy', '#surprise', 'positive', '0.59', 'sea_level', 'high_tide', 'springtide', 'direct_tide', 'tide']
senticnet['neap_tide'] = ['0.632', '-0.61', '0', '0.525', '#joy', '#surprise', 'positive', '0.59', 'high_tide', 'neap', 'springtide', 'direct_tide', 'tide']
senticnet['near'] = ['0.56', '0.555', '-0.21', '0.44', '#joy', '#interest', 'positive', '0.335', 'close', 'easy', 'togetherness', 'capture', 'gay']
senticnet['near-impossible'] = ['0', '0', '-1.0', '-0.95', '#fear', '#disgust', 'negative', '-0.97', 'arduous', 'gargantuan', 'laborious', 'thankless', 'herculean']
senticnet['near-record'] = ['0', '0', '-0.85', '-0.80', '#fear', '#disgust', 'negative', '-0.83', 'bargain-basement', 'super-low', 'dirt-cheap', 'rock-bottom', 'sky-high']
senticnet['near-term'] = ['-0.72', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.83', 'drawback', 'downside', 'disadvantage', 'upside', 'volatility']
senticnet['near_aswan'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['near_beach'] = ['0.773', '0.861', '0', '0', '#joy', '#interest', 'positive', '0.817', 'vast', 'along_beach', 'big_blue', 'land_sea', 'full_water']
senticnet['near_chamonix'] = ['0', '-0.03', '-0.10', '-0.07', '#fear', '#disgust', 'negative', '-0.04', 'autumn', 'winter', 'skier', 'snow_fall', 'volcano']
senticnet['near_chelmsford'] = ['0.623', '-0.56', '0', '0.974', '#joy', '#admiration', 'positive', '0.721', 'alterable', 'convertible', 'translatable', 'transformable', 'transmutable']
senticnet['near_completion'] = ['0.445', '0.37', '-0.14', '0.483', '#joy', '#admiration', 'positive', '0.289', 'proper', 'near', 'mission_accomplished', 'sufficient', 'absolute']
senticnet['near_conclusion'] = ['-0.05', '0.169', '-0.08', '0.146', '#interest', '#admiration', 'positive', '0.043', 'understand', 'direction', 'near_end', 'end', 'begin']
senticnet['near_destination'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'board_boat', 'alongside', 'board_ship', 'return_home', 'appear_beautiful']
senticnet['near_end'] = ['-0.04', '0.074', '0.019', '0.071', '#interest', '#admiration', 'positive', '0.021', 'sharpness', 'sharp_end', 'sharp_corner', 'straight_line', 'edge']
senticnet['near_fatal'] = ['-0.73', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.79', 'die_painfully', 'arrest_police', 'get_accident', 'fall_head', 'skin_knee']
senticnet['near_fatal_accident'] = ['-0.73', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.79', 'die_painfully', 'arrest_police', 'get_accident', 'fall_head', 'skin_knee']
senticnet['near_infrared_region'] = ['-0.07', '0.078', '0', '0.078', '#interest', '#admiration', 'positive', '0.028', 'red_blue', 'red_orange', 'green_yellow', 'hue_tint', 'yellow_green']
senticnet['near_infrared_spectrum'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'eye', 'peeper', 'oculus_sinister']
senticnet['near_mingora'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'webpage', 'store_record', 'ideal']
senticnet['near_miss'] = ['-0.62', '0', '0.507', '-0.72', '#sadness', '#disgust', 'negative', '-0.62', 'accident', 'misfortune', 'bad_luck', 'misadventure', 'derailment']
senticnet['near_mouth'] = ['0.037', '0.076', '0', '0.091', '#interest', '#admiration', 'positive', '0.068', 'face_part', 'facial_feature', 'facial_part', 'facial_organ', 'mouth_part']
senticnet['near_neath'] = ['-0.64', '0', '0.879', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'drone', 'poke', 'dawdler', 'laggard', 'straggler']
senticnet['near_newton_abbot'] = ['-0.64', '0', '0.879', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'drone', 'poke', 'dawdler', 'laggard', 'straggler']
senticnet['near_nose'] = ['0', '0.066', '0.028', '0.106', '#interest', '#admiration', 'positive', '0.048', 'face_feature', 'facial_part', 'facial_organ', 'part_of_face', 'face_part']
senticnet['near_ocean'] = ['0', '0.112', '0', '0.137', '#interest', '#admiration', 'positive', '0.124', 'island', 'popular_vacation_destination', 'land_feature', 'ocean_shore', 'oceanside']
senticnet['near_passerine_bird'] = ['-0.55', '0', '0.955', '-0.96', '#anger', '#disgust', 'negative', '-0.82', 'house_mouse', 'mouse', 'rat', 'fieldmouse', 'nude_mouse']
senticnet['near_penzance'] = ['-0.64', '0', '0.879', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'drone', 'poke', 'dawdler', 'laggard', 'straggler']
senticnet['near_reigate'] = ['0.623', '-0.56', '0', '0.974', '#joy', '#admiration', 'positive', '0.721', 'alterable', 'convertible', 'translatable', 'transformable', 'transmutable']
senticnet['near_sea'] = ['0.047', '0.11', '0', '0', '#joy', '#interest', 'positive', '0.079', 'cover_sand', 'sandy', 'sand_rock', 'near_beach', 'beach']
senticnet['near_truro'] = ['-0.64', '0', '0.879', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'drone', 'poke', 'dawdler', 'laggard', 'straggler']
senticnet['near_tv'] = ['-0.06', '0', '-0.04', '0', '#sadness', '#fear', 'negative', '-0.05', 'turn_on_tv', 'switch_tv', 'turn_television', 'rend_video', 'wear_glass']
senticnet['near_yellowstone_national'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['nearby'] = ['0.128', '0.103', '0', '0.208', '#joy', '#admiration', 'positive', '0.146', 'complex_structure', 'vicinity', 'close', 'circular', 'three_dimensions']
senticnet['nearby_attraction_include'] = ['0.034', '0.13', '-0.08', '0.258', '#interest', '#admiration', 'positive', '0.085', 'tourism', 'nice_view', 'big_monument', 'temple', 'sightsee']
senticnet['nearby_convenience_store'] = ['0.758', '0.878', '0', '0', '#joy', '#interest', 'positive', '0.818', 'tavern', 'bar', 'pub', 'martini', 'brewery']
senticnet['nearby_hamlet'] = ['-0.91', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.85', 'play', 'perpetrator', 'role_play', 'act_part', 'terrible_event']
senticnet['nearby_quarry'] = ['0', '0', '-0.85', '-0.71', '#fear', '#disgust', 'negative', '-0.78', 'fair_game', 'pit', 'stone_pit', 'target', 'prey']
senticnet['nearby_village_include'] = ['-0.91', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.85', 'play', 'perpetrator', 'role_play', 'act_part', 'terrible_event']
senticnet['nearer'] = ['0.609', '0.597', '-0.23', '0.398', '#joy', '#interest', 'positive', '0.341', 'near', 'pick_up', 'togetherness', 'gay', 'capture']
senticnet['nearest'] = ['0.609', '0.597', '-0.23', '0.398', '#joy', '#interest', 'positive', '0.341', 'near', 'pick_up', 'togetherness', 'gay', 'capture']
senticnet['nearest_bus_stop'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'train_station', 'bus', 'subway_station', 'public_place', 'pier']
senticnet['nearest_emergency_room'] = ['0', '0', '-0.73', '0', '#fear', '#fear', 'negative', '-0.73', 'clinic', 'emergency_ward', 'hospital', 'dentist_office', 'medical_office']
senticnet['nearest_hospital'] = ['0', '0', '-0.73', '0', '#fear', '#fear', 'negative', '-0.73', 'clinic', 'emergency_ward', 'hospital', 'dentist_office', 'medical_office']
senticnet['nearest_pub'] = ['0.853', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.916', 'tavern', 'cafe', 'nightclub', 'bar', 'liquor_store']
senticnet['nearest_train_station'] = ['0.623', '-0.56', '0', '0.974', '#joy', '#admiration', 'positive', '0.721', 'alterable', 'convertible', 'translatable', 'transformable', 'transmutable']
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senticnet['needle'] = ['0', '0', '0.946', '-0.90', '#anger', '#disgust', 'negative', '-0.92', 'needle_thread', 'stitch', 'suture', 'pin', 'without_space']
senticnet['needle_blight'] = ['-0.88', '0', '0.954', '-0.87', '#sadness', '#anger', 'negative', '-0.90', 'leaf_blight', 'needle_cast', 'leaf_cast', 'blight', 'fire_blight']
senticnet['needle_cast'] = ['-0.88', '0', '0.954', '-0.87', '#sadness', '#anger', 'negative', '-0.90', 'needle_blight', 'leaf_blight', 'leaf_cast', 'blight', 'fire_blight']
senticnet['needle_filament'] = ['-0.04', '-0.04', '0.078', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'string', 'bandage', 'suture', 'filament', 'thread']
senticnet['needle_insertion'] = ['-0.74', '0', '0.766', '0', '#sadness', '#anger', 'negative', '-0.75', 'use_needle', 'clothes_repair', 'woven', 'embroidery', 'thread_needle']
senticnet['needle_phobia'] = ['-0.79', '-0.71', '-0.96', '-0.95', '#fear', '#disgust', 'negative', '-0.49', 'fear', 'caution', 'deep_sadness', 'bad_feeling', 'psychoneurosis']
senticnet['needle_prick'] = ['-0.74', '0', '0.766', '0', '#sadness', '#anger', 'negative', '-0.75', 'use_needle', 'clothes_repair', 'woven', 'embroidery', 'thread_needle']
senticnet['needle_puncture'] = ['-0.74', '0', '0.766', '0', '#sadness', '#anger', 'negative', '-0.75', 'use_needle', 'clothes_repair', 'woven', 'embroidery', 'thread_needle']
senticnet['needle_stick'] = ['-0.74', '0', '0.766', '0', '#sadness', '#anger', 'negative', '-0.75', 'use_needle', 'clothes_repair', 'woven', 'embroidery', 'thread_needle']
senticnet['needle_syringe'] = ['-0.02', '0', '-0.12', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'x-ray_machine', 'medical_record', 'latex_glove', 'medicine', 'doctor_examination_room']
senticnet['needle_thread'] = ['-0.05', '-0.01', '0.012', '-0.10', '#sadness', '#disgust', 'negative', '-0.03', 'sew', 'use_needle', 'bandage', 'seam', 'stitch']
senticnet['needlefish'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'many_fishes', 'seahorse', 'starfish', 'plankton', 'sea_wee']
senticnet['needlepoint'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'building', 'carving', 'artistic_creation', 'ceramic']
senticnet['needless'] = ['-0.71', '0', '0.711', '0', '#sadness', '#anger', 'negative', '-0.71', 'vain', 'unnecessary', 'idle', 'useless', 'pointless']
senticnet['needless_litigation'] = ['0.658', '-0.52', '0', '0.626', '#joy', '#admiration', 'positive', '0.602', 'point_in_time', 'threshold', 'birth', 'commencement', 'incipience']
senticnet['needlework'] = ['-0.03', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.04', 'needle_thread', 'use_needle', 'stitch', 'sow', 'embroidery']
senticnet['needly'] = ['0.936', '0', '0', '0.994', '#joy', '#admiration', 'positive', '0.965', 'unnecessarily', 'analytic', 'needs-cost', 'necessarily', 'inevitably']
senticnet['needs-cost'] = ['0.931', '0', '0', '0.991', '#joy', '#admiration', 'positive', '0.961', 'unnecessarily', 'analytic', 'necessarily', 'needly', 'inevitably']
senticnet['needy'] = ['-0.10', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.08', 'come_house', 'desperate', 'welcome_friend', 'captain_boat', 'receive_email']
senticnet['needy_child'] = ['-0.10', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.08', 'come_house', 'desperate', 'welcome_friend', 'captain_boat', 'receive_email']
senticnet['needy_person'] = ['0', '0', '-0.94', '-0.77', '#fear', '#disgust', 'negative', '-0.85', 'lost', 'need_help', 'need_know', 'ignorant', 'need_information']
senticnet['neel_parvat'] = ['-0.73', '0', '0.55', '-0.75', '#sadness', '#disgust', 'negative', '-0.68', 'mortal', 'person', 'individual', 'mangler', 'mutilator']
senticnet['neere'] = ['-0.69', '0', '0.883', '-0.91', '#anger', '#disgust', 'negative', '-0.82', 'stick_in', 'enclose', 'insert', 'feed_in', 'put_in']
senticnet['nefaria'] = ['-0.58', '0', '0.661', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'explosive', 'cordite', 'dynamite', 'plastic_explosive', 'explosive_compound']
senticnet['nefarious'] = ['0', '0', '0.805', '-0.93', '#anger', '#disgust', 'negative', '-0.87', 'infamous', 'wicked', 'nefariousness', 'cahoot', 'sinful']
senticnet['nefariousness'] = ['0', '0', '0.796', '-0.94', '#anger', '#disgust', 'negative', '-0.86', 'infamous', 'wicked', 'nefarious', 'cahoot', 'sinful']
senticnet['neferkare'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
senticnet['negate'] = ['-0.61', '0', '0.723', '-0.74', '#anger', '#disgust', 'negative', '-0.69', 'controvert', 'refute', 'rebut', 'falsify', 'confute']
senticnet['negation'] = ['-0.90', '0', '0.92', '-0.96', '#anger', '#disgust', 'negative', '-0.93', 'negative', 'bad', 'difficulty', 'refute', 'badness']
senticnet['negative'] = ['-0.90', '0', '0.92', '-0.96', '#anger', '#disgust', 'negative', '-0.93', 'negation', 'bad', 'difficulty', 'refute', 'badness']
senticnet['negative_affective'] = ['0.971', '0.751', '0', '0.896', '#joy', '#admiration', 'positive', '0.873', 'new_friendship', 'foreplay', 'warm_fuzzy_feeling', 'become_aroused', 'erection']
senticnet['negative_affectivity'] = ['-0.93', '0', '0.586', '-0.66', '#sadness', '#disgust', 'negative', '-0.73', 'camp', 'pack', 'clique', 'coterie', 'brain_trust']
senticnet['negative_attitude_toward'] = ['-0.93', '0', '0.586', '-0.66', '#sadness', '#disgust', 'negative', '-0.73', 'camp', 'pack', 'clique', 'coterie', 'brain_trust']
senticnet['negative_binomial'] = ['-0.59', '0', '0.672', '-0.83', '#anger', '#disgust', 'negative', '-0.7', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'asymmetry', 'irregularity']
senticnet['negative_buoyancy'] = ['0.676', '0.63', '-0.23', '0.31', '#joy', '#interest', 'positive', '0.345', 'perkiness', 'walk_on_air', 'exultingly', 'exultantly', 'jubilance']
senticnet['negative_emotion'] = ['0.708', '-0.60', '0', '0.661', '#joy', '#admiration', 'positive', '0.659', 'love', 'conditioned_emotional_response', 'conditioned_emotion', 'fright', 'reverence']
senticnet['negative_emotionality'] = ['0.854', '-0.79', '0', '0.977', '#joy', '#admiration', 'positive', '0.875', 'profitableness', 'auspiciousness', 'luck', 'advantageousness', 'positivity']
senticnet['negative_environmental_impact'] = ['0', '0', '0.735', '-0.70', '#anger', '#disgust', 'negative', '-0.72', 'swing_set', 'roadway_square', 'fresh_air', 'dog_poop', 'wildflower']
senticnet['negative_fallout'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'dust', 'chalk_dust', 'radioactive_dust', 'particulate', 'particulate_matter']
senticnet['negative_feeling'] = ['-0.90', '0', '0.881', '-0.84', '#sadness', '#anger', 'negative', '-0.87', 'bad_feeling', 'feel_sorry', 'deep_sadness', 'lament', 'anguish']
senticnet['negative_mood'] = ['0.971', '0.751', '0', '0.896', '#joy', '#admiration', 'positive', '0.873', 'new_friendship', 'foreplay', 'warm_fuzzy_feeling', 'become_aroused', 'erection']
senticnet['negative_publicity'] = ['0', '0', '0.94', '0', '#anger', '#anger', 'negative', '-0.94', 'feel_angry', 'hurt_feeling', 'feel_hurt', 'ridicule', 'vengeance']
senticnet['negative_reaction'] = ['0', '0', '0.983', '-0.73', '#anger', '#disgust', 'negative', '-0.86', 'entertain', 'laugh', 'enjoy_show', 'eat_popcorn', 'negative_feeling']
senticnet['negative_reinforcement'] = ['0.347', '0.348', '-0.13', '0.394', '#interest', '#admiration', 'positive', '0.239', 'good_deed', 'benefit', 'gratification', 'self_respect', 'bonus']
senticnet['negative_reinforcer'] = ['0.583', '-0.82', '0', '0.574', '#joy', '#surprise', 'positive', '0.661', 'stimulant', 'positive_reinforcing_stimulus', 'positive_reinforcer', 'negative_reinforcing_stimulus', 'reinforcer']
senticnet['negative_reinforcing_stimulus'] = ['0.583', '-0.82', '0', '0.574', '#joy', '#surprise', 'positive', '0.661', 'stimulant', 'positive_reinforcing_stimulus', 'positive_reinforcer', 'reinforcer', 'negative_reinforcer']
senticnet['negative_response'] = ['-0.91', '0', '0.969', '-0.91', '#sadness', '#anger', 'negative', '-0.93', 'negative', 'rude_reply', 'rude_answer', 'rude_person', 'rude_man']
senticnet['negative_stigma'] = ['-0.77', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'mark', 'emblem', 'religious_sign', 'stain', 'brand']
senticnet['negative_stimulus'] = ['-0.63', '0', '0.741', '-0.61', '#sadness', '#anger', 'negative', '-0.66', 'sorrow', 'pain_in_ass', 'pain', 'pain_in_neck', 'vexation']
senticnet['negative_valence'] = ['0.583', '-0.82', '0', '0.574', '#joy', '#surprise', 'positive', '0.661', 'stimulant', 'positive_reinforcing_stimulus', 'positive_reinforcer', 'negative_reinforcing_stimulus', 'reinforcer']
senticnet['negatively_affect'] = ['-0.62', '0', '0.756', '-0.62', '#anger', '#disgust', 'negative', '-0.66', 'vitiate', 'ruin', 'maul', 'mangle', 'deface']
senticnet['negatively_buoyant'] = ['0.676', '0.63', '-0.23', '0.31', '#joy', '#interest', 'positive', '0.345', 'perkiness', 'walk_on_air', 'exultingly', 'exultantly', 'jubilance']
senticnet['negatively_impact'] = ['-0.62', '0', '0.756', '-0.62', '#anger', '#disgust', 'negative', '-0.66', 'vitiate', 'ruin', 'maul', 'mangle', 'deface']
senticnet['negativism'] = ['-0.83', '0', '0.904', '0', '#sadness', '#anger', 'negative', '-0.87', 'disappointed', 'thwarted', 'frustrated', 'foiled', 'discomfited']
senticnet['negativist'] = ['-0.52', '0', '0.95', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'skeptic', 'sceptic', 'doubter', 'defeatist', 'pessimist']
senticnet['negativistic'] = ['-0.98', '0', '0.582', '-0.56', '#sadness', '#anger', 'negative', '-0.70', 'unsocial', 'antisocial', 'lonely', 'alone', 'solitary']
senticnet['negativity'] = ['0', '-0.33', '0', '0', '#surprise', '#surprise', 'positive', '0.33', 'hopeless', 'distracted', 'diverted', 'inattentive', 'confused']
senticnet['negentropy'] = ['-0.98', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.87', 'become_fat', 'flatulence', 'feel_rejuvenated', 'tan_skin', 'make_angry']
senticnet['neglect'] = ['-0.94', '0', '0.923', '-0.98', '#sadness', '#disgust', 'negative', '-0.95', 'heap', 'push', 'accept', 'bring', 'lunge']
senticnet['neglect_chore'] = ['-0.70', '0', '0.79', '0', '#sadness', '#anger', 'negative', '-0.74', 'laugh', 'change_channel', 'distract', 'see_commercial', 'kill_brain_cell']
senticnet['neglectful'] = ['-0.56', '0', '0.743', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'negligent', 'derelict', 'inattentive', 'delinquent', 'lax']
senticnet['neglectfulness'] = ['-0.93', '0', '0.624', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'carelessness', 'willful_neglect', 'laxness', 'laxity', 'remissness']
senticnet['negligee'] = ['-0.88', '0', '0.864', '-0.72', '#sadness', '#anger', 'negative', '-0.82', 'malicious', 'venomous', 'bitchy', 'catty', 'cattish']
senticnet['negligence'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'culpable_negligence', 'evasion', 'dereliction', 'contributory_negligence']
senticnet['negligence_claim'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'culpable_negligence', 'evasion', 'dereliction', 'negligence']
senticnet['negligence_per_se'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'culpable_negligence', 'evasion', 'dereliction', 'negligence']
senticnet['negligent'] = ['-0.56', '0', '0.743', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'neglectful', 'derelict', 'inattentive', 'delinquent', 'lax']
senticnet['negligent_act'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'negligence', 'evasion', 'dereliction', 'contributory_negligence']
senticnet['negligent_conduct'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'culpable_negligence', 'evasion', 'dereliction', 'negligence']
senticnet['negligent_entrustment'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'culpable_negligence', 'evasion', 'dereliction', 'negligence']
senticnet['negligent_infliction'] = ['-0.71', '0', '-0.75', '-0.90', '#fear', '#disgust', 'negative', '-0.79', 'convent', 'monastery', 'capital_build', 'confession', 'synagogue']
senticnet['negligently'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'negligence', 'evasion', 'dereliction', 'contributory_negligence']
senticnet['negligible'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'minor', 'unimportant', 'inessential', 'petty', 'trivial']
senticnet['negociate'] = ['0.911', '-0.71', '0', '0.552', '#joy', '#surprise', 'positive', '0.726', 'deliver_goods', 'succeed', 'win', 'bring_home_bacon', 'come_through']
senticnet['negotiable'] = ['-0.99', '0', '0.641', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'navigable', 'passable', 'surmountable', 'climbable', 'traversable']
senticnet['negotiate'] = ['-0.08', '0.27', '-0.11', '0.14', '#interest', '#admiration', 'positive', '0.054', 'transact_business', 'make_profit', 'sell_stock', 'business_agreement', 'receive_money']
senticnet['negotiate_contract'] = ['-0.03', '0.177', '-0.07', '0.207', '#interest', '#admiration', 'positive', '0.067', 'buy_gas', 'ask_person', 'settle', 'economic_activity', 'ask_politely']
senticnet['negotiated_peace'] = ['0.497', '0.453', '-0.23', '0.189', '#joy', '#interest', 'positive', '0.226', 'calm', 'calmness', 'stillness', 'truce', 'love']
senticnet['negotiated_settlement'] = ['0', '0.072', '0.068', '0.074', '#interest', '#admiration', 'positive', '0.026', 'part_of_book', 'book_part', 'publication', 'book_content', 'book_paper']
senticnet['negotiating_leverage'] = ['-0.94', '0', '0.863', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'poke', 'knockout_punch', 'lick', 'punch', 'biff']
senticnet['negotiating_table'] = ['0', '0.133', '0', '0.088', '#interest', '#admiration', 'positive', '0.111', 'juror', 'decide_guilt', 'become_judge', 'arbiter', 'jury']
senticnet['negotiation'] = ['-0.02', '0.188', '-0.12', '0.192', '#interest', '#admiration', 'positive', '0.058', 'negotiate', 'receive_money', 'make_deal', 'monetary_gain', 'financial_transaction']
senticnet['negotiation_tactic'] = ['0.911', '-0.71', '0', '0.552', '#joy', '#surprise', 'positive', '0.726', 'deliver_goods', 'succeed', 'win', 'bring_home_bacon', 'come_through']
senticnet['negotiator'] = ['0', '0.133', '0', '0.088', '#interest', '#admiration', 'positive', '0.111', 'juror', 'decide_guilt', 'become_judge', 'arbiter', 'jury']
senticnet['negro_slave'] = ['0', '0', '0.8', '-0.82', '#anger', '#disgust', 'negative', '-0.81', 'personnel', 'attendant', 'manager', 'elect_official', 'gentleman']
senticnet['negroe'] = ['0', '0', '0.8', '-0.82', '#anger', '#disgust', 'negative', '-0.81', 'personnel', 'attendant', 'manager', 'elect_official', 'gentleman']
senticnet['neigh'] = ['0.027', '0.077', '0', '0.138', '#interest', '#admiration', 'positive', '0.081', 'hoof_mammal', 'hoof_animal', 'bovine', 'smart_animal', 'critter']
senticnet['neighbor'] = ['0.07', '0.105', '-0.07', '0.058', '#joy', '#interest', 'positive', '0.041', 'neighborhood', 'resident', 'girl_next_door', 'ceiling_fan', 'tenant']
senticnet['neighbor_house'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'downstairs', 'appliance_store', 'apartment', 'shack', 'motel_room']
senticnet['neighborhood'] = ['0.07', '0.105', '-0.07', '0.058', '#joy', '#interest', 'positive', '0.041', 'neighbor', 'resident', 'girl_next_door', 'ceiling_fan', 'tenant']
senticnet['neighborhood_eatery'] = ['0.165', '-0.07', '0.033', '0', '#joy', '#surprise', 'positive', '0.068', 'metal_stick', 'drinking_place', 'bart', 'meet_place', 'water_hole']
senticnet['neighborhood_grocery_store'] = ['0', '0', '0.044', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'large_supermarket', 'food_store', 'large_food_store', 'corner_shop', 'convenience_food_store']
senticnet['neighborhood_hangout'] = ['0.165', '-0.07', '0.033', '0', '#joy', '#surprise', 'positive', '0.068', 'metal_stick', 'drinking_place', 'bart', 'meet_place', 'water_hole']
senticnet['neighborhood_map'] = ['0.087', '0.08', '0.013', '0', '#joy', '#interest', 'positive', '0.051', 'map', 'billboard', 'underground_map', 'advertise_panel', 'restaurant_entrance']
senticnet['neighborhood_safer'] = ['0.36', '0.248', '-0.08', '0.278', '#joy', '#admiration', 'positive', '0.201', 'feel_important', 'strong_relationship', 'eat_together', 'world_peace', 'express_gratitude']
senticnet['neighboring_swat_valley'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'webpage', 'store_record', 'ideal']
senticnet['neighborly'] = ['0.777', '0.972', '0', '0.913', '#interest', '#admiration', 'positive', '0.887', 'serene', 'easy', 'adoration', 'togetherness', 'console']
senticnet['neighbourhood_renewal'] = ['-0.25', '0', '-0.17', '-0.08', '#sadness', '#fear', 'negative', '-0.16', 'exclusion', 'exclusion_zone', 'isolation', 'detach', 'except']
senticnet['neighbouring_hamlet'] = ['-0.91', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.85', 'play', 'perpetrator', 'role_play', 'act_part', 'terrible_event']
senticnet['neighbouring_village'] = ['-0.91', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.85', 'play', 'perpetrator', 'role_play', 'act_part', 'terrible_event']
senticnet['neisseria'] = ['-0.60', '0', '0.721', '-0.57', '#sadness', '#anger', 'negative', '-0.63', 'tubercle_bacillus', 'leprosy_bacillus', 'eubacteria', 'eubacterium', 'true_bacteria']
senticnet['nekkid'] = ['0.305', '0.168', '-0.10', '0.299', '#joy', '#admiration', 'positive', '0.167', 'diversity', 'prosper', 'desirable', 'a_lot_of_sex', 'cherish']
senticnet['neko'] = ['0.028', '0', '0.027', '0.101', '#joy', '#admiration', 'positive', '0.034', 'animal_dwell', 'bee_home', 'live_place', 'dwell_place', 'place_to_live']
senticnet['nelfinavir'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'indinavir', 'protease_inhibitor', 'ritonavir', 'saquinavir']
senticnet['nemaline_myopathy'] = ['-0.50', '0', '0.542', '-0.51', '#anger', '#disgust', 'negative', '-0.51', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'hirschsprung_disease']
senticnet['nemam'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'white_animal', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
senticnet['nematoda'] = ['0.807', '-0.86', '0', '0.611', '#joy', '#surprise', 'positive', '0.762', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['nematomorpha'] = ['0.807', '-0.86', '0', '0.611', '#joy', '#surprise', 'positive', '0.762', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['nembutal'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'use_drug', 'do_drug', 'drug_use', 'drug', 'addiction']
senticnet['nemestrina'] = ['0.921', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.825', 'monkey', 'fiance', 'lemur', 'beaver', 'koala']
senticnet['nemophila'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
senticnet['neo_calvinist'] = ['-0.99', '0', '0.835', '-0.58', '#sadness', '#anger', 'negative', '-0.80', 'beginning', 'origin', 'root', 'wellspring', 'source']
senticnet['neo_classic'] = ['-0.54', '0', '0.7', '-0.64', '#anger', '#disgust', 'negative', '-0.62', 'flourish', 'rant', 'bombast', 'fustian', 'claptrap']
senticnet['neo_classical'] = ['0.136', '-0.11', '0.031', '0', '#joy', '#surprise', 'positive', '0.075', 'rap', 'popular_music', 'music', 'band_play', 'rock_roll']
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senticnet['neo_renaissance_style'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'adobe', 'adobe_brick', 'clinker', 'brick', 'firebrick']
senticnet['neo_soul'] = ['0.081', '-0.05', '0.052', '0.072', '#joy', '#admiration', 'positive', '0.039', 'rhythm', 'type_of_music', 'instrumental', 'lyric', 'melody']
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senticnet['neolithic_revolution'] = ['0.836', '-0.83', '0', '0.511', '#joy', '#surprise', 'positive', '0.726', 'appearance', 'start', 'casus_belli', 'beginning', 'egress']
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senticnet['neonatal_jaundice'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'radiation_therapy', 'radiation', 'actinotherapy', 'irradiation']
senticnet['neonatal_nurse_practitioner'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'infant_feeding', 'schedule_feeding', 'demand_feeding', 'breast_feeding', 'feeding']
senticnet['neonatal_period'] = ['0.95', '0', '0', '0', '#joy', '#joy', 'positive', '0.95', 'born', 'new_baby', 'birthday', 'thanksgiving', 'anniversary']
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senticnet['neosporin'] = ['-0.81', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.82', 'peanut', 'leave_over', 'floppy', 'circular_motion', 'store_vegetable']
senticnet['neotoma_floridana'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'carbon_monoxide', 'tree_animal']
senticnet['neovagina'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
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senticnet['nephew'] = ['0.02', '0.209', '-0.14', '0.202', '#interest', '#admiration', 'positive', '0.071', 'familiar', 'mum', 'familiarity_uncle', 'care_giver', 'familiarity']
senticnet['nephilim'] = ['-0.93', '0', '0.544', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'mutant', 'variation', 'sport', 'lusus_naturae', 'mutation']
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senticnet['neponset'] = ['0.914', '-0.56', '0', '0.869', '#joy', '#admiration', 'positive', '0.781', 'tabula_rasa', 'ego', 'subconscious', 'unconscious', 'psyche']
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senticnet['nere'] = ['-0.63', '0', '0.683', '-0.96', '#anger', '#disgust', 'negative', '-0.76', 'slovenly_woman', 'slut', 'trollop', 'slattern', 'litterer']
senticnet['nereocystis_luetkeana'] = ['-0.89', '0', '0.556', '-0.72', '#sadness', '#disgust', 'negative', '-0.72', 'fucus', 'rockweed', 'black_rockweed', 'bladder_fucus', 'tang']
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senticnet['nerium_oleander'] = ['-0.79', '0', '0.906', '-0.67', '#sadness', '#anger', 'negative', '-0.79', 'water_dropwort', 'hemlock_water_dropwort', 'water_hemlock', 'plant_life', 'poisonous_plant']
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senticnet['nerve_cell'] = ['0', '0', '-0.95', '-0.88', '#fear', '#disgust', 'negative', '-0.92', 'neuron', 'nervous_system', 'continue_grant', 'manager', 'developmental_neuroscience']
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senticnet['nerve_pathway'] = ['0.683', '-0.91', '0', '0.679', '#joy', '#surprise', 'positive', '0.76', 'pathway', 'corpus_callosum', 'commissure', 'tract', 'nerve_tract']
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senticnet['nerve_tract'] = ['0.683', '-0.91', '0', '0.679', '#joy', '#surprise', 'positive', '0.76', 'pathway', 'corpus_callosum', 'nerve_pathway', 'tract', 'commissure']
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senticnet['nervousness'] = ['0', '0', '0.941', '0', '#anger', '#anger', 'negative', '-0.94', 'embarrassment', 'nervous', 'shame', 'worry', 'angst']
senticnet['nervy'] = ['-0.99', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.90', 'high-strung', 'uptight', 'restive', 'overstrung', 'jumpy']
senticnet['nescience'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'metastability', 'monotony', 'invariance', 'constancy', 'unchangeability']
senticnet['nest'] = ['0.974', '0.999', '0', '0', '#joy', '#interest', 'positive', '0.986', 'atmosphere', 'wing', 'bird_fly', 'feather', 'flight']
senticnet['nest_table'] = ['0.826', '0.934', '0', '0', '#joy', '#interest', 'positive', '0.88', 'cork', 'rod', 'beam', 'wand', 'i-beam']
senticnet['nesta_carter'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'nazi', 'man', 'pervert', 'healthy_person']
senticnet['nestbox'] = ['0.042', '0.088', '0', '0', '#joy', '#interest', 'positive', '0.065', 'jump_over_obstacle', 'attempt_fly', 'aircraft', 'pilot', 'wing']
senticnet['nesting_ground'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'habitat', 'mom_house', 'windsor', 'pacific_northwest', 'wilderness']
senticnet['nesting_habitat'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'habitat', 'mom_house', 'windsor', 'pacific_northwest', 'wilderness']
senticnet['nesting_waterfowl'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'habitat', 'mom_house', 'windsor', 'pacific_northwest', 'wilderness']
senticnet['nestle'] = ['0.995', '-0.69', '0', '0.528', '#joy', '#surprise', 'positive', '0.74', 'grasp', 'hug', 'embracement', 'snuggle', 'grip']
senticnet['nestle_water_north_america'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'drinking_water', 'sugar_water']
senticnet['nestorian_schism'] = ['0.909', '0.986', '0', '0', '#joy', '#interest', 'positive', '0.948', 'separation', 'divide', 'division', 'slash', 'bagle']
senticnet['net'] = ['0', '-0.04', '0.113', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'reel', 'fish_rod', 'swimsuit', 'volleyball', 'fish_pole']
senticnet['net_effect'] = ['-0.54', '0', '0.739', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'unremunerative', 'idle', 'lean', 'dead', 'unprofitable']
senticnet['net_income'] = ['0.685', '-0.58', '0', '0.56', '#joy', '#surprise', 'positive', '0.608', 'profit', 'fast_buck', 'net', 'net_profit', 'lucre']
senticnet['net_profit'] = ['0.685', '-0.58', '0', '0.56', '#joy', '#surprise', 'positive', '0.608', 'profit', 'net_income', 'net', 'fast_buck', 'lucre']
senticnet['netbackup'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_musical_sound', 'play_orchestra', 'make_noise', 'play_music', 'make_drawing']
senticnet['netball_club'] = ['0.728', '0.83', '0', '0', '#joy', '#interest', 'positive', '0.779', 'ball', 'bounce', 'ball_bounce', 'soccer', 'play_football']
senticnet['netcycler'] = ['-0.05', '0.241', '-0.06', '0.286', '#interest', '#admiration', 'positive', '0.103', 'give_take', 'barter', 'exchange_goods', 'transaction', 'lower_price']
senticnet['netfind'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'busy_bee', 'actor', 'doer', 'worker']
senticnet['nether_region'] = ['-0.72', '0', '0.759', '-0.54', '#sadness', '#anger', 'negative', '-0.67', 'hellfire', 'red_region', 'imaginary_place', 'mythical_place', 'fictitious_place']
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senticnet['netherese'] = ['0.086', '-0.10', '0.064', '0', '#joy', '#surprise', 'positive', '0.043', 'art_piece', 'lifetime', 'museum', 'vintage', 'collectible']
senticnet['netherlandish_artist'] = ['-0.52', '0', '0.506', '-0.94', '#sadness', '#disgust', 'negative', '-0.65', 'gouger', 'swindler', 'defrauder', 'fraud', 'welcher']
senticnet['netherworld'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'animal_home', 'habitat', 'beehive']
senticnet['netiquette'] = ['0', '0', '0', '0.99', '#admiration', '#admiration', 'positive', '0.99', 'politeness', 'good_behavior', 'good_etiquette', 'manner', 'hint']
senticnet['netnews'] = ['0.826', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.792', 'edu', 'news', 'washington', 'uwm', 'gatech']
senticnet['netroots_nation'] = ['-0.79', '0', '0.948', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['netscape_mozilla'] = ['-0.89', '0', '0.672', '-0.69', '#sadness', '#disgust', 'negative', '-0.75', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['nettle'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['nettle_rash'] = ['-0.63', '0', '0.591', '-0.87', '#sadness', '#disgust', 'negative', '-0.69', 'hypersensitivity_reaction', 'rash', 'efflorescence', 'roseola', 'skin_rash']
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senticnet['network_infrastructure'] = ['0', '0.912', '0', '0.735', '#interest', '#admiration', 'positive', '0.824', 'telecommunication', 'award', 'computer_science_engineering', 'standard_grant', 'connection']
senticnet['network_latency'] = ['-0.9', '0', '0.841', '-0.73', '#sadness', '#anger', 'negative', '-0.82', 'nubbiness', 'tweediness', 'harshness', 'graininess', 'raggedness']
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senticnet['network_topology'] = ['0.861', '0.764', '0', '0', '#joy', '#interest', 'positive', '0.813', 'topographic_anatomy', 'regional_anatomy', 'analysis_situs', 'topology', 'different_application']
senticnet['network_uptime'] = ['0.637', '-0.91', '0', '0.757', '#surprise', '#admiration', 'positive', '0.768', 'tractability', 'manageableness', 'docility', 'obedience', 'amenability']
senticnet['networked_storage'] = ['0.637', '-0.91', '0', '0.757', '#surprise', '#admiration', 'positive', '0.768', 'tractability', 'manageableness', 'docility', 'obedience', 'amenability']
senticnet['networker'] = ['0.749', '0.724', '0', '0', '#joy', '#interest', 'positive', '0.736', 'mouth_noise', 'vocal_cord', 'vocalization', 'vocal_sound', 'loud_soft']
senticnet['networking'] = ['0.936', '-0.80', '0', '0', '#joy', '#surprise', 'positive', '0.869', 'network', 'telecommunication', 'award', 'student', 'project']
senticnet['networking_infrastructure'] = ['0.021', '0', '0', '0.058', '#joy', '#admiration', 'positive', '0.04', 'architecture', 'route', 'structure', 'car_path', 'building']
senticnet['networking_research'] = ['0', '0.744', '0', '0.794', '#interest', '#admiration', 'positive', '0.769', 'telecommunication', 'sponsor', 'award', 'standard_grant', 'computer_science_engineering']
senticnet['neural'] = ['0', '0.786', '0', '0.728', '#interest', '#admiration', 'positive', '0.757', 'hold_brain', 'inside_head', 'cranium', 'skull', 'mind']
senticnet['neural_activation'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'excitation', 'excitement', 'arousal', 'anger']
senticnet['neural_activity'] = ['0.971', '0.751', '0', '0.896', '#joy', '#admiration', 'positive', '0.873', 'new_friendship', 'foreplay', 'warm_fuzzy_feeling', 'become_aroused', 'erection']
senticnet['neural_arch'] = ['0.886', '-0.99', '0', '0.542', '#joy', '#surprise', 'positive', '0.808', 'jowl', 'lower_jaw', 'mandible', 'lantern_jaw', 'lantern_jawr_bone']
senticnet['neural_circuitry'] = ['0', '0.786', '0', '0.728', '#interest', '#admiration', 'positive', '0.757', 'hold_brain', 'inside_head', 'cranium', 'skull', 'mind']
senticnet['neural_interface'] = ['-0.62', '0', '0.996', '-0.68', '#anger', '#disgust', 'negative', '-0.76', 'pack', 'face_pack', 'pickup', 'prosthesis', 'corrective']
senticnet['neural_mechanism'] = ['0.583', '-0.82', '0', '0.574', '#joy', '#surprise', 'positive', '0.661', 'stimulant', 'positive_reinforcing_stimulus', 'reinforcer', 'negative_reinforcing_stimulus', 'negative_reinforcer']
senticnet['neural_pathway'] = ['0', '0.786', '0', '0.728', '#interest', '#admiration', 'positive', '0.757', 'hold_brain', 'inside_head', 'cranium', 'skull', 'mind']
senticnet['neural_representation'] = ['0.693', '-0.52', '0', '0.82', '#joy', '#admiration', 'positive', '0.681', 'embodiment', 'shape', 'representation', 'concrete_representation', 'internal_representation']
senticnet['neural_spine'] = ['0.886', '-0.99', '0', '0.542', '#joy', '#surprise', 'positive', '0.808', 'jowl', 'lower_jaw', 'mandible', 'lantern_jaw', 'lantern_jawr_bone']
senticnet['neural_substrate'] = ['0.998', '-0.63', '0', '0.574', '#joy', '#surprise', 'positive', '0.736', 'cleavable', 'divisible', 'separable', 'severable', 'dissociative']
senticnet['neural_tube'] = ['0', '0.067', '0', '0.07', '#interest', '#admiration', 'positive', '0.069', 'peck', 'bushel', 'chicken_egg', 'ovum', 'egg']
senticnet['neural_tube_defect'] = ['-0.91', '0', '0.961', '-0.55', '#sadness', '#anger', 'negative', '-0.81', 'hypopigmentation', 'albinism', 'congenital_anomaly', 'congenital_defect', 'congenital_disorder']
senticnet['neuralgia'] = ['-0.54', '0', '0.543', '-0.63', '#sadness', '#disgust', 'negative', '-0.57', 'hurting', 'pain', 'trigeminal_neuralgia', 'tic_douloureux', 'sciatica']
senticnet['neuralgy'] = ['-0.64', '0', '0.924', '-0.79', '#anger', '#disgust', 'negative', '-0.78', 'hurting', 'pain', 'trigeminal_neuralgia', 'tic_douloureux', 'sciatica']
senticnet['neurapraxia'] = ['-0.98', '0', '0.678', '-0.95', '#sadness', '#disgust', 'negative', '-0.87', 'rubor', 'redness', 'inflammation', 'tennis_elbow', 'lateral_epicondylitis']
senticnet['neurasthenia'] = ['0', '0', '0.821', '-0.94', '#anger', '#disgust', 'negative', '-0.88', 'debility', 'hypochondria', 'neurosis', 'beriberi', 'pellagra']
senticnet['neurasthenic'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'bad_tempered', 'nervous', 'cranky', 'irritable', 'testy']
senticnet['neuraxial'] = ['-0.65', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'put_to_sleep', 'operation', 'anesthesia', 'surgery', 'local_anesthesia']
senticnet['neuritis'] = ['-0.65', '0', '0.873', '-0.63', '#sadness', '#anger', 'negative', '-0.72', 'polyneuritis', 'multiple_neuritis', 'rubor', 'redness', 'inflammation']
senticnet['neuro_endocrine'] = ['0.672', '-0.55', '0', '0.992', '#joy', '#admiration', 'positive', '0.739', 'pressor', 'hormone', 'adrenalin', 'internal_secretion', 'catecholamine']
senticnet['neuroanatomic'] = ['0.998', '-0.63', '0', '0.574', '#joy', '#surprise', 'positive', '0.736', 'cleavable', 'divisible', 'separable', 'severable', 'dissociative']
senticnet['neurobehavioral'] = ['0.627', '-0.65', '0', '0.968', '#surprise', '#admiration', 'positive', '0.749', 'overwhelm', 'overpower', 'sweep_over', 'whelm', 'overcome']
senticnet['neurobiological_disorder'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'acknowledgeable', 'specifiable', 'recognizable', 'identifiable', 'classifiable']
senticnet['neurobiologist'] = ['0', '0.909', '0', '0.799', '#interest', '#admiration', 'positive', '0.854', 'research', 'research_topic', 'research_subject', 'search', 'student']
senticnet['neurobiology'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'college_major', 'geology', 'physic', 'chemistry', 'subject_school']
senticnet['neurocognitive'] = ['0.874', '-0.73', '0', '0.608', '#joy', '#surprise', 'positive', '0.74', 'growth', 'growing', 'maturation', 'development', 'ontogeny']
senticnet['neurocognitive_function'] = ['0.627', '-0.65', '0', '0.968', '#surprise', '#admiration', 'positive', '0.749', 'overwhelm', 'overpower', 'sweep_over', 'whelm', 'overcome']
senticnet['neurodegeneration'] = ['-0.78', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.80', 'deterioration', 'decline', 'decay', 'degradation', 'slide']
senticnet['neurodegenerative_condition'] = ['-0.75', '0', '0.515', '-0.93', '#sadness', '#disgust', 'negative', '-0.73', 'sclerosis', 'induration', 'disseminated_sclerosis', 'autoimmune_disease', 'autoimmune_disorder']
senticnet['neurodegenerative_disease'] = ['-0.95', '0', '0.621', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'disease', 'brain_disorder', 'brain_disease', 'ill', 'epilepsy']
senticnet['neurodegenerative_disorder'] = ['-0.95', '0', '0.621', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'disease', 'brain_disorder', 'brain_disease', 'ill', 'epilepsy']
senticnet['neurodevelopment'] = ['0.874', '-0.73', '0', '0.608', '#joy', '#surprise', 'positive', '0.74', 'growth', 'growing', 'maturation', 'development', 'ontogeny']
senticnet['neurodevelopmental'] = ['0.874', '-0.73', '0', '0.608', '#joy', '#surprise', 'positive', '0.74', 'growth', 'growing', 'maturation', 'development', 'ontogeny']
senticnet['neurodevelopmental_disorder'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'acknowledgeable', 'specifiable', 'recognizable', 'identifiable', 'classifiable']
senticnet['neurodevelopmental_outcome'] = ['0.874', '-0.73', '0', '0.608', '#joy', '#surprise', 'positive', '0.74', 'growth', 'growing', 'maturation', 'development', 'ontogeny']
senticnet['neuroendocrine'] = ['0.672', '-0.55', '0', '0.992', '#joy', '#admiration', 'positive', '0.739', 'pressor', 'hormone', 'adrenalin', 'internal_secretion', 'catecholamine']
senticnet['neuroendocrine_control'] = ['0.672', '-0.55', '0', '0.992', '#joy', '#admiration', 'positive', '0.739', 'pressor', 'hormone', 'adrenalin', 'internal_secretion', 'catecholamine']
senticnet['neuroendocrine_function'] = ['0.672', '-0.55', '0', '0.992', '#joy', '#admiration', 'positive', '0.739', 'pressor', 'hormone', 'adrenalin', 'internal_secretion', 'catecholamine']
senticnet['neuroendocrine_regulation'] = ['0.672', '-0.55', '0', '0.992', '#joy', '#admiration', 'positive', '0.739', 'pressor', 'hormone', 'adrenalin', 'internal_secretion', 'catecholamine']
senticnet['neuroendocrine_system'] = ['0.672', '-0.55', '0', '0.992', '#joy', '#admiration', 'positive', '0.739', 'pressor', 'hormone', 'adrenalin', 'internal_secretion', 'catecholamine']
senticnet['neuroendocrinology'] = ['0.836', '0.949', '0', '0', '#joy', '#interest', 'positive', '0.892', 'neuroscie', 'integrative_biology', 'brain', 'different_application', 'white_ibn']
senticnet['neurofeedback'] = ['0.627', '-0.65', '0', '0.968', '#surprise', '#admiration', 'positive', '0.749', 'overwhelm', 'overpower', 'sweep_over', 'whelm', 'overcome']
senticnet['neurofibrillary_tangle'] = ['-0.78', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.80', 'deterioration', 'decline', 'decay', 'degradation', 'slide']
senticnet['neurofibromatosis_type'] = ['-0.95', '0', '0.684', '-0.76', '#sadness', '#disgust', 'negative', '-0.79', 'innate', 'nurtural', 'inborn', 'nontransmissible', 'nonheritable']
senticnet['neurogenic_bladder'] = ['-0.52', '0', '0.839', '-0.86', '#anger', '#disgust', 'negative', '-0.74', 'folie', 'upset', 'psychosomatic_disorder', 'conversion_disorder', 'conversion_reaction']
senticnet['neurohormone'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'gastrointestinal_hormone', 'secretin', 'ghrelin']
senticnet['neuroimmunology'] = ['0.672', '-0.55', '0', '0.992', '#joy', '#admiration', 'positive', '0.739', 'pressor', 'hormone', 'adrenalin', 'internal_secretion', 'catecholamine']
senticnet['neurokinin'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'gastrointestinal_hormone', 'secretin', 'ghrelin']
senticnet['neuroleptic'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['neurologic_damage'] = ['0.753', '-0.74', '0', '0.587', '#joy', '#surprise', 'positive', '0.695', 'draining', 'exhausting', 'debilitating', 'enervating', 'enfeebling']
senticnet['neurologic_disorder'] = ['-0.65', '0', '0.873', '-0.63', '#sadness', '#anger', 'negative', '-0.72', 'neuritis', 'multiple_neuritis', 'rubor', 'redness', 'inflammation']
senticnet['neurologic_examination'] = ['0.798', '-0.51', '0', '0.519', '#joy', '#admiration', 'positive', '0.611', 'averageness', 'commonness', 'normalcy', 'typicality', 'condition']
senticnet['neurologic_impairment'] = ['0.753', '-0.74', '0', '0.587', '#joy', '#surprise', 'positive', '0.695', 'draining', 'exhausting', 'debilitating', 'enervating', 'enfeebling']
senticnet['neurologic_involvement'] = ['-0.87', '0', '0.531', '-0.68', '#sadness', '#disgust', 'negative', '-0.69', 'rubor', 'redness', 'dermatomyositis', 'inclusion_body_myositis', 'inflammation']
senticnet['neurological'] = ['0', '0', '0.888', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'psychosomatic', 'neurology', 'obstetrics', 'disorder', 'psychological']
senticnet['neurological_condition'] = ['-0.94', '0', '0.821', '-0.75', '#sadness', '#anger', 'negative', '-0.84', 'predementedness', 'insanity', 'dementedness', 'senile_psychosis', 'alcoholic_dementia']
senticnet['neurological_disease'] = ['-0.95', '0', '0.621', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'disease', 'brain_disorder', 'brain_disease', 'ill', 'epilepsy']
senticnet['neurological_disorder'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'nominal_aphasia', 'anomic_aphasia']
senticnet['neurological_impairment'] = ['0.874', '-0.73', '0', '0.608', '#joy', '#surprise', 'positive', '0.74', 'growth', 'growing', 'maturation', 'development', 'ontogeny']
senticnet['neurological_injury'] = ['-0.88', '0', '-0.79', '-0.85', '#sadness', '#disgust', 'negative', '-0.84', 'pain', 'severe_pain', 'agony', 'physical_pain', 'suffer']
senticnet['neurological_rehabilitation'] = ['0.641', '-0.78', '0', '0.721', '#surprise', '#admiration', 'positive', '0.715', 'correctional_rehabilitation', 'physical_rehabilitation', 'physical_restoration', 'therapeutic_rehabilitation', 'vocational_rehabilitation']
senticnet['neurology'] = ['0', '0', '0.887', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'neurological', 'psychosomatic', 'obstetrics', 'disorder', 'psychological']
senticnet['neuromotor'] = ['0.874', '-0.73', '0', '0.608', '#joy', '#surprise', 'positive', '0.74', 'growth', 'growing', 'maturation', 'development', 'ontogeny']
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senticnet['nicotine'] = ['-0.98', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.86', 'tobacco', 'cigar', 'cigarette_smoke', 'tobacco_product', 'cigarette']
senticnet['nicotine_abstinence'] = ['0.627', '-0.65', '0', '0.968', '#surprise', '#admiration', 'positive', '0.749', 'overwhelm', 'overpower', 'sweep_over', 'whelm', 'overcome']
senticnet['nictitating_membrane'] = ['-0.79', '0', '0.769', '-0.79', '#sadness', '#disgust', 'negative', '-0.78', 'flap', 'eyelid', 'lid', 'palpebra', 'protective_fold']
senticnet['nida_grantee'] = ['0.627', '-0.65', '0', '0.968', '#surprise', '#admiration', 'positive', '0.749', 'overwhelm', 'overpower', 'sweep_over', 'whelm', 'overcome']
senticnet['niddm'] = ['0.504', '-0.93', '0', '0.797', '#surprise', '#admiration', 'positive', '0.747', 'diabetes', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['niddry_street_south'] = ['-0.02', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'tunnel', 'dark_place', 'ski_resort', 'top_tree', 'compost_pile']
senticnet['niece'] = ['0', '0.95', '0', '0.766', '#interest', '#admiration', 'positive', '0.858', 'female_human', 'female_person', 'damsel', 'female', 'woman']
senticnet['niech'] = ['-0.63', '0', '0.791', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'little', 'pinch', 'modicum', 'small_indefinite_amount', 'indefinite_quantity']
senticnet['niekro'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'dicky', 'impaired', 'lessened', 'diminished']
senticnet['niemand'] = ['-0.11', '0', '-0.09', '0.082', '#sadness', '#fear', 'negative', '-0.04', 'stare', 'ear', 'facial_expression', 'mouth', 'dimple']
senticnet['niemann_pick_disease'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'dystrophy', 'myotonic_dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['nietzsche'] = ['0.95', '-0.77', '0', '0.578', '#joy', '#surprise', 'positive', '0.767', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['nietzschean'] = ['0.95', '-0.77', '0', '0.578', '#joy', '#surprise', 'positive', '0.767', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['nieuwe_pekela'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'concupiscence', 'lustfulness', 'amorousness']
senticnet['niff'] = ['-0.74', '0', '0.579', '-0.97', '#sadness', '#disgust', 'negative', '-0.76', 'malodor', 'stench', 'stink', 'fetor', 'bad_smell']
senticnet['nifty'] = ['0.748', '0.794', '0', '0', '#joy', '#interest', 'positive', '0.771', 'bang-up', 'dandy', 'corking', 'cracking', 'bully']
senticnet['nifty_little'] = ['-0.97', '0', '-0.97', '-0.84', '#sadness', '#fear', 'negative', '-0.93', 'school', 'school_system', 'class', 'college', 'college_class']
senticnet['nigel_slater'] = ['0.897', '-0.96', '0', '0.785', '#joy', '#surprise', 'positive', '0.881', 'erotic', 'voluptuary', 'epicure', 'gourmet', 'sensualist']
senticnet['nigel_spackman'] = ['-0.9', '0', '0.749', '-0.71', '#sadness', '#anger', 'negative', '-0.78', 'chute', 'slide', 'slideway', 'sloping_trough', 'gutter']
senticnet['nigga'] = ['-0.66', '0', '0.595', '-0.83', '#sadness', '#disgust', 'negative', '-0.7', 'bastard', 'dickhead', 'disagreeable_person', 'motherfucker', 'son_of_bitch']
senticnet['niggard'] = ['0', '0', '0.815', '-0.81', '#anger', '#disgust', 'negative', '-0.81', 'mean', 'miserliness', 'miser', 'stingy', 'miserly']
senticnet['niggardly'] = ['-0.91', '0', '0.598', '-0.52', '#sadness', '#anger', 'negative', '-0.68', 'parsimonious', 'stingy', 'scrimy', 'beggarly', 'chintzy']
senticnet['nigger'] = ['0.537', '-0.51', '0', '0.807', '#joy', '#admiration', 'positive', '0.619', 'lesbian', 'fairy', 'fagot', 'homo', 'fag']
senticnet['nigger_lover'] = ['0.537', '-0.51', '0', '0.807', '#joy', '#admiration', 'positive', '0.619', 'lesbian', 'fairy', 'fagot', 'homo', 'fag']
senticnet['niggle'] = ['-0.69', '0', '0.768', '-0.67', '#sadness', '#anger', 'negative', '-0.71', 'dither', 'pother', 'worry', 'flap', 'scruple']
senticnet['niggling'] = ['0', '0', '-0.76', '-0.81', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'picayune']
senticnet['niggly'] = ['-0.69', '0', '0.768', '-0.67', '#sadness', '#anger', 'negative', '-0.71', 'dither', 'pother', 'worry', 'flap', 'scruple']
senticnet['night'] = ['-0.70', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.72', 'blackness', 'blackout', 'darkness', 'dark', 'obscure']
senticnet['night_blindness'] = ['-0.82', '0', '0.996', '-0.61', '#sadness', '#anger', 'negative', '-0.81', 'visual_impairment', 'visual_defect', 'vision_defect', 'visual_disorder', 'avitaminosis']
senticnet['night_club'] = ['-0.90', '0', '0.848', '-0.94', '#sadness', '#disgust', 'negative', '-0.89', 'cabaret', 'nightclub', 'dive', 'club', 'nightspot']
senticnet['night_dress'] = ['0.859', '0', '0', '0.91', '#joy', '#admiration', 'positive', '0.885', 'woman_dress', 'party_dress', 'fancy_dress', 'piece_of_clothing', 'robe']
senticnet['night_hag'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'jawless_fish', 'osteostracan', 'cephalaspid', 'ostracoderm', 'heterostracan']
senticnet['night_heron'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'amoeba', 'single_celled']
senticnet['night_shift'] = ['-0.07', '0', '-0.17', '-0.05', '#sadness', '#fear', 'negative', '-0.10', 'feel_sleepy', 'drowsiness', 'drink_coffee', 'need_rest', 'feel_little_tired']
senticnet['night_sky'] = ['-0.64', '0', '0.524', '-0.59', '#sadness', '#disgust', 'negative', '-0.58', 'bum', 'dreamer', 'daydreamer', 'escapist', 'wishful_thinker']
senticnet['night_sleep'] = ['-0.07', '0', '-0.17', '-0.05', '#sadness', '#fear', 'negative', '-0.10', 'night_shift', 'drowsiness', 'drink_coffee', 'need_rest', 'feel_little_tired']
senticnet['night_stalker'] = ['0.864', '-0.54', '0', '0.613', '#joy', '#admiration', 'positive', '0.675', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['night_sun'] = ['0.032', '0.134', '0', '0.105', '#interest', '#admiration', 'positive', '0.09', 'blackness', 'around_earth', 'star_planet', 'outer', 'void']
senticnet['night_thrasher'] = ['0.02', '0.131', '0.749', '-0.67', '#anger', '#disgust', 'negative', '-0.31', 'belligerence', 'belligerency', 'belligerent', 'war', 'rancor']
senticnet['night_time'] = ['-0.08', '0', '-0.04', '0', '#sadness', '#fear', 'negative', '-0.06', 'get_dark', 'dark_outside', 'day_end', 'sundown', 'dusk']
senticnet['night_vision'] = ['0', '0.214', '-0.11', '0.137', '#interest', '#admiration', 'positive', '0.079', 'imaginative', 'hallucinate', 'lucid', 'fantasy', 'dream']
senticnet['night_watchman'] = ['0', '0.115', '0', '0.105', '#interest', '#admiration', 'positive', '0.11', 'farrier', 'miner', 'horseshoer', 'blacksmith', 'smith']
senticnet['nightclub'] = ['0.763', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.75', 'pub', 'disco', 'dance_club', 'cafe', 'tavern']
senticnet['nightclub_owner'] = ['0.763', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.75', 'pub', 'disco', 'dance_club', 'cafe', 'tavern']
senticnet['nightclub_performer'] = ['0', '0.793', '0', '0.91', '#interest', '#admiration', 'positive', '0.852', 'superstar', 'vip', 'accomplishment', 'performance', 'concert']
senticnet['nightclub_scene'] = ['0.763', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.75', 'pub', 'disco', 'dance_club', 'cafe', 'tavern']
senticnet['nightclub_singer'] = ['-0.80', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.79', 'whorehouse', 'brothel', 'massage_parlor', 'bagnio', 'house_of_prostitution']
senticnet['nightfall'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'dusk', 'day_end', 'sundown', 'sunset', 'twilight']
senticnet['nightgown'] = ['0.032', '0.075', '0', '0', '#joy', '#interest', 'positive', '0.053', 'apparel', 'garment', 'clothes', 'dress', 'shirt']
senticnet['nightie'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'duvet_cover', 'pillow', 'comforter', 'pillowcase', 'blanket']
senticnet['nightingale'] = ['0.039', '0.089', '-0.08', '0.143', '#interest', '#admiration', 'positive', '0.048', 'hen', 'bird', 'stork', 'eagle', 'rooster']
senticnet['nightlife'] = ['-0.72', '0', '-0.98', '0', '#sadness', '#fear', 'negative', '-0.85', 'nightclub', 'attraction', 'restaurant', 'pub', 'clubbing']
senticnet['nightlife_agenda'] = ['0.925', '0.993', '0', '0.832', '#joy', '#interest', 'positive', '0.917', 'dance_samba', 'dance', 'skate', 'jump_rope', 'play_frisbee']
senticnet['nightly'] = ['-0.70', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.72', 'blackness', 'blackout', 'darkness', 'dark', 'obscure']
senticnet['nightly_entertainment'] = ['0.951', '0', '0', '0.731', '#joy', '#admiration', 'positive', '0.841', 'pastime', 'enjoyment', 'pleasure', 'enjoy', 'fun']
senticnet['nightly_ritual'] = ['-0.03', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.04', 'rabbit', 'squirrel', 'mole', 'chipmunk', 'nymph']
senticnet['nightly_turndown'] = ['0.722', '-0.98', '0', '0.832', '#surprise', '#admiration', 'positive', '0.845', 'laudatory', 'praiseful', 'praising', 'encomiastic', 'eulogistic']
senticnet['nightmare'] = ['-0.08', '0', '-0.04', '-0.15', '#sadness', '#disgust', 'negative', '-0.09', 'unsettle', 'sleepwalk', 'hallucinate', 'get_rest', 'reflect']
senticnet['nightmarish'] = ['-0.94', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.84', 'horrifying', 'terrifying', 'hellish', 'horrific', 'frightening']
senticnet['nightmarish_vision'] = ['-0.96', '0', '0.108', '-0.90', '#sadness', '#disgust', 'negative', '-0.66', 'evil', 'darkness', 'dark', 'sun_down', 'shadow']
senticnet['nightmarishly'] = ['-0.99', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.96', 'horrifying', 'atrocious', 'dreadfully', 'horrified', 'horribly']
senticnet['nightshade'] = ['-0.66', '0', '0.859', '-0.82', '#anger', '#disgust', 'negative', '-0.78', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['nightshirt'] = ['0.024', '0', '0', '0.074', '#joy', '#admiration', 'positive', '0.049', 'sheet', 'cover', 'quilt', 'comforter', 'modesty']
senticnet['nightspot'] = ['-0.90', '0', '0.848', '-0.94', '#sadness', '#disgust', 'negative', '-0.89', 'cabaret', 'nightclub', 'night_club', 'club', 'dive']
senticnet['nightstand'] = ['0', '-0.02', '0.041', '-0.10', '#anger', '#disgust', 'negative', '-0.03', 'separate_shower', 'shampoo_conditioner', 'towel_bar', 'shower_head', 'toilet_brush']
senticnet['nightstick'] = ['-0.91', '0', '0.931', '-0.72', '#sadness', '#anger', 'negative', '-0.85', 'hit_with_baton', 'get_hurt', 'whip', 'arrow', 'hurt']
senticnet['nighttime'] = ['-0.70', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.72', 'blackness', 'blackout', 'darkness', 'dark', 'obscure']
senticnet['nighttime_heartburn'] = ['0', '0', '-0.70', '-0.76', '#fear', '#disgust', 'negative', '-0.73', 'choke', 'become_full', 'get_full', 'burp', 'sickness']
senticnet['nighy'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'role', 'theatrical_role', 'part', 'persona']
senticnet['nigiri_sushi'] = ['0', '-0.03', '0.046', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'pasta', 'green_bean', 'mayonnaise', 'frozen_food', 'sour_milk']
senticnet['nigrosin'] = ['0', '-0.01', '0.046', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'varnish', 'brush', 'dye', 'pigment', 'canvas']
senticnet['nigrum'] = ['-0.56', '0', '0.892', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'borecole', 'colewort', 'kail', 'cole', 'kale']
senticnet['nih_grant'] = ['0.865', '-0.84', '0', '0.915', '#joy', '#admiration', 'positive', '0.875', 'contract', 'sale_in_gross', 'contract_of_hazard', 'adhesion_contract', 'contract_of_adhesion']
senticnet['nihancan'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'gnome', 'elf', 'goblin', 'undersized', 'small_person']
senticnet['nihil'] = ['-0.95', '0', '0.653', '-0.83', '#sadness', '#disgust', 'negative', '-0.81', 'null', 'zero', 'naught', 'zip', 'nothing']
senticnet['nihil_obstat'] = ['0.751', '-0.98', '0', '0.989', '#surprise', '#admiration', 'positive', '0.909', 'okay', 'approval', 'commendation', 'okey', 'okeh']
senticnet['nihilism'] = ['-0.50', '0', '0.641', '-0.89', '#anger', '#disgust', 'negative', '-0.68', 'delusion_of_persecution', 'hallucination', 'delusion_of_grandeur', 'nihilistic_delusion', 'delusion']
senticnet['nihilist'] = ['0.95', '-0.77', '0', '0.578', '#joy', '#surprise', 'positive', '0.767', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['nihilistic'] = ['0.95', '-0.77', '0', '0.578', '#joy', '#surprise', 'positive', '0.767', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['nihilistic_delusion'] = ['-0.50', '0', '0.641', '-0.89', '#anger', '#disgust', 'negative', '-0.68', 'delusion_of_persecution', 'hallucination', 'delusion_of_grandeur', 'delusion', 'nihilism']
senticnet['nijnsel'] = ['-0.91', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.85', 'play', 'perpetrator', 'role_play', 'act_part', 'terrible_event']
senticnet['nikah'] = ['0', '0.819', '0', '0.987', '#interest', '#admiration', 'positive', '0.903', 'celebrate', 'wedding_ceremony', 'propose_marriage', 'engage', 'engagement']
senticnet['nikki'] = ['-0.74', '0', '0.877', '-0.97', '#anger', '#disgust', 'negative', '-0.86', 'miss', 'girl', 'chachka', 'young_lady', 'young_woman']
senticnet['nikki_beach'] = ['0.763', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.75', 'pub', 'disco', 'dance_club', 'cafe', 'tavern']
senticnet['nikon_camera'] = ['0', '-0.02', '0.048', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'telescope', 'read_glass', 'eyeglass', 'binoculars', 'monocle']
senticnet['nil'] = ['0.027', '0.102', '0.081', '0.16', '#interest', '#admiration', 'positive', '0.052', 'zero', 'nothing', 'empty', 'void', 'null']
senticnet['nile'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['nile_caulder'] = ['-0.53', '0', '0.637', '-0.92', '#anger', '#disgust', 'negative', '-0.69', 'incorporeal', 'unembodied', 'disembodied', 'bodiless', 'unbodied']
senticnet['nile_crocodile'] = ['-0.02', '0', '0.044', '0', '#sadness', '#anger', 'negative', '-0.03', 'large_cat', 'fast_person', 'household_pet', 'big_cat', 'good_pet']
senticnet['nile_delta'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['nile_flood'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['nile_river'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['nile_river_valley'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['nile_valley'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['nilometer'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['nimble'] = ['0.939', '0', '0', '0.706', '#joy', '#admiration', 'positive', '0.823', 'agile', 'dexterous', 'responsive', 'lithe', 'maneuverable']
senticnet['nimble_finger'] = ['-0.07', '0', '0.065', '-0.05', '#sadness', '#anger', 'negative', '-0.06', 'language_skill', 'quick_movement', 'use_finger', 'debug', 'use_keyboard']
senticnet['nimbly'] = ['0', '-0.89', '0', '0.816', '#surprise', '#admiration', 'positive', '0.856', 'adroitly', 'deftly', 'adeptly', 'lithely', 'dexterously']
senticnet['nimby'] = ['-0.93', '0', '0.688', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'dissident', 'dissenter', 'protester', 'objector', 'contestant']
senticnet['nimh_battery'] = ['0', '0', '-1', '-1', '#fear', '#disgust', 'negative', '-1.0', 'moon_gravity', 'force', 'coercion', 'watt', 'power_source']
senticnet['nimue'] = ['-0.63', '0', '0.674', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'baneful', 'evil', 'baleful', 'malevolent', 'malign']
senticnet['nincompoop'] = ['-0.85', '0', '0.938', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'menial', 'lackey', 'servant', 'flunkey', 'familiar']
senticnet['nine'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'hundred_year', 'twelve', 'day_month', 'time_measurement']
senticnet['nine_feet_tall'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'gnome', 'elf', 'goblin', 'undersized', 'small_person']
senticnet['nine_volt_battery'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'smart_technology', 'electric_shaver', 'hair_dryer', 'stay_healthy', 'wash_tooth']
senticnet['nine_yard_line'] = ['0.855', '0.741', '0', '0.862', '#joy', '#admiration', 'positive', '0.819', 'further_goal', 'pass_ball', 'pass_glass', 'help', 'pass_exam']
senticnet['ninebark'] = ['-0.52', '0', '0.527', '-0.78', '#anger', '#disgust', 'negative', '-0.61', 'herb', 'black_snakeroot', 'heartleaf', 'asarabacca', 'herbaceous_plant']
senticnet['ninespine_stickleback'] = ['0', '-0.02', '0.044', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'haddock', 'mermaid', 'fish_rod', 'tidal_wave', 'dryad']
senticnet['nineteen'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'hundred_year', 'dozen', 'day_month', 'time_measurement']
senticnet['nineteenth_amendment'] = ['0', '0.089', '0', '0.129', '#interest', '#admiration', 'positive', '0.109', 'passageway', 'connect_room', 'entrance_room', 'corridor', 'walk_through']
senticnet['nineteenth_century'] = ['0.978', '-0.60', '0', '0.895', '#joy', '#admiration', 'positive', '0.825', 'period', 'quattrocento', 'twentieth_century', 'time_period', 'period_of_time']
senticnet['nineteenth_episode'] = ['0.918', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.827', 'encounter', 'reveal', 'programme', 'lose', 'sequel']
senticnet['ninety'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'hundred_year', 'dozen', 'day_month', 'time_measurement']
senticnet['ninety_degree'] = ['0.07', '0.106', '0.026', '0.187', '#interest', '#admiration', 'positive', '0.084', 'left_right', 'rotate', 'sharpness', 'around_corner', 'go_around']
senticnet['ninja'] = ['0.021', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'plastic_bottle', 'party', 'use_party', 'lighter_air', 'compress_air']
senticnet['ninja_clan'] = ['0.021', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'plastic_bottle', 'party', 'use_party', 'lighter_air', 'compress_air']
senticnet['ninja_master'] = ['-0.04', '0.138', '-0.08', '0.111', '#interest', '#admiration', 'positive', '0.031', 'geometry', 'craft', 'blade_geometry', 'blade', 'collect_item']
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senticnet['njls_saga'] = ['0.036', '0.219', '0', '0.19', '#interest', '#admiration', 'positive', '0.148', 'story', 'long_story', 'novel', 'fable', 'fiction']
senticnet['nk_cell_activity'] = ['-0.78', '0', '0.6', '-0.61', '#sadness', '#disgust', 'negative', '-0.66', 'suggestibility', 'sensitivity', 'capacity', 'reactivity', 'predisposition']
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senticnet['no-nonsense'] = ['0', '0', '0.751', '-0.86', '#anger', '#disgust', 'negative', '-0.80', 'down-to-earth', 'straight-talking', 'straight-forward', 'hard-nosed', 'take-no-prisoners']
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senticnet['nocturnal_emission'] = ['0.531', '0.673', '-0.27', '0.275', '#joy', '#interest', 'positive', '0.301', 'amorous', 'amorousness', 'amatory', 'enamoredness', 'puppy_love']
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senticnet['nocturne'] = ['0.101', '-0.04', '0.036', '0', '#joy', '#surprise', 'positive', '0.038', 'song', 'rap', 'jingle', 'chord', 'lyric']
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senticnet['nodachi'] = ['-0.04', '0.138', '-0.08', '0.111', '#interest', '#admiration', 'positive', '0.031', 'geometry', 'craft', 'blade_geometry', 'blade', 'collect_item']
senticnet['nodal_agency'] = ['0.851', '-0.65', '0', '0.66', '#joy', '#admiration', 'positive', '0.723', 'omen', 'portent', 'presage', 'prognostic', 'prognostication']
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senticnet['noddle'] = ['0.914', '-0.56', '0', '0.869', '#joy', '#admiration', 'positive', '0.781', 'tabula_rasa', 'ego', 'subconscious', 'unconscious', 'psyche']
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senticnet['node'] = ['-0.67', '0', '0.97', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'joint', 'connexion', 'articulation', 'join', 'juncture']
senticnet['node_mobility'] = ['0', '0', '0', '0.144', '#admiration', '#admiration', 'positive', '0.144', 'go_elsewhere', 'transport_person', 'get_around', 'locomotion', 'trucking']
senticnet['node_semiterete'] = ['-0.50', '0', '0.573', '-0.89', '#anger', '#disgust', 'negative', '-0.65', 'petallike', 'bowery', 'leafy', 'unifoliate', 'bifoliate']
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senticnet['nodosaurid'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'hag', 'osteostracan', 'cephalaspid', 'ostracoderm', 'jawless_fish']
senticnet['nodular'] = ['0', '0.716', '0', '0.94', '#interest', '#admiration', 'positive', '0.828', 'hyperplasia', 'nodule', 'goiter', 'lesion', 'carcinoma']
senticnet['nodularity'] = ['-0.99', '0', '0.964', '-0.70', '#sadness', '#anger', 'negative', '-0.88', 'blepharitis', 'diverticulitis', 'epiglottitis', 'inflammation', 'prostatitis']
senticnet['nodule'] = ['0', '0.707', '0', '0.932', '#interest', '#admiration', 'positive', '0.82', 'hyperplasia', 'nodular', 'goiter', 'lesion', 'carcinoma']
senticnet['nodus'] = ['-0.90', '0', '0.772', '0', '#sadness', '#anger', 'negative', '-0.84', 'block', 'obstacle', 'hitch', 'impediment', 'obstruction']
senticnet['noel_gallagher_high'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet']
senticnet['noerr_pennington_doctrine'] = ['0', '0', '0', '0.816', '#admiration', '#admiration', 'positive', '0.816', 'resistance', 'protection', 'untouchable', 'invulnerable', 'sacrosanct']
senticnet['noetic'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'metastability', 'monotony', 'invariance', 'constancy', 'unchangeability']
senticnet['nogaret'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquirer', 'enquirer', 'questioner', 'querier', 'asker']
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senticnet['noir_ish'] = ['-0.54', '0', '0.845', '-0.95', '#anger', '#disgust', 'negative', '-0.78', 'face', 'base', 'underside', 'sole', 'bilge']
senticnet['noir_thriller'] = ['-0.54', '0', '0.845', '-0.95', '#anger', '#disgust', 'negative', '-0.78', 'face', 'base', 'underside', 'sole', 'bilge']
senticnet['noirish'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'anxiety', 'apprehension', 'insecurity', 'tension', 'horror_movie']
senticnet['noise'] = ['0.03', '-0.01', '0.023', '0.053', '#joy', '#admiration', 'positive', '0.02', 'sound', 'animal_sound', 'loudness', 'decibel', 'loud_sound']
senticnet['noise_abatement'] = ['0', '0', '0.735', '-0.70', '#anger', '#disgust', 'negative', '-0.72', 'swing_set', 'roadway_square', 'fresh_air', 'dog_poop', 'wildflower']
senticnet['noise_attenuation'] = ['0', '0', '0.735', '-0.70', '#anger', '#disgust', 'negative', '-0.72', 'swing_set', 'roadway_square', 'fresh_air', 'dog_poop', 'wildflower']
senticnet['noise_disturbance'] = ['0', '0', '0.735', '-0.70', '#anger', '#disgust', 'negative', '-0.72', 'swing_set', 'roadway_square', 'fresh_air', 'dog_poop', 'wildflower']
senticnet['noise_immunity'] = ['0.558', '-0.60', '0', '0.779', '#surprise', '#admiration', 'positive', '0.646', 'benefit', 'pianism', 'show', 'routine', 'histrionics']
senticnet['noise_maker'] = ['0', '0', '0.772', '-0.85', '#anger', '#disgust', 'negative', '-0.81', 'jingle', 'chime', 'school_end', 'bell_noise', 'loud_music']
senticnet['noise_mitigation'] = ['0', '0', '0.735', '-0.70', '#anger', '#disgust', 'negative', '-0.72', 'swing_set', 'roadway_square', 'fresh_air', 'dog_poop', 'wildflower']
senticnet['noise_nuisance'] = ['0', '0', '0.735', '-0.70', '#anger', '#disgust', 'negative', '-0.72', 'swing_set', 'roadway_square', 'fresh_air', 'dog_poop', 'wildflower']
senticnet['noise_pollution'] = ['0', '0', '0.735', '-0.70', '#anger', '#disgust', 'negative', '-0.72', 'swing_set', 'roadway_square', 'fresh_air', 'dog_poop', 'wildflower']
senticnet['noise_ratio'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'short_circuit', 'error', 'mistake', 'undesired', 'trouble']
senticnet['noise_ratio_cnr'] = ['0', '0.089', '0.01', '0.118', '#interest', '#admiration', 'positive', '0.066', 'crate', 'mom_house', 'sack', 'poker_game', 'safekeep']
senticnet['noise_ratio_snr'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'short_circuit', 'error', 'mistake', 'undesired', 'trouble']
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senticnet['noiselessly'] = ['0.979', '-0.69', '0', '0.576', '#joy', '#surprise', 'positive', '0.749', 'big_cheese', 'supremo', 'important_person', 'influential_person', 'personage']
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senticnet['noisome'] = ['-0.06', '0.128', '0.707', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'sickish', 'turn_stomach', 'vile', 'sickening', 'nauseous']
senticnet['noisomeness'] = ['-0.72', '0', '0.71', '-0.9', '#sadness', '#disgust', 'negative', '-0.77', 'perniciousness', 'harmfulness', 'toxicity', 'unwholesomeness', 'noxiousness']
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senticnet['noisy_place'] = ['0.021', '0', '0', '0.113', '#joy', '#admiration', 'positive', '0.067', 'big_car', 'large_automobile', 'large_car', 'long_car', 'expensive_car']
senticnet['noisy_protest'] = ['0.669', '-0.58', '0', '0.803', '#joy', '#admiration', 'positive', '0.685', 'pep_rally', 'revival', 'revival_meeting', 'gathering', 'assemblage']
senticnet['nokia_phone'] = ['0.035', '-0.01', '0.027', '0.085', '#joy', '#admiration', 'positive', '0.026', 'telephone', 'find_phone_number', 'telephone_room', 'phone', 'use_telephone']
senticnet['nolan'] = ['-0.69', '0', '0.89', '-0.70', '#anger', '#disgust', 'negative', '-0.76', 'still', 'suppress', 'moderate', 'hush_up', 'quell']
senticnet['nollan'] = ['-0.62', '0', '0.7', '-0.83', '#anger', '#disgust', 'negative', '-0.72', 'take', 'pull', 'return', 'payback', 'proceeds']
senticnet['nomad'] = ['0.141', '0', '-0.06', '0.086', '#joy', '#admiration', 'positive', '0.055', 'dancer', 'stout', 'old_dance', 'climb_rock', 'jump_around']
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senticnet['nomadic_civilization'] = ['0', '-0.02', '-0.08', '-0.05', '#fear', '#disgust', 'negative', '-0.03', 'mattress', 'sleep_furniture', 'master_bedroom', 'small_bed', 'pillow']
senticnet['nomadic_desert'] = ['0.141', '0', '-0.06', '0.086', '#joy', '#admiration', 'positive', '0.055', 'dancer', 'stout', 'old_dance', 'climb_rock', 'jump_around']
senticnet['nomadic_lifestyle'] = ['0.141', '0', '-0.06', '0.086', '#joy', '#admiration', 'positive', '0.055', 'dancer', 'stout', 'old_dance', 'climb_rock', 'jump_around']
senticnet['nomadic_pastoral'] = ['0.141', '0', '-0.06', '0.086', '#joy', '#admiration', 'positive', '0.055', 'dancer', 'stout', 'old_dance', 'climb_rock', 'jump_around']
senticnet['nomadic_pastoralism'] = ['0.735', '-0.71', '0', '0.566', '#joy', '#surprise', 'positive', '0.672', 'improvement', 'betterment', 'habituation', 'adjustment', 'shakedown']
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senticnet['nomadic_tribe'] = ['0.141', '0', '-0.06', '0.086', '#joy', '#admiration', 'positive', '0.055', 'dancer', 'stout', 'old_dance', 'climb_rock', 'jump_around']
senticnet['nomadic_tribesman'] = ['0.141', '0', '-0.06', '0.086', '#joy', '#admiration', 'positive', '0.055', 'dancer', 'stout', 'old_dance', 'climb_rock', 'jump_around']
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senticnet['nominal'] = ['-0.95', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.90', 'figure', 'figurehead', 'ship', 'front', 'power']
senticnet['nominal_aphasia'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'aphasia', 'anomic_aphasia']
senticnet['nominal_pore_size'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'water_inside', 'water_spray', 'teapot', 'bodily_waste', 'different_type']
senticnet['nominal_value'] = ['-0.02', '0.216', '0.06', '0.141', '#interest', '#admiration', 'positive', '0.068', 'total_number', 'tally', 'total', 'list_number', 'count']
senticnet['nominal_voltage'] = ['0', '0', '-1', '-1', '#fear', '#disgust', 'negative', '-1.0', 'moon_gravity', 'force', 'coercion', 'watt', 'power_source']
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senticnet['nominate'] = ['0.027', '0.24', '-0.07', '0.257', '#interest', '#admiration', 'positive', '0.113', 'list', 'assign', 'edict', 'selection', 'presidential']
senticnet['nominating_committee'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'seeker', 'commuter']
senticnet['nomination'] = ['0', '0.745', '0', '0.832', '#interest', '#admiration', 'positive', '0.788', 'meet_up', 'year', 'rendezvous', 'meet_friend', 'make_appointment']
senticnet['nominative'] = ['0.843', '-0.60', '0', '0.97', '#joy', '#admiration', 'positive', '0.805', 'election', 'concern', 'matter', 'grammar', 'subject']
senticnet['nominative_accusative'] = ['-0.82', '0', '0.81', '-0.61', '#sadness', '#anger', 'negative', '-0.75', 'oblique', 'genitive', 'envy', 'possessive', 'jealous']
senticnet['nominative_form'] = ['-0.82', '0', '0.81', '-0.61', '#sadness', '#anger', 'negative', '-0.75', 'oblique', 'genitive', 'envy', 'possessive', 'jealous']
senticnet['nominee'] = ['0.031', '0.218', '-0.09', '0.299', '#interest', '#admiration', 'positive', '0.113', 'nominate', 'axiom', 'supreme', 'govern', 'tenet']
senticnet['nomological'] = ['0.863', '-0.94', '0', '0.986', '#surprise', '#admiration', 'positive', '0.932', 'postulate', 'posit', 'assume', 'presume', 'take_for_granted']
senticnet['nomura'] = ['0', '0', '-0.99', '-0.76', '#fear', '#disgust', 'negative', '-0.88', 'security_firm', 'brokerage_firm', 'brokerage_house', 'brokerage', 'scandal']
senticnet['non-abrasive'] = ['0', '0', '-0.99', '-0.89', '#fear', '#disgust', 'negative', '-0.94', 'non-porous', 'non-absorbent', 'porous', 'impermeable', 'nonporous']
senticnet['non-absorbent'] = ['0', '0', '-1.0', '-0.89', '#fear', '#disgust', 'negative', '-0.94', 'non-porous', 'nonporous', 'porous', 'impermeable', 'non-abrasive']
senticnet['non-availability'] = ['0', '0', '-0.93', '-0.74', '#fear', '#disgust', 'negative', '-0.84', 'unavailability', 'inaccessibility', 'interruption', 'discontinuance', 'absence']
senticnet['non-believer'] = ['0', '0.935', '0', '0', '#interest', '#interest', 'positive', '0.935', 'atheist', 'doubter', 'agnostic', 'freethinker', 'sceptic']
senticnet['non-blooming'] = ['0.758', '0', '0', '0.964', '#joy', '#admiration', 'positive', '0.861', 'early-ripening', 'crapshooter', 'quick-growing', 'late-flowering', 'early-blooming']
senticnet['non-canonical'] = ['0.804', '0.877', '0', '0', '#joy', '#interest', 'positive', '0.841', 'gospel', 'apocrypha', 'gnostic', 'apocryphal', 'epistle']
senticnet['non-combustible'] = ['0', '0', '0.794', '-0.80', '#anger', '#disgust', 'negative', '-0.8', 'non-flammable', 'nontoxic', 'odorless', 'nonabrasive', 'nonflammable']
senticnet['non-compliance'] = ['0', '0', '0.839', '-0.86', '#anger', '#disgust', 'negative', '-0.85', 'noncompliance', 'violation', 'willful', 'deficiency', 'non-payment']
senticnet['non-conductive'] = ['-0.94', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.93', 'non-metallic', 'quarrying', 'non-ferrous', 'nonferrous', 'nonmetallic']
senticnet['non-conformist'] = ['0', '0', '0.801', '-0.85', '#anger', '#disgust', 'negative', '-0.82', 'nonconformist', 'conformist', 'protestant', 'dissenter', 'clergyman']
senticnet['non-confrontational'] = ['0', '0.705', '0', '0.893', '#interest', '#admiration', 'positive', '0.799', 'business-like', 'matter-of-fact', 'tactful', 'businesslike', 'gentlemanly']
senticnet['non-corrosive'] = ['0', '0', '0.834', '-0.76', '#anger', '#disgust', 'negative', '-0.8', 'high-strength', 'corrosion-resistant', 'rustproof', 'wear-resistant', 'powder-coated']
senticnet['non-deadly'] = ['0', '0', '-0.86', '-0.75', '#fear', '#disgust', 'negative', '-0.81', 'non-restrictive', 'appositive', 'python-like', 'per-computer', 'nonrestrictive']
senticnet['non-defense'] = ['0', '0', '-0.82', '-0.75', '#fear', '#disgust', 'negative', '-0.78', 'cost', 'non-discretionary', 'expenditure', 'discretionary', 'fund']
senticnet['non-denominational'] = ['-0.75', '0', '0.93', '0', '#sadness', '#anger', 'negative', '-0.84', 'nondenominational', 'inter-denominational', 'interdenominational', 'non-religious', 'pentecostal']
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senticnet['non-discretionary'] = ['0', '0', '-0.82', '-0.75', '#fear', '#disgust', 'negative', '-0.78', 'cost', 'discretionary', 'expenditure', 'non-defense', 'fund']
senticnet['non-empty'] = ['-0.72', '0', '0.71', '0', '#sadness', '#anger', 'negative', '-0.71', 'subset', 'nonempty', 'isomorphic', 'disjoint', 'vertex']
senticnet['non-fatal'] = ['-0.90', '0', '-0.89', '0', '#sadness', '#fear', 'negative', '-0.89', 'nonfatal', 'all-cause', 'infarction', 'hospitalization', 'nonlethal']
senticnet['non-ferrous'] = ['-0.95', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.93', 'non-metallic', 'quarrying', 'nonmetallic', 'nonferrous', 'non-conductive']
senticnet['non-fictional'] = ['-0.91', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.81', 'nonfictional', 'non-literary', 'semi-fictional', 'prose', 'fictionalized']
senticnet['non-flammable'] = ['0', '0', '0.965', '-0.76', '#anger', '#disgust', 'negative', '-0.86', 'non-toxic', 'odorless', 'water-based', 'nontoxic', 'biodegradable']
senticnet['non-friable'] = ['-0.99', '0', '0.944', '0', '#sadness', '#anger', 'negative', '-0.97', 'friable', 'loamy', 'silty', 'compacted', 'loam']
senticnet['non-functioning'] = ['-0.85', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.85', 'inoperable', 'unusable', 'malfunctioning', 'inoperative', 'deactivated']
senticnet['non-infectious'] = ['-0.79', '0', '0', '-0.91', '#sadness', '#disgust', 'negative', '-0.85', 'noninfectious', 'non-infective', 'immune-mediated', 'infective', 'nonmalignant']
senticnet['non-infective'] = ['-0.80', '0', '0', '-0.91', '#sadness', '#disgust', 'negative', '-0.85', 'non-infectious', 'noninfectious', 'immune-mediated', 'infective', 'nonmalignant']
senticnet['non-interference'] = ['0', '0', '0.843', '-0.79', '#anger', '#disgust', 'negative', '-0.82', 'non-intervention', 'nonintervention', 'noninterference', 'non-interventionism', 'non-interventionist']
senticnet['non-intervention'] = ['0', '0', '0.836', '-0.79', '#anger', '#disgust', 'negative', '-0.81', 'nonintervention', 'non-interference', 'noninterference', 'non-interventionism', 'non-interventionist']
senticnet['non-interventionism'] = ['0', '0', '0.844', '-0.79', '#anger', '#disgust', 'negative', '-0.82', 'non-intervention', 'non-interference', 'noninterference', 'nonintervention', 'non-interventionist']
senticnet['non-interventionist'] = ['0', '0', '0.84', '-0.79', '#anger', '#disgust', 'negative', '-0.81', 'non-intervention', 'non-interference', 'noninterference', 'non-interventionism', 'nonintervention']
senticnet['non-intrusive'] = ['0', '0', '-0.78', '-0.99', '#fear', '#disgust', 'negative', '-0.88', 'non-invasive', 'angiography', 'tomography', 'invasive', 'noninvasive']
senticnet['non-invasive'] = ['0', '0', '-0.78', '-0.99', '#fear', '#disgust', 'negative', '-0.88', 'noninvasive', 'angiography', 'tomography', 'invasive', 'non-intrusive']
senticnet['non-issue'] = ['-0.93', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.82', 'after-thought', 'afterthought', 'afterthought', 'tacked', 'inconsequential']
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senticnet['non-lethal'] = ['0', '0', '0.911', '-0.92', '#anger', '#disgust', 'negative', '-0.91', 'nonlethal', 'lethal', 'less-lethal', 'less-than-lethal', 'incapacitating']
senticnet['non-linearly'] = ['0.976', '0.8', '0', '0', '#joy', '#interest', 'positive', '0.888', 'linearly', 'logarithmically', 'inversely', 'quadratically', 'monotonically']
senticnet['non-linguistic'] = ['-0.84', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.79', 'nonlinguistic', 'paralinguistic', 'extra-linguistic', 'language-like', 'socio-environmental']
senticnet['non-literary'] = ['-0.91', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.81', 'non-fictional', 'nonfictional', 'semi-fictional', 'prose', 'fictionalized']
senticnet['non-metallic'] = ['-0.94', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.93', 'nonmetallic', 'quarrying', 'non-ferrous', 'nonferrous', 'non-conductive']
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senticnet['non-negotiable'] = ['0.9', '0.976', '0', '0', '#joy', '#interest', 'positive', '0.938', 'nonnegotiable', 'sacrosanct', 'unchangeable', 'negotiable', 'inviolable']
senticnet['non-operative'] = ['0', '0', '0.933', '-0.95', '#anger', '#disgust', 'negative', '-0.94', 'non-surgical', 'minimally-invasive', 'nonsurgical', 'non-invasive', 'nonoperative']
senticnet['non-paid'] = ['-0.77', '0', '0.779', '0', '#sadness', '#anger', 'negative', '-0.77', 'nonpaying', 'unpaid', 'bidder', 'low-paying', 'non-paying']
senticnet['non-paying'] = ['-0.76', '0', '0.784', '0', '#sadness', '#anger', 'negative', '-0.77', 'nonpaying', 'unpaid', 'bidder', 'low-paying', 'non-paid']
senticnet['non-payment'] = ['0', '0', '0.809', '-0.91', '#anger', '#disgust', 'negative', '-0.86', 'nonpayment', 'underpayment', 'arrears', 'non-performance', 'past-due']
senticnet['non-performance'] = ['0', '0', '0.812', '-0.91', '#anger', '#disgust', 'negative', '-0.86', 'non-payment', 'underpayment', 'arrears', 'nonpayment', 'past-due']
senticnet['non-porous'] = ['0', '0', '-1', '-0.89', '#fear', '#disgust', 'negative', '-0.94', 'nonporous', 'non-absorbent', 'porous', 'impermeable', 'non-abrasive']
senticnet['non-public'] = ['0', '0', '-0.73', '-0.86', '#fear', '#disgust', 'negative', '-0.80', 'nonpublic', 'disclosing', 'disclose', 'divulge', 'identifiable']
senticnet['non-receptive'] = ['0', '0', '0.703', '-0.92', '#anger', '#disgust', 'negative', '-0.81', 'unreceptive', 'unappreciative', 'unenthusiastic', 'unwelcoming', 'unsympathetic']
senticnet['non-recoverable'] = ['-0.91', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.95', 'irrecoverable', 'unrecoverable', 'unreadable', 'overwritten', 'unusable']
senticnet['non-religious'] = ['-0.75', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.84', 'non-denominational', 'inter-denominational', 'interdenominational', 'nondenominational', 'pentecostal']
senticnet['non-renewable_resource'] = ['-0.09', '-0.00', '-0.16', '-0.09', '#fear', '#disgust', 'negative', '-0.08', 'dig_up', 'decay', 'fuel_source', 'radiation', 'ore']
senticnet['non-representative'] = ['-1', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.89', 'unrepresentative', 'undemocratic', 'unscientific', 'self-selecting', 'unaccountable']
senticnet['non-resident'] = ['-0.99', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.92', 'nonresident', 'non-residents', 'out-of-state', 'foreign', 'domiciled']
senticnet['non-residents'] = ['-0.98', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.92', 'non-resident', 'nonresident', 'out-of-state', 'foreign', 'domiciled']
senticnet['non-restrictive'] = ['0', '0', '-0.86', '-0.75', '#fear', '#disgust', 'negative', '-0.81', 'nonrestrictive', 'appositive', 'python-like', 'per-computer', 'non-deadly']
senticnet['non-standard'] = ['-0.72', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.78', 'nonstandard', 'ordinal', 'domain-specific', 'colloquial', 'syntactical']
senticnet['non-surgical'] = ['0', '0', '0.933', '-0.95', '#anger', '#disgust', 'negative', '-0.94', 'nonsurgical', 'minimally-invasive', 'non-operative', 'non-invasive', 'nonoperative']
senticnet['non-tax'] = ['0', '0.866', '0', '0.801', '#interest', '#admiration', 'positive', '0.834', 'close-out', 'write-off', 'delinquent', 'write-off', 'receivables']
senticnet['non-taxable'] = ['0', '0', '0.944', '-0.94', '#anger', '#disgust', 'negative', '-0.94', 'taxable', 'nontaxable', 'allocable', 'chargeable', 'tax-free']
senticnet['non-toxic'] = ['0', '0', '0.963', '-0.76', '#anger', '#disgust', 'negative', '-0.86', 'nontoxic', 'odorless', 'water-based', 'non-flammable', 'biodegradable']
senticnet['non-violence'] = ['0', '0', '0.853', '-0.89', '#anger', '#disgust', 'negative', '-0.87', 'non-violent', 'nonviolence', 'nonviolent', 'disobedience', 'activism']
senticnet['non-violent'] = ['0', '0', '0.861', '-0.89', '#anger', '#disgust', 'negative', '-0.88', 'nonviolent', 'nonviolence', 'non-violence', 'disobedience', 'activism']
senticnet['nonabrasive'] = ['0', '0', '0.799', '-0.80', '#anger', '#disgust', 'negative', '-0.8', 'non-flammable', 'nontoxic', 'odorless', 'nonflammable', 'non-combustible']
senticnet['nonabsorbency'] = ['-0.52', '0', '0.596', '-0.76', '#anger', '#disgust', 'negative', '-0.62', 'retention', 'retentiveness', 'retentivity', 'solidness', 'impermeability']
senticnet['nonadhesive'] = ['-0.73', '0', '0.957', '-0.60', '#sadness', '#anger', 'negative', '-0.76', 'ungummed', 'nonglutinous', 'nonviscid', 'nonresinous', 'nonresiny']
senticnet['nonagenarian'] = ['0.556', '-0.64', '0', '0.64', '#surprise', '#admiration', 'positive', '0.612', 'centenarian', 'golden_ager', 'octogenarian', 'septuagenarian', 'sexagenarian']
senticnet['nonchalance'] = ['-0.84', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.88', 'unconcern', 'apathetic', 'unconcernedly', 'apathetically', 'indifferently']
senticnet['nonchalant'] = ['-0.80', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'stolid', 'stoic', 'taciturn', 'stoical', 'soldierly']
senticnet['nonchurchgoing'] = ['-0.93', '0', '0.75', '-0.64', '#sadness', '#anger', 'negative', '-0.77', 'heathen', 'heathenish', 'pagan', 'lapsed', 'irreligious']
senticnet['noncivilized'] = ['-0.53', '0', '0.892', '-0.76', '#anger', '#disgust', 'negative', '-0.73', 'barbarous', 'primitive', 'savage', 'wild', 'preliterate']
senticnet['noncombustible'] = ['0', '0.823', '0', '0.866', '#interest', '#admiration', 'positive', '0.845', 'non-combustible', 'fire-resistant', 'non-flammable', 'nonflammable', 'combustible']
senticnet['noncompliance'] = ['0', '0', '0.839', '-0.86', '#anger', '#disgust', 'negative', '-0.85', 'non-compliance', 'violation', 'willful', 'deficiency', 'non-payment']
senticnet['nonconformance'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'nonconformity', 'iconoclasm', 'nonconformism', 'heterodoxy', 'orientation']
senticnet['nonconformism'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'nonconformity', 'iconoclasm', 'heterodoxy', 'nonconformance', 'orientation']
senticnet['nonconformist'] = ['0', '0', '0.81', '-0.84', '#anger', '#disgust', 'negative', '-0.82', 'non-conformist', 'conformist', 'protestant', 'dissenter', 'clergyman']
senticnet['nonconformity'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'heterodoxy', 'iconoclasm', 'nonconformism', 'nonconformance', 'orientation']
senticnet['noncontroversial'] = ['-0.95', '0', '0.783', '-0.65', '#sadness', '#anger', 'negative', '-0.79', 'unchallengeable', 'undisputed', 'unchallenged', 'unquestioned', 'agreed_upon']
senticnet['noncurrent'] = ['-0.90', '0', '0.517', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'outdated', 'obsolescent', 'disused', 'obsolete', 'superannuated']
senticnet['nondenominational'] = ['-0.74', '0', '0.933', '0', '#sadness', '#anger', 'negative', '-0.84', 'non-denominational', 'inter-denominational', 'interdenominational', 'non-religious', 'pentecostal']
senticnet['nondescript'] = ['-0.74', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.85', 'unpretentious', 'soft-spoken', 'self-effacing', 'unassuming', 'affable']
senticnet['nondigestible'] = ['-0.77', '0', '0.671', '-0.80', '#sadness', '#disgust', 'negative', '-0.75', 'flatulent', 'indigestible', 'undigested', 'stodgy', 'heavy']
senticnet['nondisjunction'] = ['-0.87', '0', '0.751', '0', '#sadness', '#anger', 'negative', '-0.81', 'meiosis', 'miosis', 'cell_division', 'litotes', 'understatement']
senticnet['none'] = ['-0.44', '-0.15', '0', '-0.36', '#sadness', '#disgust', 'negative', '-0.21', 'nothing', 'zero', 'nonsense', 'null', 'neglect']
senticnet['nonempty'] = ['-0.72', '0', '0.714', '0', '#sadness', '#anger', 'negative', '-0.71', 'subset', 'disjoint', 'isomorphic', 'non-empty', 'vertex']
senticnet['nonentity'] = ['-0.63', '0', '0.591', '-0.73', '#sadness', '#disgust', 'negative', '-0.65', 'irreality', 'void', 'impossibleness', 'unreality', 'impossibility']
senticnet['nonequivalent'] = ['-0.86', '0', '0.63', '-0.69', '#sadness', '#disgust', 'negative', '-0.73', 'unequalized', 'mismatched', 'unequal', 'uneven', 'unbalanced']
senticnet['nonessential'] = ['-0.81', '0', '0.987', '-0.64', '#sadness', '#anger', 'negative', '-0.81', 'unessential', 'accidental', 'incidental', 'accessorial', 'adscititious']
senticnet['nonesuch'] = ['0.805', '-0.58', '0', '0.525', '#joy', '#surprise', 'positive', '0.637', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['nonexistence'] = ['-0.77', '0', '0.908', '-0.61', '#sadness', '#anger', 'negative', '-0.76', 'irreality', 'void', 'impossibleness', 'unreality', 'impossibility']
senticnet['nonexistent'] = ['-0.98', '0', '0.801', '-0.66', '#sadness', '#anger', 'negative', '-0.81', 'lacking', 'wanting', 'nonextant', 'absent', 'missing']
senticnet['nonextant'] = ['-0.98', '0', '0.801', '-0.66', '#sadness', '#anger', 'negative', '-0.81', 'lacking', 'wanting', 'nonexistent', 'absent', 'missing']
senticnet['nonfatal'] = ['-0.89', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.89', 'non-fatal', 'all-cause', 'infarction', 'hospitalization', 'nonlethal']
senticnet['nonfederal_source'] = ['0', '0.73', '0', '0.716', '#interest', '#admiration', 'positive', '0.723', 'fund', 'grant_fund', 'grantee', 'award', 'match']
senticnet['nonferrous'] = ['-0.95', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.94', 'non-metallic', 'quarrying', 'non-ferrous', 'nonmetallic', 'non-conductive']
senticnet['nonfictional'] = ['-0.91', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.80', 'non-fictional', 'non-literary', 'semi-fictional', 'prose', 'fictionalized']
senticnet['nonflammable'] = ['0', '0', '0.802', '-0.79', '#anger', '#disgust', 'negative', '-0.8', 'non-flammable', 'nontoxic', 'odorless', 'nonabrasive', 'non-combustible']
senticnet['nonglutinous'] = ['-0.73', '0', '0.957', '-0.60', '#sadness', '#anger', 'negative', '-0.76', 'ungummed', 'nonadhesive', 'nonviscid', 'nonresinous', 'nonresiny']
senticnet['nongovernmental_organization'] = ['-0.69', '0', '0.836', '-0.73', '#anger', '#disgust', 'negative', '-0.75', 'denomination', 'committee', 'citizen_committee', 'mission', 'missionary_post']
senticnet['nonhereditary'] = ['-0.95', '0', '0.684', '-0.76', '#sadness', '#disgust', 'negative', '-0.79', 'innate', 'nurtural', 'inborn', 'nontransmissible', 'nonheritable']
senticnet['nonheritable'] = ['-0.95', '0', '0.684', '-0.76', '#sadness', '#disgust', 'negative', '-0.79', 'innate', 'nurtural', 'inborn', 'nontransmissible', 'nonhereditary']
senticnet['nonhuman'] = ['-0.93', '0', '0.508', '-0.58', '#sadness', '#disgust', 'negative', '-0.67', 'inhuman', 'bloodless', 'apelike', 'anthropoid', 'anthropoidal']
senticnet['nonhumanal'] = ['-0.94', '0', '0.501', '-0.59', '#sadness', '#disgust', 'negative', '-0.67', 'inhuman', 'bloodless', 'apelike', 'nonhuman', 'anthropoid']
senticnet['nonindulgent'] = ['0.682', '-0.55', '0', '0.985', '#joy', '#admiration', 'positive', '0.742', 'renunciant', 'renunciative', 'puritanic', 'puritanical', 'corrective']
senticnet['noninfectious'] = ['-0.79', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'non-infectious', 'non-infective', 'immune-mediated', 'infective', 'nonmalignant']
senticnet['noninheritable'] = ['-0.52', '0', '0.821', '-0.58', '#anger', '#disgust', 'negative', '-0.64', 'innate', 'nurtural', 'inborn', 'nontransmissible', 'nonhereditary']
senticnet['nonintellectual'] = ['0.849', '-0.89', '0', '0.924', '#surprise', '#admiration', 'positive', '0.89', 'philistine', 'lowbrow', 'lowbrowed', 'mindless', 'uncultivated']
senticnet['nonintellectualed'] = ['0.848', '-0.9', '0', '0.916', '#surprise', '#admiration', 'positive', '0.888', 'philistine', 'nonintellectual', 'lowbrow', 'mindless', 'uncultivated']
senticnet['noninterference'] = ['0', '0', '0.843', '-0.79', '#anger', '#disgust', 'negative', '-0.82', 'non-intervention', 'non-interference', 'nonintervention', 'non-interventionism', 'non-interventionist']
senticnet['nonintervention'] = ['0', '0', '0.846', '-0.79', '#anger', '#disgust', 'negative', '-0.82', 'non-intervention', 'non-interference', 'noninterference', 'non-interventionism', 'non-interventionist']
senticnet['noninvasive'] = ['0', '0', '-0.77', '-0.98', '#fear', '#disgust', 'negative', '-0.88', 'non-invasive', 'angiography', 'tomography', 'invasive', 'non-intrusive']
senticnet['nonlegal'] = ['-0.97', '0', '0.793', '0', '#sadness', '#anger', 'negative', '-0.88', 'non-legal', 'non-diplomatic', 'billing-related', 'public-law', 'court-related']
senticnet['nonlethal'] = ['0', '0', '0.918', '-0.92', '#anger', '#disgust', 'negative', '-0.92', 'non-lethal', 'lethal', 'less-lethal', 'less-than-lethal', 'incapacitating']
senticnet['nonlinear'] = ['0', '0', '0.776', '-0.89', '#anger', '#disgust', 'negative', '-0.83', 'isotropic', 'inhomogeneous', 'dielectric', 'anisotropic', 'anisotropy']
senticnet['nonlinguistic'] = ['-0.83', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.78', 'non-linguistic', 'paralinguistic', 'extra-linguistic', 'language-like', 'socio-environmental']
senticnet['nonmajors'] = ['0.949', '0', '0', '0.737', '#joy', '#admiration', 'positive', '0.843', 'different_application', 'student', 'project', 'lab_improve', 'education']
senticnet['nonmalignant'] = ['-0.80', '0', '0', '-0.91', '#sadness', '#disgust', 'negative', '-0.85', 'non-infectious', 'non-infective', 'immune-mediated', 'infective', 'noninfectious']
senticnet['nonmalignant_neoplasm'] = ['-0.84', '0', '0.614', '-0.89', '#sadness', '#disgust', 'negative', '-0.78', 'fibroma', 'adenoma', 'chondroma', 'enchondroma', 'myoma']
senticnet['nonmalignant_tumor'] = ['-0.97', '0', '0.787', '-0.50', '#sadness', '#anger', 'negative', '-0.75', 'fibroma', 'adenoma', 'chondroma', 'enchondroma', 'myoma']
senticnet['nonmeaningful'] = ['-0.52', '0', '0.663', '-0.60', '#anger', '#disgust', 'negative', '-0.59', 'insignificant', 'mindless', 'empty', 'nonsense', 'vacuous']
senticnet['nonmetallic'] = ['-0.94', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.93', 'non-metallic', 'quarrying', 'non-ferrous', 'nonferrous', 'non-conductive']
senticnet['nonmotile'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'immobile', 'immotile']
senticnet['nonmoving'] = ['-0.82', '0', '0.99', '-0.87', '#anger', '#disgust', 'negative', '-0.89', 'motionless', 'rigid', 'inactive', 'still', 'static']
senticnet['nonnegative'] = ['0.953', '0', '0', '0.753', '#joy', '#admiration', 'positive', '0.853', 'non-negative', 'eigenvalue', 'nonzero', 'hermitian', 'real-valued']
senticnet['nonnegotiable'] = ['0.896', '0.975', '0', '0', '#joy', '#interest', 'positive', '0.935', 'non-negotiable', 'sacrosanct', 'unchangeable', 'negotiable', 'inviolable']
senticnet['nonoperative'] = ['0', '0', '0.929', '-0.94', '#anger', '#disgust', 'negative', '-0.93', 'non-surgical', 'minimally-invasive', 'non-operative', 'non-invasive', 'nonsurgical']
senticnet['nonpareil'] = ['0.78', '-0.50', '0', '0.93', '#joy', '#admiration', 'positive', '0.739', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['nonpaying'] = ['-0.77', '0', '0.778', '0', '#sadness', '#anger', 'negative', '-0.77', 'non-paying', 'unpaid', 'bidder', 'low-paying', 'non-paid']
senticnet['nonpayment'] = ['0', '0', '0.814', '-0.91', '#anger', '#disgust', 'negative', '-0.86', 'non-payment', 'underpayment', 'arrears', 'non-performance', 'past-due']
senticnet['nonplus'] = ['-0.44', '-0.15', '0', '-0.36', '#sadness', '#disgust', 'negative', '-0.21', 'baffle', 'stupefy', 'dumbfound', 'stupefaction', 'confuse']
senticnet['nonplused'] = ['-0.83', '0', '0.917', '-1', '#anger', '#disgust', 'negative', '-0.91', 'perplexed', 'at_loss', 'confused', 'puzzled', 'bewildered']
senticnet['nonporous'] = ['0', '0', '-0.99', '-0.89', '#fear', '#disgust', 'negative', '-0.94', 'non-porous', 'non-absorbent', 'porous', 'impermeable', 'non-abrasive']
senticnet['nonpublic'] = ['0', '0', '-0.73', '-0.85', '#fear', '#disgust', 'negative', '-0.79', 'non-public', 'disclosing', 'disclose', 'divulge', 'identifiable']
senticnet['nonrational'] = ['0', '0', '-0.96', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'irrational', 'inconsequential', 'stupid', 'visceral', 'illogical']
senticnet['nonreligious_person'] = ['-0.54', '0', '0.606', '-0.73', '#anger', '#disgust', 'negative', '-0.62', 'heathen', 'pagan', 'gentile', 'infidel', 'mortal']
senticnet['nonremittal'] = ['-0.85', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.82', 'default', 'default_option', 'default_on', 'nonpayment', 'fprintfstderr']
senticnet['nonresident'] = ['-0.98', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.92', 'non-resident', 'non-residents', 'out-of-state', 'foreign', 'domiciled']
senticnet['nonresinous'] = ['-0.73', '0', '0.957', '-0.60', '#sadness', '#anger', 'negative', '-0.76', 'ungummed', 'nonglutinous', 'nonviscid', 'nonadhesive', 'nonresiny']
senticnet['nonresiny'] = ['-0.73', '0', '0.957', '-0.60', '#sadness', '#anger', 'negative', '-0.76', 'ungummed', 'nonglutinous', 'nonviscid', 'nonresinous', 'nonadhesive']
senticnet['nonrestrictive'] = ['0', '0', '-0.86', '-0.74', '#fear', '#disgust', 'negative', '-0.80', 'non-restrictive', 'appositive', 'python-like', 'per-computer', 'non-deadly']
senticnet['nonscience_major'] = ['0.827', '0', '0', '0.943', '#joy', '#admiration', 'positive', '0.885', 'different_application', 'project', 'student', 'science', 'education']
senticnet['nonsense'] = ['-0.44', '-0.15', '0', '-0.36', '#sadness', '#disgust', 'negative', '-0.21', 'nothing', 'none', 'zero', 'crazy', 'rubbish']
senticnet['nonsensical'] = ['0', '-0.79', '0', '0.797', '#surprise', '#admiration', 'positive', '0.796', 'made-up', 'fictitious', 'gibberish', 'fictional', 'nonsense']
senticnet['nonsensicality'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'nonsense', 'meaninglessness', 'bunk', 'absurdity', 'hokum']
senticnet['nonsexual'] = ['-0.50', '0', '0.763', '-0.83', '#anger', '#disgust', 'negative', '-0.70', 'agamic', 'agamous', 'agamogenetic', 'parthenogenetic', 'apomictic']
senticnet['nonstandard'] = ['-0.71', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.78', 'non-standard', 'ordinal', 'domain-specific', 'colloquial', 'syntactical']
senticnet['nonstarter'] = ['-0.94', '0', '0.551', '-0.98', '#sadness', '#disgust', 'negative', '-0.82', 'unfortunate', 'insolvent', 'underdog', 'bankrupt', 'dud']
senticnet['nonsuch'] = ['0.582', '-0.97', '0', '0.802', '#surprise', '#admiration', 'positive', '0.788', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['nonsurgical'] = ['0', '0', '0.934', '-0.94', '#anger', '#disgust', 'negative', '-0.94', 'non-surgical', 'minimally-invasive', 'non-operative', 'non-invasive', 'nonoperative']
senticnet['nontaxable'] = ['0', '0', '0.945', '-0.94', '#anger', '#disgust', 'negative', '-0.94', 'taxable', 'non-taxable', 'allocable', 'chargeable', 'tax-free']
senticnet['nontoxic'] = ['0', '0', '0.968', '-0.75', '#anger', '#disgust', 'negative', '-0.86', 'non-toxic', 'odorless', 'water-based', 'non-flammable', 'biodegradable']
senticnet['nontransferable'] = ['-0.80', '0', '0.815', '-0.98', '#anger', '#disgust', 'negative', '-0.86', 'infrangible', 'inalienable', 'unassignable', 'untransferable', 'inviolable']
senticnet['nontransmissible'] = ['-0.95', '0', '0.684', '-0.76', '#sadness', '#disgust', 'negative', '-0.79', 'innate', 'nurtural', 'inborn', 'nonheritable', 'nonhereditary']
senticnet['nonuniform'] = ['-0.82', '0', '0.756', '-0.51', '#sadness', '#anger', 'negative', '-0.69', 'inhomogeneous', 'heterogeneous', 'assorted', 'sundry', 'mixed']
senticnet['nonuniformity'] = ['0.737', '-0.83', '0', '0.641', '#joy', '#surprise', 'positive', '0.738', 'inconsistency', 'dissimilarity', 'unsimilarity', 'heterogeneity', 'heterogeneousness']
senticnet['nonviable'] = ['0', '0.76', '0', '0.86', '#interest', '#admiration', 'positive', '0.81', 'vital', 'going', 'viable', 'possible', 'viability']
senticnet['nonviolence'] = ['0', '0', '0.855', '-0.89', '#anger', '#disgust', 'negative', '-0.87', 'non-violent', 'nonviolent', 'non-violence', 'disobedience', 'activism']
senticnet['nonviolent'] = ['0', '0', '0.862', '-0.89', '#anger', '#disgust', 'negative', '-0.87', 'non-violent', 'nonviolence', 'non-violence', 'disobedience', 'activism']
senticnet['nonviscid'] = ['-0.73', '0', '0.957', '-0.60', '#sadness', '#anger', 'negative', '-0.76', 'ungummed', 'nonglutinous', 'nonadhesive', 'nonresinous', 'nonresiny']
senticnet['nonvisual'] = ['-0.51', '0', '0.979', '-0.89', '#anger', '#disgust', 'negative', '-0.79', 'occult', 'concealed', 'invisible', 'hidden', 'out_of_sight']
senticnet['nonvoluntary'] = ['-0.52', '0', '0.857', '-0.75', '#anger', '#disgust', 'negative', '-0.71', 'unwilling', 'unconscious', 'unwilled', 'senseless', 'forced']
senticnet['nonzero'] = ['0.95', '0', '0', '0.744', '#joy', '#admiration', 'positive', '0.847', 'non-negative', 'eigenvalue', 'nonnegative', 'hermitian', 'real-valued']
senticnet['noo'] = ['0.027', '0', '0.012', '0.063', '#joy', '#admiration', 'positive', '0.026', 'vegetation', 'tree_flower', 'green_growth', 'small', 'greenery']
senticnet['noob'] = ['-0.06', '-0.05', '0.009', '-0.18', '#sadness', '#disgust', 'negative', '-0.05', 'newbie', 'loser', 'computer_user', 'dummy', 'beginner']
senticnet['noodel'] = ['0.03', '0', '0', '0.076', '#joy', '#admiration', 'positive', '0.053', 'food_ingredient', 'rice', 'granulate', 'white_grain', 'wheat_flour']
senticnet['noodle'] = ['0.041', '-0.07', '0.034', '0', '#joy', '#surprise', 'positive', '0.026', 'butter', 'mayonaise', 'pie', 'bagel', 'muffin']
senticnet['noodle_dish'] = ['0', '-0.03', '0.046', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'pasta', 'green_bean', 'mayonnaise', 'frozen_food', 'sour_milk']
senticnet['noodle_kidoodle'] = ['0.914', '-0.56', '0', '0.869', '#joy', '#admiration', 'positive', '0.781', 'tabula_rasa', 'ego', 'subconscious', 'unconscious', 'psyche']
senticnet['noodle_shop'] = ['0.973', '-0.50', '0', '0.612', '#joy', '#admiration', 'positive', '0.698', 'chic', 'chicness', 'jauntiness', 'modishness', 'stylishness']
senticnet['noodles'] = ['0.711', '0', '0', '0', '#joy', '#joy', 'positive', '0.711', 'chicken_rice', 'dumpling', 'chinese_food', 'porridge', 'rice']
senticnet['nook'] = ['-0.76', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.79', 'off-the-beaten-path', 'tucked-away', 'off-the-beaten-track', 'hole-in-the-wall', 'out-of-the-way']
senticnet['nookie'] = ['-0.05', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.05', 'masturbate', 'jerk', 'jerk_off', 'watch_porn', 'porn']
senticnet['noon_edt'] = ['0.968', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.859', 'day_of_week', 'monday', 'fortnight', 'weekend', 'midweek']
senticnet['noon_hour'] = ['0.02', '0.067', '0', '0', '#joy', '#interest', 'positive', '0.044', 'sunrise', 'begin_day', 'bright_sky', 'sunny', 'part_of_day']
senticnet['noonan_syndrome'] = ['-0.87', '0', '0.873', '-0.62', '#sadness', '#anger', 'negative', '-0.78', 'dwarfism', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['noonday'] = ['0.042', '0.126', '-0.06', '0.165', '#interest', '#admiration', 'positive', '0.067', 'sparkle', 'shiny', 'shinny', 'gray', 'shine']
senticnet['noonday_sun'] = ['-0.11', '0.074', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.05', 'sun_down', 'night', 'night_time', 'moon_star', 'without_light']
senticnet['noongar_language'] = ['-0.95', '0', '0.624', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['noose'] = ['-0.06', '-0.03', '0.015', '-0.05', '#sadness', '#disgust', 'negative', '-0.02', 'suspend', 'long_arm', 'hang', 'antenna', 'long_neck']
senticnet['nope'] = ['-0.94', '0', '0.557', '-0.71', '#sadness', '#disgust', 'negative', '-0.74', 'sad', 'disappointing', 'unlucky', 'disengaged', 'cry']
senticnet['noradrenaline'] = ['0.958', '-0.72', '0', '0.72', '#joy', '#surprise', 'positive', '0.801', 'pressor', 'hormone', 'endocrine', 'internal_secretion', 'catecholamine']
senticnet['noradrenergic'] = ['0.698', '-0.61', '0', '0.619', '#joy', '#admiration', 'positive', '0.644', 'sensitivity', 'sensitiveness', 'irritability', 'sensibility', 'reactivity']
senticnet['norah'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'impaired', 'dickey', 'lessened', 'diminished']
senticnet['noreaster'] = ['-0.78', '0', '0.906', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'rainstorm', 'atmospheric_phenomenon', 'northeaster', 'storm', 'hailstorm']
senticnet['norelco'] = ['-0.08', '0.232', '0', '0.099', '#interest', '#admiration', 'positive', '0.084', 'wax_dental_floss', 'fluoride_toothpaste', 'toilet_paper', 'dental_floss', 'tartar_control_toothpaste']
senticnet['norfolk_jacket'] = ['-0.58', '0', '0.757', '-0.67', '#anger', '#disgust', 'negative', '-0.67', 'breech', 'knee_breech', 'knee_pants', 'trunk_hose', 'knickers']
senticnet['norfolk_xi'] = ['0.033', '0.088', '-0.06', '0.068', '#interest', '#admiration', 'positive', '0.032', 'earl', 'noble', 'king', 'sultan', 'royal_title']
senticnet['norifumi_suzuki'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'concupiscence', 'lustfulness', 'amorousness']
senticnet['norm'] = ['0', '0', '-0.75', '-0.94', '#fear', '#disgust', 'negative', '-0.85', 'contrary', 'unpredictable', 'wayward', 'perverse', 'deviation']
senticnet['norm_provan'] = ['0', '0.996', '0', '0.85', '#interest', '#admiration', 'positive', '0.923', 'strength', 'strong', 'physical_strength', 'athlete', 'muscle']
senticnet['norm_referenced_test'] = ['0.928', '-0.87', '0', '0.679', '#joy', '#surprise', 'positive', '0.829', 'examine', 'experiment', 'alpha_test', 'beta_test', 'road_test']
senticnet['normal'] = ['0.029', '0.228', '0', '0.145', '#interest', '#admiration', 'positive', '0.134', 'usual_ordinary', 'typical', 'usual', 'frequent', 'sameness']
senticnet['normal_attrition'] = ['-0.89', '0.168', '-0.60', '0.05', '#sadness', '#fear', 'negative', '-0.32', 'remorsefully', 'contritely', 'contrition', 'contriteness', 'ruefully']
senticnet['normal_blood_flow'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'blatancy', 'obviousness']
senticnet['normal_bmi'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'fatty', 'worse', 'health_issue', 'clinical_problem']
senticnet['normal_bowel'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'anus', 'scrotum', 'shoulder', 'liver', 'waist']
senticnet['normal_contralateral_kidney'] = ['-0.73', '0', '0.904', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'unshapely', 'shapeless', 'lumpy', 'deformed', 'malformed']
senticnet['normal_distribution'] = ['0.948', '-0.99', '0', '0.549', '#joy', '#surprise', 'positive', '0.83', 'sampling', 'equidistribution', 'frequency_distribution', 'distribution', 'sample_distribution']
senticnet['normal_gait'] = ['-0.15', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.09', 'use_leg', 'walk_quickly', 'casual', 'take_step', 'strut']
senticnet['normal_human_body'] = ['-0.04', '0.077', '-0.09', '-0.08', '#fear', '#disgust', 'negative', '-0.03', 'body', 'inside_body', 'corpse', 'dead_person', 'nervous_system']
senticnet['normal_karyotype'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'deflection', 'warp', 'aberrance']
senticnet['normal_lifespan'] = ['0.097', '0.099', '-0.09', '0.137', '#interest', '#admiration', 'positive', '0.059', 'live_long', 'long_life', 'live_healthy_lifestyle', 'increase_muscle_mass', 'feel_healthy']
senticnet['normal_map'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'illuminate_room', 'give_light', 'light', 'light_bulb', 'lantern']
senticnet['normal_modal_logic'] = ['0.734', '-0.56', '0', '0.64', '#joy', '#admiration', 'positive', '0.645', 'inferential', 'suggestive', 'connotative', 'connotational', 'connotative_of']
senticnet['normal_physiologic'] = ['-0.84', '0', '0.8', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'rigor_mortis', 'natural_process', 'natural_action', 'activity', 'action']
senticnet['normal_precipitation'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'condensation', 'water_fall', 'falling_water', 'frozen_water', 'water_drop']
senticnet['normal_rainfall'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'condensation', 'water_fall', 'falling_water', 'frozen_water', 'water_drop']
senticnet['normal_saline'] = ['0', '0', '-0.95', '-0.87', '#fear', '#disgust', 'negative', '-0.91', 'sharp_weapon', 'bayonet', 'ammunition', 'spear', 'arrow']
senticnet['normal_vector'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'illuminate_room', 'give_light', 'light', 'light_bulb', 'lantern']
senticnet['normalcy'] = ['0.798', '-0.51', '0', '0.519', '#joy', '#admiration', 'positive', '0.611', 'averageness', 'commonness', 'expectedness', 'typicality', 'condition']
senticnet['normality'] = ['0.798', '-0.51', '0', '0.519', '#joy', '#admiration', 'positive', '0.611', 'averageness', 'commonness', 'expectedness', 'typicality', 'condition']
senticnet['normality_assumption'] = ['-0.59', '0', '0.672', '-0.83', '#anger', '#disgust', 'negative', '-0.7', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'asymmetry', 'irregularity']
senticnet['normalize'] = ['-0.91', '0', '0.637', '-0.59', '#sadness', '#anger', 'negative', '-0.71', 'gauge', 'govern', 'regulate', 'standardize', 'order']
senticnet['normative'] = ['0.869', '-0.88', '0', '0.598', '#joy', '#surprise', 'positive', '0.782', 'connectedness', 'connection', 'consistency', 'continuity', 'link']
senticnet['normative_theory'] = ['0.534', '-0.86', '0', '0.676', '#surprise', '#admiration', 'positive', '0.692', 'suitability', 'suitableness', 'habitability', 'habitableness', 'qualification']
senticnet['normativity'] = ['0.534', '-0.86', '0', '0.676', '#surprise', '#admiration', 'positive', '0.692', 'suitability', 'suitableness', 'habitability', 'habitableness', 'qualification']
senticnet['normochromic'] = ['-0.64', '0', '0.903', '-0.63', '#sadness', '#anger', 'negative', '-0.72', 'jaundice_of_newborn', 'disease', 'icterus_neonatorum', 'kernicterus', 'symptom']
senticnet['norovirus_infection'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'inflammatory_disease', 'stomach_flu', 'intestinal_flu', 'pelvic_inflammatory_disease']
senticnet['norplant'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'birth_control_pill', 'condom', 'contraceptive_pill', 'sex_toy', 'prevent_pregnancy']
senticnet['norris_geyser_basin'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['norte_grande'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'dryness', 'thirstiness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['north'] = ['0', '0.157', '0', '0.094', '#interest', '#admiration', 'positive', '0.126', 'east', 'sunrise', 'sunset', 'limit', 'dawn']
senticnet['north-central'] = ['-0.83', '0', '-0.89', '0', '#sadness', '#fear', 'negative', '-0.86', 'south-central', 'south-eastern', 'west-central', 'north-western', 'north-eastern']
senticnet['north-eastern'] = ['-0.84', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.86', 'north-central', 'south-eastern', 'west-central', 'north-western', 'south-central']
senticnet['north-western'] = ['-0.83', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.86', 'north-central', 'south-eastern', 'west-central', 'south-central', 'north-eastern']
senticnet['north_anston'] = ['0.623', '-0.56', '0', '0.974', '#joy', '#admiration', 'positive', '0.721', 'alterable', 'convertible', 'translatable', 'transformable', 'transmutable']
senticnet['north_atlantic'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'pacific_ocean', 'small_lake', 'mere']
senticnet['north_atlantic_gyre'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'pacific_ocean', 'small_lake', 'mere']
senticnet['north_atlantic_ocean'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'pacific_ocean', 'small_lake', 'mere']
senticnet['north_atlantic_oscillation'] = ['-0.69', '0', '0.835', '-0.56', '#sadness', '#anger', 'negative', '-0.69', 'roughness', 'inclemency', 'bad_weather', 'inclementness', 'windiness']
senticnet['north_atlantic_oscillation_nao'] = ['-0.69', '0', '0.835', '-0.56', '#sadness', '#anger', 'negative', '-0.69', 'roughness', 'inclemency', 'bad_weather', 'inclementness', 'windiness']
senticnet['north_endzone'] = ['0.831', '-0.82', '0', '0.826', '#joy', '#admiration', 'positive', '0.826', 'deck', 'freeboard_deck', 'weather_deck', 'shelter_deck', 'boat_deck']
senticnet['north_finchley'] = ['0.623', '-0.56', '0', '0.974', '#joy', '#admiration', 'positive', '0.721', 'alterable', 'convertible', 'translatable', 'transformable', 'transmutable']
senticnet['north_flank'] = ['0.737', '-0.96', '0', '0.552', '#joy', '#surprise', 'positive', '0.75', 'prominence', 'hump', 'bump', 'swelling', 'frontal_eminence']
senticnet['north_pacific_gyre'] = ['0.078', '-0.03', '-0.04', '0.105', '#joy', '#admiration', 'positive', '0.043', 'aquarium', 'underwater', 'pacific_ocean', 'sea', 'ocean_wave']
senticnet['north_pole'] = ['0', '0', '0.856', '-0.89', '#anger', '#disgust', 'negative', '-0.87', 'arctic', 'cold_weather', 'snow', 'ice', 'cold']
senticnet['north_sea'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'harpoon', 'armory', 'lake', 'river', 'bay']
senticnet['north_south'] = ['0.046', '-0.15', '0.016', '0.167', '#surprise', '#admiration', 'positive', '0.087', 'dense_air', 'toxic_metal', 'heavy_metal', 'compass', 'tin_foil']
senticnet['northbound_entrance'] = ['0.032', '0.116', '0.016', '0', '#joy', '#interest', 'positive', '0.044', 'curb', 'crossroad', 'ring_road', 'lane', 'interstate']
senticnet['northbound_exit'] = ['0.032', '0.116', '0.016', '0', '#joy', '#interest', 'positive', '0.044', 'curb', 'crossroad', 'ring_road', 'lane', 'interstate']
senticnet['northbound_platform'] = ['0.077', '0.073', '0.013', '-0.02', '#joy', '#interest', 'positive', '0.027', 'registry_office', 'radio_studio', 'book', 'conference_auditorium', 'book_office']
senticnet['northbound_train'] = ['0.861', '-0.52', '0', '0.598', '#joy', '#admiration', 'positive', '0.663', 'exit', 'get_off', 'deplane', 'go_out', 'get_out']
senticnet['northeast_coast'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'pacific_ocean', 'small_lake', 'mere']
senticnet['northeast_corner'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'boulevard', 'lane', 'thoroughfare', 'intersection', 'curb']
senticnet['northeast_southwest'] = ['0', '0', '-0.73', '-0.79', '#fear', '#disgust', 'negative', '-0.76', 'analogue', 'analog', 'line_of_latitude', 'latitude', 'collimate']
senticnet['northeaster'] = ['-0.78', '0', '0.906', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'rainstorm', 'atmospheric_phenomenon', 'storm', 'noreaster', 'hailstorm']
senticnet['northeastern_atlantic_ocean'] = ['-0.79', '0', '0.709', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'animal', 'animate_being', 'ctenophore', 'invertebrate', 'beast']
senticnet['northeastern_boundary'] = ['0.034', '0.09', '0.039', '0.061', '#interest', '#admiration', 'positive', '0.036', 'border', 'limit', 'edge', 'indoor', 'side']
senticnet['northeastward'] = ['0', '0.21', '-0.11', '0.345', '#interest', '#admiration', 'positive', '0.147', 'direction', 'unto', 'forehead', 'upon', 'go_toward']
senticnet['northerly_breeze'] = ['0', '-0.05', '0.079', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'c_clamp', 'thumb_tack', 'pin', 'pliers', 'without_space']
senticnet['northerly_wind'] = ['-0.87', '0', '0.91', '-0.78', '#sadness', '#anger', 'negative', '-0.85', 'storm', 'rainstorm', 'rain', 'line_storm', 'equinoctial_storm']
senticnet['northern_barramundi'] = ['0.565', '-0.69', '0', '0.787', '#surprise', '#admiration', 'positive', '0.683', 'species', 'saratoga', 'spotted_barramundi', 'spotted_bonytongue', 'fish_species']
senticnet['northern_bird_migrate'] = ['-0.66', '0', '0.982', '-0.93', '#anger', '#disgust', 'negative', '-0.86', 'bother', 'barge_in', 'come_in', 'break_in', 'crash']
senticnet['northern_border'] = ['0.034', '0.09', '0.039', '0.061', '#interest', '#admiration', 'positive', '0.036', 'border', 'limit', 'edge', 'indoor', 'side']
senticnet['northern_boundary'] = ['0.034', '0.09', '0.039', '0.061', '#interest', '#admiration', 'positive', '0.036', 'border', 'limit', 'edge', 'indoor', 'side']
senticnet['northern_coast'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'pacific_ocean', 'small_lake', 'mere']
senticnet['northern_coastline'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'pacific_ocean', 'small_lake', 'mere']
senticnet['northern_constellation'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['northern_edge'] = ['0.034', '0.09', '0.039', '0.061', '#interest', '#admiration', 'positive', '0.036', 'border', 'limit', 'edge', 'indoor', 'side']
senticnet['northern_flying_squirrel'] = ['-0.54', '0', '0.768', '-0.50', '#sadness', '#anger', 'negative', '-0.60', 'polecat', 'hooded_skunk', 'hognosed_skunk', 'badger_skunk', 'rooter_skunk']
senticnet['northern_leopard_frog'] = ['-0.54', '0', '0.768', '-0.50', '#sadness', '#anger', 'negative', '-0.60', 'polecat', 'hooded_skunk', 'hognosed_skunk', 'badger_skunk', 'rooter_skunk']
senticnet['northern_mockingbird'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet']
senticnet['northern_phalarope'] = ['0.786', '-0.82', '0', '0.776', '#joy', '#surprise', 'positive', '0.794', 'red_phalarope', 'phalarope', 'shorebird', 'shore_bird', 'limicoline_bird']
senticnet['northern_pike'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'divergent_strabismus', 'exotropia', 'squint', 'abnormality', 'abnormalcy']
senticnet['northern_pin_oak'] = ['0.776', '-0.6', '0', '0.568', '#joy', '#surprise', 'positive', '0.648', 'swamp_chestnut_oak', 'chinquapin_oak', 'chinkapin_oak', 'yellow_chestnut_oak', 'basket_oak']
senticnet['northern_portion'] = ['0.595', '-0.61', '0', '0.826', '#surprise', '#admiration', 'positive', '0.679', 'completeness', 'work', 'whole_shebang', 'absolutely', 'wholly']
senticnet['northern_red_oak'] = ['0.672', '-0.69', '0', '0.831', '#surprise', '#admiration', 'positive', '0.733', 'southern_red_oak', 'swamp_red_oak', 'turkey_oak', 'red_oak', 'oak']
senticnet['northern_riffleshell'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'seal', 'walrus', 'otter', 'alligator', 'whale']
senticnet['northern_shoveler'] = ['-0.50', '0', '0.764', '-0.61', '#anger', '#disgust', 'negative', '-0.62', 'doctor', 'mountebank', 'charlatan', 'quack', 'physician']
senticnet['northern_siberia'] = ['-0.04', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.04', 'bad_luck', 'damn', 'artic', 'mammal_family', 'bad_mood']
senticnet['northern_slope'] = ['0', '0.093', '0', '0.078', '#interest', '#admiration', 'positive', '0.085', 'steep', 'landscape_feature', 'knoll', 'hill_mountain', 'incline']
senticnet['northernmost_part'] = ['0.034', '0.09', '0.039', '0.061', '#interest', '#admiration', 'positive', '0.036', 'border', 'limit', 'edge', 'indoor', 'side']
senticnet['northernmost_point'] = ['0', '0.08', '0.032', '0.096', '#interest', '#admiration', 'positive', '0.048', 'land_form', 'land_formation', 'land_feature', 'geographic_feature', 'landscape_feature']
senticnet['northernmost_section'] = ['0.595', '-0.61', '0', '0.826', '#surprise', '#admiration', 'positive', '0.679', 'completeness', 'work', 'whole_shebang', 'absolutely', 'wholly']
senticnet['northsouth'] = ['0', '0', '-0.73', '-0.79', '#fear', '#disgust', 'negative', '-0.76', 'analogue', 'analog', 'line_of_latitude', 'latitude', 'collimate']
senticnet['northsouthnet'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'identifiable', 'specifiable', 'recognizable', 'diagnosable', 'classifiable']
senticnet['northward'] = ['0', '0.21', '-0.11', '0.345', '#interest', '#admiration', 'positive', '0.147', 'direction', 'unto', 'forehead', 'upon', 'go_toward']
senticnet['northward_expansion'] = ['0', '0.229', '-0.09', '0.187', '#interest', '#admiration', 'positive', '0.106', 'increase', 'make_big', 'get_large', 'grow', 'expand']
senticnet['northwest_corner'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'boulevard', 'lane', 'thoroughfare', 'intersection', 'curb']
senticnet['northwest_southeast'] = ['0', '0', '-0.73', '-0.79', '#fear', '#disgust', 'negative', '-0.76', 'analogue', 'analog', 'line_of_latitude', 'latitude', 'collimate']
senticnet['northwest_swat_valley'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'webpage', 'store_record', 'ideal']
senticnet['northwestern_boundary'] = ['0.034', '0.09', '0.039', '0.061', '#interest', '#admiration', 'positive', '0.036', 'border', 'limit', 'edge', 'indoor', 'side']
senticnet['northwestern_pacific_railroad'] = ['-0.06', '0.21', '0', '0.076', '#interest', '#admiration', 'positive', '0.074', 'new_think', 'figure_out', 'brain_activity', 'idea', 'come_up_idea']
senticnet['northwestern_portion'] = ['0.595', '-0.61', '0', '0.826', '#surprise', '#admiration', 'positive', '0.679', 'completeness', 'work', 'whole_shebang', 'absolutely', 'wholly']
senticnet['northwestern_swat_valley'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'webpage', 'store_record', 'ideal']
senticnet['northwestsoutheast'] = ['0', '0', '-0.73', '-0.79', '#fear', '#disgust', 'negative', '-0.76', 'analogue', 'analog', 'line_of_latitude', 'latitude', 'collimate']
senticnet['northwestward'] = ['0', '0.21', '-0.11', '0.345', '#interest', '#admiration', 'positive', '0.147', 'direction', 'unto', 'forehead', 'upon', 'go_toward']
senticnet['norton_cleansweep'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'car_trunk', 'walmart', 'warehouse', 'store', 'storage_place']
senticnet['norton_ghost'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_musical_sound', 'play_orchestra', 'make_noise', 'play_music', 'make_drawing']
senticnet['noscript'] = ['-0.89', '0', '0.672', '-0.69', '#sadness', '#disgust', 'negative', '-0.75', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['noscript_extension'] = ['-0.89', '0', '0.672', '-0.69', '#sadness', '#disgust', 'negative', '-0.75', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['nose'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'mouth', 'facial_organ', 'face_feature', 'facial', 'facial_part']
senticnet['nose_dive'] = ['-0.64', '0', '0.717', '-0.98', '#anger', '#disgust', 'negative', '-0.78', 'debacle', 'licking', 'whitewash', 'finish', 'failure']
senticnet['nose_drop'] = ['-0.7', '0', '0.698', '-0.57', '#sadness', '#anger', 'negative', '-0.65', 'smack', 'hell_dust', 'diacetylmorphine', 'thunder', 'skag']
senticnet['nose_flute'] = ['0.821', '-0.53', '0', '0.785', '#joy', '#admiration', 'positive', '0.712', 'fife', 'flute', 'piccolo', 'woodwind', 'woodwind_instrument']
senticnet['nose_mouth'] = ['0', '0.076', '0', '0.052', '#interest', '#admiration', 'positive', '0.064', 'hold_brain', 'face', 'skull', 'top_face', 'eye_nose']
senticnet['nose_ring'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'bangle', 'ankle_bracelet', 'armilla']
senticnet['nose_spray'] = ['0.969', '0.817', '0', '0.828', '#joy', '#admiration', 'positive', '0.871', 'ibuprofen', 'painkiller', 'medicine', 'analgesic', 'phenobarbital']
senticnet['nose_wheel'] = ['0', '0.142', '-0.09', '0.183', '#interest', '#admiration', 'positive', '0.078', 'arise', 'rise', 'raise_up', 'go_up', 'go_upstairs']
senticnet['noseband'] = ['0', '-0.05', '0.079', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'c_clamp', 'thumb_tack', 'pin', 'pliers', 'without_space']
senticnet['nosedive'] = ['0', '0', '0.928', '-0.91', '#anger', '#disgust', 'negative', '-0.92', 'tailspin', 'disarray', 'tail-spin', 'tizzy', 'melt-down']
senticnet['nosegay'] = ['0.028', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.047', 'cowhide', 'kerchief', 'hanky', 'facial_tissue', 'zipper']
senticnet['nosepiece'] = ['0.129', '0', '0', '0', '#joy', '#joy', 'positive', '0.129', 'incline_plane', 'boulder', 'ramp', 'hill_mountain', 'rock']
senticnet['nosh'] = ['0.789', '-0.68', '0', '0.729', '#joy', '#admiration', 'positive', '0.733', 'refreshment', 'collation', 'coffee_break', 'tea_break', 'meal']
senticnet['nosiness'] = ['-0.90', '0', '0.865', '-0.77', '#sadness', '#anger', 'negative', '-0.84', 'curiousness', 'snoopiness', 'prying', 'curiosity', 'wonder']
senticnet['nostalgia'] = ['-0.87', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.85', 'eat_meal', 'eat_chip', 'eat_much_food', 'feed_child', 'eat_much']
senticnet['nostalgia_trip'] = ['0', '0.119', '-0.07', '0.189', '#interest', '#admiration', 'positive', '0.079', 'enjoy', 'good_feeling', 'excitement', 'emotion', 'good_emotion']
senticnet['nostalgic'] = ['-0.05', '-0.00', '-0.07', '-0.05', '#sadness', '#fear', 'negative', '-0.04', 'vulnerable', 'many_children', 'bald', 'newborn', 'baby_dog']
senticnet['nostoc'] = ['0.807', '-0.86', '0', '0.611', '#joy', '#surprise', 'positive', '0.762', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['nostram'] = ['0.654', '-0.75', '0', '0.78', '#surprise', '#admiration', 'positive', '0.729', 'suit', 'cause', 'lawsuit', 'occurrence', 'countersuit']
senticnet['nostril'] = ['-0.93', '0', '0.607', '-0.67', '#sadness', '#disgust', 'negative', '-0.73', 'spiracle', 'stigma', 'orifice', 'opening', 'porta']
senticnet['nostrum'] = ['-0.88', '0', '0.907', '-0.77', '#sadness', '#anger', 'negative', '-0.85', 'remedy', 'cure', 'elixir', 'therapeutic', 'curative']
senticnet['not-so-nice'] = ['-0.80', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'disparaging', 'offhanded', 'off-handed', 'laudatory', 'uncomplimentary']
senticnet['notable'] = ['0.779', '0.989', '0', '0', '#joy', '#interest', 'positive', '0.884', 'memorability', 'memorable', 'catchy', 'memory', 'nameable']
senticnet['notarized_copy'] = ['0.931', '-0.52', '0', '0.67', '#joy', '#admiration', 'positive', '0.709', 'document', 'certificate_of_incorporation', 'credential', 'diploma', 'sheepskin']
senticnet['notarized_letter'] = ['0.931', '-0.52', '0', '0.67', '#joy', '#admiration', 'positive', '0.709', 'document', 'certificate_of_incorporation', 'credential', 'diploma', 'sheepskin']
senticnet['notarized_signature'] = ['0.931', '-0.52', '0', '0.67', '#joy', '#admiration', 'positive', '0.709', 'document', 'certificate_of_incorporation', 'credential', 'diploma', 'sheepskin']
senticnet['notarized_statement'] = ['0.931', '-0.52', '0', '0.67', '#joy', '#admiration', 'positive', '0.709', 'document', 'certificate_of_incorporation', 'credential', 'diploma', 'sheepskin']
senticnet['notary_seal'] = ['-0.94', '0', '0.569', '-0.72', '#sadness', '#disgust', 'negative', '-0.74', 'bitumastic', 'veneer', 'veneering', 'adhesive', 'coating']
senticnet['notation'] = ['0.87', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.869', 'cosign', 'endorse', 'endorsement', 'insurance', 'recommendation']
senticnet['notch'] = ['0.718', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.843', 'notch', 'top', 'edge', 'excellent', 'top-notch']
senticnet['notch_filter'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'water_inside', 'water_spray', 'teapot', 'bodily_waste', 'different_type']
senticnet['notch_signaling_pathway'] = ['-0.84', '0', '0.8', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'rigor_mortis', 'natural_process', 'natural_action', 'activity', 'action']
senticnet['notchback'] = ['-0.05', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'plane_land', 'floss', 'floss_tooth', 'ornament', 'wear_dress']
senticnet['note'] = ['0.047', '0', '0', '0.132', '#joy', '#admiration', 'positive', '0.089', 'message', 'letter', 'chord', 'put_ink_paper', 'announcement']
senticnet['note_book'] = ['0.934', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.853', 'photo_copy', 'writing_paper', 'printing_paper', 'color_paper', 'white_paper']
senticnet['note_down'] = ['0', '0.077', '0', '0.154', '#interest', '#admiration', 'positive', '0.115', 'form_of_communication', 'communication_method', 'written_language', 'written_communication', 'gather']
senticnet['note_pad'] = ['0', '0', '0', '-0.77', '#disgust', '#disgust', 'negative', '-0.77', 'icon', 'bullshit', 'scrap_paper', 'attendee', 'agenda']
senticnet['notebook'] = ['-0.02', '0.094', '0', '0.097', '#interest', '#admiration', 'positive', '0.054', 'make_note', 'write', 'take_note', 'note', 'write_note']
senticnet['notebook_computer'] = ['0.113', '0.073', '0.028', '0.107', '#joy', '#admiration', 'positive', '0.066', 'pda', 'telephone_computer', 'browser', 'number_pad', 'modem']
senticnet['notebook_paper'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'gift_wrap', 'kraft', 'kraft_paper', 'wrapping_paper', 'paper']
senticnet['notebook_pc'] = ['0.113', '0.073', '0.028', '0.107', '#joy', '#admiration', 'positive', '0.066', 'pda', 'telephone_computer', 'browser', 'number_pad', 'modem']
senticnet['noted_above'] = ['0.021', '0.167', '0', '0.207', '#interest', '#admiration', 'positive', '0.132', 'size_up', 'measure_length', 'scope', 'distant', 'extension']
senticnet['notepad'] = ['0.028', '-0.03', '0.058', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'pocket_size_notebook', 'memo_pad', 'intern', 'self_adhesive_label', 'paper_clip']
senticnet['notepaper'] = ['0.805', '0.843', '0', '0', '#joy', '#interest', 'positive', '0.824', 'learn_write', 'think_write', 'paper_plane', 'find_paper', 'pen']
senticnet['notes'] = ['0.022', '-0.05', '0.079', '-0.11', '#anger', '#disgust', 'negative', '-0.02', 'stationery', 'ink', 'paper', 'refill', 'ball_point_pen']
senticnet['noteworthy'] = ['0.758', '0', '0', '0.734', '#joy', '#admiration', 'positive', '0.746', 'deserve', 'valuable', 'applaudable', 'meritable', 'sacred']
senticnet['nothing'] = ['-0.55', '0', '0.882', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'null', 'zero', 'naught', 'zip', 'bugger_all']
senticnet['nothing_better'] = ['0.937', '0.878', '0', '0.738', '#joy', '#interest', 'positive', '0.851', 'spend_time_with_friend', 'enjoyment', 'have_fun', 'pastime', 'get_out_of_house']
senticnet['nothing_else'] = ['0.77', '0', '0', '0.996', '#joy', '#admiration', 'positive', '0.883', 'bind_agreement', 'need_money', 'completely', 'earn_wage', 'truly']
senticnet['nothing_special'] = ['0', '0', '0', '-0.87', '#disgust', '#disgust', 'negative', '-0.87', 'domestic_pet', 'popular_pet', 'similar', 'common_pet', 'common']
senticnet['nothing_wrong'] = ['-0.72', '0', '0.752', '-0.81', '#anger', '#disgust', 'negative', '-0.76', 'wrongdoing', 'jail_time', 'bad_behavior', 'kidnap', 'reprimand']
senticnet['nothingness'] = ['-0.84', '0', '0.897', '0', '#sadness', '#anger', 'negative', '-0.87', 'heavenly_body', 'star_planet', 'celestial_body', 'limitless', 'around_earth']
senticnet['notice'] = ['0', '0.707', '0', '0', '#interest', '#interest', 'positive', '0.707', 'announcement', 'observation', 'comment', 'placard', 'observe']
senticnet['notice_problem'] = ['0', '0.951', '0', '0', '#interest', '#interest', 'positive', '0.951', 'arm_weapon', 'smoke_joint', 'deal_card_player', 'bridge_difference', 'explain_everything']
senticnet['noticeable'] = ['0.958', '-0.83', '0', '0.81', '#joy', '#surprise', 'positive', '0.869', 'discernible', 'perceptible', 'evident', 'detectable', 'observable']
senticnet['noticing'] = ['-0.92', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.96', 'mindless', 'thoughtlessness', 'heedless', 'unaware', 'inadvertence']
senticnet['notifiable_disease'] = ['-0.11', '0', '-0.15', '-0.24', '#fear', '#disgust', 'negative', '-0.17', 'disease', 'infectious_disease', 'polio', 'malady', 'sickness']
senticnet['notification'] = ['-0.08', '0.146', '0', '0.116', '#interest', '#admiration', 'positive', '0.059', 'musical_notation', 'bill_pay', 'musical_note', 'apprising', 'reminder']
senticnet['notify'] = ['0.532', '0.869', '0', '0.842', '#interest', '#admiration', 'positive', '0.748', 'transfer_knowledge', 'help_understand', 'inform_others', 'inform', 'inform_person']
senticnet['notion'] = ['-0.88', '0', '0.589', '-0.85', '#sadness', '#disgust', 'negative', '-0.77', 'phantom', 'shadow', 'disbelief', 'dark_magic', 'fool']
senticnet['notional'] = ['-0.67', '0', '0.716', '-0.89', '#anger', '#disgust', 'negative', '-0.76', 'imaginary', 'unreal', 'fabled', 'hallucinatory', 'illusional']
senticnet['notional_amount'] = ['0.868', '-0.84', '0', '0.673', '#joy', '#surprise', 'positive', '0.796', 'instrument', 'option', 'derivative_instrument', 'legal_document', 'legal_instrument']
senticnet['notional_value'] = ['0.868', '-0.84', '0', '0.673', '#joy', '#surprise', 'positive', '0.796', 'instrument', 'option', 'derivative_instrument', 'legal_document', 'legal_instrument']
senticnet['notoriety'] = ['-0.80', '0', '0.799', '0', '#sadness', '#anger', 'negative', '-0.80', 'status', 'seeker', 'everyone_know', 'critical', 'caste']
senticnet['notorious'] = ['-0.99', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.98', 'infamous', 'nefarious', 'notoriety', 'disreputable', 'notoriously']
senticnet['notorious_bandit'] = ['0', '0', '-0.06', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'elbow', 'arm', 'wrist', 'five_fingers', 'bendable']
senticnet['notorious_gang'] = ['0.867', '-0.84', '0', '0.876', '#joy', '#admiration', 'positive', '0.863', 'meet', 'team', 'team_up', 'embed', 'aggroup']
senticnet['notorious_highwayman'] = ['0.754', '-0.82', '0', '0.947', '#surprise', '#admiration', 'positive', '0.841', 'whorehouse', 'brothel', 'bordello', 'massage_parlor', 'house_of_prostitution']
senticnet['notorious_nazi'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'healthy_person', 'man', 'pervert', 'compatriot']
senticnet['notorious_outlaw'] = ['0', '0', '-0.06', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'elbow', 'arm', 'wrist', 'five_fingers', 'bendable']
senticnet['notorious_pirate'] = ['-0.87', '0', '0.574', '-0.73', '#sadness', '#disgust', 'negative', '-0.72', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['notoriously'] = ['-0.98', '0', '-0.98', '0', '#sadness', '#fear', 'negative', '-0.98', 'notorious', 'nefarious', 'notoriety', 'disreputable', 'infamous']
senticnet['notoriously_difficult'] = ['-0.38', '0.165', '-0.32', '0.243', '#sadness', '#fear', 'negative', '-0.07', 'hard_to_define', 'inherit', 'harmful_human', 'bad_choice', 'badness']
senticnet['notre_dame_du_sacr'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['nottale'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'general_relativity', 'general_theory_of_relativity', 'relativity', 'special_relativity', 'special_theory_of_relativity']
senticnet['nottingham_goose'] = ['0.713', '0', '0', '0.746', '#joy', '#admiration', 'positive', '0.73', 'carnival', 'circus', 'fairground', 'rodeo', 'disneyland']
senticnet['noucentisme'] = ['0.563', '-0.72', '0', '0.644', '#surprise', '#admiration', 'positive', '0.645', 'idiom', 'idiomatic_expression', 'phrasal_idiom', 'set_phrase', 'phrase']
senticnet['nougat'] = ['0.059', '-0.02', '0.025', '0.109', '#joy', '#admiration', 'positive', '0.043', 'tasty', 'bake_treat', 'bake_goods', 'bake_good', 'loaf']
senticnet['nought'] = ['-0.55', '0', '0.882', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'null', 'zero', 'bugger_all', 'zip', 'nothing']
senticnet['noumenal'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'metastability', 'monotony', 'invariance', 'constancy', 'unchangeability']
senticnet['noumenal_world'] = ['0.992', '-0.66', '0', '0.536', '#joy', '#surprise', 'positive', '0.729', 'nous', 'mind', 'brain', 'psyche', 'head']
senticnet['noumenon'] = ['-0.57', '0', '0.713', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'assured', 'conclusion', 'certainty', 'predictability', 'definiteness']
senticnet['noun'] = ['0.732', '0.8', '0', '0', '#joy', '#interest', 'positive', '0.766', 'fast', 'place', 'substance', 'substantive', 'person']
senticnet['noun_clause'] = ['0.633', '-0.67', '0', '0.776', '#surprise', '#admiration', 'positive', '0.693', 'double_entendre', 'loophole', 'ambiguity', 'amphiboly', 'parisology']
senticnet['noun_form'] = ['0', '0.192', '-0.09', '0.149', '#interest', '#admiration', 'positive', '0.083', 'mathematical', 'math_function', 'math_process', 'math_operation', 'math_action']
senticnet['noun_phrase'] = ['0.732', '0.8', '0', '0', '#joy', '#interest', 'positive', '0.766', 'fast', 'place', 'substance', 'substantive', 'person']
senticnet['noun_phrase_np'] = ['0.904', '-0.58', '0', '0.736', '#joy', '#admiration', 'positive', '0.743', 'naming', 'appellative', 'denotative', 'extensional', 'referent']
senticnet['noun_verb'] = ['0.633', '-0.67', '0', '0.776', '#surprise', '#admiration', 'positive', '0.693', 'double_entendre', 'loophole', 'ambiguity', 'amphiboly', 'parisology']
senticnet['nour_party'] = ['0.648', '0.627', '-0.26', '0.329', '#joy', '#interest', 'positive', '0.334', 'brotherly', 'friendly', 'fraternal', 'amicability', 'amicable']
senticnet['nourish'] = ['0', '0.155', '-0.05', '0.11', '#interest', '#admiration', 'positive', '0.071', 'provide_food', 'ingest_food', 'make_eat', 'animal_eat', 'satiate_hunger']
senticnet['nourishing_food'] = ['0.854', '0', '0', '0.963', '#joy', '#admiration', 'positive', '0.908', 'healthy', 'make_healthy', 'feel_energized', 'social_activity', 'keep_body_healthy']
senticnet['nourishment'] = ['0.819', '0.704', '0', '0', '#joy', '#interest', 'positive', '0.761', 'sustenance', 'eat', 'provide_food', 'transport_food', 'satisfy_hunger']
senticnet['nous'] = ['0.911', '-0.94', '0', '0.953', '#surprise', '#admiration', 'positive', '0.935', 'common_sense', 'good_sense', 'horse_sense', 'mother_wit', 'gumption']
senticnet['nouveau_record'] = ['0', '0.919', '0', '0.888', '#interest', '#admiration', 'positive', '0.903', 'surprise', 'eye-popping', 'fulgurous', 'dazzling', 'bedaze']
senticnet['nouveau_riche'] = ['0.843', '0.977', '0', '0', '#joy', '#interest', 'positive', '0.91', 'business', 'finance', 'revenue', 'wealth', 'income']
senticnet['nouvelle_beauce'] = ['-0.71', '0', '0.917', '-0.51', '#sadness', '#anger', 'negative', '-0.71', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['nouvelle_cuisine'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'blue_ribbon', 'badge', 'allegory', 'stripe', 'merit_badge']
senticnet['novaehollandiae'] = ['-0.84', '0', '0.655', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'piping_crow', 'butcherbird', 'currawong', 'bell_magpie', 'oscine']
senticnet['novecento_italiano'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'native_person', 'revolutionary', 'republic', 'revolution', 'look_ahead']
senticnet['novel'] = ['0.036', '0.219', '0', '0.19', '#interest', '#admiration', 'positive', '0.148', 'long_story', 'text', 'fiction', 'book', 'classic']
senticnet['novel_goldfinger'] = ['0.228', '0.123', '-0.09', '0', '#joy', '#interest', 'positive', '0.084', 'spend_time_with_child', 'social', 'friendship', 'companionship', 'get_close']
senticnet['novel_moby_dick'] = ['-0.93', '0', '0.544', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'mutant', 'variation', 'sport', 'lusus_naturae', 'mutation']
senticnet['novel_polymeric_alloy'] = ['0.965', '-0.84', '0', '0.599', '#joy', '#surprise', 'positive', '0.802', 'mental_faculty', 'faculty', 'module', 'remembrance', 'anamnesis']
senticnet['novel_stimulus'] = ['0.583', '-0.82', '0', '0.574', '#joy', '#surprise', 'positive', '0.661', 'stimulant', 'positive_reinforcing_stimulus', 'reinforcer', 'negative_reinforcing_stimulus', 'negative_reinforcer']
senticnet['novelist'] = ['0.163', '0.255', '-0.2', '0.31', '#interest', '#admiration', 'positive', '0.132', 'actor', 'movie', 'cctv', 'cinema', 'writer']
senticnet['novelistic'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'mediacy', 'mediateness', 'characteristic', 'indirectness']
senticnet['novelization'] = ['-0.06', '0.122', '-0.04', '0.113', '#interest', '#admiration', 'positive', '0.032', 'encounter', 'portray', 'reveal', 'episode', 'next_episode']
senticnet['novella'] = ['0', '0.952', '0', '0', '#interest', '#interest', 'positive', '0.952', 'fable', 'long_story', 'novel', 'saga', 'editorial']
senticnet['novelty'] = ['0.876', '0.854', '0', '0.79', '#joy', '#interest', 'positive', '0.84', 'new', 'timely', 'know_time', 'sec', 'current']
senticnet['novib'] = ['-0.54', '0', '0.863', '-0.76', '#anger', '#disgust', 'negative', '-0.72', 'denomination', 'committee', 'citizen_committee', 'mission', 'missionary_post']
senticnet['novice'] = ['-0.95', '0', '0.735', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'cub', 'beginner', 'tyro', 'tiro', 'initiate']
senticnet['novice_user'] = ['-0.50', '0', '0.737', '-0.55', '#anger', '#disgust', 'negative', '-0.59', 'unacquainted_with', 'unfamiliar_with', 'unknown', 'unacquainted', 'strange']
senticnet['novn'] = ['0.559', '0.656', '-0.26', '0.285', '#joy', '#interest', 'positive', '0.31', 'zealous', 'avidity', 'eagerness', 'avidness', 'thirstily']
senticnet['novocaine'] = ['-0.05', '-0.01', '-0.07', '-0.23', '#fear', '#disgust', 'negative', '-0.08', 'phenobarbital', 'ibuprofen', 'x-ray_machine', 'palliative', 'analgesic']
senticnet['now_time'] = ['0.986', '0.838', '0', '0', '#joy', '#interest', 'positive', '0.912', 'event', 'occasion', 'incident', 'present', 'accident']
senticnet['nowcast'] = ['-0.03', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.04', 'crystal_clear', 'water_sky', 'weather_condition', 'weather_phenomenon', 'sun_cover']
senticnet['nowhere'] = ['0.053', '0.163', '-0.05', '0.102', '#interest', '#admiration', 'positive', '0.067', 'utopia', 'mount_ever', 'java', 'outhouse', 'international_space_station']
senticnet['nowhere_else'] = ['0', '0.093', '0', '0.053', '#interest', '#admiration', 'positive', '0.073', 'underground', 'under_ground', 'environment', 'outpost', 'dark_place']
senticnet['nowhere_live'] = ['0', '0.208', '-0.14', '0.162', '#interest', '#admiration', 'positive', '0.076', 'much_child', 'grandchild', 'offspring', 'descendant', 'human_offspring']
senticnet['nowhere_near'] = ['0.706', '0.945', '0', '0', '#joy', '#interest', 'positive', '0.825', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['nowise'] = ['-0.04', '0.268', '-0.06', '0.227', '#interest', '#admiration', 'positive', '0.097', 'proposition', 'abstract', 'get_rid', 'theoretical', 'business_transaction']
senticnet['nowt'] = ['0.027', '0', '0.012', '0.063', '#joy', '#admiration', 'positive', '0.026', 'vegetation', 'tree_flower', 'green_growth', 'small', 'greenery']
senticnet['noxious'] = ['-0.86', '0', '0.891', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'baneful', 'pernicious', 'vesicatory', 'vesicant', 'pestilent']
senticnet['noxious_gas'] = ['-0.99', '0', '0.518', '-0.99', '#sadness', '#disgust', 'negative', '-0.83', 'holocaust', 'kill_person', 'gas', 'poison_gas', 'noxious']
senticnet['noxious_odor'] = ['-0.86', '0', '0.644', '-0.88', '#sadness', '#disgust', 'negative', '-0.79', 'frowsty', 'fetid', 'foetid', 'smelly', 'stinking']
senticnet['noxious_stimulus'] = ['0.698', '-0.61', '0', '0.619', '#joy', '#admiration', 'positive', '0.644', 'sensitivity', 'sensitiveness', 'irritability', 'sensibility', 'reactivity']
senticnet['noxious_vermin'] = ['0', '0', '0.862', '-0.79', '#anger', '#disgust', 'negative', '-0.82', 'social_insect', 'big_mouse', 'rodent', 'small_animal', 'dig_tunnel']
senticnet['noxiousness'] = ['-0.72', '0', '0.71', '-0.9', '#sadness', '#disgust', 'negative', '-0.77', 'perniciousness', 'harmfulness', 'toxicity', 'noisomeness', 'unwholesomeness']
senticnet['nozzle'] = ['0.042', '-0.02', '0.035', '0.065', '#joy', '#admiration', 'positive', '0.023', 'food_container', 'metal_cylinder', 'metal_container', 'small_ball', 'storage_container']
senticnet['nozzle_tip'] = ['0.843', '-0.99', '0', '0.706', '#joy', '#surprise', 'positive', '0.849', 'gadgetry', 'gimbal', 'contrivance', 'mod_con', 'device']
senticnet['np_complete_problem'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly']
senticnet['npov'] = ['-0.84', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.79', 'piss_pant', 'rejection', 'argue', 'make_statement', 'become_politician']
senticnet['npov_noticeboard'] = ['0.533', '0.818', '0', '0.683', '#interest', '#admiration', 'positive', '0.678', 'gain_information', 'report_news', 'solve', 'seek_answer', 'gather_information']
senticnet['nrti'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'indinavir', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['nsa'] = ['-0.53', '0', '0.884', '-0.72', '#anger', '#disgust', 'negative', '-0.71', 'bug', 'tap', 'eavesdrop', 'intercept', 'listen']
senticnet['nsaid'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucose', 'corn_sugar', 'dextrose', 'dextroglucose', 'grape_sugar']
senticnet['nsbri'] = ['0.627', '-0.65', '0', '0.968', '#surprise', '#admiration', 'positive', '0.749', 'overwhelm', 'overpower', 'sweep_over', 'whelm', 'overcome']
senticnet['nsf_grant'] = ['0', '0.097', '0', '0.092', '#interest', '#admiration', 'positive', '0.095', 'urgent', 'classify_ad', 'important', 'review', 'repetition']
senticnet['nsfwide'] = ['0.82', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.792', 'graduate_education', 'engineer', 'training', 'igert', 'challenge']
senticnet['nu_har'] = ['0', '0', '0.785', '-0.72', '#anger', '#disgust', 'negative', '-0.75', 'sharp_nail', 'sharp', 'sharp_end', 'sharp_point', 'pointy']
senticnet['nu_metal'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'explicit', 'declared', 'definitive', 'graphic']
senticnet['nuance'] = ['0.772', '-0.95', '0', '0.89', '#surprise', '#admiration', 'positive', '0.872', 'downrightness', 'straightforwardness', 'characteristic', 'directness', 'immediateness']
senticnet['nubbiness'] = ['-0.9', '0', '0.841', '-0.73', '#sadness', '#anger', 'negative', '-0.82', 'raggedness', 'tweediness', 'harshness', 'graininess', 'granularity']
senticnet['nubby'] = ['0.726', '0.87', '0', '0', '#joy', '#interest', 'positive', '0.798', 'flora', 'large_breadbox', 'domesticate', 'live_organism', 'plant']
senticnet['nubia'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['nubian'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['nubian_ibex'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'argali', 'argal', 'urial', 'white_sheep', 'wild_sheep']
senticnet['nubigena'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['nubile'] = ['-0.88', '0', '0.864', '-0.72', '#sadness', '#anger', 'negative', '-0.82', 'malicious', 'venomous', 'bitchy', 'catty', 'cattish']
senticnet['nubile_young'] = ['0.636', '-0.95', '0', '0.664', '#surprise', '#admiration', 'positive', '0.751', 'voluptuous', 'statuesque', 'sculptured', 'shapely', 'sculptural']
senticnet['nuchal_rigidity'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'stiff', 'rigid', 'inflexible', 'harsh', 'immovable']
senticnet['nuclear'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'dynamite', 'gunshot', 'missile', 'nuclear_bomb', 'fall_sky']
senticnet['nuclear_apocalypse'] = ['-0.96', '0', '0.501', '-0.59', '#sadness', '#disgust', 'negative', '-0.68', 'calamity', 'catastrophe', 'tragedy', 'disaster', 'cataclysm']
senticnet['nuclear_armament'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'dynamite', 'gunshot', 'missile', 'nuclear_bomb', 'fall_sky']
senticnet['nuclear_arsenal'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'dynamite', 'gunshot', 'missile', 'nuclear_bomb', 'fall_sky']
senticnet['nuclear_atypia'] = ['-0.58', '0', '0.814', '-0.54', '#sadness', '#anger', 'negative', '-0.64', 'abnormal', 'irregular', 'vicarious', 'supernormal', 'subnormal']
senticnet['nuclear_bomb'] = ['0', '0', '0.9', '-0.84', '#anger', '#disgust', 'negative', '-0.87', 'bomb', 'atomic_bomb', 'nuclear_weapon', 'explosive', 'grenade']
senticnet['nuclear_capability'] = ['-0.03', '-0.03', '-0.05', '-0.20', '#fear', '#disgust', 'negative', '-0.06', 'nuclear_bomb', 'bomb', 'explosive', 'atomic_bomb', 'grenade']
senticnet['nuclear_chain_reaction'] = ['-0.77', '0', '0.764', '-0.54', '#sadness', '#anger', 'negative', '-0.69', 'pullulation', 'apomixis', 'blastogenesis', 'budding', 'asexual_reproduction']
senticnet['nuclear_constabulary'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'law_enforcement_agency', 'law', 'agency']
senticnet['nuclear_deterrence'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'dynamite', 'gunshot', 'missile', 'nuclear_bomb', 'fall_sky']
senticnet['nuclear_detonation'] = ['-0.84', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.85', 'inferno', 'forest_fire', 'fire_light', 'dismissal', 'wash_mouth_soap']
senticnet['nuclear_device'] = ['0', '0', '-0.72', '-0.94', '#fear', '#disgust', 'negative', '-0.83', 'pistol', 'gun', 'hydrogen_bomb', 'handgun', 'revolver']
senticnet['nuclear_disaster'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'dust', 'chalk_dust', 'radioactive_dust', 'particulate', 'particulate_matter']
senticnet['nuclear_energy'] = ['-0.90', '0', '0.748', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'alternative_energy', 'activation_energy', 'energy_of_activation', 'atomic_energy', 'energy']
senticnet['nuclear_envoy'] = ['0', '0.133', '0', '0.088', '#interest', '#admiration', 'positive', '0.111', 'juror', 'decide_guilt', 'become_judge', 'arbiter', 'jury']
senticnet['nuclear_explosion'] = ['0', '0', '-0.72', '-0.94', '#fear', '#disgust', 'negative', '-0.83', 'pistol', 'gun', 'hydrogen_bomb', 'handgun', 'revolver']
senticnet['nuclear_explosive'] = ['-0.03', '-0.03', '-0.05', '-0.20', '#fear', '#disgust', 'negative', '-0.06', 'nuclear_bomb', 'bomb', 'explosive', 'atomic_bomb', 'grenade']
senticnet['nuclear_explosive_device'] = ['-0.03', '-0.03', '-0.05', '-0.20', '#fear', '#disgust', 'negative', '-0.06', 'nuclear_bomb', 'bomb', 'explosive', 'atomic_bomb', 'grenade']
senticnet['nuclear_fallout'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'dust', 'particulate', 'particulate_matter']
senticnet['nuclear_fission'] = ['-0.90', '0', '0.748', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'alternative_energy', 'activation_energy', 'energy_of_activation', 'energy', 'nuclear_energy']
senticnet['nuclear_fuel'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'dynamite', 'gunshot', 'missile', 'nuclear_bomb', 'fall_sky']
senticnet['nuclear_fusion'] = ['-0.77', '0', '0.764', '-0.54', '#sadness', '#anger', 'negative', '-0.69', 'pullulation', 'apomixis', 'blastogenesis', 'budding', 'asexual_reproduction']
senticnet['nuclear_holocaust'] = ['-0.96', '0', '0.501', '-0.59', '#sadness', '#disgust', 'negative', '-0.68', 'calamity', 'catastrophe', 'tragedy', 'disaster', 'cataclysm']
senticnet['nuclear_isomer'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'elementary_particle', 'neutron', 'electron', 'subatomic_particle', 'proton']
senticnet['nuclear_magneton'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'newton', 'sthene', 'force_unit']
senticnet['nuclear_power'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'dynamite', 'gunshot', 'missile', 'nuclear_bomb', 'fall_sky']
senticnet['nuclear_power_industry'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'dynamite', 'gunshot', 'missile', 'nuclear_bomb', 'fall_sky']
senticnet['nuclear_power_plant'] = ['-0.05', '-0.03', '0.018', '-0.08', '#sadness', '#disgust', 'negative', '-0.03', 'build', 'construction_site', 'skyscraper', 'tall_building', 'construct_building']
senticnet['nuclear_program'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'dynamite', 'gunshot', 'missile', 'nuclear_bomb', 'fall_sky']
senticnet['nuclear_proliferation'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'dynamite', 'gunshot', 'missile', 'nuclear_bomb', 'fall_sky']
senticnet['nuclear_reactor'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'dynamite', 'gunshot', 'missile', 'nuclear_bomb', 'fall_sky']
senticnet['nuclear_retaliation'] = ['-0.75', '0', '0.981', '-0.58', '#sadness', '#anger', 'negative', '-0.77', 'punitive', 'penitentiary', 'correctional', 'retributive', 'retributory']
senticnet['nuclear_stockpile'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'dynamite', 'gunshot', 'missile', 'nuclear_bomb', 'fall_sky']
senticnet['nuclear_threat'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'dynamite', 'gunshot', 'missile', 'nuclear_bomb', 'fall_sky']
senticnet['nuclear_translocation'] = ['0.085', '0', '0', '0.089', '#joy', '#admiration', 'positive', '0.087', 'steam', 'haze', 'mist', 'misty', 'summer_weather']
senticnet['nuclear_transmutation'] = ['-0.77', '0', '0.764', '-0.54', '#sadness', '#anger', 'negative', '-0.69', 'pullulation', 'apomixis', 'blastogenesis', 'budding', 'asexual_reproduction']
senticnet['nuclear_war'] = ['-0.93', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.84', 'kill', 'bigot', 'murder', 'death_penalty', 'execute']
senticnet['nuclear_warhead'] = ['-0.03', '-0.03', '-0.05', '-0.20', '#fear', '#disgust', 'negative', '-0.06', 'nuclear_bomb', 'bomb', 'explosive', 'atomic_bomb', 'grenade']
senticnet['nuclear_waste'] = ['0', '0', '-0.83', '-0.73', '#fear', '#disgust', 'negative', '-0.78', 'contamination', 'poison', 'kill', 'kill_earth', 'contaminant']
senticnet['nuclear_waste_management'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'dynamite', 'gunshot', 'missile', 'nuclear_bomb', 'fall_sky']
senticnet['nuclear_weapon'] = ['-0.03', '-0.03', '-0.05', '-0.20', '#fear', '#disgust', 'negative', '-0.06', 'nuclear_bomb', 'bomb', 'explosive', 'atomic_bomb', 'grenade']
senticnet['nuclear_weapon_arsenal'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'dynamite', 'gunshot', 'missile', 'nuclear_bomb', 'fall_sky']
senticnet['nuclear_weapon_capability'] = ['-0.03', '-0.03', '-0.05', '-0.20', '#fear', '#disgust', 'negative', '-0.06', 'nuclear_bomb', 'bomb', 'explosive', 'atomic_bomb', 'grenade']
senticnet['nuclear_weapon_program'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'dynamite', 'gunshot', 'missile', 'nuclear_bomb', 'fall_sky']
senticnet['nuclear_weaponry'] = ['0', '0', '-0.72', '-0.94', '#fear', '#disgust', 'negative', '-0.83', 'pistol', 'gun', 'hydrogen_bomb', 'handgun', 'revolver']
senticnet['nucleon'] = ['0.714', '0.74', '0', '0', '#joy', '#interest', 'positive', '0.727', 'antineutron', 'baryon', 'proton', 'antiproton', 'neutron']
senticnet['nucleophilic_acyl_substitution'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
senticnet['nucleophilic_addition'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
senticnet['nucleophilicity'] = ['0.695', '-0.85', '0', '0.501', '#joy', '#surprise', 'positive', '0.684', 'alternative_energy', 'energy', 'energy_of_activation', 'atomic_energy', 'nuclear_energy']
senticnet['nucleoplasm'] = ['0', '0.095', '0.016', '0.106', '#interest', '#admiration', 'positive', '0.062', 'blood_plasma', 'cytoplasm', 'germplasm', 'plasma', 'protoplasm']
senticnet['nucleoside_analog'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'indinavir', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['nucleotide'] = ['0', '0.855', '0', '0.784', '#interest', '#admiration', 'positive', '0.82', 'base', 'study', 'protein', 'function', 'mechanism']
senticnet['nucleus'] = ['0.071', '0.118', '0', '0.124', '#interest', '#admiration', 'positive', '0.104', 'atom', 'proton_neutron', 'neutron', 'proton', 'molecule']
senticnet['nucleus_accumbens_shell'] = ['0.998', '-0.63', '0', '0.574', '#joy', '#surprise', 'positive', '0.736', 'cleavable', 'divisible', 'separable', 'severable', 'dissociative']
senticnet['nude'] = ['-0.94', '0', '0.991', '-0.65', '#sadness', '#anger', 'negative', '-0.86', 'birthday_suit', 'raw', 'altogether', 'nakedness', 'undress']
senticnet['nude_body'] = ['0.024', '0', '0', '0.074', '#joy', '#admiration', 'positive', '0.049', 'sheet', 'cover', 'quilt', 'comforter', 'modesty']
senticnet['nude_dancing'] = ['0.993', '-0.96', '0', '0.917', '#joy', '#surprise', 'positive', '0.959', 'strip', 'fan_dance', 'striptease', 'strip_show', 'bubble_dance']
senticnet['nude_female'] = ['-0.94', '0', '0.991', '-0.65', '#sadness', '#anger', 'negative', '-0.86', 'birthday_suit', 'raw', 'altogether', 'nakedness', 'undress']
senticnet['nude_male'] = ['-0.94', '0', '0.991', '-0.65', '#sadness', '#anger', 'negative', '-0.86', 'birthday_suit', 'raw', 'altogether', 'nakedness', 'undress']
senticnet['nude_model'] = ['-0.94', '0', '0.991', '-0.65', '#sadness', '#anger', 'negative', '-0.86', 'birthday_suit', 'raw', 'altogether', 'nakedness', 'undress']
senticnet['nude_mouse'] = ['-0.55', '0', '0.955', '-0.96', '#anger', '#disgust', 'negative', '-0.82', 'house_mouse', 'harvest_mouse', 'rat', 'fieldmouse', 'mouse']
senticnet['nude_pictorial'] = ['-0.98', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.89', 'fairy', 'genus', 'honey_bee', 'infant', 'dove']
senticnet['nude_woman'] = ['-0.94', '0', '0.991', '-0.65', '#sadness', '#anger', 'negative', '-0.86', 'birthday_suit', 'raw', 'altogether', 'nakedness', 'undress']
senticnet['nudeness'] = ['-0.80', '0', '0.602', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'birthday_suit', 'raw', 'altogether', 'nude', 'undress']
senticnet['nudge'] = ['0.988', '0.787', '0', '0', '#joy', '#interest', 'positive', '0.887', 'tug', 'carry', 'tow', 'extend', 'drag']
senticnet['nudism'] = ['0.778', '-0.73', '0', '0.559', '#joy', '#surprise', 'positive', '0.689', 'sex', 'inversion', 'sexual_inversion', 'lesbianism', 'sapphism']
senticnet['nudity'] = ['-0.61', '0', '0.86', '-0.53', '#sadness', '#anger', 'negative', '-0.66', 'birthday_suit', 'raw', 'altogether', 'nude', 'undress']
senticnet['nudnick'] = ['-0.99', '0', '0.636', '-0.80', '#sadness', '#disgust', 'negative', '-0.81', 'pest', 'dullard', 'curse', 'blighter', 'cuss']
senticnet['nudnik'] = ['-0.53', '0', '0.973', '-0.64', '#anger', '#disgust', 'negative', '-0.71', 'pest', 'dullard', 'curse', 'blighter', 'cuss']
senticnet['nugatory'] = ['-0.97', '0', '0.675', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'infeasibility', 'unfeasibility', 'inutility', 'uselessness', 'unusefulness']
senticnet['nugget'] = ['0.03', '0', '0', '0.091', '#joy', '#admiration', 'positive', '0.06', 'part_of_meal', 'make_egg', 'good_eat', 'produce_egg', 'yum']
senticnet['nuhila'] = ['-0.93', '0', '0.791', '0', '#sadness', '#anger', 'negative', '-0.86', 'hopelessness', 'hopelessly', 'despairing', 'pessimistically', 'pessimistic']
senticnet['nuisance'] = ['-0.74', '0', '0.704', '-0.79', '#sadness', '#disgust', 'negative', '-0.75', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['nuisance_insect'] = ['-0.03', '-0.01', '0.094', '-0.05', '#anger', '#disgust', 'negative', '-0.04', 'small_insect', 'annoy_bug', 'small_bug', 'parasite', 'tiny_insect']
senticnet['nuke'] = ['0', '0', '0.977', '-0.86', '#anger', '#disgust', 'negative', '-0.92', 'waste', 'ruin', 'deface', 'shatter', 'damage']
senticnet['null'] = ['-0.55', '0', '0.882', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'bugger_all', 'zero', 'naught', 'zip', 'nothing']
senticnet['null_character'] = ['-0.04', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.06', 'chemical_reaction', 'burn', 'chemical_process', 'get_burnt', 'set_on_fire']
senticnet['null_pointer'] = ['-0.55', '0', '0.882', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'bugger_all', 'zero', 'naught', 'zip', 'nothing']
senticnet['null_terminator'] = ['-0.04', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.06', 'chemical_reaction', 'burn', 'chemical_process', 'get_burnt', 'set_on_fire']
senticnet['null_value'] = ['-0.55', '0', '0.882', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'bugger_all', 'zero', 'naught', 'zip', 'nothing']
senticnet['nullam'] = ['0.654', '-0.75', '0', '0.78', '#surprise', '#admiration', 'positive', '0.729', 'suit', 'cause', 'lawsuit', 'occurrence', 'countersuit']
senticnet['nullification'] = ['-0.55', '0', '0.965', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'interjection', 'delay', 'insert', 'disruption', 'interpolation']
senticnet['nullify'] = ['-0.92', '0', '0.762', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'stet', 'set_aside', 'break', 'cancel', 'strike_down']
senticnet['nullity'] = ['0.758', '0.87', '0', '0', '#joy', '#interest', 'positive', '0.814', 'annulment', 'annulled', 'invalidity', 'dissolution', 'decree']
senticnet['nullpointerexception'] = ['-0.55', '0', '0.882', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'bugger_all', 'zero', 'naught', 'zip', 'nothing']
senticnet['num_lock'] = ['0.893', '0.979', '0', '0', '#joy', '#interest', 'positive', '0.936', 'find_phone_number', 'dial_phone_number', 'pick_up_telephone_receiver', 'dial', 'dial_telephone']
senticnet['numb'] = ['-0.85', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.78', 'spasm', 'cure', 'forceps', 'treatment', 'operation']
senticnet['number'] = ['-0.85', '0', '0.605', '-0.73', '#sadness', '#disgust', 'negative', '-0.73', 'fewness', 'numerousness', 'numerosity', 'innumerableness', 'countlessness']
senticnet['number_claiming_jobseeker'] = ['0.924', '0.968', '0', '0.876', '#joy', '#interest', 'positive', '0.923', 'live_simply', 'help_out', 'money', 'accolade', 'generous']
senticnet['number_one'] = ['0.043', '0.118', '0.085', '0.126', '#interest', '#admiration', 'positive', '0.05', 'ordinal', 'unit', 'military_group', 'prime', 'best']
senticnet['number_pad'] = ['0', '0', '0.076', '0', '#anger', '#anger', 'negative', '-0.07', 'device', 'computer', 'browser', 'input_device', 'telephone_computer']
senticnet['number_person'] = ['0.052', '0.16', '0.061', '0.161', '#interest', '#admiration', 'positive', '0.078', 'count_person', 'small_group', 'crowd', 'large_amount', 'elite']
senticnet['number_theorist'] = ['0', '0', '0.803', '-0.75', '#anger', '#disgust', 'negative', '-0.78', 'mathematician', 'algebraist', 'probability_theorist', 'statistician', 'trigonometrician']
senticnet['number_theory'] = ['0.884', '0', '0', '0.823', '#joy', '#admiration', 'positive', '0.853', 'integer', 'whole_number', 'study', 'mathematics', 'oldest']
senticnet['numberless'] = ['0', '0.865', '0', '0.726', '#interest', '#admiration', 'positive', '0.795', 'multifarious', 'multiform', 'scad', 'umpteen', 'some-more']
senticnet['numbing_medication'] = ['-0.65', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'put_to_sleep', 'operation', 'general_anesthesia', 'surgery', 'anesthesia']
senticnet['numbingly'] = ['-0.99', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.96', 'horrifying', 'atrocious', 'dreadfully', 'horrified', 'horribly']
senticnet['numbly'] = ['0', '0', '0.737', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'glumly', 'mutely', 'dumbly', 'distractedly', 'dazedly']
senticnet['numbness'] = ['-0.21', '-0.16', '-0.10', '-0.26', '#sadness', '#disgust', 'negative', '-0.10', 'earache', 'physical_pain', 'car_wreck', 'punch_face', 'ice_cream_melt']
senticnet['numbskull'] = ['-0.76', '0', '0.904', '-0.80', '#anger', '#disgust', 'negative', '-0.82', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['numeracy'] = ['0.817', '-0.51', '0', '0.991', '#joy', '#admiration', 'positive', '0.774', 'acquirement', 'skill', 'accomplishment', 'acquisition', 'attainment']
senticnet['numeral'] = ['0.049', '0.113', '0.107', '0.162', '#interest', '#admiration', 'positive', '0.054', 'list_number', 'ordinal', 'count', 'numerical', 'enumeration']
senticnet['numeral_system'] = ['0.049', '0.113', '0.107', '0.162', '#interest', '#admiration', 'positive', '0.054', 'list_number', 'ordinal', 'count', 'numerical', 'enumeration']
senticnet['numerate'] = ['-0.93', '0', '0.8', '-0.50', '#sadness', '#anger', 'negative', '-0.74', 'miscount', 'tot', 'tally', 'find', 'add']
senticnet['numeration'] = ['0.809', '-0.71', '0', '0.708', '#joy', '#surprise', 'positive', '0.745', 'tally', 'investigating', 'tap', 'scrutiny', 'testing']
senticnet['numerator'] = ['0', '0.118', '0', '0.1', '#interest', '#admiration', 'positive', '0.109', 'divide', 'fraction', 'subtraction', 'math_action', 'division']
senticnet['numeric'] = ['0.039', '0.105', '0.099', '0.161', '#interest', '#admiration', 'positive', '0.051', 'list_number', 'ordinal', 'count', 'numeral', 'enumeration']
senticnet['numeric_keypad'] = ['0.893', '0.979', '0', '0', '#joy', '#interest', 'positive', '0.936', 'find_phone_number', 'dial_phone_number', 'pick_up_telephone_receiver', 'dial', 'dial_telephone']
senticnet['numeric_pad'] = ['0.893', '0.979', '0', '0', '#joy', '#interest', 'positive', '0.936', 'find_phone_number', 'dial_phone_number', 'pick_up_telephone_receiver', 'dial', 'dial_telephone']
senticnet['numeric_value'] = ['0.037', '0', '0.026', '0.129', '#joy', '#admiration', 'positive', '0.047', 'measurement_unit', 'many_items', 'long', 'circle_part', 'narrow']
senticnet['numerical'] = ['0.039', '0.105', '0.099', '0.161', '#interest', '#admiration', 'positive', '0.051', 'list_number', 'ordinal', 'count', 'numeral', 'enumeration']
senticnet['numerical_algorithm'] = ['0.039', '0.105', '0.099', '0.161', '#interest', '#admiration', 'positive', '0.051', 'list_number', 'ordinal', 'count', 'numeral', 'enumeration']
senticnet['numerical_algorithm_group'] = ['0', '0', '0.737', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'annoy', 'skin_rash', 'bother', 'irritation', 'great_dane']
senticnet['numerical_approximation'] = ['0.039', '0.105', '0.099', '0.161', '#interest', '#admiration', 'positive', '0.051', 'list_number', 'ordinal', 'count', 'numeral', 'enumeration']
senticnet['numerical_calculation'] = ['0.039', '0.105', '0.099', '0.161', '#interest', '#admiration', 'positive', '0.051', 'list_number', 'ordinal', 'count', 'numeral', 'enumeration']
senticnet['numerical_computation'] = ['0.039', '0.105', '0.099', '0.161', '#interest', '#admiration', 'positive', '0.051', 'list_number', 'ordinal', 'count', 'numeral', 'enumeration']
senticnet['numerical_inferiority'] = ['-0.82', '0', '0.739', '-0.50', '#sadness', '#anger', 'negative', '-0.69', 'fake', 'juke', 'maneuver', 'manoeuvre', 'feint']
senticnet['numerical_keypad'] = ['0.893', '0.979', '0', '0', '#joy', '#interest', 'positive', '0.936', 'find_phone_number', 'dial_phone_number', 'pick_up_telephone_receiver', 'dial', 'dial_telephone']
senticnet['numerical_scheme'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'assumption', 'precondition', 'provision', 'constraint', 'premiss']
senticnet['numerical_score'] = ['0.926', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.916', 'score_goal', 'accomplish', 'finalize', 'pot', 'make_point']
senticnet['numerical_superiority'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['numerical_value'] = ['0.049', '0.113', '0.107', '0.162', '#interest', '#admiration', 'positive', '0.054', 'list_number', 'ordinal', 'count', 'numerical', 'enumeration']
senticnet['numerically'] = ['0.039', '0.105', '0.099', '0.161', '#interest', '#admiration', 'positive', '0.051', 'list_number', 'ordinal', 'count', 'numeral', 'enumeration']
senticnet['numerically_superior'] = ['0.957', '-0.93', '0', '0.795', '#joy', '#surprise', 'positive', '0.896', 'outdo', 'outstrip', 'outmatch', 'outgo', 'outperform']
senticnet['numerically_superior_force'] = ['-0.05', '0', '-0.08', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'siege', 'big_fight', 'war', 'declare_war', 'combat']
senticnet['numerologist'] = ['0.083', '0', '0', '0.075', '#joy', '#admiration', 'positive', '0.079', 'carny', 'carnival_ride', 'midway', 'ferris_wheel', 'roller-coaster']
senticnet['numerosity'] = ['-0.85', '0', '0.605', '-0.73', '#sadness', '#disgust', 'negative', '-0.73', 'fewness', 'numerousness', 'number', 'innumerableness', 'countlessness']
senticnet['numerous'] = ['-0.71', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.79', 'forever', 'persistent', 'recurring', 'indefinitely', 'permanent']
senticnet['numerousness'] = ['-0.85', '0', '0.605', '-0.73', '#sadness', '#disgust', 'negative', '-0.73', 'fewness', 'number', 'numerosity', 'innumerableness', 'countlessness']
senticnet['numismatic_value'] = ['0', '0.238', '0.021', '0.152', '#interest', '#admiration', 'positive', '0.123', 'money_value', 'money_amount', 'worth', 'monetary_amount', 'importance']
senticnet['numismatics'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'collector', 'collect_item', 'coin', 'collect', 'stamp']
senticnet['numskull'] = ['-0.64', '0', '0.576', '-0.87', '#sadness', '#disgust', 'negative', '-0.69', 'stupid', 'stupid_person', 'stupe', 'dullard', 'dolt']
senticnet['nun'] = ['0.047', '0.204', '-0.13', '0.171', '#interest', '#admiration', 'positive', '0.073', 'sister', 'cousin', 'wench', 'aunt', 'parent']
senticnet['nunamiut'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'cheetah', 'giraffe', 'kangaroo', 'mule', 'horse']
senticnet['nunchaku'] = ['-0.04', '0.138', '-0.08', '0.111', '#interest', '#admiration', 'positive', '0.031', 'geometry', 'craft', 'blade_geometry', 'blade', 'collect_item']
senticnet['nuptial'] = ['0.749', '0', '0', '0.708', '#joy', '#admiration', 'positive', '0.728', 'conjugal', 'yawn-inducing', 'blamelessness', 'connubial', 'alone-time']
senticnet['nuremberg_toy'] = ['0.713', '0', '0', '0.746', '#joy', '#admiration', 'positive', '0.73', 'carnival', 'circus', 'fairground', 'rodeo', 'disneyland']
senticnet['nuremberg_zoo'] = ['-0.95', '0', '0.735', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'novice', 'beginner', 'tyro', 'tiro', 'initiate']
senticnet['nurse'] = ['0.024', '0.172', '0.073', '0.245', '#interest', '#admiration', 'positive', '0.092', 'doctor', 'cure', 'medicine', 'clinic', 'doctor_surgery']
senticnet['nurse_anesthesia'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'infant_feeding', 'schedule_feeding', 'demand_feeding', 'breast_feeding', 'feeding']
senticnet['nurse_anesthesia_program'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'infant_feeding', 'schedule_feeding', 'demand_feeding', 'breast_feeding', 'feeding']
senticnet['nurse_educator'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'infant_feeding', 'schedule_feeding', 'demand_feeding', 'breast_feeding', 'feeding']
senticnet['nurse_home'] = ['0.024', '0.172', '0.073', '0.245', '#interest', '#admiration', 'positive', '0.092', 'clinic', 'hospital', 'emergency_room', 'emergency_ward', 'medical_office']
senticnet['nurse_midwifery'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'infant_feeding', 'schedule_feeding', 'demand_feeding', 'breast_feeding', 'feeding']
senticnet['nurse_midwifery_education'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'infant_feeding', 'schedule_feeding', 'demand_feeding', 'breast_feeding', 'feeding']
senticnet['nurse_midwifery_program'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'infant_feeding', 'schedule_feeding', 'demand_feeding', 'breast_feeding', 'feeding']
senticnet['nurse_practitioner'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'infant_feeding', 'schedule_feeding', 'demand_feeding', 'breast_feeding', 'feeding']
senticnet['nurse_shark'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'many_fishes', 'seahorse', 'starfish', 'plankton', 'sea_wee']
senticnet['nursemaid'] = ['-0.62', '0', '0.649', '-0.67', '#anger', '#disgust', 'negative', '-0.64', 'handmaid', 'handmaiden', 'chambermaid', 'maid', 'parlormaid']
senticnet['nursery'] = ['0.054', '0.076', '0', '0.127', '#interest', '#admiration', 'positive', '0.086', 'take_care', 'old_folk_home', 'laboratory', 'nurse', 'compost_pile']
senticnet['nursery_maid'] = ['-0.62', '0', '0.649', '-0.67', '#anger', '#disgust', 'negative', '-0.64', 'handmaid', 'handmaiden', 'chambermaid', 'maid', 'parlormaid']
senticnet['nursery_nurse'] = ['0', '0.864', '0', '0.751', '#interest', '#admiration', 'positive', '0.807', 'family_member', 'sibling', 'parent', 'female_relative', 'relative']
senticnet['nursery_rhyme'] = ['0.95', '-0.86', '0', '0.947', '#joy', '#admiration', 'positive', '0.92', 'content', 'lore', 'folklore', 'cognitive_content', 'story']
senticnet['nursery_school'] = ['-0.04', '0.088', '0.013', '0.069', '#interest', '#admiration', 'positive', '0.024', 'kindergarden', 'daycare', 'nursery', 'child', 'asylum']
senticnet['nurseryman'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'intrepidly', 'fearlessness', 'fearlessly', 'unafraid', 'unfearing']
senticnet['nursie'] = ['0.934', '-0.57', '0', '0.767', '#joy', '#admiration', 'positive', '0.76', 'morosoph', 'simpleton', 'flibbertigibbet', 'foolish_woman', 'simple']
senticnet['nursing'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'infant_feeding', 'schedule_feeding', 'demand_feeding', 'breast_feeding', 'feeding']
senticnet['nursing_bsn'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'infant_feeding', 'schedule_feeding', 'demand_feeding', 'breast_feeding', 'feeding']
senticnet['nursing_care'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'infant_feeding', 'schedule_feeding', 'demand_feeding', 'breast_feeding', 'feeding']
senticnet['nursing_home'] = ['0.956', '-0.70', '0', '0.653', '#joy', '#surprise', 'positive', '0.771', 'centenarian', 'nonagenarian', 'octogenarian', 'septuagenarian', 'sexagenarian']
senticnet['nursing_msn'] = ['0.036', '0.111', '-0.05', '0.054', '#interest', '#admiration', 'positive', '0.038', 'despot', 'supervisor', 'chief', 'commander', 'earl']
senticnet['nursing_practice'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'infant_feeding', 'schedule_feeding', 'demand_feeding', 'breast_feeding', 'feeding']
senticnet['nursing_profession'] = ['0', '0.097', '0', '0.058', '#interest', '#admiration', 'positive', '0.077', 'occupation', 'career', 'tradesman', 'vocation', 'important_job']
senticnet['nursing_shortage'] = ['-0.83', '0', '-0.88', '-0.78', '#sadness', '#fear', 'negative', '-0.83', 'little', 'sadness', 'small_amount', 'lack', 'absence']
senticnet['nurtural'] = ['-0.95', '0', '0.684', '-0.76', '#sadness', '#disgust', 'negative', '-0.79', 'innate', 'nonheritable', 'inborn', 'nontransmissible', 'nonhereditary']
senticnet['nurturance'] = ['0.858', '0', '0', '0.8', '#joy', '#admiration', 'positive', '0.829', 'empathize', 'sympathy', 'end_conversation', 'desire', 'human_emotion']
senticnet['nurture'] = ['0.928', '0', '0', '0.712', '#joy', '#admiration', 'positive', '0.82', 'foster', 'serve', 'kindness', 'need_grocery', 'keep_alive']
senticnet['nurture_child'] = ['0.255', '0.182', '-0.06', '0.314', '#joy', '#admiration', 'positive', '0.173', 'quiet_child', 'care_child', 'feed_child', 'shoulder_responsibility', 'grow']
senticnet['nurture_talent'] = ['0.073', '0.228', '-0.10', '0.263', '#interest', '#admiration', 'positive', '0.114', 'creativity', 'blueprint', 'skill', 'expressive', 'competence']
senticnet['nurturing_environment'] = ['0.36', '0.248', '-0.08', '0.278', '#joy', '#admiration', 'positive', '0.201', 'feel_important', 'strong_relationship', 'eat_together', 'world_peace', 'express_gratitude']
senticnet['nushi'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'white_animal', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
senticnet['nut'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'palm_nut', 'palm_kernel', 'edible_nut', 'cembra_nut', 'cedar_nut']
senticnet['nut_bolt'] = ['0', '-0.01', '0.048', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'wrench', 'power_drill', 'tool', 'clamp', 'drill_bite']
senticnet['nut_bread'] = ['0.644', '-0.94', '0', '0.729', '#surprise', '#admiration', 'positive', '0.773', 'gem', 'banana_bread', 'date_bread', 'quick_bread', 'oatcake']
senticnet['nut_butter'] = ['0.056', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.053', 'leftover', 'dusty', 'mayonnaise', 'leave_over', 'savory']
senticnet['nut_house'] = ['-0.81', '0', '0.6', '-0.62', '#sadness', '#disgust', 'negative', '-0.68', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['nut_job'] = ['-0.76', '0', '0.904', '-0.80', '#anger', '#disgust', 'negative', '-0.82', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['nutball'] = ['0', '0.331', '-0.09', '0.242', '#interest', '#admiration', 'positive', '0.158', 'spread_idea', 'importance', 'tenet', 'culture', 'idea']
senticnet['nutcase'] = ['-0.64', '0', '0.781', '-0.74', '#anger', '#disgust', 'negative', '-0.72', 'weirdo', 'crazy', 'loony', 'looney', 'lunatic']
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senticnet['obama_administration'] = ['0', '0.709', '0', '0.933', '#interest', '#admiration', 'positive', '0.821', 'capitol', 'government_building', 'president', 'apartment_house', 'government']
senticnet['obama_campaign'] = ['-0.81', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.81', 'take_charge', 'anti', 'go_against', 'death_wish', 'file_complaint']
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senticnet['obedient'] = ['0.023', '0.275', '-0.18', '0.298', '#interest', '#admiration', 'positive', '0.102', 'obey', 'honor', 'duty', 'mandatory', 'confirmation']
senticnet['obediently'] = ['0.916', '0', '0', '0.868', '#joy', '#admiration', 'positive', '0.892', 'meekly', 'mutely', 'glumly', 'stoically', 'sullenly']
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senticnet['oberek'] = ['0.591', '-0.91', '0', '0.605', '#surprise', '#admiration', 'positive', '0.704', 'farandole', 'folk_dance', 'polka', 'dance', 'typical_dance']
senticnet['oberfhnrich_zur_see'] = ['0.688', '-0.90', '0', '0.877', '#surprise', '#admiration', 'positive', '0.824', 'flag_officer', 'rear_admiral', 'admiral', 'full_admiral', 'vice_admiral']
senticnet['obese'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'fatty', 'worse', 'health_issue', 'clinical_problem']
senticnet['obese_mouse'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'cholecystokinin', 'secretin', 'gastrointestinal_hormone']
senticnet['obese_woman'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'fatty', 'worse', 'health_issue', 'clinical_problem']
senticnet['obesity'] = ['-0.03', '-0.02', '-0.08', '-0.13', '#fear', '#disgust', 'negative', '-0.05', 'get_fat', 'decrease_hunger', 'feel_full', 'upset_stomach', 'overweight']
senticnet['obesity_prevalence'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'fatty', 'worse', 'health_issue', 'clinical_problem']
senticnet['obey'] = ['0.094', '0.261', '-0.24', '0.39', '#interest', '#admiration', 'positive', '0.126', 'good_behavior', 'honor', 'mandatory', 'politeness', 'obedience']
senticnet['obey_god'] = ['0.076', '0.316', '-0.18', '0.262', '#interest', '#admiration', 'positive', '0.118', 'islam', 'dogma', 'evangelist', 'faith', 'believe']
senticnet['obey_law'] = ['0.905', '0.749', '0', '0.82', '#joy', '#admiration', 'positive', '0.825', 'bridge_difference', 'respect', 'stand_alone', 'see_sky', 'market_product']
senticnet['obey_parent'] = ['0', '0.281', '-0.20', '0.302', '#interest', '#admiration', 'positive', '0.127', 'thoughtful', 'helpful', 'certainty', 'attitude', 'respect']
senticnet['obey_rule'] = ['-0.05', '0', '0.018', '0', '#sadness', '#anger', 'negative', '-0.03', 'find_museum', 'leave_house', 'decide_to_go', 'look_map', 'book_ticket']
senticnet['obey_traffic_law'] = ['0.062', '0.161', '-0.07', '0.216', '#interest', '#admiration', 'positive', '0.091', 'dampness', 'wooden_spoon', 'smoothie', 'respect', 'hold_inside']
senticnet['obfuscate'] = ['-0.63', '0', '0.712', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'upset', 'disconcert', 'discompose', 'discomfit', 'untune']
senticnet['obfuscation'] = ['-0.50', '0', '0.6', '-0.88', '#anger', '#disgust', 'negative', '-0.66', 'confusion', 'disarray', 'mental_confusion', 'confusedness', 'muddiness']
senticnet['obituary'] = ['-0.97', '0', '-0.84', '0', '#sadness', '#fear', 'negative', '-0.90', 'editorial', 'news_story', 'article', 'headline', 'newspaper_write']
senticnet['objection'] = ['-0.75', '0', '0.764', '0', '#sadness', '#anger', 'negative', '-0.75', 'expostulation', 'dissent', 'protest', 'remonstration', 'remonstrance']
senticnet['objectionable'] = ['0', '0', '0.768', '-0.77', '#anger', '#disgust', 'negative', '-0.77', 'censor', 'futz', 'arousing', 'unspeakable', 'offensive']
senticnet['objectionable_content'] = ['0.051', '0.165', '-0.06', '0.174', '#interest', '#admiration', 'positive', '0.08', 'control_access', 'prevent', 'latch', 'lock', 'gate']
senticnet['objectionably'] = ['0', '-0.58', '-1.0', '0', '#surprise', '#fear', 'negative', '-0.21', 'gratingly', 'ostentatiously', 'intrusively', 'obtrusively', 'forgivably']
senticnet['objective'] = ['-0.03', '0.152', '0', '0.16', '#interest', '#admiration', 'positive', '0.092', 'make_happen', 'set_instruction', 'mission', 'attempt', 'police_work']
senticnet['objective_caml'] = ['0', '0.202', '-0.06', '0.291', '#interest', '#admiration', 'positive', '0.142', 'vocabulary', 'translate', 'dictionary', 'translation', 'thesaurus']
senticnet['objective_correlative'] = ['-0.77', '0', '0.885', '-0.73', '#sadness', '#anger', 'negative', '-0.8', 'fallacy', 'misconception', 'paralogism', 'logical_fallacy', 'pseudoscience']
senticnet['objective_criterion'] = ['0', '0.089', '0', '0', '#interest', '#interest', 'positive', '0.089', 'ground', 'basement', 'baseboard', 'foundation', 'base']
senticnet['objective_morality'] = ['0', '0.796', '0', '0.813', '#interest', '#admiration', 'positive', '0.804', 'morality', 'first', 'ethic', 'think', 'caltech']
senticnet['objective_observer'] = ['0.828', '-0.90', '0', '0.874', '#surprise', '#admiration', 'positive', '0.869', 'perspicacity', 'judgment', 'fair', 'sound_judgment', 'fair_judgment']
senticnet['objective_reality'] = ['0', '0.928', '0', '0.969', '#interest', '#admiration', 'positive', '0.949', 'value', 'reference', 'describe', 'well', 'frame']
senticnet['objectively'] = ['0', '0.817', '0', '0.887', '#interest', '#admiration', 'positive', '0.852', 'quantitatively', 'qualitatively', 'subjectively', 'conceptually', 'substantively']
senticnet['objectively_measurable'] = ['-0.97', '0', '0.781', '-0.91', '#sadness', '#disgust', 'negative', '-0.88', 'weakness', 'fault', 'flaw', 'defect', 'wart']
senticnet['objectiveness'] = ['0.828', '-0.90', '0', '0.874', '#surprise', '#admiration', 'positive', '0.869', 'perspicacity', 'judgment', 'fair', 'sound_judgment', 'fair_judgment']
senticnet['objectivity'] = ['0.998', '-0.73', '0', '0.962', '#joy', '#admiration', 'positive', '0.899', 'perspicacity', 'judgment', 'fair', 'sound_judgment', 'fair_judgment']
senticnet['objector'] = ['-0.93', '0', '0.688', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'dissident', 'dissenter', 'protester', 'nimby', 'contestant']
senticnet['objurgate'] = ['-0.99', '0', '0.545', '-0.55', '#sadness', '#disgust', 'negative', '-0.69', 'castigate', 'tell_off', 'brush_down', 'chasten', 'bawl_out']
senticnet['objurgation'] = ['-0.98', '0', '0.537', '-0.72', '#sadness', '#disgust', 'negative', '-0.75', 'reprimand', 'wigging', 'rebuke', 'reproof', 'reproval']
senticnet['oblanceolate'] = ['-0.75', '0', '0.734', '-0.82', '#sadness', '#disgust', 'negative', '-0.77', 'puberulent', 'haired', 'furlike', 'pappose', 'pilous']
senticnet['oblate'] = ['0.925', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.934', 'ellipsoid', 'prolate', 'spheroid', 'ovoid', 'ellipsoidal']
senticnet['oblate_spheroid'] = ['0.034', '-0.07', '0.068', '0.103', '#surprise', '#admiration', 'positive', '0.035', 'use_sport', 'bouncy', 'globe', 'orb', 'disc_shaped']
senticnet['oblation'] = ['0.693', '-0.69', '0', '0.714', '#surprise', '#admiration', 'positive', '0.702', 'vow', 'dedicate', 'apply', 'rededicate', 'use']
senticnet['obligate'] = ['-0.88', '0', '0.977', '-0.81', '#sadness', '#anger', 'negative', '-0.89', 'execute', 'run', 'compel', 'oblige', 'enforce']
senticnet['obligated'] = ['0', '0', '-1.0', '-0.66', '#fear', '#disgust', 'negative', '-0.83', 'obligation', 'coercion', 'force', 'force_feed', 'enslave']
senticnet['obligately_anaerobic'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'immobile', 'immotile']
senticnet['obligation'] = ['-0.06', '0.262', '-0.20', '0.231', '#interest', '#admiration', 'positive', '0.055', 'make_happen', 'question_ask', 'task', 'ask_nicely', 'chore']
senticnet['obligatory'] = ['0.921', '-0.56', '0', '0.752', '#joy', '#admiration', 'positive', '0.745', 'bounden', 'incumbent_on', 'obligate', 'compulsory', 'mandatory']
senticnet['oblige'] = ['0.731', '-0.59', '0', '0.634', '#joy', '#admiration', 'positive', '0.652', 'accommodate', 'obey', 'conform_to', 'toe_line', 'abide_by']
senticnet['obliged'] = ['0.916', '0.766', '0', '0', '#joy', '#interest', 'positive', '0.841', 'willingly', 'knowingly', 'consented', 'voluntarily', 'refusing']
senticnet['oblique'] = ['-0.82', '0', '0.81', '-0.61', '#sadness', '#anger', 'negative', '-0.75', 'accusative', 'genitive', 'envy', 'possessive', 'jealous']
senticnet['oblique_lighting'] = ['0.906', '-0.60', '0', '0.653', '#joy', '#admiration', 'positive', '0.72', 'opaqueness', 'faintness', 'dimness', 'opacity', 'vagueness']
senticnet['oblique_muscle'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'deltoid', 'muscle', 'abductor', 'skeletal_muscle']
senticnet['oblique_projection'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'arm_movement', 'hand_gesture', 'water_motion', 'hand_signal', 'sea_movement']
senticnet['oblique_slip'] = ['0', '0', '0.869', '-0.92', '#anger', '#disgust', 'negative', '-0.89', 'accuser', 'fall_down', 'fall_off', 'collapse', 'sore_knee']
senticnet['oblique_triangle'] = ['0.763', '-0.61', '0', '0.777', '#joy', '#admiration', 'positive', '0.717', 'acute_triangle', 'wedge_shape', 'cuneus', 'wedge', 'triangle']
senticnet['oblique_view'] = ['0.838', '0.828', '0', '0', '#joy', '#interest', 'positive', '0.833', 'vista', 'spectacular', 'perspective', 'image', 'scenic']
senticnet['obliterate'] = ['0', '0', '-0.90', '-0.82', '#fear', '#disgust', 'negative', '-0.86', 'ba_lot_of_out', 'kill', 'wipe_out', 'veil', 'obscure']
senticnet['obliteration'] = ['-0.14', '-0.00', '-0.12', '-0.21', '#sadness', '#disgust', 'negative', '-0.11', 'torture', 'demolish', 'maim', 'terminate', 'hurt_person']
senticnet['obliterator'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
senticnet['oblivion'] = ['0', '0.106', '-0.05', '0.163', '#interest', '#admiration', 'positive', '0.072', 'band', 'kit', 'genesis', 'rock_band', 'high_school_band']
senticnet['oblong'] = ['0.831', '-0.84', '0', '0.586', '#joy', '#surprise', 'positive', '0.755', 'plane_figure', 'star', 'heart', 'polygon', 'polygonal_shape']
senticnet['oblong_shape'] = ['0.928', '0.703', '0', '0', '#joy', '#interest', 'positive', '0.816', 'polygon', 'fit', 'geometric_figure', 'geometric_shape', 'train_hard']
senticnet['oblongifolia'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'carnauba_palm', 'carnauba', 'wax_palm', 'palmetto', 'palmyra']
senticnet['obloquy'] = ['-0.5', '0', '0.921', '-0.87', '#anger', '#disgust', 'negative', '-0.76', 'reproach', 'ignominy', 'odium', 'dishonor', 'humiliation']
senticnet['obnoxious'] = ['-0.83', '0', '0.773', '0', '#sadness', '#anger', 'negative', '-0.80', 'odious', 'offensive', 'unpleasant', 'obnoxiousness', 'jerk']
senticnet['obnoxious_behavior'] = ['-0.69', '0', '0.792', '-0.98', '#anger', '#disgust', 'negative', '-0.82', 'outcry', 'call', 'cry', 'yell', 'shout']
senticnet['obnoxiously'] = ['-0.78', '0', '0.726', '-0.93', '#sadness', '#disgust', 'negative', '-0.81', 'smile_child', 'child', 'pass_gene', 'young_child', 'dolt']
senticnet['obnoxiousness'] = ['-0.84', '0', '0.771', '0', '#sadness', '#anger', 'negative', '-0.80', 'odious', 'offensive', 'unpleasant', 'obnoxious', 'jerk']
senticnet['oboe'] = ['0.73', '0', '0', '0.934', '#joy', '#admiration', 'positive', '0.832', 'bassoon', 'soprano', 'descant', 'recorder', 'treble']
senticnet['obovate'] = ['0', '0', '0.064', '-0.04', '#anger', '#disgust', 'negative', '-0.05', 'bird_food', 'funiculus', 'plant_product', 'fuzz', 'agricultural_product']
senticnet['obovate_leaf'] = ['-0.66', '0', '0.971', '-0.75', '#anger', '#disgust', 'negative', '-0.79', 'lyrate_leaf', 'needle', 'elliptic_leaf', 'hastate_leaf', 'simple_leaf']
senticnet['obovoid'] = ['0', '0.141', '0.01', '0.115', '#interest', '#admiration', 'positive', '0.082', 'fruit', 'rind', 'funiculus', 'seed', 'pip']
senticnet['obscene'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['obscene_graffiti'] = ['-0.12', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.07', 'bum', 'homeless_people', 'homeless_person', 'panhandler', 'homeless']
senticnet['obscene_language'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['obscene_material'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'indecency']
senticnet['obscene_publication_act'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'indecency']
senticnet['obscenely'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['obscenity'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'indecency']
senticnet['obscenity_trial'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'indecency']
senticnet['obscurantism'] = ['0', '0', '0.991', '-0.70', '#anger', '#disgust', 'negative', '-0.84', 'fanaticism', 'dogmatism', 'zealotry', 'zealot', 'bigotry']
senticnet['obscurantist'] = ['0.765', '-0.52', '0', '0.921', '#joy', '#admiration', 'positive', '0.737', 'slicker', 'beguiler', 'deceiver', 'cheat', 'cheater']
senticnet['obscure'] = ['0', '0', '-0.74', '-0.77', '#fear', '#disgust', 'negative', '-0.75', 'night', 'blackout', 'darkness', 'dark', 'blackness']
senticnet['obscurely'] = ['0.552', '-0.66', '0', '0.845', '#surprise', '#admiration', 'positive', '0.689', 'imperfection', 'imperfectness', 'bugginess', 'lameness', 'sickness']
senticnet['obscureness'] = ['-0.62', '0', '0.72', '-0.51', '#sadness', '#anger', 'negative', '-0.61', 'overcast', 'dark', 'semidarkness', 'darkness', 'dimness']
senticnet['obscurest'] = ['-0.69', '0', '0.957', '-0.92', '#anger', '#disgust', 'negative', '-0.85', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['obsequious'] = ['0.981', '-0.88', '0', '0.939', '#joy', '#admiration', 'positive', '0.936', 'slavelike', 'submissive', 'servile', 'subservient', 'slavish']
senticnet['obsequiously'] = ['0', '-0.72', '0', '0.694', '#surprise', '#admiration', 'positive', '0.71', 'demurely', 'submissively', 'deferentially', 'reverentially', 'contemptuously']
senticnet['observable'] = ['0.958', '-0.83', '0', '0.81', '#joy', '#surprise', 'positive', '0.869', 'discernible', 'perceptible', 'evident', 'detectable', 'noticeable']
senticnet['observable_universe'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'empty_room', 'a_lot_of_space', 'empty', 'empty_house', 'universe']
senticnet['observance'] = ['0.614', '-0.95', '0', '0.768', '#surprise', '#admiration', 'positive', '0.778', 'keeping', 'formality', 'honoring', 'line', 'abidance']
senticnet['observant'] = ['-0.25', '0.128', '-0.19', '0.055', '#sadness', '#fear', 'negative', '-0.06', 'look_clue', 'say_eureka', 'rational', 'make_decision', 'ask_question']
senticnet['observation'] = ['0', '0.811', '0', '0', '#interest', '#interest', 'positive', '0.811', 'observe', 'glimpse', 'look', 'stare', 'glance']
senticnet['observation_deck'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'top_floor', 'front_desk', 'concierge', 'exit', 'ground_floor']
senticnet['observation_tower'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'castle', 'bell_tower', 'spire', 'steeple', 'religious_place']
senticnet['observational'] = ['0', '0.748', '0', '0.985', '#interest', '#admiration', 'positive', '0.867', 'experimental', 'data-based', 'research', 'award', 'study']
senticnet['observational_data'] = ['0', '0.811', '0', '0', '#interest', '#interest', 'positive', '0.811', 'observe', 'glimpse', 'look', 'stare', 'glance']
senticnet['observational_humor'] = ['0.807', '-0.83', '0', '0.688', '#joy', '#surprise', 'positive', '0.776', 'folly', 'lunacy', 'craziness', 'indulgence', 'foolery']
senticnet['observatory'] = ['0.057', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'phone_booth', 'office_build', 'office', 'discount_store', 'internet_cafe']
senticnet['observe'] = ['-0.05', '0.085', '0', '0.098', '#interest', '#admiration', 'positive', '0.043', 'observation', 'look', 'stare', 'glimpse', 'use_eye']
senticnet['observe_nature'] = ['0', '0.074', '0.051', '0.105', '#interest', '#admiration', 'positive', '0.043', 'read_science_book', 'study_language', 'write_on_notebook', 'buy_textbook', 'use_telescope']
senticnet['observed_experimentally'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'constancy', 'monotony', 'invariance', 'changelessness', 'unchangeability']
senticnet['observer'] = ['0.953', '0.977', '0', '0', '#joy', '#interest', 'positive', '0.965', 'peer', 'familiar', 'well_known', 'association', 'human_group']
senticnet['observer_say'] = ['-0.94', '0', '0.863', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'poke', 'knockout_punch', 'lick', 'punch', 'biff']
senticnet['obsess'] = ['0', '0', '0.965', '-0.70', '#anger', '#disgust', 'negative', '-0.83', 'cogitate', 'fretting', 'over-much', 'overmuch', 'agonize']
senticnet['obsessed_fan'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'addicted', 'obsessed_love', 'addiction', 'obsession', 'weird']
senticnet['obsessed_love'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'addicted', 'obsession', 'addiction', 'stalker', 'weird']
senticnet['obsession'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'addicted', 'obsessed_love', 'addiction', 'stalker', 'weird']
senticnet['obsessional'] = ['0.995', '-0.73', '0', '0.83', '#joy', '#admiration', 'positive', '0.852', 'spiritism', 'otherworldliness', 'spirituality', 'spiritualism', 'internality']
senticnet['obsessive'] = ['-0.91', '0', '0.794', '-0.93', '#sadness', '#disgust', 'negative', '-0.88', 'fundamentalist', 'extremist', 'obsession', 'stubborn', 'violent']
senticnet['obsessive_attention'] = ['0.682', '-0.52', '0', '0.613', '#joy', '#admiration', 'positive', '0.607', 'nice', 'fastidious', 'dainty', 'prissy', 'pernickety']
senticnet['obsessive_compulsive_behavior'] = ['-0.79', '-0.71', '-0.96', '-0.95', '#fear', '#disgust', 'negative', '-0.49', 'fear', 'caution', 'deep_sadness', 'bad_feeling', 'psychoneurosis']
senticnet['obsessive_compulsive_disorder'] = ['-0.45', '0.186', '-0.40', '0.065', '#sadness', '#fear', 'negative', '-0.15', 'clean_often', 'mass_hysteria', 'organizational_skill', 'caution', 'maintenance']
senticnet['obsessive_secrecy'] = ['0.742', '-0.83', '0', '0.889', '#surprise', '#admiration', 'positive', '0.823', 'furtiveness', 'sneakiness', 'stealthiness', 'unsociability', 'unsociableness']
senticnet['obsessivecompulsive_disorder'] = ['-0.58', '0', '0.722', '-0.51', '#sadness', '#anger', 'negative', '-0.60', 'dissociative_disorder', 'depersonalization_neurosis', 'depersonalization', 'mental_disorder', 'mental_disturbance']
senticnet['obsessiveness'] = ['0', '0.928', '0', '0.929', '#interest', '#admiration', 'positive', '0.929', 'relentlessness', 'doggedness', 'earnestness', 'fearlessness', 'single-mindedness']
senticnet['obsolescence'] = ['0.671', '-0.56', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'macular_degeneration', 'attack', 'degeneration', 'process', 'physical_process']
senticnet['obsolescent'] = ['-0.90', '0', '0.517', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'outdated', 'noncurrent', 'disused', 'obsolete', 'superannuated']
senticnet['obsolete'] = ['-0.44', '-0.15', '0', '-0.36', '#sadness', '#disgust', 'negative', '-0.21', 'loud', 'uncontrollable', 'noisy', 'light_colored', 'last_clue']
senticnet['obsolete_equipment'] = ['-0.90', '0', '0.517', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'outdated', 'noncurrent', 'disused', 'obsolete', 'superannuated']
senticnet['obsolete_inventory'] = ['-0.50', '0', '0.89', '-0.78', '#anger', '#disgust', 'negative', '-0.72', 'immoderateness', 'degree', 'grade', 'level', 'immoderatenessiveness']
senticnet['obstacle'] = ['-0.04', '0', '0.023', '0', '#sadness', '#anger', 'negative', '-0.03', 'golf_place', 'college_class', 'school_study', 'school_class', 'school_course']
senticnet['obstacle_course'] = ['0.955', '-0.93', '0', '0', '#joy', '#surprise', 'positive', '0.943', 'tack', 'foot_part', 'casual_shoes', 'spike', 'pin']
senticnet['obstetric'] = ['0.1', '0.216', '-0.18', '0.22', '#interest', '#admiration', 'positive', '0.088', 'female', 'maternity_ward', 'motherhood', 'woman', 'mother']
senticnet['obstetric_care'] = ['0.1', '0.216', '-0.18', '0.22', '#interest', '#admiration', 'positive', '0.088', 'female', 'maternity_ward', 'motherhood', 'woman', 'mother']
senticnet['obstetrical'] = ['0.1', '0.216', '-0.18', '0.22', '#interest', '#admiration', 'positive', '0.088', 'female', 'maternity_ward', 'motherhood', 'woman', 'mother']
senticnet['obstetrical_care'] = ['0.1', '0.216', '-0.18', '0.22', '#interest', '#admiration', 'positive', '0.088', 'female', 'maternity_ward', 'motherhood', 'woman', 'mother']
senticnet['obstetrician'] = ['0.738', '-0.53', '0', '0.856', '#joy', '#admiration', 'positive', '0.71', 'doctor', 'country_doctor', 'family_doctor', 'doctor_office', 'physician']
senticnet['obstetrics'] = ['0', '0', '0.89', '-0.76', '#anger', '#disgust', 'negative', '-0.83', 'neurological', 'neurology', 'psychosomatic', 'disorder', 'psychological']
senticnet['obstinacy'] = ['-0.63', '0', '0.942', '-0.52', '#sadness', '#anger', 'negative', '-0.70', 'stubbornness', 'impenitence', 'obstinance', 'bullheadedness', 'pigheadedness']
senticnet['obstinance'] = ['-0.63', '0', '0.942', '-0.52', '#sadness', '#anger', 'negative', '-0.70', 'stubbornness', 'obstinacy', 'impenitence', 'bullheadedness', 'pigheadedness']
senticnet['obstinate'] = ['-0.51', '0', '0.559', '-0.89', '#anger', '#disgust', 'negative', '-0.65', 'persevere', 'hang_in', 'persist', 'hang_on', 'hold_on']
senticnet['obstinately'] = ['0', '0', '0.838', '-0.90', '#anger', '#disgust', 'negative', '-0.87', 'persistent', 'opinionated', 'rock-ribbed', 'wrongheaded', 'obstinate']
senticnet['obstinately_refuse'] = ['0.939', '-0.50', '0', '0.733', '#joy', '#admiration', 'positive', '0.724', 'smooth_out', 'clear', 'relieve', 'smooth', 'disembody']
senticnet['obstreperous'] = ['-0.69', '0', '0.516', '-0.94', '#sadness', '#disgust', 'negative', '-0.71', 'insubordinate', 'recalcitrant', 'defiant', 'resistant', 'resistive']
senticnet['obstruct'] = ['-0.57', '0', '0.814', '-0.80', '#anger', '#disgust', 'negative', '-0.73', 'block', 'jam', 'close_up', 'block_off', 'obturate']
senticnet['obstruct_justice'] = ['-0.99', '0', '0.581', '-0.97', '#sadness', '#disgust', 'negative', '-0.84', 'crime', 'obstruct', 'killer', 'murderer', 'thief']
senticnet['obstructed_defecation'] = ['-0.52', '0', '0.839', '-0.86', '#anger', '#disgust', 'negative', '-0.74', 'folie', 'upset', 'psychosomatic_disorder', 'conversion_disorder', 'conversion_reaction']
senticnet['obstructed_justice'] = ['-0.73', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'obstruct_justice', 'get_away_crime', 'punish', 'hurt_else', 'commit_murder']
senticnet['obstructer'] = ['-0.92', '0', '0.576', '-0.83', '#sadness', '#disgust', 'negative', '-0.77', 'naysayer', 'stonewaller', 'eristic', 'disputant', 'controversialist']
senticnet['obstructing_justice'] = ['-0.73', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'obstruct_justice', 'get_away_crime', 'punish', 'hurt_else', 'commit_murder']
senticnet['obstruction'] = ['-0.99', '0', '0.581', '-0.97', '#sadness', '#disgust', 'negative', '-0.84', 'obstruct_justice', 'football_rule', 'block_road', 'hinder', 'interfere']
senticnet['obstructionism'] = ['0', '0', '0.808', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'idle', 'lazy', 'stay', 'take_easy', 'recline']
senticnet['obstructionist'] = ['-0.97', '0', '0.62', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'obstructor', 'obstructer', 'technophobe', 'naysayer', 'resister']
senticnet['obstructive'] = ['-0.57', '0', '0.814', '-0.80', '#anger', '#disgust', 'negative', '-0.73', 'block', 'jam', 'close_up', 'block_off', 'obturate']
senticnet['obstructive_apnea'] = ['-0.03', '-0.03', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'eat_poison', 'commit_suicide', 'drug_addiction', 'drunk_drive', 'jump_off_cliff']
senticnet['obstructive_lung_disease'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchiolitis', 'bronchitis', 'respiratory_disease', 'respiratory_illness', 'respiratory_disorder']
senticnet['obstructive_sleep_apnea'] = ['-0.03', '-0.03', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'eat_poison', 'commit_suicide', 'drug_addiction', 'drunk_drive', 'jump_off_cliff']
senticnet['obstructive_sleep_apnea_osa'] = ['-0.03', '-0.03', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'eat_poison', 'commit_suicide', 'drug_addiction', 'drunk_drive', 'jump_off_cliff']
senticnet['obstructor'] = ['-0.98', '0', '0.811', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'naysayer', 'stonewaller', 'eristic', 'disputant', 'controversialist']
senticnet['obstruent'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'letter', 'vowel', 'alphabet', 'basic_speech', 'kind_gesture']
senticnet['obtain'] = ['-0.05', '0.24', '-0.13', '0.279', '#interest', '#admiration', 'positive', '0.081', 'acquire', 'business_transaction', 'get_gift', 'receive', 'hand_out']
senticnet['obtain_absolution'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitentially', 'penitent', 'penitence', 'repentant']
senticnet['obtain_bad_drug'] = ['-0.18', '-0.08', '0', '-0.20', '#sadness', '#disgust', 'negative', '-0.10', 'bad_drug_spill', 'bad_drug', 'atmosphere', 'cool_bad_drug', 'water']
senticnet['obtain_degree'] = ['-0.05', '0.24', '-0.13', '0.279', '#interest', '#admiration', 'positive', '0.081', 'get_degree', 'pass_exam', 'finish_college', 'prepare_exam', 'obtain']
senticnet['obtain_food'] = ['0', '-0.01', '0.065', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'buy_food', 'find_restaurant', 'bite_food', 'make_sandwich', 'grow_organic_garden']
senticnet['obtain_information'] = ['0.612', '0.99', '0', '0.713', '#interest', '#admiration', 'positive', '0.772', 'scan', 'comprehension', 'inform', 'gain_knowledge', 'receive_information']
senticnet['obtain_knowledge'] = ['0.731', '0', '0', '0.843', '#joy', '#admiration', 'positive', '0.787', 'increase_knowledge', 'become_knowledgeable', 'gain_knowledge', 'learn_much', 'better_understand']
senticnet['obtain_ticket'] = ['-0.03', '-0.05', '0.073', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'book_ticket', 'buy_concert_ticket', 'purchase_ticket', 'buy_ticket', 'pay_ticket']
senticnet['obtain_water'] = ['-0.18', '-0.07', '0', '-0.20', '#sadness', '#disgust', 'negative', '-0.10', 'water_spill', 'water', 'atmosphere', 'cool_water', 'bad_drug']
senticnet['obtained_experimentally'] = ['-0.54', '0', '0.909', '-0.97', '#anger', '#disgust', 'negative', '-0.81', 'suppleness', 'pliancy', 'pliantness', 'flexibility', 'flexibleness']
senticnet['obtrude'] = ['-0.53', '0', '0.662', '-0.88', '#anger', '#disgust', 'negative', '-0.69', 'intrude', 'inflict', 'clamp', 'give', 'communicate']
senticnet['obtrude_upon'] = ['-0.95', '0', '0.703', '-0.83', '#sadness', '#disgust', 'negative', '-0.83', 'raid', 'come_in', 'foray_into', 'enter', 'get_into']
senticnet['obtrusive'] = ['-0.98', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.98', 'intrusive', 'distracting', 'overbearing', 'unobtrusive', 'ostentatious']
senticnet['obtrusively'] = ['0', '-0.57', '-0.99', '0', '#surprise', '#fear', 'negative', '-0.20', 'gratingly', 'ostentatiously', 'intrusively', 'objectionably', 'forgivably']
senticnet['obtundation'] = ['-0.56', '0', '0.577', '-0.61', '#anger', '#disgust', 'negative', '-0.58', 'foggy', 'groggy', 'logy', 'lethargic', 'dazed']
senticnet['obturate'] = ['-0.57', '0', '0.814', '-0.80', '#anger', '#disgust', 'negative', '-0.73', 'block', 'jam', 'close_up', 'obstruct', 'block_off']
senticnet['obtusa'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['obtuse'] = ['-0.70', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.71', 'dense', 'purblind', 'dull', 'dumb', 'dim']
senticnet['obtusely'] = ['-0.70', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.71', 'dense', 'purblind', 'dull', 'dumb', 'dim']
senticnet['obtuseness'] = ['-0.82', '0', '0.663', '-0.50', '#sadness', '#anger', 'negative', '-0.66', 'oscitance', 'dullness', 'sleepiness', 'drowsiness', 'somnolence']
senticnet['obverse'] = ['0.586', '-0.65', '0', '0.503', '#joy', '#surprise', 'positive', '0.58', 'decoration', 'distinguished_service_cross', 'medal', 'medallion', 'palm']
senticnet['obverse_side'] = ['0.586', '-0.65', '0', '0.503', '#joy', '#surprise', 'positive', '0.58', 'decoration', 'distinguished_service_cross', 'medal', 'medallion', 'palm']
senticnet['obviate'] = ['-0.78', '0', '-0.89', '0', '#sadness', '#fear', 'negative', '-0.84', 'debar', 'rid_of', 'eliminate', 'forfend', 'forefend']
senticnet['obvious'] = ['-0.91', '0', '-0.98', '0', '#sadness', '#fear', 'negative', '-0.95', 'sudden', 'suddenly', 'lovely', 'translucent', 'situation']
senticnet['obvious_sock'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['obviously'] = ['0', '0.853', '0', '0.721', '#interest', '#admiration', 'positive', '0.787', 'patently', 'plainly', 'plain', 'manifestly', 'apparently']
senticnet['obviousness'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'patency', 'blatancy']
senticnet['occam_razor'] = ['-0.11', '0.283', '-0.16', '0.331', '#interest', '#admiration', 'positive', '0.084', 'conclude', 'question_answering', 'solution', 'jutification', 'question_starter']
senticnet['occasion'] = ['0.701', '0.867', '0', '0', '#joy', '#interest', 'positive', '0.784', 'festivity', 'special_day', 'birthday', 'special_time', 'cake_candle']
senticnet['occasion_dress'] = ['0', '0.093', '0', '0.07', '#interest', '#admiration', 'positive', '0.082', 'dress_up', 'dress', 'dress_appropriately', 'go_outdoors', 'doll_up']
senticnet['occasional'] = ['0', '0.188', '-0.04', '0.191', '#interest', '#admiration', 'positive', '0.111', 'usual_ordinary', 'normal', 'typical', 'usual', 'daily_routine']
senticnet['occasional_constipation'] = ['0', '0', '-0.70', '-0.76', '#fear', '#disgust', 'negative', '-0.73', 'choke', 'become_full', 'get_full', 'burp', 'sickness']
senticnet['occasional_indulgence'] = ['-0.75', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.82', 'become_bored', 'turn_off', 'drink', 'get_bored', 'use_electricity']
senticnet['occasional_lapse'] = ['0.617', '-0.83', '0', '0.828', '#surprise', '#admiration', 'positive', '0.761', 'brag', 'embellish', 'overemphasize', 'overstress', 'vaunt']
senticnet['occasional_lapse_into'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'venial', 'excusable', 'mercifully', 'merciful', 'mercifulness']
senticnet['occasionalism'] = ['0', '0.918', '0', '0.958', '#interest', '#admiration', 'positive', '0.938', 'marry', 'get_married', 'love', 'join', 'hook_up_with']
senticnet['occasionally'] = ['-0.97', '0', '0', '-0.86', '#sadness', '#disgust', 'negative', '-0.91', 'worst', 'optional', 'rare', 'lass', 'temporary']
senticnet['occasions'] = ['0.057', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'cheap_stuff', 'save_money', 'less_quality', 'discount_store', 'poor_people']
senticnet['occipital_artery'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['occipital_bone'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'hip_socket', 'tooth_socket', 'alveolus', 'socket', 'cotyloid_cavity']
senticnet['occipital_condyle'] = ['0.886', '-0.99', '0', '0.542', '#joy', '#surprise', 'positive', '0.808', 'jowl', 'lower_jaw', 'mandible', 'lantern_jaw', 'lantern_jawr_bone']
senticnet['occipital_cortex'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['occipital_lobe'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['occipital_nerve'] = ['-0.73', '0', '0.904', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'unshapely', 'shapeless', 'lumpy', 'deformed', 'malformed']
senticnet['occipitotemporal'] = ['0.998', '-0.63', '0', '0.574', '#joy', '#surprise', 'positive', '0.736', 'cleavable', 'divisible', 'separable', 'severable', 'dissociative']
senticnet['occlude'] = ['0', '0', '0.945', '-0.72', '#anger', '#disgust', 'negative', '-0.83', 'cease', 'repress', 'muzzle', 'discontinue', 'seal']
senticnet['occluded'] = ['-0.83', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.85', 'block', 'obstruct', 'close', 'clogged', 'shutter']
senticnet['occlusal'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'false_teeth', 'bridge', 'dental_plate', 'partial_denture', 'dental_appliance']
senticnet['occlusive_disease'] = ['-0.81', '0', '0.684', '-0.81', '#sadness', '#disgust', 'negative', '-0.77', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['occult'] = ['-0.51', '0', '0.979', '-0.89', '#anger', '#disgust', 'negative', '-0.79', 'invisible', 'concealed', 'nonvisual', 'hidden', 'out_of_sight']
senticnet['occult_bacteremia'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchitis', 'chronic_bronchitis', 'respiratory_disease', 'respiratory_illness', 'respiratory_disorder']
senticnet['occult_philosophy'] = ['0', '0.887', '0', '0.973', '#interest', '#admiration', 'positive', '0.93', 'servant', 'art_of_transmuting_metal', 'stone', 'alchemy', 'philosopher']
senticnet['occultic'] = ['-0.51', '0', '0.979', '-0.89', '#anger', '#disgust', 'negative', '-0.79', 'invisible', 'concealed', 'nonvisual', 'hidden', 'out_of_sight']
senticnet['occultism'] = ['-0.51', '0', '0.979', '-0.89', '#anger', '#disgust', 'negative', '-0.79', 'invisible', 'concealed', 'nonvisual', 'hidden', 'out_of_sight']
senticnet['occultist'] = ['0', '0.887', '0', '0.973', '#interest', '#admiration', 'positive', '0.93', 'servant', 'art_of_transmuting_metal', 'stone', 'alchemy', 'philosopher']
senticnet['occultist_aleister_crowley'] = ['-0.51', '0', '0.979', '-0.89', '#anger', '#disgust', 'negative', '-0.79', 'invisible', 'concealed', 'nonvisual', 'hidden', 'out_of_sight']
senticnet['occupant_compartment'] = ['-0.69', '0', '0.752', '-0.82', '#anger', '#disgust', 'negative', '-0.75', 'usurpation', 'trespass', 'encroachment', 'inroad', 'violation']
senticnet['occupant_protection'] = ['0.082', '0.319', '-0.10', '0.147', '#interest', '#admiration', 'positive', '0.11', 'protect', 'defend', 'secure', 'guard', 'watch_over']
senticnet['occupant_safety'] = ['0.082', '0.319', '-0.10', '0.147', '#interest', '#admiration', 'positive', '0.11', 'protect', 'defend', 'secure', 'guard', 'watch_over']
senticnet['occupation'] = ['0', '0.112', '-0.04', '0.099', '#interest', '#admiration', 'positive', '0.054', 'profession', 'career', 'vocation', 'important_job', 'laborer']
senticnet['occupational'] = ['0.032', '0.11', '0', '0.105', '#interest', '#admiration', 'positive', '0.082', 'work', 'place_work', 'implementation', 'disorganize', 'employment']
senticnet['occupational_hazard'] = ['-0.52', '0', '0.577', '-0.93', '#anger', '#disgust', 'negative', '-0.67', 'peril', 'jeopardy', 'endangerment', 'hazard', 'risk']
senticnet['occupational_health'] = ['0.032', '0.11', '0', '0.105', '#interest', '#admiration', 'positive', '0.082', 'work', 'place_work', 'implementation', 'disorganize', 'employment']
senticnet['occupational_health_safety'] = ['0.082', '0.319', '-0.10', '0.147', '#interest', '#admiration', 'positive', '0.11', 'protect', 'defend', 'secure', 'guard', 'watch_over']
senticnet['occupational_safety'] = ['0.082', '0.319', '-0.10', '0.147', '#interest', '#admiration', 'positive', '0.11', 'protect', 'defend', 'secure', 'guard', 'watch_over']
senticnet['occupational_therapist'] = ['-0.69', '0', '0.659', '-0.69', '#sadness', '#disgust', 'negative', '-0.68', 'help', 'paralegal', 'legal_assistant', 'paramedic', 'paramedical']
senticnet['occupational_therapy'] = ['0.641', '-0.78', '0', '0.721', '#surprise', '#admiration', 'positive', '0.715', 'correctional_rehabilitation', 'physical_rehabilitation', 'physical_restoration', 'therapeutic_rehabilitation', 'vocational_rehabilitation']
senticnet['occupational_therapy_assistant'] = ['-0.69', '0', '0.659', '-0.69', '#sadness', '#disgust', 'negative', '-0.68', 'help', 'paralegal', 'legal_assistant', 'paramedic', 'paramedical']
senticnet['occupational_therapy_mot'] = ['0.036', '0.111', '-0.05', '0.054', '#interest', '#admiration', 'positive', '0.038', 'despot', 'supervisor', 'chief', 'commander', 'earl']
senticnet['occupied_territory'] = ['0', '0.112', '-0.04', '0.099', '#interest', '#admiration', 'positive', '0.054', 'profession', 'career', 'vocation', 'important_job', 'laborer']
senticnet['occupier'] = ['0', '0.112', '-0.04', '0.099', '#interest', '#admiration', 'positive', '0.054', 'profession', 'career', 'vocation', 'important_job', 'laborer']
senticnet['occupy'] = ['-0.79', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.79', 'concerned', 'humility', 'desperate', 'compassionate', 'anxious']
senticnet['occupy_by_force'] = ['-0.96', '0', '0.609', '-0.99', '#sadness', '#disgust', 'negative', '-0.85', 'fight', 'battle', 'conflict', 'war', 'quarrel']
senticnet['occupy_mind'] = ['0.912', '0', '0', '0.974', '#joy', '#admiration', 'positive', '0.943', 'become_famous', 'ego_boost', 'get_excited', 'spread_idea', 'show_off']
senticnet['occupying_force'] = ['0', '0.112', '-0.04', '0.099', '#interest', '#admiration', 'positive', '0.054', 'profession', 'career', 'vocation', 'important_job', 'laborer']
senticnet['occupying_power'] = ['0', '0.112', '-0.04', '0.099', '#interest', '#admiration', 'positive', '0.054', 'profession', 'career', 'vocation', 'important_job', 'laborer']
senticnet['occur'] = ['0.879', '-0.95', '0', '0.766', '#joy', '#surprise', 'positive', '0.867', 'happen', 'shine', 'come_up', 'chance', 'break']
senticnet['occur_infrequently'] = ['0.879', '-0.95', '0', '0.766', '#joy', '#surprise', 'positive', '0.867', 'happen', 'shine', 'come_up', 'chance', 'break']
senticnet['occur_naturally'] = ['0.879', '-0.95', '0', '0.766', '#joy', '#surprise', 'positive', '0.867', 'happen', 'shine', 'come_up', 'chance', 'break']
senticnet['occur_nowhere_else'] = ['0.051', '0', '0', '0.1', '#joy', '#admiration', 'positive', '0.075', 'provide_shelter', 'live', 'get_oxygen', 'raise_family', 'live_city']
senticnet['occur_randomly'] = ['0.879', '-0.95', '0', '0.766', '#joy', '#surprise', 'positive', '0.867', 'happen', 'shine', 'come_up', 'chance', 'break']
senticnet['occur_simultaneously'] = ['0', '0.067', '0', '0.062', '#interest', '#admiration', 'positive', '0.065', 'social_skill', 'show_up', 'go_abroad', 'go_new_place', 'visit']
senticnet['occur_spontaneously'] = ['0.879', '-0.95', '0', '0.766', '#joy', '#surprise', 'positive', '0.867', 'happen', 'shine', 'come_up', 'chance', 'break']
senticnet['occurrence'] = ['0.078', '-0.03', '-0.04', '0.105', '#joy', '#admiration', 'positive', '0.043', 'special_time', 'special_day', 'special_moment', 'special_occasion', 'yearly_celebration']
senticnet['occurrent'] = ['-0.73', '0', '0.591', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'whammy', 'happening', 'occurrence', 'setback', 'natural_event']
senticnet['ocd'] = ['-0.79', '-0.71', '-0.96', '-0.95', '#fear', '#disgust', 'negative', '-0.49', 'fear', 'caution', 'deep_sadness', 'bad_feeling', 'psychoneurosis']
senticnet['ocean'] = ['0.078', '-0.03', '-0.04', '0.105', '#joy', '#admiration', 'positive', '0.043', 'aquarium', 'underwater', 'pacific_ocean', 'sea', 'ocean_wave']
senticnet['ocean-going'] = ['0', '0.833', '0', '0.754', '#interest', '#admiration', 'positive', '0.793', 'sea-going', 'seagoing', 'seafaring', 'oceangoing', 'sea-faring']
senticnet['ocean_animal'] = ['0.052', '0.066', '0', '0.106', '#interest', '#admiration', 'positive', '0.075', 'shell_fish', 'seafood', 'marine_animal', 'amphibian', 'crustacean']
senticnet['ocean_beach'] = ['0.03', '0.117', '0', '0.084', '#interest', '#admiration', 'positive', '0.077', 'beach', 'waterfront', 'ocean', 'coast', 'sea']
senticnet['ocean_bottom'] = ['0.979', '-0.75', '0', '0.764', '#joy', '#admiration', 'positive', '0.831', 'ocean_floor', 'sea_floor', 'twilight_zone', 'seabed', 'sea_bottom']
senticnet['ocean_crust'] = ['0.931', '0.84', '0', '0', '#joy', '#interest', 'positive', '0.885', 'geophysics', 'composition', 'sediment', 'process', 'abundance']
senticnet['ocean_ecosystem'] = ['0.078', '-0.03', '-0.04', '0.105', '#joy', '#admiration', 'positive', '0.043', 'aquarium', 'underwater', 'pacific_ocean', 'sea', 'ocean_wave']
senticnet['ocean_edge'] = ['0', '0.068', '0', '0', '#interest', '#interest', 'positive', '0.068', 'bottom_ocean', 'ocean_floor', 'sea_floor', 'surface_earth', 'seabed']
senticnet['ocean_eleven'] = ['-0.57', '0', '0.721', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'bush', 'native_pomegranate', 'caper_tree', 'native_orange', 'common_caper']
senticnet['ocean_floor'] = ['-0.02', '-0.01', '0.03', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'ocean_beach', 'beach', 'water_sea', 'shore', 'ground_ocean']
senticnet['ocean_ground'] = ['-0.04', '-0.01', '0.103', '0', '#sadness', '#anger', 'negative', '-0.04', 'near_beach', 'along_beach', 'full_water', 'sea_bed', 'salty']
senticnet['ocean_movement'] = ['0.738', '0.833', '0', '0', '#joy', '#interest', 'positive', '0.785', 'sea_movement', 'fast_water', 'water_movement', 'ebb', 'water_motion']
senticnet['ocean_science'] = ['0.825', '0.791', '0', '0', '#joy', '#interest', 'positive', '0.808', 'division', 'university', 'organism', 'jointly', 'project']
senticnet['ocean_shore'] = ['0', '0.112', '0', '0.137', '#interest', '#admiration', 'positive', '0.124', 'island', 'popular_vacation_destination', 'land_feature', 'near_ocean', 'oceanside']
senticnet['ocean_sunfish'] = ['0.96', '-0.77', '0', '0.929', '#joy', '#admiration', 'positive', '0.887', 'whale_louse', 'beach_flea', 'sand_hopper', 'sandhopper', 'amphipod']
senticnet['ocean_surface'] = ['-0.02', '-0.01', '0.03', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'ocean_beach', 'beach', 'water_sea', 'shore', 'ground_ocean']
senticnet['ocean_tide'] = ['0.904', '-0.71', '0', '0', '#joy', '#surprise', 'positive', '0.809', 'sea_movement', 'ocean_movement', 'fast_water', 'water_motion', 'water_movement']
senticnet['ocean_voyage'] = ['0.937', '0.8', '0', '0', '#joy', '#interest', 'positive', '0.869', 'quest', 'stunt', 'illusion', 'adventure', 'activity']
senticnet['ocean_water'] = ['-0.02', '-0.02', '0.064', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'fillet', 'fillet_fish', 'chowder', 'port', 'underwater']
senticnet['ocean_wave'] = ['0', '-0.07', '0', '0.116', '#surprise', '#admiration', 'positive', '0.097', 'ocean_movement', 'sea_movement', 'fast_water', 'water_movement', 'tsunami']
senticnet['oceanfront_resort'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'beachcomber', 'bum', 'drifter']
senticnet['oceangoing'] = ['0', '0.832', '0', '0.759', '#interest', '#admiration', 'positive', '0.795', 'sea-going', 'ocean-going', 'seafaring', 'seagoing', 'sea-faring']
senticnet['oceanic'] = ['0.078', '-0.03', '-0.04', '0.105', '#joy', '#admiration', 'positive', '0.043', 'aquarium', 'underwater', 'pacific_ocean', 'sea', 'ocean_wave']
senticnet['oceanic_whitetip_shark'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'many_fishes', 'seahorse', 'starfish', 'plankton', 'sea_wee']
senticnet['oceanographic'] = ['0', '0', '-0.71', '-0.94', '#fear', '#disgust', 'negative', '-0.83', 'hydrological', 'meteorological', 'meteorology', 'climatic', 'climatological']
senticnet['oceanographic_instrumentation'] = ['0.772', '0.718', '0', '0', '#joy', '#interest', 'positive', '0.745', 'support', 'instrument', 'university', 'equipment', 'proposal']
senticnet['oceanography'] = ['0', '0', '-0.75', '-0.99', '#fear', '#disgust', 'negative', '-0.87', 'brine', 'saltwater', 'seawater', 'ocean_science', 'isotopic_composition']
senticnet['oceanside'] = ['-0.03', '0.085', '0', '0.127', '#interest', '#admiration', 'positive', '0.061', 'ocean', 'under_water', 'underwater', 'beach', 'big_lake']
senticnet['ocellata'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'puffer', 'pufferfish', 'blowfish', 'triggerfish', 'plectognath']
senticnet['ocelot'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'unofficial', 'informal', 'loose', 'drumhead']
senticnet['och'] = ['0', '0', '0.785', '-0.72', '#anger', '#disgust', 'negative', '-0.75', 'sharp_nail', 'sharp', 'sharp_end', 'sharp_point', 'pointy']
senticnet['ochreous_brown'] = ['-0.7', '0', '0.617', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'silvery', 'achromatic', 'bluish_black', 'hueless', 'pitchy']
senticnet['ocimum_basilicum'] = ['0.632', '-0.93', '0', '0.928', '#surprise', '#admiration', 'positive', '0.831', 'herb', 'oregano', 'marjoram', 'pot_marjoram', 'origanum']
senticnet['oclock'] = ['0.02', '0.067', '0', '0', '#joy', '#interest', 'positive', '0.044', 'sunrise', 'begin_day', 'bright_sky', 'sunny', 'part_of_day']
senticnet['ocr_optical_character'] = ['0.258', '0.35', '-0.23', '0.318', '#interest', '#admiration', 'positive', '0.173', 'brownie_point', 'honor', 'reinforcement', 'positive_reinforcement', 'fame']
senticnet['ocr_software'] = ['-0.08', '0', '0.011', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'deflate', 'read_cover', 'obtain_information', 'upgrade', 'spread_out']
senticnet['oct_utc'] = ['-0.02', '0', '-0.10', '0', '#sadness', '#fear', 'negative', '-0.06', 'ads', 'boring', 'bother', 'stop_watch', 'annoying']
senticnet['octagon'] = ['0.037', '0.082', '0.055', '0.183', '#interest', '#admiration', 'positive', '0.062', 'hexagon', 'geometric_figure', 'parallelogram', 'trapezoid', 'shape']
senticnet['octagonal'] = ['0.029', '0.087', '0.065', '0.137', '#interest', '#admiration', 'positive', '0.047', 'archway', 'aisle', 'mat', 'passageway', 'right_angle']
senticnet['octagonal_base'] = ['0.029', '0.087', '0.065', '0.137', '#interest', '#admiration', 'positive', '0.047', 'archway', 'aisle', 'mat', 'passageway', 'right_angle']
senticnet['octagonal_dome'] = ['0.029', '0.087', '0.065', '0.137', '#interest', '#admiration', 'positive', '0.047', 'archway', 'aisle', 'mat', 'passageway', 'right_angle']
senticnet['octagonal_shape'] = ['0.027', '0.139', '0', '0.203', '#interest', '#admiration', 'positive', '0.123', 'horse_shoes', 'four_leaf_clover', 'rotary_phone', 'ping_pong', 'billiards']
senticnet['octagonal_structure'] = ['0.029', '0.087', '0.065', '0.137', '#interest', '#admiration', 'positive', '0.047', 'archway', 'aisle', 'mat', 'passageway', 'right_angle']
senticnet['octagonal_tower'] = ['0.026', '0.219', '0', '0.16', '#interest', '#admiration', 'positive', '0.135', 'large_build', 'palace', 'high_rise', 'structure', 'fortification']
senticnet['octahedron'] = ['0.641', '-0.6', '0', '0.957', '#joy', '#admiration', 'positive', '0.733', 'ideal_solid', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'regular_octahedron']
senticnet['octane_gasoline'] = ['0.855', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.86', 'ticket_counter', 'newspaper_stand', 'luggage_carrier', 'aircraft_roadway', 'timetable']
senticnet['octave'] = ['-0.65', '0', '0.681', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'tone', 'whole_tone', 'step', 'whole_step', 'musical_interval']
senticnet['octave_above'] = ['-0.65', '0', '0.681', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'tone', 'whole_tone', 'step', 'whole_step', 'musical_interval']
senticnet['octave_below'] = ['-0.65', '0', '0.681', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'tone', 'whole_tone', 'step', 'whole_step', 'musical_interval']
senticnet['octave_higher'] = ['-0.65', '0', '0.681', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'tone', 'whole_tone', 'step', 'whole_step', 'musical_interval']
senticnet['octave_lower'] = ['-0.65', '0', '0.681', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'tone', 'whole_tone', 'step', 'whole_step', 'musical_interval']
senticnet['octavia_hill'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['octavio_dotel'] = ['0.663', '-0.68', '0', '0.892', '#surprise', '#admiration', 'positive', '0.747', 'saphead', 'muggins', 'tomfool', 'fool', 'sap']
senticnet['octavo_volume'] = ['0.058', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.054', 'letter_slot', 'envelope', 'address_label', 'mailman', 'postage_stamp']
senticnet['octogenarian'] = ['0.556', '-0.64', '0', '0.64', '#surprise', '#admiration', 'positive', '0.612', 'centenarian', 'nonagenarian', 'golden_ager', 'septuagenarian', 'sexagenarian']
senticnet['octopod'] = ['-0.52', '0', '0.728', '-0.92', '#anger', '#disgust', 'negative', '-0.72', 'nautilus', 'cephalopod', 'cephalopod_mollusk', 'paper_nautilus', 'octopus']
senticnet['octopod_mollusk'] = ['-0.52', '0', '0.726', '-0.92', '#anger', '#disgust', 'negative', '-0.72', 'nautilus', 'octopod', 'cephalopod', 'paper_nautilus', 'octopus']
senticnet['octopus'] = ['-0.52', '0', '0.728', '-0.92', '#anger', '#disgust', 'negative', '-0.72', 'nautilus', 'cephalopod', 'cephalopod_mollusk', 'paper_nautilus', 'octopod']
senticnet['octopus_salad'] = ['0.709', '0.964', '0', '0', '#joy', '#interest', 'positive', '0.837', 'fish_trout', 'catch_butterfly', 'fish_lake', 'fish_compliment', 'fish_stream']
senticnet['octreotide'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'spironolactone', 'adrenal_cortical_steroid', 'antihypertensive', 'antihypertensive_drug']
senticnet['octroi'] = ['-0.08', '0.358', '-0.20', '0.237', '#interest', '#admiration', 'positive', '0.077', 'extortion', 'money_due', 'afford', 'bribe', 'owe']
senticnet['ocular'] = ['-0.88', '0', '0.762', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'divergent_strabismus', 'exotropia', 'walleye', 'abnormality', 'abnormalcy']
senticnet['ocular_exposure'] = ['-0.70', '0', '-0.83', '-0.98', '#fear', '#disgust', 'negative', '-0.84', 'body_hurt', 'ache', 'ouch', 'cut_skin', 'scar']
senticnet['ocular_involvement'] = ['-0.86', '0', '0.858', '-0.91', '#sadness', '#disgust', 'negative', '-0.87', 'edema', 'oedema', 'hydrops', 'dropsy', 'eye_disease']
senticnet['oculocephalic_reflex'] = ['-0.56', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.63', 'aphasic', 'inarticulate', 'speechless', 'unarticulated', 'dumb']
senticnet['oculomotor_nerve'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['oculomotor_nerve_palsy'] = ['-0.73', '0', '0.904', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'unshapely', 'shapeless', 'lumpy', 'deformed', 'malformed']
senticnet['oculus_dexter'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'eye', 'stemma', 'naked_eye', 'peeper', 'oculus_sinister']
senticnet['oculus_sinister'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'naked_eye', 'peeper', 'eye']
senticnet['odaiyakulam'] = ['0.929', '0.507', '-0.17', '0.396', '#joy', '#interest', 'positive', '0.415', 'plume', 'congratulate', 'smugness', 'smug', 'proudly']
senticnet['odbc_data'] = ['0.964', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.846', 'bulletin', 'replicate', 'current_event', 'computer_command', 'paper_write']
senticnet['odd'] = ['0.036', '0.121', '0', '0.139', '#interest', '#admiration', 'positive', '0.099', 'high_level', 'number', 'sum_total', 'maximum', 'backside']
senticnet['oddball'] = ['-0.92', '0', '0.66', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'clown', 'buffoon', 'goof', 'zany', 'stupid']
senticnet['oddest_title'] = ['0.894', '0.881', '0', '0.803', '#joy', '#interest', 'positive', '0.859', 'remittance', 'afford', 'award', 'money_due', 'amount_owe']
senticnet['oddfellows_hall'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'city_hall', 'courthouse', 'subway_station', 'public_place', 'bus_station']
senticnet['oddity'] = ['0.808', '-0.98', '0', '0.628', '#joy', '#surprise', 'positive', '0.806', 'curio', 'crank', 'curiosity', 'peculiarity', 'oddment']
senticnet['oddly'] = ['0', '0', '0.861', '-0.81', '#anger', '#disgust', 'negative', '-0.83', 'lovesick', 'quaintly', 'odd', 'queerly', 'estrangement']
senticnet['oddly_enough'] = ['0.744', '-0.76', '0', '0.669', '#joy', '#surprise', 'positive', '0.726', 'accessible', 'comprehendible', 'approachable', 'perceivable', 'intelligible']
senticnet['oddment'] = ['0.808', '-0.98', '0', '0.628', '#joy', '#surprise', 'positive', '0.806', 'curio', 'oddity', 'curiosity', 'peculiarity', 'crank']
senticnet['oddr_snorrason'] = ['-0.7', '0', '0.746', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'insufficiency', 'sparseness', 'sparsity', 'abstemiousness', 'lack_of_quantity']
senticnet['odds'] = ['0', '0', '-0.94', '-0.92', '#fear', '#disgust', 'negative', '-0.93', 'difficulty', 'dilemma', 'bad_situation', 'misplace', 'sadness']
senticnet['odds-on'] = ['0', '-0.77', '0', '0.705', '#surprise', '#admiration', 'positive', '0.74', 'morning-line', 'hands-down', 'front-runner', 'shoe-in', 'shoo-in']
senticnet['ode'] = ['0.237', '0.094', '-0.07', '0.124', '#joy', '#admiration', 'positive', '0.094', 'verse', 'song_lyric', 'lyric_poetry', 'rhyme', 'song']
senticnet['oder_der'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['odhh'] = ['0', '0.202', '-0.06', '0.291', '#interest', '#admiration', 'positive', '0.142', 'vocabulary', 'translate', 'dictionary', 'translation', 'thesaurus']
senticnet['odious'] = ['-0.84', '0', '0.764', '0', '#sadness', '#anger', 'negative', '-0.80', 'obnoxious', 'offensive', 'unpleasant', 'obnoxiousness', 'jerk']
senticnet['odiously'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'detestable']
senticnet['odiousness'] = ['-0.97', '0', '0.536', '-0.58', '#sadness', '#disgust', 'negative', '-0.69', 'lousiness', 'vileness', 'sliminess', 'unpleasantness', 'blatancy']
senticnet['odist'] = ['0.836', '-0.60', '0', '0.845', '#joy', '#admiration', 'positive', '0.761', 'poetess', 'bard', 'elegist', 'poet', 'poet_laureate']
senticnet['odium'] = ['-0.06', '0.183', '0.651', '-0.64', '#anger', '#disgust', 'negative', '-0.29', 'abhorrence', 'abomination', 'detestation', 'execration', 'loathing']
senticnet['odontiasis'] = ['-0.71', '0', '0.84', '-0.89', '#anger', '#disgust', 'negative', '-0.81', 'precocious_dentition', 'growth', 'growing', 'maturation', 'development']
senticnet['odontophoridae_family'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'lovebird', 'parroket', 'paraquet', 'paroquet']
senticnet['odor'] = ['0', '0.069', '0', '0.103', '#interest', '#admiration', 'positive', '0.086', 'stick_out', 'nose', 'sniff', 'scent', 'smell']
senticnet['odor_threshold'] = ['-0.67', '0', '0.503', '-0.67', '#sadness', '#disgust', 'negative', '-0.61', 'stink_up', 'smell_up', 'stink_out', 'incense', 'perfume']
senticnet['odoratum'] = ['-0.79', '0', '0.837', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'sunray', 'wilding', 'sagebrush_buttercup', 'wildflower', 'pasque_flower']
senticnet['odoriferous'] = ['0', '0', '0.788', '-0.98', '#anger', '#disgust', 'negative', '-0.88', 'odorous', 'malodorous', 'inflammable', 'sweet-smelling', 'pungent']
senticnet['odorize'] = ['-0.67', '0', '0.503', '-0.67', '#sadness', '#disgust', 'negative', '-0.61', 'stink_up', 'smell_up', 'stink_out', 'incense', 'perfume']
senticnet['odorless'] = ['0', '0', '0.963', '-0.75', '#anger', '#disgust', 'negative', '-0.85', 'non-toxic', 'nontoxic', 'water-based', 'non-flammable', 'biodegradable']
senticnet['odorous'] = ['0', '0', '0.788', '-0.98', '#anger', '#disgust', 'negative', '-0.88', 'odoriferous', 'malodorous', 'inflammable', 'sweet-smelling', 'pungent']
senticnet['odynophagia'] = ['-0.58', '0', '0.773', '-0.79', '#anger', '#disgust', 'negative', '-0.71', 'perspiration', 'sweating', 'polyhidrosis', 'sudation', 'hidrosis']
senticnet['odyssey'] = ['0.937', '0.8', '0', '0', '#joy', '#interest', 'positive', '0.869', 'quest', 'stunt', 'illusion', 'adventure', 'activity']
senticnet['odyssey_marine_exploration'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'succourer', 'recoverer', 'salvor', 'mortal']
senticnet['oec_taipei_wta'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['oecophylla'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'hornet', 'vespid', 'vespid_wasp']
senticnet['oedema'] = ['-0.62', '0', '0.585', '-0.56', '#sadness', '#anger', 'negative', '-0.59', 'angioedema', 'atrophedema', 'giant_hive', 'periodic_edema', 'lymphedema']
senticnet['oedipal'] = ['-0.56', '0', '0.977', '-0.86', '#anger', '#disgust', 'negative', '-0.80', 'strangeness', 'quaintness', 'abnormality', 'queerness', 'crotchet']
senticnet['oedipus_complex'] = ['-0.78', '0', '0.542', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'hysterical_neurosis', 'hysteria', 'mental_disorder', 'mental_disturbance', 'psychological_disorder']
senticnet['oedopeza'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'aphasia', 'anomic_aphasia']
senticnet['oem'] = ['0', '0.243', '0', '0.218', '#interest', '#admiration', 'positive', '0.23', 'ware', 'goods', 'result', 'good_price', 'bargain']
senticnet['oenoanda'] = ['-0.69', '0', '0.957', '-0.92', '#anger', '#disgust', 'negative', '-0.85', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['oenococcus_oeni'] = ['0', '0.104', '-0.09', '0.146', '#interest', '#admiration', 'positive', '0.052', 'thaw', 'slush', 'water_inside', 'give_water', 'obtain_water']
senticnet['oenophile'] = ['0.331', '0.119', '0.022', '0.229', '#joy', '#admiration', 'positive', '0.164', 'spice_life', 'red_light_district', 'fresh_produce', 'enough_food', 'knowledgeable']
senticnet['oenotrian'] = ['-0.69', '0', '0.957', '-0.92', '#anger', '#disgust', 'negative', '-0.85', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
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senticnet['off-the-beaten-path'] = ['-0.77', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.80', 'out-of-the-way', 'tucked-away', 'off-the-beaten-track', 'hole-in-the-wall', 'nook']
senticnet['off-the-beaten-track'] = ['-0.77', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.80', 'off-the-beaten-path', 'tucked-away', 'out-of-the-way', 'hole-in-the-wall', 'nook']
senticnet['off-the-books'] = ['0.884', '0.785', '0', '0', '#joy', '#interest', 'positive', '0.835', 'under-the-table', 'back-room', 'bribe', 'kick-backs', 'underhanded']
senticnet['off-the-record'] = ['0', '0', '0.988', '-0.88', '#anger', '#disgust', 'negative', '-0.93', 'closed-door', 'deliberation', 'round-table', 'town-hall', 'town-hall-style']
senticnet['off-the-wall'] = ['0.876', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.936', 'freaky', 'outlandish', 'flaky', 'bizarre', 'gonzo']
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senticnet['off_oversteer'] = ['0', '0.142', '-0.09', '0.183', '#interest', '#admiration', 'positive', '0.078', 'arise', 'rise', 'raise_up', 'go_up', 'go_upstairs']
senticnet['off_samar'] = ['0.084', '-0.05', '0.013', '0', '#joy', '#surprise', 'positive', '0.043', 'carnival_ride', 'carny', 'fortune_teller', 'horseback', 'ferris_wheel']
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senticnet['offensiveness'] = ['-0.56', '0', '0.829', '-0.61', '#anger', '#disgust', 'negative', '-0.67', 'lousiness', 'vileness', 'sliminess', 'unpleasantness', 'blatancy']
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senticnet['offer_accommodation'] = ['0', '0.084', '0', '0.083', '#interest', '#admiration', 'positive', '0.084', 'home_sweet_home', 'guest', 'take_care', 'host', 'show_around']
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senticnet['offer_money'] = ['0', '0', '0', '0.819', '#admiration', '#admiration', 'positive', '0.819', 'enter_store', 'complete_job', 'save_money', 'pay_fee', 'count_money']
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senticnet['offer_practical_advice'] = ['-0.03', '0.168', '-0.07', '0.139', '#interest', '#admiration', 'positive', '0.049', 'command', 'direct', 'follow', 'take_charge', 'govern']
senticnet['offer_reassurance'] = ['0.376', '0.271', '-0.19', '0.258', '#joy', '#interest', 'positive', '0.177', 'pleasant_emotion', 'bliss', 'good_feeling', 'confirm', 'healthful']
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senticnet['offer_up'] = ['0', '0.921', '0', '0.972', '#interest', '#admiration', 'positive', '0.947', 'offering', 'extend', 'offer', 'provide', 'propose']
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senticnet['offering_incense'] = ['0', '0.82', '0', '0.847', '#interest', '#admiration', 'positive', '0.833', 'reverently', 'hero-worship', 'idolize', 'veneration', 'reverentially']
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senticnet['office_build'] = ['0.057', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'phone_booth', 'internet_cafe', 'office', 'discount_store', 'observatory']
senticnet['office_depot'] = ['0.912', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.917', 'carpet_store', 'lumberyard', 'furniture_store', 'front_porch', 'shoe_shop']
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senticnet['office_stuff'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'smart_technology', 'pencil_sharpener', 'electric_pencil_sharpener', 'school_stuff', 'pencil']
senticnet['office_worker'] = ['0', '0.978', '0', '0.843', '#interest', '#admiration', 'positive', '0.91', 'manager', 'colleague', 'public_servant', 'coworker', 'secretary']
senticnet['officeholder'] = ['0.865', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.887', 'manager', 'colleague', 'office_worker', 'coworker', 'secretary']
senticnet['officemax'] = ['0.912', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.917', 'carpet_store', 'lumberyard', 'furniture_store', 'front_porch', 'shoe_shop']
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senticnet['officer_kern'] = ['-0.91', '0', '0.931', '-0.72', '#sadness', '#anger', 'negative', '-0.85', 'hit_with_baton', 'get_hurt', 'whip', 'arrow', 'hurt']
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senticnet['official_exchange_rate'] = ['-0.02', '0.264', '0', '0.095', '#interest', '#admiration', 'positive', '0.111', 'finance', 'franc', 'cent', 'banknote', 'mastercard']
senticnet['official_imprimatur'] = ['0.751', '-0.98', '0', '0.989', '#surprise', '#admiration', 'positive', '0.909', 'okay', 'approval', 'commendation', 'okey', 'okeh']
senticnet['official_letterhead'] = ['0.038', '-0.03', '0', '0.079', '#surprise', '#admiration', 'positive', '0.052', 'sender', 'message', 'note', 'post', 'email']
senticnet['official_mascot'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'unofficial', 'informal', 'loose', 'drumhead']
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senticnet['official_result_wudarski'] = ['0.964', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.846', 'bulletin', 'replicate', 'current_event', 'computer_command', 'paper_write']
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senticnet['offshore_wind'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'breeze', 'windmill', 'wind', 'wind_turbine', 'windy']
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senticnet['offsite_facility'] = ['0.024', '0.17', '0', '0.113', '#interest', '#admiration', 'positive', '0.102', 'load_dock', 'storage_place', 'lock_box', 'government_office', 'treasure_chest']
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senticnet['ogdoad'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
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senticnet['oglala'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
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senticnet['ohel'] = ['0', '0.209', '0.026', '0.184', '#interest', '#admiration', 'positive', '0.122', 'church', 'confession', 'temple', 'cathedral', 'monastery']
senticnet['ohm'] = ['0', '0', '0.122', '0', '#anger', '#anger', 'negative', '-0.12', 'mile', 'inch', 'meter', 'ounce', 'length']
senticnet['ohne'] = ['-0.11', '0', '-0.09', '0.082', '#sadness', '#fear', 'negative', '-0.04', 'stare', 'ear', 'facial_expression', 'mouth', 'dimple']
senticnet['oig'] = ['0.801', '0.86', '0', '0', '#joy', '#interest', 'positive', '0.831', 'credit_card', 'profit', 'mastercard', 'american_express', 'back_to_black']
senticnet['oig_audit'] = ['0.801', '0.86', '0', '0', '#joy', '#interest', 'positive', '0.831', 'credit_card', 'profit', 'mastercard', 'american_express', 'back_to_black']
senticnet['oil'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'lubricant', 'fuel', 'gas', 'inert_gas', 'diesel']
senticnet['oil_glut'] = ['0', '0.285', '-0.07', '0.163', '#interest', '#admiration', 'positive', '0.124', 'plus', 'importance', 'a_lot', 'bonus', 'extra']
senticnet['oil_hinge'] = ['-0.88', '0', '0', '-0.96', '#sadness', '#disgust', 'negative', '-0.92', 'marry_man', 'service_car', 'date_woman', 'man_post', 'date_man']
senticnet['oil_lamp'] = ['0', '0.232', '-0.10', '0.156', '#interest', '#admiration', 'positive', '0.094', 'light', 'eat_cake', 'birthday_party', 'candle_light', 'romantic_atmosphere']
senticnet['oil_magnate'] = ['0', '0', '-0.99', '-0.73', '#fear', '#disgust', 'negative', '-0.86', 'king', 'baron', 'business_leader', 'mogul', 'big_businessman']
senticnet['oil_paint'] = ['0', '0.084', '0', '0.095', '#interest', '#admiration', 'positive', '0.089', 'oil_painting', 'wall_art', 'artist_tool', 'artist', 'painting']
senticnet['oil_painting'] = ['0.041', '0.112', '0', '0.077', '#interest', '#admiration', 'positive', '0.077', 'oil_paint', 'artist_tool', 'painter', 'artist', 'painting']
senticnet['oil_palm'] = ['0.635', '-0.84', '0', '0.85', '#surprise', '#admiration', 'positive', '0.777', 'feather_palm', 'babassu', 'babassu_palm', 'palm', 'wax_palm']
senticnet['oil_pastel'] = ['0', '0.068', '0', '0', '#interest', '#interest', 'positive', '0.068', 'artist', 'oil_paint', 'painter', 'creative_person', 'make_paint']
senticnet['oil_sand_bitumen'] = ['0', '0', '-0.06', '0', '#fear', '#fear', 'negative', '-0.06', 'greasy', 'car_gas', 'fossil_fuel', 'liquid_fuel', 'lubrication']
senticnet['oil_sand_crude'] = ['0', '0', '-0.06', '0', '#fear', '#fear', 'negative', '-0.06', 'greasy', 'car_gas', 'fossil_fuel', 'liquid_fuel', 'lubrication']
senticnet['oil_spilled_into'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'harpoon', 'armory', 'lake', 'river', 'bay']
senticnet['oil_stain'] = ['-0.51', '0', '0.937', '-0.92', '#anger', '#disgust', 'negative', '-0.79', 'bloodstain', 'mud_stain', 'iron_mold', 'iron_mould', 'discoloration']
senticnet['oil_tanker'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'submarine', 'titanic', 'vessel', 'lifeboat', 'battleship']
senticnet['oil_tycoon'] = ['0', '0', '-0.99', '-0.73', '#fear', '#disgust', 'negative', '-0.86', 'king', 'baron', 'business_leader', 'mogul', 'big_businessman']
senticnet['oilcloth'] = ['-0.89', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.91', 'needle', 'sew_needle', 'haircloth', 'needle_thread', 'sew_machine']
senticnet['oilman'] = ['-0.78', '0', '0.944', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'loser', 'gambler', 'mortal', 'better', 'bettor']
senticnet['oilseed_crop'] = ['0.061', '0.093', '0.037', '0.114', '#interest', '#admiration', 'positive', '0.058', 'whole_grain', 'food_ingredient', 'staple_food', 'starchy_food', 'food_grain']
senticnet['oilskin'] = ['0', '-0.08', '0.105', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'underwear', 'shirt', 'panties', 'robe', 'slack']
senticnet['oily'] = ['0', '0', '-0.91', '-0.86', '#fear', '#disgust', 'negative', '-0.89', 'sebaceous', 'greasy', 'fulsome', 'oleaginous', 'buttery']
senticnet['oily_fish'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'grill', 'cook_grill', 'barbecue', 'cook', 'grilled_vegetable']
senticnet['oily_liquid'] = ['-0.67', '0', '0.503', '-0.67', '#sadness', '#disgust', 'negative', '-0.61', 'stink_up', 'smell_up', 'stink_out', 'incense', 'perfume']
senticnet['oily_road'] = ['-0.98', '0', '0.674', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'wet_floor', 'black_liquid', 'slippery', 'slick', 'dangerous']
senticnet['oily_skin'] = ['0.048', '-0.00', '0.011', '0.064', '#joy', '#admiration', 'positive', '0.027', 'become_wet', 'wash_away_dirt', 'run_out_soap', 'cleanliness', 'clean_skin']
senticnet['oily_substance'] = ['0.025', '-0.00', '0', '0.125', '#joy', '#admiration', 'positive', '0.053', 'goo', 'adhesive', 'flour_water', 'insert', 'thick_liquid']
senticnet['oily_water'] = ['-0.58', '0', '0.714', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'diversionist', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['oinochoe'] = ['0', '0.082', '-0.09', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'coffin', 'headstone', 'morgue', 'bury_body', 'casket']
senticnet['ointment'] = ['-0.81', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.82', 'peanut', 'leave_over', 'floppy', 'circular_motion', 'store_vegetable']
senticnet['oireachtas_parliament'] = ['-0.57', '0', '0.576', '-0.78', '#anger', '#disgust', 'negative', '-0.64', 'reprieve', 'respite', 'suspend', 'call', 'delay']
senticnet['oiseau'] = ['-0.81', '0', '0.885', '-0.92', '#anger', '#disgust', 'negative', '-0.87', 'miss', 'girl', 'damsel', 'maiden', 'damoiselle']
senticnet['oiseaux'] = ['-0.81', '0', '0.885', '-0.92', '#anger', '#disgust', 'negative', '-0.87', 'miss', 'girl', 'damsel', 'maiden', 'damoiselle']
senticnet['oji_cree_first'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['ojibwe'] = ['0.052', '0.099', '0', '0.067', '#interest', '#admiration', 'positive', '0.073', 'police_rank', 'head_guy', 'chief', 'leader', 'commander']
senticnet['ok'] = ['0.057', '0.141', '-0.06', '0.162', '#interest', '#admiration', 'positive', '0.074', 'belongings', 'material', 'fabric', 'guarantee', 'madonna']
senticnet['okah_release'] = ['-0.07', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.07', 'use_needle', 'doctor_tool', 'fix', 'embroidery', 'seam']
senticnet['okauchee'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['okay'] = ['0.075', '0.229', '-0.17', '0.409', '#interest', '#admiration', 'positive', '0.134', 'reciprocal', 'all_right', 'good_etiquette', 'politeness', 'good_behavior']
senticnet['okeh'] = ['0.751', '-0.98', '0', '0.989', '#surprise', '#admiration', 'positive', '0.909', 'okay', 'approval', 'commendation', 'okey', 'imprimatur']
senticnet['okey'] = ['0.751', '-0.98', '0', '0.989', '#surprise', '#admiration', 'positive', '0.909', 'okay', 'approval', 'commendation', 'imprimatur', 'okeh']
senticnet['okkadu'] = ['0', '0', '0.737', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'annoy', 'skin_rash', 'bother', 'irritation', 'great_dane']
senticnet['okkervil_river'] = ['0.304', '0.339', '-0.19', '0.333', '#interest', '#admiration', 'positive', '0.195', 'careful', 'reinforce', 'load_bear', 'go_outdoors', 'allowance']
senticnet['okonomiyaki'] = ['0.9', '0.963', '0', '0', '#joy', '#interest', 'positive', '0.931', 'form_of_food', 'japanese_food', 'uncooked', 'healthy', 'food']
senticnet['okur'] = ['0.556', '-0.79', '0', '0.755', '#surprise', '#admiration', 'positive', '0.701', 'lush', 'alcoholic', 'alky', 'dipsomaniac', 'rummy']
senticnet['ol'] = ['0.104', '0', '0', '0.088', '#joy', '#admiration', 'positive', '0.096', 'baby_flower', 'wee', 'bud', 'part_of_plant', 'leafy']
senticnet['ol_quote'] = ['0', '0.189', '0.04', '0.091', '#interest', '#admiration', 'positive', '0.08', 'combination', 'margin', 'teller', 'merge', 'finite']
senticnet['olay_regenerist'] = ['0.028', '0.138', '-0.04', '0.215', '#interest', '#admiration', 'positive', '0.083', 'water', 'dense', 'thick', 'milk', 'dense_water']
senticnet['olberman'] = ['-0.64', '0', '0.781', '-0.74', '#anger', '#disgust', 'negative', '-0.72', 'weirdo', 'crazy', 'loony', 'lunatic', 'nutcase']
senticnet['old'] = ['-0.74', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.77', 'elderly', 'grumpy', 'age', 'ancient', 'get_old']
senticnet['old-fashioned'] = ['0', '0', '-0.84', '-0.81', '#fear', '#disgust', 'negative', '-0.83', 'stodginess', 'stodgy', 'dull', 'stodgily', 'glutinous']
senticnet['old-hat'] = ['0', '0', '-0.96', '-0.71', '#fear', '#disgust', 'negative', '-0.84', 'banal', 'shopworn', 'commonplace', 'threadbare', 'hackneyed']
senticnet['old_acquaintance'] = ['0.814', '-0.50', '0', '0.622', '#joy', '#admiration', 'positive', '0.648', 'friend', 'champion', 'supporter', 'protagonist', 'endorser']
senticnet['old_adage'] = ['0.022', '0.123', '0.038', '0.129', '#interest', '#admiration', 'positive', '0.059', 'hymn', 'written_music', 'walk_beach', 'lyric', 'piece_of_music']
senticnet['old_age'] = ['-0.09', '0', '-0.19', '-0.15', '#fear', '#disgust', 'negative', '-0.14', 'drug_addiction', 'get_lung_cancer', 'suicide', 'die', 'execute']
senticnet['old_bag'] = ['-0.59', '0', '0.573', '-0.56', '#sadness', '#anger', 'negative', '-0.57', 'termagant', 'disagreeable_woman', 'shrew', 'frump', 'hellcat']
senticnet['old_book'] = ['0', '0.098', '0', '0', '#interest', '#interest', 'positive', '0.098', 'gym_equipment', 'exercise_equipment', 'plumber', 'car_stereo', 'sunscreen']
senticnet['old_building'] = ['-0.07', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.05', 'skyscraper', 'building', 'tall_building', 'nuclear_power_plant', 'government_office']
senticnet['old_burying_ground'] = ['-0.50', '0', '0.762', '-0.89', '#anger', '#disgust', 'negative', '-0.71', 'cemetery', 'graveyard', 'burial_site', 'grassland', 'burying_ground']
senticnet['old_car'] = ['0', '0', '0', '0.103', '#admiration', '#admiration', 'positive', '0.103', 'fancy_car', 'car_model', 'expensive_car', 'antique_car', 'car']
senticnet['old_chap'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'impaired', 'dickey', 'lessened', 'diminished']
senticnet['old_child'] = ['0', '0.079', '0', '0', '#interest', '#interest', 'positive', '0.079', 'descendant', 'female_child', 'human_offspring', 'male_child', 'offspring']
senticnet['old_chum'] = ['-0.90', '0.138', '-0.63', '0.071', '#sadness', '#fear', 'negative', '-0.33', 'woeful', 'execrable', 'deplorable', 'woebegone', 'plaintiveness']
senticnet['old_city'] = ['0.6', '-0.75', '0', '0.715', '#surprise', '#admiration', 'positive', '0.689', 'city', 'malaysian_city', 'asia', 'malacca', 'colony']
senticnet['old_clothes'] = ['0', '-0.01', '0.026', '-0.06', '#anger', '#disgust', 'negative', '-0.02', 'junk', 'old_stuff', 'egg_shell', 'food_scrap', 'old_paper']
senticnet['old_codger'] = ['-0.71', '0', '0.845', '-0.69', '#sadness', '#anger', 'negative', '-0.75', 'lump', 'goon', 'mortal', 'clumsy_person', 'butterfingers']
senticnet['old_coin'] = ['0', '0.066', '0.041', '0.054', '#interest', '#admiration', 'positive', '0.026', 'metal_coin', 'coin', 'money', 'monetary_unit', 'down_payment']
senticnet['old_computer'] = ['-0.03', '-0.09', '0.164', '-0.19', '#anger', '#disgust', 'negative', '-0.07', 'old_technology', 'pair_of_scissors', 'power_drill', 'push_pin', 'thumb_tack']
senticnet['old_crone'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'jawless_fish', 'osteostracan', 'cephalaspid', 'ostracoderm', 'heterostracan']
senticnet['old_dance'] = ['0.203', '-0.10', '0.013', '0', '#joy', '#surprise', 'positive', '0.097', 'dance', 'slow_dance', 'enter_contest', 'stout', 'dancer']
senticnet['old_day'] = ['-0.03', '0.096', '-0.06', '0.077', '#interest', '#admiration', 'positive', '0.019', 'history', 'past_event', 'long_ago', 'school_subject', 'subject']
senticnet['old_faithful_geyser'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['old_fart'] = ['-0.95', '0', '0.673', '-0.95', '#sadness', '#disgust', 'negative', '-0.86', 'worrier', 'fusspot', 'worrywart', 'unwelcome_person', 'killjoy']
senticnet['old_fashion'] = ['0.025', '-0.02', '0', '0.054', '#surprise', '#admiration', 'positive', '0.036', 'rock_music', 'old', 'clock_face', 'element', 'bright']
senticnet['old_fashioned'] = ['-0.12', '-0.06', '-0.09', '-0.04', '#sadness', '#fear', 'negative', '-0.04', 'passe', 'unglamorous', 'special_person', 'bloke', 'male']
senticnet['old_fashioned_way'] = ['0.76', '0.919', '0', '0', '#joy', '#interest', 'positive', '0.84', 'chore', 'make_happen', 'pick_up_trash', 'intuition', 'stop_procrastinate']
senticnet['old_folk'] = ['0', '0', '-0.05', '-0.08', '#fear', '#disgust', 'negative', '-0.06', 'grandma_house', 'chess_tournament', 'laboratory', 'computer_lab', 'old_folk_home']
senticnet['old_folk_home'] = ['0', '0', '-0.05', '-0.08', '#fear', '#disgust', 'negative', '-0.06', 'grandma_house', 'chess_tournament', 'laboratory', 'computer_lab', 'old_folk']
senticnet['old_food'] = ['-0.90', '0', '0.282', '-0.97', '#sadness', '#disgust', 'negative', '-0.72', 'trash', 'waste', 'garbage', 'rubbish', 'refuse']
senticnet['old_friend'] = ['0.86', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.798', 'director_chair', 'alcohol', 'bride_groom', 'attendee', 'coworker']
senticnet['old_game'] = ['0', '-0.07', '0', '0.081', '#surprise', '#admiration', 'positive', '0.079', 'consider_good_luck', 'game', 'form_of_gambling', 'popular_game', 'game_skill']
senticnet['old_geezer'] = ['-0.71', '0', '0.845', '-0.69', '#sadness', '#anger', 'negative', '-0.75', 'lump', 'goon', 'mortal', 'clumsy_person', 'butterfingers']
senticnet['old_gentleman'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'impaired', 'dickey', 'lessened', 'diminished']
senticnet['old_gold'] = ['-0.51', '0', '0.626', '-0.54', '#anger', '#disgust', 'negative', '-0.56', 'brownish_yellow', 'canary_yellow', 'yellowness', 'gold', 'canary']
senticnet['old_grandchild'] = ['0.837', '-0.74', '0', '0', '#joy', '#surprise', 'positive', '0.789', 'informal', 'family', 'lullaby', 'play_together', 'family_tree']
senticnet['old_hag'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'jawless_fish', 'osteostracan', 'cephalaspid', 'ostracoderm', 'heterostracan']
senticnet['old_harridan'] = ['-0.90', '0.138', '-0.63', '0.071', '#sadness', '#fear', 'negative', '-0.33', 'woeful', 'execrable', 'deplorable', 'woebegone', 'plaintiveness']
senticnet['old_house'] = ['0.101', '0.099', '0', '0.169', '#joy', '#admiration', 'positive', '0.123', 'family_home', 'small_home', 'own_house', 'appliance_store', 'residence']
senticnet['old_jewish_cemetery'] = ['0', '0.209', '0.026', '0.184', '#interest', '#admiration', 'positive', '0.122', 'church', 'confession', 'temple', 'cathedral', 'monastery']
senticnet['old_lady'] = ['0', '0', '-0.06', '-0.11', '#fear', '#disgust', 'negative', '-0.09', 'elderly_person', 'hide-a-bed', 'homosexual', 'linen_chest', 'dust_mite']
senticnet['old_lady_house'] = ['0.146', '-0.21', '-0.04', '0.139', '#joy', '#surprise', 'positive', '0.114', 'comic_book', 'cartoon', 'book', 'comic', 'comic_strip']
senticnet['old_maid'] = ['-0.62', '0', '0.649', '-0.67', '#anger', '#disgust', 'negative', '-0.64', 'handmaid', 'handmaiden', 'chambermaid', 'maid', 'parlormaid']
senticnet['old_main_university_park'] = ['0', '0.943', '0', '0.751', '#interest', '#admiration', 'positive', '0.847', 'university_park', 'sponsor', 'award', 'different_application', 'standard_grant']
senticnet['old_man'] = ['0.126', '0.187', '-0.10', '0.155', '#interest', '#admiration', 'positive', '0.09', 'female', 'female_parent', 'care_giver', 'wisdom', 'happy_together']
senticnet['old_manor_house'] = ['0.049', '0.16', '0', '0.155', '#interest', '#admiration', 'positive', '0.121', 'castle', 'palace', 'medieval', 'aristocrat', 'throne']
senticnet['old_movie'] = ['-0.04', '-0.00', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.02', 'music_school', 'civic_center', 'opus_house', 'choir', 'concert']
senticnet['old_mucker'] = ['-0.79', '0', '0.992', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['old_newspaper'] = ['-0.75', '0', '0', '-0.95', '#sadness', '#disgust', 'negative', '-0.85', 'old_paper', 'food_scrap', 'mind_child', 'moldy_food', 'egg_shell']
senticnet['old_norse_origin'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicado', 'incommunicative', 'impassive', 'inexpressive', 'silent']
senticnet['old_pal'] = ['0.86', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.798', 'director_chair', 'alcohol', 'bride_groom', 'attendee', 'coworker']
senticnet['old_paper'] = ['0', '-0.01', '0.026', '-0.06', '#anger', '#disgust', 'negative', '-0.02', 'junk', 'old_stuff', 'egg_shell', 'food_scrap', 'old_clothes']
senticnet['old_people'] = ['-0.05', '-0.02', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.05', 'old_person', 'nurse', 'elderly_person', 'dislike_person', 'sick_person']
senticnet['old_person'] = ['-0.05', '-0.02', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.05', 'old_people', 'nurse', 'elderly_person', 'dislike_person', 'sick_person']
senticnet['old_photograph'] = ['-0.04', '0', '-0.08', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'wheelchair', 'dirty_laundry', 'wife', 'hot_chick', 'mommy']
senticnet['old_reprobate'] = ['-0.90', '0.138', '-0.63', '0.071', '#sadness', '#fear', 'negative', '-0.33', 'woeful', 'execrable', 'deplorable', 'woebegone', 'plaintiveness']
senticnet['old_salt'] = ['0.587', '-0.67', '0', '0.729', '#surprise', '#admiration', 'positive', '0.662', 'mariner', 'seaman', 'tar', 'sea_lawyer', 'seafarer']
senticnet['old_schoolhouse'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'city_hall', 'courthouse', 'subway_station', 'public_place', 'bus_station']
senticnet['old_sock'] = ['0', '-0.01', '0.075', '-0.07', '#anger', '#disgust', 'negative', '-0.04', 'dirty_magazine', 'cookie_crumb', 'smelly', 'dirty_sock', 'dust_mite']
senticnet['old_software'] = ['0', '0', '0.894', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'get_old', 'become_obsolete', 'unglam', 'old_technology', 'overrated']
senticnet['old_stager'] = ['-0.79', '0', '0.992', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['old_stuff'] = ['-0.91', '0', '0.618', '-0.93', '#sadness', '#disgust', 'negative', '-0.82', 'get_stolen', 'second_hand', 'give_away', 'lose_wallet', 'away']
senticnet['old_technology'] = ['0', '0', '0.959', '-0.76', '#anger', '#disgust', 'negative', '-0.86', 'software_crash', 'old_computer', 'seventy', 'music_storage', 'disk']
senticnet['old_testament'] = ['0', '0.813', '0', '0.743', '#interest', '#admiration', 'positive', '0.778', 'cook_book', 'a_lot_of_books', 'bookmark', 'religious_tract', 'index_card_drawer']
senticnet['old_testament_prophet'] = ['0.849', '-0.89', '0', '0.924', '#surprise', '#admiration', 'positive', '0.89', 'nonintellectual', 'lowbrow', 'lowbrowed', 'mindless', 'uncultivated']
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senticnet['olin_library'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'a_lot_of_books', 'old_testament', 'cook_book', 'magazine', 'librarian']
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senticnet['olive'] = ['-0.91', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.89', 'pepper', 'garlic', 'olive', 'lemon', 'butter']
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senticnet['olmesartan'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'spironolactone', 'spironolactone_drug']
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senticnet['oltedal'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'concupiscence', 'lustfulness', 'amorousness']
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senticnet['olympiads'] = ['0', '0.069', '0', '0.111', '#interest', '#admiration', 'positive', '0.09', 'hold_every_four_years', 'event', 'important_event', 'important_game', 'important_occasion']
senticnet['olympian'] = ['0.851', '0.77', '0', '0', '#joy', '#interest', 'positive', '0.81', 'godlikeness', 'divine', 'god', 'godlike', 'immortality']
senticnet['olympic_athlete'] = ['0', '0', '0', '0.97', '#admiration', '#admiration', 'positive', '0.97', 'increase_strength', 'keep_fit', 'fit_person', 'exercise', 'strengthen_heart']
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senticnet['olympic_champion'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'nazi', 'man', 'pervert', 'healthy_person']
senticnet['olympic_event'] = ['0.884', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.817', 'athletics', 'train_hard', 'shape', 'form_of_exercise', 'good_shape']
senticnet['olympic_finalist'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'nazi', 'man', 'pervert', 'healthy_person']
senticnet['olympic_flag'] = ['-0.91', '0', '0.931', '-0.72', '#sadness', '#anger', 'negative', '-0.85', 'hit_with_baton', 'get_hurt', 'whip', 'arrow', 'hurt']
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senticnet['omegle'] = ['-0.51', '0', '0.545', '-0.91', '#anger', '#disgust', 'negative', '-0.65', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
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senticnet['ominous_shadow'] = ['-0.82', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.80', 'evil', 'darkness', 'dark', 'sun_down', 'dark_side']
senticnet['omission'] = ['-0.97', '0', '0', '-0.86', '#sadness', '#disgust', 'negative', '-0.91', 'cancel', 'elide', 'omit', 'neglect', 'overlook']
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senticnet['omnibus_legislation'] = ['0.779', '-0.88', '0', '0.521', '#joy', '#surprise', 'positive', '0.727', 'corrigible', 'correctable', 'improvable', 'reformable', 'redeemable']
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senticnet['omnigraffle'] = ['0', '0.211', '-0.12', '0.286', '#interest', '#admiration', 'positive', '0.125', 'bring_together', 'fuse', 'redo', 'fuse_together', 'request']
senticnet['omnino'] = ['0.654', '-0.75', '0', '0.78', '#surprise', '#admiration', 'positive', '0.729', 'suit', 'cause', 'lawsuit', 'occurrence', 'countersuit']
senticnet['omnipotence'] = ['0.295', '0.239', '0.049', '0.346', '#joy', '#admiration', 'positive', '0.208', 'full', 'replicate', 'infinite', 'total', 'unending']
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senticnet['omnipresent'] = ['0', '0.093', '0', '0.053', '#interest', '#admiration', 'positive', '0.073', 'underground', 'under_ground', 'environment', 'outpost', 'dark_place']
senticnet['omniscience'] = ['0.071', '0.222', '-0.10', '0.234', '#interest', '#admiration', 'positive', '0.105', 'baron', 'almighty', 'deity', 'nobility', 'god']
senticnet['omniscient'] = ['0.071', '0.222', '-0.10', '0.234', '#interest', '#admiration', 'positive', '0.105', 'baron', 'almighty', 'deity', 'nobility', 'god']
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senticnet['omniverse'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
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senticnet['omophorion'] = ['0.849', '-0.50', '0', '0.845', '#joy', '#admiration', 'positive', '0.732', 'robe', 'gown', 'veil', 'vest', 'cassock']
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senticnet['on-highway'] = ['0', '0', '-0.86', '-0.84', '#fear', '#disgust', 'negative', '-0.85', 'medium-duty', 'heavy-duty', 'diesel-powered', 'light-duty', 'off-highway']
senticnet['on_alert'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'alert', 'watchful', 'guard', 'vigilant', 'wary']
senticnet['on_cloud_nine'] = ['0.673', '0.631', '-0.23', '0.309', '#joy', '#interest', 'positive', '0.345', 'exultingly', 'exultation', 'jubilance', 'have_fun', 'walk_on_air']
senticnet['on_fence'] = ['0.778', '-0.50', '0', '0.517', '#joy', '#admiration', 'positive', '0.599', 'indecisive', 'suspensive', 'undecided', 'hesitant', 'hesitating']
senticnet['on_horseback'] = ['0.064', '0', '0', '0.123', '#joy', '#admiration', 'positive', '0.093', 'horse', 'passenger_car', 'car_action', 'conveyance', 'car_travel']
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senticnet['onboard_camera'] = ['0', '-0.02', '0.048', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'telescope', 'read_glass', 'eyeglass', 'binoculars', 'monocle']
senticnet['oncoming_train'] = ['-0.11', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.08', 'have_fun', 'spring_up', 'fall_well', 'shovel_snow', 'fly_around']
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senticnet['ondansetron'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
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senticnet['one-camera'] = ['-0.81', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'commitment-free', 'six-question', 'free-and-easy', 'three-camera', 'self-forgetful']
senticnet['one-dimensional'] = ['0', '0', '0.915', '-0.93', '#anger', '#disgust', 'negative', '-0.92', 'analog', 'linear', 'elongate', 'additive', 'analogue']
senticnet['one-eyed'] = ['-0.92', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.90', 'toothy', 'impotent', 'ineffectual', 'toothless', 'brainless']
senticnet['one-liner'] = ['0', '-0.75', '0', '0.844', '#surprise', '#admiration', 'positive', '0.797', 'quip', 'quip', 'remark', 'retort', 'sarcastic']
senticnet['one-on-one'] = ['0', '0', '-0.79', '-0.80', '#fear', '#disgust', 'negative', '-0.79', 'in-person', 'face-to-face', 'one-to-one', 'conversation', 'meeting']
senticnet['one-run'] = ['0', '0', '0.782', '-0.75', '#anger', '#disgust', 'negative', '-0.77', 'shutout', 'inning', 'hitless', 'strikeout', 'scoreless']
senticnet['one-to-one'] = ['0', '0', '-0.78', '-0.80', '#fear', '#disgust', 'negative', '-0.79', 'in-person', 'one-on-one', 'face-to-face', 'conversation', 'meeting']
senticnet['one_bedroom_apartment'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'home', 'furniture', 'kitchen', 'flat']
senticnet['one_bedroom_condo'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'home', 'furniture', 'kitchen', 'flat']
senticnet['one_bedroom_suite'] = ['0.716', '0.96', '0', '0', '#joy', '#interest', 'positive', '0.838', 'airline_seat', 'small_seat', 'beanbag_chair', 'hide-a-bed_sofa', 'chair']
senticnet['one_handed_backhand'] = ['0', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.91', 'umpire', 'referee', 'legal_proceed', 'clang', 'clamor']
senticnet['one_handed_dunk'] = ['-0.81', '0', '0.747', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'vagrant', 'drifter', 'hobo', 'vagabond', 'dosser']
senticnet['one_iota'] = ['0', '0.074', '0.026', '0.105', '#interest', '#admiration', 'positive', '0.051', 'handwritten', 'scribble', 'music_part', 'written_message', 'calligraphy']
senticnet['one_night_stand'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'find_motel', 'find_bedroom', 'find_car', 'cover_pillow', 'bed']
senticnet['one_way'] = ['0', '0.071', '0', '0.143', '#interest', '#admiration', 'positive', '0.107', 'post_notice', 'gps', 'way', 'indicator', 'arrow']
senticnet['onecard'] = ['-0.05', '0.14', '0.015', '0.082', '#interest', '#admiration', 'positive', '0.039', 'cash', 'cash_pay', 'need_earn_money', 'open_wallet', 'earn_wage']
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senticnet['onenote'] = ['-0.03', '0.112', '-0.08', '0.164', '#interest', '#admiration', 'positive', '0.04', 'run', 'jump_over', 'quick', 'sprint', 'flee']
senticnet['onerous'] = ['0', '0', '-0.98', '-0.93', '#fear', '#disgust', 'negative', '-0.96', 'heavy', 'ponderous', 'load', 'onerousness', 'onus']
senticnet['onerous_task'] = ['-0.03', '0.136', '-0.09', '0.105', '#interest', '#admiration', 'positive', '0.028', 'chore', 'hire_help', 'police_work', 'career', 'job']
senticnet['onerousness'] = ['0', '0', '-0.99', '-0.94', '#fear', '#disgust', 'negative', '-0.96', 'heavy', 'ponderous', 'load', 'onerous', 'onus']
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senticnet['ongoing_debate'] = ['0', '0', '0.94', '0', '#anger', '#anger', 'negative', '-0.94', 'feel_angry', 'hurt_feeling', 'feel_hurt', 'ridicule', 'vengeance']
senticnet['ongoing_investigation'] = ['0', '0.825', '0', '0', '#interest', '#interest', 'positive', '0.825', 'query', 'interrogation', 'inquire', 'response', 'solution']
senticnet['ongoing_saga'] = ['0.036', '0.219', '0', '0.19', '#interest', '#admiration', 'positive', '0.148', 'story', 'long_story', 'novel', 'fable', 'fiction']
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senticnet['onion_powder'] = ['0.979', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.918', 'make_curry', 'cook_dinner', 'cook_meal', 'cook_curry', 'powder']
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senticnet['onion_soup'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'dainty', 'delicacy', 'treat', 'gelatin_dessert', 'jelly']
senticnet['online'] = ['0', '0.087', '0', '0', '#interest', '#interest', 'positive', '0.087', 'find_address', 'find_computer', 'internet_connection', 'hard_drive', 'buy_computer']
senticnet['online_booking'] = ['0', '0.084', '0', '0', '#interest', '#interest', 'positive', '0.084', 'reservation', 'book_hotel', 'staycation', 'rate', 'currency']
senticnet['online_chat'] = ['-0.51', '0', '0.545', '-0.91', '#anger', '#disgust', 'negative', '-0.65', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['online_comparison_shopping'] = ['0.94', '0.804', '0', '0', '#joy', '#interest', 'positive', '0.872', 'spend_a_lot_of_money', 'window_shop', 'spend_much_money', 'check_price', 'compare_price']
senticnet['online_encyclopaedia'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'comic_book', 'child_book', 'child_story', 'comic', 'disney_cartoon']
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senticnet['online_shopper'] = ['0.875', '0.706', '0', '0', '#joy', '#interest', 'positive', '0.79', 'customer', 'clerk', 'patron', 'vintner', 'hairdresser']
senticnet['online_shopping'] = ['0.875', '0.706', '0', '0', '#joy', '#interest', 'positive', '0.79', 'customer', 'clerk', 'patron', 'vintner', 'hairdresser']
senticnet['only'] = ['0', '0', '-0.74', '-0.78', '#fear', '#disgust', 'negative', '-0.76', 'sole', 'solitary', 'single', 'lonesome', 'alone']
senticnet['only_bird'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'batman']
senticnet['only_mammal'] = ['0.039', '0.086', '0', '0', '#joy', '#interest', 'positive', '0.063', 'leopard', 'warm_blooded', 'lynx', 'mane', 'hare']
senticnet['only_marginally'] = ['-0.54', '0', '0.739', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'unremunerative', 'idle', 'lean', 'dead', 'unprofitable']
senticnet['only_remaining_superpower'] = ['0.838', '0.707', '0', '0.904', '#joy', '#admiration', 'positive', '0.816', 'natural_order', 'universe', 'existence', 'creation', 'cosmos']
senticnet['only_slightly_soluble'] = ['-0.81', '0', '0.774', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'acid', 'hypophosphoric_acid', 'periodic_acid', 'oxyacid', 'phosphorous_acid']
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senticnet['onomatopoetic'] = ['0', '0.156', '-0.06', '0.241', '#interest', '#admiration', 'positive', '0.11', 'mixture', 'noisy_place', 'make_go', 'move_vehicle', 'big_car']
senticnet['onrush'] = ['-0.79', '0', '0.59', '-0.56', '#sadness', '#anger', 'negative', '-0.65', 'debris_surge', 'debris_storm', 'flowing', 'surge', 'flow']
senticnet['onscreen'] = ['-0.69', '0', '0.508', '-0.93', '#sadness', '#disgust', 'negative', '-0.71', 'dark', 'darkness', 'light', 'shadow', 'site']
senticnet['onscreen_keyboard'] = ['0.893', '0.979', '0', '0', '#joy', '#interest', 'positive', '0.936', 'find_phone_number', 'dial_phone_number', 'pick_up_telephone_receiver', 'dial', 'dial_telephone']
senticnet['onset'] = ['0.085', '0', '0', '0.089', '#joy', '#admiration', 'positive', '0.087', 'steam', 'haze', 'mist', 'misty', 'summer_weather']
senticnet['onshore_flow'] = ['-0.69', '0', '0.835', '-0.56', '#sadness', '#anger', 'negative', '-0.69', 'roughness', 'inclemency', 'bad_weather', 'inclementness', 'windiness']
senticnet['onshore_wind'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'breeze', 'windmill', 'wind', 'wind_turbine', 'windy']
senticnet['onsite_fitness_center'] = ['0.722', '-0.98', '0', '0.832', '#surprise', '#admiration', 'positive', '0.845', 'laudatory', 'praiseful', 'praising', 'encomiastic', 'eulogistic']
senticnet['onslaught'] = ['0', '0', '-0.86', '-0.99', '#fear', '#disgust', 'negative', '-0.93', 'barrage', 'battery', 'shelling', 'bombardment', 'outpouring']
senticnet['onstage'] = ['0.26', '0', '0', '0.089', '#joy', '#admiration', 'positive', '0.174', 'balcony_rail', 'movie_theater_seat', 'row_seat', 'usher', 'sticky_floor']
senticnet['ont_ouvert_le'] = ['0.926', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.916', 'score_goal', 'accomplish', 'finalize', 'pot', 'make_point']
senticnet['ontarion'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['onto'] = ['-0.75', '0', '0.785', '-0.7', '#sadness', '#anger', 'negative', '-0.74', 'disappointing', 'dissatisfactory', 'unsatisfying', 'unsatisfactory', 'unacceptable']
senticnet['onto_youtube'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'paperclip', 'clasp', 'glue_stick', 'clothespin', 'clamp']
senticnet['ontogeny'] = ['-0.59', '0', '0.983', '-0.67', '#anger', '#disgust', 'negative', '-0.75', 'growth', 'growing', 'maturation', 'development', 'caenogenesis']
senticnet['ontological'] = ['0.863', '-0.94', '0', '0.986', '#surprise', '#admiration', 'positive', '0.932', 'postulate', 'posit', 'assume', 'presume', 'take_for_granted']
senticnet['ontological_argument'] = ['-0.92', '0', '0.547', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'assumption', 'boundary_condition', 'provision', 'proviso', 'precondition']
senticnet['ontological_commitment'] = ['0.534', '-0.86', '0', '0.676', '#surprise', '#admiration', 'positive', '0.692', 'suitability', 'suitableness', 'habitability', 'habitableness', 'qualification']
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senticnet['onus'] = ['-0.81', '0', '0.979', '-0.88', '#anger', '#disgust', 'negative', '-0.89', 'vexation', 'dead_weight', 'imposition', 'worry', 'concern']
senticnet['onward'] = ['0.776', '0', '0', '0.721', '#joy', '#admiration', 'positive', '0.748', 'forrad', 'ahead', 'forward', 'forth', 'forward']
senticnet['onward_motion'] = ['0.903', '-0.88', '0', '0.556', '#joy', '#surprise', 'positive', '0.782', 'plain_sailing', 'clear_sailing', 'easy_going', 'move', 'push']
senticnet['onychophoran'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'hag', 'osteostracan', 'cephalaspid', 'ostracoderm', 'jawless_fish']
senticnet['oocyte'] = ['0', '0.067', '0', '0.07', '#interest', '#admiration', 'positive', '0.069', 'peck', 'bushel', 'chicken_egg', 'ovum', 'egg']
senticnet['oogie_boogie'] = ['-0.63', '0', '0.674', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'baneful', 'evil', 'baleful', 'malevolent', 'malign']
senticnet['ooh'] = ['-0.78', '0', '0.691', '-0.78', '#sadness', '#disgust', 'negative', '-0.75', 'express', 'utter', 'gee', 'cry_out', 'burst_in_tear']
senticnet['oolanga'] = ['-0.84', '0', '0.987', '-0.77', '#sadness', '#anger', 'negative', '-0.87', 'thorn', 'sticker', 'glochid', 'prickle', 'spikelet']
senticnet['oompah'] = ['0.699', '0.62', '-0.23', '0.323', '#joy', '#interest', 'positive', '0.353', 'joviality', 'jolity', 'josh', 'chaff', 'banter']
senticnet['oomph'] = ['0.989', '-0.80', '0', '0.569', '#joy', '#surprise', 'positive', '0.788', 'strength', 'tolerance', 'stamina', 'endurance', 'sufferance']
senticnet['oomycete'] = ['-0.93', '0', '0.699', '-0.91', '#sadness', '#disgust', 'negative', '-0.84', 'onion_smut', 'head_smut', 'stinking_smut', 'flag_smut_fungus', 'covered_smut']
senticnet['oomycota'] = ['0.807', '-0.86', '0', '0.611', '#joy', '#surprise', 'positive', '0.762', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['oophagy'] = ['0.947', '-0.81', '0', '0.656', '#joy', '#surprise', 'positive', '0.805', 'sexual_intercourse', 'intercourse', 'sex_act', 'hank_panky', 'coitus']
senticnet['oot'] = ['0.947', '0.914', '0', '0', '#joy', '#interest', 'positive', '0.93', 'ball_bounce', 'bicycle_tire', 'tire', 'increase_price', 'gyroscope']
senticnet['ootheca'] = ['0.947', '-0.81', '0', '0.656', '#joy', '#surprise', 'positive', '0.805', 'sexual_intercourse', 'intercourse', 'sex_act', 'hank_panky', 'coitus']
senticnet['ootside'] = ['0.027', '0', '0.012', '0.063', '#joy', '#admiration', 'positive', '0.026', 'vegetation', 'tree_flower', 'green_growth', 'small', 'greenery']
senticnet['ooze'] = ['-0.09', '0', '-0.07', '-0.06', '#sadness', '#fear', 'negative', '-0.07', 'blow', 'gape', 'move_air', 'mud', 'gash']
senticnet['oozed_out'] = ['-0.09', '0', '-0.07', '-0.06', '#sadness', '#fear', 'negative', '-0.07', 'blow', 'gape', 'move_air', 'mud', 'gash']
senticnet['oozing'] = ['-0.61', '0', '0.732', '-0.59', '#sadness', '#anger', 'negative', '-0.64', 'drafty', 'draughty', 'porous', 'drippy', 'leaky']
senticnet['oozy'] = ['-0.02', '-0.01', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.03', 'dung', 'manure', 'shit', 'dog_poop', 'crap']
senticnet['op_amp'] = ['0.056', '-0.01', '0.036', '0.073', '#joy', '#admiration', 'positive', '0.028', 'loud_sound', 'stereo', 'tuner', 'speaker', 'steam_engine']
senticnet['opacification'] = ['-0.86', '0', '0.858', '-0.91', '#sadness', '#disgust', 'negative', '-0.87', 'edema', 'oedema', 'hydrops', 'dropsy', 'eye_disease']
senticnet['opacity'] = ['-0.61', '0', '0.732', '-0.59', '#sadness', '#anger', 'negative', '-0.64', 'opaqueness', 'faintness', 'dimness', 'blurriness', 'vagueness']
senticnet['opalescent'] = ['0.577', '-0.69', '0', '0.645', '#surprise', '#admiration', 'positive', '0.639', 'shining', 'polished', 'shiny', 'bright', 'burnished']
senticnet['opana'] = ['0.138', '0.103', '0', '0.095', '#joy', '#interest', 'positive', '0.112', 'masturbation', 'sexual_intercourse', 'sex', 'warm_fuzzy_feeling', 'fuck']
senticnet['opaque'] = ['-0.61', '0', '0.732', '-0.59', '#sadness', '#anger', 'negative', '-0.64', 'opaqueness', 'faintness', 'dimness', 'blurriness', 'vagueness']
senticnet['opaqued'] = ['0', '0', '0.776', '-0.74', '#anger', '#disgust', 'negative', '-0.75', 'too-warm', 'self-annihilating', 'self-enforcement', 'newsier', 'viewless']
senticnet['opaqueness'] = ['-0.61', '0', '0.732', '-0.59', '#sadness', '#anger', 'negative', '-0.64', 'blurriness', 'faintness', 'dimness', 'opacity', 'vagueness']
senticnet['opel_rekord'] = ['0.035', '0.112', '-0.07', '0.195', '#interest', '#admiration', 'positive', '0.067', 'car_action', 'car_travel', 'steer_car', 'make_go', 'transportation']
senticnet['open'] = ['-0.04', '0.12', '-0.04', '0.128', '#interest', '#admiration', 'positive', '0.039', 'aperture', 'build_open', 'control_access', 'start', 'use_farm']
senticnet['open-hearted'] = ['-0.73', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.80', 'uncomplaining', 'long-suffering', 'self-sacrificing', 'stolid', 'unflappable']
senticnet['open-mouthed'] = ['0', '-0.66', '0', '0', '#surprise', '#surprise', 'positive', '0.667', 'surprised', 'dazed', 'wide-eyed', 'goggle-eyed', 'surprise']
senticnet['open_book'] = ['0', '0.093', '0.023', '0.095', '#interest', '#admiration', 'positive', '0.055', 'buy_book', 'go_to_library', 'read_book', 'visit_library', 'find_book']
senticnet['open_bottle'] = ['0.06', '-0.12', '0', '0', '#joy', '#surprise', 'positive', '0.093', 'drink_a_lot_of_booze', 'drink_excess', 'consume_alcohol', 'drink_a_lot_of_alcohol', 'buy_alcohol']
senticnet['open_business'] = ['0', '0', '0.937', '-0.87', '#anger', '#disgust', 'negative', '-0.90', 'make_porn', 'sell_new_book', 'go_off_strike', 'go_public', 'deal_drug']
senticnet['open_car_door'] = ['0', '0.128', '-0.04', '0.064', '#interest', '#admiration', 'positive', '0.048', 'open_door', 'put_key_ignition', 'find_car_key', 'stay_sober', 'start_car_engine']
senticnet['open_casket'] = ['-0.70', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.70', 'death_row', 'sad', 'loose_end', 'car_race', 'mortuary']
senticnet['open_casket_funeral'] = ['-0.70', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.70', 'death_row', 'sad', 'loose_end', 'car_race', 'mortuary']
senticnet['open_close'] = ['-0.05', '0.07', '0', '0.142', '#interest', '#admiration', 'positive', '0.054', 'house_open', 'sill', 'see_out', 'look_out', 'look_outside']
senticnet['open_close_mouth'] = ['-0.08', '0', '0.031', '-0.08', '#sadness', '#disgust', 'negative', '-0.06', 'raise_voice', 'choke_food', 'voice_opinion', 'act_out', 'standpoint']
senticnet['open_container'] = ['0', '0', '0.066', '-0.07', '#anger', '#disgust', 'negative', '-0.07', 'container_cup', 'cup', 'grease_trap', 'dish', 'salad_bowl']
senticnet['open_door'] = ['-0.09', '-0.02', '0.019', '-0.02', '#sadness', '#disgust', 'negative', '-0.03', 'close_door', 'find_location', 'drop_ball', 'try_best', 'blow_horn']
senticnet['open_enrollment'] = ['0.719', '0.757', '0', '0', '#joy', '#interest', 'positive', '0.738', 'matriculate', 'enroll_class', 'enroll_school', 'inscribe', 'register']
senticnet['open_envelope'] = ['0.038', '-0.17', '0.024', '0.141', '#surprise', '#admiration', 'positive', '0.082', 'open_letter', 'receive_mail', 'check_mail', 'write_friend', 'phone_bill']
senticnet['open_eye'] = ['0', '0', '0.848', '-0.88', '#anger', '#disgust', 'negative', '-0.86', 'wear_glass', 'near_tv', 'blur_vision', 'turn_on_tv', 'eyesight']
senticnet['open_front_door'] = ['-0.07', '-0.00', '0.009', '0', '#sadness', '#anger', 'negative', '-0.02', 'leave_house', 'decide_to_go', 'take_train', 'know_go', 'find_museum']
senticnet['open_gift'] = ['0.03', '-0.16', '0', '0.069', '#surprise', '#admiration', 'positive', '0.087', 'excitement', 'laugh_joke', 'give_assistance', 'please_parent', 'comfort_friend']
senticnet['open_golf_tournament'] = ['0.758', '0.719', '0', '0', '#joy', '#interest', 'positive', '0.738', 'plan_party', 'dance_music', 'become_adult', 'stay_fit', 'drive_crazy']
senticnet['open_grassland'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'biome', 'timberland']
senticnet['open_heathland'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'biome', 'timberland']
senticnet['open_land'] = ['0', '0.084', '0.021', '0.063', '#interest', '#admiration', 'positive', '0.042', 'open_space', 'crop_place', 'grassy', 'cow_home', 'ground']
senticnet['open_letter'] = ['-0.10', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.08', 'check_mail', 'receive_mail', 'crowd_elevator', 'find_book_library', 'page_through_book']
senticnet['open_lock'] = ['-0.15', '0.081', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.05', 'turn_key', 'free_prisoner', 'turn_car_off', 'open_door', 'start_engine']
senticnet['open_lock_door'] = ['-0.02', '0.122', '0', '0.137', '#interest', '#admiration', 'positive', '0.078', 'unlock', 'hang_door', 'enter_room', 'control_access', 'separate_room']
senticnet['open_mailbox'] = ['-0.05', '0.072', '0.012', '0.066', '#interest', '#admiration', 'positive', '0.018', 'sink_water', 'eat_cheese', 'bell_cat', 'eat', 'shoulder_burden']
senticnet['open_marriage'] = ['-0.59', '0', '0.84', '-0.80', '#anger', '#disgust', 'negative', '-0.74', 'marriage', 'matrimony', 'union', 'spousal_relationship', 'wedlock']
senticnet['open_mic'] = ['0.165', '-0.07', '0.033', '0', '#joy', '#surprise', 'positive', '0.068', 'metal_stick', 'drinking_place', 'bart', 'meet_place', 'water_hole']
senticnet['open_mic_night'] = ['-0.60', '0', '0.866', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'misbehavior', 'misdeed', 'hell', 'vandalism', 'hooliganism']
senticnet['open_mind'] = ['0.731', '0.542', '0', '0.827', '#joy', '#admiration', 'positive', '0.7', 'persistence', 'absorb_information', 'ask_expert', 'study_history', 'discover_new']
senticnet['open_minded'] = ['0.936', '-0.91', '0', '0', '#joy', '#surprise', 'positive', '0.924', 'homosexual', 'same_sex_attraction', 'gay', 'unconventional', 'gay_person']
senticnet['open_moorland'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'biome', 'timberland']
senticnet['open_mouth'] = ['-0.05', '0', '0.03', '-0.02', '#sadness', '#anger', 'negative', '-0.03', 'clear_throat', 'speak_up', 'move_tongue', 'think_first', 'open_close_mouth']
senticnet['open_night'] = ['0.116', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.076', 'meet_place', 'sticky_floor', 'row_seat', 'theater_box', 'place_to_drink']
senticnet['open_package'] = ['0.809', '-0.91', '0', '0', '#joy', '#surprise', 'positive', '0.861', 'cut_food', 'chop_vegetable', 'cut_vegetable', 'stir_pot', 'pierce']
senticnet['open_reel_tape'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['open_refrigerator'] = ['0.942', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.853', 'hungry', 'prepare_breakfast', 'set_table', 'season_food', 'prepare_dinner']
senticnet['open_seasonally'] = ['0.877', '-0.80', '0', '0', '#joy', '#surprise', 'positive', '0.843', 'convention', 'prisoner_war_camp', 'rave', 'box_ring', 'dark_alley']
senticnet['open_source'] = ['0.81', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.774', 'available', 'public', 'population', 'crowd', 'majority']
senticnet['open_source_javascript'] = ['-0.89', '0', '0.672', '-0.69', '#sadness', '#disgust', 'negative', '-0.75', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['open_source_software'] = ['0.81', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.774', 'available', 'public', 'population', 'crowd', 'majority']
senticnet['open_space'] = ['0.022', '0.069', '0.027', '0.059', '#interest', '#admiration', 'positive', '0.031', 'open_land', 'grassy', 'terrain', 'flat_land', 'land']
senticnet['open_testing_fairtest'] = ['0.713', '0', '0', '0.746', '#joy', '#admiration', 'positive', '0.73', 'carnival', 'circus', 'fairground', 'rodeo', 'disneyland']
senticnet['open_up'] = ['0', '0.087', '0', '0', '#interest', '#interest', 'positive', '0.087', 'speak_out', 'talk', 'learn_read', 'literate', 'literacy']
senticnet['open_wallet'] = ['-0.05', '0.14', '0.015', '0.082', '#interest', '#admiration', 'positive', '0.039', 'debit_card', 'cash_pay', 'need_earn_money', 'cash', 'earn_wage']
senticnet['open_wind'] = ['-0.17', '-0.05', '0', '-0.19', '#sadness', '#disgust', 'negative', '-0.10', 'cause_bleed', 'laceration', 'painful_injury', 'gash', 'cut_skin']
senticnet['open_window'] = ['-0.11', '0', '-0.04', '-0.12', '#sadness', '#disgust', 'negative', '-0.09', 'stagger', 'box_ear', 'lower_standard', 'correct_mistake', 'smoke_cigar']
senticnet['opencast'] = ['-0.78', '0', '0.699', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'battery', 'stamp_battery', 'quartz_battery', 'quartz_mill', 'factory']
senticnet['opener'] = ['0.034', '-0.02', '0.054', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'compass', 'swiss_army_knife', 'bake_pan', 'coffee_mug', 'wine_opener']
senticnet['opener_against'] = ['0.034', '-0.02', '0.054', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'compass', 'swiss_army_knife', 'bake_pan', 'coffee_mug', 'wine_opener']
senticnet['opener_gautam_gambhir'] = ['0.034', '-0.02', '0.054', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'compass', 'swiss_army_knife', 'bake_pan', 'coffee_mug', 'wine_opener']
senticnet['openhearted'] = ['0.552', '0.681', '-0.29', '0.297', '#joy', '#interest', 'positive', '0.309', 'charitable', 'good-hearted', 'large-hearted', 'benevolent', 'kindly']
senticnet['opening'] = ['-0.93', '0', '0.607', '-0.67', '#sadness', '#disgust', 'negative', '-0.73', 'blowhole', 'stigma', 'orifice', 'spiracle', 'porta']
senticnet['opening_ceremony'] = ['0.82', '0', '0', '0.885', '#joy', '#admiration', 'positive', '0.853', 'religious_event', 'sunday', 'happy_event', 'tradition', 'wedding']
senticnet['opening_faceoff'] = ['0', '0.904', '0', '0', '#interest', '#interest', 'positive', '0.904', 'act', 'part_of_play', 'part_of_movie', 'emote', 'role']
senticnet['opening_keynote'] = ['-0.79', '0', '0.948', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['opening_keynote_speech'] = ['-0.79', '0', '0.948', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['opening_montage'] = ['-0.69', '0', '0.508', '-0.93', '#sadness', '#disgust', 'negative', '-0.71', 'dark', 'darkness', 'light', 'shadow', 'site']
senticnet['opening_move'] = ['0.889', '0.852', '0', '0', '#joy', '#interest', 'positive', '0.871', 'maiden', 'opening', 'initiative', 'enterprisingness', 'inaugural']
senticnet['opening_night'] = ['0', '0.904', '0', '0', '#interest', '#interest', 'positive', '0.904', 'act', 'part_of_play', 'part_of_movie', 'emote', 'role']
senticnet['opening_plenary_session'] = ['0', '0.214', '0.012', '0.208', '#interest', '#admiration', 'positive', '0.137', 'lecture_hall', 'lecture_theater', 'lesson', 'lecture', 'school_session']
senticnet['opening_polymerization'] = ['0.052', '0', '0', '0.118', '#joy', '#admiration', 'positive', '0.085', 'diamond_ring', 'piece_of_jewelry', 'bell_sound', 'wedding_band', 'engagement']
senticnet['opening_riff'] = ['0.773', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.844', 'favorite', 'crowd', 'acclamation', 'bunch', 'large_crowd']
senticnet['opening_round'] = ['0.823', '0.982', '0', '0', '#joy', '#interest', 'positive', '0.902', 'spherical', 'three_dimensions', 'round_orb', 'coil', 'fill_with_sand']
senticnet['opening_sequence'] = ['-0.69', '0', '0.508', '-0.93', '#sadness', '#disgust', 'negative', '-0.71', 'dark', 'darkness', 'light', 'shadow', 'site']
senticnet['openly'] = ['0.998', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.887', 'administer', 'freely', 'open', 'fairly', 'implicit']
senticnet['openly_accessible'] = ['0.81', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.774', 'available', 'public', 'population', 'crowd', 'majority']
senticnet['openly_anti_semitic'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'racism', 'intolerant', 'xenophobic', 'racist', 'nazism']
senticnet['openly_avowed'] = ['0.843', '-0.93', '0', '0.952', '#surprise', '#admiration', 'positive', '0.91', 'asserted', 'declared', 'professed', 'alleged', 'announced']
senticnet['openly_criticize'] = ['0', '0', '0', '-0.87', '#disgust', '#disgust', 'negative', '-0.87', 'move_eye', 'learn_current_event', 'become_informed', 'time_waste', 'eye_strain']
senticnet['openly_defy'] = ['-0.54', '0', '0.815', '-0.83', '#anger', '#disgust', 'negative', '-0.73', 'brave_out', 'endure', 'weather', 'withstand', 'stand_firm']
senticnet['openly_hostile'] = ['0', '0.149', '0.719', '-0.66', '#anger', '#disgust', 'negative', '-0.41', 'belligerently', 'antagonism', 'class_feeling', 'aggressiveness', 'animus']
senticnet['openly_racist'] = ['0.585', '-0.7', '0', '0.586', '#surprise', '#admiration', 'positive', '0.624', 'jingoistic', 'ultranationalistic', 'superpatriotic', 'nationalistic', 'patriotic']
senticnet['openness'] = ['0.066', '0.07', '0.012', '0.095', '#interest', '#admiration', 'positive', '0.055', 'charisma', 'unlimited', 'eye_contact', 'hump_leg', 'start_conversation']
senticnet['opentv'] = ['-0.63', '0', '0.862', '-0.52', '#sadness', '#anger', 'negative', '-0.67', 'knot', 'bunch', 'tuft', 'tussock', 'agglomeration']
senticnet['openwindows'] = ['0.828', '0.805', '0', '0', '#joy', '#interest', 'positive', '0.817', 'sun', 'use', 'server', 'note', 'mit']
senticnet['opera'] = ['0.231', '0', '0', '0.109', '#joy', '#admiration', 'positive', '0.17', 'watch_comedy', 'dinner_party', 'big_home', 'family_activity', 'anarchy']
senticnet['opera_ballet'] = ['0.12', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.09', 'ballroom', 'perform_art', 'nightclub', 'ballerina', 'venue']
senticnet['opera_comique'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'dress', 'pinny', 'dirndl', 'gown', 'muumuu']
senticnet['opera_composer'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'band_play', 'songwriter', 'rock_band']
senticnet['opera_house'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'city_hall', 'courthouse', 'subway_station', 'public_place', 'bus_station']
senticnet['opera_orchestra'] = ['0.506', '0.659', '-0.24', '0.242', '#joy', '#interest', 'positive', '0.29', 'sing', 'music_studio', 'music_room', 'choir', 'symphony']
senticnet['opera_singer'] = ['0.882', '0.901', '0', '0', '#joy', '#interest', 'positive', '0.891', 'songwriter', 'vip', 'rock_group', 'guitar_player', 'violinist']
senticnet['operability'] = ['0.512', '-0.52', '0', '0.874', '#surprise', '#admiration', 'positive', '0.635', 'responsibleness', 'reproducibility', 'duplicability', 'solidness', 'responsibility']
senticnet['operable'] = ['0.986', '-0.62', '0', '0.807', '#joy', '#admiration', 'positive', '0.807', 'serviceable', 'functional', 'user_friendly', 'usable', 'operational']
senticnet['operand'] = ['-0.04', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.06', 'chemical_reaction', 'burn', 'chemical_process', 'get_burnt', 'set_on_fire']
senticnet['operant'] = ['0.583', '-0.82', '0', '0.574', '#joy', '#surprise', 'positive', '0.661', 'stimulant', 'positive_reinforcing_stimulus', 'positive_reinforcer', 'negative_reinforcing_stimulus', 'reinforcer']
senticnet['operant_behavior'] = ['0.583', '-0.82', '0', '0.574', '#joy', '#surprise', 'positive', '0.661', 'stimulant', 'positive_reinforcing_stimulus', 'positive_reinforcer', 'negative_reinforcing_stimulus', 'reinforcer']
senticnet['operate'] = ['0.926', '0.98', '0', '0', '#joy', '#interest', 'positive', '0.953', 'use', 'make_use', 'handle', 'do_it_yourself', 'activate']
senticnet['operate_autonomously'] = ['0.926', '0.98', '0', '0', '#joy', '#interest', 'positive', '0.953', 'use', 'make_use', 'handle', 'do_it_yourself', 'activate']
senticnet['operate_computer'] = ['0.957', '0.734', '0', '0', '#joy', '#interest', 'positive', '0.845', 'browse_web', 'entertainment', 'kill_time', 'research', 'surf_net']
senticnet['operate_efficiently'] = ['0.926', '0.98', '0', '0', '#joy', '#interest', 'positive', '0.953', 'use', 'make_use', 'handle', 'do_it_yourself', 'activate']
senticnet['operate_freely'] = ['0.926', '0.98', '0', '0', '#joy', '#interest', 'positive', '0.953', 'use', 'make_use', 'handle', 'do_it_yourself', 'activate']
senticnet['operate_independently'] = ['0.926', '0.98', '0', '0', '#joy', '#interest', 'positive', '0.953', 'use', 'make_use', 'handle', 'do_it_yourself', 'activate']
senticnet['operate_profitably'] = ['0.926', '0.98', '0', '0', '#joy', '#interest', 'positive', '0.953', 'use', 'make_use', 'handle', 'do_it_yourself', 'activate']
senticnet['operate_reliably'] = ['0.926', '0.98', '0', '0', '#joy', '#interest', 'positive', '0.953', 'use', 'make_use', 'handle', 'do_it_yourself', 'activate']
senticnet['operate_safely'] = ['0.926', '0.98', '0', '0', '#joy', '#interest', 'positive', '0.953', 'use', 'make_use', 'handle', 'do_it_yourself', 'activate']
senticnet['operated_independently'] = ['0.926', '0.98', '0', '0', '#joy', '#interest', 'positive', '0.953', 'use', 'make_use', 'handle', 'do_it_yourself', 'activate']
senticnet['operated_super_puma'] = ['0.228', '0.123', '-0.09', '0', '#joy', '#interest', 'positive', '0.084', 'spend_time_with_child', 'social', 'friendship', 'companionship', 'get_close']
senticnet['operatic'] = ['0.022', '0', '0', '0.109', '#joy', '#admiration', 'positive', '0.066', 'instrumental', 'singer', 'hip_hop', 'melodic_noise', 'music_player']
senticnet['operatic_repertoire'] = ['0.99', '0', '0', '0.719', '#joy', '#admiration', 'positive', '0.855', 'instrumental', 'lyric', 'lyrics', 'sing', 'melody']
senticnet['operating_base_fob'] = ['-0.66', '0', '0.789', '-0.63', '#sadness', '#anger', 'negative', '-0.69', 'humbug', 'play_trick', 'cozen', 'pull_fast_one_on', 'play_trick_on']
senticnet['operating_budget'] = ['-0.05', '0.282', '-0.04', '0.107', '#interest', '#admiration', 'positive', '0.072', 'expense', 'dollar_cent', 'dollar_sign', 'amount_pay', 'payment_amount']
senticnet['operating_expense'] = ['-0.03', '0.269', '-0.06', '0.136', '#interest', '#admiration', 'positive', '0.078', 'cost', 'money_payment', 'debit', 'price', 'money_charge']
senticnet['operating_room'] = ['0.839', '-0.89', '0', '0.687', '#joy', '#surprise', 'positive', '0.805', 'emergency_room', 'room', 'hospital_room', 'operating_theater', 'surgery']
senticnet['operating_system'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'biological_machine', 'social_creature', 'live_creature', 'powerful', 'alive']
senticnet['operating_system_android'] = ['0.027', '-0.09', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'cookie', 'pudding', 'custard', 'candy', 'muffin']
senticnet['operating_theater'] = ['0.839', '-0.89', '0', '0.687', '#joy', '#surprise', 'positive', '0.805', 'emergency_room', 'room', 'operating_room', 'hospital_room', 'surgery']
senticnet['operation'] = ['-0.07', '0.069', '-0.18', '0', '#sadness', '#fear', 'negative', '-0.06', 'medical_procedure', 'cure', 'doctor_surgery', 'treatment', 'doctor_work']
senticnet['operation_catnip'] = ['-0.78', '0', '0.91', '-0.91', '#anger', '#disgust', 'negative', '-0.87', 'castrate', 'neuter', 'alter', 'emasculate', 'desex']
senticnet['operation_center_eoc'] = ['0', '0', '-0.82', '0', '#fear', '#fear', 'negative', '-0.82', 'sickness', 'polio', 'malady', 'ailment', 'treatment']
senticnet['operation_command_afsoc'] = ['0', '0.851', '0', '0.774', '#interest', '#admiration', 'positive', '0.813', 'join_together', 'event', 'occurrence', 'special_occasion', 'function']
senticnet['operation_command_ussocom'] = ['0', '0.851', '0', '0.774', '#interest', '#admiration', 'positive', '0.813', 'join_together', 'event', 'occurrence', 'special_occasion', 'function']
senticnet['operation_fortitude'] = ['0', '0', '0.989', '-0.71', '#anger', '#disgust', 'negative', '-0.85', 'deceive', 'against_law', 'false', 'fool', 'trick']
senticnet['operation_herne'] = ['0.845', '0.916', '0', '0', '#joy', '#interest', 'positive', '0.881', 'question', 'answer', 'query', 'question_start', 'question_answering']
senticnet['operation_mincemeat'] = ['0', '0', '0.989', '-0.71', '#anger', '#disgust', 'negative', '-0.85', 'deceive', 'against_law', 'false', 'fool', 'trick']
senticnet['operation_nordwind'] = ['0.737', '-0.96', '0', '0.552', '#joy', '#surprise', 'positive', '0.75', 'prominence', 'hump', 'bump', 'swelling', 'frontal_eminence']
senticnet['operation_reinhard'] = ['-0.08', '0', '-0.15', '-0.20', '#fear', '#disgust', 'negative', '-0.14', 'casualty', 'reaper', 'wound', 'cause_death', 'gunshot']
senticnet['operation_research'] = ['0', '0.824', '0', '0.891', '#interest', '#admiration', 'positive', '0.857', 'engineering', 'rardin_dmi', 'research', 'sponsor', 'development']
senticnet['operation_tailwind'] = ['-0.90', '0', '0.529', '-0.84', '#sadness', '#disgust', 'negative', '-0.75', 'poison_gas', 'nerve_agent', 'mustard_gas', 'mustard_agent', 'blistering_agent']
senticnet['operation_varsity'] = ['0.737', '-0.96', '0', '0.552', '#joy', '#surprise', 'positive', '0.75', 'prominence', 'hump', 'bump', 'swelling', 'frontal_eminence']
senticnet['operational'] = ['0.986', '-0.62', '0', '0.807', '#joy', '#admiration', 'positive', '0.807', 'operable', 'functional', 'user_friendly', 'usable', 'serviceable']
senticnet['operational_amplifier'] = ['0.056', '-0.01', '0.036', '0.073', '#joy', '#admiration', 'positive', '0.028', 'loud_sound', 'stereo', 'tuner', 'speaker', 'steam_engine']
senticnet['operational_efficiency'] = ['0.558', '-0.60', '0', '0.779', '#surprise', '#admiration', 'positive', '0.646', 'benefit', 'pianism', 'show', 'routine', 'histrionics']
senticnet['operational_maneuver'] = ['-0.82', '0', '0.739', '-0.50', '#sadness', '#anger', 'negative', '-0.69', 'fake', 'juke', 'maneuver', 'manoeuvre', 'feint']
senticnet['operational_tempo'] = ['0.814', '-0.87', '0', '0.918', '#surprise', '#admiration', 'positive', '0.87', 'ship_company', 'company', 'work_force', 'workforce', 'full_complement']
senticnet['operationalize'] = ['0.868', '-0.95', '0', '0.548', '#joy', '#surprise', 'positive', '0.791', 'find', 'design', 'preconceive', 'discover', 'create_by_mental_act']
senticnet['operations_forces_sof'] = ['0', '0.851', '0', '0.774', '#interest', '#admiration', 'positive', '0.813', 'join_together', 'event', 'occurrence', 'special_occasion', 'function']
senticnet['operative_dentistry'] = ['-0.03', '0.079', '0.024', '0.091', '#interest', '#admiration', 'positive', '0.029', 'toothpaste', 'toothbrush', 'mouth', 'gel_toothpaste', 'tooth']
senticnet['operative_system'] = ['-0.02', '0.084', '0.036', '0.118', '#interest', '#admiration', 'positive', '0.036', 'house_open', 'computer_operative_system', 'look_out', 'look_glass', 'see_out']
senticnet['operator'] = ['0', '0.352', '-0.23', '0.307', '#interest', '#admiration', 'positive', '0.142', 'driver', 'specialist', 'employee', 'agent', 'surgeon']
senticnet['operator_cme_group'] = ['-0.05', '0.241', '-0.06', '0.286', '#interest', '#admiration', 'positive', '0.103', 'give_take', 'barter', 'exchange_goods', 'transaction', 'lower_price']
senticnet['operator_dp'] = ['-0.02', '-0.02', '0.064', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'ocean_water', 'fillet_fish', 'chowder', 'fillet', 'underwater']
senticnet['operatorship'] = ['0.59', '-0.75', '0', '0.543', '#joy', '#surprise', 'positive', '0.63', 'venture', 'sally', 'labor', 'cause', 'risk']
senticnet['operetta'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'dress', 'pinny', 'dirndl', 'gown', 'muumuu']
senticnet['operoseness'] = ['-0.64', '0', '0.928', '-0.70', '#anger', '#disgust', 'negative', '-0.75', 'difficulty', 'arduousness', 'strenuousness', 'laboriousness', 'effortfulness']
senticnet['ophel'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['ophelia'] = ['-0.91', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.85', 'play', 'perpetrator', 'role_play', 'act_part', 'terrible_event']
senticnet['ophicleide'] = ['0.821', '-0.53', '0', '0.785', '#joy', '#admiration', 'positive', '0.712', 'fife', 'nose_flute', 'piccolo', 'woodwind', 'flute']
senticnet['ophidian'] = ['-0.57', '0', '0.571', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['ophioglossum_vulgatum'] = ['-0.79', '0', '0.906', '-0.67', '#sadness', '#anger', 'negative', '-0.79', 'poisonous_plant', 'hemlock_poisonous_plant', 'water_hemlock', 'plant_life', 'poison_sumac']
senticnet['ophiopogon'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'livelong', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['ophir_energy_plc_ophr'] = ['-0.95', '0', '0.845', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'grapefruit_peel', 'lemon_peel', 'orange_peel', 'candied_fruit', 'succade']
senticnet['ophthalmia'] = ['-0.69', '0', '0.852', '-0.69', '#sadness', '#anger', 'negative', '-0.74', 'conjunctivitis', 'ophthalmitis', 'rubor', 'redness', 'inflammation']
senticnet['ophthalmia_neonatorum'] = ['-0.69', '0', '0.852', '-0.69', '#sadness', '#anger', 'negative', '-0.74', 'conjunctivitis', 'ophthalmitis', 'rubor', 'redness', 'inflammation']
senticnet['ophthalmic_plastic'] = ['0.745', '-0.55', '0', '0.597', '#joy', '#admiration', 'positive', '0.633', 'lift', 'phalloplasty', 'abdominoplasty', 'tummy_tuck', 'autoplasty']
senticnet['ophthalmic_surgeon'] = ['0.738', '-0.53', '0', '0.856', '#joy', '#admiration', 'positive', '0.71', 'doctor', 'country_doctor', 'family_doctor', 'doctor_office', 'physician']
senticnet['ophthalmitis'] = ['-0.69', '0', '0.852', '-0.69', '#sadness', '#anger', 'negative', '-0.74', 'ophthalmia', 'conjunctivitis', 'rubor', 'redness', 'inflammation']
senticnet['ophthalmoplegia'] = ['-0.86', '0', '0.858', '-0.91', '#sadness', '#disgust', 'negative', '-0.87', 'edema', 'oedema', 'hydrops', 'dropsy', 'eye_disease']
senticnet['ophthalmoscope'] = ['0.255', '0.182', '-0.06', '0.314', '#joy', '#admiration', 'positive', '0.173', 'doctor_office', 'urethroscope', 'sigmoidoscope', 'hospital', 'check_health']
senticnet['opi_opi_avoplex_nail'] = ['0.939', '-0.71', '0', '0.775', '#joy', '#admiration', 'positive', '0.81', 'vox_angelica', 'voix_celeste', 'organ_stop', 'pipe', 'diapason']
senticnet['opi_opi_natural'] = ['0', '0.078', '0.027', '0.196', '#interest', '#admiration', 'positive', '0.082', 'heel', 'ankle', 'toe', 'foot_massage', 'foot']
senticnet['opiate'] = ['0', '-0.02', '-0.09', '-0.14', '#fear', '#disgust', 'negative', '-0.07', 'cough_syrup', 'yellow_flower', 'flower', 'poisonous_plant', 'rubber_duck']
senticnet['opiate_bind'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'dejectedness', 'low-spiritedness', 'lowness', 'downheartedness', 'cheerless']
senticnet['opiate_withdrawal'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['opinion'] = ['0', '0.352', '-0.23', '0.307', '#interest', '#admiration', 'positive', '0.142', 'reason', 'forethought', 'consider', 'strong_feeling', 'choice']
senticnet['opinion_differ'] = ['0.544', '-0.69', '0', '0.658', '#surprise', '#admiration', 'positive', '0.632', 'shakable', 'shakeable', 'challengeable', 'disputable', 'contestable']
senticnet['opinion_vary'] = ['0.544', '-0.69', '0', '0.658', '#surprise', '#admiration', 'positive', '0.632', 'shakable', 'shakeable', 'challengeable', 'disputable', 'contestable']
senticnet['opinionated'] = ['0', '0', '0.83', '-0.91', '#anger', '#disgust', 'negative', '-0.87', 'persistent', 'obstinately', 'rock-ribbed', 'wrongheaded', 'obstinate']
senticnet['opinioned'] = ['0', '0', '0.93', '-0.89', '#anger', '#disgust', 'negative', '-0.91', 'dicussion', 'argue', 'conquer_opponent', 'debate_politics', 'debate']
senticnet['opioid_analgesic'] = ['0.672', '0.632', '-0.23', '0.312', '#joy', '#interest', 'positive', '0.344', 'euphoria', 'buoyancy', 'perkiness', 'jubilance', 'ecstasy']
senticnet['opioid_antagonist'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['opioid_painkiller'] = ['0.138', '0.103', '0', '0.095', '#joy', '#interest', 'positive', '0.112', 'masturbation', 'sexual_intercourse', 'sex', 'warm_fuzzy_feeling', 'fuck']
senticnet['opioid_therapy'] = ['-0.03', '-0.02', '-0.08', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'ibuprofen', 'phenobarbital', 'analgesic', 'sleep_drug', 'pill']
senticnet['opioid_treatment'] = ['-0.03', '-0.02', '-0.08', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'ibuprofen', 'phenobarbital', 'analgesic', 'sleep_drug', 'pill']
senticnet['opisthorchis_viverrini'] = ['0.688', '-0.94', '0', '0.925', '#surprise', '#admiration', 'positive', '0.852', 'flatworm', 'platyhelminth', 'trematode', 'schistosome', 'blood_fluke']
senticnet['opium'] = ['0', '0.073', '-0.09', '-0.08', '#fear', '#disgust', 'negative', '-0.03', 'cotton_boll', 'asbestos', 'corn', 'cauliflower', 'rice']
senticnet['opium_den'] = ['-0.80', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.79', 'whorehouse', 'brothel', 'massage_parlor', 'bagnio', 'house_of_prostitution']
senticnet['opobalsamum'] = ['0.784', '-0.59', '0', '0.846', '#joy', '#admiration', 'positive', '0.742', 'astuteness', 'slyness', 'wiliness', 'craftiness', 'cunning']
senticnet['opossum'] = ['0', '0', '-0.95', '-0.81', '#fear', '#disgust', 'negative', '-0.88', 'herb', 'plant', 'poisonous_plant', 'yellow_flower', 'lichen']
senticnet['opossum_shrimp'] = ['-0.79', '0', '0.709', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'animal', 'animate_being', 'ctenophore', 'invertebrate', 'beast']
senticnet['oppanakara_street'] = ['0', '0.113', '0.041', '0.058', '#interest', '#admiration', 'positive', '0.043', 'corner_shop', 'general_store', 'many_shops', 'large_supermarket', 'goods']
senticnet['oppenheimer'] = ['0', '0.186', '0', '0.075', '#interest', '#admiration', 'positive', '0.131', 'money_inside', 'check_account', 'savings', 'money_storage', 'store_money']
senticnet['oppian'] = ['-0.87', '0', '0.599', '-0.71', '#sadness', '#disgust', 'negative', '-0.73', 'hysteria', 'fury', 'delirium', 'frenzy', 'craze']
senticnet['opponent'] = ['0', '0', '0.83', '-0.9', '#anger', '#disgust', 'negative', '-0.86', 'beat_up', 'rival', 'opposition', 'duel', 'strategy']
senticnet['opportune'] = ['0.915', '-0.99', '0', '0.673', '#joy', '#surprise', 'positive', '0.859', 'ripe', 'good', 'right', 'timely', 'seasonable']
senticnet['opportunism'] = ['-0.81', '0', '0.886', '-0.65', '#sadness', '#anger', 'negative', '-0.78', 'selfishness', 'greediness', 'rapaciousness', 'voraciousness', 'expedience']
senticnet['opportunistic'] = ['0', '0', '-0.85', '-0.93', '#fear', '#disgust', 'negative', '-0.89', 'anger', 'animosity', 'hostility', 'malice', 'grudge']
senticnet['opportunistic_pathogen'] = ['-0.60', '0', '0.721', '-0.57', '#sadness', '#anger', 'negative', '-0.63', 'tubercle_bacillus', 'leprosy_bacillus', 'eubacteria', 'eubacterium', 'true_bacteria']
senticnet['opportunity'] = ['0.027', '0.254', '-0.20', '0.294', '#interest', '#admiration', 'positive', '0.092', 'contribution', 'improvement', 'receive', 'give', 'bid']
senticnet['opposable'] = ['-0.60', '0', '0.701', '-0.72', '#anger', '#disgust', 'negative', '-0.67', 'lunate_bone', 'semilunar_bone', 'carpal_bone', 'triquetral', 'triquetral_bone']
senticnet['oppose'] = ['-0.06', '0.068', '-0.08', '-0.07', '#fear', '#disgust', 'negative', '-0.04', 'fist', 'whack', 'rudeness', 'make_fist', 'hand']
senticnet['oppose_abortion'] = ['-0.90', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.86', 'death', 'feticide', 'sickness', 'miscarriage', 'spontaneous_abortion']
senticnet['oppose_capital_punishment'] = ['-0.19', '0', '-0.17', '-0.14', '#sadness', '#fear', 'negative', '-0.17', 'death_penalty', 'die', 'death_destruction', 'kill', 'murder']
senticnet['opposer'] = ['-0.77', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.79', 'gainsay', 'disagreeable', 'deny', 'contradict', 'gainsayer']
senticnet['opposes_abortion'] = ['-0.90', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.86', 'death', 'feticide', 'sickness', 'miscarriage', 'spontaneous_abortion']
senticnet['opposes_capital_punishment'] = ['-0.19', '0', '-0.17', '-0.14', '#sadness', '#fear', 'negative', '-0.17', 'death_penalty', 'die', 'death_destruction', 'kill', 'murder']
senticnet['opposing_faction'] = ['-0.50', '0', '0.989', '-0.91', '#anger', '#disgust', 'negative', '-0.80', 'camp', 'pack', 'clique', 'coterie', 'ingroup']
senticnet['opposing_pitcher'] = ['0', '0.075', '0', '0', '#interest', '#interest', 'positive', '0.075', 'cup', 'carafe', 'container_cup', 'mug', 'glasses']
senticnet['opposite'] = ['-0.80', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.77', 'contrary', 'contradiction', 'lie', 'liar', 'bad_person']
senticnet['opposite_cold'] = ['0.9', '0.9', '0', '0', '#joy', '#interest', 'positive', '0.9', 'hot', 'suppose_impartial', 'type_of_valley', 'heat', 'warm']
senticnet['opposite_conclusion'] = ['-0.04', '0.26', '-0.15', '0.239', '#interest', '#admiration', 'positive', '0.075', 'conclude', 'result', 'end_point', 'end', 'finish']
senticnet['opposite_corner'] = ['-0.80', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.77', 'contrary', 'contradiction', 'lie', 'liar', 'bad_person']
senticnet['opposite_direction'] = ['-0.80', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.77', 'contrary', 'contradiction', 'lie', 'liar', 'bad_person']
senticnet['opposite_hemisphere'] = ['0', '0', '0', '0.119', '#admiration', '#admiration', 'positive', '0.119', 'planet', 'earth', 'spherical', 'planet_solar_system', 'terrain']
senticnet['opposite_sex'] = ['0.833', '0', '0', '0.807', '#joy', '#admiration', 'positive', '0.82', 'lust', 'amiable', 'give_hug', 'giggle', 'smile']
senticnet['opposite_shore'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'driftwood', 'sea_shell', 'scenic_view', 'naked_woman', 'tidal_wave']
senticnet['opposite_side'] = ['-0.80', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.77', 'contrary', 'contradiction', 'lie', 'liar', 'bad_person']
senticnet['opposite_tack'] = ['0', '-0.05', '0.079', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'c_clamp', 'thumb_tack', 'pin', 'pliers', 'without_space']
senticnet['oppositely'] = ['-0.80', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.77', 'contrary', 'contradiction', 'lie', 'liar', 'bad_person']
senticnet['opposition'] = ['0', '0', '0.98', '-0.98', '#anger', '#disgust', 'negative', '-0.98', 'uprise', 'disagreement', 'conflict', 'fight_against', 'big_fight']
senticnet['opposition_mohamed_brahmi'] = ['0', '0.919', '0', '0.888', '#interest', '#admiration', 'positive', '0.903', 'surprise', 'eye-popping', 'fulgurous', 'dazzling', 'bedaze']
senticnet['oppositional_culture'] = ['-0.96', '0', '0.766', '-0.96', '#sadness', '#disgust', 'negative', '-0.89', 'calumniation', 'traducement', 'hatchet_job', 'defamation', 'calumny']
senticnet['oppositive'] = ['-0.66', '0', '0.502', '-0.79', '#sadness', '#disgust', 'negative', '-0.65', 'adversative', 'disjunctive', 'contrastive', 'alternative', 'separative']
senticnet['oppress'] = ['-0.50', '0', '0.936', '-0.95', '#anger', '#disgust', 'negative', '-0.79', 'quash', 'keep_down', 'reduce', 'repress', 'subdue']
senticnet['oppressed'] = ['0', '0', '0.842', '-0.92', '#anger', '#disgust', 'negative', '-0.88', 'leper', 'pariah', 'exile', 'outcast', 'demographic']
senticnet['oppresses_woman'] = ['-0.07', '0.195', '0.629', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'misogynic', 'misogyny', 'murderousness', 'amuck', 'amok']
senticnet['oppression'] = ['-0.97', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.87', 'laden', 'oppressively', 'oppressive', 'tyrannical', 'tyrannous']
senticnet['oppressive'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'oppressiveness', 'tyrannical', 'downtrodden', 'tyrannous', 'laden']
senticnet['oppressive_heat'] = ['0', '0', '0.954', '-0.76', '#anger', '#disgust', 'negative', '-0.85', 'fervid', 'ardent', 'impassioned', 'fervent', 'fiery']
senticnet['oppressive_rule'] = ['0.601', '0.647', '-0.26', '0.322', '#joy', '#interest', 'positive', '0.326', 'break_free', 'set_free', 'relief', 'freedom', 'liberty']
senticnet['oppressive_silence'] = ['0.224', '0.224', '-0.12', '0', '#joy', '#interest', 'positive', '0.108', 'hush', 'peace', 'truce', 'seek_psychiatric_help', 'quiet']
senticnet['oppressively'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'oppressiveness', 'tyrannical', 'downtrodden', 'oppressive', 'tyrannous']
senticnet['oppressiveness'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'tyrannical', 'downtrodden', 'oppressive', 'tyrannous', 'laden']
senticnet['oppressor'] = ['-0.85', '0', '0.75', '-0.95', '#sadness', '#disgust', 'negative', '-0.85', 'reign', 'legislation', 'rebel', 'legislature', 'decree']
senticnet['opprobrious'] = ['-0.95', '0', '0', '-0.81', '#sadness', '#disgust', 'negative', '-0.88', 'disgraceful', 'shameful', 'dishonorably', 'inglorious', 'dishonored']
senticnet['opprobrium'] = ['-0.16', '0.179', '0.568', '-0.61', '#anger', '#disgust', 'negative', '-0.29', 'angrily', 'choler', 'see_red', 'dander', 'hackle']
senticnet['opsonization'] = ['0.702', '-0.52', '0', '0.958', '#joy', '#admiration', 'positive', '0.728', 'somatic_cell', 'vegetative_cell', 'fixed_phagocyte', 'macrophage', 'free_phagocyte']
senticnet['opt'] = ['0.57', '0.754', '-0.32', '0.464', '#joy', '#interest', 'positive', '0.366', 'esteem', 'optimism', 'anticipation', 'adoration', 'confidence']
senticnet['optic_ataxia'] = ['-0.73', '0', '0.955', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'visual_agnosia', 'auditory_agnosia', 'brain_disorder', 'encephalopathy', 'brain_disease']
senticnet['optic_atrophy'] = ['-0.65', '0', '0.873', '-0.63', '#sadness', '#anger', 'negative', '-0.72', 'neuritis', 'multiple_neuritis', 'rubor', 'redness', 'inflammation']
senticnet['optic_chiasm'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['optic_nerve_hypoplasia'] = ['-0.88', '0', '0.762', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'divergent_strabismus', 'exotropia', 'walleye', 'abnormality', 'abnormalcy']
senticnet['optic_neuropathy'] = ['-0.86', '0', '0.858', '-0.91', '#sadness', '#disgust', 'negative', '-0.87', 'edema', 'oedema', 'hydrops', 'dropsy', 'eye_disease']
senticnet['optic_radiation'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['optic_tract'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['optical_aberration'] = ['0.906', '-0.60', '0', '0.653', '#joy', '#admiration', 'positive', '0.72', 'opaqueness', 'faintness', 'dimness', 'opacity', 'vagueness']
senticnet['optical_absorption'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'emotional_arousal', 'excitement', 'arousal', 'anger']
senticnet['optical_aid'] = ['0', '0.108', '0.004', '0.115', '#interest', '#admiration', 'positive', '0.073', 'complex_organ', 'visual_aid', 'face_feature', 'facial_organ', 'part_of_face']
senticnet['optical_cavity'] = ['0.843', '-0.99', '0', '0.706', '#joy', '#surprise', 'positive', '0.849', 'gadgetry', 'gimbal', 'contrivance', 'mod_con', 'device']
senticnet['optical_disk'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'slot', 'box', 'load_dock', 'safe_deposit_box', 'emporium']
senticnet['optical_drive'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'computer_part', 'system', 'cpu', 'pencil_pen', 'monitor']
senticnet['optical_fiber'] = ['0.085', '0', '0', '0', '#joy', '#joy', 'positive', '0.085', 'wire', 'several_types', 'gas_stove', 'tape_player', 'control_room']
senticnet['optical_fibre'] = ['0.085', '0', '0', '0', '#joy', '#joy', 'positive', '0.085', 'wire', 'several_types', 'gas_stove', 'tape_player', 'control_room']
senticnet['optical_filter'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'water_inside', 'water_spray', 'teapot', 'bodily_waste', 'different_type']
senticnet['optical_illusion'] = ['-0.94', '0', '0.866', '-0.91', '#sadness', '#disgust', 'negative', '-0.90', 'movement', 'apparent_motion', 'motion', 'apparent_movement', 'mirage']
senticnet['optical_image_stabilization'] = ['0', '-0.02', '0.048', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'telescope', 'read_glass', 'eyeglass', 'binoculars', 'monocle']
senticnet['optical_instrument'] = ['0.954', '-0.66', '0', '0.958', '#joy', '#admiration', 'positive', '0.86', 'instrument', 'spectroscope', 'prism_spectroscope', 'binoculars', 'glasses']
senticnet['optical_microscope'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'amoeba', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['optical_mouse'] = ['0', '-0.12', '0.011', '0.089', '#surprise', '#admiration', 'positive', '0.067', 'cellular_phone', 'telephone', 'wireless', 'connect', 'message']
senticnet['optical_path'] = ['-0.50', '0', '0.75', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'beam', 'beam_of_light', 'sunbeam', 'ray', 'ray_of_light']
senticnet['optical_telescope'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'binoculars', 'read_glass', 'eyeglass', 'camera', 'monocle']
senticnet['optical_viewfinder'] = ['0.021', '0.081', '0.017', '0.113', '#interest', '#admiration', 'positive', '0.05', 'aspect', 'perspective', 'look_round', 'scenic', 'look_through']
senticnet['optimal'] = ['0.917', '-0.93', '0', '0', '#joy', '#surprise', 'positive', '0.925', 'optimize', 'best', 'optimist', 'optimality', 'desirable']
senticnet['optimal_nutrition'] = ['0.939', '-0.74', '0', '0.57', '#joy', '#surprise', 'positive', '0.751', 'haleness', 'wholeness', 'rosiness', 'health', 'vitality']
senticnet['optimality'] = ['0.91', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.927', 'optimize', 'best', 'optimist', 'optimal', 'desirable']
senticnet['optimality_theory'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'integrative', 'consolidative', 'unifying', 'combinatorial']
senticnet['optimally_fluoridated_water'] = ['0', '0', '0.813', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'ice_cream_melt', 'allergy', 'toothache', 'gonorrhea', 'tastebud']
senticnet['optimedia'] = ['-0.63', '0', '0.862', '-0.52', '#sadness', '#anger', 'negative', '-0.67', 'knot', 'bunch', 'tuft', 'tussock', 'agglomeration']
senticnet['optimisation'] = ['0', '0.728', '0', '0.83', '#interest', '#admiration', 'positive', '0.779', 'optimization', 'improvement', 'development', 'design', 'research']
senticnet['optimiser'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'carbon_fiber']
senticnet['optimism'] = ['1', '0.66', '0', '0', '#joy', '#interest', 'positive', '0.83', 'hope', 'positive', 'optimistic', 'good_luck', 'bold']
senticnet['optimist'] = ['0.912', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.926', 'optimize', 'best', 'optimal', 'optimality', 'desirable']
senticnet['optimistic'] = ['0.483', '0.675', '-0.26', '0.256', '#joy', '#interest', 'positive', '0.288', 'optimistically', 'encouraging', 'encouraged', 'bucked_up', 'encouragingly']
senticnet['optimistic_attitude'] = ['0.701', '0.874', '0', '0.752', '#interest', '#admiration', 'positive', '0.776', 'self_satisfaction', 'enjoy_life', 'good_feeling', 'good_attitude', 'enjoy']
senticnet['optimistic_concurrency_control'] = ['0.792', '-0.83', '0', '0.575', '#joy', '#surprise', 'positive', '0.734', 'connectedness', 'connection', 'consistency', 'continuity', 'link']
senticnet['optimistic_outlook'] = ['-0.70', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.75', 'pessimism', 'pessimistically', 'dispiritedly', 'hopelessness', 'hopelessly']
senticnet['optimistic_view'] = ['-0.70', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.75', 'pessimism', 'pessimistically', 'dispiritedly', 'hopelessness', 'hopelessly']
senticnet['optimistically'] = ['0.483', '0.675', '-0.26', '0.256', '#joy', '#interest', 'positive', '0.288', 'optimistic', 'encouraging', 'encouraged', 'bucked_up', 'encouragingly']
senticnet['optimization'] = ['0', '0.735', '0', '0.837', '#interest', '#admiration', 'positive', '0.786', 'optimisation', 'improvement', 'development', 'design', 'research']
senticnet['optimize'] = ['0.914', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.928', 'optimal', 'best', 'optimist', 'optimality', 'desirable']
senticnet['optimize_performance'] = ['0.558', '-0.60', '0', '0.779', '#surprise', '#admiration', 'positive', '0.646', 'benefit', 'pianism', 'show', 'routine', 'histrionics']
senticnet['optimum'] = ['0.924', '0.943', '0', '0', '#joy', '#interest', 'positive', '0.933', 'optimal', 'maximum', 'maximize', 'ensures', 'ensure']
senticnet['optimum_performance'] = ['0.558', '-0.60', '0', '0.779', '#surprise', '#admiration', 'positive', '0.646', 'benefit', 'pianism', 'show', 'routine', 'histrionics']
senticnet['option'] = ['0.096', '0.296', '-0.15', '0.419', '#interest', '#admiration', 'positive', '0.163', 'conclude', 'explanation', 'possibility', 'chance', 'conclusion']
senticnet['option_menu'] = ['0.146', '0.095', '-0.04', '0', '#joy', '#interest', 'positive', '0.065', 'paper_menu', 'diner', 'cafe', 'restaurant', 'dine_room']
senticnet['optional'] = ['0.928', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.893', 'conclude', 'explanation', 'possibility', 'chance', 'conclusion']
senticnet['optional_keyword'] = ['0.777', '-0.85', '0', '0.615', '#joy', '#surprise', 'positive', '0.748', 'think', 'play', 'trifle', 'abstract', 'warm_to']
senticnet['optionee'] = ['0.069', '0.07', '0', '0.116', '#interest', '#admiration', 'positive', '0.085', 'entrepreneur', 'invest_money', 'move_around', 'traveling_person', 'guilty_person']
senticnet['optometric'] = ['-0.03', '0.079', '0.024', '0.091', '#interest', '#admiration', 'positive', '0.029', 'toothpaste', 'toothbrush', 'mouth', 'gel_toothpaste', 'tooth']
senticnet['optometry'] = ['-0.03', '0.079', '0.024', '0.091', '#interest', '#admiration', 'positive', '0.029', 'toothpaste', 'toothbrush', 'mouth', 'gel_toothpaste', 'tooth']
senticnet['optometry_admission_test'] = ['0.878', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.883', 'wheat', 'grain', 'rye', 'white_rice', 'rice']
senticnet['opulence'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dirtiness', 'dinge']
senticnet['opulent'] = ['0.839', '0.753', '0', '0', '#joy', '#interest', 'positive', '0.796', 'luxurious', 'princely', 'gilded', 'grand', 'deluxe']
senticnet['opulent_palace'] = ['0', '0.105', '0', '0.076', '#interest', '#admiration', 'positive', '0.09', 'big_house', 'mansion', 'castle', 'block_flat', 'medieval']
senticnet['opulently'] = ['0.902', '-0.72', '0', '0.919', '#joy', '#admiration', 'positive', '0.849', 'preciousness', 'costliness', 'dearness', 'cost', 'expensiveness']
senticnet['opus'] = ['0', '0.898', '0', '0', '#interest', '#interest', 'positive', '0.898', 'opus_house', 'concert_hall', 'creative_work', 'studio', 'tv_studio']
senticnet['opus_house'] = ['0', '0.898', '0', '0', '#interest', '#interest', 'positive', '0.898', 'opus', 'concert_hall', 'studio', 'creative_work', 'tv_studio']
senticnet['opus_ticket'] = ['0.203', '0.111', '0', '0.089', '#joy', '#interest', 'positive', '0.134', 'ticket_taker', 'ticket_turnstile', 'go_friend', 'movie_star', 'have_fun']
senticnet['oracle'] = ['0.686', '-0.51', '0', '0.829', '#joy', '#admiration', 'positive', '0.676', 'diviner', 'augur', 'auspex', 'prophetess', 'sibyl']
senticnet['oracleworld'] = ['-0.79', '0', '0.948', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['orakzai'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'webpage', 'store_record', 'ideal']
senticnet['oral'] = ['-0.02', '0.067', '-0.04', '0.192', '#interest', '#admiration', 'positive', '0.047', 'verbalize', 'orate', 'say', 'talkative', 'discourse']
senticnet['oral_acyclovir'] = ['-0.72', '0', '0.778', '-0.98', '#anger', '#disgust', 'negative', '-0.82', 'actinic_dermatitis', 'hypericism', 'diaper_rash', 'dermatitis', 'seborrheic_dermatitis']
senticnet['oral_allergy_syndrome'] = ['-0.96', '0', '0.848', '-0.82', '#sadness', '#anger', 'negative', '-0.87', 'sneeze', 'sign_sickness', 'runny_nose', 'flu', 'common_cold']
senticnet['oral_argument'] = ['0', '0.154', '-0.05', '0.17', '#interest', '#admiration', 'positive', '0.089', 'court_action', 'accessory', 'legal_document', 'short', 'protective_cover']
senticnet['oral_bacteria'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'plaque']
senticnet['oral_bioavailability'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'imperium', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['oral_cleaning'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'wipe', 'wipe_mouth', 'napkin', 'clean_mouth', 'toothbrush']
senticnet['oral_communication'] = ['-0.02', '0.067', '-0.04', '0.192', '#interest', '#admiration', 'positive', '0.047', 'verbalize', 'orate', 'say', 'talkative', 'discourse']
senticnet['oral_comprehensive_examination'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'organize_thought', 'scratch_head', 'know_answer', 'speak_out', 'know_talk']
senticnet['oral_copulation'] = ['0.542', '-0.83', '0', '0.927', '#surprise', '#admiration', 'positive', '0.769', 'incest', 'extramarital_sex', 'free_love', 'sexual_intercourse', 'intercourse']
senticnet['oral_defense'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'organize_thought', 'scratch_head', 'know_answer', 'speak_out', 'know_talk']
senticnet['oral_exam'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'organize_thought', 'scratch_head', 'know_answer', 'speak_out', 'know_talk']
senticnet['oral_examination'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'organize_thought', 'scratch_head', 'know_answer', 'speak_out', 'know_talk']
senticnet['oral_flora'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'plaque']
senticnet['oral_formulation'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'spironolactone', 'adrenal_cortical_steroid', 'antihypertensive', 'antihypertensive_drug']
senticnet['oral_glucose'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
senticnet['oral_glucose_tolerance'] = ['0.928', '-0.87', '0', '0.679', '#joy', '#surprise', 'positive', '0.829', 'examine', 'experiment', 'alpha_test', 'beta_test', 'road_test']
senticnet['oral_health'] = ['0', '0', '0.813', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'ice_cream_melt', 'allergy', 'toothache', 'gonorrhea', 'tastebud']
senticnet['oral_health_care'] = ['-0.03', '0.079', '0.024', '0.091', '#interest', '#admiration', 'positive', '0.029', 'toothpaste', 'toothbrush', 'mouth', 'gel_toothpaste', 'tooth']
senticnet['oral_hygiene'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'plaque']
senticnet['oral_malodor'] = ['0', '0', '0.955', '-0.78', '#anger', '#disgust', 'negative', '-0.87', 'worm_intestine', 'fur_keep_warm', 'unpleasant-smelling', 'halitosis', 'asthama']
senticnet['oral_mucosa'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'plaque']
senticnet['oral_pathology'] = ['-0.59', '0', '0.911', '-0.87', '#anger', '#disgust', 'negative', '-0.79', 'osteosclerosis', 'sclerosis', 'disseminated_sclerosis', 'multiple_sclerosis', 'induration']
senticnet['oral_polio_vaccine'] = ['-0.07', '0', '-0.05', '-0.18', '#sadness', '#disgust', 'negative', '-0.10', 'medical_therapy', 'vaccination', 'doctor', 'medicine', 'hospital']
senticnet['oral_prednisone'] = ['-0.53', '0', '0.608', '-0.93', '#anger', '#disgust', 'negative', '-0.69', 'reprieve', 'respite', 'hiatus', 'abatement', 'suspension']
senticnet['oral_preliminary_examination'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'organize_thought', 'scratch_head', 'know_answer', 'speak_out', 'know_talk']
senticnet['oral_presentation'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'organize_thought', 'scratch_head', 'know_answer', 'speak_out', 'know_talk']
senticnet['oral_qualifying_exam'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'organize_thought', 'scratch_head', 'know_answer', 'speak_out', 'know_talk']
senticnet['oral_qualifying_examination'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'organize_thought', 'scratch_head', 'know_answer', 'speak_out', 'know_talk']
senticnet['oral_sedation'] = ['-0.65', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'put_to_sleep', 'operation', 'anesthesia', 'surgery', 'local_anesthesia']
senticnet['oral_sex'] = ['0.215', '0.194', '-0.20', '0.279', '#joy', '#admiration', 'positive', '0.121', 'win_lottery', 'make_friend_happy', 'show_affection', 'make_happy', 'give_away']
senticnet['oral_snuff'] = ['-0.72', '0', '0.71', '-0.9', '#sadness', '#disgust', 'negative', '-0.77', 'perniciousness', 'unwholesomeness', 'toxicity', 'noisomeness', 'noxiousness']
senticnet['oral_steroid'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'spironolactone', 'adrenal_cortical_steroid', 'antihypertensive', 'antihypertensive_drug']
senticnet['oral_surgery'] = ['-0.03', '0.079', '0.024', '0.091', '#interest', '#admiration', 'positive', '0.029', 'toothpaste', 'toothbrush', 'mouth', 'gel_toothpaste', 'tooth']
senticnet['oral_syringe'] = ['-0.02', '0', '-0.12', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'x-ray_machine', 'medical_record', 'latex_glove', 'medicine', 'doctor_examination_room']
senticnet['oral_therapy'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'radiation_therapy', 'radiation', 'actinotherapy', 'irradiation']
senticnet['oral_thrush'] = ['-0.72', '0', '0.778', '-0.98', '#anger', '#disgust', 'negative', '-0.82', 'actinic_dermatitis', 'hypericism', 'diaper_rash', 'dermatitis', 'seborrheic_dermatitis']
senticnet['oral_tradition'] = ['0', '0.915', '0', '0.821', '#interest', '#admiration', 'positive', '0.868', 'culture', 'special_day', 'holiday', 'party_time', 'yearly_celebration']
senticnet['oral_vaccine'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'medical_therapy', 'vaccine', 'doctor', 'medicine', 'hospital']
senticnet['oralism'] = ['-0.94', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.94', 'drive_recklessly', 'discord', 'mute', 'deny', 'helpless']
senticnet['orally'] = ['-0.02', '0.067', '-0.04', '0.192', '#interest', '#admiration', 'positive', '0.047', 'verbalize', 'orate', 'say', 'talkative', 'discourse']
senticnet['orally_translate'] = ['0', '0.202', '-0.06', '0.291', '#interest', '#admiration', 'positive', '0.142', 'vocabulary', 'translate', 'dictionary', 'translation', 'thesaurus']
senticnet['orang_pendek'] = ['0', '0', '0.964', '-0.80', '#anger', '#disgust', 'negative', '-0.88', 'monster', 'wild_man', 'abominable_snowman', 'yeti', 'abominable']
senticnet['orange'] = ['0.842', '0.818', '0', '0', '#joy', '#interest', 'positive', '0.83', 'ball_shape', 'round_shape', 'circle_shape', 'cornerless', 'orb']
senticnet['orange-copper'] = ['0', '0', '0.838', '-0.71', '#anger', '#disgust', 'negative', '-0.77', 'blueish-green', 'greenish-gold', 'ruby-brown', 'golden-green', 'deep-brown']
senticnet['orange_color'] = ['0.894', '0.969', '0', '0', '#joy', '#interest', 'positive', '0.931', 'citrus_fruit', 'orange_fruit', 'citrus', 'hair_color', 'fruit']
senticnet['orange_drink'] = ['0.04', '0.095', '-0.06', '0.132', '#interest', '#admiration', 'positive', '0.052', 'healthy_drink', 'liquid_beverage', 'popular_soda', 'liquid_drink', 'cola']
senticnet['orange_dwarf'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['orange_fruit'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'ripe_apple', 'custard', 'cookie', 'candy', 'candy_cane']
senticnet['orange_fur'] = ['0.793', '0.847', '0', '0', '#joy', '#interest', 'positive', '0.82', 'cross_bun', 'hot_cross_bun', 'bear_paw', 'coffee_roll', 'bun']
senticnet['orange_glow'] = ['0', '-0.00', '-0.07', '-0.06', '#fear', '#disgust', 'negative', '-0.04', 'lighten', 'tornado', 'dynamite_explosion', 'forest_fire', 'lightning']
senticnet['orange_juice'] = ['0.953', '-0.78', '0', '0', '#joy', '#surprise', 'positive', '0.871', 'lemon_juice', 'chocolate_pudding', 'grape_juice', 'carrot_juice', 'juice']
senticnet['orange_milkwort'] = ['-0.70', '0', '0.769', '-0.70', '#sadness', '#anger', 'negative', '-0.72', 'subshrub', 'suffrutex', 'senega', 'milkwort', 'yellow_milkwort']
senticnet['orange_peel'] = ['-0.95', '0', '0.845', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'grapefruit_peel', 'lemon_peel', 'candied_citrus_peel', 'candied_fruit', 'succade']
senticnet['orange_red'] = ['-0.02', '0.068', '0.04', '0.084', '#interest', '#admiration', 'positive', '0.022', 'peelable', 'green_black', 'purple', 'red', 'edible_human']
senticnet['orange_rind'] = ['0', '0.141', '0.01', '0.115', '#interest', '#admiration', 'positive', '0.082', 'sepal', 'achene', 'funiculus', 'seed', 'pip']
senticnet['orange_rust'] = ['0', '0', '-0.94', '-0.96', '#fear', '#disgust', 'negative', '-0.95', 'say_prayer', 'fall_off_chair', 'put_box', 'feel_terrible', 'lift_leg']
senticnet['orange_shirt'] = ['-0.04', '0.1', '0', '0.152', '#interest', '#admiration', 'positive', '0.068', 'pants', 'jacket', 'clothes', 'robe', 'garment']
senticnet['orange_sneezeweed'] = ['-0.76', '0', '0.758', '-0.67', '#sadness', '#anger', 'negative', '-0.73', 'herb', 'autumn_sneezeweed', 'owlclaws', 'sneezeweed', 'rosilla']
senticnet['orange_soda'] = ['0', '0', '0', '-0.06', '#disgust', '#disgust', 'negative', '-0.06', 'newsstand', 'bookshop', 'walmart', 'shoe_store', 'hooker']
senticnet['orange_tabby'] = ['-0.72', '0', '0.619', '-0.86', '#sadness', '#disgust', 'negative', '-0.73', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['orange_yellow'] = ['0', '0.19', '-0.09', '0.175', '#interest', '#admiration', 'positive', '0.089', 'red', 'green_black', 'green_red', 'peelable', 'precious_jewel']
senticnet['orange_zest'] = ['0.829', '0.857', '0', '0', '#joy', '#interest', 'positive', '0.843', 'fruit_tree', 'baby_flower', 'carpel', 'strawberry', 'fig']
senticnet['orangish'] = ['-0.02', '0.068', '0.04', '0.084', '#interest', '#admiration', 'positive', '0.022', 'peelable', 'green_black', 'purple', 'red', 'edible_human']
senticnet['orarion'] = ['0.849', '-0.50', '0', '0.845', '#joy', '#admiration', 'positive', '0.732', 'robe', 'gown', 'veil', 'vest', 'cassock']
senticnet['orarium'] = ['0.849', '-0.50', '0', '0.845', '#joy', '#admiration', 'positive', '0.732', 'robe', 'gown', 'veil', 'vest', 'cassock']
senticnet['orat'] = ['0.055', '0.189', '-0.09', '0.113', '#interest', '#admiration', 'positive', '0.065', 'tame', 'crack', 'sense_fear', 'friend_person', 'good_manner']
senticnet['orate'] = ['0', '0', '0', '0.137', '#admiration', '#admiration', 'positive', '0.137', 'discourse', 'verbalize', 'speech', 'verbal_communication', 'verbal']
senticnet['oration'] = ['0', '-0.00', '0', '0.085', '#surprise', '#admiration', 'positive', '0.046', 'speech', 'spoken_language', 'talkative', 'orate', 'verbalize']
senticnet['orator'] = ['0.034', '-0.02', '0', '0.057', '#joy', '#admiration', 'positive', '0.039', 'female_television_connector', 'channel_selector', 'power_switch', 'tuner', 'radio']
senticnet['oratorian'] = ['-0.85', '0', '0.749', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'hairsplitter', 'fight', 'quarrel', 'obstructor', 'obstructer']
senticnet['oratorical'] = ['0', '0', '0.914', '-0.90', '#anger', '#disgust', 'negative', '-0.90', 'declamation', 'oratory', 'extemporaneous', 'elocution', 'eloquence']
senticnet['oratorical_style'] = ['-0.67', '0', '0.936', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'unprepared', 'extemporary', 'extempore', 'impromptu', 'unrehearsed']
senticnet['oratory'] = ['0', '0', '0.906', '-0.91', '#anger', '#disgust', 'negative', '-0.90', 'declamation', 'oratorical', 'extemporaneous', 'elocution', 'eloquence']
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senticnet['ordains'] = ['0.767', '0', '0', '0.866', '#joy', '#admiration', 'positive', '0.817', 'ordaining', 'ordained', 'ordain', 'consecrate', 'appoint']
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senticnet['order_lunch'] = ['0.854', '0.659', '0', '0.545', '#joy', '#interest', 'positive', '0.686', 'teeth', 'obtain_food', 'find_restaurant', 'bite_food', 'decide_eat']
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senticnet['ordinal_number'] = ['0.049', '0.113', '0.107', '0.162', '#interest', '#admiration', 'positive', '0.054', 'list_number', 'ordinal', 'count', 'numerical', 'enumeration']
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senticnet['oregano_oil'] = ['0.95', '-0.86', '0', '0.947', '#joy', '#admiration', 'positive', '0.92', 'content', 'lore', 'folklore', 'cognitive_content', 'story']
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senticnet['orexigenic'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'gastrointestinal_hormone', 'secretin', 'ghrelin']
senticnet['orexin'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'cholecystokinin', 'secretin', 'gastrointestinal_hormone']
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senticnet['organ_perfusion'] = ['0.79', '-0.62', '0', '0.674', '#joy', '#admiration', 'positive', '0.695', 'add', 'contribute', 'lend', 'breathe', 'impart']
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senticnet['organ_transplant'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'displace', 'move', 'move_away', 'bump', 'dislocate']
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senticnet['organelle'] = ['0.721', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.838', 'sarcosome', 'chondriosome', 'mitochondrion', 'protein', 'chloroplast']
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senticnet['organic_chemical_compound'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'enhance', 'intensify', 'deepen', 'potentiate', 'heighten']
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senticnet['organic_chemistry'] = ['0.879', '0', '0', '0.902', '#joy', '#admiration', 'positive', '0.891', 'chemistry', 'different_application', 'student', 'project', 'standard_grant']
senticnet['organic_coffee'] = ['0.807', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.859', 'bubble', 'steam', 'hot', 'boil_water', 'tea']
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senticnet['ornithology'] = ['-0.98', '0', '0.934', '-0.69', '#sadness', '#anger', 'negative', '-0.87', 'bird_watcher', 'ornithologist', 'hobby', 'naturalist', 'amateur']
senticnet['ornithopod'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'hag', 'osteostracan', 'cephalaspid', 'ostracoderm', 'jawless_fish']
senticnet['ornithurae'] = ['-0.68', '0', '0.956', '-0.90', '#anger', '#disgust', 'negative', '-0.84', 'giant_moa', 'anomalopteryx', 'moa', 'moa_bird', 'flightless_bird']
senticnet['orobanche'] = ['-0.93', '0', '0.699', '-0.91', '#sadness', '#disgust', 'negative', '-0.84', 'onion_smut', 'head_smut', 'stinking_smut', 'flag_smut_fungus', 'covered_smut']
senticnet['orofacial'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'part_of_face', 'facial_organ', 'face_feature', 'facial_part', 'near_nose']
senticnet['oroha'] = ['-0.84', '0', '0.579', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'squiggle', 'hand', 'chicken_scratch', 'handwriting', 'script']
senticnet['orotund'] = ['0.812', '0', '0', '0.836', '#joy', '#admiration', 'positive', '0.824', 'tumid', 'heavy', 'great', 'bombastic', 'larger']
senticnet['oroville'] = ['-0.06', '0.21', '0', '0.076', '#interest', '#admiration', 'positive', '0.074', 'new_think', 'figure_out', 'brain_activity', 'idea', 'come_up_idea']
senticnet['orphan'] = ['-0.16', '-0.05', '-0.12', '-0.13', '#sadness', '#disgust', 'negative', '-0.09', 'waif', 'orphanage', 'stray', 'ragamuffin', 'lonely']
senticnet['orphanage'] = ['-0.16', '-0.05', '-0.12', '-0.13', '#sadness', '#disgust', 'negative', '-0.09', 'waif', 'orphan', 'stray', 'ragamuffin', 'lonely']
senticnet['orphaned_polar_bear'] = ['-0.95', '0', '0.735', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'novice', 'beginner', 'tyro', 'tiro', 'initiate']
senticnet['orphenadrine'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['orpheum'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'theater', 'drive-in_movie', 'watch_movie', 'cinema', 'movie']
senticnet['orpin'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'livelong', 'herb', 'stonecrop', 'sedume', 'sedum']
senticnet['orpine'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'livelong', 'herb', 'stonecrop', 'sedum', 'orpin']
senticnet['orquesta_sinfonica'] = ['0.506', '0.659', '-0.24', '0.242', '#joy', '#interest', 'positive', '0.29', 'sing', 'music_studio', 'music_room', 'choir', 'symphony']
senticnet['orrorin'] = ['-0.93', '0', '0.508', '-0.58', '#sadness', '#disgust', 'negative', '-0.67', 'inhuman', 'bloodless', 'apelike', 'nonhuman', 'nonhumanal']
senticnet['ortaky'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'caravansary', 'caravanserai', 'khan', 'caravan_inn', 'hostel']
senticnet['orthic_triangle'] = ['0.763', '-0.61', '0', '0.777', '#joy', '#admiration', 'positive', '0.717', 'acute_triangle', 'wedge_shape', 'cuneus', 'wedge', 'oblique_triangle']
senticnet['ortho_evra_birth_control'] = ['-0.58', '0', '0.561', '-0.62', '#sadness', '#disgust', 'negative', '-0.58', 'spot', 'speckle', 'dapple', 'mark', 'fleck']
senticnet['orthocenter'] = ['0.763', '-0.61', '0', '0.777', '#joy', '#admiration', 'positive', '0.717', 'acute_triangle', 'wedge_shape', 'cuneus', 'wedge', 'oblique_triangle']
senticnet['orthodontic'] = ['-0.03', '0.079', '0.024', '0.091', '#interest', '#admiration', 'positive', '0.029', 'toothpaste', 'toothbrush', 'mouth', 'gel_toothpaste', 'tooth']
senticnet['orthodontic_treatment'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'false_teeth', 'bridge', 'bridgework', 'dental_plate', 'dental_appliance']
senticnet['orthodox'] = ['0.033', '0.278', '0', '0.275', '#interest', '#admiration', 'positive', '0.195', 'sect', 'protestant', 'religious_service', 'holy_man', 'jewish']
senticnet['orthodox_belief'] = ['0.99', '0', '0', '0.982', '#joy', '#admiration', 'positive', '0.986', 'god', 'spiritual', 'saint', 'prayer', 'angel']
senticnet['orthodox_church'] = ['0.013', '0.199', '0.027', '0.167', '#interest', '#admiration', 'positive', '0.088', 'church', 'confession', 'good_catholic', 'spirituality', 'christianity']
senticnet['orthodox_congregation'] = ['0', '0.209', '0.026', '0.184', '#interest', '#admiration', 'positive', '0.122', 'church', 'confession', 'temple', 'cathedral', 'monastery']
senticnet['orthodox_diocese'] = ['0.8', '-0.84', '0', '0.868', '#surprise', '#admiration', 'positive', '0.837', 'commonwealth', 'province', 'administrative_district', 'administrative_division', 'territorial_division']
senticnet['orthodox_doctrine'] = ['0.99', '0', '0', '0.982', '#joy', '#admiration', 'positive', '0.986', 'god', 'spiritual', 'saint', 'prayer', 'angel']
senticnet['orthodox_jew'] = ['0', '-0.00', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'hot_chick', 'atheist', 'computer_user', 'fastener_binder', 'monitor']
senticnet['orthodox_jewish_congregation'] = ['0', '0.209', '0.026', '0.184', '#interest', '#admiration', 'positive', '0.122', 'church', 'confession', 'temple', 'cathedral', 'monastery']
senticnet['orthodox_religion'] = ['-0.93', '0', '0.75', '-0.64', '#sadness', '#anger', 'negative', '-0.77', 'heathen', 'heathenish', 'pagan', 'lapsed', 'nonchurchgoing']
senticnet['orthodox_synagogue'] = ['0', '0.209', '0.026', '0.184', '#interest', '#admiration', 'positive', '0.122', 'church', 'confession', 'temple', 'cathedral', 'monastery']
senticnet['orthodox_theology'] = ['0.664', '-0.57', '0', '0.981', '#joy', '#admiration', 'positive', '0.741', 'theodicy', 'homiletics', 'discipline', 'study', 'subject']
senticnet['orthodoxly'] = ['0.857', '0.974', '0', '0', '#joy', '#interest', 'positive', '0.915', 'english', 'correctly', 'rightly', 'rightfully', 'aright']
senticnet['orthodoxy'] = ['-0.82', '0', '0.701', '0', '#sadness', '#anger', 'negative', '-0.76', 'conventionality', 'traditionalism', 'conventionalism', 'traditionality', 'strict']
senticnet['orthogonal'] = ['-0.86', '0', '0.894', '-0.72', '#sadness', '#anger', 'negative', '-0.82', 'inapplicable', 'unsuitable', 'immaterial', 'extraneous', 'irrelevant']
senticnet['orthogonal_projection'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'arm_movement', 'hand_gesture', 'water_motion', 'hand_signal', 'sea_movement']
senticnet['orthogonality'] = ['-0.75', '0', '0.679', '-0.77', '#sadness', '#disgust', 'negative', '-0.73', 'unvariedness', 'changelessness', 'unchangeability', 'unchangeableness', 'unchangingness']
senticnet['orthographic_projection'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'arm_movement', 'hand_gesture', 'water_motion', 'hand_signal', 'sea_movement']
senticnet['orthographic_view'] = ['0.777', '-0.85', '0', '0.615', '#joy', '#surprise', 'positive', '0.748', 'think', 'play', 'trifle', 'abstract', 'warm_to']
senticnet['orthographical'] = ['0.902', '-0.85', '0', '0.898', '#joy', '#admiration', 'positive', '0.885', 'discerning', 'diacritic', 'discriminating', 'discriminative', 'selective']
senticnet['orthomyxoviridae'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'leptomeningitis', 'cerebrospinal_meningitis', 'meningitis', 'brain_fever', 'cerebrospinal_fever']
senticnet['orthonama'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'art', 'art_piece', 'museum', 'artist', 'jewel']
senticnet['orthopaedic'] = ['0.745', '-0.55', '0', '0.597', '#joy', '#admiration', 'positive', '0.633', 'lift', 'phalloplasty', 'abdominoplasty', 'tummy_tuck', 'autoplasty']
senticnet['orthopaedic_surgeon'] = ['0.738', '-0.53', '0', '0.856', '#joy', '#admiration', 'positive', '0.71', 'doctor', 'country_doctor', 'family_doctor', 'doctor_office', 'physician']
senticnet['orthopaedic_surgery'] = ['0.745', '-0.55', '0', '0.597', '#joy', '#admiration', 'positive', '0.633', 'lift', 'phalloplasty', 'abdominoplasty', 'tummy_tuck', 'autoplasty']
senticnet['orthopedic_rehabilitation'] = ['0.641', '-0.78', '0', '0.721', '#surprise', '#admiration', 'positive', '0.715', 'correctional_rehabilitation', 'physical_rehabilitation', 'physical_restoration', 'therapeutic_rehabilitation', 'vocational_rehabilitation']
senticnet['orthopedic_surgery'] = ['0.745', '-0.55', '0', '0.597', '#joy', '#admiration', 'positive', '0.633', 'lift', 'phalloplasty', 'abdominoplasty', 'tummy_tuck', 'autoplasty']
senticnet['orthoplex'] = ['0.641', '-0.6', '0', '0.957', '#joy', '#admiration', 'positive', '0.733', 'ideal_solid', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'regular_octahedron']
senticnet['orthopnea'] = ['-0.20', '0', '-0.22', '-0.19', '#sadness', '#fear', 'negative', '-0.20', 'go_hospital', 'death', 'die', 'take_aspirin', 'heart_attack']
senticnet['orthosilicate'] = ['-0.03', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'lithium', 'mineral', 'potassium', 'iodine', 'manganese']
senticnet['orthostatic'] = ['-0.62', '0', '0.901', '-0.71', '#anger', '#disgust', 'negative', '-0.74', 'pressure', 'osmotic_pressure', 'hypotonicity', 'pressure_level', 'force']
senticnet['orthotic'] = ['-0.03', '0', '-0.09', '-0.14', '#fear', '#disgust', 'negative', '-0.09', 'x-ray_machine', 'artificial_arm', 'ambulance', 'syringe', 'latex_glove']
senticnet['orthotic_device'] = ['-0.03', '0', '-0.09', '-0.14', '#fear', '#disgust', 'negative', '-0.09', 'x-ray_machine', 'artificial_arm', 'ambulance', 'syringe', 'latex_glove']
senticnet['orville_harrold'] = ['0.552', '-0.61', '0', '0.588', '#surprise', '#admiration', 'positive', '0.583', 'barbershop_quartet', 'string_quartet', 'quartette', 'musical_organization', 'musical_group']
senticnet['osage_orange'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'locust_tree']
senticnet['oscillate'] = ['0', '0', '-0.85', '-0.74', '#fear', '#disgust', 'negative', '-0.80', 'vibrate', 'vacillate', 'hover', 'waver', 'fluctuate']
senticnet['oscillated'] = ['0', '0.932', '0', '0.841', '#interest', '#admiration', 'positive', '0.887', 'discontinuously', 'modulated', 'oscillates', 'non-linearly', 'sinusoidally']
senticnet['oscillates'] = ['0', '0.934', '0', '0.835', '#interest', '#admiration', 'positive', '0.885', 'discontinuously', 'modulated', 'sinusoidally', 'non-linearly', 'oscillated']
senticnet['oscillation'] = ['0.807', '-0.88', '0', '0.718', '#joy', '#surprise', 'positive', '0.802', 'sex', 'gayness', 'sexual_gayness', 'lesbianism', 'sapphism']
senticnet['oscillator'] = ['0.056', '-0.01', '0.036', '0.073', '#joy', '#admiration', 'positive', '0.028', 'loud_sound', 'stereo', 'tuner', 'speaker', 'steam_engine']
senticnet['oscillatoria'] = ['0.807', '-0.86', '0', '0.611', '#joy', '#surprise', 'positive', '0.762', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['oscillatory'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'emotional_arousal', 'excitement', 'arousal', 'anger']
senticnet['oscillatory_behavior'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'constancy', 'monotony', 'invariance', 'changelessness', 'unchangeability']
senticnet['oscillatory_motion'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotate_movement', 'rotate', 'go_around', 'manoeuvre', 'full_circle']
senticnet['oscillococcinum'] = ['0.95', '-0.86', '0', '0.947', '#joy', '#admiration', 'positive', '0.92', 'content', 'lore', 'folklore', 'cognitive_content', 'story']
senticnet['oscine'] = ['-0.84', '0', '0.655', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'piping_crow', 'butcherbird', 'currawong', 'bell_magpie', 'australian_magpie']
senticnet['oscitance'] = ['-0.82', '0', '0.663', '-0.50', '#sadness', '#anger', 'negative', '-0.66', 'obtuseness', 'dullness', 'sleepiness', 'drowsiness', 'somnolence']
senticnet['oscitancy'] = ['-0.92', '0', '0.549', '-0.65', '#sadness', '#disgust', 'negative', '-0.71', 'obtuseness', 'dullness', 'sleepiness', 'drowsiness', 'somnolence']
senticnet['oscitant'] = ['0', '0', '-0.99', '-0.91', '#fear', '#disgust', 'negative', '-0.95', 'oscitancy', 'oscitance', 'gape', 'yawning', 'drowsy']
senticnet['oshawott'] = ['0.028', '0', '0.027', '0.101', '#joy', '#admiration', 'positive', '0.034', 'animal_dwell', 'bee_home', 'live_place', 'dwell_place', 'place_to_live']
senticnet['osi_restaurant'] = ['0.841', '0.721', '0', '0.74', '#joy', '#admiration', 'positive', '0.767', 'good_manner', 'two_people', 'love', 'guardian', 'guy']
senticnet['osirian_portal'] = ['-0.57', '0', '0.571', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['osmia'] = ['0', '0', '0', '0.869', '#admiration', '#admiration', 'positive', '0.869', 'fly_animal', 'insect', 'mosquito', 'bug', 'fly_insect']
senticnet['osmolality'] = ['-0.74', '0', '0.723', '-0.59', '#sadness', '#anger', 'negative', '-0.68', 'tone', 'tautness', 'tonus', 'condition', 'status']
senticnet['osmolarity'] = ['-0.74', '0', '0.723', '-0.59', '#sadness', '#anger', 'negative', '-0.68', 'tone', 'tautness', 'tonus', 'condition', 'status']
senticnet['osmorhiza'] = ['-0.92', '0', '0.912', '-0.94', '#sadness', '#disgust', 'negative', '-0.92', 'true_blackberry', 'sand_blackberry', 'blackberry_bush', 'blackberry', 'running_blackberry']
senticnet['osmotic'] = ['-0.57', '0', '0.767', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'verboseness', 'verbosity', 'prolixity', 'turgidness', 'flatulence']
senticnet['osmotic_gradient'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'keep_alive', 'body_system', 'body_working', 'blood_pressure', 'heart']
senticnet['osmotic_pressure'] = ['-0.62', '0', '0.901', '-0.71', '#anger', '#disgust', 'negative', '-0.74', 'pressure', 'hypertonicity', 'hypotonicity', 'pressure_level', 'force']
senticnet['osmotic_regulation'] = ['-0.74', '0', '0.723', '-0.59', '#sadness', '#anger', 'negative', '-0.68', 'tone', 'tautness', 'tonus', 'condition', 'status']
senticnet['osprey'] = ['-0.74', '0', '0.963', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['osrd'] = ['-0.90', '0', '0.748', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'alternative_energy', 'activation_energy', 'energy_of_activation', 'energy', 'nuclear_energy']
senticnet['osredek'] = ['-0.91', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.85', 'play', 'perpetrator', 'role_play', 'act_part', 'terrible_event']
senticnet['osric_dane'] = ['-0.71', '0', '0.906', '-0.93', '#anger', '#disgust', 'negative', '-0.85', 'absent', 'forgetful', 'distracted', 'inattentive', 'scatty']
senticnet['ossicular_chain'] = ['0', '0', '0', '0.106', '#admiration', '#admiration', 'positive', '0.106', 'vocal_chord', 'mouth_noise', 'vocal_sound', 'human_sound', 'vocalization']
senticnet['ossification'] = ['-0.78', '0', '0.764', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'withering', 'downfall', 'weakening', 'fading', 'attenuation']
senticnet['osso_buco'] = ['0.043', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'apple_pie', 'a_lot_of_food', 'jelly', 'frozen_food', 'cottage_cheese']
senticnet['osteitis'] = ['-0.60', '0', '0.949', '-0.96', '#anger', '#disgust', 'negative', '-0.83', 'osteomyelitis', 'rubor', 'redness', 'inflammation', 'osteitis_deformans']
senticnet['osteitis_deformans'] = ['-0.60', '0', '0.949', '-0.96', '#anger', '#disgust', 'negative', '-0.83', 'osteomyelitis', 'rubor', 'redness', 'inflammation', 'osteitis']
senticnet['ostensible'] = ['-0.63', '0', '0.857', '-0.71', '#anger', '#disgust', 'negative', '-0.73', 'shallow', 'facile', 'superficial', 'sounding', 'looking']
senticnet['ostensibly'] = ['0', '0.922', '0', '0.812', '#interest', '#admiration', 'positive', '0.867', 'patently', 'plainly', 'apparently', 'plain', 'seemingly']
senticnet['ostentation'] = ['-0.81', '0', '0.936', '-0.57', '#sadness', '#anger', 'negative', '-0.77', 'pedantry', 'bluster', 'splurge', 'ritz', 'display']
senticnet['ostentatious'] = ['-0.98', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.99', 'intrusive', 'distracting', 'overbearing', 'unobtrusive', 'obtrusive']
senticnet['ostentatious_display'] = ['0', '0', '0.925', '-0.85', '#anger', '#disgust', 'negative', '-0.89', 'overweening', 'excessive', 'wasteful', 'exuberant', 'prodigal']
senticnet['ostentatiously'] = ['0', '-0.58', '-1', '0', '#surprise', '#fear', 'negative', '-0.20', 'gratingly', 'obtrusively', 'intrusively', 'objectionably', 'forgivably']
senticnet['osteoblastic'] = ['-0.53', '0', '0.982', '-0.67', '#anger', '#disgust', 'negative', '-0.73', 'meniscus', 'semilunar_cartilage', 'gristle', 'hyaline_cartilage', 'arytenoid']
senticnet['osteoderm'] = ['0.1', '-0.12', '0.05', '-0.03', '#joy', '#surprise', 'positive', '0.034', 'fancy_dress', 'blouse', 'fashion', 'dress', 'skirt']
senticnet['osteoid'] = ['0', '0', '0.025', '-0.09', '#anger', '#disgust', 'negative', '-0.06', 'body_fluid', 'artery', 'skeletal_system', 'bodily_fluid', 'near_mouth']
senticnet['osteomalacia'] = ['-0.59', '0', '0.911', '-0.87', '#anger', '#disgust', 'negative', '-0.79', 'induration', 'sclerosis', 'disseminated_sclerosis', 'multiple_sclerosis', 'pathology']
senticnet['osteomyelitis'] = ['-0.60', '0', '0.949', '-0.96', '#anger', '#disgust', 'negative', '-0.83', 'osteitis', 'rubor', 'redness', 'inflammation', 'osteitis_deformans']
senticnet['osteopath'] = ['0.738', '-0.53', '0', '0.856', '#joy', '#admiration', 'positive', '0.71', 'doctor', 'country_doctor', 'family_doctor', 'doctor_office', 'physician']
senticnet['osteopathic'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
senticnet['osteopathic_medical'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
senticnet['osteopathic_medical_school'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
senticnet['osteopathic_medicine'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
senticnet['osteopetrosis'] = ['-0.59', '0', '0.606', '-0.57', '#sadness', '#anger', 'negative', '-0.59', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['osteoporotic'] = ['-0.59', '0', '0.911', '-0.87', '#anger', '#disgust', 'negative', '-0.79', 'induration', 'sclerosis', 'disseminated_sclerosis', 'multiple_sclerosis', 'pathology']
senticnet['osteosclerosis'] = ['-0.59', '0', '0.911', '-0.87', '#anger', '#disgust', 'negative', '-0.79', 'induration', 'sclerosis', 'disseminated_sclerosis', 'multiple_sclerosis', 'pathology']
senticnet['osteosclerosis_congenita'] = ['-0.85', '0', '0.572', '-0.75', '#sadness', '#disgust', 'negative', '-0.72', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['osteostracan'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'hag', 'jawless_fish', 'cephalaspid', 'ostracoderm', 'heterostracan']
senticnet['osteostraci'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'hag', 'osteostracan', 'cephalaspid', 'ostracoderm', 'jawless_fish']
senticnet['ostinato'] = ['-0.52', '0', '0.902', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'discordant', 'inharmonious', 'unresolved', 'false', 'disharmonious']
senticnet['ostracise'] = ['-0.75', '0', '0.964', '0', '#sadness', '#anger', 'negative', '-0.85', 'move', 'displace', 'fire', 'chase_away', 'banish']
senticnet['ostracism'] = ['0', '-0.33', '0.992', '0', '#surprise', '#anger', 'negative', '-0.32', 'turn_down', 'dismissal', 'rebuff', 'repudiation', 'rejection']
senticnet['ostracize'] = ['-0.85', '0', '0.749', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'cold_foot', 'eat_worm', 'farm_bird', 'yellow_animal', 'biped']
senticnet['ostracoderm'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'hag', 'osteostracan', 'cephalaspid', 'jawless_fish', 'heterostracan']
senticnet['ostrich_feather'] = ['0', '0', '0.789', '-0.72', '#anger', '#disgust', 'negative', '-0.75', 'displease', 'bug', 'nettle', 'chafe', 'badger']
senticnet['ostrowsky'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['ostrya_virginiana'] = ['-0.79', '0', '0.906', '-0.67', '#sadness', '#anger', 'negative', '-0.79', 'water_dropwort', 'hemlock_water_dropwort', 'water_hemlock', 'plant_life', 'poisonous_plant']
senticnet['oswalt'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'dicky', 'impaired', 'lessened', 'diminished']
senticnet['oswiu'] = ['-0.92', '0', '0.659', '-0.81', '#sadness', '#disgust', 'negative', '-0.8', 'unripe', 'napping', 'off_guard', 'off_one_guard', 'unprepared']
senticnet['otago_rugby_football'] = ['-0.68', '0', '0.923', '-0.68', '#sadness', '#anger', 'negative', '-0.76', 'negative', 'veto', 'refute', 'rebut', 'controvert']
senticnet['otalgia'] = ['-0.87', '0', '0.773', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'rubor', 'redness', 'inflammation', 'otitis', 'otitis_interna']
senticnet['otaria'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'sedum', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['otarian'] = ['0.096', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.057', 'platform_rail', 'rail', 'catwalk', 'column', 'token_turnstile']
senticnet['otc_interest_rate'] = ['-0.05', '0.241', '-0.06', '0.286', '#interest', '#admiration', 'positive', '0.103', 'give_take', 'barter', 'exchange_goods', 'transaction', 'lower_price']
senticnet['others'] = ['0.099', '0.248', '-0.07', '0.282', '#interest', '#admiration', 'positive', '0.137', 'rest_of_group', 'further', 'precise', 'significant', 'description']
senticnet['others_understand'] = ['0.255', '0.182', '-0.06', '0.314', '#joy', '#admiration', 'positive', '0.173', 'much_need', 'good_sense_of_humor', 'complement', 'knowledgeable', 'taken_seriously']
senticnet['otherworldliness'] = ['0.995', '-0.73', '0', '0.83', '#joy', '#admiration', 'positive', '0.852', 'spiritism', 'internality', 'spirituality', 'spiritualism', 'introversion']
senticnet['otherworldly'] = ['0', '0', '-0.78', '-0.81', '#fear', '#disgust', 'negative', '-0.8', 'blunt', 'stern', 'crude', 'austere', 'severe']
senticnet['otic_ganglion'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['otiose'] = ['-0.78', '0', '0.651', '-0.56', '#sadness', '#anger', 'negative', '-0.66', 'rubbishy', 'sorry', 'trashy', 'worthless', 'superfluous']
senticnet['otitis'] = ['-0.87', '0', '0.773', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'rubor', 'redness', 'inflammation', 'labyrinthitis', 'otitis_interna']
senticnet['otitis_interna'] = ['-0.87', '0', '0.773', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'rubor', 'redness', 'inflammation', 'labyrinthitis', 'otitis']
senticnet['otitis_media'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchitis', 'chronic_bronchitis', 'respiratory_disease', 'respiratory_illness', 'respiratory_disorder']
senticnet['otopharynx'] = ['-0.91', '0', '0.769', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'break', 'good_luck', 'chance_eventpy_chance', 'chance_event', 'coincidence']
senticnet['otorrhea'] = ['-0.87', '0', '0.773', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'rubor', 'redness', 'inflammation', 'otitis', 'otitis_interna']
senticnet['otosclerosis'] = ['-0.56', '0', '0.577', '-0.67', '#anger', '#disgust', 'negative', '-0.60', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['otoscope'] = ['0.255', '0.182', '-0.06', '0.314', '#joy', '#admiration', 'positive', '0.173', 'doctor_office', 'urethroscope', 'sigmoidoscope', 'hospital', 'check_health']
senticnet['otter'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'seal', 'walrus', 'mussel', 'alligator', 'whale']
senticnet['otter_creek'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['otter_lutra'] = ['-0.55', '0', '0.955', '-0.96', '#anger', '#disgust', 'negative', '-0.82', 'house_mouse', 'mouse', 'rat', 'fieldmouse', 'nude_mouse']
senticnet['otter_skin'] = ['-0.58', '0', '0.632', '-0.63', '#anger', '#disgust', 'negative', '-0.61', 'skimmer', 'trilby', 'chapeau', 'busby', 'shako']
senticnet['otto_fenichel'] = ['-0.78', '0', '0.542', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'hysterical_neurosis', 'hysteria', 'mental_disorder', 'mental_disturbance', 'psychological_disorder']
senticnet['ottoman_empire'] = ['0', '0', '-0.97', '-0.86', '#fear', '#disgust', 'negative', '-0.92', 'turkish_empire', 'jew', 'turkey', 'massacre', 'book']
senticnet['ottoman_era'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['ottoman_imperial'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'caravansary', 'caravanserai', 'khan', 'caravan_inn', 'hostel']
senticnet['ottoman_infantry'] = ['0.957', '-0.93', '0', '0.795', '#joy', '#surprise', 'positive', '0.896', 'outdo', 'outstrip', 'outmatch', 'outgo', 'outperform']
senticnet['ottoman_mosque'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['ottoman_porte'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
senticnet['ottoman_suzerainty'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
senticnet['ouch'] = ['-0.16', '-0.05', '-0.12', '-0.13', '#sadness', '#disgust', 'negative', '-0.09', 'ache', 'excruciate', 'hurt', 'pain', 'cut_skin']
senticnet['oudenaarde'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'feudal_lord', 'lord', 'master', 'overlord', 'king']
senticnet['ouer'] = ['-0.11', '0.112', '0.674', '-0.66', '#anger', '#disgust', 'negative', '-0.33', 'wrathfully', 'wrathful', 'wrothful', 'lividly', 'lividity']
senticnet['ought'] = ['0.745', '0.747', '0', '0', '#joy', '#interest', 'positive', '0.746', 'indicate', 'requirement', 'needful', 'necessary', 'need']
senticnet['ouija_board'] = ['0.083', '0', '0', '0.075', '#joy', '#admiration', 'positive', '0.079', 'carny', 'carnival_ride', 'midway', 'ferris_wheel', 'roller-coaster']
senticnet['oulton_park_gold'] = ['0.093', '-0.02', '0', '0.056', '#joy', '#admiration', 'positive', '0.057', 'pitcher', 'carafe', 'container_pitcher', 'mug', 'glass_of_water']
senticnet['ounce'] = ['0.023', '0.096', '0.021', '0.175', '#interest', '#admiration', 'positive', '0.068', 'frequency', 'amplitude', 'echo', 'volume', 'loudness']
senticnet['ouput'] = ['0', '0.098', '-0.06', '0.15', '#interest', '#admiration', 'positive', '0.061', 'make_book', 'use_ink', 'make_newspaper', 'pencil_mark', 'computer_command']
senticnet['oure'] = ['-0.63', '0', '0.683', '-0.96', '#anger', '#disgust', 'negative', '-0.76', 'slovenly_woman', 'slut', 'trollop', 'slattern', 'litterer']
senticnet['oust'] = ['0', '0', '-0.84', '-0.87', '#fear', '#disgust', 'negative', '-0.85', 'throw_out', 'drum_out', 'kick_out', 'boot_out', 'expel']
senticnet['ouster'] = ['-0.87', '0', '0.764', '-0.83', '#sadness', '#disgust', 'negative', '-0.82', 'due_process', 'due_process_of_law', 'actual_eviction', 'retaliatory_eviction', 'dispossession']
senticnet['out'] = ['-0.75', '0', '0.614', '-0.74', '#sadness', '#disgust', 'negative', '-0.70', 'impermissible', 'unmentionable', 'untouchable', 'taboo', 'tabu']
senticnet['out-and-out'] = ['0', '0', '-0.75', '-0.87', '#fear', '#disgust', 'negative', '-0.81', 'absolute', 'downright', 'rank', 'right-down', 'sheer']
senticnet['out-bid'] = ['-0.97', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.90', 'underbid', 'outbid', 'underbidding', 'undersell', 'overbid']
senticnet['out-market'] = ['0', '0', '0.726', '-0.83', '#anger', '#disgust', 'negative', '-0.77', 'over-fill', 're-tweak', 'glad-hand', 'soft-soap', 'pretty-up']
senticnet['out-of-state'] = ['-0.98', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.92', 'non-resident', 'non-residents', 'nonresident', 'foreign', 'domiciled']
senticnet['out-of-the-way'] = ['-0.76', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.79', 'off-the-beaten-path', 'tucked-away', 'off-the-beaten-track', 'hole-in-the-wall', 'nook']
senticnet['out-of-touch'] = ['0', '-0.97', '0', '0.911', '#surprise', '#admiration', 'positive', '0.942', 'plutocrat', 'plutocrat', 'multimillionaire', 'super-rich', 'corporatist']
senticnet['out-patient'] = ['-0.96', '0', '0.747', '0', '#sadness', '#anger', 'negative', '-0.85', 'inpatient', 'outpatient', 'in-patient', 'ambulatory', 'psychiatric']
senticnet['out-take'] = ['-0.99', '0', '0.77', '0', '#sadness', '#anger', 'negative', '-0.88', 'outtake', 'outtake', 'blooper', 'out-takes', 'unreleased']
senticnet['out-takes'] = ['-0.99', '0', '0.776', '0', '#sadness', '#anger', 'negative', '-0.88', 'outtake', 'out-take', 'blooper', 'outtake', 'unreleased']
senticnet['out_bed'] = ['0.258', '0', '0', '0', '#joy', '#joy', 'positive', '0.258', 'good_night_sleep', 'enough_sleep', 'read_good_book', 'good_day', 'get_up_morning']
senticnet['out_breath'] = ['-0.91', '0', '-0.70', '0', '#sadness', '#fear', 'negative', '-0.80', 'blister_foot', 'breahard', 'sprain_ankle', 'trip_fall', 'twist_ankle']
senticnet['out_car'] = ['-0.09', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.07', 'plan_day', 'get_stick_traffic', 'rush_work', 'adjust_clothes', 'meet_old_friend']
senticnet['out_chair'] = ['-0.03', '0.066', '0', '0.067', '#interest', '#admiration', 'positive', '0.033', 'wear_good_shoes', 'use_leg', 'wear_running_shoes', 'find_ball', 'put_shoes']
senticnet['out_country'] = ['0.752', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.863', 'sunset', 'wood_duck', 'pink_flower', 'beau', 'autumn_leaf']
senticnet['out_door'] = ['-0.02', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.04', 'butt', 'foreign_country', 'menagerie', 'way', 'ass']
senticnet['out_focus'] = ['0.038', '0', '0', '0.066', '#joy', '#admiration', 'positive', '0.052', 'cotton', 'herringbone', 'wool', 'cotton_boll', 'fleece']
senticnet['out_gay_man'] = ['-0.11', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.11', 'speak_english', 'deal_card_player', 'bridge_difference', 'forward_email', 'shoulder_responsibility']
senticnet['out_loud_funny'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'banter', 'cross_talking', 'swordplay', 'wordplay', 'jester']
senticnet['out_money'] = ['-0.08', '0', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.06', 'spend_money', 'compare_price', 'buy_gift', 'spend_a_lot_of_money', 'spend_much_money']
senticnet['out_of_blue'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'without_warning', 'unexpected', 'unpredicted', 'unanticipated', 'without_notice']
senticnet['out_of_control'] = ['0', '0', '-0.78', '-0.74', '#fear', '#disgust', 'negative', '-0.76', 'crazy', 'nut', 'mad', 'stupid', 'nutty']
senticnet['out_of_house'] = ['0.038', '0', '0', '0.066', '#joy', '#admiration', 'positive', '0.052', 'get_out_of_house', 'see_countryside', 'show_off', 'see_new_place', 'go_outdoors']
senticnet['out_of_line'] = ['-0.96', '0', '0.877', '-0.97', '#sadness', '#disgust', 'negative', '-0.94', 'ruin', 'break_rule', 'destroy', 'damage', 'bad_person']
senticnet['out_of_sight'] = ['-0.13', '0.07', '-0.10', '0.104', '#sadness', '#fear', 'negative', '-0.01', 'unseen', 'hide', 'conceal', 'hide_evidence', 'hidden']
senticnet['out_of_stock'] = ['-0.77', '0', '0.644', '-0.68', '#sadness', '#disgust', 'negative', '-0.70', 'unobtainable', 'unprocurable', 'unavailable', 'finish', 'untouchable']
senticnet['out_of_whack'] = ['-0.73', '0', '0.787', '-0.66', '#sadness', '#anger', 'negative', '-0.72', 'wrong', 'haywire', 'bad', 'malfunctioning', 'amiss']
senticnet['out_place'] = ['0.091', '0', '0.034', '0.049', '#joy', '#admiration', 'positive', '0.035', 'summer_vacation', 'outdoor_activity', 'hard_use', 'easy_use', 'hunt_fish']
senticnet['out_shape'] = ['0.062', '0.097', '-0.09', '0.11', '#interest', '#admiration', 'positive', '0.044', 'live_long', 'long_life', 'live_long_life', 'live_healthy_lifestyle', 'stay_fit']
senticnet['out_wallet'] = ['0.728', '0.922', '0', '0.996', '#interest', '#admiration', 'positive', '0.882', 'use_money', 'enough_money', 'money_credit', 'good_credit', 'moolah']
senticnet['out_work'] = ['-0.29', '-0.09', '0', '-0.35', '#sadness', '#disgust', 'negative', '-0.18', 'death_penalty', 'threaten', 'anthrax', 'capital_punishment', 'terrorize']
senticnet['outage'] = ['-0.08', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.09', 'mediocrity', 'effect', 'suspicion', 'exclusion', 'pugilist']
senticnet['outback'] = ['0.88', '-0.80', '0', '0.696', '#joy', '#surprise', 'positive', '0.795', 'inaccessible', 'trackless', 'pathless', 'roadless', 'untracked']
senticnet['outbid'] = ['-0.97', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.90', 'out-bid', 'underbid', 'underbidding', 'undersell', 'overbid']
senticnet['outboard_motor'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'boat', 'yacht', 'big_boat', 'large_boat']
senticnet['outbound_traffic'] = ['-0.98', '0', '0.989', '-0.93', '#sadness', '#anger', 'negative', '-0.97', 'flight_accident', 'dangerous', 'danger', 'air_traffic', 'air_traffic_jam']
senticnet['outbreak'] = ['-0.11', '0.078', '-0.15', '-0.12', '#fear', '#disgust', 'negative', '-0.07', 'illiteracy', 'sexually_transmit_disease', 'disease', 'infectious_disease', 'contagious_disease']
senticnet['outburst'] = ['0', '0', '0.781', '-0.82', '#anger', '#disgust', 'negative', '-0.80', 'cry', 'acting_out', 'explosion', 'halloo', 'yell']
senticnet['outcast'] = ['0', '0', '0.877', '-0.77', '#anger', '#disgust', 'negative', '-0.82', 'abject', 'banished', 'castaway', 'pariah', 'lumpen']
senticnet['outcaste'] = ['-0.82', '0', '0.85', '-0.70', '#sadness', '#anger', 'negative', '-0.79', 'unwanted', 'casteless', 'unclaimed', 'unwelcome', 'undesired']
senticnet['outclass'] = ['0', '0', '-0.77', '-0.87', '#fear', '#disgust', 'negative', '-0.82', 'outshine', 'outclassed', 'outshone', 'outclasses', 'outplayed']
senticnet['outclassed'] = ['0', '0', '-0.78', '-0.88', '#fear', '#disgust', 'negative', '-0.83', 'outshine', 'outclass', 'outshone', 'outclasses', 'outplayed']
senticnet['outclasses'] = ['0', '0', '-0.78', '-0.88', '#fear', '#disgust', 'negative', '-0.83', 'outshine', 'outclassed', 'outshone', 'outclass', 'outplayed']
senticnet['outcome'] = ['-0.08', '0.175', '-0.16', '0.232', '#interest', '#admiration', 'positive', '0.04', 'result', 'conclusion', 'consequence', 'effect', 'end_point']
senticnet['outcompete'] = ['0.855', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.848', 'stay_alive', 'get_oxygen', 'exist', 'alive', 'crave']
senticnet['outcry'] = ['-0.86', '0', '0.516', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'bronx_cheer', 'call', 'cry', 'yell', 'shout']
senticnet['outdated'] = ['-0.91', '0', '0.517', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'obsolescent', 'noncurrent', 'disused', 'obsolete', 'superannuated']
senticnet['outdistance'] = ['0.949', '0', '0', '0.983', '#joy', '#admiration', 'positive', '0.966', 'outsell', 'trounce', 'outrun', 'outstrip', 'outpace']
senticnet['outdo'] = ['0.944', '0', '0', '0.972', '#joy', '#admiration', 'positive', '0.958', 'top', 'cap', 'trump', 'beat', 'better']
senticnet['outdone'] = ['0.838', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.887', 'proud', 'glory', 'brag', 'vaunt', 'pride']
senticnet['outdoor'] = ['0.076', '0.079', '0.027', '0.118', '#interest', '#admiration', 'positive', '0.061', 'outside', 'plant_place', 'plant_ground', 'ground', 'cultivate_yard']
senticnet['outdoor_activity'] = ['0.064', '0', '0.013', '0.138', '#joy', '#admiration', 'positive', '0.063', 'kid_activity', 'game_action', 'activity', 'water_activity', 'fun_activity']
senticnet['outdoor_adventure'] = ['0.076', '0.079', '0.027', '0.118', '#interest', '#admiration', 'positive', '0.061', 'outside', 'plant_place', 'plant_ground', 'ground', 'cultivate_yard']
senticnet['outdoor_amphitheater'] = ['0.076', '0.079', '0.027', '0.118', '#interest', '#admiration', 'positive', '0.061', 'outside', 'plant_place', 'plant_ground', 'ground', 'cultivate_yard']
senticnet['outdoor_apparel'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['outdoor_athletic'] = ['0.041', '0.086', '0.016', '0', '#joy', '#interest', 'positive', '0.037', 'team', 'play_sport', 'game', 'exercise', 'match']
senticnet['outdoor_basketball'] = ['0.041', '0.086', '0.016', '0', '#joy', '#interest', 'positive', '0.037', 'team', 'play_sport', 'game', 'exercise', 'match']
senticnet['outdoor_classroom'] = ['0.076', '0.079', '0.027', '0.118', '#interest', '#admiration', 'positive', '0.061', 'outside', 'plant_place', 'plant_ground', 'ground', 'cultivate_yard']
senticnet['outdoor_clothing'] = ['0.053', '-0.01', '-0.05', '0.121', '#joy', '#admiration', 'positive', '0.034', 'coat', 'winter_clothes', 'stylish', 'adorn', 'protective_cover']
senticnet['outdoor_courtyard'] = ['0.841', '0', '0', '0.71', '#joy', '#admiration', 'positive', '0.775', 'waiter', 'bar_service', 'beverage_service', 'concierge', 'minibar']
senticnet['outdoor_event'] = ['0', '0.102', '0', '0', '#interest', '#interest', 'positive', '0.102', 'frostbite', 'mountain_goat', 'eskimo', 'glacier', 'coldness']
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senticnet['over-cautious'] = ['0', '-0.87', '0', '0.77', '#surprise', '#admiration', 'positive', '0.82', 'overcautious', 'overambitious', 'oversensitive', 'overenthusiastic', 'over-concerned']
senticnet['over-charge'] = ['0', '-1', '0', '-0.93', '#surprise', '#disgust', 'positive', '0.031', 'overcharging', 'overcharge', 'undercharge', 'overheat', 'short-circuit']
senticnet['over-compensation'] = ['0', '0', '0.944', '-0.82', '#anger', '#disgust', 'negative', '-0.88', 'overcompensating', 'overcompensation', 'over-reaction', 'overcompensate', 'overindulgence']
senticnet['over-concerned'] = ['0', '-0.86', '0', '0.777', '#surprise', '#admiration', 'positive', '0.822', 'over-cautious', 'overambitious', 'oversensitive', 'overenthusiastic', 'overcautious']
senticnet['over-consumption'] = ['0.957', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.939', 'over-indulgence', 'overindulging', 'overindulgence', 'overconsumption', 'over-eating']
senticnet['over-cooked'] = ['-0.83', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.78', 'undercooked', 'overcooked', 'incomplete', 'underdone', 'over-done']
senticnet['over-done'] = ['-0.83', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.78', 'undercooked', 'overcooked', 'incomplete', 'over-cooked', 'underdone']
senticnet['over-eager'] = ['0', '-0.91', '0', '0.793', '#surprise', '#admiration', 'positive', '0.855', 'overeager', 'over-zealous', 'overzealous', 'overenthusiastic', 'over-anxious']
senticnet['over-eating'] = ['0.954', '-0.91', '0', '0', '#joy', '#surprise', 'positive', '0.936', 'over-indulgence', 'overindulging', 'over-consumption', 'overconsumption', 'overindulgence']
senticnet['over-expose'] = ['-0.94', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.93', 'underexpose', 'overexpose', 'overexposing', 'underexposed', 'overexposed']
senticnet['over-exposure'] = ['0', '0', '0.708', '-0.94', '#anger', '#disgust', 'negative', '-0.82', 'overexposure', 'underexposure', 'under-exposure', 'underexposed', 'overexpose']
senticnet['over-extend'] = ['0', '0', '0.747', '-0.81', '#anger', '#disgust', 'negative', '-0.77', 'overdevelop', 'misaligned', 'mis-represent', 'over-stuff', 'misalign']
senticnet['over-fill'] = ['0', '0', '0.726', '-0.83', '#anger', '#disgust', 'negative', '-0.78', 'soft-soap', 're-tweak', 'glad-hand', 'out-market', 'pretty-up']
senticnet['over-heating'] = ['-0.74', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.73', 'overheat', 'overheating', 'overheated', 'overheats', 'malfunctioning']
senticnet['over-indulgence'] = ['0.955', '-0.91', '0', '0', '#joy', '#surprise', 'positive', '0.934', 'overindulgence', 'overindulging', 'over-consumption', 'overconsumption', 'over-eating']
senticnet['over-much'] = ['0', '0', '0.971', '-0.71', '#anger', '#disgust', 'negative', '-0.84', 'cogitate', 'fretting', 'overmuch', 'obsess', 'agonize']
senticnet['over-production'] = ['0', '0', '0.715', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'overproduction', 'underproduction', 'over-saturation', 'under-production', 'stagnancy']
senticnet['over-protective'] = ['-0.92', '0', '0.984', '0', '#sadness', '#anger', 'negative', '-0.95', 'overprotective', 'overbearing', 'doting', 'domineering', 'over-zealous']
senticnet['over-reacting'] = ['0', '0', '0.791', '-0.98', '#anger', '#disgust', 'negative', '-0.88', 'over-reaction', 'overreacting', 'knee-jerk', 'overreaction', 'overacting']
senticnet['over-reaction'] = ['0', '0', '0.787', '-0.98', '#anger', '#disgust', 'negative', '-0.88', 'overreaction', 'overreacting', 'knee-jerk', 'over-reacting', 'overacting']
senticnet['over-saturation'] = ['0', '0', '0.724', '-0.72', '#anger', '#disgust', 'negative', '-0.72', 'overproduction', 'over-production', 'underproduction', 'under-production', 'stagnancy']
senticnet['over-simplify'] = ['-0.93', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.88', 'oversimplify', 'oversimplifying', 'exaggerate', 'oversimplified', 'trivialize']
senticnet['over-specialization'] = ['0', '0', '0.908', '-0.75', '#anger', '#disgust', 'negative', '-0.83', 'garrulousness', 'avariciousness', 're-localization', 'illiberality', 'over-zealousness']
senticnet['over-stayed'] = ['0', '-0.93', '0', '0.929', '#surprise', '#admiration', 'positive', '0.933', 'outstayed', 'outstaying', 'outstays', 'outstay', 'overstays']
senticnet['over-stuff'] = ['0', '0', '0.753', '-0.81', '#anger', '#disgust', 'negative', '-0.78', 'overdevelop', 'misaligned', 'mis-represent', 'misalign', 'over-extend']
senticnet['over-the-hill'] = ['-0.73', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.83', 'has-been', 'down-and-out', 'down-on-his-luck', 'washed-up', 'untalented']
senticnet['over-the-knee'] = ['0', '0', '-0.90', '-0.86', '#fear', '#disgust', 'negative', '-0.88', 'thigh-high', 'knee-high', 'mid-calf', 'heeled', 'ankle-high']
senticnet['over-the-top'] = ['0', '-0.83', '-0.99', '0', '#surprise', '#fear', 'negative', '-0.07', 'confrontational', 'in-your-face', 'brash', 'hard-hitting', 'take-no-prisoners']
senticnet['over-zealous'] = ['0', '-0.91', '0', '0.787', '#surprise', '#admiration', 'positive', '0.85', 'over-eager', 'overeager', 'overzealous', 'overenthusiastic', 'over-anxious']
senticnet['over-zealousness'] = ['0', '0', '0.908', '-0.76', '#anger', '#disgust', 'negative', '-0.83', 'garrulousness', 'avariciousness', 're-localization', 'over-specialization', 'illiberality']
senticnet['over_head'] = ['-0.87', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.93', 'cloud', 'out_of_blue', 'without_light', 'block_sun', 'nice_day']
senticnet['over_mid_wicket'] = ['-0.13', '0', '-0.11', '-0.04', '#sadness', '#fear', 'negative', '-0.1', 'concern_health', 'medical_problem', 'strengthful', 'surgery', 'injury']
senticnet['over_midwicket'] = ['-0.13', '0', '-0.11', '-0.04', '#sadness', '#fear', 'negative', '-0.1', 'concern_health', 'medical_problem', 'strengthful', 'surgery', 'injury']
senticnet['over_rough_terrain'] = ['0.064', '0', '0', '0.123', '#joy', '#admiration', 'positive', '0.093', 'horse', 'passenger_car', 'car_action', 'conveyance', 'car_travel']
senticnet['overabundance'] = ['0.89', '0', '0', '0.931', '#joy', '#admiration', 'positive', '0.911', 'surfeit', 'paucity', 'over-abundance', 'superabundance', 'dearth']
senticnet['overabundant'] = ['0', '0', '-0.91', '-0.73', '#fear', '#disgust', 'negative', '-0.82', 'rife', 'prevailing', 'plethoric', 'predominant', 'dominant']
senticnet['overachieve'] = ['0', '-0.75', '0', '0.959', '#surprise', '#admiration', 'positive', '0.857', 'overachieved', 'underachieve', 'underachieved', 'over-achieve', 'over-achieved']
senticnet['overachieved'] = ['0', '-0.76', '0', '0.957', '#surprise', '#admiration', 'positive', '0.859', 'overachieve', 'underachieve', 'underachieved', 'over-achieve', 'over-achieved']
senticnet['overachiever'] = ['0.782', '0', '0', '0.966', '#joy', '#admiration', 'positive', '0.874', 'over-achiever', 'underachiever', 'perfectionist', 'procrastinator', 'overachieving']
senticnet['overachieving'] = ['0.78', '0', '0', '0.959', '#joy', '#admiration', 'positive', '0.869', 'over-achiever', 'underachiever', 'perfectionist', 'procrastinator', 'overachiever']
senticnet['overact'] = ['-0.77', '0', '0.887', '0', '#sadness', '#anger', 'negative', '-0.83', 'hammy', 'exaggerated', 'ham', 'act', 'overplay']
senticnet['overacting'] = ['-0.93', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.86', 'woefully', 'mis-cast', 'miscast', 'well-cast', 'snubbed']
senticnet['overactive_bladder'] = ['0.992', '-0.80', '0', '0.584', '#joy', '#surprise', 'positive', '0.795', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'excretion']
senticnet['overage'] = ['0.522', '-0.88', '0', '0.965', '#surprise', '#admiration', 'positive', '0.789', 'asset', 'crown_jewel', 'security', 'capital', 'hole_card']
senticnet['overall'] = ['-0.67', '0', '0.688', '-0.89', '#anger', '#disgust', 'negative', '-0.75', 'protective_garment', 'boilersuit', 'boiler_suit', 'coverall', 'duster']
senticnet['overall_downward_trend'] = ['0.05', '0.151', '0', '0.159', '#interest', '#admiration', 'positive', '0.12', 'glamour', 'stylish', 'style', 'clothes_style', 'fancy_clothes']
senticnet['overall_mission'] = ['0', '0.938', '0', '0.773', '#interest', '#admiration', 'positive', '0.855', 'woman', 'advance', 'advancement', 'increase', 'participation']
senticnet['overall_survival'] = ['0.988', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.98', 'sustain_life', 'sustenance', 'nutrition', 'keep_alive', 'satisfy_hunger']
senticnet['overambitious'] = ['0', '-0.86', '0', '0.736', '#surprise', '#admiration', 'positive', '0.798', 'over-ambitious', 'overenthusiastic', 'overcautious', 'over-zealous', 'overeager']
senticnet['overappraisal'] = ['0.83', '-0.83', '0', '0.872', '#surprise', '#admiration', 'positive', '0.846', 'estimation', 'overvaluation', 'overestimate', 'overestimation', 'commercial_document']
senticnet['overarch'] = ['0.904', '-0.69', '0', '0.758', '#joy', '#admiration', 'positive', '0.786', 'override', 'predominate', 'preponderate', 'outweigh', 'overbalance']
senticnet['overawe'] = ['0', '0', '-0.70', '-0.73', '#fear', '#disgust', 'negative', '-0.72', 'subdue', 'control', 'cow', 'awe', 'restrain']
senticnet['overbalance'] = ['0', '0', '0.877', '-0.86', '#anger', '#disgust', 'negative', '-0.87', 'preponderate', 'outweigh', 'predominate', 'capsize', 'upset']
senticnet['overbearing'] = ['-0.92', '0', '0.982', '0', '#sadness', '#anger', 'negative', '-0.95', 'over-protective', 'overprotective', 'doting', 'domineering', 'over-zealous']
senticnet['overbearing_mother'] = ['-0.71', '0', '0.972', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'scold', 'nag', 'scolder', 'harridan', 'common_scold']
senticnet['overbearingly'] = ['0.79', '0', '0', '0.84', '#joy', '#admiration', 'positive', '0.815', 'sickeningly', 'overpoweringly', 'cloying', 'cloyingly', 'treacly']
senticnet['overbid'] = ['-0.97', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.90', 'out-bid', 'outbid', 'underbidding', 'undersell', 'underbid']
senticnet['overbite'] = ['0', '-0.02', '0.033', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'tooth', 'teeth', 'dimple', 'mouth', 'cliche']
senticnet['overblown'] = ['0.568', '-0.99', '0', '0.638', '#surprise', '#admiration', 'positive', '0.734', 'push_aside', 'brush_off', 'disregard', 'ignore', 'dismiss']
senticnet['overboard'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'lifebelt', 'save_life', 'lifeguard', 'life_jacket', 'safety_equipment']
senticnet['overborne'] = ['-0.73', '0', '0.732', '-0.53', '#sadness', '#anger', 'negative', '-0.66', 'insurmountable', 'insuperable', 'inexpugnable', 'indomitable', 'unsubduable']
senticnet['overbreadth'] = ['-0.50', '0', '0.89', '-0.78', '#anger', '#disgust', 'negative', '-0.72', 'excess', 'degree', 'grade', 'level', 'immoderateness']
senticnet['overcapacity'] = ['0', '0.285', '-0.07', '0.163', '#interest', '#admiration', 'positive', '0.124', 'plus', 'importance', 'a_lot', 'bonus', 'extra']
senticnet['overcast'] = ['0', '0.071', '0', '0.078', '#interest', '#admiration', 'positive', '0.074', 'sun_cover', 'cloudy', 'cool_temperature', 'nice_day', 'block_sun']
senticnet['overcast_day'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'hazy', 'blur', 'bleary', 'blurred', 'blurry']
senticnet['overcast_morning'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'hazy', 'blur', 'bleary', 'blurred', 'blurry']
senticnet['overcast_sky'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'hazy', 'blur', 'bleary', 'blurred', 'blurry']
senticnet['overcast_weather'] = ['0', '-0.01', '0.048', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'mild', 'breeze', 'tropical_storm', 'wind', 'air_movement']
senticnet['overcautious'] = ['0', '-0.86', '0', '0.778', '#surprise', '#admiration', 'positive', '0.821', 'over-cautious', 'overambitious', 'oversensitive', 'overenthusiastic', 'over-concerned']
senticnet['overcharge'] = ['0', '-0.99', '0', '-0.93', '#surprise', '#disgust', 'positive', '0.028', 'overcharging', 'over-charge', 'undercharge', 'overheat', 'short-circuit']
senticnet['overcharging'] = ['0', '-1.0', '0', '-0.94', '#surprise', '#disgust', 'positive', '0.028', 'overcharge', 'over-charge', 'undercharge', 'overheat', 'short-circuit']
senticnet['overclothe'] = ['-0.63', '0', '0.694', '-0.99', '#anger', '#disgust', 'negative', '-0.77', 'raiment', 'garb', 'dress', 'clothe', 'enclothe']
senticnet['overcoat'] = ['0.049', '-0.07', '0.119', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'jacket', 'coat', 'vest', 'outerwear', 'slack']
senticnet['overcollection'] = ['0.565', '-0.76', '0', '0.982', '#surprise', '#admiration', 'positive', '0.771', 'risk', 'hazard', 'jeopardy', 'health_hazard', 'peril']
senticnet['overcome'] = ['0.955', '0', '0', '0.806', '#joy', '#admiration', 'positive', '0.881', 'overcome_problem', 'overcome_obstacle', 'win_battle', 'succeed', 'win_game']
senticnet['overcome_obstacle'] = ['0.724', '0', '0', '0.913', '#joy', '#admiration', 'positive', '0.819', 'overcome_problem', 'overcome', 'win_battle', 'succeed', 'win_game']
senticnet['overcome_problem'] = ['0.855', '0', '0', '0.911', '#joy', '#admiration', 'positive', '0.883', 'overcome', 'overcome_obstacle', 'win_battle', 'succeed', 'win_game']
senticnet['overcompensate'] = ['0.846', '-0.53', '0', '0.919', '#joy', '#admiration', 'positive', '0.766', 'correct', 'compensate', 'counterbalance', 'make_up', 'balance']
senticnet['overcompensating'] = ['0', '0', '0.944', '-0.82', '#anger', '#disgust', 'negative', '-0.88', 'overcompensation', 'over-compensation', 'over-reaction', 'overcompensate', 'overindulgence']
senticnet['overcompensation'] = ['0', '0', '0.953', '-0.82', '#anger', '#disgust', 'negative', '-0.88', 'overcompensating', 'over-compensation', 'over-reaction', 'overcompensate', 'overindulgence']
senticnet['overconfidence'] = ['-0.12', '0.193', '0.597', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'upvalue', 'overvalue', 'overprize', 'overstimate', 'overprice']
senticnet['overconfident'] = ['0.586', '-0.94', '0', '0.748', '#surprise', '#admiration', 'positive', '0.76', 'vanquish', 'beat', 'beat_out', 'trounce', 'crush']
senticnet['overconsumption'] = ['0.961', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.941', 'over-indulgence', 'overindulging', 'over-consumption', 'overindulgence', 'over-eating']
senticnet['overcooked'] = ['-0.83', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.78', 'undercooked', 'underdone', 'incomplete', 'over-cooked', 'over-done']
senticnet['overcrowded_prison'] = ['0.816', '-0.91', '0', '0.944', '#surprise', '#admiration', 'positive', '0.891', 'jail', 'jailhouse', 'gaol', 'poky', 'clink']
senticnet['overcurrent'] = ['-0.09', '-0.01', '-0.10', '-0.20', '#fear', '#disgust', 'negative', '-0.09', 'violence', 'siege', 'conflict', 'bloodshed', 'fight']
senticnet['overcurrent_protection'] = ['-0.09', '-0.01', '-0.10', '-0.20', '#fear', '#disgust', 'negative', '-0.09', 'violence', 'siege', 'conflict', 'bloodshed', 'fight']
senticnet['overdevelop'] = ['0', '0', '0.752', '-0.81', '#anger', '#disgust', 'negative', '-0.78', 'misalign', 'misaligned', 'mis-represent', 'over-stuff', 'over-extend']
senticnet['overdo'] = ['0', '0', '0.745', '-0.72', '#anger', '#disgust', 'negative', '-0.73', 'present', 'green', 'overplay', 'dramatic', 'overact']
senticnet['overdose'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'use_drug', 'do_drug', 'drug_use', 'drug', 'addiction']
senticnet['overdraft'] = ['0.54', '-0.97', '0', '0.569', '#surprise', '#admiration', 'positive', '0.695', 'overstate', 'hyperbolize', 'glorify', 'exaggerate', 'aggrandize']
senticnet['overdraft_coverage'] = ['0.54', '-0.97', '0', '0.569', '#surprise', '#admiration', 'positive', '0.695', 'overstate', 'hyperbolize', 'glorify', 'exaggerate', 'aggrandize']
senticnet['overdraft_facility'] = ['0.882', '0.89', '0', '0', '#joy', '#interest', 'positive', '0.886', 'bank_money', 'hold_coin', 'invest_money', 'vault', 'bank_loan']
senticnet['overdraft_fee'] = ['0.54', '-0.97', '0', '0.569', '#surprise', '#admiration', 'positive', '0.695', 'overstate', 'hyperbolize', 'glorify', 'exaggerate', 'aggrandize']
senticnet['overdraft_limit'] = ['0.54', '-0.97', '0', '0.569', '#surprise', '#admiration', 'positive', '0.695', 'overstate', 'hyperbolize', 'glorify', 'exaggerate', 'aggrandize']
senticnet['overdraft_protection'] = ['0.54', '-0.97', '0', '0.569', '#surprise', '#admiration', 'positive', '0.695', 'overstate', 'hyperbolize', 'glorify', 'exaggerate', 'aggrandize']
senticnet['overdraw'] = ['0.54', '-0.97', '0', '0.569', '#surprise', '#admiration', 'positive', '0.695', 'overstate', 'hyperbolize', 'glorify', 'exaggerate', 'aggrandize']
senticnet['overdress'] = ['-0.85', '0', '0.853', '-0.91', '#anger', '#disgust', 'negative', '-0.87', 'raiment', 'garb', 'dress', 'clothe', 'enclothe']
senticnet['overdue'] = ['-0.77', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.74', 'delinquency', 'debt', 'offense', 'misconduct', 'relict']
senticnet['overeact'] = ['0', '0', '-0.86', '-0.90', '#fear', '#disgust', 'negative', '-0.88', 'animal_behavior', 'fixed_pattern_of_behavior', 'animal_instinct', 'instinct', 'impulse']
senticnet['overeager'] = ['0', '-0.91', '0', '0.793', '#surprise', '#admiration', 'positive', '0.852', 'over-eager', 'over-zealous', 'overzealous', 'overenthusiastic', 'over-anxious']
senticnet['overeat'] = ['0', '0', '0.814', '-0.93', '#anger', '#disgust', 'negative', '-0.87', 'eat_much', 'eat_dinner', 'eat_lunch', 'eat_excessively', 'obesity']
senticnet['overemotional'] = ['-0.56', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.63', 'aphasic', 'inarticulate', 'speechless', 'unarticulated', 'dumb']
senticnet['overemphasis_on'] = ['-0.70', '0', '0.914', '-0.72', '#anger', '#disgust', 'negative', '-0.78', 'unadaptability', 'rigidity', 'rigidness', 'unfitness', 'inability']
senticnet['overemphasise'] = ['-0.81', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.80', 'overstate', 'hyperbolize', 'exaggerate', 'overdraw', 'magnify']
senticnet['overemphasize'] = ['-0.81', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.80', 'overstate', 'hyperbolize', 'exaggerate', 'overdraw', 'magnify']
senticnet['overenthusiastic'] = ['0', '-0.83', '0', '0.931', '#surprise', '#admiration', 'positive', '0.881', 'over-zealous', 'overeager', 'overzealous', 'overexcited', 'over-eager']
senticnet['overestimate'] = ['0.83', '-0.83', '0', '0.872', '#surprise', '#admiration', 'positive', '0.846', 'overappraisal', 'overvaluation', 'estimation', 'overestimation', 'commercial_document']
senticnet['overestimation'] = ['0.83', '-0.83', '0', '0.872', '#surprise', '#admiration', 'positive', '0.846', 'overappraisal', 'overvaluation', 'overestimate', 'estimation', 'commercial_document']
senticnet['overexcited'] = ['0', '-0.83', '0', '0.923', '#surprise', '#admiration', 'positive', '0.881', 'over-zealous', 'overeager', 'overzealous', 'overenthusiastic', 'over-eager']
senticnet['overexertion'] = ['-0.50', '0', '0.681', '-0.89', '#anger', '#disgust', 'negative', '-0.69', 'sweat', 'effort', 'elbow_grease', 'exertion', 'travail']
senticnet['overexpansion'] = ['0', '0.285', '-0.07', '0.163', '#interest', '#admiration', 'positive', '0.124', 'plus', 'importance', 'a_lot', 'bonus', 'extra']
senticnet['overexploit'] = ['-0.76', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.78', 'avail', 'use', 'harness', 'utilise', 'exploit']
senticnet['overexploitation'] = ['0', '-0.02', '0.085', '-0.09', '#anger', '#disgust', 'negative', '-0.05', 'tree_wood', 'forest', 'sideways', 'dead_tree', 'wood']
senticnet['overexpose'] = ['-0.94', '0', '0', '-0.91', '#sadness', '#disgust', 'negative', '-0.92', 'underexpose', 'over-expose', 'overexposing', 'underexposed', 'overexposed']
senticnet['overexposed'] = ['-0.94', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.93', 'underexpose', 'over-expose', 'overexposing', 'underexposed', 'overexpose']
senticnet['overexposing'] = ['-0.94', '0', '0', '-0.91', '#sadness', '#disgust', 'negative', '-0.93', 'underexpose', 'over-expose', 'overexpose', 'underexposed', 'overexposed']
senticnet['overexposure'] = ['0', '0', '0.702', '-0.93', '#anger', '#disgust', 'negative', '-0.82', 'underexposure', 'over-exposure', 'under-exposure', 'underexposed', 'overexpose']
senticnet['overextension'] = ['-0.84', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.90', 'sail', 'deceive', 'overreach', 'outwit', 'overstrike']
senticnet['overfamiliar'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'venial', 'excusable', 'mercifully', 'merciful', 'mercifulness']
senticnet['overfatigue'] = ['-0.91', '0', '0.594', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'fatigue', 'wear', 'fag', 'tire', 'jade']
senticnet['overflow_audience'] = ['0.872', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.811', 'lecture_theater', 'lecture', 'conference_auditorium', 'read_book', 'text_book']
senticnet['overflow_incontinence'] = ['-0.77', '0', '0.966', '-0.51', '#sadness', '#anger', 'negative', '-0.75', 'enuresis', 'incontinency', 'incontinence', 'stress_incontinence', 'urge_incontinence']
senticnet['overflow_pipe'] = ['0.939', '0.939', '0', '0', '#joy', '#interest', 'positive', '0.939', 'puddle', 'sop', 'liquid_water', 'finger_paint', 'bread']
senticnet['overgarment'] = ['0', '-0.05', '0.092', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'piece_of_clothing', 'clothes', 'robe', 'slack', 'shirt']
senticnet['overgeneralization'] = ['-0.88', '0', '0.705', '-0.86', '#sadness', '#disgust', 'negative', '-0.82', 'wrongness', 'inappropriateness', 'indecency', 'improperness', 'indelicacy']
senticnet['overgeneralize'] = ['-0.77', '0', '0.981', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'trip_up', 'stumble', 'misjudge', 'err', 'fall_for']
senticnet['overgenerous'] = ['0', '0', '-0.89', '-0.75', '#fear', '#disgust', 'negative', '-0.82', 'unsparing', 'munificent', 'lavish', 'too-generous', 'unstinted']
senticnet['overglaze'] = ['-0.06', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.04', 'polish', 'magnify', 'see_out', 'sill', 'pane']
senticnet['overgraze'] = ['0.966', '0.881', '0', '0', '#joy', '#interest', 'positive', '0.923', 'harvest', 'crop', 'grow_crop', 'plant_crop', 'reap']
senticnet['overgrown'] = ['-0.89', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.81', 'unkempt', 'shrubbery', 'weedy', 'undergrowth', 'weed']
senticnet['overgrowth'] = ['-0.76', '0', '0.571', '-0.84', '#sadness', '#disgust', 'negative', '-0.72', 'illness', 'giantism', 'disease', 'hypertrophy', 'feel_pain']
senticnet['overhand'] = ['0', '-0.94', '0', '0.777', '#surprise', '#admiration', 'positive', '0.86', 'authority', 'command', 'masterly', 'mastery', 'dominion']
senticnet['overhand_right'] = ['0.969', '0.998', '0', '0', '#joy', '#interest', 'positive', '0.984', 'bell_sound', 'bell_noise', 'piece_of_jewelry', 'syllable', 'fastener']
senticnet['overhang'] = ['0.753', '-0.90', '0', '0.572', '#joy', '#surprise', 'positive', '0.745', 'bag', 'stick_out', 'thrust', 'push_up', 'spear']
senticnet['overhanging_rock'] = ['-0.99', '0', '0.641', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'navigable', 'negotiable', 'surmountable', 'passable', 'traversable']
senticnet['overhasty'] = ['-0.81', '0', '0.971', '0', '#sadness', '#anger', 'negative', '-0.89', 'hasty', 'come_down', 'fall', 'precipitate', 'precipitant']
senticnet['overhaul'] = ['0.723', '0.763', '0', '0', '#joy', '#interest', 'positive', '0.743', 'renovate', 'renovation', 'renovator', 'revamp', 'renew']
senticnet['overhead'] = ['-0.9', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.84', 'expense', 'excess', 'cost', 'fee', 'disadvantage']
senticnet['overhead_bin'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'hand_baggage', 'baggage', 'passenger_ticket', 'ticket_counter', 'baggage_trunk']
senticnet['overhead_compartment'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'train_station', 'government_office', 'cable_car', 'sport_stadium', 'terminal']
senticnet['overhead_console'] = ['0.725', '0.796', '0', '0', '#joy', '#interest', 'positive', '0.76', 'car_stereo', 'battery', 'seatbelt', 'spare_tire', 'brake_pedal']
senticnet['overhead_conveyor'] = ['0', '0.072', '0', '0.064', '#interest', '#admiration', 'positive', '0.068', 'zipper', 'pant', 'trousers', 'cowboy_boot', 'accessory']
senticnet['overhead_crane'] = ['0', '0.142', '-0.09', '0.183', '#interest', '#admiration', 'positive', '0.078', 'arise', 'rise', 'raise_up', 'go_up', 'go_upstairs']
senticnet['overhead_electrification'] = ['0.547', '-0.69', '0', '0.52', '#joy', '#surprise', 'positive', '0.586', 'vivification', 'invigoration', 'activity', 'animation', 'activating']
senticnet['overhead_lighting'] = ['0.988', '0', '0', '0.789', '#joy', '#admiration', 'positive', '0.889', 'brightness', 'blaze', 'brilliance', 'glitter', 'glister']
senticnet['overhead_locker'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'hand_baggage', 'baggage', 'passenger_ticket', 'ticket_counter', 'baggage_trunk']
senticnet['overhead_projection'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'floodlight', 'spotlight', 'headlight', 'light', 'teach_lesson']
senticnet['overhead_projector'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'floodlight', 'spotlight', 'headlight', 'light', 'teach_lesson']
senticnet['overheard'] = ['0.716', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.733', 'call', 'use_phone', 'orate', 'use_telephone', 'speech']
senticnet['overheat'] = ['0', '0', '-0.90', '-0.94', '#fear', '#disgust', 'negative', '-0.92', 'overheating', 'overheated', 'overheats', 'over-heating', 'short-circuit']
senticnet['overheated'] = ['-0.74', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.73', 'overheat', 'over-heating', 'overheating', 'overheats', 'malfunctioning']
senticnet['overheating'] = ['-0.73', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.72', 'overheat', 'over-heating', 'overheated', 'overheats', 'malfunctioning']
senticnet['overheats'] = ['-0.74', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.73', 'overheat', 'over-heating', 'overheated', 'overheating', 'malfunctioning']
senticnet['overhung'] = ['-0.13', '0.146', '0.623', '-0.63', '#anger', '#disgust', 'negative', '-0.31', 'outraged', 'incensed', 'indignant', 'outrage', 'indignation']
senticnet['overhydration'] = ['-0.72', '0', '0.762', '-0.79', '#anger', '#disgust', 'negative', '-0.75', 'heat_hyperpyrexia', 'heat_prostration', 'prostration', 'sunstroke', 'thermic_fever']
senticnet['overindulge'] = ['0', '0', '0.062', '0', '#anger', '#anger', 'negative', '-0.06', 'fat', 'heavy_weight', 'heavy_person', 'heavy', 'fast_food']
senticnet['overindulgence'] = ['0.961', '-0.91', '0', '0', '#joy', '#surprise', 'positive', '0.936', 'over-indulgence', 'overindulging', 'over-consumption', 'overconsumption', 'over-eating']
senticnet['overindulging'] = ['0.961', '-0.91', '0', '0', '#joy', '#surprise', 'positive', '0.937', 'over-indulgence', 'overindulgence', 'over-consumption', 'overconsumption', 'over-eating']
senticnet['overinterpret'] = ['-0.77', '0', '0.981', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'trip_up', 'stumble', 'misjudge', 'err', 'fall_for']
senticnet['overinvest'] = ['-0.12', '0.193', '0.597', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'upvalue', 'overrate', 'overprize', 'overstimate', 'overprice']
senticnet['overjealous'] = ['0.093', '0.086', '0.814', '-0.68', '#anger', '#disgust', 'negative', '-0.32', 'green-eyed_monster', 'green-eyed', 'covetous', 'grasp', 'covet']
senticnet['overkill'] = ['0', '0', '-0.97', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'enemy', 'weapon', 'unnecessary', 'goal', 'capacity']
senticnet['overladen'] = ['-0.84', '0', '0.553', '-0.89', '#sadness', '#disgust', 'negative', '-0.76', 'encumbrance', 'incumbrance', 'block', 'bar', 'blockade']
senticnet['overland_journey'] = ['0.954', '-0.87', '0', '0.583', '#joy', '#surprise', 'positive', '0.802', 'penetration', 'air_raid', 'air_attack', 'swoop', 'incursion']
senticnet['overland_stage'] = ['0.064', '0', '0', '0.123', '#joy', '#admiration', 'positive', '0.093', 'horse', 'passenger_car', 'car_action', 'conveyance', 'car_travel']
senticnet['overland_trip'] = ['0.937', '0.8', '0', '0', '#joy', '#interest', 'positive', '0.869', 'quest', 'stunt', 'illusion', 'adventure', 'activity']
senticnet['overlap'] = ['-0.58', '0', '0.715', '-0.59', '#anger', '#disgust', 'negative', '-0.63', 'fly', 'flap', 'barndoor', 'coattail', 'codpiece']
senticnet['overlap_between'] = ['-0.58', '0', '0.715', '-0.59', '#anger', '#disgust', 'negative', '-0.63', 'fly', 'flap', 'barndoor', 'coattail', 'codpiece']
senticnet['overlarge'] = ['-0.99', '0', '0.873', '0', '#sadness', '#anger', 'negative', '-0.93', 'extravagance', 'extravagantly', 'profligate', 'splurge', 'waster']
senticnet['overlay_network'] = ['0.848', '0.735', '0', '0', '#joy', '#interest', 'positive', '0.791', 'observer', 'peek', 'appearance', 'seer', 'glimpse']
senticnet['overload'] = ['-0.83', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.82', 'overloading', 'overloaded', 'overload', 'stress', 'failure']
senticnet['overload_protection'] = ['-0.09', '-0.01', '-0.10', '-0.20', '#fear', '#disgust', 'negative', '-0.09', 'violence', 'siege', 'conflict', 'bloodshed', 'fight']
senticnet['overloaded'] = ['-0.83', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.82', 'overloading', 'overload', 'overload', 'stress', 'failure']
senticnet['overloading'] = ['-0.83', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.82', 'overload', 'overloaded', 'overload', 'stress', 'failure']
senticnet['overlook'] = ['-0.93', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.92', 'investigate_fact', 'investigation', 'think_logically', 'search', 'watch_carefully']
senticnet['overlord'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'feudal_lord', 'lord']
senticnet['overlordship'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'feudal_lord', 'lord']
senticnet['overly'] = ['0.036', '0', '0.094', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'facial_hair', 'need_shave', 'need_beauty_center', 'long_beard', 'carelessness']
senticnet['overly_aggressive'] = ['0', '0', '-0.85', '-0.93', '#fear', '#disgust', 'negative', '-0.89', 'anger', 'animosity', 'hostility', 'malice', 'grudge']
senticnet['overly_bureaucratic'] = ['0', '0', '0.99', '-0.72', '#anger', '#disgust', 'negative', '-0.85', 'cumbrous', 'ungainly', 'clumsy', 'cumbersome', 'awkward']
senticnet['overly_complex'] = ['0', '0', '0.99', '-0.72', '#anger', '#disgust', 'negative', '-0.85', 'cumbrous', 'ungainly', 'clumsy', 'cumbersome', 'awkward']
senticnet['overly_critical'] = ['0', '0', '0', '-0.87', '#disgust', '#disgust', 'negative', '-0.87', 'move_eye', 'learn_current_event', 'become_informed', 'time_waste', 'eye_strain']
senticnet['overly_emotional'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'pervert', 'horny', 'uncomfortable', 'uneasy', 'short_tempered']
senticnet['overly_expensive'] = ['-0.12', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.09', 'painful', 'uncomfortable', 'violent', 'fun_learn_play', 'expensive']
senticnet['overly_harsh'] = ['-0.75', '0', '0.981', '-0.58', '#sadness', '#anger', 'negative', '-0.77', 'retaliatory', 'penitentiary', 'correctional', 'retributive', 'retributory']
senticnet['overly_optimistic'] = ['-0.70', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.75', 'pessimism', 'pessimistically', 'dispiritedly', 'hopelessness', 'hopelessly']
senticnet['overly_pessimistic'] = ['-0.70', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.75', 'pessimism', 'pessimistically', 'dispiritedly', 'hopelessness', 'hopelessly']
senticnet['overly_prescriptive'] = ['-0.80', '0', '0.507', '-0.76', '#sadness', '#disgust', 'negative', '-0.69', 'adamant', 'inexorable', 'intransigent', 'ironclad', 'brassbound']
senticnet['overly_protective'] = ['-0.56', '0', '0.743', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'negligent', 'derelict', 'inattentive', 'delinquent', 'lax']
senticnet['overly_rigid'] = ['-0.70', '0', '0.914', '-0.72', '#anger', '#disgust', 'negative', '-0.78', 'unadaptability', 'rigidity', 'rigidness', 'unfitness', 'inability']
senticnet['overly_sensitive'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'pervert', 'horny', 'uncomfortable', 'uneasy', 'short_tempered']
senticnet['overly_simplistic'] = ['0.843', '-0.6', '0', '0.698', '#joy', '#admiration', 'positive', '0.714', 'levelheaded', 'sound', 'healthy', 'sensible', 'commonsensible']
senticnet['overly_sweet'] = ['0', '0', '-0.72', '-0.92', '#fear', '#disgust', 'negative', '-0.82', 'bee_product', 'flammable', 'remove_hair', 'slick', 'really_hot']
senticnet['overly_technical'] = ['0.515', '-0.80', '0', '0.791', '#surprise', '#admiration', 'positive', '0.703', 'accessible', 'fathomable', 'approachable', 'perceivable', 'intelligible']
senticnet['overlying_stratum'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'fossil', 'oil', 'dark_liquid', 'smithsonian_institution', 'inert_gas']
senticnet['overmantel'] = ['0.768', '0.966', '0', '0', '#joy', '#interest', 'positive', '0.867', 'hob', 'mantel', 'support', 'shelf', 'berm']
senticnet['overmaster'] = ['-0.54', '0', '0.945', '-0.94', '#anger', '#disgust', 'negative', '-0.81', 'crush', 'steamroller', 'steamroll', 'trounce', 'beat']
senticnet['overmind'] = ['-0.53', '0', '0.637', '-0.92', '#anger', '#disgust', 'negative', '-0.69', 'incorporeal', 'unembodied', 'disembodied', 'bodiless', 'unbodied']
senticnet['overmuch'] = ['0', '0', '0.971', '-0.70', '#anger', '#disgust', 'negative', '-0.83', 'cogitate', 'fretting', 'over-much', 'obsess', 'agonize']
senticnet['overnight_bag'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'sports_equipment', 'sport_stuff', 'golf', 'sport_store', 'bag']
senticnet['overnight_guest'] = ['0.097', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.056', 'sleep_upon', 'sit_upon', 'sleep', 'bed_time', 'good_sleep']
senticnet['overnight_stardom'] = ['0', '0.793', '0', '0.91', '#interest', '#admiration', 'positive', '0.852', 'superstar', 'vip', 'accomplishment', 'performance', 'concert']
senticnet['overnight_stay'] = ['0.065', '0', '-0.05', '0.085', '#joy', '#admiration', 'positive', '0.032', 'join_club', 'social_skill', 'go_abroad', 'drive_around', 'ride_train']
senticnet['overoptimistic'] = ['-0.70', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.75', 'pessimism', 'pessimistically', 'dispiritedly', 'hopelessness', 'hopelessly']
senticnet['overpay'] = ['0', '0', '0.82', '-0.74', '#anger', '#disgust', 'negative', '-0.78', 'influenza', 'cripple', 'disability', 'chicken_pox', 'diabetes']
senticnet['overpayment'] = ['-0.04', '0.277', '-0.10', '0.11', '#interest', '#admiration', 'positive', '0.059', 'owe', 'tax', 'debit', 'afford', 'wage']
senticnet['overplay'] = ['0', '0', '0.755', '-0.71', '#anger', '#disgust', 'negative', '-0.73', 'present', 'green', 'overdo', 'dramatic', 'overact']
senticnet['overplus'] = ['-0.77', '0', '0.9', '-0.79', '#anger', '#disgust', 'negative', '-0.82', 'embarrassment', 'fifth_wheel', 'deadwood', 'redundance', 'plethora']
senticnet['overpopulation'] = ['-0.08', '0', '0.016', '-0.10', '#sadness', '#disgust', 'negative', '-0.06', 'large_family', 'pass_gene', 'lower_standard_live', 'venereal_disease', 'make_person']
senticnet['overpower'] = ['-0.91', '0', '-0.89', '0', '#sadness', '#fear', 'negative', '-0.90', 'keep_peace', 'defeat_enemy', 'conquer_enemy', 'gain_territory', 'kill_soldier']
senticnet['overpoweringly'] = ['0.792', '0', '0', '0.839', '#joy', '#admiration', 'positive', '0.816', 'sickeningly', 'cloyingly', 'cloying', 'overbearingly', 'treacly']
senticnet['overpraise'] = ['0.617', '-0.83', '0', '0.828', '#surprise', '#admiration', 'positive', '0.761', 'brag', 'embellish', 'overemphasize', 'overstress', 'vaunt']
senticnet['overpressure'] = ['-0.12', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.09', 'breeze', 'move_air', 'air_movement', 'gape', 'force']
senticnet['overprice'] = ['-0.12', '0.193', '0.597', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'hire', 'job', 'ask_person', 'find_job', 'expensive']
senticnet['overprize'] = ['-0.12', '0.193', '0.597', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'upvalue', 'overvalue', 'overrate', 'overstimate', 'overprice']
senticnet['overproduced'] = ['-0.87', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.80', 'inauthentic', 'me-too', 'second-rate', 'ersatz', 'anachronistic']
senticnet['overproduction'] = ['0', '0', '0.715', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'underproduction', 'over-production', 'over-saturation', 'under-production', 'stagnancy']
senticnet['overprotective'] = ['-0.92', '0', '0.991', '0', '#sadness', '#anger', 'negative', '-0.95', 'over-protective', 'overbearing', 'doting', 'domineering', 'over-zealous']
senticnet['overrate'] = ['-0.12', '0.193', '0.597', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'upvalue', 'overvalue', 'overprize', 'overstimate', 'overprice']
senticnet['overrated'] = ['0', '0', '0.89', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'get_old', 'old_software', 'become_obsolete', 'old_technology', 'unglam']
senticnet['overreach'] = ['-0.84', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.89', 'sail', 'deceive', 'overextension', 'outwit', 'overstrike']
senticnet['overreact'] = ['0.494', '0.45', '-0.11', '0.056', '#joy', '#interest', 'positive', '0.222', 'recreate', 'peacefulness', 'euphoria', 'serenity', 'closeness']
senticnet['overreacting'] = ['0', '0', '0.792', '-0.98', '#anger', '#disgust', 'negative', '-0.88', 'over-reaction', 'overreaction', 'knee-jerk', 'over-reacting', 'overacting']
senticnet['overreaction'] = ['0', '0', '0.792', '-0.97', '#anger', '#disgust', 'negative', '-0.88', 'over-reaction', 'overreacting', 'knee-jerk', 'over-reacting', 'overacting']
senticnet['overregulation'] = ['0.033', '-0.19', '0.203', '-0.22', '#anger', '#disgust', 'negative', '-0.05', 'antiquate', 'optional', 'kleenex', 'key_chain', 'sachet']
senticnet['overreliance_on'] = ['-0.70', '0', '0.914', '-0.72', '#anger', '#disgust', 'negative', '-0.78', 'unadaptability', 'rigidity', 'rigidness', 'unfitness', 'inability']
senticnet['override'] = ['0.904', '-0.69', '0', '0.758', '#joy', '#admiration', 'positive', '0.786', 'predominate', 'overarch', 'preponderate', 'outweigh', 'overbalance']
senticnet['overriding_royalty'] = ['0.964', '0.931', '0', '0', '#joy', '#interest', 'positive', '0.948', 'heel', 'foot_part', 'type_of_shoes', 'shoe_type', 'high_heel']
senticnet['overripe'] = ['0.041', '0.069', '0.032', '0.13', '#interest', '#admiration', 'positive', '0.052', 'edible_human', 'round_fruit', 'peelable', 'green_fruit', 'nutritious']
senticnet['overround'] = ['-0.78', '0', '0.944', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'loser', 'speculator', 'mortal', 'better', 'gambler']
senticnet['overruff'] = ['0.022', '-0.11', '0.014', '0.057', '#surprise', '#admiration', 'positive', '0.045', 'wood', 'many_steps', 'franchise', 'stump', 'timber']
senticnet['overrule'] = ['-0.70', '0', '0.984', '0', '#sadness', '#anger', 'negative', '-0.84', 'isolation', 'dislike', 'huffy', 'huffily', 'huffiness']
senticnet['overrule_roe'] = ['-0.74', '0', '0.748', '-0.81', '#anger', '#disgust', 'negative', '-0.77', 'coral', 'caviar', 'caviare', 'shad_roe', 'seafood']
senticnet['overrun'] = ['0', '0', '0.71', '-0.88', '#anger', '#disgust', 'negative', '-0.79', 'invade', 'flow', 'run', 'infest', 'seizing']
senticnet['oversea'] = ['0', '0.766', '0', '0', '#interest', '#interest', 'positive', '0.766', 'abroad', 'foreign', 'study_abroad', 'elsewhere', 'live_abroad']
senticnet['overseas'] = ['0', '0.766', '0', '0', '#interest', '#interest', 'positive', '0.766', 'abroad', 'foreign', 'study_abroad', 'elsewhere', 'live_abroad']
senticnet['oversee'] = ['-0.02', '0.203', '-0.09', '0.171', '#interest', '#admiration', 'positive', '0.063', 'take_charge', 'file_complaint', 'courageous', 'man_ship', 'obey_law']
senticnet['oversell'] = ['-0.75', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.81', 'shortchange', 'undersell', 'shortchanging', 'short-changing', 'short-change']
senticnet['oversensitive'] = ['0', '-0.87', '0', '0.776', '#surprise', '#admiration', 'positive', '0.825', 'over-cautious', 'overambitious', 'overcautious', 'overenthusiastic', 'over-concerned']
senticnet['overset'] = ['-0.70', '0', '0.502', '-0.51', '#sadness', '#disgust', 'negative', '-0.57', 'distress', 'straiten', 'impact', 'bear_on', 'touch']
senticnet['overshadow'] = ['0', '0', '0.78', '-0.93', '#anger', '#disgust', 'negative', '-0.85', 'overcast', 'adumbrate', 'eclipse', 'overshadowed', 'casting']
senticnet['overshadowed'] = ['0', '0', '0.776', '-0.94', '#anger', '#disgust', 'negative', '-0.86', 'overcast', 'adumbrate', 'eclipse', 'overshadow', 'casting']
senticnet['overshadowed_by'] = ['-0.11', '0.188', '0.608', '-0.63', '#anger', '#disgust', 'negative', '-0.29', 'contempt', 'disincline', 'disinclination', 'disinclined', 'indispose']
senticnet['overshoe'] = ['-0.88', '0', '0.75', '-0.85', '#sadness', '#disgust', 'negative', '-0.82', 'rubber', 'galosh', 'golosh', 'arctic', 'gumshoe']
senticnet['overshoot'] = ['0.737', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.738', 'undershoot', 'overshooting', 'overshoot', 'deceleration', 'hysteresis']
senticnet['overshooting'] = ['0.731', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.734', 'undershoot', 'overshoot', 'overshoot', 'deceleration', 'hysteresis']
senticnet['overshot_water'] = ['-0.78', '0', '0.699', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'battery', 'stamp_battery', 'quartz_battery', 'quartz_mill', 'factory']
senticnet['overshot_wheel'] = ['-0.78', '0', '0.699', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'battery', 'stamp_battery', 'quartz_battery', 'quartz_mill', 'factory']
senticnet['oversight'] = ['-0.92', '0', '0.706', '0', '#sadness', '#anger', 'negative', '-0.81', 'inadvertently', 'miss', 'inadvertence', 'fault', 'supervision']
senticnet['oversimplification'] = ['-0.99', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.85', 'oxymoron', 'overstatement', 'misconception', 'pejorative', 'misnomer']
senticnet['oversimplified'] = ['-0.93', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.88', 'over-simplify', 'oversimplifying', 'exaggerate', 'oversimplify', 'trivialize']
senticnet['oversimplify'] = ['-0.92', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.87', 'over-simplify', 'oversimplifying', 'exaggerate', 'oversimplified', 'trivialize']
senticnet['oversimplifying'] = ['-0.93', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.88', 'over-simplify', 'oversimplify', 'exaggerate', 'oversimplified', 'trivialize']
senticnet['overskirt'] = ['-0.53', '0', '0.682', '-0.74', '#anger', '#disgust', 'negative', '-0.65', 'girdle', 'cincture', 'cummerbund', 'waistband', 'waistcloth']
senticnet['oversleep'] = ['-0.14', '-0.05', '0', '-0.16', '#sadness', '#disgust', 'negative', '-0.08', 'sleep_day', 'pull_up_cover', 'toss_turn', 'miss_work', 'look_out_window']
senticnet['overspend'] = ['0', '0', '0.723', '-0.74', '#anger', '#disgust', 'negative', '-0.73', 'become_poor', 'little_money', 'lose_wallet', 'spend_much_money', 'pay_fine']
senticnet['overspread'] = ['-0.80', '0', '0.761', '-0.70', '#sadness', '#anger', 'negative', '-0.75', 'immeasurable', 'limitless', 'measureless', 'unmeasured', 'abysmal']
senticnet['overstate'] = ['-0.84', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'brag', 'embellish', 'overemphasize', 'overstress', 'vaunt']
senticnet['overstatement'] = ['-0.98', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.85', 'oxymoron', 'misnomer', 'misconception', 'pejorative', 'oversimplification']
senticnet['overstay'] = ['-0.57', '0', '0.803', '-0.77', '#anger', '#disgust', 'negative', '-0.71', 'au_pair', 'exile', 'deportee', 'gringo', 'import']
senticnet['overstays'] = ['0', '-0.94', '0', '0.93', '#surprise', '#admiration', 'positive', '0.935', 'outstayed', 'outstaying', 'outstays', 'over-stayed', 'outstay']
senticnet['overstimate'] = ['-0.12', '0.193', '0.597', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'upvalue', 'overvalue', 'overprize', 'overrate', 'overprice']
senticnet['overstimulation'] = ['0.583', '-0.82', '0', '0.574', '#joy', '#surprise', 'positive', '0.661', 'stimulant', 'positive_reinforcing_stimulus', 'positive_reinforcer', 'negative_reinforcing_stimulus', 'reinforcer']
senticnet['overstress'] = ['0.796', '-0.62', '0', '0.683', '#joy', '#admiration', 'positive', '0.701', 'overstate', 'hyperbolize', 'exaggerate', 'overdraw', 'magnify']
senticnet['overstrike'] = ['-0.84', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.90', 'sail', 'deceive', 'overextension', 'outwit', 'overreach']
senticnet['overstrung'] = ['-0.75', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.80', 'high-strung', 'uptight', 'restive', 'nervy', 'jumpy']
senticnet['overstuffed_chair'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'coach_seat', 'stool', 'seat']
senticnet['overstuffed_furniture'] = ['0.725', '-0.66', '0', '0.906', '#joy', '#admiration', 'positive', '0.765', 'set', 'pose', 'lay', 'put', 'place']
senticnet['oversupply'] = ['-0.83', '0', '-0.88', '-0.78', '#sadness', '#fear', 'negative', '-0.83', 'little', 'sadness', 'small_amount', 'lack', 'absence']
senticnet['overt'] = ['-0.69', '0', '0.857', '-0.92', '#anger', '#disgust', 'negative', '-0.82', 'bald', 'raw', 'barefaced', 'visible', 'naked']
senticnet['overt_aggression'] = ['-0.75', '0', '0.886', '-0.72', '#sadness', '#anger', 'negative', '-0.78', 'harass', 'move', 'check', 'fork', 'pin']
senticnet['overt_discrimination'] = ['-0.94', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.92', 'insanity', 'angry_emotion', 'dislike', 'psychosis', 'fight']
senticnet['overt_racism'] = ['-0.94', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.92', 'insanity', 'angry_emotion', 'dislike', 'psychosis', 'fight']
senticnet['overt_sexuality'] = ['-0.56', '0', '0.977', '-0.86', '#anger', '#disgust', 'negative', '-0.80', 'strangeness', 'quaintness', 'abnormality', 'queerness', 'crotchet']
senticnet['overtake'] = ['0.844', '0', '0', '0', '#joy', '#joy', 'positive', '0.844', 'straight', 'late_time', 'preparedness', 'past_present', 'wait']
senticnet['overthrow'] = ['-0.84', '0', '0.744', '-0.58', '#sadness', '#anger', 'negative', '-0.72', 'subversion', 'subversive_activity', 'termination', 'ending', 'conclusion']
senticnet['overtime'] = ['-0.07', '0.207', '-0.05', '0.228', '#interest', '#admiration', 'positive', '0.076', 'free', 'gift', 'abundance', 'plus', 'plentiful']
senticnet['overtime_pay'] = ['0', '0.923', '0', '0', '#interest', '#interest', 'positive', '0.923', 'salary', 'remittance', 'owe', 'payment', 'severance']
senticnet['overtire'] = ['-0.80', '0', '0.63', '-0.91', '#sadness', '#disgust', 'negative', '-0.78', 'fatigue', 'wear', 'fag', 'tire', 'jade']
senticnet['overtly_erotic'] = ['-0.9', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.80', 'filth', 'smut', 'vulgarism', 'dirt', 'obscenity']
senticnet['overtly_racist'] = ['0.585', '-0.7', '0', '0.586', '#surprise', '#admiration', 'positive', '0.624', 'jingoistic', 'ultranationalistic', 'superpatriotic', 'nationalistic', 'patriotic']
senticnet['overtly_sexual'] = ['0', '0', '-0.95', '-0.72', '#fear', '#disgust', 'negative', '-0.84', 'lubricious', 'lewd', 'lustful', 'obscene', 'prurient']
senticnet['overtone'] = ['0.843', '0.741', '0', '0', '#joy', '#interest', 'positive', '0.792', 'tone', 'harmonic', 'undertone', 'association', 'interference']
senticnet['overtravel'] = ['-0.99', '0', '0.651', '-0.51', '#sadness', '#anger', 'negative', '-0.72', 'sending', 'compulsion', 'influence', 'inducement', 'causation']
senticnet['overture'] = ['0.101', '-0.04', '0.036', '0', '#joy', '#surprise', 'positive', '0.038', 'song', 'rap', 'jingle', 'chord', 'lyric']
senticnet['overturn'] = ['0.985', '-0.76', '0', '0.96', '#joy', '#admiration', 'positive', '0.902', 'reverse', 'lift', 'go_back_on', 'vacate', 'annul']
senticnet['overturn_roe'] = ['-0.74', '0', '0.748', '-0.81', '#anger', '#disgust', 'negative', '-0.77', 'coral', 'caviar', 'caviare', 'shad_roe', 'seafood']
senticnet['overturning_roe'] = ['-0.74', '0', '0.748', '-0.81', '#anger', '#disgust', 'negative', '-0.77', 'coral', 'caviar', 'caviare', 'shad_roe', 'seafood']
senticnet['overuse'] = ['-0.73', '0', '0.94', '-0.87', '#anger', '#disgust', 'negative', '-0.85', 'edema', 'oedema', 'hydrops', 'dropsy', 'puffiness']
senticnet['overvaluation'] = ['0.83', '-0.83', '0', '0.872', '#surprise', '#admiration', 'positive', '0.846', 'overappraisal', 'estimation', 'overestimate', 'overestimation', 'commercial_document']
senticnet['overvalue'] = ['-0.12', '0.193', '0.597', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'upvalue', 'overrate', 'overprize', 'overstimate', 'overprice']
senticnet['overview'] = ['-0.03', '0.168', '-0.07', '0.139', '#interest', '#admiration', 'positive', '0.049', 'command', 'direct', 'follow', 'take_charge', 'govern']
senticnet['overvoltage'] = ['-0.08', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.09', 'mediocrity', 'effect', 'suspicion', 'exclusion', 'pugilist']
senticnet['overweary'] = ['-0.59', '0', '0.852', '-0.71', '#anger', '#disgust', 'negative', '-0.72', 'fatigue', 'wear', 'fag', 'tire', 'jade']
senticnet['overweening'] = ['0', '0', '0.924', '-0.86', '#anger', '#disgust', 'negative', '-0.89', 'extravagant', 'excessive', 'wasteful', 'exuberant', 'prodigal']
senticnet['overweight'] = ['-0.03', '0.157', '0.698', '-0.66', '#anger', '#disgust', 'negative', '-0.30', 'fat', 'look_unattractive', 'fatty', 'unhealthy', 'obesity']
senticnet['overweight_obese'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'fatty', 'worse', 'health_issue', 'clinical_problem']
senticnet['overwhelm'] = ['0.621', '-0.65', '0', '0.967', '#surprise', '#admiration', 'positive', '0.746', 'benight', 'overpower', 'sweep_over', 'whelm', 'overcome']
senticnet['overwhelming_firepower'] = ['-0.82', '0', '0.739', '-0.50', '#sadness', '#anger', 'negative', '-0.69', 'fake', 'juke', 'maneuver', 'manoeuvre', 'feint']
senticnet['overwhelming_force'] = ['-0.82', '0', '0.739', '-0.50', '#sadness', '#anger', 'negative', '-0.69', 'fake', 'juke', 'maneuver', 'manoeuvre', 'feint']
senticnet['overwhelming_superiority'] = ['0.757', '-0.82', '0', '0.982', '#surprise', '#admiration', 'positive', '0.855', 'first_class', 'choiceness', 'first_water', 'fineness', 'quality']
senticnet['overwhelmingly'] = ['0.63', '0.622', '-0.29', '0.365', '#joy', '#interest', 'positive', '0.332', 'joyful', 'favorable', 'favorably', 'agree', 'jubilantly']
senticnet['overwhelmingly_catholic'] = ['0.013', '0.199', '0.027', '0.167', '#interest', '#admiration', 'positive', '0.088', 'church', 'confession', 'good_catholic', 'spirituality', 'christianity']
senticnet['overwhelmingly_male'] = ['0.779', '0', '0', '0.892', '#joy', '#admiration', 'positive', '0.836', 'damsel_human', 'damsel_person', 'niece', 'damsel', 'woman']
senticnet['overwhelmingly_republican'] = ['0', '0.924', '0', '0.921', '#interest', '#admiration', 'positive', '0.923', 'settle_disagreement', 'hitler', 'communist', 'conservative', 'unchanging']
senticnet['overwintering_larva'] = ['-0.02', '-0.09', '0.11', '-0.13', '#anger', '#disgust', 'negative', '-0.04', 'beetle', 'gypsy_moth', 'mole_cricket', 'grasshopper', 'many_insects']
senticnet['overwork'] = ['-0.97', '0', '0.969', '-0.50', '#sadness', '#anger', 'negative', '-0.81', 'nervousness', 'tenseness', 'tension', 'nerve', 'stress']
senticnet['overwrap'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'foil', 'tin_foil', 'gold_foil']
senticnet['overwritten'] = ['-0.90', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'irrecoverable', 'non-recoverable', 'unreadable', 'unrecoverable', 'unusable']
senticnet['overwrought'] = ['-0.9', '0', '0.841', '-0.73', '#sadness', '#anger', 'negative', '-0.82', 'nubbiness', 'tweediness', 'harshness', 'graininess', 'granularity']
senticnet['overzealous'] = ['0', '-0.92', '0', '0.784', '#surprise', '#admiration', 'positive', '0.852', 'over-eager', 'over-zealous', 'overeager', 'overenthusiastic', 'over-anxious']
senticnet['oviduct'] = ['0.04', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.055', 'kidney', 'pancreas', 'liver', 'bone', 'neck']
senticnet['oviposit'] = ['0.049', '0.098', '-0.07', '0.121', '#interest', '#admiration', 'positive', '0.047', 'procreate', 'reproduce', 'start_family', 'cause_cold', 'unprotect_sex']
senticnet['oviposition'] = ['0.947', '-0.81', '0', '0.656', '#joy', '#surprise', 'positive', '0.805', 'sexual_intercourse', 'intercourse', 'sex_act', 'hank_panky', 'coitus']
senticnet['ovis_ammon'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'argali', 'argal', 'urial', 'white_sheep', 'wild_sheep']
senticnet['ovoid'] = ['0', '0', '-0.86', '-0.89', '#fear', '#disgust', 'negative', '-0.88', 'egg-shaped', 'oval', 'ovate', 'elliptical', 'prolate']
senticnet['ovoid_shape'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expandible', 'expansive', 'erectile', 'cavernous']
senticnet['ovular'] = ['0', '-0.85', '0', '0.799', '#surprise', '#admiration', 'positive', '0.828', 'diamond-shaped', 'egg-shaped', 'butterfly-like', 'block-shaped', 'saw-tooth']
senticnet['ovulate'] = ['0.97', '0.729', '0', '0.946', '#joy', '#admiration', 'positive', '0.882', 'make_baby', 'give_birth_child', 'sexual_intercourse', 'find_partner', 'make_love']
senticnet['ovulation'] = ['-0.74', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.78', 'ejaculate', 'pass_gene', 'ecstasy', 'lower_standard_live', 'survival_species']
senticnet['ovum'] = ['0', '0.067', '0', '0.07', '#interest', '#admiration', 'positive', '0.069', 'peck', 'embryo', 'chicken_egg', 'bushel', 'egg']
senticnet['ovw_southern_tag_team'] = ['-0.57', '0', '0.571', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['ow_decease'] = ['-0.69', '0', '0.692', '-0.66', '#sadness', '#anger', 'negative', '-0.68', 'wrinkle', 'crinkle', 'crease', 'crisp', 'ruck_up']
senticnet['owe'] = ['-0.05', '0.299', '-0.11', '0.134', '#interest', '#admiration', 'positive', '0.065', 'payment', 'afford', 'bribe', 'debit', 'wage']
senticnet['owe_income_tax'] = ['0.763', '-0.67', '0', '0.945', '#joy', '#admiration', 'positive', '0.795', 'set', 'income_bracket', 'bracket', 'income', 'price_bracket']
senticnet['owe_money'] = ['-0.13', '0.251', '-0.13', '0.146', '#interest', '#admiration', 'positive', '0.032', 'give_cash', 'bill_pay', 'send_money', 'money_exchange', 'money']
senticnet['owen_pallett'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'cur', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['owen_warland'] = ['0.702', '-0.94', '0', '0.582', '#joy', '#surprise', 'positive', '0.743', 'beatable', 'vanquishable', 'vincible', 'subduable', 'subjugable']
senticnet['owl'] = ['0', '0.105', '0', '0', '#interest', '#interest', 'positive', '0.105', 'beak', 'many_feathers', 'big_eye', 'bird', 'bird_mouth']
senticnet['owl-like'] = ['0', '-0.94', '0', '0.9', '#surprise', '#admiration', 'positive', '0.92', 'horn-rimmed', 'owlish', 'heavy-lidded', 'black-rimmed', 'half-lidded']
senticnet['owl_monkey'] = ['0.921', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.825', 'monkey', 'fiance', 'lemur', 'beaver', 'koala']
senticnet['owlclaws'] = ['-0.76', '0', '0.76', '-0.66', '#sadness', '#anger', 'negative', '-0.73', 'herb', 'autumn_sneezeweed', 'orange_sneezeweed', 'sneezeweed', 'rosilla']
senticnet['owlet_moth'] = ['-0.70', '0', '0.718', '-0.55', '#sadness', '#anger', 'negative', '-0.66', 'armyworm', 'underwing', 'antler_moth', 'heliothis_moth', 'moth']
senticnet['owlish'] = ['0', '-0.93', '0', '0.908', '#surprise', '#admiration', 'positive', '0.921', 'horn-rimmed', 'owl-like', 'heavy-lidded', 'black-rimmed', 'half-lidded']
senticnet['own'] = ['0', '0.276', '-0.21', '0.323', '#interest', '#admiration', 'positive', '0.129', 'maintain', 'ownership', 'possession', 'possess', 'owner']
senticnet['own_accord'] = ['-0.91', '0', '0.576', '-0.95', '#sadness', '#disgust', 'negative', '-0.81', 'stop', 'halt', 'lock_in', 'lock', 'block']
senticnet['own_bathroom'] = ['0', '0.092', '0.016', '0.074', '#interest', '#admiration', 'positive', '0.05', 'walmart', 'drugstore', 'beach_bag', 'pharmacy', 'car_trunk']
senticnet['own_benefit'] = ['0', '0.834', '0', '0.979', '#interest', '#admiration', 'positive', '0.906', 'improve_self-image', 'keep_body_healthy', 'look_attractive', 'satisfaction', 'make_healthy']
senticnet['own_car'] = ['0', '0.99', '0', '0.743', '#interest', '#admiration', 'positive', '0.867', 'storm_out_room', 'captain_boat', 'drive', 'bicycle_across_town', 'sail_around_world']
senticnet['own_computer'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'a_lot_of_time', 'buddha', 'learn_program_language', 'turn_computer_on', 'write_email']
senticnet['own_countryman'] = ['-0.57', '0', '0.803', '-0.77', '#anger', '#disgust', 'negative', '-0.71', 'au_pair', 'exile', 'deportee', 'gringo', 'import']
senticnet['own_destiny'] = ['0', '0.862', '0', '0.774', '#interest', '#admiration', 'positive', '0.818', 'enter_contract', 'believe', 'give_help', 'karma', 'fate']
senticnet['own_excrement'] = ['-0.04', '0', '-0.07', '-0.09', '#fear', '#disgust', 'negative', '-0.07', 'go_toilet', 'go_bathroom', 'go_pee', 'read_bedtime_story', 'quiet_cry_baby']
senticnet['own_flag'] = ['0.062', '0.078', '0', '0', '#joy', '#interest', 'positive', '0.07', 'country', 'type_of_government', 'kingdom', 'flag', 'citizen']
senticnet['own_freezer'] = ['0', '0.071', '0.009', '0.104', '#interest', '#admiration', 'positive', '0.055', 'retail_store', 'supermarket', 'grocery_store', 'farmer_market', 'market']
senticnet['own_house'] = ['0.101', '0.099', '0', '0.169', '#joy', '#admiration', 'positive', '0.123', 'family_home', 'small_home', 'old_house', 'appliance_store', 'residence']
senticnet['own_kitchen'] = ['0.038', '0', '0', '0.089', '#joy', '#admiration', 'positive', '0.064', 'kitchen_ware', 'container', 'storage', 'pantry', 'food_storage']
senticnet['own_land'] = ['-0.04', '0.258', '-0.13', '0.185', '#interest', '#admiration', 'positive', '0.066', 'good_investment', 'estate', 'personal_belong', 'property', 'ownership']
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senticnet['own_person'] = ['0.059', '0.16', '-0.15', '0.24', '#interest', '#admiration', 'positive', '0.076', 'owner', 'ownership', 'possession', 'own', 'trust']
senticnet['own_pet'] = ['0', '0', '0.958', '0', '#anger', '#anger', 'negative', '-0.95', 'perform_magic', 'find_love', 'complain_state_world', 'jump_off_bridge', 'live_apartment']
senticnet['own_recognizance'] = ['-0.08', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.052', 'bequest', 'excise', 'bribe', 'afford', 'owe']
senticnet['own_sake'] = ['0.978', '0.81', '0', '0.848', '#joy', '#admiration', 'positive', '0.879', 'brownie_point', 'feel_proud', 'receive_thank', 'appreciate', 'positive_reinforcement']
senticnet['own_space'] = ['0.247', '0.204', '-0.06', '0.354', '#joy', '#admiration', 'positive', '0.185', 'family_live', 'nice_house', 'live_life', 'residence', 'live_space']
senticnet['own_vomit'] = ['-0.10', '-0.04', '-0.05', '-0.15', '#sadness', '#disgust', 'negative', '-0.06', 'acid_indigestion', 'get_full', 'burp', 'indigestion', 'become_full']
senticnet['owned_subsidiary'] = ['0.111', '0.18', '0', '0.128', '#interest', '#admiration', 'positive', '0.14', 'corporation', 'partnership', 'large_business', 'business_organization', 'enterprise']
senticnet['owner'] = ['0.035', '0.213', '-0.16', '0.21', '#interest', '#admiration', 'positive', '0.072', 'own_person', 'property', 'head', 'ownership', 'own']
senticnet['owner-managed'] = ['0', '0', '-0.84', '-0.98', '#fear', '#disgust', 'negative', '-0.91', 'publicly-traded', 'closely-held', 'publicly-held', 'proprietorship', 'middle-market']
senticnet['owner_house'] = ['0', '-0.06', '0', '0.062', '#surprise', '#admiration', 'positive', '0.062', 'box', 'small_box', 'small_apartment', 'veterinarian_office', 'pet_shop']
senticnet['ownername'] = ['0.022', '-0.13', '0.153', '-0.31', '#anger', '#disgust', 'negative', '-0.07', 'codeine', 'morphine', 'infection', 'cocaine', 'nuclear_war']
senticnet['ownership'] = ['0', '0.259', '-0.22', '0.338', '#interest', '#admiration', 'positive', '0.124', 'own', 'freehold', 'possess', 'possession', 'possessive']
senticnet['ownership_tco'] = ['-0.02', '0.249', '0', '0.116', '#interest', '#admiration', 'positive', '0.112', 'price', 'expense', 'bill', 'value', 'worth']
senticnet['owns_taco_bell'] = ['0.965', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.951', 'delicious_food', 'hot_meal', 'good_food', 'fresh_produce', 'tasty_food']
senticnet['ox'] = ['-0.99', '0', '0.827', '-0.54', '#sadness', '#anger', 'negative', '-0.78', 'treat', 'cover', 'handle', 'theologize', 'deal']
senticnet['ox_cart'] = ['0.064', '0', '0', '0.123', '#joy', '#admiration', 'positive', '0.093', 'horse', 'passenger_car', 'car_action', 'conveyance', 'car_travel']
senticnet['ox_tongue'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'cook_meat', 'cook_hamburger', 'cook_food', 'cook_rice', 'brown_meat']
senticnet['oxblood_red'] = ['0.59', '-0.86', '0', '0.561', '#joy', '#surprise', 'positive', '0.672', 'claret', 'wine', 'dark_red', 'red', 'redness']
senticnet['oxbow_lake'] = ['0.97', '0.758', '0', '0', '#joy', '#interest', 'positive', '0.864', 'trot', 'step_out', 'step_aside', 'progress', 'enter_place']
senticnet['oxfam_gb'] = ['-0.54', '0', '0.863', '-0.76', '#anger', '#disgust', 'negative', '-0.72', 'denomination', 'committee', 'citizen_committee', 'mission', 'missionary_post']
senticnet['oxidase_negative'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'immobile', 'immotile']
senticnet['oxidase_positive'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'immobile', 'immotile']
senticnet['oxidation'] = ['-0.95', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.90', 'catalytic', 'catalyst', 'converter', 'hydrogenation', 'exhaust']
senticnet['oxidation_resistance'] = ['0.8', '-0.90', '0', '0.675', '#joy', '#surprise', 'positive', '0.794', 'indestructibility', 'tensile_strength', 'continuity', 'persistence', 'changelessness']
senticnet['oxidative'] = ['-0.80', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.84', 'popularization', 'profanation', 'demoralization', 'corruption', 'humiliation']
senticnet['oxidative_burst'] = ['0.702', '-0.52', '0', '0.958', '#joy', '#admiration', 'positive', '0.728', 'somatic_cell', 'vegetative_cell', 'fixed_phagocyte', 'macrophage', 'free_phagocyte']
senticnet['oxidative_degradation'] = ['-0.80', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.84', 'popularization', 'profanation', 'demoralization', 'corruption', 'humiliation']
senticnet['oxide'] = ['0.087', '-0.07', '0.062', '0', '#joy', '#surprise', 'positive', '0.033', 'small_piece', 'spangle', 'stripe', 'particle', 'dust_particle']
senticnet['oxidiser'] = ['0', '0', '0', '0.073', '#admiration', '#admiration', 'positive', '0.073', 'fire_starter', 'organic_substance', 'renewable_resource', 'use_fuel', 'tree_product']
senticnet['oxidizable'] = ['0.766', '-0.87', '0', '0.666', '#joy', '#surprise', 'positive', '0.769', 'reactive', 'unstable', 'labile', 'activated', 'excited']
senticnet['oxidize'] = ['-0.78', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.83', 'break', 'end', 'die', 'fall_apart', 'go_to_piece']
senticnet['oxidized_mercury'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'radioactive_dust', 'dust', 'dust_matter']
senticnet['oxidizer'] = ['0', '0', '0', '0.073', '#admiration', '#admiration', 'positive', '0.073', 'fire_starter', 'organic_substance', 'renewable_resource', 'use_fuel', 'tree_product']
senticnet['oxidizing_agent'] = ['0.766', '-0.87', '0', '0.666', '#joy', '#surprise', 'positive', '0.769', 'reactive', 'unstable', 'labile', 'activated', 'excited']
senticnet['oxo_cube'] = ['-0.18', '-0.00', '-0.18', '0', '#sadness', '#fear', 'negative', '-0.11', 'pulverize', 'violent_action', 'smash', 'hit_hard', 'melt_away']
senticnet['oxo_tower'] = ['-0.81', '0', '0.543', '-0.63', '#sadness', '#disgust', 'negative', '-0.66', 'caper', 'pickle', 'relish', 'dill_pickle', 'sweet_pickle']
senticnet['oxtail'] = ['-0.02', '-0.02', '0.064', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'ocean_water', 'fillet_fish', 'chowder', 'port', 'underwater']
senticnet['oxyacid'] = ['-0.81', '0', '0.774', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'acid', 'hypophosphoric_acid', 'periodic_acid', 'hypophosphorous_acid', 'phosphorous_acid']
senticnet['oxycarpa'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['oxychloride'] = ['0.766', '-0.87', '0', '0.666', '#joy', '#surprise', 'positive', '0.769', 'reactive', 'unstable', 'labile', 'activated', 'excited']
senticnet['oxycodone'] = ['0.138', '0.103', '0', '0.095', '#joy', '#interest', 'positive', '0.112', 'masturbation', 'sexual_intercourse', 'sex', 'warm_fuzzy_feeling', 'fuck']
senticnet['oxycontin'] = ['0.138', '0.103', '0', '0.095', '#joy', '#interest', 'positive', '0.112', 'masturbation', 'sexual_intercourse', 'sex', 'warm_fuzzy_feeling', 'fuck']
senticnet['oxycontin_abuse'] = ['0.138', '0.103', '0', '0.095', '#joy', '#interest', 'positive', '0.112', 'masturbation', 'sexual_intercourse', 'sex', 'warm_fuzzy_feeling', 'fuck']
senticnet['oxygen'] = ['0.909', '0.983', '0', '0', '#joy', '#interest', 'positive', '0.946', 'air', 'breathable', 'atmosphere', 'hydrogen', 'inhale_air']
senticnet['oxygen-bearing'] = ['0', '0', '0.821', '-0.93', '#anger', '#disgust', 'negative', '-0.87', 'allergy-inducing', 'alcohol-soluble', 'energy-production', 'extra-national', 'exudation']
senticnet['oxygen_acid'] = ['-0.56', '0', '0.736', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'acid', 'hypophosphoric_acid', 'periodic_acid', 'hypophosphorous_acid', 'phosphorous_acid']
senticnet['oxygen_deficient_atmosphere'] = ['0', '0', '-0.84', '-0.87', '#fear', '#disgust', 'negative', '-0.85', 'deadly', 'threat', 'bad_health', 'unsafe', 'violent']
senticnet['oxygen_depleted_water'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'dirtiness', 'befoulment', 'defilement', 'dinginess', 'dinge']
senticnet['oxygen_desaturation'] = ['-0.03', '-0.03', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'eat_poison', 'commit_suicide', 'drug_addiction', 'drunk_drive', 'jump_off_cliff']
senticnet['oxygen_mask'] = ['-0.57', '0', '0.737', '-0.85', '#anger', '#disgust', 'negative', '-0.72', 'emergency_procedure', 'emergency', 'external_respiration', 'respiration', 'ventilation']
senticnet['oxygen_partial'] = ['-0.12', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.09', 'breeze', 'move_air', 'air_movement', 'gape', 'force']
senticnet['oxygen_tension'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'keep_alive', 'body_system', 'body_working', 'blood_pressure', 'heart']
senticnet['oxygen_white_dwarf'] = ['0.053', '0.264', '-0.11', '0.23', '#interest', '#admiration', 'positive', '0.107', 'natural_resource', 'petroleum', 'fossil_fuel', 'energy_source', 'potential_energy']
senticnet['oxygenase'] = ['-0.52', '0', '0.681', '-0.76', '#anger', '#disgust', 'negative', '-0.65', 'triose', 'tetrose', 'pentose', 'simple_sugar', 'aldose']
senticnet['oxymel'] = ['0', '0', '0', '-0.96', '#disgust', '#disgust', 'negative', '-0.96', 'thick_substance', 'viscous_substance', 'mucous_substance', 'disgusting', 'disgust']
senticnet['oxymetazoline'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'spironolactone', 'adrenal_cortical_steroid', 'antihypertensive', 'antihypertensive_drug']
senticnet['oxymoron'] = ['0', '0', '-0.96', '-0.87', '#fear', '#disgust', 'negative', '-0.91', 'tautological', 'sophistry', 'syllogism', 'tautology', 'truism']
senticnet['oxymoronic'] = ['-0.88', '0', '0.663', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'powerlessness', 'impotence', 'impotency', 'inefficacy', 'inefficaciousness']
senticnet['oxyopia'] = ['0.959', '-0.97', '0', '0.633', '#joy', '#surprise', 'positive', '0.856', 'acuity', 'sight', 'top', 'vision', 'visual_sense']
senticnet['oy_vey'] = ['0.807', '-0.83', '0', '0.688', '#joy', '#surprise', 'positive', '0.776', 'folly', 'lunacy', 'craziness', 'indulgence', 'foolery']
senticnet['oyate'] = ['0.052', '0.099', '0', '0.067', '#interest', '#admiration', 'positive', '0.073', 'police_rank', 'head_guy', 'chief', 'leader', 'commander']
senticnet['oyster'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'seal', 'walrus', 'otter', 'alligator', 'whale']
senticnet['oyster_cracker'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'saltine', 'soda_cracker', 'cracker', 'water_biscuit', 'graham_cracker']
senticnet['oyster_larva'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'seal', 'walrus', 'otter', 'alligator', 'whale']
senticnet['oyster_sauce'] = ['0', '-0.03', '0.046', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'pasta', 'green_bean', 'mayonnaise', 'frozen_food', 'sour_milk']
senticnet['oyster_shell'] = ['-0.06', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.04', 'friction', 'lack_of_water', 'dirt', 'abrasive', 'ground_material']
senticnet['oystercatcher'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'amoeba', 'single_celled']
senticnet['oz'] = ['0.931', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.855', 'crystal_clear', 'blue_sky', 'imaginary', 'water_sky', 'blue_color']
senticnet['oz_caster_sugar'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'saltine', 'soda_cracker', 'cracker', 'water_biscuit', 'graham_cracker']
senticnet['ozeki'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['ozonation'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
senticnet['ozone'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'rodent', 'tree_animal']
senticnet['ozone_hole'] = ['-0.92', '0', '0.917', '-0.95', '#sadness', '#disgust', 'negative', '-0.93', 'make_pollution', 'polluted_air', 'pollution', 'create_pollution', 'pollution_factory']
senticnet['pa_du'] = ['-0.65', '0', '0.505', '-0.91', '#sadness', '#disgust', 'negative', '-0.69', 'hellhound', 'mythical_being', 'dragon', 'mythical_monster', 'amphisbaena']
senticnet['pa_encore_rpondu'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'head_hurt', 'introspection', 'lifestyle_change', 'headache', 'move_eye']
senticnet['pa_qu'] = ['0', '0.919', '0', '0.888', '#interest', '#admiration', 'positive', '0.903', 'surprise', 'eye-popping', 'fulgurous', 'dazzling', 'bedaze']
senticnet['paasche_index'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'indicator', 'retail_price_index', 'producer_price_index', 'wholesale_price_index', 'consumer_price_index']
senticnet['pablo_legorreta'] = ['0.559', '0.656', '-0.26', '0.285', '#joy', '#interest', 'positive', '0.31', 'zealous', 'avidity', 'eagerness', 'avidness', 'thirstily']
senticnet['pablum'] = ['0.739', '-0.89', '0', '0.702', '#joy', '#surprise', 'positive', '0.78', 'pap', 'content', 'drivel', 'folderol', 'codswallop']
senticnet['pabst_blue_ribbon'] = ['0.161', '-0.14', '0.031', '-0.10', '#joy', '#surprise', 'positive', '0.043', 'barrel', 'reception', 'champagne', 'beer', 'wine']
senticnet['pabulum'] = ['0.911', '0', '0', '0.939', '#joy', '#admiration', 'positive', '0.925', 'pablum', 'drivel', 'platitude', 'twaddle', 'regurgitated']
senticnet['pacaya_samiria_national'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['pace'] = ['-0.04', '0.123', '0', '0.237', '#interest', '#admiration', 'positive', '0.107', 'foot_motion', 'foot_movement', 'foot_action', 'move_ahead', 'stride']
senticnet['pacemaker'] = ['-0.83', '0', '0.987', '-0.50', '#sadness', '#anger', 'negative', '-0.77', 'heart_block', 'atrioventricular_block', 'bradycardia', 'ventricular_fibrillation', 'tachycardia']
senticnet['pacemaker_award'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['pacesetter'] = ['0.986', '-0.60', '0', '0.568', '#joy', '#surprise', 'positive', '0.721', 'beaut', 'ideal', 'beauty', 'pattern', 'exemplar']
senticnet['pachanga'] = ['0.116', '-0.02', '0.025', '0.156', '#joy', '#admiration', 'positive', '0.069', 'dance_move', 'latin_dance', 'body_movement', 'waltz', 'dance']
senticnet['pachinko'] = ['0.925', '0.993', '0', '0.832', '#joy', '#interest', 'positive', '0.917', 'dance_samba', 'dance', 'skate', 'jump_rope', 'play_frisbee']
senticnet['pachisi'] = ['0', '0', '0', '0.115', '#admiration', '#admiration', 'positive', '0.115', 'chess_knight', 'strategy', 'pawn', 'chess_queen', 'chess_rook']
senticnet['pacific'] = ['-0.60', '0', '0.694', '-0.65', '#anger', '#disgust', 'negative', '-0.65', 'appeasing', 'placating', 'placative', 'placatory', 'conciliative']
senticnet['pacific_coast_borax'] = ['-0.98', '0', '0.521', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'battery', 'stamp_battery', 'quartz_battery', 'stamp_mill', 'factory']
senticnet['pacific_herring_clupea'] = ['-0.79', '0', '0.709', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'animal', 'animate_being', 'ctenophore', 'invertebrate', 'beast']
senticnet['pacific_lamprey'] = ['0', '-0.02', '0.044', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'haddock', 'mermaid', 'fish_rod', 'tidal_wave', 'dryad']
senticnet['pacific_northwest'] = ['0.03', '0.068', '-0.06', '0.062', '#interest', '#admiration', 'positive', '0.025', 'american_forest', 'thermal', 'treetop', 'animal', 'zoo']
senticnet['pacific_ocean'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'atlantic_ocean', 'small_lake', 'mere']
senticnet['pacific_silver_fir'] = ['0.045', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.356', 'ponderosa', 'bull_pine', 'western_yellow_pine', 'yellow_pine', 'longleaf_pine']
senticnet['pacific_tectonic_plate'] = ['-0.87', '0', '0.818', '0', '#sadness', '#anger', 'negative', '-0.84', 'slow_speed', 'wifi', 'computer_internet_connection', 'internet_connection', 'boot_up_computer']
senticnet['pacific_theater'] = ['0.084', '-0.05', '0.013', '0', '#joy', '#surprise', 'positive', '0.043', 'carnival_ride', 'carny', 'fortune_teller', 'horseback', 'ferris_wheel']
senticnet['pacifically'] = ['0.621', '0.648', '-0.25', '0.296', '#joy', '#interest', 'positive', '0.328', 'repose', 'peace-loving', 'peaceably', 'peaceable', 'peace_of_mind']
senticnet['pacificism'] = ['-0.91', '0', '0.688', '-0.57', '#sadness', '#anger', 'negative', '-0.72', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['pacifier'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'lavatory', 'commode', 'potty_chair']
senticnet['pacifism'] = ['0.968', '0.864', '0', '0', '#joy', '#interest', 'positive', '0.916', 'verbal_contract', 'communication', 'weak_acid', 'good_fortune', 'sincere']
senticnet['pacifist'] = ['-0.91', '0', '0.688', '-0.57', '#sadness', '#anger', 'negative', '-0.72', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['pacifist_stance'] = ['-0.91', '0', '0.688', '-0.57', '#sadness', '#anger', 'negative', '-0.72', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['pacifistic'] = ['-0.91', '0', '0.688', '-0.57', '#sadness', '#anger', 'negative', '-0.72', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['pacify'] = ['0', '0.827', '0', '0.722', '#interest', '#admiration', 'positive', '0.774', 'tranquilize', 'tranquillize', 'calm_down', 'quieten', 'calm']
senticnet['pack'] = ['0', '0.11', '-0.05', '0.149', '#interest', '#admiration', 'positive', '0.067', 'pack_clothes', 'hold_stuff', 'carry_stuff', 'carry_book', 'carry_belongings']
senticnet['pack_animal'] = ['0.024', '0.102', '-0.06', '0.074', '#interest', '#admiration', 'positive', '0.033', 'dolphin', 'large_mammal', 'marine_mammal', 'mammal', 'smart_creature']
senticnet['pack_bag'] = ['0.956', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.846', 'travel_work', 'sightsee', 'charge_battery', 'load_stuff', 'create_pollution']
senticnet['pack_cigarette'] = ['0.987', '0.902', '0', '0', '#joy', '#interest', 'positive', '0.945', 'franc', 'dime', 'dollar', 'shilling', 'banknote']
senticnet['pack_clothes'] = ['0', '0.124', '-0.05', '0.203', '#interest', '#admiration', 'positive', '0.09', 'carry_clothes', 'carry_belongings', 'carry_book', 'travel', 'carry_stuff']
senticnet['pack_luggage'] = ['0.804', '-0.80', '0', '0', '#joy', '#surprise', 'positive', '0.805', 'pull_off_road', 'locate_place', 'discover_new_place', 'carry_luggage', 'drive_destination']
senticnet['pack_lunch'] = ['0.044', '-0.18', '0.03', '0.081', '#surprise', '#admiration', 'positive', '0.071', 'go_mountain', 'step_forward', 'catch_bass', 'catch_catfish', 'tie_shoes']
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senticnet['pack_saddle'] = ['0', '0', '0', '0.071', '#admiration', '#admiration', 'positive', '0.071', 'two_barbs', 'barb', 'mule', 'buddhist', 'ride_barb']
senticnet['package'] = ['0.808', '-0.89', '0', '0', '#joy', '#surprise', 'positive', '0.849', 'parcel', 'mail', 'electric_bill', 'box', 'give_out']
senticnet['packaged_food'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'apple_pie', 'lasagna', 'jelly', 'a_lot_of_food', 'cottage_cheese']
senticnet['packaged_meat'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'apple_pie', 'lasagna', 'jelly', 'a_lot_of_food', 'cottage_cheese']
senticnet['packaged_software'] = ['0', '0.243', '0', '0.218', '#interest', '#admiration', 'positive', '0.23', 'ware', 'goods', 'result', 'good_price', 'bargain']
senticnet['packaging_material'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'foil', 'tin_foil', 'gold_foil']
senticnet['packard_motor'] = ['-0.04', '-0.16', '0', '0.056', '#surprise', '#admiration', 'positive', '0.059', 'car_part', 'under_car', 'car_wheel', 'wheel', 'car']
senticnet['packed_crowd'] = ['-0.71', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.84', 'traffic_jam', 'accumulate_stress', 'stress', 'accumulate_stress_city', 'bad_service']
senticnet['packed_lunch'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'cafe', 'buy_sandwich', 'eat_food', 'buy_food', 'finish_job']
senticnet['packed_rod_laver'] = ['0.026', '-0.03', '0', '0.091', '#surprise', '#admiration', 'positive', '0.051', 'stadium', 'auditorium', 'television_station', 'music_hall', 'movie_theater']
senticnet['packed_tightly'] = ['-0.1', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.1', 'deflate', 'read_cover', 'look_behind', 'upgrade', 'scan']
senticnet['packet'] = ['-0.15', '0', '0.029', '-0.11', '#sadness', '#disgust', 'negative', '-0.1', 'start_over', 'front_line', 'find_line', 'take_turn', 'long_line']
senticnet['packet_sniffer'] = ['-0.83', '0', '0.665', '-0.51', '#sadness', '#anger', 'negative', '-0.67', 'percipient', 'auditor', 'perceiver', 'observer', 'beholder']
senticnet['packhorse'] = ['0.064', '0', '0', '0.123', '#joy', '#admiration', 'positive', '0.093', 'horse', 'passenger_car', 'car_action', 'conveyance', 'car_travel']
senticnet['packing_density'] = ['0.728', '0.977', '0', '0.939', '#interest', '#admiration', 'positive', '0.881', 'air_pressure', 'altitude', 'air', 'heavy_air', 'air_density']
senticnet['packing_tape'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'kraft', 'kraft_paper', 'butcher_paper', 'paper']
senticnet['packman'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'cheapjack', 'hawker', 'pitchman']
senticnet['pact'] = ['0.397', '0.245', '-0.13', '0.224', '#joy', '#interest', 'positive', '0.183', 'agreement', 'provide', 'advice', 'contribution', 'offer']
senticnet['pactum'] = ['-0.81', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.79', 'stifle', 'forgotten', 'old', 'touch_ground', 'seldom']
senticnet['pad'] = ['0.153', '0', '0', '0.149', '#joy', '#admiration', 'positive', '0.151', 'note_pad', 'stationery', 'post_note', 'intern', 'stamp_pad']
senticnet['padded_cell'] = ['-0.67', '0', '0.931', '-0.77', '#anger', '#disgust', 'negative', '-0.79', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['padded_feet'] = ['-0.06', '-0.01', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'low', 'ground', 'look_down', 'ground_level', 'under_house']
senticnet['padded_room'] = ['-0.67', '0', '0.931', '-0.77', '#anger', '#disgust', 'negative', '-0.79', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['padded_shoulder_strap'] = ['0.041', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.043', 'fit_room', 'clothes_store_change_room', 'dress_room', 'clothes_shelf', 'changing_room']
senticnet['paddhari'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['paddle'] = ['0.153', '0', '0', '0.149', '#joy', '#admiration', 'positive', '0.151', 'water_transportation', 'raft', 'canoe', 'water_vehicle', 'ship']
senticnet['paddle_attachment'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'salad_shaker', 'store_shelf', 'kitchen_tool', 'pliers', 'ladle']
senticnet['paddle_ball'] = ['0.036', '0.084', '0', '0', '#joy', '#interest', 'positive', '0.06', 'toy', 'bath_toy', 'yo-yo', 'toy_ball', 'duck']
senticnet['paddle_boat'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'boat', 'big_boat', 'ship', 'large_boat', 'rowboat']
senticnet['paddle_brush'] = ['0.063', '0', '0.01', '0.16', '#joy', '#admiration', 'positive', '0.071', 'goodbye', 'hand_gesture', 'arm_movement', 'crest', 'water_splash']
senticnet['paddling_pool'] = ['0', '0', '0', '0.964', '#admiration', '#admiration', 'positive', '0.964', 'swimming_pool', 'swim', 'swimsuit', 'lake', 'have_fun']
senticnet['paddy_ladd'] = ['-0.94', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.94', 'drive_recklessly', 'discord', 'mute', 'deny', 'helpless']
senticnet['paddy_tuimavave'] = ['0.997', '-0.84', '0', '0.927', '#joy', '#admiration', 'positive', '0.923', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['paderno_dugnano'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'expressway', 'turnpike', 'toll_road', 'highway']
senticnet['padishah_emperor'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['padlock'] = ['0', '0.069', '0', '0.083', '#interest', '#admiration', 'positive', '0.076', 'latch', 'restrict_access', 'knob', 'door_knob', 'gate']
senticnet['padmasambhava'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['paean'] = ['0', '0', '-0.91', '-0.79', '#fear', '#disgust', 'negative', '-0.85', 'encomiastic', 'encomium', 'panegyric', 'eulogy', 'pean']
senticnet['paec'] = ['-0.90', '0', '0.748', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'alternative_energy', 'activation_energy', 'energy_of_activation', 'energy', 'nuclear_energy']
senticnet['paediatric'] = ['0.738', '-0.53', '0', '0.856', '#joy', '#admiration', 'positive', '0.71', 'doctor', 'country_doctor', 'family_doctor', 'doctor_office', 'physician']
senticnet['paediatrician'] = ['0.738', '-0.53', '0', '0.856', '#joy', '#admiration', 'positive', '0.71', 'doctor', 'country_doctor', 'family_doctor', 'doctor_office', 'physician']
senticnet['paedo'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'addicted', 'obsessed_love', 'addiction', 'obsession', 'weird']
senticnet['paedophile'] = ['0.864', '-0.54', '0', '0.613', '#joy', '#admiration', 'positive', '0.675', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['paedophile_priest'] = ['-0.95', '0', '0.673', '-0.85', '#sadness', '#disgust', 'negative', '-0.82', 'discharge', 'free', 'dethrone', 'disinvest', 'unfrock']
senticnet['paedophilia'] = ['0', '0.942', '0', '0', '#interest', '#interest', 'positive', '0.942', 'guy', 'special_person', 'guardian', 'boy', 'male']
senticnet['paedophilic'] = ['0', '0.942', '0', '0', '#interest', '#interest', 'positive', '0.942', 'guy', 'special_person', 'guardian', 'boy', 'male']
senticnet['paella'] = ['0.039', '0.089', '0.023', '0', '#joy', '#interest', 'positive', '0.035', 'steak_house', 'tapa', 'french_restaurant', 'spanish_restaurant', 'restaurant_diner']
senticnet['pagan'] = ['0', '0', '0', '-0.77', '#disgust', '#disgust', 'negative', '-0.77', 'sophisticated', 'religion', 'worship', 'sing_microphone', 'guardian']
senticnet['pagan_empire'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
senticnet['pagan_rome'] = ['-0.69', '0', '0.957', '-0.92', '#anger', '#disgust', 'negative', '-0.85', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['pagan_sacrificial'] = ['-0.73', '0', '0.55', '-0.75', '#sadness', '#disgust', 'negative', '-0.68', 'mortal', 'person', 'individual', 'mangler', 'mutilator']
senticnet['pagani_zonda'] = ['0.755', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.728', 'high_class', 'expensive_car', 'ferrari', 'fancy_car', 'sports_car']
senticnet['page'] = ['0.912', '0.749', '0', '0', '#joy', '#interest', 'positive', '0.831', 'pageboy', 'varlet', 'paginate', 'foliate', 'thomas_nelson_page']
senticnet['page_doctor'] = ['-0.13', '0.088', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.05', 'needy', 'welcome_friend', 'offer_help', 'part_of_way', 'page_person']
senticnet['page_layout'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'hardcore', 'declared', 'definitive', 'explicit']
senticnet['page_pamphlet'] = ['0.058', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.054', 'letter_slot', 'envelope', 'address_label', 'mailman', 'postage_stamp']
senticnet['page_person'] = ['-0.12', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.09', 'bridge_difference', 'stand_alone', 'deal_card_player', 'forward_email', 'explain_everything']
senticnet['page_through_book'] = ['-0.12', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.10', 'check_mail', 'gain_knowledge', 'practice', 'report_crime', 'master_skill']
senticnet['pageboy'] = ['0.911', '0.743', '0', '0', '#joy', '#interest', 'positive', '0.827', 'page', 'varlet', 'paginate', 'foliate', 'thomas_nelson_page']
senticnet['paginate'] = ['0.912', '0.742', '0', '0', '#joy', '#interest', 'positive', '0.827', 'pageboy', 'varlet', 'page', 'foliate', 'thomas_nelson_page']
senticnet['pahalwan'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['pahoehoe'] = ['0', '0', '-0.92', '0', '#fear', '#fear', 'negative', '-0.92', 'water_flow', 'wind_water', 'water_stream', 'waterway', 'magma']
senticnet['pahoehoe_flow'] = ['0', '0', '-0.92', '0', '#fear', '#fear', 'negative', '-0.92', 'water_flow', 'wind_water', 'water_stream', 'waterway', 'magma']
senticnet['pahoehoe_lava'] = ['0', '0', '-0.92', '0', '#fear', '#fear', 'negative', '-0.92', 'water_flow', 'wind_water', 'water_stream', 'waterway', 'magma']
senticnet['paid'] = ['0.521', '-0.85', '0', '0.727', '#surprise', '#admiration', 'positive', '0.702', 'compensated', 'remunerated', 'paying', 'stipendiary', 'remunerative']
senticnet['paid_paternity_leave'] = ['0.1', '0.216', '-0.18', '0.22', '#interest', '#admiration', 'positive', '0.088', 'female', 'maternity_ward', 'motherhood', 'woman', 'mother']
senticnet['paid_subscription'] = ['0.687', '-0.50', '0', '0.8', '#joy', '#admiration', 'positive', '0.663', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['pail'] = ['0.035', '-0.01', '0.009', '0.063', '#joy', '#admiration', 'positive', '0.026', 'container', 'carton', 'contain', 'hold_cupcake', 'store_dish']
senticnet['pain'] = ['-0.99', '0', '-0.73', '0', '#sadness', '#fear', 'negative', '-0.86', 'hurt', 'ouch', 'ache', 'dislocation', 'excruciate']
senticnet['pain-free'] = ['-0.91', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.90', 'painless', 'uncomplicated', 'straightforward', 'procedure', 'hassle-free']
senticnet['pain_in_ass'] = ['-0.91', '0', '0.668', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'negative_stimulus', 'irritant', 'thorn', 'plague', 'nuisance']
senticnet['pain_in_neck'] = ['-0.53', '0', '0.86', '-0.65', '#anger', '#disgust', 'negative', '-0.68', 'pain_in_ass', 'pain', 'irritant', 'annoyance', 'bother']
senticnet['pain_in_stomach'] = ['-0.79', '0', '-0.92', '-0.70', '#sadness', '#fear', 'negative', '-0.80', 'feel_weak', 'famine', 'need_snack', 'order_food', 'feel_hunger']
senticnet['pain_killer'] = ['0.969', '0.817', '0', '0.828', '#joy', '#admiration', 'positive', '0.871', 'ibuprofen', 'painkiller', 'medicine', 'analgesic', 'phenobarbital']
senticnet['pain_person'] = ['-0.09', '-0.02', '0', '-0.14', '#sadness', '#disgust', 'negative', '-0.07', 'close_eye', 'sleepwalk', 'wind_down', 'miss_appointment', 'quiet_mind']
senticnet['pain_pill'] = ['-0.98', '0', '0.601', '-0.69', '#sadness', '#disgust', 'negative', '-0.76', 'hydromorphone_hydrochloride', 'hydromorphone', 'acetaminophen', 'codeine', 'medicine']
senticnet['pain_pill_hydrochloride'] = ['-0.99', '0', '0.597', '-0.70', '#sadness', '#disgust', 'negative', '-0.76', 'hydromorphone', 'pain_pill', 'acetaminophen', 'codeine', 'medicine']
senticnet['pain_relief'] = ['-0.03', '-0.02', '-0.08', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'ibuprofen', 'phenobarbital', 'analgesic', 'sleep_drug', 'pill']
senticnet['pain_reliever'] = ['0.969', '0.817', '0', '0.828', '#joy', '#admiration', 'positive', '0.871', 'ibuprofen', 'painkiller', 'medicine', 'analgesic', 'phenobarbital']
senticnet['pain_sensation'] = ['-0.92', '0', '0.782', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'pain', 'painful', 'inflict_pain', 'break_leg', 'cut_skin']
senticnet['pain_suffer'] = ['-0.34', '-0.02', '-0.14', '-0.23', '#sadness', '#disgust', 'negative', '-0.17', 'find_out_healthy', 'lose_mind', 'hurt_feeling', 'migraine', 'a_lot_of_stress']
senticnet['painful'] = ['-0.76', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.84', 'fatal', 'hurtful', 'severe', 'bloody', 'unbearable']
senticnet['painful_bite'] = ['0.876', '0.948', '0', '0', '#joy', '#interest', 'positive', '0.912', 'sting', 'tidbit', 'nip', 'tiny_amount', 'piece']
senticnet['painful_inflammation'] = ['-0.71', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.73', 'pain', 'gape', 'sunburn', 'ouch', 'cut_skin']
senticnet['painful_injury'] = ['-0.13', '-0.01', '-0.05', '-0.09', '#sadness', '#disgust', 'negative', '-0.06', 'tear_apart', 'abrasion', 'wound', 'gash', 'laceration']
senticnet['painful_menstruation'] = ['-0.73', '0', '0.555', '-0.90', '#sadness', '#disgust', 'negative', '-0.73', 'aching', 'hurting', 'labor_pain', 'neuralgia', 'neuralgy']
senticnet['painful_sensation'] = ['-0.92', '0', '0.782', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'pain', 'painful', 'inflict_pain', 'break_leg', 'cut_skin']
senticnet['painful_stimulus'] = ['0.971', '0.751', '0', '0.896', '#joy', '#admiration', 'positive', '0.873', 'new_friendship', 'foreplay', 'warm_fuzzy_feeling', 'become_aroused', 'erection']
senticnet['painful_sting'] = ['-0.25', '0.081', '-0.19', '0', '#sadness', '#fear', 'negative', '-0.12', 'hairy', 'kind_animal', 'poisonous', 'big_fish', 'graceful']
senticnet['painful_urination'] = ['-0.05', '0', '-0.05', '-0.04', '#sadness', '#fear', 'negative', '-0.05', 'empty_bladder', 'pee', 'go_potty', 'go_pee', 'go_toilet']
senticnet['painfully'] = ['-0.85', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.78', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['painfully_slow'] = ['-0.13', '0.071', '0.694', '-0.67', '#anger', '#disgust', 'negative', '-0.35', 'bedevil', 'crucify', 'dun', 'aggravate', 'exasperation']
senticnet['painkiller'] = ['0', '0', '-0.05', '-0.19', '#fear', '#disgust', 'negative', '-0.12', 'phenobarbital', 'ibuprofen', 'analgesic', 'palliative', 'aspirin']
senticnet['painkiller_hydrocodone'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'use_drug', 'do_drug', 'drug_use', 'drug', 'addiction']
senticnet['painkiller_oxycontin'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'use_drug', 'do_drug', 'drug_use', 'drug', 'addiction']
senticnet['painless'] = ['-0.91', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.89', 'pain-free', 'uncomplicated', 'straightforward', 'procedure', 'hassle-free']
senticnet['painstaking'] = ['0', '0.866', '0', '0.894', '#interest', '#admiration', 'positive', '0.88', 'trial-and-error', 'laborious', 'guesswork', 'time-consuming', 'experimentation']
senticnet['painstaking_attention'] = ['0.567', '-0.56', '0', '0.724', '#joy', '#admiration', 'positive', '0.617', 'truth', 'accuracy', 'preciseness', 'trueness', 'precision']
senticnet['painstaking_detail'] = ['0.682', '-0.52', '0', '0.613', '#joy', '#admiration', 'positive', '0.607', 'nice', 'fastidious', 'dainty', 'prissy', 'pernickety']
senticnet['painstaking_research'] = ['0.682', '-0.52', '0', '0.613', '#joy', '#admiration', 'positive', '0.607', 'nice', 'fastidious', 'dainty', 'prissy', 'pernickety']
senticnet['painstaking_work'] = ['0.682', '-0.52', '0', '0.613', '#joy', '#admiration', 'positive', '0.607', 'nice', 'fastidious', 'dainty', 'prissy', 'pernickety']
senticnet['painstakingly'] = ['0', '0.907', '0', '0.996', '#interest', '#admiration', 'positive', '0.952', 'meticulously', 'laboriously', 'methodically', 'lovingly', 'skillfully']
senticnet['painstakingness'] = ['0.512', '-0.94', '0', '0.5', '#joy', '#surprise', 'positive', '0.652', 'scrupulousness', 'carefulness', 'diligence', 'meticulousness', 'meticulosity']
senticnet['paint'] = ['-0.07', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.07', 'cartoon_character', 'use_tool', 'mould', 'use_oil_cartoon_character', 'buy_cartoon_character']
senticnet['paint_brush'] = ['0', '0.068', '0', '0', '#interest', '#interest', 'positive', '0.068', 'artist', 'oil_paint', 'painter', 'creative_person', 'make_paint']
senticnet['paint_bucket'] = ['0.883', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.864', 'stick_eraser', 'ballpoint_pen', 'intern', 'paper_clip', 'eraser_holder']
senticnet['paint_canvas'] = ['0.723', '0.816', '0', '0.922', '#interest', '#admiration', 'positive', '0.82', 'create_new_idea', 'letter_sign', 'create_music', 'make_paint', 'create']
senticnet['paint_ceiling'] = ['-0.06', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.06', 'picture_scene', 'take_photograph', 'climb_ladder', 'frame_picture', 'staircase']
senticnet['paint_face'] = ['0.051', '0.214', '0', '0.142', '#interest', '#admiration', 'positive', '0.136', 'soccer_game', 'have_fun', 'carnival', 'funny', 'joke']
senticnet['paint_fingernail'] = ['0.721', '0', '0', '0.735', '#joy', '#admiration', 'positive', '0.728', 'deal_card_player', 'forward_email', 'explain_everything', 'mind_store', 'bridge_difference']
senticnet['paint_house'] = ['0.936', '0.749', '0', '0.871', '#joy', '#admiration', 'positive', '0.852', 'paint_picture', 'tie-dye', 'create_art', 'make_patchwork_quilt', 'letter_sign']
senticnet['paint_lily'] = ['0.532', '-0.73', '0', '0.708', '#surprise', '#admiration', 'positive', '0.657', 'beautify', 'embellish', 'decorate', 'adorn', 'grace']
senticnet['paint_line'] = ['0', '0.119', '0', '0.123', '#interest', '#admiration', 'positive', '0.121', 'street_sign', 'road_sign', 'red_light', 'traffic_sign', 'halt']
senticnet['paint_nail'] = ['0.956', '0.819', '0', '0', '#joy', '#interest', 'positive', '0.887', 'clean_shoes', 'remove_paint', 'make_pretty', 'clean_floor', 'shine_shoes']
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senticnet['paint_stripper'] = ['0.76', '0.919', '0', '0', '#joy', '#interest', 'positive', '0.84', 'chore', 'make_happen', 'pick_up_trash', 'intuition', 'stop_procrastinate']
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senticnet['painted_decoration'] = ['-0.06', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.04', 'polish', 'magnify', 'see_out', 'sill', 'pane']
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senticnet['pair'] = ['0.702', '-0.65', '0', '0.788', '#joy', '#admiration', 'positive', '0.713', 'have', 'bonk', 'take', 'couple', 'fornicate']
senticnet['pair_of_pants'] = ['0.914', '-0.80', '0', '0', '#joy', '#surprise', 'positive', '0.859', 'jeans', 'panties', 'lingerie', 'clothes', 'underwear']
senticnet['pair_of_scissors'] = ['0', '-0.06', '0.092', '-0.16', '#anger', '#disgust', 'negative', '-0.06', 'thumb_tack', 'clipper', 'razor', 'scissor', 'haircut_scissor']
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senticnet['pajama'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'fabric', 'underwear', 'white_cotton', 'shirt', 'pants']
senticnet['pajama_party'] = ['0', '-0.08', '0', '0', '#surprise', '#surprise', 'positive', '0.082', 'yo-yo', 'playroom', 'backgammon', 'action_figure', 'board_game']
senticnet['pajama_top'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'duvet_cover', 'pillow', 'comforter', 'pillowcase', 'blanket']
senticnet['paji'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'white_animal', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
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senticnet['pala_dynasty'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
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senticnet['palatability'] = ['0.711', '0.929', '0', '0', '#joy', '#interest', 'positive', '0.82', 'tolerable', 'palate', 'palatableness', 'palatable', 'acceptable']
senticnet['palatable'] = ['0.715', '0.932', '0', '0', '#joy', '#interest', 'positive', '0.824', 'tolerable', 'palate', 'palatableness', 'palatability', 'acceptable']
senticnet['palatableness'] = ['0.714', '0.926', '0', '0', '#joy', '#interest', 'positive', '0.82', 'tolerable', 'palate', 'palatable', 'palatability', 'acceptable']
senticnet['palatalisation'] = ['-0.61', '0', '0.721', '-0.68', '#anger', '#disgust', 'negative', '-0.67', 'character', 'logogram', 'logograph', 'graph', 'ideogram']
senticnet['palatalization'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'letter', 'vowel', 'alphabet', 'basic_speech', 'kind_gesture']
senticnet['palate'] = ['0.714', '0.931', '0', '0', '#joy', '#interest', 'positive', '0.823', 'tolerable', 'palatable', 'palatableness', 'palatability', 'acceptable']
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senticnet['palatial_residence'] = ['0', '0.105', '0', '0.076', '#interest', '#admiration', 'positive', '0.09', 'big_house', 'mansion', 'castle', 'block_flat', 'medieval']
senticnet['palato_alveolar'] = ['-0.56', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.63', 'aphasic', 'inarticulate', 'speechless', 'unarticulated', 'dumb']
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senticnet['pale_ale'] = ['0', '0.091', '-0.05', '0.073', '#interest', '#admiration', 'positive', '0.037', 'privately', 'hold_liquid', 'make_wish', 'rinse_dish', 'find_toilet']
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senticnet['pale_bluish'] = ['0', '0.19', '-0.09', '0.175', '#interest', '#admiration', 'positive', '0.089', 'red', 'green_black', 'green_red', 'peelable', 'precious_jewel']
senticnet['pale_brown'] = ['-0.7', '0', '0.617', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'silvery', 'achromatic', 'bluish_black', 'hueless', 'pitchy']
senticnet['pale_complexion'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'blonde', 'purple', 'violet', 'dark_light', 'hue']
senticnet['pale_cream'] = ['0.028', '0.138', '-0.04', '0.215', '#interest', '#admiration', 'positive', '0.083', 'water', 'dense', 'thick', 'milk', 'dense_water']
senticnet['pale_face'] = ['-0.66', '0', '0.616', '-0.70', '#sadness', '#disgust', 'negative', '-0.66', 'flicker', 'coruscation', 'streak', 'glint', 'flash']
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senticnet['pale_grey'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'neck', 'body_part', 'henchman', 'shoulder', 'head']
senticnet['pale_lilac'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'tropical_fish', 'wood_duck', 'disney', 'pink_flower', 'beau']
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senticnet['pale_purple_coneflower'] = ['-0.79', '0', '0.837', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'sunray', 'wilding', 'sagebrush_buttercup', 'pasqueflower', 'pasque_flower']
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senticnet['pale_skin'] = ['0.352', '0.142', '0', '0.281', '#joy', '#admiration', 'positive', '0.258', 'knowledgeable', 'complement', 'much_need', 'see_ocean', 'good_sense_of_humor']
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senticnet['pale_yellow_liquid'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
senticnet['pale_yellowish'] = ['-0.7', '0', '0.617', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'silvery', 'achromatic', 'bluish_black', 'hueless', 'pitchy']
senticnet['pale_yellowish_cream'] = ['-0.50', '0', '0.573', '-0.89', '#anger', '#disgust', 'negative', '-0.65', 'petallike', 'bowery', 'leafy', 'unifoliate', 'bifoliate']
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senticnet['paleoconservative'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'economic_libertarian', 'civil_libertarian', 'advocate', 'advocator', 'proponent']
senticnet['paleofauna'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'seed_fern', 'gymnosperm']
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senticnet['paleolithic'] = ['0.735', '-0.71', '0', '0.566', '#joy', '#surprise', 'positive', '0.672', 'improvement', 'betterment', 'habituation', 'adjustment', 'shakedown']
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senticnet['paleosol'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'fossil', 'oil', 'dark_liquid', 'smithsonian_institution', 'inert_gas']
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senticnet['palest'] = ['0.577', '-0.69', '0', '0.645', '#surprise', '#admiration', 'positive', '0.639', 'shining', 'polished', 'shiny', 'bright', 'burnished']
senticnet['palette'] = ['0.041', '0.112', '0', '0.077', '#interest', '#admiration', 'positive', '0.077', 'oil_paint', 'artist', 'brush', 'artist_tool', 'canvas']
senticnet['palette_knife'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'wall_decoration', 'renovation', 'painting', 'artwork', 'new_house']
senticnet['palimony'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'bigamy', 'wedlock']
senticnet['palindrome'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'foot_motion', 'foot_action', 'foot_movement', 'stride', 'pace']
senticnet['palitoy'] = ['0.087', '0.113', '-0.08', '0', '#joy', '#interest', 'positive', '0.038', 'find_match', 'strike_against', 'cigarette_lighter', 'match', 'use_lighter']
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senticnet['pall'] = ['0', '0', '0', '-0.89', '#disgust', '#disgust', 'negative', '-0.89', 'scary', 'foreboding', 'boding', 'premonition', 'presentiment']
senticnet['pall_bearer'] = ['-0.98', '0', '0.664', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'unfortunate', 'unfortunate_person', 'weeper', 'griever', 'bearer']
senticnet['palladian_mansion'] = ['0', '0.179', '-0.05', '0.162', '#interest', '#admiration', 'positive', '0.095', 'house_land', 'real_estate', 'acreage', 'house_place', 'good_investment']
senticnet['palladium_acetate'] = ['0.918', '0.864', '0', '0', '#joy', '#interest', 'positive', '0.891', 'consequence', 'cause', 'bad_action', 'problem', 'political_statement']
senticnet['palladium_catalyst'] = ['0.766', '-0.87', '0', '0.666', '#joy', '#surprise', 'positive', '0.769', 'reactive', 'unstable', 'labile', 'activated', 'excited']
senticnet['pallas_cat'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheep', 'argal', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['pallbearer'] = ['-0.98', '0', '0.664', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'unfortunate', 'unfortunate_person', 'weeper', 'griever', 'bearer']
senticnet['pallbearer_carry'] = ['-0.70', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.70', 'death_row', 'sad', 'loose_end', 'car_race', 'mortuary']
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senticnet['palliative_care'] = ['-0.03', '-0.02', '-0.08', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'ibuprofen', 'phenobarbital', 'analgesic', 'sleep_drug', 'pill']
senticnet['palliative_medicine'] = ['-0.03', '-0.02', '-0.08', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'ibuprofen', 'phenobarbital', 'analgesic', 'sleep_drug', 'pill']
senticnet['palliative_sedation'] = ['-0.03', '-0.02', '-0.08', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'ibuprofen', 'phenobarbital', 'analgesic', 'sleep_drug', 'pill']
senticnet['palliative_therapy'] = ['-0.03', '-0.02', '-0.08', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'ibuprofen', 'phenobarbital', 'analgesic', 'sleep_drug', 'pill']
senticnet['palliative_treatment'] = ['-0.03', '-0.02', '-0.08', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'ibuprofen', 'phenobarbital', 'analgesic', 'sleep_drug', 'pill']
senticnet['pallid'] = ['-0.9', '0', '0.841', '-0.73', '#sadness', '#anger', 'negative', '-0.82', 'nubbiness', 'tweediness', 'harshness', 'graininess', 'granularity']
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senticnet['pallor'] = ['-0.66', '0', '0.782', '-0.52', '#sadness', '#anger', 'negative', '-0.65', 'lividness', 'pallidness', 'complexion', 'wanness', 'achromasia']
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senticnet['palm_hotel'] = ['0.057', '0.13', '0', '0.119', '#interest', '#admiration', 'positive', '0.102', 'friend_wedding', 'circular_route', 'spanish_restaurant', 'altar', 'monastery']
senticnet['palm_kernel'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'palm_nut', 'nut', 'edible_nut', 'cembra_nut', 'cedar_nut']
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senticnet['palm_oil_production'] = ['0', '-0.02', '0.085', '-0.09', '#anger', '#disgust', 'negative', '-0.05', 'tree_wood', 'forest', 'sideways', 'dead_tree', 'wood']
senticnet['palm_pilot'] = ['0.113', '0.073', '0.028', '0.107', '#joy', '#admiration', 'positive', '0.066', 'pda', 'telephone_computer', 'browser', 'number_pad', 'modem']
senticnet['palm_reader'] = ['0.083', '0', '0', '0.075', '#joy', '#admiration', 'positive', '0.079', 'carny', 'carnival_ride', 'midway', 'ferris_wheel', 'roller-coaster']
senticnet['palm_thatch'] = ['-0.03', '-0.00', '0.066', '0', '#sadness', '#anger', 'negative', '-0.03', 'dirt', 'allergy', 'dust', 'wet_floor', 'insect']
senticnet['palm_tree'] = ['0', '0.068', '0', '0', '#interest', '#interest', 'positive', '0.068', 'ocean_edge', 'water_beach', 'bottom_ocean', 'sea_shore', 'seashore']
senticnet['palm_upward'] = ['0.883', '-0.59', '0', '0.533', '#joy', '#surprise', 'positive', '0.67', 'hook', 'meat_hook', 'mauler', 'right', 'left']
senticnet['palm_vii'] = ['0.113', '0.073', '0.028', '0.107', '#joy', '#admiration', 'positive', '0.066', 'pda', 'telephone_computer', 'browser', 'number_pad', 'modem']
senticnet['palm_wine'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'nut', 'palm_kernel', 'edible_nut', 'cembra_nut', 'cedar_nut']
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senticnet['palmately'] = ['-0.50', '0', '0.573', '-0.89', '#anger', '#disgust', 'negative', '-0.65', 'petallike', 'bowery', 'leafy', 'unifoliate', 'bifoliate']
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senticnet['palmistry'] = ['0.083', '0', '0', '0.075', '#joy', '#admiration', 'positive', '0.079', 'carny', 'carnival_ride', 'midway', 'ferris_wheel', 'roller-coaster']
senticnet['palmoplantar'] = ['-0.89', '0', '0.998', '-0.89', '#sadness', '#anger', 'negative', '-0.93', 'erythema', 'flare', 'skin_disease', 'disease_of_skin', 'skin_disorder']
senticnet['palmoplantar_keratoderma'] = ['-0.65', '0', '0.873', '-0.63', '#sadness', '#anger', 'negative', '-0.72', 'neuritis', 'multiple_neuritis', 'rubor', 'redness', 'inflammation']
senticnet['palmtop'] = ['0.113', '0.073', '0.028', '0.107', '#joy', '#admiration', 'positive', '0.066', 'pda', 'telephone_computer', 'browser', 'number_pad', 'modem']
senticnet['palmtop_computer'] = ['0.113', '0.073', '0.028', '0.107', '#joy', '#admiration', 'positive', '0.066', 'pda', 'telephone_computer', 'browser', 'number_pad', 'modem']
senticnet['palmy'] = ['0.98', '-0.74', '0', '0.984', '#joy', '#admiration', 'positive', '0.903', 'prosperous', 'flourishing', 'successful', 'prospering', 'thriving']
senticnet['palmyra'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'carnauba_palm', 'carnauba', 'wax_palm', 'palmetto', 'fan_palm']
senticnet['palmyra_palm'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'nut', 'palm_kernel', 'edible_nut', 'cembra_nut', 'cedar_nut']
senticnet['palo_duro'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'common_bean', 'green_bean', 'wax_bean']
senticnet['palo_duro_canyon'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'common_bean', 'green_bean', 'wax_bean']
senticnet['palometa'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'crab', 'swimming_crab', 'rock_crab', 'fiddler_crab', 'pea_crab']
senticnet['palomino'] = ['-0.97', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.96', 'gelding', 'roan', 'sorrel', 'mare', 'buckskin']
senticnet['palpability'] = ['-0.63', '0', '0.694', '-0.66', '#anger', '#disgust', 'negative', '-0.66', 'tangibility', 'tangibleness', 'corporality', 'reality', 'solidness']
senticnet['palpable'] = ['0.985', '-0.81', '0', '0.673', '#joy', '#surprise', 'positive', '0.824', 'discernible', 'noticeable', 'detectable', 'perceptible', 'perceivable']
senticnet['palpable_sense'] = ['0.772', '0.742', '0', '0', '#joy', '#interest', 'positive', '0.757', 'enjoy', 'satisfaction', 'happiness', 'become_excited', 'joy']
senticnet['palpably'] = ['0.859', '0', '0', '0.944', '#joy', '#admiration', 'positive', '0.901', 'demonstrably', 'patently', 'incontrovertibly', 'self-evidently', 'manifestly']
senticnet['palpebra'] = ['-0.79', '0', '0.769', '-0.79', '#sadness', '#disgust', 'negative', '-0.78', 'flap', 'eyelid', 'lid', 'protective_fold', 'nictitating_membrane']
senticnet['palpitant'] = ['0.449', '0.305', '-0.19', '0.222', '#joy', '#interest', 'positive', '0.196', 'calm', 'peace', 'serenity', 'quiet', 'tranquility']
senticnet['palpitation'] = ['-0.82', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.79', 'impatiently', 'raring', 'anxious', 'fidgetiness', 'fretful']
senticnet['palsy'] = ['0.528', '-0.71', '0', '0.848', '#surprise', '#admiration', 'positive', '0.698', 'cachexia', 'cachexy', 'wasting', 'softness', 'unfitness']
senticnet['paltriness'] = ['-0.72', '0', '0.894', '0', '#sadness', '#anger', 'negative', '-0.81', 'woeful', 'paltry', 'trivial', 'piddling', 'shabby']
senticnet['paltry'] = ['-0.72', '0', '0.902', '0', '#sadness', '#anger', 'negative', '-0.81', 'woeful', 'paltriness', 'trivial', 'piddling', 'shabby']
senticnet['paltry_sum'] = ['0', '0.249', '0.012', '0.154', '#interest', '#admiration', 'positive', '0.13', 'utility', 'money_value', 'monetary_value', 'money_amount', 'sticker']
senticnet['palustre'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'bulrush', 'nailrod', 'cattail', 'reedmace']
senticnet['pamper'] = ['0.27', '0', '0', '0', '#joy', '#joy', 'positive', '0.27', 'bourgeois', 'social_event', 'pleasant', 'just_fun', 'break_work']
senticnet['pampered_chef'] = ['0.348', '0.23', '-0.15', '0.294', '#joy', '#admiration', 'positive', '0.18', 'happy_emotion', 'niceness', 'contagious', 'good_emotion', 'savor']
senticnet['pamphlet'] = ['0.058', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.054', 'letter_slot', 'envelope', 'address_label', 'mailman', 'postage_stamp']
senticnet['pamphleteer'] = ['-0.85', '0', '0.749', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'hairsplitter', 'fight', 'quarrel', 'obstructor', 'obstructer']
senticnet['pan'] = ['0.87', '0.707', '0', '0', '#joy', '#interest', 'positive', '0.788', 'scrumble_egg', 'heat_meal', 'beat_egg', 'breakfast', 'food']
senticnet['pan_fry'] = ['-0.83', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.78', 'picaninny', 'kid', 'minor', 'tike', 'small_picaninny']
senticnet['pan_germanism'] = ['-0.81', '-0.71', '0.843', '-0.72', '#sadness', '#anger', 'negative', '-0.41', 'lack_of_rules', 'fascism', 'despot', 'tyrant', 'anarchism']
senticnet['pan_mongolist'] = ['0', '0.793', '0', '0.847', '#interest', '#admiration', 'positive', '0.82', 'disagreement', 'fight_against', 'opposition', 'against', 'go_against']
senticnet['pan_out'] = ['0.596', '-0.99', '0', '0.593', '#joy', '#surprise', 'positive', '0.728', 'deliver_goods', 'succeed', 'win', 'bring_home_bacon', 'come_through']
senticnet['pan_tilt'] = ['0', '-0.02', '0.048', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'telescope', 'read_glass', 'eyeglass', 'binoculars', 'monocle']
senticnet['panacea'] = ['-0.75', '0', '-0.76', '0', '#sadness', '#fear', 'negative', '-0.76', 'cure-all', 'ill', 'antidote', 'remedy', 'cure']
senticnet['panache'] = ['0.828', '-0.88', '0', '0.551', '#joy', '#surprise', 'positive', '0.755', 'superiority', 'pride', 'superbia', 'imperiousness', 'domineeringness']
senticnet['panayiotopoulos_syndrome'] = ['-0.58', '0', '0.814', '-0.54', '#sadness', '#anger', 'negative', '-0.64', 'abnormal', 'irregular', 'vicarious', 'supernormal', 'subnormal']
senticnet['pancake'] = ['0', '-0.11', '0', '0', '#surprise', '#surprise', 'positive', '0.113', 'cake', 'cookie', 'mousse', 'doughnut', 'brownie']
senticnet['pancake-like'] = ['-0.84', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'pizza-like', 'bread-like', 'crepe-like', 'donut-like', 'yeast-risen']
senticnet['pancakecroc'] = ['-0.85', '0', '0.685', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'alligator', 'crocodilian', 'false_gavial', 'gator', 'caiman']
senticnet['pancam'] = ['0.052', '0.119', '0', '0.141', '#interest', '#admiration', 'positive', '0.104', 'neigh', 'domestic_animal', 'animal_type', 'animal', 'home_animal']
senticnet['panchi'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['pancreas'] = ['0.079', '-0.10', '0.046', '0', '#joy', '#surprise', 'positive', '0.047', 'nerve', 'liver', 'groin', 'scrotum', 'diaphragm']
senticnet['pancreas_gland'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'aridness', 'dryness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['pancreas_transplant'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'displace', 'move', 'move_away', 'bump', 'dislocate']
senticnet['pancreatic'] = ['0.079', '-0.10', '0.046', '0', '#joy', '#surprise', 'positive', '0.047', 'nerve', 'liver', 'groin', 'scrotum', 'diaphragm']
senticnet['pancreatic_cancer'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['pancreatic_duct'] = ['-0.56', '0', '0.806', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['pancreatic_insufficiency'] = ['-0.86', '0', '0.76', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['pancreatic_tissue'] = ['0.079', '-0.10', '0.046', '0', '#joy', '#surprise', 'positive', '0.047', 'nerve', 'liver', 'groin', 'scrotum', 'diaphragm']
senticnet['panda'] = ['0.02', '0.071', '0', '0.069', '#interest', '#admiration', 'positive', '0.053', 'many_animals', 'donkey', 'chimpanzee', 'cute_animal', 'creature']
senticnet['panda_breeding_center'] = ['0.02', '0.071', '0', '0.069', '#interest', '#admiration', 'positive', '0.053', 'many_animals', 'donkey', 'chimpanzee', 'cute_animal', 'creature']
senticnet['panda_cam'] = ['0.02', '0.071', '0', '0.069', '#interest', '#admiration', 'positive', '0.053', 'many_animals', 'donkey', 'chimpanzee', 'cute_animal', 'creature']
senticnet['panda_cub'] = ['0.02', '0.071', '0', '0.069', '#interest', '#admiration', 'positive', '0.053', 'many_animals', 'donkey', 'chimpanzee', 'cute_animal', 'creature']
senticnet['panda_mei_xiang'] = ['-0.95', '0', '0.735', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'novice', 'beginner', 'tyro', 'tiro', 'initiate']
senticnet['panda_mom'] = ['-0.95', '0', '0.735', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'novice', 'beginner', 'tyro', 'tiro', 'initiate']
senticnet['pandemic'] = ['-0.71', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.72', 'infectious', 'communicable', 'epidemic', 'contagious', 'epizootic']
senticnet['pandemic_vaccine'] = ['-0.07', '0', '-0.05', '-0.18', '#sadness', '#disgust', 'negative', '-0.10', 'medical_therapy', 'vaccination', 'doctor', 'medicine', 'hospital']
senticnet['pandemonium'] = ['0', '0', '0.923', '-0.76', '#anger', '#disgust', 'negative', '-0.84', 'tumultuous', 'hell', 'violence', 'lawless', 'loud']
senticnet['pander'] = ['0.842', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.915', 'indulge', 'gratify', 'humor', 'luxuriate', 'spend']
senticnet['panderer'] = ['-0.63', '0', '0.88', '-0.94', '#anger', '#disgust', 'negative', '-0.81', 'beguiler', 'slicker', 'perjurer', 'false_witness', 'storyteller']
senticnet['pandit_nathuram_premi_research'] = ['0.025', '0.115', '0.06', '0.183', '#interest', '#admiration', 'positive', '0.066', 'volume_measurement', 'liquid_measurement', 'mass_measure', 'geometry', 'size']
senticnet['pane'] = ['0', '0.069', '0.025', '0.074', '#interest', '#admiration', 'positive', '0.039', 'glass', 'look_glass', 'glass_of_water', 'see_thru', 'clear']
senticnet['paneer'] = ['0.979', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.918', 'make_curry', 'cook_dinner', 'cook_meal', 'cook_curry', 'powder']
senticnet['panegyric'] = ['0', '0', '-0.90', '-0.79', '#fear', '#disgust', 'negative', '-0.85', 'encomiastic', 'encomium', 'paean', 'eulogy', 'pean']
senticnet['panegyrical'] = ['0', '0', '-0.90', '-0.79', '#fear', '#disgust', 'negative', '-0.85', 'encomiastic', 'encomium', 'paean', 'eulogy', 'pean']
senticnet['panegyrici_latini'] = ['0', '0', '-0.90', '-0.79', '#fear', '#disgust', 'negative', '-0.85', 'encomiastic', 'encomium', 'paean', 'eulogy', 'pean']
senticnet['panegyrist'] = ['0', '0', '-0.90', '-0.79', '#fear', '#disgust', 'negative', '-0.85', 'encomiastic', 'encomium', 'paean', 'eulogy', 'pean']
senticnet['panel'] = ['0', '0', '0', '0.121', '#admiration', '#admiration', 'positive', '0.121', 'wood_panel', 'house_front', 'wooden_structure', 'slam', 'home_entrance']
senticnet['panel_beater'] = ['-0.64', '0', '0.879', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'drone', 'poke', 'dawdler', 'laggard', 'straggler']
senticnet['panel_discussion'] = ['0', '0.214', '0.012', '0.208', '#interest', '#admiration', 'positive', '0.137', 'lecture_hall', 'lecture_theater', 'lesson', 'lecture', 'school_session']
senticnet['paneled_door'] = ['0', '0.171', '-0.05', '0.102', '#interest', '#admiration', 'positive', '0.072', 'bed', 'mattress', 'bead', 'bed_cover', 'seduce']
senticnet['panendoscope'] = ['0.255', '0.182', '-0.06', '0.314', '#joy', '#admiration', 'positive', '0.173', 'doctor_office', 'urethroscope', 'sigmoidoscope', 'hospital', 'check_health']
senticnet['panera_bread'] = ['0.912', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.917', 'carpet_store', 'lumberyard', 'furniture_store', 'front_porch', 'shoe_shop']
senticnet['panfish'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'divergent_strabismus', 'exotropia', 'squint', 'abnormality', 'abnormalcy']
senticnet['panfish_action'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'divergent_strabismus', 'exotropia', 'squint', 'abnormality', 'abnormalcy']
senticnet['panforte'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'grapefruit_peel', 'lemon_peel', 'orange_peel', 'candied_citrus_peel', 'succade']
senticnet['pang'] = ['0', '0', '0.696', '-0.83', '#anger', '#disgust', 'negative', '-0.76', 'twinge', 'twinge', 'tinge', 'uneasiness', 'ache']
senticnet['panglossian'] = ['0.783', '-0.51', '0', '0.584', '#joy', '#admiration', 'positive', '0.627', 'appetent', 'avid', 'thirsty', 'devouring', 'envious']
senticnet['panhandler'] = ['-0.12', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.07', 'graffiti', 'homeless_people', 'homeless_person', 'bum', 'homeless']
senticnet['panhandling'] = ['0', '0', '0.951', '-0.80', '#anger', '#disgust', 'negative', '-0.87', 'drunkenness', 'loitering', 'vagrancy', 'sedition', 'bigamy']
senticnet['panic'] = ['-0.48', '0.167', '-0.30', '-0.21', '#sadness', '#fear', 'negative', '-0.20', 'anxiety', 'scare', 'confusion', 'worry', 'embarrassment']
senticnet['panic-stricken'] = ['-0.71', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.82', 'panic-struck', 'panicked', 'panicky', 'terrified', 'frightened']
senticnet['panic-struck'] = ['-0.85', '0', '-0.7', '0', '#sadness', '#fear', 'negative', '-0.77', 'panic-stricken', 'panicked', 'panicky', 'terrified', 'frightened']
senticnet['panic_attack'] = ['-0.98', '-0.93', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.33', 'fright', 'uneasiness', 'hysteria', 'terrible', 'hopeless']
senticnet['panic_mode'] = ['-0.81', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.78', 'panic-struck', 'panic-stricken', 'panicked', 'terrified', 'frightened']
senticnet['panic_stricken'] = ['-0.88', '0.178', '-0.6', '0', '#sadness', '#fear', 'negative', '-0.43', 'rush_job', 'rush_work', 'late', 'urgency', 'emergency']
senticnet['panicked'] = ['-0.86', '0', '-0.70', '0', '#sadness', '#fear', 'negative', '-0.78', 'panic-stricken', 'panic-struck', 'panicky', 'terrified', 'frightened']
senticnet['panicky'] = ['-0.81', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.78', 'panic-struck', 'panic-stricken', 'panicked', 'terrified', 'frightened']
senticnet['paniculata'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['panicum'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'meadow_grass', 'grass', 'manna_grass', 'sweet_grass', 'wood_meadowgrass']
senticnet['panicum_virgatum'] = ['0', '0.086', '0', '0', '#interest', '#interest', 'positive', '0.086', 'swim_ocean', 'weight_lift', 'sit_in_shade', 'cut_grass', 'trust_friend']
senticnet['panix'] = ['0.687', '-0.50', '0', '0.8', '#joy', '#admiration', 'positive', '0.663', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['pankau'] = ['0.025', '0.27', '-0.11', '0.243', '#interest', '#admiration', 'positive', '0.105', 'keep_safe', 'store_dry_goods', 'store_stuff', 'secure', 'save']
senticnet['panna_cotta'] = ['0.028', '0.138', '-0.04', '0.215', '#interest', '#admiration', 'positive', '0.083', 'water', 'dense', 'thick', 'milk', 'dense_water']
senticnet['panna_wisdom'] = ['0.668', '-0.87', '0', '0.606', '#joy', '#surprise', 'positive', '0.715', 'adeptness', 'adroitness', 'deftness', 'quickness', 'expertness']
senticnet['pannage'] = ['0.966', '0.881', '0', '0', '#joy', '#interest', 'positive', '0.923', 'harvest', 'crop', 'grow_crop', 'plant_crop', 'reap']
senticnet['pannone'] = ['0.053', '0.122', '-0.07', '0.086', '#interest', '#admiration', 'positive', '0.046', 'attorney', 'lawyer', 'jury', 'decide_guilt', 'prosecutor']
senticnet['panoply'] = ['0.757', '-0.63', '0', '0.501', '#joy', '#surprise', 'positive', '0.631', 'array', 'row', 'table', 'tabular_array', 'matrix']
senticnet['panoptic'] = ['0.654', '-0.71', '0', '0.624', '#joy', '#surprise', 'positive', '0.664', 'viewable', 'gross', 'telescopic', 'seeable', 'visual']
senticnet['panopticism'] = ['0.654', '-0.71', '0', '0.624', '#joy', '#surprise', 'positive', '0.664', 'viewable', 'gross', 'telescopic', 'seeable', 'visual']
senticnet['panopticon'] = ['0.654', '-0.71', '0', '0.624', '#joy', '#surprise', 'positive', '0.664', 'viewable', 'gross', 'telescopic', 'seeable', 'visual']
senticnet['panorama'] = ['0.838', '0.828', '0', '0', '#joy', '#interest', 'positive', '0.833', 'vista', 'spectacular', 'perspective', 'image', 'scenic']
senticnet['panoramic'] = ['0.838', '0.828', '0', '0', '#joy', '#interest', 'positive', '0.833', 'vista', 'spectacular', 'perspective', 'image', 'scenic']
senticnet['panoramic_camera'] = ['0.052', '0.119', '0', '0.141', '#interest', '#admiration', 'positive', '0.104', 'neigh', 'domestic_animal', 'animal_type', 'animal', 'home_animal']
senticnet['panoramic_image'] = ['0.838', '0.828', '0', '0', '#joy', '#interest', 'positive', '0.833', 'vista', 'spectacular', 'perspective', 'image', 'scenic']
senticnet['panoramic_landscape'] = ['0.838', '0.828', '0', '0', '#joy', '#interest', 'positive', '0.833', 'vista', 'spectacular', 'perspective', 'image', 'scenic']
senticnet['panoramic_photo'] = ['0.838', '0.828', '0', '0', '#joy', '#interest', 'positive', '0.833', 'vista', 'spectacular', 'perspective', 'image', 'scenic']
senticnet['panoramic_photograph'] = ['0.838', '0.828', '0', '0', '#joy', '#interest', 'positive', '0.833', 'vista', 'spectacular', 'perspective', 'image', 'scenic']
senticnet['panoramic_photography'] = ['0.838', '0.828', '0', '0', '#joy', '#interest', 'positive', '0.833', 'vista', 'spectacular', 'perspective', 'image', 'scenic']
senticnet['panoramic_picture'] = ['0.838', '0.828', '0', '0', '#joy', '#interest', 'positive', '0.833', 'vista', 'spectacular', 'perspective', 'image', 'scenic']
senticnet['panoramic_shot'] = ['0.838', '0.828', '0', '0', '#joy', '#interest', 'positive', '0.833', 'vista', 'spectacular', 'perspective', 'image', 'scenic']
senticnet['panoramic_sunroof'] = ['0.725', '0.796', '0', '0', '#joy', '#interest', 'positive', '0.76', 'car_stereo', 'battery', 'seatbelt', 'spare_tire', 'brake_pedal']
senticnet['panoramic_view'] = ['0', '0.095', '0', '0.112', '#interest', '#admiration', 'positive', '0.104', 'stare', 'look', 'observation', 'ship_part', 'see']
senticnet['pansexual'] = ['-0.97', '0', '0.971', '-0.84', '#sadness', '#anger', 'negative', '-0.93', 'fail', 'betray', 'disappoint', 'cross', 'disillusion']
senticnet['pansy'] = ['-0.76', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.81', 'intractability', 'unruliness', 'fractiousness', 'willfulness', 'against_rule']
senticnet['pant'] = ['-0.02', '-0.05', '0.092', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'gasp', 'puff', 'wheeze', 'blow', 'heave']
senticnet['pant_back_pocket'] = ['0', '0.092', '0', '0', '#interest', '#interest', 'positive', '0.092', 'man_back_pocket', 'pocket', 'back_pocket', 'purse', 'clothes_washer']
senticnet['pant_leg'] = ['0', '0', '0', '-0.06', '#disgust', '#disgust', 'negative', '-0.06', 'body', 'nervous_system', 'normal_human_body', 'human_body', 'head_shoulder']
senticnet['pant_pocket'] = ['0', '0.092', '0', '0', '#interest', '#interest', 'positive', '0.092', 'man_pocket', 'back_pocket', 'pocket', 'purse', 'clothes_washer']
senticnet['pantheist'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'non-believer', 'doubter', 'agnostic', 'freethinker', 'sceptic']
senticnet['pantheon'] = ['0.84', '-0.56', '0', '0.561', '#joy', '#surprise', 'positive', '0.654', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['panther'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'unofficial', 'informal', 'loose', 'drumhead']
senticnet['pantie'] = ['0.923', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.939', 'under_garment', 'undergarment', 'underwear', 'woman_clothes', 'lady_underwear']
senticnet['panties'] = ['0.954', '0.903', '0', '0', '#joy', '#interest', 'positive', '0.928', 'lingerie', 'underwear', 'pair_of_pants', 'robe', 'slack']
senticnet['panto_villain'] = ['-0.91', '0', '0.681', '-0.81', '#sadness', '#disgust', 'negative', '-0.80', 'chicken', 'doormat', 'softie', 'crybaby', 'wimp']
senticnet['pantomime'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'dress', 'pinny', 'dirndl', 'gown', 'muumuu']
senticnet['pantomimic'] = ['-0.87', '0', '0.53', '-0.65', '#sadness', '#disgust', 'negative', '-0.68', 'malevolence', 'malevolency', 'malice', 'murderousness', 'ferociousness']
senticnet['pantomimist'] = ['0.563', '-0.81', '0', '0.577', '#surprise', '#admiration', 'positive', '0.65', 'mortal', 'person', 'individual', 'performer', 'artist']
senticnet['pantry'] = ['-0.04', '-0.00', '0.049', '0', '#sadness', '#anger', 'negative', '-0.02', 'janitor_room', 'buy_gum', 'shop_grocery', 'buy_clothes', 'find_pantry']
senticnet['pants'] = ['-0.04', '0.1', '0', '0.152', '#interest', '#admiration', 'positive', '0.068', 'shirt', 'jacket', 'clothes', 'robe', 'garment']
senticnet['pants_pocket'] = ['0', '0.072', '0', '0', '#interest', '#interest', 'positive', '0.072', 'metal_box', 'cash_drawer', 'keyhole', 'clutch_bag', 'safe']
senticnet['pantsuit_aficionado'] = ['0.055', '0.117', '0', '0.133', '#interest', '#admiration', 'positive', '0.102', 'graphical_representation', 'pencil_pen', 'note_pad', 'marker', 'stationery']
senticnet['panty_hose'] = ['0.998', '0', '0', '0', '#joy', '#joy', 'positive', '0.998', 'panties', 'lingerie', 'pantyhose', 'slack', 'prescription_medication']
senticnet['pantyhose'] = ['0', '-0.07', '0.099', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'prescription_medication', 'lingerie', 'sachet', 'shave_kit', 'panties']
senticnet['pantyhose_voyeur'] = ['0', '-0.07', '0.099', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'prescription_medication', 'lingerie', 'sachet', 'shave_kit', 'panties']
senticnet['panzer_lehr'] = ['0.737', '-0.96', '0', '0.552', '#joy', '#surprise', 'positive', '0.75', 'prominence', 'hump', 'bump', 'swelling', 'frontal_eminence']
senticnet['panzer_lehr_division'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'start_over', 'queue', 'find_line', 'take_turn', 'long_line']
senticnet['panzerdivision'] = ['0.737', '-0.96', '0', '0.552', '#joy', '#surprise', 'positive', '0.75', 'prominence', 'hump', 'bump', 'swelling', 'frontal_eminence']
senticnet['panzuriel'] = ['-0.04', '0.166', '0.684', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'malign', 'malefic', 'maliciously', 'spitefulness', 'maliciousness']
senticnet['paolo_ferrari_archie_goodwin'] = ['0', '0', '0', '0.068', '#admiration', '#admiration', 'positive', '0.068', 'dark_corner', 'child_game', 'play_person', 'role_play', 'part_of_play']
senticnet['pap'] = ['0.739', '-0.89', '0', '0.702', '#joy', '#surprise', 'positive', '0.78', 'drivel', 'content', 'pablum', 'folderol', 'codswallop']
senticnet['papacy'] = ['0.807', '0', '0', '0.755', '#joy', '#admiration', 'positive', '0.781', 'pontificate', 'preach', 'pope', 'minister', 'bishop']
senticnet['papakura_military'] = ['0', '0.156', '-0.06', '0.198', '#interest', '#admiration', 'positive', '0.098', 'grassland', 'camping', 'camp_out', 'campsite', 'campground']
senticnet['papal_dispensation'] = ['-0.76', '0', '0.995', '-0.94', '#anger', '#disgust', 'negative', '-0.90', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'excretion']
senticnet['papal_rescript'] = ['0.751', '-0.98', '0', '0.989', '#surprise', '#admiration', 'positive', '0.909', 'okay', 'approval', 'commendation', 'okey', 'okeh']
senticnet['papal_tiara'] = ['-0.62', '0', '0.961', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'crown', 'diadem', 'jeweled_headdress', 'headdress', 'headgear']
senticnet['papalii'] = ['0', '0.996', '0', '0.85', '#interest', '#admiration', 'positive', '0.923', 'strength', 'strong', 'physical_strength', 'athlete', 'muscle']
senticnet['papaya'] = ['0.859', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.888', 'green_apple', 'apricot', 'lime', 'peach', 'cherry']
senticnet['paper'] = ['0.022', '-0.05', '0.079', '-0.11', '#anger', '#disgust', 'negative', '-0.02', 'stationery', 'ink', 'ball_point_pen', 'refill', 'note']
senticnet['paper_airplane'] = ['0.924', '0.762', '0', '0', '#joy', '#interest', 'positive', '0.843', 'make_paper_airplane', 'play', 'write_note', 'writing', 'make_note']
senticnet['paper_bag'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'toilet_paper', 'beach_towel', 'face_towel', 'hand_towel', 'tissue']
senticnet['paper_bill'] = ['0.871', '0.873', '0', '0', '#joy', '#interest', 'positive', '0.872', 'cash_bill', 'cash_coin', 'dollar_bill', 'debit_card', 'check']
senticnet['paper_birch_betula'] = ['0.776', '-0.6', '0', '0.568', '#joy', '#surprise', 'positive', '0.648', 'swamp_chestnut_oak', 'chinquapin_oak', 'chinkapin_oak', 'yellow_chestnut_oak', 'basket_oak']
senticnet['paper_book'] = ['0.024', '0.121', '-0.07', '0.164', '#interest', '#admiration', 'positive', '0.058', 'conceal', 'concealment', 'front_page', 'vocabulary', 'book_content']
senticnet['paper_chromatography'] = ['0.838', '-0.61', '0', '0.556', '#joy', '#surprise', 'positive', '0.671', 'activity', 'natural_process', 'natural_action', 'column_chromatography', 'chromatography']
senticnet['paper_clip'] = ['0', '0.106', '-0.08', '0.145', '#interest', '#admiration', 'positive', '0.056', 'paper_fastener', 'paper_punch', 'intern', 'office', 'highlighter']
senticnet['paper_crumple'] = ['0.839', '0.94', '0', '0', '#joy', '#interest', 'positive', '0.889', 'talk', 'party', 'chat', 'communicate', 'speak']
senticnet['paper_currency'] = ['0', '0.239', '0', '0.129', '#interest', '#admiration', 'positive', '0.184', 'euro', 'currency', 'money_unit', 'dollar_change', 'four_quarters']
senticnet['paper_cut'] = ['-0.82', '-0.72', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.30', 'feel_hurt', 'embarrassment', 'gonorrhea', 'hurt_feeling', 'psychoneurosis']
senticnet['paper_deacidification'] = ['-0.97', '0', '0.885', '-0.97', '#sadness', '#disgust', 'negative', '-0.94', 'protection', 'conservation', 'environmentalism', 'reservation', 'immobilization']
senticnet['paper_doll'] = ['0.71', '0.92', '0', '0', '#joy', '#interest', 'positive', '0.815', 'toy_car', 'toy_ball', 'action_figure', 'toy_marble', 'beanie_baby']
senticnet['paper_fastener'] = ['0', '0.239', '0', '0.129', '#interest', '#admiration', 'positive', '0.184', 'paper_clip', 'decorate_wall', 'intern', 'glue', 'wallpaper']
senticnet['paper_menu'] = ['0.146', '0.095', '-0.04', '0', '#joy', '#interest', 'positive', '0.065', 'dine_room', 'diner', 'cafe', 'restaurant', 'menu']
senticnet['paper_money'] = ['-0.02', '0.147', '0', '0.073', '#interest', '#admiration', 'positive', '0.065', 'money_unit', 'monetary_unit', 'note', 'paper_currency', 'money_earn']
senticnet['paper_nautilus'] = ['-0.52', '0', '0.728', '-0.92', '#anger', '#disgust', 'negative', '-0.72', 'nautilus', 'cephalopod', 'cephalopod_mollusk', 'octopod', 'octopus']
senticnet['paper_octopod'] = ['-0.53', '0', '0.725', '-0.93', '#anger', '#disgust', 'negative', '-0.72', 'octopod', 'cephalopod', 'cephalopod_mollusk', 'nautilus', 'octopus']
senticnet['paper_page'] = ['0.718', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.734', 'current_event', 'headline', 'paper_write', 'bulletin', 'many_pages']
senticnet['paper_piece'] = ['0.044', '-0.00', '0.009', '0.138', '#joy', '#admiration', 'positive', '0.045', 'flat_thin', 'white_paper', 'paper_square', 'single_paper', 'game_piece']
senticnet['paper_plane'] = ['0.808', '0.845', '0', '0', '#joy', '#interest', 'positive', '0.827', 'learn_write', 'think_write', 'notepaper', 'find_paper', 'pen']
senticnet['paper_product'] = ['-0.05', '-0.03', '0.023', '-0.09', '#sadness', '#disgust', 'negative', '-0.03', 'moldy_food', 'celery', 'spam', 'orange_juice', 'flat_thin']
senticnet['paper_pulp'] = ['0.024', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'stump', 'plank', 'timber', 'paper', 'mahogany']
senticnet['paper_punch'] = ['0.809', '0.713', '0', '0', '#joy', '#interest', 'positive', '0.761', 'intern', 'paper_fastener', 'office', 'paper_clip', 'self_adhesive_label']
senticnet['paper_sign'] = ['0.076', '0', '0', '0.104', '#joy', '#admiration', 'positive', '0.09', 'post', 'newspaper_article', 'rail', 'column', 'peg']
senticnet['paper_square'] = ['0.044', '-0.00', '0.009', '0.138', '#joy', '#admiration', 'positive', '0.045', 'flat_thin', 'white_paper', 'paper_piece', 'single_paper', 'game_piece']
senticnet['paper_towel'] = ['-0.84', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.87', 'moistener', 'steel_wool', 'toilet_paper', 'toilet_brush', 'bath_shower']
senticnet['paper_towel_dispenser'] = ['0', '0.171', '-0.05', '0.102', '#interest', '#admiration', 'positive', '0.072', 'bed', 'mattress', 'bead', 'bed_cover', 'seduce']
senticnet['paper_wasp'] = ['0.748', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.873', 'butterfly', 'moth', 'parrot', 'beetle', 'falcon']
senticnet['paper_write'] = ['0.996', '0.837', '0', '0', '#joy', '#interest', 'positive', '0.916', 'current_event', 'headline', 'bulletin', 'paper_page', 'many_pages']
senticnet['paperback'] = ['-0.04', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.08', 'data', 'awareness', 'information', 'saga', 'knowledge']
senticnet['paperback_edition'] = ['-0.04', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.08', 'data', 'awareness', 'information', 'saga', 'knowledge']
senticnet['paperback_version'] = ['-0.04', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.08', 'data', 'awareness', 'information', 'saga', 'knowledge']
senticnet['paperboard'] = ['0.024', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'stump', 'plank', 'timber', 'paper', 'mahogany']
senticnet['paperboard_box'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'foil', 'tin_foil', 'gold_foil']
senticnet['paperboy'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'mailman', 'package', 'mail', 'parcel', 'letter_slot']
senticnet['paperclip'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'clip', 'clasp', 'glue_stick', 'clothespin', 'clamp']
senticnet['papered_over'] = ['-0.13', '0.071', '0.694', '-0.67', '#anger', '#disgust', 'negative', '-0.35', 'crucify', 'dun', 'frustrating', 'aggravate', 'exasperation']
senticnet['papering_over'] = ['-0.07', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.07', 'use_needle', 'doctor_tool', 'fix', 'embroidery', 'seam']
senticnet['paperweight'] = ['0', '0', '-0.86', '-0.85', '#fear', '#disgust', 'negative', '-0.85', 'gaud', 'novelty', 'gewgaw', 'bangle', 'bauble']
senticnet['paperwork'] = ['0.044', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'sheet_paper', 'piece_of_paper', 'staple_remover', 'board', 'whiteboard']
senticnet['papery'] = ['0.824', '0', '0', '0.871', '#joy', '#admiration', 'positive', '0.847', 'leathery', 'fleshy', 'grayish', 'scaly', 'feathery']
senticnet['papery_bark'] = ['-0.75', '0', '0.734', '-0.82', '#sadness', '#disgust', 'negative', '-0.77', 'puberulent', 'haired', 'furlike', 'pappose', 'pilous']
senticnet['papilla'] = ['-0.86', '0', '0.511', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'separate', 'move', 'branch', 'diverge', 'fork']
senticnet['papillate'] = ['-0.66', '0', '0.809', '-0.61', '#sadness', '#anger', 'negative', '-0.69', 'face', 'base', 'underbelly', 'sole', 'bilge']
senticnet['papilledema'] = ['-0.86', '0', '0.858', '-0.91', '#sadness', '#disgust', 'negative', '-0.87', 'edema', 'oedema', 'hydrops', 'dropsy', 'eye_disease']
senticnet['papineau_labelle_wildlife'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['paple'] = ['-0.63', '0', '0.683', '-0.96', '#anger', '#disgust', 'negative', '-0.76', 'slovenly_woman', 'slut', 'trollop', 'slattern', 'litterer']
senticnet['pappardelle'] = ['0.763', '0.9', '0', '0', '#joy', '#interest', 'positive', '0.832', 'breakfast', 'box_cereal', 'calcium', 'drink', 'carton_milk']
senticnet['pappataci_fever'] = ['-0.75', '0', '0.69', '-0.69', '#sadness', '#disgust', 'negative', '-0.71', 'rubor', 'redness', 'sleeping_sickness', 'sleepy_sickness', 'cephalitis']
senticnet['pappose'] = ['-0.75', '0', '0.734', '-0.82', '#sadness', '#disgust', 'negative', '-0.77', 'puberulent', 'sericeous', 'furlike', 'haired', 'pilous']
senticnet['pappy'] = ['0.934', '-0.57', '0', '0.767', '#joy', '#admiration', 'positive', '0.76', 'morosoph', 'simpleton', 'flibbertigibbet', 'foolish_woman', 'simple']
senticnet['papular'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema', 'erythema_multiforme', 'windburn', 'hickey']
senticnet['papule'] = ['-0.10', '0', '-0.05', '-0.11', '#sadness', '#disgust', 'negative', '-0.08', 'jaw', 'cheek', 'dimple', 'forehead', 'beard']
senticnet['papyrifera'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'locust_tree']
senticnet['par'] = ['0.821', '0.985', '0', '0', '#joy', '#interest', 'positive', '0.903', 'equating', 'equation', 'biquadratic_equation', 'differential_equation', 'equivalence']
senticnet['par_arme_feu'] = ['0', '0', '-0.95', '-0.85', '#fear', '#disgust', 'negative', '-0.90', 'choose_victim', 'murder_weapon', 'hide_evidence', 'murder', 'shoot_gun']
senticnet['par_excellence'] = ['0.693', '-0.52', '0', '0.82', '#joy', '#admiration', 'positive', '0.681', 'concrete_representation', 'shape', 'representation', 'mental_representation', 'internal_representation']
senticnet['par_golf_course'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'table_tennis', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['par_leur'] = ['0.911', '-0.94', '0', '0.953', '#surprise', '#admiration', 'positive', '0.935', 'common_sense', 'good_sense', 'horse_sense', 'mother_wit', 'gumption']
senticnet['para_military'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'law_enforcement_agency', 'law', 'agency']
senticnet['para_transit'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'seeker', 'chairperson']
senticnet['parable'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'narrative', 'bedtime', 'bed_time', 'long_story', 'saga']
senticnet['parabola'] = ['-0.04', '0.074', '0.019', '0.071', '#interest', '#admiration', 'positive', '0.021', 'sharpness', 'sharp_end', 'sharp_corner', 'near_end', 'edge']
senticnet['parabolica'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['paraboloid'] = ['0.06', '-0.03', '0.037', '0.075', '#joy', '#admiration', 'positive', '0.034', 'semicircle', 'trapezoid', 'circle', 'triangular', 'facet']
senticnet['paracas_national'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['paraceratherium'] = ['-0.93', '0', '0.508', '-0.58', '#sadness', '#disgust', 'negative', '-0.67', 'inhuman', 'bloodless', 'apelike', 'nonhuman', 'nonhumanal']
senticnet['paracervical_block'] = ['-0.65', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'put_to_sleep', 'operation', 'general_anesthesia', 'surgery', 'anesthesia']
senticnet['paracetamol_acetaminophen'] = ['0.969', '0.817', '0', '0.828', '#joy', '#admiration', 'positive', '0.871', 'ibuprofen', 'painkiller', 'medicine', 'analgesic', 'phenobarbital']
senticnet['parachute'] = ['-0.88', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.90', 'undergarment', 'outerwear', 'lady_underwear', 'under_garment', 'slip']
senticnet['parachute_assault'] = ['-0.07', '0.069', '-0.18', '0', '#sadness', '#fear', 'negative', '-0.06', 'medical_procedure', 'cure', 'doctor_surgery', 'treatment', 'doctor_work']
senticnet['parachute_canopy'] = ['-0.88', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.90', 'undergarment', 'outerwear', 'lady_underwear', 'under_garment', 'slip']
senticnet['parachute_deployment'] = ['-0.88', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.90', 'undergarment', 'outerwear', 'lady_underwear', 'under_garment', 'slip']
senticnet['parachute_descent'] = ['-0.88', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.90', 'undergarment', 'outerwear', 'lady_underwear', 'under_garment', 'slip']
senticnet['parachute_drop'] = ['-0.07', '0.069', '-0.18', '0', '#sadness', '#fear', 'negative', '-0.06', 'medical_procedure', 'cure', 'doctor_surgery', 'treatment', 'doctor_work']
senticnet['parachute_jump'] = ['-0.88', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.90', 'undergarment', 'outerwear', 'lady_underwear', 'under_garment', 'slip']
senticnet['parachute_pack'] = ['-0.88', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.90', 'undergarment', 'outerwear', 'lady_underwear', 'under_garment', 'slip']
senticnet['parachutist'] = ['-0.88', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.90', 'undergarment', 'outerwear', 'lady_underwear', 'under_garment', 'slip']
senticnet['parade'] = ['0.957', '0.7', '0', '0', '#joy', '#interest', 'positive', '0.829', 'ballpark', 'coven', 'disneyland', 'hockey_game', 'symposium']
senticnet['parade_route'] = ['0', '0', '-0.04', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'good_band', 'band', 'kit', 'procession', 'oblivion']
senticnet['paradigm'] = ['0', '0.137', '-0.10', '0.197', '#interest', '#admiration', 'positive', '0.076', 'boom', 'circumstance', 'loud', 'stormy', 'procedure']
senticnet['paradigmatically'] = ['0', '0.918', '0', '0.958', '#interest', '#admiration', 'positive', '0.938', 'marry', 'get_married', 'love', 'join', 'hook_up_with']
senticnet['paradisal'] = ['0.732', '0', '0', '0.706', '#joy', '#admiration', 'positive', '0.719', 'pleasant_emotion', 'happy_emotion', 'happy_feeling', 'show_affection', 'good_emotion']
senticnet['paradise'] = ['0.054', '0.098', '0', '0.076', '#interest', '#admiration', 'positive', '0.076', 'sky', 'heaven', 'constellation', 'thunder_storm', 'orbit_around_earth']
senticnet['paradise_garage'] = ['0.763', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.75', 'pub', 'disco', 'dance_club', 'cafe', 'tavern']
senticnet['paradox'] = ['-0.56', '0', '0.593', '-0.59', '#anger', '#disgust', 'negative', '-0.58', 'antinomy', 'contradiction_in_term', 'falsehood', 'falsity', 'untruth']
senticnet['paradoxalement'] = ['0', '0.919', '0', '0.888', '#interest', '#admiration', 'positive', '0.903', 'surprise', 'eye-popping', 'fulgurous', 'dazzling', 'bedaze']
senticnet['paradoxical'] = ['-0.81', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.82', 'counter-intuitive', 'counterintuitive', 'disconcerting', 'far-fetched', 'implausible']
senticnet['paradoxical_nature'] = ['0.772', '-0.73', '0', '0.958', '#joy', '#admiration', 'positive', '0.82', 'quality', 'mutability', 'mutableness', 'changefulness', 'exchangeability']
senticnet['paradoxically'] = ['0.755', '0.972', '0', '0.775', '#interest', '#admiration', 'positive', '0.834', 'grasp', 'grab', 'hold', 'sensual', 'grip']
senticnet['paraeducator'] = ['-0.69', '0', '0.659', '-0.69', '#sadness', '#disgust', 'negative', '-0.68', 'help', 'paralegal', 'legal_assistant', 'paramedic', 'paramedical']
senticnet['paraesthesia'] = ['-0.65', '0', '0.873', '-0.63', '#sadness', '#anger', 'negative', '-0.72', 'polyneuritis', 'multiple_neuritis', 'rubor', 'redness', 'inflammation']
senticnet['paraffin'] = ['0.072', '0.065', '-0.06', '0.144', '#joy', '#admiration', 'positive', '0.053', 'gooey', 'bee_product', 'slick', 'goo', 'fuel_source']
senticnet['paraffin_wax'] = ['-0.04', '0.069', '0', '0.075', '#interest', '#admiration', 'positive', '0.035', 'sculpt_gel', 'hairstyle', 'hairdresser', 'cosmetic', 'hair_sculpt_gel']
senticnet['parafield'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'cheapjack', 'pitchman']
senticnet['parafilm'] = ['-0.86', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.80', 'riverbed', 'bottom_ocean', 'waterfall', 'down_drain', 'septic_tank']
senticnet['parafoil'] = ['-0.88', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.90', 'undergarment', 'outerwear', 'lady_underwear', 'under_garment', 'slip']
senticnet['paragard'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'birth_control_pill', 'condom', 'contraceptive_pill', 'sex_toy', 'prevent_pregnancy']
senticnet['paraglider'] = ['-0.88', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.90', 'undergarment', 'outerwear', 'lady_underwear', 'under_garment', 'slip']
senticnet['paragon'] = ['0.118', '0.181', '-0.07', '0.374', '#interest', '#admiration', 'positive', '0.149', 'compare', 'confrontation', 'collation', 'comparative', 'comparison']
senticnet['paragonimus_westermani'] = ['0.688', '-0.94', '0', '0.925', '#surprise', '#admiration', 'positive', '0.852', 'flatworm', 'platyhelminth', 'trematode', 'schistosome', 'blood_fluke']
senticnet['paragraph'] = ['-0.04', '0.131', '-0.05', '0.186', '#interest', '#admiration', 'positive', '0.055', 'essay', 'document_type', 'typeface', 'hold_information', 'print']
senticnet['parainfluenza_type'] = ['-0.75', '0', '0.69', '-0.69', '#sadness', '#disgust', 'negative', '-0.71', 'rubor', 'redness', 'sleeping_sickness', 'sleepy_sickness', 'cephalitis']
senticnet['parakeet'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'lovebird', 'parrakeet', 'parroket', 'paraquet', 'paroquet']
senticnet['paralanguage'] = ['0.727', '-0.62', '0', '0.57', '#joy', '#surprise', 'positive', '0.64', 'utterer', 'deipnosophist', 'interlocutor', 'conversationalist', 'speaker']
senticnet['paralegal'] = ['-0.69', '0', '0.659', '-0.69', '#sadness', '#disgust', 'negative', '-0.68', 'help', 'paraprofessional', 'legal_assistant', 'paramedic', 'paramedical']
senticnet['paralinguistic'] = ['-0.84', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.78', 'non-linguistic', 'nonlinguistic', 'extra-linguistic', 'language-like', 'socio-environmental']
senticnet['paralipomena'] = ['-0.7', '0', '0.746', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'insufficiency', 'sparseness', 'sparsity', 'abstemiousness', 'lack_of_quantity']
senticnet['parallactic_angle'] = ['0.777', '-0.85', '0', '0.615', '#joy', '#surprise', 'positive', '0.748', 'think', 'play', 'trifle', 'abstract', 'warm_to']
senticnet['parallel'] = ['0', '0', '-0.73', '-0.79', '#fear', '#disgust', 'negative', '-0.76', 'analogue', 'analog', 'line_of_latitude', 'latitude', 'collimate']
senticnet['parallel_execution'] = ['0', '0', '-0.73', '-0.79', '#fear', '#disgust', 'negative', '-0.76', 'analogue', 'analog', 'line_of_latitude', 'latitude', 'collimate']
senticnet['parallelism'] = ['0', '0', '0.951', '-0.84', '#anger', '#disgust', 'negative', '-0.89', 'rhetoric', 'device', 'elocutionary', 'eloquent', 'rhetorical']
senticnet['parallelogram'] = ['0.733', '0.752', '0', '0', '#joy', '#interest', 'positive', '0.742', 'polygon', 'geometric_figure', 'hexagon', 'geometrical_shape', 'geometric_shape']
senticnet['paralogism'] = ['-0.77', '0', '0.885', '-0.73', '#sadness', '#anger', 'negative', '-0.8', 'pathetic_fallacy', 'misconception', 'fallacy', 'logical_fallacy', 'pseudoscience']
senticnet['paralympic_athlete'] = ['-0.04', '-0.03', '0', '-0.16', '#sadness', '#disgust', 'negative', '-0.05', 'crazy', 'damage_body', 'crazy_person', 'threat', 'max']
senticnet['paralysis'] = ['-0.29', '0.095', '-0.11', '-0.13', '#sadness', '#disgust', 'negative', '-0.11', 'say_aha', 'think_hard', 'think_past', 'scratch_head', 'head_hurt']
senticnet['paralytic_disease'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'leptomeningitis', 'meningitis', 'epidemic_meningitis', 'brain_fever', 'cerebrospinal_fever']
senticnet['paralyze'] = ['-0.59', '0', '0.704', '-0.5', '#sadness', '#anger', 'negative', '-0.59', 'lay_up', 'modify', 'nobble', 'alter', 'change']
senticnet['paralyzed'] = ['-0.71', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.84', 'catatonic', 'bedridden', 'comatose', 'bed-ridden', 'coma']
senticnet['paralyzed_vocal_cord'] = ['0', '0', '0', '0.106', '#admiration', '#admiration', 'positive', '0.106', 'vocal_chord', 'mouth_noise', 'vocal_sound', 'human_sound', 'vocalization']
senticnet['paralyzed_vocal_fold'] = ['0', '0', '0', '0.106', '#admiration', '#admiration', 'positive', '0.106', 'vocal_chord', 'mouth_noise', 'vocal_sound', 'human_sound', 'vocalization']
senticnet['param_method'] = ['0.054', '0.237', '-0.15', '0.33', '#interest', '#admiration', 'positive', '0.117', 'retrieve', 'bring', 'tote', 'take', 'go_get']
senticnet['paramatman'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
senticnet['paramecium'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['paramedic'] = ['-0.69', '0', '0.659', '-0.69', '#sadness', '#disgust', 'negative', '-0.68', 'help', 'paralegal', 'legal_assistant', 'paraprofessional', 'paraprofessionalal']
senticnet['paramedical'] = ['-0.69', '0', '0.659', '-0.69', '#sadness', '#disgust', 'negative', '-0.68', 'help', 'paralegal', 'legal_assistant', 'paramedic', 'paraprofessional']
senticnet['parameterization'] = ['-0.55', '0', '0.965', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'interjection', 'delay', 'insert', 'interposition', 'disruption']
senticnet['parametric'] = ['0.83', '-0.83', '0', '0.872', '#surprise', '#admiration', 'positive', '0.846', 'overappraisal', 'overvaluation', 'overestimate', 'overestimation', 'commercial_document']
senticnet['paramilitary_force'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'law_enforcement_agency', 'law', 'agency']
senticnet['paramount'] = ['0.777', '0', '0', '0.869', '#joy', '#admiration', 'positive', '0.823', 'importance', 'cardinal', 'highest', 'foremost', 'mount']
senticnet['paramount_importance'] = ['0.062', '0.335', '0', '0.36', '#interest', '#admiration', 'positive', '0.252', 'religious', 'religious_ceremony', 'religious_service', 'church_service', 'sacred']
senticnet['paramount_theatre'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'theater', 'drive-in_movie', 'watch_movie', 'cinema', 'movie']
senticnet['paramountcy'] = ['0.94', '-0.63', '0', '0.772', '#joy', '#admiration', 'positive', '0.783', 'dominion', 'raj', 'reign', 'sovereignty', 'suzerainty']
senticnet['paramour'] = ['0.531', '0.673', '-0.27', '0.275', '#joy', '#interest', 'positive', '0.301', 'amorousness', 'amatory', 'enamoredness', 'amative', 'puppy_love']
senticnet['paraneoplastic'] = ['-0.87', '0', '0.531', '-0.68', '#sadness', '#disgust', 'negative', '-0.69', 'rubor', 'redness', 'dermatomyositis', 'inclusion_body_myositis', 'inflammation']
senticnet['paranoia'] = ['0', '0', '0.906', '-0.88', '#anger', '#disgust', 'negative', '-0.89', 'persecution', 'tribulation', 'campaign', 'persecute', 'subjugate']
senticnet['paranoiac'] = ['-0.60', '0', '0.892', '-0.92', '#anger', '#disgust', 'negative', '-0.80', 'schizophrenic', 'cataleptic', 'paranoid', 'psycho', 'sick_person']
senticnet['paranoid'] = ['-0.08', '-0.01', '0.137', '-0.20', '#anger', '#disgust', 'negative', '-0.10', 'go_trial', 'find_guilty', 'complain', 'piss_pant', 'commit_crime']
senticnet['paranoid_ideation'] = ['0.998', '-0.63', '0', '0.574', '#joy', '#surprise', 'positive', '0.736', 'cleavable', 'dissociable', 'separable', 'severable', 'divisible']
senticnet['paranoid_personality_disorder'] = ['0', '0', '0.896', '-0.96', '#anger', '#disgust', 'negative', '-0.92', 'antisocial', 'sociopathic', 'psychopathic_personality', 'disturbance', 'schizoid']
senticnet['paranoid_schizophrenia'] = ['0', '0', '0.896', '-0.96', '#anger', '#disgust', 'negative', '-0.92', 'antisocial', 'sociopathic', 'psychopathic_personality', 'disturbance', 'schizoid']
senticnet['paranoid_schizophrenic'] = ['0', '0', '0.896', '-0.96', '#anger', '#disgust', 'negative', '-0.92', 'antisocial', 'sociopathic', 'psychopathic_personality', 'disturbance', 'schizoid']
senticnet['paranormal'] = ['0', '0.871', '0', '0.861', '#interest', '#admiration', 'positive', '0.866', 'predict_future', 'psychic_phenomenon', 'parapsychology', 'clairvoyance', 'foreknowledge']
senticnet['paranormal_phenomenon'] = ['0', '0.871', '0', '0.861', '#interest', '#admiration', 'positive', '0.866', 'predict_future', 'psychic_phenomenon', 'parapsychology', 'clairvoyance', 'foreknowledge']
senticnet['paranthropus_boisei'] = ['0.537', '-0.51', '0', '0.807', '#joy', '#admiration', 'positive', '0.619', 'lesbian', 'fairy', 'fagot', 'faggot', 'fag']
senticnet['parapet'] = ['0', '0', '0.996', '-0.75', '#anger', '#disgust', 'negative', '-0.87', 'parapet', 'battlement', 'balustrade', 'railing', 'cornice']
senticnet['paraphasia'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'nominal_aphasia', 'anomic_aphasia']
senticnet['paraphilia'] = ['-0.62', '0', '0.515', '-0.63', '#sadness', '#disgust', 'negative', '-0.59', 'immodesty', 'perversion', 'exhibitionism', 'sexual_perversion', 'fetishism']
senticnet['paraphilic'] = ['0.833', '0', '0', '0.807', '#joy', '#admiration', 'positive', '0.82', 'lust', 'amiable', 'give_hug', 'giggle', 'smile']
senticnet['paraphrase'] = ['0', '0.115', '-0.10', '0.249', '#interest', '#admiration', 'positive', '0.087', 'idiom', 'remark', 'grammar_part', 'speech_part', 'sentence']
senticnet['paraphrastic'] = ['-0.62', '0', '0.657', '-0.95', '#anger', '#disgust', 'negative', '-0.74', 'altered', 'adjusted', 'changed', 'emended', 'edited']
senticnet['paraphrenia'] = ['-0.22', '-0.01', '-0.13', '-0.07', '#sadness', '#fear', 'negative', '-0.10', 'clinical_problem', 'disorder', 'mental_disorder', 'disease', 'schizophrenic_disorder']
senticnet['paraplegia'] = ['-0.29', '0.095', '-0.11', '-0.13', '#sadness', '#disgust', 'negative', '-0.11', 'say_aha', 'think_hard', 'think_past', 'scratch_head', 'head_hurt']
senticnet['paraprofessional'] = ['-0.69', '0', '0.659', '-0.69', '#sadness', '#disgust', 'negative', '-0.68', 'help', 'paralegal', 'legal_assistant', 'paramedic', 'paramedical']
senticnet['paraprofessional_staff'] = ['-0.69', '0', '0.659', '-0.69', '#sadness', '#disgust', 'negative', '-0.68', 'help', 'paralegal', 'legal_assistant', 'paramedic', 'paramedical']
senticnet['paraprofessionalal'] = ['-0.70', '0', '0.651', '-0.7', '#sadness', '#disgust', 'negative', '-0.68', 'help', 'paralegal', 'legal_assistant', 'paraprofessional', 'paramedic']
senticnet['parapsychological'] = ['0', '0.871', '0', '0.861', '#interest', '#admiration', 'positive', '0.866', 'predict_future', 'psychic_phenomenon', 'parapsychology', 'clairvoyance', 'foreknowledge']
senticnet['parapsychologist'] = ['0', '0.871', '0', '0.861', '#interest', '#admiration', 'positive', '0.866', 'predict_future', 'psychic_phenomenon', 'parapsychology', 'clairvoyance', 'foreknowledge']
senticnet['parapsychology'] = ['-0.8', '0', '0.62', '-0.71', '#sadness', '#disgust', 'negative', '-0.71', 'telekinesis', 'psychokinesis', 'clairvoyance', 'second_sight', 'extrasensory_perception']
senticnet['paraquat_poisoning'] = ['-0.72', '0', '0.884', '-0.75', '#anger', '#disgust', 'negative', '-0.78', 'ergotism', 'poisoning', 'alkali_poisoning', 'caffeinism', 'caffeine_intoxication']
senticnet['paraquet'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'parrakeet', 'parroket', 'lovebird', 'paroquet']
senticnet['parasara'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
senticnet['parasite'] = ['-0.06', '-0.02', '0.063', '-0.11', '#sadness', '#disgust', 'negative', '-0.05', 'annoy_bug', 'nuisance_insect', 'fly_away', 'tiny_insect', 'small_fly']
senticnet['parasite_infection'] = ['-0.78', '0', '0.6', '-0.61', '#sadness', '#disgust', 'negative', '-0.66', 'suggestibility', 'sensitivity', 'capacity', 'reactivity', 'predisposition']
senticnet['parasite_trypanosoma_cruzi'] = ['0.509', '-0.9', '0', '0.507', '#joy', '#surprise', 'positive', '0.639', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['parasitic_alien'] = ['-0.53', '0', '0.637', '-0.92', '#anger', '#disgust', 'negative', '-0.69', 'incorporeal', 'unembodied', 'disembodied', 'bodiless', 'unbodied']
senticnet['parasitic_capacitance'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'short_circuit', 'error', 'mistake', 'undesired', 'trouble']
senticnet['parasitic_disease'] = ['-0.73', '0', '0.599', '-0.96', '#sadness', '#disgust', 'negative', '-0.76', 'spotted_fever', 'rickettsial_disease', 'rickettsialpox', 'typhus', 'typhus_fever']
senticnet['parasitic_drag'] = ['-0.97', '0', '0.831', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'skewness', 'irregularity']
senticnet['parasitic_inductance'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'short_circuit', 'error', 'mistake', 'undesired', 'trouble']
senticnet['parasitic_infection'] = ['-0.73', '0', '0.599', '-0.96', '#sadness', '#disgust', 'negative', '-0.76', 'spotted_fever', 'trench_fever', 'rickettsial_disease', 'typhus', 'typhus_fever']
senticnet['parasitic_mite'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'hornet', 'vespid', 'vespid_wasp']
senticnet['parasitic_organism'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['parasitic_protozoa'] = ['0.995', '-0.71', '0', '0.647', '#joy', '#surprise', 'positive', '0.784', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['parasitic_roundworm'] = ['0.509', '-0.9', '0', '0.507', '#joy', '#surprise', 'positive', '0.639', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['parasitic_twin'] = ['0.706', '0', '0', '0', '#joy', '#joy', 'positive', '0.706', 'birth', 'new_baby', 'born', 'birthday', 'thanksgiving']
senticnet['parasitic_wasp'] = ['0', '-0.09', '0.123', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'gypsy_moth', 'japanese_beetle', 'cucumber_beetle', 'mosquito', 'earwig']
senticnet['parasitic_worm'] = ['-0.74', '0', '0.94', '-0.87', '#anger', '#disgust', 'negative', '-0.85', 'fungal_infection', 'mycosis', 'kerion', 'tinea_pedis', 'athlete_foot']
senticnet['parasitica'] = ['-0.93', '0', '0.699', '-0.91', '#sadness', '#disgust', 'negative', '-0.84', 'onion_smut', 'head_smut', 'stinking_smut', 'flag_smut_fungus', 'covered_smut']
senticnet['parasitize'] = ['-0.83', '0', '-0.82', '-0.86', '#sadness', '#disgust', 'negative', '-0.84', 'cause_cold', 'tourism', 'reproduce', 'gland', 'procreate']
senticnet['parasitoid'] = ['0', '-0.09', '0.123', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'gypsy_moth', 'japanese_beetle', 'cucumber_beetle', 'mosquito', 'earwig']
senticnet['parasitoid_wasp'] = ['0', '-0.09', '0.123', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'gypsy_moth', 'japanese_beetle', 'cucumber_beetle', 'mosquito', 'earwig']
senticnet['parasitoidal'] = ['-0.77', '0', '0.576', '-0.92', '#sadness', '#disgust', 'negative', '-0.75', 'insect', 'firebug', 'water_bug', 'assassin_bug', 'heteropterous_insect']
senticnet['parasitology'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'study_person', 'subject_study', 'zoo', 'zoologist', 'geology']
senticnet['parasitophorous_vacuole'] = ['0.702', '-0.52', '0', '0.958', '#joy', '#admiration', 'positive', '0.728', 'somatic_cell', 'vegetative_cell', 'fixed_phagocyte', 'macrophage', 'free_phagocyte']
senticnet['parasol'] = ['-0.70', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.79', 'block_light', 'block_sunlight', 'umbrella', 'without_light', 'shade']
senticnet['parasphenoid'] = ['0.1', '-0.12', '0.05', '-0.03', '#joy', '#surprise', 'positive', '0.034', 'fancy_dress', 'blouse', 'fashion', 'dress', 'skirt']
senticnet['parasympathetic_activity'] = ['0.698', '-0.61', '0', '0.619', '#joy', '#admiration', 'positive', '0.644', 'sensitivity', 'sensitiveness', 'irritability', 'sensibility', 'reactivity']
senticnet['parathion_poisoning'] = ['-0.98', '0', '0.717', '-0.51', '#sadness', '#anger', 'negative', '-0.73', 'pesticide_poisoning', 'malathion_poisoning', 'toxic_condition', 'poisoning', 'intoxication']
senticnet['parathormone'] = ['-0.73', '0', '0.904', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'unshapely', 'shapeless', 'lumpy', 'deformed', 'malformed']
senticnet['parathyroid_gland'] = ['0.079', '-0.10', '0.046', '0', '#joy', '#surprise', 'positive', '0.047', 'nerve', 'liver', 'groin', 'scrotum', 'diaphragm']
senticnet['paratroop_battalion'] = ['0', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.09', 'army', 'attacker', 'military', 'soldier', 'infantry']
senticnet['paratrooper'] = ['-0.88', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.90', 'undergarment', 'outerwear', 'lady_underwear', 'under_garment', 'slip']
senticnet['paratyphoid'] = ['0.998', '-0.54', '0', '0.957', '#joy', '#admiration', 'positive', '0.834', 'contagious_disease', 'contagion', 'swine_influenza', 'swine_flu', 'respiratory_disease']
senticnet['paratyphoid_fever'] = ['-0.73', '0', '0.599', '-0.96', '#sadness', '#disgust', 'negative', '-0.76', 'spotted_fever', 'rickettsial_disease', 'rickettsialpox', 'typhus', 'typhus_fever']
senticnet['paraventricular'] = ['0.905', '-0.65', '0', '0.851', '#joy', '#admiration', 'positive', '0.805', 'gonococcus', 'bacteria', 'bacteriae', 'probiotic', 'probiotic_bacterium']
senticnet['parboiled_rice'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'palm_nut', 'nut', 'edible_nut', 'cembra_nut', 'cedar_nut']
senticnet['parcel'] = ['-0.53', '0', '0.771', '-0.69', '#anger', '#disgust', 'negative', '-0.66', 'tract', 'piece_of_land', 'piece_of_ground', 'clearing_of_land', 'clearing']
senticnet['parcel_of_land'] = ['-0.53', '0', '0.771', '-0.69', '#anger', '#disgust', 'negative', '-0.66', 'tract', 'piece_of_land', 'piece_of_ground', 'clearing', 'parcel']
senticnet['parcelization'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'blowup', 'discharge', 'backfire', 'blast']
senticnet['parched'] = ['0', '0.077', '0', '0.06', '#interest', '#admiration', 'positive', '0.069', 'towel_off', 'dry_off', 'hang_up_towel', 'make_bow', 'dry_off_towel']
senticnet['parched_desert'] = ['-0.98', '0', '0.441', '-0.96', '#sadness', '#disgust', 'negative', '-0.79', 'desert_land', 'infertile', 'sand', 'barren_land', 'desert']
senticnet['parcheesi'] = ['0.801', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.796', 'verbosity', 'sport_play', 'excite_game', 'contest', 'fun_game']
senticnet['parchment'] = ['0.719', '0.757', '0', '0', '#joy', '#interest', 'positive', '0.738', 'matriculate', 'enroll_class', 'enroll_school', 'enroll', 'register']
senticnet['parchment_paper'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'gift_wrap', 'kraft', 'kraft_paper', 'wrapping_paper', 'paper']
senticnet['parcoblatta'] = ['0', '0', '-0.92', '-0.97', '#fear', '#disgust', 'negative', '-0.95', 'annoy_bug', 'insect', 'bug', 'hare', 'nymph']
senticnet['pardner'] = ['0.908', '0', '0', '0.724', '#joy', '#admiration', 'positive', '0.816', 'partner_in_crime', 'henchman', 'collaborator', 'quisling', 'collaborationist']
senticnet['pardon'] = ['0.084', '0.139', '-0.10', '0.334', '#interest', '#admiration', 'positive', '0.113', 'bless', 'faith', 'dogma', 'spiritual', 'gospel']
senticnet['pardonable'] = ['0.768', '-0.86', '0', '0.67', '#joy', '#surprise', 'positive', '0.769', 'expiable', 'excusable', 'forgivable', 'venial', 'minor']
senticnet['pared-down'] = ['0.966', '0', '0', '0.856', '#joy', '#admiration', 'positive', '0.911', 'stripped-down', 'bare-bones', 'no-frills', 'straight-ahead', 'minimalistic']
senticnet['parenchyma'] = ['-0.86', '0', '0.511', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'separate', 'move', 'branch', 'diverge', 'fork']
senticnet['parent'] = ['0', '0.853', '0', '0.942', '#interest', '#admiration', 'positive', '0.897', 'female_sibling', 'female_relative', 'father', 'mother', 'same_parent']
senticnet['parent_child'] = ['0', '0.928', '0', '0.72', '#interest', '#admiration', 'positive', '0.824', 'mom_dad', 'relate_person', 'father_mother', 'mother_father', 'brother_sister']
senticnet['parent_company'] = ['0.111', '0.18', '0', '0.128', '#interest', '#admiration', 'positive', '0.14', 'corporation', 'partnership', 'large_business', 'business_organization', 'enterprise']
senticnet['parent_compound'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'imperium', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['parent_involvement'] = ['0.063', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.065', 'look_catalog', 'make_selection', 'good_credit', 'menu', 'save_money']
senticnet['parent_wedding_anniversary'] = ['0.161', '0.155', '-0.08', '0.223', '#joy', '#admiration', 'positive', '0.114', 'family', 'happy_moment', 'aunt_uncle', 'brother_sister', 'relation']
senticnet['parentage'] = ['0.803', '0.699', '0', '0', '#joy', '#interest', 'positive', '0.751', 'paternity', 'spousal', 'custody', 'divorce', 'annulment']
senticnet['parental'] = ['0.026', '0.069', '0', '0', '#joy', '#interest', 'positive', '0.048', 'young_child', 'childhood', 'boy', 'girl', 'smile_child']
senticnet['parental_abuse'] = ['-0.94', '0', '0.923', '-0.98', '#sadness', '#disgust', 'negative', '-0.95', 'heap', 'push', 'accept', 'bring', 'lunge']
senticnet['parental_affection'] = ['-0.98', '0', '0.654', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'deprive', 'expropriate', 'disarm', 'unclothe', 'take']
senticnet['parental_authority'] = ['-0.56', '0', '0.743', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'negligent', 'derelict', 'inattentive', 'delinquent', 'lax']
senticnet['parental_choice'] = ['0', '0.35', '-0.09', '0.31', '#interest', '#admiration', 'positive', '0.188', 'decision', 'conclude', 'explanation', 'reason', 'think_process']
senticnet['parental_corporal_punishment'] = ['-0.83', '0', '0.924', '-0.79', '#sadness', '#anger', 'negative', '-0.85', 'kick_in_butt', 'licking', 'whacking', 'stoning', 'lapidation']
senticnet['parental_discipline'] = ['-0.83', '0', '0.924', '-0.79', '#sadness', '#anger', 'negative', '-0.85', 'kick_in_butt', 'licking', 'whacking', 'stoning', 'lapidation']
senticnet['parental_leave'] = ['0.1', '0.216', '-0.18', '0.22', '#interest', '#admiration', 'positive', '0.088', 'female', 'maternity_ward', 'motherhood', 'woman', 'mother']
senticnet['parental_neglect'] = ['-0.94', '0', '0.923', '-0.98', '#sadness', '#disgust', 'negative', '-0.95', 'heap', 'push', 'accept', 'bring', 'lunge']
senticnet['parental_quality'] = ['0.585', '-0.88', '0', '0.615', '#surprise', '#admiration', 'positive', '0.695', 'motherliness', 'maternal_quality', 'quality', 'maternalism', 'fatherliness']
senticnet['parental_responsibility'] = ['0.026', '0.069', '0', '0', '#joy', '#interest', 'positive', '0.048', 'young_child', 'childhood', 'boy', 'girl', 'smile_child']
senticnet['parental_substance_abuse'] = ['-0.8', '0', '0.875', '-0.74', '#sadness', '#anger', 'negative', '-0.80', 'inhuman_treatment', 'cruelty', 'child_neglect', 'mistreatment', 'child_abuse']
senticnet['parental_supervision'] = ['0.943', '-0.78', '0', '0.571', '#joy', '#surprise', 'positive', '0.767', 'master', 'ignition_key', 'key', 'passkey', 'master_key']
senticnet['parenteral'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
senticnet['parenthesis'] = ['-0.95', '0', '0.971', '0', '#sadness', '#anger', 'negative', '-0.96', 'digression', 'away', 'excursus', 'aside', 'divagation']
senticnet['parenthetical'] = ['0', '0.115', '-0.10', '0.249', '#interest', '#admiration', 'positive', '0.087', 'idiom', 'remark', 'grammar_part', 'speech_part', 'sentence']
senticnet['parenting'] = ['0.855', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.795', 'motherhood', 'fatherhood', 'fathering', 'dad', 'father']
senticnet['paresthesia'] = ['-0.58', '0', '0.773', '-0.79', '#anger', '#disgust', 'negative', '-0.71', 'perspiration', 'sweating', 'polyhidrosis', 'sudation', 'hidrosis']
senticnet['pareto_distribution'] = ['-0.59', '0', '0.672', '-0.83', '#anger', '#disgust', 'negative', '-0.7', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'asymmetry', 'irregularity']
senticnet['pareto_optimal'] = ['-0.91', '0', '0.598', '-0.52', '#sadness', '#anger', 'negative', '-0.68', 'stingy', 'penurious', 'scrimy', 'beggarly', 'chintzy']
senticnet['parfum'] = ['0.863', '0.861', '0', '0', '#joy', '#interest', 'positive', '0.862', 'aroma', 'scent', 'smell', 'odor', 'sniff']
senticnet['pargne_salariale'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'cabbage', 'lettuce', 'broccoli']
senticnet['pari_mutuel'] = ['-0.78', '0', '0.944', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'loser', 'speculator', 'mortal', 'better', 'gambler']
senticnet['paria_river'] = ['0', '0.069', '0', '0.065', '#interest', '#admiration', 'positive', '0.067', 'alp', 'high_mountain', 'pass', 'ski_resort', 'high_place']
senticnet['pariah'] = ['0', '0', '0.845', '-0.92', '#anger', '#disgust', 'negative', '-0.88', 'leper', 'oppressed', 'exile', 'outcast', 'demographic']
senticnet['pariah_dog'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'feist', 'fice', 'cur', 'domestic_dog']
senticnet['parietal_bone'] = ['0.886', '-0.99', '0', '0.542', '#joy', '#surprise', 'positive', '0.808', 'jowl', 'lower_jaw', 'mandible', 'lantern_jaw', 'lantern_jawr_bone']
senticnet['parietal_cortex'] = ['0', '0.131', '-0.08', '0.239', '#interest', '#admiration', 'positive', '0.096', 'human_sense', 'ear_activity', 'vibration', 'five_senses', 'sensor']
senticnet['parietal_fenestra'] = ['0.1', '-0.12', '0.05', '-0.03', '#joy', '#surprise', 'positive', '0.034', 'fancy_dress', 'blouse', 'fashion', 'dress', 'skirt']
senticnet['parietal_lobe'] = ['-0.73', '0', '0.955', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'visual_agnosia', 'auditory_agnosia', 'brain_disorder', 'encephalopathy', 'brain_disease']
senticnet['parimutuel'] = ['-0.78', '0', '0.944', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'loser', 'speculator', 'mortal', 'better', 'gambler']
senticnet['parinari'] = ['0.826', '0.787', '0', '0', '#joy', '#interest', 'positive', '0.806', 'fig', 'baby_flower', 'carpel', 'strawberry', 'star_anise']
senticnet['paring_knife'] = ['-0.71', '0', '0.954', '-0.56', '#sadness', '#anger', 'negative', '-0.74', 'jackknife', 'pocket_knife', 'penknife', 'switchblade', 'switchblade_knife']
senticnet['parinirvana'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['paris_opera_ballet'] = ['0.12', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.09', 'ballroom', 'perform_art', 'nightclub', 'ballerina', 'venue']
senticnet['parish'] = ['0', '0.874', '0', '0.793', '#interest', '#admiration', 'positive', '0.834', 'denominational', 'parochial', 'diocesan', 'catholic', 'school']
senticnet['parish_churchyard'] = ['-0.50', '0', '0.762', '-0.89', '#anger', '#disgust', 'negative', '-0.71', 'cemetery', 'graveyard', 'burial_site', 'grassland', 'burying_ground']
senticnet['parish_clerk'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'bodyguard', 'outrider', 'bodyguard_of_guard', 'beefeater', 'defender']
senticnet['parish_priest'] = ['0.031', '0.272', '0', '0.231', '#interest', '#admiration', 'positive', '0.178', 'minister', 'church_head', 'pastor', 'protestant', 'evangelist']
senticnet['parish_vicar'] = ['0.031', '0.272', '0', '0.231', '#interest', '#admiration', 'positive', '0.178', 'minister', 'church_head', 'pastor', 'protestant', 'evangelist']
senticnet['parishioner'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'acolyte', 'reverend', 'thurifer', 'clergyman', 'man_of_cloth']
senticnet['parisian'] = ['0.952', '0.844', '0', '0.96', '#joy', '#admiration', 'positive', '0.919', 'fashion', 'demand', 'kindness', 'better_person', 'warm_feeling']
senticnet['parisian_chic'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'elegant', 'stylish', 'smart', 'posh', 'sleek']
senticnet['parisian_fashion'] = ['0.952', '0.844', '0', '0.96', '#joy', '#admiration', 'positive', '0.919', 'fashion', 'demand', 'kindness', 'better_person', 'warm_feeling']
senticnet['parisian_restaurant'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'blue_ribbon', 'badge', 'allegory', 'stripe', 'merit_badge']
senticnet['parisian_style'] = ['0.952', '0.844', '0', '0.96', '#joy', '#admiration', 'positive', '0.919', 'fashion', 'demand', 'kindness', 'better_person', 'warm_feeling']
senticnet['parisology'] = ['0.633', '-0.67', '0', '0.776', '#surprise', '#admiration', 'positive', '0.693', 'double_entendre', 'loophole', 'amphibology', 'amphiboly', 'ambiguity']
senticnet['parity'] = ['0.059', '0.171', '0.045', '0.234', '#interest', '#admiration', 'positive', '0.105', 'add', 'average', 'tally', 'multiplication', 'count']
senticnet['parity_check'] = ['0.84', '-0.97', '0', '0.713', '#joy', '#surprise', 'positive', '0.844', 'proof', 'bed_check', 'crosscheck', 'substantiation', 'redundancy_check']
senticnet['park'] = ['0.026', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.049', 'state_park', 'arboretum', 'meadow', 'grass', 'backyard']
senticnet['park-like'] = ['0', '0.927', '0', '0.704', '#interest', '#admiration', 'positive', '0.816', 'lushly', 'nautically', 'landscaped', 'exotically', 'tropically']
senticnet['park_association_nrpa'] = ['0', '0.962', '0', '0.911', '#interest', '#admiration', 'positive', '0.936', 'play_friend', 'competition', 'break', 'watch_baseball', 'fun_exercise']
senticnet['park_avenue'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'condominium', 'rent_apartment', 'high_rise', 'condo', 'row_house']
senticnet['park_bench'] = ['0.1', '0', '0', '0', '#joy', '#joy', 'positive', '0.1', 'bench', 'jogger', 'stool', 'bar', 'oak']
senticnet['park_constabulary'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'law_enforcement_agency', 'law', 'agency']
senticnet['park_meter'] = ['0.916', '0.764', '0', '0', '#joy', '#interest', 'positive', '0.84', 'candy', 'candy_machine', 'coin_purse', 'pocket', 'purse']
senticnet['park_recreation'] = ['0', '0.962', '0', '0.911', '#interest', '#admiration', 'positive', '0.936', 'play_friend', 'competition', 'break', 'watch_baseball', 'fun_exercise']
senticnet['park_space'] = ['0.039', '0.081', '-0.05', '0.053', '#interest', '#admiration', 'positive', '0.031', 'suite', 'empty_space', 'soda_machine', 'washroom', 'place_to_sit']
senticnet['park_structure'] = ['0.057', '0.066', '0', '-0.03', '#joy', '#interest', 'positive', '0.03', 'car_park', 'semi-truck', 'taxiway', 'parking_lot', 'flight_terminal']
senticnet['park_zone'] = ['0.043', '0.087', '0', '0', '#joy', '#interest', 'positive', '0.065', 'station', 'park_structure', 'taxiway', 'parking_lot', 'car_park']
senticnet['parka'] = ['-0.84', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.86', 'duck', 'goose', 'moose', 'pheasant', 'goose']
senticnet['parked_illegally'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'trailer', 'trail_car', 'camion', 'wagon', 'towboat']
senticnet['parked_near'] = ['0', '0', '0', '-0.09', '#disgust', '#disgust', 'negative', '-0.09', 'subway', 'bus', 'train_station', 'railway_station', 'bus_stop']
senticnet['parked_nearby'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'trailer', 'trail_car', 'camion', 'wagon', 'towboat']
senticnet['parked_truck'] = ['-0.10', '-0.07', '0.032', '-0.15', '#sadness', '#disgust', 'negative', '-0.05', 'crash_tree', 'press_accelerator', 'stop_traffic_light', 'accident', 'tire_puncture']
senticnet['parked_vehicle'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'trailer', 'trail_car', 'camion', 'wagon', 'towboat']
senticnet['parking_apron'] = ['-0.70', '0', '0.621', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'flare_path', 'airfield', 'grassland', 'runway', 'road']
senticnet['parking_decal'] = ['0.739', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.814', 'receipt', 'bet', 'electric_bill', 'letter_slot', 'deal']
senticnet['parking_deck'] = ['0', '0', '0', '-0.09', '#disgust', '#disgust', 'negative', '-0.09', 'subway', 'bus', 'train_station', 'railway_station', 'bus_stop']
senticnet['parking_garage'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'top_floor', 'front_desk', 'concierge', 'exit', 'ground_floor']
senticnet['parking_lot'] = ['0', '0', '0', '-0.09', '#disgust', '#disgust', 'negative', '-0.09', 'subway', 'bus', 'train_station', 'railway_station', 'bus_stop']
senticnet['parking_lot_adjacent'] = ['0', '0', '0', '-0.09', '#disgust', '#disgust', 'negative', '-0.09', 'subway', 'bus', 'train_station', 'railway_station', 'bus_stop']
senticnet['parking_orbit'] = ['0.989', '-0.90', '0', '0.876', '#joy', '#surprise', 'positive', '0.923', 'reach', 'range', 'compass', 'scope', 'confines']
senticnet['parking_permit'] = ['0.739', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.814', 'receipt', 'bet', 'electric_bill', 'letter_slot', 'deal']
senticnet['parking_placard'] = ['0', '0.137', '0', '0.133', '#interest', '#admiration', 'positive', '0.135', 'sign', 'indication', 'post_notice', 'notice', 'red_octagon']
senticnet['parking_ramp'] = ['0', '0', '0', '-0.09', '#disgust', '#disgust', 'negative', '-0.09', 'subway', 'bus', 'train_station', 'railway_station', 'bus_stop']
senticnet['parking_space'] = ['0', '0', '0', '-0.09', '#disgust', '#disgust', 'negative', '-0.09', 'subway', 'bus', 'train_station', 'railway_station', 'bus_stop']
senticnet['parking_spot'] = ['-0.10', '-0.07', '0.032', '-0.15', '#sadness', '#disgust', 'negative', '-0.05', 'crash_tree', 'press_accelerator', 'stop_traffic_light', 'accident', 'tire_puncture']
senticnet['parking_structure'] = ['0', '0', '0', '-0.09', '#disgust', '#disgust', 'negative', '-0.09', 'subway', 'bus', 'train_station', 'railway_station', 'bus_stop']
senticnet['parking_ticket'] = ['-0.12', '0.193', '0.597', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'fee', 'speeding_ticket', 'ridiculous', 'car_wreck', 'meanness']
senticnet['parkinson'] = ['-0.95', '0', '0.621', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'disease', 'brain_disorder', 'brain_disease', 'ill', 'epilepsy']
senticnet['parkinson_disease'] = ['-0.95', '0', '0.621', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'disease', 'brain_disorder', 'brain_disease', 'ill', 'epilepsy']
senticnet['parkinson_disease_pd'] = ['-0.95', '0', '0.621', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'disease', 'brain_disorder', 'brain_disease', 'ill', 'epilepsy']
senticnet['parkinsonism'] = ['-0.95', '0', '0.621', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'disease', 'brain_disorder', 'brain_disease', 'ill', 'epilepsy']
senticnet['parkinsons_disease'] = ['-0.95', '0', '0.621', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'disease', 'brain_disorder', 'brain_disease', 'ill', 'epilepsy']
senticnet['parklife'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'hazy', 'blurry', 'bleary', 'blurryred', 'foggy']
senticnet['parkway'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'boulevard', 'lane', 'thoroughfare', 'intersection', 'curb']
senticnet['parlance'] = ['-0.04', '-0.02', '0.041', '-0.03', '#sadness', '#anger', 'negative', '-0.02', 'slang', 'lingo', 'microtext', 'chatspeak', 'unusual_language']
senticnet['parlando'] = ['-0.67', '0', '0.936', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'extemporaneous', 'unprepared', 'extempore', 'impromptu', 'unrehearsed']
senticnet['parlay'] = ['-0.89', '0', '0.783', '-0.89', '#sadness', '#disgust', 'negative', '-0.85', 'dodge', 'plant', 'scheme', 'strategy', 'wangling']
senticnet['parliament'] = ['0.024', '0.274', '-0.04', '0.195', '#interest', '#admiration', 'positive', '0.112', 'pope', 'minister', 'socialism', 'evangelist', 'rector']
senticnet['parliament_ali_larijani'] = ['0.034', '-0.02', '0', '0.057', '#joy', '#admiration', 'positive', '0.039', 'female_television_connector', 'channel_selector', 'power_switch', 'tuner', 'radio']
senticnet['parliament_nabih_berri'] = ['0.034', '-0.02', '0', '0.057', '#joy', '#admiration', 'positive', '0.039', 'female_television_connector', 'channel_selector', 'power_switch', 'tuner', 'radio']
senticnet['parliament_sharif_hassan'] = ['0.034', '-0.02', '0', '0.057', '#joy', '#admiration', 'positive', '0.039', 'female_television_connector', 'channel_selector', 'power_switch', 'tuner', 'radio']
senticnet['parliamentarian'] = ['0', '0.868', '0', '0.853', '#interest', '#admiration', 'positive', '0.861', 'country_leader', 'prime_minister', 'president', 'elect_official', 'leader']
senticnet['parliamentarianism'] = ['0.929', '-0.71', '0', '0.605', '#joy', '#surprise', 'positive', '0.748', 'incisiveness', 'trenchancy', 'efficacy', 'efficaciousness', 'power']
senticnet['parliamentary_democracy'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'ascendance', 'absolutism', 'ascendancy', 'tyrant']
senticnet['parliamentary_election'] = ['0', '0.156', '-0.15', '0.179', '#interest', '#admiration', 'positive', '0.061', 'candidate', 'presidential', 'vote', 'elect', 'appoint']
senticnet['parliamentary_enquiry'] = ['0.845', '0.916', '0', '0', '#joy', '#interest', 'positive', '0.881', 'question', 'answer', 'query', 'question_start', 'question_answering']
senticnet['parliamentary_immunity'] = ['0', '0', '0', '0.816', '#admiration', '#admiration', 'positive', '0.816', 'resistance', 'protection', 'untouchable', 'invulnerable', 'sacrosanct']
senticnet['parliamentary_inquiry'] = ['0.845', '0.916', '0', '0', '#joy', '#interest', 'positive', '0.881', 'question', 'answer', 'query', 'question_start', 'question_answering']
senticnet['parliamentary_inquiry_into'] = ['0.845', '0.916', '0', '0', '#joy', '#interest', 'positive', '0.881', 'question', 'answer', 'query', 'question_start', 'question_answering']
senticnet['parliamentary_monarchy'] = ['0.059', '0.092', '0', '0.107', '#interest', '#admiration', 'positive', '0.086', 'royalty', 'monarch', 'throne', 'emperor', 'king_queen']
senticnet['parliamentary_poll'] = ['0', '0.156', '-0.15', '0.179', '#interest', '#admiration', 'positive', '0.061', 'candidate', 'presidential', 'vote', 'elect', 'appoint']
senticnet['parliamentary_reform'] = ['-0.71', '0', '0.928', '-0.74', '#anger', '#disgust', 'negative', '-0.79', 'set_off', 'shake_up', 'stir_up', 'instigate', 'inflame']
senticnet['parliamentary_scrutiny'] = ['-0.79', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.75', 'scan', 'checkup', 'medical_checkup', 'medical_examination', 'medical_exam']
senticnet['parliamentary_seat'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'couch', 'stool', 'coach_seat']
senticnet['parliamentary_session'] = ['-0.57', '0', '0.576', '-0.78', '#anger', '#disgust', 'negative', '-0.64', 'reprieve', 'respite', 'suspend', 'call', 'delay']
senticnet['parlor_car'] = ['-0.58', '0', '0.714', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'waster', 'diversionist', 'destroyer', 'ruiner', 'undoer']
senticnet['parlor_door'] = ['0.979', '-0.69', '0', '0.576', '#joy', '#surprise', 'positive', '0.749', 'big_cheese', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['parlormaid'] = ['-0.62', '0', '0.649', '-0.67', '#anger', '#disgust', 'negative', '-0.64', 'handmaid', 'handmaiden', 'chambermaid', 'lady_maid', 'maid']
senticnet['parlour_door'] = ['0.979', '-0.69', '0', '0.576', '#joy', '#surprise', 'positive', '0.749', 'big_cheese', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['parlour_maid'] = ['-0.62', '0', '0.649', '-0.67', '#anger', '#disgust', 'negative', '-0.64', 'handmaid', 'handmaiden', 'chambermaid', 'maid', 'parlormaid']
senticnet['parlourmaid'] = ['-0.62', '0', '0.649', '-0.67', '#anger', '#disgust', 'negative', '-0.64', 'handmaid', 'handmaiden', 'chambermaid', 'maid', 'parlormaid']
senticnet['parlous'] = ['0', '0', '-0.89', '-0.97', '#fear', '#disgust', 'negative', '-0.93', 'unstable', 'precarious', 'perilous', 'touch-and-go', 'shaky']
senticnet['parlous_state'] = ['0.706', '0.945', '0', '0', '#joy', '#interest', 'positive', '0.825', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['parmenter'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
senticnet['parmesan_cheese'] = ['0.763', '0.9', '0', '0', '#joy', '#interest', 'positive', '0.832', 'breakfast', 'box_cereal', 'calcium', 'drink', 'carton_milk']
senticnet['parmesan_rind'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'saltine', 'soda_cracker', 'cracker', 'water_biscuit', 'graham_cracker']
senticnet['parnall'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'cheapjack', 'pitchman']
senticnet['parnassus_parnassia'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'bulrush', 'nailrod', 'cattail', 'reedmace']
senticnet['parnell_park'] = ['-0.09', '0.147', '-0.11', '0.205', '#interest', '#admiration', 'positive', '0.036', 'question_starter', 'resolution', 'available', 'explanation', 'request']
senticnet['parochial'] = ['0', '0.881', '0', '0.802', '#interest', '#admiration', 'positive', '0.842', 'denominational', 'parish', 'diocesan', 'catholic', 'school']
senticnet['parochial_school'] = ['-0.04', '0.088', '0.013', '0.069', '#interest', '#admiration', 'positive', '0.024', 'kindergarden', 'daycare', 'nursery', 'child', 'asylum']
senticnet['parochial_school_tuition'] = ['-0.07', '0', '0.073', '-0.26', '#sadness', '#disgust', 'negative', '-0.13', 'tuition', 'private_tuition', 'pay_tuition', 'enroll_class', 'enroll_school']
senticnet['parochialism'] = ['0', '0.818', '0', '0.735', '#interest', '#admiration', 'positive', '0.776', 'utopianism', 'pedantry', 'purism', 'primitivism', 'expressiveness']
senticnet['parodic'] = ['0', '-0.03', '0', '0.094', '#surprise', '#admiration', 'positive', '0.065', 'commercial', 'sitcom', 'presentation', 'joke', 'funny']
senticnet['parodistic'] = ['0.807', '-0.83', '0', '0.688', '#joy', '#surprise', 'positive', '0.776', 'folly', 'lunacy', 'craziness', 'indulgence', 'foolery']
senticnet['parody'] = ['0', '0', '-0.90', '-0.90', '#fear', '#disgust', 'negative', '-0.90', 'travesty', 'farce', 'lampoon', 'farce_comedy', 'spoof']
senticnet['parole'] = ['-0.96', '0', '-0.8', '0', '#sadness', '#fear', 'negative', '-0.88', 'probation', 'jail', 'felony', 'sentenced', 'offender']
senticnet['paronomasia'] = ['0.904', '-0.58', '0', '0.736', '#joy', '#admiration', 'positive', '0.743', 'naming', 'appellative', 'denotative', 'extensional', 'referent']
senticnet['paronychia'] = ['-0.57', '0', '0.873', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'fungal_infection', 'mycosis', 'kerion', 'tinea_pedis', 'athlete_foot']
senticnet['paroquet'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'parrakeet', 'parroket', 'paraquet', 'lovebird']
senticnet['paroxysm'] = ['0', '0', '1', '0', '#anger', '#anger', 'negative', '-1', 'furious', 'raging', 'stormy', 'roaring', 'wrathful']
senticnet['paroxysmal'] = ['-0.56', '0', '0.849', '-0.61', '#anger', '#disgust', 'negative', '-0.67', 'convulsive', 'unsteady', 'jerky', 'spasmodic', 'spastic']
senticnet['paroxysmal_cough'] = ['-0.99', '0', '0.859', '-0.62', '#sadness', '#anger', 'negative', '-0.82', 'lax', 'loose', 'diarrheal', 'diarrhoeal', 'unconstipated']
senticnet['paroxysmal_nocturnal_dyspnea'] = ['-0.20', '0', '-0.22', '-0.19', '#sadness', '#fear', 'negative', '-0.20', 'go_hospital', 'death', 'die', 'take_aspirin', 'heart_attack']
senticnet['parquet_floor'] = ['0', '0.096', '0', '0.112', '#interest', '#admiration', 'positive', '0.104', 'renovation', 'tile', 'floor', 'tile_roof', 'tile_wall']
senticnet['parrakeet'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'lovebird', 'parroket', 'paraquet', 'paroquet']
senticnet['parramatta'] = ['0', '0.996', '0', '0.85', '#interest', '#admiration', 'positive', '0.923', 'strength', 'strong', 'physical_strength', 'athlete', 'muscle']
senticnet['parricide'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'forgivable', 'excusable', 'mercifully', 'merciful', 'mercifulness']
senticnet['parroket'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'parrakeet', 'lovebird', 'paraquet', 'paroquet']
senticnet['parrot'] = ['-0.68', '0', '0.947', '-0.83', '#anger', '#disgust', 'negative', '-0.82', 'popinjay', 'poll', 'bird', 'lovebird', 'amazon']
senticnet['parrot_cay'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'lurking_place', 'spot', 'drop', 'hiding_place', 'den']
senticnet['parrot_fish'] = ['-0.20', '-0.10', '0.07', '-0.24', '#sadness', '#disgust', 'negative', '-0.10', 'gill', 'sea_life', 'seafood', 'swim_animal', 'fillet']
senticnet['parrotlike'] = ['-0.5', '0', '0.97', '-0.52', '#anger', '#disgust', 'negative', '-0.66', 'simulated', 'apish', 'imitative', 'apelike', 'mimic']
senticnet['parry'] = ['0', '0', '0.862', '-0.88', '#anger', '#disgust', 'negative', '-0.87', 'cut_down_tree', 'destroy_live', 'kill_life', 'destroy_city', 'destroy_home']
senticnet['parse_error'] = ['-0.98', '0', '0.869', '-0.80', '#sadness', '#anger', 'negative', '-0.88', 'error', 'syntax_error', 'semantic_error', 'programming_error', 'computer_error']
senticnet['parser'] = ['-0.95', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.84', 'hold_pencil', 'food_scrap', 'paper_product', 'waste_paper', 'cover']
senticnet['parsimonious'] = ['-0.91', '0', '0.598', '-0.52', '#sadness', '#anger', 'negative', '-0.68', 'stingy', 'penurious', 'scrimy', 'beggarly', 'chintzy']
senticnet['parsimoniously'] = ['-0.91', '0', '0.598', '-0.52', '#sadness', '#anger', 'negative', '-0.68', 'stingy', 'penurious', 'scrimy', 'beggarly', 'chintzy']
senticnet['parsimoniousness'] = ['0.533', '-0.55', '0', '0.912', '#surprise', '#admiration', 'positive', '0.668', 'parsimony', 'frugality', 'thrift', 'economy', 'prudence']
senticnet['parsimony'] = ['0.533', '-0.55', '0', '0.912', '#surprise', '#admiration', 'positive', '0.668', 'frugality', 'parsimoniousness', 'thrift', 'economy', 'prudence']
senticnet['parsimony_criterion'] = ['-0.91', '0', '0.598', '-0.52', '#sadness', '#anger', 'negative', '-0.68', 'stingy', 'penurious', 'scrimy', 'beggarly', 'chintzy']
senticnet['parsley'] = ['0.823', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.81', 'chive', 'cilantro', 'thyme', 'oregano', 'tarragon']
senticnet['parsoid'] = ['-0.89', '0', '0.672', '-0.69', '#sadness', '#disgust', 'negative', '-0.75', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['parson'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'bodyguard', 'outrider', 'bodyguard_of_guard', 'beefeater', 'defender']
senticnet['part'] = ['0.95', '0.796', '0', '0', '#joy', '#interest', 'positive', '0.873', 'segment', 'component', 'small_part', 'portion', 'fraction']
senticnet['part_of_band'] = ['0.865', '0.938', '0', '0', '#joy', '#interest', 'positive', '0.901', 'play_stringed_instrument', 'make_beautiful_music', 'keep_beat', 'make_music', 'chamber_music']
senticnet['part_of_body'] = ['0', '0.068', '0', '0.097', '#interest', '#admiration', 'positive', '0.083', 'human_part', 'chunk', 'body_organ', 'human_organ', 'sense_organ']
senticnet['part_of_book'] = ['0.053', '0', '0.025', '0.118', '#joy', '#admiration', 'positive', '0.049', 'paper_square', 'book', 'book_part', 'book_end', 'flat_thin']
senticnet['part_of_building'] = ['0.021', '0.106', '0.014', '0.172', '#interest', '#admiration', 'positive', '0.071', 'house_place', 'house', 'chunk', 'live_quarter', 'house_location']
senticnet['part_of_ceremony'] = ['0.723', '0', '0', '0.944', '#joy', '#admiration', 'positive', '0.833', 'obedience', 'suggestion', 'vow', 'good_behavior', 'behave']
senticnet['part_of_company'] = ['-0.03', '0.149', '-0.06', '0.14', '#interest', '#admiration', 'positive', '0.048', 'live_house', 'sell_house', 'address', 'house_location', 'chunk']
senticnet['part_of_day'] = ['0.02', '0.067', '0', '0', '#joy', '#interest', 'positive', '0.044', 'sunrise', 'begin_day', 'bright_sky', 'sunny', 'afternoon']
senticnet['part_of_dinner'] = ['0.99', '-0.91', '0', '0', '#joy', '#surprise', 'positive', '0.95', 'grape_juice', 'juice', 'ethanol', 'tea', 'meal']
senticnet['part_of_face'] = ['0', '0.08', '0.016', '0.103', '#interest', '#admiration', 'positive', '0.056', 'facial_part', 'facial_organ', 'face_feature', 'head_organ', 'nose']
senticnet['part_of_family'] = ['0', '0.08', '0.016', '0.103', '#interest', '#admiration', 'positive', '0.056', 'father', 'mother', 'daughter', 'son', 'grandfather']
senticnet['part_of_foot'] = ['0', '0.08', '0.016', '0.103', '#interest', '#admiration', 'positive', '0.056', 'toe', 'ankle', 'hold_canvas', 'foot', 'heel']
senticnet['part_of_group'] = ['0.242', '0.211', '-0.06', '0.352', '#joy', '#admiration', 'positive', '0.184', 'team_up', 'together', 'social_group', 'bring_together', 'membership']
senticnet['part_of_hair'] = ['-0.13', '0', '-0.05', '-0.06', '#sadness', '#disgust', 'negative', '-0.08', 'chair_committee', 'lose_key', 'age_gracefully', 'steal_store', 'wave_flag']
senticnet['part_of_history'] = ['0.046', '0', '0', '0.085', '#joy', '#admiration', 'positive', '0.066', 'action_movie', 'history', 'medieval_time', 'human_history', 'history_book']
senticnet['part_of_house'] = ['0.869', '0.731', '0', '0', '#joy', '#interest', 'positive', '0.8', 'house_part', 'house_front', 'house_open', 'chunk', 'inside_house']
senticnet['part_of_human_body'] = ['0', '0.073', '0', '0', '#interest', '#interest', 'positive', '0.073', 'organ', 'organ_body', 'internal_organ', 'vital_organ', 'body_tissue']
senticnet['part_of_meal'] = ['0.908', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.889', 'reproduction', 'survival', 'host_party', 'breed', 'put_ice']
senticnet['part_of_movie'] = ['-0.10', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.11', 'part_of_play', 'act_part', 'play_part', 'act', 'role_play']
senticnet['part_of_plant'] = ['0.046', '0', '0', '0.085', '#joy', '#admiration', 'positive', '0.066', 'leafy', 'plant_part', 'small_tree', 'leaf', 'root']
senticnet['part_of_play'] = ['0', '-0.85', '0', '0.956', '#surprise', '#admiration', 'positive', '0.907', 'part_of_movie', 'act_part', 'play_part', 'role_play', 'act']
senticnet['part_of_room'] = ['0.773', '0.842', '0', '0', '#joy', '#interest', 'positive', '0.807', 'location', 'front_rear', 'front_back', 'square', 'front']
senticnet['part_of_speech'] = ['0.021', '0', '0', '0.174', '#joy', '#admiration', 'positive', '0.097', 'grammar_part', 'bit', 'sentence', 'chunking', 'written_language']
senticnet['part_of_team'] = ['0', '0.933', '0', '0.951', '#interest', '#admiration', 'positive', '0.942', 'fit_person', 'engage_sport', 'responsible', 'support', 'leader']
senticnet['part_of_tree'] = ['0.901', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.922', 'tree_piece', 'tree_part', 'tree_limb', 'tree_arm', 'tree_branch']
senticnet['part_of_way'] = ['0.873', '0.942', '0', '0.907', '#interest', '#admiration', 'positive', '0.907', 'better_person', 'demand', 'give_help', 'end_argument', 'feel_strongly']
senticnet['part_of_whole'] = ['0', '0.72', '0', '0.916', '#interest', '#admiration', 'positive', '0.818', 'component', 'fraction', 'portion', 'segment', 'piece']
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senticnet['parviflora'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'carnauba_palm', 'carnauba', 'wax_palm', 'palmetto', 'palmyra']
senticnet['parvifolia'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'horse_chestnut', 'red_buckeye', 'particolored_buckeye']
senticnet['pasang'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['pasang_lhamu'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['pasar'] = ['0', '0.113', '0.041', '0.058', '#interest', '#admiration', 'positive', '0.043', 'corner_shop', 'general_store', 'many_shops', 'large_supermarket', 'goods']
senticnet['pascagoula'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
senticnet['pascal_programming_language'] = ['0', '-0.99', '0', '0.862', '#surprise', '#admiration', 'positive', '0.928', 'helpful_tool', 'woodworking_tool', 'helpful', 'type_of_fruit', 'hand_tool']
senticnet['paschal_candle'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'reverence', 'genuflection', 'obeisance', 'kowtow', 'mafia']
senticnet['paschal_feast'] = ['0.701', '0.867', '0', '0', '#joy', '#interest', 'positive', '0.784', 'festivity', 'special_day', 'birthday', 'special_time', 'cake_candle']
senticnet['pasder'] = ['-0.53', '0', '0.637', '-0.92', '#anger', '#disgust', 'negative', '-0.69', 'incorporeal', 'unembodied', 'disembodied', 'bodiless', 'unbodied']
senticnet['pasgt'] = ['0.939', '-0.63', '0', '0.653', '#joy', '#admiration', 'positive', '0.741', 'vest', 'waistcoat', 'bulletproof_vest', 'suit_of_armor', 'coat_of_mail']
senticnet['pasha_mosque'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'caravansary', 'caravanserai', 'khan', 'caravan_inn', 'hostel']
senticnet['pashmina'] = ['-0.53', '0', '0.751', '-0.97', '#anger', '#disgust', 'negative', '-0.75', 'guanaco', 'domestic_llama', 'llama', 'artiodactyl', 'artiodactyl_mammal']
senticnet['pasque_flower'] = ['-0.79', '0', '0.837', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'sunray', 'wilding', 'sagebrush_buttercup', 'pasqueflower', 'wildflower']
senticnet['pasqueflower'] = ['-0.79', '0', '0.837', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'sunray', 'wilding', 'sagebrush_buttercup', 'wildflower', 'pasque_flower']
senticnet['pass'] = ['0.855', '0.741', '0', '0.862', '#joy', '#admiration', 'positive', '0.819', 'further_goal', 'pass_ball', 'pass_glass', 'help', 'pass_exam']
senticnet['pass_along'] = ['0.743', '-0.51', '0', '0.551', '#joy', '#admiration', 'positive', '0.604', 'communicate', 'pass_on', 'pass', 'get_across', 'put_across']
senticnet['pass_around'] = ['0.517', '-0.67', '0', '0.549', '#surprise', '#admiration', 'positive', '0.579', 'circularize', 'circulate', 'send_around', 'pass_on', 'distribute']
senticnet['pass_away'] = ['-0.98', '0', '0.06', '-0.91', '#sadness', '#disgust', 'negative', '-0.65', 'end_of_life', 'stop_living', 'death', 'die_person', 'lose_life']
senticnet['pass_ball'] = ['0.024', '0.073', '0', '0.192', '#interest', '#admiration', 'positive', '0.096', 'throw_football', 'practice', 'jump_around', 'dance_move', 'pretzel']
senticnet['pass_by'] = ['-0.03', '0', '0.033', '-0.06', '#sadness', '#disgust', 'negative', '-0.04', 'carry_luggage', 'walk_through_door', 'meet_friend', 'pay_dinner', 'increase_value']
senticnet['pass_card'] = ['-0.07', '0.085', '-0.05', '0.137', '#interest', '#admiration', 'positive', '0.024', 'shuffle_card', 'broker', 'go_casino', 'business_agreement', 'deal']
senticnet['pass_exam'] = ['0.796', '0.843', '0', '0', '#joy', '#interest', 'positive', '0.82', 'get_degree', 'learn_knowledge', 'finish_college', 'become_educated', 'pass_test']
senticnet['pass_examination'] = ['0.807', '0', '0', '0.853', '#joy', '#admiration', 'positive', '0.83', 'get_qualification', 'prove_knowledge', 'prove_learn', 'complete_course', 'test_knowledge']
senticnet['pass_fail'] = ['-0.96', '0', '-0.81', '-0.85', '#sadness', '#disgust', 'negative', '-0.87', 'further_goal', 'flunk', 'fail', 'pass', 'cheat']
senticnet['pass_filter'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'water_inside', 'water_spray', 'teapot', 'bodily_waste', 'different_type']
senticnet['pass_gas'] = ['-0.12', '0', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.10', 'study_library', 'complete_sentence', 'guess_answer', 'master_new_skill', 'point_out']
senticnet['pass_gene'] = ['0', '0.078', '0.014', '0.059', '#interest', '#admiration', 'positive', '0.041', 'child', 'overpopulation', 'smile_child', 'young_child', 'large_family']
senticnet['pass_glass'] = ['0.849', '0.738', '0', '0.855', '#joy', '#admiration', 'positive', '0.814', 'further_goal', 'pass_ball', 'pass', 'help', 'pass_exam']
senticnet['pass_grade'] = ['0.108', '0', '0', '0.056', '#joy', '#admiration', 'positive', '0.082', 'graduate_school', 'make_parent_happy', 'show_know', 'good_grade', 'knowledge_power']
senticnet['pass_information'] = ['0.108', '0', '0', '0.056', '#joy', '#admiration', 'positive', '0.082', 'tell_information', 'information', 'report_event', 'receive_information', 'report_news']
senticnet['pass_judgment'] = ['-0.15', '0.088', '-0.10', '0', '#sadness', '#fear', 'negative', '-0.05', 'hire_lawyer', 'call_lawyer', 'contact_lawyer', 'question_witness', 'become_lawyer']
senticnet['pass_knowledge'] = ['0.108', '0', '0', '0.056', '#joy', '#admiration', 'positive', '0.082', 'gain_information', 'get_information', 'pass_information', 'find_out_information', 'increase_vocabulary']
senticnet['pass_law'] = ['0.108', '0', '0', '0.056', '#joy', '#admiration', 'positive', '0.082', 'law', 'edict', 'rule', 'supreme_court', 'legislation']
senticnet['pass_off'] = ['-0.88', '0', '0.682', '-0.60', '#sadness', '#anger', 'negative', '-0.72', 'absent', 'fall_away', 'evanesce', 'blow_over', 'disappear']
senticnet['pass_on'] = ['0.743', '-0.51', '0', '0.551', '#joy', '#admiration', 'positive', '0.604', 'communicate', 'get_across', 'pass', 'pass_along', 'put_across']
senticnet['pass_out'] = ['-0.82', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.77', 'bump_head', 'loss_ofawareness', 'collapse', 'fall_down', 'bang_head']
senticnet['pass_over'] = ['-0.92', '0', '0.743', '-0.56', '#sadness', '#anger', 'negative', '-0.74', 'miss', 'neglect', 'overlook', 'drop', 'pretermit']
senticnet['pass_test'] = ['0.035', '0.112', '-0.07', '0.195', '#interest', '#admiration', 'positive', '0.067', 'get_degree', 'study', 'take_test', 'pass_exam', 'finish_college']
senticnet['pass_through'] = ['0.839', '0.926', '0', '0', '#joy', '#interest', 'positive', '0.883', 'walk', 'find_garden', 'enter_place', 'walk_down', 'walk_around']
senticnet['passable'] = ['-0.99', '0', '0.641', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'navigable', 'negotiable', 'surmountable', 'climbable', 'traversable']
senticnet['passableness'] = ['-0.71', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.85', 'ordinariness', 'commonplaceness', 'lack_of_inspiration', 'mediocrity', 'adequacy']
senticnet['passage'] = ['0', '0.089', '0', '0.129', '#interest', '#admiration', 'positive', '0.109', 'passageway', 'connect_room', 'entrance_room', 'corridor', 'walk_through']
senticnet['passageway'] = ['0.029', '0.087', '0.065', '0.137', '#interest', '#admiration', 'positive', '0.047', 'archway', 'arch', 'mat', 'aisle', 'walkway']
senticnet['passbook'] = ['-0.05', '0.26', '-0.08', '0.142', '#interest', '#admiration', 'positive', '0.068', 'expense', 'afford', 'payment', 'cost', 'owe']
senticnet['passe'] = ['-0.12', '-0.06', '-0.08', '-0.03', '#sadness', '#fear', 'negative', '-0.04', 'old_fashioned', 'unglamorous', 'special_person', 'bloke', 'male']
senticnet['passed_unanimously'] = ['0.779', '-0.88', '0', '0.521', '#joy', '#surprise', 'positive', '0.727', 'corrigible', 'correctable', 'improvable', 'reformable', 'redeemable']
senticnet['passenger'] = ['0.069', '0.07', '0', '0.116', '#interest', '#admiration', 'positive', '0.085', 'traveller', 'tourist', 'travel', 'explorer', 'adventurer']
senticnet['passenger_aboard'] = ['0.069', '0.07', '0', '0.116', '#interest', '#admiration', 'positive', '0.085', 'traveller', 'tourist', 'travel', 'explorer', 'adventurer']
senticnet['passenger_accommodation'] = ['0.069', '0.07', '0', '0.116', '#interest', '#admiration', 'positive', '0.085', 'traveller', 'tourist', 'travel', 'explorer', 'adventurer']
senticnet['passenger_airline'] = ['0', '0.089', '0.01', '0.118', '#interest', '#admiration', 'positive', '0.066', 'crate', 'mom_house', 'sack', 'poker_game', 'safekeep']
senticnet['passenger_airliner'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'atmosphere', 'wing', 'nest', 'feather', 'bird_fly']
senticnet['passenger_automobile'] = ['-0.04', '-0.16', '0', '0.056', '#surprise', '#admiration', 'positive', '0.059', 'car_part', 'under_car', 'car_wheel', 'wheel', 'car']
senticnet['passenger_cabin'] = ['0.069', '0.07', '0', '0.116', '#interest', '#admiration', 'positive', '0.085', 'traveller', 'tourist', 'travel', 'explorer', 'adventurer']
senticnet['passenger_car'] = ['0.035', '0.112', '-0.07', '0.195', '#interest', '#admiration', 'positive', '0.067', 'car_action', 'car_travel', 'steer_car', 'make_go', 'transportation']
senticnet['passenger_carriage'] = ['0.069', '0.07', '0', '0.116', '#interest', '#admiration', 'positive', '0.085', 'traveller', 'tourist', 'travel', 'explorer', 'adventurer']
senticnet['passenger_comfort'] = ['0.069', '0.07', '0', '0.116', '#interest', '#admiration', 'positive', '0.085', 'traveller', 'tourist', 'travel', 'explorer', 'adventurer']
senticnet['passenger_compartment'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'train_station', 'government_office', 'cable_car', 'sport_stadium', 'terminal']
senticnet['passenger_elevator'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'top_floor', 'front_desk', 'concierge', 'exit', 'ground_floor']
senticnet['passenger_locomotive'] = ['0.069', '0.097', '0', '0.172', '#interest', '#admiration', 'positive', '0.113', 'type_of_engine', 'liquid_gas', 'warm_air', 'hot_air', 'steamy']
senticnet['passenger_luggage'] = ['-0.55', '0', '0.843', '-0.58', '#anger', '#disgust', 'negative', '-0.66', 'thieving', 'larceny', 'theft', 'thievery', 'stealing']
senticnet['passenger_saloon'] = ['0', '0.248', '-0.07', '0.121', '#interest', '#admiration', 'positive', '0.099', 'gym_locker', 'observatory', 'prison_cell', 'luggage_store', 'garage_sale']
senticnet['passenger_seat'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'cable_car', 'baggage_compartment', 'subway', 'amusement_park', 'terminal']
senticnet['passenger_sedan'] = ['0.035', '0.112', '-0.07', '0.195', '#interest', '#admiration', 'positive', '0.067', 'car_action', 'car_travel', 'steer_car', 'make_go', 'transportation']
senticnet['passenger_service'] = ['0.547', '-0.69', '0', '0.52', '#joy', '#surprise', 'positive', '0.586', 'vivification', 'invigoration', 'activity', 'animation', 'activating']
senticnet['passenger_terminal'] = ['0.747', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.788', 'ticket_counter', 'ticket_booth', 'ticket_window', 'ticket_turnstile', 'aircraft_roadway']
senticnet['passenger_terminal_building'] = ['0', '0.147', '0', '0.099', '#interest', '#admiration', 'positive', '0.123', 'airliner', 'airport', 'baggage_compartment', 'aircraft', 'cable_car']
senticnet['passenger_ticket'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'transit_ticket', 'ticket_counter', 'train_ticket', 'hand_luggage', 'baggage']
senticnet['passenger_train'] = ['0.861', '-0.52', '0', '0.598', '#joy', '#admiration', 'positive', '0.663', 'exit', 'get_off', 'deplane', 'go_out', 'get_out']
senticnet['passenger_vehicle'] = ['0', '-0.01', '0', '0.064', '#surprise', '#admiration', 'positive', '0.042', 'car_gas', 'car_power', 'fossil_fuel', 'run_car', 'car_engine']
senticnet['passengers_luggage'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'hand_baggage', 'baggage', 'passenger_ticket', 'ticket_counter', 'baggage_trunk']
senticnet['passer-by'] = ['-0.85', '0', '0', '-0.80', '#sadness', '#disgust', 'negative', '-0.82', 'militiaman', 'cavalryman', 'teen-ager', 'militiaman', 'sympathizer']
senticnet['passer_by'] = ['0.952', '-0.52', '0', '0.835', '#joy', '#admiration', 'positive', '0.771', 'billboard', 'hoarding', 'sandwich_board', 'scoreboard', 'shingle']
senticnet['passerby'] = ['0.706', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.768', 'train_station', 'subway_stop', 'parking_lot', 'subway_station', 'park_structure']
senticnet['passerine'] = ['-0.84', '0', '0.655', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'piping_crow', 'butcherbird', 'currawong', 'bell_magpie', 'oscine']
senticnet['passerine_bird'] = ['-0.92', '0', '0.64', '-0.52', '#sadness', '#anger', 'negative', '-0.69', 'animal_order', 'percher', 'thyreophoran', 'marginocephalian', 'euronithopod']
senticnet['passing'] = ['-0.92', '0', '0.835', '-0.68', '#sadness', '#anger', 'negative', '-0.81', 'aerial', 'lateral', 'forward_pass', 'lateral_pass', 'spot_pass']
senticnet['passing_motorist'] = ['-0.10', '-0.07', '0.032', '-0.15', '#sadness', '#disgust', 'negative', '-0.05', 'crash_tree', 'press_accelerator', 'stop_traffic_light', 'accident', 'tire_puncture']
senticnet['passing_pass'] = ['-0.92', '0', '0.83', '-0.69', '#sadness', '#anger', 'negative', '-0.81', 'aerial', 'passing', 'forward_pass', 'lateral', 'spot_pass']
senticnet['passing_resemblance'] = ['-0.88', '0', '0.813', '-0.57', '#sadness', '#anger', 'negative', '-0.75', 'affinity', 'mutual_resemblance', 'alikeness', 'likeness', 'similitude']
senticnet['passion'] = ['0.505', '-0.85', '0', '0.94', '#surprise', '#admiration', 'positive', '0.767', 'infatuation', 'ardor', 'fervor', 'fervency', 'fervidness']
senticnet['passion_fruit'] = ['0.859', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.888', 'green_apple', 'apricot', 'lime', 'peach', 'cherry']
senticnet['passionate'] = ['0.655', '0.635', '-0.23', '0.3', '#joy', '#interest', 'positive', '0.338', 'emotional_response', 'emotional_reaction', 'sentimental', 'sensitive', 'emotional']
senticnet['passionate_affair'] = ['0.531', '0.673', '-0.27', '0.275', '#joy', '#interest', 'positive', '0.301', 'amorousness', 'amatory', 'enamoredness', 'amative', 'puppy_love']
senticnet['passionate_commitment'] = ['0.505', '-0.85', '0', '0.94', '#surprise', '#admiration', 'positive', '0.767', 'infatuation', 'ardor', 'fervor', 'fervency', 'fervidness']
senticnet['passionate_desire'] = ['0.505', '-0.85', '0', '0.94', '#surprise', '#admiration', 'positive', '0.767', 'infatuation', 'ardor', 'fervor', 'fervency', 'fervidness']
senticnet['passionate_devotion'] = ['0.505', '-0.85', '0', '0.94', '#surprise', '#admiration', 'positive', '0.767', 'infatuation', 'ardor', 'fervor', 'fervency', 'fervidness']
senticnet['passionate_love'] = ['0.505', '-0.85', '0', '0.94', '#surprise', '#admiration', 'positive', '0.767', 'infatuation', 'ardor', 'fervor', 'fervency', 'fervidness']
senticnet['passionately'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'intrepidly', 'hardy', 'fearlessness', 'unafraid', 'unfearing']
senticnet['passionateness'] = ['0.571', '-0.95', '0', '0.903', '#surprise', '#admiration', 'positive', '0.809', 'infatuation', 'ardor', 'fervor', 'fervency', 'fervidness']
senticnet['passionflower'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'infective_agent', 'satisfier', 'quickener', 'agent']
senticnet['passionfruit'] = ['0.859', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.888', 'green_apple', 'apricot', 'lime', 'peach', 'cherry']
senticnet['passionless'] = ['0', '0', '0.709', '-0.92', '#anger', '#disgust', 'negative', '-0.81', 'apathetic', 'unemotionality', 'indifference', 'stolidity', 'phlegm']
senticnet['passito'] = ['0', '0.066', '-0.05', '0.089', '#interest', '#admiration', 'positive', '0.032', 'pineapple', 'kale', 'banana', 'pickle', 'tomato']
senticnet['passive'] = ['-0.77', '0', '0.842', '0', '#sadness', '#anger', 'negative', '-0.80', 'win_money', 'team_lose', 'winner_loser', 'fun', 'competitive']
senticnet['passive_aggressive'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'pervert', 'horny', 'uncomfortable', 'uneasy', 'short_tempered']
senticnet['passive_obedience'] = ['-0.55', '0', '0.965', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'interjection', 'delay', 'insert', 'disruption', 'interpolation']
senticnet['passive_recreation'] = ['0', '0.962', '0', '0.911', '#interest', '#admiration', 'positive', '0.936', 'play_friend', 'competition', 'break', 'watch_baseball', 'fun_exercise']
senticnet['passive_recreational'] = ['0', '0.962', '0', '0.911', '#interest', '#admiration', 'positive', '0.936', 'play_friend', 'competition', 'break', 'watch_baseball', 'fun_exercise']
senticnet['passive_resistance'] = ['-0.71', '0', '0.928', '-0.74', '#anger', '#disgust', 'negative', '-0.79', 'set_off', 'shake_up', 'stir_up', 'instigate', 'inflame']
senticnet['passive_resister'] = ['0.842', '-0.66', '0', '0.663', '#joy', '#admiration', 'positive', '0.722', 'reformer', 'crusader', 'reformist', 'social_reformer', 'meliorist']
senticnet['passive_smoke'] = ['0.047', '0', '-0.10', '-0.06', '#fear', '#disgust', 'negative', '-0.03', 'smoke', 'tobacco_product', 'smoke_tobacco', 'lung_cancer', 'cigar']
senticnet['passiveness'] = ['-0.70', '0', '0.569', '-0.88', '#sadness', '#disgust', 'negative', '-0.72', 'languor', 'inertia', 'phlegm', 'passivity', 'indolence']
senticnet['passivism'] = ['-0.58', '0', '0.804', '-0.56', '#sadness', '#anger', 'negative', '-0.64', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['passivity'] = ['-0.77', '0', '0.5', '-0.75', '#sadness', '#disgust', 'negative', '-0.67', 'apathy', 'indifference', 'spiritlessness', 'inertia', 'inactiveness']
senticnet['passkey'] = ['0.943', '-0.78', '0', '0.571', '#joy', '#surprise', 'positive', '0.767', 'master', 'ignition_key', 'latchkey', 'key', 'master_key']
senticnet['passo_di'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'expressway', 'turnpike', 'toll_road', 'highway']
senticnet['passphrase'] = ['0.022', '-0.13', '0.153', '-0.31', '#anger', '#disgust', 'negative', '-0.07', 'codeine', 'morphine', 'infection', 'cocaine', 'nuclear_war']
senticnet['passport'] = ['0.082', '0.184', '-0.07', '0.208', '#interest', '#admiration', 'positive', '0.1', 'identify_person', 'profile_picture', 'capitalize', 'personal_identification', 'identification_picture']
senticnet['passport_picture'] = ['0.082', '0.184', '-0.07', '0.208', '#interest', '#admiration', 'positive', '0.1', 'identify_person', 'profile_picture', 'capitalize', 'passport', 'personal_identification']
senticnet['password'] = ['0.038', '0.117', '0', '0.095', '#interest', '#admiration', 'positive', '0.083', 'browser', 'computer', 'efficient', 'telephone_computer', 'modem']
senticnet['password_hash'] = ['-0.79', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.79', 'misgiving', 'qualm', 'scruple', 'insecurity', 'insecure']
senticnet['password_protected_web_site'] = ['0', '0.08', '0', '0', '#interest', '#interest', 'positive', '0.08', 'internet', 'go_online', 'visit_web_site', 'find_information', 'choose_book']
senticnet['past'] = ['0', '0.078', '0', '0.096', '#interest', '#admiration', 'positive', '0.087', 'ancient_rome', 'ancient', 'age', 'ancient_artifact', 'century']
senticnet['past-due'] = ['0', '0', '0.805', '-0.91', '#anger', '#disgust', 'negative', '-0.85', 'non-payment', 'underpayment', 'arrears', 'non-performance', 'nonpayment']
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senticnet['patch'] = ['-0.58', '0', '0.561', '-0.62', '#sadness', '#disgust', 'negative', '-0.58', 'spot', 'speckle', 'dapple', 'mark', 'fleck']
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senticnet['pathway_activation'] = ['-0.84', '0', '0.8', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'rigor_mortis', 'natural_process', 'natural_action', 'activity', 'action']
senticnet['patience'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'fishing_rod', 'fishing', 'catch_fish', 'fish_lake', 'bait']
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senticnet['patient_serum'] = ['-0.74', '0', '0.766', '0', '#sadness', '#anger', 'negative', '-0.75', 'use_needle', 'clothes_repair', 'woven', 'embroidery', 'thread_needle']
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senticnet['patio_door'] = ['0', '0', '0', '0.077', '#admiration', '#admiration', 'positive', '0.077', 'glass_front', 'gate', 'doorway', 'use_farm', 'entranceway']
senticnet['patio_furniture'] = ['0.074', '0', '0', '0', '#joy', '#joy', 'positive', '0.074', 'hike_trail', 'ant_hill', 'compost', 'picnic', 'soccer_field']
senticnet['patisserie'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'blue_ribbon', 'badge', 'allegory', 'stripe', 'merit_badge']
senticnet['patriarch'] = ['0.713', '0', '0', '0.782', '#joy', '#admiration', 'positive', '0.748', 'matriarch', 'matriarch', 'grandmother', 'matriarchal', 'great-grandmother']
senticnet['patriarchal'] = ['0', '-0.86', '0', '0.95', '#surprise', '#admiration', 'positive', '0.905', 'antiquated', 'dignity', 'goodness', 'reverence', 'venerable']
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senticnet['patriarchal_society'] = ['-0.68', '0', '0.949', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'chauvinism', 'sexism', 'antifeminism', 'sexual_discrimination', 'discrimination']
senticnet['patriarchal_vicar'] = ['0.8', '-0.84', '0', '0.868', '#surprise', '#admiration', 'positive', '0.837', 'commonwealth', 'province', 'administrative_district', 'administrative_division', 'territorial_division']
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senticnet['patrician'] = ['0', '0', '-0.92', '-0.79', '#fear', '#disgust', 'negative', '-0.85', 'rome', 'plebe', 'plebeian', 'unwashed', 'marcellus']
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senticnet['patrilateral'] = ['0.903', '0.684', '0', '0.791', '#joy', '#admiration', 'positive', '0.793', 'relative', 'family_member', 'relationship', 'family', 'mother']
senticnet['patrilineal'] = ['0.903', '0.684', '0', '0.791', '#joy', '#admiration', 'positive', '0.793', 'relative', 'family_member', 'relationship', 'family', 'mother']
senticnet['patrilineal_descent'] = ['0.903', '0.684', '0', '0.791', '#joy', '#admiration', 'positive', '0.793', 'relative', 'family_member', 'relationship', 'family', 'mother']
senticnet['patrilocal'] = ['0.051', '0.112', '0', '0', '#joy', '#interest', 'positive', '0.082', 'neighbor', 'family_reunion', 'clan', 'desire', 'little_specific']
senticnet['patriot'] = ['0.821', '0.989', '0', '0', '#joy', '#interest', 'positive', '0.905', 'life', 'human_life', 'experience', 'proud', 'human_experience']
senticnet['patriot_act'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'address_book', 'get_rich', 'ladle', 'swiss_army_knife', 'whisk']
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senticnet['patriotic_feeling'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'motherland', 'homeland']
senticnet['patriotic_fervor'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'motherland', 'homeland']
senticnet['patriotic_pride'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'motherland', 'homeland']
senticnet['patriotic_sentiment'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'motherland', 'homeland']
senticnet['patriotic_spirit'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'motherland', 'homeland']
senticnet['patriotic_zeal'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'motherland', 'homeland']
senticnet['patriotically'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'motherland', 'homeland']
senticnet['patriotism'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'motherland', 'homeland']
senticnet['patristic'] = ['0.587', '-0.94', '0', '0.659', '#surprise', '#admiration', 'positive', '0.731', 'critique', 'review', 'criticism', 'literary_criticism', 'new_criticism']
senticnet['patristic_literature'] = ['0.587', '-0.94', '0', '0.659', '#surprise', '#admiration', 'positive', '0.731', 'critique', 'review', 'criticism', 'literary_criticism', 'new_criticism']
senticnet['patrol'] = ['0.952', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.898', 'patrol', 'police', 'patrolling', 'trooper', 'officer']
senticnet['patrol_car'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'trailer', 'trail_car', 'camion', 'wagon', 'towboat']
senticnet['patrolling'] = ['0.943', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.897', 'patrol', 'police', 'patrol', 'trooper', 'officer']
senticnet['patron'] = ['0.975', '0.908', '0', '0', '#joy', '#interest', 'positive', '0.942', 'shopper', 'clerk', 'customer', 'vintner', 'hairdresser']
senticnet['patron_saint'] = ['0', '0.248', '-0.08', '0.173', '#interest', '#admiration', 'positive', '0.113', 'god', 'minister', 'evangelist', 'rector', 'goddess']
senticnet['patronize'] = ['0', '0', '-0.75', '-0.71', '#fear', '#disgust', 'negative', '-0.73', 'put_down', 'humiliate', 'snobby', 'snooty', 'haughty']
senticnet['patsey'] = ['-0.56', '0', '0.738', '-0.61', '#anger', '#disgust', 'negative', '-0.64', 'mix_up', 'muddle', 'puddle', 'assemble', 'piece']
senticnet['patter'] = ['-0.83', '0', '0.87', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'precipitate', 'mizzle', 'shower_down', 'spit', 'rain_down']
senticnet['pattern'] = ['0.087', '0.069', '0.013', '0.052', '#joy', '#interest', 'positive', '0.049', 'style', 'design', 'curly', 'art_form', 'fashion']
senticnet['patty'] = ['-0.74', '0', '0.877', '-0.97', '#anger', '#disgust', 'negative', '-0.86', 'miss', 'girl', 'chachka', 'young_lady', 'young_woman']
senticnet['patty_melt'] = ['0.132', '0.1', '0.063', '0.159', '#joy', '#admiration', 'positive', '0.082', 'chime', 'school_end', 'ring_loudly', 'noise_maker', 'vital']
senticnet['patty_pravo'] = ['0.107', '0.11', '-0.08', '0.21', '#interest', '#admiration', 'positive', '0.086', 'live_healthy_life', 'forever_young', 'live_forever', 'live_long', 'live_good_life']
senticnet['pattypan_squash'] = ['0', '-0.01', '0.037', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'pear', 'green_apple', 'apricot', 'cherry', 'lime']
senticnet['pauci_immune'] = ['-0.85', '0', '0.657', '-0.86', '#sadness', '#disgust', 'negative', '-0.79', 'pulseless_disease', 'temporal_arteritis', 'rubor', 'redness', 'arteritis']
senticnet['pauciflora'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['paucispiral'] = ['0.969', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.772', 'aspheric', 'aspherical', 'prolate', 'rounded', 'crescent']
senticnet['paucity'] = ['-0.88', '0', '0.732', '-0.61', '#sadness', '#anger', 'negative', '-0.74', 'scarceness', 'insufficiency', 'rareness', 'infrequency', 'dearth']
senticnet['paulb'] = ['0', '0', '-0.86', '-0.99', '#fear', '#disgust', 'negative', '-0.93', 'niggle', 'quiddity', 'bicker', 'pettifog', 'cavil']
senticnet['paulet'] = ['0.582', '-0.97', '0', '0.802', '#surprise', '#admiration', 'positive', '0.788', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['pauley_pavilion'] = ['0', '-0.84', '0', '0.723', '#surprise', '#admiration', 'positive', '0.785', 'circular_route', 'stadium', 'memorial', 'station', 'row_house']
senticnet['paunch'] = ['-0.42', '-0.14', '-0.06', '-0.37', '#sadness', '#disgust', 'negative', '-0.18', 'brain_tumor', 'bad_hair_day', 'lyme_disease', 'diarrhoea', 'suffer_fool']
senticnet['paunchy'] = ['-0.64', '0', '0.632', '-0.61', '#sadness', '#anger', 'negative', '-0.62', 'bearded', 'barbate', 'bewhiskered', 'whiskered', 'whiskery']
senticnet['pauper'] = ['0', '0.838', '0', '0.751', '#interest', '#admiration', 'positive', '0.794', 'workman', 'homeowner', 'polite_person', 'man', 'compatriot']
senticnet['pauperism'] = ['-0.57', '0', '0.903', '-0.80', '#anger', '#disgust', 'negative', '-0.76', 'poverty', 'impoverishment', 'poorness', 'beggary', 'mendicancy']
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senticnet['pause'] = ['0', '0.118', '-0.09', '0.111', '#interest', '#admiration', 'positive', '0.044', 'stop', 'time_out', 'halt', 'hesitation', 'rest_time']
senticnet['pavane'] = ['0.591', '-0.91', '0', '0.605', '#surprise', '#admiration', 'positive', '0.704', 'farandole', 'folk_dance', 'polka', 'dance', 'typical_dance']
senticnet['pave'] = ['0', '0', '0.078', '0', '#anger', '#anger', 'negative', '-0.07', 'street_city', 'small_street', 'walkway', 'black_pavement', 'dark_road']
senticnet['paved_parking_lot'] = ['0', '0', '0', '-0.09', '#disgust', '#disgust', 'negative', '-0.09', 'subway', 'bus', 'train_station', 'railway_station', 'bus_stop']
senticnet['paved_path'] = ['-0.99', '0', '0.641', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'navigable', 'negotiable', 'surmountable', 'climbable', 'passable']
senticnet['paved_runway'] = ['-0.70', '0', '0.621', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'flare_path', 'airfield', 'grassland', 'runway', 'road']
senticnet['paved_shoulder'] = ['-0.99', '0', '0.641', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'navigable', 'negotiable', 'surmountable', 'climbable', 'passable']
senticnet['pavement'] = ['0', '-0.01', '0.046', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'cement', 'tar', 'concrete', 'big_city_life', 'popular_building_material']
senticnet['pavement_condition'] = ['0', '-0.01', '0.046', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'cement', 'tar', 'concrete', 'big_city_life', 'popular_building_material']
senticnet['pavement_surface'] = ['0', '-0.01', '0.046', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'cement', 'tar', 'concrete', 'big_city_life', 'popular_building_material']
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senticnet['paving'] = ['0', '0.83', '0', '0.722', '#interest', '#admiration', 'positive', '0.776', 'lawn', 'landscape', 'green', 'landscaping', 'garden']
senticnet['pavlovian'] = ['0.067', '-0.2', '0.064', '0', '#joy', '#surprise', 'positive', '0.068', 'wand', 'long_stick', 'branch', 'twig', 'stick']
senticnet['pavonazzo_marble'] = ['-0.52', '0', '0.506', '-0.94', '#sadness', '#disgust', 'negative', '-0.65', 'gouger', 'swindler', 'defrauder', 'fraud', 'welcher']
senticnet['paw'] = ['-0.03', '0.121', '0', '0.096', '#interest', '#admiration', 'positive', '0.06', 'claw', 'animal_part', 'long_tail', 'sharp_tooth', 'fang']
senticnet['pawing_through'] = ['-0.17', '0.131', '-0.20', '0.097', '#sadness', '#fear', 'negative', '-0.03', 'lost_and_found', 'tv_series', 'bad_situation', 'odds', 'risk']
senticnet['pawn'] = ['0', '0', '0', '0.115', '#admiration', '#admiration', 'positive', '0.115', 'chess_knight', 'chess_pawn', 'chess_piece', 'chess_queen', 'chess_rook']
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senticnet['pawn_shop'] = ['-0.05', '0', '0.031', '-0.02', '#sadness', '#anger', 'negative', '-0.03', 'clothes_drawer', 'clothes_store', 'department_store', 'sex_shop', 'record_store']
senticnet['pawnshop'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'card_deck', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['pax_romana'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['pay'] = ['0.75', '0.855', '0', '0', '#joy', '#interest', 'positive', '0.802', 'give_cash', 'salary', 'wage', 'remittance', 'money']
senticnet['pay_a_lot_of_money'] = ['-0.85', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.89', 'go_debt', 'negotiate', 'borrow_money', 'transact_business', 'sell_stock']
senticnet['pay_admission'] = ['-0.77', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.85', 'find_museum', 'go_airport', 'dress_nicely', 'find_out', 'obey_rule']
senticnet['pay_attention'] = ['-0.03', '0.121', '0', '0.096', '#interest', '#admiration', 'positive', '0.06', 'listen_carefully', 'focus', 'use_dictionary', 'move_eye', 'ask_question']
senticnet['pay_back'] = ['-0.1', '0.272', '-0.14', '0.15', '#interest', '#admiration', 'positive', '0.044', 'give_change', 'settle_bill', 'collect_money', 'owe_money', 'make_change']
senticnet['pay_barber'] = ['-0.10', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.08', 'short_hair', 'talk_hairdresser', 'hair_become_short', 'hair_wash', 'keep_hair_short']
senticnet['pay_bill'] = ['0.339', '0.302', '0', '0.387', '#joy', '#admiration', 'positive', '0.343', 'good_citizen', 'use_cash', 'use_credit_card', 'purchase_gift', 'pay_off_debt']
senticnet['pay_cash'] = ['-0.04', '-0.02', '0.04', '-0.03', '#sadness', '#anger', 'negative', '-0.02', 'pay_credit_card', 'purchase_gift', 'spend_money', 'buy_product', 'holiday_shop']
senticnet['pay_cashier'] = ['0.339', '0.302', '0', '0.387', '#joy', '#admiration', 'positive', '0.343', 'pay', 'pay_bill', 'make_offer', 'buy_product', 'use_cash']
senticnet['pay_check'] = ['-0.05', '-0.05', '0.063', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'pay_bill', 'purchase_cellular_phone', 'pay_credit_card', 'pay_cash', 'spend_money']
senticnet['pay_cheque'] = ['0.057', '0.181', '-0.04', '0.18', '#interest', '#admiration', 'positive', '0.092', 'small_money', 'alter', 'money_change', 'money_coin', 'small_coin']
senticnet['pay_child_support'] = ['-0.10', '0', '-0.09', '-0.07', '#sadness', '#fear', 'negative', '-0.09', 'obligation', 'fine', 'pay_dear', 'expensive', 'stress']
senticnet['pay_close_attention'] = ['0.759', '-0.80', '0', '0', '#joy', '#surprise', 'positive', '0.783', 'use_thermometer', 'change_plan', 'form_opinion', 'new_insight', 'thirst_knowledge']
senticnet['pay_credit_card'] = ['0', '-0.75', '0', '0.863', '#surprise', '#admiration', 'positive', '0.806', 'pay_cash', 'holiday_shop', 'spend_money', 'christmas_shop', 'buy_product']
senticnet['pay_dear'] = ['-0.10', '0', '-0.09', '-0.07', '#sadness', '#fear', 'negative', '-0.09', 'obligation', 'fine', 'pay_child_support', 'expensive', 'stress']
senticnet['pay_debt'] = ['0', '0.781', '0', '0.978', '#interest', '#admiration', 'positive', '0.879', 'pay_goods', 'purchase', 'establish_credit', 'reward', 'buy_goods_service']
senticnet['pay_dinner'] = ['0.896', '0', '0', '0', '#joy', '#joy', 'positive', '0.896', 'walk_exercise', 'buy_candy', 'subscribe_magazine', 'gain_interest', 'buy_soda']
senticnet['pay_employee'] = ['-0.05', '0.182', '-0.08', '0.114', '#interest', '#admiration', 'positive', '0.038', 'pay_service', 'collect_debt', 'buy_gas', 'get_money', 'make_profit']
senticnet['pay_equity'] = ['0.032', '0.11', '0', '0.105', '#interest', '#admiration', 'positive', '0.082', 'work', 'place_work', 'implementation', 'disorganize', 'employment']
senticnet['pay_fee'] = ['0', '0', '0', '0.703', '#admiration', '#admiration', 'positive', '0.703', 'choose_destination', 'book_hotel_room', 'talk_to_travel_agent', 'make_reservation', 'decide_to_go_first']
senticnet['pay_fine'] = ['-0.21', '0', '0', '-0.13', '#sadness', '#disgust', 'negative', '-0.17', 'buy_goods_service', 'cashless', 'incur_debt', 'pay_goods', 'buy']
senticnet['pay_food'] = ['-0.04', '0', '0.011', '-0.02', '#sadness', '#disgust', 'negative', '-0.03', 'look_price', 'decide_buy', 'compare_price', 'take_out_wallet', 'check_wallet']
senticnet['pay_for_meal'] = ['0.739', '0', '0', '0.917', '#joy', '#admiration', 'positive', '0.828', 'treat', 'birthday_surprise', 'birthday_treat', 'dessert', 'chocolate_dessert']
senticnet['pay_for_sex'] = ['-0.97', '0', '0.793', '-0.85', '#sadness', '#disgust', 'negative', '-0.87', 'hooker', 'prostitute', 'bad_girl', 'mistress', 'working_girl']
senticnet['pay_goods'] = ['-0.71', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.81', 'incur_debt', 'pay_bill', 'buy_goods_service', 'cashless', 'postpone_payment']
senticnet['pay_grocery'] = ['-0.04', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'get_turn', 'give_money', 'pay_meal', 'take_number', 'order_french_fries']
senticnet['pay_handsomely'] = ['0.808', '-0.83', '0', '0.548', '#joy', '#surprise', 'positive', '0.73', 'successfulness', 'luckiness', 'prosperity', 'circumstance', 'blessing']
senticnet['pay_homage'] = ['0', '0.82', '0', '0.847', '#interest', '#admiration', 'positive', '0.833', 'reverently', 'hero-worship', 'idolize', 'veneration', 'reverentially']
senticnet['pay_insurance'] = ['0', '-0.53', '0', '-0.99', '#surprise', '#disgust', 'negative', '-0.22', 'premium', 'tax', 'useless', 'waste_money', 'bureucracy']
senticnet['pay_lip_service'] = ['0.755', '0.972', '0', '0.775', '#interest', '#admiration', 'positive', '0.834', 'grasp', 'grab', 'hold', 'sensual', 'grip']
senticnet['pay_meal'] = ['0.339', '0.302', '0', '0.387', '#joy', '#admiration', 'positive', '0.343', 'order_dinner', 'pay_grocery', 'pay_bill', 'order_french_fries', 'pay_cashier']
senticnet['pay_money'] = ['-0.77', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.84', 'money_spend', 'bankruptcy', 'receive_change', 'cash_check', 'compare_price']
senticnet['pay_monthly'] = ['0.046', '0.201', '-0.05', '0.115', '#interest', '#admiration', 'positive', '0.077', 'wage', 'serve_hot', 'after_dinner', 'income', 'monetary_amount']
senticnet['pay_off'] = ['-0.09', '0.164', '-0.09', '0.127', '#interest', '#admiration', 'positive', '0.024', 'payoff', 'deserve', 'find_money', 'spend_little_money', 'high_salary']
senticnet['pay_off_debt'] = ['0.339', '0.302', '0', '0.387', '#joy', '#admiration', 'positive', '0.343', 'good_citizen', 'complement', 'positive_reinforcement', 'feel_important', 'good_sense_of_humor']
senticnet['pay_off_handsomely'] = ['0.81', '0', '0', '0.703', '#joy', '#admiration', 'positive', '0.756', 'harvest', 'crop', 'grow_crop', 'plant_crop', 'graze_cattle']
senticnet['pay_phone'] = ['0.054', '0', '0.035', '0.066', '#joy', '#admiration', 'positive', '0.028', 'phone', 'telephone', 'cellphone', 'telephone_room', 'mobile_phone']
senticnet['pay_purchase'] = ['-0.95', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.91', 'pay_goods', 'incur_debt', 'buy_goods_service', 'pay_cash', 'pay_bill']
senticnet['pay_rend'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'provide_shelter', 'wait_person', 'live_city', 'throw_party', 'live']
senticnet['pay_service'] = ['-0.07', '0.137', '-0.06', '0.095', '#interest', '#admiration', 'positive', '0.022', 'exchange_money', 'settle_bill', 'sell', 'transact_business', 'bankruptcy']
senticnet['pay_tab'] = ['0', '0.709', '0', '0', '#interest', '#interest', 'positive', '0.709', 'serve_beer', 'drink_beer', 'invite_friend', 'buy_drink', 'order_beer']
senticnet['pay_ticket'] = ['0.339', '0.302', '0', '0.387', '#joy', '#admiration', 'positive', '0.343', 'buy_ticket', 'go_to_theater', 'buy_popcorn', 'buy_happiness', 'go_to_cinema']
senticnet['pay_toll'] = ['0', '-0.00', '0', '0.065', '#surprise', '#admiration', 'positive', '0.037', 'drive_quickly', 'drive', 'drive_upon', 'get_fast', 'pay_money']
senticnet['pay_tribute'] = ['0.857', '0.824', '0', '0.708', '#joy', '#interest', 'positive', '0.796', 'mandatory', 'gentleman', 'obey', 'virtue', 'dignity']
senticnet['pay_tuition'] = ['0', '0.872', '0', '0', '#interest', '#interest', 'positive', '0.872', 'enroll_school', 'go_college', 'matriculate', 'show_up_time', 'enroll']
senticnet['pay_tv'] = ['-0.84', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.82', 'ceiling_fan', 'tv_set', 'pornography', 'red_light_district', 'sexual_activity']
senticnet['pay_well'] = ['0.955', '0.945', '0', '0.835', '#joy', '#interest', 'positive', '0.912', 'perform_service', 'provide_service', 'accomplish', 'find_job', 'responsible']
senticnet['pay_work'] = ['0.279', '0.184', '0', '0.269', '#joy', '#admiration', 'positive', '0.244', 'financial_security', 'desirable', 'prosper', 'enough_eat', 'a_lot_of_sex']
senticnet['payable'] = ['-0.89', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.80', 'account_indebtedness', 'indebtedness', 'limited_liability', 'debt', 'arrears']
senticnet['payachata'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'head_hurt', 'introspection', 'lifestyle_change', 'headache', 'move_eye']
senticnet['payback'] = ['-0.62', '0', '0.7', '-0.83', '#anger', '#disgust', 'negative', '-0.72', 'take', 'send_back', 'return', 'taking', 'proceeds']
senticnet['payback_period'] = ['-0.02', '0.249', '0', '0.116', '#interest', '#admiration', 'positive', '0.112', 'price', 'expense', 'bill', 'value', 'worth']
senticnet['paycheck'] = ['0.832', '0', '0', '0', '#joy', '#joy', 'positive', '0.832', 'earn_money', 'receive_earn_paycheck', 'earn', 'earn_paycheck', 'finish_work']
senticnet['paycheck-to-paycheck'] = ['0', '0', '-0.74', '-0.86', '#fear', '#disgust', 'negative', '-0.80', 'hand-to-mouth', 'moment-to-moment', 'hermit-like', 'eking', 'day-in-day-out']
senticnet['paycycle'] = ['-0.03', '0.112', '-0.08', '0.164', '#interest', '#admiration', 'positive', '0.04', 'run', 'jump_over', 'quick', 'sprint', 'flee']
senticnet['payee'] = ['-0.04', '0.277', '-0.10', '0.11', '#interest', '#admiration', 'positive', '0.059', 'owe', 'tax', 'debit', 'afford', 'wage']
senticnet['payer'] = ['-0.04', '0.277', '-0.10', '0.11', '#interest', '#admiration', 'positive', '0.059', 'owe', 'tax', 'debit', 'afford', 'wage']
senticnet['paying'] = ['0.521', '-0.85', '0', '0.727', '#surprise', '#admiration', 'positive', '0.702', 'compensated', 'remunerated', 'paid', 'stipendiary', 'remunerative']
senticnet['paying_tuition'] = ['-0.07', '0', '0.073', '-0.26', '#sadness', '#disgust', 'negative', '-0.13', 'tuition', 'private_tuition', 'pay_tuition', 'enroll_class', 'enroll_school']
senticnet['payment'] = ['-0.04', '0.277', '-0.10', '0.11', '#interest', '#admiration', 'positive', '0.059', 'owe', 'tax', 'debit', 'afford', 'wage']
senticnet['payment_amount'] = ['0', '0.289', '0', '0.169', '#interest', '#admiration', 'positive', '0.229', 'amount_due', 'money_payment', 'income', 'value', 'monetary']
senticnet['payment_counter'] = ['0', '0.207', '0', '0.071', '#interest', '#admiration', 'positive', '0.139', 'check', 'bill', 'afford', 'owe', 'severance']
senticnet['paynim'] = ['-0.88', '0', '0.929', '-0.84', '#sadness', '#anger', 'negative', '-0.88', 'gentile', 'nonreligious_person', 'idolizer', 'idolater', 'idol_worshiper']
senticnet['payoff'] = ['-0.04', '0.208', '-0.11', '0.204', '#interest', '#admiration', 'positive', '0.062', 'pay_off', 'spend_little_money', 'quit', 'increase_money', 'money_earn']
senticnet['payor'] = ['-0.04', '0.277', '-0.10', '0.11', '#interest', '#admiration', 'positive', '0.059', 'owe', 'tax', 'debit', 'afford', 'wage']
senticnet['payout'] = ['0.83', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.886', 'jackpot', 'jackpot', 'winning', 'lotto', 'lottery']
senticnet['paypal_account'] = ['0', '0.192', '0', '0.052', '#interest', '#admiration', 'positive', '0.122', 'bank_vault', 'wallet', 'billfold', 'hold_change', 'bank']
senticnet['payroll'] = ['0', '0.119', '0', '0', '#interest', '#interest', 'positive', '0.119', 'ledger', 'computer_disk', 'memo_pad', 'intern', 'graphite_pencil']
senticnet['pazuzu'] = ['-0.78', '0', '0.74', '-0.94', '#sadness', '#disgust', 'negative', '-0.82', 'eject', 'chuck_out', 'exclude', 'turf_out', 'boot_out']
senticnet['pb_documentary'] = ['0', '0.762', '0', '0', '#interest', '#interest', 'positive', '0.762', 'encyclopaedia', 'tropical_rainforest', 'learn_new_information', 'tropical_forest', 'child_story']
senticnet['pc_card_slot'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'computer_part', 'system', 'cpu', 'pencil_pen', 'monitor']
senticnet['pc_hard_drive'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'car_trunk', 'walmart', 'warehouse', 'store', 'storage_place']
senticnet['pc_keyboard'] = ['0.893', '0.979', '0', '0', '#joy', '#interest', 'positive', '0.936', 'find_phone_number', 'dial_phone_number', 'pick_up_telephone_receiver', 'dial', 'dial_telephone']
senticnet['pc_plum'] = ['0.036', '0.084', '0', '0', '#joy', '#interest', 'positive', '0.06', 'toy', 'bath_toy', 'yo-yo', 'toy_ball', 'duck']
senticnet['pc_regan'] = ['0.941', '-0.83', '0', '0.973', '#joy', '#admiration', 'positive', '0.917', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['pce_price_index'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'indicator', 'retail_price_index', 'producer_price_index', 'wholesale_price_index', 'consumer_price_index']
senticnet['pchela'] = ['0', '0', '0', '0.869', '#admiration', '#admiration', 'positive', '0.869', 'fly_animal', 'insect', 'mosquito', 'bug', 'fly_insect']
senticnet['pci_card'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'computer_part', 'system', 'cpu', 'pencil_pen', 'monitor']
senticnet['pci_slot'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'computer_part', 'system', 'cpu', 'pencil_pen', 'monitor']
senticnet['pcmcia_card'] = ['0.113', '0.073', '0.028', '0.107', '#joy', '#admiration', 'positive', '0.066', 'pda', 'telephone_computer', 'browser', 'number_pad', 'modem']
senticnet['pcmcia_slot'] = ['0.113', '0.073', '0.028', '0.107', '#joy', '#admiration', 'positive', '0.066', 'pda', 'telephone_computer', 'browser', 'number_pad', 'modem']
senticnet['pda'] = ['0.113', '0.073', '0.028', '0.107', '#joy', '#admiration', 'positive', '0.066', 'portable_computer', 'telephone_computer', 'browser', 'number_pad', 'modem']
senticnet['pdflatex'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'wall_cover', 'two-dimensional', 'cover_wall', 'pigment', 'dye']
senticnet['pdftex'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'wall_cover', 'two-dimensional', 'cover_wall', 'pigment', 'dye']
senticnet['pdsa'] = ['-0.02', '0', '-0.06', '-0.03', '#fear', '#disgust', 'negative', '-0.04', 'alleyway', 'animal_shelter', 'vet_office', 'home', 'doghouse']
senticnet['pdsa_petaid_hospital'] = ['-0.02', '0', '-0.06', '-0.03', '#fear', '#disgust', 'negative', '-0.04', 'alleyway', 'animal_shelter', 'vet_office', 'home', 'doghouse']
senticnet['pe_sur_nivelle'] = ['0', '0.248', '-0.08', '0.173', '#interest', '#admiration', 'positive', '0.113', 'god', 'minister', 'evangelist', 'rector', 'goddess']
senticnet['pea'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'cabbage', 'lettuce', 'broccoli']
senticnet['pea_aphid'] = ['-0.56', '0', '0.892', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'borecole', 'colewort', 'kail', 'cole', 'kale']
senticnet['pea_crab'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'stone_crab', 'swimming_crab', 'rock_crab', 'fiddler_crab', 'crab']
senticnet['pea_pisum_sativum'] = ['-0.56', '0', '0.892', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'borecole', 'colewort', 'kail', 'cole', 'kale']
senticnet['pea_protein'] = ['0.878', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.883', 'wheat', 'grain', 'rye', 'white_rice', 'rice']
senticnet['pea_soup_fog'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'hazy', 'blur', 'bleary', 'blurred', 'blurry']
senticnet['peabody_award_board'] = ['0', '0.104', '0', '0.075', '#interest', '#admiration', 'positive', '0.089', 'judge', 'negotiator', 'jury', 'arbiter', 'member_jury']
senticnet['peabody_museum'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'institution', 'tundra', 'child_story', 'natural_history_museum', 'cartoon']
senticnet['peace'] = ['0.497', '0.453', '-0.23', '0.189', '#joy', '#interest', 'positive', '0.226', 'calm', 'calmness', 'stillness', 'truce', 'love']
senticnet['peace-loving'] = ['0.621', '0.648', '-0.25', '0.296', '#joy', '#interest', 'positive', '0.328', 'pacifically', 'repose', 'peaceably', 'peaceable', 'peace_of_mind']
senticnet['peace_accord'] = ['0.397', '0.245', '-0.13', '0.224', '#joy', '#interest', 'positive', '0.183', 'agreement', 'provide', 'advice', 'contribution', 'offer']
senticnet['peace_activist'] = ['-0.97', '0', '0.62', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'obstructor', 'obstructer', 'technophobe', 'obstructionist', 'naysayer']
senticnet['peace_agreement'] = ['0.068', '0.139', '-0.09', '0.191', '#interest', '#admiration', 'positive', '0.076', 'peace', 'peace_maker', 'peacekeeping', 'peace_mission', 'keep_peace']
senticnet['peace_deal'] = ['0.397', '0.245', '-0.13', '0.224', '#joy', '#interest', 'positive', '0.183', 'agreement', 'provide', 'advice', 'contribution', 'offer']
senticnet['peace_envoy'] = ['0.763', '0', '0', '0.719', '#joy', '#admiration', 'positive', '0.741', 'mist', 'haze', 'fog', 'misty', 'drizzle']
senticnet['peace_loving_nation'] = ['0.497', '0.453', '-0.23', '0.189', '#joy', '#interest', 'positive', '0.226', 'calm', 'calmness', 'stillness', 'truce', 'love']
senticnet['peace_loving_people'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'patriotism', 'homeland']
senticnet['peace_maker'] = ['0.068', '0.139', '-0.09', '0.191', '#interest', '#admiration', 'positive', '0.076', 'peace', 'peace_agreement', 'peacekeeping', 'peace_mission', 'agreement']
senticnet['peace_mind'] = ['0.738', '0', '0', '0', '#joy', '#joy', 'positive', '0.738', 'feel_good', 'good_feeling', 'self_satisfaction', 'serenity', 'relax']
senticnet['peace_mission'] = ['0.068', '0.139', '-0.09', '0.191', '#interest', '#admiration', 'positive', '0.076', 'peace', 'peace_maker', 'peacekeeping', 'peace_agreement', 'keep_peace']
senticnet['peace_negotiator'] = ['0.763', '0', '0', '0.719', '#joy', '#admiration', 'positive', '0.741', 'mist', 'haze', 'fog', 'misty', 'drizzle']
senticnet['peace_of_mind'] = ['0.621', '0.648', '-0.25', '0.296', '#joy', '#interest', 'positive', '0.328', 'pacifically', 'repose', 'peace-loving', 'peaceably', 'peaceable']
senticnet['peace_officer'] = ['0.74', '-0.53', '0', '0.641', '#joy', '#admiration', 'positive', '0.64', 'air_marshal', 'sky_marshal', 'lawman', 'law_officer', 'marshal']
senticnet['peace_pact'] = ['0.397', '0.245', '-0.13', '0.224', '#joy', '#interest', 'positive', '0.183', 'agreement', 'provide', 'advice', 'contribution', 'offer']
senticnet['peace_quiet'] = ['0.911', '0', '0', '0', '#joy', '#joy', 'positive', '0.911', 'silent', 'lucid', 'reflection', 'cup_coffee', 'find_quiet_place']
senticnet['peace_sign'] = ['0.091', '0.08', '0.03', '0.132', '#joy', '#admiration', 'positive', '0.068', 'letter', 'sign', 'announcement', 'placard', 'indication']
senticnet['peace_treaty'] = ['0.397', '0.245', '-0.13', '0.224', '#joy', '#interest', 'positive', '0.183', 'agreement', 'provide', 'advice', 'contribution', 'offer']
senticnet['peaceable'] = ['0.621', '0.648', '-0.25', '0.296', '#joy', '#interest', 'positive', '0.328', 'pacifically', 'repose', 'peace-loving', 'peaceably', 'peace_of_mind']
senticnet['peaceable_manner'] = ['0.621', '0.648', '-0.25', '0.296', '#joy', '#interest', 'positive', '0.328', 'pacifically', 'repose', 'peace-loving', 'peaceable', 'peace_of_mind']
senticnet['peaceably'] = ['0.621', '0.648', '-0.25', '0.296', '#joy', '#interest', 'positive', '0.328', 'pacifically', 'repose', 'peace-loving', 'peaceable', 'peace_of_mind']
senticnet['peaceful'] = ['0.383', '0.246', '-0.10', '0.256', '#joy', '#admiration', 'positive', '0.194', 'beautiful', 'lovely', 'strange', 'nice', 'pleasant']
senticnet['peaceful_atmosphere'] = ['0.733', '0', '0', '0.801', '#joy', '#admiration', 'positive', '0.767', 'serenity', 'recreate', 'peace_of_mind', 'ease', 'serene']
senticnet['peaceful_coexistence'] = ['0.497', '0.453', '-0.23', '0.189', '#joy', '#interest', 'positive', '0.226', 'calm', 'calmness', 'stillness', 'truce', 'love']
senticnet['peaceful_coexistence_between'] = ['0.497', '0.453', '-0.23', '0.189', '#joy', '#interest', 'positive', '0.226', 'calm', 'calmness', 'stillness', 'truce', 'love']
senticnet['peaceful_demonstration'] = ['0.669', '-0.58', '0', '0.803', '#joy', '#admiration', 'positive', '0.685', 'pep_rally', 'revival', 'revival_meeting', 'gathering', 'assemblage']
senticnet['peaceful_negotiation'] = ['0.497', '0.453', '-0.23', '0.189', '#joy', '#interest', 'positive', '0.226', 'calm', 'calmness', 'stillness', 'truce', 'love']
senticnet['peaceful_place'] = ['0', '0.077', '0', '0.086', '#interest', '#admiration', 'positive', '0.081', 'restful', 'comfy', 'live_inside', 'place_sleep', 'reside']
senticnet['peaceful_protest'] = ['-0.93', '0', '0.688', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'dissident', 'dissenter', 'nimby', 'objector', 'contestant']
senticnet['peaceful_rally'] = ['0.669', '-0.58', '0', '0.803', '#joy', '#admiration', 'positive', '0.685', 'pep_rally', 'revival', 'revival_meeting', 'gathering', 'assemblage']
senticnet['peaceful_reconciliation'] = ['0.497', '0.453', '-0.23', '0.189', '#joy', '#interest', 'positive', '0.226', 'calm', 'calmness', 'stillness', 'truce', 'love']
senticnet['peaceful_relax'] = ['0.103', '0', '0.026', '0.145', '#joy', '#admiration', 'positive', '0.074', 'make_wood', 'beautiful', 'big', 'sail_sea', 'huge']
senticnet['peaceful_resolution'] = ['0.763', '0', '0', '0.719', '#joy', '#admiration', 'positive', '0.741', 'mist', 'haze', 'fog', 'misty', 'drizzle']
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senticnet['peacemaker'] = ['0.763', '0', '0', '0.719', '#joy', '#admiration', 'positive', '0.741', 'mist', 'haze', 'fog', 'misty', 'drizzle']
senticnet['peacenik'] = ['-0.97', '0', '0.62', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'obstructor', 'obstructer', 'technophobe', 'obstructionist', 'resister']
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senticnet['peach_leaf_curl'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'rust_fungus', 'fungus']
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senticnet['peacock'] = ['0.745', '0', '0', '0.862', '#joy', '#admiration', 'positive', '0.803', 'feather', 'butterfly', 'bird', 'dragonfly', 'teal']
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senticnet['peak_hour'] = ['-0.98', '0', '0.989', '-0.93', '#sadness', '#anger', 'negative', '-0.97', 'flight_accident', 'dangerous', 'danger', 'air_traffic', 'air_traffic_jam']
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senticnet['peanut_butter'] = ['0.056', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.053', 'leftover', 'dusty', 'mayonnaise', 'leave_over', 'savory']
senticnet['peanut_butter_sandwich'] = ['0.056', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.053', 'leftover', 'dusty', 'mayonnaise', 'leave_over', 'savory']
senticnet['peanut_sauce'] = ['0.076', '0', '0.037', '0.065', '#joy', '#admiration', 'positive', '0.035', 'serve_cold', 'tasty_good', 'tasty', 'sour', 'high_calorie']
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senticnet['pear-shaped'] = ['-0.71', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.73', 'weighty', 'rotund', 'corpulent', 'obese', 'round']
senticnet['pear_cider'] = ['0.079', '-0.02', '0.023', '0', '#joy', '#surprise', 'positive', '0.027', 'wine', 'lemonade', 'juice', 'whisky', 'soft_drink']
senticnet['pear_juice'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'lemon_juice', 'chocolate_pudding', 'grape_juice', 'orange_juice', 'juice']
senticnet['pear_shape'] = ['0.17', '0', '0.081', '0.184', '#joy', '#admiration', 'positive', '0.091', 'food_holder', 'food_server', 'crockery', 'flattery', 'round_flat']
senticnet['pear_shaped_diamond'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'art', 'art_piece', 'museum', 'artist', 'jewel']
senticnet['pear_soap'] = ['0.808', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.903', 'marketing', 'sale', 'announcement', 'billboard', 'promotion']
senticnet['pearlescent'] = ['0.577', '-0.69', '0', '0.645', '#surprise', '#admiration', 'positive', '0.639', 'shining', 'polished', 'shiny', 'bright', 'burnished']
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senticnet['pearly_white'] = ['0.352', '0.142', '0', '0.281', '#joy', '#admiration', 'positive', '0.258', 'knowledgeable', 'complement', 'much_need', 'see_ocean', 'good_sense_of_humor']
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senticnet['pearson_syndrome'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'deflection', 'warp', 'aberrance']
senticnet['peary_caribou'] = ['-0.04', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.04', 'bad_luck', 'damn', 'artic', 'mammal_family', 'bad_mood']
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senticnet['peasant_farmer'] = ['-0.08', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.11', 'serf', 'peasant', 'servant', 'poor', 'villein']
senticnet['peasant_rebellion'] = ['0', '0.793', '0', '0.847', '#interest', '#admiration', 'positive', '0.82', 'disagreement', 'fight_against', 'opposition', 'against', 'go_against']
senticnet['peasantry'] = ['-0.65', '0', '0.691', '-0.54', '#sadness', '#anger', 'negative', '-0.63', 'moronity', 'mental_deficiency', 'imbecility', 'stupidity', 'mental_defectiveness']
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senticnet['peat_bog'] = ['-0.59', '0', '0.506', '-0.52', '#sadness', '#disgust', 'negative', '-0.54', 'dump', 'garbage_dump', 'trash_dump', 'rubbish_dump', 'wasteyard']
senticnet['peavey'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'lever']
senticnet['pebbly'] = ['-0.06', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.04', 'friction', 'lack_of_water', 'dirt', 'abrasive', 'ground_material']
senticnet['pecan'] = ['0', '0', '0.777', '-0.95', '#anger', '#disgust', 'negative', '-0.86', 'semi-sweet', 'unsweetened', 'unsalted', 'semisweet', 'bittersweet']
senticnet['pecan_pie'] = ['0.092', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.071', 'bake_treat', 'round_pastry', 'bake_good', 'bake_goods', 'sweet_food']
senticnet['peck'] = ['0', '0.067', '0', '0.07', '#interest', '#admiration', 'positive', '0.069', 'bushel', 'embryo', 'chicken_egg', 'ovum', 'egg']
senticnet['pecker'] = ['0.797', '-0.95', '0', '0.921', '#surprise', '#admiration', 'positive', '0.891', 'lush', 'alcoholic', 'alky', 'dipsomaniac', 'boozer']
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senticnet['pectinatum'] = ['-0.60', '0', '0.541', '-0.52', '#sadness', '#anger', 'negative', '-0.55', 'swoon', 'pass_out', 'faint', 'zonk_out', 'change_state']
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senticnet['pectoralis_major'] = ['-0.63', '0', '0.559', '-0.82', '#sadness', '#disgust', 'negative', '-0.67', 'take_bull_by_horns', 'confront', 'front', 'encounter', 'oppose']
senticnet['pectoralis_minor'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'skeletal_muscle', 'pterygoid_muscle', 'abductor', 'abductor_muscle']
senticnet['pectus_carinatum'] = ['0.582', '-0.66', '0', '0.661', '#surprise', '#admiration', 'positive', '0.637', 'talipes_equinus', 'deformity', 'malformation', 'talipes_valgus', 'talipes_calcaneus']
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senticnet['peculiar'] = ['0.136', '0.293', '-0.11', '0.371', '#interest', '#admiration', 'positive', '0.17', 'characteristic', 'character_trait', 'good_quality', 'description', 'good_character']
senticnet['peculiar_charm'] = ['0.824', '-0.95', '0', '0.513', '#joy', '#surprise', 'positive', '0.765', 'clearcutness', 'explicitness', 'preciseness', 'perspicuity', 'perspicuousness']
senticnet['peculiarity'] = ['0.808', '-0.98', '0', '0.628', '#joy', '#surprise', 'positive', '0.806', 'curio', 'oddity', 'curiosity', 'crank', 'oddment']
senticnet['pecuniarily'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'lucrative', 'moneymaking', 'profitable', 'paying', 'paid']
senticnet['pecuniary'] = ['0.685', '-0.58', '0', '0.56', '#joy', '#surprise', 'positive', '0.608', 'profit', 'net_income', 'net', 'net_profit', 'fast_buck']
senticnet['pecuniary_benefit'] = ['0.546', '-0.74', '0', '0.581', '#surprise', '#admiration', 'positive', '0.624', 'hand', 'commend', 'reach', 'charge', 'give']
senticnet['pecuniary_decency'] = ['0.632', '-0.80', '0', '0.537', '#joy', '#surprise', 'positive', '0.658', 'incisiveness', 'trenchancy', 'efficacy', 'effectiveness', 'power']
senticnet['pecuniary_gain'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'lucrative', 'moneymaking', 'profitable', 'paying', 'paid']
senticnet['pecuniary_interest'] = ['0.546', '-0.74', '0', '0.581', '#surprise', '#admiration', 'positive', '0.624', 'hand', 'commend', 'reach', 'charge', 'give']
senticnet['pecuniary_penalty'] = ['-0.89', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.93', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['pecuniary_resource'] = ['0.534', '-0.86', '0', '0.736', '#surprise', '#admiration', 'positive', '0.713', 'fund', 'finance', 'monetary_resource', 'cash_in_hand', 'escrow_fund']
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senticnet['pedagogically'] = ['0', '-0.74', '0', '0.853', '#surprise', '#admiration', 'positive', '0.801', 'educationally', 'esthetically', 'theologically', 'doctrinally', 'tonally']
senticnet['pedagogue'] = ['0.777', '0', '0', '0.852', '#joy', '#admiration', 'positive', '0.815', 'lector', 'academician', 'schoolmaster', 'educator', 'teacher']
senticnet['pedagogy'] = ['0.695', '-0.65', '0', '0.926', '#joy', '#admiration', 'positive', '0.758', 'education', 'didactics', 'educational_activity', 'continuing_education', 'instruction']
senticnet['pedal'] = ['0.118', '-0.06', '0.075', '0.086', '#joy', '#admiration', 'positive', '0.047', 'soft_hard', 'dance_type', 'attract', 'adjust', 'malleable']
senticnet['pedal_cycle'] = ['0.984', '0.793', '0', '0', '#joy', '#interest', 'positive', '0.889', 'cycle', 'bicycle_across_town', 'bike', 'transportation', 'city']
senticnet['pedal_steel_player'] = ['0.049', '0.167', '-0.05', '0.295', '#interest', '#admiration', 'positive', '0.115', 'four_sides', 'sound_system', 'dolby_surround', 'movie', 'surround']
senticnet['pedant'] = ['0.666', '-0.87', '0', '0.736', '#surprise', '#admiration', 'positive', '0.759', 'spread', 'diffuse', 'circulate', 'circularize', 'distribute']
senticnet['pedantic'] = ['0.71', '-0.81', '0', '0.937', '#surprise', '#admiration', 'positive', '0.819', 'erudite', 'donnish', 'scholarly', 'learned', 'bookish']
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senticnet['peddle'] = ['-0.08', '-0.05', '0.037', '-0.06', '#sadness', '#disgust', 'negative', '-0.03', 'trip_fall', 'fall_off', 'fall_down', 'lose_balance', 'breahard']
senticnet['peddler'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'cheapjack', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['pederast'] = ['0', '0.942', '0', '0', '#interest', '#interest', 'positive', '0.942', 'guy', 'special_person', 'guardian', 'boy', 'male']
senticnet['pederasty'] = ['-0.84', '0', '0.882', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'perversion', 'fetishism', 'paraphilia', 'sexual_perversion', 'buggery']
senticnet['pedernales_river'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'common_bean', 'green_bean', 'wax_bean']
senticnet['pedestal'] = ['0', '-0.75', '0', '0.975', '#surprise', '#admiration', 'positive', '0.866', 'pedestal', 'sink', 'vanity', 'washbasin', 'mounted']
senticnet['pedestal_sink'] = ['0', '-0.02', '0.041', '-0.10', '#anger', '#disgust', 'negative', '-0.03', 'separate_shower', 'shampoo_conditioner', 'towel_bar', 'shower_head', 'toilet_brush']
senticnet['pedestrian'] = ['0', '0', '-0.93', '-0.95', '#fear', '#disgust', 'negative', '-0.94', 'matter-of-fact', 'commonplace', 'prosaic', 'prosy', 'earthbound']
senticnet['pedestrian_bridge'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'suspension_bridge', 'under_bridge', 'roadway', 'county_highway', 'long_road']
senticnet['pedestrian_crossing'] = ['0.508', '-0.72', '0', '0.727', '#surprise', '#admiration', 'positive', '0.654', 'crosswalk', 'pelican_crossing', 'school_crossing', 'crossing', 'crossover']
senticnet['pedestrian_entrance'] = ['0.028', '0', '0', '0.051', '#joy', '#admiration', 'positive', '0.04', 'entranceway', 'doorway', 'entrance', 'entryway', 'corridor']
senticnet['pedestrian_path'] = ['0', '0', '0', '0.085', '#admiration', '#admiration', 'positive', '0.085', 'use_farm', 'gate', 'door', 'doorway', 'entranceway']
senticnet['pedestrian_pathway'] = ['0', '0', '0', '0.085', '#admiration', '#admiration', 'positive', '0.085', 'use_farm', 'gate', 'door', 'doorway', 'entranceway']
senticnet['pedestrian_safety'] = ['0.082', '0.319', '-0.10', '0.147', '#interest', '#admiration', 'positive', '0.11', 'protect', 'defend', 'secure', 'guard', 'watch_over']
senticnet['pedestrian_spine'] = ['0.53', '-0.64', '0', '0.76', '#surprise', '#admiration', 'positive', '0.646', 'focus', 'get_focused', 'focusing', 'centering', 'particularism']
senticnet['pedestrian_traffic'] = ['-0.98', '0', '0.989', '-0.93', '#sadness', '#anger', 'negative', '-0.97', 'flight_accident', 'dangerous', 'danger', 'air_traffic', 'air_traffic_jam']
senticnet['pedestrian_walkway'] = ['0', '0', '0', '0.085', '#admiration', '#admiration', 'positive', '0.085', 'use_farm', 'gate', 'door', 'doorway', 'entranceway']
senticnet['pedestrianism'] = ['0.891', '0.835', '0', '0.721', '#joy', '#interest', 'positive', '0.816', 'hobby', 'diversion', 'challenge', 'enjoy_game', 'leisure']
senticnet['pediatric_allergy'] = ['-0.96', '0', '0.848', '-0.82', '#sadness', '#anger', 'negative', '-0.87', 'sneeze', 'sign_sickness', 'runny_nose', 'flu', 'common_cold']
senticnet['pediatric_bipolar_disorder'] = ['-0.78', '0', '0.918', '-0.96', '#anger', '#disgust', 'negative', '-0.88', 'major_depressive_episode', 'depression', 'depressive_disorder', 'clinical_depression', 'involutional_depression']
senticnet['pediatric_cancer'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['pediatric_dental'] = ['-0.03', '0.079', '0.024', '0.091', '#interest', '#admiration', 'positive', '0.029', 'toothpaste', 'toothbrush', 'mouth', 'gel_toothpaste', 'tooth']
senticnet['pediatric_emergency_department'] = ['0', '0', '-0.73', '0', '#fear', '#fear', 'negative', '-0.73', 'clinic', 'emergency_ward', 'hospital', 'dentist_office', 'medical_office']
senticnet['pediatric_emergency_room'] = ['0', '0', '-0.73', '0', '#fear', '#fear', 'negative', '-0.73', 'clinic', 'emergency_ward', 'hospital', 'dentist_office', 'medical_office']
senticnet['pediatric_endocrine'] = ['0.672', '-0.55', '0', '0.992', '#joy', '#admiration', 'positive', '0.739', 'pressor', 'hormone', 'adrenalin', 'internal_secretion', 'catecholamine']
senticnet['pediatric_endocrinology'] = ['0.672', '-0.55', '0', '0.992', '#joy', '#admiration', 'positive', '0.739', 'pressor', 'hormone', 'adrenalin', 'internal_secretion', 'catecholamine']
senticnet['pediatric_icu'] = ['0', '0', '-0.73', '0', '#fear', '#fear', 'negative', '-0.73', 'clinic', 'emergency_ward', 'hospital', 'dentist_office', 'medical_office']
senticnet['pediatric_nurse_practitioner'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'infant_feeding', 'schedule_feeding', 'demand_feeding', 'breast_feeding', 'feeding']
senticnet['pediatric_nursing'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'infant_feeding', 'schedule_feeding', 'demand_feeding', 'breast_feeding', 'feeding']
senticnet['pediatric_orthopaedic'] = ['0.745', '-0.55', '0', '0.597', '#joy', '#admiration', 'positive', '0.633', 'lift', 'phalloplasty', 'abdominoplasty', 'tummy_tuck', 'autoplasty']
senticnet['pediatric_ward'] = ['0', '0', '-0.73', '0', '#fear', '#fear', 'negative', '-0.73', 'clinic', 'emergency_ward', 'hospital', 'dentist_office', 'medical_office']
senticnet['pedicab'] = ['0.04', '0.144', '-0.07', '0.122', '#interest', '#admiration', 'positive', '0.058', 'travel', 'abroad', 'foreign_country', 'transit', 'elsewhere']
senticnet['pedicel'] = ['0', '0', '0.064', '-0.04', '#anger', '#disgust', 'negative', '-0.05', 'bird_food', 'funiculus', 'plant_product', 'fuzz', 'agricultural_product']
senticnet['pedicle_flap'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
senticnet['pediculosis'] = ['-0.90', '0', '0.96', '-0.83', '#sadness', '#anger', 'negative', '-0.90', 'pediculosis_capitis', 'head_louse', 'pediculosis_corporis', 'pediculosis_pubis', 'crab']
senticnet['pediculosis_capitis'] = ['-0.84', '0', '0.767', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'lousiness', 'head_louse', 'pediculosis_corporis', 'pediculosis_pubis', 'crab']
senticnet['pediculosis_corporis'] = ['-0.84', '0', '0.767', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'pediculosis_capitis', 'head_louse', 'lousiness', 'pediculosis_pubis', 'crab']
senticnet['pediculosis_pubis'] = ['-0.84', '0', '0.767', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'pediculosis_capitis', 'head_louse', 'pediculosis_corporis', 'lousiness', 'crab']
senticnet['pedicure'] = ['0.944', '-0.60', '0', '0.756', '#joy', '#admiration', 'positive', '0.768', 'makeover', 'beautification', 'manicure', 'beauty_treatment', 'facial']
senticnet['pedigree_cattle'] = ['0.546', '-0.96', '0', '0.533', '#joy', '#surprise', 'positive', '0.683', 'dairy_cattle', 'dairy_cow', 'milch_cow', 'ayrshire', 'milcher']
senticnet['pediment'] = ['0.029', '0.087', '0.065', '0.137', '#interest', '#admiration', 'positive', '0.047', 'archway', 'aisle', 'mat', 'passageway', 'right_angle']
senticnet['pedlar'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'cheapjack', 'packman', 'hawker', 'pitchman']
senticnet['pedophile'] = ['0', '0.942', '0', '0', '#interest', '#interest', 'positive', '0.942', 'guy', 'special_person', 'guardian', 'boy', 'male']
senticnet['pedophile_priest'] = ['-0.95', '0', '0.673', '-0.85', '#sadness', '#disgust', 'negative', '-0.82', 'discharge', 'free', 'dethrone', 'disinvest', 'unfrock']
senticnet['pedophilia'] = ['0', '0.942', '0', '0', '#interest', '#interest', 'positive', '0.942', 'guy', 'special_person', 'guardian', 'boy', 'male']
senticnet['pedophilia_scandal'] = ['-0.95', '0', '0.673', '-0.85', '#sadness', '#disgust', 'negative', '-0.82', 'discharge', 'free', 'dethrone', 'disinvest', 'unfrock']
senticnet['pedophilic'] = ['-0.75', '0', '0.886', '-0.72', '#sadness', '#anger', 'negative', '-0.78', 'harass', 'move', 'check', 'fork', 'pin']
senticnet['peduncle'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'optic_radiation', 'radiatio_optica', 'pathway', 'cerebral_pathway']
senticnet['pedunculate_oak'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'bottlebrush_buckeye', 'red_buckeye', 'particolored_buckeye']
senticnet['pedunculate_oak_quercus_robur'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'bottlebrush_buckeye', 'red_buckeye', 'particolored_buckeye']
senticnet['pee'] = ['-0.04', '0', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.07', 'urinate', 'go_toilet', 'go_potty', 'go_pee', 'bladder_full']
senticnet['pee_pants'] = ['0', '0', '0.897', '-0.71', '#anger', '#disgust', 'negative', '-0.80', 'start_laugh', 'wet_pant', 'cry', 'make_others_laugh', 'have_fun']
senticnet['pee_wee'] = ['0', '0', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.05', 'human', 'delusional', 'use_illegal_drug', 'busy_person', 'carpenter']
senticnet['pee_wee_big'] = ['0.704', '-0.80', '0', '0', '#joy', '#surprise', 'positive', '0.755', 'quest', 'stunt', 'illusion', 'journey', 'activity']
senticnet['peece'] = ['-0.69', '0', '0.883', '-0.91', '#anger', '#disgust', 'negative', '-0.82', 'stick_in', 'enclose', 'insert', 'feed_in', 'put_in']
senticnet['peek'] = ['0', '0.096', '0', '0.075', '#interest', '#admiration', 'positive', '0.085', 'stern', 'quick_look', 'glimpse', 'peep', 'stare']
senticnet['peek_behind'] = ['0', '0.096', '0', '0.075', '#interest', '#admiration', 'positive', '0.085', 'stern', 'quick_look', 'glimpse', 'peep', 'stare']
senticnet['peek_char'] = ['-0.04', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.06', 'chemical_reaction', 'burn', 'chemical_process', 'get_burnt', 'set_on_fire']
senticnet['peek_inside'] = ['0', '0.096', '0', '0.075', '#interest', '#admiration', 'positive', '0.085', 'stern', 'quick_look', 'glimpse', 'peep', 'stare']
senticnet['peek_into'] = ['0', '0.096', '0', '0.075', '#interest', '#admiration', 'positive', '0.085', 'stern', 'quick_look', 'glimpse', 'peep', 'stare']
senticnet['peek_through'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'naked_eye', 'eye', 'oculus_sinister']
senticnet['peekaboo'] = ['0', '0.096', '0', '0.075', '#interest', '#admiration', 'positive', '0.085', 'stern', 'quick_look', 'glimpse', 'peep', 'stare']
senticnet['peeking_out'] = ['0.024', '0', '0', '0.074', '#joy', '#admiration', 'positive', '0.049', 'sheet', 'cover', 'quilt', 'comforter', 'modesty']
senticnet['peeking_through'] = ['0', '0.096', '0', '0.075', '#interest', '#admiration', 'positive', '0.085', 'stern', 'quick_look', 'glimpse', 'peep', 'stare']
senticnet['peekytoe_crab'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'saltine', 'soda_cracker', 'cracker', 'water_biscuit', 'graham_cracker']
senticnet['peel_off'] = ['-0.06', '0.109', '-0.05', '0.158', '#interest', '#admiration', 'positive', '0.036', 'elasticity', 'stretch_out', 'grow_long', 'widen', 'advance_civilization']
senticnet['peel_orange'] = ['0.059', '0', '0', '0.062', '#joy', '#admiration', 'positive', '0.06', 'round_fruit', 'green_fruit', 'small_fruit', 'redness', 'red_fruit']
senticnet['peel_potato'] = ['0', '-0.13', '0', '0', '#surprise', '#surprise', 'positive', '0.133', 'stir_pot', 'burn_food', 'remove_skin', 'prepare_ingredient', 'chop_vegetable']
senticnet['peel_tower'] = ['0.021', '0.119', '0', '0.129', '#interest', '#admiration', 'positive', '0.09', 'manor', 'palace', 'medieval', 'tower', 'chess_game']
senticnet['peelable'] = ['-0.02', '0.068', '0.04', '0.084', '#interest', '#admiration', 'positive', '0.022', 'bright_red', 'green_black', 'purple', 'red', 'edible_human']
senticnet['peeling_paint'] = ['-0.61', '0', '0.732', '-0.59', '#sadness', '#anger', 'negative', '-0.64', 'leaky', 'draughty', 'porous', 'drippy', 'oozing']
senticnet['peeling_wallpaper'] = ['-0.61', '0', '0.732', '-0.59', '#sadness', '#anger', 'negative', '-0.64', 'leaky', 'draughty', 'porous', 'drippy', 'oozing']
senticnet['peep'] = ['0', '0.07', '0', '0.063', '#interest', '#admiration', 'positive', '0.067', 'microscope', 'glimpse', 'see', 'stare', 'observation']
senticnet['peep_toe'] = ['-0.54', '0', '0.612', '-0.98', '#anger', '#disgust', 'negative', '-0.71', 'scuff', 'mule', 'bootee', 'carpet_slipper', 'footwear']
senticnet['peeped_out'] = ['0.979', '-0.69', '0', '0.576', '#joy', '#surprise', 'positive', '0.749', 'big_cheese', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['peeped_through'] = ['0.979', '-0.69', '0', '0.576', '#joy', '#surprise', 'positive', '0.749', 'big_cheese', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['peeper'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'naked_eye', 'eye', 'oculus_sinister']
senticnet['peephole'] = ['0', '0.096', '0', '0.075', '#interest', '#admiration', 'positive', '0.085', 'stern', 'quick_look', 'glimpse', 'peep', 'stare']
senticnet['peeping_out'] = ['0', '0.096', '0', '0.075', '#interest', '#admiration', 'positive', '0.085', 'stern', 'quick_look', 'glimpse', 'peep', 'stare']
senticnet['peeping_through'] = ['0.979', '-0.69', '0', '0.576', '#joy', '#surprise', 'positive', '0.749', 'big_cheese', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['peer'] = ['0.848', '0.735', '0', '0', '#joy', '#interest', 'positive', '0.791', 'observer', 'peek', 'appearance', 'seer', 'glimpse']
senticnet['peer-to-peer'] = ['0.836', '0.875', '0', '0', '#joy', '#interest', 'positive', '0.855', 'person-to-person', 'human-to-human', 'skin-to-skin', 'cell-to-cell', 'fecal-oral']
senticnet['peer_cryptocurrency'] = ['0.848', '0.735', '0', '0', '#joy', '#interest', 'positive', '0.791', 'observer', 'peek', 'appearance', 'seer', 'glimpse']
senticnet['peer_interaction'] = ['0.848', '0.735', '0', '0', '#joy', '#interest', 'positive', '0.791', 'observer', 'peek', 'appearance', 'seer', 'glimpse']
senticnet['peer_overlay'] = ['0.848', '0.735', '0', '0', '#joy', '#interest', 'positive', '0.791', 'observer', 'peek', 'appearance', 'seer', 'glimpse']
senticnet['peer_overlay_network'] = ['0.848', '0.735', '0', '0', '#joy', '#interest', 'positive', '0.791', 'observer', 'peek', 'appearance', 'seer', 'glimpse']
senticnet['peer_pressure'] = ['-0.15', '0.128', '-0.32', '0', '#sadness', '#fear', 'negative', '-0.11', 'strong_emotion', 'ridicule', 'need_help', 'correct_behavior', 'uncertainty']
senticnet['peer_review'] = ['-0.79', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.75', 'scan', 'checkup', 'medical_checkup', 'medical_examination', 'medical_exam']
senticnet['peerage'] = ['0.976', '-0.5', '0', '0.853', '#joy', '#admiration', 'positive', '0.776', 'knighthood', 'elite', 'elite_group', 'noblesse', 'aristocracy']
senticnet['peering_into'] = ['0', '0.096', '0', '0.075', '#interest', '#admiration', 'positive', '0.085', 'stern', 'quick_look', 'glimpse', 'peep', 'stare']
senticnet['peering_through'] = ['0', '0.096', '0', '0.075', '#interest', '#admiration', 'positive', '0.085', 'stern', 'quick_look', 'glimpse', 'peep', 'stare']
senticnet['peerless'] = ['0.816', '-0.59', '0', '0.947', '#joy', '#admiration', 'positive', '0.787', 'matchless', 'nonpareil', 'unique', 'incomparable', 'unmatchable']
senticnet['peet_coffee'] = ['0.807', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.859', 'bubble', 'steam', 'hot', 'boil_water', 'tea']
senticnet['peeve'] = ['-0.85', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.87', 'grievance', 'irritate', 'annoyance', 'annoy', 'vex']
senticnet['peevish'] = ['0', '0', '-0.73', '-0.83', '#fear', '#disgust', 'negative', '-0.78', 'whining', 'teeny', 'crotched', 'fretful', 'harshen']
senticnet['peevishly'] = ['-0.92', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.95', 'peevish', 'way', 'yammer', 'manner', 'pettishly']
senticnet['peevishness'] = ['-0.99', '0', '0.528', '-0.95', '#sadness', '#disgust', 'negative', '-0.82', 'ill_humor', 'distemper', 'pet', 'testiness', 'touchiness']
senticnet['peewee'] = ['0', '0', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.05', 'human', 'delusional', 'use_illegal_drug', 'busy_person', 'carpenter']
senticnet['peformance'] = ['0.558', '-0.60', '0', '0.779', '#surprise', '#admiration', 'positive', '0.646', 'benefit', 'pianism', 'show', 'routine', 'histrionics']
senticnet['peg'] = ['0.076', '0', '0', '0.104', '#joy', '#admiration', 'positive', '0.09', 'post', 'newspaper_article', 'rail', 'paper_sign', 'column']
senticnet['peg-leg'] = ['0', '0', '-0.79', '-0.74', '#fear', '#disgust', 'negative', '-0.76', 'hobble', 'lame', 'embarrass', 'clop', 'wander']
senticnet['peg_down'] = ['0.519', '-0.55', '0', '0.746', '#surprise', '#admiration', 'positive', '0.606', 'narrow_down', 'concretize', 'nail_down', 'narrow', 'pin_down']
senticnet['peg_leg'] = ['0.793', '0.847', '0', '0', '#joy', '#interest', 'positive', '0.82', 'cross_bun', 'hot_cross_bun', 'bear_paw', 'coffee_roll', 'bun']
senticnet['pegasus_mail'] = ['0.028', '0', '0.027', '0.101', '#joy', '#admiration', 'positive', '0.034', 'animal_dwell', 'bee_home', 'live_place', 'dwell_place', 'place_to_live']
senticnet['pegasus_xl_rocket'] = ['-0.64', '0', '0.524', '-0.59', '#sadness', '#disgust', 'negative', '-0.58', 'bum', 'dreamer', 'daydreamer', 'escapist', 'wishful_thinker']
senticnet['peine_forte_et_dure'] = ['-0.84', '0', '0.987', '-0.77', '#sadness', '#anger', 'negative', '-0.87', 'thorn', 'sticker', 'glochid', 'prickle', 'spikelet']
senticnet['pejorative'] = ['-0.99', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.85', 'oxymoron', 'overstatement', 'misconception', 'misnomer', 'oversimplification']
senticnet['pejorative_nickname'] = ['0.072', '0.244', '-0.20', '0.252', '#interest', '#admiration', 'positive', '0.091', 'doe', 'sweet', 'live_organism', 'love', 'alive']
senticnet['pekan'] = ['0.813', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.895', 'black_cat', 'martes_pennanti', 'fisherman', 'fisher_cat', 'fisher']
senticnet['pekuah'] = ['-0.56', '0', '0.828', '-0.68', '#anger', '#disgust', 'negative', '-0.69', 'abruptness', 'precipitateness', 'precipitousness', 'hastiness', 'precipitancy']
senticnet['pelage'] = ['-0.58', '0', '0.66', '-0.8', '#anger', '#disgust', 'negative', '-0.68', 'bald', 'depilous', 'glabrescent', 'glabrous', 'balding']
senticnet['pele_tower'] = ['0.021', '0.119', '0', '0.129', '#interest', '#admiration', 'positive', '0.09', 'manor', 'palace', 'medieval', 'tower', 'chess_game']
senticnet['pelen'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'dejectedness', 'low-spiritedness', 'lowness', 'downheartedness', 'cheerless']
senticnet['pelf'] = ['0', '0', '-0.74', '-0.97', '#fear', '#disgust', 'negative', '-0.85', 'rig', 'dishonest', 'blandish', 'dishonestly', 'shill']
senticnet['pelfrey'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'dicky', 'impaired', 'lessened', 'diminished']
senticnet['pelican_crossing'] = ['0.508', '-0.72', '0', '0.727', '#surprise', '#admiration', 'positive', '0.654', 'crosswalk', 'pedestrian_crossing', 'school_crossing', 'crossing', 'crossover']
senticnet['pelican_pedestrian_crossing'] = ['0.504', '-0.73', '0', '0.721', '#surprise', '#admiration', 'positive', '0.652', 'crosswalk', 'crossing', 'school_pedestrian_crossing', 'pedestrian_crossing', 'crossover']
senticnet['pelisse'] = ['-0.84', '0', '0.633', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'grizzle', 'postiche', 'horsehair_wig', 'wig', 'peruke']
senticnet['pell_grant'] = ['-0.07', '0', '0.073', '-0.26', '#sadness', '#disgust', 'negative', '-0.13', 'tuition', 'private_tuition', 'pay_tuition', 'enroll_class', 'enroll_school']
senticnet['pell_grant_program'] = ['-0.07', '0', '0.073', '-0.26', '#sadness', '#disgust', 'negative', '-0.13', 'tuition', 'private_tuition', 'pay_tuition', 'enroll_class', 'enroll_school']
senticnet['pell_mell'] = ['-0.88', '0.178', '-0.6', '0', '#sadness', '#fear', 'negative', '-0.43', 'rush_job', 'rush_work', 'late', 'urgency', 'emergency']
senticnet['pellagra'] = ['0', '0', '0.813', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'debility', 'hypochondria', 'neurosis', 'beriberi', 'neurasthenia']
senticnet['pellet_gun'] = ['-0.75', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.75', 'hydrogen_bomb', 'rifle', 'revolver', 'pistol', 'gun']
senticnet['pellet_stove'] = ['0.02', '0.076', '0', '0', '#joy', '#interest', 'positive', '0.048', 'land_earth', 'tidal_wave', 'puddle', 'brine', 'spurt']
senticnet['pellitory'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['pellucid'] = ['0', '0', '-0.78', '-0.78', '#fear', '#disgust', 'negative', '-0.78', 'luculent', 'crystal_clear', 'limpid', 'transparent', 'crystalline']
senticnet['pellucidity'] = ['0.557', '-0.51', '0', '0.59', '#joy', '#admiration', 'positive', '0.553', 'clearcutness', 'explicitness', 'preciseness', 'perspicuity', 'perspicuousness']
senticnet['pelt'] = ['-0.58', '0', '0.517', '-0.59', '#sadness', '#disgust', 'negative', '-0.56', 'fox', 'mink', 'seal', 'astrakhan', 'bearskin']
senticnet['peltata'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['peltate'] = ['0', '0', '0.064', '-0.04', '#anger', '#disgust', 'negative', '-0.05', 'bird_food', 'funiculus', 'plant_product', 'fuzz', 'agricultural_product']
senticnet['pelting_rain'] = ['0.898', '-0.70', '0', '0.712', '#joy', '#admiration', 'positive', '0.773', 'wish', 'reception', 'welcome', 'greeting', 'regard']
senticnet['pelvic_floor_muscle'] = ['0.992', '-0.80', '0', '0.584', '#joy', '#surprise', 'positive', '0.795', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'excretion']
senticnet['pelvic_infection'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'gastroenteritis', 'stomach_flu', 'intestinal_flu', 'inflammatory_disease']
senticnet['pelvic_inflammatory_disease'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'gastroenteritis', 'stomach_flu', 'intestinal_flu', 'inflammatory_disease']
senticnet['pelvic_irradiation'] = ['-0.70', '0', '0.639', '-0.56', '#sadness', '#anger', 'negative', '-0.63', 'fibroma', 'adenoma', 'chondroma', 'enchondroma', 'benign_tumor']
senticnet['pelvic_organ_prolapse'] = ['0.992', '-0.80', '0', '0.584', '#joy', '#surprise', 'positive', '0.795', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'excretion']
senticnet['pelvic_pain'] = ['-0.73', '0', '0.555', '-0.90', '#sadness', '#disgust', 'negative', '-0.73', 'aching', 'hurting', 'labor_pain', 'neuralgia', 'neuralgy']
senticnet['pelvic_surgery'] = ['-0.99', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.78', 'angioedema', 'atrophedema', 'giant_hive', 'periodic_edema', 'dropsy']
senticnet['pemphigus'] = ['-0.97', '0', '0.995', '-0.80', '#sadness', '#anger', 'negative', '-0.92', 'skin_disease', 'disease_of_skin', 'skin_disorder', 'autoimmune_disease', 'autoimmune_disorder']
senticnet['pen'] = ['0', '0.096', '0', '0.075', '#interest', '#admiration', 'positive', '0.085', 'write', 'marker', 'sign_document', 'pencil', 'pencil_sharpener']
senticnet['pen_hadow'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'correspondent', 'humankind']
senticnet['pen_ink'] = ['0', '0.067', '0', '0.077', '#interest', '#admiration', 'positive', '0.072', 'pencil_mark', 'use_ink', 'script', 'pencil_paper', 'written_text']
senticnet['pen_knife'] = ['0.022', '-0.01', '0.062', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'electric_toothbrush', 'moistener', 'hairbrush', 'shampoo_conditioner', 'shave_kit']
senticnet['pen_register'] = ['-0.53', '0', '0.884', '-0.72', '#anger', '#disgust', 'negative', '-0.71', 'bug', 'tap', 'eavesdrop', 'intercept', 'listen']
senticnet['pen_trap'] = ['-0.53', '0', '0.884', '-0.72', '#anger', '#disgust', 'negative', '-0.71', 'bug', 'tap', 'eavesdrop', 'intercept', 'listen']
senticnet['penal'] = ['0', '0', '-1', '-0.88', '#fear', '#disgust', 'negative', '-0.94', 'penalty', 'penalize', 'penalization', 'subject', 'infringement']
senticnet['penal_code_section'] = ['-0.84', '0', '0.745', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'crime', 'against_law', 'revenge', 'vengeance', 'theft']
senticnet['penal_substitution'] = ['-0.82', '0', '0.672', '-0.54', '#sadness', '#anger', 'negative', '-0.68', 'amends', 'damage', 'indemnification', 'redress', 'restitution']
senticnet['penal_substitution_theory'] = ['-0.82', '0', '0.672', '-0.54', '#sadness', '#anger', 'negative', '-0.68', 'amends', 'damage', 'indemnification', 'redress', 'restitution']
senticnet['penal_system'] = ['-0.19', '0', '-0.17', '-0.14', '#sadness', '#fear', 'negative', '-0.17', 'death_penalty', 'die', 'death_destruction', 'kill', 'murder']
senticnet['penalise'] = ['-0.51', '0', '0.946', '-0.99', '#anger', '#disgust', 'negative', '-0.82', 'trammel', 'limit', 'restrict', 'bound', 'confine']
senticnet['penalization'] = ['0', '0', '-1', '-0.88', '#fear', '#disgust', 'negative', '-0.94', 'penalty', 'penal', 'penalize', 'subject', 'infringement']
senticnet['penalize'] = ['0', '0', '-1.0', '-0.87', '#fear', '#disgust', 'negative', '-0.93', 'penalty', 'penal', 'penalization', 'subject', 'infringement']
senticnet['penalty'] = ['-0.91', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.81', 'wrongdoing', 'fee', 'bad_behavior', 'illegal_activity', 'wrong']
senticnet['penalty_shoot'] = ['-0.98', '0', '0.334', '-0.91', '#sadness', '#disgust', 'negative', '-0.74', 'punishment', 'fault', 'probabilty_of_losing', 'yellow_card', 'red_card']
senticnet['penalty_shoot_out'] = ['-0.09', '0.147', '-0.11', '0.205', '#interest', '#admiration', 'positive', '0.036', 'question_starter', 'resolution', 'available', 'explanation', 'request']
senticnet['penalty_shootout'] = ['-0.09', '0.147', '-0.11', '0.205', '#interest', '#admiration', 'positive', '0.036', 'question_starter', 'resolution', 'available', 'explanation', 'request']
senticnet['penance'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitentially', 'penitent', 'repentantly', 'penitence']
senticnet['pencak_silat'] = ['-0.06', '-0.00', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.05', 'madman', 'trouble', 'agony', 'cause_problem', 'river_stream']
senticnet['penchant'] = ['0.558', '0.661', '-0.27', '0.302', '#joy', '#interest', 'positive', '0.313', 'predilection', 'well-disposed', 'friendliness', 'likeable', 'liking']
senticnet['pencil'] = ['0.031', '0.08', '0.023', '0.069', '#interest', '#admiration', 'positive', '0.039', 'marker', 'graphite_pencil', 'pen', 'mechanical_graphite_pencil', 'stamp_pad']
senticnet['pencil_art'] = ['0', '0.11', '0', '0.14', '#interest', '#admiration', 'positive', '0.125', 'create_image', 'artwork', 'sketch', 'use_crayon', 'artistic']
senticnet['pencil_box'] = ['0.038', '0', '0.01', '0.059', '#joy', '#admiration', 'positive', '0.029', 'paper_clip', 'eraser_holder', 'computer_desk', 'electric_pencil_sharpener', 'folder']
senticnet['pencil_eraser'] = ['0.883', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.864', 'stick_eraser', 'ballpoint_pen', 'intern', 'paper_clip', 'eraser_holder']
senticnet['pencil_holder'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'clip', 'clasp', 'glue_stick', 'clothespin', 'clamp']
senticnet['pencil_mark'] = ['0', '0', '0', '0.122', '#admiration', '#admiration', 'positive', '0.122', 'handmade', 'make_book', 'make_newspaper', 'script', 'written_text']
senticnet['pencil_paper'] = ['0.023', '0', '0', '0.066', '#joy', '#admiration', 'positive', '0.044', 'ink_paper', 'ink_cartridge', 'use_ink', 'pencil_mark', 'type_of_document']
senticnet['pencil_pen'] = ['0.055', '-0.01', '0', '0.106', '#joy', '#admiration', 'positive', '0.059', 'attendee', 'agenda', 'learn_write', 'find_paper', 'paper_plane']
senticnet['pencil_sharpener'] = ['0.031', '0.08', '0.023', '0.069', '#interest', '#admiration', 'positive', '0.039', 'pencil', 'pencil_art', 'markert', 'paper_clip', 'electric_pencil_sharpener']
senticnet['pencil_sketch'] = ['0', '0.074', '0', '0.06', '#interest', '#admiration', 'positive', '0.067', 'use_crayon', 'draw', 'pencil_art', 'make_sketch', 'outline']
senticnet['pencil_skirt'] = ['0', '-0.00', '0.047', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'cover_body', 'make_drape', 'weave', 'weave_cloth', 'make_cloth']
senticnet['penclawdd'] = ['-0.64', '0', '0.879', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'drone', 'poke', 'dawdler', 'laggard', 'straggler']
senticnet['pendant'] = ['0.088', '-0.06', '-0.08', '0.126', '#joy', '#admiration', 'positive', '0.05', 'ornament', 'ring', 'statue', 'card', 'yellow_ribbon']
senticnet['pendant_une'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'sexual_relationship', 'liaison', 'affair', 'involvement']
senticnet['pendennis_castle'] = ['0.582', '-0.97', '0', '0.802', '#surprise', '#admiration', 'positive', '0.788', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['pending'] = ['0.795', '-0.99', '0', '0.5', '#joy', '#surprise', 'positive', '0.764', 'li_pendens', 'civil_action', 'counterclaim', 'custody', 'action_at_law']
senticnet['pending_extradition'] = ['-0.08', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.052', 'bequest', 'excise', 'bribe', 'afford', 'owe']
senticnet['pending_lawsuit'] = ['-0.91', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.85', 'lawyer', 'take_court', 'call_lawyer', 'determine_guilt', 'go_to_court']
senticnet['pending_trial'] = ['-0.08', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.052', 'bequest', 'excise', 'bribe', 'afford', 'owe']
senticnet['pendulum'] = ['0.036', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'minute', 'minute_second', 'hour', 'stopwatch', 'second']
senticnet['pendulum_clock'] = ['0', '0', '0.95', '-0.86', '#anger', '#disgust', 'negative', '-0.90', 'watch', 'stopwatch', 'timepiece', 'wristwatch', 'find_pawnshop']
senticnet['penelope_shuttle'] = ['0.947', '-0.99', '0', '0.936', '#joy', '#surprise', 'positive', '0.958', 'utterer', 'deipnosophist', 'interlocutor', 'conversational_partner', 'speaker']
senticnet['peneplain'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'fossil', 'oil', 'dark_liquid', 'smithsonian_institution', 'inert_gas']
senticnet['penetrability'] = ['0.89', '-0.62', '0', '0.548', '#joy', '#surprise', 'positive', '0.686', 'breakthrough', 'interpenetration', 'foray', 'maraud', 'raid']
senticnet['penetrable'] = ['0', '0', '0.921', '-0.87', '#anger', '#disgust', 'negative', '-0.9', 'outback', 'trackless', 'pathless', 'roadless', 'untracked']
senticnet['penetrate'] = ['0.968', '-0.76', '0', '0.583', '#joy', '#surprise', 'positive', '0.773', 'riddle', 'diffuse', 'permeate', 'pervade', 'spiritize']
senticnet['penetrate_deeper'] = ['0.89', '-0.62', '0', '0.548', '#joy', '#surprise', 'positive', '0.686', 'breakthrough', 'interpenetration', 'foray', 'maraud', 'raid']
senticnet['penetrating_injury'] = ['-0.12', '-0.04', '0', '-0.17', '#sadness', '#disgust', 'negative', '-0.08', 'cause_bleed', 'open_wind', 'gash', 'lacerate', 'painful_injury']
senticnet['penetration'] = ['0.89', '-0.62', '0', '0.548', '#joy', '#surprise', 'positive', '0.686', 'breakthrough', 'interpenetration', 'foray', 'maraud', 'raid']
senticnet['penetration_depth'] = ['0.89', '-0.62', '0', '0.548', '#joy', '#surprise', 'positive', '0.686', 'breakthrough', 'interpenetration', 'foray', 'maraud', 'raid']
senticnet['penetration_rate'] = ['0.89', '-0.62', '0', '0.548', '#joy', '#surprise', 'positive', '0.686', 'breakthrough', 'interpenetration', 'foray', 'maraud', 'raid']
senticnet['penetrative'] = ['0.89', '-0.62', '0', '0.548', '#joy', '#surprise', 'positive', '0.686', 'breakthrough', 'interpenetration', 'foray', 'maraud', 'raid']
senticnet['penetrative_sex'] = ['0.733', '0.789', '0', '0', '#joy', '#interest', 'positive', '0.761', 'sex', 'show_empathy', 'safe_sex', 'fuck', 'love_back']
senticnet['penetrator'] = ['0.89', '-0.62', '0', '0.548', '#joy', '#surprise', 'positive', '0.686', 'breakthrough', 'interpenetration', 'foray', 'maraud', 'raid']
senticnet['penguin'] = ['0.86', '0.799', '0', '0', '#joy', '#interest', 'positive', '0.83', 'year', 'think', 'focus', 'analysis', 'impact']
senticnet['penguin_edition'] = ['-0.04', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.08', 'data', 'awareness', 'information', 'saga', 'knowledge']
senticnet['penguinia'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'hardy', 'fearlessness', 'fearlessly', 'unafraid', 'unfearing']
senticnet['penhale_opp_office'] = ['0', '0.935', '0', '0.859', '#interest', '#admiration', 'positive', '0.897', 'office', 'polar_program', 'polly', 'manager', 'award']
senticnet['penicillin'] = ['-0.02', '-0.02', '-0.10', '-0.14', '#fear', '#disgust', 'negative', '-0.06', 'palliative', 'antibiotic', 'ibuprofen', 'analgesic', 'phenobarbital']
senticnet['penicillinase'] = ['-0.69', '0', '0.516', '-0.94', '#sadness', '#disgust', 'negative', '-0.71', 'insubordinate', 'recalcitrant', 'obstreperous', 'defiant', 'resistive']
senticnet['penicillium_marneffei'] = ['0.509', '-0.9', '0', '0.507', '#joy', '#surprise', 'positive', '0.639', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['penile'] = ['-0.74', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.78', 'ejaculate', 'pass_gene', 'ecstasy', 'lower_standard_live', 'survival_species']
senticnet['penile_cancer'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'gastroenteritis', 'stomach_flu', 'intestinal_flu', 'inflammatory_disease']
senticnet['penile_erection'] = ['0.733', '0.789', '0', '0', '#joy', '#interest', 'positive', '0.761', 'sex', 'show_empathy', 'safe_sex', 'fuck', 'love_back']
senticnet['penile_penetration'] = ['0.733', '0.789', '0', '0', '#joy', '#interest', 'positive', '0.761', 'sex', 'show_empathy', 'safe_sex', 'fuck', 'love_back']
senticnet['penile_shaft'] = ['0', '0', '0.021', '-0.06', '#anger', '#disgust', 'negative', '-0.04', 'skin', 'skeletal_system', 'shoulder', 'cuticle', 'groin']
senticnet['peninsula'] = ['-0.95', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.91', 'isolated', 'insular', 'situate', 'surroundings', 'standoffish']
senticnet['penis_envy'] = ['0.833', '0', '0', '0.807', '#joy', '#admiration', 'positive', '0.82', 'lust', 'amiable', 'give_hug', 'giggle', 'smile']
senticnet['penitence'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitentially', 'penitent', 'repentantly', 'repentant']
senticnet['penitent'] = ['0', '0', '0.851', '-0.91', '#anger', '#disgust', 'negative', '-0.88', 'repentant', 'contrite', 'regretful', 'remorseful', 'sorry']
senticnet['penitential'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitentially', 'penitent', 'repentantly', 'repentant']
senticnet['penitentially'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitent', 'repentantly', 'penitence', 'repentant']
senticnet['penitentiary'] = ['-0.75', '0', '0.981', '-0.58', '#sadness', '#anger', 'negative', '-0.77', 'retaliatory', 'punitive', 'correctional', 'retributive', 'retributory']
senticnet['penitently'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitentially', 'penitent', 'repentantly', 'penitence', 'repentant']
senticnet['penknife'] = ['0.818', '-0.95', '0', '0.695', '#joy', '#surprise', 'positive', '0.823', 'jackknife', 'clasp_knife', 'pocket_knife', 'switchblade', 'switchblade_knife']
senticnet['penlight'] = ['0', '-0.03', '0.043', '-0.11', '#anger', '#disgust', 'negative', '-0.03', 'torch', 'safety_pin', 'laser', 'flare', 'firebomb']
senticnet['penmanship'] = ['0.584', '-0.62', '0', '0.815', '#surprise', '#admiration', 'positive', '0.675', 'squiggle', 'hand', 'cacography', 'handwriting', 'script']
senticnet['penn_jillette'] = ['0', '0.186', '0', '0.075', '#interest', '#admiration', 'positive', '0.131', 'money_inside', 'check_account', 'savings', 'money_storage', 'store_money']
senticnet['penn_teller'] = ['0', '0.186', '0', '0.075', '#interest', '#admiration', 'positive', '0.131', 'money_inside', 'check_account', 'savings', 'money_storage', 'store_money']
senticnet['pennate_muscle'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'mathematical', 'muscle', 'math_function', 'feel_sick', 'separation']
senticnet['penncard'] = ['0.739', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.814', 'receipt', 'bet', 'electric_bill', 'letter_slot', 'deal']
senticnet['penne'] = ['0.763', '0.9', '0', '0', '#joy', '#interest', 'positive', '0.832', 'breakfast', 'box_cereal', 'calcium', 'drink', 'carton_milk']
senticnet['penniless'] = ['-0.97', '0', '0.3', '-0.93', '#sadness', '#disgust', 'negative', '-0.73', 'poor', 'need_money', 'homeless', 'hungry', 'bankruptcy']
senticnet['pennisetum'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'meadow_grass', 'grass', 'manna_grass', 'sweet_grass', 'wood_meadowgrass']
senticnet['penny'] = ['0.987', '0.902', '0', '0', '#joy', '#interest', 'positive', '0.945', 'franc', 'dime', 'dollar', 'shilling', 'banknote']
senticnet['penrose_drain'] = ['0', '0', '0', '-0.17', '#disgust', '#disgust', 'negative', '-0.17', 'medicine', 'aspirin', 'blank_check', 'painkiller', 'video_tape']
senticnet['pension_off'] = ['0.661', '-0.82', '0', '0.952', '#surprise', '#admiration', 'positive', '0.811', 'displace', 'fire', 'give_notice', 'retire', 'dismiss']
senticnet['pensionary'] = ['-0.65', '0', '0.808', '-0.69', '#anger', '#disgust', 'negative', '-0.72', 'beneficiary', 'pensioner', 'devisee', 'legatee', 'recipient']
senticnet['pensioner'] = ['-0.65', '0', '0.808', '-0.69', '#anger', '#disgust', 'negative', '-0.72', 'pensionary', 'beneficiary', 'devisee', 'legatee', 'recipient']
senticnet['pensive'] = ['-0.74', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.80', 'pondering', 'ruminative', 'broody', 'pensiveness', 'meditative']
senticnet['pensively'] = ['-0.94', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.91', 'think', 'ruminative', 'broody', 'contemplative', 'meditative']
senticnet['pensiveness'] = ['-0.33', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.33', 'worry', 'worried', 'think_too_much', 'anxious', 'anxiety']
senticnet['penstock'] = ['-0.77', '0', '0.583', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'rain', 'rain_down', 'sheet', 'rain_bucket', 'rain_buckets_down']
senticnet['pent_up_anger'] = ['0.997', '-0.78', '0', '0.504', '#joy', '#surprise', 'positive', '0.762', 'dissatisfaction', 'disgruntlement', 'dysphoria', 'longing', 'yearning']
senticnet['pent_up_consumer'] = ['0.749', '0.764', '0', '0', '#joy', '#interest', 'positive', '0.756', 'obedience', 'order', 'requirement', 'permission', 'necessary']
senticnet['pent_up_demand'] = ['0', '0.285', '-0.07', '0.163', '#interest', '#admiration', 'positive', '0.124', 'plus', 'importance', 'a_lot', 'bonus', 'extra']
senticnet['pent_up_energy'] = ['0.958', '-0.72', '0', '0.72', '#joy', '#surprise', 'positive', '0.801', 'pressor', 'hormone', 'endocrine', 'internal_secretion', 'catecholamine']
senticnet['pent_up_rage'] = ['0', '0', '1', '0', '#anger', '#anger', 'negative', '-1', 'furious', 'raging', 'stormy', 'roaring', 'wrathful']
senticnet['pentachord'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['pentagon'] = ['0.067', '0.155', '0', '0.121', '#interest', '#admiration', 'positive', '0.114', 'vicinity', 'parallelogram', 'hexagon', 'trapezoid', 'small_room']
senticnet['pentagonal_bipyramid'] = ['0.641', '-0.6', '0', '0.957', '#joy', '#admiration', 'positive', '0.733', 'ideal_solid', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'regular_octahedron']
senticnet['pentagram_map'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'fragmentation', 'discharge', 'backfire', 'blast']
senticnet['pentahydrate'] = ['-0.81', '0', '0.774', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'acid', 'hypophosphoric_acid', 'periodic_acid', 'oxyacid', 'phosphorous_acid']
senticnet['pentair'] = ['0.111', '0.18', '0', '0.128', '#interest', '#admiration', 'positive', '0.14', 'corporation', 'partnership', 'large_business', 'business_organization', 'enterprise']
senticnet['pentanal'] = ['-0.67', '0', '0.503', '-0.67', '#sadness', '#disgust', 'negative', '-0.61', 'stink_up', 'smell_up', 'stink_out', 'incense', 'perfume']
senticnet['pentandra'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'carnauba_palm', 'carnauba', 'wax_palm', 'palmetto', 'palmyra']
senticnet['pentane'] = ['-0.67', '0', '0.503', '-0.67', '#sadness', '#disgust', 'negative', '-0.61', 'stink_up', 'smell_up', 'stink_out', 'incense', 'perfume']
senticnet['pentaprism'] = ['-0.82', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.84', 'block', 'obstruct', 'close', 'clogged', 'occluded']
senticnet['pentaquark'] = ['0.022', '0.073', '0', '0.14', '#interest', '#admiration', 'positive', '0.078', 'elementary_particle', 'electron', 'proton_neutron', 'neutron', 'proton']
senticnet['pentatomidae'] = ['-0.77', '0', '0.576', '-0.92', '#sadness', '#disgust', 'negative', '-0.75', 'insect', 'firebug', 'water_bug', 'assassin_bug', 'heteropterous_insect']
senticnet['pentazocine'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['pentecostal'] = ['-0.74', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.83', 'non-denominational', 'inter-denominational', 'interdenominational', 'non-religious', 'nondenominational']
senticnet['penthouse'] = ['0.591', '-0.74', '0', '0.672', '#surprise', '#admiration', 'positive', '0.669', 'room', 'chillax', 'hotel_room', 'furniture_store', 'living_room']
senticnet['penthouse_apartment'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'home', 'furniture', 'kitchen', 'flat']
senticnet['penthouse_magazine'] = ['-0.98', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.89', 'fairy', 'genus', 'honey_bee', 'infant', 'dove']
senticnet['penthouse_pet'] = ['-0.98', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.89', 'fairy', 'genus', 'honey_bee', 'infant', 'dove']
senticnet['penthouse_suite'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'lurking_place', 'spot', 'drop', 'hiding_place', 'den']
senticnet['pentium'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'computer_part', 'system', 'cpu', 'pencil_pen', 'monitor']
senticnet['pentose'] = ['-0.52', '0', '0.681', '-0.76', '#anger', '#disgust', 'negative', '-0.65', 'triose', 'tetrose', 'simple_sugar', 'hexose', 'aldose']
senticnet['pentylenetetrazol'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['penultimate'] = ['0.033', '0.158', '-0.06', '0.145', '#interest', '#admiration', 'positive', '0.067', 'finish', 'end', 'finish_complete', 'end_point', 'finalize']
senticnet['penultimate_episode'] = ['0.918', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.827', 'encounter', 'reveal', 'programme', 'lose', 'sequel']
senticnet['penultimate_hurdle'] = ['0', '-0.04', '0.085', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'claw', 'fur_keep_warm', 'tic', 'bristle', 'muzzle']
senticnet['penultimate_lap'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['penultimate_scene'] = ['-0.69', '0', '0.508', '-0.93', '#sadness', '#disgust', 'negative', '-0.71', 'dark', 'darkness', 'light', 'shadow', 'site']
senticnet['penurious'] = ['-0.91', '0', '0.598', '-0.52', '#sadness', '#anger', 'negative', '-0.68', 'parsimonious', 'stingy', 'scrimy', 'beggarly', 'chintzy']
senticnet['penury'] = ['-0.98', '0', '0.802', '-0.88', '#sadness', '#disgust', 'negative', '-0.88', 'poverty', 'impoverishment', 'poorness', 'beggary', 'mendicancy']
senticnet['penzance_borough_police'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'law_enforcement_agency', 'law', 'agency']
senticnet['peochar'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'webpage', 'store_record', 'ideal']
senticnet['peon'] = ['-0.24', '-0.03', '-0.05', '-0.13', '#sadness', '#disgust', 'negative', '-0.09', 'laborer', 'worker', 'work_much', 'drudgery', 'occupation']
senticnet['peonage'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'vassalage']
senticnet['people'] = ['0.092', '0.114', '0', '0.108', '#interest', '#admiration', 'positive', '0.105', 'team_up', 'group_of_people', 'crowd', 'mass_person', 'individual']
senticnet['people_claiming_jobseeker'] = ['0.924', '0.968', '0', '0.876', '#joy', '#interest', 'positive', '0.923', 'live_simply', 'help_out', 'money', 'accolade', 'generous']
senticnet['people_cndp'] = ['-0.85', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.86', 'attack', 'raid', 'fight_against', 'conflict', 'opposition']
senticnet['people_sleeping_rough'] = ['-0.88', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.81', 'bum', 'red_light', 'petition', 'take', 'violent_action']
senticnet['peoplehood'] = ['0.122', '0.159', '-0.05', '0.217', '#interest', '#admiration', 'positive', '0.111', 'profile_picture', 'unity', 'country_person', 'unite', 'signature']
senticnet['pep_rally'] = ['0.669', '-0.58', '0', '0.803', '#joy', '#admiration', 'positive', '0.685', 'rally', 'revival', 'revival_meeting', 'gathering', 'assemblage']
senticnet['pep_up'] = ['0.655', '0.635', '-0.23', '0.3', '#joy', '#interest', 'positive', '0.338', 'cheerfully', 'cheerful', 'jolly_up', 'hearten', 'chirk_up']
senticnet['pepcid'] = ['0', '0', '-0.70', '-0.76', '#fear', '#disgust', 'negative', '-0.73', 'choke', 'become_full', 'get_full', 'burp', 'sickness']
senticnet['pepcid_ac'] = ['0', '0', '-0.70', '-0.76', '#fear', '#disgust', 'negative', '-0.73', 'choke', 'become_full', 'get_full', 'burp', 'sickness']
senticnet['peplum'] = ['0.522', '-0.82', '0', '0.568', '#surprise', '#admiration', 'positive', '0.638', 'adornment', 'goffer', 'gauffer', 'jabot', 'ruffle']
senticnet['peponi'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'beachcomber', 'bum', 'drifter']
senticnet['pepper'] = ['-0.91', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.89', 'olive', 'garlic', 'olive', 'lemon', 'butter']
senticnet['pepper_ghost'] = ['-0.88', '0', '0.589', '-0.85', '#sadness', '#disgust', 'negative', '-0.77', 'phantom', 'shadow', 'disbelief', 'dark_magic', 'fool']
senticnet['pepper_mill'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'neat', 'kitchen_drawer', 'cutlery_drawer', 'dine_table', 'table']
senticnet['pepper_sauce'] = ['0.076', '0', '0.037', '0.065', '#joy', '#admiration', 'positive', '0.035', 'serve_cold', 'tasty_good', 'tasty', 'sour', 'high_calorie']
senticnet['pepper_spray'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'capsaicin', 'gingerol', 'piperine', 'chemical_irritant']
senticnet['peppermint'] = ['0.965', '0.775', '0', '0.968', '#joy', '#admiration', 'positive', '0.903', 'snot', 'bad_taste', 'mouth_part', 'beautiful_smile', 'near_mouth']
senticnet['peppermint_lounge'] = ['0.966', '0.921', '0', '0.892', '#joy', '#interest', 'positive', '0.926', 'disco', 'nightclub', 'shop_complex', 'dance_club', 'fast_food_restaurant']
senticnet['peppermint_oil'] = ['0.701', '-0.76', '0', '0.904', '#surprise', '#admiration', 'positive', '0.791', 'curative', 'heal', 'medicine', 'get_cured', 'take_care']
senticnet['peppery'] = ['-0.76', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.83', 'spicy', 'tangy', 'spiciness', 'pungent', 'piquant']
senticnet['peppery_spice'] = ['-0.78', '0', '0.542', '-0.94', '#sadness', '#disgust', 'negative', '-0.75', 'taste', 'taste_sensation', 'gustatory_sensation', 'taste_perception', 'gustatory_perception']
senticnet['peppery_taste'] = ['0.112', '0', '0', '0.141', '#joy', '#admiration', 'positive', '0.127', 'sweet', 'sweet_food', 'bake_good', 'round_pastry', 'eatable']
senticnet['peppy'] = ['0.65', '-0.61', '0', '0.711', '#joy', '#admiration', 'positive', '0.658', 'bouncing', 'scintillating', 'spirited', 'lively', 'zippy']
senticnet['pepsi'] = ['0.964', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.964', 'carbonated_drink', 'sugar_free', 'coke', 'diet_coke', 'beverage']
senticnet['pepsi_cola'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'glass_water', 'refreshed', 'smoothie', 'lemonade', 'cold_water']
senticnet['pepsi_cola_company'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'glass_water', 'refreshed', 'smoothie', 'lemonade', 'cold_water']
senticnet['pepsi_max'] = ['0.964', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.964', 'carbonated_drink', 'sugar_free', 'coke', 'diet_coke', 'beverage']
senticnet['pepsico_inc'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'glass_water', 'refreshed', 'smoothie', 'lemonade', 'cold_water']
senticnet['peptic_ulcer'] = ['-0.71', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.73', 'pain', 'gape', 'sunburn', 'ouch', 'cut_skin']
senticnet['peptic_ulcer_disease'] = ['-0.95', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.89', 'chronic_gastritis', 'rubor', 'redness', 'gastritis', 'inflammation']
senticnet['peptic_ulceration'] = ['-0.95', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.89', 'chronic_gastritis', 'rubor', 'redness', 'gastritis', 'inflammation']
senticnet['peptide'] = ['0.779', '0.765', '0', '0', '#joy', '#interest', 'positive', '0.772', 'endorphin', 'fibrinopeptide', 'polypeptide', 'amide', 'protein']
senticnet['peptide_cgrp'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'gastrointestinal_hormone', 'secretin', 'ghrelin']
senticnet['peptide_hormone'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'gastrointestinal_hormone', 'secretin', 'ghrelin']
senticnet['peptide_toxin'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'giant_hornet', 'hornet', 'hornet_wasp']
senticnet['peptide_yy'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'cholecystokinin', 'secretin', 'gastrointestinal_hormone']
senticnet['peptide_yy_pyy'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'cholecystokinin', 'secretin', 'gastrointestinal_hormone']
senticnet['pepto_bismol'] = ['-0.71', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.77', 'spill_milk', 'read_paper', 'drop_food', 'smell_food', 'choke']
senticnet['pequeo_black_warrior'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'find_out', 'dig_out', 'unearth', 'become_aware', 'dish_out']
senticnet['per'] = ['0.052', '0', '0', '0.322', '#joy', '#admiration', 'positive', '0.187', 'a_lot', 'abundance', 'plenty', 'superlative', 'comparative']
senticnet['per-computer'] = ['0', '0', '-0.87', '-0.75', '#fear', '#disgust', 'negative', '-0.81', 'non-restrictive', 'appositive', 'python-like', 'nonrestrictive', 'non-deadly']
senticnet['per_acre'] = ['0', '0.122', '0', '0.123', '#interest', '#admiration', 'positive', '0.122', 'acre', 'cow_home', 'crop_place', 'big_land', 'animal_house']
senticnet['per_areole'] = ['-0.50', '0', '0.573', '-0.89', '#anger', '#disgust', 'negative', '-0.65', 'petallike', 'bowery', 'leafy', 'unifoliate', 'bifoliate']
senticnet['per_barrel'] = ['0', '0', '-0.06', '0', '#fear', '#fear', 'negative', '-0.06', 'greasy', 'car_gas', 'fossil_fuel', 'liquid_fuel', 'lubrication']
senticnet['per_bend_wavy'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'dejectedness', 'low-spiritedness', 'lowness', 'downheartedness', 'cheerless']
senticnet['per_bushel'] = ['0.868', '-0.84', '0', '0.673', '#joy', '#surprise', 'positive', '0.796', 'instrument', 'option', 'derivative_instrument', 'legal_document', 'legal_instrument']
senticnet['per_cent'] = ['0.834', '0.932', '0', '0', '#joy', '#interest', 'positive', '0.883', 'dime', 'nickel', 'metal_money', 'coin', 'money']
senticnet['per_cylinder'] = ['0.042', '-0.02', '0.035', '0.065', '#joy', '#admiration', 'positive', '0.023', 'food_container', 'metal_cylinder', 'metal_container', 'small_ball', 'storage_container']
senticnet['per_day'] = ['0.052', '0', '0', '0.322', '#joy', '#admiration', 'positive', '0.187', 'a_lot', 'abundance', 'plenty', 'superlative', 'comparative']
senticnet['per_diem'] = ['0.924', '0.968', '0', '0.876', '#joy', '#interest', 'positive', '0.923', 'live_simply', 'help_out', 'money', 'accolade', 'generous']
senticnet['per_formance'] = ['0.558', '-0.60', '0', '0.779', '#surprise', '#admiration', 'positive', '0.646', 'benefit', 'pianism', 'show', 'routine', 'histrionics']
senticnet['per_fortnight'] = ['0.924', '0.968', '0', '0.876', '#joy', '#interest', 'positive', '0.923', 'live_simply', 'help_out', 'money', 'accolade', 'generous']
senticnet['per_impossibile'] = ['-0.92', '0', '0.547', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'assumption', 'boundary_condition', 'provision', 'proviso', 'precondition']
senticnet['per_inhabitant'] = ['0', '0.075', '0', '0.159', '#interest', '#admiration', 'positive', '0.117', 'maximum', 'max', 'utmost', 'top', 'extreme']
senticnet['per_litre'] = ['0.068', '0', '0.034', '0.076', '#joy', '#admiration', 'positive', '0.037', 'gallon', 'liquid_measurement', 'ounce', 'measurement', 'quart']
senticnet['per_lucha_libre'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'find_out', 'dig_out', 'unearth', 'become_aware', 'dish_out']
senticnet['per_month'] = ['-0.87', '0', '0', '-0.86', '#sadness', '#disgust', 'negative', '-0.87', 'severally', 'number', 'effect', 'respectively', 'material_research']
senticnet['per_octave'] = ['-0.65', '0', '0.681', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'tone', 'whole_tone', 'step', 'whole_step', 'musical_interval']
senticnet['per_se'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'vocabulary', 'read_material', 'book_genre', 'many_words', 'book_library']
senticnet['per_se_condemnation'] = ['0.658', '-0.52', '0', '0.626', '#joy', '#admiration', 'positive', '0.602', 'point_in_time', 'threshold', 'birth', 'commencement', 'incipience']
senticnet['per_se_rule'] = ['0.5', '-0.9', '0', '0.525', '#surprise', '#admiration', 'positive', '0.642', 'manageable', 'controllable', 'governable', 'steerable', 'dirigible']
senticnet['per_semester'] = ['0.295', '0.239', '0.049', '0.346', '#joy', '#admiration', 'positive', '0.208', 'absolute', 'complete', 'infinite', 'total', 'stuffed']
senticnet['per_steradian'] = ['-0.63', '0', '0.897', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'cd-rom', 'international_candle', 'candle', 'candlepower_unit', 'standard_candle']
senticnet['per_unit'] = ['0.052', '0', '0', '0.322', '#joy', '#admiration', 'positive', '0.187', 'a_lot', 'abundance', 'plenty', 'superlative', 'comparative']
senticnet['per_week'] = ['0.295', '0.239', '0.049', '0.346', '#joy', '#admiration', 'positive', '0.208', 'absolute', 'complete', 'infinite', 'total', 'stuffed']
senticnet['per_year'] = ['-0.85', '0', '0.584', '-0.79', '#sadness', '#disgust', 'negative', '-0.74', 'age', 'old_age', 'second_childhood', 'eld', 'geezerhood']
senticnet['peracetic_acid'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
senticnet['peradventure'] = ['-0.04', '0.268', '-0.06', '0.227', '#interest', '#admiration', 'positive', '0.097', 'proposition', 'abstract', 'get_rid', 'theoretical', 'business_transaction']
senticnet['perambulate'] = ['-0.14', '0.216', '-0.06', '0.162', '#interest', '#admiration', 'positive', '0.042', 'give_signal', 'change_position', 'walk_around', 'rub_eye', 'pull_out']
senticnet['percave'] = ['-0.63', '0', '0.683', '-0.96', '#anger', '#disgust', 'negative', '-0.76', 'slovenly_woman', 'slut', 'trollop', 'slattern', 'litterer']
senticnet['perceiue'] = ['-0.11', '0.112', '0.674', '-0.66', '#anger', '#disgust', 'negative', '-0.33', 'wrathfully', 'wrathful', 'wrothful', 'lividly', 'lividity']
senticnet['perceivable'] = ['0.744', '-0.76', '0', '0.669', '#joy', '#surprise', 'positive', '0.726', 'accessible', 'fathomable', 'approachable', 'comprehendible', 'intelligible']
senticnet['perceive'] = ['-0.05', '0.169', '-0.12', '0.174', '#interest', '#admiration', 'positive', '0.041', 'feel', 'sense', 'perception', 'empathize', 'look']
senticnet['perceived_lack'] = ['-0.50', '0', '0.807', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'powerlessness', 'impotence', 'impotency', 'inefficacy', 'inefficaciousness']
senticnet['perceived_self_efficacy'] = ['0.854', '-0.79', '0', '0.977', '#joy', '#admiration', 'positive', '0.875', 'profitableness', 'auspiciousness', 'luck', 'advantageousness', 'positivity']
senticnet['perceived_similarity'] = ['0.817', '-0.62', '0', '0.62', '#joy', '#surprise', 'positive', '0.688', 'quality', 'fascination', 'allurement', 'allure', 'temptingness']
senticnet['perceived_stigma'] = ['-0.77', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'mark', 'emblem', 'religious_sign', 'stain', 'brand']
senticnet['perceived_susceptibility'] = ['0.854', '-0.79', '0', '0.977', '#joy', '#admiration', 'positive', '0.875', 'profitableness', 'auspiciousness', 'luck', 'advantageousness', 'positivity']
senticnet['perceiver'] = ['-0.83', '0', '0.665', '-0.51', '#sadness', '#anger', 'negative', '-0.67', 'percipient', 'eavesdropper', 'auditor', 'observer', 'beholder']
senticnet['percent'] = ['0.834', '0.932', '0', '0', '#joy', '#interest', 'positive', '0.883', 'dime', 'nickel', 'metal_money', 'coin', 'money']
senticnet['percent_abv'] = ['0', '0.091', '-0.05', '0.073', '#interest', '#admiration', 'positive', '0.037', 'privately', 'hold_liquid', 'make_wish', 'rinse_dish', 'find_toilet']
senticnet['percent_annualized_rate'] = ['0.834', '0.932', '0', '0', '#joy', '#interest', 'positive', '0.883', 'dime', 'nickel', 'metal_money', 'coin', 'money']
senticnet['percent_coinsurance'] = ['0.687', '-0.98', '0', '0.666', '#joy', '#surprise', 'positive', '0.778', 'scale', 'scale_of_measurement', 'graduated_table', 'ordered_series', 'wage_scale']
senticnet['percent_tax_bracket'] = ['0.763', '-0.67', '0', '0.945', '#joy', '#admiration', 'positive', '0.795', 'set', 'income_bracket', 'bracket', 'income', 'price_bracket']
senticnet['percentage'] = ['-0.04', '0.315', '-0.05', '0.161', '#interest', '#admiration', 'positive', '0.094', 'payment_amount', 'money_relate', 'money_payment', 'monetary', 'money_charge']
senticnet['percentile_score'] = ['0.926', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.916', 'score_goal', 'accomplish', 'finalize', 'pot', 'make_point']
senticnet['percept'] = ['0.693', '-0.52', '0', '0.82', '#joy', '#admiration', 'positive', '0.681', 'embodiment', 'shape', 'representation', 'concrete_representation', 'internal_representation']
senticnet['perceptibility'] = ['0.685', '-0.91', '0', '0.566', '#joy', '#surprise', 'positive', '0.721', 'audibility', 'audibleness', 'visibleness', 'visibility', 'physical_property']
senticnet['perceptible'] = ['0.985', '-0.81', '0', '0.673', '#joy', '#surprise', 'positive', '0.824', 'discernible', 'noticeable', 'detectable', 'palpable', 'perceivable']
senticnet['perception'] = ['-0.04', '0.143', '-0.06', '0.128', '#interest', '#admiration', 'positive', '0.041', 'perceive', 'see', 'look', 'feeling', 'ship_part']
senticnet['perceptive'] = ['0', '0.738', '0', '0.802', '#interest', '#admiration', 'positive', '0.77', 'apprehensive', 'eagle-eyed', 'knowledgeable', 'locality', 'monochromatic']
senticnet['perceptively'] = ['0', '0.764', '0', '0.845', '#interest', '#admiration', 'positive', '0.804', 'empathetically', 'empathic', 'empathetic', 'kind-heartedness', 'kindheartedness']
senticnet['perceptiveness'] = ['0', '-0.72', '0', '0.885', '#surprise', '#admiration', 'positive', '0.806', 'acumen', 'cleverness', 'sharp', 'knowledge', 'learning']
senticnet['perceptual'] = ['0.693', '-0.52', '0', '0.82', '#joy', '#admiration', 'positive', '0.681', 'embodiment', 'shape', 'representation', 'concrete_representation', 'internal_representation']
senticnet['perceptual_experience'] = ['0.693', '-0.52', '0', '0.82', '#joy', '#admiration', 'positive', '0.681', 'embodiment', 'shape', 'representation', 'concrete_representation', 'internal_representation']
senticnet['perceptual_motor'] = ['0.874', '-0.73', '0', '0.608', '#joy', '#surprise', 'positive', '0.74', 'growth', 'growing', 'maturation', 'development', 'ontogeny']
senticnet['perceptual_psychology'] = ['-0.02', '0', '-0.04', '-0.03', '#fear', '#disgust', 'negative', '-0.03', 'studio', 'art_piece', 'art_gallery', 'artist', 'museum']
senticnet['perceptually'] = ['0', '0.76', '0', '0.809', '#interest', '#admiration', 'positive', '0.784', 'tonally', 'compositionally', 'semantically', 'spatially', 'temporally']
senticnet['perch_fishing'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'divergent_strabismus', 'exotropia', 'squint', 'abnormality', 'abnormalcy']
senticnet['perchance'] = ['-0.04', '0.268', '-0.06', '0.227', '#interest', '#admiration', 'positive', '0.097', 'proposition', 'abstract', 'get_rid', 'theoretical', 'business_transaction']
senticnet['perched_upon'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['percher'] = ['-0.92', '0', '0.64', '-0.52', '#sadness', '#anger', 'negative', '-0.69', 'perching_bird', 'animal_order', 'thyreophoran', 'marginocephalian', 'euronithopod']
senticnet['perching_bird'] = ['-0.92', '0', '0.64', '-0.52', '#sadness', '#anger', 'negative', '-0.69', 'animal_order', 'percher', 'thyreophoran', 'marginocephalian', 'euronithopod']
senticnet['perciform_fish'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'puffer', 'pufferfish', 'blowfish', 'triggerfish', 'plectognath']
senticnet['percipient'] = ['-0.83', '0', '0.665', '-0.51', '#sadness', '#anger', 'negative', '-0.67', 'auditor', 'eavesdropper', 'perceiver', 'observer', 'beholder']
senticnet['percival_gull'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'cheapjack', 'pitchman']
senticnet['percocet'] = ['0.138', '0.103', '0', '0.095', '#joy', '#interest', 'positive', '0.112', 'masturbation', 'sexual_intercourse', 'sex', 'warm_fuzzy_feeling', 'fuck']
senticnet['percussion'] = ['0', '-0.02', '0.055', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'djembe', 'make_musicr', 'use_jazz_music', 'tune_instrument', 'drummer']
senticnet['percussion_instrument'] = ['0.821', '-0.53', '0', '0.785', '#joy', '#admiration', 'positive', '0.712', 'fife', 'nose_flute', 'piccolo', 'woodwind', 'flute']
senticnet['percussionist'] = ['0', '-0.02', '0.055', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'djembe', 'make_musicr', 'use_jazz_music', 'tune_instrument', 'percussion']
senticnet['percussive'] = ['-0.52', '0', '0.902', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'discordant', 'inharmonious', 'unresolved', 'false', 'disharmonious']
senticnet['perdition'] = ['-0.70', '0', '0.566', '-0.95', '#sadness', '#disgust', 'negative', '-0.74', 'hellfire', 'red_region', 'imaginary_place', 'mythical_place', 'fictitious_place']
senticnet['perdurable'] = ['-0.95', '0', '0.799', '-0.67', '#sadness', '#anger', 'negative', '-0.81', 'imputrescible', 'undestroyable', 'indestructible', 'durable', 'imperishable']
senticnet['peregrine_falcon'] = ['-0.74', '0', '0.963', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['peremptorily'] = ['-0.91', '0', '0.521', '-0.81', '#sadness', '#disgust', 'negative', '-0.74', 'bullying', 'domineering', 'blustery', 'roughshod', 'cavalier']
senticnet['peremptory'] = ['-0.91', '0', '0.521', '-0.81', '#sadness', '#disgust', 'negative', '-0.74', 'bullying', 'domineering', 'blustery', 'roughshod', 'cavalier']
senticnet['perenelle'] = ['0', '0.887', '0', '0.973', '#interest', '#admiration', 'positive', '0.93', 'servant', 'art_of_transmuting_metal', 'stone', 'alchemy', 'philosopher']
senticnet['perenne'] = ['-0.56', '0', '0.892', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'borecole', 'colewort', 'kail', 'cole', 'kale']
senticnet['perennial'] = ['0', '0.07', '0', '0.071', '#interest', '#admiration', 'positive', '0.071', 'tree_flower', 'bloom', 'blossom', 'poppy', 'greenery']
senticnet['perennial_border'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'livelong', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['perennial_flowering_plant'] = ['0.598', '-0.72', '0', '0.941', '#surprise', '#admiration', 'positive', '0.753', 'sicklepod', 'tower_rock_cress', 'tower_mustard', 'rock_cress', 'rock_cress_plant']
senticnet['perennial_garden'] = ['0.427', '0.227', '0', '0.37', '#joy', '#admiration', 'positive', '0.341', 'taken_seriously', 'complement', 'much_need', 'remission', 'knowledgeable']
senticnet['perennial_herb'] = ['-0.80', '0', '0.555', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'bush', 'wormwood', 'milkwort', 'germander', 'centaury']
senticnet['perennial_herb_up'] = ['-0.80', '0', '0.555', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'bush', 'wormwood', 'milkwort', 'germander', 'centaury']
senticnet['perennial_herbaceous_plant'] = ['-0.80', '0', '0.555', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'bush', 'wormwood', 'milkwort', 'germander', 'centaury']
senticnet['perennial_plant'] = ['-0.80', '0', '0.555', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'bush', 'wormwood', 'milkwort', 'germander', 'centaury']
senticnet['perennial_problem'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'problem', 'serious_issue', 'overrate', 'prohibitionist_society', 'blade_weapon']
senticnet['perennial_shrub'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'livelong', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['perennial_vine'] = ['0', '0', '-0.14', '-0.03', '#fear', '#disgust', 'negative', '-0.09', 'pine_cone', 'fallen_leaf', 'poison_ivy', 'detritus', 'tress']
senticnet['perfect'] = ['0', '0.794', '0', '0.941', '#interest', '#admiration', 'positive', '0.867', 'best', 'excellent', 'goodness', 'greatness', 'great']
senticnet['perfect_accompaniment'] = ['0.721', '-0.78', '0', '0', '#joy', '#surprise', 'positive', '0.754', 'delicious', 'luscious', 'pleasant-tasting', 'scrumptious', 'toothsome']
senticnet['perfect_composure'] = ['-0.80', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'difficulty', 'awkwardness', 'flea_bite', 'difficultness', 'cumbersomeness']
senticnet['perfect_excuse'] = ['0.857', '-0.68', '0', '0.971', '#joy', '#admiration', 'positive', '0.839', 'revitalize', 'refurbish', 'reconstruct', 'renovate', 'renew']
senticnet['perfect_foil'] = ['0.582', '-0.78', '0', '0.795', '#surprise', '#admiration', 'positive', '0.72', 'elfish', 'elvish', 'playful', 'frisky', 'sportive']
senticnet['perfect_getaway'] = ['0.024', '0.152', '-0.06', '0.236', '#interest', '#admiration', 'positive', '0.087', 'vacation', 'exit', 'stance', 'take_break', 'holiday']
senticnet['perfect_harmony'] = ['0.826', '-0.71', '0', '0.913', '#joy', '#admiration', 'positive', '0.819', 'true', 'pure', 'symphonious', 'harmonical', 'harmonized']
senticnet['perfect_pitch'] = ['0.785', '-0.99', '0', '0.981', '#surprise', '#admiration', 'positive', '0.92', 'ear', 'absolute_pitch', 'audition', 'modality', 'sense_modality']
senticnet['perfect_score'] = ['0.926', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.916', 'score_goal', 'accomplish', 'finalize', 'pot', 'make_point']
senticnet['perfect_skill'] = ['0.979', '0.887', '0', '0.825', '#joy', '#interest', 'positive', '0.897', 'play_tune', 'play_stringed_instrument', 'make_music', 'annoy_mom', 'produce_musical_sound']
senticnet['perfect_sphere'] = ['0', '0.794', '0', '0.941', '#interest', '#admiration', 'positive', '0.867', 'round_shape', 'circle', 'perfect', 'perfection', 'sphere']
senticnet['perfect_surface'] = ['0.022', '0.069', '0.027', '0.059', '#interest', '#admiration', 'positive', '0.031', 'plane', 'flat_land', 'flat_surface', 'crockery', 'plate']
senticnet['perfect_tonic'] = ['0.857', '-0.68', '0', '0.971', '#joy', '#admiration', 'positive', '0.839', 'revitalize', 'refurbish', 'reconstruct', 'renovate', 'renew']
senticnet['perfect_world'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'mythical', 'tale', 'dictionary', 'magic', 'fairy_tale']
senticnet['perfectibility'] = ['0.946', '-0.64', '0', '0.602', '#joy', '#surprise', 'positive', '0.73', 'grasp', 'capableness', 'imcapableness', 'reach', 'aptitude']
senticnet['perfection'] = ['0', '0.706', '0', '0.934', '#interest', '#admiration', 'positive', '0.82', 'value', 'good_value', 'ideal', 'flawless', 'ultimate']
senticnet['perfectionalist'] = ['0.84', '0', '0', '0.911', '#joy', '#admiration', 'positive', '0.875', 'purist', 'traditionalist', 'formalist', 'hard-core', 'pragmatist']
senticnet['perfectionist'] = ['0.775', '0', '0', '0.963', '#joy', '#admiration', 'positive', '0.869', 'over-achiever', 'underachiever', 'overachiever', 'procrastinator', 'overachieving']
senticnet['perfectly'] = ['0.899', '0', '0', '0.888', '#joy', '#admiration', 'positive', '0.893', 'absolutely', 'dead', 'utterly', 'think', 'well']
senticnet['perfectly_acceptable'] = ['0.054', '0.221', '-0.12', '0.236', '#interest', '#admiration', 'positive', '0.098', 'abstract', 'exclamation', 'versatile', 'nice', 'subjective']
senticnet['perfectly_calm'] = ['-0.71', '0', '0.906', '-0.93', '#anger', '#disgust', 'negative', '-0.85', 'absent', 'forgetful', 'distracted', 'inattentive', 'scatty']
senticnet['perfectly_capable'] = ['0.028', '0.138', '-0.04', '0.215', '#interest', '#admiration', 'positive', '0.083', 'skilled', 'expert', 'tin', 'dense_water', 'metal_container']
senticnet['perfectly_circular'] = ['0.034', '-0.07', '0.068', '0.103', '#surprise', '#admiration', 'positive', '0.035', 'use_sport', 'bouncy', 'globe', 'orb', 'disc_shaped']
senticnet['perfectly_clear'] = ['-0.66', '0', '0.721', '-0.53', '#sadness', '#anger', 'negative', '-0.64', 'unlimited', 'outright', 'unconditional', 'unqualified', 'unqualifiedal']
senticnet['perfectly_consistent'] = ['-0.97', '0', '0.781', '-0.91', '#sadness', '#disgust', 'negative', '-0.88', 'weakness', 'fault', 'flaw', 'defect', 'wart']
senticnet['perfectly_flat'] = ['0.022', '0.069', '0.027', '0.059', '#interest', '#admiration', 'positive', '0.031', 'perfect_surface', 'plane', 'flat_land', 'flat', 'floor']
senticnet['perfectly_happy'] = ['0.894', '0', '0', '0', '#joy', '#joy', 'positive', '0.894', 'happiness', 'good_feeling', 'happy_feeling', 'joy', 'good_emotion']
senticnet['perfectly_legal'] = ['0', '0', '0.76', '-0.92', '#anger', '#disgust', 'negative', '-0.84', 'ambiguity', 'prevarication', 'equivocation', 'unclearness', 'equivocalness']
senticnet['perfectly_ripe'] = ['0.937', '0', '0', '0.828', '#joy', '#admiration', 'positive', '0.883', 'bakery', 'produce_section', 'thanksgiving_dinner', 'dinner_meal', 'fast_food']
senticnet['perfectly_safe'] = ['0.033', '0.186', '0', '0', '#joy', '#interest', 'positive', '0.11', 'bank', 'bank_vault', 'wallet', 'government_agreement', 'coffer']
senticnet['perfectly_sane'] = ['0.203', '0.077', '0', '0.189', '#joy', '#admiration', 'positive', '0.156', 'sea_shell', 'coast', 'seaside', 'sediment', 'shell']
senticnet['perfectly_spherical'] = ['0.034', '-0.07', '0.068', '0.103', '#surprise', '#admiration', 'positive', '0.035', 'use_sport', 'bouncy', 'globe', 'orb', 'disc_shaped']
senticnet['perfervid'] = ['0', '0', '0.943', '-0.84', '#anger', '#disgust', 'negative', '-0.89', 'impassioned', 'fervent', 'fervid', 'ardent', 'fiery']
senticnet['perfidious'] = ['-0.66', '0', '0.709', '-0.88', '#anger', '#disgust', 'negative', '-0.75', 'untrue', 'apostate', 'treacherous', 'unfaithful', 'punic']
senticnet['perfidiousness'] = ['0.927', '-0.56', '0', '0.8', '#joy', '#admiration', 'positive', '0.764', 'disloyalty', 'subversiveness', 'traitorousness', 'perfidy', 'treachery']
senticnet['perfidy'] = ['0.927', '-0.56', '0', '0.8', '#joy', '#admiration', 'positive', '0.764', 'perfidiousness', 'subversiveness', 'traitorousness', 'disloyalty', 'treachery']
senticnet['perfomance'] = ['0.558', '-0.60', '0', '0.779', '#surprise', '#admiration', 'positive', '0.646', 'benefit', 'pianism', 'show', 'routine', 'histrionics']
senticnet['perfor_mance'] = ['0.558', '-0.60', '0', '0.779', '#surprise', '#admiration', 'positive', '0.646', 'benefit', 'pianism', 'show', 'routine', 'histrionics']
senticnet['perforate'] = ['0', '0', '0.873', '-0.92', '#anger', '#disgust', 'negative', '-0.89', 'punch', 'sting', 'disable', 'ache', 'vex']
senticnet['perforated_metal'] = ['0.067', '0.088', '-0.07', '0.246', '#interest', '#admiration', 'positive', '0.081', 'multiple_purposes', 'revolve', 'start_run', 'wiggle', 'twist']
senticnet['perforation'] = ['0', '0', '0.873', '-0.92', '#anger', '#disgust', 'negative', '-0.89', 'punch', 'sting', 'disable', 'ache', 'vex']
senticnet['perform'] = ['0.861', '0.928', '0', '0', '#joy', '#interest', 'positive', '0.895', 'act_play', 'go_stage', 'pretend', 'play_violin', 'play_guitar']
senticnet['perform_action'] = ['0.86', '-0.9', '0', '0', '#joy', '#surprise', 'positive', '0.88', 'run_errand', 'pay_credit_card', 'pay_bill', 'go_market', 'go_store']
senticnet['perform_art'] = ['0.12', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.09', 'ballroom', 'ballet', 'nightclub', 'ballerina', 'venue']
senticnet['perform_fellatio'] = ['-0.74', '0', '0.677', '-0.77', '#sadness', '#disgust', 'negative', '-0.73', 'whore', 'jazz', 'hump', 'bonk', 'screw']
senticnet['perform_magic'] = ['0', '0', '0.958', '0', '#anger', '#anger', 'negative', '-0.95', 'complain_state_world', 'find_love', 'own_pet', 'jump_off_bridge', 'live_apartment']
senticnet['perform_of_music'] = ['0', '0.706', '0', '0.934', '#interest', '#admiration', 'positive', '0.82', 'play_tune', 'play_orchestra', 'create_music', 'play_music', 'music']
senticnet['perform_on_stage'] = ['0', '0.706', '0', '0.934', '#interest', '#admiration', 'positive', '0.82', 'compose_music', 'sing_song', 'play_piano', 'use_microphone', 'enjoy']
senticnet['perform_oral_sex'] = ['-0.04', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.05', 'fill_bathtub', 'fill_bathtub_water', 'run_bath', 'turn_water', 'take_clothes_off']
senticnet['perform_play'] = ['0', '-0.94', '0', '0.887', '#surprise', '#admiration', 'positive', '0.914', 'show_play', 'present_play', 'stage_play', 'drama', 'performance']
senticnet['perform_poorly'] = ['0', '0.994', '0', '0.88', '#interest', '#admiration', 'positive', '0.937', 'exceed', 'outshout', 'outstrip', 'outshine', 'outsail']
senticnet['perform_service'] = ['-0.02', '0.147', '0', '0.068', '#interest', '#admiration', 'positive', '0.063', 'go_work_to_earn_money', 'responsible', 'provide_service', 'dance_joy', 'need_earn_money']
senticnet['perform_surgery'] = ['0', '0', '0', '0.857', '#admiration', '#admiration', 'positive', '0.857', 'surgery', 'compose_music', 'beep', 'sound_alarm', 'lift_leg']
senticnet['perform_task'] = ['-0.04', '0.068', '0', '0.078', '#interest', '#admiration', 'positive', '0.035', 'job_offer', 'make_life_easy', 'settle', 'ask_person', 'job']
senticnet['perform_well'] = ['0.808', '0.939', '0', '0.933', '#interest', '#admiration', 'positive', '0.893', 'go_rock_concert', 'sit_stand', 'execute_well', 'make_difference', 'enjoy_challenge']
senticnet['performability'] = ['0.512', '-0.52', '0', '0.874', '#surprise', '#admiration', 'positive', '0.635', 'responsibleness', 'reproducibility', 'duplicability', 'solidness', 'responsibility']
senticnet['performace'] = ['0.558', '-0.60', '0', '0.779', '#surprise', '#admiration', 'positive', '0.646', 'benefit', 'pianism', 'show', 'routine', 'histrionics']
senticnet['performance'] = ['0.558', '-0.60', '0', '0.779', '#surprise', '#admiration', 'positive', '0.646', 'benefit', 'pianism', 'show', 'routine', 'histrionics']
senticnet['performance_measurement'] = ['0.558', '-0.60', '0', '0.779', '#surprise', '#admiration', 'positive', '0.646', 'benefit', 'pianism', 'show', 'routine', 'histrionics']
senticnet['performant'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'carbon_fiber']
senticnet['performative'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'indirectness', 'mediateness', 'characteristic', 'allusiveness']
senticnet['performed_acoustically'] = ['0.773', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.844', 'favorite', 'crowd', 'acclamation', 'bunch', 'large_crowd']
senticnet['performed_laparoscopically'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
senticnet['performed_onstage'] = ['0.773', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.844', 'favorite', 'crowd', 'acclamation', 'bunch', 'large_crowd']
senticnet['performer'] = ['0.976', '0', '0', '0.923', '#joy', '#admiration', 'positive', '0.95', 'movie_star', 'actress', 'entertainer', 'actor', 'preview']
senticnet['performing_art'] = ['0.584', '-0.78', '0', '0.546', '#joy', '#surprise', 'positive', '0.638', 'acting', 'musical_performance', 'dancing', 'dance', 'terpsichore']
senticnet['performing_art_theater'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'theater', 'drive-in_movie', 'watch_movie', 'cinema', 'movie']
senticnet['performing_art_vpa'] = ['-0.02', '0', '-0.04', '-0.03', '#fear', '#disgust', 'negative', '-0.03', 'studio', 'art_piece', 'art_gallery', 'artist', 'museum']
senticnet['performing_cpr'] = ['-0.57', '0', '0.737', '-0.85', '#anger', '#disgust', 'negative', '-0.72', 'emergency_procedure', 'emergency', 'external_respiration', 'respiration', 'ventilation']
senticnet['performing_professionally'] = ['0.592', '-0.93', '0', '0.935', '#surprise', '#admiration', 'positive', '0.819', 'child', 'small_fry', 'tyke', 'tike', 'kid']
senticnet['perfume'] = ['0.066', '0.083', '-0.06', '0.09', '#interest', '#admiration', 'positive', '0.044', 'aroma', 'female_perfume', 'fragrance', 'bad_odor', 'saliva']
senticnet['perfume_counter'] = ['-0.05', '-0.04', '0.017', '-0.05', '#sadness', '#disgust', 'negative', '-0.02', 'shoe_shop', 'hamper', 'attic', 'obstruct', 'closet']
senticnet['perfumed_oil'] = ['0.827', '0', '0', '0', '#joy', '#joy', 'positive', '0.827', 'wrathful', 'wrathfully', 'wroth', 'wrothful', 'lividity']
senticnet['perfumery'] = ['0.863', '0.861', '0', '0', '#joy', '#interest', 'positive', '0.862', 'aroma', 'scent', 'smell', 'odor', 'sniff']
senticnet['perfumier'] = ['0.863', '0.861', '0', '0', '#joy', '#interest', 'positive', '0.862', 'aroma', 'scent', 'smell', 'odor', 'sniff']
senticnet['perfusion_pressure'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'keep_alive', 'body_system', 'body_working', 'blood_pressure', 'heart']
senticnet['pergamene'] = ['-0.69', '0', '0.957', '-0.92', '#anger', '#disgust', 'negative', '-0.85', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['pergamum'] = ['-0.69', '0', '0.957', '-0.92', '#anger', '#disgust', 'negative', '-0.85', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['pergolide'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['peri'] = ['0.992', '-0.91', '0', '0.851', '#joy', '#surprise', 'positive', '0.92', 'girl', 'miss', 'missy', 'young_lady', 'young_woman']
senticnet['periander'] = ['0.8', '-0.79', '0', '0.751', '#joy', '#surprise', 'positive', '0.782', 'elder_statesman', 'stateswoman', 'politician', 'politico', 'pol']
senticnet['perianth'] = ['0', '0', '0.064', '-0.04', '#anger', '#disgust', 'negative', '-0.05', 'bird_food', 'funiculus', 'plant_product', 'fuzz', 'agricultural_product']
senticnet['perianth_tube'] = ['0', '0', '0.064', '-0.04', '#anger', '#disgust', 'negative', '-0.05', 'bird_food', 'funiculus', 'plant_product', 'fuzz', 'agricultural_product']
senticnet['periapical'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'plaque']
senticnet['periarticular'] = ['0', '0', '0.025', '-0.09', '#anger', '#disgust', 'negative', '-0.06', 'body_fluid', 'artery', 'skeletal_system', 'bodily_fluid', 'near_mouth']
senticnet['pericardial'] = ['0.759', '-0.72', '0', '0.893', '#joy', '#admiration', 'positive', '0.793', 'manifestation', 'cry', 'reflection', 'acting_out', 'explosion']
senticnet['pericardial_effusion'] = ['0.759', '-0.72', '0', '0.893', '#joy', '#admiration', 'positive', '0.793', 'manifestation', 'cry', 'reflection', 'acting_out', 'explosion']
senticnet['pericardial_fluid'] = ['0.759', '-0.72', '0', '0.893', '#joy', '#admiration', 'positive', '0.793', 'manifestation', 'cry', 'reflection', 'acting_out', 'explosion']
senticnet['pericardial_sac'] = ['0.759', '-0.72', '0', '0.893', '#joy', '#admiration', 'positive', '0.793', 'manifestation', 'cry', 'reflection', 'acting_out', 'explosion']
senticnet['pericarditis'] = ['-0.54', '0', '0.67', '-0.92', '#anger', '#disgust', 'negative', '-0.71', 'carditis', 'rubor', 'redness', 'endocarditis', 'inflammation']
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senticnet['pericarp'] = ['0', '0.141', '0.01', '0.115', '#interest', '#admiration', 'positive', '0.082', 'sepal', 'achene', 'funiculus', 'seed', 'pip']
senticnet['perichondrium'] = ['0', '0', '0.025', '-0.09', '#anger', '#disgust', 'negative', '-0.06', 'body_fluid', 'artery', 'skeletal_system', 'bodily_fluid', 'near_mouth']
senticnet['pericyclic_reaction'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
senticnet['periderm'] = ['0', '0.097', '0', '0.085', '#interest', '#admiration', 'positive', '0.091', 'lunula', 'arm_part', 'leave_hand', 'ten_fingers', 'five_fingers']
senticnet['peridiole'] = ['-0.55', '0', '0.869', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'air_bubble', 'foam', 'froth', 'soap_bubble', 'bubble']
senticnet['peridium'] = ['-0.66', '0', '0.809', '-0.61', '#sadness', '#anger', 'negative', '-0.69', 'face', 'base', 'underbelly', 'sole', 'bilge']
senticnet['peridot'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'art', 'art_piece', 'museum', 'artist', 'jewel']
senticnet['perigee'] = ['0.989', '-0.90', '0', '0.876', '#joy', '#surprise', 'positive', '0.923', 'reach', 'range', 'compass', 'scope', 'confines']
senticnet['peril'] = ['0.565', '-0.76', '0', '0.982', '#surprise', '#admiration', 'positive', '0.771', 'risk', 'hazard', 'jeopardy', 'endangerment', 'health_hazard']
senticnet['perilous'] = ['0', '0', '-0.89', '-0.98', '#fear', '#disgust', 'negative', '-0.93', 'unstable', 'parlous', 'precarious', 'touch-and-go', 'shaky']
senticnet['perilous_journey'] = ['0.954', '-0.87', '0', '0.583', '#joy', '#surprise', 'positive', '0.802', 'penetration', 'air_raid', 'air_attack', 'swoop', 'incursion']
senticnet['perilously'] = ['-0.64', '0', '0.717', '-0.98', '#anger', '#disgust', 'negative', '-0.78', 'debacle', 'licking', 'whitewash', 'finish', 'failure']
senticnet['perimenopause'] = ['0.995', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.918', 'tell_everyone', 'reason', 'clarify', 'forethought', 'opinion']
senticnet['perinatal_death'] = ['0.95', '0', '0', '0', '#joy', '#joy', 'positive', '0.95', 'born', 'new_baby', 'birthday', 'thanksgiving', 'anniversary']
senticnet['perindopril'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'spironolactone', 'spironolactone_drug']
senticnet['perineal_pain'] = ['-0.73', '0', '0.555', '-0.90', '#sadness', '#disgust', 'negative', '-0.73', 'aching', 'hurting', 'labor_pain', 'neuralgia', 'neuralgy']
senticnet['perineum'] = ['0', '-0.89', '0', '0.868', '#surprise', '#admiration', 'positive', '0.879', 'g-spot', 'clitoris', 'clitoral', 'erogenous', 'genitals']
senticnet['period'] = ['0.906', '-0.62', '0', '0.941', '#joy', '#admiration', 'positive', '0.825', 'honeymoon', 'heyday', 'time', 'efflorescence', 'millennium']
senticnet['period_of_time'] = ['0.978', '-0.60', '0', '0.895', '#joy', '#admiration', 'positive', '0.825', 'period', 'quattrocento', 'twentieth_century', 'time_period', 'century']
senticnet['periodic'] = ['0.09', '0.07', '0', '0.246', '#joy', '#admiration', 'positive', '0.135', 'hand_motion', 'water_form', 'water_relate', 'hand_gesture', 'arm_movement']
senticnet['periodic_acid'] = ['-0.81', '0', '0.774', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'acid', 'hypophosphoric_acid', 'oxyacid', 'hypophosphorous_acid', 'phosphorous_acid']
senticnet['periodic_edema'] = ['-0.99', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.78', 'angioedema', 'atrophedema', 'giant_hive', 'dropsy', 'lymphedema']
senticnet['periodic_event'] = ['0.949', '0.939', '0', '0', '#joy', '#interest', 'positive', '0.944', 'people', 'migration', 'immigration', 'motion', 'emigration']
senticnet['periodic_newsletter'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'current_event', 'headline', 'paper_write', 'paper_page', 'many_pages']
senticnet['periodic_payment'] = ['-0.04', '0.277', '-0.10', '0.11', '#interest', '#admiration', 'positive', '0.059', 'owe', 'tax', 'debit', 'afford', 'wage']
senticnet['periodic_review'] = ['0.09', '0.07', '0', '0.246', '#joy', '#admiration', 'positive', '0.135', 'hand_motion', 'water_form', 'water_relate', 'hand_gesture', 'arm_movement']
senticnet['periodical'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'current_event', 'headline', 'paper_write', 'paper_page', 'many_pages']
senticnet['periodically'] = ['0.09', '0.07', '0', '0.246', '#joy', '#admiration', 'positive', '0.135', 'hand_motion', 'water_form', 'water_relate', 'hand_gesture', 'arm_movement']
senticnet['periodically_check'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'computer_part', 'system', 'motherboard', 'pencil_pen', 'cpu']
senticnet['periodically_review'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'computer_part', 'system', 'motherboard', 'pencil_pen', 'cpu']
senticnet['periodicity'] = ['0.09', '0.07', '0', '0.246', '#joy', '#admiration', 'positive', '0.135', 'hand_motion', 'water_form', 'water_relate', 'hand_gesture', 'arm_movement']
senticnet['periodontal'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'plaque']
senticnet['periodontal_disease'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'plaque']
senticnet['periodontal_gum_disease'] = ['0', '0', '0.813', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'ice_cream_melt', 'allergy', 'toothache', 'gonorrhea', 'tastebud']
senticnet['periodontal_ligament'] = ['0', '0', '0.025', '-0.09', '#anger', '#disgust', 'negative', '-0.06', 'body_fluid', 'artery', 'skeletal_system', 'bodily_fluid', 'near_mouth']
senticnet['periodontal_pocket'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'plaque']
senticnet['periodontium'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'plaque']
senticnet['periodontology'] = ['-0.03', '0.079', '0.024', '0.091', '#interest', '#admiration', 'positive', '0.029', 'toothpaste', 'toothbrush', 'mouth', 'gel_toothpaste', 'tooth']
senticnet['perioeci'] = ['-0.69', '0', '0.957', '-0.92', '#anger', '#disgust', 'negative', '-0.85', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['periorbital'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'part_of_face', 'facial_organ', 'face_feature', 'facial_part', 'near_nose']
senticnet['periorbital_edema'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'stiff', 'rigid', 'inflexible', 'harsh', 'immovable']
senticnet['periosteum'] = ['0', '0', '0.025', '-0.09', '#anger', '#disgust', 'negative', '-0.06', 'body_fluid', 'artery', 'skeletal_system', 'bodily_fluid', 'near_mouth']
senticnet['periostracum'] = ['-0.03', '0', '0', '-0.11', '#sadness', '#disgust', 'negative', '-0.07', 'store_cloth', 'clothes_factory', 'stock', 'sword', 'gun_store']
senticnet['peripety'] = ['0.701', '0', '0', '0.756', '#joy', '#admiration', 'positive', '0.728', 'astonishingly', 'amazingly', 'alteration', 'surprisingly', 'bombshell']
senticnet['peripheral'] = ['0', '0', '0.878', '-0.97', '#anger', '#disgust', 'negative', '-0.92', 'periphery', 'add-on', 'scanner', 'peripherally', 'polling']
senticnet['peripheral_circulation'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'keep_alive', 'body_system', 'body_working', 'blood_pressure', 'heart']
senticnet['peripheral_edema'] = ['-0.58', '0', '0.773', '-0.79', '#anger', '#disgust', 'negative', '-0.71', 'perspiration', 'sweating', 'polyhidrosis', 'sudation', 'hidrosis']
senticnet['peripheral_eosinophilia'] = ['-0.53', '0', '0.608', '-0.93', '#anger', '#disgust', 'negative', '-0.69', 'reprieve', 'respite', 'hiatus', 'abatement', 'suspension']
senticnet['peripheral_equipment'] = ['-0.02', '-0.05', '0', '0.069', '#surprise', '#admiration', 'positive', '0.032', 'fixture', 'endowment', 'material', 'stuff', 'kit']
senticnet['peripheral_sensory'] = ['-0.78', '0', '0.669', '-0.89', '#sadness', '#disgust', 'negative', '-0.78', 'pain_sensation', 'painful_sensation', 'prickling', 'tingling', 'pain']
senticnet['peripheral_vascular_resistance'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'keep_alive', 'body_system', 'body_working', 'blood_pressure', 'heart']
senticnet['peripheral_vasodilation'] = ['-0.62', '0', '0.901', '-0.71', '#anger', '#disgust', 'negative', '-0.74', 'pressure', 'osmotic_pressure', 'hypotonicity', 'pressure_level', 'force']
senticnet['peripheral_vision'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'divergent_strabismus', 'exotropia', 'walleye', 'abnormality', 'abnormalcy']
senticnet['peripherally'] = ['0', '0', '0.871', '-0.98', '#anger', '#disgust', 'negative', '-0.92', 'periphery', 'add-on', 'scanner', 'peripheral', 'polling']
senticnet['periphery'] = ['0', '0', '0.874', '-0.97', '#anger', '#disgust', 'negative', '-0.92', 'peripheral', 'add-on', 'scanner', 'peripherally', 'polling']
senticnet['periphrastic'] = ['-0.57', '0', '0.713', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'assured', 'conclusion', 'certainty', 'determinateness', 'predictability']
senticnet['periplaneta_americana'] = ['0', '0', '-0.92', '-0.97', '#fear', '#disgust', 'negative', '-0.95', 'annoy_bug', 'insect', 'bug', 'hare', 'nymph']
senticnet['perish'] = ['-0.75', '0', '-1.0', '0', '#sadness', '#fear', 'negative', '-0.87', 'give_in', 'submit', 'accept', 'defer', 'yield']
senticnet['perish_miserably'] = ['-0.86', '0', '0.891', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'baneful', 'pernicious', 'vesicatory', 'vesicant', 'noxious']
senticnet['perishability'] = ['0.774', '-0.82', '0', '0.63', '#joy', '#surprise', 'positive', '0.744', 'quality', 'unsatisfactoriness', 'perishableness', 'inadequateness', 'inadequacy']
senticnet['perishableness'] = ['0.774', '-0.82', '0', '0.63', '#joy', '#surprise', 'positive', '0.744', 'quality', 'perishability', 'unsatisfactoriness', 'inadequateness', 'inadequacy']
senticnet['perisheth'] = ['0.741', '-0.84', '0', '0.543', '#joy', '#surprise', 'positive', '0.711', 'integrity', 'righteousness', 'scrupulousness', 'incorruptness', 'honestness']
senticnet['peritoneal'] = ['0.759', '-0.72', '0', '0.893', '#joy', '#admiration', 'positive', '0.793', 'manifestation', 'cry', 'reflection', 'acting_out', 'explosion']
senticnet['peritoneal_cavity'] = ['-0.78', '0', '0.855', '-0.99', '#anger', '#disgust', 'negative', '-0.87', 'blood_blister', 'pustule', 'water_blister', 'vesicle', 'cyst']
senticnet['peritrichous_flagellum'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'immobile', 'immotile']
senticnet['periungual'] = ['-0.66', '0', '0.911', '-0.99', '#anger', '#disgust', 'negative', '-0.85', 'liver_spot', 'spot', 'freckle', 'macule', 'plague_spot']
senticnet['periwig'] = ['-0.84', '0', '0.633', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'grizzle', 'postiche', 'horsehair_wig', 'wig', 'peruke']
senticnet['perjured_testimony'] = ['-0.73', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'obstruct_justice', 'get_away_crime', 'punish', 'hurt_else', 'commit_murder']
senticnet['perjurer'] = ['-0.63', '0', '0.88', '-0.94', '#anger', '#disgust', 'negative', '-0.81', 'beguiler', 'slicker', 'prevaricator', 'false_witness', 'storyteller']
senticnet['perjury'] = ['0', '0', '-0.86', '-0.71', '#fear', '#disgust', 'negative', '-0.79', 'indictment', 'felony', 'acquitted', 'treason', 'forgery']
senticnet['perjury_charge'] = ['-0.73', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'obstruct_justice', 'get_away_crime', 'punish', 'hurt_else', 'commit_murder']
senticnet['perk'] = ['0.844', '-0.77', '0', '0.546', '#joy', '#surprise', 'positive', '0.722', 'benefit', 'appanage', 'provision', 'gratuity', 'tip']
senticnet['perk_up'] = ['0.815', '-0.75', '0', '0.744', '#joy', '#surprise', 'positive', '0.772', 'perk', 'convalesce', 'improve', 'look_better', 'meliorate']
senticnet['perked_up'] = ['0.815', '-0.75', '0', '0.744', '#joy', '#surprise', 'positive', '0.772', 'perk', 'convalesce', 'improve', 'look_better', 'meliorate']
senticnet['perkier'] = ['0.815', '-0.75', '0', '0.744', '#joy', '#surprise', 'positive', '0.772', 'perk', 'convalesce', 'improve', 'look_better', 'meliorate']
senticnet['perkily'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'hankie', 'kleenex', 'facial_tissue', 'handkerchief', 'tissue']
senticnet['perkiness'] = ['0.676', '0.63', '-0.23', '0.31', '#joy', '#interest', 'positive', '0.345', 'buoyancy', 'walk_on_air', 'exultingly', 'exultantly', 'jubilance']
senticnet['perking_up'] = ['0.815', '-0.75', '0', '0.744', '#joy', '#surprise', 'positive', '0.772', 'perk', 'convalesce', 'improve', 'look_better', 'meliorate']
senticnet['perkins_brailler'] = ['0', '0', '0.128', '0', '#anger', '#anger', 'negative', '-0.12', 'grid', 'efficient', 'calculator', 'computer', 'personal_computer']
senticnet['perl'] = ['0', '-0.99', '0', '0.862', '#surprise', '#admiration', 'positive', '0.928', 'helpful_tool', 'woodworking_tool', 'helpful', 'type_of_fruit', 'hand_tool']
senticnet['perl_module'] = ['-0.95', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.84', 'hold_pencil', 'food_scrap', 'paper_product', 'waste_paper', 'cover']
senticnet['perlocutionary'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'mediacy', 'mediateness', 'characteristic', 'allusiveness']
senticnet['permafrost'] = ['-0.04', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.04', 'bad_luck', 'damn', 'artic', 'mammal_family', 'bad_mood']
senticnet['permafrost_degradation'] = ['0.942', '-0.79', '0', '0.901', '#joy', '#admiration', 'positive', '0.88', 'backup', 'accretion', 'deposition', 'deposit', 'increase']
senticnet['permanence'] = ['0.772', '-0.73', '0', '0.958', '#joy', '#admiration', 'positive', '0.82', 'quality', 'mutability', 'mutableness', 'changefulness', 'exchangeability']
senticnet['permanent'] = ['-0.81', '0', '0.921', '-0.80', '#sadness', '#anger', 'negative', '-0.84', 'immovability', 'unchanging', 'lodgment', 'fixity', 'lodging']
senticnet['permanent_brain_damage'] = ['-0.03', '-0.00', '-0.07', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'injury', 'get_hurt', 'bruise', 'lose_game', 'get_injured']
senticnet['permanent_collection'] = ['0.07', '0', '0', '0', '#joy', '#joy', 'positive', '0.07', 'artist', 'artwork', 'sculpture', 'tapestry', 'drawing']
senticnet['permanent_damage'] = ['-0.98', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.97', 'harm', 'hurt', 'cut_skin', 'bloody', 'ouch']
senticnet['permanent_disability'] = ['-0.77', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.78', 'chicken_pox', 'diabetes', 'lyme_disease', 'brain_tumor', 'suffer_fool']
senticnet['permanent_easement'] = ['0.621', '-0.60', '0', '0.545', '#joy', '#surprise', 'positive', '0.59', 'privilege', 'perquisite', 'public_easement', 'right_of_way', 'prerogative']
senticnet['permanent_endowment'] = ['0.608', '-0.55', '0', '0.726', '#joy', '#admiration', 'positive', '0.628', 'genius', 'raw_talent', 'flair', 'gift', 'knack']
senticnet['permanent_endowment_fund'] = ['0.608', '-0.55', '0', '0.726', '#joy', '#admiration', 'positive', '0.628', 'genius', 'raw_talent', 'flair', 'gift', 'knack']
senticnet['permanent_fixture'] = ['-0.77', '0', '0.967', '-0.70', '#sadness', '#anger', 'negative', '-0.81', 'champion', 'friend', 'supporter', 'protagonist', 'admirer']
senticnet['permanent_limp'] = ['-0.13', '0', '0.013', '-0.08', '#sadness', '#disgust', 'negative', '-0.07', 'blind', 'dig_tunnel', 'underground_animal', 'big_mouse', 'small_rodent']
senticnet['permanent_magnet'] = ['0.102', '0.078', '0', '0.087', '#joy', '#admiration', 'positive', '0.089', 'gold', 'platinum', 'iron_ore', 'silver', 'adjustable']
senticnet['permanent_marker'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'gift_wrap', 'kraft', 'kraft_paper', 'wrapping_paper', 'paper']
senticnet['permanent_pacemaker'] = ['-0.83', '0', '0.987', '-0.50', '#sadness', '#anger', 'negative', '-0.77', 'heart_block', 'atrioventricular_block', 'bradycardia', 'ventricular_fibrillation', 'tachycardia']
senticnet['permanent_placement'] = ['0', '0.098', '0.012', '0.276', '#interest', '#admiration', 'positive', '0.121', 'position', 'job', 'find_own_place_in_world', 'character_trait', 'place']
senticnet['permanent_residence'] = ['0', '-0.91', '0', '0.806', '#surprise', '#admiration', 'positive', '0.861', 'clean_shoes', 'black_stuff', 'paint_nail', 'ethnicity', 'black_goo']
senticnet['permanent_resident'] = ['0.071', '0.115', '-0.08', '0.167', '#interest', '#admiration', 'positive', '0.066', 'live_inside', 'inhabit', 'live_life', 'protection_weather', 'raise_family']
senticnet['permanent_resident_card'] = ['0.931', '-0.52', '0', '0.67', '#joy', '#admiration', 'positive', '0.709', 'document', 'certificate_of_incorporation', 'credential', 'diploma', 'sheepskin']
senticnet['permanent_scar'] = ['0', '0', '0', '-0.93', '#disgust', '#disgust', 'negative', '-0.93', 'injury', 'broken_bone', 'bad_knee', 'strengthful', 'physical_pain']
senticnet['permanent_seating_capacity'] = ['0.953', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.929', 'hockey_fan', 'goalie', 'competitor', 'spectator', 'hockey_stick']
senticnet['permanent_secretariat'] = ['0.851', '-0.65', '0', '0.66', '#joy', '#admiration', 'positive', '0.723', 'omen', 'portent', 'presage', 'prognostic', 'prognostication']
senticnet['permanently'] = ['-0.96', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.87', 'hesitation', 'stay', 'rest_foot', 'pause', 'rest_leg']
senticnet['permanently_cease'] = ['-0.91', '0', '0.576', '-0.95', '#sadness', '#disgust', 'negative', '-0.81', 'stop', 'halt', 'lock_in', 'lock', 'block']
senticnet['permanently_frozen'] = ['-0.04', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.04', 'bad_luck', 'damn', 'artic', 'mammal_family', 'bad_mood']
senticnet['permanently_ineligible'] = ['-0.60', '0', '0.832', '-0.53', '#sadness', '#anger', 'negative', '-0.65', 'unsuitable', 'undesirable', 'unqualified', 'disqualified', 'unentitled']
senticnet['permanently_reside'] = ['0.071', '0.115', '-0.08', '0.167', '#interest', '#admiration', 'positive', '0.066', 'live_inside', 'inhabit', 'live_life', 'protection_weather', 'raise_family']
senticnet['permanganate'] = ['0.766', '-0.87', '0', '0.666', '#joy', '#surprise', 'positive', '0.769', 'reactive', 'unstable', 'labile', 'activated', 'excited']
senticnet['permeability'] = ['0.89', '-0.62', '0', '0.548', '#joy', '#surprise', 'positive', '0.686', 'breakthrough', 'interpenetration', 'foray', 'maraud', 'raid']
senticnet['permeable'] = ['-0.52', '0', '0.596', '-0.76', '#anger', '#disgust', 'negative', '-0.62', 'retention', 'retentiveness', 'retentivity', 'solidness', 'nonabsorbency']
senticnet['permeate'] = ['0.968', '-0.76', '0', '0.583', '#joy', '#surprise', 'positive', '0.773', 'riddle', 'diffuse', 'spiritize', 'pervade', 'penetrate']
senticnet['permeation'] = ['0.789', '-0.92', '0', '0.964', '#surprise', '#admiration', 'positive', '0.893', 'ammonification', 'carbonation', 'impregnation', 'saturation', 'diffusion']
senticnet['permethrin'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['permissible'] = ['0', '-0.70', '0', '0.874', '#surprise', '#admiration', 'positive', '0.79', 'permit', 'impermissible', 'permissibly', 'caber', 'tolerable']
senticnet['permissibly'] = ['0', '-0.71', '0', '0.865', '#surprise', '#admiration', 'positive', '0.788', 'permit', 'impermissible', 'permissible', 'caber', 'tolerable']
senticnet['permission'] = ['-0.03', '0.308', '-0.23', '0.406', '#interest', '#admiration', 'positive', '0.113', 'let', 'take', 'suggest', 'request', 'compel']
senticnet['permissiveness'] = ['0.787', '-0.88', '0', '0.858', '#surprise', '#admiration', 'positive', '0.843', 'lenience', 'leniency', 'toleration', 'disposition', 'temperament']
senticnet['permit'] = ['0', '0.243', '-0.17', '0.292', '#interest', '#admiration', 'positive', '0.121', 'citation', 'concession', 'deliver', 'allow', 'permission']
senticnet['permit_awardees'] = ['0', '0.968', '0', '0.819', '#interest', '#admiration', 'positive', '0.893', 'fellowship', 'grant', 'mathematics', 'award', 'maximal_impact']
senticnet['permit_denial'] = ['0', '0', '0.736', '-0.99', '#anger', '#disgust', 'negative', '-0.86', 'get_fired', 'unbelief', 'extreme_fear', 'psychoneurosis', 'death_love']
senticnet['permitted'] = ['-0.76', '0', '0.76', '0', '#sadness', '#anger', 'negative', '-0.76', 'describe', 'objectionable', 'unutterable', 'unspeakable', 'ineffable']
senticnet['permittee'] = ['-0.92', '0', '0.757', '-0.90', '#sadness', '#disgust', 'negative', '-0.86', 'let_up', 'far', 'slack_off', 'die_away', 'die']
senticnet['permutability'] = ['0.912', '-0.75', '0', '0.512', '#joy', '#surprise', 'positive', '0.727', 'transferability', 'convertibility', 'changeableness', 'changeability', 'exchangeability']
senticnet['permutable'] = ['0.552', '-0.58', '0', '0.762', '#surprise', '#admiration', 'positive', '0.633', 'transposable', 'exchangeable', 'fungible', 'commutable', 'substitutable']
senticnet['permutation'] = ['0.898', '-0.87', '0', '0.929', '#joy', '#admiration', 'positive', '0.9', 'make', 'transposition', 'reversal', 'passing', 'reordering']
senticnet['permutohedron'] = ['0.042', '-0.00', '0.027', '0.099', '#joy', '#admiration', 'positive', '0.031', 'chess_knight', 'chess_queen', 'chess_pawn', 'pawn', 'chess_bishop']
senticnet['pernicious'] = ['-0.86', '0', '0.891', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'baneful', 'noxious', 'vesicatory', 'vesicant', 'pestilent']
senticnet['pernicious_anemia'] = ['-0.54', '0', '0.92', '-0.84', '#anger', '#disgust', 'negative', '-0.76', 'dearth', 'demand', 'mineral_deficiency', 'famine', 'need']
senticnet['perniciousness'] = ['-0.72', '0', '0.71', '-0.9', '#sadness', '#disgust', 'negative', '-0.77', 'unwholesomeness', 'harmfulness', 'toxicity', 'noisomeness', 'noxiousness']
senticnet['pernickety'] = ['0.682', '-0.52', '0', '0.613', '#joy', '#admiration', 'positive', '0.607', 'nice', 'meticulous', 'dainty', 'prissy', 'fastidious']
senticnet['pernon'] = ['0.033', '0.088', '-0.06', '0.068', '#interest', '#admiration', 'positive', '0.032', 'earl', 'noble', 'king', 'sultan', 'royal_title']
senticnet['peroneal_nerve'] = ['-0.98', '0', '0.678', '-0.95', '#sadness', '#disgust', 'negative', '-0.87', 'rubor', 'redness', 'inflammation', 'tennis_elbow', 'lateral_epicondylitis']
senticnet['peronospora'] = ['-0.93', '0', '0.699', '-0.91', '#sadness', '#disgust', 'negative', '-0.84', 'onion_smut', 'head_smut', 'stinking_smut', 'flag_smut_fungus', 'covered_smut']
senticnet['perorate'] = ['0.584', '-0.93', '0', '0.927', '#surprise', '#admiration', 'positive', '0.816', 'elocute', 'scan', 'execute', 'declaim', 'perform']
senticnet['peroration'] = ['-0.84', '0', '0.572', '-0.68', '#sadness', '#disgust', 'negative', '-0.69', 'reply', 'response', 'sass', 'sassing', 'backtalk']
senticnet['peroxide'] = ['0.071', '0', '-0.08', '-0.09', '#fear', '#disgust', 'negative', '-0.03', 'gunk', 'rotten_food', 'sour_cream', 'glass_of_milk', 'lemon_juice']
senticnet['perpendicular'] = ['-0.97', '0', '0.865', '-0.54', '#sadness', '#anger', 'negative', '-0.79', 'piping', 'lace', 'lacing', 'cord', 'slack']
senticnet['perpendicular_direction'] = ['-0.04', '0.074', '0.019', '0.071', '#interest', '#admiration', 'positive', '0.021', 'sharpness', 'sharp_end', 'sharp_corner', 'near_end', 'edge']
senticnet['perpendicular_distance'] = ['-0.04', '0.074', '0.019', '0.071', '#interest', '#admiration', 'positive', '0.021', 'sharpness', 'sharp_end', 'sharp_corner', 'near_end', 'edge']
senticnet['perpendicular_style'] = ['0.578', '-0.95', '0', '0.979', '#surprise', '#admiration', 'positive', '0.838', 'perpendicular', 'gothic_architecture', 'architectural_style', 'style_of_architecture', 'type_of_architecture']
senticnet['perpendicularly'] = ['-0.80', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.77', 'contrary', 'contradiction', 'lie', 'liar', 'bad_person']
senticnet['perpetrate'] = ['-0.89', '0', '0.826', '-0.50', '#sadness', '#anger', 'negative', '-0.74', 'wrong', 'delude', 'deceive', 'cozen', 'rip_off']
senticnet['perpetrated_against'] = ['-0.89', '0', '0.826', '-0.50', '#sadness', '#anger', 'negative', '-0.74', 'wrong', 'delude', 'deceive', 'cozen', 'rip_off']
senticnet['perpetration'] = ['-0.06', '0', '-0.15', '0', '#sadness', '#fear', 'negative', '-0.10', 'heroism', 'role_play', 'repeat', 'act', 'part_of_movie']
senticnet['perpetrator'] = ['-0.06', '0', '-0.15', '0', '#sadness', '#fear', 'negative', '-0.10', 'heroism', 'role_play', 'repeat', 'act', 'part_of_movie']
senticnet['perpetual'] = ['-0.60', '0', '0.509', '-0.67', '#sadness', '#disgust', 'negative', '-0.59', 'dogging', 'persisting', 'sustained', 'consecutive', 'continuous']
senticnet['perpetual_abstinence'] = ['-0.74', '0', '0.585', '-0.58', '#sadness', '#anger', 'negative', '-0.63', 'lack_of_quality', 'meagreness', 'poorness', 'scantiness', 'scantness']
senticnet['perpetual_chastity'] = ['0.088', '0.317', '-0.29', '0.379', '#interest', '#admiration', 'positive', '0.122', 'oath', 'commitment', 'marriage', 'obedience', 'enter_contract']
senticnet['perpetual_curacy'] = ['0.031', '0.272', '0', '0.231', '#interest', '#admiration', 'positive', '0.178', 'minister', 'church_head', 'pastor', 'protestant', 'evangelist']
senticnet['perpetual_curate'] = ['0.031', '0.272', '0', '0.231', '#interest', '#admiration', 'positive', '0.178', 'minister', 'church_head', 'pastor', 'protestant', 'evangelist']
senticnet['perpetual_easement'] = ['0.621', '-0.60', '0', '0.545', '#joy', '#surprise', 'positive', '0.59', 'privilege', 'perquisite', 'public_easement', 'right_of_way', 'prerogative']
senticnet['perpetual_endowment'] = ['0.608', '-0.55', '0', '0.726', '#joy', '#admiration', 'positive', '0.628', 'genius', 'raw_talent', 'flair', 'gift', 'knack']
senticnet['perpetual_flux'] = ['-0.81', '0', '0.945', '-0.51', '#sadness', '#anger', 'negative', '-0.75', 'occult', 'concealed', 'nonvisual', 'hidden', 'out_of_sight']
senticnet['perpetual_imprisonment'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquirer', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['perpetual_peace'] = ['0.497', '0.453', '-0.23', '0.189', '#joy', '#interest', 'positive', '0.226', 'calm', 'calmness', 'stillness', 'truce', 'love']
senticnet['perpetually'] = ['0', '0', '0.033', '-0.09', '#anger', '#disgust', 'negative', '-0.06', 'need_exercise', 'train_dog', 'tired', 'feel_energetic', 'need_fresh_air']
senticnet['perpetuate'] = ['-0.89', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.81', 'leave_out', 'lose_team', 'cause_suffer', 'disrespect', 'loathing']
senticnet['perpetuate_poverty'] = ['-0.56', '0', '-0.49', '-0.09', '#sadness', '#fear', 'negative', '-0.38', 'lack', 'absence', 'poverty', 'emptiness', 'hunger']
senticnet['perpetuation'] = ['0.988', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.98', 'sustain_life', 'sustenance', 'nutrition', 'keep_alive', 'satisfy_hunger']
senticnet['perpetuity'] = ['-0.60', '0', '0.509', '-0.67', '#sadness', '#disgust', 'negative', '-0.59', 'dogging', 'persisting', 'sustained', 'consecutive', 'continuous']
senticnet['perplex'] = ['0', '-0.85', '0', '0', '#surprise', '#surprise', 'positive', '0.85', 'awestricken', 'awestruck', 'awed', 'astonish', 'astonished']
senticnet['perplexed'] = ['-0.83', '0', '0.915', '-1', '#anger', '#disgust', 'negative', '-0.91', 'at_loss', 'nonplused', 'confused', 'puzzled', 'bewildered']
senticnet['perplexity'] = ['-0.60', '0', '0.631', '-0.68', '#anger', '#disgust', 'negative', '-0.63', 'quandary', 'snarl', 'dilemma', 'confusion', 'secret']
senticnet['perquisite'] = ['0.83', '-0.86', '0', '0.862', '#surprise', '#admiration', 'positive', '0.853', 'benefit', 'appanage', 'provision', 'gratuity', 'tip']
senticnet['perranarworthal'] = ['-0.64', '0', '0.879', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'drone', 'poke', 'dawdler', 'laggard', 'straggler']
senticnet['perri_shake_drayton'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'nazi', 'man', 'pervert', 'healthy_person']
senticnet['perrigo'] = ['0.559', '0.656', '-0.26', '0.285', '#joy', '#interest', 'positive', '0.31', 'zealous', 'avidity', 'eagerness', 'avidness', 'thirstily']
senticnet['persea_americana'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'cabbage', 'lettuce', 'broccoli']
senticnet['persecute'] = ['0', '0', '0.796', '-0.88', '#anger', '#disgust', 'negative', '-0.84', 'persecutor', 'witch-hunt', 'downtrodden', 'martyr', 'persecution']
senticnet['persecution'] = ['0', '0', '0.907', '-0.87', '#anger', '#disgust', 'negative', '-0.89', 'paranoia', 'tribulation', 'campaign', 'persecute', 'subjugate']
senticnet['persecutor'] = ['0', '0', '0.795', '-0.89', '#anger', '#disgust', 'negative', '-0.84', 'persecute', 'witch-hunt', 'downtrodden', 'martyr', 'persecution']
senticnet['perseverance'] = ['0', '-0.75', '0', '0.835', '#surprise', '#admiration', 'positive', '0.794', 'plan', 'insistence', 'action', 'continuing', 'determination']
senticnet['perseverant'] = ['0.667', '-0.97', '0', '0.608', '#joy', '#surprise', 'positive', '0.748', 'unruliness', 'fractiousness', 'willfulness', 'perverseness', 'cussedness']
senticnet['persevere'] = ['0.808', '0.749', '0', '0', '#joy', '#interest', 'positive', '0.778', 'obstinate', 'persist', 'uphold', 'plug', 'bear']
senticnet['persevered'] = ['0.898', '0', '0', '0.902', '#joy', '#admiration', 'positive', '0.9', 'persevere', 'diligent', 'perseverance', 'steadfast', 'persevering']
senticnet['perseverence'] = ['0.605', '-0.93', '0', '0.742', '#surprise', '#admiration', 'positive', '0.761', 'road_sense', 'sagacity', 'sagaciousness', 'logic', 'nous']
senticnet['persevering'] = ['0.898', '0', '0', '0.903', '#joy', '#admiration', 'positive', '0.901', 'persevere', 'diligent', 'perseverance', 'steadfast', 'persevered']
senticnet['persian_cat'] = ['0', '0', '0.022', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'kind_animal', 'wild_animal', 'predator', 'hound', 'fang']
senticnet['persian_gulf'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'harpoon', 'armory', 'lake', 'river', 'bay']
senticnet['persicaria'] = ['0', '0', '-0.14', '-0.03', '#fear', '#disgust', 'negative', '-0.09', 'pine_cone', 'fallen_leaf', 'poison_ivy', 'detritus', 'tress']
senticnet['persil'] = ['0', '0.095', '0', '0.074', '#interest', '#admiration', 'positive', '0.084', 'buy_wash_powder', 'water_soap', 'turn_faucet', 'take_off', 'fill_bathtub_water']
senticnet['persimmon'] = ['0.956', '-0.74', '0', '0', '#joy', '#surprise', 'positive', '0.853', 'persimmon', 'pomegranate', 'apricot', 'nectarine', 'peach']
senticnet['persist'] = ['-0.97', '0', '0.988', '-0.50', '#sadness', '#anger', 'negative', '-0.82', 'persevere', 'hang_in', 'endure', 'hang_on', 'hold_on']
senticnet['persistence'] = ['-0.06', '0.203', '-0.10', '0.147', '#interest', '#admiration', 'positive', '0.045', 'dedication', 'effort', 'eat_properly', 'physical_effort', 'study_language']
senticnet['persistent'] = ['0', '0', '-0.90', '0', '#fear', '#fear', 'negative', '-0.90', 'obstinate', 'stubborn', 'pigheaded', 'mulish', 'headstrong']
senticnet['persistent_cough'] = ['-0.06', '0', '-0.15', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'severe_pain', 'discomfort', 'physical_reaction', 'choke_death', 'embarrassment']
senticnet['persistent_diarrhea'] = ['0.753', '-0.74', '0', '0.587', '#joy', '#surprise', 'positive', '0.695', 'draining', 'exhausting', 'debilitating', 'enervating', 'enfeebling']
senticnet['persistent_drought'] = ['-0.98', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.89', 'dry', 'lose_team', 'leave_out', 'disrespect', 'cause_suffer']
senticnet['persistent_poverty'] = ['-0.25', '0', '-0.17', '-0.08', '#sadness', '#fear', 'negative', '-0.16', 'exclusion', 'exclusion_zone', 'isolation', 'detach', 'except']
senticnet['persistent_unemployment'] = ['0', '0', '0.948', '-0.96', '#anger', '#disgust', 'negative', '-0.95', 'work_much', 'lack_money', 'wage_slavery', 'buy_tool', 'get_paycheck']
senticnet['persistently'] = ['0', '0', '-0.96', '-0.71', '#fear', '#disgust', 'negative', '-0.84', 'abjure', 'screen', 'shunning', 'shun', 'sky']
senticnet['persisting'] = ['-0.60', '0', '0.509', '-0.67', '#sadness', '#disgust', 'negative', '-0.59', 'dogging', 'continuous', 'sustained', 'consecutive', 'perpetual']
senticnet['persists_despite'] = ['-0.50', '0', '0.807', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'powerlessness', 'impotence', 'impotency', 'ineffectiveness', 'inefficaciousness']
senticnet['persnickety'] = ['0', '0', '-0.94', '-0.87', '#fear', '#disgust', 'negative', '-0.90', 'snooty', 'uppish', 'arrogant', 'bigheaded', 'stuck-up']
senticnet['person'] = ['0.921', '0.884', '0', '0', '#joy', '#interest', 'positive', '0.903', 'mortal', 'soul', 'individual', 'human_being', 'creature']
senticnet['person-to-person'] = ['0.846', '0.881', '0', '0', '#joy', '#interest', 'positive', '0.863', 'peer-to-peer', 'human-to-human', 'skin-to-skin', 'cell-to-cell', 'fecal-oral']
senticnet['person_living_alone'] = ['0.051', '0.112', '0', '0', '#joy', '#interest', 'positive', '0.082', 'neighbor', 'family_reunion', 'clan', 'desire', 'little_specific']
senticnet['person_reflexive_pronoun'] = ['-0.84', '0', '0.579', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'squiggle', 'hand', 'chicken_scratch', 'handwriting', 'script']
senticnet['persona'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'role', 'theatrical_role', 'part', 'baddie']
senticnet['personable'] = ['0', '-0.73', '0', '0.88', '#surprise', '#admiration', 'positive', '0.808', 'liked', 'friendly', 'likable', 'amiable', 'unlikable']
senticnet['personage'] = ['0.979', '-0.69', '0', '0.576', '#joy', '#surprise', 'positive', '0.749', 'knocker', 'supremo', 'important_person', 'influential_person', 'big_cheese']
senticnet['personal'] = ['0.946', '0.738', '0', '0', '#joy', '#interest', 'positive', '0.842', 'owner', 'belong', 'own', 'close', 'asset']
senticnet['personal_adornment'] = ['0.059', '0', '0', '0.161', '#joy', '#admiration', 'positive', '0.11', 'accessory', 'make_gold', 'piece_of_jewelry', 'gold', 'jewel']
senticnet['personal_aggrandizement'] = ['0.54', '-0.97', '0', '0.569', '#surprise', '#admiration', 'positive', '0.695', 'overstate', 'hyperbolize', 'glorify', 'exaggerate', 'overdraw']
senticnet['personal_animosity'] = ['-0.12', '0.193', '0.593', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'distaste', 'aversion', 'disapproval', 'disapprove', 'disapproving']
senticnet['personal_belong'] = ['0', '0.211', '-0.17', '0.218', '#interest', '#admiration', 'positive', '0.086', 'ownership', 'own', 'possession', 'house_land', 'holding']
senticnet['personal_bodyguard'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'yeoman', 'outrider', 'yeoman_of_guard', 'beefeater', 'defender']
senticnet['personal_career'] = ['0.961', '0.937', '0', '0', '#joy', '#interest', 'positive', '0.949', 'personal_gain', 'earn_money', 'become_rich', 'finish_work', 'go_work_to_earn_money']
senticnet['personal_cleanliness'] = ['0.024', '-0.00', '0.009', '0.071', '#joy', '#admiration', 'positive', '0.022', 'clean', 'cleanness', 'cleanliness', 'wash_away_dirt', 'run_out_soap']
senticnet['personal_computer'] = ['0', '0', '0.128', '0', '#anger', '#anger', 'negative', '-0.12', 'grid', 'efficient', 'typewriter', 'computer', 'calculator']
senticnet['personal_enjoyment'] = ['0', '0.8', '0', '0.791', '#interest', '#admiration', 'positive', '0.796', 'develop_skill', 'entertainment', 'kill_time', 'pastime', 'play']
senticnet['personal_equation'] = ['-0.9', '0', '0.747', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'variedness', 'variability', 'variegation', 'changeableness', 'changeability']
senticnet['personal_flotation_device'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'lifebelt', 'save_life', 'lifeguard', 'life_jacket', 'safety_equipment']
senticnet['personal_fortune'] = ['0.976', '0.811', '0', '0', '#joy', '#interest', 'positive', '0.893', 'deposit', 'bank_loan', 'wealth', 'finance', 'account']
senticnet['personal_fulfillment'] = ['0.961', '0.937', '0', '0', '#joy', '#interest', 'positive', '0.949', 'create_new_life', 'make_beautiful', 'stimulation', 'lower_stress', 'leisure_time']
senticnet['personal_fulfilment'] = ['0.961', '0.937', '0', '0', '#joy', '#interest', 'positive', '0.949', 'create_new_life', 'make_beautiful', 'stimulation', 'lower_stress', 'leisure_time']
senticnet['personal_gain'] = ['0.961', '0.937', '0', '0', '#joy', '#interest', 'positive', '0.949', 'personal_career', 'earn_money', 'become_rich', 'finish_work', 'go_work_to_earn_money']
senticnet['personal_groom'] = ['0', '0.872', '0', '0.913', '#interest', '#admiration', 'positive', '0.893', 'new_hair_style', 'style_hair', 'fix_hair', 'change_hair_style', 'groom']
senticnet['personal_growth'] = ['0.836', '0.836', '0', '0.72', '#joy', '#interest', 'positive', '0.797', 'make_world_better_place', 'help_others', 'fame', 'improve_world', 'personal_improvement']
senticnet['personal_grudge'] = ['0', '0', '0.901', '-0.92', '#anger', '#disgust', 'negative', '-0.91', 'animosity', 'jealous', 'bitterness', 'grudging', 'enviousness']
senticnet['personal_grudge_against'] = ['0', '0', '0.901', '-0.92', '#anger', '#disgust', 'negative', '-0.91', 'animosity', 'jealous', 'bitterness', 'grudging', 'enviousness']
senticnet['personal_hygiene'] = ['0.076', '-0.00', '0', '0', '#joy', '#surprise', 'positive', '0.042', 'clean_body', 'feel_fresh', 'remove_dirt_body', 'shower', 'feel_refreshed']
senticnet['personal_identification'] = ['0.082', '0.184', '-0.07', '0.208', '#interest', '#admiration', 'positive', '0.1', 'identify_person', 'profile_picture', 'capitalize', 'passport', 'passport_picture']
senticnet['personal_improvement'] = ['0.957', '0', '0', '0', '#joy', '#joy', 'positive', '0.957', 'earn_degree', 'graduate_school', 'get_better_job', 'good_job', 'better_job']
senticnet['personal_income_tax'] = ['-0.83', '0', '0.555', '-0.64', '#sadness', '#disgust', 'negative', '-0.67', 'single_tax', 'income_tax', 'taxation', 'capital_levy', 'departure_tax']
senticnet['personal_invective'] = ['-0.57', '0', '0.79', '-0.89', '#anger', '#disgust', 'negative', '-0.75', 'abuse', 'vilification', 'insult', 'revilement', 'contumely']
senticnet['personal_life'] = ['0.946', '0.738', '0', '0', '#joy', '#interest', 'positive', '0.842', 'owner', 'belong', 'own', 'close', 'asset']
senticnet['personal_manner'] = ['0.646', '-0.69', '0', '0.609', '#joy', '#surprise', 'positive', '0.651', 'good_manner', 'mildness', 'courtesy', 'comportment', 'mien']
senticnet['personal_preference'] = ['0.054', '0.221', '-0.12', '0.236', '#interest', '#admiration', 'positive', '0.098', 'abstract', 'exclamation', 'versatile', 'nice', 'subjective']
senticnet['personal_pronoun'] = ['0', '0.156', '-0.06', '0.241', '#interest', '#admiration', 'positive', '0.11', 'mixture', 'noisy_place', 'make_go', 'move_vehicle', 'big_car']
senticnet['personal_pronoun_distinguish'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'concupiscence', 'lustfulness', 'amorousness']
senticnet['personal_recognizance'] = ['-0.08', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.052', 'bequest', 'excise', 'bribe', 'afford', 'owe']
senticnet['personal_recognizance_bond'] = ['-0.08', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.052', 'bequest', 'excise', 'bribe', 'afford', 'owe']
senticnet['personal_relation'] = ['0.988', '-0.60', '0', '0.7', '#joy', '#admiration', 'positive', '0.766', 'indebtedness', 'bonding', 'obligation', 'relationship', 'human_relationship']
senticnet['personal_relationship'] = ['0.526', '-0.86', '0', '0.954', '#surprise', '#admiration', 'positive', '0.783', 'indebtedness', 'bonding', 'obligation', 'relationship', 'human_relationship']
senticnet['personal_satisfaction'] = ['0.801', '0.779', '0', '0', '#joy', '#interest', 'positive', '0.79', 'demonstrate_skill', 'learn_new_skill', 'educate', 'further_career', 'improve_skill']
senticnet['personal_shopper'] = ['0.875', '0.706', '0', '0', '#joy', '#interest', 'positive', '0.79', 'customer', 'clerk', 'patron', 'vintner', 'hairdresser']
senticnet['personal_trainer'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'coach', 'instructor', 'trainer', 'fit_person', 'train']
senticnet['personal_transport'] = ['0', '0', '0', '0.154', '#admiration', '#admiration', 'positive', '0.154', 'travel', 'locomotion', 'get_place', 'movement', 'go_fast']
senticnet['personal_vendetta'] = ['-0.84', '0', '0.506', '-0.62', '#sadness', '#disgust', 'negative', '-0.66', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['personalism'] = ['-0.99', '0', '0.835', '-0.58', '#sadness', '#anger', 'negative', '-0.80', 'beginning', 'origin', 'root', 'wellspring', 'source']
senticnet['personalist'] = ['-0.99', '0', '0.835', '-0.58', '#sadness', '#anger', 'negative', '-0.80', 'beginning', 'origin', 'root', 'wellspring', 'source']
senticnet['personality'] = ['0', '0.805', '0', '0.79', '#interest', '#admiration', 'positive', '0.798', 'character', 'strong_personality', 'computer_type', 'riddle', 'mystery']
senticnet['personality_disorder'] = ['0', '0', '0.896', '-0.96', '#anger', '#disgust', 'negative', '-0.92', 'antisocial', 'sociopathic', 'psychopathic_personality', 'disturbance', 'schizoid']
senticnet['personality_trait'] = ['-0.04', '0.226', '-0.14', '0.255', '#interest', '#admiration', 'positive', '0.074', 'alive', 'plot', 'always_read', 'animate', 'play_role']
senticnet['personalize'] = ['0.946', '0.738', '0', '0', '#joy', '#interest', 'positive', '0.842', 'owner', 'belong', 'own', 'close', 'asset']
senticnet['personalized_customer_service'] = ['0.971', '0.76', '0', '0', '#joy', '#interest', 'positive', '0.865', 'shopper', 'clerk', 'patron', 'vintner', 'hairdresser']
senticnet['personally'] = ['0.946', '0.738', '0', '0', '#joy', '#interest', 'positive', '0.842', 'owner', 'belong', 'own', 'close', 'asset']
senticnet['personally_meaningful'] = ['0.519', '-0.55', '0', '0.746', '#surprise', '#admiration', 'positive', '0.606', 'narrow_down', 'peg_down', 'nail_down', 'narrow', 'pin_down']
senticnet['personally_responsible'] = ['0.955', '0.945', '0', '0.835', '#joy', '#interest', 'positive', '0.912', 'support_family', 'provide_service', 'accomplish', 'find_job', 'pay_well']
senticnet['personally_thank'] = ['0.561', '-0.79', '0', '0.89', '#surprise', '#admiration', 'positive', '0.748', 'superlative', 'compliment', 'testimonial', 'praise', 'approval']
senticnet['personification'] = ['0.84', '-0.56', '0', '0.561', '#joy', '#surprise', 'positive', '0.654', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['personify'] = ['0.693', '-0.52', '0', '0.82', '#joy', '#admiration', 'positive', '0.681', 'concrete_representation', 'shape', 'representation', 'mental_representation', 'internal_representation']
senticnet['personnel'] = ['0.877', '0.986', '0', '0', '#joy', '#interest', 'positive', '0.931', 'construction_worker', 'duty', 'policeman', 'vocation', 'laborer']
senticnet['personology'] = ['-0.84', '0', '0.585', '-0.85', '#sadness', '#disgust', 'negative', '-0.76', 'subject', 'discipline', 'study', 'concern', 'research']
senticnet['perspective'] = ['0', '0.139', '0', '0.18', '#interest', '#admiration', 'positive', '0.16', 'aspect', 'vista', 'scenic', 'spectacular', 'view']
senticnet['perspective_distortion'] = ['0.906', '-0.60', '0', '0.653', '#joy', '#admiration', 'positive', '0.72', 'opaqueness', 'faintness', 'dimness', 'opacity', 'vagueness']
senticnet['perspective_projection'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'arm_movement', 'hand_gesture', 'water_motion', 'hand_signal', 'sea_movement']
senticnet['perspectivism'] = ['0.534', '-0.86', '0', '0.676', '#surprise', '#admiration', 'positive', '0.692', 'suitability', 'suitableness', 'habitability', 'habitableness', 'qualification']
senticnet['perspicacious'] = ['0.904', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.873', 'perspicaciously', 'perspicacity', 'perspective', 'lucidity', 'perspicuously']
senticnet['perspicaciously'] = ['0.896', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.873', 'perspicuously', 'perspicacity', 'perspective', 'lucidity', 'perspicacious']
senticnet['perspicaciousness'] = ['0.555', '-0.97', '0', '0.974', '#surprise', '#admiration', 'positive', '0.835', 'foxiness', 'slyness', 'wiliness', 'craftiness', 'cunning']
senticnet['perspicacity'] = ['0.626', '-0.96', '0', '0.788', '#surprise', '#admiration', 'positive', '0.794', 'foxiness', 'slyness', 'wiliness', 'craftiness', 'cunning']
senticnet['perspicuity'] = ['0.743', '-0.94', '0', '0.674', '#joy', '#surprise', 'positive', '0.788', 'lucidness', 'clarity', 'clearness', 'lucidity', 'pellucidity']
senticnet['perspicuous'] = ['0', '0.95', '0', '0.975', '#interest', '#admiration', 'positive', '0.962', 'perspicuity', 'express', 'perspicacious', 'conclusion', 'perspicuousness']
senticnet['perspicuously'] = ['0.905', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.873', 'perspicaciously', 'perspicacity', 'perspective', 'lucidity', 'perspicacious']
senticnet['perspicuousness'] = ['0.634', '-0.61', '0', '0.873', '#joy', '#admiration', 'positive', '0.708', 'lucidness', 'clarity', 'clearness', 'lucidity', 'pellucidity']
senticnet['perspiration'] = ['-0.07', '-0.02', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.04', 'sweat', 'breathlessness', 'get_sweaty', 'pull_muscle', 'tired_leg']
senticnet['perspire'] = ['-0.05', '-0.05', '0.057', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'pull_muscle', 'sweat', 'breahard', 'get_tired', 'heavy_breathe']
senticnet['persuade'] = ['-0.09', '-0.03', '0', '-0.11', '#sadness', '#disgust', 'negative', '-0.05', 'exchange_idea', 'share_idea', 'misunderstanding', 'transfer_knowledge', 'exchange_information']
senticnet['persuasion'] = ['0', '0.325', '-0.18', '0.335', '#interest', '#admiration', 'positive', '0.16', 'get_rid', 'cause_change', 'interaction', 'suggest', 'movement_towards']
senticnet['persuasive'] = ['0', '-0.80', '0', '0.793', '#surprise', '#admiration', 'positive', '0.8', 'unpersuasive', 'convincing', 'cogent', 'persuade', 'persuasiveness']
senticnet['persuasive_argument'] = ['0.715', '-0.82', '0', '0.659', '#joy', '#surprise', 'positive', '0.734', 'reasoning', 'logical_thinking', 'abstract_thought', 'casuistry', 'line_of_inquiry']
senticnet['persuasive_evidence'] = ['0.84', '-0.97', '0', '0.713', '#joy', '#surprise', 'positive', '0.844', 'proof', 'bed_check', 'crosscheck', 'parity_check', 'redundancy_check']
senticnet['persuasive_speech'] = ['-0.67', '0', '0.936', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'unprepared', 'extemporary', 'extempore', 'impromptu', 'unrehearsed']
senticnet['persuasively'] = ['0', '0.764', '0', '0.845', '#interest', '#admiration', 'positive', '0.804', 'empathetically', 'empathic', 'empathetic', 'kind-heartedness', 'kindheartedness']
senticnet['persuasiveness'] = ['0', '-0.81', '0', '0.787', '#surprise', '#admiration', 'positive', '0.799', 'unpersuasive', 'convincing', 'cogent', 'persuade', 'persuasive']
senticnet['perswade'] = ['-0.79', '0', '0.607', '-0.92', '#sadness', '#disgust', 'negative', '-0.77', 'stubbornness', 'obstinacy', 'obstinance', 'bullheadedness', 'pigheadedness']
senticnet['pert'] = ['-0.87', '0', '0', '-0.95', '#sadness', '#disgust', 'negative', '-0.91', 'lath', 'shoot', 'memorandum', 'chit', 'sprout']
senticnet['pertain'] = ['0', '0', '0.942', '-0.84', '#anger', '#disgust', 'negative', '-0.89', 'relate', 'bear_on', 'come_to', 'concern', 'refer']
senticnet['pertenue'] = ['-0.99', '0', '0.859', '-0.62', '#sadness', '#anger', 'negative', '-0.82', 'lax', 'loose', 'diarrheal', 'diarrhoeal', 'unconstipated']
senticnet['perth_wildcat_anniversary'] = ['0.977', '-0.93', '0', '0.583', '#joy', '#surprise', 'positive', '0.831', 'giant', 'shinny', 'supernova', 'supergiant', 'binary_star']
senticnet['pertinacious'] = ['-0.96', '0', '0.858', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'contrarious', 'dogged', 'unregenerate', 'cantankerous', 'bolshy']
senticnet['pertinaciously'] = ['0', '0', '-0.97', '-0.96', '#fear', '#disgust', 'negative', '-0.97', 'stubbornly', 'stubborn', 'obduracy', 'opinionated', 'stickle']
senticnet['pertinacity'] = ['0.922', '0.762', '0', '0', '#joy', '#interest', 'positive', '0.842', 'controvert', 'refute', 'confutation', 'negate', 'rebut']
senticnet['pertinence'] = ['0', '0.811', '0', '0.953', '#interest', '#admiration', 'positive', '0.882', 'applicability', 'pertinency', 'research', 'study', 'project']
senticnet['pertinency'] = ['0', '0.811', '0', '0.955', '#interest', '#admiration', 'positive', '0.883', 'pertinence', 'applicability', 'research', 'study', 'project']
senticnet['pertinent'] = ['0', '0.764', '0', '0.737', '#interest', '#admiration', 'positive', '0.75', 'relative', 'salient', 'impertinent', 'relevant', 'relevance']
senticnet['pertinent_information'] = ['0.539', '0.874', '0', '0.843', '#interest', '#admiration', 'positive', '0.752', 'gain_information', 'gain_knowledge', 'increase_knowledge', 'learn_much', 'learn']
senticnet['perturb'] = ['-0.83', '0', '0.564', '-0.79', '#sadness', '#disgust', 'negative', '-0.73', 'upset', 'trouble', 'worry', 'disturb', 'vex']
senticnet['perturbation'] = ['0.807', '-0.88', '0', '0.718', '#joy', '#surprise', 'positive', '0.802', 'sex', 'gayness', 'sexual_gayness', 'lesbianism', 'sapphism']
senticnet['perturbing'] = ['-0.74', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.74', 'disquieted', 'distressing', 'disturbing', 'troubling', 'worrisome']
senticnet['pertussis'] = ['0', '0', '-0.88', '-0.81', '#fear', '#disgust', 'negative', '-0.84', 'polio', 'measles', 'diphtheria', 'smallpox', 'poliomyelitis']
senticnet['pertussis_vaccination'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'medical_therapy', 'vaccine', 'doctor', 'medicine', 'hospital']
senticnet['peruke'] = ['-0.84', '0', '0.633', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'grizzle', 'postiche', 'horsehair_wig', 'periwig', 'wig']
senticnet['perusal'] = ['0.572', '-0.72', '0', '0.521', '#joy', '#surprise', 'positive', '0.606', 'browse', 'reading', 'perusing', 'poring_over', 'studying']
senticnet['peruse'] = ['0', '0.096', '0', '0.075', '#interest', '#admiration', 'positive', '0.085', 'stern', 'quick_look', 'glimpse', 'peep', 'stare']
senticnet['perusing'] = ['0.572', '-0.72', '0', '0.521', '#joy', '#surprise', 'positive', '0.606', 'browse', 'perusal', 'reading', 'poring_over', 'studying']
senticnet['peruvian_cuisine'] = ['0.882', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.822', 'ferment_grape_juice', 'chef_special', 'bottle_wine', 'fine_dining', 'decanter']
senticnet['pervade'] = ['0.968', '-0.76', '0', '0.583', '#joy', '#surprise', 'positive', '0.773', 'riddle', 'diffuse', 'permeate', 'spiritize', 'penetrate']
senticnet['pervading'] = ['0', '0', '0.967', '-0.74', '#anger', '#disgust', 'negative', '-0.85', 'pervasively', 'pervasive', 'wall-to-wall', 'affecting', 'colored']
senticnet['pervading_principle'] = ['0.774', '-0.82', '0', '0.63', '#joy', '#surprise', 'positive', '0.744', 'quality', 'perishability', 'perishableness', 'unsatisfactoriness', 'inadequacy']
senticnet['pervading_sense'] = ['0.772', '-0.95', '0', '0.89', '#surprise', '#admiration', 'positive', '0.872', 'downrightness', 'straightforwardness', 'characteristic', 'immediacy', 'directness']
senticnet['pervasive'] = ['0', '0', '0.976', '-0.73', '#anger', '#disgust', 'negative', '-0.85', 'pervasively', 'pervading', 'wall-to-wall', 'affecting', 'colored']
senticnet['pervasive_corruption'] = ['-0.50', '0', '0.807', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'powerlessness', 'impotence', 'impotency', 'inefficacy', 'inefficaciousness']
senticnet['pervasive_poverty'] = ['-0.84', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.82', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecure']
senticnet['pervasively'] = ['0', '0', '0.966', '-0.73', '#anger', '#disgust', 'negative', '-0.84', 'pervasive', 'pervading', 'wall-to-wall', 'affecting', 'colored']
senticnet['perverse'] = ['0', '0', '0.917', '-0.91', '#anger', '#disgust', 'negative', '-0.91', 'perversely', 'unnatural', 'awkward', 'awry', 'evidence']
senticnet['perverse_incentive'] = ['0.716', '0.813', '0', '0', '#joy', '#interest', 'positive', '0.764', 'bonus', 'reinforce', 'promotion', 'meritocracy', 'gratification']
senticnet['perversely'] = ['0', '0', '0.913', '-0.92', '#anger', '#disgust', 'negative', '-0.91', 'perverse', 'unnatural', 'awkward', 'awry', 'evidence']
senticnet['perverseness'] = ['0.667', '-0.97', '0', '0.608', '#joy', '#surprise', 'positive', '0.748', 'unruliness', 'fractiousness', 'willfulness', 'perseverant', 'cussedness']
senticnet['perversion'] = ['-0.62', '0', '0.515', '-0.63', '#sadness', '#disgust', 'negative', '-0.59', 'immodesty', 'paraphilia', 'exhibitionism', 'sexual_paraphilia', 'fetishism']
senticnet['perversity'] = ['0.885', '-0.65', '0', '0.674', '#joy', '#admiration', 'positive', '0.739', 'unruliness', 'fractiousness', 'willfulness', 'perseverant', 'cussedness']
senticnet['pervert'] = ['0', '0', '0', '0.075', '#admiration', '#admiration', 'positive', '0.075', 'healthy_person', 'philanthropist', 'nazi', 'housekeeper', 'programmer']
senticnet['peseta'] = ['-0.02', '0.264', '0', '0.095', '#interest', '#admiration', 'positive', '0.111', 'finance', 'franc', 'cent', 'banknote', 'mastercard']
senticnet['peshkopi'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
senticnet['pesky'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'miffed', 'galling', 'annoying', 'vexatious', 'teasing']
senticnet['pesme'] = ['-0.63', '0', '0.791', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'little', 'pinch', 'modicum', 'small_indefinite_amount', 'indefinite_quantity']
senticnet['peso'] = ['-0.02', '0.264', '0', '0.095', '#interest', '#admiration', 'positive', '0.111', 'finance', 'franc', 'cent', 'banknote', 'mastercard']
senticnet['peso_coin'] = ['0.81', '0.763', '0', '0', '#joy', '#interest', 'positive', '0.786', 'dollar_bill', 'franc', 'euro', 'dollar', 'cent']
senticnet['peso_devaluation'] = ['-0.02', '0.264', '0', '0.095', '#interest', '#admiration', 'positive', '0.111', 'finance', 'franc', 'cent', 'banknote', 'mastercard']
senticnet['peso_dollar'] = ['-0.02', '0.264', '0', '0.095', '#interest', '#admiration', 'positive', '0.111', 'finance', 'franc', 'cent', 'banknote', 'mastercard']
senticnet['peso_fuerte'] = ['-0.02', '0.264', '0', '0.095', '#interest', '#admiration', 'positive', '0.111', 'finance', 'franc', 'cent', 'banknote', 'mastercard']
senticnet['peso_moneda_nacional'] = ['-0.02', '0.264', '0', '0.095', '#interest', '#admiration', 'positive', '0.111', 'finance', 'franc', 'cent', 'banknote', 'mastercard']
senticnet['peso_per'] = ['-0.02', '0.264', '0', '0.095', '#interest', '#admiration', 'positive', '0.111', 'finance', 'franc', 'cent', 'banknote', 'mastercard']
senticnet['pessary'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'birth_control_pill', 'condom', 'contraceptive_pill', 'sex_toy', 'prevent_pregnancy']
senticnet['pessimism'] = ['-0.71', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.75', 'pessimistically', 'pessimistic', 'dispiritedly', 'hopelessness', 'hopelessly']
senticnet['pessimist'] = ['-0.52', '0', '0.95', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'skeptic', 'sceptic', 'doubter', 'defeatist', 'negativist']
senticnet['pessimistic'] = ['-0.70', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.75', 'pessimism', 'pessimistically', 'dispiritedly', 'hopelessness', 'hopelessly']
senticnet['pessimistic_outlook'] = ['-0.70', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.75', 'pessimism', 'pessimistically', 'dispiritedly', 'hopelessness', 'hopelessly']
senticnet['pessimistic_view'] = ['-0.70', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.75', 'pessimism', 'pessimistically', 'dispiritedly', 'hopelessness', 'hopelessly']
senticnet['pessimistically'] = ['-0.88', '0', '-0.8', '0', '#sadness', '#fear', 'negative', '-0.84', 'pessimism', 'pessimistic', 'dispiritedly', 'hopelessness', 'hopelessly']
senticnet['pest'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'nuisance', 'annoyance', 'irritant', 'pain_in_ass', 'troublesome']
senticnet['pest_insect'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'nuisance', 'annoyance', 'irritant', 'pain_in_ass', 'troublesome']
senticnet['pester'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'annoy', 'irritate', 'trouble', 'nag', 'bother']
senticnet['pestering'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['pesthole'] = ['-0.55', '0', '0.996', '-0.50', '#sadness', '#anger', 'negative', '-0.68', 'breeding_ground', 'rookery', 'tract', 'piece_of_land', 'piece_of_ground']
senticnet['pesthouse'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'lazaret', 'lazarette', 'lazar_house', 'infirmary', 'lazaretto']
senticnet['pesticide_poisoning'] = ['-0.98', '0', '0.717', '-0.51', '#sadness', '#anger', 'negative', '-0.73', 'parathion_poisoning', 'malathion_poisoning', 'toxic_condition', 'poisoning', 'intoxication']
senticnet['pestiferous'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['pestilence'] = ['-0.95', '0', '0.876', '-0.89', '#sadness', '#disgust', 'negative', '-0.90', 'bubonic_plague', 'pestis_bubonica', 'glandular_plague', 'septicemic_plague', 'pneumonic_plague']
senticnet['pestilent'] = ['-0.86', '0', '0.891', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'baneful', 'pernicious', 'vesicatory', 'vesicant', 'noxious']
senticnet['pestilential'] = ['-0.63', '0', '0.674', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'maleficent', 'evil', 'baleful', 'malevolent', 'malign']
senticnet['pestis'] = ['-0.71', '0', '0.604', '-0.77', '#sadness', '#disgust', 'negative', '-0.69', 'bubonic_plague', 'pestis_bubonica', 'glandular_plague', 'septicemic_plague', 'pneumonic_plague']
senticnet['pestis_bubonica'] = ['-0.68', '0', '0.755', '-0.68', '#sadness', '#anger', 'negative', '-0.70', 'pestis', 'plague', 'pestilence', 'pest', 'ambulant_plague']
senticnet['pet'] = ['-0.51', '0', '0.723', '-0.57', '#anger', '#disgust', 'negative', '-0.60', 'animal', 'animate_being', 'beast', 'creature', 'brute']
senticnet['pet_adoption'] = ['-0.78', '0', '0.91', '-0.91', '#anger', '#disgust', 'negative', '-0.87', 'castrate', 'neuter', 'alter', 'emasculate', 'desex']
senticnet['pet_cat'] = ['0', '0', '0', '-0.97', '#disgust', '#disgust', 'negative', '-0.97', 'sit_on_beach', 'clean_sink', 'absorb_moisture', 'mow_grass', 'wash_window']
senticnet['pet_crocodile'] = ['-0.88', '0', '0.864', '-0.72', '#sadness', '#anger', 'negative', '-0.82', 'vixenish', 'venomous', 'bitchy', 'catty', 'malicious']
senticnet['pet_dander'] = ['-0.15', '0.162', '0.596', '-0.62', '#anger', '#disgust', 'negative', '-0.30', 'hackle', 'umbrage', 'offense', 'ire', 'see_red']
senticnet['pet_die'] = ['-0.87', '0', '0.973', '0', '#sadness', '#anger', 'negative', '-0.92', 'complication', 'gonorrhea', 'toothache', 'brain_tumor', 'hear_bad_news']
senticnet['pet_dog'] = ['0.065', '0.215', '0.011', '0.199', '#interest', '#admiration', 'positive', '0.117', 'domesticate_dog', 'domesticate_animal', 'gog', 'cat', 'pet']
senticnet['pet_food'] = ['0.076', '0', '0', '0', '#joy', '#joy', 'positive', '0.076', 'attract_dog', 'food', 'dog', 'cat_food', 'dog_food']
senticnet['pet_fur'] = ['-0.15', '0.162', '0.596', '-0.62', '#anger', '#disgust', 'negative', '-0.30', 'hackle', 'umbrage', 'offense', 'ire', 'see_red']
senticnet['pet_overpopulation'] = ['-0.78', '0', '0.91', '-0.91', '#anger', '#disgust', 'negative', '-0.87', 'castrate', 'neuter', 'alter', 'emasculate', 'desex']
senticnet['pet_owner'] = ['-0.02', '0', '-0.06', '-0.03', '#fear', '#disgust', 'negative', '-0.04', 'alleyway', 'animal_shelter', 'vet_office', 'home', 'doghouse']
senticnet['pet_peeve'] = ['-0.52', '0', '0.607', '-0.54', '#anger', '#disgust', 'negative', '-0.56', 'bewail', 'bemoan', 'mutter', 'scold', 'quetch']
senticnet['pet_rabbit'] = ['-0.88', '0', '0.864', '-0.72', '#sadness', '#anger', 'negative', '-0.82', 'vixenish', 'venomous', 'bitchy', 'catty', 'malicious']
senticnet['pet_rat'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'naked_eye', 'eye', 'oculus_sinister']
senticnet['pet_shop'] = ['0', '-0.06', '0', '0.062', '#surprise', '#admiration', 'positive', '0.062', 'box', 'small_box', 'small_apartment', 'veterinarian_office', 'owner_house']
senticnet['pet_sitter'] = ['-0.02', '0', '-0.06', '-0.03', '#fear', '#disgust', 'negative', '-0.04', 'alleyway', 'animal_shelter', 'vet_office', 'home', 'doghouse']
senticnet['pet_store'] = ['0', '0', '0', '0.087', '#admiration', '#admiration', 'positive', '0.087', 'windsor', 'lair', 'habitat', 'house', 'bee_hive']
senticnet['pet_tarantula'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'naked_eye', 'eye', 'oculus_sinister']
senticnet['peta_nocona'] = ['-0.18', '-0.12', '0.049', '-0.24', '#sadness', '#disgust', 'negative', '-0.09', 'tree_trunk', 'plank', 'artificial_leg', 'hardwood', 'tree_wood']
senticnet['petaid_hospital'] = ['-0.02', '0', '-0.06', '-0.03', '#fear', '#disgust', 'negative', '-0.04', 'alleyway', 'animal_shelter', 'vet_office', 'home', 'doghouse']
senticnet['petal'] = ['0.98', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.916', 'perfume', 'scent', 'candle', 'incense', 'potpourri']
senticnet['petallike'] = ['-0.50', '0', '0.573', '-0.89', '#anger', '#disgust', 'negative', '-0.65', 'leafy', 'bowery', 'foliolate', 'unifoliate', 'bifoliate']
senticnet['petanque'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'golf', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['petar_janjatovi_book'] = ['0.648', '0.627', '-0.26', '0.329', '#joy', '#interest', 'positive', '0.334', 'brotherly', 'friendly', 'fraternal', 'amicability', 'amicable']
senticnet['pete_dragon'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'child_movie', 'child_story', 'funny_moment', 'childhood']
senticnet['pete_dye'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'table_tennis', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['pete_laney'] = ['0.034', '-0.02', '0', '0.057', '#joy', '#admiration', 'positive', '0.039', 'female_television_connector', 'channel_selector', 'power_switch', 'tuner', 'radio']
senticnet['pete_rouse'] = ['0', '0', '0', '0.876', '#admiration', '#admiration', 'positive', '0.876', 'good_manner', 'courteous', 'good_behavior', 'handle_carefully', 'politeness']
senticnet['pete_sake'] = ['-0.98', '0', '0.93', '-0.50', '#sadness', '#anger', 'negative', '-0.80', 'fink', 'snitch', 'snitcher', 'sneak', 'stoolpigeon']
senticnet['petechia'] = ['-0.64', '0', '0.903', '-0.63', '#sadness', '#anger', 'negative', '-0.72', 'jaundice_of_newborn', 'disease', 'icterus_neonatorum', 'kernicterus', 'symptom']
senticnet['petechial'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema', 'erythema_multiforme', 'windburn', 'hickey']
senticnet['peterborough_lift'] = ['0', '0.069', '0', '0.083', '#interest', '#admiration', 'positive', '0.076', 'latch', 'restrict_access', 'knob', 'door_knob', 'gate']
senticnet['petiet'] = ['-0.93', '0', '0.861', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'knotty', 'contorted', 'writhed', 'crooked', 'anfractuous']
senticnet['petit_bourgeoisie'] = ['0.127', '0.069', '0.029', '0.16', '#joy', '#admiration', 'positive', '0.082', 'basic_human_need', 'construct', 'set_down', 'make', 'build_process']
senticnet['petit_larceny'] = ['-0.84', '0', '0.745', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'crime', 'against_law', 'revenge', 'vengeance', 'theft']
senticnet['petit_mal'] = ['-0.05', '0.122', '-0.12', '0.129', '#interest', '#admiration', 'positive', '0.02', 'bad_odor', 'smell_odor', 'inhale', 'whine', 'blow_nose']
senticnet['petit_trianon'] = ['0', '0.105', '0', '0.076', '#interest', '#admiration', 'positive', '0.09', 'big_house', 'mansion', 'castle', 'block_flat', 'medieval']
senticnet['petite_bourgeoisie'] = ['0.127', '0.069', '0.029', '0.16', '#joy', '#admiration', 'positive', '0.082', 'basic_human_need', 'construct', 'set_down', 'make', 'build_process']
senticnet['petitio'] = ['0.623', '-0.53', '0', '0.873', '#joy', '#admiration', 'positive', '0.676', 'logical_fallacy_principii', 'logical_fallacy', 'hysteron_proteron', 'fallacy', 'false_belief']
senticnet['petitio_principii'] = ['0.623', '-0.53', '0', '0.873', '#joy', '#admiration', 'positive', '0.676', 'logical_fallacy', 'petitio', 'hysteron_proteron', 'fallacy', 'false_belief']
senticnet['petition'] = ['-0.03', '0.22', '-0.09', '0.21', '#interest', '#admiration', 'positive', '0.074', 'class_action', 'political_statement', 'require', 'go_against', 'posture']
senticnet['petition_drive'] = ['-0.81', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.81', 'take_charge', 'anti', 'go_against', 'death_wish', 'file_complaint']
senticnet['petitioner'] = ['0', '0', '-0.99', '-0.96', '#fear', '#disgust', 'negative', '-0.98', 'suppliant', 'requester', 'supplicant', 'supplicatory', 'prayer']
senticnet['petitioner_argument'] = ['-0.50', '0', '0.89', '-0.78', '#anger', '#disgust', 'negative', '-0.72', 'excess', 'degree', 'grade', 'level', 'immoderateness']
senticnet['petitioner_contention'] = ['-0.91', '0', '0.795', '-0.94', '#sadness', '#disgust', 'negative', '-0.88', 'ineluctability', 'assured', 'predictability', 'slam_dunk', 'inevitability']
senticnet['petitioner_conviction'] = ['-0.61', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.69', 'irresponsibility', 'untrustiness', 'undependability', 'undependableness', 'unreliability']
senticnet['petitioner_culpability'] = ['-0.61', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.69', 'irresponsibility', 'untrustiness', 'undependability', 'undependableness', 'unreliability']
senticnet['petitioner_pled_guilty'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'fraud', 'swindle', 'rig']
senticnet['petr_kopfstein'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['petri_dish'] = ['-0.86', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.80', 'riverbed', 'bottom_ocean', 'waterfall', 'down_drain', 'septic_tank']
senticnet['petri_plate'] = ['-0.86', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.80', 'riverbed', 'bottom_ocean', 'waterfall', 'down_drain', 'septic_tank']
senticnet['petrie_dish'] = ['-0.86', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.80', 'riverbed', 'bottom_ocean', 'waterfall', 'down_drain', 'septic_tank']
senticnet['petrie_polygon'] = ['0.042', '-0.00', '0.027', '0.099', '#joy', '#admiration', 'positive', '0.031', 'chess_knight', 'chess_queen', 'chess_pawn', 'pawn', 'chess_bishop']
senticnet['petrifaction'] = ['0', '0', '0.847', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'mineral', 'petrify', 'permeate', 'petrifying', 'organic']
senticnet['petrify'] = ['0', '0', '0.852', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'mineral', 'petrifaction', 'permeate', 'petrifying', 'organic']
senticnet['petrifying'] = ['0', '0', '0.849', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'mineral', 'petrifaction', 'permeate', 'petrify', 'organic']
senticnet['petrol'] = ['0', '0', '-0.08', '0', '#fear', '#fear', 'negative', '-0.08', 'butane', 'diesel', 'kerosene', 'fuel', 'gasoline']
senticnet['petrol_engine'] = ['0.068', '0', '0.034', '0.076', '#joy', '#admiration', 'positive', '0.037', 'gallon', 'liquid_measurement', 'ounce', 'measurement', 'quart']
senticnet['petrol_kiosk'] = ['0', '0.088', '0', '0', '#interest', '#interest', 'positive', '0.088', 'buy_food', 'convenience', 'grocery_store', 'corner_shop', 'convenience_store']
senticnet['petrol_station'] = ['0.059', '0.212', '0', '0.143', '#interest', '#admiration', 'positive', '0.138', 'christmas_shop', 'go_grocery_shop', 'pay_credit_card', 'pay_cash', 'holiday_shop']
senticnet['petrolatum'] = ['-0.81', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.82', 'peanut', 'leave_over', 'floppy', 'circular_motion', 'store_vegetable']
senticnet['petroleum'] = ['0', '-0.24', '-0.04', '0.193', '#surprise', '#admiration', 'positive', '0.129', 'fuel_source', 'fossil_fuel', 'fuel', 'energy_source', 'natural_resource']
senticnet['petroleum_jelly'] = ['-0.81', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.82', 'peanut', 'leave_over', 'floppy', 'circular_motion', 'store_vegetable']
senticnet['petrolhead'] = ['0.746', '-0.67', '0', '0.529', '#joy', '#surprise', 'positive', '0.651', 'labor', 'cinch', 'undertaking', 'project', 'task']
senticnet['petrology'] = ['0', '0.831', '0', '0.959', '#interest', '#admiration', 'positive', '0.895', 'lithology', 'geochemistry', 'different_application', 'geological_science', 'award']
senticnet['petsmart'] = ['0.912', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.917', 'carpet_store', 'lumberyard', 'furniture_store', 'front_porch', 'shoe_shop']
senticnet['petticoat'] = ['-0.86', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.89', 'underskirt', 'crinoline', 'tulle', 'camisole', 'corset']
senticnet['petticoated'] = ['-0.53', '0', '0.932', '-0.81', '#anger', '#disgust', 'negative', '-0.76', 'gowned', 'clad', 'surpliced', 'arrayed', 'caparisoned']
senticnet['pettifog'] = ['0', '0', '-0.87', '-1', '#fear', '#disgust', 'negative', '-0.93', 'niggle', 'quiddity', 'bicker', 'quibble', 'cavil']
senticnet['pettifoggery'] = ['-0.99', '0', '0.966', '-0.82', '#sadness', '#anger', 'negative', '-0.93', 'wrangle', 'dustup', 'quarrel', 'row', 'dispute']
senticnet['petting_zoo'] = ['0.877', '-0.80', '0', '0', '#joy', '#surprise', 'positive', '0.843', 'convention', 'prisoner_war_camp', 'rave', 'box_ring', 'dark_alley']
senticnet['pettish'] = ['0.695', '0.623', '-0.23', '0.319', '#joy', '#interest', 'positive', '0.351', 'uproarious', 'hilariously', 'hilarious', 'screaming', 'uproariously']
senticnet['pettishly'] = ['-0.93', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.95', 'peevish', 'way', 'yammer', 'manner', 'peevishly']
senticnet['pettishness'] = ['-0.59', '0', '0.911', '-0.83', '#anger', '#disgust', 'negative', '-0.78', 'biliousness', 'peevishness', 'querulousness', 'snappishness', 'surliness']
senticnet['petts_wood'] = ['0.623', '-0.56', '0', '0.974', '#joy', '#admiration', 'positive', '0.721', 'alterable', 'convertible', 'translatable', 'transformable', 'transmutable']
senticnet['petty'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'minor', 'unimportant', 'inessential', 'negligible', 'trivial']
senticnet['petty_bourgeoisie'] = ['0.127', '0.069', '0.029', '0.16', '#joy', '#admiration', 'positive', '0.082', 'basic_human_need', 'construct', 'set_down', 'make', 'build_process']
senticnet['petty_criminal'] = ['0', '0', '0.821', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'student', 'serious_student', 'addiction', 'graduate_student', 'hard_worker']
senticnet['petty_crook'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['petty_larceny'] = ['-0.75', '0', '0.94', '0', '#sadness', '#anger', 'negative', '-0.84', 'steal_money', 'theft', 'stick_up', 'robbery', 'rob']
senticnet['petty_theft'] = ['-0.75', '0', '0.94', '0', '#sadness', '#anger', 'negative', '-0.84', 'steal_money', 'theft', 'stick_up', 'robbery', 'rob']
senticnet['petty_thief'] = ['-0.87', '0', '0.574', '-0.73', '#sadness', '#disgust', 'negative', '-0.72', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['petulance'] = ['-0.53', '0', '0.512', '-0.82', '#sadness', '#disgust', 'negative', '-0.62', 'ill_humor', 'distemper', 'pet', 'testiness', 'touchiness']
senticnet['petulant'] = ['0', '0', '-0.77', '-0.96', '#fear', '#disgust', 'negative', '-0.87', 'nettlesome', 'fractious', 'peevish', 'irritable', 'cranky']
senticnet['petulantly'] = ['-0.13', '0.083', '0.682', '-0.66', '#anger', '#disgust', 'negative', '-0.35', 'frustrating', 'dun', 'crucify', 'bedevil', 'displeasingly']
senticnet['peugeot_car'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'fuel', 'car', 'fossil_fuel', 'transport', 'natural_resource']
senticnet['pew'] = ['0.065', '0.215', '0.011', '0.199', '#interest', '#admiration', 'positive', '0.117', 'fundamentalist', 'altar', 'confessional', 'chapel', 'pulpit']
senticnet['pewter'] = ['-0.75', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.83', 'plated', 'antiqued', 'brass', 'silver', 'pendant']
senticnet['peyote'] = ['-0.73', '0', '0.944', '-0.94', '#anger', '#disgust', 'negative', '-0.87', 'lysergic_acid_diethylamide', 'mescaline', 'psychedelic_drug', 'phencyclidine', 'drug']
senticnet['peyrol'] = ['-0.44', '-0.15', '0', '-0.36', '#sadness', '#disgust', 'negative', '-0.21', 'baffle', 'stupefy', 'dumbfound', 'stupefaction', 'confuse']
senticnet['pfa_player'] = ['0.232', '0.178', '-0.17', '0.223', '#joy', '#admiration', 'positive', '0.115', 'fame', 'alms', 'find_money', 'better_life', 'small_detail']
senticnet['pfa_young_player'] = ['0.232', '0.178', '-0.17', '0.223', '#joy', '#admiration', 'positive', '0.115', 'fame', 'alms', 'find_money', 'better_life', 'small_detail']
senticnet['pfannkuchen'] = ['0', '-0.11', '0', '0', '#surprise', '#surprise', 'positive', '0.113', 'cake', 'cookie', 'mousse', 'doughnut', 'brownie']
senticnet['pfizer_pfe'] = ['0.559', '0.656', '-0.26', '0.285', '#joy', '#interest', 'positive', '0.31', 'zealous', 'avidity', 'eagerness', 'avidness', 'thirstily']
senticnet['pga_golf'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'table_tennis', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['pga_professional'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'table_tennis', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['pga_tour_player'] = ['0.758', '0.719', '0', '0', '#joy', '#interest', 'positive', '0.738', 'plan_party', 'dance_music', 'become_adult', 'stay_fit', 'drive_crazy']
senticnet['pgce'] = ['-0.04', '0.097', '0.06', '0.146', '#interest', '#admiration', 'positive', '0.036', 'vector', 'ratio', 'length_measurement', 'measurement_unit', 'unit_of_measurement']
senticnet['pge_park'] = ['0', '-0.84', '0', '0.723', '#surprise', '#admiration', 'positive', '0.785', 'circular_route', 'stadium', 'memorial', 'station', 'row_house']
senticnet['ph_level'] = ['0.045', '0', '0', '0.051', '#joy', '#admiration', 'positive', '0.048', 'science', 'battery', 'chemistry', 'alkaline', 'chemical']
senticnet['phablet'] = ['0.027', '-0.09', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'cookie', 'pudding', 'custard', 'candy', 'muffin']
senticnet['phaeolus_schweinitzii'] = ['-0.60', '0', '0.541', '-0.52', '#sadness', '#anger', 'negative', '-0.55', 'swoon', 'pass_out', 'faint', 'zonk_out', 'change_state']
senticnet['phaeomelanin'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['phaerimm'] = ['0.594', '-0.54', '0', '0.518', '#joy', '#surprise', 'positive', '0.554', 'exorcist', 'necromancer', 'magus', 'sorceress', 'witch_doctor']
senticnet['phaethontidae'] = ['-0.68', '0', '0.956', '-0.90', '#anger', '#disgust', 'negative', '-0.84', 'giant_moa', 'anomalopteryx', 'moa', 'moa_bird', 'flightless_bird']
senticnet['phagocyte'] = ['0.702', '-0.52', '0', '0.958', '#joy', '#admiration', 'positive', '0.728', 'somatic_cell', 'vegetative_cell', 'fixed_phagocyte', 'macrophage', 'free_phagocyte']
senticnet['phagocytic'] = ['-0.66', '0', '0.877', '-0.64', '#sadness', '#anger', 'negative', '-0.72', 'somatic_cell', 'mastocyte', 'labrocyte', 'target_cell', 'phagocyte']
senticnet['phagocytic_cell'] = ['0.702', '-0.52', '0', '0.958', '#joy', '#admiration', 'positive', '0.728', 'somatic_cell', 'vegetative_cell', 'fixed_phagocyte', 'macrophage', 'free_phagocyte']
senticnet['phagocytose'] = ['0.702', '-0.52', '0', '0.958', '#joy', '#admiration', 'positive', '0.728', 'somatic_cell', 'vegetative_cell', 'fixed_phagocyte', 'macrophage', 'free_phagocyte']
senticnet['phagolysosome'] = ['0.702', '-0.52', '0', '0.958', '#joy', '#admiration', 'positive', '0.728', 'somatic_cell', 'vegetative_cell', 'fixed_phagocyte', 'macrophage', 'free_phagocyte']
senticnet['phagosome'] = ['0.702', '-0.52', '0', '0.958', '#joy', '#admiration', 'positive', '0.728', 'somatic_cell', 'vegetative_cell', 'fixed_phagocyte', 'macrophage', 'free_phagocyte']
senticnet['phalangeal'] = ['-0.60', '0', '0.701', '-0.72', '#anger', '#disgust', 'negative', '-0.67', 'lunate_bone', 'semilunar_bone', 'carpal_bone', 'triquetral', 'triquetral_bone']
senticnet['phalarope'] = ['0.786', '-0.82', '0', '0.776', '#joy', '#surprise', 'positive', '0.794', 'red_phalarope', 'northern_phalarope', 'shorebird', 'shore_bird', 'limicoline_bird']
senticnet['phalguna'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['phallic_stage'] = ['0.833', '0', '0', '0.807', '#joy', '#admiration', 'positive', '0.82', 'lust', 'amiable', 'give_hug', 'giggle', 'smile']
senticnet['phallocentric'] = ['0.911', '0', '0', '0.85', '#joy', '#admiration', 'positive', '0.881', 'sophisticated', 'lovely', 'elegant', 'contrary', 'common_man']
senticnet['phalloplasty'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'plastic_surgery', 'abdominoplasty', 'tummy_tuck', 'autoplasty']
senticnet['phantasmagoric'] = ['-0.93', '0', '0.94', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'delusive', 'unrealistic', 'unrealistical', 'surrealistic', 'wild']
senticnet['phantasmagorical'] = ['-0.93', '0', '0.94', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'delusive', 'phantasmagoric', 'unrealistic', 'surrealistic', 'wild']
senticnet['phantasmal'] = ['-0.53', '0', '0.637', '-0.92', '#anger', '#disgust', 'negative', '-0.69', 'discorporate', 'unembodied', 'disembodied', 'incorporeal', 'unbodied']
senticnet['phantogram'] = ['0.304', '0.339', '-0.19', '0.333', '#interest', '#admiration', 'positive', '0.195', 'careful', 'reinforce', 'load_bear', 'go_outdoors', 'allowance']
senticnet['phantom'] = ['-0.88', '0', '0.589', '-0.85', '#sadness', '#disgust', 'negative', '-0.77', 'illusion', 'shadow', 'disbelief', 'dark_magic', 'fool']
senticnet['phantom_lady'] = ['-0.58', '0', '0.661', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'explosive', 'cordite', 'dynamite', 'plastic_explosive', 'explosive_compound']
senticnet['phantom_thunder'] = ['-0.07', '0.069', '-0.18', '0', '#sadness', '#fear', 'negative', '-0.06', 'medical_procedure', 'cure', 'doctor_surgery', 'treatment', 'doctor_work']
senticnet['phar_lap_heart'] = ['-0.73', '0', '0.55', '-0.75', '#sadness', '#disgust', 'negative', '-0.68', 'mortal', 'person', 'individual', 'mangler', 'mutilator']
senticnet['pharisaic'] = ['0.889', '-0.69', '0', '0.908', '#joy', '#admiration', 'positive', '0.83', 'pious', 'piousal', 'sanctimonious', 'reverent', 'worshipful']
senticnet['pharisaical'] = ['0.889', '-0.69', '0', '0.908', '#joy', '#admiration', 'positive', '0.83', 'pharisaic', 'pious', 'sanctimonious', 'reverent', 'worshipful']
senticnet['pharisaism'] = ['0.889', '-0.69', '0', '0.908', '#joy', '#admiration', 'positive', '0.83', 'pharisaic', 'pious', 'sanctimonious', 'reverent', 'worshipful']
senticnet['pharmaceutical'] = ['-0.12', '0', '-0.12', '-0.08', '#sadness', '#fear', 'negative', '-0.11', 'hospital', 'check_up', 'check_doctor', 'check_pulse', 'check_blood_pressure']
senticnet['pharmaceutical_chemist'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'druggist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['pharmaceutical_inc'] = ['0.559', '0.656', '-0.26', '0.285', '#joy', '#interest', 'positive', '0.31', 'zealous', 'avidity', 'eagerness', 'avidness', 'thirstily']
senticnet['pharmaceutically'] = ['0', '0.784', '0', '0.849', '#interest', '#admiration', 'positive', '0.817', 'physiologically', 'therapeutically', 'pharmacologically', 'biologically', 'chemically']
senticnet['pharmacist'] = ['0.546', '-0.83', '0', '0.973', '#surprise', '#admiration', 'positive', '0.784', 'pharmacologist', 'pharmaceutical_chemist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['pharmacodynamic'] = ['0.798', '-0.51', '0', '0.519', '#joy', '#admiration', 'positive', '0.611', 'averageness', 'commonness', 'normalcy', 'typicality', 'condition']
senticnet['pharmacologic_therapy'] = ['-0.05', '0', '-0.22', '-0.06', '#fear', '#disgust', 'negative', '-0.11', 'medicine', 'cure', 'medical_procedure', 'sleep_drug', 'nurse']
senticnet['pharmacologic_treatment'] = ['-0.05', '0', '-0.22', '-0.06', '#fear', '#disgust', 'negative', '-0.11', 'medicine', 'cure', 'medical_procedure', 'sleep_drug', 'nurse']
senticnet['pharmacological_activity'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'imperium', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['pharmacological_effect'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'imperium', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['pharmacological_profile'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'imperium', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['pharmacological_therapy'] = ['-0.05', '0', '-0.22', '-0.06', '#fear', '#disgust', 'negative', '-0.11', 'medicine', 'cure', 'medical_procedure', 'sleep_drug', 'nurse']
senticnet['pharmacologically'] = ['0', '0.791', '0', '0.857', '#interest', '#admiration', 'positive', '0.824', 'physiologically', 'therapeutically', 'pharmaceutically', 'biologically', 'chemically']
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senticnet['pharmacology'] = ['0.76', '0.946', '0', '0', '#joy', '#interest', 'positive', '0.853', 'toxicology', 'microbiology', 'biochemistry', 'toxicological', 'pathology']
senticnet['pharmacopoeia'] = ['0.701', '-0.76', '0', '0.904', '#surprise', '#admiration', 'positive', '0.791', 'curative', 'heal', 'medicine', 'get_cured', 'take_care']
senticnet['pharmacotherapy'] = ['-0.03', '-0.02', '-0.08', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'ibuprofen', 'phenobarbital', 'analgesic', 'sleep_drug', 'pill']
senticnet['pharmacy'] = ['-0.02', '-0.00', '-0.07', '-0.11', '#fear', '#disgust', 'negative', '-0.05', 'bathroom_cabinet', 'medicine_cabinet', 'medicine', 'drugstore', 'medicine_chest']
senticnet['pharyngitis'] = ['-0.82', '0', '0.609', '-0.89', '#sadness', '#disgust', 'negative', '-0.77', 'inflammatory_disease', 'streptococcal_sore_throat', 'strep_throat', 'streptococcus_tonsilitis', 'septic_sore_throat']
senticnet['pharyngoesophageal'] = ['0.777', '-0.89', '0', '0.508', '#joy', '#surprise', 'positive', '0.727', 'stretching', 'enlarge', 'enlargement', 'get_bigger', 'distension']
senticnet['pharyngula'] = ['0', '0.331', '-0.09', '0.242', '#interest', '#admiration', 'positive', '0.158', 'spread_idea', 'importance', 'tenet', 'culture', 'idea']
senticnet['pharynx'] = ['0.718', '-0.88', '0', '0.83', '#surprise', '#admiration', 'positive', '0.81', 'throat', 'tubular_cavity', 'cavity', 'bodily_cavity', 'cavum']
senticnet['phase'] = ['0.794', '0.833', '0', '0', '#joy', '#interest', 'positive', '0.814', 'dispersing_phase', 'generation', 'leptotene', 'safe_period', 'diplotene']
senticnet['phase_change'] = ['-0.78', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.87', 'state_change', 'phase_transition', 'physical_change', 'number', 'material_research']
senticnet['phase_equilibrium'] = ['0.976', '0.786', '0', '0', '#joy', '#interest', 'positive', '0.881', 'combustible', 'state_matter', 'natural_resource', 'liquid_fuel', 'fossil_fuel']
senticnet['phase_inverter'] = ['0.056', '-0.01', '0.036', '0.073', '#joy', '#admiration', 'positive', '0.028', 'loud_sound', 'stereo', 'tuner', 'speaker', 'steam_engine']
senticnet['phase_lag'] = ['-0.97', '0', '0.831', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'skewness', 'irregularity']
senticnet['phase_separation'] = ['-0.98', '0', '0.516', '-0.90', '#sadness', '#disgust', 'negative', '-0.80', 'devastation', 'annihilation', 'pulverization', 'vaporization', 'destruction']
senticnet['phase_shift'] = ['0.807', '-0.88', '0', '0.718', '#joy', '#surprise', 'positive', '0.802', 'sex', 'gayness', 'sexual_gayness', 'lesbianism', 'sapphism']
senticnet['phase_transition'] = ['-0.77', '0', '0', '-0.96', '#sadness', '#disgust', 'negative', '-0.87', 'state_change', 'phase_change', 'physical_change', 'number', 'material_research']
senticnet['phasianella'] = ['0.969', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.772', 'aspheric', 'aspherical', 'prolate', 'rounded', 'crescent']
senticnet['phasic'] = ['-0.56', '0', '0.849', '-0.61', '#anger', '#disgust', 'negative', '-0.67', 'convulsive', 'unsteady', 'jerky', 'spasmodic', 'spastic']
senticnet['phat'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['phd'] = ['0', '0.794', '0', '0.986', '#interest', '#admiration', 'positive', '0.89', 'doctoral_degree', 'get_phd', 'get_job', 'pursue_research', 'rich']
senticnet['phd_candidate'] = ['0', '0.909', '0', '0.799', '#interest', '#admiration', 'positive', '0.854', 'research', 'research_topic', 'research_subject', 'search', 'student']
senticnet['phd_degree'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'typeface', 'essay', 'thesis', 'hold_information', 'continue_story']
senticnet['phd_dissertation'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'typeface', 'essay', 'thesis', 'hold_information', 'continue_story']
senticnet['phd_student'] = ['0', '0.909', '0', '0.799', '#interest', '#admiration', 'positive', '0.854', 'research', 'research_topic', 'research_subject', 'search', 'student']
senticnet['pheasant'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'grouse', 'quail', 'partridge', 'wildfowl']
senticnet['phelonion'] = ['0.849', '-0.50', '0', '0.845', '#joy', '#admiration', 'positive', '0.732', 'robe', 'gown', 'veil', 'vest', 'cassock']
senticnet['phencyclidine'] = ['-0.73', '0', '0.944', '-0.94', '#anger', '#disgust', 'negative', '-0.87', 'lysergic_acid_diethylamide', 'mescaline', 'peyote', 'psychedelic_drug', 'drug']
senticnet['phendimetrazine'] = ['-0.72', '0', '0.973', '-0.57', '#sadness', '#anger', 'negative', '-0.76', 'corpulency', 'blubber', 'fat', 'embonpoint', 'stoutness']
senticnet['phenmetrazine'] = ['0.672', '0.632', '-0.23', '0.312', '#joy', '#interest', 'positive', '0.344', 'euphoria', 'buoyancy', 'perkiness', 'jubilance', 'ecstasy']
senticnet['phenobarbital'] = ['0', '-0.02', '-0.06', '-0.19', '#fear', '#disgust', 'negative', '-0.08', 'palliative', 'ibuprofen', 'analgesic', 'painkiller', 'novocaine']
senticnet['phenolic_resin'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'foil', 'tin_foil', 'gold_foil']
senticnet['phenolphthalein'] = ['-0.50', '0', '0.582', '-0.57', '#anger', '#disgust', 'negative', '-0.55', 'purgative', 'cathartic', 'physic', 'aperient', 'laxative']
senticnet['phenomenal'] = ['0', '-0.88', '0', '0.851', '#surprise', '#admiration', 'positive', '0.868', 'incredible', 'amazing', 'remarkable', 'fantastic', 'unbelievable']
senticnet['phenomenal_character'] = ['-0.87', '0', '0.858', '-0.77', '#sadness', '#anger', 'negative', '-0.83', 'pass_off', 'fall_out', 'occur', 'happen', 'hap']
senticnet['phenomenalism'] = ['-0.55', '0', '0.58', '-0.98', '#anger', '#disgust', 'negative', '-0.70', 'fatalist', 'determinist', 'predestinarian', 'predestinationist', 'philosopher']
senticnet['phenomenological'] = ['0.558', '-0.76', '0', '0.568', '#surprise', '#admiration', 'positive', '0.63', 'indefiniteness', 'indeterminateness', 'indefinity', 'indetermination', 'inconclusiveness']
senticnet['phenomenological_approach'] = ['0.534', '-0.86', '0', '0.676', '#surprise', '#admiration', 'positive', '0.692', 'suitability', 'suitableness', 'habitability', 'habitableness', 'qualification']
senticnet['phenomenology'] = ['0.51', '-0.87', '0', '0.775', '#surprise', '#admiration', 'positive', '0.72', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['phenomenon'] = ['0', '0.137', '-0.10', '0.197', '#interest', '#admiration', 'positive', '0.076', 'boom', 'circumstance', 'loud', 'stormy', 'procedure']
senticnet['phenothiazine'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['phenotypic_expression'] = ['-0.78', '0', '0.6', '-0.61', '#sadness', '#disgust', 'negative', '-0.66', 'suggestibility', 'sensitivity', 'capacity', 'reactivity', 'predisposition']
senticnet['phenotypic_heterogeneity'] = ['0.504', '-0.93', '0', '0.797', '#surprise', '#admiration', 'positive', '0.747', 'diabetes', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['phenoxybenzamine'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'spironolactone', 'adrenal_cortical_steroid', 'antihypertensive', 'antihypertensive_drug']
senticnet['phentolamine'] = ['-0.59', '0', '0.537', '-0.57', '#sadness', '#disgust', 'negative', '-0.57', 'virility_drug', 'sildenafil', 'sildenafil_citrate', 'tadalafil', 'vardenafil']
senticnet['phenyl_group'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'enhance', 'intensify', 'deepen', 'potentiate', 'heighten']
senticnet['phenylephrine'] = ['0.887', '-0.9', '0', '0.852', '#joy', '#surprise', 'positive', '0.88', 'pressor', 'adrenergic', 'phenylpropanolamine', 'vasoconstrictor', 'vasoconstrictive']
senticnet['phenylketonuria'] = ['-0.86', '0', '0.76', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['phenylmagnesium_bromide'] = ['0.032', '-0.02', '-0.04', '-0.08', '#fear', '#disgust', 'negative', '-0.01', 'liquid', 'carbonate_drink', 'chemical', 'soda', 'bromide']
senticnet['phenylpropanolamine'] = ['0.887', '-0.9', '0', '0.852', '#joy', '#surprise', 'positive', '0.88', 'pressor', 'phenylephrine', 'adrenergic', 'vasoconstrictor', 'vasoconstrictive']
senticnet['pheomelanin'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['phi_iota_alpha'] = ['0.605', '0.647', '-0.26', '0.323', '#joy', '#interest', 'positive', '0.327', 'friendly', 'brotherly', 'amicableness', 'amicably', 'amicable']
senticnet['phial'] = ['-0.55', '0', '0.5', '-0.55', '#sadness', '#disgust', 'negative', '-0.53', 'remedy', 'cure', 'catholicon', 'therapeutic', 'curative']
senticnet['phiale'] = ['0', '0.082', '-0.09', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'coffin', 'headstone', 'morgue', 'bury_body', 'casket']
senticnet['phil'] = ['-0.75', '0', '0.847', '-0.78', '#anger', '#disgust', 'negative', '-0.79', 'steal', 'theft', 'steal_money', 'burglary', 'stick_up']
senticnet['phil_cornwell'] = ['-0.89', '0', '0.58', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'resemblance', 'comparison', 'equivalence', 'reflection', 'alikeness']
senticnet['phil_urich'] = ['-0.98', '0', '0.714', '-0.90', '#sadness', '#disgust', 'negative', '-0.86', 'stupid', 'stupid_person', 'stupe', 'dullard', 'dolt']
senticnet['philanderer'] = ['-0.63', '0', '0.88', '-0.94', '#anger', '#disgust', 'negative', '-0.81', 'beguiler', 'slicker', 'perjurer', 'false_witness', 'storyteller']
senticnet['philanthropic'] = ['0.979', '0.768', '0', '0', '#joy', '#interest', 'positive', '0.873', 'misogyny', 'misandry', 'altruist', 'altruistic', 'altruistically']
senticnet['philanthropist'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'healthy_person', 'nazi', 'man', 'pervert', 'compatriot']
senticnet['philanthropy'] = ['0.547', '-0.98', '0', '0.977', '#surprise', '#admiration', 'positive', '0.835', 'grant', 'gift', 'social_welfare', 'public_assistance', 'economic_aid']
senticnet['philharmonic'] = ['0.506', '0.659', '-0.24', '0.242', '#joy', '#interest', 'positive', '0.29', 'sing', 'music_studio', 'music_room', 'choir', 'symphony']
senticnet['philharmonic_orchestra'] = ['0.506', '0.659', '-0.24', '0.242', '#joy', '#interest', 'positive', '0.29', 'sing', 'music_studio', 'music_room', 'choir', 'symphony']
senticnet['philia'] = ['0.923', '-0.68', '0', '0.85', '#joy', '#admiration', 'positive', '0.821', 'protectiveness', 'regard', 'respect', 'soft_spot', 'attachment']
senticnet['philidor_position'] = ['0', '0', '0', '0.115', '#admiration', '#admiration', 'positive', '0.115', 'chess_knight', 'strategy', 'pawn', 'chess_queen', 'chess_rook']
senticnet['philip_sonicare'] = ['-0.05', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'plane_land', 'station_wagon', 'floss_tooth', 'ornament', 'wear_dress']
senticnet['philistine'] = ['0.849', '-0.89', '0', '0.924', '#surprise', '#admiration', 'positive', '0.89', 'nonintellectual', 'lowbrow', 'lowbrowed', 'mindless', 'uncultivated']
senticnet['philistinism'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'nonsense', 'meaninglessness', 'bunk', 'absurdity', 'hokum']
senticnet['phillips_screwdriver'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'smart_technology', 'electric_toothbrush', 'hair_dryer', 'stay_healthy', 'wash_tooth']
senticnet['philly'] = ['-0.94', '0', '0.492', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'make_noise', 'disturb', 'confusion', 'hustle_and_bustle', 'bustle']
senticnet['philly_cheesesteak'] = ['0.914', '-0.56', '0', '0.869', '#joy', '#admiration', 'positive', '0.781', 'tabula_rasa', 'ego', 'subconscious', 'unconscious', 'psyche']
senticnet['phillyrea_latifolia'] = ['0', '0.086', '0', '0', '#interest', '#interest', 'positive', '0.086', 'swim_ocean', 'weight_lift', 'sit_in_shade', 'cut_grass', 'trust_friend']
senticnet['philo_farnsworth'] = ['-0.03', '0.201', '-0.13', '0.171', '#interest', '#admiration', 'positive', '0.049', 'scientific_statement', 'hypothetical', 'conjecture', 'theory', 'fact']
senticnet['philological'] = ['0.587', '-0.94', '0', '0.659', '#surprise', '#admiration', 'positive', '0.731', 'critique', 'review', 'criticism', 'literary_criticism', 'new_criticism']
senticnet['philopoemen'] = ['0.8', '-0.79', '0', '0.751', '#joy', '#surprise', 'positive', '0.782', 'elder_statesman', 'stateswoman', 'politician', 'politico', 'pol']
senticnet['philosopher'] = ['0.922', '-0.94', '0', '0.965', '#surprise', '#admiration', 'positive', '0.945', 'utilitarian', 'egalitarian', 'equalitarian', 'elitist', 'moralist']
senticnet['philosopher_stone'] = ['0', '0.887', '0', '0.973', '#interest', '#admiration', 'positive', '0.93', 'servant', 'art_of_transmuting_metal', 'stone', 'alchemy', 'philosopher']
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senticnet['phone_conversation'] = ['0.716', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.733', 'call', 'use_phone', 'orate', 'use_telephone', 'speech']
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senticnet['phone_number'] = ['0.164', '0.13', '0.031', '0.209', '#joy', '#admiration', 'positive', '0.118', 'leave_message', 'write_note', 'leave_document', 'send_email', 'message']
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senticnet['phonemail'] = ['0.164', '0.13', '0.031', '0.209', '#joy', '#admiration', 'positive', '0.118', 'leave_message', 'write_note', 'leave_document', 'send_email', 'message']
senticnet['phoneme'] = ['-0.61', '0', '0.721', '-0.68', '#anger', '#disgust', 'negative', '-0.67', 'character', 'logogram', 'logograph', 'graph', 'ideogram']
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senticnet['phonetic_pronunciation'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicado', 'incommunicative', 'impassive', 'inexpressive', 'silent']
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senticnet['phonetic_symbol'] = ['0.902', '-0.85', '0', '0.898', '#joy', '#admiration', 'positive', '0.885', 'discerning', 'diacritic', 'discriminating', 'discriminative', 'selective']
senticnet['phonetic_value'] = ['-0.86', '0', '0.619', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'character', 'logogram', 'logograph', 'graph', 'grapheme']
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senticnet['phonetically_identical'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicado', 'incommunicative', 'impassive', 'inexpressive', 'silent']
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senticnet['phoney'] = ['0.844', '-0.71', '0', '0.61', '#joy', '#surprise', 'positive', '0.724', 'pretender', 'phony', 'charmer', 'hypocrite', 'dissembler']
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senticnet['phonological_disorder'] = ['-0.56', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.63', 'inarticulate', 'aphonic', 'speechless', 'unarticulated', 'dumb']
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senticnet['phosphate-buffered'] = ['-0.78', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.86', 'icy-cold', 'chilled', 'ice-cold', 'margarita', 'sipping']
senticnet['phosphate_buffered_saline'] = ['-0.04', '0.069', '0', '0.075', '#interest', '#admiration', 'positive', '0.035', 'sculpt_gel', 'hairstyle', 'hairdresser', 'cosmetic', 'hair_sculpt_gel']
senticnet['phosphate_fertilizer'] = ['0.071', '0', '0.031', '0.064', '#joy', '#admiration', 'positive', '0.035', 'organic_matter', 'decompose', 'recycle', 'soil', 'compost_pile']
senticnet['phosphatidylethanolamine'] = ['-0.52', '0', '0.681', '-0.76', '#anger', '#disgust', 'negative', '-0.65', 'triose', 'tetrose', 'pentose', 'simple_sugar', 'aldose']
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senticnet['phosphor_bronze'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'useful_tool']
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senticnet['phosphorus_oxychloride'] = ['0.032', '-0.02', '-0.04', '-0.08', '#fear', '#disgust', 'negative', '-0.01', 'liquid', 'carbonate_drink', 'chemical', 'soda', 'bromide']
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senticnet['phosphorylation'] = ['0.945', '0.748', '0', '0', '#joy', '#interest', 'positive', '0.847', 'protein', 'activation', 'mechanism', 'cell', 'function']
senticnet['phosphorylation_dependent'] = ['-0.84', '0', '0.8', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'rigor_mortis', 'natural_process', 'natural_action', 'activity', 'action']
senticnet['photo'] = ['0.031', '0.103', '0', '0.06', '#interest', '#admiration', 'positive', '0.065', 'graphical_representation', 'crayon', 'artwork', 'photograph', 'varnish']
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senticnet['photo_collage'] = ['0.034', '0.075', '0', '0.143', '#interest', '#admiration', 'positive', '0.084', 'photograph', 'art', 'painting', 'artwork', 'mural']
senticnet['photo_copy'] = ['0.871', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.919', 'color_paper', 'writing_paper', 'printing_paper', 'note_book', 'white_paper']
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senticnet['photo_id'] = ['0.739', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.814', 'receipt', 'bet', 'electric_bill', 'letter_slot', 'deal']
senticnet['photo_id_card'] = ['0.739', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.814', 'receipt', 'bet', 'electric_bill', 'letter_slot', 'deal']
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senticnet['photo_identification_card'] = ['0.739', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.814', 'receipt', 'bet', 'electric_bill', 'letter_slot', 'deal']
senticnet['photo_montage'] = ['0.034', '0.075', '0', '0.143', '#interest', '#admiration', 'positive', '0.084', 'photograph', 'art', 'painting', 'artwork', 'mural']
senticnet['photo_oxidation'] = ['-0.80', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.84', 'popularization', 'profanation', 'demoralization', 'corruption', 'humiliation']
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senticnet['photocatalyst'] = ['0.918', '0.864', '0', '0', '#joy', '#interest', 'positive', '0.891', 'consequence', 'cause', 'bad_action', 'problem', 'political_statement']
senticnet['photocell'] = ['-0.50', '0', '0.75', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'beam', 'beam_of_light', 'sunbeam', 'ray', 'ray_of_light']
senticnet['photochemotherapy'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'radiation_therapy', 'radiation', 'actinotherapy', 'irradiation']
senticnet['photocollage'] = ['0', '0.074', '0', '0.06', '#interest', '#admiration', 'positive', '0.067', 'use_crayon', 'draw', 'pencil_art', 'make_sketch', 'outline']
senticnet['photocopier'] = ['0', '0', '0.128', '0', '#anger', '#anger', 'negative', '-0.12', 'grid', 'efficient', 'calculator', 'computer', 'personal_computer']
senticnet['photocopy'] = ['0.839', '0.927', '0', '0', '#joy', '#interest', 'positive', '0.883', 'fax', 'facsimile', 'telephone', 'copy', 'duplicate']
senticnet['photocurrent'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'emotional_arousal', 'excitement', 'arousal', 'anger']
senticnet['photocyclization'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
senticnet['photodamage'] = ['0', '0', '-0.66', '0', '#fear', '#fear', 'negative', '-0.66', 'anger', 'rage', 'annoyance', 'apprehension', 'fear']
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senticnet['photodiode'] = ['-0.50', '0', '0.75', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'beam', 'beam_of_light', 'sunbeam', 'ray', 'ray_of_light']
senticnet['photodissociation'] = ['0', '0.073', '-0.05', '0.093', '#interest', '#admiration', 'positive', '0.038', 'vapor', 'water_vapor', 'evaporate', 'condense_water', 'bubble']
senticnet['photodynamic'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'radiation_therapy', 'radiation', 'actinotherapy', 'irradiation']
senticnet['photodynamic_therapy'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'radiation_therapy', 'radiation', 'actinotherapy', 'irradiation']
senticnet['photodynamic_therapy_pdt'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'radiation_therapy', 'radiation', 'actinotherapy', 'irradiation']
senticnet['photoelectron'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'emotional_arousal', 'excitement', 'arousal', 'anger']
senticnet['photoemission'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'emotional_arousal', 'excitement', 'arousal', 'anger']
senticnet['photoexcitation'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'emotional_arousal', 'excitement', 'arousal', 'anger']
senticnet['photofrin'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'radiation_therapy', 'radiation', 'actinotherapy', 'irradiation']
senticnet['photograph'] = ['0', '0.074', '-0.04', '0.127', '#interest', '#admiration', 'positive', '0.051', 'picture', 'drawing', 'art', 'painting', 'artwork']
senticnet['photographer'] = ['0.095', '0', '-0.04', '0.062', '#joy', '#admiration', 'positive', '0.037', 'liquid_water', 'water_spray', 'moisture', 'rise_water', 'droplet']
senticnet['photographer_bilal_hussein'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicative', 'unpronounceable', 'impassive', 'inexpressive', 'silent']
senticnet['photographic_image'] = ['0.034', '0.075', '0', '0.143', '#interest', '#admiration', 'positive', '0.084', 'photograph', 'art', 'painting', 'artwork', 'mural']
senticnet['photographic_imagery'] = ['-0.02', '0', '-0.04', '-0.03', '#fear', '#disgust', 'negative', '-0.03', 'studio', 'art_piece', 'art_gallery', 'artist', 'museum']
senticnet['photographic_print'] = ['0.034', '0.075', '0', '0.143', '#interest', '#admiration', 'positive', '0.084', 'photograph', 'art', 'painting', 'artwork', 'mural']
senticnet['photography'] = ['0.044', '0', '0', '0.065', '#joy', '#admiration', 'positive', '0.054', 'fillet_fish', 'clon', 'price_product', 'sculpt', 'demo']
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senticnet['photojournalist'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'adventurer', 'humankind']
senticnet['photoluminescence'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'emotional_arousal', 'excitement', 'arousal', 'anger']
senticnet['photomask'] = ['-0.93', '0', '0.739', '-0.96', '#sadness', '#disgust', 'negative', '-0.88', 'prevarication', 'fairytale', 'fairy_tale', 'fairy_story', 'play']
senticnet['photomedia'] = ['-0.02', '0', '-0.04', '-0.03', '#fear', '#disgust', 'negative', '-0.03', 'studio', 'art_piece', 'art_gallery', 'artist', 'museum']
senticnet['photomerge'] = ['0.838', '0.828', '0', '0', '#joy', '#interest', 'positive', '0.833', 'vista', 'spectacular', 'perspective', 'image', 'scenic']
senticnet['photometric_accuracy'] = ['0', '-0.90', '0', '0.791', '#surprise', '#admiration', 'positive', '0.849', 'precise', 'accuracy', 'correct', 'performance', 'measure']
senticnet['photomicrograph'] = ['0.71', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.85', 'fruit_tree', 'baby_flower', 'carpel', 'strawberry', 'star_anise']
senticnet['photomultiplier_tube'] = ['-0.50', '0', '0.75', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'beam', 'beam_of_light', 'sunbeam', 'ray', 'ray_of_light']
senticnet['photon'] = ['0.807', '0', '0', '0.939', '#joy', '#admiration', 'positive', '0.873', 'ray', 'light', 'electromagnetic_radiation', 'illumination', 'brightness']
senticnet['photon_absorption'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'emotional_arousal', 'excitement', 'arousal', 'anger']
senticnet['photon_emission'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'emotional_arousal', 'excitement', 'arousal', 'anger']
senticnet['photon_energy'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'emotional_arousal', 'excitement', 'arousal', 'anger']
senticnet['photoperiod'] = ['-0.75', '0', '0.512', '-0.75', '#sadness', '#disgust', 'negative', '-0.67', 'inactivity', 'inactiveness', 'slumber', 'inaction', 'hibernation']
senticnet['photophobia'] = ['-0.82', '0', '0.996', '-0.61', '#sadness', '#anger', 'negative', '-0.81', 'visual_impairment', 'visual_defect', 'vision_defect', 'visual_disorder', 'avitaminosis']
senticnet['photophone'] = ['-0.03', '0.201', '-0.13', '0.171', '#interest', '#admiration', 'positive', '0.049', 'scientific_statement', 'hypothetical', 'conjecture', 'theory', 'fact']
senticnet['photopic'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'incandescence', 'luminescence', 'brightness_level']
senticnet['photopic_vision'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'incandescence', 'luminescence', 'brightness_level']
senticnet['photorealistic'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'hardcore', 'declared', 'definitive', 'explicit']
senticnet['photoreceptor_cell_death'] = ['-0.78', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.80', 'deterioration', 'decline', 'decay', 'degradation', 'slide']
senticnet['photosensitivity'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'radiation_therapy', 'radiation', 'actinotherapy', 'irradiation']
senticnet['photosensitizer'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'radiation_therapy', 'radiation', 'actinotherapy', 'irradiation']
senticnet['photoshoot'] = ['-0.98', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.89', 'fairy', 'genus', 'honey_bee', 'infant', 'dove']
senticnet['photosphere'] = ['0.931', '-0.82', '0', '0.709', '#joy', '#surprise', 'positive', '0.822', 'light', 'aureole', 'radiance', 'visible_light', 'visible_radiation']
senticnet['photostability'] = ['0', '0', '-0.66', '0', '#fear', '#fear', 'negative', '-0.66', 'anger', 'rage', 'annoyance', 'apprehension', 'fear']
senticnet['photosynthetic_activity'] = ['-0.93', '0', '0.906', '-0.53', '#sadness', '#anger', 'negative', '-0.79', 'juvenility', 'state', 'immatureness', 'callowness', 'jejuneness']
senticnet['photosynthetic_capacity'] = ['-0.57', '0', '0.767', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'verboseness', 'verbosity', 'prolixity', 'turgidness', 'flatulence']
senticnet['photosynthetic_machinery'] = ['-0.57', '0', '0.767', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'verboseness', 'verbosity', 'prolixity', 'turgidness', 'flatulence']
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senticnet['phototoxicity'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'radiation_therapy', 'radiation', 'actinotherapy', 'irradiation']
senticnet['photovoltaic'] = ['-0.79', '0', '0.997', '-0.68', '#sadness', '#anger', 'negative', '-0.82', 'energy', 'alternative_energy', 'solar_power', 'wind_generation', 'wind_power']
senticnet['photovoltaic_energy'] = ['-0.79', '0', '0.997', '-0.68', '#sadness', '#anger', 'negative', '-0.82', 'energy', 'alternative_energy', 'solar_power', 'wind_generation', 'wind_power']
senticnet['photovoltaic_pv'] = ['-0.79', '0', '0.997', '-0.68', '#sadness', '#anger', 'negative', '-0.82', 'energy', 'alternative_energy', 'solar_power', 'wind_generation', 'wind_power']
senticnet['photovoltaic_solar'] = ['-0.79', '0', '0.997', '-0.68', '#sadness', '#anger', 'negative', '-0.82', 'energy', 'alternative_energy', 'solar_power', 'wind_generation', 'wind_power']
senticnet['photovoltaic_solar_energy'] = ['-0.79', '0', '0.997', '-0.68', '#sadness', '#anger', 'negative', '-0.82', 'energy', 'alternative_energy', 'solar_power', 'wind_generation', 'wind_power']
senticnet['photovoltaic_system'] = ['-0.79', '0', '0.997', '-0.68', '#sadness', '#anger', 'negative', '-0.82', 'energy', 'alternative_energy', 'solar_power', 'wind_generation', 'wind_power']
senticnet['photovoltaic_technology'] = ['-0.79', '0', '0.997', '-0.68', '#sadness', '#anger', 'negative', '-0.82', 'energy', 'alternative_energy', 'solar_power', 'wind_generation', 'wind_power']
senticnet['phragmidium'] = ['-0.93', '0', '0.699', '-0.91', '#sadness', '#disgust', 'negative', '-0.84', 'onion_smut', 'head_smut', 'stinking_smut', 'flag_smut_fungus', 'covered_smut']
senticnet['phragmites_australis_common'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'cattail', 'nailrod', 'reed_mace', 'reedmace']
senticnet['phragmocone'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expandible', 'expansive', 'erectile', 'cavernous']
senticnet['phrasal'] = ['0.904', '-0.58', '0', '0.736', '#joy', '#admiration', 'positive', '0.743', 'naming', 'appellative', 'denotative', 'extensional', 'referent']
senticnet['phrasal_idiom'] = ['0.563', '-0.72', '0', '0.644', '#surprise', '#admiration', 'positive', '0.645', 'idiom', 'idiomatic_expression', 'ruralism', 'set_phrase', 'phrase']
senticnet['phrase'] = ['0', '0.115', '-0.10', '0.249', '#interest', '#admiration', 'positive', '0.087', 'idiom', 'remark', 'grammar_part', 'speech_part', 'sentence']
senticnet['phraseology'] = ['-0.61', '0', '0.73', '-0.85', '#anger', '#disgust', 'negative', '-0.73', 'diction', 'phrasing', 'verbiage', 'wording', 'verbalization']
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senticnet['phreatic'] = ['0.614', '-0.95', '0', '0.584', '#joy', '#surprise', 'positive', '0.718', 'mother_lode', 'champion_lode', 'deposit', 'sedimentation', 'lode']
senticnet['phrenitis'] = ['-0.73', '0', '0.775', '-0.92', '#anger', '#disgust', 'negative', '-0.81', 'rubor', 'redness', 'sleeping_sickness', 'sleepy_sickness', 'epidemic_encephalitis']
senticnet['phrenological'] = ['0', '0.782', '0', '0.763', '#interest', '#admiration', 'positive', '0.772', 'physiognomy', 'pseudoscience', 'mesmerism', 'pseudo-science', 'phrenology']
senticnet['phrenologist'] = ['0.854', '-0.66', '0', '0.867', '#joy', '#admiration', 'positive', '0.797', 'slicker', 'beguiler', 'quack', 'mountebank', 'craniologist']
senticnet['phrenology'] = ['0', '0.787', '0', '0.764', '#interest', '#admiration', 'positive', '0.776', 'physiognomy', 'pseudoscience', 'mesmerism', 'pseudo-science', 'phrenological']
senticnet['phrygian_cap'] = ['0.586', '-0.65', '0', '0.503', '#joy', '#surprise', 'positive', '0.58', 'decoration', 'distinguished_service_cross', 'medal', 'medallion', 'palm']
senticnet['phrygian_mode'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['phthalic_anhydride'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'carboxylic_acid', 'succinic_acid', 'valeric_acid']
senticnet['phthora_nana'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['phto_copy'] = ['0.896', '-0.74', '0', '0', '#joy', '#surprise', 'positive', '0.823', 'white_paper', 'writing_paper', 'printing_paper', 'note_book', 'color_paper']
senticnet['phulambri'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['phulkari'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'anklet', 'ankle_bracelet', 'armilla']
senticnet['phycology'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'study_person', 'subject_study', 'zoo', 'zoologist', 'geology']
senticnet['phylicia_rashad'] = ['0', '0.066', '-0.05', '0.089', '#interest', '#admiration', 'positive', '0.032', 'pineapple', 'kale', 'banana', 'pickle', 'tomato']
senticnet['phyllo'] = ['0', '-0.11', '0', '0', '#surprise', '#surprise', 'positive', '0.113', 'cake', 'cookie', 'mousse', 'doughnut', 'brownie']
senticnet['phyllo_dough'] = ['0', '-0.11', '0', '0', '#surprise', '#surprise', 'positive', '0.113', 'cake', 'cookie', 'mousse', 'doughnut', 'brownie']
senticnet['phylloscopidae_family'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'lovebird', 'parroket', 'paraquet', 'paroquet']
senticnet['phyllosilicate_mineral'] = ['-0.03', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'lithium', 'mineral', 'potassium', 'iodine', 'manganese']
senticnet['phyllospadix'] = ['-0.89', '0', '0.556', '-0.72', '#sadness', '#disgust', 'negative', '-0.72', 'fucus', 'rockweed', 'black_rockweed', 'bladder_fucus', 'tang']
senticnet['phyllosticta'] = ['-0.93', '0', '0.699', '-0.91', '#sadness', '#disgust', 'negative', '-0.84', 'onion_smut', 'head_smut', 'stinking_smut', 'flag_smut_fungus', 'covered_smut']
senticnet['phylogenetic'] = ['-0.83', '0', '0.546', '-0.88', '#sadness', '#disgust', 'negative', '-0.75', 'difference', 'allomerism', 'deviation', 'variation', 'departure']
senticnet['phylogenetic_analysis'] = ['0.89', '0.998', '0', '0', '#joy', '#interest', 'positive', '0.944', 'specie', 'genus', 'evolution', 'study', 'provide']
senticnet['phylogenetic_lineage'] = ['0.504', '-0.93', '0', '0.797', '#surprise', '#admiration', 'positive', '0.747', 'diabetes', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['phylogeny'] = ['0', '0.869', '0', '0.772', '#interest', '#admiration', 'positive', '0.821', 'university', 'study', 'project', 'analysis', 'molecular_data']
senticnet['phylum_annelida'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'annelid', 'annelid_worm', 'leech']
senticnet['phylum_apicomplexa'] = ['0.995', '-0.71', '0', '0.647', '#joy', '#surprise', 'positive', '0.784', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['phylum_nematoda'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'annelid', 'annelid_worm', 'leech']
senticnet['physic'] = ['0', '0.084', '0', '0.065', '#interest', '#admiration', 'positive', '0.075', 'college_major', 'chemistry', 'science', 'geology', 'subject_school']
senticnet['physical'] = ['-0.06', '0.145', '-0.06', '0.115', '#interest', '#admiration', 'positive', '0.034', 'good_shape', 'physical_condition', 'healthy_activity', 'touch_toe', 'elongation']
senticnet['physical_abnormality'] = ['0.789', '-0.99', '0', '0.54', '#joy', '#surprise', 'positive', '0.776', 'imperfection', 'imperfectness', 'bugginess', 'lameness', 'sickness']
senticnet['physical_abuse'] = ['-0.94', '0', '0.923', '-0.98', '#sadness', '#disgust', 'negative', '-0.95', 'heap', 'push', 'accept', 'bring', 'lunge']
senticnet['physical_action'] = ['-0.93', '0', '0.949', '-0.98', '#anger', '#disgust', 'negative', '-0.95', 'fight', 'kick', 'punch', 'slap', 'beat']
senticnet['physical_activity'] = ['0', '0.348', '-0.32', '0.382', '#interest', '#admiration', 'positive', '0.136', 'go_to_gym', 'maintain_muscle_strength', 'release_energy', 'prove_physical_endurance', 'physical_endurance']
senticnet['physical_aggression'] = ['-0.91', '0', '0.451', '-0.98', '#sadness', '#disgust', 'negative', '-0.78', 'slave', 'psychological_aggression', 'female_slave', 'aggression', 'verbal_aggression']
senticnet['physical_altercation'] = ['-0.60', '0', '0.777', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'batrachomyomachia', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['physical_anthropology'] = ['0.822', '0.986', '0', '0', '#joy', '#interest', 'positive', '0.904', 'ethnology', 'social_anthropology', 'archeology', 'anthropology', 'social_science']
senticnet['physical_appearance'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'blonde', 'purple', 'violet', 'dark_light', 'hue']
senticnet['physical_attraction'] = ['0.638', '-0.51', '0', '0.656', '#joy', '#admiration', 'positive', '0.603', 'love', 'aphrodisia', 'lecherousness', 'lustfulness', 'amorousness']
senticnet['physical_barrier'] = ['-0.93', '0', '-0.99', '-0.92', '#sadness', '#fear', 'negative', '-0.95', 'language_barrier', 'shut', 'shut_down', 'close_down', 'obstacle']
senticnet['physical_change'] = ['-0.78', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.87', 'state_change', 'phase_change', 'phase_transition', 'number', 'material_research']
senticnet['physical_chastisement'] = ['-0.83', '0', '0.924', '-0.79', '#sadness', '#anger', 'negative', '-0.85', 'kick_in_butt', 'licking', 'whacking', 'stoning', 'lapidation']
senticnet['physical_chemical_oceanography'] = ['0.885', '0.707', '0', '0', '#joy', '#interest', 'positive', '0.796', 'award', 'continue_grant', 'sponsor', 'chemical_oceanography', 'project']
senticnet['physical_chemistry'] = ['0.771', '0', '0', '0.889', '#joy', '#admiration', 'positive', '0.83', 'chemistry', 'physic', 'spectroscopy', 'project', 'different_application']
senticnet['physical_condition'] = ['0', '0.077', '0', '0.077', '#interest', '#admiration', 'positive', '0.077', 'good_shape', 'fitness', 'physical', 'bodily', 'cause_dehydration']
senticnet['physical_confrontation'] = ['-0.60', '0', '0.777', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'batrachomyomachia', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['physical_contact'] = ['0.704', '0.908', '0', '0', '#joy', '#interest', 'positive', '0.806', 'grab', 'reach_out', 'grasp', 'reach', 'extend_arm']
senticnet['physical_deformity'] = ['-0.13', '0', '0.013', '-0.08', '#sadness', '#disgust', 'negative', '-0.07', 'blind', 'dig_tunnel', 'underground_animal', 'big_mouse', 'small_rodent']
senticnet['physical_desecration'] = ['0.053', '0.068', '-0.05', '0.06', '#interest', '#admiration', 'positive', '0.032', 'linen', 'star_stripe', 'velvet', 'nation', 'burlap']
senticnet['physical_deterioration'] = ['-0.94', '0', '0.923', '-0.98', '#sadness', '#disgust', 'negative', '-0.95', 'heap', 'push', 'accept', 'bring', 'lunge']
senticnet['physical_disability'] = ['-0.77', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.78', 'chicken_pox', 'diabetes', 'lyme_disease', 'brain_tumor', 'suffer_fool']
senticnet['physical_discomfort'] = ['-0.50', '0', '0.681', '-0.89', '#anger', '#disgust', 'negative', '-0.69', 'sweat', 'effort', 'elbow_grease', 'exertion', 'travail']
senticnet['physical_effort'] = ['0', '0', '0', '0.946', '#admiration', '#admiration', 'positive', '0.946', 'persistence', 'effort', 'eat_properly', 'healthy_environment', 'dedication']
senticnet['physical_embodiment'] = ['0.693', '-0.52', '0', '0.82', '#joy', '#admiration', 'positive', '0.681', 'concrete_representation', 'shape', 'representation', 'mental_representation', 'internal_representation']
senticnet['physical_endurance'] = ['0', '0.348', '-0.32', '0.382', '#interest', '#admiration', 'positive', '0.136', 'go_to_gym', 'maintain_muscle_strength', 'release_energy', 'physical_activity', 'prove_physical_endurance']
senticnet['physical_exam'] = ['-0.07', '0', '-0.13', '0', '#sadness', '#fear', 'negative', '-0.10', 'know_healthy', 'nervous_breakdown', 'check_vital_sign', 'examination', 'epileptic_fit']
senticnet['physical_examination'] = ['-0.07', '0', '-0.13', '0', '#sadness', '#fear', 'negative', '-0.10', 'know_healthy', 'nervous_breakdown', 'physical_exam', 'check_vital_sign', 'epileptic_fit']
senticnet['physical_exercise'] = ['-0.02', '0', '0.052', '0', '#sadness', '#anger', 'negative', '-0.04', 'exercise', 'exercise_regularly', 'antioxidant', 'moderation', 'drink_plenty_water']
senticnet['physical_exertion'] = ['-0.05', '-0.03', '0.061', '0', '#sadness', '#anger', 'negative', '-0.02', 'lose_energy', 'expend_energy', 'strain_muscle', 'sweaty', 'expend']
senticnet['physical_exhaustion'] = ['0', '0', '0', '-0.33', '#disgust', '#disgust', 'negative', '-0.33', 'bored', 'wearied', 'tired', 'passive', 'inactive']
senticnet['physical_extermination'] = ['-0.08', '0', '-0.15', '-0.20', '#fear', '#disgust', 'negative', '-0.14', 'casualty', 'reaper', 'wound', 'cause_death', 'gunshot']
senticnet['physical_fitness'] = ['0.051', '-0.03', '0.014', '0', '#joy', '#surprise', 'positive', '0.024', 'exercise', 'keep_shape', 'strengthen_heart', 'keep_fit', 'exercise_dog']
senticnet['physical_gratification'] = ['0.978', '0.81', '0', '0.848', '#joy', '#admiration', 'positive', '0.879', 'brownie_point', 'feel_proud', 'receive_thank', 'appreciate', 'positive_reinforcement']
senticnet['physical_harm'] = ['0.565', '-0.76', '0', '0.982', '#surprise', '#admiration', 'positive', '0.771', 'risk', 'hazard', 'jeopardy', 'health_hazard', 'peril']
senticnet['physical_impairment'] = ['-0.77', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.78', 'chicken_pox', 'diabetes', 'lyme_disease', 'brain_tumor', 'suffer_fool']
senticnet['physical_incapacity'] = ['-0.93', '0', '0.75', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'unhealthiness', 'injury', 'affliction', 'biliousness', 'harm']
senticnet['physical_infirmity'] = ['0.528', '-0.71', '0', '0.848', '#surprise', '#admiration', 'positive', '0.698', 'cachexia', 'cachexy', 'wasting', 'softness', 'unfitness']
senticnet['physical_injury'] = ['-0.12', '-0.05', '0', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'contusion', 'muscle_cramp', 'become_exhausted', 'pull_muscle', 'leg_hurt']
senticnet['physical_interaction'] = ['-0.70', '0', '0.716', '0', '#sadness', '#anger', 'negative', '-0.71', 'uprise', 'anti', 'go_against', 'fight_against', 'disagreement']
senticnet['physical_intimacy'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'liaison', 'affair', 'involvement']
senticnet['physical_manifestation'] = ['0.693', '-0.52', '0', '0.82', '#joy', '#admiration', 'positive', '0.681', 'concrete_representation', 'shape', 'representation', 'mental_representation', 'internal_representation']
senticnet['physical_pain'] = ['-0.88', '0', '-0.73', '0', '#sadness', '#fear', 'negative', '-0.81', 'broken_finger', 'bad_knee', 'car_wreck', 'ill_health', 'suffer']
senticnet['physical_phenomenon'] = ['0.054', '-0.05', '0', '0.098', '#surprise', '#admiration', 'positive', '0.07', 'magnetism', 'boom', 'boil', 'spray', 'vapor']
senticnet['physical_process'] = ['0.671', '-0.56', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'macular_degeneration', 'attack', 'obsolescence', 'process', 'degeneration']
senticnet['physical_property'] = ['-0.81', '0', '0.83', '-0.97', '#anger', '#disgust', 'negative', '-0.87', 'deadness', 'rigidity', 'rigidness', 'stiffness', 'inelasticity']
senticnet['physical_proximity'] = ['0.609', '0.597', '-0.23', '0.398', '#joy', '#interest', 'positive', '0.341', 'near', 'pick_up', 'togetherness', 'gay', 'capture']
senticnet['physical_punishment'] = ['-0.83', '0', '0.924', '-0.79', '#sadness', '#anger', 'negative', '-0.85', 'kick_in_butt', 'licking', 'whacking', 'stoning', 'lapidation']
senticnet['physical_quantity'] = ['-0.06', '0.255', '-0.15', '0.202', '#interest', '#admiration', 'positive', '0.059', 'human_trait', 'quantity', 'movement_towards', 'physic', 'velocity']
senticnet['physical_reaction'] = ['-0.88', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.85', 'painful', 'body_reaction', 'bad_feeling', 'fall_sick', 'stomach_pain']
senticnet['physical_rehabilitation'] = ['0.641', '-0.78', '0', '0.721', '#surprise', '#admiration', 'positive', '0.715', 'correctional_rehabilitation', 'rehabilitation', 'physical_restoration', 'therapeutic_rehabilitation', 'vocational_rehabilitation']
senticnet['physical_resemblance'] = ['-0.88', '0', '0.813', '-0.57', '#sadness', '#anger', 'negative', '-0.75', 'affinity', 'mutual_resemblance', 'alikeness', 'likeness', 'similitude']
senticnet['physical_restoration'] = ['0.641', '-0.78', '0', '0.721', '#surprise', '#admiration', 'positive', '0.715', 'correctional_rehabilitation', 'physical_rehabilitation', 'rehabilitation', 'therapeutic_rehabilitation', 'vocational_rehabilitation']
senticnet['physical_restraint'] = ['-0.83', '0', '0.924', '-0.79', '#sadness', '#anger', 'negative', '-0.85', 'kick_in_butt', 'licking', 'whacking', 'stoning', 'lapidation']
senticnet['physical_science'] = ['0.818', '0', '0', '0.778', '#joy', '#admiration', 'positive', '0.798', 'natural_science', 'physic', 'acoustic', 'directorate', 'science']
senticnet['physical_sens'] = ['0', '0.109', '0.006', '0.087', '#interest', '#admiration', 'positive', '0.063', 'sense_organ', 'hold_glass', 'head_organ', 'organ_body', 'sensory_organ']
senticnet['physical_strength'] = ['0', '0.712', '0', '0.802', '#interest', '#admiration', 'positive', '0.757', 'strength', 'strong', 'manly', 'athlete', 'muscle']
senticnet['physical_therapy'] = ['0.641', '-0.78', '0', '0.721', '#surprise', '#admiration', 'positive', '0.715', 'correctional_rehabilitation', 'physical_rehabilitation', 'physical_restoration', 'therapeutic_rehabilitation', 'vocational_rehabilitation']
senticnet['physical_therapy_profession'] = ['0', '0.097', '0', '0.058', '#interest', '#admiration', 'positive', '0.077', 'occupation', 'career', 'tradesman', 'vocation', 'important_job']
senticnet['physical_trauma'] = ['-0.88', '0', '-0.79', '-0.85', '#sadness', '#disgust', 'negative', '-0.84', 'pain', 'severe_pain', 'agony', 'physical_pain', 'suffer']
senticnet['physical_urticaria'] = ['-0.55', '0', '0.536', '-0.72', '#sadness', '#disgust', 'negative', '-0.60', 'hypersensitivity_reaction', 'rash', 'efflorescence', 'roseola', 'skin_rash']
senticnet['physical_vapor_deposition'] = ['-0.93', '0', '0.739', '-0.96', '#sadness', '#disgust', 'negative', '-0.88', 'prevarication', 'fairytale', 'fairy_tale', 'fairy_story', 'play']
senticnet['physicalism'] = ['-0.92', '0', '0.547', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'assumption', 'boundary_condition', 'provision', 'proviso', 'precondition']
senticnet['physicalist'] = ['-0.55', '0', '0.58', '-0.98', '#anger', '#disgust', 'negative', '-0.70', 'fatalist', 'determinist', 'predestinarian', 'predestinationist', 'philosopher']
senticnet['physicality'] = ['0.772', '-0.95', '0', '0.89', '#surprise', '#admiration', 'positive', '0.872', 'downrightness', 'straightforwardness', 'characteristic', 'directness', 'immediateness']
senticnet['physically'] = ['0.978', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.884', 'inhere', 'cohesion', 'cohere', 'figuratively', 'coherence']
senticnet['physically_abusive'] = ['-0.56', '0', '0.743', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'negligent', 'derelict', 'inattentive', 'delinquent', 'lax']
senticnet['physically_active'] = ['-0.06', '-0.02', '0.021', '-0.06', '#sadness', '#disgust', 'negative', '-0.03', 'clean_window', 'vacuum_carpet', 'repetitive_task', 'run_long_time', 'exercise_daily']
senticnet['physically_aggressive'] = ['0', '0', '-0.85', '-0.93', '#fear', '#disgust', 'negative', '-0.89', 'anger', 'animosity', 'hostility', 'malice', 'grudge']
senticnet['physically_attractive'] = ['0.817', '-0.62', '0', '0.62', '#joy', '#surprise', 'positive', '0.688', 'quality', 'fascination', 'allurement', 'allure', 'temptingness']
senticnet['physically_handicapped_lbph'] = ['-0.22', '-0.03', '0.026', '-0.13', '#sadness', '#disgust', 'negative', '-0.08', 'blind_man', 'blind_person', 'shade', 'need_help', 'shadow']
senticnet['physically_impossible'] = ['-0.80', '0', '-0.80', '-0.96', '#sadness', '#disgust', 'negative', '-0.85', 'unable', 'absurd', 'difficult', 'impractical', 'unimaginable']
senticnet['physically_inactive'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'fatty', 'worse', 'health_issue', 'clinical_problem']
senticnet['physically_painful'] = ['-0.97', '0', '0.969', '-0.50', '#sadness', '#anger', 'negative', '-0.81', 'nervousness', 'tenseness', 'tension', 'nerve', 'stress']
senticnet['physically_present'] = ['0.071', '0.115', '-0.08', '0.167', '#interest', '#admiration', 'positive', '0.066', 'live_inside', 'inhabit', 'live_life', 'protection_weather', 'raise_family']
senticnet['physicalness'] = ['-0.59', '0', '0.747', '-0.89', '#anger', '#disgust', 'negative', '-0.74', 'tangibility', 'tangibleness', 'palpability', 'reality', 'solidness']
senticnet['physician'] = ['0', '0', '-0.16', '-0.12', '#fear', '#disgust', 'negative', '-0.14', 'nurse', 'surgeon', 'doctor', 'medical_doctor', 'cure']
senticnet['physician_assistant_program'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'infant_feeding', 'schedule_feeding', 'demand_feeding', 'breast_feeding', 'feeding']
senticnet['physicist'] = ['0.707', '0', '0', '0', '#joy', '#joy', 'positive', '0.707', 'chemist', 'expert', 'scientist', 'esaminatore', 'analyst']
senticnet['physiognomy'] = ['0', '0.784', '0', '0.754', '#interest', '#admiration', 'positive', '0.769', 'phrenology', 'pseudoscience', 'mesmerism', 'pseudo-science', 'phrenological']
senticnet['physiologic'] = ['0', '0', '-0.93', '-0.95', '#fear', '#disgust', 'negative', '-0.94', 'homeostasis', 'homeostatic', 'circadian', 'plasticity', 'physiological']
senticnet['physiologic_response'] = ['-0.62', '0', '0.901', '-0.71', '#anger', '#disgust', 'negative', '-0.74', 'pressure', 'osmotic_pressure', 'hypotonicity', 'pressure_level', 'force']
senticnet['physiological'] = ['0', '0', '-0.93', '-0.95', '#fear', '#disgust', 'negative', '-0.94', 'homeostasis', 'physiologic', 'circadian', 'plasticity', 'homeostatic']
senticnet['physiological_arousal'] = ['0.971', '0.751', '0', '0.896', '#joy', '#admiration', 'positive', '0.873', 'new_friendship', 'foreplay', 'warm_fuzzy_feeling', 'become_aroused', 'erection']
senticnet['physiological_condition'] = ['0.633', '-0.63', '0', '0.507', '#joy', '#surprise', 'positive', '0.591', 'drug_addiction', 'white_plague', 'physical_condition', 'physiological_state', 'dependance']
senticnet['physiological_dormancy'] = ['-0.75', '0', '0.512', '-0.75', '#sadness', '#disgust', 'negative', '-0.67', 'inactivity', 'inactiveness', 'slumber', 'inaction', 'hibernation']
senticnet['physiological_maturity'] = ['0', '0.075', '0', '0.159', '#interest', '#admiration', 'positive', '0.117', 'many_plants', 'blossom', 'development', 'poppy', 'adult']
senticnet['physiological_reactivity'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'excitation', 'excitement', 'arousal', 'anger']
senticnet['physiological_response'] = ['0.971', '0.751', '0', '0.896', '#joy', '#admiration', 'positive', '0.873', 'new_friendship', 'foreplay', 'warm_fuzzy_feeling', 'become_aroused', 'erection']
senticnet['physiological_state'] = ['0.633', '-0.63', '0', '0.507', '#joy', '#surprise', 'positive', '0.591', 'drug_addiction', 'white_plague', 'physical_condition', 'dependance', 'physiological_condition']
senticnet['physiologically'] = ['0', '0.789', '0', '0.85', '#interest', '#admiration', 'positive', '0.82', 'pharmacologically', 'therapeutically', 'pharmaceutically', 'biologically', 'chemically']
senticnet['physiology'] = ['0.769', '0.958', '0', '0', '#joy', '#interest', 'positive', '0.863', 'bodily_property', 'biology', 'hemodynamics', 'neurophysiology', 'myology']
senticnet['physiopathology'] = ['-0.59', '0', '0.911', '-0.87', '#anger', '#disgust', 'negative', '-0.79', 'osteosclerosis', 'sclerosis', 'disseminated_sclerosis', 'multiple_sclerosis', 'induration']
senticnet['physiotherapist'] = ['0', '0.859', '0', '0.944', '#interest', '#admiration', 'positive', '0.901', 'podiatry', 'dermatologist', 'chiropractor', 'podiatrist', 'foot']
senticnet['physiotherapy'] = ['0.641', '-0.78', '0', '0.721', '#surprise', '#admiration', 'positive', '0.715', 'correctional_rehabilitation', 'physical_rehabilitation', 'physical_restoration', 'therapeutic_rehabilitation', 'vocational_rehabilitation']
senticnet['physique'] = ['0', '0.115', '0', '0.115', '#interest', '#admiration', 'positive', '0.115', 'human_shape', 'below_neck', 'shape', 'human_part', 'hand_foot']
senticnet['phytophthora_capsici'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'apple_rust', 'fungus']
senticnet['phytophthora_cinnamomi'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['phytophthora_root_rot'] = ['-0.83', '0', '0.754', '-0.97', '#sadness', '#disgust', 'negative', '-0.85', 'brown_rot_gummosis', 'ring_rot', 'ring_disease', 'tobacco_wilt', 'gummosis']
senticnet['phytoplankton'] = ['-0.92', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.90', 'biological_oceanography', 'oceanography', 'number', 'ocean_science', 'polar_programsrelated']
senticnet['phytoplankton_bloom'] = ['0', '0.184', '-0.05', '0.179', '#interest', '#admiration', 'positive', '0.104', 'rich', 'a_lot', 'abundant', 'wealth', 'plentiful']
senticnet['phytoplankton_productivity'] = ['0', '0.184', '-0.05', '0.179', '#interest', '#admiration', 'positive', '0.104', 'rich', 'a_lot', 'abundant', 'wealth', 'plentiful']
senticnet['phytoplasma'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'apple_rust', 'fungus']
senticnet['phytotron'] = ['-0.12', '0.066', '-0.08', '-0.06', '#sadness', '#fear', 'negative', '-0.05', 'garden_yard', 'see_sky', 'plant_tree', 'hair', 'seed_lawn']
senticnet['pi_bond'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'elementary_particle', 'neutron', 'electron', 'subatomic_particle', 'proton']
senticnet['pia_lot_of_airplane'] = ['0', '0.727', '0', '0', '#interest', '#interest', 'positive', '0.727', 'stand_alone', 'deal_card_player', 'forward_email', 'bridge_difference', 'rush_job']
senticnet['pia_lot_of_seat'] = ['0.808', '0.761', '0', '0', '#joy', '#interest', 'positive', '0.784', 'airplane_seat', 'small_seat', 'seat_chair', 'airplane', 'pilot']
senticnet['piaculum'] = ['-0.97', '0', '0.772', '-0.67', '#sadness', '#anger', 'negative', '-0.80', 'expiation', 'atonement', 'deliverance', 'redemption', 'rescue']
senticnet['pianism'] = ['0.552', '-0.60', '0', '0.77', '#surprise', '#admiration', 'positive', '0.644', 'benefit', 'performance', 'show', 'routine', 'histrionics']
senticnet['pianist'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'band_play', 'songwriter', 'rock_band']
senticnet['pianistic'] = ['0.607', '-0.99', '0', '0.847', '#surprise', '#admiration', 'positive', '0.817', 'eristic', 'puppetry', 'homiletics', 'aviation', 'airmanship']
senticnet['piano'] = ['-0.98', '0', '0.614', '-0.90', '#sadness', '#disgust', 'negative', '-0.83', 'grand', 'upright', 'grand_piano', 'mechanical_piano', 'player_piano']
senticnet['piano_ballad'] = ['-0.74', '0.292', '-0.42', '-0.06', '#sadness', '#fear', 'negative', '-0.23', 'lowness', 'dispiritedness', 'low-spiritedness', 'dejectedness', 'downheartedness']
senticnet['piano_bar'] = ['0.841', '0', '0', '0.71', '#joy', '#admiration', 'positive', '0.775', 'waiter', 'bar_service', 'beverage_service', 'concierge', 'minibar']
senticnet['piano_doubling_celesta'] = ['-0.95', '0', '0.845', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'grapefruit_peel', 'lemon_peel', 'orange_peel', 'candied_fruit', 'succade']
senticnet['piano_forte'] = ['-0.75', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.80', 'high-strung', 'uptight', 'restive', 'nervy', 'jumpy']
senticnet['piano_intro'] = ['0.023', '0.303', '-0.08', '0.261', '#interest', '#admiration', 'positive', '0.125', 'school_choral', 'drum_set', 'choral', 'string_quartet', 'band']
senticnet['piano_maker'] = ['-0.75', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.80', 'high-strung', 'uptight', 'restive', 'nervy', 'jumpy']
senticnet['piano_melody'] = ['0.023', '0.303', '-0.08', '0.261', '#interest', '#admiration', 'positive', '0.125', 'school_choral', 'drum_set', 'choral', 'string_quartet', 'band']
senticnet['piano_pedagogy'] = ['0.536', '-0.56', '0', '0.567', '#surprise', '#admiration', 'positive', '0.557', 'recreation', 'diversion', 'move', 'motility', 'movement']
senticnet['piano_prodigy'] = ['0.592', '-0.93', '0', '0.935', '#surprise', '#admiration', 'positive', '0.819', 'child', 'small_fry', 'tyke', 'tike', 'kid']
senticnet['piano_repertoire'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'classical_music', 'make_beautiful_music', 'create_music', 'part_of_band', 'make_music']
senticnet['piano_riff'] = ['0.023', '0.303', '-0.08', '0.261', '#interest', '#admiration', 'positive', '0.125', 'school_choral', 'drum_set', 'choral', 'string_quartet', 'band']
senticnet['piano_soloist'] = ['0.506', '0.659', '-0.24', '0.242', '#joy', '#interest', 'positive', '0.29', 'sing', 'music_studio', 'music_room', 'choir', 'symphony']
senticnet['piano_teacher'] = ['0.592', '-0.93', '0', '0.935', '#surprise', '#admiration', 'positive', '0.819', 'child', 'small_fry', 'tyke', 'tike', 'kid']
senticnet['piano_tuner'] = ['0', '0.115', '0', '0.105', '#interest', '#admiration', 'positive', '0.11', 'farrier', 'miner', 'horseshoer', 'blacksmith', 'smith']
senticnet['piano_vocal'] = ['0', '0.176', '0', '0.216', '#interest', '#admiration', 'positive', '0.196', 'choir_stall', 'worshiper', 'pew', 'fundamentalist', 'pulpit']
senticnet['piano_wire'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'hanger', 'hang_up_clothes', 'hang_clothes', 'storage_space', 'laundry']
senticnet['pianoforte'] = ['-0.75', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.80', 'high-strung', 'uptight', 'restive', 'nervy', 'jumpy']
senticnet['pianola'] = ['-0.75', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.80', 'high-strung', 'uptight', 'restive', 'nervy', 'jumpy']
senticnet['piazzale'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'expressway', 'turnpike', 'toll_road', 'highway']
senticnet['pic'] = ['0', '0.703', '0', '0.763', '#interest', '#admiration', 'positive', '0.733', 'picture', 'photograph', 'vulnerability', 'photo', 'exposure']
senticnet['pica'] = ['-0.84', '0', '0.907', '0', '#sadness', '#anger', 'negative', '-0.87', 'pop', 'unexpected', 'unexpectedly', 'impulsion', 'unawares']
senticnet['picaninny'] = ['-0.83', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.78', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['picante'] = ['0.116', '-0.02', '0.025', '0.156', '#joy', '#admiration', 'positive', '0.069', 'dance_move', 'latin_dance', 'body_movement', 'waltz', 'dance']
senticnet['picaridin'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['picasso_portrait'] = ['-0.52', '0', '0.506', '-0.94', '#sadness', '#disgust', 'negative', '-0.65', 'gouger', 'swindler', 'defrauder', 'fraud', 'welcher']
senticnet['picayune'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
senticnet['piccalilli'] = ['-0.81', '0', '0.543', '-0.63', '#sadness', '#disgust', 'negative', '-0.66', 'caper', 'gherkin', 'relish', 'dill_pickle', 'sweet_pickle']
senticnet['piccaninny'] = ['-0.95', '0', '0.624', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['piccolo'] = ['0.82', '-0.54', '0', '0.782', '#joy', '#admiration', 'positive', '0.714', 'fife', 'nose_flute', 'woodwind_instrument', 'woodwind', 'flute']
senticnet['piccolo_trumpet'] = ['0.821', '-0.53', '0', '0.785', '#joy', '#admiration', 'positive', '0.712', 'fife', 'nose_flute', 'piccolo', 'woodwind', 'flute']
senticnet['picea_engelmannii'] = ['-0.94', '0', '0.608', '-0.95', '#sadness', '#disgust', 'negative', '-0.83', 'wild_licorice', 'wild_liquorice', 'alpine_milk_vetch', 'purple_milk_vetch', 'milk_vetch']
senticnet['picea_glauca'] = ['0.776', '-0.6', '0', '0.568', '#joy', '#surprise', 'positive', '0.648', 'swamp_chestnut_oak', 'chinquapin_oak', 'chinkapin_oak', 'yellow_chestnut_oak', 'basket_oak']
senticnet['pick'] = ['0', '0.26', '-0.13', '0.236', '#interest', '#admiration', 'positive', '0.119', 'select', 'selection', 'choose', 'decide', 'compel']
senticnet['pick_apart'] = ['-0.70', '0', '0.769', '-0.62', '#sadness', '#anger', 'negative', '-0.7', 'call_on_carpet', 'take_to_task', 'chide', 'berate', 'bawl_out']
senticnet['pick_at'] = ['-0.82', '0', '0.835', '-0.67', '#sadness', '#anger', 'negative', '-0.77', 'disparage', 'belittle', 'deflate', 'puncture', 'depreciate']
senticnet['pick_axe'] = ['-0.50', '0', '0.937', '-0.59', '#anger', '#disgust', 'negative', '-0.67', 'restorative', 'medicine', 'medication', 'medicament', 'medicinal_drug']
senticnet['pick_berry'] = ['-0.08', '-0.01', '0.017', '-0.06', '#sadness', '#disgust', 'negative', '-0.03', 'jump_air', 'run_far', 'fly', 'monitor_heart_rate', 'pick_flower']
senticnet['pick_flower'] = ['-0.08', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.07', 'find_flower', 'smoke_cigar', 'cross_bridge', 'blow_horn', 'plant_tree']
senticnet['pick_lock'] = ['0', '0.129', '-0.04', '0.07', '#interest', '#admiration', 'positive', '0.05', 'nose', 'lock', 'vision', 'pick', 'stick_out']
senticnet['pick_nose'] = ['-0.71', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'disgust', 'impolite', 'bad_manner', 'odium', 'uneducated']
senticnet['pick_out'] = ['0.837', '-0.89', '0', '0.544', '#joy', '#surprise', 'positive', '0.758', 'choose', 'define', 'screen', 'nominate', 'think_of']
senticnet['pick_team'] = ['0.715', '0.801', '0', '0', '#joy', '#interest', 'positive', '0.758', 'sell_car', 'throw_frisbee', 'market_product', 'price_goods', 'deal_card_player']
senticnet['pick_tooth'] = ['0.723', '0.854', '0', '0', '#joy', '#interest', 'positive', '0.788', 'read_human', 'read', 'find_library', 'borrow_book', 'use_internet']
senticnet['pick_up'] = ['0.573', '0.616', '-0.24', '0.389', '#joy', '#interest', 'positive', '0.333', 'lift_up', 'uplift', 'togetherness', 'exhilarate', 'urge']
senticnet['pick_up_brush'] = ['0.165', '0.069', '0.028', '0.057', '#joy', '#interest', 'positive', '0.066', 'make_paint', 'stir_paint_brush', 'wall_art', 'stir_paint', 'buy_paint']
senticnet['pick_up_litter'] = ['0.935', '0', '0', '0', '#joy', '#joy', 'positive', '0.935', 'clean_up_kitchen', 'feel_dirty', 'remove_stain', 'stink', 'stink_mess']
senticnet['pick_up_pen'] = ['0.972', '0.913', '0', '0', '#joy', '#interest', 'positive', '0.943', 'proofread', 'think_write', 'write_on_paper', 'learn_write', 'run_out_ink']
senticnet['pick_up_telephone_receiver'] = ['0.715', '0.754', '0', '0', '#joy', '#interest', 'positive', '0.734', 'find_phone_number', 'dial_phone_number', 'dial', 'keypad', 'dial_telephone']
senticnet['pick_up_trash'] = ['-0.89', '0', '0', '-0.95', '#sadness', '#disgust', 'negative', '-0.92', 'chore', 'make_happen', 'elbow_grease', 'intuition', 'stop_procrastinate']
senticnet['pick_up_truck'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'trailer', 'trail_car', 'camion', 'wagon', 'towboat']
senticnet['pick_up_woman'] = ['0.896', '0.849', '0', '0', '#joy', '#interest', 'positive', '0.873', 'meet_new_person', 'looser', 'get_wasted', 'drink_beer', 'get_drink']
senticnet['pickaninny'] = ['-0.97', '0', '0.58', '-0.66', '#sadness', '#disgust', 'negative', '-0.74', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['pickaxe'] = ['-0.52', '0', '0.506', '-0.94', '#sadness', '#disgust', 'negative', '-0.65', 'gouger', 'swindler', 'defrauder', 'fraud', 'welcher']
senticnet['pickerel'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['pickerel_frog'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'amphibian', 'aquatic']
senticnet['picket'] = ['0', '0', '0.781', '-0.92', '#anger', '#disgust', 'negative', '-0.85', 'go_against', 'anti', 'political_statement', 'rally', 'angry_mob']
senticnet['picket_sign'] = ['0.021', '0.108', '0', '0', '#joy', '#interest', 'positive', '0.065', 'angry_mob', 'public_gathering', 'several_people', 'picket', 'protest']
senticnet['pickguard'] = ['0.748', '-0.90', '0', '0.99', '#surprise', '#admiration', 'positive', '0.882', 'shakiness', 'quivering', 'shaking', 'vibration', 'quiver']
senticnet['pickin'] = ['0.591', '-0.76', '0', '0.66', '#surprise', '#admiration', 'positive', '0.673', 'darling', 'pet', 'favorite', 'first_choice', 'macushla']
senticnet['picking_up'] = ['-0.75', '0', '0.785', '-0.7', '#sadness', '#anger', 'negative', '-0.74', 'disappointing', 'dissatisfactory', 'unsatisfying', 'unsatisfactory', 'unacceptable']
senticnet['picking_up_trash'] = ['0.935', '0', '0', '0', '#joy', '#joy', 'positive', '0.935', 'clean_up_kitchen', 'feel_dirty', 'remove_stain', 'stink', 'stink_mess']
senticnet['pickle'] = ['-0.81', '0', '0.543', '-0.63', '#sadness', '#disgust', 'negative', '-0.66', 'caper', 'gherkin', 'relish', 'dill_pickle', 'sweet_pickle']
senticnet['pickle_relish'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'horseradish', 'box_cereal', 'bottle_water']
senticnet['pickled_cabbage'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'roll', 'custard', 'bread', 'biscuit']
senticnet['pickled_cucumber'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'roll', 'custard', 'bread', 'biscuit']
senticnet['pickled_fish'] = ['0.9', '0.963', '0', '0', '#joy', '#interest', 'positive', '0.931', 'form_of_food', 'japanese_food', 'uncooked', 'healthy', 'food']
senticnet['pickled_mustard'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'roll', 'custard', 'bread', 'biscuit']
senticnet['pickled_onion'] = ['0.644', '-0.94', '0', '0.729', '#surprise', '#admiration', 'positive', '0.773', 'gem', 'banana_bread', 'date_bread', 'nut_bread', 'quick_bread']
senticnet['pickled_shark'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'hip', 'joint', 'above_leg', 'knee']
senticnet['picklepuss'] = ['-0.87', '0', '0.693', '-0.64', '#sadness', '#anger', 'negative', '-0.73', 'sourpuss', 'unwelcome_person', 'troublemaker', 'trouble_maker', 'troubler']
senticnet['pickpocket'] = ['0', '-0.03', '0.107', '-0.14', '#anger', '#disgust', 'negative', '-0.07', 'acquire_goods', 'buy_meat', 'thief', 'get_need', 'buy_goods']
senticnet['pickup'] = ['-0.62', '0', '0.996', '-0.68', '#anger', '#disgust', 'negative', '-0.76', 'pack', 'face_pack', 'corrective', 'prosthesis', 'prosthetic_device']
senticnet['pickup_bed'] = ['-0.95', '0', '0.793', '0', '#sadness', '#anger', 'negative', '-0.87', 'block', 'obstacle', 'nodus', 'impediment', 'obstruction']
senticnet['pickup_truck'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'trailer', 'trail_car', 'camion', 'wagon', 'towboat']
senticnet['picnic'] = ['0.074', '0', '0', '0', '#joy', '#joy', 'positive', '0.074', 'hike_trail', 'ant_hill', 'compost', 'picnic_table', 'soccer_field']
senticnet['picnic_basket'] = ['0', '0.11', '-0.05', '0.149', '#interest', '#admiration', 'positive', '0.067', 'pack_clothes', 'hold_stuff', 'carry_stuff', 'carry_book', 'carry_belongings']
senticnet['picnic_bench'] = ['0.074', '0', '0', '0', '#joy', '#joy', 'positive', '0.074', 'hike_trail', 'ant_hill', 'compost', 'picnic', 'soccer_field']
senticnet['picnic_blanket'] = ['0.074', '0', '0', '0', '#joy', '#joy', 'positive', '0.074', 'hike_trail', 'ant_hill', 'compost', 'picnic', 'soccer_field']
senticnet['picnic_grove'] = ['0.056', '-0.01', '0.036', '0.073', '#joy', '#admiration', 'positive', '0.028', 'thrill_ride', 'have_fun', 'drag_race', 'enjoyment', 'roller_coaster']
senticnet['picnic_lunch'] = ['0.074', '0', '0', '0', '#joy', '#joy', 'positive', '0.074', 'hike_trail', 'ant_hill', 'compost', 'picnic', 'soccer_field']
senticnet['picnic_pavilion'] = ['0.074', '0', '0', '0', '#joy', '#joy', 'positive', '0.074', 'hike_trail', 'ant_hill', 'compost', 'picnic', 'soccer_field']
senticnet['picnic_shelter'] = ['0.074', '0', '0', '0', '#joy', '#joy', 'positive', '0.074', 'hike_trail', 'ant_hill', 'compost', 'picnic', 'soccer_field']
senticnet['picnic_table'] = ['0.074', '0', '0', '0', '#joy', '#joy', 'positive', '0.074', 'hike_trail', 'ant_hill', 'compost', 'picnic', 'soccer_field']
senticnet['picrotoxin'] = ['0.95', '-0.95', '0', '0.769', '#joy', '#surprise', 'positive', '0.89', 'adrenocorticotropic', 'adrenocorticotrophic', 'stimulant', 'stimulating', 'stimulative']
senticnet['pict_file'] = ['-0.55', '0', '0.912', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'wrangle', 'dustup', 'quarrel', 'row', 'squabble']
senticnet['pictionary'] = ['0.801', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.796', 'verbosity', 'sport_play', 'excite_game', 'contest', 'fun_game']
senticnet['pictographic'] = ['-0.86', '0', '0.619', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'character', 'logogram', 'logograph', 'graph', 'grapheme']
senticnet['pictorial'] = ['0.804', '0', '0', '0.945', '#joy', '#admiration', 'positive', '0.875', 'lifelike', 'graphic', 'vivid', 'bright', 'brilliant']
senticnet['pictorial_representation'] = ['0.777', '-0.66', '0', '0.839', '#joy', '#admiration', 'positive', '0.76', 'elaborate', 'lucubrate', 'expatiate', 'exposit', 'enlarge']
senticnet['pictorial_space'] = ['-0.88', '0', '0.589', '-0.85', '#sadness', '#disgust', 'negative', '-0.77', 'phantom', 'shadow', 'disbelief', 'dark_magic', 'fool']
senticnet['picture'] = ['0.034', '0.075', '0', '0.143', '#interest', '#admiration', 'positive', '0.084', 'photograph', 'art', 'painting', 'artwork', 'mural']
senticnet['picture_frame'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'frame', 'artwork', 'mural', 'tile', 'smoke_alarm']
senticnet['picture_of_family'] = ['0.901', '0.961', '0', '0', '#joy', '#interest', 'positive', '0.931', 'wife', 'old_photograph', 'family_picture', 'mum', 'photo_album']
senticnet['picture_person'] = ['0', '0.164', '0', '0', '#interest', '#interest', 'positive', '0.164', 'equity', 'buy_paint_brush', 'buy_paint', 'pick_up_brush', 'use_oil_paint']
senticnet['picture_scene'] = ['0.903', '0.932', '0', '0', '#joy', '#interest', 'positive', '0.917', 'take_photograph', 'climb_ladder', 'photo', 'frame_picture', 'knock_door']
senticnet['pictured'] = ['0.709', '-0.78', '0', '0.534', '#joy', '#surprise', 'positive', '0.675', 'depicted', 'diagrammatic', 'portrayed', 'described', 'delineated']
senticnet['picturesque'] = ['0.842', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.806', 'specular', 'unpicturesque', 'chocolate-box', 'idyllic', 'painting']
senticnet['picturesque_fishing'] = ['0.203', '0.077', '0', '0.189', '#joy', '#admiration', 'positive', '0.156', 'good_life', 'coast', 'relax', 'sediment', 'shell']
senticnet['picturesque_scenery'] = ['0.896', '0', '0', '0.709', '#joy', '#admiration', 'positive', '0.802', 'perspective', 'vista', 'view', 'aspect', 'see_view']
senticnet['picturesque_town'] = ['0.203', '0.077', '0', '0.189', '#joy', '#admiration', 'positive', '0.156', 'good_life', 'coast', 'relax', 'sediment', 'shell']
senticnet['piddle'] = ['0', '0', '-0.77', '-0.91', '#fear', '#disgust', 'negative', '-0.84', 'dalliance', 'trifling', 'wanton_away', 'wanton', 'dawdling']
senticnet['piddling'] = ['-0.73', '0', '0.895', '0', '#sadness', '#anger', 'negative', '-0.81', 'woeful', 'paltriness', 'trivial', 'paltry', 'shabby']
senticnet['pidgin'] = ['0.947', '0.815', '0', '0', '#joy', '#interest', 'positive', '0.881', 'functional', 'utility', 'practice', 'utilitarian', 'practical']
senticnet['pie'] = ['0.039', '-0.09', '0', '0', '#joy', '#surprise', 'positive', '0.068', 'cookie', 'muffin', 'bagel', 'pudding', 'brownie']
senticnet['pie-in-the-sky'] = ['0.813', '0', '0', '0.918', '#joy', '#admiration', 'positive', '0.865', 'sunshiny', 'blue-sky', 'drizzly', 'cloudless', 'sun-soaked']
senticnet['pie_crust'] = ['0', '-0.11', '0', '0', '#surprise', '#surprise', 'positive', '0.113', 'cake', 'cookie', 'mousse', 'doughnut', 'brownie']
senticnet['pie_dish'] = ['0.03', '-0.05', '0', '0.059', '#surprise', '#admiration', 'positive', '0.046', 'platter', 'fork', 'spoon', 'tablespoon', 'bowl']
senticnet['pie_pan'] = ['0.03', '-0.05', '0', '0.059', '#surprise', '#admiration', 'positive', '0.046', 'platter', 'fork', 'spoon', 'tablespoon', 'bowl']
senticnet['pie_plate'] = ['0.03', '-0.05', '0', '0.059', '#surprise', '#admiration', 'positive', '0.046', 'platter', 'fork', 'spoon', 'tablespoon', 'bowl']
senticnet['piece'] = ['0.871', '0.947', '0', '0', '#joy', '#interest', 'positive', '0.909', 'sting', 'tidbit', 'nip', 'bite', 'tiny_amount']
senticnet['piece_of_art'] = ['0', '0.939', '0', '0.847', '#interest', '#admiration', 'positive', '0.893', 'current_event', 'cnn', 'piece_of_paper', 'paper_piece', 'paper']
senticnet['piece_of_cake'] = ['0.795', '-0.79', '0', '0.79', '#joy', '#surprise', 'positive', '0.792', 'labor', 'doddle', 'undertaking', 'project', 'task']
senticnet['piece_of_change'] = ['0.957', '0.93', '0', '0', '#joy', '#interest', 'positive', '0.944', 'cash_bill', 'paper_bill', 'debit_card', 'dollar_bill', 'cash_coin']
senticnet['piece_of_clothing'] = ['0.893', '0.782', '0', '0', '#joy', '#interest', 'positive', '0.838', 'clothes', 'fashion', 'robe', 'dress', 'shirt']
senticnet['piece_of_ground'] = ['-0.55', '0', '0.996', '-0.50', '#sadness', '#anger', 'negative', '-0.68', 'pesthole', 'rookery', 'tract', 'piece_of_land', 'breeding_ground']
senticnet['piece_of_information'] = ['0', '0.111', '0', '0.07', '#interest', '#admiration', 'positive', '0.09', 'data', 'system_abstract_think', 'eiffel_tower', 'landmark', 'monument']
senticnet['piece_of_jewelry'] = ['0.039', '-0.00', '0.031', '0.154', '#joy', '#admiration', 'positive', '0.041', 'precious', 'bell_sound', 'wedding_band', 'jewelry', 'jewel']
senticnet['piece_of_land'] = ['-0.55', '0', '0.996', '-0.50', '#sadness', '#anger', 'negative', '-0.68', 'pesthole', 'rookery', 'tract', 'breeding_ground', 'piece_of_ground']
senticnet['piece_of_material'] = ['-0.03', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'measure_inch', 'scale', 'measure_tool', 'ruler', 'measurement_tool']
senticnet['piece_of_meat'] = ['-0.03', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.04', 'keep_fresh', 'appetizer', 'archeology', 'porridge', 'food']
senticnet['piece_of_metal'] = ['-0.09', '-0.00', '-0.05', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'gunpowder', 'projectile', 'ammunition', 'lethal', 'automatic']
senticnet['piece_of_music'] = ['0.54', '-0.57', '0', '0.678', '#surprise', '#admiration', 'positive', '0.598', 'musical_composition', 'opus', 'composition', 'piece', 'adagio']
senticnet['piece_of_paper'] = ['0.047', '-0.10', '0.026', '-0.04', '#joy', '#surprise', 'positive', '0.021', 'paper', 'fasten_paper_together', 'bind_paper_together', 'narrowness', 'keep_paper_together']
senticnet['piece_of_story'] = ['0', '0.111', '0', '0.07', '#interest', '#admiration', 'positive', '0.09', 'beautiful_view', 'tourist_attraction', 'iconic_place', 'eiffel_tower', 'monument']
senticnet['piece_of_wood'] = ['0', '-0.06', '0.046', '0.09', '#surprise', '#admiration', 'positive', '0.038', 'dog_fetch', 'wood', 'adhere', 'long_wood', 'branch']
senticnet['piece_together'] = ['0', '0.203', '0.036', '0.204', '#interest', '#admiration', 'positive', '0.124', 'assemble', 'feel_heat', 'see_sky', 'close_together', 'fly_around']
senticnet['pieced_together'] = ['0', '0.211', '-0.12', '0.286', '#interest', '#admiration', 'positive', '0.125', 'bring_together', 'fuse', 'redo', 'fuse_together', 'request']
senticnet['piecewise'] = ['-0.85', '0', '-0.76', '0', '#sadness', '#fear', 'negative', '-0.80', 'polynomial', 'quadratic', 'equation', 'polynomial', 'approximation']
senticnet['piecing_together'] = ['0', '0.211', '-0.12', '0.286', '#interest', '#admiration', 'positive', '0.125', 'bring_together', 'fuse', 'redo', 'fuse_together', 'request']
senticnet['pied_cormorant'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'lovebird', 'parroket', 'paraquet', 'paroquet']
senticnet['pied_flycatcher'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['pied_kingfisher'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'amoeba', 'single_celled']
senticnet['pied_oystercatcher'] = ['-0.84', '0', '0.655', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'piping_crow', 'butcherbird', 'currawong', 'bell_magpie', 'oscine']
senticnet['pied_wagtail'] = ['-0.84', '0', '0.655', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'piping_crow', 'butcherbird', 'currawong', 'bell_magpie', 'oscine']
senticnet['piedra'] = ['-0.63', '0', '0.897', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'cd-rom', 'international_candle', 'candle', 'candlepower_unit', 'standard_candle']
senticnet['pier'] = ['0.024', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.08', 'train_station', 'bus', 'subway_station', 'public_place', 'bus_station']
senticnet['pierce'] = ['-0.09', '-0.01', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.06', 'cut_food', 'spread_butter', 'open_package', 'pick_out', 'lacerate']
senticnet['pierce_arrow'] = ['-0.04', '-0.16', '0', '0.056', '#surprise', '#admiration', 'positive', '0.059', 'car_part', 'under_car', 'car_wheel', 'wheel', 'car']
senticnet['piercing'] = ['0', '0', '0.865', '-0.97', '#anger', '#disgust', 'negative', '-0.92', 'incisive', 'thrust', 'pierce', 'acute', 'discriminating']
senticnet['piercing_cry'] = ['0', '0', '-0.91', '-0.72', '#fear', '#disgust', 'negative', '-0.81', 'loudness', 'vibration', 'echo', 'detect', 'decibel']
senticnet['piercing_scream'] = ['0', '0', '-0.80', '0', '#fear', '#fear', 'negative', '-0.80', 'shout', 'yell', 'annoy', 'groan', 'cry_help']
senticnet['piercing_shriek'] = ['0', '0', '-0.91', '-0.72', '#fear', '#disgust', 'negative', '-0.81', 'loudness', 'vibration', 'echo', 'detect', 'decibel']
senticnet['piero_manzoni'] = ['-0.52', '0', '0.506', '-0.94', '#sadness', '#disgust', 'negative', '-0.65', 'gouger', 'swindler', 'defrauder', 'fraud', 'welcher']
senticnet['pierogi'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'roll', 'custard', 'bread', 'biscuit']
senticnet['pierroth'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'find_out', 'dig_out', 'unearth', 'become_aware', 'dish_out']
senticnet['pierrothito'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'find_out', 'dig_out', 'unearth', 'become_aware', 'dish_out']
senticnet['pierzynski'] = ['0.663', '-0.68', '0', '0.892', '#surprise', '#admiration', 'positive', '0.747', 'saphead', 'muggins', 'tomfool', 'fool', 'sap']
senticnet['pieter_bruegel'] = ['0', '0', '0', '0.79', '#admiration', '#admiration', 'positive', '0.79', 'greatness', 'absolute', 'sec', 'respectful', 'sentence_end']
senticnet['pieter_brueghel'] = ['0', '0', '0', '0.79', '#admiration', '#admiration', 'positive', '0.79', 'greatness', 'absolute', 'sec', 'respectful', 'sentence_end']
senticnet['pietism'] = ['-0.83', '0', '0.554', '-0.53', '#sadness', '#anger', 'negative', '-0.63', 'religiosity', 'religionism', 'religiousism', 'religiousness', 'piety']
senticnet['pietistic'] = ['0', '0', '-0.74', '-0.85', '#fear', '#disgust', 'negative', '-0.80', 'pietistical', 'sanctimonious', 'pharisaic', 'holier-than-thou', 'pharisaical']
senticnet['pietistical'] = ['0', '0', '-0.75', '-0.86', '#fear', '#disgust', 'negative', '-0.80', 'sanctimonious', 'pietistic', 'pharisaic', 'holier-than-thou', 'pharisaical']
senticnet['pietroassa'] = ['0.052', '0', '0', '0.118', '#joy', '#admiration', 'positive', '0.085', 'diamond_ring', 'piece_of_jewelry', 'bell_sound', 'wedding_band', 'engagement']
senticnet['piett'] = ['0.688', '-0.90', '0', '0.877', '#surprise', '#admiration', 'positive', '0.824', 'flag_officer', 'rear_admiral', 'admiral', 'full_admiral', 'vice_admiral']
senticnet['piety'] = ['0.681', '-0.68', '0', '0.938', '#surprise', '#admiration', 'positive', '0.768', 'righteousness', 'godliness', 'dutifulness', 'devoutness', 'religiousness']
senticnet['piezoelectric_actuator'] = ['-0.54', '0', '0.909', '-0.97', '#anger', '#disgust', 'negative', '-0.81', 'suppleness', 'pliancy', 'pliantness', 'flexibility', 'flexibleness']
senticnet['piezoelectric_effect'] = ['0.102', '0.078', '0', '0.087', '#joy', '#admiration', 'positive', '0.089', 'gold', 'platinum', 'iron_ore', 'silver', 'adjustable']
senticnet['piezoelectric_transducer'] = ['0.048', '0.084', '0', '0.209', '#interest', '#admiration', 'positive', '0.114', 'move', 'motion', 'motility', 'movement', 'flicker']
senticnet['piffle'] = ['0.714', '-0.63', '0', '0.692', '#joy', '#admiration', 'positive', '0.68', 'nonsense', 'meaninglessness', 'bunk', 'nonsensicality', 'hokum']
senticnet['pig'] = ['-0.72', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.77', 'pig', 'cow', 'rabbit', 'hog', 'sheep']
senticnet['pig_breeding'] = ['0.546', '-0.96', '0', '0.533', '#joy', '#surprise', 'positive', '0.683', 'dairy_cattle', 'dairy_cow', 'milch_cow', 'ayrshire', 'milcher']
senticnet['pig_meat'] = ['0.039', '-0.00', '0.018', '0.075', '#joy', '#admiration', 'positive', '0.026', 'high_protein', 'red_meat', 'meat_cut', 'pork', 'dairy_product']
senticnet['pig_snout'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'naked_eye', 'eye', 'oculus_sinister']
senticnet['pigalle'] = ['-0.80', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.79', 'whorehouse', 'brothel', 'massage_parlor', 'bagnio', 'house_of_prostitution']
senticnet['pigeon'] = ['0', '0', '-0.83', '-0.76', '#fear', '#disgust', 'negative', '-0.80', 'dragon', 'dragoon', 'bidet', 'cavalryman', 'horse']
senticnet['pigeon_breast'] = ['-0.85', '0', '0.695', '-0.65', '#sadness', '#anger', 'negative', '-0.73', 'affliction', 'deformity', 'chicken_breast', 'clubfoot', 'talipes']
senticnet['pigeon_columba'] = ['-0.03', '-0.01', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'shit', 'wildflower', 'poop', 'wild_flower', 'dog_poop']
senticnet['pigeon_pea'] = ['0.061', '0.093', '0.037', '0.114', '#interest', '#admiration', 'positive', '0.058', 'whole_grain', 'food_ingredient', 'staple_food', 'starchy_food', 'food_grain']
senticnet['pigging_out'] = ['-0.98', '0', '0.594', '-0.98', '#sadness', '#disgust', 'negative', '-0.85', 'fast_food', 'deep_fried', 'unhealthy_food', 'reuse_oil', 'junk_food']
senticnet['piggly_wiggly'] = ['0.056', '0.157', '0', '0.154', '#interest', '#admiration', 'positive', '0.122', 'home_depot', 'lumberyard', 'carpet_store', 'front_porch', 'shoe_shop']
senticnet['piggy'] = ['0.934', '-0.57', '0', '0.767', '#joy', '#admiration', 'positive', '0.76', 'morosoph', 'simpleton', 'flibbertigibbet', 'foolish_woman', 'simple']
senticnet['piggy_bank'] = ['0', '0.187', '0', '0', '#interest', '#interest', 'positive', '0.187', 'billfold', 'wallet', 'bank', 'coffer', 'bank_vault']
senticnet['piggybank'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'rudeness', 'discourtesy', 'insolence', 'ccheekiness', 'cheekiness']
senticnet['pigheaded'] = ['0', '0', '-0.90', '0', '#fear', '#fear', 'negative', '-0.90', 'obstinate', 'persistent', 'stubborn', 'mulish', 'headstrong']
senticnet['pigheadedness'] = ['-0.63', '0', '0.942', '-0.52', '#sadness', '#anger', 'negative', '-0.70', 'stubbornness', 'obstinacy', 'obstinance', 'bullheadedness', 'impenitence']
senticnet['piglet'] = ['0.778', '0.925', '0', '0', '#joy', '#interest', 'positive', '0.852', 'suckle', 'suckling', 'suckled', 'lamb', 'weaned']
senticnet['pigment'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['pigment_melanin'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['pigmentation'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['pigmy'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'gnome', 'elf', 'goblin', 'undersized', 'small_person']
senticnet['pignut_hickory'] = ['0.776', '-0.6', '0', '0.568', '#joy', '#surprise', 'positive', '0.648', 'swamp_chestnut_oak', 'chinquapin_oak', 'chinkapin_oak', 'yellow_chestnut_oak', 'basket_oak']
senticnet['pigpen'] = ['-0.93', '0', '0.791', '0', '#sadness', '#anger', 'negative', '-0.86', 'hopelessness', 'hopelessly', 'despairing', 'pessimistically', 'pessimistic']
senticnet['pigtail'] = ['-0.06', '-0.01', '0.05', '-0.07', '#sadness', '#disgust', 'negative', '-0.04', 'embroidery', 'woven', 'cover_wind', 'torn', 'use_needle']
senticnet['pihkal'] = ['-0.73', '0', '0.944', '-0.94', '#anger', '#disgust', 'negative', '-0.87', 'lysergic_acid_diethylamide', 'mescaline', 'peyote', 'phencyclidine', 'drug']
senticnet['pikachu'] = ['0.028', '0', '0.027', '0.101', '#joy', '#admiration', 'positive', '0.034', 'animal_dwell', 'bee_home', 'live_place', 'dwell_place', 'place_to_live']
senticnet['pike'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['pilaster'] = ['0.029', '0.087', '0.065', '0.137', '#interest', '#admiration', 'positive', '0.047', 'archway', 'aisle', 'mat', 'passageway', 'right_angle']
senticnet['pilates_method'] = ['0', '0', '0', '0.93', '#admiration', '#admiration', 'positive', '0.93', 'lift_weight', 'release_stress', 'move_body', 'take_hike', 'change_color']
senticnet['pile'] = ['-0.02', '-0.01', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.03', 'dung', 'manure', 'shit', 'dog_poop', 'crap']
senticnet['pile_up'] = ['-0.59', '0', '0.604', '-0.95', '#anger', '#disgust', 'negative', '-0.71', 'run_up', 'roll_up', 'collect', 'accumulate', 'amass']
senticnet['piled_high'] = ['0', '0', '0.943', '-0.8', '#anger', '#disgust', 'negative', '-0.87', 'feel_dirty', 'unclean', 'dirty', 'yucky', 'filthy']
senticnet['piles'] = ['-0.64', '0', '0.87', '-0.74', '#anger', '#disgust', 'negative', '-0.75', 'disease', 'sick', 'discomfort', 'pain_in_ass', 'pain']
senticnet['pileup'] = ['-0.90', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.86', 'trauma', 'car_wreck', 'broken_bone', 'bad_knee', 'wreck']
senticnet['pilewise'] = ['0.03', '0', '0.017', '0.138', '#joy', '#admiration', 'positive', '0.05', 'up_down', 'curl', 'bump', 'hand_gesture', 'goodbye']
senticnet['pilfer'] = ['0', '0', '-0.77', '-0.88', '#fear', '#disgust', 'negative', '-0.83', 'cabbage', 'pinch', 'purloin', 'abstract', 'snarf']
senticnet['pilferage'] = ['-0.55', '0', '0.843', '-0.58', '#anger', '#disgust', 'negative', '-0.66', 'thieving', 'larceny', 'theft', 'thievery', 'stealing']
senticnet['pilgrimage'] = ['0.937', '0.8', '0', '0', '#joy', '#interest', 'positive', '0.869', 'quest', 'stunt', 'illusion', 'adventure', 'activity']
senticnet['pill'] = ['-0.05', '-0.01', '-0.08', '-0.19', '#fear', '#disgust', 'negative', '-0.08', 'sleep_drug', 'analgesic', 'ibuprofen', 'palliative', 'medicine']
senticnet['pill_dispenser'] = ['0', '0', '0', '0.109', '#admiration', '#admiration', 'positive', '0.109', 'knob', 'door_knob', 'door_handle', 'handle', 'hinge']
senticnet['pill_pusher'] = ['0.638', '-0.88', '0', '0.843', '#surprise', '#admiration', 'positive', '0.787', 'pharmacologist', 'pharmaceutical_chemist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['pill_roller'] = ['0.584', '-0.62', '0', '0.989', '#surprise', '#admiration', 'positive', '0.732', 'pharmacologist', 'pharmaceutical_chemist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['pillage'] = ['0', '0', '-0.85', '-0.83', '#fear', '#disgust', 'negative', '-0.84', 'booty', 'loot', 'plunder', 'swag', 'prize']
senticnet['pillar'] = ['0', '0.082', '0.009', '0.135', '#interest', '#admiration', 'positive', '0.069', 'vertical', 'support_structure', 'column', 'temple', 'backside']
senticnet['pillar_box'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'mailman', 'package', 'mail', 'parcel', 'letter_slot']
senticnet['pillared'] = ['0.891', '-0.98', '0', '0.692', '#joy', '#surprise', 'positive', '0.857', 'amphistylar', 'colonnaded', 'columnar', 'columned', 'columniform']
senticnet['pillaton'] = ['0.049', '0.16', '0', '0.155', '#interest', '#admiration', 'positive', '0.121', 'castle', 'palace', 'medieval', 'aristocrat', 'throne']
senticnet['pillion_passenger'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'fuel', 'car', 'fossil_fuel', 'transport', 'natural_resource']
senticnet['pillock'] = ['-0.73', '0', '0.836', '-0.69', '#sadness', '#anger', 'negative', '-0.75', 'dunce', 'dunderhead', 'numskull', 'blockhead', 'bonehead']
senticnet['pillory'] = ['-0.79', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'abuse', 'framework', 'harsh', 'humiliation', 'stock']
senticnet['pillow'] = ['0', '0.936', '0', '0', '#interest', '#interest', 'positive', '0.936', 'duvet_cover', 'bed_sheet', 'comforter', 'pillowcase', 'blanket']
senticnet['pillow_fight'] = ['0', '-0.08', '0', '0', '#surprise', '#surprise', 'positive', '0.082', 'yo-yo', 'playroom', 'backgammon', 'action_figure', 'board_game']
senticnet['pillow_lava'] = ['0', '0', '-0.92', '0', '#fear', '#fear', 'negative', '-0.92', 'water_flow', 'wind_water', 'water_stream', 'waterway', 'magma']
senticnet['pillowcase'] = ['0', '0.082', '0.009', '0.135', '#interest', '#admiration', 'positive', '0.069', 'duvet_cover', 'pillow', 'mattress', 'bed_sheet', 'duvet']
senticnet['pilocarpine'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['pilomotor_reflex'] = ['-0.84', '0', '0.804', '-0.84', '#sadness', '#disgust', 'negative', '-0.83', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['pilosella'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['pilosula'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'giant_hornet', 'hornet', 'hornet_wasp']
senticnet['pilot'] = ['0.039', '0.081', '0', '0', '#joy', '#interest', 'positive', '0.06', 'jump_over_obstacle', 'attempt_fly', 'aircraft', 'chick', 'wing']
senticnet['pilot_chute'] = ['-0.88', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.90', 'undergarment', 'outerwear', 'lady_underwear', 'under_garment', 'slip']
senticnet['pilot_cockpit'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'train_station', 'government_office', 'cable_car', 'sport_stadium', 'terminal']
senticnet['pilot_episode'] = ['0.918', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.827', 'encounter', 'reveal', 'programme', 'lose', 'sequel']
senticnet['pilot_experiment'] = ['0', '0.747', '0', '0.823', '#interest', '#admiration', 'positive', '0.785', 'michelson-morley_experiment', 'control_experiment', 'test', 'experimentation', 'trial']
senticnet['pilot_phase'] = ['0', '0.097', '0', '0.092', '#interest', '#admiration', 'positive', '0.095', 'urgent', 'classify_ad', 'important', 'review', 'repetition']
senticnet['pilot_program'] = ['0', '0.097', '0', '0.092', '#interest', '#admiration', 'positive', '0.095', 'urgent', 'classify_ad', 'important', 'review', 'repetition']
senticnet['pilot_project'] = ['0', '0.097', '0', '0.092', '#interest', '#admiration', 'positive', '0.095', 'urgent', 'classify_ad', 'important', 'review', 'repetition']
senticnet['pilot_whale'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'many_fishes', 'seahorse', 'starfish', 'plankton', 'sea_wee']
senticnet['pilotage'] = ['0.764', '-0.96', '0', '0.883', '#surprise', '#admiration', 'positive', '0.87', 'boatmanship', 'acquirement', 'skill', 'accomplishment', 'acquisition']
senticnet['pilous'] = ['-0.75', '0', '0.734', '-0.82', '#sadness', '#disgust', 'negative', '-0.77', 'puberulent', 'sericeous', 'furlike', 'pappose', 'haired']
senticnet['pilsner'] = ['0', '0.091', '-0.05', '0.073', '#interest', '#admiration', 'positive', '0.037', 'privately', 'hold_liquid', 'make_wish', 'rinse_dish', 'find_toilet']
senticnet['pimiento'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'cabbage', 'lettuce', 'broccoli']
senticnet['pimozide'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['pimp'] = ['0.82', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.862', 'pervert', 'healthy_person', 'philanthropist', 'arsonist', 'nazi']
senticnet['pimped-out'] = ['0', '0', '-0.97', '-0.77', '#fear', '#disgust', 'negative', '-0.87', 'souped-up', 'tricked-out', 'souped', 'race-ready', 'pumped-up']
senticnet['pimpinella_anisum'] = ['0.632', '-0.93', '0', '0.928', '#surprise', '#admiration', 'positive', '0.831', 'herb', 'oregano', 'marjoram', 'pot_marjoram', 'origanum']
senticnet['pimple'] = ['-0.10', '0', '-0.05', '-0.11', '#sadness', '#disgust', 'negative', '-0.08', 'jaw', 'cheek', 'dimple', 'forehead', 'beard']
senticnet['pimpled'] = ['-0.85', '0', '0.801', '-0.85', '#sadness', '#disgust', 'negative', '-0.83', 'scarred', 'blemished', 'marred', 'acned', 'flyblown']
senticnet['pimply'] = ['0.724', '-0.70', '0', '0.571', '#joy', '#surprise', 'positive', '0.666', 'spinnable', 'susceptible', 'easy', 'pliant', 'plastic']
senticnet['pin'] = ['-0.75', '0', '0.886', '-0.72', '#sadness', '#anger', 'negative', '-0.78', 'harass', 'move', 'check', 'fork', 'aggress']
senticnet['pin_compatible'] = ['-0.83', '0', '0.521', '-0.71', '#sadness', '#disgust', 'negative', '-0.69', 'stool', 'dejection', 'droppings', 'cow_droppings', 'cow_dung']
senticnet['pin_count'] = ['-0.83', '0', '0.521', '-0.71', '#sadness', '#disgust', 'negative', '-0.69', 'stool', 'dejection', 'droppings', 'cow_droppings', 'cow_dung']
senticnet['pin_cushion'] = ['0.022', '-0.01', '0.062', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'electric_toothbrush', 'moistener', 'hairbrush', 'shampoo_conditioner', 'shave_kit']
senticnet['pin_down'] = ['0.519', '-0.55', '0', '0.746', '#surprise', '#admiration', 'positive', '0.606', 'narrow_down', 'peg_down', 'nail_down', 'narrow', 'concretize']
senticnet['pin_needle'] = ['0.738', '0.881', '0', '0', '#joy', '#interest', 'positive', '0.809', 'pointy', 'without_space', 'pin', 'dart', 'blade']
senticnet['pin_sharp'] = ['-0.05', '0.07', '0', '0.062', '#interest', '#admiration', 'positive', '0.027', 'salty', 'fine_grain', 'powder', 'granulate', 'food_flavor']
senticnet['pin_up_girl'] = ['0', '0', '-0.94', '-0.93', '#fear', '#disgust', 'negative', '-0.94', 'thumb_tack', 'push_pin', 'glue_stick', 'letter_opener', 'pair_of_scissors']
senticnet['pinaceae'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'seed_fern', 'pteridosperm']
senticnet['pinafore'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'dress', 'pinny', 'dirndl', 'gown', 'muumuu']
senticnet['pinajian'] = ['-0.52', '0', '0.506', '-0.94', '#sadness', '#disgust', 'negative', '-0.65', 'gouger', 'swindler', 'defrauder', 'fraud', 'welcher']
senticnet['pinball_wizard'] = ['0.773', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.844', 'favorite', 'crowd', 'acclamation', 'bunch', 'large_crowd']
senticnet['pince_nez'] = ['-0.84', '0', '0.633', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'grizzle', 'postiche', 'horsehair_wig', 'wig', 'peruke']
senticnet['pincer'] = ['0', '0', '-0.73', '-0.96', '#fear', '#disgust', 'negative', '-0.84', 'claw', 'muzzle', 'tic', 'whisker', 'bed_bug']
senticnet['pinch'] = ['-0.14', '0', '-0.14', '-0.04', '#sadness', '#fear', 'negative', '-0.11', 'break_apart', 'pulse', 'learn_lesson', 'fall_down_flight_stair', 'break_glass']
senticnet['pinched'] = ['0', '0', '-0.9', '-0.86', '#fear', '#disgust', 'negative', '-0.88', 'bony', 'cadaverous', 'emaciated', 'gaunt', 'haggard']
senticnet['pinched_nerve'] = ['0.94', '0.78', '0', '0', '#joy', '#interest', 'positive', '0.86', 'breaheavily', 'heavy_breathe', 'get_exhausted', 'exhaustion', 'see_neighbor']
senticnet['pinched_nose'] = ['0', '0', '0.883', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'get_old', 'dry_skin', 'become_wet', 'clothes_shrink', 'wrinkle']
senticnet['pincher'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'rascal', 'rapscallion', 'scalawag', 'holy_terror', 'monkey']
senticnet['pinchernone'] = ['-0.53', '0', '0.637', '-0.92', '#anger', '#disgust', 'negative', '-0.69', 'incorporeal', 'unembodied', 'disembodied', 'bodiless', 'unbodied']
senticnet['pindaan'] = ['-0.63', '0', '0.791', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'little', 'pinch', 'modicum', 'small_indefinite_amount', 'indefinite_quantity']
senticnet['pine'] = ['-0.88', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.87', 'spruce', 'fir', 'hemlock', 'balsam', 'larch']
senticnet['pine_avenue'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'boulevard', 'lane', 'thoroughfare', 'intersection', 'curb']
senticnet['pine_away'] = ['-0.99', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.86', 'waste', 'ache', 'yearn', 'yen', 'languish']
senticnet['pine_bark_beetle'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['pine_beetle'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['pine_brewing_company'] = ['0', '0.091', '-0.05', '0.073', '#interest', '#admiration', 'positive', '0.037', 'privately', 'hold_liquid', 'make_wish', 'rinse_dish', 'find_toilet']
senticnet['pine_cone'] = ['0', '0', '-0.14', '-0.03', '#fear', '#disgust', 'negative', '-0.09', 'creeper', 'fallen_leaf', 'poison_ivy', 'detritus', 'tress']
senticnet['pine_lumber'] = ['-0.69', '0', '0.725', '-0.89', '#anger', '#disgust', 'negative', '-0.77', 'ponderosa', 'ponderosa_pine', 'western_yellow_pine', 'bull_pine', 'longleaf_pine']
senticnet['pine_resin'] = ['0.037', '0.084', '0', '0', '#joy', '#interest', 'positive', '0.06', 'leave_over', 'leftover', 'cellulose', 'cane', 'remainder']
senticnet['pine_ridge'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['pine_ridge_agency'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['pine_scented_air'] = ['0.786', '0', '0', '0', '#joy', '#joy', 'positive', '0.786', 'camping', 'outdoor', 'tent', 'bonfire', 'camp_out']
senticnet['pine_timber'] = ['-0.69', '0', '0.725', '-0.89', '#anger', '#disgust', 'negative', '-0.77', 'ponderosa', 'ponderosa_pine', 'western_yellow_pine', 'bull_pine', 'longleaf_pine']
senticnet['pine_tree'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'black_locust']
senticnet['pine_vole'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'carbon_monoxide', 'tree_animal']
senticnet['pine_wilt'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['pineal_melatonin'] = ['-0.56', '0', '0.849', '-0.61', '#anger', '#disgust', 'negative', '-0.67', 'convulsive', 'unsteady', 'jerky', 'spasmodic', 'spastic']
senticnet['pineapple'] = ['0', '0.064', '-0.06', '0.085', '#interest', '#admiration', 'positive', '0.028', 'raisin', 'kale', 'banana', 'pickle', 'tomato']
senticnet['pineapple_juice'] = ['0.077', '0', '0', '0', '#joy', '#joy', 'positive', '0.077', 'dip', 'garlic', 'oat', 'savory', 'rye']
senticnet['pineapple_upside_down'] = ['0.029', '-0.16', '0', '0', '#joy', '#surprise', 'positive', '0.095', 'mousse', 'brownie', 'pancake', 'custard', 'doughnut']
senticnet['pined_away'] = ['-0.86', '0', '0.738', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'amyotrophia', 'amyotrophy', 'tabes', 'kraurosis', 'symptom']
senticnet['pinelands_national'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['ping_pong'] = ['0.037', '0.148', '0', '0.212', '#interest', '#admiration', 'positive', '0.132', 'horseshoe', 'four_leaf_clover', 'rotary_phone', 'horse_shoes', 'billiards']
senticnet['ping_pong_ball'] = ['0', '-0.03', '0.042', '-0.07', '#anger', '#disgust', 'negative', '-0.02', 'ball', 'ping_pong_show', 'dandruff', 'solid_water', 'empty_bottle']
senticnet['ping_pong_paddle'] = ['0.963', '-0.91', '0', '0', '#joy', '#surprise', 'positive', '0.941', 'yo-yo', 'ball', 'tennis_ball', 'sport_ball', 'basketball']
senticnet['ping_pong_show'] = ['0', '-0.03', '0.042', '-0.07', '#anger', '#disgust', 'negative', '-0.02', 'ball', 'ping_pong_ball', 'dandruff', 'solid_water', 'empty_bottle']
senticnet['ping_pong_table'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'golf', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['pingpong_ball'] = ['0', '-0.03', '0.042', '-0.07', '#anger', '#disgust', 'negative', '-0.02', 'ball', 'ping_pong_show', 'dandruff', 'solid_water', 'empty_bottle']
senticnet['pinhead'] = ['-0.03', '-0.03', '0.081', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'ladybug', 'mote', 'grain_sand', 'mite', 'sand']
senticnet['pinion_gear'] = ['0', '0.142', '0', '0.125', '#interest', '#admiration', 'positive', '0.134', 'weight_machine', 'power_drill', 'screw', 'screwdriver', 'air_pump']
senticnet['pink'] = ['0', '0.069', '0', '0.094', '#interest', '#admiration', 'positive', '0.082', 'purple', 'colorful', 'green_black', 'red_green', 'red']
senticnet['pink_blush'] = ['0.784', '-0.59', '0', '0.846', '#joy', '#admiration', 'positive', '0.742', 'astuteness', 'slyness', 'wiliness', 'craftiness', 'cunning']
senticnet['pink_color'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['pink_diamond'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'art', 'art_piece', 'museum', 'artist', 'jewel']
senticnet['pink_flamingo'] = ['0.036', '0.084', '0', '0', '#joy', '#interest', 'positive', '0.06', 'toy', 'bath_toy', 'yo-yo', 'toy_ball', 'duck']
senticnet['pink_flower'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'tropical_fish', 'wood_duck', 'disney', 'autumn_leaf', 'beau']
senticnet['pink_hair'] = ['0', '0.097', '0', '0.146', '#interest', '#admiration', 'positive', '0.121', 'hair_style', 'hair', 'appearance', 'hold', 'look']
senticnet['pink_lady'] = ['0.02', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'many_fruits', 'peach', 'buy_fruit', 'pineapple', 'tomato']
senticnet['pink_lemonade'] = ['0.023', '0.141', '-0.13', '0.174', '#interest', '#admiration', 'positive', '0.052', 'candy', 'push_wind', 'exhalation', 'breaout', 'mouth_action']
senticnet['pink_lipstick'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'sexual_excitement', 'sexy', 'wet_lip', 'kiss', 'clean_up']
senticnet['pink_noise'] = ['-0.95', '0', '-0.73', '-0.73', '#sadness', '#fear', 'negative', '-0.80', 'decibel', 'noise', 'loud_soft', 'sound', 'audio']
senticnet['pink_pigeon'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet']
senticnet['pink_shirt'] = ['-0.04', '0.1', '0', '0.152', '#interest', '#admiration', 'positive', '0.068', 'pants', 'jacket', 'clothes', 'robe', 'garment']
senticnet['pink_shrimp'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'seal', 'walrus', 'otter', 'alligator', 'whale']
senticnet['pink_silk'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'pinafore', 'pinny', 'dirndl', 'dress', 'muumuu']
senticnet['pink_tourmaline'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'art', 'art_piece', 'museum', 'artist', 'jewel']
senticnet['pink_vinyl'] = ['-0.73', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'unwed', 'unwedded', 'divorced', 'unmarried', 'widowed']
senticnet['pinkeye'] = ['-0.60', '0', '0.8', '-0.60', '#anger', '#disgust', 'negative', '-0.66', 'ophthalmia', 'ophthalmitis', 'rubor', 'redness', 'inflammation']
senticnet['pinkie_finger'] = ['0', '0', '-0.06', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'bandit', 'arm', 'wrist', 'five_fingers', 'bendable']
senticnet['pinkie_sanchez'] = ['-0.57', '0', '0.571', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['pinkish_purple'] = ['0', '0.19', '-0.09', '0.175', '#interest', '#admiration', 'positive', '0.089', 'red', 'green_black', 'green_red', 'peelable', 'precious_jewel']
senticnet['pinkroot'] = ['0.604', '-0.51', '0', '0.795', '#joy', '#admiration', 'positive', '0.637', 'cotton_ball', 'plant_disease', 'disease', 'rust', 'scorch']
senticnet['pinky'] = ['0', '0', '0.068', '0', '#anger', '#anger', 'negative', '-0.06', 'extremity', 'human_appendage', 'body_appendage', 'appendage', 'body_member']
senticnet['pinky_finger'] = ['0', '0', '-0.06', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'bandit', 'arm', 'wrist', 'five_fingers', 'bendable']
senticnet['pinna'] = ['-0.58', '0', '0.66', '-0.8', '#anger', '#disgust', 'negative', '-0.68', 'bald', 'depilous', 'glabrescent', 'glabrous', 'balding']
senticnet['pinnacle'] = ['0.031', '0.126', '0.03', '0.111', '#interest', '#admiration', 'positive', '0.059', 'top', 'peak', 'mountain_top', 'summit', 'height']
senticnet['pinned_beneath'] = ['-0.95', '0', '0.712', '-0.52', '#sadness', '#anger', 'negative', '-0.73', 'uncut', 'unharmed', 'unscathed', 'unwounded', 'uninjured']
senticnet['pinned_underneath'] = ['-0.95', '0', '0.712', '-0.52', '#sadness', '#anger', 'negative', '-0.73', 'uncut', 'unharmed', 'unscathed', 'unwounded', 'uninjured']
senticnet['pinning_down'] = ['0.273', '0.226', '-0.13', '0.332', '#joy', '#admiration', 'positive', '0.173', 'morality', 'cagey', 'uncommon', 'evasive', 'capitalize']
senticnet['pinny'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'pinafore', 'dress', 'dirndl', 'gown', 'muumuu']
senticnet['pinocchio'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'lordosis', 'kyphosis', 'humpback', 'spinal_curvature', 'abnormality']
senticnet['pinochle'] = ['0.934', '-0.57', '0', '0.767', '#joy', '#admiration', 'positive', '0.76', 'morosoph', 'simpleton', 'flibbertigibbet', 'foolish_woman', 'simple']
senticnet['pinoresinol'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'capsaicin', 'chemical_irritant', 'piperine', 'pepper_spray']
senticnet['pinpoint'] = ['0.91', '0', '0', '0.745', '#joy', '#admiration', 'positive', '0.828', 'discern', 'obtain', 'find', 'spot', 'perceive']
senticnet['pinprick'] = ['-0.85', '0', '0.564', '-0.92', '#sadness', '#disgust', 'negative', '-0.77', 'miff', 'impatience', 'snit', 'bummer', 'botheration']
senticnet['pinstripe'] = ['0.067', '0', '0', '0', '#joy', '#joy', 'positive', '0.067', 'adornment', 'stripe', 'hatband', 'neckband', 'cigar_band']
senticnet['pinstripe_suit'] = ['0', '-0.02', '0.048', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'telescope', 'read_glass', 'eyeglass', 'camera', 'binoculars']
senticnet['pint'] = ['0.092', '0.07', '0.036', '0.068', '#joy', '#interest', 'positive', '0.049', 'gallon', 'quart', 'lite', 'ounce', 'liquid_measurement']
senticnet['pint_bottle'] = ['0.161', '-0.14', '0.031', '-0.10', '#joy', '#surprise', 'positive', '0.043', 'barrel', 'reception', 'champagne', 'beer', 'wine']
senticnet['pint_size'] = ['0.936', '-0.81', '0', '0.83', '#joy', '#admiration', 'positive', '0.86', 'cherubic', 'lovely', 'lovable', 'endearing', 'angelic']
senticnet['pintel'] = ['0.934', '-0.57', '0', '0.767', '#joy', '#admiration', 'positive', '0.76', 'morosoph', 'simpleton', 'flibbertigibbet', 'foolish_woman', 'simple']
senticnet['pinup'] = ['-0.94', '0', '0.991', '-0.65', '#sadness', '#anger', 'negative', '-0.86', 'birthday_suit', 'raw', 'altogether', 'nakedness', 'undress']
senticnet['pinus_nigra'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'bottlebrush_buckeye', 'red_buckeye', 'particolored_buckeye']
senticnet['pinwheel'] = ['0.98', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.868', 'pinwheel', 'doily', 'snowflake', 'swirl', 'swirly']
senticnet['pinworm'] = ['-0.74', '0', '0.94', '-0.87', '#anger', '#disgust', 'negative', '-0.85', 'fungal_infection', 'mycosis', 'kerion', 'tinea_pedis', 'athlete_foot']
senticnet['pinyon'] = ['0.045', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.356', 'ponderosa', 'bull_pine', 'western_yellow_pine', 'yellow_pine', 'longleaf_pine']
senticnet['pinyon_juniper'] = ['0.045', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.356', 'ponderosa', 'bull_pine', 'western_yellow_pine', 'yellow_pine', 'longleaf_pine']
senticnet['pinyon_juniper_woodland'] = ['0.045', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.356', 'ponderosa', 'bull_pine', 'western_yellow_pine', 'yellow_pine', 'longleaf_pine']
senticnet['pinyon_pine'] = ['0.045', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.356', 'ponderosa', 'bull_pine', 'western_yellow_pine', 'yellow_pine', 'longleaf_pine']
senticnet['pioche'] = ['-0.98', '0', '0.521', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'battery', 'stamp_battery', 'quartz_battery', 'stamp_mill', 'factory']
senticnet['pioggia'] = ['0.107', '0.11', '-0.08', '0.21', '#interest', '#admiration', 'positive', '0.086', 'live_healthy_life', 'forever_young', 'live_forever', 'live_long', 'live_good_life']
senticnet['pioneer'] = ['0', '0', '0', '0.972', '#admiration', '#admiration', 'positive', '0.972', 'egalitarian', 'discovery', 'connoisseur', 'inventor', 'scientist']
senticnet['pioneer_cemetery'] = ['-0.50', '0', '0.762', '-0.89', '#anger', '#disgust', 'negative', '-0.71', 'cemetery', 'graveyard', 'burial_site', 'grassland', 'burying_ground']
senticnet['piotrkwek'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'aphasia', 'anomic_aphasia']
senticnet['pious'] = ['0.889', '-0.69', '0', '0.908', '#joy', '#admiration', 'positive', '0.83', 'pharisaic', 'pharisaical', 'sanctimonious', 'reverent', 'worshipful']
senticnet['pious_fraud'] = ['-0.63', '0', '0.581', '-0.84', '#sadness', '#disgust', 'negative', '-0.68', 'malignment', 'vilification', 'smear', 'character_assassination', 'libel']
senticnet['pious_platitude'] = ['-0.61', '0', '0.981', '-0.98', '#anger', '#disgust', 'negative', '-0.85', 'talk', 'nothingness', 'jazz', 'malarkey', 'malarky']
senticnet['piousal'] = ['0.88', '-0.69', '0', '0.902', '#joy', '#admiration', 'positive', '0.827', 'pious', 'pharisaic', 'sanctimonious', 'reverent', 'worshipful']
senticnet['piously'] = ['0.8', '0', '0', '0.906', '#joy', '#admiration', 'positive', '0.853', 'hypocritically', 'arrogantly', 'sanctimoniously', 'pompously', 'self-righteously']
senticnet['piousness'] = ['0.838', '-0.55', '0', '0.622', '#joy', '#admiration', 'positive', '0.67', 'righteousness', 'godliness', 'dutifulness', 'devoutness', 'religiousness']
senticnet['pip'] = ['0', '0.132', '0.009', '0.106', '#interest', '#admiration', 'positive', '0.076', 'fruit', 'rind', 'funiculus', 'seed', 'achene']
senticnet['pipe'] = ['0.932', '-0.72', '0', '0.769', '#joy', '#admiration', 'positive', '0.809', 'vox_angelica', 'voix_celeste', 'dulciana', 'organ_stop', 'diapason']
senticnet['pipe_bomb'] = ['0', '-0.03', '-0.04', '-0.19', '#fear', '#disgust', 'negative', '-0.06', 'nuclear_bomb', 'explosive', 'grenade', 'nuclear_weapon', 'aerial_bomb']
senticnet['pipe_cleaner'] = ['0.063', '0', '0.01', '0.16', '#joy', '#admiration', 'positive', '0.071', 'goodbye', 'hand_gesture', 'arm_movement', 'crest', 'water_splash']
senticnet['pipe_dream'] = ['0.783', '-0.51', '0', '0.584', '#joy', '#admiration', 'positive', '0.627', 'appetent', 'avid', 'thirsty', 'devouring', 'envious']
senticnet['pipe_tobacco'] = ['0.031', '0', '-0.08', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'smoke_cigarette', 'smoke', 'cinder', 'tobacco_product', 'smoke_pipe']
senticnet['pipe_wrench'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'pair_of_scissors', 'c_clamp', 'tool_box', 'salad_shaker', 'ladle']
senticnet['piped_water'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'bottled_water', 'sugar_water']
senticnet['pipedreamer'] = ['0', '-0.04', '0.085', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'claw', 'fur_keep_warm', 'tic', 'bristle', 'muzzle']
senticnet['pipefish'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'puffer', 'pufferfish', 'blowfish', 'triggerfish', 'plectognath']
senticnet['pipefitter'] = ['0', '0.115', '0', '0.105', '#interest', '#admiration', 'positive', '0.11', 'farrier', 'miner', 'horseshoer', 'blacksmith', 'smith']
senticnet['piper_jaffray'] = ['0.711', '0', '0', '0', '#joy', '#joy', 'positive', '0.711', 'chemist', 'expert', 'scientist', 'esaminatore', 'physicist']
senticnet['piperacillin'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['piperacillin_tazobactam'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['piperidine'] = ['0.049', '0', '0', '0.113', '#joy', '#admiration', 'positive', '0.081', 'solid', 'hard_material', 'construction_material', 'hard', 'calcium']
senticnet['piperine'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'capsaicin', 'gingerol', 'chemical_irritant', 'pepper_spray']
senticnet['piperonyl_butoxide'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['pipette'] = ['-0.86', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.80', 'riverbed', 'bottom_ocean', 'waterfall', 'down_drain', 'septic_tank']
senticnet['piping'] = ['-0.97', '0', '0.865', '-0.54', '#sadness', '#anger', 'negative', '-0.79', 'cord', 'lace', 'lacing', 'perpendicular', 'slack']
senticnet['piping_bag'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'address_book', 'get_rich', 'blank_check', 'swiss_army_knife', 'whisk']
senticnet['piping_crow'] = ['-0.84', '0', '0.655', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'australian_magpie', 'butcherbird', 'currawong', 'bell_magpie', 'oscine']
senticnet['pippa'] = ['0', '0.864', '0', '0.751', '#interest', '#admiration', 'positive', '0.807', 'family_member', 'sibling', 'parent', 'female_relative', 'relative']
senticnet['piprahwa'] = ['-0.59', '0', '0.506', '-0.52', '#sadness', '#disgust', 'negative', '-0.54', 'dump', 'garbage_dump', 'trash_dump', 'rubbish_dump', 'wasteyard']
senticnet['piquance'] = ['0.735', '-0.50', '0', '0.959', '#joy', '#admiration', 'positive', '0.734', 'zest', 'sharpness', 'hotness', 'spiciness', 'piquancy']
senticnet['piquancy'] = ['0.741', '-0.51', '0', '0.953', '#joy', '#admiration', 'positive', '0.735', 'zest', 'sharpness', 'hotness', 'piquance', 'spiciness']
senticnet['piquant'] = ['-0.77', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.83', 'spicy', 'tangy', 'spiciness', 'pungent', 'peppery']
senticnet['pique'] = ['-0.13', '0.083', '0.682', '-0.66', '#anger', '#disgust', 'negative', '-0.35', 'frustrating', 'dun', 'crucify', 'bedevil', 'displeasingly']
senticnet['piquet_deck'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'jewelry_store', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['piracy'] = ['0', '0', '0.034', '-0.08', '#anger', '#disgust', 'negative', '-0.06', 'lifeboat', 'mast', 'bad_man', 'keel', 'yacht']
senticnet['pirate'] = ['0', '0', '0.034', '-0.08', '#anger', '#disgust', 'negative', '-0.06', 'lifeboat', 'mast', 'bad_man', 'keel', 'yacht']
senticnet['pirate_adventure'] = ['0.704', '-0.80', '0', '0', '#joy', '#surprise', 'positive', '0.755', 'quest', 'stunt', 'illusion', 'journey', 'activity']
senticnet['pirate_captain'] = ['0', '0', '0.034', '-0.08', '#anger', '#disgust', 'negative', '-0.06', 'lifeboat', 'mast', 'bad_man', 'keel', 'yacht']
senticnet['pirate_crew'] = ['0', '0', '0.034', '-0.08', '#anger', '#disgust', 'negative', '-0.06', 'lifeboat', 'mast', 'bad_man', 'keel', 'yacht']
senticnet['pirate_gang'] = ['0', '0', '0.034', '-0.08', '#anger', '#disgust', 'negative', '-0.06', 'lifeboat', 'mast', 'bad_man', 'keel', 'yacht']
senticnet['pirate_haven'] = ['0', '0', '0.034', '-0.08', '#anger', '#disgust', 'negative', '-0.06', 'lifeboat', 'mast', 'bad_man', 'keel', 'yacht']
senticnet['pirate_hunter'] = ['0', '0', '0.034', '-0.08', '#anger', '#disgust', 'negative', '-0.06', 'lifeboat', 'mast', 'bad_man', 'keel', 'yacht']
senticnet['pirate_ship'] = ['-0.06', '-0.02', '-0.05', '-0.11', '#sadness', '#disgust', 'negative', '-0.05', 'radio_station', 'old_movie', 'movie', 'chinatown', 'military_base']
senticnet['pirate_starter_zach'] = ['0.033', '0.088', '-0.06', '0.068', '#interest', '#admiration', 'positive', '0.032', 'earl', 'noble', 'king', 'sultan', 'royal_title']
senticnet['pirate_treasure'] = ['0', '0.177', '0', '0.126', '#interest', '#admiration', 'positive', '0.151', 'treasure', 'storage_facility', 'shrine', 'lock_box', 'jewelry_box']
senticnet['pirated_material'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'intend', 'mimic', 'imitation', 'copy', 'heap']
senticnet['piratical'] = ['0', '0', '0.034', '-0.08', '#anger', '#disgust', 'negative', '-0.06', 'lifeboat', 'mast', 'bad_man', 'keel', 'yacht']
senticnet['pirelli_calendar'] = ['-0.98', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.89', 'fairy', 'genus', 'honey_bee', 'infant', 'dove']
senticnet['piriformis_muscle'] = ['-0.41', '0', '-0.28', '-0.05', '#sadness', '#fear', 'negative', '-0.25', 'numbness', 'carpal_tunnel_syndrome', 'weakness', 'pain', 'muscle_damage']
senticnet['pirogi'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'roll', 'custard', 'bread', 'biscuit']
senticnet['pirouette'] = ['-0.68', '0', '0.545', '-0.68', '#sadness', '#disgust', 'negative', '-0.64', 'stunt', 'tumble', 'split', 'walkover', 'acrobatic_feat']
senticnet['pis_aller'] = ['-0.97', '0', '0.84', '-0.52', '#sadness', '#anger', 'negative', '-0.78', 'expedient', 'last_resort', 'temporary_expedient', 'makeshift', 'stopgap']
senticnet['pisa_airport'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'expressway', 'turnpike', 'toll_road', 'highway']
senticnet['piscivore'] = ['-0.53', '0', '0.648', '-0.70', '#anger', '#disgust', 'negative', '-0.63', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['pish_tush'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'dress', 'pinny', 'dirndl', 'gown', 'muumuu']
senticnet['pisiform'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'hip_socket', 'tooth_socket', 'alveolus', 'socket', 'cotyloid_cavity']
senticnet['piss'] = ['-0.06', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.06', 'relieve', 'make_coffee', 'go_toilet', 'go_pee', 'defecate']
senticnet['piss_off'] = ['0', '0', '0.86', '0', '#anger', '#anger', 'negative', '-0.86', 'overwhelm', 'black_eye', 'die_painfully', 'feel_anger', 'punch_face']
senticnet['piss_pant'] = ['-0.13', '0.133', '-0.16', '0', '#sadness', '#fear', 'negative', '-0.05', 'testify', 'give_testimony', 'disagree', 'give_verdict', 'afraid']
senticnet['pissed'] = ['0', '0', '0.65', '0', '#anger', '#anger', 'negative', '-0.65', 'angry', 'anger', 'mad', 'bad_temper', 'boiling']
senticnet['pissed_off'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['pisser'] = ['-0.53', '0', '0.508', '-0.74', '#sadness', '#disgust', 'negative', '-0.59', 'bedwetter', 'bed_wetter', 'mortal', 'wetter', 'person']
senticnet['pissing_off'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['pistil'] = ['0', '0', '0.064', '-0.04', '#anger', '#disgust', 'negative', '-0.05', 'bird_food', 'funiculus', 'plant_product', 'fuzz', 'agricultural_product']
senticnet['pistol'] = ['0', '0', '0.79', '-0.85', '#anger', '#disgust', 'negative', '-0.82', 'rifle', 'gun', 'revolver', 'hydrogen_bomb', 'firearm']
senticnet['pistol_cartridge'] = ['0.887', '-0.95', '0', '0.913', '#surprise', '#admiration', 'positive', '0.917', 'high_quality', 'superiority', 'level', 'grade', 'degree']
senticnet['pistol_duel'] = ['0', '-0.01', '-0.08', '-0.16', '#fear', '#disgust', 'negative', '-0.07', 'siege', 'big_fight', 'battle', 'declare_war', 'conflict']
senticnet['pistol_marksmanship'] = ['0.93', '-0.95', '0', '0.909', '#joy', '#surprise', 'positive', '0.93', 'acquirement', 'skill', 'accomplishment', 'acquisition', 'attainment']
senticnet['pistol_pete'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'unofficial', 'informal', 'loose', 'drumhead']
senticnet['pistol_shot'] = ['0', '-0.01', '-0.08', '-0.16', '#fear', '#disgust', 'negative', '-0.07', 'siege', 'big_fight', 'battle', 'declare_war', 'conflict']
senticnet['piston'] = ['-0.75', '0', '-0.75', '-0.99', '#sadness', '#disgust', 'negative', '-0.83', 'tease', 'upset', 'lubricant', 'vaseline', 'upset_person']
senticnet['piston_crown'] = ['0.042', '-0.02', '0.035', '0.065', '#joy', '#admiration', 'positive', '0.023', 'food_container', 'metal_cylinder', 'metal_container', 'small_ball', 'storage_container']
senticnet['pisum_sativum'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'cabbage', 'lettuce', 'broccoli']
senticnet['pit'] = ['0', '0', '-0.85', '-0.71', '#fear', '#disgust', 'negative', '-0.78', 'fair_game', 'quarry', 'stone_pit', 'target', 'prey']
senticnet['pit_lane'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['pit_stop'] = ['-0.10', '-0.07', '0.032', '-0.15', '#sadness', '#disgust', 'negative', '-0.05', 'crash_tree', 'press_accelerator', 'stop_traffic_light', 'accident', 'tire_puncture']
senticnet['pit_viper'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'death_adder', 'ophidian', 'serpent', 'elapid', 'taipan']
senticnet['pitch'] = ['0', '0', '0', '0.107', '#admiration', '#admiration', 'positive', '0.107', 'detect', 'squeak', 'hear_noise', 'rattle', 'loud_soft']
senticnet['pitch-black'] = ['-0.89', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.86', 'pitch-dark', 'half-dark', 'semi-dark', 'rain-swept', 'half-lit']
senticnet['pitch-dark'] = ['-0.88', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.86', 'pitch-black', 'half-dark', 'semi-dark', 'rain-swept', 'half-lit']
senticnet['pitch-perfect'] = ['0', '-0.98', '0', '0.925', '#surprise', '#admiration', 'positive', '0.955', 'spot-on', 'dead-on', 'bang-on', 'uncannily', 'impersonation']
senticnet['pitch_angle'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'aberrance', 'warp', 'chromosomal_aberration']
senticnet['pitch_black'] = ['-0.60', '0', '0.895', '-0.54', '#sadness', '#anger', 'negative', '-0.68', 'coal_black', 'jet_black', 'black', 'soot_black', 'achromatic_color']
senticnet['pitch_canker'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['pitch_invader'] = ['-0.71', '0', '0.83', '-0.68', '#sadness', '#anger', 'negative', '-0.74', 'gag', 'jest', 'jape', 'joke', 'laugh']
senticnet['pitch_invasion'] = ['-0.99', '0', '0.603', '-0.82', '#sadness', '#disgust', 'negative', '-0.80', 'batrachomyomachia', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['pitch_perfect'] = ['0', '0.119', '-0.07', '0.189', '#interest', '#admiration', 'positive', '0.079', 'enjoy', 'good_feeling', 'excitement', 'emotion', 'good_emotion']
senticnet['pitch_tent'] = ['0', '0.075', '0', '0', '#interest', '#interest', 'positive', '0.075', 'sleep_tent', 'hot_summer', 'close_together', 'shelter', 'blow_away']
senticnet['pitched_battle'] = ['-0.05', '0', '-0.08', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'siege', 'big_fight', 'war', 'declare_war', 'combat']
senticnet['pitched_roof'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'adobe', 'adobe_brick', 'clinker', 'brick', 'firebrick']
senticnet['pitcher'] = ['0', '0.075', '0', '0', '#interest', '#interest', 'positive', '0.075', 'cup', 'carafe', 'container_cup', 'mug', 'glasses']
senticnet['pitchfork'] = ['0.7', '-0.62', '0', '0.58', '#joy', '#surprise', 'positive', '0.636', 'white_magic', 'thaumaturgy', 'juju', 'conjuration', 'supernaturalism']
senticnet['pitchfork_media'] = ['0.7', '-0.62', '0', '0.58', '#joy', '#surprise', 'positive', '0.636', 'white_magic', 'thaumaturgy', 'juju', 'conjuration', 'supernaturalism']
senticnet['pitchman'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'hawker', 'cheapjack']
senticnet['pitchy'] = ['-0.7', '0', '0.617', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'silvery', 'blackish', 'bluish_black', 'hueless', 'achromatic']
senticnet['piteous'] = ['-0.79', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'suffering', 'miserably', 'pitiable', 'hapless', 'pitiful']
senticnet['piteously'] = ['-0.81', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.86', 'impassiveness', 'impassivity', 'emotionlessness', 'unemotionality', 'stolidity']
senticnet['pitfall'] = ['0', '0', '-0.86', '-0.76', '#fear', '#disgust', 'negative', '-0.81', 'pitfall', 'trap', 'shortcoming', 'avoiding', 'drawback']
senticnet['pith'] = ['0', '0', '0.996', '-0.73', '#anger', '#disgust', 'negative', '-0.86', 'marrow', 'kernel', 'centre', 'core', 'substance']
senticnet['pith_helmet'] = ['0', '-0.02', '0.048', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'telescope', 'read_glass', 'eyeglass', 'camera', 'binoculars']
senticnet['pithily'] = ['0', '-0.89', '0', '0.915', '#surprise', '#admiration', 'positive', '0.904', 'pith', 'sententious', 'concise', 'epigram', 'pithy']
senticnet['pithiness'] = ['-0.66', '0', '0.812', '-0.88', '#anger', '#disgust', 'negative', '-0.78', 'laconism', 'laconicism', 'conciseness', 'concision', 'terseness']
senticnet['pithoragarh_district'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['pithy'] = ['0', '-0.89', '0', '0.923', '#surprise', '#admiration', 'positive', '0.907', 'pith', 'sententious', 'concise', 'epigram', 'pithily']
senticnet['pitiable'] = ['-0.86', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.87', 'suffering', 'miserably', 'piteous', 'hapless', 'pitiful']
senticnet['pitiably'] = ['-0.63', '0', '0.591', '-0.73', '#sadness', '#disgust', 'negative', '-0.65', 'irreality', 'void', 'impossibleness', 'unreality', 'impossibility']
senticnet['pitiful'] = ['-0.86', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.87', 'suffering', 'miserably', 'pitiable', 'piteous', 'pitilessness']
senticnet['pitifully'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'lamentably', 'deplorably', 'bereaved', 'bereft', 'grief-stricken']
senticnet['pitifully_small'] = ['-0.94', '0', '0.952', '-0.83', '#sadness', '#anger', 'negative', '-0.91', 'miserable', 'scanty', 'exiguous', 'spare', 'hardscrabble']
senticnet['pitiless'] = ['-0.79', '0', '0.691', '-0.88', '#sadness', '#disgust', 'negative', '-0.79', 'cold', 'inhumane', 'cruel', 'unkind', 'vicious']
senticnet['pitilessly'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'intrepidly', 'hardy', 'fearlessness', 'unafraid', 'unfearing']
senticnet['pitilessness'] = ['0', '0', '0.793', '-0.92', '#anger', '#disgust', 'negative', '-0.85', 'mercilessness', 'unkind', 'ruthlessness', 'cruelly', 'unassertively']
senticnet['pitlane'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['pittalavanipalem'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['pittance'] = ['-0.91', '0', '0.598', '-0.52', '#sadness', '#anger', 'negative', '-0.68', 'parsimonious', 'stingy', 'scrimy', 'beggarly', 'chintzy']
senticnet['pittsburgh'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
senticnet['pittsburgh_courier'] = ['-0.05', '0.106', '0', '0.06', '#interest', '#admiration', 'positive', '0.036', 'buy', 'buy_newspaper', 'read_buy_newspaper', 'buy_book', 'read_news']
senticnet['pittsburgh_post_gazette'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquisitor', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['pittsburgh_suburb'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
senticnet['pittsburgh_symphony_orchestra'] = ['0.506', '0.659', '-0.24', '0.242', '#joy', '#interest', 'positive', '0.29', 'sing', 'music_studio', 'music_room', 'choir', 'symphony']
senticnet['pittsburgh_tribune_review'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquisitor', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['pituitary_adenoma'] = ['0.905', '-0.65', '0', '0.851', '#joy', '#admiration', 'positive', '0.805', 'gonococcus', 'bacteria', 'bacteriae', 'probiotic', 'probiotic_bacterium']
senticnet['pituitary_stalk'] = ['0.905', '-0.65', '0', '0.851', '#joy', '#admiration', 'positive', '0.805', 'gonococcus', 'bacteria', 'bacteriae', 'probiotic', 'probiotic_bacterium']
senticnet['pitviper'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'death_adder', 'ophidian', 'serpent', 'elapid', 'taipan']
senticnet['pity'] = ['-0.77', '0', '0.907', '0', '#sadness', '#anger', 'negative', '-0.83', 'shame', 'compassion', 'commiseration', 'remorse', 'mercy']
senticnet['pityriasis_rosea'] = ['-0.57', '0', '0.873', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'fungal_infection', 'mycosis', 'kerion', 'tinea_pedis', 'athlete_foot']
senticnet['pityriasis_versicolor'] = ['-0.57', '0', '0.873', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'fungal_infection', 'mycosis', 'kerion', 'tinea_pedis', 'athlete_foot']
senticnet['pius_iv'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquirer', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['pivot'] = ['0.752', '0', '0', '0.928', '#joy', '#admiration', 'positive', '0.84', 'influence', 'pry', 'leverage', 'rigger', 'advantage']
senticnet['pivot_pin'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'peavey']
senticnet['pivotal'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'baddie', 'theatrical_baddie', 'part', 'persona']
senticnet['pivotal_moment'] = ['-0.67', '0', '0.97', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'joint', 'node', 'articulation', 'join', 'connexion']
senticnet['pivotal_role'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'baddie', 'theatrical_baddie', 'part', 'persona']
senticnet['pivotal_scene'] = ['-0.69', '0', '0.508', '-0.93', '#sadness', '#disgust', 'negative', '-0.71', 'dark', 'darkness', 'light', 'shadow', 'site']
senticnet['pixel'] = ['0.737', '0.813', '0', '0', '#joy', '#interest', 'positive', '0.775', 'file', 'select', 'byte', 'first', 'count']
senticnet['pixel_size'] = ['0.734', '0.884', '0', '0', '#joy', '#interest', 'positive', '0.809', 'dimension', 'size', 'mass_measure', 'length_dimension', 'gauge']
senticnet['pixel_wide'] = ['0.891', '0.961', '0', '0', '#joy', '#interest', 'positive', '0.926', 'square', 'four_sides', 'rhombus', 'four_corners', 'cube']
senticnet['pixelation'] = ['0.973', '-0.77', '0', '0.983', '#joy', '#admiration', 'positive', '0.911', 'opaqueness', 'faintness', 'dimness', 'opacity', 'vagueness']
senticnet['pixie'] = ['-0.73', '0', '0.72', '-0.93', '#sadness', '#disgust', 'negative', '-0.79', 'imp', 'elf', 'hob', 'gremlin', 'leprechaun']
senticnet['pixie_cut'] = ['0', '0.097', '0', '0.146', '#interest', '#admiration', 'positive', '0.121', 'hair_style', 'hair', 'appearance', 'hold', 'look']
senticnet['pixilated'] = ['0', '0', '-0.92', '-0.84', '#fear', '#disgust', 'negative', '-0.88', 'mischievous', 'prankish', 'implike', 'arch', 'impish']
senticnet['pizazz'] = ['0.827', '-0.55', '0', '0.679', '#joy', '#admiration', 'positive', '0.687', 'spirit', 'spirit_up', 'inspirit', 'energize', 'jazz_up']
senticnet['pizza'] = ['0.132', '0.1', '0.063', '0.159', '#joy', '#admiration', 'positive', '0.082', 'chime', 'school_end', 'ring_loudly', 'noise_maker', 'vital']
senticnet['pizza-like'] = ['-0.85', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'pancake-like', 'bread-like', 'crepe-like', 'donut-like', 'yeast-risen']
senticnet['pizza_box'] = ['0', '-0.02', '0', '0.051', '#surprise', '#admiration', 'positive', '0.037', 'cardboard_box', 'bag', 'package', 'box_cereal', 'bin']
senticnet['pizza_crust'] = ['0', '-0.11', '0', '0', '#surprise', '#surprise', 'positive', '0.113', 'cake', 'cookie', 'mousse', 'doughnut', 'brownie']
senticnet['pizza_delivery_driver'] = ['0.964', '-0.57', '0', '0.827', '#joy', '#admiration', 'positive', '0.789', 'mortal', 'murder_suspect', 'suspect', 'rape_suspect', 'person']
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senticnet['planning_grant'] = ['0', '0.88', '0', '0.733', '#interest', '#admiration', 'positive', '0.806', 'award', 'standard_grant', 'project', 'education', 'development']
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senticnet['plant_growth'] = ['0', '0.719', '0', '0.727', '#interest', '#admiration', 'positive', '0.723', 'development', 'function', 'research', 'sponsor', 'project']
senticnet['plant_life'] = ['-0.83', '0', '0.531', '-0.72', '#sadness', '#disgust', 'negative', '-0.69', 'poisonous_plant', 'escape', 'wilding', 'perennial', 'rock_plant']
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senticnet['plant_matter'] = ['0.102', '0', '0', '0', '#joy', '#joy', 'positive', '0.102', 'fresh_vegetable', 'cabbage', 'fruit_vegetable', 'cut_flower', 'fruit']
senticnet['plant_part'] = ['0.02', '0', '0', '0.057', '#joy', '#admiration', 'positive', '0.038', 'large_plant', 'small_tree', 'tall_person', 'green_leave', 'leafy']
senticnet['plant_pathogen'] = ['-0.93', '0', '0.699', '-0.91', '#sadness', '#disgust', 'negative', '-0.84', 'onion_smut', 'head_smut', 'stinking_smut', 'flag_smut_fungus', 'covered_smut']
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senticnet['plant_seed'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'seed_plant', 'grow_flower', 'plant_garden', 'plant_flower', 'many_plants']
senticnet['plant_tree'] = ['0.737', '0.787', '0', '0.915', '#interest', '#admiration', 'positive', '0.813', 'find_flower', 'shoe_horse', 'seed_lawn', 'blue_steel', 'dig_grave']
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senticnet['plantago_maritima'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'bulrush', 'nailrod', 'cattail', 'reedmace']
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senticnet['plantar_flexion'] = ['-0.15', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.09', 'use_leg', 'walk_quickly', 'casual', 'take_step', 'strut']
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senticnet['planted_acreage'] = ['0', '0.122', '0', '0.123', '#interest', '#admiration', 'positive', '0.122', 'acre', 'cow_home', 'crop_place', 'big_land', 'animal_house']
senticnet['plantigrade'] = ['0.098', '0.084', '0.819', '-0.68', '#anger', '#disgust', 'negative', '-0.33', 'grasp', 'covet', 'covetousness', 'avaricious', 'grabby']
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senticnet['plasma_clearance'] = ['0.992', '-0.80', '0', '0.584', '#joy', '#surprise', 'positive', '0.795', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'incontinence']
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senticnet['plasma_volume'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'keep_alive', 'body_system', 'body_working', 'blood_pressure', 'heart']
senticnet['plasmalemmal'] = ['-0.84', '0', '0.8', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'rigor_mortis', 'natural_process', 'natural_action', 'activity', 'action']
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senticnet['plasmodium_falciparum'] = ['-0.55', '0', '0.757', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'rubor', 'redness', 'cephalitis', 'sleepy_sickness', 'epidemic_encephalitis']
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senticnet['plaster_saint'] = ['-0.71', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.80', 'hypocrite', 'humbug', 'pretender', 'phoney', 'imposter']
senticnet['plaster_wall'] = ['0', '0.096', '0', '0.112', '#interest', '#admiration', 'positive', '0.104', 'renovation', 'tile', 'floor', 'tile_roof', 'tile_wall']
senticnet['plasterer'] = ['-0.64', '0', '0.879', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'drone', 'poke', 'dawdler', 'laggard', 'straggler']
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senticnet['plasterwork'] = ['0', '0.128', '-0.12', '0.183', '#interest', '#admiration', 'positive', '0.063', 'support_roof', 'furnish', 'ceiling', 'window_door', 'frame']
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senticnet['plastic_bottle'] = ['0.021', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'compress_air', 'party', 'use_party', 'lighter_air', 'ninja']
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senticnet['plug_into'] = ['0', '-0.12', '0.011', '0.089', '#surprise', '#admiration', 'positive', '0.067', 'cellular_phone', 'telephone', 'wireless', 'connect', 'message']
senticnet['pluggability'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'carbon_fiber']
senticnet['plum'] = ['0', '0.076', '0', '0.107', '#interest', '#admiration', 'positive', '0.091', 'apricot', 'cranberry', 'prune', 'grapefruit', 'cherry']
senticnet['plum_headed_parakeet'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet']
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senticnet['plumb'] = ['0.876', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.853', 'butt', 'squarely', 'square', 'direct', 'honest']
senticnet['plumb_line'] = ['0.865', '0.84', '0', '0', '#joy', '#interest', 'positive', '0.853', 'travel', 'find_way', 'help_travelling', 'compass_direction', 'use_compass']
senticnet['plumber'] = ['0', '0.744', '0', '0', '#interest', '#interest', 'positive', '0.744', 'top_dog', 'give_order', 'status_symbol', 'german_car', 'army_officer']
senticnet['plumbing_fixture'] = ['0', '0', '0.865', '-0.95', '#anger', '#disgust', 'negative', '-0.90', 'dirty_dish_overflow', 'hot_dirty_dish', 'hot_tap', 'dirty_dish', 'washcloth']
senticnet['plumbing_shop'] = ['0', '0.115', '0', '0.105', '#interest', '#admiration', 'positive', '0.11', 'farrier', 'miner', 'horseshoer', 'blacksmith', 'smith']
senticnet['plumbing_supply'] = ['0.101', '0.099', '0', '0.169', '#joy', '#admiration', 'positive', '0.123', 'family_home', 'apartment', 'renovation', 'kitchen', 'electronics']
senticnet['plume'] = ['0.929', '0.507', '-0.17', '0.396', '#joy', '#interest', 'positive', '0.415', 'pridefulness', 'congratulate', 'smugness', 'smug', 'proudly']
senticnet['plumed_hat'] = ['-0.84', '0', '0.633', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'grizzle', 'postiche', 'horsehair_wig', 'wig', 'peruke']
senticnet['plummet'] = ['0', '0', '-0.79', '-0.74', '#fear', '#disgust', 'negative', '-0.76', 'plank', 'plonk', 'flump', 'plop', 'plump']
senticnet['plummy'] = ['0.947', '0', '0', '0.788', '#joy', '#admiration', 'positive', '0.867', 'delectable', 'preferred', 'lovable', 'desired', 'enviably']
senticnet['plumose'] = ['-0.02', '-0.05', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.02', 'motor_oil', 'make_toy', 'engine', 'emission', 'castor']
senticnet['plump'] = ['0', '0', '-0.78', '-0.73', '#fear', '#disgust', 'negative', '-0.76', 'plank', 'plonk', 'flump', 'plop', 'plummet']
senticnet['plump_for'] = ['0.793', '-0.94', '0', '0.953', '#surprise', '#admiration', 'positive', '0.898', 'okay', 'warrant', 'sanction', 'approve', 'champion']
senticnet['plumpest'] = ['-0.88', '0', '0.864', '-0.72', '#sadness', '#anger', 'negative', '-0.82', 'vixenish', 'venomous', 'bitchy', 'catty', 'malicious']
senticnet['plunder'] = ['-1', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.97', 'despoiler', 'plunderer', 'raider', 'pillage', 'shaver']
senticnet['plunderage'] = ['-0.51', '0', '0.717', '-0.75', '#anger', '#disgust', 'negative', '-0.66', 'raid', 'thieving', 'embezzlement', 'larceny', 'theft']
senticnet['plunderer'] = ['-0.99', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.96', 'despoiler', 'plunder', 'raider', 'pillage', 'shaver']
senticnet['plunge'] = ['0.062', '0', '0.019', '0.073', '#joy', '#admiration', 'positive', '0.039', 'go_underwater', 'dive', 'jump', 'swim', 'get_boat']
senticnet['plunge_into'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'come_across', 'tripped_over', 'lose_balance', 'stammer', 'stutter']
senticnet['plunge_pool'] = ['0', '0', '0', '0.964', '#admiration', '#admiration', 'positive', '0.964', 'swimming_pool', 'swim', 'swimsuit', 'lake', 'have_fun']
senticnet['plunger'] = ['0.978', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.84', 'separate_shower', 'electric_razor', 'shampoo_conditioner', 'bath_shower', 'tub']
senticnet['plunging_neckline'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'pinafore', 'pinny', 'dirndl', 'dress', 'muumuu']
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senticnet['plunk_for'] = ['0.566', '-0.62', '0', '0.551', '#joy', '#surprise', 'positive', '0.58', 'okay', 'warrant', 'sanction', 'approve', 'champion']
senticnet['plunkett'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
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senticnet['plural_form'] = ['0.732', '0.8', '0', '0', '#joy', '#interest', 'positive', '0.766', 'fast', 'place', 'substance', 'substantive', 'person']
senticnet['plural_marriage'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'spousal_relationship', 'wedlock']
senticnet['plural_noun'] = ['0.732', '0.8', '0', '0', '#joy', '#interest', 'positive', '0.766', 'fast', 'place', 'substance', 'substantive', 'person']
senticnet['pluralism'] = ['-0.81', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'pluralistic', 'egalitarian', 'pluralist', 'communitarian', 'individualistic']
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senticnet['plurality'] = ['0', '0.157', '0', '0.147', '#interest', '#admiration', 'positive', '0.152', 'wall_hang', 'depiction', 'illustration', 'artwork', 'wall_decoration']
senticnet['pluralize'] = ['0', '-0.98', '0', '0.937', '#surprise', '#admiration', 'positive', '0.963', 'transliterating', 'transliteration', 'transliterated', 'translate', 'transliterate']
senticnet['pluripotent'] = ['-0.98', '0', '0.737', '-0.92', '#sadness', '#disgust', 'negative', '-0.88', 'separate', 'severalize', 'contrast', 'decouple', 'dissociate']
senticnet['plus'] = ['0', '0.273', '-0.05', '0.388', '#interest', '#admiration', 'positive', '0.201', 'good', 'add', 'comparison', 'join', 'extra']
senticnet['plus_accrued_interest'] = ['-0.89', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.80', 'account_indebtedness', 'indebtedness', 'limited_liability', 'debt', 'arrears']
senticnet['plus_complte'] = ['0.502', '-0.82', '0', '0.779', '#surprise', '#admiration', 'positive', '0.703', 'mortal', 'person', 'individual', 'free_liver', 'agent']
senticnet['plus_qu_un'] = ['0.667', '-0.98', '0', '0.61', '#joy', '#surprise', 'positive', '0.753', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['plush'] = ['0.904', '0.977', '0', '0', '#joy', '#interest', 'positive', '0.941', 'plushy', 'fluffy', 'plushly', 'plushness', 'carpet']
senticnet['plush_doll'] = ['0.71', '0.92', '0', '0', '#joy', '#interest', 'positive', '0.815', 'toy_car', 'toy_ball', 'action_figure', 'toy_marble', 'beanie_baby']
senticnet['plush_toy'] = ['0.71', '0.92', '0', '0', '#joy', '#interest', 'positive', '0.815', 'toy_car', 'toy_ball', 'action_figure', 'toy_marble', 'beanie_baby']
senticnet['plushest'] = ['0.522', '-0.88', '0', '0.965', '#surprise', '#admiration', 'positive', '0.789', 'overage', 'asset', 'security', 'business', 'hole_card']
senticnet['plushly'] = ['0.895', '0.971', '0', '0', '#joy', '#interest', 'positive', '0.933', 'plushy', 'fluffy', 'plush', 'plushness', 'carpet']
senticnet['plushness'] = ['0.902', '0.973', '0', '0', '#joy', '#interest', 'positive', '0.938', 'plushy', 'fluffy', 'plushly', 'plush', 'carpet']
senticnet['plushy'] = ['0.899', '0.974', '0', '0', '#joy', '#interest', 'positive', '0.936', 'plush', 'fluffy', 'plushly', 'plushness', 'carpet']
senticnet['plutarch'] = ['0.8', '-0.79', '0', '0.751', '#joy', '#surprise', 'positive', '0.782', 'elder_statesman', 'stateswoman', 'politician', 'politico', 'pol']
senticnet['pluto_atmosphere'] = ['0.095', '0', '-0.04', '0.062', '#joy', '#admiration', 'positive', '0.037', 'photographer', 'water_spray', 'moisture', 'rise_water', 'droplet']
senticnet['plutocracy'] = ['0.028', '0.12', '0.068', '0.176', '#interest', '#admiration', 'positive', '0.064', 'elite', 'number_person', 'count_person', 'small_group', 'enumeration']
senticnet['plutocrat'] = ['0', '-0.96', '0', '0.92', '#surprise', '#admiration', 'positive', '0.944', 'out-of-touch', 'plutocrat', 'multimillionaire', 'super-rich', 'corporatist']
senticnet['plutocratic'] = ['0.028', '0.12', '0.068', '0.176', '#interest', '#admiration', 'positive', '0.064', 'elite', 'number_person', 'count_person', 'small_group', 'enumeration']
senticnet['plutonium'] = ['0', '0', '0.732', '-0.77', '#anger', '#disgust', 'negative', '-0.75', 'bomb-grade', 'weapons-grade', 'weapon-grade', 'fissile', 'uranium']
senticnet['plutonium_bomb'] = ['0', '0', '-0.72', '-0.94', '#fear', '#disgust', 'negative', '-0.83', 'pistol', 'gun', 'hydrogen_bomb', 'handgun', 'revolver']
senticnet['plymouth_barracuda'] = ['-0.73', '0', '0.72', '-0.93', '#sadness', '#disgust', 'negative', '-0.79', 'imp', 'elf', 'hob', 'leprechaun', 'pixie']
senticnet['plymouth_fury'] = ['-0.73', '0', '0.72', '-0.93', '#sadness', '#disgust', 'negative', '-0.79', 'imp', 'elf', 'hob', 'leprechaun', 'pixie']
senticnet['plymouth_savoy'] = ['0.909', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.952', 'displace', 'swing', 'snowshoe', 'shuttle', 'bang']
senticnet['plymouth_sundance'] = ['-0.82', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.80', 'evil', 'darkness', 'dark', 'sun_down', 'dark_side']
senticnet['plymouth_voyager'] = ['-0.05', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'plane_land', 'floss', 'floss_tooth', 'ornament', 'wear_dress']
senticnet['plyometric'] = ['-0.68', '0', '0.545', '-0.68', '#sadness', '#disgust', 'negative', '-0.64', 'stunt', 'tumble', 'split', 'walkover', 'acrobatic_feat']
senticnet['plywood'] = ['-0.69', '0', '0.725', '-0.89', '#anger', '#disgust', 'negative', '-0.77', 'ponderosa', 'ponderosa_pine', 'western_yellow_pine', 'bull_pine', 'longleaf_pine']
senticnet['pmi_index'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'mathematical', 'muscle', 'math_function', 'feel_sick', 'separation']
senticnet['pmi_survey'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'mathematical', 'muscle', 'math_function', 'feel_sick', 'separation']
senticnet['pna_pattern'] = ['-0.68', '0', '0.946', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'abnormality', 'abnormalcy', 'birth_defect', 'congenital_anomaly', 'congenital_defect']
senticnet['pneuma'] = ['0', '0.098', '-0.09', '0.115', '#interest', '#admiration', 'positive', '0.04', 'push_wind', 'breath', 'breaout', 'body_reaction', 'mouth_action']
senticnet['pneumatic'] = ['0.878', '0', '0', '0.847', '#joy', '#admiration', 'positive', '0.863', 'hydraulically', 'actuated', 'mechanically', 'electrically', 'pneumatically']
senticnet['pneumatically'] = ['0.885', '0', '0', '0.857', '#joy', '#admiration', 'positive', '0.871', 'hydraulically', 'actuated', 'mechanically', 'electrically', 'pneumatic']
senticnet['pneumococcal_infection'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchitis', 'chronic_bronchitis', 'respiratory_disease', 'respiratory_illness', 'respiratory_disorder']
senticnet['pneumococcal_pneumonia'] = ['-0.92', '0', '0.824', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'bronchopneumonia', 'bronchial_pneumonia', 'viral_pneumonia', 'pleuropneumonia', 'pneumonia']
senticnet['pneumocystis_carinii'] = ['-0.60', '0', '0.721', '-0.57', '#sadness', '#anger', 'negative', '-0.63', 'tubercle_bacillus', 'leprosy_bacillus', 'eubacteria', 'eubacterium', 'true_bacteria']
senticnet['pneumonia'] = ['-0.92', '0', '0.824', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'bronchopneumonia', 'bronchial_pneumonia', 'viral_pneumonia', 'pleuropneumonia', 'pneumococcal_pneumonia']
senticnet['pneumonic'] = ['-0.95', '0', '0.876', '-0.89', '#sadness', '#disgust', 'negative', '-0.90', 'bubonic_plague', 'pestis_bubonica', 'glandular_plague', 'septicemic_plague', 'pestilence']
senticnet['pneumonic_form'] = ['-0.95', '0', '0.876', '-0.89', '#sadness', '#disgust', 'negative', '-0.90', 'bubonic_plague', 'pestis_bubonica', 'glandular_plague', 'septicemic_plague', 'pestilence']
senticnet['pneumonic_plague'] = ['-0.95', '0', '0.876', '-0.89', '#sadness', '#disgust', 'negative', '-0.90', 'bubonic_plague', 'pestis_bubonica', 'glandular_plague', 'septicemic_plague', 'pestilence']
senticnet['pneumoperitoneum'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'blatancy', 'obviousness']
senticnet['pngcrush'] = ['0', '0', '-0.1', '0', '#fear', '#fear', 'negative', '-0.1', 'grow_yard', 'heat', 'burn', 'corrode', 'stain_clothes']
senticnet['poach'] = ['0', '0', '0.926', '-0.99', '#anger', '#disgust', 'negative', '-0.95', 'poached', 'braise', 'simmer', 'boil', 'poaching']
senticnet['poached'] = ['0', '0', '0.923', '-0.99', '#anger', '#disgust', 'negative', '-0.95', 'poach', 'braise', 'simmer', 'boil', 'poaching']
senticnet['poacher'] = ['0', '0', '-0.11', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'hunting_season', 'hound', 'cougar', 'outlaw', 'dragon']
senticnet['poacher_kill'] = ['-0.73', '0', '0.55', '-0.75', '#sadness', '#disgust', 'negative', '-0.68', 'mortal', 'person', 'individual', 'mangler', 'mutilator']
senticnet['poaching'] = ['0', '0', '0.923', '-0.99', '#anger', '#disgust', 'negative', '-0.95', 'poached', 'braise', 'simmer', 'boil', 'poach']
senticnet['pocket'] = ['0', '0.092', '0', '0', '#interest', '#interest', 'positive', '0.092', 'man_back_pocket', 'pant_back_pocket', 'back_pocket', 'purse', 'clothes_washer']
senticnet['pocket_billiard'] = ['-0.68', '0', '0.923', '-0.68', '#sadness', '#anger', 'negative', '-0.76', 'negative', 'veto', 'refute', 'rebut', 'controvert']
senticnet['pocket_dimension'] = ['0.086', '-0.10', '0.064', '0', '#joy', '#surprise', 'positive', '0.043', 'art_piece', 'lifetime', 'museum', 'vintage', 'collectible']
senticnet['pocket_flask'] = ['0.619', '-0.83', '0', '0.912', '#surprise', '#admiration', 'positive', '0.788', 'hipflask', 'flask', 'ampulla', 'canteen', 'vacuum_flask']
senticnet['pocket_gamer_uk'] = ['-0.97', '0', '-0.97', '-0.84', '#sadness', '#fear', 'negative', '-0.93', 'school', 'school_system', 'class', 'college', 'college_class']
senticnet['pocket_gopher'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'carbon_monoxide', 'tree_animal']
senticnet['pocket_guide'] = ['-0.03', '0.168', '-0.07', '0.139', '#interest', '#admiration', 'positive', '0.049', 'command', 'direct', 'follow', 'take_charge', 'govern']
senticnet['pocket_handkerchief'] = ['-0.84', '0', '0.633', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'grizzle', 'postiche', 'horsehair_wig', 'wig', 'peruke']
senticnet['pocket_knife'] = ['0.818', '-0.95', '0', '0.695', '#joy', '#surprise', 'positive', '0.823', 'jackknife', 'clasp_knife', 'penknife', 'switchblade', 'switchblade_knife']
senticnet['pocket_money'] = ['0.057', '0.181', '-0.04', '0.18', '#interest', '#admiration', 'positive', '0.092', 'small_money', 'alter', 'money_change', 'money_coin', 'small_coin']
senticnet['pocket_mouse'] = ['-0.55', '0', '0.955', '-0.96', '#anger', '#disgust', 'negative', '-0.82', 'house_mouse', 'mouse', 'rat', 'fieldmouse', 'nude_mouse']
senticnet['pocket_notebook'] = ['0', '-0.04', '0.061', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'index_card', 'ring_binder', 'highlighter_pen', 'memo_pad', 'notepad']
senticnet['pocket_pc'] = ['0.113', '0.073', '0.028', '0.107', '#joy', '#admiration', 'positive', '0.066', 'pda', 'telephone_computer', 'browser', 'number_pad', 'modem']
senticnet['pocket_protector'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'book_worm', 'nerd', 'loser', 'underdog', 'computer_lover']
senticnet['pocket_size_notebook'] = ['0', '-0.04', '0.072', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'notepad', 'memo_pad', 'self_adhesive_label', 'intern', 'paper_clip']
senticnet['pocket_veto'] = ['-0.81', '0', '0.513', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'ballot', 'vote', 'voting', 'balloting', 'veto']
senticnet['pocket_watch'] = ['0.992', '0.847', '0', '0', '#joy', '#interest', 'positive', '0.919', 'watch', 'find_pawnshop', 'wristwatch', 'stopwatch', 'clock']
senticnet['pocketknife'] = ['0.759', '-0.65', '0', '0.716', '#joy', '#admiration', 'positive', '0.71', 'jackknife', 'clasp_knife', 'penknife', 'switchblade', 'switchblade_knife']
senticnet['pocky'] = ['0.023', '0.141', '-0.13', '0.174', '#interest', '#admiration', 'positive', '0.052', 'candy', 'push_wind', 'exhalation', 'breaout', 'mouth_action']
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senticnet['pogonophora'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'hag', 'osteostracan', 'cephalaspid', 'ostracoderm', 'jawless_fish']
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senticnet['point_tally'] = ['0.926', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.916', 'score_goal', 'accomplish', 'finalize', 'pot', 'make_point']
senticnet['point_weapon'] = ['0.806', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.875', 'intention', 'aim', 'life_goal', 'objective', 'target']
senticnet['pointe_coupee_parish'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
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senticnet['poisonous_carbon_monoxide'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'rodent', 'tree_animal']
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senticnet['poisonous_snake'] = ['-0.02', '-0.00', '-0.05', '-0.08', '#fear', '#disgust', 'negative', '-0.03', 'red-bellied_black_snake', 'eastern_brown_snake', 'lizard', 'snake', 'shark']
senticnet['poisonous_spider'] = ['0.876', '0.948', '0', '0', '#joy', '#interest', 'positive', '0.912', 'sting', 'tidbit', 'nip', 'tiny_amount', 'piece']
senticnet['poisonous_sting'] = ['-0.25', '0.081', '-0.19', '0', '#sadness', '#fear', 'negative', '-0.12', 'hairy', 'kind_animal', 'poisonous', 'big_fish', 'graceful']
senticnet['poisonous_substance'] = ['0.049', '0', '0', '0.113', '#joy', '#admiration', 'positive', '0.081', 'solid', 'hard_material', 'construction_material', 'hard', 'calcium']
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senticnet['poke_fun_at'] = ['0', '-0.03', '0', '0.094', '#surprise', '#admiration', 'positive', '0.065', 'commercial', 'sitcom', 'presentation', 'joke', 'funny']
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senticnet['poker_countenance'] = ['0.713', '-0.59', '0', '0.737', '#joy', '#admiration', 'positive', '0.68', 'face', 'look', 'countenance', 'expression', 'aspect']
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senticnet['political_philosopher_edmund'] = ['-0.69', '0', '0.89', '-0.70', '#anger', '#disgust', 'negative', '-0.76', 'still', 'suppress', 'moderate', 'hush_up', 'quell']
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senticnet['political_science'] = ['0', '0.902', '0', '0.785', '#interest', '#admiration', 'positive', '0.844', 'politics', 'government', 'different_application', 'standard_grant', 'economic_science']
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senticnet['polk_automotive'] = ['0.997', '0.796', '0', '0.89', '#joy', '#admiration', 'positive', '0.894', 'devotion', 'bravery', 'adoration', 'zeal', 'charm']
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senticnet['polka_dot'] = ['0.793', '0.847', '0', '0', '#joy', '#interest', 'positive', '0.82', 'cross_bun', 'hot_cross_bun', 'bear_paw', 'coffee_roll', 'bun']
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senticnet['pollaxe'] = ['0.93', '-0.78', '0', '0.873', '#joy', '#admiration', 'positive', '0.861', 'blade', 'brand', 'steel', 'claymore', 'sword']
senticnet['pollen'] = ['0.814', '0.84', '0', '0', '#joy', '#interest', 'positive', '0.827', 'blossom', 'flower', 'daffodil', 'bloom', 'dandelion']
senticnet['pollen_allergy'] = ['-0.96', '0', '0.848', '-0.82', '#sadness', '#anger', 'negative', '-0.87', 'sneeze', 'sign_sickness', 'runny_nose', 'flu', 'common_cold']
senticnet['pollination'] = ['0', '0.954', '0', '0.968', '#interest', '#admiration', 'positive', '0.961', 'superfetation', 'superfecundation', 'fertilization', 'dressing', 'top_dressing']
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senticnet['polling_day'] = ['0', '0.156', '-0.15', '0.179', '#interest', '#admiration', 'positive', '0.061', 'candidate', 'presidential', 'vote', 'elect', 'appoint']
senticnet['pollinosis'] = ['-0.60', '0', '0.931', '-0.92', '#anger', '#disgust', 'negative', '-0.82', 'rhinitis', 'coryza', 'hay_fever', 'allergic_rhinitis', 'hypersensitivity_reaction']
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senticnet['pollute_air'] = ['0', '0', '0.748', '-0.75', '#anger', '#disgust', 'negative', '-0.75', 'leave_out', 'feel_sick', 'screw_up', 'terminal_illness', 'cause_suffer']
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senticnet['polluted_air'] = ['-0.92', '0', '0.917', '-0.95', '#sadness', '#disgust', 'negative', '-0.93', 'ozone_hole', 'make_pollution', 'pollution', 'create_pollution', 'pollution_factory']
senticnet['polluted_groundwater'] = ['0.935', '0', '0', '0', '#joy', '#joy', 'positive', '0.935', 'clean_up_kitchen', 'feel_dirty', 'remove_stain', 'stink', 'stink_mess']
senticnet['polluted_water'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'bottled_water', 'sugar_water']
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senticnet['pollution_factory'] = ['-0.92', '0', '0.917', '-0.95', '#sadness', '#disgust', 'negative', '-0.93', 'ozone_hole', 'polluted_air', 'pollution', 'create_pollution', 'make_pollution']
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senticnet['pollyannaish'] = ['0.783', '-0.51', '0', '0.584', '#joy', '#admiration', 'positive', '0.627', 'appetent', 'avid', 'thirsty', 'devouring', 'envious']
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senticnet['polo_shirt'] = ['-0.79', '0', '0.599', '-0.93', '#sadness', '#disgust', 'negative', '-0.77', 'uniform', 'battle_dress', 'dress_uniform', 'fatigue', 'military_uniform']
senticnet['polonaise'] = ['0.591', '-0.91', '0', '0.605', '#surprise', '#admiration', 'positive', '0.704', 'farandole', 'folk_dance', 'polka', 'dance', 'typical_dance']
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senticnet['polskiego'] = ['0', '0', '0', '0.1', '#admiration', '#admiration', 'positive', '0.1', 'find_earth', 'wax', 'plutonium', 'shine', 'melt']
senticnet['poltroon'] = ['-0.55', '0', '0.947', '-0.77', '#anger', '#disgust', 'negative', '-0.75', 'bemock', 'deride', 'twit', 'tease', 'razz']
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senticnet['poly_olbion'] = ['-0.7', '0', '0.746', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'insufficiency', 'sparseness', 'sparsity', 'abstemiousness', 'lack_of_quantity']
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senticnet['polyacrylamide_gel'] = ['-0.04', '0.069', '0', '0.075', '#interest', '#admiration', 'positive', '0.035', 'sculpt_gel', 'hairstyle', 'hairdresser', 'cosmetic', 'hair_sculpt_gel']
senticnet['polyadic'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'integrative', 'consolidative', 'unifying', 'combinatorial']
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senticnet['polyalphabetic_cipher'] = ['-0.52', '0', '0.821', '-0.65', '#anger', '#disgust', 'negative', '-0.66', 'work_out', 'reckon', 'calculate', 'average_out', 'cypher']
senticnet['polyarteritis'] = ['-0.85', '0', '0.657', '-0.86', '#sadness', '#disgust', 'negative', '-0.79', 'pulseless_disease', 'temporal_arteritis', 'rubor', 'redness', 'arteritis']
senticnet['polyarteritis_nodosa'] = ['-0.85', '0', '0.657', '-0.86', '#sadness', '#disgust', 'negative', '-0.79', 'pulseless_disease', 'temporal_arteritis', 'rubor', 'redness', 'arteritis']
senticnet['polycarbonate'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'gold', 'bronze', 'alloy', 'medal', 'copper']
senticnet['polychaeta'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'annelid', 'annelid_worm', 'leech']
senticnet['polychaete_worm'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'annelid', 'annelid_worm', 'leech']
senticnet['polychrome'] = ['-0.06', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.04', 'polish', 'magnify', 'see_out', 'sill', 'pane']
senticnet['polychromy'] = ['0.876', '-0.76', '0', '0.963', '#joy', '#admiration', 'positive', '0.867', 'housecraft', 'tradecraft', 'watercraft', 'acquirement', 'skill']
senticnet['polycystic'] = ['-0.99', '0', '0.964', '-0.70', '#sadness', '#anger', 'negative', '-0.88', 'blepharitis', 'diverticulitis', 'epiglottitis', 'inflammation', 'prostatitis']
senticnet['polycystic_kidney'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'dystrophy', 'myotonic_dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['polycystic_kidney_disease'] = ['0.576', '-0.57', '0', '0.768', '#joy', '#admiration', 'positive', '0.639', 'uropathy', 'polyuria', 'kidney_disease', 'renal_failure', 'kidney_failure']
senticnet['polydactyly'] = ['-0.61', '0', '0.981', '-0.52', '#sadness', '#anger', 'negative', '-0.70', 'birth_defect', 'congenital_anomaly', 'congenital_defect', 'congenital_disorder', 'congenital_abnormality']
senticnet['polydimethylsiloxane'] = ['-0.04', '0.069', '0', '0.075', '#interest', '#admiration', 'positive', '0.035', 'sculpt_gel', 'hairstyle', 'hairdresser', 'cosmetic', 'hair_sculpt_gel']
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senticnet['polyester_fabric'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'foil', 'tin_foil', 'gold_foil']
senticnet['polyester_resin'] = ['0.085', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.074', 'soap', 'drizzle', 'bubbly', 'solid_water', 'ivory']
senticnet['polyethylene_film'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'foil', 'tin_foil', 'gold_foil']
senticnet['polyethylene_foam'] = ['0.085', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.074', 'soap', 'drizzle', 'bubbly', 'solid_water', 'ivory']
senticnet['polyethylene_glycol'] = ['-0.04', '0.069', '0', '0.075', '#interest', '#admiration', 'positive', '0.035', 'sculpt_gel', 'hairstyle', 'hairdresser', 'cosmetic', 'hair_sculpt_gel']
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senticnet['polygonal_shape'] = ['0.831', '-0.84', '0', '0.586', '#joy', '#surprise', 'positive', '0.755', 'oblong', 'star', 'heart', 'polygon', 'plane_figure']
senticnet['polygonum'] = ['-0.79', '0', '0.906', '-0.67', '#sadness', '#anger', 'negative', '-0.79', 'poisonous_plant', 'hemlock_poisonous_plant', 'water_hemlock', 'plant_life', 'poison_sumac']
senticnet['polygyne'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'giant_hornet', 'hornet', 'hornet_wasp']
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senticnet['polyhidrosis'] = ['-0.58', '0', '0.773', '-0.79', '#anger', '#disgust', 'negative', '-0.71', 'perspiration', 'sweating', 'diaphoresis', 'sudation', 'hidrosis']
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senticnet['polymer'] = ['0.948', '0.715', '0', '0', '#joy', '#interest', 'positive', '0.831', 'award', 'material_research', 'research', 'continue_grant', 'sponsor']
senticnet['polymer_banknote'] = ['0.81', '0.763', '0', '0', '#joy', '#interest', 'positive', '0.786', 'dollar_bill', 'franc', 'euro', 'dollar', 'cent']
senticnet['polymer_chain'] = ['0.096', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.057', 'platform_rail', 'rail', 'catwalk', 'column', 'token_turnstile']
senticnet['polymer_clay'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'building', 'carving', 'artistic_creation', 'ceramic']
senticnet['polymer_matrix'] = ['-0.04', '0.069', '0', '0.075', '#interest', '#admiration', 'positive', '0.035', 'sculpt_gel', 'hairstyle', 'hairdresser', 'cosmetic', 'hair_sculpt_gel']
senticnet['polymerization'] = ['-0.03', '0.169', '-0.07', '0.218', '#interest', '#admiration', 'positive', '0.071', 'elongate', 'elasticity', 'extension', 'grow_long', 'widen']
senticnet['polymerize'] = ['-0.78', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.83', 'break', 'end', 'die', 'fall_apart', 'go_to_piece']
senticnet['polymicrobial'] = ['-0.53', '0', '0.671', '-0.55', '#anger', '#disgust', 'negative', '-0.58', 'dirty', 'putrefactive', 'putrefacient', 'abscessed', 'contaminative']
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senticnet['polymorphic_type'] = ['0.777', '-0.68', '0', '0.601', '#joy', '#surprise', 'positive', '0.686', 'lucidness', 'clarity', 'clearness', 'lucidity', 'pellucidity']
senticnet['polymyalgia_rheumatica'] = ['-0.87', '0', '0.531', '-0.68', '#sadness', '#disgust', 'negative', '-0.69', 'rubor', 'redness', 'dermatomyositis', 'myositis', 'inflammation']
senticnet['polymyositis_pm'] = ['-0.87', '0', '0.531', '-0.68', '#sadness', '#disgust', 'negative', '-0.69', 'rubor', 'redness', 'dermatomyositis', 'myositis', 'inflammation']
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senticnet['polynomial_time'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly']
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senticnet['polyp_stage'] = ['-0.87', '0', '0.983', '-0.91', '#anger', '#disgust', 'negative', '-0.92', 'thorn', 'sticker', 'pricker', 'prickle', 'spikelet']
senticnet['polypeptide'] = ['0.772', '0.758', '0', '0', '#joy', '#interest', 'positive', '0.765', 'endorphin', 'fibrinopeptide', 'peptide', 'amide', 'protein']
senticnet['polypeptide_chain'] = ['0.096', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.057', 'platform_rail', 'rail', 'catwalk', 'column', 'token_turnstile']
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senticnet['polyphonic_music'] = ['0.99', '0', '0', '0.719', '#joy', '#admiration', 'positive', '0.855', 'instrumental', 'lyric', 'lyrics', 'sing', 'melody']
senticnet['polyphonic_spree'] = ['0.304', '0.339', '-0.19', '0.333', '#interest', '#admiration', 'positive', '0.195', 'careful', 'reinforce', 'load_bear', 'go_outdoors', 'allowance']
senticnet['polyphony'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
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senticnet['polypore'] = ['-0.60', '0', '0.541', '-0.52', '#sadness', '#anger', 'negative', '-0.55', 'swoon', 'pass_out', 'faint', 'zonk_out', 'change_state']
senticnet['polypropylene_mesh'] = ['-0.81', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.85', 'bandage', 'painkiller', 'gauze', 'aspirin', 'contraceptive_device']
senticnet['polyradiculopathy'] = ['-0.65', '0', '0.873', '-0.63', '#sadness', '#anger', 'negative', '-0.72', 'polyneuritis', 'multiple_neuritis', 'rubor', 'redness', 'inflammation']
senticnet['polysemic'] = ['0.519', '-0.55', '0', '0.746', '#surprise', '#admiration', 'positive', '0.606', 'narrow_down', 'peg_down', 'nail_down', 'narrow', 'pin_down']
senticnet['polysemy'] = ['0.973', '-0.77', '0', '0.983', '#joy', '#admiration', 'positive', '0.911', 'opaqueness', 'faintness', 'dimness', 'opacity', 'vagueness']
senticnet['polysomy'] = ['0.636', '-0.92', '0', '0.636', '#joy', '#surprise', 'positive', '0.731', 'birth_defect', 'congenital_anomaly', 'congenital_defect', 'congenital_disorder', 'congenital_abnormality']
senticnet['polystichum'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['polystichum_setiferum'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'clematis', 'legume', 'leguminous_plant', 'climber', 'root_climber']
senticnet['polystyrene'] = ['0.085', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.074', 'soap', 'drizzle', 'bubbly', 'solid_water', 'ivory']
senticnet['polystyrene_foam'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'foil', 'tin_foil', 'gold_foil']
senticnet['polysyllabic'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicado', 'incommunicative', 'impassive', 'inexpressive', 'silent']
senticnet['polysynaptic'] = ['0.698', '-0.61', '0', '0.619', '#joy', '#admiration', 'positive', '0.644', 'sensitivity', 'sensitiveness', 'irritability', 'sensibility', 'reactivity']
senticnet['polytechnic_institute'] = ['0', '0.709', '0', '0.76', '#interest', '#admiration', 'positive', '0.734', 'technical_school', 'tech', 'school', 'different_application', 'chemistry']
senticnet['polythene'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'foil', 'tin_foil', 'gold_foil']
senticnet['polytonal'] = ['-0.52', '0', '0.902', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'discordant', 'inharmonious', 'unresolved', 'false', 'disharmonious']
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senticnet['polytunnel'] = ['-0.12', '0.066', '-0.08', '-0.06', '#sadness', '#fear', 'negative', '-0.05', 'garden_yard', 'see_sky', 'plant_tree', 'hair', 'seed_lawn']
senticnet['polyurethane'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'performant']
senticnet['polyurethane_condom'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'wall_cover', 'two-dimensional', 'cover_wall', 'pigment', 'dye']
senticnet['polyurethane_foam'] = ['0.085', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.074', 'soap', 'drizzle', 'bubbly', 'solid_water', 'ivory']
senticnet['polyuria'] = ['0.576', '-0.57', '0', '0.765', '#joy', '#admiration', 'positive', '0.639', 'uropathy', 'kidney_disease', 'polycystic_kidney_disease', 'renal_failure', 'kidney_failure']
senticnet['polyvinyl_alcohol'] = ['-0.04', '0.069', '0', '0.075', '#interest', '#admiration', 'positive', '0.035', 'sculpt_gel', 'hairstyle', 'hairdresser', 'cosmetic', 'hair_sculpt_gel']
senticnet['polyvinylidene_fluoride'] = ['-0.04', '0.069', '0', '0.075', '#interest', '#admiration', 'positive', '0.035', 'sculpt_gel', 'hairstyle', 'hairdresser', 'cosmetic', 'hair_sculpt_gel']
senticnet['pomade'] = ['0.824', '0.86', '0', '0.983', '#interest', '#admiration', 'positive', '0.889', 'look_smart', 'social_acceptance', 'improve_appearance', 'improve_look', 'look_good']
senticnet['pomaded'] = ['0.882', '-0.68', '0', '0.908', '#joy', '#admiration', 'positive', '0.826', 'tidy', 'kempt', 'sleek', 'groomed', 'brushed']
senticnet['pomander'] = ['-0.93', '0', '0.861', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'knotty', 'contorted', 'writhed', 'crooked', 'anfractuous']
senticnet['pomegranate'] = ['0.951', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.851', 'persimmon', 'persimmon', 'apricot', 'nectarine', 'peach']
senticnet['pomelo'] = ['0.859', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.888', 'green_apple', 'apricot', 'lime', 'peach', 'cherry']
senticnet['pommel'] = ['0', '0', '0.723', '-0.82', '#anger', '#disgust', 'negative', '-0.77', 'lacerate', 'displease', 'annoy', 'harm', 'discomfit']
senticnet['pommel_horse'] = ['0.964', '0.713', '0', '0', '#joy', '#interest', 'positive', '0.839', 'martial_art', 'judo', 'jump_around', 'go_underwater', 'athletics']
senticnet['pomp'] = ['0.902', '-0.72', '0', '0.919', '#joy', '#admiration', 'positive', '0.849', 'preciousness', 'costliness', 'dearness', 'cost', 'expensiveness']
senticnet['pompano'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'crab', 'swimming_crab', 'rock_crab', 'fiddler_crab', 'pea_crab']
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senticnet['pompilia'] = ['-0.76', '0', '0.995', '-0.94', '#anger', '#disgust', 'negative', '-0.90', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'excretion']
senticnet['pompom'] = ['0.793', '0.847', '0', '0', '#joy', '#interest', 'positive', '0.82', 'cross_bun', 'hot_cross_bun', 'bear_paw', 'coffee_roll', 'bun']
senticnet['pomposity'] = ['-0.9', '0', '0.841', '-0.73', '#sadness', '#anger', 'negative', '-0.82', 'nubbiness', 'tweediness', 'harshness', 'graininess', 'granularity']
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senticnet['pompously'] = ['0.793', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.854', 'hypocritically', 'arrogantly', 'sanctimoniously', 'self-righteously', 'piously']
senticnet['poncirus_trifoliata'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'intrepidly', 'fearlessness', 'fearlessly', 'unafraid', 'unfearing']
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senticnet['pond_inlet'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'cheetah', 'giraffe', 'kangaroo', 'mule', 'horse']
senticnet['ponder'] = ['0', '0.957', '0', '0.812', '#interest', '#admiration', 'positive', '0.885', 'think', 'rational', 'cogito_ergo_sum', 'reason', 'make_decision']
senticnet['ponderable'] = ['0.876', '-0.68', '0', '0.629', '#joy', '#surprise', 'positive', '0.728', 'conceivable', 'presumable', 'supposable', 'surmisable', 'thinkable']
senticnet['pondering'] = ['0.89', '0.839', '0', '0', '#joy', '#interest', 'positive', '0.865', 'ruminative', 'broody', 'contemplative', 'meditative', 'think']
senticnet['ponderosa'] = ['0.037', '-0.66', '0', '0.359', '#surprise', '#admiration', 'positive', '0.355', 'bull_pine', 'ponderosa_pine', 'western_yellow_pine', 'yellow_pine', 'longleaf_pine']
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senticnet['ponderosa_pine_pinus_ponderosa'] = ['-0.94', '0', '0.608', '-0.95', '#sadness', '#disgust', 'negative', '-0.83', 'wild_licorice', 'wild_liquorice', 'alpine_milk_vetch', 'purple_milk_vetch', 'milk_vetch']
senticnet['ponderous'] = ['0', '0', '-0.98', '-0.93', '#fear', '#disgust', 'negative', '-0.96', 'heavy', 'onerous', 'load', 'onerousness', 'onus']
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senticnet['pondy_bazaar'] = ['0', '0.113', '0.041', '0.058', '#interest', '#admiration', 'positive', '0.043', 'corner_shop', 'general_store', 'many_shops', 'large_supermarket', 'goods']
senticnet['pong'] = ['-0.89', '0', '0.897', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'malodor', 'stench', 'stink', 'fetor', 'bad_smell']
senticnet['pongamia_pinnata'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'nut', 'palm_kernel', 'edible_nut', 'cembra_nut', 'cedar_nut']
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senticnet['poniewozik'] = ['0.761', '0.566', '-0.21', '0.293', '#joy', '#interest', 'positive', '0.352', 'gratify', 'jubilation', 'gloat', 'gloating', 'gloatingly']
senticnet['pontiac'] = ['0.909', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.952', 'displace', 'swing', 'snowshoe', 'shuttle', 'bang']
senticnet['pontiac_bonneville'] = ['-0.05', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'plane_land', 'floss', 'floss_tooth', 'ornament', 'wear_dress']
senticnet['pontiac_gto'] = ['0.909', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.952', 'displace', 'swing', 'snowshoe', 'shuttle', 'bang']
senticnet['pontiac_sunbird'] = ['0.909', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.952', 'displace', 'swing', 'snowshoe', 'shuttle', 'bang']
senticnet['pontiac_tempest'] = ['0.909', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.952', 'displace', 'swing', 'snowshoe', 'shuttle', 'bang']
senticnet['pontificate'] = ['0.815', '0', '0', '0.76', '#joy', '#admiration', 'positive', '0.787', 'papacy', 'preach', 'pope', 'minister', 'bishop']
senticnet['pontification'] = ['-0.87', '0', '0.838', '-0.69', '#sadness', '#anger', 'negative', '-0.80', 'prate', 'blither', 'gibber', 'blabber', 'gabble']
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senticnet['pontoon'] = ['0.84', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.798', 'big_breadbox', 'water_vehicle', 'military_ship', 'ship', 'small_boat']
senticnet['pontoon_boat'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'boat', 'yacht', 'big_boat', 'large_boat']
senticnet['pontoon_bridge'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'suspension_bridge', 'under_bridge', 'roadway', 'county_highway', 'long_road']
senticnet['pontryagin_duality'] = ['0.875', '-0.65', '0', '0.861', '#joy', '#admiration', 'positive', '0.797', 'completeness', 'work', 'whole_shebang', 'essential', 'whole']
senticnet['pony'] = ['0', '-0.11', '0.014', '0.053', '#surprise', '#admiration', 'positive', '0.053', 'donkey', 'small_horse', 'male_cow', 'hoof_animal', 'little_horse']
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senticnet['ponytail'] = ['0', '-0.01', '0.044', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'fur', 'two_eyes', 'fur_keep_warm', 'hold_hair', 'facial_hair']
senticnet['ponzi_scheme_operator'] = ['0.025', '0.27', '-0.11', '0.243', '#interest', '#admiration', 'positive', '0.105', 'keep_safe', 'store_dry_goods', 'store_stuff', 'secure', 'save']
senticnet['ponzi_schemer'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'gouger', 'swindler', 'confidence_man', 'conman', 'fraud']
senticnet['ponzu'] = ['0.9', '0.963', '0', '0', '#joy', '#interest', 'positive', '0.931', 'form_of_food', 'japanese_food', 'uncooked', 'healthy', 'food']
senticnet['poo'] = ['-0.03', '0.065', '0', '0.054', '#interest', '#admiration', 'positive', '0.028', 'courthouse_look_public_record', 'transportation_device', 'new_caledonia', 'minor_planet', 'human_memory']
senticnet['pooch'] = ['-0.72', '0', '0.619', '-0.86', '#sadness', '#disgust', 'negative', '-0.73', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['poodle'] = ['-0.72', '0', '0.619', '-0.86', '#sadness', '#disgust', 'negative', '-0.73', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['poofter'] = ['-0.35', '-0.13', '-0.05', '-0.38', '#sadness', '#disgust', 'negative', '-0.16', 'dick', 'idiot', 'asshole', 'fuck', 'motherfucker']
senticnet['pool'] = ['0', '0', '0', '0.964', '#admiration', '#admiration', 'positive', '0.964', 'swimming_pool', 'swim', 'swimsuit', 'lake', 'have_fun']
senticnet['pool_deck'] = ['0', '0', '0', '0.964', '#admiration', '#admiration', 'positive', '0.964', 'swimming_pool', 'swim', 'swimsuit', 'lake', 'have_fun']
senticnet['pool_hall'] = ['0.103', '0', '0', '0', '#joy', '#joy', 'positive', '0.103', 'restaurant', 'brewery', 'club', 'men_club', 'strip_club']
senticnet['pool_heater'] = ['0.02', '0.076', '0', '0', '#joy', '#interest', 'positive', '0.048', 'land_earth', 'tidal_wave', 'puddle', 'brine', 'spurt']
senticnet['pool_party'] = ['0.755', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.728', 'take_leak', 'family_fun', 'pool', 'swim', 'go_underwater']
senticnet['pooled_fund_study'] = ['0', '0.097', '0', '0.092', '#interest', '#admiration', 'positive', '0.095', 'urgent', 'classify_ad', 'important', 'review', 'repetition']
senticnet['poolside_bar'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'drink', 'party', 'friend_gathering', 'rest_area', 'relax_time']
senticnet['poop'] = ['-0.03', '-0.01', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'shit', 'wildflower', 'skink', 'wild_flower', 'dog_poop']
senticnet['poop_out'] = ['0.891', '-0.55', '0', '0.996', '#joy', '#admiration', 'positive', '0.814', 'weary', 'tire', 'jade', 'pall', 'fatigue']
senticnet['pooper_scooper'] = ['-0.03', '-0.01', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'shit', 'wildflower', 'skink', 'wild_flower', 'dog_poop']
senticnet['poor'] = ['-0.56', '0', '-0.49', '-0.09', '#sadness', '#fear', 'negative', '-0.38', 'lack', 'absence', 'poverty', 'emptiness', 'hunger']
senticnet['poor_english'] = ['-0.94', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.83', 'mistake', 'error', 'grammar_mistake', 'misspell', 'bad_writing']
senticnet['poor_eyesight'] = ['-0.24', '-0.08', '0.03', '-0.36', '#sadness', '#disgust', 'negative', '-0.13', 'criticize', 'time_waste', 'move_eye', 'learn', 'bad_vision']
senticnet['poor_fish'] = ['-0.88', '0', '0.613', '-0.61', '#sadness', '#disgust', 'negative', '-0.70', 'dunce', 'dunderhead', 'numskull', 'blockhead', 'bonehead']
senticnet['poor_glycemic_control'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'illness', 'ill', 'tension', 'bad_health', 'malignant_hypertension']
senticnet['poor_health'] = ['-0.3', '-0.07', '-0.22', '-0.21', '#sadness', '#fear', 'negative', '-0.16', 'feel_pain', 'experience_pain', 'schizophrenia', 'take_aspirin', 'ill_health']
senticnet['poor_hygiene'] = ['-0.93', '0', '0.75', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'invalidism', 'injury', 'affliction', 'biliousness', 'harm']
senticnet['poor_lad'] = ['-0.85', '0.21', '-0.56', '0', '#sadness', '#fear', 'negative', '-0.40', 'doleful', 'sadly', 'dolefully', 'ruefulness', 'wretched']
senticnet['poor_nutrition'] = ['-0.93', '0', '0.75', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'invalidism', 'injury', 'affliction', 'biliousness', 'harm']
senticnet['poor_oral_hygiene'] = ['0', '0', '0.813', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'ice_cream_melt', 'allergy', 'toothache', 'gonorrhea', 'tastebud']
senticnet['poor_people'] = ['0.057', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'cheap_stuff', 'save_money', 'less_quality', 'occasion', 'discount_store']
senticnet['poor_person'] = ['-0.05', '0', '-0.11', '-0.04', '#sadness', '#fear', 'negative', '-0.06', 'doctor', 'doctor_surgery', 'inventor', 'sick_person', 'nurse']
senticnet['poor_personal_hygiene'] = ['0.076', '-0.00', '0', '0', '#joy', '#surprise', 'positive', '0.042', 'clean_body', 'feel_fresh', 'remove_dirt_body', 'shower', 'feel_refreshed']
senticnet['poor_posture'] = ['-0.50', '0', '0.681', '-0.89', '#anger', '#disgust', 'negative', '-0.69', 'sweat', 'effort', 'elbow_grease', 'exertion', 'travail']
senticnet['poor_quality'] = ['-0.92', '0', '0.256', '-0.98', '#sadness', '#disgust', 'negative', '-0.72', 'lousy', 'bad_quality', 'failure', 'low_quality', 'defective']
senticnet['poor_sanitation'] = ['-0.93', '0', '0.75', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'invalidism', 'injury', 'affliction', 'biliousness', 'harm']
senticnet['poor_sight'] = ['-0.89', '0', '-0.84', '0', '#sadness', '#fear', 'negative', '-0.87', 'blind', 'disease', 'poor_vision', 'cataract', 'handicap']
senticnet['poor_strategy'] = ['-0.08', '-0.03', '0.043', '-0.12', '#sadness', '#disgust', 'negative', '-0.05', 'lose_game', 'bad_luck', 'lose', 'lose_focus', 'miss_point']
senticnet['poor_ventilation'] = ['0.718', '0', '0', '0', '#joy', '#joy', 'positive', '0.718', 'water_container', 'still_water', 'clean_water', 'water', 'cool_water']
senticnet['poor_visibility'] = ['-0.09', '0', '-0.05', '-0.24', '#sadness', '#disgust', 'negative', '-0.13', 'smoke', 'smog', 'haze', 'mist', 'cloudy']
senticnet['poor_vision'] = ['-0.89', '0', '-0.84', '0', '#sadness', '#fear', 'negative', '-0.86', 'blind', 'disease', 'poor_sight', 'cataract', 'handicap']
senticnet['poor_visual_perception'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'walking_stick', 'sunglasses', 'disabled', 'blind_person', 'vision_loss']
senticnet['poor_weight_gain'] = ['-0.56', '0', '0.906', '-0.80', '#anger', '#disgust', 'negative', '-0.76', 'jaundice_of_newborn', 'disease', 'icterus_neonatorum', 'kernicterus', 'symptom']
senticnet['poor_workmanship'] = ['-0.92', '0', '0.256', '-0.98', '#sadness', '#disgust', 'negative', '-0.72', 'lousy', 'bad_quality', 'failure', 'poor_quality', 'low_quality']
senticnet['poor_wretch'] = ['-0.87', '0', '0.882', '-0.56', '#sadness', '#anger', 'negative', '-0.77', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['poorer'] = ['-0.56', '0', '-0.49', '-0.09', '#sadness', '#fear', 'negative', '-0.38', 'lack', 'absence', 'poverty', 'emptiness', 'hunger']
senticnet['poorest'] = ['-0.56', '0', '-0.49', '-0.09', '#sadness', '#fear', 'negative', '-0.38', 'lack', 'absence', 'poverty', 'emptiness', 'hunger']
senticnet['poorhouse'] = ['0', '0', '0.964', '-0.81', '#anger', '#disgust', 'negative', '-0.88', 'asylum', 'almshouse', 'rat-race', 'gaol', 'workhouse']
senticnet['poorly'] = ['-0.22', '0.115', '-0.20', '0.12', '#sadness', '#fear', 'negative', '-0.04', 'get_back', 'concede', 'bad_grade', 'payoff', 'loss']
senticnet['poorly_paid'] = ['-0.94', '0', '0.952', '-0.83', '#sadness', '#anger', 'negative', '-0.91', 'miserable', 'scanty', 'exiguous', 'spare', 'hardscrabble']
senticnet['poorness'] = ['-0.61', '0', '0.694', '-0.81', '#anger', '#disgust', 'negative', '-0.70', 'destitution', 'deprivation', 'indigence', 'penury', 'pauperism']
senticnet['pop'] = ['0.098', '0.103', '-0.06', '0.134', '#interest', '#admiration', 'positive', '0.068', 'candy', 'ethanol', 'foodstuff', 'bake_bean', 'mountain_dew']
senticnet['pop_album_chart'] = ['0.997', '0.892', '0', '0', '#joy', '#interest', 'positive', '0.945', 'advertisement', 'large_outdoor_board', 'motorway', 'expressway', 'train_track']
senticnet['pop_anthem'] = ['-0.95', '0', '0.941', '-0.57', '#sadness', '#anger', 'negative', '-0.82', 'reply', 'response', 'comeback', 'comebacking', 'backtalk']
senticnet['pop_balloon'] = ['-0.10', '-0.05', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.05', 'scratch_back', 'knock_door', 'make_face', 'wave_hand', 'call_out']
senticnet['pop_chart'] = ['0.094', '0.091', '0', '0.125', '#joy', '#admiration', 'positive', '0.103', 'diagram', 'graph', 'medium', 'fragment', 'representation']
senticnet['pop_confection'] = ['-0.95', '0', '0.941', '-0.57', '#sadness', '#anger', 'negative', '-0.82', 'reply', 'response', 'comeback', 'comebacking', 'backtalk']
senticnet['pop_crossover'] = ['0.916', '-0.93', '0', '0.674', '#joy', '#surprise', 'positive', '0.841', 'smash', 'bang', 'smasher', 'strike', 'hit']
senticnet['pop_culture_phenomenon'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'intend', 'mimic', 'imitation', 'copy', 'heap']
senticnet['pop_diva'] = ['0.056', '0.139', '-0.07', '0.154', '#interest', '#admiration', 'positive', '0.07', 'belongings', 'material', 'fabric', 'guarantee', 'stuff']
senticnet['pop_group'] = ['-0.50', '0', '0.902', '-0.54', '#anger', '#disgust', 'negative', '-0.65', 'gamelan', 'jug_band', 'ensemble', 'mariachi', 'skiffle_group']
senticnet['pop_imap'] = ['0.031', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.062', 'letter_slot', 'postal_counter', 'mailbox', 'letter_scale', 'envelope']
senticnet['pop_music'] = ['0.136', '-0.09', '0.087', '-0.03', '#joy', '#surprise', 'positive', '0.027', 'style_music', 'form_of_music', 'tune', 'rhythm', 'music']
senticnet['pop_musician'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
senticnet['pop_orchestra'] = ['0.506', '0.659', '-0.24', '0.242', '#joy', '#interest', 'positive', '0.29', 'sing', 'music_studio', 'music_room', 'choir', 'symphony']
senticnet['pop_popcorn'] = ['-0.12', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.10', 'play_tape', 'scrub_floor', 'fill_bottle', 'arrive_work', 'train_open_mind']
senticnet['pop_punk'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'explicit', 'declared', 'definitive', 'graphic']
senticnet['pop_quiz'] = ['0.861', '0.925', '0', '0', '#joy', '#interest', 'positive', '0.893', 'multiple_choices', 'final_exam', 'pass_fail', 'stressful', 'exam']
senticnet['pop_rap'] = ['0.081', '-0.05', '0.052', '0.072', '#joy', '#admiration', 'positive', '0.039', 'rhythm', 'type_of_music', 'instrumental', 'lyric', 'melody']
senticnet['pop_rock_singer'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
senticnet['pop_sensation'] = ['0.882', '0.901', '0', '0', '#joy', '#interest', 'positive', '0.891', 'songwriter', 'vip', 'rock_group', 'guitar_player', 'violinist']
senticnet['pop_sensibility'] = ['0.847', '-0.65', '0', '0.955', '#joy', '#admiration', 'positive', '0.82', 'bright', 'sparkle', 'twinkle', 'spark', 'energy']
senticnet['pop_singer'] = ['0.882', '0.901', '0', '0', '#joy', '#interest', 'positive', '0.891', 'songwriter', 'vip', 'rock_group', 'guitar_player', 'violinist']
senticnet['pop_singer_madonna'] = ['0.056', '0.139', '-0.07', '0.154', '#interest', '#admiration', 'positive', '0.07', 'belongings', 'material', 'fabric', 'guarantee', 'stuff']
senticnet['pop_singer_songwriter'] = ['0.882', '0.901', '0', '0', '#joy', '#interest', 'positive', '0.891', 'songwriter', 'vip', 'rock_group', 'guitar_player', 'violinist']
senticnet['pop_song_chart'] = ['0.997', '0.892', '0', '0', '#joy', '#interest', 'positive', '0.945', 'advertisement', 'large_outdoor_board', 'motorway', 'expressway', 'train_track']
senticnet['pop_star'] = ['0.882', '0.901', '0', '0', '#joy', '#interest', 'positive', '0.891', 'songwriter', 'vip', 'rock_group', 'guitar_player', 'violinist']
senticnet['pop_star_madonna'] = ['0.056', '0.139', '-0.07', '0.154', '#interest', '#admiration', 'positive', '0.07', 'belongings', 'material', 'fabric', 'guarantee', 'stuff']
senticnet['pop_stardom'] = ['0', '0.793', '0', '0.91', '#interest', '#admiration', 'positive', '0.852', 'superstar', 'vip', 'accomplishment', 'performance', 'concert']
senticnet['pop_the_question'] = ['0', '0.982', '0', '0.748', '#interest', '#admiration', 'positive', '0.865', 'aim', 'purport', 'proposer', 'propose', 'offer']
senticnet['pop_tune'] = ['0.65', '-0.61', '0', '0.711', '#joy', '#admiration', 'positive', '0.658', 'bouncing', 'scintillating', 'spirited', 'lively', 'zippy']
senticnet['pop_up'] = ['-0.09', '-0.04', '0.039', '-0.24', '#sadness', '#disgust', 'negative', '-0.08', 'learn_new_idea', 'learn_quickly', 'point_out', 'look_picture', 'jot_down_note']
senticnet['pop_vocalist'] = ['0.882', '0.901', '0', '0', '#joy', '#interest', 'positive', '0.891', 'songwriter', 'vip', 'rock_group', 'guitar_player', 'violinist']
senticnet['pop_warner'] = ['0.728', '0.83', '0', '0', '#joy', '#interest', 'positive', '0.779', 'ball', 'bounce', 'ball_bounce', 'soccer', 'play_football']
senticnet['popcorn'] = ['0', '-0.04', '-0.05', '-0.10', '#fear', '#disgust', 'negative', '-0.03', 'projector', 'sticky_floor', 'usher', 'theater_ticket', 'movie_ticket']
senticnet['popcorn_flick'] = ['-0.85', '0', '0.514', '-0.73', '#sadness', '#disgust', 'negative', '-0.70', 'cinema_verite', 'movie', 'film', 'picture', 'moving_picture']
senticnet['popcorn_movie'] = ['-0.85', '0', '0.514', '-0.73', '#sadness', '#disgust', 'negative', '-0.70', 'cinema_verite', 'movie', 'film', 'picture', 'moving_picture']
senticnet['popdose'] = ['0.7', '-0.62', '0', '0.58', '#joy', '#surprise', 'positive', '0.636', 'white_magic', 'thaumaturgy', 'juju', 'conjuration', 'supernaturalism']
senticnet['pope'] = ['0.036', '0.219', '0', '0.19', '#interest', '#admiration', 'positive', '0.148', 'minister', 'evangelist', 'rector', 'parliament', 'goddess']
senticnet['popemobile'] = ['0', '0', '-0.04', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'good_band', 'band', 'kit', 'procession', 'oblivion']
senticnet['popery'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'amateur', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
senticnet['popinjay'] = ['-0.68', '0', '0.947', '-0.83', '#anger', '#disgust', 'negative', '-0.82', 'parrot', 'poll', 'bird', 'lovebird', 'amazon']
senticnet['poplar_street'] = ['0.032', '0.116', '0.016', '0', '#joy', '#interest', 'positive', '0.044', 'curb', 'crossroad', 'ring_road', 'lane', 'interstate']
senticnet['poplarville'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
senticnet['popoto'] = ['-0.91', '0', '0.769', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'break', 'good_luck', 'chance_eventpy_chance', 'chance_event', 'coincidence']
senticnet['popped_up'] = ['-0.09', '-0.04', '0.039', '-0.24', '#sadness', '#disgust', 'negative', '-0.08', 'learn_new_idea', 'learn_quickly', 'point_out', 'look_picture', 'jot_down_note']
senticnet['poppet'] = ['0', '0', '-0.92', '-0.78', '#fear', '#disgust', 'negative', '-0.85', 'doll', 'lathe', 'marionette', 'puppet', 'puppeteer']
senticnet['popping_out'] = ['0', '0.096', '0', '0.075', '#interest', '#admiration', 'positive', '0.085', 'stern', 'quick_look', 'glimpse', 'peep', 'stare']
senticnet['popping_up'] = ['-0.09', '-0.04', '0.039', '-0.24', '#sadness', '#disgust', 'negative', '-0.08', 'learn_new_idea', 'learn_quickly', 'point_out', 'look_picture', 'jot_down_note']
senticnet['poppy'] = ['0.932', '0.918', '0', '0', '#joy', '#interest', 'positive', '0.925', 'flower', 'yellow_flower', 'flower_bloom', 'poisonous_plant', 'sign_love']
senticnet['poppy_seed'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'roll', 'custard', 'bread', 'biscuit']
senticnet['poppycock'] = ['-0.78', '0', '0.92', '-0.69', '#sadness', '#anger', 'negative', '-0.8', 'meaninglessness', 'nonsense', 'bunk', 'nonsensicality', 'hokum']
senticnet['popsicle'] = ['0.023', '0.141', '-0.13', '0.174', '#interest', '#admiration', 'positive', '0.052', 'candy', 'push_wind', 'exhalation', 'breaout', 'mouth_action']
senticnet['popsicle_stick'] = ['0', '0', '-0.91', '-0.70', '#fear', '#disgust', 'negative', '-0.81', 'wax_dental_floss', 'tartar_control_toothpaste', 'toothpaste', 'gel_toothpaste', 'toothbrush']
senticnet['popular'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'important', 'universal', 'vip', 'prestige', 'status']
senticnet['popular_acclaim'] = ['0.853', '0.828', '0', '0.98', '#joy', '#admiration', 'positive', '0.887', 'endorse', 'recommend', 'stimulate_mind', 'acculturation', 'support']
senticnet['popular_aquarium_fish'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'autumn_leaf', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['popular_attraction'] = ['0.034', '0.13', '-0.08', '0.258', '#interest', '#admiration', 'positive', '0.085', 'tourism', 'nice_view', 'big_monument', 'temple', 'sightsee']
senticnet['popular_building_material'] = ['0', '-0.01', '0.046', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'cement', 'tar', 'pavement', 'big_city_life', 'concrete']
senticnet['popular_catchphrase'] = ['0.88', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.828', 'ambiguity', 'loophole', 'amphibology', 'amphiboly', 'parisology']
senticnet['popular_culture'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'important', 'universal', 'vip', 'prestige', 'status']
senticnet['popular_destination'] = ['0.034', '0.13', '-0.08', '0.258', '#interest', '#admiration', 'positive', '0.085', 'tourism', 'nice_view', 'big_monument', 'temple', 'sightsee']
senticnet['popular_discontent'] = ['-0.77', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.84', 'jumpiness', 'jitteriness', 'restive', 'nervy', 'jumpy']
senticnet['popular_eatery'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'buy_sandwich', 'lunch', 'eat_food', 'buy_food', 'finish_job']
senticnet['popular_food'] = ['0.053', '0', '0.036', '0.069', '#joy', '#admiration', 'positive', '0.029', 'cereal_grain', 'japanese_food', 'tree_fruit', 'form_of_food', 'tasty']
senticnet['popular_game'] = ['0', '-0.07', '0', '0.092', '#surprise', '#admiration', 'positive', '0.082', 'game_skill', 'pastime', 'rough_sport', 'team_sport', 'violent_sport']
senticnet['popular_hangout'] = ['0.966', '0.921', '0', '0.892', '#joy', '#interest', 'positive', '0.926', 'disco', 'nightclub', 'shop_complex', 'dance_club', 'fast_food_restaurant']
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senticnet['popular_music'] = ['0.133', '-0.10', '0.06', '0', '#joy', '#surprise', 'positive', '0.06', 'classical', 'rap', 'form_of_music', 'rock_roll', 'style_music']
senticnet['popular_nightspot'] = ['0.966', '0.921', '0', '0.892', '#joy', '#interest', 'positive', '0.926', 'disco', 'nightclub', 'shop_complex', 'dance_club', 'fast_food_restaurant']
senticnet['popular_pastime'] = ['0.891', '0.835', '0', '0.721', '#joy', '#interest', 'positive', '0.816', 'hobby', 'diversion', 'challenge', 'enjoy_game', 'leisure']
senticnet['popular_pet'] = ['0', '0', '0', '-0.87', '#disgust', '#disgust', 'negative', '-0.87', 'domestic_pet', 'common_pet', 'similar', 'nothing_special', 'common']
senticnet['popular_recreation'] = ['0.877', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.806', 'trekking', 'exercise', 'exercise_regularly', 'moderation', 'nice_weather']
senticnet['popular_resentment'] = ['-0.77', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.84', 'jumpiness', 'jitteriness', 'restive', 'nervy', 'jumpy']
senticnet['popular_revolt'] = ['-0.84', '0', '0.744', '-0.58', '#sadness', '#anger', 'negative', '-0.72', 'subversion', 'subversive_activity', 'termination', 'ending', 'conclusion']
senticnet['popular_seaside_resort'] = ['0.203', '0.077', '0', '0.189', '#joy', '#admiration', 'positive', '0.156', 'good_life', 'coast', 'relax', 'sediment', 'shell']
senticnet['popular_sentiment'] = ['0.63', '0.622', '-0.29', '0.365', '#joy', '#interest', 'positive', '0.332', 'joyful', 'favorable', 'favorably', 'agree', 'jubilantly']
senticnet['popular_soda'] = ['0.045', '0', '-0.05', '-0.08', '#fear', '#disgust', 'negative', '-0.02', 'healthy_drink', 'cola', 'diuretic', 'beverage', 'soft_drink']
senticnet['popular_sovereignty'] = ['0.926', '0.937', '0', '0.975', '#interest', '#admiration', 'positive', '0.946', 'monument', 'statue', 'freedom', 'idol', 'sculpture']
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senticnet['popular_tourist'] = ['0.034', '0.13', '-0.08', '0.258', '#interest', '#admiration', 'positive', '0.085', 'tourism', 'nice_view', 'big_monument', 'temple', 'sightsee']
senticnet['popular_tourist_attraction'] = ['0.034', '0.13', '-0.08', '0.258', '#interest', '#admiration', 'positive', '0.085', 'tourism', 'nice_view', 'big_monument', 'temple', 'sightsee']
senticnet['popular_tourist_destination'] = ['0.034', '0.13', '-0.08', '0.258', '#interest', '#admiration', 'positive', '0.085', 'tourism', 'nice_view', 'big_monument', 'temple', 'sightsee']
senticnet['popular_tourist_spot'] = ['0.034', '0.13', '-0.08', '0.258', '#interest', '#admiration', 'positive', '0.085', 'tourism', 'nice_view', 'big_monument', 'temple', 'sightsee']
senticnet['popular_uprising_against'] = ['-0.84', '0', '0.744', '-0.58', '#sadness', '#anger', 'negative', '-0.72', 'subversion', 'subversive_activity', 'termination', 'ending', 'conclusion']
senticnet['popular_vacation_destination'] = ['0.795', '0.949', '0', '0', '#joy', '#interest', 'positive', '0.872', 'enjoy_nature', 'near_ocean', 'land_feature', 'ocean_shore', 'oceanside']
senticnet['popular_visitor_attraction'] = ['0.034', '0.13', '-0.08', '0.258', '#interest', '#admiration', 'positive', '0.085', 'tourism', 'nice_view', 'big_monument', 'temple', 'sightsee']
senticnet['popular_vote'] = ['0', '0.156', '-0.15', '0.179', '#interest', '#admiration', 'positive', '0.061', 'candidate', 'presidential', 'vote', 'elect', 'appoint']
senticnet['popularity'] = ['0.882', '0', '0', '0.863', '#joy', '#admiration', 'positive', '0.873', 'have_fun', 'humor', 'attract_opposite_sex', 'happiness', 'feel_pleasure']
senticnet['popularization'] = ['-0.81', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.85', 'degradation', 'profanation', 'demoralization', 'corruption', 'humiliation']
senticnet['popularize'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'important', 'universal', 'vip', 'prestige', 'status']
senticnet['popularly'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'important', 'universal', 'vip', 'prestige', 'status']
senticnet['populate'] = ['0', '0.142', '-0.06', '0.143', '#interest', '#admiration', 'positive', '0.075', 'own_lot', 'group_of_people', 'bird_group', 'asset', 'urban']
senticnet['populate_area'] = ['0.089', '0', '0', '0.13', '#joy', '#admiration', 'positive', '0.11', 'metropolis', 'county', 'good_view', 'complex_structure', 'beautiful_place']
senticnet['population'] = ['0.038', '0.073', '0', '0', '#joy', '#interest', 'positive', '0.055', 'majority', 'bunch', 'multitude', 'colony', 'big_group']
senticnet['population_density'] = ['0.728', '0.977', '0', '0.939', '#interest', '#admiration', 'positive', '0.881', 'air_pressure', 'altitude', 'air', 'heavy_air', 'air_density']
senticnet['population_dynamic'] = ['-0.81', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.79', 'globec', 'effect', 'oceanography', 'predator', 'investigate']
senticnet['population_growth'] = ['0', '0.229', '-0.09', '0.187', '#interest', '#admiration', 'positive', '0.106', 'increase', 'make_big', 'get_large', 'grow', 'expand']
senticnet['populist_politician'] = ['0', '0.868', '0', '0.853', '#interest', '#admiration', 'positive', '0.861', 'country_leader', 'prime_minister', 'president', 'elect_official', 'leader']
senticnet['popup'] = ['-0.09', '-0.04', '0.039', '-0.24', '#sadness', '#disgust', 'negative', '-0.08', 'learn_new_idea', 'learn_quickly', 'point_out', 'look_picture', 'jot_down_note']
senticnet['popup_blocker'] = ['-0.09', '-0.04', '0.039', '-0.24', '#sadness', '#disgust', 'negative', '-0.08', 'learn_new_idea', 'learn_quickly', 'point_out', 'look_picture', 'jot_down_note']
senticnet['popup_menu'] = ['0.055', '0.117', '0', '0.133', '#interest', '#admiration', 'positive', '0.102', 'graphical_representation', 'pencil_pen', 'note_pad', 'marker', 'stationery']
senticnet['popup_window'] = ['-0.09', '-0.04', '0.039', '-0.24', '#sadness', '#disgust', 'negative', '-0.08', 'learn_new_idea', 'learn_quickly', 'point_out', 'look_picture', 'jot_down_note']
senticnet['popupredirtoplinks_pg'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'head_hurt', 'introspection', 'lifestyle_change', 'headache', 'move_eye']
senticnet['porbeagle'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'many_fishes', 'seahorse', 'starfish', 'plankton', 'sea_wee']
senticnet['porcelain'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'fabrication', 'carving', 'decoupage', 'artistic_creation']
senticnet['porcelain_skin'] = ['0.636', '-0.95', '0', '0.664', '#surprise', '#admiration', 'positive', '0.751', 'voluptuous', 'statuesque', 'sculptured', 'buxom', 'sculptural']
senticnet['porch'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'vestibule', 'door', 'central_passage', 'gate', 'door_handle']
senticnet['porcine_reproductive'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'leptomeningitis', 'cerebrospinal_meningitis', 'meningitis', 'brain_fever', 'cerebrospinal_fever']
senticnet['porcupine'] = ['0', '0', '-0.95', '-0.81', '#fear', '#disgust', 'negative', '-0.88', 'herb', 'plant', 'poisonous_plant', 'yellow_flower', 'lichen']
senticnet['porcupine_caribou'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'cheetah', 'giraffe', 'kangaroo', 'mule', 'horse']
senticnet['porcupine_caribou_herd'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'cheetah', 'giraffe', 'kangaroo', 'mule', 'horse']
senticnet['porcupine_erethizon_dorsatum'] = ['0', '0', '-0.95', '-0.81', '#fear', '#disgust', 'negative', '-0.88', 'herb', 'plant', 'poisonous_plant', 'yellow_flower', 'lichen']
senticnet['porcupine_fish'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'plectognath', 'plectognathfish', 'blowfish', 'triggerfish', 'filefish']
senticnet['porcupine_herd'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'cheetah', 'giraffe', 'kangaroo', 'mule', 'horse']
senticnet['porcupine_river'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'cheetah', 'giraffe', 'kangaroo', 'mule', 'horse']
senticnet['porcupinefish'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'plectognath', 'plectognathfish', 'blowfish', 'triggerfish', 'filefish']
senticnet['pore'] = ['0', '0', '0.019', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'worm_intestine', 'bristle', 'fur_keep_warm', 'animal', 'whisker']
senticnet['pore_formation'] = ['0', '0', '0.019', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'worm_intestine', 'bristle', 'fur_keep_warm', 'animal', 'whisker']
senticnet['pore_structure'] = ['-0.52', '0', '0.596', '-0.76', '#anger', '#disgust', 'negative', '-0.62', 'retention', 'retentiveness', 'retentivity', 'solidness', 'nonabsorbency']
senticnet['pore_water_pressure'] = ['-0.52', '0', '0.596', '-0.76', '#anger', '#disgust', 'negative', '-0.62', 'retention', 'retentiveness', 'retentivity', 'solidness', 'nonabsorbency']
senticnet['porfiry_petrovitch'] = ['-0.80', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'difficulty', 'awkwardness', 'flea_bite', 'difficultness', 'cumbersomeness']
senticnet['poring_over'] = ['0.572', '-0.72', '0', '0.521', '#joy', '#surprise', 'positive', '0.606', 'browse', 'perusal', 'perusing', 'reading', 'studying']
senticnet['pork'] = ['-0.08', '-0.05', '-0.05', '-0.15', '#sadness', '#disgust', 'negative', '-0.06', 'mince', 'brisket', 'cleaver', 'chop', 'cut']
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senticnet['pork_butcher'] = ['0.053', '0.085', '0.019', '0', '#joy', '#interest', 'positive', '0.04', 'mexican_restaurant', 'spanish_restaurant', 'potato', 'french_restaurant', 'toxic_waste']
senticnet['pork_butt'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'obese', 'worse', 'health_issue', 'clinical_problem']
senticnet['pork_fat'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'cook_meat', 'cook_hamburger', 'cook_food', 'cook_rice', 'brown_meat']
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senticnet['pork_loin'] = ['0.043', '0.091', '0', '0.113', '#interest', '#admiration', 'positive', '0.082', 'sweet_sour', 'tasty', 'yummy', 'popular_food', 'form_of_food']
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senticnet['pork_pie_hat'] = ['0.08', '0.106', '-0.04', '0.112', '#interest', '#admiration', 'positive', '0.063', 'helmet', 'head_gear', 'cap', 'head_wear', 'head_protection']
senticnet['pork_roast'] = ['0.043', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'apple_pie', 'a_lot_of_food', 'jelly', 'frozen_food', 'cottage_cheese']
senticnet['pork_sausage'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'roll', 'custard', 'bread', 'biscuit']
senticnet['pork_tapeworm'] = ['-0.74', '0', '0.94', '-0.87', '#anger', '#disgust', 'negative', '-0.85', 'fungal_infection', 'mycosis', 'kerion', 'tinea_pedis', 'athlete_foot']
senticnet['pork_tenderloin'] = ['0.043', '0.091', '0', '0.113', '#interest', '#admiration', 'positive', '0.082', 'sweet_sour', 'tasty', 'yummy', 'popular_food', 'form_of_food']
senticnet['porkpie_hat'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'facial_tissue', 'kleenex', 'hanky', 'handkerchief', 'tissue']
senticnet['porn'] = ['0.842', '0.878', '0', '0', '#joy', '#interest', 'positive', '0.86', 'pornography', 'entertainment', 'sex', 'red_light_district', 'sexual_activity']
senticnet['porn_actor'] = ['0.82', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.862', 'pervert', 'healthy_person', 'philanthropist', 'arsonist', 'nazi']
senticnet['porn_star'] = ['0.82', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.862', 'pervert', 'healthy_person', 'philanthropist', 'arsonist', 'nazi']
senticnet['porneia'] = ['-0.52', '0', '0.621', '-0.91', '#anger', '#disgust', 'negative', '-0.68', 'deviltry', 'devilry', 'irreverence', 'foul_play', 'iniquity']
senticnet['porno'] = ['0', '0.752', '0', '0.909', '#interest', '#admiration', 'positive', '0.831', 'pornography', 'erotica', 'smut', 'porn', 'picture']
senticnet['pornographer'] = ['0.081', '0.095', '0', '0', '#joy', '#interest', 'positive', '0.088', 'build_tower_out_toy_block', 'a_lot_of_sex', 'play_hockey', 'play_baseball', 'play_football']
senticnet['pornographic'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'indecency']
senticnet['pornographic_content'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['pornographic_literature'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'indecency']
senticnet['pornographic_magazine'] = ['-0.98', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.89', 'fairy', 'genus', 'honey_bee', 'infant', 'dove']
senticnet['pornographic_material'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'indecency']
senticnet['pornography'] = ['0.991', '0.822', '0', '0', '#joy', '#interest', 'positive', '0.906', 'porn', 'sex', 'entertainment', 'red_light_district', 'sexual_activity']
senticnet['pornstar'] = ['0.902', '0.995', '0', '0', '#joy', '#interest', 'positive', '0.949', 'beautiful_girl', 'hen_baby', 'pretty_woman', 'hen_child', 'bird']
senticnet['porosity'] = ['0.89', '-0.62', '0', '0.548', '#joy', '#surprise', 'positive', '0.686', 'breakthrough', 'interpenetration', 'foray', 'maraud', 'raid']
senticnet['porous'] = ['0.887', '0.715', '0', '0', '#joy', '#interest', 'positive', '0.801', 'tree_product', 'rectangular', 'wood_product', 'make_wood', 'flat_thin']
senticnet['porous_limestone'] = ['-0.52', '0', '0.596', '-0.76', '#anger', '#disgust', 'negative', '-0.62', 'retention', 'retentiveness', 'retentivity', 'solidness', 'nonabsorbency']
senticnet['porous_material'] = ['-0.05', '0.068', '-0.10', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'tooth_decay', 'body_ache', 'symptom', 'pierce', 'tooth']
senticnet['porous_media'] = ['0.976', '0.786', '0', '0', '#joy', '#interest', 'positive', '0.881', 'combustible', 'state_matter', 'natural_resource', 'liquid_fuel', 'fossil_fuel']
senticnet['porous_rock'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'fossil', 'oil', 'dark_liquid', 'smithsonian_institution', 'inert_gas']
senticnet['porous_solid'] = ['0.976', '0.786', '0', '0', '#joy', '#interest', 'positive', '0.881', 'combustible', 'state_matter', 'natural_resource', 'liquid_fuel', 'fossil_fuel']
senticnet['porphyria'] = ['-0.57', '0', '0.973', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['porphyria_cutanea_tarda'] = ['-0.65', '0', '0.873', '-0.63', '#sadness', '#anger', 'negative', '-0.72', 'neuritis', 'multiple_neuritis', 'rubor', 'redness', 'inflammation']
senticnet['porphyria_cutanea_tarda_pct'] = ['-0.57', '0', '0.973', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['porpoise'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'many_fishes', 'seahorse', 'starfish', 'plankton', 'sea_wee']
senticnet['porridge'] = ['0.056', '0', '0.017', '0.058', '#joy', '#admiration', 'positive', '0.032', 'dairy_product', 'food', 'milk', 'healthy', 'morning_food']
senticnet['porringer'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'wallet', 'billfold', 'bank', 'piggy_bank', 'bank_vault']
senticnet['porsche'] = ['0.755', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.728', 'high_class', 'expensive_car', 'ferrari', 'fancy_car', 'sports_car']
senticnet['porsche_carrera'] = ['0.755', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.728', 'high_class', 'expensive_car', 'ferrari', 'fancy_car', 'sports_car']
senticnet['porsche_cayenne'] = ['0.755', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.728', 'high_class', 'expensive_car', 'ferrari', 'fancy_car', 'sports_car']
senticnet['porsche_panamera'] = ['0.755', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.728', 'high_class', 'expensive_car', 'ferrari', 'fancy_car', 'sports_car']
senticnet['port'] = ['-0.02', '-0.02', '0.064', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'ocean_water', 'fillet_fish', 'chowder', 'fillet', 'underwater']
senticnet['port_orford_cedar'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['port_sorell'] = ['-0.95', '0', '0.624', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['port_stephen_local'] = ['0', '0', '-0.11', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'hunting_season', 'hound', 'cougar', 'outlaw', 'dragon']
senticnet['port_sulphur'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
senticnet['port_tack'] = ['0', '-0.05', '0.079', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'c_clamp', 'thumb_tack', 'pin', 'pliers', 'without_space']
senticnet['port_unreachable'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'identifiable', 'specifiable', 'recognizable', 'diagnosable', 'classifiable']
senticnet['port_wine_stain'] = ['-0.66', '0', '0.911', '-0.99', '#anger', '#disgust', 'negative', '-0.85', 'liver_spot', 'spot', 'freckle', 'macule', 'plague_spot']
senticnet['porta'] = ['-0.93', '0', '0.607', '-0.67', '#sadness', '#disgust', 'negative', '-0.73', 'blowhole', 'stigma', 'orifice', 'opening', 'spiracle']
senticnet['porta_nuova'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'expressway', 'turnpike', 'toll_road', 'highway']
senticnet['portability'] = ['0.663', '-0.88', '0', '0.729', '#surprise', '#admiration', 'positive', '0.76', 'mobility', 'looseness', 'movability', 'play', 'unsteadiness']
senticnet['portable'] = ['0.194', '0.307', '-0.10', '0.338', '#interest', '#admiration', 'positive', '0.182', 'hold_stuff', 'carry_around', 'tote', 'hold_up', 'take']
senticnet['portable_computer'] = ['0.113', '0.073', '0.028', '0.107', '#joy', '#admiration', 'positive', '0.066', 'pda', 'telephone_computer', 'browser', 'number_pad', 'modem']
senticnet['portable_crib'] = ['-0.06', '0', '0.01', '0', '#sadness', '#anger', 'negative', '-0.03', 'youth', 'young_child', 'small_child', 'lad', 'tiny']
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senticnet['portal'] = ['0.02', '0', '0', '0.148', '#joy', '#admiration', 'positive', '0.084', 'build_open', 'home_entrance', 'house_open', 'house_front', 'aperture']
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senticnet['porter'] = ['0.861', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.845', 'alcohol', 'minibar', 'concierge', 'beer_mug', 'bar_stool']
senticnet['portfolio'] = ['0', '0.189', '0.04', '0.091', '#interest', '#admiration', 'positive', '0.08', 'combination', 'margin', 'teller', 'merge', 'finite']
senticnet['portfolio_manager'] = ['0.711', '0', '0', '0', '#joy', '#joy', 'positive', '0.711', 'chemist', 'expert', 'scientist', 'esaminatore', 'physicist']
senticnet['portfolio_strategist'] = ['0.711', '0', '0', '0', '#joy', '#joy', 'positive', '0.711', 'chemist', 'expert', 'scientist', 'esaminatore', 'physicist']
senticnet['portico'] = ['0.028', '0', '0', '0.051', '#joy', '#admiration', 'positive', '0.04', 'entranceway', 'doorway', 'entrance', 'entryway', 'corridor']
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senticnet['portion'] = ['0', '0.715', '0', '0.911', '#interest', '#admiration', 'positive', '0.813', 'component', 'fraction', 'part_of_whole', 'segment', 'piece']
senticnet['portkey'] = ['0.105', '-0.17', '0.057', '0.079', '#joy', '#surprise', 'positive', '0.076', 'cue', 'twig', 'long_stick', 'stick', 'adhere']
senticnet['portmanteau'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicado', 'incommunicative', 'impassive', 'inexpressive', 'silent']
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senticnet['portrait_bust'] = ['0.085', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.079', 'sculpture', 'creative', 'yellow_ribbon', 'artwork', 'monument']
senticnet['portraitist'] = ['0.847', '0', '0', '0.955', '#joy', '#admiration', 'positive', '0.901', 'miniaturist', 'watercolorist', 'printmaker', 'lutenist', 'horologist']
senticnet['portray'] = ['0.725', '0', '0', '0.964', '#joy', '#admiration', 'positive', '0.845', 'act', 'act_part', 'part_of_play', 'imitate', 'play_part']
senticnet['portrayal'] = ['0', '0', '-1', '-0.88', '#fear', '#disgust', 'negative', '-0.94', 'delineation', 'picture', 'word-painting', 'depiction', 'characterization']
senticnet['portrayed'] = ['0.712', '-0.79', '0', '0.534', '#joy', '#surprise', 'positive', '0.679', 'depicted', 'pictured', 'diagrammatic', 'described', 'delineated']
senticnet['portrayer'] = ['0', '0.797', '0', '0.804', '#interest', '#admiration', 'positive', '0.8', 'director', 'shooting_movie', 'director_chair', 'important_person', 'movie']
senticnet['pose'] = ['0.044', '-0.64', '0', '0.38', '#surprise', '#admiration', 'positive', '0.356', 'dumfounded', 'dumbstricken', 'mystify', 'baffle', 'dumbstruck']
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senticnet['posh_restaurant'] = ['0.866', '-0.91', '0', '0.893', '#surprise', '#admiration', 'positive', '0.892', 'mooch', 'freeload', 'grub', 'sponge', 'bum']
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senticnet['position'] = ['0.058', '0.184', '0', '0.21', '#interest', '#admiration', 'positive', '0.151', 'placement', 'administration', 'command', 'status', 'confirmation']
senticnet['position_furniture'] = ['0.992', '0.883', '0', '0', '#joy', '#interest', 'positive', '0.938', 'forward_email', 'stand_alone', 'deal_card_player', 'explain_everything', 'bridge_difference']
senticnet['positional_isomer'] = ['0.672', '0.632', '-0.23', '0.312', '#joy', '#interest', 'positive', '0.344', 'euphoria', 'buoyancy', 'perkiness', 'jubilance', 'ecstasy']
senticnet['positional_play'] = ['0', '0.127', '-0.04', '0.194', '#interest', '#admiration', 'positive', '0.092', 'quick', 'fast', 'speedy', 'quick_movement', 'speed']
senticnet['positional_sense'] = ['0', '0.127', '-0.04', '0.194', '#interest', '#admiration', 'positive', '0.092', 'quick', 'fast', 'speedy', 'quick_movement', 'speed']
senticnet['positionality'] = ['0.122', '0.159', '-0.05', '0.217', '#interest', '#admiration', 'positive', '0.111', 'profile_picture', 'unity', 'country_person', 'unite', 'signature']
senticnet['positioning_accuracy'] = ['0', '-0.90', '0', '0.791', '#surprise', '#admiration', 'positive', '0.849', 'precise', 'accuracy', 'correct', 'performance', 'measure']
senticnet['positioning_system'] = ['0', '0.071', '0', '0.143', '#interest', '#admiration', 'positive', '0.107', 'post_notice', 'indicator', 'way', 'one_way', 'arrow']
senticnet['positive'] = ['0.572', '0.668', '-0.34', '0.485', '#joy', '#interest', 'positive', '0.346', 'adoration', 'esteem', 'encouragement', 'optimism', 'confidence']
senticnet['positive_attitude'] = ['0.701', '0.874', '0', '0.752', '#interest', '#admiration', 'positive', '0.776', 'self_satisfaction', 'enjoy_life', 'good_feeling', 'good_attitude', 'enjoy']
senticnet['positive_attitude_toward'] = ['0.701', '0.874', '0', '0.752', '#interest', '#admiration', 'positive', '0.776', 'self_satisfaction', 'enjoy_life', 'good_feeling', 'good_attitude', 'enjoy']
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senticnet['positive_emotionality'] = ['0.995', '-0.73', '0', '0.83', '#joy', '#admiration', 'positive', '0.852', 'spiritism', 'otherworldliness', 'spirituality', 'spiritualism', 'internality']
senticnet['positive_feedback'] = ['0.347', '0.348', '-0.13', '0.394', '#interest', '#admiration', 'positive', '0.239', 'good_deed', 'benefit', 'gratification', 'self_respect', 'bonus']
senticnet['positive_fraud'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'fraud', 'mail_fraud', 'swindle', 'rig']
senticnet['positive_impact'] = ['0.085', '0.365', '-0.20', '0.362', '#interest', '#admiration', 'positive', '0.153', 'bonus', 'monetary_gain', 'financial_gain', 'return', 'give']
senticnet['positive_outlook'] = ['0.701', '0.874', '0', '0.752', '#interest', '#admiration', 'positive', '0.776', 'self_satisfaction', 'enjoy_life', 'good_feeling', 'good_attitude', 'enjoy']
senticnet['positive_peer_pressure'] = ['0.347', '0.348', '-0.13', '0.394', '#interest', '#admiration', 'positive', '0.239', 'good_deed', 'benefit', 'gratification', 'self_respect', 'bonus']
senticnet['positive_reinforcement'] = ['0.347', '0.348', '-0.13', '0.394', '#interest', '#admiration', 'positive', '0.239', 'good_deed', 'benefit', 'gratification', 'self_respect', 'bonus']
senticnet['positive_reinforcer'] = ['0.583', '-0.82', '0', '0.574', '#joy', '#surprise', 'positive', '0.661', 'stimulant', 'positive_reinforcing_stimulus', 'reinforcer', 'negative_reinforcing_stimulus', 'negative_reinforcer']
senticnet['positive_reinforcing_stimulus'] = ['0.583', '-0.82', '0', '0.574', '#joy', '#surprise', 'positive', '0.661', 'stimulant', 'reinforcer', 'positive_reinforcer', 'negative_reinforcing_stimulus', 'negative_reinforcer']
senticnet['positive_scalar_curvature'] = ['0', '0.203', '0.036', '0.204', '#interest', '#admiration', 'positive', '0.124', 'amount', 'count', 'total', 'quantity', 'total_amount']
senticnet['positive_self_esteem'] = ['0.936', '0.757', '0', '0.927', '#joy', '#admiration', 'positive', '0.873', 'attitude', 'better', 'trait', 'blue_ribbon', 'exclamation']
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senticnet['positive_transcendence'] = ['-0.57', '0', '0.713', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'assured', 'conclusion', 'certainty', 'predictability', 'definiteness']
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senticnet['positively_affect'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'compound', 'intensify', 'deepen', 'potentiate', 'heighten']
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senticnet['positively_charged'] = ['0.618', '-0.60', '0', '0.759', '#joy', '#admiration', 'positive', '0.662', 'positive', 'charged', 'electropositive', 'live', 'polar']
senticnet['positively_harmful'] = ['0.632', '-0.80', '0', '0.537', '#joy', '#surprise', 'positive', '0.658', 'incisiveness', 'trenchancy', 'efficacy', 'effectiveness', 'power']
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senticnet['positivism'] = ['-0.98', '0', '0.891', '-0.60', '#sadness', '#anger', 'negative', '-0.82', 'exorcism', 'dispossession', 'magic', 'thaumaturgy', 'belief']
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senticnet['posse'] = ['0.867', '-0.84', '0', '0.876', '#joy', '#admiration', 'positive', '0.863', 'meet', 'team', 'team_up', 'embed', 'aggroup']
senticnet['posse_superhuman_strength'] = ['0', '0.839', '0', '0.963', '#interest', '#admiration', 'positive', '0.901', 'imputrescible', 'undestroyable', 'imperishable', 'durable', 'perdurable']
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senticnet['possessed_superhuman_strength'] = ['0', '0.839', '0', '0.963', '#interest', '#admiration', 'positive', '0.901', 'imputrescible', 'undestroyable', 'imperishable', 'durable', 'perdurable']
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senticnet['possessive_pronoun'] = ['0.732', '0.8', '0', '0', '#joy', '#interest', 'positive', '0.766', 'fast', 'place', 'substance', 'substantive', 'person']
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senticnet['possibility'] = ['0', '0.163', '-0.09', '0.232', '#interest', '#admiration', 'positive', '0.099', 'random', 'unpredictable', 'unsure', 'possible', 'question_starter']
senticnet['possibility_to_die'] = ['-0.96', '0', '0.172', '-0.99', '#sadness', '#disgust', 'negative', '-0.71', 'severe_cold', 'cold', 'hypothermia', 'cold_winter', 'freeze']
senticnet['possible'] = ['0', '0.163', '-0.09', '0.232', '#interest', '#admiration', 'positive', '0.099', 'random', 'unpredictable', 'unsure', 'possibility', 'question_starter']
senticnet['possible_death_sentence'] = ['0.654', '-0.59', '0', '0.774', '#joy', '#admiration', 'positive', '0.673', 'instrument', 'indictment', 'murder_indictment', 'true_bill', 'legal_document']
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senticnet['possit'] = ['0.654', '-0.75', '0', '0.78', '#surprise', '#admiration', 'positive', '0.729', 'suit', 'cause', 'lawsuit', 'occurrence', 'countersuit']
senticnet['possum'] = ['0.03', '0.068', '-0.04', '0.093', '#interest', '#admiration', 'positive', '0.037', 'beast', 'equine', 'live_creature', 'furry_creature', 'domesticate_animal']
senticnet['post'] = ['0.721', '0.892', '0', '0', '#joy', '#interest', 'positive', '0.806', 'letter', 'message', 'parcel', 'snail_mail', 'announcement']
senticnet['post-impressionism'] = ['0', '0.964', '0', '0.876', '#interest', '#admiration', 'positive', '0.92', 'expressionism', 'cubism', 'impressionism', 'fauvism', 'surrealism']
senticnet['post-it_note'] = ['0.038', '0', '0', '0.097', '#joy', '#admiration', 'positive', '0.068', 'blank_check', 'glue_stick', 'address_book', 'ball_point_pen', 'paper_clip']
senticnet['post-summit'] = ['0.921', '-0.81', '0', '0', '#joy', '#surprise', 'positive', '0.867', 'full-dress', 'six-voice', 'white-and-black', 'company-run', 'post-victory']
senticnet['post-victory'] = ['0.927', '-0.81', '0', '0', '#joy', '#surprise', 'positive', '0.87', 'post-summit', 'six-voice', 'white-and-black', 'company-run', 'full-dress']
senticnet['post_apocalyptic'] = ['-0.96', '0', '0.501', '-0.59', '#sadness', '#disgust', 'negative', '-0.68', 'calamity', 'catastrophe', 'tragedy', 'disaster', 'cataclysm']
senticnet['post_apocalyptic_earth'] = ['0.141', '0', '-0.06', '0.086', '#joy', '#admiration', 'positive', '0.055', 'dancer', 'stout', 'old_dance', 'climb_rock', 'jump_around']
senticnet['post_apocalyptic_fiction'] = ['-0.96', '0', '0.501', '-0.59', '#sadness', '#disgust', 'negative', '-0.68', 'calamity', 'catastrophe', 'tragedy', 'disaster', 'cataclysm']
senticnet['post_apocalyptic_future'] = ['-0.96', '0', '0.501', '-0.59', '#sadness', '#disgust', 'negative', '-0.68', 'calamity', 'catastrophe', 'tragedy', 'disaster', 'cataclysm']
senticnet['post_apocalyptic_world'] = ['-0.96', '0', '0.501', '-0.59', '#sadness', '#disgust', 'negative', '-0.68', 'calamity', 'catastrophe', 'tragedy', 'disaster', 'cataclysm']
senticnet['post_baccalaureate_program'] = ['0.719', '0.757', '0', '0', '#joy', '#interest', 'positive', '0.738', 'matriculate', 'enroll_class', 'enroll_school', 'inscribe', 'register']
senticnet['post_bop'] = ['0.901', '0', '0', '0', '#joy', '#joy', 'positive', '0.901', 'music', 'vapor', 'blues', 'megrim', 'demoralization']
senticnet['post_coital'] = ['0.947', '-0.81', '0', '0.656', '#joy', '#surprise', 'positive', '0.805', 'sexual_intercourse', 'intercourse', 'sex_act', 'copulation', 'hank_panky']
senticnet['post_copulative'] = ['-0.57', '0', '0.713', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'assured', 'conclusion', 'certainty', 'determinateness', 'predictability']
senticnet['post_doc'] = ['0.846', '-0.75', '0', '0.69', '#joy', '#surprise', 'positive', '0.764', 'experimenter', 'boffin', 'fieldworker', 'postdoc', 'investigator']
senticnet['post_doctoral_fellow'] = ['0', '0.909', '0', '0.799', '#interest', '#admiration', 'positive', '0.854', 'research', 'research_topic', 'research_subject', 'search', 'student']
senticnet['post_doctoral_research'] = ['0.102', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.076', 'adjust_volume', 'tap_toe', 'enter_contest', 'tap_foot', 'dance_well']
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senticnet['post_hardcore'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'explicit', 'declared', 'definitive', 'graphic']
senticnet['post_harvest'] = ['-0.08', '-0.04', '0.023', '-0.07', '#sadness', '#disgust', 'negative', '-0.03', 'tier', 'make_toast', 'house_smell_good', 'get_hot', 'bread_crumb']
senticnet['post_haste'] = ['0.911', '-0.71', '0', '0.552', '#joy', '#surprise', 'positive', '0.726', 'deliver_goods', 'succeed', 'win', 'bring_home_bacon', 'come_through']
senticnet['post_letter'] = ['0', '0.163', '-0.09', '0.232', '#interest', '#admiration', 'positive', '0.099', 'receive_letter', 'receive_email', 'letter', 'post', 'postcard']
senticnet['post_message'] = ['0.076', '0', '0', '0', '#joy', '#joy', 'positive', '0.076', 'send_email', 'display_image', 'access_internet', 'prepare_report', 'enter_data']
senticnet['post_nasal_drip'] = ['-0.76', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.74', 'decrease_hunger', 'feel_full', 'stomach_problem', 'get_fat', 'a_little_hungry']
senticnet['post_natal'] = ['0.1', '0.216', '-0.18', '0.22', '#interest', '#admiration', 'positive', '0.088', 'female', 'maternity_ward', 'motherhood', 'woman', 'mother']
senticnet['post_natal_care'] = ['0.1', '0.216', '-0.18', '0.22', '#interest', '#admiration', 'positive', '0.088', 'female', 'maternity_ward', 'motherhood', 'woman', 'mother']
senticnet['post_nicene'] = ['-0.22', '0', '-0.09', '-0.16', '#sadness', '#disgust', 'negative', '-0.16', 'inequality', 'reject', 'discard', 'difference', 'gap']
senticnet['post_note'] = ['0', '-0.04', '0.081', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'intern', 'white-out', 'post-it_note', 'letter_scale', 'paper_punch']
senticnet['post_notice'] = ['0', '0', '0', '0.074', '#admiration', '#admiration', 'positive', '0.074', 'placard', 'sign', 'banner', 'post', 'notice']
senticnet['post_operative_nausea'] = ['0', '0', '-0.87', '-0.96', '#fear', '#disgust', 'negative', '-0.92', 'disgust', 'offend', 'despise', 'isolation', 'malice']
senticnet['post_operative_pain'] = ['-0.65', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'put_to_sleep', 'operation', 'anesthesia', 'surgery', 'local_anesthesia']
senticnet['post_partum_care'] = ['0.1', '0.216', '-0.18', '0.22', '#interest', '#admiration', 'positive', '0.088', 'female', 'maternity_ward', 'motherhood', 'woman', 'mother']
senticnet['post_partum_depression'] = ['-0.99', '0', '0.982', '-0.62', '#sadness', '#anger', 'negative', '-0.86', 'dysthymia', 'dysthymic_depression', 'affective_disorder', 'major_affective_disorder', 'emotional_disorder']
senticnet['post_pubertal'] = ['0.995', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.918', 'tell_everyone', 'reason', 'clarify', 'forethought', 'opinion']
senticnet['post_pubescent_female'] = ['-0.63', '0', '0.559', '-0.82', '#sadness', '#disgust', 'negative', '-0.67', 'take_bull_by_horns', 'confront', 'front', 'encounter', 'oppose']
senticnet['post_secondary'] = ['0.97', '0', '0.773', '0.878', '#joy', '#admiration', 'positive', '0.358', 'educate', 'get_knowledge', 'new_knowledge', 'expand_knowledge', 'find_out_world']
senticnet['post_secondary_education'] = ['0.97', '0', '0.773', '0.878', '#joy', '#admiration', 'positive', '0.358', 'educate', 'get_knowledge', 'new_knowledge', 'expand_knowledge', 'find_out_world']
senticnet['post_structuralism'] = ['0.587', '-0.94', '0', '0.659', '#surprise', '#admiration', 'positive', '0.731', 'critique', 'review', 'criticism', 'textual_criticism', 'new_criticism']
senticnet['post_structuralist'] = ['0.587', '-0.94', '0', '0.659', '#surprise', '#admiration', 'positive', '0.731', 'critique', 'review', 'criticism', 'textual_criticism', 'new_criticism']
senticnet['post_transplant'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'displace', 'move', 'move_away', 'bump', 'dislocate']
senticnet['post_traumatic'] = ['-0.88', '0', '-0.79', '-0.85', '#sadness', '#disgust', 'negative', '-0.84', 'pain', 'severe_pain', 'agony', 'physical_pain', 'suffer']
senticnet['post_traumatic_stress_disorder'] = ['-0.88', '0', '-0.79', '-0.85', '#sadness', '#disgust', 'negative', '-0.84', 'pain', 'severe_pain', 'agony', 'physical_pain', 'suffer']
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senticnet['post_world_war'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'dictatorship', 'dictator', 'colony', 'world_domination', 'oppression']
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senticnet['postage'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'send_mail', 'snail_mail', 'stamp', 'buy_stamp', 'put_envelope']
senticnet['postage_stamp'] = ['0.058', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.054', 'letter_slot', 'envelope', 'pamphlet', 'mailman', 'address_label']
senticnet['postal_carrier'] = ['0.031', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.062', 'letter_slot', 'postal_counter', 'mailbox', 'letter_scale', 'envelope']
senticnet['postal_clerk'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'mailman', 'package', 'mail', 'parcel', 'letter_slot']
senticnet['postal_counter'] = ['0.035', '0.076', '0', '0.067', '#interest', '#admiration', 'positive', '0.059', 'clutter', 'paperwork', 'coworker', 'white-out', 'stapler']
senticnet['postal_inspection_service'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'constabulary', 'law', 'agency']
senticnet['postal_questionnaire'] = ['-0.04', '0.1', '0.051', '0.137', '#interest', '#admiration', 'positive', '0.035', 'find_number', 'regularly', 'list_number', 'calculator', 'navigation']
senticnet['postal_service'] = ['0.865', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.91', 'send', 'mail_delivery', 'mail', 'delivery', 'package']
senticnet['postal_stamp'] = ['0.058', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.054', 'letter_slot', 'envelope', 'pamphlet', 'mailman', 'address_label']
senticnet['postal_worker'] = ['0.031', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.062', 'letter_slot', 'postal_counter', 'mailbox', 'letter_scale', 'envelope']
senticnet['postalveolar'] = ['-0.71', '0', '0.998', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'tone', 'quality', 'twang', 'nasal_twang', 'timber']
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senticnet['postcanine_teeth'] = ['0.1', '-0.12', '0.05', '-0.03', '#joy', '#surprise', 'positive', '0.034', 'fancy_dress', 'blouse', 'fashion', 'dress', 'skirt']
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senticnet['postcoital_contraception'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'birth_control_pill', 'condom', 'contraceptive_pill', 'sex_toy', 'prevent_pregnancy']
senticnet['postcolonial_criticism'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'indirectness', 'mediateness', 'characteristic', 'allusiveness']
senticnet['postcolonial_theory'] = ['0.587', '-0.94', '0', '0.659', '#surprise', '#admiration', 'positive', '0.731', 'critique', 'review', 'criticism', 'textual_criticism', 'new_criticism']
senticnet['postconsumer'] = ['0', '0.065', '0', '0.146', '#interest', '#admiration', 'positive', '0.105', 'drink_container', 'storage_container', 'opaque', 'drink_holder', 'food_container']
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senticnet['postdoctoral_fellowship'] = ['0.742', '0', '0', '0.85', '#joy', '#admiration', 'positive', '0.796', 'chemistry', 'fellowship', 'research', 'broaden', 'experience']
senticnet['postdoctoral_level'] = ['0.996', '0.792', '0', '0', '#joy', '#interest', 'positive', '0.894', 'biological_informatics', 'training', 'fellowship', 'research', 'education']
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senticnet['postdoctoral_research_fellowship'] = ['-0.74', '0', '0', '-0.86', '#sadness', '#disgust', 'negative', '-0.80', 'number', 'chemistry_postdoctoral_fellow', 'supervision', 'chemistry_type_award', 'doctoral_degree']
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senticnet['poste_restante'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'mailman', 'package', 'mail', 'parcel', 'letter_slot']
senticnet['posted_bail'] = ['-0.08', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.052', 'bequest', 'excise', 'bribe', 'afford', 'owe']
senticnet['poster'] = ['0.808', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.903', 'marketing', 'sale', 'announcement', 'billboard', 'promotion']
senticnet['poster_bed'] = ['0', '0.171', '-0.05', '0.102', '#interest', '#admiration', 'positive', '0.072', 'bed', 'mattress', 'bead', 'bed_cover', 'seduce']
senticnet['poster_boy'] = ['-0.61', '0', '0.738', '-0.80', '#anger', '#disgust', 'negative', '-0.71', 'dissident', 'beat', 'bohemian', 'maverick', 'recusant']
senticnet['poster_child'] = ['0.994', '-0.97', '0', '0.936', '#joy', '#surprise', 'positive', '0.969', 'child', 'small_fry', 'tyke', 'tike', 'kid']
senticnet['posterboard'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'gift_wrap', 'kraft', 'kraft_paper', 'wrapping_paper', 'paper']
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senticnet['posterior_auricular_artery'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['posterior_chamber'] = ['-0.78', '0', '0.855', '-0.99', '#anger', '#disgust', 'negative', '-0.87', 'blood_blister', 'pustule', 'water_blister', 'vesicle', 'cyst']
senticnet['posterior_cingulate'] = ['0.998', '-0.63', '0', '0.574', '#joy', '#surprise', 'positive', '0.736', 'cleavable', 'divisible', 'separable', 'severable', 'dissociative']
senticnet['posterior_cingulate_cortex'] = ['0.998', '-0.63', '0', '0.574', '#joy', '#surprise', 'positive', '0.736', 'cleavable', 'divisible', 'separable', 'severable', 'dissociative']
senticnet['posterior_communicating_artery'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['posterior_cruciate_ligament'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'deltoid', 'muscle', 'abductor', 'skeletal_muscle']
senticnet['posterior_interosseous_nerve'] = ['-0.41', '0', '-0.28', '-0.05', '#sadness', '#fear', 'negative', '-0.25', 'numbness', 'carpal_tunnel_syndrome', 'weakness', 'pain', 'muscle_damage']
senticnet['posterior_margin'] = ['-0.66', '0', '0.809', '-0.61', '#sadness', '#anger', 'negative', '-0.69', 'face', 'base', 'underbelly', 'sole', 'bilge']
senticnet['posterior_notch'] = ['0.969', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.772', 'aspheric', 'aspherical', 'prolate', 'rounded', 'crescent']
senticnet['posterior_parietal_cortex'] = ['0', '0.131', '-0.08', '0.239', '#interest', '#admiration', 'positive', '0.096', 'human_sense', 'ear_activity', 'vibration', 'five_senses', 'sensor']
senticnet['posterior_pharyngeal_wall'] = ['0', '0', '0', '0.106', '#admiration', '#admiration', 'positive', '0.106', 'vocal_chord', 'mouth_noise', 'vocal_sound', 'human_sound', 'vocalization']
senticnet['posterior_surface'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'hip_socket', 'tooth_socket', 'alveolus', 'socket', 'cotyloid_cavity']
senticnet['posterior_teeth'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'false_teeth', 'bridge', 'dental_plate', 'partial_denture', 'dental_appliance']
senticnet['posterior_wall'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'hip_socket', 'tooth_socket', 'alveolus', 'socket', 'cotyloid_cavity']
senticnet['posteriori_probability'] = ['-0.96', '0', '0.603', '-0.91', '#sadness', '#disgust', 'negative', '-0.82', 'potential', 'potency', 'conceivableness', 'conceivability', 'possibleness']
senticnet['posteritie'] = ['-0.03', '0.157', '0.698', '-0.66', '#anger', '#disgust', 'negative', '-0.30', 'vengefulness', 'vindictively', 'vengefully', 'revengefully', 'vindictive']
senticnet['posterity'] = ['0.911', '0.785', '0', '0', '#joy', '#interest', 'positive', '0.848', 'biogenesis', 'multiplication', 'people', 'propagation', 'generation']
senticnet['posterolateral'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'skeletal_muscle', 'pterygoid_muscle', 'abductor', 'abductor_muscle']
senticnet['postfix'] = ['0.031', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.062', 'letter_slot', 'postal_counter', 'mailbox', 'letter_scale', 'envelope']
senticnet['postgame_locker_room'] = ['-0.85', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.88', 'oppression', 'enslavement', 'slave', 'captivity', 'misery']
senticnet['postgame_news_conference'] = ['-0.85', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.88', 'oppression', 'enslavement', 'slave', 'captivity', 'misery']
senticnet['postgame_press_conference'] = ['-0.85', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.88', 'oppression', 'enslavement', 'slave', 'captivity', 'misery']
senticnet['postglacial'] = ['0.614', '-0.95', '0', '0.584', '#joy', '#surprise', 'positive', '0.718', 'mother_lode', 'champion_lode', 'deposit', 'sedimentation', 'lode']
senticnet['postgraduate'] = ['0.963', '0.858', '0', '0', '#joy', '#interest', 'positive', '0.91', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'collegian']
senticnet['postgraduate_degree'] = ['-0.75', '0', '0.589', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'lowness', 'grind', 'immoderation', 'immoderateness', 'amplitude_level']
senticnet['postharvest'] = ['-0.08', '-0.04', '0.023', '-0.07', '#sadness', '#disgust', 'negative', '-0.03', 'tier', 'make_toast', 'house_smell_good', 'get_hot', 'bread_crumb']
senticnet['postherpetic_neuralgia'] = ['-0.73', '0', '-0.85', '-0.72', '#sadness', '#fear', 'negative', '-0.77', 'lay_off', 'hurt', 'pain_suffer', 'lose_mind', 'forget_everything']
senticnet['posthuman'] = ['0.992', '-0.66', '0', '0.536', '#joy', '#surprise', 'positive', '0.729', 'nous', 'mind', 'brain', 'psyche', 'head']
senticnet['posthumous_medal'] = ['0.857', '0.824', '0', '0.708', '#joy', '#interest', 'positive', '0.796', 'mandatory', 'gentleman', 'obey', 'virtue', 'dignity']
senticnet['posthumous_promotion'] = ['0.688', '-0.90', '0', '0.877', '#surprise', '#admiration', 'positive', '0.824', 'flag_officer', 'rear_admiral', 'admiral', 'full_admiral', 'vice_admiral']
senticnet['posthumous_son'] = ['-0.70', '0', '0.919', '-0.73', '#anger', '#disgust', 'negative', '-0.78', 'unfruitful', 'unsuccessful', 'acarpous', 'abortive', 'stillborn']
senticnet['postiche'] = ['-0.81', '0', '0.599', '-0.67', '#sadness', '#disgust', 'negative', '-0.69', 'attire', 'garb', 'wig', 'dress', 'switch']
senticnet['postictal'] = ['-0.56', '0', '0.577', '-0.61', '#anger', '#disgust', 'negative', '-0.58', 'foggy', 'groggy', 'logy', 'lethargic', 'dazed']
senticnet['postie'] = ['0.031', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.062', 'letter_slot', 'postal_counter', 'mailbox', 'letter_scale', 'envelope']
senticnet['postindustrial'] = ['0', '0', '-0.74', '-0.93', '#fear', '#disgust', 'negative', '-0.84', 'generosity', 'oral_sex', 'win_lottery', 'gift_wrap', 'give_away']
senticnet['posting_bail'] = ['-0.08', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.052', 'bequest', 'excise', 'bribe', 'afford', 'owe']
senticnet['postman'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'mailman', 'package', 'mail', 'parcel', 'letter_slot']
senticnet['postmark_date'] = ['0.862', '0.709', '0', '0', '#joy', '#interest', 'positive', '0.785', 'invoice', 'bill', 'check', 'mastercard', 'payment_counter']
senticnet['postmedial'] = ['-0.7', '0', '0.617', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'silvery', 'achromatic', 'bluish_black', 'hueless', 'pitchy']
senticnet['postmeiotic'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'nonmotile', 'immobile']
senticnet['postmodern'] = ['0.807', '-0.83', '0', '0.688', '#joy', '#surprise', 'positive', '0.776', 'folly', 'lunacy', 'craziness', 'indulgence', 'foolery']
senticnet['postmodernism'] = ['0.587', '-0.94', '0', '0.659', '#surprise', '#admiration', 'positive', '0.731', 'critique', 'review', 'criticism', 'textual_criticism', 'new_criticism']
senticnet['postmortem'] = ['-0.64', '0', '0.684', '-0.71', '#anger', '#disgust', 'negative', '-0.68', 'consecutive', 'succeeding', 'ensuing', 'following', 'undermentioned']
senticnet['postmortem_inspection'] = ['-0.67', '0', '0.938', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'massacre', 'mass_murder', 'carnage', 'bloodbath', 'butchery']
senticnet['postnasal_drip'] = ['-0.06', '0.183', '0.651', '-0.64', '#anger', '#disgust', 'negative', '-0.29', 'allergic_reaction', 'chew', 'pierce', 'mastication', 'put_mouth']
senticnet['postnatal_care'] = ['0.1', '0.216', '-0.18', '0.22', '#interest', '#admiration', 'positive', '0.088', 'female', 'maternity_ward', 'motherhood', 'woman', 'mother']
senticnet['postnatal_development'] = ['-0.84', '0', '0.8', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'rigor_mortis', 'natural_process', 'natural_action', 'activity', 'action']
senticnet['postoffice'] = ['0', '-0.12', '0', '0.095', '#surprise', '#admiration', 'positive', '0.112', 'desk_drawer', 'hang_wall', 'stationery_store', 'mail_package', 'desk']
senticnet['postoperative'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'blatancy', 'obviousness']
senticnet['postoperative_pain'] = ['-0.65', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'put_to_sleep', 'operation', 'anesthesia', 'surgery', 'local_anesthesia']
senticnet['postoperative_period'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'blatancy', 'obviousness']
senticnet['postoperatively'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'blatancy', 'obviousness']
senticnet['postorbital'] = ['0.1', '-0.12', '0.05', '-0.03', '#joy', '#surprise', 'positive', '0.034', 'fancy_dress', 'blouse', 'fashion', 'dress', 'skirt']
senticnet['postpaid'] = ['0.687', '-0.50', '0', '0.8', '#joy', '#admiration', 'positive', '0.663', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['postpartum_care'] = ['0.1', '0.216', '-0.18', '0.22', '#interest', '#admiration', 'positive', '0.088', 'female', 'maternity_ward', 'motherhood', 'woman', 'mother']
senticnet['postpartum_depression'] = ['-0.99', '0', '0.982', '-0.62', '#sadness', '#anger', 'negative', '-0.86', 'dysthymia', 'dysthymic_depression', 'affective_disorder', 'major_affective_disorder', 'emotional_disorder']
senticnet['postpone'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'put_off', 'delay', 'retard', 'procrastination', 'lag']
senticnet['postpone_indefinitely'] = ['0.779', '-0.88', '0', '0.521', '#joy', '#surprise', 'positive', '0.727', 'corrigible', 'correctable', 'improvable', 'reformable', 'redeemable']
senticnet['postpone_payment'] = ['-0.05', '0.165', '-0.05', '0.103', '#interest', '#admiration', 'positive', '0.041', 'cashless', 'buy_goods_service', 'debit', 'pay_goods', 'incur_debt']
senticnet['postponement'] = ['0.972', '-0.72', '0', '0.862', '#joy', '#admiration', 'positive', '0.854', 'hold', 'delay', 'wait', 'time_lag', 'moratorium']
senticnet['postprandial'] = ['0.795', '0', '0', '0.787', '#joy', '#admiration', 'positive', '0.791', 'entree', 'soup', 'wine_glass', 'salt_pepper_shaker', 'plate_silverware']
senticnet['postproduction'] = ['0', '0.164', '-0.08', '0.215', '#interest', '#admiration', 'positive', '0.098', 'manufacture', 'factory_output', 'creation', 'assembly_line', 'make']
senticnet['postscript'] = ['-0.55', '0', '0.912', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'wrangle', 'dustup', 'quarrel', 'row', 'squabble']
senticnet['postsecondary_degree'] = ['0', '0.893', '0', '0', '#interest', '#interest', 'positive', '0.893', 'salary', 'wage', 'send_money', 'earnings', 'pay']
senticnet['postsecondary_education'] = ['0.97', '0', '0.773', '0.878', '#joy', '#admiration', 'positive', '0.358', 'educate', 'get_knowledge', 'new_knowledge', 'expand_knowledge', 'find_out_world']
senticnet['poststructuralism'] = ['0.587', '-0.94', '0', '0.659', '#surprise', '#admiration', 'positive', '0.731', 'critique', 'review', 'criticism', 'textual_criticism', 'new_criticism']
senticnet['postsynaptic_cell'] = ['-0.99', '0', '0.651', '-0.51', '#sadness', '#anger', 'negative', '-0.72', 'sending', 'compulsion', 'influence', 'inducement', 'causation']
senticnet['postsynaptic_neuron'] = ['-0.99', '0', '0.651', '-0.51', '#sadness', '#anger', 'negative', '-0.72', 'sending', 'compulsion', 'influence', 'inducement', 'causation']
senticnet['posttraumatic'] = ['-0.88', '0', '-0.79', '-0.85', '#sadness', '#disgust', 'negative', '-0.84', 'pain', 'severe_pain', 'agony', 'physical_pain', 'suffer']
senticnet['posttraumatic_stress_disorder'] = ['-0.75', '0', '0.87', '-0.51', '#sadness', '#anger', 'negative', '-0.71', 'battle_fatigue', 'combat_fatigue', 'combat_neurosis', 'shell_shock', 'anxiety_disorder']
senticnet['postulate'] = ['-0.66', '0', '0.813', '-0.94', '#anger', '#disgust', 'negative', '-0.80', 'demand', 'need', 'require', 'take', 'cry_for']
senticnet['postural'] = ['-0.15', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.09', 'use_leg', 'walk_quickly', 'casual', 'take_step', 'strut']
senticnet['postural_alignment'] = ['0.84', '0.76', '0', '0.894', '#joy', '#admiration', 'positive', '0.831', 'keep_skin_healthy', 'stay_healthy', 'stay_fit', 'maintain_good_health', 'eat_healthily']
senticnet['postural_control'] = ['-0.15', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.09', 'use_leg', 'walk_quickly', 'casual', 'take_step', 'strut']
senticnet['postural_hypotension'] = ['-0.85', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.82', 'faint', 'headache', 'feel_sick', 'low_blood_pressure', 'giddy']
senticnet['postural_instability'] = ['-0.75', '0', '0.504', '-0.77', '#sadness', '#disgust', 'negative', '-0.67', 'disablement', 'anorgasmia', 'hearing_impairment', 'hearing_disorder', 'bow_leg']
senticnet['posture'] = ['0', '0.199', '-0.11', '0.317', '#interest', '#admiration', 'positive', '0.134', 'body_language', 'gentleman', 'gesture', 'petition', 'take']
senticnet['posturing'] = ['-0.84', '0', '0.969', '-0.68', '#sadness', '#anger', 'negative', '-0.83', 'conceitedness', 'swelled_head', 'egotism', 'boastfulness', 'vainglory']
senticnet['postwar'] = ['0', '0.104', '0', '0.124', '#interest', '#admiration', 'positive', '0.114', 'good_trait', 'constant', 'time_increment', 'time_designation', 'time_length']
senticnet['postwar_era'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'absolutism', 'autocracy', 'ascendancy', 'tyrant']
senticnet['postwar_period'] = ['0', '0.104', '0', '0.124', '#interest', '#admiration', 'positive', '0.114', 'good_trait', 'constant', 'time_increment', 'time_designation', 'time_length']
senticnet['postwoman'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'mailman', 'package', 'mail', 'parcel', 'letter_slot']
senticnet['pot'] = ['0.011', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'kitchen_tool', 'equipment', 'kitchen_cupboard', 'kitchen_knife', 'kitchen_stuff']
senticnet['pot-luck'] = ['0.996', '-0.78', '0', '0', '#joy', '#surprise', 'positive', '0.889', 'potluck', 'get-together', 'brunch', 'cookout', 'dinner']
senticnet['pot_au_feu'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'blue_ribbon', 'badge', 'allegory', 'stripe', 'merit_badge']
senticnet['pot_belly'] = ['-0.42', '-0.14', '-0.06', '-0.37', '#sadness', '#disgust', 'negative', '-0.18', 'brain_tumor', 'bad_hair_day', 'lyme_disease', 'diarrhoea', 'suffer_fool']
senticnet['pot_marjoram'] = ['0.632', '-0.93', '0', '0.928', '#surprise', '#admiration', 'positive', '0.831', 'herb', 'oregano', 'marjoram', 'origanum', 'wild_marjoram']
senticnet['pot_metal'] = ['0.706', '-0.94', '0', '0.622', '#joy', '#surprise', 'positive', '0.756', 'alloy_iron', 'alloy_cast_iron', 'cast_iron', 'iron', 'metal']
senticnet['pot_noodle'] = ['0.547', '-0.96', '0', '0.89', '#surprise', '#admiration', 'positive', '0.799', 'pudding', 'ice_cream', 'chocolate', 'chocolate_pudding', 'chocolate_dessert']
senticnet['pot_origanum'] = ['0.622', '-0.93', '0', '0.921', '#surprise', '#admiration', 'positive', '0.827', 'herb', 'oregano', 'origanum', 'marjoram', 'wild_origanum']
senticnet['pot_pie'] = ['0.028', '-0.07', '0', '0', '#joy', '#surprise', 'positive', '0.05', 'casserole', 'delicious_meal', 'hot_dog_bun', 'hamburger', 'hot_dog']
senticnet['pot_roast'] = ['0.771', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.769', 'make_flour', 'bake_bread', 'bake_food', 'bake', 'bake_cookie']
senticnet['pot_shot_at'] = ['-0.86', '0', '0.675', '-0.84', '#sadness', '#disgust', 'negative', '-0.79', 'shenanigan', 'chicanery', 'wile', 'dupery', 'trickery']
senticnet['pot_smoker'] = ['0', '0.931', '0', '0', '#interest', '#interest', 'positive', '0.931', 'football', 'rake_leave', 'spray_paint_grafitti', 'juggle', 'play_hide_seek']
senticnet['potable'] = ['0.795', '-0.83', '0', '0.503', '#joy', '#surprise', 'positive', '0.712', 'smoothie', 'drinking_water', 'nutrient', 'milk', 'refresher']
senticnet['potable_drinking_water'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'bottled_water', 'sugar_water']
senticnet['potable_liquid'] = ['0.121', '0.113', '-0.04', '0.114', '#joy', '#admiration', 'positive', '0.075', 'gulp', 'better_cold', 'sip', 'liquid_beverage', 'ingest']
senticnet['potable_water'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'bottled_water', 'sugar_water']
senticnet['potable_water_supply'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'bottled_water', 'sugar_water']
senticnet['potash'] = ['0.071', '0', '0.031', '0.064', '#joy', '#admiration', 'positive', '0.035', 'organic_matter', 'decompose', 'recycle', 'soil', 'compost_pile']
senticnet['potassium'] = ['-0.03', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.06', 'lithium', 'mineral', 'magnesium', 'iodine', 'manganese']
senticnet['potassium-rich'] = ['0', '0', '0.819', '-0.75', '#anger', '#disgust', 'negative', '-0.78', 'alkaline-forming', 'acid-producing', 'alkalizing', 'acid-forming', 'gas-forming']
senticnet['potassium_bicarbonate'] = ['-0.81', '0', '0.774', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'acid', 'hypophosphoric_acid', 'periodic_acid', 'hypooxyacid', 'oxyacid']
senticnet['potassium_bitartrate'] = ['0.936', '0.773', '0', '0', '#joy', '#interest', 'positive', '0.855', 'calculus', 'tartar', 'mongol_tatar', 'tatar', 'tophus']
senticnet['potassium_carbonate'] = ['0.032', '-0.02', '-0.04', '-0.08', '#fear', '#disgust', 'negative', '-0.01', 'liquid', 'carbonate_drink', 'chemical', 'soda', 'bromide']
senticnet['potassium_channel'] = ['0', '0', '0.019', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'worm_intestine', 'bristle', 'fur_keep_warm', 'animal', 'whisker']
senticnet['potassium_citrate'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
senticnet['potassium_concentration'] = ['-0.74', '0', '0.723', '-0.59', '#sadness', '#anger', 'negative', '-0.68', 'tone', 'tautness', 'tonus', 'condition', 'status']
senticnet['potassium_excretion'] = ['0.992', '-0.80', '0', '0.584', '#joy', '#surprise', 'positive', '0.795', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'incontinence']
senticnet['potassium_ferricyanide'] = ['0.071', '0', '-0.08', '-0.09', '#fear', '#disgust', 'negative', '-0.03', 'gunk', 'rotten_food', 'sour_cream', 'glass_of_milk', 'lemon_juice']
senticnet['potassium_fertilizer'] = ['0.071', '0', '0.031', '0.064', '#joy', '#admiration', 'positive', '0.035', 'organic_matter', 'decompose', 'recycle', 'soil', 'compost_pile']
senticnet['potassium_hydroxide'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
senticnet['potassium_metabisulfite'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
senticnet['potassium_permanganate'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
senticnet['potassium_phosphate'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
senticnet['potassium_sulfate'] = ['-0.03', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'lithium', 'mineral', 'potassium', 'iodine', 'manganese']
senticnet['potato'] = ['0.529', '-0.68', '0', '0.983', '#surprise', '#admiration', 'positive', '0.732', 'white_potato', 'chips', 'murphy', 'spud', 'tater']
senticnet['potato_cake'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'dainty', 'delicacy', 'treat', 'gelatin_dessert', 'jelly']
senticnet['potato_chip'] = ['0.274', '-0.01', '0.012', '0.132', '#joy', '#admiration', 'positive', '0.102', 'dance_type', 'swirl', 'throw_football', 'jump_around', 'dance_move']
senticnet['potato_farm'] = ['0.053', '0.094', '0', '0.136', '#interest', '#admiration', 'positive', '0.094', 'barn_animal', 'domesticate', 'grassland', 'sheep', 'cow']
senticnet['potato_farmer'] = ['-0.08', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.11', 'serf', 'peasant', 'servant', 'poor', 'villein']
senticnet['potato_flour'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'dainty', 'delicacy', 'treat', 'jelly', 'aspic']
senticnet['potato_fungus'] = ['-0.87', '0', '0.723', '-0.94', '#sadness', '#disgust', 'negative', '-0.84', 'potato_wart_fungus', 'bottom_rot_fungus', 'fungus', 'black_root_rot_fungus', 'sclerotinia']
senticnet['potato_gratin'] = ['0.041', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.043', 'meatloaf', 'salad', 'delicious_meal', 'casserole', 'apple_pie']
senticnet['potato_leaf'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'poppy_seed', 'custard', 'bread', 'biscuit']
senticnet['potato_leafhopper'] = ['-0.02', '-0.09', '0.11', '-0.13', '#anger', '#disgust', 'negative', '-0.04', 'beetle', 'gypsy_moth', 'mole_cricket', 'grasshopper', 'many_insects']
senticnet['potato_masher'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'salad_shaker', 'store_shelf', 'kitchen_tool', 'pliers', 'ladle']
senticnet['potato_pancake'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'roll', 'custard', 'bread', 'biscuit']
senticnet['potato_peeler'] = ['-0.08', '0.232', '0', '0.099', '#interest', '#admiration', 'positive', '0.084', 'wax_dental_floss', 'fluoride_toothpaste', 'toilet_paper', 'dental_floss', 'tartar_control_toothpaste']
senticnet['potato_pie'] = ['0.644', '-0.94', '0', '0.729', '#surprise', '#admiration', 'positive', '0.773', 'gem', 'banana_bread', 'date_bread', 'nut_bread', 'quick_bread']
senticnet['potato_scone'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'saltine', 'soda_cracker', 'cracker', 'water_biscuit', 'graham_cracker']
senticnet['potato_solanum_tuberosum'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'cabbage', 'lettuce', 'broccoli']
senticnet['potato_starch'] = ['-0.08', '0.106', '0', '0.125', '#interest', '#admiration', 'positive', '0.047', 'sugar', 'treacle', 'black_treacle', 'add_sugar', 'sugar_substitute']
senticnet['potato_stew'] = ['0.52', '-0.69', '0', '0.639', '#surprise', '#admiration', 'positive', '0.617', 'black_bread', 'pumpernickel', 'limpa', 'bread', 'rye_bread']
senticnet['potato_wart_fungus'] = ['-0.87', '0', '0.723', '-0.94', '#sadness', '#disgust', 'negative', '-0.84', 'fungus', 'bottom_rot_fungus', 'potato_fungus', 'black_root_rot_fungus', 'sclerotinia']
senticnet['potbellied_stove'] = ['-0.03', '-0.00', '0.066', '0', '#sadness', '#anger', 'negative', '-0.03', 'dirt', 'allergy', 'dust', 'wet_floor', 'insect']
senticnet['potboiler'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'anxiety', 'apprehension', 'insecurity', 'tension', 'horror_movie']
senticnet['poteen'] = ['0.899', '0.991', '0', '0', '#joy', '#interest', 'positive', '0.945', 'bourbon', 'scotch', 'vodka', 'brandy', 'wine']
senticnet['potency'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'imperium', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['potent'] = ['0.796', '0.929', '0', '0', '#joy', '#interest', 'positive', '0.863', 'potentate', 'procreate', 'heraldic', 'potency', 'potentiality']
senticnet['potent_anti_inflammatory'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'chemical_irritant', 'gingerol', 'piperine', 'pepper_spray']
senticnet['potent_neurotoxin'] = ['-0.18', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.16', 'hazardous_health', 'dangerous_health', 'bad_health', 'gross', 'harmful']
senticnet['potent_venom'] = ['-0.25', '0.081', '-0.19', '0', '#sadness', '#fear', 'negative', '-0.12', 'hairy', 'kind_animal', 'poisonous', 'big_fish', 'graceful']
senticnet['potentate'] = ['0.792', '0.924', '0', '0', '#joy', '#interest', 'positive', '0.858', 'potent', 'procreate', 'heraldic', 'potency', 'potentiality']
senticnet['potential'] = ['0.949', '0.851', '0', '0', '#joy', '#interest', 'positive', '0.9', 'potentiality', 'ground', 'ability', 'whipsaw', 'brainwave']
senticnet['potential_aggressor'] = ['-0.82', '0', '0.739', '-0.50', '#sadness', '#anger', 'negative', '-0.69', 'fake', 'juke', 'maneuver', 'manoeuvre', 'feint']
senticnet['potential_application'] = ['0', '0.701', '0', '0.831', '#interest', '#admiration', 'positive', '0.766', 'research', 'mathematics', 'different_application', 'sponsor', 'mathematical_physic']
senticnet['potential_danger'] = ['-0.87', '-0.79', '0.729', '-0.97', '#sadness', '#disgust', 'negative', '-0.44', 'radiation', 'cancer', 'sickness', 'electromagnetic_radiation', 'x_ray']
senticnet['potential_energy'] = ['0', '-0.82', '0', '0.988', '#surprise', '#admiration', 'positive', '0.906', 'potential', 'radiation', 'electromagnetic_radiation', 'petroleum', 'kinetic_energy']
senticnet['potential_hazard'] = ['0', '0', '-0.84', '-0.87', '#fear', '#disgust', 'negative', '-0.85', 'deadly', 'threat', 'bad_health', 'unsafe', 'violent']
senticnet['potential_juror'] = ['0', '0.104', '0', '0.075', '#interest', '#admiration', 'positive', '0.089', 'judge', 'negotiator', 'jury', 'arbiter', 'member_jury']
senticnet['potential_vorticity'] = ['0.807', '-0.88', '0', '0.718', '#joy', '#surprise', 'positive', '0.802', 'sex', 'gayness', 'sexual_gayness', 'lesbianism', 'sapphism']
senticnet['potentiality'] = ['0.554', '-0.78', '0', '0.792', '#surprise', '#admiration', 'positive', '0.709', 'prospect', 'chance', 'possibility', 'possibleness', 'latency']
senticnet['potentially'] = ['0', '0.94', '0', '0.861', '#interest', '#admiration', 'positive', '0.9', 'research', 'project', 'study', 'result', 'well']
senticnet['potentially_beneficial'] = ['0.53', '-0.55', '0', '0.912', '#surprise', '#admiration', 'positive', '0.667', 'advantageous', 'positive', 'good', 'plus', 'discriminatory']
senticnet['potentially_costly'] = ['-0.12', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.09', 'painful', 'uncomfortable', 'violent', 'fun_learn_play', 'expensive']
senticnet['potentially_deadly'] = ['0', '0', '-0.71', '0', '#fear', '#fear', 'negative', '-0.71', 'hazardous', 'violent', 'take_life', 'threat', 'lethal']
senticnet['potentially_deadly_disease'] = ['-0.85', '0', '0.549', '-0.93', '#sadness', '#disgust', 'negative', '-0.78', 'coccidioidomycosis', 'coccidiomycosis', 'mycosis', 'desert_rheumatism', 'cryptococcosis']
senticnet['potentially_discriminatory'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
senticnet['potentially_fatal_disease'] = ['-0.85', '0', '0.549', '-0.93', '#sadness', '#disgust', 'negative', '-0.78', 'coccidioidomycosis', 'coccidiomycosis', 'mycosis', 'desert_rheumatism', 'cryptococcosis']
senticnet['potentially_fraudulent'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'mail_fraud', 'swindle', 'rig']
senticnet['potentially_harmful'] = ['-0.74', '0', '0.781', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'reprimand', 'caution', 'big_monster', 'kidnap', 'mock']
senticnet['potentially_insecure'] = ['-0.79', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.79', 'misgiving', 'qualm', 'scruple', 'insecurity', 'insecure']
senticnet['potentially_lethal'] = ['0', '0', '-0.71', '0', '#fear', '#fear', 'negative', '-0.71', 'hazardous', 'violent', 'take_life', 'threat', 'lethal']
senticnet['potentially_lucrative'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'profitable', 'moneymaking', 'gainful', 'paying', 'paid']
senticnet['potentially_offensive'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['potentially_problematic'] = ['0', '0', '0.837', '-0.87', '#anger', '#disgust', 'negative', '-0.85', 'difficult', 'hard_to_solve', 'hard_to_understand', 'tough', 'hard']
senticnet['potentially_profitable'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'profitable', 'moneymaking', 'gainful', 'paying', 'paid']
senticnet['potentially_toxic'] = ['-0.18', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.16', 'hazardous_health', 'dangerous_health', 'bad_health', 'gross', 'harmful']
senticnet['potentially_unsafe'] = ['-0.74', '0', '0.781', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'reprimand', 'caution', 'big_monster', 'kidnap', 'mock']
senticnet['potentially_useful'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'woodworking_tool']
senticnet['potentiate'] = ['0.734', '-0.81', '0', '0.604', '#joy', '#surprise', 'positive', '0.716', 'compound', 'intensify', 'deepen', 'enhance', 'heighten']
senticnet['potentiation'] = ['0.662', '-0.79', '0', '0.917', '#surprise', '#admiration', 'positive', '0.791', 'activity', 'natural_process', 'natural_action', 'synergism', 'action']
senticnet['potentilla_erecta'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
senticnet['potentiodynamic'] = ['0.965', '-0.84', '0', '0.599', '#joy', '#surprise', 'positive', '0.802', 'mental_faculty', 'faculty', 'module', 'remembrance', 'anamnesis']
senticnet['potently'] = ['0', '-0.75', '0', '0.85', '#surprise', '#admiration', 'positive', '0.8', 'transiently', 'diffusely', 'focally', 'reproducibly', 'lastingly']
senticnet['potest'] = ['0.654', '-0.75', '0', '0.78', '#surprise', '#admiration', 'positive', '0.729', 'suit', 'cause', 'lawsuit', 'occurrence', 'countersuit']
senticnet['pothead'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['pother'] = ['-0.69', '0', '0.768', '-0.67', '#sadness', '#anger', 'negative', '-0.71', 'dither', 'niggle', 'worry', 'flap', 'scruple']
senticnet['pothole'] = ['0.754', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.867', 'american_flag', 'asphalt', 'rubble', 'pavement', 'pave']
senticnet['pothole_repair'] = ['0.754', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.867', 'american_flag', 'asphalt', 'rubble', 'pavement', 'pave']
senticnet['pothouse'] = ['-0.76', '0', '0.75', '-0.56', '#sadness', '#anger', 'negative', '-0.69', 'public_house', 'pub', 'saloon', 'free_house', 'gin_mill']
senticnet['potion'] = ['0.105', '-0.17', '0.057', '0.079', '#joy', '#surprise', 'positive', '0.076', 'cue', 'twig', 'long_stick', 'stick', 'adhere']
senticnet['potiphar'] = ['-0.62', '0', '0.645', '-0.67', '#anger', '#disgust', 'negative', '-0.65', 'lady_maid', 'handmaiden', 'chambermaid', 'maid', 'parlormaid']
senticnet['potluck'] = ['0.999', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.885', 'pot-luck', 'get-together', 'brunch', 'cookout', 'dinner']
senticnet['potluck_dinner'] = ['0.161', '0.155', '-0.08', '0.223', '#joy', '#admiration', 'positive', '0.114', 'family', 'happy_moment', 'aunt_uncle', 'brother_sister', 'relation']
senticnet['potluck_supper'] = ['0.161', '0.155', '-0.08', '0.223', '#joy', '#admiration', 'positive', '0.114', 'family', 'happy_moment', 'aunt_uncle', 'brother_sister', 'relation']
senticnet['potpourri'] = ['0.99', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.916', 'perfume', 'scent', 'candle', 'incense', 'petal']
senticnet['potsherd'] = ['-0.59', '0', '0.506', '-0.52', '#sadness', '#disgust', 'negative', '-0.54', 'dump', 'garbage_dump', 'trash_dump', 'rubbish_dump', 'wasteyard']
senticnet['pott_disease'] = ['-0.69', '0', '0.852', '-0.69', '#sadness', '#anger', 'negative', '-0.74', 'conjunctivitis', 'ophthalmitis', 'rubor', 'redness', 'inflammation']
senticnet['potted_meat'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'green_bread', 'rotten', 'disgusting', 'taste_awful', 'mouldy']
senticnet['pottery'] = ['0', '0', '0.07', '0', '#anger', '#anger', 'negative', '-0.07', 'price_product', 'demo', 'create_sound', 'music', 'create_new_idea']
senticnet['pottery_manufacture'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'fabrication', 'carving', 'decoupage', 'artistic_creation']
senticnet['potting_compost'] = ['-0.12', '0', '-0.10', '-0.03', '#sadness', '#fear', 'negative', '-0.08', 'dog_poop', 'picnic', 'hike_trail', 'detritus', 'wildlife']
senticnet['potting_media'] = ['-0.12', '0', '-0.10', '-0.03', '#sadness', '#fear', 'negative', '-0.08', 'dog_poop', 'picnic', 'hike_trail', 'detritus', 'wildlife']
senticnet['potting_mix'] = ['-0.12', '0', '-0.10', '-0.03', '#sadness', '#fear', 'negative', '-0.08', 'dog_poop', 'picnic', 'hike_trail', 'detritus', 'wildlife']
senticnet['potting_soil'] = ['-0.12', '0', '-0.10', '-0.03', '#sadness', '#fear', 'negative', '-0.08', 'dog_poop', 'picnic', 'hike_trail', 'detritus', 'wildlife']
senticnet['potty'] = ['0.976', '-0.90', '0', '0.959', '#joy', '#admiration', 'positive', '0.947', 'inconsequent', 'unimportant', 'unimportantial', 'trivial', 'lightweight']
senticnet['potty_chair'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'lavatory', 'potty_seat', 'commode']
senticnet['potty_seat'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'lavatory', 'commode', 'potty_chair']
senticnet['pouch'] = ['0', '0', '0.036', '-0.11', '#anger', '#disgust', 'negative', '-0.07', 'credit_card_wallet', 'clutch_bag', 'checkbook_holder', 'checkbook_cover', 'coin_purse']
senticnet['pouf'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'sexual_excitement', 'sexy', 'wet_lip', 'kiss', 'clean_up']
senticnet['poultice'] = ['-0.81', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.82', 'peanut', 'leave_over', 'floppy', 'circular_motion', 'store_vegetable']
senticnet['poultry'] = ['-0.90', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.88', 'fowl', 'domestic_fowl', 'ban', 'fear', 'reuters']
senticnet['poultry_farm'] = ['0.053', '0.094', '0', '0.136', '#interest', '#admiration', 'positive', '0.094', 'barn_animal', 'domesticate', 'grassland', 'sheep', 'cow']
senticnet['pounce'] = ['0', '-0.70', '0', '0.878', '#surprise', '#admiration', 'positive', '0.79', 'prowling', 'prowler', 'prowl', 'prowl', 'roam']
senticnet['pounced_upon'] = ['-0.75', '0', '0.794', '-0.58', '#sadness', '#anger', 'negative', '-0.71', 'jerk', 'weightlift', 'military_press', 'clean', 'bench_press']
senticnet['pound'] = ['-0.65', '0', '0.67', '-0.71', '#anger', '#disgust', 'negative', '-0.67', 'newton', 'dyne', 'force_unit', 'sthene', 'poundal']
senticnet['pound_bomb'] = ['0', '-0.03', '-0.04', '-0.19', '#fear', '#disgust', 'negative', '-0.06', 'nuclear_bomb', 'explosive', 'grenade', 'nuclear_weapon', 'aerial_bomb']
senticnet['pound_coin'] = ['0.81', '0.763', '0', '0', '#joy', '#interest', 'positive', '0.786', 'dollar_bill', 'franc', 'euro', 'dollar', 'cent']
senticnet['pound_kg'] = ['0.764', '0.985', '0', '0', '#joy', '#interest', 'positive', '0.875', 'heavy_weight', 'weigh', 'weight_measurement', 'heaviness', 'proportion']
senticnet['pound_overweight'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'fatty', 'worse', 'health_issue', 'clinical_problem']
senticnet['pound_pumpkin'] = ['0', '-0.01', '0.037', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'pear', 'green_apple', 'apricot', 'cherry', 'lime']
senticnet['pound_weight'] = ['0.764', '0.985', '0', '0', '#joy', '#interest', 'positive', '0.875', 'heavy_weight', 'weigh', 'weight_measurement', 'heaviness', 'proportion']
senticnet['poundal'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'newton', 'sthene', 'force_unit']
senticnet['pounding'] = ['0', '0', '0.974', '-0.79', '#anger', '#disgust', 'negative', '-0.88', 'pulsing', 'pulsating', 'throbbing', 'aching', 'cock']
senticnet['pounding_headache'] = ['-0.85', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.82', 'faint', 'headache', 'feel_sick', 'low_blood_pressure', 'giddy']
senticnet['pounding_heart'] = ['0.958', '-0.72', '0', '0.72', '#joy', '#surprise', 'positive', '0.801', 'pressor', 'hormone', 'endocrine', 'internal_secretion', 'catecholamine']
senticnet['pounding_rain'] = ['-0.87', '0', '0.91', '-0.78', '#sadness', '#anger', 'negative', '-0.85', 'storm', 'thundershower', 'rain', 'line_storm', 'equinoctial_storm']
senticnet['pounding_surf'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'driftwood', 'sea_shell', 'scenic_view', 'naked_woman', 'tidal_wave']
senticnet['poundmaker'] = ['0', '0', '-0.95', '-0.81', '#fear', '#disgust', 'negative', '-0.88', 'herb', 'plant', 'poisonous_plant', 'yellow_flower', 'lichen']
senticnet['pour'] = ['0', '0.084', '-0.06', '0.156', '#interest', '#admiration', 'positive', '0.057', 'stream_ocean', 'reflect_image', 'pour_liquid', 'stream', 'evaporate']
senticnet['pour_cela'] = ['0.911', '-0.94', '0', '0.953', '#surprise', '#admiration', 'positive', '0.935', 'common_sense', 'good_sense', 'horse_sense', 'mother_wit', 'gumption']
senticnet['pour_endiguer'] = ['0', '0.919', '0', '0.888', '#interest', '#admiration', 'positive', '0.903', 'surprise', 'eye-popping', 'fulgurous', 'dazzling', 'bedaze']
senticnet['pour_fabriquer'] = ['-0.58', '0', '0.661', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'explosive', 'cordite', 'dynamite', 'plastic_explosive', 'explosive_compound']
senticnet['pour_forth'] = ['0.643', '0.64', '-0.24', '0.3', '#joy', '#interest', 'positive', '0.336', 'high_spirits', 'elating', 'exhilarating', 'elated', 'cheerful']
senticnet['pour_fraude_fiscale'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'sexual_relationship', 'liaison', 'affair', 'involvement']
senticnet['pour_gasoline'] = ['0', '-0.03', '-0.05', '-0.06', '#fear', '#disgust', 'negative', '-0.03', 'find_campsite', 'burn_house', 'cause_fire', 'die_out', 'find_wood']
senticnet['pour_homme'] = ['0.863', '0.861', '0', '0', '#joy', '#interest', 'positive', '0.862', 'aroma', 'scent', 'smell', 'odor', 'sniff']
senticnet['pour_hot_coffee_mug'] = ['-0.05', '-0.06', '0', '0.09', '#surprise', '#admiration', 'positive', '0.034', 'bluetooth', 'stuff_use_calculation', 'insure', 'complete_thesis', 'low_blood_pressure']
senticnet['pour_leur'] = ['0.911', '-0.94', '0', '0.953', '#surprise', '#admiration', 'positive', '0.935', 'common_sense', 'good_sense', 'horse_sense', 'mother_wit', 'gumption']
senticnet['pour_liquid'] = ['0.819', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.877', 'move_liquid', 'hand_hold', 'fill_in_glass', 'pour', 'push']
senticnet['pour_mixture'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'address_book', 'get_rich', 'blank_check', 'swiss_army_knife', 'whisk']
senticnet['pour_petrol'] = ['-0.80', '0', '0.962', '-0.55', '#sadness', '#anger', 'negative', '-0.77', 'sop', 'bedraggle', 'dowse', 'drench', 'soak']
senticnet['pour_trancher'] = ['0', '0.919', '0', '0.888', '#interest', '#admiration', 'positive', '0.903', 'surprise', 'eye-popping', 'fulgurous', 'dazzling', 'bedaze']
senticnet['pour_water'] = ['-0.05', '0', '-0.07', '0', '#sadness', '#fear', 'negative', '-0.06', 'wear_bath_suit', 'water_spill', 'malfunction', 'put_swimsuit', 'go_pool']
senticnet['poured_gasoline'] = ['-0.80', '0', '0.962', '-0.55', '#sadness', '#anger', 'negative', '-0.77', 'sop', 'bedraggle', 'dowse', 'drench', 'soak']
senticnet['poured_gasoline_over'] = ['-0.80', '0', '0.962', '-0.55', '#sadness', '#anger', 'negative', '-0.77', 'sop', 'bedraggle', 'dowse', 'drench', 'soak']
senticnet['poured_petrol'] = ['0.057', '0', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'huge_fire', 'fire', 'rapid_oxidation', 'heat_light', 'flame']
senticnet['poured_petrol_over'] = ['0.641', '-0.73', '0', '0.798', '#surprise', '#admiration', 'positive', '0.724', 'ablaze', 'aflame', 'afire', 'aflare', 'lighted']
senticnet['pouring_gasoline'] = ['0', '-0.03', '-0.05', '-0.06', '#fear', '#disgust', 'negative', '-0.03', 'find_campsite', 'burn_house', 'cause_fire', 'die_out', 'find_wood']
senticnet['pouring_petrol'] = ['0.057', '0', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'huge_fire', 'fire', 'rapid_oxidation', 'heat_light', 'flame']
senticnet['pouring_petrol_over'] = ['0.641', '-0.73', '0', '0.798', '#surprise', '#admiration', 'positive', '0.724', 'ablaze', 'aflame', 'afire', 'aflare', 'lighted']
senticnet['pouring_rain'] = ['-0.87', '0', '0.91', '-0.78', '#sadness', '#anger', 'negative', '-0.85', 'storm', 'thundershower', 'rain', 'line_storm', 'equinoctial_storm']
senticnet['pournami_full_moon'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['pours_forth'] = ['0', '0', '-0.78', '-0.85', '#fear', '#disgust', 'negative', '-0.81', 'liquid', 'crystal_clear', 'pellucid', 'crystalline', 'lucid']
senticnet['poursuit_le'] = ['0', '0.919', '0', '0.888', '#interest', '#admiration', 'positive', '0.903', 'surprise', 'eye-popping', 'fulgurous', 'dazzling', 'bedaze']
senticnet['pout'] = ['-0.84', '-0.89', '-0.91', '-0.91', '#fear', '#disgust', 'negative', '-0.44', 'jealous', 'envious', 'enviously', 'covetously', 'jealously']
senticnet['pouvoir_en'] = ['0.667', '-0.98', '0', '0.61', '#joy', '#surprise', 'positive', '0.753', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['pov'] = ['0.92', '0.794', '0', '0', '#joy', '#interest', 'positive', '0.857', 'move_mouse', 'reboot', 'type_of_keyboard', 'enter_address', 'keep_track_appointment']
senticnet['poverty'] = ['0', '0', '0', '-0.97', '#disgust', '#disgust', 'negative', '-0.97', 'emotional_pain', 'get_fired', 'paper_cut', 'feel_bad', 'anguish']
senticnet['poverty-stricken'] = ['-0.96', '0', '0.975', '0', '#sadness', '#anger', 'negative', '-0.96', 'impoverished', 'needy', 'necessitous', 'indigent', 'destitute']
senticnet['poverty_alleviation'] = ['0.812', '-0.91', '0', '0.647', '#joy', '#surprise', 'positive', '0.79', 'assistance', 'relief', 'economic_aid', 'financial_aid', 'social_insurance']
senticnet['poverty_rate'] = ['0', '0', '0.948', '-0.96', '#anger', '#disgust', 'negative', '-0.95', 'work_much', 'lack_money', 'wage_slavery', 'buy_tool', 'get_paycheck']
senticnet['poverty_stricken'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'oppressiveness', 'tyrannical', 'oppressive', 'tyrannous', 'laden']
senticnet['pow'] = ['-0.78', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.87', 'jailbird', 'captive', 'imprisoned', 'convict', 'inmate']
senticnet['pow_camp'] = ['0', '0.156', '-0.06', '0.198', '#interest', '#admiration', 'positive', '0.098', 'grassland', 'camping', 'camp_out', 'campsite', 'campground']
senticnet['powder'] = ['0.973', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.917', 'make_curry', 'cook_dinner', 'cook_meal', 'cook_curry', 'curry_powder']
senticnet['powder-coated'] = ['0', '0', '-0.75', '-0.86', '#fear', '#disgust', 'negative', '-0.80', 'rust-resistant', 'rust-proof', 'heavy-gauge', 'rustproof', 'corrosion-resistant']
senticnet['powder_blush'] = ['-0.93', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.85', 'protect_from_sun', 'shade', 'meaning', 'corrector', 'sunglasses']
senticnet['powdered_hair'] = ['-0.84', '0', '0.633', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'grizzle', 'postiche', 'horsehair_wig', 'wig', 'peruke']
senticnet['powdered_milk'] = ['0.053', '0.085', '0.019', '0', '#joy', '#interest', 'positive', '0.04', 'farm', 'countryside', 'farm_animal', 'cow', 'healthy']
senticnet['powdered_sugar'] = ['0.059', '-0.02', '0.025', '0.109', '#joy', '#admiration', 'positive', '0.043', 'tasty', 'bake_treat', 'bake_goods', 'bake_good', 'loaf']
senticnet['powdered_wig'] = ['-0.84', '0', '0.633', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'grizzle', 'postiche', 'horsehair_wig', 'wig', 'peruke']
senticnet['powderiness'] = ['-0.64', '0', '0.835', '-0.50', '#sadness', '#anger', 'negative', '-0.66', 'sleekness', 'slickness', 'silkiness', 'smoothness', 'burnish']
senticnet['powdery_mildew'] = ['-0.83', '0', '0.754', '-0.97', '#sadness', '#disgust', 'negative', '-0.85', 'brown_rot_gummosis', 'ring_rot', 'ring_disease', 'tobacco_wilt', 'gummosis']
senticnet['powdery_snow'] = ['-0.09', '0', '-0.05', '-0.24', '#sadness', '#disgust', 'negative', '-0.13', 'smoke', 'smog', 'haze', 'mist', 'cloudy']
senticnet['powdery_substance'] = ['0.029', '-0.03', '0.066', '-0.08', '#anger', '#disgust', 'negative', '-0.02', 'sheet', 'drawer', 'file_box', 'desk_tray', 'letter_scale']
senticnet['powell_peralta'] = ['-0.18', '0', '-0.14', '-0.11', '#sadness', '#fear', 'negative', '-0.14', 'fly_kite', 'walk_dog', 'ride_roller_coaster', 'rake_leave', 'jump_up_down']
senticnet['power'] = ['0', '0.962', '0', '0.864', '#interest', '#admiration', 'positive', '0.913', 'force', 'ability', 'increase_tax', 'form_of_energy', 'watt']
senticnet['power_adapter'] = ['0', '-0.03', '0', '0.093', '#surprise', '#admiration', 'positive', '0.063', 'work_well', 'internal_combustion_engine', 'broken_car', 'mode_transportation', 'machine']
senticnet['power_amplifier'] = ['0.036', '-0.04', '0.027', '0.063', '#surprise', '#admiration', 'positive', '0.028', 'radio', 'speaker', 'channel_selector', 'power_switch', 'female_television_connector']
senticnet['power_conferred_upon'] = ['0.657', '-0.88', '0', '0.631', '#joy', '#surprise', 'positive', '0.723', 'cognisance', 'knowingness', 'awareness', 'feel', 'consciousness']
senticnet['power_consumption'] = ['0.558', '-0.60', '0', '0.779', '#surprise', '#admiration', 'positive', '0.646', 'benefit', 'pianism', 'show', 'routine', 'histrionics']
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senticnet['priest'] = ['0', '0', '0', '0.829', '#admiration', '#admiration', 'positive', '0.829', 'clergy', 'church_head', 'preacher', 'monk', 'friar']
senticnet['priest_fr'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'acolyte', 'reverend', 'thurifer', 'clergyman', 'man_of_cloth']
senticnet['priest_sex_abuse'] = ['-0.95', '0', '0.673', '-0.85', '#sadness', '#disgust', 'negative', '-0.82', 'discharge', 'free', 'dethrone', 'disinvest', 'unfrock']
senticnet['priestcraft'] = ['-0.63', '0', '0.674', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'maleficent', 'evil', 'baleful', 'malevolent', 'malign']
senticnet['priesthood'] = ['0', '0', '-0.73', '-0.93', '#fear', '#disgust', 'negative', '-0.83', 'celibate', 'vow', 'monogamy', 'celibacy', 'ordination']
senticnet['priestly_abuse'] = ['-0.95', '0', '0.673', '-0.85', '#sadness', '#disgust', 'negative', '-0.82', 'discharge', 'free', 'dethrone', 'disinvest', 'unfrock']
senticnet['priestly_celibacy'] = ['0.839', '-0.70', '0', '0.93', '#joy', '#admiration', 'positive', '0.824', 'virtuous', 'chaste', 'pure', 'continent', 'virginal']
senticnet['priestly_source'] = ['0', '0.106', '-0.05', '0.163', '#interest', '#admiration', 'positive', '0.072', 'band', 'kit', 'oblivion', 'rock_band', 'high_school_band']
senticnet['prig'] = ['-0.76', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.81', 'intractability', 'unruliness', 'fractiousness', 'willfulness', 'against_rule']
senticnet['priggish'] = ['0', '0', '0.937', '-0.86', '#anger', '#disgust', 'negative', '-0.9', 'prissy', 'puritanical', 'prudish', 'prim', 'square-toed']
senticnet['prilep'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
senticnet['prilosec'] = ['0', '0', '-0.70', '-0.76', '#fear', '#disgust', 'negative', '-0.73', 'choke', 'become_full', 'get_full', 'burp', 'sickness']
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senticnet['prim_out'] = ['0.645', '-0.69', '0', '0.566', '#joy', '#surprise', 'positive', '0.636', 'dress', 'garb', 'raiment', 'clothe', 'enclothe']
senticnet['prim_up'] = ['0.968', '-0.85', '0', '0.734', '#joy', '#surprise', 'positive', '0.851', 'dress', 'garb', 'raiment', 'clothe', 'enclothe']
senticnet['prima_ballerina'] = ['0.12', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.09', 'ballroom', 'perform_art', 'nightclub', 'ballerina', 'venue']
senticnet['prima_facie'] = ['-0.72', '0', '0.742', '-0.80', '#anger', '#disgust', 'negative', '-0.75', 'illicitness', 'illegality', 'illegitimacy', 'shadiness', 'quality']
senticnet['prima_materia'] = ['-0.71', '0', '0.509', '-0.92', '#sadness', '#disgust', 'negative', '-0.71', 'natural_process', 'natural_action', 'activity', 'curing', 'congelation']
senticnet['primacy'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'ascendance', 'autocracy', 'absolutism', 'tyrant']
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senticnet['primarch'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['primarily'] = ['-0.96', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.97', 'chiefly', 'mainly', 'in_the_first_place', 'in_the_main', 'principally']
senticnet['primarily_arboreal'] = ['0', '0', '-0.81', '-0.92', '#fear', '#disgust', 'negative', '-0.87', 'tree-living', 'dendroidal', 'arborous', 'arboriform', 'arborary']
senticnet['primarily_diurnal'] = ['0.967', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.91', 'furry_creature', 'hairy', 'furry', 'woodland_creature', 'animal_furry']
senticnet['primarily_nocturnal'] = ['0.967', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.91', 'furry_creature', 'hairy', 'furry', 'woodland_creature', 'animal_furry']
senticnet['primary'] = ['0.899', '0.979', '0', '0', '#joy', '#interest', 'positive', '0.939', 'principal', 'elementary', 'main', 'master', 'chief']
senticnet['primary_aggressor'] = ['-0.06', '0', '-0.15', '0', '#sadness', '#fear', 'negative', '-0.10', 'heroism', 'role_play', 'repeat', 'act', 'part_of_movie']
senticnet['primary_aldosteronism'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'illness', 'ill', 'tension', 'bad_health', 'malignant_hypertension']
senticnet['primary_amenorrhea'] = ['-0.73', '0', '0.555', '-0.90', '#sadness', '#disgust', 'negative', '-0.73', 'aching', 'hurting', 'labor_pain', 'neuralgia', 'neuralgy']
senticnet['primary_antagonist'] = ['-0.95', '0', '0.774', '0', '#sadness', '#anger', 'negative', '-0.86', 'argue', 'rival', 'disagreement', 'opposition', 'anti']
senticnet['primary_beneficiary'] = ['0.851', '0.708', '0', '0', '#joy', '#interest', 'positive', '0.779', 'devisee', 'legatee', 'recipient', 'receiver', 'pensioner']
senticnet['primary_care'] = ['-0.12', '0', '-0.12', '-0.08', '#sadness', '#fear', 'negative', '-0.11', 'hospital', 'check_up', 'check_doctor', 'check_pulse', 'check_blood_pressure']
senticnet['primary_care_physician'] = ['0', '0.945', '0', '0.778', '#interest', '#admiration', 'positive', '0.861', 'doc', 'abortionist', 'angiologist', 'commerce', 'intern']
senticnet['primary_care_provider'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'pharmaceutical_chemist', 'health_professional', 'druggist', 'health_care_provider']
senticnet['primary_caregiver'] = ['0.026', '0.069', '0', '0', '#joy', '#interest', 'positive', '0.048', 'young_child', 'childhood', 'boy', 'girl', 'smile_child']
senticnet['primary_caretaker'] = ['0.026', '0.069', '0', '0', '#joy', '#interest', 'positive', '0.048', 'young_child', 'childhood', 'boy', 'girl', 'smile_child']
senticnet['primary_ciliary_dyskinesia'] = ['-0.50', '0', '0.542', '-0.51', '#anger', '#disgust', 'negative', '-0.51', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'hirschsprung_disease']
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senticnet['primary_color'] = ['0.723', '0', '0', '0.762', '#joy', '#admiration', 'positive', '0.742', 'color_rainbow', 'secondary_color', 'color', 'happiness', 'bright_color']
senticnet['primary_dysmenorrhea'] = ['-0.73', '0', '0.555', '-0.90', '#sadness', '#disgust', 'negative', '-0.73', 'aching', 'hurting', 'labor_pain', 'neuralgia', 'neuralgy']
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senticnet['primary_goal'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'compound', 'intensify', 'deepen', 'potentiate', 'heighten']
senticnet['primary_hyperaldosteronism'] = ['-0.60', '0', '0.544', '-0.53', '#sadness', '#anger', 'negative', '-0.56', 'acute_renal_failure', 'acute_kidney_failure', 'chronic_renal_failure', 'chronic_kidney_failure', 'kidney_failure']
senticnet['primary_immunodeficiency'] = ['-0.50', '0', '0.542', '-0.51', '#anger', '#disgust', 'negative', '-0.51', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'hirschsprung_disease']
senticnet['primary_motivation'] = ['0.851', '0', '0', '0.957', '#joy', '#admiration', 'positive', '0.904', 'effort', 'go_meet', 'make_time', 'ambition', 'preparation']
senticnet['primary_objective'] = ['0.899', '0.979', '0', '0', '#joy', '#interest', 'positive', '0.939', 'principal', 'elementary', 'main', 'master', 'chief']
senticnet['primary_pneumonic_plague'] = ['-0.95', '0', '0.876', '-0.89', '#sadness', '#disgust', 'negative', '-0.90', 'bubonic_plague', 'pestis_bubonica', 'glandular_plague', 'septicemic_plague', 'pestilence']
senticnet['primary_production'] = ['0', '0', '-0.98', '-0.93', '#fear', '#disgust', 'negative', '-0.95', 'oceanography', 'biological_oceanography', 'flux', 'effect', 'variability']
senticnet['primary_progressive_aphasia'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'nominal_aphasia', 'anomic_aphasia']
senticnet['primary_purpose'] = ['0.899', '0.979', '0', '0', '#joy', '#interest', 'positive', '0.939', 'principal', 'elementary', 'main', 'master', 'chief']
senticnet['primary_reinforcer'] = ['0.583', '-0.82', '0', '0.574', '#joy', '#surprise', 'positive', '0.661', 'stimulant', 'positive_reinforcing_stimulus', 'reinforcer', 'negative_reinforcing_stimulus', 'negative_reinforcer']
senticnet['primary_residence'] = ['0.071', '0.115', '-0.08', '0.167', '#interest', '#admiration', 'positive', '0.066', 'live_inside', 'inhabit', 'live_life', 'protection_weather', 'raise_family']
senticnet['primary_responsibility'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'baddie', 'theatrical_baddie', 'part', 'persona']
senticnet['primary_school'] = ['-0.04', '0.088', '0.013', '0.069', '#interest', '#admiration', 'positive', '0.024', 'kindergarden', 'daycare', 'nursery', 'child', 'asylum']
senticnet['primary_source'] = ['0.899', '0.979', '0', '0', '#joy', '#interest', 'positive', '0.939', 'principal', 'elementary', 'main', 'master', 'chief']
senticnet['primary_thoroughfare'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'artery', 'skeletal_system', 'groin', 'neck', 'muscle']
senticnet['primate'] = ['0.913', '-0.57', '0', '0.898', '#joy', '#admiration', 'positive', '0.795', 'hominid', 'homo', 'man', 'human_being', 'human']
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senticnet['prime_contractor'] = ['0.865', '-0.84', '0', '0.915', '#joy', '#admiration', 'positive', '0.875', 'grant', 'sale_in_gross', 'contract_of_hazard', 'adhesion_contract', 'contract_of_adhesion']
senticnet['prime_farmland'] = ['0', '0.122', '0', '0.123', '#interest', '#admiration', 'positive', '0.122', 'acre', 'cow_home', 'crop_place', 'big_land', 'animal_house']
senticnet['prime_minister'] = ['0.039', '0.113', '0', '0.069', '#interest', '#admiration', 'positive', '0.074', 'country_leader', 'elect_official', 'officer', 'ceo', 'emperor']
senticnet['prime_real_estate'] = ['0.522', '-0.88', '0', '0.965', '#surprise', '#admiration', 'positive', '0.789', 'overage', 'asset', 'security', 'business', 'hole_card']
senticnet['prime_requisite'] = ['0.909', '-0.85', '0', '0.846', '#joy', '#surprise', 'positive', '0.87', 'respectability', 'reputability', 'venerability', 'venerableness', 'righteousness']
senticnet['prime_rib'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'grocery_cart', 'super_market', 'fruit_stand', 'candy_store', 'produce_store']
senticnet['prime_suspect'] = ['0', '0', '-0.88', '-0.99', '#fear', '#disgust', 'negative', '-0.93', 'murder_suspect', 'robbery_suspect', 'rape_suspect', 'murder', 'doubt']
senticnet['prime_time'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'theater_seat', 'theater_box', 'sticky_floor', 'stage_entrance', 'balcony']
senticnet['primer_paint'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'wall_decoration', 'renovation', 'painting', 'artwork', 'new_house']
senticnet['primeval'] = ['-0.56', '0', '0.746', '-0.62', '#anger', '#disgust', 'negative', '-0.64', 'contrariness', 'fractiousness', 'intractability', 'intractableness', 'perversity']
senticnet['priming_effect'] = ['0.43', '0.657', '-0.28', '0.244', '#joy', '#interest', 'positive', '0.262', 'expectation', 'look_for', 'amour_propre', 'self-love', 'conceit']
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senticnet['primitive_streak'] = ['0', '0.067', '0', '0.07', '#interest', '#admiration', 'positive', '0.069', 'peck', 'bushel', 'chicken_egg', 'ovum', 'egg']
senticnet['primitive_tribe'] = ['-0.17', '0', '-0.16', '-0.09', '#sadness', '#fear', 'negative', '-0.14', 'murder', 'hurt_others', 'death_penalty', 'death_destruction', 'end_live']
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senticnet['prince_siddhartha'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
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senticnet['princely_sum'] = ['0', '0.249', '0.012', '0.154', '#interest', '#admiration', 'positive', '0.13', 'utility', 'money_value', 'monetary_value', 'money_amount', 'sticker']
senticnet['princess'] = ['0.885', '0', '0', '0.982', '#joy', '#admiration', 'positive', '0.933', 'king', 'queen', 'lady', 'wench', 'baron']
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senticnet['princeton_alumnus'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
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senticnet['principal_investigator'] = ['0', '0.909', '0', '0.799', '#interest', '#admiration', 'positive', '0.854', 'research', 'research_topic', 'research_subject', 'search', 'student']
senticnet['principal_negotiator'] = ['0', '0.133', '0', '0.088', '#interest', '#admiration', 'positive', '0.111', 'juror', 'decide_guilt', 'become_judge', 'arbiter', 'jury']
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senticnet['principal_stockholder'] = ['0.111', '0.18', '0', '0.128', '#interest', '#admiration', 'positive', '0.14', 'corporation', 'partnership', 'large_business', 'business_organization', 'enterprise']
senticnet['principal_thoroughfare'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'artery', 'skeletal_system', 'groin', 'neck', 'muscle']
senticnet['principally'] = ['-0.97', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.97', 'chiefly', 'mainly', 'in_the_first_place', 'in_the_main', 'primarily']
senticnet['principate'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['principia'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'force_unit', 'sthene', 'poundal']
senticnet['principle'] = ['0', '0.272', '-0.07', '0.232', '#interest', '#admiration', 'positive', '0.142', 'axiom', 'hypothetical', 'govern', 'tenet', 'government_person']
senticnet['principle_set_forth'] = ['-0.68', '0', '0.967', '-0.65', '#sadness', '#anger', 'negative', '-0.76', 'depart', 'start', 'part', 'start_out', 'sally_forth']
senticnet['principled'] = ['-0.83', '0', '0.762', '0', '#sadness', '#anger', 'negative', '-0.79', 'uncompromising', 'unwilling', 'truculent', 'concession', 'inflexible']
senticnet['principled_leadership'] = ['0.053', '0.136', '0', '0.057', '#interest', '#admiration', 'positive', '0.082', 'boss', 'supervisor', 'leader', 'head_person', 'head_guy']
senticnet['pringle_silktrimmed_wool_skirt'] = ['0.939', '-0.71', '0', '0.775', '#joy', '#admiration', 'positive', '0.81', 'vox_angelica', 'voix_celeste', 'organ_stop', 'pipe', 'diapason']
senticnet['prink'] = ['-0.88', '0', '0.896', '-0.79', '#sadness', '#anger', 'negative', '-0.86', 'overdress', 'attire', 'dress_up', 'dizen', 'fig_out']
senticnet['print'] = ['0.722', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.802', 'font', 'headline', 'many_words', 'edition', 'false_statement']
senticnet['print_advertisement'] = ['0.808', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.903', 'marketing', 'sale', 'announcement', 'billboard', 'promotion']
senticnet['print_material'] = ['0.036', '0.219', '0', '0.19', '#interest', '#admiration', 'positive', '0.148', 'novel', 'fiction', 'classic', 'long_story', 'literature']
senticnet['print_media'] = ['0.889', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.807', 'compression', 'push_hard', 'print_newspaper', 'journalist', 'make_newspaper']
senticnet['print_medium'] = ['0', '0', '0', '0.078', '#admiration', '#admiration', 'positive', '0.078', 'printer', 'book_write', 'readable', 'book_page', 'book_content']
senticnet['print_newspaper'] = ['0.802', '0.778', '0', '0', '#joy', '#interest', 'positive', '0.79', 'press', 'make_newspaper', 'push_hard', 'print', 'journalist']
senticnet['print_paper'] = ['-0.12', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.12', 'written_paper', 'make_out_paper', 'purchase_book', 'explain_problem', 'sell_book']
senticnet['print_printer'] = ['0.783', '0.766', '0', '0', '#joy', '#interest', 'positive', '0.774', 'post_message', 'send_mail', 'enter_data', 'use_computer', 'send_email']
senticnet['print_queue'] = ['-0.15', '0', '0.029', '-0.11', '#sadness', '#disgust', 'negative', '-0.1', 'start_over', 'front_line', 'find_line', 'take_turn', 'long_line']
senticnet['print_shop'] = ['0.026', '0.082', '0', '0', '#joy', '#interest', 'positive', '0.054', 'account_department', 'office', 'accountant_office', 'shirt_pocket', 'desk_drawer']
senticnet['printable_version'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'swat', 'store_record', 'ideal']
senticnet['printed_copy'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'slot', 'box', 'load_dock', 'safe_deposit_box', 'emporium']
senticnet['printed_material'] = ['0', '0', '0', '0.12', '#admiration', '#admiration', 'positive', '0.12', 'written_communication', 'written_message', 'handwritten', 'message', 'correlation']
senticnet['printer'] = ['0.757', '0.895', '0', '0', '#joy', '#interest', 'positive', '0.826', 'printing_machine', 'pressman', 'student', 'project', 'use']
senticnet['printer_friendly_version'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'swat', 'store_record', 'ideal']
senticnet['printing_machine'] = ['0.753', '0.889', '0', '0', '#joy', '#interest', 'positive', '0.821', 'printer', 'pressman', 'student', 'project', 'use']
senticnet['printing_paper'] = ['0.893', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.824', 'white_paper', 'writing_paper', 'phto_copy', 'note_book', 'color_paper']
senticnet['printing_shop'] = ['0.026', '0.082', '0', '0', '#joy', '#interest', 'positive', '0.054', 'account_department', 'office', 'accountant_office', 'shirt_pocket', 'desk_drawer']
senticnet['printmaker'] = ['0.853', '0', '0', '0.955', '#joy', '#admiration', 'positive', '0.904', 'portraitist', 'watercolorist', 'miniaturist', 'lutenist', 'horologist']
senticnet['printout'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'slot', 'box', 'load_dock', 'safe_deposit_box', 'emporium']
senticnet['prinzmetal_angina'] = ['-0.20', '0', '-0.22', '-0.19', '#sadness', '#fear', 'negative', '-0.20', 'go_hospital', 'death', 'die', 'take_aspirin', 'heart_attack']
senticnet['prion_disease'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'agent', 'satisfier', 'quickener', 'invigorator']
senticnet['prion_infection'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'agent', 'satisfier', 'quickener', 'invigorator']
senticnet['priori'] = ['0.295', '0.239', '0.049', '0.346', '#joy', '#admiration', 'positive', '0.208', 'full', 'replicate', 'infinite', 'total', 'unending']
senticnet['prioritize'] = ['0', '0.894', '0', '0.72', '#interest', '#admiration', 'positive', '0.807', 'ponder', 'priority', 'put_two_together', 'think_creatively', 'make_right_decision']
senticnet['priority'] = ['0', '0.891', '0', '0.717', '#interest', '#admiration', 'positive', '0.804', 'ponder', 'prioritize', 'put_two_together', 'think_creatively', 'make_right_decision']
senticnet['priority_queue'] = ['-0.15', '0', '0.029', '-0.11', '#sadness', '#disgust', 'negative', '-0.1', 'start_over', 'front_line', 'find_line', 'take_turn', 'long_line']
senticnet['priory'] = ['0.049', '0.16', '0', '0.155', '#interest', '#admiration', 'positive', '0.121', 'castle', 'palace', 'medieval', 'aristocrat', 'throne']
senticnet['prisaca_river'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'aphasia', 'anomic_aphasia']
senticnet['prise_open'] = ['0.829', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.908', 'cut_food', 'drink_beer', 'serve_drink', 'drink_much', 'drink_beverage']
senticnet['prises_en_compte'] = ['-0.95', '0', '0.845', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'grapefruit_peel', 'lemon_peel', 'orange_peel', 'candied_fruit', 'succade']
senticnet['prism-shaped'] = ['0', '0.833', '0', '0.89', '#interest', '#admiration', 'positive', '0.861', 'wall-like', 'reinforced-concrete', 'ring-like', 'trapezoidal-shaped', 'ringlike']
senticnet['prism_spectroscope'] = ['0.954', '-0.66', '0', '0.958', '#joy', '#admiration', 'positive', '0.86', 'instrument', 'spectroscope', 'optical_instrument', 'binoculars', 'glasses']
senticnet['prismacolor'] = ['0', '0.067', '0', '0.077', '#interest', '#admiration', 'positive', '0.072', 'pencil_mark', 'use_ink', 'script', 'pencil_paper', 'written_text']
senticnet['prison'] = ['-0.02', '0', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.06', 'crack_house', 'jail', 'memorial', 'bowl_alley', 'homeless_shelter']
senticnet['prison_breaker'] = ['-0.93', '0', '0.945', '-0.93', '#anger', '#disgust', 'negative', '-0.93', 'break_rule', 'escape_prison', 'illegal', 'escape_justice', 'jail_break']
senticnet['prison_camp'] = ['0', '0.156', '-0.06', '0.198', '#interest', '#admiration', 'positive', '0.098', 'grassland', 'camping', 'camp_out', 'campsite', 'campground']
senticnet['prison_cell'] = ['0', '0', '0.995', '-0.89', '#anger', '#disgust', 'negative', '-0.94', 'shoe_store', 'chess_tournament', 'dress_room', 'front_desk', 'locker_room']
senticnet['prison_guard'] = ['-0.78', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.87', 'jailbird', 'captive', 'imprisoned', 'convict', 'inmate']
senticnet['prison_reformer'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['prison_sentence'] = ['-0.31', '0', '-0.40', '0.169', '#sadness', '#fear', 'negative', '-0.18', 'wrong', 'wrongdoing', 'vengeance', 'mock', 'bad_behavior']
senticnet['prison_warden'] = ['-0.78', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.87', 'jailbird', 'captive', 'imprisoned', 'convict', 'inmate']
senticnet['prisoner'] = ['-0.78', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.87', 'jailbird', 'captive', 'imprisoned', 'convict', 'inmate']
senticnet['prisoner_war_camp'] = ['-0.03', '0', '-0.08', '-0.15', '#fear', '#disgust', 'negative', '-0.09', 'dark_alley', 'war_museum', 'funeral_home', 'battlefield', 'shoot_range']
senticnet['prissy'] = ['0.68', '-0.53', '0', '0.607', '#joy', '#admiration', 'positive', '0.607', 'nice', 'fastidious', 'dainty', 'meticulous', 'pernickety']
senticnet['pristilomatidae'] = ['-0.80', '0', '0.815', '-0.98', '#anger', '#disgust', 'negative', '-0.86', 'inalienable', 'nontransferable', 'unassignable', 'untransferable', 'inviolable']
senticnet['pristine'] = ['0.998', '0', '0', '0.926', '#joy', '#admiration', 'positive', '0.962', 'unspoiled', 'untouched', 'beach', 'breathtaking', 'unspoilt']
senticnet['pristine_beach'] = ['0.203', '0.077', '0', '0.189', '#joy', '#admiration', 'positive', '0.156', 'good_life', 'coast', 'relax', 'sediment', 'shell']
senticnet['pristine_beauty'] = ['0.733', '0', '0', '0.801', '#joy', '#admiration', 'positive', '0.767', 'serenity', 'recreate', 'peace_of_mind', 'ease', 'serene']
senticnet['pristine_glory'] = ['0.85', '-0.72', '0', '0.989', '#joy', '#admiration', 'positive', '0.854', 'excellence', 'stateliness', 'loftiness', 'expansivity', 'expansiveness']
senticnet['prithee'] = ['-0.85', '0', '0.583', '-0.71', '#sadness', '#disgust', 'negative', '-0.71', 'muddle', 'rummage', 'mare_nest', 'welter', 'clutter']
senticnet['privacy'] = ['0', '0.828', '0', '0.805', '#interest', '#admiration', 'positive', '0.817', 'keep_bathroom_dry', 'sexual_activity', 'store_belonging', 'red_light_district', 'sexual_pleasure']
senticnet['privacy_act'] = ['-0.02', '0.175', '-0.11', '0.148', '#interest', '#admiration', 'positive', '0.045', 'tolerant', 'phrase', 'inquiry', 'explain', 'question']
senticnet['privacy_intrusion'] = ['-0.69', '0', '0.752', '-0.82', '#anger', '#disgust', 'negative', '-0.75', 'usurpation', 'trespass', 'encroachment', 'inroad', 'violation']
senticnet['private'] = ['0', '0.801', '0', '0.989', '#interest', '#admiration', 'positive', '0.895', 'general', 'military_rank', 'army_boss', 'high_rank', 'police_rank']
senticnet['private_airstrip'] = ['-0.70', '0', '0.621', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'flare_path', 'airfield', 'grassland', 'runway', 'road']
senticnet['private_balcony'] = ['0', '-0.02', '-0.08', '-0.05', '#fear', '#disgust', 'negative', '-0.03', 'mattress', 'sleep_furniture', 'cradle', 'small_bed', 'pillow']
senticnet['private_citizen'] = ['0.865', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.887', 'manager', 'colleague', 'office_worker', 'coworker', 'secretary']
senticnet['private_jet'] = ['0.028', '0', '0.013', '0.061', '#joy', '#admiration', 'positive', '0.025', 'truck', 'vehicle', 'big_car', 'car', 'lorry']
senticnet['private_place'] = ['0', '0.08', '0', '0.091', '#interest', '#admiration', 'positive', '0.085', 'privacy', 'part_of_building', 'personal', 'house_place', 'place_house']
senticnet['private_plunge_pool'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'lurking_place', 'spot', 'drop', 'hiding_place', 'den']
senticnet['private_property'] = ['-0.62', '0', '0.7', '-0.83', '#anger', '#disgust', 'negative', '-0.72', 'take', 'pull', 'return', 'payback', 'proceeds']
senticnet['private_sector'] = ['0', '0.312', '-0.10', '0.207', '#interest', '#admiration', 'positive', '0.137', 'infuse', 'loan', 'lend', 'increase_money', 'wealth']
senticnet['private_sector_investment'] = ['0', '0.312', '-0.10', '0.207', '#interest', '#admiration', 'positive', '0.137', 'infuse', 'loan', 'lend', 'increase_money', 'wealth']
senticnet['private_tuition'] = ['-0.07', '0', '0.073', '-0.26', '#sadness', '#disgust', 'negative', '-0.13', 'college_tuition', 'tuition', 'pay_tuition', 'enroll_class', 'enroll_school']
senticnet['private_yacht'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'boat', 'yacht', 'big_boat', 'large_boat']
senticnet['privateer'] = ['0', '0', '0.034', '-0.08', '#anger', '#disgust', 'negative', '-0.06', 'lifeboat', 'mast', 'bad_man', 'keel', 'yacht']
senticnet['privately'] = ['0', '0.088', '-0.06', '0.068', '#interest', '#admiration', 'positive', '0.032', 'brew', 'hold_liquid', 'make_wish', 'rinse_dish', 'find_toilet']
senticnet['privately_owned_airfield'] = ['-0.70', '0', '0.621', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'flare_path', 'airfield', 'grassland', 'runway', 'road']
senticnet['privately_owned_land'] = ['0.716', '-0.51', '0', '0.647', '#joy', '#admiration', 'positive', '0.624', 'state', 'nation', 'country', 'ally', 'commonwealth']
senticnet['privateness'] = ['0.779', '-0.75', '0', '0.526', '#joy', '#surprise', 'positive', '0.687', 'hiddenness', 'covertness', 'bosom', 'isolation', 'confidentiality']
senticnet['privation'] = ['0', '0', '-0.76', '-0.99', '#fear', '#disgust', 'negative', '-0.87', 'loss', 'deprivation', 'dispossession', 'bereavement', 'demise']
senticnet['privatisation'] = ['-0.90', '0', '0.933', '0', '#sadness', '#anger', 'negative', '-0.91', 'privatization', 'denationalization', 'denationalisation', 'lagardere', 'thomson']
senticnet['privatization'] = ['-0.90', '0', '0.927', '0', '#sadness', '#anger', 'negative', '-0.91', 'privatisation', 'denationalization', 'denationalisation', 'lagardere', 'thomson']
senticnet['privie'] = ['0.967', '-0.98', '0', '0.64', '#joy', '#surprise', 'positive', '0.863', 'club', 'social_club', 'society', 'guild', 'jockey_club']
senticnet['privilege'] = ['0.619', '-0.60', '0', '0.539', '#joy', '#surprise', 'positive', '0.588', 'easement', 'perquisite', 'public_easement', 'right_of_way', 'prerogative']
senticnet['privilege_escalation'] = ['-0.02', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.07', 'army', 'military', 'infantry', 'samurai', 'soldier']
senticnet['privity'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'culpable_negligence', 'evasion', 'dereliction', 'negligence']
senticnet['privy_council'] = ['-0.62', '0', '0.961', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'jeweled_headdress', 'diadem', 'tiara', 'headdress', 'headgear']
senticnet['privy_purse'] = ['0.059', '0.076', '-0.05', '0.079', '#interest', '#admiration', 'positive', '0.039', 'noble', 'king', 'earl', 'duchess', 'duke']
senticnet['prix_fixe_menu'] = ['0.128', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.085', 'banquet', 'rave', 'auction', 'limbo', 'loose_end']
senticnet['priya_elan'] = ['0.7', '-0.62', '0', '0.58', '#joy', '#surprise', 'positive', '0.636', 'white_magic', 'thaumaturgy', 'juju', 'conjuration', 'supernaturalism']
senticnet['prize'] = ['0.894', '0.881', '0', '0.803', '#joy', '#interest', 'positive', '0.859', 'remittance', 'afford', 'award', 'money_due', 'amount_owe']
senticnet['prize_awarded_annually'] = ['0.894', '0.881', '0', '0.803', '#joy', '#interest', 'positive', '0.859', 'remittance', 'afford', 'award', 'money_due', 'amount_owe']
senticnet['prize_fighter'] = ['0.833', '-0.97', '0', '0.91', '#surprise', '#admiration', 'positive', '0.906', 'combatant', 'fencer', 'belligerent', 'fighter', 'scrapper']
senticnet['prize_winner'] = ['0.894', '0.881', '0', '0.803', '#joy', '#interest', 'positive', '0.859', 'remittance', 'afford', 'award', 'money_due', 'amount_owe']
senticnet['prized'] = ['0', '-0.85', '0', '0.757', '#surprise', '#admiration', 'positive', '0.803', 'in-demand', 'coveted', 'renowned', 'sought-after', 'prestigious']
senticnet['prizefighter'] = ['0.833', '-0.97', '0', '0.91', '#surprise', '#admiration', 'positive', '0.906', 'combatant', 'fencer', 'belligerent', 'fighter', 'scrapper']
senticnet['prizewinner'] = ['0.894', '0.881', '0', '0.803', '#joy', '#interest', 'positive', '0.859', 'remittance', 'afford', 'award', 'money_due', 'amount_owe']
senticnet['prmet'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
senticnet['prnewswire_firstcall_netgear'] = ['-0.63', '0', '0.862', '-0.52', '#sadness', '#anger', 'negative', '-0.67', 'knot', 'bunch', 'tuft', 'tussock', 'agglomeration']
senticnet['pro'] = ['0.036', '0.243', '-0.09', '0.274', '#interest', '#admiration', 'positive', '0.114', 'objective', 'confirmation', 'honor', 'etiquette', 'bottom_line']
senticnet['pro_abortion'] = ['-0.90', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.86', 'death', 'feticide', 'sickness', 'miscarriage', 'spontaneous_abortion']
senticnet['pro_active'] = ['0', '0', '-0.85', '-0.93', '#fear', '#disgust', 'negative', '-0.89', 'anger', 'animosity', 'hostility', 'malice', 'grudge']
senticnet['pro_athlete'] = ['0', '0', '0', '0.97', '#admiration', '#admiration', 'positive', '0.97', 'increase_strength', 'keep_fit', 'fit_person', 'exercise', 'strengthen_heart']
senticnet['pro_basketball_player'] = ['0', '0', '0', '0.97', '#admiration', '#admiration', 'positive', '0.97', 'increase_strength', 'keep_fit', 'fit_person', 'exercise', 'strengthen_heart']
senticnet['pro_boer'] = ['-0.91', '0', '0.688', '-0.57', '#sadness', '#anger', 'negative', '-0.72', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['pro_bono_attorney'] = ['0', '0.146', '-0.05', '0.077', '#interest', '#admiration', 'positive', '0.057', 'lawyer', 'negotiator', 'arbiter', 'judge_jury', 'decide_guilt']
senticnet['pro_box_lacrosse'] = ['0', '0.105', '0', '0', '#interest', '#interest', 'positive', '0.105', 'beak', 'many_feathers', 'big_eye', 'bird', 'bird_mouth']
senticnet['pro_choice'] = ['-0.90', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.86', 'death', 'feticide', 'sickness', 'miscarriage', 'spontaneous_abortion']
senticnet['pro_communist'] = ['0', '0', '-0.70', '-0.83', '#fear', '#disgust', 'negative', '-0.76', 'settle_disagreement', 'hitler', 'republican', 'conservative', 'unchanging']
senticnet['pro_football_player'] = ['0', '0', '0', '0.97', '#admiration', '#admiration', 'positive', '0.97', 'increase_strength', 'keep_fit', 'fit_person', 'exercise', 'strengthen_heart']
senticnet['pro_forma_session'] = ['-0.81', '0', '0.513', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'ballot', 'vote', 'voting', 'balloting', 'veto']
senticnet['pro_golfer'] = ['0.758', '0.719', '0', '0', '#joy', '#interest', 'positive', '0.738', 'plan_party', 'dance_music', 'become_adult', 'stay_fit', 'drive_crazy']
senticnet['pro_inflammatory'] = ['-0.66', '0', '0.877', '-0.64', '#sadness', '#anger', 'negative', '-0.72', 'somatic_cell', 'mastocyte', 'labrocyte', 'target_cell', 'phagocyte']
senticnet['pro_lgbt'] = ['0.778', '-0.73', '0', '0.559', '#joy', '#surprise', 'positive', '0.689', 'sex', 'inversion', 'sexual_inversion', 'lesbianism', 'sapphism']
senticnet['pro_life'] = ['-0.90', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.86', 'death', 'feticide', 'sickness', 'miscarriage', 'spontaneous_abortion']
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senticnet['profuse_watery_diarrhea'] = ['-0.97', '0', '0.584', '-0.78', '#sadness', '#disgust', 'negative', '-0.78', 'decreased', 'diminished', 'weakened', 'faded', 'small']
senticnet['profuseness'] = ['0.581', '-0.65', '0', '0.959', '#surprise', '#admiration', 'positive', '0.732', 'wilderness', 'overgrowth', 'greenness', 'verdancy', 'verdure']
senticnet['profusion'] = ['0.689', '-0.51', '0', '0.632', '#joy', '#admiration', 'positive', '0.613', 'wilderness', 'overgrowth', 'greenness', 'verdancy', 'verdure']
senticnet['prog_magazine'] = ['0.7', '-0.62', '0', '0.58', '#joy', '#surprise', 'positive', '0.636', 'white_magic', 'thaumaturgy', 'juju', 'conjuration', 'supernaturalism']
senticnet['progeny'] = ['0.81', '0', '0', '0.949', '#joy', '#admiration', 'positive', '0.879', 'orphan', 'become_parent', 'take_child', 'have_much', 'love_one']
senticnet['prognosis'] = ['0.993', '-0.65', '0', '0.743', '#joy', '#admiration', 'positive', '0.797', 'weather_forecast', 'weather_outlook', 'financial_forecast', 'prediction', 'foretelling']
senticnet['prognostic'] = ['0.844', '-0.66', '0', '0.653', '#joy', '#admiration', 'positive', '0.719', 'omen', 'portent', 'presage', 'auspice', 'prognostication']
senticnet['prognostic_significance'] = ['0.798', '-0.51', '0', '0.519', '#joy', '#admiration', 'positive', '0.611', 'averageness', 'commonness', 'normalcy', 'typicality', 'condition']
senticnet['prognosticate'] = ['0.52', '-0.98', '0', '0.846', '#surprise', '#admiration', 'positive', '0.784', 'auspicate', 'prefigure', 'bode', 'portend', 'foreshow']
senticnet['prognostication'] = ['0.847', '-0.66', '0', '0.655', '#joy', '#admiration', 'positive', '0.723', 'omen', 'portent', 'presage', 'prognostic', 'auspice']
senticnet['program'] = ['0.715', '0.617', '-0.23', '0.329', '#joy', '#interest', 'positive', '0.358', 'program_computer', 'write_program', 'use_computer', 'write_code', 'source_code']
senticnet['program_computer'] = ['0.829', '0', '0', '0.973', '#joy', '#admiration', 'positive', '0.901', 'write_program', 'add_up_column_number', 'compute_sum', 'use_calculator', 'calculate_quickly']
senticnet['program_coordinator'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'sexual_relationship', 'affair', 'involvement']
senticnet['program_language'] = ['0', '0', '0', '0.074', '#admiration', '#admiration', 'positive', '0.074', 'write_message', 'grammar', 'run_out_paper', 'english', 'computer_language']
senticnet['program_vpp'] = ['-0.02', '0.1', '-0.07', '0.089', '#interest', '#admiration', 'positive', '0.023', 'functionary', 'employment', 'labor', 'employ', 'toil']
senticnet['programmable'] = ['0', '0', '-0.93', '-0.84', '#fear', '#disgust', 'negative', '-0.89', 'special-purpose', 'widely-used', 'high-performance', 'microprocessor', 'general-purpose']
senticnet['programmable_computer'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'biological_machine', 'social_creature', 'live_creature', 'powerful', 'alive']
senticnet['programmable_thermostat'] = ['0.02', '0.076', '0', '0', '#joy', '#interest', 'positive', '0.048', 'land_earth', 'tidal_wave', 'puddle', 'brine', 'spurt']
senticnet['programme'] = ['0.708', '0.901', '0', '0', '#joy', '#interest', 'positive', '0.804', 'episode', 'jeopardy', 'sitcom', 'replay', 'describe']
senticnet['programmer'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'healthy_person', 'philanthropist', 'nazi', 'pervert', 'housekeeper']
senticnet['programming_bug'] = ['-0.04', '0.121', '0', '0.178', '#interest', '#admiration', 'positive', '0.084', 'uppercase_bug', 'bug', 'code', 'developer', 'bad']
senticnet['programming_error'] = ['-0.98', '0', '0.869', '-0.80', '#sadness', '#anger', 'negative', '-0.88', 'error', 'syntax_error', 'semantic_error', 'runtime_error', 'computer_error']
senticnet['programming_language'] = ['0', '-0.99', '0', '0.862', '#surprise', '#admiration', 'positive', '0.928', 'helpful_tool', 'woodworking_tool', 'helpful', 'type_of_fruit', 'hand_tool']
senticnet['progress'] = ['-0.06', '0.191', '-0.09', '0.223', '#interest', '#admiration', 'positive', '0.065', 'advance', 'enter_place', 'meander', 'go_ahead', 'lunge']
senticnet['progress_rpp'] = ['0.669', '-0.58', '0', '0.803', '#joy', '#admiration', 'positive', '0.685', 'pep_rally', 'revival', 'revival_meeting', 'gathering', 'assemblage']
senticnet['progress_toward_tenure'] = ['-0.69', '0', '0.806', '-0.74', '#anger', '#disgust', 'negative', '-0.74', 'hypothesis', 'theory', 'base', 'possibility', 'presumption']
senticnet['progression'] = ['0.499', '-0.51', '0', '0.676', '#surprise', '#admiration', 'positive', '0.564', 'forward_motion', 'onward_motion', 'clear_sailing', 'procession', 'progress']
senticnet['progressive_bulbar_palsy'] = ['-0.75', '0', '0.515', '-0.93', '#sadness', '#disgust', 'negative', '-0.73', 'sclerosis', 'induration', 'disseminated_sclerosis', 'autoimmune_disease', 'autoimmune_disorder']
senticnet['progressive_degeneration'] = ['-0.78', '0', '0.764', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'withering', 'downfall', 'weakening', 'fading', 'attenuation']
senticnet['progressive_dementia'] = ['-0.94', '0', '0.821', '-0.75', '#sadness', '#anger', 'negative', '-0.84', 'predementedness', 'insanity', 'dementedness', 'senile_psychosis', 'alcoholic_dementia']
senticnet['progressive_deterioration'] = ['-0.78', '0', '0.764', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'withering', 'downfall', 'weakening', 'fading', 'attenuation']
senticnet['progressive_jackpot'] = ['-0.78', '0', '0.944', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'loser', 'speculator', 'mortal', 'better', 'gambler']
senticnet['progressive_supranuclear_palsy'] = ['-0.94', '0', '0.821', '-0.75', '#sadness', '#anger', 'negative', '-0.84', 'predementedness', 'insanity', 'dementedness', 'senile_psychosis', 'alcoholic_dementia']
senticnet['progressive_vaccinia'] = ['0.753', '-0.74', '0', '0.587', '#joy', '#surprise', 'positive', '0.695', 'draining', 'exhausting', 'debilitating', 'enervating', 'enfeebling']
senticnet['progymnosperm'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'gymnosperm', 'welwitschia', 'cycad', 'seed_fern', 'pteridosperm']
senticnet['prohibit'] = ['-0.27', '-0.18', '0.02', '-0.36', '#sadness', '#disgust', 'negative', '-0.12', 'interruption', 'criticism', 'anthrax', 'deny', 'lay_off']
senticnet['prohibit_abortion'] = ['-0.90', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.86', 'death', 'feticide', 'sickness', 'miscarriage', 'spontaneous_abortion']
senticnet['prohibited_discrimination'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
senticnet['prohibited_firearm'] = ['-0.84', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.86', 'handgun', 'rifle', 'weapon', 'pistol', 'gun']
senticnet['prohibited_weapon'] = ['-0.84', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.86', 'handgun', 'rifle', 'weapon', 'pistol', 'gun']
senticnet['prohibition'] = ['0', '0', '0.973', '-0.8', '#anger', '#disgust', 'negative', '-0.88', 'bigot', 'broken_finger', 'schizophrenia', 'censorship', 'black_eye']
senticnet['prohibition_against'] = ['0', '0', '-0.9', '-0.79', '#fear', '#disgust', 'negative', '-0.84', 'forbid', 'prohibit', 'veto', 'disallow', 'interdict']
senticnet['prohibitionist_society'] = ['-0.03', '0.14', '-0.08', '0.111', '#interest', '#admiration', 'positive', '0.033', 'use_game_chess', 'make_up_person', 'blade_weapon', 'find_airplane', 'overrate']
senticnet['prohibitive'] = ['-0.97', '0', '0.789', '0', '#sadness', '#anger', 'negative', '-0.88', 'costly', 'prohibit', 'prohibitively', 'disallow', 'budget']
senticnet['prohibitively'] = ['-0.98', '0', '0.787', '0', '#sadness', '#anger', 'negative', '-0.88', 'costly', 'prohibit', 'prohibitive', 'disallow', 'budget']
senticnet['prohibitively_costly'] = ['-0.12', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.09', 'painful', 'uncomfortable', 'violent', 'fun_learn_play', 'expensive']
senticnet['prohibitively_expensive'] = ['-0.12', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.09', 'painful', 'uncomfortable', 'violent', 'fun_learn_play', 'expensive']
senticnet['prohibitory'] = ['0.509', '-0.62', '0', '0.786', '#surprise', '#admiration', 'positive', '0.64', 'agreeable', 'accordant', 'consentient', 'unanimous', 'according']
senticnet['proinflammatory'] = ['-0.66', '0', '0.877', '-0.64', '#sadness', '#anger', 'negative', '-0.72', 'somatic_cell', 'mastocyte', 'labrocyte', 'target_cell', 'phagocyte']
senticnet['project'] = ['0', '0.097', '0', '0.092', '#interest', '#admiration', 'positive', '0.095', 'urgent', 'classify_ad', 'important', 'review', 'repetition']
senticnet['project_aim'] = ['0', '1.0', '0', '0.749', '#interest', '#admiration', 'positive', '0.875', 'award', 'standard_grant', 'collaboration', 'university', 'phase']
senticnet['project_already_underway'] = ['0', '0.097', '0', '0.092', '#interest', '#admiration', 'positive', '0.095', 'urgent', 'classify_ad', 'important', 'review', 'repetition']
senticnet['project_brings_together'] = ['0', '0.756', '0', '0.804', '#interest', '#admiration', 'positive', '0.78', 'together', 'effort', 'laboratory', 'complementary_expertise', 'research_capability']
senticnet['project_focus'] = ['0', '0.792', '0', '0.76', '#interest', '#admiration', 'positive', '0.776', 'development', 'different_application', 'project', 'study', 'small_business_phase']
senticnet['project_seek'] = ['0', '0.978', '0', '0.793', '#interest', '#admiration', 'positive', '0.885', 'science', 'small_business_phase', 'well', 'project', 'standard_grant']
senticnet['project_support'] = ['0', '0.903', '0', '0.883', '#interest', '#admiration', 'positive', '0.893', 'standard_grant', 'award', 'different_application', 'student', 'project']
senticnet['project_take_advantage'] = ['0', '0.783', '0', '0.987', '#interest', '#admiration', 'positive', '0.885', 'research', 'objective', 'university', 'investigator', 'western_europe']
senticnet['project_underway'] = ['0', '0.097', '0', '0.092', '#interest', '#admiration', 'positive', '0.095', 'urgent', 'classify_ad', 'important', 'review', 'repetition']
senticnet['projected_deficit'] = ['-0.05', '0.282', '-0.04', '0.107', '#interest', '#admiration', 'positive', '0.072', 'expense', 'dollar_cent', 'dollar_sign', 'amount_pay', 'payment_amount']
senticnet['projected_onto'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'arm_movement', 'hand_gesture', 'water_motion', 'hand_signal', 'sea_movement']
senticnet['projected_trilogy'] = ['0.036', '0.219', '0', '0.19', '#interest', '#admiration', 'positive', '0.148', 'story', 'long_story', 'novel', 'fable', 'fiction']
senticnet['projectile'] = ['0.801', '0.774', '0', '0', '#joy', '#interest', 'positive', '0.788', 'ammunition', 'bullet', 'pistol', 'shotgun', 'rifle']
senticnet['projectile_point'] = ['0.063', '0.089', '0.036', '0.146', '#interest', '#admiration', 'positive', '0.066', 'horn', 'jew_harp', 'brass_instrument', 'trumpet', 'crash_cymbal']
senticnet['projectile_weapon'] = ['-0.98', '0', '0.338', '-0.97', '#sadness', '#disgust', 'negative', '-0.76', 'gun', 'bullet', 'rifle', 'shoot', 'pistol']
senticnet['projection'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'arm_movement', 'hand_gesture', 'water_motion', 'hand_signal', 'sea_movement']
senticnet['projection_booth'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'theater', 'drive-in_movie', 'watch_movie', 'cinema', 'movie']
senticnet['projection_equipment'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'floodlight', 'spotlight', 'headlight', 'light', 'teach_lesson']
senticnet['projection_onto'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'arm_movement', 'hand_gesture', 'water_motion', 'hand_signal', 'sea_movement']
senticnet['projection_plane'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'arm_movement', 'hand_gesture', 'water_motion', 'hand_signal', 'sea_movement']
senticnet['projection_screen'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'arm_movement', 'hand_gesture', 'water_motion', 'hand_signal', 'sea_movement']
senticnet['projectivity'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'integrative', 'consolidative', 'unifying', 'combinatorial']
senticnet['projector'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'floodlight', 'spotlight', 'headlight', 'light', 'teach_lesson']
senticnet['projector_screen'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'floodlight', 'spotlight', 'headlight', 'light', 'teach_lesson']
senticnet['projectthe'] = ['0', '0.097', '0', '0.092', '#interest', '#admiration', 'positive', '0.095', 'urgent', 'classify_ad', 'important', 'review', 'repetition']
senticnet['prokuplje'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
senticnet['prolactin'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'cholecystokinin', 'secretin', 'gastrointestinal_hormone']
senticnet['prolactinoma'] = ['-0.73', '0', '0.904', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'unshapely', 'shapeless', 'lumpy', 'deformed', 'malformed']
senticnet['prolate'] = ['0.962', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.771', 'aspheric', 'aspherical', 'auriform', 'rounded', 'crescent']
senticnet['prolate_spheroid'] = ['0.928', '0.703', '0', '0', '#joy', '#interest', 'positive', '0.816', 'polygon', 'fit', 'geometric_figure', 'geometric_shape', 'train_hard']
senticnet['prolene'] = ['-0.81', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.85', 'bandage', 'painkiller', 'gauze', 'aspirin', 'contraceptive_device']
senticnet['proletarian'] = ['0.127', '0.069', '0.029', '0.16', '#joy', '#admiration', 'positive', '0.082', 'basic_human_need', 'construct', 'set_down', 'make', 'build_process']
senticnet['proletarian_internationalism'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'patriotism', 'homeland']
senticnet['proletarian_revolution'] = ['-0.67', '0', '0.85', '-0.59', '#sadness', '#anger', 'negative', '-0.70', 'rack', 'boot', 'whip', 'iron_boot', 'instrument_of_torture']
senticnet['proletariat'] = ['0.127', '0.069', '0.029', '0.16', '#joy', '#admiration', 'positive', '0.082', 'basic_human_need', 'construct', 'set_down', 'make', 'build_process']
senticnet['proliferate'] = ['0.709', '0', '0', '0.709', '#joy', '#admiration', 'positive', '0.709', 'inflate', 'lengthen', 'boost', 'thicken', 'multiply']
senticnet['proliferating'] = ['-0.97', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.92', 'voltage', 'inrush', 'sinusoidal', 'quiescent', 'steady-state']
senticnet['proliferation'] = ['0.836', '-0.83', '0', '0.511', '#joy', '#surprise', 'positive', '0.726', 'appearance', 'start', 'casus_belli', 'beginning', 'egress']
senticnet['proliferative_response'] = ['-0.66', '0', '0.877', '-0.64', '#sadness', '#anger', 'negative', '-0.72', 'somatic_cell', 'mastocyte', 'labrocyte', 'target_cell', 'phagocyte']
senticnet['prolific'] = ['0.903', '-0.89', '0', '0', '#joy', '#surprise', 'positive', '0.901', 'abundance', 'work', 'prolifically', 'offspring', 'producing']
senticnet['prolific_composer'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'band_play', 'songwriter', 'rock_band']
senticnet['prolific_designer'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'maker']
senticnet['prolific_inventor'] = ['-0.03', '0.201', '-0.13', '0.171', '#interest', '#admiration', 'positive', '0.049', 'scientific_statement', 'hypothetical', 'conjecture', 'theory', 'fact']
senticnet['prolific_songwriter'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
senticnet['prolificacy'] = ['0.635', '-0.81', '0', '0.674', '#surprise', '#admiration', 'positive', '0.707', 'quality', 'richness', 'fruitfulness', 'fertility', 'productiveness']
senticnet['prolifically'] = ['0.9', '-0.9', '0', '0', '#joy', '#surprise', 'positive', '0.9', 'abundance', 'work', 'prolific', 'offspring', 'producing']
senticnet['prolix'] = ['-0.89', '0', '0.987', '-0.90', '#anger', '#disgust', 'negative', '-0.93', 'cacoethes', 'logomania', 'agromania', 'pyromania', 'necrophilia']
senticnet['prolixity'] = ['-0.57', '0', '0.767', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'verboseness', 'verbosity', 'turgidity', 'turgidness', 'flatulence']
senticnet['prolixness'] = ['-0.62', '0', '0.955', '-0.98', '#anger', '#disgust', 'negative', '-0.85', 'verboseness', 'verbosity', 'turgidity', 'turgidness', 'flatulence']
senticnet['prolog'] = ['0', '0.202', '-0.06', '0.291', '#interest', '#admiration', 'positive', '0.142', 'vocabulary', 'translate', 'dictionary', 'translation', 'thesaurus']
senticnet['prologue'] = ['0.075', '0.146', '-0.12', '0.219', '#interest', '#admiration', 'positive', '0.078', 'begin', 'edition', 'introduction', 'start_end', 'false_statement']
senticnet['prolong'] = ['0.853', '0', '0', '0.938', '#joy', '#admiration', 'positive', '0.895', 'augment', 'sharpen', 'exaggerate', 'escalate', 'amplify']
senticnet['prolong_life'] = ['0.133', '0.1', '-0.12', '0.144', '#joy', '#admiration', 'positive', '0.064', 'improve_self-image', 'keep_body_healthy', 'much_energy', 'healthy', 'long_life']
senticnet['prolonged_absence'] = ['-0.56', '0', '-0.49', '-0.09', '#sadness', '#fear', 'negative', '-0.38', 'lack', 'poor', 'poverty', 'emptiness', 'hunger']
senticnet['prolonged_capillary_refill'] = ['-0.72', '0', '0.762', '-0.79', '#anger', '#disgust', 'negative', '-0.75', 'heat_hyperpyrexia', 'heat_prostration', 'prostration', 'sunstroke', 'thermic_fever']
senticnet['prolonged_detention_without'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicative', 'unpronounceable', 'impassive', 'inexpressive', 'silent']
senticnet['prolonged_drought'] = ['-0.98', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.89', 'dry', 'lose_team', 'leave_out', 'disrespect', 'cause_suffer']
senticnet['prolonged_immersion'] = ['-0.57', '0', '0.767', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'verboseness', 'verbosity', 'prolixity', 'turgidness', 'flatulence']
senticnet['prolonged_period'] = ['-0.87', '0', '0.915', '-0.95', '#anger', '#disgust', 'negative', '-0.91', 'inactivity', 'inactiveness', 'slumber', 'inaction', 'hibernation']
senticnet['prolonged_qt_interval'] = ['-0.83', '0', '0.987', '-0.50', '#sadness', '#anger', 'negative', '-0.77', 'heart_block', 'atrioventricular_block', 'bradycardia', 'ventricular_fibrillation', 'tachycardia']
senticnet['prolonged_recession'] = ['-0.13', '-0.05', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'monitor_heart_rate', 'feel_tired', 'bump_head', 'exhaustion', 'become_fatigued']
senticnet['prolonged_spell'] = ['0', '0', '0.954', '-0.76', '#anger', '#disgust', 'negative', '-0.85', 'fervid', 'ardent', 'impassioned', 'fervent', 'fiery']
senticnet['prolonged_survival'] = ['0.988', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.98', 'sustain_life', 'sustenance', 'nutrition', 'keep_alive', 'satisfy_hunger']
senticnet['prolonged_therapy'] = ['-0.53', '0', '0.608', '-0.93', '#anger', '#disgust', 'negative', '-0.69', 'reprieve', 'respite', 'hiatus', 'abatement', 'suspension']
senticnet['prolonging_life'] = ['-0.03', '-0.02', '-0.08', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'ibuprofen', 'phenobarbital', 'analgesic', 'sleep_drug', 'pill']
senticnet['prom'] = ['0.088', '-0.00', '0', '0.077', '#joy', '#admiration', 'positive', '0.056', 'ballerina', 'ballroom', 'ballet', 'party', 'nightclub']
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senticnet['promoting_atheism'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'non-believer', 'doubter', 'agnostic', 'freethinker', 'sceptic']
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senticnet['pronghorn_antelope'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'argali', 'argal', 'urial', 'white_sheep', 'wild_sheep']
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senticnet['pronounceable'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicado', 'incommunicative', 'impassive', 'inexpressive', 'silent']
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senticnet['pronounced_identically'] = ['-0.61', '0', '0.721', '-0.68', '#anger', '#disgust', 'negative', '-0.67', 'character', 'logogram', 'logograph', 'graph', 'ideogram']
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senticnet['proof_of_purchase'] = ['0.029', '0.226', '-0.09', '0.174', '#interest', '#admiration', 'positive', '0.084', 'proof', 'movie_ticket', 'purchase', 'receipt', 'bill']
senticnet['proof_theoretic'] = ['0.777', '-0.68', '0', '0.601', '#joy', '#surprise', 'positive', '0.686', 'lucidness', 'clarity', 'clearness', 'lucidity', 'pellucidity']
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senticnet['proofread'] = ['0.642', '-0.53', '0', '0.575', '#joy', '#admiration', 'positive', '0.583', 'check', 'assure', 'see', 'insure', 'examine']
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senticnet['proopiomelanocortin_pomc'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'gastrointestinal_hormone', 'secretin', 'ghrelin']
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senticnet['prop_up'] = ['0.169', '0.223', '-0.10', '0.219', '#interest', '#admiration', 'positive', '0.126', 'ferment', 'vibrate', 'blend', 'fill_glass', 'jiggle']
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senticnet['propagation_velocity'] = ['-0.03', '0.178', '0', '0.168', '#interest', '#admiration', 'positive', '0.104', 'speed', 'fast', 'fast_and_furious', 'speedy', 'fleet']
senticnet['propagative'] = ['0.531', '0.673', '-0.27', '0.275', '#joy', '#interest', 'positive', '0.301', 'amorous', 'amorousness', 'amatory', 'enamoredness', 'puppy_love']
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senticnet['propane_heater'] = ['0.088', '0.076', '0.016', '0.094', '#joy', '#admiration', 'positive', '0.06', 'copper_wire', 'stove', 'several_types', 'oven', 'suspect']
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senticnet['propane_tank'] = ['0.886', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.881', 'fire_hydrant', 'stair_rail', 'christian_fundamentalist', 'extinguisher', 'reception_desk']
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senticnet['propel'] = ['0', '0.142', '-0.09', '0.183', '#interest', '#admiration', 'positive', '0.078', 'arise', 'rise', 'raise_up', 'go_up', 'go_upstairs']
senticnet['propellant'] = ['0.74', '-0.76', '0', '0.809', '#surprise', '#admiration', 'positive', '0.771', 'changing', 'propellent', 'propelling', 'dynamical', 'propulsive']
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senticnet['propeller'] = ['0', '0.941', '0', '0', '#interest', '#interest', 'positive', '0.941', 'boat_part', 'hull', 'cleat', 'gunwale', 'stern']
senticnet['propeller-like'] = ['0.756', '-0.95', '0', '0', '#joy', '#surprise', 'positive', '0.857', 'mandala-like', 'turbine-like', 'cross-like', 'donut-like', 'wheel-like']
senticnet['propelling'] = ['0.737', '-0.75', '0', '0.806', '#surprise', '#admiration', 'positive', '0.767', 'propellant', 'propellent', 'changing', 'dynamical', 'propulsive']
senticnet['propelling_charge'] = ['0.13', '0.097', '-0.07', '0.198', '#joy', '#admiration', 'positive', '0.087', 'jiggle', 'back_forth', 'vibrate', 'blend', 'mixture']
senticnet['propene'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'carboxylic_acid', 'succinic_acid', 'valeric_acid']
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senticnet['proper_dental_care'] = ['0', '0', '0.813', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'ice_cream_melt', 'allergy', 'toothache', 'gonorrhea', 'tastebud']
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senticnet['proper_equipment'] = ['0', '0.086', '0.04', '0.106', '#interest', '#admiration', 'positive', '0.051', 'tumble', 'up_hill', 'go_mountain', 'move_up', 'equipment']
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senticnet['proper_footwear'] = ['0.84', '0.76', '0', '0.894', '#joy', '#admiration', 'positive', '0.831', 'keep_skin_healthy', 'stay_healthy', 'stay_fit', 'maintain_good_health', 'eat_healthily']
senticnet['proper_hygiene'] = ['0.076', '-0.00', '0', '0', '#joy', '#surprise', 'positive', '0.042', 'clean_body', 'feel_fresh', 'remove_dirt_body', 'shower', 'feel_refreshed']
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senticnet['proper_nutrition'] = ['0.076', '-0.00', '0', '0', '#joy', '#surprise', 'positive', '0.042', 'clean_body', 'feel_fresh', 'remove_dirt_body', 'shower', 'feel_refreshed']
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senticnet['proper_pronunciation'] = ['-0.02', '0.186', '0', '0.213', '#interest', '#admiration', 'positive', '0.125', 'description', 'mumble', 'bummer', 'numerical', 'proverb']
senticnet['proper_sanitation'] = ['0.076', '-0.00', '0', '0', '#joy', '#surprise', 'positive', '0.042', 'clean_body', 'feel_fresh', 'remove_dirt_body', 'shower', 'feel_refreshed']
senticnet['proper_ventilation'] = ['0.718', '0', '0', '0', '#joy', '#joy', 'positive', '0.718', 'water_container', 'still_water', 'clean_water', 'water', 'cool_water']
senticnet['properly'] = ['0.89', '0', '0', '0.841', '#joy', '#admiration', 'positive', '0.865', 'aright', 'aptly', 'gradely', 'meetly', 'proper']
senticnet['properness'] = ['0.837', '-0.90', '0', '0.822', '#joy', '#surprise', 'positive', '0.854', 'decency', 'decorum', 'decorousness', 'correctness', 'seemliness']
senticnet['property'] = ['0.024', '0.199', '-0.11', '0.168', '#interest', '#admiration', 'positive', '0.068', 'estate', 'own_land', 'real_estate', 'good_investment', 'owner']
senticnet['property_bubble'] = ['-0.55', '0', '0.869', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'air_bubble', 'foam', 'froth', 'soap_bubble', 'globule']
senticnet['property_damage'] = ['-0.81', '0', '0.884', '-0.87', '#anger', '#disgust', 'negative', '-0.85', 'lose_war', 'beaten', 'cost_fortune', 'bad_grade', 'military_action']
senticnet['property_magnate'] = ['0', '0', '-0.99', '-0.73', '#fear', '#disgust', 'negative', '-0.86', 'king', 'baron', 'business_leader', 'mogul', 'big_businessman']
senticnet['property_owner'] = ['0.621', '-0.60', '0', '0.545', '#joy', '#surprise', 'positive', '0.59', 'privilege', 'perquisite', 'public_easement', 'right_of_way', 'prerogative']
senticnet['prophecy'] = ['0.958', '-0.50', '0', '0.527', '#joy', '#admiration', 'positive', '0.664', 'divination', 'anticipation', 'prevision', 'crystal_gazing', 'foretelling']
senticnet['prophesy'] = ['0.927', '-0.89', '0', '0.604', '#joy', '#surprise', 'positive', '0.809', 'vaticinate', 'call', 'enlighten', 'irradiate', 'predict']
senticnet['prophet'] = ['0', '0.264', '-0.07', '0.219', '#interest', '#admiration', 'positive', '0.136', 'bless', 'pastor', 'gospel', 'saint', 'church_head']
senticnet['prophet_mosque'] = ['0', '0.224', '0', '0.197', '#interest', '#admiration', 'positive', '0.211', 'church', 'temple', 'cathedral', 'chapel', 'confession']
senticnet['prophetess'] = ['0.676', '-0.51', '0', '0.821', '#joy', '#admiration', 'positive', '0.671', 'diviner', 'oracle', 'auspex', 'augur', 'sibyl']
senticnet['prophetic'] = ['-0.95', '0', '0.739', '-0.59', '#sadness', '#anger', 'negative', '-0.76', 'foreboding', 'precursory', 'premonitory', 'apocalyptic', 'apocalyptical']
senticnet['prophetic_religion'] = ['-0.57', '0', '0.791', '-0.74', '#anger', '#disgust', 'negative', '-0.70', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['prophetical'] = ['-0.81', '0', '0.871', '-0.94', '#anger', '#disgust', 'negative', '-0.87', 'foreboding', 'precursory', 'premonitory', 'apocalyptic', 'apocalyptical']
senticnet['prophetically'] = ['0.951', '0', '0', '0.731', '#joy', '#admiration', 'positive', '0.841', 'metaphorically', 'symbolically', 'figuratively', 'allegorically', 'abstractly']
senticnet['prophylactic'] = ['-0.60', '0', '0.544', '-0.62', '#sadness', '#disgust', 'negative', '-0.59', 'remedy', 'cure', 'therapeutic', 'antidote', 'atropine']
senticnet['prophylactic_therapy'] = ['-0.05', '0', '-0.22', '-0.06', '#fear', '#disgust', 'negative', '-0.11', 'medicine', 'cure', 'medical_procedure', 'sleep_drug', 'nurse']
senticnet['prophylactic_treatment'] = ['-0.05', '0', '-0.22', '-0.06', '#fear', '#disgust', 'negative', '-0.11', 'medicine', 'cure', 'medical_procedure', 'sleep_drug', 'nurse']
senticnet['propionic_acidemia'] = ['-0.54', '0', '0.92', '-0.84', '#anger', '#disgust', 'negative', '-0.76', 'dearth', 'demand', 'mineral_deficiency', 'famine', 'need']
senticnet['propitiate'] = ['-0.80', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.84', 'pacify', 'appease', 'placate', 'reconcile', 'resignedly']
senticnet['propitiation'] = ['-0.97', '0', '0.772', '-0.67', '#sadness', '#anger', 'negative', '-0.80', 'expiation', 'atonement', 'deliverance', 'redemption', 'rescue']
senticnet['propitiatory'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'reverence', 'obeisance', 'genuflexion', 'kowtow', 'mafia']
senticnet['propitiatory_sacrifice'] = ['-0.97', '0', '0.772', '-0.67', '#sadness', '#anger', 'negative', '-0.80', 'expiation', 'atonement', 'deliverance', 'redemption', 'rescue']
senticnet['propitious'] = ['0.736', '-0.66', '0', '0.777', '#joy', '#admiration', 'positive', '0.725', 'lucky', 'gracious', 'prosperous', 'favorable', 'good_luck']
senticnet['propitiousness'] = ['0.905', '-0.59', '0', '0.648', '#joy', '#admiration', 'positive', '0.716', 'profitableness', 'favorableness', 'luck', 'advantageousness', 'positivity']
senticnet['proponent'] = ['0.668', '-0.53', '0', '0.706', '#joy', '#admiration', 'positive', '0.637', 'economic_libertarian', 'libertarian', 'advocate', 'advocator', 'civil_libertarian']
senticnet['proponent_say'] = ['0.808', '-0.83', '0', '0.548', '#joy', '#surprise', 'positive', '0.73', 'successfulness', 'luckiness', 'prosperity', 'circumstance', 'blessing']
senticnet['proportion'] = ['0', '0.931', '0', '0.746', '#interest', '#admiration', 'positive', '0.839', 'symmetricalness', 'correspondence', 'symmetry', 'isotropy', 'balance']
senticnet['proportionable'] = ['0.902', '-0.72', '0', '0.919', '#joy', '#admiration', 'positive', '0.849', 'preciousness', 'costliness', 'expensiveness', 'cost', 'lavishness']
senticnet['proportionably'] = ['0.902', '-0.72', '0', '0.919', '#joy', '#admiration', 'positive', '0.849', 'preciousness', 'costliness', 'expensiveness', 'cost', 'lavishness']
senticnet['proportional'] = ['0', '0.119', '0', '0.106', '#interest', '#admiration', 'positive', '0.112', 'divide', 'numerator', 'subtraction', 'math_action', 'division']
senticnet['proportional_representation'] = ['0', '0.157', '0', '0.147', '#interest', '#admiration', 'positive', '0.152', 'wall_hang', 'depiction', 'illustration', 'artwork', 'wall_decoration']
senticnet['proportionality_constant'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'newton', 'sthene', 'force_unit']
senticnet['proportionality_principle'] = ['0.5', '-0.9', '0', '0.525', '#surprise', '#admiration', 'positive', '0.642', 'manageable', 'controllable', 'governable', 'steerable', 'dirigible']
senticnet['proportionally'] = ['0', '0.119', '0', '0.106', '#interest', '#admiration', 'positive', '0.112', 'divide', 'numerator', 'subtraction', 'math_action', 'division']
senticnet['proportionate'] = ['0', '0', '0.868', '-0.78', '#anger', '#disgust', 'negative', '-0.82', 'distemper', 'disproportionate', 'unfair', 'proportion', 'disproportional']
senticnet['proportionate_share'] = ['-0.04', '0.268', '0', '0.159', '#interest', '#admiration', 'positive', '0.127', 'total', 'amount', 'a_lot', 'aggregate', 'bill']
senticnet['proportionately'] = ['0.918', '0.967', '0', '0', '#joy', '#interest', 'positive', '0.943', 'unequally', 'apportioned', 'apportion', 'equitably', 'unevenly']
senticnet['proposal'] = ['-0.02', '0.168', '-0.14', '0.329', '#interest', '#admiration', 'positive', '0.081', 'engage', 'suggest', 'interaction', 'promise', 'engagement']
senticnet['proposal_focus'] = ['0', '0.812', '0', '0.785', '#interest', '#admiration', 'positive', '0.798', 'development', 'sponsor', 'telecommunication', 'standard_grant', 'proposal']
senticnet['proposal_plan'] = ['0', '0.878', '0', '0.776', '#interest', '#admiration', 'positive', '0.827', 'telecommunication', 'network_infrastructure', 'support', 'university', 'proposal']
senticnet['propose'] = ['0', '0.82', '0', '0.707', '#interest', '#admiration', 'positive', '0.763', 'cope', 'promise', 'oath', 'ask', 'affirmation']
senticnet['propose_marriage'] = ['0', '0.738', '0', '0.744', '#interest', '#admiration', 'positive', '0.741', 'celebrate', 'display_wealth', 'engagement', 'symbolism', 'marriage_ceremony']
senticnet['propose_woman'] = ['0.702', '0', '0', '0.928', '#joy', '#admiration', 'positive', '0.815', 'commit', 'kiss', 'commit_to_memory', 'love', 'marriage']
senticnet['propose_work'] = ['0', '0.762', '0', '0.989', '#interest', '#admiration', 'positive', '0.875', 'development', 'study', 'result', 'determine', 'objective']
senticnet['proposed_fy_budget'] = ['-0.05', '0.282', '-0.04', '0.107', '#interest', '#admiration', 'positive', '0.072', 'expense', 'dollar_cent', 'dollar_sign', 'amount_pay', 'payment_amount']
senticnet['proposed_research'] = ['0.837', '0.76', '0', '0', '#joy', '#interest', 'positive', '0.798', 'objective', 'research', 'development', 'determine', 'study']
senticnet['proposed_study'] = ['0', '0.99', '0', '0.923', '#interest', '#admiration', 'positive', '0.957', 'study', 'determine', 'result', 'well', 'sponsor']
senticnet['proposed_work'] = ['-0.99', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.96', 'work', 'number', 'effect', 'investigate', 'role']
senticnet['proposer'] = ['0', '0.985', '0', '0.749', '#interest', '#admiration', 'positive', '0.867', 'aim', 'purport', 'pop_the_question', 'propose', 'offer']
senticnet['proposition'] = ['0.021', '0.286', '-0.21', '0.387', '#interest', '#admiration', 'positive', '0.119', 'tell_everyone', 'make_better', 'make_right', 'practice', 'suggest']
senticnet['propositional'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'integrative', 'consolidative', 'unifying', 'combinatorial']
senticnet['propositional_content'] = ['0.633', '-0.67', '0', '0.776', '#surprise', '#admiration', 'positive', '0.693', 'double_entendre', 'loophole', 'ambiguity', 'amphiboly', 'parisology']
senticnet['propositional_representation'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'integrative', 'consolidative', 'unifying', 'combinatorial']
senticnet['propped_against'] = ['0.071', '0.127', '0', '0.197', '#interest', '#admiration', 'positive', '0.132', 'place_work', 'workplace', 'office', 'work', 'hang_wall']
senticnet['propped_open'] = ['0.829', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.908', 'cut_food', 'drink_beer', 'serve_drink', 'drink_much', 'drink_beverage']
senticnet['propped_upright'] = ['0', '0.171', '-0.05', '0.102', '#interest', '#admiration', 'positive', '0.072', 'bed', 'mattress', 'bead', 'bed_cover', 'seduce']
senticnet['propre'] = ['0.911', '-0.94', '0', '0.953', '#surprise', '#admiration', 'positive', '0.935', 'common_sense', 'good_sense', 'horse_sense', 'mother_wit', 'gumption']
senticnet['proprietary'] = ['0', '0.243', '0', '0.218', '#interest', '#admiration', 'positive', '0.23', 'ware', 'goods', 'result', 'good_price', 'bargain']
senticnet['proprietary_technology'] = ['0', '0.243', '0', '0.218', '#interest', '#admiration', 'positive', '0.23', 'ware', 'goods', 'result', 'good_price', 'bargain']
senticnet['proprietor'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'pharmaceutical_chemist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['proprietorship'] = ['0', '0', '-0.83', '-0.98', '#fear', '#disgust', 'negative', '-0.90', 'publicly-traded', 'owner-managed', 'publicly-held', 'closely-held', 'middle-market']
senticnet['propriety'] = ['0.509', '-0.63', '0', '0.77', '#surprise', '#admiration', 'positive', '0.639', 'decency', 'decorum', 'decorousness', 'correctness', 'seemliness']
senticnet['proprioception'] = ['0', '0.131', '-0.08', '0.239', '#interest', '#admiration', 'positive', '0.096', 'human_sense', 'ear_activity', 'vibration', 'five_senses', 'sensor']
senticnet['proprioceptive'] = ['0', '0.131', '-0.08', '0.239', '#interest', '#admiration', 'positive', '0.096', 'human_sense', 'ear_activity', 'vibration', 'five_senses', 'sensor']
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senticnet['propter'] = ['0.654', '-0.75', '0', '0.78', '#surprise', '#admiration', 'positive', '0.729', 'suit', 'cause', 'lawsuit', 'occurrence', 'countersuit']
senticnet['propulsion'] = ['0', '0', '0', '0.135', '#admiration', '#admiration', 'positive', '0.135', 'water_activity', 'boat_type', 'athletic_activity', 'outdoor_activity', 'backstroke']
senticnet['propulsion_system'] = ['0', '0', '0', '0.073', '#admiration', '#admiration', 'positive', '0.073', 'fire_starter', 'organic_substance', 'renewable_resource', 'use_fuel', 'tree_product']
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senticnet['propulsive_force'] = ['0.676', '0.63', '-0.23', '0.31', '#joy', '#interest', 'positive', '0.345', 'perkiness', 'walk_on_air', 'exultingly', 'exultantly', 'jubilance']
senticnet['propylene_glycol'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
senticnet['propylene_oxide'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'carboxylic_acid', 'succinic_acid', 'valeric_acid']
senticnet['propylparaben'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
senticnet['prorogation'] = ['-0.57', '0', '0.576', '-0.78', '#anger', '#disgust', 'negative', '-0.64', 'reprieve', 'respite', 'suspend', 'call', 'delay']
senticnet['prorogue'] = ['-0.57', '0', '0.576', '-0.78', '#anger', '#disgust', 'negative', '-0.64', 'reprieve', 'respite', 'suspend', 'call', 'delay']
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senticnet['prosaic'] = ['0', '0', '-0.92', '-0.95', '#fear', '#disgust', 'negative', '-0.94', 'matter-of-fact', 'commonplace', 'pedestrian', 'prosy', 'earthbound']
senticnet['prosaicness'] = ['0.74', '-0.74', '0', '0.665', '#joy', '#surprise', 'positive', '0.718', 'commonplaceness', 'mundanity', 'prosiness', 'mundaneness', 'ordinariness']
senticnet['proscribe'] = ['0', '0', '-0.9', '-0.79', '#fear', '#disgust', 'negative', '-0.84', 'forbid', 'prohibit', 'veto', 'disallow', 'interdict']
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senticnet['prose'] = ['0.048', '0', '0', '0.058', '#joy', '#admiration', 'positive', '0.053', 'poetry', 'scripture', 'chapter', 'rhyme', 'print_material']
senticnet['prose_fiction'] = ['0.587', '-0.94', '0', '0.659', '#surprise', '#admiration', 'positive', '0.731', 'critique', 'review', 'criticism', 'textual_criticism', 'new_criticism']
senticnet['prose_style'] = ['0.99', '0', '0', '0.716', '#joy', '#admiration', 'positive', '0.853', 'encyclopedism', 'learning', 'scholarship', 'learnedness', 'eruditeness']
senticnet['prose_stylist'] = ['0.99', '0', '0', '0.716', '#joy', '#admiration', 'positive', '0.853', 'encyclopedism', 'learning', 'scholarship', 'learnedness', 'eruditeness']
senticnet['prosecco'] = ['0.161', '-0.14', '0.031', '-0.10', '#joy', '#surprise', 'positive', '0.043', 'alcohol', 'reception', 'champagne', 'beer', 'drink']
senticnet['prosecute'] = ['-0.98', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.88', 'prosecution', 'fiscal', 'proceeding', 'persecute', 'prosecutor']
senticnet['prosecuting_attorney'] = ['0', '0.146', '-0.05', '0.077', '#interest', '#admiration', 'positive', '0.057', 'lawyer', 'negotiator', 'arbiter', 'judge_jury', 'decide_guilt']
senticnet['prosecution'] = ['-0.99', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.88', 'prosecute', 'fiscal', 'proceeding', 'persecute', 'prosecutor']
senticnet['prosecutor'] = ['0.024', '0.141', '0', '0.082', '#interest', '#admiration', 'positive', '0.082', 'arbiter', 'jury', 'gavel', 'judge_jury', 'lawyer']
senticnet['prosecutorial_agency'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'constabulary', 'law', 'agency']
senticnet['prosecutorial_misconduct'] = ['-0.86', '0', '0.897', '-0.81', '#sadness', '#anger', 'negative', '-0.85', 'disregard', 'ignore', 'brush_off', 'push_aside', 'flout']
senticnet['proselyte'] = ['0', '-0.00', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'hot_chick', 'atheist', 'computer_user', 'fastener_binder', 'monitor']
senticnet['proselytize'] = ['0.738', '0.898', '0', '0.731', '#joy', '#interest', 'positive', '0.789', 'plan_trip', 'desire_social_interaction', 'see_different', 'buy_airline_ticket', 'improve_knowledge']
senticnet['proser'] = ['-0.89', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.89', 'tediously', 'spout', 'tedious', 'irksomely', 'wearisomely']
senticnet['prosimian'] = ['0.921', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.825', 'monkey', 'fiance', 'lemur', 'beaver', 'koala']
senticnet['prosiness'] = ['0.739', '-0.74', '0', '0.659', '#joy', '#surprise', 'positive', '0.715', 'commonplaceness', 'mundanity', 'mundaneness', 'prosaicness', 'ordinariness']
senticnet['prosocial'] = ['-0.93', '0', '0.586', '-0.66', '#sadness', '#disgust', 'negative', '-0.73', 'camp', 'pack', 'clique', 'coterie', 'brain_trust']
senticnet['prosocial_behavior'] = ['0.936', '0.757', '0', '0.927', '#joy', '#admiration', 'positive', '0.873', 'attitude', 'better', 'trait', 'blue_ribbon', 'exclamation']
senticnet['prosodic'] = ['-0.67', '0', '0.97', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'joint', 'node', 'connexion', 'join', 'juncture']
senticnet['prosody'] = ['-0.67', '0', '0.97', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'joint', 'node', 'connexion', 'join', 'juncture']
senticnet['prosopagnosia'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'nominal_aphasia', 'anomic_aphasia']
senticnet['prospect'] = ['0.909', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.876', 'coyote', 'player', 'drift', 'hopeful', 'team']
senticnet['prospect_avenue'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'boulevard', 'lane', 'thoroughfare', 'intersection', 'curb']
senticnet['prospective_bride'] = ['0', '0.819', '0', '0.987', '#interest', '#admiration', 'positive', '0.903', 'celebrate', 'wedding_ceremony', 'propose_marriage', 'engage', 'engagement']
senticnet['prospective_employer'] = ['-0.06', '0.149', '-0.09', '0.181', '#interest', '#admiration', 'positive', '0.044', 'ask_nicely', 'job_offer', 'question_ask', 'make_happen', 'make_effort']
senticnet['prospective_juror'] = ['0', '0.104', '0', '0.075', '#interest', '#admiration', 'positive', '0.089', 'judge', 'negotiator', 'jury', 'arbiter', 'member_jury']
senticnet['prospective_participant'] = ['0.072', '0', '0', '0.078', '#joy', '#admiration', 'positive', '0.075', 'participate', 'examine', 'voyeur', 'influence_public_decision', 'question_everything']
senticnet['prospectively'] = ['0', '0.889', '0', '0.944', '#interest', '#admiration', 'positive', '0.916', 'retroactive', 'retrospectively', 'reinstated', 'retroactively', 'reinstate']
senticnet['prospector'] = ['-0.62', '0', '0.504', '-0.79', '#sadness', '#disgust', 'negative', '-0.64', 'green_gold', 'pure_gold', 'gold', 'guinea_gold', 'noble_metal']
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senticnet['prosper_economically'] = ['0.837', '0', '0', '0', '#joy', '#joy', 'positive', '0.837', 'a_lot_of_sex', 'desirable', 'give_birth_child', 'sexual_intercourse', 'diversity']
senticnet['prospering'] = ['0.972', '-0.75', '0', '0.982', '#joy', '#admiration', 'positive', '0.902', 'prosperous', 'flourishing', 'successful', 'palmy', 'thriving']
senticnet['prosperity'] = ['0.989', '0', '0', '0', '#joy', '#joy', 'positive', '0.989', 'look_beautiful', 'luck', 'good_luck', 'consistency', 'fame']
senticnet['prosperous'] = ['0.973', '-0.74', '0', '0.974', '#joy', '#admiration', 'positive', '0.898', 'palmy', 'flourishing', 'successful', 'prospering', 'thriving']
senticnet['prosperous_future'] = ['0.837', '0', '0', '0', '#joy', '#joy', 'positive', '0.837', 'a_lot_of_sex', 'desirable', 'give_birth_child', 'sexual_intercourse', 'diversity']
senticnet['prosperous_nation'] = ['0.716', '-0.51', '0', '0.647', '#joy', '#admiration', 'positive', '0.624', 'state', 'nation', 'ally', 'land', 'commonwealth']
senticnet['prosperously'] = ['0.621', '0.648', '-0.25', '0.296', '#joy', '#interest', 'positive', '0.328', 'pacifically', 'repose', 'peace-loving', 'peaceably', 'peace_of_mind']
senticnet['prostate_enlargement'] = ['-0.85', '0', '-0.71', '-0.86', '#sadness', '#disgust', 'negative', '-0.81', 'get_exercise', 'train_marathon', 'run_marathon', 'run_long_distance', 'go_gym']
senticnet['prostate_gland'] = ['0.087', '-0.03', '0', '0.063', '#joy', '#admiration', 'positive', '0.062', 'puppet', 'bouncy', 'curly', 'throw_object', 'glove_leather']
senticnet['prostate_stimulation'] = ['0.733', '0.789', '0', '0', '#joy', '#interest', 'positive', '0.761', 'sex', 'show_empathy', 'safe_sex', 'fuck', 'love_back']
senticnet['prostatitis'] = ['-0.99', '0', '0.964', '-0.70', '#sadness', '#anger', 'negative', '-0.88', 'blepharitis', 'diverticulitis', 'epiglottitis', 'orchitis', 'inflammation']
senticnet['prosthesis'] = ['-0.03', '0', '-0.09', '-0.14', '#fear', '#disgust', 'negative', '-0.09', 'x-ray_machine', 'artificial_arm', 'ambulance', 'syringe', 'latex_glove']
senticnet['prosthetic'] = ['-0.03', '0', '-0.09', '-0.14', '#fear', '#disgust', 'negative', '-0.09', 'x-ray_machine', 'artificial_arm', 'ambulance', 'syringe', 'latex_glove']
senticnet['prosthetic_arm'] = ['-0.62', '0', '0.996', '-0.68', '#anger', '#disgust', 'negative', '-0.76', 'pack', 'face_pack', 'pickup', 'prosthesis', 'corrective']
senticnet['prosthetic_device'] = ['-0.62', '0', '0.996', '-0.68', '#anger', '#disgust', 'negative', '-0.76', 'pack', 'face_pack', 'pickup', 'prosthesis', 'corrective']
senticnet['prosthetic_feet'] = ['-0.03', '0', '-0.09', '-0.14', '#fear', '#disgust', 'negative', '-0.09', 'x-ray_machine', 'artificial_arm', 'ambulance', 'syringe', 'latex_glove']
senticnet['prosthetic_foot'] = ['-0.03', '0', '-0.09', '-0.14', '#fear', '#disgust', 'negative', '-0.09', 'x-ray_machine', 'artificial_arm', 'ambulance', 'syringe', 'latex_glove']
senticnet['prosthetic_hand'] = ['-0.62', '0', '0.996', '-0.68', '#anger', '#disgust', 'negative', '-0.76', 'pack', 'face_pack', 'pickup', 'prosthesis', 'corrective']
senticnet['prosthetic_leg'] = ['-0.03', '0', '-0.09', '-0.14', '#fear', '#disgust', 'negative', '-0.09', 'x-ray_machine', 'artificial_arm', 'ambulance', 'syringe', 'latex_glove']
senticnet['prosthetic_limb'] = ['-0.03', '0', '-0.09', '-0.14', '#fear', '#disgust', 'negative', '-0.09', 'x-ray_machine', 'artificial_arm', 'ambulance', 'syringe', 'latex_glove']
senticnet['prosthetic_tail'] = ['-0.20', '-0.10', '0.07', '-0.24', '#sadness', '#disgust', 'negative', '-0.10', 'gill', 'sea_life', 'seafood', 'swim_animal', 'fillet']
senticnet['prosthodontic'] = ['-0.03', '0.079', '0.024', '0.091', '#interest', '#admiration', 'positive', '0.029', 'toothpaste', 'toothbrush', 'mouth', 'gel_toothpaste', 'tooth']
senticnet['prostitute'] = ['-0.88', '0', '0.606', '-0.73', '#sadness', '#disgust', 'negative', '-0.73', 'slattern', 'camp_follower', 'call_girl', 'comfort_woman', 'ianfu']
senticnet['prostitution'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'vassalage']
senticnet['prostrate'] = ['-1', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.89', 'devastate', 'devastating', 'devastator', 'languish', 'pestilence']
senticnet['prostrate_form'] = ['0', '0', '-0.14', '-0.03', '#fear', '#disgust', 'negative', '-0.09', 'pine_cone', 'fallen_leaf', 'poison_ivy', 'detritus', 'tress']
senticnet['prostrate_shrub'] = ['-0.80', '0', '0.555', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'bush', 'wormwood', 'milkwort', 'germander', 'centaury']
senticnet['prostration'] = ['-0.93', '0', '0.807', '-0.95', '#sadness', '#disgust', 'negative', '-0.89', 'heatstroke', 'heat_hyperpyrexia', 'malady', 'breakdown', 'shock']
senticnet['prostyle'] = ['-0.65', '0', '0.878', '-0.86', '#anger', '#disgust', 'negative', '-0.80', 'amphistylar', 'amphiprostylar', 'amphiapteral', 'porticoed', 'apteral']
senticnet['prosy'] = ['0', '0', '-0.93', '-0.95', '#fear', '#disgust', 'negative', '-0.94', 'matter-of-fact', 'commonplace', 'pedestrian', 'prosaic', 'earthbound']
senticnet['protagonist'] = ['0', '0', '-0.99', '-0.96', '#fear', '#disgust', 'negative', '-0.97', 'champion', 'admirer', 'booster', 'friend', 'supporter']
senticnet['protandrous_hermaphrodite'] = ['-0.87', '0', '0.983', '-0.91', '#anger', '#disgust', 'negative', '-0.92', 'thorn', 'sticker', 'pricker', 'prickle', 'spikelet']
senticnet['protean'] = ['0.273', '0.226', '-0.13', '0.332', '#joy', '#admiration', 'positive', '0.173', 'morality', 'cagey', 'uncommon', 'evasive', 'capitalize']
senticnet['protease_activity'] = ['0.772', '-0.56', '0', '0.824', '#joy', '#admiration', 'positive', '0.719', 'neutrophil', 'leukocyte', 'leucocyte', 'white_blood_cell', 'white_cell']
senticnet['protease_inhibitor'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'indinavir', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['protease_inhibitor_pi'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'indinavir', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['proteasomal_degradation'] = ['-0.84', '0', '0.8', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'rigor_mortis', 'natural_process', 'natural_action', 'activity', 'action']
senticnet['protect'] = ['0', '0', '0', '0.802', '#admiration', '#admiration', 'positive', '0.802', 'secure', 'defend', 'watch_over', 'guard', 'stronghold']
senticnet['protect_body'] = ['-0.03', '-0.05', '0.104', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'make_cloth', 'make_clothes', 'weave', 'weave_cloth', 'wear']
senticnet['protect_child'] = ['0', '0', '0', '0.802', '#admiration', '#admiration', 'positive', '0.802', 'raise_child', 'teach_child', 'lock_front_door', 'understand_idea', 'protection']
senticnet['protect_country'] = ['0', '0', '0', '0.802', '#admiration', '#admiration', 'positive', '0.802', 'kill_enemy', 'protection', 'conquer_enemy', 'self_defense', 'shield']
senticnet['protect_environment'] = ['0', '0.738', '0', '0.896', '#interest', '#admiration', 'positive', '0.817', 'protection', 'offer_help', 'shield', 'greenpeace', 'activist']
senticnet['protect_eye_sun'] = ['0', '0', '0', '0.802', '#admiration', '#admiration', 'positive', '0.802', 'shade_eye', 'cover_window', 'protection', 'block_light', 'shade']
senticnet['protect_family'] = ['0.852', '0', '0', '0', '#joy', '#joy', 'positive', '0.852', 'protection', 'brave', 'gain_power', 'shield', 'patriot']
senticnet['protect_foot'] = ['0', '0', '0', '0.802', '#admiration', '#admiration', 'positive', '0.802', 'keep_foot_warm', 'look_stylish', 'high_heel', 'walk_upon', 'stand_upon']
senticnet['protect_from_sun'] = ['-0.93', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.86', 'concealer', 'shade', 'meaning', 'corrector', 'sunglasses']
senticnet['protect_head'] = ['0', '0.933', '0', '0.817', '#interest', '#admiration', 'positive', '0.875', 'prevent_sunburn', 'cover_head', 'keep_hair_place', 'shade_eye', 'cover_body']
senticnet['protect_home'] = ['0', '0', '0', '0.802', '#admiration', '#admiration', 'positive', '0.802', 'purpose_container', 'home_improvement', 'protection', 'please_eye', 'protect']
senticnet['protect_house'] = ['0', '0', '0', '0.802', '#admiration', '#admiration', 'positive', '0.802', 'record', 'wall_decoration', 'form_of_art', 'creative_activity', 'ideal']
senticnet['protect_king'] = ['0', '0', '0', '0.802', '#admiration', '#admiration', 'positive', '0.802', 'play_game', 'play_card', 'play_chess', 'play_game_chess', 'play_blackjack']
senticnet['protect_place'] = ['-0.73', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.76', 'seed_lawn', 'fence_yard', 'hurry_work', 'air_grievance', 'sail_around_world']
senticnet['protect_rain'] = ['0', '0', '0', '0.802', '#admiration', '#admiration', 'positive', '0.802', 'modesty', 'make_dress', 'cover_table', 'hide', 'protect_body']
senticnet['protect_surface'] = ['0', '0.841', '0', '0.872', '#interest', '#admiration', 'positive', '0.857', 'make_pretty', 'cover_table', 'cover', 'protection', 'paint_nail']
senticnet['protect_wildlife_habitat'] = ['0', '0.243', '-0.11', '0.201', '#interest', '#admiration', 'positive', '0.108', 'upkeep', 'contain', 'keep_safe', 'keep', 'prevent']
senticnet['protect_wood'] = ['0', '0', '0', '0.802', '#admiration', '#admiration', 'positive', '0.802', 'dry', 'coat_wall', 'artist_tool', 'palette', 'coat_surface']
senticnet['protectant'] = ['-0.69', '0', '0.516', '-0.94', '#sadness', '#disgust', 'negative', '-0.71', 'insubordinate', 'recalcitrant', 'obstreperous', 'defiant', 'resistive']
senticnet['protected'] = ['-0.9', '0', '0.627', '-0.55', '#sadness', '#anger', 'negative', '-0.69', 'shielded', 'weatherproof', 'covert', 'stormproof', 'fortified']
senticnet['protected_cruiser'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'submarine', 'lifeboat', 'titanic', 'large_ship', 'canoe']
senticnet['protected_nature_reserve'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['protecting_biodiversity'] = ['0', '0', '0', '0.857', '#admiration', '#admiration', 'positive', '0.857', 'say', 'beckon', 'call', 'conserve', 'retort']
senticnet['protecting_free_speech'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'economic_libertarian', 'libertarian', 'advocate', 'advocator', 'proponent']
senticnet['protection'] = ['0', '0.995', '0', '0.99', '#interest', '#admiration', 'positive', '0.992', 'defend', 'shield', 'take_care', 'watch_over', 'protect']
senticnet['protection_sun'] = ['0', '0.818', '0', '0.819', '#interest', '#admiration', 'positive', '0.819', 'hide', 'keep_body_warm', 'outerwear', 'make_fashion_statement', 'protection']
senticnet['protection_weather'] = ['0', '0', '0', '0.802', '#admiration', '#admiration', 'positive', '0.802', 'protection', 'umbrella', 'raise_family', 'live_life', 'live_inside']
senticnet['protective'] = ['0', '0', '0', '0.802', '#admiration', '#admiration', 'positive', '0.802', 'protectiveness', 'protectively', 'protection', 'fondness', 'shield']
senticnet['protective_armor'] = ['0.939', '-0.63', '0', '0.653', '#joy', '#admiration', 'positive', '0.741', 'vest', 'waistcoat', 'bulletproof_vest', 'suit_of_armor', 'coat_of_mail']
senticnet['protective_barrier'] = ['-0.93', '0', '-0.99', '-0.92', '#sadness', '#fear', 'negative', '-0.95', 'language_barrier', 'shut', 'shut_down', 'close_down', 'obstacle']
senticnet['protective_body_armor'] = ['0.939', '-0.63', '0', '0.653', '#joy', '#admiration', 'positive', '0.741', 'vest', 'waistcoat', 'bulletproof_vest', 'suit_of_armor', 'coat_of_mail']
senticnet['protective_coloration'] = ['0.615', '-0.6', '0', '0.961', '#joy', '#admiration', 'positive', '0.725', 'aposematic_coloration', 'warning_coloration', 'apatetic_coloration', 'cryptic_coloration', 'coloration']
senticnet['protective_cover'] = ['0', '0.888', '0', '0.997', '#interest', '#admiration', 'positive', '0.943', 'mask', 'blind', 'cover', 'protection', 'protective']
senticnet['protective_covering'] = ['0.935', '-0.80', '0', '0.626', '#joy', '#surprise', 'positive', '0.79', 'protective_cover', 'cladding', 'protection', 'revetment', 'cover']
senticnet['protective_equipment'] = ['0.082', '0.319', '-0.10', '0.147', '#interest', '#admiration', 'positive', '0.11', 'protect', 'defend', 'secure', 'guard', 'watch_over']
senticnet['protective_fold'] = ['0', '0', '0', '0.802', '#admiration', '#admiration', 'positive', '0.802', 'flap', 'eyelid', 'lid', 'palpebra', 'protection']
senticnet['protective_garment'] = ['0', '0', '0', '0.802', '#admiration', '#admiration', 'positive', '0.802', 'wear', 'biohazard_suit', 'coverall', 'shoulder_pad', 'wearable']
senticnet['protective_gear'] = ['0', '0', '0', '0.802', '#admiration', '#admiration', 'positive', '0.802', 'knight_protection', 'gold', 'chrome', 'foil', 'protection']
senticnet['protective_helmet'] = ['-0.97', '0', '0.54', '-0.55', '#sadness', '#disgust', 'negative', '-0.69', 'ablate', 'ablate_off', 'ablate_out', 'ablate_down', 'ablate_thin']
senticnet['protective_immunity'] = ['0', '0', '0', '0.816', '#admiration', '#admiration', 'positive', '0.816', 'resistance', 'protection', 'untouchable', 'invulnerable', 'sacrosanct']
senticnet['protective_mask'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'wear', 'protective_garment', 'coverall', 'shoulder_pad', 'wearable']
senticnet['protective_sheath'] = ['-0.03', '0', '0', '-0.11', '#sadness', '#disgust', 'negative', '-0.07', 'store_cloth', 'clothes_factory', 'stock', 'sword', 'gun_store']
senticnet['protective_suit'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'wear', 'protective_garment', 'coverall', 'shoulder_pad', 'wearable']
senticnet['protective_vest'] = ['0.939', '-0.63', '0', '0.653', '#joy', '#admiration', 'positive', '0.741', 'vest', 'waistcoat', 'bulletproof_vest', 'suit_of_armor', 'coat_of_mail']
senticnet['protectively'] = ['0', '0', '0', '0.802', '#admiration', '#admiration', 'positive', '0.802', 'protectiveness', 'soft_spot', 'fond_regard', 'affectionateness', 'warmness']
senticnet['protectiveness'] = ['0', '0', '0', '0.802', '#admiration', '#admiration', 'positive', '0.802', 'protectively', 'soft_spot', 'fond_regard', 'affectionateness', 'warmness']
senticnet['protector'] = ['0', '0', '0', '0.841', '#admiration', '#admiration', 'positive', '0.841', 'almighty', 'omniscient', 'deity', 'god', 'baron']
senticnet['proteg'] = ['0.814', '-0.50', '0', '0.622', '#joy', '#admiration', 'positive', '0.648', 'friend', 'champion', 'supporter', 'protagonist', 'endorser']
senticnet['protegee'] = ['0.814', '-0.50', '0', '0.622', '#joy', '#admiration', 'positive', '0.648', 'friend', 'champion', 'supporter', 'protagonist', 'endorser']
senticnet['protein'] = ['0.829', '0.878', '0', '0', '#joy', '#interest', 'positive', '0.853', 'study', 'function', 'determine', 'project', 'structure']
senticnet['protein_adsorption'] = ['0.89', '-0.62', '0', '0.548', '#joy', '#surprise', 'positive', '0.686', 'breakthrough', 'interpenetration', 'foray', 'maraud', 'raid']
senticnet['protein_calorie_malnutrition'] = ['-0.71', '0', '0.74', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'malnutrition', 'marasmus', 'avitaminosis', 'hypovitaminosis', 'deficiency_disease']
senticnet['protein_deficiency'] = ['-0.54', '0', '0.92', '-0.84', '#anger', '#disgust', 'negative', '-0.76', 'dearth', 'demand', 'mineral_deficiency', 'famine', 'need']
senticnet['protein_losing_enteropathy'] = ['-0.71', '0', '0.74', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'kwashiorkor', 'marasmus', 'malnutrition', 'hypovitaminosis', 'deficiency_disease']
senticnet['protein_malnutrition'] = ['-0.71', '0', '0.74', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'kwashiorkor', 'marasmus', 'malnutrition', 'hypovitaminosis', 'deficiency_disease']
senticnet['protein_metabolism'] = ['0.992', '-0.80', '0', '0.584', '#joy', '#surprise', 'positive', '0.795', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'incontinence']
senticnet['protein_synthesis'] = ['0.905', '0.717', '0', '0', '#joy', '#interest', 'positive', '0.811', 'protein', 'regulation', 'mechanism', 'cell', 'mitochondrion']
senticnet['proteobacterium'] = ['0.807', '-0.86', '0', '0.611', '#joy', '#surprise', 'positive', '0.762', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['protest'] = ['0', '0', '0.971', '0', '#anger', '#anger', 'negative', '-0.97', 'angry_mob', 'against', 'gang', 'riot', 'picket_sign']
senticnet['protest_organizer'] = ['-0.93', '0', '0.688', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'dissident', 'dissenter', 'nimby', 'objector', 'contestant']
senticnet['protest_rally'] = ['0.669', '-0.58', '0', '0.803', '#joy', '#admiration', 'positive', '0.685', 'pep_rally', 'revival', 'revival_meeting', 'gathering', 'assemblage']
senticnet['protest_rally_against'] = ['0.669', '-0.58', '0', '0.803', '#joy', '#admiration', 'positive', '0.685', 'pep_rally', 'revival', 'revival_meeting', 'gathering', 'assemblage']
senticnet['protestant'] = ['0', '0.306', '-0.05', '0.287', '#interest', '#admiration', 'positive', '0.18', 'church_head', 'pastor', 'prayer', 'religion', 'religious_official']
senticnet['protestant_ascendancy'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'ascendance', 'autocracy', 'absolutism', 'tyrant']
senticnet['protestant_church'] = ['0.013', '0.199', '0.027', '0.167', '#interest', '#admiration', 'positive', '0.088', 'church', 'confession', 'good_catholic', 'spirituality', 'christianity']
senticnet['protestant_congregation'] = ['0.081', '0.146', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'cult', 'sect', 'conservative', 'worshiper', 'pastor']
senticnet['protestant_orthodoxy'] = ['-0.99', '0', '0.835', '-0.58', '#sadness', '#anger', 'negative', '-0.80', 'beginning', 'origin', 'root', 'wellspring', 'source']
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senticnet['protestant_theologian'] = ['-0.85', '0', '0.749', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'hairsplitter', 'fight', 'quarrel', 'obstructor', 'obstructer']
senticnet['protestant_theological'] = ['0.664', '-0.57', '0', '0.981', '#joy', '#admiration', 'positive', '0.741', 'theodicy', 'homiletics', 'discipline', 'study', 'subject']
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senticnet['protestantism'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'nonconformity', 'iconoclasm', 'heterodoxy', 'nonconformance', 'orientation']
senticnet['protester'] = ['-0.93', '0', '0.688', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'dissident', 'dissenter', 'nimby', 'objector', 'contestant']
senticnet['protester_gather'] = ['-0.93', '0', '0.688', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'dissident', 'dissenter', 'nimby', 'objector', 'contestant']
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senticnet['provision_set_forth'] = ['-0.68', '0', '0.967', '-0.65', '#sadness', '#anger', 'negative', '-0.76', 'depart', 'start', 'part', 'start_out', 'sally_forth']
senticnet['provisional_battalion'] = ['-0.9', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.9', 'withdrawal', 'indifferently', 'apathetic', 'indifferent', 'concerned']
senticnet['provisional_suspension'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'steer_wheel', 'gear_shift', 'chassis', 'brake_pedal', 'air_vent']
senticnet['proviso'] = ['-0.92', '0', '0.547', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'assumption', 'boundary_condition', 'provision', 'precondition', 'premiss']
senticnet['provocateur'] = ['0', '0', '0.997', '0', '#anger', '#anger', 'negative', '-0.99', 'stand_out', 'outstand', 'broadcast', 'egalitarian', 'pioneer']
senticnet['provocation'] = ['-0.75', '0', '0.981', '-0.58', '#sadness', '#anger', 'negative', '-0.77', 'punitive', 'penitentiary', 'correctional', 'retributive', 'retributory']
senticnet['provocative'] = ['-0.09', '-0.15', '0.52', '-0.63', '#anger', '#disgust', 'negative', '-0.27', 'anger', 'injury', 'sore', 'strengthful', 'bruise']
senticnet['provocative_discussion'] = ['-0.96', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.83', 'annoying', 'uncomfortable_situation', 'bad_experience', 'shy', 'become_red']
senticnet['provoke'] = ['-0.59', '0.162', '0.221', '-0.50', '#sadness', '#disgust', 'negative', '-0.28', 'chivvy', 'harass', 'beset', 'annoy', 'poke']
senticnet['provoke_anger'] = ['-0.12', '0.13', '0.647', '-0.64', '#anger', '#disgust', 'negative', '-0.32', 'exasperating', 'infuriating', 'maddening', 'furiously', 'dudgeon']
senticnet['provoke_discussion'] = ['0.853', '-0.71', '0', '0.92', '#joy', '#admiration', 'positive', '0.831', 'find_desk', 'learn_information', 'become_educated', 'learn', 'listen_teacher']
senticnet['provoke_urine'] = ['-0.16', '0.179', '0.568', '-0.61', '#anger', '#disgust', 'negative', '-0.29', 'angrily', 'ire', 'see_red', 'dander', 'hackle']
senticnet['provoked'] = ['-0.12', '0.065', '0.694', '-0.67', '#anger', '#disgust', 'negative', '-0.35', 'exacerbate', 'aggravate', 'aggravated', 'exasperation', 'displeased']
senticnet['provoker'] = ['-0.71', '0', '0.512', '-0.82', '#sadness', '#disgust', 'negative', '-0.68', 'troublemaker', 'trouble_maker', 'troubler', 'bad_hat', 'ringleader']
senticnet['provokes_urine'] = ['0', '0', '0', '-0.96', '#disgust', '#disgust', 'negative', '-0.96', 'thick_substance', 'viscous_substance', 'mucous_substance', 'disgusting', 'disgust']
senticnet['provoking'] = ['0', '-0.72', '0', '0.698', '#surprise', '#admiration', 'positive', '0.712', 'insightful', 'thought-provoking', 'enlightening', 'provocative', 'eye-opening']
senticnet['provost'] = ['0.973', '0.825', '0', '0', '#joy', '#interest', 'positive', '0.899', 'chancellor', 'vice-chancellor', 'vice-president', 'vice', 'faculty']
senticnet['provostship'] = ['0.031', '0.272', '0', '0.231', '#interest', '#admiration', 'positive', '0.178', 'minister', 'church_head', 'pastor', 'protestant', 'evangelist']
senticnet['prowess'] = ['0.858', '-0.95', '0', '0.599', '#joy', '#surprise', 'positive', '0.803', 'eristic', 'puppetry', 'homiletics', 'aviation', 'airmanship']
senticnet['prowl'] = ['0', '-0.70', '0', '0.88', '#surprise', '#admiration', 'positive', '0.79', 'prowling', 'prowler', 'prowl', 'pounce', 'roam']
senticnet['prowler'] = ['0', '-0.71', '0', '0.873', '#surprise', '#admiration', 'positive', '0.791', 'prowling', 'prowl', 'prowl', 'pounce', 'roam']
senticnet['prowling'] = ['0', '-0.70', '0', '0.876', '#surprise', '#admiration', 'positive', '0.791', 'prowl', 'prowler', 'prowl', 'pounce', 'roam']
senticnet['proxemics'] = ['0', '0.848', '0', '0.709', '#interest', '#admiration', 'positive', '0.778', 'sociology', 'home_economics', 'social_science', 'economics', 'anthropology']
senticnet['proximal_end'] = ['-0.03', '0', '0', '-0.11', '#sadness', '#disgust', 'negative', '-0.07', 'store_cloth', 'clothes_factory', 'stock', 'sword', 'gun_store']
senticnet['proximal_fragment'] = ['-0.74', '0', '0.598', '-0.75', '#sadness', '#disgust', 'negative', '-0.69', 'lunate_bone', 'semilunar_bone', 'carpal_bone', 'carpal', 'bone']
senticnet['proximal_phalanx'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'socket', 'tooth_socket', 'alveolus', 'acetabulum', 'cotyloid_cavity']
senticnet['proximal_row'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'hip_socket', 'tooth_socket', 'alveolus', 'socket', 'cotyloid_cavity']
senticnet['proximal_small_bowel'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'anus', 'scrotum', 'shoulder', 'liver', 'waist']
senticnet['proximal_tubule'] = ['0.992', '-0.80', '0', '0.584', '#joy', '#surprise', 'positive', '0.795', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'incontinence']
senticnet['proximate_causation'] = ['-0.91', '0', '0.795', '-0.94', '#sadness', '#disgust', 'negative', '-0.88', 'ineluctability', 'assured', 'predictability', 'slam_dunk', 'inevitability']
senticnet['proximate_cause'] = ['-0.91', '0', '0.795', '-0.94', '#sadness', '#disgust', 'negative', '-0.88', 'ineluctability', 'assured', 'predictability', 'slam_dunk', 'inevitability']
senticnet['proximity'] = ['0.158', '0.22', '0', '0.298', '#interest', '#admiration', 'positive', '0.225', 'long_distance', 'compass_direction', 'afar', 'direction', 'neighbor']
senticnet['proxy'] = ['0', '0.285', '-0.07', '0.281', '#interest', '#admiration', 'positive', '0.162', 'restraint', 'restriction', 'confirmation', 'axiom', 'standard']
senticnet['proxy_credential'] = ['0.026', '0.165', '-0.12', '0.292', '#interest', '#admiration', 'positive', '0.09', 'fetch', 'find', 'desire_win', 'take', 'obtain']
senticnet['proxy_solicitation'] = ['0.069', '0.07', '0', '0.116', '#interest', '#admiration', 'positive', '0.085', 'entrepreneur', 'invest_money', 'move_around', 'traveling_person', 'guilty_person']
senticnet['prsidente'] = ['0.667', '-0.98', '0', '0.61', '#joy', '#surprise', 'positive', '0.753', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['prude'] = ['-0.74', '0', '0.677', '-0.77', '#sadness', '#disgust', 'negative', '-0.73', 'whore', 'jazz', 'hump', 'bonk', 'screw']
senticnet['prudence'] = ['0', '0.89', '0', '0.811', '#interest', '#admiration', 'positive', '0.851', 'discretion', 'frugality', 'frugalness', 'providence', 'judgement']
senticnet['prudent'] = ['0.625', '-0.88', '0', '0.782', '#surprise', '#admiration', 'positive', '0.762', 'judicious', 'discreet', 'prudential', 'heady', 'circumspect']
senticnet['prudential'] = ['0.619', '-0.88', '0', '0.781', '#surprise', '#admiration', 'positive', '0.762', 'judicious', 'prudent', 'discreet', 'heady', 'circumspect']
senticnet['prudential_judgment'] = ['0.632', '-0.80', '0', '0.537', '#joy', '#surprise', 'positive', '0.658', 'incisiveness', 'trenchancy', 'efficacy', 'effectiveness', 'power']
senticnet['prudently'] = ['-0.92', '0', '0.876', '-0.96', '#sadness', '#disgust', 'negative', '-0.92', 'bigmouthed', 'blabby', 'blabbermouthed', 'talkative', 'indiscreet']
senticnet['prudery'] = ['-0.88', '0', '0.666', '-0.63', '#sadness', '#anger', 'negative', '-0.73', 'clannishness', 'cliquishness', 'exclusiveness', 'arrogance', 'haughtiness']
senticnet['prudish'] = ['0', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.90', 'prissy', 'puritanical', 'priggish', 'prim', 'square-toed']
senticnet['prufrock'] = ['-0.77', '0', '0.885', '-0.73', '#sadness', '#anger', 'negative', '-0.8', 'fallacy', 'misconception', 'paralogism', 'logical_fallacy', 'pseudoscience']
senticnet['pruinose'] = ['-0.66', '0', '0.809', '-0.61', '#sadness', '#anger', 'negative', '-0.69', 'face', 'base', 'underbelly', 'sole', 'bilge']
senticnet['prune'] = ['0.76', '-0.72', '0', '0', '#joy', '#surprise', 'positive', '0.744', 'onion', 'tomato', 'pea', 'berry', 'fig']
senticnet['prune_juice'] = ['0.95', '-0.86', '0', '0.947', '#joy', '#admiration', 'positive', '0.92', 'content', 'lore', 'folklore', 'cognitive_content', 'story']
senticnet['prune_tree'] = ['-0.88', '0', '0.778', '-0.79', '#sadness', '#disgust', 'negative', '-0.82', 'climb_mountain', 'skate', 'chop_down_tree', 'ski', 'jump_out_of_window']
senticnet['pruno'] = ['-0.83', '0', '0.647', '-0.94', '#sadness', '#disgust', 'negative', '-0.80', 'intoxicant', 'hootch', 'alcohol', 'alcoholic_drink', 'alcoholic_beverage']
senticnet['prunus_persica'] = ['0.861', '-0.87', '0', '0.597', '#joy', '#surprise', 'positive', '0.779', 'herb', 'cultivated_parsnip', 'wild_parsnip', 'madnep', 'herbaceous_plant']
senticnet['prunus_serotina'] = ['-0.79', '0', '0.906', '-0.67', '#sadness', '#anger', 'negative', '-0.79', 'water_dropwort', 'hemlock_water_dropwort', 'water_hemlock', 'plant_life', 'poisonous_plant']
senticnet['prunus_virginiana'] = ['0.861', '-0.87', '0', '0.597', '#joy', '#surprise', 'positive', '0.779', 'herb', 'cultivated_parsnip', 'wild_parsnip', 'madnep', 'herbaceous_plant']
senticnet['prurience'] = ['0.996', '-0.86', '0', '0.673', '#joy', '#surprise', 'positive', '0.846', 'amorousness', 'sexiness', 'amativeness', 'eroticism', 'erotism']
senticnet['pruriency'] = ['0.811', '-0.89', '0', '0.858', '#surprise', '#admiration', 'positive', '0.854', 'amorousness', 'sexiness', 'amativeness', 'eroticism', 'erotism']
senticnet['prurient'] = ['0', '0', '-0.96', '-0.73', '#fear', '#disgust', 'negative', '-0.84', 'lubricious', 'lewd', 'lustful', 'obscene', 'salacious']
senticnet['pruritic'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema', 'erythema_multiforme', 'windburn', 'hickey']
senticnet['pruritus'] = ['-0.50', '0', '0.868', '-0.79', '#anger', '#disgust', 'negative', '-0.72', 'itch', 'itchiness', 'itching', 'pruritus_ani', 'pruritus_vulvae']
senticnet['pruritus_ani'] = ['-0.50', '0', '0.868', '-0.79', '#anger', '#disgust', 'negative', '-0.72', 'itch', 'itchiness', 'itching', 'pruritus', 'pruritus_vulvae']
senticnet['pruritus_vulvae'] = ['-0.50', '0', '0.868', '-0.79', '#anger', '#disgust', 'negative', '-0.72', 'itch', 'itchiness', 'itching', 'pruritus_ani', 'pruritus']
senticnet['prutot'] = ['0.987', '0.902', '0', '0', '#joy', '#interest', 'positive', '0.945', 'franc', 'dime', 'dollar', 'shilling', 'banknote']
senticnet['pry'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'lever', 'key', 'peavey']
senticnet['pry_bar'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'pair_of_scissors', 'c_clamp', 'tool_box', 'salad_shaker', 'ladle']
senticnet['pry_loose'] = ['0.829', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.908', 'cut_food', 'drink_beer', 'serve_drink', 'drink_much', 'drink_beverage']
senticnet['pry_open'] = ['0.829', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.908', 'cut_food', 'drink_beer', 'serve_drink', 'drink_much', 'drink_beverage']
senticnet['prying'] = ['-0.90', '0', '0.865', '-0.77', '#sadness', '#anger', 'negative', '-0.84', 'nosiness', 'snoopiness', 'curiousness', 'curiosity', 'wonder']
senticnet['prying_open'] = ['0.829', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.908', 'cut_food', 'drink_beer', 'serve_drink', 'drink_much', 'drink_beverage']
senticnet['psalm'] = ['0.075', '-0.01', '0.019', '0.067', '#joy', '#admiration', 'positive', '0.034', 'rhyme', 'paragraph', 'song_lyric', 'lyrics', 'stanza']
senticnet['psalm_verse'] = ['0.075', '-0.01', '0.019', '0.067', '#joy', '#admiration', 'positive', '0.034', 'rhyme', 'paragraph', 'song_lyric', 'lyrics', 'stanza']
senticnet['psaltery'] = ['-0.93', '0', '0.861', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'knotty', 'contorted', 'writhed', 'crooked', 'anfractuous']
senticnet['pseud'] = ['-0.61', '0', '0.764', '-0.72', '#anger', '#disgust', 'negative', '-0.70', 'beguiler', 'slicker', 'poser', 'ringer', 'deceiver']
senticnet['pseudo'] = ['-0.66', '0', '0.918', '-0.62', '#sadness', '#anger', 'negative', '-0.73', 'beguiler', 'slicker', 'hidden', 'ringer', 'deceiver']
senticnet['pseudo-science'] = ['0', '0.785', '0', '0.764', '#interest', '#admiration', 'positive', '0.774', 'physiognomy', 'pseudoscience', 'mesmerism', 'phrenology', 'phrenological']
senticnet['pseudo_aristotle'] = ['-0.7', '0', '0.746', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'insufficiency', 'sparseness', 'sparsity', 'abstemiousness', 'lack_of_quantity']
senticnet['pseudo_documentary'] = ['-0.85', '0', '0.514', '-0.73', '#sadness', '#disgust', 'negative', '-0.70', 'flick', 'movie', 'film', 'picture', 'moving_picture']
senticnet['pseudo_intellectual'] = ['-0.55', '0', '0.947', '-0.77', '#anger', '#disgust', 'negative', '-0.75', 'blackguard', 'bemock', 'twit', 'tease', 'razz']
senticnet['pseudo_medieval'] = ['0.747', '0.85', '0', '0', '#joy', '#interest', 'positive', '0.798', 'night_vision', 'mystery', 'plot', 'imagine', 'myth']
senticnet['pseudo_science'] = ['-0.66', '0', '0.918', '-0.62', '#sadness', '#anger', 'negative', '-0.73', 'beguiler', 'slicker', 'hidden', 'ringer', 'deceiver']
senticnet['pseudo_scientific'] = ['-0.66', '0', '0.918', '-0.62', '#sadness', '#anger', 'negative', '-0.73', 'beguiler', 'slicker', 'hidden', 'ringer', 'deceiver']
senticnet['pseudoaneurysm'] = ['-0.56', '0', '0.806', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['pseudocopulation'] = ['0.049', '0.098', '-0.07', '0.121', '#interest', '#admiration', 'positive', '0.047', 'procreate', 'reproduce', 'start_family', 'cause_cold', 'unprotect_sex']
senticnet['pseudocyst'] = ['-0.56', '0', '0.806', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['pseudodaphnella'] = ['-0.80', '0', '0.815', '-0.98', '#anger', '#disgust', 'negative', '-0.86', 'inalienable', 'nontransferable', 'unassignable', 'untransferable', 'inviolable']
senticnet['pseudohyphae'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'immobile', 'immotile']
senticnet['pseudoknot'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['pseudomembrane'] = ['-0.52', '0', '0.538', '-0.95', '#anger', '#disgust', 'negative', '-0.67', 'sanies', 'pus', 'purulence', 'gleet', 'ichor']
senticnet['pseudomonas_sp'] = ['-0.08', '-0.03', '-0.07', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'injury', 'soreness', 'pain', 'hurt', 'dislocation']
senticnet['pseudonym'] = ['0', '0', '-0.76', '-0.96', '#fear', '#disgust', 'negative', '-0.86', 'figment', 'fictitiously', 'phantom', 'fictitious', 'pseudonymous']
senticnet['pseudonymous'] = ['0', '0', '-0.76', '-0.96', '#fear', '#disgust', 'negative', '-0.86', 'figment', 'fictitiously', 'phantom', 'pseudonym', 'fictitious']
senticnet['pseudophakic'] = ['-0.86', '0', '0.858', '-0.91', '#sadness', '#disgust', 'negative', '-0.87', 'edema', 'oedema', 'hydrops', 'dropsy', 'eye_disease']
senticnet['pseudopodium'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['pseudoscience'] = ['-0.77', '0', '0.885', '-0.73', '#sadness', '#anger', 'negative', '-0.8', 'pathetic_fallacy', 'misconception', 'paralogism', 'logical_fallacy', 'fallacy']
senticnet['pseudoscientific'] = ['-0.61', '0', '0.738', '-0.80', '#anger', '#disgust', 'negative', '-0.71', 'babbling', 'double_talk', 'gibber', 'babble', 'lallation']
senticnet['pseudotsuga_menziesii'] = ['-0.94', '0', '0.608', '-0.95', '#sadness', '#disgust', 'negative', '-0.83', 'wild_licorice', 'wild_liquorice', 'alpine_milk_vetch', 'purple_milk_vetch', 'milk_vetch']
senticnet['pseudotumor_cerebri'] = ['-0.73', '0', '0.904', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'unshapely', 'shapeless', 'lumpy', 'deformed', 'malformed']
senticnet['pseudoxanthoma_elasticum'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'dystrophy', 'myotonic_dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['psfig'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'wall_cover', 'two-dimensional', 'cover_wall', 'pigment', 'dye']
senticnet['pshe'] = ['0.924', '-0.55', '0', '0.756', '#joy', '#admiration', 'positive', '0.744', 'bounden', 'incumbent_on', 'obligate', 'obligatory', 'mandatory']
senticnet['psi_phenomenon'] = ['0.728', '-0.72', '0', '0.881', '#joy', '#admiration', 'positive', '0.779', 'predict_future', 'psychic_phenomenon', 'parapsychology', 'precognition', 'foreknowledge']
senticnet['psidium_guajava'] = ['0.861', '-0.87', '0', '0.597', '#joy', '#surprise', 'positive', '0.779', 'herb', 'cultivated_parsnip', 'wild_parsnip', 'madnep', 'herbaceous_plant']
senticnet['psilocybin'] = ['-0.73', '0', '0.944', '-0.94', '#anger', '#disgust', 'negative', '-0.87', 'lysergic_acid_diethylamide', 'mescaline', 'peyote', 'phencyclidine', 'drug']
senticnet['psionic'] = ['0.034', '0.161', '-0.05', '0.172', '#interest', '#admiration', 'positive', '0.079', 'mental', 'psychical', 'psychologist', 'mind_power', 'mind']
senticnet['psionic_energy'] = ['-0.59', '0', '0.704', '-0.5', '#sadness', '#anger', 'negative', '-0.59', 'lay_up', 'modify', 'nobble', 'alter', 'change']
senticnet['psittaculidae_family'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'lovebird', 'parroket', 'paraquet', 'paroquet']
senticnet['psoas_major'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'skeletal_muscle', 'pterygoid_muscle', 'abductor', 'abductor_muscle']
senticnet['psoralea'] = ['0.861', '-0.87', '0', '0.597', '#joy', '#surprise', 'positive', '0.779', 'herb', 'cultivated_parsnip', 'wild_parsnip', 'madnep', 'herbaceous_plant']
senticnet['psoralen'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'radiation_therapy', 'radiation', 'actinotherapy', 'irradiation']
senticnet['psoriasiform'] = ['-0.89', '0', '0.998', '-0.89', '#sadness', '#anger', 'negative', '-0.93', 'erythema', 'flare', 'skin_disease', 'disease_of_skin', 'skin_disorder']
senticnet['psoriatic'] = ['-0.89', '0', '0.998', '-0.89', '#sadness', '#anger', 'negative', '-0.93', 'erythema', 'flare', 'skin_disease', 'disease_of_skin', 'skin_disorder']
senticnet['psoriatic_arthritis'] = ['-0.89', '0', '0.989', '-0.59', '#sadness', '#anger', 'negative', '-0.82', 'arthritis', 'rheumatoid_arthritis', 'juvenile_rheumatoid_arthritis', 'autoimmune_disease', 'autoimmune_disorder']
senticnet['pspace_complete'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly']
senticnet['pst_file'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'car_trunk', 'walmart', 'warehouse', 'store', 'storage_place']
senticnet['psuedo'] = ['-0.66', '0', '0.918', '-0.62', '#sadness', '#anger', 'negative', '-0.73', 'beguiler', 'slicker', 'hidden', 'ringer', 'deceiver']
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senticnet['psychiatric_comorbidity'] = ['0.627', '-0.65', '0', '0.968', '#surprise', '#admiration', 'positive', '0.749', 'overwhelm', 'overpower', 'sweep_over', 'whelm', 'overcome']
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senticnet['psychiatric_ward'] = ['-0.67', '0', '0.931', '-0.77', '#anger', '#disgust', 'negative', '-0.79', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
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senticnet['psychiatry'] = ['-0.59', '0', '0.911', '-0.87', '#anger', '#disgust', 'negative', '-0.79', 'osteosclerosis', 'sclerosis', 'disseminated_sclerosis', 'multiple_sclerosis', 'induration']
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senticnet['psychic_ability'] = ['0', '0.871', '0', '0.861', '#interest', '#admiration', 'positive', '0.866', 'predict_future', 'psychic_phenomenon', 'parapsychology', 'clairvoyance', 'foreknowledge']
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senticnet['psychical_research'] = ['0.728', '-0.72', '0', '0.881', '#joy', '#admiration', 'positive', '0.779', 'predict_future', 'psychic_phenomenon', 'parapsychology', 'precognition', 'foreknowledge']
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senticnet['psycho'] = ['-0.60', '0', '0.892', '-0.92', '#anger', '#disgust', 'negative', '-0.80', 'schizophrenic', 'cataleptic', 'paranoid', 'paranoiac', 'sick_person']
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senticnet['psychoacoustic'] = ['-0.95', '0', '-0.73', '-0.73', '#sadness', '#fear', 'negative', '-0.80', 'decibel', 'noise', 'loud_soft', 'sound', 'audio']
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senticnet['psychoactivity'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'imperium', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['psychoanalytic'] = ['-0.78', '0', '0.542', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'hysterical_neurosis', 'hysteria', 'mental_disorder', 'mental_disturbance', 'psychological_disorder']
senticnet['psychoanalytic_criticism'] = ['0.587', '-0.94', '0', '0.659', '#surprise', '#admiration', 'positive', '0.731', 'critique', 'review', 'criticism', 'textual_criticism', 'new_criticism']
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senticnet['psychobabble'] = ['-0.61', '0', '0.738', '-0.80', '#anger', '#disgust', 'negative', '-0.71', 'babbling', 'double_talk', 'gibber', 'babble', 'lallation']
senticnet['psychobilly'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'explicit', 'declared', 'definitive', 'graphic']
senticnet['psychobiography'] = ['0.701', '0.939', '0', '0.718', '#interest', '#admiration', 'positive', '0.786', 'literature', 'bed_time_story', 'book_story', 'fiction', 'life_story']
senticnet['psychodrama'] = ['0.722', '-0.60', '0', '0.596', '#joy', '#surprise', 'positive', '0.641', 'medicament', 'medicinal_drug', 'medication', 'aloe', 'bitter_aloe']
senticnet['psychogenesis'] = ['0.96', '-0.63', '0', '0.663', '#joy', '#admiration', 'positive', '0.754', 'growth', 'growing', 'maturation', 'development', 'ontogeny']
senticnet['psychogenic'] = ['-0.55', '0', '0.736', '-0.71', '#anger', '#disgust', 'negative', '-0.66', 'neurosis', 'neuroticism', 'psychoneurosis', 'hysterocatalepsy', 'anxiety_hysteria']
senticnet['psychogenic_amnesia'] = ['-0.58', '0', '0.722', '-0.51', '#sadness', '#anger', 'negative', '-0.60', 'dissociative_disorder', 'depersonalization_neurosis', 'depersonalization', 'mental_disorder', 'mental_disturbance']
senticnet['psychokinesis'] = ['-0.8', '0', '0.62', '-0.71', '#sadness', '#disgust', 'negative', '-0.71', 'telekinesis', 'parapsychology', 'clairvoyance', 'second_sight', 'extrasensory_perception']
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senticnet['psychological_abuse'] = ['-0.83', '0', '0.924', '-0.79', '#sadness', '#anger', 'negative', '-0.85', 'kick_in_butt', 'licking', 'whacking', 'stoning', 'lapidation']
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senticnet['psychological_aggression'] = ['-0.91', '0', '0.451', '-0.98', '#sadness', '#disgust', 'negative', '-0.78', 'slave', 'female_slave', 'physical_aggression', 'aggression', 'verbal_aggression']
senticnet['psychological_barrier'] = ['-0.93', '0', '-0.99', '-0.92', '#sadness', '#fear', 'negative', '-0.95', 'language_barrier', 'shut', 'shut_down', 'close_down', 'obstacle']
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senticnet['psychological_horror'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'anxiety', 'apprehension', 'insecurity', 'tension', 'horror_movie']
senticnet['psychological_insight'] = ['0.99', '0', '0', '0.716', '#joy', '#admiration', 'positive', '0.853', 'encyclopedism', 'learning', 'scholarship', 'learnedness', 'eruditeness']
senticnet['psychological_manipulation'] = ['0', '0', '0.989', '-0.71', '#anger', '#disgust', 'negative', '-0.85', 'deceive', 'against_law', 'false', 'fool', 'trick']
senticnet['psychological_phenomena'] = ['0', '0.137', '-0.10', '0.197', '#interest', '#admiration', 'positive', '0.076', 'boom', 'circumstance', 'loud', 'stormy', 'procedure']
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senticnet['psychological_pressure'] = ['-0.08', '0', '-0.15', '-0.20', '#fear', '#disgust', 'negative', '-0.14', 'ask_money', 'exaction', 'wringing', 'intimidate', 'extortion']
senticnet['psychological_reactance'] = ['0.854', '-0.79', '0', '0.977', '#joy', '#admiration', 'positive', '0.875', 'profitableness', 'auspiciousness', 'luck', 'advantageousness', 'positivity']
senticnet['psychological_realism'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'mediacy', 'mediateness', 'characteristic', 'indirectness']
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senticnet['psychological_terror'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'mobbing', 'violence', 'physical_pain', 'boss_around', 'bully']
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senticnet['psychology_professor'] = ['0', '0.909', '0', '0.799', '#interest', '#admiration', 'positive', '0.854', 'research', 'research_topic', 'research_subject', 'search', 'student']
senticnet['psychometric'] = ['-0.84', '0', '0.585', '-0.85', '#sadness', '#disgust', 'negative', '-0.76', 'subject', 'discipline', 'study', 'concern', 'research']
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senticnet['psychometry'] = ['0', '0.871', '0', '0.861', '#interest', '#admiration', 'positive', '0.866', 'predict_future', 'psychic_phenomenon', 'parapsychology', 'clairvoyance', 'foreknowledge']
senticnet['psychomotor_development'] = ['0.874', '-0.73', '0', '0.608', '#joy', '#surprise', 'positive', '0.74', 'growth', 'growing', 'maturation', 'development', 'ontogeny']
senticnet['psychomotor_developmental'] = ['0.874', '-0.73', '0', '0.608', '#joy', '#surprise', 'positive', '0.74', 'growth', 'growing', 'maturation', 'development', 'ontogeny']
senticnet['psychomotor_retardation'] = ['-0.78', '0', '0.764', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'withering', 'downfall', 'weakening', 'fading', 'attenuation']
senticnet['psychonavigation'] = ['0.502', '-0.82', '0', '0.779', '#surprise', '#admiration', 'positive', '0.703', 'mortal', 'person', 'individual', 'free_liver', 'agent']
senticnet['psychoneuroimmunology'] = ['0.627', '-0.65', '0', '0.968', '#surprise', '#admiration', 'positive', '0.749', 'overwhelm', 'overpower', 'sweep_over', 'whelm', 'overcome']
senticnet['psychoneurosis'] = ['-0.37', '-0.03', '-0.39', '-0.15', '#sadness', '#fear', 'negative', '-0.22', 'gonorrhea', 'feel_bad', 'severe_pain', 'anguish', 'learn_disease']
senticnet['psychoneurotic'] = ['-0.74', '0', '0.839', '-0.90', '#anger', '#disgust', 'negative', '-0.83', 'sick_person', 'diseased_person', 'sufferer', 'claustrophobe', 'hysteric']
senticnet['psychopath'] = ['0', '0', '-0.96', '-0.80', '#fear', '#disgust', 'negative', '-0.88', 'get_rid_of_enemy', 'lunatic', 'hurt_others', 'harden_criminal', 'end_live']
senticnet['psychopathic'] = ['0', '0', '0.729', '-0.93', '#anger', '#disgust', 'negative', '-0.83', 'seek_revenge', 'die', 'take_life', 'threaten', 'cause_death']
senticnet['psychopathic_killer'] = ['-0.74', '0', '0.604', '-0.88', '#sadness', '#disgust', 'negative', '-0.74', 'weirdo', 'crazy', 'loony', 'looney', 'nutcase']
senticnet['psychopathic_personality'] = ['0', '0', '0.896', '-0.96', '#anger', '#disgust', 'negative', '-0.92', 'antisocial', 'sociopathic', 'personality_disorder', 'disturbance', 'schizoid']
senticnet['psychopathological'] = ['-0.55', '0', '0.736', '-0.71', '#anger', '#disgust', 'negative', '-0.66', 'neurosis', 'neuroticism', 'psychoneurosis', 'hysterocatalepsy', 'anxiety_hysteria']
senticnet['psychopathology'] = ['-0.78', '0', '0.542', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'hysterical_neurosis', 'hysteria', 'mental_disorder', 'mental_disturbance', 'psychological_disorder']
senticnet['psychopathy'] = ['0.618', '-0.59', '0', '0.773', '#joy', '#admiration', 'positive', '0.662', 'insanity', 'psychosis', 'megalomania', 'psychological_state', 'psychological_condition']
senticnet['psychophysical'] = ['0.685', '-0.91', '0', '0.566', '#joy', '#surprise', 'positive', '0.721', 'audibility', 'audibleness', 'visibleness', 'visibility', 'physical_property']
senticnet['psychophysiological'] = ['0.461', '0.674', '-0.26', '0.247', '#joy', '#interest', 'positive', '0.28', 'affectionateness', 'warmness', 'philia', 'affectional', 'emotive']
senticnet['psychosexual'] = ['0.857', '0', '0', '0', '#joy', '#joy', 'positive', '0.857', 'date', 'accuracy', 'sexy_woman', 'woo', 'enjoy_person']
senticnet['psychosis'] = ['0', '0', '-0.97', '0', '#fear', '#fear', 'negative', '-0.97', 'angry_emotion', 'dislike', 'hurt_feeling', 'feel_hurt', 'mental_disorder']
senticnet['psychosocial_care'] = ['0.641', '-0.78', '0', '0.721', '#surprise', '#admiration', 'positive', '0.715', 'correctional_rehabilitation', 'physical_rehabilitation', 'physical_restoration', 'therapeutic_rehabilitation', 'vocational_rehabilitation']
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senticnet['psychosocial_rehabilitation'] = ['0.641', '-0.78', '0', '0.721', '#surprise', '#admiration', 'positive', '0.715', 'correctional_rehabilitation', 'physical_rehabilitation', 'physical_restoration', 'therapeutic_rehabilitation', 'vocational_rehabilitation']
senticnet['psychosocial_treatment'] = ['0.627', '-0.65', '0', '0.968', '#surprise', '#admiration', 'positive', '0.749', 'overwhelm', 'overpower', 'sweep_over', 'whelm', 'overcome']
senticnet['psychosomatic'] = ['0', '0', '0.892', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'neurological', 'neurology', 'obstetrics', 'disorder', 'psychological']
senticnet['psychosomatic_disorder'] = ['-0.68', '0', '0.969', '-0.94', '#anger', '#disgust', 'negative', '-0.86', 'mental_disorder', 'mental_disturbance', 'psychological_disorder', 'folie', 'disturbance']
senticnet['psychostimulant'] = ['0.672', '0.632', '-0.23', '0.312', '#joy', '#interest', 'positive', '0.344', 'euphoria', 'buoyancy', 'perkiness', 'jubilance', 'ecstasy']
senticnet['psychosurgery'] = ['0.542', '-0.63', '0', '0.912', '#surprise', '#admiration', 'positive', '0.695', 'implosion_therapy', 'flooding', 'reciprocal_inhibition', 'behavior_modification', 'desensitization_technique']
senticnet['psychotherapeutic_treatment'] = ['0.542', '-0.63', '0', '0.912', '#surprise', '#admiration', 'positive', '0.695', 'implosion_therapy', 'flooding', 'reciprocal_inhibition', 'behavior_modification', 'desensitization_technique']
senticnet['psychotherapy'] = ['0.542', '-0.63', '0', '0.912', '#surprise', '#admiration', 'positive', '0.695', 'implosion_therapy', 'flooding', 'reciprocal_inhibition', 'behavior_modification', 'desensitization_technique']
senticnet['psychotic'] = ['-0.67', '0', '0.963', '-0.95', '#anger', '#disgust', 'negative', '-0.86', 'schizophrenic', 'cataleptic', 'paranoid', 'paranoiac', 'sick_person']
senticnet['psychotic_behavior'] = ['-0.74', '0', '0.893', '-0.58', '#sadness', '#anger', 'negative', '-0.73', 'encopresis', 'upset', 'psychosomatic_disorder', 'conversion_disorder', 'conversion_reaction']
senticnet['psychotic_depression'] = ['-0.78', '0', '0.918', '-0.96', '#anger', '#disgust', 'negative', '-0.88', 'major_depressive_episode', 'depression', 'depressive_disorder', 'clinical_depression', 'involutional_depression']
senticnet['psychotic_disorder'] = ['-0.78', '0', '0.918', '-0.96', '#anger', '#disgust', 'negative', '-0.88', 'major_depressive_episode', 'depression', 'depressive_disorder', 'clinical_depression', 'involutional_depression']
senticnet['psychotic_episode'] = ['-0.61', '0', '0.527', '-0.57', '#sadness', '#disgust', 'negative', '-0.57', 'affective_disorder', 'major_affective_disorder', 'emotional_disorder', 'emotional_disturbance', 'cyclothymia']
senticnet['psychotic_killer'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'addicted', 'obsessed_love', 'addiction', 'obsession', 'weird']
senticnet['psychotic_person'] = ['-0.8', '0', '0.788', '-0.95', '#sadness', '#disgust', 'negative', '-0.84', 'schizophrenic', 'cataleptic', 'paranoid', 'paranoiac', 'sick_person']
senticnet['psychoticism'] = ['0.995', '-0.73', '0', '0.83', '#joy', '#admiration', 'positive', '0.852', 'spiritism', 'otherworldliness', 'spirituality', 'spiritualism', 'internality']
senticnet['psychotomimetic'] = ['-0.73', '0', '0.944', '-0.94', '#anger', '#disgust', 'negative', '-0.87', 'lysergic_acid_diethylamide', 'mescaline', 'peyote', 'phencyclidine', 'drug']
senticnet['psychotropic'] = ['0', '0', '-0.74', '-0.80', '#fear', '#disgust', 'negative', '-0.77', 'hallucinogenic', 'psychoactive', 'mood-altering', 'mind-altering', 'psychedelics']
senticnet['psylla'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'rust_fungus', 'fungus']
senticnet['psyllid'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'nuisance', 'annoyance', 'irritant', 'pain_in_ass', 'troublesome']
senticnet['psyllium'] = ['-0.50', '0', '0.582', '-0.57', '#anger', '#disgust', 'negative', '-0.55', 'purgative', 'cathartic', 'physic', 'aperient', 'phenolphthalein']
senticnet['psylocke'] = ['0.02', '0.131', '0.749', '-0.67', '#anger', '#disgust', 'negative', '-0.31', 'belligerence', 'belligerency', 'belligerent', 'war', 'rancor']
senticnet['pt_barnum'] = ['0.563', '-0.81', '0', '0.577', '#surprise', '#admiration', 'positive', '0.65', 'mortal', 'person', 'individual', 'performer', 'artist']
senticnet['ptanque'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'golf', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['ptarmigan'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['pte_chou'] = ['0.059', '-0.02', '0.025', '0.109', '#joy', '#admiration', 'positive', '0.043', 'tasty', 'bake_treat', 'bake_goods', 'bake_good', 'loaf']
senticnet['pteridosperm'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'seed_fern', 'gymnosperm']
senticnet['pteridospermatophyta'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'gymnosperm', 'pteridosperm']
senticnet['pterodactyl'] = ['-0.99', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.89', 'triceratops', 'pterodactyl', 'dinosaur', 'bird-like', 'stegosaurus']
senticnet['pterospermum'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'seed_fern', 'pteridosperm']
senticnet['pterygoid'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'hip_socket', 'tooth_socket', 'alveolus', 'socket', 'cotyloid_cavity']
senticnet['pterygoid_canal'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['pterygoid_muscle'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'deltoid', 'skeletal_muscle', 'abductor', 'abductor_muscle']
senticnet['pterygopalatine_ganglion'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['ptisserie'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'blue_ribbon', 'badge', 'allegory', 'stripe', 'merit_badge']
senticnet['ptissier'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'blue_ribbon', 'badge', 'allegory', 'stripe', 'merit_badge']
senticnet['ptolemaic_period'] = ['-0.09', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.10', 'funeral_celebration', 'memorial', 'funeral', 'memento', 'cry']
senticnet['ptomaine'] = ['-0.53', '0', '0.678', '-0.73', '#anger', '#disgust', 'negative', '-0.64', 'malady', 'mushroom_poisoning', 'botulism', 'food_poisoning', 'poison']
senticnet['ptomaine_poisoning'] = ['-0.53', '0', '0.678', '-0.73', '#anger', '#disgust', 'negative', '-0.64', 'malady', 'mushroom_poisoning', 'botulism', 'ptomaine', 'food_poisoning']
senticnet['pu_uhonua'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'habitat', 'mom_house', 'windsor', 'pacific_northwest', 'wilderness']
senticnet['pub'] = ['0.853', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.916', 'tavern', 'cafe', 'nightclub', 'bar', 'liquor_store']
senticnet['pub_chain'] = ['0.853', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.916', 'tavern', 'cafe', 'nightclub', 'bar', 'liquor_store']
senticnet['pub_landlord'] = ['0.853', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.916', 'tavern', 'cafe', 'nightclub', 'bar', 'liquor_store']
senticnet['pub_next_door'] = ['0.853', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.916', 'tavern', 'cafe', 'nightclub', 'bar', 'liquor_store']
senticnet['pubertal'] = ['0', '0.075', '0', '0.159', '#interest', '#admiration', 'positive', '0.117', 'many_plants', 'blossom', 'development', 'poppy', 'adult']
senticnet['pubertal_development'] = ['0.995', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.918', 'tell_everyone', 'reason', 'clarify', 'forethought', 'opinion']
senticnet['pubertal_growth_spurt'] = ['0.995', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.918', 'tell_everyone', 'reason', 'clarify', 'forethought', 'opinion']
senticnet['puberty'] = ['0.995', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.918', 'tell_everyone', 'reason', 'clarify', 'forethought', 'opinion']
senticnet['puberulent'] = ['-0.75', '0', '0.734', '-0.82', '#sadness', '#disgust', 'negative', '-0.77', 'haired', 'sericeous', 'furlike', 'pappose', 'pilous']
senticnet['pubescent'] = ['-0.77', '0', '0.626', '-0.78', '#sadness', '#disgust', 'negative', '-0.72', 'bumpy', 'unsmooth', 'lepidote', 'leprose', 'roughish']
senticnet['pubescent_hairy'] = ['-0.75', '0', '0.734', '-0.82', '#sadness', '#disgust', 'negative', '-0.77', 'puberulent', 'haired', 'furlike', 'pappose', 'pilous']
senticnet['pubic_arch'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'hip_socket', 'tooth_socket', 'alveolus', 'socket', 'cotyloid_cavity']
senticnet['pubic_bone'] = ['0.087', '-0.03', '0', '0.063', '#joy', '#admiration', 'positive', '0.062', 'puppet', 'bouncy', 'curly', 'throw_object', 'glove_leather']
senticnet['public'] = ['0.826', '0.77', '0', '0', '#joy', '#interest', 'positive', '0.798', 'available', 'freely_available', 'population', 'crowd', 'majority']
senticnet['public-law'] = ['-0.97', '0', '0.786', '0', '#sadness', '#anger', 'negative', '-0.88', 'non-legal', 'non-diplomatic', 'billing-related', 'nonlegal', 'court-related']
senticnet['public_aquarium'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'autumn_leaf', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['public_assistance'] = ['0.753', '-0.62', '0', '0.959', '#joy', '#admiration', 'positive', '0.778', 'assistance', 'relief', 'economic_aid', 'financial_aid', 'social_insurance']
senticnet['public_confidence'] = ['0.997', '0', '0', '0.972', '#joy', '#admiration', 'positive', '0.984', 'sympathetic', 'adoration', 'confident', 'enthusiasm', 'grateful']
senticnet['public_defender'] = ['0', '0.146', '-0.05', '0.077', '#interest', '#admiration', 'positive', '0.057', 'lawyer', 'negotiator', 'arbiter', 'judge_jury', 'decide_guilt']
senticnet['public_diplomacy'] = ['0', '0.098', '0', '0.08', '#interest', '#admiration', 'positive', '0.089', 'talk_person', 'wisdom', 'tact', 'statecraft', 'greet_person']
senticnet['public_domain'] = ['0', '0.72', '0', '0.834', '#interest', '#admiration', 'positive', '0.777', 'information', 'symposium', 'workshop', 'ripem', 'data']
senticnet['public_easement'] = ['0.621', '-0.60', '0', '0.545', '#joy', '#surprise', 'positive', '0.59', 'privilege', 'perquisite', 'easement', 'right_of_way', 'prerogative']
senticnet['public_embarrassment'] = ['-0.06', '0.128', '0.707', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'embarrassment', 'peer_pressure', 'sudden', 'bad_feeling', 'scandal']
senticnet['public_executioner'] = ['-0.62', '0', '0.94', '-0.99', '#anger', '#disgust', 'negative', '-0.85', 'poisoner', 'choker', 'mortal', 'executioner', 'slayer']
senticnet['public_exposure'] = ['0.928', '0', '0', '0.786', '#joy', '#admiration', 'positive', '0.857', 'diffusion', 'dispersal', 'spreading', 'dissemination', 'dispersion']
senticnet['public_gathering'] = ['0.076', '0', '0', '0', '#joy', '#joy', 'positive', '0.076', 'gathering', 'group_gathering', 'mob', 'gather_person', 'several_people']
senticnet['public_health'] = ['0.812', '-0.91', '0', '0.647', '#joy', '#surprise', 'positive', '0.79', 'assistance', 'relief', 'economic_aid', 'financial_aid', 'social_insurance']
senticnet['public_house'] = ['-0.76', '0', '0.75', '-0.56', '#sadness', '#anger', 'negative', '-0.69', 'free_house', 'pub', 'saloon', 'pothouse', 'gin_mill']
senticnet['public_humiliation'] = ['-0.91', '0', '-0.72', '-0.88', '#sadness', '#disgust', 'negative', '-0.84', 'death_love', 'bad_feeling', 'anguish', 'consequence_war', 'feel_stressed']
senticnet['public_knowledge'] = ['0.986', '-0.97', '0', '0.766', '#joy', '#surprise', 'positive', '0.909', 'light', 'open', 'common_knowledge', 'surface', 'cognition']
senticnet['public_outcry'] = ['0', '0', '0.94', '0', '#anger', '#anger', 'negative', '-0.94', 'feel_angry', 'hurt_feeling', 'feel_hurt', 'ridicule', 'vengeance']
senticnet['public_outcry_over'] = ['-0.86', '0', '0.516', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'bronx_cheer', 'call', 'cry', 'yell', 'shout']
senticnet['public_outrage'] = ['-0.16', '0.179', '0.568', '-0.61', '#anger', '#disgust', 'negative', '-0.29', 'angrily', 'choler', 'see_red', 'dander', 'hackle']
senticnet['public_persona'] = ['0.929', '-0.79', '0', '0.915', '#joy', '#admiration', 'positive', '0.88', 'privacy', 'seclusion', 'privateness', 'solitariness', 'loneliness']
senticnet['public_place'] = ['0', '0.118', '0.023', '0.053', '#interest', '#admiration', 'positive', '0.049', 'whorehouse', 'skyscraper', 'tall_building', 'city_hall', 'memorial']
senticnet['public_presentation'] = ['0.925', '-0.61', '0', '0.686', '#joy', '#admiration', 'positive', '0.741', 'benefit', 'pianism', 'show', 'routine', 'histrionics']
senticnet['public_purse'] = ['-0.03', '0.269', '-0.06', '0.136', '#interest', '#admiration', 'positive', '0.078', 'cost', 'money_payment', 'debit', 'price', 'money_charge']
senticnet['public_restroom'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'commode', 'potty_seat', 'potty_chair']
senticnet['public_scrutiny'] = ['-0.79', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.75', 'scan', 'checkup', 'medical_checkup', 'medical_examination', 'medical_exam']
senticnet['public_sector'] = ['0.812', '-0.91', '0', '0.647', '#joy', '#surprise', 'positive', '0.79', 'assistance', 'relief', 'economic_aid', 'financial_aid', 'social_insurance']
senticnet['public_servant'] = ['0.865', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.887', 'manager', 'colleague', 'office_worker', 'coworker', 'secretary']
senticnet['public_slayer'] = ['-0.63', '0', '0.93', '-1', '#anger', '#disgust', 'negative', '-0.85', 'poisoner', 'choker', 'mortal', 'slayer', 'executioner']
senticnet['public_switched_telephone'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'long_distance_relationship', 'distant', 'kilometer', 'length_measurement', 'distance_measure']
senticnet['public_toilet'] = ['0.026', '0.096', '0.02', '0', '#joy', '#interest', 'positive', '0.034', 'accountant_office', 'account_department', 'toilet', 'office_build', 'office']
senticnet['public_transit'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'seeker', 'chairperson']
senticnet['public_transport'] = ['-0.04', '-0.01', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.03', 'wait_train', 'board_subway_train', 'wait', 'find_end_line', 'travel']
senticnet['public_transport_hub'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'train_station', 'bus', 'subway_station', 'public_place', 'pier']
senticnet['public_transportation'] = ['0', '0', '0', '0.076', '#admiration', '#admiration', 'positive', '0.076', 'travel', 'work_car', 'stay_sober', 'find_job', 'settle']
senticnet['public_trust'] = ['0.997', '0', '0', '0.972', '#joy', '#admiration', 'positive', '0.984', 'sympathetic', 'adoration', 'confident', 'enthusiasm', 'grateful']
senticnet['public_urination'] = ['-0.53', '0', '0.825', '-0.87', '#anger', '#disgust', 'negative', '-0.74', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['publically_available'] = ['0.81', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.774', 'available', 'public', 'population', 'crowd', 'majority']
senticnet['publication'] = ['0', '0.839', '0', '0.76', '#interest', '#admiration', 'positive', '0.799', 'reissue', 'impression', 'tip_sheet', 'reference', 'read']
senticnet['publicist_liz_rosenberg'] = ['0.056', '0.139', '-0.07', '0.154', '#interest', '#admiration', 'positive', '0.07', 'belongings', 'material', 'fabric', 'guarantee', 'stuff']
senticnet['publicity'] = ['0', '0.144', '-0.05', '0.173', '#interest', '#admiration', 'positive', '0.088', 'create_form', 'creationism', 'clothes_style', 'creation', 'floral']
senticnet['publicity_campaign'] = ['-0.81', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.81', 'take_charge', 'anti', 'go_against', 'death_wish', 'file_complaint']
senticnet['publicity_hound'] = ['-0.80', '0', '0.56', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'gossip', 'gossiper', 'blabbermouth', 'talkative', 'rumormonger']
senticnet['publicity_hungry'] = ['0.534', '-0.69', '0', '0.724', '#surprise', '#admiration', 'positive', '0.65', 'knocker', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['publicity_stunt'] = ['0', '0.786', '0', '0.917', '#interest', '#admiration', 'positive', '0.852', 'act', 'portray', 'play_ping_pong', 'school_play', 'dangerous']
senticnet['publicize'] = ['0.539', '0.874', '0', '0.843', '#interest', '#admiration', 'positive', '0.752', 'gain_information', 'gain_knowledge', 'increase_knowledge', 'learn_much', 'learn']
senticnet['publicly-held'] = ['0', '0', '-0.83', '-0.98', '#fear', '#disgust', 'negative', '-0.91', 'publicly-traded', 'owner-managed', 'closely-held', 'proprietorship', 'middle-market']
senticnet['publicly-traded'] = ['0', '0', '-0.83', '-0.99', '#fear', '#disgust', 'negative', '-0.91', 'closely-held', 'owner-managed', 'publicly-held', 'proprietorship', 'middle-market']
senticnet['publicly_accessible'] = ['0.81', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.774', 'available', 'public', 'population', 'crowd', 'majority']
senticnet['publicly_accessible_peer'] = ['0.848', '0.735', '0', '0', '#joy', '#interest', 'positive', '0.791', 'observer', 'peek', 'appearance', 'seer', 'glimpse']
senticnet['publicly_apologize'] = ['0.911', '-0.71', '0', '0.552', '#joy', '#surprise', 'positive', '0.726', 'deliver_goods', 'succeed', 'win', 'bring_home_bacon', 'come_through']
senticnet['publicly_available'] = ['0.81', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.774', 'available', 'public', 'population', 'crowd', 'majority']
senticnet['publicly_available_information'] = ['-0.02', '0.175', '-0.11', '0.148', '#interest', '#admiration', 'positive', '0.045', 'tolerant', 'phrase', 'inquiry', 'explain', 'question']
senticnet['publicly_criticize'] = ['0', '0', '0', '-0.87', '#disgust', '#disgust', 'negative', '-0.87', 'move_eye', 'learn_current_event', 'become_informed', 'time_waste', 'eye_strain']
senticnet['publicly_disclose'] = ['-0.02', '0.175', '-0.11', '0.148', '#interest', '#admiration', 'positive', '0.045', 'tolerant', 'phrase', 'inquiry', 'explain', 'question']
senticnet['publicly_funded_healthcare'] = ['-0.12', '0', '-0.12', '-0.08', '#sadness', '#fear', 'negative', '-0.11', 'hospital', 'check_up', 'check_doctor', 'check_pulse', 'check_blood_pressure']
senticnet['publicly_funded_research'] = ['0.81', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.774', 'available', 'public', 'population', 'crowd', 'majority']
senticnet['publicly_owned_land'] = ['0.621', '-0.60', '0', '0.545', '#joy', '#surprise', 'positive', '0.59', 'privilege', 'perquisite', 'public_easement', 'right_of_way', 'prerogative']
senticnet['publicly_thank'] = ['0.819', '0', '0', '0.746', '#joy', '#admiration', 'positive', '0.782', 'proudly', 'smug', 'congratulation', 'self-satisfied', 'applause']
senticnet['publicly_traded_company'] = ['0.111', '0.18', '0', '0.128', '#interest', '#admiration', 'positive', '0.14', 'corporation', 'partnership', 'large_business', 'business_organization', 'enterprise']
senticnet['publicly_traded_corporation'] = ['0.08', '0.195', '0', '0.137', '#interest', '#admiration', 'positive', '0.137', 'company', 'enterprise', 'business', 'partnership', 'large_business']
senticnet['publicly_traded_stock'] = ['0.069', '0.07', '0', '0.116', '#interest', '#admiration', 'positive', '0.085', 'entrepreneur', 'invest_money', 'move_around', 'traveling_person', 'guilty_person']
senticnet['publike'] = ['-0.63', '0', '0.683', '-0.96', '#anger', '#disgust', 'negative', '-0.76', 'slovenly_woman', 'slut', 'trollop', 'slattern', 'litterer']
senticnet['publinx'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'sciolist', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
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senticnet['publishable_quality'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'typeface', 'essay', 'thesis', 'hold_information', 'continue_story']
senticnet['published_anonymously'] = ['0.058', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.054', 'letter_slot', 'envelope', 'address_label', 'mailman', 'postage_stamp']
senticnet['published_bi_monthly'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'current_event', 'headline', 'paper_write', 'paper_page', 'many_pages']
senticnet['published_bi_weekly'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'current_event', 'headline', 'paper_write', 'paper_page', 'many_pages']
senticnet['published_bimonthly'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'current_event', 'headline', 'paper_write', 'paper_page', 'many_pages']
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senticnet['publisher'] = ['0.889', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.807', 'compression', 'push_hard', 'print_newspaper', 'journalist', 'make_newspaper']
senticnet['puccinia'] = ['-0.93', '0', '0.699', '-0.91', '#sadness', '#disgust', 'negative', '-0.84', 'onion_smut', 'head_smut', 'stinking_smut', 'flag_smut_fungus', 'covered_smut']
senticnet['puck'] = ['0.729', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.734', 'hockey_puck', 'hockey_stick', 'disk', 'loop', 'bullseye']
senticnet['pucker'] = ['-0.92', '0', '0.71', '-0.82', '#sadness', '#disgust', 'negative', '-0.81', 'flexure', 'fold', 'crease', 'plication', 'crimp']
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senticnet['puddin'] = ['-0.53', '0', '0.508', '-0.74', '#sadness', '#disgust', 'negative', '-0.59', 'bedwetter', 'bed_wetter', 'mortal', 'wetter', 'person']
senticnet['pudding'] = ['0.053', '-0.09', '0', '0', '#joy', '#surprise', 'positive', '0.072', 'cookie', 'muffin', 'custard', 'biscuit', 'chocolate_dessert']
senticnet['pudding_basin'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'neat', 'kitchen_drawer', 'cutlery_drawer', 'dine_table', 'table']
senticnet['pudding_berry'] = ['0.773', '-0.55', '0', '0.949', '#joy', '#admiration', 'positive', '0.757', 'bunchberry', 'dwarf_cornel', 'crackerberry', 'cornel', 'common_white_dogwood']
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senticnet['pudding_mix'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'salad_shaker', 'store_shelf', 'kitchen_tool', 'pliers', 'ladle']
senticnet['puddle'] = ['0', '-0.04', '0.059', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'squirt', 'dirty_water', 'mud', 'slosh', 'sludge']
senticnet['pudendal_nerve'] = ['-0.41', '0', '-0.28', '-0.05', '#sadness', '#fear', 'negative', '-0.25', 'numbness', 'carpal_tunnel_syndrome', 'weakness', 'pain', 'muscle_damage']
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senticnet['puerile'] = ['0', '0', '0.886', '-0.74', '#anger', '#disgust', 'negative', '-0.81', 'infantile', 'childish', 'immature', 'spastic', 'narcissistic']
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senticnet['puerperal_fever'] = ['0.579', '-0.81', '0', '0.833', '#surprise', '#admiration', 'positive', '0.741', 'murine_typhus', 'rat_typhus', 'urban_typhus', 'endemic_typhus', 'rickettsial_disease']
senticnet['puff'] = ['-0.03', '-0.05', '0.083', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'gasp', 'pant', 'wheeze', 'blow', 'heave']
senticnet['puff_adder'] = ['-0.68', '0', '0.611', '-0.59', '#sadness', '#anger', 'negative', '-0.63', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['puff_daddy'] = ['-0.19', '0', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.15', 'bad_child', 'misbehavior', 'bad_kid', 'bad_action', 'difficulty']
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senticnet['puff_pastry'] = ['0', '-0.11', '0', '0', '#surprise', '#surprise', 'positive', '0.113', 'cake', 'cookie', 'mousse', 'doughnut', 'brownie']
senticnet['puff_piece'] = ['0.73', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.851', 'sting', 'nip', 'bite', 'flea', 'least']
senticnet['puffball'] = ['-0.93', '0', '0', '-0.86', '#sadness', '#disgust', 'negative', '-0.9', 'kingdom', 'gastroboletus_scabrosus', 'slime_mold', 'fungi', 'agaric']
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senticnet['puffed_out'] = ['0', '0', '0.789', '-0.72', '#anger', '#disgust', 'negative', '-0.75', 'displease', 'bug', 'nettle', 'chafe', 'badger']
senticnet['puffed_rice'] = ['-0.92', '0', '0.609', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'cold_cereal', 'granola', 'raisin_bran', 'corn_flake', 'bran_flake']
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senticnet['puffed_wheat'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'veal', 'beef', 'food_group', 'animal_flesh', 'curry']
senticnet['puffer'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'plectognath', 'plectognathfish', 'blowfish', 'triggerfish', 'filefish']
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senticnet['puffery'] = ['0', '-0.75', '0', '0.906', '#surprise', '#admiration', 'positive', '0.829', 'sophistry', 'bombast', 'self-aggrandizement', 'bluster', 'self-aggrandizing']
senticnet['puffiness'] = ['-0.73', '0', '0.94', '-0.87', '#anger', '#disgust', 'negative', '-0.85', 'edema', 'oedema', 'hydrops', 'dropsy', 'bloat']
senticnet['puffing_away'] = ['0.047', '0', '-0.10', '-0.06', '#fear', '#disgust', 'negative', '-0.03', 'smoke', 'tobacco_product', 'smoke_tobacco', 'lung_cancer', 'cigar']
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senticnet['pug'] = ['-0.72', '0', '0.619', '-0.86', '#sadness', '#disgust', 'negative', '-0.73', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['pug_nose'] = ['0', '0', '0.883', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'get_old', 'dry_skin', 'become_wet', 'clothes_shrink', 'wrinkle']
senticnet['pugilism'] = ['0', '0', '-0.79', '-0.89', '#fear', '#disgust', 'negative', '-0.84', 'attack', 'impugnable', 'repugnant', 'impugn', 'pugnacious']
senticnet['pugilist'] = ['-0.08', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.09', 'mediocrity', 'effect', 'suspicion', 'exclusion', 'disconnection']
senticnet['pugnacious'] = ['-0.97', '0', '0.877', '0', '#sadness', '#anger', 'negative', '-0.92', 'pugnaciousness', 'belligerent', 'repugnant', 'combative', 'poniard']
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senticnet['pugnacity'] = ['-0.50', '0', '0.817', '-0.59', '#anger', '#disgust', 'negative', '-0.63', 'disagreeableness', 'truculence', 'truculency', 'bellicosity', 'bellicoseness']
senticnet['puh'] = ['-0.63', '0', '0.791', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'little', 'pinch', 'modicum', 'small_indefinite_amount', 'indefinite_quantity']
senticnet['puir'] = ['0.947', '0.914', '0', '0', '#joy', '#interest', 'positive', '0.93', 'ball_bounce', 'bicycle_tire', 'tire', 'increase_price', 'gyroscope']
senticnet['puissant'] = ['0.735', '0.757', '0', '0', '#joy', '#interest', 'positive', '0.746', 'almighty', 'power', 'mighty', 'excellent', 'mare']
senticnet['pujara'] = ['-0.62', '0', '0.713', '-0.98', '#anger', '#disgust', 'negative', '-0.77', 'muddy_up', 'muck_up', 'foul', 'blemish', 'smear']
senticnet['puke'] = ['0', '0', '0', '-0.79', '#disgust', '#disgust', 'negative', '-0.79', 'vomit', 'blackout', 'get_sick', 'throw_up', 'hangover']
senticnet['pulchra'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'sedum', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['pule'] = ['-0.85', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.84', 'mewl', 'whimper', 'sniffle', 'cry', 'intermittent']
senticnet['pull'] = ['0.988', '0.787', '0', '0', '#joy', '#interest', 'positive', '0.887', 'tug', 'carry', 'tow', 'extend', 'drag']
senticnet['pull_ahead'] = ['0.949', '-0.85', '0', '0.926', '#joy', '#admiration', 'positive', '0.91', 'tally', 'steal', 'score', 'hit', 'rack_up']
senticnet['pull_apart'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'tear_apart', 'painful_injury', 'break_apart', 'pulverize', 'small_cut']
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senticnet['pull_back'] = ['-0.52', '0', '0.524', '-0.69', '#anger', '#disgust', 'negative', '-0.57', 'retreat', 'retrograde', 'back_up', 'move', 'give_up']
senticnet['pull_car'] = ['-0.06', '0.164', '-0.11', '0.238', '#interest', '#admiration', 'positive', '0.055', 'jerk', 'gain_speed', 'pull', 'movement', 'turn_around']
senticnet['pull_cart'] = ['-0.05', '0.088', '0', '0.07', '#interest', '#admiration', 'positive', '0.034', 'play_dead', 'jump_over_fence', 'many_different_colors', 'jump_fence', 'group_animal']
senticnet['pull_down_pant'] = ['-0.05', '0', '-0.08', '-0.08', '#fear', '#disgust', 'negative', '-0.07', 'unzip_pant', 'find_restroom', 'find_toilet', 'serve_beer', 'full_bladder']
senticnet['pull_fast_one_on'] = ['-0.69', '0', '0.893', '-0.77', '#anger', '#disgust', 'negative', '-0.78', 'delude', 'deceive', 'cozen', 'snooker', 'lead_on']
senticnet['pull_leg_of'] = ['-0.62', '0', '0.9', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'dupe', 'slang', 'put_on', 'cod', 'take_in']
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senticnet['pull_off'] = ['0', '0.263', '-0.14', '0.295', '#interest', '#admiration', 'positive', '0.139', 'keep', 'hold', 'retain', 'hold_stuff', 'upkeep']
senticnet['pull_off_road'] = ['0.938', '0.891', '0', '0', '#joy', '#interest', 'positive', '0.914', 'carry_luggage', 'locate_place', 'get_plane', 'get_bus', 'arrive_late']
senticnet['pull_out'] = ['-0.06', '0.109', '-0.05', '0.158', '#interest', '#admiration', 'positive', '0.036', 'elasticity', 'stretch_out', 'grow_long', 'widen', 'advance_civilization']
senticnet['pull_string'] = ['0.086', '-0.14', '0.184', '-0.15', '#anger', '#disgust', 'negative', '-0.02', 'appreciate_music', 'string_break', 'sound_good', 'entertain_audience', 'entertain_others']
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senticnet['pull_tooth'] = ['-0.04', '-0.00', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.04', 'dentist_work', 'order_french_fries', 'take_number', 'wait_patiently', 'wait']
senticnet['pull_trigger'] = ['-0.25', '0', '-0.19', '-0.16', '#sadness', '#fear', 'negative', '-0.20', 'end_life', 'feel_anger', 'stab_knife', 'feel_remorse', 'arrest_police']
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senticnet['pull_up_cover'] = ['-0.03', '0', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'toss_turn', 'sleep_day', 'wear_pyjamas', 'oversleep', 'close_eye']
senticnet['pull_wagon'] = ['-0.08', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.07', 'drive_around', 'near_destination', 'fly_around', 'notice_problem', 'shovel_snow']
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senticnet['pulled_apart'] = ['-0.11', '0.095', '-0.11', '0.071', '#sadness', '#fear', 'negative', '-0.01', 'violent_action', 'pulverize', 'break_glass', 'tear_apart', 'hit_hard']
senticnet['pulled_off'] = ['0.749', '0', '0', '0.952', '#joy', '#admiration', 'positive', '0.851', 'high_class', 'citation', 'suspicion', 'goodness', 'prosperity']
senticnet['pulled_out'] = ['-0.06', '0.109', '-0.05', '0.158', '#interest', '#admiration', 'positive', '0.036', 'elasticity', 'stretch_out', 'grow_long', 'widen', 'advance_civilization']
senticnet['pulled_pork'] = ['0.028', '-0.07', '0', '0', '#joy', '#surprise', 'positive', '0.05', 'casserole', 'delicious_meal', 'hot_dog_bun', 'hamburger', 'hot_dog']
senticnet['pullet'] = ['0.039', '0.089', '-0.08', '0.143', '#interest', '#admiration', 'positive', '0.048', 'bird', 'nightingale', 'stork', 'eagle', 'rooster']
senticnet['pulley'] = ['0', '0.142', '0', '0.125', '#interest', '#admiration', 'positive', '0.134', 'weight_machine', 'power_drill', 'screw', 'screwdriver', 'air_pump']
senticnet['pulley_system'] = ['0', '0.142', '0', '0.125', '#interest', '#admiration', 'positive', '0.134', 'weight_machine', 'power_drill', 'screw', 'screwdriver', 'air_pump']
senticnet['pulley_wheel'] = ['0', '0.142', '0', '0.125', '#interest', '#admiration', 'positive', '0.134', 'weight_machine', 'power_drill', 'screw', 'screwdriver', 'air_pump']
senticnet['pulling_out'] = ['-0.06', '0.109', '-0.05', '0.158', '#interest', '#admiration', 'positive', '0.036', 'elasticity', 'stretch_out', 'grow_long', 'widen', 'advance_civilization']
senticnet['pulling_together'] = ['0', '0.211', '-0.12', '0.286', '#interest', '#admiration', 'positive', '0.125', 'bring_together', 'fuse', 'redo', 'fuse_together', 'request']
senticnet['pullman_car'] = ['-0.58', '0', '0.714', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'waster', 'diversionist', 'destroyer', 'ruiner', 'undoer']
senticnet['pullout'] = ['-0.06', '0.109', '-0.05', '0.158', '#interest', '#admiration', 'positive', '0.036', 'elasticity', 'stretch_out', 'grow_long', 'widen', 'advance_civilization']
senticnet['pullout_sofa'] = ['-0.58', '0', '0.714', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'waster', 'diversionist', 'destroyer', 'ruiner', 'undoer']
senticnet['pullover'] = ['0.811', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.773', 'sweater', 'cardigan', 'turtleneck', 'long-sleeved', 'sleeveless']
senticnet['pulls_together'] = ['0', '0.211', '-0.12', '0.286', '#interest', '#admiration', 'positive', '0.125', 'bring_together', 'fuse', 'redo', 'fuse_together', 'request']
senticnet['pullulate'] = ['0.776', '0', '0', '0.87', '#joy', '#admiration', 'positive', '0.823', 'spread', 'dilate', 'supplement', 'triple', 'annex']
senticnet['pullulation'] = ['-0.77', '0', '0.764', '-0.54', '#sadness', '#anger', 'negative', '-0.69', 'asexual_reproduction', 'apomixis', 'blastogenesis', 'budding', 'fission']
senticnet['pulmonary_anthrax'] = ['-0.95', '0', '0.876', '-0.89', '#sadness', '#disgust', 'negative', '-0.90', 'bubonic_plague', 'pestis_bubonica', 'glandular_plague', 'septicemic_plague', 'pestilence']
senticnet['pulmonary_artery_catheter'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'blatancy', 'obviousness']
senticnet['pulmonary_blood_flow'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'blatancy', 'obviousness']
senticnet['pulmonary_capillary_wedge'] = ['-0.12', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.09', 'breeze', 'move_air', 'air_movement', 'gape', 'force']
senticnet['pulmonary_consumption'] = ['-0.93', '0', '0.75', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'unhealthiness', 'injury', 'affliction', 'biliousness', 'harm']
senticnet['pulmonary_embolism'] = ['-0.98', '0', '0.827', '-0.94', '#sadness', '#disgust', 'negative', '-0.91', 'congestive_heart_failure', 'heart_attack', 'failure', 'heart_disease', 'cardiopathy']
senticnet['pulmonary_emphysema'] = ['-0.92', '0', '0.824', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'bronchopneumonia', 'bronchial_pneumonia', 'pneumonia', 'pleuropneumonia', 'pneumococcal_pneumonia']
senticnet['pulmonary_failure'] = ['-0.92', '0', '0.824', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'bronchopneumonia', 'bronchial_pneumonia', 'pneumonia', 'pleuropneumonia', 'pneumococcal_pneumonia']
senticnet['pulmonary_hemorrhage'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchitis', 'chronic_bronchitis', 'respiratory_disease', 'respiratory_illness', 'respiratory_disorder']
senticnet['pulmonary_hypertension'] = ['-0.98', '0', '0.827', '-0.94', '#sadness', '#disgust', 'negative', '-0.91', 'congestive_heart_failure', 'heart_attack', 'failure', 'heart_disease', 'cardiopathy']
senticnet['pulmonary_infection'] = ['0.998', '-0.54', '0', '0.957', '#joy', '#admiration', 'positive', '0.834', 'contagious_disease', 'contagion', 'swine_influenza', 'swine_flu', 'respiratory_disease']
senticnet['pulmonary_involvement'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchitis', 'chronic_bronchitis', 'respiratory_disease', 'respiratory_illness', 'respiratory_disorder']
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senticnet['puma_concolor'] = ['0', '-0.97', '0', '0', '#surprise', '#surprise', 'positive', '0.977', 'cougar', 'skunk', 'lion', 'tiger', 'lynx']
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senticnet['pumpkin_patch'] = ['0', '-0.01', '0.037', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'pear', 'green_apple', 'apricot', 'cherry', 'lime']
senticnet['pumpkin_pie'] = ['0.972', '0', '0', '0', '#joy', '#joy', 'positive', '0.972', 'chocolate_dessert', 'unhealthy_food', 'food_bake', 'ice_cream', 'chewy']
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senticnet['pumpkin_risotto'] = ['0.084', '-0.05', '0', '-0.04', '#joy', '#surprise', 'positive', '0.031', 'entree', 'feast', 'satiation', 'breakfast', 'supper']
senticnet['pumpkin_seed'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'roll', 'custard', 'bread', 'biscuit']
senticnet['pumpkinvine'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
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senticnet['puncak_kasih'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'identifiable', 'specifiable', 'recognizable', 'diagnosable', 'classifiable']
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senticnet['punch_line'] = ['0.867', '0.949', '0', '0', '#joy', '#interest', 'positive', '0.908', 'tell_funny_joke', 'tell_joke', 'funny_joke', 'jest', 'witticism']
senticnet['punched_tape'] = ['0', '0', '0.128', '0', '#anger', '#anger', 'negative', '-0.12', 'grid', 'efficient', 'calculator', 'computer', 'personal_computer']
senticnet['puncher'] = ['0.969', '0.998', '0', '0', '#joy', '#interest', 'positive', '0.984', 'bell_sound', 'bell_noise', 'piece_of_jewelry', 'syllable', 'fastener']
senticnet['punching_bag'] = ['-0.91', '0', '0.681', '-0.81', '#sadness', '#disgust', 'negative', '-0.80', 'chicken', 'softy', 'softie', 'crybaby', 'wimp']
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senticnet['punctilio'] = ['0.553', '-0.84', '0', '0.586', '#surprise', '#admiration', 'positive', '0.66', 'abidance', 'compliance', 'conformation', 'conformity', 'honoring']
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senticnet['punctual'] = ['0', '0.986', '0', '0.988', '#interest', '#admiration', 'positive', '0.987', 'ethic', 'diversity', 'new_experience', 'self_esteem', 'involve']
senticnet['punctuality'] = ['-0.58', '0', '0.52', '-0.99', '#sadness', '#disgust', 'negative', '-0.69', 'quickness', 'celerity', 'rapidity', 'rapidness', 'speediness']
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senticnet['punctuation'] = ['-0.03', '0.144', '0', '0.173', '#interest', '#admiration', 'positive', '0.096', 'reason_why', 'finger_action', 'indicate', 'sport_score', 'full_stop']
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senticnet['puncture_resistance'] = ['0.8', '-0.90', '0', '0.675', '#joy', '#surprise', 'positive', '0.794', 'indestructibility', 'tensile_strength', 'continuity', 'persistence', 'changelessness']
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senticnet['pungent_aroma'] = ['0.843', '0', '0', '0.971', '#joy', '#admiration', 'positive', '0.907', 'aroma', 'fragrance', 'smell', 'smell_good', 'odor']
senticnet['pungent_flavor'] = ['-0.78', '0', '0.542', '-0.94', '#sadness', '#disgust', 'negative', '-0.75', 'taste', 'taste_sensation', 'gustatory_sensation', 'taste_perception', 'gustatory_perception']
senticnet['pungent_ingredient'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'chemical_irritant', 'gingerol', 'piperine', 'pepper_spray']
senticnet['pungent_odor'] = ['0.843', '0', '0', '0.971', '#joy', '#admiration', 'positive', '0.907', 'aroma', 'fragrance', 'smell', 'smell_good', 'odor']
senticnet['pungent_scent'] = ['0.843', '0', '0', '0.971', '#joy', '#admiration', 'positive', '0.907', 'aroma', 'fragrance', 'smell', 'smell_good', 'odor']
senticnet['pungent_smell'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'poison', 'rotten', 'disgusting', 'taste_awful', 'green_bread']
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senticnet['punitive_measure_against'] = ['-0.69', '0', '0.516', '-0.94', '#sadness', '#disgust', 'negative', '-0.71', 'insubordinate', 'defiant', 'obstreperous', 'resistant', 'resistive']
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senticnet['punk_metal'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'explicit', 'declared', 'definitive', 'graphic']
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senticnet['punk_outfit'] = ['0.797', '-0.95', '0', '0.921', '#surprise', '#admiration', 'positive', '0.891', 'lush', 'alcoholic', 'alky', 'dipsomaniac', 'boozer']
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senticnet['punk_rock'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'explicit', 'declared', 'definitive', 'graphic']
senticnet['punkah'] = ['0.619', '-0.96', '0', '0.672', '#surprise', '#admiration', 'positive', '0.753', 'electric_fan', 'blower', 'fan', 'exhaust_fan', 'device']
senticnet['punkish'] = ['0.26', '0', '0', '0.089', '#joy', '#admiration', 'positive', '0.174', 'balcony_rail', 'movie_theater_seat', 'row_seat', 'usher', 'sticky_floor']
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senticnet['punxsutawney_phil_saw'] = ['-0.82', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.80', 'evil', 'darkness', 'dark', 'sun_down', 'dark_side']
senticnet['pup'] = ['0.924', '0', '0', '0.956', '#joy', '#admiration', 'positive', '0.94', 'goof', 'magical', 'bloodhound', 'chance', 'karma']
senticnet['pupal'] = ['0.642', '-0.58', '0', '0.751', '#joy', '#admiration', 'positive', '0.659', 'change', 'become_better', 'change_aspect', 'look_better', 'become_bigger']
senticnet['pupal_stage'] = ['0.642', '-0.58', '0', '0.751', '#joy', '#admiration', 'positive', '0.659', 'change', 'become_better', 'change_aspect', 'look_better', 'become_bigger']
senticnet['pupil'] = ['-0.08', '-0.02', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'professor', 'group_student', 'school_division', 'good_student', 'college_student']
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senticnet['pupillary'] = ['-0.78', '0', '0.669', '-0.89', '#sadness', '#disgust', 'negative', '-0.78', 'pain_sensation', 'painful_sensation', 'prickling', 'tingling', 'pain']
senticnet['pupillary_dilation'] = ['-0.73', '0', '0.904', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'unshapely', 'shapeless', 'lumpy', 'deformed', 'malformed']
senticnet['pupillary_reflex'] = ['-0.92', '0', '0.923', '-0.59', '#sadness', '#anger', 'negative', '-0.81', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['puppet'] = ['0', '0', '-0.91', '-0.77', '#fear', '#disgust', 'negative', '-0.84', 'doll', 'lathe', 'marionette', 'poppet', 'puppeteer']
senticnet['puppet_ruler'] = ['0.029', '0.079', '0', '0.063', '#interest', '#admiration', 'positive', '0.057', 'king', 'duchess', 'sultan', 'royal_leader', 'emperor']
senticnet['puppeteer'] = ['0', '0', '-0.75', '-0.73', '#fear', '#disgust', 'negative', '-0.74', 'manipulator', 'handler', 'move', 'arrange', 'marionette']
senticnet['puppetmaster'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['puppetry'] = ['0', '0.964', '0', '0.753', '#interest', '#admiration', 'positive', '0.859', 'dramaturgy', 'theatricality', 'costuming', 'theatrics', 'stagecraft']
senticnet['puppy'] = ['0.139', '0.133', '-0.04', '0.231', '#joy', '#admiration', 'positive', '0.114', 'baby_dog', 'vulnerable', 'need_care', 'young_animal', 'newborn']
senticnet['puppy_dog'] = ['-0.91', '0', '0.681', '-0.81', '#sadness', '#disgust', 'negative', '-0.80', 'chicken', 'doormat', 'softie', 'crybaby', 'wimp']
senticnet['puppy_fat'] = ['-0.42', '-0.14', '-0.06', '-0.37', '#sadness', '#disgust', 'negative', '-0.18', 'brain_tumor', 'bad_hair_day', 'lyme_disease', 'diarrhoea', 'suffer_fool']
senticnet['puppy_love'] = ['0.522', '0.674', '-0.27', '0.27', '#joy', '#interest', 'positive', '0.297', 'calf_love', 'infatuation', 'amatory', 'amorousness', 'amorous']
senticnet['puppycat'] = ['0', '0', '0', '0.869', '#admiration', '#admiration', 'positive', '0.869', 'fly_animal', 'insect', 'mosquito', 'bug', 'fly_insect']
senticnet['puppyish'] = ['0.85', '-0.71', '0', '0.617', '#joy', '#surprise', 'positive', '0.728', 'pretender', 'phony', 'phoney', 'hypocrite', 'dissembler']
senticnet['puppyishly'] = ['-0.79', '0', '0.992', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['purblind'] = ['-0.70', '0', '-0.73', '0', '#sadness', '#fear', 'negative', '-0.71', 'dense', 'obtuse', 'dull', 'dumb', 'dim']
senticnet['purchase'] = ['-0.05', '0.175', '0', '0.117', '#interest', '#admiration', 'positive', '0.078', 'establish_credit', 'cash_check', 'pay_goods', 'pay_debt', 'buy_goods_service']
senticnet['purchase_book'] = ['-0.06', '-0.01', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.02', 'choose_book', 'bad_read', 'store_book', 'sell_book', 'hold_book']
senticnet['purchase_car'] = ['0.06', '-0.12', '0', '0', '#joy', '#surprise', 'positive', '0.093', 'car', 'buy_motorbike', 'buy', 'buy_car', 'buy_object']
senticnet['purchase_cellular_phone'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'pay_bill', 'pay_check', 'pay_credit_card', 'pay_cash', 'spend_money']
senticnet['purchase_food'] = ['-0.03', '-0.00', '0.036', '0', '#sadness', '#anger', 'negative', '-0.02', 'buy_bread', 'buy_grocery', 'get_grocery', 'buy_food', 'shop_grocery']
senticnet['purchase_gift'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'buy_gift', 'gift', 'christmas_gift', 'birthday_gift', 'buy_happiness']
senticnet['purchase_goods'] = ['0', '0.095', '-0.05', '0.132', '#interest', '#admiration', 'positive', '0.058', 'buy_gum', 'buy', 'shop', 'fresh_breathe', 'spread_love']
senticnet['purchase_grocery'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'buy_milk', 'buy_vegetable', 'shop', 'shop_grocery', 'buy_grocery']
senticnet['purchase_house'] = ['0.601', '0.647', '-0.26', '0.322', '#joy', '#interest', 'positive', '0.326', 'family_nest', 'love_back', 'protection', 'shelter', 'buy_home']
senticnet['purchase_pharmacy'] = ['-0.03', '-0.04', '0.074', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'find_ranch', 'require_good_health', 'buy', 'find_animal', 'ranch']
senticnet['purchase_price'] = ['-0.04', '0.268', '0', '0.159', '#interest', '#admiration', 'positive', '0.127', 'total', 'amount', 'a_lot', 'aggregate', 'bill']
senticnet['purchase_ticket'] = ['-0.06', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.04', 'buy_ticket', 'book_ticket', 'find_out_play', 'money_ticket', 'buy_ticket_theater']
senticnet['purchasing'] = ['0.978', '0', '0', '0.81', '#joy', '#admiration', 'positive', '0.894', 'purchase', 'buy', 'buying', 'chemistry', 'different_application']
senticnet['purchasing_manager_survey'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'mathematical', 'muscle', 'math_function', 'feel_sick', 'separation']
senticnet['purdue_pharma'] = ['0.138', '0.103', '0', '0.095', '#joy', '#interest', 'positive', '0.112', 'masturbation', 'sexual_intercourse', 'sex', 'warm_fuzzy_feeling', 'fuck']
senticnet['pure'] = ['0.053', '0.162', '-0.09', '0.297', '#interest', '#admiration', 'positive', '0.103', 'godliness', 'soft', 'white', 'capitalize', 'refined']
senticnet['pure_actuality'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'metastability', 'monotony', 'invariance', 'constancy', 'unchangeability']
senticnet['pure_adrenaline'] = ['0.958', '-0.72', '0', '0.72', '#joy', '#surprise', 'positive', '0.801', 'pressor', 'hormone', 'endocrine', 'internal_secretion', 'catecholamine']
senticnet['pure_alexia'] = ['-0.56', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.63', 'inarticulate', 'aphonic', 'speechless', 'unarticulated', 'dumb']
senticnet['pure_cocaine'] = ['-0.63', '0', '0.819', '-0.54', '#sadness', '#anger', 'negative', '-0.66', 'smack', 'hell_dust', 'nose_drop', 'thunder', 'skag']
senticnet['pure_delight'] = ['0.695', '0.623', '-0.23', '0.319', '#joy', '#interest', 'positive', '0.351', 'uproarious', 'hilariously', 'hilarious', 'screaming', 'uproariously']
senticnet['pure_drinking_water'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'bottled_water', 'sugar_water']
senticnet['pure_ethanol'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
senticnet['pure_evil'] = ['-0.96', '0', '0.108', '-0.90', '#sadness', '#disgust', 'negative', '-0.66', 'evil', 'darkness', 'dark', 'sun_down', 'shadow']
senticnet['pure_fantasy'] = ['0.744', '-0.76', '0', '0.669', '#joy', '#surprise', 'positive', '0.726', 'accessible', 'comprehendible', 'approachable', 'perceivable', 'intelligible']
senticnet['pure_fiction'] = ['0.744', '-0.76', '0', '0.669', '#joy', '#surprise', 'positive', '0.726', 'accessible', 'comprehendible', 'approachable', 'perceivable', 'intelligible']
senticnet['pure_flix'] = ['0.951', '0', '0', '0.731', '#joy', '#admiration', 'positive', '0.841', 'pastime', 'enjoyment', 'pleasure', 'enjoy', 'fun']
senticnet['pure_gold'] = ['-0.62', '0', '0.504', '-0.79', '#sadness', '#disgust', 'negative', '-0.64', 'green_gold', 'gold', 'gold_dust', 'guinea_gold', 'noble_metal']
senticnet['pure_heroin'] = ['-0.63', '0', '0.819', '-0.54', '#sadness', '#anger', 'negative', '-0.66', 'smack', 'hell_dust', 'nose_drop', 'thunder', 'skag']
senticnet['pure_hydrogen'] = ['0', '0', '-0.06', '0', '#fear', '#fear', 'negative', '-0.06', 'greasy', 'car_gas', 'fossil_fuel', 'crude', 'lubrication']
senticnet['pure_immanence'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'metastability', 'monotony', 'invariance', 'constancy', 'unchangeability']
senticnet['pure_intellect'] = ['0.651', '-0.73', '0', '0.975', '#surprise', '#admiration', 'positive', '0.788', 'profoundness', 'profundity', 'sagacity', 'sagaciousness', 'discernment']
senticnet['pure_joy'] = ['-0.83', '0', '0.703', '-0.85', '#sadness', '#disgust', 'negative', '-0.79', 'hurly_burly', 'kerfuffle', 'commotion', 'disruption', 'disturbance']
senticnet['pure_luck'] = ['-0.43', '-0.14', '-0.06', '-0.33', '#sadness', '#disgust', 'negative', '-0.17', 'brain_tumor', 'lyme_disease', 'cause_suffer', 'lose_team', 'bad_hair_day']
senticnet['pure_magnesium'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'gold', 'bronze', 'alloy', 'medal', 'copper']
senticnet['pure_mdma'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'use_drug', 'do_drug', 'drug_use', 'drug', 'addiction']
senticnet['pure_thc'] = ['0.049', '0', '0', '0.113', '#joy', '#admiration', 'positive', '0.081', 'solid', 'hard_material', 'construction_material', 'hard', 'calcium']
senticnet['pure_titanium'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'gold', 'bronze', 'alloy', 'medal', 'copper']
senticnet['pure_tone'] = ['-0.95', '0', '-0.73', '-0.73', '#sadness', '#fear', 'negative', '-0.80', 'decibel', 'noise', 'loud_soft', 'sound', 'audio']
senticnet['pure_water'] = ['0.78', '0.844', '0', '0', '#joy', '#interest', 'positive', '0.812', 'hygroscope', 'hold_water', 'absorb_water', 'molecule', 'rain_water']
senticnet['purell'] = ['0.878', '0.869', '0', '0', '#joy', '#interest', 'positive', '0.873', 'fluoride_mouthwash', 'hair_conditioner', 'sculpt_gel', 'dandruff_shampoo', 'hair_gel']
senticnet['purely'] = ['0', '0.861', '0', '0.751', '#interest', '#admiration', 'positive', '0.806', 'strictly', 'study', 'mathematics', 'whole_number', 'number_theory']
senticnet['purely_arbitrary'] = ['-0.72', '0', '0.552', '-0.76', '#sadness', '#disgust', 'negative', '-0.68', 'break', 'good_luck', 'happy_chance', 'hap', 'coincidence']
senticnet['purely_decorative'] = ['0.921', '-0.90', '0', '0.608', '#joy', '#surprise', 'positive', '0.811', 'overstate', 'hyperbolize', 'glorify', 'exaggerate', 'overdraw']
senticnet['purely_defensive'] = ['-0.82', '0', '0.739', '-0.50', '#sadness', '#anger', 'negative', '-0.69', 'fake', 'juke', 'maneuver', 'manoeuvre', 'feint']
senticnet['purely_imaginary'] = ['0.075', '0.207', '-0.06', '0.12', '#interest', '#admiration', 'positive', '0.085', 'false', 'unreal', 'pretend', 'fictional', 'fictitious']
senticnet['purely_intellectual'] = ['0.838', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.835', 'pondering', 'broody', 'contemplative', 'meditative', 'think']
senticnet['purely_logical'] = ['-0.99', '0', '0.878', '-0.95', '#sadness', '#disgust', 'negative', '-0.94', 'adequacy', 'satisfactoriness', 'acceptability', 'acceptableness', 'quality']
senticnet['purely_mental'] = ['0.838', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.835', 'pondering', 'broody', 'contemplative', 'meditative', 'think']
senticnet['purely_physical'] = ['0.531', '0.673', '-0.27', '0.275', '#joy', '#interest', 'positive', '0.301', 'amorous', 'amorousness', 'amatory', 'enamoredness', 'puppy_love']
senticnet['purely_quantitative'] = ['-0.97', '0', '0.781', '-0.91', '#sadness', '#disgust', 'negative', '-0.88', 'weakness', 'fault', 'flaw', 'defect', 'wart']
senticnet['purely_rational'] = ['-0.99', '0', '0.878', '-0.95', '#sadness', '#disgust', 'negative', '-0.94', 'adequacy', 'satisfactoriness', 'acceptability', 'acceptableness', 'quality']
senticnet['purely_secular'] = ['0.783', '0.804', '0', '0.826', '#interest', '#admiration', 'positive', '0.804', 'religious_purpose', 'spiritual', 'faith', 'religion', 'judaism']
senticnet['purely_spiritual'] = ['-0.86', '0', '0.945', '-0.71', '#sadness', '#anger', 'negative', '-0.84', 'sweat', 'effort', 'elbow_grease', 'exertion', 'travail']
senticnet['purely_subjective'] = ['0.828', '-0.90', '0', '0.874', '#surprise', '#admiration', 'positive', '0.869', 'perspicacity', 'judgment', 'fair', 'sound_judgment', 'fair_judgment']
senticnet['purely_syntactic'] = ['0.777', '-0.68', '0', '0.601', '#joy', '#surprise', 'positive', '0.686', 'lucidness', 'clarity', 'clearness', 'lucidity', 'pellucidity']
senticnet['purely_visual'] = ['-0.63', '0', '0.694', '-0.66', '#anger', '#disgust', 'negative', '-0.66', 'corporality', 'tangibleness', 'palpability', 'reality', 'solidness']
senticnet['purely_voluntary'] = ['-0.02', '0.1', '-0.07', '0.089', '#interest', '#admiration', 'positive', '0.023', 'functionary', 'employment', 'labor', 'employ', 'toil']
senticnet['purest_gold'] = ['0', '-0.75', '0', '0.794', '#surprise', '#admiration', 'positive', '0.772', 'solid', 'brown', 'red_stone', 'opaque', 'generic']
senticnet['purgation'] = ['0.581', '-0.88', '0', '0.534', '#joy', '#surprise', 'positive', '0.667', 'better', 'improve', 'pure', 'meliorate', 'amend']
senticnet['purgative'] = ['0.945', '-0.99', '0', '0.873', '#joy', '#surprise', 'positive', '0.939', 'medicament', 'medicinal_drug', 'medication', 'aloe', 'bitter_aloe']
senticnet['purgatorial'] = ['-0.5', '0', '0.982', '-0.85', '#anger', '#disgust', 'negative', '-0.77', 'unpeaceable', 'stormy', 'tempestuous', 'militant', 'belligerent']
senticnet['purgatory'] = ['-0.88', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.91', 'limbo', 'heaven', 'damnation', 'hell', 'soul']
senticnet['purge'] = ['0.988', '-0.86', '0', '0.964', '#joy', '#admiration', 'positive', '0.938', 'pronounce', 'exonerate', 'whitewash', 'vindicate', 'label']
senticnet['purgeth'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'lecherousness', 'lustfulness', 'amorousness']
senticnet['purging_disorder'] = ['-0.52', '0', '0.786', '-0.92', '#anger', '#disgust', 'negative', '-0.74', 'upset', 'bulimarexia', 'bulima_nervosa', 'eating_disorder', 'disorder']
senticnet['purification'] = ['0.883', '0.813', '0', '0', '#joy', '#interest', 'positive', '0.848', 'filtration', 'purifying', 'purified', 'purify', 'cleansing']
senticnet['purification_rite'] = ['-0.93', '0', '0.807', '-0.95', '#sadness', '#disgust', 'negative', '-0.89', 'heatstroke', 'heat_hyperpyrexia', 'malady', 'breakdown', 'shock']
senticnet['purification_ritual'] = ['0.807', '-0.92', '0', '0.796', '#joy', '#surprise', 'positive', '0.841', 'agape', 'confirmation', 'libation', 'rite', 'service']
senticnet['purified'] = ['0.877', '0.806', '0', '0', '#joy', '#interest', 'positive', '0.842', 'filtration', 'purifying', 'purification', 'purify', 'cleansing']
senticnet['purified_protein_derivative'] = ['-0.74', '0', '0.766', '0', '#sadness', '#anger', 'negative', '-0.75', 'use_needle', 'clothes_repair', 'woven', 'embroidery', 'thread_needle']
senticnet['purified_water'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'drinking_water', 'sugar_water']
senticnet['purifier'] = ['0.581', '-0.88', '0', '0.534', '#joy', '#surprise', 'positive', '0.667', 'better', 'improve', 'pure', 'meliorate', 'amend']
senticnet['purify'] = ['0.581', '-0.88', '0', '0.534', '#joy', '#surprise', 'positive', '0.667', 'better', 'improve', 'pure', 'meliorate', 'amend']
senticnet['purify_water'] = ['0.581', '-0.88', '0', '0.534', '#joy', '#surprise', 'positive', '0.667', 'better', 'improve', 'pure', 'meliorate', 'amend']
senticnet['purifying'] = ['0.875', '0.811', '0', '0', '#joy', '#interest', 'positive', '0.843', 'filtration', 'purification', 'purified', 'purify', 'cleansing']
senticnet['purifying_water'] = ['0.581', '-0.88', '0', '0.534', '#joy', '#surprise', 'positive', '0.667', 'better', 'improve', 'pure', 'meliorate', 'amend']
senticnet['purinox'] = ['0.855', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.86', 'ticket_counter', 'newspaper_stand', 'luggage_carrier', 'aircraft_roadway', 'timetable']
senticnet['purism'] = ['0', '0.826', '0', '0.739', '#interest', '#admiration', 'positive', '0.782', 'utopianism', 'pedantry', 'parochialism', 'primitivism', 'expressiveness']
senticnet['purist'] = ['0.84', '0', '0', '0.915', '#joy', '#admiration', 'positive', '0.877', 'perfectionalist', 'traditionalist', 'formalist', 'hard-core', 'pragmatist']
senticnet['puritan'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'nonconformity', 'iconoclasm', 'heterodoxy', 'nonconformance', 'orientation']
senticnet['puritan_clergy'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'nonconformity', 'iconoclasm', 'heterodoxy', 'nonconformance', 'orientation']
senticnet['puritan_movement'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'nonconformity', 'iconoclasm', 'heterodoxy', 'nonconformance', 'orientation']
senticnet['puritan_preacher'] = ['-0.85', '0', '0.749', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'hairsplitter', 'fight', 'quarrel', 'obstructor', 'obstructer']
senticnet['puritanic'] = ['0.674', '-0.56', '0', '0.985', '#joy', '#admiration', 'positive', '0.742', 'renunciant', 'renunciative', 'nonindulgent', 'puritanical', 'corrective']
senticnet['puritanical'] = ['0.675', '-0.56', '0', '0.975', '#joy', '#admiration', 'positive', '0.739', 'renunciant', 'renunciative', 'puritanic', 'nonindulgent', 'corrective']
senticnet['puritanism'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'nonconformity', 'iconoclasm', 'heterodoxy', 'nonconformance', 'orientation']
senticnet['purity'] = ['0', '0.08', '0', '0.144', '#interest', '#admiration', 'positive', '0.112', 'cloud', 'mist', 'precipitate', 'fog', 'initiation']
senticnet['purlie'] = ['0', '0.066', '-0.05', '0.089', '#interest', '#admiration', 'positive', '0.032', 'pineapple', 'kale', 'banana', 'pickle', 'tomato']
senticnet['purloin'] = ['0', '0', '-0.77', '-0.88', '#fear', '#disgust', 'negative', '-0.82', 'cabbage', 'pinch', 'pilfer', 'abstract', 'snarf']
senticnet['purloined_letter'] = ['-0.89', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.80', 'riddle', 'obituary', 'editorial', 'news_story', 'article']
senticnet['purple'] = ['0.591', '-0.55', '0', '0.514', '#joy', '#surprise', 'positive', '0.553', 'violet', 'mauve', 'lavender', 'reddish_purple', 'royal_purple']
senticnet['purple_cloak'] = ['-0.62', '0', '0.961', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'crown', 'jeweled_headdress', 'tiara', 'headdress', 'headgear']
senticnet['purple_color'] = ['-0.13', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.13', 'yellow_blue', 'blue_yellow', 'green', 'violet', 'purple']
senticnet['purple_coneflower'] = ['0.632', '-0.93', '0', '0.928', '#surprise', '#admiration', 'positive', '0.831', 'herb', 'oregano', 'marjoram', 'pot_marjoram', 'origanum']
senticnet['purple_dragon'] = ['-0.64', '0', '0.524', '-0.59', '#sadness', '#disgust', 'negative', '-0.58', 'bum', 'dreamer', 'daydreamer', 'escapist', 'wishful_thinker']
senticnet['purple_flower'] = ['0', '0.07', '0', '0.071', '#interest', '#admiration', 'positive', '0.071', 'tree_flower', 'bloom', 'perennial', 'poppy', 'greenery']
senticnet['purple_gown'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'pinafore', 'pinny', 'dirndl', 'dress', 'muumuu']
senticnet['purple_hair'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'naked_eye', 'eye', 'oculus_sinister']
senticnet['purple_heron'] = ['0.797', '-0.71', '0', '0.736', '#joy', '#admiration', 'positive', '0.751', 'wood_ibis', 'wood_stork', 'ibis', 'wading_bird', 'wader']
senticnet['purple_heron_ardea_purpurea'] = ['0.797', '-0.71', '0', '0.736', '#joy', '#admiration', 'positive', '0.751', 'wood_ibis', 'wood_stork', 'ibis', 'wading_bird', 'wader']
senticnet['purple_milk_vetch'] = ['-0.94', '0', '0.608', '-0.95', '#sadness', '#disgust', 'negative', '-0.83', 'wild_licorice', 'wild_liquorice', 'alpine_milk_vetch', 'milk_vetch', 'woody_plant']
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senticnet['put_clothes'] = ['0', '0.076', '-0.06', '0.205', '#interest', '#admiration', 'positive', '0.072', 'cover_body', 'bring_present', 'christmas', 'carry_book', 'move_away']
senticnet['put_clothes_back'] = ['-0.04', '0', '-0.07', '-0.10', '#fear', '#disgust', 'negative', '-0.07', 'clock_out', 'sit_back_relax', 'quit_work', 'make_sex', 'stop_work']
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senticnet['put_effort'] = ['-0.05', '0.08', '-0.05', '0.158', '#interest', '#admiration', 'positive', '0.033', 'hold_out', 'resist', 'strike_against', 'hold_match', 'change_plan']
senticnet['put_envelope'] = ['-0.03', '0.091', '0', '0.097', '#interest', '#admiration', 'positive', '0.051', 'write_text', 'find_computer', 'body_language', 'check_spelling', 'write_message']
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senticnet['put_food'] = ['0', '0', '0', '0.881', '#admiration', '#admiration', 'positive', '0.881', 'food', 'prepare_meal', 'cook', 'serve_food', 'prepare_food']
senticnet['put_food_into_mouth'] = ['0.769', '0', '0', '0', '#joy', '#joy', 'positive', '0.769', 'make_breakfast', 'enjoy_meal', 'appetite', 'prepare_meal', 'prepare_breakfast']
senticnet['put_food_mouth'] = ['0.026', '0.069', '0', '0', '#joy', '#interest', 'positive', '0.048', 'eat_food', 'eat_beef', 'eat_dinner', 'chew_food', 'eat_much']
senticnet['put_food_table'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'tip_waiter', 'finish_job', 'eat_food', 'buy_food', 'buy_sandwich']
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senticnet['put_hand'] = ['0', '0.133', '-0.05', '0.149', '#interest', '#admiration', 'positive', '0.076', 'restrict_access', 'door_knob', 'hold', 'handle', 'control_access']
senticnet['put_helmet'] = ['-0.08', '-0.00', '0.099', '0', '#sadness', '#anger', 'negative', '-0.05', 'find_ball', 'good_balance', 'wear_good_shoes', 'use_leg', 'buy_shoes']
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senticnet['put_in'] = ['-0.69', '0', '0.883', '-0.91', '#anger', '#disgust', 'negative', '-0.82', 'stick_in', 'enclose', 'insert', 'inclose', 'feed_in']
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senticnet['pyrotechnic_device'] = ['0.13', '0.097', '-0.07', '0.198', '#joy', '#admiration', 'positive', '0.087', 'jiggle', 'back_forth', 'vibrate', 'blend', 'mixture']
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senticnet['pyroxylin'] = ['-0.88', '0', '0.87', '-0.76', '#sadness', '#anger', 'negative', '-0.83', 'cellulose_ester', 'cellulose_nitrate', 'cellulose_nitratee', 'pyrocellulose', 'nitrate']
senticnet['pyroxyline'] = ['-0.88', '0', '0.87', '-0.76', '#sadness', '#anger', 'negative', '-0.83', 'cellulose_ester', 'pyroxylin', 'cellulose_nitrate', 'pyrocellulose', 'nitrate']
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senticnet['pyrus_calleryana'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'horse_chestnut', 'red_buckeye', 'particolored_buckeye']
senticnet['pythagorean_comma'] = ['-0.65', '0', '0.681', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'tone', 'whole_tone', 'step', 'whole_step', 'musical_interval']
senticnet['python'] = ['0', '0.11', '0', '0.077', '#interest', '#admiration', 'positive', '0.093', 'snake', 'cobra', 'reptile', 'animal', 'rattlesnake']
senticnet['python-like'] = ['0', '0', '-0.86', '-0.75', '#fear', '#disgust', 'negative', '-0.80', 'non-restrictive', 'appositive', 'nonrestrictive', 'per-computer', 'non-deadly']
senticnet['python_code'] = ['-0.95', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.84', 'hold_pencil', 'food_scrap', 'paper_product', 'waste_paper', 'cover']
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senticnet['python_programming_language'] = ['0', '0', '0.737', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'annoy', 'skin_rash', 'bother', 'irritation', 'great_dane']
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senticnet['qas'] = ['0', '0.722', '0', '0.763', '#interest', '#admiration', 'positive', '0.742', 'white_house_announcement', 'text', 'edu', 'distribution', 'messageid']
senticnet['qasr_amra'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
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senticnet['qdisc'] = ['-0.15', '0', '0.029', '-0.11', '#sadness', '#disgust', 'negative', '-0.1', 'start_over', 'front_line', 'find_line', 'take_turn', 'long_line']
senticnet['qif'] = ['-0.03', '0.112', '-0.08', '0.164', '#interest', '#admiration', 'positive', '0.04', 'run', 'jump_over', 'quick', 'sprint', 'flee']
senticnet['qif_file'] = ['-0.03', '0.112', '-0.08', '0.164', '#interest', '#admiration', 'positive', '0.04', 'run', 'jump_over', 'quick', 'sprint', 'flee']
senticnet['qif_format'] = ['-0.03', '0.112', '-0.08', '0.164', '#interest', '#admiration', 'positive', '0.04', 'run', 'jump_over', 'quick', 'sprint', 'flee']
senticnet['qing_jao'] = ['0.702', '-0.94', '0', '0.582', '#joy', '#surprise', 'positive', '0.743', 'beatable', 'vanquishable', 'vincible', 'subduable', 'subjugable']
senticnet['qiviut'] = ['-0.53', '0', '0.751', '-0.97', '#anger', '#disgust', 'negative', '-0.75', 'guanaco', 'domestic_llama', 'llama', 'artiodactyl', 'artiodactyl_mammal']
senticnet['qmail'] = ['0.031', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.062', 'letter_slot', 'postal_counter', 'mailbox', 'letter_scale', 'envelope']
senticnet['qmail_smtpd'] = ['0.031', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.062', 'letter_slot', 'postal_counter', 'mailbox', 'letter_scale', 'envelope']
senticnet['qq_plot'] = ['-0.59', '0', '0.672', '-0.83', '#anger', '#disgust', 'negative', '-0.7', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'asymmetry', 'irregularity']
senticnet['qr_code'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expansive', 'expansible', 'erectile', 'cavernous']
senticnet['qrs_complex'] = ['0.04', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.055', 'measurement', 'time_interval', 'length', 'time', 'spatial_dimension']
senticnet['qt_interval'] = ['-0.83', '0', '0.987', '-0.50', '#sadness', '#anger', 'negative', '-0.77', 'heart_block', 'atrioventricular_block', 'bradycardia', 'ventricular_fibrillation', 'tachycardia']
senticnet['quack'] = ['-0.50', '0', '0.764', '-0.61', '#anger', '#disgust', 'negative', '-0.62', 'doctor', 'mountebank', 'charlatan', 'duck', 'physician']
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senticnet['quad_bike'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'fuel', 'car', 'fossil_fuel', 'transport', 'natural_resource']
senticnet['quad_injury'] = ['-0.69', '0', '0.768', '-0.67', '#sadness', '#anger', 'negative', '-0.71', 'dither', 'pother', 'worry', 'flap', 'scruple']
senticnet['quad_muscle'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'deltoid', 'muscle', 'abductor', 'skeletal_muscle']
senticnet['quadrate_bone'] = ['0.886', '-0.99', '0', '0.542', '#joy', '#surprise', 'positive', '0.808', 'jowl', 'lower_jaw', 'mandible', 'lantern_jaw', 'lantern_jawr_bone']
senticnet['quadratic'] = ['-0.84', '0', '-0.76', '0', '#sadness', '#fear', 'negative', '-0.80', 'polynomial', 'polynomial', 'equation', 'piecewise', 'approximation']
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senticnet['quadratically'] = ['0.973', '0.799', '0', '0', '#joy', '#interest', 'positive', '0.886', 'linearly', 'non-linearly', 'inversely', 'logarithmically', 'monotonically']
senticnet['quadriceps'] = ['-0.74', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.86', 'hamstring', 'groin', 'sprained', 'sidelined', 'knee']
senticnet['quadriceps_injury'] = ['0.94', '0.78', '0', '0', '#joy', '#interest', 'positive', '0.86', 'breaheavily', 'heavy_breathe', 'get_exhausted', 'exhaustion', 'see_neighbor']
senticnet['quadrilateral'] = ['0', '0', '0', '0.085', '#admiration', '#admiration', 'positive', '0.085', 'shape', 'inside_house', 'house_part', 'four_walls', 'square_shape']
senticnet['quadrille'] = ['0.591', '-0.91', '0', '0.605', '#surprise', '#admiration', 'positive', '0.704', 'farandole', 'folk_dance', 'polka', 'dance', 'typical_dance']
senticnet['quadripoint'] = ['0.034', '0.09', '0.039', '0.061', '#interest', '#admiration', 'positive', '0.036', 'border', 'limit', 'edge', 'indoor', 'side']
senticnet['quadrumana'] = ['-0.93', '0', '0.508', '-0.58', '#sadness', '#disgust', 'negative', '-0.67', 'inhuman', 'bloodless', 'apelike', 'nonhuman', 'nonhumanal']
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senticnet['quadrupedal_locomotion'] = ['0', '0', '-0.81', '-0.92', '#fear', '#disgust', 'negative', '-0.87', 'tree-living', 'dendroidal', 'arborous', 'arboriform', 'arborary']
senticnet['quadrupedalism'] = ['0', '0', '-0.81', '-0.92', '#fear', '#disgust', 'negative', '-0.87', 'tree-living', 'dendroidal', 'arborous', 'arboriform', 'arborary']
senticnet['quadrupedally'] = ['0', '0', '-0.81', '-0.92', '#fear', '#disgust', 'negative', '-0.87', 'tree-living', 'dendroidal', 'arborous', 'arboriform', 'arborary']
senticnet['quadruple'] = ['0.824', '-0.71', '0', '0', '#joy', '#surprise', 'positive', '0.77', 'double', 'triple', 'single', 'dual', 'twin']
senticnet['quadruple_double'] = ['0.749', '0', '0', '0.952', '#joy', '#admiration', 'positive', '0.851', 'high_class', 'citation', 'suspicion', 'goodness', 'prosperity']
senticnet['quadrupolar'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'elementary_particle', 'neutron', 'electron', 'subatomic_particle', 'proton']
senticnet['quae'] = ['0.654', '-0.75', '0', '0.78', '#surprise', '#admiration', 'positive', '0.729', 'suit', 'cause', 'lawsuit', 'occurrence', 'countersuit']
senticnet['quaff'] = ['0', '0.091', '-0.05', '0.073', '#interest', '#admiration', 'positive', '0.037', 'privately', 'hold_liquid', 'make_wish', 'rinse_dish', 'find_toilet']
senticnet['quaffer'] = ['0.784', '-0.59', '0', '0.846', '#joy', '#admiration', 'positive', '0.742', 'astuteness', 'slyness', 'wiliness', 'craftiness', 'cunning']
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senticnet['quail'] = ['0.561', '-0.94', '0', '0.65', '#surprise', '#admiration', 'positive', '0.717', 'bird', 'grouse', 'partridge', 'wildfowl', 'pheasant']
senticnet['quail_egg'] = ['0.041', '0.069', '0.032', '0.13', '#interest', '#admiration', 'positive', '0.052', 'edible_human', 'round_fruit', 'peelable', 'green_fruit', 'nutritious']
senticnet['quaint'] = ['-0.65', '0', '0.858', '-0.83', '#anger', '#disgust', 'negative', '-0.78', 'turmoil', 'convulsion', 'tempest', 'disorder', 'earthquake']
senticnet['quaintly'] = ['0', '-0.83', '0', '0.995', '#surprise', '#admiration', 'positive', '0.916', 'incongruously', 'improbably', 'amusingly', 'grandly', 'intriguingly']
senticnet['quaintness'] = ['-0.56', '0', '0.977', '-0.86', '#anger', '#disgust', 'negative', '-0.80', 'freakishness', 'strangeness', 'abnormality', 'queerness', 'crotchet']
senticnet['quake'] = ['-0.89', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.91', 'avalanche', 'tremor', 'rumble', 'electric_shock', 'bump']
senticnet['quaker'] = ['0.77', '0', '0', '0.844', '#joy', '#admiration', 'positive', '0.807', 'trembler', 'friend', 'charge', 'comment', 'business']
senticnet['quaker_burial_ground'] = ['-0.50', '0', '0.762', '-0.89', '#anger', '#disgust', 'negative', '-0.71', 'cemetery', 'graveyard', 'burial_site', 'grassland', 'burying_ground']
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senticnet['quaker_hill_nursing_home'] = ['0.057', '0', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'huge_fire', 'fire', 'rapid_oxidation', 'heat_light', 'flame']
senticnet['quaker_oatmeal'] = ['0.964', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.964', 'carbonated_drink', 'sugar_free', 'coke', 'diet_coke', 'beverage']
senticnet['quaking_aspen'] = ['0.045', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.356', 'ponderosa', 'bull_pine', 'western_yellow_pine', 'yellow_pine', 'longleaf_pine']
senticnet['qualification'] = ['0.034', '0.119', '0', '0.075', '#interest', '#admiration', 'positive', '0.076', 'diploma', 'certificate', 'grade', 'graduate', 'celsius']
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senticnet['qualified_personnel'] = ['0.814', '-0.87', '0', '0.918', '#surprise', '#admiration', 'positive', '0.87', 'ship_company', 'company', 'work_force', 'workforce', 'full_complement']
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senticnet['qualified_solicitor'] = ['0.053', '0.122', '-0.07', '0.086', '#interest', '#admiration', 'positive', '0.046', 'attorney', 'lawyer', 'jury', 'decide_guilt', 'prosecutor']
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senticnet['qualifying_exam'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'organize_thought', 'scratch_head', 'know_answer', 'speak_out', 'know_talk']
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senticnet['quam_oblationem'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'reverence', 'genuflection', 'obeisance', 'kowtow', 'mafia']
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senticnet['quantifiable'] = ['0', '0.238', '0.021', '0.152', '#interest', '#admiration', 'positive', '0.123', 'money_value', 'money_amount', 'worth', 'monetary_amount', 'importance']
senticnet['quantification'] = ['-0.97', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.93', 'narrowness', 'limitation', 'regulation', 'restraint', 'limit']
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senticnet['quantile_function'] = ['-0.59', '0', '0.672', '-0.83', '#anger', '#disgust', 'negative', '-0.7', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'asymmetry', 'irregularity']
senticnet['quantitative'] = ['0.806', '0.864', '0', '0', '#joy', '#interest', 'positive', '0.835', 'qualitative', 'empirical', 'methodology', 'analysis', 'analytical']
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senticnet['quantization_noise'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'short_circuit', 'error', 'mistake', 'undesired', 'trouble']
senticnet['quantum'] = ['0', '0.798', '0', '0.835', '#interest', '#admiration', 'positive', '0.817', 'well', 'study', 'research', 'award', 'sponsor']
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senticnet['quantum_cosmology'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'general_relativity', 'general_theory_of_relativity', 'relativity', 'special_relativity', 'special_theory_of_relativity']
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senticnet['quilt_square'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'gift_wrap', 'kraft', 'kraft_paper', 'wrapping_paper', 'paper']
senticnet['quilted_bedspread'] = ['0.883', '-0.91', '0', '0.991', '#surprise', '#admiration', 'positive', '0.929', 'spread', 'bedspread', 'bedcover', 'bed_cover', 'bed_covering']
senticnet['quilted_northern'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'hankie', 'kleenex', 'hanky', 'handkerchief', 'tissue']
senticnet['quince'] = ['0.02', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'many_fruits', 'peach', 'buy_fruit', 'pineapple', 'tomato']
senticnet['quince_rust'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'rust_fungus', 'fungus']
senticnet['quinn_rooney'] = ['0.034', '0.075', '0', '0.143', '#interest', '#admiration', 'positive', '0.084', 'photograph', 'art', 'painting', 'artwork', 'mural']
senticnet['quinoa_salad'] = ['0.783', '-0.63', '0', '0.829', '#joy', '#admiration', 'positive', '0.749', 'leg_of_lamb', 'gigot', 'joint', 'roast', 'rack_of_lamb']
senticnet['quinoline'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'enhance', 'intensify', 'deepen', 'potentiate', 'heighten']
senticnet['quinstreet'] = ['-0.63', '0', '0.862', '-0.52', '#sadness', '#anger', 'negative', '-0.67', 'knot', 'bunch', 'tuft', 'tussock', 'agglomeration']
senticnet['quinsy'] = ['-0.69', '0', '0.852', '-0.69', '#sadness', '#anger', 'negative', '-0.74', 'conjunctivitis', 'ophthalmitis', 'rubor', 'redness', 'inflammation']
senticnet['quintessence'] = ['0.879', '-0.51', '0', '0.496', '#joy', '#admiration', 'positive', '0.629', 'content', 'inwardness', 'stuff', 'bare_bone', 'hypostasis']
senticnet['quintessential'] = ['0.858', '0', '0', '0.995', '#joy', '#admiration', 'positive', '0.926', 'quintessentially', 'epitome', 'iconic', 'epitomizes', 'timeless']
senticnet['quintessentially'] = ['0.848', '0', '0', '0.986', '#joy', '#admiration', 'positive', '0.917', 'quintessential', 'epitome', 'iconic', 'epitomizes', 'timeless']
senticnet['quintessentially_english'] = ['-0.87', '0', '0.83', '-0.61', '#sadness', '#anger', 'negative', '-0.77', 'out', 'dated', 'stodgy', 'prehistoric', 'antique']
senticnet['quintet'] = ['0.899', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.813', 'concert', 'measure_angle', 'make_drawing', 'make_beautiful_music', 'listen_heart']
senticnet['quip'] = ['0', '-0.74', '0', '0.846', '#surprise', '#admiration', 'positive', '0.794', 'quip', 'one-liner', 'remark', 'retort', 'sarcastic']
senticnet['quirkier'] = ['-0.62', '0', '0.988', '-0.59', '#sadness', '#anger', 'negative', '-0.73', 'motley', 'mixture', 'variety', 'assortment', 'alphabet_soup']
senticnet['quirky'] = ['-0.85', '0', '0', '-0.91', '#sadness', '#disgust', 'negative', '-0.88', 'kitschy', 'retro', 'nostalgic', 'kitsch', 'chic']
senticnet['quirky_humor'] = ['0.741', '-0.74', '0', '0.666', '#joy', '#surprise', 'positive', '0.718', 'commonplaceness', 'mundanity', 'prosiness', 'prosaicness', 'ordinariness']
senticnet['quirt'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'impaired', 'dickey', 'lessened', 'diminished']
senticnet['quisling'] = ['0.91', '0', '0', '0.72', '#joy', '#admiration', 'positive', '0.815', 'partner_in_crime', 'henchman', 'pardner', 'collaborator', 'collaborationist']
senticnet['quisque'] = ['0', '0.079', '0', '0', '#interest', '#interest', 'positive', '0.079', 'change_diaper', 'lower_standard_live', 'become_pregnant', 'find_mate', 'make_baby']
senticnet['quit'] = ['-0.03', '0.131', '-0.04', '0.107', '#interest', '#admiration', 'positive', '0.039', 'payoff', 'pay_off', 'spend_little_money', 'get_back', 'poorly']
senticnet['quit_job'] = ['-0.09', '0.072', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.03', 'leave_work', 'floss_tooth', 'set_clock', 'out_bed', 'sell_vegetable']
senticnet['quit_work'] = ['-0.98', '0', '-0.82', '-0.79', '#sadness', '#fear', 'negative', '-0.87', 'finish_work', 'move_furniture', 'floss_tooth', 'work_home', 'work_team']
senticnet['quitclaim'] = ['0', '0', '-0.82', '-0.78', '#fear', '#disgust', 'negative', '-0.80', 'abdication', 'apostasy', 'renunciation', 'reject', 'repudiation']
senticnet['quitclaim_deed'] = ['0.621', '-0.60', '0', '0.545', '#joy', '#surprise', 'positive', '0.59', 'privilege', 'perquisite', 'public_easement', 'right_of_way', 'prerogative']
senticnet['quitter'] = ['-0.72', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.85', 'quit', 'wound', 'ooze', 'sore', 'deliverer']
senticnet['quiver'] = ['0', '0', '-0.70', '0', '#fear', '#fear', 'negative', '-0.70', 'shudder', 'tingle', 'attachment', 'frisson', 'exuberance']
senticnet['quivering'] = ['0.739', '-0.91', '0', '0.986', '#surprise', '#admiration', 'positive', '0.878', 'shakiness', 'tremolo', 'shaking', 'vibration', 'quiver']
senticnet['quivering_voice'] = ['-0.96', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.96', 'weep', 'bawl', 'shed_tear', 'cry_hard', 'wail']
senticnet['quixotic'] = ['0.273', '0.226', '-0.13', '0.332', '#joy', '#admiration', 'positive', '0.173', 'morality', 'cagey', 'uncommon', 'evasive', 'capitalize']
senticnet['quiz'] = ['-0.06', '0.135', '-0.10', '0.124', '#interest', '#admiration', 'positive', '0.022', 'multiple_choices', 'pop_quiz', 'final', 'final_exam', 'evaluation']
senticnet['quizzical'] = ['-0.71', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.74', 'incredulous', 'rueful', 'bemused', 'puzzled', 'sidelong']
senticnet['quizzically'] = ['0', '-0.89', '0', '0.928', '#surprise', '#admiration', 'positive', '0.911', 'questioningly', 'worriedly', 'incredulously', 'inquisitively', 'blankly']
senticnet['quna'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'sedum', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['quo'] = ['0', '0.079', '0', '0', '#interest', '#interest', 'positive', '0.079', 'change_diaper', 'lower_standard_live', 'become_pregnant', 'find_mate', 'make_baby']
senticnet['quod'] = ['0.654', '-0.75', '0', '0.78', '#surprise', '#admiration', 'positive', '0.729', 'suit', 'cause', 'lawsuit', 'occurrence', 'countersuit']
senticnet['quoin'] = ['0.754', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.83', 'cornerstone', 'noteworthy', 'prominent', 'central', 'fundamental']
senticnet['quoniam'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'reverence', 'genuflection', 'obeisance', 'kowtow', 'mafia']
senticnet['quonset_hut'] = ['-0.97', '0', '0.84', '-0.52', '#sadness', '#anger', 'negative', '-0.78', 'pis_aller', 'last_resort', 'temporary_expedient', 'expedient', 'stopgap']
senticnet['quonset_style'] = ['-0.12', '0.066', '-0.08', '-0.06', '#sadness', '#fear', 'negative', '-0.05', 'garden_yard', 'see_sky', 'plant_tree', 'hair', 'seed_lawn']
senticnet['quota'] = ['0.725', '-0.84', '0', '0.559', '#joy', '#surprise', 'positive', '0.708', 'share', 'reallocation', 'allocation', 'portion', 'part']
senticnet['quotability'] = ['0.759', '-0.95', '0', '0.503', '#joy', '#surprise', 'positive', '0.74', 'goodness', 'praiseworthiness', 'worthiness', 'laudableness', 'laudability']
senticnet['quotable'] = ['0.71', '-0.81', '0', '0.937', '#surprise', '#admiration', 'positive', '0.819', 'scholarly', 'donnish', 'pedantic', 'learned', 'bookish']
senticnet['quotation'] = ['0.857', '0', '0', '0.941', '#joy', '#admiration', 'positive', '0.899', 'citation', 'inverted_comma', 'quotation_mark', 'cite', 'quote']
senticnet['quotation_mark'] = ['0.851', '0', '0', '0.943', '#joy', '#admiration', 'positive', '0.897', 'citation', 'inverted_comma', 'quote', 'cite', 'quotation']
senticnet['quotative'] = ['0.633', '-0.67', '0', '0.776', '#surprise', '#admiration', 'positive', '0.693', 'double_entendre', 'loophole', 'ambiguity', 'amphiboly', 'parisology']
senticnet['quote'] = ['0.857', '0', '0', '0.948', '#joy', '#admiration', 'positive', '0.902', 'citation', 'inverted_comma', 'quotation_mark', 'cite', 'quotation']
senticnet['quotidian'] = ['0', '0', '-0.81', '-0.80', '#fear', '#disgust', 'negative', '-0.80', 'workaday', 'unremarkable', 'routine', 'everyday', 'mundane']
senticnet['qupa'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'head_hurt', 'introspection', 'lifestyle_change', 'headache', 'move_eye']
senticnet['qwerty_keyboard'] = ['0.893', '0.979', '0', '0', '#joy', '#interest', 'positive', '0.936', 'find_phone_number', 'dial_phone_number', 'pick_up_telephone_receiver', 'dial', 'dial_telephone']
senticnet['ra_ra_riot'] = ['0.304', '0.339', '-0.19', '0.333', '#interest', '#admiration', 'positive', '0.195', 'careful', 'reinforce', 'load_bear', 'go_outdoors', 'allowance']
senticnet['raaf_officer'] = ['0.74', '-0.53', '0', '0.641', '#joy', '#admiration', 'positive', '0.64', 'marshal', 'sky_marshal', 'lawman', 'law_officer', 'peace_officer']
senticnet['raah'] = ['-0.63', '0', '0.791', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'little', 'pinch', 'modicum', 'small_indefinite_amount', 'indefinite_quantity']
senticnet['rabbet'] = ['0', '0.089', '0', '0', '#interest', '#interest', 'positive', '0.089', 'ground', 'basement', 'base', 'foundation', 'basis']
senticnet['rabbi'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'priest', 'clergy', 'jewish', 'preacher', 'goddess']
senticnet['rabbi_abba_hillel'] = ['0', '0.931', '0', '0.947', '#interest', '#admiration', 'positive', '0.939', 'gold', 'metal', 'ash_gray', 'fluent', 'eloquent']
senticnet['rabbi_akiba'] = ['-0.88', '0', '0.929', '-0.84', '#sadness', '#anger', 'negative', '-0.88', 'paynim', 'nonreligious_person', 'idolizer', 'gentile', 'idol_worshiper']
senticnet['rabbinical_scholar'] = ['0', '-0.00', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'hot_chick', 'atheist', 'computer_user', 'fastener_binder', 'monitor']
senticnet['rabbit'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'nymph', 'mole', 'nightly_ritual', 'impairment_person', 'squirrel']
senticnet['rabbit_food'] = ['0.175', '0.101', '0.035', '0.195', '#joy', '#admiration', 'positive', '0.109', 'leafy_green', 'lawn_green', 'fruit_plant', 'vegetarian_food', 'green_vegetable']
senticnet['rabbit_fur'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'animal_hair', 'dog_hair', 'animal_fur', 'dog_cat', 'bite_dog']
senticnet['rabbit_on'] = ['0.984', '-0.63', '0', '0.731', '#joy', '#admiration', 'positive', '0.784', 'utter', 'talk', 'speak', 'mouth', 'verbalize']
senticnet['rabbit_punch'] = ['0.969', '0.998', '0', '0', '#joy', '#interest', 'positive', '0.984', 'bell_sound', 'bell_noise', 'piece_of_jewelry', 'syllable', 'fastener']
senticnet['rabbit_skin'] = ['-0.58', '0', '0.632', '-0.63', '#anger', '#disgust', 'negative', '-0.61', 'skimmer', 'trilby', 'chapeau', 'busby', 'shako']
senticnet['rabbit_stew'] = ['0.866', '-0.91', '0', '0.893', '#surprise', '#admiration', 'positive', '0.892', 'mooch', 'cadge', 'freeload', 'sponge', 'bum']
senticnet['rabbitfish'] = ['0.96', '-0.77', '0', '0.929', '#joy', '#admiration', 'positive', '0.887', 'whale_louse', 'beach_flea', 'sand_hopper', 'sandhopper', 'amphipod']
senticnet['rabble-rouser'] = ['0.924', '0', '0', '0.735', '#joy', '#admiration', 'positive', '0.83', 'polemicist', 'bomb-thrower', 'tree-hugger', 'demagogue', 'hanger-on']
senticnet['rabble_rouser'] = ['-0.97', '0', '0.62', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'obstructor', 'obstructer', 'technophobe', 'obstructionist', 'resister']
senticnet['rabelaisian'] = ['-0.77', '0', '0.626', '-0.78', '#sadness', '#disgust', 'negative', '-0.72', 'bumpy', 'unsmooth', 'lepidote', 'leprose', 'roughish']
senticnet['rabid'] = ['-0.91', '0', '0.794', '-0.93', '#sadness', '#disgust', 'negative', '-0.88', 'fundamentalist', 'extremist', 'obsession', 'stubborn', 'violent']
senticnet['rabidity'] = ['0.606', '-0.62', '0', '0.588', '#joy', '#surprise', 'positive', '0.606', 'spirit', 'lyricism', 'rabidness', 'sprightliness', 'ebullience']
senticnet['rabidly'] = ['-0.91', '0', '0.794', '-0.93', '#sadness', '#disgust', 'negative', '-0.88', 'fundamentalist', 'extremist', 'obsession', 'stubborn', 'violent']
senticnet['rabidness'] = ['0.608', '-0.61', '0', '0.587', '#joy', '#surprise', 'positive', '0.604', 'spirit', 'rabidity', 'lyricism', 'sprightliness', 'ebullience']
senticnet['rabies'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'helpless', 'unwanted', 'big_rat', 'small_cat', 'freckle']
senticnet['rabinovitch'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['rabshakeh'] = ['0.849', '-0.89', '0', '0.924', '#surprise', '#admiration', 'positive', '0.89', 'nonintellectual', 'lowbrow', 'lowbrowed', 'mindless', 'uncultivated']
senticnet['raccoon'] = ['0', '0', '-0.10', '0', '#fear', '#fear', 'negative', '-0.10', 'lonely_person', 'vegetarian', 'hermit', 'lab_rat', 'user']
senticnet['raccoon_dog'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'animal_hair', 'dog_hair', 'animal_fur', 'dog_cat', 'bite_dog']
senticnet['raccoon_dog_fur'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'animal_hair', 'dog_hair', 'animal_fur', 'dog_cat', 'bite_dog']
senticnet['raccoon_fur'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'animal_hair', 'dog_hair', 'animal_fur', 'dog_cat', 'bite_dog']
senticnet['raccoon_procyon_lotor'] = ['-0.55', '0', '0.955', '-0.96', '#anger', '#disgust', 'negative', '-0.82', 'house_mouse', 'mouse', 'rat', 'fieldmouse', 'nude_mouse']
senticnet['race'] = ['0', '0', '0', '0.108', '#admiration', '#admiration', 'positive', '0.108', 'fast', 'flee', 'sprint', 'marathon', 'good_form_exercise']
senticnet['race-ready'] = ['0', '0', '-0.97', '-0.78', '#fear', '#disgust', 'negative', '-0.87', 'souped-up', 'pimped-out', 'souped', 'tricked-out', 'pumped-up']
senticnet['race_hare'] = ['-0.04', '0.118', '-0.06', '0.102', '#interest', '#admiration', 'positive', '0.026', 'a_lot_of_practice', 'air_tank', 'live_old', 'hide_shell', 'grasp']
senticnet['racemosa'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'carnauba_palm', 'carnauba', 'wax_palm', 'palmetto', 'palmyra']
senticnet['racetrack'] = ['-0.05', '0', '0.023', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'track', 'run_event', 'run', 'course', 'run_place']
senticnet['racewalker'] = ['-0.04', '-0.03', '0', '-0.16', '#sadness', '#disgust', 'negative', '-0.05', 'crazy', 'damage_body', 'crazy_person', 'threat', 'max']
senticnet['racial'] = ['-0.94', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.92', 'insanity', 'angry_emotion', 'dislike', 'psychosis', 'fight']
senticnet['racial_bigotry'] = ['-0.94', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.92', 'insanity', 'angry_emotion', 'dislike', 'psychosis', 'fight']
senticnet['racial_discrimination'] = ['-0.94', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.92', 'insanity', 'angry_emotion', 'dislike', 'psychosis', 'fight']
senticnet['racial_epithet'] = ['0.537', '-0.51', '0', '0.807', '#joy', '#admiration', 'positive', '0.619', 'lesbian', 'fairy', 'fagot', 'homo', 'fag']
senticnet['racial_hierarchy'] = ['-0.57', '0', '0.53', '-0.63', '#sadness', '#disgust', 'negative', '-0.58', 'low_status', 'lowness', 'backseat', 'lowliness', 'subordinateness']
senticnet['racial_identity'] = ['0.122', '0.159', '-0.05', '0.217', '#interest', '#admiration', 'positive', '0.111', 'profile_picture', 'unity', 'country_person', 'unite', 'signature']
senticnet['racial_inequality'] = ['0.871', '-0.74', '0', '0.981', '#joy', '#admiration', 'positive', '0.867', 'unrighteousness', 'unjustness', 'unfairness', 'wrong', 'wrongfulness']
senticnet['racial_inferiority'] = ['0.757', '-0.82', '0', '0.982', '#surprise', '#admiration', 'positive', '0.855', 'first_class', 'choiceness', 'first_water', 'fineness', 'quality']
senticnet['racial_intolerance'] = ['-0.94', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.92', 'insanity', 'angry_emotion', 'dislike', 'psychosis', 'fight']
senticnet['racial_prejudice'] = ['-0.94', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.92', 'insanity', 'angry_emotion', 'dislike', 'psychosis', 'fight']
senticnet['racial_separatism'] = ['-0.96', '0', '0.766', '-0.96', '#sadness', '#disgust', 'negative', '-0.89', 'calumniation', 'traducement', 'hatchet_job', 'defamation', 'calumny']
senticnet['racial_slur'] = ['0.537', '-0.51', '0', '0.807', '#joy', '#admiration', 'positive', '0.619', 'lesbian', 'fairy', 'fagot', 'homo', 'fag']
senticnet['racial_superiority'] = ['0.757', '-0.82', '0', '0.982', '#surprise', '#admiration', 'positive', '0.855', 'first_class', 'choiceness', 'first_water', 'fineness', 'quality']
senticnet['racial_supremacy'] = ['-0.91', '0', '0.688', '-0.57', '#sadness', '#anger', 'negative', '-0.72', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['racialism'] = ['-0.91', '0', '0.688', '-0.57', '#sadness', '#anger', 'negative', '-0.72', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['racialist'] = ['0.585', '-0.7', '0', '0.586', '#surprise', '#admiration', 'positive', '0.624', 'jingoistic', 'ultranationalistic', 'superpatriotic', 'nationalistic', 'patriotic']
senticnet['racially_discriminatory'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
senticnet['racially_inferior'] = ['-0.98', '0', '0.582', '-0.56', '#sadness', '#anger', 'negative', '-0.70', 'unsocial', 'antisocial', 'lonely', 'alone', 'solitary']
senticnet['racier'] = ['0', '0', '-0.95', '-0.72', '#fear', '#disgust', 'negative', '-0.84', 'lubricious', 'lewd', 'lustful', 'obscene', 'prurient']
senticnet['raciness'] = ['-0.69', '0', '0.597', '-0.56', '#sadness', '#anger', 'negative', '-0.62', 'improperness', 'impropriety', 'indelicacy', 'spiciness', 'gaminess']
senticnet['racing_bike'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'fuel', 'car', 'fossil_fuel', 'transport', 'natural_resource']
senticnet['racing_heartbeat'] = ['-0.85', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.82', 'faint', 'headache', 'feel_sick', 'low_blood_pressure', 'giddy']
senticnet['racing_yacht'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'boat', 'yacht', 'big_boat', 'large_boat']
senticnet['racism'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'racist', 'intolerant', 'xenophobic', 'antisemitism', 'nazism']
senticnet['racist'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'racism', 'intolerant', 'xenophobic', 'antisemitism', 'nazism']
senticnet['racist_graffiti'] = ['-0.12', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.07', 'bum', 'homeless_people', 'homeless_person', 'panhandler', 'homeless']
senticnet['rack'] = ['-0.67', '0', '0.85', '-0.59', '#sadness', '#anger', 'negative', '-0.70', 'instrument_of_torture', 'boot', 'whip', 'iron_boot', 'iron_heel']
senticnet['rack_of_lamb'] = ['0.783', '-0.63', '0', '0.829', '#joy', '#admiration', 'positive', '0.749', 'leg_of_lamb', 'gigot', 'joint', 'roast', 'lamb_roast']
senticnet['rack_up'] = ['0.572', '-0.60', '0', '0.892', '#surprise', '#admiration', 'positive', '0.689', 'tally', 'steal', 'score', 'hit', 'get_ahead']
senticnet['racket'] = ['0', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.91', 'umpire', 'referee', 'legal_proceed', 'clang', 'clamor']
senticnet['racket_abuse'] = ['0', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.91', 'umpire', 'referee', 'legal_proceed', 'clang', 'clamor']
senticnet['raconteur'] = ['0.563', '-0.81', '0', '0.577', '#surprise', '#admiration', 'positive', '0.65', 'mortal', 'person', 'individual', 'performer', 'artist']
senticnet['racoon'] = ['0', '0', '0.08', '0', '#anger', '#anger', 'negative', '-0.08', 'tree_animal', 'small_furry', 'rodent', 'big_mouse', 'small_rodent']
senticnet['racquet'] = ['0.027', '-0.01', '0', '0.094', '#joy', '#admiration', 'positive', '0.045', 'action_figure', 'toy_car', 'game_card', 'toy_marble', 'yo-yo']
senticnet['racquetball'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'golf', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['racquetball_court'] = ['0.041', '0.086', '0.016', '0', '#joy', '#interest', 'positive', '0.037', 'team', 'play_sport', 'game', 'exercise', 'match']
senticnet['racy'] = ['0', '-0.92', '0', '0.944', '#surprise', '#admiration', 'positive', '0.934', 'scandalmongering', 'sensationalistic', 'sensationally', 'inflame', 'fire_up']
senticnet['radar_backscatter'] = ['-0.50', '0', '0.552', '-0.93', '#anger', '#disgust', 'negative', '-0.66', 'nubbiness', 'tweediness', 'harshness', 'graininess', 'granularity']
senticnet['radar_imagery'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'hurricane', 'natural_disaster', 'flood', 'atmospheric_pressure', 'tropical_storm']
senticnet['radar_seeker'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'chairperson', 'commuter']
senticnet['radclyffe_hall'] = ['-0.77', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.78', 'unhappiness', 'melancholy', 'shame', 'unhappy', 'sad']
senticnet['radial'] = ['0.06', '-0.03', '0.037', '0.075', '#joy', '#admiration', 'positive', '0.034', 'semicircle', 'trapezoid', 'circle', 'triangular', 'facet']
senticnet['radial_artery'] = ['-0.41', '0', '-0.28', '-0.05', '#sadness', '#fear', 'negative', '-0.25', 'numbness', 'carpal_tunnel_syndrome', 'weakness', 'pain', 'muscle_damage']
senticnet['radial_asymmetry'] = ['-0.59', '0', '0.6', '-0.51', '#sadness', '#anger', 'negative', '-0.57', 'asymmetry', 'dissymmetry', 'imbalance', 'directivity', 'directionality']
senticnet['radial_blur'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'hazy', 'blurry', 'bleary', 'blurryred', 'foggy']
senticnet['radial_density'] = ['0', '0.189', '0.04', '0.091', '#interest', '#admiration', 'positive', '0.08', 'combination', 'margin', 'teller', 'merge', 'finite']
senticnet['radial_deviation'] = ['0.582', '-0.66', '0', '0.661', '#surprise', '#admiration', 'positive', '0.637', 'talipes_equinus', 'deformity', 'malformation', 'talipes_valgus', 'talipes_calcaneus']
senticnet['radial_direction'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'aberrance', 'warp', 'chromosomal_aberration']
senticnet['radial_flow'] = ['-0.98', '0', '0.516', '-0.90', '#sadness', '#disgust', 'negative', '-0.80', 'devastation', 'annihilation', 'pulverization', 'vaporization', 'destruction']
senticnet['radial_nerve'] = ['-0.41', '0', '-0.28', '-0.05', '#sadness', '#fear', 'negative', '-0.25', 'numbness', 'carpal_tunnel_syndrome', 'weakness', 'pain', 'muscle_damage']
senticnet['radial_sesamoid'] = ['-0.60', '0', '0.701', '-0.72', '#anger', '#disgust', 'negative', '-0.67', 'lunate_bone', 'semilunar_bone', 'carpal_bone', 'triquetral', 'triquetral_bone']
senticnet['radially'] = ['0.867', '0.779', '0', '0', '#joy', '#interest', 'positive', '0.823', 'axially', 'circumferentially', 'longitudinally', 'laterally', 'transversely']
senticnet['radian_per_second'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'newton', 'sthene', 'force_unit']
senticnet['radiance'] = ['0.931', '-0.82', '0', '0.709', '#joy', '#surprise', 'positive', '0.822', 'light', 'aureole', 'corona', 'visible_light', 'visible_radiation']
senticnet['radiancy'] = ['0.916', '-0.99', '0', '0.984', '#surprise', '#admiration', 'positive', '0.964', 'gleaming', 'luster', 'polish', 'glow', 'burnish']
senticnet['radiant'] = ['0.04', '0.101', '0', '0.153', '#interest', '#admiration', 'positive', '0.098', 'source_light', 'celestial_body', 'far_away', 'heavenly_body', 'around_earth']
senticnet['radiant_beauty'] = ['0.785', '0', '0', '0.706', '#joy', '#admiration', 'positive', '0.746', 'clever', 'smart', 'imagination', 'creativity', 'creativeness']
senticnet['radiant_flux'] = ['-0.63', '0', '0.897', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'cd-rom', 'international_candle', 'candle', 'candlepower_unit', 'standard_candle']
senticnet['radiant_intensity'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'incandescence', 'luminescence', 'brightness_level']
senticnet['radiantly'] = ['0.54', '-0.64', '0', '0.728', '#surprise', '#admiration', 'positive', '0.638', 'gleaming', 'luster', 'polish', 'glow', 'burnish']
senticnet['radiately_striate'] = ['0.969', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.772', 'aspheric', 'aspherical', 'prolate', 'rounded', 'crescent']
senticnet['radiatio_optica'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'optic_radiation', 'pathway', 'peduncle', 'cerebral_peduncle']
senticnet['radiation'] = ['0', '0', '-0.15', '0', '#fear', '#fear', 'negative', '-0.15', 'photon', 'potential_energy', 'electromagnetic_radiation', 'energy_source', 'petroleum']
senticnet['radiation_contamination'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'dust', 'particulate', 'particulate_matter']
senticnet['radiation_therapy'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'phototherapy', 'radiation', 'actinotherapy', 'irradiation']
senticnet['radiation_therapy_oncology'] = ['0.065', '0.113', '0', '0.1', '#interest', '#admiration', 'positive', '0.093', 'big_group', 'clique', 'group_of_people', 'multitude', 'large_group']
senticnet['radiative'] = ['0.03', '0.068', '-0.06', '0.062', '#interest', '#admiration', 'positive', '0.025', 'pacific_northwest', 'american_forest', 'treetop', 'bath', 'hot_air']
senticnet['radiator'] = ['0.046', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'cold', 'electricity', 'circulate_air', 'blow_air', 'breeze']
senticnet['radical'] = ['0.219', '-0.14', '0.04', '0', '#joy', '#surprise', 'positive', '0.107', 'just_fun', 'enjoyable', 'taboo', 'creepy', 'fun']
senticnet['radical_cell'] = ['0.942', '-0.79', '0', '0.561', '#joy', '#surprise', 'positive', '0.767', 'sleeper_cell', 'terrorist_cell', 'cadre', 'political_unit', 'political_entity']
senticnet['radical_change'] = ['-0.98', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.92', 'thrilling', 'electrifying', 'sensational', 'sensationally', 'scandalmongering']
senticnet['radical_chic'] = ['-0.61', '0', '0.738', '-0.80', '#anger', '#disgust', 'negative', '-0.71', 'dissident', 'beat', 'bohemian', 'maverick', 'recusant']
senticnet['radical_faction'] = ['-0.50', '0', '0.989', '-0.91', '#anger', '#disgust', 'negative', '-0.80', 'camp', 'pack', 'clique', 'coterie', 'ingroup']
senticnet['radical_feminism'] = ['-0.68', '0', '0.949', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'chauvinism', 'sexism', 'antifeminism', 'sexual_discrimination', 'discrimination']
senticnet['radical_feminist'] = ['-0.68', '0', '0.949', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'chauvinism', 'sexism', 'antifeminism', 'sexual_discrimination', 'discrimination']
senticnet['radical_ideology'] = ['0', '0', '0', '0.986', '#admiration', '#admiration', 'positive', '0.986', 'idea', 'decision', 'consider', 'polarize', 'make_right_decision']
senticnet['radical_individualism'] = ['0', '0', '-0.74', '-0.93', '#fear', '#disgust', 'negative', '-0.84', 'generosity', 'oral_sex', 'win_lottery', 'gift_wrap', 'give_away']
senticnet['radical_libertarian'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'economic_libertarian', 'civil_libertarian', 'advocate', 'advocator', 'proponent']
senticnet['radical_overhaul'] = ['-0.98', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.92', 'thrilling', 'electrifying', 'sensational', 'sensationally', 'scandalmongering']
senticnet['radical_perineal_prostatectomy'] = ['-0.70', '0', '0.639', '-0.56', '#sadness', '#anger', 'negative', '-0.63', 'fibroma', 'adenoma', 'chondroma', 'enchondroma', 'benign_tumor']
senticnet['radical_redesign'] = ['0.047', '0.171', '-0.10', '0.212', '#interest', '#admiration', 'positive', '0.081', 'novelty', 'relevant', 'contemporary', 'timely', 'current']
senticnet['radical_reform'] = ['-0.98', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.92', 'thrilling', 'electrifying', 'sensational', 'sensationally', 'scandalmongering']
senticnet['radical_shake'] = ['-0.98', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.92', 'thrilling', 'electrifying', 'sensational', 'sensationally', 'scandalmongering']
senticnet['radical_skepticism'] = ['0', '0', '0.928', '-0.70', '#anger', '#disgust', 'negative', '-0.81', 'doubt', 'incertitude', 'dubiety', 'doubtfulness', 'dubiousness']
senticnet['radical_transformation'] = ['0.642', '-0.58', '0', '0.751', '#joy', '#admiration', 'positive', '0.659', 'change', 'become_better', 'change_aspect', 'look_better', 'become_bigger']
senticnet['radicalism'] = ['0', '0', '0', '0.986', '#admiration', '#admiration', 'positive', '0.986', 'idea', 'decision', 'consider', 'polarize', 'make_right_decision']
senticnet['radically'] = ['0', '0.839', '0', '0.752', '#interest', '#admiration', 'positive', '0.795', 'dramatically', 'significantly', 'considerably', 'drastically', 'markedly']
senticnet['radically_alter'] = ['0.7', '-0.79', '0', '0.66', '#joy', '#surprise', 'positive', '0.719', 'influence', 'determine', 'shape', 'mold', 'regulate']
senticnet['radically_change'] = ['0.7', '-0.79', '0', '0.66', '#joy', '#surprise', 'positive', '0.719', 'influence', 'determine', 'shape', 'mold', 'regulate']
senticnet['radically_transform'] = ['0.7', '-0.79', '0', '0.66', '#joy', '#surprise', 'positive', '0.719', 'influence', 'determine', 'shape', 'mold', 'regulate']
senticnet['radicchio'] = ['0.636', '-0.79', '0', '0.552', '#joy', '#surprise', 'positive', '0.661', 'lettuce', 'endive', 'green', 'salad_green', 'food']
senticnet['radicular'] = ['-0.81', '0', '0.789', '-0.51', '#sadness', '#anger', 'negative', '-0.70', 'rubor', 'redness', 'inflammation', 'synovitis', 'tendosynovitis']
senticnet['radicular_pain'] = ['-0.84', '0', '0.829', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'upset', 'dysarthria', 'defect_of_speech', 'disorder', 'lallation']
senticnet['radiculopathy'] = ['-0.87', '0', '0.773', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'rubor', 'redness', 'inflammation', 'otitis', 'otitis_interna']
senticnet['radio'] = ['0.064', '-0.06', '0.054', '0', '#joy', '#surprise', 'positive', '0.026', 'stereo', 'tuner', 'speaker', 'record_library', 'amplifier']
senticnet['radio-control_helicopter'] = ['0.703', '0.76', '0', '0', '#joy', '#interest', 'positive', '0.732', 'manga', 'cartoon', 'anime', 'beat_off', 'toy']
senticnet['radio_airplay_chart'] = ['0.094', '0.091', '0', '0.125', '#joy', '#admiration', 'positive', '0.103', 'diagram', 'graph', 'medium', 'fragment', 'representation']
senticnet['radio_alarm_clock'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'barrette', 'hair_gel', 'blow_dryer', 'dry_hair', 'wet_hair']
senticnet['radio_frequency_spectrum'] = ['-0.07', '0.078', '0', '0.078', '#interest', '#admiration', 'positive', '0.028', 'red_blue', 'red_orange', 'green_yellow', 'hue_tint', 'yellow_green']
senticnet['radio_interview'] = ['0.716', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.733', 'call', 'use_phone', 'orate', 'use_telephone', 'speech']
senticnet['radio_mast'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'castle', 'bell_tower', 'spire', 'steeple', 'religious_place']
senticnet['radio_spectrum'] = ['-0.07', '0.078', '0', '0.078', '#interest', '#admiration', 'positive', '0.028', 'red_blue', 'red_orange', 'green_yellow', 'hue_tint', 'yellow_green']
senticnet['radio_station'] = ['-0.06', '-0.02', '-0.05', '-0.11', '#sadness', '#disgust', 'negative', '-0.05', 'pirate_ship', 'old_movie', 'movie', 'chinatown', 'military_base']
senticnet['radio_studio'] = ['0.077', '0.073', '0.013', '-0.02', '#joy', '#interest', 'positive', '0.027', 'registry_office', 'book_office', 'music_studio', 'conference_auditorium', 'mezzanine']
senticnet['radio_telescope'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'binoculars', 'read_glass', 'eyeglass', 'camera', 'monocle']
senticnet['radio_tuner'] = ['0.036', '-0.04', '0.027', '0.063', '#surprise', '#admiration', 'positive', '0.028', 'radio', 'speaker', 'channel_selector', 'power_switch', 'female_television_connector']
senticnet['radio_wave'] = ['0', '0', '-0.07', '-0.16', '#fear', '#disgust', 'negative', '-0.12', 'cosmic_ray', 'sun', 'solar_cell', 'x-ray', 'gamma_ray']
senticnet['radioactive'] = ['-0.02', '0', '0.047', '-0.11', '#anger', '#disgust', 'negative', '-0.06', 'hold_trash', 'put_garbage', 'rubbish', 'put_trash', 'store_trash']
senticnet['radioactive_caesium'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'dust', 'particulate', 'particulate_matter']
senticnet['radioactive_cesium'] = ['-0.02', '0', '0.047', '-0.11', '#anger', '#disgust', 'negative', '-0.06', 'hold_trash', 'put_garbage', 'rubbish', 'put_trash', 'store_trash']
senticnet['radioactive_cloud'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'dust', 'particulate', 'particulate_matter']
senticnet['radioactive_cobalt'] = ['-0.02', '0', '0.047', '-0.11', '#anger', '#disgust', 'negative', '-0.06', 'hold_trash', 'put_garbage', 'rubbish', 'put_trash', 'store_trash']
senticnet['radioactive_contamination'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'dust', 'particulate', 'particulate_matter']
senticnet['radioactive_decay'] = ['-0.77', '0', '0.764', '-0.54', '#sadness', '#anger', 'negative', '-0.69', 'pullulation', 'apomixis', 'blastogenesis', 'budding', 'asexual_reproduction']
senticnet['radioactive_dust'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'dust', 'particulate', 'particulate_matter']
senticnet['radioactive_fallout'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'dust', 'particulate', 'particulate_matter']
senticnet['radioactive_fuel'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'dust', 'particulate', 'particulate_matter']
senticnet['radioactive_iodine'] = ['-0.02', '0', '0.047', '-0.11', '#anger', '#disgust', 'negative', '-0.06', 'hold_trash', 'put_garbage', 'rubbish', 'put_trash', 'store_trash']
senticnet['radioactive_isotope'] = ['-0.02', '0', '0.047', '-0.11', '#anger', '#disgust', 'negative', '-0.06', 'hold_trash', 'put_garbage', 'rubbish', 'put_trash', 'store_trash']
senticnet['radioactive_liquid'] = ['-0.58', '0', '0.714', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'diversionist', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['radioactive_material'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'dust', 'particulate', 'particulate_matter']
senticnet['radioactive_metal'] = ['-0.02', '0', '0.047', '-0.11', '#anger', '#disgust', 'negative', '-0.06', 'hold_trash', 'put_garbage', 'rubbish', 'put_trash', 'store_trash']
senticnet['radioactive_nucleus'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'elementary_particle', 'neutron', 'electron', 'subatomic_particle', 'proton']
senticnet['radioactive_plume'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'dust', 'particulate', 'particulate_matter']
senticnet['radioactive_plutonium'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'dust', 'particulate', 'particulate_matter']
senticnet['radioactive_pollution'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'dust', 'particulate', 'particulate_matter']
senticnet['radioactive_residue'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'dust', 'particulate', 'particulate_matter']
senticnet['radioactive_strontium'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'dust', 'particulate', 'particulate_matter']
senticnet['radioactive_substance'] = ['-0.02', '0', '0.047', '-0.11', '#anger', '#disgust', 'negative', '-0.06', 'hold_trash', 'put_garbage', 'rubbish', 'put_trash', 'store_trash']
senticnet['radioactive_waste'] = ['-0.02', '0', '0.047', '-0.11', '#anger', '#disgust', 'negative', '-0.06', 'hold_trash', 'put_garbage', 'rubbish', 'put_trash', 'store_trash']
senticnet['radioactive_waste_compact'] = ['-0.12', '-0.05', '0', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'diminish', 'reduce', 'lower', 'lower_music', 'decrease']
senticnet['radioactive_waste_disposal'] = ['-0.12', '-0.05', '0', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'diminish', 'reduce', 'lower', 'lower_music', 'decrease']
senticnet['radioactive_waste_llrw'] = ['-0.12', '-0.05', '0', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'diminish', 'reduce', 'lower', 'lower_music', 'decrease']
senticnet['radioactively'] = ['-0.02', '0', '0.047', '-0.11', '#anger', '#disgust', 'negative', '-0.06', 'hold_trash', 'put_garbage', 'rubbish', 'put_trash', 'store_trash']
senticnet['radioactivity'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'dust', 'particulate', 'particulate_matter']
senticnet['radiocarpal_joint'] = ['-0.74', '0', '0.598', '-0.75', '#sadness', '#disgust', 'negative', '-0.69', 'lunate_bone', 'semilunar_bone', 'carpal_bone', 'carpal', 'bone']
senticnet['radiographic_appearance'] = ['-0.58', '0', '0.814', '-0.54', '#sadness', '#anger', 'negative', '-0.64', 'abnormal', 'irregular', 'vicarious', 'supernormal', 'subnormal']
senticnet['radiographic_evidence'] = ['-0.53', '0', '0.608', '-0.93', '#anger', '#disgust', 'negative', '-0.69', 'reprieve', 'respite', 'hiatus', 'abatement', 'suspension']
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senticnet['rainstorm'] = ['-0.87', '0', '0.91', '-0.78', '#sadness', '#anger', 'negative', '-0.85', 'storm', 'thundershower', 'rain', 'line_storm', 'equinoctial_storm']
senticnet['rainwear'] = ['0.053', '-0.01', '-0.05', '0.121', '#joy', '#admiration', 'positive', '0.034', 'coat', 'winter_clothes', 'stylish', 'adorn', 'protective_cover']
senticnet['rainy'] = ['-0.17', '-0.07', '0', '-0.15', '#sadness', '#disgust', 'negative', '-0.08', 'snow_storm', 'rain_snow', 'cold_hot', 'natural_phenomenon', 'weather_type']
senticnet['rainy_day'] = ['-0.09', '0', '-0.15', '-0.16', '#fear', '#disgust', 'negative', '-0.13', 'emotional_pain', 'desire_entertain', 'feel_depressed', 'desire_entertainment', 'embarrassment']
senticnet['rainy_morning'] = ['-0.09', '0', '-0.15', '-0.16', '#fear', '#disgust', 'negative', '-0.13', 'emotional_pain', 'desire_entertain', 'feel_depressed', 'desire_entertainment', 'embarrassment']
senticnet['rainy_night'] = ['-0.70', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.72', 'blackness', 'blackout', 'darkness', 'dark', 'obscure']
senticnet['rainy_weather'] = ['-0.02', '-0.02', '0.076', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'wear_raincoat', 'carry_umbrella', 'feel_cold', 'camp_out', 'splash_water']
senticnet['raise'] = ['0', '0.275', '-0.15', '0.222', '#interest', '#admiration', 'positive', '0.115', 'lift', 'arise', 'rise', 'go_up', 'pull_up']
senticnet['raise_animal'] = ['0', '0.084', '0', '0.083', '#interest', '#admiration', 'positive', '0.084', 'grow_crop', 'agriculture', 'plant_crop', 'plough', 'grow_food']
senticnet['raise_arm'] = ['0.072', '0', '0', '0', '#joy', '#joy', 'positive', '0.072', 'lift_arm', 'hold_hand', 'raise_money', 'try_forget', 'pull_out']
senticnet['raise_awareness_among'] = ['0.098', '0.165', '-0.13', '0.284', '#interest', '#admiration', 'positive', '0.103', 'see_different', 'eye_strain', 'know', 'learn', 'insight']
senticnet['raise_cash'] = ['-0.03', '0.139', '0', '0.109', '#interest', '#admiration', 'positive', '0.07', 'exchange_money', 'purchase', 'sell_goods', 'make_rich', 'incur_debt']
senticnet['raise_child'] = ['-0.07', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.06', 'protect_child', 'teach_child', 'read_bedtime_story', 'understand_idea', 'look_closely']
senticnet['raise_crop'] = ['0', '0.071', '0.024', '0.065', '#interest', '#admiration', 'positive', '0.037', 'grow_food', 'raise_animal', 'agriculture', 'grow_stuff', 'grow_crop']
senticnet['raise_family'] = ['0.098', '0.087', '-0.07', '0.074', '#joy', '#interest', 'positive', '0.045', 'live_life', 'inhabit', 'reside', 'house_person', 'family_live']
senticnet['raise_flag'] = ['0', '0.886', '0', '0', '#interest', '#interest', 'positive', '0.886', 'respect', 'arm', 'right_injustice', 'market_product', 'bridge_difference']
senticnet['raise_hand'] = ['0.934', '0.878', '0', '0', '#joy', '#interest', 'positive', '0.906', 'think_first', 'think_say', 'speak_mind', 'know_talk', 'speak_up']
senticnet['raise_hell'] = ['-0.14', '0', '-0.08', '-0.04', '#sadness', '#fear', 'negative', '-0.08', 'serve_beer', 'drink_juice', 'wear_hat', 'drink_much_beer', 'spill_food_clothes']
senticnet['raise_money'] = ['-0.03', '-0.01', '0.044', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'experience_joy', 'drive_around', 'join_club', 'pitch', 'read_bedtime_story']
senticnet['raise_right_hand'] = ['-0.12', '0.28', '-0.11', '0.174', '#interest', '#admiration', 'positive', '0.053', 'stubborn', 'interested', 'become_politician', 'make_decision', 'tell_why']
senticnet['raise_self_esteem'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'demonstrate_skill', 'improve_skill', 'kill_time', 'compete_others', 'golfer']
senticnet['raise_up'] = ['0.057', '0.248', '-0.12', '0.288', '#interest', '#admiration', 'positive', '0.116', 'move_upward', 'go_up', 'upward_movement', 'rise_up', 'ascend']
senticnet['raise_voice'] = ['-0.08', '-0.03', '0.039', '-0.12', '#sadness', '#disgust', 'negative', '-0.05', 'open_close_mouth', 'use_voice', 'annoy', 'dry_mouth', 'hunt_duck']
senticnet['raised_eyebrow'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'simper', 'please', 'content', 'satisfy', 'gratify']
senticnet['raisin'] = ['0', '0.066', '-0.05', '0.089', '#interest', '#admiration', 'positive', '0.032', 'pineapple', 'kale', 'banana', 'pickle', 'tomato']
senticnet['raisin_administrative'] = ['0', '0.066', '-0.05', '0.089', '#interest', '#admiration', 'positive', '0.032', 'pineapple', 'kale', 'banana', 'pickle', 'tomato']
senticnet['raisin_bran'] = ['-0.92', '0', '0.609', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'puffed_rice', 'granola', 'cold_cereal', 'corn_flake', 'bran_flake']
senticnet['raisin_bread'] = ['0', '0.066', '-0.05', '0.089', '#interest', '#admiration', 'positive', '0.032', 'pineapple', 'kale', 'banana', 'pickle', 'tomato']
senticnet['raisin_reserve'] = ['0', '0.066', '-0.05', '0.089', '#interest', '#admiration', 'positive', '0.032', 'pineapple', 'kale', 'banana', 'pickle', 'tomato']
senticnet['raising'] = ['0.858', '-0.77', '0', '0.735', '#joy', '#surprise', 'positive', '0.79', 'breeding', 'training', 'education', 'upbringing', 'nurture']
senticnet['raising_beef_cattle'] = ['0.546', '-0.96', '0', '0.533', '#joy', '#surprise', 'positive', '0.683', 'dairy_cattle', 'dairy_cow', 'milch_cow', 'ayrshire', 'milcher']
senticnet['raising_cattle'] = ['0.053', '0.094', '0', '0.136', '#interest', '#admiration', 'positive', '0.094', 'barn_animal', 'domesticate', 'grassland', 'sheep', 'cow']
senticnet['raising_livestock'] = ['0.053', '0.094', '0', '0.136', '#interest', '#admiration', 'positive', '0.094', 'barn_animal', 'domesticate', 'grassland', 'sheep', 'cow']
senticnet['raivo'] = ['0.975', '-0.66', '0', '0.813', '#joy', '#admiration', 'positive', '0.816', 'cry', 'yell', 'scream', 'shrill', 'call']
senticnet['raizo'] = ['0.021', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'plastic_bottle', 'party', 'use_party', 'lighter_air', 'compress_air']
senticnet['raj'] = ['0.935', '-0.64', '0', '0.766', '#joy', '#admiration', 'positive', '0.78', 'dominion', 'paramountcy', 'reign', 'sovereignty', 'suzerainty']
senticnet['raj_rajaratnam'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'busy_bee', 'gopher', 'actor', 'doer', 'worker']
senticnet['rajaratnam'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'busy_bee', 'gopher', 'actor', 'doer', 'worker']
senticnet['rajat_gupta'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'busy_bee', 'gopher', 'actor', 'doer', 'worker']
senticnet['rajgarh'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['rajon_rondo'] = ['0.556', '-0.79', '0', '0.755', '#surprise', '#admiration', 'positive', '0.701', 'lush', 'alcoholic', 'alky', 'dipsomaniac', 'rummy']
senticnet['rake'] = ['0.899', '0', '0', '0.703', '#joy', '#admiration', 'positive', '0.801', 'absterge', 'hackle', 'purge', 'cleanse', 'shampoo']
senticnet['rake_leave'] = ['-0.10', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.07', 'fancy_girl', 'kiss_girl', 'wedge_door', 'jump_off_bridge', 'date_girl']
senticnet['rakish'] = ['0', '0', '-0.97', '-0.74', '#fear', '#disgust', 'negative', '-0.86', 'dashing', 'raffish', 'jaunty', 'natty', 'dapper']
senticnet['rakish_angle'] = ['0.08', '0.106', '-0.04', '0.112', '#interest', '#admiration', 'positive', '0.063', 'helmet', 'head_gear', 'cap', 'head_wear', 'head_protection']
senticnet['rakishly'] = ['0', '0', '0', '0.963', '#admiration', '#admiration', 'positive', '0.963', 'wear', 'furnishing', 'wearable_technology', 'wearable', 'belt']
senticnet['rakishness'] = ['0.848', '-0.77', '0', '0.965', '#joy', '#admiration', 'positive', '0.862', 'elegance', 'jauntiness', 'dapperness', 'nattiness', 'chicness']
senticnet['rally'] = ['0.669', '-0.58', '0', '0.803', '#joy', '#admiration', 'positive', '0.685', 'pep_rally', 'revival', 'revival_meeting', 'gathering', 'assemblage']
senticnet['rally_costa_brava'] = ['0.669', '-0.58', '0', '0.803', '#joy', '#admiration', 'positive', '0.685', 'pep_rally', 'revival', 'revival_meeting', 'gathering', 'assemblage']
senticnet['rallying_cry'] = ['0.022', '0.123', '0.038', '0.129', '#interest', '#admiration', 'positive', '0.059', 'hymn', 'written_music', 'walk_beach', 'lyric', 'piece_of_music']
senticnet['rallying_point'] = ['0.53', '-0.64', '0', '0.76', '#surprise', '#admiration', 'positive', '0.646', 'focus', 'get_focused', 'focusing', 'centering', 'particularism']
senticnet['ram'] = ['-0.92', '0', '-0.73', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'push', 'jostle', 'barge', 'bang', 'shove_around']
senticnet['ram_weigh'] = ['0', '0.075', '0', '0.159', '#interest', '#admiration', 'positive', '0.117', 'many_plants', 'blossom', 'development', 'poppy', 'adult']
senticnet['ramalina'] = ['-0.60', '0', '0.541', '-0.52', '#sadness', '#anger', 'negative', '-0.55', 'swoon', 'pass_out', 'faint', 'zonk_out', 'change_state']
senticnet['rambabu'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['ramble'] = ['0', '0', '-0.91', '-0.76', '#fear', '#disgust', 'negative', '-0.84', 'meander', 'jog', 'swan', 'roll', 'wander']
senticnet['rambler_american'] = ['0.035', '0.112', '-0.07', '0.195', '#interest', '#admiration', 'positive', '0.067', 'car_action', 'car_travel', 'steer_car', 'make_go', 'transportation']
senticnet['rambunctious'] = ['-0.64', '0', '0.941', '-0.66', '#anger', '#disgust', 'negative', '-0.74', 'rowdy', 'boisterous', 'unruly', 'disorderly', 'robustious']
senticnet['ramekin'] = ['0.059', '-0.02', '0.025', '0.109', '#joy', '#admiration', 'positive', '0.043', 'tasty', 'bake_treat', 'bake_goods', 'bake_good', 'loaf']
senticnet['ramification'] = ['0', '0', '-0.84', '-0.77', '#fear', '#disgust', 'negative', '-0.80', 'fork', 'branching', 'branch', 'forking', 'leg']
senticnet['ramify'] = ['-0.86', '0', '0.511', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'separate', 'move', 'branch', 'diverge', 'fork']
senticnet['ramin_bahrani'] = ['-0.80', '0', '0.718', '-0.79', '#sadness', '#disgust', 'negative', '-0.77', 'walk_exercise', 'lose_wallet', 'go_debt', 'pay_dinner', 'spend_much_money']
senticnet['ramipril'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'spironolactone', 'spironolactone_drug']
senticnet['ramla'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['ramlinsburg'] = ['0.929', '0.507', '-0.17', '0.396', '#joy', '#interest', 'positive', '0.415', 'plume', 'congratulate', 'smugness', 'smug', 'proudly']
senticnet['rammed_into'] = ['-0.90', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.86', 'trauma', 'car_wreck', 'broken_bone', 'bad_knee', 'wreck']
senticnet['ramp'] = ['0.12', '0', '0', '0', '#joy', '#joy', 'positive', '0.12', 'incline_plane', 'boulder', 'slide', 'hill_mountain', 'rock']
senticnet['ramp_up'] = ['0.66', '-0.85', '0', '0.744', '#surprise', '#admiration', 'positive', '0.752', 'further', 'promote', 'encourage', 'conduce', 'advance']
senticnet['rampage'] = ['-0.67', '0', '0.938', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'massacre', 'mass_murder', 'carnage', 'slaughter', 'butchery']
senticnet['rampage_through'] = ['0.02', '0.131', '0.749', '-0.67', '#anger', '#disgust', 'negative', '-0.31', 'belligerence', 'belligerency', 'belligerent', 'war', 'rancor']
senticnet['rampant'] = ['0', '0', '-0.91', '-0.73', '#fear', '#disgust', 'negative', '-0.82', 'overabundant', 'prevailing', 'plethoric', 'predominant', 'dominant']
senticnet['rampant_consumerism'] = ['0', '0', '-0.74', '-0.93', '#fear', '#disgust', 'negative', '-0.84', 'generosity', 'oral_sex', 'win_lottery', 'gift_wrap', 'give_away']
senticnet['rampant_corruption'] = ['0', '0', '-0.77', '-0.86', '#fear', '#disgust', 'negative', '-0.82', 'abduction', 'punishment', 'emergency', 'illiteracy', 'heal']
senticnet['rampant_deforestation'] = ['0', '-0.02', '0.085', '-0.09', '#anger', '#disgust', 'negative', '-0.05', 'tree_wood', 'forest', 'sideways', 'dead_tree', 'wood']
senticnet['rampant_graft'] = ['0', '0', '-0.77', '-0.86', '#fear', '#disgust', 'negative', '-0.82', 'abduction', 'punishment', 'emergency', 'illiteracy', 'heal']
senticnet['rampant_lion'] = ['0.586', '-0.65', '0', '0.503', '#joy', '#surprise', 'positive', '0.58', 'decoration', 'distinguished_service_cross', 'medal', 'medallion', 'palm']
senticnet['rampant_racism'] = ['-0.96', '0', '0.766', '-0.96', '#sadness', '#disgust', 'negative', '-0.89', 'calumniation', 'traducement', 'hatchet_job', 'defamation', 'calumny']
senticnet['rampart'] = ['0', '0', '-0.86', '-0.78', '#fear', '#disgust', 'negative', '-0.82', 'wall', 'mole', 'groin', 'bulwark', 'breakwater']
senticnet['rampaul'] = ['-0.62', '0', '0.713', '-0.98', '#anger', '#disgust', 'negative', '-0.77', 'muddy_up', 'muck_up', 'foul', 'blemish', 'smear']
senticnet['ramping_up'] = ['0.66', '-0.85', '0', '0.744', '#surprise', '#admiration', 'positive', '0.752', 'further', 'promote', 'encourage', 'conduce', 'advance']
senticnet['ramsay_hunt_syndrome'] = ['-0.87', '0', '0.773', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'rubor', 'redness', 'inflammation', 'otitis', 'otitis_interna']
senticnet['ramsey_theory'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'combinatory', 'consolidative', 'unifying', 'integrative']
senticnet['ramshackle'] = ['-0.99', '0', '0', '-0.91', '#sadness', '#disgust', 'negative', '-0.95', 'dilapidated', 'rundown', 'run-down', 'decrepit', 'derelict']
senticnet['ramularia'] = ['-0.93', '0', '0.699', '-0.91', '#sadness', '#disgust', 'negative', '-0.84', 'onion_smut', 'head_smut', 'stinking_smut', 'flag_smut_fungus', 'covered_smut']
senticnet['rana_temporaria'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'amphibian', 'aquatic']
senticnet['ranch'] = ['0.031', '0', '0', '0.061', '#joy', '#admiration', 'positive', '0.046', 'veal', 'milk_product', 'salad_dress', 'dairy', 'food_group']
senticnet['ranch_rodeo'] = ['0.938', '0.779', '0', '0', '#joy', '#interest', 'positive', '0.859', 'carnival', 'circus', 'fairground', 'fair', 'disneyland']
senticnet['rancher'] = ['0', '0.067', '0', '0.058', '#interest', '#admiration', 'positive', '0.063', 'plant_crop', 'grow_crop', 'raise_animal', 'cultivation', 'grow_food']
senticnet['ranching_operation'] = ['0.053', '0.094', '0', '0.136', '#interest', '#admiration', 'positive', '0.094', 'barn_animal', 'domesticate', 'grassland', 'sheep', 'cow']
senticnet['rancid_smell'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dirtiness', 'dinge']
senticnet['rancidification'] = ['-0.08', '-0.04', '0.023', '-0.07', '#sadness', '#disgust', 'negative', '-0.03', 'tier', 'make_toast', 'house_smell_good', 'get_hot', 'bread_crumb']
senticnet['rancidity'] = ['-0.08', '-0.04', '0.023', '-0.07', '#sadness', '#disgust', 'negative', '-0.03', 'tier', 'make_toast', 'house_smell_good', 'get_hot', 'bread_crumb']
senticnet['rancor'] = ['0.034', '0.126', '0.758', '-0.67', '#anger', '#disgust', 'negative', '-0.31', 'resentfully', 'gall', 'seek_revenge', 'resentful', 'warpath']
senticnet['randalli'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'plectognath', 'plectognathfish', 'blowfish', 'triggerfish', 'filefish']
senticnet['randian'] = ['0.95', '-0.77', '0', '0.578', '#joy', '#surprise', 'positive', '0.767', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['randle_cotgrave'] = ['-0.93', '0', '0.861', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'knotty', 'contorted', 'writhed', 'crooked', 'anfractuous']
senticnet['randolph'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'dicky', 'impaired', 'lessened', 'diminished']
senticnet['random'] = ['0', '0.713', '0', '0.767', '#interest', '#admiration', 'positive', '0.74', 'right_now', 'unsure', 'now_time', 'unpredictable', 'contemporary']
senticnet['random_assignment'] = ['0.628', '-0.96', '0', '0.682', '#surprise', '#admiration', 'positive', '0.759', 'taboo', 'tabu', 'tendentiousness', 'irrational_hostility', 'bias']
senticnet['random_sample'] = ['-0.04', '0.1', '0.051', '0.137', '#interest', '#admiration', 'positive', '0.035', 'find_number', 'regularly', 'list_number', 'calculator', 'navigation']
senticnet['random_sample_survey'] = ['-0.04', '0.1', '0.051', '0.137', '#interest', '#admiration', 'positive', '0.035', 'find_number', 'regularly', 'list_number', 'calculator', 'navigation']
senticnet['random_sampling'] = ['0.956', '-0.83', '0', '0.755', '#joy', '#surprise', 'positive', '0.85', 'choice', 'option', 'sampling', 'stratified_sampling', 'representative_sampling']
senticnet['random_walk_model'] = ['-0.89', '0', '0.972', '-0.50', '#sadness', '#anger', 'negative', '-0.79', 'unsteadiness', 'haphazardness', 'unregularity', 'variability', 'fitfulness']
senticnet['randomization'] = ['0.628', '-0.96', '0', '0.682', '#surprise', '#admiration', 'positive', '0.759', 'taboo', 'tabu', 'tendentiousness', 'irrational_hostility', 'bias']
senticnet['randomized_experiment'] = ['0.628', '-0.96', '0', '0.682', '#surprise', '#admiration', 'positive', '0.759', 'taboo', 'tabu', 'tendentiousness', 'irrational_hostility', 'bias']
senticnet['randomly_divided_into'] = ['0.065', '0.113', '0', '0.1', '#interest', '#admiration', 'positive', '0.093', 'big_group', 'clique', 'group_of_people', 'multitude', 'large_group']
senticnet['randomly_selected_sample'] = ['-0.04', '0.1', '0.051', '0.137', '#interest', '#admiration', 'positive', '0.035', 'find_number', 'regularly', 'list_number', 'calculator', 'navigation']
senticnet['rangareddi_district'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['rangatira_chief'] = ['-0.91', '0', '0.769', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'break', 'good_luck', 'chance_eventpy_chance', 'chance_event', 'coincidence']
senticnet['range'] = ['0.828', '-0.89', '0', '0.693', '#joy', '#surprise', 'positive', '0.806', 'confines', 'expanse', 'view', 'internationality', 'purview']
senticnet['range_cooker'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'kitchen_cabinet', 'kitchen_cupboard', 'dishwasher', 'cupboard', 'own_kitchen']
senticnet['range_extender'] = ['0', '0', '-1', '-1', '#fear', '#disgust', 'negative', '-1.0', 'moon_gravity', 'force', 'coercion', 'watt', 'power_source']
senticnet['range_rover'] = ['0.028', '0', '0.013', '0.061', '#joy', '#admiration', 'positive', '0.025', 'truck', 'vehicle', 'big_car', 'car', 'lorry']
senticnet['ranged_weapon'] = ['-0.59', '0', '0.704', '-0.5', '#sadness', '#anger', 'negative', '-0.59', 'lay_up', 'modify', 'nobble', 'alter', 'change']
senticnet['ranger'] = ['-0.86', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.84', 'forest_fire_fighter', 'texas_ranger', 'fire_warden', 'commando', 'game']
senticnet['ranging_from'] = ['0.828', '-0.89', '0', '0.693', '#joy', '#surprise', 'positive', '0.806', 'confines', 'expanse', 'view', 'internationality', 'purview']
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senticnet['rank_commensurate'] = ['0', '0.745', '0', '0.832', '#interest', '#admiration', 'positive', '0.788', 'meet_up', 'year', 'rendezvous', 'meet_friend', 'make_appointment']
senticnet['rank_hypocrisy'] = ['0', '0', '-0.90', '-0.90', '#fear', '#disgust', 'negative', '-0.90', 'parody', 'farce', 'lampoon', 'farce_comedy', 'spoof']
senticnet['rank_ninth'] = ['-0.53', '0', '0.977', '-0.54', '#anger', '#disgust', 'negative', '-0.68', 'rank', 'surpass', 'shine_at', 'excel_at', 'exceed']
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senticnet['ranked_fifth'] = ['0', '0.075', '0', '0.159', '#interest', '#admiration', 'positive', '0.117', 'maximum', 'max', 'utmost', 'top', 'extreme']
senticnet['ranked_fourth'] = ['0', '0.075', '0', '0.159', '#interest', '#admiration', 'positive', '0.117', 'maximum', 'max', 'utmost', 'top', 'extreme']
senticnet['ranked_sixth'] = ['0', '0.075', '0', '0.159', '#interest', '#admiration', 'positive', '0.117', 'maximum', 'max', 'utmost', 'top', 'extreme']
senticnet['ranking'] = ['0.825', '-0.82', '0', '0.982', '#joy', '#admiration', 'positive', '0.878', 'standing', 'importance', 'honor', 'laurel', 'prominence']
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senticnet['ransom'] = ['0.945', '0', '0', '0.709', '#joy', '#admiration', 'positive', '0.827', 'retrieval', 'recovery', 'computer_operation', 'reclamation', 'stovepiping']
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senticnet['rant_against'] = ['0', '0', '-0.93', '-0.88', '#fear', '#disgust', 'negative', '-0.91', 'rebuke', 'take_to_task', 'flog', 'call_on_carpet', 'cane']
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senticnet['ranunculus_flammula'] = ['0', '0.086', '0', '0', '#interest', '#interest', 'positive', '0.086', 'swim_ocean', 'weight_lift', 'sit_in_shade', 'cut_grass', 'trust_friend']
senticnet['raound'] = ['-0.85', '0', '0.583', '-0.71', '#sadness', '#disgust', 'negative', '-0.71', 'muddle', 'rummage', 'mare_nest', 'welter', 'clutter']
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senticnet['rap_artist'] = ['0', '0.712', '0', '0.844', '#interest', '#admiration', 'positive', '0.778', 'dollar_change', 'small_currency', 'currency_unit', 'metal_money', 'american_money']
senticnet['rap_collective'] = ['0', '0.712', '0', '0.844', '#interest', '#admiration', 'positive', '0.778', 'dollar_change', 'small_currency', 'currency_unit', 'metal_money', 'american_money']
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senticnet['rap_scene'] = ['0', '0.712', '0', '0.844', '#interest', '#admiration', 'positive', '0.778', 'dollar_change', 'small_currency', 'currency_unit', 'metal_money', 'american_money']
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senticnet['rapaciousness'] = ['-0.81', '0', '0.877', '-0.66', '#sadness', '#anger', 'negative', '-0.78', 'opportunism', 'selfishness', 'greediness', 'voraciousness', 'expedience']
senticnet['rapacity'] = ['-0.89', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.85', 'unkind', 'ruthlessness', 'cruelly', 'pitilessness', 'unassertively']
senticnet['rapala'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'catch_fish', 'fishing', 'patience', 'fish_lake', 'bait']
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senticnet['rape_survivor'] = ['0.864', '-0.54', '0', '0.613', '#joy', '#admiration', 'positive', '0.675', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['rape_suspect'] = ['0.964', '-0.57', '0', '0.827', '#joy', '#admiration', 'positive', '0.789', 'mortal', 'murder_suspect', 'robbery_suspect', 'suspect', 'person']
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senticnet['raphanus_raphanistrum'] = ['0', '0.086', '0', '0', '#interest', '#interest', 'positive', '0.086', 'swim_ocean', 'weight_lift', 'sit_in_shade', 'cut_grass', 'trust_friend']
senticnet['raphe'] = ['0.719', '-0.57', '0', '0.651', '#joy', '#admiration', 'positive', '0.647', 'chap', 'break', 'slit', 'gap', 'shift']
senticnet['rapid'] = ['0.03', '0.117', '-0.04', '0.153', '#interest', '#admiration', 'positive', '0.064', 'quick', 'haste', 'fast', 'swift', 'quickness']
senticnet['rapid_acting_insulin'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
senticnet['rapid_buildup'] = ['0.942', '-0.79', '0', '0.901', '#joy', '#admiration', 'positive', '0.88', 'backup', 'accretion', 'deposition', 'deposit', 'increase']
senticnet['rapid_cycling_bipolar'] = ['0.504', '-0.93', '0', '0.797', '#surprise', '#admiration', 'positive', '0.747', 'diabetes', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['rapid_decline'] = ['-0.13', '-0.05', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'monitor_heart_rate', 'feel_tired', 'bump_head', 'exhaustion', 'become_fatigued']
senticnet['rapid_deforestation'] = ['0', '-0.02', '0.085', '-0.09', '#anger', '#disgust', 'negative', '-0.05', 'tree_wood', 'forest', 'sideways', 'dead_tree', 'wood']
senticnet['rapid_degradation'] = ['-0.80', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.84', 'popularization', 'profanation', 'demoralization', 'corruption', 'humiliation']
senticnet['rapid_deployment'] = ['0.814', '-0.87', '0', '0.918', '#surprise', '#admiration', 'positive', '0.87', 'ship_company', 'company', 'work_force', 'workforce', 'manpower']
senticnet['rapid_depreciation'] = ['-0.82', '0', '0.835', '-0.67', '#sadness', '#anger', 'negative', '-0.77', 'disparage', 'belittle', 'deflate', 'puncture', 'pick_at']
senticnet['rapid_diffusion'] = ['0.89', '-0.62', '0', '0.548', '#joy', '#surprise', 'positive', '0.686', 'breakthrough', 'interpenetration', 'foray', 'maraud', 'raid']
senticnet['rapid_disintegration'] = ['-0.13', '-0.05', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'monitor_heart_rate', 'feel_tired', 'bump_head', 'exhaustion', 'become_fatigued']
senticnet['rapid_economic_growth'] = ['0.067', '0.172', '0', '0.121', '#interest', '#admiration', 'positive', '0.12', 'economic_activity', 'business', 'support_family', 'work_done', 'earn_money']
senticnet['rapid_emergence'] = ['0.836', '-0.83', '0', '0.511', '#joy', '#surprise', 'positive', '0.726', 'appearance', 'start', 'casus_belli', 'beginning', 'egress']
senticnet['rapid_erosion'] = ['-0.16', '0', '-0.18', '-0.23', '#fear', '#disgust', 'negative', '-0.19', 'eat_off', 'remove', 'diminish', 'decrease', 'reduce']
senticnet['rapid_escalation'] = ['0.942', '-0.79', '0', '0.901', '#joy', '#admiration', 'positive', '0.88', 'backup', 'accretion', 'deposition', 'deposit', 'increase']
senticnet['rapid_evaporation'] = ['0', '-0.03', '0.05', '-0.14', '#anger', '#disgust', 'negative', '-0.05', 'humidity', 'wetness', 'damp', 'humid', 'dank']
senticnet['rapid_evolution'] = ['0.836', '-0.83', '0', '0.511', '#joy', '#surprise', 'positive', '0.726', 'appearance', 'start', 'casus_belli', 'beginning', 'egress']
senticnet['rapid_expansion'] = ['0.836', '-0.83', '0', '0.511', '#joy', '#surprise', 'positive', '0.726', 'appearance', 'start', 'casus_belli', 'beginning', 'egress']
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senticnet['rapid_increase'] = ['0', '0.229', '-0.09', '0.187', '#interest', '#admiration', 'positive', '0.106', 'increase', 'make_big', 'get_large', 'grow', 'expand']
senticnet['rapid_modernization'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'absolutism', 'autocracy', 'ascendancy', 'tyrant']
senticnet['rapid_oxidation'] = ['0.021', '0', '-0.10', '-0.04', '#fear', '#disgust', 'negative', '-0.04', 'huge_fire', 'heat_light', 'fire', 'flame', 'cinder']
senticnet['rapid_pace'] = ['-0.04', '0.123', '0', '0.237', '#interest', '#admiration', 'positive', '0.107', 'foot_motion', 'foot_movement', 'foot_action', 'move_ahead', 'stride']
senticnet['rapid_pulse'] = ['-0.58', '0', '0.773', '-0.79', '#anger', '#disgust', 'negative', '-0.71', 'perspiration', 'sweating', 'polyhidrosis', 'sudation', 'hidrosis']
senticnet['rapid_response'] = ['0', '0', '-0.82', '0', '#fear', '#fear', 'negative', '-0.82', 'sickness', 'polio', 'malady', 'ailment', 'treatment']
senticnet['rapid_rise'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'absolutism', 'autocracy', 'ascendancy', 'tyrant']
senticnet['rapid_solidification'] = ['-0.98', '0', '0.516', '-0.90', '#sadness', '#disgust', 'negative', '-0.80', 'devastation', 'annihilation', 'pulverization', 'vaporization', 'destruction']
senticnet['rapid_spread'] = ['0.836', '-0.83', '0', '0.511', '#joy', '#surprise', 'positive', '0.726', 'appearance', 'start', 'casus_belli', 'beginning', 'egress']
senticnet['rapid_transformation'] = ['0.836', '-0.83', '0', '0.511', '#joy', '#surprise', 'positive', '0.726', 'appearance', 'start', 'casus_belli', 'beginning', 'egress']
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senticnet['rapid_transit_station'] = ['0', '0.147', '0', '0.099', '#interest', '#admiration', 'positive', '0.123', 'airport', 'subway', 'bus_stop', 'bus', 'control_tower']
senticnet['rapidity'] = ['-0.59', '0', '0.51', '-0.99', '#sadness', '#disgust', 'negative', '-0.69', 'quickness', 'celerity', 'promptness', 'rapidness', 'speediness']
senticnet['rapidly'] = ['0', '0.934', '0', '0.792', '#interest', '#admiration', 'positive', '0.863', 'chop-chop', 'speedily', 'apace', 'quickly', 'study']
senticnet['rapidly_becoming_obsolete'] = ['-0.90', '0', '0.517', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'outdated', 'noncurrent', 'disused', 'obsolete', 'superannuated']
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senticnet['rapidly_changing_technological'] = ['-0.03', '0.086', '0', '0.114', '#interest', '#admiration', 'positive', '0.054', 'deduce', 'figure_out', 'arrive_conclusion', 'come_conclusion', 'come_up_new_idea']
senticnet['rapidly_changing_world'] = ['-0.03', '0.086', '0', '0.114', '#interest', '#admiration', 'positive', '0.054', 'deduce', 'figure_out', 'arrive_conclusion', 'come_conclusion', 'come_up_new_idea']
senticnet['rapidly_growing_demand'] = ['0.749', '0.764', '0', '0', '#joy', '#interest', 'positive', '0.756', 'obedience', 'order', 'requirement', 'permission', 'necessary']
senticnet['rapidly_mixing_markov'] = ['0.096', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.057', 'platform_rail', 'rail', 'catwalk', 'column', 'token_turnstile']
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senticnet['rapier'] = ['0', '0.707', '0', '0.936', '#interest', '#admiration', 'positive', '0.822', 'slender', 'slimline', 'sliver', 'sylphic', 'dun']
senticnet['rapine'] = ['0', '0', '-0.84', '-0.90', '#fear', '#disgust', 'negative', '-0.87', 'steal_money', 'theft', 'burglary', 'robbery', 'thief']
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senticnet['rapist'] = ['0.864', '-0.54', '0', '0.613', '#joy', '#admiration', 'positive', '0.675', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['rappel'] = ['-0.99', '0', '0.641', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'navigable', 'negotiable', 'surmountable', 'passable', 'traversable']
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senticnet['rapper_actor'] = ['-0.94', '0', '0.492', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'make_noise', 'disturb', 'confusion', 'hustle_and_bustle', 'bustle']
senticnet['rapper_cent'] = ['0', '0.712', '0', '0.844', '#interest', '#admiration', 'positive', '0.778', 'dollar_change', 'small_currency', 'currency_unit', 'metal_money', 'american_money']
senticnet['rapper_mc'] = ['0', '0', '0.876', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'untune', 'discompose', 'discomfit', 'disconcert', 'upset']
senticnet['rapper_producer'] = ['0', '0.712', '0', '0.844', '#interest', '#admiration', 'positive', '0.778', 'dollar_change', 'small_currency', 'currency_unit', 'metal_money', 'american_money']
senticnet['rapper_singer'] = ['0', '0.712', '0', '0.844', '#interest', '#admiration', 'positive', '0.778', 'dollar_change', 'small_currency', 'currency_unit', 'metal_money', 'american_money']
senticnet['rapport'] = ['0.988', '0.74', '0', '0', '#joy', '#interest', 'positive', '0.864', 'chemistry', 'mutual', 'relationship', 'trust', 'respect']
senticnet['rapprochement'] = ['0', '0.098', '0', '0.08', '#interest', '#admiration', 'positive', '0.089', 'talk_person', 'wisdom', 'tact', 'statecraft', 'greet_person']
senticnet['rapscallion'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'rascal', 'holy_terror', 'scalawag', 'scallywag', 'monkey']
senticnet['rapt'] = ['0.865', '0.73', '0', '0', '#joy', '#interest', 'positive', '0.797', 'joyful', 'ecstatic', 'enjoyousured', 'joyousurous', 'joyous']
senticnet['raptly'] = ['-0.73', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.77', 'impatient', 'impatience', 'restlessness', 'nervous', 'humility']
senticnet['raptor'] = ['-0.74', '0', '0.963', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['raptor_center'] = ['-0.74', '0', '0.963', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['raptorial'] = ['0.098', '0.084', '0.819', '-0.68', '#anger', '#disgust', 'negative', '-0.33', 'grasp', 'covet', 'covetousness', 'avaricious', 'grabby']
senticnet['raptorial_bird'] = ['-0.53', '0', '0.648', '-0.70', '#anger', '#disgust', 'negative', '-0.63', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
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senticnet['rapturous'] = ['0.859', '0.853', '0', '0', '#joy', '#interest', 'positive', '0.856', 'joyful', 'ecstatic', 'enraptured', 'joyous', 'rapt']
senticnet['rapturous_applause'] = ['0.653', '0.635', '-0.24', '0.294', '#joy', '#interest', 'positive', '0.335', 'merrily', 'happily', 'mirthfully', 'gayly', 'blithely']
senticnet['rapturously'] = ['0.044', '-0.65', '0', '0.364', '#surprise', '#admiration', 'positive', '0.356', 'awestricken', 'awed', 'wondrous', 'wondrously', 'terrific']
senticnet['raptus'] = ['0', '0', '-0.78', '-0.86', '#fear', '#disgust', 'negative', '-0.82', 'convulsion', 'raptus_hemorrhagicus', 'focal_seizure', 'absence', 'seizure']
senticnet['raptus_hemorrhagicus'] = ['0.637', '-0.56', '0', '0.761', '#joy', '#admiration', 'positive', '0.655', 'convulsion', 'raptus', 'focal_seizure', 'absence', 'absence_seizure']
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senticnet['rare_neurological_disorder'] = ['-0.94', '0', '0.821', '-0.75', '#sadness', '#anger', 'negative', '-0.84', 'predementedness', 'insanity', 'dementedness', 'senile_psychosis', 'alcoholic_dementia']
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senticnet['rasher'] = ['0.914', '-0.56', '0', '0.869', '#joy', '#admiration', 'positive', '0.781', 'tabula_rasa', 'ego', 'subconscious', 'unconscious', 'psyche']
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senticnet['raskolnikov'] = ['-0.75', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.83', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
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senticnet['raspberry'] = ['0.751', '-0.78', '0', '0', '#joy', '#surprise', 'positive', '0.768', 'rhubarb', 'strawberry', 'herb', 'blueberry', 'custard']
senticnet['raspberry_fruit'] = ['0.02', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'many_fruits', 'peach', 'buy_fruit', 'pineapple', 'tomato']
senticnet['raspberry_jam'] = ['0.937', '0', '0', '0.828', '#joy', '#admiration', 'positive', '0.883', 'bakery', 'produce_section', 'thanksgiving_dinner', 'dinner_meal', 'fast_food']
senticnet['raspberry_sauce'] = ['0.092', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.071', 'bake_treat', 'round_pastry', 'bake_good', 'bake_goods', 'sweet_food']
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senticnet['raster_image_format'] = ['-0.55', '0', '0.912', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'wrangle', 'dustup', 'quarrel', 'row', 'squabble']
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senticnet['rasterization'] = ['0.865', '-0.80', '0', '0.52', '#joy', '#surprise', 'positive', '0.73', 'set', 'pose', 'lay', 'intersperse', 'put']
senticnet['rat'] = ['-0.13', '0', '0.013', '-0.08', '#sadness', '#disgust', 'negative', '-0.07', 'mouse', 'dig_tunnel', 'underground_animal', 'big_mouse', 'small_rodent']
senticnet['rat-like'] = ['0', '0', '-0.77', '-0.89', '#fear', '#disgust', 'negative', '-0.83', 'ratlike', 'gargoyle-like', 'squirrel-like', 'goblin-like', 'scorpion-like']
senticnet['rat-race'] = ['0', '0', '0.956', '-0.81', '#anger', '#disgust', 'negative', '-0.88', 'asylum', 'almshouse', 'poorhouse', 'gaol', 'workhouse']
senticnet['rat_aortic'] = ['0.052', '0', '0', '0.118', '#joy', '#admiration', 'positive', '0.085', 'diamond_ring', 'piece_of_jewelry', 'bell_sound', 'wedding_band', 'engagement']
senticnet['rat_bite_fever'] = ['-0.95', '0', '0.876', '-0.89', '#sadness', '#disgust', 'negative', '-0.90', 'bubonic_plague', 'pestis_bubonica', 'glandular_plague', 'septicemic_plague', 'pestilence']
senticnet['rat_catcher'] = ['0', '0', '0.862', '-0.79', '#anger', '#disgust', 'negative', '-0.82', 'social_insect', 'big_mouse', 'rodent', 'small_animal', 'dig_tunnel']
senticnet['rat_flea'] = ['-0.86', '0', '0.825', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'pestis', 'plague', 'pestilence', 'pest', 'ambulant_plague']
senticnet['rat_meat'] = ['0.965', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.951', 'delicious_food', 'hot_meal', 'good_food', 'fresh_produce', 'tasty_food']
senticnet['rat_race'] = ['0.544', '-0.91', '0', '0.941', '#surprise', '#admiration', 'positive', '0.8', 'rut', 'groove', 'modus_operandi', 'procedure', 'process']
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senticnet['rat_tat'] = ['0.979', '-0.69', '0', '0.576', '#joy', '#surprise', 'positive', '0.749', 'big_cheese', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['rat_typhus'] = ['0.579', '-0.81', '0', '0.833', '#surprise', '#admiration', 'positive', '0.741', 'murine_typhus', 'typhus', 'urban_typhus', 'endemic_typhus', 'rickettsial_disease']
senticnet['ratability'] = ['0', '0', '0.822', '-0.73', '#anger', '#disgust', 'negative', '-0.77', 'liability', 'susceptibility', 'susceptibleness', 'taxability', 'rateability']
senticnet['ratafee'] = ['0.739', '-0.52', '0', '0.833', '#joy', '#admiration', 'positive', '0.699', 'anisette', 'maraschino', 'ratafia', 'pastis', 'liqueur']
senticnet['ratafia'] = ['0.739', '-0.53', '0', '0.838', '#joy', '#admiration', 'positive', '0.702', 'anisette', 'maraschino', 'liqueur', 'pastis', 'ratafee']
senticnet['ratchet_screwdriver'] = ['0.808', '0.818', '0', '0', '#joy', '#interest', 'positive', '0.813', 'screwdriver', 'glass_front_display_cabinet', 'tool', 'paint', 'power_screwdriver']
senticnet['ratchet_up'] = ['-0.10', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.10', 'read_cover', 'spread_out', 'upgrade', 'look_behind', 'scan']
senticnet['rate'] = ['0', '0.281', '0', '0.206', '#interest', '#admiration', 'positive', '0.243', 'amount', 'quantity', 'merit', 'value', 'price']
senticnet['rate_of_interest'] = ['0.838', '0.948', '0', '0', '#joy', '#interest', 'positive', '0.893', 'interest_rate', 'bank', 'market', 'model', 'analyst']
senticnet['rate_tax_payer'] = ['0.763', '-0.67', '0', '0.945', '#joy', '#admiration', 'positive', '0.795', 'set', 'income_bracket', 'bracket', 'income', 'price_bracket']
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senticnet['rated_voltage'] = ['0.052', '0', '0', '0.322', '#joy', '#admiration', 'positive', '0.187', 'a_lot', 'abundance', 'plenty', 'superlative', 'comparative']
senticnet['rathke_pouch'] = ['0.905', '-0.65', '0', '0.851', '#joy', '#admiration', 'positive', '0.805', 'gonococcus', 'bacteria', 'bacteriae', 'probiotic', 'probiotic_bacterium']
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senticnet['rathskeller'] = ['0.165', '-0.07', '0.033', '0', '#joy', '#surprise', 'positive', '0.068', 'metal_stick', 'drinking_place', 'bart', 'meet_place', 'water_hole']
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senticnet['rating'] = ['0.798', '-0.52', '0', '0.693', '#joy', '#admiration', 'positive', '0.673', 'overvaluation', 'grade', 'undervaluation', 'pricing', 'reevaluation']
senticnet['ratio'] = ['-0.04', '0.09', '0.057', '0.14', '#interest', '#admiration', 'positive', '0.031', 'vector', 'maths', 'length_measurement', 'measurement_unit', 'unit_of_measurement']
senticnet['ratiocination'] = ['0.721', '-0.76', '0', '0.506', '#joy', '#surprise', 'positive', '0.663', 'abstract_thought', 'logical_argument', 'line_of_reasoning', 'thinking', 'thought']
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senticnet['rational_explanation'] = ['-0.11', '0.283', '-0.16', '0.331', '#interest', '#admiration', 'positive', '0.084', 'conclude', 'question_answering', 'solution', 'jutification', 'question_starter']
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senticnet['rationalism'] = ['-0.99', '0', '0.835', '-0.58', '#sadness', '#anger', 'negative', '-0.80', 'beginning', 'origin', 'root', 'wellspring', 'source']
senticnet['rationalist'] = ['0.95', '-0.77', '0', '0.578', '#joy', '#surprise', 'positive', '0.767', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
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senticnet['rationalize'] = ['-0.77', '0', '0.832', '0', '#sadness', '#anger', 'negative', '-0.80', 'scientific', 'equation', 'expression', 'root', 'radical']
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senticnet['rationalness'] = ['0.765', '-0.60', '0', '0.596', '#joy', '#surprise', 'positive', '0.656', 'rationality', 'quality', 'logicalness', 'completeness', 'consistency']
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senticnet['ratsnake'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'death_adder', 'ophidian', 'serpent', 'elapid', 'taipan']
senticnet['rattail'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'puffer', 'pufferfish', 'blowfish', 'triggerfish', 'plectognath']
senticnet['rattan'] = ['0.609', '-0.74', '0', '0.716', '#surprise', '#admiration', 'positive', '0.69', 'malacca', 'sword', 'swagger_stick', 'sword_cane', 'sword_stick']
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senticnet['rattle_snake'] = ['-0.68', '0', '0.611', '-0.59', '#sadness', '#anger', 'negative', '-0.63', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['rattlesnake'] = ['0', '0.1', '0', '0.071', '#interest', '#admiration', 'positive', '0.085', 'snake', 'cobra', 'reptile', 'animal', 'python']
senticnet['rattlesnake_bite'] = ['0', '0', '-0.73', '-0.81', '#fear', '#disgust', 'negative', '-0.77', 'flab', 'bad_hair_day', 'lose_friend', 'alcoholism', 'brain_tumor']
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senticnet['rattling_noise'] = ['0.098', '0', '-0.04', '0.112', '#joy', '#admiration', 'positive', '0.054', 'squeak', 'clang', 'vibration', 'loud_yell', 'tremor']
senticnet['rattling_sound'] = ['0.098', '0', '-0.04', '0.112', '#joy', '#admiration', 'positive', '0.054', 'squeak', 'clang', 'vibration', 'loud_yell', 'tremor']
senticnet['ratty'] = ['-0.92', '0', '0', '-0.86', '#sadness', '#disgust', 'negative', '-0.89', 'scruffy', 'beat-up', 'rumpled', 'mangy', 'frumpy']
senticnet['ratzinger_deputy'] = ['-0.95', '0', '0.673', '-0.85', '#sadness', '#disgust', 'negative', '-0.82', 'discharge', 'free', 'dethrone', 'disinvest', 'unfrock']
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senticnet['raucous_laughter'] = ['-0.85', '0', '0.583', '-0.71', '#sadness', '#disgust', 'negative', '-0.71', 'muddle', 'rummage', 'mare_nest', 'welter', 'clutter']
senticnet['raucously'] = ['0.26', '0', '0', '0.089', '#joy', '#admiration', 'positive', '0.174', 'balcony_rail', 'movie_theater_seat', 'row_seat', 'usher', 'sticky_floor']
senticnet['raunch'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'high_temperature', 'hot', 'hot_weather', 'fire_product', 'heat']
senticnet['raunchy'] = ['0', '0', '-0.77', '-0.75', '#fear', '#disgust', 'negative', '-0.76', 'grubby', 'begrimed', 'dingy', 'grimy', 'dirty']
senticnet['raunchy_comedy'] = ['-0.90', '0.152', '-0.62', '0.061', '#sadness', '#fear', 'negative', '-0.33', 'guilt_feeling', 'guilty', 'dolor', 'grieve', 'penitentially']
senticnet['raunchy_sex'] = ['0.081', '0.095', '0', '0', '#joy', '#interest', 'positive', '0.088', 'build_tower_out_toy_block', 'a_lot_of_sex', 'play_hockey', 'play_baseball', 'play_football']
senticnet['ravage'] = ['-0.71', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.81', 'ravager', 'wreak', 'devastation', 'rapid', 'depredation']
senticnet['ravager'] = ['-0.72', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.82', 'ravage', 'wreak', 'devastation', 'rapid', 'depredation']
senticnet['rave'] = ['0.098', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.079', 'banquet', 'carnival', 'gift_box', 'fun_place', 'rave_party']
senticnet['rave_master'] = ['-0.67', '0', '0.931', '-0.77', '#anger', '#disgust', 'negative', '-0.79', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['rave_party'] = ['0.098', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.079', 'banquet', 'carnival', 'gift_box', 'fun_place', 'rave']
senticnet['raven'] = ['-0.03', '-0.02', '0.027', '-0.10', '#sadness', '#disgust', 'negative', '-0.03', 'crow', 'dove', 'water_bird', 'bird_prey', 'bird_type']
senticnet['raven_hair'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'hair_color', 'purple', 'violet', 'dark_light', 'hue']
senticnet['ravenous'] = ['-0.71', '0', '0.692', '-0.81', '#sadness', '#disgust', 'negative', '-0.74', 'famished', 'starved', 'hungry', 'esurient', 'peckish']
senticnet['ravenous_hunger'] = ['-0.87', '0', '-0.71', '-0.96', '#sadness', '#disgust', 'negative', '-0.85', 'avid', 'eagerly', 'eagerness', 'avidity', 'avidness']
senticnet['ravenously'] = ['-0.71', '0', '0.692', '-0.81', '#sadness', '#disgust', 'negative', '-0.74', 'famished', 'starved', 'hungry', 'esurient', 'peckish']
senticnet['ravi_rampaul'] = ['0.034', '-0.02', '0.054', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'compass', 'swiss_army_knife', 'bake_pan', 'coffee_mug', 'wine_opener']
senticnet['ravine'] = ['0', '0.093', '0', '0.078', '#interest', '#admiration', 'positive', '0.085', 'steep', 'landscape_feature', 'knoll', 'hill_mountain', 'incline']
senticnet['raving'] = ['-0.91', '0', '0.704', '-0.70', '#sadness', '#anger', 'negative', '-0.77', 'rant', 'declamation', 'harangue', 'ranting', 'tirade']
senticnet['ravioli'] = ['0.043', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'apple_pie', 'a_lot_of_food', 'jelly', 'frozen_food', 'cottage_cheese']
senticnet['ravish'] = ['0', '0', '0.845', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'zap', 'suppress', 'quell', 'extinguish', 'ravage']
senticnet['ravisher'] = ['-0.54', '0', '0.691', '-0.96', '#anger', '#disgust', 'negative', '-0.73', 'slut', 'trollop', 'libertine', 'adulteress', 'fornicatress']
senticnet['ravishingly'] = ['0', '0', '-0.78', '-0.85', '#fear', '#disgust', 'negative', '-0.81', 'liquid', 'crystal_clear', 'pellucid', 'crystalline', 'lucid']
senticnet['raw'] = ['0', '0', '0.071', '0', '#anger', '#anger', 'negative', '-0.07', 'ripe_apple', 'corn', 'rice', 'sausage', 'maroon']
senticnet['raw_emotion'] = ['0.708', '-0.60', '0', '0.661', '#joy', '#admiration', 'positive', '0.659', 'love', 'conditioned_emotional_response', 'conditioned_emotion', 'fright', 'reverence']
senticnet['raw_fish'] = ['0.9', '0.963', '0', '0', '#joy', '#interest', 'positive', '0.931', 'form_of_food', 'japanese_food', 'uncooked', 'healthy', 'food']
senticnet['raw_flesh'] = ['-0.73', '0', '0.734', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'primitive', 'headhunter', 'barbarian', 'anthropophagus', 'anthropophagite']
senticnet['raw_footage'] = ['0', '0', '0', '-0.17', '#disgust', '#disgust', 'negative', '-0.17', 'medicine', 'aspirin', 'blank_check', 'painkiller', 'gauze']
senticnet['raw_material'] = ['0.024', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'stump', 'plank', 'timber', 'paper', 'mahogany']
senticnet['raw_milk'] = ['0.057', '0', '0.018', '0.058', '#joy', '#admiration', 'positive', '0.032', 'porridge', 'food', 'milk', 'healthy', 'morning_food']
senticnet['raw_onion'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'cook_meat', 'cook_hamburger', 'cook_food', 'cook_rice', 'brown_meat']
senticnet['raw_score'] = ['0.926', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.916', 'score_goal', 'accomplish', 'finalize', 'pot', 'make_point']
senticnet['raw_seafood'] = ['0.9', '0.963', '0', '0', '#joy', '#interest', 'positive', '0.931', 'form_of_food', 'japanese_food', 'uncooked', 'healthy', 'food']
senticnet['raw_shrimp'] = ['0.9', '0.963', '0', '0', '#joy', '#interest', 'positive', '0.931', 'form_of_food', 'japanese_food', 'uncooked', 'healthy', 'food']
senticnet['raw_sugar'] = ['-0.08', '0.106', '0', '0.125', '#interest', '#admiration', 'positive', '0.047', 'sugar', 'treacle', 'black_treacle', 'add_sugar', 'sugar_substitute']
senticnet['raw_talent'] = ['0.608', '-0.55', '0', '0.726', '#joy', '#admiration', 'positive', '0.628', 'genius', 'endowment', 'flair', 'gift', 'knack']
senticnet['raw_throat'] = ['-0.59', '0', '0.883', '-0.75', '#anger', '#disgust', 'negative', '-0.74', 'inflammatory_disease', 'streptococcal_sore_throat', 'strep_throat', 'streptococcus_tonsilitis', 'septic_sore_throat']
senticnet['raw_tuna'] = ['0.9', '0.963', '0', '0', '#joy', '#interest', 'positive', '0.931', 'form_of_food', 'japanese_food', 'uncooked', 'healthy', 'food']
senticnet['raw_vegetable'] = ['0.026', '0.08', '0.043', '0.064', '#interest', '#admiration', 'positive', '0.032', 'eat_rice', 'white_brown', 'asian', 'eat_pasta', 'staple_food']
senticnet['raw_weather'] = ['-0.92', '0', '0.929', '-0.56', '#sadness', '#anger', 'negative', '-0.80', 'overcast', 'weather', 'inclemency', 'storminess', 'cloudiness']
senticnet['rawls_theory'] = ['-0.91', '0', '0.795', '-0.94', '#sadness', '#disgust', 'negative', '-0.88', 'ineluctability', 'assured', 'predictability', 'slam_dunk', 'inevitability']
senticnet['ray'] = ['0.726', '0.971', '0', '0', '#joy', '#interest', 'positive', '0.849', 'sunlight', 'bright', 'light', 'brightness', 'sunshine']
senticnet['ray_dvd_combo'] = ['0', '0.11', '-0.05', '0.149', '#interest', '#admiration', 'positive', '0.067', 'pack_clothes', 'hold_stuff', 'carry_stuff', 'carry_book', 'carry_belongings']
senticnet['ray_gun'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
senticnet['ray_harford'] = ['-0.9', '0', '0.749', '-0.71', '#sadness', '#anger', 'negative', '-0.78', 'chute', 'slide', 'slideway', 'sloping_trough', 'gutter']
senticnet['ray_hyman'] = ['0.728', '-0.72', '0', '0.881', '#joy', '#admiration', 'positive', '0.779', 'predict_future', 'psychic_phenomenon', 'parapsychology', 'precognition', 'foreknowledge']
senticnet['ray_krone'] = ['0', '0', '0.869', '0', '#anger', '#anger', 'negative', '-0.86', 'convoke', 'loose_end', 'car_race', 'auction', 'mortuary']
senticnet['ray_of_light'] = ['-0.50', '0', '0.75', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'beam', 'beam_of_light', 'light_beam', 'ray', 'sunbeam']
senticnet['ray_ramirez'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'coach', 'instructor', 'personal_trainer', 'fit_person', 'train']
senticnet['ray_reardon'] = ['-0.66', '0', '0.771', '-0.95', '#anger', '#disgust', 'negative', '-0.79', 'delude', 'deceive', 'cozen', 'play_trick_on', 'lead_on']
senticnet['ray_winstone'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'role', 'theatrical_role', 'part', 'persona']
senticnet['rayado'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'common_bean', 'green_bean', 'wax_bean']
senticnet['rayleigh_distribution'] = ['-0.59', '0', '0.672', '-0.83', '#anger', '#disgust', 'negative', '-0.7', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'asymmetry', 'irregularity']
senticnet['raymon'] = ['-0.56', '0', '0.828', '-0.68', '#anger', '#disgust', 'negative', '-0.69', 'abruptness', 'precipitateness', 'precipitousness', 'hastiness', 'precipitancy']
senticnet['rayquaza'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
senticnet['raze'] = ['-0.07', '-0.03', '-0.06', '-0.16', '#sadness', '#disgust', 'negative', '-0.07', 'destroy', 'smash', 'damage', 'destruction', 'cause_injury']
senticnet['raze_to_ground'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'raze', 'destroy', 'smash', 'kill', 'rase']
senticnet['razor'] = ['0', '-0.06', '0.071', '-0.17', '#anger', '#disgust', 'negative', '-0.06', 'double_edge_razor', 'clipper', 'pair_of_scissors', 'knife', 'razor_blade']
senticnet['razor-sharp'] = ['0', '0.938', '0', '0.724', '#interest', '#admiration', 'positive', '0.831', 'sharpened', 'ultra-sharp', 'sharpness', 'super-sharp', 'sharpest']
senticnet['razor_blade'] = ['0.023', '-0.01', '0.079', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'knife', 'thumb_tack', 'razor', 'knife_sharp', 'pin']
senticnet['razor_burn'] = ['0.048', '-0.00', '0.011', '0.064', '#joy', '#admiration', 'positive', '0.027', 'become_wet', 'wash_away_dirt', 'run_out_soap', 'cleanliness', 'clean_skin']
senticnet['razor_clam'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'seal', 'walrus', 'otter', 'alligator', 'whale']
senticnet['razor_sharp'] = ['0', '0.074', '0.045', '0.075', '#interest', '#admiration', 'positive', '0.035', 'sharp_end', 'sharpness', 'sharp_corner', 'sharp_edge', 'sport_score']
senticnet['razor_sharp_teeth'] = ['0.876', '0.948', '0', '0', '#joy', '#interest', 'positive', '0.912', 'sting', 'tidbit', 'nip', 'tiny_amount', 'piece']
senticnet['razor_tie_label'] = ['-0.73', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'unwed', 'unwedded', 'divorced', 'unmarried', 'widowed']
senticnet['razorback'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'unofficial', 'informal', 'loose', 'drumhead']
senticnet['razz'] = ['-0.99', '0', '0.974', '-0.8', '#sadness', '#anger', 'negative', '-0.92', 'outcry', 'call', 'cry', 'yell', 'shout']
senticnet['razzing'] = ['-0.72', '0', '0.542', '-0.79', '#sadness', '#disgust', 'negative', '-0.68', 'outcry', 'call', 'cry', 'yell', 'shout']
senticnet['razzle_dazzle'] = ['-0.94', '0', '0.492', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'make_noise', 'disturb', 'confusion', 'hustle_and_bustle', 'bustle']
senticnet['rboc'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'long_distance_relationship', 'distant', 'kilometer', 'length_measurement', 'distance_measure']
senticnet['rcession_et'] = ['0', '0.919', '0', '0.888', '#interest', '#admiration', 'positive', '0.903', 'surprise', 'eye-popping', 'fulgurous', 'dazzling', 'bedaze']
senticnet['rd_engine'] = ['0', '0', '0', '0.073', '#admiration', '#admiration', 'positive', '0.073', 'fire_starter', 'organic_substance', 'renewable_resource', 'use_fuel', 'tree_product']
senticnet['re'] = ['0.047', '0.171', '-0.10', '0.212', '#interest', '#admiration', 'positive', '0.081', 'novelty', 'relevant', 'contemporary', 'timely', 'current']
senticnet['re-articulation'] = ['0', '0', '0.83', '-0.97', '#anger', '#disgust', 'negative', '-0.90', 'hastily-written', 'connectors-electric', 'checks-printing', 'benedictory', 'curtain-call']
senticnet['re-assure'] = ['0', '-0.83', '0', '0.831', '#surprise', '#admiration', 'positive', '0.835', 'disabused', 'disabusing', 'emasculate', 'disabuse', 'dishearten']
senticnet['re-create'] = ['-0.92', '0', '-0.73', '0', '#sadness', '#fear', 'negative', '-0.83', 'recreate', 'recreating', 're-creating', 're-created', 'replicate']
senticnet['re-created'] = ['-0.93', '0', '-0.73', '0', '#sadness', '#fear', 'negative', '-0.83', 'recreate', 'recreating', 're-creating', 're-create', 'replicate']
senticnet['re-creating'] = ['-0.93', '0', '-0.73', '0', '#sadness', '#fear', 'negative', '-0.83', 'recreate', 'recreating', 're-create', 're-created', 'replicate']
senticnet['re-double'] = ['0', '0', '-0.78', '-0.91', '#fear', '#disgust', 'negative', '-0.85', 'redoubled', 'redoubling', 'redouble', 'reaffirm', 're-focus']
senticnet['re-educate'] = ['0', '0.763', '0', '0.781', '#interest', '#admiration', 'positive', '0.772', 're-train', 'retrained', 'retraining', 'retrain', 're-training']
senticnet['re-engineer'] = ['0.835', '0', '0', '0.713', '#joy', '#admiration', 'positive', '0.774', 'respond', 'response', 'stimulus', 'susceptible', 'responsive']
senticnet['re-focus'] = ['0', '0', '-0.77', '-0.91', '#fear', '#disgust', 'negative', '-0.84', 'redoubled', 'redoubling', 're-double', 'reaffirm', 'redouble']
senticnet['re-hash'] = ['-0.83', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.83', 'rehashing', 'rehash', 'rehash', 'rehashed', 're-hashing']
senticnet['re-hashing'] = ['-0.83', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.83', 'rehashing', 're-hash', 'rehash', 'rehashed', 'rehash']
senticnet['re-incorporate'] = ['0.976', '0', '0', '0.706', '#joy', '#admiration', 'positive', '0.841', 'integrate', 'incorporate', 'unify', 'desegregate', 'build_in']
senticnet['re-localization'] = ['0', '0', '0.912', '-0.76', '#anger', '#disgust', 'negative', '-0.83', 'garrulousness', 'avariciousness', 'illiberality', 'over-specialization', 'over-zealousness']
senticnet['re-posting'] = ['-0.75', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.81', 'republishing', 're-printing', 'reprinting', 'reprint', 're-publishing']
senticnet['re-printing'] = ['-0.75', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.80', 'republishing', 'reprinting', 're-posting', 'reprint', 're-publishing']
senticnet['re-publishing'] = ['-0.74', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.80', 'republishing', 're-printing', 're-posting', 'reprint', 'reprinting']
senticnet['re-train'] = ['0', '0.769', '0', '0.787', '#interest', '#admiration', 'positive', '0.778', 'retrain', 'retrained', 'retraining', 're-educate', 're-training']
senticnet['re-training'] = ['0', '0.763', '0', '0.783', '#interest', '#admiration', 'positive', '0.773', 're-train', 'retrained', 'retraining', 're-educate', 'retrain']
senticnet['re-tweak'] = ['0', '0', '0.726', '-0.83', '#anger', '#disgust', 'negative', '-0.78', 'over-fill', 'soft-soap', 'glad-hand', 'out-market', 'pretty-up']
senticnet['re_affirm'] = ['0.854', '-0.70', '0', '0.912', '#joy', '#admiration', 'positive', '0.823', 'shew', 'verify', 'back', 'prove', 'demonstrate']
senticnet['re_animate'] = ['0.8', '-0.97', '0', '0.512', '#joy', '#surprise', 'positive', '0.763', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['re_election'] = ['-0.72', '0', '0.98', '0', '#sadness', '#anger', 'negative', '-0.85', 'resignedly', 'abject', 'unhopeful', 'abjectly', 'reconcile']
senticnet['re_election_campaign'] = ['-0.81', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.81', 'take_charge', 'anti', 'go_against', 'death_wish', 'file_complaint']
senticnet['re_emerge'] = ['-0.88', '0', '0.682', '-0.60', '#sadness', '#anger', 'negative', '-0.72', 'absent', 'fall_away', 'evanesce', 'blow_over', 'pass_off']
senticnet['re_emergence'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'absolutism', 'autocracy', 'ascendancy', 'tyrant']
senticnet['re_energise'] = ['0.845', '-0.59', '0', '0.926', '#joy', '#admiration', 'positive', '0.789', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['re_energize'] = ['0.845', '-0.59', '0', '0.926', '#joy', '#admiration', 'positive', '0.789', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['re_enroll'] = ['0.719', '0.757', '0', '0', '#joy', '#interest', 'positive', '0.738', 'matriculate', 'enroll_class', 'enroll_school', 'inscribe', 'register']
senticnet['re_envision'] = ['0.7', '-0.79', '0', '0.66', '#joy', '#surprise', 'positive', '0.719', 'influence', 'determine', 'shape', 'mold', 'regulate']
senticnet['re_establish'] = ['-0.91', '0', '0.637', '-0.59', '#sadness', '#anger', 'negative', '-0.71', 'gauge', 'govern', 'regulate', 'standardize', 'order']
senticnet['re_examination'] = ['-0.07', '0', '-0.13', '0', '#sadness', '#fear', 'negative', '-0.10', 'know_healthy', 'nervous_breakdown', 'physical_exam', 'check_vital_sign', 'epileptic_fit']
senticnet['re_examine'] = ['0.056', '-0.03', '0', '0.054', '#joy', '#admiration', 'positive', '0.048', 'analyze', 'stargazing', 'influence_public_decision', 'question_everything', 'see_artifact']
senticnet['re_exportation'] = ['-0.03', '0.223', '-0.15', '0.35', '#interest', '#admiration', 'positive', '0.097', 'return', 'gain', 'cheap_buy', 'receive', 'bequest']
senticnet['re_extensa'] = ['0.838', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.835', 'pondering', 'broody', 'contemplative', 'meditative', 'think']
senticnet['re_ignite'] = ['0.845', '-0.59', '0', '0.926', '#joy', '#admiration', 'positive', '0.789', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['re_inflate'] = ['-0.10', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.10', 'read_cover', 'spread_out', 'upgrade', 'look_behind', 'scan']
senticnet['re_interment'] = ['-0.12', '0.091', '-0.19', '-0.10', '#sadness', '#fear', 'negative', '-0.08', 'headstone', 'grave', 'bury_body', 'bury', 'bury_dead']
senticnet['re_invigorate'] = ['0.845', '-0.59', '0', '0.926', '#joy', '#admiration', 'positive', '0.789', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['re_ipsa_loquitur'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'culpable_negligence', 'evasion', 'dereliction', 'negligence']
senticnet['re_judicata'] = ['-0.55', '0', '0.741', '-0.76', '#anger', '#disgust', 'negative', '-0.68', 'illogicality', 'illogicalness', 'illogic', 'inconsequence', 'fallaciousness']
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senticnet['reading_room'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'a_lot_of_books', 'old_testament', 'cook_book', 'magazine', 'librarian']
senticnet['readjustment'] = ['0.665', '-0.89', '0', '0.911', '#surprise', '#admiration', 'positive', '0.822', 'adjustment', 'activity', 'tuning', 'registration', 'calibration']
senticnet['readmit'] = ['-0.63', '0', '0.872', '-0.88', '#anger', '#disgust', 'negative', '-0.79', 'retreat', 'retrograde', 'back_up', 'move', 'give_up']
senticnet['readout'] = ['-0.08', '0', '0.011', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'deflate', 'read_cover', 'obtain_information', 'upgrade', 'spread_out']
senticnet['ready'] = ['0', '0.124', '-0.07', '0.207', '#interest', '#admiration', 'positive', '0.086', 'start', 'motivation', 'likely', 'trust', 'available']
senticnet['ready_to_hand'] = ['0.859', '-0.81', '0', '0.615', '#joy', '#surprise', 'positive', '0.761', 'get_table', 'approachable', 'reachable', 'handy', 'accessible']
senticnet['ready_troop'] = ['0.506', '0.659', '-0.24', '0.242', '#joy', '#interest', 'positive', '0.29', 'kill_enemy', 'win_war', 'good_strategy', 'gain_territory', 'conquer_enemy']
senticnet['ready_wit'] = ['0.99', '0', '0', '0.716', '#joy', '#admiration', 'positive', '0.853', 'encyclopedism', 'learning', 'scholarship', 'learnedness', 'eruditeness']
senticnet['ready_work'] = ['0', '0.068', '0', '0', '#interest', '#interest', 'positive', '0.068', 'write_resume', 'look_job', 'fill_out_application', 'go_interview', 'go_job']
senticnet['reaffirm'] = ['0', '0', '-0.78', '-0.92', '#fear', '#disgust', 'negative', '-0.85', 'redoubled', 'redoubling', 're-double', 'redouble', 're-focus']
senticnet['reaffirmation'] = ['0.98', '0.857', '0', '0.966', '#joy', '#admiration', 'positive', '0.934', 'sincere', 'brownie_point', 'positive_reinforcement', 'acceptance', 'mutual_understand']
senticnet['reafon'] = ['-0.89', '0.161', '-0.61', '0.053', '#sadness', '#fear', 'negative', '-0.32', 'heartrending', 'heartbreaking', 'grievous', 'grievously', 'brokenheartedness']
senticnet['real'] = ['0.092', '0.205', '-0.08', '0.249', '#interest', '#admiration', 'positive', '0.115', 'good_investment', 'actual', 'estate', 'genuine', 'house_place']
senticnet['real-valued'] = ['0.951', '0', '0', '0.752', '#joy', '#admiration', 'positive', '0.851', 'non-negative', 'eigenvalue', 'nonzero', 'hermitian', 'nonnegative']
senticnet['real-world'] = ['0', '0.821', '0', '0.863', '#interest', '#admiration', 'positive', '0.842', 'verifiable', 'verified', 'verifiableness', 'confirmed', 'boolean']
senticnet['real_ale'] = ['0.079', '-0.02', '0.023', '0', '#joy', '#surprise', 'positive', '0.027', 'wine', 'lemonade', 'juice', 'whisky', 'soft_drink']
senticnet['real_algebraic_geometry'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'combinatory', 'consolidative', 'unifying', 'integrative']
senticnet['real_danger'] = ['-0.99', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.93', 'complacency', 'self-complacency', 'complacence', 'self_satisfaction', 'self_complacent']
senticnet['real_disposable_income'] = ['0.881', '0', '0', '0.875', '#joy', '#admiration', 'positive', '0.878', 'minor', 'meek', 'low', 'humble', 'small']
senticnet['real_duck'] = ['0', '0.07', '0', '0', '#interest', '#interest', 'positive', '0.07', 'sunken_treasure', 'scuba_diver', 'urchin', 'mussel', 'gold_fish']
senticnet['real_estate'] = ['0', '0.163', '-0.04', '0.13', '#interest', '#admiration', 'positive', '0.081', 'house_land', 'estate', 'place_to_live', 'dwell_place', 'live_place']
senticnet['real_estate_bubble'] = ['-0.55', '0', '0.869', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'air_bubble', 'foam', 'froth', 'soap_bubble', 'globule']
senticnet['real_estate_developer'] = ['0', '0', '-0.99', '-0.73', '#fear', '#disgust', 'negative', '-0.86', 'king', 'baron', 'business_leader', 'mogul', 'big_businessman']
senticnet['real_estate_magnate'] = ['0', '0', '-0.99', '-0.73', '#fear', '#disgust', 'negative', '-0.86', 'king', 'baron', 'business_leader', 'mogul', 'big_businessman']
senticnet['real_estate_mogul'] = ['0', '0', '-0.99', '-0.73', '#fear', '#disgust', 'negative', '-0.86', 'king', 'baron', 'business_leader', 'mogul', 'big_businessman']
senticnet['real_estate_speculation'] = ['-0.78', '0', '0.944', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'loser', 'gambler', 'mortal', 'better', 'bettor']
senticnet['real_estate_speculator'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'maker']
senticnet['real_estate_tycoon'] = ['0', '0', '-0.99', '-0.73', '#fear', '#disgust', 'negative', '-0.86', 'king', 'baron', 'business_leader', 'mogul', 'big_businessman']
senticnet['real_fact'] = ['0', '-0.03', '0', '0.097', '#surprise', '#admiration', 'positive', '0.068', 'analyze', 'examine', 'find_truth', 'determine_truth', 'discover_truth']
senticnet['real_stuff'] = ['0.726', '-0.82', '0', '0.966', '#surprise', '#admiration', 'positive', '0.839', 'original', 'authenticity', 'credibility', 'credibleness', 'believability']
senticnet['real_world'] = ['0.995', '0.801', '0', '0.968', '#joy', '#admiration', 'positive', '0.921', 'wisdom', 'learn', 'know_world', 'find_out_truth', 'smart']
senticnet['realign'] = ['-0.75', '0', '0.514', '-0.67', '#sadness', '#disgust', 'negative', '-0.64', 'align', 'realine', 'focus', 'true', 'address']
senticnet['realignment'] = ['-0.02', '0.119', '-0.06', '0.216', '#interest', '#admiration', 'positive', '0.062', 'go_forward', 'locomotion', 'movement', 'move', 'move_ahead']
senticnet['realine'] = ['-0.75', '0', '0.514', '-0.67', '#sadness', '#disgust', 'negative', '-0.64', 'realign', 'align', 'focus', 'true', 'address']
senticnet['realise'] = ['-0.05', '0.178', '-0.12', '0.142', '#interest', '#admiration', 'positive', '0.033', 'recognize', 'investigation', 'deduce', 'aware', 'cognition']
senticnet['realism'] = ['0.982', '-0.88', '0', '0.825', '#joy', '#surprise', 'positive', '0.898', 'functionality', 'sensibleness', 'viability', 'usefulness', 'practicality']
senticnet['realist'] = ['0.922', '-0.94', '0', '0.965', '#surprise', '#admiration', 'positive', '0.945', 'utilitarian', 'egalitarian', 'equalitarian', 'elitist', 'philosopher']
senticnet['realist_novel'] = ['0.036', '0.219', '0', '0.19', '#interest', '#admiration', 'positive', '0.148', 'book_genre', 'literature', 'concoction', 'untruth', 'falsehood']
senticnet['realist_tradition'] = ['0', '0.915', '0', '0.821', '#interest', '#admiration', 'positive', '0.868', 'culture', 'special_day', 'holiday', 'party_time', 'yearly_celebration']
senticnet['realistic'] = ['0.703', '0', '0', '0.884', '#joy', '#admiration', 'positive', '0.793', 'earthy', 'real', 'practical', 'true', 'truthful']
senticnet['realistic_fiction'] = ['0.036', '0.219', '0', '0.19', '#interest', '#admiration', 'positive', '0.148', 'book_genre', 'literature', 'concoction', 'untruth', 'falsehood']
senticnet['realistically_achievable'] = ['-0.96', '0', '0.603', '-0.91', '#sadness', '#disgust', 'negative', '-0.82', 'potential', 'potency', 'conceivableness', 'conceivability', 'possibleness']
senticnet['reality'] = ['-0.63', '0', '0.694', '-0.66', '#anger', '#disgust', 'negative', '-0.66', 'tangibility', 'tangibleness', 'palpability', 'corporality', 'solidness']
senticnet['reality_show'] = ['-0.08', '-0.03', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'graduate_student', 'student', 'serious_student', 'competition', 'hard_worker']
senticnet['realizability'] = ['-0.96', '0', '0.603', '-0.91', '#sadness', '#disgust', 'negative', '-0.82', 'potential', 'potency', 'conceivableness', 'conceivability', 'possibleness']
senticnet['realization'] = ['0', '0.937', '0', '0.707', '#interest', '#admiration', 'positive', '0.822', 'innovation', 'figure_out', 'come_conclusion', 'arrive_conclusion', 'resolve_problem']
senticnet['realize'] = ['-0.05', '0.178', '-0.12', '0.142', '#interest', '#admiration', 'positive', '0.033', 'recognize', 'investigation', 'deduce', 'aware', 'cognition']
senticnet['reallocation'] = ['0.725', '-0.83', '0', '0.559', '#joy', '#surprise', 'positive', '0.707', 'share', 'allocation', 'quota', 'portion', 'part']
senticnet['really'] = ['0.705', '0', '0', '0.851', '#joy', '#admiration', 'positive', '0.778', 'well', 'think', 'yes', 'people', 'time']
senticnet['really_appreciate'] = ['0', '0.211', '-0.12', '0.286', '#interest', '#admiration', 'positive', '0.125', 'bring_together', 'fuse', 'redo', 'fuse_together', 'request']
senticnet['really_big'] = ['0.033', '0.08', '0.044', '0.241', '#interest', '#admiration', 'positive', '0.077', 'large', 'big', 'huge', 'tall', 'wide']
senticnet['really_cool'] = ['0.057', '0.141', '-0.06', '0.162', '#interest', '#admiration', 'positive', '0.074', 'belongings', 'material', 'fabric', 'guarantee', 'madonna']
senticnet['really_good'] = ['0.704', '0', '0', '0.878', '#joy', '#admiration', 'positive', '0.791', 'high_quality', 'great', 'better_good', 'well_done', 'high_standard']
senticnet['really_hot'] = ['0.247', '0.204', '-0.06', '0.354', '#joy', '#admiration', 'positive', '0.185', 'burn_heat', 'hot', 'fire_burn', 'burn_hot', 'chemical_reaction']
senticnet['really_neat'] = ['0', '0.211', '-0.12', '0.286', '#interest', '#admiration', 'positive', '0.125', 'bring_together', 'fuse', 'redo', 'fuse_together', 'request']
senticnet['really_nice'] = ['0.123', '0.072', '-0.07', '0.152', '#joy', '#admiration', 'positive', '0.067', 'movie_star', 'two_people', 'parent', 'novelist', 'impression']
senticnet['really_nice_guy'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'male', 'bloke', 'boy', 'man_child', 'man']
senticnet['really_small'] = ['-0.74', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.78', 'small', 'make_up_water', 'proton', 'spherical', 'tiny']
senticnet['really_weird'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['realm'] = ['0', '0.169', '-0.07', '0.121', '#interest', '#admiration', 'positive', '0.073', 'democracy', 'empire', 'amendment', 'unite', 'international']
senticnet['realnetworks_rhapsody'] = ['0.101', '-0.04', '0.036', '0', '#joy', '#surprise', 'positive', '0.038', 'song', 'rap', 'jingle', 'chord', 'lyric']
senticnet['realpolitik'] = ['0', '0.098', '0', '0.08', '#interest', '#admiration', 'positive', '0.089', 'talk_person', 'wisdom', 'tact', 'statecraft', 'greet_person']
senticnet['realtor'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'maker']
senticnet['reanimate'] = ['0.8', '-0.97', '0', '0.512', '#joy', '#surprise', 'positive', '0.763', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['reanimated_corpse'] = ['0.8', '-0.97', '0', '0.512', '#joy', '#surprise', 'positive', '0.763', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['reanimation'] = ['0.8', '-0.97', '0', '0.512', '#joy', '#surprise', 'positive', '0.763', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['reanimator'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amuck', 'murderousness', 'misocainea', 'misogyny']
senticnet['reap'] = ['0.81', '0', '0', '0.703', '#joy', '#admiration', 'positive', '0.756', 'harvest', 'crop', 'grow_crop', 'plant_crop', 'graze_cattle']
senticnet['reap_huge'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'profitable', 'moneymaking', 'gainful', 'paying', 'paid']
senticnet['reap_substantial'] = ['0.81', '0', '0', '0.703', '#joy', '#admiration', 'positive', '0.756', 'harvest', 'crop', 'grow_crop', 'plant_crop', 'graze_cattle']
senticnet['reaped'] = ['0.906', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.89', 'windfall', 'profit', 'windfall', 'tax', 'subsidy']
senticnet['reaper'] = ['-0.74', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.80', 'casualty', 'extermination', 'cause_death', 'abduction', 'die']
senticnet['reaping_huge'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'profitable', 'moneymaking', 'gainful', 'paying', 'paid']
senticnet['reaping_machine'] = ['-0.03', '0.201', '-0.13', '0.171', '#interest', '#admiration', 'positive', '0.049', 'scientific_statement', 'hypothetical', 'conjecture', 'theory', 'fact']
senticnet['reappear'] = ['-0.88', '0', '0.682', '-0.60', '#sadness', '#anger', 'negative', '-0.72', 'absent', 'fall_away', 'evanesce', 'blow_over', 'pass_off']
senticnet['reapply'] = ['0.857', '-0.68', '0', '0.971', '#joy', '#admiration', 'positive', '0.839', 'revitalize', 'refurbish', 'reconstruct', 'renovate', 'freshen_up']
senticnet['reappoint'] = ['0', '0.745', '0', '0.832', '#interest', '#admiration', 'positive', '0.788', 'meet_up', 'year', 'rendezvous', 'meet_friend', 'make_appointment']
senticnet['reappointment'] = ['0', '0.745', '0', '0.832', '#interest', '#admiration', 'positive', '0.788', 'meet_up', 'year', 'rendezvous', 'meet_friend', 'make_appointment']
senticnet['reappropriate'] = ['0.734', '-0.64', '0', '0.933', '#joy', '#admiration', 'positive', '0.771', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['rear'] = ['-0.1', '0.184', '-0.14', '0.179', '#interest', '#admiration', 'positive', '0.029', 'rear_end', 'flipside', 'backside', 'go_backwards', 'exception']
senticnet['rear_admiral'] = ['0.688', '-0.90', '0', '0.877', '#surprise', '#admiration', 'positive', '0.824', 'fleet_admiral', 'flag_officer', 'admiral', 'full_admiral', 'vice_admiral']
senticnet['rear_admiral_sir'] = ['0.688', '-0.90', '0', '0.877', '#surprise', '#admiration', 'positive', '0.824', 'fleet_admiral', 'rear_admiral', 'admiral', 'full_admiral', 'flag_officer']
senticnet['rear_bench'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'train_station', 'government_office', 'cable_car', 'sport_stadium', 'terminal']
senticnet['rear_brake'] = ['0', '-0.09', '0.044', '0.055', '#surprise', '#admiration', 'positive', '0.036', 'two_wheels', 'gear', 'axle', 'engine', 'steer_wheel']
senticnet['rear_bumper'] = ['0.725', '0.796', '0', '0', '#joy', '#interest', 'positive', '0.76', 'car_stereo', 'battery', 'seatbelt', 'spare_tire', 'brake_pedal']
senticnet['rear_cabin'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'train_station', 'government_office', 'cable_car', 'sport_stadium', 'terminal']
senticnet['rear_cargo'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'train_station', 'government_office', 'cable_car', 'sport_stadium', 'terminal']
senticnet['rear_compartment'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'train_station', 'government_office', 'cable_car', 'sport_stadium', 'terminal']
senticnet['rear_deck'] = ['0', '0.248', '-0.07', '0.121', '#interest', '#admiration', 'positive', '0.099', 'gym_locker', 'observatory', 'prison_cell', 'luggage_store', 'garage_sale']
senticnet['rear_door'] = ['0', '0', '0', '0.077', '#admiration', '#admiration', 'positive', '0.077', 'glass_front', 'gate', 'doorway', 'use_farm', 'entranceway']
senticnet['rear_echelon'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'start_over', 'queue', 'find_line', 'take_turn', 'long_line']
senticnet['rear_elevation'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'top_floor', 'front_desk', 'concierge', 'exit', 'elevator']
senticnet['rear_end'] = ['-0.04', '0.182', '-0.10', '0.203', '#interest', '#admiration', 'positive', '0.059', 'exception', 'go_backwards', 'rear', 'backside', 'flipside']
senticnet['rear_facade'] = ['0.028', '0', '0', '0.051', '#joy', '#admiration', 'positive', '0.04', 'entranceway', 'doorway', 'entrance', 'entryway', 'corridor']
senticnet['rear_facing_camera'] = ['0', '-0.02', '0.048', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'telescope', 'read_glass', 'eyeglass', 'binoculars', 'monocle']
senticnet['rear_fender'] = ['0', '0.075', '0', '0.2', '#interest', '#admiration', 'positive', '0.138', 'stride', 'foot_motion', 'gait', 'foot_action', 'foot_movement']
senticnet['rear_flag_officer'] = ['0.681', '-0.91', '0', '0.868', '#surprise', '#admiration', 'positive', '0.821', 'fleet_flag_officer', 'admiral', 'flag_officer', 'full_flag_officer', 'vice_flag_officer']
senticnet['rear_guard'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['rear_guard_action'] = ['-0.05', '0', '-0.08', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'siege', 'big_fight', 'war', 'declare_war', 'combat']
senticnet['rear_hatch'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'train_station', 'government_office', 'cable_car', 'sport_stadium', 'terminal']
senticnet['rear_naked_choke_submission'] = ['0.823', '0.982', '0', '0', '#joy', '#interest', 'positive', '0.902', 'spherical', 'three_dimensions', 'round_orb', 'coil', 'fill_with_sand']
senticnet['rear_overhang'] = ['0', '0.248', '-0.07', '0.121', '#interest', '#admiration', 'positive', '0.099', 'gym_locker', 'observatory', 'prison_cell', 'luggage_store', 'garage_sale']
senticnet['rear_passenger'] = ['0', '0.248', '-0.07', '0.121', '#interest', '#admiration', 'positive', '0.099', 'gym_locker', 'observatory', 'prison_cell', 'luggage_store', 'garage_sale']
senticnet['rear_projection'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'arm_movement', 'hand_gesture', 'water_motion', 'hand_signal', 'sea_movement']
senticnet['rear_projection_screen'] = ['0.044', '0.08', '0', '-0.03', '#joy', '#interest', 'positive', '0.031', 'screen', 'pixel', 'visual_information', 'image', 'computer_monitor']
senticnet['rear_seat'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'train_station', 'government_office', 'cable_car', 'sport_stadium', 'terminal']
senticnet['rear_seat_fold'] = ['0', '0', '0.865', '-0.95', '#anger', '#disgust', 'negative', '-0.90', 'bucket_overflow', 'dirty_floor', 'spill_over', 'float', 'spoil_wood_floor']
senticnet['rear_seat_passenger'] = ['0.069', '0.07', '0', '0.116', '#interest', '#admiration', 'positive', '0.085', 'traveller', 'tourist', 'travel', 'explorer', 'adventurer']
senticnet['rear_tailgate'] = ['0', '0.248', '-0.07', '0.121', '#interest', '#admiration', 'positive', '0.099', 'gym_locker', 'observatory', 'prison_cell', 'luggage_store', 'garage_sale']
senticnet['rear_tire'] = ['0.03', '0', '0.017', '0.138', '#joy', '#admiration', 'positive', '0.05', 'up_down', 'curl', 'crest', 'hand_gesture', 'goodbye']
senticnet['rear_tyre'] = ['0', '0', '0.873', '-0.92', '#anger', '#disgust', 'negative', '-0.89', 'punch', 'sting', 'disable', 'ache', 'vex']
senticnet['rear_verandah'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'top_floor', 'front_desk', 'concierge', 'exit', 'elevator']
senticnet['rear_wall'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'vestibule', 'door', 'central_passage', 'gate', 'door_handle']
senticnet['rear_wheel'] = ['0', '-0.09', '0.044', '0.055', '#surprise', '#admiration', 'positive', '0.036', 'two_wheels', 'gear', 'axle', 'engine', 'steer_wheel']
senticnet['reargue'] = ['-0.86', '0', '0.897', '-0.81', '#sadness', '#anger', 'negative', '-0.85', 'disregard', 'ignore', 'brush_off', 'push_aside', 'flout']
senticnet['rearrange'] = ['0.048', '0.092', '-0.08', '0.216', '#interest', '#admiration', 'positive', '0.067', 'gather', 'group', 'organization', 'clique', 'collection']
senticnet['rearrangement_reaction'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
senticnet['rearrest'] = ['-0.43', '0.107', '-0.42', '-0.03', '#sadness', '#fear', 'negative', '-0.19', 'send_to_jail', 'go_jail', 'jail_time', 'go_to_prison', 'injustice']
senticnet['rearview_camera'] = ['0', '0', '0', '0.109', '#admiration', '#admiration', 'positive', '0.109', 'knob', 'door_knob', 'door_handle', 'handle', 'hinge']
senticnet['reason'] = ['-0.09', '0.286', '-0.18', '0.227', '#interest', '#admiration', 'positive', '0.058', 'consider', 'choice', 'explanation', 'rational', 'decide']
senticnet['reason_unenforceable'] = ['-0.91', '0', '-0.77', '-0.75', '#sadness', '#fear', 'negative', '-0.81', 'emptiness', 'star_planet', 'nothingness', 'outer', 'around_earth']
senticnet['reason_why'] = ['0', '0.256', '-0.05', '0.228', '#interest', '#admiration', 'positive', '0.144', 'end_point', 'end', 'conclusion', 'punctuation', 'bottom_line']
senticnet['reasonability'] = ['0.828', '-0.90', '0', '0.874', '#surprise', '#admiration', 'positive', '0.869', 'perspicacity', 'judgment', 'fair', 'sound_judgment', 'fair_judgment']
senticnet['reasonable'] = ['-0.08', '0', '-0.13', '0.085', '#sadness', '#fear', 'negative', '-0.04', 'job_offer', 'rejection', 'lucky', 'borrow_money', 'quit_work']
senticnet['reasonable_accommodation'] = ['-0.77', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.78', 'chicken_pox', 'diabetes', 'lyme_disease', 'brain_tumor', 'suffer_fool']
senticnet['reasonable_amount'] = ['-0.08', '0', '-0.13', '0.085', '#sadness', '#fear', 'negative', '-0.04', 'job_offer', 'rejection', 'lucky', 'borrow_money', 'quit_work']
senticnet['reasonable_assurance'] = ['0.036', '0.292', '0', '0.337', '#interest', '#admiration', 'positive', '0.222', 'importance', 'quality', 'total', 'excellence', 'frivolity']
senticnet['reasonable_chastisement'] = ['-0.83', '0', '0.924', '-0.79', '#sadness', '#anger', 'negative', '-0.85', 'kick_in_butt', 'licking', 'whacking', 'stoning', 'lapidation']
senticnet['reasonable_estimate'] = ['-0.02', '0.248', '0', '0.31', '#interest', '#admiration', 'positive', '0.178', 'assurance', 'total', 'quality', 'importance', 'amount']
senticnet['reasonable_explanation'] = ['-0.11', '0.283', '-0.16', '0.331', '#interest', '#admiration', 'positive', '0.084', 'conclude', 'question_answering', 'solution', 'jutification', 'question_starter']
senticnet['reasonable_interpretation'] = ['-0.84', '0', '0.627', '-0.76', '#sadness', '#disgust', 'negative', '-0.74', 'difficulty', 'inspiration', 'determinant', 'revelation', 'divine_guidance']
senticnet['reasonableness'] = ['0.545', '-0.73', '0', '0.643', '#surprise', '#admiration', 'positive', '0.64', 'inexpensiveness', 'modestness', 'cost', 'cut_rate', 'cut_price']
senticnet['reasonableness_standard'] = ['0.5', '-0.9', '0', '0.525', '#surprise', '#admiration', 'positive', '0.642', 'manageable', 'controllable', 'governable', 'steerable', 'dirigible']
senticnet['reasonably'] = ['0.976', '0.958', '0', '0', '#joy', '#interest', 'positive', '0.967', 'satisfactorily', 'fairly', 'reason', 'sane', 'well']
senticnet['reasonably_necessary'] = ['0', '0.824', '0', '0.788', '#interest', '#admiration', 'positive', '0.806', 'true', 'rule-governed', 'legitimate', 'licit', 'rightful']
senticnet['reasonably_safe'] = ['-0.74', '0', '0.781', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'reprimand', 'caution', 'big_monster', 'kidnap', 'mock']
senticnet['reasoned_argument'] = ['0.715', '-0.82', '0', '0.659', '#joy', '#surprise', 'positive', '0.734', 'reasoning', 'logical_thinking', 'abstract_thought', 'casuistry', 'line_of_inquiry']
senticnet['reasoned_decision'] = ['-0.06', '0.32', '-0.10', '0.248', '#interest', '#admiration', 'positive', '0.101', 'decide', 'select', 'selection', 'make_choice', 'choice']
senticnet['reasoning'] = ['0.807', '-0.71', '0', '0.877', '#joy', '#admiration', 'positive', '0.798', 'ratiocination', 'logical_argument', 'line_of_reasoning', 'thinking', 'thought']
senticnet['reasonless'] = ['-0.98', '0', '0.646', '-0.98', '#sadness', '#disgust', 'negative', '-0.87', 'indefensible', 'unreasonable', 'untenable', 'senseless', 'unwarranted']
senticnet['reassemble'] = ['-0.11', '0.095', '-0.11', '0.071', '#sadness', '#fear', 'negative', '-0.01', 'violent_action', 'pulverize', 'break_glass', 'tear_apart', 'hit_hard']
senticnet['reassembly'] = ['0', '0', '0.804', '-0.99', '#anger', '#disgust', 'negative', '-0.90', 'rebuild', 'reconstructive_memory', 'interpretation', 'reconstruction', 'makeover']
senticnet['reassertion'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'absolutism', 'autocracy', 'ascendancy', 'tyrant']
senticnet['reassignment'] = ['-0.02', '0.119', '-0.06', '0.216', '#interest', '#admiration', 'positive', '0.062', 'go_forward', 'locomotion', 'movement', 'move', 'move_ahead']
senticnet['reassurance'] = ['0.376', '0.271', '-0.19', '0.258', '#joy', '#interest', 'positive', '0.177', 'pleasant_emotion', 'bliss', 'good_feeling', 'confirm', 'healthful']
senticnet['reassure'] = ['0.52', '0.666', '-0.26', '0.269', '#joy', '#interest', 'positive', '0.298', 'reassuring', 'reassured', 'reassuringly', 'assure', 'likeable']
senticnet['reassured'] = ['0.513', '0.659', '-0.27', '0.26', '#joy', '#interest', 'positive', '0.291', 'reassuring', 'assure', 'reassuringly', 'reassure', 'likeable']
senticnet['reassuring'] = ['0.52', '0.666', '-0.26', '0.269', '#joy', '#interest', 'positive', '0.298', 'reassured', 'reassuringly', 'reassure', 'assure', 'likeable']
senticnet['reassuringly'] = ['0.52', '0.666', '-0.26', '0.269', '#joy', '#interest', 'positive', '0.298', 'reassuring', 'reassured', 'reassure', 'assure', 'likeable']
senticnet['reassuringly_expensive'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['reauthorize'] = ['0.857', '-0.68', '0', '0.971', '#joy', '#admiration', 'positive', '0.839', 'revitalize', 'refurbish', 'reconstruct', 'renovate', 'freshen_up']
senticnet['reaver'] = ['0.826', '-0.65', '0', '0.587', '#joy', '#surprise', 'positive', '0.689', 'scoffer', 'vegetarian', 'eater', 'diner', 'dunker']
senticnet['reawaken'] = ['-0.74', '0', '0.966', '0', '#sadness', '#anger', 'negative', '-0.85', 'move', 'displace', 'fire', 'chase_away', 'ostracise']
senticnet['rebarbative'] = ['0.71', '-0.81', '0', '0.937', '#surprise', '#admiration', 'positive', '0.819', 'erudite', 'scholarly', 'pedantic', 'learned', 'bookish']
senticnet['rebel'] = ['0', '0', '0.732', '-0.75', '#anger', '#disgust', 'negative', '-0.74', 'decree', 'impose', 'raid', 'get_revenge', 'tyranny']
senticnet['rebel_checkpoint'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'corner_two_streets', 'street_corner', 'roadside', 'city_street', 'schoolyard']
senticnet['rebel_faction'] = ['-0.50', '0', '0.989', '-0.91', '#anger', '#disgust', 'negative', '-0.80', 'camp', 'pack', 'clique', 'coterie', 'ingroup']
senticnet['rebelling_against'] = ['-0.54', '0', '0.815', '-0.83', '#anger', '#disgust', 'negative', '-0.73', 'brave_out', 'endure', 'weather', 'withstand', 'stand_firm']
senticnet['rebellion'] = ['0', '0', '0.859', '-0.80', '#anger', '#disgust', 'negative', '-0.83', 'uprise', 'opposition', 'against', 'disagreement', 'riot']
senticnet['rebellion_against'] = ['-0.84', '0', '0.744', '-0.58', '#sadness', '#anger', 'negative', '-0.72', 'subversion', 'subversive_activity', 'termination', 'ending', 'conclusion']
senticnet['rebellious'] = ['-0.65', '0', '0.795', '-0.96', '#anger', '#disgust', 'negative', '-0.80', 'contumacious', 'disobedient', 'unruly', 'insubordinate', 'mutinous']
senticnet['rebellious_daughter'] = ['0.584', '-0.59', '0', '0.753', '#surprise', '#admiration', 'positive', '0.643', 'ungracious', 'discourteous', 'impolite', 'brattish', 'unparliamentary']
senticnet['rebellious_streak'] = ['-0.84', '0', '0.506', '-0.62', '#sadness', '#disgust', 'negative', '-0.66', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['rebellious_teen'] = ['0.584', '-0.59', '0', '0.753', '#surprise', '#admiration', 'positive', '0.643', 'ungracious', 'discourteous', 'impolite', 'brattish', 'unparliamentary']
senticnet['rebirth'] = ['0.782', '0', '0', '0.923', '#joy', '#admiration', 'positive', '0.853', 'reincarnation', 'renascence', 'revive', 'fact', 'view']
senticnet['reboot'] = ['-0.05', '0.168', '-0.04', '0.263', '#interest', '#admiration', 'positive', '0.084', 'move_mouse', 'keep_track_appointment', 'type_of_keyboard', 'train_open_mind', 'enter_address']
senticnet['reborn'] = ['0', '0', '0', '0.816', '#admiration', '#admiration', 'positive', '0.816', 'immortality', 'live_forever', 'deathless', 'eternal_life', 'everlasting_life']
senticnet['rebound'] = ['0.798', '0', '0', '0.879', '#joy', '#admiration', 'positive', '0.839', 'reply', 'response', 'comeback', 'backtalk', 'revival']
senticnet['rebound_basket'] = ['-0.81', '0', '0.747', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'vagrant', 'drifter', 'hobo', 'vagabond', 'dosser']
senticnet['rebounder'] = ['0', '0.127', '-0.04', '0.194', '#interest', '#admiration', 'positive', '0.092', 'quick', 'fast', 'speedy', 'quick_movement', 'speed']
senticnet['rebuff'] = ['-0.75', '0', '0.666', '-0.51', '#sadness', '#anger', 'negative', '-0.64', 'discourtesy', 'snub', 'cold_shoulder', 'offense', 'cut']
senticnet['rebuild'] = ['0.749', '0', '0', '0.939', '#joy', '#admiration', 'positive', '0.844', 'rejuvenate', 'renovate', 'conciliate', 'meliorate', 'revivify']
senticnet['rebuild_confidence'] = ['-0.07', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.07', 'use_needle', 'doctor_tool', 'fix', 'embroidery', 'seam']
senticnet['rebuke'] = ['-0.50', '0', '0.543', '-0.62', '#anger', '#disgust', 'negative', '-0.55', 'bawling_out', 'chewing_out', 'upbraiding', 'dressing_down', 'berating']
senticnet['reburial'] = ['-0.12', '0.091', '-0.19', '-0.10', '#sadness', '#fear', 'negative', '-0.08', 'headstone', 'grave', 'bury_body', 'bury', 'bury_dead']
senticnet['rebut'] = ['-0.96', '0', '0.885', '-0.75', '#sadness', '#anger', 'negative', '-0.86', 'controvert', 'answer', 'disown', 'renounce', 'repudiate']
senticnet['rebuttal'] = ['-0.08', '0.101', '-0.07', '0.169', '#interest', '#admiration', 'positive', '0.028', 'send_back', 'question_answer', 'retort', 'write_back', 'elaborate']
senticnet['rebuttal_evidence'] = ['-0.96', '0', '0.885', '-0.75', '#sadness', '#anger', 'negative', '-0.86', 'controvert', 'answer', 'disown', 'renounce', 'repudiate']
senticnet['recalcitrance'] = ['-0.69', '0', '0.516', '-0.94', '#sadness', '#disgust', 'negative', '-0.71', 'insubordinate', 'defiant', 'obstreperous', 'resistant', 'resistive']
senticnet['recalcitrant'] = ['-0.69', '0', '0.516', '-0.94', '#sadness', '#disgust', 'negative', '-0.71', 'insubordinate', 'defiant', 'obstreperous', 'resistant', 'resistive']
senticnet['recalcitrate'] = ['-0.68', '0', '0.765', '-0.54', '#sadness', '#anger', 'negative', '-0.66', 'fight', 'oppose', 'fight_back', 'fight_down', 'defend']
senticnet['recall'] = ['0.888', '0.816', '0', '0', '#joy', '#interest', 'positive', '0.852', 'recollection', 'brain_activity', 'memorization', 'memory', 'call_center']
senticnet['recall_message'] = ['0.109', '0.129', '0', '0.098', '#joy', '#interest', 'positive', '0.112', 'leave_message', 'message', 'take_message', 'send_message', 'read_message']
senticnet['recanalization'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'blatancy', 'obviousness']
senticnet['recant'] = ['-0.97', '0', '0.616', '-0.58', '#sadness', '#anger', 'negative', '-0.72', 'abjure', 'repudiate', 'forswear', 'deny', 'reject']
senticnet['recap'] = ['-0.10', '0', '0', '-0.13', '#sadness', '#disgust', 'negative', '-0.11', 'reflect', 'stare_off_space', 'summarize', 'think_past', 'mind_boggle']
senticnet['recapitalisation'] = ['-0.12', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'escape', 'escape_situation', 'cancel_meeting', 'absent', 'cancel_appointment']
senticnet['recapitalisation_deal'] = ['-0.04', '0.274', '-0.15', '0.278', '#interest', '#admiration', 'positive', '0.089', 'business_deal', 'trade', 'exchange', 'bargain', 'offer']
senticnet['recapitalise'] = ['-0.12', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.08', 'escape', 'escape_situation', 'cancel_meeting', 'absent', 'cancel_appointment']
senticnet['recd'] = ['-0.69', '0', '0.883', '-0.91', '#anger', '#disgust', 'negative', '-0.82', 'stick_in', 'enclose', 'insert', 'feed_in', 'put_in']
senticnet['recede'] = ['-0.54', '0', '0.644', '-0.90', '#anger', '#disgust', 'negative', '-0.69', 'retreat', 'retrograde', 'back_up', 'move', 'give_up']
senticnet['receding'] = ['-0.72', '0', '0.754', '0', '#sadness', '#anger', 'negative', '-0.73', 'recession', 'recessional', 'ceremonial', 'bankruptcy', 'depressed']
senticnet['receding_brown_hair'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'plato', 'natural_world', 'hair', 'feature', 'stick_out']
senticnet['receding_hairline'] = ['0', '0', '0.841', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'ancient', 'many_years', 'year', 'old', 'teen']
senticnet['receding_tide'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'driftwood', 'sea_shell', 'scenic_view', 'naked_woman', 'tidal_wave']
senticnet['receipt'] = ['0.862', '0.709', '0', '0', '#joy', '#interest', 'positive', '0.785', 'invoice', 'bill', 'check', 'mastercard', 'payment_counter']
senticnet['receivables'] = ['0', '0.873', '0', '0.803', '#interest', '#admiration', 'positive', '0.838', 'close-out', 'write-off', 'delinquent', 'non-tax', 'write-off']
senticnet['receive'] = ['0.714', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.839', 'give', 'cope', 'offer', 'accept', 'return']
senticnet['receive_applause'] = ['0', '-0.75', '0', '0.764', '#surprise', '#admiration', 'positive', '0.76', 'exaggerate', 'wave_arm', 'show_off', 'laugh', 'applause']
senticnet['receive_change'] = ['-0.78', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.78', 'go_broke', 'bankruptcy', 'run_out_money', 'compare_price', 'incur_debt']
senticnet['receive_earn_paycheck'] = ['0.826', '0', '0', '0', '#joy', '#joy', 'positive', '0.826', 'earn_money', 'paycheck', 'earn', 'earn_paycheck', 'finish_work']
senticnet['receive_email'] = ['-0.05', '0.168', '-0.04', '0.263', '#interest', '#admiration', 'positive', '0.084', 'email', 'receive', 'use_internet', 'use_computer', 'receive_information']
senticnet['receive_financial_aid'] = ['0.719', '0.757', '0', '0', '#joy', '#interest', 'positive', '0.738', 'matriculate', 'enroll_class', 'enroll_school', 'inscribe', 'register']
senticnet['receive_gift'] = ['0.95', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.948', 'surprise_friend', 'act_foolish', 'blow_out_candle', 'tear_paper', 'say_love']
senticnet['receive_grade'] = ['-0.09', '-0.03', '0', '-0.14', '#sadness', '#disgust', 'negative', '-0.06', 'show_know', 'get_grade', 'complete_course', 'test_knowledge', 'graduate']
senticnet['receive_help'] = ['0.214', '0.258', '-0.09', '0.2', '#joy', '#interest', 'positive', '0.146', 'gather_evidence', 'testify', 'crime_scene', 'give_testimony', 'contact_police']
senticnet['receive_holy_communion'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'reverence', 'obeisance', 'genuflexion', 'kowtow', 'mafia']
senticnet['receive_information'] = ['0.75', '0.973', '0', '0.901', '#interest', '#admiration', 'positive', '0.875', 'gain_knowledge', 'share_knowledge', 'increase_understand', 'know_story', 'gain_information']
senticnet['receive_letter'] = ['0', '0.771', '0', '0', '#interest', '#interest', 'positive', '0.771', 'post_letter', 'receive_email', 'hunt_duck', 'answer_telephone', 'needy']
senticnet['receive_mail'] = ['0', '0.982', '0', '0', '#interest', '#interest', 'positive', '0.982', 'open_letter', 'write_friend', 'receive_email', 'post_letter', 'open_envelope']
senticnet['receive_money'] = ['0.025', '0.228', '-0.07', '0.117', '#interest', '#admiration', 'positive', '0.073', 'work_for_money', 'give_money', 'earn_income', 'paycheck', 'compensation']
senticnet['receive_notification'] = ['0.031', '0.218', '-0.09', '0.299', '#interest', '#admiration', 'positive', '0.113', 'nominate', 'axiom', 'supreme', 'govern', 'tenet']
senticnet['receive_paycheck'] = ['0', '0.982', '0', '0', '#interest', '#interest', 'positive', '0.982', 'earn_paycheck', 'paycheck', 'earn_money', 'receive_money', 'compensation']
senticnet['receive_phone_call'] = ['0.903', '0.759', '0', '0', '#joy', '#interest', 'positive', '0.831', 'learn_listen', 'answer_phone', 'receive_letter', 'call_mom', 'answer_telephone']
senticnet['receive_punishment'] = ['-0.31', '0.095', '-0.23', '-0.03', '#sadness', '#fear', 'negative', '-0.12', 'tell_lie', 'end_life', 'right_wrong', 'conceal_evidence', 'commit_crime']
senticnet['receive_reward'] = ['0.559', '0.313', '-0.21', '0.261', '#joy', '#interest', 'positive', '0.23', 'good_emotion', 'satisfaction', 'joy', 'elation', 'happy']
senticnet['receive_royal_assent'] = ['-0.57', '0', '0.576', '-0.78', '#anger', '#disgust', 'negative', '-0.64', 'reprieve', 'respite', 'suspend', 'call', 'delay']
senticnet['receive_service'] = ['0.033', '0.179', '-0.12', '0.251', '#interest', '#admiration', 'positive', '0.086', 'cash', 'cash_check', 'receive_change', 'incur_debt', 'bankruptcy']
senticnet['receive_signal'] = ['0.107', '0.098', '0', '0.135', '#joy', '#admiration', 'positive', '0.113', 'listen_news', 'perform_on_stage', 'send_signal', 'play_cd', 'send_message']
senticnet['receive_ssi'] = ['-0.60', '0', '0.832', '-0.53', '#sadness', '#anger', 'negative', '-0.65', 'unsuitable', 'undesirable', 'unqualified', 'disqualified', 'unentitled']
senticnet['receive_thank'] = ['0.713', '0', '0', '0.875', '#joy', '#admiration', 'positive', '0.794', 'feel_happy', 'good_karma', 'good_feeling', 'warm_feeling', 'jollify']
senticnet['received'] = ['0.838', '-0.64', '0', '0.907', '#joy', '#admiration', 'positive', '0.798', 'conventional', 'customary', 'formulaic', 'stuffy', 'stodgy']
senticnet['received_acclaim'] = ['0.853', '0.828', '0', '0.98', '#joy', '#admiration', 'positive', '0.887', 'endorse', 'recommend', 'stimulate_mind', 'acculturation', 'support']
senticnet['received_critical_acclaim'] = ['0.853', '0.828', '0', '0.98', '#joy', '#admiration', 'positive', '0.887', 'endorse', 'recommend', 'stimulate_mind', 'acculturation', 'support']
senticnet['receiver'] = ['0', '0', '0', '0.168', '#admiration', '#admiration', 'positive', '0.168', 'handset', 'tuner', 'speaker', 'channel_selector', 'power_switch']
senticnet['receiveth'] = ['0.741', '-0.84', '0', '0.543', '#joy', '#surprise', 'positive', '0.711', 'integrity', 'righteousness', 'scrupulousness', 'incorruptness', 'honestness']
senticnet['receiving_afdc'] = ['-0.60', '0', '0.832', '-0.53', '#sadness', '#anger', 'negative', '-0.65', 'unsuitable', 'undesirable', 'unqualified', 'disqualified', 'unentitled']
senticnet['receiving_communion'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'reverence', 'obeisance', 'genuflexion', 'kowtow', 'mafia']
senticnet['receiving_holy_communion'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'reverence', 'obeisance', 'genuflexion', 'kowtow', 'mafia']
senticnet['receiving_oral_sex'] = ['0.305', '0.168', '-0.10', '0.299', '#joy', '#admiration', 'positive', '0.167', 'diversity', 'prosper', 'desirable', 'a_lot_of_sex', 'cherish']
senticnet['receiving_royal_assent'] = ['0', '0.089', '0', '0.129', '#interest', '#admiration', 'positive', '0.109', 'passageway', 'connect_room', 'entrance_room', 'corridor', 'walk_through']
senticnet['receiving_ssi'] = ['-0.60', '0', '0.832', '-0.53', '#sadness', '#anger', 'negative', '-0.65', 'unsuitable', 'undesirable', 'unqualified', 'disqualified', 'unentitled']
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senticnet['receptively'] = ['0.823', '0', '0', '0.937', '#joy', '#admiration', 'positive', '0.88', 'receive', 'receipt', 'receptive', 'open', 'receptiveness']
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senticnet['recharge_battery'] = ['0.843', '0.864', '0', '0.898', '#interest', '#admiration', 'positive', '0.868', 'rest', 'recover', 'sleep', 'sleep_long_time', 'sleep_bed']
senticnet['rechargeable'] = ['0.928', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.832', 'replaceable', 'commercial-grade', 'long-life', 'long-lasting', 'high-output']
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senticnet['reciprocating_motion'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotate_movement', 'rotate', 'go_around', 'manoeuvre', 'full_circle']
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senticnet['recirculated_air'] = ['0.786', '0', '0', '0', '#joy', '#joy', 'positive', '0.786', 'camping', 'outdoor', 'tent', 'bonfire', 'camp_out']
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senticnet['reckless_driving_charge'] = ['-0.31', '-0.02', '0', '-0.24', '#sadness', '#disgust', 'negative', '-0.17', 'pollution', 'screw_up', 'car_wreck', 'parking_ticket', 'feel_stressed']
senticnet['reckless_manslaughter'] = ['0.654', '-0.59', '0', '0.774', '#joy', '#admiration', 'positive', '0.673', 'instrument', 'indictment', 'murder_indictment', 'true_bill', 'legal_document']
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senticnet['reclaim'] = ['0.93', '-0.89', '0', '0', '#joy', '#surprise', 'positive', '0.913', 'reclamation', 'claim', 'domesticate', 'recycle', 'reform']
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senticnet['reclassification'] = ['0.03', '0.167', '-0.10', '0.19', '#interest', '#admiration', 'positive', '0.071', 'designation', 'social_class', 'classification', 'military_order', 'champion']
senticnet['recline'] = ['0', '0', '0.808', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'inaction', 'lazy', 'stay', 'inactive', 'idle']
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senticnet['recommend'] = ['0.719', '0.902', '0', '0.701', '#joy', '#interest', 'positive', '0.774', 'expand_horizon', 'escape_reality', 'stimulate_mind', 'acculturation', 'support']
senticnet['recommendation'] = ['0.864', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.869', 'cosign', 'endorse', 'notation', 'insurance', 'endorsement']
senticnet['recommended'] = ['0.715', '0.723', '0', '0', '#joy', '#interest', 'positive', '0.719', 'capable', 'advisable', 'advise', 'willing', 'advisably']
senticnet['recompence'] = ['0.685', '-0.58', '0', '0.56', '#joy', '#surprise', 'positive', '0.608', 'profit', 'net_income', 'net', 'net_profit', 'fast_buck']
senticnet['recompense'] = ['0', '0', '0.765', '-0.90', '#anger', '#disgust', 'negative', '-0.83', 'reparation', 'restitution', 'redress', 'expiation', 'reparation']
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senticnet['reconstitute'] = ['0.853', '0', '0', '0.981', '#joy', '#admiration', 'positive', '0.917', 'restructure', 'restructuring', 'research', 'project', 'first']
senticnet['reconstruct'] = ['0.622', '-0.82', '0', '0.776', '#surprise', '#admiration', 'positive', '0.742', 'hypothesize', 'theorize', 'suppose', 'conjecture', 'speculate']
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senticnet['recontamination'] = ['-0.08', '-0.04', '0.023', '-0.07', '#sadness', '#disgust', 'negative', '-0.03', 'tier', 'make_toast', 'house_smell_good', 'get_hot', 'bread_crumb']
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senticnet['record-breaking'] = ['0.998', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.96', 'record-setting', 'unprecedented', 'near-record', 'record-high', 'record-shattering']
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senticnet['record_breaking_snowfall'] = ['0', '0', '-0.93', '-0.82', '#fear', '#disgust', 'negative', '-0.88', 'thunderstorm', 'snow_storm', 'storm', 'weather', 'rainy']
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senticnet['record_library'] = ['0.064', '-0.06', '0.054', '0', '#joy', '#surprise', 'positive', '0.026', 'stereo', 'tuner', 'speaker', 'radio', 'amplifier']
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senticnet['record_producer'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
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senticnet['record_studio'] = ['0.847', '-0.72', '0', '0', '#joy', '#surprise', 'positive', '0.785', 'studio', 'opus', 'concert_hall', 'tv_studio', 'opus_house']
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senticnet['recordable_cd'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotary_file', 'cd-rom', 'database', 'business_card', 'calendar_book']
senticnet['recorder'] = ['0.728', '0', '0', '0.937', '#joy', '#admiration', 'positive', '0.833', 'bassoon', 'soprano', 'oboe', 'descant', 'treble']
senticnet['recording'] = ['0', '0.104', '0', '0.196', '#interest', '#admiration', 'positive', '0.15', 'source_electricity', 'read_instruction_manual', 'switch', 'find_tape', 'turn_television_set']
senticnet['recording_artist'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
senticnet['records_label'] = ['0', '0.115', '0.036', '0.207', '#interest', '#admiration', 'positive', '0.095', 'total', 'amount', 'merit', 'award', 'excellence']
senticnet['recounting'] = ['0.751', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.829', 'minute-by-minute', 'eye-witness', 'play-by-play', 'round-by-round', 'blow-by-blow']
senticnet['recourse'] = ['-0.91', '0', '0.818', '0', '#sadness', '#anger', 'negative', '-0.86', 'recur', 'appeal', 'betake', 'resort', 'recurrence']
senticnet['recover'] = ['0', '0.955', '0', '0.76', '#interest', '#admiration', 'positive', '0.857', 'recovery', 'recoverer', 'redeem', 'retrievable', 'revive']
senticnet['recover_illness'] = ['0', '0', '0', '0.795', '#admiration', '#admiration', 'positive', '0.795', 'clear_head', 'read_good_book', 'relax_mind', 'clear_mind', 'reduce_stress']
senticnet['recovered_sufficiently'] = ['0', '0.877', '0', '0', '#interest', '#interest', 'positive', '0.877', 'get_rest', 'rejuvenation', 'feel_refreshed', 'take_nap', 'get_sleep']
senticnet['recoverer'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'succourer', 'salvager', 'salvor', 'mortal']
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senticnet['recreating'] = ['-0.93', '0', '-0.73', '0', '#sadness', '#fear', 'negative', '-0.83', 'recreate', 're-create', 're-creating', 're-created', 'replicate']
senticnet['recreation'] = ['0', '0.962', '0', '0.911', '#interest', '#admiration', 'positive', '0.936', 'play_friend', 'competition', 'break', 'watch_baseball', 'fun_exercise']
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senticnet['rector_palace'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'fantastic', 'amaze', 'pose', 'wondrous', 'wonderfully']
senticnet['rectorship'] = ['0.031', '0.272', '0', '0.231', '#interest', '#admiration', 'positive', '0.178', 'minister', 'church_head', 'pastor', 'protestant', 'evangelist']
senticnet['rectum'] = ['-0.99', '0', '0.871', '-0.55', '#sadness', '#anger', 'negative', '-0.80', 'endure', 'brook', 'tolerate', 'bear_up', 'abide']
senticnet['rectus_abdominis_muscle'] = ['-0.53', '0', '0.982', '-0.67', '#anger', '#disgust', 'negative', '-0.73', 'meniscus', 'semilunar_cartilage', 'gristle', 'hyaline_cartilage', 'arytenoid']
senticnet['recuperate'] = ['0', '0.877', '0', '0', '#interest', '#interest', 'positive', '0.877', 'get_rest', 'rejuvenation', 'feel_refreshed', 'take_nap', 'get_sleep']
senticnet['recuperation'] = ['0.831', '0.782', '0', '0', '#joy', '#interest', 'positive', '0.806', 'restore', 'power', 'jacobean', 'restoration', 'monarchy']
senticnet['recur'] = ['0', '0.128', '-0.06', '0.172', '#interest', '#admiration', 'positive', '0.078', 'replicate', 'repeated', 'frequent', 'usual_ordinary', 'redo']
senticnet['recurrence'] = ['-0.92', '0', '0.815', '0', '#sadness', '#anger', 'negative', '-0.86', 'recur', 'appeal', 'betake', 'resort', 'recourse']
senticnet['recurrence_relation'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly']
senticnet['recurrent'] = ['0.016', '0.076', '0', '0.152', '#interest', '#admiration', 'positive', '0.081', 'often', 'infinite', 'everlasting', 'unending', 'frequently']
senticnet['recurrent_aspiration'] = ['-0.87', '0', '0.773', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'rubor', 'redness', 'inflammation', 'otitis', 'otitis_interna']
senticnet['recurrent_depression'] = ['-0.78', '0', '0.918', '-0.96', '#anger', '#disgust', 'negative', '-0.88', 'major_depressive_episode', 'depression', 'depressive_disorder', 'clinical_depression', 'involutional_depression']
senticnet['recurrent_drought'] = ['-0.98', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.89', 'dry', 'lose_team', 'leave_out', 'disrespect', 'cause_suffer']
senticnet['recurrent_infection'] = ['-0.72', '0', '0.778', '-0.98', '#anger', '#disgust', 'negative', '-0.82', 'actinic_dermatitis', 'hypericism', 'diaper_rash', 'dermatitis', 'seborrheic_dermatitis']
senticnet['recurrent_laryngeal_nerve'] = ['0', '0', '0', '0.106', '#admiration', '#admiration', 'positive', '0.106', 'vocal_chord', 'mouth_noise', 'vocal_sound', 'human_sound', 'vocalization']
senticnet['recurrent_otitis_media'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchitis', 'chronic_bronchitis', 'respiratory_disease', 'respiratory_illness', 'respiratory_disorder']
senticnet['recurrent_pneumonia'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchitis', 'chronic_bronchitis', 'respiratory_disease', 'respiratory_illness', 'respiratory_disorder']
senticnet['recurrent_pregnancy'] = ['-0.81', '0', '0.884', '-0.87', '#anger', '#disgust', 'negative', '-0.85', 'lose_war', 'beaten', 'cost_fortune', 'bad_grade', 'military_action']
senticnet['recurring'] = ['-0.71', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.79', 'forever', 'persistent', 'indefinitely', 'numerous', 'permanent']
senticnet['recurring_antagonist'] = ['-0.95', '0', '0.774', '0', '#sadness', '#anger', 'negative', '-0.86', 'argue', 'rival', 'disagreement', 'opposition', 'anti']
senticnet['recurring_cast'] = ['0', '0', '0', '0.068', '#admiration', '#admiration', 'positive', '0.068', 'dark_corner', 'child_game', 'play_person', 'role_play', 'part_of_play']
senticnet['recurring_drought'] = ['-0.98', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.89', 'dry', 'lose_team', 'leave_out', 'disrespect', 'cause_suffer']
senticnet['recurring_nightmare'] = ['-0.08', '0', '-0.04', '-0.15', '#sadness', '#disgust', 'negative', '-0.09', 'unsettle', 'sleepwalk', 'hallucinate', 'get_rest', 'reflect']
senticnet['recurring_problem'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'problem', 'serious_issue', 'overrate', 'prohibitionist_society', 'blade_weapon']
senticnet['recurring_role'] = ['0', '0', '0.785', '-0.72', '#anger', '#disgust', 'negative', '-0.75', 'sharp_nail', 'sharp', 'sharp_end', 'sharp_point', 'pointy']
senticnet['recurring_villain'] = ['-0.95', '0', '0.774', '0', '#sadness', '#anger', 'negative', '-0.86', 'argue', 'rival', 'disagreement', 'opposition', 'anti']
senticnet['recursive_function'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly']
senticnet['recursively_enumerable'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly']
senticnet['recursively_enumerable_set'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly']
senticnet['recurve_bow'] = ['0', '-0.07', '0.091', '-0.19', '#anger', '#disgust', 'negative', '-0.07', 'projectile', 'use_hunt', 'weapon', 'ammunition', 'firearm']
senticnet['recusal'] = ['-0.88', '0', '0.727', '-0.82', '#sadness', '#disgust', 'negative', '-0.81', 'defervescence', 'subsidence', 'break', 'remission', 'abatement']
senticnet['recusancy'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'heterodoxy', 'iconoclasm', 'nonconformism', 'nonconformance', 'orientation']
senticnet['recusant'] = ['0.742', '-0.96', '0', '0.58', '#joy', '#surprise', 'positive', '0.762', 'dissident', 'beat', 'bohemian', 'maverick', 'enfant_terrible']
senticnet['recyclability'] = ['0.8', '-0.90', '0', '0.675', '#joy', '#surprise', 'positive', '0.794', 'indestructibility', 'tensile_strength', 'continuity', 'persistence', 'changelessness']
senticnet['recyclable'] = ['0', '0.065', '0', '0.146', '#interest', '#admiration', 'positive', '0.105', 'drink_container', 'storage_container', 'opaque', 'drink_holder', 'food_container']
senticnet['recyclable_material'] = ['0', '0.065', '0', '0.146', '#interest', '#admiration', 'positive', '0.105', 'drink_container', 'storage_container', 'opaque', 'drink_holder', 'food_container']
senticnet['recyclable_paper'] = ['0', '0.065', '0', '0.146', '#interest', '#admiration', 'positive', '0.105', 'drink_container', 'storage_container', 'opaque', 'drink_holder', 'food_container']
senticnet['recyclable_plastic'] = ['0', '0.065', '0', '0.146', '#interest', '#admiration', 'positive', '0.105', 'drink_container', 'storage_container', 'opaque', 'drink_holder', 'food_container']
senticnet['recycle'] = ['0.071', '0', '0.026', '0.06', '#joy', '#admiration', 'positive', '0.035', 'organic_matter', 'decompose', 'fertiliser', 'soil', 'compost_pile']
senticnet['recycle_bin'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'car_trunk', 'walmart', 'warehouse', 'store', 'storage_place']
senticnet['recycle_paper'] = ['0', '0', '0', '0.739', '#admiration', '#admiration', 'positive', '0.739', 'spring_action', 'see_star_night', 'state_opinion', 'visit_grandmother', 'forward_email']
senticnet['recycle_waste'] = ['0', '0.065', '0', '0.146', '#interest', '#admiration', 'positive', '0.105', 'drink_container', 'storage_container', 'opaque', 'drink_holder', 'food_container']
senticnet['recycled_aluminum'] = ['0', '0.065', '0', '0.146', '#interest', '#admiration', 'positive', '0.105', 'drink_container', 'storage_container', 'opaque', 'drink_holder', 'food_container']
senticnet['recycled_content'] = ['0', '0.065', '0', '0.146', '#interest', '#admiration', 'positive', '0.105', 'drink_container', 'storage_container', 'opaque', 'drink_holder', 'food_container']
senticnet['recycled_fiber'] = ['0.024', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'stump', 'plank', 'timber', 'paper', 'mahogany']
senticnet['recycled_material'] = ['0', '0.065', '0', '0.146', '#interest', '#admiration', 'positive', '0.105', 'drink_container', 'storage_container', 'opaque', 'drink_holder', 'food_container']
senticnet['recycled_paper'] = ['0.024', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'stump', 'plank', 'timber', 'paper', 'mahogany']
senticnet['recycled_pet'] = ['0', '0.065', '0', '0.146', '#interest', '#admiration', 'positive', '0.105', 'drink_container', 'storage_container', 'opaque', 'drink_holder', 'food_container']
senticnet['recycled_plastic'] = ['0', '0.065', '0', '0.146', '#interest', '#admiration', 'positive', '0.105', 'drink_container', 'storage_container', 'opaque', 'drink_holder', 'food_container']
senticnet['recycled_pulp'] = ['0.024', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'stump', 'plank', 'timber', 'paper', 'mahogany']
senticnet['recycling_bin'] = ['0.864', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.924', 'trash', 'trash_container', 'junk_store', 'coffee_cup', 'trash_receptacle']
senticnet['recycling_container'] = ['-0.04', '0', '0.041', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'back_sofa', 'home_improvement_store', 'closet', 'under_carpet', 'shoe_shop']
senticnet['red'] = ['-0.07', '0.101', '0', '0.105', '#interest', '#admiration', 'positive', '0.043', 'green_black', 'bright_red', 'purple', 'red_green', 'precious_jewel']
senticnet['red-bellied_black_snake'] = ['-0.02', '-0.00', '-0.05', '-0.08', '#fear', '#disgust', 'negative', '-0.03', 'eastern_brown_snake', 'poisonous_snake', 'lizard', 'snake', 'shark']
senticnet['red-blooded'] = ['0', '-0.78', '0', '0.718', '#surprise', '#admiration', 'positive', '0.752', 'right-thinking', 'self-respecting', 'fair-minded', 'god-fearing', 'die-hard']
senticnet['red-hot'] = ['0.898', '0.857', '0', '0', '#joy', '#interest', 'positive', '0.877', 'white-hot', 'glowed', 'searing', 'blazing', 'smoldering']
senticnet['red_ale'] = ['0', '0.091', '-0.05', '0.073', '#interest', '#admiration', 'positive', '0.037', 'privately', 'hold_liquid', 'make_wish', 'rinse_dish', 'find_toilet']
senticnet['red_apple'] = ['-0.05', '0', '0.037', '0', '#sadness', '#anger', 'negative', '-0.04', 'color', 'bright_color', 'secondary_color', 'light_red', 'primary_color']
senticnet['red_arrow'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'bayonet', 'spear', 'shotgun', 'bullet', 'hydrogen_bomb']
senticnet['red_backed_vole'] = ['-0.55', '0', '0.955', '-0.96', '#anger', '#disgust', 'negative', '-0.82', 'house_mouse', 'mouse', 'rat', 'fieldmouse', 'nude_mouse']
senticnet['red_bandanna'] = ['-0.18', '-0.12', '0.049', '-0.24', '#sadness', '#disgust', 'negative', '-0.09', 'tree_trunk', 'plank', 'artificial_leg', 'hardwood', 'tree_wood']
senticnet['red_bean_paste'] = ['0', '-0.03', '0.046', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'pasta', 'green_bean', 'mayonnaise', 'frozen_food', 'sour_milk']
senticnet['red_bell_pepper'] = ['0.636', '-0.79', '0', '0.552', '#joy', '#surprise', 'positive', '0.661', 'lettuce', 'endive', 'green', 'salad_green', 'food']
senticnet['red_berried'] = ['-0.86', '0', '0.665', '-0.61', '#sadness', '#anger', 'negative', '-0.71', 'wooded', 'braky', 'rushy', 'scrubby', 'forested']
senticnet['red_blue'] = ['-0.04', '0.079', '0', '0.073', '#interest', '#admiration', 'positive', '0.035', 'red_green', 'red_orange', 'spectrum', 'yellow_green', 'black_green']
senticnet['red_bluff'] = ['-0.06', '0.21', '0', '0.076', '#interest', '#admiration', 'positive', '0.074', 'new_think', 'figure_out', 'brain_activity', 'idea', 'come_up_idea']
senticnet['red_blush'] = ['0', '0.141', '0.01', '0.115', '#interest', '#admiration', 'positive', '0.082', 'sepal', 'achene', 'funiculus', 'seed', 'pip']
senticnet['red_bone_marrow'] = ['0.73', '-0.72', '0', '0.653', '#joy', '#surprise', 'positive', '0.703', 'red_marrow', 'bone_marrow', 'yellow_marrow', 'yellow_bone_marrow', 'connective_tissue']
senticnet['red_breasted'] = ['-0.02', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.04', 'wren', 'bird', 'quail', 'sparrow', 'chick']
senticnet['red_breasted_merganser'] = ['-0.50', '0', '0.764', '-0.61', '#anger', '#disgust', 'negative', '-0.62', 'doctor', 'mountebank', 'charlatan', 'quack', 'physician']
senticnet['red_brick_tower'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'castle', 'bell_tower', 'spire', 'steeple', 'religious_place']
senticnet['red_buckeye'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'bottlebrush_buckeye', 'horse_chestnut', 'particolohorse_chestnut']
senticnet['red_bull_vettel'] = ['0.844', '0', '0', '0', '#joy', '#joy', 'positive', '0.844', 'straight', 'late_time', 'preparedness', 'past_present', 'wait']
senticnet['red_cabbage'] = ['0', '0.067', '-0.09', '-0.09', '#fear', '#disgust', 'negative', '-0.04', 'cotton_boll', 'asbestos', 'corn', 'opium', 'rice']
senticnet['red_cadeaux'] = ['0.744', '-0.77', '0', '0.661', '#joy', '#surprise', 'positive', '0.725', 'sugarcoat', 'honey', 'glaze', 'change_taste', 'sugar']
senticnet['red_card'] = ['-0.98', '0', '0.334', '-0.91', '#sadness', '#disgust', 'negative', '-0.74', 'punishment', 'fault', 'probabilty_of_losing', 'yellow_card', 'penalty_shoot']
senticnet['red_carpet'] = ['0.796', '0.851', '0', '0', '#joy', '#interest', 'positive', '0.824', 'expensive_dress', 'dress', 'fashion', 'gala', 'fancy_dress']
senticnet['red_carpeted'] = ['0', '0', '-0.76', '-0.82', '#fear', '#disgust', 'negative', '-0.79', 'stage', 'thespian', 'actor', 'theatre', 'drama']
senticnet['red_cedar'] = ['-0.69', '0', '0.725', '-0.89', '#anger', '#disgust', 'negative', '-0.77', 'ponderosa', 'ponderosa_pine', 'western_yellow_pine', 'bull_pine', 'longleaf_pine']
senticnet['red_cellophane'] = ['0', '-0.03', '0.043', '-0.11', '#anger', '#disgust', 'negative', '-0.03', 'torch', 'safety_pin', 'laser', 'flare', 'firebomb']
senticnet['red_chalk'] = ['0', '0.074', '0', '0.06', '#interest', '#admiration', 'positive', '0.067', 'use_crayon', 'draw', 'pencil_art', 'make_sketch', 'outline']
senticnet['red_cheeked'] = ['-0.88', '0', '0.864', '-0.72', '#sadness', '#anger', 'negative', '-0.82', 'vixenish', 'venomous', 'bitchy', 'catty', 'malicious']
senticnet['red_chilli'] = ['0.979', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.918', 'make_curry', 'cook_dinner', 'cook_meal', 'cook_curry', 'powder']
senticnet['red_chilli_powder'] = ['0.979', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.918', 'make_curry', 'cook_dinner', 'cook_meal', 'cook_curry', 'powder']
senticnet['red_clad'] = ['-0.71', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.84', 'traffic_jam', 'accumulate_stress', 'stress', 'accumulate_stress_city', 'bad_service']
senticnet['red_cloak'] = ['0.08', '0.106', '-0.04', '0.112', '#interest', '#admiration', 'positive', '0.063', 'helmet', 'head_gear', 'cap', 'head_wear', 'head_protection']
senticnet['red_cloth'] = ['0.024', '0', '0', '0.074', '#joy', '#admiration', 'positive', '0.049', 'sheet', 'cover', 'quilt', 'comforter', 'modesty']
senticnet['red_cloud'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['red_clover_trifolium_pratense'] = ['-0.56', '0', '0.892', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'borecole', 'colewort', 'kail', 'cole', 'kale']
senticnet['red_color'] = ['0', '0.114', '0', '0.122', '#interest', '#admiration', 'positive', '0.118', 'red', 'delicate', 'often_red', 'orifice', 'bright_red']
senticnet['red_color_scheme'] = ['-0.45', '0.081', '-0.25', '0', '#sadness', '#fear', 'negative', '-0.20', 'green_yellow', 'color', 'blue_green', 'cyan', 'purple']
senticnet['red_cross'] = ['0', '0.066', '-0.05', '0.095', '#interest', '#admiration', 'positive', '0.034', 'interjection', 'rejection', 'avoid', 'traffic_cop', 'gas_electric']
senticnet['red_cross_victorian_bushfire'] = ['0.732', '0.729', '0', '0', '#joy', '#interest', 'positive', '0.73', 'feel_relief', 'good_feeling', 'feel_ok', 'feel_relieved', 'feel_better']
senticnet['red_currant'] = ['0.05', '0', '0.047', '0.068', '#joy', '#admiration', 'positive', '0.024', 'tree_fruit', 'yellow_fruit', 'small_fruit', 'vine_fruit', 'red_fruit']
senticnet['red_cyan'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'indigo', 'yellow', 'water_color', 'red', 'violet']
senticnet['red_dragon'] = ['-0.75', '0', '0.708', '0', '#sadness', '#anger', 'negative', '-0.73', 'hound', 'cage_animal', 'kind_animal', 'hunter', 'animal']
senticnet['red_dwarf_star'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['red_enamel'] = ['0.586', '-0.65', '0', '0.503', '#joy', '#surprise', 'positive', '0.58', 'decoration', 'distinguished_service_cross', 'medal', 'medallion', 'palm']
senticnet['red_enameled'] = ['0.586', '-0.65', '0', '0.503', '#joy', '#surprise', 'positive', '0.58', 'decoration', 'distinguished_service_cross', 'medal', 'medallion', 'palm']
senticnet['red_ensign'] = ['0.053', '0.068', '-0.05', '0.06', '#interest', '#admiration', 'positive', '0.032', 'linen', 'star_stripe', 'velvet', 'nation', 'burlap']
senticnet['red_eye'] = ['-0.19', '-0.01', '-0.09', '-0.03', '#sadness', '#fear', 'negative', '-0.07', 'easter_bunny', 'cute_animal', 'domestic_pet', 'household_pet', 'large_mouse']
senticnet['red_eyed_vireo'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['red_fescue'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'meadow_grass', 'grass', 'manna_grass', 'sweet_grass', 'wood_meadowgrass']
senticnet['red_fescue_festuca_rubra'] = ['-0.79', '0', '0.837', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'sunray', 'wilding', 'sagebrush_buttercup', 'wildflower', 'pasque_flower']
senticnet['red_fir'] = ['0.045', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.356', 'ponderosa', 'bull_pine', 'western_yellow_pine', 'yellow_pine', 'longleaf_pine']
senticnet['red_fleshed'] = ['0.02', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'many_fruits', 'buy_fruit', 'cherry', 'pineapple', 'tomato']
senticnet['red_footed_booby'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet']
senticnet['red_fox'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'fox', 'kit_fox', 'prairie_fox', 'white_fox']
senticnet['red_fruit'] = ['0.049', '0.078', '0.05', '0.121', '#interest', '#admiration', 'positive', '0.05', 'green_fruit', 'tree_fruit', 'round_fruit', 'crunchy', 'redness']
senticnet['red_glow'] = ['0', '0', '-0.94', '-0.91', '#fear', '#disgust', 'negative', '-0.92', 'night', 'blackout', 'darkness', 'dark', 'obscure']
senticnet['red_green'] = ['-0.04', '0.076', '0.009', '0.087', '#interest', '#admiration', 'positive', '0.027', 'black_green', 'yellow_green', 'blue_green', 'purple', 'red_orange']
senticnet['red_grouper'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'crab', 'swimming_crab', 'rock_crab', 'fiddler_crab', 'pea_crab']
senticnet['red_grouse'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['red_hair'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'hair_color', 'purple', 'violet', 'dark_light', 'hue']
senticnet['red_haired'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'hair_color', 'purple', 'violet', 'dark_light', 'hue']
senticnet['red_handkerchief'] = ['-0.11', '0.115', '0.67', '-0.65', '#anger', '#disgust', 'negative', '-0.33', 'lividity', 'livid', 'wrathfully', 'wrothful', 'wroth']
senticnet['red_hartebeest'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'argali', 'argal', 'urial', 'white_sheep', 'wild_sheep']
senticnet['red_hat'] = ['0', '0', '0.737', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'annoy', 'skin_rash', 'bother', 'irritation', 'great_dane']
senticnet['red_hood'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'only_bird']
senticnet['red_hue'] = ['0.577', '-0.69', '0', '0.645', '#surprise', '#admiration', 'positive', '0.639', 'shining', 'polished', 'shiny', 'bright', 'burnished']
senticnet['red_indian'] = ['0', '-0.91', '0', '0.835', '#surprise', '#admiration', 'positive', '0.873', 'indigenous', 'far_west', 'live_place', 'family_live', 'original']
senticnet['red_iron_oxide'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['red_jungle_fowl'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'animal', 'animate_being', 'pet', 'creature']
senticnet['red_lettering'] = ['0', '0.137', '0', '0.133', '#interest', '#admiration', 'positive', '0.135', 'sign', 'indication', 'post_notice', 'notice', 'red_octagon']
senticnet['red_light'] = ['-0.07', '0', '0.115', '-0.15', '#anger', '#disgust', 'negative', '-0.11', 'stop', 'halt', 'alarm', 'alert', 'danger']
senticnet['red_light_district'] = ['0.167', '0.092', '-0.04', '0.13', '#joy', '#admiration', 'positive', '0.086', 'homosexual', 'pornography', 'ceiling_fan', 'sexual_act', 'storage_box']
senticnet['red_lion_pub'] = ['0.853', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.916', 'tavern', 'cafe', 'nightclub', 'bar', 'liquor_store']
senticnet['red_lion_rampant'] = ['0.03', '0', '0.017', '0.138', '#joy', '#admiration', 'positive', '0.05', 'up_down', 'curl', 'bump', 'hand_gesture', 'goodbye']
senticnet['red_lionfish'] = ['-0.79', '0', '0.709', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'animal', 'animate_being', 'ctenophore', 'invertebrate', 'beast']
senticnet['red_lip'] = ['0.081', '0', '0', '0.086', '#joy', '#admiration', 'positive', '0.083', 'white_tooth', 'feel_clean', 'sexy', 'smell_better', 'look_good']
senticnet['red_lipstick'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'sexual_excitement', 'sexy', 'wet_lip', 'kiss', 'clean_up']
senticnet['red_maple_acer_rubrum'] = ['-0.94', '0', '0.608', '-0.95', '#sadness', '#disgust', 'negative', '-0.83', 'wild_licorice', 'wild_liquorice', 'alpine_milk_vetch', 'purple_milk_vetch', 'milk_vetch']
senticnet['red_maple_leaf'] = ['0.053', '0.068', '-0.05', '0.06', '#interest', '#admiration', 'positive', '0.032', 'linen', 'star_stripe', 'velvet', 'nation', 'burlap']
senticnet['red_marrow'] = ['0.73', '-0.72', '0', '0.653', '#joy', '#surprise', 'positive', '0.703', 'bone_marrow', 'red_bone_marrow', 'yellow_marrow', 'yellow_bone_marrow', 'connective_tissue']
senticnet['red_meat'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'grill', 'cook_grill', 'barbecue', 'cook', 'grilled_vegetable']
senticnet['red_mullet'] = ['0.709', '0.964', '0', '0', '#joy', '#interest', 'positive', '0.837', 'fish_trout', 'catch_butterfly', 'fish_lake', 'fish_compliment', 'fish_stream']
senticnet['red_necked_stint'] = ['0.797', '-0.71', '0', '0.736', '#joy', '#admiration', 'positive', '0.751', 'wood_ibis', 'wood_stork', 'ibis', 'wading_bird', 'wader']
senticnet['red_nosed'] = ['0.71', '0.618', '-0.23', '0.327', '#joy', '#interest', 'positive', '0.356', 'jovial', 'jocund', 'mirthful', 'joviality', 'jolity']
senticnet['red_oak'] = ['0.672', '-0.69', '0', '0.831', '#surprise', '#admiration', 'positive', '0.733', 'southern_red_oak', 'swamp_red_oak', 'turkey_oak', 'northern_red_oak', 'oak']
senticnet['red_octagon'] = ['0.021', '0.093', '0', '0.185', '#interest', '#admiration', 'positive', '0.1', 'octagon', 'indication', 'eight_sides', 'red_light', 'placard']
senticnet['red_orange'] = ['-0.05', '0.082', '0', '0.073', '#interest', '#admiration', 'positive', '0.032', 'spectrum', 'green_yellow', 'red_blue', 'yellow_green', 'hue_tint']
senticnet['red_osier_dogwood'] = ['0', '0.086', '0', '0', '#interest', '#interest', 'positive', '0.086', 'swim_ocean', 'weight_lift', 'sit_in_shade', 'cut_grass', 'trust_friend']
senticnet['red_panda'] = ['0.02', '0.071', '0', '0.069', '#interest', '#admiration', 'positive', '0.053', 'many_animals', 'donkey', 'chimpanzee', 'cute_animal', 'creature']
senticnet['red_phalarope'] = ['0.786', '-0.82', '0', '0.776', '#joy', '#surprise', 'positive', '0.794', 'phalarope', 'northern_phalarope', 'shorebird', 'shore_bird', 'limicoline_bird']
senticnet['red_pigment'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['red_pigmentation'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'helpless', 'unwanted', 'rabies', 'small_cat', 'big_rat']
senticnet['red_pitcher'] = ['0', '0.075', '0', '0', '#interest', '#interest', 'positive', '0.075', 'cup', 'carafe', 'container_cup', 'mug', 'glasses']
senticnet['red_planet'] = ['0.884', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.919', 'ufo', 'milky', 'space_junk', 'galaxy', 'solar_system']
senticnet['red_raspberry'] = ['0.02', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'many_fruits', 'buy_fruit', 'cherry', 'pineapple', 'tomato']
senticnet['red_region'] = ['-0.92', '0', '0.687', '-0.75', '#sadness', '#disgust', 'negative', '-0.78', 'hellfire', 'infernal_region', 'imaginary_place', 'mythical_place', 'fictitious_place']
senticnet['red_riding_hood'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'mythical', 'tale', 'dictionary', 'magic', 'perfect_world']
senticnet['red_rise'] = ['0', '0.996', '0', '0.929', '#interest', '#admiration', 'positive', '0.963', 'hug', 'go_kiss', 'kiss', 'trust', 'ferment']
senticnet['red_rose'] = ['0.882', '0', '0', '0.944', '#joy', '#admiration', 'positive', '0.913', 'kiss', 'rise_plant', 'flower', 'sexuality', 'see_beautiful_girl']
senticnet['red_sash'] = ['-0.58', '0', '0.632', '-0.63', '#anger', '#disgust', 'negative', '-0.61', 'skimmer', 'trilby', 'chapeau', 'busby', 'shako']
senticnet['red_satin'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'facial_tissue', 'kleenex', 'hanky', 'handkerchief', 'tissue']
senticnet['red_sauce'] = ['0.043', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'apple_pie', 'a_lot_of_food', 'jelly', 'frozen_food', 'cottage_cheese']
senticnet['red_scroll_inscribed'] = ['0.03', '0', '0.017', '0.138', '#joy', '#admiration', 'positive', '0.05', 'up_down', 'curl', 'bump', 'hand_gesture', 'goodbye']
senticnet['red_sea'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['red_shouldered_hawk'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['red_skin'] = ['-0.07', '0', '0.115', '-0.15', '#anger', '#disgust', 'negative', '-0.11', 'crispy', 'skin_injury', 'make_hot', 'burn_skin', 'make_drunk']
senticnet['red_snapper'] = ['0.709', '0.964', '0', '0', '#joy', '#interest', 'positive', '0.837', 'fish_trout', 'catch_butterfly', 'fish_lake', 'fish_compliment', 'fish_stream']
senticnet['red_spider_mite'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'rust_fungus', 'fungus']
senticnet['red_spot'] = ['-0.96', '0', '0.064', '-0.96', '#sadness', '#disgust', 'negative', '-0.66', 'disease', 'illness', 'sickness', 'itchy_skin', 'sick']
senticnet['red_stain'] = ['0', '-0.01', '0.046', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'varnish', 'brush', 'dye', 'pigment', 'canvas']
senticnet['red_star_belgrade'] = ['0.954', '-0.90', '0', '0.867', '#joy', '#surprise', 'positive', '0.91', 'friend', 'sports_fan', 'rooter', 'fan', 'nut']
senticnet['red_stone'] = ['0.999', '0', '0', '0.781', '#joy', '#admiration', 'positive', '0.89', 'solid', 'brown', 'opaque', 'precious_jewel', 'generic']
senticnet['red_stripe'] = ['0.942', '-0.61', '0', '0.908', '#joy', '#admiration', 'positive', '0.82', 'adornment', 'cigar_band', 'hatband', 'neckband', 'pinstripe']
senticnet['red_supergiant'] = ['-0.55', '0', '0.869', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'air_bubble', 'foam', 'froth', 'soap_bubble', 'bubble']
senticnet['red_sweater'] = ['-0.02', '0.256', '0', '0.166', '#interest', '#admiration', 'positive', '0.132', 'coat', 'shirt', 'garment', 'dress', 'vest']
senticnet['red_tailed'] = ['-0.03', '-0.01', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'shit', 'wildflower', 'poop', 'wild_flower', 'dog_poop']
senticnet['red_tailed_hawk'] = ['0', '-0.97', '0', '0', '#surprise', '#surprise', 'positive', '0.977', 'cougar', 'skunk', 'lion', 'tiger', 'lynx']
senticnet['red_tailed_hawk_buteo'] = ['-0.74', '0', '0.963', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['red_tailed_tropicbird'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet']
senticnet['red_tape'] = ['0', '0', '0', '-0.94', '#disgust', '#disgust', 'negative', '-0.94', 'boring', 'fill_form', 'annoying', 'form', 'fill_out_form']
senticnet['red_tile_roof'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'adobe', 'adobe_brick', 'clinker', 'brick', 'firebrick']
senticnet['red_tiled_roof'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'adobe', 'adobe_brick', 'clinker', 'brick', 'firebrick']
senticnet['red_tracer'] = ['0.744', '-0.77', '0', '0.661', '#joy', '#surprise', 'positive', '0.725', 'sugarcoat', 'honey', 'glaze', 'change_taste', 'sugar']
senticnet['red_tunic'] = ['-0.84', '0', '0.633', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'grizzle', 'postiche', 'wig', 'periwig', 'peruke']
senticnet['red_wattlebird'] = ['-0.84', '0', '0.655', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'piping_crow', 'butcherbird', 'currawong', 'bell_magpie', 'oscine']
senticnet['red_welt'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'lymph', 'exoskeleton', 'breast_pocket', 'toward', 'squint']
senticnet['red_whiskered_bulbul'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'hold_egg', 'baby_bird', 'hatch', 'lay_egg', 'bird_home']
senticnet['red_white_blue'] = ['0', '0.963', '0', '0.809', '#interest', '#admiration', 'positive', '0.886', 'blue_green', 'star_stripe', 'hue', 'hue_tint', 'purple']
senticnet['red_wig'] = ['-0.76', '0', '0.672', '-0.88', '#sadness', '#disgust', 'negative', '-0.77', 'attire', 'garb', 'dress', 'fancy_dress', 'masquerade']
senticnet['red_wine'] = ['0.122', '-0.15', '0.071', '-0.06', '#joy', '#surprise', 'positive', '0.034', 'bar', 'tavern', 'brandy', 'rum', 'liquor_store']
senticnet['red_wine_sauce'] = ['-0.02', '-0.02', '0.064', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'ocean_water', 'fillet_fish', 'chowder', 'port', 'underwater']
senticnet['red_wine_vinegar'] = ['0.07', '0.084', '0.023', '0', '#joy', '#interest', 'positive', '0.044', 'hearty', 'porridge', 'hot_liquid', 'liquid_food', 'meat_vegetable']
senticnet['red_winged'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet']
senticnet['red_yeast_rice'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucose', 'corn_sugar', 'dextrose', 'dextroglucose', 'grape_sugar']
senticnet['redact'] = ['0', '-0.86', '0', '0.897', '#surprise', '#admiration', 'positive', '0.88', 'declassifying', 'declassification', 'declassified', 'declassify', 'unseal']
senticnet['redacted_version'] = ['0', '0', '0', '0.091', '#admiration', '#admiration', 'positive', '0.091', 'correspondence', 'handwritten', 'written_message', 'written_communication', 'write_note']
senticnet['redaction_criticism'] = ['0.587', '-0.94', '0', '0.659', '#surprise', '#admiration', 'positive', '0.731', 'critique', 'review', 'criticism', 'literary_criticism', 'new_criticism']
senticnet['redbook'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'library', 'old_testament', 'cook_book', 'a_lot_of_books', 'librarian']
senticnet['redbud'] = ['0.958', '-0.82', '0', '0.924', '#joy', '#admiration', 'positive', '0.901', 'bunchberry', 'dwarf_cornel', 'crackerberry', 'pudding_berry', 'common_white_dogwood']
senticnet['redcedar'] = ['-0.69', '0', '0.725', '-0.89', '#anger', '#disgust', 'negative', '-0.77', 'ponderosa', 'ponderosa_pine', 'western_yellow_pine', 'bull_pine', 'longleaf_pine']
senticnet['reddish'] = ['-0.86', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.85', 'activity', 'rust', 'canker', 'rusty', 'recent']
senticnet['reddish-black'] = ['0', '0', '-0.78', '-0.97', '#fear', '#disgust', 'negative', '-0.88', 'white-fleshed', 'trophy-sized', 'small-mouth', 'algae-eating', 'scaleless']
senticnet['reddish_bark'] = ['-0.60', '0', '0.541', '-0.52', '#sadness', '#anger', 'negative', '-0.55', 'swoon', 'pass_out', 'faint', 'zonk_out', 'change_state']
senticnet['reddish_brown_fur'] = ['-0.58', '0', '0.66', '-0.8', '#anger', '#disgust', 'negative', '-0.68', 'bald', 'depilous', 'glabrescent', 'glabrous', 'balding']
senticnet['reddish_brown_hair'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'helpless', 'unwanted', 'rabies', 'small_cat', 'big_rat']
senticnet['reddish_color'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['reddish_fur'] = ['-0.58', '0', '0.66', '-0.8', '#anger', '#disgust', 'negative', '-0.68', 'bald', 'depilous', 'glabrescent', 'glabrous', 'balding']
senticnet['reddish_glow'] = ['0', '-0.00', '-0.07', '-0.06', '#fear', '#disgust', 'negative', '-0.04', 'lighten', 'tornado', 'dynamite_explosion', 'forest_fire', 'lightning']
senticnet['reddish_hair'] = ['0.352', '0.142', '0', '0.281', '#joy', '#admiration', 'positive', '0.258', 'knowledgeable', 'complement', 'much_need', 'see_ocean', 'good_sense_of_humor']
senticnet['reddish_orange'] = ['-0.02', '0.068', '0.04', '0.084', '#interest', '#admiration', 'positive', '0.022', 'peelable', 'green_black', 'purple', 'red', 'edible_human']
senticnet['reddish_pink'] = ['-0.02', '0.068', '0.04', '0.084', '#interest', '#admiration', 'positive', '0.022', 'peelable', 'green_black', 'purple', 'red', 'edible_human']
senticnet['reddish_purple'] = ['0.591', '-0.55', '0', '0.514', '#joy', '#surprise', 'positive', '0.553', 'violet', 'mauve', 'lavender', 'purple', 'royal_purple']
senticnet['rede'] = ['0.499', '-0.60', '0', '0.896', '#surprise', '#admiration', 'positive', '0.667', 'explicate', 'clarify', 'clear_up', 'elucidate', 'naturalize']
senticnet['rededicate'] = ['0.689', '-0.69', '0', '0.709', '#surprise', '#admiration', 'positive', '0.699', 'vow', 'dedicate', 'apply', 'consecrate', 'use']
senticnet['rededication_ceremony'] = ['0.82', '0', '0', '0.885', '#joy', '#admiration', 'positive', '0.853', 'religious_event', 'sunday', 'happy_event', 'tradition', 'wedding']
senticnet['redeem'] = ['0', '0.948', '0', '0.755', '#interest', '#admiration', 'positive', '0.851', 'recovery', 'recoverer', 'recover', 'retrievable', 'revive']
senticnet['redeemable'] = ['0.772', '-0.88', '0', '0.517', '#joy', '#surprise', 'positive', '0.724', 'corrigible', 'correctable', 'improvable', 'reformable', 'amendable']
senticnet['redeemer'] = ['0.05', '0.363', '-0.09', '0.307', '#interest', '#admiration', 'positive', '0.157', 'faith', 'christ', 'prayer', 'spiritual', 'religious_purpose']
senticnet['redeeming_quality'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'venial', 'excusable', 'mercifully', 'merciful', 'mercifulness']
senticnet['redefine'] = ['0.755', '0.972', '0', '0.775', '#interest', '#admiration', 'positive', '0.834', 'grasp', 'grab', 'hold', 'sensual', 'grip']
senticnet['redemption'] = ['0.936', '0', '0', '0.954', '#joy', '#admiration', 'positive', '0.945', 'expiation', 'atonement', 'propitiation', 'rescue', 'achievment']
senticnet['redemptioner'] = ['0', '0', '-0.79', '-0.74', '#fear', '#disgust', 'negative', '-0.77', 'servitude', 'slavery', 'serf', 'bond', 'slave']
senticnet['redemptive'] = ['0.921', '0', '0', '0.905', '#joy', '#admiration', 'positive', '0.913', 'foreknowledge', 'fortuitous', 'providential', 'prescience', 'providentially']
senticnet['redeploy'] = ['-0.06', '0.109', '-0.05', '0.158', '#interest', '#admiration', 'positive', '0.036', 'elasticity', 'stretch_out', 'grow_long', 'widen', 'advance_civilization']
senticnet['redeposition'] = ['0.965', '-0.84', '0', '0.599', '#joy', '#surprise', 'positive', '0.802', 'mental_faculty', 'faculty', 'module', 'remembrance', 'anamnesis']
senticnet['redesign'] = ['0.047', '0.171', '-0.10', '0.212', '#interest', '#admiration', 'positive', '0.081', 'novelty', 'relevant', 'contemporary', 'timely', 'current']
senticnet['redesigned_web_site'] = ['0', '0.08', '0', '0', '#interest', '#interest', 'positive', '0.08', 'internet', 'go_online', 'visit_web_site', 'find_information', 'choose_book']
senticnet['redevelop'] = ['-0.08', '0', '-0.08', '-0.14', '#sadness', '#disgust', 'negative', '-0.10', 'violence', 'assault', 'combat', 'siege', 'use_military']
senticnet['redevelopment'] = ['-0.08', '0', '-0.08', '-0.14', '#sadness', '#disgust', 'negative', '-0.10', 'violence', 'assault', 'combat', 'siege', 'use_military']
senticnet['redevelopment_project'] = ['-0.08', '0', '-0.08', '-0.14', '#sadness', '#disgust', 'negative', '-0.10', 'violence', 'assault', 'combat', 'siege', 'use_military']
senticnet['redevelopment_scheme'] = ['-0.08', '0', '-0.08', '-0.14', '#sadness', '#disgust', 'negative', '-0.10', 'violence', 'assault', 'combat', 'siege', 'use_military']
senticnet['redfish'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'crab', 'swimming_crab', 'rock_crab', 'fiddler_crab', 'pea_crab']
senticnet['redhakhol'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'head_hurt', 'introspection', 'lifestyle_change', 'headache', 'move_eye']
senticnet['redhead'] = ['0.901', '-0.91', '0', '0', '#joy', '#surprise', 'positive', '0.909', 'lustful', 'horny', 'slutty', 'lusty', 'naughty']
senticnet['redirect_request_bd'] = ['0.939', '-0.71', '0', '0.775', '#joy', '#admiration', 'positive', '0.81', 'vox_angelica', 'voix_celeste', 'organ_stop', 'pipe', 'diapason']
senticnet['redirect_stderr'] = ['0.028', '0', '0.027', '0.101', '#joy', '#admiration', 'positive', '0.034', 'animal_dwell', 'bee_home', 'live_place', 'dwell_place', 'place_to_live']
senticnet['redirecting'] = ['0', '0.87', '0', '0.806', '#interest', '#admiration', 'positive', '0.838', 'diverts', 'diverted', 'diversion', 'siphoning', 'diverting']
senticnet['redisclosure'] = ['-0.02', '0.175', '-0.11', '0.148', '#interest', '#admiration', 'positive', '0.045', 'tolerant', 'phrase', 'inquiry', 'explain', 'question']
senticnet['rediscover'] = ['0.755', '0.972', '0', '0.775', '#interest', '#admiration', 'positive', '0.834', 'grasp', 'grab', 'hold', 'sensual', 'grip']
senticnet['redistributable'] = ['0.81', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.774', 'available', 'public', 'population', 'crowd', 'majority']
senticnet['redistribute_income'] = ['-0.89', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.81', 'leave_out', 'lose_team', 'cause_suffer', 'disrespect', 'loathing']
senticnet['redistribute_wealth'] = ['-0.89', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.81', 'leave_out', 'lose_team', 'cause_suffer', 'disrespect', 'loathing']
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senticnet['redly'] = ['0', '0', '-0.94', '-0.91', '#fear', '#disgust', 'negative', '-0.92', 'night', 'blackout', 'darkness', 'dark', 'obscure']
senticnet['redneck'] = ['-0.96', '0', '0.924', '-0.86', '#sadness', '#anger', 'negative', '-0.91', 'rustic', 'yokel', 'rube', 'hick', 'hayseed']
senticnet['redness'] = ['0.947', '0.791', '0', '0', '#joy', '#interest', 'positive', '0.869', 'red_fruit', 'crunchy', 'green_fruit', 'vegetarian_food', 'small_fruit']
senticnet['redo'] = ['0', '0.203', '-0.12', '0.279', '#interest', '#admiration', 'positive', '0.12', 'bring_together', 'fuse', 'put_together', 'fuse_together', 'request']
senticnet['redolence'] = ['0.61', '-0.78', '0', '0.851', '#surprise', '#admiration', 'positive', '0.747', 'scent', 'aroma', 'olfactory_property', 'smell', 'odor']
senticnet['redolent'] = ['0', '0', '-0.82', '-0.79', '#fear', '#disgust', 'negative', '-0.80', 'resonant', 'remindful', 'smelling', 'reminiscent', 'evocative']
senticnet['redouble'] = ['0', '0', '-0.77', '-0.91', '#fear', '#disgust', 'negative', '-0.84', 'redoubled', 'redoubling', 're-double', 'reaffirm', 're-focus']
senticnet['redoubled'] = ['0', '0', '-0.77', '-0.91', '#fear', '#disgust', 'negative', '-0.84', 'redouble', 'redoubling', 're-double', 'reaffirm', 're-focus']
senticnet['redoubling'] = ['0', '0', '-0.77', '-0.91', '#fear', '#disgust', 'negative', '-0.84', 'redoubled', 'redouble', 're-double', 'reaffirm', 're-focus']
senticnet['redoubtable'] = ['0', '0', '0.804', '-0.84', '#anger', '#disgust', 'negative', '-0.82', 'forbidding', 'formidable', 'daunting', 'alarming', 'frightening']
senticnet['redound'] = ['0.81', '0', '0', '0.703', '#joy', '#admiration', 'positive', '0.756', 'harvest', 'crop', 'grow_crop', 'plant_crop', 'graze_cattle']
senticnet['redox_reaction'] = ['0.695', '-0.85', '0', '0.501', '#joy', '#surprise', 'positive', '0.684', 'alternative_energy', 'energy', 'energy_of_activation', 'atomic_energy', 'nuclear_energy']
senticnet['redress'] = ['-0.88', '0', '0.811', '-0.51', '#sadness', '#anger', 'negative', '-0.73', 'atonement', 'expiation', 'punitive_damage', 'exemplary_damage', 'compensation']
senticnet['redside_shiner'] = ['0', '-0.02', '0.044', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'haddock', 'mermaid', 'fish_rod', 'tidal_wave', 'dryad']
senticnet['redtape'] = ['0', '0', '0', '-0.94', '#disgust', '#disgust', 'negative', '-0.94', 'boring', 'fill_form', 'annoying', 'form', 'fill_out_form']
senticnet['reduce'] = ['-0.12', '-0.05', '0', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'diminish', 'decrease', 'lower', 'lower_music', 'low_level']
senticnet['reduce_churn'] = ['0.886', '-0.77', '0', '0.815', '#joy', '#admiration', 'positive', '0.826', 'countenance', 'legitimate', 'permit', 'allow', 'let']
senticnet['reduce_friction'] = ['0', '0', '-0.11', '0', '#fear', '#fear', 'negative', '-0.11', 'chemical_reaction', 'set_fire', 'grow_yard', 'heat_light', 'burn_down_house']
senticnet['reduce_glare'] = ['0.988', '0', '0', '0.789', '#joy', '#admiration', 'positive', '0.889', 'brightness', 'blaze', 'brilliance', 'glitter', 'glister']
senticnet['reduce_hunger'] = ['0.724', '0', '0', '0.956', '#joy', '#admiration', 'positive', '0.84', 'fill_stomach', 'satisfy_hunger', 'sustenance', 'relieve_hunger', 'enjoy_eat']
senticnet['reduce_illegal_immigration'] = ['-0.91', '0', '0.637', '-0.59', '#sadness', '#anger', 'negative', '-0.71', 'gauge', 'govern', 'regulate', 'standardize', 'order']
senticnet['reduce_intensity'] = ['0.803', '-0.70', '0', '0.736', '#joy', '#admiration', 'positive', '0.747', 'mortal', 'set_to_rest', 'allayer', 'calm', 'pacify']
senticnet['reduce_noise_pollution'] = ['0', '0', '0.735', '-0.70', '#anger', '#disgust', 'negative', '-0.72', 'swing_set', 'roadway_square', 'fresh_air', 'dog_poop', 'wildflower']
senticnet['reduce_pollution'] = ['0.107', '0.098', '0', '0.135', '#joy', '#admiration', 'positive', '0.113', 'go_green', 'respect_nature', 'save_water', 'go_on_foot', 'spread_seed']
senticnet['reduce_recidivism'] = ['-0.43', '0.107', '-0.42', '-0.03', '#sadness', '#fear', 'negative', '-0.19', 'send_to_jail', 'go_jail', 'jail_time', 'go_to_prison', 'injustice']
senticnet['reduce_stigma'] = ['-0.77', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'mark', 'emblem', 'religious_sign', 'stain', 'brand']
senticnet['reduce_stress'] = ['0.939', '0.865', '0', '0', '#joy', '#interest', 'positive', '0.902', 'stress_reduction', 'relaxation', 'relax', 'stress_relief', 'lower_stress']
senticnet['reduce_temperature'] = ['0.413', '0.239', '-0.09', '0.137', '#joy', '#interest', 'positive', '0.174', 'lower_temperature', 'remove_heat', 'cool_room', 'rich_house', 'nice_house']
senticnet['reduce_tooth_decay'] = ['0', '0', '0.813', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'ice_cream_melt', 'allergy', 'toothache', 'gonorrhea', 'tastebud']
senticnet['reduce_violent_crime'] = ['-0.47', '0.174', '-0.44', '0.105', '#sadness', '#fear', 'negative', '-0.16', 'wrong', 'wrongdoing', 'bad_choice', 'harmful_human', 'bad_behavior']
senticnet['reduced'] = ['-0.05', '0', '-0.12', '-0.17', '#fear', '#disgust', 'negative', '-0.11', 'small', 'tiny', 'small_size', 'narrow', 'poor']
senticnet['reduced_fat_milk'] = ['-0.03', '0.086', '0', '0.076', '#interest', '#admiration', 'positive', '0.042', 'drink_water', 'drink_espresso', 'drink', 'drink_coffee', 'drink_hot_chocolate']
senticnet['reduced_lung_function'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchiolitis', 'chronic_bronchitis', 'respiratory_disease', 'bronchitis', 'respiratory_disorder']
senticnet['reduced_planck_constant'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'newton', 'sthene', 'force_unit']
senticnet['reduced_recidivism'] = ['-0.43', '0.107', '-0.42', '-0.03', '#sadness', '#fear', 'negative', '-0.19', 'send_to_jail', 'go_jail', 'jail_time', 'go_to_prison', 'injustice']
senticnet['reduced_soil_erosion'] = ['-0.56', '0', '0.655', '-0.85', '#anger', '#disgust', 'negative', '-0.69', 'slovenly_woman', 'slut', 'trollop', 'slattern', 'slob']
senticnet['reduced_visual_acuity'] = ['-0.82', '0', '0.996', '-0.61', '#sadness', '#anger', 'negative', '-0.81', 'visual_impairment', 'visual_defect', 'vision_defect', 'visual_disorder', 'avitaminosis']
senticnet['reduces_drag'] = ['0.676', '0.63', '-0.23', '0.31', '#joy', '#interest', 'positive', '0.345', 'perkiness', 'walk_on_air', 'exultingly', 'exultantly', 'jubilance']
senticnet['reduces_friction'] = ['0', '0', '-0.11', '0', '#fear', '#fear', 'negative', '-0.11', 'chemical_reaction', 'set_fire', 'grow_yard', 'heat_light', 'burn_down_house']
senticnet['reduces_glare'] = ['0.988', '0', '0', '0.789', '#joy', '#admiration', 'positive', '0.889', 'brightness', 'blaze', 'brilliance', 'glitter', 'glister']
senticnet['reduces_inflammation'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'chemical_irritant', 'gingerol', 'piperine', 'pepper_spray']
senticnet['reducible'] = ['0.863', '-0.94', '0', '0.986', '#surprise', '#admiration', 'positive', '0.932', 'postulate', 'posit', 'assume', 'presume', 'take_for_granted']
senticnet['reducing'] = ['0', '0.728', '0', '0.904', '#interest', '#admiration', 'positive', '0.816', 'simplification', 'streamline', 'simplify', 'model', 'component']
senticnet['reducing_friction'] = ['0', '0', '-0.11', '0', '#fear', '#fear', 'negative', '-0.11', 'chemical_reaction', 'set_fire', 'grow_yard', 'heat_light', 'burn_down_house']
senticnet['reducing_glare'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'change_tire', 'fix_flat_tire', 'play_rugby', 'wear_silly_hat', 'become_adult']
senticnet['reducing_pesticide'] = ['-0.56', '0', '0.655', '-0.85', '#anger', '#disgust', 'negative', '-0.69', 'slovenly_woman', 'slut', 'trollop', 'slattern', 'slob']
senticnet['reducing_pollution'] = ['-0.56', '0', '0.655', '-0.85', '#anger', '#disgust', 'negative', '-0.69', 'slovenly_woman', 'slut', 'trollop', 'slattern', 'slob']
senticnet['reducing_recidivism'] = ['-0.43', '0.107', '-0.42', '-0.03', '#sadness', '#fear', 'negative', '-0.19', 'send_to_jail', 'go_jail', 'jail_time', 'go_to_prison', 'injustice']
senticnet['reducing_soil_erosion'] = ['-0.56', '0', '0.655', '-0.85', '#anger', '#disgust', 'negative', '-0.69', 'slovenly_woman', 'slut', 'trollop', 'slattern', 'slob']
senticnet['reducing_tooth_decay'] = ['0', '0', '0.813', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'ice_cream_melt', 'allergy', 'toothache', 'gonorrhea', 'tastebud']
senticnet['reductant'] = ['0.918', '0.864', '0', '0', '#joy', '#interest', 'positive', '0.891', 'consequence', 'cause', 'bad_action', 'problem', 'political_statement']
senticnet['reductio'] = ['0.715', '-0.82', '0', '0.659', '#joy', '#surprise', 'positive', '0.734', 'reasoning', 'logical_thinking', 'abstract_thought', 'casuistry', 'line_of_inquiry']
senticnet['reductio_ad_absurdum'] = ['0.715', '-0.82', '0', '0.659', '#joy', '#surprise', 'positive', '0.734', 'reasoning', 'logical_thinking', 'abstract_thought', 'casuistry', 'line_of_inquiry']
senticnet['reductio_ad_impossibile'] = ['-0.92', '0', '0.547', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'assumption', 'boundary_condition', 'provision', 'proviso', 'precondition']
senticnet['reduction'] = ['-0.12', '-0.05', '0', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'decrement', 'diminution', 'decrease', 'inferior', 'reduce']
senticnet['reduction_agency_dtra'] = ['-0.04', '0', '-0.06', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'make_war', 'conquer_enemy', 'use_military', 'gain_territory', 'keep_peace']
senticnet['reduction_mammoplasty'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
senticnet['reductionist'] = ['0', '0', '-0.90', '-0.85', '#fear', '#disgust', 'negative', '-0.88', 'mechanistic', 'empirical', 'elucidate', 'biochemical', 'causal']
senticnet['reductionist_view'] = ['-0.58', '0', '0.535', '-0.72', '#sadness', '#disgust', 'negative', '-0.61', 'byproduct', 'effect', 'result', 'consequence', 'outcome']
senticnet['reductive'] = ['-0.86', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.91', 'simplistically', 'barbarianism', 'simple', 'simplistic', 'simpleton']
senticnet['redundance'] = ['-0.77', '0', '0.9', '-0.79', '#anger', '#disgust', 'negative', '-0.82', 'embarrassment', 'fifth_wheel', 'deadwood', 'overplus', 'plethora']
senticnet['redundancy'] = ['0', '0', '-0.82', '-0.82', '#fear', '#disgust', 'negative', '-0.82', 'circuit', 'dismissal', 'needle', 'superfluity', 'language']
senticnet['redundancy_check'] = ['0.84', '-0.97', '0', '0.713', '#joy', '#surprise', 'positive', '0.844', 'proof', 'bed_check', 'crosscheck', 'parity_check', 'substantiation']
senticnet['redundant'] = ['0', '0', '-0.96', '-0.73', '#fear', '#disgust', 'negative', '-0.84', 'spare', 'supererogatory', 'extra', 'superfluous', 'excess']
senticnet['reduviid'] = ['-0.77', '0', '0.576', '-0.92', '#sadness', '#disgust', 'negative', '-0.75', 'insect', 'firebug', 'water_bug', 'assassin_bug', 'heteropterous_insect']
senticnet['reduviidae'] = ['-0.77', '0', '0.576', '-0.92', '#sadness', '#disgust', 'negative', '-0.75', 'insect', 'firebug', 'water_bug', 'assassin_bug', 'heteropterous_insect']
senticnet['redwall_limestone'] = ['0', '0.069', '0', '0.065', '#interest', '#admiration', 'positive', '0.067', 'alp', 'high_mountain', 'pass', 'ski_resort', 'high_place']
senticnet['reed_instrument'] = ['-0.03', '-0.00', '0.018', '-0.08', '#sadness', '#disgust', 'negative', '-0.03', 'use_jazz_music', 'stringed_musical_instrument', 'instrument_music', 'musical_instrument', 'listen_music']
senticnet['reed_mace'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'bulrush', 'nailrod', 'cattail', 'reedmace']
senticnet['reed_organ'] = ['-0.75', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.80', 'high-strung', 'uptight', 'restive', 'nervy', 'jumpy']
senticnet['reed_pipe'] = ['0.821', '-0.53', '0', '0.785', '#joy', '#admiration', 'positive', '0.712', 'fife', 'nose_flute', 'piccolo', 'woodwind', 'flute']
senticnet['reedemer'] = ['0.869', '0', '0', '0.89', '#joy', '#admiration', 'positive', '0.879', 'succorer', 'rescuer', 'salvager', 'salvor', 'saver']
senticnet['reedmace'] = ['0.648', '-0.64', '0', '0.841', '#joy', '#admiration', 'positive', '0.71', 'bullrush', 'cattail', 'nailrod', 'reed_mace', 'bulrush']
senticnet['reeducated'] = ['-0.94', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.84', 'worn-down', 'beaten-up', 'dive-bombed', 'knocked-off', 'knocked-out']
senticnet['reedy_voice'] = ['-0.71', '0', '0.998', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'tone', 'quality', 'twang', 'nasality', 'timber']
senticnet['reef'] = ['0', '0', '-0.06', '-0.03', '#fear', '#disgust', 'negative', '-0.05', 'tidal_pool', 'saltwater', 'fish_market', 'sea', 'coral_reef']
senticnet['reef_aquaria'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'autumn_leaf', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['reef_aquarium'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'autumn_leaf', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['reef_crest'] = ['-0.04', '-0.01', '0.103', '0', '#sadness', '#anger', 'negative', '-0.04', 'near_beach', 'along_beach', 'full_water', 'ocean_ground', 'salty']
senticnet['reek'] = ['-0.60', '0', '0.985', '-0.52', '#sadness', '#anger', 'negative', '-0.70', 'niff', 'pong', 'smell', 'odor', 'olfactory_sensation']
senticnet['reel'] = ['0', '-0.04', '0.113', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'net', 'fish_rod', 'swimsuit', 'volleyball', 'fish_pole']
senticnet['reel_mower'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'mop_floor', 'absorb_moisture', 'sit_on_beach', 'scrub_floor', 'cut_grass']
senticnet['reel_tape'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['reelection'] = ['0', '0.156', '-0.15', '0.179', '#interest', '#admiration', 'positive', '0.061', 'candidate', 'presidential', 'vote', 'elect', 'appoint']
senticnet['reelection_campaign'] = ['-0.81', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.81', 'take_charge', 'anti', 'go_against', 'death_wish', 'file_complaint']
senticnet['reemerge'] = ['-0.88', '0', '0.682', '-0.60', '#sadness', '#anger', 'negative', '-0.72', 'absent', 'fall_away', 'evanesce', 'blow_over', 'pass_off']
senticnet['reemergence'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'absolutism', 'autocracy', 'ascendancy', 'tyrant']
senticnet['reemployment'] = ['0.85', '0', '0', '0', '#joy', '#joy', 'positive', '0.85', 'task', 'labor', 'police_work', 'employ', 'career']
senticnet['reenergize'] = ['0.845', '-0.59', '0', '0.926', '#joy', '#admiration', 'positive', '0.789', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['reenforce'] = ['0.846', '-0.94', '0', '0.797', '#joy', '#surprise', 'positive', '0.861', 'strengthen', 'buttress', 'support', 'line', 'bolster']
senticnet['reengineer'] = ['0.7', '-0.79', '0', '0.66', '#joy', '#surprise', 'positive', '0.719', 'influence', 'determine', 'shape', 'mold', 'regulate']
senticnet['reenlistment'] = ['0.937', '0.756', '0', '0', '#joy', '#interest', 'positive', '0.847', 'army', 'enlistment', 'active-duty', 'bonus', 'obtain']
senticnet['reenroll'] = ['0.719', '0.757', '0', '0', '#joy', '#interest', 'positive', '0.738', 'matriculate', 'enroll_class', 'enroll_school', 'inscribe', 'register']
senticnet['reeperbahn'] = ['0.966', '0.921', '0', '0.892', '#joy', '#interest', 'positive', '0.926', 'disco', 'nightclub', 'shop_complex', 'dance_club', 'fast_food_restaurant']
senticnet['rees_cyclopdia'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'comic_book', 'child_book', 'child_story', 'comic', 'disney_cartoon']
senticnet['reese_river_valley'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'common_bean', 'green_bean', 'wax_bean']
senticnet['reestablish'] = ['-0.91', '0', '0.637', '-0.59', '#sadness', '#anger', 'negative', '-0.71', 'gauge', 'govern', 'regulate', 'standardize', 'order']
senticnet['reevaluation'] = ['0.79', '-0.53', '0', '0.688', '#joy', '#admiration', 'positive', '0.67', 'overvaluation', 'grade', 'undervaluation', 'pricing', 'rating']
senticnet['reexamine'] = ['0.056', '-0.03', '0', '0.054', '#joy', '#admiration', 'positive', '0.048', 'analyze', 'stargazing', 'influence_public_decision', 'question_everything', 'see_artifact']
senticnet['reexport'] = ['-0.03', '0.223', '-0.15', '0.35', '#interest', '#admiration', 'positive', '0.097', 'return', 'gain', 'cheap_buy', 'receive', 'bequest']
senticnet['refashion'] = ['0.734', '-0.64', '0', '0.933', '#joy', '#admiration', 'positive', '0.771', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['refasten'] = ['0', '0', '0', '0.09', '#admiration', '#admiration', 'positive', '0.09', 'keep_paper_together', 'bind', 'frighten_person', 'carry_paper', 'beatles_song']
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senticnet['remain_intact'] = ['-0.96', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.87', 'hesitation', 'stay', 'rest_foot', 'pause', 'rest_leg']
senticnet['remain_legal_tender'] = ['0.81', '0.763', '0', '0', '#joy', '#interest', 'positive', '0.786', 'dollar_bill', 'franc', 'euro', 'dollar', 'cent']
senticnet['remain_loyal'] = ['0.997', '0.796', '0', '0.89', '#joy', '#admiration', 'positive', '0.894', 'devotion', 'bravery', 'adoration', 'zeal', 'charm']
senticnet['remain_murky'] = ['-0.79', '0.241', '-0.52', '-0.11', '#sadness', '#fear', 'negative', '-0.29', 'disoriented', 'obscure', 'unsure', 'uncertain', 'unfair']
senticnet['remain_neutral'] = ['-0.99', '0', '0.581', '-0.97', '#sadness', '#disgust', 'negative', '-0.84', 'obstruct_justice', 'football_rule', 'block_road', 'hinder', 'obstruction']
senticnet['remain_optimistic'] = ['0.819', '-0.87', '0', '0.902', '#surprise', '#admiration', 'positive', '0.865', 'tally', 'steal', 'score', 'hit', 'rack_up']
senticnet['remain_silent'] = ['0.224', '0.224', '-0.12', '0', '#joy', '#interest', 'positive', '0.108', 'hush', 'peace', 'truce', 'seek_psychiatric_help', 'quiet']
senticnet['remain_skeptical'] = ['0', '0', '-0.99', '-0.99', '#fear', '#disgust', 'negative', '-0.99', 'unbelieving', 'sceptical', 'doubting', 'disbelieving', 'questioning']
senticnet['remain_stable'] = ['-0.96', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.87', 'hesitation', 'stay', 'rest_foot', 'pause', 'rest_leg']
senticnet['remain_uncertain'] = ['-0.79', '0.241', '-0.52', '-0.11', '#sadness', '#fear', 'negative', '-0.29', 'disoriented', 'obscure', 'unsure', 'uncertain', 'unfair']
senticnet['remain_unclear'] = ['-0.09', '0', '0', '-0.19', '#sadness', '#disgust', 'negative', '-0.14', 'add_knowledge', 'learn', 'become_smart', 'discover', 'make_out_atom']
senticnet['remain_viable'] = ['0.855', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.848', 'stay_alive', 'get_oxygen', 'exist', 'alive', 'crave']
senticnet['remain_vigilant'] = ['0', '1', '0', '0', '#interest', '#interest', 'positive', '1', 'confident', 'confidence', 'optimistic', 'assured', 'engrossed']
senticnet['remainder'] = ['0.04', '0.084', '0', '0.093', '#interest', '#admiration', 'positive', '0.072', 'inactivity', 'left_over', 'work_break', 'count_sheep', 'middle_day']
senticnet['remainder_beneficiary'] = ['0.851', '0.708', '0', '0', '#joy', '#interest', 'positive', '0.779', 'devisee', 'legatee', 'recipient', 'receiver', 'pensioner']
senticnet['remained_adamant'] = ['-0.80', '0', '0.507', '-0.76', '#sadness', '#disgust', 'negative', '-0.69', 'inflexible', 'inexorable', 'intransigent', 'ironclad', 'brassbound']
senticnet['remained_celibate'] = ['0.839', '-0.70', '0', '0.93', '#joy', '#admiration', 'positive', '0.824', 'virtuous', 'chaste', 'pure', 'continent', 'virginal']
senticnet['remained_defiant'] = ['-0.80', '0', '0.507', '-0.76', '#sadness', '#disgust', 'negative', '-0.69', 'inflexible', 'inexorable', 'intransigent', 'ironclad', 'brassbound']
senticnet['remained_derelict'] = ['-0.08', '0', '-0.08', '-0.14', '#sadness', '#disgust', 'negative', '-0.10', 'violence', 'assault', 'combat', 'siege', 'use_military']
senticnet['remained_elusive'] = ['0.273', '0.226', '-0.13', '0.332', '#joy', '#admiration', 'positive', '0.173', 'morality', 'cagey', 'uncommon', 'evasive', 'capitalize']
senticnet['remained_loyal'] = ['0.997', '0.796', '0', '0.89', '#joy', '#admiration', 'positive', '0.894', 'devotion', 'bravery', 'adoration', 'zeal', 'charm']
senticnet['remained_silent'] = ['0.224', '0.224', '-0.12', '0', '#joy', '#interest', 'positive', '0.108', 'hush', 'peace', 'truce', 'seek_psychiatric_help', 'quiet']
senticnet['remained_skeptical'] = ['0', '0', '-0.99', '-0.99', '#fear', '#disgust', 'negative', '-0.99', 'unbelieving', 'sceptical', 'doubting', 'disbelieving', 'questioning']
senticnet['remained_tense'] = ['-0.17', '0.154', '-0.28', '0.073', '#sadness', '#fear', 'negative', '-0.05', 'doctor_visit', 'bad_feeling', 'consequence', 'symptom', 'physical_reaction']
senticnet['remained_uncertain'] = ['-0.79', '0.241', '-0.52', '-0.11', '#sadness', '#fear', 'negative', '-0.29', 'disoriented', 'obscure', 'unsure', 'uncertain', 'unfair']
senticnet['remained_unclear'] = ['-0.79', '0.241', '-0.52', '-0.11', '#sadness', '#fear', 'negative', '-0.29', 'disoriented', 'obscure', 'unsure', 'uncertain', 'unfair']
senticnet['remained_upright'] = ['-0.11', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.08', 'have_fun', 'spring_up', 'fall_well', 'shovel_snow', 'fly_around']
senticnet['remaineth'] = ['0.741', '-0.84', '0', '0.543', '#joy', '#surprise', 'positive', '0.711', 'integrity', 'righteousness', 'scrupulousness', 'incorruptness', 'honestness']
senticnet['remaining_repechage_opponent'] = ['0.763', '-0.67', '0', '0.945', '#joy', '#admiration', 'positive', '0.795', 'set', 'income_bracket', 'tax_bracket', 'income_tax_bracket', 'price_bracket']
senticnet['remaining_silent'] = ['0', '0', '0.808', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'idle', 'lazy', 'stay', 'take_easy', 'recline']
senticnet['remains'] = ['-0.92', '0', '0.718', '-0.98', '#sadness', '#disgust', 'negative', '-0.87', 'collectible', 'fossil', 'endure', 'archeological_remains', 'last']
senticnet['remains_adamant'] = ['-0.80', '0', '0.507', '-0.76', '#sadness', '#disgust', 'negative', '-0.69', 'inflexible', 'inexorable', 'intransigent', 'ironclad', 'brassbound']
senticnet['remains_elusive'] = ['0.273', '0.226', '-0.13', '0.332', '#joy', '#admiration', 'positive', '0.173', 'morality', 'cagey', 'uncommon', 'evasive', 'capitalize']
senticnet['remains_enigmatic'] = ['-0.09', '0', '0', '-0.19', '#sadness', '#disgust', 'negative', '-0.14', 'add_knowledge', 'learn', 'become_smart', 'discover', 'make_out_atom']
senticnet['remains_intact'] = ['-0.96', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.87', 'hesitation', 'stay', 'rest_foot', 'pause', 'rest_leg']
senticnet['remains_stationary'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotate_movement', 'rotate', 'go_around', 'manoeuvre', 'full_circle']
senticnet['remains_uncertain'] = ['-0.09', '0', '0', '-0.19', '#sadness', '#disgust', 'negative', '-0.14', 'add_knowledge', 'learn', 'become_smart', 'discover', 'make_out_atom']
senticnet['remains_unclear'] = ['-0.09', '0', '0', '-0.19', '#sadness', '#disgust', 'negative', '-0.14', 'add_knowledge', 'learn', 'become_smart', 'discover', 'make_out_atom']
senticnet['remains_unknown'] = ['-0.09', '0', '0', '-0.19', '#sadness', '#disgust', 'negative', '-0.14', 'add_knowledge', 'learn', 'become_smart', 'discover', 'make_out_atom']
senticnet['remake'] = ['-0.06', '0.122', '-0.04', '0.113', '#interest', '#admiration', 'positive', '0.032', 'encounter', 'portray', 'reveal', 'episode', 'next_episode']
senticnet['remand_centre'] = ['0.816', '-0.91', '0', '0.944', '#surprise', '#admiration', 'positive', '0.891', 'jail', 'jailhouse', 'gaol', 'poky', 'clink']
senticnet['remand_prison'] = ['0', '0', '0.995', '-0.89', '#anger', '#disgust', 'negative', '-0.94', 'shoe_store', 'chess_tournament', 'dress_room', 'front_desk', 'locker_room']
senticnet['remark'] = ['-0.03', '0.114', '-0.09', '0.234', '#interest', '#admiration', 'positive', '0.054', 'reply', 'mention', 'comment', 'retort', 'announcement']
senticnet['remarkable'] = ['0', '-0.88', '0', '0.849', '#surprise', '#admiration', 'positive', '0.867', 'incredible', 'amazing', 'phenomenal', 'fantastic', 'unbelievable']
senticnet['remarkable_achievement'] = ['0.562', '-0.69', '0', '0.883', '#surprise', '#admiration', 'positive', '0.714', 'majestic', 'formidable', 'awesome', 'awing', 'brilliant']
senticnet['remarkable_feat'] = ['0.749', '0', '0', '0.952', '#joy', '#admiration', 'positive', '0.851', 'high_class', 'citation', 'suspicion', 'goodness', 'prosperity']
senticnet['remarkable_resemblance'] = ['-0.88', '0', '0.813', '-0.57', '#sadness', '#anger', 'negative', '-0.75', 'affinity', 'mutual_resemblance', 'alikeness', 'likeness', 'similitude']
senticnet['remarkable_similarity'] = ['-0.88', '0', '0.813', '-0.57', '#sadness', '#anger', 'negative', '-0.75', 'affinity', 'mutual_resemblance', 'alikeness', 'likeness', 'similitude']
senticnet['remarkable_talent'] = ['0.626', '-0.96', '0', '0.788', '#surprise', '#admiration', 'positive', '0.794', 'foxiness', 'slyness', 'wiliness', 'craftiness', 'cunning']
senticnet['remarkably'] = ['0.045', '-0.66', '0', '0.363', '#surprise', '#admiration', 'positive', '0.357', 'amazingly', 'surprised', 'surprising', 'surprisedly', 'surprisingly']
senticnet['remarkably_diverse'] = ['0.057', '0', '0', '0.153', '#joy', '#admiration', 'positive', '0.105', 'multiple', 'cute', 'variety', 'hairy', 'large_breadbox']
senticnet['remarkably_similar'] = ['0.14', '0.345', '-0.20', '0.378', '#interest', '#admiration', 'positive', '0.165', 'good_etiquette', 'same', 'similarity', 'band_together', 'niceness']
senticnet['remarkably_successful'] = ['0.98', '-0.74', '0', '0.984', '#joy', '#admiration', 'positive', '0.903', 'prosperous', 'flourishing', 'palmy', 'prospering', 'thriving']
senticnet['remarriage'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'bigamy', 'wedlock']
senticnet['remarry'] = ['-0.70', '0', '0.919', '-0.73', '#anger', '#disgust', 'negative', '-0.78', 'unfruitful', 'unsuccessful', 'acarpous', 'abortive', 'stillborn']
senticnet['remastered_dvd'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['rematch'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['rematch_against'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['rembrandt_self_portrait'] = ['-0.52', '0', '0.506', '-0.94', '#sadness', '#disgust', 'negative', '-0.65', 'gouger', 'swindler', 'defrauder', 'fraud', 'welcher']
senticnet['remediable'] = ['0.774', '0', '0', '0.762', '#joy', '#admiration', 'positive', '0.768', 'reparable', 'reversible', 'repairable', 'rectifiable', 'revocable']
senticnet['remedial'] = ['0.609', '-0.60', '0', '0.746', '#joy', '#admiration', 'positive', '0.654', 'corrective', 'amelioratory', 'ameliorating', 'ameliorative', 'bettering']
senticnet['remedial_action'] = ['0.931', '0.947', '0', '0', '#joy', '#interest', 'positive', '0.939', 'antidote', 'cure', 'therapeutic', 'curative', 'repair']
senticnet['remedial_legislation'] = ['0.931', '0.947', '0', '0', '#joy', '#interest', 'positive', '0.939', 'antidote', 'cure', 'therapeutic', 'curative', 'repair']
senticnet['remedial_massage'] = ['-0.13', '-0.07', '0.05', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'try_best', 'loose', 'try_hard', 'strategize', 'sore_knee']
senticnet['remediate'] = ['0.935', '0', '0', '0', '#joy', '#joy', 'positive', '0.935', 'clean_up_kitchen', 'feel_dirty', 'remove_stain', 'stink', 'stink_mess']
senticnet['remedy'] = ['0.931', '0.947', '0', '0', '#joy', '#interest', 'positive', '0.939', 'antidote', 'cure', 'therapeutic', 'curative', 'repair']
senticnet['remember'] = ['0.812', '0.872', '0', '0', '#joy', '#interest', 'positive', '0.842', 'cogitate', 'recollection', 'remember_phone_number', 'contemplate', 'mathematical_skill']
senticnet['remember_childhood'] = ['0', '-0.09', '0.105', '-0.14', '#anger', '#disgust', 'negative', '-0.05', 'make_music', 'play_song', 'create_music', 'perform_of_music', 'play_orchestra']
senticnet['remember_date'] = ['0.601', '0.647', '-0.26', '0.322', '#joy', '#interest', 'positive', '0.326', 'anniversary', 'special_occasion', 'good_memory', 'caring', 'love']
senticnet['remember_event'] = ['0.773', '0.901', '0', '0', '#joy', '#interest', 'positive', '0.837', 'think_creatively', 'brain_function', 'scratch_head', 'give_signal', 'brainstorm']
senticnet['remember_fact'] = ['0.689', '0.893', '0', '0.787', '#interest', '#admiration', 'positive', '0.79', 'remember_information', 'absorb_information', 'gain_understand', 'read_out_loud', 'use_dictionary']
senticnet['remember_information'] = ['0.69', '-0.89', '0', '0.88', '#surprise', '#admiration', 'positive', '0.82', 'remember_fact', 'review_note', 'read_book_subject', 'ask_expert', 'use_telescope']
senticnet['remember_late'] = ['-0.12', '-0.00', '0.052', '-0.12', '#sadness', '#disgust', 'negative', '-0.07', 'recite', 'learn_fact', 'remember', 'become_expert', 'forget_else']
senticnet['remember_past'] = ['0.985', '0.729', '0', '0', '#joy', '#interest', 'positive', '0.857', 'remember', 'contemplation', 'commemorate_event', 'use_memory', 'honor_person']
senticnet['remember_phone_number'] = ['-0.06', '0.145', '0.03', '0.062', '#interest', '#admiration', 'positive', '0.029', 'cogitate', 'remember', 'contemplate', 'use_memory', 'system_abstract_think']
senticnet['remember_stuff'] = ['0', '0.084', '0', '0.078', '#interest', '#admiration', 'positive', '0.081', 'hold_document', 'make_note', 'hold_paper', 'write_paper', 'store_pen']
senticnet['remembrance'] = ['0.959', '-0.84', '0', '0.593', '#joy', '#surprise', 'positive', '0.8', 'mental_faculty', 'faculty', 'module', 'retentivity', 'anamnesis']
senticnet['remf'] = ['-0.66', '0', '0.595', '-0.83', '#sadness', '#disgust', 'negative', '-0.7', 'bastard', 'dickhead', 'disagreeable_person', 'motherfucker', 'son_of_bitch']
senticnet['remind'] = ['0.111', '0.187', '-0.09', '0.161', '#interest', '#admiration', 'positive', '0.09', 'express_opinion', 'nod_head', 'new_information', 'provide_information', 'sincere']
senticnet['remind_of'] = ['0.824', '0.71', '0', '0', '#joy', '#interest', 'positive', '0.767', 'recall', 'remember', 'game_part', 'memory', 'push_together']
senticnet['reminder'] = ['-0.02', '0.096', '0', '0.114', '#interest', '#admiration', 'positive', '0.061', 'musical_symbol', 'music_part', 'musical_notation', 'written_message', 'rhyme']
senticnet['remindful'] = ['0', '0', '-0.83', '-0.80', '#fear', '#disgust', 'negative', '-0.81', 'resonant', 'redolent', 'smelling', 'reminiscent', 'evocative']
senticnet['remineralisation'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'plaque']
senticnet['remington_cartridge'] = ['0.887', '-0.95', '0', '0.913', '#surprise', '#admiration', 'positive', '0.917', 'high_quality', 'superiority', 'level', 'grade', 'degree']
senticnet['remington_typewriter'] = ['0', '0', '0.128', '0', '#anger', '#anger', 'negative', '-0.12', 'grid', 'efficient', 'calculator', 'computer', 'personal_computer']
senticnet['reminisce'] = ['0.119', '-0.13', '-0.06', '0', '#joy', '#surprise', 'positive', '0.063', 'gala', 'celebration', 'special_event', 'special_happen', 'special_moment']
senticnet['reminiscent'] = ['0', '0', '-0.82', '-0.80', '#fear', '#disgust', 'negative', '-0.81', 'resonant', 'remindful', 'smelling', 'redolent', 'evocative']
senticnet['remission'] = ['0.392', '0.196', '0', '0.345', '#joy', '#admiration', 'positive', '0.311', 'complement', 'much_need', 'taken_seriously', 'good_sense_of_humor', 'knowledgeable']
senticnet['remissness'] = ['-0.93', '0', '0.624', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'carelessness', 'willful_neglect', 'laxness', 'laxity', 'neglectfulness']
senticnet['remit'] = ['0', '0', '0.709', '-0.92', '#anger', '#disgust', 'negative', '-0.81', 'diminish', 'taper', 'drop_off', 'contract', 'break']
senticnet['remittal'] = ['-0.88', '0', '0.727', '-0.82', '#sadness', '#disgust', 'negative', '-0.81', 'defervescence', 'subsidence', 'break', 'remission', 'abatement']
senticnet['remittance'] = ['-0.05', '0.267', '-0.12', '0.154', '#interest', '#admiration', 'positive', '0.061', 'wage', 'money_due', 'owe', 'payment', 'income']
senticnet['remittent'] = ['-0.97', '0', '0.584', '-0.78', '#sadness', '#disgust', 'negative', '-0.78', 'decreased', 'diminished', 'weakened', 'faded', 'small']
senticnet['remittent_fever'] = ['-0.97', '0', '0.584', '-0.78', '#sadness', '#disgust', 'negative', '-0.78', 'decreased', 'diminished', 'weakened', 'faded', 'small']
senticnet['remizidae_family'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'lovebird', 'parroket', 'paraquet', 'paroquet']
senticnet['remobilization'] = ['0.942', '-0.79', '0', '0.901', '#joy', '#admiration', 'positive', '0.88', 'backup', 'accretion', 'deposition', 'deposit', 'increase']
senticnet['remodeler'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'maker']
senticnet['remold'] = ['0.7', '-0.79', '0', '0.66', '#joy', '#surprise', 'positive', '0.719', 'influence', 'determine', 'shape', 'mold', 'regulate']
senticnet['remonstrance'] = ['-0.76', '0', '0.763', '0', '#sadness', '#anger', 'negative', '-0.76', 'expostulation', 'dissent', 'protest', 'remonstration', 'objection']
senticnet['remonstrant'] = ['-0.85', '0', '0.749', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'hairsplitter', 'fight', 'quarrel', 'obstructor', 'obstructer']
senticnet['remonstrated_against'] = ['-0.56', '0', '0.828', '-0.68', '#anger', '#disgust', 'negative', '-0.69', 'abruptness', 'precipitateness', 'precipitousness', 'precipitance', 'hastiness']
senticnet['remonstration'] = ['-0.75', '0', '0.754', '0', '#sadness', '#anger', 'negative', '-0.75', 'expostulation', 'dissent', 'protest', 'objection', 'remonstrance']
senticnet['remorse'] = ['-1', '0', '0', '-0.66', '#sadness', '#disgust', 'negative', '-0.83', 'regret', 'powerless', 'resentful', 'miss_chance', 'sad']
senticnet['remorseful'] = ['-0.88', '0.178', '-0.6', '0', '#sadness', '#fear', 'negative', '-0.43', 'contrite', 'rueful', 'ruthful', 'bereaved', 'sorrowing']
senticnet['remorsefully'] = ['-0.89', '0.168', '-0.60', '0.05', '#sadness', '#fear', 'negative', '-0.32', 'contritely', 'contrition', 'contriteness', 'ruefully', 'attrition']
senticnet['remorseless'] = ['-0.96', '0', '0', '-1', '#sadness', '#disgust', 'negative', '-0.98', 'cold-hearted', 'cold-blooded', 'dispassionate', 'stone-cold', 'dyed-in-the-wool']
senticnet['remorselessly'] = ['-0.89', '0', '0.844', '0', '#sadness', '#anger', 'negative', '-0.86', 'stern', 'relentless', 'unrelenting', 'grim', 'merciless']
senticnet['remote'] = ['-0.05', '0.169', '-0.11', '0.157', '#interest', '#admiration', 'positive', '0.039', 'control', 'take_charge', 'strict', 'power', 'command']
senticnet['remote_attacker'] = ['-0.02', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.07', 'army', 'military', 'infantry', 'samurai', 'soldier']
senticnet['remote_control'] = ['0.906', '0.911', '0', '0', '#joy', '#interest', 'positive', '0.909', 'chatter_tooth', 'air_pump', 'safety_pin', 'slinky', 'typewriter_ribbon']
senticnet['remote_host'] = ['0.078', '0.103', '-0.09', '0.191', '#interest', '#admiration', 'positive', '0.069', 'connect', 'aunt_uncle', 'short_form', 'relationship', 'kinship']
senticnet['remote_intruder'] = ['-0.02', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.07', 'army', 'military', 'infantry', 'samurai', 'soldier']
senticnet['remote_keyless_entry'] = ['0', '0', '0', '0.109', '#admiration', '#admiration', 'positive', '0.109', 'knob', 'door_knob', 'door_handle', 'handle', 'hinge']
senticnet['remote_location'] = ['0', '0', '0.921', '-0.87', '#anger', '#disgust', 'negative', '-0.9', 'outback', 'trackless', 'pathless', 'roadless', 'untracked']
senticnet['remote_server'] = ['0.078', '0.103', '-0.09', '0.191', '#interest', '#admiration', 'positive', '0.069', 'connect', 'aunt_uncle', 'short_form', 'relationship', 'kinship']
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senticnet['removable_partial_denture'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'false_teeth', 'bridge', 'bridgework', 'dental_plate', 'dental_appliance']
senticnet['removable_storage'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotary_file', 'cd-rom', 'database', 'business_card', 'calendar_book']
senticnet['removal'] = ['0', '0', '0.889', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'dismissal', 'discharge', 'elimination', 'destitution', 'forced_removal']
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senticnet['remove_hydrogen_sulfide'] = ['-0.67', '0', '0.503', '-0.67', '#sadness', '#disgust', 'negative', '-0.61', 'stink_up', 'smell_up', 'stink_out', 'incense', 'perfume']
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senticnet['renewable_resource'] = ['0.032', '0', '0', '0.078', '#joy', '#admiration', 'positive', '0.055', 'large_plant', 'tall_person', 'tree_piece', 'small_tree', 'part_of_tree']
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senticnet['renowned'] = ['0', '0.849', '0', '0.71', '#interest', '#admiration', 'positive', '0.779', 'world-renowned', 'top-notch', 'world-class', 'state-of-the-art', 'unparalleled']
senticnet['renowned_composer'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'band_play', 'songwriter', 'rock_band']
senticnet['renowned_expert'] = ['0.891', '0.721', '0', '0.921', '#joy', '#admiration', 'positive', '0.844', 'inventor', 'connoisseur', 'medical_doctor', 'teacher', 'judge']
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senticnet['rent_apartment'] = ['0.788', '0.796', '0', '0', '#joy', '#interest', 'positive', '0.792', 'brownstone', 'condominium', 'high_rise', 'condo', 'row_house']
senticnet['rent_house'] = ['0.036', '0.138', '0.058', '0.223', '#interest', '#admiration', 'positive', '0.085', 'home_sweet_home', 'house', 'rent_room', 'live_apartment', 'need_companionship']
senticnet['rent_out'] = ['-0.05', '0.116', '-0.04', '0.102', '#interest', '#admiration', 'positive', '0.029', 'house', 'dwelling', 'multi-story_build', 'lease', 'large_house']
senticnet['rent_room'] = ['0.036', '0.138', '0.058', '0.223', '#interest', '#admiration', 'positive', '0.085', 'home_sweet_home', 'house', 'live_apartment', 'rent_house', 'need_companionship']
senticnet['rental_apartment'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'home', 'furniture', 'kitchen', 'flat']
senticnet['rental_car'] = ['0.04', '0.144', '-0.07', '0.122', '#interest', '#admiration', 'positive', '0.058', 'travel', 'abroad', 'foreign_country', 'transit', 'elsewhere']
senticnet['rental_truck'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'trailer', 'trail_car', 'camion', 'wagon', 'towboat']
senticnet['rented_apartment'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'apartment', 'furniture', 'kitchen', 'flat']
senticnet['rented_flat'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'apartment', 'furniture', 'kitchen', 'flat']
senticnet['rentier'] = ['-0.78', '0', '0.944', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'loser', 'gambler', 'mortal', 'better', 'bettor']
senticnet['rentless'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'lack_of_judgment', 'lack_of_discreetness', 'lack_of_discernment', 'reckless', 'injudiciousness']
senticnet['renunciant'] = ['0.677', '-0.56', '0', '0.975', '#joy', '#admiration', 'positive', '0.737', 'nonindulgent', 'renunciative', 'puritanic', 'puritanical', 'corrective']
senticnet['renunciate'] = ['-0.94', '0', '0.779', '-0.85', '#sadness', '#disgust', 'negative', '-0.86', 'eremitic', 'eremitical', 'cloistered', 'anchoritic', 'unworldly']
senticnet['renunciation'] = ['0', '0', '-0.82', '-0.78', '#fear', '#disgust', 'negative', '-0.80', 'abdication', 'apostasy', 'quitclaim', 'reject', 'repudiation']
senticnet['renunciative'] = ['0.679', '-0.56', '0', '0.98', '#joy', '#admiration', 'positive', '0.742', 'renunciant', 'nonindulgent', 'puritanic', 'puritanical', 'corrective']
senticnet['renwick_gallery'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'institution', 'tundra', 'child_story', 'natural_history_museum', 'cartoon']
senticnet['renzo_palmer_inspector_cramer'] = ['0', '0', '0', '0.068', '#admiration', '#admiration', 'positive', '0.068', 'dark_corner', 'child_game', 'play_person', 'role_play', 'part_of_play']
senticnet['reobligation'] = ['-0.88', '0', '0.977', '-0.81', '#sadness', '#anger', 'negative', '-0.89', 'execute', 'run', 'compel', 'oblige', 'enforce']
senticnet['reoccupation'] = ['0', '0.112', '-0.04', '0.099', '#interest', '#admiration', 'positive', '0.054', 'profession', 'career', 'vocation', 'important_job', 'laborer']
senticnet['reordering'] = ['0.898', '-0.87', '0', '0.929', '#joy', '#admiration', 'positive', '0.9', 'make', 'transposition', 'reversal', 'passing', 'permutation']
senticnet['reorganize'] = ['0.7', '-0.79', '0', '0.66', '#joy', '#surprise', 'positive', '0.719', 'influence', 'determine', 'shape', 'mold', 'regulate']
senticnet['reorient'] = ['0.7', '-0.79', '0', '0.66', '#joy', '#surprise', 'positive', '0.719', 'influence', 'determine', 'shape', 'mold', 'regulate']
senticnet['rep_murtha'] = ['-0.86', '0', '0.675', '-0.84', '#sadness', '#disgust', 'negative', '-0.79', 'shenanigan', 'chicanery', 'wile', 'dupery', 'trickery']
senticnet['repack'] = ['0', '0.11', '-0.05', '0.149', '#interest', '#admiration', 'positive', '0.067', 'pack_clothes', 'hold_stuff', 'carry_stuff', 'carry_book', 'carry_belongings']
senticnet['repair'] = ['0.924', '0.939', '0', '0', '#joy', '#interest', 'positive', '0.931', 'antidote', 'cure', 'therapeutic', 'curative', 'remedy']
senticnet['repair_car'] = ['0', '0.71', '0', '0.799', '#interest', '#admiration', 'positive', '0.754', 'car_workshop', 'repair_shop', 'workshop', 'repair_motorcycle', 'check_up_car']
senticnet['repair_motorcycle'] = ['0', '0.71', '0', '0.799', '#interest', '#admiration', 'positive', '0.754', 'repair_car', 'repair_shop', 'workshop', 'car_workshop', 'check_up_car']
senticnet['repair_shop'] = ['0.711', '0.82', '0', '0', '#joy', '#interest', 'positive', '0.765', 'garage', 'garbage_dump', 'bicycle_shop', 'tool', 'fix_panel']
senticnet['repair_umbrella'] = ['0', '-0.01', '0.078', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'fix_computer', 'write_program', 'program_computer', 'design_software', 'compute_sum']
senticnet['repairability'] = ['0.8', '-0.90', '0', '0.675', '#joy', '#surprise', 'positive', '0.794', 'indestructibility', 'tensile_strength', 'continuity', 'persistence', 'changelessness']
senticnet['repairable'] = ['0.774', '0', '0', '0.76', '#joy', '#admiration', 'positive', '0.767', 'reparable', 'remediable', 'reversible', 'rectifiable', 'revocable']
senticnet['repaired'] = ['-0.95', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.83', 'amended', 'cured', 'irretrievable', 'irreparable', 'rectified']
senticnet['repairman'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'kitchen_cabinet', 'kitchen_cupboard', 'kitchen', 'cupboard', 'own_kitchen']
senticnet['repairmen'] = ['0', '0.115', '0', '0.105', '#interest', '#admiration', 'positive', '0.11', 'farrier', 'miner', 'horseshoer', 'blacksmith', 'smith']
senticnet['reparable'] = ['0.779', '0', '0', '0.756', '#joy', '#admiration', 'positive', '0.768', 'reversible', 'remediable', 'repairable', 'rectifiable', 'revocable']
senticnet['reparation'] = ['0', '0', '0.77', '-0.9', '#anger', '#disgust', 'negative', '-0.83', 'reparation', 'restitution', 'redress', 'expiation', 'recompense']
senticnet['reparative_therapy'] = ['0.542', '-0.63', '0', '0.912', '#surprise', '#admiration', 'positive', '0.695', 'implosion_therapy', 'flooding', 'reciprocal_inhibition', 'behavior_modification', 'desensitization_technique']
senticnet['repartee'] = ['-0.85', '0', '0.855', '-0.87', '#anger', '#disgust', 'negative', '-0.86', 'banter', 'raillery', 'backchat', 'wit', 'humor']
senticnet['repast'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'green_bread', 'rotten', 'disgusting', 'taste_awful', 'mouldy']
senticnet['repatriation'] = ['-0.02', '0.119', '-0.06', '0.216', '#interest', '#admiration', 'positive', '0.062', 'go_forward', 'locomotion', 'movement', 'move', 'move_ahead']
senticnet['repay'] = ['0', '0.715', '0', '0.924', '#interest', '#admiration', 'positive', '0.82', 'payback', 'money', 'money_owe', 'money_pay', 'money_due']
senticnet['repayable'] = ['-0.89', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.80', 'account_indebtedness', 'indebtedness', 'limited_liability', 'debt', 'arrears']
senticnet['repayment'] = ['-0.04', '0.277', '-0.10', '0.11', '#interest', '#admiration', 'positive', '0.059', 'owe', 'tax', 'debit', 'afford', 'wage']
senticnet['repeal'] = ['0', '0.089', '0', '0.129', '#interest', '#admiration', 'positive', '0.109', 'passageway', 'connect_room', 'entrance_room', 'corridor', 'walk_through']
senticnet['repeat'] = ['-0.1', '0.242', '-0.13', '0.196', '#interest', '#admiration', 'positive', '0.052', 'go_over', 'realize', 'deduce', 'become_aware', 'cognition']
senticnet['repeat_action'] = ['0.825', '0.744', '0', '0', '#joy', '#interest', 'positive', '0.784', 'turn_away', 'shine_light', 'spring_up', 'repeat', 'replicate']
senticnet['repeat_maltreatment'] = ['-0.8', '0', '0.875', '-0.74', '#sadness', '#anger', 'negative', '-0.80', 'inhuman_treatment', 'cruelty', 'child_neglect', 'mistreatment', 'child_abuse']
senticnet['repeat_offender'] = ['-0.89', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.93', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['repeatability'] = ['0', '0', '0', '0.76', '#admiration', '#admiration', 'positive', '0.76', 'big_paycheck', 'self_confidence', 'balance_check_account', 'look_beautiful', 'much_money']
senticnet['repeated'] = ['0', '0.127', '-0.06', '0.168', '#interest', '#admiration', 'positive', '0.076', 'replicate', 'recur', 'frequent', 'usual_ordinary', 'redo']
senticnet['repeater'] = ['0.756', '-0.83', '0', '0.995', '#surprise', '#admiration', 'positive', '0.861', 'mortal', 'person', 'individual', 'replicate', 'duplicate']
senticnet['repeating_rifle'] = ['0', '0', '-0.87', '-0.82', '#fear', '#disgust', 'negative', '-0.84', 'pistol', 'gun', 'hydrogen_bomb', 'rifle', 'shotgun']
senticnet['repel'] = ['-0.07', '0.133', '0.697', '-0.67', '#anger', '#disgust', 'negative', '-0.32', 'sicken', 'nauseate', 'gross_out', 'churn_up', 'loathsome']
senticnet['repellant'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['repellency'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['repellent'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['repelling'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['repels_water'] = ['0.025', '-0.00', '0', '0.125', '#joy', '#admiration', 'positive', '0.053', 'goo', 'adhesive', 'flour_water', 'insert', 'thick_liquid']
senticnet['repent'] = ['-0.84', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.81', 'penitence', 'penitent', 'penitently', 'repentantly', 'penitentially']
senticnet['repentance'] = ['-0.92', '0', '0', '-0.8', '#sadness', '#disgust', 'negative', '-0.86', 'repent', 'penitentially', 'repentant', 'penitent', 'sorrow']
senticnet['repentant'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitentially', 'penitent', 'repentantly', 'penitence']
senticnet['repentantly'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitentially', 'penitent', 'penitence', 'repentant']
senticnet['repentence'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitentially', 'penitent', 'repentantly', 'repentant']
senticnet['repentigny'] = ['-0.71', '0', '0.917', '-0.51', '#sadness', '#anger', 'negative', '-0.71', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['repertoire'] = ['0.022', '0', '0', '0.109', '#joy', '#admiration', 'positive', '0.066', 'instrumental', 'singer', 'hip_hop', 'melodic_noise', 'music_player']
senticnet['repetition'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'repetitive', 'repetitive_action', 'repetitive_task', 'repeat', 'monotony']
senticnet['repetitiousness'] = ['-0.56', '0', '0.617', '-0.80', '#anger', '#disgust', 'negative', '-0.66', 'prolixity', 'prolixness', 'wordiness', 'repetitiveness', 'verboseness']
senticnet['repetitive'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'do_again', 'repetitive_action', 'repetitive_task', 'repeat', 'repetition']
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senticnet['repetitive_motion'] = ['-0.50', '0', '0.681', '-0.89', '#anger', '#disgust', 'negative', '-0.69', 'sweat', 'effort', 'elbow_grease', 'exertion', 'travail']
senticnet['repetitive_strain'] = ['-0.50', '0', '0.681', '-0.89', '#anger', '#disgust', 'negative', '-0.69', 'sweat', 'effort', 'elbow_grease', 'exertion', 'travail']
senticnet['repetitive_task'] = ['-0.28', '0', '-0.11', '-0.15', '#sadness', '#disgust', 'negative', '-0.18', 'monotony', 'run_long_time', 'unpleasantness', 'repetition', 'hardship']
senticnet['repetitiveness'] = ['-0.56', '0', '0.617', '-0.80', '#anger', '#disgust', 'negative', '-0.66', 'prolixity', 'prolixness', 'wordiness', 'verboseness', 'repetitiousness']
senticnet['rephlex'] = ['0', '0.115', '0.036', '0.207', '#interest', '#admiration', 'positive', '0.095', 'total', 'amount', 'merit', 'award', 'excellence']
senticnet['rephrase'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquirer', 'enquirer', 'inquisitor', 'querier', 'asker']
senticnet['repine'] = ['0.689', '0.627', '-0.23', '0.315', '#joy', '#interest', 'positive', '0.35', 'jubilate', 'exuberant', 'carefree', 'lightsomeness', 'carefreeness']
senticnet['replace'] = ['0.902', '0.801', '0', '0', '#joy', '#interest', 'positive', '0.852', 'line_place', 'placement', 'stance', 'place', 'position']
senticnet['replace_defective'] = ['-0.92', '0', '0.256', '-0.98', '#sadness', '#disgust', 'negative', '-0.72', 'lousy', 'bad_quality', 'failure', 'poor_quality', 'low_quality']
senticnet['replaceable'] = ['0', '0', '0.724', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'common', 'unimportant', 'usual', 'dispensable', 'inessential']
senticnet['replacement'] = ['0', '0.124', '0', '0.055', '#interest', '#admiration', 'positive', '0.089', 'part', 'replace', 'put_back', 'extent', 'order']
senticnet['replay'] = ['-0.09', '0.147', '-0.11', '0.205', '#interest', '#admiration', 'positive', '0.036', 'question_starter', 'resolution', 'available', 'explanation', 'request']
senticnet['replayed_match'] = ['-0.09', '0.147', '-0.11', '0.205', '#interest', '#admiration', 'positive', '0.036', 'question_starter', 'resolution', 'available', 'explanation', 'request']
senticnet['replenish'] = ['0.72', '-0.76', '0', '0.695', '#joy', '#surprise', 'positive', '0.726', 'suck_up', 'blot', 'wipe_up', 'mop', 'sponge_up']
senticnet['replenish_energy'] = ['0.858', '0.983', '0', '0', '#joy', '#interest', 'positive', '0.92', 'munch', 'satisfy_appetite', 'nourishment', 'able_eat', 'sustenance']
senticnet['replete'] = ['-0.80', '0', '0.542', '-0.94', '#sadness', '#disgust', 'negative', '-0.76', 'pall', 'take_in', 'take', 'ingest', 'have']
senticnet['replete_with'] = ['0', '0', '-0.91', '-0.73', '#fear', '#disgust', 'negative', '-0.82', 'overabundant', 'prevailing', 'plethoric', 'predominant', 'dominant']
senticnet['repletion'] = ['0.795', '0', '0', '0.787', '#joy', '#admiration', 'positive', '0.791', 'entree', 'soup', 'wine_glass', 'salt_pepper_shaker', 'plate_silverware']
senticnet['replicability'] = ['0.99', '-0.88', '0', '0.831', '#joy', '#surprise', 'positive', '0.9', 'functionality', 'sensibleness', 'viability', 'usefulness', 'realism']
senticnet['replicate'] = ['-0.02', '0.167', '-0.06', '0.217', '#interest', '#admiration', 'positive', '0.075', 'clone', 'imitation', 'make_copy', 'source', 'duplicate']
senticnet['replication_fork'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['reply'] = ['0.743', '0.919', '0', '0', '#joy', '#interest', 'positive', '0.831', 'remark', 'answer', 'question', 'question_answering', 'get_response']
senticnet['reply_question'] = ['-0.11', '0.075', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.04', 'desire_help', 'revert', 'voice_opinion', 'lower_expectation', 'receive_email']
senticnet['repo'] = ['-0.05', '0.241', '-0.06', '0.286', '#interest', '#admiration', 'positive', '0.103', 'give_take', 'barter', 'exchange_goods', 'transaction', 'lower_price']
senticnet['repo_transaction'] = ['-0.05', '0.241', '-0.06', '0.286', '#interest', '#admiration', 'positive', '0.103', 'give_take', 'barter', 'exchange_goods', 'transaction', 'lower_price']
senticnet['reponsible'] = ['0.955', '0.945', '0', '0.835', '#joy', '#interest', 'positive', '0.912', 'support_family', 'provide_service', 'accomplish', 'find_job', 'pay_well']
senticnet['repopulate'] = ['-0.81', '0', '0.504', '-0.96', '#sadness', '#disgust', 'negative', '-0.76', 'freshen', 'air_out', 'air', 'renew', 'vent']
senticnet['report'] = ['0', '0', '0.716', '-0.94', '#anger', '#disgust', 'negative', '-0.83', 'explanation', 'check', 'record', 'recital', 'disquisition']
senticnet['report_crime'] = ['-0.13', '-0.01', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.06', 'complete_sentence', 'point_out', 'state_fact', 'make_complain', 'page_through_book']
senticnet['report_event'] = ['0', '0.934', '0', '0.898', '#interest', '#admiration', 'positive', '0.916', 'review_book', 'spread_news', 'pass_information', 'report_news', 'pass_knowledge']
senticnet['report_news'] = ['0', '-0.86', '0', '0.981', '#surprise', '#admiration', 'positive', '0.924', 'seek_answer', 'analyze', 'know_world', 'inform', 'read_out_loud']
senticnet['reportable_disease'] = ['-0.11', '0', '-0.15', '-0.24', '#fear', '#disgust', 'negative', '-0.17', 'disease', 'infectious_disease', 'polio', 'malady', 'sickness']
senticnet['reportage'] = ['-0.95', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.95', 'reporting', 'coverage', 'insurance_coverage', 'abortion', 'murder']
senticnet['reportedly'] = ['0', '0', '-0.93', '-0.83', '#fear', '#disgust', 'negative', '-0.88', 'whisper', 'rumor', 'rumormonger', 'skinny', 'statement']
senticnet['reporter'] = ['0.713', '0.812', '0', '0', '#joy', '#interest', 'positive', '0.762', 'architect', 'public_servant', 'manager', 'hero', 'construction_worker']
senticnet['reporting'] = ['-0.95', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.95', 'coverage', 'reportage', 'insurance_coverage', 'abortion', 'murder']
senticnet['repose'] = ['0.621', '0.648', '-0.25', '0.296', '#joy', '#interest', 'positive', '0.328', 'pacifically', 'peace-loving', 'peaceably', 'peaceable', 'peace_of_mind']
senticnet['repose_on'] = ['-0.95', '0', '0.51', '-0.96', '#sadness', '#disgust', 'negative', '-0.80', 'ride', 'owe', 'depend_on', 'depend_upon', 'turn_on']
senticnet['reposeful'] = ['0.579', '0.652', '-0.24', '0.276', '#joy', '#interest', 'positive', '0.315', 'tranquilly', 'tranquility', 'quietness', 'tranquil', 'unagitated']
senticnet['reposition'] = ['0', '0.174', '-0.08', '0.302', '#interest', '#admiration', 'positive', '0.131', 'make_big', 'flourish', 'get_large', 'get_big', 'expand']
senticnet['reprehend'] = ['0.565', '-0.73', '0', '0.542', '#joy', '#surprise', 'positive', '0.615', 'say', 'asseverate', 'predicate', 'insist', 'take_firm_stand']
senticnet['reprehensible'] = ['-0.85', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.79', 'reprehension', 'base', 'deserve', 'scum', 'bad']
senticnet['reprehensibly'] = ['-0.94', '0', '0.926', '0', '#sadness', '#anger', 'negative', '-0.93', 'disgraceful', 'shameful', 'dishonorably', 'inglorious', 'disgrace']
senticnet['reprehension'] = ['-0.66', '0', '0.722', '-0.54', '#sadness', '#anger', 'negative', '-0.64', 'bawling_out', 'chewing_out', 'upbraiding', 'dressing_down', 'berating']
senticnet['represent'] = ['0', '0.128', '-0.11', '0.228', '#interest', '#admiration', 'positive', '0.08', 'portray', 'heap', 'appear', 'juggler', 'charade']
senticnet['represent_idea'] = ['0.827', '0', '0', '0', '#joy', '#joy', 'positive', '0.827', 'convey_message', 'communicate_thought', 'presentation', 'record_history', 'store_knowledge']
senticnet['representation'] = ['0', '0.157', '0', '0.147', '#interest', '#admiration', 'positive', '0.152', 'wall_hang', 'depiction', 'illustration', 'artwork', 'wall_decoration']
senticnet['representation_theory'] = ['0', '0', '0.839', '-0.93', '#anger', '#disgust', 'negative', '-0.88', 'quantum_group', 'lie_group', 'recently', 'problem', 'algebranumber_theoryand_com']
senticnet['representational'] = ['0.693', '-0.52', '0', '0.82', '#joy', '#admiration', 'positive', '0.681', 'embodiment', 'shape', 'representation', 'concrete_representation', 'internal_representation']
senticnet['representational_art'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'design', 'carving', 'decoupage', 'ceramic']
senticnet['representational_content'] = ['0.693', '-0.52', '0', '0.82', '#joy', '#admiration', 'positive', '0.681', 'embodiment', 'shape', 'representation', 'concrete_representation', 'internal_representation']
senticnet['representational_theory'] = ['0.693', '-0.52', '0', '0.82', '#joy', '#admiration', 'positive', '0.681', 'embodiment', 'shape', 'representation', 'concrete_representation', 'internal_representation']
senticnet['representationalism'] = ['0.693', '-0.52', '0', '0.82', '#joy', '#admiration', 'positive', '0.681', 'embodiment', 'shape', 'representation', 'concrete_representation', 'internal_representation']
senticnet['representationalist'] = ['0.534', '-0.86', '0', '0.676', '#surprise', '#admiration', 'positive', '0.692', 'suitability', 'suitableness', 'habitability', 'habitableness', 'qualification']
senticnet['representative'] = ['0.959', '0.924', '0', '0', '#joy', '#interest', 'positive', '0.942', 'government', 'president', 'rogue', 'politician', 'ambassador']
senticnet['representative_democracy'] = ['-0.55', '0', '0.931', '-0.74', '#anger', '#disgust', 'negative', '-0.74', 'totalitarian', 'authoritarian', 'dictatorial', 'despotic', 'monarchal']
senticnet['representative_payee'] = ['0.851', '0.708', '0', '0', '#joy', '#interest', 'positive', '0.779', 'devisee', 'legatee', 'recipient', 'receiver', 'pensioner']
senticnet['representative_sample'] = ['-0.04', '0.1', '0.051', '0.137', '#interest', '#admiration', 'positive', '0.035', 'find_number', 'regularly', 'list_number', 'calculator', 'navigation']
senticnet['representative_sampling'] = ['0.956', '-0.83', '0', '0.755', '#joy', '#surprise', 'positive', '0.85', 'choice', 'option', 'random_sampling', 'stratified_sampling', 'sampling']
senticnet['repress'] = ['-0.50', '0', '0.936', '-0.95', '#anger', '#disgust', 'negative', '-0.79', 'quash', 'keep_down', 'reduce', 'oppress', 'subdue']
senticnet['repressed'] = ['-0.83', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.81', 'suppress', 'suppression', 'neocolonialism', 'reversion', 'suppressing']
senticnet['repressed_memory'] = ['-0.77', '0', '0.981', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'trip_up', 'stumble', 'misjudge', 'err', 'fall_for']
senticnet['repression'] = ['-0.77', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.84', 'jumpiness', 'jitteriness', 'restive', 'nervy', 'jumpy']
senticnet['repressive'] = ['-0.82', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.77', 'repress', 'repressively', 'repressiveness', 'clampdown', 'suppress']
senticnet['repressive_racist_regime'] = ['-0.79', '0', '0.691', '-0.88', '#sadness', '#disgust', 'negative', '-0.79', 'cold', 'pitiless', 'cruel', 'unkind', 'vicious']
senticnet['repressive_regime'] = ['-0.73', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.72', 'royal_leader', 'despot', 'emperor', 'monarch', 'king']
senticnet['repressively'] = ['-0.82', '0', '-0.73', '0', '#sadness', '#fear', 'negative', '-0.77', 'repress', 'repressive', 'repressiveness', 'clampdown', 'suppress']
senticnet['repressiveness'] = ['-0.82', '0', '-0.73', '0', '#sadness', '#fear', 'negative', '-0.77', 'repress', 'repressively', 'repressive', 'clampdown', 'suppress']
senticnet['reprieve'] = ['-0.53', '0', '0.608', '-0.93', '#anger', '#disgust', 'negative', '-0.69', 'defervescence', 'respite', 'hiatus', 'abatement', 'suspension']
senticnet['reprimand'] = ['-0.50', '-0.02', '-0.28', '-0.10', '#sadness', '#fear', 'negative', '-0.21', 'unsafe', 'feel_stressed', 'stress', 'wrongdoing', 'screw_up']
senticnet['reprint'] = ['-0.75', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.80', 'republishing', 're-printing', 're-posting', 'reprinting', 're-publishing']
senticnet['reprinting'] = ['-0.74', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.80', 'republishing', 're-printing', 're-posting', 'reprint', 're-publishing']
senticnet['reprisal'] = ['-0.88', '0', '0.733', '-0.60', '#sadness', '#anger', 'negative', '-0.74', 'vengeance', 'payback', 'retribution', 'retaliation', 'return']
senticnet['reprisal_against'] = ['-0.88', '0', '0.733', '-0.60', '#sadness', '#anger', 'negative', '-0.74', 'vengeance', 'payback', 'retribution', 'reprisal', 'return']
senticnet['reprise'] = ['0', '0', '0', '0.068', '#admiration', '#admiration', 'positive', '0.068', 'dark_corner', 'child_game', 'play_person', 'role_play', 'part_of_play']
senticnet['reprivatisation'] = ['-0.98', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.92', 'thrilling', 'electrifying', 'sensational', 'sensationally', 'scandalmongering']
senticnet['reproach'] = ['-0.69', '0', '0.585', '-0.81', '#sadness', '#disgust', 'negative', '-0.69', 'blame', 'rap', 'reprimand', 'rebuke', 'reproof']
senticnet['reproachful'] = ['-0.74', '0', '0.982', '-0.81', '#anger', '#disgust', 'negative', '-0.84', 'inauspicious', 'admonishing', 'unfavorable', 'reproving', 'untoward']
senticnet['reproachfully'] = ['-0.75', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.83', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['reprobate'] = ['-0.75', '0', '0.985', '-0.75', '#sadness', '#anger', 'negative', '-0.83', 'scapegrace', 'black_sheep', 'degenerate', 'wretch', 'pervert']
senticnet['reprobation'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'forgivable', 'excusable', 'mercifully', 'merciful', 'mercifulness']
senticnet['reproduce'] = ['0.039', '0.109', '-0.07', '0.147', '#interest', '#admiration', 'positive', '0.055', 'procreate', 'cause_cold', 'copulate', 'start_family', 'give_birth_child']
senticnet['reproduce_rapidly'] = ['0.855', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.848', 'stay_alive', 'get_oxygen', 'exist', 'alive', 'crave']
senticnet['reproducibility'] = ['0.511', '-0.52', '0', '0.873', '#surprise', '#admiration', 'positive', '0.636', 'responsibleness', 'dependability', 'duplicability', 'solidness', 'responsibility']
senticnet['reproducibly'] = ['0', '-0.74', '0', '0.846', '#surprise', '#admiration', 'positive', '0.796', 'transiently', 'diffusely', 'potently', 'focally', 'lastingly']
senticnet['reproduction'] = ['0.905', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.891', 'part_of_meal', 'survival', 'host_party', 'breed', 'put_ice']
senticnet['reproductive'] = ['0.825', '-0.71', '0', '0', '#joy', '#surprise', 'positive', '0.771', 'graduate_education', 'training', 'collaborative_research', 'fertile_environment', 'cultural_change']
senticnet['reproductive_age'] = ['0.97', '0.729', '0', '0.946', '#joy', '#admiration', 'positive', '0.882', 'make_baby', 'give_birth_child', 'sexual_intercourse', 'find_partner', 'make_love']
senticnet['reproductive_capacity'] = ['0.988', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.98', 'sustain_life', 'sustenance', 'nutrition', 'keep_alive', 'satisfy_hunger']
senticnet['reproductive_cycle'] = ['0.947', '-0.81', '0', '0.656', '#joy', '#surprise', 'positive', '0.805', 'sexual_intercourse', 'intercourse', 'sex_act', 'hank_panky', 'coitus']
senticnet['reproductive_endocrine'] = ['0.672', '-0.55', '0', '0.992', '#joy', '#admiration', 'positive', '0.739', 'pressor', 'hormone', 'adrenalin', 'internal_secretion', 'catecholamine']
senticnet['reproductive_endocrinology'] = ['0.672', '-0.55', '0', '0.992', '#joy', '#admiration', 'positive', '0.739', 'pressor', 'hormone', 'adrenalin', 'internal_secretion', 'catecholamine']
senticnet['reproductive_function'] = ['0.672', '-0.55', '0', '0.992', '#joy', '#admiration', 'positive', '0.739', 'pressor', 'hormone', 'adrenalin', 'internal_secretion', 'catecholamine']
senticnet['reproductive_health'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'safe_sex', 'sex', 'prevent_pregnancy', 'avoid_pregnancy', 'become_aroused']
senticnet['reproductive_health_care'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'safe_sex', 'sex', 'prevent_pregnancy', 'avoid_pregnancy', 'become_aroused']
senticnet['reproductive_healthcare'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'safe_sex', 'sex', 'prevent_pregnancy', 'avoid_pregnancy', 'become_aroused']
senticnet['reproductive_output'] = ['0.988', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.98', 'sustain_life', 'sustenance', 'nutrition', 'keep_alive', 'satisfy_hunger']
senticnet['reproductive_physiology'] = ['0.672', '-0.55', '0', '0.992', '#joy', '#admiration', 'positive', '0.739', 'pressor', 'hormone', 'adrenalin', 'internal_secretion', 'catecholamine']
senticnet['reproof'] = ['-0.88', '0', '0.822', '-0.52', '#sadness', '#anger', 'negative', '-0.74', 'bawling_out', 'chewing_out', 'upbraiding', 'dressing_down', 'berating']
senticnet['reproval'] = ['-0.59', '0', '0.741', '-0.58', '#sadness', '#anger', 'negative', '-0.64', 'bawling_out', 'chewing_out', 'upbraiding', 'dressing_down', 'berating']
senticnet['reprove'] = ['-0.72', '0', '0.737', '-0.60', '#sadness', '#anger', 'negative', '-0.68', 'call_on_carpet', 'take_to_task', 'chide', 'berate', 'bawl_out']
senticnet['reproving'] = ['-0.74', '0', '0.982', '-0.81', '#anger', '#disgust', 'negative', '-0.84', 'inauspicious', 'admonishing', 'reproachful', 'unfavorable', 'untoward']
senticnet['reprsentent_le'] = ['0.502', '-0.82', '0', '0.779', '#surprise', '#admiration', 'positive', '0.703', 'mortal', 'person', 'individual', 'free_liver', 'agent']
senticnet['rept'] = ['0.049', '0.067', '0', '0.18', '#interest', '#admiration', 'positive', '0.099', 'static', 'unity', 'large_state', 'frequency', 'preparedness']
senticnet['reptile'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'alligator', 'animal', 'predator', 'meat_eater', 'river']
senticnet['repub_party'] = ['-0.10', '0', '-0.05', '-0.24', '#sadness', '#disgust', 'negative', '-0.13', 'adulate', 'suck_up', 'toady', 'blandish', 'kowtow']
senticnet['republic'] = ['0.041', '0.106', '0', '0.054', '#interest', '#admiration', 'positive', '0.067', 'nation_state', 'democratic_country', 'free_country', 'large_country', 'international']
senticnet['republic_gar'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'fantastic', 'amaze', 'pose', 'wondrous', 'wonderfully']
senticnet['republican'] = ['0', '0.924', '0', '0.921', '#interest', '#admiration', 'positive', '0.923', 'settle_disagreement', 'hitler', 'communist', 'conservative', 'unchanging']
senticnet['republican_candidate'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['republican_challenger'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['republican_congressional'] = ['0', '0.924', '0', '0.921', '#interest', '#admiration', 'positive', '0.923', 'settle_disagreement', 'hitler', 'communist', 'conservative', 'unchanging']
senticnet['republican_congressman'] = ['0', '0.924', '0', '0.921', '#interest', '#admiration', 'positive', '0.923', 'settle_disagreement', 'hitler', 'communist', 'conservative', 'unchanging']
senticnet['republican_congressmen'] = ['0', '0.924', '0', '0.921', '#interest', '#admiration', 'positive', '0.923', 'settle_disagreement', 'hitler', 'communist', 'conservative', 'unchanging']
senticnet['republican_gubernatorial'] = ['0', '0.924', '0', '0.921', '#interest', '#admiration', 'positive', '0.923', 'settle_disagreement', 'hitler', 'communist', 'conservative', 'unchanging']
senticnet['republican_leadership'] = ['0', '0.924', '0', '0.921', '#interest', '#admiration', 'positive', '0.923', 'settle_disagreement', 'hitler', 'communist', 'conservative', 'unchanging']
senticnet['republican_legislator'] = ['0', '0.924', '0', '0.921', '#interest', '#admiration', 'positive', '0.923', 'settle_disagreement', 'hitler', 'communist', 'conservative', 'unchanging']
senticnet['republican_nominee'] = ['0', '0.924', '0', '0.921', '#interest', '#admiration', 'positive', '0.923', 'settle_disagreement', 'hitler', 'communist', 'conservative', 'unchanging']
senticnet['republican_party'] = ['0', '0.924', '0', '0.921', '#interest', '#admiration', 'positive', '0.923', 'settle_disagreement', 'hitler', 'communist', 'conservative', 'unchanging']
senticnet['republican_party_chairman'] = ['0', '0.924', '0', '0.921', '#interest', '#admiration', 'positive', '0.923', 'settle_disagreement', 'hitler', 'communist', 'conservative', 'unchanging']
senticnet['republican_politician'] = ['0', '0.868', '0', '0.853', '#interest', '#admiration', 'positive', '0.861', 'country_leader', 'prime_minister', 'president', 'elect_official', 'leader']
senticnet['republican_senator'] = ['0', '0.924', '0', '0.921', '#interest', '#admiration', 'positive', '0.923', 'settle_disagreement', 'hitler', 'communist', 'conservative', 'unchanging']
senticnet['republican_senator_kit'] = ['0.228', '0.123', '-0.09', '0', '#joy', '#interest', 'positive', '0.084', 'spend_time_with_child', 'social', 'friendship', 'companionship', 'get_close']
senticnet['republicanism'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'ascendance', 'autocracy', 'absolutism', 'tyrant']
senticnet['republicrat'] = ['-0.78', '0', '0.915', '-0.99', '#anger', '#disgust', 'negative', '-0.89', 'bodge', 'botch_up', 'flub', 'ball_up', 'go_wrong']
senticnet['republishing'] = ['-0.75', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.80', 'reprinting', 're-printing', 're-posting', 'reprint', 're-publishing']
senticnet['repudiate'] = ['-0.97', '0', '0.616', '-0.58', '#sadness', '#anger', 'negative', '-0.72', 'abjure', 'recant', 'forswear', 'deny', 'reject']
senticnet['repudiation'] = ['0', '0', '-0.82', '-0.78', '#fear', '#disgust', 'negative', '-0.80', 'abdication', 'apostasy', 'quitclaim', 'reject', 'renunciation']
senticnet['repugnance'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['repugnancy'] = ['-0.97', '0', '0.675', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'infeasibility', 'unfeasibility', 'inutility', 'uselessness', 'unusefulness']
senticnet['repugnant'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'detestably', 'abominably', 'odiously', 'detestable']
senticnet['repuke'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'rudeness', 'discourtesy', 'insolence', 'ccheekiness', 'cheekiness']
senticnet['repulse'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['repulsion'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['repulsive'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repugnant', 'detestably', 'abominably', 'odiously', 'detestable']
senticnet['repulsive_force'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'empty_room', 'a_lot_of_space', 'empty', 'empty_house', 'universe']
senticnet['repulsive_forces_between'] = ['0.538', '-0.72', '0', '0.606', '#surprise', '#admiration', 'positive', '0.622', 'attraction', 'captivate', 'complementary', 'attract', 'magnetism']
senticnet['repulsively'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['reputability'] = ['0.901', '-0.86', '0', '0.842', '#joy', '#surprise', 'positive', '0.869', 'respectability', 'honorableness', 'venerability', 'venerableness', 'righteousness']
senticnet['reputable'] = ['0.991', '-0.98', '0', '0.588', '#joy', '#surprise', 'positive', '0.856', 'estimable', 'respected', 'esteemed', 'honored', 'prestigious']
senticnet['reputation'] = ['0.728', '0.977', '0', '0.939', '#interest', '#admiration', 'positive', '0.881', 'toxic_metal', 'respect', 'dense_air', 'heavy_metal', 'soft_metal']
senticnet['repute'] = ['0.852', '-0.75', '0', '0.763', '#joy', '#admiration', 'positive', '0.789', 'honor', 'laurel', 'fame', 'stock', 'character']
senticnet['request'] = ['0', '0.734', '0', '0', '#interest', '#interest', 'positive', '0.734', 'ask', 'suggest', 'proclamation', 'question_starter', 'tell_why']
senticnet['requested_clarification'] = ['-0.12', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.09', 'understand', 'teach_others', 'trivia', 'notify', 'seek_understanding']
senticnet['requester'] = ['0', '0', '-1', '-0.95', '#fear', '#disgust', 'negative', '-0.97', 'suppliant', 'supplicant', 'petitioner', 'supplicatory', 'prayer']
senticnet['requesting_clarification'] = ['0.932', '0.787', '0', '0', '#joy', '#interest', 'positive', '0.86', 'learn_patience', 'discuss', 'understand_each_other', 'conformist_attitude', 'avoid_conflict']
senticnet['requesting_cu'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['requestor'] = ['0.862', '0.709', '0', '0', '#joy', '#interest', 'positive', '0.785', 'invoice', 'bill', 'check', 'mastercard', 'payment_counter']
senticnet['requiem_shark'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'puffer', 'pufferfish', 'blowfish', 'triggerfish', 'plectognath']
senticnet['require'] = ['0', '0.904', '0', '0.896', '#interest', '#admiration', 'positive', '0.9', 'needful', 'necessary', 'compel', 'requirement', 'strong_desire']
senticnet['require_electricity'] = ['0.066', '0', '0.049', '0.086', '#joy', '#admiration', 'positive', '0.034', 'go_stage', 'perform', 'eye_up_down', 'act_play', 'hear_crowd_go_mad']
senticnet['require_general_anesthesia'] = ['-0.65', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'put_to_sleep', 'operation', 'general_anesthesia', 'surgery', 'anesthesia']
senticnet['require_good_health'] = ['0.939', '0.961', '0', '0', '#joy', '#interest', 'positive', '0.95', 'harvest_fall', 'mall', 'find_mall', 'eat', 'exersice']
senticnet['require_sedation'] = ['-0.65', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'put_to_sleep', 'operation', 'anesthesia', 'surgery', 'local_anesthesia']
senticnet['require_signature'] = ['0', '0.181', '0', '0.055', '#interest', '#admiration', 'positive', '0.118', 'keep_money', 'put_money', 'money_holder', 'store_money', 'sign_contract']
senticnet['requirement'] = ['0.704', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.72', 'use', 'necessary', 'needful', 'need', 'demand']
senticnet['requirement_set_forth'] = ['-0.68', '0', '0.967', '-0.65', '#sadness', '#anger', 'negative', '-0.76', 'depart', 'start', 'part', 'start_out', 'sally_forth']
senticnet['requiring_disclosure'] = ['-0.02', '0.175', '-0.11', '0.148', '#interest', '#admiration', 'positive', '0.045', 'tolerant', 'phrase', 'inquiry', 'explain', 'question']
senticnet['requisite'] = ['-0.99', '0', '0.712', '-0.85', '#sadness', '#disgust', 'negative', '-0.85', 'need', 'obligation', 'requirement', 'desideratum', 'important']
senticnet['requisite_intent'] = ['0.546', '-0.74', '0', '0.581', '#surprise', '#admiration', 'positive', '0.624', 'hand', 'commend', 'reach', 'charge', 'give']
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senticnet['requisite_men_rea'] = ['-0.92', '0', '0.682', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'contrariness', 'wildness', 'intractability', 'intractableness', 'perversity']
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senticnet['rescind'] = ['0', '0', '-0.89', '-0.72', '#fear', '#disgust', 'negative', '-0.81', 'lift', 'countermand', 'revoke', 'reverse', 'annul']
senticnet['rescue'] = ['0', '0', '0', '0.912', '#admiration', '#admiration', 'positive', '0.912', 'defend', 'steal', 'steal_money', 'protect', 'fight_disease']
senticnet['rescue_boat'] = ['0', '-0.02', '0.046', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'sail_ship', 'canoe', 'ship', 'submarine', 'yacht']
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senticnet['rescuer'] = ['0.865', '0', '0', '0.889', '#joy', '#admiration', 'positive', '0.877', 'succorer', 'saver', 'salvager', 'salvor', 'reedemer']
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senticnet['research_component'] = ['0', '0.753', '0', '0.778', '#interest', '#admiration', 'positive', '0.766', 'project', 'research', 'development', 'study', 'award']
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senticnet['resemblance'] = ['-0.88', '0', '0.813', '-0.57', '#sadness', '#anger', 'negative', '-0.75', 'affinity', 'mutual_resemblance', 'alikeness', 'likeness', 'similitude']
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senticnet['resemble_miniature'] = ['-0.05', '0', '-0.12', '-0.16', '#fear', '#disgust', 'negative', '-0.11', 'reduced', 'small', 'small_size', 'narrow', 'poor']
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senticnet['resend'] = ['0.949', '0.866', '0', '0', '#joy', '#interest', 'positive', '0.907', 'snail_mail', 'postage', 'send_package', 'buy_stamp', 'send_letter']
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senticnet['reservation'] = ['0', '0.084', '0', '0', '#interest', '#interest', 'positive', '0.084', 'book_hotel', 'call_restaurant', 'reserve', 'reserve_table', 'reserved']
senticnet['reserve'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['reserve_buoyancy'] = ['0.676', '0.63', '-0.23', '0.31', '#joy', '#interest', 'positive', '0.345', 'perkiness', 'walk_on_air', 'exultingly', 'exultantly', 'jubilance']
senticnet['reserve_chute'] = ['-0.88', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.90', 'undergarment', 'outerwear', 'lady_underwear', 'under_garment', 'slip']
senticnet['reserve_parachute'] = ['-0.88', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.90', 'undergarment', 'outerwear', 'lady_underwear', 'under_garment', 'slip']
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senticnet['reserved'] = ['0', '0.082', '0', '0', '#interest', '#interest', 'positive', '0.082', 'call_restaurant', 'reservation', 'reserve', 'dinner', 'enquiry']
senticnet['reserved_exclusively'] = ['0', '0.851', '0', '0.774', '#interest', '#admiration', 'positive', '0.813', 'join_together', 'event', 'occurrence', 'special_occasion', 'function']
senticnet['reserves'] = ['-0.99', '0', '0.949', '-0.89', '#sadness', '#anger', 'negative', '-0.94', 'force', 'trainband', 'military_unit', 'military_force', 'military_group']
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senticnet['reshape'] = ['0.7', '-0.79', '0', '0.66', '#joy', '#surprise', 'positive', '0.719', 'influence', 'determine', 'shape', 'mold', 'regulate']
senticnet['reshuffle'] = ['-0.98', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.92', 'thrilling', 'electrifying', 'sensational', 'sensationally', 'scandalmongering']
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senticnet['reside_permanently'] = ['0.071', '0.115', '-0.08', '0.167', '#interest', '#admiration', 'positive', '0.066', 'live_inside', 'inhabit', 'live_life', 'protection_weather', 'raise_family']
senticnet['residence'] = ['0.038', '0.098', '0', '0.154', '#interest', '#admiration', 'positive', '0.097', 'live_place', 'home', 'domicile', 'dwell_place', 'domestic_dwell']
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senticnet['resident'] = ['0', '0.715', '0', '0.76', '#interest', '#admiration', 'positive', '0.738', 'tenant', 'ceiling_fan', 'citizen', 'stair_rail', 'roommate']
senticnet['resident_choreographer'] = ['0.12', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.09', 'ballroom', 'perform_art', 'nightclub', 'ballerina', 'venue']
senticnet['resident_conductor'] = ['0.506', '0.659', '-0.24', '0.242', '#joy', '#interest', 'positive', '0.29', 'sing', 'music_studio', 'music_room', 'choir', 'symphony']
senticnet['resident_dj'] = ['0.763', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.75', 'pub', 'disco', 'dance_club', 'cafe', 'tavern']
senticnet['residential_burglary'] = ['-0.75', '0', '0.94', '0', '#sadness', '#anger', 'negative', '-0.84', 'steal_money', 'theft', 'stick_up', 'robbery', 'rob']
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senticnet['residual'] = ['0', '0', '-0.98', '-0.98', '#fear', '#disgust', 'negative', '-0.98', 'potential', 'latent', 'inherent', 'intrinsic', 'innate']
senticnet['residual_chlorine'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
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senticnet['residual_tysabri_royalty'] = ['0.559', '0.656', '-0.26', '0.285', '#joy', '#interest', 'positive', '0.31', 'zealous', 'avidity', 'eagerness', 'avidness', 'thirstily']
senticnet['residual_urine'] = ['-0.77', '0', '0.966', '-0.51', '#sadness', '#anger', 'negative', '-0.75', 'enuresis', 'incontinency', 'incontinence', 'stress_incontinence', 'urge_incontinence']
senticnet['residual_value'] = ['0', '0.238', '0.021', '0.152', '#interest', '#admiration', 'positive', '0.123', 'money_value', 'money_amount', 'worth', 'monetary_amount', 'importance']
senticnet['residuary_estate'] = ['0', '0.179', '-0.05', '0.162', '#interest', '#admiration', 'positive', '0.095', 'house_land', 'real_estate', 'acreage', 'house_place', 'good_investment']
senticnet['residue'] = ['0.059', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.04', 'rest', 'remainder', 'sleep_night', 'take_break', 'use_chair']
senticnet['residuum'] = ['-0.67', '0', '0.863', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'incompressibility', 'substance', 'firmness', 'consistency', 'hardness']
senticnet['resign'] = ['-0.86', '0', '0.838', '0', '#sadness', '#anger', 'negative', '-0.85', 'resignedly', 'abject', 'unhopeful', 'abjectly', 'resignation']
senticnet['resign_amid'] = ['-0.72', '0', '0.98', '0', '#sadness', '#anger', 'negative', '-0.85', 'resignedly', 'abject', 'unhopeful', 'abjectly', 'reconcile']
senticnet['resign_en_masse'] = ['-0.78', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.83', 'break', 'end', 'die', 'fall_apart', 'go_to_piece']
senticnet['resign_voluntarily'] = ['-0.72', '0', '0.98', '0', '#sadness', '#anger', 'negative', '-0.85', 'resignedly', 'abject', 'unhopeful', 'abjectly', 'reconcile']
senticnet['resignation'] = ['-0.72', '0', '0.98', '0', '#sadness', '#anger', 'negative', '-0.85', 'resignedly', 'abject', 'unhopeful', 'abjectly', 'reconcile']
senticnet['resignation_letter'] = ['-0.72', '0', '0.98', '0', '#sadness', '#anger', 'negative', '-0.85', 'resignedly', 'abject', 'unhopeful', 'abjectly', 'reconcile']
senticnet['resigned_abruptly'] = ['-0.72', '0', '0.98', '0', '#sadness', '#anger', 'negative', '-0.85', 'resignedly', 'abject', 'unhopeful', 'abjectly', 'reconcile']
senticnet['resigned_amid'] = ['-0.72', '0', '0.98', '0', '#sadness', '#anger', 'negative', '-0.85', 'resignedly', 'abject', 'unhopeful', 'abjectly', 'reconcile']
senticnet['resigned_en_masse'] = ['-0.72', '0', '0.98', '0', '#sadness', '#anger', 'negative', '-0.85', 'resignedly', 'abject', 'unhopeful', 'abjectly', 'reconcile']
senticnet['resigned_unexpectedly'] = ['-0.72', '0', '0.98', '0', '#sadness', '#anger', 'negative', '-0.85', 'resignedly', 'abject', 'unhopeful', 'abjectly', 'reconcile']
senticnet['resignedly'] = ['-0.73', '0', '0.986', '0', '#sadness', '#anger', 'negative', '-0.85', 'abject', 'unhopeful', 'abjectly', 'resignation', 'reconcile']
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senticnet['resilient'] = ['0', '0.728', '0', '0.707', '#interest', '#admiration', 'positive', '0.718', 'hardy', 'sturdy', 'stalwart', 'durable', 'robust']
senticnet['resilient_packet'] = ['0.052', '0', '0', '0.118', '#joy', '#admiration', 'positive', '0.085', 'diamond_ring', 'piece_of_jewelry', 'bell_sound', 'wedding_band', 'engagement']
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senticnet['resiniferatoxin'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'chemical_irritant', 'gingerol', 'piperine', 'pepper_spray']
senticnet['resinosa'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'locust_tree']
senticnet['resinous_luster'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'harsh', 'common', 'rough-cut', 'uncouth', 'vulgar']
senticnet['resist'] = ['-0.06', '0.076', '-0.05', '0.155', '#interest', '#admiration', 'positive', '0.028', 'hold_out', 'hold_match', 'strike_against', 'put_effort', 'change_plan']
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senticnet['resist_corrosion'] = ['0.8', '-0.90', '0', '0.675', '#joy', '#surprise', 'positive', '0.794', 'indestructibility', 'tensile_strength', 'continuity', 'persistence', 'changelessness']
senticnet['resistance'] = ['-0.78', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.81', 'quarrel', 'dispute', 'fight', 'siege', 'violence']
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senticnet['retaliating_against'] = ['-0.75', '0', '0.981', '-0.58', '#sadness', '#anger', 'negative', '-0.77', 'punitive', 'penitentiary', 'correctional', 'retributive', 'retributory']
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senticnet['retaliation_against'] = ['-0.75', '0', '0.981', '-0.58', '#sadness', '#anger', 'negative', '-0.77', 'punitive', 'penitentiary', 'correctional', 'retributive', 'retributory']
senticnet['retaliatory'] = ['-0.75', '0', '0.981', '-0.58', '#sadness', '#anger', 'negative', '-0.77', 'punitive', 'penitentiary', 'correctional', 'retributive', 'retributory']
senticnet['retaliatory_action'] = ['-0.75', '0', '0.981', '-0.58', '#sadness', '#anger', 'negative', '-0.77', 'punitive', 'penitentiary', 'correctional', 'retributive', 'retributory']
senticnet['retaliatory_action_against'] = ['-0.88', '0', '0.733', '-0.60', '#sadness', '#anger', 'negative', '-0.74', 'vengeance', 'payback', 'retribution', 'reprisal', 'return']
senticnet['retaliatory_attack'] = ['-0.75', '0', '0.981', '-0.58', '#sadness', '#anger', 'negative', '-0.77', 'punitive', 'penitentiary', 'correctional', 'retributive', 'retributory']
senticnet['retaliatory_eviction'] = ['-0.87', '0', '0.765', '-0.82', '#sadness', '#disgust', 'negative', '-0.82', 'due_process', 'due_process_of_law', 'ouster', 'dispossession', 'actual_eviction']
senticnet['retaliatory_strike'] = ['-0.75', '0', '0.981', '-0.58', '#sadness', '#anger', 'negative', '-0.77', 'punitive', 'penitentiary', 'correctional', 'retributive', 'retributory']
senticnet['retaliatory_violence'] = ['-0.88', '0', '0.733', '-0.60', '#sadness', '#anger', 'negative', '-0.74', 'vengeance', 'payback', 'retribution', 'reprisal', 'return']
senticnet['retard'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'put_off', 'delay', 'procrastination', 'postpone', 'lag']
senticnet['retardation'] = ['-0.60', '0', '0.827', '-0.76', '#anger', '#disgust', 'negative', '-0.73', 'slowness', 'mental_amentia', 'backwardness', 'subnormality', 'amentia']
senticnet['retarded'] = ['-0.98', '0', '0.674', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'slow', 'slow_pace', 'stupid', 'slow_car', 'slow_vehicle']
senticnet['retargetable'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'carbon_fiber']
senticnet['retch'] = ['0', '0', '-0.79', '-0.79', '#fear', '#disgust', 'negative', '-0.79', 'puke', 'salivate', 'barf', 'retching', 'vomit']
senticnet['retching'] = ['0', '0', '-0.79', '-0.80', '#fear', '#disgust', 'negative', '-0.79', 'puke', 'salivate', 'barf', 'retch', 'vomit']
senticnet['retell'] = ['0', '0.952', '0', '0', '#interest', '#interest', 'positive', '0.952', 'fable', 'long_story', 'novel', 'saga', 'editorial']
senticnet['retention'] = ['0.105', '0.226', '-0.08', '0.176', '#interest', '#admiration', 'positive', '0.106', 'control', 'hold_onto', 'glass', 'drink_cup', 'make_apple']
senticnet['retention_rate'] = ['0.105', '0.226', '-0.08', '0.176', '#interest', '#admiration', 'positive', '0.106', 'control', 'hold_onto', 'glass', 'drink_cup', 'make_apple']
senticnet['retentive'] = ['0.802', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.869', 'tenacious', 'long', 'dogged', 'dour', 'recollective']
senticnet['retentive_memory'] = ['0.99', '0', '0', '0.716', '#joy', '#admiration', 'positive', '0.853', 'encyclopedism', 'learning', 'scholarship', 'learnedness', 'eruditeness']
senticnet['retentiveness'] = ['0.685', '-0.89', '0', '0.609', '#joy', '#surprise', 'positive', '0.73', 'mental_faculty', 'faculty', 'module', 'remembrance', 'anamnesis']
senticnet['retentivity'] = ['0.965', '-0.84', '0', '0.599', '#joy', '#surprise', 'positive', '0.802', 'mental_faculty', 'faculty', 'module', 'remembrance', 'anamnesis']
senticnet['retest'] = ['0.928', '-0.87', '0', '0.679', '#joy', '#surprise', 'positive', '0.829', 'examine', 'experiment', 'alpha_test', 'beta_test', 'road_test']
senticnet['rethink'] = ['0.755', '0.972', '0', '0.775', '#interest', '#admiration', 'positive', '0.834', 'grasp', 'grab', 'hold', 'sensual', 'grip']
senticnet['rethuglican'] = ['-0.63', '0', '0.712', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'upset', 'disconcert', 'discompose', 'discomfit', 'untune']
senticnet['reticence'] = ['0', '0', '0.928', '-0.70', '#anger', '#disgust', 'negative', '-0.81', 'doubt', 'incertitude', 'dubiety', 'doubtfulness', 'dubiousness']
senticnet['reticent'] = ['-0.77', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.76', 'retiring', 'restrained', 'unemotional', 'untalkative', 'self-effacing']
senticnet['reticulate'] = ['-0.66', '0', '0.809', '-0.61', '#sadness', '#anger', 'negative', '-0.69', 'face', 'base', 'underbelly', 'sole', 'bilge']
senticnet['reticulated_python'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'death_adder', 'ophidian', 'serpent', 'elapid', 'taipan']
senticnet['retinal_degeneration'] = ['-0.82', '0', '0.996', '-0.61', '#sadness', '#anger', 'negative', '-0.81', 'visual_impairment', 'visual_defect', 'vision_defect', 'visual_disorder', 'avitaminosis']
senticnet['retinal_detachment'] = ['-0.86', '0', '0.858', '-0.91', '#sadness', '#disgust', 'negative', '-0.87', 'edema', 'oedema', 'hydrops', 'dropsy', 'eye_disease']
senticnet['retinal_ganglion_cell'] = ['-0.78', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.80', 'deterioration', 'decline', 'decay', 'degradation', 'slide']
senticnet['retinal_image'] = ['0.685', '-0.91', '0', '0.566', '#joy', '#surprise', 'positive', '0.721', 'audibility', 'audibleness', 'visibleness', 'visibility', 'physical_property']
senticnet['retinal_pigment_epithelium'] = ['0.905', '-0.65', '0', '0.851', '#joy', '#admiration', 'positive', '0.805', 'gonococcus', 'bacteria', 'bacteriae', 'probiotic', 'probiotic_bacterium']
senticnet['retinitis_pigmentosa'] = ['-0.82', '0', '0.996', '-0.61', '#sadness', '#anger', 'negative', '-0.81', 'visual_impairment', 'visual_defect', 'vision_defect', 'visual_disorder', 'avitaminosis']
senticnet['retinitis_pigmentosa_rp'] = ['-0.82', '0', '0.996', '-0.61', '#sadness', '#anger', 'negative', '-0.81', 'visual_impairment', 'visual_defect', 'vision_defect', 'visual_disorder', 'avitaminosis']
senticnet['retinopathy'] = ['-0.53', '0', '0.777', '-0.61', '#anger', '#disgust', 'negative', '-0.64', 'eye_disease', 'bad_eyesight', 'macular_degeneration', 'eye_problem', 'disease']
senticnet['retire'] = ['0.078', '0.088', '-0.07', '0', '#joy', '#interest', 'positive', '0.032', 'inactivity', 'recover', 'avoid_accident', 'middle_day', 'need_food']
senticnet['retired_lieutenant_general'] = ['0.74', '-0.53', '0', '0.641', '#joy', '#admiration', 'positive', '0.64', 'marshal', 'sky_marshal', 'lawman', 'law_officer', 'peace_officer']
senticnet['retired_professional_wrestler'] = ['0.052', '0', '0', '0.118', '#joy', '#admiration', 'positive', '0.085', 'diamond_ring', 'piece_of_jewelry', 'bell_sound', 'wedding_band', 'engagement']
senticnet['retirement'] = ['0.706', '0.865', '0', '0', '#joy', '#interest', 'positive', '0.785', 'make_gift', 'new_relationship', 'new_life', 'celebrate_holiday', 'win_lottery']
senticnet['retirement_plan'] = ['0.851', '0.708', '0', '0', '#joy', '#interest', 'positive', '0.779', 'devisee', 'legatee', 'recipient', 'receiver', 'pensioner']
senticnet['retiring'] = ['-0.78', '0', '-0.76', '0', '#sadness', '#fear', 'negative', '-0.77', 'reticent', 'restrained', 'unemotional', 'untalkative', 'self-effacing']
senticnet['retold'] = ['0', '0.952', '0', '0', '#interest', '#interest', 'positive', '0.952', 'fable', 'long_story', 'novel', 'saga', 'editorial']
senticnet['retool'] = ['0.7', '-0.79', '0', '0.66', '#joy', '#surprise', 'positive', '0.719', 'influence', 'determine', 'shape', 'mold', 'regulate']
senticnet['retort'] = ['-0.06', '0.089', '-0.08', '0.236', '#interest', '#admiration', 'positive', '0.044', 'answer_back', 'write_back', 'question_answer', 'response', 'send_back']
senticnet['retrace'] = ['0.606', '-0.90', '0', '0.711', '#surprise', '#admiration', 'positive', '0.739', 'reconstruct', 'hypothesize', 'explicate', 'expect', 'formulate']
senticnet['retract'] = ['0.914', '-0.88', '0', '0.69', '#joy', '#surprise', 'positive', '0.83', 'attract', 'catch', 'arrest', 'tug', 'get']
senticnet['retractable'] = ['0.067', '0.088', '-0.07', '0.246', '#interest', '#admiration', 'positive', '0.081', 'multiple_purposes', 'revolve', 'start_run', 'wiggle', 'twist']
senticnet['retractile'] = ['0.795', '0', '0', '0.996', '#joy', '#admiration', 'positive', '0.895', 'undescended', 'rope-like', 'blade-like', 'whip-like', 'dwarfish']
senticnet['retractor'] = ['-0.58', '0', '0.715', '-0.59', '#anger', '#disgust', 'negative', '-0.63', 'fly', 'overlap', 'barndoor', 'coattail', 'codpiece']
senticnet['retrain'] = ['0', '0.77', '0', '0.789', '#interest', '#admiration', 'positive', '0.78', 're-train', 'retrained', 'retraining', 're-educate', 're-training']
senticnet['retrained'] = ['0', '0.76', '0', '0.782', '#interest', '#admiration', 'positive', '0.771', 're-train', 'retrain', 'retraining', 're-educate', 're-training']
senticnet['retraining'] = ['0', '0.763', '0', '0.787', '#interest', '#admiration', 'positive', '0.775', 're-train', 'retrained', 'retrain', 're-educate', 're-training']
senticnet['retral'] = ['0.505', '-0.57', '0', '0.783', '#surprise', '#admiration', 'positive', '0.621', 'backward', 'reverse', 'retrograde', 'reflexive', 'inverse']
senticnet['retransmission_payload'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'identifiable', 'specifiable', 'recognizable', 'diagnosable', 'classifiable']
senticnet['retreat'] = ['-0.37', '0.11', '-0.21', '-0.06', '#sadness', '#fear', 'negative', '-0.13', 'military_retreat', 'downgrade', 'lose_battle', 'lose_war', 'retrocede']
senticnet['retreive'] = ['0.026', '0.165', '-0.12', '0.292', '#interest', '#admiration', 'positive', '0.09', 'fetch', 'find', 'desire_win', 'take', 'obtain']
senticnet['retrenchment'] = ['0.947', '-0.65', '0', '0.856', '#joy', '#admiration', 'positive', '0.821', 'economy_of_scale', 'saving', 'curtailment', 'downsizing', 'action']
senticnet['retrial'] = ['0.654', '-0.59', '0', '0.774', '#joy', '#admiration', 'positive', '0.673', 'instrument', 'indictment', 'murder_indictment', 'true_bill', 'legal_document']
senticnet['retribution'] = ['-0.37', '0.11', '-0.21', '-0.06', '#sadness', '#fear', 'negative', '-0.13', 'bad_behavior', 'correct_behavior', 'vengeance', 'judge_decision', 'put_in_jail']
senticnet['retributive'] = ['-0.75', '0', '0.981', '-0.58', '#sadness', '#anger', 'negative', '-0.77', 'retaliatory', 'penitentiary', 'correctional', 'punitive', 'retributory']
senticnet['retributory'] = ['-0.75', '0', '0.981', '-0.58', '#sadness', '#anger', 'negative', '-0.77', 'retaliatory', 'penitentiary', 'correctional', 'retributive', 'punitive']
senticnet['retrievable'] = ['-0.91', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.93', 'irrecoverable', 'incurable', 'shipwreck', 'irretrievable', 'irreparable']
senticnet['retrieval'] = ['0.945', '0', '0', '0.717', '#joy', '#admiration', 'positive', '0.831', 'ransom', 'recovery', 'computer_operation', 'reclamation', 'stovepiping']
senticnet['retrieval_cue'] = ['0.067', '-0.2', '0.064', '0', '#joy', '#surprise', 'positive', '0.068', 'wand', 'long_stick', 'branch', 'twig', 'stick']
senticnet['retrieve'] = ['0.026', '0.165', '-0.12', '0.292', '#interest', '#admiration', 'positive', '0.09', 'fetch', 'find', 'desire_win', 'take', 'obtain']
senticnet['retro'] = ['-0.85', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.88', 'kitschy', 'kitsch', 'nostalgic', 'quirky', 'chic']
senticnet['retro_chic'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'elegant', 'stylish', 'smart', 'posh', 'sleek']
senticnet['retro_futuristic'] = ['0.843', '-0.99', '0', '0.706', '#joy', '#surprise', 'positive', '0.849', 'contrivance', 'gimbal', 'injector', 'mod_con', 'device']
senticnet['retro_soul'] = ['-0.95', '0', '0.941', '-0.57', '#sadness', '#anger', 'negative', '-0.82', 'reply', 'response', 'comeback', 'comebacking', 'backtalk']
senticnet['retroactive'] = ['0', '0.887', '0', '0.941', '#interest', '#admiration', 'positive', '0.914', 'retroactively', 'retrospectively', 'reinstated', 'prospectively', 'reinstate']
senticnet['retroactive_application'] = ['-0.50', '0', '0.89', '-0.78', '#anger', '#disgust', 'negative', '-0.72', 'excess', 'degree', 'grade', 'level', 'immoderateness']
senticnet['retroactive_immunity'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'address_book', 'get_rich', 'ladle', 'swiss_army_knife', 'whisk']
senticnet['retroactively'] = ['0', '0.89', '0', '0.95', '#interest', '#admiration', 'positive', '0.92', 'retroactive', 'retrospectively', 'reinstated', 'prospectively', 'reinstate']
senticnet['retroactivity'] = ['-0.50', '0', '0.89', '-0.78', '#anger', '#disgust', 'negative', '-0.72', 'excess', 'degree', 'grade', 'level', 'immoderateness']
senticnet['retrocede'] = ['-0.37', '0.11', '-0.21', '-0.06', '#sadness', '#fear', 'negative', '-0.13', 'military_retreat', 'downgrade', 'lose_battle', 'lose_war', 'retreat']
senticnet['retrocognition'] = ['0', '0.871', '0', '0.861', '#interest', '#admiration', 'positive', '0.866', 'predict_future', 'psychic_phenomenon', 'parapsychology', 'clairvoyance', 'foreknowledge']
senticnet['retrofit'] = ['0.716', '0.701', '0', '0', '#joy', '#interest', 'positive', '0.708', 'retrofitting', 'retrofit', 'retrofitted', 'energy-saving', 'energy-efficient']
senticnet['retrofitted'] = ['0.708', '0.697', '0', '0', '#joy', '#interest', 'positive', '0.702', 'retrofitting', 'retrofit', 'retrofit', 'energy-saving', 'energy-efficient']
senticnet['retrofitting'] = ['0.708', '0.696', '0', '0', '#joy', '#interest', 'positive', '0.702', 'retrofit', 'retrofit', 'retrofitted', 'energy-saving', 'energy-efficient']
senticnet['retroflex'] = ['-0.71', '0', '0.998', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'tone', 'quality', 'twang', 'nasal_twang', 'timber']
senticnet['retrograde'] = ['-0.52', '0', '0.516', '-0.7', '#anger', '#disgust', 'negative', '-0.57', 'retreat', 'pull_back', 'back_up', 'move', 'give_up']
senticnet['retrograde_condensation'] = ['-0.71', '0', '0.509', '-0.92', '#sadness', '#disgust', 'negative', '-0.71', 'natural_process', 'natural_action', 'activity', 'curing', 'congelation']
senticnet['retrograde_ejaculation'] = ['-0.77', '0', '0.966', '-0.51', '#sadness', '#anger', 'negative', '-0.75', 'enuresis', 'incontinency', 'incontinence', 'stress_incontinence', 'urge_incontinence']
senticnet['retrograde_inversion'] = ['0.807', '-0.88', '0', '0.718', '#joy', '#surprise', 'positive', '0.802', 'sex', 'gayness', 'sexual_gayness', 'lesbianism', 'sapphism']
senticnet['retrograde_orbit'] = ['0.989', '-0.90', '0', '0.876', '#joy', '#surprise', 'positive', '0.923', 'reach', 'range', 'compass', 'scope', 'confines']
senticnet['retrogress'] = ['-0.77', '0', '0.785', '-0.72', '#sadness', '#anger', 'negative', '-0.76', 'recede', 'worsen', 'decline', 'drop_off', 'fall_back']
senticnet['retrogression'] = ['-0.94', '0', '0.678', '-0.83', '#sadness', '#disgust', 'negative', '-0.81', 'reversion', 'change_of_state', 'regression', 'regress', 'reversal']
senticnet['retropubic'] = ['-0.70', '0', '0.639', '-0.56', '#sadness', '#anger', 'negative', '-0.63', 'fibroma', 'adenoma', 'chondroma', 'enchondroma', 'benign_tumor']
senticnet['retrospect'] = ['0', '0.117', '-0.06', '0.177', '#interest', '#admiration', 'positive', '0.076', 'start', 'thumb', 'knuckle', 'extend_arm', 'reach']
senticnet['retrospection'] = ['0.08', '0.153', '-0.12', '0.224', '#interest', '#admiration', 'positive', '0.083', 'meditation', 'self_contemplation', 'soul_searching', 'self_examination', 'self_observation']
senticnet['retrospective'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'hardcore', 'declared', 'explicit', 'graphic']
senticnet['retrospective_exhibit'] = ['0.07', '0', '0', '0', '#joy', '#joy', 'positive', '0.07', 'artist', 'artwork', 'sculpture', 'tapestry', 'drawing']
senticnet['retrospectively'] = ['0', '0.885', '0', '0.943', '#interest', '#admiration', 'positive', '0.914', 'retroactive', 'retroactively', 'reinstated', 'prospectively', 'reinstate']
senticnet['retrovert'] = ['-0.9', '0', '0.837', '-0.80', '#sadness', '#anger', 'negative', '-0.84', 'revert', 'return', 'recidivate', 'regress', 'turn_back']
senticnet['retsuden'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amuck', 'murderousness', 'misocainea', 'misogyny']
senticnet['return'] = ['-0.07', '0.354', '-0.21', '0.366', '#interest', '#admiration', 'positive', '0.107', 'receive', 'offer', 'gain', 'excise', 'transfer']
senticnet['return_false'] = ['-0.55', '0', '0.882', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'bugger_all', 'zero', 'naught', 'zip', 'nothing']
senticnet['return_goods'] = ['-0.06', '0.097', '0', '0.076', '#interest', '#admiration', 'positive', '0.036', 'buy_stuff', 'refund', 'buy_goods', 'conversion', 'exchange']
senticnet['return_home'] = ['0.777', '0.769', '0', '0', '#joy', '#interest', 'positive', '0.773', 'drink_espresso', 'think_future', 'hang_picture_on_wall', 'gather_berry', 'relax']
senticnet['return_irr'] = ['0.821', '0.922', '0', '0', '#joy', '#interest', 'positive', '0.871', 'power_plant', 'radiation', 'fire_burn', 'chemical_process', 'combustion']
senticnet['return_null'] = ['-0.55', '0', '0.882', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'bugger_all', 'zero', 'naught', 'zip', 'nothing']
senticnet['return_ticket'] = ['0', '0', '0.014', '-0.11', '#anger', '#disgust', 'negative', '-0.06', 'bus_pass', 'transit_card', 'passenger_ticket', 'train_ticket', 'move_sidewalk']
senticnet['return_undef'] = ['-0.98', '0', '0.869', '-0.80', '#sadness', '#anger', 'negative', '-0.88', 'error', 'programming_error', 'semantic_error', 'runtime_error', 'computer_error']
senticnet['return_work'] = ['0.229', '0.127', '0', '0', '#joy', '#interest', 'positive', '0.178', 'begin_work', 'start_project', 'work_box_office', 'write_program', 'go_work']
senticnet['returning_home'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'apartment', 'furniture', 'kitchen', 'flat']
senticnet['retweet'] = ['0', '0.331', '-0.09', '0.242', '#interest', '#admiration', 'positive', '0.158', 'spread_idea', 'importance', 'tenet', 'culture', 'idea']
senticnet['retype'] = ['0.642', '-0.53', '0', '0.575', '#joy', '#admiration', 'positive', '0.583', 'check', 'assure', 'see', 'insure', 'examine']
senticnet['reunion'] = ['0.119', '-0.13', '-0.06', '0', '#joy', '#surprise', 'positive', '0.063', 'gala', 'celebration', 'special_event', 'special_happen', 'special_moment']
senticnet['reunion_concert'] = ['0.119', '-0.13', '-0.06', '0', '#joy', '#surprise', 'positive', '0.063', 'gala', 'celebration', 'special_event', 'special_happen', 'special_moment']
senticnet['reunion_dinner'] = ['0.119', '-0.13', '-0.06', '0', '#joy', '#surprise', 'positive', '0.063', 'gala', 'celebration', 'special_event', 'special_happen', 'special_moment']
senticnet['reunion_weekend'] = ['0.119', '-0.13', '-0.06', '0', '#joy', '#surprise', 'positive', '0.063', 'gala', 'celebration', 'special_event', 'special_happen', 'special_moment']
senticnet['reunite'] = ['0.119', '-0.13', '-0.06', '0', '#joy', '#surprise', 'positive', '0.063', 'gala', 'celebration', 'special_event', 'special_happen', 'special_moment']
senticnet['reusable'] = ['0.932', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.959', 'operational', 'functional', 'operable', 'available', 'useable']
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senticnet['reward'] = ['0.789', '0.848', '0', '0', '#joy', '#interest', 'positive', '0.819', 'tax', 'payment', 'bribe', 'owe', 'divorce_money']
senticnet['rewarding'] = ['0.929', '0.818', '0', '0', '#joy', '#interest', 'positive', '0.873', 'reinforce', 'repay', 'honor', 'pay_back', 'award']
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senticnet['rgb_color'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'indigo', 'yellow', 'water_color', 'red', 'violet']
senticnet['rgb_color_model'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'indigo', 'yellow', 'water_color', 'red', 'violet']
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senticnet['rhesus_macaque'] = ['0.921', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.825', 'monkey', 'fiance', 'lemur', 'beaver', 'koala']
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senticnet['rhetorical_skill'] = ['0.99', '0', '0', '0.716', '#joy', '#admiration', 'positive', '0.853', 'encyclopedism', 'learning', 'scholarship', 'learnedness', 'eruditeness']
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senticnet['rheumatic_heart_disease'] = ['0.852', '-0.98', '0', '0.67', '#joy', '#surprise', 'positive', '0.837', 'cardiac_arrhythmia', 'arrhythmia', 'cardiomyopathy', 'heart_disease', 'cardiopathy']
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senticnet['rhinestone'] = ['0', '0.075', '0', '0.2', '#interest', '#admiration', 'positive', '0.138', 'stride', 'foot_motion', 'gait', 'foot_action', 'foot_movement']
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senticnet['rhinoceros_beetle'] = ['0.96', '-0.77', '0', '0.929', '#joy', '#admiration', 'positive', '0.887', 'whale_louse', 'beach_flea', 'sand_hopper', 'sandhopper', 'amphipod']
senticnet['rhinoplasty'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
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senticnet['rhizoctonia_solani'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'apple_rust', 'fungus']
senticnet['rhizopogon'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'gymnosperm', 'pteridosperm']
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senticnet['rhombic_triacontahedron'] = ['0.641', '-0.6', '0', '0.957', '#joy', '#admiration', 'positive', '0.733', 'ideal_solid', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'regular_octahedron']
senticnet['rhombicosidodecahedron'] = ['0.82', '-0.65', '0', '0.826', '#joy', '#admiration', 'positive', '0.766', 'regular_tetrahedron', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'regular_octahedron']
senticnet['rhombicuboctahedron'] = ['0.641', '-0.6', '0', '0.957', '#joy', '#admiration', 'positive', '0.733', 'ideal_solid', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'regular_octahedron']
senticnet['rhombus'] = ['0.887', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.921', 'square', 'four_sides', 'rectangle', 'four_corners', 'cube']
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senticnet['rhythm'] = ['0.96', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.932', 'lyric', 'tune', 'melody', 'lyrics', 'song']
senticnet['rhythm_disturbance'] = ['-0.56', '0', '0.849', '-0.61', '#anger', '#disgust', 'negative', '-0.67', 'convulsive', 'unsteady', 'jerky', 'spasmodic', 'spastic']
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senticnet['rhythmic_precision'] = ['-0.54', '0', '0.909', '-0.97', '#anger', '#disgust', 'negative', '-0.81', 'bendability', 'pliancy', 'pliantness', 'flexibility', 'flexibleness']
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senticnet['rhytidectomy'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
senticnet['rib'] = ['-0.96', '0', '-0.84', '0', '#sadness', '#fear', 'negative', '-0.90', 'rib', 'neck', 'ribbed', 'steak', 'knit']
senticnet['rib-tickling'] = ['0.763', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.87', 'side-splitting', 'uproarious', 'gut-busting', 'sidesplitting', 'ribald']
senticnet['rib_cage'] = ['-0.99', '0', '0.871', '-0.55', '#sadness', '#anger', 'negative', '-0.80', 'endure', 'brook', 'tolerate', 'bear_up', 'abide']
senticnet['rib_cartilage'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'socket', 'tooth_socket', 'alveolus', 'acetabulum', 'cotyloid_cavity']
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senticnet['rib_eye_steak'] = ['0.041', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.043', 'meatloaf', 'salad', 'delicious_meal', 'casserole', 'apple_pie']
senticnet['rib_knit'] = ['-0.99', '0', '0.806', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'wear', 'biohazard_suit', 'coverall', 'protective_garment', 'wearable']
senticnet['ribald'] = ['-0.94', '0', '0', '-0.95', '#sadness', '#disgust', 'negative', '-0.95', 'bawdy', 'uproarious', 'side-splitting', 'sardonic', 'off-color']
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senticnet['ribbon_cable'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'computer_part', 'system', 'cpu', 'pencil_pen', 'monitor']
senticnet['ribbon_cutting_ceremony'] = ['0.82', '0', '0', '0.885', '#joy', '#admiration', 'positive', '0.853', 'religious_event', 'sunday', 'happy_event', 'tradition', 'wedding']
senticnet['ribcage'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'socket', 'tooth_socket', 'alveolus', 'acetabulum', 'cotyloid_cavity']
senticnet['ribulose'] = ['-0.52', '0', '0.681', '-0.76', '#anger', '#disgust', 'negative', '-0.65', 'triose', 'tetrose', 'pentose', 'simple_sugar', 'aldose']
senticnet['ricasso'] = ['-0.81', '0', '0.844', '-0.76', '#sadness', '#anger', 'negative', '-0.80', 'hold', 'grip', 'basket_hilt', 'handle', 'handgrip']
senticnet['rice'] = ['0.701', '0', '0', '0', '#joy', '#joy', 'positive', '0.701', 'noodles', 'chicken_rice', 'chinese_food', 'porridge', 'dumpling']
senticnet['rice_beer'] = ['0', '0.815', '0', '0.846', '#interest', '#admiration', 'positive', '0.831', 'sake', 'saki', 'interest', 'right', 'first']
senticnet['rice_bran'] = ['-0.08', '0.106', '0', '0.125', '#interest', '#admiration', 'positive', '0.047', 'sugar', 'treacle', 'black_treacle', 'add_sugar', 'sugar_substitute']
senticnet['rice_cereal'] = ['-0.03', '0.086', '0', '0.076', '#interest', '#admiration', 'positive', '0.042', 'drink_water', 'drink_espresso', 'drink', 'drink_coffee', 'drink_hot_chocolate']
senticnet['rice_husk'] = ['0.037', '0.084', '0', '0', '#joy', '#interest', 'positive', '0.06', 'leave_over', 'leftover', 'cellulose', 'cane', 'remainder']
senticnet['rice_noodle'] = ['0', '-0.03', '0.046', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'pasta', 'green_bean', 'mayonnaise', 'frozen_food', 'sour_milk']
senticnet['rice_rat'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'carbon_monoxide', 'tree_animal']
senticnet['rice_vermicelli'] = ['0', '-0.03', '0.046', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'pasta', 'green_bean', 'mayonnaise', 'frozen_food', 'sour_milk']
senticnet['rice_vinegar'] = ['0', '-0.03', '0.046', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'pasta', 'green_bean', 'mayonnaise', 'frozen_food', 'sour_milk']
senticnet['rice_wine_vinegar'] = ['0.979', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.918', 'make_curry', 'cook_dinner', 'cook_meal', 'cook_curry', 'powder']
senticnet['ricercare'] = ['0.045', '0', '0.033', '0.089', '#joy', '#admiration', 'positive', '0.034', 'spanish', 'vocalization', 'verbal_communication', 'vocal', 'mouth_noise']
senticnet['rich'] = ['0.843', '0.977', '0', '0', '#joy', '#interest', 'positive', '0.91', 'business', 'finance', 'revenue', 'wealth', 'income']
senticnet['rich_alluvial_soil'] = ['0.702', '-0.95', '0', '0.997', '#surprise', '#admiration', 'positive', '0.883', 'fertilizable', 'fecund', 'conceptive', 'impregnable', 'rank']
senticnet['rich_aroma'] = ['0.933', '-0.87', '0', '0.766', '#joy', '#surprise', 'positive', '0.858', 'scent', 'aroma', 'olfactory_property', 'smell', 'odor']
senticnet['rich_array'] = ['0.057', '0', '0', '0.153', '#joy', '#admiration', 'positive', '0.105', 'multiple', 'cute', 'variety', 'hairy', 'large_breadbox']
senticnet['rich_buckler'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'encyclopaedia', 'child_story', 'comic', 'disney_cartoon']
senticnet['rich_cultural_diversity'] = ['-0.05', '0.096', '0', '0.066', '#interest', '#admiration', 'positive', '0.037', 'hue_tint', 'spectrum', 'red_blue', 'green_yellow', 'tint']
senticnet['rich_cultural_heritage'] = ['-0.05', '0.096', '0', '0.066', '#interest', '#admiration', 'positive', '0.037', 'hue_tint', 'spectrum', 'red_blue', 'green_yellow', 'tint']
senticnet['rich_diversity'] = ['0.057', '0', '0', '0.153', '#joy', '#admiration', 'positive', '0.105', 'multiple', 'cute', 'variety', 'hairy', 'large_breadbox']
senticnet['rich_edson'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'kit_fox', 'prairie_fox', 'white_fox']
senticnet['rich_farmland'] = ['0.702', '-0.95', '0', '0.997', '#surprise', '#admiration', 'positive', '0.883', 'fertilizable', 'fecund', 'conceptive', 'impregnable', 'rank']
senticnet['rich_grassland'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'biome', 'timberland']
senticnet['rich_heritage'] = ['-0.05', '0.096', '0', '0.066', '#interest', '#admiration', 'positive', '0.037', 'hue_tint', 'spectrum', 'red_blue', 'green_yellow', 'tint']
senticnet['rich_house'] = ['0.216', '0.154', '-0.05', '0.342', '#joy', '#admiration', 'positive', '0.164', 'own_space', 'nice_house', 'decorate_house', 'comfortable', 'place_to_live']
senticnet['rich_peatland'] = ['0.053', '0.264', '-0.11', '0.23', '#interest', '#admiration', 'positive', '0.107', 'natural_resource', 'petroleum', 'fossil_fuel', 'energy_source', 'potential_energy']
senticnet['rich_person'] = ['0.883', '0.759', '0', '0.718', '#joy', '#interest', 'positive', '0.787', 'management', 'ambassador', 'supervisor', 'servant', 'master']
senticnet['rich_quick'] = ['0.685', '-0.58', '0', '0.56', '#joy', '#surprise', 'positive', '0.608', 'profit', 'net_income', 'net', 'net_profit', 'lucre']
senticnet['rich_swann'] = ['-0.57', '0', '0.571', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['rich_tapestry'] = ['-0.05', '0.096', '0', '0.066', '#interest', '#admiration', 'positive', '0.037', 'hue_tint', 'spectrum', 'red_blue', 'green_yellow', 'tint']
senticnet['richardsonian_romanesque'] = ['0.578', '-0.95', '0', '0.979', '#surprise', '#admiration', 'positive', '0.838', 'perpendicular', 'perpendicular_style', 'gothic_architecture', 'style_of_architecture', 'type_of_architecture']
senticnet['richardsonian_romanesque_style'] = ['0.578', '-0.95', '0', '0.979', '#surprise', '#admiration', 'positive', '0.838', 'perpendicular', 'perpendicular_style', 'gothic_architecture', 'style_of_architecture', 'type_of_architecture']
senticnet['riche_en'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'indweller', 'invillager', 'villager', 'dweller', 'denizen']
senticnet['richer'] = ['0.843', '0.977', '0', '0', '#joy', '#interest', 'positive', '0.91', 'business', 'finance', 'revenue', 'wealth', 'income']
senticnet['riches'] = ['0.068', '0.171', '0', '0.117', '#interest', '#admiration', 'positive', '0.119', 'wealthy', 'richness', 'much_money', 'slogan', 'immoral']
senticnet['richest_man'] = ['0.976', '0.811', '0', '0', '#joy', '#interest', 'positive', '0.893', 'deposit', 'bank_loan', 'wealth', 'finance', 'account']
senticnet['richest_men'] = ['0.976', '0.811', '0', '0', '#joy', '#interest', 'positive', '0.893', 'deposit', 'bank_loan', 'wealth', 'finance', 'account']
senticnet['richest_woman'] = ['0.976', '0.811', '0', '0', '#joy', '#interest', 'positive', '0.893', 'deposit', 'bank_loan', 'wealth', 'finance', 'account']
senticnet['richie_barnett'] = ['0.997', '-0.84', '0', '0.927', '#joy', '#admiration', 'positive', '0.923', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['richly'] = ['0.843', '0.977', '0', '0', '#joy', '#interest', 'positive', '0.91', 'business', 'finance', 'revenue', 'wealth', 'income']
senticnet['richly_comic'] = ['-0.77', '0', '0.537', '-0.64', '#sadness', '#disgust', 'negative', '-0.65', 'grim', 'saturnine', 'sardonic', 'sarcastic', 'black']
senticnet['richly_deserve'] = ['0.762', '0', '0', '0.741', '#joy', '#admiration', 'positive', '0.752', 'noteworthy', 'valuable', 'applaudable', 'meritable', 'sacred']
senticnet['richly_diverse'] = ['0.057', '0', '0', '0.153', '#joy', '#admiration', 'positive', '0.105', 'multiple', 'cute', 'variety', 'hairy', 'large_breadbox']
senticnet['richness'] = ['0.144', '0.266', '-0.05', '0.269', '#interest', '#admiration', 'positive', '0.157', 'riches', 'slogan', 'valuable', 'immoral', 'extra_money']
senticnet['rickettsia_conorii'] = ['0.995', '-0.71', '0', '0.647', '#joy', '#surprise', 'positive', '0.784', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['rickettsia_prowazekii'] = ['-0.73', '0', '0.599', '-0.96', '#sadness', '#disgust', 'negative', '-0.76', 'spotted_fever', 'rickettsial_disease', 'rickettsialpox', 'typhus', 'typhus_fever']
senticnet['rickettsia_rickettsii'] = ['0.995', '-0.71', '0', '0.647', '#joy', '#surprise', 'positive', '0.784', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['rickettsial_disease'] = ['-0.73', '0', '0.599', '-0.96', '#sadness', '#disgust', 'negative', '-0.76', 'spotted_fever', 'trench_fever', 'rickettsialpox', 'typhus', 'typhus_fever']
senticnet['rickettsialpox'] = ['-0.73', '0', '0.599', '-0.96', '#sadness', '#disgust', 'negative', '-0.76', 'spotted_fever', 'trench_fever', 'rickettsial_disease', 'typhus', 'typhus_fever']
senticnet['rickettsiosis'] = ['-0.58', '0', '0.797', '-0.93', '#anger', '#disgust', 'negative', '-0.77', 'spotted_fever', 'trench_fever', 'rickettsialpox', 'typhus', 'typhus_fever']
senticnet['rickety'] = ['-0.86', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.84', 'dilapidated', 'decrepit', 'broken-down', 'beat-up', 'worn-out']
senticnet['rickety_old'] = ['0', '0', '0.943', '-0.8', '#anger', '#disgust', 'negative', '-0.87', 'feel_dirty', 'unclean', 'dirty', 'yucky', 'filthy']
senticnet['rickinghall'] = ['0.623', '-0.56', '0', '0.974', '#joy', '#admiration', 'positive', '0.721', 'alterable', 'convertible', 'translatable', 'transformable', 'transmutable']
senticnet['rickshaw'] = ['-0.80', '0', '0.718', '-0.79', '#sadness', '#disgust', 'negative', '-0.77', 'walk_exercise', 'lose_wallet', 'go_debt', 'pay_dinner', 'spend_much_money']
senticnet['rickshaw_driver'] = ['-0.80', '0', '0.718', '-0.79', '#sadness', '#disgust', 'negative', '-0.77', 'walk_exercise', 'lose_wallet', 'go_debt', 'pay_dinner', 'spend_much_money']
senticnet['rico_conspiracy'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'fraud', 'swindle', 'rig']
senticnet['rico_petrocelli'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'dicky', 'impaired', 'lessened', 'diminished']
senticnet['ricochet'] = ['0.757', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.831', 'baseball_bat', 'water_gun', 'goalie', 'hockey_puck', 'hockey_stick']
senticnet['ricochet_off'] = ['0.793', '0.915', '0', '0', '#joy', '#interest', 'positive', '0.854', 'ball', 'ball_bounce', 'football', 'soccer', 'basketball']
senticnet['ricocheted'] = ['0', '0.75', '0', '0.836', '#interest', '#admiration', 'positive', '0.793', 'harmlessly', 'deflected', 'bounced', 'crossbar', 'uselessly']
senticnet['ricochets_off'] = ['0.793', '0.915', '0', '0', '#joy', '#interest', 'positive', '0.854', 'ball', 'ball_bounce', 'football', 'soccer', 'basketball']
senticnet['ricotta_cheese'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'grapefruit_peel', 'lemon_peel', 'orange_peel', 'candied_citrus_peel', 'succade']
senticnet['ricotta_cheesecake'] = ['0.092', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.071', 'bake_treat', 'round_pastry', 'bake_good', 'bake_goods', 'sweet_food']
senticnet['rictus_grin'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'simper', 'please', 'content', 'satisfy', 'gratify']
senticnet['rid'] = ['0.712', '-0.85', '0', '0.67', '#joy', '#surprise', 'positive', '0.745', 'smooth_out', 'clear', 'relieve', 'smooth', 'disembody']
senticnet['rid_of'] = ['-0.98', '0', '0.383', '-0.93', '#sadness', '#disgust', 'negative', '-0.77', 'lose', 'destroy', 'rid', 'defeat', 'kill']
senticnet['riddle'] = ['0.967', '-0.76', '0', '0.576', '#joy', '#surprise', 'positive', '0.771', 'diffuse', 'spiritize', 'permeate', 'pervade', 'penetrate']
senticnet['riddled_with'] = ['-0.13', '0.071', '0.694', '-0.67', '#anger', '#disgust', 'negative', '-0.35', 'crucify', 'dun', 'frustrating', 'aggravate', 'exasperation']
senticnet['ride'] = ['0.885', '0.838', '0', '0', '#joy', '#interest', 'positive', '0.861', 'transport', 'trucking', 'connect_city', 'transportation', 'car_travel']
senticnet['ride_airplane'] = ['0.748', '0.843', '0', '0', '#joy', '#interest', 'positive', '0.795', 'sit_airplane', 'work_pile_up', 'traveling_long_distance', 'come_back_home', 'fall_asleep']
senticnet['ride_barb'] = ['0', '0', '0', '0.062', '#admiration', '#admiration', 'positive', '0.062', 'two_barbs', 'barb', 'mule', 'buddhist', 'horse']
senticnet['ride_bicycle'] = ['-0.02', '0', '0', '-0.11', '#sadness', '#disgust', 'negative', '-0.07', 'car', 'police_car', 'traffic_cop', 'bomber', 'lorry']
senticnet['ride_bicycle_work'] = ['-0.08', '0.089', '0', '0.097', '#interest', '#admiration', 'positive', '0.034', 'shout_loudly', 'take_hike', 'stop_smoke', 'scream_real_loud', 'break_record']
senticnet['ride_bus'] = ['0.71', '0.868', '0', '0', '#joy', '#interest', 'positive', '0.789', 'bus', 'board_bus', 'ride', 'catch_bus', 'ride_airplane']
senticnet['ride_car'] = ['0.11', '0', '0', '0.095', '#joy', '#admiration', 'positive', '0.103', 'play_fetch', 'chew_bone', 'dog', 'play_human', 'run']
senticnet['ride_comfort'] = ['-0.53', '0', '0.765', '-0.94', '#anger', '#disgust', 'negative', '-0.74', 'jaggedness', 'unregularity', 'unevenness', 'irregularity', 'patchiness']
senticnet['ride_horse'] = ['0', '0', '0', '0.071', '#admiration', '#admiration', 'positive', '0.071', 'two_horses', 'horse', 'mule', 'buddhist', 'barb']
senticnet['ride_motorbike'] = ['-0.04', '0', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.06', 'five_fingers', 'palm', 'arm_part', 'five_digits', 'wear_glove']
senticnet['ride_roller_coaster'] = ['-0.18', '0', '-0.14', '-0.11', '#sadness', '#fear', 'negative', '-0.14', 'fly_kite', 'walk_dog', 'skateboard', 'rake_leave', 'jump_up_down']
senticnet['ride_subway'] = ['-0.05', '0.176', '-0.06', '0.125', '#interest', '#admiration', 'positive', '0.044', 'bus_ride', 'buy_vending_machine', 'take_trip', 'ride_train', 'acquire']
senticnet['ride_train'] = ['-0.05', '0.176', '-0.06', '0.125', '#interest', '#admiration', 'positive', '0.044', 'bus_ride', 'buy_vending_machine', 'take_trip', 'ride_subway', 'acquire']
senticnet['ride_work'] = ['0.045', '0.102', '0', '0', '#joy', '#interest', 'positive', '0.073', 'save_time', 'enjoy_food', 'enjoy_view', 'meet_new_friend', 'speed_up']
senticnet['rideable'] = ['-0.99', '0', '0.641', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'navigable', 'negotiable', 'surmountable', 'climbable', 'passable']
senticnet['rider'] = ['0.969', '0.894', '0', '0', '#joy', '#interest', 'positive', '0.931', 'pertinent', 'relevant', 'hydride', 'germane', 'applicable']
senticnet['rider_kick'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
senticnet['ridge'] = ['0.029', '0.096', '0.013', '0', '#joy', '#interest', 'positive', '0.037', 'knoll', 'steep', 'hill', 'land_form', 'mountain']
senticnet['ridge_tillage'] = ['0.689', '-0.56', '0', '0.74', '#joy', '#admiration', 'positive', '0.665', 'crease', 'crinkle', 'wrinkle', 'heart_line', 'seam']
senticnet['ridgeback'] = ['-0.73', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.82', 'minor_planet', 'dwarf_planet', 'insure', 'human_memory', 'competitive_activity']
senticnet['ridgelike'] = ['0.03', '0', '0.017', '0.138', '#joy', '#admiration', 'positive', '0.05', 'up_down', 'curl', 'bump', 'hand_gesture', 'goodbye']
senticnet['ridicule'] = ['0', '0', '0.808', '0', '#anger', '#anger', 'negative', '-0.80', 'feel_hurt', 'hurt_feeling', 'get_hurt', 'feel_bad', 'feel_guilty']
senticnet['ridiculous'] = ['-0.38', '-0.08', '0', '-0.34', '#sadness', '#disgust', 'negative', '-0.21', 'terminal_illness', 'ill_health', 'car_wreck', 'meanness', 'bad_hair_day']
senticnet['ridiculously'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['ridiculously_cheap'] = ['-0.04', '0.201', '-0.06', '0.312', '#interest', '#admiration', 'positive', '0.101', 'save_money', 'good_price', 'inexpensive', 'bargain', 'discount']
senticnet['ridiculously_expensive'] = ['-0.04', '0.201', '-0.06', '0.312', '#interest', '#admiration', 'positive', '0.101', 'save_money', 'good_price', 'inexpensive', 'bargain', 'discount']
senticnet['riding_accident'] = ['-0.73', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.79', 'die_painfully', 'arrest_police', 'get_accident', 'fall_head', 'skin_knee']
senticnet['riding_crop'] = ['-0.24', '0', '-0.19', '0', '#sadness', '#fear', 'negative', '-0.22', 'mock', 'prison_sentence', 'wrongdoing', 'jail_time', 'violent_action']
senticnet['riding_habit'] = ['-0.84', '0', '0.633', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'grizzle', 'postiche', 'horsehair_wig', 'wig', 'peruke']
senticnet['riding_horseback'] = ['0.064', '0', '0', '0.123', '#joy', '#admiration', 'positive', '0.093', 'horse', 'passenger_car', 'car_action', 'conveyance', 'car_travel']
senticnet['riding_mower'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'mop_floor', 'absorb_moisture', 'sit_on_beach', 'scrub_floor', 'cut_grass']
senticnet['riding_pillion'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'fuel', 'car', 'fossil_fuel', 'transport', 'natural_resource']
senticnet['riedell'] = ['0.914', '-0.56', '0', '0.869', '#joy', '#admiration', 'positive', '0.781', 'tabula_rasa', 'ego', 'subconscious', 'unconscious', 'psyche']
senticnet['riemannian_metric'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'width', 'size', 'mass_measure', 'measure', 'gauge']
senticnet['rifat'] = ['0.798', '-0.51', '0', '0.973', '#joy', '#admiration', 'positive', '0.763', 'inexperienced_person', 'boy_scout', 'dear', 'virgin', 'mortal']
senticnet['rife'] = ['0', '0', '-0.91', '-0.73', '#fear', '#disgust', 'negative', '-0.82', 'overabundant', 'prevailing', 'plethoric', 'predominant', 'dominant']
senticnet['riffage'] = ['0.26', '0', '0', '0.089', '#joy', '#admiration', 'positive', '0.174', 'balcony_rail', 'movie_theater_seat', 'row_seat', 'usher', 'sticky_floor']
senticnet['riffy'] = ['0.584', '-0.62', '0', '0.815', '#surprise', '#admiration', 'positive', '0.675', 'squiggle', 'hand', 'cacography', 'handwriting', 'script']
senticnet['rifle'] = ['0', '0', '0.954', '-0.74', '#anger', '#disgust', 'negative', '-0.85', 'gun', 'pistol', 'revolver', 'firearm', 'handgun']
senticnet['rifle_ammunition'] = ['0', '0', '-0.92', '-0.91', '#fear', '#disgust', 'negative', '-0.92', 'automatic', 'firearm', 'gun', 'weapon', 'projectile']
senticnet['rifle_barrel'] = ['-0.97', '0', '0.677', '-0.95', '#sadness', '#disgust', 'negative', '-0.86', 'stunt', 'game', 'mutant', 'invertebrate', 'animate_being']
senticnet['rifle_battalion'] = ['-0.9', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.9', 'withdrawal', 'indifferently', 'apathetic', 'indifferent', 'concerned']
senticnet['rifle_cartridge'] = ['-0.97', '0', '0.677', '-0.95', '#sadness', '#disgust', 'negative', '-0.86', 'stunt', 'game', 'mutant', 'invertebrate', 'animate_being']
senticnet['rifle_fire'] = ['0', '0', '-0.85', '-0.99', '#fear', '#disgust', 'negative', '-0.92', 'onslaught', 'battery', 'shelling', 'bombardment', 'outpouring']
senticnet['rifle_grenade'] = ['0', '0', '0.818', '-0.99', '#anger', '#disgust', 'negative', '-0.90', 'weapon', 'aerial_bomb', 'machine_gun', 'cannon', 'handgun']
senticnet['rifle_marksman'] = ['0.93', '-0.95', '0', '0.909', '#joy', '#surprise', 'positive', '0.93', 'acquirement', 'skill', 'accomplishment', 'acquisition', 'attainment']
senticnet['rifle_marksmanship'] = ['0.93', '-0.95', '0', '0.909', '#joy', '#surprise', 'positive', '0.93', 'acquirement', 'skill', 'accomplishment', 'acquisition', 'attainment']
senticnet['rifle_platoon'] = ['0', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.09', 'army', 'attacker', 'military', 'soldier', 'infantry']
senticnet['rifle_range'] = ['0.93', '-0.95', '0', '0.909', '#joy', '#surprise', 'positive', '0.93', 'acquirement', 'skill', 'accomplishment', 'acquisition', 'attainment']
senticnet['rifled_barrel'] = ['-0.97', '0', '0.677', '-0.95', '#sadness', '#disgust', 'negative', '-0.86', 'stunt', 'game', 'mutant', 'invertebrate', 'animate_being']
senticnet['rifleman'] = ['0.791', '-0.87', '0', '0.915', '#surprise', '#admiration', 'positive', '0.861', 'crack_shot', 'deadeye', 'sniper', 'shot', 'shooter']
senticnet['riflery'] = ['0.93', '-0.95', '0', '0.909', '#joy', '#surprise', 'positive', '0.93', 'acquirement', 'skill', 'accomplishment', 'acquisition', 'attainment']
senticnet['rift_valley_fever'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'leptomeningitis', 'meningitis', 'epidemic_meningitis', 'brain_fever', 'cerebrospinal_fever']
senticnet['rift_valley_fever_rvf'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'leptomeningitis', 'meningitis', 'epidemic_meningitis', 'brain_fever', 'cerebrospinal_fever']
senticnet['riftwar'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'cur', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['riftwar_cycle'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'cur', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['riftwar_saga'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'cur', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['rig'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'mail_fraud', 'swindle', 'fraud']
senticnet['rigamarole'] = ['-0.92', '0', '0.648', '-0.83', '#sadness', '#disgust', 'negative', '-0.80', 'meaninglessness', 'nonsense', 'bunk', 'nonsensicality', 'hokum']
senticnet['rigatoni'] = ['0.043', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'apple_pie', 'a_lot_of_food', 'jelly', 'frozen_food', 'cottage_cheese']
senticnet['rigaudon'] = ['0.591', '-0.91', '0', '0.605', '#surprise', '#admiration', 'positive', '0.704', 'farandole', 'folk_dance', 'polka', 'dance', 'typical_dance']
senticnet['rigger'] = ['0.757', '0', '0', '0.93', '#joy', '#admiration', 'positive', '0.844', 'influence', 'pry', 'pivot', 'leverage', 'advantage']
senticnet['riggu'] = ['-0.98', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.9', 'jailbird', 'prisoner', 'imprisoned', 'convict', 'inmate']
senticnet['right'] = ['0.961', '0.916', '0', '0.901', '#joy', '#interest', 'positive', '0.926', 'good', 'correct', 'band_together', 'song_title', 'plus']
senticnet['right-down'] = ['0', '0', '-0.75', '-0.87', '#fear', '#disgust', 'negative', '-0.81', 'absolute', 'out-and-out', 'rank', 'downright', 'sheer']
senticnet['right-footed'] = ['0.803', '-0.95', '0', '0', '#joy', '#surprise', 'positive', '0.881', 'smoke', 'skillfully', 'left-handed', 'skillful', 'handily']
senticnet['right-hand'] = ['0', '0.811', '0', '0.94', '#interest', '#admiration', 'positive', '0.875', 'leftmost', 'left-most', 'right-most', 'rightmost', 'left-hand']
senticnet['right-most'] = ['0', '0.807', '0', '0.944', '#interest', '#admiration', 'positive', '0.875', 'leftmost', 'left-most', 'rightmost', 'right-hand', 'left-hand']
senticnet['right-thinking'] = ['0', '-0.78', '0', '0.722', '#surprise', '#admiration', 'positive', '0.754', 'self-respecting', 'red-blooded', 'fair-minded', 'god-fearing', 'die-hard']
senticnet['right_achilles_tendon'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'deltoid', 'muscle', 'abductor', 'skeletal_muscle']
senticnet['right_analog_stick'] = ['-0.03', '0.11', '0', '0.128', '#interest', '#admiration', 'positive', '0.069', 'freestyle', 'take_leak', 'jump_air', 'backstroke', 'go_underwater']
senticnet['right_angle'] = ['0.049', '0.088', '0.071', '0.078', '#interest', '#admiration', 'positive', '0.036', 'part_of_room', 'corner', 'sharpness', 'side', 'ninety_degree']
senticnet['right_angled_triangle'] = ['0.763', '-0.61', '0', '0.777', '#joy', '#admiration', 'positive', '0.717', 'acute_triangle', 'wedge_shape', 'cuneus', 'wedge', 'oblique_triangle']
senticnet['right_ankle'] = ['-0.83', '0', '0.509', '-0.84', '#sadness', '#disgust', 'negative', '-0.72', 'wrench', 'injury', 'harm', 'hurt', 'trauma']
senticnet['right_arm'] = ['0', '0', '-0.06', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'bandit', 'arm', 'wrist', 'five_fingers', 'bendable']
senticnet['right_atrial'] = ['0.777', '-0.89', '0', '0.508', '#joy', '#surprise', 'positive', '0.727', 'stretching', 'enlarge', 'enlargement', 'get_bigger', 'distension']
senticnet['right_clavicle'] = ['-0.12', '-0.04', '0', '-0.17', '#sadness', '#disgust', 'negative', '-0.08', 'cause_bleed', 'open_wind', 'gash', 'lacerate', 'painful_injury']
senticnet['right_direction'] = ['0', '0.21', '-0.11', '0.345', '#interest', '#admiration', 'positive', '0.147', 'direction', 'unto', 'forehead', 'upon', 'go_toward']
senticnet['right_elbow'] = ['0', '0', '-0.06', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'bandit', 'arm', 'wrist', 'five_fingers', 'bendable']
senticnet['right_fielder'] = ['0', '0.075', '0', '0', '#interest', '#interest', 'positive', '0.075', 'cup', 'carafe', 'container_cup', 'mug', 'glasses']
senticnet['right_forearm'] = ['0', '0', '-0.06', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'bandit', 'arm', 'wrist', 'five_fingers', 'bendable']
senticnet['right_hand'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'bayonet', 'spear', 'shotgun', 'bullet', 'hydrogen_bomb']
senticnet['right_handed_batter'] = ['0', '0.075', '0', '0', '#interest', '#interest', 'positive', '0.075', 'cup', 'carafe', 'container_cup', 'mug', 'glasses']
senticnet['right_handed_pitcher'] = ['0', '0.075', '0', '0', '#interest', '#interest', 'positive', '0.075', 'cup', 'carafe', 'container_cup', 'mug', 'glasses']
senticnet['right_handed_starting_pitcher'] = ['0', '0.075', '0', '0', '#interest', '#interest', 'positive', '0.075', 'cup', 'carafe', 'container_cup', 'mug', 'glasses']
senticnet['right_hander'] = ['0', '0.075', '0', '0', '#interest', '#interest', 'positive', '0.075', 'cup', 'carafe', 'container_cup', 'mug', 'glasses']
senticnet['right_index_finger'] = ['0', '0', '-0.06', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'bandit', 'arm', 'wrist', 'five_fingers', 'bendable']
senticnet['right_injustice'] = ['-0.85', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.78', 'raise_flag', 'bridge_difference', 'arm', 'market_product', 'deal_card_player']
senticnet['right_knee'] = ['-0.83', '0', '0.509', '-0.84', '#sadness', '#disgust', 'negative', '-0.72', 'wrench', 'injury', 'harm', 'hurt', 'trauma']
senticnet['right_middle_finger'] = ['0', '0', '-0.06', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'bandit', 'arm', 'wrist', 'five_fingers', 'bendable']
senticnet['right_mouse_click'] = ['0.055', '0.117', '0', '0.133', '#interest', '#admiration', 'positive', '0.102', 'graphical_representation', 'pencil_pen', 'note_pad', 'marker', 'stationery']
senticnet['right_now'] = ['0', '0.885', '0', '0.889', '#interest', '#admiration', 'positive', '0.887', 'now_time', 'immediately', 'contemporary', 'timely', 'time']
senticnet['right_of_way'] = ['0.621', '-0.60', '0', '0.545', '#joy', '#surprise', 'positive', '0.59', 'privilege', 'perquisite', 'public_easement', 'easement', 'prerogative']
senticnet['right_pane'] = ['0.055', '0.117', '0', '0.133', '#interest', '#admiration', 'positive', '0.102', 'graphical_representation', 'pencil_pen', 'note_pad', 'marker', 'stationery']
senticnet['right_parietal_lobe'] = ['-0.73', '0', '0.955', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'visual_agnosia', 'auditory_agnosia', 'brain_disorder', 'encephalopathy', 'brain_disease']
senticnet['right_rear_tire'] = ['-0.10', '-0.07', '0.032', '-0.15', '#sadness', '#disgust', 'negative', '-0.05', 'crash_tree', 'press_accelerator', 'stop_traffic_light', 'accident', 'tire_puncture']
senticnet['right_rear_tyre'] = ['0', '0', '0.873', '-0.92', '#anger', '#disgust', 'negative', '-0.89', 'punch', 'sting', 'disable', 'ache', 'vex']
senticnet['right_ring_finger'] = ['0', '0', '-0.06', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'bandit', 'arm', 'wrist', 'five_fingers', 'bendable']
senticnet['right_shoulder'] = ['0', '0', '-0.06', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'bandit', 'arm', 'wrist', 'five_fingers', 'bendable']
senticnet['right_shoulder_blade'] = ['0', '0', '0', '-0.93', '#disgust', '#disgust', 'negative', '-0.93', 'injury', 'broken_bone', 'bad_knee', 'strengthful', 'physical_pain']
senticnet['right_shoulder_injury'] = ['-0.78', '0', '0.554', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'rubor', 'redness', 'inflammation', 'tennis_elbow', 'lateral_epicondylitis']
senticnet['right_tdr'] = ['-0.1', '0.273', '-0.09', '0.219', '#interest', '#admiration', 'positive', '0.074', 'money_exchange', 'money_transfer', 'financial_transaction', 'owe_money', 'money']
senticnet['right_temporal_lobe'] = ['-0.78', '0', '0.669', '-0.89', '#sadness', '#disgust', 'negative', '-0.78', 'pain_sensation', 'painful_sensation', 'prickling', 'tingling', 'pain']
senticnet['right_testicle'] = ['0.087', '-0.03', '0', '0.063', '#joy', '#admiration', 'positive', '0.062', 'puppet', 'bouncy', 'curly', 'throw_object', 'glove_leather']
senticnet['right_thigh'] = ['0', '0', '-0.06', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'bandit', 'arm', 'wrist', 'five_fingers', 'bendable']
senticnet['right_uppercut'] = ['0.969', '0.998', '0', '0', '#joy', '#interest', 'positive', '0.984', 'bell_sound', 'bell_noise', 'piece_of_jewelry', 'syllable', 'fastener']
senticnet['right_ventricular'] = ['0.777', '-0.89', '0', '0.508', '#joy', '#surprise', 'positive', '0.727', 'stretching', 'enlarge', 'enlargement', 'get_bigger', 'distension']
senticnet['right_ventricular_function'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'blatancy', 'obviousness']
senticnet['right_ventricular_hypertrophy'] = ['0.777', '-0.89', '0', '0.508', '#joy', '#surprise', 'positive', '0.727', 'stretching', 'enlarge', 'enlargement', 'get_bigger', 'distension']
senticnet['right_whale'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'lordosis', 'kyphosis', 'spinal_curvature', 'hunchback', 'abnormality']
senticnet['right_wing_agenda'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'rudeness', 'discourtesy', 'insolence', 'ccheekiness', 'cheekiness']
senticnet['right_wing_extremism'] = ['-0.81', '-0.71', '0.843', '-0.72', '#sadness', '#anger', 'negative', '-0.41', 'lack_of_rules', 'fascism', 'despot', 'tyrant', 'anarchism']
senticnet['right_wing_faction'] = ['-0.50', '0', '0.989', '-0.91', '#anger', '#disgust', 'negative', '-0.80', 'camp', 'pack', 'clique', 'coterie', 'ingroup']
senticnet['right_wing_ideology'] = ['0.633', '-0.92', '0', '0.869', '#surprise', '#admiration', 'positive', '0.808', 'advocate', 'bigot', 'dogmatist', 'partisan', 'advocator']
senticnet['right_wing_nationalist'] = ['-0.50', '0', '0.989', '-0.91', '#anger', '#disgust', 'negative', '-0.80', 'camp', 'pack', 'clique', 'coterie', 'ingroup']
senticnet['right_wrist'] = ['0', '0', '-0.06', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'bandit', 'arm', 'wrist', 'five_fingers', 'bendable']
senticnet['right_wrong'] = ['0.889', '0.885', '0', '0', '#joy', '#interest', 'positive', '0.887', 'weigh_consequence', 'make_plan', 'good_plan', 'vengeance', 'plan_carefully']
senticnet['righteous_anger'] = ['0', '0', '1', '0', '#anger', '#anger', 'negative', '-1', 'furious', 'raging', 'stormy', 'roaring', 'wrathful']
senticnet['righteous_indignation'] = ['-0.57', '0', '0.79', '-0.89', '#anger', '#disgust', 'negative', '-0.75', 'abuse', 'vilification', 'insult', 'revilement', 'contumely']
senticnet['righteous_judgment'] = ['0.879', '0', '0', '0.986', '#joy', '#admiration', 'positive', '0.932', 'mercifully', 'merciful', 'ruth', 'commiseration', 'commiserate']
senticnet['righteous_man'] = ['0.71', '0.882', '0', '0.9', '#interest', '#admiration', 'positive', '0.831', 'positive_reinforcement', 'brownie_point', 'benefit', 'give', 'gratification']
senticnet['righteousness'] = ['0.998', '-0.89', '0', '0.667', '#joy', '#surprise', 'positive', '0.853', 'honorableness', 'honorable', 'morality', 'rectitude', 'honor']
senticnet['rightful'] = ['0', '0.82', '0', '0.784', '#interest', '#admiration', 'positive', '0.802', 'true', 'rule-governed', 'legitimate', 'licit', 'lawful']
senticnet['rightful_role'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'baddie', 'theatrical_baddie', 'part', 'persona']
senticnet['rightful_ruler'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'feudal_lord', 'lord']
senticnet['rightfully'] = ['0.841', '0.861', '0', '0', '#joy', '#interest', 'positive', '0.851', 'just', 'correctly', 'fairly', 'rightly', 'justifiably']
senticnet['righting_moment'] = ['0.676', '0.63', '-0.23', '0.31', '#joy', '#interest', 'positive', '0.345', 'perkiness', 'walk_on_air', 'exultingly', 'exultantly', 'jubilance']
senticnet['rightist'] = ['0', '0', '-0.70', '-0.83', '#fear', '#disgust', 'negative', '-0.76', 'settle_disagreement', 'hitler', 'republican', 'conservative', 'unchanging']
senticnet['rightly'] = ['0.86', '0.973', '0', '0', '#joy', '#interest', 'positive', '0.916', 'english', 'orthodoxly', 'correctly', 'rightfully', 'aright']
senticnet['rightmost'] = ['0', '0.812', '0', '0.945', '#interest', '#admiration', 'positive', '0.879', 'leftmost', 'left-most', 'right-most', 'right-hand', 'left-hand']
senticnet['rightside_up'] = ['0.969', '0.843', '0', '0', '#joy', '#interest', 'positive', '0.906', 'tree_house', 'same_again', 'reverse', 'spot', 'clothes_accessory']
senticnet['rightward'] = ['0', '0.21', '-0.11', '0.345', '#interest', '#admiration', 'positive', '0.147', 'direction', 'unto', 'forehead', 'upon', 'go_toward']
senticnet['rightwinger'] = ['-0.97', '0', '0.62', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'obstructor', 'obstructer', 'technophobe', 'obstructionist', 'resister']
senticnet['rigid'] = ['0', '0.066', '0.056', '0.111', '#interest', '#admiration', 'positive', '0.04', 'durable', 'hard', 'hard_material', 'solid', 'inflexible']
senticnet['rigid_adherence'] = ['-0.70', '0', '0.914', '-0.72', '#anger', '#disgust', 'negative', '-0.78', 'unadaptability', 'rigidity', 'rigidness', 'unfitness', 'inability']
senticnet['rigid_airship'] = ['-0.04', '0.092', '0', '0.062', '#interest', '#admiration', 'positive', '0.036', 'hangar', 'aircraft_carrier', 'hot_air_balloon', 'war_museum', 'spaceship']
senticnet['rigid_disciplinarian'] = ['0.909', '-0.85', '0', '0.846', '#joy', '#surprise', 'positive', '0.87', 'respectability', 'reputability', 'venerability', 'venerableness', 'righteousness']
senticnet['rigid_foam'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'foil', 'tin_foil', 'gold_foil']
senticnet['rigid_pavement'] = ['0', '-0.01', '0.046', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'cement', 'tar', 'concrete', 'big_city_life', 'popular_building_material']
senticnet['rigidification'] = ['-0.84', '0', '0.8', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'rigor_mortis', 'natural_process', 'natural_action', 'activity', 'action']
senticnet['rigidify'] = ['0.712', '0', '0', '0.774', '#joy', '#admiration', 'positive', '0.743', 'anchor', 'settle', 'resolve', 'solve', 'establish']
senticnet['rigidifying'] = ['-0.96', '0', '0.897', '-0.72', '#sadness', '#anger', 'negative', '-0.86', 'rigor_mortis', 'natural_process', 'natural_action', 'activity', 'action']
senticnet['rigidity'] = ['-0.81', '0', '0.83', '-0.97', '#anger', '#disgust', 'negative', '-0.87', 'deadness', 'inelasticity', 'rigidness', 'stiffness', 'physical_property']
senticnet['rigidly'] = ['-0.80', '0', '0.507', '-0.76', '#sadness', '#disgust', 'negative', '-0.69', 'adamant', 'inexorable', 'intransigent', 'ironclad', 'brassbound']
senticnet['rigidness'] = ['-0.81', '0', '0.83', '-0.97', '#anger', '#disgust', 'negative', '-0.87', 'deadness', 'rigidity', 'inelasticity', 'stiffness', 'physical_property']
senticnet['rigmarole'] = ['-0.80', '0', '0.54', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'meaninglessness', 'nonsense', 'bunk', 'nonsensicality', 'hokum']
senticnet['rigor'] = ['0.52', '-0.76', '0', '0.573', '#surprise', '#admiration', 'positive', '0.618', 'sternness', 'grimness', 'asperity', 'hardship', 'rigorousness']
senticnet['rigor_mortis'] = ['-0.84', '0', '0.8', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'rigidification', 'natural_process', 'natural_action', 'activity', 'action']
senticnet['rigorism'] = ['0.922', '-0.94', '0', '0.965', '#surprise', '#admiration', 'positive', '0.945', 'utilitarian', 'egalitarian', 'equalitarian', 'elitist', 'philosopher']
senticnet['rigorous_enforcement'] = ['-0.88', '0', '0.977', '-0.81', '#sadness', '#anger', 'negative', '-0.89', 'execute', 'run', 'compel', 'oblige', 'obligate']
senticnet['rigorous_evaluation'] = ['-0.06', '0.18', '0', '0.143', '#interest', '#admiration', 'positive', '0.086', 'grade', 'multiple_choices', 'final', 'final_exam', 'school_grade']
senticnet['rigorous_examination'] = ['-0.79', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.75', 'scan', 'checkup', 'medical_checkup', 'medical_examination', 'medical_exam']
senticnet['rigorous_intellectual'] = ['0.03', '0.358', '-0.25', '0.299', '#interest', '#admiration', 'positive', '0.109', 'attitude', 'certainty', 'strong_feeling', 'prefer', 'tend']
senticnet['rigorous_oversight'] = ['-0.79', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.75', 'scan', 'checkup', 'medical_checkup', 'medical_examination', 'medical_exam']
senticnet['rigorous_scrutiny'] = ['-0.79', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.75', 'scan', 'checkup', 'medical_checkup', 'medical_examination', 'medical_exam']
senticnet['rigorously_define'] = ['-0.99', '0', '0.878', '-0.95', '#sadness', '#disgust', 'negative', '-0.94', 'adequacy', 'satisfactoriness', 'acceptability', 'acceptableness', 'quality']
senticnet['rigorousness'] = ['0.523', '-0.76', '0', '0.572', '#surprise', '#admiration', 'positive', '0.618', 'sternness', 'grimness', 'rigor', 'hardship', 'asperity']
senticnet['rigour'] = ['0', '0.783', '0', '0.737', '#interest', '#admiration', 'positive', '0.76', 'lustiness', 'validness', 'robustness', 'rigor', 'validity']
senticnet['rigueur'] = ['0.911', '-0.94', '0', '0.953', '#surprise', '#admiration', 'positive', '0.935', 'common_sense', 'good_sense', 'horse_sense', 'mother_wit', 'gumption']
senticnet['rihanna'] = ['0.056', '0.139', '-0.07', '0.154', '#interest', '#admiration', 'positive', '0.07', 'belongings', 'material', 'fabric', 'guarantee', 'stuff']
senticnet['rik_mayall'] = ['-0.89', '0', '0.58', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'resemblance', 'comparison', 'equivalence', 'reflection', 'alikeness']
senticnet['rikers_island'] = ['0.816', '-0.91', '0', '0.944', '#surprise', '#admiration', 'positive', '0.891', 'jail', 'jailhouse', 'gaol', 'poky', 'clink']
senticnet['rikimaru'] = ['-0.04', '0.138', '-0.08', '0.111', '#interest', '#admiration', 'positive', '0.031', 'geometry', 'craft', 'blade_geometry', 'blade', 'collect_item']
senticnet['rile'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['rile_up'] = ['-0.63', '0', '0.712', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'upset', 'disconcert', 'discompose', 'discomfit', 'untune']
senticnet['riled_up'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['rim'] = ['0', '0', '0.919', '-0.74', '#anger', '#disgust', 'negative', '-0.83', 'knife_sharp', 'decline', 'dart', 'unpleasantness', 'dullness']
senticnet['rim_blackberry'] = ['0.807', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.881', 'fruit', 'mobile_phone', 'drinkable', 'savory', 'eat_fruit']
senticnet['rim_trail'] = ['0', '0.069', '0', '0.065', '#interest', '#admiration', 'positive', '0.067', 'alp', 'high_mountain', 'pass', 'ski_resort', 'high_place']
senticnet['rimfire'] = ['0.887', '-0.95', '0', '0.913', '#surprise', '#admiration', 'positive', '0.917', 'high_quality', 'superiority', 'level', 'grade', 'degree']
senticnet['rimless_cartridge'] = ['0.887', '-0.95', '0', '0.913', '#surprise', '#admiration', 'positive', '0.917', 'high_quality', 'superiority', 'level', 'grade', 'degree']
senticnet['rimm_tsx_rim'] = ['0.807', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.881', 'fruit', 'mobile_phone', 'drinkable', 'savory', 'eat_fruit']
senticnet['rimowa'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'hand_baggage', 'baggage', 'passenger_ticket', 'ticket_counter', 'baggage_trunk']
senticnet['rind'] = ['0', '0.141', '0.01', '0.115', '#interest', '#admiration', 'positive', '0.082', 'sepal', 'achene', 'funiculus', 'seed', 'pip']
senticnet['rinderpest'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'leptomeningitis', 'meningitis', 'epidemic_meningitis', 'brain_fever', 'cerebrospinal_fever']
senticnet['ring'] = ['0.052', '0', '0', '0.118', '#joy', '#admiration', 'positive', '0.085', 'diamond_ring', 'piece_of_jewelry', 'bell_sound', 'wedding_band', 'engagement']
senticnet['ring-like'] = ['0', '0.835', '0', '0.886', '#interest', '#admiration', 'positive', '0.861', 'wall-like', 'reinforced-concrete', 'ringlike', 'trapezoidal-shaped', 'prism-shaped']
senticnet['ring_apron'] = ['0.052', '0', '0', '0.118', '#joy', '#admiration', 'positive', '0.085', 'diamond_ring', 'piece_of_jewelry', 'bell_sound', 'wedding_band', 'engagement']
senticnet['ring_binder'] = ['0.707', '0.902', '0', '0', '#joy', '#interest', 'positive', '0.804', 'paper_clip', 'paper_punch', 'highlighter', 'office', 'pencil_sharpener']
senticnet['ring_disease'] = ['-0.83', '0', '0.754', '-0.97', '#sadness', '#disgust', 'negative', '-0.85', 'brown_rot_gummosis', 'ring_rot', 'brown_rot', 'tobacco_wilt', 'gummosis']
senticnet['ring_doorbell'] = ['-0.15', '0', '-0.05', '-0.03', '#sadness', '#fear', 'negative', '-0.08', 'roll_ball', 'laugh_clown', 'trade_baseball_card', 'throw_fit', 'ring']
senticnet['ring_finger'] = ['0.048', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.071', 'ring', 'ring_loudly', 'diamond_ring', 'necklace', 'jewelry']
senticnet['ring_generalship'] = ['0.969', '0.998', '0', '0', '#joy', '#interest', 'positive', '0.984', 'bell_sound', 'bell_noise', 'piece_of_jewelry', 'syllable', 'fastener']
senticnet['ring_loudly'] = ['0.033', '0.095', '0.062', '0.167', '#interest', '#admiration', 'positive', '0.058', 'chime', 'school_end', 'ring', 'wedding_ring', 'bell_sound']
senticnet['ring_necked_pheasant'] = ['-0.54', '0', '0.768', '-0.50', '#sadness', '#anger', 'negative', '-0.60', 'polecat', 'hooded_skunk', 'hognosed_skunk', 'badger_skunk', 'rooter_skunk']
senticnet['ring_road'] = ['0.032', '0.116', '0.016', '0', '#joy', '#interest', 'positive', '0.044', 'curb', 'crossroad', 'intersection', 'lane', 'interstate']
senticnet['ring_rot'] = ['-0.83', '0', '0.754', '-0.97', '#sadness', '#disgust', 'negative', '-0.85', 'brown_rot_gummosis', 'brown_rot', 'ring_disease', 'tobacco_wilt', 'gummosis']
senticnet['ring_shaped'] = ['0.052', '0', '0', '0.118', '#joy', '#admiration', 'positive', '0.085', 'diamond_ring', 'piece_of_jewelry', 'bell_sound', 'wedding_band', 'engagement']
senticnet['ring_snake'] = ['-0.51', '0', '0.625', '-0.98', '#anger', '#disgust', 'negative', '-0.70', 'common_water_snake', 'banded_water_snake', 'water_moccasin', 'grass_snake', 'water_snake']
senticnet['ring_tailed_cat'] = ['-0.03', '0', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.06', 'lion', 'leopard', 'mountain_lion', 'craven', 'wolf']
senticnet['ring_tone'] = ['0.035', '-0.01', '0.027', '0.085', '#joy', '#admiration', 'positive', '0.026', 'telephone', 'find_phone_number', 'telephone_room', 'phone', 'use_telephone']
senticnet['ringbone'] = ['0.789', '-0.99', '0', '0.54', '#joy', '#surprise', 'positive', '0.776', 'imperfection', 'imperfectness', 'bugginess', 'defectiveness', 'sickness']
senticnet['ringed_seal'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'seal', 'otter', 'mussel', 'alligator', 'whale']
senticnet['ringer'] = ['-0.97', '0', '0.728', '-0.78', '#sadness', '#disgust', 'negative', '-0.82', 'beguiler', 'slicker', 'phony', 'faker', 'deceiver']
senticnet['ringer_lactate_solution'] = ['0.79', '-0.62', '0', '0.674', '#joy', '#admiration', 'positive', '0.695', 'add', 'contribute', 'lend', 'breathe', 'impart']
senticnet['ringer_switch'] = ['-0.97', '0', '0.728', '-0.78', '#sadness', '#disgust', 'negative', '-0.82', 'beguiler', 'slicker', 'phony', 'faker', 'deceiver']
senticnet['ringer_volume'] = ['-0.97', '0', '0.728', '-0.78', '#sadness', '#disgust', 'negative', '-0.82', 'beguiler', 'slicker', 'phony', 'faker', 'deceiver']
senticnet['ringing'] = ['0', '0.974', '0', '0.764', '#interest', '#admiration', 'positive', '0.869', 'resonance', 'sonorousness', 'plangency', 'reverberance', 'vibrancy']
senticnet['ringing_phone'] = ['0.062', '-0.03', '0.116', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'bad_music', 'noise', 'stress', 'a_lot_of_work', 'stressful_situation']
senticnet['ringing_telephone'] = ['0.062', '-0.03', '0.116', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'bad_music', 'noise', 'stress', 'a_lot_of_work', 'stressful_situation']
senticnet['ringleader'] = ['-0.59', '0', '0.79', '-0.55', '#sadness', '#anger', 'negative', '-0.64', 'troublemaker', 'trouble_maker', 'troubler', 'bad_hat', 'inciter']
senticnet['ringlet_erebia'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'sedum', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['ringlike'] = ['0', '0.841', '0', '0.895', '#interest', '#admiration', 'positive', '0.868', 'wall-like', 'reinforced-concrete', 'ring-like', 'trapezoidal-shaped', 'prism-shaped']
senticnet['ringling_brother_barnum'] = ['0.954', '0', '0', '0', '#joy', '#joy', 'positive', '0.954', 'clown', 'halloween', 'entertainment', 'fun', 'happiness']
senticnet['ringmaster'] = ['0', '0', '0', '0.987', '#admiration', '#admiration', 'positive', '0.987', 'emote', 'bold', 'form_of_social_interaction', 'act', 'stage_play']
senticnet['ringrose'] = ['0.997', '-0.84', '0', '0.927', '#joy', '#admiration', 'positive', '0.923', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['ringside'] = ['0.052', '0', '0', '0.118', '#joy', '#admiration', 'positive', '0.085', 'diamond_ring', 'piece_of_jewelry', 'bell_sound', 'wedding_band', 'engagement']
senticnet['ringtone'] = ['-0.97', '0', '0.728', '-0.78', '#sadness', '#disgust', 'negative', '-0.82', 'beguiler', 'slicker', 'phony', 'faker', 'deceiver']
senticnet['ringuet'] = ['0.502', '-0.82', '0', '0.779', '#surprise', '#admiration', 'positive', '0.703', 'mortal', 'person', 'individual', 'free_liver', 'agent']
senticnet['ringworm'] = ['-0.96', '0', '0.694', '-0.62', '#sadness', '#anger', 'negative', '-0.76', 'fungal_infection', 'mycosis', 'kerion', 'tinea_pedis', 'athlete_foot']
senticnet['rink'] = ['0.02', '0.244', '0', '0.225', '#interest', '#admiration', 'positive', '0.163', 'game', 'disneyland', 'ballpark', 'hockey_game', 'sport_event']
senticnet['rinse'] = ['0.093', '-0.03', '0.025', '0', '#joy', '#surprise', 'positive', '0.035', 'get_water', 'catch_trout', 'bathe', 'bath', 'take_shower']
senticnet['rinse_dish'] = ['0', '0.091', '-0.05', '0.073', '#interest', '#admiration', 'positive', '0.037', 'privately', 'hold_liquid', 'brew', 'make_wish', 'find_toilet']
senticnet['rinse_thoroughly'] = ['0.648', '0.618', '-0.26', '0.332', '#joy', '#interest', 'positive', '0.334', 'exhilarate', 'teased', 'titillating', 'titillate', 'titillation']
senticnet['rinsed_off'] = ['0', '-0.01', '0.046', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'varnish', 'brush', 'dye', 'pigment', 'canvas']
senticnet['rio_carnival'] = ['0.957', '0.7', '0', '0', '#joy', '#interest', 'positive', '0.829', 'ballpark', 'coven', 'disneyland', 'hockey_game', 'symposium']
senticnet['rio_samba'] = ['0.957', '0.7', '0', '0', '#joy', '#interest', 'positive', '0.829', 'ballpark', 'coven', 'disneyland', 'hockey_game', 'symposium']
senticnet['rio_vista'] = ['-0.06', '0.21', '0', '0.076', '#interest', '#admiration', 'positive', '0.074', 'new_think', 'figure_out', 'brain_activity', 'idea', 'come_up_idea']
senticnet['rionne_fujiwara'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['rioport'] = ['0.101', '-0.04', '0.036', '0', '#joy', '#surprise', 'positive', '0.038', 'song', 'rap', 'jingle', 'chord', 'lyric']
senticnet['riot'] = ['-0.89', '0', '0.829', '-0.92', '#sadness', '#disgust', 'negative', '-0.88', 'against', 'uprise', 'dispute', 'quarrel', 'opposition']
senticnet['riot_act'] = ['0.662', '-0.84', '0', '0.709', '#surprise', '#admiration', 'positive', '0.74', 'rebuke', 'reproof', 'reproval', 'reprehension', 'reprimand']
senticnet['riot_policeman'] = ['-0.93', '0', '0.688', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'dissident', 'dissenter', 'nimby', 'objector', 'contestant']
senticnet['rioter'] = ['-0.93', '0', '0.688', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'dissident', 'dissenter', 'nimby', 'objector', 'contestant']
senticnet['riotous'] = ['0', '0', '-0.97', '-0.72', '#fear', '#disgust', 'negative', '-0.85', 'disruptive', 'troubled', 'tumultuous', 'turbulent', 'churning']
senticnet['riotously'] = ['0.512', '0.658', '-0.25', '0.256', '#joy', '#interest', 'positive', '0.293', 'exuberantly', 'expansively', 'ebulliently', 'zestfulness', 'gusto']
senticnet['riotously_funny'] = ['-0.90', '0.138', '-0.63', '0.071', '#sadness', '#fear', 'negative', '-0.33', 'woeful', 'execrable', 'deplorable', 'woebegone', 'plaintiveness']
senticnet['rip'] = ['-0.11', '-0.00', '0', '-0.16', '#sadness', '#disgust', 'negative', '-0.09', 'pierce', 'spread_butter', 'cut_food', 'lacerate', 'bleed']
senticnet['rip-off'] = ['-0.75', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.74', 'rip-off', 'ripoff', 'ripoffs', 'scam', 'fraud']
senticnet['rip-roaring'] = ['-0.9', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.82', 'slam-bang', 'edge-of-your-seat', 'edge-of-the-seat', 'nail-biting', 'thrill-a-minute']
senticnet['rip_apart'] = ['-0.11', '0.095', '-0.11', '0.071', '#sadness', '#fear', 'negative', '-0.01', 'violent_action', 'pulverize', 'break_glass', 'tear_apart', 'hit_hard']
senticnet['rip_cord'] = ['-0.88', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.90', 'undergarment', 'outerwear', 'lady_underwear', 'under_garment', 'slip']
senticnet['rip_off'] = ['-0.89', '0', '0.826', '-0.50', '#sadness', '#anger', 'negative', '-0.74', 'wrong', 'delude', 'deceive', 'cozen', 'victimize']
senticnet['rip_out'] = ['-0.91', '0', '0.656', '-0.81', '#sadness', '#disgust', 'negative', '-0.79', 'withdraw', 'squeeze_out', 'take', 'wring_out', 'demodulate']
senticnet['rip_sawyer'] = ['0.028', '0.138', '-0.04', '0.215', '#interest', '#admiration', 'positive', '0.083', 'water', 'dense', 'thick', 'milk', 'dense_water']
senticnet['rip_up'] = ['-0.57', '0', '0.852', '-0.74', '#anger', '#disgust', 'negative', '-0.72', 'tease', 'bust', 'tear', 'rupture', 'snap']
senticnet['ripcord'] = ['-0.88', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.90', 'undergarment', 'outerwear', 'lady_underwear', 'under_garment', 'slip']
senticnet['ripe'] = ['0', '0.138', '-0.06', '0.221', '#interest', '#admiration', 'positive', '0.098', 'neat', 'neat_tidy', 'mature', 'tidy_up', 'tidy']
senticnet['ripe_apple'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'candy_cane', 'custard', 'cookie', 'candy', 'orange_fruit']
senticnet['ripe_fruit'] = ['0.05', '0', '0.047', '0.068', '#joy', '#admiration', 'positive', '0.024', 'tree_fruit', 'yellow_fruit', 'small_fruit', 'vine_fruit', 'red_fruit']
senticnet['ripe_rot'] = ['-0.83', '0', '0.754', '-0.97', '#sadness', '#disgust', 'negative', '-0.85', 'brown_rot_gummosis', 'ring_rot', 'ring_disease', 'tobacco_wilt', 'gummosis']
senticnet['ripe_tomato'] = ['0.937', '0', '0', '0.828', '#joy', '#admiration', 'positive', '0.883', 'bakery', 'produce_section', 'thanksgiving_dinner', 'dinner_meal', 'fast_food']
senticnet['ripem'] = ['0', '0.716', '0', '0.83', '#interest', '#admiration', 'positive', '0.773', 'information', 'symposium', 'workshop', 'public_domain', 'data']
senticnet['ripen'] = ['0.696', '-0.65', '0', '0.948', '#joy', '#admiration', 'positive', '0.765', 'find', 'maturate', 'find_self', 'grow_up', 'develop']
senticnet['ripened_into'] = ['0.608', '0.647', '-0.26', '0.325', '#joy', '#interest', 'positive', '0.328', 'amicably', 'amicable', 'amicableness', 'brotherly', 'friendly']
senticnet['ripeness_doctrine'] = ['-0.62', '0', '0.7', '-0.83', '#anger', '#disgust', 'negative', '-0.72', 'take', 'pull', 'return', 'payback', 'proceeds']
senticnet['ripoff'] = ['-0.75', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.74', 'rip-off', 'rip-off', 'ripoffs', 'scam', 'fraud']
senticnet['ripoffs'] = ['-0.75', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.74', 'rip-off', 'ripoff', 'rip-off', 'scam', 'fraud']
senticnet['riposte'] = ['-0.87', '0', '0.872', '-0.83', '#sadness', '#anger', 'negative', '-0.86', 'reply', 'response', 'comeback', 'sassing', 'backtalk']
senticnet['ripped'] = ['0.905', '0.979', '0', '0', '#joy', '#interest', 'positive', '0.942', 'old', 'elderly', 'age_well', 'matured', 'mentality']
senticnet['rippling'] = ['0', '0', '0.951', '-0.94', '#anger', '#disgust', 'negative', '-0.94', 'undulation', 'undulating', 'undulated', 'undulation', 'wavelike']
senticnet['riri'] = ['0.973', '-0.50', '0', '0.612', '#joy', '#admiration', 'positive', '0.698', 'chic', 'chicness', 'jauntiness', 'modishness', 'stylishness']
senticnet['rise'] = ['0', '0.124', '-0.07', '0.115', '#interest', '#admiration', 'positive', '0.055', 'arise', 'lift', 'store_garage', 'go_upstairs', 'climb']
senticnet['rise_plant'] = ['0.042', '0.166', '-0.12', '0.178', '#interest', '#admiration', 'positive', '0.067', 'flower_type', 'rose', 'rise', 'symbol_love', 'rosebud']
senticnet['rise_up'] = ['0', '0.229', '-0.11', '0.243', '#interest', '#admiration', 'positive', '0.119', 'raise_up', 'pull_up', 'go_up', 'move_upward', 'move_up']
senticnet['rise_water'] = ['0', '0', '0', '0.09', '#admiration', '#admiration', 'positive', '0.09', 'water_spray', 'liquid_water', 'water_inside', 'humid', 'water_relate']
senticnet['risible'] = ['0', '0', '0.765', '-0.84', '#anger', '#disgust', 'negative', '-0.80', 'costless', 'complimentary', 'needless', 'gratis', 'free']
senticnet['rising'] = ['0', '0.728', '0', '0.878', '#interest', '#admiration', 'positive', '0.803', 'assertive', 'muscular', 'bullishly', 'hopeful', 'bullish']
senticnet['rising_anti_semitism'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'racism', 'intolerant', 'xenophobic', 'racist', 'nazism']
senticnet['rising_demand'] = ['-0.83', '0', '-0.88', '-0.78', '#sadness', '#fear', 'negative', '-0.83', 'little', 'sadness', 'small_amount', 'lack', 'absence']
senticnet['rising_discontent'] = ['-0.77', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.84', 'jumpiness', 'jitteriness', 'restive', 'nervy', 'jumpy']
senticnet['rising_factorial'] = ['0.042', '-0.00', '0.027', '0.099', '#joy', '#admiration', 'positive', '0.031', 'chess_knight', 'chess_queen', 'chess_pawn', 'pawn', 'chess_bishop']
senticnet['rising_incidence'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'problem', 'serious_issue', 'overrate', 'prohibitionist_society', 'blade_weapon']
senticnet['rising_intonation'] = ['0', '0.156', '-0.06', '0.241', '#interest', '#admiration', 'positive', '0.11', 'mixture', 'noisy_place', 'make_go', 'move_vehicle', 'big_car']
senticnet['rising_magma'] = ['0', '0', '-0.92', '0', '#fear', '#fear', 'negative', '-0.92', 'water_flow', 'wind_water', 'water_stream', 'waterway', 'lava']
senticnet['rising_star_award'] = ['0.232', '0.178', '-0.17', '0.223', '#joy', '#admiration', 'positive', '0.115', 'fame', 'alms', 'find_money', 'better_life', 'small_detail']
senticnet['rising_tension'] = ['-0.2', '-0.03', '0', '-0.23', '#sadness', '#disgust', 'negative', '-0.13', 'beat_up', 'fight', 'conflict', 'violence', 'fight_against']
senticnet['rising_unemployment'] = ['0', '0', '0.948', '-0.96', '#anger', '#disgust', 'negative', '-0.95', 'work_much', 'lack_money', 'wage_slavery', 'buy_tool', 'get_paycheck']
senticnet['risk'] = ['-0.91', '0', '-0.75', '-0.87', '#sadness', '#disgust', 'negative', '-0.84', 'jeopardy', 'bad_situation', 'adversity', 'dare', 'deception']
senticnet['risk_assessment'] = ['-0.06', '0.18', '0', '0.143', '#interest', '#admiration', 'positive', '0.086', 'grade', 'multiple_choices', 'final', 'final_exam', 'school_grade']
senticnet['risk_aversion'] = ['0', '0', '0', '-0.70', '#disgust', '#disgust', 'negative', '-0.70', 'uncertain_future', 'uncertain_environment', 'insecure', 'blur', 'anxiety']
senticnet['risk_management'] = ['0.036', '0.292', '0', '0.337', '#interest', '#admiration', 'positive', '0.222', 'importance', 'quality', 'total', 'excellence', 'frivolity']
senticnet['risk_mitigation'] = ['0.036', '0.292', '0', '0.337', '#interest', '#admiration', 'positive', '0.222', 'importance', 'quality', 'total', 'excellence', 'frivolity']
senticnet['risk_premia'] = ['0', '0', '0', '-0.70', '#disgust', '#disgust', 'negative', '-0.70', 'uncertain_future', 'uncertain_environment', 'insecure', 'blur', 'anxiety']
senticnet['risk_to_lose'] = ['-0.92', '0', '0.44', '-0.94', '#sadness', '#disgust', 'negative', '-0.77', 'chance', 'risk', 'last_chance', 'dangerous', 'danger']
senticnet['risky'] = ['-0.15', '-0.06', '-0.21', '-0.05', '#sadness', '#fear', 'negative', '-0.08', 'feel_scared', 'concussion', 'loss_ofbreath', 'unsafe', 'hardship']
senticnet['risky_sexual_behavior'] = ['0', '0', '0.973', '-0.78', '#anger', '#disgust', 'negative', '-0.87', 'chastity', 'ignore_conception', 'restrain', 'abortion', 'stop']
senticnet['risotto'] = ['0.043', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'apple_pie', 'a_lot_of_food', 'jelly', 'frozen_food', 'cottage_cheese']
senticnet['risperidone_risperdal'] = ['-0.72', '0', '0.973', '-0.57', '#sadness', '#anger', 'negative', '-0.76', 'corpulency', 'blubber', 'fat', 'embonpoint', 'stoutness']
senticnet['risqu'] = ['0', '0', '-0.95', '-0.72', '#fear', '#disgust', 'negative', '-0.84', 'lubricious', 'lewd', 'lustful', 'obscene', 'prurient']
senticnet['risso_dolphin'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'many_fishes', 'seahorse', 'starfish', 'plankton', 'sea_wee']
senticnet['rite'] = ['0', '0', '0', '0.86', '#admiration', '#admiration', 'positive', '0.86', 'religious_event', 'ceremony', 'happy_event', 'occasion', 'christmas']
senticnet['rite_aid'] = ['0.912', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.917', 'carpet_store', 'lumberyard', 'furniture_store', 'front_porch', 'shoe_shop']
senticnet['ritonavir'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'indinavir', 'nelfinavir', 'protease_inhibitor', 'saquinavir']
senticnet['ritonavir_norvir'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'indinavir', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['ritornello'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['ritu_kumar'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'anklet', 'ankle_bracelet', 'armilla']
senticnet['ritual'] = ['0', '0', '0', '0.813', '#admiration', '#admiration', 'positive', '0.813', 'church_service', 'prayer', 'religious', 'faith', 'pardon']
senticnet['ritual_burial'] = ['-0.09', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.10', 'funeral_celebration', 'memorial', 'funeral', 'memento', 'cry']
senticnet['ritual_cannibalism'] = ['-0.73', '0', '0.734', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'primitive', 'headhunter', 'barbarian', 'anthropophagus', 'anthropophagite']
senticnet['ritual_dance'] = ['0.807', '-0.92', '0', '0.796', '#joy', '#surprise', 'positive', '0.841', 'agape', 'confirmation', 'libation', 'rite', 'service']
senticnet['ritual_humiliation'] = ['-0.58', '0.282', '-0.41', '-0.16', '#sadness', '#fear', 'negative', '-0.21', 'shame', 'upset', 'despair', 'fright', 'unhappiness']
senticnet['ritual_impurity'] = ['-0.69', '0', '0.858', '-0.83', '#anger', '#disgust', 'negative', '-0.79', 'draggled', 'befouled', 'filthy', 'nasty', 'scummy']
senticnet['ritual_murder'] = ['-0.17', '0', '-0.16', '-0.09', '#sadness', '#fear', 'negative', '-0.14', 'murder', 'hurt_others', 'death_penalty', 'death_destruction', 'end_live']
senticnet['ritual_purification'] = ['0.807', '-0.92', '0', '0.796', '#joy', '#surprise', 'positive', '0.841', 'agape', 'confirmation', 'libation', 'rite', 'service']
senticnet['ritual_purity'] = ['-0.69', '0', '0.858', '-0.83', '#anger', '#disgust', 'negative', '-0.79', 'draggled', 'befouled', 'filthy', 'nasty', 'scummy']
senticnet['ritual_sacrifice'] = ['-0.17', '0', '-0.16', '-0.09', '#sadness', '#fear', 'negative', '-0.14', 'murder', 'hurt_others', 'death_penalty', 'death_destruction', 'end_live']
senticnet['ritual_significance'] = ['-0.09', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.10', 'funeral_celebration', 'memorial', 'funeral', 'memento', 'cry']
senticnet['ritual_slaughter'] = ['-0.67', '0', '0.938', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'massacre', 'mass_murder', 'carnage', 'bloodbath', 'butchery']
senticnet['ritualism'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'nonconformity', 'iconoclasm', 'heterodoxy', 'nonconformance', 'orientation']
senticnet['ritualistic'] = ['0.807', '-0.92', '0', '0.796', '#joy', '#surprise', 'positive', '0.841', 'agape', 'confirmation', 'libation', 'rite', 'service']
senticnet['ritually'] = ['0.807', '-0.92', '0', '0.796', '#joy', '#surprise', 'positive', '0.841', 'agape', 'confirmation', 'libation', 'rite', 'service']
senticnet['ritually_clean'] = ['-0.69', '0', '0.858', '-0.83', '#anger', '#disgust', 'negative', '-0.79', 'draggled', 'befouled', 'filthy', 'nasty', 'scummy']
senticnet['ritually_impure'] = ['-0.69', '0', '0.858', '-0.83', '#anger', '#disgust', 'negative', '-0.79', 'draggled', 'befouled', 'filthy', 'nasty', 'scummy']
senticnet['ritually_pure'] = ['0.581', '-0.88', '0', '0.534', '#joy', '#surprise', 'positive', '0.667', 'better', 'improve', 'pure', 'meliorate', 'amend']
senticnet['ritually_sacrifice'] = ['-0.73', '0', '0.55', '-0.75', '#sadness', '#disgust', 'negative', '-0.68', 'mortal', 'person', 'individual', 'mangler', 'mutilator']
senticnet['ritually_unclean'] = ['-0.69', '0', '0.858', '-0.83', '#anger', '#disgust', 'negative', '-0.79', 'draggled', 'befouled', 'filthy', 'nasty', 'scummy']
senticnet['ritz'] = ['-0.81', '0', '0.936', '-0.57', '#sadness', '#anger', 'negative', '-0.77', 'pedantry', 'bluster', 'splurge', 'ostentation', 'display']
senticnet['ritz_escoffier'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'blue_ribbon', 'badge', 'allegory', 'stripe', 'merit_badge']
senticnet['riva_del_garda'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'expressway', 'turnpike', 'toll_road', 'highway']
senticnet['rival'] = ['-0.12', '0.115', '-0.06', '-0.11', '#sadness', '#disgust', 'negative', '-0.04', 'opposition', 'fight_against', 'funny_look', 'uprise', 'anti']
senticnet['rival_faction'] = ['-0.50', '0', '0.989', '-0.91', '#anger', '#disgust', 'negative', '-0.80', 'camp', 'pack', 'clique', 'coterie', 'ingroup']
senticnet['rival_gang'] = ['0.867', '-0.84', '0', '0.876', '#joy', '#admiration', 'positive', '0.863', 'meet', 'team', 'team_up', 'embed', 'aggroup']
senticnet['rival_gangster'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'yeoman', 'outrider', 'yeoman_of_guard', 'beefeater', 'defender']
senticnet['rival_pepsi'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'glass_water', 'refreshed', 'smoothie', 'lemonade', 'cold_water']
senticnet['rivalry'] = ['0', '0', '1', '0.33', '#anger', '#admiration', 'negative', '-0.33', 'mockery', 'hatred', 'foe', 'enemy', 'duel']
senticnet['rivaroxaban'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'spironolactone', 'spironolactone_drug']
senticnet['riven'] = ['0', '0', '-0.91', '-0.73', '#fear', '#disgust', 'negative', '-0.82', 'overabundant', 'prevailing', 'plethoric', 'predominant', 'dominant']
senticnet['river'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'reptile', 'animal', 'predator', 'meat_eater', 'alligator']
senticnet['river_bank'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'river', 'fresh_water', 'small_stream', 'small_river', 'water']
senticnet['river_bed'] = ['-0.02', '-0.03', '0.047', '-0.08', '#anger', '#disgust', 'negative', '-0.02', 'waterfall', 'riverbed', 'surface_earth', 'septic_tank', 'bottom_ocean']
senticnet['river_birch'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'locust_tree']
senticnet['river_brue'] = ['-0.77', '0', '0.583', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'rain', 'rain_down', 'sheet', 'rain_bucket', 'rain_buckets_down']
senticnet['river_cam'] = ['0.983', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.945', 'cam', 'cam_river', 'ast', 'camera', 'article']
senticnet['river_canyon'] = ['0', '0.069', '0', '0.065', '#interest', '#admiration', 'positive', '0.067', 'alp', 'high_mountain', 'pass', 'ski_resort', 'high_place']
senticnet['river_drin'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
senticnet['river_empty_into'] = ['0', '0.08', '0.032', '0.096', '#interest', '#admiration', 'positive', '0.048', 'land_form', 'land_formation', 'land_feature', 'geographic_feature', 'landscape_feature']
senticnet['river_estuary'] = ['0.933', '0.795', '0', '0', '#joy', '#interest', 'positive', '0.864', 'water_passage', 'small_stream', 'water_flow', 'flowing_water', 'water_stream']
senticnet['river_floodplain'] = ['0.614', '-0.95', '0', '0.584', '#joy', '#surprise', 'positive', '0.718', 'mother_lode', 'champion_lode', 'deposit', 'sedimentation', 'lode']
senticnet['river_great_ouse'] = ['-0.77', '0', '0.583', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'rain', 'rain_down', 'sheet', 'rain_bucket', 'rain_buckets_down']
senticnet['river_greenway'] = ['0', '0', '0', '0.085', '#admiration', '#admiration', 'positive', '0.085', 'use_farm', 'gate', 'door', 'doorway', 'entranceway']
senticnet['river_lake'] = ['0', '-0.02', '0.064', '-0.08', '#anger', '#disgust', 'negative', '-0.04', 'land_earth', 'spurt', 'brine', 'crush_ice', 'puddle']
senticnet['river_lea'] = ['-0.77', '0', '0.583', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'rain', 'rain_down', 'sheet', 'rain_bucket', 'rain_buckets_down']
senticnet['river_mouth'] = ['0.97', '0.758', '0', '0', '#joy', '#interest', 'positive', '0.864', 'trot', 'step_out', 'step_aside', 'progress', 'enter_place']
senticnet['river_navigable'] = ['0.933', '0.795', '0', '0', '#joy', '#interest', 'positive', '0.864', 'water_passage', 'small_stream', 'water_flow', 'flowing_water', 'water_stream']
senticnet['river_nene'] = ['-0.77', '0', '0.583', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'rain', 'rain_down', 'sheet', 'rain_bucket', 'rain_buckets_down']
senticnet['river_nile'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['river_ouse'] = ['-0.77', '0', '0.583', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'rain', 'rain_down', 'sheet', 'rain_bucket', 'rain_buckets_down']
senticnet['river_parrett'] = ['-0.77', '0', '0.583', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'rain', 'rain_down', 'sheet', 'rain_bucket', 'rain_buckets_down']
senticnet['river_rother'] = ['-0.77', '0', '0.583', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'rain', 'rain_down', 'sheet', 'rain_bucket', 'rain_buckets_down']
senticnet['river_state_penitentiary'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'kit_fox', 'prairie_fox', 'white_fox']
senticnet['river_stream'] = ['-0.06', '-0.00', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.05', 'capoeira', 'trouble', 'agony', 'cause_problem', 'madman']
senticnet['river_witham'] = ['-0.77', '0', '0.583', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'rain', 'rain_down', 'sheet', 'rain_bucket', 'rain_buckets_down']
senticnet['riverbank_erosion'] = ['-0.88', '0', '0.907', '-0.52', '#sadness', '#anger', 'negative', '-0.77', 'wearing', 'corrasion', 'eroding', 'eating_away', 'wearing_away']
senticnet['riverbed'] = ['0', '0', '-0.75', '-0.72', '#fear', '#disgust', 'negative', '-0.74', 'septic_tank', 'bottom_ocean', 'waterfall', 'down_drain', 'petri_dish']
senticnet['riverside'] = ['-0.04', '0.076', '0', '0.123', '#interest', '#admiration', 'positive', '0.053', 'small_stream', 'big_lake', 'reservoir', 'dam', 'oceanside']
senticnet['riverside_amusement_park'] = ['0.089', '-0.18', '0.037', '-0.06', '#joy', '#surprise', 'positive', '0.043', 'balloon', 'ballon', 'fielder', 'kite', 'blimp']
senticnet['riverside_drive'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'boulevard', 'lane', 'thoroughfare', 'intersection', 'curb']
senticnet['riverview_park'] = ['0.089', '-0.18', '0.037', '-0.06', '#joy', '#surprise', 'positive', '0.043', 'balloon', 'ballon', 'fielder', 'kite', 'blimp']
senticnet['rivet'] = ['0.817', '-0.59', '0', '0.861', '#joy', '#admiration', 'positive', '0.757', 'focus', 'pore', 'engulf', 'concentrate', 'center']
senticnet['riveted_together'] = ['0', '0.773', '0', '0.895', '#interest', '#admiration', 'positive', '0.834', 'wooden_board', 'make_wood', 'wooden_box', 'wooden_floor', 'long_wood']
senticnet['riveting'] = ['0.996', '0.969', '0', '0', '#joy', '#interest', 'positive', '0.982', 'concentrate', 'focus', 'pore', 'centre', 'engrossing']
senticnet['riveting_story'] = ['0', '0.952', '0', '0', '#interest', '#interest', 'positive', '0.952', 'fable', 'long_story', 'novel', 'saga', 'editorial']
senticnet['rivetingly'] = ['-0.90', '0.138', '-0.63', '0.071', '#sadness', '#fear', 'negative', '-0.33', 'woeful', 'execrable', 'deplorable', 'woebegone', 'plaintiveness']
senticnet['rixensart'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'feudal_lord', 'lord', 'master', 'overlord', 'king']
senticnet['riyad'] = ['0.798', '-0.51', '0', '0.973', '#joy', '#admiration', 'positive', '0.763', 'inexperienced_person', 'boy_scout', 'dear', 'virgin', 'mortal']
senticnet['rizal_memorial_coliseum'] = ['0', '-0.84', '0', '0.723', '#surprise', '#admiration', 'positive', '0.785', 'circular_route', 'stadium', 'memorial', 'station', 'row_house']
senticnet['rkairoa'] = ['-0.91', '0', '0.769', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'break', 'good_luck', 'chance_eventpy_chance', 'chance_event', 'coincidence']
senticnet['rkczi_war'] = ['0.887', '0.748', '0', '0.954', '#joy', '#admiration', 'positive', '0.863', 'political_action', 'political', 'mediocrity', 'discrimination', 'independent']
senticnet['rle'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'baddie', 'theatrical_baddie', 'part', 'persona']
senticnet['rms_carpathia'] = ['0', '-0.02', '0.046', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'sail_ship', 'canoe', 'ship', 'submarine', 'yacht']
senticnet['rnc_chairman'] = ['0', '0.924', '0', '0.921', '#interest', '#admiration', 'positive', '0.923', 'settle_disagreement', 'hitler', 'communist', 'conservative', 'unchanging']
senticnet['rnid'] = ['-0.94', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.94', 'drive_recklessly', 'discord', 'mute', 'deny', 'helpless']
senticnet['rnk_split'] = ['0.954', '-0.90', '0', '0.867', '#joy', '#surprise', 'positive', '0.91', 'friend', 'sports_fan', 'rooter', 'fan', 'nut']
senticnet['rnli'] = ['0', '-0.02', '0.046', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'sail_ship', 'canoe', 'ship', 'submarine', 'yacht']
senticnet['rnli_crew'] = ['0', '-0.02', '0.046', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'sail_ship', 'canoe', 'ship', 'submarine', 'yacht']
senticnet['rnli_inshore_lifeboat'] = ['0', '-0.02', '0.046', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'sail_ship', 'canoe', 'ship', 'submarine', 'yacht']
senticnet['rnli_lifeboat'] = ['0', '-0.02', '0.046', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'sail_ship', 'canoe', 'ship', 'submarine', 'yacht']
senticnet['rnli_lifeboat_station'] = ['0', '-0.02', '0.046', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'sail_ship', 'canoe', 'ship', 'submarine', 'yacht']
senticnet['rnli_station'] = ['0', '-0.02', '0.046', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'sail_ship', 'canoe', 'ship', 'submarine', 'yacht']
senticnet['ro_wine'] = ['0.161', '-0.14', '0.031', '-0.10', '#joy', '#surprise', 'positive', '0.043', 'alcohol', 'reception', 'champagne', 'beer', 'drink']
senticnet['roach'] = ['0.021', '-0.10', '0.248', '-0.29', '#anger', '#disgust', 'negative', '-0.10', 'dust', 'spider', 'dust_mite', 'cookie_crumb', 'dirty_sock']
senticnet['road'] = ['-0.70', '0', '0.619', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'flare_path', 'airfield', 'grassland', 'runway', 'landing_strip']
senticnet['road_block'] = ['0', '0', '0.971', '-0.94', '#anger', '#disgust', 'negative', '-0.95', 'traffic', 'congestion', 'block_road', 'traffic_congestion', 'stop']
senticnet['road_grader'] = ['-0.99', '0', '0.827', '-0.54', '#sadness', '#anger', 'negative', '-0.78', 'treat', 'cover', 'handle', 'theologize', 'deal']
senticnet['road_map'] = ['0.525', '-0.76', '0', '0.783', '#surprise', '#admiration', 'positive', '0.691', 'guidance', 'plan', 'program', 'programme', 'counsel']
senticnet['road_rage'] = ['-0.10', '-0.04', '0', '-0.14', '#sadness', '#disgust', 'negative', '-0.06', 'adjust_seat', 'angry_behavior', 'fasten_seatbelt', 'traffic_jam', 'skid_mark']
senticnet['road_rage_incident'] = ['-0.10', '-0.04', '0', '-0.14', '#sadness', '#disgust', 'negative', '-0.06', 'adjust_seat', 'angry_behavior', 'fasten_seatbelt', 'traffic_jam', 'skid_mark']
senticnet['road_sense'] = ['0.588', '-0.73', '0', '0.788', '#surprise', '#admiration', 'positive', '0.703', 'common_sense', 'good_sense', 'horse_sense', 'mother_wit', 'gumption']
senticnet['road_sign'] = ['0.038', '0.145', '0', '0.113', '#interest', '#admiration', 'positive', '0.099', 'street_sign', 'traffic_sign', 'sign', 'red_light', 'intersection']
senticnet['road_test'] = ['0.928', '-0.87', '0', '0.679', '#joy', '#surprise', 'positive', '0.829', 'examine', 'experiment', 'alpha_test', 'beta_test', 'test']
senticnet['road_trip'] = ['0.024', '0.152', '-0.06', '0.236', '#interest', '#admiration', 'positive', '0.087', 'vacation', 'exit', 'stance', 'take_break', 'holiday']
senticnet['roadblock'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'corner_two_streets', 'street_corner', 'roadside', 'city_street', 'schoolyard']
senticnet['roadblock_clue'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'corner_two_streets', 'street_corner', 'roadside', 'city_street', 'schoolyard']
senticnet['roadies_voted_out'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['roadless'] = ['0', '0', '0.915', '-0.88', '#anger', '#disgust', 'negative', '-0.90', 'outback', 'trackless', 'pathless', 'inaccessible', 'untracked']
senticnet['roadside'] = ['0', '0', '0', '-0.13', '#disgust', '#disgust', 'negative', '-0.13', 'corner_two_streets', 'street_corner', 'roadblock', 'city_street', 'schoolyard']
senticnet['roadside_ditch'] = ['0.754', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.867', 'american_flag', 'asphalt', 'rubble', 'pavement', 'pave']
senticnet['roadside_inn'] = ['0.754', '-0.82', '0', '0.947', '#surprise', '#admiration', 'positive', '0.841', 'whorehouse', 'brothel', 'bordello', 'massage_parlor', 'house_of_prostitution']
senticnet['roadside_litter'] = ['-0.56', '0', '0.655', '-0.85', '#anger', '#disgust', 'negative', '-0.69', 'slovenly_woman', 'slut', 'trollop', 'slattern', 'slob']
senticnet['roadside_stand'] = ['0', '0', '0.044', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'large_supermarket', 'food_store', 'large_food_store', 'corner_shop', 'convenience_food_store']
senticnet['roadstead'] = ['0', '0', '0.027', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'port', 'sea', 'boat', 'ocean', 'ship']
senticnet['roadstone'] = ['0', '0', '-0.85', '-0.71', '#fear', '#disgust', 'negative', '-0.78', 'fair_game', 'pit', 'stone_pit', 'target', 'prey']
senticnet['roadtrip'] = ['0', '0', '0.828', '-0.88', '#anger', '#disgust', 'negative', '-0.85', 'outing', 'junket', 'pleasure_trip', 'excursion', 'expedition']
senticnet['roadway'] = ['0.03', '0.071', '0.025', '0.082', '#interest', '#admiration', 'positive', '0.039', 'suspension_bridge', 'under_bridge', 'county_highway', 'bridge', 'long_road']
senticnet['roadway_square'] = ['0', '0', '0.729', '-0.71', '#anger', '#disgust', 'negative', '-0.72', 'swing_set', 'noise_pollution', 'fresh_air', 'dog_poop', 'wildflower']
senticnet['roadwork'] = ['0.754', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.867', 'american_flag', 'asphalt', 'rubble', 'pavement', 'pave']
senticnet['roadworthy'] = ['-0.97', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.96', 'sea-worthy', 'maneuverable', 'seagoing', 'seaworthy', 'beached']
senticnet['roald_dahl_fantastic'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'kit_fox', 'prairie_fox', 'white_fox']
senticnet['roam'] = ['-0.66', '0', '0.949', '-0.75', '#anger', '#disgust', 'negative', '-0.78', 'gad', 'move', 'gallivant', 'jazz_around', 'travel']
senticnet['roam_around'] = ['0.884', '0.776', '0', '0', '#joy', '#interest', 'positive', '0.83', 'commute_work', 'get_place', 'sightsee', 'get_school', 'get_around_town']
senticnet['roam_free'] = ['0.118', '0.28', '-0.20', '0.216', '#interest', '#admiration', 'positive', '0.103', 'friend_person', 'sense_fear', 'mark_territory', 'trip_person', 'care_deeply']
senticnet['roaming_around'] = ['0.047', '-0.01', '0.033', '0.071', '#joy', '#admiration', 'positive', '0.024', 'get_place', 'dawdle_around', 'get_around_town', 'go_to_place', 'stroll']
senticnet['roan'] = ['-0.97', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.96', 'gelding', 'sorrel', 'palomino', 'mare', 'buckskin']
senticnet['roar'] = ['0', '0', '-0.87', '0', '#fear', '#fear', 'negative', '-0.87', 'noise', 'clamor', 'clang', 'sound', 'squeak']
senticnet['roaring'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'deafening', 'hear_loud_sound', 'ear_splitting', 'rowdy', 'noisy']
senticnet['roaring_camp'] = ['-0.64', '0', '0.667', '-0.82', '#anger', '#disgust', 'negative', '-0.71', 'novice', 'beginner', 'tyro', 'tiro', 'initiate']
senticnet['roaring_crowd'] = ['0.653', '0.635', '-0.24', '0.294', '#joy', '#interest', 'positive', '0.335', 'merrily', 'happily', 'mirthfully', 'gayly', 'blithely']
senticnet['roaring_sound'] = ['0', '-0.00', '-0.07', '-0.06', '#fear', '#disgust', 'negative', '-0.04', 'lighten', 'tornado', 'dynamite_explosion', 'forest_fire', 'lightning']
senticnet['roaring_trade'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['roast'] = ['0.032', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.05', 'bake_bread', 'bake_cookie', 'grill_food', 'bake', 'roasted_chicken']
senticnet['roast_beef_sandwich'] = ['0.132', '0.1', '0.063', '0.159', '#joy', '#admiration', 'positive', '0.082', 'chime', 'school_end', 'ring_loudly', 'noise_maker', 'vital']
senticnet['roast_chicken'] = ['0.043', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'apple_pie', 'a_lot_of_food', 'jelly', 'frozen_food', 'cottage_cheese']
senticnet['roast_dinner'] = ['0.783', '-0.63', '0', '0.829', '#joy', '#admiration', 'positive', '0.749', 'leg_of_lamb', 'gigot', 'joint', 'roast', 'rack_of_lamb']
senticnet['roast_duck'] = ['0.783', '-0.63', '0', '0.829', '#joy', '#admiration', 'positive', '0.749', 'leg_of_lamb', 'gigot', 'joint', 'roast', 'rack_of_lamb']
senticnet['roast_fowl'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'cook_meat', 'cook_hamburger', 'cook_food', 'cook_rice', 'brown_meat']
senticnet['roast_lamb'] = ['0.619', '-0.56', '0', '0.875', '#joy', '#admiration', 'positive', '0.685', 'leg_of_lamb', 'gigot', 'joint', 'roast', 'rack_of_lamb']
senticnet['roast_meat'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'cook_meat', 'cook_hamburger', 'cook_food', 'cook_rice', 'brown_meat']
senticnet['roast_pig'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'cook_meat', 'cook_hamburger', 'cook_food', 'cook_rice', 'brown_meat']
senticnet['roast_pork'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'cook_meat', 'cook_hamburger', 'cook_food', 'cook_rice', 'brown_meat']
senticnet['roast_swan'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'saltine', 'soda_cracker', 'cracker', 'water_biscuit', 'graham_cracker']
senticnet['roast_turkey'] = ['0.937', '0', '0', '0.828', '#joy', '#admiration', 'positive', '0.883', 'bakery', 'produce_section', 'fruit_bowl', 'dinner_meal', 'fast_food']
senticnet['roasted_chicken'] = ['0.032', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.05', 'bake_bread', 'bake_cookie', 'grill_food', 'bake', 'roast']
senticnet['roasted_coffee'] = ['0.807', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.859', 'bubble', 'steam', 'hot', 'boil_water', 'tea']
senticnet['roasted_corn'] = ['0.866', '-0.91', '0', '0.893', '#surprise', '#admiration', 'positive', '0.892', 'mooch', 'cadge', 'freeload', 'sponge', 'bum']
senticnet['roasted_garlic'] = ['0.043', '0.091', '0', '0.113', '#interest', '#admiration', 'positive', '0.082', 'sweet_sour', 'tasty', 'yummy', 'popular_food', 'form_of_food']
senticnet['roasted_lamb'] = ['0.783', '-0.63', '0', '0.829', '#joy', '#admiration', 'positive', '0.749', 'leg_of_lamb', 'gigot', 'joint', 'roast', 'rack_of_lamb']
senticnet['roasted_malt'] = ['0', '0.091', '-0.05', '0.073', '#interest', '#admiration', 'positive', '0.037', 'privately', 'hold_liquid', 'make_wish', 'rinse_dish', 'find_toilet']
senticnet['roasted_meat'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'cook_meat', 'cook_hamburger', 'cook_food', 'cook_rice', 'brown_meat']
senticnet['roasted_pepper'] = ['0.041', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.043', 'meatloaf', 'salad', 'delicious_meal', 'casserole', 'apple_pie']
senticnet['roasted_pork_loin'] = ['0.084', '-0.05', '0', '-0.04', '#joy', '#surprise', 'positive', '0.031', 'entree', 'feast', 'satiation', 'breakfast', 'supper']
senticnet['roasted_squab'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'saltine', 'soda_cracker', 'cracker', 'water_biscuit', 'graham_cracker']
senticnet['roasted_tomato'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'mayonnaise', 'box_cereal', 'bottle_water']
senticnet['roasted_turkey'] = ['0.043', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'apple_pie', 'a_lot_of_food', 'jelly', 'frozen_food', 'cottage_cheese']
senticnet['roastery'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'buy_sandwich', 'lunch', 'eat_food', 'buy_food', 'finish_job']
senticnet['roasting_pan'] = ['0.03', '-0.05', '0', '0.059', '#surprise', '#admiration', 'positive', '0.046', 'platter', 'fork', 'spoon', 'tablespoon', 'bowl']
senticnet['roasting_tin'] = ['0.03', '-0.05', '0', '0.059', '#surprise', '#admiration', 'positive', '0.046', 'platter', 'fork', 'spoon', 'tablespoon', 'bowl']
senticnet['roasting_tray'] = ['0.03', '-0.05', '0', '0.059', '#surprise', '#admiration', 'positive', '0.046', 'platter', 'fork', 'spoon', 'tablespoon', 'bowl']
senticnet['rob'] = ['-0.75', '0', '0.847', '-0.78', '#anger', '#disgust', 'negative', '-0.79', 'steal', 'theft', 'steal_money', 'burglary', 'stick_up']
senticnet['rob_bank'] = ['-0.71', '0', '0.717', '0', '#sadness', '#anger', 'negative', '-0.71', 'enter_store', 'play_video_game', 'go_town', 'climb_to_top', 'live_simply']
senticnet['rob_mariano'] = ['-0.08', '-0.03', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'graduate_student', 'student', 'serious_student', 'competition', 'hard_worker']
senticnet['rob_smyth'] = ['-0.71', '0', '0.83', '-0.68', '#sadness', '#anger', 'negative', '-0.74', 'gag', 'jest', 'jape', 'joke', 'laugh']
senticnet['robarts_library'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'a_lot_of_books', 'old_testament', 'cook_book', 'magazine', 'librarian']
senticnet['robben_island_prison'] = ['0', '0', '0.995', '-0.89', '#anger', '#disgust', 'negative', '-0.94', 'shoe_store', 'chess_tournament', 'dress_room', 'front_desk', 'locker_room']
senticnet['robber'] = ['-0.83', '0', '0.831', '-0.69', '#sadness', '#anger', 'negative', '-0.78', 'toughie', 'accessory', 'highbinder', 'hoodlum', 'thug']
senticnet['robber_fly'] = ['0.748', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.873', 'butterfly', 'moth', 'parrot', 'beetle', 'falcon']
senticnet['robbery'] = ['0', '0', '-0.84', '-0.90', '#fear', '#disgust', 'negative', '-0.87', 'steal_money', 'theft', 'burglary', 'rapine', 'thief']
senticnet['robbery_attempt'] = ['0.964', '-0.57', '0', '0.827', '#joy', '#admiration', 'positive', '0.789', 'mortal', 'murder_suspect', 'suspect', 'rape_suspect', 'person']
senticnet['robbery_homicide'] = ['0', '0', '-0.95', '-0.85', '#fear', '#disgust', 'negative', '-0.90', 'choose_victim', 'murder_weapon', 'hide_evidence', 'murder', 'shoot_gun']
senticnet['robbery_suspect'] = ['0.964', '-0.57', '0', '0.827', '#joy', '#admiration', 'positive', '0.789', 'mortal', 'murder_suspect', 'suspect', 'rape_suspect', 'person']
senticnet['robbie'] = ['-0.75', '0', '0.847', '-0.78', '#anger', '#disgust', 'negative', '-0.79', 'steal', 'theft', 'steal_money', 'burglary', 'stick_up']
senticnet['robe'] = ['0.707', '0', '0', '0.98', '#joy', '#admiration', 'positive', '0.843', 'garment', 'slack', 'piece_of_clothing', 'underwear', 'clothes']
senticnet['robeline'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
senticnet['robertson_stadium'] = ['0', '-0.84', '0', '0.723', '#surprise', '#admiration', 'positive', '0.785', 'circular_route', 'stadium', 'memorial', 'station', 'row_house']
senticnet['robinia_pseudoacacia'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'horse_chestnut', 'red_buckeye', 'particolored_buckeye']
senticnet['robinson'] = ['-0.69', '0', '0.89', '-0.70', '#anger', '#disgust', 'negative', '-0.76', 'still', 'suppress', 'moderate', 'hush_up', 'quell']
senticnet['robitussin'] = ['0.969', '0.817', '0', '0.828', '#joy', '#admiration', 'positive', '0.871', 'ibuprofen', 'painkiller', 'medicine', 'analgesic', 'phenobarbital']
senticnet['robot'] = ['0', '0.717', '0', '0.765', '#interest', '#admiration', 'positive', '0.741', 'helper', 'artificial_intelligence', 'goal_driven', 'proactive', 'intelligent']
senticnet['robot_controller'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'biological_machine', 'social_creature', 'live_creature', 'powerful', 'alive']
senticnet['robot_sidekick'] = ['-0.64', '0', '0.524', '-0.59', '#sadness', '#disgust', 'negative', '-0.58', 'bum', 'dreamer', 'daydreamer', 'escapist', 'wishful_thinker']
senticnet['robotic'] = ['0.907', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.881', 'eletronic', 'automatic', 'pistol', 'firearm', 'shoot_weapon']
senticnet['robotic_arm'] = ['0.052', '0.119', '0', '0.141', '#interest', '#admiration', 'positive', '0.104', 'neigh', 'domestic_animal', 'animal_type', 'animal', 'home_animal']
senticnet['robotic_device'] = ['-0.62', '0', '0.996', '-0.68', '#anger', '#disgust', 'negative', '-0.76', 'pack', 'face_pack', 'pickup', 'prosthesis', 'corrective']
senticnet['robotic_exoskeleton'] = ['-0.62', '0', '0.996', '-0.68', '#anger', '#disgust', 'negative', '-0.76', 'pack', 'face_pack', 'pickup', 'prosthesis', 'corrective']
senticnet['robotic_prosthetic'] = ['-0.62', '0', '0.996', '-0.68', '#anger', '#disgust', 'negative', '-0.76', 'pack', 'face_pack', 'pickup', 'prosthesis', 'corrective']
senticnet['robotic_rover'] = ['0.052', '0.119', '0', '0.141', '#interest', '#admiration', 'positive', '0.104', 'neigh', 'domestic_animal', 'animal_type', 'animal', 'home_animal']
senticnet['robotic_telescope'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'binoculars', 'read_glass', 'eyeglass', 'camera', 'monocle']
senticnet['robotic_vacuum'] = ['-0.04', '-0.02', '0.089', '-0.14', '#anger', '#disgust', 'negative', '-0.06', 'steam_locomotive', 'loudspeaker', 'collector', 'siren', 'activist']
senticnet['robotic_vacuum_cleaner'] = ['-0.04', '-0.02', '0.089', '-0.14', '#anger', '#disgust', 'negative', '-0.06', 'steam_locomotive', 'loudspeaker', 'collector', 'siren', 'activist']
senticnet['robotman'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
senticnet['robotnik'] = ['-0.69', '0', '0.756', '-0.99', '#anger', '#disgust', 'negative', '-0.81', 'waster', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['robuchon'] = ['0.897', '-0.96', '0', '0.785', '#joy', '#surprise', 'positive', '0.881', 'erotic', 'voluptuary', 'epicure', 'gourmet', 'sensualist']
senticnet['robust'] = ['0', '0.737', '0', '0.714', '#interest', '#admiration', 'positive', '0.726', 'hardy', 'sturdy', 'stalwart', 'durable', 'resilient']
senticnet['robust_estimation'] = ['-0.91', '0', '0.598', '-0.52', '#sadness', '#anger', 'negative', '-0.68', 'stingy', 'penurious', 'scrimy', 'beggarly', 'chintzy']
senticnet['robustious'] = ['-0.64', '0', '0.941', '-0.66', '#anger', '#disgust', 'negative', '-0.74', 'rowdy', 'boisterous', 'unruly', 'rambunctious', 'disorderly']
senticnet['robustness'] = ['0.817', '0.88', '0', '0', '#joy', '#interest', 'positive', '0.849', 'hardiness', 'validity', 'lustiness', 'research', 'project']
senticnet['robyn_beck_afp_getty'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expansive', 'expansible', 'erectile', 'cavernous']
senticnet['roc'] = ['-0.65', '0', '0.505', '-0.91', '#sadness', '#disgust', 'negative', '-0.69', 'hellhound', 'mythical_being', 'dragon', 'mythical_monster', 'amphisbaena']
senticnet['roc_air_force'] = ['-0.65', '0', '0.505', '-0.91', '#sadness', '#disgust', 'negative', '-0.69', 'hellhound', 'mythical_being', 'dragon', 'mythical_monster', 'amphisbaena']
senticnet['roc_flag'] = ['-0.65', '0', '0.505', '-0.91', '#sadness', '#disgust', 'negative', '-0.69', 'hellhound', 'mythical_being', 'dragon', 'mythical_monster', 'amphisbaena']
senticnet['roc_navy'] = ['-0.65', '0', '0.505', '-0.91', '#sadness', '#disgust', 'negative', '-0.69', 'hellhound', 'mythical_being', 'dragon', 'mythical_monster', 'amphisbaena']
senticnet['rocaberti'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquirer', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['rocawear'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['rochalimaea_quintana'] = ['-0.73', '0', '0.599', '-0.96', '#sadness', '#disgust', 'negative', '-0.76', 'spotted_fever', 'rickettsial_disease', 'rickettsialpox', 'typhus', 'typhus_fever']
senticnet['roche'] = ['0.559', '0.656', '-0.26', '0.285', '#joy', '#interest', 'positive', '0.31', 'zealous', 'avidity', 'eagerness', 'avidness', 'thirstily']
senticnet['rochester_philharmonic'] = ['0.506', '0.659', '-0.24', '0.242', '#joy', '#interest', 'positive', '0.29', 'sing', 'music_studio', 'music_room', 'choir', 'symphony']
senticnet['rocinante'] = ['0', '0', '0', '0.071', '#admiration', '#admiration', 'positive', '0.071', 'two_horses', 'horse', 'barb', 'buddhist', 'ride_horse']
senticnet['rock'] = ['0.12', '0', '0', '0', '#joy', '#joy', 'positive', '0.12', 'incline_plane', 'boulder', 'ramp', 'hill_mountain', 'slide']
senticnet['rock-bottom'] = ['0', '0', '-0.84', '-0.8', '#fear', '#disgust', 'negative', '-0.82', 'bargain-basement', 'super-low', 'dirt-cheap', 'near-record', 'sky-high']
senticnet['rock-loving'] = ['0', '0', '-0.85', '-0.97', '#fear', '#disgust', 'negative', '-0.91', 'middle-school-aged', 'bird-loving', 'limited-resource', 'like-thinking', 'newly-trained']
senticnet['rock-ribbed'] = ['0', '0', '0.832', '-0.90', '#anger', '#disgust', 'negative', '-0.87', 'persistent', 'opinionated', 'obstinately', 'wrongheaded', 'obstinate']
senticnet['rock_anthem'] = ['0.916', '-0.93', '0', '0.674', '#joy', '#surprise', 'positive', '0.841', 'smash', 'bang', 'smasher', 'strike', 'hit']
senticnet['rock_band'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'songwriter', 'band_play']
senticnet['rock_band_nine_inch'] = ['0', '0.078', '0.027', '0.196', '#interest', '#admiration', 'positive', '0.082', 'heel', 'ankle', 'toe', 'foot_massage', 'foot']
senticnet['rock_chair'] = ['-0.76', '0', '-0.76', '0', '#sadness', '#fear', 'negative', '-0.76', 'sofa_bed', 'armchair', 'sofa_hide_bed', 'chair', 'club_chair']
senticnet['rock_chick'] = ['-0.95', '0', '0.941', '-0.57', '#sadness', '#anger', 'negative', '-0.82', 'reply', 'response', 'comeback', 'comebacking', 'backtalk']
senticnet['rock_climb'] = ['0.836', '0', '0', '0.864', '#joy', '#admiration', 'positive', '0.85', 'sport', 'exercise', 'enjoy_nature', 'extreme_sport', 'mountain_climb']
senticnet['rock_climber'] = ['0.518', '-0.51', '0', '0.65', '#surprise', '#admiration', 'positive', '0.562', 'clematis', 'legume', 'leguminous_plant', 'climbing_fern', 'root_climber']
senticnet['rock_concert'] = ['0.838', '0.75', '0', '0.708', '#joy', '#interest', 'positive', '0.765', 'festival', 'party', 'banquet', 'marketplace', 'big_event']
senticnet['rock_crab'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'stone_crab', 'swimming_crab', 'crab', 'fiddler_crab', 'pea_crab']
senticnet['rock_cress'] = ['0.598', '-0.72', '0', '0.941', '#surprise', '#admiration', 'positive', '0.753', 'sicklepod', 'tower_cress', 'tower_mustard', 'cress', 'cress_plant']
senticnet['rock_cress_plant'] = ['0.594', '-0.72', '0', '0.938', '#surprise', '#admiration', 'positive', '0.752', 'sicklepod', 'tower_rock_cress', 'tower_mustard', 'rock_cress', 'cress']
senticnet['rock_crystal'] = ['0.037', '0.073', '0.027', '0.072', '#interest', '#admiration', 'positive', '0.039', 'sapphire', 'birth_stone', 'turquoise', 'gemstone', 'amethyst']
senticnet['rock_formation'] = ['0.039', '0.073', '0.038', '0.107', '#interest', '#admiration', 'positive', '0.045', 'large_rock', 'big_rock', 'geographic_feature', 'land_formation', 'geographical_feature']
senticnet['rock_group'] = ['0.032', '0.092', '0', '0.075', '#interest', '#admiration', 'positive', '0.066', 'eclectic', 'music_player', 'make_musicr', 'musical_group', 'guitar_player']
senticnet['rock_ledge'] = ['0.057', '0.075', '0.039', '0.059', '#interest', '#admiration', 'positive', '0.038', 'summit', 'big_rock', 'high_land', 'peak', 'mountain_side']
senticnet['rock_music'] = ['0.061', '0', '0.017', '0.098', '#joy', '#admiration', 'positive', '0.047', 'form_expression', 'music_type', 'valuable_gem', 'small_boulder', 'music_genre']
senticnet['rock_musician'] = ['0.882', '0.901', '0', '0', '#joy', '#interest', 'positive', '0.891', 'songwriter', 'vip', 'rock_group', 'guitar_player', 'violinist']
senticnet['rock_out'] = ['0', '0.882', '0', '0.796', '#interest', '#admiration', 'positive', '0.839', 'play_song', 'singer', 'perfect_skill', 'remember_childhood', 'play_tune']
senticnet['rock_overhang'] = ['-0.02', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'tunnel', 'dark_place', 'ski_resort', 'top_tree', 'compost_pile']
senticnet['rock_phosphate'] = ['0.071', '0', '0.031', '0.064', '#joy', '#admiration', 'positive', '0.035', 'organic_matter', 'decompose', 'recycle', 'soil', 'compost_pile']
senticnet['rock_plant'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'lithophyte', 'lithophytic_plant']
senticnet['rock_quarry'] = ['0', '0', '-0.85', '-0.71', '#fear', '#disgust', 'negative', '-0.78', 'fair_game', 'pit', 'stone_pit', 'target', 'prey']
senticnet['rock_roll'] = ['0.753', '0', '0', '0.869', '#joy', '#admiration', 'positive', '0.811', 'rhythm', 'hip_hop', 'lyric', 'type_of_music', 'tune']
senticnet['rock_rose_helianthemum'] = ['-0.86', '0', '0.665', '-0.61', '#sadness', '#anger', 'negative', '-0.71', 'brambly', 'braky', 'wooded', 'scrubby', 'forested']
senticnet['rock_samphire'] = ['-0.89', '0', '0.556', '-0.72', '#sadness', '#disgust', 'negative', '-0.72', 'fucus', 'rockweed', 'black_rockweed', 'bladder_fucus', 'tang']
senticnet['rock_star'] = ['0.979', '0.982', '0', '0.752', '#joy', '#interest', 'positive', '0.904', 'hippy', 'philanthropist', 'male_human', 'singer', 'nazi']
senticnet['rock_strewn'] = ['-0.06', '-0.01', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'low', 'ground', 'look_down', 'ground_level', 'under_house']
senticnet['rock_wallaby'] = ['-0.95', '0', '0.624', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['rockabilly_singer'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
senticnet['rocker'] = ['0.107', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.076', 'eclectic', 'gig', 'jazz', 'rock_group', 'folk_music']
senticnet['rocker_arm'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'peavey']
senticnet['rocker_switch'] = ['0.129', '0', '0', '0', '#joy', '#joy', 'positive', '0.129', 'incline_plane', 'boulder', 'ramp', 'hill_mountain', 'rock']
senticnet['rocket'] = ['-0.02', '-0.02', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.04', 'launch', 'sink_ship', 'missile', 'drop_airplane', 'assault']
senticnet['rocket_barrage'] = ['0', '0', '-0.85', '-0.99', '#fear', '#disgust', 'negative', '-0.92', 'onslaught', 'battery', 'shelling', 'bombardment', 'outpouring']
senticnet['rocket_booster'] = ['0', '0', '0', '0.073', '#admiration', '#admiration', 'positive', '0.073', 'fire_starter', 'organic_substance', 'renewable_resource', 'use_fuel', 'tree_product']
senticnet['rocket_engine'] = ['0', '0', '0', '0.073', '#admiration', '#admiration', 'positive', '0.073', 'fire_starter', 'organic_substance', 'renewable_resource', 'use_fuel', 'tree_product']
senticnet['rocket_frog'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'amphibian', 'aquatic']
senticnet['rocket_launcher'] = ['0', '0', '0.818', '-0.99', '#anger', '#disgust', 'negative', '-0.90', 'weapon', 'aerial_bomb', 'machine_gun', 'cannon', 'handgun']
senticnet['rocket_motor'] = ['0', '0', '0', '0.073', '#admiration', '#admiration', 'positive', '0.073', 'fire_starter', 'organic_substance', 'renewable_resource', 'use_fuel', 'tree_product']
senticnet['rocket_propellant'] = ['0', '0', '0', '0.073', '#admiration', '#admiration', 'positive', '0.073', 'fire_starter', 'organic_substance', 'renewable_resource', 'use_fuel', 'tree_product']
senticnet['rocket_propelled_grenade'] = ['0', '0', '0.818', '-0.99', '#anger', '#disgust', 'negative', '-0.90', 'weapon', 'aerial_bomb', 'machine_gun', 'cannon', 'handgun']
senticnet['rocket_propelled_grenade_rpg'] = ['0', '0', '0.818', '-0.99', '#anger', '#disgust', 'negative', '-0.90', 'weapon', 'aerial_bomb', 'machine_gun', 'cannon', 'handgun']
senticnet['rocket_racer'] = ['-0.84', '0', '0.96', '-0.91', '#anger', '#disgust', 'negative', '-0.90', 'housewrecker', 'housebreaker', 'knacker', 'laborer', 'manual_laborer']
senticnet['rocketship'] = ['-0.64', '0', '0.524', '-0.59', '#sadness', '#disgust', 'negative', '-0.58', 'bum', 'dreamer', 'daydreamer', 'escapist', 'wishful_thinker']
senticnet['rockface'] = ['0.057', '0.075', '0.039', '0.059', '#interest', '#admiration', 'positive', '0.038', 'summit', 'big_rock', 'high_land', 'peak', 'mountain_side']
senticnet['rockfall'] = ['0', '-0.03', '-0.10', '-0.07', '#fear', '#disgust', 'negative', '-0.04', 'autumn', 'winter', 'skier', 'snow_fall', 'volcano']
senticnet['rockfoil'] = ['-0.85', '0', '0.795', '-0.74', '#sadness', '#anger', 'negative', '-0.79', 'herb', 'yellow_mountain_saxifrage', 'meadow_saxifrage', 'mossy_saxifrage', 'western_saxifrage']
senticnet['rocking_chair'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'coach_seat', 'stool', 'seat']
senticnet['rocking_horse'] = ['0.71', '0.92', '0', '0', '#joy', '#interest', 'positive', '0.815', 'toy_car', 'toy_ball', 'action_figure', 'toy_marble', 'beanie_baby']
senticnet['rocking_motion'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotate_movement', 'rotate', 'go_around', 'manoeuvre', 'full_circle']
senticnet['rocknroll'] = ['-0.05', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.05', 'masturbate', 'jerk', 'jerk_off', 'watch_porn', 'porn']
senticnet['rockpocket'] = ['0.792', '-0.86', '0', '0.888', '#surprise', '#admiration', 'positive', '0.849', 'absurd', 'inconsequential', 'intuitive', 'visceral', 'nonrational']
senticnet['rockslide'] = ['0', '-0.03', '-0.10', '-0.07', '#fear', '#disgust', 'negative', '-0.04', 'autumn', 'winter', 'skier', 'snow_fall', 'volcano']
senticnet['rockstar_energy'] = ['0.964', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.964', 'carbonated_drink', 'sugar_free', 'coke', 'diet_coke', 'beverage']
senticnet['rocktron'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'lever']
senticnet['rockweed'] = ['-0.89', '0', '0.552', '-0.73', '#sadness', '#disgust', 'negative', '-0.72', 'fucus', 'bladderwrack', 'black_rockweed', 'bladder_fucus', 'tang']
senticnet['rocky'] = ['-0.77', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.82', 'mountain', 'stony', 'ridge', 'hill', 'rock']
senticnet['rocky_beach'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'driftwood', 'sea_shell', 'scenic_view', 'naked_woman', 'tidal_wave']
senticnet['rocky_cliff'] = ['-0.93', '0', '0.607', '-0.67', '#sadness', '#disgust', 'negative', '-0.73', 'spiracle', 'stigma', 'orifice', 'opening', 'porta']
senticnet['rocky_coast'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'driftwood', 'sea_shell', 'scenic_view', 'naked_woman', 'tidal_wave']
senticnet['rocky_desert'] = ['-0.98', '0', '0.441', '-0.96', '#sadness', '#disgust', 'negative', '-0.79', 'desert_land', 'infertile', 'sand', 'barren_land', 'desert']
senticnet['rocky_hill'] = ['0', '0.093', '0', '0.078', '#interest', '#admiration', 'positive', '0.085', 'steep', 'landscape_feature', 'knoll', 'hill_mountain', 'incline']
senticnet['rocky_horror_glee'] = ['0.761', '0.566', '-0.21', '0.293', '#joy', '#interest', 'positive', '0.352', 'gratify', 'jubilation', 'gloat', 'gloating', 'gloatingly']
senticnet['rocky_islet'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'driftwood', 'sea_shell', 'scenic_view', 'naked_woman', 'tidal_wave']
senticnet['rocky_ledge'] = ['0.057', '0.075', '0.039', '0.059', '#interest', '#admiration', 'positive', '0.038', 'summit', 'big_rock', 'high_land', 'peak', 'mountain_side']
senticnet['rocky_mountain_front'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['rocky_mountain_national'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['rocky_mountain_spotted_fever'] = ['0.579', '-0.81', '0', '0.833', '#surprise', '#admiration', 'positive', '0.741', 'typhus', 'rat_typhus', 'urban_typhus', 'endemic_typhus', 'rickettsial_disease']
senticnet['rocky_outcrop'] = ['0.057', '0.075', '0.039', '0.059', '#interest', '#admiration', 'positive', '0.038', 'summit', 'big_rock', 'high_land', 'peak', 'mountain_side']
senticnet['rocky_planet'] = ['0.095', '0', '-0.04', '0.062', '#joy', '#admiration', 'positive', '0.037', 'photographer', 'water_spray', 'moisture', 'rise_water', 'droplet']
senticnet['rocky_reef'] = ['-0.04', '-0.01', '0.103', '0', '#sadness', '#anger', 'negative', '-0.04', 'near_beach', 'along_beach', 'full_water', 'ocean_ground', 'salty']
senticnet['rocky_ridge'] = ['0', '0.093', '0', '0.078', '#interest', '#admiration', 'positive', '0.085', 'steep', 'landscape_feature', 'knoll', 'hill_mountain', 'incline']
senticnet['rocky_sharpe'] = ['-0.09', '0.147', '-0.11', '0.205', '#interest', '#admiration', 'positive', '0.036', 'question_starter', 'resolution', 'available', 'explanation', 'request']
senticnet['rocky_shore'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'driftwood', 'sea_shell', 'scenic_view', 'naked_woman', 'tidal_wave']
senticnet['rocky_shoreline'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'driftwood', 'sea_shell', 'scenic_view', 'naked_woman', 'tidal_wave']
senticnet['rocky_soil'] = ['-0.98', '0', '0.441', '-0.96', '#sadness', '#disgust', 'negative', '-0.79', 'desert_land', 'infertile', 'sand', 'barren_land', 'desert']
senticnet['rococo_architecture'] = ['-0.54', '0', '0.7', '-0.64', '#anger', '#disgust', 'negative', '-0.62', 'flourish', 'rant', 'bombast', 'fustian', 'claptrap']
senticnet['rod'] = ['0.166', '-0.11', '0.025', '0', '#joy', '#surprise', 'positive', '0.084', 'wand', 'metal_rod', 'bar', 'metal_stick', 'cue']
senticnet['rod_jetton'] = ['0.034', '-0.02', '0', '0.057', '#joy', '#admiration', 'positive', '0.039', 'female_television_connector', 'channel_selector', 'power_switch', 'tuner', 'radio']
senticnet['rod_mckuen'] = ['0', '0.176', '0', '0.216', '#interest', '#admiration', 'positive', '0.196', 'choir_stall', 'worshiper', 'pew', 'fundamentalist', 'pulpit']
senticnet['rod_serling_night'] = ['0.07', '0', '0', '0', '#joy', '#joy', 'positive', '0.07', 'artist', 'artwork', 'sculpture', 'tapestry', 'drawing']
senticnet['rod_shaped_bacteria'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'immobile', 'immotile']
senticnet['rod_shaped_bacterium'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'immobile', 'immotile']
senticnet['rod_weeder'] = ['-0.99', '0', '0.827', '-0.54', '#sadness', '#anger', 'negative', '-0.78', 'treat', 'cover', 'handle', 'theologize', 'deal']
senticnet['roddick'] = ['0', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.91', 'umpire', 'referee', 'legal_proceed', 'clang', 'clamor']
senticnet['roddy_white'] = ['0.855', '0.741', '0', '0.862', '#joy', '#admiration', 'positive', '0.819', 'further_goal', 'pass_ball', 'pass_glass', 'help', 'pass_exam']
senticnet['rodent'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'carbon_monoxide', 'tree_animal']
senticnet['rodent_infestation'] = ['0.718', '0', '0', '0', '#joy', '#joy', 'positive', '0.718', 'water_container', 'still_water', 'clean_water', 'water', 'cool_water']
senticnet['rodeo'] = ['0.938', '0.779', '0', '0', '#joy', '#interest', 'positive', '0.859', 'carnival', 'circus', 'fairground', 'fair', 'disneyland']
senticnet['rodeo_arena'] = ['0', '0', '0', '-0.1', '#disgust', '#disgust', 'negative', '-0.1', 'ice_rink', 'skate', 'room_temperature', 'loose_end', 'smooth']
senticnet['rodeo_association'] = ['0.938', '0.779', '0', '0', '#joy', '#interest', 'positive', '0.859', 'carnival', 'circus', 'fairground', 'fair', 'disneyland']
senticnet['rodeo_circuit'] = ['0.938', '0.779', '0', '0', '#joy', '#interest', 'positive', '0.859', 'carnival', 'circus', 'fairground', 'fair', 'disneyland']
senticnet['rodeo_cowboy'] = ['0.938', '0.779', '0', '0', '#joy', '#interest', 'positive', '0.859', 'carnival', 'circus', 'fairground', 'fair', 'disneyland']
senticnet['rodeo_rider'] = ['0', '0', '0', '0.987', '#admiration', '#admiration', 'positive', '0.987', 'emote', 'bold', 'form_of_social_interaction', 'act', 'stage_play']
senticnet['roderick_strong'] = ['-0.57', '0', '0.571', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['rodina_motherland'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'patriotism', 'homeland']
senticnet['rodrigo_borgia'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquirer', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['rodya'] = ['0.911', '-0.71', '0', '0.552', '#joy', '#surprise', 'positive', '0.726', 'deliver_goods', 'succeed', 'win', 'bring_home_bacon', 'come_through']
senticnet['roe'] = ['-0.74', '0', '0.748', '-0.81', '#anger', '#disgust', 'negative', '-0.77', 'coral', 'caviar', 'caviare', 'shad_roe', 'seafood']
senticnet['roer_river'] = ['0.737', '-0.96', '0', '0.552', '#joy', '#surprise', 'positive', '0.75', 'prominence', 'hump', 'bump', 'swelling', 'frontal_eminence']
senticnet['roeselare'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'feudal_lord', 'lord', 'master', 'overlord', 'king']
senticnet['rogue'] = ['0', '0', '0', '-0.90', '#disgust', '#disgust', 'negative', '-0.90', 'traitor', 'seaman', 'officer', 'leader', 'master']
senticnet['rogue_gallery'] = ['-0.96', '0', '0.581', '-0.59', '#sadness', '#disgust', 'negative', '-0.71', 'camp', 'pack', 'clique', 'coterie', 'ingroup']
senticnet['rogue_planet'] = ['0.045', '0', '0', '0.051', '#joy', '#admiration', 'positive', '0.048', 'perfect_sphere', 'pour_hot_coffee_mug', 'life', 'alkaline', 'wrest']
senticnet['rogue_regime'] = ['-0.98', '0', '0.654', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'bereave', 'expropriate', 'deprive', 'unclothe', 'take']
senticnet['roguery'] = ['-0.90', '0', '0.757', '-0.56', '#sadness', '#anger', 'negative', '-0.74', 'misbehavior', 'misdeed', 'hell', 'vandalism', 'hooliganism']
senticnet['roguish'] = ['0.85', '-0.71', '0', '0.617', '#joy', '#surprise', 'positive', '0.728', 'pretender', 'phony', 'phoney', 'hypocrite', 'dissembler']
senticnet['roguishness'] = ['0.733', '-0.89', '0', '0.626', '#joy', '#surprise', 'positive', '0.752', 'rascality', 'mischievousness', 'monkey_business', 'deviltry', 'devilry']
senticnet['rohypnol'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'use_drug', 'do_drug', 'drug_use', 'drug', 'addiction']
senticnet['roidmude'] = ['-0.53', '0', '0.637', '-0.92', '#anger', '#disgust', 'negative', '-0.69', 'incorporeal', 'unembodied', 'disembodied', 'bodiless', 'unbodied']
senticnet['roil'] = ['-0.13', '0.071', '0.694', '-0.67', '#anger', '#disgust', 'negative', '-0.35', 'crucify', 'dun', 'frustrating', 'aggravate', 'exasperation']
senticnet['roister'] = ['0.592', '-0.92', '0', '0.703', '#surprise', '#admiration', 'positive', '0.741', 'racket', 'revel', 'make_whoopie', 'make_merry', 'make_happy']
senticnet['rojo'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'cabbage', 'lettuce', 'broccoli']
senticnet['rolandine'] = ['-0.56', '0', '0.828', '-0.68', '#anger', '#disgust', 'negative', '-0.69', 'abruptness', 'precipitateness', 'precipitousness', 'hastiness', 'precipitancy']
senticnet['role'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'baddie', 'theatrical_baddie', 'part', 'persona']
senticnet['role_model'] = ['0.966', '-0.66', '0', '0.578', '#joy', '#surprise', 'positive', '0.738', 'nonesuch', 'nonsuch', 'ideal', 'nonpareil', 'apotheosis']
senticnet['role_play'] = ['0.892', '0.761', '0', '0', '#joy', '#interest', 'positive', '0.827', 'stage_play', 'play_part', 'part_of_play', 'act_part', 'drama']
senticnet['role_player'] = ['-0.92', '0', '0.54', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'beguiler', 'slicker', 'artist', 'ringer', 'deceiver']
senticnet['roll'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'poppy_seed', 'custard', 'bread', 'biscuit']
senticnet['roll_away'] = ['0.081', '0', '0', '0.065', '#joy', '#admiration', 'positive', '0.073', 'dribble', 'wind_up', 'bounce_up_down', 'go_game', 'break_window']
senticnet['roll_ball'] = ['-0.17', '0', '-0.07', '-0.04', '#sadness', '#fear', 'negative', '-0.09', 'trade_baseball_card', 'bicycle_school', 'ring_doorbell', 'laugh_clown', 'throw_fit']
senticnet['roll_call_vote'] = ['0.779', '-0.88', '0', '0.521', '#joy', '#surprise', 'positive', '0.727', 'corrigible', 'correctable', 'improvable', 'reformable', 'redeemable']
senticnet['roll_dice'] = ['0.732', '-0.88', '0', '0', '#joy', '#surprise', 'positive', '0.809', 'try_win', 'loose', 'strategize', 'keep_score', 'win']
senticnet['roll_downhill'] = ['-0.11', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.08', 'have_fun', 'spring_up', 'fall_well', 'shovel_snow', 'fly_around']
senticnet['roll_off_roro'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'poppy_seed', 'custard', 'bread', 'biscuit']
senticnet['roll_over'] = ['-0.07', '0', '-0.04', '0', '#sadness', '#fear', 'negative', '-0.06', 'collide', 'fall_off_chair', 'feel_terrible', 'fly_around', 'cross_bridge']
senticnet['roll_royce_ghost'] = ['0.755', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.728', 'high_class', 'expensive_car', 'ferrari', 'fancy_car', 'sports_car']
senticnet['roll_royce_phantom'] = ['0.028', '0', '0.013', '0.061', '#joy', '#admiration', 'positive', '0.025', 'truck', 'vehicle', 'big_car', 'car', 'lorry']
senticnet['roll_royce_silver'] = ['-0.94', '0', '0.866', '-0.91', '#sadness', '#disgust', 'negative', '-0.90', 'movement', 'apparent_motion', 'motion', 'apparent_movement', 'optical_illusion']
senticnet['roll_royce_silver_wraith'] = ['0.028', '0', '0.013', '0.061', '#joy', '#admiration', 'positive', '0.025', 'truck', 'vehicle', 'big_car', 'car', 'lorry']
senticnet['roll_up'] = ['-0.59', '0', '0.604', '-0.95', '#anger', '#disgust', 'negative', '-0.71', 'pile_up', 'run_up', 'collect', 'accumulate', 'amass']
senticnet['rolled'] = ['0.611', '-0.54', '0', '0.524', '#joy', '#surprise', 'positive', '0.559', 'convoluted', 'convolute', 'coiled', 'involute', 'wound']
senticnet['rolled_cigarette'] = ['0.031', '0', '-0.08', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'smoke_cigarette', 'smoke', 'cinder', 'tobacco_product', 'smoke_pipe']
senticnet['rolled_homogeneous_armour_rha'] = ['0.89', '-0.62', '0', '0.548', '#joy', '#surprise', 'positive', '0.686', 'breakthrough', 'interpenetration', 'foray', 'maraud', 'raid']
senticnet['rolled_onto'] = ['-0.11', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.08', 'have_fun', 'spring_up', 'fall_well', 'shovel_snow', 'fly_around']
senticnet['rolled_towel'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'lavatory', 'commode', 'potty_chair']
senticnet['roller'] = ['-0.03', '0.077', '0.048', '0.091', '#interest', '#admiration', 'positive', '0.02', 'near_beach', 'along_beach', 'round', 'near_sea', 'depth']
senticnet['roller-coaster'] = ['0', '0', '-0.71', '-0.79', '#fear', '#disgust', 'negative', '-0.75', 'ferris_wheel', 'carnival_ride', 'carny', 'merry_go_round', 'fortune_teller']
senticnet['roller_blade'] = ['0.05', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.046', 'skate', 'rollerblade', 'skateboard', 'ice_skate', 'skating']
senticnet['roller_coaster'] = ['0.05', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.046', 'have_fun', 'motorcycle', 'bike', 'sports_car', 'scooter']
senticnet['roller_coaster_ride'] = ['0', '0', '-0.86', '-0.86', '#fear', '#disgust', 'negative', '-0.86', 'amusement_park', 'have_fun', 'drag_race', 'pia_lot_of_seat', 'roller_coaster']
senticnet['roller_mill'] = ['-0.78', '0', '0.699', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'battery', 'stamp_battery', 'quartz_battery', 'quartz_mill', 'factory']
senticnet['roller_rink'] = ['0.05', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.046', 'skate', 'rollerblade', 'skateboard', 'ice_skate', 'skating']
senticnet['roller_skate'] = ['0.05', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.046', 'skate', 'rollerblade', 'skateboard', 'ice_skate', 'skating']
senticnet['roller_skating'] = ['0.964', '0.713', '0', '0', '#joy', '#interest', 'positive', '0.839', 'martial_art', 'judo', 'jump_around', 'go_underwater', 'athletics']
senticnet['roller_skating_rink'] = ['0.089', '-0.18', '0.037', '-0.06', '#joy', '#surprise', 'positive', '0.043', 'balloon', 'ballon', 'fielder', 'kite', 'blimp']
senticnet['rollerblade'] = ['0.049', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.046', 'skate', 'roller_skate', 'skateboard', 'ice_skate', 'skating']
senticnet['rollercoaster'] = ['0.086', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'carny', 'fortune_teller', 'midway', 'ferris_wheel', 'roller-coaster']
senticnet['rollercoaster_ride'] = ['0', '0', '0.954', '-0.76', '#anger', '#disgust', 'negative', '-0.85', 'fervid', 'ardent', 'impassioned', 'fervent', 'fiery']
senticnet['rollerskate'] = ['0.05', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.046', 'skate', 'rollerblade', 'skateboard', 'ice_skate', 'skating']
senticnet['rollicking'] = ['0.823', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.832', 'toe-tapping', 'foot-stomping', 'down-home', 'good-time', 'hard-rocking']
senticnet['rolling'] = ['0.901', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.885', 'extensive', 'sweeping', 'expansive', 'spacious', 'open']
senticnet['rolling_downhill'] = ['-0.11', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.08', 'have_fun', 'spring_up', 'fall_well', 'shovel_snow', 'fly_around']
senticnet['rolling_friction'] = ['0', '0', '-0.11', '0', '#fear', '#fear', 'negative', '-0.11', 'chemical_reaction', 'set_fire', 'grow_yard', 'heat_light', 'burn_down_house']
senticnet['rolling_hitch'] = ['-0.95', '0', '0.793', '0', '#sadness', '#anger', 'negative', '-0.87', 'block', 'obstacle', 'nodus', 'impediment', 'obstruction']
senticnet['rolling_mill'] = ['0.692', '-0.53', '0', '0.68', '#joy', '#admiration', 'positive', '0.634', 'steel_factory', 'factory', 'mill', 'manufacturing_plant', 'manufactory']
senticnet['rolling_resistance'] = ['0', '0', '-0.11', '0', '#fear', '#fear', 'negative', '-0.11', 'chemical_reaction', 'set_fire', 'grow_yard', 'heat_light', 'burn_down_house']
senticnet['rolling_stock'] = ['0.547', '-0.69', '0', '0.52', '#joy', '#surprise', 'positive', '0.586', 'vivification', 'invigoration', 'activity', 'animation', 'activating']
senticnet['rolling_stone'] = ['0.241', '0.185', '-0.11', '0.16', '#joy', '#interest', 'positive', '0.118', 'eclectic', 'rock_group', 'musical_group', 'sound_nice', 'music_group']
senticnet['rolling_stone_critic'] = ['0.7', '-0.62', '0', '0.58', '#joy', '#surprise', 'positive', '0.636', 'white_magic', 'thaumaturgy', 'juju', 'conjuration', 'supernaturalism']
senticnet['rolling_stone_journalist'] = ['0.7', '-0.62', '0', '0.58', '#joy', '#surprise', 'positive', '0.636', 'white_magic', 'thaumaturgy', 'juju', 'conjuration', 'supernaturalism']
senticnet['rolling_stone_magazine'] = ['0.997', '0.892', '0', '0', '#joy', '#interest', 'positive', '0.945', 'advertisement', 'large_outdoor_board', 'motorway', 'expressway', 'train_track']
senticnet['rolling_stone_writer'] = ['0.7', '-0.62', '0', '0.58', '#joy', '#surprise', 'positive', '0.636', 'white_magic', 'thaumaturgy', 'juju', 'conjuration', 'supernaturalism']
senticnet['roly-poly'] = ['0', '0', '0.95', '-0.95', '#anger', '#disgust', 'negative', '-0.95', 'overweight', 'obese', 'gross', 'a_lot_of_fat', 'heavy']
senticnet['rom_com'] = ['-0.90', '0.152', '-0.62', '0.061', '#sadness', '#fear', 'negative', '-0.33', 'guilt_feeling', 'guilty', 'dolor', 'grieve', 'penitentially']
senticnet['rom_terminus'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'expressway', 'turnpike', 'toll_road', 'highway']
senticnet['rom_tiburtina'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'expressway', 'turnpike', 'toll_road', 'highway']
senticnet['romain_pizzi'] = ['-0.02', '0', '-0.06', '-0.03', '#fear', '#disgust', 'negative', '-0.04', 'alleyway', 'animal_shelter', 'vet_office', 'home', 'doghouse']
senticnet['romaine_lettuce'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'cabbage', 'broccoli', 'pea']
senticnet['roman'] = ['-0.03', '0.092', '0.07', '0.1', '#interest', '#admiration', 'positive', '0.021', 'gladiator', 'civilization', 'church_build', 'coliseum', 'every_town']
senticnet['roman_alphabet'] = ['-0.95', '0', '0.517', '-0.53', '#sadness', '#disgust', 'negative', '-0.67', 'copperplate', 'hand', 'minuscule', 'italic', 'round_hand']
senticnet['roman_arch'] = ['0.029', '0.087', '0.065', '0.137', '#interest', '#admiration', 'positive', '0.047', 'archway', 'aisle', 'mat', 'passageway', 'right_angle']
senticnet['roman_bath_museum'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
senticnet['roman_catholic'] = ['0.013', '0.199', '0.027', '0.167', '#interest', '#admiration', 'positive', '0.088', 'church', 'confession', 'good_catholic', 'spirituality', 'christianity']
senticnet['roman_catholic_cemetery'] = ['-0.50', '0', '0.762', '-0.89', '#anger', '#disgust', 'negative', '-0.71', 'cemetery', 'graveyard', 'burial_site', 'grassland', 'burying_ground']
senticnet['roman_catholic_church'] = ['0.013', '0.199', '0.027', '0.167', '#interest', '#admiration', 'positive', '0.088', 'church', 'confession', 'good_catholic', 'spirituality', 'christianity']
senticnet['roman_catholic_clergy'] = ['0.013', '0.199', '0.027', '0.167', '#interest', '#admiration', 'positive', '0.088', 'church', 'confession', 'good_catholic', 'spirituality', 'christianity']
senticnet['roman_catholic_doctrine'] = ['0.013', '0.199', '0.027', '0.167', '#interest', '#admiration', 'positive', '0.088', 'church', 'confession', 'good_catholic', 'spirituality', 'christianity']
senticnet['roman_catholic_hierarchy'] = ['0.013', '0.199', '0.027', '0.167', '#interest', '#admiration', 'positive', '0.088', 'church', 'confession', 'good_catholic', 'spirituality', 'christianity']
senticnet['roman_catholic_priest'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'acolyte', 'reverend', 'thurifer', 'clergyman', 'man_of_cloth']
senticnet['roman_catholic_priesthood'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'acolyte', 'reverend', 'thurifer', 'clergyman', 'man_of_cloth']
senticnet['roman_catholic_theologian'] = ['-0.85', '0', '0.749', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'hairsplitter', 'fight', 'quarrel', 'obstructor', 'obstructer']
senticnet['roman_catholic_theology'] = ['0.664', '-0.57', '0', '0.981', '#joy', '#admiration', 'positive', '0.741', 'theodicy', 'homiletics', 'discipline', 'study', 'subject']
senticnet['roman_catholicism'] = ['0.013', '0.199', '0.027', '0.167', '#interest', '#admiration', 'positive', '0.088', 'church', 'confession', 'good_catholic', 'spirituality', 'christianity']
senticnet['roman_civilization'] = ['-0.03', '0.092', '0.07', '0.1', '#interest', '#admiration', 'positive', '0.021', 'gladiator', 'civilization', 'church_build', 'coliseum', 'every_town']
senticnet['roman_cursive'] = ['-0.95', '0', '0.517', '-0.53', '#sadness', '#disgust', 'negative', '-0.67', 'copperplate', 'hand', 'minuscule', 'italic', 'round_hand']
senticnet['roman_empire'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['roman_forum'] = ['-0.03', '0.092', '0.07', '0.1', '#interest', '#admiration', 'positive', '0.021', 'gladiator', 'civilization', 'church_build', 'coliseum', 'every_town']
senticnet['roman_glick'] = ['0.609', '-0.74', '0', '0.716', '#surprise', '#admiration', 'positive', '0.69', 'malacca', 'sword', 'swagger_stick', 'sword_cane', 'sword_stick']
senticnet['roman_imperial'] = ['-0.03', '0.092', '0.07', '0.1', '#interest', '#admiration', 'positive', '0.021', 'gladiator', 'civilization', 'church_build', 'coliseum', 'every_town']
senticnet['roman_inquisition'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquirer', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['roman_martyr'] = ['0', '0.248', '-0.08', '0.173', '#interest', '#admiration', 'positive', '0.113', 'god', 'minister', 'evangelist', 'rector', 'goddess']
senticnet['roman_nose'] = ['-0.18', '-0.12', '0.049', '-0.24', '#sadness', '#disgust', 'negative', '-0.09', 'tree_trunk', 'plank', 'artificial_leg', 'hardwood', 'tree_wood']
senticnet['roman_numeral'] = ['0.049', '0.113', '0.107', '0.162', '#interest', '#admiration', 'positive', '0.054', 'list_number', 'ordinal', 'count', 'numerical', 'enumeration']
senticnet['roman_pontifical'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'reverence', 'obeisance', 'genuflexion', 'kowtow', 'mafia']
senticnet['roman_republic'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['roman_triumphal_arch'] = ['0.029', '0.087', '0.065', '0.137', '#interest', '#admiration', 'positive', '0.047', 'archway', 'aisle', 'mat', 'passageway', 'right_angle']
senticnet['romance'] = ['0.974', '0.789', '0', '0.828', '#joy', '#admiration', 'positive', '0.864', 'foreplay', 'comedian', 'express_love', 'cherish', 'happy_feeling']
senticnet['romance_fiction'] = ['0.036', '0.219', '0', '0.19', '#interest', '#admiration', 'positive', '0.148', 'book_genre', 'literature', 'concoction', 'untruth', 'falsehood']
senticnet['romance_genre'] = ['0.036', '0.219', '0', '0.19', '#interest', '#admiration', 'positive', '0.148', 'book_genre', 'literature', 'concoction', 'untruth', 'falsehood']
senticnet['romance_novel'] = ['0.971', '0', '0', '0.746', '#joy', '#admiration', 'positive', '0.859', 'biography', 'torah', 'literature', 'chapter', 'news_story']
senticnet['romancer'] = ['0', '-0.98', '0', '0.923', '#surprise', '#admiration', 'positive', '0.953', 'idealized', 'romantic', 'romanticize', 'glamorize', 'romanticized']
senticnet['romanesque_architecture'] = ['0.578', '-0.95', '0', '0.979', '#surprise', '#admiration', 'positive', '0.838', 'perpendicular', 'perpendicular_style', 'gothic_architecture', 'style_of_architecture', 'type_of_architecture']
senticnet['romanesque_building'] = ['0.026', '0.219', '0', '0.16', '#interest', '#admiration', 'positive', '0.135', 'large_build', 'palace', 'high_rise', 'structure', 'fortification']
senticnet['romanesque_revival'] = ['0.578', '-0.95', '0', '0.979', '#surprise', '#admiration', 'positive', '0.838', 'perpendicular', 'perpendicular_style', 'gothic_architecture', 'style_of_architecture', 'type_of_architecture']
senticnet['romani_gypsy'] = ['-0.96', '0', '0.766', '-0.96', '#sadness', '#disgust', 'negative', '-0.89', 'calumniation', 'traducement', 'hatchet_job', 'defamation', 'calumny']
senticnet['romanian_communist'] = ['0', '0', '-0.70', '-0.83', '#fear', '#disgust', 'negative', '-0.76', 'settle_disagreement', 'hitler', 'republican', 'conservative', 'unchanging']
senticnet['romanian_politician'] = ['0', '0.868', '0', '0.853', '#interest', '#admiration', 'positive', '0.861', 'country_leader', 'prime_minister', 'president', 'elect_official', 'leader']
senticnet['romantic'] = ['0.99', '0.885', '0', '0.91', '#joy', '#admiration', 'positive', '0.928', 'serene', 'cool', 'easy', 'thrill', 'tranquility']
senticnet['romantic_adventure'] = ['0.704', '-0.80', '0', '0', '#joy', '#surprise', 'positive', '0.755', 'quest', 'stunt', 'illusion', 'journey', 'activity']
senticnet['romantic_affair'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'liaison', 'affair', 'involvement']
senticnet['romantic_atmosphere'] = ['0', '0.232', '-0.10', '0.156', '#interest', '#admiration', 'positive', '0.094', 'light', 'eat_cake', 'birthday_party', 'candle', 'candle_light']
senticnet['romantic_attraction'] = ['0.833', '0', '0', '0.807', '#joy', '#admiration', 'positive', '0.82', 'lust', 'amiable', 'give_hug', 'giggle', 'smile']
senticnet['romantic_comedy'] = ['0.206', '-0.13', '0', '0', '#joy', '#surprise', 'positive', '0.172', 'dancer', 'old_dance', 'dance', 'slow_dance', 'ballroom_dance']
senticnet['romantic_date'] = ['0.076', '0.083', '0', '-0.06', '#joy', '#interest', 'positive', '0.032', 'trattoria', 'pizzeria', 'place_eat', 'restaurant_diner', 'eatery']
senticnet['romantic_dinner'] = ['0.701', '0.8', '0', '0.762', '#interest', '#admiration', 'positive', '0.754', 'use_chair', 'weekend', 'go_sleep', 'miss_work', 'smoke_wee']
senticnet['romantic_epic'] = ['0.089', '-0.01', '0.033', '0.082', '#joy', '#admiration', 'positive', '0.039', 'informative', 'informational', 'ephemeral', 'fun_read', 'ads']
senticnet['romantic_fantasy'] = ['0.747', '0.85', '0', '0', '#joy', '#interest', 'positive', '0.798', 'night_vision', 'mystery', 'plot', 'imagine', 'myth']
senticnet['romantic_fascination'] = ['0.601', '0.647', '-0.26', '0.322', '#joy', '#interest', 'positive', '0.326', 'captivated', 'charmed', 'captivate', 'bewitch', 'enthrallment']
senticnet['romantic_getaway'] = ['0.878', '0.84', '0', '0.91', '#joy', '#admiration', 'positive', '0.876', 'stumble', 'slip', 'fall', 'trip', 'tumble']
senticnet['romantic_idealism'] = ['-0.50', '0', '0.733', '-0.55', '#anger', '#disgust', 'negative', '-0.59', 'originality', 'novelty', 'innovativeness', 'freshness', 'ability']
senticnet['romantic_intrigue'] = ['0.945', '0.996', '0', '0.737', '#joy', '#interest', 'positive', '0.893', 'become_intelligent', 'gain_knowledge', 'increase_knowledge', 'learn', 'desire_education']
senticnet['romantic_love'] = ['0.833', '0', '0', '0.807', '#joy', '#admiration', 'positive', '0.82', 'lust', 'amiable', 'give_hug', 'giggle', 'smile']
senticnet['romantic_mood'] = ['0', '1', '0', '0', '#interest', '#interest', 'positive', '1.0', 'interest', 'interesting', 'attention', 'strong_emotion', 'emotion']
senticnet['romantic_nostalgia'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'nonsense', 'meaninglessness', 'bunk', 'absurdity', 'hokum']
senticnet['romantic_relationship'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'liaison', 'affair', 'involvement']
senticnet['romantic_rival'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'addicted', 'obsessed_love', 'addiction', 'obsession', 'weird']
senticnet['romanticism'] = ['0.136', '-0.11', '0.031', '0', '#joy', '#surprise', 'positive', '0.075', 'rap', 'popular_music', 'music', 'band_play', 'rock_roll']
senticnet['romanticist'] = ['0.899', '-0.87', '0', '0.564', '#joy', '#surprise', 'positive', '0.781', 'mortal', 'person', 'individual', 'writer', 'artist']
senticnet['romanticize'] = ['0', '-0.98', '0', '0.927', '#surprise', '#admiration', 'positive', '0.955', 'idealized', 'romantic', 'romancer', 'glamorize', 'romanticized']
senticnet['romanticized'] = ['0', '-0.98', '0', '0.918', '#surprise', '#admiration', 'positive', '0.954', 'idealized', 'romantic', 'romancer', 'glamorize', 'romanticize']
senticnet['romcom'] = ['-0.90', '0.152', '-0.62', '0.061', '#sadness', '#fear', 'negative', '-0.33', 'guilt_feeling', 'guilty', 'dolor', 'grieve', 'penitentially']
senticnet['rome'] = ['0', '0', '0', '0.076', '#admiration', '#admiration', 'positive', '0.076', 'newyork', 'london', 'beijing', 'big_city', 'tokyo']
senticnet['romema'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['romina_power'] = ['0.107', '0.11', '-0.08', '0.21', '#interest', '#admiration', 'positive', '0.086', 'live_healthy_life', 'forever_young', 'live_forever', 'live_long', 'live_good_life']
senticnet['romish_church'] = ['-0.52', '0', '0.604', '-0.82', '#anger', '#disgust', 'negative', '-0.65', 'discernment', 'sagacity', 'sagaciousness', 'confidentiality', 'judgment']
senticnet['romp'] = ['0.519', '-0.85', '0', '0.593', '#surprise', '#admiration', 'positive', '0.656', 'frolic', 'gambol', 'coquetry', 'play', 'caper']
senticnet['romped_home'] = ['0', '-0.04', '0.085', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'claw', 'fur_keep_warm', 'tic', 'bristle', 'muzzle']
senticnet['ronr'] = ['0.779', '-0.88', '0', '0.521', '#joy', '#surprise', 'positive', '0.727', 'corrigible', 'correctable', 'improvable', 'reformable', 'redeemable']
senticnet['ronse'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'feudal_lord', 'lord', 'master', 'overlord', 'king']
senticnet['rood_loft'] = ['0.023', '0.218', '0.01', '0.235', '#interest', '#admiration', 'positive', '0.116', 'confessional', 'altar', 'chapel', 'temple', 'christianity']
senticnet['roof'] = ['0', '0.074', '0', '0.108', '#interest', '#admiration', 'positive', '0.091', 'wall', 'horizontal_plane', 'house_front', 'foundation', 'home_entrance']
senticnet['roof_hatch'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'chick', 'wild_bird', 'baby_bird', 'lay_egg', 'bird_home']
senticnet['roof_insulation'] = ['-0.57', '0', '0.855', '-0.95', '#anger', '#disgust', 'negative', '-0.79', 'soundproof', 'modify', 'alter', 'change', 'weatherstrip']
senticnet['roof_rack'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'train_station', 'government_office', 'cable_car', 'sport_stadium', 'terminal']
senticnet['roof_spoiler'] = ['0.725', '0.796', '0', '0', '#joy', '#interest', 'positive', '0.76', 'car_stereo', 'battery', 'seatbelt', 'spare_tire', 'brake_pedal']
senticnet['roof_terrace'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'stage_border', 'row_seat', 'stage_entrance', 'theater_seat', 'theater_box']
senticnet['roofer'] = ['-0.64', '0', '0.879', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'drone', 'poke', 'dawdler', 'laggard', 'straggler']
senticnet['roofless'] = ['-0.79', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.80', 'windowless', 'dilapidated', 'mud-brick', 'multi-story', 'ramshackle']
senticnet['rooftop'] = ['0.591', '-0.74', '0', '0.672', '#surprise', '#admiration', 'positive', '0.669', 'room', 'chillax', 'hotel_room', 'furniture_store', 'living_room']
senticnet['rooftop_bar'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'drink', 'party', 'friend_gathering', 'rest_area', 'relax_time']
senticnet['rooftop_deck'] = ['0.841', '0', '0', '0.71', '#joy', '#admiration', 'positive', '0.775', 'waiter', 'bar_service', 'beverage_service', 'concierge', 'minibar']
senticnet['rooftop_greenhouse'] = ['-0.12', '0.066', '-0.08', '-0.06', '#sadness', '#fear', 'negative', '-0.05', 'garden_yard', 'see_sky', 'plant_tree', 'hair', 'seed_lawn']
senticnet['rooftop_patio'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'lurking_place', 'spot', 'drop', 'hiding_place', 'den']
senticnet['rooftop_pool'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'beachcomber', 'bum', 'drifter']
senticnet['rooftop_restaurant'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'beachcomber', 'bum', 'drifter']
senticnet['rooftop_terrace'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'stage_border', 'row_seat', 'stage_entrance', 'theater_seat', 'theater_box']
senticnet['rook'] = ['0', '0', '0', '0.115', '#admiration', '#admiration', 'positive', '0.115', 'chess_knight', 'strategy', 'pawn', 'chess_queen', 'chess_rook']
senticnet['rook_pawn'] = ['0', '0', '0', '0.115', '#admiration', '#admiration', 'positive', '0.115', 'chess_knight', 'strategy', 'pawn', 'chess_queen', 'chess_rook']
senticnet['rookery'] = ['-0.55', '0', '0.996', '-0.50', '#sadness', '#anger', 'negative', '-0.68', 'pesthole', 'breeding_ground', 'tract', 'piece_of_land', 'piece_of_ground']
senticnet['rookie'] = ['-0.88', '0', '0.968', '-0.78', '#sadness', '#anger', 'negative', '-0.87', 'novice', 'beginner', 'tyro', 'tiro', 'initiate']
senticnet['rookie_mini'] = ['0', '0.156', '-0.06', '0.198', '#interest', '#admiration', 'positive', '0.098', 'grassland', 'camping', 'camp_out', 'campsite', 'campground']
senticnet['rookie_pitcher'] = ['0.663', '-0.68', '0', '0.892', '#surprise', '#admiration', 'positive', '0.747', 'saphead', 'muggins', 'tomfool', 'fool', 'sap']
senticnet['rool'] = ['0', '0.177', '0', '0.126', '#interest', '#admiration', 'positive', '0.151', 'treasure', 'storage_facility', 'shrine', 'lock_box', 'jewelry_box']
senticnet['room'] = ['0', '-0.07', '0', '0', '#surprise', '#surprise', 'positive', '0.078', 'home', 'apartment', 'furniture', 'kitchen', 'flat']
senticnet['room_air_conditioner'] = ['0.046', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'cold', 'electricity', 'circulate_air', 'blow_air', 'breeze']
senticnet['room_amenity_include'] = ['0.866', '0.833', '0', '0', '#joy', '#interest', 'positive', '0.849', 'alcohol', 'porter', 'concierge', 'beer_mug', 'bar_stool']
senticnet['room_coffee_maker'] = ['0.866', '0.833', '0', '0', '#joy', '#interest', 'positive', '0.849', 'alcohol', 'porter', 'concierge', 'beer_mug', 'bar_stool']
senticnet['room_divider'] = ['0', '0.089', '0', '0', '#interest', '#interest', 'positive', '0.089', 'ground', 'basement', 'base', 'foundation', 'basis']
senticnet['room_log_cabin'] = ['0', '0.113', '0', '0.111', '#interest', '#admiration', 'positive', '0.112', 'animal_house', 'cow_home', 'animal_place', 'crop_place', 'acreage']
senticnet['room_part'] = ['0.739', '0.774', '0', '0', '#joy', '#interest', 'positive', '0.756', 'horizontal_plane', 'shut', 'part_of_room', 'build_part', 'rectangular_shape']
senticnet['room_shack'] = ['-0.03', '-0.00', '0.066', '0', '#sadness', '#anger', 'negative', '-0.03', 'dirt', 'allergy', 'dust', 'wet_floor', 'insect']
senticnet['room_temperature'] = ['0', '0.103', '0', '0', '#interest', '#interest', 'positive', '0.103', 'load_dock', 'car_race', 'sea_level', 'smithsonian', 'auction']
senticnet['roomba'] = ['-0.04', '-0.02', '0.089', '-0.14', '#anger', '#disgust', 'negative', '-0.06', 'steam_locomotive', 'loudspeaker', 'collector', 'siren', 'activist']
senticnet['roomette'] = ['-0.58', '0', '0.714', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'waster', 'diversionist', 'destroyer', 'ruiner', 'undoer']
senticnet['rooming_house'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'home', 'furniture', 'kitchen', 'flat']
senticnet['roommate'] = ['0', '0.714', '0', '0.754', '#interest', '#admiration', 'positive', '0.734', 'tenant', 'ceiling_fan', 'citizen', 'stair_rail', 'resident']
senticnet['roomy'] = ['0', '0.8', '0', '0.708', '#interest', '#admiration', 'positive', '0.754', 'capacious', 'commodious', 'voluminous', 'spacious', 'ample']
senticnet['roomy_enough'] = ['0.157', '0.126', '0', '0.166', '#joy', '#admiration', 'positive', '0.15', 'comfortable', 'sleep_place', 'rest_place', 'restful', 'occur']
senticnet['roomy_khan'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'busy_bee', 'gopher', 'actor', 'doer', 'worker']
senticnet['roopa'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['roosevelt_elk'] = ['0', '-0.97', '0', '0', '#surprise', '#surprise', 'positive', '0.977', 'cougar', 'skunk', 'lion', 'tiger', 'lynx']
senticnet['rooster'] = ['0.034', '0.088', '-0.08', '0.14', '#interest', '#admiration', 'positive', '0.044', 'hen', 'bird', 'stork', 'eagle', 'nightingale']
senticnet['rooster_comb'] = ['0.793', '0.847', '0', '0', '#joy', '#interest', 'positive', '0.82', 'cross_bun', 'hot_cross_bun', 'bear_paw', 'coffee_roll', 'bun']
senticnet['root'] = ['-1.0', '0', '0.831', '-0.59', '#sadness', '#anger', 'negative', '-0.80', 'beginning', 'origin', 'rootage', 'wellspring', 'source']
senticnet['root_canal_therapy'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'false_teeth', 'bridge', 'dental_plate', 'partial_denture', 'dental_appliance']
senticnet['root_cause'] = ['0.888', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.843', 'effect', 'consequence', 'impact', 'outcome', 'conclusion']
senticnet['root_climber'] = ['0.518', '-0.51', '0', '0.65', '#surprise', '#admiration', 'positive', '0.562', 'clematis', 'legume', 'leguminous_plant', 'climbing_fern', 'climber']
senticnet['root_directory'] = ['0', '0.175', '0', '0.231', '#interest', '#admiration', 'positive', '0.203', 'list', 'schedule', 'index', 'agenda', 'register']
senticnet['root_evil'] = ['0', '0.196', '0.105', '0.167', '#interest', '#admiration', 'positive', '0.086', 'measure_unit', 'measure', 'total_number', 'count', 'spatial_dimension']
senticnet['root_on'] = ['0.655', '0.635', '-0.23', '0.3', '#joy', '#interest', 'positive', '0.338', 'cheerfully', 'cheerful', 'jolly_up', 'hearten', 'chirk_up']
senticnet['root_out'] = ['0', '0', '-0.89', '-0.98', '#fear', '#disgust', 'negative', '-0.94', 'eradicate', 'deracinate', 'extirpate', 'exterminate', 'uproot']
senticnet['root_penetration'] = ['0.89', '-0.62', '0', '0.548', '#joy', '#surprise', 'positive', '0.686', 'breakthrough', 'interpenetration', 'foray', 'maraud', 'raid']
senticnet['root_vegetable'] = ['0.071', '0', '0.031', '0.064', '#joy', '#admiration', 'positive', '0.035', 'kind_food', 'legume', 'japanese_food', 'popular_food', 'energetic']
senticnet['rootage'] = ['-0.99', '0', '0.835', '-0.58', '#sadness', '#anger', 'negative', '-0.80', 'beginning', 'origin', 'root', 'wellspring', 'source']
senticnet['rooter'] = ['0.703', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.676', 'enthusiast', 'partizan', 'partisan', 'aficionado', 'railbird']
senticnet['rooter_skunk'] = ['-0.54', '0', '0.768', '-0.50', '#sadness', '#anger', 'negative', '-0.60', 'striped_skunk', 'hooded_skunk', 'hognosed_skunk', 'badger_skunk', 'polecat']
senticnet['rooting_out'] = ['-0.94', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.88', 'combat', 'uprise', 'big_fight', 'opposition', 'violence']
senticnet['rootsy'] = ['0.881', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.937', 'granular', 'gamy', 'spirited', 'farinaceous', 'coarse-grained']
senticnet['ropa_vieja'] = ['0.116', '-0.02', '0.025', '0.156', '#joy', '#admiration', 'positive', '0.069', 'dance_move', 'latin_dance', 'body_movement', 'waltz', 'dance']
senticnet['ropati'] = ['0.997', '-0.84', '0', '0.927', '#joy', '#admiration', 'positive', '0.923', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['rope'] = ['0', '0.142', '0', '0.125', '#interest', '#admiration', 'positive', '0.134', 'pulley', 'weight_machine', 'ice_skate', 'tie', 'cord']
senticnet['rope-like'] = ['0.793', '0', '0', '0.996', '#joy', '#admiration', 'positive', '0.895', 'undescended', 'retractile', 'blade-like', 'whip-like', 'dwarfish']
senticnet['rope_tied_around'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'duvet_cover', 'pillow', 'comforter', 'pillowcase', 'blanket']
senticnet['rope_tow_beginner'] = ['0', '0.142', '-0.09', '0.183', '#interest', '#admiration', 'positive', '0.078', 'arise', 'rise', 'raise_up', 'go_up', 'go_upstairs']
senticnet['roped_into'] = ['-0.89', '0', '0.783', '-0.89', '#sadness', '#disgust', 'negative', '-0.85', 'dodge', 'plant', 'scheme', 'strategy', 'wangling']
senticnet['roper'] = ['0.518', '-0.59', '0', '0.783', '#surprise', '#admiration', 'positive', '0.631', 'slicker', 'beguiler', 'decoy', 'shill', 'deceiver']
senticnet['roper_starch_worldwide'] = ['-0.04', '0.1', '0.051', '0.137', '#interest', '#admiration', 'positive', '0.035', 'find_number', 'regularly', 'list_number', 'calculator', 'navigation']
senticnet['ropework'] = ['0.764', '-0.96', '0', '0.883', '#surprise', '#admiration', 'positive', '0.87', 'boatmanship', 'acquirement', 'skill', 'accomplishment', 'acquisition']
senticnet['roquebert'] = ['0', '0', '0.08', '0', '#anger', '#anger', 'negative', '-0.08', 'tree_animal', 'small_furry', 'rodent', 'big_mouse', 'small_rodent']
senticnet['rorippa'] = ['-0.79', '0', '0.906', '-0.67', '#sadness', '#anger', 'negative', '-0.79', 'poisonous_plant', 'hemlock_poisonous_plant', 'water_hemlock', 'plant_life', 'poison_sumac']
senticnet['rorqual'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'many_fishes', 'seahorse', 'starfish', 'plankton', 'sea_wee']
senticnet['rorschach_inkblot_test'] = ['-0.84', '0', '0.585', '-0.85', '#sadness', '#disgust', 'negative', '-0.76', 'subject', 'discipline', 'study', 'concern', 'research']
senticnet['rory_bremner'] = ['-0.89', '0', '0.58', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'resemblance', 'comparison', 'equivalence', 'reflection', 'alikeness']
senticnet['rosaceae_rose'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'black_locust']
senticnet['rosae'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'sedum', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['rosary'] = ['0.021', '0.281', '0', '0.261', '#interest', '#admiration', 'positive', '0.188', 'cathedral', 'christianity', 'catholic_church', 'church', 'chapel']
senticnet['rose'] = ['0.019', '0', '0', '0.047', '#joy', '#admiration', 'positive', '0.033', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rosebud']
senticnet['rose_cheeked'] = ['0.885', '0', '0', '0.867', '#joy', '#admiration', 'positive', '0.876', 'fortunate', 'rose_colored', 'rosy', 'blushful', 'flushed']
senticnet['rose_colored'] = ['0.887', '0', '0', '0.86', '#joy', '#admiration', 'positive', '0.873', 'fortunate', 'rosy', 'rose_cheeked', 'blushful', 'flushed']
senticnet['rose_petal'] = ['0.933', '-0.87', '0', '0.766', '#joy', '#surprise', 'positive', '0.858', 'scent', 'aroma', 'olfactory_property', 'smell', 'odor']
senticnet['rose_quartz'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'art', 'art_piece', 'museum', 'artist', 'jewel']
senticnet['rosebud'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['rosebud_creek'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['rosebud_indian_reservation'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['rosebud_reservation'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['rosebush'] = ['0', '0', '0', '0.077', '#admiration', '#admiration', 'positive', '0.077', 'apple_tree', 'ferret', 'snake', 'fox', 'mormon']
senticnet['rosedale_cemetery'] = ['-0.70', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.70', 'death_row', 'sad', 'loose_end', 'car_race', 'mortuary']
senticnet['roseola'] = ['-0.95', '0', '0.783', '-0.68', '#sadness', '#anger', 'negative', '-0.80', 'hypersensitivity_reaction', 'rash', 'efflorescence', 'hive', 'skin_rash']
senticnet['rosette'] = ['-0.69', '0', '0.776', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'little_potato', 'russet_scab', 'stem_canker', 'rhizoctinia_disease', 'plant_disease']
senticnet['rosewood'] = ['-0.76', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.82', 'mahogany', 'fingerboard', 'maple', 'fretboard', 'spruce']
senticnet['rosewood_realty_group'] = ['0.025', '0.27', '-0.11', '0.243', '#interest', '#admiration', 'positive', '0.105', 'keep_safe', 'store_dry_goods', 'store_stuff', 'secure', 'save']
senticnet['rosicrucian'] = ['-0.51', '0', '0.979', '-0.89', '#anger', '#disgust', 'negative', '-0.79', 'invisible', 'concealed', 'nonvisual', 'hidden', 'out_of_sight']
senticnet['rosicrucianism'] = ['-0.51', '0', '0.979', '-0.89', '#anger', '#disgust', 'negative', '-0.79', 'invisible', 'concealed', 'nonvisual', 'hidden', 'out_of_sight']
senticnet['rosie'] = ['-0.03', '-0.00', '0.087', '-0.05', '#anger', '#disgust', 'negative', '-0.04', 'dog_fur', 'dog_cat', 'dog_hair', 'pet', 'animal_hair']
senticnet['rosilla'] = ['-0.76', '0', '0.76', '-0.66', '#sadness', '#anger', 'negative', '-0.73', 'herb', 'autumn_sneezeweed', 'orange_sneezeweed', 'owlclaws', 'sneezeweed']
senticnet['rosiness'] = ['0.939', '-0.74', '0', '0.57', '#joy', '#surprise', 'positive', '0.752', 'haleness', 'wholeness', 'healthiness', 'health', 'vitality']
senticnet['rosita'] = ['-0.90', '0', '0.726', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'broad_leaved_centaury', 'centaury', 'lesser_centaury', 'tufted_centaury', 'seaside_centaury']
senticnet['rostraria'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'meadow_grass', 'grass', 'manna_grass', 'sweet_grass', 'wood_meadowgrass']
senticnet['rostrata'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'plectognath', 'plectognathfish', 'blowfish', 'triggerfish', 'filefish']
senticnet['rostrum'] = ['0', '0', '-0.76', '-0.82', '#fear', '#disgust', 'negative', '-0.79', 'stage', 'thespian', 'actor', 'theatre', 'drama']
senticnet['rosy'] = ['0.889', '0', '0', '0.87', '#joy', '#admiration', 'positive', '0.879', 'fortunate', 'rose_colored', 'rose_cheeked', 'blushful', 'flushed']
senticnet['rosy_faced_lovebird'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet']
senticnet['rosy_hue'] = ['-0.66', '0', '0.782', '-0.52', '#sadness', '#anger', 'negative', '-0.65', 'lividness', 'pallidness', 'pallor', 'complexion', 'achromasia']
senticnet['rosy_purple'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'tropical_fish', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['rosy_tint'] = ['-0.66', '0', '0.782', '-0.52', '#sadness', '#anger', 'negative', '-0.65', 'lividness', 'pallidness', 'pallor', 'complexion', 'achromasia']
senticnet['rot'] = ['0', '0', '-0.94', '-0.96', '#fear', '#disgust', 'negative', '-0.95', 'say_prayer', 'fall_off_chair', 'put_box', 'feel_terrible', 'lift_leg']
senticnet['rot_resistant'] = ['-0.69', '0', '0.725', '-0.89', '#anger', '#disgust', 'negative', '-0.77', 'ponderosa', 'ponderosa_pine', 'western_yellow_pine', 'bull_pine', 'longleaf_pine']
senticnet['rotary_dial'] = ['0.893', '0.979', '0', '0', '#joy', '#interest', 'positive', '0.936', 'find_phone_number', 'dial_phone_number', 'pick_up_telephone_receiver', 'dial', 'dial_telephone']
senticnet['rotary_file'] = ['0', '0.091', '0', '0', '#interest', '#interest', 'positive', '0.091', 'computer_disk', 'computer', 'general_ledger', 'cd-rom', 'personal_computer']
senticnet['rotary_hoe'] = ['-0.99', '0', '0.827', '-0.54', '#sadness', '#anger', 'negative', '-0.78', 'treat', 'cover', 'handle', 'theologize', 'deal']
senticnet['rotary_motion'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotate_movement', 'rotate', 'go_around', 'manoeuvre', 'full_circle']
senticnet['rotary_phone'] = ['0.813', '0.772', '0', '0', '#joy', '#interest', 'positive', '0.792', 'loop', 'horse_shoes', 'ring', 'gray_hair', 'dartboard']
senticnet['rotary_shutter'] = ['-0.82', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.84', 'block', 'obstruct', 'close', 'clogged', 'occluded']
senticnet['rotate'] = ['0.056', '0.104', '0', '0.231', '#interest', '#admiration', 'positive', '0.13', 'rotate_movement', 'change_direction', 'go_around', 'revolve', 'ninety_degree']
senticnet['rotate_clockwise'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotate_movement', 'rotate', 'go_around', 'manoeuvre', 'full_circle']
senticnet['rotate_movement'] = ['0.037', '0.087', '0', '0.259', '#interest', '#admiration', 'positive', '0.128', 'rotate', 'circular_motion', 'revolve', 'go_around', 'manoeuvre']
senticnet['rotating'] = ['0', '0', '0.831', '-0.97', '#anger', '#disgust', 'negative', '-0.90', 'circumvolve', 'turn_out', 'revolve', 'rotate', 'go_around']
senticnet['rotating_brush'] = ['-0.04', '-0.02', '0.089', '-0.14', '#anger', '#disgust', 'negative', '-0.06', 'steam_locomotive', 'loudspeaker', 'collector', 'siren', 'activist']
senticnet['rotating_clockwise'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotate_movement', 'rotate', 'go_around', 'manoeuvre', 'full_circle']
senticnet['rotating_cylinder'] = ['0.042', '-0.02', '0.035', '0.065', '#joy', '#admiration', 'positive', '0.023', 'food_container', 'metal_cylinder', 'metal_container', 'small_ball', 'storage_container']
senticnet['rotating_disc'] = ['0.129', '0', '0', '0', '#joy', '#joy', 'positive', '0.129', 'incline_plane', 'boulder', 'ramp', 'hill_mountain', 'rock']
senticnet['rotating_machinery'] = ['0.048', '0.084', '0', '0.209', '#interest', '#admiration', 'positive', '0.114', 'move', 'motion', 'motility', 'movement', 'flicker']
senticnet['rotating_mirror'] = ['-0.50', '0', '0.75', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'beam', 'beam_of_light', 'sunbeam', 'ray', 'ray_of_light']
senticnet['rotating_shaft'] = ['0', '0.142', '0', '0.125', '#interest', '#admiration', 'positive', '0.134', 'weight_machine', 'power_drill', 'screw', 'screwdriver', 'air_pump']
senticnet['rotation'] = ['0.826', '0.89', '0', '0', '#joy', '#interest', 'positive', '0.858', 'full_circle', 'rotate', 'rotate_movement', 'spiral', 'twist']
senticnet['rotational_motion'] = ['-0.97', '0', '0.831', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'skewness', 'irregularity']
senticnet['rotational_transform'] = ['-0.97', '0', '0.831', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'skewness', 'irregularity']
senticnet['rotational_velocity'] = ['-0.03', '0.178', '0', '0.168', '#interest', '#admiration', 'positive', '0.104', 'speed', 'fast', 'fast_and_furious', 'speedy', 'fleet']
senticnet['rotationally_symmetric'] = ['-0.97', '0', '0.831', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'skewness', 'irregularity']
senticnet['rotator_cuff'] = ['-0.53', '0', '0.982', '-0.67', '#anger', '#disgust', 'negative', '-0.73', 'gristle', 'semilunar_cartilage', 'fibrocartilage', 'hyaline_cartilage', 'arytenoid']
senticnet['rotator_cuff_injury'] = ['-0.78', '0', '0.554', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'rubor', 'redness', 'inflammation', 'tennis_elbow', 'lateral_epicondylitis']
senticnet['rotator_cuff_tear'] = ['-0.98', '0', '0.678', '-0.95', '#sadness', '#disgust', 'negative', '-0.87', 'rubor', 'redness', 'inflammation', 'epicondylitis', 'lateral_epicondylitis']
senticnet['rotavirus_infection'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'inflammatory_disease', 'stomach_flu', 'intestinal_flu', 'pelvic_inflammatory_disease']
senticnet['rotavirus_vaccine'] = ['-0.07', '0', '-0.05', '-0.18', '#sadness', '#disgust', 'negative', '-0.10', 'medical_therapy', 'vaccination', 'doctor', 'medicine', 'hospital']
senticnet['rote'] = ['-0.06', '0.237', '-0.06', '0.226', '#interest', '#admiration', 'positive', '0.085', 'recollection', 'imaginary', 'brain_function', 'think_process', 'recall']
senticnet['rote_memorization'] = ['-0.06', '0.237', '-0.06', '0.226', '#interest', '#admiration', 'positive', '0.085', 'recollection', 'imaginary', 'brain_function', 'think_process', 'recall']
senticnet['rote_memory'] = ['-0.06', '0.237', '-0.06', '0.226', '#interest', '#admiration', 'positive', '0.085', 'recollection', 'imaginary', 'brain_function', 'think_process', 'recall']
senticnet['rote_recitation'] = ['-0.06', '0.237', '-0.06', '0.226', '#interest', '#admiration', 'positive', '0.085', 'recollection', 'imaginary', 'brain_function', 'think_process', 'recall']
senticnet['rote_repetition'] = ['-0.06', '0.237', '-0.06', '0.226', '#interest', '#admiration', 'positive', '0.085', 'recollection', 'imaginary', 'brain_function', 'think_process', 'recall']
senticnet['rotgut'] = ['-0.91', '0', '0.79', '-0.68', '#sadness', '#anger', 'negative', '-0.79', 'intoxicant', 'alcohol', 'alcoholic_drink', 'alcoholic_beverage', 'inebriant']
senticnet['roth_ira'] = ['0.763', '-0.67', '0', '0.945', '#joy', '#admiration', 'positive', '0.795', 'set', 'income_bracket', 'bracket', 'income', 'price_bracket']
senticnet['rothbard'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'economic_libertarian', 'civil_libertarian', 'advocate', 'advocator', 'proponent']
senticnet['rothmund_thomson_syndrome'] = ['-0.87', '0', '0.873', '-0.62', '#sadness', '#anger', 'negative', '-0.78', 'dwarfism', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['rotifer'] = ['-0.87', '0', '0.983', '-0.91', '#anger', '#disgust', 'negative', '-0.92', 'thorn', 'sticker', 'pricker', 'prickle', 'spikelet']
senticnet['rotisserie'] = ['0.061', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.055', 'cook', 'stick_butter', 'find_recipe', 'bake_oven', 'food_bake']
senticnet['rotisserie_chicken'] = ['0.061', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.055', 'cook', 'stick_butter', 'find_recipe', 'bake_oven', 'food_bake']
senticnet['rotor'] = ['0.042', '-0.02', '0.035', '0.065', '#joy', '#admiration', 'positive', '0.023', 'food_container', 'metal_cylinder', 'metal_container', 'small_ball', 'storage_container']
senticnet['rotor_machine'] = ['-0.05', '-0.19', '-0.17', '0.219', '#surprise', '#admiration', 'positive', '0.046', 'riddle', 'mystery', 'obituary', 'editorial', 'book_genre']
senticnet['rotten'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'mouldy_bread', 'green_bread', 'disgusting', 'taste_awful', 'mouldy']
senticnet['rotten_egg_odor'] = ['-0.72', '0', '0.71', '-0.9', '#sadness', '#disgust', 'negative', '-0.77', 'perniciousness', 'harmfulness', 'toxicity', 'noisomeness', 'unwholesomeness']
senticnet['rotten_fish'] = ['0', '0', '0.943', '-0.8', '#anger', '#disgust', 'negative', '-0.87', 'fight_fire', 'filthy', 'dirty', 'stink', 'fall_mud']
senticnet['rotten_food'] = ['0', '0', '-0.84', '-0.90', '#fear', '#disgust', 'negative', '-0.87', 'dynamite_explosion', 'fall_sky', 'sour_milk', 'firebomb', 'cleaver']
senticnet['rotten_fruit'] = ['-0.23', '-0.09', '0', '-0.24', '#sadness', '#disgust', 'negative', '-0.12', 'moldy_bread', 'bathroom_wall', 'dirty_kitchen', 'rotten_tomato', 'fallen_tree']
senticnet['rotten_luck'] = ['-0.43', '-0.14', '-0.06', '-0.33', '#sadness', '#disgust', 'negative', '-0.17', 'brain_tumor', 'lyme_disease', 'cause_suffer', 'lose_team', 'bad_hair_day']
senticnet['rotten_tomato'] = ['-0.23', '-0.09', '0', '-0.24', '#sadness', '#disgust', 'negative', '-0.12', 'moldy_bread', 'bathroom_wall', 'dirty_kitchen', 'rotten_fruit', 'fallen_tree']
senticnet['rottenness'] = ['-0.55', '0', '0.605', '-0.55', '#sadness', '#anger', 'negative', '-0.57', 'decay', 'putrescence', 'putrefaction', 'corruption', 'putridness']
senticnet['rotting_away'] = ['0', '0', '-0.94', '-0.96', '#fear', '#disgust', 'negative', '-0.95', 'say_prayer', 'fall_off_chair', 'put_box', 'feel_terrible', 'lift_leg']
senticnet['rotting_fish'] = ['-0.86', '0', '0.644', '-0.88', '#sadness', '#disgust', 'negative', '-0.79', 'frowsty', 'fetid', 'foetid', 'smelly', 'stinking']
senticnet['rotting_flesh'] = ['-0.86', '0', '0.644', '-0.88', '#sadness', '#disgust', 'negative', '-0.79', 'frowsty', 'fetid', 'foetid', 'smelly', 'stinking']
senticnet['rotting_food'] = ['-0.70', '0', '0.611', '-0.69', '#sadness', '#disgust', 'negative', '-0.67', 'dump', 'garbage_dump', 'trash_dump', 'garbage_heap', 'wasteyard']
senticnet['rotting_fruit'] = ['0.025', '-0.00', '0', '0.125', '#joy', '#admiration', 'positive', '0.053', 'goo', 'adhesive', 'flour_water', 'insert', 'thick_liquid']
senticnet['rotting_garbage'] = ['-0.86', '0', '0.644', '-0.88', '#sadness', '#disgust', 'negative', '-0.79', 'frowsty', 'fetid', 'foetid', 'smelly', 'stinking']
senticnet['rotting_meat'] = ['0', '0', '0.943', '-0.8', '#anger', '#disgust', 'negative', '-0.87', 'fight_fire', 'filthy', 'dirty', 'stink', 'fall_mud']
senticnet['rotting_teeth'] = ['-0.86', '0', '0.738', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'amyotrophia', 'amyotrophy', 'tabes', 'kraurosis', 'symptom']
senticnet['rotting_vegetation'] = ['-0.02', '-0.01', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.03', 'dung', 'manure', 'shit', 'dog_poop', 'crap']
senticnet['rotund'] = ['-0.70', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.72', 'weighty', 'pear-shaped', 'corpulent', 'obese', 'round']
senticnet['rotundifolia'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['rotundity'] = ['-0.94', '0', '0.65', '-0.57', '#sadness', '#anger', 'negative', '-0.72', 'manner_of_speaking', 'undertone', 'note', 'roundness', 'tone_of_voice']
senticnet['rough'] = ['-0.71', '0', '0.8', '-0.69', '#sadness', '#anger', 'negative', '-0.73', 'approximative', 'inexact', 'approximate', 'loose', 'odd']
senticnet['rough-and-tumble'] = ['-0.82', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.82', 'free-wheeling', 'hard-nosed', 'freewheeling', 'rambunctious', 'hard-drinking']
senticnet['rough-cut'] = ['0', '0', '-0.87', '-0.96', '#fear', '#disgust', 'negative', '-0.92', 'harsh', 'common', 'coarse', 'uncouth', 'vulgar']
senticnet['rough_blazing_star'] = ['0.773', '-0.55', '0', '0.949', '#joy', '#admiration', 'positive', '0.757', 'cornel', 'dwarf_cornel', 'crackerberry', 'pudding_berry', 'common_white_dogwood']
senticnet['rough_draft'] = ['0.642', '-0.53', '0', '0.575', '#joy', '#admiration', 'positive', '0.583', 'check', 'assure', 'see', 'insure', 'examine']
senticnet['rough_estimate'] = ['-0.02', '0.248', '0', '0.31', '#interest', '#admiration', 'positive', '0.178', 'assurance', 'total', 'quality', 'importance', 'amount']
senticnet['rough_game'] = ['0', '-0.09', '0.011', '0.089', '#surprise', '#admiration', 'positive', '0.056', 'rough_sport', 'type_of_sport', 'ball_sport', 'contact_sport', 'team_sport']
senticnet['rough_grassland'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'biome', 'timberland']
senticnet['rough_hewn'] = ['0.881', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.937', 'granular', 'gamy', 'spirited', 'farinaceous', 'coarse-grained']
senticnet['rough_legged_buzzard'] = ['-0.66', '0', '0.982', '-0.93', '#anger', '#disgust', 'negative', '-0.86', 'bother', 'barge_in', 'come_in', 'break_in', 'crash']
senticnet['rough_legged_hawk'] = ['-0.66', '0', '0.982', '-0.93', '#anger', '#disgust', 'negative', '-0.86', 'bother', 'barge_in', 'come_in', 'break_in', 'crash']
senticnet['rough_outline'] = ['0', '0.074', '0', '0.06', '#interest', '#admiration', 'positive', '0.067', 'use_crayon', 'draw', 'pencil_art', 'make_sketch', 'outline']
senticnet['rough_pavement'] = ['0.03', '0', '0.017', '0.138', '#joy', '#admiration', 'positive', '0.05', 'up_down', 'curl', 'crest', 'hand_gesture', 'goodbye']
senticnet['rough_road'] = ['-0.95', '0', '0.159', '-0.94', '#sadness', '#disgust', 'negative', '-0.68', 'road', 'narrow', 'poky', 'narrow_road', 'dark_road']
senticnet['rough_sketch'] = ['0', '0.074', '0', '0.06', '#interest', '#admiration', 'positive', '0.067', 'use_crayon', 'draw', 'pencil_art', 'make_sketch', 'outline']
senticnet['rough_sport'] = ['0', '-0.08', '0', '0.086', '#surprise', '#admiration', 'positive', '0.085', 'type_of_sport', 'rough_game', 'team_sport', 'violent_sport', 'ball_sport']
senticnet['rough_terrain'] = ['-0.99', '0', '0.641', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'navigable', 'negotiable', 'surmountable', 'climbable', 'passable']
senticnet['rough_texture'] = ['0.352', '0.142', '0', '0.281', '#joy', '#admiration', 'positive', '0.258', 'knowledgeable', 'complement', 'much_need', 'see_ocean', 'good_sense_of_humor']
senticnet['rough_translation'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'foot_motion', 'foot_action', 'foot_movement', 'stride', 'pace']
senticnet['roughage'] = ['0', '0', '0.754', '-0.80', '#anger', '#disgust', 'negative', '-0.77', 'vulcanized_fiber', 'raffia', 'fibril', 'fiber', 'spindle']
senticnet['rougher'] = ['0.17', '0', '0.081', '0.184', '#joy', '#admiration', 'positive', '0.091', 'food_holder', 'food_server', 'crockery', 'flattery', 'round_flat']
senticnet['rougher_surface'] = ['-0.50', '0', '0.552', '-0.93', '#anger', '#disgust', 'negative', '-0.66', 'nubbiness', 'tweediness', 'harshness', 'graininess', 'granularity']
senticnet['roughhewn'] = ['-0.80', '0', '0.677', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'unhewn', 'unfattened', 'undressed', 'unfinished', 'raw']
senticnet['roughish'] = ['-0.77', '0', '0.626', '-0.78', '#sadness', '#disgust', 'negative', '-0.72', 'bumpy', 'scabrous', 'lepidote', 'leprose', 'unsmooth']
senticnet['roughly'] = ['-0.80', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.79', 'approximately', 'delicacy', 'inexactly', 'grossly', 'sketchily']
senticnet['roughly_bounded_by'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'boulevard', 'lane', 'thoroughfare', 'intersection', 'curb']
senticnet['roughly_conical'] = ['0.06', '-0.03', '0.037', '0.075', '#joy', '#admiration', 'positive', '0.034', 'semicircle', 'trapezoid', 'circle', 'triangular', 'facet']
senticnet['roughly_cylindrical'] = ['0.022', '0', '0.015', '0.096', '#joy', '#admiration', 'positive', '0.034', 'write_tool', 'write_implement', 'cylindric', 'write_device', 'write_paper']
senticnet['roughly_humanoid'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'gnome', 'elf', 'goblin', 'undersized', 'small_person']
senticnet['roughly_parallel'] = ['0', '0', '-0.73', '-0.79', '#fear', '#disgust', 'negative', '-0.76', 'analogue', 'analog', 'line_of_latitude', 'latitude', 'collimate']
senticnet['roughly_perpendicular'] = ['-0.97', '0', '0.865', '-0.54', '#sadness', '#anger', 'negative', '-0.79', 'piping', 'lace', 'lacing', 'cord', 'slack']
senticnet['roughly_triangular'] = ['0.027', '0.139', '0', '0.203', '#interest', '#admiration', 'positive', '0.123', 'horse_shoes', 'four_leaf_clover', 'rotary_phone', 'ping_pong', 'billiards']
senticnet['roughneck'] = ['-0.92', '0', '0.804', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'bullyboy', 'muscleman', 'muscle', 'skinhead', 'skin']
senticnet['roughness'] = ['-0.50', '0', '0.552', '-0.93', '#anger', '#disgust', 'negative', '-0.66', 'nubbiness', 'tweediness', 'harshness', 'graininess', 'granularity']
senticnet['roughshod'] = ['-0.91', '0', '0.521', '-0.81', '#sadness', '#disgust', 'negative', '-0.74', 'bullying', 'peremptory', 'blustery', 'domineering', 'cavalier']
senticnet['roulette_table'] = ['-0.78', '0', '0.944', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'loser', 'speculator', 'mortal', 'better', 'gambler']
senticnet['roulette_wheel'] = ['0.038', '0', '0.031', '0.124', '#joy', '#admiration', 'positive', '0.044', 'rotate', 'rotate_movement', 'change_direction', 'move', 'go_around']
senticnet['roulotte'] = ['0.022', '0', '0.02', '0.066', '#joy', '#admiration', 'positive', '0.023', 'large_automobile', 'expensive_car', 'limousine', 'means_of_transport', 'automatic_car']
senticnet['roum'] = ['-0.71', '0', '0.836', '0', '#sadness', '#anger', 'negative', '-0.77', 'awkward', 'unenviable', 'embarrassingly', 'embarrassing', 'sheepish']
senticnet['roumanian_american'] = ['0', '0.209', '0.026', '0.184', '#interest', '#admiration', 'positive', '0.122', 'church', 'confession', 'temple', 'cathedral', 'monastery']
senticnet['round'] = ['0.823', '0.982', '0', '0', '#joy', '#interest', 'positive', '0.902', 'spherical', 'three_dimensions', 'round_orb', 'coil', 'fill_with_sand']
senticnet['round-about'] = ['-0.73', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.79', 'signposted', 'roundabout', 'roundabout', 'motorway', 'stoplight']
senticnet['round-by-round'] = ['0.749', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.826', 'minute-by-minute', 'eye-witness', 'play-by-play', 'blow-by-blow', 'recounting']
senticnet['round-table'] = ['0', '0', '0.986', '-0.87', '#anger', '#disgust', 'negative', '-0.93', 'off-the-record', 'deliberation', 'closed-door', 'town-hall', 'town-hall-style']
senticnet['round_ball'] = ['0', '0.077', '0.039', '0.154', '#interest', '#admiration', 'positive', '0.064', 'marble', 'round_figure', 'almond', 'dome', 'sphere']
senticnet['round_bread'] = ['0.054', '-0.01', '0.036', '0.132', '#joy', '#admiration', 'positive', '0.041', 'eatable', 'bun', 'bake_treat', 'bake_goods', 'breakfast_food']
senticnet['round_brush'] = ['0', '-0.06', '0.076', '-0.13', '#anger', '#disgust', 'negative', '-0.04', 'moistener', 'tartar_control_toothpaste', 'hair_brush', 'toothpaste', 'toilet_paper']
senticnet['round_figure'] = ['0', '0.077', '0.039', '0.154', '#interest', '#admiration', 'positive', '0.064', 'marble', 'dome', 'almond', 'round_ball', 'sphere']
senticnet['round_flat'] = ['0.17', '0', '0.081', '0.184', '#joy', '#admiration', 'positive', '0.091', 'food_holder', 'food_server', 'head_tail', 'flattery', 'flatter']
senticnet['round_fruit'] = ['0.034', '0.067', '0.064', '0.106', '#interest', '#admiration', 'positive', '0.036', 'green_fruit', 'edible_human', 'crunchy', 'red_fruit', 'juicy']
senticnet['round_hand'] = ['-0.95', '0', '0.517', '-0.53', '#sadness', '#disgust', 'negative', '-0.67', 'copperplate', 'hand', 'minuscule', 'italic', 'cursive']
senticnet['round_ko'] = ['0.944', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.967', 'meeting', 'meet', 'match', 'encounter', 'round']
senticnet['round_leaved_pigface'] = ['-0.86', '0', '0.665', '-0.61', '#sadness', '#anger', 'negative', '-0.71', 'brambly', 'braky', 'wooded', 'scrubby', 'forested']
senticnet['round_leaved_sundew'] = ['-0.79', '0', '0.906', '-0.67', '#sadness', '#anger', 'negative', '-0.79', 'poisonous_plant', 'hemlock_poisonous_plant', 'water_hemlock', 'plant_life', 'poison_sumac']
senticnet['round_leaved_sundew_drosera'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
senticnet['round_metal'] = ['0', '0.172', '0.034', '0.151', '#interest', '#admiration', 'positive', '0.096', 'dense_water', 'smooth_surface', 'nickel', 'head_tail', 'shinny']
senticnet['round_orb'] = ['0.83', '0.987', '0', '0', '#joy', '#interest', 'positive', '0.908', 'spherical', 'three_dimensions', 'round', 'coil', 'fill_with_sand']
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senticnet['royal_ulster_constabulary'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'law_enforcement_agency', 'law', 'agency']
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senticnet['royalty_pharma'] = ['0.559', '0.656', '-0.26', '0.285', '#joy', '#interest', 'positive', '0.31', 'zealous', 'avidity', 'eagerness', 'avidness', 'thirstily']
senticnet['royalty_pharma_proposal'] = ['0.559', '0.656', '-0.26', '0.285', '#joy', '#interest', 'positive', '0.31', 'zealous', 'avidity', 'eagerness', 'avidness', 'thirstily']
senticnet['roz_forrester'] = ['0.756', '0', '0', '0', '#joy', '#joy', 'positive', '0.756', 'companionship', 'cherish', 'lover', 'bond', 'buddy']
senticnet['rozen_maiden'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['rpf_check'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'identifiable', 'specifiable', 'recognizable', 'diagnosable', 'classifiable']
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senticnet['rsa_encryption'] = ['0.584', '-0.72', '0', '0.742', '#surprise', '#admiration', 'positive', '0.684', 'decompression', 'cryptography', 'coding', 'secret_writing', 'steganography']
senticnet['rsa_key'] = ['0.022', '-0.13', '0.153', '-0.31', '#anger', '#disgust', 'negative', '-0.07', 'codeine', 'morphine', 'infection', 'cocaine', 'nuclear_war']
senticnet['rsa_security_conference'] = ['-0.79', '0', '0.948', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
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senticnet['rstein'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'concupiscence', 'lustfulness', 'amorousness']
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senticnet['ruanui'] = ['-0.91', '0', '0.769', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'break', 'good_luck', 'chance_eventpy_chance', 'chance_event', 'coincidence']
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senticnet['rub'] = ['0', '0.841', '0', '0.833', '#interest', '#admiration', 'positive', '0.837', 'shake', 'touch', 'multiple_purposes', 'wiggle', 'wrap_around']
senticnet['rub_along'] = ['-0.54', '0', '0.967', '-0.79', '#anger', '#disgust', 'negative', '-0.76', 'manage', 'make_out', 'cope', 'get_by', 'make_do']
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senticnet['rub_hands'] = ['0.993', '0', '0', '-0.33', '#joy', '#disgust', 'positive', '0.331', 'relish', 'crow_over', 'gloat', 'rejoice', 'glad']
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senticnet['rubber_ball'] = ['0.717', '0.818', '0', '0', '#joy', '#interest', 'positive', '0.767', 'ball', 'fielder', 'yo-yo', 'toy_ball', 'toy_marble']
senticnet['rubber_band'] = ['0.045', '0.143', '-0.11', '0.212', '#interest', '#admiration', 'positive', '0.071', 'pair_of_scissors', 'safety_pin', 'adjustable_wrench', 'clothespin', 'clip']
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senticnet['rubber_duckie'] = ['-0.93', '0', '0.77', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'hothead', 'unpleasant_person', 'disagreeable_person', 'crabby_person', 'crab']
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senticnet['rubber_mallet'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'pair_of_scissors', 'c_clamp', 'tool_box', 'salad_shaker', 'ladle']
senticnet['rubber_mask'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'wear', 'protective_garment', 'coverall', 'shoulder_pad', 'wearable']
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senticnet['rubber_ring'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'hanger', 'hang_up_clothes', 'hang_clothes', 'storage_space', 'laundry']
senticnet['rubber_sheet'] = ['0.022', '0.069', '0.027', '0.059', '#interest', '#admiration', 'positive', '0.031', 'perfect_surface', 'plane', 'flat_land', 'flat', 'floor']
senticnet['rubber_sole'] = ['-0.54', '0', '0.612', '-0.98', '#anger', '#disgust', 'negative', '-0.71', 'scuff', 'mule', 'bootee', 'carpet_slipper', 'footwear']
senticnet['rubber_spatula'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'salad_shaker', 'store_shelf', 'kitchen_tool', 'pliers', 'ladle']
senticnet['rubber_stamp'] = ['0.751', '-0.98', '0', '0.989', '#surprise', '#admiration', 'positive', '0.909', 'okay', 'approval', 'commendation', 'okey', 'okeh']
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senticnet['rubberband'] = ['0.155', '-0.11', '0.009', '-0.05', '#joy', '#surprise', 'positive', '0.05', 'dance_well', 'relate_music', 'enter_contest', 'jump_around', 'cheer_up']
senticnet['rubbermaid'] = ['0.348', '0.23', '-0.15', '0.294', '#joy', '#admiration', 'positive', '0.18', 'happy_emotion', 'niceness', 'contagious', 'good_emotion', 'savor']
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senticnet['rubberstamp'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'address_book', 'get_rich', 'ladle', 'swiss_army_knife', 'whisk']
senticnet['rubbery'] = ['-0.03', '0.114', '0', '0.243', '#interest', '#admiration', 'positive', '0.106', 'widen', 'elongate', 'elasticity', 'grow_long', 'elongation']
senticnet['rubbing_alcohol'] = ['0.878', '0.869', '0', '0', '#joy', '#interest', 'positive', '0.873', 'fluoride_mouthwash', 'hair_conditioner', 'sculpt_gel', 'dandruff_shampoo', 'hair_gel']
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senticnet['rubbish'] = ['-0.88', '0', '0.851', '-0.90', '#sadness', '#disgust', 'negative', '-0.87', 'store_trash', 'hold_trash', 'put_garbage', 'put_trash', 'garbage']
senticnet['rubbish_bin'] = ['0.864', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.924', 'trash', 'trash_container', 'junk_store', 'coffee_cup', 'trash_receptacle']
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senticnet['rubdown'] = ['0.857', '-0.68', '0', '0.971', '#joy', '#admiration', 'positive', '0.839', 'revitalize', 'refurbish', 'reconstruct', 'renovate', 'renew']
senticnet['rube'] = ['-0.96', '0', '0.924', '-0.86', '#sadness', '#anger', 'negative', '-0.91', 'redneck', 'yokel', 'rustic', 'hick', 'hayseed']
senticnet['rube_marquard'] = ['0', '0.075', '0', '0', '#interest', '#interest', 'positive', '0.075', 'cup', 'carafe', 'container_cup', 'mug', 'glasses']
senticnet['rubefacient'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'chemical_irritant', 'gingerol', 'piperine', 'pepper_spray']
senticnet['rubenesque'] = ['0.636', '-0.95', '0', '0.664', '#surprise', '#admiration', 'positive', '0.751', 'voluptuous', 'statuesque', 'sculptured', 'shapely', 'sculptural']
senticnet['rubeola'] = ['0.922', '-0.56', '0', '0.619', '#joy', '#admiration', 'positive', '0.702', 'influenza', 'flu', 'grippe', 'measles', 'contagion']
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senticnet['ruby'] = ['0.412', '0.099', '0', '0.365', '#joy', '#admiration', 'positive', '0.292', 'bloodstone', 'turquoise', 'emerald', 'sapphire', 'jade']
senticnet['ruby-brown'] = ['0', '0', '0.837', '-0.71', '#anger', '#disgust', 'negative', '-0.77', 'blueish-green', 'greenish-gold', 'golden-green', 'orange-copper', 'deep-brown']
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senticnet['ruck_up'] = ['-0.69', '0', '0.692', '-0.66', '#sadness', '#anger', 'negative', '-0.68', 'wrinkle', 'crinkle', 'crease', 'crisp', 'ruckle']
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senticnet['ruddy_face'] = ['0.209', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.118', 'old_dance', 'dance', 'slow_dance', 'dancer', 'enter_contest']
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senticnet['rudolf_laban'] = ['0.536', '-0.56', '0', '0.567', '#surprise', '#admiration', 'positive', '0.557', 'recreation', 'diversion', 'move', 'motility', 'movement']
senticnet['rudolf_von_laban'] = ['0.536', '-0.56', '0', '0.567', '#surprise', '#admiration', 'positive', '0.557', 'recreation', 'diversion', 'move', 'motility', 'movement']
senticnet['rudolph_wurlitzer_company'] = ['-0.75', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.80', 'high-strung', 'uptight', 'restive', 'nervy', 'jumpy']
senticnet['rudstone'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'harsh', 'common', 'rough-cut', 'uncouth', 'vulgar']
senticnet['rudy'] = ['0.17', '0.125', '-0.04', '0.196', '#joy', '#admiration', 'positive', '0.111', 'pal', 'single_person', 'comrade', 'good', 'companion']
senticnet['rue'] = ['-0.89', '0.167', '-0.61', '0.051', '#sadness', '#fear', 'negative', '-0.32', 'compunction', 'self-reproach', 'contriteness', 'contritely', 'contrition']
senticnet['rue_monsieur_le'] = ['0.051', '0.097', '0', '0.065', '#interest', '#admiration', 'positive', '0.071', 'king', 'queen', 'duchess', 'throne', 'lord']
senticnet['rueful'] = ['-0.88', '0.178', '-0.6', '0', '#sadness', '#fear', 'negative', '-0.43', 'contrite', 'remorseful', 'ruthful', 'bereaved', 'sorrowing']
senticnet['rueful_smile'] = ['-0.89', '0.168', '-0.60', '0.05', '#sadness', '#fear', 'negative', '-0.32', 'remorsefully', 'contritely', 'contrition', 'contriteness', 'attrition']
senticnet['ruefully'] = ['-0.89', '0.168', '-0.60', '0.05', '#sadness', '#fear', 'negative', '-0.32', 'remorsefully', 'contritely', 'contrition', 'contriteness', 'attrition']
senticnet['ruefulness'] = ['-0.86', '0.2', '-0.57', '0', '#sadness', '#fear', 'negative', '-0.41', 'regretful', 'sorrowful', 'sadly', 'sorrowfully', 'doleful']
senticnet['ruff'] = ['0.022', '-0.11', '0.014', '0.057', '#surprise', '#admiration', 'positive', '0.045', 'wood', 'many_steps', 'franchise', 'stump', 'timber']
senticnet['ruffed_grouse'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['ruffian'] = ['-0.79', '0', '0.765', '-0.91', '#sadness', '#disgust', 'negative', '-0.82', 'bullyboy', 'muscleman', 'muscle', 'skinhead', 'skin']
senticnet['ruffle'] = ['0', '0', '0.789', '-0.72', '#anger', '#disgust', 'negative', '-0.75', 'displease', 'bug', 'nettle', 'chafe', 'badger']
senticnet['ruffled'] = ['0.603', '-0.53', '0', '0.931', '#joy', '#admiration', 'positive', '0.69', 'adorned', 'buttony', 'fringed', 'tessellated', 'mounted']
senticnet['rufous_brown'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'neck', 'body_part', 'henchman', 'shoulder', 'head']
senticnet['rufus_sewell'] = ['0.794', '-0.50', '0', '0.531', '#joy', '#admiration', 'positive', '0.611', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['rug'] = ['0.802', '0.996', '0', '0', '#joy', '#interest', 'positive', '0.899', 'carpet', 'floor', 'thick_hair', 'walk_closet', 'linoleum']
senticnet['rugbrd'] = ['0.52', '-0.69', '0', '0.639', '#surprise', '#admiration', 'positive', '0.617', 'black_bread', 'pumpernickel', 'limpa', 'bread', 'breadstuff']
senticnet['rugby'] = ['0.058', '-0.05', '0.005', '0', '#joy', '#surprise', 'positive', '0.037', 'golf', 'table_tennis', 'soccer', 'football', 'play_golf']
senticnet['rugby_amateur_fira'] = ['-0.68', '0', '0.923', '-0.68', '#sadness', '#anger', 'negative', '-0.76', 'negative', 'veto', 'refute', 'rebut', 'controvert']
senticnet['rugby_football'] = ['-0.68', '0', '0.923', '-0.68', '#sadness', '#anger', 'negative', '-0.76', 'negative', 'veto', 'refute', 'rebut', 'controvert']
senticnet['rugby_league'] = ['0.728', '0.83', '0', '0', '#joy', '#interest', 'positive', '0.779', 'ball', 'bounce', 'ball_bounce', 'soccer', 'play_football']
senticnet['rugby_league_lancashire_cup'] = ['0', '0', '0', '0.879', '#admiration', '#admiration', 'positive', '0.879', 'sport_competition', 'win_competition', 'race', 'match', 'championship']
senticnet['rugby_shirt'] = ['-0.04', '0.1', '0', '0.152', '#interest', '#admiration', 'positive', '0.068', 'pants', 'jacket', 'clothes', 'robe', 'garment']
senticnet['rugby_union'] = ['0.728', '0.83', '0', '0', '#joy', '#interest', 'positive', '0.779', 'ball', 'bounce', 'ball_bounce', 'soccer', 'play_football']
senticnet['rugged'] = ['-0.97', '0', '0.813', '0', '#sadness', '#anger', 'negative', '-0.89', 'tougher', 'tough', 'sturdy', 'problematic', 'hard']
senticnet['rugged_beauty'] = ['0.896', '0', '0', '0.709', '#joy', '#admiration', 'positive', '0.802', 'perspective', 'vista', 'view', 'aspect', 'see_view']
senticnet['rugged_landscape'] = ['-0.56', '0', '0.814', '-0.88', '#anger', '#disgust', 'negative', '-0.75', 'unfriendly', 'barren', 'bleak', 'desolate', 'wild']
senticnet['rugged_terrain'] = ['-0.53', '0', '0.765', '-0.94', '#anger', '#disgust', 'negative', '-0.74', 'jaggedness', 'unregularity', 'unevenness', 'irregularity', 'patchiness']
senticnet['ruggedly'] = ['0.626', '-0.78', '0', '0.611', '#joy', '#surprise', 'positive', '0.675', 'audacious', 'swaggering', 'daring', 'venturesome', 'venturous']
senticnet['ruggedness'] = ['-0.53', '0', '0.765', '-0.94', '#anger', '#disgust', 'negative', '-0.74', 'jaggedness', 'unregularity', 'unevenness', 'irregularity', 'patchiness']
senticnet['ruggero_deodato'] = ['-0.73', '0', '0.734', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'primitive', 'headhunter', 'barbarian', 'anthropophagus', 'anthropophagite']
senticnet['rugosity'] = ['-0.50', '0', '0.552', '-0.93', '#anger', '#disgust', 'negative', '-0.66', 'nubbiness', 'tweediness', 'harshness', 'graininess', 'granularity']
senticnet['ruhr_pocket'] = ['0.737', '-0.96', '0', '0.552', '#joy', '#surprise', 'positive', '0.75', 'prominence', 'hump', 'bump', 'swelling', 'frontal_eminence']
senticnet['ruhrfisch'] = ['0.792', '-0.86', '0', '0.888', '#surprise', '#admiration', 'positive', '0.849', 'absurd', 'inconsequential', 'intuitive', 'visceral', 'nonrational']
senticnet['ruili'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['ruin'] = ['-0.09', '-0.01', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.04', 'break', 'smash', 'raze', 'wreck', 'damage']
senticnet['ruin_family'] = ['-0.65', '0', '0.553', '-0.74', '#sadness', '#disgust', 'negative', '-0.65', 'waste_money', 'jail', 'form_of_gambling', 'bankrupt', 'loan_shark']
senticnet['ruination'] = ['-0.87', '0', '0.889', '-0.62', '#sadness', '#anger', 'negative', '-0.79', 'devastation', 'ruin', 'deterioration', 'blight', 'impairment']
senticnet['ruined_abbey'] = ['0.021', '0.119', '0', '0.129', '#interest', '#admiration', 'positive', '0.09', 'manor', 'palace', 'medieval', 'tower', 'chess_game']
senticnet['ruined_castle'] = ['0.021', '0.119', '0', '0.129', '#interest', '#admiration', 'positive', '0.09', 'manor', 'palace', 'medieval', 'tower', 'chess_game']
senticnet['ruined_dinner'] = ['-0.98', '0', '0.458', '-0.94', '#sadness', '#disgust', 'negative', '-0.79', 'fire', 'ruined_meal', 'burn_food', 'ruined_lunch', 'waste_food']
senticnet['ruined_lunch'] = ['-0.98', '0', '0.458', '-0.94', '#sadness', '#disgust', 'negative', '-0.79', 'fire', 'ruined_meal', 'ruined_dinner', 'burn_food', 'waste_food']
senticnet['ruined_meal'] = ['-0.98', '0', '0.458', '-0.94', '#sadness', '#disgust', 'negative', '-0.79', 'fire', 'burn_food', 'ruined_dinner', 'ruined_lunch', 'waste_food']
senticnet['ruined_medieval_castle'] = ['0.021', '0.119', '0', '0.129', '#interest', '#admiration', 'positive', '0.09', 'manor', 'palace', 'medieval', 'tower', 'chess_game']
senticnet['ruined_tower'] = ['0.021', '0.119', '0', '0.129', '#interest', '#admiration', 'positive', '0.09', 'manor', 'palace', 'medieval', 'tower', 'chess_game']
senticnet['ruiner'] = ['-0.58', '0', '0.714', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'waster', 'sleeper', 'destroyer', 'diversionist', 'undoer']
senticnet['ruinous'] = ['-0.70', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.74', 'ruin', 'catastrophic', 'calamitous', 'dilapidated', 'destructive']
senticnet['rule'] = ['0', '0.113', '0', '0.105', '#interest', '#admiration', 'positive', '0.109', 'regulation', 'edict', 'law', 'legislation', 'respect_agreement']
senticnet['rule-governed'] = ['0', '0.816', '0', '0.782', '#interest', '#admiration', 'positive', '0.799', 'true', 'lawful', 'legitimate', 'licit', 'rightful']
senticnet['rule_country'] = ['-0.04', '0.15', '0', '0.062', '#interest', '#admiration', 'positive', '0.056', 'rule_nation', 'reign', 'protect_king', 'rule_government', 'world_domination']
senticnet['rule_governing_judicial'] = ['-0.88', '0', '0.727', '-0.82', '#sadness', '#disgust', 'negative', '-0.81', 'defervescence', 'subsidence', 'break', 'remission', 'abatement']
senticnet['rule_government'] = ['0', '0', '-0.83', '-0.96', '#fear', '#disgust', 'negative', '-0.89', 'kingdom', 'dynasty', 'aboriginal', 'queen_of_england', 'imperial']
senticnet['rule_nation'] = ['-0.04', '0.15', '0', '0.062', '#interest', '#admiration', 'positive', '0.056', 'rule_country', 'reign', 'protect_king', 'rule_government', 'world_domination']
senticnet['rule_of_thumb'] = ['0', '0.959', '0', '0.899', '#interest', '#admiration', 'positive', '0.929', 'road_map', 'guidepost', 'guideline', 'research', 'award']
senticnet['rule_set_forth'] = ['-0.68', '0', '0.967', '-0.65', '#sadness', '#anger', 'negative', '-0.76', 'depart', 'start', 'part', 'start_out', 'sally_forth']
senticnet['rule_world'] = ['0', '0.964', '0', '0.724', '#interest', '#admiration', 'positive', '0.844', 'desire_win', 'try_forget', 'experience_life', 'experience_joy', 'raise_money']
senticnet['ruled_inadmissible'] = ['-0.63', '0', '0.967', '-0.67', '#anger', '#disgust', 'negative', '-0.76', 'condemnatory', 'condemning', 'accusatory', 'accusing', 'accusive']
senticnet['ruled_ineligible'] = ['-0.60', '0', '0.832', '-0.53', '#sadness', '#anger', 'negative', '-0.65', 'unsuitable', 'undesirable', 'unqualified', 'disqualified', 'unentitled']
senticnet['rulemaking_authority'] = ['0', '0.81', '0', '0.825', '#interest', '#admiration', 'positive', '0.818', 'prudence', 'frugality', 'frugalness', 'discernment', 'natural_virtue']
senticnet['ruler'] = ['0.03', '0', '0.031', '0.089', '#joy', '#admiration', 'positive', '0.029', 'imperial', 'kingly', 'nobleman', 'blue_blood', 'nobility']
senticnet['ruleth'] = ['0.655', '-0.82', '0', '0.956', '#surprise', '#admiration', 'positive', '0.813', 'superlative', 'compliment', 'testimonial', 'praise', 'approval']
senticnet['ruling_dynasty'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
senticnet['ruling_elite'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'ascendance', 'absolutism', 'ascendancy', 'tyrant']
senticnet['ruling_monarch'] = ['0.059', '0.092', '0', '0.107', '#interest', '#admiration', 'positive', '0.086', 'royalty', 'monarch', 'throne', 'emperor', 'king_queen']
senticnet['rum'] = ['-0.04', '0.15', '0', '0.062', '#interest', '#admiration', 'positive', '0.056', 'brandy', 'scotch', 'vodka', 'bourbon', 'whisky']
senticnet['rum_runner'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'drifter', 'bum', 'sundowner']
senticnet['rumba'] = ['0.116', '-0.02', '0.025', '0.156', '#joy', '#admiration', 'positive', '0.069', 'dance_move', 'latin_dance', 'body_movement', 'waltz', 'dance']
senticnet['rumble'] = ['-0.94', '-0.91', '-0.97', '-0.81', '#sadness', '#fear', 'negative', '-0.45', 'explosion', 'dynamite_explosion', 'bright_flash', 'disaster', 'earthquake']
senticnet['rumble_strip'] = ['0.754', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.867', 'american_flag', 'asphalt', 'rubble', 'pavement', 'pave']
senticnet['rumbling'] = ['-0.80', '0', '0.737', '0', '#sadness', '#anger', 'negative', '-0.77', 'grumble', 'croak', 'grudge', 'rumble', 'growling']
senticnet['rumex'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['rumiko_takahashi'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['ruminal'] = ['0.992', '-0.80', '0', '0.584', '#joy', '#surprise', 'positive', '0.795', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'incontinence']
senticnet['ruminate'] = ['-0.73', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.72', 'think_over', 'chew_over', 'ponder', 'meditate', 'excogitate']
senticnet['rumination'] = ['0.937', '-0.93', '0', '0.933', '#joy', '#surprise', 'positive', '0.936', 'consideration', 'introspection', 'cogitation', 'study', 'speculation']
senticnet['ruminative'] = ['0.898', '0.84', '0', '0', '#joy', '#interest', 'positive', '0.869', 'pondering', 'broody', 'contemplative', 'meditative', 'think']
senticnet['rummage'] = ['-0.85', '0', '0.583', '-0.71', '#sadness', '#disgust', 'negative', '-0.71', 'muddle', 'fuddle', 'mare_nest', 'welter', 'clutter']
senticnet['rummage_sale'] = ['0.048', '0', '0', '0.092', '#joy', '#admiration', 'positive', '0.07', 'gym_locker', 'observatory', 'prison_cell', 'luggage_store', 'luggage_compartment']
senticnet['rummage_through'] = ['-0.05', '0.157', '0', '0.168', '#interest', '#admiration', 'positive', '0.09', 'totting', 'make_selection', 'buy_diamond_ring', 'compute', 'go_shopping']
senticnet['rummaged'] = ['0', '-0.96', '0', '0.832', '#surprise', '#admiration', 'positive', '0.9', 'ransacking', 'ransacked', 'vandalize', 'despoil', 'ransack']
senticnet['rummaged_through'] = ['-0.05', '0.157', '0', '0.168', '#interest', '#admiration', 'positive', '0.09', 'totting', 'make_selection', 'buy_diamond_ring', 'compute', 'go_shopping']
senticnet['rummaging_around'] = ['0.788', '-0.60', '0', '0.884', '#joy', '#admiration', 'positive', '0.759', 'gap', 'rathole', 'aperture', 'bullet_hole', 'hole']
senticnet['rummaging_through'] = ['-0.05', '0.157', '0', '0.168', '#interest', '#admiration', 'positive', '0.09', 'totting', 'make_selection', 'buy_diamond_ring', 'compute', 'go_shopping']
senticnet['rummikub'] = ['0.801', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.796', 'verbosity', 'sport_play', 'excite_game', 'contest', 'fun_game']
senticnet['rummy'] = ['0.556', '-0.79', '0', '0.755', '#surprise', '#admiration', 'positive', '0.701', 'lush', 'alcoholic', 'alky', 'dipsomaniac', 'boozer']
senticnet['rumor'] = ['0', '0', '-0.92', '-0.83', '#fear', '#disgust', 'negative', '-0.87', 'whisper', 'reportedly', 'rumormonger', 'skinny', 'statement']
senticnet['rumormonger'] = ['-0.80', '0', '0.56', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'gossip', 'gossiper', 'gossipmonger', 'talkative', 'blabbermouth']
senticnet['rumour_mill_celtic'] = ['0.941', '-0.83', '0', '0.973', '#joy', '#admiration', 'positive', '0.917', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['rumpelstiltskin'] = ['-0.63', '0', '0.674', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'baneful', 'evil', 'baleful', 'malevolent', 'malign']
senticnet['rumpled'] = ['-0.93', '0', '0', '-0.86', '#sadness', '#disgust', 'negative', '-0.89', 'scruffy', 'beat-up', 'ratty', 'mangy', 'frumpy']
senticnet['rumplestiltskin'] = ['-0.63', '0', '0.674', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'baneful', 'evil', 'baleful', 'malevolent', 'malign']
senticnet['run'] = ['-0.74', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.74', 'carry_briefcase', 'hurry_work', 'bicycle_across_town', 'sail_around_world', 'voice_concern']
senticnet['run-down'] = ['-0.99', '0', '0', '-0.91', '#sadness', '#disgust', 'negative', '-0.95', 'dilapidated', 'rundown', 'ramshackle', 'decrepit', 'derelict']
senticnet['run-of-the-mill'] = ['0', '-0.87', '0', '0.978', '#surprise', '#admiration', 'positive', '0.926', 'centrist', 'middling', 'inoffensive', 'wishy-washy', 'middle-of-the-road']
senticnet['run-on'] = ['-0.73', '0', '0.94', '0', '#sadness', '#anger', 'negative', '-0.83', 'sentence', 'run-ons', 'ungrammatical', 'grammatically', 'comma']
senticnet['run-ons'] = ['-0.73', '0', '0.931', '0', '#sadness', '#anger', 'negative', '-0.83', 'sentence', 'run-on', 'ungrammatical', 'grammatically', 'comma']
senticnet['run_afoul'] = ['-0.61', '0', '0.716', '-0.82', '#anger', '#disgust', 'negative', '-0.71', 'offend', 'violate', 'go_against', 'break', 'transgress']
senticnet['run_aground'] = ['-0.99', '0', '0.658', '-0.73', '#sadness', '#disgust', 'negative', '-0.79', 'purposeless', 'afloat', 'aimless', 'directionless', 'planless']
senticnet['run_amuck'] = ['-0.63', '0', '0.712', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'upset', 'disconcert', 'discompose', 'discomfit', 'untune']
senticnet['run_arbitrary_code'] = ['-0.02', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.07', 'army', 'military', 'infantry', 'samurai', 'soldier']
senticnet['run_around'] = ['0.979', '0.959', '0', '0', '#joy', '#interest', 'positive', '0.969', 'play', 'roll_downhill', 'exersice', 'drive_crazy', 'jog']
senticnet['run_away'] = ['-0.97', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.94', 'jump', 'jump_over', 'hide_body', 'hurry_along', 'think_food']
senticnet['run_basis'] = ['0.213', '0', '0', '0', '#joy', '#joy', 'positive', '0.213', 'hit_ball_hard', 'step_up_plate', 'team_win', 'swing_bat', 'break_record']
senticnet['run_bath'] = ['-0.07', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.05', 'soap_up', 'turn_water', 'fill_bathtub', 'dispense_water', 'launder']
senticnet['run_business'] = ['0.373', '0.185', '-0.07', '0.374', '#joy', '#admiration', 'positive', '0.215', 'complement', 'world_peace', 'good_sense_of_humor', 'strong_relationship', 'taken_seriously']
senticnet['run_car'] = ['0.711', '0.777', '0', '0', '#joy', '#interest', 'positive', '0.744', 'car_bus', 'make_go', 'vehicle', 'car_engine', 'car_steer']
senticnet['run_counter'] = ['0.843', '-0.6', '0', '0.698', '#joy', '#admiration', 'positive', '0.714', 'levelheaded', 'sound', 'healthy', 'sensible', 'commonsensible']
senticnet['run_dmc'] = ['0.081', '-0.05', '0.052', '0.072', '#joy', '#admiration', 'positive', '0.039', 'rhythm', 'type_of_music', 'instrumental', 'lyric', 'melody']
senticnet['run_errand'] = ['-0.97', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.95', 'waste_time', 'lost', 'scared', 'go_nowhere', 'anxious']
senticnet['run_event'] = ['0', '0', '0', '0.087', '#admiration', '#admiration', 'positive', '0.087', 'encounter', 'finish_line', 'run_fast', 'end', 'race']
senticnet['run_far'] = ['-0.96', '0', '-0.70', '0', '#sadness', '#fear', 'negative', '-0.83', 'scare', 'terrify', 'fear', 'terror', 'run_away']
senticnet['run_fast'] = ['0.734', '0', '0', '0.945', '#joy', '#admiration', 'positive', '0.839', 'sporty_guy', 'train_daily', 'run_lot', 'train_muscle', 'exercise']
senticnet['run_first_base'] = ['0.047', '0', '0', '0.132', '#joy', '#admiration', 'positive', '0.089', 'strike_out', 'score_point', 'minor_injury', 'throw_ball', 'loose']
senticnet['run_into'] = ['-0.53', '0', '0.57', '-0.73', '#anger', '#disgust', 'negative', '-0.61', 'bump_into', 'strike', 'hit', 'impinge_on', 'collide_with']
senticnet['run_into_difficulty'] = ['-0.92', '0', '0.429', '-0.92', '#sadness', '#disgust', 'negative', '-0.75', 'difficult', 'stress', 'anxiety', 'danger', 'dangerous']
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senticnet['run_long_time'] = ['-0.06', '-0.02', '0.021', '-0.06', '#sadness', '#disgust', 'negative', '-0.03', 'clean_window', 'vacuum_carpet', 'repetitive_task', 'regular_exercise', 'exercise_daily']
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senticnet['run_office'] = ['0.571', '0.573', '0', '0.774', '#interest', '#admiration', 'positive', '0.639', 'better_world', 'make_enemy', 'find_enemy', 'disagreement', 'religious_experience']
senticnet['run_out'] = ['-0.98', '0', '0.989', '-0.93', '#sadness', '#anger', 'negative', '-0.97', 'run_out_of_time', 'run_out_of_money', 'lack_of_food', 'finish', 'low_battery']
senticnet['run_out_booze'] = ['-0.05', '-0.05', '0', '-0.16', '#sadness', '#disgust', 'negative', '-0.05', 'forgetfulness', 'stagger', 'lurch', 'pass_out', 'loss_ofawareness']
senticnet['run_out_breath'] = ['-0.86', '0', '0', '-0.96', '#sadness', '#disgust', 'negative', '-0.91', 'breaheavily', 'get_out_breath', 'get_exhausted', 'heavy_breathe', 'become_wind']
senticnet['run_out_fuel'] = ['0.975', '0.813', '0', '0', '#joy', '#interest', 'positive', '0.894', 'first_class', 'shopping', 'travel_across_world', 'spending', 'cost_money']
senticnet['run_out_gas'] = ['0', '0', '-0.94', '-0.77', '#fear', '#disgust', 'negative', '-0.85', 'stop_red_light', 'shift_gear', 'arrive_destination', 'use_gas', 'hit_tree']
senticnet['run_out_ink'] = ['0.845', '0.896', '0', '0', '#joy', '#interest', 'positive', '0.871', 'proofread', 'think_write', 'change_pen', 'learn_write', 'pick_up_pen']
senticnet['run_out_money'] = ['-0.82', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.88', 'compare_price', 'bankruptcy', 'spend_much_money', 'comparison_shop', 'make_payment']
senticnet['run_out_of_money'] = ['-0.98', '0', '0.989', '-0.93', '#sadness', '#anger', 'negative', '-0.97', 'run_out_of_time', 'broke', 'lack_of_food', 'run_out', 'low_battery']
senticnet['run_out_of_patience'] = ['0', '0.084', '0', '0.074', '#interest', '#admiration', 'positive', '0.079', 'constant', 'time', 'time_frame', 'short_time', 'time_description']
senticnet['run_out_of_time'] = ['-0.98', '0', '0.989', '-0.93', '#sadness', '#anger', 'negative', '-0.97', 'short_time', 'run_out_of_money', 'lack_of_food', 'run_out', 'low_battery']
senticnet['run_out_paper'] = ['-0.74', '0', '0.978', '0', '#sadness', '#anger', 'negative', '-0.86', 'write_down', 'copyright', 'register', 'write_essay', 'realization']
senticnet['run_out_soap'] = ['0', '0', '0.883', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'shower', 'rain', 'wash_away_dirt', 'wet_clothes', 'become_wet']
senticnet['run_out_time'] = ['-0.73', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.80', 'win', 'strategize', 'lose', 'cost_fortune', 'loss']
senticnet['run_over_car'] = ['-0.32', '-0.06', '0', '-0.21', '#sadness', '#disgust', 'negative', '-0.15', 'step_dog_poop', 'racetrack', 'cause_suffer', 'lose_team', 'leave_out']
senticnet['run_parallel'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'artery', 'skeletal_system', 'groin', 'neck', 'muscle']
senticnet['run_place'] = ['-0.05', '0', '0.023', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'track', 'run_event', 'run', 'course', 'racetrack']
senticnet['run_president'] = ['0', '0.796', '0', '0.983', '#interest', '#admiration', 'positive', '0.889', 'tell_lie', 'obey_law', 'state_opinion', 'settle_argument', 'fee_cat']
senticnet['run_quickly'] = ['0.735', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.804', 'run_fast', 'chase_ball', 'run_out_breath', 'breath_heavily', 'wear_comfortable_clothes']
senticnet['run_race'] = ['-0.06', '-0.02', '0.035', '0', '#sadness', '#anger', 'negative', '-0.02', 'competition', 'run_long_distance', 'put_tennis_shoes', 'break_record', 'stretch_first']
senticnet['run_rampant'] = ['0', '0', '-0.91', '-0.73', '#fear', '#disgust', 'negative', '-0.82', 'overabundant', 'prevailing', 'plethoric', 'predominant', 'dominant']
senticnet['run_risk'] = ['0.893', '-0.60', '0', '0.758', '#joy', '#admiration', 'positive', '0.752', 'try', 'assay', 'essay', 'go_for_broke', 'luck']
senticnet['run_roughly_parallel'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'artery', 'skeletal_system', 'groin', 'neck', 'muscle']
senticnet['run_slowly'] = ['-0.09', '0.104', '0', '0.099', '#interest', '#admiration', 'positive', '0.037', 'use_leg', 'take_step', 'wear_running_shoes', 'wear_good_shoes', 'act_quickly']
senticnet['run_treadmill'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'bluetooth', 'complete_thesis', 'dwarf_planet', 'insure', 'ridgeback']
senticnet['run_up'] = ['-0.59', '0', '0.604', '-0.95', '#anger', '#disgust', 'negative', '-0.71', 'pile_up', 'roll_up', 'collect', 'accumulate', 'amass']
senticnet['run_water'] = ['0', '0', '0', '0.093', '#admiration', '#admiration', 'positive', '0.093', 'flowing_water', 'water_flow', 'ebb', 'water_movement', 'small_stream']
senticnet['runaway_hit'] = ['0.625', '-0.90', '0', '0.54', '#joy', '#surprise', 'positive', '0.69', 'smash', 'bang', 'smasher', 'strike', 'hit']
senticnet['runaway_slave'] = ['0', '0', '0.8', '-0.82', '#anger', '#disgust', 'negative', '-0.81', 'personnel', 'attendant', 'manager', 'elect_official', 'gentleman']
senticnet['runaway_train'] = ['0.086', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'carny', 'fortune_teller', 'midway', 'ferris_wheel', 'roller-coaster']
senticnet['runaway_youth'] = ['-0.88', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.81', 'bum', 'red_light', 'petition', 'take', 'violent_action']
senticnet['rundown'] = ['-0.99', '0', '0', '-0.91', '#sadness', '#disgust', 'negative', '-0.95', 'dilapidated', 'run-down', 'ramshackle', 'decrepit', 'derelict']
senticnet['runis_en'] = ['0.667', '-0.98', '0', '0.61', '#joy', '#surprise', 'positive', '0.753', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['runner'] = ['-0.51', '0', '0.667', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['runner_knee'] = ['-0.98', '0', '0.678', '-0.95', '#sadness', '#disgust', 'negative', '-0.87', 'rubor', 'redness', 'inflammation', 'epicondylitis', 'lateral_epicondylitis']
senticnet['runner_up'] = ['0', '0', '0', '0.834', '#admiration', '#admiration', 'positive', '0.834', 'royal_person', 'royal_title', 'top', 'master', 'rank']
senticnet['runner_up_prize'] = ['0.894', '0.881', '0', '0.803', '#joy', '#interest', 'positive', '0.859', 'remittance', 'afford', 'award', 'money_due', 'amount_owe']
senticnet['runnerup'] = ['0', '0', '0', '0.834', '#admiration', '#admiration', 'positive', '0.834', 'royal_person', 'royal_title', 'top', 'master', 'rank']
senticnet['running'] = ['0', '0', '0', '0.198', '#admiration', '#admiration', 'positive', '0.198', 'human_movement', 'human_action', 'fast_movement', 'vigorous_activity', 'body_movement']
senticnet['running_afoul'] = ['-0.53', '0', '0.825', '-0.87', '#anger', '#disgust', 'negative', '-0.74', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['running_aground'] = ['-0.99', '0', '0.658', '-0.73', '#sadness', '#disgust', 'negative', '-0.79', 'purposeless', 'afloat', 'aimless', 'directionless', 'planless']
senticnet['running_amok'] = ['0', '0', '-0.85', '-0.95', '#fear', '#disgust', 'negative', '-0.90', 'scarily', 'scared', 'scare_away', 'frighten_off', 'scare_off']
senticnet['running_blackberry'] = ['-0.92', '0', '0.912', '-0.94', '#sadness', '#disgust', 'negative', '-0.92', 'true_blackberry', 'sand_blackberry', 'dewberry', 'dewberry_bush', 'blackberry_bush']
senticnet['running_gag'] = ['-0.95', '0', '0.504', '-0.77', '#sadness', '#disgust', 'negative', '-0.74', 'stupid', 'stupid_person', 'stupe', 'dullard', 'dolt']
senticnet['running_hand'] = ['-0.63', '0', '0.7', '-0.90', '#anger', '#disgust', 'negative', '-0.74', 'copperplate', 'hand', 'minuscule', 'italic', 'round_hand']
senticnet['running_joke'] = ['0.867', '0.949', '0', '0', '#joy', '#interest', 'positive', '0.908', 'tell_funny_joke', 'tell_joke', 'funny_joke', 'jest', 'witticism']
senticnet['running_rampant'] = ['0', '0', '-0.91', '-0.73', '#fear', '#disgust', 'negative', '-0.82', 'overabundant', 'prevailing', 'plethoric', 'predominant', 'dominant']
senticnet['running_shoes'] = ['0', '0', '0', '0.093', '#admiration', '#admiration', 'positive', '0.093', 'wear_shoes', 'wear_running_shoes', 'put_shoes', 'shoe', 'sport']
senticnet['running_stitch'] = ['-0.06', '-0.01', '0.05', '-0.07', '#sadness', '#disgust', 'negative', '-0.04', 'embroidery', 'woven', 'cover_wind', 'torn', 'use_needle']
senticnet['running_suit'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'activewear']
senticnet['running_water'] = ['0', '0.082', '0', '0.214', '#interest', '#admiration', 'positive', '0.148', 'water_motion', 'water_movement', 'arm_movement', 'water_relate', 'water_action']
senticnet['runny'] = ['0', '0', '-0.72', '-0.92', '#fear', '#disgust', 'negative', '-0.82', 'bee_product', 'flammable', 'remove_hair', 'slick', 'really_hot']
senticnet['runny_nose'] = ['-0.06', '0.183', '0.651', '-0.64', '#anger', '#disgust', 'negative', '-0.29', 'allergic_reaction', 'chew', 'pierce', 'mastication', 'put_mouth']
senticnet['runoff_election'] = ['0', '0.156', '-0.15', '0.179', '#interest', '#admiration', 'positive', '0.061', 'candidate', 'presidential', 'vote', 'elect', 'appoint']
senticnet['runs_rampant'] = ['0', '0', '-0.91', '-0.73', '#fear', '#disgust', 'negative', '-0.82', 'overabundant', 'prevailing', 'plethoric', 'predominant', 'dominant']
senticnet['runtime'] = ['-0.9', '0', '0.841', '-0.73', '#sadness', '#anger', 'negative', '-0.82', 'nubbiness', 'tweediness', 'harshness', 'graininess', 'raggedness']
senticnet['runtime_error'] = ['-0.98', '0', '0.869', '-0.80', '#sadness', '#anger', 'negative', '-0.88', 'error', 'syntax_error', 'semantic_error', 'programming_error', 'computer_error']
senticnet['runtime_exception'] = ['-0.98', '0', '0.869', '-0.80', '#sadness', '#anger', 'negative', '-0.88', 'error', 'syntax_error', 'semantic_error', 'programming_error', 'computer_error']
senticnet['runtime_library'] = ['-0.95', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.84', 'hold_pencil', 'food_scrap', 'paper_product', 'waste_paper', 'cover']
senticnet['runty'] = ['0', '0', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.05', 'human', 'delusional', 'use_illegal_drug', 'busy_person', 'carpenter']
senticnet['runway'] = ['-0.70', '0', '0.619', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'flare_path', 'airfield', 'grassland', 'landing_strip', 'road']
senticnet['rupee'] = ['-0.02', '0.264', '0', '0.095', '#interest', '#admiration', 'positive', '0.111', 'finance', 'franc', 'cent', 'banknote', 'mastercard']
senticnet['rupiah'] = ['-0.02', '0.264', '0', '0.095', '#interest', '#admiration', 'positive', '0.111', 'finance', 'franc', 'cent', 'banknote', 'mastercard']
senticnet['rupture'] = ['-0.57', '0', '0.852', '-0.74', '#anger', '#disgust', 'negative', '-0.72', 'tease', 'bust', 'tear', 'rip_up', 'snap']
senticnet['ruptured_appendix'] = ['-0.92', '0', '0.824', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'bronchopneumonia', 'bronchial_pneumonia', 'pneumonia', 'pleuropneumonia', 'pneumococcal_pneumonia']
senticnet['ruptured_cyst'] = ['-0.88', '0', '0.774', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fibroma', 'fibroid', 'benign_tumor', 'nonmalignant_tumor', 'nonmalignant_neoplasm']
senticnet['ruptured_ovarian_cyst'] = ['-0.88', '0', '0.774', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fibroma', 'fibroid', 'benign_tumor', 'nonmalignant_tumor', 'nonmalignant_neoplasm']
senticnet['ruptured_spleen'] = ['-0.03', '-0.00', '-0.07', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'injury', 'get_hurt', 'bruise', 'lose_game', 'get_injured']
senticnet['rupturewort'] = ['-0.72', '0', '0.64', '-0.58', '#sadness', '#anger', 'negative', '-0.65', 'pearlwort', 'pearlweed', 'flora', 'groundcover', 'whitlowwort']
senticnet['rural'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'negotiate', 'settle_down', 'urban', 'village', 'settlement']
senticnet['rural_area'] = ['0.038', '0.079', '0.03', '0.083', '#interest', '#admiration', 'positive', '0.042', 'hat', 'country_side', 'rustic', 'big_hat', 'farm']
senticnet['rural_backwater'] = ['-0.55', '0', '0.912', '-0.65', '#anger', '#disgust', 'negative', '-0.70', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['rural_countryside'] = ['-0.55', '0', '0.912', '-0.65', '#anger', '#disgust', 'negative', '-0.70', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['rural_development'] = ['0.812', '-0.91', '0', '0.647', '#joy', '#surprise', 'positive', '0.79', 'assistance', 'relief', 'economic_aid', 'financial_aid', 'social_insurance']
senticnet['rural_poor'] = ['-0.56', '0', '-0.49', '-0.09', '#sadness', '#fear', 'negative', '-0.38', 'lack', 'absence', 'poverty', 'emptiness', 'hunger']
senticnet['rural_poverty'] = ['-0.25', '0', '-0.17', '-0.08', '#sadness', '#fear', 'negative', '-0.16', 'exclusion', 'exclusion_zone', 'isolation', 'detach', 'except']
senticnet['ruralism'] = ['0.563', '-0.72', '0', '0.644', '#surprise', '#admiration', 'positive', '0.645', 'idiom', 'idiomatic_expression', 'phrasal_idiom', 'set_phrase', 'phrase']
senticnet['ruraux'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'indweller', 'invillager', 'villager', 'dweller', 'denizen']
senticnet['ruse'] = ['0', '-0.81', '0', '0.817', '#surprise', '#admiration', 'positive', '0.814', 'behave', 'act', 'perpetrator', 'portray', 'stage_play']
senticnet['rush'] = ['-0.88', '0.178', '-0.6', '0', '#sadness', '#fear', 'negative', '-0.43', 'rush_job', 'rush_work', 'late', 'urgency', 'emergency']
senticnet['rush-hour'] = ['0', '0', '-0.77', '-0.77', '#fear', '#disgust', 'negative', '-0.77', 'gridlocked', 'stalemate', 'gridlock', 'partisanship', 'impasse']
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senticnet['rush_job'] = ['0', '0', '0', '-0.96', '#disgust', '#disgust', 'negative', '-0.96', 'stand_alone', 'deal_card_player', 'forward_email', 'explain_everything', 'position_furniture']
senticnet['rush_work'] = ['-0.08', '-0.01', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.03', 'push_limit', 'fly_plane', 'plan_day', 'start_car', 'travel_work']
senticnet['rushncap'] = ['0', '0', '0.743', '-0.94', '#anger', '#disgust', 'negative', '-0.84', 'stupid', 'increase_body_temperature', 'idiot', 'get_excited', 'sense_accomplishment']
senticnet['rushy'] = ['-0.86', '0', '0.665', '-0.61', '#sadness', '#anger', 'negative', '-0.71', 'brambly', 'braky', 'wooded', 'scrubby', 'forested']
senticnet['ruskin'] = ['-0.77', '0', '0.885', '-0.73', '#sadness', '#anger', 'negative', '-0.8', 'fallacy', 'misconception', 'paralogism', 'logical_fallacy', 'pseudoscience']
senticnet['ruskinian'] = ['-0.77', '0', '0.885', '-0.73', '#sadness', '#anger', 'negative', '-0.8', 'fallacy', 'misconception', 'paralogism', 'logical_fallacy', 'pseudoscience']
senticnet['russet'] = ['0.035', '0', '0', '0.116', '#joy', '#admiration', 'positive', '0.076', 'maroon', 'color', 'bright_color', 'hair_colored', 'cool_color']
senticnet['russet_scab'] = ['-0.69', '0', '0.776', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'little_potato', 'rhizoctinia_disease', 'stem_canker', 'rosette', 'plant_disease']
senticnet['rust'] = ['-0.86', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.84', 'activity', 'reddish', 'canker', 'rusty', 'recent']
senticnet['rust-free'] = ['0.744', '0', '0', '0.723', '#joy', '#admiration', 'positive', '0.734', 'rust-proof', 'rust-resistant', 'heavy-gauge', 'corrosion-resistant', 'rustproof']
senticnet['rust-proof'] = ['0.737', '0', '0', '0.715', '#joy', '#admiration', 'positive', '0.726', 'rust-free', 'rust-resistant', 'heavy-gauge', 'corrosion-resistant', 'rustproof']
senticnet['rust-resistant'] = ['0', '0', '-0.74', '-0.86', '#fear', '#disgust', 'negative', '-0.80', 'powder-coated', 'rust-proof', 'heavy-gauge', 'rustproof', 'corrosion-resistant']
senticnet['rust_belt'] = ['-0.55', '0', '0.912', '-0.65', '#anger', '#disgust', 'negative', '-0.70', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['rust_cronartium_ribicola'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['rust_fungi'] = ['-0.93', '0', '0.699', '-0.91', '#sadness', '#disgust', 'negative', '-0.84', 'onion_smut', 'head_smut', 'stinking_smut', 'flag_smut_fungus', 'covered_smut']
senticnet['rust_fungus'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'apple_rust', 'fungus']
senticnet['rust_puccinia'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'apple_rust', 'fungus']
senticnet['rust_resistant'] = ['-0.69', '0', '0.516', '-0.94', '#sadness', '#disgust', 'negative', '-0.71', 'insubordinate', 'recalcitrant', 'obstreperous', 'defiant', 'resistive']
senticnet['rustaq'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['rusted-out'] = ['-0.95', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.90', 'beat-up', 'beaten-down', 'weather-beaten', 'broken-down', 'beaten-up']
senticnet['rusted_iron'] = ['0', '-0.00', '0', '0.088', '#surprise', '#admiration', 'positive', '0.048', 'heavy_paper', 'lightweight_metal', 'wizard', 'metal_type', 'soft_metal']
senticnet['rusted_metal'] = ['-0.02', '-0.02', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.02', 'empty_glass', 'empty_space', 'worthless', 'empty_stomach', 'universe']
senticnet['rusted_steel'] = ['-0.87', '0', '0.682', '-0.91', '#sadness', '#disgust', 'negative', '-0.82', 'dump', 'garbage_dump', 'trash_dump', 'rubbish_dump', 'wasteyard']
senticnet['rustic'] = ['-0.96', '0', '0.924', '-0.86', '#sadness', '#anger', 'negative', '-0.91', 'redneck', 'yokel', 'rube', 'hick', 'hayseed']
senticnet['rustic_charm'] = ['-0.96', '0', '0.924', '-0.86', '#sadness', '#anger', 'negative', '-0.91', 'redneck', 'yokel', 'rube', 'hick', 'hayseed']
senticnet['rustic_chic'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'lurking_place', 'spot', 'drop', 'hiding_place', 'den']
senticnet['rustic_style'] = ['-0.96', '0', '0.924', '-0.86', '#sadness', '#anger', 'negative', '-0.91', 'redneck', 'yokel', 'rube', 'hick', 'hayseed']
senticnet['rustic_wooden'] = ['-0.96', '0', '0.924', '-0.86', '#sadness', '#anger', 'negative', '-0.91', 'redneck', 'yokel', 'rube', 'hick', 'hayseed']
senticnet['rustically'] = ['-0.96', '0', '0.924', '-0.86', '#sadness', '#anger', 'negative', '-0.91', 'redneck', 'yokel', 'rube', 'hick', 'hayseed']
senticnet['rusticate'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'debar', 'expel', 'throw_out', 'kick_out']
senticnet['rusticity'] = ['0', '0', '0.781', '-0.83', '#anger', '#disgust', 'negative', '-0.80', 'gaucherie', 'slip', 'solecism', 'gaffe', 'faux_pa']
senticnet['rustle'] = ['-0.06', '-0.01', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'low', 'ground', 'look_down', 'ground_level', 'under_house']
senticnet['rustle_up'] = ['0.866', '-0.91', '0', '0.893', '#surprise', '#admiration', 'positive', '0.892', 'mooch', 'freeload', 'grub', 'sponge', 'bum']
senticnet['rustling'] = ['0.937', '-0.67', '0', '0.628', '#joy', '#surprise', 'positive', '0.746', 'thieving', 'larceny', 'theft', 'thievery', 'stealing']
senticnet['rustproof'] = ['0.736', '0', '0', '0.713', '#joy', '#admiration', 'positive', '0.724', 'rust-proof', 'rust-resistant', 'heavy-gauge', 'corrosion-resistant', 'rust-free']
senticnet['rusty'] = ['-0.70', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.71', 'activity', 'reddish', 'canker', 'rust', 'recent']
senticnet['rusty_iron'] = ['0.979', '-0.69', '0', '0.576', '#joy', '#surprise', 'positive', '0.749', 'big_cheese', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['rusty_metal'] = ['0', '-0.00', '0', '0.088', '#surprise', '#admiration', 'positive', '0.048', 'heavy_paper', 'lightweight_metal', 'wizard', 'metal_type', 'soft_metal']
senticnet['rut'] = ['-0.05', '-0.00', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.04', 'ass', 'butt', 'foreign_country', 'dog_show', 'asshole']
senticnet['rutgers'] = ['0', '0', '-0.72', '-0.78', '#fear', '#disgust', 'negative', '-0.75', 'jvncnet', 'ansnet', 'reston', 'howland', 'newsserver']
senticnet['ruth'] = ['-0.92', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.95', 'sympathize', 'commiserate', 'commiseration', 'feel_sorry', 'pathos']
senticnet['ruth_duccini'] = ['0.931', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.855', 'crystal_clear', 'blue_sky', 'imaginary', 'water_sky', 'blue_color']
senticnet['ruth_madoff'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'gouger', 'swindler', 'confidence_man', 'conman', 'fraud']
senticnet['ruth_reichl'] = ['0.897', '-0.96', '0', '0.785', '#joy', '#surprise', 'positive', '0.881', 'erotic', 'voluptuary', 'epicure', 'gourmet', 'sensualist']
senticnet['ruthenian_catholic_church'] = ['0.8', '-0.84', '0', '0.868', '#surprise', '#admiration', 'positive', '0.837', 'commonwealth', 'province', 'administrative_district', 'administrative_division', 'territorial_division']
senticnet['ruthful'] = ['-0.88', '0.178', '-0.6', '0', '#sadness', '#fear', 'negative', '-0.43', 'contrite', 'remorseful', 'rueful', 'bereaved', 'sorrowing']
senticnet['ruthfulness'] = ['-0.89', '0.16', '-0.61', '0.055', '#sadness', '#fear', 'negative', '-0.32', 'mournfully', 'sorrowfulness', 'mournfulness', 'heartrending', 'heartbreaking']
senticnet['ruthless'] = ['-0.77', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.80', 'brutal', 'iron-fisted', 'cruel', 'ruthlessly', 'machiavellian']
senticnet['ruthless_dictator'] = ['-0.73', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.72', 'royal_leader', 'despot', 'emperor', 'monarch', 'king']
senticnet['ruthless_enemy'] = ['-0.82', '0', '0.739', '-0.50', '#sadness', '#anger', 'negative', '-0.69', 'fake', 'juke', 'maneuver', 'manoeuvre', 'feint']
senticnet['ruthless_killer'] = ['-0.52', '0', '0.981', '-0.61', '#anger', '#disgust', 'negative', '-0.70', 'criminal', 'bully', 'outlaw', 'malefactor', 'felon']
senticnet['ruthless_streak'] = ['0.827', '0.913', '0', '0.867', '#interest', '#admiration', 'positive', '0.869', 'good_habit', 'rivalry', 'competitive', 'competitive_nature', 'determination']
senticnet['ruthless_tyrant'] = ['-0.87', '0', '0.53', '-0.65', '#sadness', '#disgust', 'negative', '-0.68', 'malevolence', 'malevolency', 'malice', 'murderousness', 'ferociousness']
senticnet['ruthlessly'] = ['-0.78', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.80', 'brutal', 'iron-fisted', 'cruel', 'ruthless', 'machiavellian']
senticnet['ruthlessness'] = ['-0.93', '0', '0.763', '0', '#sadness', '#anger', 'negative', '-0.84', 'mercilessness', 'unkind', 'cruelly', 'pitilessness', 'unassertively']
senticnet['rutile'] = ['-0.05', '-0.00', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'pigment', 'many_shades', 'cyan', 'green_yellow', 'material']
senticnet['rutiodon'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'hag', 'osteostracan', 'cephalaspid', 'ostracoderm', 'jawless_fish']
senticnet['rutland_barrington'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'dress', 'pinny', 'dirndl', 'gown', 'muumuu']
senticnet['rutted_dirt'] = ['-0.99', '0', '0.641', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'navigable', 'negotiable', 'surmountable', 'climbable', 'passable']
senticnet['ruzhdi'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'white_animal', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
senticnet['rvnaf'] = ['0.814', '-0.87', '0', '0.918', '#surprise', '#admiration', 'positive', '0.87', 'ship_company', 'company', 'work_force', 'workforce', 'full_complement']
senticnet['rye'] = ['0.076', '0', '0', '0', '#joy', '#joy', 'positive', '0.076', 'dip', 'garlic', 'oat', 'savory', 'ginger']
senticnet['rye_bread'] = ['0.52', '-0.69', '0', '0.639', '#surprise', '#admiration', 'positive', '0.617', 'black_bread', 'pumpernickel', 'limpa', 'bread', 'breadstuff']
senticnet['rye_flour'] = ['-0.08', '0.106', '0', '0.125', '#interest', '#admiration', 'positive', '0.047', 'sugar', 'treacle', 'black_treacle', 'add_sugar', 'sugar_substitute']
senticnet['rye_secale_cereale'] = ['0.878', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.883', 'wheat', 'grain', 'rye', 'white_rice', 'rice']
senticnet['rye_whiskey'] = ['0.877', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.919', 'score', 'economical', 'scotch_whiskey', 'spoil', 'scottish']
senticnet['rykodisc'] = ['0.805', '-0.58', '0', '0.525', '#joy', '#surprise', 'positive', '0.637', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['ryland_blackinton'] = ['-0.86', '0', '0.675', '-0.84', '#sadness', '#disgust', 'negative', '-0.79', 'shenanigan', 'chicanery', 'wile', 'dupery', 'trickery']
senticnet['ryota_chikuzen'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['ryu'] = ['-0.04', '0.138', '-0.08', '0.111', '#interest', '#admiration', 'positive', '0.031', 'geometry', 'craft', 'blade_geometry', 'blade', 'collect_item']
senticnet['ryuga'] = ['-0.53', '0', '0.637', '-0.92', '#anger', '#disgust', 'negative', '-0.69', 'incorporeal', 'unembodied', 'disembodied', 'bodiless', 'unbodied']
senticnet['ryukyu_island'] = ['0.096', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.057', 'platform_rail', 'rail', 'catwalk', 'column', 'token_turnstile']
senticnet['ryvita'] = ['0.547', '-0.96', '0', '0.89', '#surprise', '#admiration', 'positive', '0.799', 'pudding', 'ice_cream', 'chocolate', 'chocolate_pudding', 'chocolate_dessert']
senticnet['sa_candidature'] = ['0.667', '-0.98', '0', '0.61', '#joy', '#surprise', 'positive', '0.753', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['sa_commuter'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['sa_plus_forte'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'mathematical', 'muscle', 'math_function', 'feel_sick', 'separation']
senticnet['saab_aircraft'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['saad_el_katatni'] = ['0.648', '0.627', '-0.26', '0.329', '#joy', '#interest', 'positive', '0.334', 'brotherly', 'friendly', 'fraternal', 'amicability', 'amicable']
senticnet['sabaoth'] = ['-0.52', '0', '0.701', '-0.50', '#sadness', '#anger', 'negative', '-0.57', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['sabbatai_zevi'] = ['0.065', '0.17', '-0.05', '0.143', '#interest', '#admiration', 'positive', '0.081', 'supervisor', 'native_american', 'ambassador', 'master', 'general']
senticnet['sabbatarian'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'nonconformity', 'iconoclasm', 'heterodoxy', 'nonconformance', 'orientation']
senticnet['sabbatarianism'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'nonconformity', 'iconoclasm', 'heterodoxy', 'nonconformance', 'orientation']
senticnet['sabbatical'] = ['0.783', '0.945', '0', '0.982', '#interest', '#admiration', 'positive', '0.903', 'holiday', 'day_off', 'vacation', 'time_off', 'time']
senticnet['sabena'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['saber_tooth_cat'] = ['0.969', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.96', 'extinct', 'animal', 'ice', 'elephant', 'giant']
senticnet['saber_toothed_tiger'] = ['0.969', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.96', 'extinct', 'animal', 'ice', 'elephant', 'giant']
senticnet['sabertooth'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'elapid', 'ophidian', 'serpent', 'krait', 'taipan']
senticnet['sabil'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['sabine_parish'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
senticnet['sabinene'] = ['-0.67', '0', '0.503', '-0.67', '#sadness', '#disgust', 'negative', '-0.61', 'stink_up', 'smell_up', 'stink_out', 'incense', 'perfume']
senticnet['sable_brush'] = ['0.741', '0.88', '0', '0', '#joy', '#interest', 'positive', '0.81', 'use_brush', 'hair', 'brush_hair', 'brush_tooth', 'brush']
senticnet['sabotage'] = ['0', '0', '0.818', '-0.74', '#anger', '#disgust', 'negative', '-0.78', 'counteract', 'cave', 'undermine', 'subvert', 'weaken']
senticnet['saboteur'] = ['-0.69', '0', '0.756', '-0.99', '#anger', '#disgust', 'negative', '-0.81', 'waster', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['sabre_coach_lindy'] = ['0.022', '-0.11', '0.014', '0.057', '#surprise', '#admiration', 'positive', '0.045', 'wood', 'many_steps', 'franchise', 'stump', 'timber']
senticnet['sabretooth'] = ['-0.98', '0', '0.714', '-0.90', '#sadness', '#disgust', 'negative', '-0.86', 'stupid', 'stupid_person', 'stupe', 'dullard', 'dolt']
senticnet['sac'] = ['-0.98', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.98', 'money_transfer', 'business', 'income', 'finance', 'revenue']
senticnet['sac_fry'] = ['-0.83', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.78', 'picaninny', 'kid', 'minor', 'tike', 'small_picaninny']
senticnet['sacajawea'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['sacbrood'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'hornet', 'vespid', 'vespid_wasp']
senticnet['saccade'] = ['0.067', '-0.2', '0.064', '0', '#joy', '#surprise', 'positive', '0.068', 'wand', 'long_stick', 'branch', 'twig', 'stick']
senticnet['saccadic'] = ['0.067', '-0.2', '0.064', '0', '#joy', '#surprise', 'positive', '0.068', 'wand', 'long_stick', 'branch', 'twig', 'stick']
senticnet['saccadic_eye_movement'] = ['0.067', '-0.2', '0.064', '0', '#joy', '#surprise', 'positive', '0.068', 'wand', 'long_stick', 'branch', 'twig', 'stick']
senticnet['sacchari'] = ['-0.93', '0', '0.699', '-0.91', '#sadness', '#disgust', 'negative', '-0.84', 'onion_smut', 'head_smut', 'stinking_smut', 'flag_smut_fungus', 'covered_smut']
senticnet['saccharification'] = ['0', '0.104', '-0.09', '0.146', '#interest', '#admiration', 'positive', '0.052', 'thaw', 'slush', 'water_inside', 'give_water', 'obtain_water']
senticnet['saccharin'] = ['0.523', '-0.64', '0', '0.933', '#surprise', '#admiration', 'positive', '0.701', 'aspartame', 'honey', 'refined_sugar', 'sugar', 'sweetener']
senticnet['sacculina'] = ['0.96', '-0.77', '0', '0.929', '#joy', '#admiration', 'positive', '0.887', 'whale_louse', 'beach_flea', 'sand_hopper', 'sandhopper', 'amphipod']
senticnet['sacd_digipak_by'] = ['-0.73', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'unwed', 'unwedded', 'divorced', 'unmarried', 'widowed']
senticnet['sacerdotal'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'reverence', 'obeisance', 'genuflexion', 'kowtow', 'mafia']
senticnet['sachet'] = ['0', '-0.07', '0.109', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'prescription_medication', 'pantyhose', 'underwear', 'lingerie', 'shave_kit']
senticnet['sack'] = ['-0.03', '0.059', '0', '0.057', '#interest', '#admiration', 'positive', '0.026', 'crate', 'storage', 'storage_facility', 'warehouse', 'storage_place']
senticnet['sack_coat'] = ['-0.58', '0', '0.757', '-0.67', '#anger', '#disgust', 'negative', '-0.67', 'breech', 'knee_breech', 'knee_pants', 'trunk_hose', 'knickers']
senticnet['sack_lunch'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'cafe', 'buy_sandwich', 'eat_food', 'buy_food', 'finish_job']
senticnet['sackcloth'] = ['0', '0', '0.954', '-0.99', '#anger', '#disgust', 'negative', '-0.97', 'loin', 'raiment', 'gird', 'girded', 'clothed']
senticnet['sackful'] = ['-0.62', '0', '0.504', '-0.79', '#sadness', '#disgust', 'negative', '-0.64', 'green_gold', 'pure_gold', 'gold', 'guinea_gold', 'noble_metal']
senticnet['sacking'] = ['0.572', '-0.71', '0', '0.895', '#surprise', '#admiration', 'positive', '0.728', 'material', 'burlap', 'gunny', 'fabric', 'cloth']
senticnet['sacral_vertebra'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'above_leg', 'joint', 'hip', 'knee']
senticnet['sacralize'] = ['-0.81', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.79', 'stifle', 'forgotten', 'old', 'touch_ground', 'seldom']
senticnet['sacrament'] = ['0.901', '0', '0', '0.916', '#joy', '#admiration', 'positive', '0.909', 'get_away', 'bread', 'glory', 'counsel', 'vow']
senticnet['sacrament_of_eucharist'] = ['0.823', '0', '0', '0.751', '#joy', '#admiration', 'positive', '0.787', 'holy', 'eucharist', 'holy_eucharist', 'holy_sacrament', 'liturgy']
senticnet['sacramental_absolution'] = ['0.901', '0', '0', '0.916', '#joy', '#admiration', 'positive', '0.909', 'get_away', 'bread', 'glory', 'counsel', 'vow']
senticnet['sacramental_confession'] = ['0.901', '0', '0', '0.916', '#joy', '#admiration', 'positive', '0.909', 'get_away', 'bread', 'glory', 'counsel', 'vow']
senticnet['sacramental_theology'] = ['0.664', '-0.57', '0', '0.981', '#joy', '#admiration', 'positive', '0.741', 'theodicy', 'homiletics', 'discipline', 'study', 'subject']
senticnet['sacred'] = ['0.03', '0.172', '-0.11', '0.175', '#interest', '#admiration', 'positive', '0.067', 'omega', 'begin_end', 'first', 'book_bible', 'book']
senticnet['sacred_choral_music'] = ['0.99', '0', '0', '0.719', '#joy', '#admiration', 'positive', '0.855', 'instrumental', 'lyric', 'lyrics', 'sing', 'melody']
senticnet['sacred_duty'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'patriotism', 'homeland']
senticnet['sacred_ibis'] = ['0.797', '-0.71', '0', '0.736', '#joy', '#admiration', 'positive', '0.751', 'wood_ibis', 'wood_stork', 'ibis', 'wading_bird', 'wader']
senticnet['sacred_music'] = ['0.99', '0', '0', '0.719', '#joy', '#admiration', 'positive', '0.855', 'instrumental', 'lyric', 'lyrics', 'sing', 'melody']
senticnet['sacred_oath'] = ['0.088', '0.317', '-0.29', '0.379', '#interest', '#admiration', 'positive', '0.122', 'oath', 'commitment', 'marriage', 'obedience', 'enter_contract']
senticnet['sacred_relic'] = ['-0.98', '0', '0.664', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'unfortunate', 'unfortunate_person', 'weeper', 'pallgriever', 'griever']
senticnet['sacred_ritual'] = ['0.807', '-0.92', '0', '0.796', '#joy', '#surprise', 'positive', '0.841', 'agape', 'confirmation', 'libation', 'rite', 'service']
senticnet['sacred_thread'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'bangle', 'ankle_bracelet', 'armilla']
senticnet['sacred_trinity'] = ['0', '0.909', '0', '0.788', '#interest', '#admiration', 'positive', '0.849', 'deuce-ace', 'trio', 'triplet', 'trinity', 'tercet']
senticnet['sacrifice'] = ['-0.1', '0.304', '-0.21', '0.278', '#interest', '#admiration', 'positive', '0.068', 'play_blackjack', 'play_game_chess', 'play_chess', 'wish', 'play_checker']
senticnet['sacrificial'] = ['0.807', '-0.92', '0', '0.796', '#joy', '#surprise', 'positive', '0.841', 'agape', 'confirmation', 'libation', 'rite', 'service']
senticnet['sacrificial_animal'] = ['-0.97', '0', '0.772', '-0.67', '#sadness', '#anger', 'negative', '-0.80', 'expiation', 'atonement', 'deliverance', 'redemption', 'rescue']
senticnet['sacrificial_blood'] = ['-0.97', '0', '0.772', '-0.67', '#sadness', '#anger', 'negative', '-0.80', 'expiation', 'atonement', 'deliverance', 'redemption', 'rescue']
senticnet['sacrificial_death'] = ['-0.82', '0', '0.672', '-0.54', '#sadness', '#anger', 'negative', '-0.68', 'amends', 'damage', 'indemnification', 'redress', 'restitution']
senticnet['sacrificial_knife'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'sedum', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['sacrificial_rite'] = ['-0.97', '0', '0.772', '-0.67', '#sadness', '#anger', 'negative', '-0.80', 'expiation', 'atonement', 'deliverance', 'redemption', 'rescue']
senticnet['sacrificial_ritual'] = ['0.807', '-0.92', '0', '0.796', '#joy', '#surprise', 'positive', '0.841', 'agape', 'confirmation', 'libation', 'rite', 'service']
senticnet['sacrificially'] = ['0.91', '0', '0', '0.777', '#joy', '#admiration', 'positive', '0.844', 'selflessly', 'unselfishly', 'unstintingly', 'selfishly', 'altruistically']
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senticnet['sacrococcygeal_teratoma'] = ['-0.75', '0', '0.715', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'abdominal_pregnancy', 'ectopic_gestation', 'tubal_pregnancy', 'pregnancy', 'gestation']
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senticnet['sacrosanct'] = ['0', '0.732', '0', '0.918', '#interest', '#admiration', 'positive', '0.825', 'inviolable', 'inviolate', 'inalienable', 'sanctity', 'self-evident']
senticnet['sad'] = ['-0.91', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.91', 'unhappiness', 'misery', 'melancholy', 'depression', 'sorrow']
senticnet['sad-sack'] = ['-0.84', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.81', 'hard-drinking', 'down-on-his-luck', 'washed-up', 'drug-addicted', 'down-and-out']
senticnet['sad_farewell'] = ['0.037', '0.161', '-0.15', '0.203', '#interest', '#admiration', 'positive', '0.062', 'wave', 'flow', 'salute', 'multiple_purposes', 'wave_wind']
senticnet['sad_movie'] = ['-0.05', '0', '-0.07', '0', '#sadness', '#fear', 'negative', '-0.06', 'cry', 'chop_onion', 'watch_sad_film', 'see_sad_show', 'see_chop_onion']
senticnet['sad_person'] = ['-0.09', '-0.02', '-0.11', '-0.22', '#fear', '#disgust', 'negative', '-0.10', 'good_man', 'medic', 'felon', 'demon', 'buy_weapon']
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senticnet['sadar_bazar'] = ['0', '0.113', '0.041', '0.058', '#interest', '#admiration', 'positive', '0.043', 'corner_shop', 'general_store', 'many_shops', 'large_supermarket', 'goods']
senticnet['saddam'] = ['-0.73', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.72', 'royal_leader', 'despot', 'emperor', 'monarch', 'king']
senticnet['saddam_hussein'] = ['-0.73', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.72', 'royal_leader', 'despot', 'emperor', 'monarch', 'king']
senticnet['sadden'] = ['-0.95', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.95', 'unhappy', 'dysphoric', 'forlornly', 'desolation', 'forlornness']
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senticnet['saddle'] = ['0.093', '-0.02', '0', '0.054', '#joy', '#admiration', 'positive', '0.058', 'control_horse', 'lead_rope', 'horse', 'sprinter', 'jockey']
senticnet['saddle_bag'] = ['0.706', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.806', 'shoe', 'flip_flop', 'slipper', 'casual_shoes', 'footwear']
senticnet['saddle_bronc'] = ['0.938', '0.779', '0', '0', '#joy', '#interest', 'positive', '0.859', 'carnival', 'circus', 'fairground', 'fair', 'disneyland']
senticnet['saddle_horse'] = ['0.064', '0', '0', '0.123', '#joy', '#admiration', 'positive', '0.093', 'horse', 'passenger_car', 'car_action', 'conveyance', 'car_travel']
senticnet['saddle_nose'] = ['0.582', '-0.66', '0', '0.661', '#surprise', '#admiration', 'positive', '0.637', 'talipes_equinus', 'deformity', 'malformation', 'talipes_valgus', 'talipes_calcaneus']
senticnet['saddle_stitch'] = ['-0.02', '0.095', '0', '0.117', '#interest', '#admiration', 'positive', '0.063', 'identification', 'paper_square', 'carry_money', 'money_hold', 'credit']
senticnet['saddle_tank'] = ['0.069', '0.097', '0', '0.172', '#interest', '#admiration', 'positive', '0.113', 'type_of_engine', 'liquid_gas', 'warm_air', 'hot_air', 'steamy']
senticnet['saddle_tank_locomotive'] = ['0.069', '0.097', '0', '0.172', '#interest', '#admiration', 'positive', '0.113', 'type_of_engine', 'liquid_gas', 'warm_air', 'hot_air', 'steamy']
senticnet['saddleback_mountain'] = ['0', '0.093', '0', '0.078', '#interest', '#admiration', 'positive', '0.085', 'steep', 'landscape_feature', 'knoll', 'hill_mountain', 'incline']
senticnet['saddlebag'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'neat', 'kitchen_drawer', 'cutlery_drawer', 'dine_table', 'table']
senticnet['saddlecloth'] = ['0', '0', '0', '0.071', '#admiration', '#admiration', 'positive', '0.071', 'two_barbs', 'barb', 'mule', 'buddhist', 'ride_barb']
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senticnet['sadie_cole_hq'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'hip', 'joint', 'above_leg', 'knee']
senticnet['sadira'] = ['0.086', '-0.10', '0.064', '0', '#joy', '#surprise', 'positive', '0.043', 'art_piece', 'lifetime', 'museum', 'vintage', 'collectible']
senticnet['sadism'] = ['0', '0', '-0.96', '-0.71', '#fear', '#disgust', 'negative', '-0.84', 'turpitude', 'putrefaction', 'corruption', 'depravation', 'degeneracy']
senticnet['sadist'] = ['-0.74', '0', '0.604', '-0.88', '#sadness', '#disgust', 'negative', '-0.74', 'weirdo', 'crazy', 'loony', 'looney', 'nutcase']
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senticnet['sadistic_killer'] = ['0.864', '-0.54', '0', '0.613', '#joy', '#admiration', 'positive', '0.675', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['sadistic_pleasure'] = ['-0.75', '0', '0.886', '-0.72', '#sadness', '#anger', 'negative', '-0.78', 'harass', 'move', 'check', 'fork', 'pin']
senticnet['sadistically'] = ['-0.87', '0', '0.53', '-0.65', '#sadness', '#disgust', 'negative', '-0.68', 'malevolence', 'malevolency', 'malice', 'murderousness', 'ferociousness']
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senticnet['sado_masochism'] = ['0.857', '0', '0', '0', '#joy', '#joy', 'positive', '0.857', 'date', 'accuracy', 'sexy_woman', 'woo', 'enjoy_person']
senticnet['sado_masochistic'] = ['0.857', '0', '0', '0', '#joy', '#joy', 'positive', '0.857', 'date', 'accuracy', 'sexy_woman', 'woo', 'enjoy_person']
senticnet['sado_masochistic_sex'] = ['0.081', '0.095', '0', '0', '#joy', '#interest', 'positive', '0.088', 'build_tower_out_toy_block', 'a_lot_of_sex', 'play_hockey', 'play_baseball', 'play_football']
senticnet['sadomasochism'] = ['0.081', '0.095', '0', '0', '#joy', '#interest', 'positive', '0.088', 'build_tower_out_toy_block', 'a_lot_of_sex', 'play_hockey', 'play_baseball', 'play_football']
senticnet['sadomasochistic'] = ['-0.85', '0', '0.968', '-0.89', '#anger', '#disgust', 'negative', '-0.90', 'family', 'household', 'house', 'home', 'broken_home']
senticnet['sadomasochistic_sex'] = ['0.081', '0.095', '0', '0', '#joy', '#interest', 'positive', '0.088', 'build_tower_out_toy_block', 'a_lot_of_sex', 'play_hockey', 'play_baseball', 'play_football']
senticnet['safavid_dynasty'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
senticnet['safe'] = ['0.033', '0.186', '0', '0', '#joy', '#interest', 'positive', '0.11', 'bank', 'bank_vault', 'wallet', 'government_agreement', 'coffer']
senticnet['safe_belt'] = ['-0.86', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.80', 'brake_pedal', 'emergency_brake', 'stop', 'avoid_accident', 'brake']
senticnet['safe_city'] = ['0', '0.839', '0', '0.847', '#interest', '#admiration', 'positive', '0.843', 'safety', 'prevent_massacre', 'solve_problem', 'safety_first', 'gun_control']
senticnet['safe_conduct'] = ['0', '0.942', '0', '0.789', '#interest', '#admiration', 'positive', '0.865', 'safe_practice', 'safe', 'safety', 'safety_first', 'follow_guideline']
senticnet['safe_cracker'] = ['-0.83', '0', '0.831', '-0.69', '#sadness', '#anger', 'negative', '-0.78', 'toughie', 'accessory', 'highbinder', 'hoodlum', 'thug']
senticnet['safe_deposit'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'slot', 'box', 'load_dock', 'diskette', 'emporium']
senticnet['safe_deposit_box'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'slot', 'box', 'load_dock', 'diskette', 'emporium']
senticnet['safe_distance'] = ['-0.85', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.79', 'long_distance', 'compass_direction', 'distance', 'afar', 'long_distance_relationship']
senticnet['safe_drinking_water'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'bottled_water', 'sugar_water']
senticnet['safe_drinking_water_supply'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'bottled_water', 'sugar_water']
senticnet['safe_haven'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'habitat', 'mom_house', 'windsor', 'pacific_northwest', 'wilderness']
senticnet['safe_period'] = ['0.788', '0.825', '0', '0', '#joy', '#interest', 'positive', '0.806', 'dispersing_phase', 'generation', 'leptotene', 'phase', 'diplotene']
senticnet['safe_place'] = ['0.122', '0.274', '-0.06', '0.226', '#interest', '#admiration', 'positive', '0.14', 'stronghold', 'family_live', 'domestic_dwell', 'line_place', 'fortification']
senticnet['safe_practice'] = ['0', '0.935', '0', '0.783', '#interest', '#admiration', 'positive', '0.859', 'safe_conduct', 'safe', 'safety', 'safety_first', 'follow_guideline']
senticnet['safe_sex'] = ['0.739', '0.949', '0', '0', '#joy', '#interest', 'positive', '0.844', 'sex', 'contraception', 'sexual_intercourse', 'fuck', 'become_aroused']
senticnet['safe_zone'] = ['0.069', '0.152', '0', '0.088', '#interest', '#admiration', 'positive', '0.103', 'security', 'metal_detector', 'security_airport', 'security_area', 'security_system']
senticnet['safeguard'] = ['0', '0', '0', '0.817', '#admiration', '#admiration', 'positive', '0.817', 'truthful', 'honesty', 'honest', 'unity', 'good_character']
senticnet['safeguard_against'] = ['-0.97', '0', '0.84', '-0.52', '#sadness', '#anger', 'negative', '-0.78', 'pis_aller', 'expedient', 'temporary_expedient', 'makeshift', 'stopgap']
senticnet['safekeep'] = ['0.025', '0.27', '-0.11', '0.243', '#interest', '#admiration', 'positive', '0.105', 'keep_safe', 'store_dry_goods', 'store_stuff', 'secure', 'save']
senticnet['safely'] = ['0.033', '0.186', '0', '0', '#joy', '#interest', 'positive', '0.11', 'bank', 'bank_vault', 'wallet', 'government_agreement', 'coffer']
senticnet['safely_conclude'] = ['0.509', '-0.62', '0', '0.786', '#surprise', '#admiration', 'positive', '0.64', 'agreeable', 'accordant', 'consentient', 'unanimous', 'according']
senticnet['safely_dispose'] = ['0.935', '0', '0', '0', '#joy', '#joy', 'positive', '0.935', 'clean_up_kitchen', 'feel_dirty', 'remove_stain', 'stink', 'stink_mess']
senticnet['safer_sexual_behavior'] = ['0', '0', '0.973', '-0.78', '#anger', '#disgust', 'negative', '-0.87', 'chastity', 'ignore_conception', 'restrain', 'abortion', 'stop']
senticnet['safest'] = ['0.033', '0.186', '0', '0', '#joy', '#interest', 'positive', '0.11', 'bank', 'bank_vault', 'wallet', 'government_agreement', 'coffer']
senticnet['safest_place'] = ['0.36', '0.248', '-0.08', '0.278', '#joy', '#admiration', 'positive', '0.201', 'feel_important', 'strong_relationship', 'eat_together', 'world_peace', 'express_gratitude']
senticnet['safest_seat'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'couch', 'stool', 'coach_seat']
senticnet['safest_way'] = ['0.033', '0.186', '0', '0', '#joy', '#interest', 'positive', '0.11', 'bank', 'bank_vault', 'wallet', 'government_agreement', 'coffer']
senticnet['safetie'] = ['-0.89', '0.161', '-0.61', '0.053', '#sadness', '#fear', 'negative', '-0.32', 'heartrending', 'heartbreaking', 'grievous', 'grievously', 'brokenheartedness']
senticnet['safety'] = ['0.082', '0.319', '-0.10', '0.147', '#interest', '#admiration', 'positive', '0.11', 'protect', 'defend', 'secure', 'guard', 'watch_over']
senticnet['safety_belt'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'safety', 'traffic_light', 'slow_speed', 'avoid_crash', 'car']
senticnet['safety_cap'] = ['-0.89', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.86', 'protect_head', 'helmet', 'keep_hair_place', 'cover_head', 'shade_eye']
senticnet['safety_deposit_box'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'slot', 'box', 'load_dock', 'diskette', 'emporium']
senticnet['safety_device'] = ['0.045', '0.143', '-0.11', '0.212', '#interest', '#admiration', 'positive', '0.071', 'battery', 'remove_screw', 'hand_hold', 'gas_oil', 'hyper']
senticnet['safety_equipment'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'lifebelt', 'save_life', 'lifeguard', 'life_jacket', 'life_preserver']
senticnet['safety_first'] = ['0', '0.937', '0', '0.779', '#interest', '#admiration', 'positive', '0.858', 'safe_practice', 'safe', 'safety', 'safe_conduct', 'follow_guideline']
senticnet['safety_hazard'] = ['-0.74', '0', '0.781', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'reprimand', 'caution', 'big_monster', 'kidnap', 'mock']
senticnet['safety_pin'] = ['0.045', '0.143', '-0.11', '0.212', '#interest', '#admiration', 'positive', '0.071', 'pair_of_scissors', 'rubber_band', 'adjustable_wrench', 'clothespin', 'clip']
senticnet['safety_razor'] = ['-0.08', '0.232', '0', '0.099', '#interest', '#admiration', 'positive', '0.084', 'wax_dental_floss', 'fluoride_toothpaste', 'toilet_paper', 'dental_floss', 'tartar_control_toothpaste']
senticnet['safeway'] = ['0.912', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.917', 'carpet_store', 'lumberyard', 'furniture_store', 'front_porch', 'shoe_shop']
senticnet['safeway_store'] = ['0.758', '0.878', '0', '0', '#joy', '#interest', 'positive', '0.818', 'tavern', 'bar', 'pub', 'martini', 'brewery']
senticnet['saffron_risotto'] = ['0.041', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.043', 'meatloaf', 'salad', 'delicious_meal', 'casserole', 'apple_pie']
senticnet['saffy'] = ['-0.71', '0', '0.972', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'scold', 'nag', 'scolder', 'harridan', 'common_scold']
senticnet['safranin'] = ['0', '-0.01', '0.046', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'varnish', 'brush', 'dye', 'pigment', 'canvas']
senticnet['safwat_hegazy'] = ['0.648', '0.627', '-0.26', '0.329', '#joy', '#interest', 'positive', '0.334', 'brotherly', 'friendly', 'fraternal', 'amicability', 'amicable']
senticnet['sag'] = ['-0.73', '0', '0.751', '0', '#sadness', '#anger', 'negative', '-0.74', 'bend', 'consecutive', 'vertex', 'loiter', 'unbalanced']
senticnet['sag_wagon'] = ['-0.95', '0', '0.793', '0', '#sadness', '#anger', 'negative', '-0.87', 'block', 'obstacle', 'nodus', 'impediment', 'obstruction']
senticnet['saga'] = ['0.036', '0.219', '0', '0.19', '#interest', '#admiration', 'positive', '0.148', 'story', 'long_story', 'novel', 'fable', 'fiction']
senticnet['sagaciousness'] = ['0.538', '-0.51', '0', '0.872', '#joy', '#admiration', 'positive', '0.64', 'indiscreetness', 'judiciousness', 'injudiciousness', 'common_sense', 'good_sense']
senticnet['sagacity'] = ['0.675', '-0.71', '0', '0.987', '#surprise', '#admiration', 'positive', '0.794', 'indiscreetness', 'judiciousness', 'injudiciousness', 'common_sense', 'good_sense']
senticnet['sagarmatha'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['sage_brush'] = ['-0.98', '0', '0.441', '-0.96', '#sadness', '#disgust', 'negative', '-0.79', 'desert_land', 'infertile', 'sand', 'barren_land', 'desert']
senticnet['sage_salvia'] = ['0.598', '-0.72', '0', '0.941', '#surprise', '#admiration', 'positive', '0.753', 'sicklepod', 'tower_rock_cress', 'tower_mustard', 'rock_cress', 'rock_cress_plant']
senticnet['sage_sparrow'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'fox', 'prairie_fox', 'white_fox']
senticnet['sagebrush_buttercup'] = ['-0.79', '0', '0.837', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'sunray', 'wilding', 'wildflower', 'pasqueflower', 'pasque_flower']
senticnet['sagebrush_steppe'] = ['0.045', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.356', 'ponderosa', 'bull_pine', 'western_yellow_pine', 'yellow_pine', 'longleaf_pine']
senticnet['sagely'] = ['-0.89', '0.168', '-0.60', '0.05', '#sadness', '#fear', 'negative', '-0.32', 'remorsefully', 'contritely', 'contrition', 'contriteness', 'attrition']
senticnet['sagging_skin'] = ['0', '0', '0.883', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'get_old', 'dry_skin', 'become_wet', 'clothes_shrink', 'wrinkle']
senticnet['saggy'] = ['-0.42', '-0.14', '-0.06', '-0.37', '#sadness', '#disgust', 'negative', '-0.18', 'brain_tumor', 'bad_hair_day', 'lyme_disease', 'diarrhoea', 'suffer_fool']
senticnet['saggy_skin'] = ['-0.42', '-0.14', '-0.06', '-0.37', '#sadness', '#disgust', 'negative', '-0.18', 'brain_tumor', 'bad_hair_day', 'lyme_disease', 'diarrhoea', 'suffer_fool']
senticnet['sagittal_crest'] = ['0.1', '-0.12', '0.05', '-0.03', '#joy', '#surprise', 'positive', '0.034', 'fancy_dress', 'blouse', 'fashion', 'dress', 'skirt']
senticnet['sagittal_suture'] = ['0.719', '-0.57', '0', '0.651', '#joy', '#admiration', 'positive', '0.647', 'chap', 'break', 'slit', 'gap', 'shift']
senticnet['sagittaria'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'cattail', 'nailrod', 'reed_mace', 'reedmace']
senticnet['sago_palm'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'palm_nut', 'nut', 'edible_nut', 'cembra_nut', 'cedar_nut']
senticnet['sago_starch'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'palm_nut', 'nut', 'edible_nut', 'cembra_nut', 'cedar_nut']
senticnet['saharan'] = ['0.141', '0', '-0.06', '0.086', '#joy', '#admiration', 'positive', '0.055', 'dancer', 'stout', 'old_dance', 'climb_rock', 'jump_around']
senticnet['sahetuka'] = ['-0.85', '0', '0.968', '-0.75', '#sadness', '#anger', 'negative', '-0.86', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['saiawush'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitentially', 'penitent', 'repentantly', 'penitence', 'repentant']
senticnet['saidu_sharif'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'webpage', 'store_record', 'ideal']
senticnet['saiga_antelope'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheep', 'argal', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['sail'] = ['-0.05', '0.075', '0', '0.116', '#interest', '#admiration', 'positive', '0.044', 'deep_blue', 'water', 'sail_out', 'sea', 'full_water']
senticnet['sail_around_world'] = ['0.822', '0.987', '0', '0', '#joy', '#interest', 'positive', '0.904', 'storm_out_room', 'voice_concern', 'forward_email', 'age_gracefully', 'bicycle_across_town']
senticnet['sail_boat'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'boat', 'yacht', 'big_boat', 'large_boat']
senticnet['sail_ocean'] = ['-0.03', '0.111', '-0.06', '0.084', '#interest', '#admiration', 'positive', '0.025', 'carry_liquid', 'travel_over_water', 'transport_goods', 'carry_blood', 'cross_ocean']
senticnet['sail_out'] = ['-0.05', '0.079', '0', '0.118', '#interest', '#admiration', 'positive', '0.049', 'deep_blue', 'water', 'sail', 'sea', 'full_water']
senticnet['sail_sea'] = ['0.051', '0.074', '0.049', '0.128', '#interest', '#admiration', 'positive', '0.051', 'stalin', 'water_transportation', 'power_gasoline', 'water_transport', 'travel_over_water']
senticnet['sail_ship'] = ['-0.04', '-0.01', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.03', 'sailboat', 'fish_boat', 'battleship', 'oil_tanker', 'lifeboat']
senticnet['sailboard'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'boat', 'yacht', 'big_boat', 'large_boat']
senticnet['sailboat'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'boat', 'yacht', 'big_boat', 'large_boat']
senticnet['sailing'] = ['0.032', '0.081', '0', '0.123', '#interest', '#admiration', 'positive', '0.079', 'woodworking', 'build_boat', 'workshop', 'repair', 'expensive']
senticnet['sailing_boat'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'boat', 'yacht', 'big_boat', 'large_boat']
senticnet['sailing_dinghy'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'boat', 'yacht', 'big_boat', 'large_boat']
senticnet['sailing_ship'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'boat', 'yacht', 'big_boat', 'large_boat']
senticnet['sailing_vessel'] = ['0.764', '-0.96', '0', '0.883', '#surprise', '#admiration', 'positive', '0.87', 'boatmanship', 'acquirement', 'skill', 'accomplishment', 'acquisition']
senticnet['sailing_yacht'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'boat', 'yacht', 'big_boat', 'large_boat']
senticnet['sailmaker'] = ['0.764', '-0.96', '0', '0.883', '#surprise', '#admiration', 'positive', '0.87', 'boatmanship', 'acquirement', 'skill', 'accomplishment', 'acquisition']
senticnet['sailor'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'army_man', 'army_person', 'fighter', 'man', 'workman']
senticnet['sailor_suit'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'hankie', 'kleenex', 'facial_tissue', 'handkerchief', 'tissue']
senticnet['saint'] = ['0', '0.248', '-0.08', '0.173', '#interest', '#admiration', 'positive', '0.113', 'god', 'minister', 'evangelist', 'rector', 'goddess']
senticnet['saint_basile_le'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'fantastic', 'amaze', 'pose', 'wondrous', 'wonderfully']
senticnet['saint_blaise'] = ['0', '0.248', '-0.08', '0.173', '#interest', '#admiration', 'positive', '0.113', 'god', 'minister', 'evangelist', 'rector', 'goddess']
senticnet['saint_cuthbert'] = ['0', '0.248', '-0.08', '0.173', '#interest', '#admiration', 'positive', '0.113', 'god', 'minister', 'evangelist', 'rector', 'goddess']
senticnet['saint_eustace'] = ['0', '0.248', '-0.08', '0.173', '#interest', '#admiration', 'positive', '0.113', 'god', 'minister', 'evangelist', 'rector', 'goddess']
senticnet['saint_eustache'] = ['-0.71', '0', '0.917', '-0.51', '#sadness', '#anger', 'negative', '-0.71', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['saint_gaudens_double'] = ['0', '0.105', '0', '0', '#interest', '#interest', 'positive', '0.105', 'beak', 'many_feathers', 'big_eye', 'bird', 'bird_mouth']
senticnet['saint_hilaire_le'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'fantastic', 'amaze', 'pose', 'wondrous', 'wonderfully']
senticnet['saint_hyacinthe'] = ['-0.71', '0', '0.917', '-0.51', '#sadness', '#anger', 'negative', '-0.71', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['saint_lon_le'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'fantastic', 'amaze', 'pose', 'wondrous', 'wonderfully']
senticnet['saint_luke'] = ['0.967', '-0.98', '0', '0.64', '#joy', '#surprise', 'positive', '0.863', 'club', 'social_club', 'society', 'jockey_club', 'gild']
senticnet['saint_padre_pio'] = ['0', '0.248', '-0.08', '0.173', '#interest', '#admiration', 'positive', '0.113', 'god', 'minister', 'evangelist', 'rector', 'goddess']
senticnet['saint_petersburg_conservatory'] = ['0.536', '-0.56', '0', '0.567', '#surprise', '#admiration', 'positive', '0.557', 'recreation', 'diversion', 'move', 'motility', 'movement']
senticnet['saint_petroc'] = ['0', '0.248', '-0.08', '0.173', '#interest', '#admiration', 'positive', '0.113', 'god', 'minister', 'evangelist', 'rector', 'goddess']
senticnet['saint_ursula'] = ['0', '0.248', '-0.08', '0.173', '#interest', '#admiration', 'positive', '0.113', 'god', 'minister', 'evangelist', 'rector', 'goddess']
senticnet['sainte_beuve'] = ['0.722', '-0.98', '0', '0.832', '#surprise', '#admiration', 'positive', '0.845', 'laudatory', 'praiseful', 'praising', 'encomiastic', 'complimentary']
senticnet['saintliness'] = ['0', '-0.94', '0', '0.759', '#surprise', '#admiration', 'positive', '0.85', 'blessed', 'desecrate', 'holiness', 'sacred', 'sanctity']
senticnet['saintly'] = ['0', '0.248', '-0.08', '0.173', '#interest', '#admiration', 'positive', '0.113', 'god', 'minister', 'evangelist', 'rector', 'goddess']
senticnet['saipan'] = ['0.084', '-0.05', '0.013', '0', '#joy', '#surprise', 'positive', '0.043', 'carnival_ride', 'carny', 'fortune_teller', 'horseback', 'ferris_wheel']
senticnet['saith'] = ['0.741', '-0.84', '0', '0.543', '#joy', '#surprise', 'positive', '0.711', 'integrity', 'righteousness', 'scrupulousness', 'incorruptness', 'honestness']
senticnet['sajdah'] = ['-0.93', '0', '0.807', '-0.95', '#sadness', '#disgust', 'negative', '-0.89', 'heatstroke', 'heat_hyperpyrexia', 'malady', 'breakdown', 'shock']
senticnet['sak'] = ['-0.63', '0', '0.791', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'little', 'pinch', 'modicum', 'small_indefinite_amount', 'indefinite_quantity']
senticnet['sakaar'] = ['-0.88', '0', '0.882', '-0.58', '#sadness', '#anger', 'negative', '-0.78', 'mortal', 'behemoth', 'unusual_person', 'aberrant', 'giant']
senticnet['sakana'] = ['0.973', '-0.50', '0', '0.612', '#joy', '#admiration', 'positive', '0.698', 'chic', 'chicness', 'jauntiness', 'modishness', 'stylishness']
senticnet['sake'] = ['0', '0.822', '0', '0.847', '#interest', '#admiration', 'positive', '0.835', 'rice_beer', 'saki', 'interest', 'right', 'first']
senticnet['saki'] = ['0', '0.821', '0', '0.844', '#interest', '#admiration', 'positive', '0.833', 'rice_beer', 'sake', 'interest', 'right', 'first']
senticnet['saks_fifth_avenue'] = ['0', '0.081', '0', '0.071', '#interest', '#admiration', 'positive', '0.076', 'thrift_store', 'type_of_store', 'record_store', 'many_shops', 'clothes_store']
senticnet['sakyamuni'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['sakyamuni_buddha'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['salable'] = ['-0.94', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.83', 'unsellable', 'unsaleable', 'marketable', 'unmarketable', 'unsalable']
senticnet['salacious'] = ['0', '0', '-0.95', '-0.72', '#fear', '#disgust', 'negative', '-0.84', 'lubricious', 'lewd', 'lustful', 'obscene', 'prurient']
senticnet['salacity'] = ['0', '0', '0.739', '-0.88', '#anger', '#disgust', 'negative', '-0.81', 'scape', 'libidinous', 'lewdness', 'unchastity', 'lewd']
senticnet['salad'] = ['0.033', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.041', 'meatloaf', 'garden_salad', 'delicious_meal', 'casserole', 'apple_pie']
senticnet['salad_bar'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'grocery_cart', 'super_market', 'fruit_stand', 'candy_store', 'produce_store']
senticnet['salad_bowl'] = ['0', '0', '0.066', '-0.07', '#anger', '#disgust', 'negative', '-0.07', 'container_cup', 'cup', 'grease_trap', 'dish', 'open_container']
senticnet['salad_burnet_sanguisorba_minor'] = ['-0.79', '0', '0.837', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'sunray', 'wilding', 'sagebrush_buttercup', 'wildflower', 'pasque_flower']
senticnet['salad_dress'] = ['0.046', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'dairy', 'milk_product', 'horse_farm', 'food_production', 'farm']
senticnet['salad_fork'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'neat', 'kitchen_drawer', 'cutlery_drawer', 'dine_table', 'table']
senticnet['salad_green'] = ['0.636', '-0.79', '0', '0.552', '#joy', '#surprise', 'positive', '0.661', 'lettuce', 'endive', 'green', 'curly_endive', 'food']
senticnet['salad_ingredient'] = ['0.035', '0.104', '0', '0.12', '#interest', '#admiration', 'positive', '0.086', 'fruit_vegetable', 'vegetable', 'vegetable_fruit', 'fresh_vegetable', 'vegetarian_food']
senticnet['salad_mix'] = ['0.904', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.907', 'eden', 'garden', 'usual_green', 'apple', 'leafy']
senticnet['salad_shaker'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'blender', 'store_shelf', 'kitchen_tool', 'pliers', 'ladle']
senticnet['salamandastron'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'animal_home', 'habitat', 'beehive']
senticnet['salamander'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'amphibian', 'aquatic']
senticnet['salami'] = ['0.658', '-0.63', '0', '0.625', '#joy', '#surprise', 'positive', '0.64', 'knackwurst', 'sausage', 'knockwurst', 'frank', 'hotdog']
senticnet['salary'] = ['-0.04', '0.282', '-0.08', '0.137', '#interest', '#admiration', 'positive', '0.072', 'wage', 'earn', 'earnings', 'income', 'money']
senticnet['sale'] = ['-0.03', '0.268', '-0.06', '0.178', '#interest', '#admiration', 'positive', '0.088', 'bargain', 'cheap_price', 'clearance', 'good_buy', 'afford']
senticnet['sale_in_gross'] = ['0.865', '-0.84', '0', '0.915', '#joy', '#admiration', 'positive', '0.875', 'grant', 'contract', 'contract_of_hazard', 'adhesion_contract', 'contract_of_adhesion']
senticnet['sale_price'] = ['-0.04', '0.313', '-0.05', '0.188', '#interest', '#admiration', 'positive', '0.103', 'amount_pay', 'monetary', 'amount_due', 'bill', 'payment_amount']
senticnet['salerno_reggio_calabria'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'expressway', 'turnpike', 'toll_road', 'highway']
senticnet['saleroom'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'hip', 'joint', 'above_leg', 'knee']
senticnet['salesclerk'] = ['-0.05', '-0.04', '0.017', '-0.05', '#sadness', '#disgust', 'negative', '-0.02', 'shoe_shop', 'hamper', 'attic', 'obstruct', 'closet']
senticnet['salesian_order'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'acolyte', 'reverend', 'thurifer', 'clergyman', 'man_of_cloth']
senticnet['salesman'] = ['0.969', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.936', 'attendant', 'manager', 'public_servant', 'specialist', 'accomplice']
senticnet['salesmanship'] = ['0', '0.325', '-0.18', '0.335', '#interest', '#admiration', 'positive', '0.16', 'get_rid', 'cause_change', 'interaction', 'suggest', 'movement_towards']
senticnet['salespeople'] = ['0.971', '0.76', '0', '0', '#joy', '#interest', 'positive', '0.865', 'shopper', 'clerk', 'patron', 'vintner', 'hairdresser']
senticnet['salesperson'] = ['0.971', '0.76', '0', '0', '#joy', '#interest', 'positive', '0.865', 'shopper', 'clerk', 'patron', 'vintner', 'hairdresser']
senticnet['saleswoman'] = ['0.99', '0', '0', '0.721', '#joy', '#admiration', 'positive', '0.855', 'hair', 'haircut', 'hairdresser_chair', 'scissor', 'hairstyle']
senticnet['salicifolia'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'clematis', 'legume', 'leguminous_plant', 'climber', 'root_climber']
senticnet['salicional'] = ['0.939', '-0.71', '0', '0.775', '#joy', '#admiration', 'positive', '0.81', 'vox_angelica', 'voix_celeste', 'organ_stop', 'pipe', 'diapason']
senticnet['salicornia'] = ['-0.89', '0', '0.556', '-0.72', '#sadness', '#disgust', 'negative', '-0.72', 'fucus', 'rockweed', 'black_rockweed', 'bladder_fucus', 'tang']
senticnet['salicylate'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
senticnet['salience'] = ['0.542', '-0.74', '0', '0.808', '#surprise', '#admiration', 'positive', '0.698', 'strikingness', 'glare', 'prominence', 'saliency', 'standing']
senticnet['saliency'] = ['0.542', '-0.74', '0', '0.804', '#surprise', '#admiration', 'positive', '0.698', 'strikingness', 'glare', 'salience', 'prominence', 'standing']
senticnet['saliency_map'] = ['0.685', '-0.91', '0', '0.566', '#joy', '#surprise', 'positive', '0.721', 'audibility', 'audibleness', 'visibleness', 'visibility', 'physical_property']
senticnet['salient'] = ['0', '0.762', '0', '0.734', '#interest', '#admiration', 'positive', '0.748', 'relative', 'pertinent', 'impertinent', 'relevant', 'relevance']
senticnet['salim_bin'] = ['0.798', '-0.51', '0', '0.973', '#joy', '#admiration', 'positive', '0.763', 'inexperienced_person', 'boy_scout', 'dear', 'virgin', 'mortal']
senticnet['saline_river'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['saline_solution'] = ['0', '0', '-0.95', '-0.87', '#fear', '#disgust', 'negative', '-0.91', 'sharp_weapon', 'bayonet', 'ammunition', 'spear', 'arrow']
senticnet['salinity'] = ['-0.64', '0', '0.64', '-0.73', '#sadness', '#disgust', 'negative', '-0.67', 'taste', 'taste_sensation', 'gustatory_sensation', 'taste_perception', 'gustatory_perception']
senticnet['salinization'] = ['-0.80', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.84', 'popularization', 'profanation', 'demoralization', 'corruption', 'humiliation']
senticnet['saliva'] = ['0.064', '0.076', '-0.06', '0.086', '#interest', '#admiration', 'positive', '0.04', 'aroma', 'perfume', 'fragrance', 'bad_odor', 'female_perfume']
senticnet['salivary'] = ['0.881', '0.995', '0', '0', '#joy', '#interest', 'positive', '0.938', 'saliva', 'salivate', 'eager', 'anticipation', 'salivation']
senticnet['salivary_gland'] = ['0.079', '-0.10', '0.046', '0', '#joy', '#surprise', 'positive', '0.047', 'nerve', 'liver', 'groin', 'scrotum', 'diaphragm']
senticnet['salivate'] = ['0.886', '0.999', '0', '0', '#joy', '#interest', 'positive', '0.943', 'saliva', 'salivary', 'eager', 'anticipation', 'salivation']
senticnet['salivation'] = ['-0.05', '0', '0.01', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'loss_ofmoney', 'world_hunger', 'drool', 'salivate', 'buy_steak']
senticnet['salix_alba_talk'] = ['0.792', '-0.86', '0', '0.888', '#surprise', '#admiration', 'positive', '0.849', 'absurd', 'inconsequential', 'intuitive', 'visceral', 'nonrational']
senticnet['salix_caprea'] = ['0.861', '-0.87', '0', '0.597', '#joy', '#surprise', 'positive', '0.779', 'herb', 'cultivated_parsnip', 'wild_parsnip', 'madnep', 'herbaceous_plant']
senticnet['salix_spp'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'bulrush', 'nailrod', 'reed_mace', 'reedmace']
senticnet['sallied_forth'] = ['-0.84', '0', '0.553', '-0.89', '#sadness', '#disgust', 'negative', '-0.76', 'encumbrance', 'incumbrance', 'block', 'bar', 'blockade']
senticnet['sallied_out'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'hardy', 'fearlessness', 'fearlessly', 'unafraid', 'unfearing']
senticnet['sallow'] = ['-0.29', '0.2', '0.49', '-0.61', '#anger', '#disgust', 'negative', '-0.3', 'queasy', 'outrageous', 'horrid', 'angrily', 'see_red']
senticnet['sallow_face'] = ['-0.29', '0.2', '0.49', '-0.61', '#anger', '#disgust', 'negative', '-0.3', 'queasy', 'outrageous', 'horrid', 'angrily', 'see_red']
senticnet['sallow_skin'] = ['0', '0', '0.883', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'get_old', 'dry_skin', 'become_wet', 'clothes_shrink', 'wrinkle']
senticnet['sallust'] = ['0', '0', '-0.90', '-0.79', '#fear', '#disgust', 'negative', '-0.85', 'encomiastic', 'encomium', 'paean', 'eulogy', 'pean']
senticnet['sally'] = ['0.583', '-0.76', '0', '0.543', '#joy', '#surprise', 'positive', '0.63', 'venture', 'joint_venture', 'labor', 'cause', 'risk']
senticnet['sally_carrol'] = ['-0.71', '0', '0.906', '-0.93', '#anger', '#disgust', 'negative', '-0.85', 'absent', 'forgetful', 'distracted', 'inattentive', 'scatty']
senticnet['sally_forth'] = ['-0.68', '0', '0.967', '-0.65', '#sadness', '#anger', 'negative', '-0.76', 'depart', 'start', 'part', 'start_out', 'set_forth']
senticnet['sally_hershberger'] = ['-0.02', '0.122', '0', '0.137', '#interest', '#admiration', 'positive', '0.078', 'barber_shop', 'barbershop', 'hairdressing_salon', 'beauty_salon', 'salon']
senticnet['sally_ledger'] = ['0.034', '0', '0.01', '0.087', '#joy', '#admiration', 'positive', '0.037', 'chime', 'bell_noise', 'alto', 'piano', 'ring_loudly']
senticnet['sally_out'] = ['-0.58', '0', '0.694', '-0.87', '#anger', '#disgust', 'negative', '-0.71', 'depart', 'start', 'part', 'start_out', 'set_forth']
senticnet['salmon'] = ['0.029', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.039', 'bagel', 'muffin', 'pie', 'cookie', 'toast']
senticnet['salmon_fillet'] = ['-0.02', '-0.02', '0.064', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'ocean_water', 'fillet_fish', 'chowder', 'port', 'underwater']
senticnet['salmon_fisherman'] = ['0.073', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'angler', 'informal_rod', 'archaic_fisher', 'rare_piscator', 'fishing_line']
senticnet['salmon_pink'] = ['0.035', '0', '0', '0.116', '#joy', '#admiration', 'positive', '0.076', 'maroon', 'color', 'bright_color', 'hair_colored', 'cool_color']
senticnet['salmon_roe'] = ['0.9', '0.963', '0', '0', '#joy', '#interest', 'positive', '0.931', 'form_of_food', 'japanese_food', 'uncooked', 'healthy', 'food']
senticnet['salmonberry'] = ['-0.86', '0', '0.665', '-0.61', '#sadness', '#anger', 'negative', '-0.71', 'wooded', 'braky', 'rushy', 'scrubby', 'forested']
senticnet['salmonella_typhi'] = ['-0.73', '0', '0.599', '-0.96', '#sadness', '#disgust', 'negative', '-0.76', 'spotted_fever', 'rickettsial_disease', 'rickettsialpox', 'typhus', 'typhus_fever']
senticnet['salmonid'] = ['-0.86', '0', '0.941', '-0.71', '#sadness', '#anger', 'negative', '-0.83', 'salmon', 'trout', 'go_fishing', 'fish', 'catch_fish']
senticnet['salon'] = ['-0.02', '0.122', '0', '0.137', '#interest', '#admiration', 'positive', '0.078', 'beauty_salon', 'barber_shop', 'barbershop', 'hair_salon', 'beauty_shop']
senticnet['salon_owner'] = ['0.99', '0', '0', '0.721', '#joy', '#admiration', 'positive', '0.855', 'hair', 'haircut', 'hairdresser_chair', 'scissor', 'hairstyle']
senticnet['salon_spa'] = ['-0.02', '0.122', '0', '0.137', '#interest', '#admiration', 'positive', '0.078', 'barber_shop', 'barbershop', 'hairdressing_salon', 'beauty_salon', 'salon']
senticnet['saloon'] = ['-0.76', '0', '0.75', '-0.56', '#sadness', '#anger', 'negative', '-0.69', 'public_house', 'pub', 'free_house', 'pothouse', 'gin_mill']
senticnet['saloon_bar'] = ['-0.81', '0', '0.553', '-0.92', '#sadness', '#disgust', 'negative', '-0.76', 'bar', 'sawdust_saloon', 'saloon', 'ginmill', 'taproom']
senticnet['saloon_keeper'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'pharmaceutical_chemist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['saloon_owner'] = ['-0.83', '0', '0.831', '-0.69', '#sadness', '#anger', 'negative', '-0.78', 'toughie', 'accessory', 'highbinder', 'hoodlum', 'thug']
senticnet['saloon_singer'] = ['0.794', '-0.50', '0', '0.531', '#joy', '#admiration', 'positive', '0.611', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['salpingectomy'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'birth_control_pill', 'condom', 'contraceptive_pill', 'sex_toy', 'prevent_pregnancy']
senticnet['salsa'] = ['0.116', '-0.02', '0.025', '0.156', '#joy', '#admiration', 'positive', '0.069', 'dance_move', 'latin_dance', 'body_movement', 'waltz', 'dance']
senticnet['salsa_dance'] = ['0.116', '-0.02', '0.025', '0.156', '#joy', '#admiration', 'positive', '0.069', 'dance_move', 'latin_dance', 'body_movement', 'waltz', 'dance']
senticnet['salsa_music'] = ['0.116', '-0.02', '0.025', '0.156', '#joy', '#admiration', 'positive', '0.069', 'dance_move', 'latin_dance', 'body_movement', 'waltz', 'dance']
senticnet['salsa_verde'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'mayonnaise', 'box_cereal', 'bottle_water']
senticnet['salsalate'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'spironolactone', 'spironolactone_drug']
senticnet['salsify'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'mayonnaise', 'box_cereal', 'bottle_water']
senticnet['salt'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'fulmination', 'ester', 'fulminate', 'lightning', 'explode']
senticnet['salt-water'] = ['-0.82', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.90', 'seawater', 'estuary', 'freshwater', 'saltwater', 'brackish']
senticnet['salt_beef'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'cook_meat', 'cook_hamburger', 'cook_food', 'cook_rice', 'brown_meat']
senticnet['salt_caramel'] = ['0.731', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.837', 'make_bread', 'cook_dinner', 'cook_meal', 'cook_curry', 'follow_recipe']
senticnet['salt_cellar'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'wallet', 'billfold', 'bank', 'piggy_bank', 'bank_vault']
senticnet['salt_diet'] = ['-0.85', '0', '-0.71', '-0.86', '#sadness', '#disgust', 'negative', '-0.81', 'get_exercise', 'train_marathon', 'run_marathon', 'run_long_distance', 'go_gym']
senticnet['salt_food'] = ['-0.09', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.06', 'make_tea', 'chicken', 'brown_hamburger', 'fry_chicken', 'bone_chicken']
senticnet['salt_fork'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'common_bean', 'green_bean', 'wax_bean']
senticnet['salt_lick'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'common_bean', 'green_bean', 'wax_bean']
senticnet['salt_pepper_shaker'] = ['0.78', '0', '0', '0.982', '#joy', '#admiration', 'positive', '0.881', 'entree', 'soup', 'wine_glass', 'satiation', 'plate_silverware']
senticnet['salt_pork'] = ['-0.08', '0.106', '0', '0.125', '#interest', '#admiration', 'positive', '0.047', 'sugar', 'treacle', 'black_treacle', 'add_sugar', 'sugar_substitute']
senticnet['salt_shaker'] = ['0.062', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.04', 'hostess', 'bottle_wine', 'human_intelligence', 'patron', 'table_chair']
senticnet['salt_tolerant'] = ['0', '0.985', '0', '0.794', '#interest', '#admiration', 'positive', '0.889', 'patient', 'bear_up', 'tolerate', 'endurable', 'swallow']
senticnet['salted_meat'] = ['-0.08', '0.106', '0', '0.125', '#interest', '#admiration', 'positive', '0.047', 'sugar', 'treacle', 'black_treacle', 'add_sugar', 'sugar_substitute']
senticnet['salted_pork'] = ['0.52', '-0.69', '0', '0.639', '#surprise', '#admiration', 'positive', '0.617', 'black_bread', 'pumpernickel', 'limpa', 'bread', 'rye_bread']
senticnet['salted_water'] = ['-0.66', '0', '0.992', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'cold', 'boil', 'temperature', 'low_temperature', 'frigidity']
senticnet['saltie'] = ['-0.85', '0', '0.685', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'alligator', 'crocodilian', 'false_gavial', 'gator', 'caiman']
senticnet['saltine'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'cracker', 'soda_cracker', 'oyster_cracker', 'water_biscuit', 'graham_cracker']
senticnet['saltshaker'] = ['0.028', '-0.05', '0', '0.05', '#surprise', '#admiration', 'positive', '0.046', 'fork', 'hot_table', 'chopstick', 'bowl', 'spoon']
senticnet['saltwater'] = ['0', '0', '-0.06', '-0.03', '#fear', '#disgust', 'negative', '-0.05', 'tidal_pool', 'coral_reef', 'fish_market', 'sea', 'reef']
senticnet['saltwater_aquarium'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'autumn_leaf', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['saltwater_clam'] = ['-0.79', '0', '0.709', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'animal', 'animate_being', 'ctenophore', 'invertebrate', 'beast']
senticnet['saltwater_crocodile'] = ['-0.85', '0', '0.685', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'alligator', 'crocodilian', 'false_gavial', 'gator', 'caiman']
senticnet['saltwater_fish'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'autumn_leaf', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['saltwater_pool'] = ['0', '0', '0', '0.964', '#admiration', '#admiration', 'positive', '0.964', 'swimming_pool', 'swim', 'swimsuit', 'lake', 'have_fun']
senticnet['saltwater_swimming_pool'] = ['0', '0', '0', '0.964', '#admiration', '#admiration', 'positive', '0.964', 'swimming_pool', 'swim', 'swimsuit', 'lake', 'have_fun']
senticnet['saltwater_taffy'] = ['0.037', '-0.00', '0', '0.12', '#joy', '#admiration', 'positive', '0.055', 'bread_crumb', 'candy', 'bun', 'sweet', 'gush']
senticnet['salty'] = ['-0.05', '0.07', '0', '0.062', '#interest', '#admiration', 'positive', '0.027', 'fine_grain', 'grainy', 'powder', 'granulate', 'food_flavor']
senticnet['salty_cheese'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'grapefruit_peel', 'lemon_peel', 'orange_peel', 'candied_citrus_peel', 'succade']
senticnet['salty_ocean'] = ['0.078', '-0.03', '-0.04', '0.105', '#joy', '#admiration', 'positive', '0.043', 'aquarium', 'underwater', 'pacific_ocean', 'sea', 'ocean_wave']
senticnet['salty_sweet'] = ['0.037', '-0.00', '0', '0.12', '#joy', '#admiration', 'positive', '0.055', 'bread_crumb', 'candy', 'bun', 'sweet', 'gush']
senticnet['salty_taste'] = ['-0.82', '0', '0.901', '-0.65', '#sadness', '#anger', 'negative', '-0.79', 'taste', 'taste_sensation', 'gustatory_sensation', 'taste_perception', 'gustatory_perception']
senticnet['salubrious'] = ['0.654', '-0.74', '0', '0.73', '#surprise', '#admiration', 'positive', '0.708', 'strong', 'wholesome', 'healthy', 'solid', 'alimentary']
senticnet['salus_populi_romani'] = ['0.055', '0.117', '0', '0.133', '#interest', '#admiration', 'positive', '0.102', 'graphical_representation', 'pencil_pen', 'note_pad', 'marker', 'stationery']
senticnet['salut'] = ['-0.93', '0', '0.861', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'knotty', 'contorted', 'writhed', 'crooked', 'anfractuous']
senticnet['salutary'] = ['0.722', '0.782', '0', '0', '#joy', '#interest', 'positive', '0.752', 'meliorative', 'remedial', 'wholesome', 'salute', 'medicinal']
senticnet['salutation'] = ['0.859', '0', '0', '0.943', '#joy', '#admiration', 'positive', '0.901', 'welcome', 'aye', 'present', 'invitation', 'gift']
senticnet['salute'] = ['0.728', '0.788', '0', '0', '#joy', '#interest', 'positive', '0.758', 'greet', 'wave_arm', 'smile', 'say_goodbye_to', 'attract_attention']
senticnet['saluteth'] = ['-0.52', '0', '0.701', '-0.50', '#sadness', '#anger', 'negative', '-0.57', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['salvage'] = ['0.822', '-0.64', '0', '0.777', '#joy', '#admiration', 'positive', '0.748', 'lifesaving', 'deliverance', 'redemption', 'salvation', 'recovery']
senticnet['salvage_crew'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'succourer', 'recoverer', 'salvor', 'mortal']
senticnet['salvage_operation'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'succourer', 'recoverer', 'salvor', 'mortal']
senticnet['salvage_regimen'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'indinavir', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['salvage_ship'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'succourer', 'recoverer', 'salvor', 'mortal']
senticnet['salvage_value'] = ['-0.82', '0', '0.835', '-0.67', '#sadness', '#anger', 'negative', '-0.77', 'disparage', 'belittle', 'deflate', 'puncture', 'pick_at']
senticnet['salvage_vessel'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'succourer', 'recoverer', 'salvor', 'mortal']
senticnet['salvager'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'succourer', 'recoverer', 'salvor', 'mortal']
senticnet['salvation'] = ['0.05', '0.363', '-0.09', '0.307', '#interest', '#admiration', 'positive', '0.157', 'faith', 'christ', 'prayer', 'spiritual', 'religious_purpose']
senticnet['salvatore_dimasi'] = ['0.034', '-0.02', '0', '0.057', '#joy', '#admiration', 'positive', '0.039', 'female_television_connector', 'channel_selector', 'power_switch', 'tuner', 'radio']
senticnet['salve'] = ['-0.64', '0', '0.941', '-0.92', '#anger', '#disgust', 'negative', '-0.83', 'unction', 'carron_oil', 'ointment', 'unguent', 'balm']
senticnet['salvian'] = ['0', '0', '-0.90', '-0.79', '#fear', '#disgust', 'negative', '-0.85', 'encomiastic', 'encomium', 'paean', 'eulogy', 'pean']
senticnet['salvific'] = ['0.05', '0.363', '-0.09', '0.307', '#interest', '#admiration', 'positive', '0.157', 'faith', 'christ', 'prayer', 'spiritual', 'religious_purpose']
senticnet['salving'] = ['0', '0', '-0.80', '-0.73', '#fear', '#disgust', 'negative', '-0.77', 'ointment', 'demulcent', 'softening', 'emollient', 'cream']
senticnet['salvinorin'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'imperium', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['salvo'] = ['-0.85', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.78', 'break', 'burst', 'bristle', 'fit', 'bust']
senticnet['salvor'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'succourer', 'salvager', 'recoverer', 'mortal']
senticnet['samael'] = ['-0.93', '0', '0.544', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'mutant', 'variation', 'sport', 'lusus_naturae', 'mutation']
senticnet['samanid_dynasty'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
senticnet['samantabhadra'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['samaria'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['samba_reggae'] = ['-0.06', '-0.00', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.05', 'madman', 'trouble', 'agony', 'cause_problem', 'river_stream']
senticnet['sambadrome'] = ['0.957', '0.7', '0', '0', '#joy', '#interest', 'positive', '0.829', 'ballpark', 'coven', 'disneyland', 'hockey_game', 'symposium']
senticnet['sambhajirao'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['samburu_national'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['samdhi'] = ['0.088', '0.135', '0', '0', '#joy', '#interest', 'positive', '0.112', 'watch_others', 'meditate', 'imagine', 'mantra', 'dance_music']
senticnet['same'] = ['0.137', '0.344', '-0.21', '0.369', '#interest', '#admiration', 'positive', '0.16', 'good_etiquette', 'similar', 'similarity', 'band_together', 'niceness']
senticnet['same-age'] = ['0.754', '0', '0', '0.738', '#joy', '#admiration', 'positive', '0.746', 'high-five', 'harasser', 'self-stimulation', 'self-pleasure', 'self-pleasuring']
senticnet['same_again'] = ['-0.03', '0.099', '0.034', '0.116', '#interest', '#admiration', 'positive', '0.037', 'brown', 'beautiful_well_useful', 'nocturnal', 'cute', 'small']
senticnet['same_answer'] = ['-0.96', '0', '0.978', '0', '#sadness', '#anger', 'negative', '-0.97', 'soft', 'biodegradable', 'repetitive', 'organic', 'animal_product']
senticnet['same_clue'] = ['-0.02', '0.068', '0', '0.134', '#interest', '#admiration', 'positive', '0.06', 'full_water', 'hint', 'godliness', 'use_clean', 'majestic']
senticnet['same_interest'] = ['0.701', '0.939', '0', '0.718', '#interest', '#admiration', 'positive', '0.786', 'common_good', 'group_of_people', 'community', 'similar', 'common_interest']
senticnet['same_level'] = ['0', '0.812', '0', '0.74', '#interest', '#admiration', 'positive', '0.776', 'yardstick', 'next_level', 'scale', 'measure_tool', 'level']
senticnet['same_parent'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'family_member', 'relative', 'family_relation', 'sister', 'female_sibling']
senticnet['same_sex'] = ['-0.84', '0', '0.882', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'perversion', 'fetishism', 'paraphilia', 'sexual_perversion', 'buggery']
senticnet['same_sex_attraction'] = ['0.943', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.925', 'homosexual', 'gay_person', 'gay', 'unconventional', 'open_minded']
senticnet['same_sex_domestic'] = ['0.841', '0.721', '0', '0.74', '#joy', '#admiration', 'positive', '0.767', 'good_manner', 'two_people', 'love', 'guardian', 'guy']
senticnet['same_sex_intercourse'] = ['-0.52', '0', '0.621', '-0.91', '#anger', '#disgust', 'negative', '-0.68', 'deviltry', 'devilry', 'irreverence', 'foul_play', 'iniquity']
senticnet['same_sex_sexual'] = ['-0.84', '0', '0.882', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'perversion', 'fetishism', 'paraphilia', 'sexual_perversion', 'buggery']
senticnet['same_sex_sexual_behavior'] = ['-0.98', '0', '0.662', '-0.73', '#sadness', '#disgust', 'negative', '-0.79', 'fornication', 'unlawful_carnal_knowledge', 'criminal_congress', 'adultery', 'criminal_conversation']
senticnet['same_sex_wedding'] = ['0', '0.819', '0', '0.987', '#interest', '#admiration', 'positive', '0.903', 'celebrate', 'wedding_ceremony', 'propose_marriage', 'engage', 'engagement']
senticnet['same_time'] = ['-0.04', '0.194', '-0.10', '0.124', '#interest', '#admiration', 'positive', '0.042', 'together', 'musical_note', 'music_note', 'just_in_time', 'hand_in_hand']
senticnet['same_token'] = ['0', '0', '-0.89', '-0.80', '#fear', '#disgust', 'negative', '-0.84', 'cry', 'repetitive', 'exigent', 'instant', 'clamant']
senticnet['sameness'] = ['0.039', '0.173', '0.077', '0.193', '#interest', '#admiration', 'positive', '0.082', 'typical', 'same', 'usual_ordinary', 'normal', 'usual']
senticnet['samian_ware'] = ['0.099', '-0.07', '-0.08', '0.195', '#joy', '#admiration', 'positive', '0.071', 'art', 'exhibit', 'craft', 'fine_art', 'artwork']
senticnet['sammy'] = ['0.17', '0.125', '-0.04', '0.196', '#joy', '#admiration', 'positive', '0.111', 'pal', 'single_person', 'comrade', 'good', 'companion']
senticnet['sample'] = ['0', '0.23', '-0.11', '0.206', '#interest', '#admiration', 'positive', '0.108', 'savor', 'consume', 'sweet_sour', 'mouth_activity', 'bad_odor']
senticnet['sample_distribution'] = ['0.948', '-0.99', '0', '0.549', '#joy', '#surprise', 'positive', '0.83', 'sampling', 'equidistribution', 'frequency_distribution', 'normal_distribution', 'distribution']
senticnet['sample_preparation'] = ['0.838', '-0.61', '0', '0.556', '#joy', '#surprise', 'positive', '0.671', 'activity', 'natural_process', 'natural_action', 'column_chromatography', 'paper_chromatography']
senticnet['sample_size'] = ['0', '0.23', '-0.11', '0.206', '#interest', '#admiration', 'positive', '0.108', 'savor', 'consume', 'sweet_sour', 'mouth_activity', 'bad_odor']
senticnet['sampling'] = ['0.956', '-0.83', '0', '0.755', '#joy', '#surprise', 'positive', '0.85', 'choice', 'option', 'random_sampling', 'stratified_sampling', 'representative_sampling']
senticnet['sampling_interval'] = ['0.04', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.055', 'measurement', 'time_interval', 'length', 'time', 'spatial_dimension']
senticnet['sampling_procedure'] = ['0', '0.23', '-0.11', '0.206', '#interest', '#admiration', 'positive', '0.108', 'savor', 'consume', 'sweet_sour', 'mouth_activity', 'bad_odor']
senticnet['sampling_technique'] = ['0', '0.23', '-0.11', '0.206', '#interest', '#admiration', 'positive', '0.108', 'savor', 'consume', 'sweet_sour', 'mouth_activity', 'bad_odor']
senticnet['samsara'] = ['-0.87', '0', '0.744', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
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senticnet['samsung_touchwiz'] = ['0.027', '-0.09', '0', '0', '#joy', '#surprise', 'positive', '0.061', 'cookie', 'pudding', 'custard', 'candy', 'muffin']
senticnet['samurai'] = ['0.086', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'soldier', 'army', 'attacker', 'military', 'infantry']
senticnet['samurai_sword'] = ['0.818', '-0.95', '0', '0.695', '#joy', '#surprise', 'positive', '0.823', 'jackknife', 'clasp_knife', 'penknife', 'switchblade', 'switchblade_knife']
senticnet['samvega'] = ['-0.85', '0', '0.968', '-0.75', '#sadness', '#anger', 'negative', '-0.86', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['san_andreas_fault'] = ['0', '0', '0.869', '-0.92', '#anger', '#disgust', 'negative', '-0.89', 'accuser', 'fall_down', 'fall_off', 'collapse', 'sore_knee']
senticnet['san_bernardino_strait'] = ['0.084', '-0.05', '0.013', '0', '#joy', '#surprise', 'positive', '0.043', 'carnival_ride', 'carny', 'fortune_teller', 'horseback', 'ferris_wheel']
senticnet['san_isabel'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['san_joaquin_kit'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'fox', 'prairie_fox', 'white_fox']
senticnet['san_quentin_death_row'] = ['0', '0', '0.869', '0', '#anger', '#anger', 'negative', '-0.86', 'convoke', 'loose_end', 'car_race', 'auction', 'mortuary']
senticnet['san_quentin_prison'] = ['0', '0', '0.869', '0', '#anger', '#anger', 'negative', '-0.86', 'convoke', 'loose_end', 'car_race', 'auction', 'mortuary']
senticnet['san_quentin_state'] = ['0', '0', '0.869', '0', '#anger', '#anger', 'negative', '-0.86', 'convoke', 'loose_end', 'car_race', 'auction', 'mortuary']
senticnet['san_saba'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'common_bean', 'green_bean', 'wax_bean']
senticnet['sanat_kumara'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
senticnet['sanatorium'] = ['0', '0.877', '0', '0', '#interest', '#interest', 'positive', '0.877', 'get_rest', 'rejuvenation', 'feel_refreshed', 'take_nap', 'get_sleep']
senticnet['sancai_three'] = ['-0.06', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.04', 'polish', 'magnify', 'see_out', 'sill', 'pane']
senticnet['sance'] = ['0.672', '-0.65', '0', '0.535', '#joy', '#surprise', 'positive', '0.621', 'escapologist', 'escape_expert', 'mind_reader', 'telepathist', 'performer']
senticnet['sanctification'] = ['0.99', '0', '0', '0.889', '#joy', '#admiration', 'positive', '0.94', 'glorifying', 'glorify', 'glorification', 'glorifies', 'exaltation']
senticnet['sanctifier'] = ['0.811', '0.843', '0', '0', '#joy', '#interest', 'positive', '0.827', 'purify', 'useful', 'sanctify', 'unction', 'hallowed']
senticnet['sanctify'] = ['0.818', '0.853', '0', '0', '#joy', '#interest', 'positive', '0.835', 'purify', 'useful', 'sanctifier', 'unction', 'hallowed']
senticnet['sanctimonious'] = ['0', '0', '-0.74', '-0.85', '#fear', '#disgust', 'negative', '-0.80', 'pietistical', 'pietistic', 'pharisaic', 'holier-than-thou', 'pharisaical']
senticnet['sanctimoniously'] = ['0.801', '0', '0', '0.907', '#joy', '#admiration', 'positive', '0.854', 'hypocritically', 'arrogantly', 'self-righteously', 'pompously', 'piously']
senticnet['sanctimony'] = ['0.12', '0.254', '-0.20', '0.354', '#interest', '#admiration', 'positive', '0.131', 'good', 'good_etiquette', 'similarity', 'nice', 'important']
senticnet['sanction'] = ['0.669', '-0.56', '0', '0.734', '#joy', '#admiration', 'positive', '0.657', 'okay', 'approval', 'commendation', 'okey', 'okeh']
senticnet['sanctionable'] = ['0', '0', '0.853', '-0.84', '#anger', '#disgust', 'negative', '-0.84', 'flagrant', 'cry', 'glaring', 'gross', 'rank']
senticnet['sanctioned'] = ['0.572', '0.642', '-0.27', '0.304', '#joy', '#interest', 'positive', '0.311', 'well-disposed', 'approving', 'approve', 'friendliness', 'approved']
senticnet['sanctity'] = ['0', '-0.93', '0', '0.766', '#surprise', '#admiration', 'positive', '0.853', 'blessed', 'desecrate', 'holiness', 'sacred', 'saintliness']
senticnet['sanctuary'] = ['0.02', '0.276', '0', '0.275', '#interest', '#admiration', 'positive', '0.19', 'monastery', 'holy_place', 'religious_place', 'worship_place', 'capital_build']
senticnet['sanctus_bell'] = ['0.034', '0', '0.01', '0.087', '#joy', '#admiration', 'positive', '0.037', 'chime', 'bell_noise', 'alto', 'piano', 'ring_loudly']
senticnet['sand'] = ['-0.06', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.04', 'friction', 'lack_of_water', 'dirt', 'abrasive', 'ground_material']
senticnet['sand_bar'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'driftwood', 'sea_shell', 'scenic_view', 'naked_woman', 'tidal_wave']
senticnet['sand_blackberry'] = ['-0.92', '0', '0.912', '-0.94', '#sadness', '#disgust', 'negative', '-0.92', 'true_blackberry', 'blackberry_bush', 'dewberry', 'dewberry_bush', 'running_blackberry']
senticnet['sand_dune'] = ['-0.06', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.04', 'friction', 'lack_of_water', 'dirt', 'abrasive', 'ground_material']
senticnet['sand_filtration'] = ['-0.62', '0', '0.987', '-0.74', '#anger', '#disgust', 'negative', '-0.78', 'medical_aid', 'treatment', 'hospice', 'digitalization', 'bloodletting']
senticnet['sand_flea'] = ['0.96', '-0.77', '0', '0.929', '#joy', '#admiration', 'positive', '0.887', 'whale_louse', 'beach_flea', 'sand_hopper', 'sandhopper', 'amphipod']
senticnet['sand_hopper'] = ['0.96', '-0.77', '0', '0.929', '#joy', '#admiration', 'positive', '0.887', 'whale_louse', 'beach_flea', 'amphipod', 'sandhopper', 'sand_flea']
senticnet['sand_hotel'] = ['0.057', '0.13', '0', '0.119', '#interest', '#admiration', 'positive', '0.102', 'friend_wedding', 'circular_route', 'spanish_restaurant', 'altar', 'monastery']
senticnet['sand_paper'] = ['0', '-0.01', '0.027', '-0.1', '#anger', '#disgust', 'negative', '-0.03', 'chalk_board', 'obstacle', 'golf_place', 'paper', 'school_class']
senticnet['sand_quarry'] = ['0', '0', '-0.85', '-0.71', '#fear', '#disgust', 'negative', '-0.78', 'fair_game', 'pit', 'stone_pit', 'target', 'prey']
senticnet['sand_rock'] = ['0.047', '0.11', '0', '0', '#joy', '#interest', 'positive', '0.079', 'near_sea', 'sandy', 'cover_sand', 'near_beach', 'beach']
senticnet['sand_spit'] = ['0', '0', '0.027', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'port', 'sea', 'boat', 'ocean', 'ship']
senticnet['sand_stargazer_native'] = ['-0.64', '0', '0.524', '-0.59', '#sadness', '#disgust', 'negative', '-0.58', 'bum', 'dreamer', 'daydreamer', 'escapist', 'wishful_thinker']
senticnet['sand_tiger_shark'] = ['0.96', '-0.77', '0', '0.929', '#joy', '#admiration', 'positive', '0.887', 'whale_louse', 'beach_flea', 'sand_hopper', 'sandhopper', 'amphipod']
senticnet['sand_volleyball_court'] = ['0.074', '0', '0', '0', '#joy', '#joy', 'positive', '0.074', 'hike_trail', 'ant_hill', 'compost', 'picnic', 'soccer_field']
senticnet['sand_wedge'] = ['0', '0', '0.061', '-0.03', '#anger', '#disgust', 'negative', '-0.04', 'garden_tool', 'straight_line', 'sharp', 'razor_blade', 'chock']
senticnet['sandal'] = ['-0.05', '0.068', '0', '0.102', '#interest', '#admiration', 'positive', '0.038', 'flip_flop', 'moccasin', 'shoe', 'footwear', 'shoe_type']
senticnet['sandal_paste'] = ['0.827', '0', '0', '0', '#joy', '#joy', 'positive', '0.827', 'wrathful', 'wrathfully', 'wroth', 'wrothful', 'lividity']
senticnet['sandaled'] = ['-0.79', '0', '0.864', '-0.89', '#anger', '#disgust', 'negative', '-0.85', 'booted', 'slippered', 'roughshod', 'shodden', 'wear_sandal']
senticnet['sandalwood_paste'] = ['0.827', '0', '0', '0', '#joy', '#joy', 'positive', '0.827', 'wrathful', 'wrathfully', 'wroth', 'wrothful', 'lividity']
senticnet['sandalwood_tree'] = ['0.826', '0.787', '0', '0', '#joy', '#interest', 'positive', '0.806', 'fig', 'baby_flower', 'carpel', 'strawberry', 'star_anise']
senticnet['sandbag'] = ['0.794', '0', '0', '0.759', '#joy', '#admiration', 'positive', '0.776', 'stupefy', 'daze', 'stunning', 'arresting', 'bedaze']
senticnet['sandbagger'] = ['0.69', '-0.81', '0', '0.732', '#surprise', '#admiration', 'positive', '0.747', 'slicker', 'beguiler', 'deceiver', 'cheat', 'cheater']
senticnet['sandbank'] = ['0.84', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.798', 'big_breadbox', 'water_vehicle', 'military_ship', 'ship', 'small_boat']
senticnet['sandbar'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'driftwood', 'sea_shell', 'scenic_view', 'naked_woman', 'tidal_wave']
senticnet['sandblast'] = ['0.953', '-0.58', '0', '0.85', '#joy', '#admiration', 'positive', '0.795', 'bluster', 'puff', 'puff_of_air', 'gust', 'whiff']
senticnet['sandboy'] = ['-0.79', '0', '0.992', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['sandfish'] = ['0.96', '-0.77', '0', '0.929', '#joy', '#admiration', 'positive', '0.887', 'whale_louse', 'beach_flea', 'sand_hopper', 'sandhopper', 'amphipod']
senticnet['sandfly_fever'] = ['-0.73', '0', '0.599', '-0.96', '#sadness', '#disgust', 'negative', '-0.76', 'spotted_fever', 'rickettsial_disease', 'rickettsialpox', 'typhus', 'typhus_fever']
senticnet['sandhopper'] = ['0.955', '-0.77', '0', '0.921', '#joy', '#admiration', 'positive', '0.884', 'whale_louse', 'beach_flea', 'sand_hopper', 'sand_flea', 'amphipod']
senticnet['sandifer_syndrome'] = ['-0.52', '0', '0.839', '-0.86', '#anger', '#disgust', 'negative', '-0.74', 'folie', 'upset', 'psychosomatic_disorder', 'conversion_disorder', 'conversion_reaction']
senticnet['sandman'] = ['0.52', '-0.99', '0', '0.664', '#surprise', '#admiration', 'positive', '0.727', 'faery', 'faerie', 'fairy', 'leprechaun', 'imp']
senticnet['sandpaper'] = ['0.883', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.864', 'stick_eraser', 'ballpoint_pen', 'intern', 'paper_clip', 'eraser_holder']
senticnet['sandstone_bedrock'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'fossil', 'oil', 'dark_liquid', 'smithsonian_institution', 'inert_gas']
senticnet['sandstone_cliff'] = ['0.057', '0.075', '0.039', '0.059', '#interest', '#admiration', 'positive', '0.038', 'summit', 'big_rock', 'high_land', 'peak', 'mountain_side']
senticnet['sandstone_outcrop'] = ['0.027', '0.139', '0', '0.203', '#interest', '#admiration', 'positive', '0.123', 'horse_shoes', 'four_leaf_clover', 'rotary_phone', 'ping_pong', 'billiards']
senticnet['sandstone_plateau'] = ['0', '0.08', '0.032', '0.096', '#interest', '#admiration', 'positive', '0.048', 'land_form', 'land_formation', 'land_feature', 'geographic_feature', 'landscape_feature']
senticnet['sandstone_quarry'] = ['0', '0', '-0.85', '-0.71', '#fear', '#disgust', 'negative', '-0.78', 'fair_game', 'pit', 'stone_pit', 'target', 'prey']
senticnet['sandstorm'] = ['-0.94', '0', '0.866', '-0.91', '#sadness', '#disgust', 'negative', '-0.90', 'movement', 'apparent_motion', 'motion', 'apparent_movement', 'optical_illusion']
senticnet['sandwich'] = ['0.851', '0.926', '0', '0', '#joy', '#interest', 'positive', '0.889', 'pastry', 'breakfast', 'make_toast', 'dinner_meal', 'appetizer']
senticnet['sandwich_board'] = ['0.952', '-0.52', '0', '0.835', '#joy', '#admiration', 'positive', '0.771', 'billboard', 'hoarding', 'signboard', 'scoreboard', 'shingle']
senticnet['sandwich_shop'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'candy', 'sweet', 'sugar', 'sweet_tooth', 'chocolate']
senticnet['sandwort'] = ['-0.52', '0', '0.527', '-0.78', '#anger', '#disgust', 'negative', '-0.61', 'herb', 'black_snakeroot', 'heartleaf', 'asarabacca', 'herbaceous_plant']
senticnet['sandy'] = ['0', '0.066', '0', '0.121', '#interest', '#admiration', 'positive', '0.093', 'sand_rock', 'place_camp', 'sea_shell', 'sand', 'seashell']
senticnet['sandy_beach'] = ['0', '-0.03', '0.053', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'puddle', 'beach', 'waterfall', 'lake_river', 'bay']
senticnet['sandy_gravel'] = ['-0.06', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.04', 'friction', 'lack_of_water', 'dirt', 'abrasive', 'ground_material']
senticnet['sandy_grushow'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'kit_fox', 'prairie_fox', 'white_fox']
senticnet['sandy_shore'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'driftwood', 'sea_shell', 'scenic_view', 'naked_woman', 'tidal_wave']
senticnet['sandy_spit'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'driftwood', 'sea_shell', 'scenic_view', 'naked_woman', 'tidal_wave']
senticnet['sane'] = ['0.203', '0.077', '0', '0.189', '#joy', '#admiration', 'positive', '0.156', 'sea_shell', 'coast', 'seaside', 'sediment', 'shell']
senticnet['sane_person'] = ['0.203', '0.077', '0', '0.189', '#joy', '#admiration', 'positive', '0.156', 'sea_shell', 'coast', 'seaside', 'sediment', 'shell']
senticnet['saneness'] = ['0.972', '-0.88', '0', '0.926', '#joy', '#admiration', 'positive', '0.928', 'reasonableness', 'rationality', 'lucidity', 'reason', 'mental_health']
senticnet['sanford_bernstein'] = ['0.711', '0', '0', '0', '#joy', '#joy', 'positive', '0.711', 'chemist', 'expert', 'scientist', 'esaminatore', 'physicist']
senticnet['sangfroid'] = ['0.828', '-0.88', '0', '0.551', '#joy', '#surprise', 'positive', '0.755', 'superiority', 'pride', 'superbia', 'imperiousness', 'domineeringness']
senticnet['sangma'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['sanguinary'] = ['0', '0', '-0.92', '-0.94', '#fear', '#disgust', 'negative', '-0.93', 'bloodthirsty', 'slaughterous', 'gory', 'butcherly', 'sanguineous']
senticnet['sanguine'] = ['0.507', '0.676', '-0.26', '0.272', '#joy', '#interest', 'positive', '0.297', 'sanguinity', 'sanguineness', 'optimistically', 'optimistic', 'encouragingly']
senticnet['sanguineness'] = ['0.507', '0.676', '-0.26', '0.272', '#joy', '#interest', 'positive', '0.297', 'sanguine', 'sanguinity', 'optimistically', 'optimistic', 'encouragingly']
senticnet['sanguineous'] = ['0', '0', '-0.92', '-0.95', '#fear', '#disgust', 'negative', '-0.93', 'bloodthirsty', 'slaughterous', 'gory', 'butcherly', 'sanguinary']
senticnet['sanguinity'] = ['0.507', '0.676', '-0.26', '0.272', '#joy', '#interest', 'positive', '0.297', 'sanguine', 'sanguineness', 'optimistically', 'optimistic', 'encouragingly']
senticnet['sanguinolentum'] = ['-0.60', '0', '0.541', '-0.52', '#sadness', '#anger', 'negative', '-0.55', 'swoon', 'pass_out', 'faint', 'zonk_out', 'change_state']
senticnet['sanhedria'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['sanies'] = ['-0.66', '0', '0.806', '-0.95', '#anger', '#disgust', 'negative', '-0.80', 'gleet', 'humor', 'liquid_body_substance', 'bodily_fluid', 'body_fluid']
senticnet['sanitariness'] = ['0.617', '-0.59', '0', '0.805', '#joy', '#admiration', 'positive', '0.671', 'sanitation', 'hygiene', 'sterility', 'sanitary_condition', 'asepsis']
senticnet['sanitary'] = ['0.063', '0', '0', '0.147', '#joy', '#admiration', 'positive', '0.105', 'cleanliness', 'keep_clean', 'get_rid_of_dirt', 'remove_dirt', 'dry_skin']
senticnet['sanitary_condition'] = ['-0.56', '0', '0.856', '-0.85', '#anger', '#disgust', 'negative', '-0.75', 'cleanness', 'unsanitariness', 'dirtiness', 'uncleanness', 'sanitariness']
senticnet['sanitary_landfill'] = ['-0.81', '0', '0.987', '-0.64', '#sadness', '#anger', 'negative', '-0.81', 'burn_garbage', 'eliminate_garbage', 'incinerate_waste', 'dump', 'garbage_dump']
senticnet['sanitary_napkin'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'hankie', 'kleenex', 'hanky', 'handkerchief', 'tissue']
senticnet['sanitary_sewer'] = ['-0.53', '0', '0.671', '-0.55', '#anger', '#disgust', 'negative', '-0.58', 'dirty', 'putrefactive', 'putrefacient', 'abscessed', 'contaminative']
senticnet['sanitary_sewer_system'] = ['-0.53', '0', '0.671', '-0.55', '#anger', '#disgust', 'negative', '-0.58', 'dirty', 'putrefactive', 'putrefacient', 'abscessed', 'contaminative']
senticnet['sanitation'] = ['0', '0.702', '0', '0.789', '#interest', '#admiration', 'positive', '0.746', 'hygiene', 'clean', 'remove_dirt', 'sanitary', 'ensure_proper_hygiene']
senticnet['sanitization'] = ['-0.62', '0', '0.987', '-0.74', '#anger', '#disgust', 'negative', '-0.78', 'medical_aid', 'treatment', 'hospice', 'digitalization', 'bloodletting']
senticnet['sanitize'] = ['0.863', '0', '0', '0.775', '#joy', '#admiration', 'positive', '0.819', 'mop', 'brush', 'scrape', 'sterilize', 'neaten']
senticnet['sanity'] = ['0.418', '0.103', '0', '0.373', '#joy', '#admiration', 'positive', '0.298', 'knowledgeable', 'look_night_sky', 'good_lover', 'taken_seriously', 'spice_life']
senticnet['sankharakkhandha'] = ['-0.85', '0', '0.968', '-0.75', '#sadness', '#anger', 'negative', '-0.86', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['sanofi'] = ['0.559', '0.656', '-0.26', '0.285', '#joy', '#interest', 'positive', '0.31', 'zealous', 'avidity', 'eagerness', 'avidness', 'thirstily']
senticnet['sans_serif'] = ['-0.95', '0', '0.517', '-0.53', '#sadness', '#disgust', 'negative', '-0.67', 'copperplate', 'hand', 'minuscule', 'italic', 'round_hand']
senticnet['sanskara_rite'] = ['0', '0.089', '0', '0.129', '#interest', '#admiration', 'positive', '0.109', 'passageway', 'connect_room', 'entrance_room', 'corridor', 'walk_through']
senticnet['sanskrit_scholar'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['sanssouci'] = ['0', '0.105', '0', '0.076', '#interest', '#admiration', 'positive', '0.09', 'big_house', 'mansion', 'castle', 'block_flat', 'medieval']
senticnet['sant_mat'] = ['0.065', '0.17', '-0.05', '0.143', '#interest', '#admiration', 'positive', '0.081', 'supervisor', 'native_american', 'ambassador', 'master', 'general']
senticnet['santa'] = ['0.856', '0', '0', '0', '#joy', '#joy', 'positive', '0.856', 'tree', 'evergreen_tree', 'big_tree', 'santa_claus', 'christmas']
senticnet['santa_claus'] = ['0.847', '0', '0', '0', '#joy', '#joy', 'positive', '0.847', 'tree', 'evergreen_tree', 'big_tree', 'christmas_tree', 'christmas']
senticnet['santa_cruz_beach_boardwalk'] = ['0.089', '-0.18', '0.037', '-0.06', '#joy', '#surprise', 'positive', '0.043', 'balloon', 'ballon', 'fielder', 'kite', 'blimp']
senticnet['santa_cruz_predatory_bird'] = ['-0.74', '0', '0.963', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['santa_workshop'] = ['0.71', '0.92', '0', '0', '#joy', '#interest', 'positive', '0.815', 'toy_car', 'toy_ball', 'action_figure', 'toy_marble', 'beanie_baby']
senticnet['santalaceae_family'] = ['-0.56', '0', '0.617', '-0.80', '#anger', '#disgust', 'negative', '-0.66', 'prolixity', 'prolixness', 'wordiness', 'repetitiveness', 'repetitiousness']
senticnet['santalum_album'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'nut', 'palm_kernel', 'edible_nut', 'cembra_nut', 'cedar_nut']
senticnet['santana_lopez'] = ['0.761', '0.566', '-0.21', '0.293', '#joy', '#interest', 'positive', '0.352', 'gratify', 'jubilation', 'gloat', 'gloating', 'gloatingly']
senticnet['sany_heavy'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'builder']
senticnet['sao_feng'] = ['0', '0', '0.034', '-0.08', '#anger', '#disgust', 'negative', '-0.06', 'lifeboat', 'mast', 'bad_man', 'keel', 'yacht']
senticnet['sap'] = ['0', '0', '-0.74', '-0.91', '#fear', '#disgust', 'negative', '-0.83', 'tomfool', 'fool', 'cosh', 'blackjack', 'muggins']
senticnet['saphead'] = ['0.917', '-0.73', '0', '0.876', '#joy', '#admiration', 'positive', '0.841', 'morosoph', 'simpleton', 'flibbertigibbet', 'foolish_woman', 'simple']
senticnet['saphenous_vein'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'blatancy', 'obviousness']
senticnet['sapidity'] = ['-0.62', '0', '0.512', '-0.80', '#sadness', '#disgust', 'negative', '-0.64', 'vanilla', 'taste', 'lemon', 'taste_sensation', 'gustatory_sensation']
senticnet['sapience'] = ['0.651', '-0.73', '0', '0.975', '#surprise', '#admiration', 'positive', '0.788', 'profoundness', 'profundity', 'sagacity', 'sagaciousness', 'discernment']
senticnet['sapient'] = ['0.651', '-0.73', '0', '0.975', '#surprise', '#admiration', 'positive', '0.788', 'profoundness', 'profundity', 'sagacity', 'sagaciousness', 'discernment']
senticnet['sapless'] = ['-0.93', '0', '0', '-0.91', '#sadness', '#disgust', 'negative', '-0.92', 'watery', 'weaker', 'washy', 'imperfect', 'light']
senticnet['sapling'] = ['0.855', '0', '0', '0', '#joy', '#joy', 'positive', '0.855', 'christmas_tree', 'evergreen_christmas_tree', 'big_christmas_tree', 'tree', 'christmas']
senticnet['sapodilla_tree'] = ['0.965', '0.775', '0', '0.968', '#joy', '#admiration', 'positive', '0.903', 'snot', 'bad_taste', 'mouth_part', 'beautiful_smile', 'near_mouth']
senticnet['sapota'] = ['0.859', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.888', 'green_apple', 'apricot', 'lime', 'peach', 'cherry']
senticnet['sapphire'] = ['0.418', '0.103', '0', '0.373', '#joy', '#admiration', 'positive', '0.298', 'bloodstone', 'turquoise', 'emerald', 'ruby', 'jade']
senticnet['sapphire_blue'] = ['0.577', '-0.69', '0', '0.645', '#surprise', '#admiration', 'positive', '0.639', 'shining', 'polished', 'shiny', 'bright', 'burnished']
senticnet['sapphire_ring'] = ['0.048', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.071', 'ring', 'ring_loudly', 'diamond_ring', 'necklace', 'jewelry']
senticnet['sapphism'] = ['0.8', '-0.88', '0', '0.713', '#joy', '#surprise', 'positive', '0.8', 'sex', 'gayness', 'sexual_gayness', 'lesbianism', 'inversion']
senticnet['saprolite'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'fossil', 'oil', 'dark_liquid', 'smithsonian_institution', 'inert_gas']
senticnet['saquinavir'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'indinavir', 'nelfinavir', 'ritonavir', 'protease_inhibitor']
senticnet['sarabande'] = ['0.591', '-0.91', '0', '0.605', '#surprise', '#admiration', 'positive', '0.704', 'farandole', 'folk_dance', 'polka', 'dance', 'typical_dance']
senticnet['saran_wrap'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'foil', 'tin_foil', 'gold_foil']
senticnet['saratoga'] = ['0.559', '-0.69', '0', '0.777', '#surprise', '#admiration', 'positive', '0.678', 'species', 'spotted_bonytongue', 'spotted_barramundi', 'fish_species', 'northern_barramundi']
senticnet['saravali'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
senticnet['sarcasm'] = ['-0.94', '0', '0.65', '-0.57', '#sadness', '#anger', 'negative', '-0.72', 'manner_of_speaking', 'tone_of_voice', 'note', 'roundness', 'rotundity']
senticnet['sarcastic'] = ['-0.77', '0', '0.537', '-0.64', '#sadness', '#disgust', 'negative', '-0.65', 'grim', 'saturnine', 'sardonic', 'mordant', 'black']
senticnet['sarcastic_tone'] = ['0.941', '0.822', '0', '0', '#joy', '#interest', 'positive', '0.881', 'melodic_noise', 'universal_language', 'instrumental', 'musical_note', 'audio']
senticnet['sarcastically'] = ['-0.97', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.84', 'scornfully', 'mockingly', 'sardonically', 'sardonic', 'cynically']
senticnet['sarcoid'] = ['-0.87', '0', '0.531', '-0.68', '#sadness', '#disgust', 'negative', '-0.69', 'rubor', 'redness', 'dermatomyositis', 'inclusion_body_myositis', 'inflammation']
senticnet['sarcophagus'] = ['-0.09', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.10', 'funeral_celebration', 'memorial', 'funeral', 'memento', 'cry']
senticnet['sarcosome'] = ['0.727', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.841', 'mitochondrion', 'chondriosome', 'organelle', 'protein', 'chloroplast']
senticnet['sarcotesta'] = ['0', '0.141', '0.01', '0.115', '#interest', '#admiration', 'positive', '0.082', 'sepal', 'achene', 'funiculus', 'seed', 'pip']
senticnet['sardaukar'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['sardonic'] = ['-0.77', '0', '0.537', '-0.64', '#sadness', '#disgust', 'negative', '-0.65', 'grim', 'saturnine', 'sarcastic', 'mordant', 'black']
senticnet['sardonic_humor'] = ['-0.77', '0', '0.537', '-0.64', '#sadness', '#disgust', 'negative', '-0.65', 'grim', 'saturnine', 'sardonic', 'sarcastic', 'black']
senticnet['sardonic_wit'] = ['-0.77', '0', '0.626', '-0.78', '#sadness', '#disgust', 'negative', '-0.72', 'bumpy', 'unsmooth', 'lepidote', 'leprose', 'roughish']
senticnet['sardonically'] = ['-0.97', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.84', 'scornfully', 'mockingly', 'sarcastically', 'sardonic', 'cynically']
senticnet['sardonyx'] = ['0.037', '0.073', '0.027', '0.072', '#interest', '#admiration', 'positive', '0.039', 'sapphire', 'birth_stone', 'turquoise', 'gemstone', 'amethyst']
senticnet['saree'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'anklet', 'ankle_bracelet', 'armilla']
senticnet['sarin'] = ['-0.90', '0', '0.529', '-0.84', '#sadness', '#disgust', 'negative', '-0.75', 'poison_gas', 'nerve_agent', 'mustard_gas', 'mustard_agent', 'blistering_agent']
senticnet['sarin_nerve_agent'] = ['-0.90', '0', '0.529', '-0.84', '#sadness', '#disgust', 'negative', '-0.75', 'poison_gas', 'nerve_agent', 'mustard_gas', 'mustard_agent', 'blistering_agent']
senticnet['sariputra'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['sarnie'] = ['0.129', '0.092', '0', '0.167', '#joy', '#admiration', 'positive', '0.129', 'eat_cereal', 'eat_fish', 'eat_pudding', 'meet_friend_for_lunch', 'stir_coffee']
senticnet['sarojini_naidu'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['saronno'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'expressway', 'turnpike', 'toll_road', 'highway']
senticnet['sars_infection'] = ['-0.11', '0', '-0.15', '-0.24', '#fear', '#disgust', 'negative', '-0.17', 'disease', 'infectious_disease', 'polio', 'malady', 'sickness']
senticnet['sarwan'] = ['-0.13', '0', '-0.11', '-0.04', '#sadness', '#fear', 'negative', '-0.1', 'concern_health', 'medical_problem', 'strengthful', 'surgery', 'injury']
senticnet['sarx'] = ['0.668', '-0.88', '0', '0.893', '#surprise', '#admiration', 'positive', '0.815', 'chirpiness', 'spirit', 'sprightliness', 'liveliness', 'activity']
senticnet['sash'] = ['-0.53', '0', '0.682', '-0.74', '#anger', '#disgust', 'negative', '-0.65', 'girdle', 'cincture', 'cummerbund', 'waistband', 'waistcloth']
senticnet['sashay'] = ['0', '0.075', '0', '0.2', '#interest', '#admiration', 'positive', '0.138', 'stride', 'foot_motion', 'gait', 'foot_action', 'foot_movement']
senticnet['sashimi'] = ['0.9', '0.963', '0', '0', '#joy', '#interest', 'positive', '0.931', 'form_of_food', 'japanese_food', 'uncooked', 'healthy', 'food']
senticnet['sasori'] = ['0.021', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'plastic_bottle', 'party', 'use_party', 'lighter_air', 'compress_air']
senticnet['sass'] = ['-0.95', '0', '0.941', '-0.57', '#sadness', '#anger', 'negative', '-0.82', 'reply', 'response', 'comeback', 'comebacking', 'backtalk']
senticnet['sassafras_sassafras_albidum'] = ['0.958', '-0.82', '0', '0.924', '#joy', '#admiration', 'positive', '0.901', 'bunchberry', 'dwarf_cornel', 'crackerberry', 'pudding_berry', 'common_white_dogwood']
senticnet['sassing'] = ['-0.95', '0', '0.941', '-0.57', '#sadness', '#anger', 'negative', '-0.82', 'reply', 'response', 'sass', 'comeback', 'backtalk']
senticnet['sassy'] = ['0.83', '-0.80', '0', '0.969', '#joy', '#admiration', 'positive', '0.867', 'lively', 'bold', 'full_of_spirit', 'cheeky', 'fun']
senticnet['sassy_magazine'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'library', 'old_testament', 'cook_book', 'a_lot_of_books', 'librarian']
senticnet['sasuke_uchiha'] = ['0.021', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'plastic_bottle', 'party', 'use_party', 'lighter_air', 'compress_air']
senticnet['satan'] = ['0', '0', '-0.99', '-0.74', '#fear', '#disgust', 'negative', '-0.86', 'deadly', 'evil', 'devil', 'hell', 'satanism']
senticnet['satanic'] = ['-0.75', '0', '-0.76', '0', '#sadness', '#fear', 'negative', '-0.75', 'fiendish', 'evil', 'fiend', 'fiendishly', 'infernal']
senticnet['satanism'] = ['0', '0', '-0.99', '-0.74', '#fear', '#disgust', 'negative', '-0.86', 'deadly', 'evil', 'devil', 'hell', 'satan']
senticnet['satanist'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'non-believer', 'doubter', 'agnostic', 'freethinker', 'sceptic']
senticnet['satchel'] = ['0', '-0.02', '0.018', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'shoulder_bag', 'water_bottle', 'satchel_bag', 'tote_bag', 'carry']
senticnet['satchel_bag'] = ['0', '-0.03', '0.022', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'shoulder_bag', 'satchel', 'water_bottle', 'tote_bag', 'carry']
senticnet['satchel_charge'] = ['0', '0', '0.818', '-0.99', '#anger', '#disgust', 'negative', '-0.90', 'weapon', 'aerial_bomb', 'machine_gun', 'cannon', 'handgun']
senticnet['sate'] = ['0.334', '0.331', '-0.11', '0.49', '#joy', '#admiration', 'positive', '0.261', 'attain_goal', 'inclusion', 'good_fortune', 'satisfy', 'please']
senticnet['sate_hunger'] = ['0', '0', '0', '0.867', '#admiration', '#admiration', 'positive', '0.867', 'eat_like_pig', 'eat_cookie', 'eat_breakfast_in_bed', 'eat_breakfast', 'diminish_own_hunger']
senticnet['satellite'] = ['0.741', '0.999', '0', '0', '#joy', '#interest', 'positive', '0.87', 'planet', 'orbiter', 'artificial_satellite', 'study', 'project']
senticnet['satellite_airfield'] = ['-0.97', '0', '0.737', '-0.97', '#sadness', '#disgust', 'negative', '-0.89', 'flare_path', 'airstrip', 'take_off', 'runway', 'grassland']
senticnet['satellite_navigation'] = ['0', '0.071', '0', '0.143', '#interest', '#admiration', 'positive', '0.107', 'post_notice', 'indicator', 'way', 'one_way', 'arrow']
senticnet['satellite_orbit'] = ['0.989', '-0.90', '0', '0.876', '#joy', '#surprise', 'positive', '0.923', 'reach', 'range', 'compass', 'scope', 'confines']
senticnet['satellite_phone'] = ['0.035', '-0.01', '0.027', '0.085', '#joy', '#admiration', 'positive', '0.026', 'telephone', 'find_phone_number', 'telephone_room', 'phone', 'use_telephone']
senticnet['satellite_positioning_system'] = ['0', '0.071', '0', '0.143', '#interest', '#admiration', 'positive', '0.107', 'post_notice', 'indicator', 'way', 'one_way', 'arrow']
senticnet['satellite_tv_channel'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'theater_seat', 'theater_box', 'sticky_floor', 'stage_entrance', 'balcony']
senticnet['sathurthi'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['satiable'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satisfactorily', 'comforting', 'satisfyingly', 'cheering']
senticnet['satiate'] = ['-0.63', '0', '0.608', '-0.74', '#sadness', '#disgust', 'negative', '-0.66', 'pall', 'take_in', 'take', 'ingest', 'have']
senticnet['satiate_hunger'] = ['-0.02', '0.224', '-0.07', '0.116', '#interest', '#admiration', 'positive', '0.061', 'provide_food', 'money_charge', 'make_eat', 'fee', 'payment']
senticnet['satiation'] = ['0.795', '0', '0', '0.787', '#joy', '#admiration', 'positive', '0.791', 'entree', 'soup', 'wine_glass', 'salt_pepper_shaker', 'plate_silverware']
senticnet['satiety'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'cholecystokinin', 'secretin', 'gastrointestinal_hormone']
senticnet['satiety_hormone'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'cholecystokinin', 'secretin', 'gastrointestinal_hormone']
senticnet['satin'] = ['0.053', '0.128', '0', '0.145', '#interest', '#admiration', 'positive', '0.109', 'spool', 'silk', 'velvet', 'cotton', 'web']
senticnet['satin_black'] = ['0.02', '0.085', '-0.05', '0.085', '#interest', '#admiration', 'positive', '0.034', 'gold', 'silver', 'medal', 'carat', 'medallion']
senticnet['satin_chrome'] = ['0.02', '0.085', '-0.05', '0.085', '#interest', '#admiration', 'positive', '0.034', 'gold', 'silver', 'medal', 'carat', 'medallion']
senticnet['satin_finish'] = ['0.577', '-0.69', '0', '0.645', '#surprise', '#admiration', 'positive', '0.639', 'shining', 'lustrous', 'shiny', 'bright', 'burnished']
senticnet['satin_gown'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'pinafore', 'pinny', 'dirndl', 'dress', 'muumuu']
senticnet['satin_jacket'] = ['0.793', '0.847', '0', '0', '#joy', '#interest', 'positive', '0.82', 'cross_bun', 'hot_cross_bun', 'bear_paw', 'coffee_roll', 'bun']
senticnet['satin_ribbon'] = ['-0.06', '-0.01', '0.05', '-0.07', '#sadness', '#disgust', 'negative', '-0.04', 'embroidery', 'woven', 'cover_wind', 'torn', 'use_needle']
senticnet['satin_stitch'] = ['-0.03', '-0.00', '-0.05', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'bandage', 'needle_thread', 'sew', 'spasm', 'suture']
senticnet['satiny'] = ['0.819', '0', '0', '0.831', '#joy', '#admiration', 'positive', '0.825', 'silky', 'velvety', 'supple', 'full-bodied', 'creamy']
senticnet['satire'] = ['0', '-0.03', '0', '0.094', '#surprise', '#admiration', 'positive', '0.065', 'commercial', 'sitcom', 'presentation', 'joke', 'funny']
senticnet['satiric'] = ['0', '-0.03', '0', '0.094', '#surprise', '#admiration', 'positive', '0.065', 'commercial', 'sitcom', 'presentation', 'joke', 'funny']
senticnet['satirical'] = ['0', '-0.70', '0', '0.737', '#surprise', '#admiration', 'positive', '0.722', 'light-hearted', 'lighthearted', 'irreverent', 'humorous', 'tongue-in-cheek']
senticnet['satirical_comedy'] = ['0', '-0.03', '0', '0.094', '#surprise', '#admiration', 'positive', '0.065', 'commercial', 'sitcom', 'presentation', 'joke', 'funny']
senticnet['satirical_commentary'] = ['0', '-0.03', '0', '0.094', '#surprise', '#admiration', 'positive', '0.065', 'commercial', 'sitcom', 'presentation', 'joke', 'funny']
senticnet['satirical_magazine_private'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'rascal', 'rapscallion', 'scalawag', 'holy_terror', 'monkey']
senticnet['satirical_poem'] = ['-0.81', '0', '0.52', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'affray', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['satirical_poetry'] = ['-0.81', '0', '0.52', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'affray', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['satirical_puppet_show'] = ['-0.89', '0', '0.58', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'resemblance', 'comparison', 'equivalence', 'reflection', 'alikeness']
senticnet['satirical_tale'] = ['0.807', '-0.83', '0', '0.688', '#joy', '#surprise', 'positive', '0.776', 'folly', 'lunacy', 'craziness', 'indulgence', 'foolery']
senticnet['satirical_tone'] = ['0', '-0.03', '0', '0.094', '#surprise', '#admiration', 'positive', '0.065', 'commercial', 'sitcom', 'presentation', 'joke', 'funny']
senticnet['satirically'] = ['0', '-0.03', '0', '0.094', '#surprise', '#admiration', 'positive', '0.065', 'commercial', 'sitcom', 'presentation', 'joke', 'funny']
senticnet['satirise'] = ['0', '-0.03', '0', '0.094', '#surprise', '#admiration', 'positive', '0.065', 'commercial', 'sitcom', 'presentation', 'joke', 'funny']
senticnet['satirist'] = ['-0.16', '-0.01', '-0.17', '-0.17', '#fear', '#disgust', 'negative', '-0.12', 'make_others_laugh', 'laugh', 'mean_person', 'tell_others', 'make_fun']
senticnet['satirist_lucian'] = ['-0.87', '0', '0.599', '-0.71', '#sadness', '#disgust', 'negative', '-0.73', 'hysteria', 'fury', 'delirium', 'frenzy', 'craze']
senticnet['satirize'] = ['-0.66', '0', '0.809', '-0.56', '#sadness', '#anger', 'negative', '-0.68', 'blackguard', 'roast', 'ridicule', 'guy', 'laugh_at']
senticnet['satisfaction'] = ['0.653', '0.4', '-0.20', '0.168', '#joy', '#interest', 'positive', '0.254', 'elation', 'joy', 'contentment', 'pride', 'excitement']
senticnet['satisfaction_survey'] = ['-0.04', '0.1', '0.051', '0.137', '#interest', '#admiration', 'positive', '0.035', 'find_number', 'regularly', 'list_number', 'calculator', 'navigation']
senticnet['satisfactorily'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'comforting', 'satisfyingly', 'cheering']
senticnet['satisfactoriness'] = ['-0.99', '0', '0.878', '-0.95', '#sadness', '#disgust', 'negative', '-0.94', 'adequacy', 'adequateness', 'acceptability', 'acceptableness', 'quality']
senticnet['satisfactory'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly']
senticnet['satisfactory_answer'] = ['0', '0.82', '0', '0.822', '#interest', '#admiration', 'positive', '0.821', 'answer', 'inquiry', 'question_answering', 'query', 'reply']
senticnet['satisfactory_completion'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly']
senticnet['satisfactory_explanation'] = ['-0.11', '0.283', '-0.16', '0.331', '#interest', '#admiration', 'positive', '0.084', 'conclude', 'question_answering', 'solution', 'jutification', 'question_starter']
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senticnet['sawmill'] = ['-0.78', '0', '0.699', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'battery', 'stamp_battery', 'quartz_battery', 'quartz_mill', 'factory']
senticnet['sawney_bean'] = ['-0.73', '0', '0.734', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'primitive', 'headhunter', 'barbarian', 'anthropophagus', 'anthropophagite']
senticnet['sax'] = ['-0.82', '0', '0.923', '0', '#sadness', '#anger', 'negative', '-0.87', 'tenor', 'saxophone', 'soprano', 'baritone', 'clarinet']
senticnet['sax_solo'] = ['-0.89', '0', '0.722', '-0.94', '#sadness', '#disgust', 'negative', '-0.85', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['saxe_gotha'] = ['0.033', '0.088', '-0.06', '0.068', '#interest', '#admiration', 'positive', '0.032', 'earl', 'noble', 'king', 'sultan', 'royal_title']
senticnet['saxifraga'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'sedum', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['saxifrage'] = ['-0.85', '0', '0.894', '-0.51', '#sadness', '#anger', 'negative', '-0.75', 'herb', 'yellow_mountain_saxifrage', 'meadow_saxifrage', 'mossy_saxifrage', 'western_saxifrage']
senticnet['saxophone'] = ['-0.83', '0', '0.923', '0', '#sadness', '#anger', 'negative', '-0.87', 'tenor', 'baritone', 'soprano', 'sax', 'clarinet']
senticnet['saxophone_quartet'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'classical_music', 'make_beautiful_music', 'create_music', 'part_of_band', 'make_music']
senticnet['saxophonist'] = ['0.901', '0', '0', '0', '#joy', '#joy', 'positive', '0.901', 'music', 'vapor', 'blues', 'megrim', 'demoralization']
senticnet['say'] = ['0.561', '-0.74', '0', '0.533', '#joy', '#surprise', 'positive', '0.614', 'aver', 'asseverate', 'predicate', 'insist', 'take_firm_stand']
senticnet['say_aha'] = ['0.96', '0.902', '0', '0', '#joy', '#interest', 'positive', '0.931', 'stare_off_space', 'think_past', 'scratch_head', 'brain_storm', 'come_up_idea']
senticnet['say_aloud'] = ['-0.06', '0.16', '0.075', '0.078', '#interest', '#admiration', 'positive', '0.025', 'add_two_numbers_together', 'write_down', 'addition', 'idea', 'add_number']
senticnet['say_darcey'] = ['0', '0.075', '0', '0.2', '#interest', '#admiration', 'positive', '0.138', 'stride', 'foot_motion', 'gait', 'foot_action', 'foot_movement']
senticnet['say_dismissively'] = ['-0.90', '0.138', '-0.63', '0.071', '#sadness', '#fear', 'negative', '-0.33', 'woeful', 'execrable', 'deplorable', 'woebegone', 'plaintiveness']
senticnet['say_eureka'] = ['-0.31', '0', '-0.19', '0', '#sadness', '#fear', 'negative', '-0.25', 'investigate_fact', 'look_fact', 'ask_question', 'new_insight', 'look_clue']
senticnet['say_excuse'] = ['-0.09', '0', '-0.07', '-0.13', '#sadness', '#disgust', 'negative', '-0.1', 'justify', 'quiet_mind', 'lift_leg', 'wet_pant', 'listen_bird']
senticnet['say_farewell'] = ['0', '0.152', '-0.08', '0.208', '#interest', '#admiration', 'positive', '0.091', 'departure', 'leave', 'colored_green', 'green_color', 'make_big']
senticnet['say_good_bye'] = ['-0.02', '0.087', '0', '0.107', '#interest', '#admiration', 'positive', '0.056', 'greet', 'step_aside', 'step_out', 'walk_away', 'open_mailbox']
senticnet['say_goodbye'] = ['0.951', '0.771', '0', '0', '#joy', '#interest', 'positive', '0.861', 'part_of_way', 'make_feel_better', 'farewell', 'little_hunger', 'think_funny']
senticnet['say_goodbye_to'] = ['0.854', '0.967', '0', '0', '#joy', '#interest', 'positive', '0.91', 'greet', 'salute', 'smile', 'wave_arm', 'attract_attention']
senticnet['say_grace'] = ['-0.03', '-0.05', '0.052', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'prepare_dinner', 'measure_ingredient', 'eat_soup', 'create_meal', 'prepare_meal']
senticnet['say_hello'] = ['0.021', '0.093', '-0.06', '0.119', '#interest', '#admiration', 'positive', '0.041', 'greet', 'end_conversation', 'communicate', 'make_eye_contact', 'say_hi']
senticnet['say_hi'] = ['-0.04', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.05', 'greet', 'communicate', 'become_friend', 'make_eye_contact', 'say_hello']
senticnet['say_love'] = ['0.832', '0.979', '0', '0', '#joy', '#interest', 'positive', '0.905', 'surprise_friend', 'act_foolish', 'receive_gift', 'tear_paper', 'blow_out_candle']
senticnet['say_prayer'] = ['-0.31', '0', '-0.24', '-0.10', '#sadness', '#fear', 'negative', '-0.22', 'put_box', 'dig_hole', 'see_light', 'end_relationship', 'become_depressed']
senticnet['say_sorry'] = ['-0.56', '0', '-0.58', '0.294', '#sadness', '#fear', 'negative', '-0.28', 'feel_sorry', 'get_reply', 'rely', 'make_promise', 'regret']
senticnet['say_thank'] = ['0.744', '0', '0', '0.793', '#joy', '#admiration', 'positive', '0.768', 'feel_obligated', 'make_happy', 'express_love', 'give_present', 'show_affection']
senticnet['say_welcome'] = ['0.991', '0.828', '0', '0', '#joy', '#interest', 'positive', '0.909', 'become_aroused', 'show_empathy', 'find_woman', 'greet', 'love_back']
senticnet['say_yes'] = ['-0.06', '0.25', '-0.18', '0.352', '#interest', '#admiration', 'positive', '0.089', 'consent', 'make_right', 'take', 'suggest', 'agree']
senticnet['sayde'] = ['-0.11', '0.112', '0.674', '-0.66', '#anger', '#disgust', 'negative', '-0.33', 'wrathfully', 'wrathful', 'wrothful', 'lividly', 'lividity']
senticnet['sayfawa_dynasty'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
senticnet['sayin'] = ['-0.66', '0', '0.595', '-0.83', '#sadness', '#disgust', 'negative', '-0.7', 'bastard', 'dickhead', 'disagreeable_person', 'motherfucker', 'son_of_bitch']
senticnet['saying_good_bye'] = ['0', '0.145', '0', '0.2', '#interest', '#admiration', 'positive', '0.172', 'hand_gesture', 'arm_movement', 'water_splash', 'hand_motion', 'water_form']
senticnet['saying_goodbye'] = ['0', '0.145', '0', '0.2', '#interest', '#admiration', 'positive', '0.172', 'hand_gesture', 'arm_movement', 'water_splash', 'hand_motion', 'water_form']
senticnet['saynt_hieronyme'] = ['-0.79', '0', '0.607', '-0.92', '#sadness', '#disgust', 'negative', '-0.77', 'stubbornness', 'obstinacy', 'obstinance', 'bullheadedness', 'pigheadedness']
senticnet['sayonara'] = ['0', '0.145', '0', '0.2', '#interest', '#admiration', 'positive', '0.172', 'hand_gesture', 'arm_movement', 'water_splash', 'hand_motion', 'water_form']
senticnet['says_goodbye'] = ['0', '0.145', '0', '0.2', '#interest', '#admiration', 'positive', '0.172', 'hand_gesture', 'arm_movement', 'water_splash', 'hand_motion', 'water_form']
senticnet['sayth'] = ['0.887', '-0.84', '0', '0.819', '#joy', '#surprise', 'positive', '0.851', 'arouse', 'elicit', 'enkindle', 'kindle', 'evoke']
senticnet['sazer'] = ['-0.64', '0', '0.524', '-0.59', '#sadness', '#disgust', 'negative', '-0.58', 'bum', 'dreamer', 'daydreamer', 'escapist', 'wishful_thinker']
senticnet['sbanter'] = ['-0.92', '0', '-0.73', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'witty_remark', 'cross_talking', 'wordplay', 'banter', 'jester']
senticnet['sbc_ameritech_incumbent_lec'] = ['0', '0.089', '0.01', '0.118', '#interest', '#admiration', 'positive', '0.066', 'crate', 'mom_house', 'sack', 'poker_game', 'safekeep']
senticnet['sbc_pacific'] = ['0.034', '0', '0.01', '0.087', '#joy', '#admiration', 'positive', '0.037', 'chime', 'bell_noise', 'alto', 'piano', 'ring_loudly']
senticnet['scabbard'] = ['0.93', '-0.78', '0', '0.873', '#joy', '#admiration', 'positive', '0.861', 'blade', 'brand', 'steel', 'claymore', 'sword']
senticnet['scabby'] = ['-0.99', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.91', 'hoary', 'warty', 'scaly', 'slimy', 'leathery']
senticnet['scabies'] = ['0', '0', '0.949', '-0.74', '#anger', '#disgust', 'negative', '-0.84', 'ringworm', 'louse', 'eczema', 'impetigo', 'mite']
senticnet['scabiosa'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'livelong', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['scabrous'] = ['-0.77', '0', '0.626', '-0.78', '#sadness', '#disgust', 'negative', '-0.72', 'bumpy', 'unsmooth', 'lepidote', 'leprose', 'roughish']
senticnet['scad'] = ['0', '0.863', '0', '0.725', '#interest', '#admiration', 'positive', '0.794', 'multifarious', 'numberless', 'multiform', 'umpteen', 'some-more']
senticnet['scaffolder'] = ['-0.64', '0', '0.879', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'drone', 'poke', 'dawdler', 'laggard', 'straggler']
senticnet['scaglietti'] = ['0.755', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.728', 'high_class', 'expensive_car', 'ferrari', 'fancy_car', 'sports_car']
senticnet['scalability'] = ['-0.9', '0', '0.841', '-0.73', '#sadness', '#anger', 'negative', '-0.82', 'nubbiness', 'tweediness', 'harshness', 'graininess', 'raggedness']
senticnet['scalable'] = ['0.729', '0.733', '0', '0', '#joy', '#interest', 'positive', '0.731', 'next-generation', 'high-end', 'high-performance', 'state-of-the-art', 'innovative']
senticnet['scalar'] = ['0.967', '0.991', '0', '0', '#joy', '#interest', 'positive', '0.979', 'integer', 'vector', 'quantity', 'tensor', 'constant']
senticnet['scalar_curvature'] = ['0', '0.203', '0.036', '0.204', '#interest', '#admiration', 'positive', '0.124', 'amount', 'count', 'total', 'quantity', 'total_amount']
senticnet['scalar_quantity'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'newton', 'sthene', 'force_unit']
senticnet['scalawag'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'rascal', 'rapscallion', 'holy_terror', 'scallywag', 'monkey']
senticnet['scald'] = ['-0.04', '-0.00', '-0.06', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'fire_product', 'high_temperature', 'hot', 'heat', 'hotness']
senticnet['scale'] = ['-0.99', '0', '0.786', '-0.62', '#sadness', '#anger', 'negative', '-0.80', 'measure', 'standard', 'wage_scale', 'wage_schedule', 'wind_scale']
senticnet['scale_atomic_clock'] = ['-0.83', '0', '0.521', '-0.71', '#sadness', '#disgust', 'negative', '-0.69', 'stool', 'dejection', 'droppings', 'cow_droppings', 'cow_dung']
senticnet['scale_diecast'] = ['0.087', '0.113', '-0.08', '0', '#joy', '#interest', 'positive', '0.038', 'find_match', 'strike_against', 'cigarette_lighter', 'match', 'use_lighter']
senticnet['scale_invariance'] = ['-0.75', '0', '0.679', '-0.77', '#sadness', '#disgust', 'negative', '-0.73', 'unvariedness', 'changelessness', 'unchangeability', 'unchangeableness', 'unchangingness']
senticnet['scale_invariant'] = ['-0.75', '0', '0.679', '-0.77', '#sadness', '#disgust', 'negative', '-0.73', 'unvariedness', 'changelessness', 'unchangeability', 'unchangeableness', 'unchangingness']
senticnet['scale_mount_everest'] = ['0.518', '-0.51', '0', '0.65', '#surprise', '#admiration', 'positive', '0.562', 'clematis', 'legume', 'leguminous_plant', 'climbing_fern', 'root_climber']
senticnet['scale_of_measurement'] = ['-0.55', '0', '0.796', '-0.54', '#sadness', '#anger', 'negative', '-0.63', 'measure', 'standard', 'wage_scale', 'wage_schedule', 'wind_scale']
senticnet['scale_wall'] = ['0', '0.111', '-0.05', '0.098', '#interest', '#admiration', 'positive', '0.053', 'go_up', 'move_upward', 'ascend', 'go_high', 'step_up']
senticnet['scaled'] = ['-0.89', '0', '0.945', '-0.90', '#sadness', '#anger', 'negative', '-0.91', 'armored', 'mailed', 'lightly_armored', 'lightly_armoured', 'bulletproof']
senticnet['scaled-down'] = ['-0.93', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.89', 'half-sized', 'full-sized', 'standard-sized', 'half-size', 'full-size']
senticnet['scaled_mount_everest'] = ['0', '0.076', '0', '0.063', '#interest', '#admiration', 'positive', '0.07', 'up_hill', 'elevation', 'up_mountain', 'ascend', 'upwards']
senticnet['scaled_score'] = ['0.926', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.916', 'score_goal', 'accomplish', 'finalize', 'pot', 'make_point']
senticnet['scaleless'] = ['0', '0', '-0.77', '-0.97', '#fear', '#disgust', 'negative', '-0.87', 'white-fleshed', 'trophy-sized', 'small-mouth', 'algae-eating', 'reddish-black']
senticnet['scalextric'] = ['0.087', '0.113', '-0.08', '0', '#joy', '#interest', 'positive', '0.038', 'find_match', 'strike_against', 'cigarette_lighter', 'match', 'use_lighter']
senticnet['scallion'] = ['0.718', '-0.71', '0', '0', '#joy', '#surprise', 'positive', '0.718', 'parsley', 'cilantro', 'chive', 'tarragon', 'shallot']
senticnet['scallywag'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'rascal', 'rapscallion', 'scalawag', 'holy_terror', 'monkey']
senticnet['scalp'] = ['0', '-0.04', '0.085', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'claw', 'fur_keep_warm', 'tic', 'bristle', 'muzzle']
senticnet['scalp_hair'] = ['0', '0', '0.883', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'get_old', 'dry_skin', 'become_wet', 'clothes_shrink', 'wrinkle']
senticnet['scalp_lock'] = ['-0.18', '-0.12', '0.049', '-0.24', '#sadness', '#disgust', 'negative', '-0.09', 'tree_trunk', 'plank', 'artificial_leg', 'hardwood', 'tree_wood']
senticnet['scalp_wound'] = ['-0.12', '-0.04', '0', '-0.17', '#sadness', '#disgust', 'negative', '-0.08', 'cause_bleed', 'open_wind', 'gash', 'lacerate', 'painful_injury']
senticnet['scalpel'] = ['-0.05', '-0.03', '0', '-0.22', '#sadness', '#disgust', 'negative', '-0.07', 'stethoscope', 'tourniquet', 'vaccination', 'razor', 'machete']
senticnet['scalphunter'] = ['0.02', '0.131', '0.749', '-0.67', '#anger', '#disgust', 'negative', '-0.31', 'belligerence', 'belligerency', 'belligerent', 'war', 'rancor']
senticnet['scaly'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'amphibian', 'frog', 'aquatic']
senticnet['scaly_appearance'] = ['0.041', '0', '0', '0.058', '#joy', '#admiration', 'positive', '0.05', 'bird_animal', 'wing', 'farm_bird', 'barnyard_animal', 'hen_baby']
senticnet['scaly_skin'] = ['0.048', '-0.00', '0.011', '0.064', '#joy', '#admiration', 'positive', '0.027', 'become_wet', 'wash_away_dirt', 'run_out_soap', 'cleanliness', 'clean_skin']
senticnet['scam'] = ['-0.75', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.74', 'rip-off', 'ripoff', 'ripoffs', 'rip-off', 'fraud']
senticnet['scammer'] = ['-0.09', '0.156', '0.661', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'phony', 'deception', 'false', 'against_law', 'trick']
senticnet['scamp'] = ['0.625', '-0.90', '0', '0.756', '#surprise', '#admiration', 'positive', '0.762', 'child', 'small_fry', 'tyke', 'tike', 'kid']
senticnet['scamper'] = ['0', '0', '-0.81', '-0.85', '#fear', '#disgust', 'negative', '-0.83', 'skitter', 'hatchway', 'scuttle', 'opening', 'scurry']
senticnet['scampton'] = ['-0.97', '0', '0.737', '-0.97', '#sadness', '#disgust', 'negative', '-0.89', 'flare_path', 'airstrip', 'take_off', 'runway', 'grassland']
senticnet['scan'] = ['-0.08', '0', '0.011', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'deflate', 'read_cover', 'obtain_information', 'upgrade', 'spread_out']
senticnet['scanair'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['scandal'] = ['-0.06', '0.128', '0.707', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'embarrassment', 'peer_pressure', 'sudden', 'bad_feeling', 'public_embarrassment']
senticnet['scandalization'] = ['-0.92', '0', '0.699', '-0.93', '#sadness', '#disgust', 'negative', '-0.85', 'discourtesy', 'offense', 'outrage', 'affront', 'indignity']
senticnet['scandalize'] = ['-0.98', '0', '0.733', '-0.96', '#sadness', '#disgust', 'negative', '-0.89', 'churn_up', 'sicken', 'disgust', 'nauseate', 'revolt']
senticnet['scandalizing_contempt'] = ['-0.72', '0', '0.742', '-0.80', '#anger', '#disgust', 'negative', '-0.75', 'illicitness', 'illegality', 'illegitimacy', 'shadiness', 'quality']
senticnet['scandalmongering'] = ['0', '0', '0', '-0.78', '#disgust', '#disgust', 'negative', '-0.78', 'sensationalistic', 'sensational', 'sensationally', 'inflame', 'fire_up']
senticnet['scandalous'] = ['0', '0', '0.782', '-0.74', '#anger', '#disgust', 'negative', '-0.76', 'disgraceful', 'shameful', 'dishonorably', 'inglorious', 'dishonored']
senticnet['scandalous_behavior'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'nonsense', 'meaninglessness', 'bunk', 'absurdity', 'hokum']
senticnet['scandalously'] = ['-0.71', '0', '0.995', '0', '#sadness', '#anger', 'negative', '-0.85', 'horrifying', 'atrocious', 'dreadfully', 'horrifyingly', 'horrified']
senticnet['scandinavian_airlines_system'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['scandinavian_folklore'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'jawless_fish', 'osteostracan', 'cephalaspid', 'ostracoderm', 'heterostracan']
senticnet['scanned_image'] = ['-0.08', '0', '0.011', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'deflate', 'read_cover', 'obtain_information', 'upgrade', 'spread_out']
senticnet['scanner'] = ['0', '0', '0.874', '-0.98', '#anger', '#disgust', 'negative', '-0.92', 'periphery', 'add-on', 'peripheral', 'peripherally', 'polling']
senticnet['scanning_device'] = ['-0.08', '0', '0.011', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'deflate', 'read_cover', 'obtain_information', 'upgrade', 'spread_out']
senticnet['scanning_electron_microscope'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'amoeba', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['scanning_microfilm'] = ['-0.08', '0', '0.011', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'deflate', 'read_cover', 'obtain_information', 'upgrade', 'spread_out']
senticnet['scanning_squid_microscope'] = ['-0.93', '0', '0.739', '-0.96', '#sadness', '#disgust', 'negative', '-0.88', 'prevarication', 'fairytale', 'fairy_tale', 'fairy_story', 'play']
senticnet['scanning_tunnelling_microscopy'] = ['-0.54', '0', '0.909', '-0.97', '#anger', '#disgust', 'negative', '-0.81', 'suppleness', 'pliancy', 'pliantness', 'flexibility', 'flexibleness']
senticnet['scansion'] = ['-0.61', '0', '0.73', '-0.85', '#anger', '#disgust', 'negative', '-0.73', 'verbalization', 'phrasing', 'verbiage', 'wording', 'phraseology']
senticnet['scansorial'] = ['0', '0', '-0.81', '-0.92', '#fear', '#disgust', 'negative', '-0.87', 'tree-living', 'dendroidal', 'arborous', 'arboriform', 'arborary']
senticnet['scanssh'] = ['-0.08', '0', '0.011', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'deflate', 'read_cover', 'obtain_information', 'upgrade', 'spread_out']
senticnet['scant'] = ['0.744', '-0.70', '0', '0', '#joy', '#surprise', 'positive', '0.725', 'scrimp', 'economical', 'stinting', 'frugal', 'skimp']
senticnet['scantiest'] = ['-0.54', '0', '0.739', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'unprofitable', 'idle', 'lean', 'dead', 'marginal']
senticnet['scantily'] = ['0', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.914', 'immodestly', 'scantly', 'skimpily', 'nattily', 'shabbily']
senticnet['scantily_clad'] = ['0', '-0.08', '0', '0', '#surprise', '#surprise', 'positive', '0.082', 'yo-yo', 'playroom', 'backgammon', 'action_figure', 'board_game']
senticnet['scantily_clad_woman'] = ['-0.94', '0', '0.991', '-0.65', '#sadness', '#anger', 'negative', '-0.86', 'birthday_suit', 'raw', 'altogether', 'nakedness', 'undress']
senticnet['scantily_clad_young'] = ['0', '-0.08', '0', '0', '#surprise', '#surprise', 'positive', '0.082', 'yo-yo', 'playroom', 'backgammon', 'action_figure', 'board_game']
senticnet['scantiness'] = ['-0.82', '0', '0.855', '-0.56', '#sadness', '#anger', 'negative', '-0.74', 'insufficiency', 'sparseness', 'sparsity', 'abstemiousness', 'lack_of_quantity']
senticnet['scantly'] = ['0', '-0.93', '0', '0.889', '#surprise', '#admiration', 'positive', '0.913', 'immodestly', 'skimpily', 'scantily', 'nattily', 'shabbily']
senticnet['scantness'] = ['-0.7', '0', '0.746', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'insufficiency', 'sparseness', 'sparsity', 'abstemiousness', 'lack_of_quantity']
senticnet['scanty'] = ['-0.94', '0', '0.952', '-0.83', '#sadness', '#anger', 'negative', '-0.91', 'miserable', 'meager', 'exiguous', 'spare', 'hardscrabble']
senticnet['scape'] = ['0', '0', '0.737', '-0.88', '#anger', '#disgust', 'negative', '-0.81', 'lewdness', 'libidinous', 'salacity', 'unchastity', 'lewd']
senticnet['scapegoat'] = ['0', '0', '0.935', '-0.90', '#anger', '#disgust', 'negative', '-0.92', 'error', 'buck', 'punished', 'goat', 'sin']
senticnet['scapegrace'] = ['-0.51', '0', '0.571', '-0.59', '#anger', '#disgust', 'negative', '-0.56', 'miscreant', 'black_sheep', 'degenerate', 'wretch', 'pervert']
senticnet['scaphoid'] = ['-0.74', '0', '0.598', '-0.75', '#sadness', '#disgust', 'negative', '-0.69', 'lunate_bone', 'semilunar_bone', 'carpal_bone', 'carpal', 'bone']
senticnet['scapholunate_ligament'] = ['-0.53', '0', '0.982', '-0.67', '#anger', '#disgust', 'negative', '-0.73', 'gristle', 'semilunar_cartilage', 'fibrocartilage', 'hyaline_cartilage', 'arytenoid']
senticnet['scapula'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'above_leg', 'joint', 'hip', 'knee']
senticnet['scar'] = ['-0.12', '-0.00', '-0.07', '-0.14', '#sadness', '#disgust', 'negative', '-0.08', 'laceration', 'gash', 'cut_skin', 'abrasion', 'painful_injury']
senticnet['scar_tissue'] = ['0', '0', '0.025', '-0.09', '#anger', '#disgust', 'negative', '-0.06', 'body_fluid', 'artery', 'skeletal_system', 'bodily_fluid', 'near_mouth']
senticnet['scarab'] = ['0', '0', '-0.87', '0', '#fear', '#fear', 'negative', '-0.87', 'beetle', 'many_insects', 'grasshopper', 'firefly', 'mouse']
senticnet['scarab_beetle'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'sedum', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['scaraboid'] = ['-0.94', '0', '0.569', '-0.72', '#sadness', '#disgust', 'negative', '-0.74', 'bitumastic', 'veneer', 'veneering', 'adhesive', 'coating']
senticnet['scarborough_castle'] = ['0.582', '-0.97', '0', '0.802', '#surprise', '#admiration', 'positive', '0.788', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['scarce'] = ['0', '0', '0.905', '-0.70', '#anger', '#disgust', 'negative', '-0.80', 'least', 'little', 'small_amount', 'shortage', 'a_little']
senticnet['scarcely'] = ['-0.94', '0', '0.991', '-0.65', '#sadness', '#anger', 'negative', '-0.86', 'birthday_suit', 'raw', 'nakedness', 'nude', 'undress']
senticnet['scarcely_distinguishable'] = ['-0.66', '0', '0.782', '-0.52', '#sadness', '#anger', 'negative', '-0.65', 'lividness', 'pallidness', 'pallor', 'complexion', 'achromasia']
senticnet['scarcely_ever'] = ['-0.97', '0', '0', '-0.86', '#sadness', '#disgust', 'negative', '-0.91', 'worst', 'optional', 'rare', 'lass', 'temporary']
senticnet['scarcely_perceptible'] = ['0.695', '-0.83', '0', '0.959', '#surprise', '#admiration', 'positive', '0.829', 'opaqueness', 'faintness', 'dimness', 'opacity', 'vagueness']
senticnet['scarceness'] = ['-0.88', '0', '0.732', '-0.61', '#sadness', '#anger', 'negative', '-0.74', 'paucity', 'insufficiency', 'rareness', 'infrequency', 'dearth']
senticnet['scarcity'] = ['-0.52', '0', '0.732', '-0.77', '#anger', '#disgust', 'negative', '-0.67', 'paucity', 'insufficiency', 'rareness', 'infrequency', 'dearth']
senticnet['scare'] = ['0', '0', '-0.94', '0', '#fear', '#fear', 'negative', '-0.94', 'panic', 'frighten', 'humiliation', 'terror', 'fear']
senticnet['scare_away'] = ['-0.75', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.77', 'scarily', 'scared', 'frighten_away', 'frighten_off', 'scare_off']
senticnet['scare_dog'] = ['0', '0', '-0.90', '0', '#fear', '#fear', 'negative', '-0.90', 'feel_hurt', 'get_arrested', 'feel_anger', 'grimace', 'slander']
senticnet['scare_off'] = ['-0.70', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.78', 'scarily', 'scared', 'frighten_away', 'scare_away', 'frighten_off']
senticnet['scare_person'] = ['0', '0', '-0.95', '0', '#fear', '#fear', 'negative', '-0.95', 'circle_prey', 'get_rid_of_enemy', 'psychopath', 'hurt_others', 'end_live']
senticnet['scarecrow'] = ['0.931', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.855', 'crystal_clear', 'blue_sky', 'imaginary', 'water_sky', 'blue_color']
senticnet['scared'] = ['-0.91', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.90', 'scarily', 'frighten_away', 'scare_away', 'frighten_off', 'scare_off']
senticnet['scared_of_insect'] = ['-0.02', '0', '0.09', '0', '#sadness', '#anger', 'negative', '-0.05', 'nuisance_insect', 'small_insect', 'fly_insect', 'bothersome_insect', 'annoy_bug']
senticnet['scaredy_cat'] = ['-0.91', '0', '0.681', '-0.81', '#sadness', '#disgust', 'negative', '-0.80', 'chicken', 'doormat', 'softie', 'crybaby', 'wimp']
senticnet['scares_away'] = ['0', '0', '-0.85', '-0.95', '#fear', '#disgust', 'negative', '-0.90', 'scarily', 'scared', 'scare_away', 'frighten_off', 'scare_off']
senticnet['scarf'] = ['0.702', '0', '0', '0', '#joy', '#joy', 'positive', '0.702', 'jacket', 'shirt', 'slack', 'clothes', 'sweater']
senticnet['scarface'] = ['-0.52', '0', '0.981', '-0.61', '#anger', '#disgust', 'negative', '-0.70', 'criminal', 'bully', 'outlaw', 'malefactor', 'felon']
senticnet['scarfing_down'] = ['-0.71', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.77', 'spill_milk', 'read_paper', 'drop_food', 'smell_food', 'choke']
senticnet['scarily'] = ['-0.80', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.8', 'scared', 'frighten_away', 'scare_away', 'frighten_off', 'scare_off']
senticnet['scaring_away'] = ['0', '0', '-0.85', '-0.95', '#fear', '#disgust', 'negative', '-0.90', 'scarily', 'scared', 'scare_away', 'frighten_off', 'scare_off']
senticnet['scaring_off'] = ['0', '0', '-0.85', '-0.95', '#fear', '#disgust', 'negative', '-0.90', 'scarily', 'scared', 'scare_away', 'frighten_off', 'scare_off']
senticnet['scarlatina'] = ['0.998', '-0.54', '0', '0.957', '#joy', '#admiration', 'positive', '0.834', 'contagious_disease', 'contagion', 'swine_influenza', 'swine_flu', 'respiratory_disease']
senticnet['scarlet'] = ['0.035', '0', '0', '0.116', '#joy', '#admiration', 'positive', '0.076', 'maroon', 'color', 'bright_color', 'hair_colored', 'cool_color']
senticnet['scarlet_bush'] = ['0.938', '-0.77', '0', '0.944', '#joy', '#admiration', 'positive', '0.885', 'bush', 'hamelia', 'scarlet_hamelia', 'red', 'shrub']
senticnet['scarlet_fever'] = ['0.579', '-0.81', '0', '0.833', '#surprise', '#admiration', 'positive', '0.741', 'murine_typhus', 'rat_typhus', 'urban_typhus', 'endemic_typhus', 'rickettsial_disease']
senticnet['scarlet_hamelia'] = ['0.938', '-0.77', '0', '0.944', '#joy', '#admiration', 'positive', '0.885', 'bush', 'scarlet_bush', 'hamelia', 'coloradillo', 'shrub']
senticnet['scarlet_oak'] = ['0.776', '-0.6', '0', '0.568', '#joy', '#surprise', 'positive', '0.648', 'swamp_chestnut_oak', 'chinquapin_oak', 'chinkapin_oak', 'yellow_chestnut_oak', 'basket_oak']
senticnet['scarlet_oak_quercus_coccinea'] = ['0.776', '-0.6', '0', '0.568', '#joy', '#surprise', 'positive', '0.648', 'swamp_chestnut_oak', 'chinquapin_oak', 'chinkapin_oak', 'yellow_chestnut_oak', 'basket_oak']
senticnet['scarlet_red'] = ['0.035', '0', '0', '0.116', '#joy', '#admiration', 'positive', '0.076', 'maroon', 'color', 'bright_color', 'hair_colored', 'cool_color']
senticnet['scarlet_tunic'] = ['-0.58', '0', '0.632', '-0.63', '#anger', '#disgust', 'negative', '-0.61', 'skimmer', 'trilby', 'chapeau', 'busby', 'shako']
senticnet['scarlet_wool'] = ['-0.66', '0', '0.859', '-0.82', '#anger', '#disgust', 'negative', '-0.78', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['scarred'] = ['-0.85', '0', '0.8', '-0.85', '#sadness', '#disgust', 'negative', '-0.83', 'flyblown', 'blemished', 'marred', 'acned', 'pimpled']
senticnet['scarron'] = ['-0.81', '0', '0.885', '-0.92', '#anger', '#disgust', 'negative', '-0.87', 'miss', 'girl', 'damsel', 'maiden', 'damoiselle']
senticnet['scary'] = ['0', '0', '-0.70', '0', '#fear', '#fear', 'negative', '-0.70', 'ugly', 'frighten', 'terrible', 'horrible', 'fright']
senticnet['scary_movie'] = ['0', '0', '-0.80', '0', '#fear', '#fear', 'negative', '-0.80', 'horror_movie', 'political_rally', 'ball_game', 'pool_hall', 'tourist_site']
senticnet['scatalogical'] = ['-0.9', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.80', 'filth', 'smut', 'vulgarism', 'dirt', 'obscenity']
senticnet['scathing'] = ['-0.77', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.79', 'vitriolic', 'scathingly', 'piquant', 'acerbic', 'painful']
senticnet['scathing_letter'] = ['0', '0', '0', '0.091', '#admiration', '#admiration', 'positive', '0.091', 'correspondence', 'handwritten', 'written_message', 'written_communication', 'write_note']
senticnet['scathing_review'] = ['0.811', '-0.63', '0', '0.959', '#joy', '#admiration', 'positive', '0.8', 'drama_critic', 'theater_critic', 'professional', 'art_critic', 'literary_critic']
senticnet['scathingly'] = ['-0.77', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.79', 'vitriolic', 'scathing', 'piquant', 'acerbic', 'painful']
senticnet['scatological'] = ['0', '0', '0.765', '-0.84', '#anger', '#disgust', 'negative', '-0.80', 'costless', 'complimentary', 'needless', 'gratis', 'free']
senticnet['scatological_humor'] = ['-0.9', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.80', 'filth', 'smut', 'vulgarism', 'dirt', 'obscenity']
senticnet['scatology'] = ['-0.9', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.80', 'filth', 'smut', 'vulgarism', 'dirt', 'obscenity']
senticnet['scatter'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'cleanness', 'dust_furniture', 'clean_floor', 'vacuum_floor', 'clean']
senticnet['scatter_diagram'] = ['0.854', '0.76', '0', '0', '#joy', '#interest', 'positive', '0.807', 'geometric_figure', 'polygon', 'form', 'parallelogram', 'hexagon']
senticnet['scatter_gather'] = ['0.865', '-0.80', '0', '0.52', '#joy', '#surprise', 'positive', '0.73', 'set', 'pose', 'lay', 'intersperse', 'put']
senticnet['scatterbrained'] = ['-0.9', '0', '0.754', '0', '#sadness', '#anger', 'negative', '-0.82', 'dim-witted', 'accident-prone', 'absent-minded', 'dimwitted', 'klutzy']
senticnet['scattered'] = ['-0.84', '0', '0.515', '-0.99', '#sadness', '#disgust', 'negative', '-0.78', 'unconnected', 'confused', 'incoherent', 'disordered', 'garbled']
senticnet['scattered_around'] = ['-0.1', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.1', 'deflate', 'read_cover', 'look_behind', 'upgrade', 'scan']
senticnet['scattergood'] = ['0.863', '-0.94', '0', '0.828', '#joy', '#surprise', 'positive', '0.878', 'spendthrift', 'waster', 'wastrel', 'spender', 'prodigal']
senticnet['scattering'] = ['0.842', '-0.55', '0', '0.537', '#joy', '#surprise', 'positive', '0.644', 'diffusion', 'dispersion', 'spread', 'invasion', 'irradiation']
senticnet['scatty'] = ['-0.71', '0', '0.906', '-0.93', '#anger', '#disgust', 'negative', '-0.85', 'absent', 'forgetful', 'distracted', 'distrait', 'inattentive']
senticnet['scavenge'] = ['-0.66', '0', '0.663', '-0.72', '#sadness', '#disgust', 'negative', '-0.68', 'make_clean', 'vacuum', 'preen', 'modify', 'dust']
senticnet['scavenger_cell'] = ['0.565', '-0.99', '0', '0.81', '#surprise', '#admiration', 'positive', '0.788', 'somatic_cell', 'vegetative_cell', 'fixed_phagocyte', 'macrophage', 'free_phagocyte']
senticnet['scavenger_hunt'] = ['0', '0.177', '0', '0.126', '#interest', '#admiration', 'positive', '0.151', 'treasure', 'storage_facility', 'shrine', 'lock_box', 'jewelry_box']
senticnet['scene'] = ['-0.69', '0', '0.508', '-0.93', '#sadness', '#disgust', 'negative', '-0.71', 'dark', 'darkness', 'light', 'shadow', 'site']
senticnet['scene_depict'] = ['-0.69', '0', '0.508', '-0.93', '#sadness', '#disgust', 'negative', '-0.71', 'dark', 'darkness', 'light', 'shadow', 'site']
senticnet['scene_reminiscent'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'fragmentation', 'discharge', 'backfire', 'blast']
senticnet['scene_stealer'] = ['0.85', '-0.71', '0', '0.617', '#joy', '#surprise', 'positive', '0.728', 'pretender', 'phony', 'phoney', 'hypocrite', 'dissembler']
senticnet['scenery'] = ['0.05', '-0.01', '0', '0.058', '#joy', '#admiration', 'positive', '0.041', 'vista', 'panorama', 'scenic', 'view', 'spectacular']
senticnet['scenethe'] = ['0', '0', '0', '0.963', '#admiration', '#admiration', 'positive', '0.963', 'wear', 'furnishing', 'wearable_technology', 'wearable', 'belt']
senticnet['scenic'] = ['0.896', '0', '0', '0.709', '#joy', '#admiration', 'positive', '0.802', 'perspective', 'vista', 'view', 'aspect', 'see_view']
senticnet['scenic_attraction'] = ['0.896', '0', '0', '0.709', '#joy', '#admiration', 'positive', '0.802', 'perspective', 'vista', 'view', 'aspect', 'see_view']
senticnet['scenic_backdrop'] = ['0.896', '0', '0', '0.709', '#joy', '#admiration', 'positive', '0.802', 'perspective', 'vista', 'view', 'aspect', 'see_view']
senticnet['scenic_beauty'] = ['0.896', '0', '0', '0.709', '#joy', '#admiration', 'positive', '0.802', 'perspective', 'vista', 'view', 'aspect', 'see_view']
senticnet['scenic_byway'] = ['0.896', '0', '0', '0.709', '#joy', '#admiration', 'positive', '0.802', 'perspective', 'vista', 'view', 'aspect', 'see_view']
senticnet['scenic_drive'] = ['0.896', '0', '0', '0.709', '#joy', '#admiration', 'positive', '0.802', 'perspective', 'vista', 'view', 'aspect', 'see_view']
senticnet['scenic_landscape'] = ['0.896', '0', '0', '0.709', '#joy', '#admiration', 'positive', '0.802', 'perspective', 'vista', 'view', 'aspect', 'see_view']
senticnet['scenic_overlook'] = ['0.896', '0', '0', '0.709', '#joy', '#admiration', 'positive', '0.802', 'perspective', 'vista', 'view', 'aspect', 'see_view']
senticnet['scenic_railway'] = ['0.089', '-0.18', '0.037', '-0.06', '#joy', '#surprise', 'positive', '0.043', 'balloon', 'ballon', 'fielder', 'kite', 'blimp']
senticnet['scenic_reserve'] = ['-0.95', '0', '0.624', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['scenic_ride'] = ['-0.08', '0.179', '-0.06', '0.096', '#interest', '#admiration', 'positive', '0.034', 'ride_subway', 'buy_vending_machine', 'travel', 'buy_lottery_ticket', 'acquire']
senticnet['scenic_swat_valley'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'webpage', 'store_record', 'ideal']
senticnet['scenic_trail'] = ['0.896', '0', '0', '0.709', '#joy', '#admiration', 'positive', '0.802', 'perspective', 'vista', 'view', 'aspect', 'see_view']
senticnet['scenic_view'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'driftwood', 'sea_shell', 'naked_woman', 'shore', 'tidal_wave']
senticnet['scenically'] = ['0', '-0.80', '0', '0.786', '#surprise', '#admiration', 'positive', '0.795', 'geologically', 'morphologically', 'topographically', 'climatically', 'spatially']
senticnet['scenography'] = ['-0.02', '0', '-0.04', '-0.03', '#fear', '#disgust', 'negative', '-0.03', 'studio', 'art_piece', 'art_gallery', 'artist', 'museum']
senticnet['scent'] = ['0.843', '0', '0', '0.971', '#joy', '#admiration', 'positive', '0.907', 'aroma', 'fragrance', 'smell', 'smell_good', 'odor']
senticnet['scent_trail'] = ['0.843', '0', '0', '0.971', '#joy', '#admiration', 'positive', '0.907', 'aroma', 'fragrance', 'smell', 'smell_good', 'odor']
senticnet['scepter'] = ['0.105', '-0.17', '0.057', '0.079', '#joy', '#surprise', 'positive', '0.076', 'cue', 'twig', 'long_stick', 'stick', 'adhere']
senticnet['sceptic'] = ['-0.52', '0', '0.95', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'skeptic', 'pessimist', 'doubter', 'defeatist', 'negativist']
senticnet['sceptical'] = ['0', '0', '-0.99', '-0.99', '#fear', '#disgust', 'negative', '-0.99', 'unbelieving', 'skeptical', 'doubting', 'disbelieving', 'questioning']
senticnet['scepticism'] = ['0', '0', '0.928', '-0.70', '#anger', '#disgust', 'negative', '-0.81', 'doubt', 'incertitude', 'dubiety', 'doubtfulness', 'dubiousness']
senticnet['sceptre'] = ['0.105', '-0.17', '0.057', '0.079', '#joy', '#surprise', 'positive', '0.076', 'cue', 'twig', 'long_stick', 'stick', 'adhere']
senticnet['schadenfreude'] = ['-0.88', '0', '0', '-0.86', '#sadness', '#disgust', 'negative', '-0.87', 'gloat', 'gloating', 'cheer', 'dwell', 'gloatingly']
senticnet['schalfkogel'] = ['0', '-0.03', '-0.10', '-0.07', '#fear', '#disgust', 'negative', '-0.04', 'autumn', 'winter', 'skier', 'snow_fall', 'volcano']
senticnet['schedd'] = ['-0.15', '0', '0.029', '-0.11', '#sadness', '#disgust', 'negative', '-0.1', 'start_over', 'front_line', 'find_line', 'take_turn', 'long_line']
senticnet['schedulability'] = ['-0.96', '0', '0.603', '-0.91', '#sadness', '#disgust', 'negative', '-0.82', 'potential', 'potency', 'conceivableness', 'conceivability', 'possibleness']
senticnet['schedule'] = ['0.879', '0.922', '0', '0', '#joy', '#interest', 'positive', '0.901', 'scheme', 'plan', 'eureka', 'know_start', 'timetable']
senticnet['schedule_appointment'] = ['-0.06', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.07', 'make_appointment', 'go_hair_salon', 'put_nice_clothes', 'go_barber', 'shave_face']
senticnet['schedule_appointment_doctor'] = ['-0.07', '0', '-0.10', '-0.11', '#fear', '#disgust', 'negative', '-0.09', 'make_doctor_appointment', 'wear_clean_underwear', 'turn_head_cough', 'make_appointment_with_doctor', 'find_doctor']
senticnet['schedule_feeding'] = ['-0.80', '0', '0.724', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'nursing', 'infant_feeding', 'demand_feeding', 'breast_feeding', 'feeding']
senticnet['schedule_infant_feeding'] = ['-0.80', '0', '0.719', '-0.70', '#sadness', '#anger', 'negative', '-0.74', 'nursing', 'feeding', 'demand_infant_feeding', 'breast_infant_feeding', 'infant_feeding']
senticnet['schedule_time'] = ['0', '0.89', '0', '0', '#interest', '#interest', 'positive', '0.89', 'make_arrangement', 'go_airport', 'plan_ahead', 'decide_to_go_first', 'plan']
senticnet['scheduled_passenger_train'] = ['0.861', '-0.52', '0', '0.598', '#joy', '#admiration', 'positive', '0.663', 'exit', 'get_off', 'deplane', 'go_out', 'get_out']
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senticnet['sci_fi_horror'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amuck', 'murderousness', 'misocainea', 'misogyny']
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senticnet['science_aa_degree'] = ['0.102', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.076', 'adjust_volume', 'tap_toe', 'enter_contest', 'tap_foot', 'dance_well']
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senticnet['science_education'] = ['0.895', '0', '0', '0.872', '#joy', '#admiration', 'positive', '0.883', 'science', 'education', 'project', 'mathematics', 'different_application']
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senticnet['science_icsu'] = ['0.043', '0.109', '0', '0.061', '#interest', '#admiration', 'positive', '0.071', 'nation_state', 'democratic_country', 'free_country', 'large_country', 'republic']
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senticnet['scientific_curiosity'] = ['0.601', '0.647', '-0.26', '0.322', '#joy', '#interest', 'positive', '0.326', 'captivated', 'charmed', 'captivate', 'bewitch', 'enthrallment']
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senticnet['scientific_effort'] = ['0', '0.749', '0', '0.792', '#interest', '#admiration', 'positive', '0.77', 'award', 'fellowship', 'mathematics', 'mean', 'nation']
senticnet['scientific_evidence'] = ['0.84', '-0.97', '0', '0.713', '#joy', '#surprise', 'positive', '0.844', 'proof', 'bed_check', 'crosscheck', 'parity_check', 'redundancy_check']
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senticnet['scientific_instrument'] = ['0.562', '-0.77', '0', '0.962', '#surprise', '#admiration', 'positive', '0.767', 'console', 'instrument', 'accelerator', 'particle_accelerator', 'atom_smasher']
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senticnet['scientific_knowledge'] = ['0.743', '0.9', '0', '0', '#joy', '#interest', 'positive', '0.822', 'discipline', 'knowledge_base', 'region', 'dataset', 'content']
senticnet['scientific_materialism'] = ['-0.98', '0', '0.891', '-0.60', '#sadness', '#anger', 'negative', '-0.82', 'exorcism', 'dispossession', 'magic', 'thaumaturgy', 'belief']
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senticnet['scientific_realism'] = ['0.943', '-0.75', '0', '0.515', '#joy', '#surprise', 'positive', '0.736', 'reasonableness', 'tenability', 'tenableness', 'credibility', 'credibleness']
senticnet['scientific_research'] = ['0.765', '0.767', '0', '0', '#joy', '#interest', 'positive', '0.766', 'research_project', 'research', 'experiment', 'biological_research', 'big_science']
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senticnet['scientific_substantiation'] = ['0.84', '-0.97', '0', '0.713', '#joy', '#surprise', 'positive', '0.844', 'proof', 'bed_check', 'crosscheck', 'parity_check', 'redundancy_check']
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senticnet['scimitar'] = ['0.93', '-0.78', '0', '0.873', '#joy', '#admiration', 'positive', '0.861', 'blade', 'brand', 'steel', 'claymore', 'sword']
senticnet['scimitar_horned_oryx'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'argali', 'argal', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['scincella'] = ['-0.03', '-0.01', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'shit', 'wildflower', 'poop', 'wild_flower', 'dog_poop']
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senticnet['scintillating'] = ['0.642', '-0.62', '0', '0.709', '#joy', '#admiration', 'positive', '0.657', 'bouncing', 'lively', 'spirited', 'peppy', 'zippy']
senticnet['scintillator'] = ['0.048', '0', '0', '0.092', '#joy', '#admiration', 'positive', '0.07', 'radio_wave', 'sun', 'solar_cell', 'x-ray', 'cosmic_ray']
senticnet['sciolist'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'amateur', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
senticnet['scion'] = ['0', '0', '-0.99', '-0.73', '#fear', '#disgust', 'negative', '-0.86', 'king', 'baron', 'business_leader', 'mogul', 'big_businessman']
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senticnet['sciuridae'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'carbon_monoxide', 'tree_animal']
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senticnet['sclerosis'] = ['-0.78', '0', '0.764', '-0.74', '#sadness', '#anger', 'negative', '-0.76', 'osteosclerosis', 'disease', 'disseminated_sclerosis', 'multiple_sclerosis', 'pathology']
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senticnet['sclerotium_rolfsii'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'apple_rust', 'fungus']
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senticnet['scooter'] = ['0.047', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'have_fun', 'motorcycle', 'roller_coaster', 'sports_car', 'bike']
senticnet['scope'] = ['0.016', '0.157', '0', '0.201', '#interest', '#admiration', 'positive', '0.125', 'size_up', 'measure_length', 'extent', 'distant', 'extension']
senticnet['scope_creep'] = ['-0.73', '0', '0.94', '-0.87', '#anger', '#disgust', 'negative', '-0.85', 'edema', 'oedema', 'hydrops', 'dropsy', 'puffiness']
senticnet['scope_out_information'] = ['-0.05', '0.168', '-0.04', '0.263', '#interest', '#admiration', 'positive', '0.084', 'find_information', 'enhance_knowledge', 'knowledge', 'find_out', 'information']
senticnet['scoping_study'] = ['0', '0.097', '0', '0.092', '#interest', '#admiration', 'positive', '0.095', 'urgent', 'classify_ad', 'important', 'review', 'repetition']
senticnet['scopolamine'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
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senticnet['score'] = ['0.926', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.916', 'score_goal', 'accomplish', 'finalize', 'pot', 'make_point']
senticnet['score_goal'] = ['0', '0', '0', '0.717', '#admiration', '#admiration', 'positive', '0.717', 'score', 'make_point', 'accomplish_goal', 'success', 'score_point']
senticnet['score_home_run'] = ['0.064', '0.203', '0.014', '0.202', '#interest', '#admiration', 'positive', '0.114', 'compete', 'compete_against', 'blue_ribbon', 'play_baseball', 'play_hockey']
senticnet['score_homer'] = ['-0.03', '0.085', '0', '0.078', '#interest', '#admiration', 'positive', '0.041', 'compete_against', 'conquer_opponent', 'reach_advantage', 'score_home_run', 'win_baseball_game']
senticnet['score_point'] = ['0.506', '0.659', '-0.24', '0.242', '#joy', '#interest', 'positive', '0.29', 'score_home_run', 'score_goal', 'run_first_base', 'win_game', 'strike_out']
senticnet['score_sheet'] = ['0.926', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.916', 'score_goal', 'accomplish', 'finalize', 'pot', 'make_point']
senticnet['score_touchdown'] = ['0.924', '0.968', '0', '0.876', '#joy', '#interest', 'positive', '0.923', 'win_game', 'score', 'make_crowd_wave', 'pride', 'score_goal']
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senticnet['scoreless'] = ['0', '0', '0.79', '-0.75', '#anger', '#disgust', 'negative', '-0.77', 'shutout', 'inning', 'hitless', 'strikeout', 'one-run']
senticnet['scoreless_draw'] = ['-0.09', '0.147', '-0.11', '0.205', '#interest', '#admiration', 'positive', '0.036', 'question_starter', 'resolution', 'available', 'explanation', 'request']
senticnet['scoreline'] = ['-0.09', '0.147', '-0.11', '0.205', '#interest', '#admiration', 'positive', '0.036', 'question_starter', 'resolution', 'available', 'explanation', 'request']
senticnet['scorer'] = ['0', '0.829', '0', '0.911', '#interest', '#admiration', 'positive', '0.87', 'squeeze_play', 'squeeze', 'power_play', 'first_period', 'second_period']
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senticnet['screwing_up'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'vapor', 'gaseous', 'haze', 'smoke', 'smog']
senticnet['screwloose'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'naked_eye', 'eye', 'oculus_sinister']
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senticnet['scrimp'] = ['0.741', '-0.70', '0', '0', '#joy', '#surprise', 'positive', '0.725', 'stinting', 'economical', 'scant', 'frugal', 'skimp']
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senticnet['scrimshaw'] = ['0.959', '-0.69', '0', '0.755', '#joy', '#admiration', 'positive', '0.802', 'vermiculation', 'cinquefoil', 'carving', 'glyptography', 'glyptic_art']
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senticnet['scrip'] = ['-0.05', '0.215', '-0.08', '0.293', '#interest', '#admiration', 'positive', '0.092', 'swap', 'give_take', 'exchange_goods', 'transaction', 'increase_money']
senticnet['script'] = ['-0.04', '0.069', '0', '0.075', '#interest', '#admiration', 'positive', '0.035', 'pencil_mark', 'use_ink', 'code', 'pencil_paper', 'use_pen']
senticnet['script_kiddie'] = ['0.857', '0', '0', '0.968', '#joy', '#admiration', 'positive', '0.912', 'inerrable', 'flawless', 'unfailing', 'reliable', 'sure']
senticnet['scripted_comedy'] = ['0.206', '-0.13', '0', '0', '#joy', '#surprise', 'positive', '0.172', 'dancer', 'old_dance', 'dance', 'slow_dance', 'ballroom_dance']
senticnet['scripted_television'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'theater_seat', 'theater_box', 'sticky_floor', 'stage_entrance', 'balcony']
senticnet['scriptural'] = ['0', '0.106', '-0.05', '0.163', '#interest', '#admiration', 'positive', '0.072', 'band', 'kit', 'oblivion', 'rock_band', 'high_school_band']
senticnet['scriptural_authority'] = ['-0.83', '0', '0.648', '-0.60', '#sadness', '#anger', 'negative', '-0.69', 'infallibility', 'dependability', 'dependableness', 'reliability', 'reliableness']
senticnet['scriptural_interpretation'] = ['-0.99', '0', '0.835', '-0.58', '#sadness', '#anger', 'negative', '-0.80', 'beginning', 'origin', 'root', 'wellspring', 'source']
senticnet['scripture'] = ['0', '0.083', '0', '0.116', '#interest', '#admiration', 'positive', '0.1', 'prose', 'poetry', 'text', 'chapter', 'written_text']
senticnet['scrofula'] = ['-0.90', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.92', 'illness', 'polio', 'malady', 'disease', 'ailment']
senticnet['scroll'] = ['0.022', '0', '0', '0.077', '#joy', '#admiration', 'positive', '0.05', 'wave', 'farewell', 'wave_wind', 'twist', 'wrap_around']
senticnet['scroll_beneath'] = ['0.586', '-0.65', '0', '0.503', '#joy', '#surprise', 'positive', '0.58', 'decoration', 'distinguished_service_cross', 'medal', 'medallion', 'palm']
senticnet['scrollable_cursor'] = ['0.026', '0.165', '-0.12', '0.292', '#interest', '#admiration', 'positive', '0.09', 'fetch', 'find', 'desire_win', 'take', 'obtain']
senticnet['scrollable_list'] = ['-0.09', '-0.04', '0.039', '-0.24', '#sadness', '#disgust', 'negative', '-0.08', 'learn_new_idea', 'learn_quickly', 'point_out', 'look_picture', 'jot_down_note']
senticnet['scrolling_text'] = ['-0.09', '-0.04', '0.039', '-0.24', '#sadness', '#disgust', 'negative', '-0.08', 'learn_new_idea', 'learn_quickly', 'point_out', 'look_picture', 'jot_down_note']
senticnet['scrotal'] = ['0.087', '-0.03', '0', '0.063', '#joy', '#admiration', 'positive', '0.062', 'puppet', 'bouncy', 'curly', 'throw_object', 'glove_leather']
senticnet['scrotal_skin'] = ['0.087', '-0.03', '0', '0.063', '#joy', '#admiration', 'positive', '0.062', 'puppet', 'bouncy', 'curly', 'throw_object', 'glove_leather']
senticnet['scrotum'] = ['0', '0', '0.021', '-0.06', '#anger', '#disgust', 'negative', '-0.04', 'skin', 'skeletal_system', 'shoulder', 'cuticle', 'groin']
senticnet['scrotum_cryptorchidism'] = ['0', '0', '0.021', '-0.06', '#anger', '#disgust', 'negative', '-0.04', 'skin', 'skeletal_system', 'shoulder', 'cuticle', 'groin']
senticnet['scrounge'] = ['0', '0', '-0.77', '-0.95', '#fear', '#disgust', 'negative', '-0.86', 'foraging', 'pasture', 'grass', 'eatage', 'forage']
senticnet['scrub'] = ['0', '0.066', '0', '0.067', '#interest', '#admiration', 'positive', '0.067', 'wash_body', 'wash', 'soap_up', 'sponge', 'use_water_soap']
senticnet['scrub_floor'] = ['-0.10', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.10', 'refrigerate', 'mow_grass', 'fill_bottle', 'mop_floor', 'floor']
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senticnet['scruffy_beard'] = ['-0.91', '0', '0.869', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'talk_hairdresser', 'hair_get_short', 'hair_become_short', 'keep_hair_short', 'hair_wash']
senticnet['scrumble_egg'] = ['0.714', '0.777', '0', '0', '#joy', '#interest', 'positive', '0.746', 'breakfast', 'heat_meal', 'pan', 'beat_egg', 'food']
senticnet['scrumptious'] = ['0.716', '-0.78', '0', '0', '#joy', '#surprise', 'positive', '0.752', 'delicious', 'luscious', 'pleasant-tasting', 'delectable', 'toothsome']
senticnet['scrumpy'] = ['0.079', '-0.02', '0.023', '0', '#joy', '#surprise', 'positive', '0.027', 'wine', 'lemonade', 'juice', 'whisky', 'soft_drink']
senticnet['scrunch'] = ['0.063', '0', '0.01', '0.16', '#joy', '#admiration', 'positive', '0.071', 'goodbye', 'hand_gesture', 'arm_movement', 'crest', 'water_splash']
senticnet['scrunched_up'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'facial_tissue', 'kleenex', 'hanky', 'handkerchief', 'tissue']
senticnet['scruple'] = ['0.587', '-0.83', '0', '0.907', '#surprise', '#admiration', 'positive', '0.778', 'morality', 'wee_small_voice', 'small_voice', 'voice_of_conscience', 'sense_of_shame']
senticnet['scrupulosity'] = ['0', '-0.93', '0', '0.787', '#surprise', '#admiration', 'positive', '0.859', 'punctilious', 'scrupulous', 'compunction', 'scruple', 'scrupulousness']
senticnet['scrupulous'] = ['0', '-0.92', '0', '0.791', '#surprise', '#admiration', 'positive', '0.857', 'punctilious', 'scrupulosity', 'compunction', 'scruple', 'scrupulousness']
senticnet['scrupulous_attention'] = ['0.567', '-0.56', '0', '0.724', '#joy', '#admiration', 'positive', '0.617', 'truth', 'accuracy', 'preciseness', 'trueness', 'precision']
senticnet['scrupulous_care'] = ['0.567', '-0.56', '0', '0.724', '#joy', '#admiration', 'positive', '0.617', 'truth', 'accuracy', 'preciseness', 'trueness', 'precision']
senticnet['scrupulous_honesty'] = ['0.968', '-0.87', '0', '0.992', '#joy', '#admiration', 'positive', '0.946', 'sagacity', 'sagaciousness', 'care', 'caution', 'discernment']
senticnet['scrupulously'] = ['0.765', '0.981', '0', '0', '#joy', '#interest', 'positive', '0.873', 'scruple', 'scrupulousness', 'detail', 'careful', 'scrupulous']
senticnet['scrupulously_fair'] = ['0.968', '-0.87', '0', '0.992', '#joy', '#admiration', 'positive', '0.946', 'sagacity', 'sagaciousness', 'care', 'caution', 'discernment']
senticnet['scrupulousness'] = ['0.507', '-0.94', '0', '0.496', '#joy', '#surprise', 'positive', '0.651', 'carefulness', 'painstakingness', 'diligence', 'meticulousness', 'meticulosity']
senticnet['scrutinise'] = ['0', '0', '-0.81', '-0.73', '#fear', '#disgust', 'negative', '-0.77', 'scrutinize', 'inspect', 'size_up', 'take_stock', 'audit']
senticnet['scrutinize'] = ['0', '0', '-0.81', '-0.73', '#fear', '#disgust', 'negative', '-0.77', 'scrutinise', 'inspect', 'size_up', 'take_stock', 'audit']
senticnet['scrutiny'] = ['-0.79', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.75', 'scan', 'checkup', 'medical_checkup', 'medical_examination', 'medical_exam']
senticnet['sctp_implementation'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'identifiable', 'specifiable', 'recognizable', 'diagnosable', 'classifiable']
senticnet['scuba'] = ['-0.04', '0.063', '0', '0.071', '#interest', '#admiration', 'positive', '0.029', 'scuba_gear', 'go_to_swimming_pool', 'learn_swimming', 'jump_into_water', 'enjoy_nature']
senticnet['scuba_certification'] = ['0', '0.07', '0', '0', '#interest', '#interest', 'positive', '0.07', 'sunken_treasure', 'real_duck', 'urchin', 'mussel', 'gold_fish']
senticnet['scuba_dive'] = ['0', '0.07', '0', '0', '#interest', '#interest', 'positive', '0.07', 'sunken_treasure', 'real_duck', 'urchin', 'mussel', 'gold_fish']
senticnet['scuba_diver'] = ['0', '0.07', '0', '0', '#interest', '#interest', 'positive', '0.07', 'sunken_treasure', 'real_duck', 'urchin', 'mussel', 'gold_fish']
senticnet['scuba_diving'] = ['0.856', '0.883', '0', '0', '#joy', '#interest', 'positive', '0.869', 'go_underwater', 'swim', 'fall_overboard', 'dive', 'pool_party']
senticnet['scuba_diving_instructor'] = ['0', '0.07', '0', '0', '#interest', '#interest', 'positive', '0.07', 'sunken_treasure', 'real_duck', 'urchin', 'mussel', 'gold_fish']
senticnet['scuba_gear'] = ['-0.04', '0.069', '0', '0.075', '#interest', '#admiration', 'positive', '0.035', 'go_to_swimming_pool', 'scuba', 'learn_swimming', 'jump_into_water', 'enjoy_nature']
senticnet['scuba_instructor'] = ['0', '0.07', '0', '0', '#interest', '#interest', 'positive', '0.07', 'sunken_treasure', 'real_duck', 'urchin', 'mussel', 'gold_fish']
senticnet['scuba_mask'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'lifebelt', 'save_life', 'lifeguard', 'life_jacket', 'safety_equipment']
senticnet['scuff'] = ['-0.54', '0', '0.612', '-0.98', '#anger', '#disgust', 'negative', '-0.71', 'carpet_slipper', 'mule', 'bootee', 'bootie', 'footwear']
senticnet['scuffle'] = ['-0.92', '0', '0.994', '0', '#sadness', '#anger', 'negative', '-0.96', 'conflict', 'scuffle', 'fracas', 'altercation', 'ensued']
senticnet['scuffle_between'] = ['-0.60', '0', '0.777', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'batrachomyomachia', 'wrangle', 'dustup', 'quarrel', 'row']
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senticnet['scullery'] = ['0.979', '-0.69', '0', '0.576', '#joy', '#surprise', 'positive', '0.749', 'big_cheese', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['scullery_maid'] = ['-0.62', '0', '0.649', '-0.67', '#anger', '#disgust', 'negative', '-0.64', 'handmaid', 'handmaiden', 'chambermaid', 'maid', 'parlormaid']
senticnet['sculpt'] = ['0.039', '0', '0', '0.059', '#joy', '#admiration', 'positive', '0.049', 'fillet_fish', 'clon', 'photography', 'price_product', 'demo']
senticnet['sculpt_gel'] = ['-0.04', '0.069', '0', '0.075', '#interest', '#admiration', 'positive', '0.035', 'gel', 'hairstyle', 'hairdresser', 'cosmetic', 'hair_gel']
senticnet['sculpter'] = ['-0.52', '0', '0.506', '-0.94', '#sadness', '#disgust', 'negative', '-0.65', 'gouger', 'swindler', 'defrauder', 'fraud', 'welcher']
senticnet['sculptor'] = ['0.677', '-0.57', '0', '0.625', '#joy', '#admiration', 'positive', '0.627', 'art_craft', 'carving', 'art', 'craft', 'wood_product']
senticnet['sculptural'] = ['0.628', '-0.95', '0', '0.664', '#surprise', '#admiration', 'positive', '0.75', 'voluptuous', 'statuesque', 'sculptured', 'buxom', 'shapely']
senticnet['sculpture'] = ['0.044', '0', '0', '0.084', '#joy', '#admiration', 'positive', '0.064', 'art', 'creative', 'painting', 'artwork', 'oil_paint']
senticnet['sculpture_garden'] = ['0.07', '0', '0', '0', '#joy', '#joy', 'positive', '0.07', 'artist', 'artwork', 'sculpture', 'tapestry', 'drawing']
senticnet['sculptured'] = ['0.633', '-0.96', '0', '0.657', '#surprise', '#admiration', 'positive', '0.751', 'voluptuous', 'statuesque', 'shapely', 'buxom', 'sculptural']
senticnet['scum'] = ['-0.84', '0', '-0.76', '0', '#sadness', '#fear', 'negative', '-0.80', 'reprehensible', 'scummy', 'impurity', 'scumming', 'vegetation']
senticnet['scumbag'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'douche', 'bastard', 'asshole', 'motherfucker', 'dick']
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senticnet['scummy'] = ['-0.72', '0', '0.569', '-0.86', '#sadness', '#disgust', 'negative', '-0.71', 'abject', 'scurvy', 'miserable', 'contemptible', 'pathetic']
senticnet['scupper'] = ['0', '0', '-0.89', '-0.87', '#fear', '#disgust', 'negative', '-0.88', 'ambuscade', 'lurk', 'ambush', 'waylay', 'bushwhack']
senticnet['scurit_sanitaire'] = ['-0.95', '0', '0.845', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'grapefruit_peel', 'lemon_peel', 'orange_peel', 'candied_fruit', 'succade']
senticnet['scurrility'] = ['-0.81', '0', '0.827', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'abuse', 'vilification', 'insult', 'revilement', 'contumely']
senticnet['scurry'] = ['0', '0', '-0.81', '-0.86', '#fear', '#disgust', 'negative', '-0.83', 'skitter', 'hatchway', 'scamper', 'opening', 'scuttle']
senticnet['scurvy'] = ['-0.72', '0', '0.569', '-0.86', '#sadness', '#disgust', 'negative', '-0.71', 'abject', 'contemptible', 'miserable', 'scummy', 'pathetic']
senticnet['scutage'] = ['-0.08', '0', '-0.15', '-0.20', '#fear', '#disgust', 'negative', '-0.14', 'ask_money', 'extortion', 'wringing', 'intimidate', 'psychological_pressure']
senticnet['scute'] = ['0.886', '-0.99', '0', '0.542', '#joy', '#surprise', 'positive', '0.808', 'jowl', 'lower_jaw', 'mandible', 'lantern_jaw', 'lantern_jawr_bone']
senticnet['scuttle'] = ['0', '0', '-0.80', '-0.85', '#fear', '#disgust', 'negative', '-0.82', 'skitter', 'hatchway', 'scamper', 'opening', 'scurry']
senticnet['scuttlebutt'] = ['-0.70', '0', '0.965', '-0.53', '#sadness', '#anger', 'negative', '-0.73', 'earful', 'malicious_gossip', 'talk_of_town', 'talk', 'scandal']
senticnet['scythe'] = ['-0.66', '0', '0.859', '-0.82', '#anger', '#disgust', 'negative', '-0.78', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['scythe-like'] = ['-0.70', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.85', 'chisel-shaped', 'shovel-like', 'chisel-like', 'saw-like', 'teeth-like']
senticnet['sd_card_reader'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'run_treadmill', 'complete_thesis', 'dwarf_planet', 'insure', 'ridgeback']
senticnet['sdbm_database'] = ['0', '0.069', '0', '0.083', '#interest', '#admiration', 'positive', '0.076', 'latch', 'restrict_access', 'knob', 'door_knob', 'gate']
senticnet['se_tenant'] = ['0.058', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.054', 'letter_slot', 'envelope', 'pamphlet', 'mailman', 'address_label']
senticnet['sea'] = ['0.044', '0', '0', '0.084', '#joy', '#admiration', 'positive', '0.064', 'ocean', 'atlantic_ocean', 'sea_movement', 'pacific_ocean', 'lake']
senticnet['sea-faring'] = ['0', '0.825', '0', '0.759', '#interest', '#admiration', 'positive', '0.792', 'sea-going', 'ocean-going', 'seafaring', 'oceangoing', 'seagoing']
senticnet['sea-going'] = ['0', '0.825', '0', '0.758', '#interest', '#admiration', 'positive', '0.791', 'seagoing', 'ocean-going', 'seafaring', 'oceangoing', 'sea-faring']
senticnet['sea-worthy'] = ['-0.98', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.96', 'seaworthy', 'maneuverable', 'seagoing', 'roadworthy', 'beached']
senticnet['sea_aster'] = ['-0.89', '0', '0.556', '-0.72', '#sadness', '#disgust', 'negative', '-0.72', 'fucus', 'rockweed', 'black_rockweed', 'bladder_fucus', 'tang']
senticnet['sea_bass'] = ['0.709', '0.964', '0', '0', '#joy', '#interest', 'positive', '0.837', 'fish_trout', 'catch_butterfly', 'fish_lake', 'fish_compliment', 'fish_stream']
senticnet['sea_bed'] = ['-0.04', '-0.01', '0.103', '0', '#sadness', '#anger', 'negative', '-0.04', 'near_beach', 'along_beach', 'full_water', 'ocean_ground', 'salty']
senticnet['sea_beet'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['sea_bottom'] = ['0.979', '-0.75', '0', '0.764', '#joy', '#admiration', 'positive', '0.831', 'ocean_floor', 'sea_floor', 'ocean_bottom', 'seabed', 'twilight_zone']
senticnet['sea_bream'] = ['0.709', '0.964', '0', '0', '#joy', '#interest', 'positive', '0.837', 'fish_trout', 'catch_butterfly', 'fish_lake', 'fish_compliment', 'fish_stream']
senticnet['sea_breeze'] = ['0.89', '0', '0', '0.904', '#joy', '#admiration', 'positive', '0.897', 'breeze', 'wind', 'breezy', 'air_movement', 'air']
senticnet['sea_change'] = ['0.556', '-0.92', '0', '0.762', '#surprise', '#admiration', 'positive', '0.747', 'degeneration', 'improvement', 'betterment', 'transformation', 'strengthening']
senticnet['sea_cliff'] = ['0.057', '0.075', '0.039', '0.059', '#interest', '#admiration', 'positive', '0.038', 'summit', 'big_rock', 'high_land', 'peak', 'mountain_side']
senticnet['sea_creature'] = ['-0.93', '0', '0.544', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'mutant', 'variation', 'sport', 'lusus_naturae', 'mutation']
senticnet['sea_cucumber'] = ['-0.79', '0', '0.709', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'animal', 'animate_being', 'ctenophore', 'invertebrate', 'beast']
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senticnet['sea_wee'] = ['0.746', '0.727', '0', '0', '#joy', '#interest', 'positive', '0.736', 'porpoise', 'many_fishes', 'seahorse', 'horizon', 'mermaid']
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senticnet['seal_of_approval'] = ['0.682', '-0.70', '0', '0.98', '#surprise', '#admiration', 'positive', '0.789', 'prize', 'laurel', 'honorable_mention', 'aliyah', 'cachet']
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senticnet['sealed_container'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'drink_container', 'liquid_container', 'drink_cup', 'drink_holder', 'type_of_container']
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senticnet['seamless_transition'] = ['0', '0.202', '-0.06', '0.291', '#interest', '#admiration', 'positive', '0.142', 'change_life', 'pocket_money', 'go_forward', 'evolve', 'rotate_movement']
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senticnet['search_engine'] = ['0.061', '0.128', '0', '0.151', '#interest', '#admiration', 'positive', '0.113', 'spool', 'silk', 'velvet', 'cotton', 'satin']
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senticnet['searcher'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'chairperson', 'commuter']
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senticnet['sears_roebuck'] = ['0', '0.081', '0', '0.071', '#interest', '#admiration', 'positive', '0.076', 'thrift_store', 'type_of_store', 'record_store', 'many_shops', 'clothes_store']
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senticnet['seaside_centaury'] = ['-0.90', '0', '0.726', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'broad_leaved_centaury', 'rosita', 'lesser_centaury', 'tufted_centaury', 'centaury']
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senticnet['seaside_hotel'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'lurking_place', 'spot', 'drop', 'hiding_place', 'den']
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senticnet['seaside_resort_town'] = ['0.203', '0.077', '0', '0.189', '#joy', '#admiration', 'positive', '0.156', 'good_life', 'coast', 'relax', 'sediment', 'shell']
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senticnet['seaside_village'] = ['0.203', '0.077', '0', '0.189', '#joy', '#admiration', 'positive', '0.156', 'good_life', 'coast', 'relax', 'sediment', 'shell']
senticnet['season'] = ['0.796', '-0.95', '0', '0', '#joy', '#surprise', 'positive', '0.876', 'drinking_chocolate', 'hot_chocolate', 'chocolate', 'cocoa', 'stock']
senticnet['season_dish'] = ['0.061', '0', '0', '0.078', '#joy', '#admiration', 'positive', '0.07', 'fry_fish', 'fry_egg', 'season_meal', 'fry_chicken', 'make_cake']
senticnet['season_finale'] = ['0.918', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.827', 'encounter', 'reveal', 'programme', 'lose', 'sequel']
senticnet['season_finale_episode'] = ['0.918', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.827', 'encounter', 'reveal', 'programme', 'lose', 'sequel']
senticnet['season_food'] = ['0', '-0.80', '0', '0.983', '#surprise', '#admiration', 'positive', '0.895', 'hungry', 'prepare_breakfast', 'set_table', 'open_refrigerator', 'prepare_dinner']
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senticnet['season_premiere_episode'] = ['0.918', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.827', 'encounter', 'reveal', 'programme', 'lose', 'sequel']
senticnet['season_year'] = ['-0.73', '0', '-0.98', '0', '#sadness', '#fear', 'negative', '-0.86', 'snowy', 'cool_temperature', 'coldness', 'ice_rain', 'chilly']
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senticnet['seasonal_abundance'] = ['0', '0.184', '-0.05', '0.179', '#interest', '#admiration', 'positive', '0.104', 'rich', 'a_lot', 'abundant', 'wealth', 'plentiful']
senticnet['seasonal_climate'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'weather', 'sunny', 'spring', 'snow', 'four_seasons']
senticnet['seasonal_flu_vaccine'] = ['-0.07', '0', '-0.05', '-0.18', '#sadness', '#disgust', 'negative', '-0.10', 'medical_therapy', 'vaccination', 'doctor', 'medicine', 'hospital']
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senticnet['second-best'] = ['0.997', '-0.82', '0', '0', '#joy', '#surprise', 'positive', '0.909', 'third-best', 'fourth-best', 'fifth-best', 'second-worst', 'sixth-best']
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senticnet['second_brightest_star'] = ['0.977', '-0.93', '0', '0.583', '#joy', '#surprise', 'positive', '0.831', 'giant', 'shinny', 'supernova', 'supergiant', 'binary_star']
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senticnet['second_degree_murder'] = ['0.654', '-0.59', '0', '0.774', '#joy', '#admiration', 'positive', '0.673', 'instrument', 'indictment', 'murder_indictment', 'true_bill', 'legal_document']
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senticnet['secondary_dysmenorrhea'] = ['-0.73', '0', '0.555', '-0.90', '#sadness', '#disgust', 'negative', '-0.73', 'aching', 'hurting', 'labor_pain', 'neuralgia', 'neuralgy']
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senticnet['secondary_lymphedema'] = ['0.899', '0.979', '0', '0', '#joy', '#interest', 'positive', '0.939', 'principal', 'elementary', 'main', 'master', 'chief']
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senticnet['secretary_bird'] = ['-0.81', '0', '0.913', '-0.85', '#anger', '#disgust', 'negative', '-0.86', 'bird', 'hawk', 'eagle', 'vulture', 'bird_of_prey']
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senticnet['secretary_liang_shijie'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'fantastic', 'amaze', 'pose', 'wondrous', 'wonderfully']
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senticnet['secretory_diarrhea'] = ['-0.99', '0', '0.859', '-0.62', '#sadness', '#anger', 'negative', '-0.82', 'lax', 'loose', 'diarrheal', 'diarrhoeal', 'unconstipated']
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senticnet['security_belt'] = ['0.928', '0.754', '0', '0', '#joy', '#interest', 'positive', '0.841', 'life_ring', 'lifebelt', 'save_life', 'sailor', 'life_preserver']
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senticnet['sedulous'] = ['0.75', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.808', 'hard-working', 'assiduous', 'endeavor', 'diligent', 'regular']
senticnet['sedulously'] = ['-0.74', '0', '0.585', '-0.58', '#sadness', '#anger', 'negative', '-0.63', 'lack_of_quality', 'meagreness', 'poorness', 'scantiness', 'scantness']
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senticnet['see_future'] = ['-0.05', '0.105', '0', '0.074', '#interest', '#admiration', 'positive', '0.042', 'come_up_new_idea', 'come_conclusion', 'figure_out', 'reach_conclusion', 'discuss_friend']
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senticnet['see_new_place'] = ['0.717', '0', '0', '0', '#joy', '#joy', 'positive', '0.717', 'sightsee', 'see_world', 'explore', 'pastime', 'kill_time']
senticnet['see_night'] = ['-0.10', '0', '0', '-0.1', '#sadness', '#disgust', 'negative', '-0.10', 'spread_disease', 'go_stall', 'spot_prey', 'pet', 'hunt_food']
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senticnet['see_out'] = ['0.899', '0.809', '0', '0', '#joy', '#interest', 'positive', '0.854', 'look_out', 'look_outside', 'look_through', 'see_through', 'open_close']
senticnet['see_outside'] = ['0.837', '0.793', '0', '0', '#joy', '#interest', 'positive', '0.815', 'see_out', 'look_out', 'look_outside', 'open_close', 'house_front']
senticnet['see_performance'] = ['0.806', '0.776', '0', '0.761', '#joy', '#interest', 'positive', '0.781', 'appreciate_music', 'entertainment', 'music_lover', 'listen_pleasure', 'relieve_boredom']
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senticnet['see_play'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'buy_popcorn', 'go_movie_theater', 'drive_theater', 'go_to_theater', 'watch_movie']
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senticnet['see_star_night'] = ['0.923', '0', '0', '0.777', '#joy', '#admiration', 'positive', '0.85', 'gather_berry', 'hang_picture_on_wall', 'recycle_paper', 'explain_everything', 'forward_email']
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senticnet['seed-producing'] = ['0.753', '0', '0', '0.929', '#joy', '#admiration', 'positive', '0.841', 'nectar-producing', 'frost-tender', 'nectar-rich', 'five-petaled', 'high-nutrition']
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senticnet['seek_entertainment'] = ['0.705', '0', '0', '0.758', '#joy', '#admiration', 'positive', '0.732', 'interest_art', 'mindless_entertainment', 'see_actor', 'zone_out', 'eat_popcorn']
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senticnet['seek_justice'] = ['-0.21', '0.108', '-0.13', '0', '#sadness', '#fear', 'negative', '-0.08', 'hire_lawyer', 'settle_dispute', 'verdict', 'call_lawyer', 'go_court']
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senticnet['seek_legal_advice'] = ['0.932', '0.787', '0', '0', '#joy', '#interest', 'positive', '0.86', 'learn_patience', 'discuss', 'understand_each_other', 'conformist_attitude', 'avoid_conflict']
senticnet['seek_legal_counsel'] = ['0.932', '0.787', '0', '0', '#joy', '#interest', 'positive', '0.86', 'learn_patience', 'discuss', 'understand_each_other', 'conformist_attitude', 'avoid_conflict']
senticnet['seek_out'] = ['-0.06', '0.131', '0', '0.081', '#interest', '#admiration', 'positive', '0.048', 'columbus', 'joy', 'eureka', 'find_out', 'aware']
senticnet['seek_psychiatric_help'] = ['-0.07', '0', '0', '-0.23', '#sadness', '#disgust', 'negative', '-0.15', 'prepare_go_jail', 'buy_weapon', 'find_victim', 'buy_gun', 'dangerous_weapon']
senticnet['seek_re_election'] = ['-0.89', '0', '0.925', '-0.84', '#sadness', '#anger', 'negative', '-0.88', 'vacate', 'resign', 'give_up', 'renounce', 'depart']
senticnet['seek_reelection'] = ['-0.89', '0', '0.925', '-0.84', '#sadness', '#anger', 'negative', '-0.88', 'vacate', 'resign', 'give_up', 'renounce', 'depart']
senticnet['seek_refuge'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'habitat', 'mom_house', 'windsor', 'pacific_northwest', 'wilderness']
senticnet['seek_reimbursement'] = ['0', '0', '-0.75', '-0.71', '#fear', '#disgust', 'negative', '-0.73', 'foreswear', 'relinquish', 'dispense_with', 'forgo', 'throw_overboard']
senticnet['seek_revenge'] = ['-0.30', '0', '-0.23', '-0.20', '#sadness', '#fear', 'negative', '-0.25', 'feel_remorse', 'die', 'feel_anger', 'imprisonment', 'death']
senticnet['seek_revenge_against'] = ['-0.95', '0', '0.774', '0', '#sadness', '#anger', 'negative', '-0.86', 'argue', 'rival', 'disagreement', 'opposition', 'anti']
senticnet['seek_truth'] = ['0', '-0.94', '0', '0.82', '#surprise', '#admiration', 'positive', '0.882', 'thirst_knowledge', 'teach_world', 'learn_new_fact', 'experience_new', 'learn_art']
senticnet['seek_understanding'] = ['0', '0.724', '0', '0.868', '#interest', '#admiration', 'positive', '0.796', 'educate_others', 'notify', 'learn_fact', 'understand', 'increase_understand']
senticnet['seek_vengeance'] = ['-0.78', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.85', 'revenge', 'retribution', 'go_jail', 'sin', 'incarceration']
senticnet['seekadett'] = ['0.688', '-0.90', '0', '0.877', '#surprise', '#admiration', 'positive', '0.824', 'flag_officer', 'rear_admiral', 'admiral', 'full_admiral', 'vice_admiral']
senticnet['seeker'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'chairperson', 'commuter']
senticnet['seeketh'] = ['-0.84', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.90', 'mortify', 'embarrassed', 'self-hatred', 'self-disgust', 'humiliated']
senticnet['seeking_admission'] = ['0.087', '0.248', '-0.19', '0.31', '#interest', '#admiration', 'positive', '0.113', 'enroll', 'enroll_school', 'enroll_class', 'pay_tuition', 'start_college']
senticnet['seeking_clarification'] = ['-0.12', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.09', 'understand', 'teach_others', 'trivia', 'notify', 'seek_understanding']
senticnet['seeking_legal_advice'] = ['0.682', '-0.67', '0', '0.588', '#joy', '#surprise', 'positive', '0.648', 'lawful', 'civil_action', 'counterclaim', 'occurrence', 'li_pendens']
senticnet['seeking_reassurance'] = ['0.376', '0.271', '-0.19', '0.258', '#joy', '#interest', 'positive', '0.177', 'pleasant_emotion', 'bliss', 'good_feeling', 'confirm', 'healthful']
senticnet['seeking_refuge'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'habitat', 'mom_house', 'windsor', 'pacific_northwest', 'wilderness']
senticnet['seeking_revenge'] = ['-0.89', '-0.71', '-0.76', '-0.93', '#sadness', '#disgust', 'negative', '-0.47', 'bigot', 'censorship', 'jail_time', 'reprimand', 'intolerance']
senticnet['seeking_revenge_against'] = ['-0.95', '0', '0.774', '0', '#sadness', '#anger', 'negative', '-0.86', 'argue', 'rival', 'disagreement', 'opposition', 'anti']
senticnet['seeking_vengeance'] = ['-0.78', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.85', 'revenge', 'retribution', 'go_jail', 'sin', 'incarceration']
senticnet['seelie_court'] = ['0.967', '0.768', '0', '0', '#joy', '#interest', 'positive', '0.867', 'centerfold', 'genus', 'honey_bee', 'infant', 'dove']
senticnet['seem_downright'] = ['0', '0', '-0.74', '-0.87', '#fear', '#disgust', 'negative', '-0.80', 'absolute', 'out-and-out', 'rank', 'right-down', 'sheer']
senticnet['seem_insurmountable'] = ['-0.73', '0', '0.732', '-0.53', '#sadness', '#anger', 'negative', '-0.66', 'unconquerable', 'insuperable', 'inexpugnable', 'indomitable', 'unsubduable']
senticnet['seem_plausible'] = ['0.744', '-0.76', '0', '0.669', '#joy', '#surprise', 'positive', '0.726', 'accessible', 'comprehendible', 'approachable', 'perceivable', 'intelligible']
senticnet['seem_quaint'] = ['0.843', '-0.99', '0', '0.706', '#joy', '#surprise', 'positive', '0.849', 'contrivance', 'gimbal', 'injector', 'mod_con', 'device']
senticnet['seema_deo'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['seeme'] = ['0', '0', '0.726', '-0.94', '#anger', '#disgust', 'negative', '-0.83', 'irritate', 'idiot', 'talk_shit', 'rude', 'awful']
senticnet['seemed_insurmountable'] = ['-0.73', '0', '0.732', '-0.53', '#sadness', '#anger', 'negative', '-0.66', 'unconquerable', 'insuperable', 'inexpugnable', 'indomitable', 'unsubduable']
senticnet['seemeth'] = ['-0.79', '0', '0.607', '-0.92', '#sadness', '#disgust', 'negative', '-0.77', 'stubbornness', 'obstinacy', 'obstinance', 'bullheadedness', 'pigheadedness']
senticnet['seeming_inability'] = ['-0.78', '0', '0.707', '-0.86', '#sadness', '#disgust', 'negative', '-0.78', 'mental_block', 'uncreativeness', 'stupidity', 'illiteracy', 'analphabetism']
senticnet['seemingly'] = ['0', '0.921', '0', '0.806', '#interest', '#admiration', 'positive', '0.864', 'patently', 'plainly', 'ostensibly', 'plain', 'apparently']
senticnet['seemingly_inevitable'] = ['-0.80', '0', '0.507', '-0.76', '#sadness', '#disgust', 'negative', '-0.69', 'adamant', 'inflexible', 'intransigent', 'ironclad', 'brassbound']
senticnet['seemingly_inexorable'] = ['-0.80', '0', '0.507', '-0.76', '#sadness', '#disgust', 'negative', '-0.69', 'adamant', 'inflexible', 'intransigent', 'ironclad', 'brassbound']
senticnet['seemingly_inexplicable'] = ['-0.87', '0', '0.681', '-0.82', '#sadness', '#disgust', 'negative', '-0.79', 'unexplainable', 'unaccountable', 'unexplained', 'inscrutable', 'mystifying']
senticnet['seemingly_infinite'] = ['0', '0.858', '0', '0.79', '#interest', '#admiration', 'positive', '0.824', 'heavenly_body', 'star_planet', 'celestial_body', 'around_earth', 'unlimited']
senticnet['seemingly_inhospitable'] = ['-0.56', '0', '0.814', '-0.88', '#anger', '#disgust', 'negative', '-0.75', 'unfriendly', 'barren', 'bleak', 'desolate', 'wild']
senticnet['seemingly_insatiable'] = ['-0.63', '0', '0.608', '-0.74', '#sadness', '#disgust', 'negative', '-0.66', 'pall', 'take_in', 'take', 'ingest', 'have']
senticnet['seemingly_insurmountable'] = ['-0.73', '0', '0.732', '-0.53', '#sadness', '#anger', 'negative', '-0.66', 'unconquerable', 'insuperable', 'inexpugnable', 'indomitable', 'unsubduable']
senticnet['seemingly_intractable'] = ['-0.13', '0.071', '0.694', '-0.67', '#anger', '#disgust', 'negative', '-0.35', 'crucify', 'dun', 'frustrating', 'aggravate', 'exasperation']
senticnet['seemingly_invincible'] = ['0.586', '-0.94', '0', '0.748', '#surprise', '#admiration', 'positive', '0.76', 'vanquish', 'beat', 'beat_out', 'trounce', 'crush']
senticnet['seemingly_irreconcilable'] = ['-0.97', '0', '0.712', '-0.78', '#sadness', '#disgust', 'negative', '-0.82', 'discrepant', 'inconsistent', 'scratchy', 'uneven', 'contradictory']
senticnet['seemingly_paradoxical'] = ['0.534', '-0.86', '0', '0.676', '#surprise', '#admiration', 'positive', '0.692', 'suitability', 'suitableness', 'habitability', 'habitableness', 'qualification']
senticnet['seemingly_random'] = ['-0.44', '-0.13', '-0.08', '-0.40', '#sadness', '#disgust', 'negative', '-0.19', 'disorder', 'untidy', 'mess', 'heap', 'chaos']
senticnet['seemingly_unbeatable'] = ['-0.88', '0', '0.882', '-0.58', '#sadness', '#anger', 'negative', '-0.78', 'mortal', 'behemoth', 'unusual_person', 'aberrant', 'giant']
senticnet['seemliness'] = ['0.832', '-0.90', '0', '0.817', '#joy', '#surprise', 'positive', '0.852', 'decency', 'decorum', 'decorousness', 'correctness', 'properness']
senticnet['seemly'] = ['0.787', '0.885', '0', '0', '#joy', '#interest', 'positive', '0.836', 'comme_il_faut', 'comely', 'decorous', 'becoming', 'decent']
senticnet['seems_plausible'] = ['0.943', '-0.75', '0', '0.515', '#joy', '#surprise', 'positive', '0.736', 'reasonableness', 'tenability', 'tenableness', 'credibility', 'credibleness']
senticnet['seep'] = ['-0.74', '0', '0.893', '0', '#sadness', '#anger', 'negative', '-0.81', 'slow', 'edge', 'slowly', 'tardily', 'drip']
senticnet['seeps_through'] = ['-0.09', '0', '-0.07', '-0.06', '#sadness', '#fear', 'negative', '-0.07', 'blow', 'gape', 'move_air', 'mud', 'gash']
senticnet['seer'] = ['0.841', '0.733', '0', '0', '#joy', '#interest', 'positive', '0.787', 'observer', 'peek', 'appearance', 'peer', 'glimpse']
senticnet['seeta'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['seeth'] = ['0.673', '-0.66', '0', '0.566', '#joy', '#surprise', 'positive', '0.634', 'wickedness', 'impiety', 'impiousness', 'unjustness', 'dishonor']
senticnet['seethe'] = ['-0.97', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.94', 'brew', 'seething', 'agitate', 'activity', 'buzz']
senticnet['seething'] = ['-0.98', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.94', 'brew', 'seethe', 'agitate', 'activity', 'buzz']
senticnet['seething_anger'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['segan'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['segfault'] = ['-0.98', '0', '0.869', '-0.80', '#sadness', '#anger', 'negative', '-0.88', 'error', 'syntax_error', 'semantic_error', 'programming_error', 'computer_error']
senticnet['segment'] = ['0.704', '0.751', '0', '0', '#joy', '#interest', 'positive', '0.728', 'part', 'component', 'portion', 'fraction', 'piece']
senticnet['segmental'] = ['-0.67', '0', '0.97', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'joint', 'node', 'connexion', 'join', 'juncture']
senticnet['segmental_arch'] = ['0.029', '0.087', '0.065', '0.137', '#interest', '#admiration', 'positive', '0.047', 'archway', 'aisle', 'mat', 'passageway', 'right_angle']
senticnet['segmentation_fault'] = ['-0.98', '0', '0.869', '-0.80', '#sadness', '#anger', 'negative', '-0.88', 'error', 'syntax_error', 'semantic_error', 'programming_error', 'computer_error']
senticnet['segmentation_violation'] = ['-0.98', '0', '0.869', '-0.80', '#sadness', '#anger', 'negative', '-0.88', 'error', 'syntax_error', 'semantic_error', 'programming_error', 'computer_error']
senticnet['segmented_worm'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'annelid', 'annelid_worm', 'leech']
senticnet['segregate'] = ['-0.98', '0', '0.737', '-0.92', '#sadness', '#disgust', 'negative', '-0.88', 'separate', 'severalize', 'contrast', 'decouple', 'dissociate']
senticnet['segueing_into'] = ['0.773', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.844', 'favorite', 'crowd', 'acclamation', 'bunch', 'large_crowd']
senticnet['segv'] = ['-0.98', '0', '0.869', '-0.80', '#sadness', '#anger', 'negative', '-0.88', 'error', 'syntax_error', 'semantic_error', 'programming_error', 'computer_error']
senticnet['seiden'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['seigneur'] = ['-0.97', '0', '0.747', '-0.74', '#sadness', '#anger', 'negative', '-0.82', 'liege', 'liege_lord', 'master', 'overlord', 'lord']
senticnet['seigneurial'] = ['-0.71', '0', '0.917', '-0.51', '#sadness', '#anger', 'negative', '-0.71', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['seigneurie'] = ['-0.71', '0', '0.917', '-0.51', '#sadness', '#anger', 'negative', '-0.71', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['seigneury'] = ['-0.71', '0', '0.917', '-0.51', '#sadness', '#anger', 'negative', '-0.71', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['seignior'] = ['-0.97', '0', '0.798', '-0.64', '#sadness', '#anger', 'negative', '-0.80', 'liege', 'liege_lord', 'master', 'overlord', 'lord']
senticnet['seigniorage'] = ['0', '0.238', '0.021', '0.152', '#interest', '#admiration', 'positive', '0.123', 'money_value', 'money_amount', 'worth', 'monetary_amount', 'importance']
senticnet['seigniory'] = ['-0.51', '0', '0.93', '-0.85', '#anger', '#disgust', 'negative', '-0.76', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['seignorage'] = ['0', '0.238', '0.021', '0.152', '#interest', '#admiration', 'positive', '0.123', 'money_value', 'money_amount', 'worth', 'monetary_amount', 'importance']
senticnet['seignory'] = ['-0.71', '0', '0.917', '-0.51', '#sadness', '#anger', 'negative', '-0.71', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['seine_net'] = ['0.073', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'angler', 'informal_rod', 'archaic_fisher', 'rare_piscator', 'fishing_line']
senticnet['seinen_manga'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['seismic'] = ['-0.84', '0', '-0.73', '0', '#sadness', '#fear', 'negative', '-0.78', 'discontinuous', 'lack', 'break', 'regularity', 'discontinuity']
senticnet['seismic_shift'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'absolutism', 'autocracy', 'ascendancy', 'tyrant']
senticnet['seitan'] = ['0', '-0.03', '0.046', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'pasta', 'green_bean', 'mayonnaise', 'frozen_food', 'sour_milk']
senticnet['seize'] = ['0', '0', '-0.87', '-0.84', '#fear', '#disgust', 'negative', '-0.86', 'sequester', 'seclude', 'attach', 'impound', 'confiscate']
senticnet['seize_control'] = ['-0.84', '0', '0.744', '-0.58', '#sadness', '#anger', 'negative', '-0.72', 'subversion', 'subversive_activity', 'termination', 'ending', 'conclusion']
senticnet['seize_power'] = ['-0.84', '0', '0.744', '-0.58', '#sadness', '#anger', 'negative', '-0.72', 'subversion', 'subversive_activity', 'termination', 'ending', 'conclusion']
senticnet['seize_upon'] = ['0.54', '-0.97', '0', '0.569', '#surprise', '#admiration', 'positive', '0.695', 'overstate', 'hyperbolize', 'glorify', 'exaggerate', 'overdraw']
senticnet['seized_upon'] = ['-0.72', '0', '0.755', '0', '#sadness', '#anger', 'negative', '-0.73', 'disgrace', 'shameful', 'dishonorably', 'inglorious', 'dishonored']
senticnet['seizing'] = ['0', '0', '0.703', '-0.88', '#anger', '#disgust', 'negative', '-0.79', 'invade', 'flow', 'run', 'infest', 'overrun']
senticnet['seizing_power'] = ['-0.84', '0', '0.744', '-0.58', '#sadness', '#anger', 'negative', '-0.72', 'subversion', 'subversive_activity', 'termination', 'ending', 'conclusion']
senticnet['seizure'] = ['-0.85', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.80', 'sequestration', 'kill_brain_cell', 'cry', 'bad_health', 'scared']
senticnet['seizure_activity'] = ['-0.03', '-0.03', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'eat_poison', 'commit_suicide', 'drug_addiction', 'drunk_drive', 'jump_off_cliff']
senticnet['sekitori'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['selachian'] = ['-0.54', '0', '0.983', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'elasmobranch', 'shark', 'hammerhead', 'cow_shark', 'mackerel_shark']
senticnet['selaginella'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'seed_fern', 'pteridosperm']
senticnet['selbit'] = ['0.672', '-0.65', '0', '0.535', '#joy', '#surprise', 'positive', '0.621', 'escapologist', 'escape_expert', 'mind_reader', 'telepathist', 'performer']
senticnet['selbst'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['seldom'] = ['-0.97', '0', '0', '-0.86', '#sadness', '#disgust', 'negative', '-0.91', 'worst', 'optional', 'rare', 'lass', 'temporary']
senticnet['select'] = ['-0.05', '0.301', '-0.07', '0.241', '#interest', '#admiration', 'positive', '0.103', 'selection', 'decision', 'make_choice', 'decide', 'choose']
senticnet['select_edit'] = ['0.055', '0.117', '0', '0.133', '#interest', '#admiration', 'positive', '0.102', 'graphical_representation', 'pencil_pen', 'note_pad', 'marker', 'stationery']
senticnet['select_game'] = ['0', '0.079', '-0.06', '0.131', '#interest', '#admiration', 'positive', '0.047', 'find_partner', 'win_hand', 'shake_hand', 'shuffle_card', 'choose_game']
senticnet['selection'] = ['0', '0.316', '-0.12', '0.291', '#interest', '#admiration', 'positive', '0.16', 'select', 'decision', 'decide', 'choose', 'nominate']
senticnet['selection_criterion'] = ['0.57', '0.754', '-0.32', '0.464', '#joy', '#interest', 'positive', '0.366', 'esteem', 'optimism', 'anticipation', 'adoration', 'confidence']
senticnet['selective'] = ['0', '-0.72', '0', '0.939', '#surprise', '#admiration', 'positive', '0.831', 'authority', 'choosy', 'selection', 'capability', 'selectivity']
senticnet['selective_breeding'] = ['0.735', '-0.71', '0', '0.566', '#joy', '#surprise', 'positive', '0.672', 'improvement', 'betterment', 'habituation', 'adjustment', 'shakedown']
senticnet['selective_disclosure'] = ['-0.02', '0.175', '-0.11', '0.148', '#interest', '#admiration', 'positive', '0.045', 'tolerant', 'phrase', 'inquiry', 'explain', 'question']
senticnet['selective_mutism'] = ['-0.79', '-0.71', '-0.96', '-0.95', '#fear', '#disgust', 'negative', '-0.49', 'fear', 'caution', 'deep_sadness', 'bad_feeling', 'psychoneurosis']
senticnet['selective_oxidation'] = ['0.918', '0.864', '0', '0', '#joy', '#interest', 'positive', '0.891', 'consequence', 'cause', 'bad_action', 'problem', 'political_statement']
senticnet['selectively'] = ['0.888', '0.862', '0', '0', '#joy', '#interest', 'positive', '0.875', 'successively', 'serially', 'consecutively', 'sequentially', 'sequential']
senticnet['selectivity'] = ['0', '-0.73', '0', '0.933', '#surprise', '#admiration', 'positive', '0.832', 'authority', 'choosy', 'selection', 'capability', 'selective']
senticnet['selectivity_filter'] = ['0', '0', '0.019', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'worm_intestine', 'bristle', 'fur_keep_warm', 'animal', 'whisker']
senticnet['selectman'] = ['0.98', '-0.93', '0', '0.926', '#joy', '#surprise', 'positive', '0.948', 'lame_duck', 'elected_official', 'lieutenant_governor', 'selectwoman', 'official']
senticnet['selector_lever'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'peavey']
senticnet['selectric'] = ['0', '0', '0.128', '0', '#anger', '#anger', 'negative', '-0.12', 'grid', 'efficient', 'calculator', 'computer', 'personal_computer']
senticnet['selectwoman'] = ['0.98', '-0.93', '0', '0.925', '#joy', '#surprise', 'positive', '0.946', 'lame_duck', 'elected_official', 'selectman', 'lieutenant_governor', 'official']
senticnet['selenide'] = ['-0.05', '-0.00', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'pigment', 'many_shades', 'cyan', 'green_yellow', 'material']
senticnet['selenium'] = ['-0.03', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'lithium', 'mineral', 'potassium', 'iodine', 'manganese']
senticnet['selenium_se'] = ['-0.03', '0', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.06', 'lithium', 'mineral', 'potassium', 'iodine', 'magnesium']
senticnet['selenography'] = ['0.916', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.929', 'galaxy', 'universe', 'planetary', 'sun', 'dwarf_planet']
senticnet['seleucid_kingdom'] = ['-0.69', '0', '0.957', '-0.92', '#anger', '#disgust', 'negative', '-0.85', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['self'] = ['0.825', '0.883', '0', '0', '#joy', '#interest', 'positive', '0.854', 'ego', 'research', 'messageid', 'symposium', 'behavior']
senticnet['self-abuse'] = ['0', '0', '0.789', '-0.77', '#anger', '#disgust', 'negative', '-0.78', 'self-congratulation', 'self-flagellation', 'self-delusion', 'hand-wringing', 'self-aggrandizement']
senticnet['self-adhesive'] = ['0', '0', '0.767', '-0.72', '#anger', '#disgust', 'negative', '-0.74', 'stick-on', 'self-stick', 'adhesive', 'glow-in-the-dark', 'adhesive-backed']
senticnet['self-aggrandizement'] = ['0', '-0.75', '0', '0.896', '#surprise', '#admiration', 'positive', '0.828', 'sophistry', 'bombast', 'puffery', 'bluster', 'self-aggrandizing']
senticnet['self-aggrandizing'] = ['-0.98', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.98', 'self-promoting', 'egotistical', 'self-serving', 'self-important', 'pompous']
senticnet['self-analysis'] = ['0.94', '0', '0', '0.842', '#joy', '#admiration', 'positive', '0.891', 'self-reflection', 'self-doubt', 'self-criticism', 'introspection', 'self-pity']
senticnet['self-annihilating'] = ['0', '0', '0.771', '-0.74', '#anger', '#disgust', 'negative', '-0.75', 'too-warm', 'viewless', 'self-enforcement', 'newsier', 'opaqued']
senticnet['self-assured'] = ['0.89', '0', '0', '0.767', '#joy', '#admiration', 'positive', '0.829', 'self-confident', 'self-reliant', 'assertive', 'confident', 'well-adjusted']
senticnet['self-awareness'] = ['0.972', '0', '0', '0.964', '#joy', '#admiration', 'positive', '0.968', 'self-knowledge', 'self-confidence', 'self-reflection', 'self-discovery', 'self-esteem']
senticnet['self-blame'] = ['0', '0', '-0.88', '-0.91', '#fear', '#disgust', 'negative', '-0.90', 'self-hate', 'self-hatred', 'self-punishment', 'self-flagellation', 'self-reproach']
senticnet['self-complacency'] = ['0.788', '0.592', '-0.20', '0.342', '#joy', '#interest', 'positive', '0.379', 'complacent', 'complacency', 'complacence', 'self-satisfaction', 'self-complacent']
senticnet['self-complacent'] = ['0.788', '0.592', '-0.20', '0.342', '#joy', '#interest', 'positive', '0.379', 'complacent', 'complacency', 'self-complacency', 'complacence', 'self-satisfaction']
senticnet['self-confidence'] = ['0.468', '0.219', '-0.20', '0.296', '#joy', '#admiration', 'positive', '0.194', 'good_emotion', 'faith', 'better_person', 'ecstatic', 'enjoy']
senticnet['self-confident'] = ['0.896', '0', '0', '0.77', '#joy', '#admiration', 'positive', '0.833', 'self-assured', 'self-reliant', 'assertive', 'confident', 'well-adjusted']
senticnet['self-congratulation'] = ['0', '0', '0.796', '-0.77', '#anger', '#disgust', 'negative', '-0.78', 'self-flagellation', 'self-abuse', 'self-delusion', 'hand-wringing', 'self-aggrandizement']
senticnet['self-conscious'] = ['0', '0', '0', '0.966', '#admiration', '#admiration', 'positive', '0.966', 'self-consciousness', 'uncomfortableness', 'pride', 'bashfulness', 'mortification']
senticnet['self-consciousness'] = ['0', '0', '0', '0.765', '#admiration', '#admiration', 'positive', '0.765', 'self-conscious', 'uncomfortableness', 'happiness', 'bashfulness', 'mortification']
senticnet['self-contained'] = ['0.707', '0.865', '0', '0', '#joy', '#interest', 'positive', '0.786', 'standalone', 'stand-alone', 'full-featured', 'web-based', 'integrated']
senticnet['self-criticism'] = ['0.942', '0', '0', '0.848', '#joy', '#admiration', 'positive', '0.895', 'self-reflection', 'self-doubt', 'self-analysis', 'introspection', 'self-pity']
senticnet['self-deception'] = ['0', '0', '-0.72', '-0.80', '#fear', '#disgust', 'negative', '-0.76', 'self-delusion', 'delusion', 'deceit', 'irrationality', 'gullibility']
senticnet['self-defeating'] = ['0', '0', '-0.99', '-0.87', '#fear', '#disgust', 'negative', '-0.93', 'self-destructive', 'counterproductive', 'counter-productive', 'self-fulfilling', 'wrong-headed']
senticnet['self-defence'] = ['0', '0', '-0.87', '-0.98', '#fear', '#disgust', 'negative', '-0.92', 'instinct', 'self-interest', 'self-preservation', 'instinctual', 'self-protection']
senticnet['self-delusion'] = ['0', '0', '0.796', '-0.78', '#anger', '#disgust', 'negative', '-0.79', 'self-congratulation', 'self-abuse', 'self-flagellation', 'hand-wringing', 'self-aggrandizement']
senticnet['self-deprecating'] = ['-0.98', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.96', 'self-depreciation', 'submission', 'meekness', 'humbleness', 'demeaning']
senticnet['self-depreciating'] = ['0', '-0.87', '0', '0.865', '#surprise', '#admiration', 'positive', '0.867', 'off-color', 'self-deprecating', 'off-handed', 'wisecrack', 'quip']
senticnet['self-depreciation'] = ['-0.92', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.83', 'self-deprecating', 'submission', 'meekness', 'humbleness', 'demeaning']
senticnet['self-destruction'] = ['0', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.88', 'self-destructive', 'self-delusion', 'self-hatred', 'self-discovery', 'self-indulgence']
senticnet['self-destructive'] = ['0', '0', '-0.99', '-0.88', '#fear', '#disgust', 'negative', '-0.93', 'self-defeating', 'counterproductive', 'counter-productive', 'self-fulfilling', 'wrong-headed']
senticnet['self-discipline'] = ['0.628', '0.57', '0', '0.579', '#joy', '#admiration', 'positive', '0.592', 'stressful_situation', 'part_of_band', 'beautiful_music', 'desire_information', 'chamber_music']
senticnet['self-discovery'] = ['0.964', '0', '0', '0.958', '#joy', '#admiration', 'positive', '0.961', 'self-knowledge', 'self-confidence', 'self-reflection', 'self-awareness', 'self-esteem']
senticnet['self-disgust'] = ['-0.72', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.77', 'self-hatred', 'humiliated', 'mortifying', 'mortified', 'shameful']
senticnet['self-distrust'] = ['-0.85', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.87', 'diffidently', 'self-doubt', 'diffidence', 'shyness', 'hardheartedness']
senticnet['self-doubt'] = ['-0.88', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.90', 'diffidently', 'self-distrust', 'diffidence', 'shyness', 'hardheartedness']
senticnet['self-effacing'] = ['-0.98', '0', '0.861', '0', '#sadness', '#anger', 'negative', '-0.92', 'unassuming', 'affable', 'soft-spoken', 'taciturn', 'mild-mannered']
senticnet['self-enforcement'] = ['0', '0', '0.771', '-0.74', '#anger', '#disgust', 'negative', '-0.75', 'too-warm', 'self-annihilating', 'viewless', 'newsier', 'opaqued']
senticnet['self-esteem'] = ['0.49', '0.652', '-0.24', '0.233', '#joy', '#interest', 'positive', '0.284', 'confident', 'encouragement', 'grateful', 'enthusiasm', 'thankful']
senticnet['self-evident'] = ['0', '0.73', '0', '0.918', '#interest', '#admiration', 'positive', '0.824', 'inviolable', 'inviolate', 'inalienable', 'sanctity', 'sacrosanct']
senticnet['self-evidently'] = ['0.869', '0', '0', '0.954', '#joy', '#admiration', 'positive', '0.911', 'demonstrably', 'patently', 'incontrovertibly', 'palpably', 'manifestly']
senticnet['self-exaltation'] = ['0', '-0.72', '0', '0.891', '#surprise', '#admiration', 'positive', '0.807', 'exalt', 'elevation', 'ecstasy', 'deification', 'exaltation']
senticnet['self-examination'] = ['0', '0.861', '0', '0.745', '#interest', '#admiration', 'positive', '0.803', 'self-reflection', 'self-analysis', 'introspection', 'self-criticism', 'soul-searching']
senticnet['self-flagellation'] = ['0', '0', '0.798', '-0.77', '#anger', '#disgust', 'negative', '-0.78', 'self-congratulation', 'self-abuse', 'self-delusion', 'hand-wringing', 'self-aggrandizement']
senticnet['self-forgetful'] = ['-0.80', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'commitment-free', 'six-question', 'one-camera', 'three-camera', 'free-and-easy']
senticnet['self-fulfilling'] = ['0', '0', '-0.99', '-0.87', '#fear', '#disgust', 'negative', '-0.93', 'self-destructive', 'counterproductive', 'counter-productive', 'self-defeating', 'wrong-headed']
senticnet['self-fulfillment'] = ['0.664', '0.903', '0', '0.597', '#joy', '#interest', 'positive', '0.721', 'learn_sport', 'learn_new_skill', 'stimulate_mind', 'pass_knowledge', 'get_information']
senticnet['self-governing'] = ['0', '0.908', '0', '0.829', '#interest', '#admiration', 'positive', '0.869', 'self-sustaining', 'self-sufficient', 'self-reliant', 'self-supporting', 'self-sustainable']
senticnet['self-gratification'] = ['0.924', '0', '0', '0.754', '#joy', '#admiration', 'positive', '0.839', 'self-aggrandizement', 'self-fulfillment', 'gratification', 'self-indulgence', 'aggrandizement']
senticnet['self-hate'] = ['0', '0', '-0.89', '-0.91', '#fear', '#disgust', 'negative', '-0.90', 'self-punishment', 'self-hatred', 'self-blame', 'self-flagellation', 'self-reproach']
senticnet['self-hatred'] = ['-0.84', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.91', 'mortify', 'embarrassed', 'abase', 'self-disgust', 'humiliated']
senticnet['self-hypnosis'] = ['0.839', '-0.88', '0', '0', '#joy', '#surprise', 'positive', '0.864', 'hypnosis', 'subconscious', 'affirmation', 'subliminal', 'hypnotic']
senticnet['self-identity'] = ['0.804', '0', '0', '0.889', '#joy', '#admiration', 'positive', '0.847', 'self-esteem', 'self-respect', 'self-confidence', 'self-image', 'self-worth']
senticnet['self-image'] = ['0.797', '0', '0', '0.886', '#joy', '#admiration', 'positive', '0.842', 'self-esteem', 'self-respect', 'self-confidence', 'self-worth', 'self-identity']
senticnet['self-importance'] = ['0.466', '0.663', '-0.25', '0.228', '#joy', '#interest', 'positive', '0.277', 'egotism', 'amour_propre', 'conceit', 'self-love', 'self-pride']
senticnet['self-important'] = ['-0.99', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.99', 'self-promoting', 'egotistical', 'self-serving', 'self-aggrandizing', 'pompous']
senticnet['self-indulgence'] = ['0.922', '0', '0', '0.75', '#joy', '#admiration', 'positive', '0.836', 'self-aggrandizement', 'self-fulfillment', 'gratification', 'self-gratification', 'aggrandizement']
senticnet['self-indulgent'] = ['0', '0', '0.756', '-0.83', '#anger', '#disgust', 'negative', '-0.79', 'decline', 'decadence', 'decay', 'effete', 'decadent']
senticnet['self-interest'] = ['0', '0', '-0.87', '-0.98', '#fear', '#disgust', 'negative', '-0.93', 'instinct', 'self-preservation', 'self-defence', 'instinctual', 'self-protection']
senticnet['self-interested'] = ['0', '0.934', '0', '0.788', '#interest', '#admiration', 'positive', '0.861', 'hypocritical', 'self-aggrandizing', 'self-serving', 'disingenuous', 'egotistical']
senticnet['self-justification'] = ['0', '0', '0.714', '-0.98', '#anger', '#disgust', 'negative', '-0.84', 'self-delusion', 'self-aggrandizement', 'self-justifying', 'self-righteousness', 'self-pity']
senticnet['self-justifying'] = ['0', '0', '0.707', '-0.98', '#anger', '#disgust', 'negative', '-0.84', 'self-delusion', 'self-aggrandizement', 'self-justification', 'self-righteousness', 'self-pity']
senticnet['self-knowledge'] = ['0.971', '0', '0', '0.956', '#joy', '#admiration', 'positive', '0.964', 'self-awareness', 'self-confidence', 'self-reflection', 'self-discovery', 'self-esteem']
senticnet['self-less'] = ['0.864', '0', '0', '0.867', '#joy', '#admiration', 'positive', '0.865', 'selfless', 'altruistic', 'self-sacrificing', 'unselfish', 'selflessness']
senticnet['self-love'] = ['0.469', '0.661', '-0.24', '0.23', '#joy', '#interest', 'positive', '0.278', 'amour_propre', 'conceit', 'self-pride', 'egotism', 'self-importance']
senticnet['self-made'] = ['0.778', '0', '0', '0.867', '#joy', '#admiration', 'positive', '0.823', 'successful', 'famous', 'millionaire', 'entrepreneur', 'wealthy']
senticnet['self-motivation'] = ['0.09', '0.165', '-0.05', '0', '#joy', '#interest', 'positive', '0.068', 'place_bet', 'competitive_spirit', 'deserve', 'need_job', 'down_payment']
senticnet['self-pity'] = ['-0.87', '0.182', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'mournful', 'bereaved', 'mourning', 'grieving', 'sorrowing']
senticnet['self-pleasure'] = ['0.748', '0', '0', '0.733', '#joy', '#admiration', 'positive', '0.74', 'high-five', 'harasser', 'same-age', 'self-stimulation', 'self-pleasuring']
senticnet['self-pleasuring'] = ['0.748', '0', '0', '0.737', '#joy', '#admiration', 'positive', '0.742', 'high-five', 'harasser', 'same-age', 'self-pleasure', 'self-stimulation']
senticnet['self-preservation'] = ['0', '0', '-0.86', '-0.97', '#fear', '#disgust', 'negative', '-0.92', 'instinct', 'self-interest', 'self-defence', 'instinctual', 'self-protection']
senticnet['self-pride'] = ['0.463', '0.662', '-0.24', '0.225', '#joy', '#interest', 'positive', '0.276', 'amour_propre', 'conceit', 'self-love', 'self-importance', 'egotism']
senticnet['self-priming'] = ['-0.86', '0', '-0.84', '0', '#sadness', '#fear', 'negative', '-0.85', 'impeller', 'centripetal', 'centrifugal', 'spiral', 'pump']
senticnet['self-professed'] = ['0', '0.987', '0', '0.709', '#interest', '#admiration', 'positive', '0.848', 'dyed-in-the-wool', 'red-blooded', 'true-blue', 'die-hard', 'bona-fide']
senticnet['self-promoting'] = ['-0.98', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.98', 'self-aggrandizing', 'egotistical', 'self-serving', 'self-important', 'pompous']
senticnet['self-protection'] = ['0', '0', '-0.87', '-0.97', '#fear', '#disgust', 'negative', '-0.92', 'instinct', 'self-interest', 'self-defence', 'instinctual', 'self-preservation']
senticnet['self-punishment'] = ['0', '0', '-0.88', '-0.90', '#fear', '#disgust', 'negative', '-0.89', 'self-hate', 'self-hatred', 'self-blame', 'self-flagellation', 'self-reproach']
senticnet['self-reflection'] = ['0.972', '0', '0', '0.958', '#joy', '#admiration', 'positive', '0.965', 'self-knowledge', 'self-confidence', 'self-awareness', 'self-discovery', 'self-esteem']
senticnet['self-reliant'] = ['0.956', '0', '0', '0.862', '#joy', '#admiration', 'positive', '0.909', 'self-sufficient', 'self-confident', 'resourceful', 'self-sustaining', 'well-adjusted']
senticnet['self-reproach'] = ['-0.89', '0.166', '-0.61', '0.051', '#sadness', '#fear', 'negative', '-0.32', 'compunction', 'rue', 'attrition', 'ruefully', 'contritely']
senticnet['self-respect'] = ['0.805', '0', '0', '0.889', '#joy', '#admiration', 'positive', '0.847', 'self-esteem', 'self-worth', 'self-confidence', 'self-image', 'self-identity']
senticnet['self-respecting'] = ['0', '-0.78', '0', '0.727', '#surprise', '#admiration', 'positive', '0.754', 'right-thinking', 'red-blooded', 'fair-minded', 'god-fearing', 'die-hard']
senticnet['self-restraint'] = ['0', '0.93', '0', '0.758', '#interest', '#admiration', 'positive', '0.844', 'statecraft', 'bipartisanship', 'decisiveness', 'pragmatism', 'statesmanship']
senticnet['self-righteous'] = ['0.78', '0', '0', '0.907', '#joy', '#admiration', 'positive', '0.844', 'sanctimonious', 'holier-than-thou', 'pompous', 'hypocritical', 'arrogant']
senticnet['self-righteously'] = ['0.802', '0', '0', '0.916', '#joy', '#admiration', 'positive', '0.859', 'hypocritically', 'arrogantly', 'sanctimoniously', 'pompously', 'piously']
senticnet['self-righteousness'] = ['0', '0', '0.704', '-0.98', '#anger', '#disgust', 'negative', '-0.84', 'self-delusion', 'self-aggrandizement', 'self-justifying', 'self-justification', 'self-pity']
senticnet['self-sacrifice'] = ['0.966', '0', '0', '0.864', '#joy', '#admiration', 'positive', '0.915', 'unselfish', 'self-sacrificing', 'selflessness', 'altruistic', 'selfless']
senticnet['self-sacrificing'] = ['0.965', '0', '0', '0.867', '#joy', '#admiration', 'positive', '0.916', 'unselfish', 'selfless', 'selflessness', 'altruistic', 'self-sacrifice']
senticnet['self-satisfaction'] = ['0.788', '0.592', '-0.20', '0.342', '#joy', '#interest', 'positive', '0.379', 'complacent', 'complacency', 'self-complacency', 'complacence', 'self-complacent']
senticnet['self-satisfied'] = ['0.816', '0', '0', '0.742', '#joy', '#admiration', 'positive', '0.779', 'proudly', 'smug', 'congratulation', 'congratulate', 'applause']
senticnet['self-selecting'] = ['-1', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.89', 'non-representative', 'undemocratic', 'unscientific', 'unrepresentative', 'unaccountable']
senticnet['self-serving'] = ['0', '0.938', '0', '0.792', '#interest', '#admiration', 'positive', '0.865', 'hypocritical', 'self-aggrandizing', 'self-interested', 'disingenuous', 'egotistical']
senticnet['self-stick'] = ['0', '0', '0.767', '-0.72', '#anger', '#disgust', 'negative', '-0.74', 'self-adhesive', 'stick-on', 'adhesive', 'glow-in-the-dark', 'adhesive-backed']
senticnet['self-stimulation'] = ['0.756', '0', '0', '0.742', '#joy', '#admiration', 'positive', '0.749', 'high-five', 'harasser', 'same-age', 'self-pleasure', 'self-pleasuring']
senticnet['self-sufficient'] = ['0.95', '0', '0', '0.856', '#joy', '#admiration', 'positive', '0.903', 'self-reliant', 'self-confident', 'resourceful', 'self-sustaining', 'well-adjusted']
senticnet['self-supporting'] = ['0', '0.911', '0', '0.833', '#interest', '#admiration', 'positive', '0.872', 'self-sustaining', 'self-sufficient', 'self-reliant', 'self-governing', 'self-sustainable']
senticnet['self-sustainable'] = ['0', '0.909', '0', '0.825', '#interest', '#admiration', 'positive', '0.867', 'self-sustaining', 'self-sufficient', 'self-reliant', 'self-governing', 'self-supporting']
senticnet['self-sustaining'] = ['0.948', '0', '0', '0.858', '#joy', '#admiration', 'positive', '0.903', 'self-sufficient', 'self-confident', 'resourceful', 'self-reliant', 'well-adjusted']
senticnet['self-tanning'] = ['-0.78', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.85', 'tanning', 'sunless', 'lotion', 'tanner', 'bronzing']
senticnet['self-titled'] = ['0.949', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.968', 'full-length', 'feature-length', 'debut', 'album', 'release']
senticnet['self-worth'] = ['0.807', '0', '0', '0.892', '#joy', '#admiration', 'positive', '0.85', 'self-esteem', 'self-respect', 'self-confidence', 'self-image', 'self-identity']
senticnet['self_abasement'] = ['-0.84', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.90', 'mortify', 'embarrassed', 'self-hatred', 'self-disgust', 'humiliated']
senticnet['self_abnegation'] = ['0.889', '-0.69', '0', '0.908', '#joy', '#admiration', 'positive', '0.83', 'pharisaic', 'pious', 'sanctimonious', 'reverent', 'worshipful']
senticnet['self_absorption'] = ['0.08', '0.153', '-0.12', '0.224', '#interest', '#admiration', 'positive', '0.083', 'meditation', 'self_contemplation', 'soul_searching', 'self_examination', 'self_observation']
senticnet['self_actualization'] = ['0.971', '-0.71', '0', '0.656', '#joy', '#surprise', 'positive', '0.781', 'wellbeing', 'human_flourishing', 'successfulness', 'upbeat', 'strength']
senticnet['self_adhesive'] = ['-0.81', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.85', 'bandage', 'painkiller', 'gauze', 'aspirin', 'contraceptive_device']
senticnet['self_adhesive_label'] = ['0.748', '0.983', '0', '0', '#joy', '#interest', 'positive', '0.865', 'paper_punch', 'intern', 'memo_pad', 'notepad', 'paper_clip']
senticnet['self_adhesive_stamp'] = ['0.058', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.054', 'letter_slot', 'envelope', 'pamphlet', 'mailman', 'address_label']
senticnet['self_administer_cocaine'] = ['0.583', '-0.82', '0', '0.574', '#joy', '#surprise', 'positive', '0.661', 'stimulant', 'positive_reinforcing_stimulus', 'reinforcer', 'negative_reinforcing_stimulus', 'negative_reinforcer']
senticnet['self_aggrandisement'] = ['-0.5', '0', '0.939', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'avarice', 'covetousness', 'possessiveness', 'desire', 'greed']
senticnet['self_aggrandizement'] = ['0.742', '-0.83', '0', '0.889', '#surprise', '#admiration', 'positive', '0.823', 'furtiveness', 'sneakiness', 'stealthiness', 'unsociability', 'unsociableness']
senticnet['self_annihilation'] = ['-0.77', '0', '0.5', '-0.75', '#sadness', '#disgust', 'negative', '-0.67', 'apathy', 'indifference', 'passivity', 'inertia', 'inactiveness']
senticnet['self_assemble'] = ['0.048', '0.092', '-0.08', '0.216', '#interest', '#admiration', 'positive', '0.067', 'gather', 'group', 'organization', 'clique', 'collection']
senticnet['self_assembled_monolayer'] = ['-0.93', '0', '0.739', '-0.96', '#sadness', '#disgust', 'negative', '-0.88', 'prevarication', 'fairytale', 'fairy_tale', 'fairy_story', 'play']
senticnet['self_assertion'] = ['0.936', '0.757', '0', '0.927', '#joy', '#admiration', 'positive', '0.873', 'attitude', 'better', 'trait', 'blue_ribbon', 'exclamation']
senticnet['self_assertive'] = ['0.585', '-0.7', '0', '0.586', '#surprise', '#admiration', 'positive', '0.624', 'jingoistic', 'ultranationalistic', 'superpatriotic', 'nationalistic', 'patriotic']
senticnet['self_assessment'] = ['-0.06', '0.18', '0', '0.143', '#interest', '#admiration', 'positive', '0.086', 'grade', 'multiple_choices', 'final', 'final_exam', 'school_grade']
senticnet['self_assurance'] = ['0.936', '0.757', '0', '0.927', '#joy', '#admiration', 'positive', '0.873', 'attitude', 'better', 'trait', 'blue_ribbon', 'exclamation']
senticnet['self_atman'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
senticnet['self_categorization'] = ['-0.93', '0', '0.586', '-0.66', '#sadness', '#disgust', 'negative', '-0.73', 'camp', 'pack', 'clique', 'coterie', 'brain_trust']
senticnet['self_checkout'] = ['0.813', '0.832', '0', '0.874', '#interest', '#admiration', 'positive', '0.84', 'ferris_wheel', 'concession_stand', 'food', 'carnival_ride', 'theme_park']
senticnet['self_complacency'] = ['-0.96', '0', '0.91', '-0.61', '#sadness', '#anger', 'negative', '-0.82', 'unpersuadable', 'unsuasible', 'unresponsive', 'resistant', 'incapable']
senticnet['self_complacent'] = ['-1', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.94', 'complacency', 'self-complacency', 'complacence', 'self_satisfaction', 'complacent']
senticnet['self_conceit'] = ['-0.84', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.90', 'mortify', 'embarrassed', 'self-hatred', 'self-disgust', 'humiliated']
senticnet['self_confidence'] = ['0.817', '0', '0', '0.982', '#joy', '#admiration', 'positive', '0.899', 'big_paycheck', 'look_beautiful', 'find_money', 'take_out_loan', 'slogan']
senticnet['self_confident'] = ['0.936', '0.757', '0', '0.927', '#joy', '#admiration', 'positive', '0.873', 'attitude', 'better', 'trait', 'blue_ribbon', 'exclamation']
senticnet['self_congratulatory'] = ['-0.54', '0', '0.7', '-0.64', '#anger', '#disgust', 'negative', '-0.62', 'flourish', 'rant', 'ornateness', 'fustian', 'claptrap']
senticnet['self_consciously'] = ['0', '0.915', '0', '0.704', '#interest', '#admiration', 'positive', '0.809', 'low-brow', 'highbrow', 'high-brow', 'middle-brow', 'lowbrow']
senticnet['self_consciously_literary'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'mediacy', 'mediateness', 'characteristic', 'indirectness']
senticnet['self_contained_classroom'] = ['-0.69', '0', '0.659', '-0.69', '#sadness', '#disgust', 'negative', '-0.68', 'help', 'paralegal', 'legal_assistant', 'paramedic', 'paramedical']
senticnet['self_contemplation'] = ['0.08', '0.153', '-0.12', '0.224', '#interest', '#admiration', 'positive', '0.083', 'meditation', 'introspection', 'soul_searching', 'self_examination', 'self_observation']
senticnet['self_contradiction'] = ['-0.55', '0', '0.741', '-0.76', '#anger', '#disgust', 'negative', '-0.68', 'illogicality', 'illogicalness', 'illogic', 'invalidity', 'fallaciousness']
senticnet['self_contradictory'] = ['-0.92', '0', '0.547', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'assumption', 'boundary_condition', 'provision', 'proviso', 'precondition']
senticnet['self_deceit'] = ['-0.50', '0', '0.641', '-0.89', '#anger', '#disgust', 'negative', '-0.68', 'delusion_of_persecution', 'hallucination', 'delusion_of_grandeur', 'nihilistic_delusion', 'nihilism']
senticnet['self_deception'] = ['0.08', '0.153', '-0.12', '0.224', '#interest', '#admiration', 'positive', '0.083', 'meditation', 'self_contemplation', 'soul_searching', 'self_examination', 'self_observation']
senticnet['self_defence'] = ['-0.85', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.86', 'attack', 'raid', 'fight_against', 'conflict', 'opposition']
senticnet['self_defense'] = ['0', '0', '0.997', '-0.71', '#anger', '#disgust', 'negative', '-0.85', 'target_practice', 'kill_enemy', 'kill_pest', 'murder', 'protect_country']
senticnet['self_delusion'] = ['0.75', '-0.87', '0', '0.603', '#joy', '#surprise', 'positive', '0.743', 'quality', 'futility', 'impracticality', 'worthlessness', 'impracticability']
senticnet['self_denial'] = ['0.056', '0.157', '0', '0.154', '#interest', '#admiration', 'positive', '0.122', 'exercise', 'exercise_regularly', 'live_healthy_life', 'exercise_often', 'exercise_much']
senticnet['self_deprecating_humor'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'rudeness', 'discourtesy', 'insolence', 'chutzpah', 'hutzpah']
senticnet['self_deprecating_wit'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'rudeness', 'discourtesy', 'insolence', 'chutzpah', 'hutzpah']
senticnet['self_deprecation'] = ['0.909', '-0.73', '0', '0.894', '#joy', '#admiration', 'positive', '0.846', 'creativity', 'creativeness', 'creative_thinking', 'imagination', 'resourcefulness']
senticnet['self_destruct_mechanism'] = ['-0.99', '0', '0.651', '-0.51', '#sadness', '#anger', 'negative', '-0.72', 'sending', 'compulsion', 'influence', 'inducement', 'causation']
senticnet['self_destruction'] = ['0', '0', '-0.92', '-0.88', '#fear', '#disgust', 'negative', '-0.90', 'temper', 'disgust', 'offend', 'despise', 'animosity']
senticnet['self_determination'] = ['0.887', '0.748', '0', '0.954', '#joy', '#admiration', 'positive', '0.863', 'political_action', 'political', 'mediocrity', 'discrimination', 'independent']
senticnet['self_discipline'] = ['-0.05', '0.117', '-0.11', '0.134', '#interest', '#admiration', 'positive', '0.021', 'time_effort', 'persistence', 'require', 'open_mind', 'new_experience']
senticnet['self_disgust'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['self_doubt'] = ['-0.84', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.82', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecure']
senticnet['self_effacement'] = ['0.968', '-0.87', '0', '0.992', '#joy', '#admiration', 'positive', '0.946', 'sagacity', 'sagaciousness', 'care', 'caution', 'discernment']
senticnet['self_efficacy'] = ['0.936', '0.757', '0', '0.927', '#joy', '#admiration', 'positive', '0.873', 'attitude', 'better', 'trait', 'blue_ribbon', 'exclamation']
senticnet['self_employment'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'lucrative', 'moneymaking', 'profitable', 'paying', 'paid']
senticnet['self_esteem'] = ['0', '0', '0', '0.784', '#admiration', '#admiration', 'positive', '0.784', 'live_well', 'better_life', 'accomplish_goal', 'live_long_happy_life', 'nice_place_to_live']
senticnet['self_evaluation'] = ['-0.06', '0.18', '0', '0.143', '#interest', '#admiration', 'positive', '0.086', 'grade', 'multiple_choices', 'final', 'final_exam', 'school_grade']
senticnet['self_evident'] = ['0.863', '-0.94', '0', '0.986', '#surprise', '#admiration', 'positive', '0.932', 'postulate', 'posit', 'assume', 'presume', 'take_for_granted']
senticnet['self_evidently'] = ['0.544', '-0.69', '0', '0.658', '#surprise', '#admiration', 'positive', '0.632', 'shakable', 'shakeable', 'challengeable', 'disputable', 'contestable']
senticnet['self_exaltation'] = ['-0.84', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.90', 'mortify', 'embarrassed', 'self-hatred', 'self-disgust', 'humiliated']
senticnet['self_examination'] = ['0.08', '0.153', '-0.12', '0.224', '#interest', '#admiration', 'positive', '0.083', 'meditation', 'self_contemplation', 'soul_searching', 'introspection', 'self_observation']
senticnet['self_existent'] = ['0.071', '0.222', '-0.10', '0.234', '#interest', '#admiration', 'positive', '0.105', 'baron', 'almighty', 'deity', 'nobility', 'god']
senticnet['self_expression'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'creative_expression', 'pastime', 'artistic_expression', 'date_activity', 'make_beautiful']
senticnet['self_flagellation'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'reverence', 'obeisance', 'genuflexion', 'kowtow', 'mafia']
senticnet['self_fulfilling_prophesy'] = ['-0.52', '0', '0.95', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'skeptic', 'sceptic', 'doubter', 'pessimist', 'negativist']
senticnet['self_fulfillment'] = ['0.971', '-0.71', '0', '0.656', '#joy', '#surprise', 'positive', '0.781', 'wellbeing', 'human_flourishing', 'successfulness', 'upbeat', 'strength']
senticnet['self_fulfilment'] = ['0.961', '0.937', '0', '0', '#joy', '#interest', 'positive', '0.949', 'create_new_life', 'make_beautiful', 'stimulation', 'lower_stress', 'leisure_time']
senticnet['self_glorification'] = ['-0.5', '0', '0.939', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'avarice', 'covetousness', 'possessiveness', 'desire', 'greed']
senticnet['self_governance'] = ['0.952', '-0.84', '0', '0.875', '#joy', '#admiration', 'positive', '0.89', 'administration', 'judiciary', 'governing_body', 'establishment', 'brass']
senticnet['self_governing_island'] = ['-0.65', '0', '0.505', '-0.91', '#sadness', '#disgust', 'negative', '-0.69', 'hellhound', 'mythical_being', 'dragon', 'mythical_monster', 'amphisbaena']
senticnet['self_gratification'] = ['0.978', '0.81', '0', '0.848', '#joy', '#admiration', 'positive', '0.879', 'brownie_point', 'feel_proud', 'receive_thank', 'appreciate', 'positive_reinforcement']
senticnet['self_incriminating_evidence'] = ['-0.70', '0', '0.874', '-0.75', '#anger', '#disgust', 'negative', '-0.77', 'blame', 'accusation', 'inculpation', 'complaint', 'imprecation']
senticnet['self_incrimination'] = ['-0.70', '0', '0.874', '-0.75', '#anger', '#disgust', 'negative', '-0.77', 'blame', 'accusation', 'inculpation', 'complaint', 'imprecation']
senticnet['self_incrimination_clause'] = ['-0.70', '0', '0.874', '-0.75', '#anger', '#disgust', 'negative', '-0.77', 'blame', 'accusation', 'inculpation', 'complaint', 'imprecation']
senticnet['self_indulgence'] = ['-0.75', '0', '0.683', '-0.99', '#sadness', '#disgust', 'negative', '-0.80', 'cussedness', 'orneriness', 'unruliness', 'fractiousness', 'willfulness']
senticnet['self_indulgent'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['self_inflicted_gunshot_wound'] = ['-0.08', '-0.02', '0', '-0.19', '#sadness', '#disgust', 'negative', '-0.08', 'wound', 'lethal', 'vaccination', 'destruction', 'fire_bullet']
senticnet['self_injurious_behavior'] = ['0.542', '-0.63', '0', '0.912', '#surprise', '#admiration', 'positive', '0.695', 'implosion_therapy', 'flooding', 'reciprocal_inhibition', 'behavior_modification', 'desensitization_technique']
senticnet['self_interest'] = ['0', '0.189', '0', '0.177', '#interest', '#admiration', 'positive', '0.183', 'good', 'value', 'elusive', 'uncommon', 'versatile']
senticnet['self_love'] = ['0', '0', '0.747', '-0.90', '#anger', '#disgust', 'negative', '-0.82', 'conceit', 'amour_propre', 'self_pride', 'egotism', 'self-importance']
senticnet['self_mastery'] = ['0.971', '-0.71', '0', '0.656', '#joy', '#surprise', 'positive', '0.781', 'wellbeing', 'human_flourishing', 'successfulness', 'upbeat', 'strength']
senticnet['self_medicate'] = ['0', '0.931', '0', '0', '#interest', '#interest', 'positive', '0.931', 'football', 'rake_leave', 'spray_paint_grafitti', 'juggle', 'play_hide_seek']
senticnet['self_mockery'] = ['0.918', '0.476', '-0.19', '0.417', '#joy', '#interest', 'positive', '0.404', 'merriment', 'cheery', 'gaiety', 'festal', 'plume']
senticnet['self_mortification'] = ['-0.74', '0', '0.585', '-0.58', '#sadness', '#anger', 'negative', '-0.63', 'lack_of_quality', 'meagreness', 'poorness', 'scantiness', 'scantness']
senticnet['self_motivation'] = ['0.701', '0.874', '0', '0.752', '#interest', '#admiration', 'positive', '0.776', 'self_satisfaction', 'enjoy_life', 'good_feeling', 'good_attitude', 'enjoy']
senticnet['self_mutilation'] = ['-0.17', '0', '-0.16', '-0.09', '#sadness', '#fear', 'negative', '-0.14', 'murder', 'hurt_others', 'death_penalty', 'death_destruction', 'end_live']
senticnet['self_observation'] = ['0.08', '0.153', '-0.12', '0.224', '#interest', '#admiration', 'positive', '0.083', 'meditation', 'self_contemplation', 'soul_searching', 'self_examination', 'introspection']
senticnet['self_parody'] = ['0.741', '-0.74', '0', '0.666', '#joy', '#surprise', 'positive', '0.718', 'commonplaceness', 'mundanity', 'prosiness', 'prosaicness', 'ordinariness']
senticnet['self_pity'] = ['-0.83', '0', '0.904', '0', '#sadness', '#anger', 'negative', '-0.87', 'disappointed', 'thwarted', 'frustrated', 'foiled', 'discomfited']
senticnet['self_portrait'] = ['0', '0.074', '0', '0.06', '#interest', '#admiration', 'positive', '0.067', 'use_crayon', 'draw', 'pencil_art', 'make_sketch', 'outline']
senticnet['self_portraiture'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'design', 'carving', 'decoupage', 'ceramic']
senticnet['self_preservation'] = ['0.988', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.98', 'sustain_life', 'sustenance', 'nutrition', 'keep_alive', 'satisfy_hunger']
senticnet['self_pride'] = ['0', '0', '0.752', '-0.90', '#anger', '#disgust', 'negative', '-0.82', 'self_love', 'amour_propre', 'conceit', 'egotism', 'self-importance']
senticnet['self_promoter'] = ['-0.61', '0', '0.738', '-0.80', '#anger', '#disgust', 'negative', '-0.71', 'dissident', 'beat', 'bohemian', 'maverick', 'recusant']
senticnet['self_propelled_artillery'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'tank', 'tank_destroyer', 'armored_car', 'armored_vehicle', 'armored_personnel_carrier']
senticnet['self_propelled_gun'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'tank', 'tank_destroyer', 'armored_car', 'armored_vehicle', 'armored_personnel_carrier']
senticnet['self_propelled_howitzer'] = ['-0.56', '0', '0.714', '-0.89', '#anger', '#disgust', 'negative', '-0.72', 'tank', 'tank_destroyer', 'armored_car', 'armoured_car', 'armored_personnel_carrier']
senticnet['self_protection'] = ['0.059', '0.212', '0', '0.143', '#interest', '#admiration', 'positive', '0.138', 'safe', 'protect', 'protection', 'protect_family', 'self_esteem']
senticnet['self_publicist'] = ['-0.61', '0', '0.738', '-0.80', '#anger', '#disgust', 'negative', '-0.71', 'dissident', 'beat', 'bohemian', 'maverick', 'recusant']
senticnet['self_purification'] = ['0.581', '-0.88', '0', '0.534', '#joy', '#surprise', 'positive', '0.667', 'better', 'improve', 'pure', 'meliorate', 'amend']
senticnet['self_realisation'] = ['0.971', '-0.71', '0', '0.656', '#joy', '#surprise', 'positive', '0.781', 'wellbeing', 'human_flourishing', 'successfulness', 'upbeat', 'strength']
senticnet['self_realization'] = ['0.971', '-0.71', '0', '0.656', '#joy', '#surprise', 'positive', '0.781', 'wellbeing', 'human_flourishing', 'successfulness', 'upbeat', 'strength']
senticnet['self_recrimination'] = ['0', '0', '0.782', '-0.83', '#anger', '#disgust', 'negative', '-0.80', 'amuse', 'see_actor', 'wish_entertain', 'enjoyment', 'pastime']
senticnet['self_reducibility'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly']
senticnet['self_referent'] = ['0.43', '0.657', '-0.28', '0.244', '#joy', '#interest', 'positive', '0.262', 'expectation', 'look_for', 'amour_propre', 'self-love', 'conceit']
senticnet['self_referential'] = ['-0.66', '0', '0.918', '-0.62', '#sadness', '#anger', 'negative', '-0.73', 'beguiler', 'slicker', 'hidden', 'ringer', 'deceiver']
senticnet['self_reflection'] = ['0.08', '0.153', '-0.12', '0.224', '#interest', '#admiration', 'positive', '0.083', 'meditation', 'self_contemplation', 'soul_searching', 'self_examination', 'self_observation']
senticnet['self_reflective'] = ['0.08', '0.153', '-0.12', '0.224', '#interest', '#admiration', 'positive', '0.083', 'meditation', 'self_contemplation', 'soul_searching', 'self_examination', 'self_observation']
senticnet['self_reflexivity'] = ['-0.94', '0', '0.925', '-0.97', '#sadness', '#disgust', 'negative', '-0.95', 'nonexistence', 'nonentity', 'cloud', 'falseness', 'falsity']
senticnet['self_regulation'] = ['-0.02', '0.1', '-0.07', '0.089', '#interest', '#admiration', 'positive', '0.023', 'functionary', 'employment', 'labor', 'employ', 'toil']
senticnet['self_regulatory'] = ['-0.02', '0.1', '-0.07', '0.089', '#interest', '#admiration', 'positive', '0.023', 'functionary', 'employment', 'labor', 'employ', 'toil']
senticnet['self_reliant'] = ['0.545', '-0.59', '0', '0.865', '#surprise', '#admiration', 'positive', '0.67', 'farseeing', 'longsighted', 'telescopic', 'hyperopic', 'hypermetropic']
senticnet['self_replication'] = ['-0.7', '0', '0.77', '-0.75', '#anger', '#disgust', 'negative', '-0.74', 'fake', 'unnatural', 'man_made', 'artificial_sea', 'artificial_taste']
senticnet['self_reproach'] = ['-0.51', '0', '0.584', '-0.84', '#anger', '#disgust', 'negative', '-0.64', 'ill_humor', 'distemper', 'pet', 'testiness', 'touchiness']
senticnet['self_respect'] = ['0', '0', '0', '0.816', '#admiration', '#admiration', 'positive', '0.816', 'financial_security', 'strong_relationship', 'qualify', 'happiness', 'prototype']
senticnet['self_restraint'] = ['0.968', '-0.87', '0', '0.992', '#joy', '#admiration', 'positive', '0.946', 'sagacity', 'sagaciousness', 'care', 'caution', 'discernment']
senticnet['self_righteous_indignation'] = ['-0.69', '0', '0.792', '-0.98', '#anger', '#disgust', 'negative', '-0.82', 'outcry', 'call', 'cry', 'yell', 'shout']
senticnet['self_righteously'] = ['-0.55', '0', '0.947', '-0.77', '#anger', '#disgust', 'negative', '-0.75', 'blackguard', 'bemock', 'twit', 'tease', 'razz']
senticnet['self_sacrifice'] = ['0', '0', '0.764', '-0.77', '#anger', '#disgust', 'negative', '-0.77', 'restriction', 'tragedy', 'perpetrator', 'dominate', 'control']
senticnet['self_satisfaction'] = ['0.332', '0.103', '-0.08', '0', '#joy', '#interest', 'positive', '0.116', 'good_feeling', 'happiness', 'serenity', 'feel_good', 'become_happy']
senticnet['self_satisfied'] = ['-0.71', '0', '0', '-0.96', '#sadness', '#disgust', 'negative', '-0.84', 'smugness', 'proudly', 'smug', 'preen', 'complacence']
senticnet['self_seeder'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'intrepidly', 'fearlessness', 'fearlessly', 'unafraid', 'unfearing']
senticnet['self_subsistent'] = ['-0.57', '0', '0.713', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'assured', 'conclusion', 'certainty', 'predictability', 'definiteness']
senticnet['self_timer'] = ['-0.82', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.84', 'block', 'obstruct', 'close', 'clogged', 'occluded']
senticnet['self_titled_solo_album'] = ['0.899', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.813', 'concert', 'measure_angle', 'make_drawing', 'make_beautiful_music', 'listen_heart']
senticnet['self_transcendence'] = ['0.971', '-0.71', '0', '0.656', '#joy', '#surprise', 'positive', '0.781', 'wellbeing', 'human_flourishing', 'successfulness', 'upbeat', 'strength']
senticnet['selfassembly'] = ['0', '0.829', '0', '0.744', '#interest', '#admiration', 'positive', '0.786', 'award', 'research', 'study', 'sponsor', 'project']
senticnet['selfhood'] = ['0.122', '0.159', '-0.05', '0.217', '#interest', '#admiration', 'positive', '0.111', 'profile_picture', 'unity', 'country_person', 'unite', 'signature']
senticnet['selfish'] = ['0', '0', '0', '-0.92', '#disgust', '#disgust', 'negative', '-0.92', 'ridiculous', 'ignorant', 'insane', 'medical_problem', 'end_life']
senticnet['selfish_ambition'] = ['0.053', '0.207', '-0.07', '0.165', '#interest', '#admiration', 'positive', '0.088', 'motivation', 'go_job', 'reinforce', 'resume', 'effort']
senticnet['selfish_gene'] = ['0', '0.331', '-0.09', '0.242', '#interest', '#admiration', 'positive', '0.158', 'spread_idea', 'importance', 'tenet', 'culture', 'idea']
senticnet['selfishly'] = ['0.911', '0', '0', '0.777', '#joy', '#admiration', 'positive', '0.844', 'selflessly', 'sacrificially', 'unstintingly', 'unselfishly', 'altruistically']
senticnet['selfishness'] = ['-0.81', '0', '0.886', '-0.65', '#sadness', '#anger', 'negative', '-0.78', 'opportunism', 'greediness', 'rapaciousness', 'voraciousness', 'expedience']
senticnet['selfless'] = ['0.973', '0', '0', '0.873', '#joy', '#admiration', 'positive', '0.923', 'unselfish', 'self-sacrificing', 'selflessness', 'altruistic', 'self-sacrifice']
senticnet['selfless_act'] = ['0.71', '0.882', '0', '0.9', '#interest', '#admiration', 'positive', '0.831', 'positive_reinforcement', 'brownie_point', 'benefit', 'give', 'gratification']
senticnet['selfless_bravery'] = ['-0.94', '0', '0.745', '-0.97', '#sadness', '#disgust', 'negative', '-0.89', 'nerve', 'fearlessness', 'heart', 'dauntlessness', 'intrepidity']
senticnet['selfless_commitment'] = ['0.827', '0.913', '0', '0.867', '#interest', '#admiration', 'positive', '0.869', 'good_habit', 'rivalry', 'competitive', 'competitive_nature', 'determination']
senticnet['selfless_courage'] = ['0', '0', '0.764', '-0.77', '#anger', '#disgust', 'negative', '-0.77', 'restriction', 'tragedy', 'perpetrator', 'dominate', 'control']
senticnet['selfless_dedication'] = ['0.827', '0.913', '0', '0.867', '#interest', '#admiration', 'positive', '0.869', 'good_habit', 'rivalry', 'competitive', 'competitive_nature', 'determination']
senticnet['selfless_devotion'] = ['0', '0', '0.764', '-0.77', '#anger', '#disgust', 'negative', '-0.77', 'restriction', 'tragedy', 'perpetrator', 'dominate', 'control']
senticnet['selfless_love'] = ['0.488', '0.661', '-0.25', '0.244', '#joy', '#interest', 'positive', '0.284', 'appreciativeness', 'thankfulness', 'unafraid', 'intrepidly', 'audacious']
senticnet['selfless_sacrifice'] = ['0', '0', '0.764', '-0.77', '#anger', '#disgust', 'negative', '-0.77', 'restriction', 'tragedy', 'perpetrator', 'dominate', 'control']
senticnet['selflessly'] = ['0.746', '0', '0', '0.891', '#joy', '#admiration', 'positive', '0.819', 'unselfishly', 'sacrificially', 'courageously', 'heroically', 'tirelessly']
senticnet['selflessness'] = ['0.983', '0', '0', '0.713', '#joy', '#admiration', 'positive', '0.848', 'self-sacrifice', 'unselfishness', 'humility', 'selfishness', 'bravery']
senticnet['selfsame'] = ['-0.73', '0', '0.732', '-0.53', '#sadness', '#anger', 'negative', '-0.66', 'insurmountable', 'insuperable', 'unconquerable', 'indomitable', 'unsubduable']
senticnet['selimiye'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['sell'] = ['-0.03', '0.139', '0', '0.109', '#interest', '#admiration', 'positive', '0.07', 'exchange_money', 'purchase', 'sell_goods', 'make_rich', 'incur_debt']
senticnet['sell-out'] = ['0.885', '-0.72', '0', '0', '#joy', '#surprise', 'positive', '0.803', 'sold-out', 'headlining', 'sellout', 'concert', 'standing-room-only']
senticnet['sell_beer'] = ['0.217', '-0.19', '0.096', '0', '#joy', '#surprise', 'positive', '0.106', 'earn', 'long_tube', 'fun_place', 'recreational', 'get_hammered']
senticnet['sell_book'] = ['-0.09', '-0.02', '0.018', '-0.08', '#sadness', '#disgust', 'negative', '-0.04', 'buy_magazine', 'explain_problem', 'buy_book', 'pick_tooth', 'purchase_book']
senticnet['sell_car'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'car_dealership', 'truck_stop', 'vehicle', 'insurance', 'car']
senticnet['sell_food'] = ['0', '0.115', '0', '0.106', '#interest', '#admiration', 'positive', '0.111', 'keep_food', 'keep_fresh', 'ship_goods', 'keep_safe', 'keep_food_spoil']
senticnet['sell_goods'] = ['0', '0.078', '0', '0.099', '#interest', '#admiration', 'positive', '0.088', 'exchange_money', 'buy_sell', 'sell', 'make_rich', 'acquire_goods']
senticnet['sell_grocery_store'] = ['0.04', '0', '0', '0.067', '#joy', '#admiration', 'positive', '0.054', 'find_plant', 'court_action', 'add_flavor', 'ingredient', 'preserve_food']
senticnet['sell_house'] = ['-0.03', '0.149', '-0.06', '0.14', '#interest', '#admiration', 'positive', '0.048', 'live_house', 'vend', 'address', 'house_location', 'part_of_company']
senticnet['sell_insurance'] = ['-0.07', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.06', 'finish_work', 'pay_child_support', 'work_team', 'get_out_of_bed', 'arrive_work']
senticnet['sell_item'] = ['0', '0', '0.074', '-0.03', '#anger', '#disgust', 'negative', '-0.05', 'look_buy', 'promote', 'make_purchase', 'get_need', 'acquire_goods']
senticnet['sell_merchandise'] = ['-0.03', '0.139', '0', '0.109', '#interest', '#admiration', 'positive', '0.07', 'exchange_money', 'purchase', 'sell_goods', 'make_rich', 'incur_debt']
senticnet['sell_new_book'] = ['0', '0', '0.937', '-0.87', '#anger', '#disgust', 'negative', '-0.90', 'make_porn', 'deal_drug', 'go_off_strike', 'go_public', 'open_business']
senticnet['sell_out'] = ['-0.04', '0.09', '-0.15', '-0.12', '#fear', '#disgust', 'negative', '-0.05', 'bigot', 'censorship', 'loss_oflife', 'taunt', 'sleepless_night']
senticnet['sell_popcorn'] = ['0.859', '-0.82', '0', '0', '#joy', '#surprise', 'positive', '0.841', 'enter_theater', 'show_movie', 'watch_play', 'earn_money', 'proof_of_purchase']
senticnet['sell_sandwich'] = ['0.046', '0.071', '0', '0.066', '#interest', '#admiration', 'positive', '0.061', 'go_grocery_store', 'go_supermarket', 'provide_family', 'money_buy', 'plan_meal']
senticnet['sell_stock'] = ['-0.22', '0.15', '-0.22', '0.09', '#sadness', '#fear', 'negative', '-0.05', 'borrow_money', 'count_money', 'lack_money', 'negotiate', 'close_deal']
senticnet['sell_store'] = ['0', '0.128', '0.046', '0.1', '#interest', '#admiration', 'positive', '0.061', 'store_bottle', 'ship_goods', 'increase_value', 'fill_glass', 'lick_finger']
senticnet['sell_stuff'] = ['-0.04', '0.081', '0.037', '0.084', '#interest', '#admiration', 'positive', '0.022', 'small_shop', 'store', 'stock_exchange', 'shop', 'sell_goods']
senticnet['sell_ticket'] = ['-0.08', '0.066', '-0.05', '-0.06', '#sadness', '#disgust', 'negative', '-0.03', 'collect_ticket', 'buy_ticket', 'purchase_ticket', 'pay_ticket', 'book_ticket']
senticnet['sell_vegetable'] = ['-0.03', '-0.00', '0.043', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'decide_eat', 'fix_breakfast', 'prepare_breakfast', 'piece_of_meat', 'choose_eat']
senticnet['seller'] = ['-0.03', '0.119', '0', '0.095', '#interest', '#admiration', 'positive', '0.06', 'merchant', 'exchange_goods', 'trade', 'bazaar', 'vend']
senticnet['selliah'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['selling_candy'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'candy', 'sweet', 'sugar', 'sweet_tooth', 'chocolate']
senticnet['selling_liquor'] = ['0.081', '-0.05', '0', '-0.03', '#joy', '#surprise', 'positive', '0.036', 'drink_alcohol', 'consume_alcoholic_beverage', 'drink_much', 'hit_bottle', 'meet_woman']
senticnet['selling_price'] = ['0', '0.242', '0', '0.16', '#interest', '#admiration', 'positive', '0.201', 'consider_cost', 'sticker', 'worth', 'price', 'monetary']
senticnet['sellotape'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'aluminium_foil', 'foil', 'gold_foil']
senticnet['sellout'] = ['0.887', '-0.72', '0', '0', '#joy', '#surprise', 'positive', '0.803', 'sell-out', 'headlining', 'sold-out', 'concert', 'standing-room-only']
senticnet['sellout_crowd'] = ['-0.71', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.84', 'traffic_jam', 'accumulate_stress', 'stress', 'accumulate_stress_city', 'bad_service']
senticnet['seltzer_water'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'coke', 'sprite', 'cola', 'beverage', 'soft_drink']
senticnet['selvedge'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_cloth']
senticnet['sem'] = ['0.868', '0', '0', '0.885', '#joy', '#admiration', 'positive', '0.877', 'wom_girl', 'act', 'education', 'science', 'different_application']
senticnet['semantic'] = ['0.693', '-0.52', '0', '0.82', '#joy', '#admiration', 'positive', '0.681', 'embodiment', 'shape', 'representation', 'concrete_representation', 'internal_representation']
senticnet['semantic_dementia'] = ['-0.73', '0', '0.955', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'visual_agnosia', 'auditory_agnosia', 'brain_disorder', 'encephalopathy', 'brain_disease']
senticnet['semantic_differential'] = ['0.854', '-0.79', '0', '0.977', '#joy', '#admiration', 'positive', '0.875', 'profitableness', 'auspiciousness', 'luck', 'advantageousness', 'positivity']
senticnet['semantic_error'] = ['-0.98', '0', '0.869', '-0.80', '#sadness', '#anger', 'negative', '-0.88', 'error', 'syntax_error', 'programming_error', 'runtime_error', 'computer_error']
senticnet['semantic_memory'] = ['0.693', '-0.52', '0', '0.82', '#joy', '#admiration', 'positive', '0.681', 'embodiment', 'shape', 'representation', 'concrete_representation', 'internal_representation']
senticnet['semantic_representation'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'integrative', 'consolidative', 'unifying', 'combinatorial']
senticnet['semantic_similarity'] = ['-0.54', '0', '0.979', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'unlikeness', 'dissimilitude', 'difference', 'heterology', 'disparateness']
senticnet['semantically'] = ['0', '0.847', '0', '0.903', '#interest', '#admiration', 'positive', '0.875', 'biochemically', 'sociologically', 'phonologically', 'analytically', 'mechanistically']
senticnet['semblable'] = ['-0.52', '0', '0.701', '-0.50', '#sadness', '#anger', 'negative', '-0.57', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['semblance'] = ['-0.81', '0', '0.838', '-0.75', '#sadness', '#anger', 'negative', '-0.80', 'pretence', 'pretense', 'pretext', 'simulacrum', 'face_value']
senticnet['semen'] = ['0', '0.1', '0', '0.18', '#interest', '#admiration', 'positive', '0.14', 'seed_lawn', 'seed', 'pip', 'kernel', 'pit']
senticnet['semester'] = ['0', '-0.01', '-0.04', '-0.08', '#fear', '#disgust', 'negative', '-0.03', 'division', 'end_sentence', 'undergraduate', 'class', 'long_ago']
senticnet['semester_abroad'] = ['0.875', '0', '0', '0.802', '#joy', '#admiration', 'positive', '0.839', 'foreigner', 'exile', 'deportee', 'gringo', 'import']
senticnet['semi-conducting'] = ['-0.82', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.83', 'sheet-like', 'cap-like', 'sheetlike', 'water-impermeable', 'screen-like']
senticnet['semi-dark'] = ['-0.89', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.86', 'pitch-black', 'half-dark', 'pitch-dark', 'rain-swept', 'half-lit']
senticnet['semi-fictional'] = ['-0.91', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.80', 'non-fictional', 'non-literary', 'nonfictional', 'prose', 'fictionalized']
senticnet['semi-formal'] = ['-0.83', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.83', 'dressier', 'slack', 'casual', 'dressy', 'blouse']
senticnet['semi-sweet'] = ['0', '0', '0.776', '-0.94', '#anger', '#disgust', 'negative', '-0.86', 'semisweet', 'unsweetened', 'unsalted', 'pecan', 'bittersweet']
senticnet['semi-truck'] = ['0.05', '0.086', '0', '0', '#joy', '#interest', 'positive', '0.068', 'flight_terminal', 'traffic_artery', 'fire_hydrant', 'park_zone', 'human_hair']
senticnet['semi_algebraic'] = ['0.875', '-0.65', '0', '0.861', '#joy', '#admiration', 'positive', '0.797', 'completeness', 'work', 'whole_shebang', 'essential', 'whole']
senticnet['semi_annual'] = ['0.09', '0.07', '0', '0.246', '#joy', '#admiration', 'positive', '0.135', 'hand_motion', 'water_form', 'water_relate', 'hand_gesture', 'arm_movement']
senticnet['semi_aquatic'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'frog', 'amphibian']
senticnet['semi_arboreal'] = ['0', '0', '-0.81', '-0.92', '#fear', '#disgust', 'negative', '-0.87', 'tree-living', 'dendroidal', 'arborous', 'arboriform', 'arborary']
senticnet['semi_arid_steppe'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'dryness', 'thirstiness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['semi_automatic_firearm'] = ['-0.99', '0', '0.651', '-0.51', '#sadness', '#anger', 'negative', '-0.72', 'sending', 'compulsion', 'influence', 'inducement', 'causation']
senticnet['semi_automatic_gun'] = ['0', '0', '0.729', '-0.84', '#anger', '#disgust', 'negative', '-0.78', 'rifle', 'pistol', 'revolver', 'hydrogen_bomb', 'firearm']
senticnet['semi_automatic_handgun'] = ['-0.75', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.75', 'hydrogen_bomb', 'rifle', 'revolver', 'pistol', 'gun']
senticnet['semi_automatic_pistol'] = ['-0.84', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.86', 'handgun', 'rifle', 'weapon', 'pistol', 'gun']
senticnet['semi_automatic_rifle'] = ['-0.84', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.86', 'handgun', 'rifle', 'weapon', 'pistol', 'gun']
senticnet['semi_automatic_weapon'] = ['-0.75', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.75', 'hydrogen_bomb', 'rifle', 'revolver', 'pistol', 'gun']
senticnet['semi_autonomy'] = ['0.887', '0.748', '0', '0.954', '#joy', '#admiration', 'positive', '0.863', 'political_action', 'political', 'mediocrity', 'discrimination', 'independent']
senticnet['semi_circle'] = ['0', '0', '-0.76', '-0.82', '#fear', '#disgust', 'negative', '-0.79', 'stage', 'thespian', 'actor', 'theatre', 'drama']
senticnet['semi_circular'] = ['0.029', '0.087', '0.065', '0.137', '#interest', '#admiration', 'positive', '0.047', 'archway', 'aisle', 'mat', 'passageway', 'right_angle']
senticnet['semi_circular_arch'] = ['0.029', '0.087', '0.065', '0.137', '#interest', '#admiration', 'positive', '0.047', 'archway', 'aisle', 'mat', 'passageway', 'right_angle']
senticnet['semi_clad'] = ['-0.94', '0', '0.991', '-0.65', '#sadness', '#anger', 'negative', '-0.86', 'birthday_suit', 'raw', 'altogether', 'nakedness', 'undress']
senticnet['semi_classical'] = ['0.136', '-0.11', '0.031', '0', '#joy', '#surprise', 'positive', '0.075', 'rap', 'popular_music', 'music', 'band_play', 'rock_roll']
senticnet['semi_cursive'] = ['-0.95', '0', '0.517', '-0.53', '#sadness', '#disgust', 'negative', '-0.67', 'copperplate', 'hand', 'minuscule', 'italic', 'round_hand']
senticnet['semi_derelict'] = ['-0.70', '0', '0.611', '-0.69', '#sadness', '#disgust', 'negative', '-0.67', 'dump', 'garbage_dump', 'trash_dump', 'garbage_heap', 'wasteyard']
senticnet['semi_detached_house'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'apartment', 'furniture', 'kitchen', 'flat']
senticnet['semi_elliptical'] = ['0.029', '0.087', '0.065', '0.137', '#interest', '#admiration', 'positive', '0.047', 'archway', 'aisle', 'mat', 'passageway', 'right_angle']
senticnet['semi_final'] = ['-0.09', '0.147', '-0.11', '0.205', '#interest', '#admiration', 'positive', '0.036', 'question_starter', 'resolution', 'available', 'explanation', 'request']
senticnet['semi_final_against'] = ['-0.09', '0.147', '-0.11', '0.205', '#interest', '#admiration', 'positive', '0.036', 'question_starter', 'resolution', 'available', 'explanation', 'request']
senticnet['semi_final_replay'] = ['-0.09', '0.147', '-0.11', '0.205', '#interest', '#admiration', 'positive', '0.036', 'question_starter', 'resolution', 'available', 'explanation', 'request']
senticnet['semi_formal'] = ['0.096', '0.182', '-0.09', '0.198', '#interest', '#admiration', 'positive', '0.094', 'female_parent', 'relative', 'kin', 'relate_person', 'aunt_uncle']
senticnet['semi_hollow'] = ['0.748', '-0.90', '0', '0.99', '#surprise', '#admiration', 'positive', '0.882', 'shakiness', 'quivering', 'shaking', 'vibration', 'quiver']
senticnet['semi_illiterate'] = ['-0.90', '0', '0.76', '-0.87', '#sadness', '#disgust', 'negative', '-0.84', 'analphabet', 'analphabetic', 'functional_illiterate', 'ignoramus', 'know_nothing']
senticnet['semi_infinite'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'assumption', 'precondition', 'provision', 'constraint', 'premiss']
senticnet['semi_invalid'] = ['-0.93', '0', '0.75', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'unhealthiness', 'injury', 'affliction', 'biliousness', 'harm']
senticnet['semi_literate'] = ['-0.90', '0', '0.76', '-0.87', '#sadness', '#disgust', 'negative', '-0.84', 'analphabet', 'analphabetic', 'functional_illiterate', 'ignoramus', 'know_nothing']
senticnet['semi_naked'] = ['-0.94', '0', '0.991', '-0.65', '#sadness', '#anger', 'negative', '-0.86', 'birthday_suit', 'raw', 'altogether', 'nakedness', 'undress']
senticnet['semi_nomadic'] = ['0.141', '0', '-0.06', '0.086', '#joy', '#admiration', 'positive', '0.055', 'dancer', 'stout', 'old_dance', 'climb_rock', 'jump_around']
senticnet['semi_nude'] = ['-0.94', '0', '0.991', '-0.65', '#sadness', '#anger', 'negative', '-0.86', 'birthday_suit', 'raw', 'altogether', 'nakedness', 'undress']
senticnet['semi_official'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'wildcat', 'informal', 'loose', 'drumhead']
senticnet['semi_parametric'] = ['0.83', '-0.83', '0', '0.872', '#surprise', '#admiration', 'positive', '0.846', 'overappraisal', 'overvaluation', 'overestimate', 'overestimation', 'commercial_document']
senticnet['semi_permeable'] = ['-0.52', '0', '0.596', '-0.76', '#anger', '#disgust', 'negative', '-0.62', 'retention', 'retentiveness', 'retentivity', 'solidness', 'nonabsorbency']
senticnet['semi_precious_stone'] = ['0.037', '0.073', '0.027', '0.072', '#interest', '#admiration', 'positive', '0.039', 'sapphire', 'birth_stone', 'turquoise', 'gemstone', 'amethyst']
senticnet['semi_pro'] = ['0', '0', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.05', 'human', 'delusional', 'use_illegal_drug', 'busy_person', 'carpenter']
senticnet['semi_professional'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'sciolist', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
senticnet['semi_professional_basketball'] = ['0.035', '0.155', '0', '0', '#joy', '#interest', 'positive', '0.095', 'basketball_court', 'basketball_game', 'basketball', 'ball', 'sport']
senticnet['semi_professionally'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'sciolist', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
senticnet['semi_sentient'] = ['-0.93', '0', '0.544', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'mutant', 'variation', 'sport', 'lusus_naturae', 'mutation']
senticnet['semi_sequel'] = ['-0.06', '0.122', '-0.04', '0.113', '#interest', '#admiration', 'positive', '0.032', 'encounter', 'portray', 'reveal', 'episode', 'next_episode']
senticnet['semi_skimmed_milk'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'lemon_juice', 'chocolate_pudding', 'grape_juice', 'orange_juice', 'juice']
senticnet['semi_terrestrial'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'stone_crab', 'swimming_crab', 'rock_crab', 'crab', 'pea_crab']
senticnet['semi_trailer'] = ['-0.84', '0', '0.96', '-0.91', '#anger', '#disgust', 'negative', '-0.90', 'housewrecker', 'housebreaker', 'knacker', 'laborer', 'manual_laborer']
senticnet['semi_trailer_truck'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'trailer', 'trail_car', 'camion', 'wagon', 'towboat']
senticnet['semi_truck'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'trailer', 'trail_car', 'camion', 'wagon', 'towboat']
senticnet['semi_volatile'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'radioactive_dust', 'dust', 'dust_matter']
senticnet['semiannual'] = ['0.762', '0.849', '0', '0', '#joy', '#interest', 'positive', '0.805', 'biennial', 'bi-annual', 'biannual', 'triennial', 'juried']
senticnet['semiannual_newsletter'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'current_event', 'headline', 'paper_write', 'paper_page', 'many_pages']
senticnet['semiaquatic'] = ['0', '0', '-0.81', '-0.92', '#fear', '#disgust', 'negative', '-0.87', 'tree-living', 'dendroidal', 'arborous', 'arboriform', 'arborary']
senticnet['semiarboreal'] = ['0', '0', '-0.81', '-0.92', '#fear', '#disgust', 'negative', '-0.87', 'tree-living', 'dendroidal', 'arborous', 'arboriform', 'arborary']
senticnet['semiautomatic'] = ['0.978', '-0.57', '0', '0.793', '#joy', '#admiration', 'positive', '0.782', 'pistol', 'handgun', 'side_arm', 'shooting_iron', 'semiautomatic_firearm']
senticnet['semiautomatic_firearm'] = ['0.978', '-0.57', '0', '0.793', '#joy', '#admiration', 'positive', '0.782', 'pistol', 'handgun', 'side_arm', 'shooting_iron', 'semiautomatic']
senticnet['semiautomatic_handgun'] = ['-0.75', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.75', 'hydrogen_bomb', 'rifle', 'revolver', 'pistol', 'gun']
senticnet['semiautomatic_pistol'] = ['0.646', '-0.79', '0', '0.572', '#joy', '#surprise', 'positive', '0.669', 'pistol', 'handgun', 'side_arm', 'shooting_iron', 'semiautomatic_firearm']
senticnet['semiautomatic_rifle'] = ['-0.75', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.75', 'hydrogen_bomb', 'rifle', 'revolver', 'pistol', 'gun']
senticnet['semicircle'] = ['0.932', '0.753', '0', '0', '#joy', '#interest', 'positive', '0.843', 'circle', 'loop', 'arc', 'coil', 'trapezoid']
senticnet['semicircle_around'] = ['0.932', '0.753', '0', '0', '#joy', '#interest', 'positive', '0.843', 'circle', 'loop', 'arc', 'coil', 'trapezoid']
senticnet['semicircular'] = ['0.027', '0.139', '0', '0.203', '#interest', '#admiration', 'positive', '0.123', 'horse_shoes', 'four_leaf_clover', 'rotary_phone', 'ping_pong', 'billiards']
senticnet['semicircular_arch'] = ['0.029', '0.087', '0.065', '0.137', '#interest', '#admiration', 'positive', '0.047', 'archway', 'aisle', 'mat', 'passageway', 'right_angle']
senticnet['semicircular_shape'] = ['0.027', '0.139', '0', '0.203', '#interest', '#admiration', 'positive', '0.123', 'horse_shoes', 'four_leaf_clover', 'rotary_phone', 'ping_pong', 'billiards']
senticnet['semicolon'] = ['-0.97', '0', '-0.98', '0', '#sadness', '#fear', 'negative', '-0.98', 'apostrophe', 'hyphen', 'punctuation', 'comma', 'apostrophe']
senticnet['semiconductor'] = ['-0.91', '0', '0', '-0.81', '#sadness', '#disgust', 'negative', '-0.86', 'silicon', 'semiconductor_device', 'conductor', 'chip', 'transistor']
senticnet['semiconductor_chip'] = ['-0.83', '0', '0.521', '-0.71', '#sadness', '#disgust', 'negative', '-0.69', 'stool', 'dejection', 'droppings', 'cow_droppings', 'cow_dung']
senticnet['semiconductor_device'] = ['-0.91', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.87', 'silicon', 'semiconductor', 'conductor', 'chip', 'transistor']
senticnet['semidarkness'] = ['-0.62', '0', '0.72', '-0.51', '#sadness', '#anger', 'negative', '-0.61', 'overcast', 'dark', 'obscureness', 'darkness', 'dimness']
senticnet['semifinal_loser_each'] = ['0.763', '-0.67', '0', '0.945', '#joy', '#admiration', 'positive', '0.795', 'set', 'income_bracket', 'tax_bracket', 'income_tax_bracket', 'price_bracket']
senticnet['semifreddo'] = ['0.937', '0', '0', '0.828', '#joy', '#admiration', 'positive', '0.883', 'bakery', 'produce_section', 'thanksgiving_dinner', 'dinner_meal', 'fast_food']
senticnet['semiliterate'] = ['-0.90', '0', '0.76', '-0.87', '#sadness', '#disgust', 'negative', '-0.84', 'analphabet', 'analphabetic', 'functional_illiterate', 'ignoramus', 'know_nothing']
senticnet['semilunar_bone'] = ['-0.74', '0', '0.598', '-0.75', '#sadness', '#disgust', 'negative', '-0.69', 'lunate_bone', 'carpal', 'carpal_bone', 'triquetral', 'triquetral_bone']
senticnet['semilunar_cartilage'] = ['-0.53', '0', '0.982', '-0.67', '#anger', '#disgust', 'negative', '-0.73', 'meniscus', 'gristle', 'fibrocartilage', 'hyaline_cartilage', 'arytenoid']
senticnet['seminal'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'hardcore', 'declared', 'explicit', 'graphic']
senticnet['seminal_fluid'] = ['-0.74', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.78', 'ejaculate', 'pass_gene', 'ecstasy', 'lower_standard_live', 'survival_species']
senticnet['seminar'] = ['0', '0.214', '0.012', '0.208', '#interest', '#admiration', 'positive', '0.137', 'lecture_hall', 'lecture_theater', 'lesson', 'lecture', 'school_session']
senticnet['seminarian'] = ['0', '-0.86', '0', '0.846', '#surprise', '#admiration', 'positive', '0.857', 'chorister', 'organist', 'chorister', 'choir', 'choirmaster']
senticnet['seminary'] = ['0.031', '0.272', '0', '0.231', '#interest', '#admiration', 'positive', '0.178', 'minister', 'church_head', 'pastor', 'protestant', 'evangelist']
senticnet['seminole'] = ['-0.85', '0', '0.685', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'allicrocodilian', 'crocodile', 'false_gavial', 'crocodilian', 'caiman']
senticnet['seminomadic'] = ['0.141', '0', '-0.06', '0.086', '#joy', '#admiration', 'positive', '0.055', 'dancer', 'stout', 'old_dance', 'climb_rock', 'jump_around']
senticnet['seminude'] = ['-0.94', '0', '0.991', '-0.65', '#sadness', '#anger', 'negative', '-0.86', 'birthday_suit', 'raw', 'altogether', 'nakedness', 'undress']
senticnet['semiofficial'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'wildcat', 'informal', 'loose', 'drumhead']
senticnet['semiotician'] = ['-0.62', '0', '0.69', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'deviousness', 'indirectness', 'mediateness', 'characteristic', 'allusiveness']
senticnet['semiprecocial'] = ['0.042', '0.088', '0', '0', '#joy', '#interest', 'positive', '0.065', 'jump_over_obstacle', 'attempt_fly', 'aircraft', 'pilot', 'wing']
senticnet['semipro'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'sciolist', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
senticnet['semiprofessional'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'sciolist', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
senticnet['semiquaver'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['semirigid'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expandible', 'expansive', 'erectile', 'cavernous']
senticnet['semisweet'] = ['0', '0', '0.781', '-0.94', '#anger', '#disgust', 'negative', '-0.86', 'semi-sweet', 'unsweetened', 'unsalted', 'pecan', 'bittersweet']
senticnet['semitism'] = ['0', '-0.00', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'hot_chick', 'atheist', 'computer_user', 'fastener_binder', 'monitor']
senticnet['semitone'] = ['-0.65', '0', '0.681', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'tone', 'whole_tone', 'step', 'whole_step', 'musical_interval']
senticnet['semitrailer'] = ['0', '-0.01', '0', '0.064', '#surprise', '#admiration', 'positive', '0.042', 'car_gas', 'car_power', 'fossil_fuel', 'run_car', 'car_engine']
senticnet['semivowel'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'pronunciation', 'sibilation', 'mispronunciation', 'homophony']
senticnet['sempervivum'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'livelong', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['sempurna'] = ['-0.63', '0', '0.791', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'little', 'pinch', 'modicum', 'small_indefinite_amount', 'indefinite_quantity']
senticnet['semyon_bychkov'] = ['-0.91', '0', '0.931', '-0.72', '#sadness', '#anger', 'negative', '-0.85', 'hit_with_baton', 'get_hurt', 'whip', 'arrow', 'hurt']
senticnet['sen_mr_dubhagin'] = ['0', '0.105', '0', '0.084', '#interest', '#admiration', 'positive', '0.095', 'archive', 'print_medium', 'quiet_place', 'register', 'media']
senticnet['senate'] = ['-0.70', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'lower', 'crown', 'finance_ministry', 'central_bank', 'klaus']
senticnet['senate_aide'] = ['0', '0', '0', '0.876', '#admiration', '#admiration', 'positive', '0.876', 'good_manner', 'courteous', 'good_behavior', 'handle_carefully', 'politeness']
senticnet['senate_confirmation'] = ['0', '0.745', '0', '0.832', '#interest', '#admiration', 'positive', '0.788', 'meet_up', 'year', 'rendezvous', 'meet_friend', 'make_appointment']
senticnet['senate_filibuster'] = ['-0.81', '0', '0.513', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'ballot', 'vote', 'voting', 'balloting', 'veto']
senticnet['senate_overrode'] = ['-0.81', '0', '0.513', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'ballot', 'vote', 'voting', 'balloting', 'veto']
senticnet['senate_parliamentarian'] = ['0.779', '-0.88', '0', '0.521', '#joy', '#surprise', 'positive', '0.727', 'corrigible', 'correctable', 'improvable', 'reformable', 'redeemable']
senticnet['senate_seat'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'couch', 'stool', 'coach_seat']
senticnet['senate_staffer'] = ['0', '0', '0', '0.876', '#admiration', '#admiration', 'positive', '0.876', 'good_manner', 'courteous', 'good_behavior', 'handle_carefully', 'politeness']
senticnet['senate_unanimously'] = ['-0.81', '0', '0.513', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'ballot', 'vote', 'voting', 'balloting', 'veto']
senticnet['senator'] = ['0', '0.857', '0', '0.905', '#interest', '#admiration', 'positive', '0.881', 'egalitarian', 'politician', 'pioneer', 'gentleman', 'rogue']
senticnet['senator_bonnell'] = ['0.865', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.887', 'manager', 'colleague', 'office_worker', 'coworker', 'secretary']
senticnet['senator_coburn'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'rudeness', 'discourtesy', 'insolence', 'ccheekiness', 'cheekiness']
senticnet['senator_exon'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'rudeness', 'discourtesy', 'insolence', 'ccheekiness', 'cheekiness']
senticnet['senatorial_election'] = ['0', '0.156', '-0.15', '0.179', '#interest', '#admiration', 'positive', '0.061', 'candidate', 'presidential', 'vote', 'elect', 'appoint']
senticnet['senchenko'] = ['0.969', '0.998', '0', '0', '#joy', '#interest', 'positive', '0.984', 'bell_sound', 'bell_noise', 'piece_of_jewelry', 'syllable', 'fastener']
senticnet['send'] = ['0', '0.166', '-0.13', '0.275', '#interest', '#admiration', 'positive', '0.102', 'deliver', 'give_out', 'express', 'post', 'cope']
senticnet['send_around'] = ['0.517', '-0.67', '0', '0.549', '#surprise', '#admiration', 'positive', '0.579', 'circularize', 'circulate', 'pass_around', 'pass_on', 'distribute']
senticnet['send_back'] = ['-0.07', '0.13', '-0.07', '0.249', '#interest', '#admiration', 'positive', '0.057', 'write_back', 'retort', 'question_answer', 'elaborate', 'answer_back']
senticnet['send_card'] = ['0.946', '0.817', '0', '0', '#joy', '#interest', 'positive', '0.881', 'need_communicate', 'send_flower', 'yearly_celebration', 'show_care', 'send_gift']
senticnet['send_down'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'debar', 'rusticate', 'expel', 'throw_out', 'kick_out']
senticnet['send_electronic_mail'] = ['0.949', '0.866', '0', '0', '#joy', '#interest', 'positive', '0.907', 'snail_mail', 'postage', 'send_package', 'buy_stamp', 'send_letter']
senticnet['send_email'] = ['0.934', '0.983', '0', '0', '#joy', '#interest', 'positive', '0.959', 'email', 'use_computer', 'access_internet', 'send_message', 'send_letter']
senticnet['send_flower'] = ['0.73', '0', '0', '0.873', '#joy', '#admiration', 'positive', '0.801', 'keep_secret', 'tell', 'response', 'give_answer', 'dial_phone']
senticnet['send_gift'] = ['0.976', '0', '0', '0.827', '#joy', '#admiration', 'positive', '0.901', 'need_communicate', 'send_flower', 'yearly_celebration', 'show_care', 'send_card']
senticnet['send_information'] = ['0', '-0.05', '0.011', '0.057', '#surprise', '#admiration', 'positive', '0.033', 'stay_touch', 'communicate_friend', 'spread_meme', 'communicate_person', 'communication']
senticnet['send_letter'] = ['0.96', '0.807', '0', '0', '#joy', '#interest', 'positive', '0.883', 'email', 'use_computer', 'access_internet', 'send_message', 'send_email']
senticnet['send_mail'] = ['0.949', '0.866', '0', '0', '#joy', '#interest', 'positive', '0.907', 'snail_mail', 'postage', 'send_package', 'buy_stamp', 'send_letter']
senticnet['send_message'] = ['0.107', '0.098', '0', '0.135', '#joy', '#admiration', 'positive', '0.113', 'record_data', 'make_sound', 'make_easy', 'make_noise', 'strum']
senticnet['send_money'] = ['-0.06', '0.291', '-0.11', '0.194', '#interest', '#admiration', 'positive', '0.076', 'give_cash', 'money_exchange', 'currency_exchange', 'settle_bill', 'money']
senticnet['send_package'] = ['0.71', '0.8', '0', '0', '#joy', '#interest', 'positive', '0.755', 'snail_mail', 'postage', 'send_mail', 'buy_stamp', 'send_letter']
senticnet['send_signal'] = ['0.969', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.854', 'raise_voice', 'answer_phone', 'hunt_duck', 'answer_telephone', 'communicate']
senticnet['send_to_jail'] = ['0', '0', '0', '-0.77', '#disgust', '#disgust', 'negative', '-0.77', 'go_jail', 'go_to_prison', 'arrest', 'incarceration', 'injustice']
senticnet['send_to_prison'] = ['-0.95', '0', '-0.70', '-0.96', '#sadness', '#disgust', 'negative', '-0.87', 'death_penalty', 'die', 'imprison', 'break_law', 'capital_punishment']
senticnet['sender'] = ['0', '-0.02', '0', '0.094', '#surprise', '#admiration', 'positive', '0.061', 'letter', 'announcement', 'message', 'handset', 'messenger']
senticnet['sending'] = ['-0.53', '0', '0.8', '-0.64', '#anger', '#disgust', 'negative', '-0.65', 'causing', 'causation', 'transmission', 'transmittal', 'transmitting']
senticnet['sending_email'] = ['0.949', '0.866', '0', '0', '#joy', '#interest', 'positive', '0.907', 'snail_mail', 'postage', 'send_package', 'buy_stamp', 'send_letter']
senticnet['sending_spam'] = ['-0.05', '-0.03', '0.023', '-0.09', '#sadness', '#disgust', 'negative', '-0.03', 'moldy_food', 'celery', 'paper_product', 'orange_juice', 'meat']
senticnet['sendmail'] = ['0.031', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.062', 'letter_slot', 'postal_counter', 'mailbox', 'letter_scale', 'envelope']
senticnet['sendup'] = ['0', '0', '0.715', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'mockery', 'parody', 'spoof', 'lampoon', 'burlesque']
senticnet['seneca_tribe'] = ['0.989', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.97', 'committee', 'council', 'caste', 'association', 'organization']
senticnet['senecio_jacobaea'] = ['-0.56', '0', '0.892', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'borecole', 'colewort', 'kail', 'cole', 'kale']
senticnet['senega'] = ['-0.70', '0', '0.769', '-0.70', '#sadness', '#anger', 'negative', '-0.72', 'subshrub', 'suffrutex', 'milkwort', 'orange_milkwort', 'yellow_milkwort']
senticnet['senesce'] = ['0.566', '-0.80', '0', '0.801', '#surprise', '#admiration', 'positive', '0.723', 'age', 'get_on', 'mature', 'dote', 'maturate']
senticnet['senescence'] = ['-0.75', '0', '0.512', '-0.75', '#sadness', '#disgust', 'negative', '-0.67', 'inactivity', 'inactiveness', 'slumber', 'inaction', 'hibernation']
senticnet['seneschal'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'feudal_lord', 'lord']
senticnet['sengoku'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['senile'] = ['-0.75', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.87', 'senility', 'senator', 'senior', 'seniority', 'dote']
senticnet['senile_dementia'] = ['-0.94', '0', '0.821', '-0.75', '#sadness', '#anger', 'negative', '-0.84', 'predementedness', 'insanity', 'dementedness', 'senile_psychosis', 'alcoholic_dementia']
senticnet['senile_psychosis'] = ['-0.94', '0', '0.821', '-0.75', '#sadness', '#anger', 'negative', '-0.84', 'presenile_dementia', 'insanity', 'senile_dementia', 'dementedness', 'alcoholic_dementia']
senticnet['senility'] = ['-0.57', '0', '0.766', '-0.75', '#anger', '#disgust', 'negative', '-0.69', 'age', 'old_age', 'year', 'eld', 'geezerhood']
senticnet['senior'] = ['0', '-0.02', '0', '0.094', '#surprise', '#admiration', 'positive', '0.061', 'professional', 'expert', 'experienced', 'skilled', 'trained']
senticnet['senior_adviser'] = ['-0.04', '0.189', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'label', 'percentage', 'rate', 'money_relate', 'boss']
senticnet['senior_advisor'] = ['-0.04', '0.189', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'label', 'percentage', 'rate', 'money_relate', 'boss']
senticnet['senior_aide'] = ['0', '0', '0', '0.876', '#admiration', '#admiration', 'positive', '0.876', 'good_manner', 'courteous', 'good_behavior', 'handle_carefully', 'politeness']
senticnet['senior_analyst'] = ['0.711', '0', '0', '0', '#joy', '#joy', 'positive', '0.711', 'chemist', 'expert', 'scientist', 'esaminatore', 'physicist']
senticnet['senior_assistant_attorney'] = ['0', '0.146', '-0.05', '0.077', '#interest', '#admiration', 'positive', '0.057', 'lawyer', 'negotiator', 'arbiter', 'judge_jury', 'decide_guilt']
senticnet['senior_associate'] = ['-0.04', '0.189', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'label', 'percentage', 'rate', 'money_relate', 'boss']
senticnet['senior_associate_director'] = ['-0.04', '0.189', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'label', 'percentage', 'rate', 'money_relate', 'boss']
senticnet['senior_citizen'] = ['0.956', '-0.70', '0', '0.653', '#joy', '#surprise', 'positive', '0.771', 'centenarian', 'nonagenarian', 'octogenarian', 'septuagenarian', 'sexagenarian']
senticnet['senior_consultant'] = ['0.891', '0.721', '0', '0.921', '#joy', '#admiration', 'positive', '0.844', 'inventor', 'connoisseur', 'medical_doctor', 'teacher', 'judge']
senticnet['senior_correspondent'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'adventurer', 'humankind']
senticnet['senior_diplomat'] = ['0', '0.133', '0', '0.088', '#interest', '#admiration', 'positive', '0.111', 'juror', 'decide_guilt', 'become_judge', 'arbiter', 'jury']
senticnet['senior_executive'] = ['0.046', '0.116', '-0.05', '0.076', '#interest', '#admiration', 'positive', '0.045', 'head_guy', 'boss', 'officer', 'leader', 'supervisor']
senticnet['senior_executive_vice'] = ['0.046', '0.116', '-0.05', '0.076', '#interest', '#admiration', 'positive', '0.045', 'head_guy', 'boss', 'officer', 'leader', 'supervisor']
senticnet['senior_investigator'] = ['0', '0.79', '0', '0.903', '#interest', '#admiration', 'positive', '0.847', 'graduate_student', 'researcher', 'currently', 'participant', 'university']
senticnet['senior_lecturer'] = ['0', '0', '0', '0.97', '#admiration', '#admiration', 'positive', '0.97', 'lecturer', 'teacher', 'instructor', 'college_teacher', 'teach_lesson']
senticnet['senior_managing_director'] = ['0.046', '0.116', '-0.05', '0.076', '#interest', '#admiration', 'positive', '0.045', 'head_guy', 'boss', 'officer', 'leader', 'supervisor']
senticnet['senior_meteorologist'] = ['-0.03', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.04', 'crystal_clear', 'water_sky', 'weather_condition', 'weather_phenomenon', 'sun_cover']
senticnet['senior_naval_officer'] = ['0.688', '-0.90', '0', '0.877', '#surprise', '#admiration', 'positive', '0.824', 'fleet_admiral', 'rear_admiral', 'admiral', 'full_admiral', 'flag_officer']
senticnet['senior_researcher'] = ['0', '0.909', '0', '0.799', '#interest', '#admiration', 'positive', '0.854', 'research', 'research_topic', 'research_subject', 'search', 'student']
senticnet['senior_scientist'] = ['0', '0.909', '0', '0.799', '#interest', '#admiration', 'positive', '0.854', 'research', 'research_topic', 'research_subject', 'search', 'student']
senticnet['senior_status'] = ['0.594', '-0.68', '0', '0.957', '#surprise', '#admiration', 'positive', '0.746', 'preeminence', 'eminence', 'high_status', 'higher_status', 'higher_rank']
senticnet['senior_vice_president'] = ['0.046', '0.116', '-0.05', '0.076', '#interest', '#admiration', 'positive', '0.045', 'head_guy', 'boss', 'officer', 'leader', 'supervisor']
senticnet['senior_vp'] = ['0.046', '0.116', '-0.05', '0.076', '#interest', '#admiration', 'positive', '0.045', 'head_guy', 'boss', 'officer', 'leader', 'supervisor']
senticnet['seniority'] = ['-0.76', '0', '-1', '0', '#sadness', '#fear', 'negative', '-0.88', 'senility', 'senator', 'senior', 'senile', 'dote']
senticnet['senokot'] = ['-0.50', '0', '0.582', '-0.57', '#anger', '#disgust', 'negative', '-0.55', 'purgative', 'cathartic', 'physic', 'aperient', 'phenolphthalein']
senticnet['sens_fail'] = ['0.304', '0.339', '-0.19', '0.333', '#interest', '#admiration', 'positive', '0.195', 'careful', 'reinforce', 'load_bear', 'go_outdoors', 'allowance']
senticnet['sensate'] = ['-0.94', '0', '0.925', '-0.97', '#sadness', '#disgust', 'negative', '-0.95', 'nonexistence', 'nonentity', 'cloud', 'falseness', 'falsity']
senticnet['sensation'] = ['0.775', '0.881', '0', '0', '#joy', '#interest', 'positive', '0.828', 'sensual', 'feel_action', 'touch', 'physical_reaction', 'sense']
senticnet['sensational'] = ['0', '-0.92', '0', '0.944', '#surprise', '#admiration', 'positive', '0.934', 'scandalmongering', 'sensationalistic', 'sensationally', 'inflame', 'fire_up']
senticnet['sensationalism'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'nonsense', 'meaninglessness', 'bunk', 'absurdity', 'hokum']
senticnet['sensationalist'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'nonsense', 'meaninglessness', 'bunk', 'absurdity', 'hokum']
senticnet['sensationalistic'] = ['0', '-0.96', '0', '0.994', '#surprise', '#admiration', 'positive', '0.978', 'scandalmongering', 'sensational', 'sensationally', 'inflame', 'fire_up']
senticnet['sensationalize'] = ['-0.70', '0', '0', '-0.95', '#sadness', '#disgust', 'negative', '-0.82', 'too_much', 'wave_arm', 'show_off', 'laugh', 'applause']
senticnet['sensationally'] = ['0', '-0.91', '0', '0.815', '#surprise', '#admiration', 'positive', '0.867', 'scandalmongering', 'sensationalistic', 'sensational', 'inflame', 'fire_up']
senticnet['sense'] = ['0', '0.152', '-0.09', '0.225', '#interest', '#admiration', 'positive', '0.094', 'five_senses', 'human_sense', 'perceive', 'sensory_input', 'use_ear']
senticnet['sense_accomplishment'] = ['0.712', '0.935', '0', '0.968', '#interest', '#admiration', 'positive', '0.872', 'accomplish_task', 'pass_exam', 'feel_good', 'pride', 'work_hard']
senticnet['sense_audition'] = ['0.776', '-1', '0', '0.976', '#surprise', '#admiration', 'positive', '0.917', 'ear', 'absolute_pitch', 'perfect_pitch', 'audition', 'modality']
senticnet['sense_environment'] = ['0.865', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.807', 'trip_person', 'eat_live', 'live_apartment', 'need_companionship', 'yolo']
senticnet['sense_experience'] = ['-0.85', '0', '0.936', '-0.90', '#anger', '#disgust', 'negative', '-0.89', 'masking', 'taste', 'threshold', 'perception', 'limen']
senticnet['sense_fear'] = ['-0.12', '0', '-0.06', '-0.03', '#sadness', '#fear', 'negative', '-0.07', 'follow_person', 'stay_in_house', 'mark_territory', 'cause_accident', 'guide_blind']
senticnet['sense_impression'] = ['-0.73', '0', '0.835', '-0.53', '#sadness', '#anger', 'negative', '-0.69', 'masking', 'taste', 'threshold', 'perception', 'limen']
senticnet['sense_joy'] = ['0.814', '0', '0', '0', '#joy', '#joy', 'positive', '0.814', 'experience_joy', 'lift_spirit', 'plan_party', 'think_funny', 'act_silly']
senticnet['sense_modality'] = ['0.785', '-0.99', '0', '0.981', '#surprise', '#admiration', 'positive', '0.92', 'ear', 'absolute_pitch', 'perfect_pitch', 'modality', 'audition']
senticnet['sense_of_fulfillment'] = ['0.354', '0.161', '0', '-0.04', '#joy', '#interest', 'positive', '0.157', 'share_knowledge', 'study', 'increase_intelligence', 'inspiration', 'appreciation']
senticnet['sense_of_humor'] = ['0.58', '0.651', '-0.26', '0.308', '#joy', '#interest', 'positive', '0.319', 'hear_joke', 'funny', 'laugh_loudly', 'start_laugh', 'smile']
senticnet['sense_of_shame'] = ['0.714', '-0.58', '0', '0.667', '#joy', '#admiration', 'positive', '0.655', 'morality', 'wee_small_voice', 'small_voice', 'voice_of_conscience', 'moral_sense']
senticnet['sense_organ'] = ['0', '0.097', '0', '0.118', '#interest', '#admiration', 'positive', '0.107', 'body_organ', 'ear', 'sensory_organ', 'human_organ', 'head_organ']
senticnet['sense_peace'] = ['0.933', '0', '0', '0.728', '#joy', '#admiration', 'positive', '0.831', 'stress_relief', 'feel_good', 'feel_happy', 'enjoy_show', 'feel_relaxed']
senticnet['sense_pride'] = ['0.318', '0.123', '0', '0', '#joy', '#interest', 'positive', '0.221', 'great_joy', 'callus', 'sense_rhythm', 'keep_rhythm', 'arthritis']
senticnet['sense_rhythm'] = ['0.318', '0.123', '0', '0', '#joy', '#interest', 'positive', '0.221', 'keep_rhythm', 'put_music', 'arthritis', 'rhythm', 'skill']
senticnet['sense_satisfaction'] = ['0.885', '-0.91', '0', '0', '#joy', '#surprise', 'positive', '0.899', 'appreciate', 'become_better_person', 'beautiful', 'make_happy', 'tell_friend']
senticnet['sense_sound'] = ['-0.03', '0.109', '-0.06', '0.075', '#interest', '#admiration', 'positive', '0.024', 'hear_noise', 'use_ear', 'ear_work', 'ear_activity', 'stop_look']
senticnet['senseless'] = ['-0.98', '0', '0.646', '-0.98', '#sadness', '#disgust', 'negative', '-0.87', 'indefensible', 'reasonless', 'untenable', 'unreasonable', 'unwarranted']
senticnet['sensibility'] = ['0.917', '-0.70', '0', '0.672', '#joy', '#surprise', 'positive', '0.764', 'sensitivity', 'sensitiveness', 'irritability', 'responsiveness', 'excitability']
senticnet['sensible'] = ['0.843', '-0.6', '0', '0.698', '#joy', '#admiration', 'positive', '0.714', 'levelheaded', 'sound', 'healthy', 'commonsense', 'commonsensible']
senticnet['sensible_intuition'] = ['-0.57', '0', '0.713', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'assured', 'conclusion', 'certainty', 'predictability', 'definiteness']
senticnet['sensibleness'] = ['0.983', '-0.88', '0', '0.821', '#joy', '#surprise', 'positive', '0.896', 'functionality', 'practicality', 'viability', 'usefulness', 'realism']
senticnet['sensing_intuition'] = ['0.854', '-0.79', '0', '0.977', '#joy', '#admiration', 'positive', '0.875', 'profitableness', 'auspiciousness', 'luck', 'advantageousness', 'positivity']
senticnet['sensitive'] = ['-0.88', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.81', 'sensitive_skin', 'sensitive_teeth', 'weak', 'emotional_guy', 'sensitivity']
senticnet['sensitive_detector'] = ['0.048', '0', '0', '0.092', '#joy', '#admiration', 'positive', '0.07', 'radio_wave', 'sun', 'solar_cell', 'x-ray', 'cosmic_ray']
senticnet['sensitive_skin'] = ['-0.86', '0', '0', '-0.96', '#sadness', '#disgust', 'negative', '-0.91', 'sensitive', 'sensitive_teeth', 'weak', 'emotional_guy', 'sensitivity']
senticnet['sensitive_teeth'] = ['0', '0', '0.709', '-0.93', '#anger', '#disgust', 'negative', '-0.82', 'sensitive_skin', 'sensitive', 'weak', 'emotional_guy', 'sensitivity']
senticnet['sensitively'] = ['0', '0.764', '0', '0.845', '#interest', '#admiration', 'positive', '0.804', 'empathetically', 'empathic', 'empathetic', 'kind-heartedness', 'kindheartedness']
senticnet['sensitiveness'] = ['0.919', '-0.70', '0', '0.672', '#joy', '#surprise', 'positive', '0.764', 'sensitivity', 'responsiveness', 'irritability', 'sensibility', 'excitability']
senticnet['sensitivity'] = ['0', '0', '-0.66', '0', '#fear', '#fear', 'negative', '-0.66', 'anger', 'rage', 'annoyance', 'apprehension', 'fear']
senticnet['sensitize'] = ['0.649', '-0.61', '0', '0.723', '#joy', '#admiration', 'positive', '0.664', 'bring_out', 'present', 'reintroduce', 'introduce', 'inform']
senticnet['sensodyne'] = ['0.878', '0.869', '0', '0', '#joy', '#interest', 'positive', '0.873', 'fluoride_mouthwash', 'hair_conditioner', 'sculpt_gel', 'dandruff_shampoo', 'hair_gel']
senticnet['sensor'] = ['0', '0.08', '0.036', '0.067', '#interest', '#admiration', 'positive', '0.037', 'complex_organ', 'sensory_organ', 'head_organ', 'vital_organ', 'part_of_face']
senticnet['sensorial'] = ['0.838', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.835', 'pondering', 'broody', 'contemplative', 'meditative', 'think']
senticnet['sensorimotor'] = ['0', '0.131', '-0.08', '0.239', '#interest', '#admiration', 'positive', '0.096', 'human_sense', 'ear_activity', 'vibration', 'five_senses', 'sensor']
senticnet['sensorineural_hearing_loss'] = ['-0.52', '0', '0.832', '-0.58', '#anger', '#disgust', 'negative', '-0.64', 'disability', 'disablement', 'handicap', 'impairment', 'hearing_disorder']
senticnet['sensorium'] = ['-0.63', '0', '0.694', '-0.66', '#anger', '#disgust', 'negative', '-0.66', 'corporality', 'tangibleness', 'palpability', 'reality', 'solidness']
senticnet['sensory'] = ['0.154', '0.066', '0', '0.078', '#joy', '#admiration', 'positive', '0.099', 'aerial', 'antenna', 'arthropod', 'insect', 'connection']
senticnet['sensory_cortex'] = ['0', '0.131', '-0.08', '0.239', '#interest', '#admiration', 'positive', '0.096', 'human_sense', 'ear_activity', 'vibration', 'five_senses', 'sensor']
senticnet['sensory_deficit'] = ['-0.78', '0', '0.669', '-0.89', '#sadness', '#disgust', 'negative', '-0.78', 'pain_sensation', 'painful_sensation', 'prickling', 'tingling', 'pain']
senticnet['sensory_experience'] = ['-0.94', '0', '0.925', '-0.97', '#sadness', '#disgust', 'negative', '-0.95', 'nonexistence', 'nonentity', 'cloud', 'falseness', 'falsity']
senticnet['sensory_feedback'] = ['0', '0.131', '-0.08', '0.239', '#interest', '#admiration', 'positive', '0.096', 'human_sense', 'ear_activity', 'vibration', 'five_senses', 'sensor']
senticnet['sensory_function'] = ['-0.75', '0', '0.504', '-0.77', '#sadness', '#disgust', 'negative', '-0.67', 'disablement', 'anorgasmia', 'hearing_impairment', 'hearing_disorder', 'bow_leg']
senticnet['sensory_innervation'] = ['-0.41', '0', '-0.28', '-0.05', '#sadness', '#fear', 'negative', '-0.25', 'numbness', 'carpal_tunnel_syndrome', 'weakness', 'pain', 'muscle_damage']
senticnet['sensory_input'] = ['0', '0.131', '-0.08', '0.239', '#interest', '#admiration', 'positive', '0.096', 'human_sense', 'ear_activity', 'vibration', 'five_senses', 'sensor']
senticnet['sensory_leakage'] = ['0.628', '-0.96', '0', '0.682', '#surprise', '#admiration', 'positive', '0.759', 'taboo', 'tabu', 'tendentiousness', 'irrational_hostility', 'bias']
senticnet['sensory_modality'] = ['0', '0.131', '-0.08', '0.239', '#interest', '#admiration', 'positive', '0.096', 'human_sense', 'ear_activity', 'vibration', 'five_senses', 'sensor']
senticnet['sensory_motor'] = ['0', '0.131', '-0.08', '0.239', '#interest', '#admiration', 'positive', '0.096', 'human_sense', 'ear_activity', 'vibration', 'five_senses', 'sensor']
senticnet['sensory_organ'] = ['0', '0.114', '0.013', '0.097', '#interest', '#admiration', 'positive', '0.066', 'sense_organ', 'hold_glass', 'head_organ', 'organ_body', 'physical_sens']
senticnet['sensory_overload'] = ['0.681', '-0.93', '0', '0.78', '#surprise', '#admiration', 'positive', '0.799', 'behavior', 'state', 'play', 'agency', 'activeness']
senticnet['sensory_perception'] = ['0', '0.131', '-0.08', '0.239', '#interest', '#admiration', 'positive', '0.096', 'human_sense', 'ear_activity', 'vibration', 'five_senses', 'sensor']
senticnet['sensory_receptor'] = ['0', '0.806', '0', '0.814', '#interest', '#admiration', 'positive', '0.81', 'receptor', 'sense_organ', 'study', 'cell', 'protein']
senticnet['sensory_stimulation'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'excitation', 'excitement', 'arousal', 'anger']
senticnet['sensory_stimulus'] = ['0.971', '0.751', '0', '0.896', '#joy', '#admiration', 'positive', '0.873', 'new_friendship', 'foreplay', 'warm_fuzzy_feeling', 'become_aroused', 'erection']
senticnet['sensory_substitution'] = ['0.026', '0.14', '-0.13', '0.31', '#interest', '#admiration', 'positive', '0.085', 'teach_new', 'share_idea', 'gesture', 'start_talk', 'exchange_idea']
senticnet['sensory_system'] = ['0.861', '0.774', '0', '0', '#joy', '#interest', 'positive', '0.818', 'modality', 'sense_modality', 'different_application', 'neuroscie', 'integrative_biology']
senticnet['sensual'] = ['0.788', '0', '0', '0.991', '#joy', '#admiration', 'positive', '0.889', 'feel_action', 'make_contact', 'hand_action', 'hand_movement', 'physical_contact']
senticnet['sensual_desire'] = ['-0.75', '0', '0.909', '-0.96', '#anger', '#disgust', 'negative', '-0.87', 'noisome', 'unhealthy', 'morbid', 'nauseating', 'nauseous']
senticnet['sensual_gratification'] = ['0.531', '0.673', '-0.27', '0.275', '#joy', '#interest', 'positive', '0.301', 'amorous', 'amorousness', 'amatory', 'enamoredness', 'puppy_love']
senticnet['sensual_pleasure'] = ['0.656', '-0.73', '0', '0.736', '#surprise', '#admiration', 'positive', '0.708', 'strong', 'salubrious', 'healthy', 'solid', 'alimentary']
senticnet['sensualism'] = ['0.995', '-0.69', '0', '0.528', '#joy', '#surprise', 'positive', '0.74', 'grasp', 'hug', 'nestle', 'snuggle', 'grip']
senticnet['sensualist'] = ['0.897', '-0.96', '0', '0.785', '#joy', '#surprise', 'positive', '0.881', 'erotic', 'voluptuary', 'epicure', 'gourmet', 'gastronome']
senticnet['sensuality'] = ['0.918', '0.476', '-0.19', '0.417', '#joy', '#interest', 'positive', '0.404', 'merriment', 'cheery', 'gaiety', 'festal', 'plume']
senticnet['sensuous'] = ['0.593', '-0.64', '0', '0.936', '#surprise', '#admiration', 'positive', '0.723', 'cosmetic', 'painterly', 'enhancive', 'artistic', 'aesthetical']
senticnet['sensuous_intuition'] = ['-0.57', '0', '0.713', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'assured', 'conclusion', 'certainty', 'predictability', 'definiteness']
senticnet['sensuously'] = ['0', '0', '-0.78', '-0.85', '#fear', '#disgust', 'negative', '-0.81', 'liquid', 'crystal_clear', 'pellucid', 'crystalline', 'lucid']
senticnet['sentence'] = ['0', '0.106', '-0.10', '0.249', '#interest', '#admiration', 'positive', '0.083', 'idiom', 'remark', 'grammar_part', 'speech_part', 'phrase']
senticnet['sentence_end'] = ['0', '0.107', '0', '0.102', '#interest', '#admiration', 'positive', '0.104', 'full_stop', 'dot', 'punctuation', 'period', 'sport_score']
senticnet['sentence_part'] = ['0.035', '0', '0', '0.116', '#joy', '#admiration', 'positive', '0.076', 'grammar_part', 'part_of_speech', 'written_language', 'school_paper', 'speech_part']
senticnet['sentenced'] = ['-0.96', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.88', 'parole', 'jail', 'felony', 'probation', 'offender']
senticnet['sententia'] = ['0.654', '-0.75', '0', '0.78', '#surprise', '#admiration', 'positive', '0.729', 'suit', 'cause', 'lawsuit', 'occurrence', 'countersuit']
senticnet['sentential'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'integrative', 'consolidative', 'unifying', 'combinatorial']
senticnet['sententious'] = ['0', '0.792', '0', '0.734', '#interest', '#admiration', 'positive', '0.763', 'moralizer', 'didactic', 'moralizing', 'moral', 'moralize']
senticnet['sentic_team'] = ['0.881', '-0.68', '0', '0.911', '#joy', '#admiration', 'positive', '0.826', 'senticnet', 'fun', 'research', 'great_team', 'family']
senticnet['senticnet'] = ['0.888', '-0.68', '0', '0.914', '#joy', '#admiration', 'positive', '0.828', 'sentic_team', 'fun', 'research', 'great_team', 'family']
senticnet['sentience'] = ['0.03', '0', '0', '0.119', '#joy', '#admiration', 'positive', '0.074', 'notional', 'abstract', 'wow', 'made_out_of_plastic', 'make_paper']
senticnet['sentient'] = ['0.042', '0.156', '-0.09', '0.155', '#interest', '#admiration', 'positive', '0.066', 'sentimental', 'social_creature', 'social_animal', 'fun_around', 'max']
senticnet['sentient_life'] = ['0.651', '-0.73', '0', '0.975', '#surprise', '#admiration', 'positive', '0.788', 'profoundness', 'profundity', 'sagacity', 'sagaciousness', 'discernment']
senticnet['sentiment'] = ['0.856', '0.858', '0', '0', '#joy', '#interest', 'positive', '0.857', 'gesticulation', 'feel', 'popularity', 'halo', 'sentimental']
senticnet['sentimental'] = ['0.648', '0.627', '-0.24', '0.296', '#joy', '#interest', 'positive', '0.332', 'emotional_response', 'emotional_reaction', 'passionate', 'sensitive', 'emotional']
senticnet['sentimental_value'] = ['0', '0.238', '0.021', '0.152', '#interest', '#admiration', 'positive', '0.123', 'money_value', 'money_amount', 'worth', 'monetary_amount', 'importance']
senticnet['sentimentalism'] = ['0.825', '-0.91', '0', '0.626', '#joy', '#surprise', 'positive', '0.789', 'treacle', 'glop', 'mush', 'slop', 'formulation']
senticnet['sentimentalist'] = ['0.923', '-0.88', '0', '0.825', '#joy', '#surprise', 'positive', '0.879', 'sentimentality', 'emotionality', 'emotionalism', 'love', 'feel']
senticnet['sentimentality'] = ['-0.77', '0', '0.978', '-0.54', '#sadness', '#anger', 'negative', '-0.76', 'sentimentalism', 'emotionality', 'emotionalism', 'corn', 'schmaltz']
senticnet['sentimentally'] = ['0', '0.764', '0', '0.845', '#interest', '#admiration', 'positive', '0.804', 'empathetically', 'empathic', 'empathetic', 'kind-heartedness', 'kindheartedness']
senticnet['sentinel'] = ['0', '0', '-0.75', '-0.97', '#fear', '#disgust', 'negative', '-0.86', 'watch', 'lookout', 'sentry', 'lookout_man', 'spotter']
senticnet['sentry'] = ['0', '0', '-0.75', '-0.97', '#fear', '#disgust', 'negative', '-0.86', 'watch', 'lookout', 'sentinel', 'lookout_man', 'spotter']
senticnet['sepak_takraw'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'golf', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['sepal'] = ['0', '0', '0.076', '-0.03', '#anger', '#disgust', 'negative', '-0.05', 'cat_hair', 'achene', 'follicle', 'sleep_cat', 'cat']
senticnet['separability'] = ['0.929', '-0.71', '0', '0.605', '#joy', '#surprise', 'positive', '0.748', 'incisiveness', 'trenchancy', 'efficacy', 'efficaciousness', 'power']
senticnet['separable'] = ['0.995', '-0.64', '0', '0.57', '#joy', '#surprise', 'positive', '0.735', 'cleavable', 'divisible', 'dissociable', 'severable', 'dissociative']
senticnet['separate'] = ['0', '0', '-0.77', '-0.88', '#fear', '#disgust', 'negative', '-0.83', 'disunite', 'divide', 'part', 'differentiate', 'furcate']
senticnet['separate_browser_window'] = ['-0.09', '-0.04', '0.039', '-0.24', '#sadness', '#disgust', 'negative', '-0.08', 'learn_new_idea', 'learn_quickly', 'point_out', 'look_picture', 'jot_down_note']
senticnet['separate_egg'] = ['-0.06', '0', '0.025', '0', '#sadness', '#anger', 'negative', '-0.04', 'cook_dinner', 'make_bread', 'cook_meal', 'prepare_luggage', 'press_shirt']
senticnet['separate_room'] = ['-0.70', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.78', 'enter_room', 'control_access', 'hang_door', 'go_build', 'go_through']
senticnet['separate_sheet'] = ['0.022', '-0.05', '0.079', '-0.11', '#anger', '#disgust', 'negative', '-0.02', 'stationery', 'ink', 'ball_point_pen', 'refill', 'note']
senticnet['separate_shower'] = ['0.041', '0.086', '0.016', '0', '#joy', '#interest', 'positive', '0.037', 'bath_shower', 'shower_head', 'shampoo_conditioner', 'plunger', 'tub']
senticnet['separated'] = ['-0.04', '0.116', '0.462', '-0.47', '#anger', '#disgust', 'negative', '-0.21', 'detached', 'set-apart', 'alien', 'isolated', 'sickening']
senticnet['separated_shoulder'] = ['-0.90', '0', '-0.91', '-0.73', '#sadness', '#fear', 'negative', '-0.85', 'broken_window', 'step_dog_shit', 'broken_leg', 'brain_tumor', 'bad_luck']
senticnet['separation'] = ['-0.80', '0', '-0.84', '0', '#sadness', '#fear', 'negative', '-0.82', 'split', 'slash', 'sever', 'divide', 'division']
senticnet['separation_anxiety'] = ['0.943', '-0.78', '0', '0.571', '#joy', '#surprise', 'positive', '0.767', 'master', 'ignition_key', 'key', 'passkey', 'master_key']
senticnet['separation_anxiety_disorder'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'acknowledgeable', 'specifiable', 'recognizable', 'identifiable', 'classifiable']
senticnet['separation_barrier'] = ['-0.93', '0', '-0.99', '-0.92', '#sadness', '#fear', 'negative', '-0.95', 'language_barrier', 'shut', 'shut_down', 'close_down', 'obstacle']
senticnet['separative'] = ['-0.66', '0', '0.502', '-0.79', '#sadness', '#disgust', 'negative', '-0.65', 'adversative', 'oppositive', 'contrastive', 'alternative', 'disjunctive']
senticnet['separator'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'water_inside', 'water_spray', 'teapot', 'bodily_waste', 'different_type']
senticnet['seperate'] = ['-0.95', '0', '0.652', '-0.96', '#sadness', '#disgust', 'negative', '-0.85', 'use_knife', 'use_blade', 'cut', 'sharp_tool', 'dangerous']
senticnet['sephadex'] = ['0.838', '-0.61', '0', '0.556', '#joy', '#surprise', 'positive', '0.671', 'activity', 'natural_process', 'natural_action', 'column_chromatography', 'paper_chromatography']
senticnet['sephardic_community'] = ['0', '0.209', '0.026', '0.184', '#interest', '#admiration', 'positive', '0.122', 'church', 'confession', 'temple', 'cathedral', 'monastery']
senticnet['sephardic_congregation'] = ['0', '0.209', '0.026', '0.184', '#interest', '#admiration', 'positive', '0.122', 'church', 'confession', 'temple', 'cathedral', 'monastery']
senticnet['sephardic_synagogue'] = ['0', '0.209', '0.026', '0.184', '#interest', '#admiration', 'positive', '0.122', 'church', 'confession', 'temple', 'cathedral', 'monastery']
senticnet['sepia'] = ['-0.05', '0.07', '0', '0.062', '#interest', '#admiration', 'positive', '0.027', 'salty', 'fine_grain', 'powder', 'granulate', 'food_flavor']
senticnet['sepia_tone'] = ['-0.05', '0.07', '0', '0.062', '#interest', '#admiration', 'positive', '0.027', 'salty', 'fine_grain', 'powder', 'granulate', 'food_flavor']
senticnet['sepracor'] = ['0.559', '0.656', '-0.26', '0.285', '#joy', '#interest', 'positive', '0.31', 'zealous', 'avidity', 'eagerness', 'avidness', 'thirstily']
senticnet['septation'] = ['-0.84', '0', '0.8', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'rigor_mortis', 'natural_process', 'natural_action', 'activity', 'action']
senticnet['septet'] = ['0.899', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.813', 'concert', 'measure_angle', 'make_drawing', 'make_beautiful_music', 'listen_heart']
senticnet['septic'] = ['-0.53', '0', '0.671', '-0.55', '#anger', '#disgust', 'negative', '-0.58', 'dirty', 'putrefactive', 'putrefacient', 'abscessed', 'contaminative']
senticnet['septic_sore_throat'] = ['-0.82', '0', '0.609', '-0.89', '#sadness', '#disgust', 'negative', '-0.77', 'inflammatory_disease', 'streptococcal_sore_throat', 'strep_throat', 'streptococcus_tonsilitis', 'pharyngitis']
senticnet['septic_system'] = ['-0.53', '0', '0.671', '-0.55', '#anger', '#disgust', 'negative', '-0.58', 'dirty', 'putrefactive', 'putrefacient', 'abscessed', 'contaminative']
senticnet['septic_tank'] = ['0', '0', '-0.75', '-0.71', '#fear', '#disgust', 'negative', '-0.73', 'riverbed', 'bottom_ocean', 'waterfall', 'down_drain', 'petri_dish']
senticnet['septicaemia'] = ['-0.92', '0', '0.824', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'bronchopneumonia', 'bronchial_pneumonia', 'pneumonia', 'pleuropneumonia', 'pneumococcal_pneumonia']
senticnet['septicemic'] = ['-0.95', '0', '0.876', '-0.89', '#sadness', '#disgust', 'negative', '-0.90', 'bubonic_plague', 'pestis_bubonica', 'glandular_plague', 'septicemic_plague', 'pestilence']
senticnet['septicemic_plague'] = ['-0.95', '0', '0.876', '-0.89', '#sadness', '#disgust', 'negative', '-0.90', 'bubonic_plague', 'pestis_bubonica', 'glandular_plague', 'pestilence', 'pneumonic_plague']
senticnet['septo_optic_dysplasia'] = ['-0.75', '0', '0.515', '-0.93', '#sadness', '#disgust', 'negative', '-0.73', 'sclerosis', 'induration', 'disseminated_sclerosis', 'autoimmune_disease', 'autoimmune_disorder']
senticnet['septoplasty'] = ['-0.56', '0', '0.806', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['septoria'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'apple_rust', 'fungus']
senticnet['septoria_leaf_spot'] = ['-0.83', '0', '0.754', '-0.97', '#sadness', '#disgust', 'negative', '-0.85', 'brown_rot_gummosis', 'ring_rot', 'ring_disease', 'tobacco_wilt', 'gummosis']
senticnet['septra'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['septuagenarian'] = ['0.948', '-0.71', '0', '0.649', '#joy', '#surprise', 'positive', '0.769', 'centenarian', 'nonagenarian', 'octogenarian', 'senior_citizen', 'sexagenarian']
senticnet['septuagint_translation'] = ['-0.22', '0', '-0.09', '-0.16', '#sadness', '#disgust', 'negative', '-0.16', 'inequality', 'reject', 'discard', 'difference', 'gap']
senticnet['septum'] = ['-0.05', '0.068', '-0.10', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'tooth_decay', 'body_ache', 'symptom', 'pierce', 'tooth']
senticnet['sepulcher'] = ['-0.89', '0', '0.535', '-0.68', '#sadness', '#disgust', 'negative', '-0.70', 'grave', 'tomb', 'chamber', 'monument', 'vault']
senticnet['sepulchral_chamber'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'wallet', 'billfold', 'bank', 'piggy_bank', 'bank_vault']
senticnet['sepulchre'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'wallet', 'billfold', 'bank', 'piggy_bank', 'bank_vault']
senticnet['sepulture'] = ['-0.58', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.64', 'burial_chamber', 'sepulcher', 'bunker', 'chamber', 'bombproof']
senticnet['sequatchie_river'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'common_bean', 'green_bean', 'wax_bean']
senticnet['sequel'] = ['-0.06', '0.122', '-0.04', '0.113', '#interest', '#admiration', 'positive', '0.032', 'encounter', 'portray', 'reveal', 'episode', 'next_episode']
senticnet['sequence'] = ['0.025', '0.116', '0.051', '0.163', '#interest', '#admiration', 'positive', '0.063', 'flow', 'sequential_order', 'full_stop', 'time_series', 'interval']
senticnet['sequence_divergence'] = ['-0.83', '0', '0.546', '-0.88', '#sadness', '#disgust', 'negative', '-0.75', 'difference', 'allomerism', 'deviation', 'variation', 'departure']
senticnet['sequential'] = ['0.881', '0.866', '0', '0', '#joy', '#interest', 'positive', '0.873', 'successively', 'serially', 'consecutively', 'selectively', 'sequentially']
senticnet['sequential_consistency'] = ['0.792', '-0.83', '0', '0.575', '#joy', '#surprise', 'positive', '0.734', 'connectedness', 'connection', 'consistency', 'continuity', 'link']
senticnet['sequential_order'] = ['0.025', '0.116', '0.051', '0.163', '#interest', '#admiration', 'positive', '0.063', 'flow', 'sequence', 'full_stop', 'time_series', 'interval']
senticnet['sequential_scan'] = ['-0.08', '0', '0.011', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'deflate', 'read_cover', 'obtain_information', 'upgrade', 'spread_out']
senticnet['sequentiality'] = ['0.865', '-0.80', '0', '0.52', '#joy', '#surprise', 'positive', '0.73', 'set', 'pose', 'lay', 'intersperse', 'put']
senticnet['sequentially'] = ['0.888', '0.871', '0', '0', '#joy', '#interest', 'positive', '0.879', 'successively', 'serially', 'consecutively', 'selectively', 'sequential']
senticnet['sequester'] = ['0', '0', '-0.87', '-0.84', '#fear', '#disgust', 'negative', '-0.85', 'seize', 'seclude', 'attach', 'impound', 'confiscate']
senticnet['sequester_carbon'] = ['0.053', '0.264', '-0.11', '0.23', '#interest', '#admiration', 'positive', '0.107', 'natural_resource', 'petroleum', 'fossil_fuel', 'energy_source', 'potential_energy']
senticnet['sequestration'] = ['-0.80', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.85', 'seizure', 'kill_brain_cell', 'cry', 'bad_health', 'scared']
senticnet['serai'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['seraph'] = ['0.026', '0.18', '-0.11', '0.18', '#interest', '#admiration', 'positive', '0.069', 'deity', 'god', 'angel', 'goddess', 'rabbi']
senticnet['seraphic_smile'] = ['-0.80', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'difficulty', 'awkwardness', 'flea_bite', 'difficultness', 'cumbersomeness']
senticnet['serbonian_bog'] = ['-0.71', '0', '0.704', '-0.89', '#sadness', '#disgust', 'negative', '-0.76', 'soil', 'mud', 'dirt', 'bleaching_clay', 'bleaching_earth']
senticnet['sercial'] = ['0', '0.066', '-0.05', '0.089', '#interest', '#admiration', 'positive', '0.032', 'pineapple', 'kale', 'banana', 'pickle', 'tomato']
senticnet['serdab'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'wallet', 'billfold', 'bank', 'piggy_bank', 'bank_vault']
senticnet['sere'] = ['0', '0', '-0.74', '-0.97', '#fear', '#disgust', 'negative', '-0.86', 'sear', 'withered', 'dried-up', 'shrivelled', 'shriveled']
senticnet['serena_slam'] = ['0.749', '0', '0', '0.952', '#joy', '#admiration', 'positive', '0.851', 'high_class', 'citation', 'suspicion', 'goodness', 'prosperity']
senticnet['serenade'] = ['0.101', '-0.04', '0.036', '0', '#joy', '#surprise', 'positive', '0.038', 'song', 'rap', 'jingle', 'chord', 'lyric']
senticnet['serendipitous'] = ['0.74', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.865', 'fortuitous', 'happenstance', 'coincidence', 'serendipity', 'serendipitously']
senticnet['serendipitous_discovery'] = ['0.774', '-0.63', '0', '0.567', '#joy', '#surprise', 'positive', '0.658', 'catching', 'breakthrough', 'spying', 'determination', 'tracing']
senticnet['serendipitously'] = ['0.739', '-1.0', '0', '0', '#joy', '#surprise', 'positive', '0.869', 'fortuitous', 'happenstance', 'coincidence', 'serendipity', 'serendipitous']
senticnet['serendipity'] = ['0.734', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.863', 'fortuitous', 'happenstance', 'coincidence', 'serendipitous', 'serendipitously']
senticnet['serene'] = ['0.873', '0', '0', '0.876', '#joy', '#admiration', 'positive', '0.875', 'tranquility', 'peace-loving', 'peace_of_mind', 'repose', 'heartsease']
senticnet['serene_atmosphere'] = ['0.579', '0.652', '-0.24', '0.276', '#joy', '#interest', 'positive', '0.315', 'tranquilly', 'tranquility', 'quietness', 'tranquil', 'unagitated']
senticnet['serenely'] = ['-0.77', '0', '0.855', '-0.57', '#sadness', '#anger', 'negative', '-0.73', 'unhurried', 'easygoing', 'deliberate', 'easy', 'careful']
senticnet['sereneness'] = ['0.615', '0.645', '-0.25', '0.291', '#joy', '#interest', 'positive', '0.324', 'pacifically', 'ataraxis', 'tranquility', 'harmony', 'peace']
senticnet['sereness'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'aridness', 'thirstiness', 'xerostomia', 'dry_mouth', 'dryness']
senticnet['serenity'] = ['0.33', '0', '0', '0', '#joy', '#joy', 'positive', '0.33', 'serene', 'at_ease', 'comfort', 'comfy', 'blessed']
senticnet['serf'] = ['-0.08', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.11', 'farmer', 'peasant', 'servant', 'poor', 'villein']
senticnet['serf_level_maternelle'] = ['-0.04', '0.088', '0.013', '0.069', '#interest', '#admiration', 'positive', '0.024', 'kindergarden', 'daycare', 'nursery', 'child', 'asylum']
senticnet['serfdom'] = ['-0.36', '-0.08', '0', '-0.33', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'bonded_labor', 'serfhood', 'vassalage']
senticnet['serfhood'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'bonded_labor', 'vassalage']
senticnet['sergeant'] = ['0', '0.115', '0', '0.063', '#interest', '#admiration', 'positive', '0.089', 'rank', 'military_rank', 'military_order', 'general', 'army_boss']
senticnet['sergeant_major'] = ['0.045', '0', '0', '0.088', '#joy', '#admiration', 'positive', '0.067', 'leader', 'seaman', 'captain', 'officer', 'traitor']
senticnet['sergent'] = ['0', '0', '-0.07', '-0.03', '#fear', '#disgust', 'negative', '-0.05', 'army_person', 'fighter', 'police_man', 'warrior', 'army_man']
senticnet['serial_adulterer'] = ['-0.94', '0', '0.944', '-0.90', '#sadness', '#anger', 'negative', '-0.93', 'lust', 'pregnant', 'desire', 'care_deeply', 'love']
senticnet['serial_arsonist'] = ['-0.14', '0', '-0.21', '-0.07', '#sadness', '#fear', 'negative', '-0.14', 'heat_light', 'burn_down_house', 'set_on_fire', 'rapid_oxidation', 'flame']
senticnet['serial_entrepreneur'] = ['0.046', '0.116', '-0.05', '0.076', '#interest', '#admiration', 'positive', '0.045', 'head_guy', 'boss', 'officer', 'leader', 'supervisor']
senticnet['serial_killer'] = ['-0.73', '0', '0.734', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'primitive', 'headhunter', 'barbarian', 'anthropophagus', 'anthropophagite']
senticnet['serial_murder'] = ['0', '0', '-0.95', '-0.85', '#fear', '#disgust', 'negative', '-0.90', 'choose_victim', 'murder_weapon', 'hide_evidence', 'murder', 'shoot_gun']
senticnet['serial_murderer'] = ['0.864', '-0.54', '0', '0.613', '#joy', '#admiration', 'positive', '0.675', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['serial_number'] = ['-0.97', '0', '0', '-0.81', '#sadness', '#disgust', 'negative', '-0.89', 'chip', 'clipper_chip', 'clipper', 'court_order', 'escrow_agency']
senticnet['serial_rapist'] = ['0.864', '-0.54', '0', '0.613', '#joy', '#admiration', 'positive', '0.675', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['serial_sex_offender'] = ['0.864', '-0.54', '0', '0.613', '#joy', '#admiration', 'positive', '0.675', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['serializability'] = ['0.792', '-0.83', '0', '0.575', '#joy', '#surprise', 'positive', '0.734', 'connectedness', 'connection', 'consistency', 'continuity', 'link']
senticnet['serialized_manga'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['serially'] = ['0.932', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.952', 'sequentially', 'synchronously', 'successively', 'cyclically', 'outputted']
senticnet['sericeous'] = ['-0.75', '0', '0.734', '-0.82', '#sadness', '#disgust', 'negative', '-0.77', 'puberulent', 'haired', 'furlike', 'pappose', 'pilous']
senticnet['series'] = ['0.887', '-0.81', '0', '0.613', '#joy', '#surprise', 'positive', '0.772', 'bacteria_order', 'sequence', 'genetic_code', 'genome', 'ordering']
senticnet['series_decider'] = ['0.034', '-0.02', '0.054', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'compass', 'swiss_army_knife', 'bake_pan', 'coffee_mug', 'wine_opener']
senticnet['series_finale'] = ['0.918', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.827', 'encounter', 'reveal', 'programme', 'lose', 'sequel']
senticnet['series_opener'] = ['0.034', '-0.02', '0.054', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'compass', 'swiss_army_knife', 'bake_pan', 'coffee_mug', 'wine_opener']
senticnet['seriocomic'] = ['0.807', '-0.83', '0', '0.688', '#joy', '#surprise', 'positive', '0.776', 'folly', 'lunacy', 'craziness', 'indulgence', 'foolery']
senticnet['serious'] = ['-0.89', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.82', 'wrong', 'insane', 'vengeance', 'impend_danger', 'hazardous']
senticnet['serious-minded'] = ['0', '0.841', '0', '0.869', '#interest', '#admiration', 'positive', '0.855', 'wasp-y', 'honest-hearted', 'clean-living', 'god-loving', 'levelheaded']
senticnet['serious_accident'] = ['-0.82', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.82', 'bang_head', 'automobile_accident', 'loss_ofawareness', 'brain_damage', 'unconscious']
senticnet['serious_allergic_reaction'] = ['0', '0', '-0.73', '-0.81', '#fear', '#disgust', 'negative', '-0.77', 'flab', 'bad_hair_day', 'lose_friend', 'alcoholism', 'brain_tumor']
senticnet['serious_automobile_accident'] = ['-0.73', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.79', 'die_painfully', 'arrest_police', 'get_accident', 'fall_head', 'skin_knee']
senticnet['serious_bacterial_infection'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchitis', 'chronic_bronchitis', 'respiratory_disease', 'respiratory_illness', 'respiratory_disorder']
senticnet['serious_bodily_harm'] = ['-0.70', '0', '0.874', '-0.75', '#anger', '#disgust', 'negative', '-0.77', 'blame', 'accusation', 'inculpation', 'complaint', 'imprecation']
senticnet['serious_bodily_injury'] = ['0.565', '-0.76', '0', '0.982', '#surprise', '#admiration', 'positive', '0.771', 'risk', 'hazard', 'jeopardy', 'health_hazard', 'peril']
senticnet['serious_brain_injury'] = ['-0.03', '-0.00', '-0.07', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'injury', 'get_hurt', 'bruise', 'lose_game', 'get_injured']
senticnet['serious_car_accident'] = ['-0.92', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.93', 'serious_accident', 'accident', 'hit_car', 'loss_ofawareness', 'bang_head']
senticnet['serious_concern'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'problem', 'serious_issue', 'overrate', 'prohibitionist_society', 'blade_weapon']
senticnet['serious_consideration'] = ['0.751', '-0.98', '0', '0.989', '#surprise', '#admiration', 'positive', '0.909', 'okay', 'approval', 'commendation', 'okey', 'okeh']
senticnet['serious_crime'] = ['-0.47', '0.174', '-0.44', '0.105', '#sadness', '#fear', 'negative', '-0.16', 'wrong', 'wrongdoing', 'bad_choice', 'harmful_human', 'bad_behavior']
senticnet['serious_damage'] = ['-0.98', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.97', 'harm', 'hurt', 'cut_skin', 'bloody', 'ouch']
senticnet['serious_drawback'] = ['0.554', '-0.72', '0', '0.651', '#surprise', '#admiration', 'positive', '0.644', 'reasonableness', 'modestness', 'cost', 'cut_rate', 'cut_price']
senticnet['serious_flaw'] = ['-0.07', '0.075', '-0.17', '0', '#sadness', '#fear', 'negative', '-0.06', 'protect_family', 'fault', 'mistake', 'expire', 'kill_self']
senticnet['serious_harm'] = ['0.565', '-0.76', '0', '0.982', '#surprise', '#admiration', 'positive', '0.771', 'risk', 'hazard', 'jeopardy', 'health_hazard', 'peril']
senticnet['serious_impediment'] = ['-0.88', '0', '0.944', '-0.91', '#anger', '#disgust', 'negative', '-0.91', 'difficulty', 'straitjacket', 'millstone', 'drag', 'obstruction']
senticnet['serious_injury'] = ['-0.17', '-0.04', '-0.12', '-0.25', '#sadness', '#disgust', 'negative', '-0.12', 'get_hurt', 'die', 'loss_oflife', 'feel_anger', 'arrest_police']
senticnet['serious_issue'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'problem', 'serious_problem', 'overrate', 'prohibitionist_society', 'blade_weapon']
senticnet['serious_leg_injury'] = ['-0.90', '0', '-0.91', '-0.73', '#sadness', '#fear', 'negative', '-0.85', 'broken_window', 'step_dog_shit', 'broken_leg', 'brain_tumor', 'bad_luck']
senticnet['serious_misdemeanor'] = ['-0.84', '0', '0.745', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'crime', 'against_law', 'revenge', 'vengeance', 'theft']
senticnet['serious_music'] = ['0.514', '-0.58', '0', '0.927', '#surprise', '#admiration', 'positive', '0.676', 'genre', 'fugue', 'chamber_music', 'opera', 'cantata']
senticnet['serious_neck_injury'] = ['-0.90', '0', '-0.91', '-0.73', '#sadness', '#fear', 'negative', '-0.85', 'broken_window', 'step_dog_shit', 'broken_leg', 'brain_tumor', 'bad_luck']
senticnet['serious_obstacle'] = ['-0.88', '0', '0.944', '-0.91', '#anger', '#disgust', 'negative', '-0.91', 'difficulty', 'straitjacket', 'millstone', 'drag', 'obstruction']
senticnet['serious_offense'] = ['-0.89', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.93', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['serious_pest'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'nuisance', 'annoyance', 'irritant', 'pain_in_ass', 'troublesome']
senticnet['serious_problem'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'problem', 'serious_issue', 'overrate', 'prohibitionist_society', 'blade_weapon']
senticnet['serious_shoulder_injury'] = ['-0.90', '0', '-0.91', '-0.73', '#sadness', '#fear', 'negative', '-0.85', 'broken_window', 'step_dog_shit', 'broken_leg', 'brain_tumor', 'bad_luck']
senticnet['serious_student'] = ['0', '0', '0', '0.783', '#admiration', '#admiration', 'positive', '0.783', 'student', 'graduate_student', 'good_student', 'hard_worker', 'drug_addict']
senticnet['serious_threat'] = ['-0.93', '0', '-0.91', '-0.84', '#sadness', '#fear', 'negative', '-0.89', 'destroy_live', 'take_life', 'death_destruction', 'get_rid_of_enemy', 'intimidate']
senticnet['serious_violent_crime'] = ['-0.47', '0.174', '-0.44', '0.105', '#sadness', '#fear', 'negative', '-0.16', 'wrong', 'wrongdoing', 'bad_choice', 'harmful_human', 'bad_behavior']
senticnet['seriously'] = ['-0.70', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.71', 'devout', 'earnestness', 'earnestly', 'seriousness', 'in_earnest']
senticnet['seriously_affect'] = ['-0.62', '0', '0.756', '-0.62', '#anger', '#disgust', 'negative', '-0.66', 'vitiate', 'ruin', 'maul', 'mangle', 'deface']
senticnet['seriously_hurt'] = ['-0.95', '0', '0.712', '-0.52', '#sadness', '#anger', 'negative', '-0.73', 'uncut', 'unharmed', 'unscathed', 'unwounded', 'uninjured']
senticnet['seriously_ill'] = ['-0.09', '0', '-0.16', '-0.21', '#fear', '#disgust', 'negative', '-0.15', 'sickness', 'malady', 'polio', 'disease', 'ailment']
senticnet['seriously_impair'] = ['-0.62', '0', '0.756', '-0.62', '#anger', '#disgust', 'negative', '-0.66', 'vitiate', 'ruin', 'maul', 'mangle', 'deface']
senticnet['seriously_impede'] = ['-0.05', '-0.04', '0.017', '-0.05', '#sadness', '#disgust', 'negative', '-0.02', 'shoe_shop', 'shoe_store', 'attic', 'obstruct', 'closet']
senticnet['seriously_injure'] = ['-0.59', '0', '0.704', '-0.5', '#sadness', '#anger', 'negative', '-0.59', 'lay_up', 'modify', 'nobble', 'alter', 'change']
senticnet['seriously_injured'] = ['-0.16', '0', '-0.18', '-0.23', '#fear', '#disgust', 'negative', '-0.19', 'attack_enemy', 'cause_death', 'die', 'lose_war', 'imprisonment']
senticnet['seriously_threaten'] = ['-0.16', '0', '-0.18', '-0.23', '#fear', '#disgust', 'negative', '-0.19', 'eat_off', 'remove', 'diminish', 'decrease', 'reduce']
senticnet['seriously_undermine'] = ['-0.16', '0', '-0.18', '-0.23', '#fear', '#disgust', 'negative', '-0.19', 'eat_off', 'remove', 'diminish', 'decrease', 'reduce']
senticnet['seriously_unwell'] = ['-0.71', '0', '-0.96', '-0.94', '#fear', '#disgust', 'negative', '-0.87', 'ill', 'unconscious', 'sick', 'feel_sick', 'sleep_apnea']
senticnet['seriousness'] = ['-0.92', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.89', 'seriously', 'devout', 'earnestness', 'earnestly', 'in_earnest']
senticnet['serjeanty'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'bodyguard', 'outrider', 'bodyguard_of_guard', 'beefeater', 'defender']
senticnet['sermon'] = ['0.023', '0.295', '-0.05', '0.283', '#interest', '#admiration', 'positive', '0.136', 'religion', 'religious_purpose', 'world_religion', 'gospel', 'judaism']
senticnet['sermonic'] = ['-0.67', '0', '0.936', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'unprepared', 'extemporary', 'extempore', 'impromptu', 'unrehearsed']
senticnet['sermonize'] = ['-0.75', '0', '0.991', '0', '#sadness', '#anger', 'negative', '-0.87', 'sermonizing', 'morality', 'inculcate', 'manner', 'speech']
senticnet['sermonizing'] = ['-0.75', '0', '0.984', '0', '#sadness', '#anger', 'negative', '-0.86', 'sermonize', 'morality', 'inculcate', 'manner', 'speech']
senticnet['serocomplex'] = ['-0.75', '0', '0.69', '-0.69', '#sadness', '#disgust', 'negative', '-0.71', 'rubor', 'redness', 'sleeping_sickness', 'sleepy_sickness', 'cephalitis']
senticnet['serologist'] = ['0.898', '-0.93', '0', '0.637', '#joy', '#surprise', 'positive', '0.824', 'toxicologist', 'epidemiologist', 'immunologist', 'medical_scientist', 'scientist']
senticnet['serology'] = ['0.7', '-0.63', '0', '0.922', '#joy', '#admiration', 'positive', '0.752', 'pathology', 'medicine', 'medical_science', 'radiology', 'medical_specialty']
senticnet['seronegative'] = ['-0.74', '0', '0.766', '0', '#sadness', '#anger', 'negative', '-0.75', 'use_needle', 'clothes_repair', 'woven', 'embroidery', 'thread_needle']
senticnet['serosa'] = ['-0.03', '0', '0', '-0.11', '#sadness', '#disgust', 'negative', '-0.07', 'store_cloth', 'clothes_factory', 'stock', 'sword', 'gun_store']
senticnet['serotina'] = ['-0.92', '0', '0.912', '-0.94', '#sadness', '#disgust', 'negative', '-0.92', 'true_blackberry', 'sand_blackberry', 'blackberry_bush', 'blackberry', 'running_blackberry']
senticnet['serotonergic'] = ['0.698', '-0.61', '0', '0.619', '#joy', '#admiration', 'positive', '0.644', 'sensitivity', 'sensitiveness', 'irritability', 'sensibility', 'reactivity']
senticnet['serotonergic_activity'] = ['0.698', '-0.61', '0', '0.619', '#joy', '#admiration', 'positive', '0.644', 'sensitivity', 'sensitiveness', 'irritability', 'sensibility', 'reactivity']
senticnet['serous_fluid'] = ['0.759', '-0.72', '0', '0.893', '#joy', '#admiration', 'positive', '0.793', 'manifestation', 'cry', 'reflection', 'acting_out', 'explosion']
senticnet['serous_retinal_detachment'] = ['-0.86', '0', '0.858', '-0.91', '#sadness', '#disgust', 'negative', '-0.87', 'edema', 'oedema', 'hydrops', 'dropsy', 'eye_disease']
senticnet['serow'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheep', 'argal', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['serpent'] = ['-0.76', '0', '0.676', '-0.51', '#sadness', '#anger', 'negative', '-0.65', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['serpent_crown'] = ['0.086', '-0.10', '0.064', '0', '#joy', '#surprise', 'positive', '0.043', 'art_piece', 'lifetime', 'museum', 'vintage', 'collectible']
senticnet['serpent_society'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'elapid', 'ophidian', 'serpent', 'krait', 'taipan']
senticnet['serpent_squad'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'elapid', 'ophidian', 'serpent', 'krait', 'taipan']
senticnet['serrated_blade'] = ['0', '-0.06', '0.071', '-0.17', '#anger', '#disgust', 'negative', '-0.06', 'double_edge_razor', 'clipper', 'pair_of_scissors', 'knife', 'razor_blade']
senticnet['serrated_edge'] = ['-0.71', '0', '0.954', '-0.56', '#sadness', '#anger', 'negative', '-0.74', 'jackknife', 'pocket_knife', 'penknife', 'switchblade', 'switchblade_knife']
senticnet['serrated_knife'] = ['0.03', '-0.05', '0', '0.059', '#surprise', '#admiration', 'positive', '0.046', 'platter', 'fork', 'spoon', 'tablespoon', 'bowl']
senticnet['serrated_teeth'] = ['0.1', '-0.12', '0.05', '-0.03', '#joy', '#surprise', 'positive', '0.034', 'fancy_dress', 'blouse', 'fashion', 'dress', 'skirt']
senticnet['serrated_tussock'] = ['0', '0.086', '0', '0', '#interest', '#interest', 'positive', '0.086', 'swim_ocean', 'weight_lift', 'sit_in_shade', 'cut_grass', 'trust_friend']
senticnet['serto'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'dryness', 'thirstiness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['sertoli_cell'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'nonmotile', 'immobile']
senticnet['serue'] = ['-0.69', '0', '0.883', '-0.91', '#anger', '#disgust', 'negative', '-0.82', 'stick_in', 'enclose', 'insert', 'feed_in', 'put_in']
senticnet['serum'] = ['0', '0', '0.834', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'blood_serum', 'zuma', 'anatolia', 'uunet', 'mideasttalk']
senticnet['serum_concentration'] = ['0.992', '-0.80', '0', '0.584', '#joy', '#surprise', 'positive', '0.795', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'incontinence']
senticnet['serum_sodium'] = ['-0.74', '0', '0.723', '-0.59', '#sadness', '#anger', 'negative', '-0.68', 'tone', 'tautness', 'tonus', 'condition', 'status']
senticnet['servant'] = ['-0.85', '0', '0.938', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'menial', 'lackey', 'flunky', 'flunkey', 'familiar']
senticnet['servant_boy'] = ['-0.97', '0', '0.072', '-0.91', '#sadness', '#disgust', 'negative', '-0.65', 'slave', 'female_slave', 'psychological_aggression', 'physical_aggression', 'verbal_aggression']
senticnet['servant_girl'] = ['-0.62', '0', '0.649', '-0.67', '#anger', '#disgust', 'negative', '-0.64', 'handmaid', 'handmaiden', 'chambermaid', 'maid', 'parlormaid']
senticnet['servant_leadership'] = ['0.053', '0.136', '0', '0.057', '#interest', '#admiration', 'positive', '0.082', 'boss', 'supervisor', 'leader', 'head_person', 'head_guy']
senticnet['servare'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'sedum', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['serve'] = ['0', '0.165', '-0.08', '0.179', '#interest', '#admiration', 'positive', '0.085', 'bring_food', 'take_order', 'meet_need', 'help_person', 'free_slave']
senticnet['serve_beer'] = ['0', '0.709', '0', '0', '#interest', '#interest', 'positive', '0.709', 'buy_drink', 'drink_beer', 'invite_friend', 'pay_tab', 'throw_away_trash']
senticnet['serve_coffee'] = ['0.108', '0.145', '-0.09', '0', '#joy', '#interest', 'positive', '0.054', 'drink_wine', 'choke_food', 'drink_fluid', 'drink_tea', 'suck']
senticnet['serve_cold'] = ['0.28', '0.174', '-0.05', '0.249', '#joy', '#admiration', 'positive', '0.163', 'tasty_good', 'make_fruit', 'delicious', 'high_calorie', 'soft_sweet']
senticnet['serve_dinner'] = ['0.986', '0', '0', '0.924', '#joy', '#admiration', 'positive', '0.955', 'play_card_game', 'sit_around', 'eat_cereal', 'eat_lot', 'eat_food']
senticnet['serve_dish'] = ['0.052', '-0.04', '0.024', '0', '#joy', '#surprise', 'positive', '0.023', 'table_set', 'meal_time', 'dinner', 'dinner_table', 'table']
senticnet['serve_drink'] = ['0.081', '-0.05', '0', '-0.03', '#joy', '#surprise', 'positive', '0.036', 'cut_food', 'drink_beer', 'drink_beverage', 'drink_much', 'pry_open']
senticnet['serve_food'] = ['0', '-0.17', '0', '0.244', '#surprise', '#admiration', 'positive', '0.209', 'obtain_food', 'carry_food', 'take_order', 'consumption', 'make_dinner']
senticnet['serve_guest'] = ['0.706', '0.76', '0', '0', '#joy', '#interest', 'positive', '0.733', 'have_fun', 'laugh_lot', 'change_mood', 'deafness', 'smoke_bowl']
senticnet['serve_hot'] = ['0.067', '0.089', '0', '0.094', '#interest', '#admiration', 'positive', '0.083', 'liquid_food', 'hot_liquid', 'drinkable', 'fluid', 'better_cold']
senticnet['serve_jury'] = ['-0.74', '0', '0.926', '0', '#sadness', '#anger', 'negative', '-0.83', 'bang_gavel', 'verdict', 'register_vote', 'respect', 'convict_criminal']
senticnet['serve_justice'] = ['-0.74', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'get_divorce', 'stop_marry', 'justice', 'institute_civil_action', 'divorce']
senticnet['serve_liquid'] = ['0.097', '0', '0', '0', '#joy', '#joy', 'positive', '0.097', 'consume_food', 'eat_food', 'swallow', 'fill_up', 'drink_soup']
senticnet['serve_meal'] = ['0.863', '0', '0', '0', '#joy', '#joy', 'positive', '0.863', 'eat_food_off', 'eat_pudding', 'sit_around', 'eat_food', 'munch']
senticnet['serve_wine'] = ['0.025', '0', '0.016', '0.067', '#joy', '#admiration', 'positive', '0.025', 'liquid', 'ferment_beverage', 'carbonate_drink', 'measure_liquid', 'good_drink']
senticnet['server'] = ['0.949', '0.991', '0', '0', '#joy', '#interest', 'positive', '0.97', 'wait_staff', 'manager', 'waitress', 'public_servant', 'waiter']
senticnet['server_consolidation'] = ['0.637', '-0.91', '0', '0.757', '#surprise', '#admiration', 'positive', '0.768', 'tractability', 'manageableness', 'docility', 'obedience', 'amenability']
senticnet['server_socket'] = ['0.078', '0.103', '-0.09', '0.191', '#interest', '#admiration', 'positive', '0.069', 'connect', 'aunt_uncle', 'short_form', 'relationship', 'kinship']
senticnet['server_virtualization'] = ['0.637', '-0.91', '0', '0.757', '#surprise', '#admiration', 'positive', '0.768', 'tractability', 'manageableness', 'docility', 'obedience', 'amenability']
senticnet['serveth'] = ['0.887', '-0.84', '0', '0.819', '#joy', '#surprise', 'positive', '0.851', 'arouse', 'elicit', 'enkindle', 'kindle', 'evoke']
senticnet['service'] = ['0.805', '-0.92', '0', '0.793', '#joy', '#surprise', 'positive', '0.84', 'agape', 'confirmation', 'libation', 'rite', 'religious_ritual']
senticnet['service_car'] = ['0.856', '0.746', '0', '0', '#joy', '#interest', 'positive', '0.801', 'marry_man', 'date_woman', 'oil_hinge', 'man_post', 'date_man']
senticnet['service_delivery'] = ['0.92', '-0.70', '0', '0.674', '#joy', '#surprise', 'positive', '0.765', 'sensitivity', 'sensitiveness', 'irritability', 'sensibility', 'excitability']
senticnet['service_overseas_vso'] = ['-0.02', '0.1', '-0.07', '0.089', '#interest', '#admiration', 'positive', '0.023', 'functionary', 'employment', 'labor', 'employ', 'toil']
senticnet['service_provider'] = ['0.971', '0.76', '0', '0', '#joy', '#interest', 'positive', '0.865', 'shopper', 'clerk', 'patron', 'vintner', 'hairdresser']
senticnet['service_station'] = ['0.065', '-0.02', '0.009', '0', '#joy', '#surprise', 'positive', '0.026', 'rest_area', 'motel_room', 'gas_station', 'buy_snack', 'go_toilet']
senticnet['serviceability'] = ['0.508', '-0.87', '0', '0.915', '#surprise', '#admiration', 'positive', '0.767', 'quality', 'usefulness', 'serviceableness', 'usableness', 'user_friendly']
senticnet['serviceable'] = ['0.986', '-0.62', '0', '0.807', '#joy', '#admiration', 'positive', '0.807', 'operable', 'functional', 'user_friendly', 'usable', 'operational']
senticnet['serviceableness'] = ['0.508', '-0.87', '0', '0.923', '#surprise', '#admiration', 'positive', '0.768', 'quality', 'serviceability', 'usefulness', 'usableness', 'user_friendly']
senticnet['serviceberry'] = ['-0.86', '0', '0.665', '-0.61', '#sadness', '#anger', 'negative', '-0.71', 'wooded', 'braky', 'rushy', 'scrubby', 'forested']
senticnet['serviceline'] = ['0', '0.084', '0', '0', '#interest', '#interest', 'positive', '0.084', 'call_restaurant', 'reservation', 'reserve', 'dinner', 'reserved']
senticnet['servicemembers_civil'] = ['0.732', '0.729', '0', '0', '#joy', '#interest', 'positive', '0.73', 'feel_relief', 'good_feeling', 'feel_ok', 'feel_relieved', 'feel_better']
senticnet['servicemembers_legal'] = ['-0.04', '0', '-0.06', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'make_war', 'conquer_enemy', 'use_military', 'gain_territory', 'keep_peace']
senticnet['servile'] = ['0.981', '-0.88', '0', '0.939', '#joy', '#admiration', 'positive', '0.936', 'slavelike', 'submissive', 'obsequious', 'subservient', 'slavish']
senticnet['servile_fear'] = ['-0.84', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.90', 'mortify', 'embarrassed', 'self-hatred', 'self-disgust', 'humiliated']
senticnet['servility'] = ['-0.83', '0', '0', '-0.81', '#sadness', '#disgust', 'negative', '-0.82', 'resignedly', 'unhopeful', 'abjectly', 'resignation', 'reconcile']
senticnet['serving_platter'] = ['0.03', '-0.05', '0', '0.059', '#surprise', '#admiration', 'positive', '0.046', 'platter', 'fork', 'spoon', 'tablespoon', 'bowl']
senticnet['servitor'] = ['0.036', '0.111', '-0.05', '0.054', '#interest', '#admiration', 'positive', '0.038', 'despot', 'supervisor', 'chief', 'commander', 'earl']
senticnet['servitude'] = ['0', '0', '-0.78', '-0.74', '#fear', '#disgust', 'negative', '-0.76', 'redemptioner', 'slavery', 'serf', 'bond', 'slave']
senticnet['servlet'] = ['-0.95', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.84', 'hold_pencil', 'food_scrap', 'paper_product', 'waste_paper', 'cover']
senticnet['sesame_paste'] = ['0', '-0.03', '0.046', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'pasta', 'green_bean', 'mayonnaise', 'frozen_food', 'sour_milk']
senticnet['sesame_seed'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'roll', 'custard', 'bread', 'biscuit']
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senticnet['setlist'] = ['0.773', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.844', 'favorite', 'crowd', 'acclamation', 'bunch', 'large_crowd']
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senticnet['settle_argument'] = ['0.973', '0.734', '0', '0.897', '#joy', '#admiration', 'positive', '0.868', 'arrest_criminal', 'make_judgment', 'judge_person', 'go_court', 'tell_truth']
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senticnet['seventh_chord'] = ['-0.65', '0', '0.681', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'tone', 'whole_tone', 'step', 'whole_step', 'musical_interval']
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senticnet['severe_bout'] = ['-0.09', '0', '-0.16', '-0.21', '#fear', '#disgust', 'negative', '-0.15', 'sickness', 'malady', 'polio', 'disease', 'ailment']
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senticnet['severe_brain_injury'] = ['-0.03', '-0.00', '-0.07', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'injury', 'get_hurt', 'bruise', 'lose_game', 'get_injured']
senticnet['severe_chest_pain'] = ['-0.20', '0', '-0.22', '-0.19', '#sadness', '#fear', 'negative', '-0.20', 'go_hospital', 'death', 'die', 'take_aspirin', 'heart_attack']
senticnet['severe_cold'] = ['-0.96', '0', '0.172', '-0.99', '#sadness', '#disgust', 'negative', '-0.71', 'cold_winter', 'cold', 'hypothermia', 'possibility_to_die', 'fear_to_die']
senticnet['severe_concussion'] = ['-0.03', '-0.00', '-0.07', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'injury', 'get_hurt', 'bruise', 'lose_game', 'get_injured']
senticnet['severe_congenital'] = ['-0.50', '0', '0.542', '-0.51', '#anger', '#disgust', 'negative', '-0.51', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'hirschsprung_disease']
senticnet['severe_congestion'] = ['-0.98', '0', '0.989', '-0.93', '#sadness', '#anger', 'negative', '-0.97', 'flight_accident', 'dangerous', 'danger', 'air_traffic', 'air_traffic_jam']
senticnet['severe_constipation'] = ['-0.52', '0', '0.839', '-0.86', '#anger', '#disgust', 'negative', '-0.74', 'folie', 'upset', 'psychosomatic_disorder', 'conversion_disorder', 'conversion_reaction']
senticnet['severe_cough'] = ['-0.92', '0', '0.824', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'bronchopneumonia', 'bronchial_pneumonia', 'pneumonia', 'pleuropneumonia', 'pneumococcal_pneumonia']
senticnet['severe_criticism'] = ['-0.16', '0.179', '0.568', '-0.61', '#anger', '#disgust', 'negative', '-0.29', 'angrily', 'choler', 'see_red', 'dander', 'hackle']
senticnet['severe_damage'] = ['-0.98', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.97', 'harm', 'hurt', 'cut_skin', 'bloody', 'ouch']
senticnet['severe_dehydration'] = ['-0.72', '0', '0.762', '-0.79', '#anger', '#disgust', 'negative', '-0.75', 'heat_hyperpyrexia', 'heat_prostration', 'prostration', 'sunstroke', 'thermic_fever']
senticnet['severe_dementia'] = ['-0.94', '0', '0.821', '-0.75', '#sadness', '#anger', 'negative', '-0.84', 'predementedness', 'insanity', 'dementedness', 'senile_psychosis', 'alcoholic_dementia']
senticnet['severe_depression'] = ['-0.99', '0', '0.982', '-0.62', '#sadness', '#anger', 'negative', '-0.86', 'dysthymia', 'dysthymic_depression', 'affective_disorder', 'major_affective_disorder', 'emotional_disorder']
senticnet['severe_diarrhea'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'inflammatory_disease', 'stomach_flu', 'intestinal_flu', 'pelvic_inflammatory_disease']
senticnet['severe_diarrhoea'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'inflammatory_disease', 'stomach_flu', 'intestinal_flu', 'pelvic_inflammatory_disease']
senticnet['severe_disability'] = ['-0.77', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.78', 'chicken_pox', 'diabetes', 'lyme_disease', 'brain_tumor', 'suffer_fool']
senticnet['severe_drought'] = ['-0.98', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.89', 'dry', 'lose_team', 'leave_out', 'disrespect', 'cause_suffer']
senticnet['severe_economic_downturn'] = ['0', '0', '0', '-0.70', '#disgust', '#disgust', 'negative', '-0.70', 'uncertainty', 'uncertain_environment', 'insecure', 'blur', 'anxiety']
senticnet['severe_erosion'] = ['0.614', '-0.95', '0', '0.584', '#joy', '#surprise', 'positive', '0.718', 'mother_lode', 'champion_lode', 'deposit', 'sedimentation', 'lode']
senticnet['severe_famine'] = ['-0.61', '0', '0.712', '-0.86', '#anger', '#disgust', 'negative', '-0.73', 'impoverishment', 'deprivation', 'indigence', 'penury', 'pauperism']
senticnet['severe_fatigue'] = ['-0.80', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'fatigue', 'exhaustion', 'become_tired', 'get_tired', 'feel_tired']
senticnet['severe_headache'] = ['-0.06', '0', '-0.15', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'severe_pain', 'discomfort', 'physical_reaction', 'choke_death', 'embarrassment']
senticnet['severe_heart_failure'] = ['-0.98', '0', '0.827', '-0.94', '#sadness', '#disgust', 'negative', '-0.91', 'congestive_heart_failure', 'heart_attack', 'failure', 'heart_disease', 'cardiopathy']
senticnet['severe_hypertension'] = ['-0.85', '0', '-0.71', '-0.86', '#sadness', '#disgust', 'negative', '-0.81', 'get_exercise', 'train_marathon', 'run_marathon', 'run_long_distance', 'go_gym']
senticnet['severe_hypothermia'] = ['-0.72', '0', '0.762', '-0.79', '#anger', '#disgust', 'negative', '-0.75', 'heat_hyperpyrexia', 'heat_prostration', 'prostration', 'sunstroke', 'thermic_fever']
senticnet['severe_impairment'] = ['0.874', '-0.73', '0', '0.608', '#joy', '#surprise', 'positive', '0.74', 'growth', 'growing', 'maturation', 'development', 'ontogeny']
senticnet['severe_infection'] = ['-0.92', '0', '0.824', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'bronchopneumonia', 'bronchial_pneumonia', 'pneumonia', 'pleuropneumonia', 'pneumococcal_pneumonia']
senticnet['severe_inflammation'] = ['-0.52', '0', '0.538', '-0.95', '#anger', '#disgust', 'negative', '-0.67', 'sanies', 'pus', 'purulence', 'gleet', 'ichor']
senticnet['severe_injury'] = ['-0.03', '-0.00', '-0.07', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'injury', 'get_hurt', 'bruise', 'lose_game', 'get_injured']
senticnet['severe_irritation'] = ['-0.70', '0', '-0.83', '-0.98', '#fear', '#disgust', 'negative', '-0.84', 'body_hurt', 'ache', 'ouch', 'cut_skin', 'scar']
senticnet['severe_knee_injury'] = ['-0.90', '0', '-0.91', '-0.73', '#sadness', '#fear', 'negative', '-0.85', 'broken_window', 'step_dog_shit', 'broken_leg', 'brain_tumor', 'bad_luck']
senticnet['severe_laceration'] = ['-0.12', '-0.04', '0', '-0.17', '#sadness', '#disgust', 'negative', '-0.08', 'cause_bleed', 'open_wind', 'gash', 'lacerate', 'painful_injury']
senticnet['severe_liver_disease'] = ['-0.60', '0', '0.544', '-0.53', '#sadness', '#anger', 'negative', '-0.56', 'acute_renal_failure', 'acute_kidney_failure', 'chronic_renal_failure', 'chronic_kidney_failure', 'kidney_failure']
senticnet['severe_lung_disease'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchiolitis', 'bronchitis', 'respiratory_disease', 'respiratory_illness', 'respiratory_disorder']
senticnet['severe_malaria'] = ['-0.55', '0', '0.757', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'rubor', 'redness', 'cephalitis', 'sleepy_sickness', 'epidemic_encephalitis']
senticnet['severe_malnutrition'] = ['-0.71', '0', '0.74', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'malnutrition', 'marasmus', 'avitaminosis', 'hypovitaminosis', 'deficiency_disease']
senticnet['severe_mental_retardation'] = ['0.874', '-0.73', '0', '0.608', '#joy', '#surprise', 'positive', '0.74', 'growth', 'growing', 'maturation', 'development', 'ontogeny']
senticnet['severe_myopia'] = ['-0.13', '0', '0.013', '-0.08', '#sadness', '#disgust', 'negative', '-0.07', 'blind', 'dig_tunnel', 'underground_animal', 'big_mouse', 'small_rodent']
senticnet['severe_nausea'] = ['-0.06', '0', '-0.15', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'severe_pain', 'discomfort', 'physical_reaction', 'choke_death', 'embarrassment']
senticnet['severe_neurologic'] = ['0.753', '-0.74', '0', '0.587', '#joy', '#surprise', 'positive', '0.695', 'draining', 'exhausting', 'debilitating', 'enervating', 'enfeebling']
senticnet['severe_neurological'] = ['-0.50', '0', '0.542', '-0.51', '#anger', '#disgust', 'negative', '-0.51', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'hirschsprung_disease']
senticnet['severe_obesity'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'fatty', 'worse', 'health_issue', 'clinical_problem']
senticnet['severe_obstructive_sleep'] = ['-0.03', '-0.03', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'eat_poison', 'commit_suicide', 'drug_addiction', 'drunk_drive', 'jump_off_cliff']
senticnet['severe_pain'] = ['-0.2', '0', '-0.25', '-0.15', '#sadness', '#fear', 'negative', '-0.20', 'feel_pain', 'poor_health', 'learn_disease', 'suffer', 'concern_health']
senticnet['severe_penance'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitentially', 'penitent', 'repentantly', 'penitence']
senticnet['severe_personality_disorder'] = ['0', '0', '0.896', '-0.96', '#anger', '#disgust', 'negative', '-0.92', 'antisocial', 'sociopathic', 'psychopathic_personality', 'disturbance', 'schizoid']
senticnet['severe_pneumonia'] = ['-0.92', '0', '0.824', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'bronchopneumonia', 'bronchial_pneumonia', 'pneumonia', 'pleuropneumonia', 'pneumococcal_pneumonia']
senticnet['severe_punishment'] = ['-0.83', '0', '0.924', '-0.79', '#sadness', '#anger', 'negative', '-0.85', 'kick_in_butt', 'licking', 'whacking', 'stoning', 'lapidation']
senticnet['severe_repression'] = ['0', '0.793', '0', '0.847', '#interest', '#admiration', 'positive', '0.82', 'disagreement', 'fight_against', 'opposition', 'against', 'go_against']
senticnet['severe_reprimand'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'negligence', 'evasion', 'dereliction', 'contributory_negligence']
senticnet['severe_shortage'] = ['-0.83', '0', '-0.88', '-0.78', '#sadness', '#fear', 'negative', '-0.83', 'little', 'sadness', 'small_amount', 'lack', 'absence']
senticnet['severe_snowstorm'] = ['0', '0', '-0.93', '-0.82', '#fear', '#disgust', 'negative', '-0.88', 'thunderstorm', 'snow_storm', 'storm', 'weather', 'rainy']
senticnet['severe_stomach_pain'] = ['-0.06', '0', '-0.15', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'severe_pain', 'discomfort', 'physical_reaction', 'choke_death', 'embarrassment']
senticnet['severe_storm'] = ['0', '-0.41', '-0.95', '0', '#surprise', '#fear', 'negative', '-0.26', 'blizzard', 'snow_storm', 'storm', 'danger', 'rainy']
senticnet['severe_strain'] = ['-0.08', '-0.03', '-0.07', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'injury', 'soreness', 'pain', 'hurt', 'dislocation']
senticnet['severe_structural_damage'] = ['-0.98', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.97', 'harm', 'hurt', 'cut_skin', 'bloody', 'ouch']
senticnet['severe_thunderstorm'] = ['0', '-0.41', '-0.95', '0', '#surprise', '#fear', 'negative', '-0.26', 'blizzard', 'snow_storm', 'storm', 'danger', 'rainy']
senticnet['severe_trauma'] = ['-0.88', '0', '-0.79', '-0.85', '#sadness', '#disgust', 'negative', '-0.84', 'pain', 'severe_pain', 'agony', 'physical_pain', 'suffer']
senticnet['severe_visual_impairment'] = ['-0.82', '0', '0.996', '-0.61', '#sadness', '#anger', 'negative', '-0.81', 'visual_impairment', 'visual_defect', 'vision_defect', 'visual_disorder', 'avitaminosis']
senticnet['severe_weather'] = ['-0.03', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.04', 'crystal_clear', 'water_sky', 'weather_condition', 'weather_phenomenon', 'sun_cover']
senticnet['severe_winter_storm'] = ['0', '0', '-0.93', '-0.82', '#fear', '#disgust', 'negative', '-0.88', 'thunderstorm', 'snow_storm', 'storm', 'weather', 'rainy']
senticnet['severely'] = ['0', '0', '0.81', '-0.84', '#anger', '#disgust', 'negative', '-0.82', 'abrasively', 'barely', 'faint', 'mica', 'hardly']
senticnet['severely_allergic'] = ['-0.71', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.77', 'spill_milk', 'read_paper', 'drop_food', 'smell_food', 'choke']
senticnet['severely_damage'] = ['-0.93', '0', '-0.91', '-0.84', '#sadness', '#fear', 'negative', '-0.89', 'destroy_live', 'take_life', 'death_destruction', 'get_rid_of_enemy', 'intimidate']
senticnet['severely_disabled_child'] = ['0', '0', '0', '0.7', '#admiration', '#admiration', 'positive', '0.7', 'old', 'year', 'many_years', 'teen', 'annual']
senticnet['severely_disrupt'] = ['-0.93', '0', '-0.91', '-0.84', '#sadness', '#fear', 'negative', '-0.89', 'destroy_live', 'take_life', 'death_destruction', 'get_rid_of_enemy', 'intimidate']
senticnet['severely_dyslexic'] = ['0.89', '-0.70', '0', '0.643', '#joy', '#surprise', 'positive', '0.745', 'dyslexia', 'dyscalculia', 'learning_disability', 'disorder', 'upset']
senticnet['severely_ill'] = ['-0.71', '0', '-0.96', '-0.94', '#fear', '#disgust', 'negative', '-0.87', 'ill', 'unconscious', 'sick', 'feel_sick', 'sleep_apnea']
senticnet['severely_impair'] = ['-0.62', '0', '0.756', '-0.62', '#anger', '#disgust', 'negative', '-0.66', 'vitiate', 'ruin', 'maul', 'mangle', 'deface']
senticnet['severely_injure'] = ['-0.59', '0', '0.704', '-0.5', '#sadness', '#anger', 'negative', '-0.59', 'lay_up', 'modify', 'nobble', 'alter', 'change']
senticnet['severely_injured'] = ['-0.95', '0', '0.712', '-0.52', '#sadness', '#anger', 'negative', '-0.73', 'uncut', 'unharmed', 'unscathed', 'unwounded', 'uninjured']
senticnet['severely_limit'] = ['-0.05', '-0.04', '0.017', '-0.05', '#sadness', '#disgust', 'negative', '-0.02', 'shoe_shop', 'shoe_store', 'attic', 'obstruct', 'closet']
senticnet['severely_mentally'] = ['-0.98', '0', '0.674', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'slow', 'slow_pace', 'stupid', 'slow_car', 'slow_vehicle']
senticnet['severely_mentally_retarded'] = ['-0.98', '0', '0.674', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'slow', 'slow_pace', 'stupid', 'slow_car', 'slow_vehicle']
senticnet['severely_obese'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'fatty', 'worse', 'health_issue', 'clinical_problem']
senticnet['severely_overweight'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'fatty', 'worse', 'health_issue', 'clinical_problem']
senticnet['severely_restrict'] = ['0.051', '0.165', '-0.06', '0.174', '#interest', '#admiration', 'positive', '0.08', 'control_access', 'prevent', 'latch', 'lock', 'gate']
senticnet['severely_retarded'] = ['-0.98', '0', '0.674', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'slow', 'slow_pace', 'stupid', 'slow_car', 'slow_vehicle']
senticnet['severely_undermine'] = ['-0.16', '0', '-0.18', '-0.23', '#fear', '#disgust', 'negative', '-0.19', 'eat_off', 'remove', 'diminish', 'decrease', 'reduce']
senticnet['severely_underweight'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'fatty', 'worse', 'health_issue', 'clinical_problem']
senticnet['severeness'] = ['-0.53', '0', '0.733', '-0.61', '#anger', '#disgust', 'negative', '-0.62', 'distressfulness', 'intensiveness', 'intensity', 'foulness', 'seriousness']
senticnet['severest_punishment'] = ['-0.93', '0', '0.936', '-0.88', '#sadness', '#anger', 'negative', '-0.91', 'lose_team', 'cause_suffer', 'leave_out', 'loathing', 'lyme_disease']
senticnet['severity'] = ['-0.98', '0', '0.883', '-0.85', '#sadness', '#anger', 'negative', '-0.90', 'distressfulness', 'intensiveness', 'intensity', 'foulness', 'seriousness']
senticnet['seviche'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'crab', 'swimming_crab', 'rock_crab', 'fiddler_crab', 'pea_crab']
senticnet['sew'] = ['0', '0', '-0.88', '-0.96', '#fear', '#disgust', 'negative', '-0.92', 'knit', 'thread_needle', 'needle_thread', 'use_needle', 'weave']
senticnet['sew_kit'] = ['0', '0', '-0.09', '-0.07', '#fear', '#disgust', 'negative', '-0.08', 'antibiotic', 'medicine', 'doctor', 'sleep_drug', 'medicine_chest']
senticnet['sew_machine'] = ['0', '0', '-0.87', '-0.98', '#fear', '#disgust', 'negative', '-0.93', 'needle', 'sew_needle', 'haircloth', 'needle_thread', 'oilcloth']
senticnet['sew_needle'] = ['0', '-0.02', '0.074', '-0.13', '#anger', '#disgust', 'negative', '-0.06', 'use_needle', 'needle', 'needle_thread', 'clothes_repair', 'sew_machine']
senticnet['sewage'] = ['-0.53', '0', '0.671', '-0.55', '#anger', '#disgust', 'negative', '-0.58', 'dirty', 'putrefactive', 'putrefacient', 'abscessed', 'contaminative']
senticnet['sewage_disposal'] = ['-0.53', '0', '0.671', '-0.55', '#anger', '#disgust', 'negative', '-0.58', 'dirty', 'putrefactive', 'putrefacient', 'abscessed', 'contaminative']
senticnet['sewage_pollution'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'dirtiness', 'befoulment', 'defilement', 'dinginess', 'dinge']
senticnet['sewage_system'] = ['-0.53', '0', '0.671', '-0.55', '#anger', '#disgust', 'negative', '-0.58', 'dirty', 'putrefactive', 'putrefacient', 'abscessed', 'contaminative']
senticnet['sewage_treatment_plant'] = ['-0.81', '0', '0.987', '-0.64', '#sadness', '#anger', 'negative', '-0.81', 'burn_garbage', 'eliminate_garbage', 'incinerate_waste', 'dump', 'garbage_dump']
senticnet['sewardstone'] = ['0.623', '-0.56', '0', '0.974', '#joy', '#admiration', 'positive', '0.721', 'alterable', 'convertible', 'translatable', 'transformable', 'transmutable']
senticnet['sewer'] = ['0', '-0.00', '0.01', '-0.10', '#anger', '#disgust', 'negative', '-0.03', 'dump', 'trashcan', 'tunnel', 'incinerator', 'car_wash']
senticnet['sewer_line'] = ['-0.53', '0', '0.671', '-0.55', '#anger', '#disgust', 'negative', '-0.58', 'dirty', 'putrefactive', 'putrefacient', 'abscessed', 'contaminative']
senticnet['sewer_system'] = ['-0.53', '0', '0.671', '-0.55', '#anger', '#disgust', 'negative', '-0.58', 'dirty', 'putrefactive', 'putrefacient', 'abscessed', 'contaminative']
senticnet['sewickley'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
senticnet['sewing_kit'] = ['0.022', '-0.01', '0.062', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'electric_toothbrush', 'moistener', 'hairbrush', 'shampoo_conditioner', 'shave_kit']
senticnet['sewing_needle'] = ['0.105', '-0.17', '0.057', '0.079', '#joy', '#surprise', 'positive', '0.076', 'cue', 'twig', 'long_stick', 'stick', 'adhere']
senticnet['sewing_thread'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_cloth']
senticnet['sex'] = ['0.989', '0.947', '0', '0', '#joy', '#interest', 'positive', '0.968', 'safe_sex', 'contraception', 'sexual_intercourse', 'fuck', 'become_aroused']
senticnet['sex_abuse'] = ['0', '0.942', '0', '0', '#interest', '#interest', 'positive', '0.942', 'guy', 'special_person', 'guardian', 'boy', 'male']
senticnet['sex_abuse_scandal'] = ['-0.95', '0', '0.673', '-0.85', '#sadness', '#disgust', 'negative', '-0.82', 'discharge', 'free', 'dethrone', 'disinvest', 'unfrock']
senticnet['sex_act'] = ['0.947', '-0.81', '0', '0.656', '#joy', '#surprise', 'positive', '0.805', 'sexual_intercourse', 'intercourse', 'hank_panky', 'copulation', 'coitus']
senticnet['sex_addict'] = ['0', '0', '0.821', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'student', 'serious_student', 'addiction', 'graduate_student', 'hard_worker']
senticnet['sex_appeal'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'high_temperature', 'hot', 'hot_weather', 'fire_product', 'heat']
senticnet['sex_assault'] = ['-0.64', '0', '0.854', '-0.83', '#anger', '#disgust', 'negative', '-0.77', 'crime', 'bigamy', 'regulatory_offence', 'sexual_abuse', 'sex_crime']
senticnet['sex_attacker'] = ['0.864', '-0.54', '0', '0.613', '#joy', '#admiration', 'positive', '0.675', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['sex_attractant'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['sex_crime'] = ['-0.64', '0', '0.854', '-0.83', '#anger', '#disgust', 'negative', '-0.77', 'crime', 'bigamy', 'sexual_assault', 'sexual_abuse', 'regulatory_offence']
senticnet['sex_differ'] = ['0.982', '-0.72', '0', '0.615', '#joy', '#surprise', 'positive', '0.772', 'sexual_desire', 'concupiscence', 'physical_attraction', 'prurience', 'pruriency']
senticnet['sex_kitten'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'hair_color', 'purple', 'violet', 'dark_light', 'hue']
senticnet['sex_offense'] = ['-0.06', '0', '-0.15', '0', '#sadness', '#fear', 'negative', '-0.10', 'heroism', 'role_play', 'repeat', 'act', 'part_of_movie']
senticnet['sex_offense_forcible'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'identifiable', 'specifiable', 'recognizable', 'diagnosable', 'classifiable']
senticnet['sex_same_gender'] = ['0', '0', '0.772', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'go_public', 'change_society', 'take_oath', 'give_plug', 'get_divorce']
senticnet['sex_scandal'] = ['0.54', '-0.64', '0', '0.566', '#surprise', '#admiration', 'positive', '0.582', 'pleaser', 'entertainer', 'mortal', 'busker', 'drawing_card']
senticnet['sex_shop'] = ['-0.02', '0', '0.012', '-0.04', '#sadness', '#disgust', 'negative', '-0.02', 'walmart', 'woman_purse', 'record_store', 'tailor_shop', 'sock_drawer']
senticnet['sex_slave'] = ['-0.98', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.9', 'jailbird', 'prisoner', 'imprisoned', 'convict', 'inmate']
senticnet['sex_symbol'] = ['0', '0.793', '0', '0.91', '#interest', '#admiration', 'positive', '0.852', 'superstar', 'vip', 'accomplishment', 'performance', 'concert']
senticnet['sex_toy'] = ['0', '0', '0.714', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'duvet_cover', 'naughty', 'pillowcase', 'homosexual', 't_shirt']
senticnet['sex_with_stranger'] = ['0.053', '0.127', '-0.08', '0.134', '#interest', '#admiration', 'positive', '0.058', 'temporary_home', 'stay', 'sleep_on_couch', 'stay_overnight', 'remain']
senticnet['sex_without_condom'] = ['-0.90', '0', '0.202', '-1.0', '#sadness', '#disgust', 'negative', '-0.70', 'sickness', 'unsafe_sex', 'social_disease', 'std', 'sexually_transmitted_disease']
senticnet['sexagenarian'] = ['0.953', '-0.70', '0', '0.649', '#joy', '#surprise', 'positive', '0.769', 'centenarian', 'nonagenarian', 'octogenarian', 'septuagenarian', 'senior_citizen']
senticnet['sexily'] = ['-0.88', '0', '0.864', '-0.72', '#sadness', '#anger', 'negative', '-0.82', 'malicious', 'venomous', 'bitchy', 'catty', 'cattish']
senticnet['sexiness'] = ['0.927', '-0.55', '0', '0.612', '#joy', '#admiration', 'positive', '0.699', 'sexual_desire', 'concupiscence', 'physical_attraction', 'prurience', 'pruriency']
senticnet['sexism'] = ['-0.68', '0', '0.949', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'chauvinism', 'male_chauvinism', 'antifeminism', 'sexual_discrimination', 'discrimination']
senticnet['sexist'] = ['0.585', '-0.7', '0', '0.586', '#surprise', '#admiration', 'positive', '0.624', 'jingoistic', 'ultranationalistic', 'superpatriotic', 'nationalistic', 'patriotic']
senticnet['sexist_pig'] = ['0', '0', '0.71', '-0.80', '#anger', '#disgust', 'negative', '-0.75', 'idiotic', 'stupid', 'dumb', 'idiot', 'dummy']
senticnet['sexless'] = ['0', '-0.85', '0', '0.89', '#surprise', '#admiration', 'positive', '0.871', 'friendless', 'dateless', 'hung-over', 'druggie', 'obdurate']
senticnet['sexploitation'] = ['-0.07', '0.195', '0.629', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'misogynic', 'misogyny', 'murderousness', 'amuck', 'amok']
senticnet['sexploitation_film'] = ['0.794', '-0.50', '0', '0.531', '#joy', '#admiration', 'positive', '0.611', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['sexpot'] = ['-0.19', '0', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.15', 'bad_child', 'misbehavior', 'bad_kid', 'bad_action', 'difficulty']
senticnet['sextant'] = ['0.865', '0.84', '0', '0', '#joy', '#interest', 'positive', '0.853', 'travel', 'find_way', 'help_travelling', 'compass_direction', 'use_compass']
senticnet['sexual'] = ['0', '0.095', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'fragrant', 'use_decoration', 'colorful', 'green', 'cultivate_yard']
senticnet['sexual_abstention'] = ['0', '0.821', '0', '0.724', '#interest', '#admiration', 'positive', '0.772', 'sexual_morality', 'chastity', 'celibacy', 'virtue', 'intellect']
senticnet['sexual_abstinence'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'safe_sex', 'sex', 'prevent_pregnancy', 'avoid_pregnancy', 'become_aroused']
senticnet['sexual_abuse'] = ['-0.64', '0', '0.854', '-0.83', '#anger', '#disgust', 'negative', '-0.77', 'crime', 'bigamy', 'sexual_assault', 'regulatory_offence', 'sex_crime']
senticnet['sexual_act'] = ['0.158', '0.089', '-0.05', '0.129', '#joy', '#admiration', 'positive', '0.081', 'homosexual', 'pornography', 'ceiling_fan', 'red_light_district', 'storage_box']
senticnet['sexual_activity'] = ['0.836', '0.88', '0', '0', '#joy', '#interest', 'positive', '0.858', 'ceiling_fan', 'tv_set', 'pay_tv', 'red_light_district', 'pornography']
senticnet['sexual_addiction'] = ['-0.52', '0', '0.786', '-0.92', '#anger', '#disgust', 'negative', '-0.74', 'upset', 'bulimarexia', 'bulima_nervosa', 'eating_disorder', 'disorder']
senticnet['sexual_affair'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'liaison', 'affair', 'involvement']
senticnet['sexual_aggression'] = ['-0.75', '0', '0.886', '-0.72', '#sadness', '#anger', 'negative', '-0.78', 'harass', 'move', 'check', 'fork', 'pin']
senticnet['sexual_allure'] = ['-0.56', '0', '0.977', '-0.86', '#anger', '#disgust', 'negative', '-0.80', 'strangeness', 'quaintness', 'abnormality', 'queerness', 'crotchet']
senticnet['sexual_ambiguity'] = ['-0.9', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.80', 'filth', 'smut', 'vulgarism', 'dirt', 'obscenity']
senticnet['sexual_appetite'] = ['0.978', '0.81', '0', '0.848', '#joy', '#admiration', 'positive', '0.879', 'brownie_point', 'feel_proud', 'receive_thank', 'appreciate', 'positive_reinforcement']
senticnet['sexual_arousal'] = ['0.877', '0.734', '0', '0', '#joy', '#interest', 'positive', '0.805', 'arousal', 'foreplay', 'fall_love', 'orgasm', 'make_love']
senticnet['sexual_assault'] = ['-0.64', '0', '0.854', '-0.83', '#anger', '#disgust', 'negative', '-0.77', 'crime', 'bigamy', 'regulatory_offence', 'sexual_abuse', 'sex_crime']
senticnet['sexual_assault_prevention'] = ['-0.64', '0', '0.854', '-0.83', '#anger', '#disgust', 'negative', '-0.77', 'crime', 'bigamy', 'regulatory_offence', 'sexual_abuse', 'sex_crime']
senticnet['sexual_assault_survivor'] = ['-0.64', '0', '0.854', '-0.83', '#anger', '#disgust', 'negative', '-0.77', 'crime', 'bigamy', 'regulatory_offence', 'sexual_abuse', 'sex_crime']
senticnet['sexual_assault_victim'] = ['-0.06', '0', '-0.15', '0', '#sadness', '#fear', 'negative', '-0.10', 'heroism', 'role_play', 'repeat', 'act', 'part_of_movie']
senticnet['sexual_assault_victimization'] = ['-0.64', '0', '0.854', '-0.83', '#anger', '#disgust', 'negative', '-0.77', 'crime', 'bigamy', 'regulatory_offence', 'sexual_abuse', 'sex_crime']
senticnet['sexual_attraction'] = ['0.833', '0', '0', '0.807', '#joy', '#admiration', 'positive', '0.82', 'lust', 'amiable', 'give_hug', 'giggle', 'smile']
senticnet['sexual_behavior'] = ['0', '0.095', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'fragrant', 'use_decoration', 'colorful', 'green', 'cultivate_yard']
senticnet['sexual_cannibalism'] = ['0.947', '-0.81', '0', '0.656', '#joy', '#surprise', 'positive', '0.805', 'sexual_intercourse', 'intercourse', 'sex_act', 'hank_panky', 'coitus']
senticnet['sexual_conduct'] = ['-0.84', '0', '0.882', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'perversion', 'fetishism', 'paraphilia', 'sexual_perversion', 'buggery']
senticnet['sexual_connotation'] = ['0.633', '-0.67', '0', '0.776', '#surprise', '#admiration', 'positive', '0.693', 'double_entendre', 'loophole', 'ambiguity', 'amphiboly', 'parisology']
senticnet['sexual_conquest'] = ['0.857', '0', '0', '0', '#joy', '#joy', 'positive', '0.857', 'date', 'accuracy', 'sexy_woman', 'woo', 'enjoy_person']
senticnet['sexual_contact'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'liaison', 'affair', 'involvement']
senticnet['sexual_content'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'indecency']
senticnet['sexual_depravity'] = ['0', '0', '-0.96', '-0.71', '#fear', '#disgust', 'negative', '-0.84', 'turpitude', 'putrefaction', 'corruption', 'depravation', 'degeneracy']
senticnet['sexual_desire'] = ['0.611', '-0.88', '0', '0.883', '#surprise', '#admiration', 'positive', '0.792', 'love', 'aphrodisia', 'lecherousness', 'lustfulness', 'amorousness']
senticnet['sexual_deviance'] = ['0', '0.095', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'fragrant', 'use_decoration', 'colorful', 'green', 'cultivate_yard']
senticnet['sexual_deviancy'] = ['0.542', '-0.63', '0', '0.912', '#surprise', '#admiration', 'positive', '0.695', 'implosion_therapy', 'flooding', 'reciprocal_inhibition', 'behavior_modification', 'desensitization_technique']
senticnet['sexual_deviant'] = ['0.864', '-0.54', '0', '0.613', '#joy', '#admiration', 'positive', '0.675', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['sexual_discrimination'] = ['-0.68', '0', '0.949', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'chauvinism', 'male_chauvinism', 'antifeminism', 'sexism', 'discrimination']
senticnet['sexual_dysfunction'] = ['0.611', '-0.88', '0', '0.883', '#surprise', '#admiration', 'positive', '0.792', 'love', 'aphrodisia', 'lecherousness', 'lustfulness', 'amorousness']
senticnet['sexual_encounter'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'liaison', 'affair', 'involvement']
senticnet['sexual_excitation'] = ['0.611', '-0.88', '0', '0.883', '#surprise', '#admiration', 'positive', '0.792', 'love', 'aphrodisia', 'lecherousness', 'lustfulness', 'amorousness']
senticnet['sexual_excitement'] = ['0', '0', '0', '0.752', '#admiration', '#admiration', 'positive', '0.752', 'prevent_pregnancy', 'contraception', 'safe_sex', 'sex', 'avoid_pregnancy']
senticnet['sexual_experimentation'] = ['0', '0.095', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'fragrant', 'use_decoration', 'colorful', 'green', 'cultivate_yard']
senticnet['sexual_exploitation'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'vassalage']
senticnet['sexual_expression'] = ['0', '0.095', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'fragrant', 'use_decoration', 'colorful', 'green', 'cultivate_yard']
senticnet['sexual_fantasy'] = ['0.833', '0', '0', '0.807', '#joy', '#admiration', 'positive', '0.82', 'lust', 'amiable', 'give_hug', 'giggle', 'smile']
senticnet['sexual_fetish'] = ['-0.62', '0', '0.515', '-0.63', '#sadness', '#disgust', 'negative', '-0.59', 'immodesty', 'perversion', 'paraphilia', 'sexual_perversion', 'fetishism']
senticnet['sexual_fetishism'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'concupiscence', 'lustfulness', 'amorousness']
senticnet['sexual_frustration'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'concupiscence', 'lustfulness', 'amorousness']
senticnet['sexual_gayness'] = ['0.804', '-0.88', '0', '0.716', '#joy', '#surprise', 'positive', '0.802', 'sex', 'gayness', 'inversion', 'lesbianism', 'sapphism']
senticnet['sexual_gratification'] = ['0.978', '0.81', '0', '0.848', '#joy', '#admiration', 'positive', '0.879', 'brownie_point', 'feel_proud', 'receive_thank', 'appreciate', 'positive_reinforcement']
senticnet['sexual_harassment'] = ['0.032', '0.11', '0', '0.105', '#interest', '#admiration', 'positive', '0.082', 'work', 'place_work', 'implementation', 'disorganize', 'employment']
senticnet['sexual_identity'] = ['0.122', '0.159', '-0.05', '0.217', '#interest', '#admiration', 'positive', '0.111', 'profile_picture', 'unity', 'country_person', 'unite', 'signature']
senticnet['sexual_immorality'] = ['-0.52', '0', '0.621', '-0.91', '#anger', '#disgust', 'negative', '-0.68', 'deviltry', 'devilry', 'irreverence', 'foul_play', 'iniquity']
senticnet['sexual_inequality'] = ['-0.07', '0.195', '0.629', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'misogynic', 'misogyny', 'murderousness', 'amuck', 'amok']
senticnet['sexual_infidelity'] = ['0.833', '0', '0', '0.807', '#joy', '#admiration', 'positive', '0.82', 'lust', 'amiable', 'give_hug', 'giggle', 'smile']
senticnet['sexual_innuendo'] = ['0', '0', '0.765', '-0.84', '#anger', '#disgust', 'negative', '-0.80', 'costless', 'complimentary', 'needless', 'gratis', 'free']
senticnet['sexual_instinct'] = ['0.531', '0.673', '-0.27', '0.275', '#joy', '#interest', 'positive', '0.301', 'amorous', 'amorousness', 'amatory', 'enamoredness', 'puppy_love']
senticnet['sexual_intercourse'] = ['0.164', '0.144', '-0.07', '0.135', '#joy', '#interest', 'positive', '0.093', 'find_mate', 'warm_fuzzy_feeling', 'sex', 'safe_sex', 'find_partner']
senticnet['sexual_intimacy'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'liaison', 'affair', 'involvement']
senticnet['sexual_intrigue'] = ['0.945', '0.996', '0', '0.737', '#joy', '#interest', 'positive', '0.893', 'become_intelligent', 'gain_knowledge', 'increase_knowledge', 'learn', 'desire_education']
senticnet['sexual_inversion'] = ['0.807', '-0.88', '0', '0.718', '#joy', '#surprise', 'positive', '0.802', 'sex', 'inversion', 'gayness', 'lesbianism', 'sapphism']
senticnet['sexual_jealousy'] = ['-0.75', '0', '0.886', '-0.72', '#sadness', '#anger', 'negative', '-0.78', 'harass', 'move', 'check', 'fork', 'pin']
senticnet['sexual_liaison'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'liaison', 'affair', 'involvement']
senticnet['sexual_maturation'] = ['0', '0.075', '0', '0.159', '#interest', '#admiration', 'positive', '0.117', 'many_plants', 'blossom', 'development', 'poppy', 'adult']
senticnet['sexual_maturity'] = ['0', '0.075', '0', '0.159', '#interest', '#admiration', 'positive', '0.117', 'many_plants', 'blossom', 'development', 'poppy', 'adult']
senticnet['sexual_misconduct'] = ['-0.64', '0', '0.854', '-0.83', '#anger', '#disgust', 'negative', '-0.77', 'crime', 'bigamy', 'regulatory_offence', 'sexual_abuse', 'sex_crime']
senticnet['sexual_molestation'] = ['0', '0.942', '0', '0', '#interest', '#interest', 'positive', '0.942', 'guy', 'special_person', 'guardian', 'boy', 'male']
senticnet['sexual_morality'] = ['0', '0.823', '0', '0.724', '#interest', '#admiration', 'positive', '0.773', 'chastity', 'sexual_abstention', 'celibacy', 'virtue', 'intellect']
senticnet['sexual_objectification'] = ['-0.68', '0', '0.949', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'chauvinism', 'sexism', 'antifeminism', 'sexual_discrimination', 'discrimination']
senticnet['sexual_obsession'] = ['0.857', '0', '0', '0', '#joy', '#joy', 'positive', '0.857', 'date', 'accuracy', 'sexy_woman', 'woo', 'enjoy_person']
senticnet['sexual_offence_act'] = ['0.542', '-0.83', '0', '0.927', '#surprise', '#admiration', 'positive', '0.769', 'incest', 'extramarital_sex', 'free_love', 'sexual_intercourse', 'intercourse']
senticnet['sexual_offence_amendment'] = ['0.542', '-0.83', '0', '0.927', '#surprise', '#admiration', 'positive', '0.769', 'incest', 'extramarital_sex', 'free_love', 'sexual_intercourse', 'intercourse']
senticnet['sexual_offense'] = ['-0.64', '0', '0.854', '-0.83', '#anger', '#disgust', 'negative', '-0.77', 'crime', 'bigamy', 'regulatory_offence', 'sexual_abuse', 'sex_crime']
senticnet['sexual_oppression'] = ['-0.68', '0', '0.949', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'chauvinism', 'sexism', 'antifeminism', 'sexual_discrimination', 'discrimination']
senticnet['sexual_paraphilia'] = ['-0.62', '0', '0.511', '-0.64', '#sadness', '#disgust', 'negative', '-0.59', 'immodesty', 'paraphilia', 'exhibitionism', 'perversion', 'fetishism']
senticnet['sexual_partner'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'liaison', 'affair', 'involvement']
senticnet['sexual_penetration'] = ['0.542', '-0.83', '0', '0.927', '#surprise', '#admiration', 'positive', '0.769', 'incest', 'extramarital_sex', 'free_love', 'sexual_intercourse', 'intercourse']
senticnet['sexual_perversion'] = ['-0.84', '0', '0.882', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'perversion', 'fetishism', 'paraphilia', 'sodomy', 'buggery']
senticnet['sexual_pleasure'] = ['0.801', '0.967', '0', '0', '#joy', '#interest', 'positive', '0.884', 'foreplay', 'happiness', 'feel_joy', 'sense_peace', 'sit_sun']
senticnet['sexual_potency'] = ['0.611', '-0.88', '0', '0.883', '#surprise', '#admiration', 'positive', '0.792', 'love', 'aphrodisia', 'lecherousness', 'lustfulness', 'amorousness']
senticnet['sexual_predator'] = ['0.864', '-0.54', '0', '0.613', '#joy', '#admiration', 'positive', '0.675', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['sexual_promiscuity'] = ['-0.94', '0', '0.944', '-0.90', '#sadness', '#anger', 'negative', '-0.93', 'lust', 'pregnant', 'desire', 'care_deeply', 'love']
senticnet['sexual_purity'] = ['0.956', '0', '0', '0.864', '#joy', '#admiration', 'positive', '0.91', 'honesty', 'wear_around_neck', 'type_of_dog', 'future_robot', 'trust']
senticnet['sexual_relationship'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'liaison', 'affair', 'involvement']
senticnet['sexual_repression'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'nonsense', 'meaninglessness', 'bunk', 'absurdity', 'hokum']
senticnet['sexual_satisfaction'] = ['0.655', '-0.87', '0', '0.595', '#joy', '#surprise', 'positive', '0.709', 'erection', 'excitement', 'arousal', 'kill_brain_cell', 'feel_relieved']
senticnet['sexual_slang'] = ['0.88', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.828', 'ambiguity', 'loophole', 'amphibology', 'amphiboly', 'parisology']
senticnet['sexual_slavery'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'vassalage']
senticnet['sexual_stimulation'] = ['0.733', '0.789', '0', '0', '#joy', '#interest', 'positive', '0.761', 'sex', 'show_empathy', 'safe_sex', 'fuck', 'love_back']
senticnet['sexual_stimulus'] = ['0.971', '0.751', '0', '0.896', '#joy', '#admiration', 'positive', '0.873', 'new_friendship', 'foreplay', 'warm_fuzzy_feeling', 'become_aroused', 'erection']
senticnet['sexual_tension'] = ['-0.64', '0', '0.625', '-0.88', '#sadness', '#disgust', 'negative', '-0.71', 'difficulty', 'troublesomeness', 'flea_bite', 'difficultness', 'cumbersomeness']
senticnet['sexual_trauma'] = ['-0.64', '0', '0.854', '-0.83', '#anger', '#disgust', 'negative', '-0.77', 'crime', 'bigamy', 'regulatory_offence', 'sexual_abuse', 'sex_crime']
senticnet['sexual_victimization'] = ['-0.8', '0', '0.875', '-0.74', '#sadness', '#anger', 'negative', '-0.80', 'inhuman_treatment', 'cruelty', 'child_neglect', 'mistreatment', 'child_abuse']
senticnet['sexual_violence'] = ['-0.64', '0', '0.854', '-0.83', '#anger', '#disgust', 'negative', '-0.77', 'crime', 'bigamy', 'regulatory_offence', 'sexual_abuse', 'sex_crime']
senticnet['sexual_violence_prevention'] = ['-0.64', '0', '0.854', '-0.83', '#anger', '#disgust', 'negative', '-0.77', 'crime', 'bigamy', 'regulatory_offence', 'sexual_abuse', 'sex_crime']
senticnet['sexualise'] = ['0.724', '-0.70', '0', '0.571', '#joy', '#surprise', 'positive', '0.666', 'spinnable', 'susceptible', 'easy', 'pliant', 'plastic']
senticnet['sexualit'] = ['-0.64', '0', '0.625', '-0.88', '#sadness', '#disgust', 'negative', '-0.71', 'difficulty', 'awkwardness', 'flea_bite', 'difficultness', 'cumbersomeness']
senticnet['sexuality'] = ['0.787', '0.812', '0', '0', '#joy', '#interest', 'positive', '0.8', 'love', 'lust', 'show_empathy', 'desirability', 'desire']
senticnet['sexualization'] = ['-0.07', '0.195', '0.629', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'misogynic', 'misogyny', 'murderousness', 'amuck', 'amok']
senticnet['sexually'] = ['0.819', '0.837', '0', '0', '#joy', '#interest', 'positive', '0.828', 'smart_man', 'husband_wife', 'gender', 'voyeur', 'wear_sunglasses']
senticnet['sexually_abuse'] = ['0', '0.942', '0', '0', '#interest', '#interest', 'positive', '0.942', 'guy', 'special_person', 'guardian', 'boy', 'male']
senticnet['sexually_abusing_child'] = ['0', '0.942', '0', '0', '#interest', '#interest', 'positive', '0.942', 'guy', 'special_person', 'guardian', 'boy', 'male']
senticnet['sexually_abusing_young'] = ['0', '0.942', '0', '0', '#interest', '#interest', 'positive', '0.942', 'guy', 'special_person', 'guardian', 'boy', 'male']
senticnet['sexually_abusive'] = ['-0.56', '0', '0.743', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'negligent', 'derelict', 'inattentive', 'delinquent', 'lax']
senticnet['sexually_active'] = ['0.97', '0.729', '0', '0.946', '#joy', '#admiration', 'positive', '0.882', 'make_baby', 'give_birth_child', 'sexual_intercourse', 'find_partner', 'make_love']
senticnet['sexually_aggressive'] = ['-0.75', '0', '0.886', '-0.72', '#sadness', '#anger', 'negative', '-0.78', 'harass', 'move', 'check', 'fork', 'pin']
senticnet['sexually_assault'] = ['0.864', '-0.54', '0', '0.613', '#joy', '#admiration', 'positive', '0.675', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['sexually_attractive'] = ['0.833', '0', '0', '0.807', '#joy', '#admiration', 'positive', '0.82', 'lust', 'amiable', 'give_hug', 'giggle', 'smile']
senticnet['sexually_coercive'] = ['-0.75', '0', '0.886', '-0.72', '#sadness', '#anger', 'negative', '-0.78', 'harass', 'move', 'check', 'fork', 'pin']
senticnet['sexually_explicit'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'indecency']
senticnet['sexually_explicit_content'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['sexually_explicit_material'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'indecency']
senticnet['sexually_exploit'] = ['-0.89', '0', '0.826', '-0.50', '#sadness', '#anger', 'negative', '-0.74', 'wrong', 'delude', 'deceive', 'cozen', 'rip_off']
senticnet['sexually_graphic'] = ['0', '0', '-0.95', '-0.72', '#fear', '#disgust', 'negative', '-0.84', 'lubricious', 'lewd', 'lustful', 'obscene', 'prurient']
senticnet['sexually_immature'] = ['0.702', '-0.95', '0', '0.997', '#surprise', '#admiration', 'positive', '0.883', 'fertile', 'fecund', 'conceptive', 'impregnable', 'rank']
senticnet['sexually_intimate'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'liaison', 'affair', 'involvement']
senticnet['sexually_mature'] = ['0', '0.075', '0', '0.159', '#interest', '#admiration', 'positive', '0.117', 'many_plants', 'blossom', 'development', 'poppy', 'adult']
senticnet['sexually_mature_adult'] = ['-0.87', '0', '0.983', '-0.91', '#anger', '#disgust', 'negative', '-0.92', 'thorn', 'sticker', 'pricker', 'prickle', 'spikelet']
senticnet['sexually_provocative'] = ['-0.62', '0', '0.515', '-0.63', '#sadness', '#disgust', 'negative', '-0.59', 'immodesty', 'perversion', 'paraphilia', 'sexual_perversion', 'fetishism']
senticnet['sexually_receptive'] = ['0.049', '0.098', '-0.07', '0.121', '#interest', '#admiration', 'positive', '0.047', 'procreate', 'reproduce', 'start_family', 'cause_cold', 'unprotect_sex']
senticnet['sexually_suggestive'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['sexually_transmit_disease'] = ['-0.95', '0', '-0.76', '-0.79', '#sadness', '#disgust', 'negative', '-0.83', 'cripple', 'influenza', 'infectious_disease', 'chicken_pox', 'overpay']
senticnet['sexually_transmitted_disease'] = ['-0.90', '0', '0.202', '-1.0', '#sadness', '#disgust', 'negative', '-0.70', 'sickness', 'unsafe_sex', 'social_disease', 'std', 'sex_without_condom']
senticnet['sexually_transmitted_infection'] = ['-0.73', '-0.72', '-0.86', '-0.79', '#fear', '#disgust', 'negative', '-0.41', 'venereal_disease', 'overpopulation', 'std', 'loss_offreedom', 'lower_standard_live']
senticnet['sexually_violent'] = ['-0.75', '0', '0.886', '-0.72', '#sadness', '#anger', 'negative', '-0.78', 'harass', 'move', 'check', 'fork', 'pin']
senticnet['sexy'] = ['0.137', '0.211', '-0.11', '0.212', '#interest', '#admiration', 'positive', '0.112', 'familiar', 'female_human', 'sophisticated', 'feminine', 'contrary']
senticnet['sexy_beast'] = ['-0.85', '0', '0.514', '-0.73', '#sadness', '#disgust', 'negative', '-0.70', 'cinema_verite', 'movie', 'film', 'picture', 'moving_picture']
senticnet['sexy_clothes'] = ['0.728', '0.977', '0', '0.939', '#interest', '#admiration', 'positive', '0.881', 'dress', 'night_dress', 'sexy', 'pretty', 'pleasure']
senticnet['sexy_girl'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'clean', 'polish', 'shiny', 'attractive', 'car_wash']
senticnet['sexy_woman'] = ['0.857', '0', '0', '0', '#joy', '#joy', 'positive', '0.857', 'date', 'accuracy', 'seduction', 'woo', 'enjoy_person']
senticnet['seychellois_creole'] = ['-0.84', '0', '0.579', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'squiggle', 'hand', 'chicken_scratch', 'handwriting', 'script']
senticnet['seymore'] = ['-0.64', '0', '0.781', '-0.74', '#anger', '#disgust', 'negative', '-0.72', 'weirdo', 'crazy', 'loony', 'lunatic', 'nutcase']
senticnet['seymour_duncan'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'lever']
senticnet['sf_novel'] = ['0.747', '0.85', '0', '0', '#joy', '#interest', 'positive', '0.798', 'night_vision', 'mystery', 'plot', 'imagine', 'myth']
senticnet['shabbat_dinner'] = ['0.937', '0', '0', '0.828', '#joy', '#admiration', 'positive', '0.883', 'bakery', 'produce_section', 'fruit_bowl', 'dinner_meal', 'fast_food']
senticnet['shabbily'] = ['0', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.913', 'immodestly', 'scantly', 'scantily', 'nattily', 'skimpily']
senticnet['shabby'] = ['-0.82', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.89', 'ragtag', 'mean', 'torn', 'tacky', 'poor']
senticnet['shabby_chic'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'elegant', 'stylish', 'smart', 'posh', 'sleek']
senticnet['shack'] = ['-0.02', '0', '-0.04', '-0.03', '#fear', '#disgust', 'negative', '-0.03', 'small_house', 'small_room', 'mansion', 'art_gallery', 'motel_room']
senticnet['shad_roe'] = ['-0.52', '0', '0.748', '-0.92', '#anger', '#disgust', 'negative', '-0.73', 'coral', 'caviar', 'caviare', 'hard_roe', 'seafood']
senticnet['shadder'] = ['-0.93', '0', '0.791', '0', '#sadness', '#anger', 'negative', '-0.86', 'hopelessness', 'hopelessly', 'despairing', 'pessimistically', 'pessimistic']
senticnet['shade'] = ['-0.93', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.86', 'protect_from_sun', 'concealer', 'meaning', 'corrector', 'sunglasses']
senticnet['shade-loving'] = ['0', '0', '0.751', '-0.89', '#anger', '#disgust', 'negative', '-0.82', 'moisture-loving', 'heat-loving', 'sun-loving', 'water-loving', 'hydrophilic']
senticnet['shade_cloth'] = ['-0.12', '0.066', '-0.08', '-0.06', '#sadness', '#fear', 'negative', '-0.05', 'garden_yard', 'see_sky', 'plant_tree', 'hair', 'seed_lawn']
senticnet['shade_eye'] = ['0', '0', '-0.90', '-0.87', '#fear', '#disgust', 'negative', '-0.89', 'protect_head', 'helmet', 'keep_hair_place', 'safety_cap', 'cover_head']
senticnet['shade_lighter'] = ['-0.93', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.85', 'protect_from_sun', 'shade', 'meaning', 'corrector', 'sunglasses']
senticnet['shade_sun'] = ['-0.02', '0', '-0.07', '0', '#sadness', '#fear', 'negative', '-0.05', 'branch_out', 'move_wind', 'fall_dead', 'memory_loss', 'fall_floor']
senticnet['shadia'] = ['0.798', '-0.51', '0', '0.973', '#joy', '#admiration', 'positive', '0.763', 'inexperienced_person', 'boy_scout', 'dear', 'virgin', 'mortal']
senticnet['shadiness'] = ['-0.72', '0', '0.742', '-0.80', '#anger', '#disgust', 'negative', '-0.75', 'illicitness', 'illegality', 'illegitimacy', 'unlawfulness', 'quality']
senticnet['shading'] = ['-0.91', '0', '0.796', '-0.87', '#sadness', '#disgust', 'negative', '-0.86', 'hatch', 'pattern', 'crosshatch', 'hachure', 'marking']
senticnet['shadow'] = ['-0.82', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.80', 'evil', 'darkness', 'dark', 'sun_down', 'dark_side']
senticnet['shadow_cast'] = ['-0.82', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.80', 'evil', 'darkness', 'dark', 'sun_down', 'dark_side']
senticnet['shadow_cast_by'] = ['-0.82', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.80', 'evil', 'darkness', 'dark', 'sun_down', 'dark_side']
senticnet['shadow_flicker'] = ['0', '0', '0.735', '-0.70', '#anger', '#disgust', 'negative', '-0.72', 'swing_set', 'roadway_square', 'fresh_air', 'dog_poop', 'wildflower']
senticnet['shadowhawk'] = ['-0.98', '0', '0.714', '-0.90', '#sadness', '#disgust', 'negative', '-0.86', 'stupid', 'stupid_person', 'stupe', 'dullard', 'dolt']
senticnet['shadowhunter'] = ['0.026', '0.18', '-0.11', '0.18', '#interest', '#admiration', 'positive', '0.069', 'deity', 'god', 'angel', 'goddess', 'rabbi']
senticnet['shadowpact'] = ['-0.04', '-0.02', '0.027', '-0.03', '#sadness', '#disgust', 'negative', '-0.01', 'precious_metal', 'olympic_medal', 'shiny_metal', 'many_different_kinds', 'valuable_metal']
senticnet['shadowy'] = ['-0.96', '0', '0.939', '-0.60', '#sadness', '#anger', 'negative', '-0.83', 'airy', 'insubstantial', 'stringy', 'wraithlike', 'ethereal']
senticnet['shady'] = ['0', '0', '-0.91', '-0.82', '#fear', '#disgust', 'negative', '-0.86', 'suspect', 'fishy', 'funny', 'louche', 'fly-by-night']
senticnet['shady_spot'] = ['0.074', '0', '0', '0', '#joy', '#joy', 'positive', '0.074', 'hike_trail', 'ant_hill', 'compost', 'picnic', 'soccer_field']
senticnet['shadyside'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
senticnet['shafiq_campaign'] = ['0.648', '0.627', '-0.26', '0.329', '#joy', '#interest', 'positive', '0.334', 'brotherly', 'friendly', 'fraternal', 'amicability', 'amicable']
senticnet['shaft'] = ['-0.17', '0', '-0.09', '-0.03', '#sadness', '#fear', 'negative', '-0.1', 'lever', 'terrorist_home', 'tank', 'defence', 'police_car']
senticnet['shag_carpet'] = ['0', '-0.02', '0.041', '-0.10', '#anger', '#disgust', 'negative', '-0.03', 'separate_shower', 'shampoo_conditioner', 'towel_bar', 'shower_head', 'toilet_brush']
senticnet['shagbark'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'locust_tree']
senticnet['shagbark_hickory'] = ['0.776', '-0.6', '0', '0.568', '#joy', '#surprise', 'positive', '0.648', 'swamp_chestnut_oak', 'chinquapin_oak', 'chinkapin_oak', 'yellow_chestnut_oak', 'basket_oak']
senticnet['shagbark_hickory_carya_ovata'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'horse_chestnut', 'red_buckeye', 'particolored_buckeye']
senticnet['shaggy'] = ['-0.64', '0', '0.647', '-0.55', '#sadness', '#anger', 'negative', '-0.61', 'puberulent', 'sericeous', 'furlike', 'pappose', 'pilous']
senticnet['shaggy_dog_story'] = ['-0.52', '0', '0.538', '-0.99', '#anger', '#disgust', 'negative', '-0.68', 'gag', 'jest', 'jape', 'joke', 'laugh']
senticnet['shaggy_hair'] = ['0', '0', '0.841', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'ancient', 'many_years', 'year', 'old', 'teen']
senticnet['shaggy_man'] = ['0.931', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.855', 'crystal_clear', 'blue_sky', 'imaginary', 'water_sky', 'blue_color']
senticnet['shakable'] = ['0.535', '-0.69', '0', '0.656', '#surprise', '#admiration', 'positive', '0.628', 'debatable', 'shakeable', 'challengeable', 'disputable', 'contestable']
senticnet['shake'] = ['-0.97', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.98', 'rub', 'earth_movement', 'wiggle', 'wrap_around', 'convulse']
senticnet['shake_drayton'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'nazi', 'man', 'pervert', 'healthy_person']
senticnet['shake_hand'] = ['0', '0.082', '-0.06', '0.095', '#interest', '#admiration', 'positive', '0.036', 'say_hello', 'introduce', 'hunt_duck', 'greet', 'say_hi']
senticnet['shake_off'] = ['-0.74', '0', '0.966', '0', '#sadness', '#anger', 'negative', '-0.85', 'move', 'displace', 'fire', 'chase_away', 'ostracise']
senticnet['shake_rattle'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'poppy_seed', 'custard', 'bread', 'biscuit']
senticnet['shake_up'] = ['-0.98', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.92', 'thrilling', 'electrifying', 'sensational', 'sensationally', 'scandalmongering']
senticnet['shakeable'] = ['0.54', '-0.70', '0', '0.657', '#surprise', '#admiration', 'positive', '0.633', 'shakable', 'debatable', 'challengeable', 'disputable', 'contestable']
senticnet['shakedown'] = ['0.734', '-0.72', '0', '0.558', '#joy', '#surprise', 'positive', '0.671', 'improvement', 'betterment', 'habituation', 'adjustment', 'domestication']
senticnet['shakehole'] = ['-0.02', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'tunnel', 'dark_place', 'ski_resort', 'top_tree', 'compost_pile']
senticnet['shakespeare_hamlet'] = ['-0.91', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.85', 'play', 'perpetrator', 'role_play', 'act_part', 'terrible_event']
senticnet['shakespeare_play'] = ['-0.04', '-0.02', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.03', 'present_play', 'perform_play', 'show_play', 'drama', 'stage_play']
senticnet['shakespearean_actor'] = ['0', '0.797', '0', '0.804', '#interest', '#admiration', 'positive', '0.8', 'director', 'shooting_movie', 'director_chair', 'important_person', 'movie']
senticnet['shakespearean_character'] = ['-0.91', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.85', 'play', 'perpetrator', 'role_play', 'act_part', 'terrible_event']
senticnet['shakespearian'] = ['-0.91', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.85', 'play', 'perpetrator', 'role_play', 'act_part', 'terrible_event']
senticnet['shakeup'] = ['-0.98', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.92', 'thrilling', 'electrifying', 'sensational', 'sensationally', 'scandalmongering']
senticnet['shakily'] = ['0', '0', '0.867', '-0.73', '#anger', '#disgust', 'negative', '-0.80', 'anxious', 'shaky', 'shakiness', 'unsound', 'rickety']
senticnet['shakiness'] = ['0.74', '-0.90', '0', '0.987', '#surprise', '#admiration', 'positive', '0.879', 'tremolo', 'quivering', 'shaking', 'vibration', 'quiver']
senticnet['shaking'] = ['0.741', '-0.91', '0', '0.987', '#surprise', '#admiration', 'positive', '0.881', 'shakiness', 'quivering', 'tremolo', 'vibration', 'quiver']
senticnet['shaking_off'] = ['0.568', '-0.99', '0', '0.638', '#surprise', '#admiration', 'positive', '0.734', 'push_aside', 'brush_off', 'disregard', 'ignore', 'dismiss']
senticnet['shaking_palsy'] = ['-0.95', '0', '0.621', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'disease', 'brain_disorder', 'brain_disease', 'ill', 'epilepsy']
senticnet['shako'] = ['-0.58', '0', '0.632', '-0.63', '#anger', '#disgust', 'negative', '-0.61', 'skimmer', 'trilby', 'bearskin', 'busby', 'chapeau']
senticnet['shakspeare'] = ['-0.99', '0', '0.947', '-0.88', '#sadness', '#anger', 'negative', '-0.94', 'atrociousness', 'inhumanity', 'heinousness', 'atrocity', 'barbarity']
senticnet['shaky'] = ['0', '0', '0.868', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'anxious', 'shakily', 'shakiness', 'unsound', 'rickety']
senticnet['shaky_camera'] = ['-0.85', '0', '0.514', '-0.73', '#sadness', '#disgust', 'negative', '-0.70', 'flick', 'movie', 'film', 'picture', 'moving_picture']
senticnet['shakyamuni'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['shakyamuni_buddha'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['shalbe'] = ['-0.69', '0', '0.883', '-0.91', '#anger', '#disgust', 'negative', '-0.82', 'stick_in', 'enclose', 'insert', 'feed_in', 'put_in']
senticnet['shale_bedrock'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'fossil', 'oil', 'dark_liquid', 'smithsonian_institution', 'inert_gas']
senticnet['shale_oil_plant'] = ['0', '0.164', '-0.08', '0.215', '#interest', '#admiration', 'positive', '0.098', 'manufacture', 'factory_output', 'creation', 'assembly_line', 'make']
senticnet['shallot'] = ['0.716', '-0.71', '0', '0', '#joy', '#surprise', 'positive', '0.716', 'parsley', 'cilantro', 'scallion', 'tarragon', 'chive']
senticnet['shallot_vinaigrette'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'saltine', 'soda_cracker', 'cracker', 'water_biscuit', 'graham_cracker']
senticnet['shallow'] = ['-0.05', '-0.02', '-0.07', '0', '#sadness', '#fear', 'negative', '-0.03', 'tide', 'tidal_wave', 'surf_board', 'puddle', 'pier']
senticnet['shallow_bay'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'driftwood', 'sea_shell', 'scenic_view', 'naked_woman', 'tidal_wave']
senticnet['shallow_bowl'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'address_book', 'get_rich', 'blank_check', 'swiss_army_knife', 'whisk']
senticnet['shallow_coastal'] = ['0.078', '-0.03', '-0.04', '0.105', '#joy', '#admiration', 'positive', '0.043', 'aquarium', 'underwater', 'pacific_ocean', 'sea', 'ocean_wave']
senticnet['shallow_continental_shelf'] = ['-0.04', '-0.01', '0.103', '0', '#sadness', '#anger', 'negative', '-0.04', 'near_beach', 'along_beach', 'full_water', 'ocean_ground', 'salty']
senticnet['shallow_dish'] = ['0.035', '-0.01', '0.009', '0.063', '#joy', '#admiration', 'positive', '0.026', 'container', 'carton', 'contain', 'hold_cupcake', 'store_dish']
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senticnet['sheer_courage'] = ['0.827', '0.913', '0', '0.867', '#interest', '#admiration', 'positive', '0.869', 'good_habit', 'rivalry', 'competitive', 'competitive_nature', 'determination']
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senticnet['shine_up'] = ['0.985', '-0.75', '0', '0.894', '#joy', '#admiration', 'positive', '0.879', 'cozy_up', 'cotton_up', 'ingratiate', 'sidle_up', 'play_up']
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senticnet['shinny'] = ['0.031', '0', '0.028', '0.082', '#joy', '#admiration', 'positive', '0.028', 'type_of_metal', 'shiny', 'use_jewelry', 'magnetic', 'tin']
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senticnet['shintaro'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['shinumo_creek'] = ['0', '0.069', '0', '0.065', '#interest', '#admiration', 'positive', '0.067', 'alp', 'high_mountain', 'pass', 'ski_resort', 'high_place']
senticnet['shiny'] = ['0.049', '0', '0.023', '0.095', '#joy', '#admiration', 'positive', '0.04', 'shinny', 'magnetic', 'use_jewelry', 'hard_plastic', 'tin']
senticnet['shiny_black'] = ['-0.60', '0', '0.895', '-0.54', '#sadness', '#anger', 'negative', '-0.68', 'coal_black', 'black', 'pitch_black', 'soot_black', 'achromatic_color']
senticnet['shiny_coat'] = ['0', '-0.01', '0.038', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'flesh', 'bone', 'skeletal_system', 'muscle', 'live_meat']
senticnet['shiny_hair'] = ['0.824', '0.86', '0', '0.983', '#interest', '#admiration', 'positive', '0.889', 'look_smart', 'social_acceptance', 'improve_appearance', 'improve_look', 'look_good']
senticnet['shiny_metal'] = ['-0.04', '-0.02', '0.027', '-0.03', '#sadness', '#disgust', 'negative', '-0.01', 'precious_metal', 'olympic_medal', 'many_different_kinds', 'bloodstone', 'valuable_metal']
senticnet['shiny_surface'] = ['0.734', '0', '0', '0.757', '#joy', '#admiration', 'positive', '0.746', 'type_of_material', 'material_type', 'soft_cloth', 'shiny', 'hard_rock']
senticnet['shiny_vinyl'] = ['-0.54', '0', '0.612', '-0.98', '#anger', '#disgust', 'negative', '-0.71', 'scuff', 'mule', 'bootee', 'carpet_slipper', 'footwear']
senticnet['shion'] = ['-0.53', '0', '0.637', '-0.92', '#anger', '#disgust', 'negative', '-0.69', 'incorporeal', 'unembodied', 'disembodied', 'bodiless', 'unbodied']
senticnet['shiori_asahi'] = ['-0.57', '0', '0.571', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['ship'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'yacht', 'boat', 'sailboat', 'water_vehicle', 'speed_boat']
senticnet['ship_adrift'] = ['-0.99', '0', '0.658', '-0.73', '#sadness', '#disgust', 'negative', '-0.79', 'purposeless', 'afloat', 'aimless', 'directionless', 'planless']
senticnet['ship_boat'] = ['-0.02', '-0.01', '0.069', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'harbor', 'water_body', 'water_transport', 'water_float', 'cabin']
senticnet['ship_builder'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'maker']
senticnet['ship_canal'] = ['0.933', '0.795', '0', '0', '#joy', '#interest', 'positive', '0.864', 'water_passage', 'small_stream', 'water_flow', 'flowing_water', 'water_stream']
senticnet['ship_company'] = ['0.814', '-0.87', '0', '0.918', '#surprise', '#admiration', 'positive', '0.87', 'full_complement', 'company', 'work_force', 'workforce', 'manpower']
senticnet['ship_container'] = ['0', '0', '0.088', '0', '#anger', '#anger', 'negative', '-0.08', 'carry_vessel', 'military_ship', 'big_boat', 'large_boat', 'ship']
senticnet['ship_goods'] = ['0.852', '0.937', '0', '0', '#joy', '#interest', 'positive', '0.895', 'squeeze', 'charge_credit_card', 'sit_toilet', 'choose_color', 'transport']
senticnet['ship_home'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'go_home', 'port', 'bay', 'find_sea', 'lake']
senticnet['ship_package'] = ['-0.11', '0', '0', '-0.10', '#sadness', '#disgust', 'negative', '-0.11', 'sharpen_knife', 'act_up', 'die_young', 'crowd_elevator', 'bridge_difference']
senticnet['ship_part'] = ['-0.04', '0.1', '0', '0.152', '#interest', '#admiration', 'positive', '0.068', 'look', 'glimpse', 'stare', 'quick_look', 'cabin']
senticnet['ship_sink'] = ['-0.94', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.84', 'puddle', 'sandy_beach', 'dew', 'squirt', 'rain']
senticnet['shipbuilder'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'maker']
senticnet['shipment'] = ['0', '0.933', '0', '0', '#interest', '#interest', 'positive', '0.933', 'mission', 'consignment', 'delivery', 'dispatch', 'expedition']
senticnet['shipper'] = ['0', '0.089', '0.01', '0.118', '#interest', '#admiration', 'positive', '0.066', 'crate', 'mom_house', 'sack', 'poker_game', 'safekeep']
senticnet['shipping'] = ['-0.77', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.78', 'merchant_marine', 'transportation', 'merchant_vessel', 'cargo_ship', 'embark']
senticnet['shipping_magnate'] = ['0', '0', '-0.99', '-0.73', '#fear', '#disgust', 'negative', '-0.86', 'king', 'baron', 'business_leader', 'mogul', 'big_businessman']
senticnet['shipwreck'] = ['-0.92', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.93', 'irrecoverable', 'incurable', 'irretrievable', 'retrievable', 'irreparable']
senticnet['shipwreck_cove'] = ['0', '0', '0.034', '-0.08', '#anger', '#disgust', 'negative', '-0.06', 'lifeboat', 'mast', 'bad_man', 'keel', 'yacht']
senticnet['shipwright'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'maker']
senticnet['shire_hall'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'city_hall', 'courthouse', 'subway_station', 'public_place', 'bus_station']
senticnet['shirk'] = ['0', '0', '-0.83', '-0.70', '#fear', '#disgust', 'negative', '-0.76', 'duty', 'responsibility', 'skulk', 'backslide', 'goldbrick']
senticnet['shirt'] = ['-0.04', '0.1', '0', '0.152', '#interest', '#admiration', 'positive', '0.068', 'pants', 'jacket', 'clothes', 'robe', 'garment']
senticnet['shirt_collar'] = ['0.08', '0.106', '-0.04', '0.112', '#interest', '#admiration', 'positive', '0.063', 'helmet', 'head_gear', 'cap', 'head_wear', 'head_protection']
senticnet['shirt_emblazoned_with'] = ['-0.04', '0.1', '0', '0.152', '#interest', '#admiration', 'positive', '0.068', 'pants', 'jacket', 'clothes', 'robe', 'garment']
senticnet['shirt_pocket'] = ['0.026', '0.082', '0', '0', '#joy', '#interest', 'positive', '0.054', 'account_department', 'office', 'stationery_store', 'accountant_office', 'desk_drawer']
senticnet['shit'] = ['0', '0', '0', '-0.99', '#disgust', '#disgust', 'negative', '-0.99', 'poop', 'chew_tobacco', 'detritus', 'barf', 'hike_trail']
senticnet['shite'] = ['-0.69', '0', '0.936', '-0.87', '#anger', '#disgust', 'negative', '-0.83', 'stool', 'dejection', 'fecal_matter', 'shit', 'feces']
senticnet['shithead'] = ['-0.79', '0', '0.935', '-0.70', '#sadness', '#anger', 'negative', '-0.81', 'stupid', 'stupid_person', 'stupe', 'dullard', 'dolt']
senticnet['shitting'] = ['-0.80', '0', '0.952', '-0.95', '#anger', '#disgust', 'negative', '-0.90', 'shit', 'bowel_movement', 'movement', 'dump', 'asshole']
senticnet['shitty'] = ['0', '0', '-0.77', '-0.92', '#fear', '#disgust', 'negative', '-0.84', 'crappy', 'rotten', 'lousy', 'stinking', 'icky']
senticnet['shiver'] = ['0', '0', '-0.73', '0', '#fear', '#fear', 'negative', '-0.73', 'quiver', 'tingle', 'shudder', 'chill', 'cool']
senticnet['shivery'] = ['-0.74', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.82', 'chilling', 'shuddery', 'apprehension', 'apprehensiveness', 'scare_off']
senticnet['shjo'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['shjo_manga'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['shkaku'] = ['0.084', '-0.05', '0.013', '0', '#joy', '#surprise', 'positive', '0.043', 'carnival_ride', 'carny', 'fortune_teller', 'horseback', 'ferris_wheel']
senticnet['shkoder'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
senticnet['shkodr'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
senticnet['shmoose'] = ['0.685', '-0.95', '0', '0.653', '#joy', '#surprise', 'positive', '0.764', 'schmooze', 'schmoose', 'shmooze', 'shoot_breeze', 'jawbone']
senticnet['shmooze'] = ['0.687', '-0.95', '0', '0.66', '#joy', '#surprise', 'positive', '0.766', 'schmooze', 'schmoose', 'shoot_breeze', 'shmoose', 'jawbone']
senticnet['shmuel_hanavi'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['shna'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'identifiable', 'specifiable', 'recognizable', 'diagnosable', 'classifiable']
senticnet['shnen'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['shnen_manga'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['sho'] = ['0', '0.995', '0', '0.707', '#interest', '#admiration', 'positive', '0.851', 'stick', 'car', 'brake', 'mustang', 'article']
senticnet['shoal'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'driftwood', 'sea_shell', 'scenic_view', 'naked_woman', 'tidal_wave']
senticnet['shoal_water'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'driftwood', 'sea_shell', 'scenic_view', 'naked_woman', 'tidal_wave']
senticnet['shock'] = ['-0.93', '0', '0.8', '-0.96', '#sadness', '#disgust', 'negative', '-0.9', 'heatstroke', 'heat_hyperpyrexia', 'malady', 'breakdown', 'prostration']
senticnet['shock_absorption'] = ['-0.53', '0', '0.765', '-0.94', '#anger', '#disgust', 'negative', '-0.74', 'jaggedness', 'unregularity', 'unevenness', 'irregularity', 'patchiness']
senticnet['shock_hazard'] = ['-0.09', '-0.01', '-0.10', '-0.20', '#fear', '#disgust', 'negative', '-0.09', 'violence', 'siege', 'conflict', 'bloodshed', 'fight']
senticnet['shock_resignation'] = ['-0.72', '0', '0.98', '0', '#sadness', '#anger', 'negative', '-0.85', 'resignedly', 'abject', 'unhopeful', 'abjectly', 'reconcile']
senticnet['shock_resistant'] = ['-0.53', '0', '0.765', '-0.94', '#anger', '#disgust', 'negative', '-0.74', 'jaggedness', 'unregularity', 'unevenness', 'irregularity', 'patchiness']
senticnet['shock_therapy'] = ['-0.72', '0', '0.732', '-0.59', '#sadness', '#anger', 'negative', '-0.68', 'insulin_shock', 'insulin_shock_therapy', 'insulin_shock_treatment', 'metrazol_shock', 'metrazol_shock_therapy']
senticnet['shock_therapy_treatment'] = ['-0.72', '0', '0.732', '-0.60', '#sadness', '#anger', 'negative', '-0.68', 'shock_therapy', 'shock', 'insulin_shock', 'metrazol_shock', 'metrazol_shock_therapy']
senticnet['shock_trauma'] = ['0', '0', '-0.73', '0', '#fear', '#fear', 'negative', '-0.73', 'clinic', 'emergency_ward', 'hospital', 'dentist_office', 'medical_office']
senticnet['shock_treatment'] = ['-0.96', '0', '0.52', '-0.54', '#sadness', '#disgust', 'negative', '-0.67', 'insulin_shock', 'insulin_shock_therapy', 'insulin_shock_treatment', 'metrazol_shock', 'metrazol_shock_therapy']
senticnet['shock_wave'] = ['-0.90', '0', '0.825', '-0.61', '#sadness', '#anger', 'negative', '-0.78', 'flaming', 'combustion', 'fire', 'flame', 'internal_combustion']
senticnet['shockercito'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'find_out', 'dig_out', 'unearth', 'become_aware', 'dish_out']
senticnet['shocking'] = ['-0.82', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.87', 'disgraceful', 'shameful', 'dishonorably', 'inglorious', 'dishonored']
senticnet['shocking_pink'] = ['0', '-0.73', '0', '0.787', '#surprise', '#admiration', 'positive', '0.763', 'brassy', 'flashy', 'gaudy', 'cheap', 'flash']
senticnet['shocking_revelation'] = ['0.54', '-0.64', '0', '0.566', '#surprise', '#admiration', 'positive', '0.582', 'pleaser', 'entertainer', 'mortal', 'busker', 'drawing_card']
senticnet['shockingly'] = ['0', '0', '0.928', '-0.74', '#anger', '#disgust', 'negative', '-0.83', 'extremely', 'monstrous', 'greatly', 'tremendously', 'enormously']
senticnet['shod'] = ['-0.59', '0', '0.553', '-0.76', '#sadness', '#disgust', 'negative', '-0.63', 'booted', 'slippered', 'roughshod', 'sandaled', 'wear_sandal']
senticnet['shodden'] = ['-0.79', '0', '0.865', '-0.89', '#anger', '#disgust', 'negative', '-0.84', 'booted', 'slippered', 'roughshod', 'sandaled', 'wear_sandal']
senticnet['shoddiness'] = ['-0.77', '0', '0.599', '-0.52', '#sadness', '#anger', 'negative', '-0.63', 'vanity', 'trashiness', 'damn', 'worthlessness', 'shit']
senticnet['shoddy'] = ['0', '0', '0.749', '-0.92', '#anger', '#disgust', 'negative', '-0.83', 'jerry-built', 'tawdry', 'misleading', 'deceptive', 'cheapjack']
senticnet['shoe'] = ['-0.05', '0.068', '0', '0.102', '#interest', '#admiration', 'positive', '0.038', 'flip_flop', 'moccasin', 'slipper', 'footwear', 'shoe_type']
senticnet['shoe-in'] = ['0', '-0.78', '0', '0.7', '#surprise', '#admiration', 'positive', '0.74', 'morning-line', 'hands-down', 'front-runner', 'odds-on', 'shoo-in']
senticnet['shoe_box'] = ['0', '-0.02', '0', '0.051', '#surprise', '#admiration', 'positive', '0.037', 'cardboard_box', 'bag', 'package', 'box_cereal', 'bin']
senticnet['shoe_brand'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['shoe_factory'] = ['-0.05', '-0.04', '0.017', '-0.05', '#sadness', '#disgust', 'negative', '-0.02', 'shoe_shop', 'hamper', 'attic', 'obstruct', 'closet']
senticnet['shoe_horse'] = ['0.882', '0.864', '0', '0', '#joy', '#interest', 'positive', '0.873', 'dig_grave', 'seed_lawn', 'bicycle_across_town', 'plant_flower', 'age_gracefully']
senticnet['shoe_lace'] = ['0.028', '0', '0', '0.113', '#joy', '#admiration', 'positive', '0.071', 'casino', 'propose_marriage', 'display_wealth', 'time_tracker', 'use_finger']
senticnet['shoe_maker'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'builder']
senticnet['shoe_polish'] = ['-0.75', '0', '-0.75', '-0.99', '#sadness', '#disgust', 'negative', '-0.83', 'tease', 'upset', 'lubricant', 'upset_person', 'piston']
senticnet['shoe_repair'] = ['-0.07', '0.228', '0.019', '0.089', '#interest', '#admiration', 'positive', '0.056', 'home_depot', 'lumberyard', 'furniture_store', 'front_porch', 'carpet_store']
senticnet['shoe_repair_shop'] = ['0.026', '0.082', '0', '0', '#joy', '#interest', 'positive', '0.054', 'account_department', 'office', 'accountant_office', 'shirt_pocket', 'desk_drawer']
senticnet['shoe_salesman'] = ['-0.05', '-0.04', '0.017', '-0.05', '#sadness', '#disgust', 'negative', '-0.02', 'shoe_shop', 'hamper', 'attic', 'obstruct', 'closet']
senticnet['shoe_shine'] = ['-0.80', '0', '0.718', '-0.79', '#sadness', '#disgust', 'negative', '-0.77', 'walk_exercise', 'lose_wallet', 'go_debt', 'pay_dinner', 'spend_much_money']
senticnet['shoe_shop'] = ['-0.07', '0.228', '0.019', '0.089', '#interest', '#admiration', 'positive', '0.056', 'home_depot', 'lumberyard', 'furniture_store', 'front_porch', 'carpet_store']
senticnet['shoe_store'] = ['-0.05', '-0.04', '0.017', '-0.05', '#sadness', '#disgust', 'negative', '-0.02', 'shoe_shop', 'hamper', 'attic', 'obstruct', 'closet']
senticnet['shoe_string'] = ['-0.71', '0', '0.539', '-0.88', '#sadness', '#disgust', 'negative', '-0.71', 'lace', 'shoestring', 'cord', 'bootlace', 'shoelace']
senticnet['shoe_type'] = ['-0.05', '0.068', '0', '0.102', '#interest', '#admiration', 'positive', '0.038', 'type_of_shoes', 'shoe', 'footwear', 'flip_flop', 'casual_shoes']
senticnet['shoelace'] = ['-0.05', '0.068', '0', '0.102', '#interest', '#admiration', 'positive', '0.038', 'sole', 'shoe', 'footwear', 'lace', 'string']
senticnet['shoemaker_shop'] = ['-0.07', '0.228', '0.019', '0.089', '#interest', '#admiration', 'positive', '0.056', 'home_depot', 'lumberyard', 'furniture_store', 'front_porch', 'carpet_store']
senticnet['shoeshine'] = ['0.866', '-0.91', '0', '0.893', '#surprise', '#admiration', 'positive', '0.892', 'mooch', 'freeload', 'grub', 'sponge', 'bum']
senticnet['shoestring'] = ['-0.71', '0', '0.539', '-0.88', '#sadness', '#disgust', 'negative', '-0.71', 'shoe_string', 'lace', 'cord', 'bootlace', 'shoelace']
senticnet['shogi'] = ['0', '0', '0', '0.115', '#admiration', '#admiration', 'positive', '0.115', 'chess_knight', 'strategy', 'pawn', 'chess_queen', 'chess_rook']
senticnet['shogo_makishima'] = ['-0.95', '0', '0.774', '0', '#sadness', '#anger', 'negative', '-0.86', 'argue', 'rival', 'disagreement', 'opposition', 'anti']
senticnet['shonen'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['shonen_jump'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['shoo-in'] = ['0', '-0.78', '0', '0.704', '#surprise', '#admiration', 'positive', '0.744', 'morning-line', 'hands-down', 'front-runner', 'shoe-in', 'odds-on']
senticnet['shoo_away'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'annoy', 'irritate', 'trouble', 'nag', 'bother']
senticnet['shoot'] = ['-0.06', '-0.06', '-0.04', '-0.19', '#sadness', '#disgust', 'negative', '-0.06', 'kill', 'shoot_person', 'fire_gun', 'shoot_cannon', 'fire_bullet']
senticnet['shoot_blight'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'apple_rust', 'fungus']
senticnet['shoot_breeze'] = ['0.695', '-0.94', '0', '0.662', '#joy', '#surprise', 'positive', '0.768', 'schmooze', 'schmoose', 'shmooze', 'shmoose', 'jawbone']
senticnet['shoot_bullet'] = ['-0.07', '-0.04', '-0.07', '-0.12', '#sadness', '#fear', 'negative', '-0.05', 'shoot_target', 'target_practice', 'kill_people', 'kill_pest', 'blow_up']
senticnet['shoot_cannon'] = ['0', '0', '0.716', '-0.79', '#anger', '#disgust', 'negative', '-0.75', 'hit_target', 'shoot', 'shoot_person', 'kill', 'fire_gun']
senticnet['shoot_deer'] = ['-0.99', '0', '0.696', '-0.95', '#sadness', '#disgust', 'negative', '-0.88', 'gun', 'bullet', 'kill_deer', 'hurt', 'weapon']
senticnet['shoot_down_person'] = ['-0.91', '0', '0.495', '-0.99', '#sadness', '#disgust', 'negative', '-0.8', 'gun', 'bullet', 'rifle', 'weapon', 'shoot']
senticnet['shoot_elongation'] = ['-0.75', '0', '0.512', '-0.75', '#sadness', '#disgust', 'negative', '-0.67', 'inactivity', 'inactiveness', 'slumber', 'inaction', 'hibernation']
senticnet['shoot_gun'] = ['-0.12', '-0.08', '-0.05', '-0.20', '#sadness', '#disgust', 'negative', '-0.07', 'fire_gun', 'use_gun', 'shoot', 'fire_weapon', 'load_gun']
senticnet['shoot_line'] = ['0', '0', '0.814', '-0.84', '#anger', '#disgust', 'negative', '-0.82', 'touting', 'boast', 'swash', 'gas', 'brag']
senticnet['shoot_person'] = ['-0.70', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.8', 'shoot', 'choose_victim', 'shoot_cannon', 'murder_weapon', 'kill']
senticnet['shoot_range'] = ['-0.05', '-0.01', '-0.09', '-0.22', '#fear', '#disgust', 'negative', '-0.09', 'underworld', 'gun_shop', 'combat', 'holster', 'war_zone']
senticnet['shoot_target'] = ['-0.05', '-0.05', '-0.06', '-0.15', '#fear', '#disgust', 'negative', '-0.05', 'target_practice', 'shoot_bullet', 'kill_pest', 'self_defense', 'blow_up']
senticnet['shoot_up'] = ['-0.93', '0', '0.292', '-0.94', '#sadness', '#disgust', 'negative', '-0.72', 'shoot', 'gun', 'bullet', 'rifle', 'pistol']
senticnet['shoot_weapon'] = ['0', '0', '-0.88', '0', '#fear', '#fear', 'negative', '-0.88', 'automatic', 'gun', 'firearm', 'ammunition', 'pistol']
senticnet['shooter'] = ['-0.86', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.79', 'gun', 'firearm', 'handgun', 'rifle', 'pistol']
senticnet['shootin'] = ['-0.97', '0', '0.677', '-0.95', '#sadness', '#disgust', 'negative', '-0.86', 'stunt', 'game', 'mutant', 'invertebrate', 'animate_being']
senticnet['shooting_brake'] = ['-0.53', '0', '0.905', '-0.58', '#anger', '#disgust', 'negative', '-0.67', 'beach_wagon', 'station_wagon', 'wagon', 'estate_car', 'beach_waggon']
senticnet['shooting_iron'] = ['0.978', '-0.57', '0', '0.793', '#joy', '#admiration', 'positive', '0.782', 'pistol', 'handgun', 'side_arm', 'semiautomatic', 'semiautomatic_firearm']
senticnet['shooting_movie'] = ['0.777', '0.975', '0', '0', '#joy', '#interest', 'positive', '0.876', 'director', 'director_chair', 'actor', 'important_person', 'movie']
senticnet['shop'] = ['0', '0.124', '-0.05', '0.203', '#interest', '#admiration', 'positive', '0.09', 'small_shop', 'stock_exchange', 'store', 'shop_grocery', 'music_store']
senticnet['shop_arcade'] = ['0', '0', '0.016', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'dime_store', 'deli_store', 'vend_stand', 'bookshop', 'store']
senticnet['shop_assistant'] = ['0.99', '0', '0', '0.721', '#joy', '#admiration', 'positive', '0.855', 'hair', 'haircut', 'hairdresser_chair', 'scissor', 'hairstyle']
senticnet['shop_bag'] = ['0.095', '0', '0', '0', '#joy', '#joy', 'positive', '0.095', 'plastic_bag', 'coffee_cup', 'bag', 'plastic', 'store_shelf']
senticnet['shop_complex'] = ['0.966', '0.921', '0', '0.892', '#joy', '#interest', 'positive', '0.926', 'disco', 'nightclub', 'dance_club', 'discotheque', 'fast_food_restaurant']
senticnet['shop_food'] = ['0.786', '0.724', '0', '0', '#joy', '#interest', 'positive', '0.755', 'purchase_grocery', 'buy_vegetable', 'buy_grocery', 'purchase_food', 'stock_merchandise']
senticnet['shop_grocery'] = ['0.067', '0.162', '-0.11', '0.192', '#interest', '#admiration', 'positive', '0.076', 'store', 'buy_grocery', 'purchase_grocery', 'buy_food', 'buy_snack']
senticnet['shop_keeper'] = ['0.059', '0.212', '0', '0.143', '#interest', '#admiration', 'positive', '0.138', 'christmas_shop', 'go_grocery_shop', 'pay_credit_card', 'pay_cash', 'holiday_shop']
senticnet['shop_list'] = ['0.705', '0.967', '0', '0', '#joy', '#interest', 'positive', '0.836', 'make_shop_list', 'write_note', 'make_list', 'go_shopping', 'go_to_supermarket']
senticnet['shop_mall'] = ['0.771', '0', '0', '0', '#joy', '#joy', 'positive', '0.771', 'shop', 'store', 'go_shopping', 'shopping_center', 'buy_stuff']
senticnet['shop_next_door'] = ['0', '0', '0.044', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'large_supermarket', 'food_store', 'large_food_store', 'corner_shop', 'convenience_food_store']
senticnet['shop_owner'] = ['0.026', '0.082', '0', '0', '#joy', '#interest', 'positive', '0.054', 'account_department', 'office', 'accountant_office', 'shirt_pocket', 'desk_drawer']
senticnet['shop_vac'] = ['-0.04', '-0.02', '0.089', '-0.14', '#anger', '#disgust', 'negative', '-0.06', 'steam_locomotive', 'loudspeaker', 'collector', 'siren', 'activist']
senticnet['shop_window'] = ['0.952', '-0.52', '0', '0.835', '#joy', '#admiration', 'positive', '0.771', 'billboard', 'hoarding', 'sandwich_board', 'scoreboard', 'shingle']
senticnet['shop_wisely'] = ['0.94', '0.804', '0', '0', '#joy', '#interest', 'positive', '0.872', 'spend_a_lot_of_money', 'window_shop', 'spend_much_money', 'check_price', 'compare_price']
senticnet['shopkeeper'] = ['0', '-0.76', '0', '0.705', '#surprise', '#admiration', 'positive', '0.736', 'fishmonger', 'grocer', 'greengrocer', 'fruiterer', 'newsagent']
senticnet['shoplift'] = ['-0.18', '0.138', '-0.21', '0.086', '#sadness', '#fear', 'negative', '-0.04', 'steal', 'rob', 'leave_without_pay', 'bad_citizen', 'robbery']
senticnet['shoplifter'] = ['0.041', '-0.02', '0.044', '-0.10', '#anger', '#disgust', 'negative', '-0.02', 'suntan_lotion', 'carton_milk', 'store_shelf', 'plastic_container', 'wax_dental_floss']
senticnet['shoppe'] = ['0.026', '0.082', '0', '0', '#joy', '#interest', 'positive', '0.054', 'account_department', 'office', 'accountant_office', 'shirt_pocket', 'desk_drawer']
senticnet['shopper'] = ['0.875', '0.706', '0', '0', '#joy', '#interest', 'positive', '0.79', 'customer', 'clerk', 'patron', 'vintner', 'hairdresser']
senticnet['shopper_stocked_up'] = ['0.03', '0.073', '0.027', '0.084', '#interest', '#admiration', 'positive', '0.04', 'grocery', 'store_vegetable', 'fresh_vegetable', 'vendor', 'fruit_vegetable']
senticnet['shopping'] = ['-0.05', '0', '0.021', '0', '#sadness', '#anger', 'negative', '-0.03', 'buy_clothes', 'spend_money', 'get_grocery', 'buy_goods', 'buy_stuff']
senticnet['shopping_area'] = ['0.835', '0.927', '0', '0', '#joy', '#interest', 'positive', '0.881', 'outlet', 'sour_cream', 'leave_over', 'hand_print', 'taffy']
senticnet['shopping_bag'] = ['-0.93', '0', '0.607', '-0.67', '#sadness', '#disgust', 'negative', '-0.73', 'miss', 'girl', 'missy', 'young_lady', 'young_woman']
senticnet['shopping_basket'] = ['0.875', '0.706', '0', '0', '#joy', '#interest', 'positive', '0.79', 'customer', 'clerk', 'patron', 'vintner', 'hairdresser']
senticnet['shopping_cart'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'super_market', 'salad_bar', 'fruit_stand', 'candy_store', 'produce_store']
senticnet['shopping_center'] = ['0.771', '0', '0', '0', '#joy', '#joy', 'positive', '0.771', 'shop', 'store', 'shop_mall', 'go_shopping', 'buy_stuff']
senticnet['shopping_centre'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'train_station', 'bus', 'subway_station', 'public_place', 'pier']
senticnet['shopping_complex'] = ['0.041', '0.098', '0', '0', '#joy', '#interest', 'positive', '0.07', 'utopia', 'java', 'memorial', 'nowhere', 'coffee']
senticnet['shopping_destination'] = ['0.041', '0.098', '0', '0', '#joy', '#interest', 'positive', '0.07', 'utopia', 'java', 'memorial', 'nowhere', 'coffee']
senticnet['shopping_mall'] = ['0.041', '0.098', '0', '0', '#joy', '#interest', 'positive', '0.07', 'utopia', 'java', 'memorial', 'nowhere', 'coffee']
senticnet['shopping_search_engine'] = ['0.94', '0.804', '0', '0', '#joy', '#interest', 'positive', '0.872', 'spend_a_lot_of_money', 'window_shop', 'spend_much_money', 'check_price', 'compare_price']
senticnet['shopping_trip'] = ['0.03', '0.073', '0.027', '0.084', '#interest', '#admiration', 'positive', '0.04', 'grocery', 'store_vegetable', 'fresh_vegetable', 'vendor', 'fruit_vegetable']
senticnet['shopping_trolley'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'super_market', 'salad_bar', 'fruit_stand', 'candy_store', 'produce_store']
senticnet['shopworn'] = ['0', '0', '-0.96', '-0.70', '#fear', '#disgust', 'negative', '-0.83', 'banal', 'threadbare', 'commonplace', 'old-hat', 'hackneyed']
senticnet['shore'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'driftwood', 'sea_shell', 'scenic_view', 'naked_woman', 'tidal_wave']
senticnet['shore_bird'] = ['0.786', '-0.82', '0', '0.776', '#joy', '#surprise', 'positive', '0.794', 'red_phalarope', 'northern_phalarope', 'shorebird', 'phalarope', 'limicoline_bird']
senticnet['shore_boat'] = ['-0.10', '0.07', '-0.06', '-0.08', '#sadness', '#disgust', 'negative', '-0.04', 'board_ship', 'row_boat', 'battle_cancer', 'pier', 'weather_storm']
senticnet['shore_erosion'] = ['-0.88', '0', '0.907', '-0.52', '#sadness', '#anger', 'negative', '-0.77', 'wearing', 'corrasion', 'eroding', 'eating_away', 'wearing_away']
senticnet['shore_up_wall'] = ['-0.05', '0.111', '0', '0.12', '#interest', '#admiration', 'positive', '0.059', 'build_house', 'construct', 'build_wall', 'build_bridge', 'fix_car']
senticnet['shorebird'] = ['0.785', '-0.82', '0', '0.768', '#joy', '#surprise', 'positive', '0.792', 'red_phalarope', 'phalarope', 'northern_phalarope', 'shore_bird', 'limicoline_bird']
senticnet['shoreline'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'driftwood', 'sea_shell', 'scenic_view', 'naked_woman', 'tidal_wave']
senticnet['shoreline_erosion'] = ['-0.88', '0', '0.907', '-0.52', '#sadness', '#anger', 'negative', '-0.77', 'wearing', 'corrasion', 'eroding', 'eating_away', 'wearing_away']
senticnet['shoreward'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'driftwood', 'sea_shell', 'scenic_view', 'naked_woman', 'tidal_wave']
senticnet['short'] = ['0', '0', '-0.78', '-0.71', '#fear', '#disgust', 'negative', '-0.74', 'unretentive', 'poor', 'inadequate', 'little', 'forgetful']
senticnet['short-change'] = ['-0.75', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.81', 'shortchange', 'undersell', 'shortchanging', 'short-changing', 'oversell']
senticnet['short-changing'] = ['-0.75', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.81', 'shortchange', 'undersell', 'shortchanging', 'short-change', 'oversell']
senticnet['short-circuit'] = ['0', '0', '-0.90', '-0.94', '#fear', '#disgust', 'negative', '-0.92', 'overheating', 'overheated', 'overheats', 'over-heating', 'overheat']
senticnet['short-lived'] = ['0', '0', '-0.73', '-0.96', '#fear', '#disgust', 'negative', '-0.84', 'transitory', 'transeunt', 'ephemeral', 'passing', 'transient']
senticnet['short-range'] = ['-0.77', '0', '0.78', '0', '#sadness', '#anger', 'negative', '-0.77', 'long-range', 'missile', 'medium-range', 'surface-to-surface', 'air-to-air']
senticnet['short_animated_film'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'child_movie', 'child_story', 'funny_moment', 'childhood']
senticnet['short_attention_span'] = ['0', '-0.33', '0', '0', '#surprise', '#surprise', 'positive', '0.33', 'hopeless', 'distracted', 'diverted', 'inattentive', 'confused']
senticnet['short_barreled_rifle'] = ['-0.84', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.86', 'handgun', 'rifle', 'weapon', 'pistol', 'gun']
senticnet['short_barreled_shotgun'] = ['-0.84', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.86', 'handgun', 'rifle', 'weapon', 'pistol', 'gun']
senticnet['short_blond_hair'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'plato', 'natural_world', 'hair', 'feature', 'stick_out']
senticnet['short_brown_hair'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'plato', 'natural_world', 'hair', 'feature', 'stick_out']
senticnet['short_bus_ride'] = ['-0.08', '0.179', '-0.06', '0.096', '#interest', '#admiration', 'positive', '0.034', 'ride_subway', 'buy_vending_machine', 'travel', 'buy_lottery_ticket', 'acquire']
senticnet['short_circuit'] = ['-0.09', '-0.01', '-0.10', '-0.20', '#fear', '#disgust', 'negative', '-0.09', 'violence', 'siege', 'conflict', 'bloodshed', 'fight']
senticnet['short_cut'] = ['0.709', '0', '0', '0.989', '#joy', '#admiration', 'positive', '0.849', 'theme_park', 'combo_pass', 'save_time', 'multiple_entry', 'ticket']
senticnet['short_dark_hair'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'plato', 'natural_world', 'hair', 'feature', 'stick_out']
senticnet['short_distance'] = ['0.609', '0.597', '-0.23', '0.398', '#joy', '#interest', 'positive', '0.341', 'near', 'pick_up', 'togetherness', 'gay', 'capture']
senticnet['short_documentary_film'] = ['0', '0.762', '0', '0', '#interest', '#interest', 'positive', '0.762', 'encyclopaedia', 'tropical_rainforest', 'learn_new_information', 'tropical_forest', 'child_story']
senticnet['short_duration'] = ['0', '0.188', '-0.04', '0.191', '#interest', '#admiration', 'positive', '0.111', 'usual_ordinary', 'normal', 'typical', 'usual', 'daily_routine']
senticnet['short_eared_owl'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['short_fall'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'shortage', 'scarceness', 'insufficient', 'in_dearth', 'poverty']
senticnet['short_ferry_ride'] = ['0', '0', '0.027', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'port', 'sea', 'boat', 'ocean', 'ship']
senticnet['short_fiction'] = ['0.036', '0.219', '0', '0.19', '#interest', '#admiration', 'positive', '0.148', 'book_genre', 'literature', 'concoction', 'untruth', 'falsehood']
senticnet['short_finned_pilot'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'lordosis', 'kyphosis', 'spinal_curvature', 'hunchback', 'abnormality']
senticnet['short_form'] = ['0.067', '0.162', '-0.11', '0.192', '#interest', '#admiration', 'positive', '0.076', 'happy_together', 'informal', 'belong_person', 'care_giver', 'relate_person']
senticnet['short_fuse'] = ['-0.57', '0', '0.711', '-0.99', '#anger', '#disgust', 'negative', '-0.76', 'grouch', 'grump', 'crosspatch', 'churl', 'crank']
senticnet['short_grey_hair'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'plato', 'natural_world', 'hair', 'feature', 'stick_out']
senticnet['short_hair'] = ['-0.03', '0', '-0.04', '0', '#sadness', '#fear', 'negative', '-0.04', 'hair_become_short', 'little_hair', 'get_new_hairstyle', 'keep_hair_short', 'cold_head']
senticnet['short_haircut'] = ['-0.91', '0', '0.869', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'talk_hairdresser', 'hair_get_short', 'hair_become_short', 'keep_hair_short', 'hair_wash']
senticnet['short_jumper'] = ['-0.81', '0', '0.747', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'vagrant', 'drifter', 'hobo', 'vagabond', 'dosser']
senticnet['short_line'] = ['-0.71', '0', '0', '-0.96', '#sadness', '#disgust', 'negative', '-0.84', 'sharp_corner', 'intention', 'embroidery', 'sharp_end', 'mend']
senticnet['short_nap'] = ['0.026', '0.069', '0', '0', '#joy', '#interest', 'positive', '0.048', 'eat_food', 'eat_beef', 'chew_food', 'espresso', 'eat_much']
senticnet['short_photoperiod'] = ['-0.87', '0', '0.915', '-0.95', '#anger', '#disgust', 'negative', '-0.91', 'inactivity', 'inactiveness', 'slumber', 'inaction', 'hibernation']
senticnet['short_rib'] = ['-0.02', '-0.02', '0.064', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'ocean_water', 'fillet_fish', 'chowder', 'port', 'underwater']
senticnet['short_siphonal_canal'] = ['0.969', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.772', 'aspheric', 'aspherical', 'prolate', 'rounded', 'crescent']
senticnet['short_sleep'] = ['-0.62', '0', '0.941', '-0.73', '#anger', '#disgust', 'negative', '-0.76', 'catnap', 'sleep_late', 'sleep_in', 'rest', 'practice_bundling']
senticnet['short_sleeve_shirt'] = ['-0.02', '0.256', '0', '0.166', '#interest', '#admiration', 'positive', '0.132', 'coat', 'shirt', 'garment', 'dress', 'vest']
senticnet['short_sleeved_shirt'] = ['-0.02', '0.256', '0', '0.166', '#interest', '#admiration', 'positive', '0.132', 'coat', 'shirt', 'garment', 'dress', 'vest']
senticnet['short_stroll'] = ['0.876', '0.883', '0', '0', '#joy', '#interest', 'positive', '0.879', 'walk_along', 'gait', 'take_step', 'run_slowly', 'locomotion']
senticnet['short_subject'] = ['0.789', '-0.51', '0', '0.947', '#joy', '#admiration', 'positive', '0.751', 'flick', 'cartoon', 'animated_cartoon', 'toon', 'newsreel']
senticnet['short_supply'] = ['-0.83', '0', '-0.88', '-0.78', '#sadness', '#fear', 'negative', '-0.83', 'little', 'sadness', 'small_amount', 'lack', 'absence']
senticnet['short_taxi_ride'] = ['-0.08', '0.179', '-0.06', '0.096', '#interest', '#admiration', 'positive', '0.034', 'ride_subway', 'buy_vending_machine', 'travel', 'buy_lottery_ticket', 'acquire']
senticnet['short_temper'] = ['-0.12', '0.13', '0.646', '-0.64', '#anger', '#disgust', 'negative', '-0.32', 'irascibility', 'quick_temper', 'irascible', 'choleric', 'ill_temper']
senticnet['short_tempered'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'pervert', 'horny', 'uncomfortable', 'uneasy', 'touchy']
senticnet['short_time'] = ['0', '0.069', '0', '0.092', '#interest', '#admiration', 'positive', '0.081', 'time_frame', 'time_period', 'calendar_event', 'event', 'time_length']
senticnet['short_toed_snake'] = ['-0.74', '0', '0.963', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['short_trunk_dwarfism'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'concupiscence', 'lustfulness', 'amorousness']
senticnet['short_version'] = ['-0.03', '0.271', '-0.08', '0.303', '#interest', '#admiration', 'positive', '0.114', 'plea', 'game_show', 'movement_towards', 'award', 'suggest']
senticnet['short_video_clip'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'paperclip', 'clasp', 'glue_stick', 'clothespin', 'clamp']
senticnet['short_vowel'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'letter', 'vowel', 'alphabet', 'basic_speech', 'kind_gesture']
senticnet['short_wavelength'] = ['-0.07', '0.078', '0', '0.078', '#interest', '#admiration', 'positive', '0.028', 'red_blue', 'red_orange', 'green_yellow', 'hue_tint', 'yellow_green']
senticnet['short_wheelbase'] = ['0.035', '0.112', '-0.07', '0.195', '#interest', '#admiration', 'positive', '0.067', 'car_action', 'car_travel', 'steer_car', 'make_go', 'transportation']
senticnet['shortage'] = ['-0.83', '0', '-0.88', '-0.78', '#sadness', '#fear', 'negative', '-0.83', 'little', 'sadness', 'small_amount', 'lack', 'absence']
senticnet['shortbread'] = ['0.644', '-0.94', '0', '0.729', '#surprise', '#admiration', 'positive', '0.773', 'gem', 'banana_bread', 'date_bread', 'nut_bread', 'quick_bread']
senticnet['shortchange'] = ['-0.75', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.81', 'short-change', 'undersell', 'shortchanging', 'short-changing', 'oversell']
senticnet['shortchanging'] = ['-0.75', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.82', 'shortchange', 'undersell', 'short-change', 'short-changing', 'oversell']
senticnet['shortcoming'] = ['0', '0', '-0.87', '-0.81', '#fear', '#disgust', 'negative', '-0.84', 'shortcoming', 'drawback', 'inadequacy', 'failing', 'flaw']
senticnet['shortcrust'] = ['0.059', '-0.02', '0.025', '0.109', '#joy', '#admiration', 'positive', '0.043', 'tasty', 'bake_treat', 'bake_goods', 'bake_good', 'loaf']
senticnet['shortcrust_pastry'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'roll', 'custard', 'bread', 'biscuit']
senticnet['shortcut'] = ['-0.05', '0.119', '-0.12', '0.219', '#interest', '#admiration', 'positive', '0.04', 'shorten', 'abridge', 'curtail', 'cut_short', 'abbreviate']
senticnet['shortcut_menu'] = ['0.777', '-0.85', '0', '0.615', '#joy', '#surprise', 'positive', '0.748', 'think', 'play', 'trifle', 'abstract', 'warm_to']
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senticnet['shorter_barrel'] = ['0.887', '-0.95', '0', '0.913', '#surprise', '#admiration', 'positive', '0.917', 'high_quality', 'superiority', 'level', 'grade', 'degree']
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senticnet['shorter_tail'] = ['-0.59', '0', '0.887', '-0.71', '#anger', '#disgust', 'negative', '-0.73', 'armet', 'basinet', 'helmet', 'heaume', 'morion']
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senticnet['shorthorn_cattle'] = ['0.546', '-0.96', '0', '0.533', '#joy', '#surprise', 'positive', '0.683', 'dairy_cattle', 'dairy_cow', 'milch_cow', 'ayrshire', 'milcher']
senticnet['shortie'] = ['-0.54', '0', '0.612', '-0.98', '#anger', '#disgust', 'negative', '-0.71', 'scuff', 'mule', 'bootee', 'carpet_slipper', 'footwear']
senticnet['shortish'] = ['0', '0', '-0.78', '-0.71', '#fear', '#disgust', 'negative', '-0.74', 'unretentive', 'poor', 'inadequate', 'little', 'forgetful']
senticnet['shortleaf'] = ['-0.69', '0', '0.725', '-0.89', '#anger', '#disgust', 'negative', '-0.77', 'ponderosa', 'ponderosa_pine', 'western_yellow_pine', 'bull_pine', 'longleaf_pine']
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senticnet['shortleaf_pine_pinus_echinata'] = ['-0.69', '0', '0.725', '-0.89', '#anger', '#disgust', 'negative', '-0.77', 'ponderosa', 'ponderosa_pine', 'western_yellow_pine', 'bull_pine', 'longleaf_pine']
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senticnet['shortness_breath'] = ['-0.11', '-0.04', '-0.06', '-0.12', '#sadness', '#disgust', 'negative', '-0.06', 'breathlessness', 'leg_cramp', 'get_out_breath', 'muscle_cramp', 'become_wind']
senticnet['shorts'] = ['0.835', '-0.70', '0', '0', '#joy', '#surprise', 'positive', '0.77', 'swimming_costume', 'bikini', 'jeans', 'pair_of_pants', 'dirty_magazine']
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senticnet['shortstop'] = ['0', '0.075', '0', '0', '#interest', '#interest', 'positive', '0.075', 'cup', 'carafe', 'container_cup', 'mug', 'glasses']
senticnet['shortsword'] = ['0.93', '-0.78', '0', '0.873', '#joy', '#admiration', 'positive', '0.861', 'blade', 'brand', 'steel', 'claymore', 'sword']
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senticnet['shot_fired'] = ['-0.08', '-0.02', '0', '-0.19', '#sadness', '#disgust', 'negative', '-0.08', 'wound', 'lethal', 'vaccination', 'destruction', 'fire_bullet']
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senticnet['shoujo'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['shoulder'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'bowel', 'scrotum', 'anus', 'liver', 'waist']
senticnet['shoulder_bag'] = ['0', '-0.03', '0.038', '-0.10', '#anger', '#disgust', 'negative', '-0.03', 'tote_bag', 'satchel', 'fitting_room', 'carry', 'lingerie']
senticnet['shoulder_blade'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'socket', 'tooth_socket', 'alveolus', 'acetabulum', 'cotyloid_cavity']
senticnet['shoulder_burden'] = ['-0.05', '0.072', '0.012', '0.066', '#interest', '#admiration', 'positive', '0.018', 'sink_water', 'open_mailbox', 'bell_cat', 'eat', 'eat_cheese']
senticnet['shoulder_dislocation'] = ['-0.41', '0', '-0.28', '-0.05', '#sadness', '#fear', 'negative', '-0.25', 'carpal_tunnel_syndrome', 'health_issue', 'breaking_news', 'disease', 'grade_paper']
senticnet['shoulder_girdle'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'above_leg', 'joint', 'hip', 'knee']
senticnet['shoulder_gown'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'pinafore', 'pinny', 'dirndl', 'dress', 'muumuu']
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senticnet['shoulder_injury'] = ['-0.90', '0', '-0.91', '-0.73', '#sadness', '#fear', 'negative', '-0.85', 'broken_window', 'step_dog_shit', 'broken_leg', 'brain_tumor', 'bad_luck']
senticnet['shoulder_length'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'plato', 'natural_world', 'hair', 'feature', 'stick_out']
senticnet['shoulder_pad'] = ['-0.99', '0', '0.806', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'wear', 'biohazard_suit', 'coverall', 'protective_garment', 'wearable']
senticnet['shoulder_pain'] = ['-0.98', '0', '0.678', '-0.95', '#sadness', '#disgust', 'negative', '-0.87', 'rubor', 'redness', 'inflammation', 'epicondylitis', 'lateral_epicondylitis']
senticnet['shoulder_responsibility'] = ['-0.13', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.09', 'respect', 'responsibility', 'throw_fit', 'battle_illness', 'honor_parent']
senticnet['shoulder_seam'] = ['-0.04', '-0.01', '0.026', '-0.12', '#sadness', '#disgust', 'negative', '-0.04', 'embroidery', 'cover_wind', 'use_needle', 'bandage', 'joint']
senticnet['shoulder_strain'] = ['-0.93', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.92', 'pull_muscle', 'become_exhausted', 'increased_heart_rate', 'breathlessness', 'tired_leg']
senticnet['shoulder_strap'] = ['0', '0.072', '0', '0.064', '#interest', '#admiration', 'positive', '0.068', 'zipper', 'pant', 'trousers', 'cowboy_boot', 'accessory']
senticnet['shoulder_width_apart'] = ['-0.68', '0', '0.545', '-0.68', '#sadness', '#disgust', 'negative', '-0.64', 'stunt', 'tumble', 'split', 'walkover', 'acrobatic_feat']
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senticnet['shout_loudly'] = ['-0.08', '0.089', '0', '0.097', '#interest', '#admiration', 'positive', '0.034', 'ride_bicycle_work', 'take_hike', 'stop_smoke', 'scream_real_loud', 'break_record']
senticnet['shout_top_voice'] = ['0', '0', '0', '-0.78', '#disgust', '#disgust', 'negative', '-0.78', 'bungee_jump_from_high_bridge', 'asthma_attack', 'angry', 'high_blood_pressure', 'go_gym']
senticnet['shouting'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'noisy', 'noise_pollution', 'blaring', 'make_sound', 'loud']
senticnet['shouting_match'] = ['-0.63', '0', '0.982', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'wrangle', 'dustup', 'quarrel', 'row', 'argument']
senticnet['shove'] = ['0.085', '0', '-0.05', '0.116', '#joy', '#admiration', 'positive', '0.05', 'dance_well', 'water_faucet', 'enter_contest', 'dance_type', 'dance_music']
senticnet['shove_around'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'push', 'ram', 'barge', 'bang', 'jostle']
senticnet['shoved_onto'] = ['0.706', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.768', 'train_station', 'subway_stop', 'parking_lot', 'subway_station', 'park_structure']
senticnet['shovel-like'] = ['-0.71', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.85', 'chisel-shaped', 'chisel-like', 'scythe-like', 'saw-like', 'teeth-like']
senticnet['shovel_snow'] = ['0.999', '0.826', '0', '0', '#joy', '#interest', 'positive', '0.912', 'fly_around', 'smoke_joint', 'blow_away', 'dig', 'sit_floor']
senticnet['shoveler'] = ['-0.50', '0', '0.764', '-0.61', '#anger', '#disgust', 'negative', '-0.62', 'doctor', 'mountebank', 'charlatan', 'quack', 'physician']
senticnet['shoving_match'] = ['-0.60', '0', '0.777', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'batrachomyomachia', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['show'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'cinema', 'movie', 'watch_movie', 'theater', 'cctv']
senticnet['show-off'] = ['-0.91', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.95', 'showoff', 'flaunt', 'chit-chat', 'brag', 'showboating']
senticnet['show_affection'] = ['0.809', '0', '0', '0', '#joy', '#joy', 'positive', '0.809', 'show_love', 'express_love', 'make_happy', 'show_care', 'fondness']
senticnet['show_appreciation'] = ['0.926', '0', '0', '0.868', '#joy', '#admiration', 'positive', '0.897', 'warm_feeling', 'show_love', 'demand', 'say_welcome', 'express_gratitude']
senticnet['show_around'] = ['0', '0.084', '0', '0.083', '#interest', '#admiration', 'positive', '0.084', 'home_sweet_home', 'guest', 'take_care', 'offer_accommodation', 'host']
senticnet['show_care'] = ['0.774', '0', '0', '0', '#joy', '#joy', 'positive', '0.774', 'express_appreciation', 'celebrate_birthday', 'show_affection', 'make_happy', 'get_present']
senticnet['show_emotion'] = ['-0.12', '0', '-0.15', '0.051', '#sadness', '#fear', 'negative', '-0.07', 'whine', 'jump_out', 'shed_tear', 'surprise_friend', 'eye_tear']
senticnet['show_empathy'] = ['0', '0.873', '0', '0.787', '#interest', '#admiration', 'positive', '0.83', 'protection', 'love_back', 'find_woman', 'care_deeply', 'say_welcome']
senticnet['show_feeling'] = ['0.155', '0.199', '-0.17', '0.2', '#interest', '#admiration', 'positive', '0.095', 'interaction', 'funny', 'demonstrate', 'activity', 'circus_performer']
senticnet['show_id'] = ['-0.03', '0', '0.017', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'order_hamburger', 'order_beer', 'money_credit', 'make_sure_enough_money', 'go_supermarket']
senticnet['show_know'] = ['-0.06', '-0.03', '0', '-0.11', '#sadness', '#disgust', 'negative', '-0.04', 'receive_grade', 'get_grade', 'complete_course', 'test_knowledge', 'get_qualification']
senticnet['show_love'] = ['0.728', '0', '0', '0.933', '#joy', '#admiration', 'positive', '0.831', 'show_affection', 'express_love', 'make_happy', 'warm_feeling', 'lust']
senticnet['show_movie'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'see_film', 'enter_theater', 'cinema', 'watch_movie', 'watch_film']
senticnet['show_off'] = ['0', '0', '0.981', '-0.72', '#anger', '#disgust', 'negative', '-0.85', 'get_out_of_house', 'funny_friend', 'social_bond', 'impress_girl', 'recreation']
senticnet['show_off_wealth'] = ['0.077', '-0.01', '0.045', '0.124', '#joy', '#admiration', 'positive', '0.042', 'make_pollution', 'transport_person', 'get_place', 'get_around_town', 'get_school']
senticnet['show_picture'] = ['0', '0.07', '0.041', '0.058', '#interest', '#admiration', 'positive', '0.029', 'receive_information', 'share_information', 'share_thought', 'masturbate', 'interrupt']
senticnet['show_play'] = ['0.753', '0.971', '0', '0', '#joy', '#interest', 'positive', '0.862', 'present_play', 'perform_play', 'stage_play', 'drama', 'performance']
senticnet['show_respect'] = ['0.907', '0', '0', '0.872', '#joy', '#admiration', 'positive', '0.889', 'polite', 'honor', 'stand_behind', 'fondness', 'show_affection']
senticnet['show_sample'] = ['0', '0.142', '-0.08', '0.258', '#interest', '#admiration', 'positive', '0.106', 'description', 'example', 'illustrative', 'sample', 'teach']
senticnet['show_spitting_image'] = ['-0.89', '0', '0.58', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'resemblance', 'comparison', 'equivalence', 'reflection', 'alikeness']
senticnet['show_support'] = ['0.339', '0', '-0.25', '0.419', '#joy', '#admiration', 'positive', '0.167', 'adore', 'cloud_judgment', 'feel_pleasure', 'have_fun', 'appreciate']
senticnet['show_ticket'] = ['0.577', '-0.50', '0', '0.901', '#joy', '#admiration', 'positive', '0.66', 'dress_nicely', 'dating', 'dinner', 'find_place_to_sit', 'restaurant']
senticnet['show_time'] = ['-0.04', '-0.02', '0.061', '0', '#sadness', '#anger', 'negative', '-0.02', 'timekeeper', 'time', 'swiss_watch', 'device_measure_time', 'tell_time']
senticnet['show_tooth'] = ['0.908', '0.853', '0', '0', '#joy', '#interest', 'positive', '0.881', 'contagious', 'expression', 'facial_expression', 'mouth_movement', 'sniff']
senticnet['show_up'] = ['0', '0.986', '0', '0.99', '#interest', '#admiration', 'positive', '0.988', 'show_up_time', 'go_abroad', 'enroll_school', 'available', 'sign_up']
senticnet['show_up_time'] = ['-0.07', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.06', 'enroll_school', 'study_lot', 'a_lot_of_study', 'go_class', 'study_course_material']
senticnet['showbiz'] = ['0', '0.793', '0', '0.91', '#interest', '#admiration', 'positive', '0.852', 'superstar', 'vip', 'accomplishment', 'performance', 'concert']
senticnet['showboating'] = ['-0.92', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.96', 'showoff', 'flaunt', 'chit-chat', 'brag', 'show-off']
senticnet['showcase'] = ['0', '-0.74', '0', '0.86', '#surprise', '#admiration', 'positive', '0.802', 'showcasing', 'showcase', 'showcased', 'featured', 'featuring']
senticnet['showcase_showdown'] = ['-0.08', '-0.03', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'graduate_student', 'student', 'serious_student', 'competition', 'hard_worker']
senticnet['showcased'] = ['0', '-0.75', '0', '0.857', '#surprise', '#admiration', 'positive', '0.803', 'showcasing', 'showcase', 'showcase', 'featured', 'featuring']
senticnet['showcasing'] = ['0', '-0.75', '0', '0.852', '#surprise', '#admiration', 'positive', '0.802', 'showcase', 'showcase', 'showcased', 'featured', 'featuring']
senticnet['showdown'] = ['-0.2', '-0.03', '0', '-0.23', '#sadness', '#disgust', 'negative', '-0.13', 'beat_up', 'fight', 'conflict', 'violence', 'fight_against']
senticnet['shower'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'water_garden', 'water_lawn', 'heater', 'adjust_water_temperature', 'cook']
senticnet['shower_bath'] = ['-0.53', '0', '0.508', '-0.74', '#sadness', '#disgust', 'negative', '-0.59', 'bedwetter', 'bed_wetter', 'mortal', 'wetter', 'person']
senticnet['shower_cap'] = ['0.179', '0.164', '-0.05', '0.163', '#joy', '#interest', 'positive', '0.113', 'cover_hair', 'hat', 'cap', 'head_wear', 'scarf']
senticnet['shower_cover_hair'] = ['0.173', '0.164', '-0.06', '0.155', '#joy', '#interest', 'positive', '0.108', 'cap', 'hat', 'cover_hair', 'head_wear', 'scarf']
senticnet['shower_cubicle'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'commode', 'potty_seat', 'potty_chair']
senticnet['shower_curtain'] = ['0', '-0.02', '0.041', '-0.10', '#anger', '#disgust', 'negative', '-0.03', 'separate_shower', 'shampoo_conditioner', 'towel_bar', 'shower_head', 'toilet_brush']
senticnet['shower_down'] = ['-0.83', '0', '0.87', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'precipitate', 'mizzle', 'rain_down', 'spit', 'patter']
senticnet['shower_gel'] = ['0', '0.069', '0.023', '0.052', '#interest', '#admiration', 'positive', '0.033', 'soapy_water', 'wash_self', 'soap_water', 'soap_up', 'turn_water']
senticnet['shower_head'] = ['0.041', '0.086', '0.016', '0', '#joy', '#interest', 'positive', '0.037', 'separate_shower', 'bath_shower', 'shampoo_conditioner', 'plunger', 'tub']
senticnet['shower_rod'] = ['0.024', '0', '0', '0.074', '#joy', '#admiration', 'positive', '0.049', 'sheet', 'cover', 'quilt', 'comforter', 'modesty']
senticnet['shower_room'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'commode', 'potty_seat', 'potty_chair']
senticnet['shower_scene'] = ['-0.69', '0', '0.508', '-0.93', '#sadness', '#disgust', 'negative', '-0.71', 'dark', 'darkness', 'light', 'shadow', 'site']
senticnet['shower_stall'] = ['0', '-0.02', '0.041', '-0.10', '#anger', '#disgust', 'negative', '-0.03', 'separate_shower', 'shampoo_conditioner', 'towel_bar', 'shower_head', 'toilet_brush']
senticnet['showerhead'] = ['0.939', '0.939', '0', '0', '#joy', '#interest', 'positive', '0.939', 'puddle', 'sop', 'liquid_water', 'finger_paint', 'bread']
senticnet['showery'] = ['-0.92', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.82', 'drizzly', 'muggy', 'blustery', 'squally', 'spring-like']
senticnet['showest'] = ['-0.79', '0', '0.948', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['showgirl'] = ['-0.80', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.79', 'whorehouse', 'brothel', 'massage_parlor', 'bagnio', 'house_of_prostitution']
senticnet['showground'] = ['-0.72', '0', '0.978', '-0.86', '#anger', '#disgust', 'negative', '-0.85', 'amusement_park', 'common', 'pleasure_ground', 'village_green', 'tract']
senticnet['showld'] = ['0.887', '-0.84', '0', '0.819', '#joy', '#surprise', 'positive', '0.851', 'arouse', 'elicit', 'enkindle', 'kindle', 'evoke']
senticnet['showman'] = ['0.563', '-0.81', '0', '0.577', '#surprise', '#admiration', 'positive', '0.65', 'mortal', 'person', 'individual', 'performer', 'artist']
senticnet['showmanship'] = ['0.816', '-0.59', '0', '0.538', '#joy', '#surprise', 'positive', '0.65', 'acquirement', 'skill', 'accomplishment', 'acquisition', 'attainment']
senticnet['showoff'] = ['-0.92', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.95', 'show-off', 'flaunt', 'chit-chat', 'brag', 'showboating']
senticnet['showpiece'] = ['0.507', '-0.52', '0', '0.579', '#surprise', '#admiration', 'positive', '0.538', 'curio', 'oddity', 'curiosity', 'peculiarity', 'oddment']
senticnet['showplace'] = ['0.522', '-0.88', '0', '0.965', '#surprise', '#admiration', 'positive', '0.789', 'overage', 'asset', 'security', 'business', 'hole_card']
senticnet['showroom'] = ['0.041', '0.098', '0', '0', '#joy', '#interest', 'positive', '0.07', 'utopia', 'java', 'memorial', 'nowhere', 'coffee']
senticnet['showstopper'] = ['0.654', '-0.57', '0', '0.898', '#joy', '#admiration', 'positive', '0.708', 'routine', 'bit', 'number', 'act', 'turn']
senticnet['showtime'] = ['0.813', '-0.75', '0', '0.664', '#joy', '#surprise', 'positive', '0.745', 'point_in_time', 'threshold', 'birth', 'incipiency', 'incipience']
senticnet['showy'] = ['0', '0', '-0.88', '-0.96', '#fear', '#disgust', 'negative', '-0.92', 'jazzy', 'flashy', 'splashy', 'flamboyant', 'gaudy']
senticnet['showy_lady_slipper'] = ['0.277', '0.161', '0', '0.337', '#joy', '#admiration', 'positive', '0.258', 'moccasin_flower', 'nerveroot', 'lady_slipper', 'yellow_lady_slipper', 'clustered_lady_slipper']
senticnet['shpigelman'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'busy_bee', 'gopher', 'actor', 'doer', 'worker']
senticnet['shrapnel'] = ['0', '0', '0.818', '-0.99', '#anger', '#disgust', 'negative', '-0.90', 'weapon', 'aerial_bomb', 'machine_gun', 'cannon', 'handgun']
senticnet['shrapnel_wound'] = ['-0.16', '0', '-0.18', '-0.23', '#fear', '#disgust', 'negative', '-0.19', 'attack_enemy', 'cause_death', 'die', 'lose_war', 'imprisonment']
senticnet['shredded_beef'] = ['0.076', '0', '0.037', '0.065', '#joy', '#admiration', 'positive', '0.035', 'serve_cold', 'tasty_good', 'tasty', 'sour', 'high_calorie']
senticnet['shredded_cabbage'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'horseradish', 'box_cereal', 'bottle_water']
senticnet['shredded_coconut'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'dainty', 'delicacy', 'treat', 'jelly', 'aspic']
senticnet['shredded_lettuce'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'horseradish', 'box_cereal', 'bottle_water']
senticnet['shredded_paper'] = ['0', '-0.03', '0.054', '-0.14', '#anger', '#disgust', 'negative', '-0.05', 'trash', 'junk_store', 'dustbin', 'inbox', 'trash_bin']
senticnet['shredded_pork'] = ['0.076', '0', '0.037', '0.065', '#joy', '#admiration', 'positive', '0.035', 'serve_cold', 'tasty_good', 'tasty', 'sour', 'high_calorie']
senticnet['shredded_wheat'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'veal', 'beef', 'food_group', 'animal_flesh', 'curry']
senticnet['shree_krishn'] = ['-0.71', '0', '0.836', '0', '#sadness', '#anger', 'negative', '-0.77', 'awkward', 'unenviable', 'embarrassingly', 'embarrassing', 'sheepish']
senticnet['shree_krishn_chund'] = ['-0.71', '0', '0.836', '0', '#sadness', '#anger', 'negative', '-0.77', 'awkward', 'unenviable', 'embarrassingly', 'embarrassing', 'sheepish']
senticnet['shreveport'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
senticnet['shrew'] = ['-0.59', '0', '0.573', '-0.56', '#sadness', '#anger', 'negative', '-0.57', 'termagant', 'old_bag', 'disagreeable_woman', 'frump', 'hellcat']
senticnet['shrewd'] = ['0.883', '0', '0', '0.905', '#joy', '#admiration', 'positive', '0.894', 'ingenious', 'prudent', 'gnostic', 'artful', 'resourcefulness']
senticnet['shrewd_businessman'] = ['0.976', '0.811', '0', '0', '#joy', '#interest', 'positive', '0.893', 'deposit', 'bank_loan', 'wealth', 'finance', 'account']
senticnet['shrewd_observation'] = ['-0.52', '0', '0.604', '-0.82', '#anger', '#disgust', 'negative', '-0.65', 'discernment', 'sagacity', 'sagaciousness', 'confidentiality', 'judgment']
senticnet['shrewdness'] = ['0.545', '-0.71', '0', '0.811', '#surprise', '#admiration', 'positive', '0.691', 'foxiness', 'slyness', 'wiliness', 'craftiness', 'cunning']
senticnet['shrewish'] = ['0', '0', '0.747', '-0.73', '#anger', '#disgust', 'negative', '-0.74', 'harridan', 'sharp-tongued', 'loutish', 'gold-digging', 'oafish']
senticnet['shriek'] = ['0.545', '-0.88', '0', '0.898', '#surprise', '#admiration', 'positive', '0.775', 'cry', 'yell', 'call', 'outcry', 'shout']
senticnet['shrieked'] = ['0.763', '0', '0', '0.711', '#joy', '#admiration', 'positive', '0.737', 'shrilly', 'indignantly', 'plaintively', 'mockingly', 'imperiously']
senticnet['shrieking'] = ['0.758', '-0.75', '0', '0.683', '#joy', '#surprise', 'positive', '0.731', 'cry', 'yell', 'call', 'outcry', 'shout']
senticnet['shrift'] = ['0', '0', '0.989', '-0.8', '#anger', '#disgust', 'negative', '-0.89', 'brushoff', 'tongue-lashing', 'heave-ho', 'dressing-down', 'brush-off']
senticnet['shrill'] = ['0', '0', '0.802', '-0.75', '#anger', '#disgust', 'negative', '-0.78', 'cry', 'yell', 'scream', 'hollo', 'call']
senticnet['shrill_cry'] = ['0', '0.07', '0', '0.063', '#interest', '#admiration', 'positive', '0.067', 'microscope', 'glimpse', 'see', 'stare', 'observation']
senticnet['shrill_voice'] = ['0', '0', '0.802', '-0.75', '#anger', '#disgust', 'negative', '-0.78', 'cry', 'yell', 'scream', 'hollo', 'call']
senticnet['shrill_whistle'] = ['0.041', '0', '0', '0.064', '#joy', '#admiration', 'positive', '0.053', 'squeak', 'clang', 'musical_note', 'bad_music', 'roar']
senticnet['shriller'] = ['0', '0', '0.802', '-0.75', '#anger', '#disgust', 'negative', '-0.78', 'cry', 'yell', 'scream', 'hollo', 'call']
senticnet['shrillest'] = ['0', '0', '0.802', '-0.75', '#anger', '#disgust', 'negative', '-0.78', 'cry', 'yell', 'scream', 'hollo', 'call']
senticnet['shrillness'] = ['0.502', '-0.67', '0', '0.699', '#surprise', '#admiration', 'positive', '0.627', 'color', 'newsworthiness', 'interestingness', 'vividness', 'charisma']
senticnet['shrilly'] = ['0.766', '0', '0', '0.712', '#joy', '#admiration', 'positive', '0.739', 'shrieked', 'indignantly', 'plaintively', 'mockingly', 'imperiously']
senticnet['shrimp_cocktail'] = ['0.062', '0', '0.027', '0.055', '#joy', '#admiration', 'positive', '0.03', 'porridge', 'meal', 'broth', 'hamburger', 'hearty']
senticnet['shrimp_salad'] = ['0', '0', '0.066', '-0.07', '#anger', '#disgust', 'negative', '-0.07', 'container_cup', 'cup', 'grease_trap', 'dish', 'open_container']
senticnet['shrimp_scampi'] = ['0.043', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'apple_pie', 'a_lot_of_food', 'jelly', 'frozen_food', 'cottage_cheese']
senticnet['shrimp_trawler'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'succourer', 'recoverer', 'salvor', 'mortal']
senticnet['shrimper'] = ['0.073', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'angler', 'informal_rod', 'archaic_fisher', 'rare_piscator', 'fishing_line']
senticnet['shrine'] = ['0.025', '0.251', '0.013', '0.165', '#interest', '#admiration', 'positive', '0.107', 'capital_build', 'monastery', 'sanctuary', 'temple', 'cathedral']
senticnet['shrink'] = ['-0.99', '0', '0.327', '-0.91', '#sadness', '#disgust', 'negative', '-0.74', 'shrinkage', 'clothes_shrinkage', 'become_small', 'remove_stain', 'fight_fire']
senticnet['shrinkage'] = ['-0.99', '0', '0.327', '-0.91', '#sadness', '#disgust', 'negative', '-0.74', 'shrink', 'clothes_shrink', 'become_small', 'remove_stain', 'fight_fire']
senticnet['shrivel'] = ['-0.88', '0', '0.682', '-0.60', '#sadness', '#anger', 'negative', '-0.72', 'absent', 'fall_away', 'evanesce', 'blow_over', 'pass_off']
senticnet['shrivel_up'] = ['-0.34', '0.092', '-0.36', '0.079', '#sadness', '#fear', 'negative', '-0.13', 'garden_plant', 'lifeless', 'undesirable', 'kill_self', 'need_water']
senticnet['shriveled'] = ['0', '0', '-0.75', '-0.98', '#fear', '#disgust', 'negative', '-0.86', 'sear', 'withered', 'dried-up', 'shrivelled', 'sere']
senticnet['shrivelled'] = ['0.142', '0', '0', '0.052', '#joy', '#admiration', 'positive', '0.097', 'dry_off_towel', 'lock_front_door', 'dry_towel', 'protect_body', 'put_clothes']
senticnet['shrovetide'] = ['0.957', '0.7', '0', '0', '#joy', '#interest', 'positive', '0.829', 'ballpark', 'coven', 'disneyland', 'hockey_game', 'symposium']
senticnet['shrub'] = ['0.929', '-0.77', '0', '0.94', '#joy', '#admiration', 'positive', '0.882', 'bush', 'scarlet_bush', 'scarlet_hamelia', 'hamelia', 'coloradillo']
senticnet['shrub_frog'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'amphibian', 'aquatic']
senticnet['shrub_growing_up'] = ['-0.80', '0', '0.555', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'bush', 'wormwood', 'milkwort', 'germander', 'centaury']
senticnet['shrub_native'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'carnauba_palm', 'carnauba', 'wax_palm', 'palmetto', 'palmyra']
senticnet['shrub_tundra'] = ['-0.04', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.04', 'bad_luck', 'damn', 'artic', 'mammal_family', 'bad_mood']
senticnet['shrubbery'] = ['-0.90', '0', '-0.73', '0', '#sadness', '#fear', 'negative', '-0.81', 'unkempt', 'overgrown', 'weedy', 'undergrowth', 'weed']
senticnet['shrubby_cinquefoil'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'cattail', 'nailrod', 'reed_mace', 'reedmace']
senticnet['shrug'] = ['0', '0', '-0.80', '-0.97', '#fear', '#disgust', 'negative', '-0.89', 'lunge', 'wiggle', 'push', 'progress', 'sway']
senticnet['shrug_off'] = ['0.568', '-0.99', '0', '0.638', '#surprise', '#admiration', 'positive', '0.734', 'push_aside', 'brush_off', 'disregard', 'ignore', 'dismiss']
senticnet['shrugged_off'] = ['0.568', '-0.99', '0', '0.638', '#surprise', '#admiration', 'positive', '0.734', 'push_aside', 'brush_off', 'disregard', 'ignore', 'dismiss']
senticnet['shrugging_off'] = ['0.568', '-0.99', '0', '0.638', '#surprise', '#admiration', 'positive', '0.734', 'push_aside', 'brush_off', 'disregard', 'ignore', 'dismiss']
senticnet['shrunken'] = ['-0.82', '0', '0.758', '-0.70', '#sadness', '#anger', 'negative', '-0.76', 'unclassifiable', 'unidentifiable', 'unrecognisable', 'intangible', 'undiagnosable']
senticnet['shtick'] = ['0.021', '0.179', '-0.13', '0.164', '#interest', '#admiration', 'positive', '0.058', 'nod_head', 'point_finger', 'upgrade', 'ask', 'put_bid']
senticnet['shtik'] = ['0.924', '-0.85', '0', '0.828', '#joy', '#surprise', 'positive', '0.869', 'folly', 'lunacy', 'craziness', 'indulgence', 'buffoonery']
senticnet['shtriga'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'jawless_fish', 'osteostracan', 'cephalaspid', 'ostracoderm', 'heterostracan']
senticnet['shuafat'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['shudder'] = ['0', '0', '-0.91', '0', '#fear', '#fear', 'negative', '-0.91', 'frisson', 'tingle', 'fond_regard', 'emotive', 'affective']
senticnet['shuddery'] = ['-0.80', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.79', 'chilling', 'shivery', 'apprehension', 'apprehensiveness', 'scare_off']
senticnet['shuffle'] = ['-0.03', '0.11', '0', '0.128', '#interest', '#admiration', 'positive', '0.069', 'freestyle', 'take_leak', 'jump_air', 'backstroke', 'go_underwater']
senticnet['shuffle_card'] = ['0.952', '0.957', '0', '0', '#joy', '#interest', 'positive', '0.954', 'win', 'play_game', 'play_card', 'shuffle_card_machine', 'start_card_game']
senticnet['shuffle_card_machine'] = ['0.99', '0.915', '0', '0', '#joy', '#interest', 'positive', '0.953', 'win', 'play_game', 'play_card', 'shuffle_card', 'start_card_game']
senticnet['shuffled_deck'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'jewelry_store', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['shuffling_gait'] = ['0.983', '-0.63', '0', '0.697', '#joy', '#admiration', 'positive', '0.772', 'amateurishness', 'inability', 'ineptness', 'unskillfulness', 'awkwardness']
senticnet['shug_mcgaughey'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'coach', 'instructor', 'personal_trainer', 'fit_person', 'train']
senticnet['shugo'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'overlord', 'lord']
senticnet['shukla_paksha'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['shul'] = ['0', '0.209', '0.026', '0.184', '#interest', '#admiration', 'positive', '0.122', 'church', 'confession', 'temple', 'cathedral', 'monastery']
senticnet['shulde'] = ['-0.98', '0', '0.654', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'deprive', 'expropriate', 'disarm', 'unclothe', 'take']
senticnet['shulgin'] = ['-0.73', '0', '0.944', '-0.94', '#anger', '#disgust', 'negative', '-0.87', 'lysergic_acid_diethylamide', 'mescaline', 'peyote', 'phencyclidine', 'drug']
senticnet['shun'] = ['0', '0', '-0.96', '-0.71', '#fear', '#disgust', 'negative', '-0.83', 'abjure', 'screen', 'shunning', 'persistently', 'sky']
senticnet['shunning'] = ['0.602', '-0.90', '0', '0.761', '#surprise', '#admiration', 'positive', '0.757', 'rejection', 'escape', 'abscond', 'aversion', 'averting']
senticnet['shunter'] = ['0.069', '0.097', '0', '0.172', '#interest', '#admiration', 'positive', '0.113', 'type_of_engine', 'liquid_gas', 'warm_air', 'hot_air', 'steamy']
senticnet['shunting_locomotive'] = ['0.069', '0.097', '0', '0.172', '#interest', '#admiration', 'positive', '0.113', 'type_of_engine', 'liquid_gas', 'warm_air', 'hot_air', 'steamy']
senticnet['shuowen_jiezi'] = ['-0.86', '0', '0.619', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'character', 'logogram', 'logograph', 'graph', 'grapheme']
senticnet['shuriken'] = ['0.757', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.831', 'baseball_bat', 'water_gun', 'goalie', 'hockey_puck', 'hockey_stick']
senticnet['shut'] = ['-0.93', '0', '-0.99', '-0.92', '#sadness', '#fear', 'negative', '-0.95', 'shut_down', 'close_down', 'barrier', 'language_barrier', 'obstacle']
senticnet['shut-in'] = ['-0.71', '0', '0', '-0.96', '#sadness', '#disgust', 'negative', '-0.84', 'shut-in', 'house-bound', 'home-bound', 'housebound', 'wellhead']
senticnet['shut_door'] = ['-0.03', '0.13', '-0.05', '0.126', '#interest', '#admiration', 'positive', '0.043', 'close_door', 'climb_to_top', 'lean_against', 'rush_job', 'see_sky']
senticnet['shut_down'] = ['-0.12', '0', '-0.10', '0', '#sadness', '#fear', 'negative', '-0.11', 'collide', 'amount_nothing', 'fall_off_chair', 'fly_around', 'cross_bridge']
senticnet['shut_eye'] = ['0.056', '0.083', '0', '0.051', '#joy', '#interest', 'positive', '0.063', 'rest_night', 'lay_down', 'count_sheep', 'lie_down', 'comfortable_place_sleep']
senticnet['shut_in'] = ['-0.99', '0', '0.327', '-0.91', '#sadness', '#disgust', 'negative', '-0.74', 'close', 'shut', 'hide', 'confine', 'enclose']
senticnet['shut_off'] = ['-0.08', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.09', 'mediocrity', 'effect', 'suspicion', 'exclusion', 'pugilist']
senticnet['shut_off_alarm_clock'] = ['-0.05', '0', '0.027', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'fall_out_bed', 'take_nap', 'yawn', 'asleep', 'feel_little_tired']
senticnet['shut_up'] = ['-0.04', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.05', 'stop_talk', 'pay_attention', 'dry_mouth', 'hear_sound', 'able_hear']
senticnet['shutdown'] = ['0.9', '0.785', '0', '0', '#joy', '#interest', 'positive', '0.843', 'stand-by', 'mode', 'standby', 'switching', 'back-up']
senticnet['shutoff'] = ['0.875', '-0.78', '0', '0', '#joy', '#surprise', 'positive', '0.83', 'failsafe', 'foolproof', 'fail-over', 'fool-proof', 'fail-safe']
senticnet['shutoff_valve'] = ['0', '0', '0.865', '-0.95', '#anger', '#disgust', 'negative', '-0.90', 'dirty_dish_overflow', 'hot_dirty_dish', 'hot_tap', 'dirty_dish', 'washcloth']
senticnet['shutout'] = ['0', '0', '0.78', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'scoreless', 'inning', 'hitless', 'strikeout', 'one-run']
senticnet['shutter'] = ['-0.82', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.84', 'block', 'obstruct', 'close', 'clogged', 'occluded']
senticnet['shutter_button'] = ['-0.82', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.84', 'block', 'obstruct', 'close', 'clogged', 'occluded']
senticnet['shutter_mechanism'] = ['-0.82', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.84', 'block', 'obstruct', 'close', 'clogged', 'occluded']
senticnet['shutter_release'] = ['-0.82', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.84', 'block', 'obstruct', 'close', 'clogged', 'occluded']
senticnet['shutter_release_button'] = ['-0.82', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.84', 'block', 'obstruct', 'close', 'clogged', 'occluded']
senticnet['shutter_speed_dial'] = ['-0.82', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.84', 'block', 'obstruct', 'close', 'clogged', 'occluded']
senticnet['shutter_telegraph'] = ['0.096', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.057', 'platform_rail', 'rail', 'catwalk', 'column', 'token_turnstile']
senticnet['shuttle'] = ['0.902', '-1', '0', '0', '#joy', '#surprise', 'positive', '0.951', 'displace', 'swing', 'snowshoe', 'dodge', 'bang']
senticnet['shuttle_airlock'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'chick', 'wild_bird', 'baby_bird', 'lay_egg', 'bird_home']
senticnet['shuttle_bus_service'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'seeker', 'chairperson']
senticnet['shuttle_diplomacy'] = ['0', '0.098', '0', '0.08', '#interest', '#admiration', 'positive', '0.089', 'talk_person', 'wisdom', 'tact', 'statecraft', 'greet_person']
senticnet['shuttle_external_tank'] = ['0.899', '0.803', '0', '0', '#joy', '#interest', 'positive', '0.851', 'ufo', 'milky', 'martian', 'galaxy', 'solar_system']
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senticnet['shuttle_van'] = ['-0.08', '0.179', '-0.06', '0.096', '#interest', '#admiration', 'positive', '0.034', 'ride_subway', 'buy_vending_machine', 'travel', 'buy_lottery_ticket', 'acquire']
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senticnet['shy_away_from'] = ['-0.81', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.80', 'overstate', 'hyperbolize', 'exaggerate', 'overdraw', 'magnify']
senticnet['shy_drager_syndrome'] = ['-0.75', '0', '0.515', '-0.93', '#sadness', '#disgust', 'negative', '-0.73', 'sclerosis', 'induration', 'disseminated_sclerosis', 'autoimmune_disease', 'autoimmune_disorder']
senticnet['shy_smile'] = ['0.895', '0.712', '0', '0', '#joy', '#interest', 'positive', '0.803', 'timorousness', 'timidness', 'timidity', 'shyly', 'timidly']
senticnet['shyest'] = ['0.947', '-0.99', '0', '0.936', '#joy', '#surprise', 'positive', '0.958', 'utterer', 'deipnosophist', 'interlocutor', 'conversational_partner', 'speaker']
senticnet['shyly'] = ['-0.81', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.82', 'bashfully', 'timorousness', 'timidness', 'timidity', 'timidly']
senticnet['shyness'] = ['-0.73', '0', '0', '-0.95', '#sadness', '#disgust', 'negative', '-0.84', 'diffident', 'self-doubt', 'diffidently', 'diffidence', 'self-distrust']
senticnet['shyster'] = ['0', '0', '-0.75', '-0.78', '#fear', '#disgust', 'negative', '-0.76', 'contemptuous', 'immoral', 'unscrupulously', 'whore', 'unscrupulous']
senticnet['si_cl'] = ['0.228', '0.123', '-0.09', '0', '#joy', '#interest', 'positive', '0.084', 'spend_time_with_child', 'social', 'friendship', 'companionship', 'get_close']
senticnet['si_prefix'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'newton', 'sthene', 'force_unit']
senticnet['si_quelqu_un'] = ['0', '0.919', '0', '0.888', '#interest', '#admiration', 'positive', '0.903', 'surprise', 'eye-popping', 'fulgurous', 'dazzling', 'bedaze']
senticnet['si_substrate'] = ['-0.93', '0', '0.739', '-0.96', '#sadness', '#disgust', 'negative', '-0.88', 'prevarication', 'fairytale', 'fairy_tale', 'fairy_story', 'play']
senticnet['si_tu_doi_partir'] = ['-0.73', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'unwed', 'unwedded', 'divorced', 'unmarried', 'widowed']
senticnet['si_unit'] = ['-0.63', '0', '0.897', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'cd-rom', 'international_candle', 'candle', 'candlepower_unit', 'standard_candle']
senticnet['siadh'] = ['-0.77', '0', '0.966', '-0.51', '#sadness', '#anger', 'negative', '-0.75', 'enuresis', 'incontinency', 'incontinence', 'stress_incontinence', 'urge_incontinence']
senticnet['siamese_cat'] = ['0', '0', '0.022', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'kind_animal', 'wild_animal', 'predator', 'hound', 'fang']
senticnet['siberian_ibex'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheep', 'argal', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['siberian_mammoth'] = ['0.969', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.96', 'extinct', 'animal', 'ice', 'elephant', 'giant']
senticnet['siberian_permafrost'] = ['0.969', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.96', 'extinct', 'animal', 'ice', 'elephant', 'giant']
senticnet['siberian_tiger'] = ['0.02', '0.071', '0', '0.069', '#interest', '#admiration', 'positive', '0.053', 'many_animals', 'donkey', 'chimpanzee', 'cute_animal', 'creature']
senticnet['siberian_tundra'] = ['-0.04', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.04', 'bad_luck', 'damn', 'artic', 'mammal_family', 'bad_mood']
senticnet['sibilant'] = ['-0.56', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.63', 'aphasic', 'inarticulate', 'speechless', 'unarticulated', 'dumb']
senticnet['sibilate'] = ['0.73', '-0.86', '0', '0.609', '#joy', '#surprise', 'positive', '0.735', 'pronounce', 'enounce', 'say', 'sound_out', 'enunciate']
senticnet['sibilation'] = ['0.523', '-0.75', '0', '0.925', '#surprise', '#admiration', 'positive', '0.733', 'accent', 'assibilation', 'homophony', 'mispronunciation', 'pronunciation']
senticnet['sibling'] = ['0.933', '0.752', '0', '0', '#joy', '#interest', 'positive', '0.843', 'relative', 'familial_relationship', 'family_member', 'mother_brother', 'female_relative']
senticnet['sibling_relationship'] = ['0.903', '0.684', '0', '0.791', '#joy', '#admiration', 'positive', '0.793', 'relative', 'family_member', 'relationship', 'family', 'mother']
senticnet['sibm'] = ['-0.87', '0', '0.531', '-0.68', '#sadness', '#disgust', 'negative', '-0.69', 'rubor', 'redness', 'dermatomyositis', 'myositis', 'inflammation']
senticnet['sibutramine_meridia'] = ['-0.72', '0', '0.973', '-0.57', '#sadness', '#anger', 'negative', '-0.76', 'corpulency', 'blubber', 'fat', 'embonpoint', 'stoutness']
senticnet['sibuyan_sea'] = ['0.084', '-0.05', '0.013', '0', '#joy', '#surprise', 'positive', '0.043', 'carnival_ride', 'carny', 'fortune_teller', 'horseback', 'ferris_wheel']
senticnet['sibyl'] = ['0.677', '-0.51', '0', '0.827', '#joy', '#admiration', 'positive', '0.674', 'diviner', 'oracle', 'auspex', 'prophetess', 'augur']
senticnet['sicangu_lakota'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['sick'] = ['-0.09', '0.077', '0.458', '-0.62', '#anger', '#disgust', 'negative', '-0.27', 'nausea', 'malice', 'mad', 'resentment', 'disgust']
senticnet['sick_joke'] = ['-0.79', '0', '0.732', '-0.69', '#sadness', '#anger', 'negative', '-0.74', 'gag', 'jest', 'jape', 'joke', 'laugh']
senticnet['sick_of'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'disgustful']
senticnet['sick_person'] = ['0', '0', '-0.13', '-0.17', '#fear', '#disgust', 'negative', '-0.15', 'nurse', 'doctor', 'illness', 'elderly_person', 'cure']
senticnet['sick_sinus_syndrome'] = ['-0.83', '0', '0.987', '-0.50', '#sadness', '#anger', 'negative', '-0.77', 'heart_block', 'atrioventricular_block', 'bradycardia', 'ventricular_fibrillation', 'tachycardia']
senticnet['sicken'] = ['-0.07', '0.137', '0.693', '-0.67', '#anger', '#disgust', 'negative', '-0.32', 'nauseate', 'repel', 'gross_out', 'loathsome', 'churn_up']
senticnet['sickening'] = ['-0.06', '0.128', '0.707', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'sickish', 'turn_stomach', 'vile', 'noisome', 'nauseous']
senticnet['sickening_feeling'] = ['-0.80', '0', '-0.8', '0', '#sadness', '#fear', 'negative', '-0.80', 'sinking', 'misgiving', 'qualm', 'scruple', 'insecurely']
senticnet['sickeningly'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['sickish'] = ['-0.06', '0.128', '0.707', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'turn_stomach', 'vile', 'sickening', 'noisome', 'nauseous']
senticnet['sicklepod'] = ['0.589', '-0.72', '0', '0.937', '#surprise', '#admiration', 'positive', '0.749', 'cress', 'tower_rock_cress', 'tower_mustard', 'rock_cress', 'rock_cress_plant']
senticnet['sickly'] = ['-0.96', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.97', 'ailing', 'qualm', 'frequently', 'sickness', 'wan']
senticnet['sickly_child'] = ['-0.13', '0', '0.013', '-0.08', '#sadness', '#disgust', 'negative', '-0.07', 'blind', 'dig_tunnel', 'underground_animal', 'big_mouse', 'small_rodent']
senticnet['sickly_green'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dirtiness', 'dinge']
senticnet['sickly_sweet'] = ['-0.78', '0', '0.542', '-0.94', '#sadness', '#disgust', 'negative', '-0.75', 'taste', 'taste_sensation', 'gustatory_sensation', 'taste_perception', 'gustatory_perception']
senticnet['sickness'] = ['-0.90', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.92', 'illness', 'polio', 'malady', 'disease', 'ailment']
senticnet['sickout'] = ['-0.77', '0', '0.505', '-0.50', '#sadness', '#anger', 'negative', '-0.59', 'wildcat_strike', 'sympathy_strike', 'sympathetic_strike', 'walkout', 'job_action']
senticnet['sicut'] = ['0.654', '-0.75', '0', '0.78', '#surprise', '#admiration', 'positive', '0.729', 'suit', 'cause', 'lawsuit', 'occurrence', 'countersuit']
senticnet['sicyonian'] = ['0.8', '-0.79', '0', '0.751', '#joy', '#surprise', 'positive', '0.782', 'elder_statesman', 'stateswoman', 'politician', 'politico', 'pol']
senticnet['siddle'] = ['-0.13', '0', '-0.11', '-0.04', '#sadness', '#fear', 'negative', '-0.1', 'concern_health', 'medical_problem', 'strengthful', 'surgery', 'injury']
senticnet['side'] = ['0.041', '0.09', '0.01', '0.124', '#interest', '#admiration', 'positive', '0.061', 'corner', 'sharpness', 'edge', 'right_angle', 'facet']
senticnet['side-splitting'] = ['0.762', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.87', 'sidesplitting', 'uproarious', 'gut-busting', 'rib-tickling', 'ribald']
senticnet['side_arm'] = ['0.978', '-0.57', '0', '0.793', '#joy', '#admiration', 'positive', '0.782', 'pistol', 'handgun', 'semiautomatic', 'shooting_iron', 'semiautomatic_firearm']
senticnet['side_build'] = ['0', '-0.08', '-0.05', '0.099', '#surprise', '#admiration', 'positive', '0.043', 'phone_book', 'classify_ad', 'city_center', 'letter_alphabet', 'ads']
senticnet['side_by_side'] = ['0', '0', '-0.91', '-0.96', '#fear', '#disgust', 'negative', '-0.94', 'next', 'succeeding', 'following', 'adjacent', 'future']
senticnet['side_chair'] = ['0', '-0.04', '0.068', '-0.06', '#anger', '#disgust', 'negative', '-0.02', 'box_tissue', 'stapler', 'tape_dispenser', 'box', 'ergonomic_chair']
senticnet['side_dish'] = ['0.055', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'chocolate_pudding', 'green_bean', 'sweetness', 'whip_cream', 'sushi']
senticnet['side_effect'] = ['-0.96', '0', '0.501', '-0.92', '#sadness', '#disgust', 'negative', '-0.79', 'byproduct', 'wallop', 'effect', 'dent', 'fallout']
senticnet['side_face'] = ['0', '0.089', '0.014', '0.134', '#interest', '#admiration', 'positive', '0.07', 'face_part', 'hold_glass', 'facial_feature', 'facial_part', 'near_mouth']
senticnet['side_house'] = ['-0.05', '0.081', '0', '0.06', '#interest', '#admiration', 'positive', '0.028', 'leave_table', 'stock_food', 'reciprocate', 'shut_door', 'cross_room']
senticnet['side_oat_grama'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'cattail', 'nailrod', 'reed_mace', 'reedmace']
senticnet['side_of_bacon'] = ['0.648', '-0.53', '0', '0.94', '#joy', '#admiration', 'positive', '0.709', 'bacon_strip', 'flitch', 'bacon', 'gammon', 'cut_of_pork']
senticnet['side_road'] = ['0', '0', '0', '-0.06', '#disgust', '#disgust', 'negative', '-0.06', 'roadside', 'sidewalk', 'interstate', 'junk_yard', 'street']
senticnet['sidebar'] = ['-0.09', '-0.04', '0.039', '-0.24', '#sadness', '#disgust', 'negative', '-0.08', 'learn_new_idea', 'learn_quickly', 'point_out', 'look_picture', 'jot_down_note']
senticnet['sideboard'] = ['0.045', '-0.08', '0.02', '0', '#joy', '#surprise', 'positive', '0.035', 'table', 'glass_front_cupboard', 'gate_leg_table', 'dine_table', 'dine_room_table']
senticnet['sided_dice'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'jewelry_store', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['sided_die'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'jewelry_store', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['sidekick'] = ['0', '0.782', '0', '0.775', '#interest', '#admiration', 'positive', '0.778', 'bring_home', 'bring_back_home', 'take_care', 'help', 'take_home']
senticnet['sideline'] = ['-0.72', '0', '0.918', '-0.54', '#sadness', '#anger', 'negative', '-0.73', 'designate', 'depute', 'reduce', 'demote', 'delegate']
senticnet['sidelined'] = ['-0.74', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.86', 'quadriceps', 'groin', 'sprained', 'hamstring', 'knee']
senticnet['sidelong'] = ['-0.71', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.75', 'incredulous', 'rueful', 'bemused', 'puzzled', 'quizzical']
senticnet['sidelong_look'] = ['-0.80', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'difficulty', 'awkwardness', 'flea_bite', 'difficultness', 'cumbersomeness']
senticnet['siderite'] = ['-0.03', '0', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.06', 'lithium', 'mineral', 'potassium', 'iodine', 'magnesium']
senticnet['sideroxylon'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['sideshow_performer'] = ['0', '0', '0', '0.987', '#admiration', '#admiration', 'positive', '0.987', 'emote', 'bold', 'form_of_social_interaction', 'act', 'stage_play']
senticnet['sidespin'] = ['0.038', '0', '0.031', '0.124', '#joy', '#admiration', 'positive', '0.044', 'rotate', 'rotate_movement', 'change_direction', 'move', 'go_around']
senticnet['sidesplitter'] = ['-0.92', '0', '0.631', '-0.51', '#sadness', '#anger', 'negative', '-0.68', 'gag', 'jest', 'jape', 'joke', 'laugh']
senticnet['sidesplitting'] = ['0.77', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.871', 'side-splitting', 'uproarious', 'gut-busting', 'rib-tickling', 'ribald']
senticnet['sidestep'] = ['0.568', '-0.99', '0', '0.638', '#surprise', '#admiration', 'positive', '0.734', 'push_aside', 'brush_off', 'disregard', 'ignore', 'dismiss']
senticnet['sidetrack'] = ['0', '0', '-0.92', '-0.83', '#fear', '#disgust', 'negative', '-0.88', 'digress', 'depart', 'wander', 'divagate', 'stray']
senticnet['sidewalk'] = ['0', '0', '0.033', '-0.05', '#anger', '#disgust', 'negative', '-0.04', 'freeway', 'roadside', 'avenue', 'pavement', 'boulevard']
senticnet['sidewalk_cafe'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'buy_sandwich', 'lunch', 'eat_food', 'buy_food', 'finish_job']
senticnet['sidewall'] = ['-0.04', '-0.01', '0.026', '-0.12', '#sadness', '#disgust', 'negative', '-0.04', 'embroidery', 'cover_wind', 'use_needle', 'bandage', 'joint']
senticnet['sideward'] = ['0.035', '0.115', '0', '0.209', '#interest', '#admiration', 'positive', '0.12', 'dance_move', 'anagram', 'pace', 'tread', 'foot_action']
senticnet['sideways'] = ['0', '0.103', '0', '0.08', '#interest', '#admiration', 'positive', '0.091', 'tree_wood', 'forest', 'dead_tree', 'many_steps', 'wood']
senticnet['sideways_glance'] = ['0.021', '0.179', '-0.13', '0.164', '#interest', '#admiration', 'positive', '0.058', 'nod_head', 'point_finger', 'upgrade', 'ask', 'put_bid']
senticnet['sidewinder'] = ['-0.94', '0', '0.866', '-0.91', '#sadness', '#disgust', 'negative', '-0.90', 'movement', 'apparent_motion', 'motion', 'apparent_movement', 'optical_illusion']
senticnet['sidewise'] = ['0.035', '0.115', '0', '0.209', '#interest', '#admiration', 'positive', '0.12', 'dance_move', 'anagram', 'pace', 'tread', 'foot_action']
senticnet['sidhe'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'jawless_fish', 'osteostracan', 'cephalaspid', 'ostracoderm', 'heterostracan']
senticnet['sidle_up'] = ['0.985', '-0.75', '0', '0.894', '#joy', '#admiration', 'positive', '0.879', 'cozy_up', 'cotton_up', 'shine_up', 'ingratiate', 'play_up']
senticnet['sidra_incident'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'harpoon', 'armory', 'lake', 'river', 'bay']
senticnet['sieberi'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['sieg_heil'] = ['-0.69', '0', '0.792', '-0.98', '#anger', '#disgust', 'negative', '-0.82', 'outcry', 'call', 'cry', 'yell', 'shout']
senticnet['siege'] = ['-0.07', '0', '-0.10', '-0.18', '#fear', '#disgust', 'negative', '-0.12', 'battle', 'violence', 'conflict', 'fight', 'big_fight']
senticnet['siege_artillery'] = ['-0.85', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.84', 'atomic_bomb', 'weapon_war', 'gun', 'pistol', 'hydrogen_bomb']
senticnet['siege_mentality'] = ['-0.83', '0', '0.904', '0', '#sadness', '#anger', 'negative', '-0.87', 'disappointed', 'thwarted', 'frustrated', 'foiled', 'discomfited']
senticnet['siegfried_line'] = ['0.737', '-0.96', '0', '0.552', '#joy', '#surprise', 'positive', '0.75', 'prominence', 'hump', 'bump', 'swelling', 'frontal_eminence']
senticnet['siemens_ag_quote'] = ['0', '0.189', '0.04', '0.091', '#interest', '#admiration', 'positive', '0.08', 'combination', 'margin', 'teller', 'merge', 'finite']
senticnet['siemens_quote'] = ['0', '0.189', '0.04', '0.091', '#interest', '#admiration', 'positive', '0.08', 'combination', 'margin', 'teller', 'merge', 'finite']
senticnet['siemens_westinghouse_science'] = ['0', '0', '0', '0.879', '#admiration', '#admiration', 'positive', '0.879', 'sport_competition', 'win_competition', 'race', 'match', 'championship']
senticnet['siena_blaze'] = ['-0.74', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.80', 'casualty', 'extermination', 'cause_death', 'abduction', 'die']
senticnet['siesta'] = ['-0.77', '0', '0.855', '-0.57', '#sadness', '#anger', 'negative', '-0.73', 'unhurried', 'easygoing', 'deliberate', 'easy', 'careful']
senticnet['sieve'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'water_inside', 'water_spray', 'teapot', 'bodily_waste', 'different_type']
senticnet['sift'] = ['0', '0', '-0.74', '-0.80', '#fear', '#disgust', 'negative', '-0.77', 'sifted', 'sifting', 'flour', 'whisk', 'scrape']
senticnet['sifted'] = ['0', '0', '-0.74', '-0.81', '#fear', '#disgust', 'negative', '-0.78', 'sift', 'sifting', 'flour', 'whisk', 'scrape']
senticnet['sifting'] = ['0', '0', '-0.75', '-0.81', '#fear', '#disgust', 'negative', '-0.78', 'sifted', 'sift', 'flour', 'whisk', 'scrape']
senticnet['sigh'] = ['-0.19', '-0.06', '-0.21', '0', '#sadness', '#fear', 'negative', '-0.11', 'breath', 'smell_odor', 'exhale', 'drool', 'say_excuse']
senticnet['sigh_relief'] = ['0.932', '0', '0', '0', '#joy', '#joy', 'positive', '0.932', 'relieve_tension', 'relax', 'give_thank', 'invigorate', 'feel_relaxed']
senticnet['sighed_deeply'] = ['-0.19', '-0.06', '-0.21', '0', '#sadness', '#fear', 'negative', '-0.11', 'breath', 'smell_odor', 'exhale', 'drool', 'say_excuse']
senticnet['sighed_heavily'] = ['-0.75', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.83', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['sight'] = ['-0.03', '0.081', '0', '0.091', '#interest', '#admiration', 'positive', '0.045', 'vision', 'eye_function', 'eye_sense', 'see_ability', 'aim']
senticnet['sight_gag'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'fragmentation', 'discharge', 'backfire', 'blast']
senticnet['sighted_person'] = ['-0.22', '-0.03', '0.026', '-0.13', '#sadness', '#disgust', 'negative', '-0.08', 'blind_man', 'blind_person', 'shade', 'need_help', 'shadow']
senticnet['sighting_device'] = ['0.865', '0.84', '0', '0', '#joy', '#interest', 'positive', '0.853', 'travel', 'find_way', 'help_travelling', 'compass_direction', 'use_compass']
senticnet['sighting_tube'] = ['0.865', '0.84', '0', '0', '#joy', '#interest', 'positive', '0.853', 'travel', 'find_way', 'help_travelling', 'compass_direction', 'use_compass']
senticnet['sightsee'] = ['0.932', '0', '0', '0', '#joy', '#joy', 'positive', '0.932', 'travel', 'see_new_place', 'see_sight', 'see_countryside', 'proceed']
senticnet['sightseeing_trip'] = ['0', '0', '0.828', '-0.88', '#anger', '#disgust', 'negative', '-0.85', 'outing', 'junket', 'pleasure_trip', 'excursion', 'expedition']
senticnet['sightseer'] = ['-0.65', '0', '0.943', '-0.75', '#anger', '#disgust', 'negative', '-0.78', 'rubberneck', 'holidaymaker', 'excursionist', 'tripper', 'traveler']
senticnet['sigillum'] = ['-0.94', '0', '0.569', '-0.72', '#sadness', '#disgust', 'negative', '-0.74', 'bitumastic', 'veneer', 'veneering', 'adhesive', 'coating']
senticnet['sigma_bond'] = ['0.228', '0.123', '-0.09', '0', '#joy', '#interest', 'positive', '0.084', 'spend_time_with_child', 'social', 'friendship', 'companionship', 'get_close']
senticnet['sigma_xi'] = ['0.856', '0', '0', '0.963', '#joy', '#admiration', 'positive', '0.909', 'progress', 'get_better_job', 'further_career', 'graduate_school', 'get_diploma']
senticnet['sigmatropic_rearrangement'] = ['0.753', '-0.64', '0', '0.606', '#joy', '#surprise', 'positive', '0.667', 'enthusiast', 'appreciator', 'lover', 'person', 'individual']
senticnet['sigmoidoscope'] = ['0.255', '0.182', '-0.06', '0.314', '#joy', '#admiration', 'positive', '0.173', 'doctor_office', 'urethroscope', 'panendoscope', 'hospital', 'check_health']
senticnet['sign'] = ['0.037', '0.073', '0', '0.098', '#interest', '#admiration', 'positive', '0.069', 'placard', 'indication', 'road_sign', 'post_notice', 'street_sign']
senticnet['sign_agreement'] = ['0.765', '0.794', '0', '0', '#joy', '#interest', 'positive', '0.78', 'price_goods', 'plan_retirement', 'mind_store', 'air_grievance', 'explain_everything']
senticnet['sign_autograph'] = ['0.936', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.889', 'coincidence', 'math_homework', 'character_play', 'role', 'write']
senticnet['sign_contract'] = ['-0.03', '0', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.05', 'write', 'write_poem', 'medical_examination', 'medical_exam', 'make_shop_list']
senticnet['sign_document'] = ['0.037', '0.073', '0', '0.098', '#interest', '#admiration', 'positive', '0.069', 'sign_autograph', 'sign', 'document', 'sign_signature', 'sign_up_class']
senticnet['sign_language'] = ['-0.94', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.94', 'drive_recklessly', 'discord', 'mute', 'deny', 'helpless']
senticnet['sign_language_asl'] = ['-0.94', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.94', 'drive_recklessly', 'discord', 'mute', 'deny', 'helpless']
senticnet['sign_love'] = ['0.935', '0.928', '0', '0', '#joy', '#interest', 'positive', '0.931', 'flower', 'yellow_flower', 'flower_bloom', 'poisonous_plant', 'poppy']
senticnet['sign_paper'] = ['-0.16', '-0.01', '-0.17', '0.062', '#sadness', '#fear', 'negative', '-0.06', 'heart_attack', 'criminal_activity', 'understand_fact', 'freedom', 'sign_document']
senticnet['sign_sickness'] = ['-0.96', '0', '0.848', '-0.82', '#sadness', '#anger', 'negative', '-0.87', 'sneeze', 'allergy', 'runny_nose', 'flu', 'common_cold']
senticnet['sign_signature'] = ['-0.05', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.05', 'think_need', 'flash', 'bulletin', 'identity', 'keep_file']
senticnet['sign_up'] = ['0', '0.093', '0', '0.07', '#interest', '#admiration', 'positive', '0.082', 'listen_advice', 'become_better_person', 'build_strength', 'new_experience', 'dress_success']
senticnet['sign_up_class'] = ['0', '0.074', '0.051', '0.105', '#interest', '#admiration', 'positive', '0.043', 'buy_textbook', 'read_textbook', 'read_book_subject', 'borrow_book', 'use_search_engine']
senticnet['signal'] = ['0.053', '0.071', '-0.05', '0.124', '#interest', '#admiration', 'positive', '0.049', 'warn', 'communicate', 'dictation', 'beckon', 'indication']
senticnet['signal_amplitude'] = ['-0.93', '0', '0.698', '-0.85', '#sadness', '#disgust', 'negative', '-0.83', 'candlepower', 'light_intensity', 'acoustic_power', 'sound_pressure_level', 'threshold_level']
senticnet['signal_attenuation'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'short_circuit', 'error', 'mistake', 'undesired', 'trouble']
senticnet['signal_degradation'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'short_circuit', 'error', 'mistake', 'undesired', 'trouble']
senticnet['signal_detection'] = ['0.86', '-0.82', '0', '0', '#joy', '#surprise', 'positive', '0.843', 'espial', 'sleuthing', 'spying', 'detection', 'catching']
senticnet['signal_distortion'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'short_circuit', 'error', 'mistake', 'undesired', 'trouble']
senticnet['signal_handler'] = ['-0.98', '0', '0.869', '-0.80', '#sadness', '#anger', 'negative', '-0.88', 'error', 'programming_error', 'semantic_error', 'runtime_error', 'computer_error']
senticnet['signal_integrity'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'short_circuit', 'error', 'mistake', 'undesired', 'trouble']
senticnet['signal_intensity'] = ['0', '0', '-0.66', '0', '#fear', '#fear', 'negative', '-0.66', 'anger', 'rage', 'annoyance', 'apprehension', 'fear']
senticnet['signaling_cascade'] = ['0.085', '0', '0', '0.089', '#joy', '#admiration', 'positive', '0.087', 'steam', 'haze', 'mist', 'misty', 'summer_weather']
senticnet['signalize'] = ['0.91', '-0.71', '0', '0', '#joy', '#surprise', 'positive', '0.814', 'high', 'eminently', 'eminent', 'eminence', 'prominence']
senticnet['signalized_intersection'] = ['0.032', '0.116', '0.016', '0', '#joy', '#interest', 'positive', '0.044', 'curb', 'crossroad', 'ring_road', 'lane', 'interstate']
senticnet['signally'] = ['-0.78', '0', '0.831', '0', '#sadness', '#anger', 'negative', '-0.80', 'conspicuous', 'display', 'markedly', 'conspicuously', 'pectoral']
senticnet['signature'] = ['-0.03', '0.117', '-0.04', '0.079', '#interest', '#admiration', 'positive', '0.029', 'written_text', 'written_communication', 'information_source', 'sign', 'document']
senticnet['signature_fragrance'] = ['0.863', '0.861', '0', '0', '#joy', '#interest', 'positive', '0.862', 'aroma', 'scent', 'smell', 'odor', 'sniff']
senticnet['signature_scent'] = ['0.863', '0.861', '0', '0', '#joy', '#interest', 'positive', '0.862', 'aroma', 'scent', 'smell', 'odor', 'sniff']
senticnet['signboard'] = ['0.952', '-0.52', '0', '0.835', '#joy', '#admiration', 'positive', '0.771', 'billboard', 'hoarding', 'sandwich_board', 'scoreboard', 'shingle']
senticnet['signed_integer'] = ['-0.04', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.06', 'chemical_reaction', 'burn', 'chemical_process', 'get_burnt', 'set_on_fire']
senticnet['signet_ring'] = ['0.048', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.071', 'ring', 'ring_loudly', 'diamond_ring', 'necklace', 'jewelry']
senticnet['significance'] = ['0', '0.367', '-0.11', '0.327', '#interest', '#admiration', 'positive', '0.194', 'significant', 'capitalize', 'good', 'importance', 'inclusion']
senticnet['significant'] = ['0.04', '0.296', '-0.05', '0.351', '#interest', '#admiration', 'positive', '0.159', 'significance', 'plus', 'ultimate', 'precision', 'important']
senticnet['significant_adverse_effect'] = ['0.565', '-0.76', '0', '0.982', '#surprise', '#admiration', 'positive', '0.771', 'risk', 'hazard', 'jeopardy', 'health_hazard', 'peril']
senticnet['significant_difference'] = ['0.118', '0.181', '-0.07', '0.374', '#interest', '#admiration', 'positive', '0.149', 'compare', 'confrontation', 'collation', 'comparative', 'paragon']
senticnet['significant_difference_between'] = ['0.118', '0.181', '-0.07', '0.374', '#interest', '#admiration', 'positive', '0.149', 'compare', 'confrontation', 'collation', 'comparative', 'paragon']
senticnet['significant_fraction'] = ['0', '0.119', '0', '0.106', '#interest', '#admiration', 'positive', '0.112', 'divide', 'numerator', 'subtraction', 'math_action', 'division']
senticnet['significant_obstacle'] = ['-0.73', '0', '0.732', '-0.53', '#sadness', '#anger', 'negative', '-0.66', 'unconquerable', 'insuperable', 'inexpugnable', 'indomitable', 'unsubduable']
senticnet['significant_portion'] = ['0.956', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.935', 'aggregate', 'total', 'whole', 'complete', 'integral']
senticnet['significant_structural_damage'] = ['-0.98', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.97', 'harm', 'hurt', 'cut_skin', 'bloody', 'ouch']
senticnet['significantly'] = ['0', '0.762', '0', '0.779', '#interest', '#admiration', 'positive', '0.77', 'importantly', 'project', 'research', 'study', 'well']
senticnet['significantly_affect'] = ['-0.62', '0', '0.756', '-0.62', '#anger', '#disgust', 'negative', '-0.66', 'vitiate', 'ruin', 'maul', 'mangle', 'deface']
senticnet['significantly_cheaper'] = ['-0.78', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.86', 'high_cost', 'spend_money', 'waste_money', 'high_price', 'unaffordable']
senticnet['significantly_different'] = ['0.118', '0.181', '-0.07', '0.374', '#interest', '#admiration', 'positive', '0.149', 'compare', 'confrontation', 'collation', 'comparative', 'paragon']
senticnet['significantly_enhance'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'compound', 'intensify', 'deepen', 'potentiate', 'heighten']
senticnet['significantly_expand'] = ['-0.04', '0.153', '-0.12', '0.232', '#interest', '#admiration', 'positive', '0.055', 'reach', 'elasticity', 'grow_long', 'extend_arm', 'tow']
senticnet['significantly_faster'] = ['0.729', '0.974', '0', '0', '#joy', '#interest', 'positive', '0.851', 'travel', 'fast', 'travel_fast', 'take_airplane', 'drive_fast']
senticnet['significantly_impair'] = ['-0.62', '0', '0.756', '-0.62', '#anger', '#disgust', 'negative', '-0.66', 'vitiate', 'ruin', 'maul', 'mangle', 'deface']
senticnet['significantly_improve'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'compound', 'intensify', 'deepen', 'potentiate', 'heighten']
senticnet['significantly_increase'] = ['0.66', '-0.85', '0', '0.744', '#surprise', '#admiration', 'positive', '0.752', 'further', 'promote', 'encourage', 'conduce', 'advance']
senticnet['significantly_less_expensive'] = ['-0.78', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.86', 'high_cost', 'spend_money', 'waste_money', 'high_price', 'unaffordable']
senticnet['significantly_outperform'] = ['0', '0.994', '0', '0.88', '#interest', '#admiration', 'positive', '0.937', 'exceed', 'outshout', 'outstrip', 'outshine', 'outsail']
senticnet['significantly_reduce'] = ['0.676', '-0.70', '0', '0.8', '#surprise', '#admiration', 'positive', '0.727', 'better', 'soothe', 'improve', 'alleviate_pain', 'meliorate']
senticnet['significantly_slower'] = ['0.729', '0.974', '0', '0', '#joy', '#interest', 'positive', '0.851', 'travel', 'fast', 'travel_fast', 'take_airplane', 'drive_fast']
senticnet['significantly_underestimate'] = ['-0.84', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'brag', 'embellish', 'overemphasize', 'overstress', 'vaunt']
senticnet['signification'] = ['-0.57', '0', '0.713', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'assured', 'conclusion', 'certainty', 'determinateness', 'predictability']
senticnet['signifie'] = ['-0.52', '0', '0.701', '-0.50', '#sadness', '#anger', 'negative', '-0.57', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['signifies'] = ['0', '0', '0.747', '-0.77', '#anger', '#disgust', 'negative', '-0.76', 'erroneous', 'error', 'expurgation', 'improper', 'incorrect']
senticnet['signify'] = ['-0.98', '0', '0.664', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'unfortunate', 'unfortunate_person', 'weeper', 'pallgriever', 'griever']
senticnet['signior'] = ['-0.44', '-0.15', '0', '-0.36', '#sadness', '#disgust', 'negative', '-0.21', 'baffle', 'stupefy', 'dumbfound', 'stupefaction', 'confuse']
senticnet['signor_brunoni'] = ['0.672', '-0.65', '0', '0.535', '#joy', '#surprise', 'positive', '0.621', 'escapologist', 'escape_expert', 'mind_reader', 'telepathist', 'performer']
senticnet['signposted'] = ['-0.72', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.79', 'roundabout', 'roundabout', 'round-about', 'motorway', 'stoplight']
senticnet['sigsegv'] = ['-0.98', '0', '0.869', '-0.80', '#sadness', '#anger', 'negative', '-0.88', 'error', 'syntax_error', 'semantic_error', 'programming_error', 'computer_error']
senticnet['sikth'] = ['0.028', '0', '0.027', '0.101', '#joy', '#admiration', 'positive', '0.034', 'animal_dwell', 'bee_home', 'live_place', 'dwell_place', 'place_to_live']
senticnet['silambam'] = ['-0.06', '-0.00', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.05', 'madman', 'trouble', 'agony', 'cause_problem', 'river_stream']
senticnet['silastic'] = ['-0.81', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.85', 'bandage', 'painkiller', 'gauze', 'aspirin', 'contraceptive_device']
senticnet['silat'] = ['-0.06', '-0.00', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.05', 'madman', 'trouble', 'agony', 'cause_problem', 'river_stream']
senticnet['sildenafil'] = ['-0.59', '0', '0.537', '-0.57', '#sadness', '#disgust', 'negative', '-0.57', 'phentolamine', 'virility_drug', 'virility_drug_citrate', 'tadalafil', 'vardenafil']
senticnet['sildenafil_citrate'] = ['-0.59', '0', '0.537', '-0.57', '#sadness', '#disgust', 'negative', '-0.57', 'phentolamine', 'sildenafil', 'virility_drug', 'tadalafil', 'vardenafil']
senticnet['sileby'] = ['0.623', '-0.56', '0', '0.974', '#joy', '#admiration', 'positive', '0.721', 'alterable', 'convertible', 'translatable', 'transformable', 'transmutable']
senticnet['silence'] = ['0.224', '0.224', '-0.12', '0', '#joy', '#interest', 'positive', '0.108', 'hush', 'peace', 'truce', 'seek_psychiatric_help', 'quiet']
senticnet['silence_dissent'] = ['0.033', '-0.19', '0.203', '-0.22', '#anger', '#disgust', 'negative', '-0.05', 'antiquate', 'optional', 'kleenex', 'key_chain', 'sachet']
senticnet['silenced_pistol'] = ['-0.86', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.79', 'gun', 'firearm', 'handgun', 'rifle', 'pistol']
senticnet['silent'] = ['0', '0', '-0.70', '-0.95', '#fear', '#disgust', 'negative', '-0.82', 'tacit', 'soundless', 'still', 'understand', 'mum']
senticnet['silent_adoration'] = ['0.524', '0.681', '-0.27', '0.281', '#joy', '#interest', 'positive', '0.302', 'adoring', 'devotedness', 'devoted', 'amorousness', 'amorous']
senticnet['silent_assassin'] = ['-0.86', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.79', 'gun', 'firearm', 'handgun', 'rifle', 'pistol']
senticnet['silent_disco'] = ['0.925', '0.993', '0', '0.832', '#joy', '#interest', 'positive', '0.917', 'dance_samba', 'dance', 'skate', 'jump_rope', 'play_frisbee']
senticnet['silent_film_actor'] = ['0', '0.797', '0', '0.804', '#interest', '#admiration', 'positive', '0.8', 'director', 'shooting_movie', 'director_chair', 'important_person', 'movie']
senticnet['silent_meditation'] = ['0.023', '0.295', '-0.05', '0.283', '#interest', '#admiration', 'positive', '0.136', 'religion', 'religious_purpose', 'world_religion', 'gospel', 'judaism']
senticnet['silent_movie'] = ['-0.71', '0', '0.953', '-0.76', '#anger', '#disgust', 'negative', '-0.81', 'flick', 'movie', 'film', 'picture', 'moving_picture']
senticnet['silent_picture'] = ['-0.62', '0', '0.551', '-0.74', '#sadness', '#disgust', 'negative', '-0.63', 'flick', 'movie', 'film', 'picture', 'moving_picture']
senticnet['silent_prayer'] = ['0.023', '0.295', '-0.05', '0.283', '#interest', '#admiration', 'positive', '0.136', 'religion', 'religious_purpose', 'world_religion', 'gospel', 'judaism']
senticnet['silent_protagonist'] = ['-0.95', '0', '0.774', '0', '#sadness', '#anger', 'negative', '-0.86', 'argue', 'rival', 'disagreement', 'opposition', 'anti']
senticnet['silently'] = ['-0.94', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.94', 'drive_recklessly', 'discord', 'deaf', 'deny', 'silent']
senticnet['silents'] = ['-0.71', '0', '0.618', '-0.57', '#sadness', '#anger', 'negative', '-0.63', 'flick', 'movie', 'film', 'picture', 'moving_picture']
senticnet['silesian'] = ['0', '0', '0', '0.1', '#admiration', '#admiration', 'positive', '0.1', 'find_earth', 'wax', 'plutonium', 'shine', 'melt']
senticnet['silhouetted_against'] = ['0.042', '0.126', '-0.06', '0.165', '#interest', '#admiration', 'positive', '0.067', 'sparkle', 'shiny', 'shinny', 'gray', 'shine']
senticnet['silica'] = ['-0.03', '-0.01', '0.041', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'sand', 'salt', 'grain_sand', 'sandy', 'quartz']
senticnet['silica_gel'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'drink_container', 'liquid_container', 'drink_cup', 'drink_holder', 'type_of_container']
senticnet['silicate_rock'] = ['0.095', '0', '-0.04', '0.062', '#joy', '#admiration', 'positive', '0.037', 'photographer', 'water_spray', 'moisture', 'rise_water', 'droplet']
senticnet['silicon'] = ['-0.06', '0', '0.022', '0', '#sadness', '#anger', 'negative', '-0.04', 'grainy', 'silicon_rubber', 'silica', 'salty', 'fine_grain']
senticnet['silicon_carbide'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'gold', 'bronze', 'alloy', 'medal', 'copper']
senticnet['silicon_chip'] = ['-0.83', '0', '0.521', '-0.71', '#sadness', '#disgust', 'negative', '-0.69', 'stool', 'dejection', 'droppings', 'cow_droppings', 'cow_dung']
senticnet['silicon_integrated_circuit'] = ['-0.93', '0', '0.739', '-0.96', '#sadness', '#disgust', 'negative', '-0.88', 'prevarication', 'fairytale', 'fairy_tale', 'fairy_story', 'play']
senticnet['silicon_layer'] = ['-0.93', '0', '0.739', '-0.96', '#sadness', '#disgust', 'negative', '-0.88', 'prevarication', 'fairytale', 'fairy_tale', 'fairy_story', 'play']
senticnet['silicon_monoxide'] = ['0', '0.073', '-0.05', '0.093', '#interest', '#admiration', 'positive', '0.038', 'vapor', 'water_vapor', 'evaporate', 'condense_water', 'bubble']
senticnet['silicon_nitride'] = ['-0.93', '0', '0.739', '-0.96', '#sadness', '#disgust', 'negative', '-0.88', 'prevarication', 'fairytale', 'fairy_tale', 'fairy_story', 'play']
senticnet['silicon_nitride_layer'] = ['-0.93', '0', '0.739', '-0.96', '#sadness', '#disgust', 'negative', '-0.88', 'prevarication', 'fairytale', 'fairy_tale', 'fairy_story', 'play']
senticnet['silicon_rubber'] = ['-0.06', '0', '0.022', '0', '#sadness', '#anger', 'negative', '-0.04', 'grainy', 'silicon', 'silica', 'salty', 'fine_grain']
senticnet['silicon_substrate'] = ['-0.05', '0.068', '-0.10', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'tooth_decay', 'body_ache', 'symptom', 'pierce', 'tooth']
senticnet['silicon_tetrachloride'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'carboxylic_acid', 'succinic_acid', 'valeric_acid']
senticnet['silicone_rubber'] = ['0.085', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.074', 'soap', 'drizzle', 'bubbly', 'solid_water', 'ivory']
senticnet['silique'] = ['0', '0.141', '0.01', '0.115', '#interest', '#admiration', 'positive', '0.082', 'fruit', 'rind', 'funiculus', 'seed', 'pip']
senticnet['silk'] = ['-0.03', '0', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'cotton', 'asbestos', 'wool', 'cotton_boll', 'velvet']
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senticnet['silk_tulle'] = ['0.735', '-0.54', '0', '0.897', '#joy', '#admiration', 'positive', '0.727', 'net', 'network', 'mesh', 'meshing', 'meshwork']
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senticnet['silky_smooth'] = ['0', '0.794', '0', '0.941', '#interest', '#admiration', 'positive', '0.867', 'best', 'excellent', 'goodness', 'greatness', 'great']
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senticnet['silver-tongued'] = ['0', '0', '-0.98', '-0.73', '#fear', '#disgust', 'negative', '-0.86', 'facile', 'silver', 'smooth-spoken', 'fluent', 'eloquent']
senticnet['silver_alloy'] = ['0', '0.931', '0', '0.947', '#interest', '#admiration', 'positive', '0.939', 'gold', 'metal', 'ash_gray', 'fluent', 'eloquent']
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senticnet['silver_commemorative_coin'] = ['0.81', '0.763', '0', '0', '#joy', '#interest', 'positive', '0.786', 'dollar_bill', 'franc', 'euro', 'dollar', 'cent']
senticnet['silver_cutlery'] = ['0.022', '0.069', '0.027', '0.059', '#interest', '#admiration', 'positive', '0.031', 'perfect_surface', 'flat_land', 'flat_surface', 'bowl', 'plate']
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senticnet['silver_maple_acer_saccharinum'] = ['-0.92', '0', '0.912', '-0.94', '#sadness', '#disgust', 'negative', '-0.92', 'true_blackberry', 'sand_blackberry', 'blackberry_bush', 'blackberry', 'running_blackberry']
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senticnet['silver_screen'] = ['0', '0.793', '0', '0.91', '#interest', '#admiration', 'positive', '0.852', 'superstar', 'vip', 'accomplishment', 'performance', 'concert']
senticnet['silver_stripe'] = ['0.942', '-0.61', '0', '0.908', '#joy', '#admiration', 'positive', '0.82', 'adornment', 'cigar_band', 'hatband', 'neckband', 'pinstripe']
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senticnet['silverware_drawer'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'neat', 'kitchen_drawer', 'cutlery_drawer', 'dine_table', 'table']
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senticnet['similarity_between'] = ['-0.83', '0', '0.546', '-0.88', '#sadness', '#disgust', 'negative', '-0.75', 'difference', 'allomerism', 'deviation', 'variation', 'departure']
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senticnet['similitudinem'] = ['0', '0.079', '0', '0', '#interest', '#interest', 'positive', '0.079', 'change_diaper', 'lower_standard_live', 'become_pregnant', 'find_mate', 'make_baby']
senticnet['simkin'] = ['0', '0.996', '0', '0.952', '#interest', '#admiration', 'positive', '0.974', 'manager', 'division', 'astronomical_science', 'award', 'continue_grant']
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senticnet['simmer_gently'] = ['-0.66', '0', '0.992', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'cold', 'boil', 'temperature', 'low_temperature', 'frigidity']
senticnet['simmer_slowly'] = ['-0.66', '0', '0.992', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'cold', 'boil', 'temperature', 'low_temperature', 'frigidity']
senticnet['simmering'] = ['-0.73', '0', '0.873', '0', '#sadness', '#anger', 'negative', '-0.80', 'stewing', 'boiling', 'churn', 'moil', 'seethe']
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senticnet['simple-minded'] = ['0.74', '0.931', '0', '0', '#joy', '#interest', 'positive', '0.836', 'dim-witted', 'unsubdivided', 'simple', 'dewy-eyed', 'simplest']
senticnet['simple_arithmetic'] = ['0.076', '0.096', '-0.08', '0.167', '#interest', '#admiration', 'positive', '0.063', 'statistic', 'mathematical_function', 'calculation', 'list_number', 'calculate']
senticnet['simple_cuboidal'] = ['0.905', '-0.65', '0', '0.851', '#joy', '#admiration', 'positive', '0.805', 'gonococcus', 'bacteria', 'bacteriae', 'probiotic', 'probiotic_bacterium']
senticnet['simple_fracture'] = ['-0.81', '0', '0.745', '-0.94', '#sadness', '#disgust', 'negative', '-0.83', 'injury', 'hairline_fracture', 'capillary_fracture', 'incomplete_fracture', 'fracture']
senticnet['simple_geometric'] = ['0.716', '-0.78', '0', '0.539', '#joy', '#surprise', 'positive', '0.678', 'depicted', 'pictured', 'portrayed', 'described', 'delineated']
senticnet['simple_harmonic_motion'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotate_movement', 'rotate', 'go_around', 'manoeuvre', 'full_circle']
senticnet['simple_headstone'] = ['-0.88', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.90', 'coffin', 'casket', 'morgue', 'headstone', 'burial']
senticnet['simple_leaf'] = ['-0.66', '0', '0.971', '-0.75', '#anger', '#disgust', 'negative', '-0.79', 'lyrate_leaf', 'needle', 'elliptic_leaf', 'hastate_leaf', 'obovate_leaf']
senticnet['simple_life'] = ['0.302', '0', '0.084', '0.175', '#joy', '#admiration', 'positive', '0.131', 'expressive', 'form_of_art', 'sanity', 'art', 'knowledgeable']
senticnet['simple_linear_interpolation'] = ['-0.55', '0', '0.965', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'interjection', 'delay', 'insert', 'interposition', 'disruption']
senticnet['simple_mindedness'] = ['-0.61', '0', '0.599', '-0.57', '#sadness', '#anger', 'negative', '-0.59', 'naiveness', 'simpleness', 'ingenuousness', 'artlessness', 'simplicity']
senticnet['simple_pendulum'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotate_movement', 'rotate', 'go_around', 'manoeuvre', 'full_circle']
senticnet['simple_random_sample'] = ['0', '0.23', '-0.11', '0.206', '#interest', '#admiration', 'positive', '0.108', 'savor', 'consume', 'sweet_sour', 'mouth_activity', 'bad_odor']
senticnet['simple_sugar'] = ['-0.52', '0', '0.681', '-0.76', '#anger', '#disgust', 'negative', '-0.65', 'triose', 'tetrose', 'pentose', 'hexose', 'aldose']
senticnet['simple_syrup'] = ['-0.95', '0', '0.845', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'grapefruit_peel', 'candied_citrus_peel', 'orange_peel', 'candied_fruit', 'succade']
senticnet['simpleness'] = ['-0.61', '0', '0.599', '-0.57', '#sadness', '#anger', 'negative', '-0.59', 'simple_mindedness', 'naiveness', 'ingenuousness', 'artlessness', 'simplicity']
senticnet['simplest'] = ['0.742', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.839', 'dim-witted', 'unsubdivided', 'simple', 'dewy-eyed', 'simple-minded']
senticnet['simplest_explanation'] = ['-0.11', '0.283', '-0.16', '0.331', '#interest', '#admiration', 'positive', '0.084', 'conclude', 'question_answering', 'solution', 'jutification', 'question_starter']
senticnet['simpleton'] = ['0.776', '-0.53', '0', '0.728', '#joy', '#admiration', 'positive', '0.678', 'saphead', 'muggins', 'tomfool', 'fool', 'booby']
senticnet['simpliciter'] = ['0.633', '-0.67', '0', '0.776', '#surprise', '#admiration', 'positive', '0.693', 'double_entendre', 'loophole', 'ambiguity', 'amphiboly', 'parisology']
senticnet['simplicity'] = ['0.448', '0.292', '0.129', '0.224', '#joy', '#interest', 'positive', '0.209', 'taken_seriously', 'much_need', 'knowledgeable', 'complement', 'remission']
senticnet['simplification'] = ['0', '0.724', '0', '0.908', '#interest', '#admiration', 'positive', '0.816', 'simplify', 'streamline', 'reducing', 'model', 'component']
senticnet['simplified_diagram'] = ['0.854', '0.76', '0', '0', '#joy', '#interest', 'positive', '0.807', 'geometric_figure', 'polygon', 'form', 'parallelogram', 'hexagon']
senticnet['simplify'] = ['0', '0.733', '0', '0.909', '#interest', '#admiration', 'positive', '0.821', 'simplification', 'streamline', 'reducing', 'model', 'component']
senticnet['simplistic'] = ['-0.86', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.91', 'simplistically', 'barbarianism', 'simple', 'reductive', 'simpleton']
senticnet['simplistically'] = ['-0.86', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.91', 'simplistic', 'barbarianism', 'simple', 'reductive', 'simpleton']
senticnet['simply'] = ['0', '0', '0.801', '-0.71', '#anger', '#disgust', 'negative', '-0.75', 'stupidly', 'gawk', 'stupid', 'brainlessly', 'gawker']
senticnet['simply_double_click'] = ['0.055', '0.117', '0', '0.133', '#interest', '#admiration', 'positive', '0.102', 'graphical_representation', 'pencil_pen', 'note_pad', 'marker', 'stationery']
senticnet['simply_ignore'] = ['-0.70', '0', '0.984', '0', '#sadness', '#anger', 'negative', '-0.84', 'isolation', 'dislike', 'huffy', 'huffily', 'huffiness']
senticnet['simply_typed_lambda'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'integrative', 'consolidative', 'unifying', 'combinatorial']
senticnet['simply_untrue'] = ['-0.88', '0', '0.907', '-0.84', '#sadness', '#anger', 'negative', '-0.88', 'intolerable', 'unacceptable', 'bitter', 'impossible', 'insufferable']
senticnet['simply_vanish'] = ['-0.88', '0', '0.682', '-0.60', '#sadness', '#anger', 'negative', '-0.72', 'absent', 'fall_away', 'evanesce', 'blow_over', 'pass_off']
senticnet['simply_walk_away'] = ['-0.02', '0.087', '0', '0.107', '#interest', '#admiration', 'positive', '0.056', 'say_good_bye', 'step_aside', 'step_out', 'resign', 'open_mailbox']
senticnet['simpson_desert'] = ['-0.95', '0', '0.624', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['simpson_dysmorphia_syndrome'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'concupiscence', 'lustfulness', 'amorousness']
senticnet['simpson_episode'] = ['0.918', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.827', 'encounter', 'reveal', 'programme', 'lose', 'sequel']
senticnet['simuka'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
senticnet['simulacra'] = ['0.075', '0.207', '-0.06', '0.12', '#interest', '#admiration', 'positive', '0.085', 'false', 'unreal', 'pretend', 'fictional', 'fictitious']
senticnet['simulacrum'] = ['-0.81', '0', '0.838', '-0.75', '#sadness', '#anger', 'negative', '-0.80', 'pretence', 'pretense', 'pretext', 'semblance', 'face_value']
senticnet['simulate'] = ['0.559', '-0.82', '0', '0.871', '#surprise', '#admiration', 'positive', '0.751', 'model', 'conform_to', 'pattern', 'procedure', 'mock']
senticnet['simulated'] = ['-0.50', '0', '0.968', '-0.52', '#anger', '#disgust', 'negative', '-0.66', 'imitative', 'apish', 'parrotlike', 'apelike', 'mimic']
senticnet['simulated_sex'] = ['0', '-0.08', '0', '0', '#surprise', '#surprise', 'positive', '0.082', 'yo-yo', 'playroom', 'backgammon', 'action_figure', 'board_game']
senticnet['simulation'] = ['-0.95', '0', '0.557', '-0.66', '#sadness', '#disgust', 'negative', '-0.72', 'pretence', 'feigning', 'pretense', 'pretending', 'pretend']
senticnet['simuliidae'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'annelid', 'annelid_worm', 'leech']
senticnet['simultanagnosia'] = ['-0.73', '0', '0.955', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'visual_agnosia', 'auditory_agnosia', 'brain_disorder', 'encephalopathy', 'brain_disease']
senticnet['simultaneity'] = ['0.869', '-0.88', '0', '0.598', '#joy', '#surprise', 'positive', '0.782', 'connectedness', 'connection', 'consistency', 'continuity', 'link']
senticnet['simultaneous_interpretation'] = ['0', '0.202', '-0.06', '0.291', '#interest', '#admiration', 'positive', '0.142', 'vocabulary', 'translate', 'dictionary', 'translation', 'thesaurus']
senticnet['simultaneous_measurement'] = ['0', '0.811', '0', '0', '#interest', '#interest', 'positive', '0.811', 'observe', 'glimpse', 'look', 'stare', 'glance']
senticnet['simultaneous_translation'] = ['0', '0.202', '-0.06', '0.291', '#interest', '#admiration', 'positive', '0.142', 'vocabulary', 'translate', 'dictionary', 'translation', 'thesaurus']
senticnet['simultaneously'] = ['0.028', '0.138', '-0.04', '0.215', '#interest', '#admiration', 'positive', '0.083', 'skilled', 'expert', 'tin', 'dense_water', 'metal_container']
senticnet['sin'] = ['-0.24', '0.179', '-0.28', '0', '#sadness', '#fear', 'negative', '-0.11', 'vengeance', 'kidnap', 'wrong', 'take_life', 'revenge']
senticnet['sin-cursed'] = ['0.767', '-0.89', '0', '0', '#joy', '#surprise', 'positive', '0.832', 'new-made', 'newly-made', 'soccer-loving', 'city-dwelling', 'newly-christened']
senticnet['sin_hath'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitentially', 'penitent', 'penitence', 'repentant']
senticnet['sin_mortally'] = ['0', '0', '0.753', '-0.72', '#anger', '#disgust', 'negative', '-0.73', 'sinful', 'human_emotion', 'call', 'demand', 'yearn']
senticnet['sinc_function'] = ['0.506', '-0.56', '0', '0.788', '#surprise', '#admiration', 'positive', '0.62', 'backward', 'reverse', 'retrograde', 'reflexive', 'retral']
senticnet['sincere'] = ['0', '0', '0', '0.835', '#admiration', '#admiration', 'positive', '0.835', 'mutual_understand', 'give_hint', 'end_conversation', 'acknowledgment', 'others_understand']
senticnet['sincere_affection'] = ['0.605', '0.647', '-0.26', '0.323', '#joy', '#interest', 'positive', '0.327', 'friendly', 'brotherly', 'amicableness', 'amicably', 'amicable']
senticnet['sincere_apology'] = ['-0.89', '0.168', '-0.60', '0.05', '#sadness', '#fear', 'negative', '-0.32', 'remorsefully', 'contritely', 'contriteness', 'ruefully', 'attrition']
senticnet['sincere_appreciation'] = ['0.561', '-0.79', '0', '0.89', '#surprise', '#admiration', 'positive', '0.748', 'superlative', 'compliment', 'testimonial', 'praise', 'approval']
senticnet['sincere_belief'] = ['0.909', '-0.85', '0', '0.846', '#joy', '#surprise', 'positive', '0.87', 'respectability', 'reputability', 'venerability', 'venerableness', 'righteousness']
senticnet['sincere_friendship'] = ['0.605', '0.647', '-0.26', '0.323', '#joy', '#interest', 'positive', '0.327', 'friendly', 'brotherly', 'amicableness', 'amicably', 'amicable']
senticnet['sincere_gratitude'] = ['0.798', '0.754', '0', '0.779', '#joy', '#admiration', 'positive', '0.777', 'affection', 'joy', 'glad', 'admire', 'sympathetic']
senticnet['sincere_interest'] = ['0.701', '0.874', '0', '0.752', '#interest', '#admiration', 'positive', '0.776', 'self_satisfaction', 'enjoy_life', 'good_feeling', 'good_attitude', 'enjoy']
senticnet['sincere_piety'] = ['-0.74', '0', '0.585', '-0.58', '#sadness', '#anger', 'negative', '-0.63', 'lack_of_quality', 'meagreness', 'poorness', 'scantiness', 'scantness']
senticnet['sincere_remorse'] = ['-0.89', '0.168', '-0.60', '0.05', '#sadness', '#fear', 'negative', '-0.32', 'remorsefully', 'contritely', 'contriteness', 'ruefully', 'attrition']
senticnet['sincere_repentance'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitentially', 'penitent', 'repentantly', 'repentant']
senticnet['sincerely'] = ['-0.99', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.96', 'sympathetic', 'sympathetically', 'pity', 'sympathize_with', 'sympathy']
senticnet['sincerely_believe'] = ['0.762', '0', '0', '0.741', '#joy', '#admiration', 'positive', '0.752', 'noteworthy', 'valuable', 'applaudable', 'meritable', 'sacred']
senticnet['sincerely_repentant'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitentially', 'penitent', 'penitence', 'repentant']
senticnet['sincerely_thank'] = ['0.561', '-0.79', '0', '0.89', '#surprise', '#admiration', 'positive', '0.748', 'superlative', 'compliment', 'testimonial', 'praise', 'approval']
senticnet['sincerest'] = ['-0.96', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.89', 'assassinate', 'charge', 'accuse', 'malign', 'libel']
senticnet['sincerity'] = ['0', '0.931', '0', '0.894', '#interest', '#admiration', 'positive', '0.913', 'expectantly', 'anticipant', 'anticipative', 'expectant', 'earnest']
senticnet['sine'] = ['0.717', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.836', 'electrical_unit', 'sea_movement', 'mathematic_sign', 'fast_water', 'ocean_movement']
senticnet['sine_die'] = ['-0.57', '0', '0.576', '-0.78', '#anger', '#disgust', 'negative', '-0.64', 'reprieve', 'respite', 'suspend', 'call', 'delay']
senticnet['sinecure'] = ['-0.77', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.77', 'sinecure', 'well-paid', 'cushy', 'low-paying', 'high-paid']
senticnet['sinew'] = ['0', '0', '0.869', '-0.92', '#anger', '#disgust', 'negative', '-0.89', 'sinew', 'tendon', 'gristle', 'connective', 'sinewy']
senticnet['sinewy'] = ['0.877', '0.749', '0', '0', '#joy', '#interest', 'positive', '0.813', 'fibrous', 'tendinous', 'stringy', 'unchewable', 'brawny']
senticnet['sinful'] = ['0', '0', '0.753', '-0.72', '#anger', '#disgust', 'negative', '-0.73', 'deadly_sin', 'human_emotion', 'call', 'demand', 'yearn']
senticnet['sinful_behavior'] = ['-0.52', '0', '0.621', '-0.91', '#anger', '#disgust', 'negative', '-0.68', 'deviltry', 'devilry', 'irreverence', 'foul_play', 'iniquity']
senticnet['sinful_men'] = ['0.673', '-0.66', '0', '0.566', '#joy', '#surprise', 'positive', '0.634', 'wickedness', 'impiety', 'impiousness', 'unjustness', 'dishonor']
senticnet['sinful_nature'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'lecherousness', 'lustfulness', 'amorousness']
senticnet['sing'] = ['0.917', '0', '0', '0', '#joy', '#joy', 'positive', '0.917', 'record_music', 'receive_applause', 'sing_song', 'produce_sound', 'hear_well']
senticnet['sing_along'] = ['0.98', '0', '0', '0', '#joy', '#joy', 'positive', '0.98', 'enjoy_music', 'tap_foot', 'hear_song', 'earring', 'tap_toe']
senticnet['sing_aloud'] = ['0.622', '0.641', '-0.24', '0.285', '#joy', '#interest', 'positive', '0.327', 'gleeful', 'gladsome', 'happiness', 'gladness', 'gladfulness']
senticnet['sing_bird'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'batman', 'hatch', 'vulture', 'only_bird']
senticnet['sing_choir'] = ['0', '0.797', '0', '0.923', '#interest', '#admiration', 'positive', '0.86', 'sing_group', 'church', 'performance', 'music', 'singer']
senticnet['sing_dance'] = ['0.947', '0.807', '0', '0', '#joy', '#interest', 'positive', '0.877', 'play_instrument', 'sound_beautiful', 'compose_music', 'lovely_music', 'strum']
senticnet['sing_group'] = ['0', '0.171', '0.025', '0.144', '#interest', '#admiration', 'positive', '0.097', 'church', 'confession', 'monastery', 'chapel', 'synagogue']
senticnet['sing_in_rain'] = ['0.02', '0.071', '0', '0.069', '#interest', '#admiration', 'positive', '0.053', 'funny_moment', 'crazy_moment', 'dance_in_rain', 'playful', 'dance']
senticnet['sing_karaoke'] = ['0', '0.075', '0.028', '0.173', '#interest', '#admiration', 'positive', '0.073', 'friend', 'spend_time_with_friend', 'party', 'fun', 'hang_out_with_friend']
senticnet['sing_microphone'] = ['0.191', '0.165', '-0.07', '0.114', '#joy', '#interest', 'positive', '0.098', 'masculine', 'sophisticated', 'uneducated', 'contrary', 'sing']
senticnet['sing_song'] = ['0.973', '0', '0', '0.878', '#joy', '#admiration', 'positive', '0.925', 'melody', 'perform_on_stage', 'applause', 'receive_applause', 'hum']
senticnet['sing_together'] = ['0.752', '0.855', '0', '0', '#joy', '#interest', 'positive', '0.803', 'hymn', 'chant', 'christmas_song', 'melodic_noise', 'common_language']
senticnet['sing_well'] = ['0.814', '0.773', '0', '0', '#joy', '#interest', 'positive', '0.793', 'artist', 'travel_fast_light', 'singer', 'live_ocean', 'control_weather']
senticnet['singalong'] = ['0.773', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.844', 'favorite', 'crowd', 'acclamation', 'bunch', 'large_crowd']
senticnet['singer'] = ['0.882', '0.901', '0', '0', '#joy', '#interest', 'positive', '0.891', 'songwriter', 'vip', 'rock_group', 'guitar_player', 'violinist']
senticnet['singer_dancer'] = ['0.882', '0.901', '0', '0', '#joy', '#interest', 'positive', '0.891', 'songwriter', 'vip', 'rock_group', 'guitar_player', 'violinist']
senticnet['singer_guitarist'] = ['0', '-0.02', '0.055', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'djembe', 'make_musicr', 'use_jazz_music', 'tune_instrument', 'percussion']
senticnet['singer_latha_walpola'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'head_hurt', 'introspection', 'lifestyle_change', 'headache', 'move_eye']
senticnet['singer_madonna'] = ['0.056', '0.139', '-0.07', '0.154', '#interest', '#admiration', 'positive', '0.07', 'belongings', 'material', 'fabric', 'guarantee', 'stuff']
senticnet['singer_songwriter'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
senticnet['singer_songwriter_producer'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
senticnet['singersongwriter'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
senticnet['singing'] = ['0.026', '0', '0.004', '0.085', '#joy', '#admiration', 'positive', '0.036', 'music', 'voice', 'bell', 'highest_pitch', 'alto']
senticnet['singing_duo'] = ['0.882', '0.901', '0', '0', '#joy', '#interest', 'positive', '0.891', 'songwriter', 'vip', 'rock_group', 'guitar_player', 'violinist']
senticnet['singing_karaoke'] = ['0.925', '0.993', '0', '0.832', '#joy', '#interest', 'positive', '0.917', 'dance_samba', 'dance', 'skate', 'jump_rope', 'play_frisbee']
senticnet['singing_loudly'] = ['-0.03', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.04', 'rabbit', 'squirrel', 'mole', 'chipmunk', 'nymph']
senticnet['singing_sensation'] = ['0.882', '0.901', '0', '0', '#joy', '#interest', 'positive', '0.891', 'songwriter', 'vip', 'rock_group', 'guitar_player', 'violinist']
senticnet['single'] = ['-0.02', '0.103', '0.069', '0.151', '#interest', '#admiration', 'positive', '0.039', 'do_it_yourself', 'unit_of_measurement', 'two_halves', 'enumeration', 'alone']
senticnet['single-handedly'] = ['0', '0', '-0.97', '-0.70', '#fear', '#disgust', 'negative', '-0.83', 'singlehandedly', 'handedly', 'miraculously', 'succeeded', 'heroically']
senticnet['single-mindedness'] = ['0', '0.937', '0', '0.933', '#interest', '#admiration', 'positive', '0.935', 'relentlessness', 'doggedness', 'earnestness', 'fearlessness', 'obsessiveness']
senticnet['single-parent'] = ['0', '0', '0.831', '-0.78', '#anger', '#disgust', 'negative', '-0.81', 'motherless', 'destitute', 'widow', 'fatherless', 'friendless']
senticnet['single-sex'] = ['0', '0', '-0.89', '-0.78', '#fear', '#disgust', 'negative', '-0.83', 'penitentiary', 'correctional', 'maximum-security', 'prison', 'reformatory']
senticnet['single_bullet_wound'] = ['-0.52', '0', '0.785', '-0.61', '#anger', '#disgust', 'negative', '-0.64', 'gap', 'rathole', 'aperture', 'hole', 'cranny']
senticnet['single_byte'] = ['-0.04', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.06', 'chemical_reaction', 'burn', 'chemical_process', 'get_burnt', 'set_on_fire']
senticnet['single_celled'] = ['0', '0.056', '0.011', '0.051', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'heron', 'amoeba']
senticnet['single_celled_organism'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['single_champion'] = ['0', '0', '0', '0.834', '#admiration', '#admiration', 'positive', '0.834', 'royal_person', 'royal_title', 'top', 'master', 'rank']
senticnet['single_chart'] = ['0.997', '0.892', '0', '0', '#joy', '#interest', 'positive', '0.945', 'advertisement', 'large_outdoor_board', 'motorway', 'expressway', 'train_track']
senticnet['single_chip'] = ['-0.83', '0', '0.521', '-0.71', '#sadness', '#disgust', 'negative', '-0.69', 'stool', 'dejection', 'droppings', 'cow_droppings', 'cow_dung']
senticnet['single_coil_pickup'] = ['0.748', '-0.90', '0', '0.99', '#surprise', '#admiration', 'positive', '0.882', 'shakiness', 'quivering', 'shaking', 'vibration', 'quiver']
senticnet['single_currency'] = ['0.771', '0.846', '0', '0', '#joy', '#interest', 'positive', '0.808', 'dollar_bill', 'franc', 'banknote', 'dollar', 'cent']
senticnet['single_currency_bloc'] = ['0.771', '0.846', '0', '0', '#joy', '#interest', 'positive', '0.808', 'dollar_bill', 'franc', 'banknote', 'dollar', 'cent']
senticnet['single_disc_dvd'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['single_edged_blade'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'useful_tool']
senticnet['single_engined_aircraft'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'atmosphere', 'wing', 'nest', 'feather', 'bird_fly']
senticnet['single_flagellum'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'immobile', 'immotile']
senticnet['single_grass_runway'] = ['-0.70', '0', '0.621', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'flare_path', 'airfield', 'grassland', 'runway', 'road']
senticnet['single_gunshot'] = ['-0.52', '0', '0.785', '-0.61', '#anger', '#disgust', 'negative', '-0.64', 'gap', 'rathole', 'aperture', 'hole', 'cranny']
senticnet['single_gunshot_wound'] = ['-0.52', '0', '0.785', '-0.61', '#anger', '#disgust', 'negative', '-0.64', 'gap', 'rathole', 'aperture', 'hole', 'cranny']
senticnet['single_handed_backhand'] = ['0', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.91', 'umpire', 'referee', 'legal_proceed', 'clang', 'clamor']
senticnet['single_leg_takedown'] = ['-0.94', '0', '0.863', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'poke', 'clout', 'lick', 'punch', 'biff']
senticnet['single_malt'] = ['0.877', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.919', 'score', 'economical', 'scotch_whiskey', 'spoil', 'scottish']
senticnet['single_malt_scotch'] = ['0.877', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.919', 'score', 'economical', 'scotch_whiskey', 'spoil', 'scottish']
senticnet['single_malt_whisky'] = ['0.877', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.919', 'score', 'economical', 'scotch_whiskey', 'spoil', 'scottish']
senticnet['single_minded_determination'] = ['0.053', '0.207', '-0.07', '0.165', '#interest', '#admiration', 'positive', '0.088', 'motivation', 'go_job', 'reinforce', 'resume', 'effort']
senticnet['single_minded_devotion'] = ['0.053', '0.207', '-0.07', '0.165', '#interest', '#admiration', 'positive', '0.088', 'motivation', 'go_job', 'reinforce', 'resume', 'effort']
senticnet['single_mom'] = ['0', '0.864', '0', '0.751', '#interest', '#admiration', 'positive', '0.807', 'family_member', 'sibling', 'parent', 'female_relative', 'relative']
senticnet['single_motherhood'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'spousal_relationship', 'bigamy']
senticnet['single_paper'] = ['0.044', '-0.00', '0.009', '0.138', '#joy', '#admiration', 'positive', '0.045', 'flat_thin', 'white_paper', 'paper_piece', 'paper_square', 'game_piece']
senticnet['single_parent'] = ['0.051', '0.112', '0', '0', '#joy', '#interest', 'positive', '0.082', 'neighbor', 'family_reunion', 'clan', 'desire', 'little_specific']
senticnet['single_payor'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'rudeness', 'discourtesy', 'insolence', 'ccheekiness', 'cheekiness']
senticnet['single_person'] = ['0.077', '0.092', '0', '0', '#joy', '#interest', 'positive', '0.084', 'person', 'independent', 'multiple_people', 'uneducated', 'individual']
senticnet['single_phoneme'] = ['-0.61', '0', '0.721', '-0.68', '#anger', '#disgust', 'negative', '-0.67', 'character', 'logogram', 'logograph', 'graph', 'ideogram']
senticnet['single_polar_flagellum'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'immobile', 'immotile']
senticnet['single_storey_building'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'top_floor', 'front_desk', 'concierge', 'exit', 'elevator']
senticnet['single_strand'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['single_stranded_dna'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['single_tax'] = ['0.559', '-0.84', '0', '0.92', '#surprise', '#admiration', 'positive', '0.775', 'taxation', 'income_tax', 'capital_gain_tax', 'capital_levy', 'departure_tax']
senticnet['single_woman'] = ['0.077', '0.092', '0', '0', '#joy', '#interest', 'positive', '0.084', 'single', 'woman', 'maiden', 'girl', 'unmarried']
senticnet['singlehandedly'] = ['0', '0', '-0.97', '-0.71', '#fear', '#disgust', 'negative', '-0.84', 'single-handedly', 'handedly', 'miraculously', 'succeeded', 'heroically']
senticnet['singleness'] = ['0.909', '-0.51', '0', '0.826', '#joy', '#admiration', 'positive', '0.75', 'diligence', 'industriousness', 'assiduousness', 'intentness', 'engrossment']
senticnet['singlestick'] = ['0', '-0.07', '0.091', '-0.19', '#anger', '#disgust', 'negative', '-0.07', 'projectile', 'use_hunt', 'weapon', 'ammunition', 'firearm']
senticnet['singletrack'] = ['-0.99', '0', '0.641', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'navigable', 'negotiable', 'surmountable', 'climbable', 'passable']
senticnet['sings'] = ['0.974', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.853', 'rendition', 'singing', 'song', 'rendition', 'ballad']
senticnet['singsong'] = ['0.584', '-0.62', '0', '0.815', '#surprise', '#admiration', 'positive', '0.675', 'squiggle', 'hand', 'cacography', 'handwriting', 'script']
senticnet['singstar'] = ['0.925', '0.993', '0', '0.832', '#joy', '#interest', 'positive', '0.917', 'dance_samba', 'dance', 'skate', 'jump_rope', 'play_frisbee']
senticnet['singular'] = ['0', '0.099', '0.046', '0.187', '#interest', '#admiration', 'positive', '0.08', 'single', 'only', 'skinny', 'uppercase_bug', 'alone']
senticnet['singular_form'] = ['0', '0.882', '0', '0.905', '#interest', '#admiration', 'positive', '0.893', 'single', 'unit_of_measurement', 'singular', 'measurement_unit', 'grammatical_form']
senticnet['singularity'] = ['0', '0.131', '0.078', '0.129', '#interest', '#admiration', 'positive', '0.061', 'unique', 'array', 'widow', 'alone', 'single']
senticnet['singularly'] = ['-0.64', '0', '0.52', '-0.53', '#sadness', '#disgust', 'negative', '-0.56', 'wrong', 'incorrect', 'unbecoming', 'improper', 'unseemly']
senticnet['sinister'] = ['0', '0', '-0.75', '-0.93', '#fear', '#disgust', 'negative', '-0.84', 'illegitimate', 'inauspicious', 'perverse', 'shield', 'grim']
senticnet['sinister_person'] = ['-0.99', '0', '0.905', '-0.99', '#sadness', '#disgust', 'negative', '-0.96', 'bad', 'evil', 'evil_man', 'dangerous_person', 'killer']
senticnet['sinisterly'] = ['0.973', '0', '0', '0.722', '#joy', '#admiration', 'positive', '0.847', 'winningly', 'cutely', 'winsomely', 'engagingly', 'movingly']
senticnet['sink'] = ['-0.02', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.04', 'down_drain', 'septic_tank', 'water_fountain', 'drain', 'flea_market']
senticnet['sink_drain'] = ['0.939', '0.939', '0', '0', '#joy', '#interest', 'positive', '0.939', 'puddle', 'sop', 'liquid_water', 'finger_paint', 'bread']
senticnet['sink_faucet'] = ['0.822', '0.802', '0', '0', '#joy', '#interest', 'positive', '0.812', 'electric_toothbrush', 'towel', 't_shirt', 'heat_towel_rack', 'hair_brush']
senticnet['sink_hole'] = ['-0.02', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'tunnel', 'dark_place', 'ski_resort', 'top_tree', 'compost_pile']
senticnet['sink_ship'] = ['-0.06', '-0.06', '0', '-0.19', '#sadness', '#disgust', 'negative', '-0.06', 'use_military', 'drop_airplane', 'kill_soldier', 'make_war', 'conquer_enemy']
senticnet['sink_water'] = ['0', '0.07', '0.027', '0.14', '#interest', '#admiration', 'positive', '0.061', 'spring_up', 'bail_water', 'float_through_air', 'hold_two_together', 'travel_through_space']
senticnet['sinkiang'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['sinking'] = ['-0.76', '0', '-0.73', '0', '#sadness', '#fear', 'negative', '-0.74', 'sinking_feeling', 'misgiving', 'qualm', 'scruple', 'insecurely']
senticnet['sinking_boat'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'lifebelt', 'save_life', 'lifeguard', 'life_jacket', 'safety_equipment']
senticnet['sinking_deeper'] = ['-0.71', '0', '0.704', '-0.89', '#sadness', '#disgust', 'negative', '-0.76', 'soil', 'mud', 'dirt', 'bleaching_clay', 'bleaching_earth']
senticnet['sinking_feeling'] = ['-0.80', '0', '-0.8', '0', '#sadness', '#fear', 'negative', '-0.80', 'sinking', 'misgiving', 'qualm', 'scruple', 'insecurely']
senticnet['sinking_ship'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'lifebelt', 'save_life', 'lifeguard', 'life_jacket', 'safety_equipment']
senticnet['sinking_vessel'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'lifebelt', 'save_life', 'lifeguard', 'life_jacket', 'safety_equipment']
senticnet['sinne'] = ['-0.98', '0', '0.657', '-0.95', '#sadness', '#disgust', 'negative', '-0.86', 'impeachability', 'indictability', 'blameworthiness', 'culpability', 'culpableness']
senticnet['sinned_against'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitentially', 'penitent', 'penitence', 'repentant']
senticnet['sinner'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'mind_control', 'contradiction', 'sin', 'obsession', 'evil']
senticnet['sinning'] = ['0.688', '-0.64', '0', '0.818', '#joy', '#admiration', 'positive', '0.717', 'lust', 'luxuria', 'mortal_sin', 'pride', 'superbia']
senticnet['sinning_against'] = ['0.71', '0.882', '0', '0.9', '#interest', '#admiration', 'positive', '0.831', 'positive_reinforcement', 'brownie_point', 'benefit', 'give', 'gratification']
senticnet['sino_soviet'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['sino_soviet_split'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['sino_soviet_treaty'] = ['0.397', '0.245', '-0.13', '0.224', '#joy', '#interest', 'positive', '0.183', 'agreement', 'provide', 'advice', 'contribution', 'offer']
senticnet['sinoatrial'] = ['-0.56', '0', '0.849', '-0.61', '#anger', '#disgust', 'negative', '-0.67', 'convulsive', 'unsteady', 'jerky', 'spasmodic', 'spastic']
senticnet['sinonasal'] = ['-0.88', '0', '0.774', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fibroid_tumor', 'fibroid', 'benign_tumor', 'nonmalignant_tumor', 'nonmalignant_neoplasm']
senticnet['sint_truiden'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'feudal_lord', 'lord', 'master', 'overlord', 'king']
senticnet['sinuous'] = ['0.739', '-0.70', '0', '0', '#joy', '#surprise', 'positive', '0.722', 'adagio', 'drapery', 'sail', 'sportsmanship', 'gracefully']
senticnet['sinus_arrest'] = ['-0.56', '0', '0.849', '-0.61', '#anger', '#disgust', 'negative', '-0.67', 'convulsive', 'unsteady', 'jerky', 'spasmodic', 'spastic']
senticnet['sinus_bradycardia'] = ['-0.83', '0', '0.987', '-0.50', '#sadness', '#anger', 'negative', '-0.77', 'heart_block', 'atrioventricular_block', 'bradycardia', 'ventricular_fibrillation', 'tachycardia']
senticnet['sinus_congestion'] = ['-0.76', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.74', 'decrease_hunger', 'feel_full', 'stomach_problem', 'get_fat', 'a_little_hungry']
senticnet['sinus_infection'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'stiff', 'rigid', 'inflexible', 'harsh', 'immovable']
senticnet['sinus_pain'] = ['-0.06', '0.183', '0.651', '-0.64', '#anger', '#disgust', 'negative', '-0.29', 'allergic_reaction', 'chew', 'pierce', 'mastication', 'put_mouth']
senticnet['sinus_pressure'] = ['-0.06', '0.183', '0.651', '-0.64', '#anger', '#disgust', 'negative', '-0.29', 'allergic_reaction', 'chew', 'pierce', 'mastication', 'put_mouth']
senticnet['sinus_tachycardia'] = ['-0.56', '0', '0.849', '-0.61', '#anger', '#disgust', 'negative', '-0.67', 'convulsive', 'unsteady', 'jerky', 'spasmodic', 'spastic']
senticnet['sinus_tract'] = ['-0.52', '0', '0.538', '-0.95', '#anger', '#disgust', 'negative', '-0.67', 'sanies', 'pus', 'purulence', 'gleet', 'ichor']
senticnet['sinusitis'] = ['0', '0', '0.931', '-0.87', '#anger', '#disgust', 'negative', '-0.90', 'pharyngitis', 'laryngitis', 'bronchitis', 'tonsillitis', 'strep']
senticnet['sinusoidal'] = ['-0.97', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.92', 'voltage', 'inrush', 'quiescent', 'proliferating', 'steady-state']
senticnet['sinusoidal_projection'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'arm_movement', 'hand_gesture', 'water_motion', 'hand_signal', 'sea_movement']
senticnet['sinusoidally'] = ['0', '0.941', '0', '0.841', '#interest', '#admiration', 'positive', '0.891', 'discontinuously', 'modulated', 'oscillates', 'non-linearly', 'oscillated']
senticnet['sip'] = ['0.108', '0.1', '0', '0.109', '#joy', '#admiration', 'positive', '0.106', 'better_cold', 'gulp', 'potable_liquid', 'liquid_beverage', 'ingest']
senticnet['sip_wine'] = ['0.876', '0.883', '0', '0', '#joy', '#interest', 'positive', '0.879', 'walk_along', 'gait', 'take_step', 'run_slowly', 'locomotion']
senticnet['siphon'] = ['0.648', '0.618', '-0.26', '0.332', '#joy', '#interest', 'positive', '0.334', 'exhilarate', 'teased', 'titillating', 'titillate', 'titillation']
senticnet['siphon_tube'] = ['0', '0', '0.865', '-0.95', '#anger', '#disgust', 'negative', '-0.90', 'dirty_dish_overflow', 'hot_dirty_dish', 'hot_tap', 'dirty_dish', 'washcloth']
senticnet['siphonal_notch'] = ['0.969', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.772', 'aspheric', 'aspherical', 'prolate', 'rounded', 'crescent']
senticnet['siphoning'] = ['0', '0.866', '0', '0.802', '#interest', '#admiration', 'positive', '0.834', 'diverts', 'diverted', 'diversion', 'diverting', 'redirecting']
senticnet['siphonophore'] = ['0.96', '-0.77', '0', '0.929', '#joy', '#admiration', 'positive', '0.887', 'whale_louse', 'beach_flea', 'sand_hopper', 'sandhopper', 'amphipod']
senticnet['sipped_champagne'] = ['0.075', '0', '0', '0.158', '#joy', '#admiration', 'positive', '0.116', 'sway', 'swirl', 'body_movement', 'move_around', 'dance_move']
senticnet['sippin'] = ['0.797', '-0.95', '0', '0.921', '#surprise', '#admiration', 'positive', '0.891', 'lush', 'alcoholic', 'alky', 'dipsomaniac', 'boozer']
senticnet['sipping'] = ['-0.78', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'icy-cold', 'chilled', 'phosphate-buffered', 'margarita', 'ice-cold']
senticnet['sipping_hot'] = ['0.807', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.859', 'bubble', 'steam', 'hot', 'boil_water', 'tea']
senticnet['sippy_cup'] = ['0', '0', '0', '-0.06', '#disgust', '#disgust', 'negative', '-0.06', 'newsstand', 'bookshop', 'walmart', 'shoe_store', 'hooker']
senticnet['sir'] = ['0.068', '0.134', '-0.06', '0.121', '#interest', '#admiration', 'positive', '0.065', 'nobleman', 'ceo', 'leadership_position', 'country_leader', 'head_guy']
senticnet['sir_ghulam_hussain'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['sir_harrison_birtwistle'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'band_play', 'songwriter', 'rock_band']
senticnet['sir_malcolm_sargent'] = ['-0.91', '0', '0.931', '-0.72', '#sadness', '#anger', 'negative', '-0.85', 'hit_with_baton', 'get_hurt', 'whip', 'arrow', 'hurt']
senticnet['sir_nigel_gresley'] = ['0.069', '0.097', '0', '0.172', '#interest', '#admiration', 'positive', '0.113', 'type_of_engine', 'liquid_gas', 'warm_air', 'hot_air', 'steamy']
senticnet['sir_pathrick'] = ['-0.63', '0', '0.683', '-0.96', '#anger', '#disgust', 'negative', '-0.76', 'slovenly_woman', 'slut', 'trollop', 'slattern', 'litterer']
senticnet['sir_sedley'] = ['-0.95', '0', '-0.76', '0', '#sadness', '#fear', 'negative', '-0.85', 'disquietude', 'edginess', 'edgy', 'uptight', 'restive']
senticnet['sire'] = ['0.806', '0', '0', '0.752', '#joy', '#admiration', 'positive', '0.779', 'generate', 'develop', 'engender', 'render', 'come_up']
senticnet['siren'] = ['-0.04', '-0.02', '0.089', '-0.14', '#anger', '#disgust', 'negative', '-0.06', 'vacuum_cleaner', 'loudspeaker', 'collector', 'steam_locomotive', 'activist']
senticnet['siren_call'] = ['0.817', '-0.62', '0', '0.62', '#joy', '#surprise', 'positive', '0.688', 'quality', 'fascination', 'attractivenessment', 'attractiveness', 'temptingness']
senticnet['siren_song'] = ['0.817', '-0.62', '0', '0.62', '#joy', '#surprise', 'positive', '0.688', 'quality', 'fascination', 'attractivenessment', 'attractiveness', 'temptingness']
senticnet['sirloin'] = ['-0.73', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.78', 'skinless', 'bone-in', 'boneless', 'marinated', 'loin']
senticnet['sirloin_steak'] = ['0.783', '-0.63', '0', '0.829', '#joy', '#admiration', 'positive', '0.749', 'leg_of_lamb', 'gigot', 'joint', 'roast', 'rack_of_lamb']
senticnet['sirrah'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'holy_terror', 'rapscallion', 'scalawag', 'scallywag', 'monkey']
senticnet['sirrel'] = ['-0.9', '0', '0.749', '-0.71', '#sadness', '#anger', 'negative', '-0.78', 'chute', 'slide', 'slideway', 'sloping_trough', 'gutter']
senticnet['sisera'] = ['0.849', '-0.89', '0', '0.924', '#surprise', '#admiration', 'positive', '0.89', 'nonintellectual', 'lowbrow', 'lowbrowed', 'mindless', 'uncultivated']
senticnet['siskiyou_county'] = ['-0.06', '0.21', '0', '0.076', '#interest', '#admiration', 'positive', '0.074', 'new_think', 'figure_out', 'brain_activity', 'idea', 'come_up_idea']
senticnet['sisler'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'dicky', 'impaired', 'lessened', 'diminished']
senticnet['sissified'] = ['-0.78', '0', '0.561', '-0.64', '#sadness', '#disgust', 'negative', '-0.66', 'womanish', 'effeminate', 'emasculate', 'cissy', 'unmanlike']
senticnet['sissy'] = ['-0.71', '0', '0.972', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'scold', 'nag', 'scolder', 'harridan', 'common_scold']
senticnet['sister'] = ['0', '0.726', '0', '0.985', '#interest', '#admiration', 'positive', '0.855', 'foster_kid', 'daughter', 'relation', 'cousin', 'adopt']
senticnet['sister_chromatid_exchange'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'deflection', 'warp', 'aberrance']
senticnet['sister_house'] = ['0', '0', '-0.14', '0', '#fear', '#fear', 'negative', '-0.14', 'womb', 'anus', 'asshole', 'shit', 'land_moon']
senticnet['sister_keeper'] = ['-0.90', '0.152', '-0.62', '0.061', '#sadness', '#fear', 'negative', '-0.33', 'guilt_feeling', 'guilty', 'dolor', 'grieve', 'penitentially']
senticnet['sisterhood'] = ['0.648', '0.627', '-0.26', '0.329', '#joy', '#interest', 'positive', '0.334', 'brotherly', 'friendly', 'fraternal', 'amicability', 'amicable']
senticnet['sit'] = ['0.068', '0.134', '-0.06', '0.121', '#interest', '#admiration', 'positive', '0.065', 'sit_upon', 'sit_down', 'seat_guest', 'sit_at_theater', 'sit_around']
senticnet['sit_airplane'] = ['-0.70', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.76', 'ride_airplane', 'work_pile_up', 'traveling_long_distance', 'come_back_home', 'fall_asleep']
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senticnet['sit_at_theater'] = ['0.172', '0.236', '-0.13', '0.287', '#interest', '#admiration', 'positive', '0.14', 'watch_play', 'sell_ticket', 'watch_performance', 'attend_concert', 'popcorn']
senticnet['sit_back'] = ['-0.51', '0', '0.738', '-0.83', '#anger', '#disgust', 'negative', '-0.69', 'relax', 'loosen_up', 'unbend', 'unwind', 'decompress']
senticnet['sit_back_relax'] = ['-0.04', '0', '-0.07', '-0.10', '#fear', '#disgust', 'negative', '-0.07', 'put_clothes_back', 'clock_out', 'quit_work', 'make_sex', 'stop_work']
senticnet['sit_bench'] = ['-0.06', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.07', 'stop_bleed', 'make_look_better', 'change_color', 'make_house_look_better', 'fee_animal']
senticnet['sit_chair'] = ['0.897', '0.933', '0', '0', '#joy', '#interest', 'positive', '0.915', 'sit_down', 'take_break', 'stay_bed', 'lie_on_bed', 'go_sleep']
senticnet['sit_com'] = ['0.206', '-0.13', '0', '0', '#joy', '#surprise', 'positive', '0.172', 'dancer', 'old_dance', 'dance', 'slow_dance', 'ballroom_dance']
senticnet['sit_computer'] = ['-0.1', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.07', 'rush_job', 'explain_everything', 'deal_card_player', 'stand_alone', 'forward_email']
senticnet['sit_dark'] = ['0.991', '-0.71', '0', '0', '#joy', '#surprise', 'positive', '0.851', 'watch_movie', 'laugh', 'see_show', 'eat_popcorn', 'kill_brain_cell']
senticnet['sit_down'] = ['-0.02', '-0.01', '0.018', '-0.11', '#sadness', '#disgust', 'negative', '-0.03', 'sit_chair', 'sit_upon', 'lie_on_bed', 'take_break', 'lean_back']
senticnet['sit_down_chair'] = ['0.743', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.856', 'feel_sleepy', 'put_feet_up', 'digest', 'sit', 'clean_up_kitchen']
senticnet['sit_down_dinner'] = ['0.128', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.085', 'banquet', 'rave', 'auction', 'limbo', 'loose_end']
senticnet['sit_down_meal'] = ['0.128', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.085', 'banquet', 'rave', 'auction', 'limbo', 'loose_end']
senticnet['sit_down_protest'] = ['0.669', '-0.58', '0', '0.803', '#joy', '#admiration', 'positive', '0.685', 'pep_rally', 'revival', 'revival_meeting', 'gathering', 'assemblage']
senticnet['sit_down_restaurant'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'grocery_cart', 'super_market', 'fruit_stand', 'candy_store', 'produce_store']
senticnet['sit_down_table'] = ['0.786', '0.826', '0', '0', '#joy', '#interest', 'positive', '0.806', 'hear_well', 'earring', 'tap_foot', 'sing_along', 'order_menu']
senticnet['sit_floor'] = ['-0.09', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.07', 'smoke_joint', 'shovel_snow', 'fly_around', 'blow_away', 'hurt_animal']
senticnet['sit_furniture'] = ['0.988', '-0.79', '0', '0.65', '#joy', '#surprise', 'positive', '0.811', 'sit_place', 'find_chair', 'place_to_sit', 'bend_knee', 'chair']
senticnet['sit_idle'] = ['0', '0', '0.808', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'inaction', 'lazy', 'stay', 'inactive', 'recline']
senticnet['sit_in_shade'] = ['-0.03', '-0.00', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.04', 'wear_suit', 'underwater', 'turn_aircon_on', 'hold_breath', 'think_food']
senticnet['sit_jury'] = ['0.89', '0.88', '0', '0', '#joy', '#interest', 'positive', '0.885', 'examine_evidence', 'consider_evidence', 'find_out_fact', 'investigate', 'go_courtroom']
senticnet['sit_on_beach'] = ['0.759', '0', '0', '0', '#joy', '#joy', 'positive', '0.759', 'get_tan', 'enjoy_sun', 'summer', 'sunshine', 'dry_clothes']
senticnet['sit_on_desk'] = ['0.068', '0.134', '-0.06', '0.121', '#interest', '#admiration', 'positive', '0.065', 'sit', 'sit_chair', 'sit_upon', 'lie_on_bed', 'take_break']
senticnet['sit_out'] = ['-0.90', '0', '0.729', '-0.80', '#sadness', '#disgust', 'negative', '-0.81', 'bear_up', 'take_lying_down', 'swallow', 'live_with', 'tolerate']
senticnet['sit_outside'] = ['0.955', '-0.82', '0', '0', '#joy', '#surprise', 'positive', '0.889', 'work_pile_up', 'drink_cup_coffee', 'lean_back', 'put_feet_up', 'drink_coffee']
senticnet['sit_place'] = ['0.209', '0.115', '0', '0.109', '#joy', '#interest', 'positive', '0.144', 'sit_furniture', 'comfortable_sit', 'horizontal_surface', 'comfortable', 'comfortable_chair']
senticnet['sit_quietly'] = ['-0.73', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.77', 'impatient', 'impatience', 'restlessness', 'nervous', 'humility']
senticnet['sit_rest'] = ['0.797', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.871', 'sit', 'go_home', 'take_nap', 'sit_back_relax', 'sit_airplane']
senticnet['sit_rock_chair'] = ['-0.02', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.04', 'think_future', 'quiet_mind', 'miss_appointment', 'sleep_through', 'sleep_late']
senticnet['sit_seat'] = ['0.925', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.85', 'buy_popcorn', 'get_ticket', 'find_seat', 'get_front', 'entertain']
senticnet['sit_sofa'] = ['0.811', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.879', 'see_commercial', 'watch_commercial', 'fall_asleep', 'change_channel', 'become_bored']
senticnet['sit_stand'] = ['0.722', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.859', 'see_star', 'see_far_away', 'go_art_museum', 'use_eye', 'perform_well']
senticnet['sit_still'] = ['0.027', '0.104', '0', '0', '#joy', '#interest', 'positive', '0.066', 'turn_aircon_on', 'sit_in_shade', 'take_off_shoes', 'hold_breath', 'lie_around']
senticnet['sit_sun'] = ['0.797', '0', '0', '0', '#joy', '#joy', 'positive', '0.797', 'time_think', 'enjoy_beauty', 'rest', 'good_mental_health', 'relax']
senticnet['sit_table'] = ['-0.06', '-0.00', '0.033', '-0.04', '#sadness', '#disgust', 'negative', '-0.03', 'miss_appointment', 'clear_table', 'drink_coffee', 'stop_work', 'sleep_bed']
senticnet['sit_think'] = ['0.835', '0.715', '0', '0', '#joy', '#interest', 'positive', '0.775', 'think_creatively', 'brainstorm', 'think', 'scratch_head', 'hang_up']
senticnet['sit_toilet'] = ['-0.74', '0', '0.871', '0', '#sadness', '#anger', 'negative', '-0.80', 'gather_berry', 'hang_picture_on_wall', 'see_star_night', 'charge_credit_card', 'air_grievance']
senticnet['sit_up'] = ['-0.04', '0.114', '0', '0.09', '#interest', '#admiration', 'positive', '0.054', 'feel_grateful', 'work_home', 'butter_toast', 'read_bedtime_story', 'ease_pain']
senticnet['sit_upon'] = ['0.097', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.056', 'sleep_upon', 'overnight_guest', 'sleep', 'bed_time', 'good_sleep']
senticnet['sita_mata_wildlife_sanctuary'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'head_hurt', 'introspection', 'lifestyle_change', 'headache', 'move_eye']
senticnet['sitcom'] = ['0', '-0.04', '0', '0.09', '#surprise', '#admiration', 'positive', '0.067', 'commercial', 'satire', 'presentation', 'joke', 'funny']
senticnet['site'] = ['0.091', '0.09', '0', '0.134', '#joy', '#admiration', 'positive', '0.105', 'location', 'scope', 'place', 'position', 'home']
senticnet['sith_lord'] = ['-0.96', '0', '0.108', '-0.90', '#sadness', '#disgust', 'negative', '-0.66', 'evil', 'darkness', 'dark', 'sun_down', 'shadow']
senticnet['sitosterol'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'capsaicin', 'chemical_irritant', 'piperine', 'pepper_spray']
senticnet['sitting_beside'] = ['-0.03', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.04', 'rabbit', 'squirrel', 'mole', 'chipmunk', 'nymph']
senticnet['sitting_bolt_upright'] = ['-0.29', '0.2', '0.49', '-0.61', '#anger', '#disgust', 'negative', '-0.3', 'queasy', 'outrageous', 'horrid', 'angrily', 'see_red']
senticnet['sitting_bull'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['sitting_comfortably'] = ['-0.85', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.88', 'oppression', 'enslavement', 'slave', 'captivity', 'misery']
senticnet['sitting_duck'] = ['-0.53', '0', '0.591', '-0.59', '#anger', '#disgust', 'negative', '-0.57', 'mug', 'dupe', 'easy_mark', 'stooge', 'mortal']
senticnet['sitting_idle'] = ['0', '0', '0.808', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'inaction', 'lazy', 'stay', 'inactive', 'recline']
senticnet['sitting_liberal_mp'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'couch', 'stool', 'coach_seat']
senticnet['sitting_meditation'] = ['-0.13', '-0.07', '0.05', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'try_best', 'loose', 'try_hard', 'strategize', 'sore_knee']
senticnet['sitting_mp'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'couch', 'stool', 'coach_seat']
senticnet['sitting_posture'] = ['-0.93', '0', '0.807', '-0.95', '#sadness', '#disgust', 'negative', '-0.89', 'heatstroke', 'heat_hyperpyrexia', 'malady', 'breakdown', 'shock']
senticnet['sitting_quietly'] = ['-0.73', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.77', 'impatient', 'impatience', 'restlessness', 'nervous', 'humility']
senticnet['sitting_room'] = ['0', '0.171', '-0.05', '0.102', '#interest', '#admiration', 'positive', '0.072', 'bed', 'mattress', 'bead', 'bed_cover', 'seduce']
senticnet['situ_conservation'] = ['0', '0', '0', '0.857', '#admiration', '#admiration', 'positive', '0.857', 'say', 'beckon', 'call', 'conserve', 'retort']
senticnet['situ_tem'] = ['-0.93', '0', '0.739', '-0.96', '#sadness', '#disgust', 'negative', '-0.88', 'prevarication', 'fairytale', 'fairy_tale', 'fairy_story', 'play']
senticnet['situate'] = ['-0.95', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.91', 'isolated', 'peninsula', 'insular', 'surroundings', 'standoffish']
senticnet['situated_atop'] = ['0', '0.093', '0', '0.078', '#interest', '#admiration', 'positive', '0.085', 'steep', 'landscape_feature', 'knoll', 'hill_mountain', 'incline']
senticnet['situation'] = ['0', '0.836', '0', '0.731', '#interest', '#admiration', 'positive', '0.783', 'preemptive', 'preemptively', 'interfere', 'preemption', 'deter']
senticnet['situational'] = ['0.798', '-0.51', '0', '0.519', '#joy', '#admiration', 'positive', '0.611', 'averageness', 'commonness', 'normalcy', 'typicality', 'condition']
senticnet['situational_context'] = ['0.727', '-0.62', '0', '0.57', '#joy', '#surprise', 'positive', '0.64', 'utterer', 'deipnosophist', 'interlocutor', 'conversationalist', 'speaker']
senticnet['situla'] = ['0', '0.082', '-0.09', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'coffin', 'headstone', 'morgue', 'bury_body', 'casket']
senticnet['sivananda_yoga'] = ['0', '0', '0', '0.93', '#admiration', '#admiration', 'positive', '0.93', 'lift_weight', 'release_stress', 'move_body', 'take_hike', 'change_color']
senticnet['sivudu'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['sivushka'] = ['0', '0', '0.752', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'get_hit_by_car', 'meander', 'full_moon', 'step_dog_poop', 'kick_ass']
senticnet['siwa'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['six'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'hundred_year', 'twelve', 'day_month', 'time_measurement']
senticnet['six-game'] = ['-0.85', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.84', 'unbeaten', 'five-game', 'winless', 'three-game', 'four-game']
senticnet['six-question'] = ['-0.80', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'commitment-free', 'free-and-easy', 'one-camera', 'three-camera', 'self-forgetful']
senticnet['six-voice'] = ['0.924', '-0.81', '0', '0', '#joy', '#surprise', 'positive', '0.871', 'post-summit', 'full-dress', 'white-and-black', 'company-run', 'post-victory']
senticnet['six-year-old'] = ['0.99', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.947', 'five-year-old', 'three-year-old', 'four-year-old', 'two-year-old', 'seven-year-old']
senticnet['six_cylinder_engine'] = ['0.035', '0.112', '-0.07', '0.195', '#interest', '#admiration', 'positive', '0.067', 'car_action', 'car_travel', 'steer_car', 'make_go', 'transportation']
senticnet['six_feet_tall'] = ['0.209', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.118', 'old_dance', 'dance', 'slow_dance', 'dancer', 'enter_contest']
senticnet['six_flags_america'] = ['0.086', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'carny', 'fortune_teller', 'midway', 'ferris_wheel', 'roller-coaster']
senticnet['six_flags_astroworld'] = ['0.089', '-0.18', '0.037', '-0.06', '#joy', '#surprise', 'positive', '0.043', 'balloon', 'ballon', 'fielder', 'kite', 'blimp']
senticnet['six_footer'] = ['-0.71', '0', '0.845', '-0.69', '#sadness', '#anger', 'negative', '-0.75', 'lump', 'goon', 'mortal', 'clumsy_person', 'butterfingers']
senticnet['six_furlong'] = ['-0.76', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.78', 'strangle', 'bloodshed', 'cripple', 'hear_bad_news', 'sexually_transmit_disease']
senticnet['six_inches_deep'] = ['-0.71', '0', '0.704', '-0.89', '#sadness', '#disgust', 'negative', '-0.76', 'soil', 'mire', 'dirt', 'bleaching_clay', 'bleaching_earth']
senticnet['six_lane_expressway'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['six_lane_freeway'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['six_lane_highway'] = ['0', '0', '0', '0.085', '#admiration', '#admiration', 'positive', '0.085', 'use_farm', 'gate', 'door', 'doorway', 'entranceway']
senticnet['six_legs'] = ['0', '-0.07', '0.116', '-0.08', '#anger', '#disgust', 'negative', '-0.04', 'wing_insect', 'fly', 'small_bug', 'fly_away', 'small_insect']
senticnet['six_month'] = ['-0.85', '0', '0.584', '-0.79', '#sadness', '#disgust', 'negative', '-0.74', 'age', 'old_age', 'second_childhood', 'eld', 'geezerhood']
senticnet['six_oclock'] = ['0.919', '-0.83', '0', '0', '#joy', '#surprise', 'positive', '0.877', 'breakfast', 'dry_fruit', 'carton_milk', 'wheat', 'box_cereal']
senticnet['six_shooter'] = ['-0.97', '0', '0.677', '-0.95', '#sadness', '#disgust', 'negative', '-0.86', 'stunt', 'game', 'mutant', 'invertebrate', 'animate_being']
senticnet['six_sided_dice'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'poppy_seed', 'custard', 'bread', 'biscuit']
senticnet['six_sides'] = ['0', '0.081', '0.039', '0.196', '#interest', '#admiration', 'positive', '0.079', 'four_sides', 'polygon', 'geometric_figure', 'hexagon', 'parallelogram']
senticnet['six_wicket_victory'] = ['0.034', '-0.02', '0.054', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'compass', 'swiss_army_knife', 'bake_pan', 'coffee_mug', 'wine_opener']
senticnet['sixfold'] = ['-0.02', '0.095', '0', '0.117', '#interest', '#admiration', 'positive', '0.063', 'identification', 'paper_square', 'carry_money', 'money_hold', 'credit']
senticnet['sixpence'] = ['0.057', '0.181', '-0.04', '0.18', '#interest', '#admiration', 'positive', '0.092', 'small_money', 'alter', 'money_change', 'money_coin', 'small_coin']
senticnet['sixteen'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'hundred_year', 'dozen', 'day_month', 'time_measurement']
senticnet['sixteen_year_old'] = ['0.575', '-0.62', '0', '0.596', '#surprise', '#admiration', 'positive', '0.598', 'altar_boy', 'cub', 'ball_boy', 'bat_boy', 'lad']
senticnet['sixteenth'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'home', 'position', 'time_limit', 'monthly', 'modern_home']
senticnet['sixteenth_century'] = ['0.978', '-0.60', '0', '0.895', '#joy', '#admiration', 'positive', '0.825', 'period', 'quattrocento', 'twentieth_century', 'time_period', 'period_of_time']
senticnet['sixth'] = ['0.033', '0.158', '-0.06', '0.145', '#interest', '#admiration', 'positive', '0.067', 'finish', 'end', 'finish_complete', 'end_point', 'finalize']
senticnet['sixth-best'] = ['0.987', '-0.82', '0', '0', '#joy', '#surprise', 'positive', '0.906', 'third-best', 'fourth-best', 'fifth-best', 'second-worst', 'second-best']
senticnet['sixth_century_bc'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'home', 'position', 'time_limit', 'monthly', 'modern_home']
senticnet['sixth_circuit_nomination'] = ['0.031', '0.218', '-0.09', '0.299', '#interest', '#admiration', 'positive', '0.113', 'nominate', 'axiom', 'supreme', 'govern', 'tenet']
senticnet['sixth_commandment'] = ['0', '0', '0.753', '-0.72', '#anger', '#disgust', 'negative', '-0.73', 'sinful', 'human_emotion', 'call', 'demand', 'yearn']
senticnet['sixth_dynasty'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
senticnet['sixth_form'] = ['-0.04', '0.097', '0.06', '0.146', '#interest', '#admiration', 'positive', '0.036', 'vector', 'ratio', 'length_measurement', 'measurement_unit', 'unit_of_measurement']
senticnet['sixth_grade'] = ['0.521', '0.677', '-0.27', '0.273', '#joy', '#interest', 'positive', '0.299', 'further_education', 'protect_child', 'look_closely', 'go_to_school', 'register']
senticnet['sixth_grade_teacher'] = ['0.521', '0.677', '-0.27', '0.273', '#joy', '#interest', 'positive', '0.299', 'further_education', 'protect_child', 'look_closely', 'go_to_school', 'register']
senticnet['sixth_grader'] = ['0.521', '0.677', '-0.27', '0.273', '#joy', '#interest', 'positive', '0.299', 'further_education', 'protect_child', 'look_closely', 'go_to_school', 'register']
senticnet['sixth_installment'] = ['-0.06', '0.122', '-0.04', '0.113', '#interest', '#admiration', 'positive', '0.032', 'encounter', 'portray', 'reveal', 'episode', 'next_episode']
senticnet['sixth_place'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'home', 'position', 'time_limit', 'monthly', 'modern_home']
senticnet['sixth_round'] = ['0.823', '0.982', '0', '0', '#joy', '#interest', 'positive', '0.902', 'spherical', 'three_dimensions', 'round_orb', 'coil', 'fill_with_sand']
senticnet['sixty'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'hundred_year', 'twelve', 'day_month', 'time_measurement']
senticnet['sixty_nine'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'hundred_year', 'dozen', 'day_month', 'time_measurement']
senticnet['sixty_six'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'hundred_year', 'dozen', 'day_month', 'time_measurement']
senticnet['sizable'] = ['0', '0.803', '0', '0.844', '#interest', '#admiration', 'positive', '0.824', 'copious', 'plenteous', 'plentiful', 'rich', 'ample']
senticnet['sizable_fortune'] = ['0.976', '0.811', '0', '0', '#joy', '#interest', 'positive', '0.893', 'deposit', 'bank_loan', 'wealth', 'finance', 'account']
senticnet['size'] = ['0', '0.1', '0.083', '0.152', '#interest', '#admiration', 'positive', '0.056', 'measure_unit', 'unit', 'measure', 'magnitude', 'mass_measure']
senticnet['size_bronze_statue'] = ['0.085', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.079', 'sculpture', 'creative', 'yellow_ribbon', 'artwork', 'monument']
senticnet['size_up'] = ['0', '0.14', '0.06', '0.237', '#interest', '#admiration', 'positive', '0.106', 'big_size', 'measure', 'fat', 'measure_pound', 'mass_measure']
senticnet['size_vary'] = ['-0.05', '0', '-0.12', '-0.16', '#fear', '#disgust', 'negative', '-0.11', 'reduced', 'tiny', 'small_size', 'narrow', 'poor']
senticnet['sizeable'] = ['0.881', '0', '0', '0.875', '#joy', '#admiration', 'positive', '0.878', 'minor', 'meek', 'low', 'humble', 'small']
senticnet['sized_bed'] = ['0.716', '0.96', '0', '0', '#joy', '#interest', 'positive', '0.838', 'airline_seat', 'small_seat', 'beanbag_chair', 'hide-a-bed_sofa', 'chair']
senticnet['sized_bronze_statue'] = ['0.085', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.079', 'sculpture', 'creative', 'yellow_ribbon', 'artwork', 'monument']
senticnet['sized_statue'] = ['0.085', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.079', 'sculpture', 'creative', 'yellow_ribbon', 'artwork', 'monument']
senticnet['sizeof'] = ['-0.04', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.06', 'chemical_reaction', 'burn', 'chemical_process', 'get_burnt', 'set_on_fire']
senticnet['sizzling'] = ['-0.91', '0', '0.754', '0', '#sadness', '#anger', 'negative', '-0.83', 'seductive', 'alluring', 'sensuous', 'dreamy', 'sultry']
senticnet['sizzurp'] = ['0.797', '-0.95', '0', '0.921', '#surprise', '#admiration', 'positive', '0.891', 'lush', 'alcoholic', 'alky', 'dipsomaniac', 'boozer']
senticnet['sjgren_syndrome'] = ['-0.75', '0', '0.515', '-0.93', '#sadness', '#disgust', 'negative', '-0.73', 'sclerosis', 'induration', 'degenerative_disorder', 'autoimmune_disease', 'disseminated_sclerosis']
senticnet['sjoblom'] = ['0.025', '0.27', '-0.11', '0.243', '#interest', '#admiration', 'positive', '0.105', 'keep_safe', 'store_dry_goods', 'store_stuff', 'secure', 'save']
senticnet['ska_punk'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'explicit', 'declared', 'definitive', 'graphic']
senticnet['skag'] = ['-0.7', '0', '0.698', '-0.57', '#sadness', '#anger', 'negative', '-0.65', 'smack', 'hell_dust', 'nose_drop', 'thunder', 'diacetylmorphine']
senticnet['skank'] = ['-0.05', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.05', 'masturbate', 'jerk', 'jerk_off', 'watch_porn', 'porn']
senticnet['skanky'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['skate'] = ['-0.88', '0', '0.778', '-0.79', '#sadness', '#disgust', 'negative', '-0.82', 'climb_mountain', 'chop_down_tree', 'prune_tree', 'ski', 'jump_out_of_window']
senticnet['skate_blade'] = ['0.783', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.766', 'baseball_bat', 'water_gun', 'goalie', 'hockey_stick', 'bullseye']
senticnet['skate_ice'] = ['0', '0.14', '0.06', '0.237', '#interest', '#admiration', 'positive', '0.106', 'skate', 'winter_sport', 'rollerblade', 'skateboard', 'ski']
senticnet['skate_plaza'] = ['0', '0', '0', '-0.1', '#disgust', '#disgust', 'negative', '-0.1', 'ice_rink', 'skate', 'room_temperature', 'loose_end', 'smooth']
senticnet['skate_punk'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'explicit', 'declared', 'definitive', 'graphic']
senticnet['skate_rink'] = ['0', '0', '0', '-0.1', '#disgust', '#disgust', 'negative', '-0.1', 'ice_rink', 'skate', 'room_temperature', 'loose_end', 'smooth']
senticnet['skate_shop'] = ['-0.18', '0', '-0.14', '-0.11', '#sadness', '#fear', 'negative', '-0.14', 'fly_kite', 'walk_dog', 'ride_roller_coaster', 'rake_leave', 'jump_up_down']
senticnet['skateboard'] = ['-0.18', '0', '-0.14', '-0.11', '#sadness', '#fear', 'negative', '-0.14', 'fly_kite', 'walk_dog', 'ride_roller_coaster', 'rake_leave', 'jump_up_down']
senticnet['skateboard_deck'] = ['-0.18', '0', '-0.14', '-0.11', '#sadness', '#fear', 'negative', '-0.14', 'fly_kite', 'walk_dog', 'ride_roller_coaster', 'rake_leave', 'jump_up_down']
senticnet['skateboard_park'] = ['0', '0', '0', '-0.1', '#disgust', '#disgust', 'negative', '-0.1', 'ice_rink', 'skate', 'room_temperature', 'loose_end', 'smooth']
senticnet['skateboarder'] = ['-0.18', '0', '-0.14', '-0.11', '#sadness', '#fear', 'negative', '-0.14', 'fly_kite', 'walk_dog', 'ride_roller_coaster', 'rake_leave', 'jump_up_down']
senticnet['skateboarding'] = ['-0.18', '0', '-0.14', '-0.11', '#sadness', '#fear', 'negative', '-0.14', 'fly_kite', 'walk_dog', 'ride_roller_coaster', 'rake_leave', 'jump_up_down']
senticnet['skating'] = ['0.045', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'skate', 'rollerblade', 'skateboard', 'ice_skate', 'roller_skate']
senticnet['skating_rink'] = ['0.02', '0.244', '0', '0.225', '#interest', '#admiration', 'positive', '0.163', 'game', 'disneyland', 'ballpark', 'hockey_game', 'sport_event']
senticnet['skatt'] = ['-0.63', '0', '0.791', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'little', 'pinch', 'modicum', 'small_indefinite_amount', 'indefinite_quantity']
senticnet['skdar'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'caravansary', 'caravanserai', 'khan', 'caravan_inn', 'hostel']
senticnet['skedaddle'] = ['-0.69', '0', '0.998', '-0.84', '#anger', '#disgust', 'negative', '-0.84', 'evasion', 'lam', 'break', 'escape', 'hegira']
senticnet['skee_ball'] = ['0.925', '0.993', '0', '0.832', '#joy', '#interest', 'positive', '0.917', 'dance_samba', 'dance', 'skate', 'jump_rope', 'play_frisbee']
senticnet['skeet'] = ['0', '-0.07', '0.091', '-0.19', '#anger', '#disgust', 'negative', '-0.07', 'projectile', 'use_hunt', 'weapon', 'ammunition', 'firearm']
senticnet['skeletal'] = ['-0.96', '0', '0.817', '0', '#sadness', '#anger', 'negative', '-0.88', 'gaunt', 'emaciated', 'haggard', 'bony', 'cadaverous']
senticnet['skeletal_disorder'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'dystrophy', 'myotonic_dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['skeletal_dysplasia'] = ['-0.87', '0', '0.873', '-0.62', '#sadness', '#anger', 'negative', '-0.78', 'dwarfism', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['skeletal_hypha'] = ['0', '0.097', '0', '0.085', '#interest', '#admiration', 'positive', '0.091', 'lunula', 'arm_part', 'leave_hand', 'ten_fingers', 'five_fingers']
senticnet['skeletal_muscle'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'deltoid', 'pterygoid_muscle', 'abductor', 'abductor_muscle']
senticnet['skeletal_system'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'artery', 'main_artery', 'groin', 'vein', 'muscle']
senticnet['skeleton'] = ['0', '0', '-0.04', '-0.04', '#fear', '#disgust', 'negative', '-0.04', 'inside_body', 'dog_treat', 'body', 'live_meat', 'flesh']
senticnet['skep'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'lair', 'habitat', 'animal_home']
senticnet['skeptic'] = ['-0.52', '0', '0.95', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'pessimist', 'sceptic', 'doubter', 'defeatist', 'negativist']
senticnet['skeptical'] = ['0', '0', '-0.99', '-0.99', '#fear', '#disgust', 'negative', '-0.99', 'unbelieving', 'sceptical', 'doubting', 'disbelieving', 'questioning']
senticnet['skeptical_attitude'] = ['0', '0', '0.928', '-0.70', '#anger', '#disgust', 'negative', '-0.81', 'doubt', 'incertitude', 'dubiety', 'doubtfulness', 'dubiousness']
senticnet['skeptically'] = ['0', '0', '0.928', '-0.70', '#anger', '#disgust', 'negative', '-0.81', 'doubt', 'incertitude', 'dubiety', 'doubtfulness', 'dubiousness']
senticnet['skepticism'] = ['0', '0', '0.928', '-0.70', '#anger', '#disgust', 'negative', '-0.81', 'doubt', 'incertitude', 'dubiety', 'doubtfulness', 'dubiousness']
senticnet['skepticism_toward'] = ['0', '0', '0.928', '-0.70', '#anger', '#disgust', 'negative', '-0.81', 'doubt', 'incertitude', 'dubiety', 'doubtfulness', 'dubiousness']
senticnet['sketch'] = ['0', '0.074', '0', '0.06', '#interest', '#admiration', 'positive', '0.067', 'use_crayon', 'draw', 'pencil_art', 'make_sketch', 'outline']
senticnet['sketch_artist'] = ['0', '0.074', '0', '0.06', '#interest', '#admiration', 'positive', '0.067', 'use_crayon', 'draw', 'pencil_art', 'make_sketch', 'outline']
senticnet['sketch_comedy'] = ['0.206', '-0.13', '0', '0', '#joy', '#surprise', 'positive', '0.172', 'dancer', 'old_dance', 'dance', 'slow_dance', 'ballroom_dance']
senticnet['sketch_comedy_show'] = ['0.206', '-0.13', '0', '0', '#joy', '#surprise', 'positive', '0.172', 'dancer', 'old_dance', 'dance', 'slow_dance', 'ballroom_dance']
senticnet['sketch_pad'] = ['0.031', '0.08', '0.023', '0.069', '#interest', '#admiration', 'positive', '0.039', 'marker', 'graphite_pencil', 'pen', 'mechanical_graphite_pencil', 'stamp_pad']
senticnet['sketch_show'] = ['0.206', '-0.13', '0', '0', '#joy', '#surprise', 'positive', '0.172', 'dancer', 'old_dance', 'dance', 'slow_dance', 'ballroom_dance']
senticnet['sketch_show_chewin'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'obese', 'worse', 'health_issue', 'clinical_problem']
senticnet['sketch_troupe'] = ['0.206', '-0.13', '0', '0', '#joy', '#surprise', 'positive', '0.172', 'dancer', 'old_dance', 'dance', 'slow_dance', 'ballroom_dance']
senticnet['sketchbook'] = ['0', '0.074', '0', '0.06', '#interest', '#admiration', 'positive', '0.067', 'use_crayon', 'draw', 'pencil_art', 'make_sketch', 'outline']
senticnet['sketched_out'] = ['0', '0.211', '-0.12', '0.286', '#interest', '#admiration', 'positive', '0.125', 'bring_together', 'fuse', 'redo', 'fuse_together', 'request']
senticnet['sketcher'] = ['0', '0.074', '0', '0.06', '#interest', '#admiration', 'positive', '0.067', 'use_crayon', 'draw', 'pencil_art', 'make_sketch', 'outline']
senticnet['sketchily'] = ['-0.81', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.79', 'approximately', 'delicacy', 'inexactly', 'roughly', 'grossly']
senticnet['sketchpad'] = ['0', '0.074', '0', '0.06', '#interest', '#admiration', 'positive', '0.067', 'use_crayon', 'draw', 'pencil_art', 'make_sketch', 'outline']
senticnet['sketchy'] = ['0.903', '-0.93', '0', '0.993', '#surprise', '#admiration', 'positive', '0.944', 'incomplete', 'unelaborated', 'partial', 'uncompleted', 'broken']
senticnet['skew_symmetric_matrix'] = ['0.042', '-0.00', '0.027', '0.099', '#joy', '#admiration', 'positive', '0.031', 'chess_knight', 'chess_queen', 'chess_pawn', 'pawn', 'chess_bishop']
senticnet['skewed'] = ['0', '0', '-0.83', '-0.89', '#fear', '#disgust', 'negative', '-0.86', 'gauche', 'awkward', 'gaucheness', 'bumbling', 'gauchely']
senticnet['skewer'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'aluminium_foil', 'tin_foil', 'gold_foil']
senticnet['skewness'] = ['-0.59', '0', '0.672', '-0.83', '#anger', '#disgust', 'negative', '-0.7', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'asymmetry', 'irregularity']
senticnet['ski'] = ['-0.88', '0', '0.778', '-0.79', '#sadness', '#disgust', 'negative', '-0.82', 'climb_mountain', 'skate', 'prune_tree', 'chop_down_tree', 'jump_out_of_window']
senticnet['ski_bind'] = ['0.779', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.885', 'bind', 'befriend', 'three-quarter_bind', 'keep_paper_together', 'fasten']
senticnet['ski_binding'] = ['0.715', '0', '0', '0.869', '#joy', '#admiration', 'positive', '0.792', 'binding', 'back', 'obligate', 'truss', 'book_binding']
senticnet['ski_resort'] = ['0.947', '0.925', '0', '0', '#joy', '#interest', 'positive', '0.936', 'canyon', 'high_mountain', 'beautiful_place', 'alp', 'high_place']
senticnet['ski_trip'] = ['0.977', '0.828', '0', '0', '#joy', '#interest', 'positive', '0.902', 'hunt_fish', 'out_place', 'getaway', 'outdoor_activity', 'vacation']
senticnet['ski_vacation'] = ['0.024', '0.152', '-0.06', '0.236', '#interest', '#admiration', 'positive', '0.087', 'vacation', 'exit', 'stance', 'take_break', 'holiday']
senticnet['skiagraphia'] = ['-0.87', '0', '0.599', '-0.71', '#sadness', '#disgust', 'negative', '-0.73', 'hysteria', 'fury', 'delirium', 'frenzy', 'craze']
senticnet['skid'] = ['-0.77', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.8', 'fall_over', 'lose_foot', 'lose_balance', 'stunt', 'slip']
senticnet['skid_mark'] = ['-0.95', '0', '0.932', '0', '#sadness', '#anger', 'negative', '-0.94', 'shift_gear', 'stop_red_light', 'change_gear', 'run_out_gas', 'car_crash']
senticnet['skid_resistance'] = ['0', '-0.01', '0.046', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'cement', 'tar', 'concrete', 'big_city_life', 'popular_building_material']
senticnet['skid_row'] = ['-0.88', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.81', 'bum', 'red_light', 'petition', 'take', 'violent_action']
senticnet['skiddy_von_stade'] = ['-0.69', '0', '0.692', '-0.66', '#sadness', '#anger', 'negative', '-0.68', 'wrinkle', 'crinkle', 'crease', 'crisp', 'ruck_up']
senticnet['skier'] = ['0', '-0.03', '-0.10', '-0.07', '#fear', '#disgust', 'negative', '-0.04', 'autumn', 'winter', 'avalanche', 'snow_fall', 'volcano']
senticnet['skiff'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'driftwood', 'sea_shell', 'scenic_view', 'naked_woman', 'tidal_wave']
senticnet['skiffle_group'] = ['-0.50', '0', '0.902', '-0.54', '#anger', '#disgust', 'negative', '-0.65', 'gamelan', 'jug_band', 'pop_group', 'mariachi', 'ensemble']
senticnet['skiggaret'] = ['-0.04', '0.166', '0.684', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'malign', 'malefic', 'maliciously', 'spitefulness', 'maliciousness']
senticnet['skiing_accident'] = ['-0.73', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.79', 'die_painfully', 'arrest_police', 'get_accident', 'fall_head', 'skin_knee']
senticnet['skiing_off_piste'] = ['0', '-0.03', '-0.10', '-0.07', '#fear', '#disgust', 'negative', '-0.04', 'autumn', 'winter', 'skier', 'snow_fall', 'volcano']
senticnet['skilful'] = ['0.633', '-0.50', '0', '0.59', '#joy', '#admiration', 'positive', '0.575', 'craftsman', 'manicurist', 'artisan', 'journeyman', 'beautician']
senticnet['skilfully'] = ['0', '-0.90', '0', '0.714', '#surprise', '#admiration', 'positive', '0.811', 'elegantly', 'suitably', 'gracefully', 'featly', 'graceful']
senticnet['skill'] = ['-0.03', '0.183', '0', '0.181', '#interest', '#admiration', 'positive', '0.111', 'talent', 'draw_blueprint', 'plan', 'blueprint', 'illustrate']
senticnet['skill_level'] = ['0.763', '0.921', '0', '0', '#joy', '#interest', 'positive', '0.842', 'yardstick', 'next_level', 'scale', 'measure_tool', 'same_level']
senticnet['skilled'] = ['0.023', '0.135', '-0.05', '0.206', '#interest', '#admiration', 'positive', '0.077', 'capable', 'expert', 'tin', 'dense_water', 'metal_container']
senticnet['skilled_assassin'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'yeoman', 'outrider', 'yeoman_of_guard', 'beefeater', 'defender']
senticnet['skilled_carpenter'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'maker']
senticnet['skilled_craftsman'] = ['0.876', '-0.76', '0', '0.963', '#joy', '#admiration', 'positive', '0.867', 'housecraft', 'tradecraft', 'watercraft', 'acquirement', 'skill']
senticnet['skilled_craftsmanship'] = ['0.876', '-0.76', '0', '0.963', '#joy', '#admiration', 'positive', '0.867', 'housecraft', 'tradecraft', 'watercraft', 'acquirement', 'skill']
senticnet['skilled_fighter'] = ['0.586', '-0.94', '0', '0.748', '#surprise', '#admiration', 'positive', '0.76', 'vanquish', 'beat', 'beat_out', 'trounce', 'crush']
senticnet['skilled_labor'] = ['0.814', '-0.87', '0', '0.918', '#surprise', '#admiration', 'positive', '0.87', 'ship_company', 'company', 'work_force', 'workforce', 'full_complement']
senticnet['skilled_magician'] = ['0.818', '0.913', '0', '0.825', '#interest', '#admiration', 'positive', '0.852', 'greeting', 'blackjack', 'pass_card', 'credit', 'play_blackjack']
senticnet['skilled_manpower'] = ['0.814', '-0.87', '0', '0.918', '#surprise', '#admiration', 'positive', '0.87', 'ship_company', 'company', 'work_force', 'workforce', 'full_complement']
senticnet['skilled_marksman'] = ['0.93', '-0.95', '0', '0.909', '#joy', '#surprise', 'positive', '0.93', 'acquirement', 'skill', 'accomplishment', 'acquisition', 'attainment']
senticnet['skilled_negotiator'] = ['0.865', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.887', 'manager', 'colleague', 'office_worker', 'coworker', 'secretary']
senticnet['skilled_personnel'] = ['0.814', '-0.87', '0', '0.918', '#surprise', '#admiration', 'positive', '0.87', 'ship_company', 'company', 'work_force', 'workforce', 'full_complement']
senticnet['skilled_swordsman'] = ['-0.95', '0', '0.774', '0', '#sadness', '#anger', 'negative', '-0.86', 'argue', 'rival', 'disagreement', 'opposition', 'anti']
senticnet['skilled_workman'] = ['-0.54', '0', '0.739', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'unprofitable', 'idle', 'lean', 'dead', 'marginal']
senticnet['skillful'] = ['0', '0.738', '0', '0.809', '#interest', '#admiration', 'positive', '0.774', 'dexterous', 'skill', 'legwork', 'sly', 'subtle']
senticnet['skillfully'] = ['0.804', '-0.95', '0', '0', '#joy', '#surprise', 'positive', '0.878', 'smoke', 'right-footed', 'left-handed', 'skillful', 'handily']
senticnet['skillfulness'] = ['0.668', '-0.87', '0', '0.606', '#joy', '#surprise', 'positive', '0.715', 'adeptness', 'adroitness', 'deftness', 'quickness', 'expertness']
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senticnet['skim_milk_powder'] = ['0.057', '0', '0.018', '0.058', '#joy', '#admiration', 'positive', '0.032', 'porridge', 'food', 'milk', 'healthy', 'morning_food']
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senticnet['skimmed_milk'] = ['0.053', '0.085', '0.019', '0', '#joy', '#interest', 'positive', '0.04', 'farm', 'countryside', 'farm_animal', 'cow', 'healthy']
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senticnet['skimp'] = ['0.742', '-0.70', '0', '0', '#joy', '#surprise', 'positive', '0.722', 'scrimp', 'economical', 'scant', 'frugal', 'stinting']
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senticnet['skimpole'] = ['-0.63', '0', '0.591', '-0.73', '#sadness', '#disgust', 'negative', '-0.65', 'irreality', 'void', 'impossibleness', 'unreality', 'impossibility']
senticnet['skimpy'] = ['-0.88', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.82', 'bikini', 'skin-tight', 'panties', 'undies', 'thong']
senticnet['skin'] = ['0', '0', '0.021', '-0.06', '#anger', '#disgust', 'negative', '-0.04', 'scrotum', 'skeletal_system', 'shoulder', 'cuticle', 'groin']
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senticnet['skin-to-skin'] = ['0.839', '0.871', '0', '0', '#joy', '#interest', 'positive', '0.855', 'peer-to-peer', 'human-to-human', 'person-to-person', 'cell-to-cell', 'fecal-oral']
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senticnet['skin_cancer'] = ['-0.09', '0', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.09', 'underground_animal', 'under_ground', 'bury_body', 'sickness', 'underground']
senticnet['skin_care_article'] = ['0.74', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.843', 'clipper', 'double_edge_razor', 'thumb_tack', 'pair_of_scissors', 'birth_control_pill']
senticnet['skin_cleanser'] = ['0', '0.086', '-0.06', '0.12', '#interest', '#admiration', 'positive', '0.046', 'gel_toothpaste', 'tartar_control_toothpaste', 'fluoride_mouthwash', 'toothpaste', 'hair_lotion']
senticnet['skin_color'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'blonde', 'purple', 'violet', 'dark_light', 'hue']
senticnet['skin_complexion'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'blonde', 'purple', 'violet', 'dark_light', 'hue']
senticnet['skin_condition'] = ['-0.85', '0', '0', '-0.96', '#sadness', '#disgust', 'negative', '-0.90', 'acne_medication', 'adolescent', 'pimple', 'medical_treatment', 'prescription']
senticnet['skin_cover'] = ['0', '0.066', '0.041', '0.109', '#interest', '#admiration', 'positive', '0.045', 'flexible', 'often_red', 'rigid', 'human_organ', 'different_shape']
senticnet['skin_cream'] = ['-0.81', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.82', 'peanut', 'leave_over', 'floppy', 'circular_motion', 'store_vegetable']
senticnet['skin_discoloration'] = ['-0.10', '0', '-0.05', '-0.11', '#sadness', '#disgust', 'negative', '-0.08', 'jaw', 'cheek', 'dimple', 'forehead', 'beard']
senticnet['skin_disease'] = ['-0.89', '0', '0.998', '-0.89', '#sadness', '#anger', 'negative', '-0.93', 'erythema', 'flare', 'erythroderma', 'disease_of_skin', 'skin_disorder']
senticnet['skin_disorder'] = ['-0.89', '0', '0.998', '-0.89', '#sadness', '#anger', 'negative', '-0.93', 'erythema', 'flare', 'skin_disease', 'disease_of_skin', 'erythroderma']
senticnet['skin_elasticity'] = ['0.798', '-0.61', '0', '0.899', '#joy', '#admiration', 'positive', '0.77', 'pliancy', 'pliantness', 'flexibility', 'flexibleness', 'suppleness']
senticnet['skin_flap'] = ['-0.58', '0', '0.715', '-0.59', '#anger', '#disgust', 'negative', '-0.63', 'fly', 'overlap', 'barndoor', 'coattail', 'codpiece']
senticnet['skin_friction'] = ['0.676', '0.63', '-0.23', '0.31', '#joy', '#interest', 'positive', '0.345', 'perkiness', 'walk_on_air', 'exultingly', 'exultantly', 'jubilance']
senticnet['skin_impurity'] = ['-0.02', '0', '0.048', '0', '#sadness', '#anger', 'negative', '-0.03', 'furry_creature', 'black_dot', 'big_mouse', 'underground_animal', 'small_rodent']
senticnet['skin_incision'] = ['-0.65', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'put_to_sleep', 'operation', 'general_anesthesia', 'surgery', 'anesthesia']
senticnet['skin_infection'] = ['-0.71', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.73', 'pain', 'gape', 'sunburn', 'ouch', 'cut_skin']
senticnet['skin_inflammation'] = ['-0.72', '0', '0.778', '-0.98', '#anger', '#disgust', 'negative', '-0.82', 'actinic_dermatitis', 'hypericism', 'diaper_rash', 'dermatitis', 'seborrheic_dermatitis']
senticnet['skin_injury'] = ['-0.05', '0', '-0.07', '-0.10', '#fear', '#disgust', 'negative', '-0.07', 'char', 'burn_hot', 'burn_skin', 'burn', 'grow_yard']
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senticnet['skin_irritation'] = ['-0.70', '0', '-0.83', '-0.98', '#fear', '#disgust', 'negative', '-0.84', 'body_hurt', 'ache', 'ouch', 'cut_skin', 'scar']
senticnet['skin_jaundice'] = ['-0.56', '0', '0.906', '-0.80', '#anger', '#disgust', 'negative', '-0.76', 'jaundice_of_newborn', 'disease', 'icterus_neonatorum', 'kernicterus', 'symptom']
senticnet['skin_knee'] = ['-0.2', '-0.16', '0', '-0.34', '#sadness', '#disgust', 'negative', '-0.12', 'get_hurt', 'fall_head', 'bloody_nose', 'injure', 'serious_injury']
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senticnet['skin_patch'] = ['-0.58', '0', '0.561', '-0.62', '#sadness', '#disgust', 'negative', '-0.58', 'spot', 'speckle', 'dapple', 'mark', 'fleck']
senticnet['skin_pigment'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'blonde', 'purple', 'violet', 'dark_light', 'hue']
senticnet['skin_pigmentation'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'blonde', 'purple', 'violet', 'dark_light', 'hue']
senticnet['skin_prick_test'] = ['0.928', '-0.87', '0', '0.679', '#joy', '#surprise', 'positive', '0.829', 'examine', 'experiment', 'alpha_test', 'beta_test', 'road_test']
senticnet['skin_rash'] = ['-0.12', '-0.08', '0.349', '-0.40', '#anger', '#disgust', 'negative', '-0.19', 'rash', 'irritation', 'itch', 'provocative', 'skin_irritation']
senticnet['skin_rug'] = ['0.022', '-0.01', '0.062', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'electric_toothbrush', 'moistener', 'hairbrush', 'shampoo_conditioner', 'shave_kit']
senticnet['skin_sensation'] = ['-0.72', '0', '0.501', '-0.57', '#sadness', '#disgust', 'negative', '-0.6', 'itch', 'urtication', 'itchiness', 'itching', 'tickle']
senticnet['skin_sensitization'] = ['-0.53', '0', '0.86', '-0.65', '#anger', '#disgust', 'negative', '-0.68', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['skin_sensitizer'] = ['-0.53', '0', '0.86', '-0.65', '#anger', '#disgust', 'negative', '-0.68', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['skin_texture'] = ['0.352', '0.142', '0', '0.281', '#joy', '#admiration', 'positive', '0.258', 'knowledgeable', 'complement', 'much_need', 'see_ocean', 'good_sense_of_humor']
senticnet['skin_tight'] = ['0', '0', '0', '-0.06', '#disgust', '#disgust', 'negative', '-0.06', 'body', 'nervous_system', 'normal_human_body', 'human_body', 'head_shoulder']
senticnet['skin_tone'] = ['-0.96', '0', '0.636', '-0.64', '#sadness', '#disgust', 'negative', '-0.75', 'burn', 'tan', 'hyperpigmentation', 'sunburn', 'physical_condition']
senticnet['skin_ulcer'] = ['-0.71', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.73', 'pain', 'gape', 'sunburn', 'ouch', 'cut_skin']
senticnet['skincare'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'beauty_parlor', 'lady_hair', 'beauty_shop', 'beauty_salon', 'make_up']
senticnet['skinhead'] = ['-0.89', '0', '0.933', '-0.93', '#anger', '#disgust', 'negative', '-0.92', 'bullyboy', 'muscleman', 'muscle', 'hooligan', 'skin']
senticnet['skink'] = ['-0.03', '-0.01', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'shit', 'wildflower', 'poop', 'wild_flower', 'dog_poop']
senticnet['skink_endemic'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'death_adder', 'ophidian', 'serpent', 'elapid', 'taipan']
senticnet['skinless'] = ['-0.73', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.78', 'boneless', 'bone-in', 'sirloin', 'marinated', 'loin']
senticnet['skinless_chicken'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'grill', 'cook_grill', 'barbecue', 'cook', 'grilled_vegetable']
senticnet['skinned_humanoid'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'gnome', 'elf', 'goblin', 'undersized', 'small_person']
senticnet['skinny'] = ['0', '-0.87', '0', '0.881', '#surprise', '#admiration', 'positive', '0.876', 'scrawny', 'underweight', 'boney', 'weedy', 'scraggy']
senticnet['skintight'] = ['0', '0.075', '0', '0.2', '#interest', '#admiration', 'positive', '0.138', 'stride', 'foot_motion', 'gait', 'foot_action', 'foot_movement']
senticnet['skip'] = ['-0.94', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.86', 'dribble', 'break_window', 'best_opponent', 'hit_hard', 'hit']
senticnet['skip_assignment'] = ['-0.77', '0', '0.877', '-0.94', '#anger', '#disgust', 'negative', '-0.86', 'avoid_work', 'lazy', 'reject_involvement', 'skip_class', 'avoid_responsibility']
senticnet['skip_barber_formula'] = ['0.909', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.952', 'displace', 'swing', 'snowshoe', 'shuttle', 'bang']
senticnet['skip_breakfast'] = ['-0.92', '0', '0.784', '-0.91', '#sadness', '#disgust', 'negative', '-0.87', 'bad_meal', 'eat_snack', 'eat_unhealthy_food', 'eat_expired_food', 'eat_quickly']
senticnet['skip_class'] = ['-0.96', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.87', 'absent', 'skip_work', 'bunk', 'avoid_work', 'lazy']
senticnet['skip_over'] = ['-0.88', '0', '0.812', '-0.64', '#sadness', '#anger', 'negative', '-0.77', 'miss', 'neglect', 'overlook', 'drop', 'pretermit']
senticnet['skip_rope'] = ['0.155', '-0.11', '0.009', '-0.05', '#joy', '#surprise', 'positive', '0.05', 'dance_well', 'relate_music', 'enter_contest', 'jump_around', 'cheer_up']
senticnet['skip_work'] = ['-0.71', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.76', 'skip_class', 'absent', 'bunk', 'avoid_work', 'lazy']
senticnet['skipping_breakfast'] = ['-0.92', '0', '0.784', '-0.91', '#sadness', '#disgust', 'negative', '-0.87', 'bad_meal', 'eat_snack', 'eat_unhealthy_food', 'eat_expired_food', 'eat_quickly']
senticnet['skipping_rope'] = ['0.155', '-0.11', '0.009', '-0.05', '#joy', '#surprise', 'positive', '0.05', 'dance_well', 'relate_music', 'enter_contest', 'jump_around', 'cheer_up']
senticnet['skirmish'] = ['-0.05', '0', '-0.08', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'siege', 'big_fight', 'war', 'declare_war', 'combat']
senticnet['skirt'] = ['0.098', '-0.12', '0.048', '-0.03', '#joy', '#surprise', 'positive', '0.034', 'fancy_dress', 'blouse', 'fashion', 'dress', 'frill']
senticnet['skirt_steak'] = ['0.043', '0.091', '0', '0.113', '#interest', '#admiration', 'positive', '0.082', 'sweet_sour', 'tasty', 'yummy', 'popular_food', 'form_of_food']
senticnet['skirt_suit'] = ['0', '-0.00', '0.047', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'cover_body', 'make_drape', 'weave', 'weave_cloth', 'make_cloth']
senticnet['skirting_board'] = ['0', '0.096', '0', '0.112', '#interest', '#admiration', 'positive', '0.104', 'renovation', 'tile', 'floor', 'tile_roof', 'tile_wall']
senticnet['skitaa'] = ['-0.91', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.85', 'play', 'perpetrator', 'role_play', 'act_part', 'terrible_event']
senticnet['skitter'] = ['0', '0', '-0.80', '-0.85', '#fear', '#disgust', 'negative', '-0.83', 'scuttle', 'hatchway', 'scamper', 'opening', 'scurry']
senticnet['skittish'] = ['0.957', '-0.99', '0', '0.508', '#joy', '#surprise', 'positive', '0.82', 'warm', 'flighty', 'nervous', 'excitable', 'spooky']
senticnet['skiway'] = ['-0.70', '0', '0.621', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'flare_path', 'airfield', 'grassland', 'runway', 'road']
senticnet['sklar'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['skndalo'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'find_out', 'dig_out', 'unearth', 'become_aware', 'dish_out']
senticnet['skne_nordvstra'] = ['0.502', '-0.82', '0', '0.779', '#surprise', '#admiration', 'positive', '0.703', 'mortal', 'person', 'individual', 'free_liver', 'agent']
senticnet['skolian_empire'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['skolnik'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['skool'] = ['0.104', '0', '0', '0.088', '#joy', '#admiration', 'positive', '0.096', 'baby_flower', 'wee', 'bud', 'part_of_plant', 'leafy']
senticnet['skr_against'] = ['0.771', '0.846', '0', '0', '#joy', '#interest', 'positive', '0.808', 'dollar_bill', 'franc', 'banknote', 'dollar', 'cent']
senticnet['skreigh'] = ['-0.76', '0', '0.516', '-0.85', '#sadness', '#disgust', 'negative', '-0.71', 'squall', 'call', 'cry', 'yell', 'scream']
senticnet['skronk'] = ['-0.65', '0', '0.578', '-0.76', '#sadness', '#disgust', 'negative', '-0.66', 'grating', 'rasping', 'raspy', 'jarring', 'gruff']
senticnet['skulk'] = ['-0.80', '0', '-0.98', '0', '#sadness', '#fear', 'negative', '-0.89', 'shirk', 'hide', 'conceal', 'skulker', 'fox']
senticnet['skulker'] = ['-0.80', '0', '-0.98', '0', '#sadness', '#fear', 'negative', '-0.89', 'shirk', 'hide', 'conceal', 'skulk', 'fox']
senticnet['skull'] = ['0', '0.075', '0', '0.046', '#interest', '#admiration', 'positive', '0.06', 'eye_nose', 'face', 'hold_brain', 'top_face', 'nose_mouth']
senticnet['skull_bone'] = ['-0.93', '0', '0.578', '-0.90', '#sadness', '#disgust', 'negative', '-0.80', 'skull', 'death', 'kill', 'die', 'murder']
senticnet['skull_fracture'] = ['-0.03', '-0.00', '-0.07', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'injury', 'get_hurt', 'bruise', 'lose_game', 'get_injured']
senticnet['skull_mask'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'wear', 'protective_garment', 'coverall', 'shoulder_pad', 'wearable']
senticnet['skull_roof'] = ['0.1', '-0.12', '0.05', '-0.03', '#joy', '#surprise', 'positive', '0.034', 'fancy_dress', 'blouse', 'fashion', 'dress', 'skirt']
senticnet['skullduggery'] = ['0.945', '0.996', '0', '0.737', '#joy', '#interest', 'positive', '0.893', 'become_intelligent', 'gain_knowledge', 'increase_knowledge', 'learn', 'desire_education']
senticnet['skunk'] = ['0', '-0.98', '0', '0', '#surprise', '#surprise', 'positive', '0.985', 'cougar', 'mountain_lion', 'lion', 'tiger', 'lynx']
senticnet['skunk_cabbage'] = ['-0.79', '0', '0.906', '-0.67', '#sadness', '#anger', 'negative', '-0.79', 'water_dropwort', 'hemlock_water_dropwort', 'water_hemlock', 'plant_life', 'poisonous_plant']
senticnet['skves_east_melbourne'] = ['0.552', '-0.61', '0', '0.588', '#surprise', '#admiration', 'positive', '0.583', 'barbershop_quartet', 'string_quartet', 'quartette', 'musical_organization', 'musical_group']
senticnet['sky'] = ['0.754', '0.749', '0', '0', '#joy', '#interest', 'positive', '0.752', 'water_fall', 'come_down', 'cloud_sky', 'water_drop', 'water_droplet']
senticnet['sky-high'] = ['0', '0.946', '0', '0.892', '#interest', '#admiration', 'positive', '0.919', 'skyrocketing', 'exorbitant', 'soaring', 'sky-rocketing', 'record-high']
senticnet['sky-rocketing'] = ['0', '0.946', '0', '0.884', '#interest', '#admiration', 'positive', '0.915', 'skyrocketing', 'exorbitant', 'soaring', 'sky-high', 'record-high']
senticnet['sky_blue'] = ['-0.45', '0.081', '-0.25', '0', '#sadness', '#fear', 'negative', '-0.20', 'green_yellow', 'color', 'blue_green', 'cyan', 'purple']
senticnet['sky_blue_shirt'] = ['-0.04', '0.1', '0', '0.152', '#interest', '#admiration', 'positive', '0.068', 'pants', 'jacket', 'clothes', 'robe', 'garment']
senticnet['sky_harbor'] = ['0', '0.147', '0', '0.099', '#interest', '#admiration', 'positive', '0.123', 'airliner', 'airport', 'baggage_compartment', 'aircraft', 'cable_car']
senticnet['sky_marshal'] = ['0.74', '-0.53', '0', '0.641', '#joy', '#admiration', 'positive', '0.64', 'air_marshal', 'marshal', 'lawman', 'law_officer', 'peace_officer']
senticnet['skybreak'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'chairperson', 'commuter']
senticnet['skybridge'] = ['0.077', '0.073', '0.013', '-0.02', '#joy', '#interest', 'positive', '0.027', 'registry_office', 'radio_studio', 'book', 'conference_auditorium', 'book_office']
senticnet['skycoaster'] = ['0.086', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'carny', 'fortune_teller', 'midway', 'ferris_wheel', 'roller-coaster']
senticnet['skydive'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'take_hike', 'exercise_daily', 'stay_up_night', 'climb_stair', 'staircase']
senticnet['skydiving'] = ['0', '0.1', '-0.10', '0.227', '#interest', '#admiration', 'positive', '0.073', 'sport', 'sports_expert', 'adrenaline', 'extreme', 'extreme_sport']
senticnet['skylar_astin'] = ['-0.97', '0', '0.728', '-0.78', '#sadness', '#disgust', 'negative', '-0.82', 'beguiler', 'slicker', 'phony', 'faker', 'deceiver']
senticnet['skylight'] = ['0.046', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'light', 'bay_window', 'air_conditioner', 'sky', 'lamp']
senticnet['skyline'] = ['0.838', '0.828', '0', '0', '#joy', '#interest', 'positive', '0.833', 'vista', 'spectacular', 'perspective', 'image', 'scenic']
senticnet['skylit'] = ['0.046', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'light', 'bay_window', 'air_conditioner', 'sky', 'lamp']
senticnet['skyrocket'] = ['0.952', '0.918', '0', '0', '#joy', '#interest', 'positive', '0.935', 'tack', 'foot_part', 'casual_shoes', 'gauntlet', 'pin']
senticnet['skyrocketing'] = ['0', '0.946', '0', '0.887', '#interest', '#admiration', 'positive', '0.916', 'sky-high', 'exorbitant', 'soaring', 'sky-rocketing', 'record-high']
senticnet['skyscraper'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'build', 'tall_building', 'architect', 'find_suburb', 'large_build']
senticnet['skyship'] = ['-0.04', '0.092', '0', '0.062', '#interest', '#admiration', 'positive', '0.036', 'hangar', 'aircraft_carrier', 'hot_air_balloon', 'war_museum', 'spaceship']
senticnet['skyway'] = ['0', '0.906', '0', '0.963', '#interest', '#admiration', 'positive', '0.934', 'air_lane', 'air_duct', 'flight_path', 'airway', 'airline']
senticnet['slaad_lord'] = ['-0.44', '-0.13', '-0.08', '-0.40', '#sadness', '#disgust', 'negative', '-0.19', 'disorder', 'untidy', 'mess', 'jumble', 'heap']
senticnet['slab'] = ['0', '-0.01', '0.046', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'cement', 'tar', 'concrete', 'big_city_life', 'popular_building_material']
senticnet['slab_avalanche'] = ['0', '-0.03', '-0.10', '-0.07', '#fear', '#disgust', 'negative', '-0.04', 'autumn', 'winter', 'skier', 'snow_fall', 'volcano']
senticnet['slab_hut'] = ['0', '0.113', '0', '0.111', '#interest', '#admiration', 'positive', '0.112', 'animal_house', 'cow_home', 'animal_place', 'crop_place', 'acreage']
senticnet['slack'] = ['-0.93', '0', '0.578', '-0.90', '#sadness', '#disgust', 'negative', '-0.80', 'lazy', 'slack_off', 'careless', 'terrible', 'bad_flatmate']
senticnet['slack_off'] = ['-0.92', '0', '0.757', '-0.90', '#sadness', '#disgust', 'negative', '-0.86', 'let_up', 'far', 'die_away', 'abate', 'die']
senticnet['slacken'] = ['0', '0', '0.735', '-0.91', '#anger', '#disgust', 'negative', '-0.82', 'ebb', 'curtail', 'settle', 'abate', 'drop']
senticnet['slacker'] = ['-0.04', '-0.02', '0.041', '-0.03', '#sadness', '#anger', 'negative', '-0.02', 'shoe', 'loafer', 'slipper', 'moccasin', 'flip_flop']
senticnet['slacking_off'] = ['0', '0', '0.748', '-0.81', '#anger', '#disgust', 'negative', '-0.78', 'search_information', 'look_for_information', 'check_email', 'phone_line_busy', 'distract']
senticnet['slacktivism'] = ['0', '0.331', '-0.09', '0.242', '#interest', '#admiration', 'positive', '0.158', 'spread_idea', 'importance', 'tenet', 'culture', 'idea']
senticnet['slader'] = ['0.746', '-0.88', '0', '0.723', '#joy', '#surprise', 'positive', '0.783', 'strike', 'run_into', 'hit', 'impinge_on', 'collide_with']
senticnet['slagged_off'] = ['0.739', '-0.89', '0', '0.702', '#joy', '#surprise', 'positive', '0.78', 'pap', 'content', 'pablum', 'folderol', 'drivel']
senticnet['slake'] = ['0', '0', '0.75', '-0.81', '#anger', '#disgust', 'negative', '-0.78', 'quench', 'abate', 'allay', 'assuage', 'slack']
senticnet['slaked_lime'] = ['0.037', '0.084', '0', '0', '#joy', '#interest', 'positive', '0.06', 'leave_over', 'leftover', 'cellulose', 'cane', 'remainder']
senticnet['slam'] = ['0', '0.092', '0', '0.172', '#interest', '#admiration', 'positive', '0.132', 'build_open', 'hinge', 'entry', 'home_entrance', 'house_front']
senticnet['slam-bang'] = ['-0.89', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.81', 'rip-roaring', 'edge-of-your-seat', 'edge-of-the-seat', 'nail-biting', 'thrill-a-minute']
senticnet['slam_dunk'] = ['-0.91', '0', '0.795', '-0.94', '#sadness', '#disgust', 'negative', '-0.88', 'ineluctability', 'unavoidability', 'predictability', 'shot', 'inevitability']
senticnet['slam_shut'] = ['0.829', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.908', 'cut_food', 'drink_beer', 'serve_drink', 'drink_much', 'drink_beverage']
senticnet['slammed_into'] = ['-0.90', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.86', 'trauma', 'car_wreck', 'broken_bone', 'bad_knee', 'wreck']
senticnet['slammed_shut'] = ['0.829', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.908', 'cut_food', 'drink_beer', 'serve_drink', 'drink_much', 'drink_beverage']
senticnet['slammer'] = ['-0.60', '0', '0.866', '-0.61', '#anger', '#disgust', 'negative', '-0.69', 'pokey', 'clink', 'holding_cell', 'jail', 'jailhouse']
senticnet['slammer_worm'] = ['-0.07', '0.075', '-0.17', '0', '#sadness', '#fear', 'negative', '-0.06', 'protect_family', 'fault', 'mistake', 'expire', 'kill_self']
senticnet['slamming_into'] = ['-0.12', '0', '-0.09', '-0.04', '#sadness', '#fear', 'negative', '-0.08', 'hold_two_together', 'install_window', 'float_through_air', 'spring_up', 'collision']
senticnet['slamming_shut'] = ['0.085', '0', '-0.05', '0.116', '#joy', '#admiration', 'positive', '0.05', 'dance_well', 'water_faucet', 'enter_contest', 'dance_type', 'dance_music']
senticnet['slana'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'common_bean', 'green_bean', 'wax_bean']
senticnet['slander'] = ['-0.88', '0', '0.874', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'injure', 'find_bank', 'get_hurt', 'find_subway_stop', 'die']
senticnet['slanderer'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'gouger', 'swindler', 'confidence_man', 'conman', 'fraud']
senticnet['slanderous'] = ['-0.79', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.83', 'mud', 'slanderously', 'injurious', 'vilification', 'slanderousness']
senticnet['slanderous_accusation'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'gouger', 'swindler', 'confidence_man', 'conman', 'fraud']
senticnet['slanderously'] = ['-0.80', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.84', 'mud', 'slanderous', 'injurious', 'vilification', 'slanderousness']
senticnet['slanderousness'] = ['-0.80', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.84', 'mud', 'slanderously', 'injurious', 'vilification', 'slanderous']
senticnet['slang'] = ['-0.04', '-0.02', '0.041', '-0.03', '#sadness', '#anger', 'negative', '-0.02', 'jargon', 'lingo', 'microtext', 'chatspeak', 'unusual_language']
senticnet['slang_expression'] = ['0', '0.115', '-0.10', '0.249', '#interest', '#admiration', 'positive', '0.087', 'idiom', 'remark', 'grammar_part', 'speech_part', 'sentence']
senticnet['slanging_match'] = ['-0.63', '0', '0.982', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'wrangle', 'dustup', 'quarrel', 'row', 'argument']
senticnet['slant'] = ['0', '0', '0.87', '-0.86', '#anger', '#disgust', 'negative', '-0.86', 'slump', 'collapse', 'wrench', 'warp', 'twist']
senticnet['slap'] = ['-0.04', '-0.00', '-0.09', '-0.03', '#sadness', '#fear', 'negative', '-0.04', 'hit_hard', 'make_fist', 'spank', 'learn_lesson', 'feel_hurt']
senticnet['slapdash'] = ['-0.71', '0', '-0.73', '0', '#sadness', '#fear', 'negative', '-0.72', 'foolhardiness', 'madcap', 'foolhardy', 'impetuous', 'improvident']
senticnet['slaphappy'] = ['0', '0', '0.729', '-0.93', '#anger', '#disgust', 'negative', '-0.83', 'freewheeling', 'carefree', 'irresponsible', 'unaccountable', 'loose']
senticnet['slapper_detonator'] = ['0.13', '0.097', '-0.07', '0.198', '#joy', '#admiration', 'positive', '0.087', 'jiggle', 'back_forth', 'vibrate', 'blend', 'mixture']
senticnet['slapstick'] = ['0.825', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.839', 'gross-out', 'melodrama', 'comedy', 'farce', 'comedic']
senticnet['slapstick_comedy'] = ['0.206', '-0.13', '0', '0', '#joy', '#surprise', 'positive', '0.172', 'dancer', 'old_dance', 'dance', 'slow_dance', 'ballroom_dance']
senticnet['slash'] = ['-0.08', '-0.01', '0.03', '-0.10', '#sadness', '#disgust', 'negative', '-0.05', 'sever', 'lacerate', 'slit', 'separation', 'tear_apart']
senticnet['slash_pine'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'locust_tree']
senticnet['slash_pine_pinus_elliottii'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'locust_tree']
senticnet['slash_wound'] = ['-0.12', '-0.02', '0', '-0.15', '#sadness', '#disgust', 'negative', '-0.08', 'incision', 'laceration', 'abrasion', 'cut_skin', 'painful_injury']
senticnet['slash_wrist'] = ['-0.15', '0.101', '-0.22', '-0.09', '#sadness', '#fear', 'negative', '-0.09', 'stop_breathe', 'chest_pain', 'die', 'loss_oflife', 'kill']
senticnet['slashdot'] = ['0', '0.331', '-0.09', '0.242', '#interest', '#admiration', 'positive', '0.158', 'spread_idea', 'importance', 'tenet', 'culture', 'idea']
senticnet['slasher'] = ['-0.08', '-0.01', '-0.16', '-0.13', '#fear', '#disgust', 'negative', '-0.09', 'hockey_game', 'movie', 'scary_movie', 'cinema', 'cctv']
senticnet['slasher_film'] = ['-0.97', '0', '-0.81', '-0.95', '#sadness', '#disgust', 'negative', '-0.91', 'dislike', 'loathe', 'queasy', 'horrid', 'outrageous']
senticnet['slasher_flick'] = ['-0.08', '-0.01', '-0.16', '-0.13', '#fear', '#disgust', 'negative', '-0.09', 'hockey_game', 'movie', 'scary_movie', 'cinema', 'cctv']
senticnet['slasher_movie'] = ['-0.97', '0', '-0.81', '-0.95', '#sadness', '#disgust', 'negative', '-0.91', 'dislike', 'loathe', 'queasy', 'horrid', 'outrageous']
senticnet['slat'] = ['-0.58', '0', '0.715', '-0.59', '#anger', '#disgust', 'negative', '-0.63', 'fly', 'overlap', 'barndoor', 'coattail', 'codpiece']
senticnet['slate'] = ['0.083', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.059', 'good_view', 'bird_droppings', 'gable', 'nature', 'homeowner']
senticnet['slate_blue'] = ['0.035', '0', '0', '0.116', '#joy', '#admiration', 'positive', '0.076', 'maroon', 'color', 'bright_color', 'hair_colored', 'cool_color']
senticnet['slate_gray'] = ['-0.60', '0', '0.895', '-0.54', '#sadness', '#anger', 'negative', '-0.68', 'coal_black', 'black', 'pitch_black', 'soot_black', 'achromatic_color']
senticnet['slate_quarry'] = ['0', '0', '-0.85', '-0.71', '#fear', '#disgust', 'negative', '-0.78', 'fair_game', 'pit', 'stone_pit', 'target', 'prey']
senticnet['slather'] = ['-0.96', '0', '0.636', '-0.64', '#sadness', '#disgust', 'negative', '-0.75', 'burn', 'tan', 'hyperpigmentation', 'sunburn', 'physical_condition']
senticnet['slattern'] = ['-0.86', '0', '0.926', '-0.74', '#sadness', '#anger', 'negative', '-0.84', 'tart', 'prostitute', 'harlot', 'bawd', 'sporting_lady']
senticnet['slaughter'] = ['-0.67', '0', '0.938', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'massacre', 'mass_murder', 'carnage', 'bloodbath', 'butchery']
senticnet['slaughter_cattle'] = ['-0.67', '0', '0.938', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'massacre', 'mass_murder', 'carnage', 'bloodbath', 'butchery']
senticnet['slaughtered_cattle'] = ['-0.67', '0', '0.938', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'massacre', 'mass_murder', 'carnage', 'bloodbath', 'butchery']
senticnet['slaughterer'] = ['-0.67', '0', '0.938', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'massacre', 'mass_murder', 'carnage', 'bloodbath', 'butchery']
senticnet['slaughterhouse'] = ['-0.67', '0', '0.938', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'massacre', 'mass_murder', 'carnage', 'bloodbath', 'butchery']
senticnet['slaughtering_cattle'] = ['-0.67', '0', '0.938', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'massacre', 'mass_murder', 'carnage', 'bloodbath', 'butchery']
senticnet['slaughtering_establishment'] = ['-0.67', '0', '0.938', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'massacre', 'mass_murder', 'carnage', 'bloodbath', 'butchery']
senticnet['slaughterous'] = ['0', '0', '-0.93', '-0.94', '#fear', '#disgust', 'negative', '-0.93', 'bloodthirsty', 'sanguinary', 'gory', 'butcherly', 'sanguineous']
senticnet['slave'] = ['0', '0', '0.8', '-0.82', '#anger', '#disgust', 'negative', '-0.81', 'personnel', 'attendant', 'manager', 'elect_official', 'gentleman']
senticnet['slave_collar'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'bangle', 'ankle_bracelet', 'armilla']
senticnet['slave_dealer'] = ['0', '0', '0.8', '-0.82', '#anger', '#disgust', 'negative', '-0.81', 'personnel', 'attendant', 'manager', 'elect_official', 'gentleman']
senticnet['slave_girl'] = ['-0.62', '0', '0.645', '-0.67', '#anger', '#disgust', 'negative', '-0.65', 'lady_maid', 'handmaiden', 'chambermaid', 'maid', 'parlormaid']
senticnet['slave_labor'] = ['-0.75', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.82', 'oppression', 'caged_lion', 'slave', 'captivity', 'misery']
senticnet['slave_owner'] = ['0.056', '0.075', '0', '0.069', '#interest', '#admiration', 'positive', '0.067', 'see_eye_dog', 'future_robot', 'slave', 'type_of_dog', 'dog']
senticnet['slave_trade'] = ['-0.75', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.82', 'oppression', 'caged_lion', 'slave', 'captivity', 'misery']
senticnet['slaveholder'] = ['0', '0', '0.8', '-0.82', '#anger', '#disgust', 'negative', '-0.81', 'personnel', 'attendant', 'manager', 'elect_official', 'gentleman']
senticnet['slavelike'] = ['0.971', '-0.89', '0', '0.93', '#joy', '#admiration', 'positive', '0.932', 'obsequious', 'submissive', 'servile', 'subservient', 'slavish']
senticnet['slaveowner'] = ['0', '0', '0.8', '-0.82', '#anger', '#disgust', 'negative', '-0.81', 'personnel', 'attendant', 'manager', 'elect_official', 'gentleman']
senticnet['slavery'] = ['-0.85', '0', '0.882', '-0.91', '#anger', '#disgust', 'negative', '-0.88', 'slave', 'hardship', 'responsibility', 'drudgery', 'duty']
senticnet['slavish'] = ['0.979', '-0.89', '0', '0.935', '#joy', '#admiration', 'positive', '0.935', 'slavelike', 'submissive', 'servile', 'subservient', 'obsequious']
senticnet['slavish_fear'] = ['-0.79', '0', '0.607', '-0.92', '#sadness', '#disgust', 'negative', '-0.77', 'stubbornness', 'obstinacy', 'obstinance', 'bullheadedness', 'pigheadedness']
senticnet['slavophile'] = ['0.95', '-0.77', '0', '0.578', '#joy', '#surprise', 'positive', '0.767', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['slaw'] = ['0', '-0.01', '0.037', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'pear', 'green_apple', 'apricot', 'cherry', 'lime']
senticnet['slay'] = ['-0.11', '0.079', '-0.18', '-0.04', '#sadness', '#fear', 'negative', '-0.06', 'shoot', 'homicide', 'alibi', 'use_weapon', 'use_gun']
senticnet['slayer'] = ['-0.62', '0', '0.94', '-0.99', '#anger', '#disgust', 'negative', '-0.85', 'poisoner', 'choker', 'mortal', 'executioner', 'public_executioner']
senticnet['slaying'] = ['-0.54', '0', '0.669', '-0.78', '#anger', '#disgust', 'negative', '-0.66', 'massacre', 'mass_murder', 'carnage', 'assassination', 'bloodshed']
senticnet['sleaze'] = ['-0.82', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.85', 'sleazy', 'scumbag', 'shark', 'inadequate', 'vulgar']
senticnet['sleaziness'] = ['-0.77', '0', '0.748', '-0.60', '#sadness', '#anger', 'negative', '-0.70', 'ignobleness', 'ignobility', 'dishonorableness', 'unrespectability', 'disreputability']
senticnet['sleazy'] = ['-0.82', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.84', 'sleaze', 'scumbag', 'shark', 'inadequate', 'vulgar']
senticnet['slection'] = ['0.911', '-0.94', '0', '0.953', '#surprise', '#admiration', 'positive', '0.935', 'common_sense', 'good_sense', 'horse_sense', 'mother_wit', 'gumption']
senticnet['sledge_hammer'] = ['0', '0.078', '0.027', '0.196', '#interest', '#admiration', 'positive', '0.082', 'heel', 'ankle', 'toe', 'foot_massage', 'foot']
senticnet['sleek'] = ['0.785', '0.93', '0', '0.843', '#interest', '#admiration', 'positive', '0.853', 'elegant', 'stylish', 'smart', 'posh', 'chic']
senticnet['sleek_design'] = ['-0.53', '0', '0.765', '-0.94', '#anger', '#disgust', 'negative', '-0.74', 'jaggedness', 'unregularity', 'unevenness', 'irregularity', 'patchiness']
senticnet['sleeker'] = ['0.17', '0', '0.081', '0.184', '#joy', '#admiration', 'positive', '0.091', 'food_holder', 'food_server', 'crockery', 'flattery', 'round_flat']
senticnet['sleeker_design'] = ['0.047', '0.171', '-0.10', '0.212', '#interest', '#admiration', 'positive', '0.081', 'novelty', 'relevant', 'contemporary', 'timely', 'current']
senticnet['sleekit'] = ['0.027', '0', '0.012', '0.063', '#joy', '#admiration', 'positive', '0.026', 'vegetation', 'tree_flower', 'green_growth', 'small', 'greenery']
senticnet['sleekly'] = ['0', '0', '0', '0.963', '#admiration', '#admiration', 'positive', '0.963', 'wear', 'furnishing', 'wearable_technology', 'wearable', 'belt']
senticnet['sleekness'] = ['-0.64', '0', '0.835', '-0.50', '#sadness', '#anger', 'negative', '-0.66', 'smoothness', 'slickness', 'silkiness', 'powderiness', 'burnish']
senticnet['sleep'] = ['0.06', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.056', 'go_sleep', 'dream', 'nap', 'sleep_night', 'rest']
senticnet['sleep_apnea'] = ['-0.12', '0.07', '-0.18', '-0.12', '#fear', '#disgust', 'negative', '-0.09', 'ill', 'sickness', 'unwell', 'malady', 'emergency']
senticnet['sleep_back'] = ['-0.09', '-0.07', '0.02', '-0.18', '#sadness', '#disgust', 'negative', '-0.05', 'learn_new_idea', 'choose_seat', 'fall_asleep', 'drink_cup_coffee', 'bed_time']
senticnet['sleep_bed'] = ['0.847', '0.871', '0', '0.903', '#interest', '#admiration', 'positive', '0.874', 'rest', 'recover', 'sleep', 'sleep_long_time', 'recharge_battery']
senticnet['sleep_cat'] = ['0', '-0.10', '0.112', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'cat_hair', 'cookie_crumb', 'coffee_table', 'dust_mite', 'dirty_sock']
senticnet['sleep_day'] = ['-0.05', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.05', 'oversleep', 'toss_turn', 'pull_up_cover', 'take_afternoon_nap', 'look_out_window']
senticnet['sleep_disorder'] = ['-0.88', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.81', 'medical_problem', 'ailment', 'sign_sickness', 'diagnose', 'emergency']
senticnet['sleep_disordered_breathing_sdb'] = ['-0.03', '-0.03', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'eat_poison', 'commit_suicide', 'drug_addiction', 'drunk_drive', 'jump_off_cliff']
senticnet['sleep_dog'] = ['-0.79', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'fallen_leaf', 'pine_cone', 'detritus', 'dead_leaf', 'truffle']
senticnet['sleep_drug'] = ['-0.03', '-0.00', '-0.12', '-0.20', '#fear', '#disgust', 'negative', '-0.08', 'analgesic', 'medicine', 'ibuprofen', 'pill', 'antibiotic']
senticnet['sleep_early'] = ['0.887', '0.991', '0', '0', '#joy', '#interest', 'positive', '0.939', 'set_alarm_clock', 'go_bed_early', 'set_alarm', 'deep_sleep', 'take_nap']
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senticnet['sleep_lot'] = ['0.847', '0.871', '0', '0.903', '#interest', '#admiration', 'positive', '0.874', 'rejuvenation', 'recuperate', 'rest_dream', 'feel_refreshed', 'get_rest']
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senticnet['sleep_night'] = ['0.06', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.055', 'go_sleep', 'dream', 'nap', 'sleep', 'rest']
senticnet['sleep_on_couch'] = ['0.03', '0', '0', '0.081', '#joy', '#admiration', 'positive', '0.056', 'comfy', 'occur', 'comfortable', 'stay', 'temporary_home']
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senticnet['sleep_soundly'] = ['0.1', '0.111', '-0.08', '0.142', '#interest', '#admiration', 'positive', '0.067', 'sleep_long_time', 'sense_fear', 'mark_territory', 'stay_in_house', 'follow_person']
senticnet['sleep_talking'] = ['-0.53', '0', '0.762', '-0.66', '#anger', '#disgust', 'negative', '-0.65', 'defense', 'unconscious_process', 'somniloquy', 'somniloquism', 'condensation']
senticnet['sleep_tent'] = ['0.095', '0.204', '-0.12', '0.174', '#interest', '#admiration', 'positive', '0.087', 'go_outdoors', 'careful', 'keep_dry', 'pitch_tent', 'warm_up']
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senticnet['sleep_time'] = ['0.1', '0.104', '0', '0', '#joy', '#interest', 'positive', '0.102', 'closing_time', 'rest_night', 'rest_dream', 'shut_eye', 'inactivity']
senticnet['sleep_together'] = ['0.718', '-0.57', '0', '0.991', '#joy', '#admiration', 'positive', '0.76', 'have', 'pair', 'take', 'couple', 'fornicate']
senticnet['sleep_under'] = ['0.049', '0', '0', '0.052', '#joy', '#admiration', 'positive', '0.051', 'bed_cover', 'bed', 'berth', 'cover_furniture', 'mattress']
senticnet['sleep_upon'] = ['0.701', '0.943', '0', '0', '#joy', '#interest', 'positive', '0.822', 'comfortable_place_sleep', 'place_sleep', 'shut_eye', 'laze', 'good_sleep']
senticnet['sleep_well'] = ['0.849', '0', '0', '0', '#joy', '#joy', 'positive', '0.849', 'get_rest', 'feel_little_tired', 'get_over_illness', 'get_sleep', 'lie_back']
senticnet['sleep_with'] = ['0.872', '-0.50', '0', '0.935', '#joy', '#admiration', 'positive', '0.772', 'have', 'pair', 'take', 'couple', 'fornicate']
senticnet['sleeper'] = ['-0.58', '0', '0.714', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'waster', 'diversionist', 'destroyer', 'ruiner', 'undoer']
senticnet['sleeper_car'] = ['-0.58', '0', '0.714', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'waster', 'diversionist', 'destroyer', 'ruiner', 'undoer']
senticnet['sleeper_cell'] = ['0.942', '-0.79', '0', '0.561', '#joy', '#surprise', 'positive', '0.767', 'cadre', 'terrorist_cell', 'radical_cell', 'political_unit', 'political_entity']
senticnet['sleeper_hit'] = ['0.625', '-0.90', '0', '0.54', '#joy', '#surprise', 'positive', '0.69', 'smash', 'bang', 'smasher', 'strike', 'hit']
senticnet['sleeper_sofa'] = ['0.716', '0.96', '0', '0', '#joy', '#interest', 'positive', '0.838', 'airline_seat', 'small_seat', 'beanbag_chair', 'hide-a-bed_sofa', 'chair']
senticnet['sleeper_train'] = ['-0.58', '0', '0.714', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'waster', 'diversionist', 'destroyer', 'ruiner', 'undoer']
senticnet['sleepily'] = ['-0.71', '0', '0.906', '-0.93', '#anger', '#disgust', 'negative', '-0.85', 'absent', 'forgetful', 'distracted', 'inattentive', 'scatty']
senticnet['sleepiness'] = ['0', '0', '0', '-0.76', '#disgust', '#disgust', 'negative', '-0.76', 'stop_activity', 'sleepy', 'rest_up', 'need_to_rest', 'tired']
senticnet['sleeping'] = ['0', '0', '-0.90', '0', '#fear', '#fear', 'negative', '-0.90', 'comfortable_position', 'asleep', 'take_nap', 'lie_back', 'comfort']
senticnet['sleeping_bag'] = ['0', '-0.02', '0.027', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'shoulder_bag', 'satchel', 'satchel_bag', 'tote_bag', 'carry']
senticnet['sleeping_beauty'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'mythical', 'tale', 'dictionary', 'magic', 'perfect_world']
senticnet['sleeping_loft'] = ['0.591', '-0.74', '0', '0.672', '#surprise', '#admiration', 'positive', '0.669', 'room', 'chillax', 'hotel_room', 'furniture_store', 'living_room']
senticnet['sleeping_peacefully'] = ['0.062', '-0.03', '0.116', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'bad_music', 'noise', 'stress', 'a_lot_of_work', 'stressful_situation']
senticnet['sleeping_pill'] = ['-0.88', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.87', 'lower_stress', 'use_energy', 'good_fun', 'help_sleep', 'stay_physically_fit']
senticnet['sleeping_potion'] = ['-0.55', '0', '0.5', '-0.55', '#sadness', '#disgust', 'negative', '-0.53', 'remedy', 'cure', 'catholicon', 'therapeutic', 'curative']
senticnet['sleeping_sickness'] = ['-0.55', '0', '0.757', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'rubor', 'redness', 'cephalitis', 'sleepy_sickness', 'epidemic_encephalitis']
senticnet['sleeping_soundly'] = ['0', '0.08', '0', '0.179', '#interest', '#admiration', 'positive', '0.13', 'wake_up_morning', 'up_early', 'get_up_early', 'gather_energy_tomorrow', 'sit_chair']
senticnet['sleepless'] = ['0.805', '-0.67', '0', '0.897', '#joy', '#admiration', 'positive', '0.792', 'vigilant', 'wakeful', 'watchful', 'fly', 'lidless']
senticnet['sleepless_night'] = ['-0.32', '0.072', '-0.15', '-0.17', '#sadness', '#disgust', 'negative', '-0.14', 'put_in_jail', 'injustice', 'go_jail', 'arrest', 'go_to_prison']
senticnet['sleepover'] = ['0', '-0.08', '0', '0', '#surprise', '#surprise', 'positive', '0.082', 'yo-yo', 'playroom', 'backgammon', 'action_figure', 'board_game']
senticnet['sleepwalk'] = ['-0.80', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.87', 'close_eye', 'disease', 'fall_out_bed', 'wear_pyjamas', 'count_sheep']
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senticnet['sleepy'] = ['-0.93', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.91', 'rest_up', 'need_to_rest', 'sleepiness', 'stop_activity', 'regain_energy']
senticnet['sleepy_sickness'] = ['-0.55', '0', '0.757', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'rubor', 'redness', 'sleeping_sickness', 'cephalitis', 'epidemic_encephalitis']
senticnet['sleepycat_software'] = ['0', '0', '0.737', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'annoy', 'skin_rash', 'bother', 'irritation', 'great_dane']
senticnet['sleestak'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'animal_home', 'habitat', 'beehive']
senticnet['sleet'] = ['0.721', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.816', 'dew', 'fog', 'spray', 'cold_air', 'cloudy']
senticnet['sleeve'] = ['0.021', '-0.03', '0.068', '-0.13', '#anger', '#disgust', 'negative', '-0.03', 'checkbook_holder', 'lip_balm', 'account_book', 'checkbook_cover', 'cover']
senticnet['sleeve_gastrectomy'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
senticnet['sleeveless'] = ['-0.60', '0', '0.761', '-0.84', '#anger', '#disgust', 'negative', '-0.73', 'vain', 'bootless', 'fruitless', 'futile', 'unproductive']
senticnet['sleeveless_jacket'] = ['-0.02', '0.256', '0', '0.166', '#interest', '#admiration', 'positive', '0.132', 'coat', 'shirt', 'garment', 'dress', 'vest']
senticnet['sleeveless_shirt'] = ['-0.99', '0', '0.806', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'wear', 'biohazard_suit', 'coverall', 'protective_garment', 'wearable']
senticnet['sleigh'] = ['0', '-0.03', '0.059', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'autumn', 'snow', 'put_warm_clothes', 'snow_fall', 'cold_weather']
senticnet['sleight'] = ['0.957', '-0.88', '0', '0.772', '#joy', '#surprise', 'positive', '0.872', 'skillfulness', 'touch', 'dexterity', 'manual_dexterity', 'adroitness']
senticnet['slender'] = ['0', '0.716', '0', '0.946', '#interest', '#admiration', 'positive', '0.831', 'rapier', 'slimline', 'sliver', 'sylphic', 'dun']
senticnet['slender_crescent_moon'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['slender_neck'] = ['-0.59', '0', '0.887', '-0.71', '#anger', '#disgust', 'negative', '-0.73', 'armet', 'basinet', 'helmet', 'heaume', 'morion']
senticnet['slender_rod'] = ['-0.11', '0.115', '0.67', '-0.65', '#anger', '#disgust', 'negative', '-0.33', 'lividity', 'livid', 'wrathfully', 'wrothful', 'wroth']
senticnet['sleuth'] = ['-0.89', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.80', 'riddle', 'obituary', 'editorial', 'news_story', 'article']
senticnet['sleuthing'] = ['0.852', '-0.83', '0', '0', '#joy', '#surprise', 'positive', '0.841', 'espial', 'detection', 'spying', 'signal_detection', 'catching']
senticnet['sleymaniye'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'caravansary', 'caravanserai', 'khan', 'caravan_inn', 'hostel']
senticnet['sleymaniye_mosque'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'caravansary', 'caravanserai', 'khan', 'caravan_inn', 'hostel']
senticnet['slice'] = ['-0.06', '-0.02', '0.068', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'cut_up', 'sever', 'slash', 'cut', 'separation']
senticnet['slice_meat'] = ['0.871', '0.948', '0', '0', '#joy', '#interest', 'positive', '0.909', 'meat', 'toast', 'make_flour', 'bake_bread', 'bread']
senticnet['slice_theorem'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'arm_movement', 'hand_gesture', 'water_motion', 'hand_signal', 'sea_movement']
senticnet['sliced_backhand'] = ['0', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.91', 'umpire', 'referee', 'legal_proceed', 'clang', 'clamor']
senticnet['sliced_celery'] = ['-0.83', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.78', 'picaninny', 'kid', 'minor', 'tike', 'small_picaninny']
senticnet['sliced_fruit'] = ['0', '0', '0.066', '-0.07', '#anger', '#disgust', 'negative', '-0.07', 'container_cup', 'cup', 'grease_trap', 'dish', 'open_container']
senticnet['sliced_red_onion'] = ['-0.95', '0', '0.845', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'grapefruit_peel', 'lemon_peel', 'orange_peel', 'candied_fruit', 'succade']
senticnet['slick'] = ['0', '0', '-0.72', '-0.93', '#fear', '#disgust', 'negative', '-0.83', 'bee_product', 'flammable', 'remove_hair', 'gooey', 'really_hot']
senticnet['slicked_back'] = ['0', '0', '0.841', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'ancient', 'many_years', 'year', 'old', 'teen']
senticnet['slicked_back_hair'] = ['-0.91', '0', '0.869', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'talk_hairdresser', 'hair_get_short', 'hair_become_short', 'keep_hair_short', 'hair_wash']
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senticnet['slickly'] = ['0.961', '0.794', '0', '0', '#joy', '#interest', 'positive', '0.877', 'touch_heart', 'sincere', 'others_understand', 'spread_word', 'look_sexy']
senticnet['slickness'] = ['-0.64', '0', '0.835', '-0.50', '#sadness', '#anger', 'negative', '-0.66', 'sleekness', 'smoothness', 'silkiness', 'powderiness', 'burnish']
senticnet['slickrock'] = ['-0.99', '0', '0.641', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'navigable', 'negotiable', 'surmountable', 'passable', 'traversable']
senticnet['slide'] = ['0.129', '0', '0', '0', '#joy', '#joy', 'positive', '0.129', 'incline_plane', 'boulder', 'ramp', 'hill_mountain', 'rock']
senticnet['slide_projector'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'floodlight', 'spotlight', 'headlight', 'light', 'teach_lesson']
senticnet['slide_rule'] = ['0', '-0.00', '0.034', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'celsius', 'grade', 'certificate', 'learn_tool', 'temperature_measure']
senticnet['slidell'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
senticnet['slider'] = ['0', '0', '0.748', '-0.79', '#anger', '#disgust', 'negative', '-0.76', 'finger', 'bawd', 'shiatsu', 'thumb', 'thumb-sketch']
senticnet['slideway'] = ['-0.9', '0', '0.749', '-0.71', '#sadness', '#anger', 'negative', '-0.78', 'chute', 'slide', 'gutter', 'sloping_trough', 'cullis']
senticnet['sliding_canopy'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'train_station', 'government_office', 'cable_car', 'sport_stadium', 'terminal']
senticnet['sliding_door'] = ['0', '0', '0', '0.077', '#admiration', '#admiration', 'positive', '0.077', 'glass_front', 'gate', 'doorway', 'use_farm', 'entranceway']
senticnet['sliding_down'] = ['0.129', '0', '0', '0', '#joy', '#joy', 'positive', '0.129', 'incline_plane', 'boulder', 'ramp', 'hill_mountain', 'rock']
senticnet['sliding_fee'] = ['0.687', '-0.98', '0', '0.666', '#joy', '#surprise', 'positive', '0.778', 'scale', 'scale_of_measurement', 'graduated_table', 'ordered_series', 'wage_scale']
senticnet['sliding_fee_scale'] = ['0.687', '-0.98', '0', '0.666', '#joy', '#surprise', 'positive', '0.778', 'scale', 'scale_of_measurement', 'graduated_table', 'ordered_series', 'wage_scale']
senticnet['sliding_fee_schedule'] = ['0.687', '-0.98', '0', '0.666', '#joy', '#surprise', 'positive', '0.778', 'scale', 'scale_of_measurement', 'graduated_table', 'ordered_series', 'wage_scale']
senticnet['sliding_friction'] = ['0', '0', '-0.11', '0', '#fear', '#fear', 'negative', '-0.11', 'chemical_reaction', 'set_fire', 'grow_yard', 'heat_light', 'burn_down_house']
senticnet['sliding_glass_door'] = ['0', '0.171', '-0.05', '0.102', '#interest', '#admiration', 'positive', '0.072', 'bed', 'mattress', 'bead', 'bed_cover', 'seduce']
senticnet['sliding_lid'] = ['0', '-0.02', '0', '0.051', '#surprise', '#admiration', 'positive', '0.037', 'cardboard_box', 'bag', 'package', 'box_cereal', 'bin']
senticnet['sliding_rear'] = ['0', '0.248', '-0.07', '0.121', '#interest', '#admiration', 'positive', '0.099', 'gym_locker', 'observatory', 'prison_cell', 'luggage_store', 'garage_sale']
senticnet['sliding_scale'] = ['0.687', '-0.98', '0', '0.666', '#joy', '#surprise', 'positive', '0.778', 'scale', 'scale_of_measurement', 'graduated_table', 'ordered_series', 'wage_scale']
senticnet['sliding_scale_fee'] = ['0.687', '-0.98', '0', '0.666', '#joy', '#surprise', 'positive', '0.778', 'scale', 'scale_of_measurement', 'graduated_table', 'ordered_series', 'wage_scale']
senticnet['sliding_tackle'] = ['-0.71', '0', '0.83', '-0.68', '#sadness', '#anger', 'negative', '-0.74', 'gag', 'jest', 'jape', 'joke', 'laugh']
senticnet['slight'] = ['-0.86', '0', '0.772', '-0.73', '#sadness', '#anger', 'negative', '-0.79', 'discourtesy', 'snub', 'cold_shoulder', 'offense', 'cut']
senticnet['slight_angle'] = ['0', '0', '0.061', '-0.03', '#anger', '#disgust', 'negative', '-0.04', 'garden_tool', 'straight_line', 'sharp', 'razor_blade', 'chock']
senticnet['slight_breeze'] = ['0', '-0.99', '-0.99', '0', '#surprise', '#fear', 'positive', '0.004', 'blizzard', 'snow_storm', 'storm', 'danger', 'rainy']
senticnet['slight_bump'] = ['0.03', '0', '0.017', '0.138', '#joy', '#admiration', 'positive', '0.05', 'up_down', 'curl', 'crest', 'hand_gesture', 'goodbye']
senticnet['slight_concussion'] = ['-0.90', '0', '-0.91', '-0.73', '#sadness', '#fear', 'negative', '-0.85', 'broken_window', 'step_dog_shit', 'broken_leg', 'brain_tumor', 'bad_luck']
senticnet['slight_contraction'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'mathematical', 'muscle', 'math_function', 'feel_sick', 'separation']
senticnet['slight_decline'] = ['0.05', '0.151', '0', '0.159', '#interest', '#admiration', 'positive', '0.12', 'glamour', 'stylish', 'style', 'clothes_style', 'fancy_clothes']
senticnet['slight_dip'] = ['0.952', '0.918', '0', '0', '#joy', '#interest', 'positive', '0.935', 'tack', 'foot_part', 'casual_shoes', 'gauntlet', 'pin']
senticnet['slight_disappointment'] = ['-0.83', '0', '0.912', '-0.67', '#sadness', '#anger', 'negative', '-0.80', 'dissatisfaction', 'displeasure', 'disappointment', 'boredom', 'ennui']
senticnet['slight_drop'] = ['0.978', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.865', 'tumble', 'dew', 'hailstone', 'rain', 'snowflake']
senticnet['slight_fever'] = ['-0.76', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.74', 'decrease_hunger', 'feel_full', 'stomach_problem', 'get_fat', 'a_little_hungry']
senticnet['slight_headache'] = ['0.052', '-0.04', '-0.05', '-0.20', '#fear', '#disgust', 'negative', '-0.04', 'indigestion', 'make_little_hungry', 'full_stomach', 'sick', 'become_full']
senticnet['slight_incline'] = ['-0.11', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.08', 'have_fun', 'spring_up', 'fall_well', 'shovel_snow', 'fly_around']
senticnet['slight_inconvenience'] = ['-0.89', '0', '0.712', '-0.80', '#sadness', '#disgust', 'negative', '-0.80', 'difficultness', 'severity', 'severeness', 'troublesomeness', 'worriment']
senticnet['slight_increase'] = ['0.978', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.865', 'tumble', 'dew', 'hailstone', 'rain', 'snowflake']
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senticnet['slip_away'] = ['0', '0.263', '-0.14', '0.295', '#interest', '#admiration', 'positive', '0.139', 'keep', 'hold', 'retain', 'hold_stuff', 'upkeep']
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senticnet['slippage'] = ['0', '0', '-0.72', '-0.72', '#fear', '#disgust', 'negative', '-0.72', 'misalignment', 'misaligned', 'alignment', 'mis-alignment', 'misalignment']
senticnet['slipper'] = ['0', '-0.13', '0', '0', '#surprise', '#surprise', 'positive', '0.132', 'shoe', 'moccasin', 'flip_flop', 'footwear', 'casual_shoes']
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senticnet['slipshod'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'lamentably', 'deplorably', 'bereaved', 'bereft', 'grief-stricken']
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senticnet['slop'] = ['0.824', '-0.91', '0', '0.625', '#joy', '#surprise', 'positive', '0.789', 'treacle', 'glop', 'mush', 'formulation', 'sentimentalism']
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senticnet['sloping_shoulder'] = ['0.209', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.118', 'old_dance', 'dance', 'slow_dance', 'dancer', 'enter_contest']
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senticnet['slothfulness'] = ['-0.91', '0', '0.883', '-0.63', '#sadness', '#anger', 'negative', '-0.81', 'disinclination', 'indisposition', 'hesitancy', 'reluctance', 'hesitation']
senticnet['slotted_spoon'] = ['0.03', '-0.05', '0', '0.059', '#surprise', '#admiration', 'positive', '0.046', 'platter', 'fork', 'spoon', 'tablespoon', 'bowl']
senticnet['slouch'] = ['-0.77', '0', '0.503', '-0.93', '#sadness', '#disgust', 'negative', '-0.73', 'butcher', 'fuckup', 'incompetent', 'bungler', 'blunderer']
senticnet['slouch_hat'] = ['-0.58', '0', '0.632', '-0.63', '#anger', '#disgust', 'negative', '-0.61', 'skimmer', 'trilby', 'chapeau', 'busby', 'shako']
senticnet['slouchy'] = ['-0.77', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.82', 'boxy', 'squarish', 'low-slung', 'blocky', 'cropped']
senticnet['slough'] = ['-0.68', '0', '0.916', '-0.91', '#anger', '#disgust', 'negative', '-0.83', 'dry_gangrene', 'cold_gangrene', 'mumification_necrosis', 'mummification', 'pathology']
senticnet['slough_off'] = ['-0.11', '0.095', '-0.11', '0.071', '#sadness', '#fear', 'negative', '-0.01', 'violent_action', 'pulverize', 'break_glass', 'tear_apart', 'hit_hard']
senticnet['sloven'] = ['-0.63', '0', '0.683', '-0.96', '#anger', '#disgust', 'negative', '-0.76', 'slovenly_woman', 'slut', 'trollop', 'slattern', 'litterer']
senticnet['slovenliness'] = ['-0.56', '0', '0.909', '-0.62', '#anger', '#disgust', 'negative', '-0.69', 'unkemptness', 'disorder', 'untidiness', 'sloppiness', 'disorderliness']
senticnet['slovenly'] = ['-0.9', '0', '0.841', '-0.73', '#sadness', '#anger', 'negative', '-0.82', 'nubbiness', 'tweediness', 'harshness', 'graininess', 'granularity']
senticnet['slovenly_person'] = ['-0.87', '0', '0.96', '-0.97', '#anger', '#disgust', 'negative', '-0.93', 'slovenly_woman', 'slut', 'trollop', 'slattern', 'litterer']
senticnet['slovenly_woman'] = ['-0.56', '0', '0.655', '-0.85', '#anger', '#disgust', 'negative', '-0.69', 'slob', 'slut', 'trollop', 'slattern', 'litterer']
senticnet['slow'] = ['-0.98', '0', '0.674', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'retarded', 'slow_pace', 'stupid', 'slow_car', 'slow_vehicle']
senticnet['slow-release'] = ['0', '0', '-0.83', '-0.84', '#fear', '#disgust', 'negative', '-0.83', 'time-released', 'timed-release', 'time-release', 'controlled-release', 'immediate-release']
senticnet['slow-thinking'] = ['0', '0', '0.897', '-0.74', '#anger', '#disgust', 'negative', '-0.82', 'free-flowering', 'frost-tender', 'quiet-running', 'strong-growing', 'long-flowering']
senticnet['slow_car'] = ['-0.98', '0', '0.674', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'retarded', 'slow_pace', 'stupid', 'slow', 'slow_vehicle']
senticnet['slow_cooker'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'kitchen_cabinet', 'kitchen_cupboard', 'kitchen', 'cupboard', 'own_kitchen']
senticnet['slow_dance'] = ['0.208', '-0.11', '0', '-0.03', '#joy', '#surprise', 'positive', '0.096', 'dance', 'old_dance', 'enter_contest', 'dancer', 'stout']
senticnet['slow_down'] = ['0.978', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.865', 'tumble', 'dew', 'hailstone', 'rain', 'snowflake']
senticnet['slow_drawl'] = ['-0.53', '0', '0.798', '-0.73', '#anger', '#disgust', 'negative', '-0.68', 'husky', 'hardy', 'sturdy', 'robust', 'stalwart']
senticnet['slow_food'] = ['0.06', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'eat_food', 'restaurant', 'meal', 'italian_food', 'italian_restaurant']
senticnet['slow_gait'] = ['-0.15', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.09', 'use_leg', 'walk_quickly', 'casual', 'take_step', 'strut']
senticnet['slow_heart_beat'] = ['0.869', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.872', 'feel_refreshed', 'feel_little_tired', 'rejuvenation', 'feel_good', 'relax']
senticnet['slow_learner'] = ['-0.98', '0', '0.674', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'slow', 'slow_pace', 'stupid', 'slow_car', 'slow_vehicle']
senticnet['slow_left_armer'] = ['0.034', '-0.02', '0.054', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'compass', 'swiss_army_knife', 'bake_pan', 'coffee_mug', 'wine_opener']
senticnet['slow_motion'] = ['-0.69', '0', '0.508', '-0.93', '#sadness', '#disgust', 'negative', '-0.71', 'dark', 'darkness', 'light', 'shadow', 'site']
senticnet['slow_motion_footage'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'paperclip', 'clasp', 'glue_stick', 'clothespin', 'clamp']
senticnet['slow_moving_storm'] = ['-0.78', '0', '0.906', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'rainstorm', 'atmospheric_phenomenon', 'northeaster', 'noreaster', 'hailstorm']
senticnet['slow_moving_truck'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'corner_two_streets', 'street_corner', 'roadside', 'city_street', 'schoolyard']
senticnet['slow_pace'] = ['-0.06', '-0.01', '-0.10', '0', '#sadness', '#fear', 'negative', '-0.05', 'slug', 'use_foot', 'fast_walk', 'move_forward', 'move_slow']
senticnet['slow_speed'] = ['-0.88', '0', '0.817', '0', '#sadness', '#anger', 'negative', '-0.85', 'hotspot', 'wifi', 'computer_internet_connection', 'internet_connection', 'boot_up_computer']
senticnet['slow_traffic'] = ['-0.06', '-0.01', '-0.10', '0', '#sadness', '#fear', 'negative', '-0.05', 'slow_pace', 'guard_bank', 'fight_war', 'fight_enemy', 'conquer_nation']
senticnet['slow_vehicle'] = ['-0.98', '0', '0.674', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'retarded', 'slow_pace', 'stupid', 'slow_car', 'slow']
senticnet['slowdown'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'mathematical', 'muscle', 'math_function', 'feel_sick', 'separation']
senticnet['slowed_down'] = ['-0.04', '0.123', '0', '0.237', '#interest', '#admiration', 'positive', '0.107', 'foot_motion', 'foot_movement', 'foot_action', 'move_ahead', 'stride']
senticnet['slower'] = ['-0.04', '0.123', '0', '0.237', '#interest', '#admiration', 'positive', '0.107', 'foot_motion', 'foot_movement', 'foot_action', 'move_ahead', 'stride']
senticnet['slower_growth'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'mathematical', 'muscle', 'math_function', 'feel_sick', 'separation']
senticnet['slower_pace'] = ['-0.04', '0.123', '0', '0.237', '#interest', '#admiration', 'positive', '0.107', 'foot_motion', 'foot_movement', 'foot_action', 'move_ahead', 'stride']
senticnet['slowest'] = ['-0.04', '0.123', '0', '0.237', '#interest', '#admiration', 'positive', '0.107', 'foot_motion', 'foot_movement', 'foot_action', 'move_ahead', 'stride']
senticnet['slowing_demand'] = ['0', '0.285', '-0.07', '0.163', '#interest', '#admiration', 'positive', '0.124', 'plus', 'importance', 'a_lot', 'bonus', 'extra']
senticnet['slowing_down'] = ['-0.04', '0.123', '0', '0.237', '#interest', '#admiration', 'positive', '0.107', 'foot_motion', 'foot_movement', 'foot_action', 'move_ahead', 'stride']
senticnet['slowly'] = ['-0.74', '0', '0.898', '0', '#sadness', '#anger', 'negative', '-0.82', 'slow', 'edge', 'seep', 'tardily', 'drip']
senticnet['slowly_drizzle'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'salad_shaker', 'store_shelf', 'kitchen_tool', 'pliers', 'ladle']
senticnet['slowly_exhale'] = ['0.757', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.754', 'inhale', 'cough', 'respiration', 'involuntary', 'smell_odor']
senticnet['slowly_pour'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'address_book', 'get_rich', 'blank_check', 'swiss_army_knife', 'whisk']
senticnet['slowly_progressive'] = ['-0.65', '0', '0.873', '-0.63', '#sadness', '#anger', 'negative', '-0.72', 'neuritis', 'multiple_neuritis', 'rubor', 'redness', 'inflammation']
senticnet['slowness'] = ['-0.60', '0', '0.827', '-0.76', '#anger', '#disgust', 'negative', '-0.73', 'amentia', 'mental_retardation', 'backwardness', 'subnormality', 'retardation']
senticnet['sludge'] = ['0', '-0.04', '0.059', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'squirt', 'dirty_water', 'mud', 'slosh', 'puddle']
senticnet['sludge_metal'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'explicit', 'declared', 'definitive', 'graphic']
senticnet['sluff'] = ['0.022', '-0.11', '0.014', '0.057', '#surprise', '#admiration', 'positive', '0.045', 'wood', 'many_steps', 'franchise', 'stump', 'timber']
senticnet['slug'] = ['-0.02', '-0.02', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.03', 'piece_of_metal', 'gunpowder', 'ammunition', 'lethal', 'projectile']
senticnet['slugfest'] = ['-0.94', '0', '0.863', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'poke', 'clout', 'lick', 'punch', 'biff']
senticnet['sluggard'] = ['0', '0', '0.808', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'inaction', 'lazy', 'stay', 'inactive', 'recline']
senticnet['slugger'] = ['0', '0', '-0.73', '-0.89', '#fear', '#disgust', 'negative', '-0.81', 'hit', 'batter', 'hitter', 'pain', 'cause_pain']
senticnet['sluggish'] = ['-0.89', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.81', 'logier', 'languid', 'sedentary', 'slothful', 'idle']
senticnet['sluggish_demand'] = ['0.749', '0.764', '0', '0', '#joy', '#interest', 'positive', '0.756', 'obedience', 'order', 'requirement', 'permission', 'necessary']
senticnet['sluggish_pace'] = ['-0.04', '0.123', '0', '0.237', '#interest', '#admiration', 'positive', '0.107', 'foot_motion', 'foot_movement', 'foot_action', 'move_ahead', 'stride']
senticnet['sluggishness'] = ['-0.97', '0', '0.992', '-0.86', '#sadness', '#anger', 'negative', '-0.94', 'lassitude', 'hibernation', 'lethargy', 'torpidity', 'physical_condition']
senticnet['sluice'] = ['-0.77', '0', '0.583', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'rain', 'rain_down', 'sheet', 'rain_bucket', 'rain_buckets_down']
senticnet['sluice_down'] = ['-0.77', '0', '0.583', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'rain', 'rain_down', 'sheet', 'sluice', 'rain_bucket']
senticnet['sluice_gate'] = ['-0.77', '0', '0.583', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'rain', 'rain_down', 'sheet', 'rain_bucket', 'rain_buckets_down']
senticnet['slum'] = ['-0.76', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.76', 'broken', 'home', 'homeless_shelter', 'crack_house', 'poor']
senticnet['slumber'] = ['0.821', '0.793', '0', '0', '#joy', '#interest', 'positive', '0.807', 'berth', 'sleep_under', 'use_bed', 'political_debate', 'bed']
senticnet['slumber_party'] = ['0', '-0.08', '0', '0', '#surprise', '#surprise', 'positive', '0.082', 'yo-yo', 'playroom', 'backgammon', 'action_figure', 'board_game']
senticnet['slumbercoach'] = ['-0.58', '0', '0.714', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'waster', 'diversionist', 'destroyer', 'ruiner', 'undoer']
senticnet['slump'] = ['-0.99', '0', '0.691', '-0.65', '#sadness', '#anger', 'negative', '-0.78', 'depression', 'economic_crisis', 'exigency', 'crisis', 'juncture']
senticnet['slunk'] = ['0', '0.07', '0', '0.063', '#interest', '#admiration', 'positive', '0.067', 'microscope', 'glimpse', 'see', 'stare', 'observation']
senticnet['slunk_away'] = ['-0.76', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'disgraceful', 'shameful', 'dishonorably', 'inglorious', 'dishonored']
senticnet['slunk_back'] = ['-0.85', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.88', 'oppression', 'enslavement', 'slave', 'captivity', 'misery']
senticnet['slur'] = ['0', '0', '0.761', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'articulation', 'slight', 'type', 'legato', 'note']
senticnet['slurpee'] = ['0', '0', '0', '-0.06', '#disgust', '#disgust', 'negative', '-0.06', 'newsstand', 'bookshop', 'walmart', 'shoe_store', 'hooker']
senticnet['slurpy'] = ['-0.78', '0', '0.915', '-0.99', '#anger', '#disgust', 'negative', '-0.89', 'bodge', 'botch_up', 'flub', 'ball_up', 'go_wrong']
senticnet['slurred'] = ['0', '0', '0.747', '-0.8', '#anger', '#disgust', 'negative', '-0.77', 'bloodshot', 'bleary', 'eye', 'watery', 'glassy']
senticnet['slurred_speech'] = ['0.983', '-0.63', '0', '0.697', '#joy', '#admiration', 'positive', '0.772', 'amateurishness', 'inability', 'ineptness', 'unskillfulness', 'awkwardness']
senticnet['slush'] = ['0.032', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'frozen_water', 'glacier', 'come_down', 'water_fall', 'water_drop']
senticnet['slush_around'] = ['-0.54', '0', '0.583', '-0.81', '#anger', '#disgust', 'negative', '-0.64', 'swash', 'spatter', 'splatter', 'plash', 'splash']
senticnet['slushy'] = ['0', '0', '-0.95', '-0.89', '#fear', '#disgust', 'negative', '-0.92', 'drippy', 'maudlin', 'bathetic', 'hokey', 'mawkish']
senticnet['slut'] = ['-0.54', '0', '0.691', '-0.96', '#anger', '#disgust', 'negative', '-0.73', 'adulterer', 'trollop', 'libertine', 'adulteress', 'fornicatress']
senticnet['sluttish'] = ['-0.56', '0', '0.738', '-0.61', '#anger', '#disgust', 'negative', '-0.64', 'mix_up', 'muddle', 'puddle', 'assemble', 'piece']
senticnet['slutty'] = ['0.907', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.916', 'lustful', 'horny', 'lusty', 'redhead', 'naughty']
senticnet['sly'] = ['-0.95', '0', '0.711', '0', '#sadness', '#anger', 'negative', '-0.83', 'skillful', 'subtle', 'slyness', 'snaky', 'wily']
senticnet['sly_humor'] = ['0.918', '0.476', '-0.19', '0.417', '#joy', '#interest', 'positive', '0.404', 'merriment', 'cheery', 'gaiety', 'festal', 'plume']
senticnet['sly_smile'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'simper', 'please', 'content', 'satisfy', 'gratify']
senticnet['sly_wink'] = ['0.021', '0.179', '-0.13', '0.164', '#interest', '#admiration', 'positive', '0.058', 'nod_head', 'point_finger', 'upgrade', 'ask', 'put_bid']
senticnet['sly_wit'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly', 'cheering']
senticnet['slyboots'] = ['-0.52', '0', '0.669', '-0.66', '#anger', '#disgust', 'negative', '-0.61', 'beguiler', 'slicker', 'deceiver', 'cheat', 'cheater']
senticnet['slydini'] = ['0.563', '-0.81', '0', '0.577', '#surprise', '#admiration', 'positive', '0.65', 'mortal', 'person', 'individual', 'performer', 'artist']
senticnet['slyness'] = ['0.623', '-0.97', '0', '0.785', '#surprise', '#admiration', 'positive', '0.794', 'foxiness', 'perspicacity', 'wiliness', 'craftiness', 'cunning']
senticnet['smack'] = ['-0.7', '0', '0.698', '-0.57', '#sadness', '#anger', 'negative', '-0.65', 'diacetylmorphine', 'hell_dust', 'nose_drop', 'thunder', 'skag']
senticnet['smack_down'] = ['-0.97', '0', '0.62', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'obstructor', 'obstructer', 'technophobe', 'obstructionist', 'resister']
senticnet['small'] = ['-0.05', '0', '-0.12', '-0.16', '#fear', '#disgust', 'negative', '-0.11', 'reduced', 'tiny', 'small_size', 'narrow', 'poor']
senticnet['small-arm'] = ['0', '-1', '0', '0.956', '#surprise', '#admiration', 'positive', '0.978', 'shell-fire', 'shellfire', 'gunfire', 'bombardment', 'antiaircraft']
senticnet['small-minded'] = ['-0.98', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.88', 'ungenerous', 'unchristian', 'unkind', 'mean-spirited', 'uncharitable']
senticnet['small-mouth'] = ['0', '0', '-0.78', '-0.97', '#fear', '#disgust', 'negative', '-0.87', 'white-fleshed', 'trophy-sized', 'scaleless', 'algae-eating', 'reddish-black']
senticnet['small-scale'] = ['-0.82', '0', '-0.84', '0', '#sadness', '#fear', 'negative', '-0.83', 'large-scale', 'smaller-scale', 'farming', 'larger-scale', 'medium-scale']
senticnet['small-time'] = ['-0.79', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'smalltime', 'big-time', 'two-bit', 'wanna-be', 'ex-con']
senticnet['small_adult'] = ['0', '0.074', '-0.04', '0.054', '#interest', '#admiration', 'positive', '0.027', 'young_kid', 'human_offspring', 'young_people', 'small_boy', 'little_person']
senticnet['small_amount'] = ['-0.99', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.84', 'least', 'little', 'a_little', 'shortage', 'scarce']
senticnet['small_animal'] = ['0.959', '0', '0', '0.929', '#joy', '#admiration', 'positive', '0.944', 'small_cat', 'cute', 'pet', 'baby_cat', 'curious_animal']
senticnet['small_apartment'] = ['0', '-0.06', '0', '0.062', '#surprise', '#admiration', 'positive', '0.062', 'box', 'small_box', 'owner_house', 'veterinarian_office', 'pet_shop']
senticnet['small_ball'] = ['0.042', '-0.02', '0.035', '0.065', '#joy', '#admiration', 'positive', '0.023', 'food_container', 'metal_cylinder', 'metal_container', 'cylinder', 'storage_container']
senticnet['small_bed'] = ['0', '-0.02', '-0.08', '-0.05', '#fear', '#disgust', 'negative', '-0.03', 'mattress', 'sleep_furniture', 'master_bedroom', 'cradle', 'pillow']
senticnet['small_bite'] = ['0.807', '0.763', '0', '0', '#joy', '#interest', 'positive', '0.785', 'broken', 'little_bite', 'need_eat_meal', 'find_road', 'repair']
senticnet['small_boat'] = ['0.957', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.961', 'jetty', 'water_vehicle', 'military_ship', 'ship', 'big_breadbox']
senticnet['small_book'] = ['0', '0.103', '0', '0.057', '#interest', '#admiration', 'positive', '0.08', 'tale', 'narrative', 'print_material', 'journal', 'newspaper_story']
senticnet['small_boulder'] = ['0', '0', '0.73', '-0.76', '#anger', '#disgust', 'negative', '-0.74', 'music_type', 'type_of_music', 'music_genre', 'valuable_gem', 'rock_music']
senticnet['small_bowel'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'anus', 'scrotum', 'shoulder', 'liver', 'waist']
senticnet['small_bowel_obstruction'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'anus', 'scrotum', 'shoulder', 'liver', 'waist']
senticnet['small_box'] = ['0', '-0.06', '0', '0.062', '#surprise', '#admiration', 'positive', '0.062', 'box', 'owner_house', 'small_apartment', 'veterinarian_office', 'pet_shop']
senticnet['small_boy'] = ['0.033', '0', '0', '0.055', '#joy', '#admiration', 'positive', '0.044', 'young_kid', 'little_person', 'helpless', 'small_adult', 'small_kid']
senticnet['small_breast'] = ['-0.73', '0', '0.71', '0', '#sadness', '#anger', 'negative', '-0.72', 'swamp', 'boring', 'flat_tire', 'flat_foot', 'flatness']
senticnet['small_bug'] = ['-0.04', '-0.02', '0.108', '-0.06', '#anger', '#disgust', 'negative', '-0.04', 'small_insect', 'nuisance_insect', 'small_fly', 'fly_away', 'annoy_bug']
senticnet['small_building'] = ['0', '0.108', '0', '0.175', '#interest', '#admiration', 'positive', '0.141', 'build_structure', 'construction', 'architecture', 'building', 'manufacture_plant']
senticnet['small_bus_tech_trans'] = ['0', '0.971', '0', '0.874', '#interest', '#admiration', 'positive', '0.922', 'standard_grant', 'project', 'award', 'title_sttr_phase', 'technology_transfer']
senticnet['small_business_phase'] = ['0', '0.843', '0', '0.712', '#interest', '#admiration', 'positive', '0.777', 'feasibleness', 'practicability', 'phase', 'feasibility', 'project']
senticnet['small_cat'] = ['0.959', '0', '0', '0.929', '#joy', '#admiration', 'positive', '0.944', 'baby_cat', 'little_animal', 'rodent', 'small_animal', 'feline']
senticnet['small_change'] = ['0', '0.154', '0.076', '0.088', '#interest', '#admiration', 'positive', '0.055', 'dollar_change', 'money_change', 'currency_unit', 'small_coin', 'money_coin']
senticnet['small_child'] = ['0', '0.154', '0.076', '0.088', '#interest', '#admiration', 'positive', '0.055', 'youth', 'young_child', 'childhood', 'kid', 'child']
senticnet['small_circle'] = ['0.741', '0.995', '0', '0', '#joy', '#interest', 'positive', '0.868', 'round_shape', 'make_gold', 'roundness', 'curve', 'round_flat']
senticnet['small_city'] = ['0', '0', '0', '0.104', '#admiration', '#admiration', 'positive', '0.104', 'downtown', 'city', 'village', 'govern', 'govern_city']
senticnet['small_coin'] = ['0', '0.114', '0.073', '0.11', '#interest', '#admiration', 'positive', '0.05', 'money_coin', 'small_money', 'small_currency', 'american_coin', 'money_change']
senticnet['small_creature'] = ['0.02', '0.071', '0', '0.069', '#interest', '#admiration', 'positive', '0.053', 'many_animals', 'little_animal', 'chihuahua', 'panda', 'cute_animal']
senticnet['small_currency'] = ['0', '0.117', '0.072', '0.134', '#interest', '#admiration', 'positive', '0.06', 'small_money', 'metal_money', 'money_coin', 'small_coin', 'money']
senticnet['small_cut'] = ['-0.08', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.08', 'bruise', 'mistake', 'injury', 'bloody', 'gash']
senticnet['small_detail'] = ['0.232', '0.178', '-0.17', '0.223', '#joy', '#admiration', 'positive', '0.115', 'fame', 'alms', 'find_money', 'better_life', 'accolade']
senticnet['small_dog'] = ['-0.06', '0', '-0.04', '0', '#sadness', '#fear', 'negative', '-0.05', 'dog', 'red-bellied_black_snake', 'eastern_brown_snake', 'wiener_dog', 'puma']
senticnet['small_dot'] = ['0', '0.121', '0.085', '0.122', '#interest', '#admiration', 'positive', '0.053', 'sharp_end', 'point', 'sport_score', 'sharp_corner', 'reason_why']
senticnet['small_earth'] = ['0.817', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.771', 'heavenly_body', 'celestial_body', 'around_earth', 'earth', 'hemisphere']
senticnet['small_elephant'] = ['0.711', '0.792', '0', '0.93', '#interest', '#admiration', 'positive', '0.811', 'carnivore', 'house_animal', 'household_animal', 'large_cat', 'good_pet']
senticnet['small_evergreen_tree'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'carnauba_palm', 'carnauba', 'wax_palm', 'palmetto', 'palmyra']
senticnet['small_fish'] = ['0.053', '0.073', '-0.06', '0.104', '#interest', '#admiration', 'positive', '0.041', 'coral', 'aquatic_life', 'horizon', 'ocean', 'wave']
senticnet['small_flower'] = ['0', '0.109', '0', '0.109', '#interest', '#admiration', 'positive', '0.109', 'grassland', 'garden_plant', 'plant_yard', 'use_decoration', 'use_salad']
senticnet['small_fly'] = ['-0.03', '-0.02', '0.138', '-0.07', '#anger', '#disgust', 'negative', '-0.05', 'small_bug', 'annoy_bug', 'parasite', 'fly_away', 'tiny_insect']
senticnet['small_footprint'] = ['-0.53', '0', '0.765', '-0.94', '#anger', '#disgust', 'negative', '-0.74', 'jaggedness', 'unregularity', 'unevenness', 'irregularity', 'patchiness']
senticnet['small_fraction'] = ['0', '0.119', '0', '0.106', '#interest', '#admiration', 'positive', '0.112', 'divide', 'numerator', 'subtraction', 'math_action', 'division']
senticnet['small_fruit'] = ['0.05', '0', '0.047', '0.068', '#joy', '#admiration', 'positive', '0.024', 'tree_fruit', 'yellow_fruit', 'delicious_fruit', 'vine_fruit', 'red_fruit']
senticnet['small_fry'] = ['0.592', '-0.93', '0', '0.935', '#surprise', '#admiration', 'positive', '0.819', 'child', 'child_prodigy', 'tyke', 'tike', 'kid']
senticnet['small_furry'] = ['0.046', '0.085', '0', '0.061', '#interest', '#admiration', 'positive', '0.064', 'high_animal', 'furry_creature', 'omnivore', 'small_rodent', 'dog']
senticnet['small_govern_city'] = ['0', '0', '0', '0.095', '#admiration', '#admiration', 'positive', '0.095', 'downtown', 'govern_city', 'large_town', 'govern', 'city']
senticnet['small_grain'] = ['0', '0.065', '0.064', '0.079', '#interest', '#admiration', 'positive', '0.027', 'granular', 'white_grain', 'granule', 'food_type', 'staple_food']
senticnet['small_grocery_shop'] = ['0.059', '0.212', '0', '0.143', '#interest', '#admiration', 'positive', '0.138', 'christmas_shop', 'go_grocery_shop', 'pay_credit_card', 'pay_cash', 'holiday_shop']
senticnet['small_group'] = ['0.028', '0.12', '0.068', '0.176', '#interest', '#admiration', 'positive', '0.064', 'elite', 'number_person', 'count_person', 'oligarchy', 'enumeration']
senticnet['small_hamlet'] = ['-0.91', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.85', 'play', 'perpetrator', 'role_play', 'act_part', 'terrible_event']
senticnet['small_hive_beetle'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'nuisance', 'annoyance', 'irritant', 'pain_in_ass', 'troublesome']
senticnet['small_home'] = ['0', '0', '0', '0.09', '#admiration', '#admiration', 'positive', '0.09', 'family_home', 'residence', 'home_build', 'domicile', 'dwell_place']
senticnet['small_horse'] = ['0', '-0.11', '0.022', '0.06', '#surprise', '#admiration', 'positive', '0.051', 'donkey', 'little_horse', 'male_cow', 'hoof_animal', 'pony']
senticnet['small_house'] = ['-0.02', '0', '-0.04', '-0.03', '#fear', '#disgust', 'negative', '-0.03', 'shack', 'small_room', 'mansion', 'art_gallery', 'motel_room']
senticnet['small_human'] = ['0', '0.1', '0', '0.117', '#interest', '#admiration', 'positive', '0.109', 'cute', 'duh', 'diverse', 'small_person', 'tiny_animal']
senticnet['small_incision'] = ['0.255', '0.182', '-0.06', '0.314', '#joy', '#admiration', 'positive', '0.173', 'doctor_office', 'urethroscope', 'sigmoidoscope', 'hospital', 'check_health']
senticnet['small_indefinite_amount'] = ['-0.63', '0', '0.791', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'little', 'pinch', 'modicum', 'dreg', 'indefinite_quantity']
senticnet['small_indefinite_quantity'] = ['-0.75', '0', '0.614', '-0.61', '#sadness', '#disgust', 'negative', '-0.66', 'little', 'pinch', 'modicum', 'dreg', 'indefinite_quantity']
senticnet['small_indian_civet'] = ['0', '0', '-0.95', '-0.81', '#fear', '#disgust', 'negative', '-0.88', 'herb', 'plant', 'poisonous_plant', 'yellow_flower', 'lichen']
senticnet['small_insect'] = ['-0.03', '-0.02', '0.106', '-0.05', '#anger', '#disgust', 'negative', '-0.04', 'nuisance_insect', 'small_bug', 'tiny_insect', 'annoy_bug', 'fly_insect']
senticnet['small_intestine'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'anus', 'scrotum', 'shoulder', 'liver', 'waist']
senticnet['small_kid'] = ['0.025', '0.073', '-0.05', '0.1', '#interest', '#admiration', 'positive', '0.037', 'little_person', 'boy_girl', 'small_boy', 'young_kid', 'young_people']
senticnet['small_kitchenette'] = ['0.159', '-0.00', '0.011', '0.079', '#joy', '#admiration', 'positive', '0.058', 'ceiling_fan', 'ipod', 'technologic', 'formula', 'music']
senticnet['small_lake'] = ['-0.05', '0', '0.058', '0', '#sadness', '#anger', 'negative', '-0.05', 'ocean', 'lake', 'aquarium', 'pacific_ocean', 'pond']
senticnet['small_light'] = ['-0.07', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.06', 'cover_stone', 'find_garden', 'walk', 'cover_floor', 'meander']
senticnet['small_man'] = ['-0.07', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.06', 'ridiculous', 'bad_person', 'insignificant', 'motherfucker', 'idiot']
senticnet['small_memory_footprint'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'carbon_fiber']
senticnet['small_money'] = ['0', '0.112', '0.072', '0.132', '#interest', '#admiration', 'positive', '0.057', 'small_currency', 'money_coin', 'small_coin', 'metal_money', 'money_change']
senticnet['small_ocean'] = ['-0.06', '0.07', '0.03', '0.138', '#interest', '#admiration', 'positive', '0.028', 'along_beach', 'depth', 'deep', 'oceanside', 'full_water']
senticnet['small_part'] = ['0.718', '0.97', '0', '0', '#joy', '#interest', 'positive', '0.844', 'segment', 'component', 'part', 'portion', 'fraction']
senticnet['small_particle'] = ['0', '0.081', '0.044', '0.106', '#interest', '#admiration', 'positive', '0.048', 'particle', 'tiny_particle', 'minute', 'tiny', 'small']
senticnet['small_passerine_bird'] = ['-0.92', '0', '0.64', '-0.52', '#sadness', '#anger', 'negative', '-0.69', 'animal_order', 'percher', 'thyreophoran', 'marginocephalian', 'euronithopod']
senticnet['small_path'] = ['-0.99', '0', '0.802', '0', '#sadness', '#anger', 'negative', '-0.9', 'water_passage', 'flowing_water', 'waterway', 'water_feature', 'small_stream']
senticnet['small_pebble'] = ['-0.04', '-0.03', '0.057', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'small_rock', 'small_stone', 'ground_material', 'precious_gem', 'gem_stone']
senticnet['small_percentage'] = ['0', '0.119', '0', '0.106', '#interest', '#admiration', 'positive', '0.112', 'divide', 'numerator', 'subtraction', 'math_action', 'division']
senticnet['small_perennial_herb'] = ['-0.80', '0', '0.555', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'bush', 'wormwood', 'milkwort', 'germander', 'centaury']
senticnet['small_person'] = ['0', '0.1', '0', '0.117', '#interest', '#admiration', 'positive', '0.109', 'cute', 'small_human', 'diverse', 'duh', 'tiny_animal']
senticnet['small_picaninny'] = ['-0.83', '0', '0.984', '-0.54', '#sadness', '#anger', 'negative', '-0.78', 'picaninny', 'kid', 'minor', 'tike', 'fry']
senticnet['small_piece'] = ['0.087', '-0.07', '0.062', '0', '#joy', '#surprise', 'positive', '0.033', 'dust_particle', 'spangle', 'stripe', 'particle', 'oxide']
senticnet['small_place'] = ['0', '0.101', '0.009', '0.124', '#interest', '#admiration', 'positive', '0.072', 'large_number', 'push_together', 'location', 'vicinity', 'big_place']
senticnet['small_portion'] = ['0', '0.119', '0', '0.106', '#interest', '#admiration', 'positive', '0.112', 'divide', 'numerator', 'subtraction', 'math_action', 'division']
senticnet['small_pox'] = ['0.579', '-0.81', '0', '0.833', '#surprise', '#admiration', 'positive', '0.741', 'murine_typhus', 'rat_typhus', 'urban_typhus', 'endemic_typhus', 'rickettsial_disease']
senticnet['small_river'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'river', 'fresh_water', 'small_stream', 'river_bank', 'water']
senticnet['small_rock'] = ['0', '0', '0', '-0.86', '#disgust', '#disgust', 'negative', '-0.86', 'fine_grain', 'grainy', 'small_stone', 'grit', 'powder']
senticnet['small_rodent'] = ['-0.13', '0', '0.013', '-0.08', '#sadness', '#disgust', 'negative', '-0.07', 'blind', 'dig_tunnel', 'underground_animal', 'big_mouse', 'bad_eyesight']
senticnet['small_room'] = ['0', '0.123', '0.024', '0.135', '#interest', '#admiration', 'positive', '0.078', 'hold_place', 'microscopic', 'carry_around', 'tiny_organism', 'structural_support']
senticnet['small_round'] = ['0.955', '0.937', '0', '0', '#joy', '#interest', 'positive', '0.946', 'floppy', 'sugary', 'delicious', 'eatable', 'creamy']
senticnet['small_rowboat'] = ['0.073', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'angler', 'informal_rod', 'archaic_fisher', 'rare_piscator', 'fishing_line']
senticnet['small_saltwater_clam'] = ['-0.89', '0', '0.556', '-0.72', '#sadness', '#disgust', 'negative', '-0.72', 'fucus', 'rockweed', 'black_rockweed', 'bladder_fucus', 'tang']
senticnet['small_sample_size'] = ['-0.64', '0', '0.576', '-0.83', '#sadness', '#disgust', 'negative', '-0.68', 'nisi', 'indeterminate', 'equivocal', 'ambiguous', 'indecisive']
senticnet['small_saucepan'] = ['-0.66', '0', '0.992', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'cold', 'boil', 'temperature', 'low_temperature', 'frigidity']
senticnet['small_saucepan_over'] = ['-0.66', '0', '0.992', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'cold', 'boil', 'temperature', 'low_temperature', 'frigidity']
senticnet['small_scale'] = ['-0.05', '0', '-0.12', '-0.16', '#fear', '#disgust', 'negative', '-0.11', 'reduced', 'tiny', 'small_size', 'narrow', 'poor']
senticnet['small_seat'] = ['-0.73', '0', '0', '-0.95', '#sadness', '#disgust', 'negative', '-0.84', 'sofa_bed', 'airline_seat', 'beanbag_chair', 'hide-a-bed_sofa', 'chair']
senticnet['small_shop'] = ['-0.76', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.84', 'less_item', 'carry_goods', 'hold_flower', 'buy_goods', 'hold_grocery']
senticnet['small_size'] = ['-0.71', '0', '0.88', '0', '#sadness', '#anger', 'negative', '-0.79', 'liquid_unit', 'small', 'mass_measure', 'small_amount', 'measure_unit']
senticnet['small_stellated_dodecahedron'] = ['0.641', '-0.6', '0', '0.957', '#joy', '#admiration', 'positive', '0.733', 'ideal_solid', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'regular_octahedron']
senticnet['small_stone'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'small_rock', 'ground_material', 'small_pebble', 'grainy', 'fine_grain']
senticnet['small_stream'] = ['-0.99', '0', '0.802', '0', '#sadness', '#anger', 'negative', '-0.89', 'water_passage', 'flowing_water', 'waterway', 'water_feature', 'small_path']
senticnet['small_street'] = ['0', '0', '0.078', '0', '#anger', '#anger', 'negative', '-0.07', 'street_city', 'black_pavement', 'walkway', 'pave', 'dark_road']
senticnet['small_talk'] = ['-0.51', '0', '0.545', '-0.91', '#anger', '#disgust', 'negative', '-0.65', 'gossip', 'chitchat', 'chit_chat', 'gab', 'chat']
senticnet['small_town'] = ['-0.55', '0', '0.912', '-0.65', '#anger', '#disgust', 'negative', '-0.70', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['small_tree'] = ['0.024', '0', '0.01', '0.08', '#joy', '#admiration', 'positive', '0.031', 'plant_part', 'large_plant', 'leafy', 'part_of_plant', 'foliage']
senticnet['small_unit'] = ['0.864', '0.945', '0', '0', '#joy', '#interest', 'positive', '0.904', 'microscopic', 'basic_unit', 'minute', 'tiny_organism', 'unit']
senticnet['small_voice'] = ['0.714', '-0.58', '0', '0.667', '#joy', '#admiration', 'positive', '0.655', 'morality', 'wee_moral_sense', 'moral_sense', 'voice_of_conscience', 'sense_of_shame']
senticnet['smaller'] = ['-0.73', '0', '0.843', '0', '#sadness', '#anger', 'negative', '-0.78', 'numeral', 'list_number', 'lesser', 'measure_unit', 'two_halves']
senticnet['smaller-scale'] = ['-0.82', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.84', 'large-scale', 'small-scale', 'farming', 'larger-scale', 'medium-scale']
senticnet['smaller_footprint'] = ['0.729', '0.974', '0', '0', '#joy', '#interest', 'positive', '0.851', 'travel', 'fast', 'travel_fast', 'take_airplane', 'drive_fast']
senticnet['smallest'] = ['-0.61', '0', '0.856', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'unfixed', 'indefinite', 'nebulous', 'indecisive', 'coy']
senticnet['smallest_denomination'] = ['0.81', '0.763', '0', '0', '#joy', '#interest', 'positive', '0.786', 'dollar_bill', 'franc', 'euro', 'dollar', 'cent']
senticnet['smallholder'] = ['-0.08', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.11', 'serf', 'peasant', 'servant', 'poor', 'villein']
senticnet['smallish'] = ['-0.05', '0', '-0.12', '-0.16', '#fear', '#disgust', 'negative', '-0.11', 'reduced', 'tiny', 'small_size', 'narrow', 'poor']
senticnet['smallmouth'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'divergent_strabismus', 'exotropia', 'squint', 'abnormality', 'abnormalcy']
senticnet['smallmouth_bass_fishing'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'divergent_strabismus', 'exotropia', 'squint', 'abnormality', 'abnormalcy']
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senticnet['smallpox_immunization'] = ['-0.07', '0', '-0.22', '-0.15', '#fear', '#disgust', 'negative', '-0.15', 'gonorrhea', 'infectious_disease', 'psychoneurosis', 'sickness', 'polio']
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senticnet['smallpox_vaccination'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'medical_therapy', 'vaccine', 'doctor', 'medicine', 'hospital']
senticnet['smallpox_vaccine'] = ['-0.07', '0', '-0.22', '-0.15', '#fear', '#disgust', 'negative', '-0.15', 'gonorrhea', 'infectious_disease', 'psychoneurosis', 'sickness', 'polio']
senticnet['smalltalk'] = ['0', '-0.99', '0', '0.862', '#surprise', '#admiration', 'positive', '0.928', 'helpful_tool', 'woodworking_tool', 'helpful', 'type_of_fruit', 'hand_tool']
senticnet['smalltime'] = ['-0.8', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.82', 'small-time', 'big-time', 'two-bit', 'wanna-be', 'ex-con']
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senticnet['smarmy'] = ['0', '0', '-0.98', '-0.93', '#fear', '#disgust', 'negative', '-0.95', 'buttery', 'oleaginous', 'fulsome', 'unctuous', 'oily']
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senticnet['smart_alec'] = ['-0.95', '0', '0.673', '-0.95', '#sadness', '#disgust', 'negative', '-0.86', 'worrier', 'fusspot', 'worrywart', 'unwelcome_person', 'killjoy']
senticnet['smart_aleck'] = ['-0.95', '0', '0.772', '-0.89', '#sadness', '#disgust', 'negative', '-0.87', 'upstart', 'wiseacre', 'snob', 'snooty', 'unpleasant_person']
senticnet['smart_animal'] = ['0.029', '0', '0.018', '0.059', '#joy', '#admiration', 'positive', '0.023', 'domestic_animal', 'big_animal', 'hoof_mammal', 'large_mammal', 'cat']
senticnet['smart_arse'] = ['-0.95', '0', '0.673', '-0.95', '#sadness', '#disgust', 'negative', '-0.86', 'worrier', 'fusspot', 'worrywart', 'unwelcome_person', 'killjoy']
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senticnet['smart_dog'] = ['0.039', '0', '0', '0.059', '#joy', '#admiration', 'positive', '0.049', 'big_dog', 'dog_cat', 'canine', 'pet', 'domesticated_animal']
senticnet['smart_enough'] = ['0.995', '0.801', '0', '0.968', '#joy', '#admiration', 'positive', '0.921', 'acquire_knowledge', 'comprehend', 'great', 'accumulate_knowledge', 'increase_knowledge']
senticnet['smart_guy'] = ['0.995', '0.801', '0', '0.968', '#joy', '#admiration', 'positive', '0.921', 'acquire_knowledge', 'comprehend', 'great', 'accumulate_knowledge', 'increase_knowledge']
senticnet['smart_man'] = ['-0.88', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.88', 'moan', 'common_emotion', 'depress_person', 'child_cry', 'severe_pain']
senticnet['smart_person'] = ['0', '0', '0', '0.913', '#admiration', '#admiration', 'positive', '0.913', 'good_band', 'kit', 'wiseman', 'oblivion', 'high_school_band']
senticnet['smart_phone'] = ['0.807', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.881', 'fruit', 'mobile_phone', 'drinkable', 'savory', 'eat_fruit']
senticnet['smart_technology'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'cool_moment', 'electric_fan', 'relax', 'refreshing', 'wind']
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senticnet['smartened_up'] = ['-0.82', '0', '0.758', '-0.70', '#sadness', '#anger', 'negative', '-0.76', 'unclassifiable', 'unrecognizable', 'unidentifiable', 'intangible', 'undiagnosable']
senticnet['smarter'] = ['0.995', '0.801', '0', '0.968', '#joy', '#admiration', 'positive', '0.921', 'smart', 'smartness', 'curious', 'become_wise', 'comprehend']
senticnet['smartest'] = ['0.995', '0.801', '0', '0.968', '#joy', '#admiration', 'positive', '0.921', 'smart', 'smartness', 'curious', 'become_wise', 'comprehend']
senticnet['smartest_guy'] = ['-0.85', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.88', 'oppression', 'enslavement', 'slave', 'captivity', 'misery']
senticnet['smarting_under'] = ['0.563', '-0.56', '0', '0.722', '#joy', '#admiration', 'positive', '0.615', 'mortal', 'cutthroat', 'bowelless', 'tigerish', 'pitiless']
senticnet['smartlist'] = ['0.031', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.062', 'letter_slot', 'postal_counter', 'mailbox', 'letter_scale', 'envelope']
senticnet['smartly'] = ['0.782', '0.717', '0', '0', '#joy', '#interest', 'positive', '0.75', 'intelligently', 'elegant', 'smart', 'quickly', 'fillip']
senticnet['smartness'] = ['0.995', '0.801', '0', '0.968', '#joy', '#admiration', 'positive', '0.921', 'smart', 'experience', 'wisdom', 'intelligent', 'intelligence']
senticnet['smartphone'] = ['0.807', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.881', 'fruit', 'mobile_phone', 'drinkable', 'savory', 'eat_fruit']
senticnet['smartphone_maker'] = ['0.807', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.881', 'fruit', 'mobile_phone', 'drinkable', 'savory', 'eat_fruit']
senticnet['smartrip'] = ['-0.03', '0.29', '-0.12', '0.203', '#interest', '#admiration', 'positive', '0.083', 'excise', 'severance', 'wage', 'afford', 'revenue']
senticnet['smash'] = ['-0.06', '-0.01', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.05', 'raze', 'wreck', 'destroy', 'break', 'break_apart']
senticnet['smash_hit'] = ['0.625', '-0.90', '0', '0.54', '#joy', '#surprise', 'positive', '0.69', 'smash', 'bang', 'smasher', 'strike', 'hit']
senticnet['smash_hit_single'] = ['0.625', '-0.90', '0', '0.54', '#joy', '#surprise', 'positive', '0.69', 'smash', 'bang', 'smasher', 'strike', 'hit']
senticnet['smashbox'] = ['-0.93', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.85', 'protect_from_sun', 'shade', 'meaning', 'corrector', 'sunglasses']
senticnet['smashed'] = ['0', '0', '-0.94', '-0.77', '#fear', '#disgust', 'negative', '-0.86', 'broken_engine', 'broken_car', 'flat_tyre', 'broken_shelf', 'need_fix']
senticnet['smasher'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
senticnet['smattering'] = ['0', '0', '0.994', '-0.89', '#anger', '#disgust', 'negative', '-0.94', 'smattering', 'handful', 'plethora', 'bevy', 'sprinkling']
senticnet['smear'] = ['0', '0.101', '0.039', '0.054', '#interest', '#admiration', 'positive', '0.039', 'wipe_tear', 'press_enter', 'smudge', 'push_button', 'scratch_back']
senticnet['smell'] = ['0.867', '0', '0', '0.998', '#joy', '#admiration', 'positive', '0.932', 'fragrance', 'scent', 'aroma', 'smell_flower', 'smell_good']
senticnet['smell_bad'] = ['-0.75', '0.154', '-0.64', '0.142', '#sadness', '#fear', 'negative', '-0.27', 'evil', 'awful', 'bad_person', 'bad', 'harmful_human']
senticnet['smell_better'] = ['0.085', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.077', 'ensure_proper_hygiene', 'smell_nice', 'remove_stain', 'feel_clean', 'smell_good']
senticnet['smell_clean'] = ['0', '0', '0.943', '-0.8', '#anger', '#disgust', 'negative', '-0.87', 'fight_fire', 'soapy', 'feel_dirty', 'dirty', 'filthy']
senticnet['smell_flower'] = ['0.829', '0', '0', '0', '#joy', '#joy', 'positive', '0.829', 'thank_parent', 'travel_space', 'eat_potato', 'crowd_elevator', 'check_mail']
senticnet['smell_food'] = ['-0.04', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.05', 'eat_salad', 'masticate', 'enjoy_food', 'sell_vegetable', 'drink_wine']
senticnet['smell_fruity'] = ['0.784', '-0.59', '0', '0.846', '#joy', '#admiration', 'positive', '0.742', 'astuteness', 'slyness', 'wiliness', 'craftiness', 'cunning']
senticnet['smell_good'] = ['0.081', '0', '0', '0.086', '#joy', '#admiration', 'positive', '0.083', 'smell_nice', 'feel_clean', 'sexy', 'smell_better', 'smell_clean']
senticnet['smell_good_food'] = ['0.087', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.074', 'pumpkin_pie', 'ice_cream', 'chocolate_dessert', 'delicious', 'chewy']
senticnet['smell_nice'] = ['0.118', '0', '0', '0.099', '#joy', '#admiration', 'positive', '0.108', 'feel_clean', 'smell_clean', 'smell_good', 'smell_better', 'feel_fresh']
senticnet['smell_odor'] = ['-0.08', '0', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.08', 'age_well', 'feel_heat', 'crowd_elevator', 'close_together', 'remove_splinter']
senticnet['smell_smoke'] = ['-0.10', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.08', 'explain_everything', 'deal_card_player', 'visit_grandmother', 'stand_alone', 'storm_out_room']
senticnet['smell_up'] = ['-0.67', '0', '0.503', '-0.67', '#sadness', '#disgust', 'negative', '-0.61', 'stink_up', 'odorize', 'stink_out', 'incense', 'perfume']
senticnet['smelliest'] = ['0', '0', '0.943', '-0.8', '#anger', '#disgust', 'negative', '-0.87', 'fight_fire', 'filthy', 'dirty', 'stink', 'fall_mud']
senticnet['smelling'] = ['0', '0', '-0.83', '-0.8', '#fear', '#disgust', 'negative', '-0.81', 'resonant', 'remindful', 'redolent', 'reminiscent', 'evocative']
senticnet['smelling_fresh'] = ['0', '0', '0', '0.068', '#admiration', '#admiration', 'positive', '0.068', 'become_clean', 'wash_clothes', 'take_shower', 'get_clean', 'clean_body']
senticnet['smelly'] = ['-0.52', '0', '0.943', '-0.97', '#anger', '#disgust', 'negative', '-0.81', 'frowsty', 'fetid', 'foetid', 'malodourous', 'stinking']
senticnet['smelly_breath'] = ['0', '0', '0.955', '-0.78', '#anger', '#disgust', 'negative', '-0.87', 'worm_intestine', 'fur_keep_warm', 'unpleasant-smelling', 'halitosis', 'asthama']
senticnet['smelly_feet'] = ['0', '0', '0.943', '-0.8', '#anger', '#disgust', 'negative', '-0.87', 'fight_fire', 'filthy', 'dirty', 'stink', 'fall_mud']
senticnet['smelly_foot'] = ['0', '0', '0', '-0.87', '#disgust', '#disgust', 'negative', '-0.87', 'chicken_pox', 'lose_team', 'cause_suffer', 'disrespect', 'leave_out']
senticnet['smelter'] = ['-0.78', '0', '0.699', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'battery', 'stamp_battery', 'quartz_battery', 'quartz_mill', 'factory']
senticnet['smersh'] = ['-0.69', '0', '0.756', '-0.99', '#anger', '#disgust', 'negative', '-0.81', 'waster', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['smestow'] = ['-0.77', '0', '0.583', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'rain', 'rain_down', 'sheet', 'rain_bucket', 'rain_buckets_down']
senticnet['smethwick_summit'] = ['-0.77', '0', '0.583', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'rain', 'rain_down', 'sheet', 'rain_bucket', 'rain_buckets_down']
senticnet['smidgen'] = ['-0.78', '0', '0.915', '-0.99', '#anger', '#disgust', 'negative', '-0.89', 'bodge', 'botch_up', 'flub', 'ball_up', 'go_wrong']
senticnet['smilax'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'black_locust']
senticnet['smile'] = ['0.997', '0', '0', '0', '#joy', '#joy', 'positive', '0.997', 'sneer', 'start_laugh', 'hear_joke', 'smile_laugh', 'giggle']
senticnet['smile_child'] = ['0', '0.089', '0.023', '0.053', '#interest', '#admiration', 'positive', '0.04', 'pass_gene', 'child', 'young_child', 'overpopulation', 'great_performance']
senticnet['smile_laugh'] = ['0.759', '0', '0', '0', '#joy', '#joy', 'positive', '0.759', 'start_laugh', 'sense_of_humor', 'giggle', 'smile', 'hear_joke']
senticnet['smiled_faintly'] = ['-0.80', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'difficulty', 'awkwardness', 'flea_bite', 'difficultness', 'cumbersomeness']
senticnet['smilin'] = ['0.591', '-0.76', '0', '0.66', '#surprise', '#admiration', 'positive', '0.673', 'darling', 'pet', 'favorite', 'first_choice', 'macushla']
senticnet['smiling_broadly'] = ['0', '0', '-0.76', '-0.82', '#fear', '#disgust', 'negative', '-0.79', 'stage', 'thespian', 'actor', 'theatre', 'drama']
senticnet['smilingly'] = ['-0.85', '0.21', '-0.56', '0', '#sadness', '#fear', 'negative', '-0.40', 'doleful', 'sadly', 'dolefully', 'ruefulness', 'wretched']
senticnet['smilkov'] = ['0.633', '-0.96', '0', '0.612', '#joy', '#surprise', 'positive', '0.738', 'particularism', 'absorption', 'concentration', 'engrossment', 'centering']
senticnet['smirch'] = ['-0.87', '0', '0.994', '-0.85', '#sadness', '#anger', 'negative', '-0.90', 'fingermark', 'blemish', 'ruin', 'defect', 'blotch']
senticnet['smirk'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'simper', 'please', 'content', 'satisfy', 'gratify']
senticnet['smirnoff_ice'] = ['0.951', '-0.97', '0', '0.639', '#joy', '#surprise', 'positive', '0.853', 'smoothie', 'drinking_water', 'nutrient', 'milk', 'refresher']
senticnet['smite'] = ['0', '0', '-0.90', '-0.90', '#fear', '#disgust', 'negative', '-0.90', 'smiting', 'strike', 'afflict', 'shalt', 'slay']
senticnet['smith'] = ['0', '0.115', '0', '0.097', '#interest', '#admiration', 'positive', '0.106', 'farrier', 'miner', 'horseshoer', 'blacksmith', 'locksmith']
senticnet['smith_corona'] = ['0', '0', '0.128', '0', '#anger', '#anger', 'negative', '-0.12', 'grid', 'efficient', 'calculator', 'computer', 'personal_computer']
senticnet['smith_wesson_revolver'] = ['-0.75', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.75', 'hydrogen_bomb', 'rifle', 'revolver', 'pistol', 'gun']
senticnet['smithsonian'] = ['0', '0.099', '0', '0', '#interest', '#interest', 'positive', '0.099', 'room_temperature', 'car_race', 'sea_level', 'load_dock', 'auction']
senticnet['smithsonian_institute'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'institution', 'tundra', 'child_story', 'natural_history_museum', 'cartoon']
senticnet['smithsonian_institution'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'institution', 'tundra', 'child_story', 'natural_history_museum', 'cartoon']
senticnet['smithsonian_museum'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'institution', 'tundra', 'child_story', 'natural_history_museum', 'cartoon']
senticnet['smithsonian_national'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'institution', 'tundra', 'child_story', 'natural_history_museum', 'cartoon']
senticnet['smithsonians_national_museum'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'institution', 'tundra', 'child_story', 'natural_history_museum', 'cartoon']
senticnet['smithsonite'] = ['-0.03', '0', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.06', 'lithium', 'mineral', 'potassium', 'iodine', 'magnesium']
senticnet['smiting'] = ['0', '0', '-0.90', '-0.91', '#fear', '#disgust', 'negative', '-0.90', 'smite', 'strike', 'afflict', 'shalt', 'slay']
senticnet['smle'] = ['0', '0', '0.954', '-0.74', '#anger', '#disgust', 'negative', '-0.85', 'gun', 'pistol', 'revolver', 'firearm', 'handgun']
senticnet['smog'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'vapor', 'gaseous', 'haze', 'smoke', 'bad_habit']
senticnet['smoke'] = ['-0.76', '0', '0', '-0.95', '#sadness', '#disgust', 'negative', '-0.85', 'addiction', 'drug', 'embarrassment', 'cigarette', 'disappointment']
senticnet['smoke_alarm'] = ['0', '0.101', '-0.05', '0.059', '#interest', '#admiration', 'positive', '0.037', 'make_toy', 'frame_picture', 'equity', 'buy_paint_brush', 'buy_paint']
senticnet['smoke_bomb'] = ['0.886', '-0.87', '0', '0', '#joy', '#surprise', 'positive', '0.881', 'fire_hydrant', 'stair_rail', 'christian_fundamentalist', 'extinguisher', 'reception_desk']
senticnet['smoke_bowl'] = ['-0.71', '0', '0', '-0.96', '#sadness', '#disgust', 'negative', '-0.84', 'have_fun', 'laugh_lot', 'change_mood', 'deafness', 'serve_guest']
senticnet['smoke_cigar'] = ['-0.10', '0', '-0.05', '-0.03', '#sadness', '#fear', 'negative', '-0.06', 'deal_card', 'bicycle_across_town', 'unhealthy', 'wind_watch', 'kill_specific_group']
senticnet['smoke_cigarette'] = ['0', '-0.00', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.06', 'age_prematurely', 'cause_mental_illness', 'smoke_pipe', 'light_cigar', 'light_pipe']
senticnet['smoke_crack'] = ['0', '0.931', '0', '0', '#interest', '#interest', 'positive', '0.931', 'football', 'rake_leave', 'spray_paint_grafitti', 'juggle', 'play_hide_seek']
senticnet['smoke_detector'] = ['0.02', '0.076', '0', '0', '#joy', '#interest', 'positive', '0.048', 'land_earth', 'tidal_wave', 'puddle', 'brine', 'spurt']
senticnet['smoke_dope'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['smoke_grenade'] = ['0', '0', '0.818', '-0.99', '#anger', '#disgust', 'negative', '-0.90', 'weapon', 'aerial_bomb', 'machine_gun', 'cannon', 'handgun']
senticnet['smoke_joint'] = ['0.874', '0.74', '0', '0', '#joy', '#interest', 'positive', '0.807', 'shovel_snow', 'fly_around', 'blow_away', 'feel_hungry', 'explain_everything']
senticnet['smoke_marijuana'] = ['0', '0.931', '0', '0', '#interest', '#interest', 'positive', '0.931', 'football', 'rake_leave', 'spray_paint_grafitti', 'juggle', 'play_hide_seek']
senticnet['smoke_pipe'] = ['0.031', '0', '-0.08', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'smoke_cigarette', 'smoke', 'cinder', 'tobacco_product', 'smoke_tobacco']
senticnet['smoke_pot'] = ['0.992', '0.728', '0', '0.834', '#joy', '#admiration', 'positive', '0.851', 'win_contest', 'laugh_lot', 'experience_pleasure', 'go_date', 'enjoy_performance']
senticnet['smoke_rose'] = ['0.641', '-0.73', '0', '0.798', '#surprise', '#admiration', 'positive', '0.724', 'ablaze', 'aflame', 'afire', 'aflare', 'lighted']
senticnet['smoke_tobacco'] = ['-0.90', '0', '0.713', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'smoke', 'cigarette', 'tobacco', 'cigar', 'lung_cancer']
senticnet['smoke_wee'] = ['0.949', '0.741', '0', '0', '#joy', '#interest', 'positive', '0.845', 'miss_work', 'take_break', 'lie_on_bed', 'watch_tv', 'sleep_upon']
senticnet['smoked_duck_breast'] = ['0.084', '-0.05', '0', '-0.04', '#joy', '#surprise', 'positive', '0.031', 'entree', 'feast', 'satiation', 'breakfast', 'supper']
senticnet['smoked_eel'] = ['0.709', '0.964', '0', '0', '#joy', '#interest', 'positive', '0.837', 'fish_trout', 'catch_butterfly', 'fish_lake', 'fish_compliment', 'fish_stream']
senticnet['smoked_fish'] = ['0.9', '0.963', '0', '0', '#joy', '#interest', 'positive', '0.931', 'form_of_food', 'japanese_food', 'uncooked', 'healthy', 'food']
senticnet['smoked_haddock'] = ['0.644', '-0.94', '0', '0.729', '#surprise', '#admiration', 'positive', '0.773', 'gem', 'banana_bread', 'date_bread', 'nut_bread', 'quick_bread']
senticnet['smoked_ham'] = ['0.52', '-0.69', '0', '0.639', '#surprise', '#admiration', 'positive', '0.617', 'black_bread', 'pumpernickel', 'limpa', 'bread', 'breadstuff']
senticnet['smoked_marijuana'] = ['0', '0.931', '0', '0', '#interest', '#interest', 'positive', '0.931', 'football', 'rake_leave', 'spray_paint_grafitti', 'juggle', 'play_hide_seek']
senticnet['smoked_paprika'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'high_temperature', 'hot', 'hot_weather', 'fire_product', 'heat']
senticnet['smoked_pork'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'dainty', 'delicacy', 'treat', 'gelatin_dessert', 'jelly']
senticnet['smoked_pot'] = ['0', '0.931', '0', '0', '#interest', '#interest', 'positive', '0.931', 'football', 'rake_leave', 'spray_paint_grafitti', 'juggle', 'play_hide_seek']
senticnet['smoked_salmon'] = ['0.709', '0.964', '0', '0', '#joy', '#interest', 'positive', '0.837', 'fish_trout', 'catch_butterfly', 'fish_lake', 'fish_compliment', 'fish_stream']
senticnet['smokeless_tobacco'] = ['-0.99', '0', '0.056', '-0.98', '#sadness', '#disgust', 'negative', '-0.67', 'smoke', 'cigarette', 'bad_air', 'pollution', 'cigar']
senticnet['smokeless_tobacco_product'] = ['-0.99', '0', '0.056', '-0.98', '#sadness', '#disgust', 'negative', '-0.67', 'smoke', 'cigarette', 'bad_air', 'pollution', 'cigar']
senticnet['smoker'] = ['0.056', '0', '-0.06', '0.062', '#joy', '#admiration', 'positive', '0.017', 'cyclist', 'chauffeur', 'homeowner', 'programmer', 'stalactite']
senticnet['smokescreen'] = ['-0.72', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.82', 'conceal', 'distract', 'mask', 'cover', 'troop']
senticnet['smoking_habit'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'vapor', 'gaseous', 'haze', 'smoke', 'smog']
senticnet['smoking_jacket'] = ['0', '-0.02', '0.048', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'telescope', 'read_glass', 'eyeglass', 'camera', 'binoculars']
senticnet['smoking_marijuana'] = ['0', '0.931', '0', '0', '#interest', '#interest', 'positive', '0.931', 'football', 'rake_leave', 'spray_paint_grafitti', 'juggle', 'play_hide_seek']
senticnet['smoking_pot'] = ['0', '0.931', '0', '0', '#interest', '#interest', 'positive', '0.931', 'football', 'rake_leave', 'spray_paint_grafitti', 'juggle', 'play_hide_seek']
senticnet['smoking_relapse'] = ['0.627', '-0.65', '0', '0.968', '#surprise', '#admiration', 'positive', '0.749', 'overwhelm', 'overpower', 'sweep_over', 'whelm', 'overcome']
senticnet['smoking_tobacco'] = ['-0.99', '0', '0.056', '-0.98', '#sadness', '#disgust', 'negative', '-0.67', 'smoke', 'cigarette', 'bad_air', 'pollution', 'cigar']
senticnet['smoky'] = ['0', '0', '0.943', '-0.8', '#anger', '#disgust', 'negative', '-0.87', 'feel_dirty', 'unclean', 'dirty', 'yucky', 'filthy']
senticnet['smoky_flavor'] = ['0.043', '0.091', '0', '0.113', '#interest', '#admiration', 'positive', '0.082', 'sweet_sour', 'tasty', 'yummy', 'popular_food', 'form_of_food']
senticnet['smoky_quartz'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'art', 'art_piece', 'museum', 'artist', 'jewel']
senticnet['smoky_voice'] = ['0.85', '-0.71', '0', '0.617', '#joy', '#surprise', 'positive', '0.728', 'pretender', 'phony', 'phoney', 'hypocrite', 'dissembler']
senticnet['smolder'] = ['0', '-0.03', '-0.05', '-0.06', '#fear', '#disgust', 'negative', '-0.03', 'find_campsite', 'burn_house', 'cause_fire', 'die_out', 'find_wood']
senticnet['smoldering'] = ['0.897', '0.856', '0', '0', '#joy', '#interest', 'positive', '0.877', 'red-hot', 'glowed', 'searing', 'blazing', 'white-hot']
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senticnet['soaking_wet'] = ['0.951', '0.763', '0', '0', '#joy', '#interest', 'positive', '0.857', 'pull_muscle', 'heart_rate_increase', 'heavy_breathe', 'get_exhausted', 'tire']
senticnet['soap'] = ['0.078', '-0.07', '0', '0', '#joy', '#surprise', 'positive', '0.074', 'foam', 'drizzle', 'bubbly', 'solid_water', 'ivory']
senticnet['soap_bubble'] = ['-0.55', '0', '0.869', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'air_bubble', 'foam', 'froth', 'bubble', 'globule']
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senticnet['soap_opera_actor'] = ['0', '0.797', '0', '0.804', '#interest', '#admiration', 'positive', '0.8', 'director', 'shooting_movie', 'director_chair', 'important_person', 'movie']
senticnet['soap_up'] = ['-0.07', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.05', 'dispense_water', 'turn_water', 'fill_bathtub', 'run_bath', 'launder']
senticnet['soap_water'] = ['0', '0.069', '0.023', '0.052', '#interest', '#admiration', 'positive', '0.033', 'soapy_water', 'wash_self', 'shower_gel', 'soap_up', 'turn_water']
senticnet['soapy'] = ['0', '0', '0.939', '-0.80', '#anger', '#disgust', 'negative', '-0.87', 'fight_fire', 'dirty', 'feel_dirty', 'smell_clean', 'filthy']
senticnet['soapy_smith'] = ['-0.64', '0', '0.667', '-0.82', '#anger', '#disgust', 'negative', '-0.71', 'novice', 'beginner', 'tyro', 'tiro', 'initiate']
senticnet['soapy_water'] = ['0', '0.069', '0.023', '0.052', '#interest', '#admiration', 'positive', '0.033', 'shower_gel', 'wash_self', 'soap_water', 'soap_up', 'turn_water']
senticnet['soar'] = ['-0.02', '-0.04', '0.055', '-0.07', '#anger', '#disgust', 'negative', '-0.02', 'aviation', 'wing_insect', 'bird', 'wing', 'flap_wing']
senticnet['soaring'] = ['0', '0.942', '0', '0.891', '#interest', '#admiration', 'positive', '0.916', 'skyrocketing', 'exorbitant', 'sky-high', 'sky-rocketing', 'record-high']
senticnet['soaring_demand'] = ['0.749', '0.764', '0', '0', '#joy', '#interest', 'positive', '0.756', 'obedience', 'order', 'requirement', 'permission', 'necessary']
senticnet['soaring_eagle'] = ['0', '0.105', '0', '0', '#interest', '#interest', 'positive', '0.105', 'beak', 'many_feathers', 'big_eye', 'bird', 'bird_mouth']
senticnet['soaring_unemployment'] = ['0', '0', '0.948', '-0.96', '#anger', '#disgust', 'negative', '-0.95', 'work_much', 'lack_money', 'wage_slavery', 'buy_tool', 'get_paycheck']
senticnet['sob'] = ['-0.96', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.96', 'weep', 'bawl', 'shed_tear', 'cry_hard', 'wail']
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senticnet['soberly'] = ['0', '0.764', '0', '0.845', '#interest', '#admiration', 'positive', '0.804', 'empathetically', 'empathic', 'empathetic', 'kind-heartedness', 'kindheartedness']
senticnet['soberness'] = ['-0.57', '0', '0.841', '-0.50', '#sadness', '#anger', 'negative', '-0.64', 'somberness', 'sombreness', 'gravity', 'stodginess', 'graveness']
senticnet['sobolev_space'] = ['0.637', '-0.91', '0', '0.757', '#surprise', '#admiration', 'positive', '0.768', 'manageability', 'manageableness', 'docility', 'obedience', 'tractability']
senticnet['sobriety'] = ['0.912', '-0.74', '0', '0.793', '#joy', '#admiration', 'positive', '0.817', 'natural_virtue', 'abstemiousness', 'dryness', 'temperance', 'restraint']
senticnet['sobriety_test'] = ['-0.31', '-0.02', '0', '-0.24', '#sadness', '#disgust', 'negative', '-0.17', 'pollution', 'screw_up', 'car_wreck', 'parking_ticket', 'feel_stressed']
senticnet['sobriquet'] = ['0.072', '0.244', '-0.20', '0.252', '#interest', '#admiration', 'positive', '0.091', 'doe', 'sweet', 'live_organism', 'love', 'alive']
senticnet['soc_sec'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'rudeness', 'discourtesy', 'insolence', 'ccheekiness', 'cheekiness']
senticnet['soccer'] = ['0.784', '0.912', '0', '0', '#joy', '#interest', 'positive', '0.848', 'ball', 'ball_bounce', 'football', 'bounce', 'basketball']
senticnet['soccer-loving'] = ['0.766', '-0.89', '0', '0', '#joy', '#surprise', 'positive', '0.832', 'sin-cursed', 'newly-made', 'new-made', 'city-dwelling', 'newly-christened']
senticnet['soccer_ball'] = ['0.783', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.766', 'baseball_bat', 'water_gun', 'goalie', 'hockey_stick', 'bullseye']
senticnet['soccer_field'] = ['0.048', '0.077', '0', '-0.03', '#joy', '#interest', 'positive', '0.031', 'baseball_diamond', 'outhouse', 'memorial', 'ground', 'city_park']
senticnet['soccer_game'] = ['0', '-0.00', '-0.06', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'sport_event', 'toy_box', 'hockey_game', 'stadium', 'disneyland']
senticnet['soccer_pitch'] = ['0.041', '0.086', '0.016', '0', '#joy', '#interest', 'positive', '0.037', 'team', 'play_sport', 'game', 'exercise', 'match']
senticnet['soccer_player'] = ['0', '0', '0', '0.97', '#admiration', '#admiration', 'positive', '0.97', 'increase_strength', 'keep_fit', 'fit_person', 'exercise', 'strengthen_heart']
senticnet['soccer_team'] = ['0.035', '0.155', '0', '0', '#joy', '#interest', 'positive', '0.095', 'basketball_court', 'basketball_game', 'basketball', 'ball', 'sport']
senticnet['sociability'] = ['-0.64', '0', '0.574', '-0.89', '#sadness', '#disgust', 'negative', '-0.70', 'gregariousness', 'ambiversion', 'openness', 'nakedness', 'extraversion']
senticnet['sociable'] = ['0.992', '0', '0', '0.801', '#joy', '#admiration', 'positive', '0.897', 'feel_content', 'make_feel_better', 'celebrate_special_occasion', 'survival', 'express_appreciation']
senticnet['sociableness'] = ['-0.87', '0', '0.505', '-0.84', '#sadness', '#disgust', 'negative', '-0.74', 'gregariousness', 'ambiversion', 'openness', 'nakedness', 'extraversion']
senticnet['social'] = ['0.15', '-0.07', '-0.05', '0', '#joy', '#surprise', 'positive', '0.055', 'friendship', 'share_experience', 'network', 'get_together', 'relay_information']
senticnet['social_acceptability'] = ['0.817', '-0.62', '0', '0.62', '#joy', '#surprise', 'positive', '0.688', 'quality', 'fascination', 'allurement', 'allure', 'temptingness']
senticnet['social_acceptance'] = ['0.052', '0', '-0.04', '0.07', '#joy', '#admiration', 'positive', '0.025', 'smell_better', 'ensure_proper_hygiene', 'prevent_disease', 'smell_good', 'look_presentable']
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senticnet['social_activity'] = ['0.161', '-0.09', '0', '0.073', '#joy', '#surprise', 'positive', '0.109', 'social_event', 'athletic', 'fun_exercise', 'recreational', 'healthful']
senticnet['social_affair'] = ['0.544', '-0.67', '0', '0.653', '#surprise', '#admiration', 'positive', '0.625', 'get_together', 'bee', 'love_feast', 'company', 'function']
senticnet['social_animal'] = ['0.932', '0.931', '0', '0.78', '#joy', '#interest', 'positive', '0.881', 'good_friend', 'feel_comfort', 'fun_around', 'good_pet', 'social_creature']
senticnet['social_anthropology'] = ['0.818', '0.991', '0', '0', '#joy', '#interest', 'positive', '0.904', 'ethnology', 'anthropology', 'archeology', 'physical_anthropology', 'social_science']
senticnet['social_anxiety_disorder'] = ['-0.91', '0', '0.508', '-0.89', '#sadness', '#disgust', 'negative', '-0.77', 'mental_disorder', 'mental_disturbance', 'psychological_disorder', 'folie', 'disturbance']
senticnet['social_bond'] = ['0.166', '-0.05', '0.044', '-0.04', '#joy', '#surprise', 'positive', '0.032', 'impress_girl', 'relieve_stress', 'enjoy_outdoors', 'attract_mate', 'become_popular']
senticnet['social_class'] = ['0.036', '0.129', '0', '0.068', '#interest', '#admiration', 'positive', '0.078', 'status', 'military_order', 'army_position', 'rank', 'general']
senticnet['social_climber'] = ['-0.74', '0', '0.786', '-0.96', '#anger', '#disgust', 'negative', '-0.83', 'junior', 'unpleasant_person', 'disagreeable_person', 'climber', 'arriviste']
senticnet['social_club'] = ['0.967', '-0.98', '0', '0.64', '#joy', '#surprise', 'positive', '0.863', 'club', 'jockey_club', 'society', 'guild', 'gild']
senticnet['social_cognition'] = ['0.461', '0.674', '-0.26', '0.247', '#joy', '#interest', 'positive', '0.28', 'affectionateness', 'warmness', 'philia', 'affectional', 'emotive']
senticnet['social_cohesion'] = ['-0.25', '0', '-0.17', '-0.08', '#sadness', '#fear', 'negative', '-0.16', 'exclusion', 'exclusion_zone', 'isolation', 'detach', 'except']
senticnet['social_commentary'] = ['0', '-0.03', '0', '0.094', '#surprise', '#admiration', 'positive', '0.065', 'commercial', 'sitcom', 'presentation', 'joke', 'funny']
senticnet['social_connection'] = ['-0.06', '0.291', '-0.11', '0.194', '#interest', '#admiration', 'positive', '0.076', 'communicate_idea', 'communicate_with_others', 'communicate_family', 'social_life', 'exchange_information']
senticnet['social_convention'] = ['0', '0.942', '0', '0.795', '#interest', '#admiration', 'positive', '0.869', 'enter_place', 'stand', 'cover_floor', 'meander', 'progress']
senticnet['social_creature'] = ['0.047', '0.087', '0', '0.08', '#interest', '#admiration', 'positive', '0.071', 'social_animal', 'smart_animal', 'omnivore', 'live_animal', 'large_mouse']
senticnet['social_darwinist'] = ['0.95', '-0.77', '0', '0.578', '#joy', '#surprise', 'positive', '0.767', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['social_deprivation'] = ['-0.25', '0', '-0.17', '-0.08', '#sadness', '#fear', 'negative', '-0.16', 'exclusion', 'exclusion_zone', 'isolation', 'detach', 'except']
senticnet['social_desirability'] = ['-0.93', '0', '0.586', '-0.66', '#sadness', '#disgust', 'negative', '-0.73', 'camp', 'pack', 'clique', 'coterie', 'brain_trust']
senticnet['social_disease'] = ['-0.61', '0', '0.899', '-0.62', '#anger', '#disgust', 'negative', '-0.71', 'venereal_disease', 'venereal_infection', 'dose', 'sexually_transmitted_disease', 'gonorrhoea']
senticnet['social_disintegration'] = ['-0.84', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.82', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecure']
senticnet['social_dislocation'] = ['-0.84', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.82', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecure']
senticnet['social_drink'] = ['0.879', '0.796', '0', '0', '#joy', '#interest', 'positive', '0.838', 'social_gathering', 'meet_up', 'gala', 'gathering', 'happy_time']
senticnet['social_event'] = ['0.158', '-0.10', '0.018', '-0.05', '#joy', '#surprise', 'positive', '0.048', 'get_together', 'radical', 'enjoyable', 'social_activity', 'amusement']
senticnet['social_exclusion'] = ['-0.25', '0', '-0.17', '-0.08', '#sadness', '#fear', 'negative', '-0.16', 'exclusion', 'exclusion_zone', 'isolation', 'detach', 'except']
senticnet['social_gathering'] = ['0.663', '-0.84', '0', '0.8', '#surprise', '#admiration', 'positive', '0.769', 'get_together', 'fun_time', 'social_drink', 'meet_up', 'gala']
senticnet['social_group'] = ['0.124', '0.101', '-0.04', '0.135', '#joy', '#admiration', 'positive', '0.078', 'team_up', 'group_of_people', 'organize_group', 'member', 'group']
senticnet['social_inequality'] = ['-0.25', '0', '-0.17', '-0.08', '#sadness', '#fear', 'negative', '-0.16', 'exclusion', 'exclusion_zone', 'isolation', 'detach', 'except']
senticnet['social_injustice'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'oppressiveness', 'tyrannical', 'oppressive', 'tyrannous', 'laden']
senticnet['social_insect'] = ['0', '-0.03', '0.072', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'vermin', 'arthropod', 'fly_insect', 'type_of_animal', 'small_animal']
senticnet['social_insurance'] = ['0.753', '-0.62', '0', '0.959', '#joy', '#admiration', 'positive', '0.778', 'assistance', 'relief', 'economic_aid', 'financial_aid', 'public_assistance']
senticnet['social_interaction'] = ['0.702', '0', '0', '0.785', '#joy', '#admiration', 'positive', '0.744', 'get_hammered', 'get_drunk', 'meet_member_opposite_sex', 'good_time', 'get_to_know_person']
senticnet['social_intercourse'] = ['0.569', '-0.80', '0', '0.801', '#surprise', '#admiration', 'positive', '0.726', 'mincemeat', 'stuffing', 'dressing', 'roux', 'batter']
senticnet['social_isolation'] = ['-0.77', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.78', 'unhappiness', 'melancholy', 'shame', 'unhappy', 'sad']
senticnet['social_justice'] = ['0.812', '-0.91', '0', '0.647', '#joy', '#surprise', 'positive', '0.79', 'assistance', 'relief', 'economic_aid', 'financial_aid', 'social_insurance']
senticnet['social_life'] = ['0.82', '0.997', '0', '0.973', '#interest', '#admiration', 'positive', '0.93', 'exchange_information', 'relay_information', 'exchange_idea', 'give_information', 'keep_in_touch']
senticnet['social_marginalisation'] = ['-0.25', '0', '-0.17', '-0.08', '#sadness', '#fear', 'negative', '-0.16', 'exclusion', 'exclusion_zone', 'isolation', 'detach', 'except']
senticnet['social_marginalization'] = ['-0.84', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.82', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecure']
senticnet['social_misfit'] = ['0.584', '-0.59', '0', '0.753', '#surprise', '#admiration', 'positive', '0.643', 'ungracious', 'discourteous', 'impolite', 'brattish', 'unparliamentary']
senticnet['social_networking_platform'] = ['0.886', '-0.77', '0', '0.815', '#joy', '#admiration', 'positive', '0.826', 'countenance', 'legitimate', 'permit', 'allow', 'let']
senticnet['social_occasion'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'celebrate_holiday', 'express_appreciation', 'celebrate_special_occasion', 'joke_around', 'get_sex']
senticnet['social_ostracism'] = ['-0.77', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'mark', 'emblem', 'religious_sign', 'stain', 'brand']
senticnet['social_personality_disorder'] = ['0', '0', '0.896', '-0.96', '#anger', '#disgust', 'negative', '-0.92', 'antisocial', 'sociopathic', 'psychopathic_personality', 'disturbance', 'schizoid']
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senticnet['social_rank'] = ['0', '0.916', '0', '0.741', '#interest', '#admiration', 'positive', '0.829', 'royal_title', 'royal_person', 'earl', 'duke', 'noble']
senticnet['social_realism'] = ['0.563', '-0.72', '0', '0.644', '#surprise', '#admiration', 'positive', '0.645', 'idiom', 'idiomatic_expression', 'phrasal_idiom', 'set_phrase', 'phrase']
senticnet['social_reformer'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['social_reintegration'] = ['0.641', '-0.78', '0', '0.721', '#surprise', '#admiration', 'positive', '0.715', 'correctional_rehabilitation', 'physical_rehabilitation', 'physical_restoration', 'therapeutic_rehabilitation', 'vocational_rehabilitation']
senticnet['social_responsibility_csr'] = ['0.037', '0.177', '0', '0.147', '#interest', '#admiration', 'positive', '0.12', 'business_organization', 'company', 'economic_activity', 'large_business', 'partnership']
senticnet['social_safety_net'] = ['0.812', '-0.91', '0', '0.647', '#joy', '#surprise', 'positive', '0.79', 'assistance', 'relief', 'economic_aid', 'financial_aid', 'social_insurance']
senticnet['social_satire'] = ['0', '-0.03', '0', '0.094', '#surprise', '#admiration', 'positive', '0.065', 'commercial', 'sitcom', 'presentation', 'joke', 'funny']
senticnet['social_science'] = ['0', '0.852', '0', '0.716', '#interest', '#admiration', 'positive', '0.784', 'sociology', 'home_economics', 'proxemics', 'economics', 'anthropology']
senticnet['social_security_card'] = ['0.931', '-0.52', '0', '0.67', '#joy', '#admiration', 'positive', '0.709', 'document', 'certificate_of_incorporation', 'credential', 'diploma', 'sheepskin']
senticnet['social_security_number'] = ['0.931', '-0.52', '0', '0.67', '#joy', '#admiration', 'positive', '0.709', 'document', 'certificate_of_incorporation', 'credential', 'diploma', 'sheepskin']
senticnet['social_skill'] = ['0.149', '0.09', '0', '0.158', '#joy', '#admiration', 'positive', '0.132', 'go_abroad', 'invite', 'visit', 'go_club', 'outgo_personality']
senticnet['social_status'] = ['0.044', '0.124', '-0.07', '0.124', '#interest', '#admiration', 'positive', '0.055', 'designation', 'caste', 'status', 'well_known', 'distinction']
senticnet['social_stigma'] = ['-0.77', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'mark', 'emblem', 'religious_sign', 'stain', 'brand']
senticnet['social_stigmatization'] = ['-0.77', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'mark', 'emblem', 'religious_sign', 'stain', 'brand']
senticnet['social_unrest'] = ['-0.84', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.82', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecure']
senticnet['social_upheaval'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'absolutism', 'autocracy', 'ascendancy', 'tyrant']
senticnet['social_upliftment'] = ['0.812', '-0.91', '0', '0.647', '#joy', '#surprise', 'positive', '0.79', 'assistance', 'relief', 'economic_aid', 'financial_aid', 'social_insurance']
senticnet['social_welfare'] = ['0.812', '-0.91', '0', '0.647', '#joy', '#surprise', 'positive', '0.79', 'assistance', 'relief', 'economic_aid', 'financial_aid', 'social_insurance']
senticnet['social_welfare_policy'] = ['0.812', '-0.91', '0', '0.647', '#joy', '#surprise', 'positive', '0.79', 'assistance', 'relief', 'economic_aid', 'financial_aid', 'social_insurance']
senticnet['socialise'] = ['0.992', '0', '0', '0.801', '#joy', '#admiration', 'positive', '0.897', 'feel_content', 'make_feel_better', 'celebrate_special_occasion', 'survival', 'express_appreciation']
senticnet['socialism'] = ['0.032', '0.255', '-0.08', '0.168', '#interest', '#admiration', 'positive', '0.092', 'parliament', 'feudalism', 'empire', 'democracy', 'political']
senticnet['socialist'] = ['0.863', '0.715', '0', '0', '#joy', '#interest', 'positive', '0.789', 'kill', 'leftist', 'result_death', 'eliminate', 'kill_pest']
senticnet['socialist_fatherland'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'patriotism', 'homeland']
senticnet['socialist_ideology'] = ['0', '0', '0', '0.986', '#admiration', '#admiration', 'positive', '0.986', 'idea', 'decision', 'consider', 'polarize', 'make_right_decision']
senticnet['socialist_internationalism'] = ['0.946', '-0.54', '0', '0.774', '#joy', '#admiration', 'positive', '0.754', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['socialist_motherland'] = ['0', '0.19', '-0.06', '0.093', '#interest', '#admiration', 'positive', '0.072', 'pride', 'revolution', 'patriot', 'patriotism', 'homeland']
senticnet['socialist_politician'] = ['0', '0.868', '0', '0.853', '#interest', '#admiration', 'positive', '0.861', 'country_leader', 'prime_minister', 'president', 'elect_official', 'leader']
senticnet['socialistic'] = ['0', '0', '-0.87', '-0.89', '#fear', '#disgust', 'negative', '-0.88', 'communistic', 'fascistic', 'capitalistic', 'collectivist', 'theocratic']
senticnet['socialite'] = ['0', '0', '-0.99', '-0.73', '#fear', '#disgust', 'negative', '-0.86', 'king', 'baron', 'business_leader', 'mogul', 'big_businessman']
senticnet['sociality'] = ['-0.64', '0', '0.574', '-0.89', '#sadness', '#disgust', 'negative', '-0.70', 'gregariousness', 'ambiversion', 'openness', 'nakedness', 'extraversion']
senticnet['socialization'] = ['0.96', '0.548', '0', '0.684', '#joy', '#admiration', 'positive', '0.731', 'nothing_better', 'relay_information', 'get_together_friend', 'entertainment_education', 'spread_knowledge']
senticnet['socialize'] = ['0.057', '0', '0.013', '0.087', '#joy', '#admiration', 'positive', '0.044', 'social_skill', 'invite', 'greet', 'charisma', 'communicate']
senticnet['socialize_with_friend'] = ['0.96', '0.548', '0', '0.684', '#joy', '#admiration', 'positive', '0.731', 'friend', 'socialize', 'spend_time_with_friend', 'chit_chat', 'meet_new_person']
senticnet['socialized_health_care'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'rudeness', 'discourtesy', 'insolence', 'ccheekiness', 'cheekiness']
senticnet['socially_awkward'] = ['0.943', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.93', 'high-strung', 'restive', 'overstrung', 'nervy', 'jumpy']
senticnet['socially_inept'] = ['0', '0.943', '0', '0.789', '#interest', '#admiration', 'positive', '0.866', 'energy', 'genius', 'energy_use', 'gas_oil', 'invent']
senticnet['socially_irresponsible'] = ['0', '0', '0.733', '-0.92', '#anger', '#disgust', 'negative', '-0.83', 'freewheeling', 'carefree', 'slaphappy', 'unaccountable', 'loose']
senticnet['socially_prominent'] = ['0.973', '0.707', '0', '0', '#joy', '#interest', 'positive', '0.84', 'money', 'down_payment', 'much_money', 'slogan', 'take_out_loan']
senticnet['socially_undesirable'] = ['-0.98', '0', '0.582', '-0.56', '#sadness', '#anger', 'negative', '-0.70', 'unsocial', 'antisocial', 'lonely', 'alone', 'solitary']
senticnet['socially_useful'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'lucrative', 'moneymaking', 'profitable', 'paying', 'paid']
senticnet['socially_vulnerable'] = ['0.812', '-0.91', '0', '0.647', '#joy', '#surprise', 'positive', '0.79', 'assistance', 'relief', 'economic_aid', 'financial_aid', 'social_insurance']
senticnet['socialtext'] = ['-0.63', '0', '0.862', '-0.52', '#sadness', '#anger', 'negative', '-0.67', 'knot', 'bunch', 'tuft', 'tussock', 'agglomeration']
senticnet['societal'] = ['0.913', '-0.63', '0', '0.961', '#joy', '#admiration', 'positive', '0.836', 'materialistic', 'mercenary', 'mundane', 'material', 'worldly']
senticnet['societal_prejudice'] = ['-0.77', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'mark', 'emblem', 'religious_sign', 'stain', 'brand']
senticnet['societal_stigma'] = ['-0.77', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'mark', 'emblem', 'religious_sign', 'stain', 'brand']
senticnet['society'] = ['0.092', '0.112', '-0.08', '0.052', '#joy', '#interest', 'positive', '0.042', 'egalitarian_society', 'urban', 'group_of_people', 'colony', 'social_group']
senticnet['socinian'] = ['-0.85', '0', '0.749', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'hairsplitter', 'fight', 'quarrel', 'obstructor', 'obstructer']
senticnet['socinianism'] = ['-0.85', '0', '0.749', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'hairsplitter', 'fight', 'quarrel', 'obstructor', 'obstructer']
senticnet['socio-economic'] = ['-0.89', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.94', 'demographic', 'socioeconomic', 'demographic', 'population', 'geographical']
senticnet['socio-economically'] = ['0', '0.81', '0', '0.793', '#interest', '#admiration', 'positive', '0.802', 'socioeconomically', 'ethnically', 'disadvantaged', 'linguistically', 'educationally']
senticnet['socio-environmental'] = ['-0.84', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.79', 'non-linguistic', 'paralinguistic', 'extra-linguistic', 'language-like', 'nonlinguistic']
senticnet['socio_economic'] = ['-0.25', '0', '-0.17', '-0.08', '#sadness', '#fear', 'negative', '-0.16', 'exclusion', 'exclusion_zone', 'isolation', 'detach', 'except']
senticnet['socio_economic_development'] = ['0', '0.885', '0', '0.73', '#interest', '#admiration', 'positive', '0.807', 'enlarge', 'flourish', 'growth', 'get_large', 'get_big']
senticnet['socio_economic_disadvantage'] = ['-0.25', '0', '-0.17', '-0.08', '#sadness', '#fear', 'negative', '-0.16', 'exclusion', 'exclusion_zone', 'isolation', 'detach', 'except']
senticnet['socio_emotional'] = ['0.461', '0.674', '-0.26', '0.247', '#joy', '#interest', 'positive', '0.28', 'affectionateness', 'warmness', 'philia', 'affectional', 'emotive']
senticnet['socio_political'] = ['0.836', '-0.83', '0', '0.511', '#joy', '#surprise', 'positive', '0.726', 'appearance', 'start', 'casus_belli', 'beginning', 'egress']
senticnet['socio_technical'] = ['0.654', '-0.71', '0', '0.624', '#joy', '#surprise', 'positive', '0.664', 'viewable', 'gross', 'telescopic', 'seeable', 'visual']
senticnet['socioeconomic'] = ['-0.89', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.94', 'demographic', 'demographic', 'socio-economic', 'population', 'geographical']
senticnet['socioeconomic_development'] = ['0.913', '-0.63', '0', '0.961', '#joy', '#admiration', 'positive', '0.836', 'materialistic', 'mercenary', 'mundane', 'material', 'worldly']
senticnet['socioeconomic_marginalization'] = ['-0.12', '-0.05', '0', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'diminish', 'reduce', 'lower', 'lower_music', 'decrease']
senticnet['socioeconomically'] = ['0', '0.815', '0', '0.798', '#interest', '#admiration', 'positive', '0.806', 'socio-economically', 'ethnically', 'disadvantaged', 'linguistically', 'educationally']
senticnet['sociologically'] = ['0', '0.85', '0', '0.904', '#interest', '#admiration', 'positive', '0.877', 'biochemically', 'mechanistically', 'phonologically', 'analytically', 'semantically']
senticnet['sociologist'] = ['0', '0.909', '0', '0.799', '#interest', '#admiration', 'positive', '0.854', 'research', 'research_topic', 'research_subject', 'search', 'student']
senticnet['sociology'] = ['0', '0.851', '0', '0.712', '#interest', '#admiration', 'positive', '0.781', 'social_science', 'home_economics', 'proxemics', 'economics', 'anthropology']
senticnet['sociopath'] = ['0.203', '0.077', '0', '0.189', '#joy', '#admiration', 'positive', '0.156', 'sea_shell', 'coast', 'seaside', 'sediment', 'shell']
senticnet['sociopathic'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'antisocial', 'attitude', 'disorder', 'weird_behavior', 'unfriendly']
senticnet['sock'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['sock_account'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['sock_drawer'] = ['-0.02', '0', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.03', 'woman_purse', 'suitcase', 'pocket', 'back_sofa', 'pant_pocket']
senticnet['sock_farm'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['sock_master'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['sock_meat_puppet'] = ['0.792', '-0.86', '0', '0.888', '#surprise', '#admiration', 'positive', '0.849', 'absurd', 'inconsequential', 'intuitive', 'visceral', 'nonrational']
senticnet['sock_puppet'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['sock_puppeteer'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['sock_puppetry'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['socket'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'hip_socket', 'tooth_socket', 'alveolus', 'acetabulum', 'cotyloid_cavity']
senticnet['socket_connection'] = ['0.078', '0.103', '-0.09', '0.191', '#interest', '#admiration', 'positive', '0.069', 'connect', 'aunt_uncle', 'short_form', 'relationship', 'kinship']
senticnet['socket_wrench'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'pair_of_scissors', 'c_clamp', 'tool_box', 'salad_shaker', 'ladle']
senticnet['sockfarm'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['sockmaster'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['sockpuppet'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['sockpuppet_account'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['sockpuppeteer'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['sockpuppeter'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['sockpuppetry'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['socratic_method'] = ['-0.06', '0.237', '-0.06', '0.226', '#interest', '#admiration', 'positive', '0.085', 'recollection', 'imaginary', 'brain_function', 'think_process', 'recall']
senticnet['sod'] = ['-0.13', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.10', 'grass', 'butter_bread', 'fill_bottle', 'scrub_floor', 'sward']
senticnet['soda'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'coke', 'sprite', 'cola', 'beverage', 'soft_drink']
senticnet['soda_bottle'] = ['0', '-0.02', '0.027', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'shoulder_bag', 'satchel', 'satchel_bag', 'tote_bag', 'carry']
senticnet['soda_bread'] = ['0.644', '-0.94', '0', '0.729', '#surprise', '#admiration', 'positive', '0.773', 'gem', 'banana_bread', 'date_bread', 'nut_bread', 'quick_bread']
senticnet['soda_butte_creek'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'common_bean', 'green_bean', 'wax_bean']
senticnet['soda_consumption'] = ['-0.98', '0', '0.594', '-0.98', '#sadness', '#disgust', 'negative', '-0.85', 'fast_food', 'deep_fried', 'unhealthy_food', 'reuse_oil', 'fattening_food']
senticnet['soda_cracker'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'saltine', 'cracker', 'oyster_cracker', 'water_biscuit', 'graham_cracker']
senticnet['soda_fountain'] = ['0', '0', '0.044', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'large_supermarket', 'food_store', 'large_food_store', 'corner_shop', 'convenience_food_store']
senticnet['soda_jerk'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'candy', 'sweet', 'sugar', 'sweet_tooth', 'chocolate']
senticnet['soda_machine'] = ['0', '0', '0', '-0.06', '#disgust', '#disgust', 'negative', '-0.06', 'newsstand', 'bookshop', 'walmart', 'shoe_store', 'hooker']
senticnet['soda_pop'] = ['0', '0', '0', '-0.06', '#disgust', '#disgust', 'negative', '-0.06', 'newsstand', 'bookshop', 'walmart', 'shoe_store', 'hooker']
senticnet['soda_shop'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'candy', 'sweet', 'sugar', 'sweet_tooth', 'chocolate']
senticnet['soda_water'] = ['0.877', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.919', 'score', 'economical', 'scotch_whiskey', 'spoil', 'scottish']
senticnet['sodden'] = ['-0.71', '0', '0.704', '-0.89', '#sadness', '#disgust', 'negative', '-0.76', 'soil', 'mud', 'dirt', 'bleaching_clay', 'bleaching_earth']
senticnet['sodium'] = ['-0.03', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'lithium', 'mineral', 'potassium', 'iodine', 'manganese']
senticnet['sodium_alginate'] = ['-0.04', '0.069', '0', '0.075', '#interest', '#admiration', 'positive', '0.035', 'sculpt_gel', 'hairstyle', 'hairdresser', 'cosmetic', 'hair_sculpt_gel']
senticnet['sodium_aluminum_phosphate'] = ['0.52', '-0.69', '0', '0.639', '#surprise', '#admiration', 'positive', '0.617', 'black_bread', 'pumpernickel', 'limpa', 'bread', 'rye_bread']
senticnet['sodium_bicarbonate'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
senticnet['sodium_bisulfate'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
senticnet['sodium_borohydride'] = ['0.032', '-0.02', '-0.04', '-0.08', '#fear', '#disgust', 'negative', '-0.01', 'liquid', 'carbonate_drink', 'chemical', 'soda', 'bromide']
senticnet['sodium_chloride_injection'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
senticnet['sodium_chloride_solution'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
senticnet['sodium_citrate'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
senticnet['sodium_cyclamate'] = ['0.964', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.964', 'carbonated_drink', 'sugar_free', 'coke', 'diet_coke', 'beverage']
senticnet['sodium_ethoxide'] = ['0.071', '0', '-0.08', '-0.09', '#fear', '#disgust', 'negative', '-0.03', 'gunk', 'rotten_food', 'sour_cream', 'glass_of_milk', 'lemon_juice']
senticnet['sodium_hydroxide'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
senticnet['sodium_hydroxide_naoh'] = ['-0.81', '0', '0.774', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'acid', 'hypophosphoric_acid', 'periodic_acid', 'oxyacid', 'phosphorous_acid']
senticnet['sodium_hydroxide_solution'] = ['0.071', '0', '-0.08', '-0.09', '#fear', '#disgust', 'negative', '-0.03', 'gunk', 'rotten_food', 'sour_cream', 'glass_of_milk', 'lemon_juice']
senticnet['sodium_hypochlorite'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
senticnet['sodium_methoxide'] = ['-0.81', '0', '0.774', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'acid', 'hypophosphoric_acid', 'periodic_acid', 'oxyacid', 'phosphorous_acid']
senticnet['sodium_phosphate'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
senticnet['sodium_polystyrene_sulfonate'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
senticnet['sodium_retention'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'keep_alive', 'body_system', 'body_working', 'blood_pressure', 'heart']
senticnet['sodium_salt'] = ['-0.81', '0', '0.774', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'acid', 'hypophosphoric_acid', 'periodic_acid', 'oxyacid', 'phosphorous_acid']
senticnet['sodium_sulfide'] = ['0.071', '0', '-0.08', '-0.09', '#fear', '#disgust', 'negative', '-0.03', 'gunk', 'rotten_food', 'sour_cream', 'glass_of_milk', 'lemon_juice']
senticnet['sodium_sulfite'] = ['0.071', '0', '-0.08', '-0.09', '#fear', '#disgust', 'negative', '-0.03', 'gunk', 'rotten_food', 'sour_cream', 'glass_of_milk', 'lemon_juice']
senticnet['sodium_thiosulfate'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
senticnet['sodomite'] = ['-0.84', '0', '0.882', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'perversion', 'fetishism', 'paraphilia', 'sexual_perversion', 'buggery']
senticnet['sodomitical'] = ['-0.84', '0', '0.882', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'perversion', 'fetishism', 'paraphilia', 'sexual_perversion', 'buggery']
senticnet['sodomize'] = ['-0.24', '0', '-0.19', '0', '#sadness', '#fear', 'negative', '-0.22', 'mock', 'prison_sentence', 'wrongdoing', 'jail_time', 'violent_action']
senticnet['sodomy'] = ['-0.84', '0', '0.882', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'perversion', 'fetishism', 'paraphilia', 'sexual_perversion', 'buggery']
senticnet['sodomy_law'] = ['-0.84', '0', '0.882', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'perversion', 'fetishism', 'paraphilia', 'sexual_perversion', 'buggery']
senticnet['sodomy_statute'] = ['-0.84', '0', '0.882', '-0.82', '#sadness', '#anger', 'negative', '-0.85', 'perversion', 'fetishism', 'paraphilia', 'sexual_perversion', 'buggery']
senticnet['soever'] = ['-0.10', '0.127', '-0.11', '0.174', '#interest', '#admiration', 'positive', '0.022', 'query', 'response', 'respond', 'interrogation', 'inquiry']
senticnet['sofa'] = ['0.721', '0.721', '0', '0', '#joy', '#interest', 'positive', '0.721', 'magazine_rack', 'dentist_chair', 'couch', 'dentist_chair_bed', 'mattress']
senticnet['sofa_bed'] = ['0.716', '0.96', '0', '0', '#joy', '#interest', 'positive', '0.838', 'airline_seat', 'small_seat', 'beanbag_chair', 'hide-a-bed_sofa', 'chair']
senticnet['sofa_hide_bed'] = ['0.925', '0.879', '0', '0', '#joy', '#interest', 'positive', '0.902', 'sofa_bed', 'armchair', 'club_chair', 'chair', 'rock_chair']
senticnet['sofa_sleeper'] = ['0.716', '0.96', '0', '0', '#joy', '#interest', 'positive', '0.838', 'airline_seat', 'small_seat', 'beanbag_chair', 'hide-a-bed_sofa', 'chair']
senticnet['soffit'] = ['0', '0.089', '0', '0', '#interest', '#interest', 'positive', '0.089', 'ground', 'basement', 'base', 'foundation', 'basis']
senticnet['soft'] = ['0.097', '0.07', '0.009', '0.148', '#joy', '#admiration', 'positive', '0.076', 'biodegradable', 'material_type', 'fabric_type', 'softness', 'fuzzy']
senticnet['soft-soap'] = ['0', '0', '0.734', '-0.83', '#anger', '#disgust', 'negative', '-0.78', 'over-fill', 're-tweak', 'glad-hand', 'out-market', 'pretty-up']
senticnet['soft-spoken'] = ['-0.97', '0', '0.863', '0', '#sadness', '#anger', 'negative', '-0.92', 'unassuming', 'affable', 'self-effacing', 'taciturn', 'mild-mannered']
senticnet['soft_body_armor'] = ['0.939', '-0.63', '0', '0.653', '#joy', '#admiration', 'positive', '0.741', 'vest', 'waistcoat', 'bulletproof_vest', 'suit_of_armor', 'coat_of_mail']
senticnet['soft_boiled_egg'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'green_bread', 'rotten', 'disgusting', 'taste_awful', 'mouldy']
senticnet['soft_brush'] = ['0.022', '-0.01', '0.062', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'electric_toothbrush', 'moistener', 'hairbrush', 'shampoo_conditioner', 'shave_kit']
senticnet['soft_cloth'] = ['0.03', '0.069', '0', '0.168', '#interest', '#admiration', 'positive', '0.089', 'softness', 'curly', 'texture', 'soft_fabric', 'fuzzy']
senticnet['soft_diet'] = ['-0.63', '0', '0.819', '-0.99', '#anger', '#disgust', 'negative', '-0.81', 'light_diet', 'diet', 'spoon_food', 'bland_diet', 'ulcer_diet']
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senticnet['soft_lighting'] = ['0.737', '-0.85', '0', '0.623', '#joy', '#surprise', 'positive', '0.738', 'understated', 'esthetic', 'aesthetic', 'artistic', 'unostentatious']
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senticnet['soft_porn'] = ['-0.78', '0', '0.651', '-0.56', '#sadness', '#anger', 'negative', '-0.66', 'rubbishy', 'sorry', 'worthless', 'otiose', 'superfluous']
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senticnet['soil-building'] = ['0', '0', '0.932', '-0.72', '#anger', '#disgust', 'negative', '-0.83', 'surf-fishing', 'school-improvement', 'summer-friendly', 'action-planning', 'water-safety']
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senticnet['soil_conditioner'] = ['0.071', '0', '0.031', '0.064', '#joy', '#admiration', 'positive', '0.035', 'organic_matter', 'decompose', 'recycle', 'soil', 'compost_pile']
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senticnet['soil_surface'] = ['0.689', '-0.56', '0', '0.74', '#joy', '#admiration', 'positive', '0.665', 'crease', 'crinkle', 'wrinkle', 'heart_line', 'seam']
senticnet['soilborne'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'rust_fungus', 'fungus']
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senticnet['soiled_clothing'] = ['0', '0', '0', '0.068', '#admiration', '#admiration', 'positive', '0.068', 'become_clean', 'wash_clothes', 'take_shower', 'get_clean', 'clean_body']
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senticnet['sojitz'] = ['0.59', '-0.75', '0', '0.543', '#joy', '#surprise', 'positive', '0.63', 'venture', 'sally', 'labor', 'cause', 'risk']
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senticnet['sojourner_rover'] = ['0.884', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.919', 'ufo', 'milky', 'space_junk', 'galaxy', 'solar_system']
senticnet['sol'] = ['0.061', '0', '0', '0.067', '#joy', '#admiration', 'positive', '0.064', 'star', 'celestial_body', 'sun', 'celestial_object', 'solar']
senticnet['sola_fide'] = ['-0.83', '0', '0.648', '-0.60', '#sadness', '#anger', 'negative', '-0.69', 'infallibility', 'dependability', 'dependableness', 'reliability', 'reliableness']
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senticnet['solace'] = ['0.661', '0.641', '-0.24', '0.309', '#joy', '#interest', 'positive', '0.343', 'comfortableness', 'comfortably', 'relief', 'belonging', 'euphoric']
senticnet['solanum_tuberosum'] = ['-0.56', '0', '0.892', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'borecole', 'colewort', 'kail', 'cole', 'kale']
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senticnet['solar_array_cap'] = ['-0.94', '0', '0.569', '-0.72', '#sadness', '#disgust', 'negative', '-0.74', 'bitumastic', 'veneer', 'veneering', 'adhesive', 'coating']
senticnet['solar_atmosphere'] = ['0.931', '-0.82', '0', '0.709', '#joy', '#surprise', 'positive', '0.822', 'light', 'aureole', 'radiance', 'visible_light', 'visible_radiation']
senticnet['solar_cell'] = ['0', '0', '-0.07', '-0.16', '#fear', '#disgust', 'negative', '-0.12', 'radio_wave', 'sun', 'cosmic_ray', 'x-ray', 'gamma_ray']
senticnet['solar_chromosphere'] = ['0.931', '-0.82', '0', '0.709', '#joy', '#surprise', 'positive', '0.822', 'light', 'aureole', 'radiance', 'visible_light', 'visible_radiation']
senticnet['solar_corona'] = ['0.931', '-0.82', '0', '0.709', '#joy', '#surprise', 'positive', '0.822', 'light', 'aureole', 'radiance', 'visible_light', 'visible_radiation']
senticnet['solar_disk'] = ['0', '-0.72', '0', '0.712', '#surprise', '#admiration', 'positive', '0.72', 'heavenly_body', 'around_earth', 'source_light', 'sol', 'celestial_body']
senticnet['solar_eclipse'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['solar_electric'] = ['-0.79', '0', '0.997', '-0.68', '#sadness', '#anger', 'negative', '-0.82', 'energy', 'alternative_energy', 'solar_power', 'wind_generation', 'wind_power']
senticnet['solar_electric_power'] = ['-0.79', '0', '0.997', '-0.68', '#sadness', '#anger', 'negative', '-0.82', 'energy', 'alternative_energy', 'solar_power', 'wind_generation', 'wind_power']
senticnet['solar_electricity'] = ['-0.79', '0', '0.997', '-0.68', '#sadness', '#anger', 'negative', '-0.82', 'energy', 'alternative_energy', 'solar_power', 'wind_generation', 'wind_power']
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senticnet['solar_orbit'] = ['0.989', '-0.90', '0', '0.876', '#joy', '#surprise', 'positive', '0.923', 'reach', 'range', 'compass', 'scope', 'confines']
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senticnet['solar_photovoltaic_pv'] = ['-0.79', '0', '0.997', '-0.68', '#sadness', '#anger', 'negative', '-0.82', 'energy', 'alternative_energy', 'solar_power', 'wind_generation', 'wind_power']
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senticnet['solar_pv'] = ['-0.79', '0', '0.997', '-0.68', '#sadness', '#anger', 'negative', '-0.82', 'energy', 'alternative_energy', 'solar_power', 'wind_generation', 'wind_power']
senticnet['solar_system'] = ['0.838', '0.765', '0', '0', '#joy', '#interest', 'positive', '0.801', 'ufo', 'milky', 'space_junk', 'galaxy', 'martian']
senticnet['solar_telescope'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'binoculars', 'read_glass', 'eyeglass', 'camera', 'monocle']
senticnet['solar_thermal'] = ['-0.79', '0', '0.997', '-0.68', '#sadness', '#anger', 'negative', '-0.82', 'energy', 'alternative_energy', 'solar_power', 'wind_generation', 'wind_power']
senticnet['solar_thermal_energy'] = ['-0.79', '0', '0.997', '-0.68', '#sadness', '#anger', 'negative', '-0.82', 'energy', 'alternative_energy', 'solar_power', 'wind_generation', 'wind_power']
senticnet['solar_thermal_power'] = ['-0.79', '0', '0.997', '-0.68', '#sadness', '#anger', 'negative', '-0.82', 'energy', 'alternative_energy', 'solar_power', 'wind_generation', 'wind_power']
senticnet['solar_thermal_technology'] = ['-0.79', '0', '0.997', '-0.68', '#sadness', '#anger', 'negative', '-0.82', 'energy', 'alternative_energy', 'solar_power', 'wind_generation', 'wind_power']
senticnet['solar_variability'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'weather', 'sunny', 'spring', 'snow', 'four_seasons']
senticnet['solaris'] = ['0', '0', '0', '0.087', '#admiration', '#admiration', 'positive', '0.087', 'unix', 'window', 'many_windows', 'train_window', 'doorway']
senticnet['solaris_patch'] = ['-0.58', '0', '0.561', '-0.62', '#sadness', '#disgust', 'negative', '-0.58', 'spot', 'speckle', 'dapple', 'mark', 'fleck']
senticnet['sold-out'] = ['0.894', '-0.71', '0', '0', '#joy', '#surprise', 'positive', '0.803', 'sell-out', 'headlining', 'sellout', 'concert', 'standing-room-only']
senticnet['soldadera'] = ['-0.62', '0', '0.98', '-0.81', '#anger', '#disgust', 'negative', '-0.80', 'tart', 'prostitute', 'harlot', 'bawd', 'sporting_lady']
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senticnet['soldering_iron'] = ['0.76', '0.919', '0', '0', '#joy', '#interest', 'positive', '0.84', 'chore', 'make_happen', 'pick_up_trash', 'intuition', 'stop_procrastinate']
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senticnet['soldier_ant'] = ['-0.57', '0', '0.571', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['soldier_group'] = ['0.945', '0.931', '0', '0', '#joy', '#interest', 'positive', '0.938', 'military', 'attacker', 'army', 'arm_force', 'samurai']
senticnet['soldiering'] = ['0.753', '-0.60', '0', '0.551', '#joy', '#surprise', 'positive', '0.636', 'acquirement', 'skill', 'accomplishment', 'acquisition', 'attainment']
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senticnet['soldiership'] = ['0.646', '-0.9', '0', '0.684', '#surprise', '#admiration', 'positive', '0.743', 'acquirement', 'skill', 'accomplishment', 'acquisition', 'attainment']
senticnet['sole'] = ['0.964', '0.931', '0', '0', '#joy', '#interest', 'positive', '0.948', 'heel', 'foot_part', 'type_of_shoes', 'shoe_type', 'high_heel']
senticnet['sole_arbitrator'] = ['0', '0.146', '0', '0.077', '#interest', '#admiration', 'positive', '0.111', 'negotiator', 'judge_jury', 'arbiter', 'decide_guilt', 'become_judge']
senticnet['sole_beneficiary'] = ['0.851', '0.708', '0', '0', '#joy', '#interest', 'positive', '0.779', 'devisee', 'legatee', 'recipient', 'receiver', 'pensioner']
senticnet['sole_breadwinner'] = ['0.051', '0.112', '0', '0', '#joy', '#interest', 'positive', '0.082', 'neighbor', 'family_reunion', 'clan', 'desire', 'little_specific']
senticnet['sole_discretion'] = ['0', '0.81', '0', '0.825', '#interest', '#admiration', 'positive', '0.818', 'prudence', 'frugality', 'frugalness', 'discernment', 'natural_virtue']
senticnet['sole_executor'] = ['0', '0.179', '-0.05', '0.162', '#interest', '#admiration', 'positive', '0.095', 'house_land', 'real_estate', 'acreage', 'house_place', 'good_investment']
senticnet['sole_heir'] = ['0', '0.769', '0', '0', '#interest', '#interest', 'positive', '0.769', 'get_rich', 'get_money', 'inherit_property', 'benefit', 'enjoy_life']
senticnet['sole_inventor'] = ['-0.03', '0.201', '-0.13', '0.171', '#interest', '#admiration', 'positive', '0.049', 'scientific_statement', 'hypothetical', 'conjecture', 'theory', 'fact']
senticnet['sole_legitimate'] = ['-0.65', '0', '0.505', '-0.91', '#sadness', '#disgust', 'negative', '-0.69', 'hellhound', 'mythical_being', 'dragon', 'mythical_monster', 'amphisbaena']
senticnet['sole_meunire'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'blue_ribbon', 'badge', 'allegory', 'stripe', 'merit_badge']
senticnet['sole_practitioner'] = ['0.841', '0.721', '0', '0.74', '#joy', '#admiration', 'positive', '0.767', 'good_manner', 'two_people', 'love', 'guardian', 'guy']
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senticnet['sole_responsibility'] = ['0.955', '0.945', '0', '0.835', '#joy', '#interest', 'positive', '0.912', 'support_family', 'provide_service', 'accomplish', 'find_job', 'pay_well']
senticnet['sole_shareholder'] = ['0.111', '0.18', '0', '0.128', '#interest', '#admiration', 'positive', '0.14', 'corporation', 'partnership', 'large_business', 'business_organization', 'enterprise']
senticnet['sole_source'] = ['0.964', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.846', 'bulletin', 'replicate', 'current_event', 'computer_command', 'paper_write']
senticnet['sole_source_contract'] = ['0.865', '-0.84', '0', '0.915', '#joy', '#admiration', 'positive', '0.875', 'grant', 'sale_in_gross', 'contract_of_hazard', 'adhesion_contract', 'contract_of_adhesion']
senticnet['solebay'] = ['0.582', '-0.97', '0', '0.802', '#surprise', '#admiration', 'positive', '0.788', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['solecism'] = ['0', '0', '0.776', '-0.83', '#anger', '#disgust', 'negative', '-0.80', 'rusticity', 'slip', 'gaucherie', 'gaffe', 'faux_pa']
senticnet['solely'] = ['0', '0.089', '0', '0', '#interest', '#interest', 'positive', '0.089', 'ground', 'basement', 'baseboard', 'foundation', 'base']
senticnet['solely_responsible'] = ['0.955', '0.945', '0', '0.835', '#joy', '#interest', 'positive', '0.912', 'support_family', 'provide_service', 'accomplish', 'find_job', 'pay_well']
senticnet['solemn'] = ['0', '0.767', '0', '0.711', '#interest', '#admiration', 'positive', '0.739', 'reverently', 'hero-worship', 'venerate', 'idolize', 'veneration']
senticnet['solemn_ceremony'] = ['0.82', '0', '0', '0.885', '#joy', '#admiration', 'positive', '0.853', 'religious_event', 'sunday', 'happy_event', 'tradition', 'wedding']
senticnet['solemn_commitment'] = ['0', '0.286', '-0.22', '0.383', '#interest', '#admiration', 'positive', '0.147', 'oath', 'propose', 'suggest', 'ask', 'trust']
senticnet['solemn_covenant'] = ['0.088', '0.317', '-0.29', '0.379', '#interest', '#admiration', 'positive', '0.122', 'oath', 'commitment', 'marriage', 'obedience', 'enter_contract']
senticnet['solemn_oath'] = ['0.088', '0.317', '-0.29', '0.379', '#interest', '#admiration', 'positive', '0.122', 'oath', 'commitment', 'marriage', 'obedience', 'enter_contract']
senticnet['solemn_obligation'] = ['0.594', '-0.81', '0', '0.93', '#surprise', '#admiration', 'positive', '0.78', 'hold', 'hold_on', 'persist', 'hang_on', 'maintain']
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senticnet['solemn_pledge'] = ['0.088', '0.317', '-0.29', '0.379', '#interest', '#admiration', 'positive', '0.122', 'oath', 'commitment', 'marriage', 'obedience', 'enter_contract']
senticnet['solemn_prayer'] = ['0.023', '0.295', '-0.05', '0.283', '#interest', '#admiration', 'positive', '0.136', 'religion', 'religious_purpose', 'world_religion', 'gospel', 'judaism']
senticnet['solemn_promise'] = ['0.088', '0.317', '-0.29', '0.379', '#interest', '#admiration', 'positive', '0.122', 'oath', 'commitment', 'marriage', 'obedience', 'enter_contract']
senticnet['solemn_rite'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'reverence', 'obeisance', 'genuflexion', 'kowtow', 'mafia']
senticnet['solemn_silence'] = ['0.449', '0.305', '-0.19', '0.222', '#joy', '#interest', 'positive', '0.196', 'calm', 'peace', 'serenity', 'quiet', 'tranquility']
senticnet['solemn_vow'] = ['0.088', '0.317', '-0.29', '0.379', '#interest', '#admiration', 'positive', '0.122', 'oath', 'commitment', 'marriage', 'obedience', 'enter_contract']
senticnet['solemnity'] = ['0', '0.867', '0', '0.782', '#interest', '#admiration', 'positive', '0.825', 'wistful', 'pensiveness', 'musing', 'pondering', 'contemplative']
senticnet['solemnization'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'bigamy', 'wedlock']
senticnet['solemnize'] = ['0.693', '-0.69', '0', '0.714', '#surprise', '#admiration', 'positive', '0.702', 'vow', 'dedicate', 'apply', 'rededicate', 'use']
senticnet['solemnly'] = ['-0.85', '0.21', '-0.56', '0', '#sadness', '#fear', 'negative', '-0.40', 'doleful', 'sadly', 'dolefully', 'ruefulness', 'wretched']
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senticnet['solenoid_valve'] = ['0.843', '-0.99', '0', '0.706', '#joy', '#surprise', 'positive', '0.849', 'gadgetry', 'gimbal', 'contrivance', 'mod_con', 'device']
senticnet['solenoidal'] = ['-0.97', '0', '0.831', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'skewness', 'irregularity']
senticnet['solfge'] = ['0.536', '-0.56', '0', '0.567', '#surprise', '#admiration', 'positive', '0.557', 'recreation', 'diversion', 'move', 'motility', 'movement']
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senticnet['solicitation'] = ['0.772', '-0.59', '0', '0.532', '#joy', '#surprise', 'positive', '0.632', 'appeal', 'touch', 'prayer', 'beggary', 'begging']
senticnet['solicitor'] = ['0.053', '0.122', '-0.07', '0.086', '#interest', '#admiration', 'positive', '0.046', 'attorney', 'lawyer', 'jury', 'decide_guilt', 'prosecutor']
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senticnet['solicitor_general'] = ['0.865', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.887', 'manager', 'colleague', 'office_worker', 'coworker', 'secretary']
senticnet['solicitous'] = ['0.909', '0', '0', '0.755', '#joy', '#admiration', 'positive', '0.832', 'concerned', 'caring', 'attentive', 'mindful', 'interested']
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senticnet['solid_alibi'] = ['0', '0', '-0.88', '-0.99', '#fear', '#disgust', 'negative', '-0.93', 'murder_suspect', 'robbery_suspect', 'rape_suspect', 'murder', 'doubt']
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senticnet['solid_fuel'] = ['0', '0', '0', '0.073', '#admiration', '#admiration', 'positive', '0.073', 'fire_starter', 'organic_substance', 'renewable_resource', 'use_fuel', 'tree_product']
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senticnet['solid_ground'] = ['0.703', '-0.57', '0', '0.918', '#joy', '#admiration', 'positive', '0.733', 'land', 'dry_land', 'earth', 'ground', 'wonderland']
senticnet['solid_liquid'] = ['0.976', '0.786', '0', '0', '#joy', '#interest', 'positive', '0.881', 'combustible', 'state_matter', 'natural_resource', 'liquid_fuel', 'fossil_fuel']
senticnet['solid_phase'] = ['0.838', '-0.61', '0', '0.556', '#joy', '#surprise', 'positive', '0.671', 'activity', 'natural_process', 'natural_action', 'column_chromatography', 'paper_chromatography']
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senticnet['solid_room_temperature'] = ['0.856', '0.927', '0', '0', '#joy', '#interest', 'positive', '0.891', 'hard_rock', 'hard_mineral', 'hard_plastic', 'hard_material', 'hard']
senticnet['solid_surface'] = ['-0.12', '-0.03', '0.091', '0', '#sadness', '#anger', 'negative', '-0.06', 'use_leg', 'wear_running_shoes', 'wear_good_shoes', 'take_step', 'act_quickly']
senticnet['solid_waste'] = ['-0.81', '0', '0.987', '-0.64', '#sadness', '#anger', 'negative', '-0.81', 'burn_garbage', 'eliminate_garbage', 'incinerate_waste', 'dump', 'garbage_dump']
senticnet['solid_waste_landfill'] = ['-0.81', '0', '0.987', '-0.64', '#sadness', '#anger', 'negative', '-0.81', 'burn_garbage', 'eliminate_garbage', 'incinerate_waste', 'dump', 'garbage_dump']
senticnet['solid_water'] = ['0.085', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.074', 'soap', 'drizzle', 'bubbly', 'foam', 'ivory']
senticnet['solid_work_ethic'] = ['0.701', '0.874', '0', '0.752', '#interest', '#admiration', 'positive', '0.776', 'self_satisfaction', 'enjoy_life', 'good_feeling', 'good_attitude', 'enjoy']
senticnet['solidago'] = ['-0.52', '0', '0.527', '-0.78', '#anger', '#disgust', 'negative', '-0.61', 'herb', 'black_snakeroot', 'wild_ginger', 'asarabacca', 'herbaceous_plant']
senticnet['solidarity'] = ['0.605', '0.647', '-0.26', '0.323', '#joy', '#interest', 'positive', '0.327', 'friendly', 'brotherly', 'amicableness', 'amicably', 'amicable']
senticnet['solidification'] = ['-0.55', '0', '0.689', '-0.94', '#anger', '#disgust', 'negative', '-0.72', 'natural_process', 'natural_action', 'activity', 'congealment', 'congelation']
senticnet['solidified_lava'] = ['0', '0', '-0.92', '0', '#fear', '#fear', 'negative', '-0.92', 'water_flow', 'wind_water', 'water_stream', 'waterway', 'magma']
senticnet['solidify'] = ['0.65', '-0.61', '0', '0.703', '#joy', '#admiration', 'positive', '0.657', 'gleaming', 'luster', 'polish', 'glow', 'effulgence']
senticnet['solidifying'] = ['-0.96', '0', '0.682', '-0.83', '#sadness', '#disgust', 'negative', '-0.82', 'natural_process', 'natural_action', 'activity', 'congealment', 'congelation']
senticnet['solidity'] = ['-0.63', '0', '0.694', '-0.66', '#anger', '#disgust', 'negative', '-0.66', 'corporality', 'tangibleness', 'palpability', 'reality', 'solidness']
senticnet['solidliquid'] = ['0.976', '0.786', '0', '0', '#joy', '#interest', 'positive', '0.881', 'combustible', 'state_matter', 'natural_resource', 'liquid_fuel', 'fossil_fuel']
senticnet['solidly_republican_district'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'couch', 'stool', 'coach_seat']
senticnet['solidness'] = ['-0.63', '0', '0.694', '-0.66', '#anger', '#disgust', 'negative', '-0.66', 'tangibility', 'tangibleness', 'palpability', 'reality', 'corporality']
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senticnet['solipsistic'] = ['0.08', '0.153', '-0.12', '0.224', '#interest', '#admiration', 'positive', '0.083', 'meditation', 'self_contemplation', 'soul_searching', 'self_examination', 'self_observation']
senticnet['solitaire'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'jewelry_store', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['solitaire_card_game'] = ['0.934', '-0.57', '0', '0.767', '#joy', '#admiration', 'positive', '0.76', 'morosoph', 'simpleton', 'flibbertigibbet', 'foolish_woman', 'simple']
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senticnet['solitarily'] = ['0.967', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.91', 'furry_creature', 'hairy', 'furry', 'woodland_creature', 'animal_furry']
senticnet['solitariness'] = ['0.924', '-0.80', '0', '0.912', '#joy', '#admiration', 'positive', '0.879', 'privacy', 'seclusion', 'privateness', 'reclusiveness', 'loneliness']
senticnet['solitary'] = ['-0.84', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.84', 'precision', 'small', 'alone', 'single', 'widow']
senticnet['solitary_cell'] = ['0.816', '-0.91', '0', '0.944', '#surprise', '#admiration', 'positive', '0.891', 'jail', 'jailhouse', 'gaol', 'poky', 'clink']
senticnet['solitary_confinement'] = ['0.816', '-0.91', '0', '0.944', '#surprise', '#admiration', 'positive', '0.891', 'jail', 'jailhouse', 'gaol', 'poky', 'clink']
senticnet['solitary_confinement_cell'] = ['0', '0', '0.995', '-0.89', '#anger', '#disgust', 'negative', '-0.94', 'shoe_store', 'chess_tournament', 'dress_room', 'front_desk', 'locker_room']
senticnet['solitary_existence'] = ['-0.94', '0', '0.779', '-0.85', '#sadness', '#disgust', 'negative', '-0.86', 'eremitic', 'eremitical', 'cloistered', 'anchoritic', 'unworldly']
senticnet['solitude'] = ['0.768', '0.902', '0', '0', '#joy', '#interest', 'positive', '0.835', 'alone', 'sit_dark', 'single', 'blackout', 'sociopathic']
senticnet['solitudinarian'] = ['0', '0', '-0.94', '-0.72', '#fear', '#disgust', 'negative', '-0.83', 'solitary', 'troglodyte', 'hermit', 'reclusive', 'recluse']
senticnet['sollte'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['solo'] = ['0.899', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.813', 'concert', 'measure_angle', 'make_drawing', 'make_beautiful_music', 'listen_heart']
senticnet['solo_acoustic'] = ['0.899', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.813', 'concert', 'measure_angle', 'make_drawing', 'make_beautiful_music', 'listen_heart']
senticnet['solo_album'] = ['0.899', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.813', 'concert', 'measure_angle', 'make_drawing', 'make_beautiful_music', 'listen_heart']
senticnet['solo_artist'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
senticnet['solo_ascent'] = ['0.518', '-0.51', '0', '0.65', '#surprise', '#admiration', 'positive', '0.562', 'clematis', 'legume', 'leguminous_plant', 'climbing_fern', 'root_climber']
senticnet['solo_career'] = ['0.899', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.813', 'concert', 'measure_angle', 'make_drawing', 'make_beautiful_music', 'listen_heart']
senticnet['solo_cd'] = ['0.805', '-0.58', '0', '0.525', '#joy', '#surprise', 'positive', '0.637', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['solo_climber'] = ['0.518', '-0.51', '0', '0.65', '#surprise', '#admiration', 'positive', '0.562', 'clematis', 'legume', 'leguminous_plant', 'climbing_fern', 'root_climber']
senticnet['solo_debut'] = ['0.899', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.813', 'concert', 'measure_angle', 'make_drawing', 'make_beautiful_music', 'listen_heart']
senticnet['solo_debut_album'] = ['0.899', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.813', 'concert', 'measure_angle', 'make_drawing', 'make_beautiful_music', 'listen_heart']
senticnet['solo_ep'] = ['0.899', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.813', 'concert', 'measure_angle', 'make_drawing', 'make_beautiful_music', 'listen_heart']
senticnet['solo_exhibition'] = ['0.07', '0', '0', '0', '#joy', '#joy', 'positive', '0.07', 'artist', 'artwork', 'sculpture', 'tapestry', 'drawing']
senticnet['solo_guitar'] = ['0.899', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.813', 'concert', 'measure_angle', 'make_drawing', 'make_beautiful_music', 'listen_heart']
senticnet['solo_lp'] = ['0.899', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.813', 'concert', 'measure_angle', 'make_drawing', 'make_beautiful_music', 'listen_heart']
senticnet['solo_musician'] = ['0.899', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.813', 'concert', 'measure_angle', 'make_drawing', 'make_beautiful_music', 'listen_heart']
senticnet['solo_performer'] = ['0.899', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.813', 'concert', 'measure_angle', 'make_drawing', 'make_beautiful_music', 'listen_heart']
senticnet['solo_piano'] = ['0.101', '-0.04', '0.036', '0', '#joy', '#surprise', 'positive', '0.038', 'song', 'rap', 'jingle', 'chord', 'lyric']
senticnet['solo_piano_album'] = ['0.805', '-0.58', '0', '0.525', '#joy', '#surprise', 'positive', '0.637', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['solo_piano_music'] = ['0.805', '-0.58', '0', '0.525', '#joy', '#surprise', 'positive', '0.637', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['solo_piano_piece'] = ['0.101', '-0.04', '0.036', '0', '#joy', '#surprise', 'positive', '0.038', 'song', 'rap', 'jingle', 'chord', 'lyric']
senticnet['solo_practitioner'] = ['0', '0.146', '-0.05', '0.077', '#interest', '#admiration', 'positive', '0.057', 'lawyer', 'negotiator', 'arbiter', 'judge_jury', 'decide_guilt']
senticnet['solo_rendition'] = ['0.773', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.844', 'favorite', 'crowd', 'acclamation', 'bunch', 'large_crowd']
senticnet['solo_singer'] = ['0.882', '0.901', '0', '0', '#joy', '#interest', 'positive', '0.891', 'songwriter', 'vip', 'rock_group', 'guitar_player', 'violinist']
senticnet['solo_soprano'] = ['0.023', '0.303', '-0.08', '0.261', '#interest', '#admiration', 'positive', '0.125', 'school_choral', 'drum_set', 'choral', 'string_quartet', 'band']
senticnet['solo_vocal'] = ['0.022', '0', '0', '0.109', '#joy', '#admiration', 'positive', '0.066', 'instrumental', 'singer', 'hip_hop', 'melodic_noise', 'music_player']
senticnet['soloist'] = ['0.506', '0.659', '-0.24', '0.242', '#joy', '#interest', 'positive', '0.29', 'sing', 'music_studio', 'music_room', 'choir', 'symphony']
senticnet['solon'] = ['0.8', '-0.79', '0', '0.751', '#joy', '#surprise', 'positive', '0.782', 'elder_statesman', 'stateswoman', 'politician', 'politico', 'pol']
senticnet['solstice'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['solubility'] = ['0.954', '0.875', '0', '0', '#joy', '#interest', 'positive', '0.914', 'insoluble', 'water-soluble', 'soluble', 'aqueous', 'solvent']
senticnet['soluble'] = ['0.961', '0.876', '0', '0', '#joy', '#interest', 'positive', '0.918', 'insoluble', 'water-soluble', 'solubility', 'aqueous', 'solvent']
senticnet['soluble_fiber_content'] = ['0.026', '0.08', '0.043', '0.064', '#interest', '#admiration', 'positive', '0.032', 'eat_rice', 'white_brown', 'asian', 'eat_pasta', 'staple_food']
senticnet['solukhumbu'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['solution'] = ['-0.07', '0.186', '-0.10', '0.181', '#interest', '#admiration', 'positive', '0.048', 'get_answer', 'question_start', 'explanation', 'query', 'inquire']
senticnet['solution_to_problem'] = ['0.87', '0.831', '0', '0.93', '#joy', '#admiration', 'positive', '0.877', 'know_fact', 'great_understand', 'gain_insight', 'understand', 'trivia']
senticnet['solvability'] = ['0.875', '-0.65', '0', '0.861', '#joy', '#admiration', 'positive', '0.797', 'completeness', 'work', 'whole_shebang', 'essential', 'whole']
senticnet['solvable'] = ['-0.99', '0', '0.754', '-0.50', '#sadness', '#anger', 'negative', '-0.75', 'unmalleable', 'stubborn', 'refractory', 'uncontrollable', 'balking']
senticnet['solve'] = ['0.849', '0.79', '0', '0.793', '#joy', '#admiration', 'positive', '0.811', 'compute', 'fantasize', 'mind_boggle', 'decipher', 'introspection']
senticnet['solve_big_problem'] = ['0.487', '0.675', '-0.26', '0.266', '#joy', '#interest', 'positive', '0.29', 'collective_intelligence', 'gather_intelligence', 'intelligent', 'team_work', 'clever']
senticnet['solve_equation'] = ['-0.16', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.11', 'guess_answer', 'master_skill', 'love_pet', 'master_new_skill', 'practice']
senticnet['solve_mystery'] = ['-0.10', '0.094', '0', '-0.10', '#sadness', '#disgust', 'negative', '-0.03', 'come_conclusion', 'find_answer', 'forget_else', 'study_fact', 'resolve_problem']
senticnet['solve_problem'] = ['0.849', '0.723', '0', '0', '#joy', '#interest', 'positive', '0.786', 'look_clue', 'make_decision', 'revelation', 'deduce', 'consider_option']
senticnet['solve_puzzle'] = ['-0.09', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.08', 'beat_off', 'play_board_game', 'television_view', 'smoke_marijuana', 'rake_leave']
senticnet['solvent'] = ['0.727', '-0.84', '0', '0.966', '#surprise', '#admiration', 'positive', '0.845', 'medium', 'acetone', 'propanone', 'dimethyl_ketone', 'chlorobenzene']
senticnet['somali_pirate'] = ['0', '0', '0.034', '-0.08', '#anger', '#disgust', 'negative', '-0.06', 'lifeboat', 'mast', 'bad_man', 'keel', 'yacht']
senticnet['somatic_cell'] = ['-0.66', '0', '0.877', '-0.64', '#sadness', '#anger', 'negative', '-0.72', 'mast_cell', 'mastocyte', 'labrocyte', 'target_cell', 'phagocyte']
senticnet['somatic_marker'] = ['0.583', '-0.82', '0', '0.574', '#joy', '#surprise', 'positive', '0.661', 'stimulant', 'positive_reinforcing_stimulus', 'positive_reinforcer', 'negative_reinforcing_stimulus', 'reinforcer']
senticnet['somatic_mutation'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'deflection', 'warp', 'aberrance']
senticnet['somatic_sensation'] = ['-0.78', '0', '0.669', '-0.89', '#sadness', '#disgust', 'negative', '-0.78', 'pain_sensation', 'painful_sensation', 'prickling', 'tingling', 'pain']
senticnet['somatic_symptom'] = ['-0.55', '0', '0.736', '-0.71', '#anger', '#disgust', 'negative', '-0.66', 'neurosis', 'neuroticism', 'psychoneurosis', 'hysterocatalepsy', 'anxiety_hysteria']
senticnet['somatic_symptom_disorder'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'acknowledgeable', 'specifiable', 'recognizable', 'identifiable', 'classifiable']
senticnet['somatization_disorder'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'acknowledgeable', 'specifiable', 'recognizable', 'identifiable', 'classifiable']
senticnet['somatoform'] = ['-0.55', '0', '0.736', '-0.71', '#anger', '#disgust', 'negative', '-0.66', 'neurosis', 'neuroticism', 'psychoneurosis', 'hysterocatalepsy', 'anxiety_hysteria']
senticnet['somatoform_disorder'] = ['-0.58', '0', '0.722', '-0.51', '#sadness', '#anger', 'negative', '-0.60', 'dissociative_disorder', 'depersonalization_neurosis', 'depersonalization', 'mental_disorder', 'mental_disturbance']
senticnet['somatosensory'] = ['0', '0.131', '-0.08', '0.239', '#interest', '#admiration', 'positive', '0.096', 'human_sense', 'ear_activity', 'vibration', 'five_senses', 'sensor']
senticnet['somatosensory_cortex'] = ['0', '0.131', '-0.08', '0.239', '#interest', '#admiration', 'positive', '0.096', 'human_sense', 'ear_activity', 'vibration', 'five_senses', 'sensor']
senticnet['somatosensory_system'] = ['0', '0.131', '-0.08', '0.239', '#interest', '#admiration', 'positive', '0.096', 'human_sense', 'ear_activity', 'vibration', 'five_senses', 'sensor']
senticnet['somatostatin'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'gastrointestinal_hormone', 'secretin', 'ghrelin']
senticnet['somber'] = ['-0.79', '0.262', '-0.45', '-0.04', '#sadness', '#fear', 'negative', '-0.25', 'downhearted', 'down_in_mouth', 'depressed', 'downcast', 'low-spirited']
senticnet['somber_tone'] = ['-0.79', '0.257', '-0.47', '-0.03', '#sadness', '#fear', 'negative', '-0.26', 'gloomful', 'glooming', 'darkness', 'gloominess', 'gloomily']
senticnet['somberness'] = ['-0.79', '0.257', '-0.47', '-0.03', '#sadness', '#fear', 'negative', '-0.26', 'gloomful', 'glooming', 'darkness', 'gloominess', 'gloomily']
senticnet['sombre'] = ['-0.79', '0.262', '-0.45', '-0.04', '#sadness', '#fear', 'negative', '-0.25', 'downhearted', 'down_in_mouth', 'depressed', 'downcast', 'low-spirited']
senticnet['sombreness'] = ['-0.57', '0', '0.841', '-0.50', '#sadness', '#anger', 'negative', '-0.64', 'somberness', 'stodginess', 'gravity', 'soberness', 'graveness']
senticnet['some-more'] = ['0', '0.861', '0', '0.727', '#interest', '#admiration', 'positive', '0.794', 'multifarious', 'numberless', 'scad', 'umpteen', 'multiform']
senticnet['somehow'] = ['0.768', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.806', 'magically', 'mysteriously', 'healed', 'thankfully', 'miraculously']
senticnet['someplace'] = ['-0.95', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.83', 'outer', 'radiant', 'around_earth', 'nothingness', 'heavenly_body']
senticnet['somerdale_plant'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'wrapping_paper', 'wrap_gift', 'butcher_paper', 'paper']
senticnet['somerive'] = ['-0.56', '0', '0.828', '-0.68', '#anger', '#disgust', 'negative', '-0.69', 'abruptness', 'precipitateness', 'precipitousness', 'hastiness', 'precipitancy']
senticnet['somersault'] = ['-0.03', '0.074', '-0.06', '0.176', '#interest', '#admiration', 'positive', '0.039', 'revolve', 'spring_leak', 'rotate_movement', 'wrap_around', 'turn_around']
senticnet['somerset'] = ['-0.50', '0', '0.73', '-0.68', '#anger', '#disgust', 'negative', '-0.63', 'flip', 'acrobatic_stunt', 'acrobatic_feat', 'somersault', 'tumble']
senticnet['somethin'] = ['-0.66', '0', '0.595', '-0.83', '#sadness', '#disgust', 'negative', '-0.7', 'bastard', 'dickhead', 'disagreeable_person', 'motherfucker', 'son_of_bitch']
senticnet['somewhat'] = ['0.911', '-0.91', '0', '0', '#joy', '#surprise', 'positive', '0.911', 'auspiciously', 'fully', 'plainly', 'fairly', 'neutrally']
senticnet['somewhat_aloof'] = ['0.992', '0', '0', '0.801', '#joy', '#admiration', 'positive', '0.897', 'feel_content', 'make_feel_better', 'celebrate_special_occasion', 'survival', 'express_appreciation']
senticnet['somewhat_doubtful'] = ['0.536', '-0.71', '0', '0.533', '#joy', '#surprise', 'positive', '0.594', 'incredulity', 'mental_rejection', 'skepticism', 'mistrust', 'distrust']
senticnet['somewhat_similar'] = ['0.14', '0.345', '-0.20', '0.378', '#interest', '#admiration', 'positive', '0.165', 'good_etiquette', 'same', 'similarity', 'band_together', 'niceness']
senticnet['somewhat_skeptical'] = ['0', '0', '-0.99', '-0.99', '#fear', '#disgust', 'negative', '-0.99', 'unbelieving', 'sceptical', 'doubting', 'disbelieving', 'questioning']
senticnet['somewhat_uncertain'] = ['-0.09', '0', '0', '-0.19', '#sadness', '#disgust', 'negative', '-0.14', 'add_knowledge', 'learn', 'become_smart', 'discover', 'make_out_atom']
senticnet['somewhat_unclear'] = ['-0.79', '0.241', '-0.52', '-0.11', '#sadness', '#fear', 'negative', '-0.29', 'disoriented', 'obscure', 'unsure', 'uncertain', 'unfair']
senticnet['somewhat_vague'] = ['-0.79', '0.241', '-0.52', '-0.11', '#sadness', '#fear', 'negative', '-0.29', 'disoriented', 'obscure', 'unsure', 'uncertain', 'unfair']
senticnet['somme'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'start_over', 'queue', 'find_line', 'take_turn', 'long_line']
senticnet['somme_front'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'start_over', 'queue', 'find_line', 'take_turn', 'long_line']
senticnet['somnambulant'] = ['-0.89', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.85', 'high-strung', 'uptight', 'restive', 'overstrung', 'nervy']
senticnet['somnifera'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'infective_agent', 'satisfier', 'quickener', 'agent']
senticnet['somniferous'] = ['0', '0', '-0.80', '-0.87', '#fear', '#disgust', 'negative', '-0.84', 'somnific', 'hypnotic', 'hypnogogic', 'soporific', 'soporiferous']
senticnet['somnific'] = ['0', '0', '-0.80', '-0.87', '#fear', '#disgust', 'negative', '-0.84', 'soporific', 'hypnotic', 'hypnogogic', 'somniferous', 'soporiferous']
senticnet['somniloquism'] = ['-0.53', '0', '0.762', '-0.66', '#anger', '#disgust', 'negative', '-0.65', 'defense', 'sleep_talking', 'somniloquy', 'unconscious_process', 'condensation']
senticnet['somniloquy'] = ['-0.53', '0', '0.762', '-0.66', '#anger', '#disgust', 'negative', '-0.65', 'defense', 'sleep_talking', 'unconscious_process', 'somniloquism', 'condensation']
senticnet['somnolence'] = ['-0.82', '0', '0.663', '-0.50', '#sadness', '#anger', 'negative', '-0.66', 'obtuseness', 'dullness', 'sleepiness', 'drowsiness', 'oscitance']
senticnet['somnolent'] = ['0.828', '0.832', '0', '0', '#joy', '#interest', 'positive', '0.83', 'daze', 'fulgurant', 'eye-popping', 'fulgurous', 'dazzling']
senticnet['son'] = ['0', '0.073', '0.015', '0.101', '#interest', '#admiration', 'positive', '0.053', 'father', 'mother', 'daughter', 'part_of_family', 'grandfather']
senticnet['son_attachement'] = ['0.667', '-0.98', '0', '0.61', '#joy', '#surprise', 'positive', '0.753', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['son_jarocho'] = ['0.116', '-0.02', '0.025', '0.156', '#joy', '#admiration', 'positive', '0.069', 'dance_move', 'latin_dance', 'body_movement', 'waltz', 'dance']
senticnet['son_mari'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'sexual_relationship', 'liaison', 'affair', 'involvement']
senticnet['son_of_bitch'] = ['-0.66', '0', '0.595', '-0.83', '#sadness', '#disgust', 'negative', '-0.7', 'bastard', 'dickhead', 'mother_fucker', 'motherfucker', 'disagreeable_person']
senticnet['son_pouse'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'sexual_relationship', 'liaison', 'affair', 'involvement']
senticnet['son_rocco'] = ['0.056', '0.139', '-0.07', '0.154', '#interest', '#admiration', 'positive', '0.07', 'belongings', 'material', 'fabric', 'guarantee', 'stuff']
senticnet['sonam'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['sonet_ring'] = ['0.63', '-0.55', '0', '0.946', '#joy', '#admiration', 'positive', '0.711', 'complect', 'connect', 'link', 'link_up', 'join']
senticnet['song'] = ['0.077', '-0.02', '0.049', '0.053', '#joy', '#admiration', 'positive', '0.027', 'melody', 'lyrics', 'music_note', 'lyric', 'pop_music']
senticnet['song_chart'] = ['0.997', '0.892', '0', '0', '#joy', '#interest', 'positive', '0.945', 'advertisement', 'large_outdoor_board', 'motorway', 'expressway', 'train_track']
senticnet['song_lyric'] = ['0.075', '-0.00', '0.057', '0.085', '#joy', '#admiration', 'positive', '0.028', 'lyrics', 'word', 'text', 'piece_of_music', 'melody']
senticnet['song_title'] = ['0.169', '0.13', '-0.16', '0.536', '#joy', '#admiration', 'positive', '0.167', 'song', 'inclusion', 'music', 'right', 'all_right']
senticnet['songbird'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet']
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senticnet['songful'] = ['0.898', '0.84', '0', '0', '#joy', '#interest', 'positive', '0.869', 'pondering', 'broody', 'contemplative', 'meditative', 'think']
senticnet['songwriter'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
senticnet['songwriter_producer'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
senticnet['songwriter_super_furry'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'animal', 'animate_being', 'pet', 'creature']
senticnet['songwriting_credit_include'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
senticnet['songwriting_discography'] = ['0.502', '-0.82', '0', '0.779', '#surprise', '#admiration', 'positive', '0.703', 'mortal', 'person', 'individual', 'free_liver', 'agent']
senticnet['songwriting_duo'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
senticnet['songwriting_partner'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'music', 'classical', 'composer', 'band_play', 'rock_band']
senticnet['sonic'] = ['0.036', '0', '0', '0.176', '#joy', '#admiration', 'positive', '0.106', 'vibration', 'audio', 'ear_activity', 'audible', 'decibel']
senticnet['sonic_assault'] = ['0.26', '0', '0', '0.089', '#joy', '#admiration', 'positive', '0.174', 'balcony_rail', 'movie_theater_seat', 'row_seat', 'usher', 'sticky_floor']
senticnet['sonic_screwdriver'] = ['0.105', '-0.17', '0.057', '0.079', '#joy', '#surprise', 'positive', '0.076', 'cue', 'twig', 'long_stick', 'stick', 'adhere']
senticnet['sonic_ultimate'] = ['0', '0.106', '-0.05', '0.163', '#interest', '#admiration', 'positive', '0.072', 'band', 'kit', 'oblivion', 'rock_band', 'high_school_band']
senticnet['sonicare'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'smart_technology', 'electric_shaver', 'hair_dryer', 'stay_healthy', 'wash_tooth']
senticnet['sonicare_toothbrush'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'smart_technology', 'electric_shaver', 'hair_dryer', 'stay_healthy', 'wash_tooth']
senticnet['sonication'] = ['-0.04', '0.069', '0', '0.075', '#interest', '#admiration', 'positive', '0.035', 'sculpt_gel', 'hairstyle', 'hairdresser', 'cosmetic', 'hair_sculpt_gel']
senticnet['sonnenschein_nath_rosenthal'] = ['0.841', '0.721', '0', '0.74', '#joy', '#admiration', 'positive', '0.767', 'good_manner', 'two_people', 'love', 'guardian', 'guy']
senticnet['sonnet'] = ['0.069', '0', '0', '0.124', '#joy', '#admiration', 'positive', '0.097', 'rhyme', 'delightful', 'poem', 'song_lyric', 'form_of_art']
senticnet['sonofabitch'] = ['-0.76', '0', '0.904', '-0.80', '#anger', '#disgust', 'negative', '-0.82', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['sonorant'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'letter', 'vowel', 'alphabet', 'basic_speech', 'kind_gesture']
senticnet['sonority'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['sonorous_voice'] = ['-0.71', '0', '0.998', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'tone', 'quality', 'twang', 'nasality', 'timber']
senticnet['sonorousness'] = ['0', '0.972', '0', '0.761', '#interest', '#admiration', 'positive', '0.867', 'ringing', 'resonance', 'plangency', 'reverberance', 'vibrancy']
senticnet['sonship'] = ['0.741', '-0.84', '0', '0.543', '#joy', '#surprise', 'positive', '0.711', 'integrity', 'righteousness', 'scrupulousness', 'incorruptness', 'honestness']
senticnet['sonu_walia'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['sony_aibo_robotic'] = ['0.039', '0', '0', '0.059', '#joy', '#admiration', 'positive', '0.049', 'big_dog', 'dog_cat', 'smart_dog', 'pet', 'domesticated_animal']
senticnet['sony_classical'] = ['0.805', '-0.58', '0', '0.525', '#joy', '#surprise', 'positive', '0.637', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['soon'] = ['0', '0.071', '0', '0', '#interest', '#interest', 'positive', '0.071', 'early', 'light_in_sky', 'sunrise', 'day_part', 'part_of_day']
senticnet['soot'] = ['0', '0', '-0.71', '0', '#fear', '#fear', 'negative', '-0.71', 'ash', 'cinder', 'combustion', 'flue', 'flame']
senticnet['soot_black'] = ['-0.60', '0', '0.895', '-0.54', '#sadness', '#anger', 'negative', '-0.68', 'coal_black', 'jet_black', 'pitch_black', 'black', 'achromatic_color']
senticnet['soot_formation'] = ['-0.98', '0', '0.516', '-0.90', '#sadness', '#disgust', 'negative', '-0.80', 'devastation', 'annihilation', 'pulverization', 'vaporization', 'destruction']
senticnet['sooth'] = ['0.753', '-0.59', '0', '0.705', '#joy', '#admiration', 'positive', '0.684', 'truthfulness', 'sincerity', 'honestness', 'honesty', 'veracity']
senticnet['soothe'] = ['0.712', '0', '0', '0', '#joy', '#joy', 'positive', '0.712', 'serenity', 'euphoria', 'serene', 'tranquility', 'cheer']
senticnet['soothing'] = ['0.506', '0.659', '-0.24', '0.242', '#joy', '#interest', 'positive', '0.29', 'calmly', 'quieten', 'assuasive', 'tranquillize', 'cool_down']
senticnet['soothing_balm'] = ['0.712', '0', '0', '0', '#joy', '#joy', 'positive', '0.712', 'serenity', 'euphoria', 'serene', 'tranquility', 'cheer']
senticnet['soothing_massage'] = ['0.838', '0.744', '0', '0.874', '#joy', '#admiration', 'positive', '0.819', 'reduce_stress', 'take_easy', 'take_rest', 'stress_reduction', 'relaxation']
senticnet['soothing_music'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'drink', 'party', 'friend_gathering', 'rest_area', 'relax_time']
senticnet['soothingly'] = ['-0.83', '0', '0.87', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'precipitate', 'rain_down', 'shower_down', 'spit', 'patter']
senticnet['soothsayer'] = ['0.083', '0', '0', '0.075', '#joy', '#admiration', 'positive', '0.079', 'carny', 'carnival_ride', 'midway', 'ferris_wheel', 'roller-coaster']
senticnet['sooty_blotch'] = ['-0.83', '0', '0.754', '-0.97', '#sadness', '#disgust', 'negative', '-0.85', 'brown_rot_gummosis', 'ring_rot', 'ring_disease', 'tobacco_wilt', 'gummosis']
senticnet['sop'] = ['0.783', '0.962', '0', '0', '#joy', '#interest', 'positive', '0.873', 'puddle', 'toilet_tank', 'liquid_water', 'finger_paint', 'bread']
senticnet['sophia_webster'] = ['0', '-0.03', '0.022', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'shoulder_bag', 'satchel', 'water_bottle', 'tote_bag', 'carry']
senticnet['sophism'] = ['0.922', '0.762', '0', '0', '#joy', '#interest', 'positive', '0.842', 'controvert', 'refute', 'confutation', 'negate', 'rebut']
senticnet['sophistical'] = ['0.922', '0.762', '0', '0', '#joy', '#interest', 'positive', '0.842', 'controvert', 'refute', 'confutation', 'negate', 'rebut']
senticnet['sophisticate'] = ['-0.89', '0', '0.89', '-0.77', '#sadness', '#anger', 'negative', '-0.85', 'doctor', 'doctor_up', 'corrupt', 'spoil', 'adulterate']
senticnet['sophisticated'] = ['0.783', '0.971', '0', '0', '#joy', '#interest', 'positive', '0.877', 'gorgeous', 'nice_look', 'good_look', 'belle', 'pretty_woman']
senticnet['sophistication'] = ['-0.77', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.76', 'decontamination', 'blend', 'contaminator', 'impurity', 'contamination']
senticnet['sophistry'] = ['0', '-0.75', '0', '0.906', '#surprise', '#admiration', 'positive', '0.829', 'puffery', 'bombast', 'self-aggrandizement', 'bluster', 'self-aggrandizing']
senticnet['sophomore_qb'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'unofficial', 'informal', 'loose', 'drumhead']
senticnet['sophomore_slump'] = ['-0.83', '0', '0.912', '-0.67', '#sadness', '#anger', 'negative', '-0.80', 'dissatisfaction', 'displeasure', 'disappointment', 'boredom', 'ennui']
senticnet['sophomoric'] = ['0', '0', '0.71', '-0.80', '#anger', '#disgust', 'negative', '-0.75', 'idiotic', 'stupid', 'dumb', 'idiot', 'dummy']
senticnet['sophrosyne'] = ['0.971', '-0.71', '0', '0.656', '#joy', '#surprise', 'positive', '0.781', 'wellbeing', 'human_flourishing', 'successfulness', 'upbeat', 'strength']
senticnet['sopor'] = ['-0.59', '0', '0.799', '-0.69', '#anger', '#disgust', 'negative', '-0.69', 'untroubled', 'undisturbed', 'unmolested', 'unworried', 'unconcerned']
senticnet['soporiferous'] = ['0', '0', '-0.80', '-0.87', '#fear', '#disgust', 'negative', '-0.84', 'somnific', 'hypnotic', 'hypnogogic', 'somniferous', 'soporific']
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senticnet['soppy'] = ['-0.91', '0', '0.681', '-0.81', '#sadness', '#disgust', 'negative', '-0.80', 'chicken', 'doormat', 'softie', 'crybaby', 'wimp']
senticnet['soprano'] = ['0.052', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.04', 'chord', 'singer', 'melodic_noise', 'instrumental', 'amplifier']
senticnet['sopwith'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'cheapjack', 'pitchman']
senticnet['sopwith_aviation_company'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'cheapjack', 'pitchman']
senticnet['sopwith_gnu'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'cheapjack', 'pitchman']
senticnet['sorbent'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'radioactive_dust', 'dust', 'dust_matter']
senticnet['sorbet'] = ['0.092', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.071', 'bake_treat', 'round_pastry', 'bake_good', 'bake_goods', 'sweet_food']
senticnet['sorbic_acid'] = ['0', '0', '0.709', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'debasement', 'dirtiness', 'pollution', 'taint', 'impureness']
senticnet['sorbitol'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
senticnet['sorbus_aucuparia'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'bottlebrush_buckeye', 'red_buckeye', 'particolored_buckeye']
senticnet['sorcerer'] = ['-0.62', '0', '0.964', '-0.65', '#anger', '#disgust', 'negative', '-0.74', 'exorcist', 'enchanter', 'magus', 'sorceress', 'witch_doctor']
senticnet['sorcerer_hat'] = ['0.105', '-0.17', '0.057', '0.079', '#joy', '#surprise', 'positive', '0.076', 'cue', 'twig', 'long_stick', 'stick', 'adhere']
senticnet['sorceress'] = ['0.591', '-0.55', '0', '0.512', '#joy', '#surprise', 'positive', '0.552', 'exorcist', 'necromancer', 'magus', 'enchanter', 'witch_doctor']
senticnet['sorceror'] = ['-0.94', '0', '0.866', '-0.91', '#sadness', '#disgust', 'negative', '-0.90', 'movement', 'apparent_motion', 'motion', 'apparent_movement', 'optical_illusion']
senticnet['sorcery'] = ['-0.79', '0', '0.506', '-0.93', '#sadness', '#disgust', 'negative', '-0.74', 'magic', 'thaumaturgy', 'enchantment', 'witchcraft', 'witchery']
senticnet['sordid'] = ['-0.91', '0', '0.986', '0', '#sadness', '#anger', 'negative', '-0.95', 'flyblown', 'seamy', 'seedy', 'squalid', 'sleazy']
senticnet['sore'] = ['-0.17', '0', '0.256', '-0.45', '#anger', '#disgust', 'negative', '-0.29', 'cut_skin', 'ache', 'abrasion', 'laceration', 'gash']
senticnet['sore_ankle'] = ['0.94', '0.78', '0', '0', '#joy', '#interest', 'positive', '0.86', 'breaheavily', 'heavy_breathe', 'get_exhausted', 'exhaustion', 'see_neighbor']
senticnet['sore_back'] = ['-0.97', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.97', 'back_pain', 'backache', 'broken_leg', 'become_exhausted', 'dog_bark']
senticnet['sore_butt'] = ['0.847', '0.806', '0', '0', '#joy', '#interest', 'positive', '0.827', 'win_loose', 'keep_score', 'roll_dice', 'win_game', 'play_game']
senticnet['sore_foot'] = ['0.976', '0.971', '0', '0', '#joy', '#interest', 'positive', '0.974', 'become_tired', 'leg_cramp', 'fatigue', 'tiredness', 'get_tired_leg']
senticnet['sore_knee'] = ['0.744', '0.981', '0', '0', '#joy', '#interest', 'positive', '0.863', 'get_tired', 'tiredness', 'breaheavily', 'run_out_breath', 'feel_tired']
senticnet['sore_left'] = ['-0.78', '0', '0.554', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'rubor', 'redness', 'inflammation', 'tennis_elbow', 'lateral_epicondylitis']
senticnet['sore_leg'] = ['0.94', '0.78', '0', '0', '#joy', '#interest', 'positive', '0.86', 'breaheavily', 'heavy_breathe', 'get_exhausted', 'exhaustion', 'see_neighbor']
senticnet['sore_loser'] = ['1', '0', '0', '-0.33', '#joy', '#disgust', 'positive', '0.335', 'relish', 'crow_over', 'rub_hands', 'rejoice', 'glad']
senticnet['sore_mouth'] = ['0', '0', '0.955', '-0.78', '#anger', '#disgust', 'negative', '-0.87', 'worm_intestine', 'fur_keep_warm', 'unpleasant-smelling', 'halitosis', 'asthama']
senticnet['sore_muscle'] = ['-0.93', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.92', 'pull_muscle', 'become_exhausted', 'increased_heart_rate', 'breathlessness', 'tired_leg']
senticnet['sore_neck'] = ['0.94', '0.78', '0', '0', '#joy', '#interest', 'positive', '0.86', 'breaheavily', 'heavy_breathe', 'get_exhausted', 'exhaustion', 'see_neighbor']
senticnet['sore_right'] = ['-0.78', '0', '0.554', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'rubor', 'redness', 'inflammation', 'tennis_elbow', 'lateral_epicondylitis']
senticnet['sore_shoulder'] = ['0.94', '0.78', '0', '0', '#joy', '#interest', 'positive', '0.86', 'breaheavily', 'heavy_breathe', 'get_exhausted', 'exhaustion', 'see_neighbor']
senticnet['sore_stomach'] = ['-0.02', '-0.01', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.04', 'painful', 'needle_thread', 'use_needle', 'seam', 'cut_fabric']
senticnet['sore_throat'] = ['-0.88', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.88', 'get_laugh', 'tooth_hurt', 'choke_death', 'gain_weight', 'feel_satisfied']
senticnet['sorely'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'lamentably', 'deplorably', 'bereaved', 'bereft', 'grief-stricken']
senticnet['soreness'] = ['-0.89', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.86', 'contusion', 'injury', 'concern_health', 'physical_injury', 'strengthful']
senticnet['sorghum'] = ['0.061', '0.093', '0.037', '0.114', '#interest', '#admiration', 'positive', '0.058', 'whole_grain', 'food_ingredient', 'staple_food', 'starchy_food', 'food_grain']
senticnet['sorghum_bicolor'] = ['0.061', '0.093', '0.037', '0.114', '#interest', '#admiration', 'positive', '0.058', 'whole_grain', 'food_ingredient', 'staple_food', 'starchy_food', 'food_grain']
senticnet['sororal'] = ['0.605', '0.647', '-0.26', '0.323', '#joy', '#interest', 'positive', '0.327', 'friendly', 'brotherly', 'amicableness', 'amicably', 'amicable']
senticnet['sorrah'] = ['0.96', '-0.77', '0', '0.929', '#joy', '#admiration', 'positive', '0.887', 'whale_louse', 'beach_flea', 'sand_hopper', 'sandhopper', 'amphipod']
senticnet['sorrel'] = ['-0.96', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.96', 'gelding', 'roan', 'palomino', 'mare', 'buckskin']
senticnet['sorrow'] = ['-0.8', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.8', 'heartache', 'grief', 'heartbreak', 'woe', 'guilty_conscience']
senticnet['sorrower'] = ['-0.84', '0', '0.523', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'unfortunate', 'unfortunate_person', 'weeper', 'pallbearer', 'bearer']
senticnet['sorrowful'] = ['-0.87', '0.193', '-0.58', '0', '#sadness', '#fear', 'negative', '-0.41', 'regretful', 'ruefulness', 'mournful', 'sadly', 'self-pity']
senticnet['sorrowfully'] = ['-0.85', '0.21', '-0.56', '0', '#sadness', '#fear', 'negative', '-0.40', 'doleful', 'sadly', 'dolefully', 'ruefulness', 'wretched']
senticnet['sorrowfulness'] = ['-0.89', '0.16', '-0.61', '0.055', '#sadness', '#fear', 'negative', '-0.32', 'mournfully', 'ruthfulness', 'mournfulness', 'heartrending', 'heartbreaking']
senticnet['sorrowing'] = ['-0.88', '0.175', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'bereaved', 'grief-stricken', 'bereft', 'grieving', 'mourning']
senticnet['sorry'] = ['-0.87', '0.203', '-0.6', '0.06', '#sadness', '#fear', 'negative', '-0.30', 'ruthful', 'contrite', 'remorseful', 'rueful', 'sorrowing']
senticnet['sort_clothes'] = ['-0.04', '0', '0.03', '0', '#sadness', '#anger', 'negative', '-0.03', 'use_water', 'use_soap', 'buy_wash_powder', 'clothes_dirty', 'laundry']
senticnet['sorta'] = ['-0.94', '0', '0.557', '-0.71', '#sadness', '#disgust', 'negative', '-0.74', 'sad', 'disappointing', 'unlucky', 'disengaged', 'cry']
senticnet['sortable_table'] = ['0.113', '0.126', '0', '0.11', '#joy', '#interest', 'positive', '0.116', 'file', 'pencil_pen', 'document', 'note_pad', 'scrap_paper']
senticnet['sortable_table_click'] = ['0.055', '0.117', '0', '0.133', '#interest', '#admiration', 'positive', '0.102', 'graphical_representation', 'pencil_pen', 'note_pad', 'marker', 'stationery']
senticnet['sorted_alphabetically'] = ['0.113', '0.126', '0', '0.11', '#joy', '#interest', 'positive', '0.116', 'file', 'pencil_pen', 'document', 'note_pad', 'scrap_paper']
senticnet['sortie'] = ['0.954', '-0.87', '0', '0.583', '#joy', '#surprise', 'positive', '0.802', 'penetration', 'air_raid', 'air_attack', 'swoop', 'incursion']
senticnet['sortition'] = ['0.774', '-0.82', '0', '0.63', '#joy', '#surprise', 'positive', '0.744', 'quality', 'perishability', 'perishableness', 'unsatisfactoriness', 'inadequacy']
senticnet['sot'] = ['0.749', '-0.72', '0', '0.896', '#joy', '#admiration', 'positive', '0.791', 'lush', 'alcoholic', 'alky', 'dipsomaniac', 'boozer']
senticnet['soterius_von_sachsenheim'] = ['0.975', '-0.66', '0', '0.813', '#joy', '#admiration', 'positive', '0.816', 'cry', 'yell', 'scream', 'shrill', 'call']
senticnet['sotheby_auction'] = ['0.054', '0.237', '-0.15', '0.33', '#interest', '#admiration', 'positive', '0.117', 'retrieve', 'bring', 'tote', 'take', 'go_get']
senticnet['sotos_syndrome'] = ['-0.87', '0', '0.873', '-0.62', '#sadness', '#anger', 'negative', '-0.78', 'dwarfism', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['sottish'] = ['-0.52', '0', '0.542', '-0.93', '#anger', '#disgust', 'negative', '-0.67', 'intoxication', 'drunkenness', 'inebriation', 'inebriety', 'tipsiness']
senticnet['sottishness'] = ['-0.52', '0', '0.542', '-0.93', '#anger', '#disgust', 'negative', '-0.67', 'intoxication', 'drunkenness', 'inebriation', 'inebriety', 'tipsiness']
senticnet['soubrette'] = ['0.814', '-0.93', '0', '0.781', '#joy', '#surprise', 'positive', '0.844', 'girl', 'miss', 'missy', 'young_lady', 'young_woman']
senticnet['soubriquet'] = ['0.072', '0.244', '-0.20', '0.252', '#interest', '#admiration', 'positive', '0.091', 'doe', 'sweet', 'live_organism', 'love', 'alive']
senticnet['souce'] = ['0.964', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.846', 'bulletin', 'replicate', 'current_event', 'computer_command', 'paper_write']
senticnet['soud_system'] = ['0', '0', '-0.04', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'television_set', 'remove_control', 'tv_set', 'christian_fundamentalist', 'stair_down']
senticnet['soudanese'] = ['-0.71', '0', '0.836', '0', '#sadness', '#anger', 'negative', '-0.77', 'awkward', 'unenviable', 'embarrassingly', 'embarrassing', 'sheepish']
senticnet['sought-after'] = ['0', '-0.84', '0', '0.758', '#surprise', '#admiration', 'positive', '0.8', 'in-demand', 'coveted', 'renowned', 'prized', 'prestigious']
senticnet['souk'] = ['0', '0.113', '0.041', '0.058', '#interest', '#admiration', 'positive', '0.043', 'corner_shop', 'general_store', 'many_shops', 'large_supermarket', 'goods']
senticnet['soul'] = ['0.042', '0.146', '0', '0', '#joy', '#interest', 'positive', '0.094', 'skeletal_system', 'main_artery', 'nape', 'artery', 'tendon']
senticnet['soul-crushing'] = ['-0.84', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.91', 'soul-destroying', 'soul-killing', 'soul-sucking', 'stultifying', 'mind-numbing']
senticnet['soul-destroying'] = ['-0.83', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.90', 'soul-crushing', 'soul-killing', 'soul-sucking', 'stultifying', 'mind-numbing']
senticnet['soul-killing'] = ['-0.83', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.90', 'soul-crushing', 'soul-destroying', 'soul-sucking', 'stultifying', 'mind-numbing']
senticnet['soul-searching'] = ['0', '0.854', '0', '0.742', '#interest', '#admiration', 'positive', '0.798', 'self-reflection', 'self-analysis', 'introspection', 'self-criticism', 'self-examination']
senticnet['soul-sucking'] = ['-0.83', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.90', 'soul-crushing', 'soul-killing', 'soul-destroying', 'stultifying', 'mind-numbing']
senticnet['soul_diva'] = ['-0.89', '0', '0.722', '-0.94', '#sadness', '#disgust', 'negative', '-0.85', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['soul_searching'] = ['0.08', '0.153', '-0.12', '0.224', '#interest', '#admiration', 'positive', '0.083', 'meditation', 'self_contemplation', 'introspection', 'self_examination', 'self_observation']
senticnet['soul_singer'] = ['0.882', '0.901', '0', '0', '#joy', '#interest', 'positive', '0.891', 'songwriter', 'vip', 'rock_group', 'guitar_player', 'violinist']
senticnet['soulful'] = ['-0.74', '0.292', '-0.42', '-0.06', '#sadness', '#fear', 'negative', '-0.23', 'lowness', 'dispiritedness', 'low-spiritedness', 'dejectedness', 'downheartedness']
senticnet['soulful_pop'] = ['0.946', '-0.91', '0', '0.664', '#joy', '#surprise', 'positive', '0.841', 'pretender', 'phony', 'phoney', 'hypocrite', 'dissembler']
senticnet['soulful_rendition'] = ['0.773', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.844', 'favorite', 'crowd', 'acclamation', 'bunch', 'large_crowd']
senticnet['soulful_voice'] = ['0.608', '-0.55', '0', '0.726', '#joy', '#admiration', 'positive', '0.628', 'genius', 'endowment', 'flair', 'gift', 'knack']
senticnet['sound'] = ['0.036', '0', '0', '0.11', '#joy', '#admiration', 'positive', '0.073', 'animal_sound', 'noise', 'decibel', 'loudness', 'audio']
senticnet['sound_alarm'] = ['-0.09', '0', '0.025', '-0.03', '#sadness', '#disgust', 'negative', '-0.05', 'make_a_lot_of_noise', 'compose_music', 'tune_instrument', 'perform_on_stage', 'strum']
senticnet['sound_asleep'] = ['0', '0.08', '0', '0.179', '#interest', '#admiration', 'positive', '0.13', 'wake_up_morning', 'up_early', 'get_up_early', 'gather_energy_tomorrow', 'sit_chair']
senticnet['sound_bad'] = ['0.062', '-0.13', '0.16', '-0.18', '#anger', '#disgust', 'negative', '-0.03', 'fail', 'broken', 'string_break', 'bad', 'make_sound']
senticnet['sound_beautiful'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'instrumental', 'symphony', 'concert', 'music', 'violin']
senticnet['sound_bite'] = ['0.022', '0.123', '0.038', '0.129', '#interest', '#admiration', 'positive', '0.059', 'hymn', 'written_music', 'walk_beach', 'lyric', 'piece_of_music']
senticnet['sound_control_room'] = ['0.136', '-0.05', '-0.04', '-0.03', '#joy', '#surprise', 'positive', '0.029', 'theater_hall', 'rehearse_room', 'theater_box', 'amphitheater', 'soundstage']
senticnet['sound_good'] = ['0.955', '0', '0', '0.751', '#joy', '#admiration', 'positive', '0.853', 'delight_sense', 'great_joy', 'feel_refreshed', 'happiness', 'self_satisfaction']
senticnet['sound_judgment'] = ['0.828', '-0.90', '0', '0.874', '#surprise', '#admiration', 'positive', '0.869', 'perspicacity', 'judgment', 'fair', 'objectiveness', 'fair_judgment']
senticnet['sound_level'] = ['0', '0', '0.946', '-0.81', '#anger', '#disgust', 'negative', '-0.88', 'adjust_volume', 'speaker', 'noisy', 'making_a_lot_of_noise', 'hear_song']
senticnet['sound_localization'] = ['0.685', '-0.91', '0', '0.566', '#joy', '#surprise', 'positive', '0.721', 'audibility', 'audibleness', 'visibleness', 'visibility', 'physical_property']
senticnet['sound_loud'] = ['-0.05', '-0.00', '0.059', '0', '#sadness', '#anger', 'negative', '-0.03', 'beep', 'strum', 'make_noise', 'compose_music', 'make_a_lot_of_noise']
senticnet['sound_lovely'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'agree', 'make_beautiful_music', 'chamber_music', 'play_stringed_instrument', 'make_music']
senticnet['sound_make'] = ['0.845', '0.789', '0', '0.806', '#joy', '#admiration', 'positive', '0.813', 'vocal_music', 'melody', 'lullaby', 'tune', 'lyric']
senticnet['sound_nice'] = ['0.241', '0.185', '-0.11', '0.16', '#joy', '#interest', 'positive', '0.118', 'eclectic', 'rock_group', 'musical_group', 'rolling_stone', 'music_group']
senticnet['sound_off'] = ['-0.90', '0', '0.725', '-0.86', '#sadness', '#disgust', 'negative', '-0.83', 'quetch', 'kvetch', 'complain', 'bemoan', 'plain']
senticnet['sound_out'] = ['-0.03', '0.084', '0', '0.112', '#interest', '#admiration', 'positive', '0.054', 'finite', 'spell_wrong', 'color_red', 'hue', 'bright_red']
senticnet['sound_power'] = ['0.692', '-0.86', '0', '0.499', '#joy', '#surprise', 'positive', '0.685', 'acoustic_power', 'light_intensity', 'intensity', 'sound_pressure_level', 'threshold_level']
senticnet['sound_pressure_level'] = ['-0.93', '0', '0.698', '-0.85', '#sadness', '#disgust', 'negative', '-0.83', 'candlepower', 'light_intensity', 'acoustic_power', 'intensity', 'threshold_level']
senticnet['sound_system'] = ['0.049', '0.167', '-0.05', '0.295', '#interest', '#admiration', 'positive', '0.115', 'four_sides', 'dolby_surround', 'bass', 'movie', 'surround']
senticnet['sound_wave'] = ['0.115', '0', '0.042', '0.066', '#joy', '#admiration', 'positive', '0.046', 'loud_sound', 'boom', 'decibel', 'loud_noise', 'loudness']
senticnet['soundbite'] = ['0.022', '0.123', '0.038', '0.129', '#interest', '#admiration', 'positive', '0.059', 'hymn', 'written_music', 'walk_beach', 'lyric', 'piece_of_music']
senticnet['soundcheck'] = ['0.773', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.844', 'favorite', 'crowd', 'acclamation', 'bunch', 'large_crowd']
senticnet['sounding'] = ['-0.63', '0', '0.857', '-0.71', '#anger', '#disgust', 'negative', '-0.73', 'shallow', 'facile', 'ostensible', 'superficial', 'looking']
senticnet['soundless'] = ['0', '0', '-0.70', '-0.95', '#fear', '#disgust', 'negative', '-0.82', 'tacit', 'silent', 'still', 'understand', 'mum']
senticnet['soundproof'] = ['-0.9', '0', '0.657', '-0.64', '#sadness', '#anger', 'negative', '-0.73', 'imperviable', 'mothproof', 'resistant', 'proof', 'greaseproof']
senticnet['soundstage'] = ['0.136', '-0.05', '-0.04', '-0.03', '#joy', '#surprise', 'positive', '0.029', 'theater_hall', 'rehearse_room', 'theater_box', 'sound_control_room', 'amphitheater']
senticnet['soundtrack'] = ['0.916', '-0.93', '0', '0.674', '#joy', '#surprise', 'positive', '0.841', 'smash', 'bang', 'smasher', 'strike', 'hit']
senticnet['soup'] = ['-0.08', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.08', 'needy', 'come_house', 'quiet_child', 'prepare_supper', 'salt_food']
senticnet['soup_bowl'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'address_book', 'get_rich', 'blank_check', 'swiss_army_knife', 'whisk']
senticnet['soup_kitchen'] = ['-0.10', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.08', 'come_house', 'desperate', 'welcome_friend', 'captain_boat', 'receive_email']
senticnet['soup_plate'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'address_book', 'get_rich', 'blank_check', 'swiss_army_knife', 'whisk']
senticnet['soup_pot'] = ['0.07', '0.084', '0.023', '0', '#joy', '#interest', 'positive', '0.044', 'hearty', 'porridge', 'hot_liquid', 'liquid_food', 'meat_vegetable']
senticnet['soup_spoon'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'neat', 'kitchen_drawer', 'cutlery_drawer', 'dine_table', 'table']
senticnet['souped'] = ['0', '0', '-0.97', '-0.78', '#fear', '#disgust', 'negative', '-0.87', 'souped-up', 'pimped-out', 'tricked-out', 'race-ready', 'pumped-up']
senticnet['souped-up'] = ['0', '0', '-0.97', '-0.78', '#fear', '#disgust', 'negative', '-0.88', 'tricked-out', 'pimped-out', 'souped', 'race-ready', 'pumped-up']
senticnet['souped_up'] = ['0.843', '-0.99', '0', '0.706', '#joy', '#surprise', 'positive', '0.849', 'contrivance', 'gimbal', 'injector', 'mod_con', 'device']
senticnet['soupon'] = ['0.828', '-0.88', '0', '0.551', '#joy', '#surprise', 'positive', '0.755', 'superiority', 'pride', 'superbia', 'imperiousness', 'domineeringness']
senticnet['soupy'] = ['0', '0', '-0.73', '-0.9', '#fear', '#disgust', 'negative', '-0.81', 'maudlin', 'hokey', 'drippy', 'mawkish', 'bathetic']
senticnet['souq'] = ['0', '0.113', '0.041', '0.058', '#interest', '#admiration', 'positive', '0.043', 'corner_shop', 'general_store', 'many_shops', 'large_supermarket', 'goods']
senticnet['sour'] = ['0', '0', '-0.88', '-0.83', '#fear', '#disgust', 'negative', '-0.86', 'work', 'acidity', 'ferment', 'tartness', 'turn']
senticnet['sour_apple'] = ['0.02', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'many_fruits', 'peach', 'buy_fruit', 'pineapple', 'tomato']
senticnet['sour_cherry'] = ['0', '0.066', '-0.05', '0.089', '#interest', '#admiration', 'positive', '0.032', 'pineapple', 'kale', 'banana', 'pickle', 'tomato']
senticnet['sour_cream'] = ['0.812', '0.785', '0', '0', '#joy', '#interest', 'positive', '0.798', 'tofu', 'sour_milk', 'peanut_butter', 'mayonnaise', 'cottage_cheese']
senticnet['sour_crude'] = ['0', '0', '-0.06', '0', '#fear', '#fear', 'negative', '-0.06', 'greasy', 'car_gas', 'fossil_fuel', 'liquid_fuel', 'lubrication']
senticnet['sour_crude_oil'] = ['0', '0', '-0.06', '0', '#fear', '#fear', 'negative', '-0.06', 'greasy', 'car_gas', 'fossil_fuel', 'liquid_fuel', 'lubrication']
senticnet['sour_flavor'] = ['-0.82', '0', '0.901', '-0.65', '#sadness', '#anger', 'negative', '-0.79', 'taste', 'taste_sensation', 'gustatory_sensation', 'taste_perception', 'gustatory_perception']
senticnet['sour_flavour'] = ['0.045', '0.141', '-0.05', '0.065', '#interest', '#admiration', 'positive', '0.049', 'kernel', 'starch', 'ginger', 'cereal_crop', 'pea']
senticnet['sour_milk'] = ['-0.10', '-0.04', '0', '-0.15', '#sadness', '#disgust', 'negative', '-0.07', 'lasagna', 'frozen_food', 'green_bean', 'tofu', 'horseradish']
senticnet['sour_stomach'] = ['0', '0', '-0.70', '-0.76', '#fear', '#disgust', 'negative', '-0.73', 'choke', 'become_full', 'get_full', 'burp', 'sickness']
senticnet['sour_taste'] = ['0.045', '0.141', '-0.05', '0.065', '#interest', '#admiration', 'positive', '0.049', 'kernel', 'starch', 'ginger', 'cereal_crop', 'pea']
senticnet['source'] = ['0.964', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.846', 'bulletin', 'replicate', 'current_event', 'computer_command', 'paper_write']
senticnet['source_code'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source', 'computer', 'browser', 'dvd', 'clown']
senticnet['source_code_freely_available'] = ['0.81', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.774', 'available', 'public', 'population', 'crowd', 'majority']
senticnet['source_electricity'] = ['0.033', '0.075', '0', '0.153', '#interest', '#admiration', 'positive', '0.087', 'switch', 'find_tape', 'turn_computer_on', 'read_manual', 'buy_computer']
senticnet['source_information'] = ['0.494', '0.673', '-0.26', '0.25', '#joy', '#interest', 'positive', '0.287', 'information', 'report_event', 'read_news', 'review_book', 'report_news']
senticnet['source_light'] = ['0.026', '0', '0', '0.116', '#joy', '#admiration', 'positive', '0.071', 'bright_yellow', 'celestial_body', 'limitless', 'around_earth', 'sol']
senticnet['source_tarball'] = ['-0.58', '0', '0.561', '-0.62', '#sadness', '#disgust', 'negative', '-0.58', 'spot', 'speckle', 'dapple', 'mark', 'fleck']
senticnet['sourcebook'] = ['-0.03', '0.168', '-0.07', '0.139', '#interest', '#admiration', 'positive', '0.049', 'command', 'direct', 'follow', 'take_charge', 'govern']
senticnet['soured_milk'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'dainty', 'delicacy', 'treat', 'jelly', 'aspic']
senticnet['souring'] = ['-0.93', '0', '0.997', '-0.50', '#sadness', '#anger', 'negative', '-0.81', 'decay', 'mildew', 'mold', 'mould', 'spoiling']
senticnet['sourly'] = ['-0.89', '0.168', '-0.60', '0.05', '#sadness', '#fear', 'negative', '-0.32', 'remorsefully', 'contritely', 'contrition', 'contriteness', 'attrition']
senticnet['sourness'] = ['-0.78', '0', '0.969', '-0.78', '#anger', '#disgust', 'negative', '-0.84', 'acidity', 'acidulousness', 'taste', 'taste_sensation', 'gustatory_sensation']
senticnet['sourpuss'] = ['-0.87', '0', '0.693', '-0.64', '#sadness', '#anger', 'negative', '-0.73', 'unwelcome_person', 'picklepuss', 'troublemaker', 'trouble_maker', 'troubler']
senticnet['soursop'] = ['0.826', '0.787', '0', '0', '#joy', '#interest', 'positive', '0.806', 'fig', 'baby_flower', 'carpel', 'strawberry', 'star_anise']
senticnet['sourwood'] = ['0.958', '-0.82', '0', '0.924', '#joy', '#admiration', 'positive', '0.901', 'bunchberry', 'dwarf_cornel', 'crackerberry', 'pudding_berry', 'common_white_dogwood']
senticnet['sousveillance'] = ['0.654', '-0.71', '0', '0.624', '#joy', '#surprise', 'positive', '0.664', 'viewable', 'gross', 'telescopic', 'seeable', 'visual']
senticnet['soutane'] = ['0.849', '-0.50', '0', '0.845', '#joy', '#admiration', 'positive', '0.732', 'robe', 'gown', 'veil', 'vest', 'cassock']
senticnet['south-central'] = ['-0.83', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.85', 'north-central', 'south-eastern', 'west-central', 'north-western', 'north-eastern']
senticnet['south-eastern'] = ['-0.84', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.86', 'north-central', 'south-central', 'west-central', 'north-western', 'north-eastern']
senticnet['south_bend_tribune'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['south_brega'] = ['0', '0.105', '0', '0.084', '#interest', '#admiration', 'positive', '0.095', 'archive', 'print_medium', 'quiet_place', 'register', 'media']
senticnet['south_east_asia'] = ['0', '0.766', '0', '0', '#interest', '#interest', 'positive', '0.766', 'abroad', 'foreign', 'study_abroad', 'elsewhere', 'live_abroad']
senticnet['south_luzon_expressway'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['south_stradbroke_island'] = ['0.84', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.798', 'big_breadbox', 'water_vehicle', 'military_ship', 'ship', 'small_boat']
senticnet['south_thoroughfare'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'artery', 'skeletal_system', 'groin', 'neck', 'muscle']
senticnet['south_waziristan'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'webpage', 'store_record', 'ideal']
senticnet['south_waziristan_tribal'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'webpage', 'store_record', 'ideal']
senticnet['southbound_bridge'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'suspension_bridge', 'under_bridge', 'roadway', 'county_highway', 'long_road']
senticnet['southbound_entrance'] = ['0.032', '0.116', '0.016', '0', '#joy', '#interest', 'positive', '0.044', 'curb', 'crossroad', 'ring_road', 'lane', 'interstate']
senticnet['southbound_exit'] = ['0.032', '0.116', '0.016', '0', '#joy', '#interest', 'positive', '0.044', 'curb', 'crossroad', 'ring_road', 'lane', 'interstate']
senticnet['southbound_train'] = ['0.861', '-0.52', '0', '0.598', '#joy', '#admiration', 'positive', '0.663', 'exit', 'get_off', 'deplane', 'go_out', 'get_out']
senticnet['southeast_asian_heroin'] = ['-0.63', '0', '0.819', '-0.54', '#sadness', '#anger', 'negative', '-0.66', 'smack', 'hell_dust', 'nose_drop', 'thunder', 'skag']
senticnet['southeast_coast'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'pacific_ocean', 'small_lake', 'mere']
senticnet['southeast_corner'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'boulevard', 'lane', 'thoroughfare', 'intersection', 'curb']
senticnet['southeastcon'] = ['0', '0.214', '0.012', '0.208', '#interest', '#admiration', 'positive', '0.137', 'lecture_hall', 'lecture_theater', 'lesson', 'lecture', 'school_session']
senticnet['southeasterly_flow'] = ['-0.12', '-0.05', '0', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'diminish', 'reduce', 'lower', 'lower_music', 'decrease']
senticnet['southeastern_coast'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'pacific_ocean', 'small_lake', 'mere']
senticnet['southeasternmost'] = ['0', '0.08', '0.032', '0.096', '#interest', '#admiration', 'positive', '0.048', 'land_form', 'land_formation', 'land_feature', 'geographic_feature', 'landscape_feature']
senticnet['southee'] = ['-0.62', '0', '0.713', '-0.98', '#anger', '#disgust', 'negative', '-0.77', 'muddy_up', 'muck_up', 'foul', 'blemish', 'smear']
senticnet['southerly_wind'] = ['-0.87', '0', '0.91', '-0.78', '#sadness', '#anger', 'negative', '-0.85', 'storm', 'rainstorm', 'rain', 'line_storm', 'equinoctial_storm']
senticnet['southern_blight'] = ['-0.02', '-0.09', '0.11', '-0.13', '#anger', '#disgust', 'negative', '-0.04', 'beetle', 'gypsy_moth', 'mole_cricket', 'grasshopper', 'many_insects']
senticnet['southern_border'] = ['0.034', '0.09', '0.039', '0.061', '#interest', '#admiration', 'positive', '0.036', 'border', 'limit', 'edge', 'indoor', 'side']
senticnet['southern_boundary'] = ['0.034', '0.09', '0.039', '0.061', '#interest', '#admiration', 'positive', '0.036', 'border', 'limit', 'edge', 'indoor', 'side']
senticnet['southern_coast'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'pacific_ocean', 'small_lake', 'mere']
senticnet['southern_cone'] = ['0', '0', '0', '0.119', '#admiration', '#admiration', 'positive', '0.119', 'planet', 'earth', 'spherical', 'planet_solar_system', 'terrain']
senticnet['southern_constellation'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['southern_corn_rootworm'] = ['-0.02', '-0.09', '0.11', '-0.13', '#anger', '#disgust', 'negative', '-0.04', 'beetle', 'gypsy_moth', 'mole_cricket', 'grasshopper', 'many_insects']
senticnet['southern_drawl'] = ['-0.53', '0', '0.798', '-0.73', '#anger', '#disgust', 'negative', '-0.68', 'husky', 'hardy', 'sturdy', 'robust', 'stalwart']
senticnet['southern_edge'] = ['0.034', '0.09', '0.039', '0.061', '#interest', '#admiration', 'positive', '0.036', 'border', 'limit', 'edge', 'indoor', 'side']
senticnet['southern_interscholastic'] = ['0.889', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.807', 'compression', 'push_hard', 'print_newspaper', 'journalist', 'make_newspaper']
senticnet['southern_magnolia'] = ['0.958', '-0.82', '0', '0.924', '#joy', '#admiration', 'positive', '0.901', 'bunchberry', 'dwarf_cornel', 'crackerberry', 'pudding_berry', 'common_white_dogwood']
senticnet['southern_mole_cricket'] = ['0', '-0.09', '0.123', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'gypsy_moth', 'japanese_beetle', 'cucumber_beetle', 'mosquito', 'earwig']
senticnet['southern_ocean'] = ['0.872', '0.783', '0', '0', '#joy', '#interest', 'positive', '0.828', 'carbon', 'sediment', 'water_column', 'rate', 'measurement']
senticnet['southern_paiute'] = ['0.052', '0.099', '0', '0.067', '#interest', '#admiration', 'positive', '0.073', 'police_rank', 'head_guy', 'chief', 'leader', 'commander']
senticnet['southern_pine_beetle'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['southern_portion'] = ['0.595', '-0.61', '0', '0.826', '#surprise', '#admiration', 'positive', '0.679', 'completeness', 'work', 'whole_shebang', 'absolutely', 'wholly']
senticnet['southern_red_oak'] = ['0.672', '-0.69', '0', '0.831', '#surprise', '#admiration', 'positive', '0.733', 'red_oak', 'swamp_red_oak', 'turkey_oak', 'northern_red_oak', 'oak']
senticnet['southern_redbelly_dace'] = ['0', '-0.02', '0.044', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'haddock', 'mermaid', 'fish_rod', 'tidal_wave', 'dryad']
senticnet['southern_slope'] = ['0', '0.093', '0', '0.078', '#interest', '#admiration', 'positive', '0.085', 'steep', 'landscape_feature', 'knoll', 'hill_mountain', 'incline']
senticnet['southern_tip'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'pacific_ocean', 'small_lake', 'mere']
senticnet['southern_trunk_road'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'fantastic', 'amaze', 'pose', 'wondrous', 'wonderfully']
senticnet['southernmost_portion'] = ['0.595', '-0.61', '0', '0.826', '#surprise', '#admiration', 'positive', '0.679', 'completeness', 'work', 'whole_shebang', 'absolutely', 'wholly']
senticnet['southernmost_tip'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'pacific_ocean', 'small_lake', 'mere']
senticnet['southernwood'] = ['-0.66', '0', '0.811', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'subshrub', 'suffrutex', 'old_woman', 'mugwort', 'wormwood']
senticnet['southpaw'] = ['0.969', '0.998', '0', '0', '#joy', '#interest', 'positive', '0.984', 'bell_sound', 'bell_noise', 'piece_of_jewelry', 'syllable', 'fastener']
senticnet['southpaw_stance'] = ['0.969', '0.998', '0', '0', '#joy', '#interest', 'positive', '0.984', 'bell_sound', 'bell_noise', 'piece_of_jewelry', 'syllable', 'fastener']
senticnet['southward'] = ['0', '0.21', '-0.11', '0.345', '#interest', '#admiration', 'positive', '0.147', 'direction', 'unto', 'forehead', 'upon', 'go_toward']
senticnet['southward_shift'] = ['-0.69', '0', '0.835', '-0.56', '#sadness', '#anger', 'negative', '-0.69', 'roughness', 'inclemency', 'bad_weather', 'inclementness', 'windiness']
senticnet['southwest_airline_nyse_luv'] = ['0', '0.089', '0.01', '0.118', '#interest', '#admiration', 'positive', '0.066', 'crate', 'mom_house', 'sack', 'poker_game', 'safekeep']
senticnet['southwest_corner'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'boulevard', 'lane', 'thoroughfare', 'intersection', 'curb']
senticnet['southwesterly_flow'] = ['-0.12', '-0.05', '0', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'diminish', 'reduce', 'lower', 'lower_music', 'decrease']
senticnet['southwestern_boundary'] = ['0.034', '0.09', '0.039', '0.061', '#interest', '#admiration', 'positive', '0.036', 'border', 'limit', 'edge', 'indoor', 'side']
senticnet['southwestern_coast'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'pacific_ocean', 'small_lake', 'mere']
senticnet['southwestern_extremity'] = ['0', '0.08', '0.032', '0.096', '#interest', '#admiration', 'positive', '0.048', 'land_form', 'land_formation', 'land_feature', 'geographic_feature', 'landscape_feature']
senticnet['southwestern_flank'] = ['0', '0.08', '0.032', '0.096', '#interest', '#admiration', 'positive', '0.048', 'land_form', 'land_formation', 'land_feature', 'geographic_feature', 'landscape_feature']
senticnet['southwestern_portion'] = ['0.595', '-0.61', '0', '0.826', '#surprise', '#admiration', 'positive', '0.679', 'completeness', 'work', 'whole_shebang', 'absolutely', 'wholly']
senticnet['southwestern_willow_flycatcher'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'panda', 'hunting', 'air_pollution', 'global_warming', 'sea_pollution']
senticnet['southwestward'] = ['0', '0.21', '-0.11', '0.345', '#interest', '#admiration', 'positive', '0.147', 'direction', 'unto', 'forehead', 'upon', 'go_toward']
senticnet['souvenier'] = ['-0.93', '0', '0.607', '-0.67', '#sadness', '#disgust', 'negative', '-0.73', 'miss', 'girl', 'missy', 'young_lady', 'young_woman']
senticnet['souvenir'] = ['0', '0', '-0.86', '-0.85', '#fear', '#disgust', 'negative', '-0.85', 'gaud', 'novelty', 'gewgaw', 'bangle', 'bauble']
senticnet['souvenir_sheet'] = ['0.058', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.054', 'letter_slot', 'envelope', 'pamphlet', 'mailman', 'address_label']
senticnet['souvenir_shop'] = ['0.813', '0.832', '0', '0.874', '#interest', '#admiration', 'positive', '0.84', 'ferris_wheel', 'concession_stand', 'food', 'carnival_ride', 'theme_park']
senticnet['souvenir_store'] = ['0.877', '-0.80', '0', '0', '#joy', '#surprise', 'positive', '0.843', 'convention', 'prisoner_war_camp', 'rave', 'box_ring', 'dark_alley']
senticnet['soveraign'] = ['0.731', '-0.59', '0', '0.634', '#joy', '#admiration', 'positive', '0.652', 'accommodate', 'obey', 'conform_to', 'toe_line', 'abide_by']
senticnet['sovereign'] = ['-0.62', '0', '0.961', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'jeweled_headdress', 'diadem', 'tiara', 'headdress', 'headgear']
senticnet['sovereign_authority'] = ['0.657', '-0.88', '0', '0.631', '#joy', '#surprise', 'positive', '0.723', 'cognisance', 'knowingness', 'awareness', 'feel', 'consciousness']
senticnet['sovereign_immunity'] = ['0', '0', '0', '0.816', '#admiration', '#admiration', 'positive', '0.816', 'resistance', 'protection', 'untouchable', 'invulnerable', 'sacrosanct']
senticnet['sovereign_power'] = ['-0.55', '0', '0.965', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'interjection', 'delay', 'insert', 'disruption', 'interpolation']
senticnet['sovereign_prerogative'] = ['-0.81', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.79', 'stifle', 'forgotten', 'old', 'touch_ground', 'seldom']
senticnet['sovereign_prince'] = ['0.051', '0.097', '0', '0.065', '#interest', '#admiration', 'positive', '0.071', 'king', 'queen', 'duchess', 'throne', 'lord']
senticnet['sovereignly'] = ['-0.84', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.90', 'completely', 'sheerly', 'utterly', 'roundly', 'fullest']
senticnet['sovereignty'] = ['0.934', '-0.64', '0', '0.766', '#joy', '#admiration', 'positive', '0.782', 'dominion', 'raj', 'reign', 'paramountcy', 'suzerainty']
senticnet['soviet'] = ['0', '0', '-0.70', '-0.83', '#fear', '#disgust', 'negative', '-0.76', 'settle_disagreement', 'hitler', 'republican', 'conservative', 'unchanging']
senticnet['soviet_aggression'] = ['0.828', '-0.67', '0', '0.811', '#joy', '#admiration', 'positive', '0.771', 'foolhardiness', 'brashness', 'thoughtlessness', 'desperation', 'unthoughtfulness']
senticnet['soviet_bloc'] = ['0.045', '0.286', '-0.14', '0.229', '#interest', '#admiration', 'positive', '0.104', 'socialism', 'parliament', 'elect', 'government', 'elect_president']
senticnet['soviet_bureaucracy'] = ['-0.67', '0', '0.85', '-0.59', '#sadness', '#anger', 'negative', '-0.70', 'rack', 'boot', 'whip', 'iron_boot', 'instrument_of_torture']
senticnet['soviet_communism'] = ['0.045', '0.286', '-0.14', '0.229', '#interest', '#admiration', 'positive', '0.104', 'socialism', 'parliament', 'elect', 'government', 'elect_president']
senticnet['soviet_communist_party'] = ['0', '0', '-0.70', '-0.83', '#fear', '#disgust', 'negative', '-0.76', 'settle_disagreement', 'hitler', 'republican', 'conservative', 'unchanging']
senticnet['soviet_empire'] = ['0.045', '0.286', '-0.14', '0.229', '#interest', '#admiration', 'positive', '0.104', 'socialism', 'parliament', 'elect', 'government', 'elect_president']
senticnet['soviet_encyclopedia'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'comic_book', 'child_book', 'child_story', 'comic', 'disney_cartoon']
senticnet['soviet_hegemony'] = ['0.045', '0.286', '-0.14', '0.229', '#interest', '#admiration', 'positive', '0.104', 'socialism', 'parliament', 'elect', 'government', 'elect_president']
senticnet['soviet_mole'] = ['-0.69', '0', '0.756', '-0.99', '#anger', '#disgust', 'negative', '-0.81', 'waster', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['soviet_regime'] = ['0.045', '0.286', '-0.14', '0.229', '#interest', '#admiration', 'positive', '0.104', 'socialism', 'parliament', 'elect', 'government', 'elect_president']
senticnet['soviet_socialist'] = ['0', '0.793', '0', '0.847', '#interest', '#admiration', 'positive', '0.82', 'disagreement', 'fight_against', 'opposition', 'against', 'go_against']
senticnet['soviet_union'] = ['0.045', '0.286', '-0.14', '0.229', '#interest', '#admiration', 'positive', '0.104', 'socialism', 'parliament', 'elect', 'government', 'elect_president']
senticnet['sow'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'needlework', 'hoe', 'cultivate', 'pip', 'holding']
senticnet['sow_confusion'] = ['-0.78', '0', '0.704', '-0.91', '#sadness', '#disgust', 'negative', '-0.8', 'direct', 'take', 'lead', 'conduct', 'guide']
senticnet['sow_thistle'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['soweth'] = ['0.741', '-0.84', '0', '0.543', '#joy', '#surprise', 'positive', '0.711', 'integrity', 'righteousness', 'scrupulousness', 'incorruptness', 'honestness']
senticnet['sowing_seed'] = ['0.969', '-0.86', '0', '0.631', '#joy', '#surprise', 'positive', '0.823', 'assuage', 'perfect', 'make_look_better', 'relieve', 'sublimate']
senticnet['soy_allergy'] = ['-0.86', '0', '0.76', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['soy_flour'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'veal', 'beef', 'food_group', 'animal_flesh', 'curry']
senticnet['soy_milk'] = ['0.053', '0.085', '0.019', '0', '#joy', '#interest', 'positive', '0.04', 'farm', 'countryside', 'farm_animal', 'cow', 'healthy']
senticnet['soy_sauce'] = ['0', '-0.03', '0.046', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'pasta', 'green_bean', 'mayonnaise', 'frozen_food', 'sour_milk']
senticnet['soya'] = ['-0.70', '0', '0.606', '-0.72', '#sadness', '#disgust', 'negative', '-0.67', 'common_bean', 'bean', 'goa_bean', 'mung_bean', 'mung']
senticnet['soya_bean'] = ['-0.71', '0', '0.6', '-0.72', '#sadness', '#disgust', 'negative', '-0.68', 'common_bean', 'soya', 'goa_bean', 'mung', 'bean']
senticnet['soya_milk'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'veal', 'beef', 'food_group', 'animal_flesh', 'curry']
senticnet['soybean'] = ['0.895', '0', '0', '0', '#joy', '#joy', 'positive', '0.895', 'french_toast', 'fondue', 'gnocchi', 'result_cook', 'lasagna']
senticnet['soybean_acreage'] = ['0', '0.122', '0', '0.123', '#interest', '#admiration', 'positive', '0.122', 'acre', 'cow_home', 'crop_place', 'big_land', 'animal_house']
senticnet['soybean_farmer'] = ['-0.08', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.11', 'serf', 'peasant', 'servant', 'poor', 'villein']
senticnet['soybean_production'] = ['0', '0.122', '0', '0.123', '#interest', '#admiration', 'positive', '0.122', 'acre', 'cow_home', 'crop_place', 'big_land', 'animal_house']
senticnet['soybean_seed'] = ['0.878', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.883', 'wheat', 'grain', 'rye', 'white_rice', 'rice']
senticnet['spa'] = ['0.905', '-0.8', '0', '0', '#joy', '#surprise', 'positive', '0.853', 'spa', 'resort', 'salon', 'sauna', 'massage']
senticnet['space'] = ['0.857', '-0.79', '0', '0.907', '#joy', '#admiration', 'positive', '0.853', 'round', 'pause', 'time_interval', 'lunitidal_interval', 'break']
senticnet['space_agency_xmm'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'force_unit', 'sthene', 'poundal']
senticnet['space_between'] = ['0.045', '0.092', '0.06', '0.22', '#interest', '#admiration', 'positive', '0.074', 'gap', 'distance', 'distance_measure', 'length_measurement', 'measurement_unit']
senticnet['space_filling_tessellation'] = ['0.641', '-0.6', '0', '0.957', '#joy', '#admiration', 'positive', '0.733', 'regular_tetrahedron', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'ideal_solid']
senticnet['space_heater'] = ['0.02', '0.076', '0', '0', '#joy', '#interest', 'positive', '0.048', 'land_earth', 'tidal_wave', 'puddle', 'brine', 'spurt']
senticnet['space_junk'] = ['0.899', '0.803', '0', '0', '#joy', '#interest', 'positive', '0.851', 'ufo', 'milky', 'martian', 'galaxy', 'solar_system']
senticnet['space_shuttle_solid_rocket'] = ['0', '0', '0', '0.073', '#admiration', '#admiration', 'positive', '0.073', 'fire_starter', 'organic_substance', 'renewable_resource', 'use_fuel', 'tree_product']
senticnet['space_telescope'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'binoculars', 'read_glass', 'eyeglass', 'camera', 'monocle']
senticnet['space_travel'] = ['0.076', '0.085', '-0.04', '0', '#joy', '#interest', 'positive', '0.038', 'travel', 'space', 'rock_climb', 'anarchy', 'secret_organization']
senticnet['space_vehicle'] = ['0', '0.802', '0', '0.855', '#interest', '#admiration', 'positive', '0.829', 'ballistic_capsule', 'spacecraft', 'earth', 'april', 'first']
senticnet['spacecraft'] = ['0', '0.805', '0', '0.864', '#interest', '#admiration', 'positive', '0.835', 'ballistic_capsule', 'space_vehicle', 'earth', 'april', 'first']
senticnet['spacecraft_orbit'] = ['0.989', '-0.90', '0', '0.876', '#joy', '#surprise', 'positive', '0.923', 'reach', 'range', 'compass', 'scope', 'confines']
senticnet['spaced_apart'] = ['0.067', '0.088', '-0.07', '0.246', '#interest', '#admiration', 'positive', '0.081', 'multiple_purposes', 'revolve', 'start_run', 'wiggle', 'twist']
senticnet['spaceship'] = ['-0.05', '0.086', '0', '0.058', '#interest', '#admiration', 'positive', '0.031', 'hangar', 'aircraft_carrier', 'hot_air_balloon', 'war_museum', 'blimp']
senticnet['spaceship_earth'] = ['0.045', '0', '0', '0.051', '#joy', '#admiration', 'positive', '0.048', 'perfect_sphere', 'pour_hot_coffee_mug', 'life', 'alkaline', 'wrest']
senticnet['spacetime'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'empty_room', 'a_lot_of_space', 'empty', 'empty_house', 'universe']
senticnet['spacewalking_astronaut'] = ['0.899', '0.803', '0', '0', '#joy', '#interest', 'positive', '0.851', 'ufo', 'milky', 'martian', 'galaxy', 'solar_system']
senticnet['spacing_between'] = ['0.734', '0.884', '0', '0', '#joy', '#interest', 'positive', '0.809', 'dimension', 'size', 'mass_measure', 'length_dimension', 'gauge']
senticnet['spacing_guild'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['spacious'] = ['0.075', '0.115', '0', '0.124', '#interest', '#admiration', 'positive', '0.105', 'wide_open', 'shelter', 'house_place', 'live_environment', 'natural']
senticnet['spacious_apartment'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'home', 'furniture', 'kitchen', 'flat']
senticnet['spacious_bathroom'] = ['0.716', '0.96', '0', '0', '#joy', '#interest', 'positive', '0.838', 'airline_seat', 'small_seat', 'beanbag_chair', 'hide-a-bed_sofa', 'chair']
senticnet['spacious_bedroom'] = ['0.716', '0.96', '0', '0', '#joy', '#interest', 'positive', '0.838', 'airline_seat', 'small_seat', 'beanbag_chair', 'hide-a-bed_sofa', 'chair']
senticnet['spacious_kitchen'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'kitchen_cabinet', 'kitchen_cupboard', 'dishwasher', 'cupboard', 'own_kitchen']
senticnet['spacious_living_room'] = ['0', '-0.02', '-0.08', '-0.05', '#fear', '#disgust', 'negative', '-0.03', 'mattress', 'sleep_furniture', 'cradle', 'small_bed', 'pillow']
senticnet['spacious_lobby'] = ['0.841', '0', '0', '0.71', '#joy', '#admiration', 'positive', '0.775', 'waiter', 'bar_service', 'beverage_service', 'concierge', 'minibar']
senticnet['spacious_room'] = ['0.075', '0.115', '0', '0.124', '#interest', '#admiration', 'positive', '0.105', 'wide_open', 'shelter', 'house_place', 'live_environment', 'natural']
senticnet['spade_ace'] = ['0.022', '-0.11', '0.014', '0.057', '#surprise', '#admiration', 'positive', '0.045', 'wood', 'many_steps', 'franchise', 'stump', 'timber']
senticnet['spade_deuce'] = ['0.022', '-0.11', '0.014', '0.057', '#surprise', '#admiration', 'positive', '0.045', 'wood', 'many_steps', 'franchise', 'stump', 'timber']
senticnet['spade_queen'] = ['0.022', '-0.11', '0.014', '0.057', '#surprise', '#admiration', 'positive', '0.045', 'wood', 'many_steps', 'franchise', 'stump', 'timber']
senticnet['spade_ruff'] = ['0.022', '-0.11', '0.014', '0.057', '#surprise', '#admiration', 'positive', '0.045', 'wood', 'many_steps', 'franchise', 'stump', 'timber']
senticnet['spadefoot_toad'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'amphibian', 'aquatic']
senticnet['spadeful'] = ['-0.02', '-0.01', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.03', 'dung', 'manure', 'shit', 'dog_poop', 'crap']
senticnet['spaghetti'] = ['0.763', '0.9', '0', '0', '#joy', '#interest', 'positive', '0.832', 'breakfast', 'box_cereal', 'calcium', 'drink', 'carton_milk']
senticnet['spaghetti_carbonara'] = ['0.028', '-0.07', '0', '0', '#joy', '#surprise', 'positive', '0.05', 'casserole', 'delicious_meal', 'hot_dog_bun', 'hamburger', 'hot_dog']
senticnet['spaghetti_dinner'] = ['0.937', '0', '0', '0.828', '#joy', '#admiration', 'positive', '0.883', 'bakery', 'produce_section', 'fruit_bowl', 'dinner_meal', 'fast_food']
senticnet['spaghetti_squash'] = ['0.05', '0', '0.047', '0.068', '#joy', '#admiration', 'positive', '0.024', 'tree_fruit', 'yellow_fruit', 'small_fruit', 'vine_fruit', 'red_fruit']
senticnet['spam'] = ['-0.05', '-0.03', '0.023', '-0.09', '#sadness', '#disgust', 'negative', '-0.03', 'moldy_food', 'celery', 'paper_product', 'orange_juice', 'meat']
senticnet['spam_detection'] = ['0.857', '0', '0', '0.968', '#joy', '#admiration', 'positive', '0.912', 'inerrable', 'flawless', 'unfailing', 'reliable', 'sure']
senticnet['spam_email'] = ['-0.05', '-0.03', '0.023', '-0.09', '#sadness', '#disgust', 'negative', '-0.03', 'moldy_food', 'celery', 'paper_product', 'orange_juice', 'meat']
senticnet['spam_filter'] = ['-0.05', '-0.03', '0.023', '-0.09', '#sadness', '#disgust', 'negative', '-0.03', 'moldy_food', 'celery', 'paper_product', 'orange_juice', 'meat']
senticnet['spammer'] = ['-0.05', '-0.03', '0.023', '-0.09', '#sadness', '#disgust', 'negative', '-0.03', 'moldy_food', 'celery', 'paper_product', 'orange_juice', 'meat']
senticnet['spammy'] = ['-0.02', '0', '-0.10', '0', '#sadness', '#fear', 'negative', '-0.06', 'ads', 'boring', 'bother', 'stop_watch', 'annoying']
senticnet['span'] = ['0.831', '0.989', '0', '0', '#joy', '#interest', 'positive', '0.91', 'span', 'spanning', 'spanned', 'length', 'shorter']
senticnet['spandau_prison'] = ['-0.78', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.87', 'jailbird', 'captive', 'imprisoned', 'convict', 'inmate']
senticnet['spandex'] = ['0', '-0.07', '0.099', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'prescription_medication', 'lingerie', 'sachet', 'shave_kit', 'panties']
senticnet['spandex_clad'] = ['-0.90', '0', '0.522', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'hooligan', 'ruffian', 'roughneck', 'rowdy', 'yob']
senticnet['spang'] = ['0.746', '-0.88', '0', '0.723', '#joy', '#surprise', 'positive', '0.783', 'strike', 'run_into', 'hit', 'impinge_on', 'collide_with']
senticnet['spangle'] = ['0.087', '-0.07', '0.062', '0', '#joy', '#surprise', 'positive', '0.033', 'small_piece', 'dust_particle', 'stripe', 'letter', 'oxide']
senticnet['spangly'] = ['0.951', '0.763', '0', '0', '#joy', '#interest', 'positive', '0.857', 'pull_muscle', 'heart_rate_increase', 'heavy_breathe', 'get_exhausted', 'tire']
senticnet['spaniard'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'nazi', 'man', 'pervert', 'healthy_person']
senticnet['spanish'] = ['0', '0.074', '0', '0.143', '#interest', '#admiration', 'positive', '0.108', 'idiom', 'utterance', 'rhetoric', 'whisper', 'voice']
senticnet['spanish_asamblea'] = ['0.055', '0.072', '-0.06', '0.094', '#interest', '#admiration', 'positive', '0.038', 'big_group', 'large_group', 'bunch_person', 'large_number', 'clique']
senticnet['spanish_civil_war'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'dictatorship', 'dictator', 'colony', 'world_domination', 'oppression']
senticnet['spanish_colonial_revival'] = ['0.578', '-0.95', '0', '0.979', '#surprise', '#admiration', 'positive', '0.838', 'perpendicular', 'perpendicular_style', 'gothic_architecture', 'style_of_architecture', 'type_of_architecture']
senticnet['spanish_colonial_revival_style'] = ['0.578', '-0.95', '0', '0.979', '#surprise', '#admiration', 'positive', '0.838', 'perpendicular', 'perpendicular_style', 'gothic_architecture', 'style_of_architecture', 'type_of_architecture']
senticnet['spanish_defender_gerard'] = ['-0.13', '0.083', '0.682', '-0.66', '#anger', '#disgust', 'negative', '-0.35', 'frustrating', 'dun', 'crucify', 'bedevil', 'displeasingly']
senticnet['spanish_flu'] = ['-0.86', '0', '0.825', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'pestis', 'plague', 'pestilence', 'pest', 'ambulant_plague']
senticnet['spanish_flu_epidemic'] = ['0.579', '-0.81', '0', '0.833', '#surprise', '#admiration', 'positive', '0.741', 'murine_typhus', 'rat_typhus', 'urban_typhus', 'endemic_typhus', 'rickettsial_disease']
senticnet['spanish_flu_pandemic'] = ['-0.86', '0', '0.825', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'pestis', 'plague', 'pestilence', 'pest', 'ambulant_plague']
senticnet['spanish_fluently'] = ['0', '0.202', '-0.06', '0.291', '#interest', '#admiration', 'positive', '0.142', 'vocabulary', 'translate', 'dictionary', 'translation', 'thesaurus']
senticnet['spanish_galleon'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'succourer', 'recoverer', 'salvor', 'mortal']
senticnet['spanish_immersion'] = ['0.633', '-0.96', '0', '0.612', '#joy', '#surprise', 'positive', '0.738', 'particularism', 'absorption', 'concentration', 'engrossment', 'centering']
senticnet['spanish_influenza'] = ['-0.86', '0', '0.825', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'pestis', 'plague', 'pestilence', 'pest', 'ambulant_plague']
senticnet['spanish_inquisition'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquirer', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['spanish_language_newspaper'] = ['-0.05', '0.106', '0', '0.06', '#interest', '#admiration', 'positive', '0.036', 'buy', 'buy_newspaper', 'read_buy_newspaper', 'buy_book', 'read_news']
senticnet['spanish_mackerel'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'crab', 'swimming_crab', 'rock_crab', 'fiddler_crab', 'pea_crab']
senticnet['spanish_privateer'] = ['0', '0', '0.08', '0', '#anger', '#anger', 'negative', '-0.08', 'tree_animal', 'small_furry', 'rodent', 'big_mouse', 'small_rodent']
senticnet['spanish_restaurant'] = ['0.039', '0.089', '0.023', '0', '#joy', '#interest', 'positive', '0.035', 'steak_house', 'tapa', 'french_restaurant', 'paella', 'restaurant_diner']
senticnet['spanish_tile_roof'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'adobe', 'adobe_brick', 'clinker', 'brick', 'firebrick']
senticnet['spanishamerican_war'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'dictatorship', 'dictator', 'colony', 'world_domination', 'oppression']
senticnet['spank'] = ['-0.24', '0', '-0.19', '0', '#sadness', '#fear', 'negative', '-0.22', 'mock', 'prison_sentence', 'wrongdoing', 'jail_time', 'violent_action']
senticnet['spank_rock'] = ['-0.86', '0', '0.675', '-0.84', '#sadness', '#disgust', 'negative', '-0.79', 'shenanigan', 'chicanery', 'wile', 'dupery', 'trickery']
senticnet['spanned'] = ['0.83', '0.987', '0', '0', '#joy', '#interest', 'positive', '0.908', 'span', 'spanning', 'span', 'length', 'shorter']
senticnet['spanner'] = ['0.713', '0', '0', '0.728', '#joy', '#admiration', 'positive', '0.72', 'stainless_steel', 'gold', 'strong_material', 'platinum', 'durable']
senticnet['spanning'] = ['0.831', '0.984', '0', '0', '#joy', '#interest', 'positive', '0.907', 'span', 'span', 'spanned', 'length', 'shorter']
senticnet['spanwise'] = ['-0.97', '0', '0.831', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'skewness', 'irregularity']
senticnet['spar'] = ['-0.89', '0', '0.758', '0', '#sadness', '#anger', 'negative', '-0.82', 'topsail', 'transvestite', 'hoax', 'earing', 'gaff']
senticnet['spare'] = ['0.919', '0.756', '0', '0', '#joy', '#interest', 'positive', '0.838', 'extra', 'rest', 'fresh', 'catch_breath', 'relief']
senticnet['spare_bedroom'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'apartment', 'furniture', 'kitchen', 'flat']
senticnet['spare_cash'] = ['0.057', '0.181', '-0.04', '0.18', '#interest', '#admiration', 'positive', '0.092', 'small_money', 'alter', 'money_change', 'money_coin', 'small_coin']
senticnet['spare_change'] = ['0.05', '0.139', '0.037', '0', '#joy', '#interest', 'positive', '0.051', 'bank_machine', 'piggy_bank', 'billfold', 'cash_register', 'bank_account']
senticnet['spare_enemy'] = ['0.76', '-0.59', '0', '0.713', '#joy', '#admiration', 'positive', '0.689', 'spareness', 'magnanimous', 'generous', 'forgiving', 'deal_gently']
senticnet['spare_time'] = ['0.164', '0.13', '0.031', '0.209', '#joy', '#admiration', 'positive', '0.118', 'watch_good_movie', 'good_sense_of_humor', 'able_read', 'unzip', 'strong_relationship']
senticnet['spare_tire'] = ['0.798', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.843', 'dashboard', 'battery', 'seatbelt', 'car_stereo', 'brake_pedal']
senticnet['spare_wheel'] = ['0', '0.248', '-0.07', '0.121', '#interest', '#admiration', 'positive', '0.099', 'gym_locker', 'observatory', 'prison_cell', 'luggage_store', 'garage_sale']
senticnet['spareness'] = ['0.76', '-0.59', '0', '0.713', '#joy', '#admiration', 'positive', '0.689', 'spare_enemy', 'magnanimous', 'generous', 'forgiving', 'deal_gently']
senticnet['sparganium'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'bulrush', 'nailrod', 'cattail', 'reedmace']
senticnet['sparidae'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'crab', 'swimming_crab', 'rock_crab', 'fiddler_crab', 'pea_crab']
senticnet['sparing'] = ['0.715', '-0.75', '0', '0.546', '#joy', '#surprise', 'positive', '0.671', 'saving', 'economical', 'scotch', 'frugal', 'thrifty']
senticnet['sparingly'] = ['0', '-0.93', '0', '0.847', '#surprise', '#admiration', 'positive', '0.892', 'scantily', 'reserve', 'frugality', 'sparsely', 'economize']
senticnet['spark'] = ['0.934', '0.758', '0', '0', '#joy', '#interest', 'positive', '0.846', 'flame', 'fire', 'heat_light', 'rapid_oxidation', 'huge_fire']
senticnet['sparked_outrage'] = ['-0.86', '0', '0.516', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'bronx_cheer', 'call', 'cry', 'yell', 'shout']
senticnet['sparkle'] = ['0.064', '0.07', '0', '0.184', '#interest', '#admiration', 'positive', '0.106', 'shine', 'shiny', 'recyclable', 'versatile', 'godliness']
senticnet['sparkling_blue'] = ['0.98', '-0.74', '0', '0.984', '#joy', '#admiration', 'positive', '0.903', 'prosperous', 'flourishing', 'successful', 'prospering', 'thriving']
senticnet['sparkling_clean'] = ['0.824', '0.86', '0', '0.983', '#interest', '#admiration', 'positive', '0.889', 'look_smart', 'social_acceptance', 'improve_appearance', 'improve_look', 'look_good']
senticnet['sparkling_water'] = ['0.032', '-0.02', '-0.04', '-0.08', '#fear', '#disgust', 'negative', '-0.01', 'liquid', 'carbonate_drink', 'sweet', 'soda', 'drink']
senticnet['sparkling_wine'] = ['0.161', '-0.14', '0.031', '-0.10', '#joy', '#surprise', 'positive', '0.043', 'alcohol', 'reception', 'champagne', 'beer', 'drink']
senticnet['sparkling_wit'] = ['0.99', '0', '0', '0.716', '#joy', '#admiration', 'positive', '0.853', 'encyclopedism', 'learning', 'scholarship', 'learnedness', 'eruditeness']
senticnet['sparkly'] = ['0', '0', '0.796', '-0.82', '#anger', '#disgust', 'negative', '-0.80', 'glisten', 'scintillating', 'fulgid', 'coruscant', 'glittering']
senticnet['sparrow'] = ['-0.10', '0', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.1', 'quail', 'wren', 'chicken_egg', 'chicken', 'stork']
senticnet['sparse'] = ['0.969', '-0.82', '0', '0.87', '#joy', '#admiration', 'positive', '0.887', 'mortal', 'person', 'individual', 'rare_bird', 'scarce']
senticnet['sparse_crowd'] = ['-0.71', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.84', 'traffic_jam', 'accumulate_stress', 'stress', 'accumulate_stress_city', 'bad_service']
senticnet['sparse_hair'] = ['0.352', '0.142', '0', '0.281', '#joy', '#admiration', 'positive', '0.258', 'knowledgeable', 'complement', 'much_need', 'see_ocean', 'good_sense_of_humor']
senticnet['sparsely'] = ['0', '-0.93', '0', '0.847', '#surprise', '#admiration', 'positive', '0.892', 'scantily', 'reserve', 'frugality', 'sparingly', 'economize']
senticnet['sparsely_hairy'] = ['-0.75', '0', '0.734', '-0.82', '#sadness', '#disgust', 'negative', '-0.77', 'puberulent', 'haired', 'furlike', 'pappose', 'pilous']
senticnet['sparseness'] = ['-0.57', '0', '0.769', '-0.86', '#anger', '#disgust', 'negative', '-0.73', 'lack_of_quality', 'meagreness', 'poorness', 'scantiness', 'scantness']
senticnet['sparsity'] = ['-0.81', '0', '0.986', '-0.94', '#anger', '#disgust', 'negative', '-0.91', 'lack_of_quality', 'meagreness', 'poorness', 'scantiness', 'scantness']
senticnet['sparta'] = ['0.8', '-0.79', '0', '0.751', '#joy', '#surprise', 'positive', '0.782', 'elder_statesman', 'stateswoman', 'politician', 'politico', 'pol']
senticnet['spartak_subotica'] = ['0.954', '-0.90', '0', '0.867', '#joy', '#surprise', 'positive', '0.91', 'friend', 'sports_fan', 'rooter', 'fan', 'nut']
senticnet['spartan'] = ['0.554', '-0.91', '0', '0.598', '#surprise', '#admiration', 'positive', '0.69', 'abstemious', 'abstemiousal', 'ascetic', 'austere', 'abstinent']
senticnet['spartan_stadium'] = ['0', '-0.84', '0', '0.723', '#surprise', '#admiration', 'positive', '0.785', 'circular_route', 'stadium', 'memorial', 'station', 'row_house']
senticnet['spartina'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'bulrush', 'nailrod', 'reed_mace', 'reedmace']
senticnet['spasm'] = ['-0.02', '0', '-0.09', '-0.08', '#fear', '#disgust', 'negative', '-0.06', 'stitch', 'tourniquet', 'cramp', 'bandage', 'cure']
senticnet['spasmodic'] = ['-0.87', '0', '0.722', '-0.57', '#sadness', '#anger', 'negative', '-0.72', 'stray', 'sporadic', 'unpredictable', 'isolated', 'irregular']
senticnet['spasmodic_dysphonia'] = ['-0.84', '0', '0.829', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'upset', 'dysarthria', 'defect_of_speech', 'disorder', 'lallation']
senticnet['spasmolytic'] = ['0.701', '-0.76', '0', '0.904', '#surprise', '#admiration', 'positive', '0.791', 'curative', 'heal', 'medicine', 'get_cured', 'take_care']
senticnet['spastic'] = ['-0.56', '0', '0.849', '-0.61', '#anger', '#disgust', 'negative', '-0.67', 'convulsive', 'arrhythmic', 'jerky', 'spasmodic', 'unsteady']
senticnet['spastic_cerebral_palsy'] = ['-0.75', '0', '0.515', '-0.93', '#sadness', '#disgust', 'negative', '-0.73', 'sclerosis', 'induration', 'disseminated_sclerosis', 'autoimmune_disease', 'autoimmune_disorder']
senticnet['spastic_diplegia'] = ['-0.75', '0', '0.515', '-0.93', '#sadness', '#disgust', 'negative', '-0.73', 'sclerosis', 'induration', 'disseminated_sclerosis', 'autoimmune_disease', 'autoimmune_disorder']
senticnet['spat_between'] = ['-0.63', '0', '0.982', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'wrangle', 'dustup', 'quarrel', 'row', 'argument']
senticnet['spate'] = ['-0.86', '0', '0.503', '-0.65', '#sadness', '#disgust', 'negative', '-0.67', 'large_indefinite_quantity', 'large_indefinite_amount', 'deluge', 'flood', 'inundation']
senticnet['spathulata'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['spatial_autocorrelation'] = ['-0.54', '0', '0.979', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'unlikeness', 'dissimilitude', 'difference', 'heterology', 'disparateness']
senticnet['spatial_cognition'] = ['0.693', '-0.52', '0', '0.82', '#joy', '#admiration', 'positive', '0.681', 'embodiment', 'shape', 'representation', 'concrete_representation', 'internal_representation']
senticnet['spatial_dimension'] = ['0.914', '0.983', '0', '0', '#joy', '#interest', 'positive', '0.949', 'measurement', 'time_interval', 'length', 'time', 'interval']
senticnet['spatial_disorientation'] = ['0', '0', '0.958', '-0.82', '#anger', '#disgust', 'negative', '-0.89', 'become_drunk', 'tipsy', 'act_stupid', 'forgetfulness', 'hangover']
senticnet['spatial_distribution'] = ['0.784', '0.822', '0', '0', '#joy', '#interest', 'positive', '0.803', 'determine', 'study', 'model', 'process', 'relationship']
senticnet['spatial_frequency'] = ['-0.93', '0', '0.698', '-0.85', '#sadness', '#disgust', 'negative', '-0.83', 'candlepower', 'light_intensity', 'acoustic_power', 'sound_pressure_level', 'threshold_level']
senticnet['spatial_heterogeneity'] = ['-0.89', '0', '0.972', '-0.50', '#sadness', '#anger', 'negative', '-0.79', 'unsteadiness', 'haphazardness', 'unregularity', 'variability', 'fitfulness']
senticnet['spatial_locality'] = ['-0.9', '0', '0.841', '-0.73', '#sadness', '#anger', 'negative', '-0.82', 'nubbiness', 'tweediness', 'harshness', 'graininess', 'raggedness']
senticnet['spatial_metaphor'] = ['0.654', '-0.71', '0', '0.624', '#joy', '#surprise', 'positive', '0.664', 'viewable', 'gross', 'telescopic', 'seeable', 'visual']
senticnet['spatial_proximity'] = ['-0.54', '0', '0.979', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'unlikeness', 'dissimilitude', 'difference', 'heterology', 'disparateness']
senticnet['spatial_representation'] = ['0', '0.157', '0', '0.147', '#interest', '#admiration', 'positive', '0.152', 'wall_hang', 'depiction', 'illustration', 'artwork', 'wall_decoration']
senticnet['spatial_resolution'] = ['0', '0', '-0.66', '0', '#fear', '#fear', 'negative', '-0.66', 'anger', 'rage', 'annoyance', 'apprehension', 'fear']
senticnet['spatiality'] = ['0.654', '-0.71', '0', '0.624', '#joy', '#surprise', 'positive', '0.664', 'viewable', 'gross', 'telescopic', 'seeable', 'visual']
senticnet['spatialization'] = ['0.654', '-0.71', '0', '0.624', '#joy', '#surprise', 'positive', '0.664', 'viewable', 'gross', 'telescopic', 'seeable', 'visual']
senticnet['spatially'] = ['0', '-0.79', '0', '0.789', '#surprise', '#admiration', 'positive', '0.794', 'geologically', 'morphologically', 'scenically', 'climatically', 'topographically']
senticnet['spatially_uniform'] = ['0.965', '-0.84', '0', '0.599', '#joy', '#surprise', 'positive', '0.802', 'mental_faculty', 'faculty', 'module', 'remembrance', 'anamnesis']
senticnet['spatter'] = ['-0.87', '0', '0.682', '-0.53', '#sadness', '#anger', 'negative', '-0.69', 'swash', 'slosh_around', 'splatter', 'plash', 'splash']
senticnet['spatter_cone'] = ['0', '0', '-0.92', '0', '#fear', '#fear', 'negative', '-0.92', 'water_flow', 'wind_water', 'water_stream', 'waterway', 'magma']
senticnet['spatula'] = ['0.03', '-0.05', '0', '0.059', '#surprise', '#admiration', 'positive', '0.046', 'platter', 'fork', 'spoon', 'tablespoon', 'bowl']
senticnet['spawn'] = ['0.873', '0', '0', '0.725', '#joy', '#admiration', 'positive', '0.799', 'generate', 'beget', 'engender', 'procreate', 'inaugurate']
senticnet['spay'] = ['-0.78', '0', '0.91', '-0.91', '#anger', '#disgust', 'negative', '-0.87', 'castrate', 'neuter', 'alter', 'emasculate', 'desex']
senticnet['spay_neuter'] = ['-0.78', '0', '0.91', '-0.91', '#anger', '#disgust', 'negative', '-0.87', 'castrate', 'neuter', 'alter', 'emasculate', 'desex']
senticnet['spay_neuter_clinic'] = ['-0.78', '0', '0.91', '-0.91', '#anger', '#disgust', 'negative', '-0.87', 'castrate', 'neuter', 'alter', 'emasculate', 'desex']
senticnet['spay_neuter_program'] = ['-0.78', '0', '0.91', '-0.91', '#anger', '#disgust', 'negative', '-0.87', 'castrate', 'neuter', 'alter', 'emasculate', 'desex']
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senticnet['spectral_envoy'] = ['-0.57', '0', '0.571', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['spectral_line'] = ['-0.07', '0.078', '0', '0.078', '#interest', '#admiration', 'positive', '0.028', 'red_blue', 'red_orange', 'green_yellow', 'hue_tint', 'yellow_green']
senticnet['spectral_radiance'] = ['-0.63', '0', '0.897', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'cd-rom', 'international_candle', 'candle', 'candlepower_unit', 'standard_candle']
senticnet['spectral_range'] = ['-0.07', '0.078', '0', '0.078', '#interest', '#admiration', 'positive', '0.028', 'red_blue', 'red_orange', 'green_yellow', 'hue_tint', 'yellow_green']
senticnet['spectral_reflectance'] = ['-0.93', '0', '0.906', '-0.53', '#sadness', '#anger', 'negative', '-0.79', 'juvenility', 'state', 'immatureness', 'callowness', 'jejuneness']
senticnet['spectral_resolution'] = ['-0.07', '0.078', '0', '0.078', '#interest', '#admiration', 'positive', '0.028', 'red_blue', 'red_orange', 'green_yellow', 'hue_tint', 'yellow_green']
senticnet['spectral_sensitivity'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'incandescence', 'luminescence', 'brightness_level']
senticnet['spectral_shape'] = ['-0.93', '0', '0.698', '-0.85', '#sadness', '#disgust', 'negative', '-0.83', 'candlepower', 'light_intensity', 'acoustic_power', 'sound_pressure_level', 'threshold_level']
senticnet['spectral_type'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['spectral_width'] = ['-0.93', '0', '0.698', '-0.85', '#sadness', '#disgust', 'negative', '-0.83', 'candlepower', 'light_intensity', 'acoustic_power', 'sound_pressure_level', 'threshold_level']
senticnet['spectro_temporal'] = ['0.685', '-0.91', '0', '0.566', '#joy', '#surprise', 'positive', '0.721', 'audibility', 'audibleness', 'visibleness', 'visibility', 'physical_property']
senticnet['spectrograph'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'binoculars', 'read_glass', 'eyeglass', 'camera', 'monocle']
senticnet['spectrographic_analysis'] = ['0', '0.856', '0', '0.933', '#interest', '#admiration', 'positive', '0.895', 'spectroscopic_analysis', 'spectrometry', 'spectroscopy', 'spectrum_analysis', 'different_application']
senticnet['spectrometry'] = ['0', '0.857', '0', '0.924', '#interest', '#admiration', 'positive', '0.891', 'spectroscopic_analysis', 'spectroscopy', 'spectrographic_analysis', 'spectrum_analysis', 'different_application']
senticnet['spectrophotometry'] = ['0.838', '-0.61', '0', '0.556', '#joy', '#surprise', 'positive', '0.671', 'activity', 'natural_process', 'natural_action', 'column_chromatography', 'paper_chromatography']
senticnet['spectroscope'] = ['0.949', '-0.67', '0', '0.951', '#joy', '#admiration', 'positive', '0.859', 'instrument', 'prism_spectroscope', 'optical_instrument', 'binoculars', 'glasses']
senticnet['spectroscopic_analysis'] = ['0', '0.865', '0', '0.925', '#interest', '#admiration', 'positive', '0.895', 'spectroscopy', 'spectrometry', 'spectrographic_analysis', 'spectrum_analysis', 'different_application']
senticnet['spectroscopic_binary'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['spectroscopic_binary_star'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['spectroscopic_binary_system'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['spectroscopy'] = ['0', '0.866', '0', '0.933', '#interest', '#admiration', 'positive', '0.899', 'spectroscopic_analysis', 'spectrometry', 'spectrographic_analysis', 'spectrum_analysis', 'different_application']
senticnet['spectrum'] = ['-0.07', '0.078', '0', '0.078', '#interest', '#admiration', 'positive', '0.028', 'red_blue', 'red_orange', 'green_yellow', 'hue_tint', 'yellow_green']
senticnet['spectrum_allocation'] = ['-0.07', '0.078', '0', '0.078', '#interest', '#admiration', 'positive', '0.028', 'red_blue', 'red_orange', 'green_yellow', 'hue_tint', 'yellow_green']
senticnet['spectrum_analysis'] = ['0', '0.864', '0', '0.929', '#interest', '#admiration', 'positive', '0.897', 'spectroscopic_analysis', 'spectrometry', 'spectrographic_analysis', 'spectroscopy', 'different_application']
senticnet['spectrum_disorder'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'acknowledgeable', 'specifiable', 'recognizable', 'identifiable', 'classifiable']
senticnet['specular'] = ['0', '0.961', '0', '0.886', '#interest', '#admiration', 'positive', '0.923', 'specularity', 'diffuse', 'reflectivity', 'reflectance', 'glossiness']
senticnet['specular_highlight'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'illuminate_room', 'give_light', 'light', 'light_bulb', 'lantern']
senticnet['specular_light'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'illuminate_room', 'give_light', 'light', 'light_bulb', 'lantern']
senticnet['specular_lighting'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'illuminate_room', 'give_light', 'light', 'light_bulb', 'lantern']
senticnet['specularity'] = ['0', '0.961', '0', '0.883', '#interest', '#admiration', 'positive', '0.922', 'specular', 'diffuse', 'reflectivity', 'reflectance', 'glossiness']
senticnet['speculate'] = ['-0.82', '0', '0.98', '-0.91', '#anger', '#disgust', 'negative', '-0.90', 'formulate', 'expect', 'retrace', 'explicate', 'reconstruct']
senticnet['speculation'] = ['0', '0.165', '-0.09', '0.144', '#interest', '#admiration', 'positive', '0.071', 'build_home', 'good_investment', 'small_building', 'habitation', 'own_land']
senticnet['speculative_bubble'] = ['-0.55', '0', '0.869', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'air_bubble', 'foam', 'froth', 'soap_bubble', 'globule']
senticnet['speculative_fiction'] = ['0.036', '0.219', '0', '0.19', '#interest', '#admiration', 'positive', '0.148', 'book_genre', 'literature', 'concoction', 'untruth', 'falsehood']
senticnet['speculative_frenzy'] = ['-0.55', '0', '0.869', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'air_bubble', 'foam', 'froth', 'soap_bubble', 'globule']
senticnet['speculator'] = ['-0.78', '0', '0.944', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'loser', 'gambler', 'mortal', 'better', 'bettor']
senticnet['speculum'] = ['-0.02', '0', '-0.08', '-0.12', '#fear', '#disgust', 'negative', '-0.07', 'cystoscope', 'scope_out_information', 'gastroscope', 'stethoscope', 'light_instrument']
senticnet['speech'] = ['0', '0', '0', '0.13', '#admiration', '#admiration', 'positive', '0.13', 'say', 'orate', 'narrate', 'verbal_communication', 'oration']
senticnet['speech_defect'] = ['-0.78', '0', '0.96', '-0.64', '#sadness', '#anger', 'negative', '-0.79', 'upset', 'dysarthria', 'dysphonia', 'disorder', 'lallation']
senticnet['speech_disorder'] = ['-0.51', '0', '0.822', '-0.66', '#anger', '#disgust', 'negative', '-0.66', 'upset', 'dysarthria', 'dysphonia', 'disorder', 'lallation']
senticnet['speech_impairment'] = ['-0.56', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.63', 'inarticulate', 'aphonic', 'speechless', 'unarticulated', 'dumb']
senticnet['speech_impediment'] = ['0.983', '-0.63', '0', '0.697', '#joy', '#admiration', 'positive', '0.772', 'amateurishness', 'inability', 'ineptness', 'unskillfulness', 'awkwardness']
senticnet['speech_intelligibility'] = ['-0.95', '0', '-0.73', '-0.73', '#sadness', '#fear', 'negative', '-0.80', 'decibel', 'noise', 'loud_soft', 'sound', 'audio']
senticnet['speech_part'] = ['0', '0.085', '-0.04', '0.222', '#interest', '#admiration', 'positive', '0.086', 'grammar_part', 'unit_language', 'retort', 'enunciation', 'written_language']
senticnet['speech_perception'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'nominal_aphasia', 'anomic_aphasia']
senticnet['speech_production'] = ['0.945', '-0.68', '0', '0.824', '#joy', '#admiration', 'positive', '0.818', 'whispering', 'voicelessness', 'whisper', 'susurration', 'speech']
senticnet['speechless'] = ['-0.56', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.63', 'aphasic', 'aphonic', 'inarticulate', 'unarticulated', 'dumb']
senticnet['speechtek'] = ['-0.79', '0', '0.948', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['speed'] = ['-0.03', '0.173', '0', '0.162', '#interest', '#admiration', 'positive', '0.1', 'velocity', 'fast', 'fast_and_furious', 'speedy', 'fleet']
senticnet['speed_boat'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'yacht', 'boat', 'sailboat', 'water_vehicle', 'ship']
senticnet['speed_bump'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'corner_two_streets', 'street_corner', 'roadside', 'city_street', 'schoolyard']
senticnet['speed_demon'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
senticnet['speed_dial'] = ['-0.97', '0', '0.728', '-0.78', '#sadness', '#disgust', 'negative', '-0.82', 'beguiler', 'slicker', 'phony', 'faker', 'deceiver']
senticnet['speed_skater'] = ['-0.04', '-0.03', '0', '-0.16', '#sadness', '#disgust', 'negative', '-0.05', 'crazy', 'damage_body', 'crazy_person', 'threat', 'max']
senticnet['speed_skating_oval'] = ['0.02', '0.244', '0', '0.225', '#interest', '#admiration', 'positive', '0.163', 'game', 'disneyland', 'ballpark', 'hockey_game', 'sport_event']
senticnet['speed_trap'] = ['-0.31', '-0.02', '0', '-0.24', '#sadness', '#disgust', 'negative', '-0.17', 'pollution', 'screw_up', 'car_wreck', 'parking_ticket', 'feel_stressed']
senticnet['speed_up'] = ['0.032', '0.261', '-0.15', '0.198', '#interest', '#admiration', 'positive', '0.084', 'fast', 'arrive_destination', 'pace', 'take_action', 'gain_speed']
senticnet['speedboat'] = ['0.024', '0.152', '-0.06', '0.236', '#interest', '#admiration', 'positive', '0.087', 'vacation', 'exit', 'stance', 'take_break', 'holiday']
senticnet['speeder'] = ['-0.84', '0', '0.96', '-0.91', '#anger', '#disgust', 'negative', '-0.90', 'housewrecker', 'housebreaker', 'knacker', 'laborer', 'manual_laborer']
senticnet['speedier'] = ['0.905', '0.726', '0', '0', '#joy', '#interest', 'positive', '0.816', 'expedient', 'expeditious', 'cost-efficient', 'expeditiously', 'orderly']
senticnet['speedily'] = ['0', '0.93', '0', '0.789', '#interest', '#admiration', 'positive', '0.86', 'chop-chop', 'rapidly', 'apace', 'quickly', 'study']
senticnet['speediness'] = ['-0.58', '0', '0.517', '-1', '#sadness', '#disgust', 'negative', '-0.70', 'quickness', 'celerity', 'rapidity', 'rapidness', 'promptness']
senticnet['speeding_driver'] = ['-0.31', '-0.02', '0', '-0.24', '#sadness', '#disgust', 'negative', '-0.17', 'pollution', 'screw_up', 'car_wreck', 'parking_ticket', 'feel_stressed']
senticnet['speeding_offence'] = ['-0.31', '-0.02', '0', '-0.24', '#sadness', '#disgust', 'negative', '-0.17', 'pollution', 'screw_up', 'car_wreck', 'parking_ticket', 'feel_stressed']
senticnet['speeding_ticket'] = ['-0.31', '-0.02', '0', '-0.24', '#sadness', '#disgust', 'negative', '-0.17', 'pollution', 'screw_up', 'car_wreck', 'parking_ticket', 'feel_stressed']
senticnet['speeding_train'] = ['-0.11', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.08', 'have_fun', 'spring_up', 'fall_well', 'shovel_snow', 'fly_around']
senticnet['speeding_up'] = ['0.729', '0.974', '0', '0', '#joy', '#interest', 'positive', '0.851', 'travel', 'fast', 'travel_fast', 'take_airplane', 'drive_fast']
senticnet['speedometer'] = ['0.725', '0.796', '0', '0', '#joy', '#interest', 'positive', '0.76', 'car_stereo', 'battery', 'seatbelt', 'spare_tire', 'brake_pedal']
senticnet['speedtrack'] = ['0.087', '0.113', '-0.08', '0', '#joy', '#interest', 'positive', '0.038', 'find_match', 'strike_against', 'cigarette_lighter', 'match', 'use_lighter']
senticnet['speedy'] = ['0', '0.091', '0.031', '0.181', '#interest', '#admiration', 'positive', '0.08', 'quick_movement', 'fast', 'quickness', 'quick_motion', 'quick']
senticnet['speleologist'] = ['-0.02', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'tunnel', 'dark_place', 'ski_resort', 'top_tree', 'compost_pile']
senticnet['spell'] = ['-0.05', '0', '-0.04', '0', '#sadness', '#fear', 'negative', '-0.04', 'knack', 'scribble', 'mark_paper', 'record_information', 'use_pencil']
senticnet['spell_caster'] = ['-0.79', '0', '0.506', '-0.93', '#sadness', '#disgust', 'negative', '-0.74', 'magic', 'thaumaturgy', 'enchantment', 'witchcraft', 'witchery']
senticnet['spell_check'] = ['0.642', '-0.53', '0', '0.575', '#joy', '#admiration', 'positive', '0.583', 'check', 'assure', 'see', 'insure', 'examine']
senticnet['spell_checker'] = ['0.642', '-0.53', '0', '0.575', '#joy', '#admiration', 'positive', '0.583', 'check', 'assure', 'see', 'insure', 'examine']
senticnet['spell_doom'] = ['0.808', '-0.83', '0', '0.548', '#joy', '#surprise', 'positive', '0.73', 'successfulness', 'luckiness', 'prosperity', 'circumstance', 'blessing']
senticnet['spell_wrong'] = ['-0.18', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.18', 'warm_color', 'make_mistake', 'color', 'secondary_color', 'primary_color']
senticnet['spellbind'] = ['0.75', '0.957', '0', '0', '#joy', '#interest', 'positive', '0.853', 'spellbinding', 'spell', 'fey', 'spellbound', 'mesmerized']
senticnet['spellbinding'] = ['0.744', '0.948', '0', '0', '#joy', '#interest', 'positive', '0.846', 'spellbind', 'spell', 'fey', 'spellbound', 'mesmerized']
senticnet['spellbook'] = ['0.105', '-0.17', '0.057', '0.079', '#joy', '#surprise', 'positive', '0.076', 'cue', 'twig', 'long_stick', 'stick', 'adhere']
senticnet['spellbound'] = ['0.743', '0.95', '0', '0', '#joy', '#interest', 'positive', '0.847', 'spellbinding', 'spell', 'fey', 'spellbind', 'mesmerized']
senticnet['spellcheck'] = ['0.642', '-0.53', '0', '0.575', '#joy', '#admiration', 'positive', '0.583', 'check', 'assure', 'see', 'insure', 'examine']
senticnet['spellchecker'] = ['0.642', '-0.53', '0', '0.575', '#joy', '#admiration', 'positive', '0.583', 'check', 'assure', 'see', 'insure', 'examine']
senticnet['spelled_differently'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicado', 'incommunicative', 'impassive', 'inexpressive', 'silent']
senticnet['spelling'] = ['0.749', '-0.89', '0', '0.981', '#surprise', '#admiration', 'positive', '0.873', 'review', 'check_spelling', 'double_check', 'correct_homework', 'mark_exam']
senticnet['spelling_checker'] = ['0.642', '-0.53', '0', '0.575', '#joy', '#admiration', 'positive', '0.583', 'check', 'assure', 'see', 'insure', 'examine']
senticnet['spencer_baird'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'institution', 'tundra', 'child_story', 'natural_history_museum', 'cartoon']
senticnet['spencer_fullerton_baird'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'institution', 'tundra', 'child_story', 'natural_history_museum', 'cartoon']
senticnet['spend'] = ['-0.06', '0.232', '0', '0.115', '#interest', '#admiration', 'positive', '0.095', 'bill_pay', 'owe_money', 'money', 'money_spend', 'give_cash']
senticnet['spend_a_lot_of_money'] = ['-0.03', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.04', 'spend_much_money', 'comparison_shop', 'spend_money', 'window_shop', 'check_price']
senticnet['spend_family_time'] = ['0', '0.826', '0', '0.906', '#interest', '#admiration', 'positive', '0.866', 'familial_relationship', 'spend_quality_time', 'family_relation', 'family_time', 'dad']
senticnet['spend_little_money'] = ['-0.08', '0.118', '0.017', '0.105', '#interest', '#admiration', 'positive', '0.03', 'hold_card', 'pay_off', 'dumpster_dive', 'barter', 'make_offer']
senticnet['spend_lot'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'expensive', 'high_price', 'unaffordable', 'overprice', 'immoderate']
senticnet['spend_money'] = ['-0.03', '-0.01', '0.02', '-0.03', '#sadness', '#disgust', 'negative', '-0.01', 'purchase_gift', 'buy_clothes', 'pay_cash', 'pay_credit_card', 'buy_milk']
senticnet['spend_much_money'] = ['-0.08', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.07', 'spend_a_lot_of_money', 'window_shop', 'comparison_shop', 'check_price', 'make_payment']
senticnet['spend_quality_time'] = ['0', '0.975', '0', '0.869', '#interest', '#admiration', 'positive', '0.922', 'familial_relationship', 'spend_family_time', 'family_relation', 'family_time', 'dad']
senticnet['spend_time'] = ['0.958', '0', '0', '0', '#joy', '#joy', 'positive', '0.958', 'waste_time', 'understand_culture', 'bored', 'enjoy_entertainment', 'have_fun']
senticnet['spend_time_together'] = ['0.084', '0.075', '0', '0.107', '#joy', '#admiration', 'positive', '0.089', 'play_on_grass', 'play_board_game', 'fun', 'become_adult', 'play_hide_seek']
senticnet['spend_time_with_child'] = ['0.833', '0', '0', '0.796', '#joy', '#admiration', 'positive', '0.815', 'amuse', 'enjoyment', 'spend_time_with_friend', 'keep_child_occupy', 'entertainment']
senticnet['spend_time_with_friend'] = ['0.897', '0.959', '0', '0', '#joy', '#interest', 'positive', '0.928', 'friend', 'hang_out', 'party', 'fun', 'hang_out_with_friend']
senticnet['spender'] = ['0.865', '-0.94', '0', '0.827', '#joy', '#surprise', 'positive', '0.878', 'spendthrift', 'waster', 'wastrel', 'prodigal', 'scattergood']
senticnet['spending'] = ['0.965', '0.807', '0', '0', '#joy', '#interest', 'positive', '0.886', 'first_class', 'shopping', 'travel_across_world', 'run_out_fuel', 'cost_money']
senticnet['spendthrift'] = ['0.861', '-0.94', '0', '0.827', '#joy', '#surprise', 'positive', '0.877', 'prodigal', 'waster', 'wastrel', 'spender', 'scattergood']
senticnet['sper'] = ['-0.63', '0', '0.791', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'little', 'pinch', 'modicum', 'small_indefinite_amount', 'indefinite_quantity']
senticnet['sper_comando'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'find_out', 'dig_out', 'unearth', 'become_aware', 'dish_out']
senticnet['sperm'] = ['0.038', '0.132', '-0.08', '0.095', '#interest', '#admiration', 'positive', '0.045', 'baby_born', 'sexual_intercourse', 'pregnant', 'fuck', 'mate']
senticnet['sperm_bank'] = ['0', '0.098', '0', '0', '#interest', '#interest', 'positive', '0.098', 'bed', 'mattress', 'political_debate', 'seduce', 'bead']
senticnet['sperm_capacitation'] = ['0.085', '0', '0', '0.089', '#joy', '#admiration', 'positive', '0.087', 'steam', 'haze', 'mist', 'misty', 'summer_weather']
senticnet['sperm_cell'] = ['0.706', '0', '0', '0', '#joy', '#joy', 'positive', '0.706', 'birth', 'new_baby', 'born', 'birthday', 'thanksgiving']
senticnet['sperm_tail'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'nonmotile', 'immobile']
senticnet['sperm_whale'] = ['-0.93', '0', '0.544', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'mutant', 'variation', 'sport', 'lusus_naturae', 'mutation']
senticnet['spermaceti_organ'] = ['-0.93', '0', '0.607', '-0.67', '#sadness', '#disgust', 'negative', '-0.73', 'spiracle', 'stigma', 'orifice', 'opening', 'porta']
senticnet['spermatic'] = ['0.531', '0.673', '-0.27', '0.275', '#joy', '#interest', 'positive', '0.301', 'amorous', 'amorousness', 'amatory', 'enamoredness', 'puppy_love']
senticnet['spermatic_cord'] = ['0.087', '-0.03', '0', '0.063', '#joy', '#admiration', 'positive', '0.062', 'puppet', 'bouncy', 'curly', 'throw_object', 'glove_leather']
senticnet['spermatid'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'nonmotile', 'immobile']
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senticnet['spiderhunter_arachnothera'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'lovebird', 'parroket', 'paraquet', 'paroquet']
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senticnet['spiky_hair'] = ['-0.91', '0', '0.869', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'talk_hairdresser', 'hair_get_short', 'hair_become_short', 'keep_hair_short', 'hair_wash']
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senticnet['spin_polarization'] = ['0.038', '0', '0.031', '0.124', '#joy', '#admiration', 'positive', '0.044', 'rotate', 'rotate_movement', 'change_direction', 'move', 'go_around']
senticnet['spin_silk'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'woven_fabric', 'woven_cloth']
senticnet['spin_singlet'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'elementary_particle', 'neutron', 'electron', 'subatomic_particle', 'proton']
senticnet['spin_thread'] = ['0', '0.078', '0.014', '0.108', '#interest', '#admiration', 'positive', '0.057', 'fragrant', 'sexual', 'use_decoration', 'green', 'pink']
senticnet['spina_bifida'] = ['-0.98', '0', '0.899', '-0.73', '#sadness', '#anger', 'negative', '-0.87', 'birth_defect', 'congenital_anomaly', 'congenital_defect', 'congenital_disorder', 'congenital_abnormality']
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senticnet['spiral_staircase'] = ['0', '0', '0.095', '0', '#anger', '#anger', 'negative', '-0.09', 'build_part', 'flat', 'step', 'large_rock', 'hard_surface']
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senticnet['spirantization'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'pronunciation', 'sibilation', 'mispronunciation', 'homophony']
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senticnet['spirit_rover'] = ['0.052', '0.119', '0', '0.141', '#interest', '#admiration', 'positive', '0.104', 'neigh', 'domestic_animal', 'animal_type', 'animal', 'home_animal']
senticnet['spirit_up'] = ['0.759', '-0.73', '0', '0.684', '#joy', '#surprise', 'positive', '0.726', 'enliven', 'liven', 'liven_up', 'invigorate', 'animate']
senticnet['spirited'] = ['0.647', '-0.62', '0', '0.705', '#joy', '#admiration', 'positive', '0.659', 'bouncing', 'scintillating', 'lively', 'peppy', 'zippy']
senticnet['spirited_discussion'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'discuss', 'communication', 'communicate_person', 'share', 'autograph']
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senticnet['spirits_of_wine'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'ethyl_alcohol', 'denatured_alcohol', 'alcohol', 'plant_product']
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senticnet['spiritual_attainment'] = ['0.668', '-0.87', '0', '0.606', '#joy', '#surprise', 'positive', '0.715', 'adeptness', 'adroitness', 'deftness', 'quickness', 'expertness']
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senticnet['spiritual_leader'] = ['0.763', '0', '0', '0.719', '#joy', '#admiration', 'positive', '0.741', 'mist', 'haze', 'fog', 'misty', 'drizzle']
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senticnet['spiritual_nourishment'] = ['0', '0.155', '-0.05', '0.11', '#interest', '#admiration', 'positive', '0.071', 'provide_food', 'ingest_food', 'make_eat', 'animal_eat', 'satiate_hunger']
senticnet['spiritual_perfection'] = ['0', '0.706', '0', '0.934', '#interest', '#admiration', 'positive', '0.82', 'value', 'good_value', 'ideal', 'flawless', 'ultimate']
senticnet['spiritual_purity'] = ['0.853', '-0.95', '0', '0.605', '#joy', '#surprise', 'positive', '0.805', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
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senticnet['spiritual_realization'] = ['0.668', '-0.87', '0', '0.606', '#joy', '#surprise', 'positive', '0.715', 'adeptness', 'adroitness', 'deftness', 'quickness', 'expertness']
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senticnet['spiritual_renewal'] = ['0.868', '-0.91', '0', '0.862', '#joy', '#surprise', 'positive', '0.88', 'chirpiness', 'spirit', 'sprightliness', 'liveliness', 'activity']
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senticnet['spiritualism'] = ['0.987', '-0.73', '0', '0.826', '#joy', '#admiration', 'positive', '0.849', 'spiritism', 'otherworldliness', 'spirituality', 'introversion', 'internality']
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senticnet['spirituality'] = ['0.037', '0.293', '0', '0.269', '#interest', '#admiration', 'positive', '0.2', 'religious_purpose', 'prayer', 'judaism', 'christianity', 'religion']
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senticnet['spirituous_liquor'] = ['0', '0', '0', '-0.91', '#disgust', '#disgust', 'negative', '-0.91', 'alcoholic_drink', 'get_drunk', 'ferment_beverage', 'adult_drink', 'booze']
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senticnet['spirochete'] = ['0.995', '-0.71', '0', '0.647', '#joy', '#surprise', 'positive', '0.784', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
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senticnet['spirometer'] = ['0.757', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.754', 'inhale', 'cough', 'respiration', 'involuntary', 'smell_odor']
senticnet['spironolactone'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'antihypertensive', 'antihypertensive_drug']
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senticnet['spirostomum'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['spirt'] = ['-0.59', '0', '0.753', '-0.54', '#sadness', '#anger', 'negative', '-0.63', 'discharge', 'spray', 'spritz', 'outpouring', 'run']
senticnet['spit'] = ['-0.83', '0', '0.87', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'precipitate', 'mizzle', 'shower_down', 'rain_down', 'patter']
senticnet['spit_out'] = ['-0.83', '0', '0.87', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'precipitate', 'mizzle', 'shower_down', 'rain_down', 'patter']
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senticnet['spitefully'] = ['0.089', '0.09', '0.81', '-0.68', '#anger', '#disgust', 'negative', '-0.32', 'covetous', 'envious', 'enviously', 'covetously', 'green-eyed']
senticnet['spitefulness'] = ['-0.04', '0.164', '0.685', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maliciousness', 'maliciously', 'maleficence', 'malefic', 'malign']
senticnet['spitfire'] = ['0.843', '-0.88', '0', '0.97', '#surprise', '#admiration', 'positive', '0.898', 'captive', 'mortal', 'emotional_person', 'person', 'individual']
senticnet['spiti_valley'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['spitter'] = ['-0.83', '0', '0.87', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'precipitate', 'mizzle', 'shower_down', 'rain_down', 'patter']
senticnet['spitting_cobra'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'death_adder', 'ophidian', 'serpent', 'elapid', 'taipan']
senticnet['spitting_image'] = ['-0.89', '0', '0.58', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'resemblance', 'comparison', 'equivalence', 'reflection', 'alikeness']
senticnet['spitting_out'] = ['-0.83', '0', '0.87', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'precipitate', 'mizzle', 'shower_down', 'rain_down', 'patter']
senticnet['spittle'] = ['-0.83', '0', '0.87', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'precipitate', 'mizzle', 'shower_down', 'rain_down', 'patter']
senticnet['spittlebug'] = ['-0.02', '-0.09', '0.11', '-0.13', '#anger', '#disgust', 'negative', '-0.04', 'beetle', 'gypsy_moth', 'mole_cricket', 'grasshopper', 'many_insects']
senticnet['spiv'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'rascal', 'rapscallion', 'scalawag', 'holy_terror', 'monkey']
senticnet['spl_table'] = ['0.844', '0', '0', '0', '#joy', '#joy', 'positive', '0.844', 'straight', 'late_time', 'preparedness', 'past_present', 'wait']
senticnet['splash'] = ['0.708', '0.895', '0', '0', '#joy', '#interest', 'positive', '0.801', 'wade', 'traveling_water', 'drown', 'catch_shark', 'catch_catfish']
senticnet['splash_mountain'] = ['0.056', '-0.01', '0.036', '0.073', '#joy', '#admiration', 'positive', '0.028', 'thrill_ride', 'have_fun', 'drag_race', 'enjoyment', 'roller_coaster']
senticnet['splash_pad'] = ['0.076', '0.079', '0.027', '0.118', '#interest', '#admiration', 'positive', '0.061', 'outside', 'plant_place', 'plant_ground', 'ground', 'cultivate_yard']
senticnet['splash_pool'] = ['0', '0', '0', '0.964', '#admiration', '#admiration', 'positive', '0.964', 'swimming_pool', 'swim', 'swimsuit', 'lake', 'have_fun']
senticnet['splash_screen'] = ['-0.09', '-0.04', '0.039', '-0.24', '#sadness', '#disgust', 'negative', '-0.08', 'learn_new_idea', 'learn_quickly', 'point_out', 'look_picture', 'jot_down_note']
senticnet['splash_water'] = ['-0.02', '-0.02', '0.076', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'wear_raincoat', 'wet', 'feel_cold', 'camp_out', 'carry_umbrella']
senticnet['splashy'] = ['0', '0', '-0.88', '-0.97', '#fear', '#disgust', 'negative', '-0.92', 'jazzy', 'flashy', 'showy', 'flamboyant', 'gaudy']
senticnet['splat'] = ['-0.87', '0', '0.682', '-0.53', '#sadness', '#anger', 'negative', '-0.69', 'swash', 'spatter', 'slosh_around', 'plash', 'splash']
senticnet['splatter'] = ['-0.87', '0', '0.682', '-0.53', '#sadness', '#anger', 'negative', '-0.69', 'swash', 'spatter', 'slosh_around', 'plash', 'splash']
senticnet['splatter_film'] = ['-0.87', '0', '0.682', '-0.53', '#sadness', '#anger', 'negative', '-0.69', 'swash', 'spatter', 'slosh_around', 'plash', 'splash']
senticnet['splatter_paint'] = ['0.725', '-0.66', '0', '0.906', '#joy', '#admiration', 'positive', '0.765', 'set', 'pose', 'lay', 'put', 'place']
senticnet['splay'] = ['0.726', '0', '0', '0.893', '#joy', '#admiration', 'positive', '0.809', 'splayed', 'splay', 'splaying', 'toe', 'flexed']
senticnet['splayed'] = ['0.723', '0', '0', '0.886', '#joy', '#admiration', 'positive', '0.804', 'splay', 'splay', 'splaying', 'toe', 'flexed']
senticnet['splaying'] = ['0.717', '0', '0', '0.887', '#joy', '#admiration', 'positive', '0.802', 'splayed', 'splay', 'splay', 'toe', 'flexed']
senticnet['spleen'] = ['-0.09', '0.124', '0.609', '-0.60', '#anger', '#disgust', 'negative', '-0.29', 'irascibility', 'short_temper', 'quick_temper', 'choleric', 'irascible']
senticnet['spleen_enlargement'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'bowel', 'scrotum', 'shoulder', 'anus', 'waist']
senticnet['spleen_hepatosplenomegaly'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'bowel', 'scrotum', 'shoulder', 'anus', 'waist']
senticnet['spleen_splenomegaly'] = ['-0.56', '0', '0.906', '-0.80', '#anger', '#disgust', 'negative', '-0.76', 'jaundice_of_newborn', 'disease', 'icterus_neonatorum', 'kernicterus', 'symptom']
senticnet['splendid'] = ['0.978', '0', '0', '0.941', '#joy', '#admiration', 'positive', '0.96', 'splendiferous', 'glorious', 'excellent', 'resplendent', 'first-class']
senticnet['splendidly'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'terrific', 'wondrously', 'tremendous', 'marvelous', 'wondrous']
senticnet['splendiferous'] = ['0.972', '0', '0', '0.937', '#joy', '#admiration', 'positive', '0.955', 'splendid', 'glorious', 'excellent', 'resplendent', 'first-class']
senticnet['splendor'] = ['0.899', '-0.74', '0', '0', '#joy', '#surprise', 'positive', '0.824', 'splendid', 'luster', 'majestic', 'display', 'magnificence']
senticnet['splenetic'] = ['0', '0', '-0.99', '-0.83', '#fear', '#disgust', 'negative', '-0.91', 'barbed', 'prickly', 'bristly', 'barbellate', 'waspish']
senticnet['splenial'] = ['0.886', '-0.99', '0', '0.542', '#joy', '#surprise', 'positive', '0.808', 'jowl', 'lower_jaw', 'mandible', 'lantern_jaw', 'lantern_jawr_bone']
senticnet['splenic'] = ['0.777', '-0.89', '0', '0.508', '#joy', '#surprise', 'positive', '0.727', 'stretching', 'enlarge', 'enlargement', 'get_bigger', 'distension']
senticnet['splenomegaly'] = ['-0.56', '0', '0.906', '-0.80', '#anger', '#disgust', 'negative', '-0.76', 'jaundice_of_newborn', 'disease', 'icterus_neonatorum', 'kernicterus', 'symptom']
senticnet['spline_interpolation'] = ['-0.55', '0', '0.965', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'interjection', 'delay', 'insert', 'interposition', 'disruption']
senticnet['splint'] = ['-0.03', '0', '-0.09', '-0.14', '#fear', '#disgust', 'negative', '-0.09', 'x-ray_machine', 'artificial_arm', 'ambulance', 'syringe', 'latex_glove']
senticnet['splinter_faction'] = ['-0.50', '0', '0.989', '-0.91', '#anger', '#disgust', 'negative', '-0.80', 'camp', 'pack', 'clique', 'coterie', 'ingroup']
senticnet['splinter_group'] = ['-0.50', '0', '0.989', '-0.91', '#anger', '#disgust', 'negative', '-0.80', 'camp', 'pack', 'clique', 'coterie', 'ingroup']
senticnet['splinter_party'] = ['-0.50', '0', '0.989', '-0.91', '#anger', '#disgust', 'negative', '-0.80', 'camp', 'pack', 'clique', 'coterie', 'ingroup']
senticnet['splintered_into'] = ['0', '0', '-0.89', '-0.79', '#fear', '#disgust', 'negative', '-0.84', 'blend', 'conflate', 'mix', 'flux', 'commingle']
senticnet['splintered_wood'] = ['0', '-0.00', '0', '0.088', '#surprise', '#admiration', 'positive', '0.048', 'heavy_paper', 'lightweight_metal', 'wizard', 'metal_type', 'soft_metal']
senticnet['split'] = ['0.909', '0.986', '0', '0', '#joy', '#interest', 'positive', '0.948', 'separation', 'divide', 'division', 'slash', 'bagle']
senticnet['split_apart'] = ['0.909', '0.986', '0', '0', '#joy', '#interest', 'positive', '0.948', 'separation', 'divide', 'division', 'slash', 'bagle']
senticnet['split_bamboo'] = ['0', '0.773', '0', '0.895', '#interest', '#admiration', 'positive', '0.834', 'wooden_board', 'make_wood', 'wooden_box', 'wooden_floor', 'long_wood']
senticnet['split_end'] = ['0.978', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.933', 'late', 'get_dark', 'rainy_day', 'tardiness', 'house_look_better']
senticnet['split_folding_rear'] = ['0', '0.248', '-0.07', '0.121', '#interest', '#admiration', 'positive', '0.099', 'gym_locker', 'observatory', 'prison_cell', 'luggage_store', 'garage_sale']
senticnet['split_infinitive'] = ['0', '0.156', '-0.06', '0.241', '#interest', '#admiration', 'positive', '0.11', 'mixture', 'noisy_place', 'make_go', 'move_vehicle', 'big_car']
senticnet['split_into'] = ['0.909', '0.986', '0', '0', '#joy', '#interest', 'positive', '0.948', 'separation', 'divide', 'division', 'slash', 'bagle']
senticnet['split_lip'] = ['-0.12', '-0.02', '0', '-0.15', '#sadness', '#disgust', 'negative', '-0.08', 'incision', 'laceration', 'abrasion', 'cut_skin', 'painful_injury']
senticnet['split_pea'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'roll', 'custard', 'bread', 'biscuit']
senticnet['split_personality'] = ['-0.56', '0', '0.977', '-0.86', '#anger', '#disgust', 'negative', '-0.80', 'strangeness', 'quaintness', 'abnormality', 'queerness', 'crotchet']
senticnet['split_up'] = ['0.804', '-0.95', '0', '0.813', '#surprise', '#admiration', 'positive', '0.856', 'break', 'split', 'separate', 'part', 'dissociate']
senticnet['splitting_headache'] = ['0', '0', '-0.73', '-0.79', '#fear', '#disgust', 'negative', '-0.76', 'hard_see', 'rain_fall', 'dew', 'rain', 'mist']
senticnet['splodge'] = ['0.991', '-0.69', '0', '0.822', '#joy', '#admiration', 'positive', '0.834', 'spot', 'smirch', 'daub', 'smudge', 'blot']
senticnet['splotch'] = ['0.87', '-0.5', '0', '0.584', '#joy', '#admiration', 'positive', '0.651', 'spot', 'smirch', 'daub', 'smudge', 'blot']
senticnet['splotchy'] = ['-0.92', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.84', 'blotchy', 'blotch', 'splotch', 'brownish', 'discolored']
senticnet['splurge'] = ['-0.81', '0', '0.936', '-0.57', '#sadness', '#anger', 'negative', '-0.77', 'pedantry', 'bluster', 'ostentation', 'ritz', 'display']
senticnet['spob'] = ['-0.61', '0', '0.981', '-0.98', '#anger', '#disgust', 'negative', '-0.85', 'pious_platitude', 'nothingness', 'jazz', 'talk', 'malarky']
senticnet['spodoptera'] = ['-0.02', '-0.09', '0.11', '-0.13', '#anger', '#disgust', 'negative', '-0.04', 'beetle', 'gypsy_moth', 'mole_cricket', 'grasshopper', 'many_insects']
senticnet['spoil'] = ['0.993', '0.846', '0', '0', '#joy', '#interest', 'positive', '0.919', 'kid', 'playtime', 'little_child', 'brat', 'child']
senticnet['spoil_food'] = ['0', '0', '0', '-0.72', '#disgust', '#disgust', 'negative', '-0.72', 'smelly_foot', 'cripple', 'influenza', 'chicken_pox', 'sexually_transmit_disease']
senticnet['spoil_heap'] = ['0', '0', '-0.85', '-0.71', '#fear', '#disgust', 'negative', '-0.78', 'fair_game', 'pit', 'stone_pit', 'target', 'prey']
senticnet['spoil_wood_floor'] = ['0', '0', '0.865', '-0.95', '#anger', '#disgust', 'negative', '-0.90', 'flat', 'dirty_floor', 'spill_over', 'float', 'bucket_overflow']
senticnet['spoilage'] = ['-0.08', '-0.04', '0.023', '-0.07', '#sadness', '#disgust', 'negative', '-0.03', 'tier', 'make_toast', 'house_smell_good', 'get_hot', 'bread_crumb']
senticnet['spoilage_bacteria'] = ['-0.08', '-0.04', '0.023', '-0.07', '#sadness', '#disgust', 'negative', '-0.03', 'tier', 'make_toast', 'house_smell_good', 'get_hot', 'bread_crumb']
senticnet['spoiled_brat'] = ['0.584', '-0.59', '0', '0.753', '#surprise', '#admiration', 'positive', '0.643', 'ungracious', 'discourteous', 'impolite', 'brattish', 'unparliamentary']
senticnet['spoiling'] = ['-0.93', '0', '0.997', '-0.50', '#sadness', '#anger', 'negative', '-0.81', 'decay', 'mildew', 'mold', 'mould', 'souring']
senticnet['spoilsport'] = ['-0.65', '0', '0.87', '-0.82', '#anger', '#disgust', 'negative', '-0.78', 'worrier', 'fusspot', 'worrywart', 'unwelcome_person', 'killjoy']
senticnet['spoilt'] = ['-0.62', '0', '0.863', '-0.99', '#anger', '#disgust', 'negative', '-0.82', 'destroyed', 'wrecked', 'impoverished', 'blighted', 'extinguished']
senticnet['spoked_wheel'] = ['0', '-0.09', '0.044', '0.055', '#surprise', '#admiration', 'positive', '0.036', 'two_wheels', 'gear', 'axle', 'engine', 'steer_wheel']
senticnet['spoken_language'] = ['0', '-0.04', '0.021', '0.07', '#surprise', '#admiration', 'positive', '0.032', 'talk', 'oration', 'talkative', 'voice_communication', 'make_call']
senticnet['spokesman_angus_robertson'] = ['-0.85', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.86', 'attack', 'raid', 'fight_against', 'conflict', 'opposition']
senticnet['spokeswoman_kendra_barkoff'] = ['0', '0.128', '-0.12', '0.183', '#interest', '#admiration', 'positive', '0.063', 'support_roof', 'furnish', 'ceiling', 'window_door', 'frame']
senticnet['spoliate'] = ['0', '0', '0.8', '-0.78', '#anger', '#disgust', 'negative', '-0.79', 'impair', 'crush', 'trash', 'exterminate', 'sabotage']
senticnet['spoliation'] = ['-0.98', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.88', 'tamper', 'tobacco', 'tool', 'untampered', 'caplet']
senticnet['sponge'] = ['0.865', '-0.92', '0', '0.888', '#surprise', '#admiration', 'positive', '0.892', 'mooch', 'cadge', 'freeload', 'grub', 'bum']
senticnet['sponge_bath'] = ['0', '0', '0', '0.068', '#admiration', '#admiration', 'positive', '0.068', 'become_clean', 'wash_clothes', 'take_shower', 'get_clean', 'clean_body']
senticnet['sponge_cake'] = ['0', '-0.11', '0', '0', '#surprise', '#surprise', 'positive', '0.113', 'cake', 'cookie', 'mousse', 'doughnut', 'brownie']
senticnet['sponge_up'] = ['0.72', '-0.76', '0', '0.695', '#joy', '#surprise', 'positive', '0.726', 'mop_up', 'blot', 'wipe_up', 'mop', 'suck_up']
senticnet['spongy'] = ['-0.98', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.87', 'rubbery', 'pliable', 'springy', 'squishy', 'spring-like']
senticnet['spongy_bone'] = ['-0.53', '0', '0.982', '-0.67', '#anger', '#disgust', 'negative', '-0.73', 'meniscus', 'semilunar_cartilage', 'gristle', 'hyaline_cartilage', 'arytenoid']
senticnet['spongy_texture'] = ['-0.50', '0', '0.85', '-0.82', '#anger', '#disgust', 'negative', '-0.72', 'confusion', 'disarray', 'mental_confusion', 'confusedness', 'bafflement']
senticnet['sponsor'] = ['0', '0.946', '0', '0.99', '#interest', '#admiration', 'positive', '0.968', 'chemistry', 'material_research', 'research', 'focus', 'career_award']
senticnet['sponsor_individual_award'] = ['0', '0.995', '0', '0.999', '#interest', '#admiration', 'positive', '0.997', 'award', 'different_application', 'standard_grant', 'abroad', 'office']
senticnet['sponsor_university'] = ['0', '0.933', '0', '0.729', '#interest', '#admiration', 'positive', '0.831', 'award', 'different_application', 'standard_grant', 'continue_grant', 'university']
senticnet['sponsoring_agency'] = ['-0.88', '0', '0.977', '-0.81', '#sadness', '#anger', 'negative', '-0.89', 'execute', 'run', 'compel', 'oblige', 'enforce']
senticnet['sponsoring_institution'] = ['0.072', '0', '0', '0.078', '#joy', '#admiration', 'positive', '0.075', 'participate', 'examine', 'voyeur', 'influence_public_decision', 'question_everything']
senticnet['sponsoring_laboratory'] = ['0', '0.891', '0', '0.854', '#interest', '#admiration', 'positive', '0.873', 'research', 'training_plan', 'fellowship', 'environment', 'fellowship_support']
senticnet['sponsoring_organization'] = ['0.072', '0', '0', '0.078', '#joy', '#admiration', 'positive', '0.075', 'participate', 'examine', 'voyeur', 'influence_public_decision', 'question_everything']
senticnet['sponsorship'] = ['0', '0.718', '0', '0.985', '#interest', '#admiration', 'positive', '0.851', 'chemistry', 'university', 'fellowship', 'continue_research', 'award']
senticnet['sponsorship_agreement'] = ['0.865', '-0.84', '0', '0.915', '#joy', '#admiration', 'positive', '0.875', 'grant', 'sale_in_gross', 'contract_of_hazard', 'adhesion_contract', 'contract_of_adhesion']
senticnet['spontaneity'] = ['0', '0.803', '0', '0.71', '#interest', '#admiration', 'positive', '0.756', 'novelty', 'innovativeness', 'freshness', 'ability', 'power']
senticnet['spontaneous'] = ['0.909', '0', '0', '0.761', '#joy', '#admiration', 'positive', '0.835', 'natural', 'impulsive', 'instinctive', 'unprompted', 'easy']
senticnet['spontaneous_abortion'] = ['-0.90', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.86', 'death', 'feticide', 'sickness', 'miscarriage', 'abortion']
senticnet['spontaneous_combustion'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'firework', 'bright_flash', 'nuclear_weapon', 'earthquake', 'nuclear_bomb']
senticnet['spontaneous_emission'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'emotional_arousal', 'excitement', 'arousal', 'anger']
senticnet['spontaneous_fission'] = ['-0.77', '0', '0.764', '-0.54', '#sadness', '#anger', 'negative', '-0.69', 'pullulation', 'apomixis', 'blastogenesis', 'budding', 'asexual_reproduction']
senticnet['spontaneous_speech'] = ['0.727', '-0.62', '0', '0.57', '#joy', '#surprise', 'positive', '0.64', 'utterer', 'deipnosophist', 'interlocutor', 'conversationalist', 'speaker']
senticnet['spontaneously'] = ['0', '0.959', '0', '0.856', '#interest', '#admiration', 'positive', '0.907', 'naturally', 'inherently', 'acoustic', 'drainer', 'mineral']
senticnet['spoof'] = ['0', '0', '-0.90', '-0.91', '#fear', '#disgust', 'negative', '-0.90', 'parody', 'farce', 'lampoon', 'farce_comedy', 'travesty']
senticnet['spoof_casino_royale'] = ['0.228', '0.123', '-0.09', '0', '#joy', '#interest', 'positive', '0.084', 'spend_time_with_child', 'social', 'friendship', 'companionship', 'get_close']
senticnet['spoof_video'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'paperclip', 'clasp', 'glue_stick', 'clothespin', 'clamp']
senticnet['spook'] = ['-0.16', '-0.04', '-0.31', '0', '#sadness', '#fear', 'negative', '-0.14', 'cry', 'big_monster', 'shock', 'common_emotion', 'fear']
senticnet['spookily'] = ['-0.80', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.8', 'scared', 'frighten_away', 'scare_away', 'frighten_off', 'scare_off']
senticnet['spooky'] = ['-0.70', '0', '0.761', '0', '#sadness', '#anger', 'negative', '-0.73', 'eerie', 'skittish', 'unpredictably', 'ghost', 'supernatural']
senticnet['spookyfish'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'aridness', 'dryness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['spool'] = ['0.058', '0.123', '0', '0.142', '#interest', '#admiration', 'positive', '0.108', 'web', 'silk', 'velvet', 'cotton', 'satin']
senticnet['spoon'] = ['0.03', '-0.05', '0', '0.059', '#surprise', '#admiration', 'positive', '0.046', 'fork', 'hot_table', 'chopstick', 'bowl', 'saltshaker']
senticnet['spoon_feed'] = ['0', '-0.07', '0', '0', '#surprise', '#surprise', 'positive', '0.075', 'feed', 'feed_baby', 'bake_bread', 'make_ice', 'bake_cookie']
senticnet['spoon_food'] = ['-0.63', '0', '0.819', '-0.99', '#anger', '#disgust', 'negative', '-0.81', 'light_diet', 'soft_diet', 'diet', 'bland_diet', 'ulcer_diet']
senticnet['spoon_fork'] = ['0.03', '-0.05', '0', '0.059', '#surprise', '#admiration', 'positive', '0.046', 'grease_trap', 'glass_front_cupboard', 'fork', 'spoon', 'utensil']
senticnet['spoonerism'] = ['0.88', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.828', 'ambiguity', 'loophole', 'amphibology', 'amphiboly', 'parisology']
senticnet['spoonfeed'] = ['0.629', '-0.80', '0', '0.93', '#surprise', '#admiration', 'positive', '0.788', 'reward', 'mentor', 'reinforce', 'instruct', 'lecture']
senticnet['spoonfeeding'] = ['0.546', '-0.98', '0', '0.769', '#surprise', '#admiration', 'positive', '0.767', 'education', 'teaching_reading', 'catechesis', 'lecture', 'teach']
senticnet['spoonful'] = ['0.102', '0.084', '0', '0.076', '#joy', '#interest', 'positive', '0.087', 'better_cold', 'sip', 'potable_liquid', 'ingest', 'liquid_beverage']
senticnet['spoonful_onto'] = ['0.978', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.865', 'tumble', 'dew', 'hailstone', 'rain', 'snowflake']
senticnet['sporadic'] = ['-0.87', '0', '0.722', '-0.57', '#sadness', '#anger', 'negative', '-0.72', 'stray', 'spasmodic', 'unpredictable', 'isolated', 'irregular']
senticnet['sporadically'] = ['0', '0.152', '0', '0.119', '#interest', '#admiration', 'positive', '0.136', 'replicate', 'start_end', 'everyday', 'description', 'precise']
senticnet['spore_monolete'] = ['-0.66', '0', '0.809', '-0.61', '#sadness', '#anger', 'negative', '-0.69', 'face', 'base', 'underbelly', 'sole', 'bilge']
senticnet['spore_sac'] = ['-0.66', '0', '0.809', '-0.61', '#sadness', '#anger', 'negative', '-0.69', 'face', 'base', 'underbelly', 'sole', 'bilge']
senticnet['sporogony'] = ['0.642', '-0.58', '0', '0.751', '#joy', '#admiration', 'positive', '0.659', 'change', 'become_better', 'change_aspect', 'look_better', 'become_bigger']
senticnet['sport'] = ['0.026', '0.15', '0', '0', '#joy', '#interest', 'positive', '0.088', 'basketball_court', 'basketball_game', 'basketball', 'ball', 'basketball_team']
senticnet['sport_accident'] = ['-0.93', '0', '0.643', '-0.95', '#sadness', '#disgust', 'negative', '-0.84', 'fall_down', 'car_accident', 'stumble', 'accident', 'injured']
senticnet['sport_activity'] = ['0.057', '-0.01', '0', '0.15', '#joy', '#admiration', 'positive', '0.073', 'human_motion', 'human_movement', 'water_activity', 'vigorous_activity', 'water_sport']
senticnet['sport_arena'] = ['0.058', '0', '0', '0.088', '#joy', '#admiration', 'positive', '0.073', 'school_yard', 'fashion_show', 'art', 'football_stadium', 'exhibition']
senticnet['sport_ball'] = ['0.885', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.938', 'toy_ball', 'sport_bat', 'toy_marble', 'toy_soldier', 'beanie_baby']
senticnet['sport_bat'] = ['0.848', '0.785', '0', '0', '#joy', '#interest', 'positive', '0.817', 'sport_ball', 'fielder', 'toy_ball', 'water_gun', 'toy_soldier']
senticnet['sport_coat'] = ['-0.79', '0', '0.599', '-0.93', '#sadness', '#disgust', 'negative', '-0.77', 'uniform', 'battle_dress', 'dress_uniform', 'fatigue', 'military_uniform']
senticnet['sport_competition'] = ['0', '0', '0', '0.879', '#admiration', '#admiration', 'positive', '0.879', 'competition', 'win_competition', 'race', 'match', 'championship']
senticnet['sport_coupe'] = ['-0.05', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'plane_land', 'floss', 'floss_tooth', 'ornament', 'wear_dress']
senticnet['sport_equipment'] = ['0.876', '0.724', '0', '0', '#joy', '#interest', 'positive', '0.8', 'sport_ball', 'frisbee', 'toy_ball', 'toy_marble', 'baseball']
senticnet['sport_event'] = ['0.025', '0.246', '0', '0.226', '#interest', '#admiration', 'positive', '0.166', 'game', 'disneyland', 'ballpark', 'hockey_game', 'hockey_rink']
senticnet['sport_fisherman'] = ['0.073', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.048', 'angler', 'informal_rod', 'archaic_fisher', 'rare_piscator', 'fishing_line']
senticnet['sport_game'] = ['0', '-0.08', '0.015', '0.053', '#surprise', '#admiration', 'positive', '0.042', 'team_sport', 'violent_sport', 'type_of_sport', 'rough_sport', 'game_skill']
senticnet['sport_goods_store'] = ['0', '0.306', '-0.05', '0.287', '#interest', '#admiration', 'positive', '0.18', 'luggage_compartment', 'toy_box', 'toy_store', 'girl_room', 'gym']
senticnet['sport_highlight'] = ['0.026', '0.227', '0', '0.233', '#interest', '#admiration', 'positive', '0.162', 'best_moment', 'highlight', 'football_highlight', 'television_news', 'put_in_evidence']
senticnet['sport_person'] = ['0', '-0.07', '0.024', '0.081', '#surprise', '#admiration', 'positive', '0.044', 'athlete', 'team_sport', 'american_sport', 'rough_sport', 'popular_game']
senticnet['sport_play'] = ['0.943', '0.842', '0', '0', '#joy', '#interest', 'positive', '0.892', 'excite_game', 'ball_sport', 'team_sport', 'rough_game', 'fun_sport']
senticnet['sport_score'] = ['0.991', '0', '0', '0', '#joy', '#joy', 'positive', '0.991', 'sharp_end', 'sharp_corner', 'indicate', 'result', 'full_stop']
senticnet['sport_stadium'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'train_station', 'government_office', 'cable_car', 'baggage_compartment', 'terminal']
senticnet['sport_steering_wheel'] = ['0.725', '0.796', '0', '0', '#joy', '#interest', 'positive', '0.76', 'car_stereo', 'battery', 'seatbelt', 'spare_tire', 'brake_pedal']
senticnet['sport_store'] = ['0.035', '-0.01', '0.009', '0.063', '#joy', '#admiration', 'positive', '0.026', 'daycare', 'gym', 'soccer_game', 'gym_locker', 'football_stadium']
senticnet['sport_stuff'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'sport_equipment', 'golf_bag', 'golf', 'sport_store', 'bag']
senticnet['sport_team'] = ['0.046', '0.125', '0.022', '0.184', '#interest', '#admiration', 'positive', '0.083', 'direct_person', 'stalin', 'water_vehicle', 'sail_sea', 'seagoing_vessel']
senticnet['sport_utility_vehicle'] = ['-0.05', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'plane_land', 'floss', 'floss_tooth', 'ornament', 'wear_dress']
senticnet['sport_yokozuna'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['sporting_goods'] = ['0.52', '-0.97', '0', '0.916', '#surprise', '#admiration', 'positive', '0.803', 'sports_equipment', 'weight', 'mat', 'free_weight', 'exercising_weight']
senticnet['sporting_goods_store'] = ['-0.05', '-0.04', '0.017', '-0.05', '#sadness', '#disgust', 'negative', '-0.02', 'shoe_shop', 'hamper', 'attic', 'obstruct', 'closet']
senticnet['sporting_lady'] = ['-0.60', '0', '0.994', '-0.78', '#anger', '#disgust', 'negative', '-0.79', 'slattern', 'camp_follower', 'call_girl', 'comfort_woman', 'ianfu']
senticnet['sportive'] = ['0.578', '-0.79', '0', '0.786', '#surprise', '#admiration', 'positive', '0.718', 'elfish', 'elvish', 'playful', 'frisky', 'kittenish']
senticnet['sports'] = ['-0.07', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.06', 'sweat', 'become_dirty', 'fall_mud', 'exercise', 'dirty']
senticnet['sports_apparel'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['sports_arena'] = ['0', '-0.84', '0', '0.723', '#surprise', '#admiration', 'positive', '0.785', 'circular_route', 'stadium', 'memorial', 'station', 'row_house']
senticnet['sports_bra'] = ['0', '-0.07', '0.099', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'prescription_medication', 'lingerie', 'sachet', 'shave_kit', 'panties']
senticnet['sports_car'] = ['-0.79', '0', '0.695', '-0.87', '#sadness', '#disgust', 'negative', '-0.78', 'car', 'auto', 'automobile', 'machine', 'motorcar']
senticnet['sports_equipment'] = ['0.054', '-0.05', '0.008', '0', '#joy', '#surprise', 'positive', '0.032', 'golf_bag', 'sport_stuff', 'golf', 'sport_store', 'bag']
senticnet['sports_expert'] = ['0', '0.109', '-0.10', '0.235', '#interest', '#admiration', 'positive', '0.081', 'sport', 'extreme_sport', 'adrenaline', 'extreme', 'skydiving']
senticnet['sports_fan'] = ['0.747', '-0.56', '0', '0.683', '#joy', '#admiration', 'positive', '0.664', 'enthusiast', 'partizan', 'partisan', 'aficionado', 'railbird']
senticnet['sports_illustrated_swimsuit'] = ['-0.98', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.89', 'fairy', 'genus', 'honey_bee', 'infant', 'dove']
senticnet['sports_journalist'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'adventurer', 'humankind']
senticnet['sports_luxe'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'elegant', 'stylish', 'smart', 'posh', 'sleek']
senticnet['sports_reporter'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'adventurer', 'humankind']
senticnet['sports_utility_vehicle'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'cable_car', 'baggage_compartment', 'subway', 'amusement_park', 'terminal']
senticnet['sportsmanship'] = ['0.739', '-0.70', '0', '0', '#joy', '#surprise', 'positive', '0.722', 'adagio', 'drapery', 'sail', 'gracefully', 'sinuous']
senticnet['sportswear'] = ['-0.85', '0', '0.553', '-0.63', '#sadness', '#disgust', 'negative', '-0.67', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['sportswear_brand'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['sporty'] = ['0.025', '-0.02', '0.001', '0.06', '#joy', '#admiration', 'positive', '0.027', 'go_to_gym', 'sport', 'gym', 'sport_store', 'fit']
senticnet['sporty_guy'] = ['0', '0.986', '0', '0.704', '#interest', '#admiration', 'positive', '0.845', 'run_fast', 'train_daily', 'run_lot', 'train_muscle', 'exercise']
senticnet['sporty_person'] = ['0.769', '0', '0', '0', '#joy', '#joy', 'positive', '0.769', 'health', 'become_strong', 'feel_better', 'better_health', 'increase_muscle_mass']
senticnet['sporulation'] = ['-0.75', '0', '0.512', '-0.75', '#sadness', '#disgust', 'negative', '-0.67', 'inactivity', 'inactiveness', 'slumber', 'inaction', 'hibernation']
senticnet['spot'] = ['-0.04', '0.146', '0', '0.152', '#interest', '#admiration', 'positive', '0.085', 'woolly_animal', 'same_again', 'pure', 'nocturnal', 'big_person']
senticnet['spot-on'] = ['0', '-0.98', '0', '0.924', '#surprise', '#admiration', 'positive', '0.955', 'dead-on', 'pitch-perfect', 'bang-on', 'uncannily', 'impersonation']
senticnet['spot_anthracnose'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'rust_fungus', 'fungus']
senticnet['spot_billed_pelican'] = ['0.797', '-0.71', '0', '0.736', '#joy', '#admiration', 'positive', '0.751', 'wood_ibis', 'wood_stork', 'ibis', 'wading_bird', 'wader']
senticnet['spot_blotch'] = ['0.878', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.883', 'wheat', 'grain', 'rye', 'white_rice', 'rice']
senticnet['spot_pass'] = ['-0.92', '0', '0.835', '-0.68', '#sadness', '#anger', 'negative', '-0.81', 'aerial', 'lateral', 'forward_pass', 'lateral_pass', 'passing']
senticnet['spot_prey'] = ['-0.10', '0', '0', '-0.1', '#sadness', '#disgust', 'negative', '-0.10', 'spread_disease', 'go_stall', 'see_night', 'pet', 'hunt_food']
senticnet['spot_promote'] = ['0.897', '-0.92', '0', '0.645', '#joy', '#surprise', 'positive', '0.824', 'kick_upstairs', 'promote', 'elevate', 'upgrade', 'advance']
senticnet['spotify'] = ['0.101', '-0.04', '0.036', '0', '#joy', '#surprise', 'positive', '0.038', 'song', 'rap', 'jingle', 'chord', 'lyric']
senticnet['spotless'] = ['0', '0', '-0.94', '-0.70', '#fear', '#disgust', 'negative', '-0.82', 'spic-and-span', 'spick-and-span', 'spic', 'immaculate', 'speckless']
senticnet['spotlessly'] = ['0', '-0.81', '0', '0.85', '#surprise', '#admiration', 'positive', '0.833', 'well-maintained', 'well-tended', 'manicured', 'well-kept', 'landscaped']
senticnet['spotlight'] = ['-0.03', '0.123', '-0.07', '0.21', '#interest', '#admiration', 'positive', '0.055', 'projector', 'floodlight', 'headlight', 'light', 'teach_lesson']
senticnet['spotted_barramundi'] = ['0.565', '-0.69', '0', '0.787', '#surprise', '#admiration', 'positive', '0.683', 'species', 'saratoga', 'fish_species', 'spotted_bonytongue', 'northern_barramundi']
senticnet['spotted_bonytongue'] = ['0.565', '-0.69', '0', '0.787', '#surprise', '#admiration', 'positive', '0.683', 'species', 'saratoga', 'spotted_barramundi', 'fish_species', 'northern_barramundi']
senticnet['spotted_cucumber_beetle'] = ['-0.02', '-0.09', '0.11', '-0.13', '#anger', '#disgust', 'negative', '-0.04', 'beetle', 'gypsy_moth', 'mole_cricket', 'grasshopper', 'many_insects']
senticnet['spotted_dolphin'] = ['-0.94', '0', '0.87', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'lordosis', 'kyphosis', 'spinal_curvature', 'hunchback', 'abnormality']
senticnet['spotted_dove'] = ['-0.84', '0', '0.655', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'piping_crow', 'butcherbird', 'currawong', 'bell_magpie', 'oscine']
senticnet['spotted_fever'] = ['-0.73', '0', '0.599', '-0.96', '#sadness', '#disgust', 'negative', '-0.76', 'rickettsial_disease', 'trench_fever', 'rickettsialpox', 'typhus', 'typhus_fever']
senticnet['spotted_hyena'] = ['-0.02', '0', '0.044', '0', '#sadness', '#anger', 'negative', '-0.03', 'large_cat', 'fast_person', 'household_pet', 'big_cat', 'good_pet']
senticnet['spotted_lady_beetle'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'giant_hornet', 'hornet', 'hornet_wasp']
senticnet['spotted_skipper'] = ['0.866', '0.82', '0', '0.713', '#joy', '#interest', 'positive', '0.8', 'titanium', 'bronze', 'alloy', 'medal', 'copper']
senticnet['spotted_skunk'] = ['-0.54', '0', '0.768', '-0.50', '#sadness', '#anger', 'negative', '-0.60', 'polecat', 'hooded_skunk', 'hognosed_skunk', 'badger_skunk', 'rooter_skunk']
senticnet['spotted_tail'] = ['-0.18', '-0.12', '0.049', '-0.24', '#sadness', '#disgust', 'negative', '-0.09', 'tree_trunk', 'plank', 'artificial_leg', 'hardwood', 'tree_wood']
senticnet['spotter'] = ['0', '0', '-0.76', '-0.97', '#fear', '#disgust', 'negative', '-0.87', 'watch', 'lookout', 'sentry', 'lookout_man', 'sentinel']
senticnet['spottily'] = ['-0.95', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.91', 'spot', 'spottiness', 'slang', 'quality', 'spotty']
senticnet['spottiness'] = ['-0.96', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.91', 'spot', 'spotty', 'slang', 'quality', 'spottily']
senticnet['spotty'] = ['-0.95', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.91', 'spot', 'spottiness', 'slang', 'quality', 'spottily']
senticnet['spousal'] = ['0.793', '0.701', '0', '0', '#joy', '#interest', 'positive', '0.747', 'parentage', 'paternity', 'custody', 'divorce', 'annulment']
senticnet['spousal_abuse'] = ['-0.64', '0', '0.854', '-0.83', '#anger', '#disgust', 'negative', '-0.77', 'crime', 'bigamy', 'regulatory_offence', 'sexual_abuse', 'sex_crime']
senticnet['spousal_maintenance'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'bigamy', 'wedlock']
senticnet['spousal_rape'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'spousal_relationship', 'wedlock']
senticnet['spousal_relationship'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'bigamy', 'wedlock']
senticnet['spousal_support'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'bigamy', 'wedlock']
senticnet['spousal_violence'] = ['-0.8', '0', '0.875', '-0.74', '#sadness', '#anger', 'negative', '-0.80', 'inhuman_treatment', 'cruelty', 'child_neglect', 'mistreatment', 'child_abuse']
senticnet['spouse'] = ['0.112', '0.262', '-0.16', '0.23', '#interest', '#admiration', 'positive', '0.11', 'husband', 'relationship', 'wife', 'marriage', 'male_child']
senticnet['spouse_abuse'] = ['-0.8', '0', '0.875', '-0.74', '#sadness', '#anger', 'negative', '-0.80', 'inhuman_treatment', 'cruelty', 'child_neglect', 'mistreatment', 'child_abuse']
senticnet['spout'] = ['-0.90', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.89', 'proser', 'tediously', 'tedious', 'irksomely', 'wearisomely']
senticnet['spout_off'] = ['-0.97', '0', '0.62', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'obstructor', 'obstructer', 'technophobe', 'obstructionist', 'resister']
senticnet['spouting_off'] = ['-0.86', '0', '0.675', '-0.84', '#sadness', '#disgust', 'negative', '-0.79', 'shenanigan', 'chicanery', 'wile', 'dupery', 'trickery']
senticnet['sprain'] = ['-0.83', '0', '0.509', '-0.84', '#sadness', '#disgust', 'negative', '-0.72', 'wrench', 'injury', 'harm', 'hurt', 'trauma']
senticnet['sprain_ankle'] = ['0', '0', '-0.91', '0', '#fear', '#fear', 'negative', '-0.91', 'out_breath', 'get_injured', 'pull_muscle', 'breahard', 'blister_foot']
senticnet['sprained'] = ['-0.74', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.87', 'quadriceps', 'groin', 'hamstring', 'sidelined', 'knee']
senticnet['sprained_ankle'] = ['-0.83', '0', '0.509', '-0.84', '#sadness', '#disgust', 'negative', '-0.72', 'wrench', 'injury', 'harm', 'hurt', 'trauma']
senticnet['sprawl'] = ['-0.84', '0', '0', '-0.81', '#sadness', '#disgust', 'negative', '-0.82', 'random', 'consistent', 'thorough', 'haphazardly', 'haphazard']
senticnet['sprawling_epic'] = ['0.089', '-0.01', '0.033', '0.082', '#joy', '#admiration', 'positive', '0.039', 'informative', 'informational', 'ephemeral', 'fun_read', 'ads']
senticnet['sprawling_shrub'] = ['-0.80', '0', '0.555', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'bush', 'wormwood', 'milkwort', 'germander', 'centaury']
senticnet['spray'] = ['-0.76', '0', '0.786', '-0.92', '#anger', '#disgust', 'negative', '-0.82', 'discharge', 'spurt', 'spritz', 'outpouring', 'run']
senticnet['spray_adhesive'] = ['0.852', '0.818', '0', '0', '#joy', '#interest', 'positive', '0.835', 'handout', 'fountain_pen', 'pen', 'stamp_pad', 'scrap_paper']
senticnet['spray_bottle'] = ['0.939', '0.939', '0', '0', '#joy', '#interest', 'positive', '0.939', 'puddle', 'sop', 'liquid_water', 'finger_paint', 'bread']
senticnet['spray_paint'] = ['-0.12', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.07', 'bum', 'homeless_people', 'homeless_person', 'panhandler', 'homeless']
senticnet['spray_paint_grafitti'] = ['0.983', '0', '0', '0', '#joy', '#joy', 'positive', '0.983', 'sex', 'watch_porn', 'eat_pussy', 'masturbation', 'beat_off']
senticnet['spray_painting_anti'] = ['-0.12', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.07', 'bum', 'homeless_people', 'homeless_person', 'panhandler', 'homeless']
senticnet['spray_tan'] = ['-0.96', '0', '0.636', '-0.64', '#sadness', '#disgust', 'negative', '-0.75', 'burn', 'tan', 'hyperpigmentation', 'sunburn', 'physical_condition']
senticnet['sprayed_graffiti'] = ['-0.12', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.07', 'bum', 'homeless_people', 'homeless_person', 'panhandler', 'homeless']
senticnet['spread'] = ['0.849', '-0.54', '0', '0.543', '#joy', '#surprise', 'positive', '0.646', 'diffusion', 'dispersion', 'scattering', 'invasion', 'irradiation']
senticnet['spread-eagle'] = ['0', '0.949', '0', '0.841', '#interest', '#admiration', 'positive', '0.895', 'spread-eagled', 'spreadeagled', 'belly-down', 'half-sitting', 'cross-legged']
senticnet['spread-eagled'] = ['0', '0.942', '0', '0.835', '#interest', '#admiration', 'positive', '0.888', 'spread-eagle', 'spreadeagled', 'belly-down', 'half-sitting', 'cross-legged']
senticnet['spread_butter'] = ['-0.09', '-0.08', '0.039', '-0.18', '#sadness', '#disgust', 'negative', '-0.05', 'cut_food', 'lacerate', 'pierce', 'chop_wood', 'slash']
senticnet['spread_disease'] = ['0', '-0.04', '0.063', '-0.14', '#anger', '#disgust', 'negative', '-0.05', 'fly_high', 'std', 'try_forget', 'spot_prey', 'buzz']
senticnet['spread_germ'] = ['0', '0', '0', '-0.95', '#disgust', '#disgust', 'negative', '-0.95', 'feel_comfortable', 'age_prematurely', 'use_thermometer', 'feel_warm', 'addiction']
senticnet['spread_happiness'] = ['0.991', '0', '0', '0.744', '#joy', '#admiration', 'positive', '0.867', 'get_happy', 'comedian', 'pleasure', 'give_pleasure', 'feel_different']
senticnet['spread_idea'] = ['0.686', '0.526', '0', '0.812', '#joy', '#admiration', 'positive', '0.675', 'make_money', 'explain_idea', 'ego_boost', 'get_published', 'promote']
senticnet['spread_information'] = ['0.686', '0.526', '0', '0.812', '#joy', '#admiration', 'positive', '0.675', 'wander_around', 'organize_information', 'transfer_information', 'process_data', 'communicate_with_others']
senticnet['spread_knowledge'] = ['0.686', '0.526', '0', '0.812', '#joy', '#admiration', 'positive', '0.675', 'spread_information', 'spread_news', 'get_information', 'expand_knowledge', 'knowledge']
senticnet['spread_love'] = ['0', '0.095', '-0.05', '0.132', '#interest', '#admiration', 'positive', '0.058', 'purchase_goods', 'buy', 'shop', 'purchase_gift', 'buy_gum']
senticnet['spread_meme'] = ['0.072', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.046', 'stay_touch', 'communicate_friend', 'send_information', 'give_comfort', 'communicate_thought']
senticnet['spread_news'] = ['0.551', '0.914', '0', '0.817', '#interest', '#admiration', 'positive', '0.761', 'report_event', 'pass_information', 'review_book', 'tell_information', 'offer_opinion']
senticnet['spread_out'] = ['-0.1', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.1', 'deflate', 'read_cover', 'look_behind', 'upgrade', 'scan']
senticnet['spread_outward'] = ['-0.1', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.1', 'deflate', 'read_cover', 'look_behind', 'upgrade', 'scan']
senticnet['spread_rapidly'] = ['-0.83', '0', '-0.82', '-0.86', '#sadness', '#disgust', 'negative', '-0.84', 'cause_cold', 'tourism', 'reproduce', 'gland', 'procreate']
senticnet['spread_seed'] = ['0.107', '0.098', '0', '0.135', '#joy', '#admiration', 'positive', '0.113', 'go_green', 'respect_nature', 'save_water', 'go_on_foot', 'reduce_pollution']
senticnet['spread_throughout'] = ['-0.1', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.1', 'deflate', 'read_cover', 'look_behind', 'upgrade', 'scan']
senticnet['spread_wing'] = ['0.107', '0.098', '0', '0.135', '#joy', '#admiration', 'positive', '0.113', 'fly', 'success', 'honor_parent', 'eat_potato', 'bug_person']
senticnet['spread_word'] = ['0.211', '0.114', '0', '0.262', '#joy', '#admiration', 'positive', '0.196', 'others_understand', 'a_lot', 'touch_heart', 'express_opinion', 'make_progress']
senticnet['spreadable'] = ['0.033', '0', '0', '0.203', '#joy', '#admiration', 'positive', '0.118', 'thick_liquid', 'edible_human', 'liquid_substance', 'round_fruit', 'descriptive']
senticnet['spreadeagled'] = ['0', '0.947', '0', '0.832', '#interest', '#admiration', 'positive', '0.889', 'spread-eagled', 'spread-eagle', 'belly-down', 'half-sitting', 'cross-legged']
senticnet['spreading'] = ['0.927', '0', '0', '0.788', '#joy', '#admiration', 'positive', '0.858', 'diffusion', 'dispersal', 'dissemination', 'public_exposure', 'dispersion']
senticnet['spreading_shrub'] = ['-0.80', '0', '0.555', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'bush', 'wormwood', 'milkwort', 'germander', 'centaury']
senticnet['spreadsheet'] = ['-0.84', '0', '0.689', '-0.90', '#sadness', '#disgust', 'negative', '-0.81', 'software', 'software_program', 'computer_software', 'software_system', 'authoring_language']
senticnet['spreadsheet_software'] = ['-0.84', '0', '0.689', '-0.90', '#sadness', '#disgust', 'negative', '-0.81', 'software', 'software_program', 'computer_software', 'software_system', 'authoring_language']
senticnet['spree'] = ['0.819', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.831', 'spree', 'frenzy', 'robbery', 'killing', 'shopping']
senticnet['sprig'] = ['0.967', '-0.98', '0', '0.64', '#joy', '#surprise', 'positive', '0.863', 'club', 'social_club', 'society', 'guild', 'jockey_club']
senticnet['sprightliness'] = ['0.546', '-0.73', '0', '0.583', '#surprise', '#admiration', 'positive', '0.62', 'irrepressibility', 'spiritedness', 'brio', 'bright', 'delicacy']
senticnet['sprightly'] = ['0', '-0.97', '0', '0.871', '#surprise', '#admiration', 'positive', '0.92', 'gay', 'vigorous', 'cavalier', 'vigor', 'lively']
senticnet['spring'] = ['-0.07', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.06', 'hold_two_together', 'float_through_air', 'travel_through_space', 'spring_up', 'fly_around']
senticnet['spring-blooming'] = ['0.839', '0', '0', '0.807', '#joy', '#admiration', 'positive', '0.823', 'day-neutral', 'winter-flowering', 'winter-blooming', 'summer-flowering', 'summer-blooming']
senticnet['spring-flowering'] = ['0', '-0.74', '0', '0.703', '#surprise', '#admiration', 'positive', '0.722', 'spring-blooming', 'summer-blooming', 'summer-flowering', 'shade-loving', 'fall-blooming']
senticnet['spring-like'] = ['-0.92', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.83', 'drizzly', 'muggy', 'blustery', 'squally', 'showery']
senticnet['spring_action'] = ['0', '0', '0', '0.736', '#admiration', '#admiration', 'positive', '0.736', 'state_opinion', 'see_star_night', 'recycle_paper', 'visit_grandmother', 'forward_email']
senticnet['spring_break'] = ['0', '0.734', '0', '0', '#interest', '#interest', 'positive', '0.734', 'sleep', 'go_sleep', 'relax', 'go_out', 'sleep_night']
senticnet['spring_break_trip'] = ['0.977', '0.828', '0', '0', '#joy', '#interest', 'positive', '0.902', 'hunt_fish', 'out_place', 'getaway', 'outdoor_activity', 'vacation']
senticnet['spring_calving'] = ['0.032', '0.109', '-0.07', '0.085', '#interest', '#admiration', 'positive', '0.037', 'plural', 'kin', 'relate_person', 'younger', 'young_people']
senticnet['spring_cleaning'] = ['0.76', '0.919', '0', '0', '#joy', '#interest', 'positive', '0.84', 'chore', 'make_happen', 'pick_up_trash', 'intuition', 'stop_procrastinate']
senticnet['spring_equinox'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['spring_heeled'] = ['-0.95', '0', '0.735', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'novice', 'beginner', 'cub', 'tiro', 'initiate']
senticnet['spring_leak'] = ['-0.02', '0.206', '-0.10', '0.291', '#interest', '#admiration', 'positive', '0.092', 'carry', 'raise_up', 'haul', 'tote', 'convey']
senticnet['spring_loaded'] = ['0', '0.069', '0', '0.083', '#interest', '#admiration', 'positive', '0.076', 'latch', 'restrict_access', 'knob', 'door_knob', 'gate']
senticnet['spring_loaded_lever'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'peavey']
senticnet['spring_loaded_mechanism'] = ['0.13', '0.097', '-0.07', '0.198', '#joy', '#admiration', 'positive', '0.087', 'jiggle', 'back_forth', 'vibrate', 'blend', 'mixture']
senticnet['spring_loaded_striker'] = ['-0.99', '0', '0.651', '-0.51', '#sadness', '#anger', 'negative', '-0.72', 'sending', 'compulsion', 'influence', 'inducement', 'causation']
senticnet['spring_onion'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'mayonnaise', 'box_cereal', 'bottle_water']
senticnet['spring_reverb'] = ['0.056', '-0.01', '0.036', '0.073', '#joy', '#admiration', 'positive', '0.028', 'loud_sound', 'stereo', 'tuner', 'speaker', 'steam_engine']
senticnet['spring_semester'] = ['-0.85', '0', '0.584', '-0.79', '#sadness', '#disgust', 'negative', '-0.74', 'age', 'old_age', 'second_childhood', 'eld', 'geezerhood']
senticnet['spring_sunshine'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'weather', 'four_seasons', 'fall_spring', 'rain_snow', 'climate']
senticnet['spring_up'] = ['-0.06', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.05', 'hold_two_together', 'float_through_air', 'travel_through_space', 'spring', 'fly_around']
senticnet['spring_wildflower'] = ['-0.79', '0', '0.837', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'sunray', 'wilding', 'sagebrush_buttercup', 'pasqueflower', 'pasque_flower']
senticnet['springboard'] = ['0.918', '0.864', '0', '0', '#joy', '#interest', 'positive', '0.891', 'consequence', 'cause', 'bad_action', 'problem', 'political_statement']
senticnet['springing_up'] = ['0', '0.093', '0', '0.053', '#interest', '#admiration', 'positive', '0.073', 'underground', 'under_ground', 'environment', 'outpost', 'dark_place']
senticnet['springtide'] = ['0.624', '-0.61', '0', '0.524', '#joy', '#surprise', 'positive', '0.589', 'sea_level', 'high_tide', 'neap', 'direct_tide', 'tide']
senticnet['springy'] = ['-0.97', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.86', 'rubbery', 'pliable', 'spongy', 'squishy', 'spring-like']
senticnet['sprinkling'] = ['0', '0', '0.988', '-0.89', '#anger', '#disgust', 'negative', '-0.94', 'smattering', 'handful', 'plethora', 'bevy', 'smattering']
senticnet['sprint'] = ['-0.03', '0.107', '-0.09', '0.164', '#interest', '#admiration', 'positive', '0.037', 'run', 'jump_over', 'quick', 'quicken', 'flee']
senticnet['sprint_champion'] = ['0', '0', '0', '0.834', '#admiration', '#admiration', 'positive', '0.834', 'royal_person', 'royal_title', 'top', 'master', 'rank']
senticnet['sprint_freestyle'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'chlorine', 'lifeguard', 'still_water', 'large_body', 'shallow_water']
senticnet['sprinted_clear'] = ['0', '-0.04', '0.085', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'claw', 'fur_keep_warm', 'tic', 'bristle', 'muzzle']
senticnet['sprinter'] = ['0.091', '-0.03', '0', '0.047', '#joy', '#admiration', 'positive', '0.056', 'control_horse', 'lead_rope', 'horse', 'jockey', 'saddle']
senticnet['sprite'] = ['0.071', '0', '0', '0', '#joy', '#joy', 'positive', '0.071', 'soda', 'coke', 'cola', 'soft_drink', 'cool_drink']
senticnet['spritz'] = ['-0.59', '0', '0.753', '-0.54', '#sadness', '#anger', 'negative', '-0.63', 'discharge', 'spray', 'spirt', 'outpouring', 'run']
senticnet['sprout'] = ['-0.86', '0', '0', '-0.95', '#sadness', '#disgust', 'negative', '-0.91', 'lath', 'shoot', 'memorandum', 'pert', 'chit']
senticnet['sprouting_broccoli'] = ['0', '0.067', '-0.09', '-0.09', '#fear', '#disgust', 'negative', '-0.04', 'cotton_boll', 'asbestos', 'corn', 'opium', 'rice']
senticnet['spruce'] = ['0', '0.733', '0', '0.956', '#interest', '#admiration', 'positive', '0.845', 'pine', 'birch', 'fir', 'maple', 'alder']
senticnet['spruce_bark_beetle'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['spruce_beetle'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['spruce_budworm'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['spruce_forest'] = ['-0.04', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.04', 'bad_luck', 'damn', 'artic', 'mammal_family', 'bad_mood']
senticnet['spruce_goose'] = ['-0.04', '0.092', '0', '0.062', '#interest', '#admiration', 'positive', '0.036', 'hangar', 'aircraft_carrier', 'hot_air_balloon', 'war_museum', 'spaceship']
senticnet['spruce_grouse'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['spruce_root'] = ['0.706', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.806', 'shoe', 'flip_flop', 'slipper', 'casual_shoes', 'footwear']
senticnet['spruce_wood'] = ['-0.69', '0', '0.725', '-0.89', '#anger', '#disgust', 'negative', '-0.77', 'ponderosa', 'ponderosa_pine', 'western_yellow_pine', 'bull_pine', 'longleaf_pine']
senticnet['spruced_up'] = ['0.857', '-0.68', '0', '0.971', '#joy', '#admiration', 'positive', '0.839', 'revitalize', 'refurbish', 'reconstruct', 'renovate', 'renew']
senticnet['sprucely'] = ['0.781', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.845', 'smoothly', 'featly', 'elegant', 'gracefully', 'elegantly']
senticnet['sprucing_up'] = ['0.76', '0.919', '0', '0', '#joy', '#interest', 'positive', '0.84', 'chore', 'make_happen', 'pick_up_trash', 'intuition', 'stop_procrastinate']
senticnet['spud'] = ['0.53', '-0.68', '0', '0.981', '#surprise', '#admiration', 'positive', '0.732', 'potato', 'chips', 'murphy', 'white_potato', 'tater']
senticnet['spunk'] = ['0.605', '-0.93', '0', '0.742', '#surprise', '#admiration', 'positive', '0.761', 'road_sense', 'sagacity', 'sagaciousness', 'logic', 'nous']
senticnet['spunky'] = ['0', '0', '-0.83', '-0.95', '#fear', '#disgust', 'negative', '-0.89', 'touchy', 'feisty', 'plucky', 'thin-skinned', 'huffy']
senticnet['spur'] = ['0.829', '0.924', '0', '0', '#joy', '#interest', 'positive', '0.877', 'rowel', 'column', 'fortification', 'implement', 'leafage']
senticnet['spur_economic_growth'] = ['0.66', '-0.85', '0', '0.744', '#surprise', '#admiration', 'positive', '0.752', 'further', 'promote', 'encourage', 'conduce', 'advance']
senticnet['spur_gear'] = ['0', '-0.09', '0.044', '0.055', '#surprise', '#admiration', 'positive', '0.036', 'two_wheels', 'gear', 'axle', 'engine', 'steer_wheel']
senticnet['spur_growth'] = ['0.66', '-0.85', '0', '0.744', '#surprise', '#admiration', 'positive', '0.752', 'further', 'promote', 'encourage', 'conduce', 'advance']
senticnet['spurge'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['spurious'] = ['0', '0', '-0.82', '-0.90', '#fear', '#disgust', 'negative', '-0.86', 'misbegotten', 'inauthentic', 'misbegot', 'bastardly', 'specious']
senticnet['spurn'] = ['-0.66', '0', '0.82', '-0.79', '#anger', '#disgust', 'negative', '-0.75', 'snub', 'rebuff', 'repel', 'refuse', 'decline']
senticnet['spurt'] = ['-0.76', '0', '0.794', '-0.91', '#anger', '#disgust', 'negative', '-0.82', 'discharge', 'spray', 'spritz', 'outpouring', 'run']
senticnet['spurting_blood'] = ['-0.87', '0', '0.682', '-0.53', '#sadness', '#anger', 'negative', '-0.69', 'swash', 'spatter', 'slosh_around', 'plash', 'splash']
senticnet['sputter'] = ['-0.78', '0', '0.719', '0', '#sadness', '#anger', 'negative', '-0.75', 'haste', 'hurried', 'rash', 'hasty', 'deliberate']
senticnet['sputum'] = ['0', '0', '0', '-0.96', '#disgust', '#disgust', 'negative', '-0.96', 'thick_substance', 'viscous_substance', 'mucous_substance', 'disgusting', 'disgust']
senticnet['sputum_production'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchiolitis', 'bronchitis', 'respiratory_disease', 'respiratory_illness', 'respiratory_disorder']
senticnet['spx_quote'] = ['0', '0.189', '0.04', '0.091', '#interest', '#admiration', 'positive', '0.08', 'combination', 'margin', 'teller', 'merge', 'finite']
senticnet['spy'] = ['0', '0', '0', '-0.91', '#disgust', '#disgust', 'negative', '-0.91', 'inventor', 'finish_college', 'cheat', 'hard_worker', 'biological_machine']
senticnet['spy_ring'] = ['-0.69', '0', '0.756', '-0.99', '#anger', '#disgust', 'negative', '-0.81', 'waster', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['spy_satellite'] = ['0.899', '0.803', '0', '0', '#joy', '#interest', 'positive', '0.851', 'ufo', 'milky', 'martian', 'galaxy', 'solar_system']
senticnet['spy_scandal'] = ['-0.87', '0', '0.762', '-0.92', '#sadness', '#disgust', 'negative', '-0.85', 'misreading', 'misconstrual', 'misconstruction', 'misinterpretation', 'interpretation']
senticnet['spy_thriller'] = ['-0.57', '0', '0.721', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'bush', 'native_pomegranate', 'caper_tree', 'native_orange', 'common_caper']
senticnet['spy_thriller_lust'] = ['-0.32', '0.133', '-0.38', '0', '#sadness', '#fear', 'negative', '-0.19', 'big_monster', 'unsafe', 'phobia', 'mock', 'feel_scared']
senticnet['spying'] = ['0.769', '-0.64', '0', '0.559', '#joy', '#surprise', 'positive', '0.656', 'catching', 'breakthrough', 'discovery', 'determination', 'tracing']
senticnet['spying_scandal'] = ['-0.87', '0', '0.762', '-0.92', '#sadness', '#disgust', 'negative', '-0.85', 'misreading', 'misconstrual', 'misconstruction', 'misinterpretation', 'interpretation']
senticnet['spymaster'] = ['-0.69', '0', '0.756', '-0.99', '#anger', '#disgust', 'negative', '-0.81', 'waster', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['spyware'] = ['-0.05', '-0.03', '0.023', '-0.09', '#sadness', '#disgust', 'negative', '-0.03', 'moldy_food', 'celery', 'paper_product', 'orange_juice', 'meat']
senticnet['sql_injection_attack'] = ['-0.02', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.07', 'army', 'military', 'infantry', 'samurai', 'soldier']
senticnet['squabble'] = ['-0.63', '0', '0.982', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'wrangle', 'dustup', 'quarrel', 'row', 'argument']
senticnet['squabble_between'] = ['-0.63', '0', '0.982', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'wrangle', 'dustup', 'quarrel', 'row', 'argument']
senticnet['squabble_over'] = ['-0.13', '0.071', '0.694', '-0.67', '#anger', '#disgust', 'negative', '-0.35', 'crucify', 'dun', 'frustrating', 'aggravate', 'exasperation']
senticnet['squabbles_over'] = ['-0.63', '0', '0.982', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'wrangle', 'dustup', 'quarrel', 'row', 'argument']
senticnet['squabbling'] = ['0', '0.965', '0', '0.724', '#interest', '#admiration', 'positive', '0.845', 'infighting', 'factionalism', 'in-fighting', 'bickering', 'factional']
senticnet['squabbling_among'] = ['-0.67', '0', '0.838', '-0.73', '#anger', '#disgust', 'negative', '-0.75', 'altercation', 'fracas', 'difference', 'affray', 'bickering']
senticnet['squabbling_over'] = ['-0.63', '0', '0.982', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'wrangle', 'dustup', 'quarrel', 'row', 'argument']
senticnet['squacco_heron'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'amoeba', 'single_celled']
senticnet['squad_automatic_weapon'] = ['0', '0', '0.954', '-0.74', '#anger', '#disgust', 'negative', '-0.85', 'gun', 'pistol', 'revolver', 'firearm', 'handgun']
senticnet['squad_played_professionally'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['squadron'] = ['-0.9', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.9', 'withdrawal', 'indifferently', 'apathetic', 'indifferent', 'concerned']
senticnet['squadron_commander'] = ['0.045', '0', '0', '0.088', '#joy', '#admiration', 'positive', '0.067', 'leader', 'seaman', 'captain', 'officer', 'traitor']
senticnet['squadron_leader'] = ['0.74', '-0.53', '0', '0.641', '#joy', '#admiration', 'positive', '0.64', 'marshal', 'sky_marshal', 'lawman', 'law_officer', 'peace_officer']
senticnet['squalid'] = ['-0.91', '0', '0.989', '0', '#sadness', '#anger', 'negative', '-0.95', 'flyblown', 'seamy', 'seedy', 'sordid', 'sleazy']
senticnet['squall'] = ['-0.76', '0', '0.516', '-0.85', '#sadness', '#disgust', 'negative', '-0.71', 'skreigh', 'call', 'cry', 'yell', 'scream']
senticnet['squall_line'] = ['0', '-0.41', '-0.95', '0', '#surprise', '#fear', 'negative', '-0.26', 'blizzard', 'snow_storm', 'storm', 'danger', 'rainy']
senticnet['squally'] = ['-0.85', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.89', 'blustery', 'gusty', 'showery', 'squall', 'gale-force']
senticnet['squalor'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dirtiness', 'dinge']
senticnet['squamosal'] = ['0.1', '-0.12', '0.05', '-0.03', '#joy', '#surprise', 'positive', '0.034', 'fancy_dress', 'blouse', 'fashion', 'dress', 'skirt']
senticnet['squamous_epithelium'] = ['-0.78', '0', '0.855', '-0.99', '#anger', '#disgust', 'negative', '-0.87', 'blood_blister', 'pustule', 'water_blister', 'vesicle', 'cyst']
senticnet['squander'] = ['-0.77', '0', '0', '-0.86', '#sadness', '#disgust', 'negative', '-0.81', 'profuse', 'squandering', 'squanderer', 'waster', 'misspent']
senticnet['squanderer'] = ['0.878', '-0.67', '0', '0.951', '#joy', '#admiration', 'positive', '0.835', 'spendthrift', 'waster', 'wastrel', 'spender', 'scattergood']
senticnet['squandering'] = ['-0.77', '0', '0', '-0.86', '#sadness', '#disgust', 'negative', '-0.82', 'profuse', 'squander', 'squanderer', 'waster', 'misspent']
senticnet['square'] = ['0.852', '0.943', '0', '0', '#joy', '#interest', 'positive', '0.897', 'rectangle', 'four_sides', 'rhombus', 'cube', 'four_corners']
senticnet['square-toed'] = ['0', '0', '0.944', '-0.86', '#anger', '#disgust', 'negative', '-0.90', 'prissy', 'puritanical', 'priggish', 'prim', 'prudish']
senticnet['square_dance'] = ['0.826', '0', '0', '0.848', '#joy', '#admiration', 'positive', '0.837', 'form_of_music', 'style_music', 'good_music', 'music', 'beautiful_music']
senticnet['square_diagonally'] = ['0', '0', '0', '0.115', '#admiration', '#admiration', 'positive', '0.115', 'chess_knight', 'strategy', 'pawn', 'chess_queen', 'chess_rook']
senticnet['square_foot_greenhouse'] = ['-0.12', '0.066', '-0.08', '-0.06', '#sadness', '#fear', 'negative', '-0.05', 'garden_yard', 'see_sky', 'plant_tree', 'hair', 'seed_lawn']
senticnet['square_foot_space'] = ['0.591', '-0.74', '0', '0.672', '#surprise', '#admiration', 'positive', '0.669', 'room', 'chillax', 'hotel_room', 'furniture_store', 'living_room']
senticnet['square_foot_studio'] = ['0.591', '-0.74', '0', '0.672', '#surprise', '#admiration', 'positive', '0.669', 'room', 'chillax', 'hotel_room', 'furniture_store', 'living_room']
senticnet['square_knot'] = ['-0.06', '-0.01', '0.05', '-0.07', '#sadness', '#disgust', 'negative', '-0.04', 'embroidery', 'woven', 'cover_wind', 'torn', 'use_needle']
senticnet['square_leg'] = ['-0.13', '0', '-0.11', '-0.04', '#sadness', '#fear', 'negative', '-0.1', 'concern_health', 'medical_problem', 'strengthful', 'surgery', 'injury']
senticnet['square_pedestal'] = ['0', '0.082', '-0.09', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'coffin', 'headstone', 'morgue', 'bury_body', 'casket']
senticnet['square_pyramid'] = ['0.641', '-0.6', '0', '0.957', '#joy', '#admiration', 'positive', '0.733', 'ideal_solid', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'regular_octahedron']
senticnet['square_root'] = ['0.506', '-0.56', '0', '0.788', '#surprise', '#admiration', 'positive', '0.62', 'backward', 'reverse', 'retrograde', 'reflexive', 'retral']
senticnet['square_shape'] = ['0', '0', '0', '0.092', '#admiration', '#admiration', 'positive', '0.092', 'shape', 'inside_house', 'house_part', 'four_walls', 'quadrilateral']
senticnet['square_tower'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'castle', 'bell_tower', 'spire', 'steeple', 'religious_place']
senticnet['squarely'] = ['0.877', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.859', 'butt', 'plumb', 'square', 'direct', 'honest']
senticnet['squarish'] = ['-0.76', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.82', 'slouchy', 'boxy', 'low-slung', 'blocky', 'cropped']
senticnet['squash'] = ['-0.56', '0', '0.696', '-0.96', '#anger', '#disgust', 'negative', '-0.74', 'winter_squash', 'acorn_winter_squash', 'hubbard_winter_squash', 'turban_winter_squash', 'butternut_winter_squash']
senticnet['squash_bug'] = ['-0.03', '-0.09', '0.111', '-0.20', '#anger', '#disgust', 'negative', '-0.06', 'mole_cricket', 'gypsy_moth', 'cucumber_beetle', 'japanese_beetle', 'mosquito']
senticnet['squash_court'] = ['0.024', '0.152', '-0.06', '0.236', '#interest', '#admiration', 'positive', '0.087', 'basketball_court', 'play_sport', 'tennis_game', 'playground_equipment', 'gymnasium']
senticnet['squash_racket'] = ['0.72', '0.707', '0', '0', '#joy', '#interest', 'positive', '0.714', 'lick', 'drub', 'bat', 'clobber', 'chiropteran']
senticnet['squat_lobster'] = ['-0.79', '0', '0.709', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'animal', 'animate_being', 'ctenophore', 'invertebrate', 'beast']
senticnet['squat_toilet'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'commode', 'potty_seat', 'potty_chair']
senticnet['squatina'] = ['-0.79', '0', '0.709', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'animal', 'animate_being', 'ctenophore', 'invertebrate', 'beast']
senticnet['squatter'] = ['0', '0', '0.744', '-0.99', '#anger', '#disgust', 'negative', '-0.87', 'squatter', 'slum', 'homesteader', 'squatting', 'settler']
senticnet['squatting'] = ['0', '0', '0.737', '-0.99', '#anger', '#disgust', 'negative', '-0.86', 'squatter', 'slum', 'homesteader', 'squatter', 'settler']
senticnet['squatting_position'] = ['-0.68', '0', '0.545', '-0.68', '#sadness', '#disgust', 'negative', '-0.64', 'stunt', 'tumble', 'split', 'walkover', 'acrobatic_feat']
senticnet['squaw'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
senticnet['squawk'] = ['-0.99', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.87', 'infringement', 'referee', 'infraction', 'breach', 'infringe']
senticnet['squeak'] = ['-0.17', '-0.04', '0.035', '-0.08', '#sadness', '#disgust', 'negative', '-0.06', 'bad_music', 'annoy_sound', 'high_music_note', 'bang', 'loud']
senticnet['squeak_by'] = ['-0.56', '0', '0.962', '-0.50', '#sadness', '#anger', 'negative', '-0.67', 'manage', 'make_out', 'cope', 'get_by', 'make_do']
senticnet['squeaker'] = ['-0.17', '-0.04', '0.035', '-0.08', '#sadness', '#disgust', 'negative', '-0.06', 'bad_music', 'annoy_sound', 'high_music_note', 'bang', 'loud']
senticnet['squeaky_voice'] = ['-0.91', '0', '0.681', '-0.81', '#sadness', '#disgust', 'negative', '-0.80', 'chicken', 'doormat', 'softie', 'crybaby', 'wimp']
senticnet['squeal'] = ['0.688', '-0.97', '0', '0.763', '#surprise', '#admiration', 'positive', '0.808', 'honk', 'blate', 'churr', 'let_loose', 'sibilate']
senticnet['squealer'] = ['-0.73', '0', '0.946', '-0.59', '#sadness', '#anger', 'negative', '-0.76', 'fink', 'snitch', 'snitcher', 'sneak', 'stoolpigeon']
senticnet['squeamish'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'pervert', 'horny', 'uncomfortable', 'uneasy', 'short_tempered']
senticnet['squeegee'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'wall_decoration', 'renovation', 'painting', 'artwork', 'new_house']
senticnet['squeegee_men'] = ['-0.53', '0', '0.825', '-0.87', '#anger', '#disgust', 'negative', '-0.74', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['squeel'] = ['-0.53', '0', '0.508', '-0.74', '#sadness', '#disgust', 'negative', '-0.59', 'bedwetter', 'bed_wetter', 'mortal', 'wetter', 'person']
senticnet['squeeze'] = ['0.974', '0.827', '0', '0', '#joy', '#interest', 'positive', '0.9', 'choose_color', 'charge_credit_card', 'sit_toilet', 'ship_goods', 'press']
senticnet['squeeze_bottle'] = ['0.035', '-0.01', '0.009', '0.063', '#joy', '#admiration', 'positive', '0.026', 'container', 'carton', 'contain', 'hold_cupcake', 'store_dish']
senticnet['squeeze_by'] = ['-0.94', '0', '0.899', '-0.56', '#sadness', '#anger', 'negative', '-0.80', 'manage', 'make_out', 'cope', 'get_by', 'make_do']
senticnet['squeeze_out'] = ['-0.53', '0', '0.825', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'displace', 'fire', 'give_notice', 'extract', 'dismiss']
senticnet['squeeze_play'] = ['0', '0.828', '0', '0.91', '#interest', '#admiration', 'positive', '0.869', 'power_play', 'squeeze', 'scorer', 'first_period', 'second_period']
senticnet['squelch'] = ['0.033', '-0.19', '0.203', '-0.22', '#anger', '#disgust', 'negative', '-0.05', 'antiquate', 'optional', 'kleenex', 'key_chain', 'sachet']
senticnet['squib_bomb'] = ['-0.96', '0', '0.394', '-0.90', '#sadness', '#disgust', 'negative', '-0.75', 'explosion', 'get_hurt', 'blow_up', 'detonate', 'get_bomb']
senticnet['squiggle'] = ['-0.84', '0', '0.579', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'cacography', 'hand', 'chicken_scratch', 'handwriting', 'script']
senticnet['squiggly'] = ['-0.05', '0', '-0.12', '-0.16', '#fear', '#disgust', 'negative', '-0.11', 'reduced', 'small', 'small_size', 'narrow', 'poor']
senticnet['squint'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'divergent_strabismus', 'exotropia', 'walleye', 'abnormality', 'abnormalcy']
senticnet['squire'] = ['-0.14', '0.104', '-0.23', '0.081', '#sadness', '#fear', 'negative', '-0.04', 'samurai', 'knight', 'private', 'high_rank', 'general']
senticnet['squirm'] = ['0', '0', '-0.91', '-0.98', '#fear', '#disgust', 'negative', '-0.94', 'discomfort', 'motion', 'twist', 'embarrassment', 'wiggle']
senticnet['squirrel'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'nymph', 'mole', 'nightly_ritual', 'rabbit', 'impairment_person']
senticnet['squirrel-like'] = ['0', '0', '-0.77', '-0.89', '#fear', '#disgust', 'negative', '-0.83', 'rat-like', 'gargoyle-like', 'ratlike', 'goblin-like', 'scorpion-like']
senticnet['squirrel_away'] = ['0.917', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.927', 'lay_away', 'stash', 'cache', 'hive_up', 'memory_cache']
senticnet['squirrel_hill'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
senticnet['squirrel_hill_neighborhood'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
senticnet['squirrel_monkey'] = ['0.921', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.825', 'monkey', 'fiance', 'lemur', 'beaver', 'koala']
senticnet['squirt'] = ['0.045', '0', '0.007', '0.054', '#joy', '#admiration', 'positive', '0.031', 'pier', 'rich_person', 'moat', 'puddle', 'dryad']
senticnet['squirt_gun'] = ['-0.87', '0', '0.682', '-0.53', '#sadness', '#anger', 'negative', '-0.69', 'swash', 'spatter', 'slosh_around', 'plash', 'splash']
senticnet['squishy'] = ['-0.97', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.87', 'rubbery', 'pliable', 'spongy', 'springy', 'spring-like']
senticnet['squizzy'] = ['-0.54', '0', '0.845', '-0.95', '#anger', '#disgust', 'negative', '-0.78', 'face', 'base', 'underside', 'sole', 'bilge']
senticnet['squizzy_taylor'] = ['-0.54', '0', '0.845', '-0.95', '#anger', '#disgust', 'negative', '-0.78', 'face', 'base', 'underside', 'sole', 'bilge']
senticnet['sran'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'white_animal', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
senticnet['sraosha'] = ['-0.97', '0', '0.772', '-0.67', '#sadness', '#anger', 'negative', '-0.80', 'expiation', 'atonement', 'deliverance', 'redemption', 'rescue']
senticnet['sree_moolam_popular'] = ['0.055', '0.072', '-0.06', '0.094', '#interest', '#admiration', 'positive', '0.038', 'big_group', 'large_group', 'bunch_person', 'large_number', 'clique']
senticnet['sree_narayana_guru'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['ss_panzer_division'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'start_over', 'queue', 'find_line', 'take_turn', 'long_line']
senticnet['ss_quote'] = ['0', '0.189', '0.04', '0.091', '#interest', '#admiration', 'positive', '0.08', 'combination', 'margin', 'teller', 'merge', 'finite']
senticnet['ssdna'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['ssh_connection'] = ['0.078', '0.103', '-0.09', '0.191', '#interest', '#admiration', 'positive', '0.069', 'connect', 'aunt_uncle', 'short_form', 'relationship', 'kinship']
senticnet['ssi_benefit'] = ['0.924', '0.968', '0', '0.876', '#joy', '#interest', 'positive', '0.923', 'live_simply', 'help_out', 'money', 'accolade', 'generous']
senticnet['ssi_recipient'] = ['-0.60', '0', '0.832', '-0.53', '#sadness', '#anger', 'negative', '-0.65', 'unsuitable', 'undesirable', 'unqualified', 'disqualified', 'unentitled']
senticnet['ssl_connection'] = ['0.078', '0.103', '-0.09', '0.191', '#interest', '#admiration', 'positive', '0.069', 'connect', 'aunt_uncle', 'short_form', 'relationship', 'kinship']
senticnet['ssl_encryption'] = ['-0.79', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.79', 'misgiving', 'qualm', 'scruple', 'insecurity', 'insecure']
senticnet['sslosh_around'] = ['-0.88', '0', '0.674', '-0.53', '#sadness', '#anger', 'negative', '-0.69', 'swash', 'spatter', 'splatter', 'slosh_around', 'plash']
senticnet['sst_anomaly'] = ['-0.69', '0', '0.835', '-0.56', '#sadness', '#anger', 'negative', '-0.69', 'roughness', 'inclemency', 'bad_weather', 'inclementness', 'windiness']
senticnet['st_bartholomew_hospital'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'druggist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['st_mary_church'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'city_hall', 'courthouse', 'subway_station', 'public_place', 'bus_station']
senticnet['st_mirren_defender'] = ['0.941', '-0.83', '0', '0.973', '#joy', '#admiration', 'positive', '0.917', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['sta_travel'] = ['0', '0', '0', '0.097', '#admiration', '#admiration', 'positive', '0.097', 'transport_person', 'travel_long_distance', 'get_plane', 'get_bus', 'move']
senticnet['stab'] = ['-0.16', '-0.07', '-0.07', '-0.21', '#sadness', '#disgust', 'negative', '-0.09', 'cause_injury', 'hurt_person', 'maim', 'elephant_sit_chest', 'parry']
senticnet['stab_detonator'] = ['-0.99', '0', '0.651', '-0.51', '#sadness', '#anger', 'negative', '-0.72', 'sending', 'compulsion', 'influence', 'inducement', 'causation']
senticnet['stab_knife'] = ['0.782', '0.735', '0', '0', '#joy', '#interest', 'positive', '0.758', 'feel_remorse', 'imprisonment', 'strangle', 'hide_evidence', 'feel_anger']
senticnet['stab_to_death'] = ['-1.0', '0', '0.346', '-0.97', '#sadness', '#disgust', 'negative', '-0.77', 'kill', 'murder', 'death', 'kill_person', 'violance']
senticnet['stab_wound'] = ['-0.12', '-0.04', '0', '-0.17', '#sadness', '#disgust', 'negative', '-0.08', 'cause_bleed', 'open_wind', 'gash', 'lacerate', 'painful_injury']
senticnet['stabbed_repeatedly'] = ['-0.03', '-0.02', '-0.05', '-0.15', '#fear', '#disgust', 'negative', '-0.05', 'knife', 'hatchet', 'sword', 'razor_blade', 'scalpel']
senticnet['stabbing_pain'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'stiff', 'rigid', 'inflexible', 'harsh', 'immovable']
senticnet['stabbing_weapon'] = ['0', '-0.02', '0.074', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'sharp_edge', 'without_space', 'knife_sharp', 'pin', 'machete']
senticnet['stabilator'] = ['-0.58', '0', '0.715', '-0.59', '#anger', '#disgust', 'negative', '-0.63', 'fly', 'overlap', 'barndoor', 'coattail', 'codpiece']
senticnet['stabilisation'] = ['0.963', '0', '0', '0.985', '#joy', '#admiration', 'positive', '0.974', 'settle', 'set', 'secure', 'affix', 'stable']
senticnet['stabilise'] = ['0.72', '-0.76', '0', '0.695', '#joy', '#surprise', 'positive', '0.726', 'suck_up', 'blot', 'wipe_up', 'mop', 'sponge_up']
senticnet['stability'] = ['-0.02', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.04', 'run_lot', 'ride_car', 'chew_bone', 'play_human', 'play_fetch']
senticnet['stabilization'] = ['0.963', '0', '0', '0.985', '#joy', '#admiration', 'positive', '0.974', 'settle', 'set', 'secure', 'affix', 'stable']
senticnet['stabilize'] = ['0.963', '0', '0', '0.985', '#joy', '#admiration', 'positive', '0.974', 'settle', 'set', 'secure', 'affix', 'stable']
senticnet['stabilizer_bar'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'steer_wheel', 'gear_shift', 'chassis', 'brake_pedal', 'air_vent']
senticnet['stable'] = ['0.034', '0', '0', '0.064', '#joy', '#admiration', 'positive', '0.049', 'barn', 'grassland', 'steady', 'firm', 'farmyard']
senticnet['stable_equilibrium'] = ['-0.91', '0', '0.598', '-0.52', '#sadness', '#anger', 'negative', '-0.68', 'stingy', 'penurious', 'scrimy', 'beggarly', 'chintzy']
senticnet['stable_foreman'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'coach', 'instructor', 'personal_trainer', 'fit_person', 'train']
senticnet['stable_lad'] = ['0', '0', '0', '0.071', '#admiration', '#admiration', 'positive', '0.071', 'two_barbs', 'barb', 'mule', 'buddhist', 'ride_barb']
senticnet['stable_orbit'] = ['0.989', '-0.90', '0', '0.876', '#joy', '#surprise', 'positive', '0.923', 'reach', 'range', 'compass', 'scope', 'confines']
senticnet['stablemaster'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['staccato'] = ['-0.52', '0', '0.902', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'discordant', 'inharmonious', 'unresolved', 'false', 'disharmonious']
senticnet['stack'] = ['0', '0', '0', '0.16', '#admiration', '#admiration', 'positive', '0.16', 'make_fruit', 'layer', 'soft_sweet', 'bake_goods', 'cover_ice']
senticnet['stack_brick'] = ['0.96', '-0.71', '0', '0.519', '#joy', '#surprise', 'positive', '0.731', 'brick', 'adobe', 'clinker', 'clinker_brick', 'firebrick']
senticnet['stack_interchange'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
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senticnet['stage'] = ['0.811', '0.791', '0', '0', '#joy', '#interest', 'positive', '0.801', 'thespian', 'performance', 'drama', 'performer', 'actor']
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senticnet['stage_dancing'] = ['-0.95', '0', '0.992', '-0.63', '#sadness', '#anger', 'negative', '-0.86', 'interpretive_dance', 'interpretive_dancing', 'interpretative_dance', 'interpretative_dancing', 'modern_dance']
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senticnet['stage_production'] = ['0.044', '-0.08', '0', '-0.02', '#joy', '#surprise', 'positive', '0.034', 'stage_show', 'child_game', 'fool_around', 'kid_activity', 'fun_activity']
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senticnet['stagecoach_robbery'] = ['0', '0', '-0.11', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'hunting_season', 'hound', 'cougar', 'hunter', 'dragon']
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senticnet['staggering_per_cent'] = ['0.706', '0.945', '0', '0', '#joy', '#interest', 'positive', '0.825', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['staggeringly'] = ['0', '0.799', '0', '0.914', '#interest', '#admiration', 'positive', '0.857', 'unfathomably', 'unprecedentedly', 'unacceptably', 'astoundingly', 'monstrously']
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senticnet['stagnant_water'] = ['0.718', '0', '0', '0', '#joy', '#joy', 'positive', '0.718', 'water_container', 'still_water', 'clean_water', 'water', 'cool_water']
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senticnet['stain_remover'] = ['0', '0.095', '0', '0.074', '#interest', '#admiration', 'positive', '0.084', 'buy_wash_powder', 'water_soap', 'turn_faucet', 'take_off', 'fill_bathtub_water']
senticnet['stain_resistance'] = ['0.8', '-0.90', '0', '0.675', '#joy', '#surprise', 'positive', '0.794', 'indestructibility', 'tensile_strength', 'continuity', 'persistence', 'changelessness']
senticnet['stain_resistant'] = ['-0.02', '0.078', '0', '0.08', '#interest', '#admiration', 'positive', '0.045', 'rigid', 'fragile', 'opaque', 'delicate', 'versatile']
senticnet['stainbank'] = ['0.034', '0', '0.01', '0.087', '#joy', '#admiration', 'positive', '0.037', 'chime', 'bell_noise', 'alto', 'piano', 'ring_loudly']
senticnet['stained_blue'] = ['0', '-0.01', '0.046', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'varnish', 'brush', 'dye', 'pigment', 'canvas']
senticnet['stained_glass_dome'] = ['0.046', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'light', 'bay_window', 'air_conditioner', 'sky', 'lamp']
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senticnet['stained_red'] = ['0', '-0.01', '0.046', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'varnish', 'brush', 'dye', 'pigment', 'canvas']
senticnet['stained_teeth'] = ['0', '0', '0.955', '-0.78', '#anger', '#disgust', 'negative', '-0.87', 'worm_intestine', 'fur_keep_warm', 'unpleasant-smelling', 'halitosis', 'asthama']
senticnet['stainless'] = ['0', '0.945', '0', '0.972', '#interest', '#admiration', 'positive', '0.958', 'virgin', 'steel', 'metal', 'iron', 'silver']
senticnet['stainless_steel'] = ['0.72', '0', '0', '0.733', '#joy', '#admiration', 'positive', '0.726', 'spanner', 'gold', 'strong_material', 'platinum', 'durable']
senticnet['stainless_steel_blade'] = ['0', '-0.02', '0.074', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'sharp_edge', 'without_space', 'knife_sharp', 'pin', 'machete']
senticnet['stainless_steel_bowl'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'salad_shaker', 'store_shelf', 'kitchen_tool', 'pliers', 'ladle']
senticnet['stainless_steel_canister'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expandible', 'expansive', 'erectile', 'cavernous']
senticnet['stainless_steel_kitchen'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'kitchen_cabinet', 'kitchen_cupboard', 'dishwasher', 'cupboard', 'own_kitchen']
senticnet['stainless_steel_mesh'] = ['0.085', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.074', 'soap', 'drizzle', 'bubbly', 'solid_water', 'ivory']
senticnet['stainless_steel_sink'] = ['0.822', '0.802', '0', '0', '#joy', '#interest', 'positive', '0.812', 'electric_toothbrush', 'towel', 't_shirt', 'heat_towel_rack', 'hair_brush']
senticnet['stainless_steel_tube'] = ['-0.05', '0.068', '-0.10', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'tooth_decay', 'body_ache', 'symptom', 'pierce', 'tooth']
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senticnet['stair_climber'] = ['0.094', '0', '0', '0', '#joy', '#joy', 'positive', '0.094', 'hooker', 'registry_office', 'restaurant_entrance', 'elevator', 'exit']
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senticnet['stair_rail'] = ['0.858', '0.945', '0', '0', '#joy', '#interest', 'positive', '0.901', 'fire_hydrant', 'fire_extinguisher', 'christian_fundamentalist', 'extinguisher', 'reception_desk']
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senticnet['stair_tower'] = ['0', '0', '0.095', '0', '#anger', '#anger', 'negative', '-0.09', 'build_part', 'flat', 'step', 'large_rock', 'hard_surface']
senticnet['staircase'] = ['-0.06', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.06', 'picture_scene', 'take_photograph', 'paint_ceiling', 'frame_picture', 'climb_ladder']
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senticnet['stalactite'] = ['0.061', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.04', 'workman', 'man', 'homeowner', 'pauper', 'nazi']
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senticnet['stale_air'] = ['0.757', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.754', 'inhale', 'cough', 'respiration', 'involuntary', 'smell_odor']
senticnet['stale_beer'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'poison', 'rotten', 'disgusting', 'taste_awful', 'green_bread']
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senticnet['stalemate'] = ['-0.90', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.86', 'impasse', 'deadlock', 'gridlock', 'negotiation', 'brinkmanship']
senticnet['stalin'] = ['0', '0.884', '0', '0.769', '#interest', '#admiration', 'positive', '0.827', 'superior', 'sail_sea', 'well_done', 'supervisor', 'small_ocean']
senticnet['stalin_hitler'] = ['0.397', '0.245', '-0.13', '0.224', '#joy', '#interest', 'positive', '0.183', 'agreement', 'provide', 'advice', 'contribution', 'offer']
senticnet['stalingrad'] = ['0.737', '-0.96', '0', '0.552', '#joy', '#surprise', 'positive', '0.75', 'prominence', 'hump', 'bump', 'swelling', 'frontal_eminence']
senticnet['stalinism'] = ['-0.81', '-0.71', '0.843', '-0.72', '#sadness', '#anger', 'negative', '-0.41', 'lack_of_rules', 'fascism', 'despot', 'tyrant', 'anarchism']
senticnet['stalinist'] = ['0.045', '0.286', '-0.14', '0.229', '#interest', '#admiration', 'positive', '0.104', 'socialism', 'parliament', 'elect', 'government', 'elect_president']
senticnet['stalinist_bureaucracy'] = ['-0.67', '0', '0.85', '-0.59', '#sadness', '#anger', 'negative', '-0.70', 'rack', 'boot', 'whip', 'iron_boot', 'instrument_of_torture']
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senticnet['stalker'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'addicted', 'obsessed_love', 'addiction', 'obsession', 'weird']
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senticnet['stallion'] = ['0', '0', '0', '0.071', '#admiration', '#admiration', 'positive', '0.071', 'two_barbs', 'barb', 'mule', 'buddhist', 'ride_barb']
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senticnet['staying_calm'] = ['0.494', '0.45', '-0.11', '0.056', '#joy', '#interest', 'positive', '0.222', 'recreate', 'peacefulness', 'euphoria', 'serenity', 'closeness']
senticnet['staying_healthy'] = ['0', '0.921', '0', '0.89', '#interest', '#admiration', 'positive', '0.905', 'maintain_good_health', 'eat_healthily', 'do_exercise', 'stay_fit', 'eat_well']
senticnet['staying_physically_active'] = ['-0.06', '-0.02', '0.021', '-0.06', '#sadness', '#disgust', 'negative', '-0.03', 'clean_window', 'vacuum_carpet', 'repetitive_task', 'run_long_time', 'exercise_daily']
senticnet['staying_power'] = ['0.989', '-0.80', '0', '0.569', '#joy', '#surprise', 'positive', '0.788', 'strength', 'tolerance', 'stamina', 'endurance', 'sufferance']
senticnet['staysail'] = ['0', '-0.05', '0.079', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'c_clamp', 'thumb_tack', 'pin', 'pliers', 'without_space']
senticnet['stazione'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'expressway', 'turnpike', 'toll_road', 'highway']
senticnet['std'] = ['-0.39', '-0.07', '-0.09', '-0.32', '#sadness', '#disgust', 'negative', '-0.18', 'broken_heart', 'schizophrenia', 'broken_finger', 'disability', 'diabetes']
senticnet['steadfast'] = ['0.898', '0', '0', '0.896', '#joy', '#admiration', 'positive', '0.897', 'persevere', 'diligent', 'perseverance', 'persevering', 'persevered']
senticnet['steadfast_love'] = ['0.956', '0', '0', '0.864', '#joy', '#admiration', 'positive', '0.91', 'honesty', 'wear_around_neck', 'type_of_dog', 'future_robot', 'trust']
senticnet['steadfast_loyalty'] = ['0.997', '0.796', '0', '0.89', '#joy', '#admiration', 'positive', '0.894', 'devotion', 'bravery', 'adoration', 'zeal', 'charm']
senticnet['steadfastly'] = ['0', '0.903', '0', '0.865', '#interest', '#admiration', 'positive', '0.884', 'unwaveringly', 'resolutely', 'doggedly', 'staunchly', 'obstinately']
senticnet['steadicam'] = ['0', '-0.02', '0.048', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'telescope', 'read_glass', 'eyeglass', 'binoculars', 'monocle']
senticnet['steadily'] = ['0.897', '0.937', '0', '0', '#joy', '#interest', 'positive', '0.917', 'dramatically', 'exponentially', 'considerably', 'incrementally', 'rapidly']
senticnet['steadiness'] = ['0.644', '-0.88', '0', '0.834', '#surprise', '#admiration', 'positive', '0.787', 'resoluteness', 'firmness_of_purpose', 'firmness', 'resolve', 'resolution']
senticnet['steady'] = ['0.765', '0', '0', '0.928', '#joy', '#admiration', 'positive', '0.847', 'trade', 'stet', 'farm', 'steadiness', 'consistent']
senticnet['steady-state'] = ['-0.97', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.92', 'voltage', 'inrush', 'sinusoidal', 'proliferating', 'quiescent']
senticnet['steady_barrage'] = ['0', '0', '-0.85', '-0.99', '#fear', '#disgust', 'negative', '-0.92', 'onslaught', 'battery', 'shelling', 'bombardment', 'outpouring']
senticnet['steady_decline'] = ['0.05', '0.151', '0', '0.159', '#interest', '#admiration', 'positive', '0.12', 'glamour', 'stylish', 'style', 'clothes_style', 'fancy_clothes']
senticnet['steady_erosion'] = ['-0.16', '0', '-0.18', '-0.23', '#fear', '#disgust', 'negative', '-0.19', 'eat_off', 'remove', 'diminish', 'decrease', 'reduce']
senticnet['steady_growth'] = ['0.05', '0.151', '0', '0.159', '#interest', '#admiration', 'positive', '0.12', 'glamour', 'stylish', 'style', 'clothes_style', 'fancy_clothes']
senticnet['steady_income'] = ['0.057', '0.181', '-0.04', '0.18', '#interest', '#admiration', 'positive', '0.092', 'small_money', 'alter', 'money_change', 'money_coin', 'small_coin']
senticnet['steady_increase'] = ['0.05', '0.151', '0', '0.159', '#interest', '#admiration', 'positive', '0.12', 'glamour', 'stylish', 'style', 'clothes_style', 'fancy_clothes']
senticnet['steady_pace'] = ['-0.04', '0.123', '0', '0.237', '#interest', '#admiration', 'positive', '0.107', 'foot_motion', 'foot_movement', 'foot_action', 'move_ahead', 'stride']
senticnet['steady_rain'] = ['-0.87', '0', '0.91', '-0.78', '#sadness', '#anger', 'negative', '-0.85', 'storm', 'thundershower', 'rain', 'line_storm', 'equinoctial_storm']
senticnet['steady_rise'] = ['0.05', '0.151', '0', '0.159', '#interest', '#admiration', 'positive', '0.12', 'glamour', 'stylish', 'style', 'clothes_style', 'fancy_clothes']
senticnet['steady_state'] = ['-0.87', '0', '0.915', '-0.95', '#anger', '#disgust', 'negative', '-0.91', 'inactivity', 'inactiveness', 'slumber', 'inaction', 'hibernation']
senticnet['steady_upward_trend'] = ['0.05', '0.151', '0', '0.159', '#interest', '#admiration', 'positive', '0.12', 'glamour', 'stylish', 'style', 'clothes_style', 'fancy_clothes']
senticnet['steak'] = ['0.75', '0.896', '0', '0', '#joy', '#interest', 'positive', '0.823', 'fix_dinner', 'feel_hunger', 'need_grocery', 'cook', 'famine']
senticnet['steak_au_poivre'] = ['0.783', '-0.63', '0', '0.829', '#joy', '#admiration', 'positive', '0.749', 'leg_of_lamb', 'gigot', 'joint', 'roast', 'rack_of_lamb']
senticnet['steak_house'] = ['0.039', '0.089', '0.023', '0', '#joy', '#interest', 'positive', '0.035', 'spanish_restaurant', 'tapa', 'french_restaurant', 'paella', 'restaurant_diner']
senticnet['steak_knife'] = ['-0.71', '0', '0.954', '-0.56', '#sadness', '#anger', 'negative', '-0.74', 'jackknife', 'pocket_knife', 'penknife', 'switchblade', 'switchblade_knife']
senticnet['steak_pie'] = ['0.644', '-0.94', '0', '0.729', '#surprise', '#admiration', 'positive', '0.773', 'gem', 'banana_bread', 'date_bread', 'nut_bread', 'quick_bread']
senticnet['steak_restaurant'] = ['0.06', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'eat_food', 'fast_food', 'pizza_parlor', 'pizza_store', 'kitchen_cupboard']
senticnet['steak_sandwich'] = ['0.043', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'apple_pie', 'a_lot_of_food', 'jelly', 'frozen_food', 'cottage_cheese']
senticnet['steak_sauce'] = ['-0.75', '0', '0.593', '-0.59', '#sadness', '#anger', 'negative', '-0.64', 'taco_sauce', 'chili_sauce', 'condiment', 'duck_sauce', 'hoisin_sauce']
senticnet['steak_tartare'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'cook_meat', 'cook_hamburger', 'cook_food', 'cook_rice', 'brown_meat']
senticnet['steakhouse'] = ['0.061', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.055', 'cook', 'stick_butter', 'find_recipe', 'bake_oven', 'food_bake']
senticnet['steal'] = ['-0.92', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'thief', 'steal_idea', 'steal_document', 'hide_evidence', 'bad_person']
senticnet['steal_car'] = ['-0.07', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.07', 'find_wallet', 'bring_money', 'new_car', 'find_car_key', 'license']
senticnet['steal_client'] = ['-0.92', '0', '0.784', '-0.91', '#sadness', '#disgust', 'negative', '-0.87', 'enemy', 'bad_customer', 'competitor', 'disloyal', 'buy_cheaper']
senticnet['steal_document'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'thief', 'steal', 'steal_idea', 'hide_evidence', 'bad_person']
senticnet['steal_idea'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'thief', 'steal', 'steal_document', 'hide_evidence', 'bad_person']
senticnet['steal_money'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'burglary', 'theft', 'stick_up', 'robbery', 'rob']
senticnet['steal_paint'] = ['-0.04', '-0.03', '0.069', '0', '#sadness', '#anger', 'negative', '-0.02', 'make_pretty', 'paint_nail', 'remove_paint', 'dust_furniture', 'vacuum_floor']
senticnet['steal_store'] = ['-0.12', '0.065', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.04', 'bridge_difference', 'stand_alone', 'deal_card_player', 'explain_everything', 'forward_email']
senticnet['stealing'] = ['-0.10', '0', '-0.05', '-0.24', '#sadness', '#disgust', 'negative', '-0.13', 'thieving', 'larceny', 'theft', 'thievery', 'fraudulent_intent']
senticnet['stealth'] = ['-0.82', '0', '0.739', '-0.50', '#sadness', '#anger', 'negative', '-0.69', 'fake', 'juke', 'maneuver', 'manoeuvre', 'feint']
senticnet['stealthily'] = ['0', '-0.76', '0', '0.822', '#surprise', '#admiration', 'positive', '0.792', 'inconspicuously', 'discretely', 'discreetly', 'invisibly', 'unobtrusively']
senticnet['stealthiness'] = ['0.736', '-0.84', '0', '0.883', '#surprise', '#admiration', 'positive', '0.82', 'furtiveness', 'sneakiness', 'secretiveness', 'unsociability', 'unsociableness']
senticnet['stealthy'] = ['0', '0', '-0.88', '-0.70', '#fear', '#disgust', 'negative', '-0.79', 'surreptitious', 'furtive', 'clandestine', 'sneaky', 'undercover']
senticnet['steam'] = ['0.07', '-0.03', '0.023', '0', '#joy', '#surprise', 'positive', '0.026', 'vapor', 'haze', 'mist', 'misty', 'summer_weather']
senticnet['steam_cleaner'] = ['-0.04', '-0.02', '0.089', '-0.14', '#anger', '#disgust', 'negative', '-0.06', 'steam_locomotive', 'loudspeaker', 'collector', 'siren', 'activist']
senticnet['steam_engine'] = ['0.857', '0.8', '0', '0', '#joy', '#interest', 'positive', '0.829', 'noise', 'loud_noise', 'loudness', 'amplifier', 'loud_sound']
senticnet['steam_explosion'] = ['-0.90', '0', '0.825', '-0.61', '#sadness', '#anger', 'negative', '-0.78', 'flaming', 'combustion', 'fire', 'flame', 'internal_combustion']
senticnet['steam_frigate'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'submarine', 'lifeboat', 'titanic', 'large_ship', 'canoe']
senticnet['steam_locomotive'] = ['-0.04', '-0.02', '0.089', '-0.14', '#anger', '#disgust', 'negative', '-0.06', 'vacuum_cleaner', 'loudspeaker', 'collector', 'siren', 'activist']
senticnet['steam_mill'] = ['-0.78', '0', '0.699', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'battery', 'stamp_battery', 'quartz_battery', 'quartz_mill', 'factory']
senticnet['steam_pumping_station'] = ['-0.77', '0', '0.583', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'rain', 'rain_down', 'sheet', 'rain_bucket', 'rain_buckets_down']
senticnet['steam_roller'] = ['-0.84', '0', '0.96', '-0.91', '#anger', '#disgust', 'negative', '-0.90', 'housewrecker', 'housebreaker', 'knacker', 'laborer', 'manual_laborer']
senticnet['steam_shower'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
senticnet['steam_sterilization'] = ['-0.62', '0', '0.987', '-0.74', '#anger', '#disgust', 'negative', '-0.78', 'medical_aid', 'treatment', 'hospice', 'digitalization', 'bloodletting']
senticnet['steam_turbine'] = ['-0.97', '0', '0.658', '-0.60', '#sadness', '#anger', 'negative', '-0.74', 'gas_turbine', 'hydroelectric_turbine', 'impulse_turbine', 'reaction_turbine', 'turbine']
senticnet['steamboat_captain'] = ['-0.64', '0', '0.667', '-0.82', '#anger', '#disgust', 'negative', '-0.71', 'novice', 'beginner', 'tyro', 'tiro', 'initiate']
senticnet['steamboy'] = ['-0.67', '0', '0.931', '-0.77', '#anger', '#disgust', 'negative', '-0.79', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['steamed_broccoli'] = ['0.041', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.043', 'meatloaf', 'salad', 'delicious_meal', 'casserole', 'apple_pie']
senticnet['steamed_bun'] = ['0', '-0.03', '0.046', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'pasta', 'green_bean', 'mayonnaise', 'frozen_food', 'sour_milk']
senticnet['steamed_rice'] = ['0', '-0.03', '0.046', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'pasta', 'green_bean', 'mayonnaise', 'frozen_food', 'sour_milk']
senticnet['steamer_trunk'] = ['0', '0.177', '0', '0.126', '#interest', '#admiration', 'positive', '0.151', 'treasure', 'storage_facility', 'shrine', 'lock_box', 'jewelry_box']
senticnet['steaming_coffee'] = ['0.214', '0.258', '-0.09', '0.2', '#joy', '#interest', 'positive', '0.146', 'cup', 'carafe', 'pitcher', 'container_cup', 'glasses']
senticnet['steaming_cup'] = ['0.807', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.859', 'bubble', 'steam', 'hot', 'boil_water', 'tea']
senticnet['steaming_pile'] = ['-0.82', '0', '0.89', '-0.76', '#sadness', '#anger', 'negative', '-0.82', 'stool', 'dejection', 'fecal_matter', 'shit', 'feces']
senticnet['steaming_tea'] = ['0.214', '0.258', '-0.09', '0.2', '#joy', '#interest', 'positive', '0.146', 'cup', 'carafe', 'pitcher', 'container_cup', 'glasses']
senticnet['steamroll'] = ['0.859', '-0.92', '0', '0.901', '#surprise', '#admiration', 'positive', '0.896', 'coerce', 'hale', 'pressure', 'force', 'squeeze']
senticnet['steamroller'] = ['-0.79', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.87', 'turn_up_heat', 'turn_up_pressure', 'coerce', 'steamroll', 'sandbag']
senticnet['steamy'] = ['0.041', '0.072', '-0.05', '0.091', '#interest', '#admiration', 'positive', '0.038', 'warm_air', 'gaseous', 'hot_air', 'vapor', 'steam']
senticnet['stearate'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
senticnet['steatorrhea'] = ['-0.86', '0', '0.76', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['steatotic'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'bowel', 'scrotum', 'shoulder', 'anus', 'waist']
senticnet['stede_bonnet'] = ['0', '0', '0.034', '-0.08', '#anger', '#disgust', 'negative', '-0.06', 'lifeboat', 'mast', 'bad_man', 'keel', 'yacht']
senticnet['steel'] = ['0.922', '-0.78', '0', '0.866', '#joy', '#admiration', 'positive', '0.858', 'blade', 'brand', 'broadsword', 'claymore', 'sword']
senticnet['steel_alloy'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'gold', 'bronze', 'alloy', 'medal', 'copper']
senticnet['steel_arch_bridge'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'suspension_bridge', 'under_bridge', 'roadway', 'county_highway', 'long_road']
senticnet['steel_billet'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'useful_tool']
senticnet['steel_blade'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'useful_tool']
senticnet['steel_cable'] = ['0', '0.142', '0', '0.125', '#interest', '#admiration', 'positive', '0.134', 'weight_machine', 'power_drill', 'screw', 'screwdriver', 'air_pump']
senticnet['steel_factory'] = ['0.692', '-0.53', '0', '0.68', '#joy', '#admiration', 'positive', '0.634', 'rolling_mill', 'factory', 'mill', 'manufacturing_plant', 'manufactory']
senticnet['steel_girder_bridge'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'suspension_bridge', 'under_bridge', 'roadway', 'county_highway', 'long_road']
senticnet['steel_guitarist'] = ['-0.04', '-0.04', '-0.04', '-0.04', '#fear', '#disgust', 'negative', '-0.02', 'music_shop', 'music_store', 'musical_instrument_store', 'string_quartet', 'high_school_band']
senticnet['steel_jeeg'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amuck', 'murderousness', 'misocainea', 'misogyny']
senticnet['steel_magnate'] = ['0', '0', '-0.99', '-0.73', '#fear', '#disgust', 'negative', '-0.86', 'king', 'baron', 'business_leader', 'mogul', 'big_businessman']
senticnet['steel_mill'] = ['0.641', '-0.56', '0', '0.6', '#joy', '#admiration', 'positive', '0.601', 'rolling_mill', 'factory', 'mill', 'manufacturing_plant', 'manufactory']
senticnet['steel_pen'] = ['0.023', '-0.05', '0.069', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'stamp_pad', 'ballpoint_pen', 'intern', 'paper_punch', 'paper_fastener']
senticnet['steel_plant'] = ['0.757', '-0.67', '0', '0.685', '#joy', '#admiration', 'positive', '0.706', 'rolling_mill', 'factory', 'mill', 'manufacturing_plant', 'manufactory']
senticnet['steel_rebar'] = ['0', '0.127', '-0.04', '0.194', '#interest', '#admiration', 'positive', '0.092', 'concrete', 'tar', 'pavement', 'big_city_life', 'popular_building_material']
senticnet['steel_roller_coaster'] = ['0.056', '-0.01', '0.036', '0.073', '#joy', '#admiration', 'positive', '0.028', 'thrill_ride', 'have_fun', 'drag_race', 'enjoyment', 'roller_coaster']
senticnet['steel_truss_bridge'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'suspension_bridge', 'under_bridge', 'roadway', 'county_highway', 'long_road']
senticnet['steel_wire'] = ['0.085', '0', '0', '0', '#joy', '#joy', 'positive', '0.085', 'wire', 'several_types', 'gas_stove', 'tape_player', 'control_room']
senticnet['steel_wool'] = ['0.972', '0.964', '0', '0', '#joy', '#interest', 'positive', '0.968', 'moistener', 'paper_towel', 'toilet_paper', 'toilet_brush', 'bath_shower']
senticnet['steeliness'] = ['-0.96', '0', '0.903', '0', '#sadness', '#anger', 'negative', '-0.93', 'toughness', 'difficulty', 'stressed', 'resistance', 'tough']
senticnet['steelkilt'] = ['-0.03', '0.157', '0.698', '-0.66', '#anger', '#disgust', 'negative', '-0.30', 'vengefulness', 'vindictively', 'vengefully', 'revengefully', 'vindictive']
senticnet['steelworks'] = ['0.56', '-0.55', '0', '0.807', '#joy', '#admiration', 'positive', '0.642', 'rolling_mill', 'factory', 'mill', 'manufacturing_plant', 'manufactory']
senticnet['steely_determination'] = ['0.827', '0.913', '0', '0.867', '#interest', '#admiration', 'positive', '0.869', 'good_habit', 'rivalry', 'competitive', 'competitive_nature', 'determination']
senticnet['steely_resolve'] = ['0.812', '0.937', '0', '0', '#joy', '#interest', 'positive', '0.875', 'retentive', 'long', 'dogged', 'dour', 'recollective']
senticnet['steep'] = ['0', '0.092', '0.016', '0.074', '#interest', '#admiration', 'positive', '0.05', 'incline', 'knoll', 'up_hill', 'slant', 'mountain_side']
senticnet['steep_ascent'] = ['0', '0', '-0.73', '-0.99', '#fear', '#disgust', 'negative', '-0.86', 'transom', 'track', 'crosspiece', 'crossbeam', 'traversal']
senticnet['steep_canyon'] = ['0', '0.069', '0', '0.065', '#interest', '#admiration', 'positive', '0.067', 'alp', 'high_mountain', 'pass', 'ski_resort', 'high_place']
senticnet['steep_cliff'] = ['0.057', '0.075', '0.039', '0.059', '#interest', '#admiration', 'positive', '0.038', 'summit', 'big_rock', 'high_land', 'peak', 'mountain_side']
senticnet['steep_descent'] = ['-0.11', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.08', 'have_fun', 'spring_up', 'fall_well', 'shovel_snow', 'fly_around']
senticnet['steep_discount'] = ['-0.03', '0.139', '0', '0.109', '#interest', '#admiration', 'positive', '0.07', 'exchange_money', 'purchase', 'sell_goods', 'make_rich', 'incur_debt']
senticnet['steep_drop'] = ['0.978', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.865', 'tumble', 'dew', 'hailstone', 'rain', 'snowflake']
senticnet['steep_embankment'] = ['-0.11', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.08', 'have_fun', 'spring_up', 'fall_well', 'shovel_snow', 'fly_around']
senticnet['steep_grade'] = ['-0.11', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.08', 'have_fun', 'spring_up', 'fall_well', 'shovel_snow', 'fly_around']
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senticnet['steep_learning_curve'] = ['-0.13', '0.071', '0.694', '-0.67', '#anger', '#disgust', 'negative', '-0.35', 'bedevil', 'crucify', 'dun', 'aggravate', 'exasperation']
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senticnet['steep_rise'] = ['0.952', '0.918', '0', '0', '#joy', '#interest', 'positive', '0.935', 'tack', 'foot_part', 'casual_shoes', 'gauntlet', 'pin']
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senticnet['steep_sided_valley'] = ['0', '0.08', '0.032', '0.096', '#interest', '#admiration', 'positive', '0.048', 'land_form', 'land_formation', 'land_feature', 'geographic_feature', 'landscape_feature']
senticnet['steep_slope'] = ['-0.11', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.08', 'have_fun', 'spring_up', 'fall_well', 'shovel_snow', 'fly_around']
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senticnet['steep_valley'] = ['0.97', '0.758', '0', '0', '#joy', '#interest', 'positive', '0.864', 'trot', 'step_out', 'step_aside', 'progress', 'enter_place']
senticnet['steeper'] = ['0', '0.891', '0', '0.96', '#interest', '#admiration', 'positive', '0.925', 'steep', 'precipitously', 'sloping', 'steeply', 'sloped']
senticnet['steepest_drop'] = ['0.978', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.865', 'tumble', 'dew', 'hailstone', 'rain', 'snowflake']
senticnet['steepest_fall'] = ['0.978', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.865', 'tumble', 'dew', 'hailstone', 'rain', 'snowflake']
senticnet['steeple'] = ['0.029', '0.233', '0.019', '0.246', '#interest', '#admiration', 'positive', '0.122', 'catholic_church', 'christianity', 'chapel', 'worship_place', 'cathedral']
senticnet['steeplechase_park'] = ['0.089', '-0.18', '0.037', '-0.06', '#joy', '#surprise', 'positive', '0.043', 'balloon', 'ballon', 'fielder', 'kite', 'blimp']
senticnet['steeplejack'] = ['0.9', '-0.88', '0', '0.687', '#joy', '#surprise', 'positive', '0.823', 'conjurer', 'escape_expert', 'mind_reader', 'telepathist', 'performer']
senticnet['steeply'] = ['0', '0.9', '0', '0.964', '#interest', '#admiration', 'positive', '0.932', 'steep', 'precipitously', 'sloping', 'steeper', 'sloped']
senticnet['steeply_pitched_roof'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'adobe', 'adobe_brick', 'clinker', 'brick', 'firebrick']
senticnet['steer'] = ['0.886', '0.769', '0', '0', '#joy', '#interest', 'positive', '0.828', 'car_movement', 'steer_car', 'car_action', 'navigate', 'turn_ignition']
senticnet['steer_car'] = ['0', '0', '0', '0.159', '#admiration', '#admiration', 'positive', '0.159', 'car_travel', 'car_action', 'control_car', 'car_movement', 'passenger_car']
senticnet['steer_clear'] = ['0', '0', '-0.81', '-0.75', '#fear', '#disgust', 'negative', '-0.78', 'watch_out', 'keep_one_eye_peeled', 'keep_one_eye_skinned', 'keep_one_eye_open', 'aware']
senticnet['steer_wheel'] = ['0.041', '-0.08', '0', '0.056', '#surprise', '#admiration', 'positive', '0.061', 'engine', 'wheel', 'motor', 'axle', 'two_wheels']
senticnet['steerable'] = ['0.493', '-0.90', '0', '0.516', '#surprise', '#admiration', 'positive', '0.638', 'manageable', 'controllable', 'governable', 'administrable', 'dirigible']
senticnet['steering'] = ['0.765', '-0.72', '0', '0', '#joy', '#surprise', 'positive', '0.744', 'tilting', 'tilt', 'bracket', 'swivel', 'angle']
senticnet['steering_committee'] = ['0', '0', '0.88', '-0.92', '#anger', '#disgust', 'negative', '-0.90', 'sofa_bed', 'small_seat', 'beanbag_airline_seat', 'hide-a-bed_sofa', 'airline_seat']
senticnet['steering_group'] = ['0.065', '0.113', '0', '0.1', '#interest', '#admiration', 'positive', '0.093', 'big_group', 'clique', 'group_of_people', 'multitude', 'large_group']
senticnet['steering_mechanism'] = ['0', '-0.09', '0.044', '0.055', '#surprise', '#admiration', 'positive', '0.036', 'two_wheels', 'gear', 'axle', 'engine', 'steer_wheel']
senticnet['steering_oar'] = ['0.937', '0.847', '0', '0', '#joy', '#interest', 'positive', '0.892', 'arrow', 'spear', 'buy_wood', 'baton', 'archery']
senticnet['steering_wheel'] = ['0', '-0.09', '0.044', '0.055', '#surprise', '#admiration', 'positive', '0.036', 'two_wheels', 'gear', 'axle', 'engine', 'steer_wheel']
senticnet['stefan_asbury'] = ['-0.91', '0', '0.931', '-0.72', '#sadness', '#anger', 'negative', '-0.85', 'hit_with_baton', 'get_hurt', 'whip', 'arrow', 'hurt']
senticnet['stefan_edberg_anders_jrryd'] = ['-0.56', '0', '0.617', '-0.80', '#anger', '#disgust', 'negative', '-0.66', 'prolixity', 'prolixness', 'wordiness', 'repetitiveness', 'repetitiousness']
senticnet['stefan_wolpe'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_music', 'hear_music', 'artist', 'make_drawing', 'art']
senticnet['steganography'] = ['0.577', '-0.73', '0', '0.738', '#surprise', '#admiration', 'positive', '0.682', 'decompression', 'cryptography', 'coding', 'secret_writing', 'decryption']
senticnet['stegosaurus'] = ['-1.0', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.89', 'triceratops', 'pterodactyl', 'dinosaur', 'bird-like', 'pterodactyl']
senticnet['steht'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['steiff'] = ['0.71', '0.92', '0', '0', '#joy', '#interest', 'positive', '0.815', 'toy_car', 'toy_ball', 'action_figure', 'toy_marble', 'beanie_baby']
senticnet['steinernema'] = ['0', '-0.09', '0.123', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'gypsy_moth', 'japanese_beetle', 'cucumber_beetle', 'mosquito', 'earwig']
senticnet['stele'] = ['-0.09', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.10', 'funeral_celebration', 'memorial', 'funeral', 'memento', 'cry']
senticnet['stellar'] = ['0', '-0.87', '0', '0.856', '#surprise', '#admiration', 'positive', '0.867', 'impressive', 'phenomenal', 'outstanding', 'terrific', 'excellent']
senticnet['stellar_parallax'] = ['0', '-0.72', '0', '0.712', '#surprise', '#admiration', 'positive', '0.72', 'heavenly_body', 'around_earth', 'source_light', 'sol', 'celestial_body']
senticnet['stellaria'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'bulrush', 'nailrod', 'cattail', 'reedmace']
senticnet['stellite'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'gold', 'bronze', 'alloy', 'medal', 'copper']
senticnet['stem'] = ['0', '0', '0', '0.089', '#admiration', '#admiration', 'positive', '0.089', 'green_leave', 'large_plant', 'leaf', 'plant_part', 'leave']
senticnet['stem_canker'] = ['-0.69', '0', '0.776', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'little_potato', 'russet_scab', 'rhizoctinia_disease', 'rosette', 'plant_disease']
senticnet['stem_cell_transplant'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'displace', 'move', 'move_away', 'bump', 'dislocate']
senticnet['stem_educa'] = ['0.853', '0', '0', '0.873', '#joy', '#admiration', 'positive', '0.863', 'gender_diversity', 'education', 'science', 'human_resource_development', 'award']
senticnet['stem_elongation'] = ['-0.75', '0', '0.512', '-0.75', '#sadness', '#disgust', 'negative', '-0.67', 'inactivity', 'inactiveness', 'slumber', 'inaction', 'hibernation']
senticnet['stem_loop'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['stem_rot'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'apple_rust', 'fungus']
senticnet['stem_stitch'] = ['-0.03', '-0.00', '-0.05', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'bandage', 'needle_thread', 'sew', 'spasm', 'suture']
senticnet['stemma'] = ['0.753', '-0.65', '0', '0.554', '#joy', '#surprise', 'positive', '0.653', 'oculus_dexter', 'peeper', 'naked_eye', 'eye', 'oculus_sinister']
senticnet['sten_gun'] = ['0', '0', '-0.87', '-0.82', '#fear', '#disgust', 'negative', '-0.84', 'pistol', 'gun', 'hydrogen_bomb', 'rifle', 'shotgun']
senticnet['stench'] = ['0', '0.073', '0', '0.092', '#interest', '#admiration', 'positive', '0.082', 'false_tooth', 'scent', 'waste_water', 'sniff', 'odor']
senticnet['stencil_art'] = ['-0.12', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.07', 'bum', 'homeless_people', 'homeless_person', 'panhandler', 'homeless']
senticnet['stencil_graffiti'] = ['-0.12', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.07', 'bum', 'homeless_people', 'homeless_person', 'panhandler', 'homeless']
senticnet['stenosis'] = ['-0.81', '0', '0.684', '-0.81', '#sadness', '#disgust', 'negative', '-0.77', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['stenotic'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'blatancy', 'obviousness']
senticnet['stent_placement'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'blatancy', 'obviousness']
senticnet['stentorian'] = ['0', '0', '0.802', '-0.75', '#anger', '#disgust', 'negative', '-0.78', 'cry', 'yell', 'scream', 'hollo', 'call']
senticnet['steny'] = ['0.783', '-0.63', '0', '0.829', '#joy', '#admiration', 'positive', '0.749', 'leg_of_lamb', 'lamb_roast', 'joint', 'roast', 'rack_of_lamb']
senticnet['step'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'walk_up', 'stroll', 'move', 'go_up_down', 'climb_up']
senticnet['step_aside'] = ['-0.02', '0.087', '0', '0.107', '#interest', '#admiration', 'positive', '0.056', 'say_good_bye', 'resign', 'step_out', 'walk_away', 'open_mailbox']
senticnet['step_dancing'] = ['0.818', '-0.61', '0', '0.743', '#joy', '#admiration', 'positive', '0.727', 'tap_dancing', 'tap_dance', 'dancing', 'dance', 'terpsichore']
senticnet['step_dog_poop'] = ['0', '0', '0.962', '-0.88', '#anger', '#disgust', 'negative', '-0.92', 'get_hit_by_car', 'broken_window', 'broken_wrist', 'step_dog_shit', 'trip_fall_down']
senticnet['step_dog_shit'] = ['0', '0', '0.753', '-0.88', '#anger', '#disgust', 'negative', '-0.82', 'broken_window', 'broken_wrist', 'broken_leg', 'sprain_ankle', 'bloody_nose']
senticnet['step_down'] = ['-0.89', '0', '0.925', '-0.84', '#sadness', '#anger', 'negative', '-0.88', 'vacate', 'resign', 'give_up', 'renounce', 'depart']
senticnet['step_forward'] = ['-0.08', '0.106', '0', '0.125', '#interest', '#admiration', 'positive', '0.047', 'step', 'step_out', 'brave', 'courage', 'stand_out']
senticnet['step_out'] = ['-0.08', '0.106', '0', '0.125', '#interest', '#admiration', 'positive', '0.047', 'step_aside', 'meander', 'walk_away', 'step_forward', 'stay_still']
senticnet['step_stool'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'lavatory', 'commode', 'potty_chair']
senticnet['step_toward'] = ['0', '0', '0.078', '0', '#anger', '#anger', 'negative', '-0.07', 'street_city', 'small_street', 'walkway', 'black_pavement', 'dark_road']
senticnet['step_up'] = ['0.023', '0.212', '-0.13', '0.172', '#interest', '#admiration', 'positive', '0.069', 'awaken', 'levitate', 'rise_up', 'come_up', 'raise_up']
senticnet['step_up_plate'] = ['0.048', '0', '0', '0.092', '#joy', '#admiration', 'positive', '0.07', 'competition', 'match', 'babe_ruth', 'contest', 'game_chess']
senticnet['stepdad'] = ['0', '0.864', '0', '0.751', '#interest', '#admiration', 'positive', '0.807', 'family_member', 'sibling', 'parent', 'female_relative', 'relative']
senticnet['stepdaughter'] = ['0.707', '0', '0', '0.734', '#joy', '#admiration', 'positive', '0.72', 'husband', 'mum', 'parent', 'mother', 'woman_child']
senticnet['stepfather'] = ['0.02', '0.209', '-0.14', '0.202', '#interest', '#admiration', 'positive', '0.071', 'familiar', 'mum', 'familiarity_uncle', 'care_giver', 'familiarity']
senticnet['stephen_collins_foster'] = ['0', '0.981', '0', '0.965', '#interest', '#admiration', 'positive', '0.973', 'surrogate', 'serve', 'patronize', 'stephen_foster', 'foster']
senticnet['stephen_foster'] = ['0', '0.979', '0', '0.971', '#interest', '#admiration', 'positive', '0.975', 'surrogate', 'serve', 'patronize', 'foster', 'stephen_collins_foster']
senticnet['stepmother'] = ['0.02', '0.209', '-0.14', '0.202', '#interest', '#admiration', 'positive', '0.071', 'familiar', 'mum', 'familiarity_uncle', 'care_giver', 'familiarity']
senticnet['stepmum'] = ['0', '0.864', '0', '0.751', '#interest', '#admiration', 'positive', '0.807', 'family_member', 'sibling', 'parent', 'female_relative', 'relative']
senticnet['stepparent_adoption'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'bigamy', 'wedlock']
senticnet['steppe'] = ['0.141', '0', '-0.06', '0.086', '#joy', '#admiration', 'positive', '0.055', 'dancer', 'stout', 'old_dance', 'climb_rock', 'jump_around']
senticnet['stepped-up'] = ['0.931', '-0.9', '0', '0', '#joy', '#surprise', 'positive', '0.915', 'beefed', 'beefier', 'beefed-up', 'beefing', 'much-improved']
senticnet['stepped_aside'] = ['-0.72', '0', '0.98', '0', '#sadness', '#anger', 'negative', '-0.85', 'resignedly', 'abject', 'unhopeful', 'abjectly', 'reconcile']
senticnet['stepped_down'] = ['-0.72', '0', '0.98', '0', '#sadness', '#anger', 'negative', '-0.85', 'resignedly', 'abject', 'unhopeful', 'abjectly', 'reconcile']
senticnet['stepped_down_last_month'] = ['-0.89', '0', '0.925', '-0.84', '#sadness', '#anger', 'negative', '-0.88', 'vacate', 'resign', 'give_up', 'renounce', 'depart']
senticnet['stepped_up_enforcement'] = ['-0.53', '0', '0.825', '-0.87', '#anger', '#disgust', 'negative', '-0.74', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['stepper'] = ['0.667', '-0.91', '0', '0.796', '#surprise', '#admiration', 'positive', '0.793', 'motor', 'engine', 'electric_motor', 'stepping_motor', 'machine']
senticnet['stepping_aside'] = ['-0.72', '0', '0.98', '0', '#sadness', '#anger', 'negative', '-0.85', 'resignedly', 'abject', 'unhopeful', 'abjectly', 'reconcile']
senticnet['stepping_down'] = ['-0.72', '0', '0.98', '0', '#sadness', '#anger', 'negative', '-0.85', 'resignedly', 'abject', 'unhopeful', 'abjectly', 'reconcile']
senticnet['stepping_motor'] = ['0.67', '-0.91', '0', '0.805', '#surprise', '#admiration', 'positive', '0.796', 'electric_motor', 'engine', 'stepper', 'motor', 'machine']
senticnet['stepping_stone'] = ['0', '0', '0.078', '0', '#anger', '#anger', 'negative', '-0.07', 'street_city', 'small_street', 'walkway', 'black_pavement', 'dark_road']
senticnet['stepsister'] = ['0.02', '0.209', '-0.14', '0.202', '#interest', '#admiration', 'positive', '0.071', 'familiar', 'mum', 'familiarity_uncle', 'care_giver', 'familiarity']
senticnet['stepson'] = ['0', '0.732', '0', '0.994', '#interest', '#admiration', 'positive', '0.863', 'foster_kid', 'daughter', 'relation', 'sister', 'adopt']
senticnet['steradian'] = ['-0.63', '0', '0.897', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'cd-rom', 'international_candle', 'candle', 'candlepower_unit', 'standard_candle']
senticnet['sterben'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['sterculia'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'carnauba_palm', 'carnauba', 'wax_palm', 'palmetto', 'palmyra']
senticnet['stereo'] = ['0.052', '-0.11', '0.02', '0', '#joy', '#surprise', 'positive', '0.048', 'radio', 'tuner', 'speaker', 'soprano', 'loudspeaker']
senticnet['stereo_audio'] = ['0.055', '0', '0.03', '0.136', '#joy', '#admiration', 'positive', '0.054', 'loud_soft', 'decibel', 'loudness', 'sound', 'melodic_noise']
senticnet['stereo_bluetooth'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'run_treadmill', 'complete_thesis', 'dwarf_planet', 'insure', 'ridgeback']
senticnet['stereo_headset'] = ['0', '-0.12', '0.011', '0.089', '#surprise', '#admiration', 'positive', '0.067', 'cellular_phone', 'telephone', 'wireless', 'connect', 'message']
senticnet['stereo_image'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'arm_movement', 'hand_gesture', 'water_motion', 'hand_signal', 'sea_movement']
senticnet['stereo_microscope'] = ['0.057', '0', '0.012', '0.08', '#joy', '#admiration', 'positive', '0.042', 'see_better', 'glasses', 'become_gigantic', 'magnify', 'contact_lens']
senticnet['stereo_receiver'] = ['0.036', '-0.04', '0.027', '0.063', '#surprise', '#admiration', 'positive', '0.028', 'radio', 'speaker', 'channel_selector', 'power_switch', 'female_television_connector']
senticnet['stereo_sound'] = ['0.055', '0', '0.03', '0.136', '#joy', '#admiration', 'positive', '0.054', 'loud_soft', 'decibel', 'loudness', 'sound', 'melodic_noise']
senticnet['stereogram'] = ['0.034', '0.075', '0', '0.143', '#interest', '#admiration', 'positive', '0.084', 'photograph', 'art', 'painting', 'artwork', 'mural']
senticnet['stereographic'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'arm_movement', 'hand_gesture', 'water_motion', 'hand_signal', 'sea_movement']
senticnet['stereographic_projection'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'arm_movement', 'hand_gesture', 'water_motion', 'hand_signal', 'sea_movement']
senticnet['stereoisomer'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'enhance', 'intensify', 'deepen', 'potentiate', 'heighten']
senticnet['stereomicroscope'] = ['0.057', '0', '0.012', '0.08', '#joy', '#admiration', 'positive', '0.042', 'see_better', 'glasses', 'become_gigantic', 'magnify', 'contact_lens']
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senticnet['stifling_innovation'] = ['0.033', '-0.19', '0.203', '-0.22', '#anger', '#disgust', 'negative', '-0.05', 'antiquate', 'optional', 'kleenex', 'key_chain', 'sachet']
senticnet['stigma'] = ['-0.77', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'mark', 'emblem', 'religious_sign', 'stain', 'brand']
senticnet['stigma_associated_with'] = ['-0.77', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'mark', 'emblem', 'religious_sign', 'stain', 'brand']
senticnet['stigmatisation'] = ['-0.25', '0', '-0.17', '-0.08', '#sadness', '#fear', 'negative', '-0.16', 'exclusion', 'exclusion_zone', 'isolation', 'detach', 'except']
senticnet['stigmatism'] = ['-0.77', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'mark', 'emblem', 'religious_sign', 'stain', 'brand']
senticnet['stigmatization'] = ['-0.77', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'mark', 'emblem', 'religious_sign', 'stain', 'brand']
senticnet['stigmatize'] = ['-0.88', '0', '0.871', '0', '#sadness', '#anger', 'negative', '-0.87', 'stigma', 'disgraceful', 'unstigmatized', 'brand', 'stigmatized']
senticnet['stigmatized'] = ['-0.88', '0', '0.866', '0', '#sadness', '#anger', 'negative', '-0.87', 'stigma', 'disgraceful', 'unstigmatized', 'brand', 'stigmatize']
senticnet['still'] = ['-0.82', '0', '0.99', '-0.87', '#anger', '#disgust', 'negative', '-0.89', 'motionless', 'rigid', 'inactive', 'nonmoving', 'static']
senticnet['still_alive'] = ['-0.87', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.91', 'loss_oflife', 'chest_pain', 'die', 'tumor', 'end_life']
senticnet['still_reeling_from'] = ['0', '0.909', '0', '0.948', '#interest', '#admiration', 'positive', '0.928', 'set_off', 'shake_up', 'scandalmongering', 'sensationalistic', 'sensational']
senticnet['still_smarting_from'] = ['0.568', '-0.99', '0', '0.638', '#surprise', '#admiration', 'positive', '0.734', 'push_aside', 'brush_off', 'disregard', 'ignore', 'dismiss']
senticnet['still_unclear'] = ['-0.79', '0.241', '-0.52', '-0.11', '#sadness', '#fear', 'negative', '-0.29', 'disoriented', 'obscure', 'unsure', 'uncertain', 'unfair']
senticnet['still_water'] = ['0.718', '0', '0', '0', '#joy', '#joy', 'positive', '0.718', 'water_container', 'clean_water', 'dampness', 'water', 'cool_water']
senticnet['stillborn'] = ['-0.70', '0', '0.919', '-0.73', '#anger', '#disgust', 'negative', '-0.78', 'childless', 'unsuccessful', 'acarpous', 'abortive', 'unfruitful']
senticnet['stillborn_baby'] = ['0', '0', '0', '0.939', '#admiration', '#admiration', 'positive', '0.939', 'large_family', 'small_child', 'youth', 'quiet_baby', 'deliver_baby']
senticnet['stillborn_child'] = ['0.706', '0', '0', '0', '#joy', '#joy', 'positive', '0.706', 'birth', 'new_baby', 'born', 'birthday', 'thanksgiving']
senticnet['stillborn_cub'] = ['-0.95', '0', '0.735', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'novice', 'beginner', 'tyro', 'tiro', 'initiate']
senticnet['stillborn_infant'] = ['-0.51', '0', '0.747', '-0.94', '#anger', '#disgust', 'negative', '-0.73', 'neonate', 'premature_baby', 'preterm_baby', 'premature_infant', 'preterm_infant']
senticnet['stillborn_son'] = ['0.95', '0', '0', '0', '#joy', '#joy', 'positive', '0.95', 'born', 'new_baby', 'birthday', 'thanksgiving', 'anniversary']
senticnet['stillness'] = ['0.449', '0.305', '-0.19', '0.222', '#joy', '#interest', 'positive', '0.196', 'calm', 'peace', 'serenity', 'quiet', 'tranquility']
senticnet['stilt_man'] = ['-0.98', '0', '0.714', '-0.90', '#sadness', '#disgust', 'negative', '-0.86', 'stupid', 'stupid_person', 'stupe', 'dullard', 'dolt']
senticnet['stilt_sandpiper'] = ['0.797', '-0.71', '0', '0.736', '#joy', '#admiration', 'positive', '0.751', 'wood_ibis', 'wood_stork', 'ibis', 'wading_bird', 'wader']
senticnet['stilt_walker'] = ['0', '0', '0', '0.987', '#admiration', '#admiration', 'positive', '0.987', 'emote', 'bold', 'form_of_social_interaction', 'act', 'stage_play']
senticnet['stilted_dialogue'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicado', 'unpronounceable', 'impassive', 'incommunicative', 'silent']
senticnet['stilton_cheese'] = ['0.644', '-0.94', '0', '0.729', '#surprise', '#admiration', 'positive', '0.773', 'gem', 'banana_bread', 'date_bread', 'nut_bread', 'quick_bread']
senticnet['stimulant'] = ['0.579', '-0.82', '0', '0.564', '#joy', '#surprise', 'positive', '0.657', 'negative_reinforcer', 'positive_reinforcing_stimulus', 'positive_reinforcer', 'negative_reinforcing_stimulus', 'reinforcer']
senticnet['stimulant_caffeine'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'wooden_spoon', 'cereal_bowl', 'teaspoon', 'container_cup', 'large_container']
senticnet['stimulant_drug'] = ['0.672', '0.632', '-0.23', '0.312', '#joy', '#interest', 'positive', '0.344', 'euphoria', 'buoyancy', 'perkiness', 'jubilance', 'ecstasy']
senticnet['stimulant_effect'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'wooden_spoon', 'cereal_bowl', 'teaspoon', 'container_cup', 'large_container']
senticnet['stimulant_laxative'] = ['-0.50', '0', '0.582', '-0.57', '#anger', '#disgust', 'negative', '-0.55', 'purgative', 'cathartic', 'physic', 'aperient', 'phenolphthalein']
senticnet['stimulant_methamphetamine'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'use_drug', 'do_drug', 'drug_use', 'drug', 'addiction']
senticnet['stimulate'] = ['0.88', '0.967', '0', '0', '#joy', '#interest', 'positive', '0.923', 'sensationalistic', 'sensational', 'scandalmongering', 'sensationally', 'set_off']
senticnet['stimulate_appetite'] = ['0.032', '0.075', '0', '0', '#joy', '#interest', 'positive', '0.053', 'alcohol', 'aperitif', 'beverage', 'adult_drink', 'bartender']
senticnet['stimulate_creativity'] = ['0.858', '-0.88', '0', '0.508', '#joy', '#surprise', 'positive', '0.751', 'encourage', 'stir', 'shake', 'excite', 'shake_up']
senticnet['stimulate_demand'] = ['0.66', '-0.85', '0', '0.744', '#surprise', '#admiration', 'positive', '0.752', 'further', 'promote', 'encourage', 'conduce', 'advance']
senticnet['stimulate_discussion'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'discuss', 'communication', 'communicate_person', 'share', 'autograph']
senticnet['stimulate_growth'] = ['0.66', '-0.85', '0', '0.744', '#surprise', '#admiration', 'positive', '0.752', 'further', 'promote', 'encourage', 'conduce', 'advance']
senticnet['stimulate_mind'] = ['0.868', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.929', 'get_informed', 'expand_horizon', 'acculturation', 'appreciate_art', 'enjoy_story']
senticnet['stimulated_emission'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'emotional_arousal', 'excitement', 'arousal', 'anger']
senticnet['stimulates_appetite'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'cholecystokinin', 'secretin', 'gastrointestinal_hormone']
senticnet['stimulating'] = ['0.645', '0.637', '-0.23', '0.299', '#joy', '#interest', 'positive', '0.336', 'gladdened', 'tickle_pink', 'inebriate', 'exalt', 'beatify']
senticnet['stimulating_conversation'] = ['0.858', '-0.88', '0', '0.508', '#joy', '#surprise', 'positive', '0.751', 'encourage', 'stir', 'shake', 'excite', 'shake_up']
senticnet['stimulating_environment'] = ['-0.05', '0.096', '0', '0.066', '#interest', '#admiration', 'positive', '0.037', 'hue_tint', 'spectrum', 'red_blue', 'green_yellow', 'tint']
senticnet['stimulation'] = ['0.341', '0', '0', '0.088', '#joy', '#admiration', 'positive', '0.215', 'good_lover', 'personal_fulfillment', 'lower_stress', 'sit_sun', 'look_night_sky']
senticnet['stimulative'] = ['0.95', '-0.95', '0', '0.769', '#joy', '#surprise', 'positive', '0.89', 'adrenocorticotropic', 'adrenocorticotrophic', 'stimulant', 'stimulating', 'excitant']
senticnet['stimulatory_effect'] = ['0.95', '-0.95', '0', '0.769', '#joy', '#surprise', 'positive', '0.89', 'adrenocorticotropic', 'adrenocorticotrophic', 'stimulant', 'stimulating', 'stimulative']
senticnet['stimulus'] = ['0', '0.697', '0', '0.84', '#interest', '#admiration', 'positive', '0.768', 'comment', 'remark', 'input_signal', 'input', 'stimulation']
senticnet['stimulus_generalisation'] = ['0', '0.857', '0', '0.84', '#interest', '#admiration', 'positive', '0.849', 'generalisation', 'abstraction', 'generality', 'inductive_reasoning', 'generalization']
senticnet['sting'] = ['-0.10', '-0.02', '-0.07', '-0.13', '#sadness', '#disgust', 'negative', '-0.07', 'dislocation', 'welt', 'ache', 'laceration', 'abrasion']
senticnet['sting_insect'] = ['-0.93', '0', '0.879', '-0.98', '#sadness', '#disgust', 'negative', '-0.93', 'bee', 'pain', 'painful', 'sharp_pain', 'sharp']
senticnet['sting_operation'] = ['0.931', '-0.71', '0', '0.63', '#joy', '#surprise', 'positive', '0.758', 'hustle', 'con', 'bunco', 'sting', 'bunco_game']
senticnet['sting_operation_conducted'] = ['0.931', '-0.71', '0', '0.63', '#joy', '#surprise', 'positive', '0.758', 'hustle', 'con', 'bunco', 'sting', 'bunco_game']
senticnet['sting_operation_game'] = ['0.93', '-0.72', '0', '0.622', '#joy', '#surprise', 'positive', '0.759', 'hustle', 'con', 'sting_operation', 'sting', 'bunco']
senticnet['sting_operation_involving'] = ['0.931', '-0.71', '0', '0.63', '#joy', '#surprise', 'positive', '0.758', 'hustle', 'con', 'bunco', 'sting', 'bunco_game']
senticnet['sting_ray'] = ['-0.73', '0', '0.72', '-0.93', '#sadness', '#disgust', 'negative', '-0.79', 'imp', 'elf', 'hob', 'leprechaun', 'pixie']
senticnet['stinger'] = ['0', '0', '-0.83', '-0.8', '#fear', '#disgust', 'negative', '-0.81', 'armory', 'tank', 'hydrogen_bomb', 'rifle', 'use_hunt']
senticnet['stinging_nettle'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['stinging_nettle_urtica_dioica'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['stinging_pain'] = ['0', '0', '0', '-0.93', '#disgust', '#disgust', 'negative', '-0.93', 'injury', 'broken_bone', 'bad_knee', 'strengthful', 'physical_pain']
senticnet['stinging_sensation'] = ['-0.74', '0', '0.766', '0', '#sadness', '#anger', 'negative', '-0.75', 'use_needle', 'clothes_repair', 'woven', 'embroidery', 'thread_needle']
senticnet['stingray'] = ['-0.20', '-0.10', '0.07', '-0.24', '#sadness', '#disgust', 'negative', '-0.10', 'gill', 'sea_life', 'seafood', 'swim_animal', 'fillet']
senticnet['stingy'] = ['-0.91', '0', '0.598', '-0.52', '#sadness', '#anger', 'negative', '-0.68', 'parsimonious', 'penurious', 'scrimy', 'beggarly', 'chintzy']
senticnet['stingy_defense'] = ['-0.63', '0', '0.712', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'upset', 'disconcert', 'discompose', 'discomfit', 'untune']
senticnet['stink'] = ['0', '0', '0.883', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'feel_dirty', 'remove_stain', 'clean_up', 'clean_up_mess', 'dry_hair']
senticnet['stink_bug'] = ['0', '-0.09', '0.123', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'gypsy_moth', 'japanese_beetle', 'cucumber_beetle', 'mosquito', 'earwig']
senticnet['stink_mess'] = ['0.935', '0', '0', '0', '#joy', '#joy', 'positive', '0.935', 'clean_up_kitchen', 'feel_dirty', 'remove_stain', 'stink', 'clean_up']
senticnet['stink_out'] = ['-0.67', '0', '0.503', '-0.67', '#sadness', '#disgust', 'negative', '-0.61', 'stink_up', 'smell_up', 'odorize', 'incense', 'perfume']
senticnet['stink_up'] = ['-0.67', '0', '0.503', '-0.67', '#sadness', '#disgust', 'negative', '-0.61', 'odorize', 'smell_up', 'stink_out', 'incense', 'perfume']
senticnet['stinkbug'] = ['-0.77', '0', '0.576', '-0.92', '#sadness', '#disgust', 'negative', '-0.75', 'insect', 'firebug', 'water_bug', 'assassin_bug', 'heteropterous_insect']
senticnet['stinker'] = ['-0.71', '0', '0.83', '-0.68', '#sadness', '#anger', 'negative', '-0.74', 'gag', 'jest', 'jape', 'joke', 'laugh']
senticnet['stinkin'] = ['-0.66', '0', '0.595', '-0.83', '#sadness', '#disgust', 'negative', '-0.7', 'bastard', 'dickhead', 'disagreeable_person', 'motherfucker', 'son_of_bitch']
senticnet['stinkiness'] = ['-0.97', '0', '0.537', '-0.69', '#sadness', '#disgust', 'negative', '-0.73', 'body_odor', 'aroma', 'scent', 'olfactory_property', 'smell']
senticnet['stinking'] = ['-0.86', '0', '0.644', '-0.88', '#sadness', '#disgust', 'negative', '-0.79', 'frowsty', 'fetid', 'foetid', 'smelly', 'malodorous']
senticnet['stinking_clover'] = ['-0.81', '0', '0.699', '-0.52', '#sadness', '#anger', 'negative', '-0.67', 'herb', 'spider_flower', 'spider_plant', 'cleome', 'herbaceous_plant']
senticnet['stinking_mud'] = ['-0.02', '-0.01', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.03', 'dung', 'manure', 'shit', 'dog_poop', 'crap']
senticnet['stinking_smut'] = ['-0.93', '0', '0.699', '-0.91', '#sadness', '#disgust', 'negative', '-0.84', 'onion_smut', 'head_smut', 'smut_fungus', 'flag_smut_fungus', 'covered_smut']
senticnet['stinkwood'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'black_locust']
senticnet['stinky'] = ['0', '0', '0.943', '-0.8', '#anger', '#disgust', 'negative', '-0.87', 'fight_fire', 'filthy', 'dirty', 'stink', 'fall_mud']
senticnet['stinky_feet'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['stinky_tofu'] = ['0.9', '0.963', '0', '0', '#joy', '#interest', 'positive', '0.931', 'form_of_food', 'japanese_food', 'uncooked', 'healthy', 'food']
senticnet['stinsford'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'intrepidly', 'fearlessness', 'fearlessly', 'unafraid', 'unfearing']
senticnet['stint'] = ['0', '-0.01', '0.037', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'limit_time', 'restrict', 'deadline', 'limit', 'restriction']
senticnet['stinting'] = ['0.749', '-0.70', '0', '0', '#joy', '#surprise', 'positive', '0.726', 'scrimp', 'economical', 'scant', 'frugal', 'skimp']
senticnet['stipe_base'] = ['-0.60', '0', '0.541', '-0.52', '#sadness', '#anger', 'negative', '-0.55', 'swoon', 'pass_out', 'faint', 'zonk_out', 'change_state']
senticnet['stipend'] = ['0', '0.808', '0', '0.792', '#interest', '#admiration', 'positive', '0.8', 'honorarium', 'allowance', 'fee', 'bursary', 'payment']
senticnet['stipendiary'] = ['0.519', '-0.86', '0', '0.726', '#surprise', '#admiration', 'positive', '0.702', 'compensated', 'remunerated', 'paying', 'paid', 'remunerative']
senticnet['stipulate'] = ['-0.88', '0', '0.977', '-0.81', '#sadness', '#anger', 'negative', '-0.89', 'execute', 'run', 'compel', 'oblige', 'enforce']
senticnet['stir'] = ['0.884', '0.998', '0', '0', '#joy', '#interest', 'positive', '0.941', 'sensationally', 'sensational', 'sensationalistic', 'scandalmongering', 'set_off']
senticnet['stir_coffee'] = ['0.837', '0', '0', '0', '#joy', '#joy', 'positive', '0.837', 'eat_food', 'eat_pudding', 'eat_cereal', 'satisfy_appetite', 'able_eat']
senticnet['stir_constantly'] = ['-0.66', '0', '0.992', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'cold', 'boil', 'temperature', 'low_temperature', 'frigidity']
senticnet['stir_occasionally'] = ['-0.66', '0', '0.992', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'cold', 'boil', 'temperature', 'low_temperature', 'frigidity']
senticnet['stir_paint'] = ['0.165', '0.069', '0.028', '0.057', '#joy', '#interest', 'positive', '0.066', 'make_paint', 'buy_paint_brush', 'wall_art', 'buy_paint', 'pick_up_brush']
senticnet['stir_paint_brush'] = ['0.165', '0.069', '0.028', '0.057', '#joy', '#interest', 'positive', '0.066', 'make_paint', 'buy_paint', 'wall_art', 'stir_paint', 'pick_up_brush']
senticnet['stir_pot'] = ['0', '-0.08', '0', '0', '#surprise', '#surprise', 'positive', '0.081', 'peel_potato', 'chop_vegetable', 'burn_food', 'heat_pot_water', 'cut_vegetable']
senticnet['stir_up'] = ['0.879', '0.884', '0', '0', '#joy', '#interest', 'positive', '0.881', 'incite', 'fire_up', 'instigate', 'inflame', 'tumult']
senticnet['stirling_prize'] = ['-0.81', '0', '0.543', '-0.63', '#sadness', '#disgust', 'negative', '-0.66', 'caper', 'pickle', 'relish', 'dill_pickle', 'sweet_pickle']
senticnet['stirring_constantly'] = ['-0.66', '0', '0.992', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'cold', 'boil', 'temperature', 'low_temperature', 'frigidity']
senticnet['stirring_continuously'] = ['-0.66', '0', '0.992', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'cold', 'boil', 'temperature', 'low_temperature', 'frigidity']
senticnet['stirring_frequently'] = ['-0.66', '0', '0.992', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'cold', 'boil', 'temperature', 'low_temperature', 'frigidity']
senticnet['stirring_occasionally'] = ['-0.66', '0', '0.992', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'cold', 'boil', 'temperature', 'low_temperature', 'frigidity']
senticnet['stirring_rendition'] = ['0.773', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.844', 'favorite', 'crowd', 'acclamation', 'bunch', 'large_crowd']
senticnet['stirring_up'] = ['-0.98', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.93', 'foment', 'agitated', 'tumult', 'stir_up', 'earnestness']
senticnet['stirringly'] = ['0.512', '0.658', '-0.25', '0.256', '#joy', '#interest', 'positive', '0.293', 'riotously', 'expansively', 'ebulliently', 'zestfulness', 'gusto']
senticnet['stitch'] = ['-0.03', '-0.00', '-0.05', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'bandage', 'needle_thread', 'sew', 'spasm', 'suture']
senticnet['stitch_together'] = ['0', '0.211', '-0.12', '0.286', '#interest', '#admiration', 'positive', '0.125', 'bring_together', 'fuse', 'redo', 'fuse_together', 'request']
senticnet['stitchery'] = ['0.548', '-0.61', '0', '0.556', '#surprise', '#admiration', 'positive', '0.572', 'patchwork', 'darn', 'mend', 'stitch', 'applique']
senticnet['stitchwort'] = ['-0.64', '0', '0.555', '-0.62', '#sadness', '#disgust', 'negative', '-0.60', 'herb', 'common_chickweed', 'chickweed', 'greater_chickweed', 'starwort']
senticnet['stitzer'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'wrapping_paper', 'wrap_gift', 'butcher_paper', 'paper']
senticnet['stochastic'] = ['-0.89', '0', '0.972', '-0.50', '#sadness', '#anger', 'negative', '-0.79', 'unsteadiness', 'haphazardness', 'unregularity', 'variability', 'fitfulness']
senticnet['stochastic_model'] = ['-0.89', '0', '0.972', '-0.50', '#sadness', '#anger', 'negative', '-0.79', 'unsteadiness', 'haphazardness', 'unregularity', 'variability', 'fitfulness']
senticnet['stochastic_nature'] = ['-0.89', '0', '0.972', '-0.50', '#sadness', '#anger', 'negative', '-0.79', 'unsteadiness', 'haphazardness', 'unregularity', 'variability', 'fitfulness']
senticnet['stochastic_resonance'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'constancy', 'monotony', 'invariance', 'changelessness', 'unchangeability']
senticnet['stochastically'] = ['-0.89', '0', '0.972', '-0.50', '#sadness', '#anger', 'negative', '-0.79', 'unsteadiness', 'haphazardness', 'unregularity', 'variability', 'fitfulness']
senticnet['stochasticity'] = ['-0.89', '0', '0.972', '-0.50', '#sadness', '#anger', 'negative', '-0.79', 'unsteadiness', 'haphazardness', 'unregularity', 'variability', 'fitfulness']
senticnet['stock'] = ['0.021', '0.079', '0.011', '0.118', '#interest', '#admiration', 'positive', '0.052', 'hang_hanger', 'store_dress', 'keep_clothes', 'leg_wear', 'store_shoes']
senticnet['stock-taking'] = ['0.916', '0.973', '0', '0', '#joy', '#interest', 'positive', '0.945', 'inventorying', 'inventory', 'take_stock', 'stocktaking', 'stock_list']
senticnet['stock_exchange'] = ['-0.03', '0.125', '0', '0.105', '#interest', '#admiration', 'positive', '0.064', 'switch', 'small_shop', 'shop', 'return_goods', 'sell_stuff']
senticnet['stock_food'] = ['-0.11', '0', '-0.04', '0', '#sadness', '#fear', 'negative', '-0.08', 'forward_email', 'mind_store', 'deal_card_player', 'position_furniture', 'explain_everything']
senticnet['stock_list'] = ['0.919', '0.975', '0', '0', '#joy', '#interest', 'positive', '0.947', 'inventorying', 'stock-taking', 'take_stock', 'stocktaking', 'inventory']
senticnet['stock_market'] = ['-0.02', '0.254', '0', '0.181', '#interest', '#admiration', 'positive', '0.137', 'wall_street', 'merchant', 'trade', 'exchange_goods', 'market']
senticnet['stock_market_boom'] = ['-0.55', '0', '0.869', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'air_bubble', 'foam', 'froth', 'soap_bubble', 'globule']
senticnet['stock_market_bubble'] = ['-0.55', '0', '0.869', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'air_bubble', 'foam', 'froth', 'soap_bubble', 'globule']
senticnet['stock_merchandise'] = ['-0.06', '0', '0.035', '0', '#sadness', '#anger', 'negative', '-0.04', 'buy_grocery', 'purchase_food', 'buy_vegetable', 'purchase_grocery', 'shop_food']
senticnet['stock_ownership'] = ['0.069', '0.07', '0', '0.116', '#interest', '#admiration', 'positive', '0.085', 'entrepreneur', 'invest_money', 'move_around', 'traveling_person', 'guilty_person']
senticnet['stock_raiser'] = ['-0.08', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.11', 'serf', 'peasant', 'servant', 'poor', 'villein']
senticnet['stock_speculator'] = ['-0.78', '0', '0.944', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'loser', 'gambler', 'mortal', 'better', 'bettor']
senticnet['stock_trader'] = ['-0.78', '0', '0.944', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'loser', 'gambler', 'mortal', 'better', 'bettor']
senticnet['stockade_settlement'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'castle', 'bell_tower', 'spire', 'steeple', 'religious_place']
senticnet['stockbroker'] = ['-0.78', '0', '0.944', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'loser', 'gambler', 'mortal', 'better', 'bettor']
senticnet['stockbroker_belt'] = ['0.623', '-0.56', '0', '0.974', '#joy', '#admiration', 'positive', '0.721', 'alterable', 'convertible', 'translatable', 'transformable', 'transmutable']
senticnet['stockholder'] = ['0.069', '0.07', '0', '0.116', '#interest', '#admiration', 'positive', '0.085', 'entrepreneur', 'invest_money', 'move_around', 'traveling_person', 'guilty_person']
senticnet['stockholder_approval'] = ['0.069', '0.07', '0', '0.116', '#interest', '#admiration', 'positive', '0.085', 'entrepreneur', 'invest_money', 'move_around', 'traveling_person', 'guilty_person']
senticnet['stockholder_meeting'] = ['0.069', '0.07', '0', '0.116', '#interest', '#admiration', 'positive', '0.085', 'entrepreneur', 'invest_money', 'move_around', 'traveling_person', 'guilty_person']
senticnet['stockholm_globe'] = ['0.026', '-0.03', '0', '0.091', '#surprise', '#admiration', 'positive', '0.051', 'stadium', 'auditorium', 'television_station', 'music_hall', 'movie_theater']
senticnet['stockholm_norra'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'identifiable', 'specifiable', 'recognizable', 'diagnosable', 'classifiable']
senticnet['stockier'] = ['0.17', '0', '0.081', '0.184', '#joy', '#admiration', 'positive', '0.091', 'food_holder', 'food_server', 'crockery', 'flattery', 'round_flat']
senticnet['stocking'] = ['0', '-0.04', '0.074', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'nightgown', 'garment', 'judo', 'kimono', 'apparel']
senticnet['stocking_cap'] = ['0.08', '0.106', '-0.04', '0.112', '#interest', '#admiration', 'positive', '0.063', 'helmet', 'head_gear', 'cap', 'head_wear', 'head_protection']
senticnet['stockpile'] = ['0', '0.727', '0', '0.811', '#interest', '#admiration', 'positive', '0.769', 'stockpile', 'stockpiling', 'stockpiled', 'depleted', 'uranium']
senticnet['stockpiled'] = ['0', '0.727', '0', '0.808', '#interest', '#admiration', 'positive', '0.768', 'stockpile', 'stockpiling', 'stockpile', 'depleted', 'uranium']
senticnet['stockpiling'] = ['0', '0.874', '0', '0.957', '#interest', '#admiration', 'positive', '0.915', 'store', 'collect', 'accumulate', 'hoarding', 'stashing']
senticnet['stockpoint'] = ['-0.63', '0', '0.862', '-0.52', '#sadness', '#anger', 'negative', '-0.67', 'knot', 'bunch', 'tuft', 'tussock', 'agglomeration']
senticnet['stocktaking'] = ['0.91', '0.983', '0', '0', '#joy', '#interest', 'positive', '0.947', 'inventorying', 'stock-taking', 'take_stock', 'inventory', 'stock_list']
senticnet['stocky'] = ['0.733', '0.982', '0', '0', '#joy', '#interest', 'positive', '0.857', 'thick-set', 'thickset', 'corpulent', 'barrel-chested', 'broad-shouldered']
senticnet['stocky_body'] = ['0.098', '0.084', '0.819', '-0.68', '#anger', '#disgust', 'negative', '-0.33', 'grasp', 'covet', 'covetousness', 'avaricious', 'grabby']
senticnet['stocky_build'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'plato', 'natural_world', 'hair', 'feature', 'stick_out']
senticnet['stocky_man'] = ['-0.64', '0', '0.632', '-0.61', '#sadness', '#anger', 'negative', '-0.62', 'bearded', 'barbate', 'bewhiskered', 'whiskered', 'whiskery']
senticnet['stodgily'] = ['0', '0', '-0.85', '-0.81', '#fear', '#disgust', 'negative', '-0.83', 'stodginess', 'old-fashioned', 'dull', 'stodgy', 'glutinous']
senticnet['stodginess'] = ['-0.57', '0', '0.841', '-0.50', '#sadness', '#anger', 'negative', '-0.64', 'somberness', 'sombreness', 'gravity', 'soberness', 'graveness']
senticnet['stodgy'] = ['0', '0', '-0.84', '-0.81', '#fear', '#disgust', 'negative', '-0.82', 'stodginess', 'old-fashioned', 'dull', 'stodgily', 'glutinous']
senticnet['stogie'] = ['0.031', '0', '-0.08', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'smoke_cigarette', 'smoke', 'cinder', 'tobacco_product', 'smoke_pipe']
senticnet['stoic'] = ['-0.90', '0', '0.792', '-0.96', '#sadness', '#disgust', 'negative', '-0.88', 'phlegmatic', 'unemotional', 'unblinking', 'phlegmatical', 'chilly']
senticnet['stoic_philosophy'] = ['0.971', '-0.71', '0', '0.656', '#joy', '#surprise', 'positive', '0.781', 'wellbeing', 'human_flourishing', 'successfulness', 'upbeat', 'strength']
senticnet['stoical'] = ['-0.79', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.81', 'stolid', 'stoic', 'taciturn', 'nonchalant', 'soldierly']
senticnet['stoically'] = ['0.923', '0', '0', '0.874', '#joy', '#admiration', 'positive', '0.899', 'meekly', 'mutely', 'glumly', 'obediently', 'sullenly']
senticnet['stoicism'] = ['0.595', '-0.65', '0', '0.89', '#surprise', '#admiration', 'positive', '0.713', 'tranquility', 'serenity', 'disposition', 'repose', 'placidity']
senticnet['stokes_flow'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'assumption', 'precondition', 'provision', 'constraint', 'premiss']
senticnet['stolid'] = ['0', '0.722', '0', '0.776', '#interest', '#admiration', 'positive', '0.749', 'grandfatherly', 'fatherly', 'affable', 'avuncular', 'supercilious']
senticnet['stolidity'] = ['0', '0', '0.709', '-0.92', '#anger', '#disgust', 'negative', '-0.81', 'apathetic', 'unemotionality', 'passionless', 'indifference', 'phlegm']
senticnet['stolidly'] = ['-0.81', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.86', 'impassiveness', 'impassivity', 'emotionlessness', 'unemotionality', 'stolidity']
senticnet['stomach'] = ['-0.99', '0', '0.871', '-0.55', '#sadness', '#anger', 'negative', '-0.80', 'endure', 'brook', 'tolerate', 'bear_up', 'abide']
senticnet['stomach_ache'] = ['0.052', '-0.04', '-0.05', '-0.20', '#fear', '#disgust', 'negative', '-0.04', 'indigestion', 'make_little_hungry', 'full_stomach', 'sick', 'become_full']
senticnet['stomach_acid'] = ['0', '0', '-0.70', '-0.76', '#fear', '#disgust', 'negative', '-0.73', 'choke', 'become_full', 'get_full', 'burp', 'sickness']
senticnet['stomach_ailment'] = ['-0.09', '0', '-0.16', '-0.21', '#fear', '#disgust', 'negative', '-0.15', 'sickness', 'malady', 'polio', 'disease', 'ailment']
senticnet['stomach_bug'] = ['-0.71', '0', '-0.96', '-0.94', '#fear', '#disgust', 'negative', '-0.87', 'ill', 'unconscious', 'sick', 'feel_sick', 'sleep_apnea']
senticnet['stomach_cancer'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['stomach_discomfort'] = ['-0.06', '0', '-0.15', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'severe_pain', 'discomfort', 'physical_reaction', 'choke_death', 'embarrassment']
senticnet['stomach_flu'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'gastroenteritis', 'inflammatory_disease', 'intestinal_flu', 'pelvic_inflammatory_disease']
senticnet['stomach_full'] = ['-0.08', '-0.04', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.05', 'wipe_mouth_napkin', 'tuberculosis', 'use_fork', 'swallow', 'eat']
senticnet['stomach_irritation'] = ['0', '0', '-0.70', '-0.76', '#fear', '#disgust', 'negative', '-0.73', 'choke', 'become_full', 'get_full', 'burp', 'sickness']
senticnet['stomach_muscle'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'deltoid', 'muscle', 'abductor', 'skeletal_muscle']
senticnet['stomach_pain'] = ['-0.06', '0', '-0.15', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'severe_pain', 'discomfort', 'physical_reaction', 'choke_death', 'embarrassment']
senticnet['stomach_poison'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['stomach_problem'] = ['0.763', '0.872', '0', '0', '#joy', '#interest', 'positive', '0.818', 'decrease_hunger', 'feel_full', 'upset_stomach', 'get_fat', 'a_little_hungry']
senticnet['stomach_rumble'] = ['0.796', '0.851', '0', '0', '#joy', '#interest', 'positive', '0.824', 'hungry', 'delicious_food', 'nice_meal', 'meal', 'snack']
senticnet['stomach_surgery'] = ['0.934', '-0.88', '0', '0.797', '#joy', '#surprise', 'positive', '0.872', 'scan', 'checkup', 'scrutiny', 'medical_examination', 'medical_exam']
senticnet['stomach_ulcer'] = ['-0.71', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.73', 'pain', 'gape', 'sunburn', 'ouch', 'cut_skin']
senticnet['stomach_upset'] = ['-0.06', '0', '-0.15', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'severe_pain', 'discomfort', 'physical_reaction', 'choke_death', 'embarrassment']
senticnet['stomach_wound'] = ['-0.08', '-0.02', '0', '-0.19', '#sadness', '#disgust', 'negative', '-0.08', 'wound', 'lethal', 'vaccination', 'destruction', 'fire_bullet']
senticnet['stomachache'] = ['0', '0', '-0.70', '-0.76', '#fear', '#disgust', 'negative', '-0.73', 'choke', 'become_full', 'get_full', 'burp', 'sickness']
senticnet['stomacher'] = ['-0.53', '0', '0.682', '-0.74', '#anger', '#disgust', 'negative', '-0.65', 'girdle', 'cincture', 'cummerbund', 'waistband', 'waistcloth']
senticnet['stomachic'] = ['-0.50', '0', '0.582', '-0.57', '#anger', '#disgust', 'negative', '-0.55', 'purgative', 'cathartic', 'physic', 'aperient', 'phenolphthalein']
senticnet['stomacke'] = ['-0.16', '0.179', '0.568', '-0.61', '#anger', '#disgust', 'negative', '-0.29', 'angrily', 'ire', 'see_red', 'dander', 'hackle']
senticnet['stomatal_pore'] = ['-0.57', '0', '0.767', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'verboseness', 'verbosity', 'prolixity', 'turgidness', 'flatulence']
senticnet['stomp'] = ['0.155', '-0.11', '0.009', '-0.05', '#joy', '#surprise', 'positive', '0.05', 'dance_well', 'relate_music', 'enter_contest', 'jump_around', 'cheer_up']
senticnet['stomp_box'] = ['0.748', '-0.90', '0', '0.99', '#surprise', '#admiration', 'positive', '0.882', 'shakiness', 'quivering', 'shaking', 'vibration', 'quiver']
senticnet['stone'] = ['0.559', '-0.56', '0', '0.769', '#joy', '#admiration', 'positive', '0.63', 'whole', 'dark', 'blackbody', 'full_radiator', 'black_body']
senticnet['stone-cold'] = ['-0.95', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.97', 'cold-hearted', 'cold-blooded', 'dispassionate', 'remorseless', 'dyed-in-the-wool']
senticnet['stone_adze'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'useful_tool']
senticnet['stone_altar'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'wallet', 'billfold', 'bank', 'piggy_bank', 'bank_vault']
senticnet['stone_arch'] = ['0.029', '0.087', '0.065', '0.137', '#interest', '#admiration', 'positive', '0.047', 'archway', 'aisle', 'mat', 'passageway', 'right_angle']
senticnet['stone_arch_bridge'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'suspension_bridge', 'under_bridge', 'roadway', 'county_highway', 'long_road']
senticnet['stone_barn'] = ['0', '0.113', '0', '0.111', '#interest', '#admiration', 'positive', '0.112', 'animal_house', 'cow_home', 'animal_place', 'crop_place', 'acreage']
senticnet['stone_bridge'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'suspension_bridge', 'under_bridge', 'roadway', 'county_highway', 'long_road']
senticnet['stone_clad'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'adobe', 'adobe_brick', 'clinker', 'brick', 'firebrick']
senticnet['stone_coffin'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'wallet', 'billfold', 'bank', 'piggy_bank', 'bank_vault']
senticnet['stone_cold_stunner'] = ['0.052', '0', '0', '0.118', '#joy', '#admiration', 'positive', '0.085', 'diamond_ring', 'piece_of_jewelry', 'bell_sound', 'wedding_band', 'engagement']
senticnet['stone_cottage'] = ['0', '0.113', '0', '0.111', '#interest', '#admiration', 'positive', '0.112', 'animal_house', 'cow_home', 'animal_place', 'crop_place', 'acreage']
senticnet['stone_crab'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'crab', 'swimming_crab', 'rock_crab', 'fiddler_crab', 'pea_crab']
senticnet['stone_cutter'] = ['-0.52', '0', '0.506', '-0.94', '#sadness', '#disgust', 'negative', '-0.65', 'gouger', 'swindler', 'defrauder', 'fraud', 'welcher']
senticnet['stone_dead'] = ['-0.90', '0', '0.718', '-0.90', '#sadness', '#disgust', 'negative', '-0.84', 'dead_person', 'death_bed', 'grave', 'corpse', 'coffin']
senticnet['stone_edifice'] = ['0.026', '0.219', '0', '0.16', '#interest', '#admiration', 'positive', '0.135', 'large_build', 'palace', 'high_rise', 'structure', 'fortification']
senticnet['stone_faade'] = ['0.026', '0.219', '0', '0.16', '#interest', '#admiration', 'positive', '0.135', 'large_build', 'palace', 'high_rise', 'structure', 'fortification']
senticnet['stone_facade'] = ['0.026', '0.219', '0', '0.16', '#interest', '#admiration', 'positive', '0.135', 'large_build', 'palace', 'high_rise', 'structure', 'fortification']
senticnet['stone_gossard'] = ['0.304', '0.339', '-0.19', '0.333', '#interest', '#admiration', 'positive', '0.195', 'careful', 'reinforce', 'load_bear', 'go_outdoors', 'allowance']
senticnet['stone_marker'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'dental_plaque']
senticnet['stone_mason'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'maker']
senticnet['stone_masonry'] = ['0.876', '-0.76', '0', '0.963', '#joy', '#admiration', 'positive', '0.867', 'housecraft', 'tradecraft', 'watercraft', 'acquirement', 'skill']
senticnet['stone_monument'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'dental_plaque']
senticnet['stone_pavement'] = ['0.952', '-0.52', '0', '0.835', '#joy', '#admiration', 'positive', '0.771', 'billboard', 'hoarding', 'sandwich_board', 'scoreboard', 'shingle']
senticnet['stone_pillar'] = ['0.952', '-0.52', '0', '0.835', '#joy', '#admiration', 'positive', '0.771', 'billboard', 'hoarding', 'sandwich_board', 'scoreboard', 'shingle']
senticnet['stone_pit'] = ['0', '0', '-0.86', '-0.71', '#fear', '#disgust', 'negative', '-0.78', 'fair_game', 'pit', 'quarry', 'target', 'prey']
senticnet['stone_plaque'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'dental_plaque']
senticnet['stone_quarry'] = ['0', '0', '-0.85', '-0.71', '#fear', '#disgust', 'negative', '-0.78', 'fair_game', 'pit', 'stone_pit', 'target', 'prey']
senticnet['stone_retaining_wall'] = ['0.027', '0.139', '0', '0.203', '#interest', '#admiration', 'positive', '0.123', 'horse_shoes', 'four_leaf_clover', 'rotary_phone', 'ping_pong', 'billiards']
senticnet['stone_sarcophagus'] = ['-0.09', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.10', 'funeral_celebration', 'memorial', 'funeral', 'memento', 'cry']
senticnet['stone_slab'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'wallet', 'billfold', 'bank', 'piggy_bank', 'bank_vault']
senticnet['stone_staircase'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'vestibule', 'door', 'central_passage', 'gate', 'door_handle']
senticnet['stone_tablet'] = ['0.952', '-0.52', '0', '0.835', '#joy', '#admiration', 'positive', '0.771', 'billboard', 'hoarding', 'sandwich_board', 'scoreboard', 'shingle']
senticnet['stone_trim'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'condominium', 'rent_apartment', 'high_rise', 'condo', 'row_house']
senticnet['stonechat'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'amoeba', 'single_celled']
senticnet['stonecrop'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'livelong', 'herb', 'sedum', 'orpine', 'orpin']
senticnet['stonefish'] = ['0', '0', '-0.87', '0', '#fear', '#fear', 'negative', '-0.87', 'beetle', 'many_insects', 'grasshopper', 'firefly', 'mouse']
senticnet['stonemason'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'maker']
senticnet['stoner_comedy'] = ['-0.85', '0', '0.514', '-0.73', '#sadness', '#disgust', 'negative', '-0.70', 'cinema_verite', 'movie', 'film', 'picture', 'moving_picture']
senticnet['stonewaller'] = ['-0.83', '0', '0.682', '-0.87', '#sadness', '#disgust', 'negative', '-0.79', 'obstructor', 'obstructer', 'obstructionist', 'resister', 'thwarter']
senticnet['stoneware'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'fabrication', 'carving', 'decoupage', 'artistic_creation']
senticnet['stonework'] = ['0.876', '-0.76', '0', '0.963', '#joy', '#admiration', 'positive', '0.867', 'housecraft', 'tradecraft', 'watercraft', 'acquirement', 'skill']
senticnet['stoning'] = ['-0.83', '0', '0.924', '-0.79', '#sadness', '#anger', 'negative', '-0.85', 'kick_in_butt', 'licking', 'whacking', 'corporal_punishment', 'lapidation']
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senticnet['strip_joint'] = ['0', '0', '-0.10', '0', '#fear', '#fear', 'negative', '-0.10', 'television_station', 'hockey_rink', 'movie_theater', 'movie_studio', 'wedding_ceremony']
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senticnet['striped_hyena'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'argali', 'argal', 'urial', 'white_sheep', 'mountain_sheep']
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senticnet['striped_shirt'] = ['-0.02', '0.256', '0', '0.166', '#interest', '#admiration', 'positive', '0.132', 'coat', 'shirt', 'garment', 'dress', 'vest']
senticnet['striped_skunk'] = ['-0.54', '0', '0.768', '-0.50', '#sadness', '#anger', 'negative', '-0.60', 'polecat', 'hooded_skunk', 'hognosed_skunk', 'badger_skunk', 'rooter_skunk']
senticnet['striped_sweater'] = ['0.214', '0.258', '-0.09', '0.2', '#joy', '#interest', 'positive', '0.146', 'cup', 'carafe', 'pitcher', 'mug', 'container_cup']
senticnet['stripped-down'] = ['0.976', '0', '0', '0.858', '#joy', '#admiration', 'positive', '0.917', 'pared-down', 'bare-bones', 'no-frills', 'straight-ahead', 'minimalistic']
senticnet['stripped_naked'] = ['-0.04', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.05', 'fill_bathtub', 'fill_bathtub_water', 'run_bath', 'turn_water', 'take_clothes_off']
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senticnet['stripper'] = ['0.82', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.862', 'pervert', 'healthy_person', 'philanthropist', 'arsonist', 'nazi']
senticnet['stripper_pole'] = ['0', '-0.08', '0', '0', '#surprise', '#surprise', 'positive', '0.082', 'yo-yo', 'playroom', 'backgammon', 'action_figure', 'board_game']
senticnet['stripping_off'] = ['-0.04', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.05', 'fill_bathtub', 'fill_bathtub_water', 'run_bath', 'turn_water', 'take_clothes_off']
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senticnet['stroke_rehabilitation'] = ['0.641', '-0.78', '0', '0.721', '#surprise', '#admiration', 'positive', '0.715', 'correctional_rehabilitation', 'physical_rehabilitation', 'physical_restoration', 'therapeutic_rehabilitation', 'vocational_rehabilitation']
senticnet['stroll'] = ['0.876', '0.883', '0', '0', '#joy', '#interest', 'positive', '0.879', 'walk_along', 'gait', 'take_step', 'run_slowly', 'locomotion']
senticnet['stroll_along'] = ['-0.77', '0', '0.855', '-0.57', '#sadness', '#anger', 'negative', '-0.73', 'unhurried', 'easygoing', 'deliberate', 'easy', 'careful']
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senticnet['stroll_through'] = ['0.876', '0.883', '0', '0', '#joy', '#interest', 'positive', '0.879', 'walk_along', 'gait', 'take_step', 'run_slowly', 'locomotion']
senticnet['strolled_along'] = ['0.876', '0.883', '0', '0', '#joy', '#interest', 'positive', '0.879', 'walk_along', 'gait', 'take_step', 'run_slowly', 'locomotion']
senticnet['strolled_down'] = ['0.876', '0.883', '0', '0', '#joy', '#interest', 'positive', '0.879', 'walk_along', 'gait', 'take_step', 'run_slowly', 'locomotion']
senticnet['strolled_through'] = ['0.876', '0.883', '0', '0', '#joy', '#interest', 'positive', '0.879', 'walk_along', 'gait', 'take_step', 'run_slowly', 'locomotion']
senticnet['stroller'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'lavatory', 'commode', 'potty_chair']
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senticnet['strolling_around'] = ['0.876', '0.883', '0', '0', '#joy', '#interest', 'positive', '0.879', 'walk_along', 'gait', 'take_step', 'run_slowly', 'locomotion']
senticnet['strolling_down'] = ['0.876', '0.883', '0', '0', '#joy', '#interest', 'positive', '0.879', 'walk_along', 'gait', 'take_step', 'run_slowly', 'locomotion']
senticnet['strolling_through'] = ['0.876', '0.883', '0', '0', '#joy', '#interest', 'positive', '0.879', 'walk_along', 'gait', 'take_step', 'run_slowly', 'locomotion']
senticnet['strolls_along'] = ['0.876', '0.883', '0', '0', '#joy', '#interest', 'positive', '0.879', 'walk_along', 'gait', 'take_step', 'run_slowly', 'locomotion']
senticnet['strolls_through'] = ['0.876', '0.883', '0', '0', '#joy', '#interest', 'positive', '0.879', 'walk_along', 'gait', 'take_step', 'run_slowly', 'locomotion']
senticnet['strong'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'durable', 'rigid', 'sturdy', 'hard', 'heavy']
senticnet['strong-arm'] = ['0', '-0.74', '0', '0.798', '#surprise', '#admiration', 'positive', '0.772', 'strong-armed', 'thuggish', 'underhanded', 'thuggery', 'extort']
senticnet['strong-armed'] = ['0', '-0.74', '0', '0.794', '#surprise', '#admiration', 'positive', '0.77', 'strong-arm', 'thuggish', 'underhanded', 'thuggery', 'extort']
senticnet['strong-growing'] = ['0', '0', '0.898', '-0.74', '#anger', '#disgust', 'negative', '-0.82', 'free-flowering', 'frost-tender', 'quiet-running', 'slow-thinking', 'long-flowering']
senticnet['strong-smelling'] = ['0', '-0.96', '0', '-0.81', '#surprise', '#disgust', 'positive', '0.076', 'bad_smell', 'odoriferous', 'bad-smelling', 'disgusting', 'foul-smelling']
senticnet['strong_bisimulation'] = ['0.777', '-0.68', '0', '0.601', '#joy', '#surprise', 'positive', '0.686', 'lucidness', 'clarity', 'clearness', 'lucidity', 'pellucidity']
senticnet['strong_body'] = ['0', '0', '0', '0.744', '#admiration', '#admiration', 'positive', '0.744', 'live_long_healthy_life', 'live_ripe_old_age', 'live_healthy_life', 'live_well', 'long_lifespan']
senticnet['strong_commitment'] = ['0.053', '0.136', '0', '0.057', '#interest', '#admiration', 'positive', '0.082', 'boss', 'supervisor', 'leader', 'head_person', 'head_guy']
senticnet['strong_desire'] = ['0.035', '0.388', '-0.31', '0.334', '#interest', '#admiration', 'positive', '0.112', 'demand', 'yearn', 'essential', 'dream', 'needful']
senticnet['strong_disincentive'] = ['0.716', '0.813', '0', '0', '#joy', '#interest', 'positive', '0.764', 'bonus', 'reinforce', 'promotion', 'meritocracy', 'gratification']
senticnet['strong_dislike'] = ['-0.90', '0', '0.48', '-0.99', '#sadness', '#disgust', 'negative', '-0.79', 'deep_resentment', 'hate', 'hatred', 'enemy', 'animosity']
senticnet['strong_drink'] = ['0.071', '0', '0', '0', '#joy', '#joy', 'positive', '0.071', 'alcoholic_drink', 'alcoholic_beverage', 'fizzy', 'popular_soda', 'ferment_beverage']
senticnet['strong_el_nino'] = ['-0.69', '0', '0.835', '-0.56', '#sadness', '#anger', 'negative', '-0.69', 'roughness', 'inclemency', 'bad_weather', 'inclementness', 'windiness']
senticnet['strong_emotion'] = ['0', '1', '0', '0', '#interest', '#interest', 'positive', '1.0', 'interest', 'interesting', 'mood', 'attention', 'emotion']
senticnet['strong_endurance'] = ['0.856', '0.928', '0', '0.828', '#joy', '#interest', 'positive', '0.871', 'sport', 'endurance_exercise', 'champion', 'win_marathon', 'competition']
senticnet['strong_feeling'] = ['0', '0.069', '0', '0.108', '#interest', '#admiration', 'positive', '0.088', 'desire_entertain', 'good_emotion', 'love', 'strong_emotion', 'intution']
senticnet['strong_incentive'] = ['0.716', '0.813', '0', '0', '#joy', '#interest', 'positive', '0.764', 'bonus', 'reinforce', 'promotion', 'meritocracy', 'gratification']
senticnet['strong_material'] = ['0.03', '0', '0.021', '0.065', '#joy', '#admiration', 'positive', '0.025', 'hard_rock', 'element', 'silver', 'magnetic', 'tin_aluminium']
senticnet['strong_muscle'] = ['0.068', '0', '0.027', '0.079', '#joy', '#admiration', 'positive', '0.04', 'eat_little', 'eat_right', 'exercise_regularly', 'exercise', 'eat_healthy']
senticnet['strong_odor'] = ['0', '0', '0.717', '-0.84', '#anger', '#disgust', 'negative', '-0.77', 'gymnast', 'bikini', 'underwear', 'basketball_hoop', 'dirty_sock']
senticnet['strong_oxidizing_agent'] = ['0.766', '-0.87', '0', '0.666', '#joy', '#surprise', 'positive', '0.769', 'reactive', 'unstable', 'labile', 'activated', 'excited']
senticnet['strong_pain'] = ['-0.91', '0', '0.513', '-0.93', '#sadness', '#disgust', 'negative', '-0.78', 'suffering', 'cause_bleeding', 'cause_strong_pain', 'kill_person', 'beat_person']
senticnet['strong_person'] = ['0.034', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.05', 'big_guy', 'strength', 'fearless', 'wrestler', 'body_building']
senticnet['strong_personality'] = ['0', '0.816', '0', '0.956', '#interest', '#admiration', 'positive', '0.886', 'character', 'personality', 'computer_type', 'riddle', 'mystery']
senticnet['strong_preference'] = ['0.57', '0.754', '-0.32', '0.464', '#joy', '#interest', 'positive', '0.366', 'esteem', 'optimism', 'anticipation', 'adoration', 'confidence']
senticnet['strong_relationship'] = ['0.568', '0.302', '-0.11', '0.39', '#joy', '#admiration', 'positive', '0.285', 'good_sense_of_humor', 'world_peace', 'complement', 'run_business', 'develop_talent']
senticnet['strong_resemblance'] = ['-0.88', '0', '0.813', '-0.57', '#sadness', '#anger', 'negative', '-0.75', 'affinity', 'mutual_resemblance', 'alikeness', 'likeness', 'similitude']
senticnet['strong_smell'] = ['-0.02', '-0.04', '-0.09', '-0.27', '#fear', '#disgust', 'negative', '-0.08', 'poison', 'nuclear_weapon', 'shoot', 'explosive', 'grenade']
senticnet['strong_suit'] = ['0.553', '-0.8', '0', '0.908', '#surprise', '#admiration', 'positive', '0.754', 'long_suit', 'collection', 'aggregation', 'accumulation', 'assemblage']
senticnet['strong_taste'] = ['0.152', '0.099', '-0.07', '0.107', '#joy', '#admiration', 'positive', '0.072', 'dark_beer', 'ale', 'cocktail', 'convulse', 'beer']
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senticnet['strong_work_ethic'] = ['0.701', '0.874', '0', '0.752', '#interest', '#admiration', 'positive', '0.776', 'self_satisfaction', 'enjoy_life', 'good_feeling', 'good_attitude', 'enjoy']
senticnet['strongbox'] = ['0', '0.177', '0', '0.126', '#interest', '#admiration', 'positive', '0.151', 'treasure', 'storage_facility', 'shrine', 'lock_box', 'jewelry_box']
senticnet['stronger'] = ['0.968', '0.717', '0', '0', '#joy', '#interest', 'positive', '0.843', 'firm', 'unattackable', 'secure', 'unassailable', 'stiff']
senticnet['stronger_foothold'] = ['0.733', '0.991', '0', '0.843', '#interest', '#admiration', 'positive', '0.856', 'improvement', 'advantage', 'return', 'receive', 'contribution']
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senticnet['strongest_condemnation'] = ['-0.66', '0', '0.721', '-0.53', '#sadness', '#anger', 'negative', '-0.64', 'unlimited', 'outright', 'unconditional', 'unqualified', 'unqualifiedal']
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senticnet['strongest_predictor'] = ['0.627', '-0.65', '0', '0.968', '#surprise', '#admiration', 'positive', '0.749', 'overwhelm', 'overpower', 'sweep_over', 'whelm', 'overcome']
senticnet['stronghold'] = ['0.08', '0.228', '0', '0.135', '#interest', '#admiration', 'positive', '0.148', 'secure', 'hold_coin', 'money_collector', 'safe_place', 'safe']
senticnet['strongly'] = ['0', '0.933', '0', '0.747', '#interest', '#admiration', 'positive', '0.84', 'powerfully', 'study', 'research', 'project', 'different_application']
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senticnet['strongly_worded_letter'] = ['0.038', '-0.03', '0', '0.079', '#surprise', '#admiration', 'positive', '0.052', 'sender', 'message', 'note', 'post', 'email']
senticnet['strongman'] = ['-0.73', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.72', 'royal_leader', 'despot', 'emperor', 'monarch', 'king']
senticnet['strongman_super'] = ['0', '0', '0', '0.879', '#admiration', '#admiration', 'positive', '0.879', 'sport_competition', 'win_competition', 'race', 'match', 'championship']
senticnet['strongroom'] = ['0.02', '0.205', '0', '0', '#joy', '#interest', 'positive', '0.112', 'safe', 'bank_vault', 'bank', 'piggy_bank', 'bank_account']
senticnet['strophe'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
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senticnet['structural_complexity'] = ['0.637', '-0.91', '0', '0.757', '#surprise', '#admiration', 'positive', '0.768', 'manageability', 'manageableness', 'docility', 'obedience', 'tractability']
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senticnet['subchondral_bone'] = ['0', '0', '0.025', '-0.09', '#anger', '#disgust', 'negative', '-0.06', 'body_fluid', 'artery', 'skeletal_system', 'bodily_fluid', 'near_mouth']
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senticnet['subcontract'] = ['0.963', '0.819', '0', '0', '#joy', '#interest', 'positive', '0.891', 'subcontractor', 'sub-contractor', 'contractor', 'subcontractor', 'sub-contractors']
senticnet['subcontractor'] = ['0.966', '0.826', '0', '0', '#joy', '#interest', 'positive', '0.896', 'subcontractor', 'sub-contractor', 'contractor', 'subcontract', 'sub-contractors']
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senticnet['subdean'] = ['0.031', '0.272', '0', '0.231', '#interest', '#admiration', 'positive', '0.178', 'minister', 'church_head', 'pastor', 'protestant', 'evangelist']
senticnet['subdirectory'] = ['0', '0.175', '0', '0.231', '#interest', '#admiration', 'positive', '0.203', 'list', 'schedule', 'index', 'agenda', 'register']
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senticnet['subdivision'] = ['0.965', '0.846', '0', '0', '#joy', '#interest', 'positive', '0.905', 'subdivision', 'estate', 'condominium', 'sub-division', 'zoning']
senticnet['subduable'] = ['0.698', '-0.94', '0', '0.581', '#joy', '#surprise', 'positive', '0.743', 'beatable', 'vanquishable', 'vincible', 'conquerable', 'subjugable']
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senticnet['subframe'] = ['0.033', '0', '0', '0.081', '#joy', '#admiration', 'positive', '0.057', 'door', 'door_lock', 'window_door', 'wall', 'doorway']
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senticnet['subjugable'] = ['0.696', '-0.94', '0', '0.575', '#joy', '#surprise', 'positive', '0.739', 'beatable', 'vanquishable', 'vincible', 'subduable', 'conquerable']
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senticnet['sublimely'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'terrific', 'wondrously', 'tremendous', 'marvelous', 'wondrous']
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senticnet['submission_guillotine_choke'] = ['0.823', '0.982', '0', '0', '#joy', '#interest', 'positive', '0.902', 'spherical', 'three_dimensions', 'round_orb', 'coil', 'fill_with_sand']
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senticnet['submissive_behavior'] = ['-0.75', '0', '0.886', '-0.72', '#sadness', '#anger', 'negative', '-0.78', 'harass', 'move', 'check', 'fork', 'pin']
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senticnet['subtropics'] = ['-0.83', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.82', 'tropical', 'tropic', 'subtropical', 'temperate', 'climate']
senticnet['subungual'] = ['-0.84', '0', '0.581', '-0.93', '#sadness', '#disgust', 'negative', '-0.78', 'keratosis_blennorrhagica', 'keratoderma_blennorrhagica', 'keratosis_pilaris', 'seborrheic_keratosis', 'verruca']
senticnet['suburb'] = ['0.698', '0.938', '0', '0.713', '#interest', '#admiration', 'positive', '0.783', 'book_story', 'bed', 'seduce', 'fond_memory', 'jewish_community']
senticnet['suburban_commuter'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'seeker', 'chairperson']
senticnet['suburban_handicap'] = ['-0.76', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.78', 'strangle', 'bloodshed', 'cripple', 'hear_bad_news', 'sexually_transmit_disease']
senticnet['suburban_lifestyle'] = ['0', '0.095', '0', '0.05', '#interest', '#admiration', 'positive', '0.073', 'need_earn_money', 'earn_money', 'responsible', 'perform_service', 'go_work_to_earn_money']
senticnet['suburban_rail'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'seeker', 'chairperson']
senticnet['suburban_railway'] = ['0.547', '-0.69', '0', '0.52', '#joy', '#surprise', 'positive', '0.586', 'vivification', 'invigoration', 'activity', 'animation', 'activating']
senticnet['suburban_shop_mall'] = ['0', '0', '0.051', '-0.03', '#anger', '#disgust', 'negative', '-0.04', 'large_city_shop_mall', 'mini_mall', 'corner_shop', 'corner_grocery', 'large_food_store']
senticnet['subversion'] = ['-0.85', '0', '0.739', '-0.58', '#sadness', '#anger', 'negative', '-0.72', 'overthrow', 'subversive_activity', 'termination', 'ending', 'conclusion']
senticnet['subversive'] = ['-0.98', '0', '-0.89', '0', '#sadness', '#fear', 'negative', '-0.94', 'revolutionist', 'subverter', 'insurgent', 'seditious', 'revolutionary']
senticnet['subversive_activity'] = ['-0.84', '0', '0.744', '-0.58', '#sadness', '#anger', 'negative', '-0.72', 'subversion', 'overthrow', 'termination', 'ending', 'conclusion']
senticnet['subversiveness'] = ['0.921', '-0.56', '0', '0.791', '#joy', '#admiration', 'positive', '0.76', 'perfidiousness', 'perfidy', 'traitorousness', 'disloyalty', 'treachery']
senticnet['subvert'] = ['-0.71', '0', '0.932', '-0.50', '#sadness', '#anger', 'negative', '-0.71', 'refuse', 'disobey', 'decline', 'balk', 'baulk']
senticnet['subverter'] = ['-0.99', '0', '-0.89', '0', '#sadness', '#fear', 'negative', '-0.94', 'revolutionist', 'subversive', 'insurgent', 'seditious', 'revolutionary']
senticnet['subway'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'bus_stop', 'fast_food', 'train_station', 'railway_station', 'gas_station']
senticnet['subway_car'] = ['0.706', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.768', 'train_station', 'subway_stop', 'parking_lot', 'subway_station', 'park_structure']
senticnet['subway_entrance'] = ['0.706', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.768', 'train_station', 'subway_stop', 'parking_lot', 'subway_station', 'park_structure']
senticnet['subway_fare'] = ['-0.03', '0.29', '-0.12', '0.203', '#interest', '#admiration', 'positive', '0.083', 'excise', 'severance', 'wage', 'afford', 'revenue']
senticnet['subway_graffiti'] = ['-0.12', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.07', 'bum', 'homeless_people', 'homeless_person', 'panhandler', 'homeless']
senticnet['subway_pass'] = ['0', '0', '0.024', '-0.06', '#anger', '#disgust', 'negative', '-0.04', 'transit_ticket', 'bus_pass', 'train_ticket', 'passenger_ticket', 'transit_card']
senticnet['subway_platform'] = ['0.706', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.768', 'train_station', 'subway_stop', 'parking_lot', 'subway_station', 'park_structure']
senticnet['subway_ride'] = ['-0.08', '0.179', '-0.06', '0.096', '#interest', '#admiration', 'positive', '0.034', 'ride_subway', 'buy_vending_machine', 'travel', 'buy_lottery_ticket', 'acquire']
senticnet['subway_sandwich_shop'] = ['0.758', '0.878', '0', '0', '#joy', '#interest', 'positive', '0.818', 'tavern', 'bar', 'pub', 'martini', 'brewery']
senticnet['subway_station'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'town_hall', 'courthouse', 'city_hall', 'public_place', 'bus_station']
senticnet['subway_stop'] = ['0.878', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.916', 'train_station', 'subway_platform', 'parking_lot', 'subway_station', 'park_structure']
senticnet['subway_system'] = ['0.706', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.768', 'train_station', 'subway_stop', 'parking_lot', 'subway_station', 'park_structure']
senticnet['subway_train'] = ['0.706', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.768', 'train_station', 'subway_stop', 'parking_lot', 'subway_station', 'park_structure']
senticnet['subway_tunnel'] = ['0.706', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.768', 'train_station', 'subway_stop', 'parking_lot', 'subway_station', 'park_structure']
senticnet['subwindow'] = ['0.777', '-0.85', '0', '0.615', '#joy', '#surprise', 'positive', '0.748', 'think', 'play', 'trifle', 'abstract', 'warm_to']
senticnet['subwoofer'] = ['0.036', '-0.04', '0.027', '0.063', '#surprise', '#admiration', 'positive', '0.028', 'radio', 'speaker', 'channel_selector', 'power_switch', 'female_television_connector']
senticnet['subxiphoid'] = ['-0.78', '0', '0.855', '-0.99', '#anger', '#disgust', 'negative', '-0.87', 'blood_blister', 'pustule', 'water_blister', 'vesicle', 'cyst']
senticnet['succade'] = ['-0.95', '0', '0.845', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'grapefruit_peel', 'lemon_peel', 'orange_peel', 'candied_fruit', 'candied_citrus_peel']
senticnet['succeed'] = ['0.588', '-0.75', '0', '0.55', '#joy', '#surprise', 'positive', '0.632', 'clear', 'pan_out', 'reach', 'bring_off', 'luck_out']
senticnet['succeed_carrier'] = ['0', '0.069', '0', '0.108', '#interest', '#admiration', 'positive', '0.088', 'succeed_school', 'create_question', 'create_thought', 'creative_mind', 'creative_soul']
senticnet['succeed_school'] = ['0.601', '0.647', '-0.26', '0.322', '#joy', '#interest', 'positive', '0.326', 'bicycle_school', 'love_pet', 'gain_knowledge', 'question_teacher', 'solve_equation']
senticnet['succeeded'] = ['0', '0', '-0.97', '-0.70', '#fear', '#disgust', 'negative', '-0.84', 'singlehandedly', 'handedly', 'miraculously', 'single-handedly', 'heroically']
senticnet['succeeder'] = ['0.622', '-0.88', '0', '0.915', '#surprise', '#admiration', 'positive', '0.808', 'great', 'highflier', 'highflyer', 'natural', 'mortal']
senticnet['succeeding'] = ['0.812', '0.962', '0', '0', '#joy', '#interest', 'positive', '0.887', 'consecutive', 'ensuing', 'following', 'undermentioned', 'prosper']
senticnet['succeeding_fabio_capello'] = ['0.941', '-0.83', '0', '0.973', '#joy', '#admiration', 'positive', '0.917', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['succesful'] = ['0.98', '-0.74', '0', '0.984', '#joy', '#admiration', 'positive', '0.903', 'prosperous', 'flourishing', 'palmy', 'prospering', 'thriving']
senticnet['success'] = ['0.889', '0', '0', '0', '#joy', '#joy', 'positive', '0.889', 'succeed', 'increase_self_esteem', 'increase_self-esteem', 'help_others', 'achievement']
senticnet['successful'] = ['0.98', '-0.74', '0', '0.984', '#joy', '#admiration', 'positive', '0.903', 'prosperous', 'flourishing', 'palmy', 'prospering', 'thriving']
senticnet['successful_bidder'] = ['0.865', '-0.84', '0', '0.915', '#joy', '#admiration', 'positive', '0.875', 'grant', 'sale_in_gross', 'contract_of_hazard', 'adhesion_contract', 'contract_of_adhesion']
senticnet['successful_businessman'] = ['0', '0', '-0.99', '-0.73', '#fear', '#disgust', 'negative', '-0.86', 'king', 'baron', 'business_leader', 'mogul', 'big_businessman']
senticnet['successful_completion'] = ['0', '0.098', '0.012', '0.276', '#interest', '#admiration', 'positive', '0.121', 'position', 'job', 'find_own_place_in_world', 'character_trait', 'place']
senticnet['successfully'] = ['0', '0.75', '0', '0.717', '#interest', '#admiration', 'positive', '0.734', 'project', 'student', 'research', 'different_application', 'study']
senticnet['successfully_complete'] = ['0.719', '0.757', '0', '0', '#joy', '#interest', 'positive', '0.738', 'matriculate', 'enroll_class', 'enroll_school', 'inscribe', 'register']
senticnet['successfully_fledge'] = ['0.702', '-0.95', '0', '0.997', '#surprise', '#admiration', 'positive', '0.883', 'fertile', 'fecund', 'conceptive', 'impregnable', 'rank']
senticnet['successfully_integrate'] = ['-0.04', '0.18', '-0.09', '0.247', '#interest', '#admiration', 'positive', '0.071', 'obtain', 'satisfy_need', 'business_transaction', 'get_stuff', 'return_goods']
senticnet['successfulness'] = ['0.971', '-0.71', '0', '0.656', '#joy', '#surprise', 'positive', '0.781', 'wellbeing', 'human_flourishing', 'eudaimonia', 'upbeat', 'strength']
senticnet['successive'] = ['0', '0.078', '0', '0.096', '#interest', '#admiration', 'positive', '0.087', 'ancient_rome', 'ancient', 'age', 'ancient_artifact', 'century']
senticnet['successively'] = ['0.928', '0.968', '0', '0', '#joy', '#interest', 'positive', '0.948', 'sequentially', 'synchronously', 'serially', 'cyclically', 'outputted']
senticnet['successor'] = ['0', '0.787', '0', '0.932', '#interest', '#admiration', 'positive', '0.86', 'pass_ball', 'next_legislature', 'next', 'coming_next', 'handover']
senticnet['succimer'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'radiation_therapy', 'radiation', 'actinotherapy', 'irradiation']
senticnet['succinct'] = ['0.515', '-0.80', '0', '0.791', '#surprise', '#admiration', 'positive', '0.703', 'accessible', 'fathomable', 'approachable', 'perceivable', 'intelligible']
senticnet['succinct_description'] = ['0.041', '0.16', '0', '0.3', '#interest', '#admiration', 'positive', '0.167', 'instruction', 'summary', 'same', 'narrate', 'explaination']
senticnet['succinct_statement'] = ['0', '0.154', '-0.05', '0.17', '#interest', '#admiration', 'positive', '0.089', 'court_action', 'accessory', 'legal_document', 'short', 'protective_cover']
senticnet['succinctness'] = ['0', '0', '0.812', '-0.94', '#anger', '#disgust', 'negative', '-0.87', 'concision', 'conciseness', 'terseness', 'bluntness', 'brevity']
senticnet['succinic_acid'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'adipic_acid', 'carboxylic_acid', 'valeric_acid']
senticnet['succinylcholine'] = ['-0.65', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'put_to_sleep', 'operation', 'anesthesia', 'surgery', 'local_anesthesia']
senticnet['succor'] = ['0.759', '-0.78', '0', '0.918', '#surprise', '#admiration', 'positive', '0.82', 'solace', 'assist', 'help', 'assistance', 'mercy']
senticnet['succorer'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'recoverer', 'succourer', 'salvager', 'salvor', 'mortal']
senticnet['succotash'] = ['0', '-0.01', '0.037', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'pear', 'green_apple', 'apricot', 'cherry', 'lime']
senticnet['succourer'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'recoverer', 'salvager', 'salvor', 'mortal']
senticnet['succuba'] = ['-0.79', '0', '0.92', '-0.75', '#sadness', '#anger', 'negative', '-0.82', 'evil_spirit', 'incubus', 'succubus', 'daemon', 'dybbuk']
senticnet['succubus'] = ['-0.79', '0', '0.92', '-0.75', '#sadness', '#anger', 'negative', '-0.82', 'evil_spirit', 'incubus', 'daemon', 'succuba', 'dybbuk']
senticnet['succulence'] = ['-0.79', '0', '0.992', '-0.88', '#anger', '#disgust', 'negative', '-0.89', 'flavorsomeness', 'flavoursomeness', 'savoriness', 'appetizingness', 'succulency']
senticnet['succulency'] = ['-0.79', '0', '0.992', '-0.88', '#anger', '#disgust', 'negative', '-0.89', 'flavorsomeness', 'flavoursomeness', 'savoriness', 'succulence', 'appetizingness']
senticnet['succulent'] = ['0.041', '0.069', '0.032', '0.13', '#interest', '#admiration', 'positive', '0.052', 'edible_human', 'round_fruit', 'peelable', 'green_fruit', 'nutritious']
senticnet['succulent_karoo'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'woodland', 'timberland']
senticnet['succulent_perennial'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'clematis', 'legume', 'leguminous_plant', 'climber', 'root_climber']
senticnet['succulent_vegetation'] = ['-0.86', '0', '0.665', '-0.61', '#sadness', '#anger', 'negative', '-0.71', 'brambly', 'braky', 'wooded', 'scrubby', 'forested']
senticnet['succumb'] = ['-0.75', '0', '-1.0', '0', '#sadness', '#fear', 'negative', '-0.87', 'give_in', 'submit', 'accept', 'defer', 'yield']
senticnet['suche'] = ['-0.98', '0', '0.654', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'deprive', 'expropriate', 'disarm', 'unclothe', 'take']
senticnet['suck'] = ['-0.89', '0', '0.691', '-0.83', '#sadness', '#disgust', 'negative', '-0.80', 'choke_food', 'drink_tea', 'spill_food_clothes', 'welcome_friend', 'pull']
senticnet['suck-up'] = ['-0.87', '0', '0', '-1.0', '#sadness', '#disgust', 'negative', '-0.93', 'sycophant', 'yes-man', 'lapdog', 'war-monger', 'flunky']
senticnet['suck_sap_from'] = ['-0.77', '0', '0.576', '-0.92', '#sadness', '#disgust', 'negative', '-0.75', 'insect', 'firebug', 'water_bug', 'assassin_bug', 'heteropterous_insect']
senticnet['suck_up'] = ['0.72', '-0.76', '0', '0.695', '#joy', '#surprise', 'positive', '0.726', 'mop_up', 'blot', 'wipe_up', 'mop', 'sponge_up']
senticnet['sucker'] = ['0', '0', '0.804', '-0.80', '#anger', '#disgust', 'negative', '-0.80', 'shoot', 'trematode', 'octopus', 'bucket', 'draw']
senticnet['sucker_punch'] = ['0.705', '-0.60', '0', '0.913', '#joy', '#admiration', 'positive', '0.741', 'lick', 'clout', 'poke', 'punch', 'biff']
senticnet['sucking_mud'] = ['-0.71', '0', '0.704', '-0.89', '#sadness', '#disgust', 'negative', '-0.76', 'soil', 'mud', 'dirt', 'bleaching_clay', 'bleaching_earth']
senticnet['suckle'] = ['0.773', '0.928', '0', '0', '#joy', '#interest', 'positive', '0.851', 'suckling', 'piglet', 'suckled', 'lamb', 'weaned']
senticnet['suckled'] = ['0.776', '0.922', '0', '0', '#joy', '#interest', 'positive', '0.849', 'suckle', 'piglet', 'suckling', 'lamb', 'weaned']
senticnet['suckling'] = ['0.782', '0.93', '0', '0', '#joy', '#interest', 'positive', '0.856', 'suckle', 'piglet', 'suckled', 'lamb', 'weaned']
senticnet['sucralfate'] = ['-0.53', '0', '0.954', '-0.54', '#anger', '#disgust', 'negative', '-0.67', 'ranitidine', 'histamine_blocker', 'gastric_ranitidine', 'alkalizer', 'antiacid']
senticnet['sucrose_solution'] = ['0.677', '-0.54', '0', '0.587', '#joy', '#admiration', 'positive', '0.603', 'glucosamine', 'corn_sugar', 'glucose', 'dextroglucose', 'grape_sugar']
senticnet['suction'] = ['0.72', '0.791', '0', '0', '#joy', '#interest', 'positive', '0.756', 'nothing', 'none', 'void', 'emptiness', 'outside_atmosphere']
senticnet['suction_catheter'] = ['0', '0', '0.873', '-0.92', '#anger', '#disgust', 'negative', '-0.89', 'punch', 'sting', 'disable', 'ache', 'vex']
senticnet['suction_cup'] = ['0.105', '-0.17', '0.057', '0.079', '#joy', '#surprise', 'positive', '0.076', 'cue', 'twig', 'long_stick', 'stick', 'adhere']
senticnet['suction_device'] = ['-0.02', '0', '-0.12', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'x-ray_machine', 'medical_record', 'latex_glove', 'medicine', 'doctor_examination_room']
senticnet['suction_pressure'] = ['-0.12', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.09', 'breeze', 'move_air', 'air_movement', 'gape', 'force']
senticnet['sudation'] = ['-0.58', '0', '0.773', '-0.79', '#anger', '#disgust', 'negative', '-0.71', 'perspiration', 'sweating', 'diaphoresis', 'polyhidrosis', 'hidrosis']
senticnet['sudd'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['sudden'] = ['0', '0', '0', '-0.78', '#disgust', '#disgust', 'negative', '-0.78', 'suddenly', 'precipitous', 'immediate', 'unexpected', 'sudden_asthma_attack']
senticnet['sudden_appearance'] = ['0', '0', '0', '-0.78', '#disgust', '#disgust', 'negative', '-0.78', 'suddenly', 'precipitous', 'immediate', 'unexpected', 'sudden_asthma_attack']
senticnet['sudden_asthma_attack'] = ['0', '0', '0', '-0.78', '#disgust', '#disgust', 'negative', '-0.78', 'shock', 'scared', 'afraid', 'high_blood_pressure', 'go_gym']
senticnet['sudden_burst'] = ['0', '0', '0', '-0.78', '#disgust', '#disgust', 'negative', '-0.78', 'suddenly', 'precipitous', 'immediate', 'unexpected', 'sudden_asthma_attack']
senticnet['sudden_cardiac_death'] = ['-0.98', '0', '0.827', '-0.94', '#sadness', '#disgust', 'negative', '-0.91', 'congestive_heart_failure', 'heart_attack', 'failure', 'heart_disease', 'cardiopathy']
senticnet['sudden_change'] = ['0', '0', '0', '-0.78', '#disgust', '#disgust', 'negative', '-0.78', 'suddenly', 'precipitous', 'immediate', 'unexpected', 'sudden_asthma_attack']
senticnet['sudden_collapse'] = ['0.942', '-0.79', '0', '0.901', '#joy', '#admiration', 'positive', '0.88', 'backup', 'accretion', 'deposition', 'deposit', 'increase']
senticnet['sudden_death'] = ['-0.87', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.91', 'loss_oflife', 'chest_pain', 'die', 'tumor', 'end_life']
senticnet['sudden_death_leg'] = ['0.823', '0.982', '0', '0', '#joy', '#interest', 'positive', '0.902', 'spherical', 'three_dimensions', 'round_orb', 'coil', 'fill_with_sand']
senticnet['sudden_death_overtime'] = ['0', '0.904', '0', '0', '#interest', '#interest', 'positive', '0.904', 'act', 'part_of_play', 'part_of_movie', 'emote', 'role']
senticnet['sudden_departure'] = ['-0.72', '0', '0.98', '0', '#sadness', '#anger', 'negative', '-0.85', 'resignedly', 'abject', 'unhopeful', 'abjectly', 'reconcile']
senticnet['sudden_downpour'] = ['-0.87', '0', '0.91', '-0.78', '#sadness', '#anger', 'negative', '-0.85', 'storm', 'thundershower', 'rain', 'line_storm', 'equinoctial_storm']
senticnet['sudden_drop'] = ['0.978', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.865', 'tumble', 'dew', 'hailstone', 'rain', 'snowflake']
senticnet['sudden_flash'] = ['0', '-0.00', '-0.07', '-0.06', '#fear', '#disgust', 'negative', '-0.04', 'lighten', 'tornado', 'dynamite_explosion', 'forest_fire', 'lightning']
senticnet['sudden_gust'] = ['0', '-0.99', '-0.99', '0', '#surprise', '#fear', 'positive', '0.004', 'blizzard', 'snow_storm', 'storm', 'danger', 'rainy']
senticnet['sudden_jolt'] = ['0', '0', '0', '-0.78', '#disgust', '#disgust', 'negative', '-0.78', 'suddenly', 'precipitous', 'immediate', 'unexpected', 'sudden_asthma_attack']
senticnet['sudden_outburst'] = ['0', '0', '0.781', '-0.82', '#anger', '#disgust', 'negative', '-0.80', 'cry', 'acting_out', 'explosion', 'halloo', 'yell']
senticnet['sudden_resignation'] = ['-0.72', '0', '0.98', '0', '#sadness', '#anger', 'negative', '-0.85', 'resignedly', 'abject', 'unhopeful', 'abjectly', 'reconcile']
senticnet['sudden_rise'] = ['0.952', '0.918', '0', '0', '#joy', '#interest', 'positive', '0.935', 'tack', 'foot_part', 'casual_shoes', 'gauntlet', 'pin']
senticnet['sudden_rush'] = ['-0.88', '0.178', '-0.6', '0', '#sadness', '#fear', 'negative', '-0.43', 'rush_job', 'rush_work', 'late', 'urgency', 'emergency']
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senticnet['suffer_fool'] = ['-0.44', '-0.13', '-0.04', '-0.37', '#sadness', '#disgust', 'negative', '-0.18', 'lyme_disease', 'brain_tumor', 'disrespect', 'cause_suffer', 'lose_team']
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senticnet['sufficient_detail'] = ['0', '0.136', '0', '0.196', '#interest', '#admiration', 'positive', '0.166', 'describe', 'explain', 'answer', 'description', 'example']
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senticnet['suffragist'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
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senticnet['sugar_loaf'] = ['0', '0.093', '0', '0.078', '#interest', '#admiration', 'positive', '0.085', 'steep', 'landscape_feature', 'knoll', 'hill_mountain', 'incline']
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senticnet['sugar_maple_acer_saccharum'] = ['0.776', '-0.6', '0', '0.568', '#joy', '#surprise', 'positive', '0.648', 'swamp_chestnut_oak', 'chinquapin_oak', 'chinkapin_oak', 'yellow_chestnut_oak', 'basket_oak']
senticnet['sugar_mg_sodium'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'obese', 'worse', 'health_issue', 'clinical_problem']
senticnet['sugar_substitute'] = ['-0.08', '0.106', '0', '0.125', '#interest', '#admiration', 'positive', '0.047', 'sugar', 'tracle', 'molasses', 'add_sugar', 'black_treacle']
senticnet['sugar_syrup'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'dainty', 'delicacy', 'treat', 'jelly', 'aspic']
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senticnet['sugarbeet'] = ['0.878', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.883', 'wheat', 'grain', 'rye', 'white_rice', 'rice']
senticnet['sugarcane'] = ['0.609', '-0.74', '0', '0.716', '#surprise', '#admiration', 'positive', '0.69', 'malacca', 'sword', 'swagger_stick', 'sword_cane', 'sword_stick']
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senticnet['sugarcoat'] = ['0.988', '-0.83', '0', '0.683', '#joy', '#surprise', 'positive', '0.835', 'dulcorate', 'honey', 'glaze', 'change_taste', 'sugar']
senticnet['sugared_water'] = ['0.081', '-0.05', '0', '-0.03', '#joy', '#surprise', 'positive', '0.036', 'drink_alcohol', 'consume_alcoholic_beverage', 'drink_much', 'hit_bottle', 'meet_woman']
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senticnet['sugarless_gum'] = ['0.965', '0.775', '0', '0.968', '#joy', '#admiration', 'positive', '0.903', 'snot', 'bad_taste', 'mouth_part', 'beautiful_smile', 'near_mouth']
senticnet['sugary'] = ['0.107', '0', '0', '0.187', '#joy', '#admiration', 'positive', '0.147', 'round_pastry', 'sweet', 'soft_sweet', 'bake_good', 'tasty']
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senticnet['sugary_sweet'] = ['0.112', '0', '0', '0.141', '#joy', '#admiration', 'positive', '0.127', 'aftertaste', 'sweet_food', 'bake_good', 'round_pastry', 'eatable']
senticnet['suggest'] = ['-0.03', '0.323', '-0.23', '0.437', '#interest', '#admiration', 'positive', '0.124', 'request', 'permission', 'proclamation', 'plea', 'ask']
senticnet['suggestibility'] = ['-0.78', '0', '0.6', '-0.61', '#sadness', '#disgust', 'negative', '-0.66', 'susceptibility', 'sensitivity', 'capacity', 'reactivity', 'predisposition']
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senticnet['suggestion'] = ['0', '0.313', '-0.20', '0.403', '#interest', '#admiration', 'positive', '0.17', 'suggest', 'affirmation', 'propose', 'demand', 'intend']
senticnet['suggestive'] = ['0.733', '-0.56', '0', '0.633', '#joy', '#admiration', 'positive', '0.644', 'inferential', 'connotative_of', 'implicative', 'connotational', 'connotative']
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senticnet['sui_dynasty'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
senticnet['suicidal'] = ['0.203', '0.077', '0', '0.189', '#joy', '#admiration', 'positive', '0.156', 'sea_shell', 'coast', 'seaside', 'sediment', 'shell']
senticnet['suicidal_depression'] = ['-0.99', '0', '0.982', '-0.62', '#sadness', '#anger', 'negative', '-0.86', 'dysthymia', 'dysthymic_depression', 'affective_disorder', 'major_affective_disorder', 'emotional_disorder']
senticnet['suicide'] = ['-0.10', '-0.05', '-0.12', '-0.24', '#fear', '#disgust', 'negative', '-0.10', 'old_age', 'get_lung_cancer', 'capital_punishment', 'kill', 'execute']
senticnet['suicide_note'] = ['-0.07', '-0.02', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.03', 'commit_suicide', 'suicide', 'kill', 'write_suicide_note', 'execution']
senticnet['suit'] = ['0.831', '0.715', '0', '0', '#joy', '#interest', 'positive', '0.773', 'go', 'become', 'turn', 'get', 'research']
senticnet['suit_jacket'] = ['-0.02', '0.256', '0', '0.166', '#interest', '#admiration', 'positive', '0.132', 'coat', 'shirt', 'garment', 'dress', 'vest']
senticnet['suit_of_armor'] = ['0.939', '-0.63', '0', '0.653', '#joy', '#admiration', 'positive', '0.741', 'vest', 'waistcoat', 'body_armor', 'bulletproof_vest', 'coat_of_mail']
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senticnet['suitability'] = ['0.541', '-0.59', '0', '0.527', '#joy', '#surprise', 'positive', '0.554', 'fitness', 'quality', 'fittingness', 'convenience', 'appropriateness']
senticnet['suitable'] = ['0.869', '0.805', '0', '0', '#joy', '#interest', 'positive', '0.837', 'appropriate', 'apt', 'proper', 'liable', 'likely']
senticnet['suitable_solvent'] = ['-0.78', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.83', 'break', 'end', 'die', 'fall_apart', 'go_to_piece']
senticnet['suitableness'] = ['0.958', '-0.95', '0', '0.509', '#joy', '#surprise', 'positive', '0.807', 'fitness', 'quality', 'fittingness', 'convenience', 'appropriateness']
senticnet['suitably'] = ['0', '-0.90', '0', '0.708', '#surprise', '#admiration', 'positive', '0.808', 'elegantly', 'featly', 'gracefully', 'skilfully', 'graceful']
senticnet['suitably_grand'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'fantastic', 'amaze', 'pose', 'wondrous', 'wonderfully']
senticnet['suitcase'] = ['-0.02', '0', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.03', 'woman_purse', 'sock_drawer', 'pocket', 'back_sofa', 'pant_pocket']
senticnet['suitcase_bomb'] = ['0', '-0.03', '-0.04', '-0.19', '#fear', '#disgust', 'negative', '-0.06', 'nuclear_bomb', 'explosive', 'grenade', 'nuclear_weapon', 'aerial_bomb']
senticnet['suitcase_nuke'] = ['-0.57', '0', '0.56', '-0.87', '#sadness', '#disgust', 'negative', '-0.67', 'reprimand', 'rebuke', 'reproof', 'reproval', 'reprehension']
senticnet['suite'] = ['0.029', '0.073', '-0.05', '0.043', '#interest', '#admiration', 'positive', '0.023', 'park_space', 'empty_space', 'soda_machine', 'washroom', 'place_to_sit']
senticnet['suite_feature'] = ['0.159', '-0.00', '0.011', '0.079', '#joy', '#admiration', 'positive', '0.058', 'ceiling_fan', 'ipod', 'technologic', 'formula', 'music']
senticnet['sujud'] = ['-0.93', '0', '0.807', '-0.95', '#sadness', '#disgust', 'negative', '-0.89', 'heatstroke', 'heat_hyperpyrexia', 'malady', 'breakdown', 'shock']
senticnet['suka'] = ['-0.63', '0', '0.791', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'little', 'pinch', 'modicum', 'small_indefinite_amount', 'indefinite_quantity']
senticnet['sulcate'] = ['0.969', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.772', 'aspheric', 'aspherical', 'prolate', 'rounded', 'crescent']
senticnet['sulcus'] = ['0.719', '-0.57', '0', '0.651', '#joy', '#admiration', 'positive', '0.647', 'chap', 'break', 'slit', 'gap', 'shift']
senticnet['sulejman'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'white_animal', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
senticnet['sulfa'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['sulfa_drug'] = ['0.869', '-0.72', '0', '0.764', '#joy', '#admiration', 'positive', '0.785', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['sulfacetamide'] = ['0.543', '-0.75', '0', '0.808', '#surprise', '#admiration', 'positive', '0.701', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfa']
senticnet['sulfadiazine'] = ['0.546', '-0.75', '0', '0.811', '#surprise', '#admiration', 'positive', '0.704', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfa', 'sulfacetamide']
senticnet['sulfamethoxazole'] = ['0.55', '-0.75', '0', '0.813', '#surprise', '#admiration', 'positive', '0.706', 'antibacterial', 'sulfa', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['sulfate'] = ['-0.79', '0', '-1', '0', '#sadness', '#fear', 'negative', '-0.89', 'carbonate', 'ammonium', 'phosphate', 'calcium', 'phosphate']
senticnet['sulfate_attack'] = ['-0.52', '0', '0.596', '-0.76', '#anger', '#disgust', 'negative', '-0.62', 'retention', 'retentiveness', 'retentivity', 'solidness', 'nonabsorbency']
senticnet['sulfathiazole'] = ['0.551', '-0.75', '0', '0.817', '#surprise', '#admiration', 'positive', '0.707', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['sulfide'] = ['-0.99', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.89', 'arsenic', 'poisoning', 'strychnine', 'cyanide', 'monoxide']
senticnet['sulfide_mineral'] = ['-0.03', '0', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.06', 'lithium', 'mineral', 'potassium', 'iodine', 'magnesium']
senticnet['sulfisoxazole'] = ['0.551', '-0.75', '0', '0.808', '#surprise', '#admiration', 'positive', '0.705', 'antibacterial', 'sulfamethoxazole', 'sulfa', 'sulfadiazine', 'sulfacetamide']
senticnet['sulfonamide'] = ['0.721', '-0.95', '0', '0.554', '#joy', '#surprise', 'positive', '0.742', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['sulfonic_acid'] = ['-0.81', '0', '0.774', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'acid', 'hypophosphoric_acid', 'periodic_acid', 'hypooxyacid', 'oxyacid']
senticnet['sulforaphane'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'chemical_irritant', 'gingerol', 'piperine', 'pepper_spray']
senticnet['sulfur'] = ['-0.03', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'lithium', 'mineral', 'potassium', 'iodine', 'manganese']
senticnet['sulfuric_acid'] = ['0', '0', '-0.05', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'lifeguard', 'swimmer', 'still_water', 'large_body', 'shallow_water']
senticnet['sulfurous'] = ['-0.72', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.84', 'corrosive', 'vitriolic', 'acid', 'acerbic', 'bitter']
senticnet['sulfurous_acid'] = ['-0.91', '0', '0.24', '-0.93', '#sadness', '#disgust', 'negative', '-0.69', 'acid', 'dangerous', 'danger', 'kill_person', 'poison']
senticnet['sulfuryl_chloride'] = ['-0.81', '0', '0.774', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'acid', 'hypophosphoric_acid', 'periodic_acid', 'oxyacid', 'phosphorous_acid']
senticnet['sulieman_benn'] = ['-0.62', '0', '0.713', '-0.98', '#anger', '#disgust', 'negative', '-0.77', 'muddy_up', 'muck_up', 'foul', 'blemish', 'smear']
senticnet['sulk'] = ['0.056', '0.105', '0.786', '-0.68', '#anger', '#disgust', 'negative', '-0.32', 'huffishness', 'sulkiness', 'hover', 'loom', 'bulk_large']
senticnet['sulkily'] = ['-0.71', '0', '0.906', '-0.93', '#anger', '#disgust', 'negative', '-0.85', 'absent', 'forgetful', 'distracted', 'inattentive', 'scatty']
senticnet['sulkiness'] = ['0.056', '0.105', '0.786', '-0.68', '#anger', '#disgust', 'negative', '-0.32', 'huffishness', 'sulk', 'hover', 'loom', 'bulk_large']
senticnet['sulky'] = ['-0.29', '0.2', '0.49', '-0.61', '#anger', '#disgust', 'negative', '-0.3', 'queasy', 'outrageous', 'horrid', 'angrily', 'see_red']
senticnet['sullen'] = ['-0.80', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.89', 'sulky', 'solitary', 'morose', 'glum', 'sourpuss']
senticnet['sullenly'] = ['0.921', '0', '0', '0.872', '#joy', '#admiration', 'positive', '0.897', 'meekly', 'mutely', 'glumly', 'obediently', 'stoically']
senticnet['sullenness'] = ['-0.95', '0', '0.694', '-0.94', '#sadness', '#disgust', 'negative', '-0.86', 'ill_nature', 'moroseness', 'crabbiness', 'crabbedness', 'crankiness']
senticnet['sullied'] = ['0', '0', '-0.88', '-0.91', '#fear', '#disgust', 'negative', '-0.89', 'alloy', 'meal', 'taint', 'blemish', 'unwholesome']
senticnet['sullivan'] = ['-0.69', '0', '0.89', '-0.70', '#anger', '#disgust', 'negative', '-0.76', 'still', 'suppress', 'moderate', 'hush_up', 'quell']
senticnet['sullivan_comic_opera'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'dress', 'pinny', 'dirndl', 'gown', 'muumuu']
senticnet['sullivan_opera'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'dress', 'pinny', 'dirndl', 'gown', 'muumuu']
senticnet['sullivan_operetta'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'dress', 'pinny', 'dirndl', 'gown', 'muumuu']
senticnet['sully'] = ['0', '0', '-0.94', '-0.72', '#fear', '#disgust', 'negative', '-0.83', 'sullied', 'defiled', 'slur', 'dirty', 'stain']
senticnet['sulpha'] = ['0.636', '-0.87', '0', '0.932', '#surprise', '#admiration', 'positive', '0.815', 'antibacterial', 'sulfamethoxazole', 'sulfisoxazole', 'sulfadiazine', 'sulfacetamide']
senticnet['sulphide'] = ['-0.05', '-0.00', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'pigment', 'many_shades', 'cyan', 'green_yellow', 'material']
senticnet['sulphuric_acid'] = ['0.071', '0', '-0.08', '-0.09', '#fear', '#disgust', 'negative', '-0.03', 'gunk', 'rotten_food', 'sour_cream', 'glass_of_milk', 'lemon_juice']
senticnet['sulpiride'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['sultan'] = ['0.07', '0', '0', '0.051', '#joy', '#admiration', 'positive', '0.06', 'despot', 'duke', 'king', 'duchess', 'earl']
senticnet['sultan_mosque'] = ['0', '0.224', '0', '0.197', '#interest', '#admiration', 'positive', '0.211', 'church', 'temple', 'cathedral', 'chapel', 'confession']
senticnet['sultan_palace'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['sultanahmet'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['sultry'] = ['-0.90', '0', '0.762', '0', '#sadness', '#anger', 'negative', '-0.83', 'seductive', 'alluring', 'sensuous', 'dreamy', 'sizzling']
senticnet['sum'] = ['-0.02', '0.216', '0.06', '0.141', '#interest', '#admiration', 'positive', '0.068', 'total_number', 'tally', 'total', 'list_number', 'count']
senticnet['sum_total'] = ['0', '0.175', '0.084', '0.133', '#interest', '#admiration', 'positive', '0.075', 'total_number', 'aggregate', 'sum', 'total', 'count']
senticnet['sumerology'] = ['-0.98', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.93', 'archaeology', 'protohistory', 'paleology', 'archeology', 'paleopathology']
senticnet['summarise'] = ['-0.03', '0.136', '-0.11', '0.267', '#interest', '#admiration', 'positive', '0.064', 'explain', 'question_starter', 'tell_why', 'answer', 'respond']
senticnet['summarize'] = ['-0.10', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.11', 'reflect', 'stare_off_space', 'recap', 'think_past', 'mind_boggle']
senticnet['summary'] = ['0', '0.175', '0.084', '0.133', '#interest', '#admiration', 'positive', '0.075', 'brief', 'recap', 'abridgement', 'resume', 'report']
senticnet['summary_judgement'] = ['-0.86', '0', '0.897', '-0.81', '#sadness', '#anger', 'negative', '-0.85', 'disregard', 'ignore', 'brush_off', 'push_aside', 'flout']
senticnet['summat'] = ['-0.85', '0', '0.583', '-0.71', '#sadness', '#disgust', 'negative', '-0.71', 'muddle', 'rummage', 'mare_nest', 'welter', 'clutter']
senticnet['summative_evaluation'] = ['-0.06', '0.18', '0', '0.143', '#interest', '#admiration', 'positive', '0.086', 'grade', 'multiple_choices', 'final', 'final_exam', 'school_grade']
senticnet['summer'] = ['0.723', '0', '0', '0.862', '#joy', '#admiration', 'positive', '0.792', 'student', 'faculty_mentor', 'opportunity', 'participant', 'university']
senticnet['summer-blooming'] = ['0.833', '0', '0', '0.801', '#joy', '#admiration', 'positive', '0.817', 'day-neutral', 'spring-blooming', 'winter-blooming', 'summer-flowering', 'winter-flowering']
senticnet['summer-flowering'] = ['0.834', '0', '0', '0.805', '#joy', '#admiration', 'positive', '0.82', 'day-neutral', 'spring-blooming', 'winter-blooming', 'winter-flowering', 'summer-blooming']
senticnet['summer-friendly'] = ['0', '0', '0.932', '-0.73', '#anger', '#disgust', 'negative', '-0.83', 'surf-fishing', 'school-improvement', 'soil-building', 'action-planning', 'water-safety']
senticnet['summer_blockbuster'] = ['0.705', '-0.60', '0', '0.913', '#joy', '#admiration', 'positive', '0.741', 'lick', 'clout', 'poke', 'punch', 'biff']
senticnet['summer_breeze'] = ['0.98', '-0.74', '0', '0.984', '#joy', '#admiration', 'positive', '0.903', 'prosperous', 'flourishing', 'successful', 'prospering', 'thriving']
senticnet['summer_camp'] = ['0', '0.156', '-0.06', '0.198', '#interest', '#admiration', 'positive', '0.098', 'grassland', 'camping', 'camp_out', 'campsite', 'campground']
senticnet['summer_fete'] = ['-0.72', '0', '0.978', '-0.86', '#anger', '#disgust', 'negative', '-0.85', 'amusement_park', 'common', 'pleasure_ground', 'village_green', 'tract']
senticnet['summer_getaway'] = ['0.024', '0.152', '-0.06', '0.236', '#interest', '#admiration', 'positive', '0.087', 'vacation', 'exit', 'stance', 'take_break', 'holiday']
senticnet['summer_institute'] = ['0.853', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.841', 'teacher', 'teacher_enhancement', 'institute', 'school', 'mathematics']
senticnet['summer_month'] = ['0.763', '0.759', '0', '0', '#joy', '#interest', 'positive', '0.761', 'academicyear_month', 'fellowship', 'fulltime_support', 'summer', 'support_divided']
senticnet['summer_savory'] = ['0.91', '-0.99', '0', '0.779', '#joy', '#surprise', 'positive', '0.894', 'herb', 'delicious', 'savory', 'eat_well', 'winter_savory']
senticnet['summer_session'] = ['0.719', '0.757', '0', '0', '#joy', '#interest', 'positive', '0.738', 'matriculate', 'enroll_class', 'enroll_school', 'inscribe', 'register']
senticnet['summer_solstice'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['summer_squash'] = ['0', '0.067', '-0.09', '-0.09', '#fear', '#disgust', 'negative', '-0.04', 'cotton_boll', 'asbestos', 'corn', 'opium', 'rice']
senticnet['summer_stipend'] = ['0.865', '-0.84', '0', '0.915', '#joy', '#admiration', 'positive', '0.875', 'contract', 'sale_in_gross', 'contract_of_hazard', 'adhesion_contract', 'contract_of_adhesion']
senticnet['summer_support'] = ['0', '0.773', '0', '0.834', '#interest', '#admiration', 'positive', '0.803', 'fellowship', 'summer', 'fulltime_support', 'calendar_year', 'recipient']
senticnet['summer_vacation'] = ['0.977', '0.828', '0', '0', '#joy', '#interest', 'positive', '0.902', 'hunt_fish', 'out_place', 'getaway', 'outdoor_activity', 'vacation']
senticnet['summer_weather'] = ['0.109', '0', '-0.06', '0.073', '#joy', '#admiration', 'positive', '0.038', 'steam', 'haze', 'misty', 'vapor', 'mist']
senticnet['summer_winter'] = ['0.093', '0', '0', '0', '#joy', '#joy', 'positive', '0.093', 'snowy', 'season_year', 'fall_spring', 'chilly', 'winter']
senticnet['summertime'] = ['0.697', '0.723', '0', '0', '#joy', '#interest', 'positive', '0.71', 'sunscreen', 'sunblock', 'go_to_beach', 'sunny_day', 'wear_sunscreen']
senticnet['summit'] = ['0.083', '0.073', '0.015', '0', '#joy', '#interest', 'positive', '0.047', 'peak', 'top', 'good_view', 'pinnacle', 'great_view']
senticnet['summit_brings_together'] = ['-0.79', '0', '0.948', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['summit_cairn'] = ['-0.99', '0', '0.641', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'navigable', 'negotiable', 'surmountable', 'passable', 'traversable']
senticnet['summit_crater'] = ['0.06', '-0.03', '0.037', '0.075', '#joy', '#admiration', 'positive', '0.034', 'semicircle', 'trapezoid', 'circle', 'triangular', 'facet']
senticnet['summit_everest'] = ['0.518', '-0.51', '0', '0.65', '#surprise', '#admiration', 'positive', '0.562', 'clematis', 'legume', 'leguminous_plant', 'climbing_fern', 'root_climber']
senticnet['summit_mount_everest'] = ['0.518', '-0.51', '0', '0.65', '#surprise', '#admiration', 'positive', '0.562', 'clematis', 'legume', 'leguminous_plant', 'climbing_fern', 'root_climber']
senticnet['summited_everest'] = ['0.518', '-0.51', '0', '0.65', '#surprise', '#admiration', 'positive', '0.562', 'clematis', 'legume', 'leguminous_plant', 'climbing_fern', 'root_climber']
senticnet['summited_mount_everest'] = ['0', '0.076', '0', '0.063', '#interest', '#admiration', 'positive', '0.07', 'up_hill', 'elevation', 'up_mountain', 'ascend', 'upwards']
senticnet['summon'] = ['-0.74', '0', '0.966', '0', '#sadness', '#anger', 'negative', '-0.85', 'move', 'displace', 'fire', 'chase_away', 'ostracise']
senticnet['summoner'] = ['0.826', '-0.65', '0', '0.587', '#joy', '#surprise', 'positive', '0.689', 'scoffer', 'vegetarian', 'eater', 'diner', 'dunker']
senticnet['summons'] = ['-0.48', '0.159', '-0.55', '0.397', '#sadness', '#fear', 'negative', '-0.12', 'invitation', 'request', 'tell_why', 'circus_performer', 'invitation_response']
senticnet['summum_bonum'] = ['0.971', '-0.71', '0', '0.656', '#joy', '#surprise', 'positive', '0.781', 'wellbeing', 'human_flourishing', 'successfulness', 'upbeat', 'strength']
senticnet['sumo'] = ['0.103', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.066', 'western', 'circus_act', 'rodeo', 'grand_canyon', 'have_fun']
senticnet['sumo_association'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['sumo_wrestler'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['sumptuous'] = ['0', '0.962', '0', '0.987', '#interest', '#admiration', 'positive', '0.974', 'luxurious', 'opulent', 'magnificent', 'sumptuousness', 'apparatus']
senticnet['sumptuous_dinner'] = ['0.044', '0.08', '0', '-0.03', '#joy', '#interest', 'positive', '0.031', 'cook', 'festival', 'bake_pan', 'restaurant', 'food']
senticnet['sumptuously'] = ['0.902', '-0.72', '0', '0.919', '#joy', '#admiration', 'positive', '0.849', 'preciousness', 'costliness', 'dearness', 'cost', 'expensiveness']
senticnet['sumptuousness'] = ['0', '0.96', '0', '0.985', '#interest', '#admiration', 'positive', '0.972', 'luxurious', 'opulent', 'magnificent', 'sumptuous', 'apparatus']
senticnet['sun'] = ['0.046', '0', '0', '0.085', '#joy', '#admiration', 'positive', '0.066', 'radio_wave', 'gamma_ray', 'solar_cell', 'x-ray', 'cosmic_ray']
senticnet['sun-loving'] = ['-0.75', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'shade-loving', 'heat-loving', 'easy-to-grow', 'spring-blooming', 'summer-blooming']
senticnet['sun-soaked'] = ['0.811', '0', '0', '0.921', '#joy', '#admiration', 'positive', '0.866', 'sunshiny', 'pie-in-the-sky', 'drizzly', 'cloudless', 'blue-sky']
senticnet['sun_beach'] = ['0.929', '0', '0', '0', '#joy', '#joy', 'positive', '0.929', 'shine_light', 'close_together', 'keep_dry', 'smoke_joint', 'shovel_snow']
senticnet['sun_block'] = ['0.93', '-0.91', '0', '0', '#joy', '#surprise', 'positive', '0.922', 'block_sunlight', 'shadow', 'block_light', 'parasol', 'eclipse']
senticnet['sun_burn'] = ['-0.90', '0', '0.209', '-0.92', '#sadness', '#disgust', 'negative', '-0.68', 'burn_skin', 'erythema', 'fire', 'lighting', 'hurt_skin']
senticnet['sun_chromosphere'] = ['0.931', '-0.82', '0', '0.709', '#joy', '#surprise', 'positive', '0.822', 'light', 'aureole', 'radiance', 'visible_light', 'visible_radiation']
senticnet['sun_corona'] = ['0.931', '-0.82', '0', '0.709', '#joy', '#surprise', 'positive', '0.822', 'light', 'aureole', 'radiance', 'visible_light', 'visible_radiation']
senticnet['sun_cover'] = ['0', '0.071', '0', '0.078', '#interest', '#admiration', 'positive', '0.074', 'block_sun', 'eclipse', 'cool_temperature', 'nice_day', 'overcast']
senticnet['sun_cream'] = ['-0.96', '0', '0.636', '-0.64', '#sadness', '#disgust', 'negative', '-0.75', 'burn', 'tan', 'hyperpigmentation', 'sunburn', 'physical_condition']
senticnet['sun_down'] = ['-0.09', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.08', 'night', 'darkness', 'moon_star', 'dusk', 'day_end']
senticnet['sun_exposure'] = ['-0.96', '0', '0.636', '-0.64', '#sadness', '#disgust', 'negative', '-0.75', 'burn', 'tan', 'hyperpigmentation', 'sunburn', 'physical_condition']
senticnet['sun_glare'] = ['0.988', '0', '0', '0.789', '#joy', '#admiration', 'positive', '0.889', 'brightness', 'blaze', 'brilliance', 'glitter', 'glister']
senticnet['sun_god'] = ['0.978', '-0.88', '0', '0.785', '#joy', '#surprise', 'positive', '0.881', 'daemon', 'demigod', 'sea_god', 'divinity', 'goddess']
senticnet['sun_lotion'] = ['0.03', '0', '0.07', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'checkbook_holder', 'account_book', 'checkbook_cover', 'highlighter_pen', 'shine']
senticnet['sun_lounger'] = ['0', '0.092', '0.016', '0.074', '#interest', '#admiration', 'positive', '0.05', 'armoire', 'pillowcase', 'bed_sheet', 'duvet_cover', 'comforter']
senticnet['sun_photosphere'] = ['0.931', '-0.82', '0', '0.709', '#joy', '#surprise', 'positive', '0.822', 'light', 'aureole', 'radiance', 'visible_light', 'visible_radiation']
senticnet['sun_protective_clothing'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'change_tire', 'fix_flat_tire', 'play_rugby', 'wear_silly_hat', 'become_adult']
senticnet['sun_scald'] = ['0.718', '0', '0', '0', '#joy', '#joy', 'positive', '0.718', 'water_container', 'still_water', 'clean_water', 'water', 'cool_water']
senticnet['sun_shining_through'] = ['0.085', '0.071', '0.01', '0.085', '#joy', '#admiration', 'positive', '0.058', 'blue_color', 'munchkin', 'imaginary', 'crystal_clear', 'water_sky']
senticnet['sun_sparcstation'] = ['0.526', '-0.99', '0', '0.673', '#surprise', '#admiration', 'positive', '0.732', 'faery', 'faerie', 'fairy', 'leprechaun', 'sandman']
senticnet['sun_synchronous_orbit'] = ['0.989', '-0.90', '0', '0.876', '#joy', '#surprise', 'positive', '0.923', 'reach', 'range', 'compass', 'scope', 'confines']
senticnet['sun_up'] = ['0.051', '0.16', '-0.09', '0.144', '#interest', '#admiration', 'positive', '0.065', 'come_up', 'get_up', 'rise', 'awaken', 'levitate']
senticnet['sun_worshipper'] = ['-0.96', '0', '0.636', '-0.64', '#sadness', '#disgust', 'negative', '-0.75', 'burn', 'tan', 'hyperpigmentation', 'sunburn', 'physical_condition']
senticnet['sun_wukong'] = ['-0.75', '0', '0.708', '0', '#sadness', '#anger', 'negative', '-0.73', 'hound', 'cage_animal', 'kind_animal', 'hunter', 'animal']
senticnet['sunbathe'] = ['0.099', '0', '0', '0.116', '#joy', '#admiration', 'positive', '0.108', 'question_everything', 'examine', 'bounce_up_down', 'amuse_child', 'add_two_numbers']
senticnet['sunbather'] = ['-0.05', '-0.01', '0.047', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'young_child', 'child', 'youngster', 'youth', 'sunshine']
senticnet['sunbeam'] = ['-0.50', '0', '0.75', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'beam', 'beam_of_light', 'light_beam', 'ray', 'ray_of_light']
senticnet['sunbird_aethopyga'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'lovebird', 'parroket', 'paraquet', 'paroquet']
senticnet['sunbittern'] = ['-0.68', '0', '0.956', '-0.90', '#anger', '#disgust', 'negative', '-0.84', 'giant_moa', 'anomalopteryx', 'moa', 'moa_bird', 'flightless_bird']
senticnet['sunblock'] = ['0.954', '0.885', '0', '0', '#joy', '#interest', 'positive', '0.919', 'sunscreen', 'wear_sunscreen', 'go_to_beach', 'sunny_day', 'summertime']
senticnet['sunbright_light'] = ['0.077', '0.102', '-0.08', '0', '#joy', '#interest', 'positive', '0.032', 'hot_sun', 'light', 'hot', 'hot_day', 'bright_light']
senticnet['sunburn'] = ['0', '0', '-0.79', '0', '#fear', '#fear', 'negative', '-0.79', 'dislocation', 'burn_skin', 'cut_skin', 'pain', 'ache']
senticnet['sunburned'] = ['0', '0', '0', '-0.81', '#disgust', '#disgust', 'negative', '-0.81', 'wet_clothes', 'get_cold', 'wet_hair', 'hurt_back', 'sore_back']
senticnet['sunburst'] = ['0.03', '0.085', '0', '0', '#joy', '#interest', 'positive', '0.058', 'favorite_color', 'colorful', 'different_color', 'pink', 'happy']
senticnet['suncoast'] = ['-0.85', '0', '0.685', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'allicrocodilian', 'crocodile', 'false_gavial', 'crocodilian', 'caiman']
senticnet['suncream'] = ['-0.96', '0', '0.636', '-0.64', '#sadness', '#disgust', 'negative', '-0.75', 'burn', 'tan', 'hyperpigmentation', 'sunburn', 'physical_condition']
senticnet['sundae'] = ['0', '-0.02', '0', '0.069', '#surprise', '#admiration', 'positive', '0.046', 'pastry', 'bakery', 'breadbox', 'christmas_stock', 'freezer']
senticnet['sunday'] = ['0.975', '0.92', '0', '0.868', '#joy', '#interest', 'positive', '0.921', 'religious_event', 'happy_event', 'ceremony', 'tradition', 'religious']
senticnet['sunday_afternoon'] = ['0.819', '0.861', '0', '0', '#joy', '#interest', 'positive', '0.84', 'breakfast_bed', 'hug', 'eat_together', 'make_love', 'give_birth_child']
senticnet['sunday_brunch'] = ['0.715', '0.785', '0', '0', '#joy', '#interest', 'positive', '0.75', 'scrumble_egg', 'heat_meal', 'pan', 'beat_egg', 'food']
senticnet['sunday_morning'] = ['0.02', '0.067', '0', '0', '#joy', '#interest', 'positive', '0.044', 'sunrise', 'begin_day', 'bright_sky', 'sunny', 'part_of_day']
senticnet['sunday_night'] = ['0', '0.734', '0', '0', '#interest', '#interest', 'positive', '0.734', 'sleep', 'go_sleep', 'relax', 'go_out', 'sleep_night']
senticnet['sunday_punch'] = ['-0.98', '0', '0.808', '-0.60', '#sadness', '#anger', 'negative', '-0.8', 'poke', 'clout', 'lick', 'punch', 'biff']
senticnet['sundeck'] = ['0.841', '0', '0', '0.71', '#joy', '#admiration', 'positive', '0.775', 'waiter', 'bar_service', 'beverage_service', 'concierge', 'minibar']
senticnet['sunderland_match_preview'] = ['0.941', '-0.83', '0', '0.973', '#joy', '#admiration', 'positive', '0.917', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['sundown'] = ['-0.08', '0', '-0.04', '0', '#sadness', '#fear', 'negative', '-0.06', 'get_dark', 'night_time', 'day_end', 'dark_outside', 'dusk']
senticnet['sundowner'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'beachcomber', 'bum', 'drifter']
senticnet['sundry'] = ['-0.82', '0', '0.756', '-0.51', '#sadness', '#anger', 'negative', '-0.69', 'inhomogeneous', 'nonuniform', 'assorted', 'heterogeneous', 'mixed']
senticnet['sunfire'] = ['0.02', '0.131', '0.749', '-0.67', '#anger', '#disgust', 'negative', '-0.31', 'belligerence', 'belligerency', 'belligerent', 'war', 'rancor']
senticnet['sunflower_seed'] = ['0.878', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.883', 'wheat', 'grain', 'rye', 'white_rice', 'rice']
senticnet['sungei_buloh_wetland'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['sunglasses'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'walking_stick', 'blind_person', 'disabled', 'poor_visual_perception', 'vision_loss']
senticnet['sungold'] = ['0.02', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'many_fruits', 'buy_fruit', 'cherry', 'pineapple', 'tomato']
senticnet['sungrazing_comet'] = ['0', '-0.72', '0', '0.712', '#surprise', '#admiration', 'positive', '0.72', 'heavenly_body', 'around_earth', 'source_light', 'sol', 'celestial_body']
senticnet['sunhyperpigmentation'] = ['-0.97', '0', '0.63', '-0.65', '#sadness', '#disgust', 'negative', '-0.75', 'burn', 'hyperpigmentation', 'tan', 'sunburn', 'physical_condition']
senticnet['sunken_pirate_ship'] = ['0', '0.177', '0', '0.126', '#interest', '#admiration', 'positive', '0.151', 'treasure', 'storage_facility', 'shrine', 'lock_box', 'jewelry_box']
senticnet['sunken_ship'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'succourer', 'recoverer', 'salvor', 'mortal']
senticnet['sunken_submarine'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'succourer', 'recoverer', 'salvor', 'mortal']
senticnet['sunken_treasure'] = ['-0.04', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.05', 'coral', 'fish_equipment', 'small_fish', 'aquatic_life', 'fly_fish']
senticnet['sunken_vessel'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'succourer', 'recoverer', 'salvor', 'mortal']
senticnet['sunken_wreck'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'succourer', 'recoverer', 'salvor', 'mortal']
senticnet['sunless'] = ['-0.77', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.84', 'tanning', 'self-tanning', 'lotion', 'tanner', 'bronzing']
senticnet['sunlight'] = ['0.078', '0.099', '-0.08', '0', '#joy', '#interest', 'positive', '0.031', 'hot_sun', 'bright_light', 'hot', 'hot_day', 'light']
senticnet['sunlit'] = ['-0.82', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.80', 'evil', 'darkness', 'dark', 'sun_down', 'dark_side']
senticnet['sunni_mosque'] = ['0', '0.224', '0', '0.197', '#interest', '#admiration', 'positive', '0.211', 'church', 'temple', 'cathedral', 'chapel', 'confession']
senticnet['sunnier'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'weather', 'four_seasons', 'fall_spring', 'rain_snow', 'climate']
senticnet['sunny'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'weather', 'four_seasons', 'fall_spring', 'rain_snow', 'climate']
senticnet['sunny_afternoon'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'weather', 'four_seasons', 'fall_spring', 'rain_snow', 'climate']
senticnet['sunny_climate'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'weather', 'four_seasons', 'fall_spring', 'rain_snow', 'climate']
senticnet['sunny_day'] = ['0.135', '0.122', '0', '0.089', '#joy', '#interest', 'positive', '0.115', 'hot_sun', 'good_weather', 'striped', 'bright_day', 'fluffy']
senticnet['sunny_delight'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'glass_water', 'refreshed', 'smoothie', 'lemonade', 'cold_water']
senticnet['sunny_morning'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'weather', 'four_seasons', 'fall_spring', 'rain_snow', 'climate']
senticnet['sunny_weather'] = ['0.833', '0.927', '0', '0', '#joy', '#interest', 'positive', '0.88', 'shinny', 'transparency', 'clarity', 'intelligent', 'water_color']
senticnet['sunnyhillboy'] = ['0', '-0.04', '0.085', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'claw', 'fur_keep_warm', 'tic', 'bristle', 'muzzle']
senticnet['sunray'] = ['-0.62', '0', '0.762', '-0.7', '#anger', '#disgust', 'negative', '-0.69', 'beam', 'beam_of_light', 'light_beam', 'ray', 'ray_of_light']
senticnet['sunrise'] = ['0', '0.071', '0', '0', '#interest', '#interest', 'positive', '0.071', 'sun', 'dawn', 'begin_day', 'light_in_sky', 'daylight']
senticnet['sunroom'] = ['0', '-0.02', '-0.08', '-0.05', '#fear', '#disgust', 'negative', '-0.03', 'mattress', 'sleep_furniture', 'cradle', 'small_bed', 'pillow']
senticnet['sunscald'] = ['0', '0', '-0.94', '-0.96', '#fear', '#disgust', 'negative', '-0.95', 'say_prayer', 'fall_off_chair', 'put_box', 'feel_terrible', 'lift_leg']
senticnet['sunscreen'] = ['0.698', '0.725', '0', '0', '#joy', '#interest', 'positive', '0.712', 'wear_sunscreen', 'sunblock', 'go_to_beach', 'sunny_day', 'summertime']
senticnet['sunset'] = ['0.062', '0.072', '0', '0', '#joy', '#interest', 'positive', '0.067', 'wood_duck', 'dim', 'darkness', 'dusky', 'unlit']
senticnet['sunset_clause'] = ['0.779', '-0.88', '0', '0.521', '#joy', '#surprise', 'positive', '0.727', 'corrigible', 'correctable', 'improvable', 'reformable', 'redeemable']
senticnet['sunset_cruise'] = ['0.024', '0.152', '-0.06', '0.236', '#interest', '#admiration', 'positive', '0.087', 'vacation', 'exit', 'stance', 'take_break', 'holiday']
senticnet['sunset_handicap'] = ['-0.76', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.78', 'strangle', 'bloodshed', 'cripple', 'hear_bad_news', 'sexually_transmit_disease']
senticnet['sunshine'] = ['0.779', '0', '0', '0.747', '#joy', '#admiration', 'positive', '0.763', 'just_fun', 'good_mental_health', 'sun', 'enjoyable', 'power_electricity']
senticnet['sunshiny'] = ['0.813', '0', '0', '0.917', '#joy', '#admiration', 'positive', '0.865', 'blue-sky', 'pie-in-the-sky', 'drizzly', 'cloudless', 'sun-soaked']
senticnet['sunsite'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'busy_bee', 'actor', 'doer', 'worker']
senticnet['sunstroke'] = ['0', '0', '-0.08', '-0.12', '#fear', '#disgust', 'negative', '-0.10', 'burn_skin', 'cancer', 'ultraviolet', 'erythema', 'expose_skin_sun']
senticnet['suntan'] = ['-0.96', '0', '0.636', '-0.64', '#sadness', '#disgust', 'negative', '-0.75', 'burn', 'tan', 'hyperpigmentation', 'sunburn', 'physical_condition']
senticnet['suntan_lotion'] = ['0.041', '-0.02', '0.044', '-0.10', '#anger', '#disgust', 'negative', '-0.02', 'shoplifter', 'carton_milk', 'store_shelf', 'plastic_container', 'wax_dental_floss']
senticnet['sunyata'] = ['-0.87', '0', '0.744', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['suo_jure'] = ['0.975', '-0.78', '0', '0.969', '#joy', '#admiration', 'positive', '0.911', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['suorum'] = ['0', '0.079', '0', '0', '#interest', '#interest', 'positive', '0.079', 'change_diaper', 'lower_standard_live', 'become_pregnant', 'find_mate', 'make_baby']
senticnet['super'] = ['0.117', '-0.24', '0', '0.423', '#surprise', '#admiration', 'positive', '0.263', 'better_good', 'great', 'carry_lunch', 'high_quality', 'really_good']
senticnet['super-abundant'] = ['-0.82', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.88', 'superabundant', 'superabundance', 'inexhaustible', 'measureless', 'ever-dwindling']
senticnet['super-human'] = ['0.707', '0', '0', '0.857', '#joy', '#admiration', 'positive', '0.782', 'superhuman', 'god-like', 'feat', 'ability', 'preternatural']
senticnet['super-low'] = ['0', '0', '-0.84', '-0.81', '#fear', '#disgust', 'negative', '-0.83', 'bargain-basement', 'rock-bottom', 'dirt-cheap', 'near-record', 'sky-high']
senticnet['super-rich'] = ['0', '-0.97', '0', '0.917', '#surprise', '#admiration', 'positive', '0.945', 'out-of-touch', 'plutocrat', 'multimillionaire', 'plutocrat', 'corporatist']
senticnet['super-sharp'] = ['0', '0.934', '0', '0.721', '#interest', '#admiration', 'positive', '0.828', 'sharpened', 'ultra-sharp', 'sharpness', 'razor-sharp', 'sharpest']
senticnet['super_absorbent'] = ['-0.81', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.85', 'bandage', 'painkiller', 'gauze', 'aspirin', 'contraceptive_device']
senticnet['super_adaptoid'] = ['-0.88', '0', '0.882', '-0.58', '#sadness', '#anger', 'negative', '-0.78', 'mortal', 'behemoth', 'unusual_person', 'aberrant', 'giant']
senticnet['super_angry'] = ['0', '0', '0.748', '0', '#anger', '#anger', 'negative', '-0.74', 'wet_pant', 'tell_lie', 'feel_sad', 'correct_mistake', 'feel_angry']
senticnet['super_bowl_advertisement'] = ['0.808', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.903', 'marketing', 'sale', 'announcement', 'billboard', 'promotion']
senticnet['super_cal'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'find_out', 'dig_out', 'unearth', 'become_aware', 'dish_out']
senticnet['super_constellation'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'atmosphere', 'wing', 'nest', 'feather', 'bird_fly']
senticnet['super_cup'] = ['0.093', '-0.02', '0', '0.056', '#joy', '#admiration', 'positive', '0.057', 'pitcher', 'carafe', 'container_pitcher', 'mug', 'glass_of_water']
senticnet['super_cute'] = ['0.936', '-0.81', '0', '0.83', '#joy', '#admiration', 'positive', '0.86', 'cherubic', 'lovely', 'lovable', 'endearing', 'angelic']
senticnet['super_deluxe_box'] = ['-0.73', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'unwed', 'unwedded', 'divorced', 'unmarried', 'widowed']
senticnet['super_duper'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'book_worm', 'nerd', 'loser', 'underdog', 'computer_lover']
senticnet['super_flyweight_division'] = ['0', '0', '0', '0.834', '#admiration', '#admiration', 'positive', '0.834', 'royal_person', 'royal_title', 'top', 'master', 'rank']
senticnet['super_glue'] = ['0', '0.848', '0', '0.874', '#interest', '#admiration', 'positive', '0.861', 'fix', 'glue', 'fastener_binder', 'stick_together', 'repair']
senticnet['super_intelligent'] = ['0.995', '0.801', '0', '0.968', '#joy', '#admiration', 'positive', '0.921', 'smart', 'smartness', 'curious', 'become_wise', 'comprehend']
senticnet['super_lightweight'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'carbon_fiber', 'aluminium', 'light', 'thin', 'performant']
senticnet['super_market'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'grocery_cart', 'salad_bar', 'fruit_stand', 'candy_store', 'produce_store']
senticnet['super_phosphate'] = ['0.071', '0', '0.031', '0.064', '#joy', '#admiration', 'positive', '0.035', 'organic_matter', 'decompose', 'recycle', 'soil', 'compost_pile']
senticnet['super_rich'] = ['0.843', '0.977', '0', '0', '#joy', '#interest', 'positive', '0.91', 'business', 'finance', 'revenue', 'wealth', 'income']
senticnet['super_scrabble'] = ['0.801', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.796', 'verbosity', 'sport_play', 'excite_game', 'contest', 'fun_game']
senticnet['super_sentai'] = ['0.021', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'plastic_bottle', 'party', 'use_party', 'lighter_air', 'compress_air']
senticnet['super_sexy'] = ['0.902', '0.995', '0', '0', '#joy', '#interest', 'positive', '0.949', 'beautiful_girl', 'hen_baby', 'pretty_woman', 'hen_child', 'bird']
senticnet['super_skrull'] = ['-0.88', '0', '0.882', '-0.58', '#sadness', '#anger', 'negative', '-0.78', 'mortal', 'behemoth', 'unusual_person', 'aberrant', 'giant']
senticnet['super_sonic'] = ['-0.94', '0', '0.866', '-0.91', '#sadness', '#disgust', 'negative', '-0.90', 'movement', 'apparent_motion', 'motion', 'apparent_movement', 'optical_illusion']
senticnet['super_stardom'] = ['0', '0.793', '0', '0.91', '#interest', '#admiration', 'positive', '0.852', 'superstar', 'vip', 'accomplishment', 'performance', 'concert']
senticnet['super_villain'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
senticnet['superabundance'] = ['-0.82', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.88', 'super-abundant', 'superabundant', 'inexhaustible', 'measureless', 'ever-dwindling']
senticnet['superabundant'] = ['-0.81', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.87', 'super-abundant', 'superabundance', 'inexhaustible', 'measureless', 'ever-dwindling']
senticnet['superannuated'] = ['-0.91', '0', '0.511', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'outdated', 'noncurrent', 'disused', 'obsolete', 'obsolescent']
senticnet['superb'] = ['0', '0.791', '0', '0.977', '#interest', '#admiration', 'positive', '0.884', 'superbness', 'wizard', 'bang-up', 'super', 'haughty']
senticnet['superb_storyteller'] = ['-0.90', '0.138', '-0.63', '0.071', '#sadness', '#fear', 'negative', '-0.33', 'woeful', 'execrable', 'deplorable', 'woebegone', 'plaintiveness']
senticnet['superball'] = ['0.793', '0.915', '0', '0', '#joy', '#interest', 'positive', '0.854', 'ball', 'ball_bounce', 'football', 'soccer', 'basketball']
senticnet['superbia'] = ['0.563', '-0.99', '0', '0.729', '#surprise', '#admiration', 'positive', '0.763', 'lordliness', 'mortal_sin', 'deadly_sin', 'arrogance', 'haughtiness']
senticnet['superbly'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'terrific', 'wondrously', 'tremendous', 'marvelous', 'wondrous']
senticnet['superbness'] = ['0', '0.787', '0', '0.969', '#interest', '#admiration', 'positive', '0.878', 'superb', 'wizard', 'bang-up', 'super', 'haughty']
senticnet['superboy'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'only_bird']
senticnet['superbubble'] = ['-0.55', '0', '0.869', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'air_bubble', 'foam', 'froth', 'soap_bubble', 'bubble']
senticnet['supercar'] = ['0.755', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.728', 'high_class', 'expensive_car', 'ferrari', 'fancy_car', 'sports_car']
senticnet['superceding_indictment'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'fraud', 'swindle', 'rig']
senticnet['supercelestial'] = ['-0.52', '0', '0.701', '-0.50', '#sadness', '#anger', 'negative', '-0.57', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['supercell_storm'] = ['0', '-0.41', '-0.95', '0', '#surprise', '#fear', 'negative', '-0.26', 'blizzard', 'snow_storm', 'storm', 'danger', 'rainy']
senticnet['supercell_thunderstorm'] = ['0', '-0.41', '-0.95', '0', '#surprise', '#fear', 'negative', '-0.26', 'blizzard', 'snow_storm', 'storm', 'danger', 'rainy']
senticnet['superciliary'] = ['-0.59', '0', '0.887', '-0.71', '#anger', '#disgust', 'negative', '-0.73', 'armet', 'basinet', 'helmet', 'heaume', 'morion']
senticnet['supercilious'] = ['0', '0', '-0.86', '-0.95', '#fear', '#disgust', 'negative', '-0.90', 'imperious', 'haughty', 'disdainful', 'lordly', 'overbearing']
senticnet['supercilium'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'neck', 'body_part', 'henchman', 'shoulder', 'head']
senticnet['superclub'] = ['0.763', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.75', 'pub', 'disco', 'dance_club', 'cafe', 'tavern']
senticnet['supercomm'] = ['-0.79', '0', '0.948', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['supercomputer'] = ['0', '0.711', '0', '0.898', '#interest', '#admiration', 'positive', '0.804', 'student', 'access', 'computer_science_engineering', 'telecommunication', 'network_infrastructure']
senticnet['superconductive'] = ['0.102', '0.078', '0', '0.087', '#joy', '#admiration', 'positive', '0.089', 'gold', 'platinum', 'iron_ore', 'silver', 'adjustable']
senticnet['superconductivity'] = ['0', '0.77', '0', '0.744', '#interest', '#admiration', 'positive', '0.757', 'material_research', 'material', 'number', 'role', 'investigate']
senticnet['superconductor'] = ['0.727', '0.827', '0', '0', '#joy', '#interest', 'positive', '0.777', 'material', 'material_research', 'number', 'work', 'investigation']
senticnet['supercooled_water'] = ['-0.64', '0', '0.612', '-0.84', '#sadness', '#disgust', 'negative', '-0.7', 'chilling', 'frost', 'lyophilization', 'icing', 'cooling']
senticnet['supercritical'] = ['-0.90', '0', '0.825', '-0.61', '#sadness', '#anger', 'negative', '-0.78', 'flaming', 'combustion', 'fire', 'flame', 'internal_combustion']
senticnet['supercritical_fluid'] = ['0', '0.073', '-0.05', '0.093', '#interest', '#admiration', 'positive', '0.038', 'vapor', 'water_vapor', 'evaporate', 'condense_water', 'bubble']
senticnet['supercritical_water'] = ['0', '0.073', '-0.05', '0.093', '#interest', '#admiration', 'positive', '0.038', 'vapor', 'water_vapor', 'evaporate', 'condense_water', 'bubble']
senticnet['supercup'] = ['0.093', '-0.02', '0', '0.056', '#joy', '#admiration', 'positive', '0.057', 'pitcher', 'carafe', 'container_pitcher', 'mug', 'glass_of_water']
senticnet['superelastic'] = ['-0.54', '0', '0.909', '-0.97', '#anger', '#disgust', 'negative', '-0.81', 'suppleness', 'pliancy', 'pliantness', 'flexibility', 'flexibleness']
senticnet['supererogation'] = ['-0.86', '0', '0.945', '-0.71', '#sadness', '#anger', 'negative', '-0.84', 'sweat', 'effort', 'elbow_grease', 'exertion', 'travail']
senticnet['supererogatory'] = ['0', '0', '-0.97', '-0.73', '#fear', '#disgust', 'negative', '-0.85', 'spare', 'redundant', 'extra', 'superfluous', 'excess']
senticnet['superessential'] = ['0.668', '-0.88', '0', '0.893', '#surprise', '#admiration', 'positive', '0.815', 'chirpiness', 'spirit', 'sprightliness', 'liveliness', 'activity']
senticnet['superfecta'] = ['-0.78', '0', '0.944', '-0.51', '#sadness', '#anger', 'negative', '-0.74', 'loser', 'speculator', 'mortal', 'better', 'gambler']
senticnet['superfecundation'] = ['0', '0.956', '0', '0.965', '#interest', '#admiration', 'positive', '0.96', 'superfetation', 'fertilization', 'pollination', 'dressing', 'top_dressing']
senticnet['superfetation'] = ['0', '0.957', '0', '0.968', '#interest', '#admiration', 'positive', '0.962', 'fertilization', 'superfecundation', 'pollination', 'dressing', 'top_dressing']
senticnet['superficial'] = ['-0.63', '0', '0.857', '-0.71', '#anger', '#disgust', 'negative', '-0.73', 'shallow', 'facile', 'ostensible', 'sounding', 'looking']
senticnet['superficial_charm'] = ['-0.97', '0', '0.848', '-0.58', '#sadness', '#anger', 'negative', '-0.80', 'moroseness', 'sullenness', 'ill_humor', 'glumness', 'distemper']
senticnet['superficial_damage'] = ['-0.98', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.97', 'harm', 'hurt', 'cut_skin', 'bloody', 'ouch']
senticnet['superficial_layer'] = ['-0.53', '0', '0.982', '-0.67', '#anger', '#disgust', 'negative', '-0.73', 'meniscus', 'semilunar_cartilage', 'gristle', 'hyaline_cartilage', 'arytenoid']
senticnet['superficial_resemblance'] = ['-0.88', '0', '0.813', '-0.57', '#sadness', '#anger', 'negative', '-0.75', 'affinity', 'mutual_resemblance', 'alikeness', 'likeness', 'similitude']
senticnet['superficial_similarity'] = ['-0.88', '0', '0.813', '-0.57', '#sadness', '#anger', 'negative', '-0.75', 'affinity', 'mutual_resemblance', 'alikeness', 'likeness', 'similitude']
senticnet['superficiality'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'nonsense', 'meaninglessness', 'bunk', 'absurdity', 'hokum']
senticnet['superficially'] = ['0', '0.764', '0', '0.845', '#interest', '#admiration', 'positive', '0.804', 'empathetically', 'empathic', 'empathetic', 'kind-heartedness', 'kindheartedness']
senticnet['superficially_similar'] = ['0.14', '0.345', '-0.20', '0.378', '#interest', '#admiration', 'positive', '0.165', 'good_etiquette', 'same', 'similarity', 'band_together', 'niceness']
senticnet['superfine'] = ['0', '0.997', '0', '0.967', '#interest', '#admiration', 'positive', '0.982', 'wife', 'cashmere', 'merino', 'personal', 'wool']
senticnet['superfluity'] = ['0', '0', '-0.82', '-0.83', '#fear', '#disgust', 'negative', '-0.82', 'circuit', 'dismissal', 'needle', 'redundancy', 'language']
senticnet['superfluous'] = ['-0.78', '0', '0.651', '-0.56', '#sadness', '#anger', 'negative', '-0.66', 'rubbishy', 'sorry', 'trashy', 'otiose', 'worthless']
senticnet['superfood'] = ['0.026', '0.08', '0.043', '0.064', '#interest', '#admiration', 'positive', '0.032', 'eat_rice', 'white_brown', 'asian', 'eat_pasta', 'staple_food']
senticnet['superfund_list'] = ['0.935', '0', '0', '0', '#joy', '#joy', 'positive', '0.935', 'clean_up_kitchen', 'feel_dirty', 'remove_stain', 'stink', 'stink_mess']
senticnet['supergiant'] = ['0.5', '-0.72', '0', '0.649', '#surprise', '#admiration', 'positive', '0.624', 'giant', 'huge', 'supernova', 'binary_star', 'star']
senticnet['supergiant_star'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['superglue'] = ['0', '0', '-0.91', '-0.70', '#fear', '#disgust', 'negative', '-0.81', 'wax_dental_floss', 'tartar_control_toothpaste', 'toothpaste', 'gel_toothpaste', 'toothbrush']
senticnet['supergravity'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'general_relativity', 'general_theory_of_relativity', 'relativity', 'special_relativity', 'special_theory_of_relativity']
senticnet['supergroup_plc_sgp_lse'] = ['0.025', '0.115', '0.06', '0.183', '#interest', '#admiration', 'positive', '0.066', 'volume_measurement', 'liquid_measurement', 'mass_measure', 'geometry', 'size']
senticnet['superheated'] = ['0', '0', '-0.84', '-0.79', '#fear', '#disgust', 'negative', '-0.81', 'high-pressure', 'pressurized', 'high-temperature', 'low-temperature', 'low-pressure']
senticnet['superhero'] = ['0', '0.725', '0', '0.756', '#interest', '#admiration', 'positive', '0.74', 'version', 'grizzly_bear', 'brown_bear', 'elf', 'book_poetry']
senticnet['superhero_comic'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'encyclopaedia', 'child_story', 'comic', 'disney_cartoon']
senticnet['superhero_costume'] = ['-0.76', '0', '0.672', '-0.88', '#sadness', '#disgust', 'negative', '-0.77', 'attire', 'garb', 'dress', 'fancy_dress', 'masquerade']
senticnet['superhero_flick'] = ['0.705', '-0.60', '0', '0.913', '#joy', '#admiration', 'positive', '0.741', 'lick', 'clout', 'poke', 'punch', 'biff']
senticnet['superhero_movie'] = ['0.705', '-0.60', '0', '0.913', '#joy', '#admiration', 'positive', '0.741', 'lick', 'clout', 'poke', 'punch', 'biff']
senticnet['superhighway'] = ['0.989', '-0.51', '0', '0.603', '#joy', '#admiration', 'positive', '0.701', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['superhuman'] = ['0.71', '0', '0', '0.865', '#joy', '#admiration', 'positive', '0.787', 'super-human', 'god-like', 'feat', 'ability', 'preternatural']
senticnet['superhuman_intelligence'] = ['0.651', '-0.73', '0', '0.975', '#surprise', '#admiration', 'positive', '0.788', 'profoundness', 'profundity', 'sagacity', 'sagaciousness', 'discernment']
senticnet['superhuman_strength'] = ['0', '0.839', '0', '0.963', '#interest', '#admiration', 'positive', '0.901', 'imputrescible', 'undestroyable', 'imperishable', 'durable', 'perdurable']
senticnet['superhumanly'] = ['-0.61', '0', '0.572', '-0.83', '#sadness', '#disgust', 'negative', '-0.67', 'numb', 'benumbed', 'anesthetic', 'insensible', 'asleep']
senticnet['superhumanly_strong'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
senticnet['superia'] = ['-0.98', '0', '0.714', '-0.90', '#sadness', '#disgust', 'negative', '-0.86', 'stupid', 'stupid_person', 'stupe', 'dullard', 'dolt']
senticnet['superimpose'] = ['-0.09', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.11', 'write_down_idea', 'finish_book', 'write_down', 'take_note_class', 'take_note']
senticnet['superimposition'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'arm_movement', 'hand_gesture', 'water_motion', 'hand_signal', 'sea_movement']
senticnet['superintelligence'] = ['0.651', '-0.73', '0', '0.975', '#surprise', '#admiration', 'positive', '0.788', 'profoundness', 'profundity', 'sagacity', 'sagaciousness', 'discernment']
senticnet['superintelligent'] = ['0.651', '-0.73', '0', '0.975', '#surprise', '#admiration', 'positive', '0.788', 'profoundness', 'profundity', 'sagacity', 'sagaciousness', 'discernment']
senticnet['superintendence'] = ['0', '0.898', '0', '0.771', '#interest', '#admiration', 'positive', '0.835', 'management', 'supervising', 'invigilation', 'oversight', 'supervision']
senticnet['superior'] = ['0', '0.886', '0', '0.772', '#interest', '#admiration', 'positive', '0.829', 'stalin', 'sail_sea', 'well_done', 'supervisor', 'small_ocean']
senticnet['superior_firepower'] = ['0.586', '-0.94', '0', '0.748', '#surprise', '#admiration', 'positive', '0.76', 'vanquish', 'beat', 'beat_out', 'trounce', 'crush']
senticnet['superior_intellect'] = ['0.626', '-0.96', '0', '0.788', '#surprise', '#admiration', 'positive', '0.794', 'foxiness', 'slyness', 'wiliness', 'craftiness', 'cunning']
senticnet['superior_mirage'] = ['-0.94', '0', '0.866', '-0.91', '#sadness', '#disgust', 'negative', '-0.90', 'movement', 'apparent_motion', 'motion', 'apparent_movement', 'optical_illusion']
senticnet['superior_ovary'] = ['0', '0', '0.064', '-0.04', '#anger', '#disgust', 'negative', '-0.05', 'bird_food', 'funiculus', 'plant_product', 'fuzz', 'agricultural_product']
senticnet['superior_vena_cava_syndrome'] = ['-0.99', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.78', 'angioedema', 'atrophedema', 'giant_hive', 'periodic_edema', 'dropsy']
senticnet['superior_workmanship'] = ['0.876', '-0.76', '0', '0.963', '#joy', '#admiration', 'positive', '0.867', 'housecraft', 'tradecraft', 'watercraft', 'acquirement', 'skill']
senticnet['superiority'] = ['0.757', '-0.82', '0', '0.982', '#surprise', '#admiration', 'positive', '0.855', 'first_class', 'choiceness', 'first_water', 'fineness', 'quality']
senticnet['superiority_complex'] = ['0.585', '-0.7', '0', '0.586', '#surprise', '#admiration', 'positive', '0.624', 'jingoistic', 'ultranationalistic', 'superpatriotic', 'nationalistic', 'patriotic']
senticnet['superkick'] = ['0.052', '0', '0', '0.118', '#joy', '#admiration', 'positive', '0.085', 'diamond_ring', 'piece_of_jewelry', 'bell_sound', 'wedding_band', 'engagement']
senticnet['superlative'] = ['0.112', '-0.00', '0.013', '0.383', '#joy', '#admiration', 'positive', '0.121', 'excellence', 'well_done', 'high_quality', 'ultimate', 'greatness']
senticnet['superlight'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'performant']
senticnet['superman'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'only_bird']
senticnet['superman_aquaman_hour'] = ['0.704', '-0.80', '0', '0', '#joy', '#surprise', 'positive', '0.755', 'quest', 'stunt', 'illusion', 'journey', 'activity']
senticnet['superman_costume'] = ['-0.76', '0', '0.672', '-0.88', '#sadness', '#disgust', 'negative', '-0.77', 'attire', 'garb', 'dress', 'fancy_dress', 'masquerade']
senticnet['supermarine'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'cheapjack', 'pitchman']
senticnet['supermarket'] = ['0.146', '0.113', '-0.06', '0.124', '#joy', '#admiration', 'positive', '0.08', 'food_shop', 'corner_grocery', 'food_store', 'buy_food', 'market']
senticnet['supermarket_aisle'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'super_market', 'salad_bar', 'fruit_stand', 'candy_store', 'produce_store']
senticnet['supermarket_chain'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'apple_pie', 'lasagna', 'jelly', 'a_lot_of_food', 'cottage_cheese']
senticnet['supermarket_shopping'] = ['0.875', '0.706', '0', '0', '#joy', '#interest', 'positive', '0.79', 'customer', 'clerk', 'patron', 'vintner', 'hairdresser']
senticnet['supermarket_trolley'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'super_market', 'salad_bar', 'fruit_stand', 'candy_store', 'produce_store']
senticnet['supermassive_black_hole'] = ['0', '0.706', '0', '0.847', '#interest', '#admiration', 'positive', '0.776', 'dwarf_planet', 'universe', 'planetary', 'sun', 'moon']
senticnet['supermax'] = ['0.816', '-0.91', '0', '0.944', '#surprise', '#admiration', 'positive', '0.891', 'jail', 'jailhouse', 'gaol', 'poky', 'clink']
senticnet['supermax_facility'] = ['0.816', '-0.91', '0', '0.944', '#surprise', '#admiration', 'positive', '0.891', 'jail', 'jailhouse', 'gaol', 'poky', 'clink']
senticnet['supermoon'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['supernal'] = ['0', '0', '-0.86', '-0.77', '#fear', '#disgust', 'negative', '-0.81', 'untellable', 'unspeakable', 'indescribable', 'indefinable', 'unutterable']
senticnet['supernatural'] = ['-0.70', '0', '0.754', '0', '#sadness', '#anger', 'negative', '-0.73', 'eerie', 'skittish', 'unpredictably', 'ghost', 'spooky']
senticnet['supernatural_entity'] = ['0', '0.871', '0', '0.861', '#interest', '#admiration', 'positive', '0.866', 'predict_future', 'psychic_phenomenon', 'parapsychology', 'clairvoyance', 'foreknowledge']
senticnet['supernatural_grace'] = ['-0.86', '0', '0.945', '-0.71', '#sadness', '#anger', 'negative', '-0.84', 'sweat', 'effort', 'elbow_grease', 'exertion', 'travail']
senticnet['supernatural_horror'] = ['0.747', '0.85', '0', '0', '#joy', '#interest', 'positive', '0.798', 'night_vision', 'mystery', 'plot', 'imagine', 'myth']
senticnet['supernatural_phenomenon'] = ['0', '0.871', '0', '0.861', '#interest', '#admiration', 'positive', '0.866', 'predict_future', 'psychic_phenomenon', 'parapsychology', 'clairvoyance', 'foreknowledge']
senticnet['supernatural_thriller'] = ['-0.08', '-0.01', '-0.16', '-0.13', '#fear', '#disgust', 'negative', '-0.09', 'hockey_game', 'movie', 'scary_movie', 'cinema', 'cctv']
senticnet['supernaturalism'] = ['-0.98', '0', '0.891', '-0.60', '#sadness', '#anger', 'negative', '-0.82', 'exorcism', 'dispossession', 'magic', 'thaumaturgy', 'belief']
senticnet['supernaturalist'] = ['-0.98', '0', '0.891', '-0.60', '#sadness', '#anger', 'negative', '-0.82', 'exorcism', 'dispossession', 'magic', 'thaumaturgy', 'belief']
senticnet['supernaturalistic'] = ['-0.98', '0', '0.891', '-0.60', '#sadness', '#anger', 'negative', '-0.82', 'exorcism', 'dispossession', 'magic', 'thaumaturgy', 'belief']
senticnet['supernaturally'] = ['0.962', '0', '0', '0.875', '#joy', '#admiration', 'positive', '0.918', 'sovereignly', 'providentially', 'fortuitously', 'divinely', 'wondrously']
senticnet['supernaturalness'] = ['-0.68', '0', '0.863', '-0.69', '#anger', '#disgust', 'negative', '-0.74', 'pretension', 'artificiality', 'affectedness', 'unnaturalness', 'largeness']
senticnet['supernormal'] = ['-0.58', '0', '0.814', '-0.54', '#sadness', '#anger', 'negative', '-0.64', 'atypical', 'irregular', 'vicarious', 'abnormal', 'subnormal']
senticnet['supernova'] = ['-0.70', '0', '0.924', '0', '#sadness', '#anger', 'negative', '-0.81', 'supernova', 'galaxy', 'supernova', 'explosion', 'nebula']
senticnet['superpac'] = ['-0.81', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.81', 'take_charge', 'anti', 'go_against', 'death_wish', 'file_complaint']
senticnet['superpartner'] = ['0.022', '0.073', '0', '0.14', '#interest', '#admiration', 'positive', '0.078', 'elementary_particle', 'electron', 'proton_neutron', 'neutron', 'proton']
senticnet['superpatriotic'] = ['0.585', '-0.70', '0', '0.579', '#surprise', '#admiration', 'positive', '0.624', 'jingoistic', 'ultranationalistic', 'chauvinistic', 'nationalistic', 'patriotic']
senticnet['superpatriotism'] = ['0.549', '-0.73', '0', '0.744', '#surprise', '#admiration', 'positive', '0.676', 'jingoism', 'trueness', 'loyalty', 'chauvinism', 'nationalism']
senticnet['superphosphate'] = ['0.071', '0', '0.031', '0.064', '#joy', '#admiration', 'positive', '0.035', 'organic_matter', 'decompose', 'recycle', 'soil', 'compost_pile']
senticnet['superposition'] = ['0.807', '-0.88', '0', '0.718', '#joy', '#surprise', 'positive', '0.802', 'sex', 'gayness', 'sexual_gayness', 'lesbianism', 'sapphism']
senticnet['superpower'] = ['0.838', '0.707', '0', '0.904', '#joy', '#admiration', 'positive', '0.816', 'natural_order', 'universe', 'existence', 'creation', 'cosmos']
senticnet['superscript'] = ['0.049', '0.113', '0.107', '0.162', '#interest', '#admiration', 'positive', '0.054', 'list_number', 'ordinal', 'count', 'numerical', 'enumeration']
senticnet['superscription'] = ['-0.98', '0', '0.664', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'unfortunate', 'unfortunate_person', 'weeper', 'pallgriever', 'griever']
senticnet['supersede'] = ['-0.81', '0', '0.854', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'replace', 'cover', 'supplant', 'deputize', 'supervene_upon']
senticnet['supersensible'] = ['-0.87', '0', '0.858', '-0.77', '#sadness', '#anger', 'negative', '-0.83', 'pass_off', 'fall_out', 'occur', 'happen', 'hap']
senticnet['supersonic'] = ['-0.99', '0', '0.798', '-0.71', '#sadness', '#anger', 'negative', '-0.83', 'silent', 'voiceless', 'unsounded', 'breathed', 'inaudible']
senticnet['supersonic_detonation'] = ['-0.90', '0', '0.825', '-0.61', '#sadness', '#anger', 'negative', '-0.78', 'flaming', 'combustion', 'fire', 'flame', 'internal_combustion']
senticnet['superspace'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'width', 'size', 'mass_measure', 'measure', 'gauge']
senticnet['superstar'] = ['0', '0.789', '0', '0.907', '#interest', '#admiration', 'positive', '0.848', 'stardom', 'vip', 'accomplishment', 'performance', 'concert']
senticnet['superstar_athlete'] = ['0', '0', '0', '0.97', '#admiration', '#admiration', 'positive', '0.97', 'increase_strength', 'keep_fit', 'fit_person', 'exercise', 'strengthen_heart']
senticnet['superstar_singer'] = ['0.056', '0.139', '-0.07', '0.154', '#interest', '#admiration', 'positive', '0.07', 'belongings', 'material', 'fabric', 'guarantee', 'stuff']
senticnet['superstardom'] = ['0', '0.793', '0', '0.91', '#interest', '#admiration', 'positive', '0.852', 'superstar', 'vip', 'accomplishment', 'performance', 'concert']
senticnet['superstition'] = ['0', '0.207', '-0.09', '0.143', '#interest', '#admiration', 'positive', '0.086', 'preach', 'pulpit', 'preacher', 'judaism', 'prayer']
senticnet['superstitious'] = ['0', '0', '-0.83', '-0.86', '#fear', '#disgust', 'negative', '-0.84', 'superstition', 'ignorant', 'deluded', 'irrational', 'credulous']
senticnet['superstitiously'] = ['-0.43', '-0.14', '-0.06', '-0.33', '#sadness', '#disgust', 'negative', '-0.17', 'brain_tumor', 'lyme_disease', 'cause_suffer', 'lose_team', 'bad_hair_day']
senticnet['superstore'] = ['0.041', '0.098', '0', '0', '#joy', '#interest', 'positive', '0.07', 'utopia', 'java', 'memorial', 'nowhere', 'coffee']
senticnet['superstorm'] = ['0', '0', '-0.93', '-0.82', '#fear', '#disgust', 'negative', '-0.88', 'thunderstorm', 'snow_storm', 'storm', 'weather', 'rainy']
senticnet['supertonic'] = ['-0.65', '0', '0.681', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'tone', 'whole_tone', 'step', 'whole_step', 'musical_interval']
senticnet['supervene'] = ['-0.87', '0', '0.858', '-0.77', '#sadness', '#anger', 'negative', '-0.83', 'pass_off', 'fall_out', 'occur', 'happen', 'hap']
senticnet['supervene_upon'] = ['-0.81', '0', '0.854', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'replace', 'cover', 'supplant', 'supersede', 'deputize']
senticnet['supervenience'] = ['-0.87', '0', '0.858', '-0.77', '#sadness', '#anger', 'negative', '-0.83', 'pass_off', 'fall_out', 'occur', 'happen', 'hap']
senticnet['supervenient'] = ['-0.87', '0', '0.858', '-0.77', '#sadness', '#anger', 'negative', '-0.83', 'pass_off', 'fall_out', 'occur', 'happen', 'hap']
senticnet['supervillain'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
senticnet['supervillain_prison'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
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senticnet['surcoat'] = ['-0.84', '0', '0.633', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'grizzle', 'postiche', 'horsehair_wig', 'wig', 'peruke']
senticnet['sure'] = ['0.855', '0', '0', '0.959', '#joy', '#admiration', 'positive', '0.907', 'inerrable', 'flawless', 'unfailing', 'reliable', 'foolproof']
senticnet['sure-fire'] = ['0.88', '-0.81', '0', '0', '#joy', '#surprise', 'positive', '0.848', 'surefire', 'foolproof', 'surest', 'tried-and-true', 'fool-proof']
senticnet['sure_bet'] = ['-0.63', '0', '0.712', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'upset', 'disconcert', 'discompose', 'discomfit', 'untune']
senticnet['surefire'] = ['0.878', '-0.82', '0', '0', '#joy', '#surprise', 'positive', '0.85', 'sure-fire', 'foolproof', 'surest', 'tried-and-true', 'fool-proof']
senticnet['surely'] = ['-0.94', '0', '0.991', '-0.65', '#sadness', '#anger', 'negative', '-0.86', 'birthday_suit', 'raw', 'nakedness', 'nude', 'undress']
senticnet['surest'] = ['0.88', '-0.81', '0', '0', '#joy', '#surprise', 'positive', '0.848', 'surefire', 'foolproof', 'sure-fire', 'tried-and-true', 'fool-proof']
senticnet['surety'] = ['0.036', '0.292', '0', '0.337', '#interest', '#admiration', 'positive', '0.222', 'importance', 'quality', 'total', 'excellence', 'frivolity']
senticnet['surety_bond'] = ['0.228', '0.123', '-0.09', '0', '#joy', '#interest', 'positive', '0.084', 'spend_time_with_child', 'social', 'friendship', 'companionship', 'get_close']
senticnet['surf-fishing'] = ['0', '0', '0.929', '-0.73', '#anger', '#disgust', 'negative', '-0.83', 'soil-building', 'school-improvement', 'summer-friendly', 'action-planning', 'water-safety']
senticnet['surf_avenue'] = ['0.089', '-0.18', '0.037', '-0.06', '#joy', '#surprise', 'positive', '0.043', 'balloon', 'ballon', 'fielder', 'kite', 'blimp']
senticnet['surf_board'] = ['-0.05', '-0.02', '-0.07', '0', '#sadness', '#fear', 'negative', '-0.03', 'tide', 'tidal_wave', 'shallow', 'puddle', 'pier']
senticnet['surf_internet'] = ['0.02', '0.273', '-0.10', '0.295', '#interest', '#admiration', 'positive', '0.121', 'internet', 'use_computer', 'search_information', 'check_email', 'send_email']
senticnet['surf_net'] = ['0.929', '0.902', '0', '0', '#joy', '#interest', 'positive', '0.915', 'operate_computer', 'entertainment', 'kill_time', 'research', 'browse_web']
senticnet['surf_shop'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'drifter', 'bum', 'sundowner']
senticnet['surf_spot'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'drifter', 'bum', 'sundowner']
senticnet['surf_web'] = ['-0.75', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.82', 'use_internet', 'hack', 'read_magazine', 'surf_net', 'read_letter']
senticnet['surface'] = ['-0.95', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.83', 'house', 'dim', 'wane', 'child', 'period']
senticnet['surface-active_agent'] = ['-0.76', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.77', 'surfactant', 'wetter', 'wetting_agent', 'effect', 'water']
senticnet['surface-to-surface'] = ['-0.77', '0', '0.773', '0', '#sadness', '#anger', 'negative', '-0.77', 'long-range', 'missile', 'medium-range', 'short-range', 'air-to-air']
senticnet['surface_adsorption'] = ['0.965', '-0.84', '0', '0.599', '#joy', '#surprise', 'positive', '0.802', 'mental_faculty', 'faculty', 'module', 'remembrance', 'anamnesis']
senticnet['surface_assimilation'] = ['0', '0.894', '0', '0.89', '#interest', '#admiration', 'positive', '0.892', 'adsorption', 'study', 'award', 'surface', 'result']
senticnet['surface_chemistry'] = ['0.788', '0', '0', '0.961', '#joy', '#admiration', 'positive', '0.875', 'chemistry', 'research', 'material_research', 'surface', 'trace_contaminant']
senticnet['surface_earth'] = ['0', '0', '0.012', '-0.08', '#anger', '#disgust', 'negative', '-0.04', 'riverbed', 'waterfall', 'underground', 'ground', 'lawn']
senticnet['surface_epithelium'] = ['-0.53', '0', '0.982', '-0.67', '#anger', '#disgust', 'negative', '-0.73', 'meniscus', 'semilunar_cartilage', 'gristle', 'hyaline_cartilage', 'arytenoid']
senticnet['surface_impoundment'] = ['-0.58', '0', '0.714', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'diversionist', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['surface_morphology'] = ['-0.50', '0', '0.552', '-0.93', '#anger', '#disgust', 'negative', '-0.66', 'nubbiness', 'tweediness', 'harshness', 'graininess', 'granularity']
senticnet['surface_tension'] = ['0', '0', '-0.11', '0', '#fear', '#fear', 'negative', '-0.11', 'chemical_reaction', 'set_fire', 'grow_yard', 'heat_light', 'burn_down_house']
senticnet['surface_texture'] = ['0.928', '0.703', '0', '0', '#joy', '#interest', 'positive', '0.816', 'polygon', 'fit', 'geometric_figure', 'geometric_shape', 'train_hard']
senticnet['surface_topography'] = ['-0.50', '0', '0.552', '-0.93', '#anger', '#disgust', 'negative', '-0.66', 'nubbiness', 'tweediness', 'harshness', 'graininess', 'granularity']
senticnet['surfactant'] = ['-0.76', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.77', 'surface-active_agent', 'wetter', 'wetting_agent', 'effect', 'water']
senticnet['surfboard'] = ['-0.18', '0', '-0.14', '-0.11', '#sadness', '#fear', 'negative', '-0.14', 'fly_kite', 'walk_dog', 'ride_roller_coaster', 'rake_leave', 'jump_up_down']
senticnet['surfboat'] = ['0', '-0.02', '0.046', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'sail_ship', 'canoe', 'ship', 'submarine', 'yacht']
senticnet['surfeit'] = ['0.891', '0', '0', '0.94', '#joy', '#admiration', 'positive', '0.915', 'overabundance', 'paucity', 'over-abundance', 'superabundance', 'dearth']
senticnet['surfer'] = ['0', '0', '0', '0.078', '#admiration', '#admiration', 'positive', '0.078', 'water_relate', 'water_splash', 'tsunami', 'water_form', 'hand_gesture']
senticnet['surfer_blood'] = ['0.98', '-0.74', '0', '0.984', '#joy', '#admiration', 'positive', '0.903', 'prosperous', 'flourishing', 'successful', 'prospering', 'thriving']
senticnet['surficial_sediment'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'dirtiness', 'befoulment', 'defilement', 'dinginess', 'dinge']
senticnet['surform'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'useful_tool']
senticnet['surge'] = ['-0.79', '0', '0.59', '-0.56', '#sadness', '#anger', 'negative', '-0.65', 'debris_surge', 'debris_storm', 'flowing', 'onrush', 'flow']
senticnet['surge_protector'] = ['0', '-0.03', '0', '0.093', '#surprise', '#admiration', 'positive', '0.063', 'work_well', 'internal_combustion_engine', 'broken_car', 'mode_transportation', 'machine']
senticnet['surgeon'] = ['-0.03', '0.068', '-0.16', '-0.09', '#fear', '#disgust', 'negative', '-0.05', 'physician', 'nurse', 'doctor', 'doctor_surgery', 'merlin']
senticnet['surgeon_prefer'] = ['-0.65', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'put_to_sleep', 'operation', 'general_anesthesia', 'surgery', 'anesthesia']
senticnet['surgeonfish'] = ['0.96', '-0.77', '0', '0.929', '#joy', '#admiration', 'positive', '0.887', 'whale_louse', 'beach_flea', 'sand_hopper', 'sandhopper', 'amphipod']
senticnet['surgery'] = ['-0.11', '-0.00', '-0.08', '-0.21', '#sadness', '#disgust', 'negative', '-0.10', 'hospital', 'illness', 'doctor_visit', 'heal', 'concern_health']
senticnet['surgery_mbbs_degree'] = ['0.924', '-0.55', '0', '0.756', '#joy', '#admiration', 'positive', '0.744', 'bounden', 'incumbent_on', 'obligate', 'obligatory', 'mandatory']
senticnet['surgical'] = ['0.745', '-0.55', '0', '0.597', '#joy', '#admiration', 'positive', '0.633', 'lift', 'phalloplasty', 'abdominoplasty', 'tummy_tuck', 'autoplasty']
senticnet['surgical_anesthesia'] = ['-0.65', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'put_to_sleep', 'operation', 'anesthesia', 'surgery', 'local_anesthesia']
senticnet['surgical_care'] = ['-0.03', '-0.02', '-0.08', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'ibuprofen', 'phenobarbital', 'analgesic', 'sleep_drug', 'pill']
senticnet['surgical_castration'] = ['0.542', '-0.63', '0', '0.912', '#surprise', '#admiration', 'positive', '0.695', 'implosion_therapy', 'flooding', 'reciprocal_inhibition', 'behavior_modification', 'desensitization_technique']
senticnet['surgical_clinic'] = ['0.738', '-0.53', '0', '0.856', '#joy', '#admiration', 'positive', '0.71', 'doctor', 'country_doctor', 'family_doctor', 'doctor_office', 'physician']
senticnet['surgical_contraception'] = ['-0.02', '0.1', '-0.07', '0.089', '#interest', '#admiration', 'positive', '0.023', 'functionary', 'employment', 'labor', 'employ', 'toil']
senticnet['surgical_drainage'] = ['-0.53', '0', '0.671', '-0.55', '#anger', '#disgust', 'negative', '-0.58', 'dirty', 'putrefactive', 'putrefacient', 'abscessed', 'contaminative']
senticnet['surgical_exploration'] = ['-0.56', '0', '0.806', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['surgical_incision'] = ['-0.56', '0', '0.806', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['surgical_knife'] = ['0.932', '-0.52', '0', '0.534', '#joy', '#admiration', 'positive', '0.662', 'lancet', 'catling', 'cut', 'scalpel', 'knife']
senticnet['surgical_lesion'] = ['-0.73', '0', '0.904', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'unshapely', 'shapeless', 'lumpy', 'deformed', 'malformed']
senticnet['surgical_mask'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'wear', 'protective_garment', 'coverall', 'shoulder_pad', 'wearable']
senticnet['surgical_operation'] = ['-0.50', '0', '0.596', '-0.76', '#anger', '#disgust', 'negative', '-0.62', 'operation', 'surgery', 'angioplasty', 'surgical_procedure', 'surgical_process']
senticnet['surgical_pathologist'] = ['-0.59', '0', '0.911', '-0.87', '#anger', '#disgust', 'negative', '-0.79', 'osteosclerosis', 'sclerosis', 'disseminated_sclerosis', 'multiple_sclerosis', 'induration']
senticnet['surgical_pathology'] = ['-0.59', '0', '0.911', '-0.87', '#anger', '#disgust', 'negative', '-0.79', 'osteosclerosis', 'sclerosis', 'disseminated_sclerosis', 'multiple_sclerosis', 'induration']
senticnet['surgical_procedure'] = ['-0.50', '0', '0.596', '-0.76', '#anger', '#disgust', 'negative', '-0.62', 'operation', 'surgery', 'surgical_operation', 'angioplasty', 'surgical_process']
senticnet['surgical_process'] = ['-0.50', '0', '0.596', '-0.76', '#anger', '#disgust', 'negative', '-0.62', 'operation', 'surgery', 'surgical_operation', 'surgical_procedure', 'angioplasty']
senticnet['surgical_repair'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'blatancy', 'obviousness']
senticnet['surgical_sterilization'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'safe_sex', 'sex', 'prevent_pregnancy', 'avoid_pregnancy', 'become_aroused']
senticnet['surgically_repaired_right'] = ['-0.93', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.92', 'pull_muscle', 'become_exhausted', 'increased_heart_rate', 'breathlessness', 'tired_leg']
senticnet['surging_demand'] = ['0', '0.285', '-0.07', '0.163', '#interest', '#admiration', 'positive', '0.124', 'plus', 'importance', 'a_lot', 'bonus', 'extra']
senticnet['suri_vidyasagar_college'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'head_hurt', 'introspection', 'lifestyle_change', 'headache', 'move_eye']
senticnet['surigao_strait'] = ['0.084', '-0.05', '0.013', '0', '#joy', '#surprise', 'positive', '0.043', 'carnival_ride', 'carny', 'fortune_teller', 'horseback', 'ferris_wheel']
senticnet['surjectivity'] = ['-0.75', '0', '0.679', '-0.77', '#sadness', '#disgust', 'negative', '-0.73', 'unvariedness', 'changelessness', 'unchangeability', 'unchangeableness', 'unchangingness']
senticnet['surliness'] = ['-0.59', '0', '0.911', '-0.83', '#anger', '#disgust', 'negative', '-0.78', 'biliousness', 'peevishness', 'pettishness', 'snappishness', 'querulousness']
senticnet['surly'] = ['-0.99', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.88', 'rough', 'bearishly', 'bearishness', 'bearish', 'clumsy']
senticnet['surmisable'] = ['0.877', '-0.68', '0', '0.624', '#joy', '#surprise', 'positive', '0.727', 'ponderable', 'presumable', 'supposable', 'conceivable', 'thinkable']
senticnet['surmisal'] = ['0.856', '-0.56', '0', '0.574', '#joy', '#admiration', 'positive', '0.664', 'conjecture', 'speculation', 'guess', 'divination', 'supposition']
senticnet['surmise'] = ['0', '0', '-0.88', '-0.99', '#fear', '#disgust', 'negative', '-0.93', 'murder_suspect', 'robbery_suspect', 'rape_suspect', 'murder', 'doubt']
senticnet['surmount'] = ['-0.73', '0', '0.732', '-0.53', '#sadness', '#anger', 'negative', '-0.66', 'unconquerable', 'insuperable', 'inexpugnable', 'indomitable', 'unsubduable']
senticnet['surmountable'] = ['-0.99', '0', '0.641', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'navigable', 'negotiable', 'passable', 'climbable', 'traversable']
senticnet['surpass'] = ['0.829', '0', '0', '0.793', '#joy', '#admiration', 'positive', '0.811', 'outrank', 'shine_at', 'excel_at', 'exceed', 'excel']
senticnet['surplice'] = ['0.849', '-0.50', '0', '0.845', '#joy', '#admiration', 'positive', '0.732', 'robe', 'gown', 'veil', 'vest', 'cassock']
senticnet['surpliced'] = ['-0.53', '0', '0.934', '-0.80', '#anger', '#disgust', 'negative', '-0.75', 'gowned', 'petticoated', 'clad', 'arrayed', 'caparisoned']
senticnet['surplus'] = ['-0.73', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.76', 'goods', 'excess', 'surplus', 'asset', 'supply']
senticnet['surprise'] = ['0', '-0.66', '0', '0', '#surprise', '#surprise', 'positive', '0.66', 'surprised', 'dazed', 'wide-eyed', 'goggle-eyed', 'open-mouthed']
senticnet['surprise_announcement'] = ['0.54', '-0.64', '0', '0.566', '#surprise', '#admiration', 'positive', '0.582', 'pleaser', 'entertainer', 'mortal', 'busker', 'drawing_card']
senticnet['surprise_attack'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['surprise_birthday_party'] = ['0.161', '0.155', '-0.08', '0.223', '#joy', '#admiration', 'positive', '0.114', 'family', 'happy_moment', 'aunt_uncle', 'brother_sister', 'relation']
senticnet['surprise_friend'] = ['0.743', '0.916', '0', '0', '#joy', '#interest', 'positive', '0.83', 'blow_out_candle', 'act_foolish', 'receive_gift', 'tear_paper', 'say_love']
senticnet['surprise_party'] = ['0', '-0.6', '0', '0', '#surprise', '#surprise', 'positive', '0.6', 'surprised', 'surprise', 'wow', 'uncanny', 'astonishment']
senticnet['surprise_person'] = ['-0.11', '-0.04', '0', '-0.10', '#sadness', '#disgust', 'negative', '-0.05', 'pierce', 'spread_butter', 'taste_flavor', 'digest', 'clean_up_kitchen']
senticnet['surprise_resignation'] = ['-0.72', '0', '0.98', '0', '#sadness', '#anger', 'negative', '-0.85', 'resignedly', 'abject', 'unhopeful', 'abjectly', 'reconcile']
senticnet['surprised'] = ['0.042', '-0.67', '0', '0.361', '#surprise', '#admiration', 'positive', '0.358', 'amazingly', 'astonishingly', 'surprising', 'surprisedly', 'surprisingly']
senticnet['surprisedly'] = ['0.045', '-0.66', '0', '0.363', '#surprise', '#admiration', 'positive', '0.357', 'astonishingly', 'amazingly', 'surprised', 'surprising', 'surprisingly']
senticnet['surprising'] = ['0.045', '-0.66', '0', '0.363', '#surprise', '#admiration', 'positive', '0.357', 'astonishingly', 'amazingly', 'surprise', 'surprisedly', 'surprisingly']
senticnet['surprisingly'] = ['0.71', '0', '0', '0.764', '#joy', '#admiration', 'positive', '0.737', 'astonishingly', 'amazingly', 'alteration', 'peripety', 'bombshell']
senticnet['surprisingly_easy'] = ['-0.38', '0.165', '-0.32', '0.243', '#sadness', '#fear', 'negative', '-0.07', 'hard_to_define', 'inherit', 'harmful_human', 'bad_choice', 'badness']
senticnet['surprize'] = ['-0.96', '0', '0.787', '0', '#sadness', '#anger', 'negative', '-0.87', 'disconcertion', 'untune', 'disconcertment', 'discomfiture', 'discomfit']
senticnet['surreal'] = ['-0.99', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.90', 'incongruity', 'remainder', 'dreamlike', 'incongruent', 'incongruous']
senticnet['surreal_comedy'] = ['0.206', '-0.13', '0', '0', '#joy', '#surprise', 'positive', '0.172', 'dancer', 'old_dance', 'dance', 'slow_dance', 'ballroom_dance']
senticnet['surrealism'] = ['0', '0.964', '0', '0.877', '#interest', '#admiration', 'positive', '0.92', 'expressionism', 'cubism', 'impressionism', 'post-impressionism', 'fauvism']
senticnet['surrealist_movement'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'native_person', 'revolutionary', 'republic', 'revolution', 'look_ahead']
senticnet['surrealistic'] = ['-0.93', '0', '0.94', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'delusive', 'phantasmagoric', 'phantasmagorical', 'unrealistic', 'wild']
senticnet['surrealistically'] = ['-0.94', '0', '0.866', '-0.91', '#sadness', '#disgust', 'negative', '-0.90', 'movement', 'apparent_motion', 'motion', 'apparent_movement', 'optical_illusion']
senticnet['surreality'] = ['-0.94', '0', '0.925', '-0.97', '#sadness', '#disgust', 'negative', '-0.95', 'nonexistence', 'nonentity', 'cloud', 'falseness', 'falsity']
senticnet['surreally'] = ['-0.82', '0', '0.758', '-0.70', '#sadness', '#anger', 'negative', '-0.76', 'unclassifiable', 'unrecognizable', 'unidentifiable', 'intangible', 'undiagnosable']
senticnet['surrender'] = ['-0.38', '0.212', '-0.23', '-0.11', '#sadness', '#fear', 'negative', '-0.13', 'defeat', 'terror', 'military_action', 'lose_battle', 'cruelty']
senticnet['surrender_peacefully'] = ['-0.98', '0', '0.654', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'bereave', 'expropriate', 'deprive', 'unclothe', 'take']
senticnet['surreptitious'] = ['0', '0', '-0.88', '-0.70', '#fear', '#disgust', 'negative', '-0.79', 'stealthy', 'furtive', 'clandestine', 'sneaky', 'undercover']
senticnet['surrogate'] = ['0', '0.981', '0', '0.963', '#interest', '#admiration', 'positive', '0.972', 'foster', 'serve', 'patronize', 'stephen_foster', 'stephen_collins_foster']
senticnet['surround'] = ['0.049', '0.167', '-0.05', '0.295', '#interest', '#admiration', 'positive', '0.115', 'four_sides', 'around', 'six_sides', 'neighborhood', 'dolby_surround']
senticnet['surround_ocean'] = ['0', '0', '0.082', '0', '#anger', '#anger', 'negative', '-0.08', 'surround_water', 'land_mass', 'island', 'sea', 'large_land_mass']
senticnet['surround_sound'] = ['0.055', '0', '0.03', '0.136', '#joy', '#admiration', 'positive', '0.054', 'loud_soft', 'decibel', 'loudness', 'sound', 'melodic_noise']
senticnet['surround_sound_audio'] = ['0.055', '0', '0.03', '0.136', '#joy', '#admiration', 'positive', '0.054', 'loud_soft', 'decibel', 'loudness', 'sound', 'melodic_noise']
senticnet['surround_sound_system'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'floodlight', 'spotlight', 'headlight', 'light', 'teach_lesson']
senticnet['surround_water'] = ['0', '0', '0.072', '0', '#anger', '#anger', 'negative', '-0.07', 'surround_ocean', 'land_mass', 'island', 'surround', 'large_land_mass']
senticnet['surrounded_by'] = ['0.071', '0.127', '0', '0.197', '#interest', '#admiration', 'positive', '0.132', 'place_work', 'workplace', 'office', 'work', 'hang_wall']
senticnet['surrounding'] = ['0.106', '0.101', '-0.08', '0.202', '#interest', '#admiration', 'positive', '0.081', 'environment', 'family_live', 'house_yard', 'live_environment', 'live_life']
senticnet['surrounding_nebula'] = ['-0.55', '0', '0.869', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'air_bubble', 'foam', 'froth', 'soap_bubble', 'bubble']
senticnet['surrounding_scenery'] = ['0.838', '0.828', '0', '0', '#joy', '#interest', 'positive', '0.833', 'vista', 'spectacular', 'perspective', 'image', 'scenic']
senticnet['surroundings'] = ['-0.96', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.91', 'isolated', 'peninsula', 'situate', 'insular', 'standoffish']
senticnet['surveillance'] = ['0', '0.102', '0.016', '0.076', '#interest', '#admiration', 'positive', '0.054', 'computer_part', 'system', 'motherboard', 'pencil_pen', 'cpu']
senticnet['survey'] = ['-0.04', '0.1', '0.051', '0.137', '#interest', '#admiration', 'positive', '0.035', 'find_number', 'regularly', 'list_number', 'calculator', 'navigation']
senticnet['survey_instrument'] = ['-0.04', '0.1', '0.051', '0.137', '#interest', '#admiration', 'positive', '0.035', 'find_number', 'regularly', 'list_number', 'calculator', 'navigation']
senticnet['survey_questionnaire'] = ['-0.04', '0.1', '0.051', '0.137', '#interest', '#admiration', 'positive', '0.035', 'find_number', 'regularly', 'list_number', 'calculator', 'navigation']
senticnet['survivability'] = ['0', '0', '0', '0.144', '#admiration', '#admiration', 'positive', '0.144', 'go_elsewhere', 'transport_person', 'get_around', 'locomotion', 'trucking']
senticnet['survival'] = ['0.988', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.98', 'sustain_life', 'sustenance', 'nutrition', 'keep_alive', 'satisfy_hunger']
senticnet['survival_advantage'] = ['0.988', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.98', 'sustain_life', 'sustenance', 'nutrition', 'keep_alive', 'satisfy_hunger']
senticnet['survival_rate'] = ['0.988', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.98', 'sustain_life', 'sustenance', 'nutrition', 'keep_alive', 'satisfy_hunger']
senticnet['survival_species'] = ['0', '0.946', '0', '0.895', '#interest', '#admiration', 'positive', '0.92', 'ejaculate', 'pass_gene', 'ecstasy', 'lower_standard_live', 'ejaculation']
senticnet['survive'] = ['0.855', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.848', 'stay_alive', 'get_oxygen', 'exist', 'alive', 'crave']
senticnet['survive_infancy'] = ['-0.70', '0', '0.919', '-0.73', '#anger', '#disgust', 'negative', '-0.78', 'unfruitful', 'unsuccessful', 'acarpous', 'abortive', 'stillborn']
senticnet['surviving'] = ['0', '0.818', '0', '0.714', '#interest', '#admiration', 'positive', '0.766', 'living', 'livelihood', 'exist', 'experience', 'go']
senticnet['surviving_beneficiary'] = ['0.851', '0.708', '0', '0', '#joy', '#interest', 'positive', '0.779', 'devisee', 'legatee', 'recipient', 'receiver', 'pensioner']
senticnet['surviving_son'] = ['-0.70', '0', '0.919', '-0.73', '#anger', '#disgust', 'negative', '-0.78', 'unfruitful', 'unsuccessful', 'acarpous', 'abortive', 'stillborn']
senticnet['surviving_spouse'] = ['0.851', '0.708', '0', '0', '#joy', '#interest', 'positive', '0.779', 'devisee', 'legatee', 'recipient', 'receiver', 'pensioner']
senticnet['surviving_twin'] = ['-0.87', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.91', 'loss_oflife', 'chest_pain', 'die', 'tumor', 'end_life']
senticnet['survivor'] = ['0.741', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.828', 'alive', 'escape_danger', 'survive', 'avoid_death', 'escape_massacre']
senticnet['survivor_guilt'] = ['-0.89', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.84', 'remorse', 'grief', 'regret', 'sorrow', 'heartache']
senticnet['survivorship'] = ['0.988', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.98', 'sustain_life', 'sustenance', 'nutrition', 'keep_alive', 'satisfy_hunger']
senticnet['suryakant'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['sus_scrofa'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'animal', 'animate_being', 'pet', 'creature']
senticnet['susanville'] = ['-0.06', '0.21', '0', '0.076', '#interest', '#admiration', 'positive', '0.074', 'new_think', 'figure_out', 'brain_activity', 'idea', 'come_up_idea']
senticnet['susceptibility'] = ['-0.78', '0', '0.6', '-0.61', '#sadness', '#disgust', 'negative', '-0.66', 'suggestibility', 'sensitivity', 'capacity', 'reactivity', 'predisposition']
senticnet['susceptible'] = ['0.933', '-0.75', '0', '0.89', '#joy', '#admiration', 'positive', '0.859', 'amenable', 'tractable', 'pliable', 'capable', 'vulnerable']
senticnet['susceptibleness'] = ['-0.69', '0', '0.945', '-0.65', '#sadness', '#anger', 'negative', '-0.76', 'suggestibility', 'sensitivity', 'capacity', 'reactivity', 'predisposition']
senticnet['sushi'] = ['0.049', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.034', 'chocolate_pudding', 'green_bean', 'sweetness', 'whip_cream', 'side_dish']
senticnet['sushi_restaurant'] = ['0', '0', '-0.06', '-0.03', '#fear', '#disgust', 'negative', '-0.05', 'coral_reef', 'saltwater', 'tidal_pool', 'fish_market', 'reef']
senticnet['sushi_rice'] = ['0', '-0.03', '0.046', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'pasta', 'green_bean', 'mayonnaise', 'frozen_food', 'sour_milk']
senticnet['sushi_roll'] = ['0.9', '0.963', '0', '0', '#joy', '#interest', 'positive', '0.931', 'form_of_food', 'japanese_food', 'uncooked', 'healthy', 'food']
senticnet['susie'] = ['-0.74', '0', '0.877', '-0.97', '#anger', '#disgust', 'negative', '-0.86', 'miss', 'girl', 'chachka', 'young_lady', 'young_woman']
senticnet['suskin'] = ['0', '0.746', '0', '0.872', '#interest', '#admiration', 'positive', '0.809', 'award', 'office', 'manager', 'standard_grant', 'latest']
senticnet['suspect'] = ['0', '0', '-0.88', '-0.99', '#fear', '#disgust', 'negative', '-0.93', 'murder_suspect', 'robbery_suspect', 'rape_suspect', 'murder', 'doubt']
senticnet['suspected_abuse'] = ['-0.94', '0', '0.923', '-0.98', '#sadness', '#disgust', 'negative', '-0.95', 'heap', 'push', 'accept', 'bring', 'lunge']
senticnet['suspected_arson'] = ['-0.14', '0', '-0.21', '-0.07', '#sadness', '#fear', 'negative', '-0.14', 'heat_light', 'burn_down_house', 'set_on_fire', 'rapid_oxidation', 'flame']
senticnet['suspected_burglar'] = ['0.964', '-0.57', '0', '0.827', '#joy', '#admiration', 'positive', '0.789', 'mortal', 'murder_suspect', 'suspect', 'rape_suspect', 'person']
senticnet['suspected_carcinogen'] = ['-0.18', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.16', 'hazardous_health', 'dangerous_health', 'bad_health', 'gross', 'harmful']
senticnet['suspected_child_abuse'] = ['-0.94', '0', '0.923', '-0.98', '#sadness', '#disgust', 'negative', '-0.95', 'heap', 'push', 'accept', 'bring', 'lunge']
senticnet['suspected_criminal_activity'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'constabulary', 'law', 'agency']
senticnet['suspected_drug_dealer'] = ['0.964', '-0.57', '0', '0.827', '#joy', '#admiration', 'positive', '0.789', 'mortal', 'murder_suspect', 'suspect', 'rape_suspect', 'person']
senticnet['suspected_fraud'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'mail_fraud', 'swindle', 'rig']
senticnet['suspected_gunman'] = ['-0.86', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.79', 'gun', 'firearm', 'handgun', 'rifle', 'pistol']
senticnet['suspected_killer'] = ['0', '0', '-0.88', '-0.99', '#fear', '#disgust', 'negative', '-0.93', 'murder_suspect', 'robbery_suspect', 'rape_suspect', 'murder', 'doubt']
senticnet['suspected_misconduct'] = ['-0.88', '0', '0.733', '-0.60', '#sadness', '#anger', 'negative', '-0.74', 'vengeance', 'payback', 'retribution', 'retaliation', 'return']
senticnet['suspected_perpetrator'] = ['-0.06', '0', '-0.15', '0', '#sadness', '#fear', 'negative', '-0.10', 'heroism', 'role_play', 'repeat', 'act', 'part_of_movie']
senticnet['suspected_rapist'] = ['0.864', '-0.54', '0', '0.613', '#joy', '#admiration', 'positive', '0.675', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['suspected_robber'] = ['0.964', '-0.57', '0', '0.827', '#joy', '#admiration', 'positive', '0.789', 'mortal', 'murder_suspect', 'suspect', 'rape_suspect', 'person']
senticnet['suspected_shooter'] = ['-0.86', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.79', 'gun', 'firearm', 'handgun', 'rifle', 'pistol']
senticnet['suspected_sock'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['suspected_sockpuppet'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['suspected_thief'] = ['0.964', '-0.57', '0', '0.827', '#joy', '#admiration', 'positive', '0.789', 'mortal', 'murder_suspect', 'suspect', 'rape_suspect', 'person']
senticnet['suspend'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'capital_punishment', 'destroy_building', 'reverse', 'jump_off_cliff', 'kill_self']
senticnet['suspended_animation'] = ['0.8', '-0.97', '0', '0.512', '#joy', '#surprise', 'positive', '0.763', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['suspended_indefinitely'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'steer_wheel', 'gear_shift', 'chassis', 'brake_pedal', 'air_vent']
senticnet['suspended_license'] = ['-0.31', '-0.02', '0', '-0.24', '#sadness', '#disgust', 'negative', '-0.17', 'pollution', 'screw_up', 'car_wreck', 'parking_ticket', 'feel_stressed']
senticnet['suspended_upside_down'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'hanger', 'hang_up_clothes', 'hang_clothes', 'storage_space', 'laundry']
senticnet['suspender'] = ['0', '0.072', '0', '0.064', '#interest', '#admiration', 'positive', '0.068', 'zipper', 'pant', 'trousers', 'cowboy_boot', 'accessory']
senticnet['suspender_approach'] = ['0', '0.072', '0', '0.064', '#interest', '#admiration', 'positive', '0.068', 'zipper', 'pant', 'trousers', 'cowboy_boot', 'accessory']
senticnet['suspense'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'anxiety', 'apprehension', 'insecurity', 'tension', 'horror_movie']
senticnet['suspense_thriller'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'anxiety', 'apprehension', 'insecurity', 'tension', 'horror_movie']
senticnet['suspenseful'] = ['0', '0', '0.862', '-0.79', '#anger', '#disgust', 'negative', '-0.82', 'suspensive', 'nail-biting', 'cliff-hanging', 'apathetically', 'concern']
senticnet['suspenseful_plot'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'anxiety', 'apprehension', 'insecurity', 'tension', 'horror_movie']
senticnet['suspension'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'steer_wheel', 'gear_shift', 'chassis', 'brake_pedal', 'air_vent']
senticnet['suspension_bridge'] = ['0.791', '0.857', '0', '0', '#joy', '#interest', 'positive', '0.824', 'bridge', 'under_bridge', 'golden_gate_bridge', 'side_road', 'roadside']
senticnet['suspension_feeder'] = ['-0.79', '0', '0.709', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'animal', 'animate_being', 'ctenophore', 'invertebrate', 'beast']
senticnet['suspensive'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'suspenseful', 'suspense', 'anxiety', 'tension', 'apprehension']
senticnet['suspensive_veto'] = ['-0.57', '0', '0.576', '-0.78', '#anger', '#disgust', 'negative', '-0.64', 'reprieve', 'respite', 'suspend', 'call', 'delay']
senticnet['suspicion'] = ['-0.22', '0.087', '-0.32', '0.105', '#sadness', '#fear', 'negative', '-0.09', 'citation', 'problem', 'jeopardy', 'effect', 'exclusion']
senticnet['suspicious'] = ['0', '0', '-0.81', '-0.75', '#fear', '#disgust', 'negative', '-0.78', 'mistrustful', 'untrusting', 'leery', 'fishy', 'wary']
senticnet['suspicious_resemblance'] = ['-0.88', '0', '0.813', '-0.57', '#sadness', '#anger', 'negative', '-0.75', 'affinity', 'mutual_resemblance', 'alikeness', 'likeness', 'similitude']
senticnet['suspiria'] = ['-0.79', '0', '0.885', '-0.76', '#sadness', '#anger', 'negative', '-0.81', 'infernal', 'mortal', 'dead_soul', 'dead_soule', 'living_dead']
senticnet['susquehanna_financial'] = ['0.711', '0', '0', '0', '#joy', '#joy', 'positive', '0.711', 'chemist', 'expert', 'scientist', 'esaminatore', 'physicist']
senticnet['suss_out'] = ['0.559', '-0.64', '0', '0.949', '#surprise', '#admiration', 'positive', '0.717', 'watch_over', 'keep_an_eye_on', 'watch', 'study', 'observe']
senticnet['susser'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['sustain'] = ['0', '0.983', '0', '0.797', '#interest', '#admiration', 'positive', '0.89', 'pillar', 'help_people', 'solid_basis', 'structure', 'back_bone']
senticnet['sustain_life'] = ['0.712', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.72', 'survive', 'stay_alive', 'live', 'exist', 'get_oxygen']
senticnet['sustainability'] = ['0.952', '-0.84', '0', '0.875', '#joy', '#admiration', 'positive', '0.89', 'administration', 'judiciary', 'governing_body', 'establishment', 'brass']
senticnet['sustainable'] = ['0', '0.885', '0', '0.73', '#interest', '#admiration', 'positive', '0.807', 'enlarge', 'flourish', 'growth', 'get_large', 'get_big']
senticnet['sustainable_development'] = ['0.952', '-0.84', '0', '0.875', '#joy', '#admiration', 'positive', '0.89', 'administration', 'judiciary', 'governing_body', 'establishment', 'brass']
senticnet['sustainable_energy'] = ['-0.79', '0', '0.997', '-0.68', '#sadness', '#anger', 'negative', '-0.82', 'energy', 'alternative_energy', 'solar_power', 'wind_generation', 'wind_power']
senticnet['sustained'] = ['-0.60', '0', '0.499', '-0.68', '#sadness', '#disgust', 'negative', '-0.59', 'dogging', 'persisting', 'perpetual', 'consecutive', 'continuous']
senticnet['sustained_abstinence'] = ['0', '0', '0.973', '-0.78', '#anger', '#disgust', 'negative', '-0.87', 'chastity', 'ignore_conception', 'restrain', 'abortion', 'stop']
senticnet['sustained_applause'] = ['-0.86', '0', '0.516', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'outcry', 'call', 'cry', 'yell', 'shout']
senticnet['sustained_damage'] = ['-0.98', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.97', 'harm', 'hurt', 'cut_skin', 'bloody', 'ouch']
senticnet['sustained_growth'] = ['0.067', '0.172', '0', '0.121', '#interest', '#admiration', 'positive', '0.12', 'economic_activity', 'business', 'support_family', 'work_done', 'earn_money']
senticnet['sustaining_economic_growth'] = ['0.868', '-0.91', '0', '0.862', '#joy', '#surprise', 'positive', '0.88', 'chirpiness', 'spirit', 'sprightliness', 'liveliness', 'activity']
senticnet['sustenance'] = ['0.986', '0.962', '0', '0', '#joy', '#interest', 'positive', '0.974', 'sustain_life', 'survival', 'nutrition', 'keep_alive', 'satisfy_hunger']
senticnet['sustiva'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'indinavir', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['susurration'] = ['0.937', '-0.68', '0', '0.824', '#joy', '#admiration', 'positive', '0.816', 'whispering', 'voicelessness', 'whisper', 'speech_production', 'speech']
senticnet['sutra'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['sutta'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['sutter_creek'] = ['-0.06', '0.21', '0', '0.076', '#interest', '#admiration', 'positive', '0.074', 'new_think', 'figure_out', 'brain_activity', 'idea', 'come_up_idea']
senticnet['sutter_mill'] = ['-0.06', '0.21', '0', '0.076', '#interest', '#admiration', 'positive', '0.074', 'new_think', 'figure_out', 'brain_activity', 'idea', 'come_up_idea']
senticnet['sutton'] = ['-0.20', '-0.10', '0.07', '-0.24', '#sadness', '#disgust', 'negative', '-0.10', 'flipper', 'sea_life', 'seafood', 'swim_animal', 'fillet']
senticnet['sutton_foster'] = ['0', '0', '0', '0.068', '#admiration', '#admiration', 'positive', '0.068', 'dark_corner', 'child_game', 'play_person', 'role_play', 'part_of_play']
senticnet['sutton_hoo_ship'] = ['-0.12', '0.091', '-0.19', '-0.10', '#sadness', '#fear', 'negative', '-0.08', 'headstone', 'grave', 'bury_body', 'bury', 'bury_dead']
senticnet['sutural'] = ['0.719', '-0.57', '0', '0.651', '#joy', '#admiration', 'positive', '0.647', 'chap', 'break', 'slit', 'gap', 'shift']
senticnet['suture'] = ['-0.03', '-0.04', '0.079', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'string', 'bandage', 'filament', 'thread', 'needle_thread']
senticnet['suum'] = ['0', '0.079', '0', '0', '#interest', '#interest', 'positive', '0.079', 'change_diaper', 'lower_standard_live', 'become_pregnant', 'find_mate', 'make_baby']
senticnet['suv'] = ['-0.05', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'plane_land', 'floss', 'floss_tooth', 'ornament', 'wear_dress']
senticnet['suv_driver'] = ['-0.10', '-0.04', '0', '-0.14', '#sadness', '#disgust', 'negative', '-0.06', 'adjust_seat', 'angry_behavior', 'fasten_seatbelt', 'traffic_jam', 'skid_mark']
senticnet['suzerain'] = ['-0.71', '0', '0.755', '0', '#sadness', '#anger', 'negative', '-0.73', 'subservient', 'enthrall', 'obsequious', 'inferior', 'suzerainty']
senticnet['suzerainty'] = ['-0.71', '0', '0.756', '0', '#sadness', '#anger', 'negative', '-0.73', 'suzerain', 'enthrall', 'obsequious', 'inferior', 'subservient']
senticnet['suzie'] = ['-0.74', '0', '0.877', '-0.97', '#anger', '#disgust', 'negative', '-0.86', 'miss', 'girl', 'chachka', 'young_lady', 'young_woman']
senticnet['suzuki_method'] = ['0.536', '-0.56', '0', '0.567', '#surprise', '#admiration', 'positive', '0.557', 'recreation', 'diversion', 'move', 'motility', 'movement']
senticnet['svabhava'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'metastability', 'monotony', 'invariance', 'constancy', 'unchangeability']
senticnet['svatthi'] = ['0.053', '0.248', '-0.18', '0.258', '#interest', '#admiration', 'positive', '0.093', 'slogan', 'find_money', 'wealthy', 'much_money', 'donation']
senticnet['svelte'] = ['0', '0', '-0.99', '-0.86', '#fear', '#disgust', 'negative', '-0.92', 'urbane', 'refined', 'slim', 'lithesome', 'polished']
senticnet['svtmrma'] = ['0.909', '-0.85', '0', '0.846', '#joy', '#surprise', 'positive', '0.87', 'respectability', 'reputability', 'venerability', 'venerableness', 'righteousness']
senticnet['sw_avenue'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'boulevard', 'lane', 'thoroughfare', 'intersection', 'curb']
senticnet['swab'] = ['0.711', '0', '0', '0.802', '#joy', '#admiration', 'positive', '0.756', 'rasp', 'whisk', 'edulcorate', 'depurate', 'expunge']
senticnet['swad'] = ['-0.63', '0', '0.862', '-0.52', '#sadness', '#anger', 'negative', '-0.67', 'knot', 'bunch', 'tuft', 'tussock', 'agglomeration']
senticnet['swag'] = ['0', '0', '-0.85', '-0.83', '#fear', '#disgust', 'negative', '-0.84', 'booty', 'loot', 'plunder', 'pillage', 'prize']
senticnet['swagger'] = ['-0.19', '0', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.15', 'bad_child', 'misbehavior', 'bad_kid', 'bad_action', 'difficulty']
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senticnet['sweatshop'] = ['0.041', '0.07', '0', '0.049', '#interest', '#admiration', 'positive', '0.053', 'work_in_garden', 'hold_up_office', 'workplace', 'courier', 'rendezvous']
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senticnet['sweet_drink'] = ['0.828', '0.862', '0', '0', '#joy', '#interest', 'positive', '0.845', 'water', 'bottle_water', 'beer', 'plastic_container', 'convenience_store']
senticnet['sweet_flavor'] = ['0.043', '0.091', '0', '0.113', '#interest', '#admiration', 'positive', '0.082', 'sweet_sour', 'tasty', 'yummy', 'popular_food', 'form_of_food']
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senticnet['switch_hitter'] = ['0', '0.075', '0', '0', '#interest', '#interest', 'positive', '0.075', 'cup', 'carafe', 'container_cup', 'mug', 'glasses']
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senticnet['switch_on_light'] = ['0.937', '0', '0', '0.828', '#joy', '#admiration', 'positive', '0.883', 'create_light', 'light_candle', 'light_match', 'turn_on_torch', 'create_flame']
senticnet['switch_on_radio'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'listen_cd', 'turn_on_radio', 'radio', 'listen_music', 'put_radio']
senticnet['switch_router'] = ['0.63', '-0.55', '0', '0.946', '#joy', '#admiration', 'positive', '0.711', 'complect', 'connect', 'link', 'link_up', 'join']
senticnet['switch_television'] = ['0', '0', '-0.05', '-0.04', '#fear', '#disgust', 'negative', '-0.05', 'turn_television', 'turn_tv', 'turn_television_set', 'switch_tv', 'find_tv']
senticnet['switch_tv'] = ['-0.04', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.04', 'turn_television', 'near_tv', 'turn_tv_set', 'turn_television_set', 'turn_on_tv']
senticnet['switchback_railway'] = ['0.089', '-0.18', '0.037', '-0.06', '#joy', '#surprise', 'positive', '0.043', 'balloon', 'ballon', 'fielder', 'kite', 'blimp']
senticnet['switchblade'] = ['-0.71', '0', '0.945', '-0.56', '#sadness', '#anger', 'negative', '-0.74', 'jackknife', 'pocket_knife', 'penknife', 'clasp_knife', 'switchblade_knife']
senticnet['switchblade_knife'] = ['0.818', '-0.95', '0', '0.695', '#joy', '#surprise', 'positive', '0.823', 'jackknife', 'clasp_knife', 'penknife', 'switchblade', 'pocket_knife']
senticnet['switching'] = ['0.903', '0.784', '0', '0', '#joy', '#interest', 'positive', '0.844', 'stand-by', 'mode', 'shutdown', 'standby', 'back-up']
senticnet['switching_frequency'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'short_circuit', 'error', 'mistake', 'undesired', 'trouble']
senticnet['swivel'] = ['0.769', '-0.72', '0', '0', '#joy', '#surprise', 'positive', '0.746', 'tilting', 'steering', 'bracket', 'tilt', 'angle']
senticnet['swivel_chair'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'coach_seat', 'stool', 'seat']
senticnet['swollen_eye'] = ['0', '0', '0', '-0.93', '#disgust', '#disgust', 'negative', '-0.93', 'injury', 'broken_bone', 'bad_knee', 'strengthful', 'physical_pain']
senticnet['swollen_feet'] = ['0.052', '-0.04', '-0.05', '-0.20', '#fear', '#disgust', 'negative', '-0.04', 'indigestion', 'make_little_hungry', 'full_stomach', 'sick', 'become_full']
senticnet['swollen_throat'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'stiff', 'rigid', 'inflexible', 'harsh', 'immovable']
senticnet['swoon'] = ['-0.60', '0', '0.541', '-0.52', '#sadness', '#anger', 'negative', '-0.55', 'zonk_out', 'pass_out', 'faint', 'conk', 'change_state']
senticnet['swooning_over'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['swoop'] = ['-0.93', '0', '0.712', '-0.67', '#sadness', '#anger', 'negative', '-0.77', 'penetration', 'air_raid', 'air_attack', 'maraud', 'incursion']
senticnet['swoop_down'] = ['-0.75', '0', '0.794', '-0.58', '#sadness', '#anger', 'negative', '-0.71', 'jerk', 'weightlift', 'military_press', 'clean', 'bench_press']
senticnet['swoop_up'] = ['0.981', '-0.72', '0', '0.638', '#joy', '#surprise', 'positive', '0.782', 'swoop', 'snatch_up', 'seize', 'prehend', 'clutch']
senticnet['swooping_down'] = ['-0.75', '0', '0.794', '-0.58', '#sadness', '#anger', 'negative', '-0.71', 'jerk', 'weightlift', 'military_press', 'clean', 'bench_press']
senticnet['sword'] = ['0.944', '0.816', '0', '0', '#joy', '#interest', 'positive', '0.88', 'gun_knife', 'atomic_bomb', 'hydrogen_bomb', 'hatchet', 'nuclear_bomb']
senticnet['sword_belt'] = ['-0.98', '0', '0.664', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'unfortunate', 'unfortunate_person', 'weeper', 'pallgriever', 'griever']
senticnet['sword_blade'] = ['0.93', '-0.78', '0', '0.873', '#joy', '#admiration', 'positive', '0.861', 'blade', 'brand', 'steel', 'claymore', 'sword']
senticnet['sword_cane'] = ['0.609', '-0.74', '0', '0.716', '#surprise', '#admiration', 'positive', '0.69', 'malacca', 'sword', 'swagger_stick', 'cane', 'sword_stick']
senticnet['sword_duel'] = ['0', '-0.01', '-0.08', '-0.16', '#fear', '#disgust', 'negative', '-0.07', 'siege', 'big_fight', 'battle', 'declare_war', 'conflict']
senticnet['sword_hilt'] = ['-0.50', '0', '0.937', '-0.59', '#anger', '#disgust', 'negative', '-0.67', 'restorative', 'medicine', 'medication', 'medicament', 'medicinal_drug']
senticnet['sword_stick'] = ['0.609', '-0.74', '0', '0.716', '#surprise', '#admiration', 'positive', '0.69', 'malacca', 'sword', 'swagger_stick', 'sword_cane', 'cane']
senticnet['sword_swallower'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'naked_eye', 'eye', 'oculus_sinister']
senticnet['swordfight'] = ['0', '-0.01', '-0.08', '-0.16', '#fear', '#disgust', 'negative', '-0.07', 'siege', 'big_fight', 'battle', 'declare_war', 'conflict']
senticnet['swordplay'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'witty_remark', 'cross_talking', 'banter', 'wordplay', 'jester']
senticnet['swordsman'] = ['0.771', '-0.68', '0', '0.967', '#joy', '#admiration', 'positive', '0.809', 'combatant', 'battler', 'belligerent', 'fighter', 'scrapper']
senticnet['swordsmanship'] = ['0.978', '-0.85', '0', '0.736', '#joy', '#surprise', 'positive', '0.857', 'acquirement', 'skill', 'accomplishment', 'acquisition', 'attainment']
senticnet['swordsmen'] = ['0', '-0.01', '-0.08', '-0.16', '#fear', '#disgust', 'negative', '-0.07', 'siege', 'big_fight', 'battle', 'declare_war', 'conflict']
senticnet['swordsmith'] = ['-0.04', '0.138', '-0.08', '0.111', '#interest', '#admiration', 'positive', '0.031', 'geometry', 'craft', 'blade_geometry', 'blade', 'collect_item']
senticnet['swordswoman'] = ['0', '0.081', '0', '0', '#interest', '#interest', 'positive', '0.081', 'favorite', 'guard', 'crowd', 'several_people', 'clique']
senticnet['sy_hersh'] = ['0.783', '-0.63', '0', '0.829', '#joy', '#admiration', 'positive', '0.749', 'leg_of_lamb', 'lamb_roast', 'joint', 'roast', 'rack_of_lamb']
senticnet['sybaritic'] = ['0.558', '-0.90', '0', '0.728', '#surprise', '#admiration', 'positive', '0.731', 'indulgent', 'luxuriant', 'luxurious', 'voluptuary', 'gay']
senticnet['sybill_trelawney'] = ['0.083', '0', '0', '0.075', '#joy', '#admiration', 'positive', '0.079', 'carny', 'carnival_ride', 'midway', 'ferris_wheel', 'roller-coaster']
senticnet['sycamore_maple'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'bottlebrush_buckeye', 'red_buckeye', 'particolored_buckeye']
senticnet['sycophancy'] = ['-0.76', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.81', 'intractability', 'unruliness', 'fractiousness', 'willfulness', 'against_rule']
senticnet['sycophant'] = ['-0.79', '0', '0.648', '-0.86', '#sadness', '#disgust', 'negative', '-0.76', 'apple_polisher', 'bootlicker', 'fawner', 'servant', 'groveler']
senticnet['sycophantic'] = ['0.981', '-0.88', '0', '0.939', '#joy', '#admiration', 'positive', '0.936', 'slavelike', 'submissive', 'servile', 'subservient', 'slavish']
senticnet['syd_eru'] = ['0.997', '-0.84', '0', '0.927', '#joy', '#admiration', 'positive', '0.923', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['sydenham_chorea'] = ['-0.75', '0', '0.69', '-0.69', '#sadness', '#disgust', 'negative', '-0.71', 'rubor', 'redness', 'sleeping_sickness', 'sleepy_sickness', 'cephalitis']
senticnet['sydney_camm'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'cheapjack', 'pitchman']
senticnet['sydney_funnel'] = ['-0.25', '0.081', '-0.19', '0', '#sadness', '#fear', 'negative', '-0.12', 'hairy', 'kind_animal', 'poisonous', 'big_fish', 'graceful']
senticnet['sydney_yacht_squadron'] = ['0.059', '0.076', '-0.05', '0.079', '#interest', '#admiration', 'positive', '0.039', 'noble', 'king', 'earl', 'duchess', 'duke']
senticnet['syed_ali_zaheer'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['syllable'] = ['0.055', '0', '0.028', '0.128', '#joy', '#admiration', 'positive', '0.052', 'idiom', 'remark', 'rhetoric', 'vowel_sound', 'note']
senticnet['syllable_om'] = ['0.022', '0.123', '0.038', '0.129', '#interest', '#admiration', 'positive', '0.059', 'hymn', 'written_music', 'walk_beach', 'lyric', 'piece_of_music']
senticnet['syllable_structure'] = ['-0.67', '0', '0.97', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'joint', 'node', 'connexion', 'join', 'juncture']
senticnet['syllabus'] = ['0.724', '-0.97', '0', '0.955', '#surprise', '#admiration', 'positive', '0.883', 'course_of_study', 'program', 'programme', 'curriculum', 'reading_program']
senticnet['syllogism'] = ['0', '0.774', '0', '0.726', '#interest', '#admiration', 'positive', '0.75', 'syllogism', 'syllogistic', 'fallacious', 'deductive', 'disjunctive']
senticnet['syllogistic'] = ['0', '0.768', '0', '0.719', '#interest', '#admiration', 'positive', '0.744', 'syllogism', 'syllogism', 'fallacious', 'deductive', 'disjunctive']
senticnet['sylphic'] = ['0', '0.709', '0', '0.937', '#interest', '#admiration', 'positive', '0.823', 'rapier', 'slimline', 'sliver', 'slender', 'dun']
senticnet['sylphlike'] = ['0', '0', '-0.83', '-0.85', '#fear', '#disgust', 'negative', '-0.84', 'lithe', 'supple', 'lissom', 'slender', 'svelte']
senticnet['sylvatica'] = ['0.776', '-0.6', '0', '0.568', '#joy', '#surprise', 'positive', '0.648', 'swamp_chestnut_oak', 'chinquapin_oak', 'chinkapin_oak', 'yellow_chestnut_oak', 'basket_oak']
senticnet['sylvian_fissure'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['symbol'] = ['0.043', '0.159', '0', '0.25', '#interest', '#admiration', 'positive', '0.151', 'unit_language', 'written_message', 'vowel', 'handwritten', 'punctuation']
senticnet['symbol_authority'] = ['0.041', '-0.05', '0', '0.083', '#surprise', '#admiration', 'positive', '0.058', 'uniform', 'respect_law', 'justice', 'get_away_crime', 'change_society']
senticnet['symbol_love'] = ['0.098', '0.185', '-0.17', '0.249', '#interest', '#admiration', 'positive', '0.09', 'flower_type', 'sign_love', 'upward_movement', 'rise_plant', 'levitate']
senticnet['symbol_manipulation'] = ['-0.99', '0', '0.878', '-0.95', '#sadness', '#disgust', 'negative', '-0.94', 'adequacy', 'satisfactoriness', 'acceptability', 'acceptableness', 'quality']
senticnet['symbolic'] = ['0.025', '0.238', '-0.05', '0.25', '#interest', '#admiration', 'positive', '0.115', 'creative', 'wall_hang', 'allusive', 'artwork', 'craft']
senticnet['symbolic_expression'] = ['0', '0.161', '-0.08', '0.219', '#interest', '#admiration', 'positive', '0.098', 'deliver', 'send', 'mention', 'reply', 'remark']
senticnet['symbolic_logic'] = ['0.629', '-0.79', '0', '0.758', '#surprise', '#admiration', 'positive', '0.726', 'system_of_rules', 'logical_system', 'mathematical_logic', 'formal_logic', 'system']
senticnet['symbolic_representation'] = ['0', '0.157', '0', '0.147', '#interest', '#admiration', 'positive', '0.152', 'wall_hang', 'depiction', 'illustration', 'artwork', 'wall_decoration']
senticnet['symbolical_grotesque'] = ['-0.77', '0', '0.885', '-0.73', '#sadness', '#anger', 'negative', '-0.8', 'fallacy', 'misconception', 'paralogism', 'logical_fallacy', 'pseudoscience']
senticnet['symbolically'] = ['0.947', '0', '0', '0.736', '#joy', '#admiration', 'positive', '0.841', 'metaphorically', 'allegorically', 'figuratively', 'prophetically', 'abstractly']
senticnet['symbolism'] = ['0.19', '0.203', '-0.15', '0.211', '#interest', '#admiration', 'positive', '0.112', 'propose_marriage', 'display_wealth', 'engage', 'holy_matrimony', 'similarity']
senticnet['symbolist'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'native_person', 'revolutionary', 'republic', 'revolution', 'look_ahead']
senticnet['symbyax'] = ['-0.72', '0', '0.973', '-0.57', '#sadness', '#anger', 'negative', '-0.76', 'corpulency', 'blubber', 'fat', 'embonpoint', 'stoutness']
senticnet['symmedian_point'] = ['0.763', '-0.61', '0', '0.777', '#joy', '#admiration', 'positive', '0.717', 'acute_triangle', 'wedge_shape', 'cuneus', 'wedge', 'oblique_triangle']
senticnet['symmetric'] = ['0.807', '-0.88', '0', '0.718', '#joy', '#surprise', 'positive', '0.802', 'sex', 'gayness', 'sexual_gayness', 'lesbianism', 'sapphism']
senticnet['symmetric_encryption'] = ['0.584', '-0.72', '0', '0.742', '#surprise', '#admiration', 'positive', '0.684', 'decompression', 'cryptography', 'coding', 'secret_writing', 'steganography']
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senticnet['symmetricalness'] = ['0', '0.932', '0', '0.747', '#interest', '#admiration', 'positive', '0.84', 'symmetry', 'correspondence', 'proportion', 'isotropy', 'balance']
senticnet['symmetry'] = ['0', '0.934', '0', '0.748', '#interest', '#admiration', 'positive', '0.841', 'symmetricalness', 'correspondence', 'proportion', 'isotropy', 'balance']
senticnet['sympathectomy'] = ['-0.65', '0', '0.873', '-0.63', '#sadness', '#anger', 'negative', '-0.72', 'polyneuritis', 'multiple_neuritis', 'rubor', 'redness', 'inflammation']
senticnet['sympathetic'] = ['0', '0', '0', '0.799', '#admiration', '#admiration', 'positive', '0.799', 'adoration', 'confidence', 'enthusiasm', 'grateful', 'anticipation']
senticnet['sympathetic_character'] = ['-0.61', '0', '0.572', '-0.83', '#sadness', '#disgust', 'negative', '-0.67', 'numb', 'benumbed', 'anesthetic', 'insensible', 'asleep']
senticnet['sympathetic_detonation'] = ['-0.84', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.85', 'inferno', 'forest_fire', 'fire_light', 'dismissal', 'wash_mouth_soap']
senticnet['sympathetic_ear'] = ['0.376', '0.271', '-0.19', '0.258', '#joy', '#interest', 'positive', '0.177', 'pleasant_emotion', 'bliss', 'good_feeling', 'confirm', 'healthful']
senticnet['sympathetic_magic'] = ['-0.82', '0', '0.72', '-0.53', '#sadness', '#anger', 'negative', '-0.69', 'invocation', 'charm', 'spell', 'magic_spell', 'magical_spell']
senticnet['sympathetic_nerve'] = ['-0.62', '0', '0.901', '-0.71', '#anger', '#disgust', 'negative', '-0.74', 'pressure', 'osmotic_pressure', 'hypotonicity', 'pressure_level', 'force']
senticnet['sympathetic_portrait'] = ['0', '0.764', '0', '0.845', '#interest', '#admiration', 'positive', '0.804', 'empathetically', 'empathic', 'empathetic', 'kind-heartedness', 'kindheartedness']
senticnet['sympathetic_stimulation'] = ['-0.62', '0', '0.901', '-0.71', '#anger', '#disgust', 'negative', '-0.74', 'pressure', 'osmotic_pressure', 'hypotonicity', 'pressure_level', 'force']
senticnet['sympathetic_strike'] = ['-0.77', '0', '0.505', '-0.50', '#sadness', '#anger', 'negative', '-0.59', 'wildcat_strike', 'sympathy_strike', 'work_stoppage', 'walkout', 'job_action']
senticnet['sympathetically'] = ['0', '0.764', '0', '0.845', '#interest', '#admiration', 'positive', '0.804', 'empathetically', 'empathic', 'empathetic', 'kind-heartedness', 'kindheartedness']
senticnet['sympathise'] = ['-0.95', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.95', 'unhappy', 'dysphoric', 'forlornly', 'desolation', 'forlornness']
senticnet['sympathise_with'] = ['-0.99', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.96', 'sympathetic', 'sympathetically', 'pity', 'sympathize_with', 'sympathy']
senticnet['sympathiser'] = ['0.065', '0.17', '-0.05', '0.143', '#interest', '#admiration', 'positive', '0.081', 'supervisor', 'native_american', 'ambassador', 'master', 'general']
senticnet['sympathize'] = ['-0.99', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.96', 'sympathetic', 'sympathetically', 'pity', 'sympathize_with', 'sympathy']
senticnet['sympathize_with'] = ['0.752', '0.976', '0', '0', '#joy', '#interest', 'positive', '0.864', 'compassionateness', 'feel_for', 'condole_with', 'sympathize', 'commiserate']
senticnet['sympathizer'] = ['-0.85', '0', '0', '-0.80', '#sadness', '#disgust', 'negative', '-0.82', 'militiaman', 'cavalryman', 'teen-ager', 'passer-by', 'militiaman']
senticnet['sympathizes_with'] = ['-0.99', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.96', 'sympathetic', 'sympathetically', 'pity', 'sympathize_with', 'sympathy']
senticnet['sympathomimetic'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'spironolactone', 'adrenal_cortical_steroid', 'antihypertensive', 'antihypertensive_drug']
senticnet['sympathomimetic_amine'] = ['-0.72', '0', '0.973', '-0.57', '#sadness', '#anger', 'negative', '-0.76', 'corpulency', 'blubber', 'fat', 'embonpoint', 'stoutness']
senticnet['sympathy'] = ['0', '0.806', '0', '0.71', '#interest', '#admiration', 'positive', '0.758', 'empathy', 'kindness', 'sympathize', 'sympathize_with', 'sympathetic']
senticnet['sympathy_strike'] = ['-0.77', '0', '0.505', '-0.50', '#sadness', '#anger', 'negative', '-0.59', 'wildcat_strike', 'work_stoppage', 'sympathetic_strike', 'walkout', 'job_action']
senticnet['symphonic'] = ['0.022', '0', '0', '0.109', '#joy', '#admiration', 'positive', '0.066', 'instrumental', 'singer', 'hip_hop', 'melodic_noise', 'music_player']
senticnet['symphonic_orchestra'] = ['0.506', '0.659', '-0.24', '0.242', '#joy', '#interest', 'positive', '0.29', 'sing', 'music_studio', 'music_room', 'choir', 'symphony']
senticnet['symphonious'] = ['0.823', '-0.72', '0', '0.905', '#joy', '#admiration', 'positive', '0.817', 'true', 'pure', 'harmonious', 'harmonical', 'harmonized']
senticnet['symphony'] = ['0.445', '0.37', '-0.14', '0.483', '#joy', '#admiration', 'positive', '0.289', 'sing', 'music_studio', 'music_room', 'choir_orchestra', 'choir']
senticnet['symphony_hall'] = ['0.164', '0.13', '0.031', '0.209', '#joy', '#admiration', 'positive', '0.118', 'orchestra_play', 'orchestra_seat', 'music', 'orchestra_pit', 'concert_hall']
senticnet['symphony_orchestra'] = ['0.506', '0.659', '-0.24', '0.242', '#joy', '#interest', 'positive', '0.29', 'sing', 'music_studio', 'music_room', 'choir', 'symphony']
senticnet['symposium'] = ['0.445', '0.37', '-0.14', '0.483', '#joy', '#admiration', 'positive', '0.289', 'meeting', 'conference', 'convention', 'assembly', 'reunion']
senticnet['symptom'] = ['-0.17', '0.086', '-0.18', '-0.06', '#sadness', '#fear', 'negative', '-0.08', 'sign_sickness', 'body_reaction', 'ailment', 'unpleasant', 'sign']
senticnet['symptom_management'] = ['-0.03', '-0.02', '-0.08', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'ibuprofen', 'phenobarbital', 'analgesic', 'sleep_drug', 'pill']
senticnet['symptom_onset'] = ['-0.53', '0', '0.608', '-0.93', '#anger', '#disgust', 'negative', '-0.69', 'reprieve', 'respite', 'hiatus', 'abatement', 'suspension']
senticnet['symptom_relief'] = ['-0.03', '-0.02', '-0.08', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'ibuprofen', 'phenobarbital', 'analgesic', 'sleep_drug', 'pill']
senticnet['symptom_severity'] = ['0.798', '-0.51', '0', '0.519', '#joy', '#admiration', 'positive', '0.611', 'averageness', 'commonness', 'normalcy', 'typicality', 'condition']
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senticnet['symptomatic_improvement'] = ['-0.53', '0', '0.608', '-0.93', '#anger', '#disgust', 'negative', '-0.69', 'reprieve', 'respite', 'hiatus', 'abatement', 'suspension']
senticnet['symptomatic_relief'] = ['-0.60', '0', '0.544', '-0.62', '#sadness', '#disgust', 'negative', '-0.59', 'remedy', 'cure', 'therapeutic', 'antidote', 'atropine']
senticnet['symptomatic_treatment'] = ['-0.05', '0', '-0.22', '-0.06', '#fear', '#disgust', 'negative', '-0.11', 'medicine', 'cure', 'medical_procedure', 'sleep_drug', 'nurse']
senticnet['symptomatology'] = ['-0.58', '0', '0.722', '-0.51', '#sadness', '#anger', 'negative', '-0.60', 'dissociative_disorder', 'depersonalization_neurosis', 'depersonalization', 'mental_disorder', 'mental_disturbance']
senticnet['symptomology'] = ['0.798', '-0.51', '0', '0.519', '#joy', '#admiration', 'positive', '0.611', 'averageness', 'commonness', 'normalcy', 'typicality', 'condition']
senticnet['synagogue'] = ['0', '0.209', '0.026', '0.184', '#interest', '#admiration', 'positive', '0.122', 'church', 'confession', 'temple', 'cathedral', 'monastery']
senticnet['synaptic_activity'] = ['0.698', '-0.61', '0', '0.619', '#joy', '#admiration', 'positive', '0.644', 'sensitivity', 'sensitiveness', 'irritability', 'sensibility', 'reactivity']
senticnet['synaptic_inhibition'] = ['0.698', '-0.61', '0', '0.619', '#joy', '#admiration', 'positive', '0.644', 'sensitivity', 'sensitiveness', 'irritability', 'sensibility', 'reactivity']
senticnet['syncategorematic'] = ['0.633', '-0.67', '0', '0.776', '#surprise', '#admiration', 'positive', '0.693', 'double_entendre', 'loophole', 'ambiguity', 'amphiboly', 'parisology']
senticnet['synchronised_swimming'] = ['0.964', '0.713', '0', '0', '#joy', '#interest', 'positive', '0.839', 'martial_art', 'judo', 'jump_around', 'go_underwater', 'athletics']
senticnet['synchronization'] = ['-0.9', '0', '0.841', '-0.73', '#sadness', '#anger', 'negative', '-0.82', 'nubbiness', 'tweediness', 'harshness', 'graininess', 'raggedness']
senticnet['synchronized_swimming'] = ['0.964', '0.713', '0', '0', '#joy', '#interest', 'positive', '0.839', 'martial_art', 'judo', 'jump_around', 'go_underwater', 'athletics']
senticnet['synchronously'] = ['0.931', '0.968', '0', '0', '#joy', '#interest', 'positive', '0.95', 'sequentially', 'serially', 'successively', 'cyclically', 'outputted']
senticnet['syncopate'] = ['-0.5', '0', '0.598', '-0.80', '#anger', '#disgust', 'negative', '-0.63', 'temper', 'corrupt', 'cushion', 'modify', 'spoil']
senticnet['syncope'] = ['-0.20', '0', '-0.22', '-0.19', '#sadness', '#fear', 'negative', '-0.20', 'go_hospital', 'death', 'die', 'take_aspirin', 'heart_attack']
senticnet['syndactylism'] = ['0.545', '-0.62', '0', '0.946', '#surprise', '#admiration', 'positive', '0.704', 'birth_defect', 'congenital_anomaly', 'congenital_defect', 'congenital_disorder', 'congenital_abnormality']
senticnet['syndactyly'] = ['0.618', '-0.6', '0', '0.546', '#joy', '#surprise', 'positive', '0.588', 'birth_defect', 'congenital_anomaly', 'congenital_defect', 'congenital_disorder', 'congenital_abnormality']
senticnet['syndic'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquirer', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['syndicalism'] = ['0.563', '-0.72', '0', '0.644', '#surprise', '#admiration', 'positive', '0.645', 'idiom', 'idiomatic_expression', 'phrasal_idiom', 'set_phrase', 'phrase']
senticnet['syndicated_television'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'theater_seat', 'theater_box', 'sticky_floor', 'stage_entrance', 'balcony']
senticnet['syndicated_tv'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'theater_seat', 'theater_box', 'sticky_floor', 'stage_entrance', 'balcony']
senticnet['syndromebackground'] = ['-0.58', '0', '0.724', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'myotonic_muscular_dystrophy', 'myotonic_dystrophy', 'myotonia_atrophica', 'distal_muscular_dystrophy', 'genetic_disease']
senticnet['syne'] = ['0.947', '0.914', '0', '0', '#joy', '#interest', 'positive', '0.93', 'ball_bounce', 'bicycle_tire', 'tire', 'increase_price', 'gyroscope']
senticnet['synechia'] = ['-0.78', '0', '0.855', '-0.99', '#anger', '#disgust', 'negative', '-0.87', 'blood_blister', 'pustule', 'water_blister', 'vesicle', 'cyst']
senticnet['synera'] = ['-0.58', '0', '0.561', '-0.62', '#sadness', '#disgust', 'negative', '-0.58', 'spot', 'speckle', 'dapple', 'mark', 'fleck']
senticnet['synergies_between'] = ['-0.81', '0', '0.931', '-0.77', '#sadness', '#anger', 'negative', '-0.84', 'mutualness', 'mutuality', 'reciprocity', 'correlation', 'correlativity']
senticnet['synergism'] = ['0.662', '-0.79', '0', '0.919', '#surprise', '#admiration', 'positive', '0.791', 'activity', 'natural_process', 'natural_action', 'potentiation', 'action']
senticnet['synergistic'] = ['0.03', '0.215', '-0.06', '0.18', '#interest', '#admiration', 'positive', '0.09', 'combo', 'hold_coin', 'money_storage', 'money_place', 'money_holder']
senticnet['synergistic_effect'] = ['0.03', '0.215', '-0.06', '0.18', '#interest', '#admiration', 'positive', '0.09', 'combo', 'hold_coin', 'money_storage', 'money_place', 'money_holder']
senticnet['synergy_between'] = ['-0.81', '0', '0.931', '-0.77', '#sadness', '#anger', 'negative', '-0.84', 'mutualness', 'mutuality', 'reciprocity', 'correlation', 'correlativity']
senticnet['syngrapha'] = ['-0.56', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.63', 'aphasic', 'inarticulate', 'speechless', 'unarticulated', 'dumb']
senticnet['synod'] = ['0.081', '0.146', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'cult', 'sect', 'conservative', 'worshiper', 'pastor']
senticnet['synoptic'] = ['0', '0.811', '0', '0', '#interest', '#interest', 'positive', '0.811', 'observe', 'glimpse', 'look', 'stare', 'glance']
senticnet['synoptic_scale'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'hurricane', 'natural_disaster', 'flood', 'atmospheric_pressure', 'tropical_storm']
senticnet['synovial'] = ['0', '0', '0.025', '-0.09', '#anger', '#disgust', 'negative', '-0.06', 'body_fluid', 'artery', 'skeletal_system', 'bodily_fluid', 'near_mouth']
senticnet['synovial_fluid'] = ['0', '0', '0.025', '-0.09', '#anger', '#disgust', 'negative', '-0.06', 'body_fluid', 'artery', 'skeletal_system', 'bodily_fluid', 'near_mouth']
senticnet['synovial_joint'] = ['-0.53', '0', '0.982', '-0.67', '#anger', '#disgust', 'negative', '-0.73', 'meniscus', 'semilunar_cartilage', 'gristle', 'hyaline_cartilage', 'arytenoid']
senticnet['synovial_membrane'] = ['0', '0', '0.025', '-0.09', '#anger', '#disgust', 'negative', '-0.06', 'body_fluid', 'artery', 'skeletal_system', 'bodily_fluid', 'near_mouth']
senticnet['synovitis'] = ['-0.81', '0', '0.789', '-0.51', '#sadness', '#anger', 'negative', '-0.70', 'rubor', 'redness', 'inflammation', 'tenosynovitis', 'tendosynovitis']
senticnet['synovium'] = ['0', '0', '0.025', '-0.09', '#anger', '#disgust', 'negative', '-0.06', 'body_fluid', 'artery', 'skeletal_system', 'bodily_fluid', 'near_mouth']
senticnet['syntactic'] = ['0.785', '-0.99', '0', '0.981', '#surprise', '#admiration', 'positive', '0.92', 'ear', 'absolute_pitch', 'perfect_pitch', 'audition', 'sense_audition']
senticnet['syntactic_category'] = ['0.734', '-0.56', '0', '0.64', '#joy', '#admiration', 'positive', '0.645', 'inferential', 'suggestive', 'connotative', 'connotational', 'connotative_of']
senticnet['syntactic_foam'] = ['0.085', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.074', 'soap', 'drizzle', 'bubbly', 'solid_water', 'ivory']
senticnet['syntactic_structure'] = ['-0.93', '0', '0.944', '-0.92', '#sadness', '#anger', 'negative', '-0.93', 'combinative', 'integrative', 'consolidative', 'unifying', 'combinatorial']
senticnet['syntactical'] = ['-0.72', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.79', 'non-standard', 'ordinal', 'domain-specific', 'colloquial', 'nonstandard']
senticnet['syntactically'] = ['0', '0.8', '0', '0.968', '#interest', '#admiration', 'positive', '0.884', 'semantically', 'grammatically', 'phonetically', 'morphologically', 'lexically']
senticnet['syntagma_musicum'] = ['0.033', '0', '0.009', '0.057', '#joy', '#admiration', 'positive', '0.027', 'triangle', 'baton', 'bugle', 'type_of_bow', 'horn']
senticnet['syntax_checker'] = ['-0.89', '0', '0.672', '-0.69', '#sadness', '#disgust', 'negative', '-0.75', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['syntax_error'] = ['-0.98', '0', '0.869', '-0.80', '#sadness', '#anger', 'negative', '-0.88', 'error', 'programming_error', 'semantic_error', 'runtime_error', 'computer_error']
senticnet['synth_heavy'] = ['0.857', '-0.77', '0', '0.878', '#joy', '#admiration', 'positive', '0.836', 'hand', 'round', 'approval', 'commendation', 'clapping']
senticnet['synthesis'] = ['0', '0.707', '0', '0.722', '#interest', '#admiration', 'positive', '0.714', 'deduction', 'deductive_reasoning', 'synthetic_thinking', 'research', 'chemistry']
senticnet['synthesize'] = ['0', '0', '0.814', '-0.83', '#anger', '#disgust', 'negative', '-0.82', 'distills', 'condense', 'distilling', 'distill', 'distilled']
senticnet['synthetic'] = ['-0.71', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.78', 'artificial', 'natural', 'turf', 'chemical', 'grass']
senticnet['synthetic_analog'] = ['0.672', '0.632', '-0.23', '0.312', '#joy', '#interest', 'positive', '0.344', 'euphoria', 'buoyancy', 'perkiness', 'jubilance', 'ecstasy']
senticnet['synthetic_drug'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'use_drug', 'do_drug', 'drug_use', 'drug', 'addiction']
senticnet['synthetic_fabric'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'white_cotton', 'sheepskin_material', 'woven_sheepskin', 'sheepskin', 'clothes_type']
senticnet['synthetic_fuel'] = ['0.855', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.86', 'ticket_counter', 'newspaper_stand', 'luggage_carrier', 'aircraft_roadway', 'timetable']
senticnet['synthetic_fur'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'animal_hair', 'dog_hair', 'animal_fur', 'dog_cat', 'bite_dog']
senticnet['synthetic_marijuana'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'use_drug', 'do_drug', 'drug_use', 'drug', 'addiction']
senticnet['synthetic_mesh'] = ['-0.81', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.85', 'bandage', 'painkiller', 'gauze', 'aspirin', 'contraceptive_device']
senticnet['synthetic_opioid'] = ['0.672', '0.632', '-0.23', '0.312', '#joy', '#interest', 'positive', '0.344', 'euphoria', 'buoyancy', 'perkiness', 'jubilance', 'ecstasy']
senticnet['synthetic_pyrethroid'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['synthetic_soda_ash'] = ['0', '0.164', '-0.08', '0.215', '#interest', '#admiration', 'positive', '0.098', 'manufacture', 'factory_output', 'creation', 'assembly_line', 'make']
senticnet['synthetic_substance'] = ['0.049', '0', '0', '0.113', '#joy', '#admiration', 'positive', '0.081', 'solid', 'hard_material', 'construction_material', 'hard', 'calcium']
senticnet['synthetic_thinking'] = ['0', '0.706', '0', '0.717', '#interest', '#admiration', 'positive', '0.712', 'deduction', 'deductive_reasoning', 'synthesis', 'research', 'chemistry']
senticnet['synthetic_ultramarine'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['synthetical'] = ['0.838', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.835', 'pondering', 'broody', 'contemplative', 'meditative', 'think']
senticnet['synthetically'] = ['-0.7', '0', '0.77', '-0.75', '#anger', '#disgust', 'negative', '-0.74', 'fake', 'unnatural', 'man_made', 'artificial_sea', 'artificial_taste']
senticnet['syntonic_comma'] = ['-0.65', '0', '0.681', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'tone', 'whole_tone', 'step', 'whole_step', 'musical_interval']
senticnet['syphilis'] = ['0', '0', '-0.82', '-0.97', '#fear', '#disgust', 'negative', '-0.89', 'tuberculosis', 'leper', 'cholera', 'leprosy', 'smallpox']
senticnet['syphilitic'] = ['-0.93', '0', '0.75', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'unhealthiness', 'injury', 'affliction', 'biliousness', 'harm']
senticnet['syringa'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'black_locust']
senticnet['syringe'] = ['-0.02', '0', '-0.12', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'x-ray_machine', 'medical_record', 'latex_glove', 'medicine', 'doctor_examination_room']
senticnet['syringe_barrel'] = ['-0.02', '0', '-0.12', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'x-ray_machine', 'medical_record', 'latex_glove', 'medicine', 'doctor_examination_room']
senticnet['syringe_needle'] = ['-0.02', '0', '-0.12', '-0.19', '#fear', '#disgust', 'negative', '-0.11', 'x-ray_machine', 'medical_record', 'latex_glove', 'medicine', 'doctor_examination_room']
senticnet['syringe_pump'] = ['0', '0', '-0.95', '-0.87', '#fear', '#disgust', 'negative', '-0.91', 'sharp_weapon', 'bayonet', 'ammunition', 'spear', 'arrow']
senticnet['syro_malabar'] = ['0.8', '-0.84', '0', '0.868', '#surprise', '#admiration', 'positive', '0.837', 'commonwealth', 'province', 'administrative_district', 'administrative_division', 'territorial_division']
senticnet['syro_malabar_catholic_church'] = ['0.8', '-0.84', '0', '0.868', '#surprise', '#admiration', 'positive', '0.837', 'commonwealth', 'province', 'administrative_district', 'administrative_division', 'territorial_division']
senticnet['syrup'] = ['0.871', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.856', 'agave', 'honey', 'juice', 'nectar', 'bee']
senticnet['system'] = ['0', '0.172', '0', '0.138', '#interest', '#admiration', 'positive', '0.155', 'motherboard', 'cpu', 'hard_drive', 'computer_program', 'computer_disk']
senticnet['system_abstract_think'] = ['-0.07', '0.147', '0', '0.074', '#interest', '#admiration', 'positive', '0.049', 'think_carefully', 'look_detail', 'contemplate', 'cogitate', 'logic']
senticnet['system_agency_disa'] = ['-0.04', '0', '-0.06', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'make_war', 'conquer_enemy', 'use_military', 'gain_territory', 'keep_peace']
senticnet['system_of_logic'] = ['0.73', '-0.58', '0', '0.768', '#joy', '#admiration', 'positive', '0.695', 'system_of_rules', 'symbolic_logic', 'mathematical_logic', 'formal_logic', 'system']
senticnet['system_of_rules'] = ['0.661', '-0.66', '0', '0.685', '#surprise', '#admiration', 'positive', '0.671', 'ethical_code', 'theosophy', 'anthroposophy', 'logic', 'method']
senticnet['system_tray'] = ['0.055', '0.117', '0', '0.133', '#interest', '#admiration', 'positive', '0.102', 'graphical_representation', 'pencil_pen', 'note_pad', 'marker', 'stationery']
senticnet['system_tray_icon'] = ['-0.09', '-0.04', '0.039', '-0.24', '#sadness', '#disgust', 'negative', '-0.08', 'learn_new_idea', 'learn_quickly', 'point_out', 'look_picture', 'jot_down_note']
senticnet['systematic'] = ['0.018', '0.235', '-0.07', '0.294', '#interest', '#admiration', 'positive', '0.117', 'ready', 'efficient', 'get_ready', 'preparedness', 'born_ready']
senticnet['systematic_approach'] = ['-0.03', '0.2', '-0.10', '0.298', '#interest', '#admiration', 'positive', '0.09', 'procedure', 'action', 'create_form', 'transform', 'posture']
senticnet['systematic_biology'] = ['0', '0.899', '0', '0.714', '#interest', '#admiration', 'positive', '0.806', 'different_application', 'award', 'standard_grant', 'sponsor', 'specie']
senticnet['systematic_desensitization'] = ['-0.79', '-0.71', '-0.96', '-0.95', '#fear', '#disgust', 'negative', '-0.49', 'fear', 'caution', 'deep_sadness', 'bad_feeling', 'psychoneurosis']
senticnet['systematic_destruction'] = ['-0.14', '-0.00', '-0.12', '-0.21', '#sadness', '#disgust', 'negative', '-0.11', 'torture', 'demolish', 'maim', 'terminate', 'hurt_person']
senticnet['systematic_empirical'] = ['0.632', '-0.80', '0', '0.537', '#joy', '#surprise', 'positive', '0.658', 'incisiveness', 'trenchancy', 'efficacy', 'effectiveness', 'power']
senticnet['systematic_error'] = ['-0.68', '0', '0.946', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'abnormality', 'abnormalcy', 'birth_defect', 'congenital_anomaly', 'congenital_defect']
senticnet['systematic_evaluation'] = ['-0.06', '0.18', '0', '0.143', '#interest', '#admiration', 'positive', '0.086', 'grade', 'multiple_choices', 'final', 'final_exam', 'school_grade']
senticnet['systematic_examination'] = ['0.056', '-0.03', '0', '0.054', '#joy', '#admiration', 'positive', '0.048', 'analyze', 'stargazing', 'influence_public_decision', 'question_everything', 'see_artifact']
senticnet['systematic_exploration'] = ['0.774', '-0.63', '0', '0.567', '#joy', '#surprise', 'positive', '0.658', 'catching', 'breakthrough', 'spying', 'determination', 'tracing']
senticnet['systematic_extermination'] = ['-0.08', '0', '-0.15', '-0.20', '#fear', '#disgust', 'negative', '-0.14', 'casualty', 'reaper', 'wound', 'cause_death', 'gunshot']
senticnet['systematic_genocide'] = ['-0.08', '0', '-0.15', '-0.20', '#fear', '#disgust', 'negative', '-0.14', 'casualty', 'reaper', 'wound', 'cause_death', 'gunshot']
senticnet['systematic_slaughter'] = ['-0.67', '0', '0.938', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'massacre', 'mass_murder', 'carnage', 'bloodbath', 'butchery']
senticnet['systematic_theology'] = ['0.664', '-0.57', '0', '0.981', '#joy', '#admiration', 'positive', '0.741', 'theodicy', 'homiletics', 'discipline', 'study', 'subject']
senticnet['systematic_zoology'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'study_person', 'subject_study', 'zoo', 'zoologist', 'geology']
senticnet['systematically'] = ['0', '0.91', '0', '0.95', '#interest', '#admiration', 'positive', '0.93', 'consistently', 'study', 'project', 'research', 'different_application']
senticnet['systematically_investigate'] = ['0.548', '-0.86', '0', '0.938', '#surprise', '#admiration', 'positive', '0.784', 'elaborate', 'clear', 'crystallize', 'clear_up', 'make_clear']
senticnet['systematicity'] = ['-0.99', '0', '0.878', '-0.95', '#sadness', '#disgust', 'negative', '-0.94', 'adequacy', 'satisfactoriness', 'acceptability', 'acceptableness', 'quality']
senticnet['systemic_blood_pressure'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'keep_alive', 'body_system', 'body_working', 'blood_pressure', 'heart']
senticnet['systemic_corruption'] = ['0', '0', '-0.77', '-0.86', '#fear', '#disgust', 'negative', '-0.82', 'abduction', 'punishment', 'emergency', 'illiteracy', 'heal']
senticnet['systemic_disorder'] = ['-0.87', '0', '0.531', '-0.68', '#sadness', '#disgust', 'negative', '-0.69', 'rubor', 'redness', 'dermatomyositis', 'inclusion_body_myositis', 'inflammation']
senticnet['systemic_fungicide'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'rust_fungus', 'fungus']
senticnet['systemic_hypotension'] = ['-0.03', '-0.03', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'eat_poison', 'commit_suicide', 'drug_addiction', 'drunk_drive', 'jump_off_cliff']
senticnet['systemic_infection'] = ['-0.53', '0', '0.671', '-0.55', '#anger', '#disgust', 'negative', '-0.58', 'dirty', 'putrefactive', 'putrefacient', 'abscessed', 'contaminative']
senticnet['systemic_insecticide'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['systemic_pathology'] = ['-0.59', '0', '0.911', '-0.87', '#anger', '#disgust', 'negative', '-0.79', 'osteosclerosis', 'sclerosis', 'disseminated_sclerosis', 'multiple_sclerosis', 'induration']
senticnet['systemic_problem'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'problem', 'serious_issue', 'overrate', 'prohibitionist_society', 'blade_weapon']
senticnet['systemic_racism'] = ['-0.96', '0', '0.766', '-0.96', '#sadness', '#disgust', 'negative', '-0.89', 'calumniation', 'traducement', 'hatchet_job', 'defamation', 'calumny']
senticnet['systemic_reaction'] = ['-0.99', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.78', 'dropsy', 'atrophedema', 'giant_hive', 'periodic_edema', 'lymphedema']
senticnet['systole'] = ['0', '0.098', '-0.09', '0.115', '#interest', '#admiration', 'positive', '0.04', 'push_wind', 'breath', 'breaout', 'body_reaction', 'mouth_action']
senticnet['systolic_dysfunction'] = ['-0.20', '0', '-0.22', '-0.19', '#sadness', '#fear', 'negative', '-0.20', 'go_hospital', 'death', 'die', 'take_aspirin', 'heart_attack']
senticnet['systolic_heart_failure'] = ['-0.98', '0', '0.827', '-0.94', '#sadness', '#disgust', 'negative', '-0.91', 'congestive_heart_failure', 'heart_attack', 'failure', 'heart_disease', 'cardiopathy']
senticnet['systolic_hypertension'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'illness', 'ill', 'tension', 'bad_health', 'malignant_hypertension']
senticnet['systrust'] = ['0.036', '0.292', '0', '0.337', '#interest', '#admiration', 'positive', '0.222', 'importance', 'quality', 'total', 'excellence', 'frivolity']
senticnet['syzygium'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'carnauba_palm', 'carnauba', 'wax_palm', 'palmetto', 'palmyra']
senticnet['syzygium_cumini'] = ['0.826', '0.787', '0', '0', '#joy', '#interest', 'positive', '0.806', 'fig', 'baby_flower', 'carpel', 'strawberry', 'star_anise']
senticnet['szepesi'] = ['0.975', '-0.66', '0', '0.813', '#joy', '#admiration', 'positive', '0.816', 'cry', 'yell', 'scream', 'shrill', 'call']
senticnet['t-bill'] = ['0.976', '0.97', '0', '0', '#joy', '#interest', 'positive', '0.973', 'high-yielding', 'yield', 'investment-grade', 'fixed-income', 'high-yield']
senticnet['t-shirt'] = ['0.041', '0.119', '0.017', '0', '#joy', '#interest', 'positive', '0.048', 'shirt', 'robe', 'jacket', 'underwear', 'jeans']
senticnet['t_shirt'] = ['0.82', '0.792', '0', '0', '#joy', '#interest', 'positive', '0.806', 'electric_toothbrush', 'towel', 'toilet_seat', 'heat_towel_rack', 'hair_brush']
senticnet['taas_test'] = ['-0.22', '-0.00', '-0.09', '-0.03', '#sadness', '#fear', 'negative', '-0.08', 'failure', 'bad_grade', 'a_lot_of_stress', 'fail', 'further_goal']
senticnet['tab'] = ['0.041', '0.119', '0.017', '0', '#joy', '#interest', 'positive', '0.048', 'account_book', 'fountain_pen', 'general_ledger', 'scrap_paper', 'scotch_tape']
senticnet['tabacalera'] = ['0.031', '0', '-0.08', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'smoke_cigarette', 'smoke', 'cinder', 'tobacco_product', 'smoke_pipe']
senticnet['tabbed_interface'] = ['-0.03', '0.112', '-0.08', '0.164', '#interest', '#admiration', 'positive', '0.04', 'run', 'jump_over', 'quick', 'sprint', 'flee']
senticnet['tabby'] = ['0.83', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.798', 'calico', 'tortoiseshell', 'kitten', 'critter', 'cat']
senticnet['tabby_cat'] = ['0.144', '0.134', '-0.04', '0.238', '#joy', '#admiration', 'positive', '0.118', 'cat', 'grey_cat', 'kitty', 'baby_dog', 'dog']
senticnet['tabes'] = ['-0.86', '0', '0.738', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'amyotrophia', 'amyotrophy', 'wasting_away', 'kraurosis', 'symptom']
senticnet['table'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'gate_leg_table', 'kitchen_drawer', 'silverware_drawer', 'dine_table', 'cutlery_drawer']
senticnet['table_chair'] = ['0.062', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.04', 'hostess', 'bottle_wine', 'human_intelligence', 'patron', 'salt_shaker']
senticnet['table_cloth'] = ['-0.71', '0', '0.954', '-0.56', '#sadness', '#anger', 'negative', '-0.74', 'jackknife', 'pocket_knife', 'penknife', 'switchblade', 'switchblade_knife']
senticnet['table_grape'] = ['0', '0.066', '-0.05', '0.089', '#interest', '#admiration', 'positive', '0.032', 'pineapple', 'kale', 'banana', 'pickle', 'tomato']
senticnet['table_linen'] = ['0.022', '0.069', '0.027', '0.059', '#interest', '#admiration', 'positive', '0.031', 'perfect_surface', 'flat_land', 'flat_surface', 'bowl', 'plate']
senticnet['table_manner'] = ['0', '0.727', '0', '0.984', '#interest', '#admiration', 'positive', '0.855', 'nanotechnology', 'class', 'manner', 'school_study', 'school_session']
senticnet['table_set'] = ['0.052', '-0.04', '0.024', '0', '#joy', '#surprise', 'positive', '0.023', 'dinner_table', 'meal_time', 'dinner', 'serve_dish', 'table']
senticnet['table_tennis'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'golf', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['table_tennis_champion'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'golf', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['table_tennis_competition'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'golf', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['table_tennis_federation'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'golf', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['table_tennis_federation_ittf'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'golf', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['table_tennis_player'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'golf', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['table_tennis_tournament'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'golf', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['tableau_vivant'] = ['-0.69', '0', '0.508', '-0.93', '#sadness', '#disgust', 'negative', '-0.71', 'dark', 'darkness', 'light', 'shadow', 'site']
senticnet['tablecloth'] = ['0', '-0.02', '0.041', '-0.10', '#anger', '#disgust', 'negative', '-0.03', 'separate_shower', 'shampoo_conditioner', 'towel_bar', 'shower_head', 'toilet_brush']
senticnet['tablespoon'] = ['0.03', '-0.05', '0', '0.059', '#surprise', '#admiration', 'positive', '0.046', 'spoon', 'fork', 'spatula', 'utensil', 'bowl']
senticnet['tablespoon_butter'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'salad_shaker', 'store_shelf', 'kitchen_tool', 'pliers', 'ladle']
senticnet['tablet'] = ['0.132', '0.1', '0.063', '0.159', '#joy', '#admiration', 'positive', '0.082', 'technology', 'phone', 'pad', 'use_technology', 'computer']
senticnet['tablet_pc'] = ['0.113', '0.073', '0.028', '0.107', '#joy', '#admiration', 'positive', '0.066', 'pda', 'telephone_computer', 'browser', 'number_pad', 'modem']
senticnet['tableware'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'fabrication', 'carving', 'decoupage', 'artistic_creation']
senticnet['tabloid'] = ['-0.05', '0.106', '0', '0.06', '#interest', '#admiration', 'positive', '0.036', 'buy', 'buy_newspaper', 'read_buy_newspaper', 'buy_book', 'read_news']
senticnet['tabloid_headline'] = ['0.54', '-0.64', '0', '0.566', '#surprise', '#admiration', 'positive', '0.582', 'pleaser', 'entertainer', 'mortal', 'busker', 'drawing_card']
senticnet['tabloid_journalism'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'nonsense', 'meaninglessness', 'bunk', 'absurdity', 'hokum']
senticnet['tabloid_newspaper'] = ['-0.05', '0.106', '0', '0.06', '#interest', '#admiration', 'positive', '0.036', 'buy', 'buy_newspaper', 'read_buy_newspaper', 'buy_book', 'read_news']
senticnet['tabloid_reporter'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'addicted', 'obsessed_love', 'addiction', 'obsession', 'weird']
senticnet['tabloid_speculation'] = ['0.54', '-0.64', '0', '0.566', '#surprise', '#admiration', 'positive', '0.582', 'pleaser', 'entertainer', 'mortal', 'busker', 'drawing_card']
senticnet['tabloid_style'] = ['0.088', '-0.00', '0', '0', '#joy', '#surprise', 'positive', '0.045', 'look_up_information', 'news', 'find_newspaper', 'enter_contest', 'latest_news']
senticnet['taboo'] = ['0', '0', '-0.88', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'prohibition', 'proscription', 'veto', 'interdiction', 'interdict']
senticnet['tabor_pipe'] = ['0.821', '-0.53', '0', '0.785', '#joy', '#admiration', 'positive', '0.712', 'fife', 'nose_flute', 'piccolo', 'woodwind', 'flute']
senticnet['tabreez'] = ['-0.71', '0', '0.836', '0', '#sadness', '#anger', 'negative', '-0.77', 'awkward', 'unenviable', 'embarrassingly', 'embarrassing', 'sheepish']
senticnet['tabu'] = ['-0.79', '0', '0.627', '-0.81', '#sadness', '#disgust', 'negative', '-0.74', 'taboo', 'bias', 'tendentiousness', 'irrational_hostility', 'experimenter_bias']
senticnet['tabula_rasa'] = ['0.992', '-0.66', '0', '0.536', '#joy', '#surprise', 'positive', '0.729', 'nous', 'mind', 'brain', 'psyche', 'head']
senticnet['tabular_array'] = ['0.757', '-0.63', '0', '0.501', '#joy', '#surprise', 'positive', '0.631', 'panoply', 'row', 'table', 'array', 'matrix']
senticnet['tachi'] = ['-0.04', '0.138', '-0.08', '0.111', '#interest', '#admiration', 'positive', '0.031', 'geometry', 'craft', 'blade_geometry', 'blade', 'collect_item']
senticnet['tachinid_fly'] = ['0', '-0.09', '0.123', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'gypsy_moth', 'japanese_beetle', 'cucumber_beetle', 'mosquito', 'earwig']
senticnet['tachycardia'] = ['-0.83', '0', '0.987', '-0.50', '#sadness', '#anger', 'negative', '-0.77', 'heart_block', 'atrioventricular_block', 'bradycardia', 'ventricular_fibrillation', 'arrhythmia']
senticnet['tachycardic'] = ['-0.56', '0', '0.577', '-0.61', '#anger', '#disgust', 'negative', '-0.58', 'foggy', 'groggy', 'logy', 'lethargic', 'dazed']
senticnet['tachykinin'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'gastrointestinal_hormone', 'secretin', 'ghrelin']
senticnet['tachypnea'] = ['-0.58', '0', '0.773', '-0.79', '#anger', '#disgust', 'negative', '-0.71', 'perspiration', 'sweating', 'polyhidrosis', 'sudation', 'hidrosis']
senticnet['tachypneic'] = ['-0.56', '0', '0.577', '-0.61', '#anger', '#disgust', 'negative', '-0.58', 'foggy', 'groggy', 'logy', 'lethargic', 'dazed']
senticnet['tacit'] = ['0', '0', '-0.70', '-0.95', '#fear', '#disgust', 'negative', '-0.83', 'silent', 'soundless', 'still', 'understand', 'mum']
senticnet['tacit_knowledge'] = ['0', '0.808', '0', '0.754', '#interest', '#admiration', 'positive', '0.781', 'know', 'understand', 'trivia', 'gain_insight', 'great_understand']
senticnet['tacit_support'] = ['0.751', '-0.98', '0', '0.989', '#surprise', '#admiration', 'positive', '0.909', 'okay', 'approval', 'commendation', 'okey', 'okeh']
senticnet['taciturn'] = ['-0.98', '0', '0.854', '0', '#sadness', '#anger', 'negative', '-0.91', 'unassuming', 'affable', 'self-effacing', 'soft-spoken', 'mild-mannered']
senticnet['taciturnity'] = ['-0.55', '0', '0.741', '-0.76', '#anger', '#disgust', 'negative', '-0.68', 'illogicality', 'illogicalness', 'illogic', 'invalidity', 'fallaciousness']
senticnet['tack'] = ['0', '-0.05', '0.079', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'c_clamp', 'thumb_tack', 'pin', 'pliers', 'without_space']
senticnet['tacked'] = ['-0.93', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.82', 'after-thought', 'afterthought', 'non-issue', 'afterthought', 'inconsequential']
senticnet['tackle'] = ['0.905', '0.858', '0', '0', '#joy', '#interest', 'positive', '0.881', 'swing_bat', 'injured', 'sprain_ankle', 'fall_down', 'twist_ankle']
senticnet['tackle_corruption'] = ['0', '0', '-0.77', '-0.86', '#fear', '#disgust', 'negative', '-0.82', 'abduction', 'punishment', 'emergency', 'illiteracy', 'heal']
senticnet['tackle_obesity'] = ['0.84', '0.76', '0', '0.894', '#joy', '#admiration', 'positive', '0.831', 'keep_skin_healthy', 'stay_healthy', 'stay_fit', 'maintain_good_health', 'maintain_muscle_strength']
senticnet['tackle_poverty'] = ['-0.94', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.88', 'combat', 'uprise', 'big_fight', 'opposition', 'violence']
senticnet['tackling_poverty'] = ['-0.25', '0', '-0.17', '-0.08', '#sadness', '#fear', 'negative', '-0.16', 'exclusion', 'exclusion_zone', 'isolation', 'detach', 'except']
senticnet['tackling_terrorism'] = ['-0.94', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.88', 'combat', 'uprise', 'big_fight', 'opposition', 'violence']
senticnet['tacky'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'wet', 'sticky', 'gluey', 'viscous', 'gooey']
senticnet['taco'] = ['0.132', '0.1', '0.063', '0.159', '#joy', '#admiration', 'positive', '0.082', 'chime', 'school_end', 'ring_loudly', 'noise_maker', 'vital']
senticnet['taco_bell'] = ['0.965', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.951', 'delicious_food', 'hot_meal', 'good_food', 'fresh_produce', 'tasty_food']
senticnet['taco_sauce'] = ['-0.75', '0', '0.593', '-0.59', '#sadness', '#anger', 'negative', '-0.64', 'condiment', 'chili_sauce', 'steak_sauce', 'duck_sauce', 'hoisin_sauce']
senticnet['taco_stand'] = ['0.132', '0.1', '0.063', '0.159', '#joy', '#admiration', 'positive', '0.082', 'chime', 'school_end', 'ring_loudly', 'noise_maker', 'vital']
senticnet['taco_truck'] = ['0.132', '0.1', '0.063', '0.159', '#joy', '#admiration', 'positive', '0.082', 'chime', 'school_end', 'ring_loudly', 'noise_maker', 'vital']
senticnet['tact'] = ['0.594', '-0.60', '0', '0.813', '#surprise', '#admiration', 'positive', '0.67', 'considerateness', 'delicacy', 'thoughtfulness', 'consideration', 'finesse']
senticnet['tactful'] = ['0', '0.706', '0', '0.889', '#interest', '#admiration', 'positive', '0.797', 'business-like', 'matter-of-fact', 'businesslike', 'non-confrontational', 'gentlemanly']
senticnet['tactfully'] = ['0', '0.764', '0', '0.845', '#interest', '#admiration', 'positive', '0.804', 'empathetically', 'empathic', 'empathetic', 'kind-heartedness', 'kindheartedness']
senticnet['tactfulness'] = ['0.83', '-0.89', '0', '0.987', '#surprise', '#admiration', 'positive', '0.903', 'considerateness', 'delicacy', 'thoughtfulness', 'consideration', 'finesse']
senticnet['tactic'] = ['0', '0', '0.877', '-0.94', '#anger', '#disgust', 'negative', '-0.91', 'diversion', 'diversionary', 'misdirection', 'propagandistic', 'ploy']
senticnet['tactical'] = ['-0.82', '0', '0.739', '-0.50', '#sadness', '#anger', 'negative', '-0.69', 'fake', 'juke', 'maneuver', 'manoeuvre', 'feint']
senticnet['tactical_acumen'] = ['0.724', '-0.92', '0', '0.854', '#surprise', '#admiration', 'positive', '0.833', 'tranquility', 'serenity', 'disposition', 'repose', 'placidity']
senticnet['tactical_advantage'] = ['-0.82', '0', '0.739', '-0.50', '#sadness', '#anger', 'negative', '-0.69', 'fake', 'juke', 'maneuver', 'manoeuvre', 'feint']
senticnet['tactical_feint'] = ['-0.82', '0', '0.738', '-0.51', '#sadness', '#anger', 'negative', '-0.69', 'fake', 'juke', 'feint', 'manoeuvre', 'maneuver']
senticnet['tactical_maneuver'] = ['-0.82', '0', '0.739', '-0.50', '#sadness', '#anger', 'negative', '-0.69', 'fake', 'juke', 'maneuver', 'manoeuvre', 'feint']
senticnet['tactical_mobility'] = ['0.814', '-0.87', '0', '0.918', '#surprise', '#admiration', 'positive', '0.87', 'ship_company', 'company', 'work_force', 'workforce', 'manpower']
senticnet['tactile'] = ['0.861', '-0.86', '0', '0', '#joy', '#surprise', 'positive', '0.862', 'auditory', 'visual', 'sensory', 'aural', 'kinesthetic']
senticnet['tactile_display'] = ['0.026', '0.14', '-0.13', '0.31', '#interest', '#admiration', 'positive', '0.085', 'teach_new', 'share_idea', 'gesture', 'start_talk', 'exchange_idea']
senticnet['tactile_feedback'] = ['0.026', '0.14', '-0.13', '0.31', '#interest', '#admiration', 'positive', '0.085', 'teach_new', 'share_idea', 'gesture', 'start_talk', 'exchange_idea']
senticnet['tactile_perception'] = ['0.026', '0.14', '-0.13', '0.31', '#interest', '#admiration', 'positive', '0.085', 'teach_new', 'share_idea', 'gesture', 'start_talk', 'exchange_idea']
senticnet['tactile_sensation'] = ['-0.78', '0', '0.669', '-0.89', '#sadness', '#disgust', 'negative', '-0.78', 'pain_sensation', 'painful_sensation', 'prickling', 'tingling', 'pain']
senticnet['tactile_sense'] = ['0.026', '0.14', '-0.13', '0.31', '#interest', '#admiration', 'positive', '0.085', 'teach_new', 'share_idea', 'gesture', 'start_talk', 'exchange_idea']
senticnet['tactile_sensor'] = ['0.026', '0.14', '-0.13', '0.31', '#interest', '#admiration', 'positive', '0.085', 'teach_new', 'share_idea', 'gesture', 'start_talk', 'exchange_idea']
senticnet['tactile_stimulus'] = ['0.067', '-0.2', '0.064', '0', '#joy', '#surprise', 'positive', '0.068', 'wand', 'long_stick', 'branch', 'twig', 'stick']
senticnet['tactility'] = ['-0.63', '0', '0.694', '-0.66', '#anger', '#disgust', 'negative', '-0.66', 'corporality', 'tangibleness', 'palpability', 'reality', 'solidness']
senticnet['tactual_sensation'] = ['-0.94', '0', '0.827', '-0.55', '#sadness', '#anger', 'negative', '-0.77', 'cutaneous_sensation', 'haptic_sensation', 'skin_sensation', 'perception', 'creepiness']
senticnet['tad_kubler'] = ['0.304', '0.339', '-0.19', '0.333', '#interest', '#admiration', 'positive', '0.195', 'careful', 'reinforce', 'load_bear', 'go_outdoors', 'allowance']
senticnet['tadalafil'] = ['-0.59', '0', '0.537', '-0.57', '#sadness', '#disgust', 'negative', '-0.57', 'phentolamine', 'sildenafil', 'sildenafil_citrate', 'virility_drug', 'vardenafil']
senticnet['tadaoki'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'overlord', 'lord']
senticnet['tadasuke'] = ['-0.57', '0', '0.571', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['tadeusz'] = ['0', '0', '0', '0.1', '#admiration', '#admiration', 'positive', '0.1', 'find_earth', 'wax', 'plutonium', 'shine', 'melt']
senticnet['tadpole'] = ['-0.79', '0', '-0.8', '0', '#sadness', '#fear', 'negative', '-0.79', 'frog', 'tadpole', 'jellyfish', 'duckling', 'larva']
senticnet['tadpole_shrimp'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'stone_crab', 'swimming_crab', 'rock_crab', 'crab', 'pea_crab']
senticnet['tadpole_stage'] = ['0.642', '-0.58', '0', '0.751', '#joy', '#admiration', 'positive', '0.659', 'change', 'become_better', 'change_aspect', 'look_better', 'become_bigger']
senticnet['tae'] = ['0.027', '0', '0.012', '0.063', '#joy', '#admiration', 'positive', '0.026', 'vegetation', 'tree_flower', 'green_growth', 'small', 'greenery']
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senticnet['take_medicine'] = ['-0.96', '0', '0.636', '-0.98', '#sadness', '#disgust', 'negative', '-0.86', 'sick', 'sickness', 'need_cure', 'mental_disease', 'disease']
senticnet['take_message'] = ['0.109', '0.129', '0', '0.098', '#joy', '#interest', 'positive', '0.112', 'leave_message', 'message', 'recall_message', 'send_message', 'read_message']
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senticnet['take_nap'] = ['0.921', '0.993', '0', '0', '#joy', '#interest', 'positive', '0.957', 'lie_back', 'feel_little_tired', 'nap', 'laze', 'asleep']
senticnet['take_note'] = ['0.986', '0.526', '0', '0.746', '#joy', '#admiration', 'positive', '0.753', 'take_note_class', 'collect_information', 'listen_lecture', 'finish_book', 'jot_down_note']
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senticnet['take_number'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'use_phone', 'call_number', 'write_number', 'contact', 'add_number']
senticnet['take_oath'] = ['0', '-0.04', '0.02', '0.079', '#surprise', '#admiration', 'positive', '0.034', 'take_stand', 'promise', 'marry', 'change_society', 'get_married']
senticnet['take_off'] = ['0', '0.095', '0', '0.074', '#interest', '#admiration', 'positive', '0.084', 'buy_wash_powder', 'water_soap', 'turn_faucet', 'laundry_detergent', 'fill_bathtub_water']
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senticnet['take_phone_call'] = ['0.719', '0.854', '0', '0', '#joy', '#interest', 'positive', '0.786', 'conversation', 'chat_friend', 'communicate', 'talk_far_away', 'chat']
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senticnet['take_photograph'] = ['0', '0.333', '-0.21', '0.384', '#interest', '#admiration', 'positive', '0.166', 'take_picture', 'picture', 'video', 'use_camera', 'photograph']
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senticnet['take_responsibility'] = ['0', '0.812', '0', '0.962', '#interest', '#admiration', 'positive', '0.887', 'enter_door', 'bicycle_store', 'turn_knob', 'carry_suitcase', 'broomstick']
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senticnet['take_seat'] = ['0.918', '0.738', '0', '0', '#joy', '#interest', 'positive', '0.828', 'find_seat', 'watch_commercial', 'pay_attention', 'label', 'look_picture']
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senticnet['take_temperature'] = ['0.778', '0.767', '0', '0', '#joy', '#interest', 'positive', '0.772', 'cure', 'thermometer', 'high_temperature', 'hot_weather', 'temperature_measurement']
senticnet['take_test'] = ['0', '0.769', '0', '0', '#interest', '#interest', 'positive', '0.769', 'study_test', 'pass_test', 'knowledge_power', 'get_grade', 'make_parent_happy']
senticnet['take_ticket'] = ['0', '0.769', '0', '0', '#interest', '#interest', 'positive', '0.769', 'direct_play', 'face_music', 'shade_eye', 'give_signal', 'travel']
senticnet['take_time'] = ['0', '0', '0', '0.875', '#admiration', '#admiration', 'positive', '0.875', 'meditate', 'watch_others', 'wait_bus', 'conserve_energy', 'go_home']
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senticnet['taking_refuge'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'habitat', 'mom_house', 'windsor', 'pacific_northwest', 'wilderness']
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senticnet['talipes'] = ['0.582', '-0.66', '0', '0.661', '#surprise', '#admiration', 'positive', '0.637', 'talipes_equinus', 'deformity', 'malformation', 'talipes_valgus', 'talipes_calcaneus']
senticnet['talipes_calcaneus'] = ['0.582', '-0.66', '0', '0.661', '#surprise', '#admiration', 'positive', '0.637', 'talipes_equinus', 'deformity', 'malformation', 'talipes_valgus', 'talipes']
senticnet['talipes_equinus'] = ['0.582', '-0.66', '0', '0.661', '#surprise', '#admiration', 'positive', '0.637', 'talipes', 'deformity', 'malformation', 'talipes_valgus', 'talipes_calcaneus']
senticnet['talipes_valgus'] = ['0.582', '-0.66', '0', '0.661', '#surprise', '#admiration', 'positive', '0.637', 'talipes_equinus', 'deformity', 'malformation', 'talipes', 'talipes_calcaneus']
senticnet['talisman'] = ['0.105', '-0.17', '0.057', '0.079', '#joy', '#surprise', 'positive', '0.076', 'cue', 'twig', 'long_stick', 'stick', 'adhere']
senticnet['talk'] = ['-0.61', '0', '0.981', '-0.98', '#anger', '#disgust', 'negative', '-0.85', 'pious_platitude', 'nothingness', 'jazz', 'malarkey', 'malarky']
senticnet['talk_athenean_talk'] = ['0.792', '-0.86', '0', '0.888', '#surprise', '#admiration', 'positive', '0.849', 'absurd', 'inconsequential', 'intuitive', 'visceral', 'nonrational']
senticnet['talk_back'] = ['-0.78', '0', '0.571', '-0.77', '#sadness', '#disgust', 'negative', '-0.71', 'gossip', 'gossiper', 'gossipmonger', 'taleteller', 'rumormonger']
senticnet['talk_companion'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'take_number', 'ask_menu', 'order_dinner', 'learn_patience', 'place_order']
senticnet['talk_dirty'] = ['-0.99', '0', '0.142', '-0.91', '#sadness', '#disgust', 'negative', '-0.68', 'rude', 'uneducated', 'flirt', 'scoundrel', 'satyr']
senticnet['talk_doctor'] = ['-0.18', '0', '-0.18', '-0.12', '#sadness', '#fear', 'negative', '-0.16', 'take_shirt_off', 'check_up', 'get_naked', 'see_doctor', 'find_out_healthy']
senticnet['talk_dpmuk_talk'] = ['0.792', '-0.86', '0', '0.888', '#surprise', '#admiration', 'positive', '0.849', 'absurd', 'inconsequential', 'intuitive', 'visceral', 'nonrational']
senticnet['talk_exxolon_talk'] = ['0.792', '-0.86', '0', '0.888', '#surprise', '#admiration', 'positive', '0.849', 'absurd', 'inconsequential', 'intuitive', 'visceral', 'nonrational']
senticnet['talk_family'] = ['-0.02', '-0.03', '0.038', '-0.07', '#anger', '#disgust', 'negative', '-0.02', 'stop_eat', 'eat_burger', 'order_pizza', 'use_fork', 'talk_friend']
senticnet['talk_far_away'] = ['0.911', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.942', 'conversation', 'take_phone_call', 'communicate', 'chat_friend', 'talk']
senticnet['talk_friend'] = ['0.175', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.103', 'hump_leg', 'talk_person', 'become_friend', 'start_conversation', 'chat']
senticnet['talk_hairdresser'] = ['-0.09', '0', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.06', 'hair_get_short', 'bad_haircut', 'hair_wash', 'hair_become_short', 'keep_hair_short']
senticnet['talk_interest_person'] = ['0.103', '-0.13', '0.045', '0.094', '#joy', '#surprise', 'positive', '0.071', 'express_opinion', 'engage_conversation', 'misunderstanding', 'know_story', 'exchange_idea']
senticnet['talk_kdammers_talk'] = ['0.792', '-0.86', '0', '0.888', '#surprise', '#admiration', 'positive', '0.849', 'absurd', 'inconsequential', 'intuitive', 'visceral', 'nonrational']
senticnet['talk_kww_talk'] = ['0.792', '-0.86', '0', '0.888', '#surprise', '#admiration', 'positive', '0.849', 'absurd', 'inconsequential', 'intuitive', 'visceral', 'nonrational']
senticnet['talk_lawyer'] = ['-0.04', '0.135', '0', '0.074', '#interest', '#admiration', 'positive', '0.053', 'lawyer', 'take_court', 'call_lawyer', 'seek_justice', 'settle_dispute']
senticnet['talk_lot'] = ['-0.76', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'boring_talk', 'yawn', 'dull', 'logorrheic', 'boring_activity']
senticnet['talk_loud'] = ['-0.99', '0', '0.216', '-0.92', '#sadness', '#disgust', 'negative', '-0.71', 'make_noise', 'talkative', 'annoy', 'mock', 'shout']
senticnet['talk_loudly'] = ['-0.94', '0', '0.137', '-0.94', '#sadness', '#disgust', 'negative', '-0.67', 'cry', 'shout', 'scream', 'yell', 'weep']
senticnet['talk_man'] = ['0.035', '0', '0', '0.183', '#joy', '#admiration', 'positive', '0.109', 'written_language', 'chat', 'verbal_communication', 'speak', 'speech']
senticnet['talk_msjapan_talk'] = ['0.792', '-0.86', '0', '0.888', '#surprise', '#admiration', 'positive', '0.849', 'absurd', 'inconsequential', 'intuitive', 'visceral', 'nonrational']
senticnet['talk_much'] = ['0', '0', '0', '-0.73', '#disgust', '#disgust', 'negative', '-0.73', 'convince', 'brag', 'discuss', 'transfer_knowledge', 'communicate']
senticnet['talk_neighbor'] = ['-0.13', '-0.06', '0.063', '-0.17', '#sadness', '#disgust', 'negative', '-0.07', 'grow_impatient', 'think_dog', 'boredom', 'act_bored', 'whistle_happy_tune']
senticnet['talk_nonsense'] = ['-0.95', '0', '0.479', '-0.90', '#sadness', '#disgust', 'negative', '-0.78', 'nonsense', 'crazy', 'fool', 'mental_disease', 'mental_problem']
senticnet['talk_of_town'] = ['-0.70', '0', '0.965', '-0.53', '#sadness', '#anger', 'negative', '-0.73', 'earful', 'malicious_gossip', 'scuttlebutt', 'talk', 'scandal']
senticnet['talk_on_phone'] = ['0', '0.952', '0', '0', '#interest', '#interest', 'positive', '0.952', 'friend', 'communication', 'call_friend', 'chat', 'talk_spouse']
senticnet['talk_page'] = ['0.92', '0.794', '0', '0', '#joy', '#interest', 'positive', '0.857', 'move_mouse', 'reboot', 'type_of_keyboard', 'enter_address', 'keep_track_appointment']
senticnet['talk_person'] = ['0', '0.926', '0', '0.824', '#interest', '#admiration', 'positive', '0.875', 'see_know', 'conduct_business', 'make_hotel_reservation', 'make_deal', 'send_information']
senticnet['talk_protonk_talk'] = ['0.792', '-0.86', '0', '0.888', '#surprise', '#admiration', 'positive', '0.849', 'absurd', 'inconsequential', 'intuitive', 'visceral', 'nonrational']
senticnet['talk_pschemp_talk'] = ['0.792', '-0.86', '0', '0.888', '#surprise', '#admiration', 'positive', '0.849', 'absurd', 'inconsequential', 'intuitive', 'visceral', 'nonrational']
senticnet['talk_shadowjams_talk'] = ['0.792', '-0.86', '0', '0.888', '#surprise', '#admiration', 'positive', '0.849', 'absurd', 'inconsequential', 'intuitive', 'visceral', 'nonrational']
senticnet['talk_shit'] = ['0', '0', '0.726', '-0.94', '#anger', '#disgust', 'negative', '-0.83', 'irritate', 'idiot', 'vex', 'rude', 'awful']
senticnet['talk_show'] = ['0.827', '-0.93', '0', '0.759', '#joy', '#surprise', 'positive', '0.84', 'party', 'use_party', 'birthday_party', 'gift_shop', 'gift_box']
senticnet['talk_smack'] = ['-0.10', '0', '-0.05', '-0.24', '#sadness', '#disgust', 'negative', '-0.13', 'adulate', 'suck_up', 'toady', 'blandish', 'kowtow']
senticnet['talk_spouse'] = ['0', '0.952', '0', '0', '#interest', '#interest', 'positive', '0.952', 'friend', 'communication', 'talk_on_phone', 'chat', 'call_friend']
senticnet['talk_telephone'] = ['0.704', '0.89', '0', '0', '#joy', '#interest', 'positive', '0.797', 'call_mom', 'hunt_duck', 'make_telephone_call', 'answer_phone', 'communicate']
senticnet['talk_through_one_hat'] = ['-0.72', '0', '0.791', '-0.94', '#anger', '#disgust', 'negative', '-0.82', 'dissemble', 'feign', 'sham', 'pretend', 'affect']
senticnet['talk_to_exwife'] = ['0', '0', '-0.74', '-0.82', '#fear', '#disgust', 'negative', '-0.78', 'bad_connection', 'pay_off', 'pay_fee', 'grow_impatient', 'hate']
senticnet['talk_to_travel_agent'] = ['-0.06', '0', '0.011', '-0.02', '#sadness', '#disgust', 'negative', '-0.03', 'buy_plane_ticket', 'buy_airplane_ticket', 'organize', 'plan_time', 'decide_to_go']
senticnet['talkative'] = ['0', '0', '0', '0.13', '#admiration', '#admiration', 'positive', '0.13', 'verbalize', 'verbal', 'orate', 'annoying', 'discourse']
senticnet['talkativeness'] = ['0.908', '-0.88', '0', '0.786', '#joy', '#surprise', 'positive', '0.86', 'garrulity', 'garrulousness', 'loquaciousness', 'leresis', 'loquacity']
senticnet['talker'] = ['0.749', '0.724', '0', '0', '#joy', '#interest', 'positive', '0.736', 'mouth_noise', 'vocal_cord', 'vocalization', 'vocal_sound', 'loud_soft']
senticnet['talkin_broadway'] = ['0.811', '-0.63', '0', '0.959', '#joy', '#admiration', 'positive', '0.8', 'drama_critic', 'critic', 'professional', 'art_critic', 'literary_critic']
senticnet['talking'] = ['-0.76', '0', '0.578', '-0.91', '#sadness', '#disgust', 'negative', '-0.75', 'pious_platitude', 'nothingness', 'jazz', 'malarkey', 'malarky']
senticnet['talking_to'] = ['-0.63', '0', '0.556', '-0.51', '#sadness', '#anger', 'negative', '-0.56', 'reprimand', 'rebuke', 'reproof', 'reproval', 'reprehension']
senticnet['talking_trash'] = ['-0.85', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.88', 'oppression', 'enslavement', 'slave', 'captivity', 'misery']
senticnet['talkpage'] = ['0.92', '0.794', '0', '0', '#joy', '#interest', 'positive', '0.857', 'move_mouse', 'reboot', 'type_of_keyboard', 'enter_address', 'keep_track_appointment']
senticnet['talkpage_discussion'] = ['0.533', '0.818', '0', '0.683', '#interest', '#admiration', 'positive', '0.678', 'gain_information', 'report_news', 'solve', 'seek_answer', 'gather_information']
senticnet['talky'] = ['0.944', '-0.69', '0', '0.991', '#joy', '#admiration', 'positive', '0.878', 'voluble', 'gabby', 'garrulous', 'loquacious', 'chatty']
senticnet['tall'] = ['0.036', '0.138', '0.058', '0.223', '#interest', '#admiration', 'positive', '0.085', 'big', 'really_big', 'large', 'beautiful_look', 'huge']
senticnet['tall_building'] = ['-0.05', '-0.03', '0.018', '-0.08', '#sadness', '#disgust', 'negative', '-0.03', 'build', 'nuclear_power_plant', 'skyscraper', 'construction_site', 'construct_building']
senticnet['tall_buttercup'] = ['-0.80', '0', '0.632', '-0.71', '#sadness', '#disgust', 'negative', '-0.71', 'herb', 'meadow_buttercup', 'buttercup', 'tall_crowfoot', 'common_buttercup']
senticnet['tall_crowfoot'] = ['-0.80', '0', '0.632', '-0.71', '#sadness', '#disgust', 'negative', '-0.71', 'herb', 'meadow_buttercup', 'tall_buttercup', 'buttercup', 'common_buttercup']
senticnet['tall_mallow'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'musk_mallow', 'mu_rose', 'common_mallow', 'mallow']
senticnet['tall_man'] = ['0', '0.098', '-0.06', '0.118', '#interest', '#admiration', 'positive', '0.051', 'egret', 'broad', 'wide', 'wood_structure', 'large']
senticnet['tall_person'] = ['0.072', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.072', 'large_plant', 'small_tree', 'plant_part', 'plant_big', 'green_leave']
senticnet['tall_play_basketball'] = ['0', '0.966', '0', '0.801', '#interest', '#admiration', 'positive', '0.883', 'altruistic', 'better', 'advantage', 'benifit', 'precise']
senticnet['tall_ship'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'boat', 'yacht', 'big_boat', 'large_boat']
senticnet['tall_shrub'] = ['-0.80', '0', '0.555', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'bush', 'wormwood', 'milkwort', 'germander', 'centaury']
senticnet['tall_spire'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'castle', 'bell_tower', 'spire', 'steeple', 'religious_place']
senticnet['tall_tower'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'castle', 'bell_tower', 'spire', 'steeple', 'religious_place']
senticnet['tallage'] = ['-0.08', '0', '-0.15', '-0.20', '#fear', '#disgust', 'negative', '-0.14', 'ask_money', 'extortion', 'wringing', 'intimidate', 'psychological_pressure']
senticnet['taller'] = ['0.17', '0', '0.081', '0.184', '#joy', '#admiration', 'positive', '0.091', 'food_holder', 'food_server', 'crockery', 'flattery', 'round_flat']
senticnet['taller_tower'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'castle', 'bell_tower', 'spire', 'steeple', 'religious_place']
senticnet['tallest_sculpture'] = ['-0.81', '0', '0.543', '-0.63', '#sadness', '#disgust', 'negative', '-0.66', 'caper', 'pickle', 'relish', 'dill_pickle', 'sweet_pickle']
senticnet['tallest_structure'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'castle', 'bell_tower', 'spire', 'steeple', 'religious_place']
senticnet['tallest_tower'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'castle', 'bell_tower', 'spire', 'steeple', 'religious_place']
senticnet['talley_beatty'] = ['-0.95', '0', '0.992', '-0.63', '#sadness', '#anger', 'negative', '-0.86', 'modern_dance', 'interpretive_dancing', 'interpretative_dance', 'interpretative_dancing', 'stage_dancing']
senticnet['tallil'] = ['-0.66', '0', '0.789', '-0.63', '#sadness', '#anger', 'negative', '-0.69', 'humbug', 'play_trick', 'cozen', 'pull_fast_one_on', 'play_trick_on']
senticnet['tallil_air_base'] = ['-0.66', '0', '0.789', '-0.63', '#sadness', '#anger', 'negative', '-0.69', 'humbug', 'play_trick', 'cozen', 'pull_fast_one_on', 'play_trick_on']
senticnet['tallish'] = ['0', '-0.80', '0', '0.713', '#surprise', '#admiration', 'positive', '0.758', 'heavyset', 'heavy-set', 'broad-shouldered', 'rail-thin', 'large-boned']
senticnet['tallow_candle'] = ['0', '0.232', '-0.10', '0.156', '#interest', '#admiration', 'positive', '0.094', 'light', 'eat_cake', 'birthday_party', 'candle_light', 'romantic_atmosphere']
senticnet['tally'] = ['0.024', '0.162', '0.072', '0.181', '#interest', '#admiration', 'positive', '0.074', 'count', 'total_number', 'compute', 'numeral', 'ordinal']
senticnet['talmud_torah'] = ['0', '0.209', '0.026', '0.184', '#interest', '#admiration', 'positive', '0.122', 'church', 'confession', 'temple', 'cathedral', 'monastery']
senticnet['talon'] = ['-0.74', '0', '0.963', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['talus_slope'] = ['0.057', '0.075', '0.039', '0.059', '#interest', '#admiration', 'positive', '0.038', 'summit', 'big_rock', 'high_land', 'peak', 'mountain_side']
senticnet['tamable'] = ['0.835', '-0.57', '0', '0.891', '#joy', '#admiration', 'positive', '0.768', 'ductile', 'tameable', 'manipulable', 'malleable', 'docile']
senticnet['tamarind'] = ['0.859', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.888', 'green_apple', 'apricot', 'lime', 'peach', 'cherry']
senticnet['tamarind_chutney'] = ['0.979', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.918', 'make_curry', 'cook_dinner', 'cook_meal', 'cook_curry', 'powder']
senticnet['tamarind_sauce'] = ['0.076', '0', '0.037', '0.065', '#joy', '#admiration', 'positive', '0.035', 'serve_cold', 'tasty_good', 'tasty', 'sour', 'high_calorie']
senticnet['tamarind_tamarindus_indica'] = ['0.859', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.888', 'green_apple', 'apricot', 'lime', 'peach', 'cherry']
senticnet['tamarindus_indica'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'nut', 'palm_kernel', 'edible_nut', 'cembra_nut', 'cedar_nut']
senticnet['tambopata_national'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['tambov_rebellion'] = ['0', '0.793', '0', '0.847', '#interest', '#admiration', 'positive', '0.82', 'disagreement', 'fight_against', 'opposition', 'against', 'go_against']
senticnet['tame'] = ['0.118', '0.28', '-0.20', '0.216', '#interest', '#admiration', 'positive', '0.103', 'friend_person', 'sense_fear', 'mark_territory', 'trip_person', 'care_deeply']
senticnet['tame_impala'] = ['0.304', '0.339', '-0.19', '0.333', '#interest', '#admiration', 'positive', '0.195', 'careful', 'reinforce', 'load_bear', 'go_outdoors', 'allowance']
senticnet['tameable'] = ['0.84', '-0.58', '0', '0.886', '#joy', '#admiration', 'positive', '0.771', 'tamable', 'ductile', 'manipulable', 'malleable', 'docile']
senticnet['tamed'] = ['0.027', '0.064', '-0.05', '0.091', '#interest', '#admiration', 'positive', '0.032', 'cultivated', 'domesticate_animal', 'live_creature', 'furry_creature', 'farm']
senticnet['tamp_down'] = ['0.615', '0.643', '-0.24', '0.29', '#joy', '#interest', 'positive', '0.325', 'relieve', 'easiness', 'pacifically', 'peaceable', 'repose']
senticnet['tamper'] = ['-0.97', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.87', 'spoliation', 'tobacco', 'tool', 'untampered', 'caplet']
senticnet['tamper_evident'] = ['-0.94', '0', '0.569', '-0.72', '#sadness', '#disgust', 'negative', '-0.74', 'bitumastic', 'veneer', 'veneering', 'adhesive', 'coating']
senticnet['tamper_proof'] = ['0.857', '0', '0', '0.968', '#joy', '#admiration', 'positive', '0.912', 'inerrable', 'flawless', 'unfailing', 'reliable', 'sure']
senticnet['tamper_resistance'] = ['0.857', '0', '0', '0.968', '#joy', '#admiration', 'positive', '0.912', 'inerrable', 'flawless', 'unfailing', 'reliable', 'sure']
senticnet['tamper_resistant'] = ['0.857', '0', '0', '0.968', '#joy', '#admiration', 'positive', '0.912', 'inerrable', 'flawless', 'unfailing', 'reliable', 'sure']
senticnet['tamper_with'] = ['-0.59', '0', '0.537', '-0.53', '#sadness', '#anger', 'negative', '-0.55', 'murder', 'mutilate', 'mangle', 'misrepresent', 'belie']
senticnet['tamperproof'] = ['0.857', '0', '0', '0.968', '#joy', '#admiration', 'positive', '0.912', 'inerrable', 'flawless', 'unfailing', 'reliable', 'sure']
senticnet['tamponade'] = ['0.759', '-0.72', '0', '0.893', '#joy', '#admiration', 'positive', '0.793', 'manifestation', 'cry', 'reflection', 'acting_out', 'explosion']
senticnet['tan'] = ['-0.96', '0', '0.636', '-0.64', '#sadness', '#disgust', 'negative', '-0.75', 'burn', 'hyperpigmentation', 'sunhyperpigmentation', 'sunburn', 'physical_condition']
senticnet['tan_brick'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'condominium', 'rent_apartment', 'high_rise', 'condo', 'row_house']
senticnet['tan_leather'] = ['0.028', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.047', 'handkerchief', 'kerchief', 'hanky', 'facial_tissue', 'zipper']
senticnet['tan_lotion'] = ['0.847', '0.999', '0', '0', '#joy', '#interest', 'positive', '0.923', 'suntan_lotion', 'hair_conditioner', 'dandruff_shampoo', 'hair_shape_gel', 'fluoride_toothpaste']
senticnet['tan_skin'] = ['0.784', '0.978', '0', '0', '#joy', '#interest', 'positive', '0.881', 'become_fat', 'flatulence', 'feel_rejuvenated', 'entropy', 'make_angry']
senticnet['tan_yard'] = ['-0.93', '0', '0.791', '0', '#sadness', '#anger', 'negative', '-0.86', 'hopelessness', 'hopelessly', 'despairing', 'pessimistically', 'pessimistic']
senticnet['tanager_family'] = ['-0.92', '0', '0.64', '-0.52', '#sadness', '#anger', 'negative', '-0.69', 'animal_order', 'percher', 'thyreophoran', 'marginocephalian', 'euronithopod']
senticnet['tandem_jump'] = ['-0.88', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.90', 'undergarment', 'outerwear', 'lady_underwear', 'under_garment', 'slip']
senticnet['tandem_switch'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'long_distance_relationship', 'distant', 'kilometer', 'length_measurement', 'distance_measure']
senticnet['tanf_assistance'] = ['-0.60', '0', '0.832', '-0.53', '#sadness', '#anger', 'negative', '-0.65', 'unsuitable', 'undesirable', 'unqualified', 'disqualified', 'unentitled']
senticnet['tang'] = ['0.071', '0', '0', '0', '#joy', '#joy', 'positive', '0.071', 'smoothie', 'glass_water', 'drink', 'caffeine', 'lemonade']
senticnet['tangelo'] = ['0.02', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'many_fruits', 'buy_fruit', 'cherry', 'pineapple', 'tomato']
senticnet['tangent'] = ['-0.02', '0', '0.074', '0', '#sadness', '#anger', 'negative', '-0.05', 'diagonal', 'incline_plane', 'downward', 'mountain_side', 'angle']
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senticnet['tangerine'] = ['0.028', '-0.01', '0.063', '-0.05', '#anger', '#disgust', 'negative', '-0.01', 'apple', 'cranberry', 'green_apple', 'lime', 'orange']
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senticnet['tangled'] = ['-0.78', '0', '0.782', '0', '#sadness', '#anger', 'negative', '-0.78', 'untangling', 'disentangle', 'unravel', 'tangle', 'untangle']
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senticnet['tangy_flavor'] = ['0.043', '0.091', '0', '0.113', '#interest', '#admiration', 'positive', '0.082', 'sweet_sour', 'tasty', 'yummy', 'popular_food', 'form_of_food']
senticnet['tangy_taste'] = ['0.045', '0.141', '-0.05', '0.065', '#interest', '#admiration', 'positive', '0.049', 'kernel', 'starch', 'ginger', 'cereal_crop', 'pea']
senticnet['tanis_half_elven'] = ['-0.04', '-0.02', '0.027', '-0.03', '#sadness', '#disgust', 'negative', '-0.01', 'precious_metal', 'olympic_medal', 'shiny_metal', 'many_different_kinds', 'valuable_metal']
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senticnet['tank'] = ['0.748', '0.934', '0', '0', '#joy', '#interest', 'positive', '0.841', 'cannon', 'artillery', 'grenade', 'machine_gun', 'atomic_bomb']
senticnet['tank_destroyer'] = ['-0.71', '0', '0.585', '-0.51', '#sadness', '#anger', 'negative', '-0.60', 'tank', 'armored_vehicle', 'armored_car', 'armoured_car', 'armored_personnel_carrier']
senticnet['tank_farm'] = ['0.024', '0.17', '0', '0.113', '#interest', '#admiration', 'positive', '0.102', 'load_dock', 'storage_place', 'lock_box', 'government_office', 'treasure_chest']
senticnet['tank_gunnery'] = ['0.93', '-0.95', '0', '0.909', '#joy', '#surprise', 'positive', '0.93', 'acquirement', 'skill', 'accomplishment', 'acquisition', 'attainment']
senticnet['tank_locomotive'] = ['0.069', '0.097', '0', '0.172', '#interest', '#admiration', 'positive', '0.113', 'type_of_engine', 'liquid_gas', 'warm_air', 'hot_air', 'steamy']
senticnet['tankage'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'gas', 'inert_gas', 'diesel', 'oil', 'combustible']
senticnet['tanker_truck'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'trailer', 'trail_car', 'camion', 'wagon', 'towboat']
senticnet['tankette'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'tank', 'tank_destroyer', 'armored_car', 'armored_vehicle', 'armored_personnel_carrier']
senticnet['tankless_water_heater'] = ['0.02', '0.076', '0', '0', '#joy', '#interest', 'positive', '0.048', 'land_earth', 'tidal_wave', 'puddle', 'brine', 'spurt']
senticnet['tanned_complexion'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'plato', 'natural_world', 'hair', 'feature', 'stick_out']
senticnet['tanned_face'] = ['0', '0', '0.841', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'ancient', 'many_years', 'year', 'old', 'teen']
senticnet['tanned_leather'] = ['0.028', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.047', 'handkerchief', 'kerchief', 'hanky', 'facial_tissue', 'zipper']
senticnet['tanned_skin'] = ['-0.96', '0', '0.636', '-0.64', '#sadness', '#disgust', 'negative', '-0.75', 'burn', 'tan', 'hyperpigmentation', 'sunburn', 'physical_condition']
senticnet['tanner'] = ['-0.77', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.85', 'tanning', 'self-tanning', 'lotion', 'sunless', 'bronzing']
senticnet['tannic'] = ['-0.82', '0', '0.901', '-0.65', '#sadness', '#anger', 'negative', '-0.79', 'taste', 'taste_sensation', 'gustatory_sensation', 'taste_perception', 'gustatory_perception']
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senticnet['tanning_salon'] = ['-0.96', '0', '0.636', '-0.64', '#sadness', '#disgust', 'negative', '-0.75', 'burn', 'tan', 'hyperpigmentation', 'sunburn', 'physical_condition']
senticnet['tanswell'] = ['0.941', '-0.83', '0', '0.973', '#joy', '#admiration', 'positive', '0.917', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['tanta_district'] = ['-0.13', '0', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.19', 'head_hurt', 'introspection', 'lifestyle_change', 'headache', 'move_eye']
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senticnet['tantalize'] = ['-0.80', '0', '0.512', '-0.82', '#sadness', '#disgust', 'negative', '-0.71', 'bemock', 'jeer', 'scoff', 'flout', 'chaff']
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senticnet['tantamount'] = ['0.721', '0.782', '0', '0', '#joy', '#interest', 'positive', '0.752', 'implying', 'treason', 'advocating', 'committing', 'equating']
senticnet['tantric_practice'] = ['0.668', '-0.87', '0', '0.606', '#joy', '#surprise', 'positive', '0.715', 'adeptness', 'adroitness', 'deftness', 'quickness', 'expertness']
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senticnet['tao_pueblo'] = ['0.052', '0.099', '0', '0.067', '#interest', '#admiration', 'positive', '0.073', 'police_rank', 'head_guy', 'chief', 'leader', 'commander']
senticnet['taobh'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['taoyuan_county_now'] = ['-0.65', '0', '0.505', '-0.91', '#sadness', '#disgust', 'negative', '-0.69', 'hellhound', 'mythical_being', 'dragon', 'mythical_monster', 'amphisbaena']
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senticnet['tap'] = ['0.093', '-0.02', '0', '0.056', '#joy', '#admiration', 'positive', '0.057', 'tuner', 'glass_of_water', 'rattle', 'run_water', 'vibration']
senticnet['tap_dance'] = ['0.764', '-0.52', '0', '0.866', '#joy', '#admiration', 'positive', '0.719', 'tap_dancing', 'hoofing', 'bachata', 'dance', 'salsa']
senticnet['tap_dancing'] = ['0.764', '-0.52', '0', '0.866', '#joy', '#admiration', 'positive', '0.719', 'hoofing', 'tap_dance', 'bachata', 'dance', 'salsa']
senticnet['tap_foot'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'impatience', 'impatient', 'wait_for', 'angry', 'anger']
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senticnet['tap_toe'] = ['0.767', '0', '0', '0.962', '#joy', '#admiration', 'positive', '0.865', 'tap_foot', 'sing_along', 'hear_song', 'earring', 'adjust_volume']
senticnet['tap_water'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'bottled_water', 'sugar_water']
senticnet['tapa'] = ['0.037', '0.08', '0.015', '0', '#joy', '#interest', 'positive', '0.034', 'steak_house', 'spanish_restaurant', 'french_restaurant', 'paella', 'restaurant_diner']
senticnet['tapa_restaurant'] = ['0.06', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'eat_food', 'fast_food', 'pizza_parlor', 'pizza_store', 'kitchen_cupboard']
senticnet['tape'] = ['0.875', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.923', 'magnetic_tape', 'tapeline', 'record', 'videotape', 'taping']
senticnet['tape_backup'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_musical_sound', 'play_orchestra', 'make_noise', 'play_music', 'make_drawing']
senticnet['tape_backup_system'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_musical_sound', 'play_orchestra', 'make_noise', 'play_music', 'make_drawing']
senticnet['tape_cassette'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['tape_deck'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['tape_dispenser'] = ['0', '-0.04', '0.057', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'white-out', 'letter_scale', 'paper_punch', 'stapler', 'paper_fastener']
senticnet['tape_drive'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_musical_sound', 'play_orchestra', 'make_noise', 'play_music', 'make_drawing']
senticnet['tape_machine_room'] = ['0', '0', '-0.84', '0', '#fear', '#fear', 'negative', '-0.84', 'bad_karma', 'spoil_food', 'anxiety_disorder', 'anthrax', 'bad_food']
senticnet['tape_measure'] = ['0', '-0.00', '0.034', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'celsius', 'grade', 'certificate', 'learn_tool', 'temperature_measure']
senticnet['tape_player'] = ['0.09', '-0.03', '0.015', '0', '#joy', '#surprise', 'positive', '0.036', 'dvd_player', 'several_types', 'copper_wire', 'control_room', 'technician']
senticnet['tape_television_show'] = ['-0.04', '0.082', '0', '0.066', '#interest', '#admiration', 'positive', '0.034', 'record_television_show', 'turn_on_dvd_player', 'view_film_home', 'view_video', 'watch_television_show']
senticnet['taped_conversation'] = ['0.716', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.733', 'call', 'use_phone', 'orate', 'use_telephone', 'speech']
senticnet['taped_interview'] = ['0.716', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.733', 'call', 'use_phone', 'orate', 'use_telephone', 'speech']
senticnet['tapeline'] = ['0.875', '0.971', '0', '0', '#joy', '#interest', 'positive', '0.923', 'magnetic_tape', 'tape', 'record', 'videotape', 'taping']
senticnet['taper'] = ['0', '0', '0.707', '-0.92', '#anger', '#disgust', 'negative', '-0.81', 'remit', 'diminish', 'drop_off', 'contract', 'break']
senticnet['taper_relief'] = ['-0.83', '0', '0.555', '-0.64', '#sadness', '#disgust', 'negative', '-0.67', 'single_tax', 'income_tax', 'taxation', 'capital_levy', 'departure_tax']
senticnet['tapestry'] = ['0.061', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'oil_painting', 'use_oil_paint', 'wall_art', 'yellow_ribbon', 'artist_tool']
senticnet['taping'] = ['0.874', '0.967', '0', '0', '#joy', '#interest', 'positive', '0.92', 'magnetic_tape', 'tapeline', 'record', 'videotape', 'tape']
senticnet['tapioca_starch'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'dainty', 'delicacy', 'treat', 'jelly', 'aspic']
senticnet['tappet'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'peavey']
senticnet['taproom'] = ['-0.81', '0', '0.553', '-0.92', '#sadness', '#disgust', 'negative', '-0.76', 'bar', 'barroom', 'saloon', 'ginmill', 'sawdust_saloon']
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senticnet['tapvd'] = ['-0.81', '0', '0.684', '-0.81', '#sadness', '#disgust', 'negative', '-0.77', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['taquera'] = ['0.132', '0.1', '0.063', '0.159', '#joy', '#admiration', 'positive', '0.082', 'chime', 'school_end', 'ring_loudly', 'noise_maker', 'vital']
senticnet['taqueria'] = ['0.973', '-0.50', '0', '0.612', '#joy', '#admiration', 'positive', '0.698', 'chic', 'chicness', 'jauntiness', 'modishness', 'stylishness']
senticnet['tar'] = ['0', '-0.01', '0.039', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'cement', 'pavement', 'concrete', 'big_city_life', 'popular_building_material']
senticnet['tar_paper'] = ['-0.03', '-0.00', '0.066', '0', '#sadness', '#anger', 'negative', '-0.03', 'dirt', 'allergy', 'dust', 'wet_floor', 'insect']
senticnet['taradiddle'] = ['-0.84', '0', '0.815', '-0.60', '#sadness', '#anger', 'negative', '-0.75', 'prevarication', 'fairytale', 'fairy_tale', 'fairy_story', 'play']
senticnet['tarantula'] = ['-0.89', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.91', 'tarantula', 'spider', 'cockroach', 'scorpion', 'arachnid']
senticnet['tarantula_hawk'] = ['0.748', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.873', 'butterfly', 'moth', 'parrot', 'beetle', 'falcon']
senticnet['tarantula_nebula'] = ['0', '0.706', '0', '0.847', '#interest', '#admiration', 'positive', '0.776', 'dwarf_planet', 'universe', 'planetary', 'sun', 'moon']
senticnet['tarball'] = ['-0.58', '0', '0.561', '-0.62', '#sadness', '#disgust', 'negative', '-0.58', 'spot', 'speckle', 'dapple', 'mark', 'fleck']
senticnet['tarcu_river'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'aphasia', 'anomic_aphasia']
senticnet['tardar_sauce'] = ['0', '0', '0.022', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'kind_animal', 'wild_animal', 'predator', 'hound', 'fang']
senticnet['tardily'] = ['-0.75', '0', '0.896', '0', '#sadness', '#anger', 'negative', '-0.82', 'slow', 'edge', 'seep', 'slowly', 'drip']
senticnet['tardiness'] = ['0.723', '0.72', '0', '0', '#joy', '#interest', 'positive', '0.722', 'late', 'get_dark', 'rainy_day', 'split_end', 'house_look_better']
senticnet['tardy'] = ['0.723', '0.72', '0', '0', '#joy', '#interest', 'positive', '0.722', 'late', 'get_dark', 'rainy_day', 'split_end', 'house_look_better']
senticnet['targe'] = ['0.93', '-0.78', '0', '0.873', '#joy', '#admiration', 'positive', '0.861', 'blade', 'brand', 'steel', 'claymore', 'sword']
senticnet['target'] = ['-0.02', '0', '0.016', '-0.04', '#sadness', '#disgust', 'negative', '-0.03', 'hatchet', 'sharp_weapon', 'piece_of_metal', 'aim', 'projectile']
senticnet['target_cell'] = ['-0.66', '0', '0.877', '-0.64', '#sadness', '#anger', 'negative', '-0.72', 'mast_cell', 'mastocyte', 'labrocyte', 'somatic_cell', 'phagocyte']
senticnet['target_practice'] = ['-0.81', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.86', 'shoot_target', 'shoot_bullet', 'kill_pest', 'self_defense', 'murder']
senticnet['targetmaster'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
senticnet['targhee'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'common_bean', 'green_bean', 'wax_bean']
senticnet['tarkheena'] = ['-0.73', '0', '0.55', '-0.75', '#sadness', '#disgust', 'negative', '-0.68', 'mortal', 'person', 'individual', 'mangler', 'mutilator']
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senticnet['tarnished_image'] = ['0.65', '-0.61', '0', '0.703', '#joy', '#admiration', 'positive', '0.657', 'gleaming', 'luster', 'polish', 'glow', 'effulgence']
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senticnet['tarnished_reputation'] = ['0.65', '-0.61', '0', '0.703', '#joy', '#admiration', 'positive', '0.657', 'gleaming', 'luster', 'polish', 'glow', 'effulgence']
senticnet['taro_root'] = ['0.52', '-0.69', '0', '0.639', '#surprise', '#admiration', 'positive', '0.617', 'black_bread', 'pumpernickel', 'limpa', 'bread', 'rye_bread']
senticnet['tarocchini'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'jewelry_store', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['tarock'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'jewelry_store', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['tarot_card'] = ['0', '0.083', '-0.05', '0.142', '#interest', '#admiration', 'positive', '0.057', 'credit', 'buck', 'riches', 'store_checkbook', 'psychic']
senticnet['tarot_card_reader'] = ['0.083', '0', '0', '0.075', '#joy', '#admiration', 'positive', '0.079', 'carny', 'carnival_ride', 'midway', 'ferris_wheel', 'roller-coaster']
senticnet['tarot_deck'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'jewelry_store', 'hold_change', 'bank_account', 'coffer', 'wallet']
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senticnet['tartar_control_toothpaste'] = ['0.921', '0.798', '0', '0', '#joy', '#interest', 'positive', '0.86', 'gel_toothpaste', 'wax_dental_floss', 'dental_floss', 'toothpaste', 'fluoride_toothpaste']
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senticnet['tarui_disease'] = ['-0.75', '0', '0.504', '-0.77', '#sadness', '#disgust', 'negative', '-0.67', 'disablement', 'anorgasmia', 'hearing_impairment', 'hearing_disorder', 'bow_leg']
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senticnet['task_decomposition'] = ['0.929', '-0.71', '0', '0.605', '#joy', '#surprise', 'positive', '0.748', 'incisiveness', 'trenchancy', 'efficacy', 'efficaciousness', 'power']
senticnet['taskbar'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'car_trunk', 'walmart', 'warehouse', 'store', 'storage_place']
senticnet['tasked_with'] = ['0.955', '0.945', '0', '0.835', '#joy', '#interest', 'positive', '0.912', 'support_family', 'provide_service', 'accomplish', 'find_job', 'pay_well']
senticnet['tasmanian_rugby_union'] = ['-0.68', '0', '0.923', '-0.68', '#sadness', '#anger', 'negative', '-0.76', 'negative', 'veto', 'refute', 'rebut', 'controvert']
senticnet['taste'] = ['0.732', '0.771', '0', '0', '#joy', '#interest', 'positive', '0.752', 'food', 'put_mouth', 'tastebud', 'sour_taste', 'tongue']
senticnet['taste_awful'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'mouldy_bread', 'rotten', 'disgusting', 'green_bread', 'mouldy']
senticnet['taste_bitter'] = ['0.09', '0', '0.025', '0.098', '#joy', '#admiration', 'positive', '0.054', 'sweetness', 'sweet_taste', 'flavor', 'sweet', 'bake_product']
senticnet['taste_flavor'] = ['-0.11', '-0.04', '0', '-0.10', '#sadness', '#disgust', 'negative', '-0.05', 'pierce', 'spread_butter', 'surprise_person', 'digest', 'clean_up_kitchen']
senticnet['taste_food'] = ['0.864', '0.934', '0', '0', '#joy', '#interest', 'positive', '0.899', 'eat_animal', 'eat_pasta', 'close_together', 'hurt_animal', 'butter_toast']
senticnet['taste_good'] = ['0.859', '0.867', '0', '0', '#joy', '#interest', 'positive', '0.863', 'joy', 'digestion', 'get_fat', 'fill_stomach', 'overeat']
senticnet['taste_perception'] = ['-0.93', '0', '0.537', '-0.57', '#sadness', '#disgust', 'negative', '-0.68', 'taste', 'taste_sensation', 'gustatory_sensation', 'astringence', 'gustatory_perception']
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senticnet['taste_salty'] = ['0.045', '0.141', '-0.05', '0.065', '#interest', '#admiration', 'positive', '0.049', 'kernel', 'starch', 'ginger', 'cereal_crop', 'pea']
senticnet['taste_sensation'] = ['-0.93', '0', '0.537', '-0.57', '#sadness', '#disgust', 'negative', '-0.68', 'taste', 'astringence', 'gustatory_sensation', 'taste_perception', 'gustatory_perception']
senticnet['taste_sour'] = ['0', '0', '0.084', '-0.03', '#anger', '#disgust', 'negative', '-0.06', 'love_child', 'raise_child', 'pass_gene', 'overpopulation', 'smile_child']
senticnet['taste_sugar'] = ['0.907', '0.794', '0', '0.771', '#joy', '#interest', 'positive', '0.824', 'cookie', 'fresh_food', 'good_taste_food', 'brownie', 'pie']
senticnet['taste_sweet'] = ['0.911', '0.813', '0', '0', '#joy', '#interest', 'positive', '0.862', 'enjoy_company_friend', 'enjoy_day', 'laugh_joke', 'comfort_friend', 'entertain_person']
senticnet['tastebud'] = ['0', '0', '0.804', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'ice_cream_melt', 'allergy', 'toothache', 'gonorrhea', 'tooth_decay']
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senticnet['taster'] = ['0.912', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.919', 'food', 'masterclass', 'tasting', 'refresher', 'bowl']
senticnet['tastiest'] = ['0.076', '0', '0.032', '0.106', '#joy', '#admiration', 'positive', '0.05', 'delicious', 'good_eat', 'yummy', 'edible', 'eatable']
senticnet['tasting'] = ['0.902', '-0.93', '0', '0', '#joy', '#surprise', 'positive', '0.917', 'food', 'masterclass', 'taster', 'refresher', 'bowl']
senticnet['tasty'] = ['0.076', '0', '0.032', '0.106', '#joy', '#admiration', 'positive', '0.05', 'delicious', 'good_eat', 'yummy', 'edible', 'eatable']
senticnet['tasty_food'] = ['0.157', '0', '0.014', '0.107', '#joy', '#admiration', 'positive', '0.083', 'sweet', 'sweet_treat', 'bake_good', 'sweet_food', 'bake_product']
senticnet['tasty_fruit'] = ['0.05', '0', '0.047', '0.068', '#joy', '#admiration', 'positive', '0.024', 'tree_fruit', 'yellow_fruit', 'small_fruit', 'vine_fruit', 'red_fruit']
senticnet['tasty_good'] = ['0.154', '0.112', '0', '0.193', '#joy', '#admiration', 'positive', '0.153', 'serve_cold', 'delicious', 'make_fruit', 'high_calorie', 'juicy']
senticnet['tasty_meal'] = ['0.866', '-0.91', '0', '0.893', '#surprise', '#admiration', 'positive', '0.892', 'mooch', 'cadge', 'freeload', 'sponge', 'bum']
senticnet['tasty_treat'] = ['0.866', '-0.91', '0', '0.893', '#surprise', '#admiration', 'positive', '0.892', 'mooch', 'cadge', 'freeload', 'sponge', 'bum']
senticnet['tastykake'] = ['0.274', '-0.01', '0.012', '0.132', '#joy', '#admiration', 'positive', '0.102', 'dance_type', 'swirl', 'throw_football', 'jump_around', 'dance_move']
senticnet['tatar'] = ['0.936', '0.773', '0', '0', '#joy', '#interest', 'positive', '0.855', 'calculus', 'potassium_bitartrate', 'mongol_tatar', 'tartar', 'tophus']
senticnet['tater'] = ['0.525', '-0.69', '0', '0.98', '#surprise', '#admiration', 'positive', '0.732', 'potato', 'chips', 'murphy', 'spud', 'white_potato']
senticnet['tathata'] = ['-0.85', '0', '0.968', '-0.75', '#sadness', '#anger', 'negative', '-0.86', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['tatopani'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['tatter'] = ['-0.70', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.84', 'disarray', 'shamble', 'floundering', 'disrepair', 'disgrace']
senticnet['tattle'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'annoy', 'irritate', 'trouble', 'nag', 'bother']
senticnet['tattler'] = ['-0.78', '0', '0.581', '-0.69', '#sadness', '#disgust', 'negative', '-0.68', 'gossip', 'gossiper', 'gossipmonger', 'talk_back', 'rumormonger']
senticnet['tattletale'] = ['-0.54', '0', '0.583', '-0.51', '#sadness', '#anger', 'negative', '-0.54', 'gossip', 'gossiper', 'gossipmonger', 'rumormonger', 'talk_back']
senticnet['tattletale_gray'] = ['-0.51', '0', '0.651', '-0.61', '#anger', '#disgust', 'negative', '-0.59', 'silver_gray', 'gray_hair', 'old', 'gray', 'achromatic_color']
senticnet['tatty'] = ['0', '0', '0.937', '-0.78', '#anger', '#disgust', 'negative', '-0.86', 'cheaper', 'cheap', 'cheesy', 'brassy', 'flash']
senticnet['tatty_devine'] = ['0.532', '-0.60', '0', '0.875', '#surprise', '#admiration', 'positive', '0.67', 'gem', 'adornment', 'jewel', 'bead', 'bangle']
senticnet['tau_empire'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['tau_neutrino'] = ['0.022', '0.073', '0', '0.14', '#interest', '#admiration', 'positive', '0.078', 'elementary_particle', 'electron', 'proton_neutron', 'neutron', 'proton']
senticnet['tauck_world'] = ['0.774', '-0.63', '0', '0.567', '#joy', '#surprise', 'positive', '0.658', 'catching', 'breakthrough', 'spying', 'determination', 'tracing']
senticnet['taufua'] = ['0', '0.996', '0', '0.85', '#interest', '#admiration', 'positive', '0.923', 'strength', 'strong', 'physical_strength', 'athlete', 'muscle']
senticnet['taunt'] = ['0', '0', '0.898', '-0.73', '#anger', '#disgust', 'negative', '-0.81', 'make_mad', 'win_battle', 'serious_injury', 'get_hurt', 'beaten']
senticnet['tauriel'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'gnome', 'elf', 'goblin', 'undersized', 'small_person']
senticnet['taurobolium'] = ['-0.97', '0', '0.772', '-0.67', '#sadness', '#anger', 'negative', '-0.80', 'expiation', 'atonement', 'deliverance', 'redemption', 'rescue']
senticnet['taut'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'anxiety', 'apprehension', 'insecurity', 'tension', 'horror_movie']
senticnet['taut_thriller'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'anxiety', 'apprehension', 'insecurity', 'tension', 'horror_movie']
senticnet['tautness'] = ['-0.74', '0', '0.723', '-0.59', '#sadness', '#anger', 'negative', '-0.68', 'tone', 'tonicity', 'tonus', 'condition', 'status']
senticnet['tautological'] = ['0', '0', '-0.96', '-0.88', '#fear', '#disgust', 'negative', '-0.92', 'tautology', 'sophistry', 'syllogism', 'oxymoron', 'truism']
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senticnet['tavern'] = ['0.829', '0.919', '0', '0', '#joy', '#interest', 'positive', '0.874', 'liquor_store', 'bar', 'pub', 'drinking_place', 'martini']
senticnet['tavern_keeper'] = ['0.754', '-0.82', '0', '0.947', '#surprise', '#admiration', 'positive', '0.841', 'whorehouse', 'brothel', 'bordello', 'massage_parlor', 'house_of_prostitution']
senticnet['tawdry'] = ['0', '0', '-0.96', '-0.98', '#fear', '#disgust', 'negative', '-0.97', 'flashy', 'garish', 'cheap', 'brassy', 'flash']
senticnet['tawera_nikau'] = ['0.997', '-0.84', '0', '0.927', '#joy', '#admiration', 'positive', '0.923', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['tawny_owl_strix_aluco'] = ['-0.53', '0', '0.648', '-0.70', '#anger', '#disgust', 'negative', '-0.63', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['tax'] = ['-0.28', '0', '-0.23', '-0.12', '#sadness', '#fear', 'negative', '-0.21', 'payment', 'owe', 'rend', 'bribe', 'reward']
senticnet['tax-free'] = ['0', '0', '0.936', '-0.94', '#anger', '#disgust', 'negative', '-0.94', 'taxable', 'non-taxable', 'allocable', 'chargeable', 'nontaxable']
senticnet['tax-payers'] = ['0', '0', '0.692', '-0.82', '#anger', '#disgust', 'negative', '-0.75', 'tax-paying', 'law-abiding', 'taxpaying', 'hard-working', 'citizenry']
senticnet['tax-paying'] = ['0', '0', '0.698', '-0.81', '#anger', '#disgust', 'negative', '-0.75', 'taxpaying', 'law-abiding', 'tax-payers', 'hard-working', 'citizenry']
senticnet['tax_advantaged_college'] = ['-0.07', '0', '0.073', '-0.26', '#sadness', '#disgust', 'negative', '-0.13', 'tuition', 'private_tuition', 'pay_tuition', 'enroll_class', 'enroll_school']
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senticnet['tax_bracket'] = ['0.763', '-0.67', '0', '0.945', '#joy', '#admiration', 'positive', '0.795', 'set', 'income_bracket', 'bracket', 'income', 'price_bracket']
senticnet['tax_deductible_contribution'] = ['0', '0.264', '-0.24', '0.34', '#interest', '#admiration', 'positive', '0.12', 'help_out', 'give', 'come_back', 'alms', 'lend']
senticnet['tax_deductible_donation'] = ['0', '0.264', '-0.24', '0.34', '#interest', '#admiration', 'positive', '0.12', 'help_out', 'give', 'come_back', 'alms', 'lend']
senticnet['tax_deductible_gift'] = ['0', '0.264', '-0.24', '0.34', '#interest', '#admiration', 'positive', '0.12', 'help_out', 'give', 'come_back', 'alms', 'lend']
senticnet['tax_deduction'] = ['-0.83', '0', '0.555', '-0.64', '#sadness', '#disgust', 'negative', '-0.67', 'single_tax', 'income_tax', 'taxation', 'capital_levy', 'departure_tax']
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senticnet['tax_exempt_debt'] = ['-0.82', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.78', 'go_broke', 'receive_change', 'run_out_money', 'compare_price', 'bankruptcy']
senticnet['tax_fraud'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'mail_fraud', 'swindle', 'rig']
senticnet['tax_incentive'] = ['0.716', '0.813', '0', '0', '#joy', '#interest', 'positive', '0.764', 'bonus', 'reinforce', 'promotion', 'meritocracy', 'gratification']
senticnet['tax_liability'] = ['-0.83', '0', '0.555', '-0.64', '#sadness', '#disgust', 'negative', '-0.67', 'single_tax', 'income_tax', 'taxation', 'capital_levy', 'departure_tax']
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senticnet['tax_payer_money'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'address_book', 'get_rich', 'ladle', 'swiss_army_knife', 'whisk']
senticnet['tax_rate'] = ['0.763', '-0.67', '0', '0.945', '#joy', '#admiration', 'positive', '0.795', 'set', 'income_bracket', 'bracket', 'income', 'price_bracket']
senticnet['tax_relief'] = ['-0.83', '0', '0.555', '-0.64', '#sadness', '#disgust', 'negative', '-0.67', 'single_tax', 'income_tax', 'taxation', 'capital_levy', 'departure_tax']
senticnet['tax_revenue'] = ['-0.62', '0', '0.7', '-0.83', '#anger', '#disgust', 'negative', '-0.72', 'take', 'pull', 'return', 'payback', 'proceeds']
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senticnet['taxable'] = ['0', '0', '0.942', '-0.94', '#anger', '#disgust', 'negative', '-0.94', 'nontaxable', 'non-taxable', 'allocable', 'chargeable', 'tax-free']
senticnet['taxable_gain'] = ['0', '0.238', '0.021', '0.152', '#interest', '#admiration', 'positive', '0.123', 'money_value', 'money_amount', 'worth', 'monetary_amount', 'importance']
senticnet['taxable_income'] = ['0.763', '-0.67', '0', '0.945', '#joy', '#admiration', 'positive', '0.795', 'set', 'income_bracket', 'bracket', 'income', 'price_bracket']
senticnet['taxact'] = ['-0.03', '0.112', '-0.08', '0.164', '#interest', '#admiration', 'positive', '0.04', 'run', 'jump_over', 'quick', 'sprint', 'flee']
senticnet['taxation'] = ['0.559', '-0.84', '0', '0.92', '#surprise', '#admiration', 'positive', '0.775', 'single_tax', 'income_tax', 'capital_gain_tax', 'capital_levy', 'departure_tax']
senticnet['taxcut'] = ['-0.03', '0.112', '-0.08', '0.164', '#interest', '#admiration', 'positive', '0.04', 'run', 'jump_over', 'quick', 'sprint', 'flee']
senticnet['taxi'] = ['0', '0.935', '0', '0', '#interest', '#interest', 'positive', '0.935', 'semi-truck', 'traffic_artery', 'aircraft_roadway', 'flight_terminal', 'taxiway']
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senticnet['teen_angst'] = ['-0.87', '0', '0.924', '-0.96', '#anger', '#disgust', 'negative', '-0.92', 'embarrassment', 'consternation', 'humility', 'hysteria', 'nervousness']
senticnet['teen_comedy'] = ['0.206', '-0.13', '0', '0', '#joy', '#surprise', 'positive', '0.172', 'dancer', 'old_dance', 'dance', 'slow_dance', 'ballroom_dance']
senticnet['teen_heartthrob'] = ['-0.19', '0', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.15', 'bad_child', 'misbehavior', 'bad_kid', 'bad_action', 'difficulty']
senticnet['teen_idol'] = ['-0.19', '0', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.15', 'bad_child', 'misbehavior', 'bad_kid', 'bad_action', 'difficulty']
senticnet['teen_sensation'] = ['0', '0.793', '0', '0.91', '#interest', '#admiration', 'positive', '0.852', 'superstar', 'vip', 'accomplishment', 'performance', 'concert']
senticnet['teen_sexual_activity'] = ['0', '0', '0.973', '-0.78', '#anger', '#disgust', 'negative', '-0.87', 'chastity', 'ignore_conception', 'restrain', 'abortion', 'stop']
senticnet['teen_spirit'] = ['0.916', '-0.93', '0', '0.674', '#joy', '#surprise', 'positive', '0.841', 'smash', 'bang', 'smasher', 'strike', 'hit']
senticnet['teenage'] = ['0.584', '-0.59', '0', '0.753', '#surprise', '#admiration', 'positive', '0.643', 'ungracious', 'discourteous', 'impolite', 'brattish', 'unparliamentary']
senticnet['teenage_angst'] = ['-0.87', '0', '0.924', '-0.96', '#anger', '#disgust', 'negative', '-0.92', 'embarrassment', 'consternation', 'humility', 'hysteria', 'nervousness']
senticnet['teenage_boy'] = ['0.575', '-0.62', '0', '0.596', '#surprise', '#admiration', 'positive', '0.598', 'altar_boy', 'cub', 'ball_boy', 'bat_boy', 'lad']
senticnet['teenage_daughter'] = ['0.033', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.058', 'baby_dog', 'sweet', 'puppet', 'dog', 'love']
senticnet['teenage_gang'] = ['0.867', '-0.84', '0', '0.876', '#joy', '#admiration', 'positive', '0.863', 'meet', 'team', 'team_up', 'embed', 'aggroup']
senticnet['teenage_girl'] = ['0.575', '-0.62', '0', '0.596', '#surprise', '#admiration', 'positive', '0.598', 'altar_boy', 'cub', 'ball_boy', 'bat_boy', 'lad']
senticnet['teenage_rebellion'] = ['-0.50', '0', '0.733', '-0.55', '#anger', '#disgust', 'negative', '-0.59', 'originality', 'novelty', 'innovativeness', 'freshness', 'ability']
senticnet['teenage_sex'] = ['0.081', '0.095', '0', '0', '#joy', '#interest', 'positive', '0.088', 'build_tower_out_toy_block', 'a_lot_of_sex', 'play_hockey', 'play_baseball', 'play_football']
senticnet['teenage_son'] = ['0.033', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.058', 'baby_dog', 'sweet', 'puppet', 'dog', 'love']
senticnet['teenager'] = ['0.022', '0.069', '0', '0', '#joy', '#interest', 'positive', '0.045', 'youngster', 'lad', 'junior', 'adolescent', 'maturity']
senticnet['teenbeat_sampler'] = ['-0.80', '0', '0.815', '-0.98', '#anger', '#disgust', 'negative', '-0.86', 'inalienable', 'nontransferable', 'unassignable', 'untransferable', 'inviolable']
senticnet['teensy'] = ['-0.05', '0', '-0.12', '-0.16', '#fear', '#disgust', 'negative', '-0.11', 'reduced', 'small', 'small_size', 'narrow', 'poor']
senticnet['teeny'] = ['0', '0', '-0.73', '-0.84', '#fear', '#disgust', 'negative', '-0.78', 'whining', 'peevish', 'crotched', 'fretful', 'harshen']
senticnet['teeny_tiny'] = ['-0.05', '0', '-0.12', '-0.16', '#fear', '#disgust', 'negative', '-0.11', 'reduced', 'small', 'small_size', 'narrow', 'poor']
senticnet['teeny_weeny'] = ['-0.05', '0', '-0.12', '-0.16', '#fear', '#disgust', 'negative', '-0.11', 'reduced', 'small', 'small_size', 'narrow', 'poor']
senticnet['teeter_totter'] = ['0', '-0.03', '0.042', '-0.07', '#anger', '#disgust', 'negative', '-0.02', 'ball', 'ping_pong_show', 'dandruff', 'solid_water', 'empty_bottle']
senticnet['teeth'] = ['0', '-0.02', '0.033', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'tooth', 'jaw', 'dimple', 'mouth', 'cliche']
senticnet['teeth-like'] = ['-0.70', '0', '0', '-1', '#sadness', '#disgust', 'negative', '-0.85', 'chisel-shaped', 'shovel-like', 'scythe-like', 'saw-like', 'chisel-like']
senticnet['teeth_grinding'] = ['-0.80', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'fatigue', 'exhaustion', 'become_tired', 'get_tired', 'feel_tired']
senticnet['teeth_whitening'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'beautiful_smile', 'smile', 'clean_teeth', 'white_tooth', 'dentist']
senticnet['teething'] = ['-0.54', '0', '0.696', '-0.67', '#anger', '#disgust', 'negative', '-0.63', 'precocious_dentition', 'growth', 'growing', 'maturation', 'development']
senticnet['teetotaler'] = ['0.081', '-0.05', '0', '-0.03', '#joy', '#surprise', 'positive', '0.036', 'drink_alcohol', 'consume_alcoholic_beverage', 'drink_much', 'hit_bottle', 'meet_woman']
senticnet['teevee'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'rudeness', 'discourtesy', 'insolence', 'ccheekiness', 'cheekiness']
senticnet['tefl'] = ['0.875', '0', '0', '0.802', '#joy', '#admiration', 'positive', '0.839', 'foreigner', 'exile', 'deportee', 'gringo', 'import']
senticnet['tefl_certificate'] = ['0.875', '0', '0', '0.802', '#joy', '#admiration', 'positive', '0.839', 'foreigner', 'exile', 'deportee', 'gringo', 'import']
senticnet['tegaderm'] = ['-0.81', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.85', 'bandage', 'painkiller', 'gauze', 'aspirin', 'contraceptive_device']
senticnet['tegumai'] = ['0.037', '-0.00', '0', '0.12', '#joy', '#admiration', 'positive', '0.055', 'bread_crumb', 'candy', 'bun', 'sweet', 'gush']
senticnet['tegument'] = ['0', '0.097', '0', '0.085', '#interest', '#admiration', 'positive', '0.091', 'lunula', 'arm_part', 'leave_hand', 'ten_fingers', 'five_fingers']
senticnet['tehran_bazaar'] = ['0', '0.113', '0.041', '0.058', '#interest', '#admiration', 'positive', '0.043', 'corner_shop', 'general_store', 'many_shops', 'large_supermarket', 'goods']
senticnet['tehran_evin_prison'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicative', 'unpronounceable', 'impassive', 'inexpressive', 'silent']
senticnet['tehsil_matta'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'webpage', 'store_record', 'ideal']
senticnet['teichmller_metric'] = ['-0.53', '0', '0.993', '-0.88', '#anger', '#disgust', 'negative', '-0.80', 'expansiveness', 'impressiveness', 'stateliness', 'loftiness', 'excellence']
senticnet['teilo'] = ['0', '0.248', '-0.08', '0.173', '#interest', '#admiration', 'positive', '0.113', 'god', 'minister', 'evangelist', 'rector', 'goddess']
senticnet['teinosuke_kinugasa'] = ['-0.67', '0', '0.931', '-0.77', '#anger', '#disgust', 'negative', '-0.79', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['teint'] = ['-0.58', '0', '0.661', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'explosive', 'cordite', 'dynamite', 'plastic_explosive', 'explosive_compound']
senticnet['tekke'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'caravansary', 'caravanserai', 'khan', 'caravan_inn', 'hostel']
senticnet['tekken'] = ['0.021', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'plastic_bottle', 'party', 'use_party', 'lighter_air', 'compress_air']
senticnet['tekumel'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'cur', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['tel_hai'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['telangiectasia'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema', 'erythema_multiforme', 'windburn', 'hickey']
senticnet['telangiectatic'] = ['-0.66', '0', '0.911', '-0.99', '#anger', '#disgust', 'negative', '-0.85', 'liver_spot', 'spot', 'freckle', 'macule', 'plague_spot']
senticnet['telarc'] = ['0.805', '-0.58', '0', '0.525', '#joy', '#surprise', 'positive', '0.637', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['telarc_label'] = ['0.805', '-0.58', '0', '0.525', '#joy', '#surprise', 'positive', '0.637', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['teldec'] = ['0.805', '-0.58', '0', '0.525', '#joy', '#surprise', 'positive', '0.637', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['telecom'] = ['0.767', '0.835', '0', '0', '#joy', '#interest', 'positive', '0.801', 'medium', 'telegraphy', 'telecommunication', 'telephone', 'wireless']
senticnet['telecom_bubble'] = ['-0.55', '0', '0.869', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'air_bubble', 'foam', 'froth', 'soap_bubble', 'globule']
senticnet['telecom_gear'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'builder']
senticnet['telecom_infrastructure'] = ['0.021', '0', '0', '0.058', '#joy', '#admiration', 'positive', '0.04', 'architecture', 'route', 'structure', 'car_path', 'building']
senticnet['telecommunication'] = ['0.772', '0.844', '0', '0', '#joy', '#interest', 'positive', '0.808', 'medium', 'telegraphy', 'telecom', 'telephone', 'wireless']
senticnet['telecommunication_carrier'] = ['0.687', '-0.50', '0', '0.8', '#joy', '#admiration', 'positive', '0.663', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['telecommunication_equipment'] = ['-0.02', '-0.05', '0', '0.069', '#surprise', '#admiration', 'positive', '0.032', 'fixture', 'endowment', 'material', 'stuff', 'kit']
senticnet['telecommunication_marketplace'] = ['0.838', '0.75', '0', '0.708', '#joy', '#interest', 'positive', '0.765', 'festival', 'party', 'banquet', 'big_event', 'rock_concert']
senticnet['telecommunication_provider'] = ['0.687', '-0.50', '0', '0.8', '#joy', '#admiration', 'positive', '0.663', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['telecommunication_tower'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'castle', 'bell_tower', 'spire', 'steeple', 'religious_place']
senticnet['telecommunications_equipment'] = ['-0.02', '-0.05', '0', '0.069', '#surprise', '#admiration', 'positive', '0.032', 'fixture', 'endowment', 'material', 'stuff', 'kit']
senticnet['teleconnection'] = ['-0.68', '0', '0.946', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'abnormality', 'abnormalcy', 'birth_defect', 'congenital_anomaly', 'congenital_defect']
senticnet['telegram'] = ['0.038', '-0.03', '0', '0.079', '#surprise', '#admiration', 'positive', '0.052', 'sender', 'message', 'note', 'post', 'email']
senticnet['telegraph'] = ['0.089', '-0.01', '0', '0.117', '#joy', '#admiration', 'positive', '0.074', 'email', 'telephone_kiosk', 'phone', 'fax', 'handset']
senticnet['telegraphy'] = ['0.767', '0.834', '0', '0', '#joy', '#interest', 'positive', '0.8', 'medium', 'telecommunication', 'telecom', 'telephone', 'wireless']
senticnet['telekinesis'] = ['-0.8', '0', '0.62', '-0.71', '#sadness', '#disgust', 'negative', '-0.71', 'parapsychology', 'psychokinesis', 'clairvoyance', 'second_sight', 'extrasensory_perception']
senticnet['telekinetic'] = ['0.796', '-0.75', '0', '0.941', '#joy', '#admiration', 'positive', '0.829', 'mortal', 'person', 'individual', 'attractive', 'impressive']
senticnet['telekinetically'] = ['0.8', '-0.97', '0', '0.512', '#joy', '#surprise', 'positive', '0.763', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['telemetry_equipment'] = ['-0.02', '-0.05', '0', '0.069', '#surprise', '#admiration', 'positive', '0.032', 'fixture', 'endowment', 'material', 'stuff', 'kit']
senticnet['teleological'] = ['0.863', '-0.94', '0', '0.986', '#surprise', '#admiration', 'positive', '0.932', 'postulate', 'posit', 'assume', 'presume', 'take_for_granted']
senticnet['teleological_explanation'] = ['-0.55', '0', '0.58', '-0.98', '#anger', '#disgust', 'negative', '-0.70', 'fatalist', 'determinist', 'predestinarian', 'predestinationist', 'philosopher']
senticnet['teleological_view'] = ['-0.55', '0', '0.58', '-0.98', '#anger', '#disgust', 'negative', '-0.70', 'fatalist', 'determinist', 'predestinarian', 'predestinationist', 'philosopher']
senticnet['teleology'] = ['-0.98', '0', '0.891', '-0.60', '#sadness', '#anger', 'negative', '-0.82', 'exorcism', 'dispossession', 'magic', 'thaumaturgy', 'belief']
senticnet['teleoperation'] = ['0.026', '0.14', '-0.13', '0.31', '#interest', '#admiration', 'positive', '0.085', 'teach_new', 'share_idea', 'gesture', 'start_talk', 'exchange_idea']
senticnet['telepath'] = ['0', '0.081', '0', '0', '#interest', '#interest', 'positive', '0.081', 'favorite', 'guard', 'crowd', 'several_people', 'clique']
senticnet['telepathic'] = ['0', '0.871', '0', '0.861', '#interest', '#admiration', 'positive', '0.866', 'predict_future', 'psychic_phenomenon', 'parapsychology', 'clairvoyance', 'foreknowledge']
senticnet['telepathic_link'] = ['-0.53', '0', '0.637', '-0.92', '#anger', '#disgust', 'negative', '-0.69', 'incorporeal', 'unembodied', 'disembodied', 'bodiless', 'unbodied']
senticnet['telepathically'] = ['-0.53', '0', '0.637', '-0.92', '#anger', '#disgust', 'negative', '-0.69', 'incorporeal', 'unembodied', 'disembodied', 'bodiless', 'unbodied']
senticnet['telepathist'] = ['0.899', '-0.89', '0', '0.678', '#joy', '#surprise', 'positive', '0.823', 'escapologist', 'escape_expert', 'conjurer', 'mind_reader', 'performer']
senticnet['telepathy'] = ['0', '0.871', '0', '0.861', '#interest', '#admiration', 'positive', '0.866', 'predict_future', 'psychic_phenomenon', 'parapsychology', 'clairvoyance', 'foreknowledge']
senticnet['telephone'] = ['0.814', '0.737', '0', '0', '#joy', '#interest', 'positive', '0.775', 'communicate', 'dial_number', 'make_contact', 'attention', 'ask_question']
senticnet['telephone_bidder'] = ['0.054', '0.237', '-0.15', '0.33', '#interest', '#admiration', 'positive', '0.117', 'retrieve', 'bring', 'tote', 'take', 'go_get']
senticnet['telephone_booth'] = ['0.053', '0.1', '0', '0', '#joy', '#interest', 'positive', '0.076', 'registry_office', 'lodging', 'board_room', 'cubicle', 'reception_area']
senticnet['telephone_call'] = ['0.164', '0.13', '0.031', '0.209', '#joy', '#admiration', 'positive', '0.118', 'leave_message', 'write_note', 'leave_document', 'send_email', 'message']
senticnet['telephone_computer'] = ['0.074', '0.069', '0.036', '0.055', '#joy', '#interest', 'positive', '0.041', 'computer', 'browser', 'efficient', 'computer_manufacturer', 'modem']
senticnet['telephone_conversation'] = ['0.164', '0.13', '0.031', '0.209', '#joy', '#admiration', 'positive', '0.118', 'leave_message', 'write_note', 'leave_document', 'send_email', 'message']
senticnet['telephone_conversation_between'] = ['0.716', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.733', 'call', 'use_phone', 'orate', 'use_telephone', 'speech']
senticnet['telephone_directory'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'old_testament', 'religious_tract', 'cook_book', 'magazine', 'a_lot_of_books']
senticnet['telephone_etiquette'] = ['0', '0', '0', '0.99', '#admiration', '#admiration', 'positive', '0.99', 'politeness', 'good_behavior', 'good_etiquette', 'manner', 'hint']
senticnet['telephone_helpline'] = ['0', '0.084', '0', '0', '#interest', '#interest', 'positive', '0.084', 'call_restaurant', 'reservation', 'reserve', 'dinner', 'reserved']
senticnet['telephone_interview'] = ['-0.93', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.87', 'tough_interview', 'tough_interviewer', 'seek_job', 'jobless', 'ignorant']
senticnet['telephone_keypad'] = ['0.893', '0.979', '0', '0', '#joy', '#interest', 'positive', '0.936', 'find_phone_number', 'dial_phone_number', 'pick_up_telephone_receiver', 'dial', 'dial_telephone']
senticnet['telephone_kiosk'] = ['0.089', '-0.01', '0', '0.117', '#joy', '#admiration', 'positive', '0.074', 'email', 'telegraph', 'phone', 'fax', 'handset']
senticnet['telephone_number'] = ['0', '-0.03', '0.135', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'look_up_phone_number', 'read_email', 'find_phone_number', 'pass_information', 'give_information']
senticnet['telephone_pole'] = ['0.087', '-0.14', '0.06', '0', '#joy', '#surprise', 'positive', '0.058', 'phone', 'communication', 'cork', 'stick_paper', 'birch']
senticnet['telephone_reassurance'] = ['0.956', '-0.70', '0', '0.653', '#joy', '#surprise', 'positive', '0.771', 'centenarian', 'nonagenarian', 'octogenarian', 'septuagenarian', 'sexagenarian']
senticnet['telephone_ring'] = ['0', '-0.04', '0.018', '0.072', '#surprise', '#admiration', 'positive', '0.032', 'dial_phone', 'phone_conversation', 'speak_loudly', 'call', 'use_phone']
senticnet['telephone_room'] = ['0.035', '-0.01', '0.027', '0.085', '#joy', '#admiration', 'positive', '0.026', 'telephone', 'cellphone', 'find_phone_number', 'phone', 'use_telephone']
senticnet['telephone_service_provider'] = ['0.687', '-0.50', '0', '0.8', '#joy', '#admiration', 'positive', '0.663', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['telephone_survey'] = ['-0.04', '0.1', '0.051', '0.137', '#interest', '#admiration', 'positive', '0.035', 'find_number', 'regularly', 'list_number', 'calculator', 'navigation']
senticnet['telephone_wire'] = ['0.085', '0', '0', '0', '#joy', '#joy', 'positive', '0.085', 'wire', 'several_types', 'gas_stove', 'tape_player', 'control_room']
senticnet['telephony'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'long_distance_relationship', 'distant', 'kilometer', 'length_measurement', 'distance_measure']
senticnet['telephony_interface'] = ['0.749', '0.724', '0', '0', '#joy', '#interest', 'positive', '0.736', 'mouth_noise', 'vocal_cord', 'vocalization', 'vocal_sound', 'loud_soft']
senticnet['telephoto'] = ['0.021', '0.081', '0.017', '0.113', '#interest', '#admiration', 'positive', '0.05', 'aspect', 'perspective', 'look_round', 'scenic', 'look_through']
senticnet['telepod'] = ['-0.53', '0', '0.637', '-0.92', '#anger', '#disgust', 'negative', '-0.69', 'incorporeal', 'unembodied', 'disembodied', 'bodiless', 'unbodied']
senticnet['teleport'] = ['0.026', '0.165', '-0.12', '0.292', '#interest', '#admiration', 'positive', '0.09', 'fetch', 'find', 'desire_win', 'take', 'obtain']
senticnet['teleportation_device'] = ['0.8', '-0.97', '0', '0.512', '#joy', '#surprise', 'positive', '0.763', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['teleported_away'] = ['0.8', '-0.97', '0', '0.512', '#joy', '#surprise', 'positive', '0.763', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['teleporter'] = ['0.026', '0.165', '-0.12', '0.292', '#interest', '#admiration', 'positive', '0.09', 'fetch', 'find', 'desire_win', 'take', 'obtain']
senticnet['teleports_away'] = ['-0.04', '-0.02', '0.027', '-0.03', '#sadness', '#disgust', 'negative', '-0.01', 'precious_metal', 'olympic_medal', 'shiny_metal', 'many_different_kinds', 'valuable_metal']
senticnet['telescope'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'binoculars', 'read_glass', 'eyeglass', 'camera', 'monocle']
senticnet['telescope_array'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'binoculars', 'read_glass', 'eyeglass', 'camera', 'monocle']
senticnet['telescope_mount'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'binoculars', 'read_glass', 'eyeglass', 'camera', 'monocle']
senticnet['telescope_tube'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'binoculars', 'read_glass', 'eyeglass', 'camera', 'monocle']
senticnet['telescopic'] = ['0.539', '-0.60', '0', '0.859', '#surprise', '#admiration', 'positive', '0.667', 'farsighted', 'longsighted', 'farseeing', 'hyperopic', 'hypermetropic']
senticnet['telescript'] = ['0', '0.717', '0', '0.765', '#interest', '#admiration', 'positive', '0.741', 'helper', 'artificial_intelligence', 'goal_driven', 'proactive', 'intelligent']
senticnet['teletubbyland'] = ['0.036', '0.084', '0', '0', '#joy', '#interest', 'positive', '0.06', 'toy', 'bath_toy', 'yo-yo', 'toy_ball', 'duck']
senticnet['teletype'] = ['0', '0', '0.128', '0', '#anger', '#anger', 'negative', '-0.12', 'grid', 'efficient', 'calculator', 'computer', 'personal_computer']
senticnet['teletype_machine'] = ['0', '0', '0.128', '0', '#anger', '#anger', 'negative', '-0.12', 'grid', 'efficient', 'calculator', 'computer', 'personal_computer']
senticnet['televise'] = ['0.053', '-0.05', '0.01', '0', '#joy', '#surprise', 'positive', '0.034', 'radio-control_helicopter', 'football', 'spray_paint_grafitti', 'marijuana', 'fricative']
senticnet['televised_concert'] = ['0.773', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.844', 'favorite', 'crowd', 'acclamation', 'bunch', 'large_crowd']
senticnet['television'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'theater_seat', 'theater_box', 'sticky_floor', 'stage_entrance', 'balcony']
senticnet['television_advert'] = ['0.808', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.903', 'marketing', 'sale', 'announcement', 'billboard', 'promotion']
senticnet['television_advertisement'] = ['0.808', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.903', 'marketing', 'sale', 'announcement', 'billboard', 'promotion']
senticnet['television_broadcast'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'theater_seat', 'theater_box', 'sticky_floor', 'stage_entrance', 'balcony']
senticnet['television_broadcaster'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'theater_seat', 'theater_box', 'sticky_floor', 'stage_entrance', 'balcony']
senticnet['television_channel'] = ['-0.09', '0.166', '-0.14', '0.198', '#interest', '#admiration', 'positive', '0.032', 'television_station', 'tv_studio', 'watch_tv', 'radio_station', 'tv_station']
senticnet['television_documentary'] = ['0', '0.762', '0', '0', '#interest', '#interest', 'positive', '0.762', 'encyclopaedia', 'tropical_rainforest', 'learn_new_information', 'tropical_forest', 'child_story']
senticnet['television_news'] = ['0.026', '0.227', '0', '0.233', '#interest', '#admiration', 'positive', '0.162', 'best_moment', 'sport_highlight', 'football_highlight', 'highlight', 'put_in_evidence']
senticnet['television_newscast'] = ['0.088', '-0.00', '0', '0', '#joy', '#surprise', 'positive', '0.045', 'look_up_information', 'find_newspaper', 'headlines', 'enter_contest', 'latest_find_newspaper']
senticnet['television_program'] = ['0.162', '-0.20', '0', '0.232', '#surprise', '#admiration', 'positive', '0.2', 'tv_show', 'cartoon', 'child_story', 'tv_series', 'flower_shop']
senticnet['television_series_glee'] = ['0.761', '0.566', '-0.21', '0.293', '#joy', '#interest', 'positive', '0.352', 'gratify', 'jubilation', 'gloat', 'gloating', 'gloatingly']
senticnet['television_set'] = ['0', '0', '-0.04', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'soud_system', 'remove_control', 'tv_set', 'christian_fundamentalist', 'stair_down']
senticnet['television_station'] = ['0', '0.07', '0', '0.071', '#interest', '#admiration', 'positive', '0.071', 'movie_studio', 'broadcast', 'stadium', 'film_studio', 'record_studio']
senticnet['television_view'] = ['-0.09', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.08', 'beat_off', 'play_board_game', 'solve_puzzle', 'smoke_marijuana', 'rake_leave']
senticnet['televison'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'theater_seat', 'theater_box', 'sticky_floor', 'stage_entrance', 'balcony']
senticnet['telework'] = ['0.032', '0.11', '0', '0.105', '#interest', '#admiration', 'positive', '0.082', 'work', 'place_work', 'implementation', 'disorganize', 'employment']
senticnet['telfa'] = ['0', '0', '0', '-0.17', '#disgust', '#disgust', 'negative', '-0.17', 'medicine', 'aspirin', 'blank_check', 'painkiller', 'video_tape']
senticnet['telipna'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'dejectedness', 'low-spiritedness', 'lowness', 'downheartedness', 'cheerless']
senticnet['tell'] = ['0.074', '0.129', '-0.10', '0.209', '#interest', '#admiration', 'positive', '0.076', 'draw_diagram', 'form_of_communication', 'tell_love', 'explain_detail', 'make_sale']
senticnet['tell_apart'] = ['0.933', '-0.63', '0', '0.59', '#joy', '#surprise', 'positive', '0.72', 'contradistinguish', 'severalize', 'differentiate', 'secern', 'secernate']
senticnet['tell_day'] = ['0', '0.827', '0', '0.84', '#interest', '#admiration', 'positive', '0.833', 'impress_person', 'get_know', 'look_sexy', 'find_new_friend', 'visit_friend']
senticnet['tell_everyone'] = ['0.074', '0.359', '-0.22', '0.337', '#interest', '#admiration', 'positive', '0.137', 'newscaster', 'identify', 'make_right', 'proposition', 'request']
senticnet['tell_friend'] = ['0.997', '0.929', '0', '0.941', '#joy', '#admiration', 'positive', '0.956', 'lose_track_time', 'get_excited', 'boredom', 'bored', 'kill_brain_cell']
senticnet['tell_funny_joke'] = ['0.759', '0', '0', '0.975', '#joy', '#admiration', 'positive', '0.867', 'find_funny', 'tell_joke', 'laugh', 'sense_of_humor', 'good_joke']
senticnet['tell_information'] = ['0.972', '0.734', '0', '0.517', '#joy', '#interest', 'positive', '0.741', 'pass_information', 'share_knowledge', 'offer_opinion', 'notify', 'transfer_knowledge']
senticnet['tell_joke'] = ['0.721', '0', '0', '0.969', '#joy', '#admiration', 'positive', '0.845', 'tell_funny_joke', 'applause', 'smile_laugh', 'chuckle', 'talk_much']
senticnet['tell_lie'] = ['-0.94', '0', '0.507', '-0.93', '#sadness', '#disgust', 'negative', '-0.79', 'tell_untruth', 'untruth', 'dishonesty', 'deceit', 'fraud']
senticnet['tell_love'] = ['0.953', '0.709', '0', '0', '#joy', '#interest', 'positive', '0.831', 'say_love', 'make_sale', 'draw_diagram', 'stay_happy', 'receive_gift']
senticnet['tell_off'] = ['-0.85', '0', '0.631', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'call_on_carpet', 'take_to_task', 'chide', 'berate', 'bawl_out']
senticnet['tell_on'] = ['0', '0', '-0.74', '-0.86', '#fear', '#disgust', 'negative', '-0.80', 'betray', 'stigmatize', 'denounce', 'brand', 'mark']
senticnet['tell_others'] = ['0.058', '-0.06', '-0.14', '-0.07', '#fear', '#disgust', 'negative', '-0.02', 'make_others_laugh', 'laugh', 'make_fun', 'start_laugh', 'laugh_out_loud']
senticnet['tell_pov_ray'] = ['0.777', '-0.85', '0', '0.615', '#joy', '#surprise', 'positive', '0.748', 'think', 'play', 'trifle', 'abstract', 'warm_to']
senticnet['tell_secret'] = ['0', '0', '0', '0.881', '#admiration', '#admiration', 'positive', '0.881', 'reply_question', 'state_fact', 'voice_opinion', 'share_opinion', 'communicate']
senticnet['tell_story'] = ['0.047', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.026', 'convey_information', 'evoke_emotion', 'express_idea', 'recite_poem', 'share_thought']
senticnet['tell_tale'] = ['-0.76', '0', '0.573', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'gossip', 'gossiper', 'gossipmonger', 'talk_back', 'rumormonger']
senticnet['tell_tale_sign'] = ['-0.76', '0', '0.573', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'gossip', 'gossiper', 'gossipmonger', 'talk_back', 'rumormonger']
senticnet['tell_time'] = ['0', '0.184', '-0.06', '0.235', '#interest', '#admiration', 'positive', '0.118', 'swiss_watch', 'timekeeper', 'measure_passage_time', 'timekeep', 'time_event']
senticnet['tell_truth'] = ['-0.10', '0.227', '-0.19', '0.248', '#interest', '#admiration', 'positive', '0.044', 'tell_story', 'make_judgment', 'testify', 'arrest_criminal', 'call_police']
senticnet['tell_untruth'] = ['-0.93', '0', '0.049', '-0.98', '#sadness', '#disgust', 'negative', '-0.65', 'lie', 'fraud', 'dishonesty', 'untruth', 'deceit']
senticnet['tell_why'] = ['-0.12', '0.179', '-0.14', '0.204', '#interest', '#admiration', 'positive', '0.028', 'elaborate', 'inquire', 'request', 'explain', 'question']
senticnet['teller'] = ['0', '0.186', '0', '0.075', '#interest', '#admiration', 'positive', '0.131', 'money_inside', 'check_account', 'savings', 'money_storage', 'store_money']
senticnet['teller_counter'] = ['0', '0.186', '0', '0.075', '#interest', '#admiration', 'positive', '0.131', 'money_inside', 'check_account', 'savings', 'money_storage', 'store_money']
senticnet['teller_machine'] = ['0', '0.186', '0', '0.075', '#interest', '#admiration', 'positive', '0.131', 'money_inside', 'check_account', 'savings', 'money_storage', 'store_money']
senticnet['teller_window'] = ['0', '0.186', '0', '0.075', '#interest', '#admiration', 'positive', '0.131', 'money_inside', 'check_account', 'savings', 'money_storage', 'store_money']
senticnet['telling'] = ['0', '0', '-0.84', '-0.84', '#fear', '#disgust', 'negative', '-0.84', 'grave', 'weighty', 'heavy', 'cogent', 'grievous']
senticnet['tellingly'] = ['0.964', '-0.88', '0', '0', '#joy', '#surprise', 'positive', '0.926', 'convincing', 'pan', 'conviction', 'convince', 'convincingly']
senticnet['telltale'] = ['-0.76', '0', '0.573', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'gossip', 'gossiper', 'gossipmonger', 'talk_back', 'rumormonger']
senticnet['telltale_sign'] = ['-0.76', '0', '0.573', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'gossip', 'gossiper', 'gossipmonger', 'talk_back', 'rumormonger']
senticnet['telly_monster'] = ['-0.93', '0', '0.77', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'hothead', 'unpleasant_person', 'disagreeable_person', 'crabby_person', 'crab']
senticnet['telnet'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'busy_bee', 'actor', 'doer', 'worker']
senticnet['telnet_connection'] = ['0.078', '0.103', '-0.09', '0.191', '#interest', '#admiration', 'positive', '0.069', 'connect', 'aunt_uncle', 'short_form', 'relationship', 'kinship']
senticnet['telomere'] = ['0.839', '0.941', '0', '0', '#joy', '#interest', 'positive', '0.89', 'chromosome', 'function', 'structure', 'end', 'first']
senticnet['telomere_length'] = ['0.097', '0.099', '-0.09', '0.137', '#interest', '#admiration', 'positive', '0.059', 'live_long', 'long_life', 'live_healthy_lifestyle', 'increase_muscle_mass', 'feel_healthy']
senticnet['tem_sample'] = ['-0.93', '0', '0.739', '-0.96', '#sadness', '#disgust', 'negative', '-0.88', 'prevarication', 'fairytale', 'fairy_tale', 'fairy_story', 'play']
senticnet['temerity'] = ['0.894', '-0.74', '0', '0.598', '#joy', '#surprise', 'positive', '0.747', 'daredevilry', 'daredeviltry', 'hardihood', 'audacity', 'audaciousness']
senticnet['temet'] = ['-0.84', '0', '0.579', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'squiggle', 'hand', 'chicken_scratch', 'handwriting', 'script']
senticnet['temminckii'] = ['-0.03', '-0.01', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'shit', 'wildflower', 'poop', 'wild_flower', 'dog_poop']
senticnet['temnospondyl'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'hag', 'osteostracan', 'cephalaspid', 'ostracoderm', 'jawless_fish']
senticnet['temp'] = ['0', '0.125', '-0.05', '0.078', '#interest', '#admiration', 'positive', '0.049', 'measure_heat', 'temperature_measurement', 'measure_degree', 'temperature_measure', 'celsius']
senticnet['temp_agency'] = ['0.907', '0', '0', '0.79', '#joy', '#admiration', 'positive', '0.849', 'work', 'task', 'hard_work', 'accomplish', 'find_accomplish']
senticnet['temp_file'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'car_trunk', 'walmart', 'warehouse', 'store', 'storage_place']
senticnet['temp_folder'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'car_trunk', 'walmart', 'warehouse', 'store', 'storage_place']
senticnet['temp_job'] = ['0.907', '0', '0', '0.79', '#joy', '#admiration', 'positive', '0.849', 'work', 'task', 'hard_work', 'accomplish', 'find_accomplish']
senticnet['tempeh'] = ['0', '-0.03', '0.046', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'pasta', 'green_bean', 'mayonnaise', 'frozen_food', 'sour_milk']
senticnet['temper'] = ['0', '0', '0.889', '-0.85', '#anger', '#disgust', 'negative', '-0.87', 'mood', 'humor', 'irritation', 'chasten', 'anneal']
senticnet['temper_tantrum'] = ['0.494', '0.45', '-0.11', '0.056', '#joy', '#interest', 'positive', '0.222', 'recreate', 'peacefulness', 'euphoria', 'serenity', 'closeness']
senticnet['tempera_paint'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'gift_wrap', 'kraft', 'kraft_paper', 'wrapping_paper', 'paper']
senticnet['temperament'] = ['-0.92', '0', '0.53', '-0.67', '#sadness', '#disgust', 'negative', '-0.70', 'ill_nature', 'disagreeableness', 'unfriendliness', 'bloodthirstiness', 'aloneness']
senticnet['temperamental'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'pervert', 'horny', 'uncomfortable', 'uneasy', 'short_tempered']
senticnet['temperance'] = ['0.916', '-0.73', '0', '0.797', '#joy', '#admiration', 'positive', '0.817', 'natural_virtue', 'abstemiousness', 'dryness', 'sobriety', 'restraint']
senticnet['temperance_campaigner'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['temperate'] = ['0.952', '-0.83', '0', '0', '#joy', '#surprise', 'positive', '0.895', 'excessive', 'temporize', 'clematis', 'hibiscus', 'sedate']
senticnet['temperate_broadleaf'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'woodland', 'timberland']
senticnet['temperate_deciduous_forest'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'woodland', 'timberland']
senticnet['temperate_forest'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'woodland', 'timberland']
senticnet['temperature'] = ['-0.66', '0', '0.992', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'cold', 'boil', 'simmer', 'low_temperature', 'frigidity']
senticnet['temperature_anomaly'] = ['-0.68', '0', '0.946', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'abnormality', 'abnormalcy', 'birth_defect', 'congenital_anomaly', 'congenital_defect']
senticnet['temperature_dependence'] = ['0.695', '-0.85', '0', '0.501', '#joy', '#surprise', 'positive', '0.684', 'alternative_energy', 'energy', 'energy_of_activation', 'atomic_energy', 'nuclear_energy']
senticnet['temperature_measure'] = ['0.053', '0.097', '0.036', '0.052', '#joy', '#interest', 'positive', '0.041', 'temperature_measurement', 'celsius', 'measure_heat', 'heat_measure', 'measure_degree']
senticnet['temperature_measurement'] = ['0.053', '0.092', '0.027', '0.062', '#interest', '#admiration', 'positive', '0.045', 'temperature_measure', 'measure_heat', 'celsius', 'heat_measure', 'measure_degree']
senticnet['temperature_soar'] = ['0', '0', '0.954', '-0.76', '#anger', '#disgust', 'negative', '-0.85', 'fervid', 'ardent', 'impassioned', 'fervent', 'fiery']
senticnet['tempest'] = ['-0.96', '0', '0.581', '-0.90', '#sadness', '#disgust', 'negative', '-0.81', 'hurly_burly', 'kerfuffle', 'commotion', 'disruption', 'disturbance']
senticnet['tempestuous'] = ['-0.5', '0', '0.982', '-0.85', '#anger', '#disgust', 'negative', '-0.77', 'unpeaceable', 'stormy', 'unpeaceful', 'militant', 'belligerent']
senticnet['templar_order'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquirer', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['template'] = ['0.92', '0.794', '0', '0', '#joy', '#interest', 'positive', '0.857', 'move_mouse', 'reboot', 'type_of_keyboard', 'enter_address', 'keep_track_appointment']
senticnet['template_strand'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
senticnet['temple'] = ['0', '0.247', '0.025', '0.218', '#interest', '#admiration', 'positive', '0.147', 'cathedral', 'chapel', 'church', 'catholic_church', 'confession']
senticnet['temple_emanu_el'] = ['0', '0.209', '0.026', '0.184', '#interest', '#admiration', 'positive', '0.122', 'church', 'confession', 'temple', 'cathedral', 'monastery']
senticnet['temple_mount'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['tempo'] = ['0.914', '0.942', '0', '0', '#joy', '#interest', 'positive', '0.928', 'upbeat', 'tone', 'lively', 'positive', 'unaccented']
senticnet['temporal'] = ['0.716', '-0.62', '0', '0.896', '#joy', '#admiration', 'positive', '0.746', 'constitutional', 'integral', 'intimate', 'inner', 'inbuilt']
senticnet['temporal_arteritis'] = ['-0.85', '0', '0.657', '-0.86', '#sadness', '#disgust', 'negative', '-0.79', 'pulseless_disease', 'arteritis', 'rubor', 'redness', 'polyarteritis']
senticnet['temporal_coherency'] = ['0.792', '-0.83', '0', '0.575', '#joy', '#surprise', 'positive', '0.734', 'connectedness', 'connection', 'consistency', 'continuity', 'link']
senticnet['temporal_dimension'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'width', 'size', 'mass_measure', 'measure', 'gauge']
senticnet['temporal_lobe_epilepsy'] = ['-0.58', '0', '0.722', '-0.51', '#sadness', '#anger', 'negative', '-0.60', 'dissociative_disorder', 'depersonalization_neurosis', 'depersonalization', 'mental_disorder', 'mental_disturbance']
senticnet['temporal_locality'] = ['-0.9', '0', '0.841', '-0.73', '#sadness', '#anger', 'negative', '-0.82', 'nubbiness', 'tweediness', 'harshness', 'graininess', 'raggedness']
senticnet['temporal_punishment'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'forgivable', 'excusable', 'mercifully', 'merciful', 'mercifulness']
senticnet['temporal_relationship_between'] = ['0.798', '-0.51', '0', '0.519', '#joy', '#admiration', 'positive', '0.611', 'averageness', 'commonness', 'normalcy', 'typicality', 'condition']
senticnet['temporal_variation'] = ['0', '0.184', '-0.05', '0.179', '#interest', '#admiration', 'positive', '0.104', 'rich', 'a_lot', 'abundant', 'wealth', 'plentiful']
senticnet['temporality'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'metastability', 'monotony', 'invariance', 'constancy', 'unchangeability']
senticnet['temporally'] = ['0', '0.756', '0', '0.803', '#interest', '#admiration', 'positive', '0.78', 'tonally', 'compositionally', 'semantically', 'spatially', 'perceptually']
senticnet['temporarily_freeze'] = ['-0.64', '0', '0.612', '-0.84', '#sadness', '#disgust', 'negative', '-0.7', 'chilling', 'frost', 'lyophilization', 'icing', 'cooling']
senticnet['temporarily_incapacitate'] = ['-0.59', '0', '0.704', '-0.5', '#sadness', '#anger', 'negative', '-0.59', 'lay_up', 'modify', 'nobble', 'alter', 'change']
senticnet['temporarily_relocate'] = ['-0.02', '0.119', '-0.06', '0.216', '#interest', '#admiration', 'positive', '0.062', 'go_forward', 'locomotion', 'movement', 'move', 'move_ahead']
senticnet['temporarily_stepped_down'] = ['-0.72', '0', '0.98', '0', '#sadness', '#anger', 'negative', '-0.85', 'resignedly', 'abject', 'unhopeful', 'abjectly', 'reconcile']
senticnet['temporarily_stun'] = ['0', '0', '-0.85', '-0.95', '#fear', '#disgust', 'negative', '-0.90', 'scarily', 'scared', 'scare_away', 'frighten_off', 'scare_off']
senticnet['temporarily_unavailable'] = ['-0.77', '0', '0.644', '-0.68', '#sadness', '#disgust', 'negative', '-0.70', 'unobtainable', 'unprocurable', 'out_of_stock', 'bunk', 'untouchable']
senticnet['temporariness'] = ['-0.87', '0', '0.744', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'impermanence', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['temporary'] = ['0.101', '-0.23', '0.031', '0.105', '#surprise', '#admiration', 'positive', '0.103', 'end_soon', 'stick', 'seldom', 'walk_stick', 'lift']
senticnet['temporary_expedient'] = ['-0.97', '0', '0.84', '-0.52', '#sadness', '#anger', 'negative', '-0.78', 'pis_aller', 'last_resort', 'expedient', 'makeshift', 'stopgap']
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senticnet['temporary_shelter'] = ['0.196', '0.077', '0', '0.064', '#joy', '#interest', 'positive', '0.112', 'lower_temperature', 'reduce_temperature', 'remove_heat', 'entertain_company', 'get_meal']
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senticnet['temporomandibular_joint_pain'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'aphasia', 'anomic_aphasia']
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senticnet['tenant_farmer'] = ['0.053', '0.094', '0', '0.136', '#interest', '#admiration', 'positive', '0.094', 'barn_animal', 'domesticate', 'grassland', 'sheep', 'cow']
senticnet['tenchu'] = ['0.021', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'plastic_bottle', 'party', 'use_party', 'lighter_air', 'compress_air']
senticnet['tend'] = ['0', '0.37', '-0.27', '0.283', '#interest', '#admiration', 'positive', '0.126', 'thoughtful', 'attitude', 'swing', 'certainty', 'type_of_bear']
senticnet['tendency'] = ['0.843', '0.822', '0', '0', '#joy', '#interest', 'positive', '0.833', 'understanding', 'propensity', 'leaning', 'favor', 'proclivity']
senticnet['tendency_toward'] = ['-0.63', '0', '0.822', '-0.95', '#anger', '#disgust', 'negative', '-0.80', 'uniformness', 'homogeneousness', 'consistency', 'consistence', 'similarity']
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senticnet['tendentiousness'] = ['-0.79', '0', '0.627', '-0.81', '#sadness', '#disgust', 'negative', '-0.74', 'taboo', 'tabu', 'bias', 'irrational_hostility', 'experimenter_bias']
senticnet['tender'] = ['0', '0.834', '0', '0.74', '#interest', '#admiration', 'positive', '0.787', 'affectionate', 'lovesome', 'fond', 'contemplative', 'broody']
senticnet['tender_age'] = ['0.592', '-0.93', '0', '0.935', '#surprise', '#admiration', 'positive', '0.819', 'child', 'small_fry', 'tyke', 'tike', 'kid']
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senticnet['tender_itt'] = ['0', '0.852', '0', '0', '#interest', '#interest', 'positive', '0.852', 'streamer', 'tribute', 'salutation', 'gift', 'anniversary_gift']
senticnet['tender_locomotive'] = ['0.069', '0.097', '0', '0.172', '#interest', '#admiration', 'positive', '0.113', 'type_of_engine', 'liquid_gas', 'warm_air', 'hot_air', 'steamy']
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senticnet['tenderest'] = ['0.808', '-0.69', '0', '0.938', '#joy', '#admiration', 'positive', '0.815', 'spinnable', 'susceptible', 'easy', 'pliant', 'plastic']
senticnet['tenderfeet'] = ['-0.64', '0', '0.667', '-0.82', '#anger', '#disgust', 'negative', '-0.71', 'novice', 'beginner', 'tyro', 'tiro', 'initiate']
senticnet['tenderfoot'] = ['-0.64', '0', '0.667', '-0.82', '#anger', '#disgust', 'negative', '-0.71', 'novice', 'beginner', 'tyro', 'tiro', 'initiate']
senticnet['tenderheartedness'] = ['0.856', '0', '0', '0.721', '#joy', '#admiration', 'positive', '0.788', 'tenderly', 'feel_for', 'compassionateness', 'sympathize_with', 'condole_with']
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senticnet['tending_bar'] = ['0.023', '0.085', '0', '0', '#joy', '#interest', 'positive', '0.054', 'public_place', 'front_build', 'rest_area', 'memorial', 'nuclear_power_plant']
senticnet['tendinitis'] = ['-0.78', '0', '0.554', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'rubor', 'redness', 'inflammation', 'tennis_elbow', 'lateral_epicondylitis']
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senticnet['tendinous'] = ['0.876', '0.742', '0', '0', '#joy', '#interest', 'positive', '0.809', 'fibrous', 'sinewy', 'stringy', 'unchewable', 'brawny']
senticnet['tendon'] = ['0', '0', '0.868', '-0.92', '#anger', '#disgust', 'negative', '-0.89', 'sinew', 'sinew', 'gristle', 'connective', 'sinewy']
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senticnet['tendonitis'] = ['-0.41', '0', '-0.28', '-0.05', '#sadness', '#fear', 'negative', '-0.25', 'carpal_tunnel_syndrome', 'health_issue', 'breaking_news', 'disease', 'grade_paper']
senticnet['tendosynovitis'] = ['-0.81', '0', '0.789', '-0.51', '#sadness', '#anger', 'negative', '-0.70', 'rubor', 'redness', 'inflammation', 'tenosynovitis', 'synovitis']
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senticnet['tenebre'] = ['-0.79', '0', '0.885', '-0.76', '#sadness', '#anger', 'negative', '-0.81', 'infernal', 'mortal', 'dead_soul', 'dead_soule', 'living_dead']
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senticnet['tengboche'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['tengwar'] = ['0.902', '-0.85', '0', '0.898', '#joy', '#admiration', 'positive', '0.885', 'discerning', 'diacritic', 'discriminating', 'discriminative', 'selective']
senticnet['tennis'] = ['0', '0.249', '-0.14', '0.299', '#interest', '#admiration', 'positive', '0.136', 'sport', 'match_point', 'set_point', 'violent_action', 'excitement']
senticnet['tennis_ball'] = ['0.951', '0.803', '0', '0', '#joy', '#interest', 'positive', '0.877', 'yo-yo', 'ball', 'basketball', 'sport_ball', 'ping_pong_paddle']
senticnet['tennis_court'] = ['0.041', '0.086', '0.016', '0', '#joy', '#interest', 'positive', '0.037', 'team', 'play_sport', 'game', 'exercise', 'match']
senticnet['tennis_elbow'] = ['-0.98', '0', '0.678', '-0.95', '#sadness', '#disgust', 'negative', '-0.87', 'rubor', 'redness', 'inflammation', 'epicondylitis', 'lateral_epicondylitis']
senticnet['tennis_game'] = ['0.024', '0.152', '-0.06', '0.236', '#interest', '#admiration', 'positive', '0.087', 'basketball_court', 'play_sport', 'playground_equipment', 'squash_court', 'gymnasium']
senticnet['tennis_match'] = ['0', '0.11', '0', '0.061', '#interest', '#admiration', 'positive', '0.085', 'middle_road', 'room_temperature', 'car_race', 'tennis', 'loose_end']
senticnet['tennis_player'] = ['0', '0', '0', '0.97', '#admiration', '#admiration', 'positive', '0.97', 'increase_strength', 'keep_fit', 'fit_person', 'exercise', 'strengthen_heart']
senticnet['tennis_racket'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'sports_equipment', 'sport_stuff', 'golf', 'sport_store', 'bag']
senticnet['tennis_racquet'] = ['0', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.91', 'umpire', 'referee', 'legal_proceed', 'clang', 'clamor']
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senticnet['tenofovir_viread'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'indinavir', 'nelfinavir', 'ritonavir', 'saquinavir']
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senticnet['tenor'] = ['-0.82', '0', '0.923', '0', '#sadness', '#anger', 'negative', '-0.87', 'baritone', 'saxophone', 'soprano', 'sax', 'clarinet']
senticnet['tenor_aria'] = ['0.023', '0.303', '-0.08', '0.261', '#interest', '#admiration', 'positive', '0.125', 'school_choral', 'drum_set', 'choral', 'string_quartet', 'band']
senticnet['tenor_bell'] = ['0.034', '0', '0.01', '0.087', '#joy', '#admiration', 'positive', '0.037', 'chime', 'bell_noise', 'alto', 'piano', 'ring_loudly']
senticnet['tenor_sax'] = ['0.049', '0.167', '-0.05', '0.295', '#interest', '#admiration', 'positive', '0.115', 'four_sides', 'sound_system', 'dolby_surround', 'movie', 'surround']
senticnet['tenor_solo'] = ['0.023', '0.303', '-0.08', '0.261', '#interest', '#admiration', 'positive', '0.125', 'school_choral', 'drum_set', 'choral', 'string_quartet', 'band']
senticnet['tenor_voice'] = ['-0.71', '0', '0.998', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'tone', 'quality', 'twang', 'nasality', 'timber']
senticnet['tenormin'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'spironolactone', 'spironolactone_drug']
senticnet['tenosynovitis'] = ['-0.81', '0', '0.789', '-0.51', '#sadness', '#anger', 'negative', '-0.70', 'rubor', 'redness', 'inflammation', 'synovitis', 'tendosynovitis']
senticnet['tense'] = ['-0.17', '0.154', '-0.28', '0.073', '#sadness', '#fear', 'negative', '-0.05', 'doctor_visit', 'bad_feeling', 'consequence', 'symptom', 'physical_reaction']
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senticnet['tense_finale'] = ['0.034', '-0.02', '0.054', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'compass', 'swiss_army_knife', 'bake_pan', 'coffee_mug', 'wine_opener']
senticnet['tense_mood'] = ['-0.17', '0.154', '-0.28', '0.073', '#sadness', '#fear', 'negative', '-0.05', 'doctor_visit', 'bad_feeling', 'consequence', 'symptom', 'physical_reaction']
senticnet['tense_relationship'] = ['0.72', '-0.72', '0', '0.506', '#joy', '#surprise', 'positive', '0.651', 'standoffishness', 'withdrawnness', 'closeness', 'secretiveness', 'remoteness']
senticnet['tense_situation'] = ['-0.2', '-0.03', '0', '-0.23', '#sadness', '#disgust', 'negative', '-0.13', 'beat_up', 'fight', 'conflict', 'violence', 'fight_against']
senticnet['tense_standoff'] = ['-0.2', '-0.03', '0', '-0.23', '#sadness', '#disgust', 'negative', '-0.13', 'beat_up', 'fight', 'conflict', 'violence', 'fight_against']
senticnet['tensely'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicado', 'unpronounceable', 'incommunicative', 'inexpressive', 'silent']
senticnet['tenseness'] = ['-0.84', '0', '0.982', '-0.92', '#anger', '#disgust', 'negative', '-0.91', 'tone', 'tonicity', 'tonus', 'condition', 'status']
senticnet['tensest'] = ['-0.17', '0.154', '-0.28', '0.073', '#sadness', '#fear', 'negative', '-0.05', 'doctor_visit', 'bad_feeling', 'consequence', 'symptom', 'physical_reaction']
senticnet['tensile'] = ['0', '0', '-1', '-0.80', '#fear', '#disgust', 'negative', '-0.90', 'fictile', 'malleable', 'pliable', 'ductile', 'pliant']
senticnet['tensile_force'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'aberrance', 'warp', 'chromosomal_aberration']
senticnet['tensile_strain'] = ['-0.54', '0', '0.909', '-0.97', '#anger', '#disgust', 'negative', '-0.81', 'suppleness', 'pliancy', 'pliantness', 'flexibility', 'flexibleness']
senticnet['tensile_strength'] = ['0.8', '-0.90', '0', '0.675', '#joy', '#surprise', 'positive', '0.794', 'indestructibility', 'durability', 'continuity', 'persistence', 'changelessness']
senticnet['tensile_test'] = ['-0.54', '0', '0.909', '-0.97', '#anger', '#disgust', 'negative', '-0.81', 'suppleness', 'pliancy', 'pliantness', 'flexibility', 'flexibleness']
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senticnet['tension_pneumothorax'] = ['0.759', '-0.72', '0', '0.893', '#joy', '#admiration', 'positive', '0.793', 'manifestation', 'cry', 'reflection', 'acting_out', 'explosion']
senticnet['tensioner'] = ['0', '0.142', '0', '0.125', '#interest', '#admiration', 'positive', '0.134', 'weight_machine', 'power_drill', 'screw', 'screwdriver', 'air_pump']
senticnet['tensity'] = ['-0.62', '0', '0.81', '-0.61', '#sadness', '#anger', 'negative', '-0.68', 'tone', 'tonicity', 'tonus', 'condition', 'status']
senticnet['tensor'] = ['0.959', '0.988', '0', '0', '#joy', '#interest', 'positive', '0.974', 'integer', 'vector', 'quantity', 'scalar', 'constant']
senticnet['tent'] = ['0.783', '0', '0', '0', '#joy', '#joy', 'positive', '0.783', 'camping', 'outdoor', 'camp_out', 'bonfire', 'fresh_air']
senticnet['tent_camp'] = ['0', '0.156', '-0.06', '0.198', '#interest', '#admiration', 'positive', '0.098', 'grassland', 'camping', 'camp_out', 'campsite', 'campground']
senticnet['tentative'] = ['-0.60', '0', '0.722', '-0.84', '#anger', '#disgust', 'negative', '-0.72', 'undetermined', 'undecided', 'unsettled', 'unresolved', 'open']
senticnet['tentative_conclusion'] = ['-0.04', '0.26', '-0.15', '0.239', '#interest', '#admiration', 'positive', '0.075', 'conclude', 'result', 'end_point', 'end', 'finish']
senticnet['tentative_contract'] = ['-0.77', '0', '0.505', '-0.50', '#sadness', '#anger', 'negative', '-0.59', 'wildcat_strike', 'sympathy_strike', 'sympathetic_strike', 'walkout', 'job_action']
senticnet['tentative_deal'] = ['-0.04', '0.274', '-0.15', '0.278', '#interest', '#admiration', 'positive', '0.089', 'business_deal', 'trade', 'exchange', 'bargain', 'offer']
senticnet['tentatively_set'] = ['0', '0.067', '0', '0.062', '#interest', '#admiration', 'positive', '0.065', 'social_skill', 'show_up', 'go_abroad', 'go_new_place', 'visit']
senticnet['tented_camp'] = ['0', '0.156', '-0.06', '0.198', '#interest', '#admiration', 'positive', '0.098', 'grassland', 'camping', 'camp_out', 'campsite', 'campground']
senticnet['tenth_consecutive'] = ['-0.85', '0', '0.584', '-0.79', '#sadness', '#disgust', 'negative', '-0.74', 'age', 'old_age', 'second_childhood', 'eld', 'geezerhood']
senticnet['tenth_episode'] = ['0.918', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.827', 'encounter', 'reveal', 'programme', 'lose', 'sequel']
senticnet['tenth_grader'] = ['0.994', '0.759', '0', '0', '#joy', '#interest', 'positive', '0.877', 'study', 'do_homework', 'student', 'attend_class', 'write']
senticnet['tentorium_cerebelli'] = ['0.719', '-0.57', '0', '0.651', '#joy', '#admiration', 'positive', '0.647', 'chap', 'break', 'slit', 'gap', 'shift']
senticnet['tenuifolia'] = ['-0.52', '0', '0.527', '-0.78', '#anger', '#disgust', 'negative', '-0.61', 'herb', 'black_snakeroot', 'wild_ginger', 'asarabacca', 'herbaceous_plant']
senticnet['tenuifolium'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'bulrush', 'nailrod', 'cattail', 'reedmace']
senticnet['tenuous'] = ['-0.70', '0', '0.723', '0', '#sadness', '#anger', 'negative', '-0.71', 'gaseous', 'tenuousness', 'insubstantial', 'ethereal', 'attenuation']
senticnet['tenuous_atmosphere'] = ['0.095', '0', '-0.04', '0.062', '#joy', '#admiration', 'positive', '0.037', 'photographer', 'water_spray', 'moisture', 'rise_water', 'droplet']
senticnet['tenuousness'] = ['-0.71', '0', '0.721', '0', '#sadness', '#anger', 'negative', '-0.71', 'gaseous', 'tenuous', 'insubstantial', 'ethereal', 'attenuation']
senticnet['tenure'] = ['0', '0.745', '0', '0.832', '#interest', '#admiration', 'positive', '0.788', 'meet_up', 'year', 'rendezvous', 'meet_friend', 'make_appointment']
senticnet['tenured_appointment'] = ['-0.69', '0', '0.806', '-0.74', '#anger', '#disgust', 'negative', '-0.74', 'hypothesis', 'theory', 'base', 'possibility', 'presumption']
senticnet['tenured_associate_professor'] = ['0', '0', '0', '0.97', '#admiration', '#admiration', 'positive', '0.97', 'lecturer', 'teacher', 'instructor', 'college_teacher', 'teach_lesson']
senticnet['tenured_faculty_member'] = ['-0.69', '0', '0.806', '-0.74', '#anger', '#disgust', 'negative', '-0.74', 'hypothesis', 'theory', 'base', 'possibility', 'presumption']
senticnet['tenured_professor'] = ['0', '0', '0', '0.97', '#admiration', '#admiration', 'positive', '0.97', 'lecturer', 'teacher', 'instructor', 'college_teacher', 'teach_lesson']
senticnet['tenzing_norgay'] = ['0', '0.076', '0', '0.063', '#interest', '#admiration', 'positive', '0.07', 'up_hill', 'elevation', 'up_mountain', 'ascend', 'upwards']
senticnet['tepal_white'] = ['-0.50', '0', '0.573', '-0.89', '#anger', '#disgust', 'negative', '-0.65', 'petallike', 'bowery', 'leafy', 'unifoliate', 'bifoliate']
senticnet['tepee'] = ['0.706', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.806', 'shoe', 'flip_flop', 'slipper', 'casual_shoes', 'footwear']
senticnet['tepelen'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
senticnet['tepid'] = ['-0.62', '0', '0.988', '-0.59', '#sadness', '#anger', 'negative', '-0.73', 'motley', 'mixture', 'variety', 'assortment', 'alphabet_soup']
senticnet['tequila'] = ['0.899', '0.991', '0', '0', '#joy', '#interest', 'positive', '0.945', 'bourbon', 'scotch', 'vodka', 'brandy', 'wine']
senticnet['tequila_bar'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'beachcomber', 'bum', 'drifter']
senticnet['tera'] = ['0.578', '-0.51', '0', '0.514', '#joy', '#admiration', 'positive', '0.535', 'fetus', 'baby', 'monster', 'abortus', 'vertebrate']
senticnet['terahertz_thz'] = ['-0.07', '0.078', '0', '0.078', '#interest', '#admiration', 'positive', '0.028', 'red_blue', 'red_orange', 'green_yellow', 'hue_tint', 'yellow_green']
senticnet['terbinafine'] = ['0.562', '-0.54', '0', '0.949', '#joy', '#admiration', 'positive', '0.685', 'antifungal', 'antifungal_agent', 'fungicide', 'antimycotic', 'antimycotic_agent']
senticnet['terbium'] = ['-0.05', '-0.00', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'pigment', 'many_shades', 'cyan', 'green_yellow', 'material']
senticnet['tercentenary'] = ['0.701', '0.867', '0', '0', '#joy', '#interest', 'positive', '0.784', 'festivity', 'special_day', 'birthday', 'special_time', 'cake_candle']
senticnet['tercentenary_theatre'] = ['0', '0', '-0.76', '-0.82', '#fear', '#disgust', 'negative', '-0.79', 'stage', 'thespian', 'actor', 'theatre', 'drama']
senticnet['tercet'] = ['0', '0.911', '0', '0.783', '#interest', '#admiration', 'positive', '0.847', 'deuce-ace', 'trio', 'triplet', 'sacred_trinity', 'trinity']
senticnet['terenty'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'impaired', 'dickey', 'lessened', 'diminished']
senticnet['terephthalic_acid'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'carboxylic_acid', 'succinic_acid', 'valeric_acid']
senticnet['teres_major'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'skeletal_muscle', 'pterygoid_muscle', 'abductor', 'abductor_muscle']
senticnet['teres_minor'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'skeletal_muscle', 'pterygoid_muscle', 'abductor', 'abductor_muscle']
senticnet['terete'] = ['-0.75', '0', '0.734', '-0.82', '#sadness', '#disgust', 'negative', '-0.77', 'puberulent', 'haired', 'furlike', 'pappose', 'pilous']
senticnet['tergiversate'] = ['-0.97', '0', '0.737', '-0.55', '#sadness', '#anger', 'negative', '-0.75', 'deceive', 'sandbag', 'betray', 'lead_astray', 'misinform']
senticnet['teriyaki_sauce'] = ['0', '0', '-0.98', '-0.97', '#fear', '#disgust', 'negative', '-0.97', 'fat', 'obese', 'worse', 'health_issue', 'clinical_problem']
senticnet['terlingua'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'common_bean', 'green_bean', 'wax_bean']
senticnet['termagant'] = ['-0.59', '0', '0.573', '-0.56', '#sadness', '#anger', 'negative', '-0.57', 'disagreeable_woman', 'old_bag', 'shrew', 'frump', 'hellcat']
senticnet['terminal'] = ['0.027', '-0.03', '0.017', '0.1', '#surprise', '#admiration', 'positive', '0.037', 'train_station', 'government_office', 'cable_car', 'baggage_compartment', 'sport_stadium']
senticnet['terminal_bronchiole'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expandible', 'expansive', 'erectile', 'cavernous']
senticnet['terminal_differentiation'] = ['0.085', '0', '0', '0.089', '#joy', '#admiration', 'positive', '0.087', 'steam', 'haze', 'mist', 'misty', 'summer_weather']
senticnet['terminal_disease'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'broken_heart', 'std', 'foolish', 'bad_feeling', 'complication']
senticnet['terminal_illness'] = ['-0.97', '0', '-0.94', '-0.76', '#sadness', '#fear', 'negative', '-0.89', 'ill_health', 'influenza', 'ridiculous', 'chicken_pox', 'pollute_air']
senticnet['terminal_sedation'] = ['-0.03', '-0.02', '-0.08', '-0.18', '#fear', '#disgust', 'negative', '-0.07', 'ibuprofen', 'phenobarbital', 'analgesic', 'sleep_drug', 'pill']
senticnet['terminalia_bellirica'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'nut', 'palm_kernel', 'edible_nut', 'cembra_nut', 'cedar_nut']
senticnet['terminalia_catappa'] = ['-0.59', '0', '0.945', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'nut', 'palm_kernel', 'edible_nut', 'cembra_nut', 'cedar_nut']
senticnet['terminate'] = ['-0.22', '0.086', '-0.22', '0', '#sadness', '#fear', 'negative', '-0.11', 'demolish', 'maim', 'unbearable', 'eliminate', 'take_life']
senticnet['terminating_null'] = ['-0.04', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.06', 'chemical_reaction', 'burn', 'chemical_process', 'get_burnt', 'set_on_fire']
senticnet['termination'] = ['-0.84', '0', '0.744', '-0.58', '#sadness', '#anger', 'negative', '-0.72', 'subversion', 'subversive_activity', 'overthrow', 'ending', 'conclusion']
senticnet['termination_clause'] = ['0.865', '-0.84', '0', '0.915', '#joy', '#admiration', 'positive', '0.875', 'grant', 'sale_in_gross', 'contract_of_hazard', 'adhesion_contract', 'contract_of_adhesion']
senticnet['terminative'] = ['-0.57', '0', '0.713', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'assured', 'conclusion', 'certainty', 'determinateness', 'predictability']
senticnet['terminological'] = ['0.973', '-0.77', '0', '0.983', '#joy', '#admiration', 'positive', '0.911', 'opaqueness', 'faintness', 'dimness', 'opacity', 'vagueness']
senticnet['terminology'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'vocabulary', 'read_material', 'book_genre', 'many_words', 'book_library']
senticnet['terminus_post_quem'] = ['-0.7', '0', '0.746', '-0.78', '#anger', '#disgust', 'negative', '-0.74', 'insufficiency', 'sparseness', 'sparsity', 'abstemiousness', 'lack_of_quantity']
senticnet['termite_damage'] = ['-0.98', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.97', 'harm', 'hurt', 'cut_skin', 'bloody', 'ouch']
senticnet['termite_infestation'] = ['0.718', '0', '0', '0', '#joy', '#joy', 'positive', '0.718', 'water_container', 'still_water', 'clean_water', 'water', 'cool_water']
senticnet['tern_sterna'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'stone_crab', 'swimming_crab', 'rock_crab', 'crab', 'pea_crab']
senticnet['ternational'] = ['0.043', '0.109', '0', '0.061', '#interest', '#admiration', 'positive', '0.071', 'nation_state', 'democratic_country', 'free_country', 'large_country', 'republic']
senticnet['terpinene'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'capsaicin', 'chemical_irritant', 'piperine', 'pepper_spray']
senticnet['terpsichore'] = ['0.817', '-0.62', '0', '0.742', '#joy', '#admiration', 'positive', '0.729', 'tap_dancing', 'tap_dance', 'dancing', 'dance', 'step_dancing']
senticnet['terra_del'] = ['0.964', '0.931', '0', '0', '#joy', '#interest', 'positive', '0.948', 'heel', 'foot_part', 'type_of_shoes', 'shoe_type', 'high_heel']
senticnet['terrace'] = ['0', '0.103', '0', '0.073', '#interest', '#admiration', 'positive', '0.088', 'palace', 'mansion', 'castle', 'bungalow', 'big_house']
senticnet['terrain'] = ['-0.03', '0.091', '0', '0.094', '#interest', '#admiration', 'positive', '0.051', 'earth', 'flat_land', 'geographical_feature', 'dirt_soil', 'acreage']
senticnet['terran'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['terran_empire'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'potency', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['terrazzo_floor'] = ['0.077', '0.073', '0.013', '-0.02', '#joy', '#interest', 'positive', '0.027', 'registry_office', 'radio_studio', 'book', 'conference_auditorium', 'book_office']
senticnet['terrestrial'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'frog', 'amphibian']
senticnet['terrestrial_biome'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'woodland', 'timberland']
senticnet['terrestrial_ecoregion'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'woodland', 'timberland']
senticnet['terrestrial_habitat'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'frog', 'amphibian']
senticnet['terrestrial_lifestyle'] = ['0', '0', '-0.81', '-0.92', '#fear', '#disgust', 'negative', '-0.87', 'tree-living', 'dendroidal', 'arborous', 'arboriform', 'arborary']
senticnet['terrestrial_prey'] = ['0.812', '0.872', '0', '0', '#joy', '#interest', 'positive', '0.842', 'insect', 'fly_away', 'small_insect', 'maggot', 'chihuahua']
senticnet['terrestrial_wildlife'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'frog', 'amphibian']
senticnet['terrible'] = ['-0.96', '0', '-0.96', '-0.78', '#sadness', '#fear', 'negative', '-0.90', 'dread', 'fearful', 'horrible', 'hysteria', 'embarrass']
senticnet['terrible_accident'] = ['-0.91', '0', '0.905', '-0.78', '#sadness', '#anger', 'negative', '-0.86', 'pity', 'shame', 'misfortune', 'mischance', 'misadventure']
senticnet['terrible_affliction'] = ['-0.72', '0.304', '-0.41', '-0.07', '#sadness', '#fear', 'negative', '-0.22', 'suffering', 'miserably', 'pitiable', 'piteous', 'hapless']
senticnet['terrible_car_accident'] = ['-0.73', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.79', 'die_painfully', 'arrest_police', 'get_accident', 'fall_head', 'skin_knee']
senticnet['terrible_crime'] = ['-0.92', '0', '0.095', '-0.91', '#sadness', '#disgust', 'negative', '-0.64', 'mess', 'distressingly_bad', 'awful_event', 'bad_influence', 'terrible_loss']
senticnet['terrible_devastation'] = ['-0.87', '0', '0.889', '-0.62', '#sadness', '#anger', 'negative', '-0.79', 'ruination', 'ruin', 'deterioration', 'blight', 'impairment']
senticnet['terrible_event'] = ['-0.86', '0', '-0.92', '-0.91', '#fear', '#disgust', 'negative', '-0.90', 'tragedy', 'hamlet', 'perpetrator', 'play_person', 'play_in_theater']
senticnet['terrible_headache'] = ['0.052', '-0.04', '-0.05', '-0.20', '#fear', '#disgust', 'negative', '-0.04', 'indigestion', 'make_little_hungry', 'full_stomach', 'sick', 'become_full']
senticnet['terrible_injury'] = ['-0.92', '0', '0.742', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'painful', 'injury', 'hurt', 'pain', 'blood']
senticnet['terrible_loss'] = ['-0.92', '0', '0.095', '-0.91', '#sadness', '#disgust', 'negative', '-0.64', 'mess', 'distressingly_bad', 'terrible_crime', 'bad_influence', 'awful_event']
senticnet['terrible_misunderstand'] = ['0.838', '0.742', '0', '0', '#joy', '#interest', 'positive', '0.79', 'make_fun', 'laughter', 'laugh', 'silliness', 'forget_trouble']
senticnet['terrible_nightmare'] = ['-0.08', '0', '-0.04', '-0.15', '#sadness', '#disgust', 'negative', '-0.09', 'unsettle', 'sleepwalk', 'hallucinate', 'get_rest', 'reflect']
senticnet['terrible_plague'] = ['-0.86', '0', '0.825', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'pestis', 'plague', 'pestilence', 'pest', 'ambulant_plague']
senticnet['terrible_stench'] = ['0', '0.073', '0', '0.092', '#interest', '#admiration', 'positive', '0.082', 'false_tooth', 'scent', 'waste_water', 'sniff', 'odor']
senticnet['terrible_vengeance'] = ['-0.78', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.85', 'revenge', 'retribution', 'go_jail', 'sin', 'incarceration']
senticnet['terribly'] = ['0', '0', '-0.80', '-0.83', '#fear', '#disgust', 'negative', '-0.82', 'crashingly', 'very', 'terror', 'awfully', 'awe']
senticnet['terribly_ill'] = ['-0.71', '0', '-0.96', '-0.94', '#fear', '#disgust', 'negative', '-0.87', 'ill', 'unconscious', 'sick', 'feel_sick', 'sleep_apnea']
senticnet['terrier'] = ['0', '0', '0', '0.059', '#admiration', '#admiration', 'positive', '0.059', 'video_recorder', 'live_human', 'vertebrate', 'elf', 'ice_skater']
senticnet['terrier_mix'] = ['-0.72', '0', '0.619', '-0.86', '#sadness', '#disgust', 'negative', '-0.73', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['terrific'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'wondrously', 'tremendous', 'awesome', 'wondrous', 'marvelous']
senticnet['terrifically'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'terrific', 'wondrously', 'tremendous', 'marvelous', 'wondrous']
senticnet['terrified'] = ['-0.86', '0', '-0.70', '0', '#sadness', '#fear', 'negative', '-0.78', 'panic-stricken', 'panicked', 'panicky', 'panic-struck', 'frightened']
senticnet['terrify'] = ['-0.99', '0', '-0.74', '-0.94', '#sadness', '#disgust', 'negative', '-0.89', 'car_wreck', 'get_hurt', 'bad_hair_day', 'ridiculous', 'dismay']
senticnet['terrifying'] = ['-0.94', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.84', 'horrifying', 'nightmarish', 'hellish', 'horrific', 'frightening']
senticnet['terrifyingly'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['terrine'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'dainty', 'delicacy', 'treat', 'gelatin_dessert', 'jelly']
senticnet['territorial_autonomy'] = ['0.887', '0.748', '0', '0.954', '#joy', '#admiration', 'positive', '0.863', 'political_action', 'political', 'mediocrity', 'discrimination', 'independent']
senticnet['territorial_division'] = ['0.858', '-0.89', '0', '0.719', '#joy', '#surprise', 'positive', '0.823', 'mandatory', 'administrative_district', 'administrative_division', 'district', 'borderland']
senticnet['territorial_dominion'] = ['0.737', '-0.55', '0', '0.884', '#joy', '#admiration', 'positive', '0.725', 'mandatory', 'administrative_district', 'administrative_division', 'territorial_division', 'borderland']
senticnet['territorial_expansion'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'ascendance', 'autocracy', 'absolutism', 'tyrant']
senticnet['territorial_integrity'] = ['0.887', '0.748', '0', '0.954', '#joy', '#admiration', 'positive', '0.863', 'political_action', 'political', 'mediocrity', 'discrimination', 'independent']
senticnet['territorial_jurisdiction'] = ['0.657', '-0.88', '0', '0.631', '#joy', '#surprise', 'positive', '0.723', 'cognisance', 'knowingness', 'awareness', 'feel', 'consciousness']
senticnet['territory'] = ['0', '0.076', '0', '0', '#interest', '#interest', 'positive', '0.076', 'region', 'land', 'land_mass', 'political_unit', 'landmass']
senticnet['terror'] = ['0', '0', '-1', '0', '#fear', '#fear', 'negative', '-1', 'terrified', 'horror', 'dread', 'dreadful', 'fright']
senticnet['terror_stricken'] = ['-0.87', '0', '0.882', '-0.56', '#sadness', '#anger', 'negative', '-0.77', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['terrorise'] = ['-0.89', '0.161', '-0.61', '0.053', '#sadness', '#fear', 'negative', '-0.32', 'heartrending', 'heartbreaking', 'grievous', 'grievously', 'brokenheartedness']
senticnet['terrorism'] = ['-0.88', '0', '-0.98', '-0.72', '#sadness', '#fear', 'negative', '-0.86', 'kill_person', 'violence', 'eliminate', 'kill', 'kill_man']
senticnet['terrorist'] = ['-0.91', '0', '-0.72', '-0.90', '#sadness', '#disgust', 'negative', '-0.84', 'soldier', 'medic', 'enemy', 'warrior', 'victim']
senticnet['terrorist_action'] = ['-0.97', '0', '0.775', '-0.95', '#sadness', '#disgust', 'negative', '-0.89', 'die', 'cut_throat', 'death', 'kill', 'dead_person']
senticnet['terrorist_attack'] = ['-0.97', '0', '0.7', '-0.96', '#sadness', '#disgust', 'negative', '-0.87', 'strike', 'difficult', 'complicated', 'dangerous', 'problem']
senticnet['terrorist_bomb'] = ['-0.57', '0', '0.56', '-0.87', '#sadness', '#disgust', 'negative', '-0.67', 'reprimand', 'rebuke', 'reproof', 'reproval', 'reprehension']
senticnet['terrorist_cell'] = ['0.942', '-0.79', '0', '0.561', '#joy', '#surprise', 'positive', '0.767', 'sleeper_cell', 'cadre', 'radical_cell', 'political_unit', 'political_entity']
senticnet['terrorist_corporation'] = ['-0.94', '0', '0.622', '-0.91', '#sadness', '#disgust', 'negative', '-0.82', 'bad_team', 'terrorist_group', 'bad_company', 'terrorism', 'bad_guy']
senticnet['terrorist_group'] = ['-0.94', '0', '0.622', '-0.91', '#sadness', '#disgust', 'negative', '-0.82', 'bad_team', 'bad_company', 'terrorist_corporation', 'terrorism', 'bad_guy']
senticnet['terrorist_home'] = ['-0.91', '0', '0.453', '-0.90', '#sadness', '#disgust', 'negative', '-0.75', 'war', 'bomb', 'fight_war', 'kill_person', 'weapon']
senticnet['terrorist_organization'] = ['-0.94', '0', '0.622', '-0.91', '#sadness', '#disgust', 'negative', '-0.82', 'bad_organization', 'terrorist_group', 'bad_company', 'terrorism', 'terrorist_corporation']
senticnet['terrorist_suspect'] = ['0', '0', '-0.88', '-0.99', '#fear', '#disgust', 'negative', '-0.93', 'murder_suspect', 'robbery_suspect', 'rape_suspect', 'murder', 'doubt']
senticnet['terrorize'] = ['-0.78', '0', '-0.77', '-0.95', '#sadness', '#disgust', 'negative', '-0.83', 'hurt_others', 'destroy_live', 'terrorism', 'death_destruction', 'murder']
senticnet['terse'] = ['-0.61', '0', '0.73', '-0.85', '#anger', '#disgust', 'negative', '-0.73', 'diction', 'phrasing', 'verbalization', 'wording', 'phraseology']
senticnet['terseness'] = ['-0.66', '0', '0.812', '-0.88', '#anger', '#disgust', 'negative', '-0.78', 'laconism', 'laconicism', 'conciseness', 'concision', 'pithiness']
senticnet['tertiary'] = ['0.899', '0.979', '0', '0', '#joy', '#interest', 'positive', '0.939', 'principal', 'elementary', 'main', 'master', 'chief']
senticnet['tertiary_care'] = ['0.899', '0.979', '0', '0', '#joy', '#interest', 'positive', '0.939', 'principal', 'elementary', 'main', 'master', 'chief']
senticnet['tertiary_education'] = ['0.97', '0', '0.773', '0.878', '#joy', '#admiration', 'positive', '0.358', 'educate', 'get_knowledge', 'new_knowledge', 'expand_knowledge', 'find_out_world']
senticnet['tertiary_prevention'] = ['0.899', '0.979', '0', '0', '#joy', '#interest', 'positive', '0.939', 'principal', 'elementary', 'main', 'master', 'chief']
senticnet['tesco_everyday'] = ['0.064', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.067', 'apple_pie', 'lasagna', 'jelly', 'a_lot_of_food', 'cottage_cheese']
senticnet['teshekpuk_lake'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'habitat', 'mom_house', 'windsor', 'pacific_northwest', 'wilderness']
senticnet['tessellated'] = ['0.603', '-0.53', '0', '0.922', '#joy', '#admiration', 'positive', '0.686', 'adorned', 'ruffled', 'fringed', 'buttony', 'mounted']
senticnet['tesseractic_honeycomb'] = ['0.641', '-0.6', '0', '0.957', '#joy', '#admiration', 'positive', '0.733', 'regular_tetrahedron', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'ideal_solid']
senticnet['test'] = ['0.928', '-0.87', '0', '0.679', '#joy', '#surprise', 'positive', '0.829', 'examine', 'experiment', 'alpha_test', 'beta_test', 'road_test']
senticnet['test_hypothesis'] = ['0.763', '0', '0', '0.719', '#joy', '#admiration', 'positive', '0.741', 'hypothesis', 'big_bang', 'theory', 'prove_theory', 'experiment']
senticnet['test_knowledge'] = ['-0.09', '-0.00', '-0.05', '-0.06', '#sadness', '#disgust', 'negative', '-0.05', 'show_know', 'complete_course', 'get_grade', 'get_qualification', 'receive_grade']
senticnet['test_mettle'] = ['0.927', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.931', 'spirit', 'competition', 'victory', 'winner_loser', 'win_money']
senticnet['test_statistic'] = ['-0.59', '0', '0.672', '-0.83', '#anger', '#disgust', 'negative', '-0.7', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'asymmetry', 'irregularity']
senticnet['test_strength'] = ['0.032', '0', '0', '0.052', '#joy', '#admiration', 'positive', '0.042', 'prove_physical_endurance', 'maintain_muscle_strength', 'release_energy', 'physical_activity', 'get_fit']
senticnet['test_suite'] = ['0.928', '-0.87', '0', '0.679', '#joy', '#surprise', 'positive', '0.829', 'examine', 'experiment', 'alpha_test', 'beta_test', 'road_test']
senticnet['test_taker'] = ['0.928', '-0.87', '0', '0.679', '#joy', '#surprise', 'positive', '0.829', 'examine', 'experiment', 'alpha_test', 'beta_test', 'road_test']
senticnet['test_tube'] = ['0.032', '0', '0', '0.052', '#joy', '#admiration', 'positive', '0.042', 'bunsen', 'chemistry_equipment', 'test', 'chemistry', 'tube']
senticnet['testability'] = ['0.512', '-0.52', '0', '0.874', '#surprise', '#admiration', 'positive', '0.635', 'responsibleness', 'reproducibility', 'duplicability', 'solidness', 'responsibility']
senticnet['testament'] = ['0.962', '0.984', '0', '0', '#joy', '#interest', 'positive', '0.973', 'proof', 'bible', 'scripture', 'evidence', 'testimony']
senticnet['testamentary_trust'] = ['0.851', '0.708', '0', '0', '#joy', '#interest', 'positive', '0.779', 'devisee', 'legatee', 'recipient', 'receiver', 'pensioner']
senticnet['testarossa'] = ['-0.73', '0', '0.72', '-0.93', '#sadness', '#disgust', 'negative', '-0.79', 'imp', 'elf', 'hob', 'leprechaun', 'pixie']
senticnet['testate'] = ['-0.83', '0', '0.941', '0', '#sadness', '#anger', 'negative', '-0.88', 'decedent', 'intestate', 'intestacy', 'testator', 'heir']
senticnet['testator'] = ['-0.83', '0', '0.938', '0', '#sadness', '#anger', 'negative', '-0.88', 'decedent', 'testate', 'intestacy', 'intestate', 'heir']
senticnet['tested'] = ['0.943', '-0.79', '0', '0.696', '#joy', '#surprise', 'positive', '0.812', 'dependable', 'tried', 'trust', 'sure', 'undeviating']
senticnet['tester'] = ['0.928', '-0.87', '0', '0.679', '#joy', '#surprise', 'positive', '0.829', 'examine', 'experiment', 'alpha_test', 'beta_test', 'road_test']
senticnet['testicle'] = ['0.087', '-0.03', '0', '0.063', '#joy', '#admiration', 'positive', '0.062', 'puppet', 'bouncy', 'curly', 'throw_object', 'glove_leather']
senticnet['testicular'] = ['0.087', '-0.03', '0', '0.063', '#joy', '#admiration', 'positive', '0.062', 'puppet', 'bouncy', 'curly', 'throw_object', 'glove_leather']
senticnet['testicular_atrophy'] = ['-0.99', '0', '0.964', '-0.70', '#sadness', '#anger', 'negative', '-0.88', 'blepharitis', 'diverticulitis', 'epiglottitis', 'inflammation', 'prostatitis']
senticnet['testicular_biopsy'] = ['-0.75', '0', '0.715', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'abdominal_pregnancy', 'ectopic_gestation', 'tubal_pregnancy', 'pregnancy', 'gestation']
senticnet['testicular_pain'] = ['-0.99', '0', '0.964', '-0.70', '#sadness', '#anger', 'negative', '-0.88', 'blepharitis', 'diverticulitis', 'epiglottitis', 'inflammation', 'prostatitis']
senticnet['testicular_sperm'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'nonmotile', 'immobile']
senticnet['testicular_tissue'] = ['0.087', '-0.03', '0', '0.063', '#joy', '#admiration', 'positive', '0.062', 'puppet', 'bouncy', 'curly', 'throw_object', 'glove_leather']
senticnet['testified_falsely'] = ['-0.73', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'obstruct_justice', 'get_away_crime', 'punish', 'hurt_else', 'commit_murder']
senticnet['testified_under_oath'] = ['-0.73', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'obstruct_justice', 'get_away_crime', 'punish', 'hurt_else', 'commit_murder']
senticnet['testify'] = ['-0.21', '0.16', '-0.19', '0.057', '#sadness', '#fear', 'negative', '-0.04', 'murder', 'make_judgment', 'call_police', 'arrest_criminal', 'tell_truth']
senticnet['testify_against'] = ['-0.73', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'obstruct_justice', 'get_away_crime', 'punish', 'hurt_else', 'commit_murder']
senticnet['testify_falsely'] = ['-0.73', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'obstruct_justice', 'get_away_crime', 'punish', 'hurt_else', 'commit_murder']
senticnet['testify_truthfully'] = ['-0.73', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'obstruct_justice', 'get_away_crime', 'punish', 'hurt_else', 'commit_murder']
senticnet['testify_under_oath'] = ['-0.73', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'obstruct_justice', 'get_away_crime', 'punish', 'hurt_else', 'commit_murder']
senticnet['testifying_against'] = ['-0.73', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'obstruct_justice', 'get_away_crime', 'punish', 'hurt_else', 'commit_murder']
senticnet['testimonia'] = ['0.587', '-0.94', '0', '0.659', '#surprise', '#admiration', 'positive', '0.731', 'critique', 'review', 'criticism', 'literary_criticism', 'new_criticism']
senticnet['testimonial'] = ['0.56', '-0.79', '0', '0.882', '#surprise', '#admiration', 'positive', '0.747', 'superlative', 'compliment', 'congratulations', 'praise', 'approval']
senticnet['testimonial_evidence'] = ['-0.63', '0', '0.967', '-0.67', '#anger', '#disgust', 'negative', '-0.76', 'condemnatory', 'condemning', 'accusatory', 'accusing', 'accusive']
senticnet['testimony'] = ['0', '0.759', '0', '0.841', '#interest', '#admiration', 'positive', '0.8', 'trial', 'justice', 'negotiator', 'jury', 'judgment']
senticnet['testimony_under_oath'] = ['-0.21', '0.16', '-0.19', '0.057', '#sadness', '#fear', 'negative', '-0.04', 'murder', 'make_judgment', 'call_police', 'arrest_criminal', 'tell_truth']
senticnet['testiness'] = ['-0.89', '0', '0.943', '-0.56', '#sadness', '#anger', 'negative', '-0.79', 'ill_humor', 'distemper', 'pet', 'crossness', 'touchiness']
senticnet['testing'] = ['0.805', '-0.72', '0', '0.699', '#joy', '#surprise', 'positive', '0.744', 'tally', 'numeration', 'tap', 'scrutiny', 'investigating']
senticnet['testoderm'] = ['-0.58', '0', '0.561', '-0.62', '#sadness', '#disgust', 'negative', '-0.58', 'spot', 'speckle', 'dapple', 'mark', 'fleck']
senticnet['testosterone'] = ['0.958', '-0.72', '0', '0.72', '#joy', '#surprise', 'positive', '0.801', 'pressor', 'hormone', 'endocrine', 'internal_secretion', 'catecholamine']
senticnet['testy'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'bad_tempered', 'nervous', 'neurasthenic', 'irritable', 'cranky']
senticnet['tetanus_vaccine'] = ['-0.07', '0', '-0.05', '-0.18', '#sadness', '#disgust', 'negative', '-0.10', 'medical_therapy', 'vaccination', 'doctor', 'medicine', 'hospital']
senticnet['tetchy'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'pervert', 'horny', 'uncomfortable', 'uneasy', 'short_tempered']
senticnet['tetherball'] = ['0.783', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.766', 'baseball_bat', 'water_gun', 'goalie', 'hockey_stick', 'bullseye']
senticnet['tethered_balloon'] = ['-0.04', '0.092', '0', '0.062', '#interest', '#admiration', 'positive', '0.036', 'hangar', 'aircraft_carrier', 'hot_air_balloon', 'war_museum', 'spaceship']
senticnet['tetley_trophy'] = ['0.997', '-0.84', '0', '0.927', '#joy', '#admiration', 'positive', '0.923', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['teton_range'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['tetrachord'] = ['0.524', '-0.75', '0', '0.932', '#surprise', '#admiration', 'positive', '0.736', 'accent', 'assibilation', 'sibilation', 'mispronunciation', 'pronunciation']
senticnet['tetracyclic'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'enhance', 'intensify', 'deepen', 'potentiate', 'heighten']
senticnet['tetragonal_form'] = ['-0.71', '0', '0.509', '-0.92', '#sadness', '#disgust', 'negative', '-0.71', 'natural_process', 'natural_action', 'activity', 'curing', 'congelation']
senticnet['tetrahedral_symmetry'] = ['0.641', '-0.6', '0', '0.957', '#joy', '#admiration', 'positive', '0.733', 'ideal_solid', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'regular_octahedron']
senticnet['tetrahedron'] = ['0.641', '-0.6', '0', '0.957', '#joy', '#admiration', 'positive', '0.733', 'ideal_solid', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'regular_octahedron']
senticnet['tetrahydrocannabinol'] = ['0.049', '0', '0', '0.113', '#joy', '#admiration', 'positive', '0.081', 'solid', 'hard_material', 'construction_material', 'hard', 'calcium']
senticnet['tetrahydrocannabinol_thc'] = ['0.672', '0.632', '-0.23', '0.312', '#joy', '#interest', 'positive', '0.344', 'euphoria', 'buoyancy', 'perkiness', 'jubilance', 'ecstasy']
senticnet['tetrahydrofuran'] = ['-0.81', '0', '0.774', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'acid', 'hypophosphoric_acid', 'periodic_acid', 'oxyacid', 'phosphorous_acid']
senticnet['tetramer'] = ['-0.02', '0.095', '0', '0.117', '#interest', '#admiration', 'positive', '0.063', 'identification', 'paper_square', 'carry_money', 'money_hold', 'credit']
senticnet['tetranychus_urticae'] = ['0', '-0.09', '0.123', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'gypsy_moth', 'japanese_beetle', 'cucumber_beetle', 'mosquito', 'earwig']
senticnet['tetraodon'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'plectognath', 'plectognathfish', 'blowfish', 'triggerfish', 'filefish']
senticnet['tetrose'] = ['-0.52', '0', '0.681', '-0.76', '#anger', '#disgust', 'negative', '-0.65', 'triose', 'simple_sugar', 'pentose', 'hexose', 'aldose']
senticnet['tex_avery'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'child_movie', 'child_story', 'funny_moment', 'childhood']
senticnet['tex_file'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'wall_cover', 'two-dimensional', 'cover_wall', 'pigment', 'dye']
senticnet['tex_latex'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'wall_cover', 'two-dimensional', 'cover_wall', 'pigment', 'dye']
senticnet['tex_mex'] = ['0.116', '-0.02', '0.025', '0.156', '#joy', '#admiration', 'positive', '0.069', 'dance_move', 'latin_dance', 'body_movement', 'waltz', 'dance']
senticnet['tex_typesetting_system'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'wall_cover', 'two-dimensional', 'cover_wall', 'pigment', 'dye']
senticnet['texas_chainsaw_massacre'] = ['-0.08', '-0.01', '-0.16', '-0.13', '#fear', '#disgust', 'negative', '-0.09', 'hockey_game', 'movie', 'scary_movie', 'cinema', 'cctv']
senticnet['texas_death_row'] = ['0', '0', '0.869', '0', '#anger', '#anger', 'negative', '-0.86', 'convoke', 'loose_end', 'car_race', 'auction', 'mortuary']
senticnet['texas_ranger'] = ['-0.87', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.85', 'forest_fire_fighter', 'ranger', 'fire_warden', 'commando', 'game']
senticnet['texinfo'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'wall_cover', 'two-dimensional', 'cover_wall', 'pigment', 'dye']
senticnet['text'] = ['0', '-0.73', '0', '0.792', '#surprise', '#admiration', 'positive', '0.762', 'cohesive', 'cohering', 'growing', 'system', 'cohesion']
senticnet['text_book'] = ['0.83', '0.795', '0', '0', '#joy', '#interest', 'positive', '0.813', 'note_book', 'classroom_chair', 'learn_tool', 'atheist', 'lecturer']
senticnet['text_box'] = ['0', '-0.12', '0', '0', '#surprise', '#surprise', 'positive', '0.127', 'chop', 'glue', 'carve', 'bandage', 'split']
senticnet['text_chat'] = ['-0.51', '0', '0.545', '-0.91', '#anger', '#disgust', 'negative', '-0.65', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['text_editor'] = ['0', '-0.12', '0', '0', '#surprise', '#surprise', 'positive', '0.127', 'chop', 'glue', 'carve', 'bandage', 'split']
senticnet['text_file'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'slot', 'box', 'load_dock', 'safe_deposit_box', 'emporium']
senticnet['text_message'] = ['0.035', '-0.01', '0.027', '0.085', '#joy', '#admiration', 'positive', '0.026', 'telephone', 'find_phone_number', 'telephone_room', 'phone', 'use_telephone']
senticnet['textbook'] = ['0', '0.074', '0.051', '0.105', '#interest', '#admiration', 'positive', '0.043', 'science_book', 'note_book', 'lecture_hall', 'school_book', 'book_magazine']
senticnet['textedit'] = ['0', '-0.12', '0', '0', '#surprise', '#surprise', 'positive', '0.127', 'chop', 'glue', 'carve', 'bandage', 'split']
senticnet['textile_design'] = ['-0.02', '0', '-0.04', '-0.03', '#fear', '#disgust', 'negative', '-0.03', 'studio', 'art_piece', 'art_gallery', 'artist', 'museum']
senticnet['textile_fabric'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_cloth']
senticnet['textile_machinery'] = ['0.966', '0.799', '0', '0', '#joy', '#interest', 'positive', '0.883', 'mechanical_device', 'move_part', 'machine', 'work_well', 'make_car']
senticnet['textual'] = ['0.587', '-0.94', '0', '0.659', '#surprise', '#admiration', 'positive', '0.731', 'critique', 'review', 'criticism', 'literary_criticism', 'new_criticism']
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senticnet['theatrical_flair'] = ['0.608', '-0.55', '0', '0.726', '#joy', '#admiration', 'positive', '0.628', 'genius', 'raw_talent', 'endowment', 'knack', 'talent']
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senticnet['theban_dynasty'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
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senticnet['thegither'] = ['0.027', '0', '0.012', '0.063', '#joy', '#admiration', 'positive', '0.026', 'vegetation', 'tree_flower', 'green_growth', 'small', 'greenery']
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senticnet['thelazia'] = ['-0.74', '0', '0.94', '-0.87', '#anger', '#disgust', 'negative', '-0.85', 'fungal_infection', 'mycosis', 'kerion', 'tinea_pedis', 'athlete_foot']
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senticnet['thelemite'] = ['-0.51', '0', '0.979', '-0.89', '#anger', '#disgust', 'negative', '-0.79', 'invisible', 'concealed', 'nonvisual', 'hidden', 'out_of_sight']
senticnet['thelstan'] = ['-0.92', '0', '0.659', '-0.81', '#sadness', '#disgust', 'negative', '-0.8', 'unripe', 'napping', 'off_guard', 'off_one_guard', 'unprepared']
senticnet['thematic'] = ['0.869', '-0.88', '0', '0.598', '#joy', '#surprise', 'positive', '0.782', 'connectedness', 'connection', 'consistency', 'continuity', 'link']
senticnet['thematic_material'] = ['-0.5', '0', '0.97', '-0.52', '#anger', '#disgust', 'negative', '-0.66', 'simulated', 'apish', 'parrotlike', 'apelike', 'mimic']
senticnet['thematically'] = ['0', '0.839', '0', '0.721', '#interest', '#admiration', 'positive', '0.78', 'stylistically', 'chronologically', 'conceptually', 'narratively', 'tonally']
senticnet['theme_park'] = ['0.709', '0', '0', '0.989', '#joy', '#admiration', 'positive', '0.849', 'combo_pass', 'short_cut', 'save_time', 'multiple_entry', 'ticket']
senticnet['themed_episode'] = ['0.918', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.827', 'encounter', 'reveal', 'programme', 'lose', 'sequel']
senticnet['themed_restaurant'] = ['-0.80', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.79', 'whorehouse', 'brothel', 'massage_parlor', 'bagnio', 'house_of_prostitution']
senticnet['themed_wedding'] = ['0.057', '0.13', '0', '0.119', '#interest', '#admiration', 'positive', '0.102', 'friend_wedding', 'circular_route', 'spanish_restaurant', 'altar', 'monastery']
senticnet['themself'] = ['0.921', '0.884', '0', '0', '#joy', '#interest', 'positive', '0.903', 'mortal', 'soul', 'individual', 'human_being', 'creature']
senticnet['thenar'] = ['-0.41', '0', '-0.28', '-0.05', '#sadness', '#fear', 'negative', '-0.25', 'numbness', 'carpal_tunnel_syndrome', 'weakness', 'pain', 'muscle_damage']
senticnet['thenceforward'] = ['-0.60', '0', '0.509', '-0.67', '#sadness', '#disgust', 'negative', '-0.59', 'dogging', 'persisting', 'sustained', 'consecutive', 'continuous']
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senticnet['theobald_boehm'] = ['-0.75', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.80', 'high-strung', 'uptight', 'restive', 'nervy', 'jumpy']
senticnet['theobromine'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'wooden_spoon', 'cereal_bowl', 'teaspoon', 'container_cup', 'large_container']
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senticnet['theocratic'] = ['0', '0', '-0.87', '-0.89', '#fear', '#disgust', 'negative', '-0.88', 'socialistic', 'fascistic', 'capitalistic', 'collectivist', 'communistic']
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senticnet['theodolite'] = ['0.865', '0.84', '0', '0', '#joy', '#interest', 'positive', '0.853', 'travel', 'find_way', 'help_travelling', 'compass_direction', 'use_compass']
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senticnet['theorbo'] = ['0.033', '0', '0.009', '0.057', '#joy', '#admiration', 'positive', '0.027', 'triangle', 'baton', 'bugle', 'type_of_bow', 'horn']
senticnet['theorem'] = ['0.76', '0.889', '0', '0', '#joy', '#interest', 'positive', '0.825', 'theorem', 'equation', 'approximation', 'lemma', 'algebraic']
senticnet['theoretic_framework'] = ['-0.96', '0', '0.603', '-0.91', '#sadness', '#disgust', 'negative', '-0.82', 'potential', 'potency', 'conceivableness', 'conceivability', 'possibleness']
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senticnet['theoretical_framework'] = ['0.868', '-0.95', '0', '0.548', '#joy', '#surprise', 'positive', '0.791', 'find', 'design', 'preconceive', 'discover', 'create_by_mental_act']
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senticnet['theoretically_capable'] = ['0.028', '0.138', '-0.04', '0.215', '#interest', '#admiration', 'positive', '0.083', 'skilled', 'expert', 'tin', 'dense_water', 'metal_container']
senticnet['theoretically_possible'] = ['-0.80', '0', '-0.80', '-0.96', '#sadness', '#disgust', 'negative', '-0.85', 'unable', 'absurd', 'difficult', 'impractical', 'unimaginable']
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senticnet['theory'] = ['0', '0.2', '-0.06', '0.186', '#interest', '#admiration', 'positive', '0.107', 'supposition', 'hypothesis', 'hypothetical', 'experiment', 'scientific_statement']
senticnet['theosophic'] = ['-0.73', '0', '0.732', '-0.53', '#sadness', '#anger', 'negative', '-0.66', 'insurmountable', 'insuperable', 'unconquerable', 'indomitable', 'unsubduable']
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senticnet['theosophy'] = ['0.656', '-0.66', '0', '0.681', '#surprise', '#admiration', 'positive', '0.668', 'ethical_code', 'system_of_rules', 'anthroposophy', 'logic', 'method']
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senticnet['therapeutic_effect'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'imperium', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['therapeutic_efficacy'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'imperium', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['therapeutic_index'] = ['0.742', '-0.92', '0', '0.564', '#joy', '#surprise', 'positive', '0.744', 'imperium', 'lordship', 'power_of_appointment', 'carte_blanche', 'command']
senticnet['therapeutic_modality'] = ['-0.05', '0', '-0.22', '-0.06', '#fear', '#disgust', 'negative', '-0.11', 'medicine', 'cure', 'medical_procedure', 'sleep_drug', 'nurse']
senticnet['therapeutic_recreation'] = ['0', '0.962', '0', '0.911', '#interest', '#admiration', 'positive', '0.936', 'play_friend', 'competition', 'break', 'watch_baseball', 'fun_exercise']
senticnet['therapeutic_regimen'] = ['-0.05', '0', '-0.22', '-0.06', '#fear', '#disgust', 'negative', '-0.11', 'medicine', 'cure', 'medical_procedure', 'sleep_drug', 'nurse']
senticnet['therapeutic_rehabilitation'] = ['0.641', '-0.78', '0', '0.721', '#surprise', '#admiration', 'positive', '0.715', 'correctional_rehabilitation', 'physical_rehabilitation', 'physical_restoration', 'rehabilitation', 'vocational_rehabilitation']
senticnet['therapeutically'] = ['0', '0.785', '0', '0.852', '#interest', '#admiration', 'positive', '0.819', 'physiologically', 'pharmacologically', 'pharmaceutically', 'biologically', 'chemically']
senticnet['therapist'] = ['0', '0.705', '0', '0.942', '#interest', '#admiration', 'positive', '0.823', 'nurse', 'emergency_ward', 'doctor', 'doctor_examination_room', 'clinic']
senticnet['therapsid'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'hag', 'osteostracan', 'cephalaspid', 'ostracoderm', 'jawless_fish']
senticnet['therapy'] = ['0.053', '-0.30', '0', '0', '#joy', '#surprise', 'positive', '0.18', 'electrocution', 'spread_happiness', 'pleasure', 'solution', 'enjoyment']
senticnet['therapy_cbt'] = ['0.542', '-0.63', '0', '0.912', '#surprise', '#admiration', 'positive', '0.695', 'implosion_therapy', 'flooding', 'reciprocal_inhibition', 'aversion_therapy', 'desensitization_technique']
senticnet['theravance'] = ['0.559', '0.656', '-0.26', '0.285', '#joy', '#interest', 'positive', '0.31', 'zealous', 'avidity', 'eagerness', 'avidness', 'thirstily']
senticnet['therebetween'] = ['-0.02', '0.103', '0.069', '0.151', '#interest', '#admiration', 'positive', '0.039', 'single', 'unit_of_measurement', 'do_it_yourself', 'enumeration', 'alone']
senticnet['thereby'] = ['0.028', '0.138', '-0.04', '0.215', '#interest', '#admiration', 'positive', '0.083', 'skilled', 'expert', 'tin', 'dense_water', 'metal_container']
senticnet['therefor'] = ['-0.99', '0', '0.712', '-0.85', '#sadness', '#disgust', 'negative', '-0.85', 'need', 'obligation', 'requirement', 'desideratum', 'important']
senticnet['therein'] = ['0.546', '-0.74', '0', '0.581', '#surprise', '#admiration', 'positive', '0.624', 'hand', 'commend', 'reach', 'charge', 'give']
senticnet['thereof'] = ['-0.99', '0', '0.712', '-0.85', '#sadness', '#disgust', 'negative', '-0.85', 'need', 'obligation', 'requirement', 'desideratum', 'important']
senticnet['therethrough'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expandible', 'expansive', 'erectile', 'cavernous']
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senticnet['thereunder'] = ['-0.68', '0', '0.967', '-0.65', '#sadness', '#anger', 'negative', '-0.76', 'depart', 'start', 'part', 'start_out', 'sally_forth']
senticnet['thereupon'] = ['-0.91', '0', '0.521', '-0.81', '#sadness', '#disgust', 'negative', '-0.74', 'bullying', 'domineering', 'blustery', 'roughshod', 'cavalier']
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senticnet['thermae_bath_spa'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
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senticnet['thermal_conductivity'] = ['0.728', '0.977', '0', '0.939', '#interest', '#admiration', 'positive', '0.881', 'air_pressure', 'altitude', 'air', 'heavy_air', 'air_density']
senticnet['thermal_decomposition'] = ['0', '0.073', '-0.05', '0.093', '#interest', '#admiration', 'positive', '0.038', 'vapor', 'water_vapor', 'evaporate', 'condense_water', 'bubble']
senticnet['thermal_degradation'] = ['-0.80', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.84', 'popularization', 'profanation', 'demoralization', 'corruption', 'humiliation']
senticnet['thermal_energy'] = ['-0.98', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.87', 'become_fat', 'flatulence', 'feel_rejuvenated', 'tan_skin', 'make_angry']
senticnet['thermal_equilibrium'] = ['-0.98', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.87', 'become_fat', 'flatulence', 'feel_rejuvenated', 'tan_skin', 'make_angry']
senticnet['thermal_expansion'] = ['0', '0', '-0.11', '0', '#fear', '#fear', 'negative', '-0.11', 'chemical_reaction', 'set_fire', 'grow_yard', 'heat_light', 'burn_down_house']
senticnet['thermal_fuse'] = ['-0.09', '-0.01', '-0.10', '-0.20', '#fear', '#disgust', 'negative', '-0.09', 'violence', 'siege', 'conflict', 'bloodshed', 'fight']
senticnet['thermal_instability'] = ['0.965', '-0.84', '0', '0.599', '#joy', '#surprise', 'positive', '0.802', 'mental_faculty', 'faculty', 'module', 'remembrance', 'anamnesis']
senticnet['thermal_insulation'] = ['-0.57', '0', '0.855', '-0.95', '#anger', '#disgust', 'negative', '-0.79', 'soundproof', 'modify', 'alter', 'change', 'weatherstrip']
senticnet['thermal_isolation'] = ['0.03', '0.068', '-0.06', '0.062', '#interest', '#admiration', 'positive', '0.025', 'pacific_northwest', 'american_forest', 'treetop', 'bath', 'hot_air']
senticnet['thermal_neutron'] = ['0.048', '0', '0', '0.092', '#joy', '#admiration', 'positive', '0.07', 'radio_wave', 'sun', 'solar_cell', 'x-ray', 'cosmic_ray']
senticnet['thermal_noise'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'short_circuit', 'error', 'mistake', 'undesired', 'trouble']
senticnet['thermal_overload'] = ['-0.09', '-0.01', '-0.10', '-0.20', '#fear', '#disgust', 'negative', '-0.09', 'violence', 'siege', 'conflict', 'bloodshed', 'fight']
senticnet['thermal_runaway'] = ['-0.09', '-0.01', '-0.10', '-0.20', '#fear', '#disgust', 'negative', '-0.09', 'violence', 'siege', 'conflict', 'bloodshed', 'fight']
senticnet['thermal_shock'] = ['0.03', '0.068', '-0.06', '0.062', '#interest', '#admiration', 'positive', '0.025', 'pacific_northwest', 'american_forest', 'treetop', 'bath', 'hot_air']
senticnet['thermal_shutdown'] = ['-0.09', '-0.01', '-0.10', '-0.20', '#fear', '#disgust', 'negative', '-0.09', 'violence', 'siege', 'conflict', 'bloodshed', 'fight']
senticnet['thermal_stability'] = ['0', '0', '-0.66', '0', '#fear', '#fear', 'negative', '-0.66', 'anger', 'rage', 'annoyance', 'apprehension', 'fear']
senticnet['thermal_underwear'] = ['0.998', '0', '0', '0', '#joy', '#joy', 'positive', '0.998', 'panties', 'lingerie', 'pantyhose', 'slack', 'prescription_medication']
senticnet['thermalization'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'constancy', 'monotony', 'invariance', 'changelessness', 'unchangeability']
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senticnet['thermic_fever'] = ['-0.72', '0', '0.762', '-0.79', '#anger', '#disgust', 'negative', '-0.75', 'heat_exhaustion', 'heat_prostration', 'prostration', 'sunstroke', 'heat_hyperpyrexia']
senticnet['thermo'] = ['0.03', '0.068', '-0.06', '0.062', '#interest', '#admiration', 'positive', '0.025', 'pacific_northwest', 'american_forest', 'treetop', 'bath', 'hot_air']
senticnet['thermo_mechanical'] = ['0.03', '0.068', '-0.06', '0.062', '#interest', '#admiration', 'positive', '0.025', 'pacific_northwest', 'american_forest', 'treetop', 'bath', 'hot_air']
senticnet['thermodynamic'] = ['-0.98', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.87', 'become_fat', 'flatulence', 'feel_rejuvenated', 'tan_skin', 'make_angry']
senticnet['thermodynamic_entropy'] = ['-0.98', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.87', 'become_fat', 'flatulence', 'feel_rejuvenated', 'tan_skin', 'make_angry']
senticnet['thermodynamic_equilibrium'] = ['-0.98', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.87', 'become_fat', 'flatulence', 'feel_rejuvenated', 'tan_skin', 'make_angry']
senticnet['thermodynamic_limit'] = ['-0.98', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.87', 'become_fat', 'flatulence', 'feel_rejuvenated', 'tan_skin', 'make_angry']
senticnet['thermodynamic_process'] = ['-0.65', '0', '0.929', '-0.52', '#sadness', '#anger', 'negative', '-0.70', 'extinction', 'adiabatic_process', 'hardening', 'decay', 'absorption']
senticnet['thermodynamical'] = ['-0.98', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.87', 'become_fat', 'flatulence', 'feel_rejuvenated', 'tan_skin', 'make_angry']
senticnet['thermodynamically'] = ['0', '-0.95', '0', '0.883', '#surprise', '#admiration', 'positive', '0.92', 'kinetically', 'biochemically', 'climatically', 'dimensionally', 'metastable']
senticnet['thermodynamically_favorable'] = ['0.695', '-0.85', '0', '0.501', '#joy', '#surprise', 'positive', '0.684', 'alternative_energy', 'energy', 'energy_of_activation', 'atomic_energy', 'nuclear_energy']
senticnet['thermodynamically_stable'] = ['0.695', '-0.85', '0', '0.501', '#joy', '#surprise', 'positive', '0.684', 'alternative_energy', 'energy', 'energy_of_activation', 'atomic_energy', 'nuclear_energy']
senticnet['thermoelastic'] = ['0.03', '0.068', '-0.06', '0.062', '#interest', '#admiration', 'positive', '0.025', 'pacific_northwest', 'american_forest', 'treetop', 'bath', 'hot_air']
senticnet['thermomechanical'] = ['0.03', '0.068', '-0.06', '0.062', '#interest', '#admiration', 'positive', '0.025', 'pacific_northwest', 'american_forest', 'treetop', 'bath', 'hot_air']
senticnet['thermometer'] = ['0.808', '0.714', '0', '0', '#joy', '#interest', 'positive', '0.761', 'heal', 'take_temperature', 'high_temperature', 'hot_weather', 'temperature_measurement']
senticnet['thermonuclear'] = ['0', '0', '0.853', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'kiloton', 'warhead', 'megaton', 'megaton', 'nuke']
senticnet['thermonuclear_bomb'] = ['0', '0', '-0.72', '-0.94', '#fear', '#disgust', 'negative', '-0.83', 'pistol', 'gun', 'hydrogen_bomb', 'handgun', 'revolver']
senticnet['thermonuclear_device'] = ['0', '0', '-0.72', '-0.94', '#fear', '#disgust', 'negative', '-0.83', 'pistol', 'gun', 'hydrogen_bomb', 'handgun', 'revolver']
senticnet['thermonuclear_fusion'] = ['-0.77', '0', '0.764', '-0.54', '#sadness', '#anger', 'negative', '-0.69', 'pullulation', 'apomixis', 'blastogenesis', 'budding', 'asexual_reproduction']
senticnet['thermonuclear_war'] = ['-0.14', '-0.08', '0.031', '-0.22', '#sadness', '#disgust', 'negative', '-0.07', 'war', 'attack_enemy', 'heart_beat_fast', 'bomb', 'missile']
senticnet['thermonuclear_weapon'] = ['0', '0', '-0.72', '-0.94', '#fear', '#disgust', 'negative', '-0.83', 'pistol', 'gun', 'hydrogen_bomb', 'handgun', 'revolver']
senticnet['thermoplastic_material'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'foil', 'tin_foil', 'gold_foil']
senticnet['thermostat'] = ['0.02', '0.076', '0', '0', '#joy', '#interest', 'positive', '0.048', 'land_earth', 'tidal_wave', 'puddle', 'brine', 'spurt']
senticnet['thermotherapy'] = ['-0.60', '0', '0.544', '-0.62', '#sadness', '#disgust', 'negative', '-0.59', 'remedy', 'cure', 'therapeutic', 'antidote', 'atropine']
senticnet['thermotolerance'] = ['-0.78', '0', '0.6', '-0.61', '#sadness', '#disgust', 'negative', '-0.66', 'suggestibility', 'sensitivity', 'capacity', 'reactivity', 'predisposition']
senticnet['therof'] = ['-0.69', '0', '0.883', '-0.91', '#anger', '#disgust', 'negative', '-0.82', 'stick_in', 'enclose', 'insert', 'feed_in', 'put_in']
senticnet['thesaurus'] = ['-0.05', '0.084', '0', '0.062', '#interest', '#admiration', 'positive', '0.03', 'vocabulary', 'translation', 'book_library', 'book_poetry', 'book_genre']
senticnet['thesis'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'typeface', 'essay', 'dissertation', 'hold_information', 'continue_story']
senticnet['thesis_advisor'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'typeface', 'essay', 'thesis', 'hold_information', 'continue_story']
senticnet['thesis_dissertation'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'typeface', 'essay', 'thesis', 'hold_information', 'continue_story']
senticnet['thesis_proposal'] = ['0', '0.899', '0', '0.846', '#interest', '#admiration', 'positive', '0.873', 'organize_thought', 'scratch_head', 'know_answer', 'speak_out', 'know_talk']
senticnet['thesis_supervisor'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'typeface', 'essay', 'thesis', 'hold_information', 'continue_story']
senticnet['thesp'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'role', 'theatrical_role', 'part', 'persona']
senticnet['thespian'] = ['-0.02', '-0.01', '-0.10', '0', '#sadness', '#fear', 'negative', '-0.03', 'actor', 'balcony_seat', 'performance', 'stage_entrance', 'sticky_floor']
senticnet['theurgic'] = ['-0.82', '0', '0.72', '-0.53', '#sadness', '#anger', 'negative', '-0.69', 'invocation', 'charm', 'spell', 'magic_spell', 'magical_spell']
senticnet['theurgy'] = ['-0.82', '0', '0.72', '-0.53', '#sadness', '#anger', 'negative', '-0.69', 'invocation', 'charm', 'spell', 'magic_spell', 'magical_spell']
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senticnet['thoracostomy_tube'] = ['0.759', '-0.72', '0', '0.893', '#joy', '#admiration', 'positive', '0.793', 'manifestation', 'cry', 'reflection', 'acting_out', 'explosion']
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senticnet['thorough_examination'] = ['-0.07', '0', '-0.13', '0', '#sadness', '#fear', 'negative', '-0.10', 'know_healthy', 'nervous_breakdown', 'physical_exam', 'check_vital_sign', 'epileptic_fit']
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senticnet['three-set'] = ['0', '-0.85', '0', '0.98', '#surprise', '#admiration', 'positive', '0.916', 'hard-fought', 'come-from-behind', 'victory', 'low-scoring', 'nail-biting']
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senticnet['three_dimensional_space'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'width', 'size', 'mass_measure', 'measure', 'gauge']
senticnet['three_dimensionality'] = ['-0.63', '0', '0.694', '-0.66', '#anger', '#disgust', 'negative', '-0.66', 'corporality', 'tangibleness', 'palpability', 'reality', 'solidness']
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senticnet['three_disc_dvd'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['three_fifths_majority'] = ['0.779', '-0.88', '0', '0.521', '#joy', '#surprise', 'positive', '0.727', 'corrigible', 'correctable', 'improvable', 'reformable', 'redeemable']
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senticnet['threesome'] = ['-0.85', '0', '0.968', '-0.89', '#anger', '#disgust', 'negative', '-0.90', 'family', 'household', 'house', 'home', 'broken_home']
senticnet['threeyear_period'] = ['0.807', '0', '0', '0.866', '#joy', '#admiration', 'positive', '0.837', 'fellowship', 'fulltime_support', 'support', 'halftime_support', 'recipient']
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senticnet['threshold_function'] = ['0.858', '0.993', '0', '0', '#joy', '#interest', 'positive', '0.925', 'function', 'map', 'mathematical_function', 'represent', 'mapping']
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senticnet['thrift_shop'] = ['0.048', '0', '0', '0.092', '#joy', '#admiration', 'positive', '0.07', 'gym_locker', 'observatory', 'prison_cell', 'luggage_store', 'luggage_compartment']
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senticnet['thrill-a-minute'] = ['-0.89', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.82', 'rip-roaring', 'edge-of-your-seat', 'edge-of-the-seat', 'nail-biting', 'slam-bang']
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senticnet['throw_ball'] = ['0.054', '0.081', '0', '0.113', '#interest', '#admiration', 'positive', '0.083', 'play_bowling', 'play_game', 'catch_ball', 'play_baseball', 'play_basketball']
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senticnet['throw_object'] = ['0.96', '0.912', '0', '0', '#joy', '#interest', 'positive', '0.936', 'use_sport', 'spherical_shape', 'bouncy', 'aim', 'child_toy']
senticnet['throw_out'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'debar', 'kick_out']
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senticnet['throw_up'] = ['0.714', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.816', 'tipsy', 'forgetfulness', 'hangover', 'run_out_booze', 'stagger']
senticnet['throwaway'] = ['-0.92', '0', '0', '-0.86', '#sadness', '#disgust', 'negative', '-0.89', 'throw-away', 'forgettable', 'tongue-in-cheek', 'banal', 'insipid']
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senticnet['throwing_mud'] = ['-0.86', '0', '0.675', '-0.84', '#sadness', '#disgust', 'negative', '-0.79', 'shenanigan', 'chicanery', 'wile', 'dupery', 'trickery']
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senticnet['thrumming'] = ['0.704', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.851', 'thud', 'thumping', 'thudded', 'clattering', 'thudding']
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senticnet['thrust_belt'] = ['-0.02', '0.095', '0', '0.117', '#interest', '#admiration', 'positive', '0.063', 'identification', 'paper_square', 'carry_money', 'money_hold', 'credit']
senticnet['thrust_chamber'] = ['0.843', '-0.99', '0', '0.706', '#joy', '#surprise', 'positive', '0.849', 'gadgetry', 'gimbal', 'contrivance', 'mod_con', 'device']
senticnet['thrust_fault'] = ['0', '0', '0.869', '-0.92', '#anger', '#disgust', 'negative', '-0.89', 'accuser', 'fall_down', 'fall_off', 'collapse', 'sore_knee']
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senticnet['thudding'] = ['0.709', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.849', 'thud', 'thumping', 'thudded', 'clattering', 'thrumming']
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senticnet['thuggish'] = ['0', '-0.74', '0', '0.796', '#surprise', '#admiration', 'positive', '0.771', 'strong-armed', 'strong-arm', 'underhanded', 'thuggery', 'extort']
senticnet['thuin'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'feudal_lord', 'lord', 'master', 'overlord', 'king']
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senticnet['thuja'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'bottlebrush_buckeye', 'red_buckeye', 'particolored_buckeye']
senticnet['thumb'] = ['0', '0', '0.754', '-0.78', '#anger', '#disgust', 'negative', '-0.77', 'finger', 'bawd', 'shiatsu', 'slider', 'thumb-sketch']
senticnet['thumb-sketch'] = ['0', '0', '0.744', '-0.78', '#anger', '#disgust', 'negative', '-0.76', 'finger', 'bawd', 'shiatsu', 'slider', 'thumb']
senticnet['thumb_down'] = ['0', '0', '0.852', '-0.99', '#anger', '#disgust', 'negative', '-0.92', 'boo', 'complaint', 'bad', 'dislike', 'sad']
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senticnet['thumbwheel'] = ['0.129', '0', '0', '0', '#joy', '#joy', 'positive', '0.129', 'incline_plane', 'boulder', 'ramp', 'hill_mountain', 'rock']
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senticnet['thumping_beat'] = ['0.023', '0.303', '-0.08', '0.261', '#interest', '#admiration', 'positive', '0.125', 'school_choral', 'drum_set', 'choral', 'string_quartet', 'band']
senticnet['thumping_techno'] = ['0.857', '-0.77', '0', '0.878', '#joy', '#admiration', 'positive', '0.836', 'hand', 'round', 'approval', 'commendation', 'clapping']
senticnet['thums_up'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'glass_water', 'refreshed', 'smoothie', 'lemonade', 'cold_water']
senticnet['thunder'] = ['0.045', '0', '0', '0.088', '#joy', '#admiration', 'positive', '0.067', 'loud_sound', 'boom', 'rain_storm', 'sound_wave', 'rain_wind']
senticnet['thunder_butte'] = ['0.029', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.04', 'flower_type', 'rise_plant', 'symbol_love', 'sign_love', 'rose']
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senticnet['thunder_storm'] = ['0', '-0.99', '-0.99', '0', '#surprise', '#fear', 'positive', '0.004', 'blizzard', 'snow_storm', 'storm', 'danger', 'rainy']
senticnet['thunderclap'] = ['0', '-0.00', '-0.07', '-0.06', '#fear', '#disgust', 'negative', '-0.04', 'lighten', 'tornado', 'dynamite_explosion', 'forest_fire', 'lightning']
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senticnet['thundering_herd'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'unofficial', 'informal', 'loose', 'drumhead']
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senticnet['thunderous_dunk'] = ['-0.76', '0', '0.794', '-0.91', '#anger', '#disgust', 'negative', '-0.82', 'discharge', 'spray', 'spritz', 'outpouring', 'run']
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senticnet['thunderously'] = ['-0.83', '0', '0.703', '-0.85', '#sadness', '#disgust', 'negative', '-0.79', 'hurly_burly', 'kerfuffle', 'commotion', 'disruption', 'disturbance']
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senticnet['thunderstorm'] = ['0', '-0.41', '-0.95', '0', '#surprise', '#fear', 'negative', '-0.26', 'blizzard', 'snow_storm', 'storm', 'danger', 'rainy']
senticnet['thunderstorm_activity'] = ['0', '-0.41', '-0.95', '0', '#surprise', '#fear', 'negative', '-0.26', 'blizzard', 'snow_storm', 'storm', 'danger', 'rainy']
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senticnet['thunderstruck'] = ['0', '-0.70', '-0.94', '0', '#surprise', '#fear', 'negative', '-0.11', 'thunderstorm', 'blizzard', 'snow_storm', 'storm', 'rainy']
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senticnet['thurifer'] = ['-0.67', '0', '0.994', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'altar_boy', 'reverend', 'acolyte', 'clergyman', 'man_of_cloth']
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senticnet['thy_bridegroom'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitentially', 'penitent', 'penitence', 'repentant']
senticnet['thy_divine'] = ['0.655', '-0.82', '0', '0.956', '#surprise', '#admiration', 'positive', '0.813', 'superlative', 'compliment', 'testimonial', 'praise', 'approval']
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senticnet['thy_heart'] = ['0.635', '0.639', '-0.24', '0.291', '#joy', '#interest', 'positive', '0.331', 'gladsomeness', 'gladfulness', 'gladsome', 'beaming', 'gladden']
senticnet['thy_heavenly'] = ['0.622', '0.641', '-0.24', '0.285', '#joy', '#interest', 'positive', '0.327', 'gleeful', 'gladsome', 'happiness', 'gladness', 'gladfulness']
senticnet['thy_holy'] = ['0.903', '0', '0', '0', '#joy', '#joy', 'positive', '0.903', 'gospel', 'religion', 'prayer', 'faith', 'world_religion']
senticnet['thy_only_begotten'] = ['0.741', '-0.84', '0', '0.543', '#joy', '#surprise', 'positive', '0.711', 'integrity', 'righteousness', 'scrupulousness', 'incorruptness', 'honestness']
senticnet['thy_revelation'] = ['0.65', '-0.61', '0', '0.703', '#joy', '#admiration', 'positive', '0.657', 'gleaming', 'luster', 'polish', 'glow', 'burnish']
senticnet['thy_sake'] = ['-0.76', '0', '0.998', '-0.63', '#sadness', '#anger', 'negative', '-0.79', 'quetch', 'kvetch', 'complain', 'sound_off', 'plain']
senticnet['thy_salvation'] = ['0.622', '0.641', '-0.24', '0.285', '#joy', '#interest', 'positive', '0.327', 'gleeful', 'gladsome', 'happiness', 'gladness', 'gladfulness']
senticnet['thy_servant'] = ['0.622', '0.641', '-0.24', '0.285', '#joy', '#interest', 'positive', '0.327', 'gleeful', 'gladsome', 'happiness', 'gladness', 'gladfulness']
senticnet['thy_soul'] = ['0.622', '0.641', '-0.24', '0.285', '#joy', '#interest', 'positive', '0.327', 'gleeful', 'gladsome', 'happiness', 'gladness', 'gladfulness']
senticnet['thy_spirit'] = ['0.622', '0.641', '-0.24', '0.285', '#joy', '#interest', 'positive', '0.327', 'gleeful', 'gladsome', 'happiness', 'gladness', 'gladfulness']
senticnet['thy_throne'] = ['-0.52', '0', '0.701', '-0.50', '#sadness', '#anger', 'negative', '-0.57', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['thy_wrath'] = ['-0.84', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.90', 'mortify', 'embarrassed', 'self-hatred', 'self-disgust', 'humiliated']
senticnet['thylacoleo_carnifex'] = ['-0.02', '0', '0.044', '0', '#sadness', '#anger', 'negative', '-0.03', 'large_cat', 'fast_person', 'household_pet', 'big_cat', 'good_pet']
senticnet['thyme'] = ['0.818', '-0.80', '0', '0', '#joy', '#surprise', 'positive', '0.811', 'chive', 'cilantro', 'parsley', 'oregano', 'tarragon']
senticnet['thymol'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
senticnet['thymus_serpyllum'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
senticnet['thyreophoran'] = ['-0.92', '0', '0.64', '-0.52', '#sadness', '#anger', 'negative', '-0.69', 'perching_bird', 'percher', 'animal_order', 'marginocephalian', 'euronithopod']
senticnet['thyroid'] = ['-0.97', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.91', 'hypothyroidism', 'adrenal', 'hormone', 'gland', 'hyperthyroidism']
senticnet['thyroid_cancer'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'weak_body', 'death', 'feel_sick']
senticnet['thyroid_cartilage'] = ['0', '0', '0', '0.106', '#admiration', '#admiration', 'positive', '0.106', 'vocal_chord', 'mouth_noise', 'vocal_sound', 'human_sound', 'vocalization']
senticnet['thyroid_gland'] = ['0.079', '-0.10', '0.046', '0', '#joy', '#surprise', 'positive', '0.047', 'nerve', 'liver', 'groin', 'scrotum', 'diaphragm']
senticnet['thyroid_tissue'] = ['0.079', '-0.10', '0.046', '0', '#joy', '#surprise', 'positive', '0.047', 'nerve', 'liver', 'groin', 'scrotum', 'diaphragm']
senticnet['thyroplasty'] = ['0', '0', '0', '0.106', '#admiration', '#admiration', 'positive', '0.106', 'vocal_chord', 'mouth_noise', 'vocal_sound', 'human_sound', 'vocalization']
senticnet['thyrotoxicosis'] = ['-0.53', '0', '0.667', '-0.55', '#anger', '#disgust', 'negative', '-0.58', 'exophthalmic_goiter', 'glandular_disease', 'gland_disease', 'glandular_disorder', 'adenosis']
senticnet['thysanuran_insect'] = ['-0.70', '0', '0.994', '-0.99', '#anger', '#disgust', 'negative', '-0.89', 'insect', 'bristletail', 'silverfish', 'jumping_bristletail', 'machilid']
senticnet['thysanuron'] = ['-0.73', '0', '0.774', '-0.94', '#anger', '#disgust', 'negative', '-0.81', 'insect', 'bristletail', 'silverfish', 'jumping_bristletail', 'machilid']
senticnet['ti_true'] = ['-0.03', '0.157', '0.698', '-0.66', '#anger', '#disgust', 'negative', '-0.30', 'vengefulness', 'vindictively', 'vengefully', 'revengefully', 'vindictive']
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senticnet['tiananmen_gate'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['tianqiao'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['tiara'] = ['-0.62', '0', '0.961', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'crown', 'diadem', 'jeweled_headdress', 'headdress', 'headgear']
senticnet['tiberian_system'] = ['0.902', '-0.85', '0', '0.898', '#joy', '#admiration', 'positive', '0.885', 'discerning', 'diacritic', 'discriminating', 'discriminative', 'selective']
senticnet['tibetan_antelope'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheep', 'argal', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['tibetan_medicine'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
senticnet['tibetan_terrier'] = ['0', '0', '0.022', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'kind_animal', 'wild_animal', 'predator', 'hound', 'fang']
senticnet['tibia'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'above_leg', 'joint', 'hip', 'knee']
senticnet['tibial'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'socket', 'tooth_socket', 'alveolus', 'acetabulum', 'cotyloid_cavity']
senticnet['tibial_nerve'] = ['-0.41', '0', '-0.28', '-0.05', '#sadness', '#fear', 'negative', '-0.25', 'numbness', 'carpal_tunnel_syndrome', 'weakness', 'pain', 'muscle_damage']
senticnet['tibial_plateau'] = ['-0.53', '0', '0.982', '-0.67', '#anger', '#disgust', 'negative', '-0.73', 'gristle', 'semilunar_cartilage', 'fibrocartilage', 'hyaline_cartilage', 'arytenoid']
senticnet['tibialis_posterior'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'skeletal_muscle', 'pterygoid_muscle', 'abductor', 'abductor_muscle']
senticnet['tibor_dry'] = ['0.975', '-0.66', '0', '0.813', '#joy', '#admiration', 'positive', '0.816', 'cry', 'yell', 'scream', 'shrill', 'call']
senticnet['tic'] = ['0.932', '0.713', '0', '0', '#joy', '#interest', 'positive', '0.823', 'worm_intestine', 'fur_keep_warm', 'bald', 'bad_breath', 'claw']
senticnet['tic_disorder'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'acknowledgeable', 'specifiable', 'recognizable', 'identifiable', 'classifiable']
senticnet['tic_douloureux'] = ['-0.54', '0', '0.543', '-0.63', '#sadness', '#disgust', 'negative', '-0.57', 'hurting', 'pain', 'trigeminal_neuralgia', 'neuralgia', 'sciatica']
senticnet['tic_tac'] = ['0.023', '0.141', '-0.13', '0.174', '#interest', '#admiration', 'positive', '0.052', 'candy', 'push_wind', 'exhalation', 'breaout', 'mouth_action']
senticnet['tic_tac_toe'] = ['0', '0', '0', '0.115', '#admiration', '#admiration', 'positive', '0.115', 'chess_knight', 'strategy', 'pawn', 'chess_queen', 'chess_rook']
senticnet['tick_fever'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'leptomeningitis', 'meningitis', 'epidemic_meningitis', 'brain_fever', 'cerebrospinal_fever']
senticnet['tick_vector'] = ['0.579', '-0.81', '0', '0.833', '#surprise', '#admiration', 'positive', '0.741', 'typhus', 'rat_typhus', 'urban_typhus', 'endemic_typhus', 'rickettsial_disease']
senticnet['ticket'] = ['0.895', '0', '0', '0', '#joy', '#joy', 'positive', '0.895', 'enjoy_concert', 'enjoy_club', 'free_ticket', 'free', 'enjoy_football_match']
senticnet['ticket_booth'] = ['0', '0', '-0.06', '-0.09', '#fear', '#disgust', 'negative', '-0.07', 'ticket_office', 'ticket_window', 'ticket_box', 'box_office', 'passenger_terminal']
senticnet['ticket_box'] = ['0', '-0.00', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.04', 'ticket_office', 'box_office', 'sticky_floor', 'row_seat', 'ticket_booth']
senticnet['ticket_counter'] = ['0.961', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.939', 'newspaper_stand', 'taxiway', 'runway', 'flight_terminal', 'aircraft_roadway']
senticnet['ticket_dispenser'] = ['0.813', '0.832', '0', '0.874', '#interest', '#admiration', 'positive', '0.84', 'ferris_wheel', 'concession_stand', 'food', 'carnival_ride', 'theme_park']
senticnet['ticket_kiosk'] = ['0.813', '0.832', '0', '0.874', '#interest', '#admiration', 'positive', '0.84', 'ferris_wheel', 'concession_stand', 'food', 'carnival_ride', 'theme_park']
senticnet['ticket_office'] = ['0', '0', '-0.07', '-0.09', '#fear', '#disgust', 'negative', '-0.08', 'ticket_box', 'ticket_booth', 'box_office', 'ticket_window', 'theater_ticket']
senticnet['ticket_price'] = ['-0.03', '0.29', '-0.12', '0.203', '#interest', '#admiration', 'positive', '0.083', 'excise', 'severance', 'wage', 'afford', 'revenue']
senticnet['ticket_taker'] = ['0.89', '0.806', '0', '0', '#joy', '#interest', 'positive', '0.848', 'sound_control_room', 'beat_box', 'microphone_boom', 'speaker', 'contralto']
senticnet['ticket_tout'] = ['-0.89', '0', '0.783', '-0.89', '#sadness', '#disgust', 'negative', '-0.85', 'dodge', 'plant', 'scheme', 'strategy', 'wangling']
senticnet['ticket_turnstile'] = ['0.203', '0.111', '0', '0.089', '#joy', '#interest', 'positive', '0.134', 'ticket_taker', 'have_fun', 'go_friend', 'movie_star', 'opus_ticket']
senticnet['ticket_vending_machine'] = ['0.813', '0.832', '0', '0.874', '#interest', '#admiration', 'positive', '0.84', 'ferris_wheel', 'concession_stand', 'food', 'carnival_ride', 'theme_park']
senticnet['ticket_window'] = ['0', '0', '-0.06', '-0.06', '#fear', '#disgust', 'negative', '-0.06', 'ticket_booth', 'ticket_box', 'box_office', 'ticket_office', 'movie_ticket']
senticnet['ticking_away'] = ['0', '0', '0.95', '-0.86', '#anger', '#disgust', 'negative', '-0.90', 'watch', 'stopwatch', 'timepiece', 'wristwatch', 'find_pawnshop']
senticnet['ticking_loudly'] = ['0', '0', '0.95', '-0.86', '#anger', '#disgust', 'negative', '-0.90', 'watch', 'stopwatch', 'timepiece', 'wristwatch', 'find_pawnshop']
senticnet['tickle'] = ['0.755', '0', '0', '0.757', '#joy', '#admiration', 'positive', '0.756', 'entertain', 'amuse', 'hear_funny_joke', 'laugh_joke', 'howl_laughter']
senticnet['tickle_pink'] = ['0.645', '0.637', '-0.23', '0.299', '#joy', '#interest', 'positive', '0.336', 'gladdened', 'stimulating', 'inebriate', 'exalt', 'beatify']
senticnet['ticklish'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'pervert', 'horny', 'uncomfortable', 'uneasy', 'short_tempered']
senticnet['tidal_bay'] = ['-0.79', '0', '0.992', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['tidal_dissipation'] = ['-0.68', '0', '0.946', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'abnormality', 'abnormalcy', 'birth_defect', 'congenital_anomaly', 'congenital_defect']
senticnet['tidal_estuary'] = ['0.933', '0.795', '0', '0', '#joy', '#interest', 'positive', '0.864', 'water_passage', 'small_stream', 'water_flow', 'flowing_water', 'water_stream']
senticnet['tidal_pool'] = ['0', '0', '-0.06', '-0.03', '#fear', '#disgust', 'negative', '-0.05', 'coral_reef', 'saltwater', 'fish_market', 'sushi_restaurant', 'reef']
senticnet['tidal_surge'] = ['-0.78', '0', '0.906', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'rainstorm', 'atmospheric_phenomenon', 'northeaster', 'noreaster', 'hailstorm']
senticnet['tidal_volume'] = ['0', '0.098', '-0.09', '0.115', '#interest', '#admiration', 'positive', '0.04', 'push_wind', 'breath', 'breaout', 'body_reaction', 'mouth_action']
senticnet['tidal_wave'] = ['0.02', '0.076', '0', '0', '#joy', '#interest', 'positive', '0.048', 'land_earth', 'brine', 'puddle', 'hot_water_heater', 'spurt']
senticnet['tidbit'] = ['0.73', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.851', 'sting', 'nip', 'bite', 'flea', 'least']
senticnet['tide'] = ['0.631', '-0.62', '0', '0.519', '#joy', '#surprise', 'positive', '0.59', 'sea_level', 'high_tide', 'springtide', 'direct_tide', 'neap']
senticnet['tide_detergent'] = ['0', '0.095', '0', '0.074', '#interest', '#admiration', 'positive', '0.084', 'buy_wash_powder', 'water_soap', 'turn_faucet', 'take_off', 'fill_bathtub_water']
senticnet['tidewater_railroad'] = ['-0.98', '0', '0.521', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'battery', 'stamp_battery', 'quartz_battery', 'stamp_mill', 'factory']
senticnet['tidiness'] = ['-0.09', '-0.04', '-0.04', '-0.15', '#sadness', '#disgust', 'negative', '-0.06', 'spider', 'back_pain', 'house_look_better', 'asthma', 'barf']
senticnet['tidy'] = ['0.862', '0', '0', '0.99', '#joy', '#admiration', 'positive', '0.926', 'neat', 'tidy_up', 'cleanness', 'personal_cleanliness', 'cleanliness']
senticnet['tidy_profit'] = ['-0.03', '0.139', '0', '0.109', '#interest', '#admiration', 'positive', '0.07', 'exchange_money', 'purchase', 'sell_goods', 'make_rich', 'incur_debt']
senticnet['tidy_room'] = ['0.72', '0.799', '0', '0.753', '#interest', '#admiration', 'positive', '0.757', 'clean_room', 'check_breathe', 'feel_emotion', 'stop_bleed', 'think_future']
senticnet['tidy_sum'] = ['0', '0.249', '0.012', '0.154', '#interest', '#admiration', 'positive', '0.13', 'utility', 'money_value', 'monetary_value', 'money_amount', 'sticker']
senticnet['tidy_up'] = ['0.981', '0.732', '0', '0.846', '#joy', '#admiration', 'positive', '0.853', 'neat', 'tidy', 'expect_guest', 'use_vacuum_cleaner', 'vacuum_floor']
senticnet['tidying'] = ['0.708', '0', '0', '0.961', '#joy', '#admiration', 'positive', '0.835', 'preening', 'primped', 'primping', 'preened', 'schmoozing']
senticnet['tie'] = ['0', '-0.91', '0', '0.739', '#surprise', '#admiration', 'positive', '0.827', 'make_coat', 'make_out_leather', 'overcoat', 'make_ball', 't-shirt']
senticnet['tie-dye'] = ['0.929', '0.746', '0', '0.869', '#joy', '#admiration', 'positive', '0.848', 'paint_picture', 'create_art', 'paint_house', 'make_patchwork_quilt', 'letter_sign']
senticnet['tie_bow'] = ['0', '0.087', '-0.07', '0.1', '#interest', '#admiration', 'positive', '0.039', 'cover_body', 'knit_sweater', 'weave', 'make_drape', 'organize_clothes']
senticnet['tie_down'] = ['0.624', '-0.65', '0', '0.521', '#joy', '#surprise', 'positive', '0.598', 'hold', 'faggot', 'fagot', 'chain_up', 'faggot_up']
senticnet['tie_game'] = ['-0.03', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.03', 'use_crayon', 'sketch', 'create_image', 'pencil_art', 'outline']
senticnet['tie_knot'] = ['0.726', '0.808', '0', '0.861', '#interest', '#admiration', 'positive', '0.798', 'need_companionship', 'ease_pain', 'cup_hand', 'gather_berry', 'hang_picture_on_wall']
senticnet['tie_score'] = ['0.886', '0.744', '0', '0', '#joy', '#interest', 'positive', '0.815', 'competitiveness', 'draw', 'win_money', 'keep_score', 'funny_friend']
senticnet['tie_shoelace'] = ['-0.02', '-0.02', '0.062', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'jump_up_down', 'stand_up', 'jump_rope', 'hike', 'skate']
senticnet['tie_shoes'] = ['-0.1', '-0.03', '0.065', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'put_sneaker', 'bring_dog', 'go_park', 'move_leg', 'put_jogging_shoes']
senticnet['tie_together'] = ['0', '0.123', '-0.06', '0.176', '#interest', '#admiration', 'positive', '0.077', 'bring_together', 'inclusion', 'fuse', 'commence', 'merge']
senticnet['tie_up'] = ['-0.20', '-0.04', '0', '-0.18', '#sadness', '#disgust', 'negative', '-0.11', 'reach_high_shelf', 'lift_foot', 'step_up', 'go_up_down', 'go_upstairs']
senticnet['tied_tightly'] = ['0', '0', '0', '0.09', '#admiration', '#admiration', 'positive', '0.09', 'keep_paper_together', 'bind', 'frighten_person', 'carry_paper', 'beatles_song']
senticnet['tier'] = ['-0.08', '-0.04', '0.023', '-0.07', '#sadness', '#disgust', 'negative', '-0.03', 'spoilage', 'make_toast', 'house_smell_good', 'get_hot', 'bread_crumb']
senticnet['tier_etat'] = ['0.774', '-0.82', '0', '0.63', '#joy', '#surprise', 'positive', '0.744', 'quality', 'perishability', 'perishableness', 'unsatisfactoriness', 'inadequacy']
senticnet['tiered_storage'] = ['0.637', '-0.91', '0', '0.757', '#surprise', '#admiration', 'positive', '0.768', 'tractability', 'manageableness', 'docility', 'obedience', 'amenability']
senticnet['tierra_caliente'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'dryness', 'thirstiness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['tif_file'] = ['-0.55', '0', '0.912', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'wrangle', 'dustup', 'quarrel', 'row', 'squabble']
senticnet['tif_format'] = ['-0.55', '0', '0.912', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'wrangle', 'dustup', 'quarrel', 'row', 'squabble']
senticnet['tiff'] = ['-0.55', '0', '0.912', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'wrangle', 'dustup', 'quarrel', 'row', 'squabble']
senticnet['tiff_ep'] = ['-0.55', '0', '0.912', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'wrangle', 'dustup', 'quarrel', 'row', 'squabble']
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senticnet['tiny_amount'] = ['0.879', '0.951', '0', '0', '#joy', '#interest', 'positive', '0.915', 'sting', 'tidbit', 'nip', 'bite', 'piece']
senticnet['tiny_animal'] = ['0', '0', '0.091', '0', '#anger', '#anger', 'negative', '-0.09', 'tiny_insect', 'small_insect', 'critter', 'furry_creature', 'fly']
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senticnet['tiny_fraction'] = ['0', '0.119', '0', '0.106', '#interest', '#admiration', 'positive', '0.112', 'divide', 'numerator', 'subtraction', 'math_action', 'division']
senticnet['tiny_incision'] = ['0.255', '0.182', '-0.06', '0.314', '#joy', '#admiration', 'positive', '0.173', 'doctor_office', 'urethroscope', 'sigmoidoscope', 'hospital', 'check_health']
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senticnet['tiny_person'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'kid_toy', 'small', 'mannequin', 'build_block', 'plaything']
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senticnet['tipped_cane'] = ['0', '0.931', '0', '0.947', '#interest', '#admiration', 'positive', '0.939', 'gold', 'metal', 'ash_gray', 'fluent', 'eloquent']
senticnet['tipped_off'] = ['0.931', '-0.71', '0', '0.63', '#joy', '#surprise', 'positive', '0.758', 'hustle', 'con', 'bunco', 'sting', 'bunco_game']
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senticnet['tipranavir'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'indinavir', 'nelfinavir', 'ritonavir', 'saquinavir']
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senticnet['tired_eye'] = ['-0.16', '0', '0', '-0.23', '#sadness', '#disgust', 'negative', '-0.20', 'lifestyle_change', 'headache', 'mind_boggle', 'relieve_pain', 'head_hurt']
senticnet['tired_foot'] = ['0.778', '0.961', '0', '0', '#joy', '#interest', 'positive', '0.869', 'lack_of_sleep', 'gyroscope', 'get_tired', 'exhaustion', 'sedative']
senticnet['tired_iron'] = ['0', '-0.06', '0.077', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'lose_ball', 'increase_price', 'ball_bounce', 'punch_bag', 'buy_ball']
senticnet['tired_leg'] = ['-0.78', '0', '0', '-0.80', '#sadness', '#disgust', 'negative', '-0.79', 'dehydration', 'heavy_breathe', 'sweat', 'shin_splint', 'get_sweaty']
senticnet['tired_of'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['tired_person'] = ['-0.90', '0', '0.877', '-0.91', '#sadness', '#disgust', 'negative', '-0.89', 'boredom', 'bored_person', 'annoy_person', 'boring_movie', 'boring_show']
senticnet['tiredness'] = ['-0.80', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.76', 'fatigue', 'exhaustion', 'become_tired', 'get_tired', 'feel_tired']
senticnet['tireless'] = ['0', '0', '0.799', '-0.82', '#anger', '#disgust', 'negative', '-0.81', 'untiring', 'indefatigable', 'unflagging', 'unstinting', 'unremitting']
senticnet['tireless_advocate'] = ['0.865', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.887', 'manager', 'colleague', 'office_worker', 'coworker', 'secretary']
senticnet['tireless_work_ethic'] = ['0.608', '-0.55', '0', '0.726', '#joy', '#admiration', 'positive', '0.628', 'genius', 'endowment', 'flair', 'gift', 'knack']
senticnet['tireless_worker'] = ['0.865', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.887', 'manager', 'colleague', 'office_worker', 'coworker', 'secretary']
senticnet['tirelessly'] = ['0.742', '0', '0', '0.885', '#joy', '#admiration', 'positive', '0.814', 'unselfishly', 'sacrificially', 'courageously', 'heroically', 'selflessly']
senticnet['tiresome'] = ['-0.66', '0', '0.594', '-0.67', '#sadness', '#disgust', 'negative', '-0.64', 'boring', 'deadening', 'irksome', 'uninteresting', 'wearisome']
senticnet['tiresomely'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['tiring'] = ['-0.82', '0', '0.58', '-0.53', '#sadness', '#anger', 'negative', '-0.64', 'effortful', 'wearing', 'wearying', 'hard', 'arduous']
senticnet['tiro'] = ['-0.95', '0', '0.735', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'novice', 'beginner', 'tyro', 'cub', 'initiate']
senticnet['tirthankara'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['tissue'] = ['0.051', '0.066', '0.024', '0', '#joy', '#interest', 'positive', '0.031', 'skeleton', 'skin', 'skin_cover', 'internal_organ', 'flesh']
senticnet['tissue_expander'] = ['-0.81', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.85', 'bandage', 'painkiller', 'gauze', 'aspirin', 'contraceptive_device']
senticnet['tissue_layer'] = ['0.954', '0.993', '0', '0', '#joy', '#interest', 'positive', '0.974', 'membrane', 'protein', 'study', 'cell', 'project']
senticnet['tissue_paper'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'kraft', 'kraft_paper', 'butcher_paper', 'paper']
senticnet['tissue_perfusion'] = ['-0.57', '0', '0.767', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'verboseness', 'verbosity', 'prolixity', 'turgidness', 'flatulence']
senticnet['tissue_repair'] = ['-0.81', '0', '0.504', '-0.96', '#sadness', '#disgust', 'negative', '-0.76', 'freshen', 'air_out', 'air', 'renew', 'vent']
senticnet['tit'] = ['0.818', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.892', 'cunt', 'cock', 'ass', 'pussy', 'fucked']
senticnet['titan'] = ['-0.92', '0', '0.702', '0', '#sadness', '#anger', 'negative', '-0.81', 'giant', 'colossus', 'heavyweight', 'behemoth', 'nasagov']
senticnet['titanate'] = ['-0.05', '-0.00', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'pigment', 'many_shades', 'cyan', 'green_yellow', 'material']
senticnet['titanian'] = ['-0.94', '0', '0.866', '-0.91', '#sadness', '#disgust', 'negative', '-0.90', 'movement', 'apparent_motion', 'motion', 'apparent_movement', 'optical_illusion']
senticnet['titanic'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'submarine', 'lifeboat', 'battleship', 'large_ship', 'canoe']
senticnet['titanic_violin'] = ['0.054', '0.237', '-0.15', '0.33', '#interest', '#admiration', 'positive', '0.117', 'retrieve', 'bring', 'tote', 'take', 'go_get']
senticnet['titanium'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'gold', 'bronze', 'alloy', 'medal', 'copper']
senticnet['titanium_alloy'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'gold', 'bronze', 'alloy', 'medal', 'copper']
senticnet['titanium_carbide'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'gold', 'bronze', 'alloy', 'medal', 'copper']
senticnet['titanium_metal'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'gold', 'bronze', 'alloy', 'medal', 'copper']
senticnet['titanium_ore'] = ['-0.05', '-0.00', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'pigment', 'many_shades', 'cyan', 'green_yellow', 'material']
senticnet['titanium_tetrachloride'] = ['0.766', '-0.87', '0', '0.666', '#joy', '#surprise', 'positive', '0.769', 'reactive', 'unstable', 'labile', 'activated', 'excited']
senticnet['titanocene'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'enhance', 'intensify', 'deepen', 'potentiate', 'heighten']
senticnet['titch'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'rascal', 'rapscallion', 'scalawag', 'holy_terror', 'monkey']
senticnet['tite_kubo'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['tithe'] = ['-0.04', '0.418', '-0.23', '0.35', '#interest', '#admiration', 'positive', '0.122', 'charity', 'excise', 'give', 'extortion', 'return']
senticnet['tithi'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['titillate'] = ['0.676', '0.63', '-0.23', '0.314', '#joy', '#interest', 'positive', '0.346', 'titillated', 'teased', 'titillating', 'titillation', 'perkiness']
senticnet['titillated'] = ['0.671', '0.62', '-0.23', '0.305', '#joy', '#interest', 'positive', '0.34', 'titillate', 'titillating', 'teased', 'titillation', 'perkiness']
senticnet['titillating'] = ['0.676', '0.63', '-0.23', '0.314', '#joy', '#interest', 'positive', '0.346', 'titillate', 'titillated', 'teased', 'titillation', 'perkiness']
senticnet['titillation'] = ['0.676', '0.63', '-0.23', '0.314', '#joy', '#interest', 'positive', '0.346', 'titillate', 'titillated', 'teased', 'titillating', 'perkiness']
senticnet['title'] = ['0.07', '0.134', '-0.07', '0.155', '#interest', '#admiration', 'positive', '0.072', 'royal_person', 'blue_blood', 'royal_title', 'kingly', 'leader']
senticnet['title_acquisition'] = ['1.0', '0', '0', '0.768', '#joy', '#admiration', 'positive', '0.884', 'standard_grant', 'acquisition', 'chemistry', 'material_research', 'department']
senticnet['title_bout'] = ['0.944', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.967', 'meeting', 'meet', 'match', 'encounter', 'round']
senticnet['title_development'] = ['0', '0.797', '0', '0.705', '#interest', '#admiration', 'positive', '0.751', 'award', 'standard_grant', 'continue_grant', 'different_application', 'education']
senticnet['title_dissertation_research'] = ['0', '0.838', '0', '0.814', '#interest', '#admiration', 'positive', '0.826', 'award', 'standard_grant', 'different_application', 'sponsor', 'manager']
senticnet['title_elementary_secondary'] = ['0.797', '0', '0', '0.958', '#joy', '#admiration', 'positive', '0.877', 'award', 'education', 'elem_secondary_informal_sci', 'different_application', 'continue_grant']
senticnet['title_eliminator'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['title_holder'] = ['0', '0', '0', '0.834', '#admiration', '#admiration', 'positive', '0.834', 'royal_person', 'royal_title', 'top', 'master', 'rank']
senticnet['title_hope_alive'] = ['0.844', '0', '0', '0', '#joy', '#joy', 'positive', '0.844', 'straight', 'late_time', 'preparedness', 'past_present', 'wait']
senticnet['title_improvement'] = ['0', '0.741', '0', '0.81', '#interest', '#admiration', 'positive', '0.776', 'award', 'standard_grant', 'undergraduate_education', 'education', 'different_application']
senticnet['title_page'] = ['0.044', '0', '0', '0.206', '#joy', '#admiration', 'positive', '0.125', 'begin_end', 'book', 'book_content', 'book_end', 'frontspiece']
senticnet['title_presidential_young'] = ['0', '0.889', '0', '0.842', '#interest', '#admiration', 'positive', '0.865', 'award', 'continue_grant', 'award_type_award', 'sponsor', 'material_research']
senticnet['title_purchase'] = ['0.833', '0.796', '0', '0', '#joy', '#interest', 'positive', '0.815', 'chemistry', 'department', 'facility_crif', 'standard_grant', 'equipment']
senticnet['title_renovation'] = ['0', '0.974', '0', '0.958', '#interest', '#admiration', 'positive', '0.966', 'award', 'standard_grant', 'office', 'manager', 'green_oia_office']
senticnet['title_sttr_phase'] = ['0', '0.968', '0', '0.868', '#interest', '#admiration', 'positive', '0.918', 'standard_grant', 'project', 'award', 'small_bus_tech_trans', 'technology_transfer']
senticnet['title_track'] = ['0.625', '-0.90', '0', '0.54', '#joy', '#surprise', 'positive', '0.69', 'smash', 'bang', 'smasher', 'strike', 'hit']
senticnet['titleist'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'sports_equipment', 'sport_stuff', 'golf', 'sport_store', 'bag']
senticnet['tito_puente'] = ['0.116', '-0.02', '0.025', '0.156', '#joy', '#admiration', 'positive', '0.069', 'dance_move', 'latin_dance', 'body_movement', 'waltz', 'dance']
senticnet['titter'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'simper', 'please', 'content', 'satisfy', 'gratify']
senticnet['tittle'] = ['0', '0.074', '0.026', '0.105', '#interest', '#admiration', 'positive', '0.051', 'handwritten', 'scribble', 'music_part', 'written_message', 'calligraphy']
senticnet['tittle-tattle'] = ['0', '0', '-0.77', '-0.90', '#fear', '#disgust', 'negative', '-0.83', 'prate', 'gibbering', 'palaver', 'piffle', 'chatter']
senticnet['titular'] = ['-0.96', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.88', 'protagonist', 'villainous', 'nominal', 'eponymous', 'enigmatic']
senticnet['titular_character'] = ['-0.95', '0', '0.774', '0', '#sadness', '#anger', 'negative', '-0.86', 'argue', 'rival', 'disagreement', 'opposition', 'anti']
senticnet['titular_hero'] = ['-0.95', '0', '0.774', '0', '#sadness', '#anger', 'negative', '-0.86', 'argue', 'rival', 'disagreement', 'opposition', 'anti']
senticnet['tizzard'] = ['-0.79', '0', '0.992', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['tizzy'] = ['0', '0', '0.933', '-0.91', '#anger', '#disgust', 'negative', '-0.92', 'nosedive', 'disarray', 'tail-spin', 'tailspin', 'melt-down']
senticnet['tj_maxx'] = ['0.912', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.917', 'carpet_store', 'lumberyard', 'furniture_store', 'front_porch', 'shoe_shop']
senticnet['tko'] = ['0.944', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.967', 'meeting', 'meet', 'match', 'encounter', 'round']
senticnet['tko_due'] = ['0.944', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.967', 'meeting', 'meet', 'match', 'encounter', 'round']
senticnet['tko_victory'] = ['-0.94', '0', '0.863', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'poke', 'clout', 'lick', 'punch', 'biff']
senticnet['tl_connection'] = ['0.078', '0.103', '-0.09', '0.191', '#interest', '#admiration', 'positive', '0.069', 'connect', 'aunt_uncle', 'short_form', 'relationship', 'kinship']
senticnet['tmoin'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'sexual_relationship', 'liaison', 'affair', 'involvement']
senticnet['tmp_worldwide'] = ['-0.63', '0', '0.862', '-0.52', '#sadness', '#anger', 'negative', '-0.67', 'knot', 'bunch', 'tuft', 'tussock', 'agglomeration']
senticnet['tmr_programme'] = ['0', '0', '0', '0.144', '#admiration', '#admiration', 'positive', '0.144', 'go_elsewhere', 'transport_person', 'get_around', 'locomotion', 'trucking']
senticnet['tnf_alpha'] = ['-0.66', '0', '0.877', '-0.64', '#sadness', '#anger', 'negative', '-0.72', 'somatic_cell', 'mastocyte', 'labrocyte', 'target_cell', 'phagocyte']
senticnet['tnt_equivalent'] = ['-0.84', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.85', 'inferno', 'forest_fire', 'fire_light', 'dismissal', 'wash_mouth_soap']
senticnet['to'] = ['0.633', '-0.79', '0', '0.935', '#surprise', '#admiration', 'positive', '0.789', 'reward', 'mentor', 'reinforce', 'spoonfeed', 'lecture']
senticnet['to-do'] = ['-0.80', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.79', 'task', 'reminder', 'to-do', 'to-do-list', 'chore']
senticnet['to-do-list'] = ['-0.80', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.79', 'task', 'reminder', 'to-do', 'to-do', 'chore']
senticnet['to_go'] = ['0', '0.152', '-0.08', '0.208', '#interest', '#admiration', 'positive', '0.091', 'colored_green', 'plant', 'depart', 'green_color', 'tree']
senticnet['to_the_full'] = ['0.702', '0', '0', '0.965', '#joy', '#admiration', 'positive', '0.833', 'in_full', 'fully', 'full', 'amply', 'project']
senticnet['toad'] = ['-0.68', '0', '0.611', '-0.59', '#sadness', '#anger', 'negative', '-0.63', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['toadfish'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'plectognath', 'plectognathfish', 'blowfish', 'triggerfish', 'filefish']
senticnet['toady'] = ['-0.82', '0', '0.612', '-0.51', '#sadness', '#anger', 'negative', '-0.64', 'apple_polisher', 'bootlicker', 'fawner', 'servant', 'groveler']
senticnet['toast'] = ['0.022', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'bread', 'muffin', 'cookie', 'loaf', 'bagel']
senticnet['toasted_bread'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'horseradish', 'box_cereal', 'bottle_water']
senticnet['toasted_coconut'] = ['0.028', '0.138', '-0.04', '0.215', '#interest', '#admiration', 'positive', '0.083', 'water', 'dense', 'thick', 'milk', 'dense_water']
senticnet['toaster'] = ['0.868', '-0.75', '0', '0.795', '#joy', '#admiration', 'positive', '0.805', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['toaster_oven'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'kitchen_cabinet', 'kitchen_cupboard', 'kitchen', 'cupboard', 'own_kitchen']
senticnet['toasty_warm'] = ['0.808', '0', '0', '0', '#joy', '#joy', 'positive', '0.808', 'fall_asleep', 'relax_mind', 'curl_up', 'make_out', 'bed_time']
senticnet['tobacco'] = ['-0.99', '0', '0.056', '-0.98', '#sadness', '#disgust', 'negative', '-0.67', 'smoke', 'cigarette', 'bad_air', 'pollution', 'cigar']
senticnet['tobacco_consumption'] = ['-0.99', '0', '0.056', '-0.98', '#sadness', '#disgust', 'negative', '-0.67', 'smoke', 'cigarette', 'bad_air', 'pollution', 'cigar']
senticnet['tobacco_control'] = ['-0.99', '0', '0.056', '-0.98', '#sadness', '#disgust', 'negative', '-0.67', 'smoke', 'cigarette', 'bad_air', 'pollution', 'cigar']
senticnet['tobacco_farmer'] = ['-0.56', '0', '0.655', '-0.85', '#anger', '#disgust', 'negative', '-0.69', 'slovenly_woman', 'slut', 'trollop', 'slattern', 'slob']
senticnet['tobacco_industry'] = ['-0.99', '0', '0.056', '-0.98', '#sadness', '#disgust', 'negative', '-0.67', 'smoke', 'cigarette', 'bad_air', 'pollution', 'cigar']
senticnet['tobacco_juice'] = ['-0.83', '0', '0.87', '-0.97', '#anger', '#disgust', 'negative', '-0.89', 'precipitate', 'mizzle', 'shower_down', 'rain_down', 'patter']
senticnet['tobacco_leaf'] = ['-0.99', '0', '0.056', '-0.98', '#sadness', '#disgust', 'negative', '-0.67', 'smoke', 'cigarette', 'bad_air', 'pollution', 'cigar']
senticnet['tobacco_pouch'] = ['-0.18', '-0.12', '0.049', '-0.24', '#sadness', '#disgust', 'negative', '-0.09', 'tree_trunk', 'plank', 'artificial_leg', 'hardwood', 'tree_wood']
senticnet['tobacco_product'] = ['0.047', '0', '-0.10', '-0.06', '#fear', '#disgust', 'negative', '-0.03', 'smoke', 'cigarette_smoke', 'smoke_tobacco', 'lung_cancer', 'cigar']
senticnet['tobacco_smoke'] = ['0.047', '0', '-0.10', '-0.06', '#fear', '#disgust', 'negative', '-0.03', 'smoke', 'tobacco_product', 'smoke_tobacco', 'lung_cancer', 'cigar']
senticnet['tobacco_use'] = ['-0.99', '0', '0.056', '-0.98', '#sadness', '#disgust', 'negative', '-0.67', 'smoke', 'cigarette', 'bad_air', 'pollution', 'cigar']
senticnet['tobacco_wilt'] = ['-0.83', '0', '0.754', '-0.97', '#sadness', '#disgust', 'negative', '-0.85', 'brown_rot_gummosis', 'ring_rot', 'ring_disease', 'brown_rot', 'gummosis']
senticnet['tobacconist'] = ['0.026', '0.082', '0', '0', '#joy', '#interest', 'positive', '0.054', 'account_department', 'office', 'accountant_office', 'shirt_pocket', 'desk_drawer']
senticnet['tobiko_flying_fish'] = ['-0.74', '0', '0.748', '-0.81', '#anger', '#disgust', 'negative', '-0.77', 'coral', 'caviar', 'caviare', 'shad_roe', 'seafood']
senticnet['tobin_bell'] = ['-0.82', '0', '0.958', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'character', 'role', 'theatrical_role', 'part', 'persona']
senticnet['toblerone'] = ['0.547', '-0.96', '0', '0.89', '#surprise', '#admiration', 'positive', '0.799', 'pudding', 'ice_cream', 'chocolate', 'chocolate_pudding', 'chocolate_dessert']
senticnet['toboggan'] = ['0.05', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.046', 'skate', 'rollerblade', 'skateboard', 'ice_skate', 'skating']
senticnet['toby'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'impaired', 'dickey', 'lessened', 'diminished']
senticnet['toby_riddle'] = ['0', '0.202', '-0.06', '0.291', '#interest', '#admiration', 'positive', '0.142', 'vocabulary', 'translate', 'dictionary', 'translation', 'thesaurus']
senticnet['toccata'] = ['0.62', '-0.73', '0', '0.511', '#joy', '#surprise', 'positive', '0.621', 'musical_composition', 'opus', 'composition', 'piece', 'piece_of_music']
senticnet['tochiazuma'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['tocino'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'cabbage', 'lettuce', 'broccoli']
senticnet['today'] = ['0.935', '-0.80', '0', '0.547', '#joy', '#surprise', 'positive', '0.761', 'day', 'day_of_month', 'date', 'tomorrow', 'yesterday']
senticnet['today_global_marketplace'] = ['0.837', '0', '0', '0', '#joy', '#joy', 'positive', '0.837', 'a_lot_of_sex', 'desirable', 'give_birth_child', 'sexual_intercourse', 'diversity']
senticnet['today_savannah_guthrie'] = ['0.54', '-0.64', '0', '0.566', '#surprise', '#admiration', 'positive', '0.582', 'pleaser', 'entertainer', 'mortal', 'busker', 'drawing_card']
senticnet['toddle'] = ['-0.74', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.81', 'coggle', 'paddle', 'dodder', 'totter', 'waddle']
senticnet['toddler'] = ['-0.06', '0', '0.01', '0', '#sadness', '#anger', 'negative', '-0.03', 'youth', 'young_child', 'small_child', 'lad', 'tiny']
senticnet['toddler_son'] = ['-0.06', '0', '0.01', '0', '#sadness', '#anger', 'negative', '-0.03', 'youth', 'young_child', 'small_child', 'lad', 'tiny']
senticnet['toe'] = ['-0.03', '-0.02', '0.065', '-0.02', '#sadness', '#anger', 'negative', '-0.02', 'leg', 'ankle', 'hold_canvas', 'foot', 'heel']
senticnet['toe-tapping'] = ['0.819', '-0.83', '0', '0', '#joy', '#surprise', 'positive', '0.827', 'good-time', 'foot-stomping', 'down-home', 'rollicking', 'hard-rocking']
senticnet['toe_claw'] = ['-0.60', '0', '0.701', '-0.72', '#anger', '#disgust', 'negative', '-0.67', 'lunate_bone', 'semilunar_bone', 'carpal_bone', 'triquetral', 'triquetral_bone']
senticnet['toe_injury'] = ['-0.90', '0', '-0.91', '-0.73', '#sadness', '#fear', 'negative', '-0.85', 'broken_window', 'step_dog_shit', 'broken_leg', 'brain_tumor', 'bad_luck']
senticnet['toe_line'] = ['0.731', '-0.59', '0', '0.634', '#joy', '#admiration', 'positive', '0.652', 'accommodate', 'obey', 'conform_to', 'abide_by', 'oblige']
senticnet['toe_nail'] = ['0', '0.952', '0', '0', '#interest', '#interest', 'positive', '0.952', 'make_up', 'bathroom_wall', 'fallen_tree', 'old_food', 'moldy_bread']
senticnet['toefl_minimum'] = ['0.926', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.916', 'score_goal', 'accomplish', 'finalize', 'pot', 'make_point']
senticnet['toefl_preparation'] = ['0.633', '-0.96', '0', '0.612', '#joy', '#surprise', 'positive', '0.738', 'particularism', 'absorption', 'concentration', 'engrossment', 'centering']
senticnet['toehold'] = ['0.961', '-0.84', '0', '0.958', '#joy', '#admiration', 'positive', '0.923', 'pause', 'break', 'intermission', 'suspension', 'rest_period']
senticnet['toei_animation'] = ['-0.67', '0', '0.931', '-0.77', '#anger', '#disgust', 'negative', '-0.79', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['toenail'] = ['0', '0', '-0.91', '-0.92', '#fear', '#disgust', 'negative', '-0.91', 'ingrown', 'claw', 'hair', 'callus', 'ingrowing']
senticnet['toffee'] = ['-0.71', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.74', 'butterscotch', 'caramel', 'pecan', 'fudge', 'praline']
senticnet['tofu'] = ['0', '-0.03', '0.046', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'pasta', 'green_bean', 'mayonnaise', 'frozen_food', 'sour_milk']
senticnet['tofu_dish'] = ['-0.88', '0', '0.864', '-0.72', '#sadness', '#anger', 'negative', '-0.82', 'vixenish', 'venomous', 'bitchy', 'catty', 'malicious']
senticnet['toga_party'] = ['0', '-0.08', '0', '0', '#surprise', '#surprise', 'positive', '0.082', 'yo-yo', 'playroom', 'backgammon', 'action_figure', 'board_game']
senticnet['togedere'] = ['-0.63', '0', '0.791', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'little', 'pinch', 'modicum', 'small_indefinite_amount', 'indefinite_quantity']
senticnet['together'] = ['0.211', '0.103', '-0.08', '0.256', '#joy', '#admiration', 'positive', '0.122', 'come_together', 'group_of_people', 'bring_together', 'part_of_group', 'team_up']
senticnet['together_with'] = ['0.787', '0.948', '0', '0', '#joy', '#interest', 'positive', '0.867', 'collectively', 'jointly', 'conjointly', 'graduate_education', 'award']
senticnet['togetherness'] = ['0.676', '0.695', '-0.26', '0.402', '#joy', '#interest', 'positive', '0.377', 'trust', 'capture', 'comfortableness', 'comfortably', 'solace']
senticnet['toggle'] = ['-0.03', '0.11', '0', '0.128', '#interest', '#admiration', 'positive', '0.069', 'freestyle', 'take_leak', 'jump_air', 'backstroke', 'go_underwater']
senticnet['toggle_button'] = ['0.777', '-0.85', '0', '0.615', '#joy', '#surprise', 'positive', '0.748', 'think', 'play', 'trifle', 'abstract', 'warm_to']
senticnet['toil'] = ['-0.22', '0.182', '-0.27', '0.162', '#sadness', '#fear', 'negative', '-0.04', 'labor', 'employment', 'task', 'employ', 'obligation']
senticnet['toilet'] = ['0.036', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.051', 'bathroom_drawer', 'bathroom', 'beauty_parlor', 'hotel_bathroom', 'bath']
senticnet['toilet_block'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'commode', 'potty_seat', 'potty_chair']
senticnet['toilet_bowl'] = ['0', '-0.02', '0.041', '-0.10', '#anger', '#disgust', 'negative', '-0.03', 'separate_shower', 'shampoo_conditioner', 'towel_bar', 'shower_head', 'toilet_brush']
senticnet['toilet_brush'] = ['0.821', '0.864', '0', '0', '#joy', '#interest', 'positive', '0.843', 'shampoo_conditioner', 'toothpaste', 'electric_razor', 'gel_toothpaste', 'tartar_control_toothpaste']
senticnet['toilet_cubicle'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'commode', 'potty_seat', 'potty_chair']
senticnet['toilet_flush'] = ['0.822', '0.802', '0', '0', '#joy', '#interest', 'positive', '0.812', 'electric_toothbrush', 'towel', 't_shirt', 'heat_towel_rack', 'hair_brush']
senticnet['toilet_humor'] = ['-0.9', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.80', 'filth', 'smut', 'vulgarism', 'dirt', 'obscenity']
senticnet['toilet_lid'] = ['0.822', '0.802', '0', '0', '#joy', '#interest', 'positive', '0.812', 'electric_toothbrush', 'towel', 't_shirt', 'heat_towel_rack', 'hair_brush']
senticnet['toilet_paper'] = ['0.831', '0.971', '0', '0', '#joy', '#interest', 'positive', '0.901', 'towel', 'wax_dental_floss', 'disposable_razor', 'fluoride_toothpaste', 'hair_brush']
senticnet['toilet_paper_dispenser'] = ['0.822', '0.802', '0', '0', '#joy', '#interest', 'positive', '0.812', 'electric_toothbrush', 'towel', 't_shirt', 'heat_towel_rack', 'hair_brush']
senticnet['toilet_seat'] = ['0.822', '0.802', '0', '0', '#joy', '#interest', 'positive', '0.812', 'electric_toothbrush', 'towel', 't_shirt', 'heat_towel_rack', 'hair_brush']
senticnet['toilet_seat_cover'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'lavatory', 'commode', 'potty_chair']
senticnet['toilet_stall'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'commode', 'potty_seat', 'potty_chair']
senticnet['toilet_tank'] = ['0.939', '0.939', '0', '0', '#joy', '#interest', 'positive', '0.939', 'puddle', 'sop', 'liquid_water', 'finger_paint', 'bread']
senticnet['toilet_tissue'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'hankie', 'kleenex', 'hanky', 'handkerchief', 'tissue']
senticnet['toiletry_kit'] = ['0', '-0.02', '0.027', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'shoulder_bag', 'satchel', 'satchel_bag', 'tote_bag', 'carry']
senticnet['toilette'] = ['0.625', '-0.88', '0', '0.782', '#surprise', '#admiration', 'positive', '0.762', 'judicious', 'prudent', 'prudential', 'heady', 'circumspect']
senticnet['toilsome'] = ['-0.60', '0', '0.761', '-0.84', '#anger', '#disgust', 'negative', '-0.73', 'vain', 'unproductive', 'fruitless', 'futile', 'sleeveless']
senticnet['token'] = ['0.02', '0.079', '0', '0.069', '#interest', '#admiration', 'positive', '0.056', 'paper_bill', 'metal_coin', 'cash_bill', 'cash_coin', 'coin']
senticnet['token_booth'] = ['0.706', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.768', 'train_station', 'subway_stop', 'parking_lot', 'subway_station', 'park_structure']
senticnet['token_opposition'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['token_slot'] = ['0.035', '0.078', '0.025', '0', '#joy', '#interest', 'positive', '0.029', 'coin_slot', 'transit_ticket', 'passenger_ticket', 'train_ticket', 'museum_ticket']
senticnet['token_turnstile'] = ['0.096', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.057', 'chain', 'platform_rail', 'catwalk', 'column', 'rail']
senticnet['toki_clan'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'overlord', 'lord']
senticnet['tokitsukaze_stable'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['tokomaru_bay'] = ['-0.91', '0', '0.769', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'break', 'good_luck', 'chance_eventpy_chance', 'chance_event', 'coincidence']
senticnet['tokyo'] = ['0', '0', '0', '0.073', '#admiration', '#admiration', 'positive', '0.073', 'newyork', 'london', 'beijing', 'rome', 'big_city']
senticnet['tokyo_electron'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'builder']
senticnet['tokyo_fm_radio'] = ['0', '0.069', '0', '0.083', '#interest', '#admiration', 'positive', '0.076', 'latch', 'restrict_access', 'knob', 'door_knob', 'gate']
senticnet['tokyo_string_quartet'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'classical_music', 'make_beautiful_music', 'create_music', 'part_of_band', 'make_music']
senticnet['tokyo_subway_system'] = ['-0.90', '0', '0.529', '-0.84', '#sadness', '#disgust', 'negative', '-0.75', 'poison_gas', 'nerve_agent', 'mustard_gas', 'mustard_agent', 'blistering_agent']
senticnet['tokyo_ueno_zoo'] = ['0.02', '0.071', '0', '0.069', '#interest', '#admiration', 'positive', '0.053', 'many_animals', 'donkey', 'chimpanzee', 'cute_animal', 'creature']
senticnet['tolazamide'] = ['-0.72', '0', '0.973', '-0.57', '#sadness', '#anger', 'negative', '-0.76', 'corpulency', 'blubber', 'fat', 'embonpoint', 'stoutness']
senticnet['tolerable'] = ['0', '0.726', '0', '0.914', '#interest', '#admiration', 'positive', '0.82', 'patient', 'bear_up', 'tolerate', 'bearable', 'swallow']
senticnet['tolerably'] = ['0.621', '0.648', '-0.25', '0.296', '#joy', '#interest', 'positive', '0.328', 'pacifically', 'repose', 'peace-loving', 'peaceably', 'peace_of_mind']
senticnet['tolerably_well'] = ['-0.54', '0', '0.739', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'unprofitable', 'idle', 'lean', 'dead', 'marginal']
senticnet['tolerance'] = ['0', '0.828', '0', '0.75', '#interest', '#admiration', 'positive', '0.789', 'patient', 'bear_up', 'tolerate', 'live_with', 'swallow']
senticnet['tolerant'] = ['0', '0.985', '0', '0.794', '#interest', '#admiration', 'positive', '0.889', 'patient', 'bear_up', 'tolerate', 'endurable', 'swallow']
senticnet['tolerate'] = ['0', '0.809', '0', '0.889', '#interest', '#admiration', 'positive', '0.849', 'patient', 'bear_up', 'stand', 'live_with', 'swallow']
senticnet['tolerate_drought'] = ['0', '0.985', '0', '0.794', '#interest', '#admiration', 'positive', '0.889', 'patient', 'bear_up', 'tolerate', 'endurable', 'swallow']
senticnet['tolerate_sexual_assault'] = ['-0.64', '0', '0.854', '-0.83', '#anger', '#disgust', 'negative', '-0.77', 'crime', 'bigamy', 'regulatory_offence', 'sexual_abuse', 'sex_crime']
senticnet['toleration'] = ['0', '-0.81', '0', '0.863', '#surprise', '#admiration', 'positive', '0.837', 'tolerance', 'tolerant', 'forbearance', 'religion', 'endurance']
senticnet['toleration_act'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'heterodoxy', 'iconoclasm', 'nonconformism', 'nonconformance', 'orientation']
senticnet['toll'] = ['-0.28', '0', '-0.23', '-0.12', '#sadness', '#fear', 'negative', '-0.21', 'tax', 'rend', 'bill', 'owe', 'reward']
senticnet['toll_bridge'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'suspension_bridge', 'under_bridge', 'roadway', 'county_highway', 'long_road']
senticnet['toll_expressway'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['toll_highway'] = ['0', '0', '0.024', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'toll_road', 'national_highway', 'superhighway', 'international_highway', 'motorway']
senticnet['toll_motorway'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'expressway', 'turnpike', 'toll_road', 'highway']
senticnet['toll_plaza'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['toll_road'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'autostrada', 'turnpike', 'expressway', 'highway']
senticnet['tollway'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['toluidine_blue'] = ['0', '-0.01', '0.046', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'varnish', 'brush', 'dye', 'pigment', 'canvas']
senticnet['tomahawk'] = ['0', '0', '-0.83', '-0.8', '#fear', '#disgust', 'negative', '-0.81', 'armory', 'tank', 'hydrogen_bomb', 'rifle', 'use_hunt']
senticnet['tomato'] = ['0.968', '-0.83', '0', '0', '#joy', '#surprise', 'positive', '0.903', 'sauce', 'soup', 'chili', 'chicken', 'garlic']
senticnet['tomato_basil'] = ['0.783', '-0.63', '0', '0.829', '#joy', '#admiration', 'positive', '0.749', 'leg_of_lamb', 'gigot', 'joint', 'roast', 'rack_of_lamb']
senticnet['tomato_bisque'] = ['0.084', '-0.05', '0', '-0.04', '#joy', '#surprise', 'positive', '0.031', 'entree', 'feast', 'satiation', 'breakfast', 'supper']
senticnet['tomato_fruit'] = ['-0.57', '0', '0.767', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'verboseness', 'verbosity', 'prolixity', 'turgidness', 'flatulence']
senticnet['tomato_juice'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'lemon_juice', 'chocolate_pudding', 'grape_juice', 'orange_juice', 'juice']
senticnet['tomato_ketchup'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'horseradish', 'box_cereal', 'bottle_water']
senticnet['tomato_plant'] = ['0.904', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.907', 'eden', 'garden', 'usual_green', 'apple', 'leafy']
senticnet['tomato_salad'] = ['0.041', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.043', 'meatloaf', 'salad', 'delicious_meal', 'casserole', 'apple_pie']
senticnet['tomato_salsa'] = ['0', '0', '0.066', '-0.07', '#anger', '#disgust', 'negative', '-0.07', 'container_cup', 'cup', 'grease_trap', 'dish', 'open_container']
senticnet['tomato_sauce'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'horseradish', 'box_cereal', 'bottle_water']
senticnet['tomato_soup'] = ['0.937', '0', '0', '0.828', '#joy', '#admiration', 'positive', '0.883', 'bakery', 'produce_section', 'thanksgiving_dinner', 'dinner_meal', 'fast_food']
senticnet['tomatoey'] = ['0.076', '0', '0.037', '0.065', '#joy', '#admiration', 'positive', '0.035', 'serve_cold', 'tasty_good', 'tasty', 'sour', 'high_calorie']
senticnet['tomatoey_sauce'] = ['0.084', '-0.05', '0', '-0.04', '#joy', '#surprise', 'positive', '0.031', 'entree', 'feast', 'satiation', 'breakfast', 'supper']
senticnet['tomb'] = ['-0.92', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.91', 'bury_body', 'coffin', 'headstone', 'morgue', 'tombstone']
senticnet['tomb_monument'] = ['-0.92', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.91', 'bury_body', 'coffin', 'headstone', 'morgue', 'tombstone']
senticnet['tomb_tt'] = ['-0.92', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.91', 'bury_body', 'coffin', 'headstone', 'morgue', 'tombstone']
senticnet['tomboy'] = ['0.619', '-0.56', '0', '0.523', '#joy', '#surprise', 'positive', '0.57', 'girl', 'miss', 'missy', 'young_lady', 'young_woman']
senticnet['tomboyish'] = ['0.584', '-0.59', '0', '0.753', '#surprise', '#admiration', 'positive', '0.643', 'ungracious', 'discourteous', 'impolite', 'brattish', 'unparliamentary']
senticnet['tombstone'] = ['-0.92', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.91', 'bury_body', 'coffin', 'headstone', 'morgue', 'tomb']
senticnet['tome'] = ['-0.04', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.08', 'data', 'awareness', 'information', 'saga', 'knowledge']
senticnet['tomentosa'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['tomentose'] = ['-0.58', '0', '0.66', '-0.8', '#anger', '#disgust', 'negative', '-0.68', 'bald', 'depilous', 'glabrescent', 'glabrous', 'balding']
senticnet['tomfool'] = ['0.512', '-0.92', '0', '0.688', '#surprise', '#admiration', 'positive', '0.709', 'morosoph', 'simpleton', 'flibbertigibbet', 'foolish_woman', 'simple']
senticnet['tomfoolery'] = ['0.931', '-0.84', '0', '0.829', '#joy', '#surprise', 'positive', '0.869', 'folly', 'lunacy', 'craziness', 'indulgence', 'shtik']
senticnet['tomica'] = ['0.087', '0.113', '-0.08', '0', '#joy', '#interest', 'positive', '0.038', 'find_match', 'strike_against', 'cigarette_lighter', 'match', 'use_lighter']
senticnet['tomograph'] = ['-0.93', '0', '0.739', '-0.96', '#sadness', '#disgust', 'negative', '-0.88', 'prevarication', 'fairytale', 'fairy_tale', 'fairy_story', 'play']
senticnet['tomography'] = ['0', '0', '-0.77', '-0.99', '#fear', '#disgust', 'negative', '-0.88', 'non-invasive', 'angiography', 'noninvasive', 'invasive', 'non-intrusive']
senticnet['tomorrow'] = ['0.93', '-0.80', '0', '0.542', '#joy', '#surprise', 'positive', '0.759', 'today', 'day_of_month', 'date', 'day', 'yesterday']
senticnet['tomorrow_morning'] = ['0.02', '0.067', '0', '0', '#joy', '#interest', 'positive', '0.044', 'sunrise', 'begin_day', 'bright_sky', 'sunny', 'part_of_day']
senticnet['ton'] = ['-0.64', '0', '0.87', '-0.74', '#anger', '#disgust', 'negative', '-0.75', 'infinitude', 'piles', 'mess', 'large_number', 'large_indefinite_amount']
senticnet['tonal'] = ['-0.52', '0', '0.902', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'discordant', 'inharmonious', 'unresolved', 'false', 'disharmonious']
senticnet['tonal_harmony'] = ['0.136', '-0.11', '0.031', '0', '#joy', '#surprise', 'positive', '0.075', 'rap', 'popular_music', 'music', 'band_play', 'rock_roll']
senticnet['tonality'] = ['-0.52', '0', '0.902', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'discordant', 'inharmonious', 'unresolved', 'false', 'disharmonious']
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senticnet['tone'] = ['0.941', '0.822', '0', '0', '#joy', '#interest', 'positive', '0.881', 'melodic_noise', 'universal_language', 'instrumental', 'musical_note', 'audio']
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senticnet['topic_covered_include'] = ['0.04', '0.22', '-0.17', '0.362', '#interest', '#admiration', 'positive', '0.113', 'come', 'inside', 'allow', 'tow', 'accept']
senticnet['topic_include'] = ['0.04', '0.22', '-0.17', '0.362', '#interest', '#admiration', 'positive', '0.113', 'come', 'inside', 'allow', 'tow', 'accept']
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senticnet['topical_antibiotic'] = ['-0.81', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.82', 'peanut', 'leave_over', 'floppy', 'circular_motion', 'store_vegetable']
senticnet['topical_application'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'chemical_irritant', 'gingerol', 'piperine', 'pepper_spray']
senticnet['topical_corticosteroid'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'radiation_therapy', 'radiation', 'actinotherapy', 'irradiation']
senticnet['topical_cream'] = ['-0.81', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.82', 'peanut', 'leave_over', 'floppy', 'circular_motion', 'store_vegetable']
senticnet['topical_index'] = ['0.113', '0.126', '0', '0.11', '#joy', '#interest', 'positive', '0.116', 'file', 'pencil_pen', 'document', 'note_pad', 'scrap_paper']
senticnet['topical_ointment'] = ['-0.81', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.82', 'peanut', 'leave_over', 'floppy', 'circular_motion', 'store_vegetable']
senticnet['topical_steroid'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'radiation_therapy', 'radiation', 'actinotherapy', 'irradiation']
senticnet['topical_therapy'] = ['-0.05', '0', '-0.22', '-0.06', '#fear', '#disgust', 'negative', '-0.11', 'medicine', 'cure', 'medical_procedure', 'sleep_drug', 'nurse']
senticnet['topically'] = ['0', '0.845', '0', '0.921', '#interest', '#admiration', 'positive', '0.883', 'locally', 'study', 'different_application', 'project', 'manifold']
senticnet['topkap_palace'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'caravansary', 'caravanserai', 'khan', 'caravan_inn', 'hostel']
senticnet['topkapi_palace'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['topknot'] = ['0', '-0.01', '0.044', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'fur', 'two_eyes', 'fur_keep_warm', 'hold_hair', 'facial_hair']
senticnet['topleft'] = ['0.777', '-0.85', '0', '0.615', '#joy', '#surprise', 'positive', '0.748', 'think', 'play', 'trifle', 'abstract', 'warm_to']
senticnet['topless_bar'] = ['0.82', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.862', 'pervert', 'healthy_person', 'philanthropist', 'arsonist', 'nazi']
senticnet['topless_dancer'] = ['0.82', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.862', 'pervert', 'healthy_person', 'philanthropist', 'arsonist', 'nazi']
senticnet['topless_woman'] = ['-0.94', '0', '0.991', '-0.65', '#sadness', '#anger', 'negative', '-0.86', 'birthday_suit', 'raw', 'altogether', 'nakedness', 'undress']
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senticnet['topnotch'] = ['0.781', '0', '0', '0.706', '#joy', '#admiration', 'positive', '0.744', 'goodness', 'best', 'quality', 'great', 'well_done']
senticnet['topographic_anatomy'] = ['0.865', '0.757', '0', '0', '#joy', '#interest', 'positive', '0.811', 'topology', 'regional_anatomy', 'analysis_situs', 'network_topology', 'different_application']
senticnet['topographic_feature'] = ['0', '0.08', '0.032', '0.096', '#interest', '#admiration', 'positive', '0.048', 'land_form', 'land_formation', 'land_feature', 'geographic_feature', 'landscape_feature']
senticnet['topographical_feature'] = ['0', '0.08', '0.032', '0.096', '#interest', '#admiration', 'positive', '0.048', 'land_form', 'land_formation', 'land_feature', 'geographic_feature', 'landscape_feature']
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senticnet['topography'] = ['0.614', '-0.95', '0', '0.584', '#joy', '#surprise', 'positive', '0.718', 'mother_lode', 'champion_lode', 'deposit', 'sedimentation', 'lode']
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senticnet['topological_conjugacy'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'fragmentation', 'discharge', 'backfire', 'blast']
senticnet['topology'] = ['0.867', '0.766', '0', '0', '#joy', '#interest', 'positive', '0.817', 'topographic_anatomy', 'regional_anatomy', 'analysis_situs', 'network_topology', 'different_application']
senticnet['toponym'] = ['0', '0.08', '0.032', '0.096', '#interest', '#admiration', 'positive', '0.048', 'land_form', 'land_formation', 'land_feature', 'geographic_feature', 'landscape_feature']
senticnet['topped_billboard'] = ['0.997', '0.892', '0', '0', '#joy', '#interest', 'positive', '0.945', 'advertisement', 'large_outdoor_board', 'motorway', 'expressway', 'train_track']
senticnet['topple'] = ['-0.53', '0', '0.752', '-0.89', '#anger', '#disgust', 'negative', '-0.72', 'fall', 'keel_over', 'descend', 'go_down', 'come_down']
senticnet['topple_assad'] = ['-0.53', '0', '0.752', '-0.89', '#anger', '#disgust', 'negative', '-0.72', 'fall', 'keel_over', 'descend', 'go_down', 'come_down']
senticnet['topsail'] = ['-0.89', '0', '0.766', '0', '#sadness', '#anger', 'negative', '-0.83', 'gaff', 'transvestite', 'hoax', 'earing', 'spar']
senticnet['topshop_unique'] = ['0', '-0.03', '0.022', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'shoulder_bag', 'satchel', 'water_bottle', 'tote_bag', 'carry']
senticnet['topspin_forehand'] = ['0', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.91', 'umpire', 'referee', 'legal_proceed', 'clang', 'clamor']
senticnet['topwater'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'catch_fish', 'fishing', 'patience', 'fish_lake', 'bait']
senticnet['tora_prison'] = ['0', '0', '0.995', '-0.89', '#anger', '#disgust', 'negative', '-0.94', 'shoe_store', 'chess_tournament', 'dress_room', 'front_desk', 'locker_room']
senticnet['torah'] = ['0.071', '0.171', '0', '0.154', '#interest', '#admiration', 'positive', '0.132', 'biography', 'chapter', 'text', 'scripture', 'essay']
senticnet['torah_ark'] = ['0', '0.209', '0.026', '0.184', '#interest', '#admiration', 'positive', '0.122', 'church', 'confession', 'temple', 'cathedral', 'monastery']
senticnet['torch'] = ['-0.03', '-0.02', '-0.11', '-0.08', '#fear', '#disgust', 'negative', '-0.05', 'firebomb', 'fire_light', 'master_control', 'flare', 'flash_light']
senticnet['torchiere'] = ['-0.63', '0', '0.897', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'cd-rom', 'international_candle', 'candle', 'candlepower_unit', 'standard_candle']
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senticnet['tormentil'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['torn'] = ['-0.06', '-0.01', '0.05', '-0.07', '#sadness', '#disgust', 'negative', '-0.04', 'embroidery', 'woven', 'cover_wind', 'braid', 'use_needle']
senticnet['torn_away'] = ['-0.84', '0', '0.827', '-0.81', '#sadness', '#anger', 'negative', '-0.83', 'sop', 'douse', 'dowse', 'drench', 'soak']
senticnet['torn_down'] = ['-0.08', '0', '-0.08', '-0.14', '#sadness', '#disgust', 'negative', '-0.10', 'violence', 'assault', 'combat', 'siege', 'use_military']
senticnet['torn_flesh'] = ['-0.87', '0', '0.682', '-0.53', '#sadness', '#anger', 'negative', '-0.69', 'swash', 'spatter', 'slosh_around', 'plash', 'splash']
senticnet['torn_muscle'] = ['0.934', '-0.62', '0', '0.79', '#joy', '#admiration', 'positive', '0.781', 'voluntary_muscle', 'deltoid', 'muscle', 'abductor', 'skeletal_muscle']
senticnet['torn_tendon'] = ['-0.78', '0', '0.554', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'rubor', 'redness', 'inflammation', 'tennis_elbow', 'lateral_epicondylitis']
senticnet['tornado'] = ['0', '-0.01', '-0.08', '-0.13', '#fear', '#disgust', 'negative', '-0.06', 'lightning', 'lighten', 'bright_flash', 'flood', 'natural_disaster']
senticnet['toroweap'] = ['0', '0.069', '0', '0.065', '#interest', '#admiration', 'positive', '0.067', 'alp', 'high_mountain', 'pass', 'ski_resort', 'high_place']
senticnet['torpedo'] = ['0.077', '0.089', '-0.06', '0.064', '#joy', '#interest', 'positive', '0.041', 'married_couple', 'presidential_election', 'grown_up', 'use_binoculars', 'intelligent_person']
senticnet['torpedo_boat'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'submarine', 'lifeboat', 'titanic', 'large_ship', 'canoe']
senticnet['torpedo_plane'] = ['0.084', '-0.05', '0.013', '0', '#joy', '#surprise', 'positive', '0.043', 'carnival_ride', 'carny', 'fortune_teller', 'horseback', 'ferris_wheel']
senticnet['torpedo_room'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'chick', 'wild_bird', 'baby_bird', 'lay_egg', 'bird_home']
senticnet['torpedo_tube'] = ['0', '0', '-0.83', '-0.8', '#fear', '#disgust', 'negative', '-0.81', 'armory', 'tank', 'hydrogen_bomb', 'rifle', 'use_hunt']
senticnet['torpid'] = ['-0.84', '0', '-0.76', '0', '#sadness', '#fear', 'negative', '-0.80', 'soggy', 'dormant', 'inert', 'sluggish', 'hibernating']
senticnet['torpidity'] = ['-0.97', '0', '0.992', '-0.86', '#sadness', '#anger', 'negative', '-0.94', 'lassitude', 'hibernation', 'lethargy', 'sluggishness', 'physical_condition']
senticnet['torpor'] = ['-0.79', '0', '0.837', '-0.82', '#anger', '#disgust', 'negative', '-0.81', 'lassitude', 'hibernation', 'lethargy', 'sluggishness', 'physical_condition']
senticnet['torque'] = ['0', '0', '-0.11', '0', '#fear', '#fear', 'negative', '-0.11', 'chemical_reaction', 'set_fire', 'grow_yard', 'heat_light', 'burn_down_house']
senticnet['torquemada'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquirer', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['torquigener'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'plectognath', 'plectognathfish', 'blowfish', 'triggerfish', 'filefish']
senticnet['torrent'] = ['0', '0', '-0.85', '-0.99', '#fear', '#disgust', 'negative', '-0.92', 'onslaught', 'battery', 'shelling', 'bombardment', 'outpouring']
senticnet['torrential'] = ['0.791', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.792', 'downpour', 'rainstorm', 'rain', 'flood', 'storm']
senticnet['torrential_downpour'] = ['-0.87', '0', '0.91', '-0.78', '#sadness', '#anger', 'negative', '-0.85', 'storm', 'thundershower', 'rain', 'line_storm', 'equinoctial_storm']
senticnet['torrential_rain'] = ['-0.87', '0', '0.91', '-0.78', '#sadness', '#anger', 'negative', '-0.85', 'storm', 'thundershower', 'rain', 'line_storm', 'equinoctial_storm']
senticnet['torrential_rainfall'] = ['-0.78', '0', '0.906', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'rainstorm', 'atmospheric_phenomenon', 'northeaster', 'noreaster', 'hailstorm']
senticnet['torrid'] = ['0', '0', '0.954', '-0.76', '#anger', '#disgust', 'negative', '-0.85', 'fervid', 'ardent', 'impassioned', 'fervent', 'fiery']
senticnet['torrid_pace'] = ['-0.04', '0.123', '0', '0.237', '#interest', '#admiration', 'positive', '0.107', 'foot_motion', 'foot_movement', 'foot_action', 'move_ahead', 'stride']
senticnet['torrid_start'] = ['0', '0', '0.954', '-0.76', '#anger', '#disgust', 'negative', '-0.85', 'fervid', 'ardent', 'impassioned', 'fervent', 'fiery']
senticnet['torrid_zone'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'dryness', 'thirstiness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['torse'] = ['0.03', '0', '0.017', '0.138', '#joy', '#admiration', 'positive', '0.05', 'up_down', 'curl', 'bump', 'hand_gesture', 'goodbye']
senticnet['torsion'] = ['0.695', '-0.51', '0', '0.789', '#joy', '#admiration', 'positive', '0.667', 'tortuousness', 'gnarl', 'warp', 'tortuosity', 'distorted_shape']
senticnet['torsion_spring'] = ['0', '0.142', '0', '0.125', '#interest', '#admiration', 'positive', '0.134', 'weight_machine', 'power_drill', 'screw', 'screwdriver', 'air_pump']
senticnet['torsional'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'aberrance', 'warp', 'chromosomal_aberration']
senticnet['torsional_strain'] = ['-0.08', '-0.03', '-0.07', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'injury', 'soreness', 'pain', 'hurt', 'dislocation']
senticnet['torsional_vibration'] = ['0.048', '0.084', '0', '0.209', '#interest', '#admiration', 'positive', '0.114', 'move', 'motion', 'motility', 'movement', 'flicker']
senticnet['torso'] = ['0.082', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.066', 'hoist', 'control_room', 'raise', 'platform_rail', 'forklift']
senticnet['tort'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'culpable_negligence', 'evasion', 'dereliction', 'negligence']
senticnet['tort_action'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'culpable_negligence', 'evasion', 'dereliction', 'negligence']
senticnet['tort_claim'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'culpable_negligence', 'evasion', 'dereliction', 'negligence']
senticnet['tort_claim_act'] = ['0', '0', '0', '0.816', '#admiration', '#admiration', 'positive', '0.816', 'resistance', 'protection', 'untouchable', 'invulnerable', 'sacrosanct']
senticnet['tort_law'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'culpable_negligence', 'evasion', 'dereliction', 'negligence']
senticnet['tort_liability'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'culpable_negligence', 'evasion', 'dereliction', 'negligence']
senticnet['tortellini'] = ['0.043', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'apple_pie', 'a_lot_of_food', 'jelly', 'frozen_food', 'cottage_cheese']
senticnet['tortfeasor'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'culpable_negligence', 'evasion', 'dereliction', 'negligence']
senticnet['tortious_conduct'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'culpable_negligence', 'evasion', 'dereliction', 'negligence']
senticnet['tortoise_shell'] = ['0.022', '-0.01', '0.062', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'electric_toothbrush', 'moistener', 'hairbrush', 'shampoo_conditioner', 'shave_kit']
senticnet['tortoiseshell'] = ['0.838', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.801', 'tabby', 'calico', 'kitten', 'critter', 'cat']
senticnet['tortuga'] = ['0', '0', '0.034', '-0.08', '#anger', '#disgust', 'negative', '-0.06', 'lifeboat', 'mast', 'bad_man', 'keel', 'yacht']
senticnet['tortuosity'] = ['0.692', '-0.51', '0', '0.791', '#joy', '#admiration', 'positive', '0.665', 'tortuousness', 'gnarl', 'warp', 'distorted_shape', 'torsion']
senticnet['tortuous'] = ['0', '0', '0.749', '-0.89', '#anger', '#disgust', 'negative', '-0.82', 'convoluted', 'knotty', 'tangled', 'involved', 'byzantine']
senticnet['tortuousness'] = ['0.694', '-0.51', '0', '0.783', '#joy', '#admiration', 'positive', '0.665', 'distorted_shape', 'gnarl', 'warp', 'tortuosity', 'torsion']
senticnet['torture'] = ['0', '0', '-0.79', '-0.90', '#fear', '#disgust', 'negative', '-0.84', 'destruction', 'seek_revenge', 'suffer', 'stab_knife', 'feel_anger']
senticnet['torture_chamber'] = ['-0.67', '0', '0.931', '-0.77', '#anger', '#disgust', 'negative', '-0.79', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['torture_memo'] = ['0', '0', '0', '0.091', '#admiration', '#admiration', 'positive', '0.091', 'correspondence', 'handwritten', 'written_message', 'written_communication', 'write_note']
senticnet['torture_porn'] = ['-0.08', '-0.01', '-0.16', '-0.13', '#fear', '#disgust', 'negative', '-0.09', 'hockey_game', 'movie', 'scary_movie', 'cinema', 'cctv']
senticnet['tortured'] = ['0', '0', '0.796', '-0.78', '#anger', '#disgust', 'negative', '-0.79', 'pain', 'hurt', 'tormented', 'anguished', 'rage']
senticnet['tortured_soul'] = ['-0.96', '0', '0.108', '-0.90', '#sadness', '#disgust', 'negative', '-0.66', 'evil', 'darkness', 'dark', 'sun_down', 'shadow']
senticnet['torturing'] = ['-0.70', '0', '0.806', '0', '#sadness', '#anger', 'negative', '-0.75', 'excruciating', 'harrowing', 'agonizing', 'worry', 'fret']
senticnet['torturous'] = ['0.718', '0.704', '0', '0', '#joy', '#interest', 'positive', '0.711', 'unending', 'never-ending', 'drawn-out', 'agonizing', 'interminable']
senticnet['torus'] = ['-0.05', '0.069', '0.064', '0.115', '#interest', '#admiration', 'positive', '0.017', 'round_figure', 'orb', 'great_invention', 'circle_shape', 'ball_shape']
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senticnet['trabecula'] = ['-0.53', '0', '0.982', '-0.67', '#anger', '#disgust', 'negative', '-0.73', 'meniscus', 'semilunar_cartilage', 'gristle', 'hyaline_cartilage', 'arytenoid']
senticnet['trabecular_bone'] = ['-0.59', '0', '0.911', '-0.87', '#anger', '#disgust', 'negative', '-0.79', 'induration', 'sclerosis', 'disseminated_sclerosis', 'multiple_sclerosis', 'pathology']
senticnet['trabecular_meshwork'] = ['-0.78', '0', '0.855', '-0.99', '#anger', '#disgust', 'negative', '-0.87', 'blood_blister', 'pustule', 'water_blister', 'vesicle', 'cyst']
senticnet['trace'] = ['-0.04', '0.114', '0', '0.086', '#interest', '#admiration', 'positive', '0.053', 'pencil_art', 'sketch', 'detect', 'representation', 'illustration']
senticnet['trace_contaminant'] = ['0', '0', '-0.74', '-0.73', '#fear', '#disgust', 'negative', '-0.74', 'contaminant', 'pollution', 'poison', 'danger', 'death']
senticnet['traceable'] = ['0.502', '-0.60', '0', '0.574', '#surprise', '#admiration', 'positive', '0.559', 'due', 'imputable', 'ascribable', 'attributable', 'referable']
senticnet['tracer'] = ['0', '0', '-0.74', '-0.98', '#fear', '#disgust', 'negative', '-0.86', 'tracer', 'photon', 'bullet', 'trace', 'positron']
senticnet['tracer_ammunition'] = ['0', '0', '-0.92', '-0.91', '#fear', '#disgust', 'negative', '-0.92', 'automatic', 'firearm', 'gun', 'weapon', 'projectile']
senticnet['tracer_fire'] = ['0', '-0.00', '-0.07', '-0.06', '#fear', '#disgust', 'negative', '-0.04', 'lighten', 'tornado', 'dynamite_explosion', 'forest_fire', 'lightning']
senticnet['tracheal_intubation'] = ['-0.65', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'put_to_sleep', 'operation', 'anesthesia', 'surgery', 'local_anesthesia']
senticnet['trachelospermum'] = ['0', '0.086', '0', '0', '#interest', '#interest', 'positive', '0.086', 'swim_ocean', 'weight_lift', 'sit_in_shade', 'cut_grass', 'trust_friend']
senticnet['tracheomalacia'] = ['-0.56', '0', '0.806', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['tracheotomy'] = ['-0.57', '0', '0.737', '-0.85', '#anger', '#disgust', 'negative', '-0.72', 'emergency_procedure', 'emergency', 'external_respiration', 'respiration', 'ventilation']
senticnet['trachoma'] = ['-0.69', '0', '0.852', '-0.69', '#sadness', '#anger', 'negative', '-0.74', 'conjunctivitis', 'ophthalmitis', 'rubor', 'redness', 'inflammation']
senticnet['trachycarpus_fortunei'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'intrepidly', 'fearlessness', 'fearlessly', 'unafraid', 'unfearing']
senticnet['tracie_bennett'] = ['0.03', '0.085', '0', '0', '#joy', '#interest', 'positive', '0.058', 'favorite_color', 'colorful', 'different_color', 'pink', 'happy']
senticnet['tracing'] = ['0.773', '-0.63', '0', '0.565', '#joy', '#surprise', 'positive', '0.657', 'catching', 'breakthrough', 'spying', 'determination', 'discovery']
senticnet['tracing_paper'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'gift_wrap', 'kraft', 'kraft_paper', 'wrapping_paper', 'paper']
senticnet['track'] = ['-0.04', '0.114', '0', '0.086', '#interest', '#admiration', 'positive', '0.053', 'street', 'road', 'railway', 'racetrack', 'pathway']
senticnet['track_gauge'] = ['0', '0.721', '0', '0.731', '#interest', '#admiration', 'positive', '0.726', 'dimension', 'measure', 'tackle', 'length', 'measurement']
senticnet['track_record'] = ['0.989', '-0.80', '0', '0.569', '#joy', '#surprise', 'positive', '0.788', 'strength', 'tolerance', 'stamina', 'endurance', 'sufferance']
senticnet['track_speed_skater'] = ['0', '0', '-0.78', '-0.71', '#fear', '#disgust', 'negative', '-0.74', 'unretentive', 'poor', 'inadequate', 'little', 'forgetful']
senticnet['track_speed_skating'] = ['0', '0', '-0.78', '-0.71', '#fear', '#disgust', 'negative', '-0.74', 'unretentive', 'poor', 'inadequate', 'little', 'forgetful']
senticnet['track_time'] = ['0', '0', '0.724', '-0.83', '#anger', '#disgust', 'negative', '-0.78', 'learn_foreign_language', 'buy_souvenir', 'memory', 'carry_luggage', 'learn_experience']
senticnet['tracked_armored_personnel'] = ['0', '0.089', '0.01', '0.118', '#interest', '#admiration', 'positive', '0.066', 'crate', 'mom_house', 'sack', 'poker_game', 'safekeep']
senticnet['tracked_armoured_personnel'] = ['0', '0.089', '0.01', '0.118', '#interest', '#admiration', 'positive', '0.066', 'crate', 'mom_house', 'sack', 'poker_game', 'safekeep']
senticnet['tracked_vehicle'] = ['-0.56', '0', '0.714', '-0.89', '#anger', '#disgust', 'negative', '-0.72', 'tank', 'tank_destroyer', 'armored_car', 'armoured_car', 'armored_personnel_carrier']
senticnet['tracking_device'] = ['-0.86', '0', '0.831', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'jewelry', 'jewellery', 'bangle', 'ankle_bracelet', 'armilla']
senticnet['trackless'] = ['0', '0', '0.915', '-0.87', '#anger', '#disgust', 'negative', '-0.89', 'outback', 'inaccessible', 'pathless', 'roadless', 'untracked']
senticnet['tracksuit'] = ['-0.04', '0.1', '0', '0.152', '#interest', '#admiration', 'positive', '0.068', 'pants', 'jacket', 'clothes', 'robe', 'garment']
senticnet['tracksuit_top'] = ['-0.04', '0.1', '0', '0.152', '#interest', '#admiration', 'positive', '0.068', 'pants', 'jacket', 'clothes', 'robe', 'garment']
senticnet['tracle'] = ['-0.09', '0.097', '0', '0.117', '#interest', '#admiration', 'positive', '0.04', 'sugar', 'sugar_substitute', 'molasses', 'add_sugar', 'black_treacle']
senticnet['tract'] = ['-0.55', '0', '0.996', '-0.50', '#sadness', '#anger', 'negative', '-0.68', 'pesthole', 'rookery', 'breeding_ground', 'piece_of_land', 'piece_of_ground']
senticnet['tractability'] = ['0.637', '-0.91', '0', '0.757', '#surprise', '#admiration', 'positive', '0.768', 'manageability', 'manageableness', 'docility', 'obedience', 'amenability']
senticnet['tractable'] = ['0.604', '-0.61', '0', '0.536', '#joy', '#surprise', 'positive', '0.584', 'tamable', 'tameable', 'ductile', 'malleable', 'docile']
senticnet['tractableness'] = ['0.518', '-0.92', '0', '0.527', '#surprise', '#admiration', 'positive', '0.656', 'manageability', 'manageableness', 'docility', 'obedience', 'amenability']
senticnet['tractarian'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'nonconformity', 'iconoclasm', 'heterodoxy', 'nonconformance', 'orientation']
senticnet['tractarian_movement'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'nonconformity', 'iconoclasm', 'heterodoxy', 'nonconformance', 'orientation']
senticnet['tractarianism'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'heterodoxy', 'iconoclasm', 'nonconformism', 'nonconformance', 'orientation']
senticnet['traction_battery'] = ['0', '0', '-1', '-1', '#fear', '#disgust', 'negative', '-1.0', 'moon_gravity', 'force', 'coercion', 'watt', 'power_source']
senticnet['tractor'] = ['-0.99', '0', '0.827', '-0.54', '#sadness', '#anger', 'negative', '-0.78', 'treat', 'cover', 'handle', 'theologize', 'deal']
senticnet['tractor_pull'] = ['0.938', '0.779', '0', '0', '#joy', '#interest', 'positive', '0.859', 'carnival', 'circus', 'fairground', 'fair', 'disneyland']
senticnet['tractor_trailer'] = ['0.969', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.936', 'attendant', 'manager', 'public_servant', 'specialist', 'accomplice']
senticnet['tractor_trailer_driver'] = ['0.969', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.936', 'attendant', 'manager', 'public_servant', 'specialist', 'accomplice']
senticnet['tractor_unit'] = ['-0.84', '0', '0.96', '-0.91', '#anger', '#disgust', 'negative', '-0.90', 'housewrecker', 'housebreaker', 'knacker', 'laborer', 'manual_laborer']
senticnet['trade'] = ['-0.04', '0.278', '-0.08', '0.202', '#interest', '#admiration', 'positive', '0.087', 'exchange', 'bargain', 'business_deal', 'sale', 'deal']
senticnet['trade_agreement'] = ['0.397', '0.245', '-0.13', '0.224', '#joy', '#interest', 'positive', '0.183', 'agreement', 'provide', 'advice', 'contribution', 'offer']
senticnet['trade_baseball_card'] = ['-0.18', '0', '-0.08', '-0.05', '#sadness', '#fear', 'negative', '-0.10', 'roll_ball', 'ring_doorbell', 'wear_silly_hat', 'bicycle_school', 'promise_good']
senticnet['trade_bloc'] = ['0', '0', '0', '0.119', '#admiration', '#admiration', 'positive', '0.119', 'planet', 'earth', 'spherical', 'planet_solar_system', 'terrain']
senticnet['trade_card'] = ['-0.04', '0.278', '-0.08', '0.202', '#interest', '#admiration', 'positive', '0.087', 'trade', 'card', 'collection', 'collect_item', 'business_card']
senticnet['trade_cbot'] = ['0.868', '-0.84', '0', '0.673', '#joy', '#surprise', 'positive', '0.796', 'instrument', 'option', 'derivative_instrument', 'legal_document', 'legal_instrument']
senticnet['trade_mart'] = ['0', '0', '-0.04', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'good_band', 'band', 'kit', 'procession', 'oblivion']
senticnet['trade_negotiator'] = ['0', '0.133', '0', '0.088', '#interest', '#admiration', 'positive', '0.111', 'juror', 'decide_guilt', 'become_judge', 'arbiter', 'jury']
senticnet['trade_pact'] = ['0.397', '0.245', '-0.13', '0.224', '#joy', '#interest', 'positive', '0.183', 'agreement', 'provide', 'advice', 'contribution', 'offer']
senticnet['trade_protection'] = ['-0.70', '0', '0.552', '-0.68', '#sadness', '#disgust', 'negative', '-0.64', 'protection', 'regimentation', 'taxation', 'imposition', 'reimposition']
senticnet['trade_stock'] = ['0.022', '0.08', '0', '0.076', '#interest', '#admiration', 'positive', '0.059', 'trade', 'stock', 'wall_street', 'earn_money', 'business']
senticnet['trade_unionist'] = ['0', '0.868', '0', '0.853', '#interest', '#admiration', 'positive', '0.861', 'country_leader', 'prime_minister', 'president', 'elect_official', 'leader']
senticnet['tradecraft'] = ['0.873', '-0.77', '0', '0.961', '#joy', '#admiration', 'positive', '0.869', 'housecraft', 'workmanship', 'watercraft', 'acquirement', 'skill']
senticnet['trademark'] = ['-0.80', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.86', 'trademark', 'patent', 'infringement', 'copyright', 'trademarked']
senticnet['trademarked'] = ['-0.81', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.87', 'trademark', 'patent', 'infringement', 'copyright', 'trademark']
senticnet['tradeoff'] = ['0.979', '0.713', '0', '0', '#joy', '#interest', 'positive', '0.846', 'infectious_agent', 'microorganism', 'herbivore', 'host', 'pathogen']
senticnet['tradeoff_among'] = ['-0.96', '0', '0.603', '-0.91', '#sadness', '#disgust', 'negative', '-0.82', 'potential', 'potency', 'conceivableness', 'conceivability', 'possibleness']
senticnet['tradeoff_between'] = ['0.554', '-0.72', '0', '0.651', '#surprise', '#admiration', 'positive', '0.644', 'reasonableness', 'modestness', 'cost', 'cut_rate', 'cut_price']
senticnet['trader'] = ['-0.70', '0', '0.774', '0', '#sadness', '#anger', 'negative', '-0.73', 'bargainer', 'monger', 'dealer', 'tonne', 'far']
senticnet['trader_vic'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'drifter', 'bum', 'sundowner']
senticnet['tradeshow'] = ['-0.79', '0', '0.948', '-0.82', '#anger', '#disgust', 'negative', '-0.85', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['tradesman'] = ['0.022', '0.08', '0', '0.076', '#interest', '#admiration', 'positive', '0.059', 'profession', 'occupation', 'job', 'important_job', 'hire_help']
senticnet['trading_card'] = ['0.031', '-0.10', '0', '0', '#joy', '#surprise', 'positive', '0.07', 'teddy_bear', 'beanie_baby', 'yo-yo', 'board_game', 'toy']
senticnet['tradition'] = ['0', '0.915', '0', '0.821', '#interest', '#admiration', 'positive', '0.868', 'culture', 'special_day', 'holiday', 'party_time', 'yearly_celebration']
senticnet['tradition_dating_back'] = ['0', '0.915', '0', '0.821', '#interest', '#admiration', 'positive', '0.868', 'culture', 'special_day', 'holiday', 'party_time', 'yearly_celebration']
senticnet['traditional'] = ['-0.06', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'cultural', 'political_state', 'large_land', 'large_state', 'political_unit']
senticnet['traditional_afternoon_tea'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'beachcomber', 'bum', 'drifter']
senticnet['traditional_ayurvedic_medicine'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'infective_agent', 'satisfier', 'quickener', 'agent']
senticnet['traditional_cel_animation'] = ['0.895', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.823', 'liveliness', 'excitement', 'art_form', 'art', 'film']
senticnet['traditional_chinese'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
senticnet['traditional_folk_song'] = ['0', '0.176', '0', '0.216', '#interest', '#admiration', 'positive', '0.196', 'choir_stall', 'worshiper', 'pew', 'fundamentalist', 'pulpit']
senticnet['traditional_ira'] = ['0.763', '-0.67', '0', '0.945', '#joy', '#admiration', 'positive', '0.795', 'set', 'income_bracket', 'bracket', 'income', 'price_bracket']
senticnet['traditional_knowledge'] = ['0.634', '-0.97', '0', '0.523', '#joy', '#surprise', 'positive', '0.709', 'content', 'old_wives_tale', 'folklore', 'cognitive_content', 'information']
senticnet['traditional_navajo'] = ['0.052', '0.099', '0', '0.067', '#interest', '#admiration', 'positive', '0.073', 'police_rank', 'head_guy', 'chief', 'leader', 'commander']
senticnet['traditional_oriental_medicine'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
senticnet['traditional_thanksgiving_meal'] = ['0.937', '0', '0', '0.828', '#joy', '#admiration', 'positive', '0.883', 'bakery', 'produce_section', 'fruit_bowl', 'dinner_meal', 'fast_food']
senticnet['traditionalism'] = ['0.64', '-0.93', '0', '0.812', '#surprise', '#admiration', 'positive', '0.795', 'orthodoxy', 'conventionality', 'conventionalism', 'traditionality', 'unoriginality']
senticnet['traditionalist'] = ['0', '0.897', '0', '0.773', '#interest', '#admiration', 'positive', '0.835', 'traditionalist', 'reactionary', 'fundamentalist', 'ultra-conservative', 'conservative']
senticnet['traditionalist_catholic'] = ['0.013', '0.199', '0.027', '0.167', '#interest', '#admiration', 'positive', '0.088', 'church', 'confession', 'good_catholic', 'spirituality', 'christianity']
senticnet['traditionality'] = ['0.638', '-0.92', '0', '0.806', '#surprise', '#admiration', 'positive', '0.789', 'orthodoxy', 'traditionalism', 'conventionalism', 'conventionality', 'unoriginality']
senticnet['traditionally_feminine'] = ['0.911', '0', '0', '0.85', '#joy', '#admiration', 'positive', '0.881', 'sophisticated', 'lovely', 'elegant', 'contrary', 'common_man']
senticnet['traditionally_masculine'] = ['0.911', '0', '0', '0.85', '#joy', '#admiration', 'positive', '0.881', 'sophisticated', 'lovely', 'elegant', 'contrary', 'common_man']
senticnet['traditionary'] = ['-0.99', '0', '0.947', '-0.88', '#sadness', '#anger', 'negative', '-0.94', 'atrociousness', 'inhumanity', 'heinousness', 'atrocity', 'barbarity']
senticnet['traduce'] = ['0', '0', '0.924', '-0.91', '#anger', '#disgust', 'negative', '-0.91', 'denigrate', 'traducer', 'malign', 'false', 'malicious']
senticnet['traducement'] = ['-0.61', '0', '0.749', '-0.54', '#sadness', '#anger', 'negative', '-0.63', 'malignment', 'vilification', 'smear', 'character_assassination', 'libel']
senticnet['traducer'] = ['0', '0', '0.92', '-0.91', '#anger', '#disgust', 'negative', '-0.91', 'denigrate', 'traduce', 'malign', 'false', 'malicious']
senticnet['traduzione'] = ['0.107', '0.11', '-0.08', '0.21', '#interest', '#admiration', 'positive', '0.086', 'live_healthy_life', 'forever_young', 'live_forever', 'live_long', 'live_good_life']
senticnet['traffic'] = ['-0.98', '0', '0.989', '-0.93', '#sadness', '#anger', 'negative', '-0.97', 'flight_accident', 'dangerous', 'danger', 'air_traffic', 'air_traffic_jam']
senticnet['traffic_accident'] = ['-0.08', '-0.05', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.03', 'press_accelerator', 'get_lose', 'use_gas', 'arrive_late', 'shift_gear']
senticnet['traffic_artery'] = ['0.04', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.055', 'intersection', 'interstate', 'parking_lot', 'expressway', 'crossroad']
senticnet['traffic_circle'] = ['0.032', '0.116', '0.016', '0', '#joy', '#interest', 'positive', '0.044', 'curb', 'crossroad', 'ring_road', 'lane', 'interstate']
senticnet['traffic_collision'] = ['-0.90', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.86', 'trauma', 'car_wreck', 'broken_bone', 'bad_knee', 'wreck']
senticnet['traffic_congestion'] = ['0', '0', '0.913', '-0.85', '#anger', '#disgust', 'negative', '-0.88', 'traffic', 'congestion', 'block_road', 'road_block', 'stop']
senticnet['traffic_control'] = ['0.032', '-0.00', '0', '0.09', '#joy', '#admiration', 'positive', '0.044', 'umpire', 'referee', 'equestrian_sport', 'backgammon', 'croquet']
senticnet['traffic_cop'] = ['0', '0.066', '-0.05', '0.095', '#interest', '#admiration', 'positive', '0.034', 'interjection', 'strict', 'red_cross', 'careful', 'gas_electric']
senticnet['traffic_flow'] = ['-0.98', '0', '0.989', '-0.93', '#sadness', '#anger', 'negative', '-0.97', 'flight_accident', 'dangerous', 'danger', 'air_traffic', 'air_traffic_jam']
senticnet['traffic_infraction'] = ['0', '-0.01', '0', '0.064', '#surprise', '#admiration', 'positive', '0.042', 'car_gas', 'car_power', 'fossil_fuel', 'run_car', 'car_engine']
senticnet['traffic_jam'] = ['-0.15', '-0.13', '0', '-0.25', '#sadness', '#disgust', 'negative', '-0.09', 'road_rage', 'car_crash', 'skid_mark', 'pollution', 'stop_red_light']
senticnet['traffic_light'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'safety', 'avoid_crash', 'drive_safe', 'safety_belt', 'respect_rule']
senticnet['traffic_police'] = ['0', '0.769', '0', '0', '#interest', '#interest', 'positive', '0.769', 'stop_suspect', 'direct_traffic', 'traffic_jam', 'stop_crime', 'fine_violator']
senticnet['traffic_rule'] = ['0.038', '0', '0.01', '0.059', '#joy', '#admiration', 'positive', '0.029', 'control_road', 'develop_city', 'bicycle_area', 'red_light', 'yellow_line']
senticnet['traffic_sign'] = ['0', '0.119', '0', '0.123', '#interest', '#admiration', 'positive', '0.121', 'street_sign', 'road_sign', 'red_light', 'paint_line', 'halt']
senticnet['traffic_signal'] = ['0.06', '0', '0', '0.059', '#joy', '#admiration', 'positive', '0.059', 'electrical_circuit', 'mailbox', 'parking_lot', 'hand_luggage', 'message_board']
senticnet['traffic_volume'] = ['-0.98', '0', '0.989', '-0.93', '#sadness', '#anger', 'negative', '-0.97', 'flight_accident', 'dangerous', 'danger', 'air_traffic', 'air_traffic_jam']
senticnet['trafficked_woman'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'vassalage']
senticnet['trafficker'] = ['0.805', '-0.58', '0', '0.641', '#joy', '#admiration', 'positive', '0.677', 'seller', 'cosmetician', 'vender', 'vendor', 'marketer']
senticnet['trafficking_ring'] = ['0.931', '-0.71', '0', '0.63', '#joy', '#surprise', 'positive', '0.758', 'hustle', 'con', 'bunco', 'sting', 'bunco_game']
senticnet['tragedie'] = ['-0.93', '0', '0.861', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'knotty', 'contorted', 'writhed', 'crooked', 'anfractuous']
senticnet['tragedienne'] = ['-0.71', '0', '0.884', '0', '#sadness', '#anger', 'negative', '-0.8', 'iliad', 'tragically', 'tragic', 'tragedy', 'sadness']
senticnet['tragedy'] = ['-0.23', '-0.04', '-0.18', '-0.03', '#sadness', '#fear', 'negative', '-0.10', 'terrible_event', 'perpetrator', 'drama', 'broken_arm', 'deception']
senticnet['tragic'] = ['-0.70', '0', '0.888', '0', '#sadness', '#anger', 'negative', '-0.79', 'iliad', 'tragically', 'tragedienne', 'tragedy', 'sadness']
senticnet['tragic_accident'] = ['-0.91', '0', '0.905', '-0.78', '#sadness', '#anger', 'negative', '-0.86', 'pity', 'shame', 'misfortune', 'mischance', 'misadventure']
senticnet['tragic_car_accident'] = ['-0.73', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.79', 'die_painfully', 'arrest_police', 'get_accident', 'fall_head', 'skin_knee']
senticnet['tragic_death'] = ['-0.87', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.91', 'loss_oflife', 'chest_pain', 'die', 'tumor', 'end_life']
senticnet['tragic_escalation'] = ['-0.88', '0', '0.733', '-0.60', '#sadness', '#anger', 'negative', '-0.74', 'vengeance', 'payback', 'retribution', 'retaliation', 'return']
senticnet['tragic_situation'] = ['-0.87', '0', '0.673', '-0.55', '#sadness', '#anger', 'negative', '-0.7', 'hot_water', 'difficulty', 'care', 'corner', 'box']
senticnet['tragic_story'] = ['0.036', '0.219', '0', '0.19', '#interest', '#admiration', 'positive', '0.148', 'story', 'long_story', 'novel', 'fable', 'fiction']
senticnet['tragic_tale'] = ['0.036', '0.219', '0', '0.19', '#interest', '#admiration', 'positive', '0.148', 'story', 'long_story', 'novel', 'fable', 'fiction']
senticnet['tragical'] = ['-0.64', '0', '0.625', '-0.88', '#sadness', '#disgust', 'negative', '-0.71', 'difficulty', 'awkwardness', 'flea_bite', 'difficultness', 'cumbersomeness']
senticnet['tragically'] = ['-0.71', '0', '0.883', '0', '#sadness', '#anger', 'negative', '-0.79', 'iliad', 'tragic', 'tragedienne', 'tragedy', 'sadness']
senticnet['tragicomic'] = ['0.807', '-0.83', '0', '0.688', '#joy', '#surprise', 'positive', '0.776', 'folly', 'lunacy', 'craziness', 'indulgence', 'foolery']
senticnet['trail'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'path', 'roadside', 'car_path', 'intersection', 'under_bridge']
senticnet['trail_car'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'tow_truck', 'trailer', 'camion', 'wagon', 'towboat']
senticnet['trail_mix'] = ['0.895', '0.946', '0', '0', '#joy', '#interest', 'positive', '0.92', 'need_food', 'a_little_hungry', 'dead_hungry', 'prepare_food', 'fill_stomach']
senticnet['trailblazing'] = ['0.709', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.809', 'innovativeness', 'innovative', 'groundbreaking', 'new-wave', 'ground-breaking']
senticnet['trailer'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'tow_truck', 'trail_car', 'camion', 'wagon', 'towboat']
senticnet['trailer_hitch'] = ['-0.95', '0', '0.793', '0', '#sadness', '#anger', 'negative', '-0.87', 'block', 'obstacle', 'nodus', 'impediment', 'obstruction']
senticnet['trailer_trash'] = ['-0.78', '0', '0.651', '-0.56', '#sadness', '#anger', 'negative', '-0.66', 'rubbishy', 'sorry', 'worthless', 'otiose', 'superfluous']
senticnet['train'] = ['0.781', '-0.72', '0', '0.587', '#joy', '#surprise', 'positive', '0.699', 'take', 'direct', 'aim', 'take_aim', 'draw_bead_on']
senticnet['train_collided_with'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'firework', 'bright_flash', 'nuclear_weapon', 'earthquake', 'nuclear_bomb']
senticnet['train_collision'] = ['-0.90', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.86', 'trauma', 'car_wreck', 'broken_bone', 'bad_knee', 'wreck']
senticnet['train_daily'] = ['0', '0.812', '0', '0.811', '#interest', '#admiration', 'positive', '0.812', 'sporty_guy', 'run_fast', 'run_lot', 'train_muscle', 'exercise']
senticnet['train_depart'] = ['0.861', '-0.52', '0', '0.598', '#joy', '#admiration', 'positive', '0.663', 'exit', 'get_off', 'deplane', 'go_out', 'get_out']
senticnet['train_depot'] = ['0', '0.106', '0', '0', '#interest', '#interest', 'positive', '0.106', 'train_station', 'park_meter', 'bus_station', 'factory', 'baggage_compartment']
senticnet['train_dog'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'teach', 'restless', 'train_pet', 'feel_energetic', 'need_fresh_air']
senticnet['train_hard'] = ['0', '0.728', '0', '0.703', '#interest', '#admiration', 'positive', '0.716', 'olympic_event', 'athletics', 'shape', 'form_of_exercise', 'good_shape']
senticnet['train_marathon'] = ['-0.85', '0', '-0.71', '-0.86', '#sadness', '#disgust', 'negative', '-0.81', 'get_exercise', 'high_blood_pressure', 'run_marathon', 'run_long_distance', 'go_gym']
senticnet['train_muscle'] = ['0', '0.884', '0', '0.855', '#interest', '#admiration', 'positive', '0.869', 'sporty_guy', 'train_daily', 'run_lot', 'run_fast', 'exercise']
senticnet['train_open_mind'] = ['0', '0.951', '0', '0', '#interest', '#interest', 'positive', '0.951', 'enter_data_computer', 'add_number', 'write_computer_program', 'add_two_numbers_together', 'calculation']
senticnet['train_pet'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'teach', 'restless', 'train_dog', 'feel_energetic', 'need_fresh_air']
senticnet['train_ride'] = ['-0.08', '0.179', '-0.06', '0.096', '#interest', '#admiration', 'positive', '0.034', 'ride_subway', 'buy_vending_machine', 'travel', 'buy_lottery_ticket', 'acquire']
senticnet['train_robber'] = ['-0.83', '0', '0.831', '-0.69', '#sadness', '#anger', 'negative', '-0.78', 'toughie', 'accessory', 'highbinder', 'hoodlum', 'thug']
senticnet['train_seat'] = ['0.863', '0.856', '0', '0', '#joy', '#interest', 'positive', '0.859', 'coach_seat', 'snack_bar', 'airplane_seat', 'sofa_bed', 'first_class_airplane_seat']
senticnet['train_slammed_into'] = ['0.706', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.768', 'train_station', 'subway_stop', 'parking_lot', 'subway_station', 'park_structure']
senticnet['train_station'] = ['0', '0.147', '0', '0.099', '#interest', '#admiration', 'positive', '0.123', 'airport', 'railway_station', 'subway', 'airport_terminal', 'depot']
senticnet['train_terminating_at'] = ['0.861', '-0.52', '0', '0.598', '#joy', '#admiration', 'positive', '0.663', 'exit', 'get_off', 'deplane', 'go_out', 'get_out']
senticnet['train_ticket'] = ['0', '0.093', '0', '0', '#interest', '#interest', 'positive', '0.093', 'subway_pass', 'bus_pass', 'transit_ticket', 'passenger_ticket', 'transit_card']
senticnet['train_track'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'railway', 'track', 'train', 'rail', 'pathway']
senticnet['train_window'] = ['0', '0', '0', '0.087', '#admiration', '#admiration', 'positive', '0.087', 'unix', 'window', 'many_windows', 'solaris', 'doorway']
senticnet['train_wreck'] = ['-0.69', '0', '0.941', '-0.75', '#anger', '#disgust', 'negative', '-0.8', 'flub', 'blunder', 'blooper', 'bungle', 'botch']
senticnet['trainband'] = ['-0.63', '0', '0.575', '-0.59', '#sadness', '#disgust', 'negative', '-0.60', 'force', 'militia', 'military_unit', 'military_force', 'military_group']
senticnet['trained'] = ['0', '-0.03', '0', '0.089', '#surprise', '#admiration', 'positive', '0.063', 'professional', 'expert', 'experienced', 'skilled', 'senior']
senticnet['trained_colt'] = ['-0.76', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.78', 'strangle', 'bloodshed', 'cripple', 'hear_bad_news', 'sexually_transmit_disease']
senticnet['trained_facilitator'] = ['0.072', '0', '0', '0.078', '#joy', '#admiration', 'positive', '0.075', 'participate', 'examine', 'voyeur', 'influence_public_decision', 'question_everything']
senticnet['trained_filly'] = ['0', '-0.04', '0.085', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'claw', 'fur_keep_warm', 'tic', 'bristle', 'muzzle']
senticnet['trained_manpower'] = ['0.814', '-0.87', '0', '0.918', '#surprise', '#admiration', 'positive', '0.87', 'ship_company', 'company', 'work_force', 'workforce', 'full_complement']
senticnet['trained_personnel'] = ['0.814', '-0.87', '0', '0.918', '#surprise', '#admiration', 'positive', '0.87', 'ship_company', 'company', 'work_force', 'workforce', 'full_complement']
senticnet['trainee'] = ['-0.88', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.91', 'vacancy', 'job', 'recruitment', 'employment', 'vacancy']
senticnet['trainer'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'coach', 'instructor', 'personal_trainer', 'fit_person', 'train']
senticnet['training'] = ['0.908', '-0.83', '0', '0.822', '#joy', '#surprise', 'positive', '0.856', 'practice', 'activity', 'discipline', 'athletic_training', 'drill']
senticnet['training_body'] = ['0.877', '0.832', '0', '0.791', '#joy', '#interest', 'positive', '0.833', 'enjoy_challenge', 'win_competition', 'enjoy_game', 'leader', 'engage_sport']
senticnet['training_corp_afjrotc'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['training_corp_jrotc'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['training_corp_rotc'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['training_plan'] = ['0', '0.881', '0', '0.852', '#interest', '#admiration', 'positive', '0.867', 'research', 'sponsoring_laboratory', 'fellowship', 'environment', 'fellowship_support']
senticnet['traipse'] = ['-0.82', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.83', 'trudging', 'trudged', 'plodded', 'trudge', 'slogged']
senticnet['traipse_through'] = ['0.047', '-0.01', '0.033', '0.071', '#joy', '#admiration', 'positive', '0.024', 'get_place', 'dawdle_around', 'get_around_town', 'go_to_place', 'stroll']
senticnet['trait'] = ['0.797', '0.964', '0', '0', '#joy', '#interest', 'positive', '0.881', 'peculiar', 'good_character', 'personality_trait', 'character_trait', 'behavior']
senticnet['trait_anxiety'] = ['0.627', '-0.65', '0', '0.968', '#surprise', '#admiration', 'positive', '0.749', 'overwhelm', 'overpower', 'sweep_over', 'whelm', 'overcome']
senticnet['traitor'] = ['0', '0', '0.85', '-0.88', '#anger', '#disgust', 'negative', '-0.86', 'rogue', 'seaman', 'lieutenant', 'officer', 'captain']
senticnet['traitorously'] = ['-0.04', '0.164', '0.685', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maliciousness', 'spitefulness', 'maleficence', 'malefic', 'malign']
senticnet['traitorousness'] = ['0.921', '-0.57', '0', '0.799', '#joy', '#admiration', 'positive', '0.764', 'perfidiousness', 'subversiveness', 'perfidy', 'disloyalty', 'treachery']
senticnet['trajan_column'] = ['-0.03', '0.092', '0.07', '0.1', '#interest', '#admiration', 'positive', '0.021', 'gladiator', 'civilization', 'church_build', 'coliseum', 'every_town']
senticnet['trajectory'] = ['0', '0.175', '0', '0.231', '#interest', '#admiration', 'positive', '0.203', 'compass_direction', 'go_straight', 'end_point', 'eight_sides', 'red_light']
senticnet['tram'] = ['0.991', '0.943', '0', '0', '#joy', '#interest', 'positive', '0.967', 'bus', 'metro', 'train', 'transport', 'transportation']
senticnet['tram_flap'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
senticnet['tram_network'] = ['0.547', '-0.69', '0', '0.52', '#joy', '#surprise', 'positive', '0.586', 'vivification', 'invigoration', 'activity', 'animation', 'activating']
senticnet['tram_ride'] = ['-0.08', '0.179', '-0.06', '0.096', '#interest', '#admiration', 'positive', '0.034', 'ride_subway', 'buy_vending_machine', 'travel', 'buy_lottery_ticket', 'acquire']
senticnet['tram_stop'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'train_station', 'bus', 'subway_station', 'public_place', 'pier']
senticnet['tram_system'] = ['0.547', '-0.69', '0', '0.52', '#joy', '#surprise', 'positive', '0.586', 'vivification', 'invigoration', 'activity', 'animation', 'activating']
senticnet['tramatza'] = ['0.939', '-0.71', '0', '0.775', '#joy', '#admiration', 'positive', '0.81', 'vox_angelica', 'voix_celeste', 'organ_stop', 'pipe', 'diapason']
senticnet['trammel'] = ['-0.51', '0', '0.946', '-0.99', '#anger', '#disgust', 'negative', '-0.82', 'clamp_down', 'limit', 'restrict', 'bound', 'confine']
senticnet['tramp'] = ['-0.64', '0', '0.712', '-0.78', '#anger', '#disgust', 'negative', '-0.71', 'vagrant', 'drifter', 'floater', 'vagabond', 'dosser']
senticnet['tramp_steamer'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'succourer', 'recoverer', 'salvor', 'mortal']
senticnet['trample'] = ['0', '0', '0.81', '-0.81', '#anger', '#disgust', 'negative', '-0.81', 'maul', 'slate', 'ill', 'harshly', 'hardly']
senticnet['trample_upon'] = ['-0.95', '0', '0.703', '-0.83', '#sadness', '#disgust', 'negative', '-0.83', 'raid', 'come_in', 'foray_into', 'enter', 'get_into']
senticnet['trampoline'] = ['0.155', '-0.11', '0.009', '-0.05', '#joy', '#surprise', 'positive', '0.05', 'dance_well', 'relate_music', 'enter_contest', 'jump_around', 'cheer_up']
senticnet['tramway'] = ['0.547', '-0.69', '0', '0.52', '#joy', '#surprise', 'positive', '0.586', 'vivification', 'invigoration', 'activity', 'animation', 'activating']
senticnet['tramway_network'] = ['0.547', '-0.69', '0', '0.52', '#joy', '#surprise', 'positive', '0.586', 'vivification', 'invigoration', 'activity', 'animation', 'activating']
senticnet['trance'] = ['0.636', '0.631', '-0.28', '0.36', '#joy', '#interest', 'positive', '0.337', 'enchant', 'comfortableness', 'comfortably', 'solace', 'capture']
senticnet['trancelike'] = ['-0.83', '0', '0.703', '-0.85', '#sadness', '#disgust', 'negative', '-0.79', 'hurly_burly', 'kerfuffle', 'commotion', 'disruption', 'disturbance']
senticnet['tranchet_flake'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'useful_tool']
senticnet['tranfer'] = ['0.979', '0.921', '0', '0.714', '#joy', '#interest', 'positive', '0.871', 'collaborate', 'information_transfer', 'inform_person', 'inform_others', 'information']
senticnet['tranquil'] = ['0.549', '0.656', '-0.24', '0.264', '#joy', '#interest', 'positive', '0.305', 'unagitated', 'unruffled', 'placidly', 'placid', 'placidness']
senticnet['tranquil_atmosphere'] = ['0.733', '0', '0', '0.801', '#joy', '#admiration', 'positive', '0.767', 'serenity', 'recreate', 'peace_of_mind', 'ease', 'serene']
senticnet['tranquil_beauty'] = ['0.733', '0', '0', '0.801', '#joy', '#admiration', 'positive', '0.767', 'serenity', 'recreate', 'peace_of_mind', 'ease', 'serene']
senticnet['tranquility'] = ['0.634', '0.649', '-0.24', '0.3', '#joy', '#interest', 'positive', '0.334', 'serene', 'repose', 'ataraxis', 'pacifically', 'peaceably']
senticnet['tranquilize'] = ['0.506', '0.659', '-0.24', '0.242', '#joy', '#interest', 'positive', '0.29', 'calmly', 'quieten', 'soothing', 'assuasive', 'tranquility']
senticnet['tranquilizer'] = ['-0.86', '0', '0.646', '-0.88', '#sadness', '#disgust', 'negative', '-0.8', 'minor_tranquilizer', 'put_to_sleep', 'antianxiety_drug', 'anxiolytic', 'anxiolytic_drug']
senticnet['tranquilizer_dart'] = ['0', '-0.97', '0', '0', '#surprise', '#surprise', 'positive', '0.977', 'cougar', 'skunk', 'lion', 'tiger', 'lynx']
senticnet['tranquilizer_drug'] = ['-0.87', '0', '0.639', '-0.89', '#sadness', '#disgust', 'negative', '-0.80', 'minor_tranquilizer', 'put_to_sleep', 'antianxiety_drug', 'tranquilizer', 'anxiolytic']
senticnet['tranquilizer_gun'] = ['-0.97', '0', '0.677', '-0.95', '#sadness', '#disgust', 'negative', '-0.86', 'stunt', 'game', 'mutant', 'invertebrate', 'animate_being']
senticnet['tranquillity'] = ['-0.65', '0', '0.716', '-0.94', '#anger', '#disgust', 'negative', '-0.77', 'bad_hat', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['tranquillize'] = ['0.962', '-0.73', '0', '0.995', '#joy', '#admiration', 'positive', '0.899', 'lenify', 'placate', 'gruntle', 'pacify', 'conciliate']
senticnet['tranquilly'] = ['0.579', '0.652', '-0.24', '0.276', '#joy', '#interest', 'positive', '0.315', 'tranquility', 'quietness', 'quietude', 'tranquil', 'unagitated']
senticnet['trans_atlantic_slave_trade'] = ['-0.75', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.82', 'oppression', 'caged_lion', 'slave', 'captivity', 'misery']
senticnet['trans_dimensional'] = ['-0.03', '0.118', '0.472', '-0.46', '#anger', '#disgust', 'negative', '-0.21', 'detached', 'set-apart', 'separated', 'isolated', 'sickening']
senticnet['transabdominal'] = ['-0.70', '0', '0.639', '-0.56', '#sadness', '#anger', 'negative', '-0.63', 'fibroma', 'adenoma', 'chondroma', 'enchondroma', 'benign_tumor']
senticnet['transact_business'] = ['0.84', '0.813', '0', '0.915', '#joy', '#admiration', 'positive', '0.856', 'increase_income', 'able_use', 'resolve_dispute', 'time_consume', 'promote_business']
senticnet['transaction'] = ['-0.05', '0.265', '-0.06', '0.233', '#interest', '#admiration', 'positive', '0.093', 'business_transaction', 'money_exchange', 'financial_transaction', 'purchase', 'lower_price']
senticnet['transactional_immunity'] = ['0', '0', '0', '0.816', '#admiration', '#admiration', 'positive', '0.816', 'resistance', 'protection', 'untouchable', 'invulnerable', 'sacrosanct']
senticnet['transantarctic_mountain'] = ['-0.95', '0', '0.966', '0', '#sadness', '#anger', 'negative', '-0.96', 'polar_programsrelated', 'antarctic_geology_geophysics', 'antarctica', 'award_provided', 'geophysics']
senticnet['transatlantic_flight'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'atmosphere', 'wing', 'nest', 'feather', 'bird_fly']
senticnet['transcend'] = ['0', '0.858', '0', '0.79', '#interest', '#admiration', 'positive', '0.824', 'heavenly_body', 'star_planet', 'celestial_body', 'around_earth', 'unlimited']
senticnet['transcendence'] = ['0.506', '-0.84', '0', '0.981', '#surprise', '#admiration', 'positive', '0.777', 'superiority', 'supremacy', 'transcendency', 'dominance', 'ascendance']
senticnet['transcendency'] = ['0.51', '-0.84', '0', '0.983', '#surprise', '#admiration', 'positive', '0.779', 'superiority', 'transcendence', 'supremacy', 'dominance', 'ascendance']
senticnet['transcendent'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'metastability', 'monotony', 'invariance', 'constancy', 'unchangeability']
senticnet['transcendent_reality'] = ['0.779', '-0.75', '0', '0.526', '#joy', '#surprise', 'positive', '0.687', 'privateness', 'covertness', 'bosom', 'isolation', 'confidentiality']
senticnet['transcendental'] = ['0', '0', '-0.86', '-0.77', '#fear', '#disgust', 'negative', '-0.81', 'untellable', 'unspeakable', 'indescribable', 'indefinable', 'unutterable']
senticnet['transcendental_principle'] = ['0.789', '-0.99', '0', '0.54', '#joy', '#surprise', 'positive', '0.776', 'imperfection', 'imperfectness', 'bugginess', 'lameness', 'sickness']
senticnet['transcontinental_flight'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'atmosphere', 'wing', 'nest', 'feather', 'bird_fly']
senticnet['transcontinental_journey'] = ['0.937', '0.8', '0', '0', '#joy', '#interest', 'positive', '0.869', 'quest', 'stunt', 'illusion', 'adventure', 'activity']
senticnet['transcortical_sensory_aphasia'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'nominal_aphasia', 'anomic_aphasia']
senticnet['transcribe'] = ['0.642', '-0.53', '0', '0.575', '#joy', '#admiration', 'positive', '0.583', 'check', 'assure', 'see', 'insure', 'examine']
senticnet['transcriber'] = ['0.88', '0.87', '0', '0', '#joy', '#interest', 'positive', '0.875', 'adaptor', 'arranger', 'adapter', 'drive', 'use']
senticnet['transcription'] = ['0', '0.991', '0', '0.982', '#interest', '#admiration', 'positive', '0.986', 'arranging', 'recording', 'arrangement', 'written_text', 'protein']
senticnet['transducer'] = ['0.736', '0.956', '0', '0', '#joy', '#interest', 'positive', '0.846', 'ultrasonic', 'laser', 'sound', 'ultrasound', 'infrared']
senticnet['transeunt'] = ['0', '0', '-0.72', '-0.96', '#fear', '#disgust', 'negative', '-0.84', 'transitory', 'transient', 'ephemeral', 'passing', 'short-lived']
senticnet['transfer'] = ['-0.1', '0.273', '-0.09', '0.219', '#interest', '#admiration', 'positive', '0.074', 'money_exchange', 'money_transfer', 'financial_transaction', 'owe_money', 'money']
senticnet['transfer_information'] = ['0.574', '0.561', '0', '0.795', '#joy', '#admiration', 'positive', '0.643', 'take_note', 'collect_information', 'take_note_class', 'search_information', 'listen_lecture']
senticnet['transfer_knowledge'] = ['-0.1', '0.273', '-0.09', '0.219', '#interest', '#admiration', 'positive', '0.074', 'share_idea', 'knowledge', 'exchange_knowledge', 'exchange_idea', 'share_knowledge']
senticnet['transferability'] = ['0.53', '-0.99', '0', '0.584', '#surprise', '#admiration', 'positive', '0.702', 'interchangeability', 'convertibility', 'changeableness', 'changeability', 'permutability']
senticnet['transferable'] = ['-0.04', '0.18', '-0.09', '0.247', '#interest', '#admiration', 'positive', '0.071', 'obtain', 'satisfy_need', 'business_transaction', 'get_stuff', 'return_goods']
senticnet['transferee'] = ['-0.1', '0.273', '-0.09', '0.219', '#interest', '#admiration', 'positive', '0.074', 'money_exchange', 'money_transfer', 'financial_transaction', 'owe_money', 'money']
senticnet['transferor'] = ['0.851', '0.708', '0', '0', '#joy', '#interest', 'positive', '0.779', 'devisee', 'legatee', 'recipient', 'receiver', 'pensioner']
senticnet['transfigure'] = ['0.734', '-0.64', '0', '0.933', '#joy', '#admiration', 'positive', '0.771', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['transfixed'] = ['0.743', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.855', 'enthrall', 'mesmerize', 'mesmerized', 'planer', 'mesmerism']
senticnet['transform'] = ['-0.03', '0.197', '-0.10', '0.292', '#interest', '#admiration', 'positive', '0.087', 'procedure', 'action', 'create_form', 'process', 'posture']
senticnet['transformable'] = ['0.62', '-0.57', '0', '0.97', '#joy', '#admiration', 'positive', '0.721', 'alterable', 'convertible', 'translatable', 'commutable', 'transmutable']
senticnet['transformation'] = ['0.556', '-0.92', '0', '0.762', '#surprise', '#admiration', 'positive', '0.747', 'degeneration', 'improvement', 'betterment', 'sea_change', 'strengthening']
senticnet['transformational'] = ['-0.03', '0.081', '0', '0.095', '#interest', '#admiration', 'positive', '0.047', 'sight', 'eye_function', 'eye_sense', 'see_ability', 'aim']
senticnet['transformational_change'] = ['-0.03', '0.081', '0', '0.095', '#interest', '#admiration', 'positive', '0.047', 'sight', 'eye_function', 'eye_sense', 'see_ability', 'aim']
senticnet['transformational_leadership'] = ['0.053', '0.136', '0', '0.057', '#interest', '#admiration', 'positive', '0.082', 'boss', 'supervisor', 'leader', 'head_person', 'head_guy']
senticnet['transformative'] = ['-0.03', '0.081', '0', '0.095', '#interest', '#admiration', 'positive', '0.047', 'sight', 'eye_function', 'eye_sense', 'see_ability', 'aim']
senticnet['transformed_into'] = ['0.857', '0', '0', '0.875', '#joy', '#admiration', 'positive', '0.866', 'documented', 'echt', 'attested', 'authenticated', 'genuine']
senticnet['transformer'] = ['0.718', '0.899', '0', '0', '#joy', '#interest', 'positive', '0.808', 'yo-yo', 'action_figure', 'toy_marble', 'kite', 'toy_car']
senticnet['transformer_dark'] = ['0.916', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.929', 'galaxy', 'universe', 'planetary', 'sun', 'dwarf_planet']
senticnet['transforms_into'] = ['-0.73', '0', '0.72', '-0.93', '#sadness', '#disgust', 'negative', '-0.79', 'imp', 'elf', 'hob', 'leprechaun', 'pixie']
senticnet['transfuse'] = ['0.79', '-0.62', '0', '0.674', '#joy', '#admiration', 'positive', '0.695', 'add', 'contribute', 'lend', 'breathe', 'impart']
senticnet['transfused_blood'] = ['0.79', '-0.62', '0', '0.674', '#joy', '#admiration', 'positive', '0.695', 'add', 'contribute', 'lend', 'breathe', 'impart']
senticnet['transfused_into'] = ['0.79', '-0.62', '0', '0.674', '#joy', '#admiration', 'positive', '0.695', 'add', 'contribute', 'lend', 'breathe', 'impart']
senticnet['transfused_red'] = ['0.79', '-0.62', '0', '0.674', '#joy', '#admiration', 'positive', '0.695', 'add', 'contribute', 'lend', 'breathe', 'impart']
senticnet['transfusion'] = ['0.79', '-0.62', '0', '0.674', '#joy', '#admiration', 'positive', '0.695', 'add', 'contribute', 'lend', 'breathe', 'impart']
senticnet['transfusion_reaction'] = ['0.79', '-0.62', '0', '0.674', '#joy', '#admiration', 'positive', '0.695', 'add', 'contribute', 'lend', 'breathe', 'impart']
senticnet['transfusion_therapy'] = ['0.79', '-0.62', '0', '0.674', '#joy', '#admiration', 'positive', '0.695', 'add', 'contribute', 'lend', 'breathe', 'impart']
senticnet['transgender'] = ['-0.97', '0', '0.971', '-0.84', '#sadness', '#anger', 'negative', '-0.93', 'fail', 'betray', 'disappoint', 'cross', 'disillusion']
senticnet['transgress'] = ['-0.93', '0', '0.669', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'offend', 'violate', 'go_against', 'break', 'contravene']
senticnet['transgression'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'forgivable', 'excusable', 'mercifully', 'merciful', 'mercifulness']
senticnet['transgressor'] = ['-0.54', '0', '0.691', '-0.96', '#anger', '#disgust', 'negative', '-0.73', 'slut', 'trollop', 'libertine', 'adulteress', 'fornicatress']
senticnet['transhipment'] = ['-0.55', '0', '0.843', '-0.58', '#anger', '#disgust', 'negative', '-0.66', 'thieving', 'larceny', 'theft', 'thievery', 'stealing']
senticnet['transhistorical'] = ['0.519', '-0.55', '0', '0.746', '#surprise', '#admiration', 'positive', '0.606', 'narrow_down', 'peg_down', 'nail_down', 'narrow', 'pin_down']
senticnet['transhuman'] = ['0.992', '-0.66', '0', '0.536', '#joy', '#surprise', 'positive', '0.729', 'nous', 'mind', 'brain', 'psyche', 'head']
senticnet['transience'] = ['-0.87', '0', '0.744', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'temporariness', 'impermanence', 'transiency', 'transitoriness', 'mortality']
senticnet['transiency'] = ['-0.87', '0', '0.744', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'temporariness', 'transience', 'impermanence', 'transitoriness', 'mortality']
senticnet['transient'] = ['0', '0', '-0.72', '-0.95', '#fear', '#disgust', 'negative', '-0.84', 'transitory', 'transeunt', 'ephemeral', 'passing', 'short-lived']
senticnet['transient_bacteremia'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'plaque']
senticnet['transient_thermal'] = ['0.03', '0.068', '-0.06', '0.062', '#interest', '#admiration', 'positive', '0.025', 'pacific_northwest', 'american_forest', 'treetop', 'bath', 'hot_air']
senticnet['transiently'] = ['0', '-0.75', '0', '0.848', '#surprise', '#admiration', 'positive', '0.799', 'focally', 'diffusely', 'potently', 'reproducibly', 'lastingly']
senticnet['transistor'] = ['-0.91', '0', '0', '-0.81', '#sadness', '#disgust', 'negative', '-0.86', 'silicon', 'semiconductor_device', 'conductor', 'chip', 'semiconductor']
senticnet['transistor_count'] = ['-0.83', '0', '0.521', '-0.71', '#sadness', '#disgust', 'negative', '-0.69', 'stool', 'dejection', 'droppings', 'cow_droppings', 'cow_dung']
senticnet['transistor_radio'] = ['0.087', '0.113', '-0.08', '0', '#joy', '#interest', 'positive', '0.038', 'find_match', 'strike_against', 'cigarette_lighter', 'match', 'use_lighter']
senticnet['transit'] = ['0.04', '0.144', '-0.07', '0.122', '#interest', '#admiration', 'positive', '0.058', 'taxi_cab', 'rut', 'foreign_country', 'travel', 'move']
senticnet['transit_card'] = ['0.04', '0.144', '-0.07', '0.122', '#interest', '#admiration', 'positive', '0.058', 'transit', 'bus_pass', 'travel_card', 'transit_ticket', 'transport_card']
senticnet['transit_corridor'] = ['0', '0', '0', '0.085', '#admiration', '#admiration', 'positive', '0.085', 'use_farm', 'gate', 'door', 'doorway', 'entranceway']
senticnet['transit_hub'] = ['0', '0.147', '0', '0.099', '#interest', '#admiration', 'positive', '0.123', 'airport', 'subway', 'bus_stop', 'bus', 'control_tower']
senticnet['transit_ticket'] = ['0', '0.093', '0', '0', '#interest', '#interest', 'positive', '0.093', 'subway_pass', 'train_ticket', 'bus_pass', 'passenger_ticket', 'transit_card']
senticnet['transiting_planet'] = ['0', '-0.72', '0', '0.712', '#surprise', '#admiration', 'positive', '0.72', 'heavenly_body', 'around_earth', 'source_light', 'sol', 'celestial_body']
senticnet['transition'] = ['0', '0.202', '-0.06', '0.291', '#interest', '#admiration', 'positive', '0.142', 'change_life', 'pocket_money', 'go_forward', 'evolve', 'rotate_movement']
senticnet['transition_period'] = ['0', '0.202', '-0.06', '0.291', '#interest', '#admiration', 'positive', '0.142', 'change_life', 'pocket_money', 'go_forward', 'evolve', 'rotate_movement']
senticnet['transition_zone'] = ['0.034', '0.09', '0.039', '0.061', '#interest', '#admiration', 'positive', '0.036', 'border', 'limit', 'edge', 'indoor', 'side']
senticnet['transitional'] = ['0', '0.202', '-0.06', '0.291', '#interest', '#admiration', 'positive', '0.142', 'change_life', 'pocket_money', 'go_forward', 'evolve', 'rotate_movement']
senticnet['transitional_housing_facility'] = ['-0.88', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.81', 'bum', 'red_light', 'petition', 'take', 'violent_action']
senticnet['transitional_housing_program'] = ['-0.88', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.81', 'bum', 'red_light', 'petition', 'take', 'violent_action']
senticnet['transitional_period'] = ['0', '0.202', '-0.06', '0.291', '#interest', '#admiration', 'positive', '0.142', 'change_life', 'pocket_money', 'go_forward', 'evolve', 'rotate_movement']
senticnet['transitional_phase'] = ['0', '0.202', '-0.06', '0.291', '#interest', '#admiration', 'positive', '0.142', 'change_life', 'pocket_money', 'go_forward', 'evolve', 'rotate_movement']
senticnet['transitive_closure'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly']
senticnet['transitive_relation'] = ['0.734', '-0.56', '0', '0.64', '#joy', '#admiration', 'positive', '0.645', 'inferential', 'suggestive', 'connotative', 'connotational', 'connotative_of']
senticnet['transitive_verb'] = ['0.732', '0.8', '0', '0', '#joy', '#interest', 'positive', '0.766', 'fast', 'place', 'substance', 'substantive', 'person']
senticnet['transitoriness'] = ['-0.87', '0', '0.744', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'temporariness', 'transience', 'transiency', 'impermanence', 'mortality']
senticnet['transitory'] = ['0', '0', '-0.73', '-0.96', '#fear', '#disgust', 'negative', '-0.84', 'transient', 'transeunt', 'ephemeral', 'passing', 'short-lived']
senticnet['transitory_nature'] = ['-0.87', '0', '0.744', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['transitway'] = ['0', '0', '0', '0.085', '#admiration', '#admiration', 'positive', '0.085', 'use_farm', 'gate', 'door', 'doorway', 'entranceway']
senticnet['transkeian'] = ['0.774', '-0.82', '0', '0.63', '#joy', '#surprise', 'positive', '0.744', 'quality', 'perishability', 'perishableness', 'unsatisfactoriness', 'inadequacy']
senticnet['translatable'] = ['0.621', '-0.56', '0', '0.972', '#joy', '#admiration', 'positive', '0.72', 'alterable', 'convertible', 'commutable', 'transformable', 'transmutable']
senticnet['translate'] = ['0', '0.193', '-0.06', '0.284', '#interest', '#admiration', 'positive', '0.136', 'vocabulary', 'translation', 'dictionary', 'interpreter', 'thesaurus']
senticnet['translate_into'] = ['0.81', '0', '0', '0.703', '#joy', '#admiration', 'positive', '0.756', 'harvest', 'crop', 'grow_crop', 'plant_crop', 'graze_cattle']
senticnet['translation'] = ['0', '0.202', '-0.06', '0.291', '#interest', '#admiration', 'positive', '0.142', 'vocabulary', 'translate', 'dictionary', 'interpreter', 'thesaurus']
senticnet['translation_initiation'] = ['0.085', '0', '0', '0.089', '#joy', '#admiration', 'positive', '0.087', 'steam', 'haze', 'mist', 'misty', 'summer_weather']
senticnet['translation_invariance'] = ['-0.75', '0', '0.679', '-0.77', '#sadness', '#disgust', 'negative', '-0.73', 'unvariedness', 'changelessness', 'unchangeability', 'unchangeableness', 'unchangingness']
senticnet['translation_invariant'] = ['-0.75', '0', '0.679', '-0.77', '#sadness', '#disgust', 'negative', '-0.73', 'unvariedness', 'changelessness', 'unchangeability', 'unchangeableness', 'unchangingness']
senticnet['translational_motion'] = ['-0.97', '0', '0.831', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'skewness', 'irregularity']
senticnet['translator'] = ['0', '0.202', '-0.06', '0.291', '#interest', '#admiration', 'positive', '0.142', 'vocabulary', 'translate', 'dictionary', 'translation', 'thesaurus']
senticnet['translator_interpreter'] = ['0', '0.202', '-0.06', '0.291', '#interest', '#admiration', 'positive', '0.142', 'vocabulary', 'translate', 'dictionary', 'translation', 'thesaurus']
senticnet['transliterate'] = ['0', '-0.98', '0', '0.938', '#surprise', '#admiration', 'positive', '0.959', 'transliterating', 'transliteration', 'transliterated', 'translate', 'pluralize']
senticnet['transliterated'] = ['0', '-0.98', '0', '0.937', '#surprise', '#admiration', 'positive', '0.962', 'transliterating', 'transliteration', 'transliterate', 'translate', 'pluralize']
senticnet['transliterating'] = ['0', '-0.98', '0', '0.938', '#surprise', '#admiration', 'positive', '0.961', 'transliterate', 'transliteration', 'transliterated', 'translate', 'pluralize']
senticnet['transliteration'] = ['0', '-0.98', '0', '0.929', '#surprise', '#admiration', 'positive', '0.956', 'transliterating', 'transliterate', 'transliterated', 'translate', 'pluralize']
senticnet['translucence'] = ['-0.63', '0', '0.694', '-0.66', '#anger', '#disgust', 'negative', '-0.66', 'corporality', 'tangibleness', 'palpability', 'reality', 'solidness']
senticnet['translucent'] = ['0', '0.075', '0', '0.119', '#interest', '#admiration', 'positive', '0.097', 'transparent', 'see_thru', 'clear_solid', 'transparency', 'see_through']
senticnet['translucent_powder'] = ['-0.93', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.85', 'protect_from_sun', 'shade', 'meaning', 'corrector', 'sunglasses']
senticnet['translucent_skin'] = ['0.352', '0.142', '0', '0.281', '#joy', '#admiration', 'positive', '0.258', 'knowledgeable', 'complement', 'much_need', 'see_ocean', 'good_sense_of_humor']
senticnet['transmedia'] = ['0.951', '0', '0', '0.731', '#joy', '#admiration', 'positive', '0.841', 'pastime', 'enjoyment', 'pleasure', 'enjoy', 'fun']
senticnet['transmembrane'] = ['0', '0', '0.019', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'worm_intestine', 'bristle', 'fur_keep_warm', 'animal', 'whisker']
senticnet['transmissible_agent'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'agent', 'satisfier', 'quickener', 'invigorator']
senticnet['transmissible_disease'] = ['-0.11', '0', '-0.15', '-0.24', '#fear', '#disgust', 'negative', '-0.17', 'disease', 'infectious_disease', 'polio', 'malady', 'sickness']
senticnet['transmission'] = ['-0.53', '0', '0.8', '-0.64', '#anger', '#disgust', 'negative', '-0.65', 'causing', 'causation', 'sending', 'transmittal', 'transmitting']
senticnet['transmission_line'] = ['0.98', '0.823', '0', '0', '#joy', '#interest', 'positive', '0.901', 'cable_television', 'wire', 'telegraph', 'cable_television_service', 'cable']
senticnet['transmit_disease'] = ['-0.07', '0', '-0.17', '-0.09', '#fear', '#disgust', 'negative', '-0.11', 'get_trouble', 'end_war', 'feel_love', 'go_scuba_dive', 'sense_environment']
senticnet['transmit_information'] = ['0.504', '0.617', '0', '0.753', '#interest', '#admiration', 'positive', '0.625', 'convey_information', 'body_language', 'express_idea', 'give_comfort', 'write_novel']
senticnet['transmit_signal'] = ['0.764', '0.719', '0', '0', '#joy', '#interest', 'positive', '0.742', 'lean', 'bury_dead', 'wooden_box', 'inside_body', 'body']
senticnet['transmittal'] = ['-0.53', '0', '0.8', '-0.64', '#anger', '#disgust', 'negative', '-0.65', 'causing', 'causation', 'transmission', 'sending', 'transmitting']
senticnet['transmittal_form'] = ['0.862', '0.709', '0', '0', '#joy', '#interest', 'positive', '0.785', 'invoice', 'bill', 'check', 'mastercard', 'payment_counter']
senticnet['transmittance'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'incandescence', 'luminescence', 'brightness_level']
senticnet['transmitting'] = ['-0.53', '0', '0.8', '-0.64', '#anger', '#disgust', 'negative', '-0.65', 'causing', 'causation', 'transmission', 'transmittal', 'sending']
senticnet['transmontana'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'dryness', 'thirstiness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['transmural'] = ['0.777', '-0.89', '0', '0.508', '#joy', '#surprise', 'positive', '0.727', 'stretching', 'enlarge', 'enlargement', 'get_bigger', 'distension']
senticnet['transmutable'] = ['0.618', '-0.57', '0', '0.965', '#joy', '#admiration', 'positive', '0.719', 'alterable', 'convertible', 'translatable', 'transformable', 'commutable']
senticnet['transmute'] = ['0.581', '-0.88', '0', '0.534', '#joy', '#surprise', 'positive', '0.667', 'better', 'improve', 'pure', 'meliorate', 'amend']
senticnet['transmuted_into'] = ['0.734', '-0.64', '0', '0.933', '#joy', '#admiration', 'positive', '0.771', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['transnational'] = ['0.043', '0.109', '0', '0.061', '#interest', '#admiration', 'positive', '0.071', 'nation_state', 'democratic_country', 'free_country', 'large_country', 'republic']
senticnet['transnational_citizenship'] = ['0.122', '0.159', '-0.05', '0.217', '#interest', '#admiration', 'positive', '0.111', 'profile_picture', 'unity', 'country_person', 'unite', 'signature']
senticnet['transnationalization'] = ['0.836', '-0.83', '0', '0.511', '#joy', '#surprise', 'positive', '0.726', 'appearance', 'start', 'casus_belli', 'beginning', 'egress']
senticnet['transnusa'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['transom'] = ['0', '0', '-0.73', '-0.99', '#fear', '#disgust', 'negative', '-0.86', 'traverse', 'track', 'crosspiece', 'crossbeam', 'traversal']
senticnet['transparence'] = ['0.811', '-0.67', '0', '0.898', '#joy', '#admiration', 'positive', '0.795', 'quality', 'transparentness', 'distinctness', 'transparency', 'uncloudedness']
senticnet['transparency'] = ['0.617', '-0.84', '0', '0.868', '#surprise', '#admiration', 'positive', '0.778', 'see_thru', 'translucent', 'transparent', 'clear_solid', 'sunny_weather']
senticnet['transparent'] = ['0.617', '-0.84', '0', '0.868', '#surprise', '#admiration', 'positive', '0.778', 'translucent', 'see_thru', 'clear_solid', 'see_through', 'clear']
senticnet['transparent_glaze'] = ['-0.06', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.04', 'polish', 'magnify', 'see_out', 'sill', 'pane']
senticnet['transparent_manner'] = ['0.837', '-0.99', '0', '0.559', '#joy', '#surprise', 'positive', '0.797', 'disinterested', 'dispassionate', 'indifferent', 'unbiased', 'fair']
senticnet['transparent_plastic'] = ['0', '-0.02', '0', '0.051', '#surprise', '#admiration', 'positive', '0.037', 'cardboard_box', 'bag', 'package', 'box_cereal', 'bin']
senticnet['transparentness'] = ['0.617', '-0.84', '0', '0.868', '#surprise', '#admiration', 'positive', '0.778', 'clearness', 'clarity', 'uncloudedness', 'pellucidity', 'limpidity']
senticnet['transphobic'] = ['0.778', '-0.73', '0', '0.559', '#joy', '#surprise', 'positive', '0.689', 'sex', 'inversion', 'sexual_inversion', 'lesbianism', 'sapphism']
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senticnet['trellick_tower'] = ['-0.81', '0', '0.543', '-0.63', '#sadness', '#disgust', 'negative', '-0.66', 'caper', 'pickle', 'relish', 'dill_pickle', 'sweet_pickle']
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senticnet['trematode'] = ['0.688', '-0.94', '0', '0.925', '#surprise', '#admiration', 'positive', '0.852', 'flatworm', 'platyhelminth', 'liver_fluke', 'schistosome', 'blood_fluke']
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senticnet['trembler'] = ['0.761', '0', '0', '0.839', '#joy', '#admiration', 'positive', '0.8', 'quaker', 'friend', 'charge', 'comment', 'business']
senticnet['trembling_hand'] = ['0.883', '-0.59', '0', '0.533', '#joy', '#surprise', 'positive', '0.67', 'hook', 'meat_hook', 'mauler', 'right', 'left']
senticnet['trembling_voice'] = ['0.591', '-0.76', '0', '0.66', '#surprise', '#admiration', 'positive', '0.673', 'darling', 'pet', 'favorite', 'first_choice', 'macushla']
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senticnet['tremendous_enthusiasm'] = ['0.887', '0', '0', '0', '#joy', '#joy', 'positive', '0.887', 'thankful', 'openhearted', 'good-hearted', 'kindly', 'benevolent']
senticnet['tremendous_explosion'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'firework', 'bright_flash', 'nuclear_weapon', 'earthquake', 'nuclear_bomb']
senticnet['tremendous_growth'] = ['0', '0.229', '-0.09', '0.187', '#interest', '#admiration', 'positive', '0.106', 'increase', 'make_big', 'get_large', 'grow', 'expand']
senticnet['tremendous_popularity'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'important', 'universal', 'vip', 'prestige', 'status']
senticnet['tremendous_potential'] = ['0', '0.286', '-0.22', '0.383', '#interest', '#admiration', 'positive', '0.147', 'oath', 'propose', 'suggest', 'ask', 'trust']
senticnet['tremendous_pressure'] = ['-0.12', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.09', 'breeze', 'move_air', 'air_movement', 'gape', 'force']
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senticnet['tremendously_successful'] = ['0.889', '0', '0', '0', '#joy', '#joy', 'positive', '0.889', 'succeed', 'increase_self_esteem', 'increase_self-esteem', 'help_others', 'achievement']
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senticnet['trench_fever'] = ['-0.73', '0', '0.599', '-0.96', '#sadness', '#disgust', 'negative', '-0.76', 'spotted_fever', 'rickettsial_disease', 'rickettsialpox', 'typhus', 'typhus_fever']
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senticnet['trenchcoat'] = ['0', '-0.08', '0.105', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'underwear', 'shirt', 'panties', 'robe', 'slack']
senticnet['trend'] = ['0.05', '0.151', '0', '0.159', '#interest', '#admiration', 'positive', '0.12', 'glamour', 'stylish', 'style', 'clothes_style', 'fancy_clothes']
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senticnet['trend_upward'] = ['0.05', '0.151', '0', '0.159', '#interest', '#admiration', 'positive', '0.12', 'glamour', 'stylish', 'style', 'clothes_style', 'fancy_clothes']
senticnet['trendiest'] = ['0.952', '0.844', '0', '0.96', '#joy', '#admiration', 'positive', '0.919', 'fashion', 'demand', 'kindness', 'better_person', 'warm_feeling']
senticnet['trendy'] = ['0.039', '-0.02', '0.032', '0.059', '#joy', '#admiration', 'positive', '0.022', 'glamour', 'fancy_clothes', 'frilly', 'woman_clothes', 'girl_clothes']
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senticnet['trentsevern_waterway'] = ['0.933', '0.795', '0', '0', '#joy', '#interest', 'positive', '0.864', 'water_passage', 'small_stream', 'water_flow', 'flowing_water', 'water_stream']
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senticnet['treo_smartphone'] = ['0.807', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.881', 'fruit', 'mobile_phone', 'drinkable', 'savory', 'eat_fruit']
senticnet['trepid'] = ['0.761', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.744', 'timorously', 'timorous', 'fear', 'timorousation', 'presentiment']
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senticnet['trepidly'] = ['0.939', '0.908', '0', '0', '#joy', '#interest', 'positive', '0.923', 'boding', 'premonition', 'presentiment', 'foreboding', 'presage']
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senticnet['trever_miller'] = ['0.663', '-0.68', '0', '0.892', '#surprise', '#admiration', 'positive', '0.747', 'saphead', 'muggins', 'tomfool', 'fool', 'sap']
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senticnet['trevor_fitzroy'] = ['0.02', '0.131', '0.749', '-0.67', '#anger', '#disgust', 'negative', '-0.31', 'belligerence', 'belligerency', 'belligerent', 'war', 'rancor']
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senticnet['tri_oval'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'pin', 'barrette', 'haircut_scissor', 'hair_gel', 'double_edge_razor']
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senticnet['trial-and-error'] = ['0', '0.875', '0', '0.898', '#interest', '#admiration', 'positive', '0.887', 'painstaking', 'laborious', 'guesswork', 'time-consuming', 'experimentation']
senticnet['trial_court'] = ['0.817', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.824', 'witness', 'prosecutor', 'legal_process', 'lawyer', 'jury']
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senticnet['triazole'] = ['-0.81', '0', '0.774', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'acid', 'hypophosphoric_acid', 'periodic_acid', 'hypooxyacid', 'oxyacid']
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senticnet['tribal'] = ['0.989', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.97', 'committee', 'council', 'caste', 'association', 'organization']
senticnet['tribal_affiliation'] = ['0', '-0.91', '0', '0.806', '#surprise', '#admiration', 'positive', '0.861', 'clean_shoes', 'black_stuff', 'paint_nail', 'ethnicity', 'black_goo']
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senticnet['tribal_law_enforcement'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'constabulary', 'law', 'agency']
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senticnet['tribe_meliponini'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'giant_hornet', 'hornet', 'hornet_wasp']
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senticnet['tribosphenic_pattern'] = ['-0.56', '0', '0.617', '-0.80', '#anger', '#disgust', 'negative', '-0.66', 'prolixity', 'prolixness', 'wordiness', 'repetitiveness', 'repetitiousness']
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senticnet['tripped_over'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'come_across', 'stumble', 'lose_balance', 'stammer', 'stutter']
senticnet['tripped_up'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'come_across', 'tripped_over', 'lose_balance', 'stammer', 'stutter']
senticnet['tripper'] = ['-0.65', '0', '0.943', '-0.75', '#anger', '#disgust', 'negative', '-0.78', 'rubberneck', 'sightseer', 'excursionist', 'holidaymaker', 'traveler']
senticnet['tripping_over'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'come_across', 'tripped_over', 'lose_balance', 'stammer', 'stutter']
senticnet['trippingly'] = ['-0.11', '0', '-0.09', '0.082', '#sadness', '#fear', 'negative', '-0.04', 'stare', 'ear', 'facial_expression', 'mouth', 'dimple']
senticnet['triptan'] = ['-0.73', '0', '-0.85', '-0.72', '#sadness', '#fear', 'negative', '-0.77', 'lay_off', 'hurt', 'pain_suffer', 'lose_mind', 'forget_everything']
senticnet['tripurantakam'] = ['0.929', '0.507', '-0.17', '0.396', '#joy', '#interest', 'positive', '0.415', 'plume', 'congratulate', 'smugness', 'smug', 'proudly']
senticnet['triquetral'] = ['-0.74', '0', '0.598', '-0.75', '#sadness', '#disgust', 'negative', '-0.69', 'lunate_bone', 'semilunar_bone', 'carpal_bone', 'carpal', 'bone']
senticnet['triquetral_bone'] = ['-0.74', '0', '0.598', '-0.75', '#sadness', '#disgust', 'negative', '-0.69', 'lunate_bone', 'semilunar_bone', 'carpal_bone', 'triquetral', 'carpal']
senticnet['triquetrum'] = ['-0.74', '0', '0.598', '-0.75', '#sadness', '#disgust', 'negative', '-0.69', 'lunate_bone', 'semilunar_bone', 'carpal_bone', 'carpal', 'bone']
senticnet['trisagion'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'reverence', 'genuflection', 'obeisance', 'kowtow', 'mafia']
senticnet['trisect'] = ['0', '-0.00', '0.034', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'celsius', 'grade', 'certificate', 'learn_tool', 'temperature_measure']
senticnet['trisetum'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'meadow_grass', 'grass', 'manna_grass', 'sweet_grass', 'wood_meadowgrass']
senticnet['trisha_brown'] = ['-0.95', '0', '0.992', '-0.63', '#sadness', '#anger', 'negative', '-0.86', 'modern_dance', 'interpretive_dancing', 'interpretative_dance', 'interpretative_dancing', 'stage_dancing']
senticnet['trishuli'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['trisomy'] = ['-0.82', '0', '0.582', '-0.74', '#sadness', '#disgust', 'negative', '-0.71', 'down_syndrome', 'birth_defect', 'congenital_anomaly', 'congenital_defect', 'congenital_disorder']
senticnet['tristan_murail'] = ['0.045', '0', '0.033', '0.089', '#joy', '#admiration', 'positive', '0.034', 'spanish', 'vocalization', 'verbal_communication', 'vocal', 'mouth_noise']
senticnet['tristram_risdon'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'bodyguard', 'outrider', 'bodyguard_of_guard', 'beefeater', 'defender']
senticnet['trisul'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['trite'] = ['0', '0', '-0.74', '-0.93', '#fear', '#disgust', 'negative', '-0.84', 'banal', 'commonplace', 'old-hat', 'hackneyed', 'shopworn']
senticnet['triterpene'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'enhance', 'intensify', 'deepen', 'potentiate', 'heighten']
senticnet['triterpenoid'] = ['0.737', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.719', 'enhance', 'intensify', 'deepen', 'potentiate', 'heighten']
senticnet['triticale'] = ['0.878', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.883', 'wheat', 'grain', 'rye', 'white_rice', 'rice']
senticnet['triticeae'] = ['0.878', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.883', 'wheat', 'grain', 'rye', 'white_rice', 'rice']
senticnet['triticum_aestivum'] = ['0.878', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.883', 'wheat', 'grain', 'rye', 'white_rice', 'rice']
senticnet['tritone'] = ['-0.52', '0', '0.902', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'discordant', 'inharmonious', 'unresolved', 'false', 'disharmonious']
senticnet['triumph'] = ['0.721', '0.805', '0', '0.738', '#interest', '#admiration', 'positive', '0.755', 'triumphal', 'prideful', 'exulting', 'exultant', 'triumphant']
senticnet['triumphal'] = ['0.732', '0.614', '-0.22', '0.329', '#joy', '#interest', 'positive', '0.363', 'exulting', 'prideful', 'exultant', 'triumphant', 'rejoicing']
senticnet['triumphal_arch'] = ['0.029', '0.087', '0.065', '0.137', '#interest', '#admiration', 'positive', '0.047', 'archway', 'aisle', 'mat', 'passageway', 'right_angle']
senticnet['triumphalism'] = ['-0.83', '0', '0.904', '0', '#sadness', '#anger', 'negative', '-0.87', 'disappointed', 'thwarted', 'frustrated', 'foiled', 'discomfited']
senticnet['triumphalist'] = ['-0.52', '0', '0.95', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'skeptic', 'sceptic', 'doubter', 'pessimist', 'negativist']
senticnet['triumphant'] = ['0.732', '0.614', '-0.22', '0.329', '#joy', '#interest', 'positive', '0.363', 'exulting', 'prideful', 'triumphal', 'exultant', 'rejoicing']
senticnet['triumphant_return'] = ['0.954', '-0.87', '0', '0.583', '#joy', '#surprise', 'positive', '0.802', 'penetration', 'air_raid', 'air_attack', 'swoop', 'incursion']
senticnet['triumphantly'] = ['0.724', '0.617', '-0.22', '0.331', '#joy', '#interest', 'positive', '0.361', 'rejoicing', 'triumphal', 'prideful', 'exulting', 'triumphant']
senticnet['triumphed_over'] = ['0.702', '-0.94', '0', '0.582', '#joy', '#surprise', 'positive', '0.743', 'beatable', 'vanquishable', 'vincible', 'subduable', 'subjugable']
senticnet['trivia'] = ['0.098', '0.165', '-0.13', '0.284', '#interest', '#admiration', 'positive', '0.103', 'understand', 'gain_insight', 'knowledge', 'know', 'solution_to_problem']
senticnet['trivial'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'minor', 'unimportant', 'inessential', 'negligible', 'petty']
senticnet['trivial_pursuit'] = ['0.801', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.796', 'verbosity', 'sport_play', 'excite_game', 'contest', 'fun_game']
senticnet['triviality'] = ['-0.55', '0', '0.741', '-0.76', '#anger', '#disgust', 'negative', '-0.68', 'illogicality', 'illogicalness', 'illogic', 'invalidity', 'fallaciousness']
senticnet['trivialization'] = ['-0.76', '0', '0.519', '-0.56', '#sadness', '#disgust', 'negative', '-0.61', 'scurrility', 'billingsgate', 'disrespect', 'discourtesy', 'vituperation']
senticnet['trivialize'] = ['-0.93', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.88', 'over-simplify', 'oversimplifying', 'exaggerate', 'oversimplified', 'oversimplify']
senticnet['trixie'] = ['-0.03', '-0.00', '0.087', '-0.05', '#anger', '#disgust', 'negative', '-0.04', 'dog_fur', 'dog_cat', 'dog_hair', 'pet', 'animal_hair']
senticnet['trocar'] = ['0.255', '0.182', '-0.06', '0.314', '#joy', '#admiration', 'positive', '0.173', 'doctor_office', 'urethroscope', 'sigmoidoscope', 'hospital', 'check_health']
senticnet['trochanter'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'hip_socket', 'tooth_socket', 'alveolus', 'socket', 'cotyloid_cavity']
senticnet['trochlea'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'hip_socket', 'tooth_socket', 'alveolus', 'socket', 'cotyloid_cavity']
senticnet['trochlear_nerve'] = ['-0.73', '0', '0.897', '-0.77', '#anger', '#disgust', 'negative', '-0.80', 'commissure', 'pathway', 'radiatio_optica', 'peduncle', 'cerebral_peduncle']
senticnet['trochophore_larva'] = ['0.642', '-0.58', '0', '0.751', '#joy', '#admiration', 'positive', '0.659', 'change', 'become_better', 'change_aspect', 'look_better', 'become_bigger']
senticnet['troff'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'wall_cover', 'two-dimensional', 'cover_wall', 'pigment', 'dye']
senticnet['troglodyte'] = ['0', '0', '-0.94', '-0.72', '#fear', '#disgust', 'negative', '-0.83', 'solitary', 'recluse', 'hermit', 'reclusive', 'solitudinarian']
senticnet['troglofauna'] = ['0.051', '0', '0', '0.1', '#joy', '#admiration', 'positive', '0.075', 'provide_shelter', 'live', 'get_oxygen', 'raise_family', 'live_city']
senticnet['trojan'] = ['-0.86', '0', '0.769', '-0.88', '#sadness', '#disgust', 'negative', '-0.84', 'malevolent_program', 'malevolent_program_horse', 'virus', 'worm', 'computer_virus']
senticnet['trojan_horse'] = ['-0.86', '0', '0.769', '-0.88', '#sadness', '#disgust', 'negative', '-0.84', 'trojan', 'malevolent_program', 'virus', 'worm', 'computer_virus']
senticnet['trokosi'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'vassalage']
senticnet['troll'] = ['-0.89', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.84', 'grotesqueness', 'form', 'style', 'grotesque', 'grotesquely']
senticnet['trolley'] = ['0.038', '0.155', '0', '0.074', '#interest', '#admiration', 'positive', '0.089', 'merchandise', 'cashier', 'goods', 'shopper', 'grocery']
senticnet['trolley_ride'] = ['-0.08', '0.179', '-0.06', '0.096', '#interest', '#admiration', 'positive', '0.034', 'ride_subway', 'buy_vending_machine', 'travel', 'buy_lottery_ticket', 'acquire']
senticnet['trollop'] = ['-0.54', '0', '0.691', '-0.96', '#anger', '#disgust', 'negative', '-0.73', 'slut', 'adulterer', 'libertine', 'adulteress', 'fornicatress']
senticnet['trombicula'] = ['-0.77', '0', '0.576', '-0.92', '#sadness', '#disgust', 'negative', '-0.75', 'insect', 'firebug', 'water_bug', 'assassin_bug', 'heteropterous_insect']
senticnet['troop'] = ['0', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.09', 'army', 'attacker', 'military', 'soldier', 'infantry']
senticnet['troop_commander'] = ['0', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.09', 'army', 'attacker', 'military', 'soldier', 'infantry']
senticnet['troop_drawdown'] = ['0.942', '-0.79', '0', '0.901', '#joy', '#admiration', 'positive', '0.88', 'backup', 'accretion', 'deposition', 'deposit', 'increase']
senticnet['troop_leader'] = ['0', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.09', 'army', 'attacker', 'military', 'soldier', 'infantry']
senticnet['troop_presence'] = ['0.942', '-0.79', '0', '0.901', '#joy', '#admiration', 'positive', '0.88', 'backup', 'accretion', 'deposition', 'deposit', 'increase']
senticnet['troop_reduction'] = ['0.942', '-0.79', '0', '0.901', '#joy', '#admiration', 'positive', '0.88', 'backup', 'accretion', 'deposition', 'deposit', 'increase']
senticnet['troop_strength'] = ['0.814', '-0.87', '0', '0.918', '#surprise', '#admiration', 'positive', '0.87', 'ship_company', 'company', 'work_force', 'workforce', 'full_complement']
senticnet['troop_surge_strategy'] = ['-0.82', '0', '0.739', '-0.50', '#sadness', '#anger', 'negative', '-0.69', 'fake', 'juke', 'maneuver', 'manoeuvre', 'feint']
senticnet['troop_train'] = ['0.861', '-0.52', '0', '0.598', '#joy', '#admiration', 'positive', '0.663', 'exit', 'get_off', 'deplane', 'go_out', 'get_out']
senticnet['trooper'] = ['0.945', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.898', 'patrol', 'police', 'patrolling', 'patrol', 'officer']
senticnet['trophoblastic_disease'] = ['-0.75', '0', '0.715', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'abdominal_pregnancy', 'ectopic_gestation', 'tubal_pregnancy', 'pregnancy', 'gestation']
senticnet['trophozoite'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['trophy'] = ['0.041', '0.116', '-0.05', '0.089', '#interest', '#admiration', 'positive', '0.047', 'medal', 'medallion', 'gold', 'titanium', 'carat']
senticnet['trophy-sized'] = ['0', '0', '-0.78', '-0.97', '#fear', '#disgust', 'negative', '-0.87', 'white-fleshed', 'scaleless', 'small-mouth', 'algae-eating', 'reddish-black']
senticnet['tropic'] = ['-0.82', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.81', 'tropical', 'subtropics', 'subtropical', 'temperate', 'climate']
senticnet['tropical'] = ['-0.83', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.82', 'tropic', 'subtropics', 'subtropical', 'temperate', 'climate']
senticnet['tropical_atlantic'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'pacific_ocean', 'small_lake', 'mere']
senticnet['tropical_convection'] = ['-0.69', '0', '0.835', '-0.56', '#sadness', '#anger', 'negative', '-0.69', 'roughness', 'inclemency', 'bad_weather', 'inclementness', 'windiness']
senticnet['tropical_cyclone'] = ['-0.96', '0', '0.014', '-0.95', '#sadness', '#disgust', 'negative', '-0.64', 'storm', 'gale', 'bad_weather', 'hurricane', 'rain']
senticnet['tropical_cyclone_activity'] = ['-0.69', '0', '0.835', '-0.56', '#sadness', '#anger', 'negative', '-0.69', 'roughness', 'inclemency', 'bad_weather', 'inclementness', 'windiness']
senticnet['tropical_deforestation'] = ['0', '-0.02', '0.085', '-0.09', '#anger', '#disgust', 'negative', '-0.05', 'tree_wood', 'forest', 'sideways', 'dead_tree', 'wood']
senticnet['tropical_downpour'] = ['-0.87', '0', '0.91', '-0.78', '#sadness', '#anger', 'negative', '-0.85', 'storm', 'thundershower', 'rain', 'line_storm', 'equinoctial_storm']
senticnet['tropical_dry'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'woodland', 'timberland']
senticnet['tropical_fish'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'autumn_leaf', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['tropical_forest'] = ['0', '0.762', '0', '0', '#interest', '#interest', 'positive', '0.762', 'encyclopaedia', 'tropical_rainforest', 'learn_new_information', 'documentary', 'child_story']
senticnet['tropical_fruit'] = ['0.859', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.888', 'green_apple', 'apricot', 'lime', 'peach', 'cherry']
senticnet['tropical_hardwood'] = ['-0.69', '0', '0.725', '-0.89', '#anger', '#disgust', 'negative', '-0.77', 'ponderosa', 'ponderosa_pine', 'western_yellow_pine', 'bull_pine', 'longleaf_pine']
senticnet['tropical_hideaway'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'lurking_place', 'spot', 'drop', 'hiding_place', 'den']
senticnet['tropical_island'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'lurking_place', 'spot', 'drop', 'hiding_place', 'den']
senticnet['tropical_paradise'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'lurking_place', 'spot', 'drop', 'hiding_place', 'den']
senticnet['tropical_rain_forest'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'woodland', 'timberland']
senticnet['tropical_rainforest'] = ['0', '0.762', '0', '0', '#interest', '#interest', 'positive', '0.762', 'encyclopaedia', 'documentary', 'learn_new_information', 'tropical_forest', 'child_story']
senticnet['tropical_savanna'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'woodland', 'timberland']
senticnet['tropical_sst'] = ['-0.68', '0', '0.946', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'abnormality', 'abnormalcy', 'birth_defect', 'congenital_anomaly', 'congenital_defect']
senticnet['tropical_storm'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'hurricane', 'mild', 'breeze', 'natural', 'atmospheric_phenomenon']
senticnet['tropical_storm_fay'] = ['-0.78', '0', '0.906', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'rainstorm', 'atmospheric_phenomenon', 'northeaster', 'noreaster', 'hailstorm']
senticnet['tropical_storm_hanna'] = ['-0.78', '0', '0.906', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'rainstorm', 'atmospheric_phenomenon', 'northeaster', 'noreaster', 'hailstorm']
senticnet['tropically'] = ['0', '0.929', '0', '0.704', '#interest', '#admiration', 'positive', '0.817', 'lushly', 'nautically', 'landscaped', 'exotically', 'park-like']
senticnet['tropicana_hotel'] = ['0.057', '0.13', '0', '0.119', '#interest', '#admiration', 'positive', '0.102', 'friend_wedding', 'circular_route', 'spanish_restaurant', 'altar', 'monastery']
senticnet['trot'] = ['0', '0', '0.752', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'get_hit_by_car', 'meander', 'full_moon', 'step_dog_poop', 'kick_ass']
senticnet['trot_out'] = ['0.58', '-0.82', '0', '0.77', '#surprise', '#admiration', 'positive', '0.724', 'show', 'expose', 'bring_out', 'unfold', 'disclose']
senticnet['trotsky'] = ['-0.67', '0', '0.85', '-0.59', '#sadness', '#anger', 'negative', '-0.70', 'rack', 'boot', 'whip', 'iron_boot', 'instrument_of_torture']
senticnet['trotskyist'] = ['0', '0', '-0.70', '-0.83', '#fear', '#disgust', 'negative', '-0.76', 'settle_disagreement', 'hitler', 'republican', 'conservative', 'unchanging']
senticnet['trotted_along'] = ['-0.93', '0', '0.791', '0', '#sadness', '#anger', 'negative', '-0.86', 'hopelessness', 'hopelessly', 'despairing', 'pessimistically', 'pessimistic']
senticnet['trotted_off'] = ['0', '0', '0.752', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'get_hit_by_car', 'meander', 'full_moon', 'step_dog_poop', 'kick_ass']
senticnet['trotted_out'] = ['-0.08', '-0.05', '0.037', '-0.06', '#sadness', '#disgust', 'negative', '-0.03', 'trip_fall', 'fall_off', 'fall_down', 'lose_balance', 'breahard']
senticnet['trotting'] = ['0', '0', '-0.82', '-0.93', '#fear', '#disgust', 'negative', '-0.88', 'trot', 'canter', 'galloping', 'dart', 'gallop']
senticnet['trotting_along'] = ['0', '0', '0.752', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'get_hit_by_car', 'meander', 'full_moon', 'step_dog_poop', 'kick_ass']
senticnet['trouble'] = ['-0.37', '0.184', '-0.34', '-0.06', '#sadness', '#fear', 'negative', '-0.15', 'cause_problem', 'caution', 'unsafe', 'big_monster', 'intolerance']
senticnet['trouble-free'] = ['-0.72', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.75', 'hassle-free', 'worry-free', 'problem-free', 'stress-free', 'effortless']
senticnet['trouble_breathe'] = ['0', '0', '-0.74', '-0.87', '#fear', '#disgust', 'negative', '-0.80', 'go_hospital', 'chest_pain', 'brain_damage', 'heart_attack', 'health_problem']
senticnet['trouble_falling_asleep'] = ['-0.07', '0', '-0.17', '-0.05', '#sadness', '#fear', 'negative', '-0.10', 'night_shift', 'drowsiness', 'drink_coffee', 'need_rest', 'feel_little_tired']
senticnet['trouble_maker'] = ['-0.84', '0', '0.506', '-0.62', '#sadness', '#disgust', 'negative', '-0.66', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['troubled'] = ['-0.70', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.84', 'awful', 'incapacitated', 'stricken', 'removed', 'disabled']
senticnet['troubled_relationship'] = ['0.72', '-0.72', '0', '0.506', '#joy', '#surprise', 'positive', '0.651', 'standoffishness', 'withdrawnness', 'closeness', 'secretiveness', 'remoteness']
senticnet['troubled_swat_valley'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'webpage', 'store_record', 'ideal']
senticnet['troubled_teenager'] = ['0.943', '-0.78', '0', '0.571', '#joy', '#surprise', 'positive', '0.767', 'master', 'ignition_key', 'key', 'passkey', 'master_key']
senticnet['troubled_youth'] = ['0.943', '-0.78', '0', '0.571', '#joy', '#surprise', 'positive', '0.767', 'master', 'ignition_key', 'key', 'passkey', 'master_key']
senticnet['troublemaker'] = ['-0.98', '0', '0.693', '-0.76', '#sadness', '#disgust', 'negative', '-0.81', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['troubler'] = ['-0.52', '0', '0.701', '-0.50', '#sadness', '#anger', 'negative', '-0.57', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['troubleshoot'] = ['-0.88', '0', '0.688', '-0.93', '#sadness', '#disgust', 'negative', '-0.83', 'fix', 'bushel', 'doctor', 'furbish_up', 'repair']
senticnet['troublesome'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'nuisance', 'annoyance', 'irritant', 'pain_in_ass', 'pest']
senticnet['troublesome_left_knee'] = ['-0.78', '0', '0.554', '-0.87', '#sadness', '#disgust', 'negative', '-0.73', 'rubor', 'redness', 'inflammation', 'tennis_elbow', 'lateral_epicondylitis']
senticnet['troublesomeness'] = ['-0.64', '0', '0.625', '-0.88', '#sadness', '#disgust', 'negative', '-0.71', 'difficulty', 'awkwardness', 'flea_bite', 'difficultness', 'cumbersomeness']
senticnet['troubling'] = ['0.732', '0.887', '0', '0', '#joy', '#interest', 'positive', '0.809', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'worrisome']
senticnet['troubling_trend'] = ['0.05', '0.151', '0', '0.159', '#interest', '#admiration', 'positive', '0.12', 'glamour', 'stylish', 'style', 'clothes_style', 'fancy_clothes']
senticnet['trounce'] = ['0.946', '-0.63', '0', '0.889', '#joy', '#admiration', 'positive', '0.824', 'flagellate', 'leather', 'birch', 'beat', 'scourge']
senticnet['troupe'] = ['0', '0', '0', '0.987', '#admiration', '#admiration', 'positive', '0.987', 'emote', 'bold', 'form_of_social_interaction', 'act', 'stage_play']
senticnet['trouser_leg'] = ['-0.99', '0', '0.806', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'wear', 'biohazard_suit', 'coverall', 'protective_garment', 'wearable']
senticnet['trouser_suit'] = ['-0.88', '0', '0.864', '-0.72', '#sadness', '#anger', 'negative', '-0.82', 'malicious', 'venomous', 'bitchy', 'catty', 'cattish']
senticnet['trousers'] = ['0', '-0.05', '0.092', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'pant', 'man_clothes', 'slack', 'shirt', 'scarf']
senticnet['trout'] = ['-0.86', '0', '0.941', '-0.71', '#sadness', '#anger', 'negative', '-0.83', 'salmon', 'salmonid', 'go_fishing', 'fish', 'catch_fish']
senticnet['trout_brook'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['trout_lily'] = ['-0.52', '0', '0.527', '-0.78', '#anger', '#disgust', 'negative', '-0.61', 'herb', 'black_snakeroot', 'heartleaf', 'asarabacca', 'herbaceous_plant']
senticnet['trove'] = ['0', '0.177', '0', '0.126', '#interest', '#admiration', 'positive', '0.151', 'treasure', 'storage_facility', 'shrine', 'lock_box', 'jewelry_box']
senticnet['trowel'] = ['0', '0.068', '0', '0', '#interest', '#interest', 'positive', '0.068', 'artist', 'oil_paint', 'painter', 'creative_person', 'make_paint']
senticnet['truancy'] = ['0.723', '0.72', '0', '0', '#joy', '#interest', 'positive', '0.722', 'late', 'get_dark', 'rainy_day', 'split_end', 'house_look_better']
senticnet['truant'] = ['-0.51', '0', '0.63', '-0.84', '#anger', '#disgust', 'negative', '-0.66', 'hooky_player', 'absentee', 'defaulter', 'exile', 'expatriate']
senticnet['truce'] = ['0.268', '0.199', '-0.12', '0', '#joy', '#interest', 'positive', '0.115', 'peace', 'respite', 'ceasefire', 'treaty', 'civil_war']
senticnet['truck'] = ['0.903', '-0.89', '0', '0.919', '#joy', '#admiration', 'positive', '0.905', 'camion', 'rig', 'wrecker', 'dump_truck', 'dumper']
senticnet['truck_bed'] = ['-0.95', '0', '0.793', '0', '#sadness', '#anger', 'negative', '-0.87', 'block', 'obstacle', 'nodus', 'impediment', 'obstruction']
senticnet['truck_bomb'] = ['0', '-0.03', '-0.04', '-0.19', '#fear', '#disgust', 'negative', '-0.06', 'nuclear_bomb', 'explosive', 'grenade', 'nuclear_weapon', 'aerial_bomb']
senticnet['truck_driver'] = ['0.969', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.936', 'attendant', 'manager', 'public_servant', 'specialist', 'accomplice']
senticnet['truck_stop'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'sell_car', 'car_dealership', 'vehicle', 'insurance', 'car']
senticnet['truck_tire'] = ['0', '-0.07', '0', '0', '#surprise', '#surprise', 'positive', '0.075', 'gyroscope', 'air_pump', 'ball_bounce', 'motor_oil', 'tire']
senticnet['truck_tractor'] = ['0', '-0.01', '0', '0.064', '#surprise', '#admiration', 'positive', '0.042', 'car_gas', 'car_power', 'fossil_fuel', 'run_car', 'car_engine']
senticnet['truck_traffic'] = ['-0.98', '0', '0.989', '-0.93', '#sadness', '#anger', 'negative', '-0.97', 'flight_accident', 'dangerous', 'danger', 'air_traffic', 'air_traffic_jam']
senticnet['truckee'] = ['-0.06', '0.21', '0', '0.076', '#interest', '#admiration', 'positive', '0.074', 'new_think', 'figure_out', 'brain_activity', 'idea', 'come_up_idea']
senticnet['trucker'] = ['0.969', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.936', 'attendant', 'manager', 'public_servant', 'specialist', 'accomplice']
senticnet['truckie'] = ['-0.84', '0', '0.96', '-0.91', '#anger', '#disgust', 'negative', '-0.90', 'housewrecker', 'housebreaker', 'knacker', 'laborer', 'manual_laborer']
senticnet['trucking'] = ['0', '0.082', '-0.05', '0.201', '#interest', '#admiration', 'positive', '0.075', 'car_travel', 'transportation', 'go_elsewhere', 'transport_person', 'steer_car']
senticnet['trucking_company'] = ['0.969', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.936', 'attendant', 'manager', 'public_servant', 'specialist', 'accomplice']
senticnet['trucking_firm'] = ['0.969', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.936', 'attendant', 'manager', 'public_servant', 'specialist', 'accomplice']
senticnet['truculence'] = ['-0.50', '0', '0.817', '-0.59', '#anger', '#disgust', 'negative', '-0.63', 'disagreeableness', 'pugnacity', 'truculency', 'bellicosity', 'bellicoseness']
senticnet['truculency'] = ['-0.50', '0', '0.817', '-0.59', '#anger', '#disgust', 'negative', '-0.63', 'disagreeableness', 'truculence', 'pugnacity', 'bellicosity', 'bellicoseness']
senticnet['truculent'] = ['-0.83', '0', '0.756', '0', '#sadness', '#anger', 'negative', '-0.79', 'principled', 'unwilling', 'uncompromising', 'concession', 'inflexible']
senticnet['trudge'] = ['-0.82', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.83', 'trudging', 'trudged', 'plodded', 'traipse', 'slogged']
senticnet['trudged'] = ['-0.82', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.83', 'trudging', 'trudge', 'plodded', 'traipse', 'slogged']
senticnet['trudged_along'] = ['-0.93', '0', '0.791', '0', '#sadness', '#anger', 'negative', '-0.86', 'hopelessness', 'hopelessly', 'despairing', 'pessimistically', 'pessimistic']
senticnet['trudging'] = ['-0.82', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.83', 'trudge', 'trudged', 'plodded', 'traipse', 'slogged']
senticnet['true'] = ['0.117', '0.266', '0', '0.215', '#interest', '#admiration', 'positive', '0.199', 'genuine', 'truthful', 'smartness', 'honesty', 'truth']
senticnet['true-blue'] = ['0', '0.996', '0', '0.713', '#interest', '#admiration', 'positive', '0.855', 'dyed-in-the-wool', 'red-blooded', 'self-professed', 'die-hard', 'bona-fide']
senticnet['true_bacteria'] = ['-0.90', '0', '0.908', '-0.53', '#sadness', '#anger', 'negative', '-0.78', 'tubercle_bacillus', 'leprosy_bacillus', 'eubacteria', 'eubacterium', 'mycobacteria']
senticnet['true_believer'] = ['0', '0.211', '-0.08', '0.183', '#interest', '#admiration', 'positive', '0.102', 'believer', 'cleric', 'worshiper', 'religious_person', 'religious_leader']
senticnet['true_bill'] = ['0.654', '-0.59', '0', '0.774', '#joy', '#admiration', 'positive', '0.673', 'instrument', 'murder_charge', 'murder_indictment', 'indictment', 'legal_document']
senticnet['true_blackberry'] = ['-0.92', '0', '0.912', '-0.94', '#sadness', '#disgust', 'negative', '-0.92', 'blackberry_bush', 'sand_blackberry', 'dewberry', 'dewberry_bush', 'running_blackberry']
senticnet['true_identity'] = ['0.122', '0.159', '-0.05', '0.217', '#interest', '#admiration', 'positive', '0.111', 'profile_picture', 'unity', 'country_person', 'unite', 'signature']
senticnet['true_love'] = ['0.801', '0', '0', '0.912', '#joy', '#admiration', 'positive', '0.857', 'cherish', 'love_parent', 'run_business', 'express_gratitude', 'world_peace']
senticnet['true_patriot'] = ['0.865', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.887', 'manager', 'colleague', 'office_worker', 'coworker', 'secretary']
senticnet['true_repentance'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitentially', 'penitent', 'repentantly', 'repentant']
senticnet['trueness'] = ['0.559', '-0.56', '0', '0.721', '#joy', '#admiration', 'positive', '0.616', 'truth', 'accuracy', 'preciseness', 'exactitude', 'precision']
senticnet['trueque'] = ['-0.05', '0.215', '-0.08', '0.293', '#interest', '#admiration', 'positive', '0.092', 'swap', 'give_take', 'exchange_goods', 'transaction', 'increase_money']
senticnet['truest'] = ['0.135', '0.225', '-0.12', '0.432', '#interest', '#admiration', 'positive', '0.167', 'excellent', 'goodness', 'well_done', 'great', 'good_quality']
senticnet['truffle'] = ['0.893', '0.937', '0', '0', '#joy', '#interest', 'positive', '0.915', 'fallen_leaf', 'pine_cone', 'detritus', 'dead_leaf', 'sleep_dog']
senticnet['truffle_dog'] = ['0.059', '0.073', '0', '0', '#joy', '#interest', 'positive', '0.066', 'chihuahua', 'dog', 'domesticate_dog', 'domesticate_animal', 'bloodhound']
senticnet['truffle_oil'] = ['0.084', '-0.05', '0', '-0.04', '#joy', '#surprise', 'positive', '0.031', 'entree', 'feast', 'satiation', 'breakfast', 'supper']
senticnet['truism'] = ['0.524', '-0.64', '0', '0.675', '#surprise', '#admiration', 'positive', '0.613', 'truth', 'platitude', 'cliche', 'banality', 'commonplace']
senticnet['truly'] = ['0', '0.74', '0', '0.814', '#interest', '#admiration', 'positive', '0.777', 'indeed', 'frank', 'amen', 'faithfully', 'accurately']
senticnet['truly_appreciate'] = ['0.819', '0', '0', '0.816', '#joy', '#admiration', 'positive', '0.817', 'self_satisfaction', 'praise', 'good_feeling', 'happiness', 'applaud']
senticnet['truly_awful'] = ['-0.93', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.96', 'dire', 'fearsome', 'dreaded', 'direful', 'frightening']
senticnet['truly_exceptional'] = ['0.728', '-0.72', '0', '0.881', '#joy', '#admiration', 'positive', '0.779', 'howling', 'wonderful', 'wondrous', 'bonzer', 'rattling']
senticnet['truly_extraordinary'] = ['0.728', '-0.72', '0', '0.881', '#joy', '#admiration', 'positive', '0.779', 'howling', 'wonderful', 'wondrous', 'bonzer', 'rattling']
senticnet['truly_grateful'] = ['0.561', '-0.79', '0', '0.89', '#surprise', '#admiration', 'positive', '0.748', 'superlative', 'compliment', 'testimonial', 'praise', 'approval']
senticnet['truly_horrible'] = ['-0.93', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.96', 'dire', 'fearsome', 'dreaded', 'direful', 'frightening']
senticnet['truly_horrific'] = ['-0.93', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.96', 'dire', 'fearsome', 'dreaded', 'direful', 'frightening']
senticnet['truly_impressive'] = ['0.562', '-0.69', '0', '0.883', '#surprise', '#admiration', 'positive', '0.714', 'majestic', 'formidable', 'awesome', 'awing', 'brilliant']
senticnet['truly_magical'] = ['0.88', '0.86', '0', '0', '#joy', '#interest', 'positive', '0.87', 'amaze', 'grand', 'wonderful', 'pose', 'howling']
senticnet['truly_memorable'] = ['0.88', '0.86', '0', '0', '#joy', '#interest', 'positive', '0.87', 'amaze', 'grand', 'wonderful', 'pose', 'howling']
senticnet['truly_needy'] = ['-0.10', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.08', 'come_house', 'desperate', 'welcome_friend', 'captain_boat', 'receive_email']
senticnet['truly_pathetic'] = ['-0.71', '0', '0.83', '-0.68', '#sadness', '#anger', 'negative', '-0.74', 'gag', 'jest', 'jape', 'joke', 'laugh']
senticnet['truly_remarkable'] = ['0.728', '-0.72', '0', '0.881', '#joy', '#admiration', 'positive', '0.779', 'howling', 'wonderful', 'wondrous', 'bonzer', 'rattling']
senticnet['truly_repent'] = ['-0.90', '0.142', '-0.63', '0.07', '#sadness', '#fear', 'negative', '-0.33', 'penitently', 'penitentially', 'penitent', 'penitence', 'repentant']
senticnet['truly_stunning'] = ['0.794', '0', '0', '0.76', '#joy', '#admiration', 'positive', '0.777', 'stupefy', 'daze', 'sandbag', 'arresting', 'bedaze']
senticnet['truly_unforgettable'] = ['0.88', '0.86', '0', '0', '#joy', '#interest', 'positive', '0.87', 'amaze', 'grand', 'wonderful', 'pose', 'howling']
senticnet['truly_wonderful'] = ['0.88', '0.86', '0', '0', '#joy', '#interest', 'positive', '0.87', 'amaze', 'grand', 'wonderful', 'pose', 'howling']
senticnet['trump'] = ['0.697', '-0.70', '0', '0.582', '#joy', '#surprise', 'positive', '0.66', 'best', 'outmaneuver', 'outflank', 'scoop', 'outdo']
senticnet['trump_suit'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'jewelry_store', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['trump_trick'] = ['0.022', '-0.11', '0.014', '0.057', '#surprise', '#admiration', 'positive', '0.045', 'wood', 'many_steps', 'franchise', 'stump', 'timber']
senticnet['trumped'] = ['0', '0', '-0.80', '-0.82', '#fear', '#disgust', 'negative', '-0.81', 'trumped-up', 'baseless', 'politically-motivated', 'sedition', 'treason']
senticnet['trumped-up'] = ['0', '0', '-0.79', '-0.81', '#fear', '#disgust', 'negative', '-0.80', 'trumped', 'baseless', 'politically-motivated', 'sedition', 'treason']
senticnet['trumpery'] = ['-0.84', '0', '0.553', '-0.89', '#sadness', '#disgust', 'negative', '-0.76', 'encumbrance', 'incumbrance', 'block', 'bar', 'blockade']
senticnet['trumpet'] = ['0.908', '0.913', '0', '0', '#joy', '#interest', 'positive', '0.911', 'trumpeter', 'brass', 'daffodil', 'orchestra', 'loudly']
senticnet['trumpet_interchange'] = ['0.579', '-0.89', '0', '0.878', '#surprise', '#admiration', 'positive', '0.783', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['trumpet_solo'] = ['0.023', '0.303', '-0.08', '0.261', '#interest', '#admiration', 'positive', '0.125', 'school_choral', 'drum_set', 'choral', 'string_quartet', 'band']
senticnet['trumpeter'] = ['0.906', '0.912', '0', '0', '#joy', '#interest', 'positive', '0.909', 'trumpet', 'brass', 'daffodil', 'orchestra', 'loudly']
senticnet['trumpkin'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'gnome', 'elf', 'goblin', 'undersized', 'small_person']
senticnet['truncal'] = ['0.722', '-0.87', '0', '0.696', '#joy', '#surprise', 'positive', '0.763', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['truncata'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'seed_fern', 'pteridosperm']
senticnet['truncated_cone'] = ['0', '0', '0.061', '-0.03', '#anger', '#disgust', 'negative', '-0.04', 'garden_tool', 'straight_line', 'sharp', 'razor_blade', 'chock']
senticnet['truncated_cuboctahedron'] = ['0.641', '-0.6', '0', '0.957', '#joy', '#admiration', 'positive', '0.733', 'ideal_solid', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'regular_octahedron']
senticnet['truncated_icosidodecahedron'] = ['0.641', '-0.6', '0', '0.957', '#joy', '#admiration', 'positive', '0.733', 'ideal_solid', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'regular_octahedron']
senticnet['truncated_octahedron'] = ['0.641', '-0.6', '0', '0.957', '#joy', '#admiration', 'positive', '0.733', 'ideal_solid', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'regular_octahedron']
senticnet['truncation_error'] = ['-0.55', '0', '0.965', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'interjection', 'delay', 'insert', 'interposition', 'disruption']
senticnet['truncheon'] = ['-0.91', '0', '0.931', '-0.72', '#sadness', '#anger', 'negative', '-0.85', 'hit_with_baton', 'get_hurt', 'whip', 'arrow', 'hurt']
senticnet['trunk'] = ['0.898', '0.964', '0', '0', '#joy', '#interest', 'positive', '0.931', 'body', 'luggage_compartment', 'proboscis', 'bole', 'tree_trunk']
senticnet['trunk_hose'] = ['-0.58', '0', '0.757', '-0.67', '#anger', '#disgust', 'negative', '-0.67', 'breech', 'knee_breech', 'knee_pants', 'knickerbockers', 'knickers']
senticnet['trunk_lid'] = ['0.725', '0.796', '0', '0', '#joy', '#interest', 'positive', '0.76', 'car_stereo', 'battery', 'seatbelt', 'spare_tire', 'brake_pedal']
senticnet['trunklid'] = ['0.02', '0.085', '-0.05', '0.085', '#interest', '#admiration', 'positive', '0.034', 'gold', 'silver', 'medal', 'carat', 'medallion']
senticnet['truss'] = ['0', '0.963', '0', '0.922', '#interest', '#admiration', 'positive', '0.943', 'truss', 'girder', 'joist', 'cantilever', 'screw']
senticnet['truss_bridge'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'suspension_bridge', 'under_bridge', 'roadway', 'county_highway', 'long_road']
senticnet['truss_span'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'suspension_bridge', 'under_bridge', 'roadway', 'county_highway', 'long_road']
senticnet['trussed_up'] = ['0.92', '-0.57', '0', '0.758', '#joy', '#admiration', 'positive', '0.751', 'bail_out', 'unspell', 'bail', 'unchain', 'run']
senticnet['trust'] = ['0', '0', '0', '0.66', '#admiration', '#admiration', 'positive', '0.66', 'assurance', 'entrust', 'reliance', 'credible', 'trustworthy']
senticnet['trust_friend'] = ['-0.04', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.04', 'think_food', 'sense_fear', 'guide_blind_person', 'play_outside', 'cause_accident']
senticnet['trust_god'] = ['0.782', '0', '0', '0.752', '#joy', '#admiration', 'positive', '0.767', 'plan_wedding', 'believe_god', 'belief_god', 'believe', 'kneel']
senticnet['trusted_adult'] = ['0.36', '0.248', '-0.08', '0.278', '#joy', '#admiration', 'positive', '0.201', 'feel_important', 'strong_relationship', 'eat_together', 'world_peace', 'express_gratitude']
senticnet['trusted_aide'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'yeoman', 'outrider', 'yeoman_of_guard', 'beefeater', 'defender']
senticnet['trusted_colleague'] = ['0.05', '0.141', '-0.07', '0.132', '#interest', '#admiration', 'positive', '0.063', 'public_servant', 'worker', 'manager', 'laborer', 'office_job']
senticnet['trusted_friend'] = ['0.05', '0.141', '-0.07', '0.132', '#interest', '#admiration', 'positive', '0.063', 'public_servant', 'worker', 'manager', 'laborer', 'office_job']
senticnet['trusteth'] = ['0.887', '-0.84', '0', '0.819', '#joy', '#surprise', 'positive', '0.851', 'arouse', 'elicit', 'enkindle', 'kindle', 'evoke']
senticnet['trustiness'] = ['0.879', '-0.52', '0', '0.76', '#joy', '#admiration', 'positive', '0.72', 'trustworthiness', 'responsibleness', 'dependability', 'dependableness', 'reliability']
senticnet['trusting'] = ['0', '0', '-0.94', '-0.83', '#fear', '#disgust', 'negative', '-0.89', 'hope', 'swear', 'rely', 'bank', 'believe']
senticnet['trustor'] = ['0.851', '0.708', '0', '0', '#joy', '#interest', 'positive', '0.779', 'devisee', 'legatee', 'recipient', 'receiver', 'pensioner']
senticnet['trustworthiness'] = ['0', '-0.98', '0', '0.997', '#surprise', '#admiration', 'positive', '0.991', 'insure', 'reliable', 'assure', 'trustworthy', 'trust']
senticnet['trustworthy'] = ['0', '0.774', '0', '0.767', '#interest', '#admiration', 'positive', '0.77', 'medical_person', 'medical', 'diagnose', 'medical_professional', 'doctor_patient']
senticnet['truth'] = ['0.768', '0.935', '0', '0.996', '#interest', '#admiration', 'positive', '0.9', 'integrity', 'ethic', 'certain', 'impact', 'investigation']
senticnet['truth_teller'] = ['-0.63', '0', '0.88', '-0.94', '#anger', '#disgust', 'negative', '-0.81', 'beguiler', 'slicker', 'perjurer', 'false_witness', 'storyteller']
senticnet['truthful'] = ['0.76', '0.998', '0', '0.75', '#joy', '#interest', 'positive', '0.836', 'honesty', 'honest', 'integrity', 'virtue', 'good_character']
senticnet['truthful_answer'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquirer', 'enquirer', 'inquisitor', 'querier', 'asker']
senticnet['truthful_testimony'] = ['-0.21', '0.16', '-0.19', '0.057', '#sadness', '#fear', 'negative', '-0.04', 'murder', 'make_judgment', 'call_police', 'arrest_criminal', 'tell_truth']
senticnet['truthfully'] = ['0.729', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.849', 'genuinely', 'frankly', 'forsooth', 'veraciously', 'truly']
senticnet['truthfulness'] = ['0.76', '-0.59', '0', '0.713', '#joy', '#admiration', 'positive', '0.689', 'sooth', 'sincerity', 'honestness', 'honesty', 'veracity']
senticnet['try'] = ['-0.10', '0.171', '-0.1', '0.17', '#interest', '#admiration', 'positive', '0.034', 'ask_nicely', 'ask_politely', 'attempt', 'make_effort', 'question_ask']
senticnet['try_again'] = ['-0.12', '0', '-0.07', '0', '#sadness', '#fear', 'negative', '-0.09', 'smoke_cigar', 'cross_bridge', 'hope_peace', 'deal_card', 'sell_car']
senticnet['try_best'] = ['-0.07', '-0.00', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.04', 'strategize', 'loose', 'win', 'stand_around', 'lose_game']
senticnet['try_calm_down'] = ['-0.07', '0', '0.073', '-0.26', '#sadness', '#disgust', 'negative', '-0.13', 'find_victim', 'buy_gun', 'buy_weapon', 'plan_attack', 'short_circuit']
senticnet['try_clothes'] = ['0.973', '0.848', '0', '0', '#joy', '#interest', 'positive', '0.91', 'dress_room', 'fitting_room', 'changing_room', 'jeans', 'clothes_shelf']
senticnet['try_forget'] = ['0.993', '0', '0', '0', '#joy', '#joy', 'positive', '0.993', 'experience_pleasure', 'listen_rock_music', 'trade_stock', 'think_funny', 'experience_joy']
senticnet['try_hard'] = ['-0.10', '0', '0.02', '0', '#sadness', '#anger', 'negative', '-0.06', 'try_best', 'win', 'strategize', 'get_back', 'loose']
senticnet['try_new'] = ['0', '0.069', '0', '0.108', '#interest', '#admiration', 'positive', '0.088', 'appreciate', 'curious', 'new_insight', 'desire_travel', 'become_distracted']
senticnet['try_new_clothes'] = ['0.813', '0.86', '0', '0', '#joy', '#interest', 'positive', '0.837', 'changing_room', 'lock_front_door', 'dry_off_towel', 'put_clothes', 'dress_occasion']
senticnet['try_on'] = ['0.745', '-0.71', '0', '0.709', '#joy', '#surprise', 'positive', '0.722', 'fitting_room', 'put_on', 'wear', 'assume', 'get_into']
senticnet['try_out'] = ['0.777', '-0.88', '0', '0.79', '#surprise', '#admiration', 'positive', '0.819', 'verify', 'control', 'float', 'evaluate', 'pass_judgment']
senticnet['try_to_find'] = ['0.43', '0.657', '-0.28', '0.244', '#joy', '#interest', 'positive', '0.262', 'look_for', 'expectancy', 'amour_propre', 'self-love', 'conceit']
senticnet['try_understand'] = ['0', '0.925', '0', '0.96', '#interest', '#admiration', 'positive', '0.943', 'take_picture', 'point_out', 'work_hard', 'complete_sentence', 'read_bible']
senticnet['try_win'] = ['0', '0.95', '0', '0', '#interest', '#interest', 'positive', '0.95', 'loose', 'roll_dice', 'win', 'try_hard', 'minor_injury']
senticnet['try_win_game'] = ['0.807', '0', '0', '0', '#joy', '#joy', 'positive', '0.807', 'buy_stock', 'play_video_game', 'double_money', 'lose_money', 'desire_win']
senticnet['tryin'] = ['-0.66', '0', '0.595', '-0.83', '#sadness', '#disgust', 'negative', '-0.7', 'bastard', 'dickhead', 'disagreeable_person', 'motherfucker', 'son_of_bitch']
senticnet['trying'] = ['-0.86', '0', '0.547', '-0.67', '#sadness', '#disgust', 'negative', '-0.69', 'dreadful', 'vexing', 'harsh', 'stressful', 'disagreeable']
senticnet['trying_luck'] = ['-0.08', '0.179', '-0.06', '0.096', '#interest', '#admiration', 'positive', '0.034', 'gamble', 'buy_lottery_ticket', 'spend_money', 'hope', 'finger_crossed']
senticnet['tryout'] = ['0.615', '-0.51', '0', '0.573', '#joy', '#admiration', 'positive', '0.566', 'examine', 'experiment', 'alpha_test', 'beta_test', 'road_test']
senticnet['trypanosoma'] = ['-0.74', '0', '0.94', '-0.87', '#anger', '#disgust', 'negative', '-0.85', 'fungal_infection', 'mycosis', 'kerion', 'tinea_pedis', 'athlete_foot']
senticnet['trypanosoma_brucei_gambiense'] = ['-0.55', '0', '0.757', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'rubor', 'redness', 'cephalitis', 'sleepy_sickness', 'epidemic_encephalitis']
senticnet['trypanosoma_evansi'] = ['0.688', '-0.94', '0', '0.925', '#surprise', '#admiration', 'positive', '0.852', 'flatworm', 'platyhelminth', 'trematode', 'schistosome', 'blood_fluke']
senticnet['trypophobia'] = ['-0.79', '-0.71', '-0.96', '-0.95', '#fear', '#disgust', 'negative', '-0.49', 'fear', 'caution', 'deep_sadness', 'bad_feeling', 'psychoneurosis']
senticnet['trypsin_inhibitor'] = ['0.878', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.883', 'wheat', 'grain', 'rye', 'white_rice', 'rice']
senticnet['tryptamine'] = ['-0.73', '0', '0.944', '-0.94', '#anger', '#disgust', 'negative', '-0.87', 'lysergic_acid_diethylamide', 'mescaline', 'peyote', 'phencyclidine', 'drug']
senticnet['tryptase'] = ['-0.66', '0', '0.877', '-0.64', '#sadness', '#anger', 'negative', '-0.72', 'somatic_cell', 'mastocyte', 'labrocyte', 'target_cell', 'phagocyte']
senticnet['trysallia'] = ['-0.75', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.83', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['tsarism'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'ascendance', 'absolutism', 'ascendancy', 'tyrant']
senticnet['tsarist_regime'] = ['0', '0.793', '0', '0.847', '#interest', '#admiration', 'positive', '0.82', 'disagreement', 'fight_against', 'opposition', 'against', 'go_against']
senticnet['tsatske'] = ['-0.77', '0', '0.737', '-0.56', '#sadness', '#anger', 'negative', '-0.69', 'miss', 'girl', 'missy', 'young_lady', 'young_woman']
senticnet['tseiha'] = ['-0.50', '0', '0.989', '-0.91', '#anger', '#disgust', 'negative', '-0.80', 'camp', 'pack', 'clique', 'coterie', 'ingroup']
senticnet['tsetse_fly'] = ['-0.55', '0', '0.757', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'rubor', 'redness', 'cephalitis', 'sleepy_sickness', 'epidemic_encephalitis']
senticnet['tsewang'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'wild_goat', 'markhor', 'goat', 'ibex']
senticnet['tshatshke'] = ['-0.92', '0', '0.994', '-0.64', '#sadness', '#anger', 'negative', '-0.85', 'miss', 'girl', 'missy', 'young_lady', 'young_woman']
senticnet['tshirt'] = ['-0.54', '0', '0.612', '-0.98', '#anger', '#disgust', 'negative', '-0.71', 'scuff', 'mule', 'bootee', 'carpet_slipper', 'footwear']
senticnet['tshogdu'] = ['0.774', '-0.82', '0', '0.63', '#joy', '#surprise', 'positive', '0.744', 'quality', 'perishability', 'perishableness', 'unsatisfactoriness', 'inadequacy']
senticnet['tsongkhapa'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['tsp_bicarbonate'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'coke', 'sprite', 'cola', 'beverage', 'soft_drink']
senticnet['tsp_ground'] = ['0.979', '-0.85', '0', '0', '#joy', '#surprise', 'positive', '0.918', 'make_curry', 'cook_dinner', 'cook_meal', 'cook_curry', 'powder']
senticnet['tsubasa_reservoir_chronicle'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['tsugaru_clan'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'overlord', 'lord']
senticnet['tsunami'] = ['-0.07', '0', '0.073', '-0.26', '#sadness', '#disgust', 'negative', '-0.13', 'disaster', 'catastrophe', 'earthquake', 'hurricane', 'flood']
senticnet['tsurugi'] = ['-0.04', '0.138', '-0.08', '0.111', '#interest', '#admiration', 'positive', '0.031', 'geometry', 'craft', 'blade_geometry', 'blade', 'collect_item']
senticnet['tswalu_kalahari'] = ['0.049', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.086', 'protect', 'robbery', 'keep', 'steal_money', 'prevent']
senticnet['tub'] = ['0.041', '0.077', '0.011', '0', '#joy', '#interest', 'positive', '0.036', 'separate_shower', 'bath_shower', 'shampoo_conditioner', 'plunger', 'shower_head']
senticnet['tub_shower'] = ['0.716', '0.96', '0', '0', '#joy', '#interest', 'positive', '0.838', 'airline_seat', 'small_seat', 'beanbag_chair', 'hide-a-bed_sofa', 'chair']
senticnet['tubal_ectopic_gestation'] = ['-0.75', '0', '0.711', '-0.73', '#sadness', '#disgust', 'negative', '-0.73', 'abdominal_ectopic_gestation', 'ovarian_ectopic_gestation', 'pregnancy', 'ectopic_gestation', 'gestation']
senticnet['tubal_infertility'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'gastroenteritis', 'stomach_flu', 'intestinal_flu', 'inflammatory_disease']
senticnet['tubal_ligation'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'birth_control_pill', 'condom', 'contraceptive_pill', 'sex_toy', 'prevent_pregnancy']
senticnet['tubal_pregnancy'] = ['-0.75', '0', '0.715', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'abdominal_pregnancy', 'ovarian_pregnancy', 'ectopic_gestation', 'pregnancy', 'gestation']
senticnet['tubby'] = ['0', '0', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.05', 'human', 'delusional', 'use_illegal_drug', 'busy_person', 'carpenter']
senticnet['tube'] = ['0.05', '-0.08', '0', '-0.03', '#joy', '#surprise', 'positive', '0.032', 'plastic_bag', 'refrigerator', 'storage_furniture', 'water_bottle', 'electric_fan']
senticnet['tube_alloy_project'] = ['-0.90', '0', '0.748', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'alternative_energy', 'activation_energy', 'energy_of_activation', 'energy', 'nuclear_energy']
senticnet['tube_amplifier'] = ['0.056', '-0.01', '0.036', '0.073', '#joy', '#admiration', 'positive', '0.028', 'loud_sound', 'stereo', 'tuner', 'speaker', 'steam_engine']
senticnet['tube_worm'] = ['0.96', '-0.77', '0', '0.929', '#joy', '#admiration', 'positive', '0.887', 'whale_louse', 'beach_flea', 'sand_hopper', 'sandhopper', 'amphipod']
senticnet['tubercle_bacillus'] = ['-0.90', '0', '0.908', '-0.53', '#sadness', '#anger', 'negative', '-0.78', 'mycobacteria', 'leprosy_bacillus', 'eubacteria', 'eubacterium', 'true_bacteria']
senticnet['tubercular'] = ['-0.86', '0', '0.738', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'amyotrophia', 'amyotrophy', 'tabes', 'kraurosis', 'symptom']
senticnet['tuberculate'] = ['-0.66', '0', '0.809', '-0.61', '#sadness', '#anger', 'negative', '-0.69', 'face', 'base', 'underbelly', 'sole', 'bilge']
senticnet['tuberculin'] = ['-0.74', '0', '0.766', '0', '#sadness', '#anger', 'negative', '-0.75', 'use_needle', 'clothes_repair', 'woven', 'embroidery', 'thread_needle']
senticnet['tuberculosis'] = ['-0.08', '-0.04', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.05', 'wipe_mouth_napkin', 'stomach_full', 'use_fork', 'swallow', 'eat']
senticnet['tuberculosis_tb'] = ['-0.88', '0', '-0.84', '-0.95', '#sadness', '#disgust', 'negative', '-0.89', 'polio', 'infectious_disease', 'malady', 'sickness', 'ailment']
senticnet['tuberose'] = ['0.933', '-0.87', '0', '0.766', '#joy', '#surprise', 'positive', '0.858', 'scent', 'aroma', 'olfactory_property', 'smell', 'odor']
senticnet['tuberosity'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'hip_socket', 'tooth_socket', 'alveolus', 'socket', 'cotyloid_cavity']
senticnet['tubular'] = ['0.022', '0', '0.015', '0.096', '#joy', '#admiration', 'positive', '0.034', 'write_tool', 'write_implement', 'cylindric', 'write_device', 'write_paper']
senticnet['tubular_atrophy'] = ['-0.73', '0', '0.904', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'unshapely', 'shapeless', 'lumpy', 'deformed', 'malformed']
senticnet['tubular_cavity'] = ['0.718', '-0.88', '0', '0.83', '#surprise', '#admiration', 'positive', '0.81', 'throat', 'pharynx', 'cavity', 'bodily_cavity', 'cavum']
senticnet['tubular_lumen'] = ['0.992', '-0.80', '0', '0.584', '#joy', '#surprise', 'positive', '0.795', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'incontinence']
senticnet['tubular_secretion'] = ['0.992', '-0.80', '0', '0.584', '#joy', '#surprise', 'positive', '0.795', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'incontinence']
senticnet['tubular_structure'] = ['-0.03', '0', '0', '-0.11', '#sadness', '#disgust', 'negative', '-0.07', 'store_cloth', 'clothes_factory', 'stock', 'sword', 'gun_store']
senticnet['tubule'] = ['0', '0', '0.019', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'worm_intestine', 'bristle', 'fur_keep_warm', 'animal', 'whisker']
senticnet['tucanae'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['tuck_away'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'lavatory', 'commode', 'potty_chair']
senticnet['tucked-away'] = ['-0.77', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.80', 'off-the-beaten-path', 'out-of-the-way', 'off-the-beaten-track', 'hole-in-the-wall', 'nook']
senticnet['tucked_away'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'lurking_place', 'spot', 'drop', 'hiding_place', 'den']
senticnet['tucked_into'] = ['-0.05', '0', '-0.12', '-0.16', '#fear', '#disgust', 'negative', '-0.11', 'reduced', 'small', 'small_size', 'narrow', 'poor']
senticnet['tucker'] = ['-0.80', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.79', 'wear_out', 'tucker_out', 'exhaust', 'beat', 'overtire']
senticnet['tucker_out'] = ['0.828', '-0.96', '0', '0.555', '#joy', '#surprise', 'positive', '0.784', 'wear_upon', 'tire_out', 'outwear', 'fag_out', 'weary']
senticnet['tucuxi'] = ['0.024', '0.102', '-0.06', '0.074', '#interest', '#admiration', 'positive', '0.033', 'marine_mammal', 'large_mammal', 'pack_animal', 'mammal', 'smart_creature']
senticnet['tudhaliya_iv'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
senticnet['tudor_manor_house'] = ['0', '0.179', '-0.05', '0.162', '#interest', '#admiration', 'positive', '0.095', 'house_land', 'real_estate', 'acreage', 'house_place', 'good_investment']
senticnet['tudor_revival'] = ['0.578', '-0.95', '0', '0.979', '#surprise', '#admiration', 'positive', '0.838', 'perpendicular', 'perpendicular_style', 'gothic_architecture', 'style_of_architecture', 'type_of_architecture']
senticnet['tudor_rose'] = ['-0.62', '0', '0.961', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'jeweled_headdress', 'diadem', 'tiara', 'headdress', 'headgear']
senticnet['tuface'] = ['0', '0', '0.876', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'untune', 'discompose', 'discomfit', 'disconcert', 'upset']
senticnet['tuff_ring'] = ['0', '0', '-0.92', '0', '#fear', '#fear', 'negative', '-0.92', 'water_flow', 'wind_water', 'water_stream', 'waterway', 'magma']
senticnet['tuft'] = ['-0.63', '0', '0.862', '-0.52', '#sadness', '#anger', 'negative', '-0.67', 'knot', 'swad', 'bunch', 'tussock', 'agglomeration']
senticnet['tufted_centaury'] = ['-0.90', '0', '0.726', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'broad_leaved_centaury', 'rosita', 'lesser_centaury', 'centaury', 'seaside_centaury']
senticnet['tufted_duck'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['tufted_duck_aythya_fuligula'] = ['0.797', '-0.71', '0', '0.736', '#joy', '#admiration', 'positive', '0.751', 'wood_ibis', 'wood_stork', 'ibis', 'wading_bird', 'wader']
senticnet['tug'] = ['0', '0.179', '-0.09', '0.279', '#interest', '#admiration', 'positive', '0.12', 'drag', 'go_build', 'enter', 'hang_door', 'invade']
senticnet['tugged'] = ['0.99', '-0.95', '0', '0', '#joy', '#surprise', 'positive', '0.974', 'heart-strings', 'heartstrings', 'tugging', 'tug', 'cockle']
senticnet['tugging'] = ['0.995', '-0.95', '0', '0', '#joy', '#surprise', 'positive', '0.976', 'heart-strings', 'tugged', 'heartstrings', 'tug', 'cockle']
senticnet['tui_na'] = ['0', '-0.04', '0.098', '-0.11', '#anger', '#disgust', 'negative', '-0.05', 'grapefruit', 'plum', 'lemon_juice', 'green_apple', 'apricot']
senticnet['tuileries_palace'] = ['0', '0.105', '0', '0.076', '#interest', '#admiration', 'positive', '0.09', 'big_house', 'mansion', 'castle', 'block_flat', 'medieval']
senticnet['tuilleadh'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'feist', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['tuition'] = ['-0.07', '0', '0.073', '-0.26', '#sadness', '#disgust', 'negative', '-0.13', 'college_tuition', 'private_tuition', 'pay_tuition', 'enroll_class', 'enroll_school']
senticnet['tuition_assistance'] = ['-0.07', '0', '0.073', '-0.26', '#sadness', '#disgust', 'negative', '-0.13', 'tuition', 'private_tuition', 'pay_tuition', 'enroll_class', 'enroll_school']
senticnet['tuition_waiver'] = ['0.295', '0.239', '0.049', '0.346', '#joy', '#admiration', 'positive', '0.208', 'absolute', 'complete', 'infinite', 'total', 'stuffed']
senticnet['tuk'] = ['-0.63', '0', '0.791', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'little', 'pinch', 'modicum', 'small_indefinite_amount', 'indefinite_quantity']
senticnet['tuktoyaktuk'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'cheetah', 'giraffe', 'kangaroo', 'mule', 'horse']
senticnet['tulagi'] = ['0.084', '-0.05', '0.013', '0', '#joy', '#surprise', 'positive', '0.043', 'carnival_ride', 'carny', 'fortune_teller', 'horseback', 'ferris_wheel']
senticnet['tularemia'] = ['-0.95', '0', '0.876', '-0.89', '#sadness', '#disgust', 'negative', '-0.90', 'bubonic_plague', 'pestis_bubonica', 'glandular_plague', 'septicemic_plague', 'pestilence']
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senticnet['tule_elk'] = ['0', '0', '-0.70', '-0.77', '#fear', '#disgust', 'negative', '-0.73', 'animal', 'brown_bear', 'forest', 'creature', 'polar_bear']
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senticnet['tulip'] = ['0', '0.067', '0', '0.05', '#interest', '#admiration', 'positive', '0.059', 'bloom', 'flower', 'clover', 'rose', 'daffodil']
senticnet['tulip_poplar'] = ['0.958', '-0.82', '0', '0.924', '#joy', '#admiration', 'positive', '0.901', 'bunchberry', 'dwarf_cornel', 'crackerberry', 'pudding_berry', 'common_white_dogwood']
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senticnet['tulle'] = ['0.735', '-0.54', '0', '0.897', '#joy', '#admiration', 'positive', '0.727', 'net', 'network', 'mesh', 'meshing', 'meshwork']
senticnet['tullia'] = ['-0.76', '0', '0.995', '-0.94', '#anger', '#disgust', 'negative', '-0.90', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'excretion']
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senticnet['tumble_down'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'come_across', 'tripped_over', 'lose_balance', 'stammer', 'stutter']
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senticnet['tumeric'] = ['0.077', '0', '0', '0', '#joy', '#joy', 'positive', '0.077', 'dip', 'garlic', 'oat', 'savory', 'rye']
senticnet['tumesce'] = ['-0.63', '0', '0.737', '-0.75', '#anger', '#disgust', 'negative', '-0.70', 'bloat', 'blister', 'vesicate', 'belly', 'belly_out']
senticnet['tumid'] = ['0.818', '0', '0', '0.838', '#joy', '#admiration', 'positive', '0.828', 'larger', 'heavy', 'great', 'bombastic', 'orotund']
senticnet['tummy'] = ['0', '0', '0', '0.967', '#admiration', '#admiration', 'positive', '0.967', 'belly', 'nervous_system', 'human_body', 'stomach', 'digestive_system']
senticnet['tummy_ache'] = ['0.052', '-0.04', '-0.05', '-0.20', '#fear', '#disgust', 'negative', '-0.04', 'indigestion', 'make_little_hungry', 'full_stomach', 'sick', 'become_full']
senticnet['tummy_tuck'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'abdominoplasty', 'plastic_surgery', 'autoplasty']
senticnet['tumor'] = ['-0.71', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.80', 'loss_oflife', 'chest_pain', 'die', 'death', 'end_life']
senticnet['tumult'] = ['-0.84', '-0.74', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.35', 'foment', 'agitated', 'agitate', 'stir_up', 'fire_up']
senticnet['tumultuous'] = ['0', '0', '0.92', '-0.77', '#anger', '#disgust', 'negative', '-0.84', 'pandemonium', 'hell', 'violence', 'lawless', 'loud']
senticnet['tumultuousness'] = ['-0.83', '0', '0.703', '-0.85', '#sadness', '#disgust', 'negative', '-0.79', 'hurly_burly', 'kerfuffle', 'commotion', 'disruption', 'disturbance']
senticnet['tuna_belly'] = ['0.041', '0.069', '0.032', '0.13', '#interest', '#admiration', 'positive', '0.052', 'edible_human', 'round_fruit', 'peelable', 'green_fruit', 'nutritious']
senticnet['tuna_salad'] = ['0.043', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'apple_pie', 'a_lot_of_food', 'jelly', 'frozen_food', 'cottage_cheese']
senticnet['tuna_tartare'] = ['-0.81', '0', '0.543', '-0.63', '#sadness', '#disgust', 'negative', '-0.66', 'caper', 'gherkin', 'relish', 'dill_pickle', 'sweet_pickle']
senticnet['tuna_tataki'] = ['0.084', '-0.05', '0', '-0.04', '#joy', '#surprise', 'positive', '0.031', 'entree', 'feast', 'satiation', 'breakfast', 'supper']
senticnet['tunability'] = ['0.03', '0.215', '-0.06', '0.18', '#interest', '#admiration', 'positive', '0.09', 'combo', 'hold_coin', 'money_storage', 'money_place', 'money_holder']
senticnet['tundra'] = ['-0.04', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.04', 'bad_luck', 'damn', 'artic', 'mammal_family', 'bad_mood']
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senticnet['tune'] = ['0.802', '0', '0', '0', '#joy', '#joy', 'positive', '0.802', 'classical_music', 'melody', 'harmony', 'type_of_music', 'lyric']
senticnet['tune_instrument'] = ['-0.04', '0', '0.058', '-0.05', '#anger', '#disgust', 'negative', '-0.05', 'make_a_lot_of_noise', 'sound_alarm', 'strum', 'perform_on_stage', 'pluck_string']
senticnet['tune_up'] = ['0.71', '-0.57', '0', '0.721', '#joy', '#admiration', 'positive', '0.669', 'tweak', 'correct', 'adjust', 'set', 'service']
senticnet['tuned_circuit'] = ['0.056', '-0.01', '0.036', '0.073', '#joy', '#admiration', 'positive', '0.028', 'loud_sound', 'stereo', 'tuner', 'speaker', 'steam_engine']
senticnet['tuneful'] = ['0.75', '0.71', '0', '0', '#joy', '#interest', 'positive', '0.73', 'spherical_shape', 'use_sport', 'flexible', 'ball', 'fill_air']
senticnet['tuner'] = ['0.036', '-0.04', '0.027', '0.063', '#surprise', '#admiration', 'positive', '0.028', 'radio', 'speaker', 'channel_selector', 'power_switch', 'female_television_connector']
senticnet['tung'] = ['0', '0.936', '0', '0.833', '#interest', '#admiration', 'positive', '0.885', 'largest', 'beijing', 'democracy', 'democratic_party', 'people']
senticnet['tungstate'] = ['-0.03', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'lithium', 'mineral', 'potassium', 'iodine', 'manganese']
senticnet['tungsten'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'gold', 'bronze', 'alloy', 'medal', 'copper']
senticnet['tungsten_carbide'] = ['0.87', '0.822', '0', '0.717', '#joy', '#interest', 'positive', '0.803', 'gold', 'bronze', 'alloy', 'medal', 'copper']
senticnet['tungsten_filament'] = ['-0.03', '-0.04', '0.079', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'string', 'bandage', 'suture', 'thread', 'needle_thread']
senticnet['tungsten_halogen'] = ['0.637', '-0.71', '0', '0.932', '#surprise', '#admiration', 'positive', '0.761', 'glow', 'illumination', 'incandescence', 'luminescence', 'brightness_level']
senticnet['tungsten_incandescent'] = ['-0.63', '0', '0.897', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'cd-rom', 'international_candle', 'candle', 'candlepower_unit', 'standard_candle']
senticnet['tungsten_wire'] = ['-0.03', '-0.04', '0.079', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'string', 'bandage', 'suture', 'thread', 'needle_thread']
senticnet['tunic'] = ['0.849', '-0.50', '0', '0.845', '#joy', '#admiration', 'positive', '0.732', 'robe', 'gown', 'veil', 'vest', 'cassock']
senticnet['tunica_albuginea'] = ['-0.53', '0', '0.982', '-0.67', '#anger', '#disgust', 'negative', '-0.73', 'meniscus', 'semilunar_cartilage', 'gristle', 'hyaline_cartilage', 'arytenoid']
senticnet['tunica_intima'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'chest', 'artery', 'vein', 'internal_organ', 'under_head']
senticnet['tunicata'] = ['-0.71', '0', '0.605', '-0.62', '#sadness', '#disgust', 'negative', '-0.64', 'medicinal_leech', 'horseleech', 'annelid', 'annelid_worm', 'leech']
senticnet['tunicle'] = ['0.849', '-0.50', '0', '0.845', '#joy', '#admiration', 'positive', '0.732', 'robe', 'gown', 'veil', 'vest', 'cassock']
senticnet['tuning'] = ['0.658', '-0.89', '0', '0.918', '#surprise', '#admiration', 'positive', '0.823', 'adjustment', 'activity', 'calibration', 'registration', 'readjustment']
senticnet['tunnel'] = ['-0.04', '0', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'underground_passage', 'rut', 'cave', 'golf_course', 'room_temperature']
senticnet['tunneler'] = ['-0.74', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.80', 'casualty', 'extermination', 'cause_death', 'abduction', 'die']
senticnet['tunng'] = ['0.946', '-0.91', '0', '0.664', '#joy', '#surprise', 'positive', '0.841', 'pretender', 'phony', 'phoney', 'hypocrite', 'dissembler']
senticnet['tuohy_needle'] = ['0', '0.721', '0', '0.731', '#interest', '#admiration', 'positive', '0.726', 'dimension', 'measure', 'tackle', 'length', 'measurement']
senticnet['tuolumne_county'] = ['-0.06', '0.21', '0', '0.076', '#interest', '#admiration', 'positive', '0.074', 'new_think', 'figure_out', 'brain_activity', 'idea', 'come_up_idea']
senticnet['tup'] = ['0.652', '-0.81', '0', '0.905', '#surprise', '#admiration', 'positive', '0.791', 'black_sheep', 'ewe', 'ram', 'sheep', 'wether']
senticnet['tupelo'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
senticnet['tuple'] = ['0.022', '-0.13', '0.153', '-0.31', '#anger', '#disgust', 'negative', '-0.07', 'codeine', 'morphine', 'infection', 'cocaine', 'nuclear_war']
senticnet['tuppence'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'impaired', 'dickey', 'lessened', 'diminished']
senticnet['tupperware'] = ['0.348', '0.23', '-0.15', '0.294', '#joy', '#admiration', 'positive', '0.18', 'happy_emotion', 'niceness', 'contagious', 'good_emotion', 'savor']
senticnet['tupperware_box'] = ['-0.52', '0', '0.978', '-0.55', '#anger', '#disgust', 'negative', '-0.68', 'dump', 'garbage_dump', 'trash_dump', 'rubbish_dump', 'wasteyard']
senticnet['tupperware_container'] = ['0.036', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.044', 'neat', 'cutlery_drawer', 'silverware_drawer', 'dine_table', 'table']
senticnet['tupperware_party'] = ['0.348', '0.23', '-0.15', '0.294', '#joy', '#admiration', 'positive', '0.18', 'happy_emotion', 'niceness', 'contagious', 'good_emotion', 'savor']
senticnet['tuqtuqucha'] = ['0.039', '0.089', '-0.08', '0.143', '#interest', '#admiration', 'positive', '0.048', 'bird', 'nightingale', 'stork', 'eagle', 'rooster']
senticnet['turano_lodigiano'] = ['0.939', '-0.71', '0', '0.775', '#joy', '#admiration', 'positive', '0.81', 'vox_angelica', 'voix_celeste', 'organ_stop', 'pipe', 'diapason']
senticnet['turban'] = ['0.08', '0.106', '-0.04', '0.112', '#interest', '#admiration', 'positive', '0.063', 'helmet', 'head_gear', 'cap', 'head_wear', 'head_protection']
senticnet['turban_squash'] = ['-0.56', '0', '0.696', '-0.96', '#anger', '#disgust', 'negative', '-0.74', 'squash', 'acorn_squash', 'hubbard_squash', 'winter_squash', 'butternut_squash']
senticnet['turban_winter_squash'] = ['-0.57', '0', '0.692', '-0.96', '#anger', '#disgust', 'negative', '-0.74', 'winter_squash', 'acorn_winter_squash', 'hubbard_winter_squash', 'squash', 'butternut_winter_squash']
senticnet['turbid'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'dirtiness', 'befoulment', 'defilement', 'dinginess', 'dinge']
senticnet['turbid_water'] = ['-0.50', '0', '0.85', '-0.82', '#anger', '#disgust', 'negative', '-0.72', 'confusion', 'disarray', 'mental_confusion', 'confusedness', 'bafflement']
senticnet['turbinado'] = ['-0.08', '0.106', '0', '0.125', '#interest', '#admiration', 'positive', '0.047', 'sugar', 'treacle', 'black_treacle', 'add_sugar', 'sugar_substitute']
senticnet['turbine'] = ['-0.97', '0', '0.658', '-0.60', '#sadness', '#anger', 'negative', '-0.74', 'gas_turbine', 'hydroelectric_turbine', 'impulse_turbine', 'reaction_turbine', 'steam_turbine']
senticnet['turbine-like'] = ['0.751', '-0.95', '0', '0', '#joy', '#surprise', 'positive', '0.855', 'mandala-like', 'wheel-like', 'cross-like', 'donut-like', 'propeller-like']
senticnet['turbo_charge'] = ['0.66', '-0.85', '0', '0.744', '#surprise', '#admiration', 'positive', '0.752', 'further', 'promote', 'encourage', 'conduce', 'advance']
senticnet['turbo_diesel'] = ['0.068', '0', '0.034', '0.076', '#joy', '#admiration', 'positive', '0.037', 'gallon', 'liquid_measurement', 'ounce', 'measurement', 'quart']
senticnet['turbo_pascal'] = ['0', '-0.99', '0', '0.862', '#surprise', '#admiration', 'positive', '0.928', 'helpful_tool', 'woodworking_tool', 'helpful', 'type_of_fruit', 'hand_tool']
senticnet['turbo_pump'] = ['0.843', '-0.99', '0', '0.706', '#joy', '#surprise', 'positive', '0.849', 'gadgetry', 'gimbal', 'contrivance', 'mod_con', 'device']
senticnet['turbodiesel'] = ['0.068', '0', '0.034', '0.076', '#joy', '#admiration', 'positive', '0.037', 'gallon', 'liquid_measurement', 'ounce', 'measurement', 'quart']
senticnet['turbot'] = ['0.709', '0.964', '0', '0', '#joy', '#interest', 'positive', '0.837', 'fish_trout', 'catch_butterfly', 'fish_lake', 'fish_compliment', 'fish_stream']
senticnet['turbotax'] = ['-0.03', '0.112', '-0.08', '0.164', '#interest', '#admiration', 'positive', '0.04', 'run', 'jump_over', 'quick', 'sprint', 'flee']
senticnet['turbulence'] = ['-0.74', '0', '0', '-0.95', '#sadness', '#disgust', 'negative', '-0.84', 'upheaval', 'turbulency', 'sturm_und_drang', 'role', 'effect']
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senticnet['turbulent'] = ['0', '0', '-0.97', '-0.71', '#fear', '#disgust', 'negative', '-0.84', 'disruptive', 'troubled', 'tumultuous', 'riotous', 'churning']
senticnet['turbulent_flame'] = ['-0.90', '0', '0.825', '-0.61', '#sadness', '#anger', 'negative', '-0.78', 'flaming', 'combustion', 'fire', 'flame', 'internal_combustion']
senticnet['turd'] = ['-0.82', '0', '0.89', '-0.76', '#sadness', '#anger', 'negative', '-0.82', 'stool', 'dejection', 'fecal_matter', 'shit', 'feces']
senticnet['turd_blossom'] = ['-0.91', '0', '0.681', '-0.81', '#sadness', '#disgust', 'negative', '-0.80', 'chicken', 'softy', 'softie', 'crybaby', 'wimp']
senticnet['turf'] = ['-0.72', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.79', 'artificial', 'natural', 'synthetic', 'chemical', 'grass']
senticnet['turf_out'] = ['-0.71', '0', '0.954', '-0.56', '#sadness', '#anger', 'negative', '-0.74', 'eject', 'chuck_out', 'exclude', 'evict', 'boot_out']
senticnet['turf_runway'] = ['-0.70', '0', '0.621', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'flare_path', 'airfield', 'grassland', 'runway', 'road']
senticnet['turf_toe'] = ['-0.83', '0', '0.509', '-0.84', '#sadness', '#disgust', 'negative', '-0.72', 'wrench', 'injury', 'harm', 'hurt', 'trauma']
senticnet['turf_war'] = ['-0.63', '0', '0.982', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'wrangle', 'dustup', 'quarrel', 'row', 'argument']
senticnet['turgid'] = ['-0.89', '0', '0.987', '-0.90', '#anger', '#disgust', 'negative', '-0.93', 'cacoethes', 'logomania', 'agromania', 'pyromania', 'necrophilia']
senticnet['turgidity'] = ['-0.57', '0', '0.767', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'verboseness', 'verbosity', 'prolixity', 'turgidness', 'flatulence']
senticnet['turgidness'] = ['-0.57', '0', '0.767', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'verboseness', 'verbosity', 'turgidity', 'prolixity', 'flatulence']
senticnet['turgor'] = ['-0.57', '0', '0.767', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'verboseness', 'verbosity', 'prolixity', 'turgidness', 'flatulence']
senticnet['turgor_pressure'] = ['-0.57', '0', '0.767', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'verboseness', 'verbosity', 'prolixity', 'turgidness', 'flatulence']
senticnet['turilli'] = ['0.101', '-0.04', '0.036', '0', '#joy', '#surprise', 'positive', '0.038', 'song', 'rap', 'jingle', 'chord', 'lyric']
senticnet['turkey'] = ['0', '0', '-0.98', '-0.86', '#fear', '#disgust', 'negative', '-0.92', 'turkish_empire', 'jew', 'ottoman_empire', 'massacre', 'book']
senticnet['turkey_baster'] = ['-0.86', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.80', 'riverbed', 'bottom_ocean', 'waterfall', 'down_drain', 'septic_tank']
senticnet['turkey_cock'] = ['-0.54', '0', '0.811', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'bird_of_prey', 'raptor', 'raptorial_bird', 'parrot', 'bird']
senticnet['turkey_oak'] = ['0.672', '-0.69', '0', '0.831', '#surprise', '#admiration', 'positive', '0.733', 'southern_red_oak', 'swamp_red_oak', 'red_oak', 'northern_red_oak', 'oak']
senticnet['turkey_sandwich'] = ['-0.92', '0', '0.609', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'puffed_rice', 'granola', 'raisin_bran', 'corn_flake', 'bran_flake']
senticnet['turkey_twizzler'] = ['-0.98', '0', '0.594', '-0.98', '#sadness', '#disgust', 'negative', '-0.85', 'fast_food', 'deep_fried', 'unhealthy_food', 'reuse_oil', 'junk_food']
senticnet['turkey_vulture'] = ['-0.74', '0', '0.963', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['turkish_bath'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
senticnet['turkish_empire'] = ['0', '0', '-0.97', '-0.87', '#fear', '#disgust', 'negative', '-0.92', 'ottoman_empire', 'jew', 'turkey', 'massacre', 'book']
senticnet['turkish_genocide'] = ['0', '0', '0.73', '-0.80', '#anger', '#disgust', 'negative', '-0.76', 'formally', 'officially', 'victim', 'refuse', 'tonne']
senticnet['turkish_traveler_evliya'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'caravansary', 'caravanserai', 'khan', 'caravan_inn', 'hostel']
senticnet['turmeric'] = ['0', '0', '-0.76', '-0.71', '#fear', '#disgust', 'negative', '-0.73', 'cumin', 'cilantro', 'cardamom', 'coriander', 'clove']
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senticnet['tv_studio'] = ['0', '0.07', '0', '0.071', '#interest', '#admiration', 'positive', '0.071', 'theater', 'cinema', 'opus', 'film_studio', 'opus_house']
senticnet['tv_tuner'] = ['0.036', '-0.04', '0.027', '0.063', '#surprise', '#admiration', 'positive', '0.028', 'radio', 'speaker', 'channel_selector', 'power_switch', 'female_television_connector']
senticnet['tvline'] = ['0.761', '0.566', '-0.21', '0.293', '#joy', '#interest', 'positive', '0.352', 'gratify', 'jubilation', 'gloat', 'gloating', 'gloatingly']
senticnet['tw_quote'] = ['0', '0.189', '0.04', '0.091', '#interest', '#admiration', 'positive', '0.08', 'combination', 'margin', 'teller', 'merge', 'finite']
senticnet['twaddle'] = ['0.906', '0', '0', '0.936', '#joy', '#admiration', 'positive', '0.921', 'pablum', 'drivel', 'platitude', 'pabulum', 'regurgitated']
senticnet['twang'] = ['-0.71', '0', '0.998', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'tone', 'quality', 'nasality', 'nasal_nasality', 'timber']
senticnet['twangy_guitar'] = ['0.857', '-0.77', '0', '0.878', '#joy', '#admiration', 'positive', '0.836', 'hand', 'round', 'approval', 'commendation', 'clapping']
senticnet['twat'] = ['-0.72', '0', '0.728', '-0.68', '#sadness', '#anger', 'negative', '-0.71', 'sap', 'fool', 'saphead', 'muggins', 'tomfool']
senticnet['twayblade'] = ['-0.52', '0', '0.527', '-0.78', '#anger', '#disgust', 'negative', '-0.61', 'herb', 'black_snakeroot', 'wild_ginger', 'asarabacca', 'herbaceous_plant']
senticnet['tweak'] = ['0.703', '-0.58', '0', '0.714', '#joy', '#admiration', 'positive', '0.666', 'tune_up', 'correct', 'adjust', 'set', 'service']
senticnet['twee'] = ['0.606', '-0.96', '0', '0.531', '#joy', '#surprise', 'positive', '0.699', 'mincing', 'gentlemanly', 'gentlemanlike', 'refined', 'ladylike']
senticnet['tweed_coat'] = ['0.923', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.939', 'under_garment', 'undergarment', 'underwear', 'woman_clothes', 'lady_underwear']
senticnet['tweed_jacket'] = ['-0.84', '0', '0.633', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'grizzle', 'postiche', 'wig', 'periwig', 'peruke']
senticnet['tweed_skirt'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'facial_tissue', 'kleenex', 'hanky', 'handkerchief', 'tissue']
senticnet['tweed_suit'] = ['-0.84', '0', '0.633', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'grizzle', 'postiche', 'horsehair_wig', 'wig', 'peruke']
senticnet['tweediness'] = ['-0.9', '0', '0.841', '-0.73', '#sadness', '#anger', 'negative', '-0.82', 'nubbiness', 'raggedness', 'harshness', 'graininess', 'granularity']
senticnet['tweety_bird'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'naked_eye', 'eye', 'oculus_sinister']
senticnet['tweezer'] = ['0.844', '0.717', '0', '0', '#joy', '#interest', 'positive', '0.78', 'chisel', 'power_drill', 'razor_blade', 'knife', 'vise']
senticnet['twelfth'] = ['0.082', '0.191', '0', '0.198', '#interest', '#admiration', 'positive', '0.157', 'regal', 'imperial', 'greatness', 'nobleman', 'magnitude']
senticnet['twelfth_amendment'] = ['0.779', '-0.88', '0', '0.521', '#joy', '#surprise', 'positive', '0.727', 'corrigible', 'correctable', 'improvable', 'reformable', 'redeemable']
senticnet['twelfth_century'] = ['0.978', '-0.60', '0', '0.895', '#joy', '#admiration', 'positive', '0.825', 'period', 'quattrocento', 'twentieth_century', 'time_period', 'period_of_time']
senticnet['twelfth_dynasty'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
senticnet['twelfth_episode'] = ['0.918', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.827', 'encounter', 'reveal', 'programme', 'lose', 'sequel']
senticnet['twelve'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'hundred_year', 'dozen', 'day_month', 'time_measurement']
senticnet['twelve-year-old'] = ['0', '0', '0.944', '-0.74', '#anger', '#disgust', 'negative', '-0.84', 'mentally-challenged', 'deaf-mute', 'blond-haired', 'eleven-year-old', 'thirteen-year-old']
senticnet['twelve_consecutive'] = ['-0.85', '0', '0.584', '-0.79', '#sadness', '#disgust', 'negative', '-0.74', 'age', 'old_age', 'second_childhood', 'eld', 'geezerhood']
senticnet['twelve_inch'] = ['0.711', '0.976', '0', '0', '#joy', '#interest', 'positive', '0.843', 'sole', 'type_of_shoes', 'foot', 'foot_part', 'heel']
senticnet['twelve_month'] = ['0.295', '0.239', '0.049', '0.346', '#joy', '#admiration', 'positive', '0.208', 'absolute', 'complete', 'infinite', 'total', 'stuffed']
senticnet['twelve_months'] = ['0', '0', '0', '0.7', '#admiration', '#admiration', 'positive', '0.7', 'yearly_celebration', 'year', 'annual', 'age', 'centennial']
senticnet['twelve_pence'] = ['0.902', '-0.72', '0', '0.919', '#joy', '#admiration', 'positive', '0.849', 'preciousness', 'costliness', 'expensiveness', 'cost', 'lavishness']
senticnet['twelve_tone_row'] = ['0.045', '0', '0.033', '0.089', '#joy', '#admiration', 'positive', '0.034', 'spanish', 'vocalization', 'verbal_communication', 'vocal', 'mouth_noise']
senticnet['twelve_tone_technique'] = ['0.045', '0', '0.033', '0.089', '#joy', '#admiration', 'positive', '0.034', 'spanish', 'vocalization', 'verbal_communication', 'vocal', 'mouth_noise']
senticnet['twelve_year_old'] = ['0.575', '-0.62', '0', '0.596', '#surprise', '#admiration', 'positive', '0.598', 'altar_boy', 'cub', 'ball_boy', 'bat_boy', 'lad']
senticnet['twelvemonth'] = ['0.726', '0.925', '0', '0', '#joy', '#interest', 'positive', '0.826', 'friday', 'monday', 'midweek', 'weekend', 'day_of_week']
senticnet['twentieth'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'home', 'position', 'time_limit', 'monthly', 'modern_home']
senticnet['twentieth_century'] = ['0.978', '-0.60', '0', '0.895', '#joy', '#admiration', 'positive', '0.825', 'period', 'quattrocento', 'century', 'time_period', 'period_of_time']
senticnet['twenty'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'hundred_year', 'twelve', 'day_month', 'time_measurement']
senticnet['twenty-eight'] = ['0.715', '-1', '0', '0', '#joy', '#surprise', 'positive', '0.857', 'twenty-three', 'twenty-two', 'twenty-six', 'twenty-seven', 'thirty-four']
senticnet['twenty-seven'] = ['0.716', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.856', 'twenty-three', 'twenty-eight', 'twenty-six', 'twenty-two', 'thirty-four']
senticnet['twenty-six'] = ['0.722', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.859', 'twenty-three', 'twenty-eight', 'twenty-two', 'twenty-seven', 'thirty-four']
senticnet['twenty-three'] = ['0.719', '-1.0', '0', '0', '#joy', '#surprise', 'positive', '0.859', 'twenty-two', 'twenty-eight', 'twenty-six', 'twenty-seven', 'thirty-four']
senticnet['twenty-two'] = ['0.725', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.86', 'twenty-three', 'twenty-eight', 'twenty-six', 'twenty-seven', 'thirty-four']
senticnet['twenty_eight'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'hundred_year', 'twelve', 'day_month', 'time_measurement']
senticnet['twenty_fifth'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'home', 'position', 'time_limit', 'monthly', 'modern_home']
senticnet['twenty_fifth_amendment'] = ['-0.81', '0', '0.513', '-0.71', '#sadness', '#disgust', 'negative', '-0.68', 'ballot', 'vote', 'voting', 'balloting', 'veto']
senticnet['twenty_five'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'hundred_year', 'twelve', 'day_month', 'time_measurement']
senticnet['twenty_five_cents_per'] = ['0.987', '0.902', '0', '0', '#joy', '#interest', 'positive', '0.945', 'franc', 'dime', 'dollar', 'shilling', 'banknote']
senticnet['twenty_nine'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'hundred_year', 'dozen', 'day_month', 'time_measurement']
senticnet['twenty_seven'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'hundred_year', 'dozen', 'day_month', 'time_measurement']
senticnet['twenty_six'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'hundred_year', 'dozen', 'day_month', 'time_measurement']
senticnet['twenty_sixth'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'home', 'position', 'time_limit', 'monthly', 'modern_home']
senticnet['twere'] = ['-0.63', '0', '0.683', '-0.96', '#anger', '#disgust', 'negative', '-0.76', 'slovenly_woman', 'slut', 'trollop', 'slattern', 'litterer']
senticnet['twerp'] = ['-0.71', '0', '0.845', '-0.69', '#sadness', '#anger', 'negative', '-0.75', 'lump', 'goon', 'mortal', 'clumsy_person', 'butterfingers']
senticnet['twerton'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
senticnet['twiddle_thumb'] = ['0.873', '0.864', '0', '0', '#joy', '#interest', 'positive', '0.869', 'learn_patience', 'collect_data', 'wait_patiently', 'take_number', 'wait_service']
senticnet['twig'] = ['0.781', '0.858', '0', '0', '#joy', '#interest', 'positive', '0.82', 'tree_limb', 'tree_part', 'tree_arm', 'part_of_tree', 'tree_branch']
senticnet['twilight'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'dusk', 'nightfall', 'sundown', 'sunset', 'day_end']
senticnet['twilight_sequel_new'] = ['0.916', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.929', 'galaxy', 'universe', 'planetary', 'sun', 'dwarf_planet']
senticnet['twilight_zone'] = ['0.979', '-0.75', '0', '0.764', '#joy', '#admiration', 'positive', '0.831', 'ocean_floor', 'sea_floor', 'ocean_bottom', 'seabed', 'sea_bottom']
senticnet['twill_weave'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_fabric']
senticnet['twilly'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['twilo'] = ['0.763', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.75', 'pub', 'disco', 'dance_club', 'cafe', 'tavern']
senticnet['twin'] = ['0', '0.142', '0.057', '0.168', '#interest', '#admiration', 'positive', '0.084', 'duplicate', 'brother', 'multiply', 'two_halves', 'equal']
senticnet['twin-engined'] = ['0', '0', '-0.96', '-0.80', '#fear', '#disgust', 'negative', '-0.88', 'tail-less', 'stout-bodied', 'bipedal', 'tailless', 'legless']
senticnet['twin_bed'] = ['0.716', '0.96', '0', '0', '#joy', '#interest', 'positive', '0.838', 'airline_seat', 'small_seat', 'beanbag_chair', 'hide-a-bed_sofa', 'chair']
senticnet['twin_brother'] = ['0', '0.732', '0', '0.994', '#interest', '#admiration', 'positive', '0.863', 'foster_kid', 'daughter', 'relation', 'sister', 'adopt']
senticnet['twin_engine_aircraft'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'atmosphere', 'wing', 'nest', 'feather', 'bird_fly']
senticnet['twin_engine_turboprop'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'atmosphere', 'wing', 'nest', 'feather', 'bird_fly']
senticnet['twin_otter_aircraft'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'atmosphere', 'wing', 'nest', 'feather', 'bird_fly']
senticnet['twin_paradox'] = ['-0.69', '0', '0.83', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'general_relativity', 'general_theory_of_relativity', 'general_relativity_theory', 'relativity', 'special_theory_of_relativity']
senticnet['twin_span'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'suspension_bridge', 'under_bridge', 'roadway', 'county_highway', 'long_road']
senticnet['twin_turboprop'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'atmosphere', 'wing', 'nest', 'feather', 'bird_fly']
senticnet['twin_turret'] = ['-0.90', '0', '0.517', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'outdated', 'noncurrent', 'disused', 'obsolete', 'superannuated']
senticnet['twined_around'] = ['0.92', '-0.57', '0', '0.758', '#joy', '#admiration', 'positive', '0.751', 'bail_out', 'unspell', 'bail', 'unchain', 'run']
senticnet['twinflower'] = ['0.773', '-0.55', '0', '0.949', '#joy', '#admiration', 'positive', '0.757', 'cornel', 'dwarf_cornel', 'crackerberry', 'pudding_berry', 'common_white_dogwood']
senticnet['twinge'] = ['0', '0', '0.701', '-0.83', '#anger', '#disgust', 'negative', '-0.76', 'twinge', 'pang', 'tinge', 'uneasiness', 'ache']
senticnet['twinkie'] = ['0.274', '-0.01', '0.012', '0.132', '#joy', '#admiration', 'positive', '0.102', 'dance_type', 'swirl', 'throw_football', 'jump_around', 'dance_move']
senticnet['twinkle'] = ['0.841', '-0.66', '0', '0.946', '#joy', '#admiration', 'positive', '0.816', 'bright', 'sparkle', 'verve', 'spark', 'energy']
senticnet['twinkle_twinkle_little'] = ['0.977', '-0.93', '0', '0.583', '#joy', '#surprise', 'positive', '0.831', 'giant', 'shinny', 'supernova', 'supergiant', 'binary_star']
senticnet['twinkly'] = ['0.936', '-0.81', '0', '0.83', '#joy', '#admiration', 'positive', '0.86', 'cherubic', 'lovely', 'lovable', 'endearing', 'angelic']
senticnet['twirl'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'turn_around', 'float_through_air', 'pull_car', 'revolve', 'spring_up']
senticnet['twirl_around'] = ['0.075', '0', '0', '0.158', '#joy', '#admiration', 'positive', '0.116', 'sway', 'swirl', 'body_movement', 'move_around', 'dance_move']
senticnet['twirled_around'] = ['0.155', '-0.11', '0.009', '-0.05', '#joy', '#surprise', 'positive', '0.05', 'dance_well', 'relate_music', 'enter_contest', 'jump_around', 'cheer_up']
senticnet['twirler'] = ['-0.92', '0', '0.767', '-0.85', '#sadness', '#disgust', 'negative', '-0.84', 'closer', 'finisher', 'pitcher', 'hurler', 'relief_pitcher']
senticnet['twirling_around'] = ['0.075', '0', '0', '0.158', '#joy', '#admiration', 'positive', '0.116', 'sway', 'swirl', 'body_movement', 'move_around', 'dance_move']
senticnet['twist'] = ['0.06', '0', '0', '0.182', '#joy', '#admiration', 'positive', '0.121', 'turn', 'wrap_around', 'revolve', 'salsa', 'rotation']
senticnet['twist_ankle'] = ['0', '0', '-0.94', '-0.85', '#fear', '#disgust', 'negative', '-0.90', 'trip_fall', 'out_breath', 'blister_foot', 'breahard', 'tire']
senticnet['twist_drill_bit'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'useful_tool']
senticnet['twist_tie'] = ['0', '0', '-0.91', '-0.70', '#fear', '#disgust', 'negative', '-0.81', 'wax_dental_floss', 'tartar_control_toothpaste', 'toothpaste', 'gel_toothpaste', 'toothbrush']
senticnet['twisted_ankle'] = ['0.94', '0.78', '0', '0', '#joy', '#interest', 'positive', '0.86', 'breaheavily', 'heavy_breathe', 'get_exhausted', 'exhaustion', 'see_neighbor']
senticnet['twisted_rope'] = ['-0.06', '-0.01', '0.05', '-0.07', '#sadness', '#disgust', 'negative', '-0.04', 'embroidery', 'woven', 'cover_wind', 'torn', 'use_needle']
senticnet['twister'] = ['-0.03', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'abrasion', 'wind', 'welt', 'gash', 'cut_skin']
senticnet['twisting_yurchenko'] = ['0.02', '0.205', '0', '0', '#joy', '#interest', 'positive', '0.112', 'safe', 'bank_vault', 'bank', 'piggy_bank', 'bank_account']
senticnet['twisty_road'] = ['0.825', '0', '0', '0.703', '#joy', '#admiration', 'positive', '0.764', 'pay_toll', 'get_fast', 'access', 'fast_travel', 'access_quickly']
senticnet['twit'] = ['-0.55', '0', '0.947', '-0.77', '#anger', '#disgust', 'negative', '-0.75', 'blackguard', 'deride', 'bemock', 'tease', 'razz']
senticnet['twitch'] = ['-0.69', '0', '0.849', '-0.71', '#anger', '#disgust', 'negative', '-0.75', 'muscle_spasm', 'spasm', 'cramp', 'tic', 'blepharism']
senticnet['twitching'] = ['-0.65', '0', '0.713', '-0.66', '#anger', '#disgust', 'negative', '-0.67', 'muscle_spasm', 'spasm', 'cramp', 'tic', 'blepharism']
senticnet['twitter_rant'] = ['0', '0', '0.781', '-0.82', '#anger', '#disgust', 'negative', '-0.80', 'cry', 'acting_out', 'explosion', 'halloo', 'yell']
senticnet['twitter_tirade'] = ['0', '0', '0.781', '-0.82', '#anger', '#disgust', 'negative', '-0.80', 'cry', 'acting_out', 'explosion', 'halloo', 'yell']
senticnet['twix'] = ['0.023', '0.141', '-0.13', '0.174', '#interest', '#admiration', 'positive', '0.052', 'candy', 'push_wind', 'exhalation', 'breaout', 'mouth_action']
senticnet['two'] = ['0', '-0.00', '0.063', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'thirty', 'hundred_year', 'twelve', 'day_month', 'time_measurement']
senticnet['two-bit'] = ['-0.79', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.82', 'smalltime', 'big-time', 'small-time', 'wanna-be', 'ex-con']
senticnet['two-dimensional'] = ['-0.02', '0.091', '0', '0.152', '#interest', '#admiration', 'positive', '0.073', 'latex', 'wall_cover', 'dimensional', 'thick_liquid', 'cover_wall']
senticnet['two-handed'] = ['0', '0', '-0.74', '-0.93', '#fear', '#disgust', 'negative', '-0.84', 'duplicitous', 'deceitful', 'double-dealing', 'janus-faced', 'ambidextrous']
senticnet['two-party'] = ['0', '0.795', '0', '0.785', '#interest', '#admiration', 'positive', '0.79', 'lock-step', 'corporatist', 'lockstep', 'blindly', 'arm-in-arm']
senticnet['two-year-old'] = ['0.988', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.946', 'five-year-old', 'three-year-old', 'six-year-old', 'four-year-old', 'seven-year-old']
senticnet['two_apparently_contradictory'] = ['-0.97', '0', '0.712', '-0.78', '#sadness', '#disgust', 'negative', '-0.82', 'discrepant', 'inconsistent', 'scratchy', 'uneven', 'contradictory']
senticnet['two_arms'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'two_ears', 'two_eyes', 'body_part', 'hormone', 'liver']
senticnet['two_barbs'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'horse', 'barb', 'mule', 'buddhist', 'ride_barb']
senticnet['two_bedroom_apartment'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'apartment', 'furniture', 'kitchen', 'flat']
senticnet['two_children'] = ['0.029', '0.076', '0', '0', '#joy', '#interest', 'positive', '0.052', 'youngster', 'adolescent', 'grandchild', 'lad', 'child']
senticnet['two_colorspaces_define'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'illuminate_room', 'give_light', 'light', 'light_bulb', 'lantern']
senticnet['two_dimensional'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'arm_movement', 'hand_gesture', 'water_motion', 'hand_signal', 'sea_movement']
senticnet['two_disc_dvd'] = ['0', '0', '0', '0.07', '#admiration', '#admiration', 'positive', '0.07', 'source_code', 'computer', 'browser', 'buffoon', 'clown']
senticnet['two_door_coupe'] = ['-0.05', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'plane_land', 'floss', 'floss_tooth', 'ornament', 'wear_dress']
senticnet['two_door_hardtop'] = ['-0.05', '0', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.08', 'plane_land', 'floss', 'floss_tooth', 'ornament', 'wear_dress']
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senticnet['two_liter_bottle'] = ['0.078', '0', '0', '0', '#joy', '#joy', 'positive', '0.078', 'coke', 'sprite', 'cola', 'beverage', 'soft_drink']
senticnet['two_parter'] = ['0.918', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.827', 'encounter', 'reveal', 'programme', 'lose', 'sequel']
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senticnet['two_wheeled_cart'] = ['0', '0', '0', '0.071', '#admiration', '#admiration', 'positive', '0.071', 'two_horses', 'horse', 'barb', 'buddhist', 'ride_horse']
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senticnet['twopence'] = ['0.902', '-0.72', '0', '0.919', '#joy', '#admiration', 'positive', '0.849', 'preciousness', 'costliness', 'expensiveness', 'cost', 'lavishness']
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senticnet['tympanic_membrane_perforation'] = ['-0.53', '0', '0.671', '-0.55', '#anger', '#disgust', 'negative', '-0.58', 'dirty', 'putrefactive', 'putrefacient', 'abscessed', 'contaminative']
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senticnet['type_of_dog'] = ['0', '0', '-0.80', '-0.88', '#fear', '#disgust', 'negative', '-0.84', 'future_robot', 'drive_recklessly', 'dog', 'dangerous', 'drive']
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senticnet['type_of_keyboard'] = ['0', '0.951', '0', '0', '#interest', '#interest', 'positive', '0.951', 'enter_data_computer', 'train_open_mind', 'write_computer_program', 'move_mouse', 'add_number']
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senticnet['typecode'] = ['-0.55', '0', '0.882', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'bugger_all', 'zero', 'naught', 'zip', 'nothing']
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senticnet['uk_confectioner'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'wrapping_paper', 'wrap_gift', 'butcher_paper', 'paper']
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senticnet['ultimate_goal'] = ['0', '0.718', '0', '0.779', '#interest', '#admiration', 'positive', '0.748', 'project', 'research', 'study', 'development', 'objective']
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senticnet['ultra-conservative'] = ['0', '0.892', '0', '0.767', '#interest', '#admiration', 'positive', '0.83', 'traditionalist', 'reactionary', 'fundamentalist', 'traditionalist', 'conservative']
senticnet['ultra-sharp'] = ['0', '0.938', '0', '0.714', '#interest', '#admiration', 'positive', '0.826', 'sharpened', 'razor-sharp', 'sharpness', 'super-sharp', 'sharpest']
senticnet['ultra_cheap'] = ['-0.04', '0.201', '-0.06', '0.312', '#interest', '#admiration', 'positive', '0.101', 'save_money', 'good_price', 'inexpensive', 'bargain', 'discount']
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senticnet['ultra_violet_radiation'] = ['-0.72', '0', '0.71', '-0.9', '#sadness', '#disgust', 'negative', '-0.77', 'perniciousness', 'harmfulness', 'toxicity', 'noisomeness', 'unwholesomeness']
senticnet['ultra_wealthy'] = ['0.973', '0.707', '0', '0', '#joy', '#interest', 'positive', '0.84', 'money', 'down_payment', 'much_money', 'slogan', 'take_out_loan']
senticnet['ultra_wideband'] = ['-0.07', '0.078', '0', '0.078', '#interest', '#admiration', 'positive', '0.028', 'red_blue', 'red_orange', 'green_yellow', 'hue_tint', 'yellow_green']
senticnet['ultrafine'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'radioactive_dust', 'dust', 'dust_matter']
senticnet['ultramantis_black'] = ['-0.57', '0', '0.571', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'viper', 'elapid', 'elapid_snake', 'worm_snake', 'blind_snake']
senticnet['ultramarathoner'] = ['-0.04', '-0.03', '0', '-0.16', '#sadness', '#disgust', 'negative', '-0.05', 'crazy', 'damage_body', 'crazy_person', 'threat', 'max']
senticnet['ultramicroscopic'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'amoeba', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['ultramontanism'] = ['0.013', '0.199', '0.027', '0.167', '#interest', '#admiration', 'positive', '0.088', 'church', 'confession', 'good_catholic', 'spirituality', 'christianity']
senticnet['ultranationalistic'] = ['0.575', '-0.70', '0', '0.577', '#surprise', '#admiration', 'positive', '0.618', 'jingoistic', 'chauvinistic', 'superpatriotic', 'nationalistic', 'patriotic']
senticnet['ultrapatriotic'] = ['0.582', '-0.71', '0', '0.583', '#surprise', '#admiration', 'positive', '0.625', 'jingoistic', 'nationalistic', 'superpatriotic', 'patriotic', 'chauvinistic']
senticnet['ultrasmall'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'amoeba', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['ultrasonic'] = ['0.738', '0.962', '0', '0', '#joy', '#interest', 'positive', '0.85', 'transducer', 'laser', 'sound', 'ultrasound', 'infrared']
senticnet['ultrasonic_probe'] = ['0.255', '0.182', '-0.06', '0.314', '#joy', '#admiration', 'positive', '0.173', 'doctor_office', 'urethroscope', 'sigmoidoscope', 'hospital', 'check_health']
senticnet['ultrasound'] = ['0.736', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.845', 'transducer', 'laser', 'sound', 'ultrasonic', 'infrared']
senticnet['ultrasound_guidance'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'blatancy', 'obviousness']
senticnet['ultrasound_transducer'] = ['0', '0.825', '0', '0', '#interest', '#interest', 'positive', '0.825', 'query', 'interrogation', 'inquire', 'response', 'solution']
senticnet['ultrastructural_level'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'nonmotile', 'immobile']
senticnet['ultratop'] = ['0.094', '0.091', '0', '0.125', '#joy', '#admiration', 'positive', '0.103', 'diagram', 'graph', 'medium', 'fragment', 'representation']
senticnet['ultraviolent'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amuck', 'murderousness', 'misocainea', 'misogyny']
senticnet['ultraviolet'] = ['0', '-0.01', '-0.07', '-0.06', '#fear', '#disgust', 'negative', '-0.04', 'sun', 'lantern', 'illumination', 'light', 'ray']
senticnet['ululate'] = ['-0.50', '0', '0.611', '-0.75', '#anger', '#disgust', 'negative', '-0.62', 'howl', 'wail', 'waul', 'roar', 'yawl']
senticnet['ulva_lactuca'] = ['-0.89', '0', '0.556', '-0.72', '#sadness', '#disgust', 'negative', '-0.72', 'fucus', 'rockweed', 'black_rockweed', 'bladder_fucus', 'tang']
senticnet['ulysses_bloodstone'] = ['-0.04', '-0.02', '0.027', '-0.03', '#sadness', '#disgust', 'negative', '-0.01', 'precious_metal', 'olympic_medal', 'shiny_metal', 'many_different_kinds', 'valuable_metal']
senticnet['umai'] = ['-0.63', '0', '0.791', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'little', 'pinch', 'modicum', 'small_indefinite_amount', 'indefinite_quantity']
senticnet['umami_taste'] = ['0.16', '0', '0.039', '0.121', '#joy', '#admiration', 'positive', '0.081', 'taste_bitter', 'sweetness', 'sweet', 'sweet_food', 'after_taste']
senticnet['umayyad_mosque'] = ['0', '0.224', '0', '0.197', '#interest', '#admiration', 'positive', '0.211', 'church', 'temple', 'cathedral', 'chapel', 'confession']
senticnet['umbellata'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'carnauba_palm', 'carnauba', 'wax_palm', 'palmetto', 'palmyra']
senticnet['umberleigh'] = ['0.049', '0.16', '0', '0.155', '#interest', '#admiration', 'positive', '0.121', 'castle', 'palace', 'medieval', 'aristocrat', 'throne']
senticnet['umberto_boccioni'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'native_person', 'revolutionary', 'republic', 'revolution', 'look_ahead']
senticnet['umberto_lenzi'] = ['-0.79', '0', '0.885', '-0.76', '#sadness', '#anger', 'negative', '-0.81', 'infernal', 'mortal', 'dead_soul', 'dead_soule', 'living_dead']
senticnet['umbilical'] = ['0.919', '-0.99', '0', '0', '#joy', '#surprise', 'positive', '0.959', 'placenta', 'placental', 'fetal', 'fetus', 'amniotic']
senticnet['umbilical_cord'] = ['0.706', '0', '0', '0', '#joy', '#joy', 'positive', '0.706', 'birth', 'new_baby', 'born', 'birthday', 'thanksgiving']
senticnet['umbilicate'] = ['0.969', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.772', 'aspheric', 'aspherical', 'prolate', 'rounded', 'crescent']
senticnet['umbra'] = ['-0.82', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.80', 'evil', 'darkness', 'dark', 'sun_down', 'dark_side']
senticnet['umbrage'] = ['-0.14', '0.148', '0.62', '-0.63', '#anger', '#disgust', 'negative', '-0.31', 'offense', 'incensed', 'indignant', 'umbrageous', 'outraged']
senticnet['umbrageous'] = ['-0.13', '0.146', '0.623', '-0.63', '#anger', '#disgust', 'negative', '-0.31', 'outraged', 'incensed', 'indignant', 'outrage', 'indignation']
senticnet['umbrella'] = ['0.106', '0.162', '0.037', '0.32', '#interest', '#admiration', 'positive', '0.138', 'block_sun', 'waterproof_jacket', 'waterproof_boot', 'rain_equipment', 'block_rain']
senticnet['umbrella_group'] = ['0.065', '0.113', '0', '0.1', '#interest', '#admiration', 'positive', '0.093', 'big_group', 'clique', 'group_of_people', 'multitude', 'large_group']
senticnet['umbrella_organization'] = ['0.065', '0.113', '0', '0.1', '#interest', '#admiration', 'positive', '0.093', 'big_group', 'clique', 'group_of_people', 'multitude', 'large_group']
senticnet['umeboshi'] = ['0.9', '0.963', '0', '0', '#joy', '#interest', 'positive', '0.931', 'form_of_food', 'japanese_food', 'uncooked', 'healthy', 'food']
senticnet['umesh'] = ['0', '0', '0.737', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'annoy', 'skin_rash', 'bother', 'irritation', 'great_dane']
senticnet['umiak'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'seal', 'otter', 'mussel', 'alligator', 'whale']
senticnet['umlaut'] = ['-0.61', '0', '0.721', '-0.68', '#anger', '#disgust', 'negative', '-0.67', 'character', 'logogram', 'logograph', 'graph', 'ideogram']
senticnet['umpire'] = ['0', '0', '0.844', '-0.99', '#anger', '#disgust', 'negative', '-0.92', 'racket', 'referee', 'legal_proceed', 'clang', 'clamor']
senticnet['umpteen'] = ['0', '0.862', '0', '0.732', '#interest', '#admiration', 'positive', '0.797', 'multifarious', 'numberless', 'scad', 'multiform', 'some-more']
senticnet['un_amore'] = ['0.107', '0.11', '-0.08', '0.21', '#interest', '#admiration', 'positive', '0.086', 'live_healthy_life', 'forever_young', 'live_forever', 'live_long', 'live_good_life']
senticnet['un_dlai'] = ['0.667', '-0.98', '0', '0.61', '#joy', '#surprise', 'positive', '0.753', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['un_gilet'] = ['-0.58', '0', '0.661', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'explosive', 'cordite', 'dynamite', 'plastic_explosive', 'explosive_compound']
senticnet['un_homme'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'sexual_relationship', 'liaison', 'affair', 'involvement']
senticnet['un_mariage'] = ['-0.85', '0', '0.968', '-0.89', '#anger', '#disgust', 'negative', '-0.90', 'family', 'household', 'house', 'home', 'broken_home']
senticnet['un_pistolet'] = ['-0.58', '0', '0.661', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'explosive', 'cordite', 'dynamite', 'plastic_explosive', 'explosive_compound']
senticnet['un_ventuel_accord'] = ['0.667', '-0.98', '0', '0.61', '#joy', '#surprise', 'positive', '0.753', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['una_notte'] = ['0.107', '0.11', '-0.08', '0.21', '#interest', '#admiration', 'positive', '0.086', 'live_healthy_life', 'forever_young', 'live_forever', 'live_long', 'live_good_life']
senticnet['una_stanza'] = ['0.107', '0.11', '-0.08', '0.21', '#interest', '#admiration', 'positive', '0.086', 'live_healthy_life', 'forever_young', 'live_forever', 'live_long', 'live_good_life']
senticnet['unabashedly'] = ['0.512', '0.658', '-0.25', '0.256', '#joy', '#interest', 'positive', '0.293', 'riotously', 'expansively', 'ebulliently', 'zestfulness', 'gusto']
senticnet['unable'] = ['-0.80', '0', '-0.80', '-0.96', '#sadness', '#disgust', 'negative', '-0.85', 'impossible', 'absurd', 'difficult', 'impractical', 'unimaginable']
senticnet['unaccented'] = ['-0.90', '0', '0.969', '-0.80', '#sadness', '#anger', 'negative', '-0.89', 'feminine', 'weak', 'light', 'unstressed', 'unemphatic']
senticnet['unacceptable'] = ['-0.88', '0', '0.907', '-0.84', '#sadness', '#anger', 'negative', '-0.88', 'unsupportable', 'intolerable', 'bitter', 'impossible', 'insufferable']
senticnet['unacceptably'] = ['0', '0.795', '0', '0.909', '#interest', '#admiration', 'positive', '0.852', 'unfathomably', 'staggeringly', 'unprecedentedly', 'astoundingly', 'monstrously']
senticnet['unacceptably_high'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'problem', 'serious_issue', 'overrate', 'prohibitionist_society', 'blade_weapon']
senticnet['unaccessible'] = ['0.743', '-0.66', '0', '0.535', '#joy', '#surprise', 'positive', '0.649', 'outback', 'trackless', 'pathless', 'roadless', 'untracked']
senticnet['unaccountability'] = ['0.742', '-0.83', '0', '0.889', '#surprise', '#admiration', 'positive', '0.823', 'furtiveness', 'sneakiness', 'stealthiness', 'unsociability', 'unsociableness']
senticnet['unaccountable'] = ['-0.62', '0', '0.825', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'unexplainable', 'incomprehensible', 'unexplained', 'inscrutable', 'mystifying']
senticnet['unaccountably'] = ['0', '-0.85', '0', '-0.84', '#surprise', '#disgust', 'positive', '0.007', 'deservedly', 'unjustifiably', 'inexcusably', 'undeservedly', 'deservingly']
senticnet['unaccustomed'] = ['-0.22', '-0.21', '0.126', '-0.33', '#sadness', '#disgust', 'negative', '-0.11', 'uncommon', 'strange', 'unpleasant', 'unfamiliar', 'unusual']
senticnet['unacknowledged'] = ['-0.75', '0', '0.561', '-0.99', '#sadness', '#disgust', 'negative', '-0.77', 'unsung', 'unrecognized', 'unappreciated', 'unvalued', 'unconfessed']
senticnet['unacquainted'] = ['-0.51', '0', '0.737', '-0.55', '#anger', '#disgust', 'negative', '-0.60', 'unacquainted_with', 'unfamiliar_with', 'unknown', 'unfamiliar', 'strange']
senticnet['unacquainted_with'] = ['-0.50', '0', '0.737', '-0.55', '#anger', '#disgust', 'negative', '-0.59', 'unfamiliar', 'unfamiliar_with', 'unknown', 'unacquainted', 'strange']
senticnet['unadaptability'] = ['-0.70', '0', '0.914', '-0.72', '#anger', '#disgust', 'negative', '-0.78', 'inflexibility', 'rigidity', 'rigidness', 'unfitness', 'inability']
senticnet['unadorned'] = ['-0.75', '0', '0.864', '-0.73', '#sadness', '#anger', 'negative', '-0.78', 'spare', 'unembellished', 'unornamented', 'bare', 'plain']
senticnet['unadventurous'] = ['-0.84', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.78', 'unambitious', 'unimaginative', 'uncreative', 'unexciting', 'uninspired']
senticnet['unadvisedly'] = ['-0.82', '0', '0.749', '0', '#sadness', '#anger', 'negative', '-0.78', 'divulge', 'thought', 'inconsiderately', 'blurt', 'flirt']
senticnet['unaerated'] = ['-0.59', '0', '0.586', '-0.98', '#anger', '#disgust', 'negative', '-0.72', 'unvented', 'unventilated', 'fuggy', 'unoxygenated', 'stuffy']
senticnet['unaesthetic'] = ['0.036', '0', '0.094', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'facial_hair', 'need_shave', 'need_beauty_center', 'long_beard', 'carelessness']
senticnet['unaffected'] = ['-0.92', '0', '0.724', '-0.85', '#sadness', '#disgust', 'negative', '-0.83', 'unimpressed', 'untouched', 'uninfluenced', 'unswayed', 'immune']
senticnet['unaffected_simplicity'] = ['0.992', '-0.82', '0', '0.901', '#joy', '#admiration', 'positive', '0.906', 'gracility', 'grace', 'lissomeness', 'litheness', 'suppleness']
senticnet['unaffectedly'] = ['0', '0', '0', '0.994', '#admiration', '#admiration', 'positive', '0.994', 'seriousness', 'in_earnest', 'heartfelt', 'devout', 'earnestly']
senticnet['unaffectionate'] = ['-0.56', '0', '0.578', '-0.74', '#anger', '#disgust', 'negative', '-0.62', 'cold', 'frigid', 'loveless', 'detached', 'unloving']
senticnet['unaffordable'] = ['-0.78', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.86', 'high_cost', 'spend_money', 'waste_money', 'high_price', 'expensive']
senticnet['unafraid'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'intrepidly', 'hardy', 'fearlessness', 'fearlessly', 'unfearing']
senticnet['unagi'] = ['0.9', '0.963', '0', '0', '#joy', '#interest', 'positive', '0.931', 'form_of_food', 'japanese_food', 'uncooked', 'healthy', 'food']
senticnet['unagitated'] = ['0.526', '0.651', '-0.25', '0.25', '#joy', '#interest', 'positive', '0.294', 'imperturbability', 'tranquil', 'imperturbableness', 'placidly', 'placidity']
senticnet['unaided_eye'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'eye', 'peeper', 'oculus_sinister']
senticnet['unaired_episode'] = ['0.918', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.827', 'encounter', 'reveal', 'programme', 'lose', 'sequel']
senticnet['unaired_pilot'] = ['0.918', '0.736', '0', '0', '#joy', '#interest', 'positive', '0.827', 'encounter', 'reveal', 'programme', 'lose', 'sequel']
senticnet['unalienable'] = ['-0.54', '0', '0.812', '-0.86', '#anger', '#disgust', 'negative', '-0.74', 'infrangible', 'nontransferable', 'unassignable', 'untransferable', 'inviolable']
senticnet['unalienable_right'] = ['0.926', '0.937', '0', '0.975', '#interest', '#admiration', 'positive', '0.946', 'monument', 'statue', 'freedom', 'idol', 'sculpture']
senticnet['unalterability'] = ['-0.83', '0', '0.696', '-0.98', '#sadness', '#disgust', 'negative', '-0.84', 'immutability', 'agelessness', 'changelessness', 'unchangeability', 'unchangeableness']
senticnet['unalterable'] = ['-0.90', '0', '0.653', '-0.99', '#sadness', '#disgust', 'negative', '-0.85', 'unchangeable', 'static', 'changeless', 'confirmed', 'fixed']
senticnet['unambiguity'] = ['0.777', '-0.68', '0', '0.601', '#joy', '#surprise', 'positive', '0.686', 'lucidness', 'clarity', 'clearness', 'lucidity', 'pellucidity']
senticnet['unambiguous'] = ['0', '0', '0', '0.892', '#admiration', '#admiration', 'positive', '0.892', 'organize_life', 'clear_think', 'neatness', 'declutter', 'organizational_skill']
senticnet['unambiguously'] = ['0', '0.748', '0', '0.939', '#interest', '#admiration', 'positive', '0.843', 'uniquely', 'study', 'result', 'student', 'tool']
senticnet['unambitious'] = ['0', '0.882', '0', '0.998', '#interest', '#admiration', 'positive', '0.94', 'unadventurous', 'unimaginative', 'over-ambitious', 'uncreative', 'dim-witted']
senticnet['unambivalent'] = ['0.929', '0.507', '-0.17', '0.396', '#joy', '#interest', 'positive', '0.415', 'plume', 'congratulate', 'smugness', 'smug', 'proudly']
senticnet['unamiable'] = ['-0.54', '0', '0.706', '-0.62', '#anger', '#disgust', 'negative', '-0.62', 'alienated', 'alienation', 'disaffection', 'alienate', 'disoriented']
senticnet['unanimous'] = ['0', '-0.84', '0', '0.865', '#surprise', '#admiration', 'positive', '0.856', 'solid', 'view', 'opinion', 'animus', 'harmony']
senticnet['unanimous_approval'] = ['0.779', '-0.88', '0', '0.521', '#joy', '#surprise', 'positive', '0.727', 'corrigible', 'correctable', 'improvable', 'reformable', 'redeemable']
senticnet['unanimous_consent'] = ['0.779', '-0.88', '0', '0.521', '#joy', '#surprise', 'positive', '0.727', 'corrigible', 'correctable', 'improvable', 'reformable', 'redeemable']
senticnet['unanimous_critical_acclaim'] = ['0.853', '0.828', '0', '0.98', '#joy', '#admiration', 'positive', '0.887', 'endorse', 'recommend', 'stimulate_mind', 'acculturation', 'support']
senticnet['unanimous_decision'] = ['0.944', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.967', 'meeting', 'meet', 'match', 'encounter', 'round']
senticnet['unanimous_decision_victory'] = ['-0.94', '0', '0.863', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'poke', 'clout', 'lick', 'punch', 'biff']
senticnet['unanimous_decision_win'] = ['0.944', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.967', 'meeting', 'meet', 'match', 'encounter', 'round']
senticnet['unanswerable'] = ['-0.63', '0', '0.745', '-0.52', '#sadness', '#anger', 'negative', '-0.63', 'watertight', 'incontrovertible', 'incontestable', 'bulletproof', 'unassailable']
senticnet['unanticipated'] = ['0.038', '-0.66', '0', '0.358', '#surprise', '#admiration', 'positive', '0.354', 'without_warning', 'unexpected', 'unpredicted', 'without_notice', 'out_of_blue']
senticnet['unapologetic'] = ['-0.67', '0', '0.715', '-0.89', '#anger', '#disgust', 'negative', '-0.76', 'obstinate', 'unregenerated', 'impenitent', 'unconverted', 'unpersuaded']
senticnet['unapologetically'] = ['0.512', '0.658', '-0.25', '0.256', '#joy', '#interest', 'positive', '0.293', 'riotously', 'expansively', 'ebulliently', 'zestfulness', 'gusto']
senticnet['unappealing'] = ['-0.82', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.82', 'unattractive', 'unappetizing', 'uninteresting', 'uninspiring', 'off-putting']
senticnet['unappetizing'] = ['0', '-0.83', '0', '-0.94', '#surprise', '#disgust', 'negative', '-0.05', 'unwelcoming', 'unappealing', 'inhospitable', 'uninviting', 'cheerless']
senticnet['unappreciated'] = ['-0.75', '0', '0.56', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'unacknowledged', 'unrecognized', 'unsung', 'unvalued', 'unconfessed']
senticnet['unappreciative'] = ['0', '0', '0.711', '-0.92', '#anger', '#disgust', 'negative', '-0.81', 'non-receptive', 'unreceptive', 'unenthusiastic', 'unwelcoming', 'unsympathetic']
senticnet['unapproachable'] = ['-0.95', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.94', 'standoffish', 'aloof', 'uncommunicative', 'inscrutable', 'ineffable']
senticnet['unarmed'] = ['-0.57', '0', '0.65', '-0.99', '#anger', '#disgust', 'negative', '-0.74', 'defenseless', 'defenceless', 'barehanded', 'clean', 'weaponless']
senticnet['unarmed_combat'] = ['0.93', '-0.95', '0', '0.909', '#joy', '#surprise', 'positive', '0.93', 'acquirement', 'skill', 'accomplishment', 'acquisition', 'attainment']
senticnet['unarmed_robbery'] = ['-0.75', '0', '0.94', '0', '#sadness', '#anger', 'negative', '-0.84', 'steal_money', 'theft', 'stick_up', 'robbery', 'rob']
senticnet['unarticulate'] = ['-0.66', '0', '0.989', '-0.68', '#anger', '#disgust', 'negative', '-0.78', 'aphasic', 'aphonic', 'speechless', 'unarticulated', 'dumb']
senticnet['unarticulated'] = ['-0.67', '0', '0.983', '-0.69', '#anger', '#disgust', 'negative', '-0.78', 'aphasic', 'aphonic', 'speechless', 'unarticulate', 'dumb']
senticnet['unary_relation'] = ['0.042', '-0.00', '0.027', '0.099', '#joy', '#admiration', 'positive', '0.031', 'chess_knight', 'chess_queen', 'chess_pawn', 'pawn', 'chess_bishop']
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senticnet['unassertiveness'] = ['0.722', '0.998', '0', '0', '#joy', '#interest', 'positive', '0.86', 'unassertively', 'unassertive', 'hesitantly', 'hesitatingly', 'hesitancy']
senticnet['unassignable'] = ['-0.80', '0', '0.815', '-0.98', '#anger', '#disgust', 'negative', '-0.86', 'infrangible', 'nontransferable', 'inalienable', 'untransferable', 'inviolable']
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senticnet['unassuming_man'] = ['0.856', '0', '0', '0.862', '#joy', '#admiration', 'positive', '0.859', 'calm', 'calmness', 'serenity', 'soothe', 'peacefulness']
senticnet['unassumingly'] = ['0', '0.999', '0', '0.925', '#interest', '#admiration', 'positive', '0.962', 'wholesomely', 'readably', 'incongruously', 'cozily', 'fetchingly']
senticnet['unattached'] = ['-0.66', '0', '0.939', '-0.87', '#anger', '#disgust', 'negative', '-0.82', 'unconnected', 'isolated', 'separate', 'asternal', 'separated']
senticnet['unattackable'] = ['0.968', '0.717', '0', '0', '#joy', '#interest', 'positive', '0.843', 'firm', 'stronger', 'secure', 'unassailable', 'stiff']
senticnet['unattainable'] = ['0.273', '0.226', '-0.13', '0.332', '#joy', '#admiration', 'positive', '0.173', 'morality', 'cagey', 'uncommon', 'evasive', 'capitalize']
senticnet['unattainableness'] = ['-0.66', '0', '0.984', '-0.55', '#sadness', '#anger', 'negative', '-0.73', 'inconceivability', 'inconceivableness', 'nonexistence', 'nonentity', 'impossibility']
senticnet['unattended_stimulus'] = ['0.685', '-0.91', '0', '0.566', '#joy', '#surprise', 'positive', '0.721', 'audibility', 'audibleness', 'visibleness', 'visibility', 'physical_property']
senticnet['unattractive'] = ['0', '0', '0', '-0.70', '#disgust', '#disgust', 'negative', '-0.70', 'leave_out', 'loathing', 'cause_suffer', 'lose_team', 'disrespect']
senticnet['unattractiveness'] = ['-0.87', '0', '0.584', '-0.71', '#sadness', '#disgust', 'negative', '-0.72', 'unsightliness', 'eyesore', 'hideousness', 'ugliness', 'garishness']
senticnet['unauthorised_overdraft'] = ['0.54', '-0.97', '0', '0.569', '#surprise', '#admiration', 'positive', '0.695', 'overstate', 'hyperbolize', 'glorify', 'exaggerate', 'aggrandize']
senticnet['unauthorized'] = ['-0.90', '0', '0.562', '-0.97', '#sadness', '#disgust', 'negative', '-0.81', 'wildcat', 'unofficial', 'informal', 'loose', 'drumhead']
senticnet['unauthorized_biography'] = ['0.701', '0.939', '0', '0.718', '#interest', '#admiration', 'positive', '0.786', 'literature', 'bed_time_story', 'book_story', 'fiction', 'life_story']
senticnet['unauthorized_disclosure'] = ['-0.02', '0.175', '-0.11', '0.148', '#interest', '#admiration', 'positive', '0.045', 'tolerant', 'phrase', 'inquiry', 'explain', 'question']
senticnet['unauthorized_entry'] = ['-0.55', '0', '0.843', '-0.58', '#anger', '#disgust', 'negative', '-0.66', 'thieving', 'larceny', 'theft', 'thievery', 'stealing']
senticnet['unauthorized_intrusion'] = ['-0.69', '0', '0.752', '-0.82', '#anger', '#disgust', 'negative', '-0.75', 'usurpation', 'trespass', 'encroachment', 'inroad', 'violation']
senticnet['unavailability'] = ['0', '0', '-0.93', '-0.73', '#fear', '#disgust', 'negative', '-0.83', 'non-availability', 'inaccessibility', 'interruption', 'discontinuance', 'absence']
senticnet['unavailable'] = ['-0.77', '0', '0.644', '-0.68', '#sadness', '#disgust', 'negative', '-0.70', 'unobtainable', 'unprocurable', 'out_of_stock', 'bunk', 'untouchable']
senticnet['unavailing'] = ['-0.83', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.88', 'unpersuasive', 'futile', 'fruitless', 'ineffectual', 'entreaty']
senticnet['unavoidability'] = ['-0.91', '0', '0.795', '-0.94', '#sadness', '#disgust', 'negative', '-0.88', 'ineluctability', 'assured', 'predictability', 'slam_dunk', 'inevitability']
senticnet['unavoidable'] = ['-0.77', '0', '0.784', '0', '#sadness', '#anger', 'negative', '-0.78', 'voidable', 'inevitable', 'impossible', 'obbligato', 'necessity']
senticnet['unaware'] = ['-0.93', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.96', 'mindless', 'thoughtlessness', 'noticing', 'heedless', 'inadvertence']
senticnet['unawares'] = ['-0.83', '0', '0.913', '0', '#sadness', '#anger', 'negative', '-0.87', 'pop', 'unexpected', 'pica', 'impulsion', 'unexpectedly']
senticnet['unbalance'] = ['0.992', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.874', 'unequal', 'instability', 'imbalance', 'disequilibrium', 'astasia']
senticnet['unbalanced'] = ['-0.87', '0', '0.623', '-0.70', '#sadness', '#disgust', 'negative', '-0.73', 'unequalized', 'mismatched', 'nonequivalent', 'uneven', 'unequal']
senticnet['unbearable'] = ['-0.17', '0.079', '-0.18', '0', '#sadness', '#fear', 'negative', '-0.09', 'terminate', 'bloody', 'maim', 'hurtful', 'severe']
senticnet['unbearable_pain'] = ['-0.97', '0', '0.969', '-0.50', '#sadness', '#anger', 'negative', '-0.81', 'nervousness', 'tenseness', 'tension', 'nerve', 'stress']
senticnet['unbearably'] = ['0', '0', '-0.74', '-0.87', '#fear', '#disgust', 'negative', '-0.80', 'absolute', 'out-and-out', 'rank', 'right-down', 'sheer']
senticnet['unbearably_hot'] = ['0.953', '0.792', '0', '0', '#joy', '#interest', 'positive', '0.873', 'snowy', 'cool_temperature', 'coldness', 'ice_rain', 'season_year']
senticnet['unbeatable'] = ['0', '0.794', '0', '0.941', '#interest', '#admiration', 'positive', '0.867', 'best', 'excellent', 'goodness', 'greatness', 'great']
senticnet['unbeaten'] = ['-0.85', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.84', 'winless', 'five-game', 'six-game', 'three-game', 'four-game']
senticnet['unbecoming'] = ['-0.64', '0', '0.52', '-0.53', '#sadness', '#disgust', 'negative', '-0.56', 'wrong', 'incorrect', 'improper', 'uncomely', 'unseemly']
senticnet['unbeknownst'] = ['-0.89', '-0.71', '-0.76', '-0.93', '#sadness', '#disgust', 'negative', '-0.47', 'bigot', 'censorship', 'jail_time', 'reprimand', 'intolerance']
senticnet['unbelief'] = ['-0.55', '-0.1', '-0.41', '-0.15', '#sadness', '#fear', 'negative', '-0.25', 'angry_mob', 'protest', 'denial', 'against', 'rebellion']
senticnet['unbelievable'] = ['-0.54', '0', '0.646', '-0.62', '#anger', '#disgust', 'negative', '-0.60', 'improbable', 'undreamed', 'undreamed_of', 'undreamt', 'undreamt_of']
senticnet['unbelievable_of'] = ['-0.54', '0', '0.641', '-0.62', '#anger', '#disgust', 'negative', '-0.60', 'improbable', 'undreamed', 'undreamed_of', 'unbelievable', 'undreamt']
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senticnet['unbeliever'] = ['0', '0.325', '-0.09', '0.21', '#interest', '#admiration', 'positive', '0.146', 'saint', 'priest', 'christian', 'holy_man', 'friar']
senticnet['unbelieving'] = ['0', '0', '-0.99', '-0.99', '#fear', '#disgust', 'negative', '-0.99', 'skeptical', 'sceptical', 'doubting', 'disbelieving', 'questioning']
senticnet['unbelieving_world'] = ['0.889', '-0.69', '0', '0.908', '#joy', '#admiration', 'positive', '0.83', 'pharisaic', 'pious', 'sanctimonious', 'reverent', 'worshipful']
senticnet['unbeloved'] = ['-0.86', '0', '0.653', '-0.67', '#sadness', '#disgust', 'negative', '-0.73', 'unloved', 'lovelorn', 'bereft', 'estranged', 'disinherited']
senticnet['unbend'] = ['-0.51', '0', '0.738', '-0.83', '#anger', '#disgust', 'negative', '-0.69', 'relax', 'loosen_up', 'sit_back', 'unwind', 'decompress']
senticnet['unbiased'] = ['0.829', '-1.0', '0', '0.558', '#joy', '#surprise', 'positive', '0.796', 'disinterested', 'impartial', 'indifferent', 'dispassionate', 'fair']
senticnet['unbiased_judgment'] = ['0.837', '-0.99', '0', '0.559', '#joy', '#surprise', 'positive', '0.797', 'disinterested', 'dispassionate', 'indifferent', 'unbiased', 'fair']
senticnet['unbiased_manner'] = ['0.837', '-0.99', '0', '0.559', '#joy', '#surprise', 'positive', '0.797', 'disinterested', 'dispassionate', 'indifferent', 'unbiased', 'fair']
senticnet['unbiasedness'] = ['0.729', '0.749', '0', '0', '#joy', '#interest', 'positive', '0.739', 'candor', 'disinterest', 'justice', 'impartiality', 'fairness']
senticnet['unbidden'] = ['0', '0', '0', '-0.78', '#disgust', '#disgust', 'negative', '-0.78', 'suddenly', 'precipitous', 'immediate', 'unexpected', 'sudden_asthma_attack']
senticnet['unblemished_character'] = ['0.852', '-0.75', '0', '0.763', '#joy', '#admiration', 'positive', '0.789', 'honor', 'laurel', 'fame', 'stock', 'character']
senticnet['unblinking'] = ['-0.89', '0', '0.789', '-0.96', '#sadness', '#disgust', 'negative', '-0.88', 'phlegmatic', 'unemotional', 'phlegmatical', 'stoic', 'chilly']
senticnet['unbodied'] = ['-0.53', '0', '0.634', '-0.92', '#anger', '#disgust', 'negative', '-0.69', 'discorporate', 'unembodied', 'disembodied', 'incorporeal', 'bodiless']
senticnet['unbolt'] = ['0.979', '-0.69', '0', '0.576', '#joy', '#surprise', 'positive', '0.749', 'big_cheese', 'supremo', 'important_person', 'influential_person', 'personage']
senticnet['unborn'] = ['-0.99', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.96', 'fetus', 'embroyo', 'abortion', 'baby', 'womb']
senticnet['unborn_baby'] = ['0', '0', '0', '0.939', '#admiration', '#admiration', 'positive', '0.939', 'large_family', 'small_child', 'youth', 'quiet_baby', 'deliver_baby']
senticnet['unborn_child'] = ['-0.90', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.86', 'death', 'feticide', 'sickness', 'miscarriage', 'spontaneous_abortion']
senticnet['unborn_infant'] = ['0.706', '0', '0', '0', '#joy', '#joy', 'positive', '0.706', 'birth', 'new_baby', 'born', 'birthday', 'thanksgiving']
senticnet['unbrace'] = ['0.501', '-0.51', '0', '0.676', '#joy', '#surprise', 'positive', '0.563', 'unlace', 'unfold', 'untie', 'undo', 'spread']
senticnet['unbreakable'] = ['0.123', '0.207', '0', '0.237', '#interest', '#admiration', 'positive', '0.189', 'good_quality', 'premium', 'last_long', 'steel', 'strong']
senticnet['unbridgeable'] = ['0', '0', '0.878', '-0.92', '#anger', '#disgust', 'negative', '-0.90', 'uncrossable', 'chasm', 'irreconcilable', 'irresolvable', 'gulf']
senticnet['unbridled_discretion'] = ['-0.52', '0', '0.561', '-0.90', '#anger', '#disgust', 'negative', '-0.66', 'difference', 'tolerance', 'allowance', 'discrepancy', 'margin']
senticnet['unbridled_enthusiasm'] = ['0.772', '0.742', '0', '0', '#joy', '#interest', 'positive', '0.757', 'enjoy', 'satisfaction', 'happiness', 'become_excited', 'joy']
senticnet['unbroken'] = ['-0.63', '0', '0.991', '-0.73', '#anger', '#disgust', 'negative', '-0.78', 'undomesticated', 'feral', 'ferine', 'savage', 'untamed']
senticnet['unburden'] = ['0.712', '0', '0', '0', '#joy', '#joy', 'positive', '0.712', 'serenity', 'euphoria', 'serene', 'tranquility', 'cheer']
senticnet['unburnt'] = ['0.037', '0.084', '0', '0', '#joy', '#interest', 'positive', '0.06', 'leave_over', 'leftover', 'cellulose', 'cane', 'remainder']
senticnet['unbutton'] = ['-0.04', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.05', 'fill_bathtub', 'fill_bathtub_water', 'run_bath', 'turn_water', 'take_clothes_off']
senticnet['uncandid'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'amateur', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
senticnet['uncannily'] = ['0', '-0.99', '0', '0.931', '#surprise', '#admiration', 'positive', '0.961', 'spot-on', 'pitch-perfect', 'bang-on', 'dead-on', 'impersonation']
senticnet['uncanny'] = ['0', '0', '0', '-0.80', '#disgust', '#disgust', 'negative', '-0.80', 'uneducated', 'homophobic', 'guilty_crime', 'weird', 'strange']
senticnet['uncanny_resemblance'] = ['-0.88', '0', '0.813', '-0.57', '#sadness', '#anger', 'negative', '-0.75', 'affinity', 'mutual_resemblance', 'alikeness', 'likeness', 'similitude']
senticnet['uncanny_sense'] = ['0.772', '-0.95', '0', '0.89', '#surprise', '#admiration', 'positive', '0.872', 'downrightness', 'straightforwardness', 'characteristic', 'immediacy', 'directness']
senticnet['uncaring'] = ['-0.70', '0', '0.823', '0', '#sadness', '#anger', 'negative', '-0.76', 'thickened', 'tougheneded', 'heartless', 'unfeeling', 'callous']
senticnet['unceasing'] = ['0', '0', '-0.71', '-0.96', '#fear', '#disgust', 'negative', '-0.83', 'incessant', 'never-ending', 'constant', 'ceaseless', 'perpetual']
senticnet['unceasingly'] = ['0', '0.82', '0', '0.853', '#interest', '#admiration', 'positive', '0.837', 'incessantly', 'ceaselessly', 'continuously', 'endlessly', 'unendingly']
senticnet['unceremonious'] = ['0', '0', '0.831', '-1', '#anger', '#disgust', 'negative', '-0.91', 'discourteous', 'brusque', 'brusk', 'abrupt', 'curt']
senticnet['unceremoniously'] = ['0.864', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.924', 'trash', 'trash_container', 'junk_store', 'coffee_cup', 'trash_receptacle']
senticnet['uncertain'] = ['-0.87', '0.184', '-0.58', '0', '#sadness', '#fear', 'negative', '-0.42', 'unclear', 'unsure', 'unpredictable', 'random', 'vague']
senticnet['uncertain_environment'] = ['0', '0', '0', '-0.70', '#disgust', '#disgust', 'negative', '-0.70', 'uncertain_future', 'uncertainty', 'insecure', 'blur', 'anxiety']
senticnet['uncertain_fate'] = ['0', '0', '0', '-0.70', '#disgust', '#disgust', 'negative', '-0.70', 'uncertainty', 'uncertain_environment', 'insecure', 'blur', 'anxiety']
senticnet['uncertain_future'] = ['0', '0', '0', '-0.70', '#disgust', '#disgust', 'negative', '-0.70', 'uncertainty', 'uncertain_environment', 'insecure', 'blur', 'anxiety']
senticnet['uncertain_taxonomic_placement'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'welwitschia', 'cycad', 'gymnosperm', 'pteridosperm']
senticnet['uncertainness'] = ['0.756', '-0.86', '0', '0.809', '#surprise', '#admiration', 'positive', '0.81', 'unlikelihood', 'unlikeliness', 'uncertainty', 'precariousness', 'improbableness']
senticnet['uncertainties_inherent'] = ['0', '0', '0', '-0.70', '#disgust', '#disgust', 'negative', '-0.70', 'uncertain_future', 'uncertain_environment', 'insecure', 'blur', 'anxiety']
senticnet['uncertainty'] = ['0', '0', '0', '-0.70', '#disgust', '#disgust', 'negative', '-0.70', 'uncertain_future', 'uncertain_environment', 'insecure', 'blur', 'anxiety']
senticnet['uncertainty_inherent'] = ['0', '0', '0', '-0.70', '#disgust', '#disgust', 'negative', '-0.70', 'uncertain_future', 'uncertain_environment', 'insecure', 'blur', 'anxiety']
senticnet['uncertainty_principle'] = ['-0.69', '0', '0.83', '-0.99', '#anger', '#disgust', 'negative', '-0.83', 'general_relativity', 'general_theory_of_relativity', 'general_relativity_theory', 'relativity', 'special_theory_of_relativity']
senticnet['unchain'] = ['0.915', '-0.57', '0', '0.755', '#joy', '#admiration', 'positive', '0.749', 'bail_out', 'unspell', 'bail', 'unloosen', 'run']
senticnet['unchallengeable'] = ['-0.95', '0', '0.783', '-0.65', '#sadness', '#anger', 'negative', '-0.79', 'noncontroversial', 'undisputed', 'unchallenged', 'unquestioned', 'agreed_upon']
senticnet['unchallenged'] = ['-0.96', '0', '0.776', '-0.66', '#sadness', '#anger', 'negative', '-0.80', 'unchallengeable', 'undisputed', 'noncontroversial', 'unquestioned', 'agreed_upon']
senticnet['unchangeability'] = ['0.948', '-0.55', '0', '0.592', '#joy', '#admiration', 'positive', '0.699', 'metastability', 'monotony', 'invariance', 'constancy', 'changelessness']
senticnet['unchangeable'] = ['-0.90', '0', '0.653', '-0.99', '#sadness', '#disgust', 'negative', '-0.85', 'unalterable', 'static', 'changeless', 'confirmed', 'fixed']
senticnet['unchangeableness'] = ['-0.83', '0', '0.696', '-0.98', '#sadness', '#disgust', 'negative', '-0.84', 'unalterability', 'agelessness', 'changelessness', 'unchangeability', 'immutability']
senticnet['unchangeably'] = ['-0.90', '0', '0.653', '-0.99', '#sadness', '#disgust', 'negative', '-0.85', 'unalterable', 'static', 'changeless', 'confirmed', 'fixed']
senticnet['unchanged'] = ['0', '0.725', '0', '0.876', '#interest', '#admiration', 'positive', '0.8', 'preserved', 'remains', 'undamaged', 'intact', 'untouched']
senticnet['unchanging'] = ['-0.81', '0', '0.921', '-0.80', '#sadness', '#anger', 'negative', '-0.84', 'immovability', 'fixity', 'lodgment', 'permanent', 'lodging']
senticnet['unchanging_reality'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'metastability', 'monotony', 'invariance', 'constancy', 'unchangeability']
senticnet['unchangingness'] = ['-0.93', '0', '0.749', '-0.68', '#sadness', '#anger', 'negative', '-0.79', 'unvariedness', 'changelessness', 'unchangeability', 'unchangeableness', 'invariableness']
senticnet['uncharacteristic'] = ['0.983', '-0.63', '0', '0.697', '#joy', '#admiration', 'positive', '0.772', 'amateurishness', 'inability', 'ineptness', 'unskillfulness', 'awkwardness']
senticnet['uncharitable'] = ['-0.98', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.87', 'ungenerous', 'unchristian', 'unkind', 'mean-spirited', 'small-minded']
senticnet['unchaste'] = ['-0.93', '0', '0.936', '-0.88', '#sadness', '#anger', 'negative', '-0.91', 'lose_team', 'cause_suffer', 'leave_out', 'loathing', 'lyme_disease']
senticnet['unchastity'] = ['0', '0', '0.74', '-0.88', '#anger', '#disgust', 'negative', '-0.81', 'scape', 'libidinous', 'salacity', 'lewdness', 'lewd']
senticnet['unchecked_power'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'address_book', 'get_rich', 'ladle', 'swiss_army_knife', 'whisk']
senticnet['uncheerful'] = ['-0.75', '0.282', '-0.42', '-0.06', '#sadness', '#fear', 'negative', '-0.24', 'depressing', 'cheerless', 'low-spiritedness', 'lowness', 'dispiritedness']
senticnet['uncheerfulness'] = ['-0.71', '0.307', '-0.40', '-0.07', '#sadness', '#fear', 'negative', '-0.22', 'cheerlessness', 'cheerlessly', 'lachrymose', 'weeping', 'dolorous']
senticnet['unchewable'] = ['0.875', '0.742', '0', '0', '#joy', '#interest', 'positive', '0.808', 'fibrous', 'tendinous', 'stringy', 'sinewy', 'brawny']
senticnet['unchristian'] = ['-0.99', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.88', 'ungenerous', 'uncharitable', 'unkind', 'mean-spirited', 'small-minded']
senticnet['uncial'] = ['-0.95', '0', '0.517', '-0.53', '#sadness', '#disgust', 'negative', '-0.67', 'copperplate', 'hand', 'minuscule', 'italic', 'round_hand']
senticnet['uncivil'] = ['-0.78', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.80', 'complaisant', 'discourteous', 'uncivilly', 'civil', 'savage']
senticnet['uncivilly'] = ['-0.79', '0', '-0.84', '0', '#sadness', '#fear', 'negative', '-0.81', 'complaisant', 'discourteous', 'uncivil', 'civil', 'savage']
senticnet['unclaimed'] = ['-0.83', '0', '0.843', '-0.71', '#sadness', '#anger', 'negative', '-0.79', 'unwanted', 'casteless', 'outcaste', 'unwelcome', 'undesired']
senticnet['unclasp'] = ['0.92', '-0.57', '0', '0.758', '#joy', '#admiration', 'positive', '0.751', 'bail_out', 'unspell', 'bail', 'unchain', 'run']
senticnet['unclassifiable'] = ['-0.82', '0', '0.758', '-0.70', '#sadness', '#anger', 'negative', '-0.76', 'unidentifiable', 'unrecognizable', 'unrecognisable', 'intangible', 'undiagnosable']
senticnet['unclassified_version'] = ['0', '0', '0', '0.091', '#admiration', '#admiration', 'positive', '0.091', 'correspondence', 'handwritten', 'written_message', 'written_communication', 'write_note']
senticnet['uncle'] = ['0', '0.765', '0', '0.75', '#interest', '#admiration', 'positive', '0.758', 'family_member', 'dad', 'brother', 'mother_brother', 'daddy']
senticnet['uncle_alec'] = ['0', '0', '-0.06', '-0.11', '#fear', '#disgust', 'negative', '-0.09', 'elderly_person', 'hide-a-bed', 'homosexual', 'linen_chest', 'dust_mite']
senticnet['unclean'] = ['-0.69', '0', '0.858', '-0.83', '#anger', '#disgust', 'negative', '-0.79', 'draggled', 'befouled', 'filthy', 'nasty', 'scummy']
senticnet['unclean_animal'] = ['-0.69', '0', '0.858', '-0.83', '#anger', '#disgust', 'negative', '-0.79', 'draggled', 'befouled', 'filthy', 'nasty', 'scummy']
senticnet['uncleanliness'] = ['0', '0', '0.86', '-0.98', '#anger', '#disgust', 'negative', '-0.92', 'dirtiness', 'unattractiveness', 'uncleanness', 'grossness', 'unhealthiness']
senticnet['uncleanness'] = ['0.732', '-0.95', '0', '0.856', '#surprise', '#admiration', 'positive', '0.848', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dinge']
senticnet['unclear'] = ['-0.79', '0.241', '-0.52', '-0.11', '#sadness', '#fear', 'negative', '-0.29', 'disoriented', 'obscure', 'unsure', 'uncertain', 'unfair']
senticnet['unclear_exactly'] = ['-0.79', '0.241', '-0.52', '-0.11', '#sadness', '#fear', 'negative', '-0.29', 'disoriented', 'obscure', 'unsure', 'uncertain', 'unfair']
senticnet['unclear_why'] = ['-0.79', '0.241', '-0.52', '-0.11', '#sadness', '#fear', 'negative', '-0.29', 'disoriented', 'obscure', 'unsure', 'uncertain', 'unfair']
senticnet['unclearness'] = ['-0.58', '0', '0.871', '-0.70', '#anger', '#disgust', 'negative', '-0.72', 'elusiveness', 'inexplicitness', 'ambiguity', 'equivocalness', 'vagueness']
senticnet['unclimbable'] = ['-0.99', '0', '0.641', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'navigable', 'negotiable', 'surmountable', 'passable', 'traversable']
senticnet['unclothe'] = ['-0.98', '0', '0.654', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'bereave', 'expropriate', 'disarm', 'deprive', 'take']
senticnet['uncloudedness'] = ['0.818', '-0.67', '0', '0.906', '#joy', '#admiration', 'positive', '0.799', 'quality', 'transparentness', 'distinctness', 'transparency', 'transparence']
senticnet['uncoerced'] = ['0.829', '0.754', '0', '0', '#joy', '#interest', 'positive', '0.791', 'unforced', 'leave', 'bequeath', 'volition', 'willing']
senticnet['uncomely'] = ['-0.64', '0', '0.52', '-0.53', '#sadness', '#disgust', 'negative', '-0.56', 'wrong', 'incorrect', 'unbecoming', 'improper', 'unseemly']
senticnet['uncomfortable'] = ['0', '0', '0.898', '-0.90', '#anger', '#disgust', 'negative', '-0.90', 'painful', 'ignorant', 'unpleasant', 'smelly', 'physical_reaction']
senticnet['uncomfortable_chair'] = ['-0.97', '0', '0.135', '-0.92', '#sadness', '#disgust', 'negative', '-0.67', 'tight_shoe', 'uncomfortable', 'uncomfy', 'uncomfortable_seat', 'uncomfortable_shoe']
senticnet['uncomfortable_resemblance'] = ['-0.92', '0', '0.946', '-0.56', '#sadness', '#anger', 'negative', '-0.81', 'forcefulness', 'intensiveness', 'impulse', 'vigor', 'energy']
senticnet['uncomfortable_seat'] = ['-0.97', '0', '0.135', '-0.92', '#sadness', '#disgust', 'negative', '-0.67', 'tight_shoe', 'uncomfortable', 'uncomfortable_chair', 'uncomfy', 'uncomfortable_shoe']
senticnet['uncomfortable_sensation'] = ['-0.89', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.82', 'fretful', 'antsy', 'fidgety', 'stewing', 'impatiently']
senticnet['uncomfortable_shoe'] = ['-0.97', '0', '0.135', '-0.92', '#sadness', '#disgust', 'negative', '-0.67', 'tight_shoe', 'uncomfortable', 'uncomfortable_chair', 'uncomfortable_seat', 'uncomfy']
senticnet['uncomfortable_silence'] = ['-0.85', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.88', 'oppression', 'enslavement', 'slave', 'captivity', 'misery']
senticnet['uncomfortable_situation'] = ['-0.96', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.83', 'uneasiness', 'become_red', 'bad_experience', 'shy', 'provocative_discussion']
senticnet['uncomfortableness'] = ['0.937', '0.73', '0', '0', '#joy', '#interest', 'positive', '0.834', 'self-conscious', 'self-consciousness', 'pride', 'bashfulness', 'mortification']
senticnet['uncomfortably'] = ['-0.89', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.85', 'high-strung', 'uptight', 'restive', 'overstrung', 'nervy']
senticnet['uncomfortably_hot'] = ['0.953', '0.792', '0', '0', '#joy', '#interest', 'positive', '0.873', 'snowy', 'cool_temperature', 'coldness', 'ice_rain', 'season_year']
senticnet['uncomfy'] = ['-0.97', '0', '0.135', '-0.92', '#sadness', '#disgust', 'negative', '-0.67', 'tight_shoe', 'uncomfortable', 'uncomfortable_chair', 'uncomfortable_seat', 'uncomfortable_shoe']
senticnet['uncommon'] = ['0', '0.857', '0', '0.929', '#interest', '#admiration', 'positive', '0.893', 'good', 'unique', 'tasty_good', 'special', 'niceness']
senticnet['uncommon_valor'] = ['0', '0', '0.764', '-0.77', '#anger', '#disgust', 'negative', '-0.77', 'restriction', 'tragedy', 'perpetrator', 'dominate', 'control']
senticnet['uncommonly'] = ['0.045', '-0.66', '0', '0.363', '#surprise', '#admiration', 'positive', '0.357', 'amazingly', 'surprised', 'surprising', 'surprisedly', 'surprisingly']
senticnet['uncommunicative'] = ['-0.96', '0', '0.861', '-0.90', '#sadness', '#disgust', 'negative', '-0.90', 'incommunicado', 'unpronounceable', 'impassive', 'inexpressive', 'silent']
senticnet['uncommunicativeness'] = ['0.609', '-0.82', '0', '0.756', '#surprise', '#admiration', 'positive', '0.73', 'secretiveness', 'trait', 'secrecy', 'reserve', 'silence']
senticnet['uncomparable'] = ['0.603', '-0.80', '0', '0.555', '#joy', '#surprise', 'positive', '0.654', 'matchless', 'nonpareil', 'unique', 'peerless', 'unmatchable']
senticnet['uncompensated'] = ['-0.97', '0', '0.524', '-0.67', '#sadness', '#disgust', 'negative', '-0.72', 'buckshee', 'unsalaried', 'unpaid', 'undischarged', 'complimentary']
senticnet['uncomplaining'] = ['-0.72', '0', '0', '-0.86', '#sadness', '#disgust', 'negative', '-0.79', 'open-hearted', 'long-suffering', 'self-sacrificing', 'stolid', 'unflappable']
senticnet['uncomplete'] = ['0.538', '-0.72', '0', '0.606', '#surprise', '#admiration', 'positive', '0.622', 'sketchy', 'unelaborated', 'partial', 'uncompleted', 'broken']
senticnet['uncompleted'] = ['-0.95', '0', '0.749', '0', '#sadness', '#anger', 'negative', '-0.85', 'inventory', 'unused', 'unshared', 'vacant', 'unsold']
senticnet['uncomplicated'] = ['0.894', '0', '0', '0.96', '#joy', '#admiration', 'positive', '0.927', 'straightforward', 'easy', 'simple', 'uninvolved', 'effortless']
senticnet['uncomplimentary'] = ['-0.8', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.86', 'disparaging', 'offhanded', 'off-handed', 'laudatory', 'not-so-nice']
senticnet['uncomprehending'] = ['0', '0', '0.992', '-0.78', '#anger', '#disgust', 'negative', '-0.88', 'disbelieving', 'expressionless', 'incomprehension', 'unseeing', 'incredulous']
senticnet['uncomprehensible'] = ['-0.99', '0', '0.797', '-0.56', '#sadness', '#anger', 'negative', '-0.78', 'dark', 'impenetrable', 'lost', 'missed', 'unfathomable']
senticnet['uncompressed_data'] = ['0', '0', '-0.1', '0', '#fear', '#fear', 'negative', '-0.1', 'grow_yard', 'heat', 'burn', 'corrode', 'stain_clothes']
senticnet['uncompromising'] = ['-0.82', '0', '0.762', '0', '#sadness', '#anger', 'negative', '-0.79', 'principled', 'unwilling', 'truculent', 'concession', 'inflexible']
senticnet['unconcern'] = ['0', '0', '0.898', '-0.90', '#anger', '#disgust', 'negative', '-0.90', 'unconcerned', 'unconcernedly', 'apathy', 'indifference', 'carelessness']
senticnet['unconcerned'] = ['-0.59', '0', '0.79', '-0.70', '#anger', '#disgust', 'negative', '-0.69', 'untroubled', 'undisturbed', 'unmolested', 'unworried', 'dreamless']
senticnet['unconcernedly'] = ['0', '0', '0.898', '-0.90', '#anger', '#disgust', 'negative', '-0.90', 'unconcern', 'unconcerned', 'apathy', 'indifference', 'carelessness']
senticnet['unconditional'] = ['0.873', '0', '0', '0.984', '#joy', '#admiration', 'positive', '0.928', 'unlimited', 'unqualified', 'categoric', 'categorical', 'entire']
senticnet['unconditional_acceptance'] = ['0.731', '0', '0', '0.96', '#joy', '#admiration', 'positive', '0.845', 'ask', 'inquiry', 'plea', 'suggest', 'question_start']
senticnet['unconditional_bail'] = ['-0.08', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.052', 'bequest', 'excise', 'bribe', 'afford', 'owe']
senticnet['unconditional_love'] = ['0.954', '0', '0', '0.964', '#joy', '#admiration', 'positive', '0.959', 'feel_for', 'sympathize_with', 'compassionateness', 'condole_with', 'commiserate']
senticnet['unconditional_loyalty'] = ['0.997', '0.796', '0', '0.89', '#joy', '#admiration', 'positive', '0.894', 'devotion', 'bravery', 'adoration', 'zeal', 'charm']
senticnet['unconditional_positive'] = ['0.632', '-0.80', '0', '0.537', '#joy', '#surprise', 'positive', '0.658', 'incisiveness', 'trenchancy', 'efficacy', 'effectiveness', 'power']
senticnet['unconditional_submission'] = ['-0.91', '0', '0.521', '-0.81', '#sadness', '#disgust', 'negative', '-0.74', 'bullying', 'domineering', 'blustery', 'roughshod', 'cavalier']
senticnet['unconditionally'] = ['-0.91', '0', '0.576', '-0.95', '#sadness', '#disgust', 'negative', '-0.81', 'stop', 'halt', 'lock_in', 'lock', 'block']
senticnet['unconditionally_secure'] = ['-0.83', '0', '0.665', '-0.51', '#sadness', '#anger', 'negative', '-0.67', 'percipient', 'auditor', 'perceiver', 'observer', 'beholder']
senticnet['unconfessed'] = ['-0.76', '0', '0.563', '-0.99', '#sadness', '#disgust', 'negative', '-0.77', 'unacknowledged', 'unrecognized', 'unappreciated', 'unvalued', 'unsung']
senticnet['unconfident'] = ['-0.81', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.89', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecurity']
senticnet['uncongenial'] = ['-0.87', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.93', 'congenial', 'discomfiting', 'unpromising', 'unglamorous', 'unexciting']
senticnet['unconnected'] = ['-0.65', '0', '0.941', '-0.87', '#anger', '#disgust', 'negative', '-0.82', 'asternal', 'isolated', 'separate', 'unattached', 'separated']
senticnet['unconquerable'] = ['-0.73', '0', '0.732', '-0.53', '#sadness', '#anger', 'negative', '-0.66', 'insurmountable', 'insuperable', 'inexpugnable', 'indomitable', 'unsubduable']
senticnet['unconscientiousness'] = ['-0.93', '0', '0.689', '-0.85', '#sadness', '#disgust', 'negative', '-0.82', 'neglectfulness', 'inattentiveness', 'negligence', 'neglect', 'carelessness']
senticnet['unconscionability'] = ['-0.72', '0', '0.742', '-0.80', '#anger', '#disgust', 'negative', '-0.75', 'illicitness', 'illegality', 'illegitimacy', 'shadiness', 'quality']
senticnet['unconscionable'] = ['0', '0', '-0.97', '-0.99', '#fear', '#disgust', 'negative', '-0.98', 'reprehensible', 'unjustifiable', 'disgraceful', 'abhorrent', 'egregious']
senticnet['unconscious'] = ['-0.96', '0', '0.907', '-0.95', '#sadness', '#disgust', 'negative', '-0.94', 'brain_damage', 'loss_ofawareness', 'concussion', 'amnesia', 'death']
senticnet['unconscious_process'] = ['-0.53', '0', '0.762', '-0.66', '#anger', '#disgust', 'negative', '-0.65', 'defense', 'sleep_talking', 'somniloquy', 'somniloquism', 'condensation']
senticnet['unconsciously'] = ['0.052', '0', '0', '0.131', '#joy', '#admiration', 'positive', '0.091', 'run_treadmill', 'dangerous_drive', 'helpful_tool', 'profitable', 'creepy']
senticnet['unconsciousness'] = ['-0.1', '0', '-0.13', '-0.15', '#fear', '#disgust', 'negative', '-0.13', 'senseless', 'unconscious', 'stop_move', 'serious_accident', 'become_unconscious']
senticnet['unconsidered'] = ['0.556', '-0.91', '0', '0.562', '#surprise', '#admiration', 'positive', '0.677', 'unthinking', 'inconsiderate', 'unreflective', 'unthoughtful', 'thoughtless']
senticnet['unconsolidated_material'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'fossil', 'oil', 'dark_liquid', 'smithsonian_institution', 'inert_gas']
senticnet['unconstipated'] = ['-0.99', '0', '0.856', '-0.63', '#sadness', '#anger', 'negative', '-0.82', 'lax', 'loose', 'diarrheal', 'diarrhoeal', 'diarrhetic']
senticnet['unconstitutional'] = ['0', '0', '0.741', '-0.95', '#anger', '#disgust', 'negative', '-0.84', 'institution', 'unconstitutionality', 'constitution', 'government', 'unconstitutionally']
senticnet['unconstitutionality'] = ['0', '0', '0.731', '-0.95', '#anger', '#disgust', 'negative', '-0.84', 'institution', 'unconstitutional', 'constitution', 'government', 'unconstitutionally']
senticnet['unconstitutionally'] = ['0', '0', '0.741', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'institution', 'unconstitutionality', 'constitution', 'government', 'unconstitutional']
senticnet['unconstitutionally_cruel'] = ['-0.19', '0', '-0.17', '-0.14', '#sadness', '#fear', 'negative', '-0.17', 'death_penalty', 'die', 'death_destruction', 'kill', 'murder']
senticnet['uncontrollable'] = ['-0.21', '0.147', '-0.27', '0.131', '#sadness', '#fear', 'negative', '-0.05', 'hazardous_health', 'bad_health', 'dangerous_health', 'unhealthy', 'destructive']
senticnet['uncontrollable_rage'] = ['-0.87', '0', '0.53', '-0.65', '#sadness', '#disgust', 'negative', '-0.68', 'malevolence', 'malevolency', 'malice', 'murderousness', 'cruelness']
senticnet['uncontrolled'] = ['-0.89', '0', '0.669', '-0.62', '#sadness', '#anger', 'negative', '-0.72', 'undisciplined', 'anarchic', 'anarchical', 'irrepressible', 'uncontrollable']
senticnet['uncontrolled_hypertension'] = ['-0.85', '0', '-0.71', '-0.86', '#sadness', '#disgust', 'negative', '-0.81', 'get_exercise', 'train_marathon', 'run_marathon', 'run_long_distance', 'go_gym']
senticnet['uncontrolledal'] = ['-0.9', '0', '0.672', '-0.62', '#sadness', '#anger', 'negative', '-0.73', 'undisciplined', 'uncontrolled', 'anarchic', 'irrepressible', 'uncontrollable']
senticnet['uncontroversial'] = ['-0.81', '0', '0.899', '-0.70', '#sadness', '#anger', 'negative', '-0.80', 'unchallengeable', 'undisputed', 'unchallenged', 'unquestioned', 'agreed_upon']
senticnet['uncontroverted_testimony'] = ['-0.63', '0', '0.967', '-0.67', '#anger', '#disgust', 'negative', '-0.76', 'condemnatory', 'condemning', 'accusatory', 'accusing', 'accusive']
senticnet['unconventional'] = ['0', '-0.77', '0', '0.883', '#surprise', '#admiration', 'positive', '0.827', 'unorthodox', 'untraditional', 'idiosyncratic', 'inventive', 'heretical']
senticnet['unconventional_lifestyle'] = ['0.929', '-0.79', '0', '0.915', '#joy', '#admiration', 'positive', '0.88', 'privacy', 'seclusion', 'privateness', 'solitariness', 'loneliness']
senticnet['unconventionality'] = ['-0.50', '0', '0.733', '-0.55', '#anger', '#disgust', 'negative', '-0.59', 'originality', 'novelty', 'innovativeness', 'freshness', 'ability']
senticnet['unconverted'] = ['-0.68', '0', '0.714', '-0.89', '#anger', '#disgust', 'negative', '-0.76', 'obstinate', 'unregenerated', 'impenitent', 'unrepentant', 'unpersuaded']
senticnet['unconvincing'] = ['-0.72', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.71', 'implausible', 'unsatisfying', 'unimpressive', 'contrived', 'unpersuasive']
senticnet['unconvincingly'] = ['0', '-0.63', '-0.99', '0', '#surprise', '#fear', 'negative', '-0.18', 'disingenuously', 'lamely', 'coyly', 'apologetically', 'cheekily']
senticnet['uncooked'] = ['0.897', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.925', 'form_of_food', 'japanese_food', 'raw_fish', 'healthy', 'food']
senticnet['uncool'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'book_worm', 'nerd', 'loser', 'underdog', 'computer_lover']
senticnet['uncooperative'] = ['-0.69', '0', '0.516', '-0.94', '#sadness', '#disgust', 'negative', '-0.71', 'insubordinate', 'defiant', 'obstreperous', 'resistant', 'resistive']
senticnet['uncorrectable'] = ['-0.83', '0', '0.855', '-0.89', '#anger', '#disgust', 'negative', '-0.86', 'incorrigible', 'unreformable', 'unregenerate', 'uncontrollable', 'unmanageable']
senticnet['uncorroborated_testimony'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'gouger', 'swindler', 'confidence_man', 'conman', 'fraud']
senticnet['uncouth'] = ['0', '0', '0.857', '-0.75', '#anger', '#disgust', 'negative', '-0.80', 'roughneck', 'unfamiliar', 'uncouthness', 'unrefined', 'foreign']
senticnet['uncouthness'] = ['0', '0', '0.856', '-0.76', '#anger', '#disgust', 'negative', '-0.80', 'roughneck', 'unfamiliar', 'uncouth', 'unrefined', 'foreign']
senticnet['uncover'] = ['0.92', '0', '0', '0.902', '#joy', '#admiration', 'positive', '0.911', 'espy', 'notice', 'locate', 'unearth', 'observe']
senticnet['uncover_fraud'] = ['-0.91', '0', '0.85', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'fraud_in_fact', 'positive_fraud', 'mail_fraud', 'swindle', 'rig']
senticnet['uncreated_light'] = ['0', '0', '-0.86', '-0.77', '#fear', '#disgust', 'negative', '-0.81', 'untellable', 'unspeakable', 'indescribable', 'indefinable', 'unutterable']
senticnet['uncreative'] = ['-0.85', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.78', 'unambitious', 'unimaginative', 'unadventurous', 'unexciting', 'uninspired']
senticnet['uncreativeness'] = ['-0.78', '0', '0.707', '-0.86', '#sadness', '#disgust', 'negative', '-0.78', 'mental_block', 'inability', 'stupidity', 'illiteracy', 'analphabetism']
senticnet['uncredited_extra'] = ['0', '0.797', '0', '0.804', '#interest', '#admiration', 'positive', '0.8', 'director', 'shooting_movie', 'director_chair', 'important_person', 'movie']
senticnet['uncritical'] = ['0', '0', '0.928', '-0.70', '#anger', '#disgust', 'negative', '-0.81', 'doubt', 'incertitude', 'dubiety', 'doubtfulness', 'dubiousness']
senticnet['uncritical_acceptance'] = ['-0.88', '0', '0.907', '-0.84', '#sadness', '#anger', 'negative', '-0.88', 'intolerable', 'unacceptable', 'bitter', 'impossible', 'insufferable']
senticnet['uncritically'] = ['-0.72', '0', '0.755', '0', '#sadness', '#anger', 'negative', '-0.73', 'disgrace', 'shameful', 'dishonorably', 'inglorious', 'dishonored']
senticnet['uncrossable'] = ['0', '0', '0.875', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'unbridgeable', 'chasm', 'irreconcilable', 'irresolvable', 'gulf']
senticnet['unction'] = ['-0.64', '0', '0.941', '-0.92', '#anger', '#disgust', 'negative', '-0.83', 'carron_oil', 'salve', 'ointment', 'unguent', 'balm']
senticnet['unctuous'] = ['0', '0', '-0.97', '-0.93', '#fear', '#disgust', 'negative', '-0.95', 'buttery', 'oleaginous', 'fulsome', 'smarmy', 'oily']
senticnet['uncultivated'] = ['0.848', '-0.89', '0', '0.924', '#surprise', '#admiration', 'positive', '0.89', 'nonintellectual', 'lowbrow', 'lowbrowed', 'mindless', 'philistine']
senticnet['uncultivated_land'] = ['0.716', '-0.51', '0', '0.647', '#joy', '#admiration', 'positive', '0.624', 'state', 'nation', 'country', 'ally', 'commonwealth']
senticnet['uncurl'] = ['0.92', '-0.57', '0', '0.758', '#joy', '#admiration', 'positive', '0.751', 'bail_out', 'unspell', 'bail', 'unchain', 'run']
senticnet['uncut'] = ['-0.95', '0', '0.712', '-0.52', '#sadness', '#anger', 'negative', '-0.73', 'uninjured', 'unharmed', 'unscathed', 'unwounded', 'unhurt']
senticnet['uncut_magazine'] = ['0.7', '-0.62', '0', '0.58', '#joy', '#surprise', 'positive', '0.636', 'white_magic', 'thaumaturgy', 'juju', 'conjuration', 'supernaturalism']
senticnet['und_dem'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['und_den'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['und_der'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['und_die'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
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senticnet['under_local_anaesthetic'] = ['-0.65', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.78', 'put_to_sleep', 'operation', 'general_anesthesia', 'surgery', 'anesthesia']
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senticnet['under_umbrella'] = ['0', '0.094', '0', '0', '#interest', '#interest', 'positive', '0.094', 'shade', 'cavern', 'cooler', 'parasol', 'stick_wall']
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senticnet['underachiever'] = ['0.772', '0', '0', '0.966', '#joy', '#admiration', 'positive', '0.869', 'over-achiever', 'overachiever', 'perfectionist', 'procrastinator', 'overachieving']
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senticnet['underexpose'] = ['-0.95', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.93', 'overexpose', 'over-expose', 'overexposing', 'underexposed', 'overexposed']
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senticnet['undergird'] = ['0.66', '-0.81', '0', '0.505', '#joy', '#surprise', 'positive', '0.66', 'strengthen', 'secure', 'reenforce', 'vitalize', 'reinforce']
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senticnet['undergo_fission'] = ['-0.77', '0', '0.764', '-0.54', '#sadness', '#anger', 'negative', '-0.69', 'pullulation', 'apomixis', 'blastogenesis', 'budding', 'asexual_reproduction']
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senticnet['undergoes_surgery'] = ['0.934', '-0.88', '0', '0.797', '#joy', '#surprise', 'positive', '0.872', 'scan', 'checkup', 'scrutiny', 'medical_examination', 'medical_exam']
senticnet['undergoing_treatment'] = ['0.934', '-0.88', '0', '0.797', '#joy', '#surprise', 'positive', '0.872', 'scan', 'checkup', 'scrutiny', 'medical_examination', 'medical_exam']
senticnet['undergone_cholecystectomy'] = ['-0.95', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.89', 'chronic_gastritis', 'rubor', 'redness', 'gastritis', 'inflammation']
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senticnet['undergraduate'] = ['0.963', '0.858', '0', '0', '#joy', '#interest', 'positive', '0.91', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'collegian']
senticnet['undergraduate_advisor'] = ['-0.04', '-0.02', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.02', 'professor', 'lecturer', 'teacher', 'freshman', 'pupil']
senticnet['undergraduate_curriculum'] = ['0.772', '0.943', '0', '0', '#joy', '#interest', 'positive', '0.857', 'lab_improve', 'undergraduate_education', 'student', 'chemistry', 'education']
senticnet['undergraduate_degree'] = ['0.087', '0.248', '-0.19', '0.31', '#interest', '#admiration', 'positive', '0.113', 'enroll', 'enroll_school', 'enroll_class', 'pay_tuition', 'start_college']
senticnet['undergraduate_education'] = ['0.962', '0', '0', '0.954', '#joy', '#admiration', 'positive', '0.958', 'education', 'lab_improve', 'different_application', 'standard_grant', 'student']
senticnet['undergraduate_research'] = ['0.858', '0', '0', '0.925', '#joy', '#admiration', 'positive', '0.891', 'student', 'different_application', 'award', 'project', 'education']
senticnet['undergraduate_site'] = ['0', '0.723', '0', '0.928', '#interest', '#admiration', 'positive', '0.826', 'provide_fund', 'fellowship', 'university', 'agriculture', 'special_project']
senticnet['undergraduate_student'] = ['0.768', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.825', 'graduate', 'student', 'research', 'university', 'project']
senticnet['undergraduate_tuition'] = ['-0.07', '0', '0.073', '-0.26', '#sadness', '#disgust', 'negative', '-0.13', 'tuition', 'private_tuition', 'pay_tuition', 'enroll_class', 'enroll_school']
senticnet['underground'] = ['-0.04', '0', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'golf_course', 'surface_earth', 'ground', 'under_ground', 'tunnel']
senticnet['underground_animal'] = ['-0.13', '0', '0.013', '-0.08', '#sadness', '#disgust', 'negative', '-0.07', 'blind', 'dig_tunnel', 'bad_eyesight', 'big_mouse', 'small_rodent']
senticnet['underground_area'] = ['0', '0', '-0.05', '-0.09', '#fear', '#disgust', 'negative', '-0.07', 'basement', 'master_control', 'hold_point', 'tunnel', 'metal_detector']
senticnet['underground_cave'] = ['-0.02', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'tunnel', 'dark_place', 'ski_resort', 'top_tree', 'compost_pile']
senticnet['underground_cavern'] = ['-0.96', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.96', 'tunnel', 'international_space_station', 'stick_wall', 'car_race', 'auction']
senticnet['underground_chamber'] = ['-0.02', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'tunnel', 'dark_place', 'ski_resort', 'top_tree', 'compost_pile']
senticnet['underground_hip_hop'] = ['0.081', '-0.05', '0.052', '0.072', '#joy', '#admiration', 'positive', '0.039', 'rhythm', 'type_of_music', 'instrumental', 'lyric', 'melody']
senticnet['underground_lair'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'animal_home', 'habitat', 'beehive']
senticnet['underground_map'] = ['0.087', '0.08', '0.013', '0', '#joy', '#interest', 'positive', '0.051', 'advertise_company', 'billboard', 'advertise_panel', 'neighborhood_map', 'restaurant_entrance']
senticnet['underground_neighborhood_map'] = ['0.086', '0.073', '0.012', '0', '#joy', '#interest', 'positive', '0.049', 'neighborhood_map', 'billboard', 'map', 'advertise_panel', 'restaurant_entrance']
senticnet['underground_parking_garage'] = ['0', '0', '0', '-0.09', '#disgust', '#disgust', 'negative', '-0.09', 'subway', 'bus', 'train_station', 'railway_station', 'bus_stop']
senticnet['underground_passage'] = ['-0.04', '0', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'tunnel', 'rut', 'cave', 'golf_course', 'room_temperature']
senticnet['underground_rap'] = ['0.081', '-0.05', '0.052', '0.072', '#joy', '#admiration', 'positive', '0.039', 'rhythm', 'type_of_music', 'instrumental', 'lyric', 'melody']
senticnet['underground_rapper'] = ['0', '0.712', '0', '0.844', '#interest', '#admiration', 'positive', '0.778', 'dollar_change', 'small_currency', 'currency_unit', 'metal_money', 'american_money']
senticnet['underground_resistance'] = ['-0.97', '0', '0.62', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'obstructor', 'obstructer', 'technophobe', 'obstructionist', 'naysayer']
senticnet['underground_station'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'train_station', 'bus', 'subway_station', 'public_place', 'pier']
senticnet['underground_storage'] = ['0.024', '0.17', '0', '0.113', '#interest', '#admiration', 'positive', '0.102', 'load_dock', 'storage_place', 'lock_box', 'government_office', 'treasure_chest']
senticnet['underground_storage_facility'] = ['0.024', '0.17', '0', '0.113', '#interest', '#admiration', 'positive', '0.102', 'load_dock', 'storage_place', 'lock_box', 'government_office', 'treasure_chest']
senticnet['underground_vault'] = ['0.02', '0.205', '0', '0', '#joy', '#interest', 'positive', '0.112', 'safe', 'bank_vault', 'bank', 'piggy_bank', 'bank_account']
senticnet['undergrowth'] = ['-0.89', '0', '-0.73', '0', '#sadness', '#fear', 'negative', '-0.81', 'unkempt', 'shrubbery', 'weedy', 'overgrown', 'weed']
senticnet['underhanded'] = ['0', '-0.75', '0', '0.795', '#surprise', '#admiration', 'positive', '0.772', 'strong-armed', 'thuggish', 'strong-arm', 'thuggery', 'extort']
senticnet['underinclusive'] = ['0', '0', '-0.9', '-0.79', '#fear', '#disgust', 'negative', '-0.84', 'forbid', 'prohibit', 'veto', 'disallow', 'interdict']
senticnet['underlain_by'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'fossil', 'oil', 'dark_liquid', 'smithsonian_institution', 'inert_gas']
senticnet['underlie'] = ['0.548', '-0.86', '0', '0.938', '#surprise', '#admiration', 'positive', '0.784', 'elaborate', 'clear', 'crystallize', 'clear_up', 'make_clear']
senticnet['underlying_asset'] = ['0.868', '-0.84', '0', '0.673', '#joy', '#surprise', 'positive', '0.796', 'instrument', 'option', 'derivative_instrument', 'legal_document', 'legal_instrument']
senticnet['underlying_bedrock'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'fossil', 'oil', 'dark_liquid', 'smithsonian_institution', 'inert_gas']
senticnet['underlying_cause'] = ['0.888', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.843', 'effect', 'consequence', 'impact', 'outcome', 'conclusion']
senticnet['underlying_commodity'] = ['0.868', '-0.84', '0', '0.673', '#joy', '#surprise', 'positive', '0.796', 'instrument', 'option', 'derivative_instrument', 'legal_document', 'legal_instrument']
senticnet['underlying_future_contract'] = ['0.868', '-0.84', '0', '0.673', '#joy', '#surprise', 'positive', '0.796', 'instrument', 'option', 'derivative_instrument', 'legal_document', 'legal_instrument']
senticnet['underlying_geology'] = ['0.614', '-0.95', '0', '0.584', '#joy', '#surprise', 'positive', '0.718', 'mother_lode', 'champion_lode', 'deposit', 'sedimentation', 'lode']
senticnet['underlying_lung_disease'] = ['-0.78', '0', '0.993', '-0.65', '#sadness', '#anger', 'negative', '-0.81', 'bronchitis', 'chronic_bronchitis', 'respiratory_disease', 'respiratory_illness', 'respiratory_disorder']
senticnet['underlying_stratum'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'fossil', 'oil', 'dark_liquid', 'smithsonian_institution', 'inert_gas']
senticnet['undermanned'] = ['-0.58', '0', '0.995', '-0.85', '#anger', '#disgust', 'negative', '-0.81', 'wanting', 'understaffed', 'deficient', 'lacking', 'inadequate']
senticnet['undermentioned'] = ['-0.65', '0', '0.676', '-0.72', '#anger', '#disgust', 'negative', '-0.68', 'consecutive', 'postmortem', 'ensuing', 'succeeding', 'following']
senticnet['undermine'] = ['0', '0', '0.827', '-0.73', '#anger', '#disgust', 'negative', '-0.78', 'counteract', 'cave', 'sabotage', 'subvert', 'weaken']
senticnet['undermine_confidence'] = ['-0.03', '0', '0', '-0.03', '#sadness', '#disgust', 'negative', '-0.03', 'cobalt', 'use_construction', 'many_shades', 'dye', 'gray']
senticnet['undermines_democracy'] = ['-0.55', '0', '0.931', '-0.74', '#anger', '#disgust', 'negative', '-0.74', 'totalitarian', 'authoritarian', 'dictatorial', 'despotic', 'monarchal']
senticnet['undermining'] = ['-0.86', '0', '0.811', '0', '#sadness', '#anger', 'negative', '-0.83', 'destroy', 'substance', 'corrosively', 'fret', 'corrosive']
senticnet['undermining_democracy'] = ['-0.55', '0', '0.931', '-0.74', '#anger', '#disgust', 'negative', '-0.74', 'totalitarian', 'authoritarian', 'dictatorial', 'despotic', 'monarchal']
senticnet['underneath'] = ['-0.74', '0.241', '-0.42', '0', '#sadness', '#fear', 'negative', '-0.31', 'low', 'grim', 'sorry', 'gloominess', 'gloomily']
senticnet['undernourished'] = ['-0.61', '0', '0.763', '-0.75', '#anger', '#disgust', 'negative', '-0.70', 'unfed', 'underfed', 'starving', 'starved', 'malnourished']
senticnet['undernourishment'] = ['-0.93', '0', '0.75', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'invalidism', 'injury', 'affliction', 'biliousness', 'harm']
senticnet['undernutrition'] = ['-0.71', '0', '0.74', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'malnutrition', 'marasmus', 'avitaminosis', 'hypovitaminosis', 'deficiency_disease']
senticnet['underpants'] = ['-0.73', '0', '0.997', '-0.86', '#anger', '#disgust', 'negative', '-0.86', 'scanty', 'unmentionable', 'bikini_pants', 'bloomer', 'pants']
senticnet['underpayment'] = ['0', '0', '0.81', '-0.91', '#anger', '#disgust', 'negative', '-0.86', 'non-payment', 'nonpayment', 'arrears', 'non-performance', 'past-due']
senticnet['underpayment_attributable'] = ['-0.93', '0', '0.624', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'carelessness', 'neglectfulness', 'laxness', 'laxity', 'remissness']
senticnet['underperform'] = ['0', '0.994', '0', '0.88', '#interest', '#admiration', 'positive', '0.937', 'exceed', 'outshout', 'outstrip', 'outshine', 'outsail']
senticnet['underplanted_with'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'livelong', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['underplay'] = ['-0.12', '0.193', '0.597', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'upvalue', 'overvalue', 'overprize', 'overstimate', 'overprice']
senticnet['underprivileged_child'] = ['-0.10', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.08', 'come_house', 'desperate', 'welcome_friend', 'captain_boat', 'receive_email']
senticnet['underproduction'] = ['0', '0', '0.724', '-0.72', '#anger', '#disgust', 'negative', '-0.72', 'overproduction', 'over-production', 'over-saturation', 'under-production', 'stagnancy']
senticnet['underrate'] = ['-0.12', '0.193', '0.597', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'upvalue', 'overvalue', 'overprize', 'overstimate', 'overprice']
senticnet['underrating'] = ['-0.53', '0', '0.543', '-0.99', '#anger', '#disgust', 'negative', '-0.68', 'underestimation', 'approximation', 'underreckoning', 'dead_reckoning', 'underestimate']
senticnet['underreckoning'] = ['-0.53', '0', '0.543', '-0.99', '#anger', '#disgust', 'negative', '-0.68', 'underestimation', 'underrating', 'approximation', 'dead_reckoning', 'underestimate']
senticnet['underreport'] = ['-0.84', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'brag', 'embellish', 'overemphasize', 'overstress', 'vaunt']
senticnet['underrepresented_group'] = ['-0.93', '0', '0.717', '0', '#sadness', '#anger', 'negative', '-0.82', 'minority', 'unfair', 'illegal', 'bad_management', 'discrimination']
senticnet['undersea'] = ['-0.02', '-0.01', '0.03', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'ocean_beach', 'beach', 'water_sea', 'shore', 'ground_ocean']
senticnet['undersea_mountain'] = ['-0.02', '-0.01', '0.03', '-0.04', '#anger', '#disgust', 'negative', '-0.02', 'ocean_beach', 'beach', 'water_sea', 'shore', 'ground_ocean']
senticnet['undersell'] = ['-0.97', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.90', 'out-bid', 'outbid', 'underbidding', 'underbid', 'overbid']
senticnet['underseller'] = ['-0.77', '0', '0.528', '-0.51', '#sadness', '#anger', 'negative', '-0.60', 'seller', 'marketer', 'vender', 'vendor', 'trafficker']
senticnet['underserved_youth'] = ['0.025', '0.073', '-0.05', '0.1', '#interest', '#admiration', 'positive', '0.037', 'young_age', 'energy', 'hope', 'young_life', 'healthy']
senticnet['undershirt'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'duvet_cover', 'pillow', 'comforter', 'pillowcase', 'blanket']
senticnet['undershoot'] = ['0.733', '0.738', '0', '0', '#joy', '#interest', 'positive', '0.736', 'overshoot', 'overshooting', 'overshoot', 'deceleration', 'hysteresis']
senticnet['undershot_wheel'] = ['-0.78', '0', '0.699', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'battery', 'stamp_battery', 'quartz_battery', 'quartz_mill', 'factory']
senticnet['underside'] = ['-0.54', '0', '0.845', '-0.95', '#anger', '#disgust', 'negative', '-0.78', 'face', 'base', 'underbelly', 'sole', 'bilge']
senticnet['undersized'] = ['0', '-0.03', '-0.06', '-0.13', '#fear', '#disgust', 'negative', '-0.05', 'gnome', 'elf', 'goblin', 'dwarf', 'small_person']
senticnet['underskirt'] = ['-0.86', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.89', 'crinoline', 'petticoat', 'tulle', 'camisole', 'corset']
senticnet['underspecification'] = ['0.777', '-0.68', '0', '0.601', '#joy', '#surprise', 'positive', '0.686', 'lucidness', 'clarity', 'clearness', 'lucidity', 'pellucidity']
senticnet['understaffed'] = ['-0.59', '0', '0.995', '-0.84', '#anger', '#disgust', 'negative', '-0.81', 'undermanned', 'wanting', 'deficient', 'lacking', 'inadequate']
senticnet['understand'] = ['0.098', '0.165', '-0.13', '0.284', '#interest', '#admiration', 'positive', '0.103', 'know', 'know_fact', 'great_understand', 'trivia', 'gain_insight']
senticnet['understand_better'] = ['0.875', '0.945', '0', '0', '#joy', '#interest', 'positive', '0.91', 'communicate', 'transfer_knowledge', 'answer_question', 'supply_answer', 'commit_to_memory']
senticnet['understand_culture'] = ['0.97', '0', '0.773', '0.878', '#joy', '#admiration', 'positive', '0.358', 'get_informed', 'get_news', 'stimulate_mind', 'expand_horizon', 'appreciate_art']
senticnet['understand_current_event'] = ['0.97', '0', '0.773', '0.878', '#joy', '#admiration', 'positive', '0.358', 'keep_up_current_event', 'learn_entertainment', 'take_up_time', 'know_happen_world', 'expand_knowledge']
senticnet['understand_each_other'] = ['0.932', '0.787', '0', '0', '#joy', '#interest', 'positive', '0.86', 'learn_patience', 'discuss', 'avoid_conflict', 'conformist_attitude', 'seek_clarification']
senticnet['understand_fact'] = ['-0.16', '-0.01', '-0.17', '0.062', '#sadness', '#fear', 'negative', '-0.06', 'heart_attack', 'sign_paper', 'criminal_activity', 'freedom', 'sign_document']
senticnet['understand_idea'] = ['0', '0', '0', '0.881', '#admiration', '#admiration', 'positive', '0.881', 'educate_others', 'listen_teacher', 'share_opinion', 'discuss', 'seek_understanding']
senticnet['understand_new_culture'] = ['0.97', '0', '0.773', '0.878', '#joy', '#admiration', 'positive', '0.358', 'find_out', 'find_out_world', 'discover', 'explore', 'learn']
senticnet['understand_plot'] = ['0.935', '0.775', '0', '0', '#joy', '#interest', 'positive', '0.855', 'watch_commercial', 'eat_popcorn', 'laugh', 'neglect_chore', 'see_advertisement']
senticnet['understand_problem'] = ['0.839', '0.773', '0', '0', '#joy', '#interest', 'positive', '0.806', 'trivia', 'understand', 'find_answer', 'intelligence', 'gain_insight']
senticnet['understand_rule'] = ['0.928', '0.754', '0', '0', '#joy', '#interest', 'positive', '0.841', 'resolve_conflict', 'look_truth', 'learn_world', 'logic', 'look_evidence']
senticnet['understand_why'] = ['-0.70', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.71', 'devout', 'earnestness', 'earnestly', 'seriousness', 'in_earnest']
senticnet['understand_world'] = ['0.041', '0', '0', '0.109', '#joy', '#admiration', 'positive', '0.075', 'good_idea', 'party', 'use_party', 'hold_beer', 'relax_friend']
senticnet['understandability'] = ['0', '0', '0', '0.76', '#admiration', '#admiration', 'positive', '0.76', 'big_paycheck', 'self_confidence', 'balance_check_account', 'look_beautiful', 'much_money']
senticnet['understandable'] = ['0.515', '-0.80', '0', '0.791', '#surprise', '#admiration', 'positive', '0.703', 'accessible', 'fathomable', 'approachable', 'perceivable', 'intelligible']
senticnet['understandably'] = ['-0.99', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.93', 'complacency', 'self-complacency', 'complacence', 'self_satisfaction', 'self_complacent']
senticnet['understanding'] = ['0.971', '-0.87', '0', '0.704', '#joy', '#surprise', 'positive', '0.85', 'inclination', 'propensity', 'leaning', 'favor', 'proclivity']
senticnet['understate'] = ['-0.70', '0', '0', '-0.95', '#sadness', '#disgust', 'negative', '-0.82', 'too_much', 'wave_arm', 'show_off', 'laugh', 'applause']
senticnet['understated'] = ['0.733', '-0.86', '0', '0.622', '#joy', '#surprise', 'positive', '0.739', 'tasteful', 'esthetic', 'aesthetic', 'artistic', 'unostentatious']
senticnet['understated_elegance'] = ['0.737', '-0.85', '0', '0.623', '#joy', '#surprise', 'positive', '0.738', 'understated', 'esthetic', 'aesthetic', 'artistic', 'unostentatious']
senticnet['understatement'] = ['-0.87', '0', '0.751', '0', '#sadness', '#anger', 'negative', '-0.81', 'nondisjunction', 'miosis', 'cell_division', 'litotes', 'meiosis']
senticnet['understory'] = ['0.045', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.356', 'ponderosa', 'bull_pine', 'western_yellow_pine', 'yellow_pine', 'longleaf_pine']
senticnet['understory_vegetation'] = ['-0.94', '0', '0.608', '-0.95', '#sadness', '#disgust', 'negative', '-0.83', 'wild_licorice', 'wild_liquorice', 'alpine_milk_vetch', 'purple_milk_vetch', 'milk_vetch']
senticnet['undersupply'] = ['-0.83', '0', '-0.88', '-0.78', '#sadness', '#fear', 'negative', '-0.83', 'little', 'sadness', 'small_amount', 'lack', 'absence']
senticnet['undersurface'] = ['-0.66', '0', '0.809', '-0.61', '#sadness', '#anger', 'negative', '-0.69', 'face', 'base', 'underbelly', 'sole', 'bilge']
senticnet['undertail'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'neck', 'body_part', 'henchman', 'shoulder', 'head']
senticnet['undertake'] = ['-0.04', '0.194', '-0.10', '0.124', '#interest', '#admiration', 'positive', '0.042', 'control', 'recruit', 'consideration', 'review', 'business_person']
senticnet['undertaking'] = ['0.744', '-0.68', '0', '0.521', '#joy', '#surprise', 'positive', '0.65', 'labor', 'doddle', 'cinch', 'project', 'task']
senticnet['underthrown'] = ['0.855', '0.741', '0', '0.862', '#joy', '#admiration', 'positive', '0.819', 'further_goal', 'pass_ball', 'pass_glass', 'help', 'pass_exam']
senticnet['undertone'] = ['-0.94', '0', '0.65', '-0.57', '#sadness', '#anger', 'negative', '-0.72', 'manner_of_speaking', 'tone_of_voice', 'note', 'roundness', 'rotundity']
senticnet['undervaluation'] = ['0.797', '-0.52', '0', '0.689', '#joy', '#admiration', 'positive', '0.671', 'overvaluation', 'grade', 'rating', 'pricing', 'reevaluation']
senticnet['undervalue'] = ['-0.12', '0.193', '0.597', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'upvalue', 'overvalue', 'overprize', 'overstimate', 'overprice']
senticnet['undervoltage'] = ['-0.09', '-0.01', '-0.10', '-0.20', '#fear', '#disgust', 'negative', '-0.09', 'violence', 'siege', 'conflict', 'bloodshed', 'fight']
senticnet['underwater'] = ['-0.03', '-0.00', '0.024', '-0.05', '#sadness', '#disgust', 'negative', '-0.02', 'under_water', 'bottom_sea', 'water_sea', 'ocean', 'sea']
senticnet['underwater_adventure'] = ['-0.64', '0', '0.524', '-0.59', '#sadness', '#disgust', 'negative', '-0.58', 'bum', 'dreamer', 'daydreamer', 'escapist', 'wishful_thinker']
senticnet['underwater_archaeologist'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'succourer', 'recoverer', 'salvor', 'mortal']
senticnet['underwater_cave'] = ['-0.02', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'tunnel', 'dark_place', 'ski_resort', 'top_tree', 'compost_pile']
senticnet['underwater_explosion'] = ['-0.84', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.85', 'inferno', 'forest_fire', 'fire_light', 'dismissal', 'wash_mouth_soap']
senticnet['underwater_photographer'] = ['0', '0.07', '0', '0', '#interest', '#interest', 'positive', '0.07', 'sunken_treasure', 'real_duck', 'urchin', 'mussel', 'gold_fish']
senticnet['underwater_photography'] = ['0', '0.07', '0', '0', '#interest', '#interest', 'positive', '0.07', 'sunken_treasure', 'real_duck', 'urchin', 'mussel', 'gold_fish']
senticnet['underwater_topography'] = ['-0.04', '-0.01', '0.103', '0', '#sadness', '#anger', 'negative', '-0.04', 'near_beach', 'along_beach', 'full_water', 'ocean_ground', 'salty']
senticnet['underway'] = ['0', '0.067', '0', '0.062', '#interest', '#admiration', 'positive', '0.065', 'social_skill', 'show_up', 'go_abroad', 'go_new_place', 'visit']
senticnet['underwear'] = ['0.753', '0.897', '0', '0', '#joy', '#interest', 'positive', '0.825', 'robe', 'clothes', 'garment', 'piece_of_clothing', 'slack']
senticnet['underweight'] = ['0', '0', '0.967', '-0.85', '#anger', '#disgust', 'negative', '-0.90', 'scraggy', 'boney', 'scrawny', 'skinny', 'weedy']
senticnet['underweight_baby'] = ['-0.51', '0', '0.747', '-0.94', '#anger', '#disgust', 'negative', '-0.73', 'stillborn_infant', 'neonate', 'preterm_baby', 'premature_infant', 'preterm_infant']
senticnet['underwhelming_performance'] = ['-0.83', '0', '0.912', '-0.67', '#sadness', '#anger', 'negative', '-0.80', 'dissatisfaction', 'displeasure', 'disappointment', 'boredom', 'ennui']
senticnet['underwing'] = ['-0.53', '0', '0.8', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'armyworm', 'noctuid', 'antler_moth', 'heliothis_moth', 'moth']
senticnet['underworld'] = ['0', '0', '-0.97', '-0.86', '#fear', '#disgust', 'negative', '-0.92', 'war', 'arsenal', 'gun_store', 'war_museum', 'war_zone']
senticnet['undescended'] = ['0.793', '0', '0', '0.988', '#joy', '#admiration', 'positive', '0.891', 'retractile', 'rope-like', 'blade-like', 'whip-like', 'dwarfish']
senticnet['undeservedly'] = ['0', '-0.85', '0', '-0.83', '#surprise', '#disgust', 'positive', '0.01', 'deservedly', 'unjustifiably', 'inexcusably', 'unaccountably', 'deservingly']
senticnet['undesirable'] = ['-0.55', '0', '-0.40', '-0.08', '#sadness', '#fear', 'negative', '-0.35', 'death_love', 'negative_feeling', 'bad_feeling', 'lament', 'deep_sadness']
senticnet['undesirable_behavior'] = ['0.583', '-0.82', '0', '0.574', '#joy', '#surprise', 'positive', '0.661', 'stimulant', 'positive_reinforcing_stimulus', 'positive_reinforcer', 'negative_reinforcing_stimulus', 'reinforcer']
senticnet['undesired'] = ['-0.92', '0', '-0.73', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'short_circuit', 'error', 'mistake', 'crosstalk', 'trouble']
senticnet['undesired_behavior'] = ['0.347', '0.348', '-0.13', '0.394', '#interest', '#admiration', 'positive', '0.239', 'good_deed', 'benefit', 'gratification', 'self_respect', 'bonus']
senticnet['undestroyable'] = ['-0.95', '0', '0.799', '-0.67', '#sadness', '#anger', 'negative', '-0.81', 'imputrescible', 'imperishable', 'indestructible', 'durable', 'perdurable']
senticnet['undetermined'] = ['-0.61', '0', '0.721', '-0.84', '#anger', '#disgust', 'negative', '-0.72', 'tentative', 'undecided', 'unsettled', 'unresolved', 'open']
senticnet['undeveloped_land'] = ['0.716', '-0.51', '0', '0.647', '#joy', '#admiration', 'positive', '0.624', 'state', 'nation', 'country', 'ally', 'commonwealth']
senticnet['undeviating'] = ['0.943', '-0.79', '0', '0.694', '#joy', '#surprise', 'positive', '0.811', 'tested', 'tried', 'trust', 'sure', 'dependable']
senticnet['undiagnosable'] = ['-0.82', '0', '0.758', '-0.70', '#sadness', '#anger', 'negative', '-0.76', 'unclassifiable', 'unrecognizable', 'unrecognisable', 'intangible', 'unidentifiable']
senticnet['undies'] = ['-0.88', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.82', 'bikini', 'skin-tight', 'panties', 'skimpy', 'thong']
senticnet['undigested'] = ['-0.78', '0', '0.662', '-0.80', '#sadness', '#disgust', 'negative', '-0.75', 'indigestible', 'nondigestible', 'flatulent', 'stodgy', 'heavy']
senticnet['undigested_food'] = ['-0.99', '0', '0.871', '-0.55', '#sadness', '#anger', 'negative', '-0.80', 'endure', 'brook', 'tolerate', 'bear_up', 'abide']
senticnet['undignified'] = ['-0.9', '0', '0', '-0.80', '#sadness', '#disgust', 'negative', '-0.85', 'degrade', 'demean', 'disgrace', 'dehumanize', 'inglorious']
senticnet['undiplomatic'] = ['-0.95', '0', '0.771', '-0.67', '#sadness', '#anger', 'negative', '-0.8', 'inculpative', 'condemning', 'accusatory', 'accusing', 'accusive']
senticnet['undischarged'] = ['-0.98', '0', '0.528', '-0.67', '#sadness', '#disgust', 'negative', '-0.72', 'uncompensated', 'unsalaried', 'unpaid', 'buckshee', 'complimentary']
senticnet['undiscipline'] = ['-0.96', '0', '0.942', '-0.85', '#sadness', '#anger', 'negative', '-0.92', 'indulgence', 'incontinence', 'trait', 'dissoluteness', 'unrestraint']
senticnet['undisciplined'] = ['-0.9', '0', '0.671', '-0.62', '#sadness', '#anger', 'negative', '-0.73', 'anarchical', 'anarchic', 'uncontrolled', 'irrepressible', 'uncontrollable']
senticnet['undiscovered'] = ['-0.81', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.78', 'lesser-known', 'obscure', 'little-known', 'little-used', 'unheralded']
senticnet['undismayed'] = ['0.516', '-0.87', '0', '0.497', '#joy', '#surprise', 'positive', '0.628', 'resolute', 'unshaken', 'undaunted', 'firm', 'stout']
senticnet['undisputed'] = ['-0.96', '0', '0.783', '-0.66', '#sadness', '#anger', 'negative', '-0.80', 'unchallengeable', 'noncontroversial', 'unchallenged', 'unquestioned', 'agreed_upon']
senticnet['undisputed_fact'] = ['-0.96', '0', '0.885', '-0.75', '#sadness', '#anger', 'negative', '-0.86', 'controvert', 'answer', 'disown', 'renounce', 'repudiate']
senticnet['undistinguishable'] = ['-0.52', '0', '0.902', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'discordant', 'dissonant', 'unresolved', 'false', 'disharmonious']
senticnet['undistinguished'] = ['-0.84', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'unremarkable', 'unexciting', 'unspectacular', 'unexceptional', 'unmemorable']
senticnet['undistributed'] = ['-0.93', '0', '0.853', '-0.64', '#sadness', '#anger', 'negative', '-0.81', 'monolithic', 'undiversified', 'unanimous', 'whole', 'solid']
senticnet['undistributed_middle'] = ['0.633', '-0.67', '0', '0.776', '#surprise', '#admiration', 'positive', '0.693', 'double_entendre', 'loophole', 'ambiguity', 'amphiboly', 'parisology']
senticnet['undisturbed'] = ['-0.59', '0', '0.793', '-0.7', '#anger', '#disgust', 'negative', '-0.69', 'untroubled', 'dreamless', 'unmolested', 'unworried', 'unconcerned']
senticnet['undisturbed_possession'] = ['0.621', '0.648', '-0.25', '0.296', '#joy', '#interest', 'positive', '0.328', 'pacifically', 'repose', 'peace-loving', 'peaceable', 'peace_of_mind']
senticnet['undiversified'] = ['-0.93', '0', '0.851', '-0.64', '#sadness', '#anger', 'negative', '-0.81', 'undistributed', 'monolithic', 'unanimous', 'whole', 'solid']
senticnet['undivided'] = ['-0.77', '0', '0.573', '-0.78', '#sadness', '#disgust', 'negative', '-0.71', 'exclusive', 'unshared', 'sole', 'individual', 'single']
senticnet['undivided_loyalty'] = ['0.997', '0.796', '0', '0.89', '#joy', '#admiration', 'positive', '0.894', 'devotion', 'bravery', 'adoration', 'zeal', 'charm']
senticnet['undo'] = ['0.507', '-0.50', '0', '0.676', '#joy', '#surprise', 'positive', '0.563', 'unlace', 'unfold', 'untie', 'unbrace', 'spread']
senticnet['undoer'] = ['-0.58', '0', '0.714', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'waster', 'sleeper', 'destroyer', 'ruiner', 'diversionist']
senticnet['undogmatic'] = ['0.633', '-0.92', '0', '0.869', '#surprise', '#admiration', 'positive', '0.808', 'advocate', 'bigot', 'dogmatist', 'partisan', 'advocator']
senticnet['undomesticated'] = ['-0.63', '0', '0.991', '-0.74', '#anger', '#disgust', 'negative', '-0.79', 'ferine', 'feral', 'untamed', 'savage', 'unbroken']
senticnet['undone'] = ['-0.98', '0', '0.811', '-0.95', '#sadness', '#disgust', 'negative', '-0.91', 'unmethodical', 'upset', 'confused', 'scrambled', 'disorganized']
senticnet['undoubtedly'] = ['-0.67', '0', '0.97', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'joint', 'node', 'articulation', 'join', 'connexion']
senticnet['undp'] = ['-0.54', '0', '0.863', '-0.76', '#anger', '#disgust', 'negative', '-0.72', 'denomination', 'committee', 'citizen_committee', 'mission', 'missionary_post']
senticnet['undreamed'] = ['-0.55', '0', '0.641', '-0.62', '#anger', '#disgust', 'negative', '-0.60', 'improbable', 'undreamed_of', 'unbelievable', 'undreamt', 'undreamt_of']
senticnet['undreamed_of'] = ['-0.54', '0', '0.646', '-0.62', '#anger', '#disgust', 'negative', '-0.60', 'improbable', 'undreamed', 'unbelievable', 'undreamt', 'undreamt_of']
senticnet['undreamt'] = ['-0.54', '0', '0.646', '-0.62', '#anger', '#disgust', 'negative', '-0.60', 'improbable', 'undreamed', 'undreamed_of', 'unbelievable', 'unbelievable_of']
senticnet['undreamt_of'] = ['-0.54', '0', '0.646', '-0.62', '#anger', '#disgust', 'negative', '-0.60', 'improbable', 'undreamed', 'undreamed_of', 'undreamt', 'unbelievable']
senticnet['undress'] = ['0.899', '0.776', '0', '0', '#joy', '#interest', 'positive', '0.838', 'disorder', 'messy', 'worst', 'mess_up', 'disarray']
senticnet['undressed'] = ['-0.81', '0', '0.673', '-0.81', '#sadness', '#disgust', 'negative', '-0.76', 'unfinished', 'unfattened', 'unhewn', 'roughhewn', 'raw']
senticnet['undue'] = ['-0.80', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.78', 'excessive', 'sufficient', 'emancipate', 'illegal', 'payable']
senticnet['undue_burden_standard'] = ['-0.74', '0', '0.748', '-0.81', '#anger', '#disgust', 'negative', '-0.77', 'coral', 'caviar', 'caviare', 'shad_roe', 'seafood']
senticnet['undue_pressure'] = ['-0.81', '0', '0.979', '-0.88', '#anger', '#disgust', 'negative', '-0.89', 'vexation', 'dead_weight', 'imposition', 'worry', 'concern']
senticnet['undulant_fever'] = ['0.998', '-0.54', '0', '0.957', '#joy', '#admiration', 'positive', '0.834', 'contagious_disease', 'contagion', 'swine_influenza', 'swine_flu', 'respiratory_disease']
senticnet['undulated'] = ['0', '0', '0.947', '-0.94', '#anger', '#disgust', 'negative', '-0.94', 'undulation', 'undulating', 'undulation', 'rippling', 'wavelike']
senticnet['undulating'] = ['-0.97', '0', '-0.70', '0', '#sadness', '#fear', 'negative', '-0.84', 'curly', 'frizzy', 'hair', 'hairstyle', 'wavy']
senticnet['undulation'] = ['0', '0', '0.956', '-0.94', '#anger', '#disgust', 'negative', '-0.94', 'undulation', 'undulating', 'undulated', 'rippling', 'wavelike']
senticnet['unduly'] = ['-0.80', '0', '0.507', '-0.76', '#sadness', '#disgust', 'negative', '-0.69', 'adamant', 'inexorable', 'intransigent', 'ironclad', 'brassbound']
senticnet['unduly_harsh'] = ['-0.75', '0', '0.981', '-0.58', '#sadness', '#anger', 'negative', '-0.77', 'retaliatory', 'penitentiary', 'correctional', 'retributive', 'retributory']
senticnet['unduly_narrow'] = ['-0.50', '0', '0.89', '-0.78', '#anger', '#disgust', 'negative', '-0.72', 'excess', 'degree', 'grade', 'level', 'immoderateness']
senticnet['unduly_optimistic'] = ['-0.70', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.75', 'pessimism', 'pessimistically', 'dispiritedly', 'hopelessness', 'hopelessly']
senticnet['unduly_restrict'] = ['-0.62', '0', '0.756', '-0.62', '#anger', '#disgust', 'negative', '-0.66', 'vitiate', 'ruin', 'maul', 'mangle', 'deface']
senticnet['unduly_restrictive'] = ['-0.97', '0', '0.712', '-0.78', '#sadness', '#disgust', 'negative', '-0.82', 'discrepant', 'unreconciled', 'scratchy', 'uneven', 'contradictory']
senticnet['undutiful'] = ['0', '0', '0.817', '-0.74', '#anger', '#disgust', 'negative', '-0.77', 'undeferential', 'disrespectful', 'insolent', 'bareface', 'brazen']
senticnet['undutifulness'] = ['0.9', '-0.80', '0', '0.582', '#joy', '#surprise', 'positive', '0.763', 'unrighteousness', 'irreligiousness', 'godlessness', 'ungodliness', 'impiety']
senticnet['undying'] = ['0', '0', '-0.87', '-0.73', '#fear', '#disgust', 'negative', '-0.80', 'unwavering', 'devotion', 'admiration', 'unending', 'affection']
senticnet['undying_loyalty'] = ['0.997', '0.796', '0', '0.89', '#joy', '#admiration', 'positive', '0.894', 'devotion', 'bravery', 'adoration', 'zeal', 'charm']
senticnet['une_arme'] = ['-0.58', '0', '0.661', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'explosive', 'cordite', 'dynamite', 'plastic_explosive', 'explosive_compound']
senticnet['une_enqute'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'sexual_relationship', 'liaison', 'affair', 'involvement']
senticnet['une_lettre'] = ['0.667', '-0.98', '0', '0.61', '#joy', '#surprise', 'positive', '0.753', 'friend', 'champion', 'supporter', 'protagonist', 'admirer']
senticnet['une_peine'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'sexual_relationship', 'liaison', 'affair', 'involvement']
senticnet['unearned_increment'] = ['-0.91', '0', '0.598', '-0.52', '#sadness', '#anger', 'negative', '-0.68', 'parsimonious', 'stingy', 'scrimy', 'beggarly', 'chintzy']
senticnet['unearth'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'find_out', 'unmask', 'dig_out', 'become_aware', 'dish_out']
senticnet['unearthed'] = ['-0.86', '0', '-0.76', '0', '#sadness', '#fear', 'negative', '-0.81', 'relic', 'artifact', 'ruin', 'relic', 'treasure']
senticnet['unearthly'] = ['0.449', '0.305', '-0.19', '0.222', '#joy', '#interest', 'positive', '0.196', 'calm', 'peace', 'serenity', 'quiet', 'tranquility']
senticnet['unease'] = ['-0.84', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.82', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecure']
senticnet['uneasily'] = ['-0.82', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.76', 'anxiously', 'apprehensively', 'apprehensive', 'worried', 'solicitousness']
senticnet['uneasiness'] = ['-0.97', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.86', 'hysteria', 'dreadful', 'dread', 'scary', 'anxious']
senticnet['uneasy'] = ['-0.70', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.82', 'unquiet', 'sinking_feeling', 'sinking', 'scruple', 'misgiving']
senticnet['uneasy_suspicion'] = ['-0.90', '0.138', '-0.63', '0.071', '#sadness', '#fear', 'negative', '-0.33', 'woeful', 'execrable', 'deplorable', 'woebegone', 'plaintiveness']
senticnet['uneaten'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'green_bread', 'rotten', 'disgusting', 'taste_awful', 'mouldy']
senticnet['uneconomic'] = ['-0.54', '0', '0.739', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'unremunerative', 'idle', 'lean', 'dead', 'unprofitable']
senticnet['uneconomical'] = ['-0.12', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.09', 'painful', 'uncomfortable', 'violent', 'fun_learn_play', 'expensive']
senticnet['uneducable'] = ['-0.90', '0', '0.76', '-0.87', '#sadness', '#disgust', 'negative', '-0.84', 'analphabet', 'analphabetic', 'functional_illiterate', 'ignoramus', 'know_nothing']
senticnet['uneducated'] = ['-0.95', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.96', 'homophobic', 'guilty_crime', 'precise', 'strange', 'lovely']
senticnet['unelaborated'] = ['0.897', '-0.94', '0', '0.988', '#surprise', '#admiration', 'positive', '0.943', 'sketchy', 'incomplete', 'partial', 'uncompleted', 'broken']
senticnet['unembellished'] = ['-0.76', '0', '0.867', '-0.74', '#sadness', '#anger', 'negative', '-0.79', 'spare', 'bare', 'unornamented', 'unadorned', 'plain']
senticnet['unembodied'] = ['-0.53', '0', '0.628', '-0.93', '#anger', '#disgust', 'negative', '-0.7', 'discorporate', 'bodiless', 'disembodied', 'incorporeal', 'unbodied']
senticnet['unemotional'] = ['0.716', '-0.63', '0', '0.848', '#joy', '#admiration', 'positive', '0.733', 'phlegmatic', 'phlegmatical', 'unblinking', 'stoic', 'chilly']
senticnet['unemotionalal'] = ['-0.89', '0', '0.789', '-0.96', '#sadness', '#disgust', 'negative', '-0.88', 'unemotional', 'phlegmatic', 'unblinking', 'stoic', 'chilly']
senticnet['unemotionality'] = ['0', '0', '0.701', '-0.93', '#anger', '#disgust', 'negative', '-0.81', 'apathetic', 'indifference', 'passionless', 'stolidity', 'phlegm']
senticnet['unemphatic'] = ['-0.9', '0', '0.972', '-0.79', '#sadness', '#anger', 'negative', '-0.88', 'feminine', 'weak', 'light', 'unstressed', 'unaccented']
senticnet['unemployed'] = ['-0.96', '0', '0.155', '-0.98', '#sadness', '#disgust', 'negative', '-0.7', 'fired', 'need_new_job', 'become_poor', 'need_money', 'jobless']
senticnet['unemployment'] = ['0', '0', '0.948', '-0.96', '#anger', '#disgust', 'negative', '-0.95', 'work_much', 'lack_money', 'wage_slavery', 'buy_tool', 'get_paycheck']
senticnet['unemployment_insurance'] = ['0.85', '0', '0', '0', '#joy', '#joy', 'positive', '0.85', 'task', 'labor', 'police_work', 'employ', 'career']
senticnet['unemployment_rate'] = ['0', '0', '0.948', '-0.96', '#anger', '#disgust', 'negative', '-0.95', 'work_much', 'lack_money', 'wage_slavery', 'buy_tool', 'get_paycheck']
senticnet['unemployment_rose'] = ['0', '0', '0.948', '-0.96', '#anger', '#disgust', 'negative', '-0.95', 'work_much', 'lack_money', 'wage_slavery', 'buy_tool', 'get_paycheck']
senticnet['unending'] = ['0', '0.093', '0', '0.146', '#interest', '#admiration', 'positive', '0.119', 'constant', 'past_present', 'eternity', 'irrelevant', 'replicate']
senticnet['unendingly'] = ['0', '0.822', '0', '0.858', '#interest', '#admiration', 'positive', '0.84', 'incessantly', 'ceaselessly', 'unceasingly', 'endlessly', 'continuously']
senticnet['unendurable'] = ['-0.85', '0', '0.898', '-0.92', '#anger', '#disgust', 'negative', '-0.89', 'unsupportable', 'unacceptable', 'bitter', 'impossible', 'insufferable']
senticnet['unenergetic'] = ['-0.74', '0', '0.994', '-0.85', '#anger', '#disgust', 'negative', '-0.86', 'foggy', 'groggy', 'logy', 'stuporous', 'dazed']
senticnet['unenforceability'] = ['-0.55', '0', '0.741', '-0.76', '#anger', '#disgust', 'negative', '-0.68', 'illogicality', 'illogicalness', 'illogic', 'inconsequence', 'fallaciousness']
senticnet['unenforceable'] = ['-0.91', '0', '-0.77', '-0.75', '#sadness', '#fear', 'negative', '-0.81', 'emptiness', 'star_planet', 'nothingness', 'outer', 'around_earth']
senticnet['unenjoyable'] = ['0.036', '0', '0.094', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'facial_hair', 'need_shave', 'need_beauty_center', 'long_beard', 'carelessness']
senticnet['unenthusiastic'] = ['0', '0', '0.711', '-0.92', '#anger', '#disgust', 'negative', '-0.81', 'non-receptive', 'unappreciative', 'unreceptive', 'unwelcoming', 'unsympathetic']
senticnet['unentitled'] = ['-0.61', '0', '0.825', '-0.54', '#sadness', '#anger', 'negative', '-0.66', 'unsuitable', 'undesirable', 'unqualified', 'disqualified', 'ineligible']
senticnet['unenviable'] = ['-0.81', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'awkward', 'abash', 'embarrassingly', 'embarrassing', 'sheepish']
senticnet['unequal'] = ['-0.86', '0', '0.63', '-0.69', '#sadness', '#disgust', 'negative', '-0.73', 'unequalized', 'mismatched', 'nonequivalent', 'uneven', 'unbalanced']
senticnet['unequal_distribution'] = ['0.871', '-0.74', '0', '0.981', '#joy', '#admiration', 'positive', '0.867', 'unrighteousness', 'unjustness', 'unfairness', 'wrong', 'wrongfulness']
senticnet['unequal_treatment'] = ['0.871', '-0.74', '0', '0.981', '#joy', '#admiration', 'positive', '0.867', 'unrighteousness', 'unjustness', 'unfairness', 'wrong', 'wrongfulness']
senticnet['unequalized'] = ['-0.87', '0', '0.627', '-0.70', '#sadness', '#disgust', 'negative', '-0.73', 'unequal', 'mismatched', 'nonequivalent', 'uneven', 'unbalanced']
senticnet['unequally'] = ['0.915', '0.966', '0', '0', '#joy', '#interest', 'positive', '0.941', 'equitably', 'apportioned', 'apportion', 'proportionately', 'unevenly']
senticnet['unequivocal'] = ['0.743', '-0.70', '0', '0', '#joy', '#surprise', 'positive', '0.725', 'clear', 'unmistakable', 'unquestionable', 'equivocation', 'unambiguous']
senticnet['unequivocal_evidence'] = ['-0.64', '0', '0.576', '-0.83', '#sadness', '#disgust', 'negative', '-0.68', 'nisi', 'indeterminate', 'equivocal', 'ambiguous', 'indecisive']
senticnet['unequivocal_statement'] = ['-0.66', '0', '0.721', '-0.53', '#sadness', '#anger', 'negative', '-0.64', 'unlimited', 'outright', 'unconditional', 'unqualified', 'unqualifiedal']
senticnet['unequivocal_support'] = ['-0.66', '0', '0.721', '-0.53', '#sadness', '#anger', 'negative', '-0.64', 'unlimited', 'outright', 'unconditional', 'unqualified', 'unqualifiedal']
senticnet['unequivocally'] = ['-0.66', '0', '0.721', '-0.53', '#sadness', '#anger', 'negative', '-0.64', 'unlimited', 'outright', 'unconditional', 'unqualified', 'unqualifiedal']
senticnet['unequivocalness'] = ['0.823', '-0.53', '0', '0.583', '#joy', '#admiration', 'positive', '0.646', 'lucidness', 'clarity', 'clearness', 'lucidity', 'pellucidity']
senticnet['unerect'] = ['-0.78', '0', '0.95', '-0.73', '#sadness', '#anger', 'negative', '-0.82', 'drooping', 'crooked', 'weeping', 'prone', 'supine']
senticnet['unerring'] = ['-0.69', '0', '0.679', '-0.89', '#sadness', '#disgust', 'negative', '-0.75', 'inerrable', 'inerrant', 'infallible', 'unfailing', 'foolproof']
senticnet['unesco_prize'] = ['0.894', '0.881', '0', '0.803', '#joy', '#interest', 'positive', '0.859', 'remittance', 'afford', 'award', 'money_due', 'amount_owe']
senticnet['unessential'] = ['-0.81', '0', '0.987', '-0.64', '#sadness', '#anger', 'negative', '-0.81', 'nonessential', 'accidental', 'incidental', 'accessorial', 'adscititious']
senticnet['unethical'] = ['0', '0', '0', '-0.95', '#disgust', '#disgust', 'negative', '-0.95', 'immoral', 'bad', 'obscene', 'indecent', 'shameless']
senticnet['unethical_behavior'] = ['-0.98', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.97', 'flunk', 'a_lot_of_stress', 'further_goal', 'failure', 'shuffle_card']
senticnet['uneulogized'] = ['0', '0.943', '0', '0.718', '#interest', '#admiration', 'positive', '0.831', 'celebrate', 'eulogistic', 'formal', 'eloquent', 'eulogize']
senticnet['uneven'] = ['0', '0', '0.726', '-0.94', '#anger', '#disgust', 'negative', '-0.83', 'seldom', 'alpha', 'capoeira', 'convert_energy', 'animal_sound']
senticnet['uneven_bounce'] = ['0.793', '0.915', '0', '0', '#joy', '#interest', 'positive', '0.854', 'ball', 'ball_bounce', 'football', 'soccer', 'basketball']
senticnet['uneven_skin_tone'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'helpless', 'unwanted', 'rabies', 'small_cat', 'big_rat']
senticnet['uneven_terrain'] = ['-0.99', '0', '0.641', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'navigable', 'negotiable', 'surmountable', 'climbable', 'passable']
senticnet['unevenly'] = ['0.92', '0.962', '0', '0', '#joy', '#interest', 'positive', '0.941', 'unequally', 'apportioned', 'apportion', 'proportionately', 'equitably']
senticnet['unevenness'] = ['-0.53', '0', '0.765', '-0.94', '#anger', '#disgust', 'negative', '-0.74', 'jaggedness', 'unregularity', 'ruggedness', 'irregularity', 'patchiness']
senticnet['uneventful'] = ['0', '0', '0', '-0.76', '#disgust', '#disgust', 'negative', '-0.76', 'fevered', 'feverish', 'feverishly', 'buck_fever', 'expectantly']
senticnet['unexceptionable'] = ['-0.91', '0', '0.521', '-0.81', '#sadness', '#disgust', 'negative', '-0.74', 'bullying', 'domineering', 'blustery', 'roughshod', 'cavalier']
senticnet['unexceptional'] = ['-0.84', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'unremarkable', 'unexciting', 'unspectacular', 'undistinguished', 'unmemorable']
senticnet['unexchangeability'] = ['-0.56', '0', '0.574', '-0.69', '#anger', '#disgust', 'negative', '-0.61', 'incommutability', 'irreplaceableness', 'changelessness', 'unchangeability', 'unchangeableness']
senticnet['unexciting'] = ['-0.85', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'unremarkable', 'unexceptional', 'unspectacular', 'undistinguished', 'unmemorable']
senticnet['unexcused_absence'] = ['0.723', '0', '0', '0.762', '#joy', '#admiration', 'positive', '0.742', 'presence', 'feel_important', 'existence', 'feel_satisfied', 'feel_presence']
senticnet['unexpected'] = ['0', '0', '0', '-0.78', '#disgust', '#disgust', 'negative', '-0.78', 'suddenly', 'precipitous', 'immediate', 'sudden', 'sudden_asthma_attack']
senticnet['unexpected_surge'] = ['0.952', '0.918', '0', '0', '#joy', '#interest', 'positive', '0.935', 'tack', 'foot_part', 'casual_shoes', 'gauntlet', 'pin']
senticnet['unexpectedly'] = ['-0.83', '0', '0.913', '0', '#sadness', '#anger', 'negative', '-0.87', 'pop', 'unexpected', 'pica', 'impulsion', 'unawares']
senticnet['unexplainable'] = ['-0.62', '0', '0.825', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'incomprehensible', 'unaccountable', 'unexplained', 'inscrutable', 'mystifying']
senticnet['unexplained'] = ['-0.87', '0', '0.675', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'unexplainable', 'unaccountable', 'inexplicable', 'inscrutable', 'mystifying']
senticnet['unexplained_fever'] = ['-0.53', '0', '0.608', '-0.93', '#anger', '#disgust', 'negative', '-0.69', 'reprieve', 'respite', 'hiatus', 'abatement', 'suspension']
senticnet['unfailing'] = ['-0.69', '0', '0.679', '-0.89', '#sadness', '#disgust', 'negative', '-0.75', 'inerrable', 'inerrant', 'unerring', 'infallible', 'foolproof']
senticnet['unfailing_sense'] = ['0.947', '-0.99', '0', '0.936', '#joy', '#surprise', 'positive', '0.958', 'utterer', 'deipnosophist', 'interlocutor', 'conversational_partner', 'speaker']
senticnet['unfailingly'] = ['0.03', '0.358', '-0.25', '0.299', '#interest', '#admiration', 'positive', '0.109', 'attitude', 'certainty', 'strong_feeling', 'prefer', 'tend']
senticnet['unfair'] = ['-0.97', '0', '0.7', '-0.96', '#sadness', '#disgust', 'negative', '-0.87', 'rude', 'uneducated', 'fragile', 'mean', 'cheat_on']
senticnet['unfair_discrimination'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
senticnet['unfair_treatment'] = ['-0.88', '0', '0.733', '-0.60', '#sadness', '#anger', 'negative', '-0.74', 'vengeance', 'payback', 'retribution', 'retaliation', 'return']
senticnet['unfairly'] = ['-0.96', '0', '-0.84', '0', '#sadness', '#fear', 'negative', '-0.90', 'unequally', 'just', 'unfair', 'stacked', 'poach']
senticnet['unfairly_prejudicial'] = ['-0.70', '0', '0.874', '-0.75', '#anger', '#disgust', 'negative', '-0.77', 'blame', 'accusation', 'inculpation', 'complaint', 'imprecation']
senticnet['unfairness'] = ['0.863', '-0.75', '0', '0.974', '#joy', '#admiration', 'positive', '0.863', 'unrighteousness', 'unjustness', 'inequity', 'wrong', 'wrongfulness']
senticnet['unfaithful'] = ['-0.66', '0', '0.709', '-0.88', '#anger', '#disgust', 'negative', '-0.75', 'untrue', 'apostate', 'treacherous', 'perfidious', 'punic']
senticnet['unfaithful_wife'] = ['-0.94', '0', '0.944', '-0.90', '#sadness', '#anger', 'negative', '-0.93', 'lust', 'pregnant', 'desire', 'care_deeply', 'love']
senticnet['unfaithfulness'] = ['0.667', '-0.71', '0', '0.775', '#surprise', '#admiration', 'positive', '0.72', 'quality', 'disloyalty', 'inconstancy', 'falseness', 'faithlessness']
senticnet['unfaltering'] = ['0', '0.824', '0', '0.711', '#interest', '#admiration', 'positive', '0.767', 'untiring', 'unswerving', 'unwavering', 'unflagging', 'unfailing']
senticnet['unfamiliar'] = ['-0.50', '0', '0.737', '-0.55', '#anger', '#disgust', 'negative', '-0.59', 'unacquainted_with', 'unfamiliar_with', 'unknown', 'unacquainted', 'strange']
senticnet['unfamiliar_territory'] = ['-0.50', '0', '0.737', '-0.55', '#anger', '#disgust', 'negative', '-0.59', 'unacquainted_with', 'unfamiliar_with', 'unknown', 'unacquainted', 'strange']
senticnet['unfamiliar_with'] = ['-0.50', '0', '0.737', '-0.55', '#anger', '#disgust', 'negative', '-0.59', 'unacquainted_with', 'unfamiliar', 'unknown', 'unacquainted', 'strange']
senticnet['unfamiliarity'] = ['-0.65', '0', '0.98', '-0.68', '#anger', '#disgust', 'negative', '-0.77', 'freakishness', 'quaintness', 'abnormality', 'queerness', 'crotchet']
senticnet['unfashionable'] = ['-0.87', '0', '0.83', '-0.61', '#sadness', '#anger', 'negative', '-0.77', 'out', 'dated', 'stodgy', 'prehistoric', 'antique']
senticnet['unfashionably'] = ['-0.87', '0', '0.83', '-0.61', '#sadness', '#anger', 'negative', '-0.77', 'out', 'dated', 'stodgy', 'prehistoric', 'antique']
senticnet['unfasten'] = ['0.829', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.908', 'cut_food', 'drink_beer', 'serve_drink', 'drink_much', 'drink_beverage']
senticnet['unfathomable'] = ['-0.99', '0', '0.797', '-0.56', '#sadness', '#anger', 'negative', '-0.78', 'dark', 'impenetrable', 'lost', 'missed', 'uncomprehensible']
senticnet['unfathomably'] = ['0', '0.795', '0', '0.912', '#interest', '#admiration', 'positive', '0.854', 'unprecedentedly', 'staggeringly', 'unacceptably', 'astoundingly', 'monstrously']
senticnet['unfattened'] = ['-0.81', '0', '0.671', '-0.81', '#sadness', '#disgust', 'negative', '-0.76', 'unfinished', 'unhewn', 'undressed', 'roughhewn', 'raw']
senticnet['unfavorable'] = ['-0.74', '0', '0.982', '-0.81', '#anger', '#disgust', 'negative', '-0.84', 'inauspicious', 'admonishing', 'reproachful', 'reproving', 'untoward']
senticnet['unfavorable_foreign_exchange'] = ['0.702', '-0.54', '0', '0.614', '#joy', '#admiration', 'positive', '0.62', 'point_in_time', 'threshold', 'birth', 'incipiency', 'incipience']
senticnet['unfavorable_judgment'] = ['-0.54', '0', '0.884', '-0.71', '#anger', '#disgust', 'negative', '-0.71', 'reprimand', 'static', 'stricture', 'disapproval', 'faultfinding']
senticnet['unfavorable_position'] = ['-0.62', '0', '0.708', '-0.61', '#sadness', '#anger', 'negative', '-0.64', 'unfavorableness', 'tough_luck', 'disadvantage', 'unprofitableness', 'unprofitability']
senticnet['unfavorableness'] = ['-0.62', '0', '0.708', '-0.61', '#sadness', '#anger', 'negative', '-0.64', 'disadvantage', 'bad_luck', 'unfavorable_position', 'unprofitableness', 'unprofitability']
senticnet['unfearing'] = ['0.476', '0.657', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.28', 'intrepidly', 'hardy', 'fearlessness', 'unafraid', 'fearlessly']
senticnet['unfeasibility'] = ['-0.97', '0', '0.675', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'infeasibility', 'impracticability', 'inutility', 'uselessness', 'unusefulness']
senticnet['unfeasible'] = ['-0.77', '0', '0.644', '-0.68', '#sadness', '#disgust', 'negative', '-0.70', 'unobtainable', 'unprocurable', 'out_of_stock', 'bunk', 'untouchable']
senticnet['unfed'] = ['-0.61', '0', '0.761', '-0.75', '#anger', '#disgust', 'negative', '-0.70', 'starving', 'underfed', 'undernourished', 'starved', 'malnourished']
senticnet['unfeeling'] = ['-0.70', '0', '0.822', '0', '#sadness', '#anger', 'negative', '-0.76', 'thickened', 'tougheneded', 'heartless', 'callous', 'uncaring']
senticnet['unfeelingness'] = ['-0.79', '0', '0.636', '-0.75', '#sadness', '#disgust', 'negative', '-0.72', 'insensibility', 'tin_ear', 'hardness', 'unfitness', 'insensitiveness']
senticnet['unfelt'] = ['-0.69', '0.32', '-0.38', '-0.08', '#sadness', '#fear', 'negative', '-0.21', 'forlornly', 'desolation', 'forlorn', 'weepiness', 'tearful']
senticnet['unfeminine'] = ['0.911', '0', '0', '0.85', '#joy', '#admiration', 'positive', '0.881', 'sophisticated', 'lovely', 'elegant', 'contrary', 'common_man']
senticnet['unfermentable'] = ['-0.08', '0.106', '0', '0.125', '#interest', '#admiration', 'positive', '0.047', 'sugar', 'treacle', 'black_treacle', 'add_sugar', 'sugar_substitute']
senticnet['unfertile'] = ['0.702', '-0.95', '0', '0.997', '#surprise', '#admiration', 'positive', '0.883', 'fertilizable', 'fecund', 'conceptive', 'impregnable', 'rank']
senticnet['unfettered_discretion'] = ['-0.52', '0', '0.561', '-0.90', '#anger', '#disgust', 'negative', '-0.66', 'difference', 'tolerance', 'allowance', 'discrepancy', 'margin']
senticnet['unfiltered_internet'] = ['0.051', '0.165', '-0.06', '0.174', '#interest', '#admiration', 'positive', '0.08', 'control_access', 'prevent', 'latch', 'lock', 'gate']
senticnet['unfinished'] = ['-0.81', '0', '0.677', '-0.81', '#sadness', '#disgust', 'negative', '-0.76', 'unhewn', 'unfattened', 'undressed', 'roughhewn', 'raw']
senticnet['unfinished_basement'] = ['-0.61', '0', '0.732', '-0.59', '#sadness', '#anger', 'negative', '-0.64', 'leaky', 'draughty', 'porous', 'drippy', 'oozing']
senticnet['unfinished_manuscript'] = ['-0.04', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.08', 'data', 'awareness', 'information', 'saga', 'knowledge']
senticnet['unfirm'] = ['-0.69', '0', '0.695', '-0.71', '#sadness', '#disgust', 'negative', '-0.70', 'unfixed', 'unsteady', 'detached', 'free', 'floating']
senticnet['unfit'] = ['-0.95', '0', '0', '-0.81', '#sadness', '#disgust', 'negative', '-0.88', 'blister_foot', 'trip_fall', 'breafast', 'run_out_breath', 'breahard']
senticnet['unfitly'] = ['0', '0', '0.964', '-0.82', '#anger', '#disgust', 'negative', '-0.89', 'illicitly', 'jackass', 'political', 'inappropriately', 'inappropriate']
senticnet['unfitness'] = ['-0.82', '0', '0.698', '-0.94', '#sadness', '#disgust', 'negative', '-0.82', 'disability', 'disablement', 'ill_health', 'unhealthiness', 'health_problem']
senticnet['unfixable'] = ['-0.95', '0', '0.721', '-0.75', '#sadness', '#disgust', 'negative', '-0.81', 'cook_up', 'ready', 'make', 'cook', 'prepare']
senticnet['unfixed'] = ['-0.61', '0', '0.855', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'one', 'indefinite', 'nebulous', 'indecisive', 'coy']
senticnet['unflagging'] = ['0', '0.825', '0', '0.715', '#interest', '#admiration', 'positive', '0.77', 'untiring', 'unswerving', 'unwavering', 'unfaltering', 'unfailing']
senticnet['unflappability'] = ['0.724', '-0.92', '0', '0.854', '#surprise', '#admiration', 'positive', '0.833', 'tranquility', 'serenity', 'disposition', 'repose', 'placidity']
senticnet['unflappable'] = ['-0.73', '0', '0', '-0.87', '#sadness', '#disgust', 'negative', '-0.80', 'open-hearted', 'long-suffering', 'self-sacrificing', 'stolid', 'uncomplaining']
senticnet['unflattering'] = ['-0.82', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.90', 'flattering', 'embarrassing', 'disparaging', 'unattractive', 'demeaning']
senticnet['unflattering_nickname'] = ['0.072', '0.244', '-0.20', '0.252', '#interest', '#admiration', 'positive', '0.091', 'doe', 'sweet', 'live_organism', 'love', 'alive']
senticnet['unflinching_courage'] = ['0', '0', '0.764', '-0.77', '#anger', '#disgust', 'negative', '-0.77', 'restriction', 'tragedy', 'perpetrator', 'dominate', 'control']
senticnet['unflinchingly'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'intrepidly', 'hardy', 'fearlessness', 'unafraid', 'unfearing']
senticnet['unfold'] = ['0.574', '-0.82', '0', '0.761', '#surprise', '#admiration', 'positive', '0.719', 'show', 'expose', 'trot_out', 'disclose', 'bring_out']
senticnet['unforced'] = ['0.835', '0.756', '0', '0', '#joy', '#interest', 'positive', '0.795', 'willing', 'leave', 'bequeath', 'volition', 'uncoerced']
senticnet['unforeseeable'] = ['-0.80', '0', '0.945', '-0.63', '#sadness', '#anger', 'negative', '-0.79', 'fancy', 'conceive_of', 'fantasy', 'daydream', 'woolgather']
senticnet['unforeseen'] = ['-0.80', '0', '0.945', '-0.63', '#sadness', '#anger', 'negative', '-0.79', 'fancy', 'conceive_of', 'fantasy', 'daydream', 'woolgather']
senticnet['unforesightful'] = ['-0.63', '0', '0.867', '-0.57', '#sadness', '#anger', 'negative', '-0.69', 'myopic', 'short', 'shortsighted', 'improvident', 'thriftless']
senticnet['unforgettable'] = ['0.709', '-0.61', '0', '0.762', '#joy', '#admiration', 'positive', '0.695', 'careful', 'heedful', 'resonant', 'mindful', 'redolent']
senticnet['unforgettably'] = ['0.88', '0', '0', '0.748', '#joy', '#admiration', 'positive', '0.814', 'memorably', 'enchantingly', 'terrifically', 'indescribably', 'hauntingly']
senticnet['unforgivable'] = ['-0.53', '0', '0.901', '-0.51', '#sadness', '#anger', 'negative', '-0.64', 'inexpiable', 'inexcusable', 'deadly', 'unpardonable', 'mortal']
senticnet['unforgivably'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'lamentably', 'deplorably', 'bereaved', 'bereft', 'grief-stricken']
senticnet['unforgiveable'] = ['-0.84', '0', '0.828', '-0.57', '#sadness', '#anger', 'negative', '-0.75', 'bobble', 'error', 'misstep', 'fumble', 'stumble']
senticnet['unforgiving_environment'] = ['-0.56', '0', '0.814', '-0.88', '#anger', '#disgust', 'negative', '-0.75', 'unfriendly', 'barren', 'bleak', 'desolate', 'wild']
senticnet['unformed'] = ['-0.65', '0', '0.556', '-0.74', '#sadness', '#disgust', 'negative', '-0.64', 'formless', 'amorphous', 'shapeless', 'unshaped', 'unshapen']
senticnet['unfortified_wine'] = ['0', '0', '0', '-0.91', '#disgust', '#disgust', 'negative', '-0.91', 'alcoholic_drink', 'get_drunk', 'ferment_beverage', 'adult_drink', 'booze']
senticnet['unfortunate'] = ['-0.67', '0', '0.838', '-0.86', '#anger', '#disgust', 'negative', '-0.79', 'unsuccessful_person', 'loser', 'abandoned_person', 'desperate', 'jonah']
senticnet['unfortunate_accident'] = ['-0.91', '0', '0.905', '-0.78', '#sadness', '#anger', 'negative', '-0.86', 'pity', 'shame', 'misfortune', 'mischance', 'misadventure']
senticnet['unfortunate_person'] = ['-0.80', '0', '0.603', '-0.71', '#sadness', '#disgust', 'negative', '-0.70', 'unsuccessful_person', 'loser', 'abandoned_person', 'desperate', 'jonah']
senticnet['unfortunately'] = ['0', '0', '0.751', '-0.75', '#anger', '#disgust', 'negative', '-0.75', 'ala', 'unluckily', 'regrettably', 'number', 'problem']
senticnet['unfounded'] = ['-0.98', '0', '0.584', '-0.93', '#sadness', '#disgust', 'negative', '-0.83', 'idle', 'unsupported', 'groundless', 'baseless', 'unwarranted']
senticnet['unfreeze'] = ['-0.64', '0', '0.612', '-0.84', '#sadness', '#disgust', 'negative', '-0.7', 'chilling', 'frost', 'lyophilization', 'icing', 'cooling']
senticnet['unfriendliness'] = ['-0.12', '0.193', '0.597', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'unfriendly', 'inimical', 'disapproval', 'disapprove', 'disapproving']
senticnet['unfriendly'] = ['-0.12', '0.193', '0.597', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'inimical', 'unfriendliness', 'disapproval', 'disapprove', 'disapproving']
senticnet['unfriendly_staff'] = ['-0.98', '0', '0.058', '-0.92', '#sadness', '#disgust', 'negative', '-0.65', 'bad_staff', 'cheap_hotel', 'cheap_restaurant', 'bad_service', 'long_waiting_time']
senticnet['unfrock'] = ['-0.95', '0', '0.673', '-0.85', '#sadness', '#disgust', 'negative', '-0.82', 'discharge', 'free', 'dethrone', 'defrock', 'disinvest']
senticnet['unfrozen'] = ['-0.64', '0', '0.612', '-0.84', '#sadness', '#disgust', 'negative', '-0.7', 'chilling', 'frost', 'lyophilization', 'icing', 'cooling']
senticnet['unfruitful'] = ['-0.70', '0', '0.919', '-0.73', '#anger', '#disgust', 'negative', '-0.78', 'childless', 'unsuccessful', 'acarpous', 'abortive', 'stillborn']
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senticnet['unfunctional'] = ['-0.75', '0', '0.832', '-0.69', '#sadness', '#anger', 'negative', '-0.76', 'impractical', 'useless', 'stupid', 'difficult', 'hard']
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senticnet['unhook'] = ['0.988', '0.787', '0', '0', '#joy', '#interest', 'positive', '0.887', 'tug', 'carry', 'tow', 'extend', 'drag']
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senticnet['uniformness'] = ['-0.63', '0', '0.822', '-0.95', '#anger', '#disgust', 'negative', '-0.80', 'homogeneity', 'homogeneousness', 'consistency', 'consistence', 'similarity']
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senticnet['unilateral_imposition'] = ['-0.70', '0', '0.552', '-0.68', '#sadness', '#disgust', 'negative', '-0.64', 'protection', 'regimentation', 'taxation', 'trade_protection', 'reimposition']
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senticnet['unilateral_neglect'] = ['-0.73', '0', '0.955', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'visual_agnosia', 'auditory_agnosia', 'brain_disorder', 'encephalopathy', 'brain_disease']
senticnet['unilateralism'] = ['-0.50', '0', '0.734', '-0.99', '#anger', '#disgust', 'negative', '-0.74', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
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senticnet['unimodal'] = ['-0.59', '0', '0.672', '-0.83', '#anger', '#disgust', 'negative', '-0.7', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'asymmetry', 'irregularity']
senticnet['unimodal_distribution'] = ['-0.59', '0', '0.672', '-0.83', '#anger', '#disgust', 'negative', '-0.7', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'asymmetry', 'irregularity']
senticnet['unimpaired_capital'] = ['-0.50', '0', '0.89', '-0.78', '#anger', '#disgust', 'negative', '-0.72', 'immoderateness', 'degree', 'grade', 'level', 'immoderatenessiveness']
senticnet['unimpeachable'] = ['-0.71', '0', '0.785', '-0.55', '#sadness', '#anger', 'negative', '-0.68', 'unquestionable', 'acknowledged', 'for_sure', 'mathematical', 'beyond_doubt']
senticnet['unimpeachable_integrity'] = ['-0.71', '0', '0.785', '-0.55', '#sadness', '#anger', 'negative', '-0.68', 'unquestionable', 'acknowledged', 'for_sure', 'mathematical', 'beyond_doubt']
senticnet['unimportant'] = ['-0.91', '0', '0.829', '0', '#sadness', '#anger', 'negative', '-0.87', 'inconsequent', 'inconsequential', 'trivial', 'lightweight', 'insignificant']
senticnet['unimportantial'] = ['0.975', '-0.90', '0', '0.955', '#joy', '#admiration', 'positive', '0.946', 'potty', 'unimportant', 'inconsequent', 'trivial', 'lightweight']
senticnet['unimpressed'] = ['-0.92', '0', '0.721', '-0.85', '#sadness', '#disgust', 'negative', '-0.83', 'immune', 'untouched', 'uninfluenced', 'unswayed', 'unaffected']
senticnet['unimpressive'] = ['-0.72', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.72', 'implausible', 'unsatisfying', 'unconvincing', 'contrived', 'unpersuasive']
senticnet['unimproved_grassland'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'biome', 'timberland']
senticnet['unimproved_land'] = ['0.716', '-0.51', '0', '0.647', '#joy', '#admiration', 'positive', '0.624', 'state', 'nation', 'country', 'ally', 'commonwealth']
senticnet['unincorporated_community'] = ['-0.91', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.85', 'play', 'perpetrator', 'role_play', 'act_part', 'terrible_event']
senticnet['uninfluenced'] = ['-0.92', '0', '0.722', '-0.85', '#sadness', '#disgust', 'negative', '-0.83', 'unimpressed', 'untouched', 'immune', 'unswayed', 'unaffected']
senticnet['uninfluenced_by'] = ['-0.96', '0', '0.91', '-0.61', '#sadness', '#anger', 'negative', '-0.82', 'unpersuadable', 'unsuasible', 'unresponsive', 'resistant', 'incapable']
senticnet['uninformative'] = ['-0.91', '0', '0.829', '0', '#sadness', '#anger', 'negative', '-0.87', 'inconsequent', 'inconsequential', 'trivial', 'lightweight', 'insignificant']
senticnet['uninformed'] = ['-0.85', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.82', 'unskilled', 'amateur', 'uninitiated', 'inexperienced', 'inexpert']
senticnet['uninhabitable'] = ['-0.56', '0', '0.814', '-0.88', '#anger', '#disgust', 'negative', '-0.75', 'unfriendly', 'barren', 'bleak', 'desolate', 'wild']
senticnet['uninitiated'] = ['-0.85', '0', '0', '-0.80', '#sadness', '#disgust', 'negative', '-0.82', 'unskilled', 'amateur', 'inexperienced', 'uninformed', 'inexpert']
senticnet['uninjured'] = ['-0.96', '0', '0.706', '-0.53', '#sadness', '#anger', 'negative', '-0.73', 'uncut', 'unharmed', 'unscathed', 'unwounded', 'unhurt']
senticnet['uninspired'] = ['0', '-0.91', '0', '-0.89', '#surprise', '#disgust', 'positive', '0.007', 'uninspiring', 'unimpressive', 'unexciting', 'uninteresting', 'unimaginative']
senticnet['uninspiring'] = ['0', '-0.90', '0', '-0.89', '#surprise', '#disgust', 'positive', '0.006', 'uninspired', 'unimpressive', 'unexciting', 'uninteresting', 'unimaginative']
senticnet['uninstaller'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'car_trunk', 'walmart', 'warehouse', 'store', 'storage_place']
senticnet['uninsured_motor_vehicle'] = ['0', '-0.01', '0', '0.064', '#surprise', '#admiration', 'positive', '0.042', 'car_gas', 'car_power', 'fossil_fuel', 'run_car', 'car_engine']
senticnet['unintelligent'] = ['-0.98', '0', '0.674', '-0.93', '#sadness', '#disgust', 'negative', '-0.86', 'slow', 'slow_pace', 'stupid', 'slow_car', 'slow_vehicle']
senticnet['unintelligibility'] = ['0.813', '-0.66', '0', '0.65', '#joy', '#surprise', 'positive', '0.708', 'nonsense', 'meaninglessness', 'rubbish', 'bunk', 'nonsensicality']
senticnet['unintelligible'] = ['0', '0', '-0.72', '-0.79', '#fear', '#disgust', 'negative', '-0.76', 'deep', 'fathomless', 'abysmal', 'unfathomable', 'incomprehensible']
senticnet['unintended'] = ['-0.84', '0', '0.5', '-0.80', '#sadness', '#disgust', 'negative', '-0.71', 'unwitting', 'unplanned', 'unintentional', 'accidental', 'inadvertent']
senticnet['unintended_pregnancy'] = ['0.97', '0.729', '0', '0.946', '#joy', '#admiration', 'positive', '0.882', 'make_baby', 'give_birth_child', 'sexual_intercourse', 'find_partner', 'make_love']
senticnet['unintentional'] = ['-0.83', '0', '0.508', '-0.79', '#sadness', '#disgust', 'negative', '-0.71', 'unwitting', 'unplanned', 'unintended', 'accidental', 'inadvertent']
senticnet['unintentional_injury'] = ['-0.71', '0', '0.748', '-0.55', '#sadness', '#anger', 'negative', '-0.67', 'casualty', 'fatal_accident', 'killing', 'violent_death', 'death']
senticnet['unintentionally'] = ['-0.04', '0.164', '0.685', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maliciousness', 'spitefulness', 'maleficence', 'malefic', 'malign']
senticnet['unintentionally_funny'] = ['-0.78', '0', '0.651', '-0.56', '#sadness', '#anger', 'negative', '-0.66', 'rubbishy', 'sorry', 'worthless', 'otiose', 'superfluous']
senticnet['uninterested'] = ['-0.62', '0', '0.761', '-0.97', '#anger', '#disgust', 'negative', '-0.78', 'apathetic', 'benumbed', 'indifferent', 'dismissive', 'dulled']
senticnet['uninteresting'] = ['-0.66', '0', '0.594', '-0.67', '#sadness', '#disgust', 'negative', '-0.64', 'boring', 'deadening', 'irksome', 'tiresome', 'wearisome']
senticnet['uninterestingness'] = ['-0.68', '0', '0.93', '-0.85', '#anger', '#disgust', 'negative', '-0.82', 'powerlessness', 'ponderousness', 'impotence', 'impotency', 'dullness']
senticnet['uninterrupted_sleep'] = ['-0.07', '0', '-0.17', '-0.05', '#sadness', '#fear', 'negative', '-0.10', 'night_shift', 'drowsiness', 'drink_coffee', 'need_rest', 'feel_little_tired']
senticnet['uninterruptible_power'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'make_musical_sound', 'play_orchestra', 'make_noise', 'play_music', 'make_drawing']
senticnet['uninterruptible_power_supply'] = ['0', '-0.03', '0', '0.093', '#surprise', '#admiration', 'positive', '0.063', 'work_well', 'internal_combustion_engine', 'broken_car', 'mode_transportation', 'machine']
senticnet['unintuitive'] = ['0.036', '0', '0.094', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'facial_hair', 'need_shave', 'need_beauty_center', 'long_beard', 'carelessness']
senticnet['uninucleate'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'unmovable', 'nonmotile', 'immobile']
senticnet['uninviting'] = ['0', '-0.82', '0', '-0.94', '#surprise', '#disgust', 'negative', '-0.05', 'unwelcoming', 'unappealing', 'inhospitable', 'unappetizing', 'cheerless']
senticnet['uninvolved'] = ['0.895', '0', '0', '0.967', '#joy', '#admiration', 'positive', '0.931', 'straightforward', 'easy', 'uncomplicated', 'simple', 'effortless']
senticnet['uninvolved_editor'] = ['0.533', '0.818', '0', '0.683', '#interest', '#admiration', 'positive', '0.678', 'gain_information', 'report_news', 'solve', 'seek_answer', 'gather_information']
senticnet['uninvolved_user'] = ['0.533', '0.818', '0', '0.683', '#interest', '#admiration', 'positive', '0.678', 'gain_information', 'report_news', 'solve', 'seek_answer', 'gather_information']
senticnet['union'] = ['0.075', '0.216', '-0.16', '0.211', '#interest', '#admiration', 'positive', '0.084', 'association', 'agency', 'team_up', 'join', 'egalitarian_society']
senticnet['union_flag'] = ['0.053', '0.068', '-0.05', '0.06', '#interest', '#admiration', 'positive', '0.032', 'linen', 'star_stripe', 'velvet', 'nation', 'burlap']
senticnet['union_internationale_pour'] = ['0', '0', '0', '0.857', '#admiration', '#admiration', 'positive', '0.857', 'say', 'beckon', 'call', 'conserve', 'retort']
senticnet['union_square_greenmarket'] = ['0', '0', '0.044', '-0.03', '#anger', '#disgust', 'negative', '-0.03', 'large_supermarket', 'food_store', 'large_food_store', 'corner_shop', 'convenience_food_store']
senticnet['unionidae'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'seal', 'walrus', 'otter', 'alligator', 'whale']
senticnet['unipolar_depression'] = ['-0.78', '0', '0.918', '-0.96', '#anger', '#disgust', 'negative', '-0.88', 'major_depressive_episode', 'depression', 'depressive_disorder', 'clinical_depression', 'involutional_depression']
senticnet['unique'] = ['0.957', '0', '0', '0.759', '#joy', '#admiration', 'positive', '0.858', 'hard_find', 'nice', 'compliment', 'lovely', 'fair']
senticnet['unique_animal'] = ['0.816', '0.756', '0', '0.747', '#joy', '#interest', 'positive', '0.773', 'attractive', 'become_extinct', 'exotic', 'nocturnal', 'beautiful_creature']
senticnet['unique_blend'] = ['-0.05', '0.096', '0', '0.066', '#interest', '#admiration', 'positive', '0.037', 'hue_tint', 'spectrum', 'red_blue', 'green_yellow', 'tint']
senticnet['unique_combination'] = ['0.03', '0.215', '-0.06', '0.18', '#interest', '#admiration', 'positive', '0.09', 'combo', 'hold_coin', 'money_storage', 'money_place', 'money_holder']
senticnet['unique_day'] = ['0', '0', '-0.71', '-0.9', '#fear', '#disgust', 'negative', '-0.80', 'get_out_of_bed', 'find_job', 'hard_work', 'different_day', 'start_new_day']
senticnet['unique_feature'] = ['0.957', '0', '0', '0.759', '#joy', '#admiration', 'positive', '0.858', 'hard_find', 'nice', 'compliment', 'lovely', 'fair']
senticnet['unique_flavor'] = ['0.054', '0.068', '0', '0.074', '#interest', '#admiration', 'positive', '0.065', 'eat_food', 'sweetness', 'food_flavor', 'eatable', 'tasty']
senticnet['unique_insight_into'] = ['-0.06', '-0.02', '0.02', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'eye_strain', 'new_perspective', 'move_eye', 'gain_insight', 'great_understand']
senticnet['unique_opportunity'] = ['0', '0.976', '0', '0.781', '#interest', '#admiration', 'positive', '0.879', 'explore', 'school', 'student', 'benefit', 'link']
senticnet['unique_perspective'] = ['-0.06', '-0.02', '0.02', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'eye_strain', 'new_perspective', 'move_eye', 'gain_insight', 'great_understand']
senticnet['unique_treble'] = ['0.749', '0', '0', '0.952', '#joy', '#admiration', 'positive', '0.851', 'high_class', 'citation', 'suspicion', 'goodness', 'prosperity']
senticnet['uniquely'] = ['0', '0.751', '0', '0.944', '#interest', '#admiration', 'positive', '0.847', 'unambiguously', 'study', 'result', 'student', 'tool']
senticnet['uniquely_capable'] = ['0.028', '0.138', '-0.04', '0.215', '#interest', '#admiration', 'positive', '0.083', 'skilled', 'expert', 'tin', 'dense_water', 'metal_container']
senticnet['uniqueness'] = ['0.611', '-0.75', '0', '0.621', '#surprise', '#admiration', 'positive', '0.662', 'individualism', 'singularity', 'specialty', 'special', 'distinctiveness']
senticnet['uniqueness_theorem'] = ['0.875', '-0.65', '0', '0.861', '#joy', '#admiration', 'positive', '0.797', 'completeness', 'work', 'whole_shebang', 'essential', 'whole']
senticnet['unisex_fragrance'] = ['-0.73', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'unwed', 'unwedded', 'divorced', 'unmarried', 'widowed']
senticnet['unit'] = ['0.052', '0.109', '0.086', '0.116', '#interest', '#admiration', 'positive', '0.048', 'size', 'magnitude', 'ounce', 'measure', 'measurement']
senticnet['unit_citation'] = ['0.827', '0.85', '0', '0.743', '#joy', '#interest', 'positive', '0.807', 'chivalry', 'honor', 'almighty', 'admire', 'respect']
senticnet['unit_language'] = ['-0.03', '0.119', '-0.11', '0.216', '#interest', '#admiration', 'positive', '0.049', 'snail_mail', 'message', 'grammar_part', 'speech_part', 'dialect']
senticnet['unit_of_measure'] = ['0', '0.126', '0.104', '0.172', '#interest', '#admiration', 'positive', '0.065', 'mass_measure', 'measurement_unit', 'imperial_measurement', 'liquid_measurement', 'measure_unit']
senticnet['unit_of_measurement'] = ['0.86', '0.859', '0', '0', '#joy', '#interest', 'positive', '0.859', 'measurement_unit', 'length_measurement', 'imperial_measurement', 'length', 'single']
senticnet['unit_si'] = ['0', '0.203', '0.036', '0.204', '#interest', '#admiration', 'positive', '0.124', 'amount', 'count', 'total', 'quantity', 'total_amount']
senticnet['unitard'] = ['-0.76', '0', '0.672', '-0.88', '#sadness', '#disgust', 'negative', '-0.77', 'attire', 'garb', 'dress', 'fancy_dress', 'masquerade']
senticnet['unitarian_church'] = ['0.081', '0.146', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'cult', 'sect', 'conservative', 'worshiper', 'pastor']
senticnet['unitarian_congregation'] = ['0.081', '0.146', '0', '0.167', '#interest', '#admiration', 'positive', '0.131', 'cult', 'sect', 'conservative', 'worshiper', 'pastor']
senticnet['unitarian_minister'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['unitarianism'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'nonconformity', 'iconoclasm', 'heterodoxy', 'nonconformance', 'orientation']
senticnet['unitary'] = ['0', '0.157', '0', '0.147', '#interest', '#admiration', 'positive', '0.152', 'wall_hang', 'depiction', 'illustration', 'artwork', 'wall_decoration']
senticnet['unitary_representation'] = ['0.774', '-0.91', '0', '0.95', '#surprise', '#admiration', 'positive', '0.88', 'big_bang', 'fragmentation', 'discharge', 'backfire', 'blast']
senticnet['unitary_state'] = ['0.887', '0.748', '0', '0.954', '#joy', '#admiration', 'positive', '0.863', 'political_action', 'political', 'mediocrity', 'discrimination', 'independent']
senticnet['unitary_transformation'] = ['0.807', '-0.88', '0', '0.718', '#joy', '#surprise', 'positive', '0.802', 'sex', 'gayness', 'sexual_gayness', 'lesbianism', 'sapphism']
senticnet['unite'] = ['0.084', '0.122', '-0.08', '0.137', '#interest', '#admiration', 'positive', '0.064', 'democratic_country', 'pledge_allegiance', 'political_unit', 'state', 'free_country']
senticnet['unite_country'] = ['0.041', '0.104', '0', '0.072', '#interest', '#admiration', 'positive', '0.072', 'political_entity', 'political_unit', 'political_state', 'pledge_allegiance', 'democratic_country']
senticnet['uniting'] = ['0', '0.984', '0', '0.852', '#interest', '#admiration', 'positive', '0.918', 'merger', 'fusion', 'unification', 'amalgamation', 'deal']
senticnet['unitive'] = ['0.531', '0.673', '-0.27', '0.275', '#joy', '#interest', 'positive', '0.301', 'amorous', 'amorousness', 'amatory', 'enamoredness', 'puppy_love']
senticnet['unity'] = ['0.099', '0.073', '0', '0.19', '#joy', '#admiration', 'positive', '0.121', 'country_person', 'political_state', 'large_state', 'political_division', 'political_unit']
senticnet['unity_module'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'chick', 'wild_bird', 'baby_bird', 'lay_egg', 'bird_home']
senticnet['univac'] = ['0', '-0.99', '0', '0.862', '#surprise', '#admiration', 'positive', '0.928', 'helpful_tool', 'woodworking_tool', 'helpful', 'type_of_fruit', 'hand_tool']
senticnet['univalent'] = ['0', '0', '0.937', '-0.75', '#anger', '#disgust', 'negative', '-0.84', 'bivalent', 'multivalent', 'divalent', 'meromorphic', 'monovalent']
senticnet['univariate'] = ['-0.59', '0', '0.672', '-0.83', '#anger', '#disgust', 'negative', '-0.7', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'asymmetry', 'irregularity']
senticnet['universal'] = ['0.196', '0.129', '-0.06', '0.184', '#joy', '#admiration', 'positive', '0.111', 'pleasant', 'pleasurable', 'bourgeois', 'important_human', 'good']
senticnet['universal_acceptance'] = ['0.196', '0.129', '-0.06', '0.184', '#joy', '#admiration', 'positive', '0.111', 'pleasant', 'pleasurable', 'bourgeois', 'important_human', 'good']
senticnet['universal_acclaim'] = ['0.853', '0.828', '0', '0.98', '#joy', '#admiration', 'positive', '0.887', 'endorse', 'recommend', 'stimulate_mind', 'acculturation', 'support']
senticnet['universal_affirmative'] = ['-0.92', '0', '0.547', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'assumption', 'boundary_condition', 'provision', 'proviso', 'precondition']
senticnet['universal_benevolence'] = ['0.657', '-0.56', '0', '0.749', '#joy', '#admiration', 'positive', '0.655', 'naturalness', 'condescension', 'condescendingness', 'mellowness', 'friendliness']
senticnet['universal_conscription'] = ['0.786', '0.702', '0', '0', '#joy', '#interest', 'positive', '0.744', 'attacker', 'army', 'samurai', 'infantry', 'military_force']
senticnet['universal_critical_acclaim'] = ['0.853', '0.828', '0', '0.98', '#joy', '#admiration', 'positive', '0.887', 'endorse', 'recommend', 'stimulate_mind', 'acculturation', 'support']
senticnet['universal_death'] = ['0', '0', '-0.80', '-0.88', '#fear', '#disgust', 'negative', '-0.84', 'doom', 'destruction', 'doomsday', 'widespread_disease', 'death']
senticnet['universal_garage_door'] = ['0.034', '-0.02', '0.054', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'compass', 'swiss_army_knife', 'bake_pan', 'coffee_mug', 'wine_opener']
senticnet['universal_gravitation'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'force_unit', 'sthene', 'poundal']
senticnet['universal_immunization'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'medical_therapy', 'vaccine', 'doctor', 'medicine', 'hospital']
senticnet['universal_language'] = ['0.08', '0', '0.048', '0.108', '#joy', '#admiration', 'positive', '0.047', 'instrumental', 'melodic_noise', 'common_language', 'vocal_music', 'form_expression']
senticnet['universal_praise'] = ['0.853', '0.828', '0', '0.98', '#joy', '#admiration', 'positive', '0.887', 'endorse', 'recommend', 'stimulate_mind', 'acculturation', 'support']
senticnet['universal_preschool'] = ['0.063', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.065', 'look_catalog', 'make_selection', 'good_credit', 'menu', 'save_money']
senticnet['universal_quantification'] = ['0.633', '-0.67', '0', '0.776', '#surprise', '#admiration', 'positive', '0.693', 'double_entendre', 'loophole', 'ambiguity', 'amphiboly', 'parisology']
senticnet['universal_vaccination'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'medical_therapy', 'vaccine', 'doctor', 'medicine', 'hospital']
senticnet['universalism'] = ['-0.94', '0', '0.687', '-0.75', '#sadness', '#disgust', 'negative', '-0.79', 'clannishness', 'cliquishness', 'snobbery', 'arrogance', 'haughtiness']
senticnet['universalistic'] = ['0.995', '-0.69', '0', '0.528', '#joy', '#surprise', 'positive', '0.74', 'grasp', 'hug', 'nestle', 'snuggle', 'grip']
senticnet['universality'] = ['-0.63', '0', '0.822', '-0.95', '#anger', '#disgust', 'negative', '-0.80', 'uniformness', 'homogeneousness', 'consistency', 'consistence', 'similarity']
senticnet['universally'] = ['0.196', '0.129', '-0.06', '0.184', '#joy', '#admiration', 'positive', '0.111', 'pleasant', 'pleasurable', 'bourgeois', 'important_human', 'good']
senticnet['universally_applicable'] = ['0.196', '0.129', '-0.06', '0.184', '#joy', '#admiration', 'positive', '0.111', 'pleasant', 'pleasurable', 'bourgeois', 'important_human', 'good']
senticnet['universally_valid'] = ['0.863', '-0.94', '0', '0.986', '#surprise', '#admiration', 'positive', '0.932', 'postulate', 'posit', 'assume', 'presume', 'take_for_granted']
senticnet['universe'] = ['0.837', '0.704', '0', '0.894', '#joy', '#admiration', 'positive', '0.812', 'natural_order', 'world', 'existence', 'creation', 'cosmos']
senticnet['universe_classic_toyline'] = ['0.036', '0.111', '-0.05', '0.054', '#interest', '#admiration', 'positive', '0.038', 'despot', 'supervisor', 'chief', 'commander', 'earl']
senticnet['university'] = ['0', '0.711', '0', '0.979', '#interest', '#admiration', 'positive', '0.845', 'chemistry', 'sponsorship', 'fellowship', 'continue_research', 'award']
senticnet['university_endowment'] = ['0.608', '-0.55', '0', '0.726', '#joy', '#admiration', 'positive', '0.628', 'genius', 'raw_talent', 'flair', 'gift', 'knack']
senticnet['university_park'] = ['0', '0.937', '0', '0.747', '#interest', '#admiration', 'positive', '0.842', 'old_main_university_park', 'sponsor', 'award', 'different_application', 'standard_grant']
senticnet['univocal'] = ['0.863', '-0.94', '0', '0.986', '#surprise', '#admiration', 'positive', '0.932', 'postulate', 'posit', 'assume', 'presume', 'take_for_granted']
senticnet['univocally'] = ['0.633', '-0.67', '0', '0.776', '#surprise', '#admiration', 'positive', '0.693', 'double_entendre', 'loophole', 'ambiguity', 'amphiboly', 'parisology']
senticnet['univox'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'lever']
senticnet['unix'] = ['0', '0', '0', '0.09', '#admiration', '#admiration', 'positive', '0.09', 'solaris', 'window', 'many_windows', 'train_window', 'doorway']
senticnet['unix_operating_system'] = ['0', '-0.99', '0', '0.862', '#surprise', '#admiration', 'positive', '0.928', 'helpful_tool', 'woodworking_tool', 'helpful', 'type_of_fruit', 'hand_tool']
senticnet['unix_pine'] = ['0.031', '0', '0', '0.093', '#joy', '#admiration', 'positive', '0.062', 'letter_slot', 'postal_counter', 'mailbox', 'letter_scale', 'envelope']
senticnet['unjust'] = ['-0.74', '0', '0.934', '-0.85', '#anger', '#disgust', 'negative', '-0.84', 'dirty', 'below_belt', 'foul', 'cheating', 'raw']
senticnet['unjustifiable'] = ['-0.73', '0', '0.846', '-0.68', '#sadness', '#anger', 'negative', '-0.75', 'indefensible', 'unwarranted', 'insupportable', 'inexcusable', 'unwarrantable']
senticnet['unjustifiable_hardship'] = ['-0.81', '0', '0.979', '-0.88', '#anger', '#disgust', 'negative', '-0.89', 'vexation', 'dead_weight', 'imposition', 'worry', 'concern']
senticnet['unjustifiable_risk'] = ['-0.61', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.69', 'irresponsibility', 'untrustiness', 'undependability', 'undependableness', 'unreliability']
senticnet['unjustifiably'] = ['0', '-0.85', '0', '-0.84', '#surprise', '#disgust', 'positive', '0.008', 'deservedly', 'undeservedly', 'inexcusably', 'unaccountably', 'deservingly']
senticnet['unjustly'] = ['0', '0', '0.772', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'unjust', 'tyrant', 'libel', 'victimize', 'wrongly']
senticnet['unjustness'] = ['0.871', '-0.74', '0', '0.981', '#joy', '#admiration', 'positive', '0.867', 'unrighteousness', 'inequity', 'unfairness', 'wrong', 'wrongfulness']
senticnet['unkempt'] = ['0', '0', '0.74', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'seediness', 'french', 'seedy', 'run-down', 'brandy']
senticnet['unkempt_beard'] = ['-0.64', '0', '0.632', '-0.61', '#sadness', '#anger', 'negative', '-0.62', 'bearded', 'barbate', 'bewhiskered', 'whiskered', 'whiskery']
senticnet['unkempt_hair'] = ['-0.64', '0', '0.632', '-0.61', '#sadness', '#anger', 'negative', '-0.62', 'bearded', 'barbate', 'bewhiskered', 'whiskered', 'whiskery']
senticnet['unkemptness'] = ['-0.56', '0', '0.909', '-0.62', '#anger', '#disgust', 'negative', '-0.69', 'untidiness', 'disorder', 'slovenliness', 'sloppiness', 'disorderliness']
senticnet['unkind'] = ['-0.72', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.80', 'mercilessness', 'ruthlessness', 'cruelly', 'pitilessness', 'unassertively']
senticnet['unkindly'] = ['-0.03', '0.157', '0.698', '-0.66', '#anger', '#disgust', 'negative', '-0.30', 'vengefulness', 'vengefully', 'revengefully', 'vindictive', 'spiteful']
senticnet['unkindness'] = ['0.814', '-0.95', '0', '0.918', '#surprise', '#admiration', 'positive', '0.894', 'inconsideration', 'inconsiderateness', 'thoughtlessness', 'unhelpfulness', 'insensitivity']
senticnet['unknowability'] = ['0.772', '-0.73', '0', '0.958', '#joy', '#admiration', 'positive', '0.82', 'quality', 'mutability', 'mutableness', 'changefulness', 'exchangeability']
senticnet['unknowable'] = ['0', '0', '-0.86', '-0.77', '#fear', '#disgust', 'negative', '-0.81', 'untellable', 'unspeakable', 'indescribable', 'indefinable', 'unutterable']
senticnet['unknowing'] = ['0', '0', '-0.90', '-0.82', '#fear', '#disgust', 'negative', '-0.86', 'unsuspecting', 'ensnare', 'entrap', 'unwary', 'gullible']
senticnet['unknown'] = ['-0.09', '0', '0', '-0.19', '#sadness', '#disgust', 'negative', '-0.14', 'add_knowledge', 'learn', 'become_smart', 'discover', 'make_out_atom']
senticnet['unknown_etiology'] = ['-0.85', '0', '0.657', '-0.86', '#sadness', '#disgust', 'negative', '-0.79', 'pulseless_disease', 'temporal_arteritis', 'rubor', 'redness', 'arteritis']
senticnet['unknown_origin'] = ['-0.09', '0', '0', '-0.19', '#sadness', '#disgust', 'negative', '-0.14', 'add_knowledge', 'learn', 'become_smart', 'discover', 'make_out_atom']
senticnet['unkown'] = ['-0.09', '0', '0', '-0.19', '#sadness', '#disgust', 'negative', '-0.14', 'add_knowledge', 'learn', 'become_smart', 'discover', 'make_out_atom']
senticnet['unlace'] = ['0.506', '-0.50', '0', '0.666', '#joy', '#surprise', 'positive', '0.56', 'undo', 'unfold', 'untie', 'unbrace', 'spread']
senticnet['unladylike'] = ['0.585', '-0.88', '0', '0.615', '#surprise', '#admiration', 'positive', '0.695', 'parental_quality', 'maternal_quality', 'quality', 'maternalism', 'fatherliness']
senticnet['unlatch'] = ['0', '0.069', '0', '0.083', '#interest', '#admiration', 'positive', '0.076', 'latch', 'restrict_access', 'knob', 'door_knob', 'gate']
senticnet['unlawful'] = ['0', '0', '-0.83', '-0.97', '#fear', '#disgust', 'negative', '-0.90', 'illegitimate', 'unconventional', 'illicit', 'wrongful', 'improper']
senticnet['unlawful_arrest'] = ['-0.70', '0', '0.874', '-0.75', '#anger', '#disgust', 'negative', '-0.77', 'blame', 'accusation', 'inculpation', 'complaint', 'imprecation']
senticnet['unlawful_assault'] = ['0.542', '-0.83', '0', '0.927', '#surprise', '#admiration', 'positive', '0.769', 'incest', 'extramarital_sex', 'free_love', 'sexual_intercourse', 'intercourse']
senticnet['unlawful_carnal_knowledge'] = ['0.542', '-0.83', '0', '0.927', '#surprise', '#admiration', 'positive', '0.769', 'incest', 'extramarital_sex', 'free_love', 'sexual_intercourse', 'intercourse']
senticnet['unlawful_cohabitation'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'spousal_relationship', 'wedlock']
senticnet['unlawful_conduct'] = ['0.658', '-0.52', '0', '0.626', '#joy', '#admiration', 'positive', '0.602', 'point_in_time', 'threshold', 'birth', 'commencement', 'incipience']
senticnet['unlawful_discrimination'] = ['-0.86', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.90', 'homophobic', 'racist', 'jaundiced', 'antiblack', 'invidious']
senticnet['unlawfully'] = ['-0.04', '0.164', '0.685', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maliciousness', 'spitefulness', 'maleficence', 'malefic', 'malign']
senticnet['unlawfulness'] = ['-0.72', '0', '0.742', '-0.80', '#anger', '#disgust', 'negative', '-0.75', 'illicitness', 'illegality', 'illegitimacy', 'shadiness', 'quality']
senticnet['unleaded_petrol'] = ['0.068', '0', '0.034', '0.076', '#joy', '#admiration', 'positive', '0.037', 'gallon', 'liquid_measurement', 'ounce', 'measurement', 'quart']
senticnet['unlearn'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'vapor', 'gaseous', 'haze', 'smoke', 'smog']
senticnet['unleash'] = ['-0.90', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.85', 'unleashed', 'kick', 'release', 'precipitate', 'leash']
senticnet['unleashed'] = ['-0.90', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.85', 'unleash', 'kick', 'release', 'precipitate', 'leash']
senticnet['unleashing'] = ['-0.91', '0', '-0.89', '0', '#sadness', '#fear', 'negative', '-0.90', 'juggernaut', 'relentless', 'invincible', 'unstoppable', 'unrelenting']
senticnet['unlesse'] = ['-0.79', '0', '0.607', '-0.92', '#sadness', '#disgust', 'negative', '-0.77', 'stubbornness', 'obstinacy', 'obstinance', 'bullheadedness', 'pigheadedness']
senticnet['unlicensed_driver'] = ['0', '-0.01', '0', '0.064', '#surprise', '#admiration', 'positive', '0.042', 'car_gas', 'car_power', 'fossil_fuel', 'run_car', 'car_engine']
senticnet['unlicensed_spectrum'] = ['-0.07', '0.078', '0', '0.078', '#interest', '#admiration', 'positive', '0.028', 'red_blue', 'red_orange', 'green_yellow', 'hue_tint', 'yellow_green']
senticnet['unlikable'] = ['0', '-0.73', '0', '0.873', '#surprise', '#admiration', 'positive', '0.806', 'liked', 'friendly', 'personable', 'amiable', 'likable']
senticnet['unlike_conventional'] = ['0.84', '-0.64', '0', '0.912', '#joy', '#admiration', 'positive', '0.799', 'received', 'customary', 'formulaic', 'stuffy', 'stodgy']
senticnet['unlikeable'] = ['-0.61', '0', '0.572', '-0.83', '#sadness', '#disgust', 'negative', '-0.67', 'numb', 'benumbed', 'anesthetic', 'insensible', 'asleep']
senticnet['unlikelihood'] = ['0.758', '-0.87', '0', '0.815', '#surprise', '#admiration', 'positive', '0.816', 'improbableness', 'unlikeliness', 'uncertainty', 'precariousness', 'uncertainness']
senticnet['unlikeliness'] = ['0.758', '-0.86', '0', '0.817', '#surprise', '#admiration', 'positive', '0.813', 'unlikelihood', 'improbableness', 'uncertainty', 'precariousness', 'uncertainness']
senticnet['unlikely'] = ['0', '0', '-0.93', '-0.91', '#fear', '#disgust', 'negative', '-0.92', 'farfetched', 'unbelievable', 'cockamamie', 'cockamamy', 'implausible']
senticnet['unlikely_hero'] = ['0.833', '-0.97', '0', '0.91', '#surprise', '#admiration', 'positive', '0.906', 'combatant', 'fencer', 'belligerent', 'fighter', 'scrapper']
senticnet['unlikeness'] = ['-0.54', '0', '0.979', '-0.85', '#anger', '#disgust', 'negative', '-0.79', 'dissimilarity', 'dissimilitude', 'difference', 'heterology', 'disparateness']
senticnet['unlimited'] = ['0', '0.857', '0', '0.786', '#interest', '#admiration', 'positive', '0.822', 'heavenly_body', 'star_planet', 'celestial_body', 'around_earth', 'limitless']
senticnet['unlit'] = ['0.056', '0.063', '0', '0', '#joy', '#interest', 'positive', '0.059', 'wood_duck', 'dim', 'darkness', 'dusky', 'sunset']
senticnet['unload'] = ['0.832', '0', '0', '0', '#joy', '#joy', 'positive', '0.832', 'murder', 'death_destruction', 'kill', 'hurt_others', 'get_rid_of_enemy']
senticnet['unloading'] = ['0.883', '0.849', '0', '0', '#joy', '#interest', 'positive', '0.866', 'exhaust', 'discharge', 'drainage', 'waste', 'refuse']
senticnet['unlock'] = ['-0.02', '0.122', '0', '0.137', '#interest', '#admiration', 'positive', '0.078', 'open_lock_door', 'hang_door', 'enter_room', 'control_access', 'separate_room']
senticnet['unlock_door'] = ['-0.10', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.10', 'sell_car', 'door_open', 'own_car', 'shine_car', 'shoulder_burden']
senticnet['unlogical'] = ['0.792', '-0.86', '0', '0.888', '#surprise', '#admiration', 'positive', '0.849', 'absurd', 'inconsequential', 'intuitive', 'visceral', 'nonrational']
senticnet['unloose'] = ['0.714', '-0.57', '0', '0.873', '#joy', '#admiration', 'positive', '0.719', 'bail_out', 'unspell', 'bail', 'unchain', 'run']
senticnet['unloosen'] = ['0.92', '-0.57', '0', '0.758', '#joy', '#admiration', 'positive', '0.751', 'bail_out', 'unspell', 'bail', 'unchain', 'run']
senticnet['unlovable'] = ['-0.56', '0', '0.743', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'negligent', 'derelict', 'inattentive', 'delinquent', 'lax']
senticnet['unlove'] = ['-0.73', '0', '0', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'betray', 'hear_bad_news', 'cause_suffer', 'lose_team', 'hate']
senticnet['unloved'] = ['-0.86', '0', '0.651', '-0.68', '#sadness', '#disgust', 'negative', '-0.73', 'lovelorn', 'unbeloved', 'bereft', 'estranged', 'disinherited']
senticnet['unlovely'] = ['-0.04', '0.153', '0.69', '-0.66', '#anger', '#disgust', 'negative', '-0.31', 'fat', 'overweight', 'fatty', 'unhealthy', 'look_unattractive']
senticnet['unloving'] = ['-0.56', '0', '0.578', '-0.74', '#anger', '#disgust', 'negative', '-0.62', 'cold', 'frigid', 'loveless', 'detached', 'unaffectionate']
senticnet['unluckiest'] = ['0', '0', '-0.90', '-0.93', '#fear', '#disgust', 'negative', '-0.92', 'luckless', 'ill-omened', 'ill-starred', 'doomed', 'ill-fated']
senticnet['unluckily'] = ['0', '0', '0.749', '-0.76', '#anger', '#disgust', 'negative', '-0.75', 'ala', 'unfortunately', 'regrettably', 'number', 'problem']
senticnet['unlucky'] = ['0', '0', '-0.90', '-0.93', '#fear', '#disgust', 'negative', '-0.92', 'luckless', 'ill-omened', 'ill-starred', 'doomed', 'ill-fated']
senticnet['unlucky_enough'] = ['0', '0', '-0.90', '-0.93', '#fear', '#disgust', 'negative', '-0.92', 'luckless', 'ill-omened', 'ill-starred', 'doomed', 'ill-fated']
senticnet['unmaintainable'] = ['0.036', '0', '0.094', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'facial_hair', 'need_shave', 'need_beauty_center', 'long_beard', 'carelessness']
senticnet['unmalleable'] = ['-0.99', '0', '0.754', '-0.50', '#sadness', '#anger', 'negative', '-0.75', 'intractable', 'stubborn', 'refractory', 'uncontrollable', 'balking']
senticnet['unmanageable'] = ['-0.96', '0', '0.636', '-0.85', '#sadness', '#disgust', 'negative', '-0.81', 'awkward', 'bunglesome', 'clumsy', 'cumbersome', 'ungainly']
senticnet['unmanaged_code'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'carbon_fiber']
senticnet['unmanful'] = ['-0.63', '0', '0.986', '-0.94', '#anger', '#disgust', 'negative', '-0.85', 'womanish', 'effeminate', 'emasculate', 'cissy', 'sissified']
senticnet['unmanifest'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'metastability', 'monotony', 'invariance', 'constancy', 'unchangeability']
senticnet['unmanlike'] = ['-0.77', '0', '0.566', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'womanish', 'effeminate', 'emasculate', 'cissy', 'sissified']
senticnet['unmanly'] = ['-0.95', '0', '0.682', '-0.89', '#sadness', '#disgust', 'negative', '-0.84', 'womanish', 'effeminate', 'emasculate', 'cissy', 'sissified']
senticnet['unmannerly'] = ['0', '0', '0.874', '-0.77', '#anger', '#disgust', 'negative', '-0.82', 'ungracious', 'ungentlemanly', 'ill-mannered', 'loutish', 'discourteous']
senticnet['unmarked_car'] = ['0', '0', '0', '-0.12', '#disgust', '#disgust', 'negative', '-0.12', 'corner_two_streets', 'street_corner', 'roadside', 'city_street', 'schoolyard']
senticnet['unmarked_grave'] = ['-0.88', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.90', 'coffin', 'casket', 'morgue', 'headstone', 'burial']
senticnet['unmarketable'] = ['-0.94', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.83', 'unsellable', 'unsaleable', 'marketable', 'salable', 'unsalable']
senticnet['unmarried'] = ['-0.73', '0', '0.726', '-0.99', '#sadness', '#disgust', 'negative', '-0.81', 'unwed', 'unwedded', 'divorced', 'mateless', 'widowed']
senticnet['unmarried_couple'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'bigamy', 'wedlock']
senticnet['unmarried_mother'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'spousal_relationship', 'bigamy']
senticnet['unmarried_woman'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'spousal_relationship', 'bigamy']
senticnet['unmask'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'find_out', 'dig_out', 'unearth', 'become_aware', 'dish_out']
senticnet['unmatchable'] = ['0.807', '-0.60', '0', '0.937', '#joy', '#admiration', 'positive', '0.784', 'matchless', 'nonpareil', 'unique', 'incomparable', 'peerless']
senticnet['unmatched'] = ['-0.85', '0', '0.722', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'mismated', 'unsuited', 'incompatible', 'odd', 'mismatched']
senticnet['unmatched_reliability'] = ['0.814', '-0.87', '0', '0.918', '#surprise', '#admiration', 'positive', '0.87', 'ship_company', 'company', 'work_force', 'workforce', 'manpower']
senticnet['unmeasurable'] = ['-0.97', '0', '0.66', '-0.88', '#sadness', '#disgust', 'negative', '-0.84', 'illimitable', 'limitless', 'measureless', 'unmeasured', 'abysmal']
senticnet['unmeasured'] = ['-0.63', '0', '0.86', '-0.65', '#anger', '#disgust', 'negative', '-0.71', 'illimitable', 'limitless', 'measureless', 'immensurable', 'abysmal']
senticnet['unmelodious'] = ['-0.88', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.93', 'non-musical', 'mellifluous', 'uncouth', 'unmusical', 'improvident']
senticnet['unmemorable'] = ['-0.85', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.81', 'unremarkable', 'unexciting', 'unspectacular', 'undistinguished', 'unexceptional']
senticnet['unmentionable'] = ['-0.75', '0', '0.614', '-0.74', '#sadness', '#disgust', 'negative', '-0.70', 'out', 'impermissible', 'untouchable', 'taboo', 'tabu']
senticnet['unmerciful'] = ['0.563', '-0.56', '0', '0.722', '#joy', '#admiration', 'positive', '0.615', 'mortal', 'cutthroat', 'bowelless', 'tigerish', 'pitiless']
senticnet['unmercifully'] = ['-0.76', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'disgraceful', 'shameful', 'dishonorably', 'inglorious', 'dishonored']
senticnet['unmet_demand'] = ['0.749', '0.764', '0', '0', '#joy', '#interest', 'positive', '0.756', 'obedience', 'order', 'requirement', 'permission', 'necessary']
senticnet['unmethodical'] = ['-0.98', '0', '0.811', '-0.95', '#sadness', '#disgust', 'negative', '-0.91', 'disorganized', 'upset', 'confused', 'scrambled', 'undone']
senticnet['unmindful'] = ['-0.02', '0.125', '0.672', '-0.66', '#anger', '#disgust', 'negative', '-0.31', 'venom', 'malice', 'offensive', 'animosity', 'despise']
senticnet['unmistakable'] = ['0.739', '-0.71', '0', '0', '#joy', '#surprise', 'positive', '0.726', 'clear', 'unequivocal', 'unquestionable', 'equivocation', 'unambiguous']
senticnet['unmistakable_stamp'] = ['-0.92', '0', '0.946', '-0.56', '#sadness', '#anger', 'negative', '-0.81', 'forcefulness', 'intensiveness', 'impulse', 'vigor', 'energy']
senticnet['unmistakably'] = ['-0.9', '0', '0.841', '-0.73', '#sadness', '#anger', 'negative', '-0.82', 'nubbiness', 'tweediness', 'harshness', 'graininess', 'granularity']
senticnet['unmodern'] = ['-0.87', '0', '0.83', '-0.61', '#sadness', '#anger', 'negative', '-0.77', 'out', 'dated', 'stodgy', 'prehistoric', 'antique']
senticnet['unmolested'] = ['-0.59', '0', '0.796', '-0.70', '#anger', '#disgust', 'negative', '-0.69', 'untroubled', 'undisturbed', 'dreamless', 'unworried', 'unconcerned']
senticnet['unmolested_enjoyment'] = ['0.774', '-0.82', '0', '0.63', '#joy', '#surprise', 'positive', '0.744', 'quality', 'perishability', 'perishableness', 'unsatisfactoriness', 'inadequacy']
senticnet['unmovable'] = ['-0.57', '0', '0.97', '-0.76', '#anger', '#disgust', 'negative', '-0.76', 'immovable', 'immoveable', 'immobile', 'nonmotile', 'immotile']
senticnet['unmoved_mover'] = ['0.949', '-0.55', '0', '0.595', '#joy', '#admiration', 'positive', '0.699', 'metastability', 'monotony', 'invariance', 'constancy', 'unchangeability']
senticnet['unmoving'] = ['-0.80', '0', '0.547', '-0.86', '#sadness', '#disgust', 'negative', '-0.73', 'motionless', 'rigid', 'inactive', 'still', 'static']
senticnet['unmusical'] = ['-0.87', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.93', 'non-musical', 'mellifluous', 'uncouth', 'unmelodious', 'improvident']
senticnet['unmute'] = ['-0.94', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.94', 'drive_recklessly', 'discord', 'deaf', 'deny', 'silent']
senticnet['unnamable'] = ['-0.81', '0', '0.945', '-0.51', '#sadness', '#anger', 'negative', '-0.75', 'occult', 'concealed', 'nonvisual', 'hidden', 'out_of_sight']
senticnet['unnatural'] = ['-0.7', '0', '0.77', '-0.75', '#anger', '#disgust', 'negative', '-0.74', 'fake', 'artificial', 'man_made', 'artificial_sea', 'artificial_taste']
senticnet['unnatural_vice'] = ['-0.52', '0', '0.621', '-0.91', '#anger', '#disgust', 'negative', '-0.68', 'deviltry', 'devilry', 'irreverence', 'foul_play', 'iniquity']
senticnet['unnaturally'] = ['-0.88', '0', '0.762', '0', '#sadness', '#anger', 'negative', '-0.82', 'absurdly', 'inordinately', 'intolerably', 'unjustifiably', 'monstrously']
senticnet['unnaturalness'] = ['-0.68', '0', '0.863', '-0.69', '#anger', '#disgust', 'negative', '-0.74', 'pretension', 'artificiality', 'affectedness', 'supernaturalness', 'largeness']
senticnet['unnavigable'] = ['0.933', '0.795', '0', '0', '#joy', '#interest', 'positive', '0.864', 'water_passage', 'small_stream', 'water_flow', 'flowing_water', 'water_stream']
senticnet['unneccessary'] = ['-0.73', '0', '0.94', '-0.87', '#anger', '#disgust', 'negative', '-0.85', 'edema', 'oedema', 'hydrops', 'dropsy', 'puffiness']
senticnet['unnecessarily'] = ['0.932', '0', '0', '0.993', '#joy', '#admiration', 'positive', '0.963', 'necessarily', 'analytic', 'needs-cost', 'needly', 'inevitably']
senticnet['unnecessarily_complex'] = ['0', '0', '0.99', '-0.72', '#anger', '#disgust', 'negative', '-0.85', 'cumbrous', 'ungainly', 'clumsy', 'cumbersome', 'awkward']
senticnet['unnecessarily_cruel'] = ['-0.79', '0', '0.691', '-0.88', '#sadness', '#disgust', 'negative', '-0.79', 'cold', 'pitiless', 'cruel', 'unkind', 'vicious']
senticnet['unnecessary'] = ['-0.71', '0', '0.711', '0', '#sadness', '#anger', 'negative', '-0.71', 'needless', 'vain', 'idle', 'useless', 'pointless']
senticnet['unnecessary_complexity'] = ['-0.73', '0', '0.94', '-0.87', '#anger', '#disgust', 'negative', '-0.85', 'edema', 'oedema', 'hydrops', 'dropsy', 'puffiness']
senticnet['unnecessary_delay'] = ['-0.89', '0', '0.712', '-0.80', '#sadness', '#disgust', 'negative', '-0.80', 'difficultness', 'severity', 'severeness', 'troublesomeness', 'worriment']
senticnet['unnecessary_expense'] = ['-0.12', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.09', 'painful', 'uncomfortable', 'violent', 'fun_learn_play', 'expensive']
senticnet['unnecessary_overhead'] = ['-0.73', '0', '0.94', '-0.87', '#anger', '#disgust', 'negative', '-0.85', 'edema', 'oedema', 'hydrops', 'dropsy', 'puffiness']
senticnet['unnerve'] = ['0', '0', '0.75', '-0.88', '#anger', '#disgust', 'negative', '-0.81', 'upset', 'faze', 'discompose', 'discomfit', 'disconcert']
senticnet['unnervingly'] = ['-0.80', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.8', 'scared', 'frighten_away', 'scare_away', 'frighten_off', 'scare_off']
senticnet['unnoticed'] = ['0.758', '-0.78', '0', '0', '#joy', '#surprise', 'positive', '0.772', 'unheard-of', 'unheard', 'virtually', 'unimaginable', 'unseen']
senticnet['unobligated_balance'] = ['-0.04', '0.268', '0', '0.159', '#interest', '#admiration', 'positive', '0.127', 'total', 'amount', 'a_lot', 'aggregate', 'bill']
senticnet['unobservable'] = ['-0.78', '0', '0.792', '-0.61', '#sadness', '#anger', 'negative', '-0.73', 'incognizable', 'incognoscible', 'insensible', 'imperceptible', 'indiscernible']
senticnet['unobservant'] = ['-0.71', '0', '0.906', '-0.93', '#anger', '#disgust', 'negative', '-0.85', 'absent', 'forgetful', 'distracted', 'inattentive', 'scatty']
senticnet['unobtainable'] = ['-0.77', '0', '0.644', '-0.68', '#sadness', '#disgust', 'negative', '-0.70', 'unavailable', 'unprocurable', 'out_of_stock', 'finish', 'untouchable']
senticnet['unobtrusive'] = ['-0.71', '0', '-0.74', '0', '#sadness', '#fear', 'negative', '-0.73', 'non-intrusive', 'unpretentious', 'obtrusive', 'inconspicuous', 'discreet']
senticnet['unobtrusively'] = ['0', '-0.75', '0', '0.825', '#surprise', '#admiration', 'positive', '0.79', 'inconspicuously', 'discretely', 'discreetly', 'invisibly', 'stealthily']
senticnet['unoccupied_land'] = ['0.716', '-0.51', '0', '0.647', '#joy', '#admiration', 'positive', '0.624', 'state', 'nation', 'country', 'ally', 'commonwealth']
senticnet['unofficial'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'wildcat', 'informal', 'loose', 'drumhead']
senticnet['unofficial_mascot'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'unofficial', 'informal', 'loose', 'drumhead']
senticnet['unofficial_nickname'] = ['0.072', '0.244', '-0.20', '0.252', '#interest', '#admiration', 'positive', '0.091', 'doe', 'sweet', 'live_organism', 'love', 'alive']
senticnet['unofficial_patch'] = ['-0.58', '0', '0.561', '-0.62', '#sadness', '#disgust', 'negative', '-0.58', 'spot', 'speckle', 'dapple', 'mark', 'fleck']
senticnet['unofficially'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'wildcat', 'informal', 'loose', 'drumhead']
senticnet['unoriginal'] = ['-0.65', '0', '0.804', '-0.94', '#anger', '#disgust', 'negative', '-0.80', 'cold', 'cliched', 'dusty', 'slavish', 'banal']
senticnet['unoriginality'] = ['0.644', '-0.93', '0', '0.807', '#surprise', '#admiration', 'positive', '0.794', 'orthodoxy', 'traditionalism', 'conventionalism', 'traditionality', 'conventionality']
senticnet['unornamented'] = ['-0.75', '0', '0.867', '-0.74', '#sadness', '#anger', 'negative', '-0.78', 'spare', 'unembellished', 'bare', 'unadorned', 'plain']
senticnet['unorthodox'] = ['0', '-0.76', '0', '0.893', '#surprise', '#admiration', 'positive', '0.83', 'unconventional', 'untraditional', 'idiosyncratic', 'inventive', 'heretical']
senticnet['unostentatious'] = ['0.728', '-0.86', '0', '0.615', '#joy', '#surprise', 'positive', '0.736', 'understated', 'esthetic', 'aesthetic', 'artistic', 'tasteful']
senticnet['unoxygenated'] = ['-0.58', '0', '0.587', '-0.98', '#anger', '#disgust', 'negative', '-0.72', 'unvented', 'unventilated', 'unaerated', 'fuggy', 'stuffy']
senticnet['unpack'] = ['0', '0.11', '-0.05', '0.149', '#interest', '#admiration', 'positive', '0.067', 'pack_clothes', 'hold_stuff', 'carry_stuff', 'carry_book', 'carry_belongings']
senticnet['unpaid'] = ['-0.97', '0', '0.53', '-0.66', '#sadness', '#disgust', 'negative', '-0.72', 'uncompensated', 'unsalaried', 'buckshee', 'undischarged', 'complimentary']
senticnet['unpaid_balance'] = ['-0.89', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.80', 'account_indebtedness', 'indebtedness', 'limited_liability', 'debt', 'arrears']
senticnet['unpaid_maternity_leave'] = ['0.1', '0.216', '-0.18', '0.22', '#interest', '#admiration', 'positive', '0.088', 'female', 'maternity_ward', 'motherhood', 'woman', 'mother']
senticnet['unpaired_electron'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'elementary_particle', 'neutron', 'electron', 'subatomic_particle', 'proton']
senticnet['unpalatability'] = ['-0.52', '0', '0.703', '-0.71', '#anger', '#disgust', 'negative', '-0.64', 'distastefulness', 'taste_property', 'disgustingness', 'unsavoriness', 'nauseatingness']
senticnet['unpalatableness'] = ['-0.77', '0', '0.717', '-0.54', '#sadness', '#anger', 'negative', '-0.68', 'distastefulness', 'taste_property', 'disgustingness', 'unsavoriness', 'nauseatingness']
senticnet['unparalleled'] = ['0', '0.856', '0', '0.71', '#interest', '#admiration', 'positive', '0.783', 'world-renowned', 'top-notch', 'renowned', 'state-of-the-art', 'world-class']
senticnet['unparalleled_expertise'] = ['0', '0.983', '0', '0', '#interest', '#interest', 'positive', '0.983', 'find_problem', 'analyze', 'expert_report', 'examination', 'check']
senticnet['unpardonable'] = ['-0.53', '0', '0.901', '-0.51', '#sadness', '#anger', 'negative', '-0.64', 'inexpiable', 'inexcusable', 'deadly', 'unforgivable', 'mortal']
senticnet['unpardonable_sin'] = ['0', '0', '0.753', '-0.72', '#anger', '#disgust', 'negative', '-0.73', 'sinful', 'human_emotion', 'call', 'demand', 'yearn']
senticnet['unparliamentary'] = ['0.582', '-0.60', '0', '0.752', '#surprise', '#admiration', 'positive', '0.645', 'ungracious', 'discourteous', 'impolite', 'brattish', 'bratty']
senticnet['unparticled_matter'] = ['0.668', '-0.88', '0', '0.893', '#surprise', '#admiration', 'positive', '0.815', 'chirpiness', 'spirit', 'sprightliness', 'liveliness', 'activity']
senticnet['unpasteurised_milk'] = ['0.053', '0.085', '0.019', '0', '#joy', '#interest', 'positive', '0.04', 'farm', 'countryside', 'farm_animal', 'cow', 'healthy']
senticnet['unpatriotic'] = ['0', '0', '0', '-0.87', '#disgust', '#disgust', 'negative', '-0.87', 'move_eye', 'learn_current_event', 'become_informed', 'time_waste', 'eye_strain']
senticnet['unpaved_runway'] = ['-0.70', '0', '0.621', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'flare_path', 'airfield', 'grassland', 'runway', 'road']
senticnet['unpeaceable'] = ['-0.5', '0', '0.982', '-0.85', '#anger', '#disgust', 'negative', '-0.77', 'unpeaceful', 'stormy', 'tempestuous', 'militant', 'belligerent']
senticnet['unpeaceful'] = ['-0.5', '0', '0.982', '-0.85', '#anger', '#disgust', 'negative', '-0.77', 'unpeaceable', 'stormy', 'tempestuous', 'militant', 'belligerent']
senticnet['unperceivable'] = ['-0.53', '0', '0.661', '-0.83', '#anger', '#disgust', 'negative', '-0.67', 'incognizable', 'incognoscible', 'insensible', 'unobservable', 'indiscernible']
senticnet['unpersuadable'] = ['-0.77', '0', '0.639', '-0.79', '#sadness', '#disgust', 'negative', '-0.73', 'insusceptible', 'unsuasible', 'unresponsive', 'resistant', 'incapable']
senticnet['unpersuaded'] = ['-0.67', '0', '0.711', '-0.89', '#anger', '#disgust', 'negative', '-0.76', 'obstinate', 'unregenerated', 'impenitent', 'unconverted', 'unrepentant']
senticnet['unpersuasive'] = ['-0.84', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.89', 'unavailing', 'futile', 'fruitless', 'ineffectual', 'entreaty']
senticnet['unpersuasively'] = ['0.536', '-0.71', '0', '0.533', '#joy', '#surprise', 'positive', '0.594', 'incredulity', 'mental_rejection', 'skepticism', 'mistrust', 'distrust']
senticnet['unpersuasiveness'] = ['-0.96', '0', '0.531', '-0.51', '#sadness', '#anger', 'negative', '-0.67', 'impuissance', 'weakness', 'impotence', 'uninterestingness', 'helplessness']
senticnet['unphilosophical'] = ['-0.96', '0', '0.91', '-0.61', '#sadness', '#anger', 'negative', '-0.82', 'unpersuadable', 'unsuasible', 'unresponsive', 'resistant', 'incapable']
senticnet['unphysical'] = ['0.807', '-0.88', '0', '0.718', '#joy', '#surprise', 'positive', '0.802', 'sex', 'gayness', 'sexual_gayness', 'lesbianism', 'sapphism']
senticnet['unpicturesque'] = ['0.834', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.805', 'specular', 'picturesque', 'chocolate-box', 'idyllic', 'painting']
senticnet['unplaced'] = ['-0.75', '0', '0.785', '-0.58', '#sadness', '#anger', 'negative', '-0.70', 'lose', 'unsuccessful', 'out', 'pointless', 'defeated']
senticnet['unplanned'] = ['-0.84', '0', '0.508', '-0.79', '#sadness', '#disgust', 'negative', '-0.71', 'unintended', 'unwitting', 'unintentional', 'accidental', 'inadvertent']
senticnet['unpleasant'] = ['-0.70', '0', '0.986', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'body_reaction', 'symptom', 'painful', 'mock', 'sign_sickness']
senticnet['unpleasant-smelling'] = ['0', '0', '0.955', '-0.79', '#anger', '#disgust', 'negative', '-0.87', 'worm_intestine', 'fur_keep_warm', 'bad_breath', 'halitosis', 'asthama']
senticnet['unpleasant_impression'] = ['-0.75', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.83', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['unpleasant_odor'] = ['-0.53', '0', '0.86', '-0.65', '#anger', '#disgust', 'negative', '-0.68', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['unpleasant_person'] = ['-0.85', '0', '0.644', '-0.98', '#sadness', '#disgust', 'negative', '-0.82', 'bastard', 'dickhead', 'mother_fucker', 'motherfucker', 'son_of_bitch']
senticnet['unpleasant_smell'] = ['0', '0', '0.717', '-0.84', '#anger', '#disgust', 'negative', '-0.77', 'gymnast', 'bikini', 'underwear', 'basketball_hoop', 'dirty_sock']
senticnet['unpleasant_taste'] = ['0.95', '-0.86', '0', '0.947', '#joy', '#admiration', 'positive', '0.92', 'content', 'lore', 'folklore', 'cognitive_content', 'story']
senticnet['unpleasant_woman'] = ['-0.55', '0', '0.956', '-0.54', '#sadness', '#anger', 'negative', '-0.68', 'termagant', 'old_bag', 'shrew', 'frump', 'hellcat']
senticnet['unpleasantly'] = ['-0.9', '0', '0.841', '-0.73', '#sadness', '#anger', 'negative', '-0.82', 'nubbiness', 'tweediness', 'harshness', 'graininess', 'granularity']
senticnet['unpleasantness'] = ['-0.71', '0', '0', '-0.81', '#sadness', '#disgust', 'negative', '-0.76', 'repetitive_task', 'monotony', 'screw_up', 'wrongdoing', 'reprimand']
senticnet['unplug'] = ['-0.07', '-0.01', '0', '-0.15', '#sadness', '#disgust', 'negative', '-0.06', 'stop_move', 'remove', 'become_unconscious', 'lose_consciousness', 'stop_think']
senticnet['unplugged_acoustic'] = ['0.773', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.844', 'favorite', 'crowd', 'acclamation', 'bunch', 'large_crowd']
senticnet['unpoetic'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'nonsense', 'meaninglessness', 'bunk', 'absurdity', 'hokum']
senticnet['unpolarized_light'] = ['-0.50', '0', '0.75', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'beam', 'beam_of_light', 'sunbeam', 'ray', 'ray_of_light']
senticnet['unpopular'] = ['-0.10', '0.176', '0.629', '-0.64', '#anger', '#disgust', 'negative', '-0.3', 'alienated', 'alienation', 'disaffection', 'estrange', 'disoriented']
senticnet['unpopularity'] = ['0', '0.958', '0', '0.781', '#interest', '#admiration', 'positive', '0.869', 'disaffection', 'disenchantment', 'dissatisfaction', 'ineptness', 'disillusionment']
senticnet['unpractical'] = ['0.036', '0', '0.094', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'facial_hair', 'need_shave', 'need_beauty_center', 'long_beard', 'carelessness']
senticnet['unprecedented'] = ['0.994', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.96', 'record-setting', 'record-breaking', 'near-record', 'record-high', 'record-shattering']
senticnet['unprecedented_devastation'] = ['-0.87', '0', '0.889', '-0.62', '#sadness', '#anger', 'negative', '-0.79', 'ruination', 'ruin', 'deterioration', 'blight', 'impairment']
senticnet['unprecedented_feat'] = ['0.749', '0', '0', '0.952', '#joy', '#admiration', 'positive', '0.851', 'high_class', 'citation', 'suspicion', 'goodness', 'prosperity']
senticnet['unprecedented_scrutiny'] = ['-0.79', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.75', 'scan', 'checkup', 'medical_checkup', 'medical_examination', 'medical_exam']
senticnet['unprecedentedly'] = ['0', '0.8', '0', '0.918', '#interest', '#admiration', 'positive', '0.859', 'unfathomably', 'staggeringly', 'unacceptably', 'astoundingly', 'monstrously']
senticnet['unpredictability'] = ['-0.52', '0', '0.959', '-0.70', '#anger', '#disgust', 'negative', '-0.72', 'irresoluteness', 'indecisiveness', 'indecision', 'trait', 'volatility']
senticnet['unpredictable'] = ['-0.73', '0', '0', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'uncontrollable', 'unstable', 'incalculable', 'uncertain', 'unreliable']
senticnet['unpredictably'] = ['-0.70', '0', '0.756', '0', '#sadness', '#anger', 'negative', '-0.73', 'eerie', 'skittish', 'spooky', 'ghost', 'supernatural']
senticnet['unpredicted'] = ['0.036', '-0.66', '0', '0.355', '#surprise', '#admiration', 'positive', '0.353', 'without_warning', 'unexpected', 'without_notice', 'unanticipated', 'out_of_blue']
senticnet['unpremeditated_murder'] = ['0.654', '-0.59', '0', '0.774', '#joy', '#admiration', 'positive', '0.673', 'instrument', 'indictment', 'murder_indictment', 'true_bill', 'legal_document']
senticnet['unprepared'] = ['-0.93', '0', '0.651', '-0.81', '#sadness', '#disgust', 'negative', '-0.80', 'unripe', 'napping', 'off_guard', 'off_one_guard', 'unready']
senticnet['unpretending'] = ['0', '-0.72', '0', '0.877', '#surprise', '#admiration', 'positive', '0.8', 'unpretentious', 'homespun', 'understated', 'pretentious', 'humble']
senticnet['unpretentious'] = ['0', '-0.72', '0', '0.883', '#surprise', '#admiration', 'positive', '0.803', 'unpretending', 'homespun', 'understated', 'pretentious', 'humble']
senticnet['unprintable'] = ['0.93', '0.883', '0', '0', '#joy', '#interest', 'positive', '0.907', 'unmentionable', 'expletive', 'four-letter', 'undecipherable', 'untranslatable']
senticnet['unproblematic'] = ['0.807', '0.8', '0', '0', '#joy', '#interest', 'positive', '0.804', 'simple', 'uncomplicated', 'elementary', 'basic', 'primary']
senticnet['unprocurable'] = ['-0.77', '0', '0.644', '-0.68', '#sadness', '#disgust', 'negative', '-0.70', 'unobtainable', 'unavailable', 'out_of_stock', 'finish', 'untouchable']
senticnet['unproductive'] = ['-0.60', '0', '0.761', '-0.84', '#anger', '#disgust', 'negative', '-0.73', 'vain', 'bootless', 'fruitless', 'futile', 'sleeveless']
senticnet['unprofessional'] = ['-0.88', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.88', 'disrespectful', 'unethical', 'dishonest', 'amateurish', 'rude']
senticnet['unprofitability'] = ['-0.62', '0', '0.708', '-0.61', '#sadness', '#anger', 'negative', '-0.64', 'unfavorableness', 'bad_luck', 'unfavorable_position', 'unprofitableness', 'disadvantage']
senticnet['unprofitable'] = ['-0.54', '0', '0.739', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'unremunerative', 'idle', 'lean', 'dead', 'marginal']
senticnet['unprofitableness'] = ['-0.62', '0', '0.708', '-0.61', '#sadness', '#anger', 'negative', '-0.64', 'unfavorableness', 'black_cat', 'unfavorable_position', 'disadvantage', 'unprofitability']
senticnet['unprogressive'] = ['-0.99', '0', '0.658', '-0.73', '#sadness', '#disgust', 'negative', '-0.79', 'adrift', 'afloat', 'aimless', 'directionless', 'purposeless']
senticnet['unpromising'] = ['-0.73', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.83', 'inauspicious', 'uninviting', 'unappetizing', 'unimpressive', 'uninspiring']
senticnet['unprompted'] = ['0.9', '0', '0', '0.753', '#joy', '#admiration', 'positive', '0.827', 'natural', 'impulsive', 'instinctive', 'spontaneous', 'easy']
senticnet['unpronounceable'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicado', 'incommunicative', 'impassive', 'inexpressive', 'silent']
senticnet['unprotect_sex'] = ['0.049', '0.098', '-0.07', '0.121', '#interest', '#admiration', 'positive', '0.047', 'procreate', 'reproduce', 'start_family', 'cause_cold', 'copulate']
senticnet['unprotected_intercourse'] = ['0.97', '0.729', '0', '0.946', '#joy', '#admiration', 'positive', '0.882', 'make_baby', 'give_birth_child', 'sexual_intercourse', 'find_partner', 'make_love']
senticnet['unprotected_oral_sex'] = ['-0.73', '-0.72', '-0.86', '-0.79', '#fear', '#disgust', 'negative', '-0.41', 'venereal_disease', 'overpopulation', 'std', 'loss_offreedom', 'lower_standard_live']
senticnet['unprotected_sex'] = ['0.97', '0.729', '0', '0.946', '#joy', '#admiration', 'positive', '0.882', 'make_baby', 'give_birth_child', 'sexual_intercourse', 'find_partner', 'make_love']
senticnet['unprotected_speech'] = ['-0.52', '0', '0.648', '-0.96', '#anger', '#disgust', 'negative', '-0.71', 'improperness', 'outrageousness', 'impropriety', 'immodesty', 'indecency']
senticnet['unproved'] = ['0', '0.835', '0', '0.727', '#interest', '#admiration', 'positive', '0.781', 'untested', 'unsubstantiated', 'untried', 'unproven', 'unfounded']
senticnet['unproven'] = ['0', '0.837', '0', '0.733', '#interest', '#admiration', 'positive', '0.785', 'untested', 'unsubstantiated', 'untried', 'unproved', 'unfounded']
senticnet['unpublished_novel'] = ['-0.04', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.08', 'data', 'awareness', 'information', 'saga', 'knowledge']
senticnet['unqualified'] = ['-0.67', '0', '0.714', '-0.54', '#sadness', '#anger', 'negative', '-0.64', 'unlimited', 'outright', 'unconditional', 'categoric', 'unqualifiedal']
senticnet['unqualified_sense'] = ['-0.57', '0', '0.713', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'assured', 'conclusion', 'certainty', 'predictability', 'definiteness']
senticnet['unqualifiedal'] = ['-0.67', '0', '0.715', '-0.54', '#sadness', '#anger', 'negative', '-0.64', 'unlimited', 'outright', 'unconditional', 'unqualified', 'categoric']
senticnet['unquenchable'] = ['-0.72', '0', '0.933', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'unsatisfied', 'quenchless', 'insatiable', 'unsated', 'unsatiated']
senticnet['unquenchable_fire'] = ['-0.70', '0', '0.566', '-0.95', '#sadness', '#disgust', 'negative', '-0.74', 'hellfire', 'red_region', 'imaginary_place', 'mythical_place', 'fictitious_place']
senticnet['unquestionability'] = ['0.904', '-0.80', '0', '0.611', '#joy', '#surprise', 'positive', '0.773', 'demonstrability', 'provability', 'positivity', 'positiveness', 'certainty']
senticnet['unquestionable'] = ['0', '0.89', '0', '0.943', '#interest', '#admiration', 'positive', '0.916', 'unimpeachable', 'for_sure', 'mathematical', 'beyond_doubt', 'sure']
senticnet['unquestionable_integrity'] = ['0.865', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.887', 'manager', 'colleague', 'office_worker', 'coworker', 'secretary']
senticnet['unquestionableness'] = ['0.946', '-0.89', '0', '0.942', '#joy', '#admiration', 'positive', '0.928', 'demonstrability', 'provability', 'positivity', 'positiveness', 'certainty']
senticnet['unquestioned'] = ['-0.96', '0', '0.774', '-0.65', '#sadness', '#anger', 'negative', '-0.79', 'unchallengeable', 'undisputed', 'unchallenged', 'noncontroversial', 'agreed_upon']
senticnet['unquestioned_integrity'] = ['0.865', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.887', 'manager', 'colleague', 'office_worker', 'coworker', 'secretary']
senticnet['unquestioning_acceptance'] = ['0.536', '-0.71', '0', '0.533', '#joy', '#surprise', 'positive', '0.594', 'incredulity', 'mental_rejection', 'skepticism', 'mistrust', 'distrust']
senticnet['unquestioning_loyalty'] = ['0.997', '0.796', '0', '0.89', '#joy', '#admiration', 'positive', '0.894', 'devotion', 'bravery', 'adoration', 'zeal', 'charm']
senticnet['unquestioning_obedience'] = ['0.997', '0.796', '0', '0.89', '#joy', '#admiration', 'positive', '0.894', 'devotion', 'bravery', 'adoration', 'zeal', 'charm']
senticnet['unquiet'] = ['-0.94', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.83', 'uneasy', 'sinking_feeling', 'sinking', 'scruple', 'misgiving']
senticnet['unravel'] = ['-0.78', '0', '0.787', '0', '#sadness', '#anger', 'negative', '-0.78', 'untangling', 'disentangle', 'untangle', 'tangle', 'tangled']
senticnet['unreachable'] = ['-0.77', '0', '0.644', '-0.68', '#sadness', '#disgust', 'negative', '-0.70', 'unobtainable', 'unprocurable', 'out_of_stock', 'bunk', 'untouchable']
senticnet['unreactive'] = ['-0.54', '0', '0.868', '-0.96', '#anger', '#disgust', 'negative', '-0.79', 'inactive', 'inert', 'indifferent', 'stable', 'neutral']
senticnet['unread'] = ['0.864', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.924', 'trash', 'trash_container', 'junk_store', 'coffee_cup', 'trash_receptacle']
senticnet['unreadable'] = ['-0.91', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.95', 'irrecoverable', 'non-recoverable', 'unrecoverable', 'overwritten', 'unusable']
senticnet['unready'] = ['-0.92', '0', '0.659', '-0.81', '#sadness', '#disgust', 'negative', '-0.8', 'unripe', 'napping', 'off_guard', 'off_one_guard', 'unprepared']
senticnet['unreal'] = ['-0.79', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.77', 'fabled', 'fictitious', 'pretend', 'make_believe', 'made_up']
senticnet['unrealistic'] = ['-0.93', '0', '0.94', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'delusive', 'phantasmagoric', 'phantasmagorical', 'surrealistic', 'wild']
senticnet['unrealistical'] = ['-0.93', '0', '0.934', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'delusive', 'unrealistic', 'phantasmagoric', 'surrealistic', 'wild']
senticnet['unreality'] = ['-0.95', '0', '0.909', '-0.97', '#sadness', '#disgust', 'negative', '-0.94', 'incorporeality', 'insubstantiality', 'quality', 'intangibility', 'intangibleness']
senticnet['unrealizable'] = ['0.783', '-0.51', '0', '0.584', '#joy', '#admiration', 'positive', '0.627', 'appetent', 'avid', 'thirsty', 'devouring', 'envious']
senticnet['unreason'] = ['0', '0', '-0.94', '-0.72', '#fear', '#disgust', 'negative', '-0.83', 'irrationality', 'irrational', 'faculty', 'fatuity', 'dada']
senticnet['unreasonable'] = ['-0.98', '0', '0.646', '-0.98', '#sadness', '#disgust', 'negative', '-0.87', 'indefensible', 'reasonless', 'untenable', 'senseless', 'unwarranted']
senticnet['unreasonable_delay'] = ['-0.88', '0', '0.727', '-0.82', '#sadness', '#disgust', 'negative', '-0.81', 'defervescence', 'subsidence', 'break', 'remission', 'abatement']
senticnet['unreasonably'] = ['-0.92', '0', '-0.89', '0', '#sadness', '#fear', 'negative', '-0.90', 'switch', 'crank', 'stale', 'crowd', 'unreasonable']
senticnet['unreceptive'] = ['0', '0', '0.713', '-0.92', '#anger', '#disgust', 'negative', '-0.81', 'non-receptive', 'unappreciative', 'unenthusiastic', 'unwelcoming', 'unsympathetic']
senticnet['unrecognisable'] = ['-0.82', '0', '0.758', '-0.70', '#sadness', '#anger', 'negative', '-0.76', 'unclassifiable', 'unrecognizable', 'unidentifiable', 'intangible', 'undiagnosable']
senticnet['unrecognisably'] = ['-0.82', '0', '0.758', '-0.70', '#sadness', '#anger', 'negative', '-0.76', 'unclassifiable', 'unrecognizable', 'unidentifiable', 'intangible', 'undiagnosable']
senticnet['unrecognizable'] = ['-0.82', '0', '0.758', '-0.70', '#sadness', '#anger', 'negative', '-0.76', 'unclassifiable', 'unidentifiable', 'unrecognisable', 'intangible', 'undiagnosable']
senticnet['unrecognized'] = ['-0.75', '0', '0.559', '-0.99', '#sadness', '#disgust', 'negative', '-0.76', 'unacknowledged', 'unsung', 'unappreciated', 'unvalued', 'unconfessed']
senticnet['unreconciled'] = ['-0.97', '0', '0.712', '-0.78', '#sadness', '#disgust', 'negative', '-0.82', 'discrepant', 'inconsistent', 'scratchy', 'uneven', 'contradictory']
senticnet['unrecoverable'] = ['-0.90', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'irrecoverable', 'non-recoverable', 'unreadable', 'overwritten', 'unusable']
senticnet['unrectifiable'] = ['0', '0', '0.757', '-0.78', '#anger', '#disgust', 'negative', '-0.77', 'irreparable', 'irremediable', 'beyond_repair', 'irreversible', 'irrevocable']
senticnet['unredeemable'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'venial', 'excusable', 'mercifully', 'merciful', 'mercifulness']
senticnet['unrefined'] = ['0', '0', '0.852', '-0.76', '#anger', '#disgust', 'negative', '-0.80', 'roughneck', 'unfamiliar', 'uncouthness', 'uncouth', 'foreign']
senticnet['unreflective'] = ['-0.73', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.75', 'unthinking', 'thoughtless', 'inconsiderate', 'unthoughtful', 'selfish']
senticnet['unreformable'] = ['-0.83', '0', '0.855', '-0.89', '#anger', '#disgust', 'negative', '-0.86', 'uncorrectable', 'incorrigible', 'unregenerate', 'uncontrollable', 'unmanageable']
senticnet['unregenerate'] = ['-0.96', '0', '0.858', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'contrarious', 'dogged', 'pertinacious', 'cantankerous', 'bolshy']
senticnet['unregenerated'] = ['-0.68', '0', '0.709', '-0.89', '#anger', '#disgust', 'negative', '-0.76', 'obstinate', 'unrepentant', 'impenitent', 'unconverted', 'unpersuaded']
senticnet['unregistered_vehicle'] = ['0', '-0.01', '0', '0.064', '#surprise', '#admiration', 'positive', '0.042', 'car_gas', 'car_power', 'fossil_fuel', 'run_car', 'car_engine']
senticnet['unregularity'] = ['-0.89', '0', '0.972', '-0.50', '#sadness', '#anger', 'negative', '-0.79', 'unsteadiness', 'haphazardness', 'stochasticity', 'variability', 'fitfulness']
senticnet['unrehearsed'] = ['-0.68', '0', '0.931', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'unprepared', 'extemporary', 'extempore', 'impromptu', 'extemporaneous']
senticnet['unreleased'] = ['-0.99', '0', '0.769', '0', '#sadness', '#anger', 'negative', '-0.88', 'outtake', 'out-take', 'blooper', 'out-takes', 'outtake']
senticnet['unrelenting'] = ['0', '0', '0.95', '-0.84', '#anger', '#disgust', 'negative', '-0.89', 'relent', 'inclement', 'hard', 'rigid', 'stern']
senticnet['unrelentingly'] = ['-0.99', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.96', 'horrifying', 'atrocious', 'dreadfully', 'horrified', 'horribly']
senticnet['unreliability'] = ['-0.87', '0', '0.508', '-0.94', '#sadness', '#disgust', 'negative', '-0.77', 'irresponsibility', 'irresponsibleness', 'instability', 'irreproducibility', 'fallibility']
senticnet['unreliable'] = ['-0.73', '0', '0', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'uncontrollable', 'unpredictable', 'unstable', 'incalculable', 'uncertain']
senticnet['unreliable_source'] = ['0.964', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.846', 'bulletin', 'replicate', 'current_event', 'computer_command', 'paper_write']
senticnet['unreliableness'] = ['-0.91', '0', '0.795', '-0.66', '#sadness', '#anger', 'negative', '-0.78', 'irresponsibility', 'irresponsibleness', 'instability', 'irreproducibility', 'fallibility']
senticnet['unreliably'] = ['-0.77', '0', '0.644', '-0.68', '#sadness', '#disgust', 'negative', '-0.70', 'unobtainable', 'unprocurable', 'out_of_stock', 'bunk', 'untouchable']
senticnet['unremarkable'] = ['-0.85', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'unexceptional', 'unexciting', 'unspectacular', 'undistinguished', 'unmemorable']
senticnet['unremitting'] = ['0', '0', '0.801', '-0.82', '#anger', '#disgust', 'negative', '-0.81', 'tireless', 'indefatigable', 'unflagging', 'unstinting', 'untiring']
senticnet['unremunerative'] = ['-0.54', '0', '0.739', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'unprofitable', 'idle', 'lean', 'dead', 'marginal']
senticnet['unrepentant'] = ['-0.67', '0', '0.715', '-0.89', '#anger', '#disgust', 'negative', '-0.76', 'obstinate', 'unregenerated', 'impenitent', 'unconverted', 'unpersuaded']
senticnet['unrepentantly'] = ['-0.80', '0', '0.56', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'gossip', 'gossiper', 'blabbermouth', 'talkative', 'rumormonger']
senticnet['unrepresentative'] = ['-0.99', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.89', 'non-representative', 'undemocratic', 'unscientific', 'self-selecting', 'unaccountable']
senticnet['unrequited_love'] = ['-0.77', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.78', 'unhappiness', 'melancholy', 'shame', 'unhappy', 'sad']
senticnet['unresolvable'] = ['-0.99', '0', '0.754', '-0.50', '#sadness', '#anger', 'negative', '-0.75', 'unmalleable', 'stubborn', 'refractory', 'uncontrollable', 'balking']
senticnet['unresolved'] = ['-0.53', '0', '0.898', '-0.74', '#anger', '#disgust', 'negative', '-0.72', 'discordant', 'inharmonious', 'dissonant', 'false', 'disharmonious']
senticnet['unrespectability'] = ['-0.77', '0', '0.748', '-0.60', '#sadness', '#anger', 'negative', '-0.70', 'ignobleness', 'ignobility', 'sleaziness', 'dishonorableness', 'disreputability']
senticnet['unrespectable'] = ['0.666', '-0.83', '0', '0.896', '#surprise', '#admiration', 'positive', '0.798', 'nice', 'decent', 'solid', 'presentable', 'upstanding']
senticnet['unresponsive'] = ['-0.77', '0', '0.639', '-0.79', '#sadness', '#disgust', 'negative', '-0.73', 'unpersuadable', 'unsuasible', 'insusceptible', 'resistant', 'incapable']
senticnet['unrest'] = ['-0.78', '0', '0.994', '-0.76', '#sadness', '#anger', 'negative', '-0.84', 'turmoil', 'tumult', 'foment', 'agitate', 'agitated']
senticnet['unrestrained'] = ['0', '0', '-0.73', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'hallucinating', 'excited', 'frantic', 'mad', 'delirious']
senticnet['unrestraint'] = ['-0.83', '0', '0.531', '-0.59', '#sadness', '#disgust', 'negative', '-0.65', 'indulgence', 'incontinence', 'trait', 'dissoluteness', 'indiscipline']
senticnet['unrestricted_endowment'] = ['0.608', '-0.55', '0', '0.726', '#joy', '#admiration', 'positive', '0.628', 'genius', 'raw_talent', 'flair', 'gift', 'knack']
senticnet['unretentive'] = ['0', '0', '-0.78', '-0.72', '#fear', '#disgust', 'negative', '-0.75', 'short', 'poor', 'inadequate', 'little', 'forgetful']
senticnet['unreturnable'] = ['0', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.91', 'umpire', 'referee', 'legal_proceed', 'clang', 'clamor']
senticnet['unreviewable_discretion'] = ['0.964', '0.931', '0', '0', '#joy', '#interest', 'positive', '0.948', 'heel', 'foot_part', 'type_of_shoes', 'shoe_type', 'high_heel']
senticnet['unrighteousness'] = ['0.673', '-0.66', '0', '0.566', '#joy', '#surprise', 'positive', '0.634', 'wickedness', 'impiety', 'impiousness', 'unjustness', 'dishonor']
senticnet['unripe'] = ['-0.92', '0', '0.659', '-0.81', '#sadness', '#disgust', 'negative', '-0.8', 'unready', 'napping', 'off_guard', 'off_one_guard', 'rest']
senticnet['unruffled'] = ['0.552', '0.65', '-0.25', '0.269', '#joy', '#interest', 'positive', '0.304', 'placid', 'placidly', 'placidness', 'placidity', 'tranquil']
senticnet['unruh_effect'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'general_relativity', 'general_theory_of_relativity', 'relativity', 'special_relativity', 'special_theory_of_relativity']
senticnet['unruliness'] = ['-0.66', '0', '0.649', '-0.76', '#sadness', '#disgust', 'negative', '-0.69', 'contrariness', 'wildness', 'intractability', 'intractableness', 'perversity']
senticnet['unruly'] = ['-0.64', '0', '0.941', '-0.66', '#anger', '#disgust', 'negative', '-0.74', 'rowdy', 'boisterous', 'disorderly', 'rambunctious', 'robustious']
senticnet['unruly_hair'] = ['0', '0', '0.841', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'ancient', 'many_years', 'year', 'old', 'teen']
senticnet['unrung'] = ['0.034', '0', '0.01', '0.087', '#joy', '#admiration', 'positive', '0.037', 'chime', 'bell_noise', 'alto', 'piano', 'ring_loudly']
senticnet['unsafe'] = ['-0.74', '0', '0.781', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'reprimand', 'caution', 'big_monster', 'kidnap', 'mock']
senticnet['unsafe_condition'] = ['-0.74', '0', '0.781', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'reprimand', 'caution', 'big_monster', 'kidnap', 'mock']
senticnet['unsafe_lane'] = ['-0.31', '-0.02', '0', '-0.24', '#sadness', '#disgust', 'negative', '-0.17', 'pollution', 'screw_up', 'car_wreck', 'parking_ticket', 'feel_stressed']
senticnet['unsafe_manner'] = ['-0.74', '0', '0.781', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'reprimand', 'caution', 'big_monster', 'kidnap', 'mock']
senticnet['unsafe_sex'] = ['-0.90', '0', '0.202', '-1.0', '#sadness', '#disgust', 'negative', '-0.70', 'sickness', 'sexually_transmitted_disease', 'social_disease', 'std', 'sex_without_condom']
senticnet['unsafely'] = ['-0.74', '0', '0.781', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'reprimand', 'caution', 'big_monster', 'kidnap', 'mock']
senticnet['unsalable'] = ['-0.94', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.83', 'unsellable', 'unsaleable', 'marketable', 'salable', 'unmarketable']
senticnet['unsalaried'] = ['-0.97', '0', '0.524', '-0.66', '#sadness', '#disgust', 'negative', '-0.72', 'uncompensated', 'buckshee', 'unpaid', 'undischarged', 'complimentary']
senticnet['unsaleable'] = ['-0.95', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.83', 'unsellable', 'unmarketable', 'marketable', 'salable', 'unsalable']
senticnet['unsalted'] = ['0', '0', '0.779', '-0.95', '#anger', '#disgust', 'negative', '-0.86', 'semi-sweet', 'unsweetened', 'semisweet', 'pecan', 'bittersweet']
senticnet['unsanitariness'] = ['-0.68', '0', '0.529', '-0.69', '#sadness', '#disgust', 'negative', '-0.63', 'nastiness', 'filth', 'filthiness', 'dunghill', 'sanitary_condition']
senticnet['unsanitary'] = ['-0.74', '0', '0.781', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'reprimand', 'caution', 'big_monster', 'kidnap', 'mock']
senticnet['unsated'] = ['-0.73', '0', '0.925', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'unsatisfied', 'quenchless', 'unquenchable', 'insatiable', 'unsatiated']
senticnet['unsatiable'] = ['-0.67', '0', '0.931', '-0.69', '#anger', '#disgust', 'negative', '-0.76', 'unsatisfied', 'quenchless', 'unquenchable', 'unsated', 'unsatiated']
senticnet['unsatiated'] = ['-0.73', '0', '0.929', '-0.51', '#sadness', '#anger', 'negative', '-0.72', 'unsatisfied', 'quenchless', 'unquenchable', 'unsated', 'insatiable']
senticnet['unsatisfactoriness'] = ['0.774', '-0.82', '0', '0.63', '#joy', '#surprise', 'positive', '0.744', 'quality', 'perishability', 'perishableness', 'inadequateness', 'inadequacy']
senticnet['unsatisfactory'] = ['-0.75', '0', '0.785', '-0.7', '#sadness', '#anger', 'negative', '-0.74', 'disappointing', 'dissatisfactory', 'unsatisfying', 'off', 'unacceptable']
senticnet['unsatisfactory_performance'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly']
senticnet['unsatisfiability'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly']
senticnet['unsatisfiable'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'gratifyingly', 'satiable', 'satisfactorily', 'comforting', 'satisfyingly']
senticnet['unsatisfied'] = ['-0.89', '0', '0.708', '-0.92', '#sadness', '#disgust', 'negative', '-0.84', 'ungratified', 'discontented', 'malcontent', 'disaffected', 'restless']
senticnet['unsatisfying'] = ['-0.75', '0', '0.785', '-0.7', '#sadness', '#anger', 'negative', '-0.74', 'disappointing', 'dissatisfactory', 'unsatisfactory', 'off', 'unacceptable']
senticnet['unsavoriness'] = ['-0.52', '0', '0.703', '-0.71', '#anger', '#disgust', 'negative', '-0.64', 'distastefulness', 'taste_property', 'disgustingness', 'unpalatability', 'nauseatingness']
senticnet['unsavory'] = ['0', '0', '0.968', '-0.79', '#anger', '#disgust', 'negative', '-0.88', 'disreputable', 'distasteful', 'savory', 'undesirable', 'character']
senticnet['unsayable'] = ['-0.81', '0', '0.945', '-0.51', '#sadness', '#anger', 'negative', '-0.75', 'occult', 'concealed', 'nonvisual', 'hidden', 'out_of_sight']
senticnet['unscalable'] = ['0', '0', '0.921', '-0.87', '#anger', '#disgust', 'negative', '-0.9', 'outback', 'trackless', 'pathless', 'roadless', 'untracked']
senticnet['unscathed'] = ['-0.96', '0', '0.704', '-0.52', '#sadness', '#anger', 'negative', '-0.73', 'uncut', 'unharmed', 'unhurt', 'unwounded', 'uninjured']
senticnet['unscholarly'] = ['0.536', '-0.71', '0', '0.533', '#joy', '#surprise', 'positive', '0.594', 'incredulity', 'mental_rejection', 'skepticism', 'mistrust', 'distrust']
senticnet['unscientific'] = ['-1', '0', '0', '-0.80', '#sadness', '#disgust', 'negative', '-0.90', 'non-representative', 'undemocratic', 'unrepresentative', 'self-selecting', 'unaccountable']
senticnet['unscrew'] = ['0.062', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.046', 'twirl_around', 'dance_well', 'upgrade', 'turn_around', 'adjust_volume']
senticnet['unscriptural'] = ['0.889', '-0.69', '0', '0.908', '#joy', '#admiration', 'positive', '0.83', 'pharisaic', 'pious', 'sanctimonious', 'reverent', 'worshipful']
senticnet['unscrupulous'] = ['0', '0', '-0.75', '-0.78', '#fear', '#disgust', 'negative', '-0.76', 'contemptuous', 'immoral', 'unscrupulously', 'whore', 'shyster']
senticnet['unscrupulous_businessmen'] = ['0.685', '-0.58', '0', '0.56', '#joy', '#surprise', 'positive', '0.608', 'profit', 'net_income', 'net', 'net_profit', 'lucre']
senticnet['unscrupulously'] = ['0', '0', '-0.75', '-0.78', '#fear', '#disgust', 'negative', '-0.77', 'contemptuous', 'immoral', 'unscrupulous', 'whore', 'shyster']
senticnet['unseal'] = ['0', '-0.86', '0', '0.902', '#surprise', '#admiration', 'positive', '0.882', 'declassifying', 'declassification', 'declassified', 'redact', 'declassify']
senticnet['unsearchable'] = ['-0.81', '0', '0.945', '-0.51', '#sadness', '#anger', 'negative', '-0.75', 'occult', 'concealed', 'nonvisual', 'hidden', 'out_of_sight']
senticnet['unseasonable'] = ['0', '0', '-0.70', '-0.91', '#fear', '#disgust', 'negative', '-0.81', 'wrong', 'prematurely', 'untimely', 'premature', 'ill-timed']
senticnet['unseasonably'] = ['0', '0.792', '0', '0.905', '#interest', '#admiration', 'positive', '0.849', 'answerable', 'granting', 'accordance', 'consensus', 'coincident']
senticnet['unseasonably_cold'] = ['0.953', '0.792', '0', '0', '#joy', '#interest', 'positive', '0.873', 'snowy', 'cool_temperature', 'coldness', 'ice_rain', 'season_year']
senticnet['unseasonably_mild'] = ['0.953', '0.792', '0', '0', '#joy', '#interest', 'positive', '0.873', 'snowy', 'cool_temperature', 'coldness', 'ice_rain', 'season_year']
senticnet['unseasonably_warm'] = ['0.953', '0.792', '0', '0', '#joy', '#interest', 'positive', '0.873', 'snowy', 'cool_temperature', 'coldness', 'ice_rain', 'season_year']
senticnet['unseasonably_warm_weather'] = ['0.15', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.087', 'winter', 'winter_time', 'snow_fall', 'snowy', 'put_warm_clothes']
senticnet['unseasonal_weather'] = ['0', '0', '0.954', '-0.76', '#anger', '#disgust', 'negative', '-0.85', 'fervid', 'ardent', 'impassioned', 'fervent', 'fiery']
senticnet['unseasoned'] = ['-0.72', '0', '0.555', '-0.95', '#sadness', '#disgust', 'negative', '-0.74', 'flavourless', 'unsalted', 'flavorless', 'tasteless', 'savorless']
senticnet['unseat'] = ['-0.53', '0', '0.752', '-0.89', '#anger', '#disgust', 'negative', '-0.72', 'fall', 'keel_over', 'descend', 'go_down', 'come_down']
senticnet['unseat_president_obama'] = ['0.65', '-0.61', '0', '0.703', '#joy', '#admiration', 'positive', '0.657', 'gleaming', 'luster', 'polish', 'glow', 'effulgence']
senticnet['unsecure'] = ['-0.79', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.79', 'misgiving', 'qualm', 'scruple', 'insecurity', 'insecure']
senticnet['unsecured_bond'] = ['-0.08', '0.277', '-0.15', '0.168', '#interest', '#admiration', 'positive', '0.052', 'bequest', 'excise', 'bribe', 'afford', 'owe']
senticnet['unseeable'] = ['-0.81', '0', '0.945', '-0.51', '#sadness', '#anger', 'negative', '-0.75', 'occult', 'concealed', 'nonvisual', 'hidden', 'out_of_sight']
senticnet['unseeing'] = ['0', '0', '0.99', '-0.79', '#anger', '#disgust', 'negative', '-0.89', 'disbelieving', 'expressionless', 'incomprehension', 'uncomprehending', 'incredulous']
senticnet['unseemly'] = ['-0.64', '0', '0.52', '-0.53', '#sadness', '#disgust', 'negative', '-0.56', 'wrong', 'incorrect', 'unbecoming', 'uncomely', 'improper']
senticnet['unseen'] = ['-0.14', '0.061', '-0.11', '0.101', '#sadness', '#fear', 'negative', '-0.02', 'out_of_sight', 'hide', 'conceal', 'hide_evidence', 'hidden']
senticnet['unselfish'] = ['0.864', '0', '0', '0.875', '#joy', '#admiration', 'positive', '0.869', 'selfless', 'altruistic', 'self-sacrificing', 'self-less', 'selflessness']
senticnet['unselfish_devotion'] = ['0.857', '0.824', '0', '0.708', '#joy', '#interest', 'positive', '0.796', 'mandatory', 'gentleman', 'obey', 'virtue', 'dignity']
senticnet['unselfishly'] = ['0.915', '0', '0', '0.779', '#joy', '#admiration', 'positive', '0.847', 'selflessly', 'sacrificially', 'unstintingly', 'selfishly', 'altruistically']
senticnet['unselfishness'] = ['0.764', '0', '0', '0.748', '#joy', '#admiration', 'positive', '0.756', 'selflessness', 'self-sacrifice', 'magnanimity', 'humbleness', 'fearlessness']
senticnet['unsellable'] = ['-0.94', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.83', 'unmarketable', 'unsaleable', 'marketable', 'salable', 'unsalable']
senticnet['unsentimental'] = ['0', '0.764', '0', '0.845', '#interest', '#admiration', 'positive', '0.804', 'empathetically', 'empathic', 'empathetic', 'kind-heartedness', 'kindheartedness']
senticnet['unsentimentally'] = ['0', '0.764', '0', '0.845', '#interest', '#admiration', 'positive', '0.804', 'empathetically', 'empathic', 'empathetic', 'kind-heartedness', 'kindheartedness']
senticnet['unsere'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['unserviceable'] = ['-0.90', '0', '0.517', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'outdated', 'noncurrent', 'disused', 'obsolete', 'superannuated']
senticnet['unsettle'] = ['-0.14', '0', '-0.15', '-0.14', '#fear', '#disgust', 'negative', '-0.14', 'nightmare', 'sleepwalk', 'reflect', 'pleasant_thought', 'doze_off']
senticnet['unsettled'] = ['-0.60', '0', '0.717', '-0.84', '#anger', '#disgust', 'negative', '-0.72', 'undetermined', 'undecided', 'tentative', 'unresolved', 'open']
senticnet['unsettlingly'] = ['-0.80', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.8', 'scared', 'frighten_away', 'scare_away', 'frighten_off', 'scare_off']
senticnet['unshakable'] = ['0.779', '0.813', '0', '0', '#joy', '#interest', 'positive', '0.796', 'incontrovertible', 'bulletproof', 'incontestable', 'armored', 'unassailable']
senticnet['unshaken'] = ['0.516', '-0.87', '0', '0.492', '#joy', '#surprise', 'positive', '0.626', 'undismayed', 'resolute', 'undaunted', 'firm', 'stout']
senticnet['unshaped'] = ['-0.64', '0', '0.556', '-0.74', '#sadness', '#disgust', 'negative', '-0.64', 'formless', 'amorphous', 'shapeless', 'unshapen', 'unformed']
senticnet['unshapely'] = ['-0.73', '0', '0.904', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'acromegalic', 'shapeless', 'lumpy', 'deformed', 'malformed']
senticnet['unshapen'] = ['-0.64', '0', '0.557', '-0.73', '#sadness', '#disgust', 'negative', '-0.64', 'formless', 'amorphous', 'shapeless', 'unshaped', 'unformed']
senticnet['unshared'] = ['-0.94', '0', '0.752', '0', '#sadness', '#anger', 'negative', '-0.84', 'inventory', 'unused', 'unsold', 'vacant', 'uncompleted']
senticnet['unsharp'] = ['0.973', '-0.77', '0', '0.983', '#joy', '#admiration', 'positive', '0.911', 'opaqueness', 'faintness', 'dimness', 'opacity', 'vagueness']
senticnet['unsharpened'] = ['-0.04', '-0.04', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.02', 'blunted', 'dull', 'insensitive', 'blunt', 'edgeless']
senticnet['unshaved'] = ['-0.94', '0', '0.837', '-0.92', '#sadness', '#disgust', 'negative', '-0.90', 'bearded', 'barbate', 'bewhiskered', 'whiskered', 'whiskery']
senticnet['unshaven'] = ['-0.64', '0', '0.632', '-0.61', '#sadness', '#anger', 'negative', '-0.62', 'bearded', 'barbate', 'bewhiskered', 'whiskered', 'whiskery']
senticnet['unshod'] = ['-0.84', '0', '0.553', '-0.89', '#sadness', '#disgust', 'negative', '-0.76', 'encumbrance', 'incumbrance', 'block', 'bar', 'blockade']
senticnet['unshowy'] = ['0.76', '-0.59', '0', '0.713', '#joy', '#admiration', 'positive', '0.689', 'spare_enemy', 'magnanimous', 'generous', 'forgiving', 'deal_gently']
senticnet['unsightliness'] = ['-0.87', '0', '0.584', '-0.71', '#sadness', '#disgust', 'negative', '-0.72', 'ugliness', 'eyesore', 'hideousness', 'unattractiveness', 'garishness']
senticnet['unsightly'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dirtiness', 'dinge']
senticnet['unsigned_char'] = ['-0.04', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.06', 'chemical_reaction', 'burn', 'chemical_process', 'get_burnt', 'set_on_fire']
senticnet['unsigned_int'] = ['-0.04', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.06', 'chemical_reaction', 'burn', 'chemical_process', 'get_burnt', 'set_on_fire']
senticnet['unsigned_integer'] = ['-0.04', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.06', 'chemical_reaction', 'burn', 'chemical_process', 'get_burnt', 'set_on_fire']
senticnet['unsigned_letter'] = ['0.038', '-0.03', '0', '0.079', '#surprise', '#admiration', 'positive', '0.052', 'sender', 'message', 'note', 'post', 'email']
senticnet['unsimilarity'] = ['-0.75', '0', '0.69', '-0.69', '#sadness', '#disgust', 'negative', '-0.71', 'unlikeness', 'dissimilitude', 'difference', 'heterology', 'disparateness']
senticnet['unskilful'] = ['-0.68', '0', '0.82', '-0.96', '#anger', '#disgust', 'negative', '-0.82', 'deliberateness', 'leisureliness', 'procrastination', 'rate', 'pace']
senticnet['unskilled'] = ['-0.99', '0', '0.501', '-0.83', '#sadness', '#disgust', 'negative', '-0.77', 'lowly', 'bungled', 'botched', 'fumbling', 'bungling']
senticnet['unskillful'] = ['0.983', '-0.63', '0', '0.697', '#joy', '#admiration', 'positive', '0.772', 'amateurishness', 'inability', 'ineptness', 'clumsiness', 'awkwardness']
senticnet['unskillfulness'] = ['0.983', '-0.63', '0', '0.697', '#joy', '#admiration', 'positive', '0.772', 'amateurishness', 'inability', 'ineptness', 'clumsiness', 'awkwardness']
senticnet['unsmiling'] = ['-0.82', '0', '0', '-0.86', '#sadness', '#disgust', 'negative', '-0.84', 'expressionless', 'impassive', 'stolid', 'uncomprehending', 'scowling']
senticnet['unsmooth'] = ['-0.77', '0', '0.626', '-0.78', '#sadness', '#disgust', 'negative', '-0.72', 'bumpy', 'scabrous', 'lepidote', 'leprose', 'roughish']
senticnet['unsociability'] = ['0.72', '-0.72', '0', '0.506', '#joy', '#surprise', 'positive', '0.651', 'standoffishness', 'withdrawnness', 'closeness', 'secretiveness', 'remoteness']
senticnet['unsociable'] = ['0', '0', '-0.70', '-0.95', '#fear', '#disgust', 'negative', '-0.83', 'moody', 'dour', 'glum', 'dark', 'glowering']
senticnet['unsociableness'] = ['0.831', '-0.98', '0', '0.868', '#surprise', '#admiration', 'positive', '0.894', 'standoffishness', 'withdrawnness', 'closeness', 'secretiveness', 'remoteness']
senticnet['unsocial'] = ['-0.98', '0', '0.582', '-0.56', '#sadness', '#anger', 'negative', '-0.70', 'asocial', 'antisocial', 'lonely', 'alone', 'solitary']
senticnet['unsold'] = ['-0.94', '0', '0.753', '0', '#sadness', '#anger', 'negative', '-0.84', 'inventory', 'unused', 'unshared', 'vacant', 'uncompleted']
senticnet['unsold_inventory'] = ['-0.03', '0.139', '0', '0.109', '#interest', '#admiration', 'positive', '0.07', 'exchange_money', 'purchase', 'sell_goods', 'make_rich', 'incur_debt']
senticnet['unsolicited_bulk_email'] = ['-0.05', '-0.03', '0.023', '-0.09', '#sadness', '#disgust', 'negative', '-0.03', 'moldy_food', 'celery', 'paper_product', 'orange_juice', 'meat']
senticnet['unsolicited_commercial_email'] = ['-0.05', '-0.03', '0.023', '-0.09', '#sadness', '#disgust', 'negative', '-0.03', 'moldy_food', 'celery', 'paper_product', 'orange_juice', 'meat']
senticnet['unsolicited_email'] = ['-0.05', '-0.03', '0.023', '-0.09', '#sadness', '#disgust', 'negative', '-0.03', 'moldy_food', 'celery', 'paper_product', 'orange_juice', 'meat']
senticnet['unsolicited_mail'] = ['-0.05', '-0.03', '0.023', '-0.09', '#sadness', '#disgust', 'negative', '-0.03', 'moldy_food', 'celery', 'paper_product', 'orange_juice', 'meat']
senticnet['unsolvable'] = ['0.898', '0', '0', '0.951', '#joy', '#admiration', 'positive', '0.924', 'insurmountable', 'insuperable', 'surmountable', 'unsurmountable', 'insolvable']
senticnet['unsolved_mystery'] = ['-0.89', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.80', 'riddle', 'obituary', 'editorial', 'news_story', 'article']
senticnet['unsound'] = ['-0.89', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.85', 'unsoundness', 'defective', 'unwholesome', 'shaky', 'diseased']
senticnet['unsound_banking_practice'] = ['-0.74', '0', '0.781', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'reprimand', 'caution', 'big_monster', 'kidnap', 'mock']
senticnet['unsound_manner'] = ['-0.74', '0', '0.781', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'reprimand', 'caution', 'big_monster', 'kidnap', 'mock']
senticnet['unsound_practice'] = ['-0.74', '0', '0.781', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'reprimand', 'caution', 'big_monster', 'kidnap', 'mock']
senticnet['unsounded'] = ['-1', '0', '0.793', '-0.71', '#sadness', '#anger', 'negative', '-0.83', 'silent', 'voiceless', 'supersonic', 'breathed', 'inaudible']
senticnet['unsoundness'] = ['-0.9', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.86', 'unsound', 'defective', 'unwholesome', 'shaky', 'diseased']
senticnet['unsparing'] = ['0', '0', '-0.90', '-0.74', '#fear', '#disgust', 'negative', '-0.82', 'munificent', 'overgenerous', 'lavish', 'too-generous', 'unstinted']
senticnet['unsparingly'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'intrepidly', 'hardy', 'fearlessness', 'unafraid', 'unfearing']
senticnet['unspeakable'] = ['-0.75', '0', '0.765', '0', '#sadness', '#anger', 'negative', '-0.76', 'describe', 'objectionable', 'unutterable', 'permitted', 'ineffable']
senticnet['unspeakable_horror'] = ['-0.97', '0', '-0.81', '-0.95', '#sadness', '#disgust', 'negative', '-0.91', 'dislike', 'loathe', 'queasy', 'horrid', 'outrageous']
senticnet['unspeakably'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'lamentably', 'woefully', 'bereaved', 'bereft', 'grief-stricken']
senticnet['unspecific'] = ['0', '0.987', '0', '0.721', '#interest', '#admiration', 'positive', '0.854', 'wide', 'across-the-board', 'full', 'liberal', 'broader']
senticnet['unspectacular'] = ['-0.85', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.80', 'unremarkable', 'unexciting', 'unexceptional', 'undistinguished', 'unmemorable']
senticnet['unspell'] = ['0.915', '-0.89', '0', '0.936', '#joy', '#admiration', 'positive', '0.916', 'free', 'unloose', 'unloosen', 'liberate', 'release']
senticnet['unspiritual'] = ['-0.52', '0', '0.604', '-0.82', '#anger', '#disgust', 'negative', '-0.65', 'discernment', 'sagacity', 'sagaciousness', 'confidentiality', 'judgment']
senticnet['unsplit'] = ['0.909', '0.986', '0', '0', '#joy', '#interest', 'positive', '0.948', 'separation', 'divide', 'division', 'slash', 'bagle']
senticnet['unspoiled'] = ['0.701', '0', '0', '0.812', '#joy', '#admiration', 'positive', '0.756', 'unspoilt', 'idyllic', 'picturesque', 'tranquil', 'pristine']
senticnet['unspoilt'] = ['0.703', '0', '0', '0.816', '#joy', '#admiration', 'positive', '0.759', 'unspoiled', 'idyllic', 'picturesque', 'tranquil', 'pristine']
senticnet['unspoken'] = ['0.66', '-0.58', '0', '0.931', '#joy', '#admiration', 'positive', '0.724', 'tacit', 'understand', 'unvoiced', 'silent', 'inexplicit']
senticnet['unstable'] = ['-0.22', '-0.20', '0.134', '-0.32', '#sadness', '#disgust', 'negative', '-0.12', 'rocky', 'foolish', 'naughty', 'small_boat', 'big_boat']
senticnet['unstable_angina'] = ['-0.20', '0', '-0.22', '-0.19', '#sadness', '#fear', 'negative', '-0.20', 'go_hospital', 'death', 'die', 'take_aspirin', 'heart_attack']
senticnet['unstable_nucleus'] = ['-0.77', '0', '0.764', '-0.54', '#sadness', '#anger', 'negative', '-0.69', 'pullulation', 'apomixis', 'blastogenesis', 'budding', 'asexual_reproduction']
senticnet['unsteadily'] = ['-0.98', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.87', 'fitfully', 'shake', 'unsteady', 'totter', 'vacillate']
senticnet['unsteadiness'] = ['-0.91', '0', '0.887', '0', '#sadness', '#anger', 'negative', '-0.90', 'yaw', 'falter', 'fluctuation', 'giddy', 'unsteady']
senticnet['unsteady'] = ['-0.56', '0', '0.849', '-0.61', '#anger', '#disgust', 'negative', '-0.67', 'convulsive', 'arrhythmic', 'jerky', 'spasmodic', 'spastic']
senticnet['unsteady_gait'] = ['0.983', '-0.63', '0', '0.697', '#joy', '#admiration', 'positive', '0.772', 'amateurishness', 'inability', 'ineptness', 'unskillfulness', 'awkwardness']
senticnet['unstick'] = ['-0.63', '0', '0.829', '-0.79', '#anger', '#disgust', 'negative', '-0.75', 'stuffiness', 'blocked', 'problem', 'traffic', 'congestion']
senticnet['unstigmatized'] = ['-0.88', '0', '0.867', '0', '#sadness', '#anger', 'negative', '-0.87', 'stigma', 'disgraceful', 'stigmatize', 'brand', 'stigmatized']
senticnet['unstinted'] = ['0', '0', '-0.89', '-0.74', '#fear', '#disgust', 'negative', '-0.82', 'unsparing', 'overgenerous', 'lavish', 'too-generous', 'munificent']
senticnet['unstinting'] = ['0', '0', '0.801', '-0.83', '#anger', '#disgust', 'negative', '-0.81', 'tireless', 'indefatigable', 'unflagging', 'untiring', 'unremitting']
senticnet['unstintingly'] = ['0.91', '0', '0', '0.772', '#joy', '#admiration', 'positive', '0.841', 'selflessly', 'sacrificially', 'unselfishly', 'selfishly', 'altruistically']
senticnet['unstoppable'] = ['-0.91', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.89', 'juggernaut', 'relentless', 'invincible', 'unleashing', 'unrelenting']
senticnet['unstressed'] = ['-0.90', '0', '0.966', '-0.79', '#sadness', '#anger', 'negative', '-0.89', 'feminine', 'weak', 'light', 'unemphatic', 'unaccented']
senticnet['unstructured_peer'] = ['0.848', '0.735', '0', '0', '#joy', '#interest', 'positive', '0.791', 'observer', 'peek', 'appearance', 'seer', 'glimpse']
senticnet['unstrung'] = ['0.92', '-0.57', '0', '0.758', '#joy', '#admiration', 'positive', '0.751', 'bail_out', 'unspell', 'bail', 'unchain', 'run']
senticnet['unstuck'] = ['-0.63', '0', '0.829', '-0.79', '#anger', '#disgust', 'negative', '-0.75', 'stuffiness', 'blocked', 'problem', 'traffic', 'congestion']
senticnet['unsuasible'] = ['-0.77', '0', '0.639', '-0.79', '#sadness', '#disgust', 'negative', '-0.73', 'unpersuadable', 'insusceptible', 'unresponsive', 'resistant', 'incapable']
senticnet['unsubdivided'] = ['0.735', '0.93', '0', '0', '#joy', '#interest', 'positive', '0.833', 'dim-witted', 'simplest', 'simple', 'dewy-eyed', 'simple-minded']
senticnet['unsubduable'] = ['-0.73', '0', '0.732', '-0.53', '#sadness', '#anger', 'negative', '-0.66', 'insurmountable', 'insuperable', 'inexpugnable', 'indomitable', 'unconquerable']
senticnet['unsubmissive'] = ['-0.98', '0', '0.537', '-0.72', '#sadness', '#disgust', 'negative', '-0.75', 'reprimand', 'wigging', 'rebuke', 'reproof', 'reproval']
senticnet['unsubscribe'] = ['0.949', '0.866', '0', '0', '#joy', '#interest', 'positive', '0.907', 'snail_mail', 'postage', 'send_package', 'buy_stamp', 'send_letter']
senticnet['unsubsidized_employment'] = ['0.85', '0', '0', '0', '#joy', '#joy', 'positive', '0.85', 'task', 'labor', 'police_work', 'employ', 'career']
senticnet['unsubstantial'] = ['-0.86', '0', '0.632', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'airy', 'shadowy', 'stringy', 'wraithlike', 'ethereal']
senticnet['unsubstantiated'] = ['0', '0.828', '0', '0.726', '#interest', '#admiration', 'positive', '0.777', 'untested', 'unproven', 'untried', 'unproved', 'unfounded']
senticnet['unsuccessful'] = ['-0.74', '0', '0.792', '-0.57', '#sadness', '#anger', 'negative', '-0.70', 'lose', 'unplaced', 'out', 'pointless', 'defeated']
senticnet['unsuccessful_attempt'] = ['0.954', '-0.87', '0', '0.583', '#joy', '#surprise', 'positive', '0.802', 'penetration', 'air_raid', 'air_attack', 'swoop', 'incursion']
senticnet['unsuccessful_coup'] = ['-0.84', '0', '0.744', '-0.58', '#sadness', '#anger', 'negative', '-0.72', 'subversion', 'subversive_activity', 'termination', 'ending', 'conclusion']
senticnet['unsuccessful_person'] = ['-0.57', '0', '0.57', '-0.94', '#sadness', '#disgust', 'negative', '-0.69', 'unfortunate', 'insolvent', 'underdog', 'bankrupt', 'dud']
senticnet['unsuitability'] = ['0.605', '-0.83', '0', '0.935', '#surprise', '#admiration', 'positive', '0.792', 'quality', 'unfitness', 'inappropriateness', 'inconvenience', 'unworthiness']
senticnet['unsuitable'] = ['-0.60', '0', '0.832', '-0.53', '#sadness', '#anger', 'negative', '-0.65', 'ineligible', 'undesirable', 'unqualified', 'disqualified', 'unentitled']
senticnet['unsuitableness'] = ['0.93', '-0.50', '0', '0.694', '#joy', '#admiration', 'positive', '0.709', 'quality', 'unfitness', 'inappropriateness', 'inconvenience', 'unworthiness']
senticnet['unsuited'] = ['-0.84', '0', '0.72', '-0.77', '#sadness', '#disgust', 'negative', '-0.77', 'mismated', 'incompatible', 'unmatched', 'odd', 'mismatched']
senticnet['unsung'] = ['-0.75', '0', '0.563', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'unacknowledged', 'unrecognized', 'unappreciated', 'unvalued', 'unconfessed']
senticnet['unsupportable'] = ['-0.88', '0', '0.907', '-0.84', '#sadness', '#anger', 'negative', '-0.88', 'intolerable', 'unacceptable', 'bitter', 'impossible', 'insufferable']
senticnet['unsupported'] = ['-0.97', '0', '0.586', '-0.93', '#sadness', '#disgust', 'negative', '-0.83', 'idle', 'baseless', 'groundless', 'unfounded', 'unwarranted']
senticnet['unsupportive'] = ['-0.56', '0', '0.743', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'negligent', 'derelict', 'inattentive', 'delinquent', 'lax']
senticnet['unsure'] = ['-0.73', '0', '0.786', '-0.75', '#anger', '#disgust', 'negative', '-0.75', 'diffidence', 'self-distrust', 'self-doubt', 'diffidently', 'unassertive']
senticnet['unsurmountable'] = ['0.907', '0', '0', '0.959', '#joy', '#admiration', 'positive', '0.933', 'insurmountable', 'insuperable', 'surmountable', 'unsolvable', 'insolvable']
senticnet['unsurpassable'] = ['0.816', '-0.59', '0', '0.947', '#joy', '#admiration', 'positive', '0.787', 'matchless', 'nonpareil', 'unique', 'incomparable', 'unmatchable']
senticnet['unsurpassed'] = ['0', '0', '-1', '-0.91', '#fear', '#disgust', 'negative', '-0.95', 'exceptional', 'superb', 'craftsmanship', 'impeccable', 'unparalleled']
senticnet['unsurprisingly'] = ['0.706', '0.945', '0', '0', '#joy', '#interest', 'positive', '0.825', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['unsusceptibility'] = ['0.725', '-0.57', '0', '0.613', '#joy', '#admiration', 'positive', '0.636', 'amnesty', 'indemnity', 'diplomatic_immunity', 'exemption', 'impunity']
senticnet['unsusceptible'] = ['-0.96', '0', '0.91', '-0.61', '#sadness', '#anger', 'negative', '-0.82', 'unpersuadable', 'unsuasible', 'unresponsive', 'resistant', 'incapable']
senticnet['unsuspecting'] = ['-0.83', '0', '0.773', '0', '#sadness', '#anger', 'negative', '-0.80', 'lack', 'swarming', 'suspicious', 'unsuspectingly', 'suspicion']
senticnet['unsuspecting_prey'] = ['0.812', '0.872', '0', '0', '#joy', '#interest', 'positive', '0.842', 'insect', 'fly_away', 'small_insect', 'maggot', 'chihuahua']
senticnet['unsuspecting_user'] = ['-0.02', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.07', 'army', 'military', 'infantry', 'samurai', 'soldier']
senticnet['unsuspectingly'] = ['-0.83', '0', '0.769', '0', '#sadness', '#anger', 'negative', '-0.80', 'lack', 'swarming', 'suspicious', 'unsuspecting', 'suspicion']
senticnet['unswayed'] = ['-0.92', '0', '0.723', '-0.85', '#sadness', '#disgust', 'negative', '-0.83', 'unimpressed', 'untouched', 'uninfluenced', 'immune', 'unaffected']
senticnet['unsweetened'] = ['0', '0', '0.78', '-0.94', '#anger', '#disgust', 'negative', '-0.86', 'semi-sweet', 'semisweet', 'unsalted', 'pecan', 'bittersweet']
senticnet['unswerving'] = ['0', '0.825', '0', '0.712', '#interest', '#admiration', 'positive', '0.768', 'untiring', 'unflagging', 'unwavering', 'unfaltering', 'unfailing']
senticnet['unswerving_devotion'] = ['0', '0', '0.764', '-0.77', '#anger', '#disgust', 'negative', '-0.77', 'restriction', 'tragedy', 'perpetrator', 'dominate', 'control']
senticnet['unswerving_loyalty'] = ['0.997', '0.796', '0', '0.89', '#joy', '#admiration', 'positive', '0.894', 'devotion', 'bravery', 'adoration', 'zeal', 'charm']
senticnet['unsympathetic'] = ['0', '0', '0.706', '-0.93', '#anger', '#disgust', 'negative', '-0.81', 'non-receptive', 'unappreciative', 'unenthusiastic', 'unwelcoming', 'unreceptive']
senticnet['untalented'] = ['-0.73', '0', '0', '-0.94', '#sadness', '#disgust', 'negative', '-0.84', 'has-been', 'down-and-out', 'down-on-his-luck', 'over-the-hill', 'washed-up']
senticnet['untalkative'] = ['-0.78', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.76', 'retiring', 'restrained', 'unemotional', 'reticent', 'self-effacing']
senticnet['untamable'] = ['0.118', '0.28', '-0.20', '0.216', '#interest', '#admiration', 'positive', '0.103', 'friend_person', 'sense_fear', 'mark_territory', 'trip_person', 'care_deeply']
senticnet['untameable'] = ['0.118', '0.28', '-0.20', '0.216', '#interest', '#admiration', 'positive', '0.103', 'friend_person', 'sense_fear', 'mark_territory', 'trip_person', 'care_deeply']
senticnet['untamed'] = ['-0.63', '0', '0.991', '-0.73', '#anger', '#disgust', 'negative', '-0.78', 'undomesticated', 'feral', 'ferine', 'savage', 'unbroken']
senticnet['untamed_nature'] = ['-0.56', '0', '0.746', '-0.62', '#anger', '#disgust', 'negative', '-0.64', 'contrariness', 'fractiousness', 'intractability', 'intractableness', 'perversity']
senticnet['untampered'] = ['-0.98', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.87', 'spoliation', 'tobacco', 'tool', 'tamper', 'caplet']
senticnet['untangle'] = ['-0.77', '0', '0.792', '0', '#sadness', '#anger', 'negative', '-0.78', 'untangling', 'disentangle', 'unravel', 'tangle', 'tangled']
senticnet['untangling'] = ['-0.78', '0', '0.782', '0', '#sadness', '#anger', 'negative', '-0.78', 'untangle', 'disentangle', 'unravel', 'tangle', 'tangled']
senticnet['untapped_talent'] = ['0.073', '0.228', '-0.10', '0.263', '#interest', '#admiration', 'positive', '0.114', 'creativity', 'blueprint', 'skill', 'expressive', 'competence']
senticnet['untaught'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'amateur', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
senticnet['untellable'] = ['0', '0', '-0.87', '-0.77', '#fear', '#disgust', 'negative', '-0.82', 'ineffable', 'unspeakable', 'indescribable', 'indefinable', 'unutterable']
senticnet['untenable'] = ['-0.98', '0', '0.646', '-0.98', '#sadness', '#disgust', 'negative', '-0.87', 'indefensible', 'reasonless', 'unreasonable', 'senseless', 'unwarranted']
senticnet['untenable_position'] = ['-0.81', '0', '0.979', '-0.88', '#anger', '#disgust', 'negative', '-0.89', 'vexation', 'dead_weight', 'imposition', 'worry', 'concern']
senticnet['untermenschen'] = ['-0.98', '0', '0.582', '-0.56', '#sadness', '#anger', 'negative', '-0.70', 'unsocial', 'antisocial', 'lonely', 'alone', 'solitary']
senticnet['untested'] = ['0.96', '0', '0', '0.792', '#joy', '#admiration', 'positive', '0.876', 'new', 'young', 'youthful', 'offspring', 'immature']
senticnet['unthankful'] = ['0.563', '-0.56', '0', '0.722', '#joy', '#admiration', 'positive', '0.615', 'mortal', 'cutthroat', 'bowelless', 'tigerish', 'pitiless']
senticnet['unthinkable'] = ['0', '0', '0.97', '-0.89', '#anger', '#disgust', 'negative', '-0.93', 'unimaginable', 'improbable', 'incredible', 'unthinkably', 'ludicrous']
senticnet['unthinkably'] = ['0', '0', '0.965', '-0.89', '#anger', '#disgust', 'negative', '-0.93', 'unimaginable', 'improbable', 'incredible', 'unthinkable', 'ludicrous']
senticnet['unthinking'] = ['-0.72', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.74', 'inconsiderate', 'thoughtless', 'unreflective', 'unthoughtful', 'selfish']
senticnet['unthinkingly'] = ['0.653', '0.635', '-0.24', '0.294', '#joy', '#interest', 'positive', '0.335', 'merrily', 'jubilantly', 'happily', 'mirthfully', 'gayly']
senticnet['unthoughtful'] = ['-0.72', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.74', 'unthinking', 'thoughtless', 'unreflective', 'inconsiderate', 'selfish']
senticnet['unthoughtfulness'] = ['-0.93', '0', '0.672', '-0.94', '#sadness', '#disgust', 'negative', '-0.85', 'recklessness', 'foolhardiness', 'rashness', 'trait', 'impulsiveness']
senticnet['untidiness'] = ['-0.56', '0', '0.909', '-0.62', '#anger', '#disgust', 'negative', '-0.69', 'unkemptness', 'disorder', 'slovenliness', 'sloppiness', 'disorderliness']
senticnet['untidy'] = ['-0.85', '0', '0.985', '-0.71', '#sadness', '#anger', 'negative', '-0.84', 'messy', 'mussy', 'disheveled', 'dirty', 'frowzled']
senticnet['untie'] = ['0.502', '-0.51', '0', '0.669', '#joy', '#surprise', 'positive', '0.561', 'unlace', 'unfold', 'undo', 'unbrace', 'spread']
senticnet['untimely'] = ['0', '0', '-0.70', '-0.91', '#fear', '#disgust', 'negative', '-0.80', 'wrong', 'prematurely', 'unseasonable', 'premature', 'ill-timed']
senticnet['untimely_death'] = ['-0.87', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.91', 'loss_oflife', 'chest_pain', 'die', 'tumor', 'end_life']
senticnet['untiring'] = ['0', '0', '0.808', '-0.82', '#anger', '#disgust', 'negative', '-0.81', 'tireless', 'indefatigable', 'unflagging', 'unstinting', 'unremitting']
senticnet['untiring_energy'] = ['0.497', '0.594', '-0.2', '0.263', '#joy', '#interest', 'positive', '0.288', 'imperturbability', 'imperturbableness', 'placid', 'placidness', 'placidly']
senticnet['untiring_industry'] = ['0.646', '-0.9', '0', '0.684', '#surprise', '#admiration', 'positive', '0.743', 'acquirement', 'skill', 'accomplishment', 'acquisition', 'attainment']
senticnet['untiring_zeal'] = ['0.215', '0.194', '-0.20', '0.279', '#joy', '#admiration', 'positive', '0.121', 'use_match', 'gather_kindle', 'combustible_material', 'rub_two_stick_together', 'lighter']
senticnet['unto'] = ['0', '0.209', '-0.11', '0.338', '#interest', '#admiration', 'positive', '0.143', 'direction', 'toward', 'forehead', 'upon', 'go_toward']
senticnet['unto_christ'] = ['0.741', '-0.84', '0', '0.543', '#joy', '#surprise', 'positive', '0.711', 'integrity', 'righteousness', 'scrupulousness', 'incorruptness', 'honestness']
senticnet['unto_god'] = ['0.741', '-0.84', '0', '0.543', '#joy', '#surprise', 'positive', '0.711', 'integrity', 'righteousness', 'scrupulousness', 'incorruptness', 'honestness']
senticnet['unto_hym'] = ['-0.52', '0', '0.701', '-0.50', '#sadness', '#anger', 'negative', '-0.57', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['unto_salvation'] = ['0.082', '0.17', '-0.07', '0.291', '#interest', '#admiration', 'positive', '0.117', 'sanitary', 'sterile', 'pure', 'use_clean', 'refined']
senticnet['untold'] = ['0', '0.834', '0', '0.914', '#interest', '#admiration', 'positive', '0.874', 'secret', 'secret_agent', 'secret_organization', 'mysterious', 'mystic']
senticnet['untouchability'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'vassalage']
senticnet['untouchable'] = ['-0.75', '0', '0.606', '-0.74', '#sadness', '#disgust', 'negative', '-0.70', 'out', 'impermissible', 'unmentionable', 'taboo', 'tabu']
senticnet['untouched'] = ['-0.92', '0', '0.73', '-0.85', '#sadness', '#disgust', 'negative', '-0.83', 'unimpressed', 'immune', 'uninfluenced', 'unswayed', 'unaffected']
senticnet['untoward'] = ['-0.74', '0', '0.982', '-0.81', '#anger', '#disgust', 'negative', '-0.84', 'inauspicious', 'admonishing', 'reproachful', 'reproving', 'unfavorable']
senticnet['untracked'] = ['0', '0', '0.916', '-0.88', '#anger', '#disgust', 'negative', '-0.89', 'outback', 'trackless', 'pathless', 'roadless', 'inaccessible']
senticnet['untraditional'] = ['0', '-0.77', '0', '0.887', '#surprise', '#admiration', 'positive', '0.831', 'unconventional', 'unorthodox', 'idiosyncratic', 'inventive', 'heretical']
senticnet['untransferable'] = ['-0.80', '0', '0.815', '-0.98', '#anger', '#disgust', 'negative', '-0.86', 'infrangible', 'nontransferable', 'unassignable', 'inalienable', 'inviolable']
senticnet['untranslatable'] = ['0.926', '0.873', '0', '0', '#joy', '#interest', 'positive', '0.899', 'unmentionable', 'expletive', 'four-letter', 'undecipherable', 'unprintable']
senticnet['untransparent'] = ['-0.55', '0', '0.931', '-0.74', '#anger', '#disgust', 'negative', '-0.74', 'totalitarian', 'authoritarian', 'dictatorial', 'despotic', 'monarchal']
senticnet['untreated_chlamydia'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'gastroenteritis', 'stomach_flu', 'intestinal_flu', 'inflammatory_disease']
senticnet['untreated_tooth_decay'] = ['0', '0', '0.813', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'ice_cream_melt', 'allergy', 'toothache', 'gonorrhea', 'tastebud']
senticnet['untreated_waste'] = ['-0.58', '0', '0.714', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'diversionist', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['untrendy'] = ['-0.87', '0', '0.83', '-0.61', '#sadness', '#anger', 'negative', '-0.77', 'out', 'dated', 'stodgy', 'prehistoric', 'antique']
senticnet['untried'] = ['0', '0.837', '0', '0.732', '#interest', '#admiration', 'positive', '0.784', 'untested', 'unsubstantiated', 'unproven', 'unproved', 'unfounded']
senticnet['untrodden'] = ['-0.80', '0', '0.761', '-0.70', '#sadness', '#anger', 'negative', '-0.75', 'immeasurable', 'limitless', 'measureless', 'unmeasured', 'abysmal']
senticnet['untroubled'] = ['-0.59', '0', '0.798', '-0.70', '#anger', '#disgust', 'negative', '-0.69', 'dreamless', 'undisturbed', 'unmolested', 'unworried', 'unconcerned']
senticnet['untrue'] = ['-0.67', '0', '0.701', '-0.88', '#anger', '#disgust', 'negative', '-0.75', 'perfidious', 'apostate', 'treacherous', 'unfaithful', 'punic']
senticnet['untruly'] = ['0.617', '-0.83', '0', '0.828', '#surprise', '#admiration', 'positive', '0.761', 'brag', 'embellish', 'overemphasize', 'overstress', 'vaunt']
senticnet['untrustiness'] = ['-0.61', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.69', 'untrustworthiness', 'irresponsibility', 'undependability', 'undependableness', 'unreliability']
senticnet['untrusting'] = ['0', '0', '-0.82', '-0.75', '#fear', '#disgust', 'negative', '-0.78', 'mistrustful', 'suspicious', 'leery', 'fishy', 'wary']
senticnet['untrustworthiness'] = ['-0.61', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.69', 'irresponsibility', 'untrustiness', 'undependability', 'undependableness', 'unreliability']
senticnet['untrustworthy'] = ['0', '0', '-0.98', '-0.99', '#fear', '#disgust', 'negative', '-0.98', 'shiftless', 'blackguard', 'deserve', 'lowlife', 'slick']
senticnet['untruth'] = ['-0.15', '-0.01', '-0.14', '0.066', '#sadness', '#fear', 'negative', '-0.05', 'dishonesty', 'falsehood', 'deceit', 'fallacy', 'slumber']
senticnet['untruthful'] = ['-0.79', '0', '0.734', '0', '#sadness', '#anger', 'negative', '-0.76', 'mendaciousness', 'mendacity', 'deceit', 'dishonesty', 'mendacious']
senticnet['untruthfully'] = ['-0.81', '0', '0.97', '0', '#sadness', '#anger', 'negative', '-0.89', 'fable', 'forgery', 'oleaginous', 'misspeaking', 'falsely']
senticnet['untune'] = ['0.591', '-0.59', '0', '0.751', '#joy', '#surprise', 'positive', '0.644', 'enervate', 'faze', 'unnerve', 'unsettle', 'dissolve']
senticnet['unusable'] = ['-0.86', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.85', 'inoperable', 'inoperative', 'malfunctioning', 'non-functioning', 'deactivated']
senticnet['unused'] = ['-0.95', '0', '0.751', '0', '#sadness', '#anger', 'negative', '-0.85', 'inventory', 'unsold', 'unshared', 'vacant', 'uncompleted']
senticnet['unusefulness'] = ['0.526', '-0.73', '0', '0.951', '#surprise', '#admiration', 'positive', '0.739', 'quality', 'futility', 'impracticality', 'worthlessness', 'impracticability']
senticnet['unusquisque'] = ['-0.64', '0', '0.625', '-0.88', '#sadness', '#disgust', 'negative', '-0.71', 'difficulty', 'awkwardness', 'flea_bite', 'difficultness', 'cumbersomeness']
senticnet['unusual'] = ['-0.22', '-0.20', '0.134', '-0.32', '#sadness', '#disgust', 'negative', '-0.12', 'uncommon', 'strange', 'unpleasant', 'unfamiliar', 'unaccustomed']
senticnet['unusual_face'] = ['-0.08', '0', '0.015', '0', '#sadness', '#anger', 'negative', '-0.05', 'use_hand_gesture', 'make_unusual_face', 'move_tongue', 'make_gesture', 'share_idea']
senticnet['unusual_feat'] = ['0.749', '0', '0', '0.952', '#joy', '#admiration', 'positive', '0.851', 'high_class', 'citation', 'suspicion', 'goodness', 'prosperity']
senticnet['unusual_language'] = ['-0.04', '-0.02', '0.041', '-0.03', '#sadness', '#anger', 'negative', '-0.02', 'jargon', 'lingo', 'microtext', 'chatspeak', 'slang']
senticnet['unusual_person'] = ['-0.88', '0', '0.882', '-0.58', '#sadness', '#anger', 'negative', '-0.78', 'mortal', 'behemoth', 'colossus', 'aberrant', 'giant']
senticnet['unusually'] = ['0', '0', '-0.84', '-0.86', '#fear', '#disgust', 'negative', '-0.85', 'outsize', 'premium', 'unusual', 'unwontedly', 'grubber']
senticnet['unusually_aggressive'] = ['0', '0', '-0.85', '-0.93', '#fear', '#disgust', 'negative', '-0.89', 'anger', 'animosity', 'hostility', 'malice', 'grudge']
senticnet['unusually_broad'] = ['0', '0.098', '-0.06', '0.118', '#interest', '#admiration', 'positive', '0.051', 'egret', 'tall_man', 'wide', 'wood_structure', 'large']
senticnet['unusually_cold'] = ['0.15', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.087', 'winter', 'winter_time', 'snow_fall', 'snowy', 'put_warm_clothes']
senticnet['unusually_cold_weather'] = ['0.15', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.087', 'winter', 'winter_time', 'snow_fall', 'snowy', 'put_warm_clothes']
senticnet['unusually_dry'] = ['-0.98', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.89', 'dry', 'lose_team', 'leave_out', 'disrespect', 'cause_suffer']
senticnet['unusually_heavy'] = ['-0.82', '0.226', '-0.46', '-0.05', '#sadness', '#fear', 'negative', '-0.27', 'heaviness', 'heavy_weight', 'heavy_person', 'heavy_breathe', 'heavy_air']
senticnet['unusually_long'] = ['0', '0', '-0.78', '-0.71', '#fear', '#disgust', 'negative', '-0.74', 'unretentive', 'poor', 'inadequate', 'little', 'forgetful']
senticnet['unusually_rich'] = ['0.843', '0.977', '0', '0', '#joy', '#interest', 'positive', '0.91', 'business', 'finance', 'revenue', 'wealth', 'income']
senticnet['unutterable'] = ['0', '0', '-0.86', '-0.77', '#fear', '#disgust', 'negative', '-0.82', 'untellable', 'unspeakable', 'indescribable', 'indefinable', 'ineffable']
senticnet['unutterably'] = ['-0.63', '0', '0.674', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'baneful', 'evil', 'maleficent', 'malevolent', 'malign']
senticnet['unvalued'] = ['-0.76', '0', '0.562', '-0.99', '#sadness', '#disgust', 'negative', '-0.77', 'unacknowledged', 'unrecognized', 'unappreciated', 'unsung', 'unconfessed']
senticnet['unvariedness'] = ['-0.83', '0', '0.757', '-0.65', '#sadness', '#anger', 'negative', '-0.74', 'invariability', 'invariableness', 'invariance', 'sameness', 'monotony']
senticnet['unvarying'] = ['-0.90', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.83', 'constant', 'invariant', 'changeless', 'work', 'number']
senticnet['unveil_truth'] = ['0.781', '0.942', '0', '0', '#joy', '#interest', 'positive', '0.861', 'add_knowledge', 'desire_education', 'learn_much', 'gain_knowledge', 'become_smart']
senticnet['unveiling_ceremony'] = ['0.82', '0', '0', '0.885', '#joy', '#admiration', 'positive', '0.853', 'religious_event', 'sunday', 'happy_event', 'tradition', 'wedding']
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senticnet['unviable'] = ['-0.54', '0', '0.739', '-0.64', '#anger', '#disgust', 'negative', '-0.64', 'unprofitable', 'idle', 'lean', 'dead', 'marginal']
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senticnet['unwanted'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'helpless', 'big_rat', 'rabies', 'small_cat', 'freckle']
senticnet['unwanted_email'] = ['-0.05', '-0.03', '0.023', '-0.09', '#sadness', '#disgust', 'negative', '-0.03', 'moldy_food', 'celery', 'paper_product', 'orange_juice', 'meat']
senticnet['unwanted_mail'] = ['-0.05', '-0.03', '0.023', '-0.09', '#sadness', '#disgust', 'negative', '-0.03', 'moldy_food', 'celery', 'paper_product', 'orange_juice', 'meat']
senticnet['unwanted_outcome'] = ['-0.93', '0', '0.812', '-0.96', '#sadness', '#disgust', 'negative', '-0.90', 'problem', 'trouble', 'punishment', 'exam_failed', 'job_interview_failed']
senticnet['unwanted_pregnancy'] = ['0.97', '0.729', '0', '0.946', '#joy', '#admiration', 'positive', '0.882', 'make_baby', 'give_birth_child', 'sexual_intercourse', 'find_partner', 'make_love']
senticnet['unwanted_sexual_contact'] = ['-0.64', '0', '0.854', '-0.83', '#anger', '#disgust', 'negative', '-0.77', 'crime', 'bigamy', 'regulatory_offence', 'sexual_abuse', 'sex_crime']
senticnet['unwanted_spam'] = ['-0.05', '-0.03', '0.023', '-0.09', '#sadness', '#disgust', 'negative', '-0.03', 'moldy_food', 'celery', 'paper_product', 'orange_juice', 'meat']
senticnet['unwarlike'] = ['-0.84', '0', '0.553', '-0.89', '#sadness', '#disgust', 'negative', '-0.76', 'encumbrance', 'incumbrance', 'block', 'bar', 'blockade']
senticnet['unwarrantable'] = ['-0.73', '0', '0.846', '-0.68', '#sadness', '#anger', 'negative', '-0.75', 'indefensible', 'unwarranted', 'insupportable', 'unjustifiable', 'inexcusable']
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senticnet['unwarranted_interference'] = ['-0.99', '0', '0.581', '-0.97', '#sadness', '#disgust', 'negative', '-0.84', 'obstruct_justice', 'football_rule', 'block_road', 'hinder', 'obstruction']
senticnet['unwary'] = ['0', '0', '-0.90', '-0.82', '#fear', '#disgust', 'negative', '-0.86', 'unsuspecting', 'ensnare', 'entrap', 'unknowing', 'gullible']
senticnet['unwashed'] = ['0', '0', '-0.92', '-0.79', '#fear', '#disgust', 'negative', '-0.85', 'rome', 'plebe', 'patrician', 'plebeian', 'marcellus']
senticnet['unwavering'] = ['0', '0.862', '0', '0.795', '#interest', '#admiration', 'positive', '0.829', 'unswerving', 'steadfast', 'unyielding', 'uncompromising', 'unshakable']
senticnet['unwavering_loyalty'] = ['0.997', '0.796', '0', '0.89', '#joy', '#admiration', 'positive', '0.894', 'devotion', 'bravery', 'adoration', 'zeal', 'charm']
senticnet['unwaveringly'] = ['0', '0.905', '0', '0.874', '#interest', '#admiration', 'positive', '0.889', 'steadfastly', 'resolutely', 'doggedly', 'staunchly', 'obstinately']
senticnet['unwed'] = ['-0.73', '0', '0.723', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'mateless', 'unwedded', 'divorced', 'unmarried', 'widowed']
senticnet['unwed_father'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'spousal_relationship', 'bigamy']
senticnet['unwed_motherhood'] = ['-0.87', '0', '0.524', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'marriage', 'matrimony', 'union', 'spousal_relationship', 'bigamy']
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senticnet['unwelcome'] = ['-0.82', '0', '0.85', '-0.70', '#sadness', '#anger', 'negative', '-0.79', 'outcaste', 'casteless', 'unclaimed', 'unwanted', 'undesired']
senticnet['unwelcome_attention'] = ['-0.16', '0.179', '0.568', '-0.61', '#anger', '#disgust', 'negative', '-0.29', 'angrily', 'choler', 'see_red', 'dander', 'hackle']
senticnet['unwelcome_person'] = ['-0.87', '0', '0.693', '-0.64', '#sadness', '#anger', 'negative', '-0.73', 'sourpuss', 'picklepuss', 'troublemaker', 'trouble_maker', 'troubler']
senticnet['unwelcoming'] = ['0', '-0.83', '0', '-0.94', '#surprise', '#disgust', 'negative', '-0.05', 'uninviting', 'unappealing', 'inhospitable', 'unappetizing', 'cheerless']
senticnet['unwell'] = ['-0.71', '0', '-0.96', '-0.94', '#fear', '#disgust', 'negative', '-0.87', 'ill', 'unconscious', 'sick', 'feel_sick', 'sleep_apnea']
senticnet['unwholesome'] = ['-0.75', '0', '0.909', '-0.96', '#anger', '#disgust', 'negative', '-0.87', 'noisome', 'unhealthy', 'morbid', 'nauseating', 'nauseous']
senticnet['unwholesome_food'] = ['-0.93', '0', '0.75', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'invalidism', 'injury', 'affliction', 'biliousness', 'harm']
senticnet['unwholesomeness'] = ['-0.72', '0', '0.71', '-0.9', '#sadness', '#disgust', 'negative', '-0.77', 'perniciousness', 'harmfulness', 'toxicity', 'noisomeness', 'noxiousness']
senticnet['unwieldly'] = ['0', '0', '0.99', '-0.72', '#anger', '#disgust', 'negative', '-0.85', 'cumbrous', 'ungainly', 'clumsy', 'cumbersome', 'awkward']
senticnet['unwieldy'] = ['0', '0', '0.99', '-0.72', '#anger', '#disgust', 'negative', '-0.85', 'cumbrous', 'ungainly', 'clumsy', 'cumbersome', 'awkward']
senticnet['unwilled'] = ['-0.65', '0', '0.91', '-0.86', '#anger', '#disgust', 'negative', '-0.80', 'unwilling', 'unconscious', 'unvoluntary', 'senseless', 'forced']
senticnet['unwilling'] = ['-0.52', '0', '0.857', '-0.75', '#anger', '#disgust', 'negative', '-0.71', 'nonvoluntary', 'unconscious', 'unwilled', 'senseless', 'forced']
senticnet['unwillingly'] = ['-0.84', '0', '0.82', '0', '#sadness', '#anger', 'negative', '-0.83', 'cooperate', 'aversely', 'disgorge', 'loathly', 'unwilling']
senticnet['unwillingness'] = ['-0.99', '0', '0.907', '-0.61', '#sadness', '#anger', 'negative', '-0.83', 'resistance', 'disinclination', 'indisposition', 'temperament', 'hesitancy']
senticnet['unwind'] = ['0.759', '0.8', '0', '0', '#joy', '#interest', 'positive', '0.78', 'relaxation', 'stress_reduction', 'waste_time', 'enjoy_down_time', 'distraction']
senticnet['unwinnable'] = ['-0.82', '0', '0.739', '-0.50', '#sadness', '#anger', 'negative', '-0.69', 'fake', 'juke', 'maneuver', 'manoeuvre', 'feint']
senticnet['unwisdom'] = ['-0.50', '0', '0.807', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'powerlessness', 'impotence', 'impotency', 'ineffectiveness', 'inefficaciousness']
senticnet['unwise'] = ['-0.91', '0', '0.743', '0', '#sadness', '#anger', 'negative', '-0.82', 'injudiciously', 'imprudent', 'injudicious', 'injudiciousness', 'judge']
senticnet['unwisely'] = ['-0.92', '0', '0.876', '-0.96', '#sadness', '#disgust', 'negative', '-0.92', 'bigmouthed', 'blabby', 'blabbermouthed', 'talkative', 'indiscreet']
senticnet['unwiseness'] = ['0.661', '-0.63', '0', '0.873', '#joy', '#admiration', 'positive', '0.721', 'injudiciousness', 'asininity', 'silliness', 'fatuity', 'fatuousness']
senticnet['unwitting'] = ['-0.83', '0', '0.508', '-0.79', '#sadness', '#disgust', 'negative', '-0.71', 'unintended', 'unplanned', 'unintentional', 'accidental', 'inadvertent']
senticnet['unwomanly'] = ['0.525', '-0.85', '0', '0.599', '#surprise', '#admiration', 'positive', '0.66', 'frolic', 'gambol', 'romp', 'play', 'caper']
senticnet['unwontedly'] = ['0', '0', '-0.84', '-0.86', '#fear', '#disgust', 'negative', '-0.85', 'outsize', 'premium', 'unusual', 'unusually', 'grubber']
senticnet['unworkable'] = ['0.779', '-0.81', '0', '0', '#joy', '#surprise', 'positive', '0.797', 'work', 'workable', 'workableness', 'workably', 'smith']
senticnet['unworldly'] = ['-0.94', '0', '0.779', '-0.85', '#sadness', '#disgust', 'negative', '-0.86', 'eremitic', 'eremitical', 'cloistered', 'anchoritic', 'hermitic']
senticnet['unworldlyal'] = ['-0.95', '0', '0.772', '-0.86', '#sadness', '#disgust', 'negative', '-0.86', 'unworldly', 'eremitic', 'cloistered', 'anchoritic', 'hermitic']
senticnet['unworried'] = ['-0.59', '0', '0.794', '-0.70', '#anger', '#disgust', 'negative', '-0.69', 'untroubled', 'undisturbed', 'unmolested', 'dreamless', 'unconcerned']
senticnet['unworthily'] = ['-0.64', '0', '0.594', '-0.94', '#sadness', '#disgust', 'negative', '-0.73', 'scurrility', 'billingsgate', 'disrespect', 'discourtesy', 'vituperation']
senticnet['unworthiness'] = ['0.604', '-0.84', '0', '0.927', '#surprise', '#admiration', 'positive', '0.79', 'quality', 'unfitness', 'inappropriateness', 'inconvenience', 'unsuitability']
senticnet['unworthy'] = ['0', '0', '-0.77', '-0.99', '#fear', '#disgust', 'negative', '-0.88', 'slimy', 'despicable', 'ugly', 'slimed', 'vile']
senticnet['unwounded'] = ['-0.95', '0', '0.704', '-0.53', '#sadness', '#anger', 'negative', '-0.73', 'uncut', 'unharmed', 'unscathed', 'unhurt', 'uninjured']
senticnet['unwrap'] = ['-0.96', '0', '0.69', '-0.84', '#sadness', '#disgust', 'negative', '-0.83', 'bring_out', 'break', 'let_out', 'expose', 'bewray']
senticnet['unwritten_code'] = ['0', '0', '0', '0.99', '#admiration', '#admiration', 'positive', '0.99', 'politeness', 'good_behavior', 'good_etiquette', 'manner', 'hint']
senticnet['unyielding'] = ['0', '0.858', '0', '0.79', '#interest', '#admiration', 'positive', '0.824', 'unswerving', 'steadfast', 'unwavering', 'uncompromising', 'unshakable']
senticnet['unyieldingness'] = ['-0.87', '0', '0.814', '-0.97', '#sadness', '#disgust', 'negative', '-0.89', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['unzip'] = ['0.155', '0.124', '0.026', '0.208', '#joy', '#admiration', 'positive', '0.115', 'watch_good_movie', 'good_sense_of_humor', 'able_read', 'spare_time', 'strong_relationship']
senticnet['unzip_pant'] = ['0', '0.071', '-0.07', '-0.08', '#fear', '#disgust', 'negative', '-0.02', 'find_restroom', 'pull_down_pant', 'find_toilet', 'full_bladder', 'serve_beer']
senticnet['up'] = ['0.877', '0.902', '0', '0', '#joy', '#interest', 'positive', '0.889', 'out', 'down', 'put', 'get', 'just']
senticnet['up-to-date'] = ['0', '0.957', '0', '0.738', '#interest', '#admiration', 'positive', '0.847', 'comprehensive', 'up-to-the-minute', 'update', 'updated', 'accurate']
senticnet['up-to-the-minute'] = ['0', '0.954', '0', '0.734', '#interest', '#admiration', 'positive', '0.844', 'comprehensive', 'up-to-date', 'update', 'updated', 'accurate']
senticnet['up_date'] = ['0.032', '0.205', '-0.11', '0.294', '#interest', '#admiration', 'positive', '0.103', 'electrical_unit', 'relevant', 'contemporary', 'particular', 'actual']
senticnet['up_down'] = ['0.03', '0.12', '-0.04', '0.26', '#interest', '#admiration', 'positive', '0.091', 'hand_gesture', 'crest', 'goodbye', 'hand_motion', 'hand_movement']
senticnet['up_early'] = ['0', '0.08', '0', '0.179', '#interest', '#admiration', 'positive', '0.13', 'wake_up_morning', 'awake', 'get_awake', 'gather_energy_tomorrow', 'sit_chair']
senticnet['up_front'] = ['0', '0.09', '-0.07', '0.148', '#interest', '#admiration', 'positive', '0.053', 'forepart', 'clear', 'front', 'forehead', 'part_of_face']
senticnet['up_high'] = ['-0.04', '0.078', '0', '0.174', '#interest', '#admiration', 'positive', '0.069', 'high_up', 'block_sun', 'crystal_clear', 'sun_cover', 'water_particle']
senticnet['up_hill'] = ['0', '0.1', '0', '0.113', '#interest', '#admiration', 'positive', '0.107', 'steep', 'incline', 'slant', 'elevation', 'incline_plane']
senticnet['up_impressionable_libertarian'] = ['0', '0.786', '0', '0.917', '#interest', '#admiration', 'positive', '0.852', 'act', 'portray', 'play_ping_pong', 'school_play', 'dangerous']
senticnet['up_morning'] = ['-0.03', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.04', 'up_early', 'get_out_of_bed', 'start_new_day', 'out_bed', 'wake_up_morning']
senticnet['up_mountain'] = ['0', '0.137', '-0.04', '0.125', '#interest', '#admiration', 'positive', '0.072', 'elevation', 'mountaineer', 'move_upward', 'up_hill', 'ascend']
senticnet['up_roar'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'agitation', 'anxiety', 'commotion', 'disturbance', 'upset']
senticnet['up_to'] = ['0.889', '-0.90', '0', '0.559', '#joy', '#surprise', 'positive', '0.784', 'competent', 'adequate', 'adequate_to', 'equal_to', 'capable']
senticnet['upaya'] = ['0.668', '-0.87', '0', '0.606', '#joy', '#surprise', 'positive', '0.715', 'adeptness', 'adroitness', 'deftness', 'quickness', 'expertness']
senticnet['upbeat'] = ['0.919', '0.947', '0', '0', '#joy', '#interest', 'positive', '0.933', 'tempo', 'tone', 'lively', 'positive', 'unaccented']
senticnet['upbeat_attitude'] = ['0.701', '0.874', '0', '0.752', '#interest', '#admiration', 'positive', '0.776', 'self_satisfaction', 'enjoy_life', 'good_feeling', 'good_attitude', 'enjoy']
senticnet['upbraid'] = ['-0.70', '0', '0.905', '-0.84', '#anger', '#disgust', 'negative', '-0.81', 'reproach', 'criminate', 'arraign', 'charge', 'accuse']
senticnet['upbraiding'] = ['-0.55', '0', '0.95', '-0.68', '#anger', '#disgust', 'negative', '-0.73', 'reprimand', 'rebuke', 'reproof', 'reproval', 'reprehension']
senticnet['upbringing'] = ['0.858', '-0.77', '0', '0.735', '#joy', '#surprise', 'positive', '0.79', 'breeding', 'training', 'education', 'raising', 'nurture']
senticnet['upcoming'] = ['0.89', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.826', 'forthcoming', 'anticipated', 'debut', 'release', 'imminent']
senticnet['upcoming_debut_album'] = ['0.946', '-0.91', '0', '0.664', '#joy', '#surprise', 'positive', '0.841', 'pretender', 'phony', 'phoney', 'hypocrite', 'dissembler']
senticnet['upcoming_election'] = ['0', '0.156', '-0.15', '0.179', '#interest', '#admiration', 'positive', '0.061', 'candidate', 'presidential', 'vote', 'elect', 'appoint']
senticnet['upcoming_film_adaptation'] = ['-0.06', '0.122', '-0.04', '0.113', '#interest', '#admiration', 'positive', '0.032', 'encounter', 'portray', 'reveal', 'episode', 'next_episode']
senticnet['upcoming_fiscal_year'] = ['-0.05', '0.282', '-0.04', '0.107', '#interest', '#admiration', 'positive', '0.072', 'expense', 'dollar_cent', 'dollar_sign', 'amount_pay', 'payment_amount']
senticnet['upcoming_sequel'] = ['-0.06', '0.122', '-0.04', '0.113', '#interest', '#admiration', 'positive', '0.032', 'encounter', 'portray', 'reveal', 'episode', 'next_episode']
senticnet['upcott'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'bodyguard', 'outrider', 'bodyguard_of_guard', 'beefeater', 'defender']
senticnet['upcourt'] = ['-0.81', '0', '0.747', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'vagrant', 'drifter', 'hobo', 'vagabond', 'dosser']
senticnet['update'] = ['-0.07', '-0.01', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.05', 'up_date', 'happen_now', 'latest', 'relevant', 'timely']
senticnet['updated'] = ['0', '0.954', '0', '0.73', '#interest', '#admiration', 'positive', '0.842', 'comprehensive', 'up-to-the-minute', 'update', 'up-to-date', 'accurate']
senticnet['updated_edition'] = ['-0.04', '0', '-0.11', '0', '#sadness', '#fear', 'negative', '-0.08', 'data', 'awareness', 'information', 'saga', 'knowledge']
senticnet['updated_hourly'] = ['-0.03', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.04', 'crystal_clear', 'water_sky', 'weather_condition', 'weather_phenomenon', 'sun_cover']
senticnet['updraft'] = ['0', '-0.41', '-0.95', '0', '#surprise', '#fear', 'negative', '-0.26', 'blizzard', 'snow_storm', 'storm', 'danger', 'rainy']
senticnet['upekkha'] = ['0.668', '-0.87', '0', '0.606', '#joy', '#surprise', 'positive', '0.715', 'adeptness', 'adroitness', 'deftness', 'quickness', 'expertness']
senticnet['upend'] = ['-0.98', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.92', 'thrilling', 'electrifying', 'sensational', 'sensationally', 'scandalmongering']
senticnet['upfront_cost'] = ['-0.02', '0.249', '0', '0.116', '#interest', '#admiration', 'positive', '0.112', 'price', 'expense', 'bill', 'value', 'worth']
senticnet['upgrade'] = ['-0.06', '0.113', '-0.09', '0.131', '#interest', '#admiration', 'positive', '0.02', 'deflate', 'spread_out', 'read_cover', 'scan', 'look_behind']
senticnet['upheaval'] = ['-0.59', '0', '0.978', '-0.97', '#anger', '#disgust', 'negative', '-0.85', 'disorder', 'agitation', 'unrest', 'violence', 'roller_coaster']
senticnet['upheld'] = ['0.594', '-0.81', '0', '0.93', '#surprise', '#admiration', 'positive', '0.78', 'hold', 'hold_on', 'persist', 'hang_on', 'maintain']
senticnet['uphill'] = ['-0.74', '0.241', '-0.42', '0', '#sadness', '#fear', 'negative', '-0.31', 'low', 'grim', 'sorry', 'gloominess', 'gloomily']
senticnet['uphold'] = ['0.594', '-0.81', '0', '0.93', '#surprise', '#admiration', 'positive', '0.78', 'hold', 'hold_on', 'persist', 'hang_on', 'maintain']
senticnet['upholstered'] = ['0', '0', '-0.91', '-0.70', '#fear', '#disgust', 'negative', '-0.80', 'strapless', 'backless', 'stool', 'sleeveless', 'barstools']
senticnet['upkeep'] = ['0', '0.235', '-0.12', '0.274', '#interest', '#admiration', 'positive', '0.127', 'preserve', 'keep', 'hold_onto', 'prevent', 'hold']
senticnet['upland_forest'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'biome', 'timberland']
senticnet['uplift'] = ['0.654', '0.617', '-0.25', '0.328', '#joy', '#interest', 'positive', '0.335', 'joyful', 'euphoric', 'euphoria', 'euphoriant', 'elating']
senticnet['upliftment'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'oppressiveness', 'tyrannical', 'oppressive', 'tyrannous', 'laden']
senticnet['uploadable'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expansive', 'expansible', 'erectile', 'cavernous']
senticnet['upmarket'] = ['0.952', '0.844', '0', '0.96', '#joy', '#admiration', 'positive', '0.919', 'fashion', 'demand', 'kindness', 'better_person', 'warm_feeling']
senticnet['upnor_castle'] = ['0.582', '-0.97', '0', '0.802', '#surprise', '#admiration', 'positive', '0.788', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['upon'] = ['0', '0.2', '-0.11', '0.345', '#interest', '#admiration', 'positive', '0.143', 'direction', 'unto', 'forehead', 'toward', 'go_toward']
senticnet['upon_arrival'] = ['0', '0.152', '-0.08', '0.208', '#interest', '#admiration', 'positive', '0.091', 'departure', 'leave', 'colored_green', 'green_color', 'make_big']
senticnet['upon_completion'] = ['0.086', '0.148', '0', '0.157', '#interest', '#admiration', 'positive', '0.13', 'end_point', 'commence', 'success', 'finish', 'mission_accomplished']
senticnet['upon_expiration'] = ['0.857', '-0.68', '0', '0.971', '#joy', '#admiration', 'positive', '0.839', 'revitalize', 'refurbish', 'reconstruct', 'renovate', 'freshen_up']
senticnet['upon_graduation'] = ['0', '0.893', '0', '0', '#interest', '#interest', 'positive', '0.893', 'salary', 'wage', 'send_money', 'earnings', 'pay']
senticnet['upon_receipt'] = ['0.862', '0.709', '0', '0', '#joy', '#interest', 'positive', '0.785', 'invoice', 'bill', 'check', 'mastercard', 'payment_counter']
senticnet['upon_request'] = ['0', '0.851', '0', '0.774', '#interest', '#admiration', 'positive', '0.813', 'join_together', 'event', 'occurrence', 'special_occasion', 'function']
senticnet['upon_satisfactory_completion'] = ['0.086', '0.148', '0', '0.157', '#interest', '#admiration', 'positive', '0.13', 'end_point', 'commence', 'success', 'finish', 'mission_accomplished']
senticnet['upon_successful_completion'] = ['0.086', '0.148', '0', '0.157', '#interest', '#admiration', 'positive', '0.13', 'end_point', 'commence', 'success', 'finish', 'mission_accomplished']
senticnet['upper'] = ['0.022', '0.075', '0', '0.117', '#interest', '#admiration', 'positive', '0.071', 'status', 'part', 'designation', 'upper_lower', 'social_status']
senticnet['upper-division'] = ['0', '0.831', '0', '0.83', '#interest', '#admiration', 'positive', '0.831', 'lower-level', 'upper-level', 'college-level', 'graduate-level', 'mid-level']
senticnet['upper-level'] = ['0', '0.832', '0', '0.836', '#interest', '#admiration', 'positive', '0.834', 'lower-level', 'upper-division', 'college-level', 'graduate-level', 'mid-level']
senticnet['upper_abdominal_pain'] = ['-0.20', '0', '-0.22', '-0.19', '#sadness', '#fear', 'negative', '-0.20', 'go_hospital', 'death', 'die', 'take_aspirin', 'heart_attack']
senticnet['upper_airway_obstruction'] = ['-0.84', '0', '0.829', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'upset', 'dysarthria', 'defect_of_speech', 'disorder', 'lallation']
senticnet['upper_arm'] = ['0', '0', '0', '-0.08', '#disgust', '#disgust', 'negative', '-0.08', 'bowel', 'scrotum', 'shoulder', 'liver', 'anus']
senticnet['upper_atmospheric'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'hurricane', 'natural_disaster', 'flood', 'atmospheric_pressure', 'tropical_storm']
senticnet['upper_balcony'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'stage_border', 'row_seat', 'stage_entrance', 'theater_seat', 'theater_box']
senticnet['upper_bound'] = ['0.04', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.055', 'measurement', 'time_interval', 'length', 'time', 'spatial_dimension']
senticnet['upper_bound_estimate'] = ['-0.02', '0.248', '0', '0.31', '#interest', '#admiration', 'positive', '0.178', 'assurance', 'total', 'quality', 'importance', 'amount']
senticnet['upper_chest'] = ['-0.63', '0', '0.559', '-0.82', '#sadness', '#disgust', 'negative', '-0.67', 'take_bull_by_horns', 'confront', 'front', 'encounter', 'oppose']
senticnet['upper_concourse'] = ['0.831', '-0.82', '0', '0.826', '#joy', '#admiration', 'positive', '0.826', 'deck', 'freeboard_deck', 'weather_deck', 'shelter_deck', 'boat_deck']
senticnet['upper_crust'] = ['-0.02', '0', '0', '-0.06', '#sadness', '#disgust', 'negative', '-0.04', 'snobby', 'look_down_on', 'snob', 'french', 'full_of_shit']
senticnet['upper_deck'] = ['0.831', '-0.82', '0', '0.826', '#joy', '#admiration', 'positive', '0.826', 'deck', 'freeboard_deck', 'weather_deck', 'shelter_deck', 'boat_deck']
senticnet['upper_echelon'] = ['-0.53', '0', '0.977', '-0.54', '#anger', '#disgust', 'negative', '-0.68', 'rank', 'surpass', 'shine_at', 'excel_at', 'exceed']
senticnet['upper_extremity'] = ['-0.81', '0', '0.789', '-0.51', '#sadness', '#anger', 'negative', '-0.70', 'rubor', 'redness', 'inflammation', 'synovitis', 'tendosynovitis']
senticnet['upper_eyelid'] = ['-0.58', '0', '0.715', '-0.59', '#anger', '#disgust', 'negative', '-0.63', 'fly', 'overlap', 'barndoor', 'coattail', 'codpiece']
senticnet['upper_floor'] = ['0', '0', '0.095', '0', '#anger', '#anger', 'negative', '-0.09', 'build_part', 'flat', 'step', 'large_rock', 'hard_surface']
senticnet['upper_garment'] = ['-0.11', '0.115', '0.67', '-0.65', '#anger', '#disgust', 'negative', '-0.33', 'lividity', 'livid', 'wrathfully', 'wrothful', 'wroth']
senticnet['upper_genital_tract'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'gastroenteritis', 'stomach_flu', 'intestinal_flu', 'inflammatory_disease']
senticnet['upper_geyser_basin'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['upper_hand'] = ['0.11', '0.209', '0', '0.238', '#interest', '#admiration', 'positive', '0.186', 'extremity', 'hand', 'human_appendage', 'arm_part', 'body_limb']
senticnet['upper_intestine'] = ['0.04', '0.07', '0', '0', '#joy', '#interest', 'positive', '0.055', 'kidney', 'pancreas', 'liver', 'bone', 'neck']
senticnet['upper_jaw'] = ['0', '-0.02', '0.033', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'tooth', 'teeth', 'dimple', 'mouth', 'cliche']
senticnet['upper_limit'] = ['0.052', '0', '0', '0.322', '#joy', '#admiration', 'positive', '0.187', 'a_lot', 'abundance', 'plenty', 'superlative', 'comparative']
senticnet['upper_lip'] = ['0', '-0.02', '0.033', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'tooth', 'teeth', 'dimple', 'mouth', 'cliche']
senticnet['upper_lower'] = ['0', '0.104', '0.05', '0.117', '#interest', '#admiration', 'positive', '0.057', 'upper', 'singular', 'lower', 'crate', 'lid']
senticnet['upper_mandible'] = ['-0.59', '0', '0.887', '-0.71', '#anger', '#disgust', 'negative', '-0.73', 'armet', 'basinet', 'helmet', 'heaume', 'morion']
senticnet['upper_mantle'] = ['0', '0', '-0.92', '0', '#fear', '#fear', 'negative', '-0.92', 'water_flow', 'wind_water', 'water_stream', 'waterway', 'lava']
senticnet['upper_molar'] = ['0.886', '-0.99', '0', '0.542', '#joy', '#surprise', 'positive', '0.808', 'jowl', 'lower_jaw', 'mandible', 'lantern_jaw', 'lantern_jawr_bone']
senticnet['upper_motor_neuron'] = ['-0.78', '0', '0.764', '-0.88', '#sadness', '#disgust', 'negative', '-0.81', 'withering', 'downfall', 'weakening', 'fading', 'attenuation']
senticnet['upper_nubia'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['upper_ossory'] = ['0.975', '-0.78', '0', '0.969', '#joy', '#admiration', 'positive', '0.911', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['upper_palaeolithic'] = ['0.099', '-0.07', '-0.08', '0.195', '#joy', '#admiration', 'positive', '0.071', 'art', 'exhibit', 'craft', 'fine_art', 'artwork']
senticnet['upper_respiratory_infection'] = ['-0.06', '0.183', '0.651', '-0.64', '#anger', '#disgust', 'negative', '-0.29', 'allergic_reaction', 'chew', 'pierce', 'mastication', 'put_mouth']
senticnet['upper_saint_clair'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'saltine', 'soda_cracker', 'cracker', 'water_biscuit', 'graham_cracker']
senticnet['upper_secondary'] = ['0.924', '-0.55', '0', '0.756', '#joy', '#admiration', 'positive', '0.744', 'bounden', 'incumbent_on', 'obligate', 'obligatory', 'mandatory']
senticnet['upper_secondary_education'] = ['0.924', '-0.55', '0', '0.756', '#joy', '#admiration', 'positive', '0.744', 'bounden', 'incumbent_on', 'obligate', 'obligatory', 'mandatory']
senticnet['upper_stigma'] = ['-0.95', '0', '0.892', '-0.81', '#sadness', '#anger', 'negative', '-0.88', 'blurry', 'foggy', 'hazy', 'muzzy', 'blurred']
senticnet['upper_storey'] = ['0', '0', '0.095', '0', '#anger', '#anger', 'negative', '-0.09', 'build_part', 'flat', 'step', 'large_rock', 'hard_surface']
senticnet['upper_surface'] = ['-0.66', '0', '0.809', '-0.61', '#sadness', '#anger', 'negative', '-0.69', 'face', 'base', 'underbelly', 'sole', 'bilge']
senticnet['upper_thigh'] = ['0', '0', '-0.06', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'bandit', 'arm', 'wrist', 'five_fingers', 'bendable']
senticnet['upper_tropospheric'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'hurricane', 'natural_disaster', 'flood', 'atmospheric_pressure', 'tropical_storm']
senticnet['uppercase'] = ['0.049', '0.113', '0.107', '0.162', '#interest', '#admiration', 'positive', '0.054', 'list_number', 'ordinal', 'count', 'numerical', 'enumeration']
senticnet['uppercase_bug'] = ['-0.04', '0.121', '0', '0.178', '#interest', '#admiration', 'positive', '0.084', 'programming_bug', 'bug', 'code', 'developer', 'bad']
senticnet['uppercase_letter'] = ['0.049', '0.113', '0.107', '0.162', '#interest', '#admiration', 'positive', '0.054', 'list_number', 'ordinal', 'count', 'numerical', 'enumeration']
senticnet['uppercut'] = ['0.969', '0.998', '0', '0', '#joy', '#interest', 'positive', '0.984', 'bell_sound', 'bell_noise', 'piece_of_jewelry', 'syllable', 'fastener']
senticnet['uppish'] = ['0', '0', '-0.94', '-0.88', '#fear', '#disgust', 'negative', '-0.91', 'snooty', 'persnickety', 'arrogant', 'bigheaded', 'stuck-up']
senticnet['uppity'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'pervert', 'horny', 'uncomfortable', 'uneasy', 'short_tempered']
senticnet['uppon'] = ['-0.69', '0', '0.883', '-0.91', '#anger', '#disgust', 'negative', '-0.82', 'stick_in', 'enclose', 'insert', 'feed_in', 'put_in']
senticnet['upright'] = ['0', '0.193', '-0.09', '0.214', '#interest', '#admiration', 'positive', '0.104', 'vertical', 'standing', 'position', 'trust', 'support']
senticnet['upright_conscience'] = ['-0.64', '0', '0.962', '-0.68', '#anger', '#disgust', 'negative', '-0.76', 'beguiler', 'slicker', 'leader', 'deceiver', 'cheat']
senticnet['upright_piano'] = ['-0.75', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.80', 'high-strung', 'uptight', 'restive', 'nervy', 'jumpy']
senticnet['upright_posture'] = ['-0.15', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.09', 'use_leg', 'walk_quickly', 'casual', 'take_step', 'strut']
senticnet['upright_vacuum'] = ['-0.04', '-0.02', '0.089', '-0.14', '#anger', '#disgust', 'negative', '-0.06', 'steam_locomotive', 'loudspeaker', 'collector', 'siren', 'activist']
senticnet['upright_vacuum_cleaner'] = ['-0.04', '-0.02', '0.089', '-0.14', '#anger', '#disgust', 'negative', '-0.06', 'steam_locomotive', 'loudspeaker', 'collector', 'siren', 'activist']
senticnet['uprightly'] = ['0.621', '0.648', '-0.25', '0.296', '#joy', '#interest', 'positive', '0.328', 'pacifically', 'repose', 'peace-loving', 'peaceable', 'peace_of_mind']
senticnet['uprise'] = ['0', '0.793', '0', '0.847', '#interest', '#admiration', 'positive', '0.82', 'disagreement', 'fight_against', 'opposition', 'against', 'go_against']
senticnet['uprising_against'] = ['0', '0.793', '0', '0.847', '#interest', '#admiration', 'positive', '0.82', 'disagreement', 'fight_against', 'opposition', 'against', 'go_against']
senticnet['uproar'] = ['-0.50', '0', '0.863', '-0.57', '#anger', '#disgust', 'negative', '-0.64', 'hurly_burly', 'kerfuffle', 'commotion', 'disruption', 'disturbance']
senticnet['uproar_among'] = ['0', '0', '0.94', '0', '#anger', '#anger', 'negative', '-0.94', 'feel_angry', 'hurt_feeling', 'feel_hurt', 'ridicule', 'vengeance']
senticnet['uproar_over'] = ['-0.86', '0', '0.516', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'bronx_cheer', 'call', 'cry', 'yell', 'shout']
senticnet['uproarious'] = ['0.893', '0.881', '0', '0', '#joy', '#interest', 'positive', '0.887', 'screaming', 'hilariously', 'uproariously', 'hilarious', 'hilarity']
senticnet['uproariously'] = ['0.695', '0.623', '-0.23', '0.319', '#joy', '#interest', 'positive', '0.351', 'uproarious', 'screaming', 'hilariously', 'hilarious', 'hilarity']
senticnet['uproot'] = ['0', '0', '-0.90', '-0.98', '#fear', '#disgust', 'negative', '-0.94', 'eradicate', 'deracinate', 'root_out', 'exterminate', 'extirpate']
senticnet['uprose'] = ['-0.71', '0', '0.836', '0', '#sadness', '#anger', 'negative', '-0.77', 'awkward', 'unenviable', 'embarrassingly', 'embarrassing', 'sheepish']
senticnet['upscale'] = ['0', '-0.79', '0', '0.8', '#surprise', '#admiration', 'positive', '0.795', 'size', 'increase', 'wealthy', 'tony', 'quality']
senticnet['upscale_grocery'] = ['0.096', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.057', 'platform_rail', 'rail', 'catwalk', 'column', 'token_turnstile']
senticnet['upscale_restaurant'] = ['0.06', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'eat_food', 'fast_food', 'pizza_parlor', 'pizza_store', 'kitchen_cupboard']
senticnet['upsell'] = ['0.971', '0.76', '0', '0', '#joy', '#interest', 'positive', '0.865', 'shopper', 'clerk', 'patron', 'vintner', 'hairdresser']
senticnet['upset'] = ['-0.87', '0', '-0.74', '-0.72', '#sadness', '#fear', 'negative', '-0.78', 'shame', 'humiliation', 'embarrassment', 'fright', 'dread']
senticnet['upset_person'] = ['-0.75', '0', '-0.75', '-0.99', '#sadness', '#disgust', 'negative', '-0.83', 'tease', 'upset', 'lubricant', 'vaseline', 'piston']
senticnet['upset_stomach'] = ['-0.76', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.74', 'decrease_hunger', 'feel_full', 'stomach_problem', 'get_fat', 'a_little_hungry']
senticnet['upshot'] = ['0', '0', '-0.75', '-0.83', '#fear', '#disgust', 'negative', '-0.79', 'consequence', 'result', 'outcome', 'effect', 'event']
senticnet['upside'] = ['-0.72', '0', '-0.94', '0', '#sadness', '#fear', 'negative', '-0.83', 'drawback', 'near-term', 'disadvantage', 'downside', 'volatility']
senticnet['upside_down'] = ['0.969', '0.843', '0', '0', '#joy', '#interest', 'positive', '0.906', 'tree_house', 'same_again', 'reverse', 'spot', 'clothes_accessory']
senticnet['upsidedown'] = ['0.969', '0.843', '0', '0', '#joy', '#interest', 'positive', '0.906', 'tree_house', 'same_again', 'reverse', 'spot', 'clothes_accessory']
senticnet['upsize'] = ['0', '0.134', '0.053', '0.232', '#interest', '#admiration', 'positive', '0.104', 'size_up', 'big_size', 'big_meal', 'big', 'quantity']
senticnet['upstairs_apartment'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'home', 'furniture', 'kitchen', 'flat']
senticnet['upstairs_balcony'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'stage_border', 'row_seat', 'stage_entrance', 'theater_seat', 'theater_box']
senticnet['upstairs_bathroom'] = ['0', '0.171', '-0.05', '0.102', '#interest', '#admiration', 'positive', '0.072', 'bed', 'mattress', 'bead', 'bed_cover', 'seduce']
senticnet['upstairs_bedroom'] = ['0', '0.171', '-0.05', '0.102', '#interest', '#admiration', 'positive', '0.072', 'bed', 'mattress', 'bead', 'bed_cover', 'seduce']
senticnet['upstairs_gallery'] = ['0.07', '0', '0', '0', '#joy', '#joy', 'positive', '0.07', 'artist', 'artwork', 'sculpture', 'tapestry', 'drawing']
senticnet['upstairs_loft'] = ['0.591', '-0.74', '0', '0.672', '#surprise', '#admiration', 'positive', '0.669', 'room', 'chillax', 'hotel_room', 'furniture_store', 'living_room']
senticnet['upstairs_lounge'] = ['0.841', '0', '0', '0.71', '#joy', '#admiration', 'positive', '0.775', 'waiter', 'bar_service', 'beverage_service', 'concierge', 'minibar']
senticnet['upstanding'] = ['0.664', '-0.83', '0', '0.895', '#surprise', '#admiration', 'positive', '0.799', 'nice', 'respectable', 'solid', 'presentable', 'decent']
senticnet['upstart'] = ['-0.95', '0', '0.772', '-0.89', '#sadness', '#disgust', 'negative', '-0.87', 'smart_aleck', 'wiseacre', 'snob', 'snooty', 'unpleasant_person']
senticnet['upstream_migration'] = ['-0.93', '0', '-0.99', '-0.92', '#sadness', '#fear', 'negative', '-0.95', 'language_barrier', 'shut', 'shut_down', 'close_down', 'obstacle']
senticnet['upsurge'] = ['-0.52', '0', '0.884', '-0.94', '#anger', '#disgust', 'negative', '-0.78', 'debris_surge', 'debris_storm', 'flowing', 'onrush', 'flow']
senticnet['uptake'] = ['0', '0.839', '0', '0.702', '#interest', '#admiration', 'positive', '0.77', 'intake', 'ingestion', 'consumption', 'research', 'determine']
senticnet['uptick'] = ['0.952', '0.918', '0', '0', '#joy', '#interest', 'positive', '0.935', 'tack', 'foot_part', 'casual_shoes', 'gauntlet', 'pin']
senticnet['uptight'] = ['0.943', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.93', 'high-strung', 'restive', 'overstrung', 'nervy', 'jumpy']
senticnet['uptime'] = ['0.637', '-0.91', '0', '0.757', '#surprise', '#admiration', 'positive', '0.768', 'tractability', 'manageableness', 'docility', 'obedience', 'amenability']
senticnet['uptown_theatre'] = ['0', '0', '0', '-0.1', '#disgust', '#disgust', 'negative', '-0.1', 'ice_rink', 'skate', 'room_temperature', 'loose_end', 'smooth']
senticnet['uptrend'] = ['0.05', '0.151', '0', '0.159', '#interest', '#admiration', 'positive', '0.12', 'glamour', 'stylish', 'style', 'clothes_style', 'fancy_clothes']
senticnet['upturned_boat'] = ['0', '-0.02', '0.046', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'sail_ship', 'canoe', 'ship', 'submarine', 'yacht']
senticnet['upturned_hull'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'lifebelt', 'save_life', 'lifeguard', 'life_jacket', 'safety_equipment']
senticnet['upturned_nose'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'helpless', 'unwanted', 'rabies', 'small_cat', 'big_rat']
senticnet['upvalue'] = ['-0.12', '0.193', '0.597', '-0.62', '#anger', '#disgust', 'negative', '-0.28', 'overrate', 'overvalue', 'overprize', 'overstimate', 'overprice']
senticnet['upward'] = ['0', '0.21', '-0.12', '0.224', '#interest', '#admiration', 'positive', '0.104', 'upwards', 'move_upward', 'raise_up', 'go_up', 'go_high']
senticnet['upward_mobility'] = ['0.961', '0.937', '0', '0', '#joy', '#interest', 'positive', '0.949', 'create_new_life', 'make_beautiful', 'stimulation', 'lower_stress', 'leisure_time']
senticnet['upward_motion'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotate_movement', 'rotate', 'go_around', 'manoeuvre', 'full_circle']
senticnet['upward_movement'] = ['0', '0.22', '-0.11', '0.282', '#interest', '#admiration', 'positive', '0.131', 'move_upward', 'raise_up', 'go_up', 'levitate', 'rise_up']
senticnet['upward_pressure'] = ['-0.12', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.09', 'breeze', 'move_air', 'air_movement', 'gape', 'force']
senticnet['upward_spike'] = ['0.952', '0.918', '0', '0', '#joy', '#interest', 'positive', '0.935', 'tack', 'foot_part', 'casual_shoes', 'gauntlet', 'pin']
senticnet['upward_toward'] = ['0', '0.21', '-0.11', '0.345', '#interest', '#admiration', 'positive', '0.147', 'direction', 'unto', 'forehead', 'upon', 'go_toward']
senticnet['upward_trend'] = ['0.05', '0.151', '0', '0.159', '#interest', '#admiration', 'positive', '0.12', 'glamour', 'stylish', 'style', 'clothes_style', 'fancy_clothes']
senticnet['upwardly'] = ['0.839', '0', '0', '0.95', '#joy', '#admiration', 'positive', '0.894', 'inwardly', 'radially', 'outwardly', 'downwardly', 'axially']
senticnet['upwards'] = ['-0.08', '0.127', '-0.06', '0.149', '#interest', '#admiration', 'positive', '0.032', 'high', 'high_land', 'upward', 'mountaineer', 'cover_snow']
senticnet['uraei'] = ['-0.62', '0', '0.961', '-0.54', '#sadness', '#anger', 'negative', '-0.71', 'crown', 'jeweled_headdress', 'tiara', 'headdress', 'headgear']
senticnet['urango'] = ['-0.94', '0', '0.863', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'poke', 'clout', 'lick', 'punch', 'biff']
senticnet['uranium'] = ['0', '0', '0.731', '-0.77', '#anger', '#disgust', 'negative', '-0.75', 'bomb-grade', 'plutonium', 'weapon-grade', 'fissile', 'weapons-grade']
senticnet['uranium_atom'] = ['-0.77', '0', '0.764', '-0.54', '#sadness', '#anger', 'negative', '-0.69', 'pullulation', 'apomixis', 'blastogenesis', 'budding', 'asexual_reproduction']
senticnet['uranium_bomb'] = ['0', '0', '-0.72', '-0.94', '#fear', '#disgust', 'negative', '-0.83', 'pistol', 'gun', 'hydrogen_bomb', 'handgun', 'revolver']
senticnet['uranium_enrichment'] = ['-0.03', '-0.03', '-0.05', '-0.20', '#fear', '#disgust', 'negative', '-0.06', 'nuclear_bomb', 'bomb', 'explosive', 'atomic_bomb', 'grenade']
senticnet['uranium_nucleus'] = ['-0.77', '0', '0.764', '-0.54', '#sadness', '#anger', 'negative', '-0.69', 'pullulation', 'apomixis', 'blastogenesis', 'budding', 'asexual_reproduction']
senticnet['uranium_oxide_dust'] = ['-0.95', '0', '0.738', '-0.58', '#sadness', '#anger', 'negative', '-0.75', 'fallout', 'chalk_dust', 'dust', 'particulate', 'particulate_matter']
senticnet['uranology'] = ['0', '0.818', '0', '0.976', '#interest', '#admiration', 'positive', '0.897', 'astronomy', 'cosmos', 'award', 'astronomical_science', 'sponsor']
senticnet['uranoplasty'] = ['0.703', '-0.55', '0', '0.605', '#joy', '#admiration', 'positive', '0.622', 'operation', 'surgery', 'surgical_operation', 'surgical_procedure', 'surgical_process']
senticnet['uranverein_uranium_club'] = ['-0.90', '0', '0.748', '-0.90', '#sadness', '#disgust', 'negative', '-0.85', 'alternative_energy', 'activation_energy', 'energy_of_activation', 'energy', 'nuclear_energy']
senticnet['uranyl_acetate'] = ['0', '-0.01', '0.046', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'varnish', 'brush', 'dye', 'pigment', 'canvas']
senticnet['urartu'] = ['0', '0.886', '0', '0.81', '#interest', '#admiration', 'positive', '0.848', 'edu', 'thinkcom', 'article', 'think', 'politics']
senticnet['urate'] = ['0.992', '-0.80', '0', '0.584', '#joy', '#surprise', 'positive', '0.795', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'incontinence']
senticnet['urban'] = ['0.077', '0.125', '0', '0.076', '#joy', '#interest', 'positive', '0.093', 'many_cities', 'group_of_people', 'community', 'big_city_life', 'metropolis']
senticnet['urban_chic'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'elegant', 'stylish', 'smart', 'posh', 'sleek']
senticnet['urban_fabric'] = ['0.563', '-0.72', '0', '0.644', '#surprise', '#admiration', 'positive', '0.645', 'idiom', 'idiomatic_expression', 'phrasal_idiom', 'set_phrase', 'phrase']
senticnet['urban_forester'] = ['-0.56', '0', '0.655', '-0.85', '#anger', '#disgust', 'negative', '-0.69', 'slovenly_woman', 'slut', 'trollop', 'slattern', 'slob']
senticnet['urban_legend'] = ['0.95', '-0.86', '0', '0.947', '#joy', '#admiration', 'positive', '0.92', 'content', 'lore', 'folklore', 'cognitive_content', 'story']
senticnet['urban_myth'] = ['0.95', '-0.86', '0', '0.947', '#joy', '#admiration', 'positive', '0.92', 'content', 'lore', 'folklore', 'cognitive_content', 'story']
senticnet['urban_renewal_project'] = ['-0.08', '0', '-0.08', '-0.14', '#sadness', '#disgust', 'negative', '-0.10', 'violence', 'assault', 'combat', 'siege', 'use_military']
senticnet['urban_sprawl'] = ['0', '0', '0.735', '-0.70', '#anger', '#disgust', 'negative', '-0.72', 'swing_set', 'roadway_square', 'fresh_air', 'dog_poop', 'wildflower']
senticnet['urban_typhus'] = ['0.579', '-0.81', '0', '0.833', '#surprise', '#admiration', 'positive', '0.741', 'murine_typhus', 'rat_typhus', 'typhus', 'endemic_typhus', 'rickettsial_disease']
senticnet['urbane'] = ['0', '0', '-0.99', '-0.86', '#fear', '#disgust', 'negative', '-0.93', 'svelte', 'refined', 'slim', 'lithesome', 'polished']
senticnet['urbanism'] = ['0.563', '-0.72', '0', '0.644', '#surprise', '#admiration', 'positive', '0.645', 'idiom', 'idiomatic_expression', 'phrasal_idiom', 'set_phrase', 'phrase']
senticnet['urbanist'] = ['0.563', '-0.72', '0', '0.644', '#surprise', '#admiration', 'positive', '0.645', 'idiom', 'idiomatic_expression', 'phrasal_idiom', 'set_phrase', 'phrase']
senticnet['urbanite'] = ['0', '0.095', '0', '0.05', '#interest', '#admiration', 'positive', '0.073', 'need_earn_money', 'earn_money', 'responsible', 'perform_service', 'go_work_to_earn_money']
senticnet['urbanity'] = ['0.598', '-0.99', '0', '0.64', '#surprise', '#admiration', 'positive', '0.745', 'good_manners', 'niceness', 'politeness', 'gallantry', 'politesse']
senticnet['urchin'] = ['0', '0', '-0.06', '0', '#fear', '#fear', 'negative', '-0.06', 'mussel', 'fish', 'barnacle', 'scuba_diver', 'seal']
senticnet['urdu_shabnam'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'white_animal', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
senticnet['urea_cycle_disorder'] = ['-0.54', '0', '0.92', '-0.84', '#anger', '#disgust', 'negative', '-0.76', 'dearth', 'demand', 'mineral_deficiency', 'famine', 'need']
senticnet['urea_fertilizer'] = ['0.071', '0', '0.031', '0.064', '#joy', '#admiration', 'positive', '0.035', 'organic_matter', 'decompose', 'recycle', 'soil', 'compost_pile']
senticnet['ureaplasma_urealyticum'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'gastroenteritis', 'stomach_flu', 'intestinal_flu', 'inflammatory_disease']
senticnet['ureter'] = ['-0.56', '0', '0.806', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['ureteral'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'blatancy', 'obviousness']
senticnet['ureteral_stent'] = ['-0.56', '0', '0.806', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['ureteric'] = ['-0.56', '0', '0.806', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['ureteroscope'] = ['0.255', '0.182', '-0.06', '0.314', '#joy', '#admiration', 'positive', '0.173', 'doctor_office', 'urethroscope', 'sigmoidoscope', 'hospital', 'check_health']
senticnet['urethane'] = ['0.085', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.074', 'soap', 'drizzle', 'bubbly', 'solid_water', 'ivory']
senticnet['urethane_foam'] = ['0.085', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.074', 'soap', 'drizzle', 'bubbly', 'solid_water', 'ivory']
senticnet['urethra'] = ['0.087', '-0.03', '0', '0.063', '#joy', '#admiration', 'positive', '0.062', 'puppet', 'bouncy', 'curly', 'throw_object', 'glove_leather']
senticnet['urethral_catheter'] = ['-0.56', '0', '0.806', '-0.76', '#anger', '#disgust', 'negative', '-0.70', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['urethral_lumen'] = ['-0.78', '0', '0.855', '-0.99', '#anger', '#disgust', 'negative', '-0.87', 'blood_blister', 'pustule', 'water_blister', 'vesicle', 'cyst']
senticnet['urethral_mucosa'] = ['-0.78', '0', '0.855', '-0.99', '#anger', '#disgust', 'negative', '-0.87', 'blood_blister', 'pustule', 'water_blister', 'vesicle', 'cyst']
senticnet['urethral_obstruction'] = ['-0.77', '0', '0.966', '-0.51', '#sadness', '#anger', 'negative', '-0.75', 'enuresis', 'incontinency', 'incontinence', 'stress_incontinence', 'urge_incontinence']
senticnet['urethral_prolapse'] = ['-0.52', '0', '0.839', '-0.86', '#anger', '#disgust', 'negative', '-0.74', 'folie', 'upset', 'psychosomatic_disorder', 'conversion_disorder', 'conversion_reaction']
senticnet['urethral_stricture'] = ['-0.52', '0', '0.839', '-0.86', '#anger', '#disgust', 'negative', '-0.74', 'folie', 'upset', 'psychosomatic_disorder', 'conversion_disorder', 'conversion_reaction']
senticnet['urethroscope'] = ['0.251', '0.174', '-0.06', '0.306', '#joy', '#admiration', 'positive', '0.167', 'doctor_office', 'cystoscope', 'sigmoidoscope', 'hospital', 'check_health']
senticnet['urge'] = ['0.655', '0.681', '-0.3', '0.39', '#joy', '#interest', 'positive', '0.357', 'favor', 'exhilarate', 'joyful', 'wish', 'comfortableness']
senticnet['urge_caution'] = ['-0.32', '0.133', '-0.38', '0', '#sadness', '#fear', 'negative', '-0.19', 'big_monster', 'unsafe', 'phobia', 'mock', 'feel_scared']
senticnet['urge_incontinence'] = ['-0.77', '0', '0.966', '-0.51', '#sadness', '#anger', 'negative', '-0.75', 'overflow_incontinence', 'incontinency', 'incontinence', 'stress_incontinence', 'enuresis']
senticnet['urge_on'] = ['0.655', '0.635', '-0.23', '0.3', '#joy', '#interest', 'positive', '0.338', 'cheerfully', 'cheerful', 'jolly_up', 'hearten', 'chirk_up']
senticnet['urgency'] = ['-0.89', '0.168', '-0.60', '0.05', '#sadness', '#fear', 'negative', '-0.32', 'emergency', 'emergency_situation', 'urgent', 'needful', 'alarm']
senticnet['urgent'] = ['0', '0.092', '0', '0.091', '#interest', '#admiration', 'positive', '0.091', 'project', 'classify_ad', 'important', 'review', 'repetition']
senticnet['urgent_care'] = ['0', '0', '-0.73', '0', '#fear', '#fear', 'negative', '-0.73', 'clinic', 'emergency_ward', 'hospital', 'dentist_office', 'medical_office']
senticnet['urgent_necessity'] = ['-0.99', '0', '0.691', '-0.65', '#sadness', '#anger', 'negative', '-0.78', 'depression', 'economic_crisis', 'crisis', 'slump', 'juncture']
senticnet['urging'] = ['0', '0', '0.968', '-0.85', '#anger', '#disgust', 'negative', '-0.91', 'prod', 'spur', 'needle', 'goad', 'prick']
senticnet['urhannagh'] = ['0.929', '0.507', '-0.17', '0.396', '#joy', '#interest', 'positive', '0.415', 'plume', 'congratulate', 'smugness', 'smug', 'proudly']
senticnet['uri_geller'] = ['0.818', '0.913', '0', '0.825', '#interest', '#admiration', 'positive', '0.852', 'greeting', 'blackjack', 'pass_card', 'credit', 'play_blackjack']
senticnet['urial'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'argali', 'argal', 'wild_sheep', 'white_sheep', 'mountain_sheep']
senticnet['urinal'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'commode', 'potty_seat', 'potty_chair']
senticnet['urinary_calculus'] = ['0.976', '0.804', '0', '0', '#joy', '#interest', 'positive', '0.89', 'nephrolith', 'kidney_stone', 'renal_calculus', 'help', 'stone']
senticnet['urinary_excretion'] = ['0.992', '-0.80', '0', '0.584', '#joy', '#surprise', 'positive', '0.795', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'incontinence']
senticnet['urinary_frequency'] = ['-0.05', '0', '-0.05', '-0.04', '#sadness', '#fear', 'negative', '-0.05', 'empty_bladder', 'pee', 'go_potty', 'go_pee', 'go_toilet']
senticnet['urinary_incontinence'] = ['-0.88', '0', '0.525', '-0.92', '#sadness', '#disgust', 'negative', '-0.77', 'overflow_incontinence', 'incontinency', 'incontinence', 'stress_incontinence', 'urge_incontinence']
senticnet['urinary_leakage'] = ['0.992', '-0.80', '0', '0.584', '#joy', '#surprise', 'positive', '0.795', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'excretion']
senticnet['urinary_obstruction'] = ['-0.77', '0', '0.966', '-0.51', '#sadness', '#anger', 'negative', '-0.75', 'enuresis', 'incontinency', 'incontinence', 'stress_incontinence', 'urge_incontinence']
senticnet['urinary_retention'] = ['-0.77', '0', '0.966', '-0.51', '#sadness', '#anger', 'negative', '-0.75', 'enuresis', 'incontinency', 'incontinence', 'stress_incontinence', 'urge_incontinence']
senticnet['urinary_stream'] = ['-0.05', '0', '-0.05', '-0.04', '#sadness', '#fear', 'negative', '-0.05', 'empty_bladder', 'pee', 'go_potty', 'go_pee', 'go_toilet']
senticnet['urinary_tract_infection'] = ['-0.99', '0', '0.964', '-0.70', '#sadness', '#anger', 'negative', '-0.88', 'blepharitis', 'inflammation', 'epiglottitis', 'orchitis', 'prostatitis']
senticnet['urinary_urgency'] = ['-0.05', '0', '-0.05', '-0.04', '#sadness', '#fear', 'negative', '-0.05', 'empty_bladder', 'pee', 'go_potty', 'go_pee', 'go_toilet']
senticnet['urinate'] = ['-0.05', '0', '-0.05', '-0.04', '#sadness', '#fear', 'negative', '-0.05', 'empty_bladder', 'pee', 'go_potty', 'go_pee', 'go_toilet']
senticnet['urinate_frequently'] = ['-0.05', '0', '-0.05', '-0.04', '#sadness', '#fear', 'negative', '-0.05', 'empty_bladder', 'pee', 'go_potty', 'go_pee', 'go_toilet']
senticnet['urination'] = ['-0.04', '0', '-0.07', '-0.09', '#fear', '#disgust', 'negative', '-0.07', 'go_toilet', 'go_bathroom', 'go_pee', 'read_bedtime_story', 'quiet_cry_baby']
senticnet['urine'] = ['-0.05', '0', '0', '-0.11', '#sadness', '#disgust', 'negative', '-0.08', 'piss', 'pee', 'sour_milk', 'lemon', 'lifeguard']
senticnet['urine_flow'] = ['-0.89', '0', '0.925', '-0.58', '#sadness', '#anger', 'negative', '-0.80', 'shit', 'laxation', 'movement', 'dump', 'asshole']
senticnet['urine_leakage'] = ['-0.05', '0', '-0.05', '-0.04', '#sadness', '#fear', 'negative', '-0.05', 'empty_bladder', 'pee', 'go_potty', 'go_pee', 'go_toilet']
senticnet['url'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'swat', 'store_record', 'ideal']
senticnet['urltoarea_river'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'aphasia', 'anomic_aphasia']
senticnet['urn'] = ['0', '0.082', '-0.09', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'coffin', 'headstone', 'morgue', 'bury_body', 'casket']
senticnet['uropathy'] = ['0.762', '-0.84', '0', '0.831', '#surprise', '#admiration', 'positive', '0.812', 'kidney_disease', 'renal_disorder', 'nephropathy', 'pathology', 'nephrosis']
senticnet['urpmi'] = ['0.965', '0.936', '0', '0', '#joy', '#interest', 'positive', '0.951', 'delicious_food', 'hot_meal', 'good_food', 'fresh_produce', 'tasty_food']
senticnet['ursolic_acid'] = ['-0.82', '0', '0.642', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'substance', 'capsaicin', 'chemical_irritant', 'piperine', 'pepper_spray']
senticnet['ursula_holden'] = ['-0.20', '-0.10', '0.07', '-0.24', '#sadness', '#disgust', 'negative', '-0.10', 'flipper', 'sea_life', 'seafood', 'swim_animal', 'fillet']
senticnet['urtica'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['urtica_dioica'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['urticaria'] = ['-0.55', '0', '0.536', '-0.72', '#sadness', '#disgust', 'negative', '-0.60', 'hypersensitivity_reaction', 'rash', 'efflorescence', 'roseola', 'skin_rash']
senticnet['urtication'] = ['-0.78', '0', '0.872', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'hypersensitivity_reaction', 'rash', 'efflorescence', 'roseola', 'skin_rash']
senticnet['uruki'] = ['0.089', '0.09', '0.81', '-0.68', '#anger', '#disgust', 'negative', '-0.32', 'covetous', 'envious', 'enviously', 'covetously', 'green-eyed']
senticnet['urushiol'] = ['-0.53', '0', '0.86', '-0.65', '#anger', '#disgust', 'negative', '-0.68', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['usability'] = ['0.932', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.959', 'operational', 'functional', 'operable', 'available', 'useable']
senticnet['usable'] = ['0.932', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.959', 'operational', 'functional', 'operable', 'available', 'useable']
senticnet['usableness'] = ['0.505', '-0.88', '0', '0.921', '#surprise', '#admiration', 'positive', '0.769', 'quality', 'serviceability', 'serviceableness', 'usefulness', 'user_friendly']
senticnet['usac_silver_crown'] = ['0', '0', '-0.05', '-0.05', '#fear', '#disgust', 'negative', '-0.05', 'human', 'delusional', 'use_illegal_drug', 'busy_person', 'carpenter']
senticnet['usage'] = ['0.917', '-0.98', '0', '0.613', '#joy', '#surprise', 'positive', '0.837', 'practice', 'activity', 'play', 'misuse', 'abuse']
senticnet['usaid'] = ['-0.54', '0', '0.863', '-0.76', '#anger', '#disgust', 'negative', '-0.72', 'denomination', 'committee', 'citizen_committee', 'mission', 'missionary_post']
senticnet['usama_fayyad'] = ['-0.63', '0', '0.862', '-0.52', '#sadness', '#anger', 'negative', '-0.67', 'knot', 'bunch', 'tuft', 'tussock', 'agglomeration']
senticnet['usb'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'run_treadmill', 'complete_thesis', 'dwarf_planet', 'insure', 'ridgeback']
senticnet['usb_connection'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'run_treadmill', 'complete_thesis', 'dwarf_planet', 'insure', 'ridgeback']
senticnet['usb_drive'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotary_file', 'cd-rom', 'database', 'business_card', 'calendar_book']
senticnet['usb_flash_drive'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'rotary_file', 'cd-rom', 'database', 'business_card', 'calendar_book']
senticnet['usb_otg'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'run_treadmill', 'complete_thesis', 'dwarf_planet', 'insure', 'ridgeback']
senticnet['usb_pen'] = ['0.825', '0', '0', '0.703', '#joy', '#admiration', 'positive', '0.764', 'pay_toll', 'get_fast', 'access', 'fast_travel', 'access_quickly']
senticnet['usb_plug'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'clip', 'clasp', 'glue_stick', 'clothespin', 'clamp']
senticnet['usb_port'] = ['0', '0', '0', '0.072', '#admiration', '#admiration', 'positive', '0.072', 'run_treadmill', 'complete_thesis', 'dwarf_planet', 'insure', 'ridgeback']
senticnet['usc_thornton_school'] = ['0.937', '0', '0', '0', '#joy', '#joy', 'positive', '0.937', 'band_play', 'classical', 'composer', 'songwriter', 'rock_band']
senticnet['usda_seal'] = ['-0.94', '0', '0.569', '-0.72', '#sadness', '#disgust', 'negative', '-0.74', 'bitumastic', 'veneer', 'veneering', 'adhesive', 'coating']
senticnet['usda_zone'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'intrepidly', 'fearlessness', 'fearlessly', 'unafraid', 'unfearing']
senticnet['use'] = ['-0.04', '0.141', '-0.09', '0.21', '#interest', '#admiration', 'positive', '0.053', 'make_use', 'exploit', 'creation', 'make_stuff', 'employ']
senticnet['use_army'] = ['0', '0', '-0.06', '-0.15', '#fear', '#disgust', 'negative', '-0.10', 'battle', 'soldier', 'declare_war', 'warrior', 'siege']
senticnet['use_atm'] = ['-0.07', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.07', 'carry_credit_card', 'carry_wallet', 'window_shop', 'buy_coffee', 'out_money']
senticnet['use_bathroom'] = ['0.838', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.896', 'go_restroom', 'leave_tip', 'charge_credit_card', 'spill_food_clothes', 'ask_menu']
senticnet['use_bed'] = ['0.165', '0', '0.032', '0.114', '#joy', '#admiration', 'positive', '0.082', 'good_sleep', 'shut_eye', 'place_sleep', 'comfortable_place_sleep', 'lie_down_bed']
senticnet['use_binoculars'] = ['0.021', '0.206', '-0.11', '0.148', '#interest', '#admiration', 'positive', '0.066', 'classy', 'female_person', 'female_human', 'elegant', 'relate']
senticnet['use_blade'] = ['-0.94', '0', '0.655', '-0.95', '#sadness', '#disgust', 'negative', '-0.85', 'use_knife', 'seperate', 'cut', 'sharp_tool', 'dangerous']
senticnet['use_board_game'] = ['-0.03', '0.192', '-0.14', '0.148', '#interest', '#admiration', 'positive', '0.039', 'inclusion', 'list', 'band_together', 'commence', 'enlist']
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senticnet['use_calculator'] = ['0.031', '0.07', '0.025', '0.07', '#interest', '#admiration', 'positive', '0.037', 'use_computer', 'use_technology', 'calculator', 'study_math', 'math_exercise']
senticnet['use_camera'] = ['0', '0.333', '-0.21', '0.384', '#interest', '#admiration', 'positive', '0.166', 'take_picture', 'picture', 'video', 'take_photograph', 'photograph']
senticnet['use_car'] = ['0', '0.738', '0', '0.857', '#interest', '#admiration', 'positive', '0.797', 'decide_to_go', 'turn_ignition', 'burn_gasoline', 'start_engine', 'know_go']
senticnet['use_cash'] = ['0.339', '0.302', '0', '0.387', '#joy', '#admiration', 'positive', '0.343', 'good_citizen', 'pay_bill', 'use_credit_card', 'purchase_gift', 'pay_off_debt']
senticnet['use_chair'] = ['0.059', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.04', 'rest', 'sit', 'sleep_night', 'take_break', 'residue']
senticnet['use_clean'] = ['0.048', '0', '0', '0.104', '#joy', '#admiration', 'positive', '0.076', 'find_home', 'clean_agent', 'clean_product', 'clean_cloth', 'tv_drama']
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senticnet['use_computer'] = ['0.777', '0.761', '0', '0', '#joy', '#interest', 'positive', '0.769', 'post_message', 'send_mail', 'print_printer', 'enter_data', 'send_email']
senticnet['use_condom'] = ['0.06', '0.422', '-0.17', '0.41', '#interest', '#admiration', 'positive', '0.179', 'safe_sex', 'meet_girl', 'prevention', 'playboy', 'dirty_magazine']
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senticnet['use_cook'] = ['0', '0', '0', '0.09', '#admiration', '#admiration', 'positive', '0.09', 'essential_life', 'slippery', 'spicy', 'liquid_substance', 'fluid']
senticnet['use_crayon'] = ['-0.03', '0.077', '0', '0.077', '#interest', '#admiration', 'positive', '0.039', 'sketch', 'draw', 'pencil_art', 'make_sketch', 'create_image']
senticnet['use_credit_card'] = ['0', '0.707', '0', '0.845', '#interest', '#admiration', 'positive', '0.776', 'run_out_money', 'pay_money', 'compare_price', 'make_profit', 'spend_much_money']
senticnet['use_decoration'] = ['0', '0.106', '0', '0.165', '#interest', '#admiration', 'positive', '0.136', 'fragrant', 'sexual', 'cultivate_yard', 'garden_plant', 'green']
senticnet['use_dictionary'] = ['-0.10', '-0.03', '0.068', '-0.22', '#sadness', '#disgust', 'negative', '-0.09', 'turn_page', 'remember_fact', 'move_eye', 'gain_insight', 'better_understand']
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senticnet['use_electricity'] = ['0.886', '0.876', '0', '0', '#joy', '#interest', 'positive', '0.881', 'change_channel', 'see_commercial', 'tell_friend', 'kill_brain_cell', 'drink_soda']
senticnet['use_energy'] = ['0.708', '0.842', '0', '0', '#joy', '#interest', 'positive', '0.775', 'exercise', 'enjoy_outdoors', 'fit_person', 'relieve_stress', 'learn_teamwork']
senticnet['use_eye'] = ['0', '0', '0', '0.881', '#admiration', '#admiration', 'positive', '0.881', 'vision', 'eye', 'contact_lens', 'correct_vision', 'improve_vision']
senticnet['use_farm'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'door', 'glass_front', 'gate', 'corridor', 'build_open']
senticnet['use_finger'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'dexterity', 'manual_dexterity', 'use_hand', 'coordination', 'bend_over']
senticnet['use_foot'] = ['-0.08', '0.102', '0', '0.153', '#interest', '#admiration', 'positive', '0.056', 'move_foot', 'move_forward', 'foot_motion', 'move_ahead', 'walk']
senticnet['use_fork'] = ['-0.04', '-0.01', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.02', 'burp', 'drink_much_beer', 'acid_indigestion', 'add_spice', 'wipe_mouth_napkin']
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senticnet['use_game_chess'] = ['-0.04', '0.141', '-0.07', '0.107', '#interest', '#admiration', 'positive', '0.032', 'use_garden', 'chess', 'make_up_person', 'find_airplane', 'overrate']
senticnet['use_garden'] = ['-0.04', '0.139', '-0.07', '0.106', '#interest', '#admiration', 'positive', '0.032', 'garden', 'relax', 'chillax', 'chill_out', 'outdoor']
senticnet['use_gas'] = ['0.032', '0.261', '-0.15', '0.198', '#interest', '#admiration', 'positive', '0.084', 'stop_red_light', 'get_to_destination', 'run_out_gas', 'arrive_destination', 'control_car']
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senticnet['use_hand_gesture'] = ['-0.08', '0', '0.015', '0', '#sadness', '#anger', 'negative', '-0.05', 'make_unusual_face', 'unusual_face', 'move_tongue', 'make_gesture', 'share_idea']
senticnet['use_hunt'] = ['0.702', '0.867', '0', '0', '#joy', '#interest', 'positive', '0.784', 'rifle', 'gun', 'revolver', 'pistol', 'firearm']
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senticnet['use_imagination'] = ['0', '0.128', '-0.05', '0.098', '#interest', '#admiration', 'positive', '0.058', 'mental_activity', 'fantasize', 'use_brain', 'imagination', 'dream']
senticnet['use_ink'] = ['-0.03', '0.093', '0', '0.083', '#interest', '#admiration', 'positive', '0.047', 'pencil_mark', 'script', 'use_pen', 'write_poem', 'mark']
senticnet['use_internet'] = ['-0.06', '-0.05', '0.049', '-0.06', '#sadness', '#disgust', 'negative', '-0.03', 'read_article', 'borrow_book', 'keep_open_mind', 'hack', 'use_system_abstract_think']
senticnet['use_jazz_music'] = ['-0.03', '-0.00', '0.018', '-0.08', '#sadness', '#disgust', 'negative', '-0.03', 'reed_instrument', 'stringed_musical_instrument', 'instrument_music', 'musical_instrument', 'listen_music']
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senticnet['use_language'] = ['-0.05', '0.101', '0.015', '0.058', '#interest', '#admiration', 'positive', '0.022', 'ability_communicate', 'know_language', 'know_talk', 'make_unusual_face', 'fact_straight']
senticnet['use_leg'] = ['-0.09', '0.104', '0', '0.099', '#interest', '#admiration', 'positive', '0.037', 'act_quickly', 'take_step', 'wear_running_shoes', 'wear_good_shoes', 'run_slowly']
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senticnet['use_logic'] = ['0', '0', '0', '0.878', '#admiration', '#admiration', 'positive', '0.878', 'guess_answer', 'love_pet', 'state_fact', 'miss_bus', 'point_out']
senticnet['use_machine'] = ['0', '0.08', '0', '0.179', '#interest', '#admiration', 'positive', '0.13', 'scrub', 'soap_up', 'launder', 'use_water_soap', 'use_soap']
senticnet['use_map'] = ['0.853', '-0.71', '0', '0.92', '#joy', '#admiration', 'positive', '0.831', 'point_out', 'use_compass', 'find', 'direction', 'find_direction']
senticnet['use_match'] = ['0.215', '0.194', '-0.20', '0.279', '#joy', '#admiration', 'positive', '0.121', 'diligence', 'gather_kindle', 'combustible_material', 'rub_two_stick_together', 'lighter']
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senticnet['use_microphone'] = ['0.028', '-0.60', '0', '0.354', '#surprise', '#admiration', 'positive', '0.328', 'perform_on_stage', 'listen', 'sing', 'compose_music', 'loud']
senticnet['use_military'] = ['-0.06', '-0.02', '-0.07', '-0.19', '#fear', '#disgust', 'negative', '-0.07', 'make_war', 'find_airport', 'conquer_enemy', 'invade', 'kill_soldier']
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senticnet['use_mouth'] = ['0.029', '0.074', '0', '0.187', '#interest', '#admiration', 'positive', '0.097', 'mouth_movement', 'mouth_action', 'verbal_communication', 'mouth_noise', 'human_noise']
senticnet['use_muscle'] = ['0', '0.069', '0', '0.1', '#interest', '#admiration', 'positive', '0.085', 'limber_up', 'move_body', 'expend_energy', 'fitness', 'stretch_muscle']
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senticnet['use_paint'] = ['-0.02', '0.132', '0', '0.134', '#interest', '#admiration', 'positive', '0.081', 'pencil_art', 'wall_hang', 'artwork', 'create_image', 'wall_decoration']
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senticnet['use_pen'] = ['0.463', '0.561', '-0.29', '0.281', '#joy', '#interest', 'positive', '0.253', 'write_date', 'digitate_date', 'use_computer', 'impress_date', 'date']
senticnet['use_pencil'] = ['0.078', '0.229', '-0.11', '0.209', '#interest', '#admiration', 'positive', '0.1', 'misspell', 'record_information', 'definite', 'documentation', 'think_idea']
senticnet['use_phone'] = ['0', '-0.00', '0', '0.104', '#surprise', '#admiration', 'positive', '0.053', 'phone_conversation', 'use_telephone', 'call', 'make_call', 'telephone_ring']
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senticnet['use_restroom'] = ['-0.06', '-0.01', '0', '-0.14', '#sadness', '#disgust', 'negative', '-0.06', 'eat_french_fries', 'ask_menu', 'order_dinner', 'read_menu', 'order_french_fries']
senticnet['use_salad'] = ['0', '0.115', '0.02', '0.189', '#interest', '#admiration', 'positive', '0.095', 'leafy_green', 'cultivate_yard', 'rabbit_food', 'red_fruit', 'nutritious']
senticnet['use_school'] = ['0', '0.071', '0.043', '0.067', '#interest', '#admiration', 'positive', '0.032', 'go_to_school', 'student', 'teacher', 'learn_somthing_new', 'hard_surface']
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senticnet['use_screwdriver'] = ['-0.10', '-0.05', '0.09', '-0.20', '#sadness', '#disgust', 'negative', '-0.08', 'take_apart', 'medical_equipment', 'doctor_tool', 'reboot', 'make_error']
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senticnet['use_shower'] = ['-0.13', '0.098', '-0.15', '0', '#sadness', '#fear', 'negative', '-0.06', 'pour_water', 'remove_clothes', 'wear_bath_suit', 'malfunction', 'change_lifestyle']
senticnet['use_skill'] = ['0', '0.074', '0.051', '0.105', '#interest', '#admiration', 'positive', '0.043', 'skill', 'talent', 'ability', 'genius', 'earn_money']
senticnet['use_soap'] = ['0', '0.133', '0', '0.206', '#interest', '#admiration', 'positive', '0.169', 'scrub', 'cleanse', 'body_clean', 'shower', 'clean_laundry']
senticnet['use_solar_oven'] = ['0.034', '0', '-0.05', '-0.09', '#fear', '#disgust', 'negative', '-0.03', 'empty', 'hungry', 'need_grocery', 'hunger', 'fee']
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senticnet['use_talent'] = ['0.981', '0.791', '0', '0', '#joy', '#interest', 'positive', '0.886', 'entertain_audience', 'sound_good', 'give_pleasure', 'entertainment', 'entertain_crowd']
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senticnet['use_telephone'] = ['0.024', '-0.01', '0.011', '0.085', '#joy', '#admiration', 'positive', '0.029', 'use_phone', 'phone_conversation', 'make_call', 'speak_loudly', 'call']
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senticnet['use_thermometer'] = ['-0.07', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.11', 'catch_fire', 'age_prematurely', 'light_cigar', 'light_pipe', 'cause_mental_illness']
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senticnet['use_tissue'] = ['0', '0.101', '0.039', '0.054', '#interest', '#admiration', 'positive', '0.039', 'smear', 'cheer_up', 'wipe_tear', 'push_button', 'scratch_back']
senticnet['use_toilet'] = ['-0.04', '0', '0.023', '-0.04', '#sadness', '#disgust', 'negative', '-0.03', 'wash_up', 'go_potty', 'go_pee', 'change_clothes', 'take_shower']
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senticnet['use_tooth'] = ['0.808', '0', '0', '0', '#joy', '#joy', 'positive', '0.808', 'wash_first', 'take_bite', 'buy_market', 'chew', 'put_napkin_lap']
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senticnet['use_vacuum_cleaner'] = ['-0.02', '0.165', '-0.08', '0.14', '#interest', '#admiration', 'positive', '0.05', 'maintenance', 'vacuum_carpet', 'clean_often', 'tidy_up', 'vacuum_floor']
senticnet['use_vehicle'] = ['0', '0', '0', '0.165', '#admiration', '#admiration', 'positive', '0.165', 'get_around', 'passenger_car', 'transportation', 'car_travel', 'car_go']
senticnet['use_voice'] = ['-0.04', '0', '0.032', '0', '#sadness', '#anger', 'negative', '-0.03', 'clear_throat', 'whisper', 'hear_voice', 'mouth_move', 'move_tongue']
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senticnet['use_washcloth'] = ['0.813', '0', '0', '0', '#joy', '#joy', 'positive', '0.813', 'wash_body', 'wash_dog', 'lather', 'clean', 'stay_fresh']
senticnet['use_water'] = ['0', '0.095', '0', '0.074', '#interest', '#admiration', 'positive', '0.084', 'laundry_detergent', 'water_soap', 'clothes_dirty', 'laundry', 'buy_wash_powder']
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senticnet['use_weapon'] = ['-0.18', '0', '-0.12', '-0.22', '#sadness', '#disgust', 'negative', '-0.17', 'use_gun', 'fire_weapon', 'attack_enemy', 'plan_attack', 'hide_evidence']
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senticnet['useage'] = ['0.917', '-0.98', '0', '0.613', '#joy', '#surprise', 'positive', '0.837', 'practice', 'activity', 'play', 'misuse', 'abuse']
senticnet['used_bayer_aspirin'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'safe_sex', 'sex', 'prevent_pregnancy', 'avoid_pregnancy', 'become_aroused']
senticnet['used_capsicum_spray'] = ['-0.91', '0', '0.931', '-0.72', '#sadness', '#anger', 'negative', '-0.85', 'hit_with_baton', 'get_hurt', 'whip', 'arrow', 'hurt']
senticnet['used_car_dealership'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'clean', 'polish', 'shiny', 'water', 'sexy_girl']
senticnet['used_car_salesman'] = ['-0.08', '-0.05', '0.037', '-0.06', '#sadness', '#disgust', 'negative', '-0.03', 'trip_fall', 'fall_off', 'fall_down', 'lose_balance', 'breahard']
senticnet['used_interchangeably'] = ['0.917', '-0.98', '0', '0.613', '#joy', '#surprise', 'positive', '0.837', 'practice', 'activity', 'play', 'misuse', 'abuse']
senticnet['used_medicinally'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'infective_agent', 'satisfier', 'quickener', 'agent']
senticnet['used_pepper_spray'] = ['-0.13', '0.113', '-0.16', '0', '#sadness', '#fear', 'negative', '-0.06', 'question_witness', 'legal_process', 'find_guilty', 'convict_criminal', 'custody']
senticnet['used_recreationally'] = ['0.672', '0.632', '-0.23', '0.312', '#joy', '#interest', 'positive', '0.344', 'euphoria', 'buoyancy', 'perkiness', 'jubilance', 'ecstasy']
senticnet['used_topically'] = ['0.701', '-0.76', '0', '0.904', '#surprise', '#admiration', 'positive', '0.791', 'curative', 'heal', 'medicine', 'get_cured', 'take_care']
senticnet['useful'] = ['0', '0.081', '0', '0.112', '#interest', '#admiration', 'positive', '0.097', 'dead', 'versatile', 'hard', 'expensive', 'hard_material']
senticnet['useful_starting_point'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'woodworking_tool']
senticnet['useful_tool'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'woodworking_tool']
senticnet['usefulness'] = ['0.513', '-0.87', '0', '0.924', '#surprise', '#admiration', 'positive', '0.77', 'quality', 'serviceability', 'serviceableness', 'usableness', 'user_friendly']
senticnet['useless'] = ['0', '0', '0', '-0.78', '#disgust', '#disgust', 'negative', '-0.78', 'hazardous', 'bad_choice', 'wrong', 'violent', 'serious']
senticnet['useless_info'] = ['-0.78', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.80', 'annoy', 'ad', 'annoy_user', 'advertising', 'useless_information']
senticnet['useless_information'] = ['-0.78', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.81', 'annoy', 'ad', 'annoy_user', 'useless_info', 'advertising']
senticnet['uselessly'] = ['0', '0.753', '0', '0.837', '#interest', '#admiration', 'positive', '0.795', 'ricocheted', 'deflected', 'bounced', 'crossbar', 'harmlessly']
senticnet['uselessness'] = ['0.75', '-0.87', '0', '0.603', '#joy', '#surprise', 'positive', '0.743', 'quality', 'futility', 'impracticality', 'worthlessness', 'impracticability']
senticnet['usemap'] = ['0.777', '-0.85', '0', '0.615', '#joy', '#surprise', 'positive', '0.748', 'think', 'play', 'trifle', 'abstract', 'warm_to']
senticnet['user'] = ['-0.84', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.82', 'anon', 'comment', 'reply', 'anonymous', 'coward']
senticnet['user_athenean_athenean_user'] = ['0.792', '-0.86', '0', '0.888', '#surprise', '#admiration', 'positive', '0.849', 'absurd', 'inconsequential', 'intuitive', 'visceral', 'nonrational']
senticnet['user_blnguyen_blnguyen_user'] = ['0.792', '-0.86', '0', '0.888', '#surprise', '#admiration', 'positive', '0.849', 'absurd', 'inconsequential', 'intuitive', 'visceral', 'nonrational']
senticnet['user_callable'] = ['0', '0', '0.737', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'annoy', 'skin_rash', 'bother', 'irritation', 'great_dane']
senticnet['user_finlay_mcwalter'] = ['0.792', '-0.86', '0', '0.888', '#surprise', '#admiration', 'positive', '0.849', 'absurd', 'inconsequential', 'intuitive', 'visceral', 'nonrational']
senticnet['user_friendly'] = ['0.986', '-0.62', '0', '0.807', '#joy', '#admiration', 'positive', '0.807', 'operable', 'functional', 'serviceable', 'usable', 'operational']
senticnet['user_friendly_format'] = ['-0.03', '0.168', '-0.07', '0.139', '#interest', '#admiration', 'positive', '0.049', 'command', 'direct', 'follow', 'take_charge', 'govern']
senticnet['user_friendly_guide'] = ['-0.03', '0.168', '-0.07', '0.139', '#interest', '#admiration', 'positive', '0.049', 'command', 'direct', 'follow', 'take_charge', 'govern']
senticnet['user_friendly_interface'] = ['-0.84', '0', '0.689', '-0.90', '#sadness', '#disgust', 'negative', '-0.81', 'software', 'software_program', 'computer_software', 'software_system', 'authoring_language']
senticnet['user_friendly_tool'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'woodworking_tool']
senticnet['user_interface'] = ['0', '0.876', '0', '0.942', '#interest', '#admiration', 'positive', '0.909', 'interface', 'port', 'iui', 'material_research', 'work']
senticnet['user_interface_toolkit'] = ['0.044', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.355', 'lightweight', 'aluminium', 'light', 'thin', 'carbon_fiber']
senticnet['user_makerocketgonow'] = ['-0.08', '0', '0.011', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'deflate', 'read_cover', 'obtain_information', 'upgrade', 'spread_out']
senticnet['user_momusufan_momusufan_user'] = ['0.792', '-0.86', '0', '0.888', '#surprise', '#admiration', 'positive', '0.849', 'absurd', 'inconsequential', 'intuitive', 'visceral', 'nonrational']
senticnet['user_mouse_over'] = ['0.777', '-0.85', '0', '0.615', '#joy', '#surprise', 'positive', '0.748', 'think', 'play', 'trifle', 'abstract', 'warm_to']
senticnet['user_protonk_protonk_user'] = ['0.792', '-0.86', '0', '0.888', '#surprise', '#admiration', 'positive', '0.849', 'absurd', 'inconsequential', 'intuitive', 'visceral', 'nonrational']
senticnet['user_selectable'] = ['0.044', '0.08', '0', '-0.03', '#joy', '#interest', 'positive', '0.031', 'screen', 'pixel', 'visual_information', 'image', 'computer_monitor']
senticnet['user_spui'] = ['0.792', '-0.86', '0', '0.888', '#surprise', '#admiration', 'positive', '0.849', 'absurd', 'inconsequential', 'intuitive', 'visceral', 'nonrational']
senticnet['user_talk_page'] = ['0.92', '0.794', '0', '0', '#joy', '#interest', 'positive', '0.857', 'move_mouse', 'reboot', 'type_of_keyboard', 'enter_address', 'keep_track_appointment']
senticnet['user_talk_silver_seren'] = ['0', '0.931', '0', '0.947', '#interest', '#admiration', 'positive', '0.939', 'gold', 'metal', 'ash_gray', 'fluent', 'eloquent']
senticnet['user_volunteer_marek'] = ['0.533', '0.818', '0', '0.683', '#interest', '#admiration', 'positive', '0.678', 'gain_information', 'report_news', 'solve', 'seek_answer', 'gather_information']
senticnet['useragent'] = ['-0.89', '0', '0.672', '-0.69', '#sadness', '#disgust', 'negative', '-0.75', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['username'] = ['0.92', '0.794', '0', '0', '#joy', '#interest', 'positive', '0.857', 'move_mouse', 'reboot', 'type_of_keyboard', 'enter_address', 'keep_track_appointment']
senticnet['userpage'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['useth'] = ['-0.98', '0', '0.654', '-0.84', '#sadness', '#disgust', 'negative', '-0.82', 'deprive', 'expropriate', 'disarm', 'unclothe', 'take']
senticnet['usher'] = ['0.256', '0', '0', '0.084', '#joy', '#admiration', 'positive', '0.17', 'balcony_rail', 'movie_theater_seat', 'row_seat', 'mosh_pit', 'sticky_floor']
senticnet['ushort'] = ['-0.04', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.06', 'chemical_reaction', 'burn', 'chemical_process', 'get_burnt', 'set_on_fire']
senticnet['using'] = ['-0.82', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.87', 'protest', 'buying', 'boycott', 'blockade', 'expression']
senticnet['using_gzip'] = ['0', '0', '-0.1', '0', '#fear', '#fear', 'negative', '-0.1', 'grow_yard', 'heat', 'burn', 'corrode', 'stain_clothes']
senticnet['using_transmission_electron'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'amoeba', 'tiny_organism', 'microbe', 'heron', 'single_celled']
senticnet['usman_khawaja_dismissal'] = ['-0.71', '0', '0.83', '-0.68', '#sadness', '#anger', 'negative', '-0.74', 'gag', 'jest', 'jape', 'joke', 'laugh']
senticnet['usn_ret'] = ['0.688', '-0.90', '0', '0.877', '#surprise', '#admiration', 'positive', '0.824', 'flag_officer', 'rear_admiral', 'admiral', 'full_admiral', 'vice_admiral']
senticnet['usr'] = ['0', '0.175', '0', '0.231', '#interest', '#admiration', 'positive', '0.203', 'list', 'schedule', 'index', 'agenda', 'register']
senticnet['ussuri_river'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['ustilago'] = ['-0.93', '0', '0.699', '-0.91', '#sadness', '#disgust', 'negative', '-0.84', 'onion_smut', 'head_smut', 'stinking_smut', 'flag_smut_fungus', 'covered_smut']
senticnet['ustinov_studio'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
senticnet['usual'] = ['0.035', '0.236', '-0.07', '0.218', '#interest', '#admiration', 'positive', '0.103', 'typical', 'usual_ordinary', 'normal', 'frequent', 'sameness']
senticnet['usual_green'] = ['0', '0.08', '0.019', '0.076', '#interest', '#admiration', 'positive', '0.046', 'green', 'leafy', 'vegetation', 'green_growth', 'tree_flower']
senticnet['usual_ordinary'] = ['0.071', '0.222', '-0.06', '0.264', '#interest', '#admiration', 'positive', '0.124', 'typical', 'normal', 'usual', 'frequent', 'sameness']
senticnet['usum'] = ['0.654', '-0.75', '0', '0.78', '#surprise', '#admiration', 'positive', '0.729', 'suit', 'cause', 'lawsuit', 'occurrence', 'countersuit']
senticnet['usurer'] = ['0.685', '-0.58', '0', '0.56', '#joy', '#surprise', 'positive', '0.608', 'profit', 'net_income', 'net', 'net_profit', 'fast_buck']
senticnet['usurious'] = ['-0.89', '0', '0.6', '-0.92', '#sadness', '#disgust', 'negative', '-0.80', 'outrageous', 'exorbitant', 'extortionate', 'immoderate', 'abnormal']
senticnet['usurp'] = ['-0.79', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'raid', 'take', 'appropriate', 'capture', 'annex']
senticnet['usurp_power'] = ['-0.98', '0', '-0.87', '0', '#sadness', '#fear', 'negative', '-0.93', 'foment', 'agitated', 'tumult', 'stir_up', 'earnestness']
senticnet['usurpation'] = ['-0.63', '0', '0.728', '-0.58', '#sadness', '#anger', 'negative', '-0.65', 'misconduct', 'inroad', 'wrongdoing', 'wrongful_conduct', 'actus_reus']
senticnet['usurper'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'feudal_lord', 'lord']
senticnet['utau'] = ['0.028', '0', '0.027', '0.101', '#joy', '#admiration', 'positive', '0.034', 'animal_dwell', 'bee_home', 'live_place', 'dwell_place', 'place_to_live']
senticnet['utensil'] = ['0.02', '-0.05', '0', '0.051', '#surprise', '#admiration', 'positive', '0.043', 'grease_trap', 'glass_front_cupboard', 'fork', 'spoon', 'spoon_fork']
senticnet['uterine_cavity'] = ['0.255', '0.182', '-0.06', '0.314', '#joy', '#admiration', 'positive', '0.173', 'doctor_office', 'urethroscope', 'sigmoidoscope', 'hospital', 'check_health']
senticnet['uterine_contraction'] = ['-0.62', '0', '0.901', '-0.71', '#anger', '#disgust', 'negative', '-0.74', 'pressure', 'osmotic_pressure', 'hypotonicity', 'pressure_level', 'force']
senticnet['uterine_fibroid'] = ['-0.88', '0', '0.774', '-0.99', '#sadness', '#disgust', 'negative', '-0.88', 'fibroma', 'fibroid', 'benign_tumor', 'nonmalignant_tumor', 'nonmalignant_neoplasm']
senticnet['uterine_myoma'] = ['-0.73', '0', '0.904', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'unshapely', 'shapeless', 'lumpy', 'deformed', 'malformed']
senticnet['uterine_wall'] = ['0.706', '0', '0', '0', '#joy', '#joy', 'positive', '0.706', 'birth', 'new_baby', 'born', 'birthday', 'thanksgiving']
senticnet['utero'] = ['0.706', '0', '0', '0', '#joy', '#joy', 'positive', '0.706', 'birth', 'new_baby', 'born', 'birthday', 'thanksgiving']
senticnet['uterovaginal'] = ['-0.75', '0', '0.715', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'abdominal_pregnancy', 'ectopic_gestation', 'tubal_pregnancy', 'pregnancy', 'gestation']
senticnet['uterus'] = ['0.158', '0.067', '-0.08', '0.114', '#joy', '#admiration', 'positive', '0.064', 'stomach', 'womb', 'pregnant', 'lap', 'gravid']
senticnet['utica_avenue'] = ['0.706', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.768', 'train_station', 'subway_stop', 'parking_lot', 'subway_station', 'park_structure']
senticnet['utilisation'] = ['0.917', '-0.98', '0', '0.613', '#joy', '#surprise', 'positive', '0.837', 'practice', 'activity', 'play', 'misuse', 'abuse']
senticnet['utilise'] = ['-0.76', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.77', 'avail', 'use', 'harness', 'exploit', 'overexploit']
senticnet['utilitarian'] = ['0.949', '0.818', '0', '0', '#joy', '#interest', 'positive', '0.883', 'functional', 'utility', 'practice', 'pidgin', 'practical']
senticnet['utilitarian_approach'] = ['0.843', '-0.6', '0', '0.698', '#joy', '#admiration', 'positive', '0.714', 'levelheaded', 'sound', 'healthy', 'sensible', 'commonsensible']
senticnet['utilitarian_pottery'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'fabrication', 'carving', 'decoupage', 'artistic_creation']
senticnet['utilitarian_principle'] = ['0.841', '-0.65', '0', '0.645', '#joy', '#surprise', 'positive', '0.714', 'precept', 'rule', 'caveat_emptor', 'higher_law', 'hypothetical_imperative']
senticnet['utilitarianism'] = ['0.726', '-0.80', '0', '0.73', '#surprise', '#admiration', 'positive', '0.754', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['utility'] = ['-0.02', '0.186', '-0.04', '0.098', '#interest', '#admiration', 'positive', '0.053', 'spend', 'give_money', 'owe_money', 'pay_debt', 'pay']
senticnet['utility_easement'] = ['0.621', '-0.60', '0', '0.545', '#joy', '#surprise', 'positive', '0.59', 'privilege', 'perquisite', 'public_easement', 'right_of_way', 'prerogative']
senticnet['utility_grid'] = ['0.986', '0.897', '0', '0', '#joy', '#interest', 'positive', '0.942', 'potential_energy', 'watt', 'electrical', 'power_source', 'volt']
senticnet['utility_knife'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'useful_tool']
senticnet['utility_maximization'] = ['-0.91', '0', '0.598', '-0.52', '#sadness', '#anger', 'negative', '-0.68', 'stingy', 'penurious', 'scrimy', 'beggarly', 'chintzy']
senticnet['utility_muffin'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'kitchen_cabinet', 'kitchen_cupboard', 'dishwasher', 'cupboard', 'own_kitchen']
senticnet['utility_relocation'] = ['-0.02', '0.119', '-0.06', '0.216', '#interest', '#admiration', 'positive', '0.062', 'go_forward', 'locomotion', 'movement', 'move', 'move_ahead']
senticnet['utility_room'] = ['-0.03', '-0.00', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.02', 'tool_shed', 'garage', 'hardware_store', 'antique_store', 'service_station']
senticnet['utilization'] = ['0.105', '0.226', '-0.08', '0.176', '#interest', '#admiration', 'positive', '0.106', 'control', 'hold_onto', 'glass', 'drink_cup', 'make_apple']
senticnet['utilize'] = ['-0.04', '0.153', '-0.12', '0.232', '#interest', '#admiration', 'positive', '0.055', 'reach', 'elasticity', 'grow_long', 'extend_arm', 'tow']
senticnet['utilize_knowledge'] = ['-0.03', '0', '0.066', '-0.10', '#anger', '#disgust', 'negative', '-0.06', 'use_brain', 'use_mind', 'solve_mystery', 'come_conclusion', 'focus_attention']
senticnet['utmost'] = ['0', '0.844', '0', '0.884', '#interest', '#admiration', 'positive', '0.864', 'distant', 'outermost', 'ultimate', 'limit', 'quantity']
senticnet['utmost_care'] = ['-0.32', '0.133', '-0.38', '0', '#sadness', '#fear', 'negative', '-0.19', 'big_monster', 'unsafe', 'phobia', 'mock', 'feel_scared']
senticnet['utmost_caution'] = ['-0.32', '0.133', '-0.38', '0', '#sadness', '#fear', 'negative', '-0.19', 'big_monster', 'unsafe', 'phobia', 'mock', 'feel_scared']
senticnet['utmost_difficulty'] = ['-0.71', '0', '0.836', '0', '#sadness', '#anger', 'negative', '-0.77', 'awkward', 'unenviable', 'embarrassingly', 'embarrassing', 'sheepish']
senticnet['utmost_importance'] = ['0.062', '0.335', '0', '0.36', '#interest', '#admiration', 'positive', '0.252', 'religious', 'religious_ceremony', 'religious_service', 'church_service', 'sacred']
senticnet['utmost_integrity'] = ['0.865', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.887', 'manager', 'colleague', 'office_worker', 'coworker', 'secretary']
senticnet['utmost_latitude'] = ['0.774', '-0.82', '0', '0.63', '#joy', '#surprise', 'positive', '0.744', 'quality', 'perishability', 'perishableness', 'unsatisfactoriness', 'inadequacy']
senticnet['utmost_precision'] = ['0.567', '-0.56', '0', '0.724', '#joy', '#admiration', 'positive', '0.617', 'truth', 'accuracy', 'preciseness', 'trueness', 'precision']
senticnet['utmost_vigilance'] = ['0', '1', '0', '0', '#interest', '#interest', 'positive', '1', 'confident', 'confidence', 'optimistic', 'assured', 'engrossed']
senticnet['utne_reader'] = ['0', '0.076', '0', '0.066', '#interest', '#admiration', 'positive', '0.071', 'library', 'old_testament', 'cook_book', 'a_lot_of_books', 'librarian']
senticnet['utopia'] = ['0.087', '0.151', '0', '0.112', '#interest', '#admiration', 'positive', '0.117', 'java', 'memorial', 'nowhere', 'mount_ever', 'international_space_station']
senticnet['utopia_planitia'] = ['0.884', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.919', 'ufo', 'milky', 'space_junk', 'galaxy', 'solar_system']
senticnet['utopian_socialism'] = ['0.563', '-0.72', '0', '0.644', '#surprise', '#admiration', 'positive', '0.645', 'idiom', 'idiomatic_expression', 'phrasal_idiom', 'set_phrase', 'phrase']
senticnet['utopianism'] = ['0', '0.821', '0', '0.737', '#interest', '#admiration', 'positive', '0.779', 'purism', 'pedantry', 'parochialism', 'primitivism', 'expressiveness']
senticnet['utter'] = ['0', '0.985', '0', '0.845', '#interest', '#admiration', 'positive', '0.915', 'say', 'verbal', 'beckon', 'talkative', 'orate']
senticnet['utter_contempt'] = ['-0.10', '0.188', '0.62', '-0.63', '#anger', '#disgust', 'negative', '-0.29', 'contemn', 'contempt', 'despite', 'alienated', 'estranged']
senticnet['utter_despair'] = ['-0.77', '0', '0.797', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'unhappiness', 'sorrow', 'remorse', 'melancholy', 'guilt']
senticnet['utter_destruction'] = ['-0.14', '-0.00', '-0.12', '-0.21', '#sadness', '#disgust', 'negative', '-0.11', 'torture', 'demolish', 'maim', 'terminate', 'hurt_person']
senticnet['utter_devastation'] = ['-0.87', '0', '0.889', '-0.62', '#sadness', '#anger', 'negative', '-0.79', 'ruination', 'ruin', 'deterioration', 'blight', 'impairment']
senticnet['utter_folly'] = ['0.909', '-0.85', '0', '0.846', '#joy', '#surprise', 'positive', '0.87', 'respectability', 'reputability', 'venerability', 'venerableness', 'righteousness']
senticnet['utter_ignorance'] = ['0.909', '-0.85', '0', '0.846', '#joy', '#surprise', 'positive', '0.87', 'respectability', 'reputability', 'venerability', 'venerableness', 'righteousness']
senticnet['utter_indifference'] = ['-0.76', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.81', 'intractability', 'unruliness', 'fractiousness', 'willfulness', 'against_rule']
senticnet['utter_joy'] = ['0.66', '0', '0', '0', '#joy', '#joy', 'positive', '0.66', 'happiness', 'happy', 'joyful', 'pleasure', 'delight']
senticnet['utter_lack'] = ['0.604', '-0.57', '0', '0.935', '#joy', '#admiration', 'positive', '0.705', 'affirmativeness', 'deference', 'compliance', 'disposition', 'complaisance']
senticnet['utter_nonsense'] = ['0.739', '-0.89', '0', '0.702', '#joy', '#surprise', 'positive', '0.78', 'pap', 'content', 'pablum', 'folderol', 'drivel']
senticnet['utter_poverty'] = ['-0.83', '0.214', '-0.39', '-0.10', '#sadness', '#fear', 'negative', '-0.28', 'oppressiveness', 'tyrannical', 'oppressive', 'tyrannous', 'laden']
senticnet['utter_rubbish'] = ['0.739', '-0.89', '0', '0.702', '#joy', '#surprise', 'positive', '0.78', 'pap', 'content', 'pablum', 'folderol', 'drivel']
senticnet['utter_ruin'] = ['-0.98', '0', '0.802', '-0.88', '#sadness', '#disgust', 'negative', '-0.88', 'poverty', 'impoverishment', 'poorness', 'beggary', 'mendicancy']
senticnet['utter_silence'] = ['-0.94', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.94', 'drive_recklessly', 'discord', 'deaf', 'deny', 'silent']
senticnet['utterance'] = ['0', '0.992', '0', '0.704', '#interest', '#admiration', 'positive', '0.848', 'call', 'speech', 'voice', 'verbal_communication', 'phone_conversation']
senticnet['utterer'] = ['0.718', '-0.62', '0', '0.568', '#joy', '#surprise', 'positive', '0.637', 'conversational_partner', 'deipnosophist', 'interlocutor', 'conversationalist', 'speaker']
senticnet['utterly'] = ['-0.84', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.90', 'completely', 'sheerly', 'sovereignly', 'roundly', 'fullest']
senticnet['utterly_alien'] = ['0.744', '-0.76', '0', '0.669', '#joy', '#surprise', 'positive', '0.726', 'accessible', 'comprehendible', 'approachable', 'perceivable', 'intelligible']
senticnet['utterly_destroy'] = ['-0.72', '0', '0.737', '-0.60', '#sadness', '#anger', 'negative', '-0.68', 'call_on_carpet', 'take_to_task', 'chide', 'berate', 'bawl_out']
senticnet['utterly_devoid'] = ['-0.77', '0', '0.5', '-0.75', '#sadness', '#disgust', 'negative', '-0.67', 'apathy', 'indifference', 'passivity', 'inertia', 'inactiveness']
senticnet['utterly_false'] = ['-0.24', '0.147', '-0.25', '0.215', '#sadness', '#fear', 'negative', '-0.03', 'dishonest', 'against_law', 'falsehood', 'deceit', 'deception']
senticnet['utterly_forgotten'] = ['0', '0', '0.935', '-0.76', '#anger', '#disgust', 'negative', '-0.84', 'hear_bad_news', 'death_love', 'disrespect', 'forget_everything', 'toothache']
senticnet['utterly_impossible'] = ['-0.80', '0', '-0.80', '-0.96', '#sadness', '#disgust', 'negative', '-0.85', 'unable', 'absurd', 'difficult', 'impractical', 'unimaginable']
senticnet['utterly_null'] = ['-0.91', '0', '-0.77', '-0.75', '#sadness', '#fear', 'negative', '-0.81', 'emptiness', 'star_planet', 'nothingness', 'outer', 'around_earth']
senticnet['utterly_reject'] = ['-0.69', '0', '0.806', '-0.74', '#anger', '#disgust', 'negative', '-0.74', 'hypothesis', 'theory', 'base', 'possibility', 'presumption']
senticnet['utterly_unlike'] = ['0.744', '-0.76', '0', '0.669', '#joy', '#surprise', 'positive', '0.726', 'accessible', 'comprehendible', 'approachable', 'perceivable', 'intelligible']
senticnet['utterly_void'] = ['-0.91', '0', '-0.77', '-0.75', '#sadness', '#fear', 'negative', '-0.81', 'emptiness', 'star_planet', 'nothingness', 'outer', 'around_earth']
senticnet['uttermost'] = ['0.976', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.873', 'highest', 'greatest', 'utmost', 'farthest', 'extent']
senticnet['uunet'] = ['-0.97', '0', '0', '-0.70', '#sadness', '#disgust', 'negative', '-0.84', 'news_bulletin', 'flash', 'newsbreak', 'concordia', 'newsflash']
senticnet['uv_degradation'] = ['-0.69', '0', '0.516', '-0.94', '#sadness', '#disgust', 'negative', '-0.71', 'insubordinate', 'recalcitrant', 'obstreperous', 'defiant', 'resistive']
senticnet['uv_disinfection'] = ['-0.62', '0', '0.987', '-0.74', '#anger', '#disgust', 'negative', '-0.78', 'medical_aid', 'treatment', 'hospice', 'digitalization', 'bloodletting']
senticnet['uv_protection'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'change_tire', 'fix_flat_tire', 'play_rugby', 'wear_silly_hat', 'become_adult']
senticnet['uv_protective'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'change_tire', 'fix_flat_tire', 'play_rugby', 'wear_silly_hat', 'become_adult']
senticnet['uv_resistance'] = ['0.8', '-0.90', '0', '0.675', '#joy', '#surprise', 'positive', '0.794', 'indestructibility', 'tensile_strength', 'continuity', 'persistence', 'changelessness']
senticnet['uv_resistant'] = ['-0.02', '0.078', '0', '0.08', '#interest', '#admiration', 'positive', '0.045', 'rigid', 'fragile', 'opaque', 'delicate', 'versatile']
senticnet['uva_radiation'] = ['-0.72', '0', '0.71', '-0.9', '#sadness', '#disgust', 'negative', '-0.77', 'perniciousness', 'harmfulness', 'toxicity', 'noisomeness', 'unwholesomeness']
senticnet['uvalde_county'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'common_bean', 'green_bean', 'wax_bean']
senticnet['uvaria'] = ['0.861', '-0.87', '0', '0.597', '#joy', '#surprise', 'positive', '0.779', 'herb', 'cultivated_parsnip', 'wild_parsnip', 'madnep', 'herbaceous_plant']
senticnet['uvb'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'radiation_therapy', 'radiation', 'actinotherapy', 'irradiation']
senticnet['uvb_light'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'radiation_therapy', 'radiation', 'actinotherapy', 'irradiation']
senticnet['uvular'] = ['-0.71', '0', '0.998', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'tone', 'quality', 'twang', 'nasal_twang', 'timber']
senticnet['uwire'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['uwm'] = ['0.817', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.79', 'edu', 'news', 'washington', 'netnews', 'gatech']
senticnet['uy'] = ['0.104', '0', '0', '0.088', '#joy', '#admiration', 'positive', '0.096', 'baby_flower', 'wee', 'bud', 'part_of_plant', 'leafy']
senticnet['uyghur_latin_alphabet'] = ['-0.84', '0', '0.579', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'squiggle', 'hand', 'chicken_scratch', 'handwriting', 'script']
senticnet['v_shark'] = ['0', '0.105', '0', '0', '#interest', '#interest', 'positive', '0.105', 'beak', 'many_feathers', 'big_eye', 'bird', 'bird_mouth']
senticnet['vaati'] = ['-0.63', '0', '0.674', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'baneful', 'evil', 'baleful', 'malevolent', 'malign']
senticnet['vacancy'] = ['-0.88', '0', '-0.92', '0', '#sadness', '#fear', 'negative', '-0.90', 'vacancy', 'job', 'recruitment', 'employment', 'trainee']
senticnet['vacancy_occasioned_by'] = ['-0.91', '0', '-0.77', '-0.75', '#sadness', '#fear', 'negative', '-0.81', 'emptiness', 'star_planet', 'nothingness', 'outer', 'around_earth']
senticnet['vacancy_temporarily'] = ['-0.91', '0', '-0.77', '-0.75', '#sadness', '#fear', 'negative', '-0.81', 'emptiness', 'star_planet', 'nothingness', 'outer', 'around_earth']
senticnet['vacant'] = ['-0.95', '0', '0.744', '0', '#sadness', '#anger', 'negative', '-0.84', 'inventory', 'unused', 'unshared', 'unsold', 'uncompleted']
senticnet['vacant_apartment'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.069', 'room', 'home', 'furniture', 'kitchen', 'flat']
senticnet['vacant_congressional_district'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'couch', 'stool', 'coach_seat']
senticnet['vacant_land'] = ['0.716', '-0.51', '0', '0.647', '#joy', '#admiration', 'positive', '0.624', 'state', 'nation', 'country', 'ally', 'commonwealth']
senticnet['vacant_lot'] = ['0', '0', '0', '-0.09', '#disgust', '#disgust', 'negative', '-0.09', 'subway', 'bus', 'train_station', 'railway_station', 'bus_stop']
senticnet['vacant_position'] = ['0', '0.745', '0', '0.832', '#interest', '#admiration', 'positive', '0.788', 'meet_up', 'year', 'rendezvous', 'meet_friend', 'make_appointment']
senticnet['vacant_seat'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'couch', 'stool', 'coach_seat']
senticnet['vacant_wbc'] = ['0.944', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.967', 'meeting', 'meet', 'match', 'encounter', 'round']
senticnet['vacantly'] = ['-0.71', '0', '0.906', '-0.93', '#anger', '#disgust', 'negative', '-0.85', 'absent', 'forgetful', 'distracted', 'inattentive', 'scatty']
senticnet['vacate'] = ['-0.78', '0', '0.801', '-0.87', '#anger', '#disgust', 'negative', '-0.81', 'resign', 'renounce', 'step_down', 'abdicate', 'leave_office']
senticnet['vacated_seat'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'couch', 'stool', 'coach_seat']
senticnet['vacation'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'avoid_crash', 'summer_vacation', 'reach_destination', 'go_holiday', 'temporary_home']
senticnet['vacation_getaway'] = ['0.024', '0.152', '-0.06', '0.236', '#interest', '#admiration', 'positive', '0.087', 'vacation', 'exit', 'stance', 'take_break', 'holiday']
senticnet['vacation_resort'] = ['0.803', '-0.89', '0', '0.94', '#surprise', '#admiration', 'positive', '0.879', 'lurking_place', 'spot', 'drop', 'hiding_place', 'den']
senticnet['vacation_spot'] = ['0.024', '0.152', '-0.06', '0.236', '#interest', '#admiration', 'positive', '0.087', 'vacation', 'exit', 'stance', 'take_break', 'holiday']
senticnet['vacation_time'] = ['0.777', '0.994', '0', '0.964', '#interest', '#admiration', 'positive', '0.912', 'make_difference', 'save_world', 'make_good_decision', 'feel_important', 'enjoy_beauty']
senticnet['vacationer'] = ['0.024', '0.152', '-0.06', '0.236', '#interest', '#admiration', 'positive', '0.087', 'vacation', 'exit', 'stance', 'take_break', 'holiday']
senticnet['vaccinate'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'medical_therapy', 'vaccine', 'doctor', 'medicine', 'hospital']
senticnet['vaccinated_against_smallpox'] = ['-0.07', '0', '-0.22', '-0.15', '#fear', '#disgust', 'negative', '-0.15', 'gonorrhea', 'infectious_disease', 'psychoneurosis', 'sickness', 'polio']
senticnet['vaccination'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'medical_therapy', 'vaccine', 'doctor', 'medicine', 'hospital']
senticnet['vaccination_campaign'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'medical_therapy', 'vaccine', 'doctor', 'medicine', 'hospital']
senticnet['vaccination_programme'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'medical_therapy', 'vaccine', 'doctor', 'medicine', 'hospital']
senticnet['vaccination_schedule'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'medical_therapy', 'vaccine', 'doctor', 'medicine', 'hospital']
senticnet['vaccine'] = ['-0.07', '0', '-0.05', '-0.18', '#sadness', '#disgust', 'negative', '-0.10', 'medical_therapy', 'vaccination', 'doctor', 'medicine', 'hospital']
senticnet['vaccinii'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'apple_rust', 'fungus']
senticnet['vaccinium'] = ['-0.92', '0', '0.912', '-0.94', '#sadness', '#disgust', 'negative', '-0.92', 'true_blackberry', 'sand_blackberry', 'blackberry_bush', 'blackberry', 'running_blackberry']
senticnet['vaccinium_arboreum'] = ['-0.92', '0', '0.912', '-0.94', '#sadness', '#disgust', 'negative', '-0.92', 'true_blackberry', 'sand_blackberry', 'blackberry_bush', 'blackberry', 'running_blackberry']
senticnet['vacillate'] = ['0', '0', '-0.84', '-0.74', '#fear', '#disgust', 'negative', '-0.79', 'vibrate', 'oscillate', 'hover', 'waver', 'fluctuate']
senticnet['vacillate_between'] = ['0.604', '-0.57', '0', '0.935', '#joy', '#admiration', 'positive', '0.705', 'affirmativeness', 'deference', 'compliance', 'disposition', 'complaisance']
senticnet['vacillated_between'] = ['-0.97', '0', '0.712', '-0.78', '#sadness', '#disgust', 'negative', '-0.82', 'discrepant', 'inconsistent', 'scratchy', 'uneven', 'contradictory']
senticnet['vacillation'] = ['-0.83', '0', '0.904', '0', '#sadness', '#anger', 'negative', '-0.87', 'disappointed', 'thwarted', 'frustrated', 'foiled', 'discomfited']
senticnet['vacnat'] = ['-0.62', '0', '0.735', '-0.80', '#anger', '#disgust', 'negative', '-0.72', 'empty_room', 'empty_space', 'empty', 'empty_house', 'aimless']
senticnet['vacuity'] = ['-0.92', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.90', 'pointless', 'lack', 'meaning', 'void', 'inane']
senticnet['vacuolization'] = ['-0.57', '0', '0.767', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'verboseness', 'verbosity', 'prolixity', 'turgidness', 'flatulence']
senticnet['vacuous'] = ['-0.78', '0', '0.783', '-0.62', '#sadness', '#anger', 'negative', '-0.72', 'insignificant', 'mindless', 'empty', 'nonsense', 'meaningless']
senticnet['vacuum'] = ['0', '0.727', '0', '0.774', '#interest', '#admiration', 'positive', '0.75', 'clean_floor', 'clean_carpet', 'keep_house_clean', 'expect_guest', 'clean_bathroom']
senticnet['vacuum_carpet'] = ['0', '0.083', '0', '0.148', '#interest', '#admiration', 'positive', '0.115', 'mission_accomplished', 'maintenance', 'clean_often', 'achieve_goal', 'achievement']
senticnet['vacuum_chamber'] = ['-0.05', '0.068', '-0.10', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'tooth_decay', 'body_ache', 'symptom', 'pierce', 'tooth']
senticnet['vacuum_cleaner'] = ['-0.04', '-0.02', '0.089', '-0.14', '#anger', '#disgust', 'negative', '-0.06', 'steam_locomotive', 'loudspeaker', 'collector', 'siren', 'activist']
senticnet['vacuum_flask'] = ['0.619', '-0.83', '0', '0.912', '#surprise', '#admiration', 'positive', '0.788', 'hipflask', 'pocket_flask', 'ampulla', 'canteen', 'flask']
senticnet['vacuum_floor'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'cleanness', 'dust_furniture', 'sweep', 'clean_floor', 'clean']
senticnet['vacuum_fluorescent_display'] = ['0.044', '0.08', '0', '-0.03', '#joy', '#interest', 'positive', '0.031', 'screen', 'pixel', 'visual_information', 'image', 'computer_monitor']
senticnet['vacuum_gauge'] = ['0', '0.721', '0', '0.731', '#interest', '#admiration', 'positive', '0.726', 'dimension', 'measure', 'tackle', 'length', 'measurement']
senticnet['vacuum_hose'] = ['-0.04', '-0.02', '0.089', '-0.14', '#anger', '#disgust', 'negative', '-0.06', 'steam_locomotive', 'loudspeaker', 'collector', 'siren', 'activist']
senticnet['vacuum_permittivity'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'newton', 'sthene', 'force_unit']
senticnet['vacuum_pump'] = ['0.042', '-0.02', '0.035', '0.065', '#joy', '#admiration', 'positive', '0.023', 'food_container', 'metal_cylinder', 'metal_container', 'small_ball', 'storage_container']
senticnet['vacuum_vessel'] = ['0.843', '-0.99', '0', '0.706', '#joy', '#surprise', 'positive', '0.849', 'gadgetry', 'gimbal', 'contrivance', 'mod_con', 'device']
senticnet['vade_mecum'] = ['0.756', '0', '0', '0', '#joy', '#joy', 'positive', '0.756', 'companionship', 'cherish', 'lover', 'bond', 'buddy']
senticnet['vadgaon'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['vadm'] = ['0.688', '-0.90', '0', '0.877', '#surprise', '#admiration', 'positive', '0.824', 'flag_officer', 'rear_admiral', 'admiral', 'full_admiral', 'vice_admiral']
senticnet['vagabond'] = ['-0.81', '0', '0.746', '-0.89', '#sadness', '#disgust', 'negative', '-0.81', 'vagrant', 'drifter', 'hobo', 'floater', 'dosser']
senticnet['vagal'] = ['-0.62', '0', '0.901', '-0.71', '#anger', '#disgust', 'negative', '-0.74', 'pressure', 'osmotic_pressure', 'hypotonicity', 'pressure_level', 'force']
senticnet['vagal_tone'] = ['0.698', '-0.61', '0', '0.619', '#joy', '#admiration', 'positive', '0.644', 'sensitivity', 'sensitiveness', 'irritability', 'sensibility', 'reactivity']
senticnet['vagenda'] = ['-0.07', '0.195', '0.629', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'misogynic', 'misogyny', 'murderousness', 'amuck', 'amok']
senticnet['vagina'] = ['0.655', '-0.87', '0', '0.595', '#joy', '#surprise', 'positive', '0.709', 'erection', 'excitement', 'arousal', 'kill_brain_cell', 'feel_relieved']
senticnet['vagina_dentata'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['vaginal_atresia'] = ['-0.75', '0', '0.715', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'abdominal_pregnancy', 'ectopic_gestation', 'tubal_pregnancy', 'pregnancy', 'gestation']
senticnet['vaginal_atrophy'] = ['-0.73', '0', '0.555', '-0.90', '#sadness', '#disgust', 'negative', '-0.73', 'aching', 'hurting', 'labor_pain', 'neuralgia', 'neuralgy']
senticnet['vaginal_discharge'] = ['-0.89', '0', '0.925', '-0.58', '#sadness', '#anger', 'negative', '-0.80', 'shit', 'laxation', 'movement', 'dump', 'asshole']
senticnet['vaginal_hysterectomy'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
senticnet['vaginal_infection'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'gastroenteritis', 'stomach_flu', 'intestinal_flu', 'inflammatory_disease']
senticnet['vaginal_intercourse'] = ['-0.74', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.78', 'ejaculate', 'pass_gene', 'ecstasy', 'lower_standard_live', 'survival_species']
senticnet['vaginal_lubrication'] = ['0.655', '-0.87', '0', '0.595', '#joy', '#surprise', 'positive', '0.709', 'erection', 'excitement', 'arousal', 'kill_brain_cell', 'feel_relieved']
senticnet['vaginal_orgasm'] = ['0.655', '-0.87', '0', '0.595', '#joy', '#surprise', 'positive', '0.709', 'erection', 'excitement', 'arousal', 'kill_brain_cell', 'feel_relieved']
senticnet['vaginal_penetration'] = ['0.655', '-0.87', '0', '0.595', '#joy', '#surprise', 'positive', '0.709', 'erection', 'excitement', 'arousal', 'kill_brain_cell', 'feel_relieved']
senticnet['vaginal_ring'] = ['-0.58', '0', '0.561', '-0.62', '#sadness', '#disgust', 'negative', '-0.58', 'spot', 'speckle', 'dapple', 'mark', 'fleck']
senticnet['vaginal_surgery'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
senticnet['vaginal_vault'] = ['-0.75', '0', '0.715', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'abdominal_pregnancy', 'ectopic_gestation', 'tubal_pregnancy', 'pregnancy', 'gestation']
senticnet['vaginal_wall'] = ['-0.03', '0', '0', '-0.11', '#sadness', '#disgust', 'negative', '-0.07', 'store_cloth', 'clothes_factory', 'stock', 'sword', 'gun_store']
senticnet['vaginally'] = ['0', '0', '-0.87', '-0.95', '#fear', '#disgust', 'negative', '-0.91', 'birth_control_pill', 'condom', 'contraceptive_pill', 'sex_toy', 'prevent_pregnancy']
senticnet['vaginoplasty'] = ['0.96', '-0.83', '0', '0.566', '#joy', '#surprise', 'positive', '0.788', 'lift', 'phalloplasty', 'plastic_surgery', 'tummy_tuck', 'autoplasty']
senticnet['vagrancy'] = ['0', '0', '0.953', '-0.79', '#anger', '#disgust', 'negative', '-0.87', 'drunkenness', 'loitering', 'panhandling', 'sedition', 'bigamy']
senticnet['vagrant'] = ['-0.91', '0', '0.768', '-0.95', '#sadness', '#disgust', 'negative', '-0.88', 'hobo', 'tramp', 'beachcomber', 'bum', 'sundowner']
senticnet['vague'] = ['-0.81', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.81', 'obscure', 'unsure', 'uncertain', 'disoriented', 'unclear']
senticnet['vague_resemblance'] = ['-0.88', '0', '0.813', '-0.57', '#sadness', '#anger', 'negative', '-0.75', 'affinity', 'mutual_resemblance', 'alikeness', 'likeness', 'similitude']
senticnet['vaguely'] = ['-0.62', '0', '0.825', '-0.73', '#anger', '#disgust', 'negative', '-0.72', 'unexplainable', 'unaccountable', 'unexplained', 'incomprehensible', 'mystifying']
senticnet['vagueness'] = ['0.973', '-0.78', '0', '0.98', '#joy', '#admiration', 'positive', '0.913', 'opaqueness', 'faintness', 'dimness', 'opacity', 'fuzziness']
senticnet['vagueness_doctrine'] = ['-0.91', '0', '-0.77', '-0.75', '#sadness', '#fear', 'negative', '-0.81', 'emptiness', 'star_planet', 'nothingness', 'outer', 'around_earth']
senticnet['vain'] = ['-0.71', '0', '0.711', '0', '#sadness', '#anger', 'negative', '-0.71', 'needless', 'unnecessary', 'idle', 'useless', 'pointless']
senticnet['vain_glory'] = ['0.657', '-0.56', '0', '0.749', '#joy', '#admiration', 'positive', '0.655', 'naturalness', 'condescension', 'condescendingness', 'mellowness', 'friendliness']
senticnet['vainglory'] = ['-0.84', '0', '0.969', '-0.68', '#sadness', '#anger', 'negative', '-0.83', 'posturing', 'swelled_head', 'egotism', 'boastfulness', 'conceitedness']
senticnet['vainly'] = ['0.901', '0', '0', '0.812', '#joy', '#admiration', 'positive', '0.857', 'futilely', 'feebly', 'ineffectually', 'half-heartedly', 'timidly']
senticnet['vairocana'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['vaishnavi'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['vajrapani'] = ['0.063', '0.099', '0', '0.119', '#interest', '#admiration', 'positive', '0.094', 'happiness', 'ecstasy', 'happy', 'peace_of_mind', 'peace']
senticnet['vakf'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'caravansary', 'caravanserai', 'khan', 'caravan_inn', 'hostel']
senticnet['val_lewton'] = ['-0.08', '-0.01', '-0.16', '-0.13', '#fear', '#disgust', 'negative', '-0.09', 'hockey_game', 'movie', 'scary_movie', 'cinema', 'cctv']
senticnet['valea_larg_river'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'aphasia', 'anomic_aphasia']
senticnet['valea_seac_river'] = ['-0.93', '0', '0.861', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'knotty', 'contorted', 'writhed', 'crooked', 'anfractuous']
senticnet['valeant'] = ['0.559', '0.656', '-0.26', '0.285', '#joy', '#interest', 'positive', '0.31', 'zealous', 'avidity', 'eagerness', 'avidness', 'thirstily']
senticnet['valediction'] = ['0.972', '0.958', '0', '0', '#joy', '#interest', 'positive', '0.965', 'proud', 'valiantly', 'doughty', 'valiant', 'franc']
senticnet['valedictory'] = ['0.037', '0.161', '-0.15', '0.203', '#interest', '#admiration', 'positive', '0.062', 'wave', 'flow', 'salute', 'multiple_purposes', 'wave_wind']
senticnet['valentine_day'] = ['0.158', '0.207', '-0.20', '0.278', '#interest', '#admiration', 'positive', '0.11', 'go_kiss', 'love', 'infidelity', 'amiable', 'lust']
senticnet['valerate'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
senticnet['valeric_acid'] = ['-0.65', '0', '0.935', '-0.96', '#anger', '#disgust', 'negative', '-0.85', 'acid', 'hexanedioic_acid', 'adipic_acid', 'succinic_acid', 'carboxylic_acid']
senticnet['valery_gergiev'] = ['-0.91', '0', '0.931', '-0.72', '#sadness', '#anger', 'negative', '-0.85', 'hit_with_baton', 'get_hurt', 'whip', 'arrow', 'hurt']
senticnet['valery_hache_afp_getty'] = ['-0.98', '0', '0.702', '-0.70', '#sadness', '#disgust', 'negative', '-0.79', 'expandable', 'expansive', 'expansible', 'erectile', 'cavernous']
senticnet['valet'] = ['0', '0.078', '0', '0.068', '#interest', '#admiration', 'positive', '0.073', 'go_bar', 'designate_driver', 'drink_a_lot_of_beer', 'go_party', 'drink_alcohol']
senticnet['valet_laundry'] = ['0.722', '-0.98', '0', '0.832', '#surprise', '#admiration', 'positive', '0.845', 'laudatory', 'praiseful', 'praising', 'encomiastic', 'eulogistic']
senticnet['valet_service'] = ['0.722', '-0.98', '0', '0.832', '#surprise', '#admiration', 'positive', '0.845', 'laudatory', 'praiseful', 'praising', 'encomiastic', 'eulogistic']
senticnet['valetudinarian'] = ['0', '0', '0.851', '-0.98', '#anger', '#disgust', 'negative', '-0.91', 'frail', 'ill', 'infirm', 'doddering', 'irresolute']
senticnet['valetudinarianism'] = ['-0.83', '0', '0.874', '-0.71', '#sadness', '#anger', 'negative', '-0.80', 'infirmity', 'frailty', 'debility', 'frailness', 'asthenia']
senticnet['valiance'] = ['-0.94', '0', '0.745', '-0.97', '#sadness', '#disgust', 'negative', '-0.89', 'nerve', 'fearlessness', 'heart', 'dauntlessness', 'intrepidity']
senticnet['valiant'] = ['0.973', '0.959', '0', '0', '#joy', '#interest', 'positive', '0.966', 'proud', 'valiantly', 'doughty', 'valediction', 'franc']
senticnet['valiant_warrior'] = ['-0.94', '0', '0.745', '-0.97', '#sadness', '#disgust', 'negative', '-0.89', 'nerve', 'fearlessness', 'heart', 'dauntlessness', 'intrepidity']
senticnet['valiantly'] = ['0.967', '0.95', '0', '0', '#joy', '#interest', 'positive', '0.958', 'proud', 'valiant', 'doughty', 'valediction', 'franc']
senticnet['valid'] = ['0.622', '-0.72', '0', '0.799', '#surprise', '#admiration', 'positive', '0.714', 'sound', 'logical', 'validated', 'effectual', 'legitimate']
senticnet['valid_driver_license'] = ['0', '-0.01', '0', '0.064', '#surprise', '#admiration', 'positive', '0.042', 'car_gas', 'car_power', 'fossil_fuel', 'run_car', 'car_engine']
senticnet['valid_excuse'] = ['0.723', '0.72', '0', '0', '#joy', '#interest', 'positive', '0.722', 'late', 'get_dark', 'rainy_day', 'split_end', 'house_look_better']
senticnet['valid_id_card'] = ['0.739', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.814', 'receipt', 'bet', 'electric_bill', 'letter_slot', 'deal']
senticnet['valid_inference'] = ['-0.99', '0', '0.878', '-0.95', '#sadness', '#disgust', 'negative', '-0.94', 'adequacy', 'satisfactoriness', 'acceptability', 'acceptableness', 'quality']
senticnet['valid_license'] = ['0', '-0.01', '0', '0.064', '#surprise', '#admiration', 'positive', '0.042', 'car_gas', 'car_power', 'fossil_fuel', 'run_car', 'car_engine']
senticnet['valid_photo_id'] = ['0.739', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.814', 'receipt', 'bet', 'electric_bill', 'letter_slot', 'deal']
senticnet['valid_photo_identification'] = ['0.739', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.814', 'receipt', 'bet', 'electric_bill', 'letter_slot', 'deal']
senticnet['validate'] = ['0.928', '-0.87', '0', '0.679', '#joy', '#surprise', 'positive', '0.829', 'examine', 'experiment', 'alpha_test', 'beta_test', 'road_test']
senticnet['validated'] = ['0.616', '-0.72', '0', '0.797', '#surprise', '#admiration', 'positive', '0.712', 'sound', 'logical', 'effectual', 'valid', 'legitimate']
senticnet['validation'] = ['-0.03', '0.2', '-0.10', '0.298', '#interest', '#admiration', 'positive', '0.09', 'procedure', 'action', 'create_form', 'transform', 'posture']
senticnet['validation_sticker'] = ['0.739', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.814', 'receipt', 'bet', 'electric_bill', 'letter_slot', 'deal']
senticnet['validity'] = ['0', '0.792', '0', '0.742', '#interest', '#admiration', 'positive', '0.767', 'lustiness', 'validness', 'robustness', 'rigor', 'rigour']
senticnet['validly'] = ['-0.55', '0', '0.741', '-0.76', '#anger', '#disgust', 'negative', '-0.68', 'illogicality', 'illogicalness', 'illogic', 'inconsequence', 'fallaciousness']
senticnet['validness'] = ['0', '0.785', '0', '0.734', '#interest', '#admiration', 'positive', '0.76', 'lustiness', 'validity', 'robustness', 'rigor', 'rigour']
senticnet['valio'] = ['0.057', '0', '0.018', '0.058', '#joy', '#admiration', 'positive', '0.032', 'porridge', 'food', 'milk', 'healthy', 'morning_food']
senticnet['valise'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'hand_baggage', 'baggage', 'passenger_ticket', 'ticket_counter', 'baggage_trunk']
senticnet['vallecula'] = ['0.719', '-0.57', '0', '0.651', '#joy', '#admiration', 'positive', '0.647', 'chap', 'break', 'slit', 'gap', 'shift']
senticnet['valles_caldera_national'] = ['0', '0.243', '-0.11', '0.201', '#interest', '#admiration', 'positive', '0.108', 'upkeep', 'contain', 'keep_safe', 'keep', 'prevent']
senticnet['valley'] = ['-0.75', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.84', 'lowland', 'grassland', 'subtropical', 'temperate', 'lowland']
senticnet['valley_fever'] = ['-0.85', '0', '0.549', '-0.93', '#sadness', '#disgust', 'negative', '-0.78', 'coccidioidomycosis', 'coccidiomycosis', 'mycosis', 'desert_rheumatism', 'cryptococcosis']
senticnet['valley_floor'] = ['0', '0.069', '0', '0.065', '#interest', '#admiration', 'positive', '0.067', 'alp', 'high_mountain', 'pass', 'ski_resort', 'high_place']
senticnet['valley_indian_reservation'] = ['-0.50', '0', '0.764', '-0.61', '#anger', '#disgust', 'negative', '-0.62', 'doctor', 'mountebank', 'charlatan', 'quack', 'physician']
senticnet['vallone_sr'] = ['0.034', '-0.02', '0', '0.057', '#joy', '#admiration', 'positive', '0.039', 'female_television_connector', 'channel_selector', 'power_switch', 'tuner', 'radio']
senticnet['valor'] = ['0.827', '0.85', '0', '0.743', '#joy', '#interest', 'positive', '0.807', 'chivalry', 'honor', 'almighty', 'admire', 'respect']
senticnet['valorization'] = ['0.995', '-0.69', '0', '0.528', '#joy', '#surprise', 'positive', '0.74', 'grasp', 'hug', 'nestle', 'snuggle', 'grip']
senticnet['valrhona_chocolate'] = ['0.731', '-0.94', '0', '0', '#joy', '#surprise', 'positive', '0.837', 'make_bread', 'cook_dinner', 'cook_meal', 'cook_curry', 'follow_recipe']
senticnet['valsartan'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'spironolactone', 'spironolactone_drug']
senticnet['valse'] = ['0.591', '-0.91', '0', '0.605', '#surprise', '#admiration', 'positive', '0.704', 'farandole', 'folk_dance', 'polka', 'dance', 'typical_dance']
senticnet['valtrex'] = ['-0.73', '-0.72', '-0.86', '-0.79', '#fear', '#disgust', 'negative', '-0.41', 'venereal_disease', 'overpopulation', 'std', 'loss_offreedom', 'lower_standard_live']
senticnet['valuable'] = ['0.027', '0.281', '-0.07', '0.195', '#interest', '#admiration', 'positive', '0.107', 'dollar_change', 'currency', 'richness', 'small_change', 'wealth']
senticnet['valuable_commodity'] = ['0.808', '-0.83', '0', '0.548', '#joy', '#surprise', 'positive', '0.73', 'successfulness', 'luckiness', 'prosperity', 'circumstance', 'blessing']
senticnet['valuable_contribution'] = ['0.851', '-0.67', '0', '0.854', '#joy', '#admiration', 'positive', '0.792', 'fertile', 'breeding', 'prolific', 'bountiful', 'productive']
senticnet['valuable_gem'] = ['0', '0.974', '0', '0.942', '#interest', '#admiration', 'positive', '0.958', 'music_type', 'type_of_music', 'small_boulder', 'music_genre', 'rock_music']
senticnet['valuable_insight'] = ['-0.06', '-0.02', '0.02', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'eye_strain', 'new_perspective', 'move_eye', 'gain_insight', 'great_understand']
senticnet['valuable_insight_into'] = ['-0.06', '-0.02', '0.02', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'eye_strain', 'new_perspective', 'move_eye', 'gain_insight', 'great_understand']
senticnet['valuable_metal'] = ['-0.04', '-0.02', '0.027', '-0.03', '#sadness', '#disgust', 'negative', '-0.01', 'precious_metal', 'olympic_medal', 'shiny_metal', 'bloodstone', 'many_different_kinds']
senticnet['valuable_resource'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'woodworking_tool']
senticnet['valuable_tool'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'woodworking_tool']
senticnet['valuation'] = ['0', '0.238', '0.021', '0.152', '#interest', '#admiration', 'positive', '0.123', 'money_value', 'money_amount', 'worth', 'monetary_amount', 'importance']
senticnet['value'] = ['0', '0.717', '0', '0.765', '#interest', '#admiration', 'positive', '0.741', 'worth', 'price', 'value_amount', 'utility', 'amount']
senticnet['value-added_tax'] = ['0.921', '0.846', '0', '0', '#joy', '#interest', 'positive', '0.883', 'tub', 'vat', 'ad_valorem_tax', 'president', 'level']
senticnet['value_amount'] = ['-0.03', '0.249', '0.011', '0.146', '#interest', '#admiration', 'positive', '0.088', 'worth', 'sticker', 'bill', 'dollar_sign', 'amount_money']
senticnet['value_chain'] = ['0.943', '-0.78', '0', '0.571', '#joy', '#surprise', 'positive', '0.767', 'master', 'ignition_key', 'latchkey', 'passkey', 'master_key']
senticnet['value_for_money'] = ['0', '0.21', '-0.05', '0.138', '#interest', '#admiration', 'positive', '0.096', 'price', 'value', 'amount_charge', 'amount', 'bill']
senticnet['value_proposition'] = ['0', '0.243', '0', '0.218', '#interest', '#admiration', 'positive', '0.23', 'ware', 'goods', 'result', 'good_price', 'bargain']
senticnet['value_study'] = ['0', '0.804', '0', '0.971', '#interest', '#admiration', 'positive', '0.887', 'ethical_consideration', 'research', 'human_subject', 'science', 'project']
senticnet['valueclick'] = ['-0.63', '0', '0.862', '-0.52', '#sadness', '#anger', 'negative', '-0.67', 'knot', 'bunch', 'tuft', 'tussock', 'agglomeration']
senticnet['valued'] = ['0', '0.801', '0', '0.788', '#interest', '#admiration', 'positive', '0.794', 'valuable', 'costly', 'expensive', 'precious', 'special']
senticnet['valued_cadbury'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'wrapping_paper', 'wrap_gift', 'butcher_paper', 'paper']
senticnet['valued_colleague'] = ['0.05', '0.141', '-0.07', '0.132', '#interest', '#admiration', 'positive', '0.063', 'public_servant', 'worker', 'manager', 'laborer', 'office_job']
senticnet['valvate'] = ['0', '0', '0.064', '-0.04', '#anger', '#disgust', 'negative', '-0.05', 'bird_food', 'funiculus', 'plant_product', 'fuzz', 'agricultural_product']
senticnet['valve'] = ['-0.71', '0', '-0.90', '0', '#sadness', '#fear', 'negative', '-0.81', 'push', 'pumping', 'pump', 'fuel', 'heater']
senticnet['valve_stem'] = ['0', '-0.07', '0', '0', '#surprise', '#surprise', 'positive', '0.075', 'gyroscope', 'air_pump', 'ball_bounce', 'motor_oil', 'tire']
senticnet['vampire'] = ['-0.79', '0', '0.92', '-0.75', '#sadness', '#anger', 'negative', '-0.82', 'evil_spirit', 'incubus', 'daemon', 'succuba', 'dybbuk']
senticnet['vampire_bat'] = ['0', '0', '-0.92', '-0.97', '#fear', '#disgust', 'negative', '-0.95', 'annoy_bug', 'insect', 'bug', 'hare', 'nymph']
senticnet['vampire_blood'] = ['-0.55', '0', '0.5', '-0.55', '#sadness', '#disgust', 'negative', '-0.53', 'remedy', 'cure', 'catholicon', 'therapeutic', 'curative']
senticnet['vampire_hunter'] = ['-0.79', '0', '0.92', '-0.75', '#sadness', '#anger', 'negative', '-0.82', 'evil_spirit', 'incubus', 'daemon', 'succuba', 'dybbuk']
senticnet['vampire_movie'] = ['-0.08', '-0.01', '-0.16', '-0.13', '#fear', '#disgust', 'negative', '-0.09', 'hockey_game', 'movie', 'scary_movie', 'cinema', 'cctv']
senticnet['vampire_queen'] = ['-0.63', '0', '0.674', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'baneful', 'evil', 'baleful', 'malevolent', 'malign']
senticnet['vampire_teeth'] = ['0.057', '0.111', '0', '0.143', '#interest', '#admiration', 'positive', '0.104', 'hankie', 'kleenex', 'facial_tissue', 'handkerchief', 'tissue']
senticnet['vampiric'] = ['0.8', '-0.97', '0', '0.512', '#joy', '#surprise', 'positive', '0.763', 'energize', 'perk_up', 'brace', 'stimulate', 'arouse']
senticnet['vampirism'] = ['-0.17', '0', '-0.16', '-0.09', '#sadness', '#fear', 'negative', '-0.14', 'murder', 'hurt_others', 'death_penalty', 'death_destruction', 'end_live']
senticnet['vampish'] = ['0.582', '-0.78', '0', '0.795', '#surprise', '#admiration', 'positive', '0.72', 'elfish', 'elvish', 'playful', 'frisky', 'sportive']
senticnet['vampy'] = ['0.902', '0.995', '0', '0', '#joy', '#interest', 'positive', '0.949', 'beautiful_girl', 'hen_baby', 'pretty_woman', 'hen_child', 'bird']
senticnet['van'] = ['0.77', '0', '0', '0.802', '#joy', '#admiration', 'positive', '0.786', 'caravan', 'vanguard', 'avant-garde', 'new_wave', 'bos']
senticnet['van_den_hoogenband'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'nazi', 'man', 'pervert', 'healthy_person']
senticnet['van_der_waals_attraction'] = ['-0.97', '0', '0.831', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'skewness', 'irregularity']
senticnet['van_der_waals_force'] = ['0.538', '-0.72', '0', '0.606', '#surprise', '#admiration', 'positive', '0.622', 'attraction', 'captivate', 'complementary', 'attract', 'magnetism']
senticnet['van_der_woude_syndrome'] = ['0.504', '-0.93', '0', '0.797', '#surprise', '#admiration', 'positive', '0.747', 'diabetes', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['van_driver'] = ['0.969', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.936', 'attendant', 'manager', 'public_servant', 'specialist', 'accomplice']
senticnet['van_handel'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'acolyte', 'reverend', 'thurifer', 'clergyman', 'man_of_cloth']
senticnet['van_krevel'] = ['-0.64', '0', '0.879', '-0.98', '#anger', '#disgust', 'negative', '-0.83', 'drone', 'poke', 'dawdler', 'laggard', 'straggler']
senticnet['van_tuyl'] = ['0.746', '-0.88', '0', '0.723', '#joy', '#surprise', 'positive', '0.783', 'strike', 'run_into', 'hit', 'impinge_on', 'collide_with']
senticnet['vanadate'] = ['-0.03', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'lithium', 'mineral', 'potassium', 'iodine', 'manganese']
senticnet['vanadium'] = ['-0.03', '0', '0', '-0.09', '#sadness', '#disgust', 'negative', '-0.06', 'lithium', 'mineral', 'potassium', 'iodine', 'magnesium']
senticnet['vandal'] = ['0.754', '0.909', '0', '0', '#joy', '#interest', 'positive', '0.832', 'foot', 'footwear', 'leg_wear', 'shoe', 'man_clothes']
senticnet['vandal_savage'] = ['0.02', '0.131', '0.749', '-0.67', '#anger', '#disgust', 'negative', '-0.31', 'belligerence', 'belligerency', 'belligerent', 'war', 'rancor']
senticnet['vandalism'] = ['-0.51', '0', '0.617', '-0.69', '#anger', '#disgust', 'negative', '-0.61', 'devilment', 'roguery', 'mischief', 'shenanigan', 'deviltry']
senticnet['vandalize'] = ['0', '-0.96', '0', '0.829', '#surprise', '#admiration', 'positive', '0.896', 'ransacking', 'ransacked', 'ransack', 'despoil', 'rummaged']
senticnet['vander'] = ['0.746', '-0.88', '0', '0.723', '#joy', '#surprise', 'positive', '0.783', 'strike', 'run_into', 'hit', 'impinge_on', 'collide_with']
senticnet['vanderwerff'] = ['0.761', '0.566', '-0.21', '0.293', '#joy', '#interest', 'positive', '0.352', 'gratify', 'jubilation', 'gloat', 'gloating', 'gloatingly']
senticnet['vane'] = ['0.144', '0.107', '-0.06', '0.126', '#joy', '#admiration', 'positive', '0.079', 'roof', 'foundation', 'exterior', 'umbrella', 'find_outside']
senticnet['vanguard'] = ['0.76', '0', '0', '0.794', '#joy', '#admiration', 'positive', '0.777', 'caravan', 'van', 'avant-garde', 'new_wave', 'bos']
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senticnet['vaniyambalam_railway_station'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'concupiscence', 'lustfulness', 'amorousness']
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senticnet['vanquishable'] = ['0.7', '-0.95', '0', '0.572', '#joy', '#surprise', 'positive', '0.741', 'beatable', 'conquerable', 'vincible', 'subduable', 'subjugable']
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senticnet['vantage_point'] = ['0', '0.139', '0', '0.18', '#interest', '#admiration', 'positive', '0.16', 'aspect', 'vista', 'scenic', 'spectacular', 'view']
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senticnet['vapor_barrier'] = ['-0.57', '0', '0.855', '-0.95', '#anger', '#disgust', 'negative', '-0.79', 'soundproof', 'modify', 'alter', 'change', 'weatherstrip']
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senticnet['vapor_water'] = ['0.837', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.859', 'movie', 'wet_air', 'condense_water', 'movie_title', 'hazy']
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senticnet['vapour'] = ['0', '0.098', '-0.09', '0.115', '#interest', '#admiration', 'positive', '0.04', 'push_wind', 'breath', 'breaout', 'body_reaction', 'mouth_action']
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senticnet['variability'] = ['-0.9', '0', '0.747', '-0.63', '#sadness', '#anger', 'negative', '-0.75', 'variedness', 'personal_equation', 'variegation', 'changeableness', 'changeability']
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senticnet['varicella_vaccination'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'medical_therapy', 'vaccine', 'doctor', 'medicine', 'hospital']
senticnet['varicella_vaccine'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'medical_therapy', 'vaccine', 'doctor', 'medicine', 'hospital']
senticnet['varicocele'] = ['0.087', '-0.03', '0', '0.063', '#joy', '#admiration', 'positive', '0.062', 'puppet', 'bouncy', 'curly', 'throw_object', 'glove_leather']
senticnet['varicose_vein'] = ['-0.36', '-0.08', '0', '-0.32', '#sadness', '#disgust', 'negative', '-0.20', 'disease', 'body_disease', 'need_cure', 'need_doctor', 'leg_pain']
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senticnet['variegate_porphyria'] = ['-0.57', '0', '0.973', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['variegated_cutworm'] = ['-0.02', '-0.09', '0.11', '-0.13', '#anger', '#disgust', 'negative', '-0.04', 'beetle', 'gypsy_moth', 'mole_cricket', 'grasshopper', 'many_insects']
senticnet['variegated_foliage'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'tropical_fish', 'wood_duck', 'disney', 'pink_flower', 'beau']
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senticnet['varies_between'] = ['0.828', '-0.89', '0', '0.693', '#joy', '#surprise', 'positive', '0.806', 'confines', 'expanse', 'view', 'internationality', 'purview']
senticnet['varies_considerably'] = ['0.295', '0.072', '0', '0.136', '#joy', '#admiration', 'positive', '0.168', 'live_environment', 'beautiful', 'lovely', 'important_man', 'peaceful']
senticnet['varies_depending_on'] = ['0.295', '0.072', '0', '0.136', '#joy', '#admiration', 'positive', '0.168', 'live_environment', 'beautiful', 'lovely', 'important_man', 'peaceful']
senticnet['varies_greatly'] = ['0.295', '0.072', '0', '0.136', '#joy', '#admiration', 'positive', '0.168', 'live_environment', 'beautiful', 'lovely', 'important_man', 'peaceful']
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senticnet['variety_resistant'] = ['-0.69', '0', '0.516', '-0.94', '#sadness', '#disgust', 'negative', '-0.71', 'insubordinate', 'recalcitrant', 'obstreperous', 'defiant', 'resistive']
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senticnet['varitone'] = ['0.748', '-0.90', '0', '0.99', '#surprise', '#admiration', 'positive', '0.882', 'shakiness', 'quivering', 'shaking', 'vibration', 'quiver']
senticnet['varlet'] = ['0.906', '0.74', '0', '0', '#joy', '#interest', 'positive', '0.823', 'pageboy', 'page', 'paginate', 'foliate', 'thomas_nelson_page']
senticnet['varment'] = ['-0.62', '0', '0.588', '-0.80', '#sadness', '#disgust', 'negative', '-0.67', 'animal', 'animate_being', 'beast', 'creature', 'brute']
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senticnet['varsity_baseball_team'] = ['0.035', '0.155', '0', '0', '#joy', '#interest', 'positive', '0.095', 'basketball_court', 'basketball_game', 'basketball', 'ball', 'sport']
senticnet['varsity_basketball'] = ['0.035', '0.155', '0', '0', '#joy', '#interest', 'positive', '0.095', 'basketball_court', 'basketball_game', 'basketball', 'ball', 'sport']
senticnet['varsity_basketball_team'] = ['0.035', '0.155', '0', '0', '#joy', '#interest', 'positive', '0.095', 'basketball_court', 'basketball_game', 'basketball', 'ball', 'sport']
senticnet['varsity_football'] = ['0.728', '0.83', '0', '0', '#joy', '#interest', 'positive', '0.779', 'ball', 'bounce', 'ball_bounce', 'soccer', 'play_football']
senticnet['varsity_soccer_team'] = ['0.035', '0.155', '0', '0', '#joy', '#interest', 'positive', '0.095', 'basketball_court', 'basketball_game', 'basketball', 'ball', 'sport']
senticnet['varsity_squad'] = ['0.035', '0.155', '0', '0', '#joy', '#interest', 'positive', '0.095', 'basketball_court', 'basketball_game', 'basketball', 'ball', 'sport']
senticnet['varsity_team'] = ['0.035', '0.155', '0', '0', '#joy', '#interest', 'positive', '0.095', 'basketball_court', 'basketball_game', 'basketball', 'ball', 'sport']
senticnet['varsity_volleyball_team'] = ['0.035', '0.155', '0', '0', '#joy', '#interest', 'positive', '0.095', 'basketball_court', 'basketball_game', 'basketball', 'ball', 'sport']
senticnet['varsson'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'concupiscence', 'lustfulness', 'amorousness']
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senticnet['vary_considerably'] = ['0.295', '0.072', '0', '0.136', '#joy', '#admiration', 'positive', '0.168', 'live_environment', 'beautiful', 'lovely', 'important_man', 'peaceful']
senticnet['vary_depending_on'] = ['0.295', '0.072', '0', '0.136', '#joy', '#admiration', 'positive', '0.168', 'live_environment', 'beautiful', 'lovely', 'important_man', 'peaceful']
senticnet['vary_depending_upon'] = ['0.295', '0.072', '0', '0.136', '#joy', '#admiration', 'positive', '0.168', 'live_environment', 'beautiful', 'lovely', 'important_man', 'peaceful']
senticnet['vary_dramatically'] = ['0.295', '0.072', '0', '0.136', '#joy', '#admiration', 'positive', '0.168', 'live_environment', 'beautiful', 'lovely', 'important_man', 'peaceful']
senticnet['vary_enormously'] = ['0.295', '0.072', '0', '0.136', '#joy', '#admiration', 'positive', '0.168', 'live_environment', 'beautiful', 'lovely', 'important_man', 'peaceful']
senticnet['vary_greatly'] = ['0.295', '0.072', '0', '0.136', '#joy', '#admiration', 'positive', '0.168', 'live_environment', 'beautiful', 'lovely', 'important_man', 'peaceful']
senticnet['vary_significantly'] = ['0.295', '0.072', '0', '0.136', '#joy', '#admiration', 'positive', '0.168', 'live_environment', 'beautiful', 'lovely', 'important_man', 'peaceful']
senticnet['vary_slightly'] = ['0.295', '0.072', '0', '0.136', '#joy', '#admiration', 'positive', '0.168', 'live_environment', 'beautiful', 'lovely', 'important_man', 'peaceful']
senticnet['vary_somewhat'] = ['0.295', '0.072', '0', '0.136', '#joy', '#admiration', 'positive', '0.168', 'live_environment', 'beautiful', 'lovely', 'important_man', 'peaceful']
senticnet['vary_substantially'] = ['0.295', '0.072', '0', '0.136', '#joy', '#admiration', 'positive', '0.168', 'live_environment', 'beautiful', 'lovely', 'important_man', 'peaceful']
senticnet['vary_widely'] = ['0.295', '0.072', '0', '0.136', '#joy', '#admiration', 'positive', '0.168', 'live_environment', 'beautiful', 'lovely', 'important_man', 'peaceful']
senticnet['vary_widely_among'] = ['0.295', '0.072', '0', '0.136', '#joy', '#admiration', 'positive', '0.168', 'live_environment', 'beautiful', 'lovely', 'important_man', 'peaceful']
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senticnet['varying_intensity'] = ['-0.93', '0', '0.698', '-0.85', '#sadness', '#disgust', 'negative', '-0.83', 'candlepower', 'light_intensity', 'acoustic_power', 'sound_pressure_level', 'threshold_level']
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senticnet['varying_width'] = ['0.734', '0.884', '0', '0', '#joy', '#interest', 'positive', '0.809', 'dimension', 'size', 'mass_measure', 'length_dimension', 'gauge']
senticnet['vasco_rossi'] = ['0.107', '0.11', '-0.08', '0.21', '#interest', '#admiration', 'positive', '0.086', 'live_healthy_life', 'forever_young', 'live_forever', 'live_long', 'live_good_life']
senticnet['vascular_bed'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'keep_alive', 'body_system', 'body_working', 'blood_pressure', 'heart']
senticnet['vascular_bundle'] = ['-0.86', '0', '0.511', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'separate', 'move', 'branch', 'diverge', 'fork']
senticnet['vascular_congestion'] = ['0.759', '-0.72', '0', '0.893', '#joy', '#admiration', 'positive', '0.793', 'manifestation', 'cry', 'reflection', 'acting_out', 'explosion']
senticnet['vascular_dementia'] = ['-0.94', '0', '0.821', '-0.75', '#sadness', '#anger', 'negative', '-0.84', 'predementedness', 'insanity', 'dementedness', 'senile_psychosis', 'alcoholic_dementia']
senticnet['vascular_discoloration'] = ['-0.57', '0', '0.767', '-0.61', '#anger', '#disgust', 'negative', '-0.65', 'verboseness', 'verbosity', 'prolixity', 'turgidness', 'flatulence']
senticnet['vascular_disease'] = ['-0.96', '0', '0.261', '-0.95', '#sadness', '#disgust', 'negative', '-0.72', 'illness', 'ill', 'tension', 'bad_health', 'malignant_hypertension']
senticnet['vascular_permeability'] = ['-0.66', '0', '0.877', '-0.64', '#sadness', '#anger', 'negative', '-0.72', 'somatic_cell', 'mastocyte', 'labrocyte', 'target_cell', 'phagocyte']
senticnet['vascular_proliferative'] = ['-0.73', '0', '0.904', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'unshapely', 'shapeless', 'lumpy', 'deformed', 'malformed']
senticnet['vascular_resistance'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'keep_alive', 'body_system', 'body_working', 'blood_pressure', 'heart']
senticnet['vascular_ring'] = ['-0.81', '0', '0.684', '-0.81', '#sadness', '#disgust', 'negative', '-0.77', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['vascular_tone'] = ['-0.74', '0', '0.723', '-0.59', '#sadness', '#anger', 'negative', '-0.68', 'tone', 'tautness', 'tonus', 'condition', 'status']
senticnet['vascularity'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'blatancy', 'obviousness']
senticnet['vasculature'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'chest', 'artery', 'vein', 'internal_organ', 'under_head']
senticnet['vasculitic'] = ['-0.85', '0', '0.657', '-0.86', '#sadness', '#disgust', 'negative', '-0.79', 'pulseless_disease', 'temporal_arteritis', 'rubor', 'redness', 'arteritis']
senticnet['vasculopathy'] = ['-0.85', '0', '0.657', '-0.86', '#sadness', '#disgust', 'negative', '-0.79', 'pulseless_disease', 'temporal_arteritis', 'rubor', 'redness', 'arteritis']
senticnet['vase'] = ['0', '0.082', '-0.09', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'coffin', 'headstone', 'morgue', 'bury_body', 'casket']
senticnet['vaseline'] = ['-0.75', '0', '-0.75', '-0.99', '#sadness', '#disgust', 'negative', '-0.83', 'tease', 'upset', 'lubricant', 'upset_person', 'piston']
senticnet['vasoactive'] = ['-0.74', '0', '0.723', '-0.59', '#sadness', '#anger', 'negative', '-0.68', 'tone', 'tautness', 'tonus', 'condition', 'status']
senticnet['vasoactive_intestinal_peptide'] = ['0.685', '-0.89', '0', '0.765', '#surprise', '#admiration', 'positive', '0.783', 'motilin', 'gastrin', 'gastrointestinal_hormone', 'secretin', 'ghrelin']
senticnet['vasoconstrictive'] = ['0.882', '-0.90', '0', '0.849', '#joy', '#surprise', 'positive', '0.877', 'pressor', 'phenylephrine', 'adrenergic', 'vasoconstrictor', 'phenylpropanolamine']
senticnet['vasoconstrictor'] = ['0.882', '-0.90', '0', '0.846', '#joy', '#surprise', 'positive', '0.876', 'pressor', 'phenylephrine', 'adrenergic', 'phenylpropanolamine', 'vasoconstrictive']
senticnet['vasodilatation'] = ['-0.62', '0', '0.901', '-0.71', '#anger', '#disgust', 'negative', '-0.74', 'pressure', 'osmotic_pressure', 'hypotonicity', 'pressure_level', 'force']
senticnet['vasodilatory'] = ['-0.62', '0', '0.901', '-0.71', '#anger', '#disgust', 'negative', '-0.74', 'pressure', 'osmotic_pressure', 'hypotonicity', 'pressure_level', 'force']
senticnet['vasomotor'] = ['-0.62', '0', '0.901', '-0.71', '#anger', '#disgust', 'negative', '-0.74', 'pressure', 'osmotic_pressure', 'hypotonicity', 'pressure_level', 'force']
senticnet['vasotec'] = ['-0.76', '0', '0.681', '-0.57', '#sadness', '#anger', 'negative', '-0.67', 'corticosteroid', 'corticoid', 'adrenal_cortical_steroid', 'spironolactone', 'spironolactone_drug']
senticnet['vassal'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'feudal_lord', 'lord']
senticnet['vassalage'] = ['-0.37', '-0.08', '0', '-0.33', '#sadness', '#disgust', 'negative', '-0.20', 'thrall', 'servitude', 'serfdom', 'serfhood', 'bonded_labor']
senticnet['vast'] = ['-0.04', '0.099', '0', '0.177', '#interest', '#admiration', 'positive', '0.077', 'wide', 'large', 'big', 'really_big', 'huge']
senticnet['vast_array'] = ['0.057', '0', '0', '0.153', '#joy', '#admiration', 'positive', '0.105', 'multiple', 'cute', 'variety', 'hairy', 'large_breadbox']
senticnet['vast_cavern'] = ['-0.96', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.96', 'tunnel', 'international_space_station', 'stick_wall', 'car_race', 'auction']
senticnet['vast_continent'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'dryness', 'thirstiness', 'xerostomia', 'dry_mouth', 'sereness']
senticnet['vast_erudition'] = ['0.99', '0', '0', '0.716', '#joy', '#admiration', 'positive', '0.853', 'encyclopedism', 'learning', 'scholarship', 'learnedness', 'eruditeness']
senticnet['vast_fortune'] = ['0.976', '0.811', '0', '0', '#joy', '#interest', 'positive', '0.893', 'deposit', 'bank_loan', 'wealth', 'finance', 'account']
senticnet['vast_land'] = ['0', '0.061', '0.015', '0.062', '#interest', '#admiration', 'positive', '0.036', 'large_land', 'big_land', 'immense', 'large', 'land']
senticnet['vast_range'] = ['0.828', '-0.89', '0', '0.693', '#joy', '#surprise', 'positive', '0.806', 'confines', 'expanse', 'view', 'internationality', 'purview']
senticnet['vast_superiority'] = ['0.757', '-0.82', '0', '0.982', '#surprise', '#admiration', 'positive', '0.855', 'first_class', 'choiceness', 'first_water', 'fineness', 'quality']
senticnet['vast_tract'] = ['0.716', '-0.51', '0', '0.647', '#joy', '#admiration', 'positive', '0.624', 'state', 'nation', 'country', 'ally', 'commonwealth']
senticnet['vast_wealth'] = ['0.843', '0.977', '0', '0', '#joy', '#interest', 'positive', '0.91', 'business', 'finance', 'revenue', 'wealth', 'income']
senticnet['vaster'] = ['0', '0.858', '0', '0.79', '#interest', '#admiration', 'positive', '0.824', 'heavenly_body', 'star_planet', 'celestial_body', 'around_earth', 'unlimited']
senticnet['vastly_superior'] = ['0', '0.886', '0', '0.772', '#interest', '#admiration', 'positive', '0.829', 'stalin', 'sail_sea', 'well_done', 'supervisor', 'small_ocean']
senticnet['vasundra'] = ['-0.97', '0', '0.549', '-0.97', '#sadness', '#disgust', 'negative', '-0.83', 'firmness', 'resolve', 'resoluteness', 'firmness_of_purpose', 'resolution']
senticnet['vat'] = ['0.931', '0.849', '0', '0', '#joy', '#interest', 'positive', '0.89', 'tub', 'value-added_tax', 'ad_valorem_tax', 'president', 'level']
senticnet['vatican'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'jerusalem', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['vatican_congregation'] = ['-0.95', '0', '0.673', '-0.85', '#sadness', '#disgust', 'negative', '-0.82', 'discharge', 'free', 'dethrone', 'disinvest', 'unfrock']
senticnet['vatican_hierarchy'] = ['0.013', '0.199', '0.027', '0.167', '#interest', '#admiration', 'positive', '0.088', 'church', 'confession', 'good_catholic', 'spirituality', 'christianity']
senticnet['vaticinate'] = ['0.677', '-0.55', '0', '0.831', '#joy', '#admiration', 'positive', '0.686', 'vaticination', 'call', 'enlighten', 'irradiate', 'predict']
senticnet['vaticination'] = ['0.675', '-0.55', '0', '0.83', '#joy', '#admiration', 'positive', '0.685', 'vaticinate', 'call', 'enlighten', 'irradiate', 'predict']
senticnet['vattaru'] = ['-0.84', '0', '0.579', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'squiggle', 'hand', 'chicken_scratch', 'handwriting', 'script']
senticnet['vaudeville'] = ['0.954', '0', '0', '0', '#joy', '#joy', 'positive', '0.954', 'clown', 'halloween', 'entertainment', 'fun', 'happiness']
senticnet['vaudeville_theater'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'theater', 'drive-in_movie', 'watch_movie', 'cinema', 'movie']
senticnet['vaudevillian'] = ['0', '0', '0', '0.987', '#admiration', '#admiration', 'positive', '0.987', 'emote', 'bold', 'form_of_social_interaction', 'act', 'stage_play']
senticnet['vaudreuil'] = ['-0.71', '0', '0.917', '-0.51', '#sadness', '#anger', 'negative', '-0.71', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['vault'] = ['0.02', '0.205', '0', '0', '#joy', '#interest', 'positive', '0.112', 'safe', 'bank_vault', 'bank', 'piggy_bank', 'bank_account']
senticnet['vault_door'] = ['0.02', '0.205', '0', '0', '#joy', '#interest', 'positive', '0.112', 'safe', 'bank_vault', 'bank', 'piggy_bank', 'bank_account']
senticnet['vault_toilet'] = ['0.074', '0', '0', '0', '#joy', '#joy', 'positive', '0.074', 'hike_trail', 'ant_hill', 'compost', 'picnic', 'soccer_field']
senticnet['vaulted_roof'] = ['0.026', '0.219', '0', '0.16', '#interest', '#admiration', 'positive', '0.135', 'large_build', 'palace', 'high_rise', 'structure', 'fortification']
senticnet['vaunt'] = ['-0.85', '0', '0', '-0.84', '#sadness', '#disgust', 'negative', '-0.85', 'brag', 'embellish', 'overemphasize', 'overstress', 'overstate']
senticnet['vaunter'] = ['0', '0', '0.982', '-0.71', '#anger', '#disgust', 'negative', '-0.84', 'boaster', 'braggart', 'blowhard', 'line-shooter', 'bragger']
senticnet['vauxhall_astra_sport'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'chick', 'wild_bird', 'baby_bird', 'lay_egg', 'bird_home']
senticnet['vax'] = ['0.526', '-0.99', '0', '0.673', '#surprise', '#admiration', 'positive', '0.732', 'faery', 'faerie', 'fairy', 'leprechaun', 'sandman']
senticnet['vdata'] = ['0.761', '0.603', '-0.21', '0.333', '#joy', '#interest', 'positive', '0.371', 'illuminate_room', 'give_light', 'light', 'light_bulb', 'lantern']
senticnet['veal'] = ['0', '0.077', '0', '0', '#interest', '#interest', 'positive', '0.077', 'cereal', 'beef', 'food_group', 'animal_flesh', 'curry']
senticnet['veal_stock'] = ['0.07', '0.084', '0.023', '0', '#joy', '#interest', 'positive', '0.044', 'hearty', 'porridge', 'hot_liquid', 'liquid_food', 'meat_vegetable']
senticnet['vecna'] = ['0.594', '-0.54', '0', '0.518', '#joy', '#surprise', 'positive', '0.554', 'exorcist', 'enchanter', 'magus', 'sorceress', 'witch_doctor']
senticnet['vector'] = ['-0.04', '0.097', '0.06', '0.146', '#interest', '#admiration', 'positive', '0.036', 'maths', 'ratio', 'length_measurement', 'measurement_unit', 'unit_of_measurement']
senticnet['vector_graphic'] = ['0.501', '-0.99', '0', '0.68', '#surprise', '#admiration', 'positive', '0.726', 'stated', 'hardcore', 'declared', 'definitive', 'explicit']
senticnet['vector_meson'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'elementary_particle', 'neutron', 'electron', 'subatomic_particle', 'proton']
senticnet['vedana'] = ['-0.87', '0', '0.744', '-0.54', '#sadness', '#anger', 'negative', '-0.72', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['veer'] = ['-0.64', '0', '0.717', '-0.98', '#anger', '#disgust', 'negative', '-0.78', 'debacle', 'licking', 'whitewash', 'finish', 'failure']
senticnet['vegan'] = ['-0.91', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.89', 'vegetarian', 'gluten-free', 'gluten', 'veggie', 'dairy-free']
senticnet['vege_out'] = ['0.502', '-0.78', '0', '0.73', '#surprise', '#admiration', 'positive', '0.672', 'relax', 'loosen_up', 'unbend', 'unwind', 'decompress']
senticnet['vegetable'] = ['0.035', '0.095', '0', '0.118', '#interest', '#admiration', 'positive', '0.083', 'fruit_vegetable', 'leafy_vegetable', 'vegetable_fruit', 'fresh_vegetable', 'vegetarian_food']
senticnet['vegetable_broth'] = ['0.07', '0.084', '0.023', '0', '#joy', '#interest', 'positive', '0.044', 'hearty', 'porridge', 'hot_liquid', 'liquid_food', 'meat_vegetable']
senticnet['vegetable_brush'] = ['0.986', '0', '0', '0', '#joy', '#joy', 'positive', '0.986', 'personal_cleanliness', 'cleanness', 'wipe_up_spill', 'smell_good', 'clean_carpet']
senticnet['vegetable_curry'] = ['-0.81', '0', '0.543', '-0.63', '#sadness', '#disgust', 'negative', '-0.66', 'caper', 'gherkin', 'relish', 'dill_pickle', 'sweet_pickle']
senticnet['vegetable_diet'] = ['0.509', '-0.62', '0', '0.786', '#surprise', '#admiration', 'positive', '0.64', 'agreeable', 'accordant', 'consentient', 'unanimous', 'according']
senticnet['vegetable_farmer'] = ['-0.08', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.11', 'serf', 'peasant', 'servant', 'poor', 'villein']
senticnet['vegetable_fruit'] = ['0.035', '0.104', '0', '0.12', '#interest', '#admiration', 'positive', '0.086', 'fruit_vegetable', 'vegetable', 'leafy_vegetable', 'fresh_vegetable', 'vegetarian_food']
senticnet['vegetable_garden'] = ['0.427', '0.227', '0', '0.37', '#joy', '#admiration', 'positive', '0.341', 'taken_seriously', 'complement', 'much_need', 'remission', 'knowledgeable']
senticnet['vegetable_grow'] = ['0', '0.09', '0.014', '0.085', '#interest', '#admiration', 'positive', '0.054', 'grow_place', 'plant_ground', 'grow_stuff', 'crop_place', 'grass']
senticnet['vegetable_grower'] = ['-0.08', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.11', 'serf', 'peasant', 'servant', 'poor', 'villein']
senticnet['vegetable_juice'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'lemon_juice', 'chocolate_pudding', 'grape_juice', 'orange_juice', 'juice']
senticnet['vegetable_kingdom'] = ['0.509', '-0.62', '0', '0.786', '#surprise', '#admiration', 'positive', '0.64', 'agreeable', 'accordant', 'consentient', 'unanimous', 'according']
senticnet['vegetable_market'] = ['0', '0.113', '0.041', '0.058', '#interest', '#admiration', 'positive', '0.043', 'corner_shop', 'general_store', 'many_shops', 'large_supermarket', 'goods']
senticnet['vegetable_matter'] = ['-0.12', '0', '-0.10', '-0.03', '#sadness', '#fear', 'negative', '-0.08', 'dog_poop', 'picnic', 'hike_trail', 'compost', 'wildlife']
senticnet['vegetable_mixture'] = ['-0.83', '0', '0.987', '-0.53', '#sadness', '#anger', 'negative', '-0.78', 'picaninny', 'kid', 'minor', 'tike', 'small_picaninny']
senticnet['vegetable_peeler'] = ['0.03', '-0.05', '0', '0.059', '#surprise', '#admiration', 'positive', '0.046', 'platter', 'fork', 'spoon', 'tablespoon', 'bowl']
senticnet['vegetable_protein'] = ['0.026', '0.08', '0.043', '0.064', '#interest', '#admiration', 'positive', '0.032', 'eat_rice', 'white_brown', 'asian', 'eat_pasta', 'staple_food']
senticnet['vegetable_seller'] = ['-0.80', '0', '0.718', '-0.79', '#sadness', '#disgust', 'negative', '-0.77', 'walk_exercise', 'lose_wallet', 'go_debt', 'pay_dinner', 'spend_much_money']
senticnet['vegetable_soup'] = ['0', '0', '0.066', '-0.07', '#anger', '#disgust', 'negative', '-0.07', 'container_cup', 'cup', 'grease_trap', 'dish', 'open_container']
senticnet['vegetable_stock'] = ['0.07', '0.084', '0.023', '0', '#joy', '#interest', 'positive', '0.044', 'hearty', 'porridge', 'hot_liquid', 'liquid_food', 'meat_vegetable']
senticnet['vegetable_tempura'] = ['0.52', '-0.69', '0', '0.639', '#surprise', '#admiration', 'positive', '0.617', 'black_bread', 'pumpernickel', 'limpa', 'bread', 'rye_bread']
senticnet['vegetable_vendor'] = ['-0.80', '0', '0.718', '-0.79', '#sadness', '#disgust', 'negative', '-0.77', 'walk_exercise', 'lose_wallet', 'go_debt', 'pay_dinner', 'spend_much_money']
senticnet['vegetarian'] = ['0.817', '-0.65', '0', '0.58', '#joy', '#surprise', 'positive', '0.684', 'scoffer', 'devourer', 'eater', 'diner', 'dunker']
senticnet['vegetarian_food'] = ['0.175', '0.101', '0.035', '0.195', '#joy', '#admiration', 'positive', '0.109', 'leafy_green', 'rabbit_food', 'fruit_plant', 'lawn_green', 'green_vegetable']
senticnet['vegetate'] = ['-0.03', '-0.07', '0.152', '-0.08', '#anger', '#disgust', 'negative', '-0.04', 'lazy', 'immobile', 'motionless', 'stationary', 'inactive']
senticnet['vegetation'] = ['0', '0.066', '0.007', '0.081', '#interest', '#admiration', 'positive', '0.047', 'usual_green', 'green_growth', 'green', 'green_garden', 'plant_big']
senticnet['vegetative_cell'] = ['-0.89', '0', '0.506', '-0.52', '#sadness', '#disgust', 'negative', '-0.64', 'mast_cell', 'mastocyte', 'labrocyte', 'target_cell', 'phagocyte']
senticnet['vegetative_growth'] = ['-0.75', '0', '0.512', '-0.75', '#sadness', '#disgust', 'negative', '-0.67', 'inactivity', 'inactiveness', 'slumber', 'inaction', 'hibernation']
senticnet['vegetative_stage'] = ['0', '0.075', '0', '0.159', '#interest', '#admiration', 'positive', '0.117', 'many_plants', 'blossom', 'development', 'poppy', 'adult']
senticnet['vegeterian'] = ['0', '-0.78', '0', '0.851', '#surprise', '#admiration', 'positive', '0.818', 'animal', 'ecology', 'interaction', 'herbivore', 'vegan']
senticnet['veggie'] = ['0.04', '0.091', '-0.06', '0.066', '#interest', '#admiration', 'positive', '0.033', 'cereal_grain', 'food_ingredient', 'vegetable', 'food_grain', 'whole_grain']
senticnet['veggie_burger'] = ['0', '0.074', '0', '0.071', '#interest', '#admiration', 'positive', '0.072', 'grocery_cart', 'super_market', 'fruit_stand', 'candy_store', 'produce_store']
senticnet['veggie_garden'] = ['0.427', '0.227', '0', '0.37', '#joy', '#admiration', 'positive', '0.341', 'taken_seriously', 'complement', 'much_need', 'remission', 'knowledgeable']
senticnet['vehemence'] = ['0', '0', '-0.73', '-0.96', '#fear', '#disgust', 'negative', '-0.85', 'fury', 'emphasis', 'fierceness', 'wildness', 'ferocity']
senticnet['vehement'] = ['-0.91', '0', '0.521', '-0.81', '#sadness', '#disgust', 'negative', '-0.74', 'bullying', 'domineering', 'blustery', 'roughshod', 'cavalier']
senticnet['vehement_anti'] = ['0.778', '-0.73', '0', '0.559', '#joy', '#surprise', 'positive', '0.689', 'sex', 'inversion', 'sexual_inversion', 'lesbianism', 'sapphism']
senticnet['vehemently'] = ['0', '0.149', '0.719', '-0.66', '#anger', '#disgust', 'negative', '-0.41', 'belligerently', 'antagonism', 'class_feeling', 'aggressiveness', 'animus']
senticnet['vehemently_disagree'] = ['-0.84', '0', '0', '-0.74', '#sadness', '#disgust', 'negative', '-0.79', 'piss_pant', 'rejection', 'argue', 'make_statement', 'become_politician']
senticnet['vehicle'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'sell_car', 'truck_stop', 'car_dealership', 'insurance', 'car']
senticnet['vehicle_convoy'] = ['0', '0', '-0.04', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'good_band', 'band', 'kit', 'procession', 'oblivion']
senticnet['vehicle_crash'] = ['-0.90', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.86', 'trauma', 'car_wreck', 'broken_bone', 'bad_knee', 'wreck']
senticnet['vehicle_exhaust'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'rodent', 'tree_animal']
senticnet['vehicle_impoundment'] = ['-0.53', '0', '0.825', '-0.87', '#anger', '#disgust', 'negative', '-0.74', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['vehicle_registration'] = ['0', '-0.01', '0', '0.064', '#surprise', '#admiration', 'positive', '0.042', 'car_gas', 'car_power', 'fossil_fuel', 'run_car', 'car_engine']
senticnet['vehicle_theft'] = ['0', '-0.01', '0', '0.064', '#surprise', '#admiration', 'positive', '0.042', 'car_gas', 'car_power', 'fossil_fuel', 'run_car', 'car_engine']
senticnet['vehicle_type'] = ['-0.10', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.10', 'mope', 'truck', 'lorry', 'vehicle', 'unicycle']
senticnet['vehicular'] = ['0', '0', '0', '0.144', '#admiration', '#admiration', 'positive', '0.144', 'go_elsewhere', 'transport_person', 'get_around', 'locomotion', 'trucking']
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senticnet['vehicular_traffic'] = ['-0.98', '0', '0.989', '-0.93', '#sadness', '#anger', 'negative', '-0.97', 'flight_accident', 'dangerous', 'danger', 'air_traffic', 'air_traffic_jam']
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senticnet['velar'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'letter', 'vowel', 'alphabet', 'basic_speech', 'kind_gesture']
senticnet['velar_fricative'] = ['-0.02', '0.186', '0', '0.213', '#interest', '#admiration', 'positive', '0.125', 'description', 'mumble', 'bummer', 'numerical', 'proverb']
senticnet['velar_nasal'] = ['-0.61', '0', '0.721', '-0.68', '#anger', '#disgust', 'negative', '-0.67', 'character', 'logogram', 'logograph', 'graph', 'ideogram']
senticnet['velcro'] = ['0.04', '0.091', '-0.06', '0.066', '#interest', '#admiration', 'positive', '0.033', 'nylon', 'nylon_product', 'clothes', 'shoe', 'pants']
senticnet['velcro_closure'] = ['0.775', '0.835', '0', '0', '#joy', '#interest', 'positive', '0.805', 'belt', 'leather_jacket', 'backpack', 'pouch', 'bag']
senticnet['velcro_product'] = ['0.039', '0.086', '-0.06', '0.056', '#interest', '#admiration', 'positive', '0.028', 'velcro', 'nylon', 'clothes', 'shoe', 'pants']
senticnet['velcro_strip'] = ['-0.58', '0', '0.715', '-0.59', '#anger', '#disgust', 'negative', '-0.63', 'fly', 'overlap', 'barndoor', 'coattail', 'codpiece']
senticnet['veliger'] = ['-0.87', '0', '0.983', '-0.91', '#anger', '#disgust', 'negative', '-0.92', 'thorn', 'sticker', 'pricker', 'prickle', 'spikelet']
senticnet['veliger_larva'] = ['-0.87', '0', '0.983', '-0.91', '#anger', '#disgust', 'negative', '-0.92', 'thorn', 'sticker', 'pricker', 'prickle', 'spikelet']
senticnet['vellication'] = ['-0.96', '0', '0.586', '-0.55', '#sadness', '#anger', 'negative', '-0.70', 'muscle_spasm', 'spasm', 'cramp', 'tic', 'blepharism']
senticnet['velociraptor'] = ['-0.74', '0', '0.963', '-0.76', '#anger', '#disgust', 'negative', '-0.82', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['velocity'] = ['-0.03', '0.178', '0', '0.168', '#interest', '#admiration', 'positive', '0.104', 'speed', 'fast', 'fast_and_furious', 'speedy', 'fleet']
senticnet['velocity_anomaly'] = ['-0.87', '0', '0.818', '0', '#sadness', '#anger', 'negative', '-0.84', 'slow_speed', 'wifi', 'computer_internet_connection', 'internet_connection', 'boot_up_computer']
senticnet['velocity_gradient'] = ['0.807', '-0.88', '0', '0.718', '#joy', '#surprise', 'positive', '0.802', 'sex', 'gayness', 'sexual_gayness', 'lesbianism', 'sapphism']
senticnet['velocity_profile'] = ['-0.03', '0.178', '0', '0.168', '#interest', '#admiration', 'positive', '0.104', 'speed', 'fast', 'fast_and_furious', 'speedy', 'fleet']
senticnet['velocity_shear'] = ['-0.97', '0', '0.831', '-0.87', '#sadness', '#disgust', 'negative', '-0.89', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'skewness', 'irregularity']
senticnet['velocity_vector'] = ['-0.03', '0.178', '0', '0.168', '#interest', '#admiration', 'positive', '0.104', 'speed', 'fast', 'fast_and_furious', 'speedy', 'fleet']
senticnet['velopharyngeal'] = ['0', '0', '0', '0.106', '#admiration', '#admiration', 'positive', '0.106', 'vocal_chord', 'mouth_noise', 'vocal_sound', 'human_sound', 'vocalization']
senticnet['velopharyngeal_closure'] = ['-0.56', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.63', 'aphasic', 'inarticulate', 'speechless', 'unarticulated', 'dumb']
senticnet['velu'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheepi', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['velutina'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['velvet'] = ['-0.03', '0', '0', '-0.04', '#sadness', '#disgust', 'negative', '-0.04', 'cotton', 'silk', 'wool', 'cotton_boll', 'asbestos']
senticnet['velvet_ant'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'hornet', 'vespid', 'vespid_wasp']
senticnet['velvet_carpet'] = ['-0.73', '0', '0.729', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'unwed', 'unwedded', 'divorced', 'unmarried', 'widowed']
senticnet['velvet_coat'] = ['-0.84', '0', '0.633', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'grizzle', 'postiche', 'horsehair_wig', 'wig', 'peruke']
senticnet['velvet_collar'] = ['-0.84', '0', '0.633', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'grizzle', 'postiche', 'wig', 'periwig', 'peruke']
senticnet['velvet_gown'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'pinafore', 'pinny', 'dirndl', 'dress', 'muumuu']
senticnet['velvet_leaf'] = ['0', '0.086', '0', '0', '#interest', '#interest', 'positive', '0.086', 'swim_ocean', 'weight_lift', 'sit_in_shade', 'cut_grass', 'trust_friend']
senticnet['velvet_suit'] = ['-0.76', '0', '0.672', '-0.88', '#sadness', '#disgust', 'negative', '-0.77', 'attire', 'garb', 'dress', 'fancy_dress', 'masquerade']
senticnet['velveteen_coat'] = ['-0.11', '0.115', '0.67', '-0.65', '#anger', '#disgust', 'negative', '-0.33', 'lividity', 'livid', 'wrathfully', 'wrothful', 'wroth']
senticnet['velvety'] = ['0.821', '0', '0', '0.84', '#joy', '#admiration', 'positive', '0.831', 'silky', 'satiny', 'supple', 'full-bodied', 'creamy']
senticnet['vena'] = ['0.61', '-0.89', '0', '0.504', '#joy', '#surprise', 'positive', '0.668', 'nerve', 'external_jugular_vein', 'internal_jugular_vein', 'vein', 'jugular']
senticnet['venal'] = ['0', '0.771', '0', '0.981', '#interest', '#admiration', 'positive', '0.876', 'self-serving', 'mendacious', 'duplicitous', 'avaricious', 'conniving']
senticnet['venality'] = ['-0.61', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.69', 'untrustworthiness', 'untrustiness', 'undependability', 'undependableness', 'unreliability']
senticnet['vend'] = ['-0.08', '0.196', '0', '0.194', '#interest', '#admiration', 'positive', '0.101', 'business_transaction', 'good_price', 'trade', 'transaction', 'cheap_price']
senticnet['vend_stand'] = ['0', '0', '0.016', '-0.08', '#anger', '#disgust', 'negative', '-0.05', 'dime_store', 'shop_arcade', 'deli_store', 'bookshop', 'store']
senticnet['vender'] = ['0.804', '-0.58', '0', '0.64', '#joy', '#admiration', 'positive', '0.676', 'seller', 'cosmetician', 'marketer', 'vendor', 'trafficker']
senticnet['vendetta'] = ['0.526', '-0.80', '0', '0.826', '#surprise', '#admiration', 'positive', '0.72', 'conflict', 'struggle', 'battle', 'feud', 'blood_feud']
senticnet['vendetta_against'] = ['0', '0', '0.901', '-0.92', '#anger', '#disgust', 'negative', '-0.91', 'animosity', 'jealous', 'bitterness', 'grudging', 'enviousness']
senticnet['vending_cart'] = ['-0.80', '0', '0.718', '-0.79', '#sadness', '#disgust', 'negative', '-0.77', 'walk_exercise', 'lose_wallet', 'go_debt', 'pay_dinner', 'spend_much_money']
senticnet['vending_machine'] = ['0', '0', '0', '-0.06', '#disgust', '#disgust', 'negative', '-0.06', 'newsstand', 'bookshop', 'walmart', 'shoe_store', 'hooker']
senticnet['vendor'] = ['0.024', '-0.06', '0', '0.126', '#surprise', '#admiration', 'positive', '0.071', 'grocery', 'groceries', 'fresh_vegetable', 'cold_drink', 'goods']
senticnet['veneer'] = ['-0.94', '0', '0.569', '-0.72', '#sadness', '#disgust', 'negative', '-0.74', 'bitumastic', 'coating', 'coatinging', 'covering', 'seal']
senticnet['veneering'] = ['-0.94', '0', '0.569', '-0.72', '#sadness', '#disgust', 'negative', '-0.74', 'bitumastic', 'veneer', 'coating', 'covering', 'seal']
senticnet['venepuncture'] = ['-0.74', '0', '0.766', '0', '#sadness', '#anger', 'negative', '-0.75', 'use_needle', 'clothes_repair', 'woven', 'embroidery', 'thread_needle']
senticnet['venerability'] = ['0.909', '-0.85', '0', '0.845', '#joy', '#surprise', 'positive', '0.87', 'respectability', 'reputability', 'honorableness', 'venerableness', 'righteousness']
senticnet['venerable'] = ['0', '-0.85', '0', '0.954', '#surprise', '#admiration', 'positive', '0.904', 'antiquated', 'dignity', 'goodness', 'reverence', 'patriarchal']
senticnet['venerable_tradition'] = ['0', '0.915', '0', '0.821', '#interest', '#admiration', 'positive', '0.868', 'culture', 'special_day', 'holiday', 'party_time', 'yearly_celebration']
senticnet['venerableness'] = ['0.906', '-0.86', '0', '0.838', '#joy', '#surprise', 'positive', '0.869', 'respectability', 'reputability', 'venerability', 'honorableness', 'righteousness']
senticnet['venerate'] = ['0', '0.82', '0', '0.847', '#interest', '#admiration', 'positive', '0.833', 'reverently', 'hero-worship', 'idolize', 'veneration', 'reverentially']
senticnet['veneration'] = ['0', '0.815', '0', '0.782', '#interest', '#admiration', 'positive', '0.798', 'reverently', 'hero-worship', 'venerate', 'idolize', 'reverentially']
senticnet['venereal_disease'] = ['-0.83', '0', '-0.81', '-0.84', '#sadness', '#disgust', 'negative', '-0.83', 'herpes', 'overpopulation', 'large_family', 'loss_offreedom', 'youth']
senticnet['venereal_infection'] = ['-0.97', '0', '0.135', '-0.92', '#sadness', '#disgust', 'negative', '-0.67', 'contagious_disease', 'contagion', 'disease', 'granuloma_inguinale', 'die']
senticnet['venery'] = ['-0.84', '0', '0.553', '-0.89', '#sadness', '#disgust', 'negative', '-0.76', 'encumbrance', 'incumbrance', 'block', 'bar', 'blockade']
senticnet['venetian_gothic_style'] = ['0.025', '0.136', '0', '0', '#joy', '#interest', 'positive', '0.081', 'city_hall', 'courthouse', 'subway_station', 'public_place', 'bus_station']
senticnet['venetian_hotel'] = ['0.057', '0.13', '0', '0.119', '#interest', '#admiration', 'positive', '0.102', 'friend_wedding', 'circular_route', 'spanish_restaurant', 'altar', 'monastery']
senticnet['venetian_resort_hotel_casino'] = ['0.057', '0.13', '0', '0.119', '#interest', '#admiration', 'positive', '0.102', 'friend_wedding', 'circular_route', 'spanish_restaurant', 'altar', 'monastery']
senticnet['venetian_window'] = ['0', '0', '0.095', '0', '#anger', '#anger', 'negative', '-0.09', 'build_part', 'flat', 'step', 'large_rock', 'hard_surface']
senticnet['vengeance'] = ['-0.78', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.85', 'revenge', 'retribution', 'go_jail', 'sin', 'incarceration']
senticnet['vengeful'] = ['0.978', '0.967', '0', '0', '#joy', '#interest', 'positive', '0.972', 'revenge', 'experience_emotion', 'take_revenge', 'bitter', 'waste_time']
senticnet['vengefully'] = ['-0.03', '0.157', '0.698', '-0.66', '#anger', '#disgust', 'negative', '-0.30', 'vengefulness', 'vindictively', 'revengefully', 'vindictive', 'spiteful']
senticnet['vengefulness'] = ['-0.03', '0.157', '0.698', '-0.66', '#anger', '#disgust', 'negative', '-0.30', 'vindictively', 'vengefully', 'revengefully', 'vindictive', 'spiteful']
senticnet['veni_yan'] = ['-0.66', '0', '0.859', '-0.82', '#anger', '#disgust', 'negative', '-0.78', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['venial'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'forgivable', 'excusable', 'mercifully', 'merciful', 'mercifulness']
senticnet['venial_sin'] = ['0.027', '-0.03', '0.027', '0.105', '#surprise', '#admiration', 'positive', '0.035', 'forgivable', 'excusable', 'mercifully', 'merciful', 'mercifulness']
senticnet['venipuncture'] = ['-0.74', '0', '0.766', '0', '#sadness', '#anger', 'negative', '-0.75', 'use_needle', 'clothes_repair', 'woven', 'embroidery', 'thread_needle']
senticnet['venire'] = ['0', '0.104', '0', '0.075', '#interest', '#admiration', 'positive', '0.089', 'judge', 'negotiator', 'jury', 'arbiter', 'member_jury']
senticnet['veniremen'] = ['0', '0.104', '0', '0.075', '#interest', '#admiration', 'positive', '0.089', 'judge', 'negotiator', 'jury', 'arbiter', 'member_jury']
senticnet['venison'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'hip', 'joint', 'above_leg', 'knee']
senticnet['venison_gallery'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'hip', 'joint', 'above_leg', 'knee']
senticnet['venison_steak'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'hip', 'joint', 'above_leg', 'knee']
senticnet['venite'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'reverence', 'genuflection', 'obeisance', 'kowtow', 'mafia']
senticnet['venom'] = ['-0.93', '0', '-0.99', '-0.92', '#sadness', '#fear', 'negative', '-0.95', 'offensive', 'malice', 'despise', 'disgust', 'scorn']
senticnet['venomous'] = ['-0.25', '0.081', '-0.19', '0', '#sadness', '#fear', 'negative', '-0.12', 'hairy', 'kind_animal', 'poisonous', 'big_fish', 'graceful']
senticnet['venomous_bite'] = ['0.876', '0.948', '0', '0', '#joy', '#interest', 'positive', '0.912', 'sting', 'tidbit', 'nip', 'tiny_amount', 'piece']
senticnet['venomous_snake'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'death_adder', 'ophidian', 'serpent', 'elapid', 'taipan']
senticnet['venomously'] = ['-0.76', '0', '0.824', '-0.86', '#anger', '#disgust', 'negative', '-0.81', 'intractability', 'unruliness', 'fractiousness', 'willfulness', 'against_rule']
senticnet['venous_blood'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'keep_alive', 'body_system', 'body_working', 'blood_pressure', 'heart']
senticnet['venous_drainage'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'blatancy', 'obviousness']
senticnet['venous_flow'] = ['0.759', '-0.72', '0', '0.893', '#joy', '#admiration', 'positive', '0.793', 'manifestation', 'cry', 'reflection', 'acting_out', 'explosion']
senticnet['venous_hypertension'] = ['-0.77', '0', '0.966', '-0.51', '#sadness', '#anger', 'negative', '-0.75', 'enuresis', 'incontinency', 'incontinence', 'stress_incontinence', 'urge_incontinence']
senticnet['venous_insufficiency'] = ['-0.99', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.78', 'angioedema', 'atrophedema', 'giant_hive', 'periodic_edema', 'dropsy']
senticnet['venous_outflow'] = ['0.777', '-0.89', '0', '0.508', '#joy', '#surprise', 'positive', '0.727', 'stretching', 'enlarge', 'dilatation', 'get_bigger', 'enlargement']
senticnet['venous_patency'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'blatancy', 'obviousness']
senticnet['venous_pressure'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'keep_alive', 'body_system', 'body_working', 'blood_pressure', 'heart']
senticnet['venous_reflux'] = ['-0.99', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.78', 'angioedema', 'atrophedema', 'giant_hive', 'periodic_edema', 'dropsy']
senticnet['venous_return'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'keep_alive', 'body_system', 'body_working', 'blood_pressure', 'heart']
senticnet['venous_system'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'chest', 'artery', 'vein', 'internal_organ', 'under_head']
senticnet['vent'] = ['-0.81', '0', '0.504', '-0.96', '#sadness', '#disgust', 'negative', '-0.76', 'regenerate', 'air_out', 'air', 'renew', 'freshen']
senticnet['vent_pipe'] = ['0.628', '-0.96', '0', '0.682', '#surprise', '#admiration', 'positive', '0.759', 'electric_fan', 'blower', 'fan', 'punkah', 'device']
senticnet['vent_valve'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'chick', 'wild_bird', 'baby_bird', 'lay_egg', 'bird_home']
senticnet['ventilate'] = ['0.628', '-0.96', '0', '0.682', '#surprise', '#admiration', 'positive', '0.759', 'electric_fan', 'blower', 'fan', 'punkah', 'device']
senticnet['ventilation'] = ['-0.58', '0', '0.732', '-0.85', '#anger', '#disgust', 'negative', '-0.72', 'emergency_procedure', 'emergency', 'external_respiration', 'respiration', 'artificial_respiration']
senticnet['ventilation_fan'] = ['0.628', '-0.96', '0', '0.682', '#surprise', '#admiration', 'positive', '0.759', 'electric_fan', 'blower', 'fan', 'punkah', 'device']
senticnet['ventilation_shaft'] = ['-0.96', '0', '-0.95', '0', '#sadness', '#fear', 'negative', '-0.96', 'tunnel', 'international_space_station', 'stick_wall', 'car_race', 'auction']
senticnet['ventilation_system'] = ['0.628', '-0.96', '0', '0.682', '#surprise', '#admiration', 'positive', '0.759', 'electric_fan', 'blower', 'fan', 'punkah', 'device']
senticnet['ventilatory_failure'] = ['-0.03', '-0.03', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'eat_poison', 'commit_suicide', 'drug_addiction', 'drunk_drive', 'jump_off_cliff']
senticnet['ventilatory_support'] = ['-0.03', '-0.03', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'eat_poison', 'commit_suicide', 'drug_addiction', 'drunk_drive', 'jump_off_cliff']
senticnet['ventral_aspect'] = ['-0.86', '0', '0.511', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'separate', 'move', 'branch', 'diverge', 'fork']
senticnet['ventral_surface'] = ['-0.66', '0', '0.809', '-0.61', '#sadness', '#anger', 'negative', '-0.69', 'face', 'base', 'underbelly', 'sole', 'bilge']
senticnet['ventricle'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'mathematical', 'muscle', 'math_function', 'feel_sick', 'separation']
senticnet['ventricular'] = ['-0.56', '0', '0.849', '-0.61', '#anger', '#disgust', 'negative', '-0.67', 'convulsive', 'unsteady', 'jerky', 'spasmodic', 'spastic']
senticnet['ventricular_arrhythmia'] = ['-0.20', '0', '-0.22', '-0.19', '#sadness', '#fear', 'negative', '-0.20', 'go_hospital', 'death', 'die', 'take_aspirin', 'heart_attack']
senticnet['ventricular_contraction'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'mathematical', 'muscle', 'math_function', 'feel_sick', 'separation']
senticnet['ventricular_depolarization'] = ['-0.56', '0', '0.849', '-0.61', '#anger', '#disgust', 'negative', '-0.67', 'convulsive', 'unsteady', 'jerky', 'spasmodic', 'spastic']
senticnet['ventricular_dilation'] = ['-0.73', '0', '0.904', '-0.53', '#sadness', '#anger', 'negative', '-0.72', 'unshapely', 'shapeless', 'lumpy', 'deformed', 'malformed']
senticnet['ventricular_dysfunction'] = ['-0.54', '0', '0.67', '-0.92', '#anger', '#disgust', 'negative', '-0.71', 'pericarditis', 'rubor', 'redness', 'endocarditis', 'inflammation']
senticnet['ventricular_escape'] = ['-0.56', '0', '0.849', '-0.61', '#anger', '#disgust', 'negative', '-0.67', 'convulsive', 'unsteady', 'jerky', 'spasmodic', 'spastic']
senticnet['ventricular_fibrillation'] = ['-0.83', '0', '0.987', '-0.50', '#sadness', '#anger', 'negative', '-0.77', 'heart_block', 'atrioventricular_block', 'bradycardia', 'arrhythmia', 'tachycardia']
senticnet['ventricular_fibrillation_vf'] = ['-0.56', '0', '0.849', '-0.61', '#anger', '#disgust', 'negative', '-0.67', 'convulsive', 'unsteady', 'jerky', 'spasmodic', 'spastic']
senticnet['ventricular_function'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'distastefulness', 'blatancy', 'obviousness']
senticnet['ventricular_repolarization'] = ['-0.56', '0', '0.849', '-0.61', '#anger', '#disgust', 'negative', '-0.67', 'convulsive', 'unsteady', 'jerky', 'spasmodic', 'spastic']
senticnet['ventricular_septal_defect'] = ['-0.81', '0', '0.684', '-0.81', '#sadness', '#disgust', 'negative', '-0.77', 'rhinostenosis', 'enterostenosis', 'laryngostenosis', 'aortic_stenosis', 'mitral_stenosis']
senticnet['ventricular_system'] = ['-0.86', '0', '0.511', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'separate', 'move', 'branch', 'diverge', 'fork']
senticnet['ventricular_tachycardia'] = ['-0.83', '0', '0.987', '-0.50', '#sadness', '#anger', 'negative', '-0.77', 'heart_block', 'atrioventricular_block', 'bradycardia', 'ventricular_fibrillation', 'tachycardia']
senticnet['ventriloquism'] = ['0.672', '-0.65', '0', '0.535', '#joy', '#surprise', 'positive', '0.621', 'escapologist', 'escape_expert', 'mind_reader', 'telepathist', 'performer']
senticnet['ventriloquist'] = ['0.672', '-0.65', '0', '0.535', '#joy', '#surprise', 'positive', '0.621', 'escapologist', 'escape_expert', 'mind_reader', 'telepathist', 'performer']
senticnet['ventriloquist_dummy'] = ['0.753', '-0.64', '0', '0.556', '#joy', '#surprise', 'positive', '0.652', 'oculus_dexter', 'stemma', 'naked_eye', 'eye', 'oculus_sinister']
senticnet['ventromedial_prefrontal_cortex'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'excitation', 'excitement', 'arousal', 'anger']
senticnet['venture'] = ['0.59', '-0.75', '0', '0.543', '#joy', '#surprise', 'positive', '0.63', 'joint_venture', 'sally', 'labor', 'cause', 'risk']
senticnet['venture_capital'] = ['0', '0.312', '-0.10', '0.207', '#interest', '#admiration', 'positive', '0.137', 'infuse', 'loan', 'lend', 'increase_money', 'wealth']
senticnet['ventured_into'] = ['0.954', '-0.87', '0', '0.583', '#joy', '#surprise', 'positive', '0.802', 'penetration', 'air_raid', 'air_attack', 'swoop', 'incursion']
senticnet['venturesome'] = ['0.617', '-0.79', '0', '0.608', '#joy', '#surprise', 'positive', '0.672', 'audacious', 'swaggering', 'daring', 'adventurous', 'venturous']
senticnet['venturing'] = ['0', '0', '0.963', '-0.73', '#anger', '#disgust', 'negative', '-0.84', 'embark', 'venture', 'guess', 'hazard', 'pretend']
senticnet['venturing_into'] = ['0.954', '-0.87', '0', '0.583', '#joy', '#surprise', 'positive', '0.802', 'penetration', 'air_raid', 'air_attack', 'swoop', 'incursion']
senticnet['venturous'] = ['0.619', '-0.79', '0', '0.604', '#joy', '#surprise', 'positive', '0.673', 'audacious', 'swaggering', 'daring', 'venturesome', 'adventurous']
senticnet['venturously'] = ['0.963', '0.807', '0', '0', '#joy', '#interest', 'positive', '0.885', 'able', 'entrepreneurial', 'enterprising', 'business', 'ingenious']
senticnet['venue'] = ['0.115', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.09', 'ballroom', 'perform_art', 'nightclub', 'ballerina', 'ballet']
senticnet['venus_aversa'] = ['0', '-0.02', '-0.08', '-0.05', '#fear', '#disgust', 'negative', '-0.03', 'mattress', 'sleep_furniture', 'master_bedroom', 'small_bed', 'pillow']
senticnet['venus_orbit'] = ['0.989', '-0.90', '0', '0.876', '#joy', '#surprise', 'positive', '0.923', 'reach', 'range', 'compass', 'scope', 'confines']
senticnet['venusian'] = ['0.884', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.919', 'ufo', 'milky', 'space_junk', 'galaxy', 'solar_system']
senticnet['veraciously'] = ['0.72', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.849', 'genuinely', 'frankly', 'forsooth', 'truthfully', 'truly']
senticnet['veracity'] = ['0.754', '-0.6', '0', '0.713', '#joy', '#admiration', 'positive', '0.689', 'sooth', 'sincerity', 'honestness', 'honesty', 'truthfulness']
senticnet['veranda'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'stage_border', 'row_seat', 'stage_entrance', 'theater_seat', 'theater_box']
senticnet['verandah'] = ['0', '0', '0.095', '0', '#anger', '#anger', 'negative', '-0.09', 'build_part', 'flat', 'step', 'large_rock', 'hard_surface']
senticnet['verb'] = ['0', '0.115', '-0.10', '0.249', '#interest', '#admiration', 'positive', '0.087', 'idiom', 'remark', 'grammar_part', 'speech_part', 'sentence']
senticnet['verbage'] = ['-0.61', '0', '0.73', '-0.85', '#anger', '#disgust', 'negative', '-0.73', 'diction', 'phrasing', 'verbalization', 'wording', 'phraseology']
senticnet['verbal'] = ['-0.02', '0.067', '-0.04', '0.192', '#interest', '#admiration', 'positive', '0.047', 'verbalize', 'orate', 'say', 'talkative', 'discourse']
senticnet['verbal_adjective'] = ['0.633', '-0.67', '0', '0.776', '#surprise', '#admiration', 'positive', '0.693', 'double_entendre', 'loophole', 'ambiguity', 'amphiboly', 'parisology']
senticnet['verbal_aggression'] = ['-0.91', '0', '0.451', '-0.98', '#sadness', '#disgust', 'negative', '-0.78', 'slave', 'psychological_aggression', 'physical_aggression', 'aggression', 'female_slave']
senticnet['verbal_agreement'] = ['0.865', '-0.84', '0', '0.915', '#joy', '#admiration', 'positive', '0.875', 'grant', 'sale_in_gross', 'contract_of_hazard', 'adhesion_contract', 'contract_of_adhesion']
senticnet['verbal_altercation'] = ['-0.60', '0', '0.777', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'batrachomyomachia', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['verbal_assault'] = ['0', '0', '0.781', '-0.82', '#anger', '#disgust', 'negative', '-0.80', 'cry', 'acting_out', 'explosion', 'halloo', 'yell']
senticnet['verbal_communication'] = ['0', '0', '0', '0.141', '#admiration', '#admiration', 'positive', '0.141', 'orate', 'chat', 'speech', 'mouth_move', 'verbalize']
senticnet['verbal_confrontation'] = ['-0.60', '0', '0.777', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'batrachomyomachia', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['verbal_contract'] = ['0.977', '0.868', '0', '0', '#joy', '#interest', 'positive', '0.922', 'pacifism', 'communication', 'weak_acid', 'good_fortune', 'sincere']
senticnet['verbal_description'] = ['0.716', '-0.78', '0', '0.539', '#joy', '#surprise', 'positive', '0.678', 'depicted', 'pictured', 'portrayed', 'described', 'delineated']
senticnet['verbal_dexterity'] = ['0.772', '-0.95', '0', '0.89', '#surprise', '#admiration', 'positive', '0.872', 'downrightness', 'straightforwardness', 'characteristic', 'immediacy', 'directness']
senticnet['verbal_expression'] = ['0.772', '-0.95', '0', '0.89', '#surprise', '#admiration', 'positive', '0.872', 'downrightness', 'straightforwardness', 'characteristic', 'immediacy', 'directness']
senticnet['verbal_fluency'] = ['-0.02', '0.067', '-0.04', '0.192', '#interest', '#admiration', 'positive', '0.047', 'verbalize', 'orate', 'say', 'talkative', 'discourse']
senticnet['verbal_harassment'] = ['-0.75', '0', '0.886', '-0.72', '#sadness', '#anger', 'negative', '-0.78', 'harass', 'move', 'check', 'fork', 'pin']
senticnet['verbal_inspiration'] = ['-0.83', '0', '0.648', '-0.60', '#sadness', '#anger', 'negative', '-0.69', 'infallibility', 'dependability', 'dependableness', 'reliability', 'reliableness']
senticnet['verbal_iq'] = ['0', '0.943', '0', '0.789', '#interest', '#admiration', 'positive', '0.866', 'energy', 'genius', 'energy_use', 'gas_oil', 'invent']
senticnet['verbal_noun'] = ['0.732', '0.8', '0', '0', '#joy', '#interest', 'positive', '0.766', 'fast', 'place', 'substance', 'substantive', 'person']
senticnet['verbal_tic'] = ['-0.89', '0', '0.987', '-0.90', '#anger', '#disgust', 'negative', '-0.93', 'cacoethes', 'logomania', 'agromania', 'pyromania', 'necrophilia']
senticnet['verbal_tirade'] = ['0', '0', '0.781', '-0.82', '#anger', '#disgust', 'negative', '-0.80', 'cry', 'acting_out', 'explosion', 'halloo', 'yell']
senticnet['verbalisation'] = ['-0.02', '0.067', '-0.04', '0.192', '#interest', '#admiration', 'positive', '0.047', 'verbalize', 'orate', 'say', 'talkative', 'discourse']
senticnet['verbalization'] = ['-0.61', '0', '0.73', '-0.85', '#anger', '#disgust', 'negative', '-0.73', 'diction', 'phrasing', 'verbiage', 'wording', 'phraseology']
senticnet['verbalize'] = ['0.932', '0.827', '0', '0', '#joy', '#interest', 'positive', '0.879', 'verbal', 'orate', 'converse', 'chat', 'talkative']
senticnet['verbally'] = ['-0.02', '0.067', '-0.04', '0.192', '#interest', '#admiration', 'positive', '0.047', 'verbalize', 'orate', 'say', 'talkative', 'discourse']
senticnet['verbally_abuse'] = ['-0.24', '0', '-0.19', '0', '#sadness', '#fear', 'negative', '-0.22', 'mock', 'prison_sentence', 'wrongdoing', 'jail_time', 'violent_action']
senticnet['verbally_abusive'] = ['-0.56', '0', '0.743', '-0.77', '#anger', '#disgust', 'negative', '-0.69', 'negligent', 'derelict', 'inattentive', 'delinquent', 'lax']
senticnet['verbally_aggressive'] = ['-0.75', '0', '0.886', '-0.72', '#sadness', '#anger', 'negative', '-0.78', 'harass', 'move', 'check', 'fork', 'pin']
senticnet['verbiage'] = ['-0.61', '0', '0.73', '-0.85', '#anger', '#disgust', 'negative', '-0.73', 'diction', 'phrasing', 'verbalization', 'wording', 'phraseology']
senticnet['verbose'] = ['-0.61', '0', '0.73', '-0.85', '#anger', '#disgust', 'negative', '-0.73', 'diction', 'phrasing', 'verbalization', 'wording', 'phraseology']
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senticnet['verbosity'] = ['0.04', '-0.03', '0', '0.115', '#joy', '#admiration', 'positive', '0.064', 'sport_play', 'scrabble', 'excite_game', 'fun_game', 'rough_game']
senticnet['verdancy'] = ['0.575', '-0.65', '0', '0.954', '#surprise', '#admiration', 'positive', '0.729', 'wilderness', 'overgrowth', 'greenness', 'profuseness', 'verdure']
senticnet['verdant'] = ['-0.93', '0', '0.906', '-0.53', '#sadness', '#anger', 'negative', '-0.79', 'juvenility', 'state', 'immatureness', 'callowness', 'jejuneness']
senticnet['verde_island'] = ['0', '0.089', '0', '0.129', '#interest', '#admiration', 'positive', '0.109', 'passageway', 'connect_room', 'entrance_room', 'corridor', 'walk_through']
senticnet['verdict'] = ['0.782', '0.934', '0', '0', '#joy', '#interest', 'positive', '0.858', 'go_court', 'serve_jury', 'question_witness', 'call_police', 'make_judgment']
senticnet['verdure'] = ['0.574', '-0.65', '0', '0.956', '#surprise', '#admiration', 'positive', '0.729', 'wilderness', 'overgrowth', 'greenness', 'verdancy', 'profuseness']
senticnet['verenestra'] = ['-0.04', '0.166', '0.684', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'malign', 'malefic', 'maliciously', 'spitefulness', 'maliciousness']
senticnet['verging_on'] = ['0', '0', '-0.74', '-0.87', '#fear', '#disgust', 'negative', '-0.80', 'absolute', 'out-and-out', 'rank', 'right-down', 'sheer']
senticnet['veridical'] = ['0', '0', '-0.71', '-0.97', '#fear', '#disgust', 'negative', '-0.84', 'illusion', 'fallacious', 'illusionary', 'imaginary', 'illusory']
senticnet['veridicality'] = ['0.943', '-0.75', '0', '0.515', '#joy', '#surprise', 'positive', '0.736', 'reasonableness', 'tenability', 'tenableness', 'credibility', 'credibleness']
senticnet['verie'] = ['-0.52', '0', '0.542', '-0.93', '#anger', '#disgust', 'negative', '-0.67', 'intoxication', 'drunkenness', 'inebriation', 'inebriety', 'tipsiness']
senticnet['veriest'] = ['-0.99', '0', '0.658', '-0.73', '#sadness', '#disgust', 'negative', '-0.79', 'adrift', 'afloat', 'aimless', 'directionless', 'purposeless']
senticnet['verifiable'] = ['0', '0.822', '0', '0.869', '#interest', '#admiration', 'positive', '0.845', 'real-world', 'verified', 'verifiableness', 'confirmed', 'boolean']
senticnet['verifiable_evidence'] = ['0.84', '-0.97', '0', '0.713', '#joy', '#surprise', 'positive', '0.844', 'proof', 'bed_check', 'crosscheck', 'parity_check', 'redundancy_check']
senticnet['verifiableness'] = ['0', '0.816', '0', '0.868', '#interest', '#admiration', 'positive', '0.842', 'real-world', 'verified', 'verifiable', 'confirmed', 'boolean']
senticnet['verification'] = ['0.626', '-0.85', '0', '0.551', '#joy', '#surprise', 'positive', '0.677', 'proof', 'bed_check', 'crosscheck', 'parity_check', 'redundancy_check']
senticnet['verified'] = ['0', '0.815', '0', '0.863', '#interest', '#admiration', 'positive', '0.839', 'real-world', 'verifiable', 'verifiableness', 'confirmed', 'boolean']
senticnet['verify'] = ['0.547', '-0.96', '0', '0.89', '#surprise', '#admiration', 'positive', '0.799', 'check', 'support', 'assure', 'affirm', 'check_off']
senticnet['verily'] = ['-0.04', '0.268', '-0.06', '0.227', '#interest', '#admiration', 'positive', '0.097', 'proposition', 'abstract', 'get_rid', 'theoretical', 'business_transaction']
senticnet['verisimilitude'] = ['0', '0.888', '0', '0.926', '#interest', '#admiration', 'positive', '0.907', 'believability', 'plausibility', 'realism', 'artificiality', 'profundity']
senticnet['veritable'] = ['0.857', '0', '0', '0.875', '#joy', '#admiration', 'positive', '0.866', 'documented', 'echt', 'attested', 'authenticated', 'genuine']
senticnet['veritable_inferno'] = ['0.864', '-0.98', '0', '0', '#joy', '#surprise', 'positive', '0.924', 'trash', 'trash_container', 'junk_store', 'coffee_cup', 'trash_receptacle']
senticnet['veritably'] = ['0.857', '0', '0', '0.875', '#joy', '#admiration', 'positive', '0.866', 'documented', 'echt', 'attested', 'authenticated', 'genuine']
senticnet['vermicide'] = ['0.98', '-0.70', '0', '0.625', '#joy', '#surprise', 'positive', '0.771', 'agent', 'medicament', 'medicinal_drug', 'medication', 'medicine']
senticnet['vermicompost'] = ['-0.12', '0', '-0.10', '-0.03', '#sadness', '#fear', 'negative', '-0.08', 'dog_poop', 'picnic', 'hike_trail', 'detritus', 'wildlife']
senticnet['vermiculation'] = ['0.96', '-0.69', '0', '0.749', '#joy', '#admiration', 'positive', '0.801', 'glyptic_art', 'cinquefoil', 'carving', 'glyptography', 'scrimshaw']
senticnet['vermifuge'] = ['0.639', '-0.79', '0', '0.562', '#joy', '#surprise', 'positive', '0.666', 'thiabendazole', 'medicament', 'medicinal_drug', 'medication', 'medicine']
senticnet['vermin'] = ['0', '0', '0.862', '-0.79', '#anger', '#disgust', 'negative', '-0.82', 'social_insect', 'big_mouse', 'rodent', 'small_animal', 'dig_tunnel']
senticnet['vermuyden'] = ['-0.77', '0', '0.583', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'rain', 'rain_down', 'sheet', 'rain_bucket', 'rain_buckets_down']
senticnet['vernaccia'] = ['0.784', '-0.59', '0', '0.846', '#joy', '#admiration', 'positive', '0.742', 'astuteness', 'slyness', 'wiliness', 'craftiness', 'cunning']
senticnet['vernacular_architecture'] = ['0.578', '-0.95', '0', '0.979', '#surprise', '#admiration', 'positive', '0.838', 'perpendicular', 'perpendicular_style', 'gothic_architecture', 'style_of_architecture', 'type_of_architecture']
senticnet['vernacular_style'] = ['0.578', '-0.95', '0', '0.979', '#surprise', '#admiration', 'positive', '0.838', 'perpendicular', 'perpendicular_style', 'gothic_architecture', 'style_of_architecture', 'type_of_architecture']
senticnet['vernal_equinox'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['vernal_hanging_parrot'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'lovebird', 'parroket', 'paraquet', 'paroquet']
senticnet['vernalization'] = ['-0.75', '0', '0.512', '-0.75', '#sadness', '#disgust', 'negative', '-0.67', 'inactivity', 'inactiveness', 'slumber', 'inaction', 'hibernation']
senticnet['vernatt_drain'] = ['-0.77', '0', '0.583', '-0.60', '#sadness', '#disgust', 'negative', '-0.65', 'rain', 'rain_down', 'sheet', 'rain_bucket', 'rain_buckets_down']
senticnet['vernier_acuity'] = ['0.685', '-0.91', '0', '0.566', '#joy', '#surprise', 'positive', '0.721', 'audibility', 'audibleness', 'visibleness', 'visibility', 'physical_property']
senticnet['vernier_caliper'] = ['0', '-0.00', '0.034', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'celsius', 'grade', 'certificate', 'learn_tool', 'temperature_measure']
senticnet['vernier_scale'] = ['0', '-0.00', '0.034', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'celsius', 'grade', 'certificate', 'learn_tool', 'temperature_measure']
senticnet['verrone'] = ['0.967', '-0.98', '0', '0.64', '#joy', '#surprise', 'positive', '0.863', 'club', 'social_club', 'society', 'jockey_club', 'gild']
senticnet['verruca'] = ['-0.58', '0', '0.727', '-0.59', '#anger', '#disgust', 'negative', '-0.63', 'blemish', 'keratosis', 'infection', 'defect', 'common_wart']
senticnet['versatile'] = ['0.085', '0.218', '-0.07', '0.308', '#interest', '#admiration', 'positive', '0.135', 'delicate', 'useful', 'good', 'light_colored', 'beautiful']
senticnet['versatile_actor'] = ['0', '0.797', '0', '0.804', '#interest', '#admiration', 'positive', '0.8', 'director', 'shooting_movie', 'director_chair', 'important_person', 'movie']
senticnet['versatile_tool'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'woodworking_tool']
senticnet['versatility'] = ['-0.53', '0', '0.765', '-0.94', '#anger', '#disgust', 'negative', '-0.74', 'jaggedness', 'unregularity', 'unevenness', 'irregularity', 'patchiness']
senticnet['verse'] = ['0.075', '-0.01', '0.019', '0.067', '#joy', '#admiration', 'positive', '0.034', 'rhyme', 'paragraph', 'song_lyric', 'lyrics', 'stanza']
senticnet['versification'] = ['-0.61', '0', '0.73', '-0.85', '#anger', '#disgust', 'negative', '-0.73', 'diction', 'phrasing', 'verbiage', 'wording', 'verbalization']
senticnet['versifier'] = ['0.584', '-0.93', '0', '0.927', '#surprise', '#admiration', 'positive', '0.816', 'elocute', 'perorate', 'perform', 'orate', 'spout']
senticnet['version'] = ['0', '0.893', '0', '0.947', '#interest', '#admiration', 'positive', '0.92', 'adjustment', 'adaption', 'adaptation', 'project', 'study']
senticnet['vert_ramp'] = ['-0.18', '0', '-0.14', '-0.11', '#sadness', '#fear', 'negative', '-0.14', 'fly_kite', 'walk_dog', 'ride_roller_coaster', 'rake_leave', 'jump_up_down']
senticnet['vertebra'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'socket', 'tooth_socket', 'alveolus', 'acetabulum', 'cotyloid_cavity']
senticnet['vertebrate'] = ['0', '0', '0', '0.068', '#admiration', '#admiration', 'positive', '0.068', 'video_recorder', 'terrier', 'live_human', 'elf', 'ice_skater']
senticnet['vertebrate_zoology'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'study_person', 'subject_study', 'zoo', 'zoologist', 'geology']
senticnet['verteporfin'] = ['0.789', '-0.81', '0', '0.591', '#joy', '#surprise', 'positive', '0.732', 'radiotherapy', 'radiation_therapy', 'radiation', 'actinotherapy', 'irradiation']
senticnet['vertex'] = ['-0.72', '0', '0.712', '0', '#sadness', '#anger', 'negative', '-0.71', 'subset', 'nonempty', 'isomorphic', 'non-empty', 'disjoint']
senticnet['vertex_figure'] = ['0.641', '-0.6', '0', '0.957', '#joy', '#admiration', 'positive', '0.733', 'regular_tetrahedron', 'cube', 'regular_hexahedron', 'regular_dodecahedron', 'ideal_solid']
senticnet['vertical'] = ['0.746', '0.889', '0', '0', '#joy', '#interest', 'positive', '0.818', 'pillar', 'build_support', 'load_bear', 'build_part', 'empty_place']
senticnet['vertical_bar'] = ['0.049', '0.113', '0.107', '0.162', '#interest', '#admiration', 'positive', '0.054', 'list_number', 'ordinal', 'count', 'numerical', 'enumeration']
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senticnet['via_ko'] = ['0.823', '0.982', '0', '0', '#joy', '#interest', 'positive', '0.902', 'spherical', 'three_dimensions', 'round_orb', 'coil', 'fill_with_sand']
senticnet['via_latina'] = ['-0.69', '0', '0.957', '-0.92', '#anger', '#disgust', 'negative', '-0.85', 'meager', 'meagre', 'meagerly', 'scrimpy', 'stingy']
senticnet['via_smtp'] = ['-0.79', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.79', 'misgiving', 'qualm', 'scruple', 'insecurity', 'insecure']
senticnet['via_spiga'] = ['0', '-0.03', '0.022', '-0.09', '#anger', '#disgust', 'negative', '-0.02', 'shoulder_bag', 'satchel', 'water_bottle', 'tote_bag', 'carry']
senticnet['via_ssl'] = ['-0.79', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.79', 'misgiving', 'qualm', 'scruple', 'insecurity', 'insecure']
senticnet['via_submission_armbar'] = ['0.823', '0.982', '0', '0', '#joy', '#interest', 'positive', '0.902', 'spherical', 'three_dimensions', 'round_orb', 'coil', 'fill_with_sand']
senticnet['via_submission_guillotine'] = ['-0.10', '-0.04', '-0.05', '-0.15', '#sadness', '#disgust', 'negative', '-0.06', 'acid_indigestion', 'get_full', 'burp', 'indigestion', 'become_full']
senticnet['via_tcp_ip'] = ['0.078', '0.103', '-0.09', '0.191', '#interest', '#admiration', 'positive', '0.069', 'connect', 'aunt_uncle', 'short_form', 'relationship', 'kinship']
senticnet['via_technical_knockout'] = ['0.823', '0.982', '0', '0', '#joy', '#interest', 'positive', '0.902', 'spherical', 'three_dimensions', 'round_orb', 'coil', 'fill_with_sand']
senticnet['via_telnet'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'busy_bee', 'actor', 'doer', 'worker']
senticnet['via_tko'] = ['0.944', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.967', 'meeting', 'meet', 'match', 'encounter', 'round']
senticnet['via_webcam'] = ['-0.51', '0', '0.545', '-0.91', '#anger', '#disgust', 'negative', '-0.65', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['viability'] = ['0', '0.866', '0', '0.842', '#interest', '#admiration', 'positive', '0.854', 'addling', 'ability', 'viable', 'addle', 'live']
senticnet['viable'] = ['0', '0.767', '0', '0.868', '#interest', '#admiration', 'positive', '0.818', 'vital', 'going', 'nonviable', 'possible', 'viability']
senticnet['viable_alternative'] = ['0.02', '0.277', '-0.11', '0.336', '#interest', '#admiration', 'positive', '0.129', 'different', 'similar', 'substitute', 'option', 'exception']
senticnet['viable_option'] = ['-0.97', '0', '0.84', '-0.52', '#sadness', '#anger', 'negative', '-0.78', 'pis_aller', 'expedient', 'temporary_expedient', 'makeshift', 'stopgap']
senticnet['viaduct'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'suspension_bridge', 'under_bridge', 'roadway', 'county_highway', 'long_road']
senticnet['viaggio'] = ['0.107', '0.11', '-0.08', '0.21', '#interest', '#admiration', 'positive', '0.086', 'live_healthy_life', 'forever_young', 'live_forever', 'live_long', 'live_good_life']
senticnet['viagra'] = ['0.962', '-0.89', '0', '0.855', '#joy', '#surprise', 'positive', '0.905', 'infectious_agent', 'infective_agent', 'satisfier', 'quickener', 'agent']
senticnet['vial'] = ['0.026', '0.067', '0', '0', '#joy', '#interest', 'positive', '0.046', 'beaker', 'glass_container', 'bottle', 'drink_holder', 'flask']
senticnet['vibe'] = ['0.7', '-0.62', '0', '0.58', '#joy', '#surprise', 'positive', '0.636', 'white_magic', 'thaumaturgy', 'juju', 'conjuration', 'supernaturalism']
senticnet['vibrancy'] = ['0', '0.97', '0', '0.764', '#interest', '#admiration', 'positive', '0.867', 'ringing', 'sonorousness', 'plangency', 'reverberance', 'resonance']
senticnet['vibrant'] = ['-0.05', '0.096', '0', '0.066', '#interest', '#admiration', 'positive', '0.037', 'hue_tint', 'spectrum', 'red_blue', 'green_yellow', 'tint']
senticnet['vibrant_democracy'] = ['0.716', '-0.51', '0', '0.647', '#joy', '#admiration', 'positive', '0.624', 'state', 'nation', 'ally', 'land', 'commonwealth']
senticnet['vibrant_downtown'] = ['-0.05', '0.096', '0', '0.066', '#interest', '#admiration', 'positive', '0.037', 'hue_tint', 'spectrum', 'red_blue', 'green_yellow', 'tint']
senticnet['vibrant_economy'] = ['0.837', '0', '0', '0', '#joy', '#joy', 'positive', '0.837', 'a_lot_of_sex', 'desirable', 'give_birth_child', 'sexual_intercourse', 'diversity']
senticnet['vibrant_intellectual'] = ['-0.05', '0.096', '0', '0.066', '#interest', '#admiration', 'positive', '0.037', 'hue_tint', 'spectrum', 'red_blue', 'green_yellow', 'tint']
senticnet['vibrant_nightlife'] = ['-0.05', '0.096', '0', '0.066', '#interest', '#admiration', 'positive', '0.037', 'hue_tint', 'spectrum', 'red_blue', 'green_yellow', 'tint']
senticnet['vibrant_private_sector'] = ['0.837', '0', '0', '0', '#joy', '#joy', 'positive', '0.837', 'a_lot_of_sex', 'desirable', 'give_birth_child', 'sexual_intercourse', 'diversity']
senticnet['vibrantly'] = ['-0.05', '0.096', '0', '0.066', '#interest', '#admiration', 'positive', '0.037', 'hue_tint', 'spectrum', 'red_blue', 'green_yellow', 'tint']
senticnet['vibraphonist'] = ['0.901', '0', '0', '0', '#joy', '#joy', 'positive', '0.901', 'music', 'vapor', 'blues', 'megrim', 'demoralization']
senticnet['vibrate'] = ['0.746', '0.744', '0', '0', '#joy', '#interest', 'positive', '0.745', 'jiggle', 'back_forth', 'vibration', 'vibrator', 'bail_water']
senticnet['vibrate_mode'] = ['-0.97', '0', '0.728', '-0.78', '#sadness', '#disgust', 'negative', '-0.82', 'beguiler', 'slicker', 'phony', 'faker', 'deceiver']
senticnet['vibrating_tuning_fork'] = ['-0.56', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.63', 'aphasic', 'inarticulate', 'speechless', 'unarticulated', 'dumb']
senticnet['vibration'] = ['0.048', '0.084', '0', '0.209', '#interest', '#admiration', 'positive', '0.114', 'move', 'motion', 'motility', 'movement', 'flicker']
senticnet['vibration_frequency'] = ['0.048', '0.084', '0', '0.209', '#interest', '#admiration', 'positive', '0.114', 'move', 'motion', 'motility', 'movement', 'flicker']
senticnet['vibration_isolation'] = ['0.048', '0.084', '0', '0.209', '#interest', '#admiration', 'positive', '0.114', 'move', 'motion', 'motility', 'movement', 'flicker']
senticnet['vibration_measurement'] = ['0.048', '0.084', '0', '0.209', '#interest', '#admiration', 'positive', '0.114', 'move', 'motion', 'motility', 'movement', 'flicker']
senticnet['vibrational'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'emotional_arousal', 'excitement', 'arousal', 'anger']
senticnet['vibrational_energy'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'emotional_arousal', 'excitement', 'arousal', 'anger']
senticnet['vibrato'] = ['0.941', '0.822', '0', '0', '#joy', '#interest', 'positive', '0.881', 'melodic_noise', 'universal_language', 'instrumental', 'musical_note', 'audio']
senticnet['vibrator'] = ['0.737', '0.742', '0', '0', '#joy', '#interest', 'positive', '0.74', 'jiggle', 'back_forth', 'vibration', 'vibrate', 'bail_water']
senticnet['vibratory_sensation'] = ['-0.78', '0', '0.669', '-0.89', '#sadness', '#disgust', 'negative', '-0.78', 'pain_sensation', 'painful_sensation', 'prickling', 'tingling', 'pain']
senticnet['vibrio_cholera'] = ['-0.99', '0', '0.859', '-0.62', '#sadness', '#anger', 'negative', '-0.82', 'lax', 'loose', 'diarrheal', 'diarrhoeal', 'unconstipated']
senticnet['vibrissa'] = ['0.098', '0.084', '0.819', '-0.68', '#anger', '#disgust', 'negative', '-0.33', 'grasp', 'covet', 'covetousness', 'avaricious', 'grabby']
senticnet['vibroblade'] = ['-0.50', '0', '0.937', '-0.59', '#anger', '#disgust', 'negative', '-0.67', 'restorative', 'medicine', 'medication', 'medicament', 'medicinal_drug']
senticnet['vibronic'] = ['0.705', '-0.93', '0', '0.89', '#surprise', '#admiration', 'positive', '0.843', 'fervor', 'emotional_arousal', 'excitement', 'arousal', 'anger']
senticnet['viburnum'] = ['0.958', '-0.82', '0', '0.924', '#joy', '#admiration', 'positive', '0.901', 'bunchberry', 'dwarf_cornel', 'crackerberry', 'pudding_berry', 'common_white_dogwood']
senticnet['viburnum_edule'] = ['0.773', '-0.55', '0', '0.949', '#joy', '#admiration', 'positive', '0.757', 'cornel', 'dwarf_cornel', 'crackerberry', 'pudding_berry', 'common_white_dogwood']
senticnet['vicar'] = ['0.023', '0.244', '-0.04', '0.212', '#interest', '#admiration', 'positive', '0.109', 'pastor', 'church_head', 'protestant', 'monk', 'prayer']
senticnet['vicar_general'] = ['0.031', '0.272', '0', '0.231', '#interest', '#admiration', 'positive', '0.178', 'minister', 'church_head', 'pastor', 'protestant', 'evangelist']
senticnet['vicariance'] = ['-0.83', '0', '0.546', '-0.88', '#sadness', '#disgust', 'negative', '-0.75', 'difference', 'allomerism', 'deviation', 'variation', 'departure']
senticnet['vicariate'] = ['0.8', '-0.84', '0', '0.868', '#surprise', '#admiration', 'positive', '0.837', 'commonwealth', 'province', 'administrative_district', 'administrative_division', 'territorial_division']
senticnet['vicarious'] = ['-0.58', '0', '0.814', '-0.54', '#sadness', '#anger', 'negative', '-0.64', 'atypical', 'irregular', 'abnormal', 'supernormal', 'subnormal']
senticnet['vicarious_atonement'] = ['-0.82', '0', '0.672', '-0.54', '#sadness', '#anger', 'negative', '-0.68', 'amends', 'damage', 'indemnification', 'redress', 'restitution']
senticnet['vicarious_experience'] = ['-0.58', '0', '0.814', '-0.54', '#sadness', '#anger', 'negative', '-0.64', 'atypical', 'irregular', 'abnormal', 'supernormal', 'subnormal']
senticnet['vicarious_leisure'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'lucrative', 'moneymaking', 'profitable', 'paying', 'paid']
senticnet['vicarious_liability'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'culpable_negligence', 'evasion', 'dereliction', 'negligence']
senticnet['vicarious_sacrifice'] = ['-0.82', '0', '0.672', '-0.54', '#sadness', '#anger', 'negative', '-0.68', 'amends', 'damage', 'indemnification', 'redress', 'restitution']
senticnet['vicarious_trauma'] = ['-0.88', '0', '-0.79', '-0.85', '#sadness', '#disgust', 'negative', '-0.84', 'pain', 'severe_pain', 'agony', 'physical_pain', 'suffer']
senticnet['vicarious_traumatization'] = ['-0.88', '0', '-0.79', '-0.85', '#sadness', '#disgust', 'negative', '-0.84', 'pain', 'severe_pain', 'agony', 'physical_pain', 'suffer']
senticnet['vicariously'] = ['-0.58', '0', '0.814', '-0.54', '#sadness', '#anger', 'negative', '-0.64', 'atypical', 'irregular', 'abnormal', 'supernormal', 'subnormal']
senticnet['vice'] = ['-0.82', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.79', 'bad_habit', 'smoke_cigarette', 'drink_alcohol', 'gambling_addiction', 'gambling']
senticnet['vice-chancellor'] = ['0.964', '0.823', '0', '0', '#joy', '#interest', 'positive', '0.893', 'chancellor', 'provost', 'vice-president', 'vice', 'faculty']
senticnet['vice-president'] = ['0.966', '0.823', '0', '0', '#joy', '#interest', 'positive', '0.894', 'chancellor', 'vice-chancellor', 'provost', 'vice', 'faculty']
senticnet['vice_addiction'] = ['-0.79', '0', '0', '-0.91', '#sadness', '#disgust', 'negative', '-0.85', 'bad_habit', 'smoke_cigarette', 'drink_alcohol', 'gambling', 'vice']
senticnet['vice_admiral'] = ['0.688', '-0.90', '0', '0.877', '#surprise', '#admiration', 'positive', '0.824', 'fleet_admiral', 'rear_admiral', 'admiral', 'full_admiral', 'flag_officer']
senticnet['vice_admiral_sir'] = ['0.688', '-0.90', '0', '0.877', '#surprise', '#admiration', 'positive', '0.824', 'fleet_admiral', 'rear_admiral', 'admiral', 'full_admiral', 'flag_officer']
senticnet['vice_chair'] = ['0', '0', '0.88', '-0.92', '#anger', '#disgust', 'negative', '-0.90', 'sofa_bed', 'small_seat', 'beanbag_airline_seat', 'hide-a-bed_sofa', 'airline_seat']
senticnet['vice_chairman'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'seeker', 'commuter']
senticnet['vice_chairperson'] = ['-0.02', '0', '0', '-0.07', '#sadness', '#disgust', 'negative', '-0.05', 'manager', 'investigator', 'slut', 'seeker', 'commuter']
senticnet['vice_champion'] = ['0', '0', '0', '0.834', '#admiration', '#admiration', 'positive', '0.834', 'royal_person', 'royal_title', 'top', 'master', 'rank']
senticnet['vice_chancellor'] = ['0', '0.745', '0', '0.832', '#interest', '#admiration', 'positive', '0.788', 'meet_up', 'year', 'rendezvous', 'meet_friend', 'make_appointment']
senticnet['vice_chief'] = ['0.74', '-0.53', '0', '0.641', '#joy', '#admiration', 'positive', '0.64', 'marshal', 'sky_marshal', 'lawman', 'law_officer', 'peace_officer']
senticnet['vice_flag_officer'] = ['0.683', '-0.91', '0', '0.867', '#surprise', '#admiration', 'positive', '0.821', 'fleet_flag_officer', 'rear_flag_officer', 'flag_officer', 'full_flag_officer', 'admiral']
senticnet['vice_president'] = ['0.046', '0.116', '-0.05', '0.076', '#interest', '#admiration', 'positive', '0.045', 'head_guy', 'boss', 'officer', 'leader', 'supervisor']
senticnet['vice_president_biden'] = ['0', '0.709', '0', '0.933', '#interest', '#admiration', 'positive', '0.821', 'capitol', 'government_building', 'president', 'apartment_house', 'government']
senticnet['vice_president_cheney'] = ['0', '0.709', '0', '0.933', '#interest', '#admiration', 'positive', '0.821', 'capitol', 'government_building', 'president', 'apartment_house', 'government']
senticnet['vice_president_dick_cheney'] = ['0', '0.709', '0', '0.933', '#interest', '#admiration', 'positive', '0.821', 'capitol', 'government_building', 'president', 'apartment_house', 'government']
senticnet['vice_president_president_elect'] = ['0', '0', '0.88', '-0.92', '#anger', '#disgust', 'negative', '-0.90', 'sofa_bed', 'small_seat', 'beanbag_airline_seat', 'hide-a-bed_sofa', 'airline_seat']
senticnet['vice_presidential_vacancy'] = ['-0.91', '0', '-0.77', '-0.75', '#sadness', '#fear', 'negative', '-0.81', 'emptiness', 'star_planet', 'nothingness', 'outer', 'around_earth']
senticnet['vice_rector'] = ['0.031', '0.272', '0', '0.231', '#interest', '#admiration', 'positive', '0.178', 'minister', 'church_head', 'pastor', 'protestant', 'evangelist']
senticnet['vice_versa'] = ['-0.80', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.77', 'contrary', 'contradiction', 'lie', 'liar', 'bad_person']
senticnet['viceroyal_sign_manual'] = ['-0.94', '0', '0.569', '-0.72', '#sadness', '#disgust', 'negative', '-0.74', 'bitumastic', 'veneer', 'veneering', 'adhesive', 'coating']
senticnet['vichyssoise'] = ['-0.81', '0', '0.543', '-0.63', '#sadness', '#disgust', 'negative', '-0.66', 'caper', 'gherkin', 'relish', 'dill_pickle', 'sweet_pickle']
senticnet['vicia_cracca'] = ['0.861', '-0.87', '0', '0.597', '#joy', '#surprise', 'positive', '0.779', 'herb', 'cultivated_parsnip', 'wild_parsnip', 'madnep', 'herbaceous_plant']
senticnet['vicia_faba'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'cabbage', 'lettuce', 'broccoli']
senticnet['vicinity'] = ['0.088', '0.161', '0', '0.22', '#interest', '#admiration', 'positive', '0.156', 'location', 'absent', 'nearby', 'small_place', 'proximity']
senticnet['vicious'] = ['-0.15', '0', '-0.12', '-0.09', '#sadness', '#fear', 'negative', '-0.12', 'feel_ashamed', 'hunt_animal', 'play_dead', 'feel_confident', 'crazy']
senticnet['vicious_circle'] = ['0.886', '-0.58', '0', '0.792', '#joy', '#admiration', 'positive', '0.755', 'argument', 'straw_man', 'strawman', 'specious_argument', 'special_pleading']
senticnet['vicious_civil_war'] = ['-0.67', '0', '0.938', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'massacre', 'mass_murder', 'carnage', 'slaughter', 'butchery']
senticnet['vicious_gang'] = ['0.867', '-0.84', '0', '0.876', '#joy', '#admiration', 'positive', '0.863', 'meet', 'team', 'team_up', 'embed', 'aggroup']
senticnet['vicious_killer'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'addicted', 'obsessed_love', 'addiction', 'obsession', 'weird']
senticnet['viciously'] = ['-0.87', '0', '-0.76', '0', '#sadness', '#fear', 'negative', '-0.82', 'extremely', 'intensely', 'savage', 'ferociously', 'aggressive']
senticnet['vicissitude'] = ['-0.73', '0', '0.732', '-0.53', '#sadness', '#anger', 'negative', '-0.66', 'insurmountable', 'insuperable', 'unconquerable', 'indomitable', 'unsubduable']
senticnet['vickers_machine_gun'] = ['0', '0', '-0.92', '-0.91', '#fear', '#disgust', 'negative', '-0.92', 'automatic', 'firearm', 'gun', 'weapon', 'projectile']
senticnet['vickers_vildebeest'] = ['-0.90', '0', '0.517', '-0.92', '#sadness', '#disgust', 'negative', '-0.78', 'outdated', 'noncurrent', 'disused', 'obsolete', 'superannuated']
senticnet['vicodin'] = ['0.138', '0.103', '0', '0.095', '#joy', '#interest', 'positive', '0.112', 'masturbation', 'sexual_intercourse', 'sex', 'warm_fuzzy_feeling', 'fuck']
senticnet['vicomte'] = ['-0.81', '0', '0.885', '-0.92', '#anger', '#disgust', 'negative', '-0.87', 'miss', 'girl', 'damsel', 'maiden', 'damoiselle']
senticnet['vicryl'] = ['-0.81', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.85', 'bandage', 'painkiller', 'gauze', 'aspirin', 'contraceptive_device']
senticnet['victim'] = ['-0.85', '0', '0.872', '-0.94', '#anger', '#disgust', 'negative', '-0.89', 'bloodshed', 'buy_gun', 'assault', 'enemy', 'siege']
senticnet['victim_crime'] = ['-0.76', '0', '0.742', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'pass_judgment', 'justice', 'juror', 'judge', 'punish_criminal']
senticnet['victimise'] = ['-0.89', '0', '0.826', '-0.50', '#sadness', '#anger', 'negative', '-0.74', 'wrong', 'delude', 'deceive', 'cozen', 'rip_off']
senticnet['victimization'] = ['-0.8', '0', '0.875', '-0.74', '#sadness', '#anger', 'negative', '-0.80', 'inhuman_treatment', 'cruelty', 'child_neglect', 'mistreatment', 'child_abuse']
senticnet['victimize'] = ['-0.89', '0', '0.826', '-0.50', '#sadness', '#anger', 'negative', '-0.74', 'wrong', 'delude', 'deceive', 'cozen', 'rip_off']
senticnet['victimless_crime'] = ['-0.53', '0', '0.825', '-0.87', '#anger', '#disgust', 'negative', '-0.74', 'criminal', 'outlaw', 'malefactor', 'felon', 'crook']
senticnet['victorian_architecture'] = ['-0.54', '0', '0.7', '-0.64', '#anger', '#disgust', 'negative', '-0.62', 'flourish', 'rant', 'bombast', 'fustian', 'claptrap']
senticnet['victorian_era'] = ['0.952', '0.844', '0', '0.96', '#joy', '#admiration', 'positive', '0.919', 'fashion', 'demand', 'kindness', 'better_person', 'warm_feeling']
senticnet['victorian_fiction'] = ['0.036', '0.219', '0', '0.19', '#interest', '#admiration', 'positive', '0.148', 'book_genre', 'literature', 'concoction', 'untruth', 'falsehood']
senticnet['victorianism'] = ['-0.88', '0', '0.666', '-0.63', '#sadness', '#anger', 'negative', '-0.73', 'clannishness', 'cliquishness', 'exclusiveness', 'arrogance', 'haughtiness']
senticnet['victorie'] = ['-0.52', '0', '0.701', '-0.50', '#sadness', '#anger', 'negative', '-0.57', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['victorinox'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'builder']
senticnet['victorious'] = ['0.82', '0.765', '0', '0.967', '#joy', '#admiration', 'positive', '0.851', 'enjoy_challenge', 'win_bet', 'occupy_mind', 'triumph', 'win_money']
senticnet['victoriously'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'hardy', 'fearlessness', 'fearlessly', 'unafraid', 'unfearing']
senticnet['victory'] = ['0.82', '0.792', '0', '0.757', '#joy', '#interest', 'positive', '0.79', 'win_battle', 'test_mettle', 'life', 'win_war', 'win']
senticnet['victory_defeat'] = ['-0.08', '-0.04', '-0.05', '-0.25', '#sadness', '#disgust', 'negative', '-0.08', 'lose_battle', 'beaten', 'defeat', 'lose_war', 'win_battle']
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senticnet['video_camcorder'] = ['0', '-0.02', '0.048', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'telescope', 'read_glass', 'eyeglass', 'binoculars', 'monocle']
senticnet['video_camera'] = ['0.995', '0.713', '0', '0.766', '#joy', '#admiration', 'positive', '0.825', 'watch_news', 'watch_movie', 'watch_show', 'record_movie', 'enjoy_movie']
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senticnet['video_clip'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'paperclip', 'clasp', 'glue_stick', 'clothespin', 'clamp']
senticnet['video_footage'] = ['0', '0', '0', '-0.17', '#disgust', '#disgust', 'negative', '-0.17', 'medicine', 'aspirin', 'blank_check', 'painkiller', 'gauze']
senticnet['video_game'] = ['0.082', '-0.01', '0.011', '0', '#joy', '#surprise', 'positive', '0.029', 'yo-yo', 'action_figure', 'ball_game', 'playroom', 'board_game']
senticnet['video_game_adaptation'] = ['-0.06', '0.122', '-0.04', '0.113', '#interest', '#admiration', 'positive', '0.032', 'encounter', 'portray', 'reveal', 'episode', 'next_episode']
senticnet['video_interlude'] = ['0.773', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.844', 'favorite', 'crowd', 'acclamation', 'bunch', 'large_crowd']
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senticnet['video_montage'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'paperclip', 'clasp', 'glue_stick', 'clothespin', 'clamp']
senticnet['video_projection'] = ['-0.03', '0.133', '-0.07', '0.214', '#interest', '#admiration', 'positive', '0.06', 'floodlight', 'spotlight', 'headlight', 'light', 'teach_lesson']
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senticnet['video_recorder'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'live_human', 'terrier', 'vertebrate', 'elf', 'ice_skater']
senticnet['video_replay'] = ['-0.09', '0.147', '-0.11', '0.205', '#interest', '#admiration', 'positive', '0.036', 'question_starter', 'resolution', 'available', 'explanation', 'request']
senticnet['video_scoreboard'] = ['0.953', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.929', 'hockey_fan', 'goalie', 'competitor', 'spectator', 'hockey_stick']
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senticnet['videocamera'] = ['0', '-0.02', '0.048', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'telescope', 'read_glass', 'eyeglass', 'binoculars', 'monocle']
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senticnet['videotape'] = ['0.873', '0.964', '0', '0', '#joy', '#interest', 'positive', '0.918', 'magnetic_tape', 'tapeline', 'record', 'tape', 'taping']
senticnet['videotaped_interview'] = ['0.716', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.733', 'call', 'use_phone', 'orate', 'use_telephone', 'speech']
senticnet['videotex'] = ['-0.09', '-0.03', '0.061', '0', '#sadness', '#anger', 'negative', '-0.04', 'bad_reception', 'wifi', 'bad_connection', 'phone_call', 'underground']
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senticnet['view_art'] = ['0.082', '-0.01', '0.011', '0', '#joy', '#surprise', 'positive', '0.029', 'art', 'paint', 'painting', 'picture', 'opera']
senticnet['view_film'] = ['0.826', '0.894', '0', '0', '#joy', '#interest', 'positive', '0.86', 'press_play', 'rend_movie', 'insert_dvd_in_player', 'view_movie', 'hear_funny']
senticnet['view_film_home'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'view_video', 'watch_tv_show', 'watch_film', 'take_film', 'watch_movie']
senticnet['view_movie'] = ['0.741', '0.868', '0', '0', '#joy', '#interest', 'positive', '0.804', 'use_remote_control', 'rewind', 'see_film', 'jitter', 'view_film']
senticnet['view_nature'] = ['0.865', '0.998', '0', '0', '#joy', '#interest', 'positive', '0.931', 'great_exercise', 'enjoy_nature', 'wear_out_shoes', 'physical_exertion', 'exercise_dog']
senticnet['view_video'] = ['0.988', '0.954', '0', '0', '#joy', '#interest', 'positive', '0.971', 'record_film', 'proof_of_purchase', 'watch_television', 'use_television', 'store_film']
senticnet['viewable'] = ['0.649', '-0.72', '0', '0.623', '#joy', '#surprise', 'positive', '0.665', 'panoptic', 'gross', 'telescopic', 'seeable', 'visual']
senticnet['viewed_skeptically'] = ['0', '0', '-0.99', '-0.99', '#fear', '#disgust', 'negative', '-0.99', 'unbelieving', 'sceptical', 'doubting', 'disbelieving', 'questioning']
senticnet['viewer'] = ['0', '0.075', '0.047', '0.101', '#interest', '#admiration', 'positive', '0.043', 'watcher', 'part_of_face', 'facial_organ', 'face_feature', 'head_organ']
senticnet['viewfinder'] = ['0.021', '0.081', '0.017', '0.113', '#interest', '#admiration', 'positive', '0.05', 'aspect', 'perspective', 'look_round', 'scenic', 'look_through']
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senticnet['viewing_angle'] = ['0.057', '0', '0.012', '0.08', '#joy', '#admiration', 'positive', '0.042', 'see_better', 'glasses', 'become_gigantic', 'magnify', 'contact_lens']
senticnet['viewless'] = ['0', '0', '0.778', '-0.73', '#anger', '#disgust', 'negative', '-0.75', 'too-warm', 'self-annihilating', 'self-enforcement', 'newsier', 'opaqued']
senticnet['viewliner'] = ['-0.58', '0', '0.714', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'waster', 'diversionist', 'destroyer', 'ruiner', 'undoer']
senticnet['viewpoint'] = ['-0.02', '0.272', '-0.08', '0.236', '#interest', '#admiration', 'positive', '0.101', 'polarize', 'imaginative', 'acknowledge', 'idea', 'brain_function']
senticnet['viewport'] = ['0.033', '0', '0', '0.081', '#joy', '#admiration', 'positive', '0.057', 'door', 'door_lock', 'window_door', 'wall', 'doorway']
senticnet['vigenre_cipher'] = ['-0.52', '0', '0.821', '-0.65', '#anger', '#disgust', 'negative', '-0.66', 'work_out', 'reckon', 'calculate', 'average_out', 'cypher']
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senticnet['vigilance'] = ['0', '1', '0', '0', '#interest', '#interest', 'positive', '1', 'confident', 'confidence', 'optimistic', 'assured', 'engrossed']
senticnet['vigilant'] = ['0.992', '0', '0', '0.863', '#joy', '#admiration', 'positive', '0.927', 'assiduousness', 'careful', 'conscientious', 'meticulous', 'big']
senticnet['vigilant_eye'] = ['0', '1', '0', '0', '#interest', '#interest', 'positive', '1', 'confident', 'confidence', 'optimistic', 'assured', 'engrossed']
senticnet['vigilant_oversight'] = ['0', '1', '0', '0', '#interest', '#interest', 'positive', '1', 'confident', 'confidence', 'optimistic', 'assured', 'engrossed']
senticnet['vigilant_resolve'] = ['-0.07', '0.069', '-0.18', '0', '#sadness', '#fear', 'negative', '-0.06', 'medical_procedure', 'cure', 'doctor_surgery', 'treatment', 'doctor_work']
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senticnet['vigna_unguiculata'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'cabbage', 'lettuce', 'broccoli']
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senticnet['vigorous'] = ['0', '0.866', '0', '0.735', '#interest', '#admiration', 'positive', '0.8', 'bouncing', 'high-powered', 'munch', 'spicy', 'strident']
senticnet['vigorous_activity'] = ['0', '0', '0', '0.202', '#admiration', '#admiration', 'positive', '0.202', 'human_movement', 'human_action', 'fast_movement', 'running', 'body_movement']
senticnet['vigorous_campaign'] = ['-0.81', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.81', 'take_charge', 'anti', 'go_against', 'death_wish', 'file_complaint']
senticnet['vigorous_competition'] = ['0.838', '0.75', '0', '0.708', '#joy', '#interest', 'positive', '0.765', 'festival', 'party', 'banquet', 'big_event', 'rock_concert']
senticnet['vigorous_defender'] = ['0.677', '-0.52', '0', '0.714', '#joy', '#admiration', 'positive', '0.64', 'economic_libertarian', 'libertarian', 'advocate', 'advocator', 'proponent']
senticnet['vigorous_exercise'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'physical_exertion', 'wear_out_shoes', 'become_physically_fit', 'much_exercise', 'strain_muscle']
senticnet['vigorously'] = ['0.859', '0.715', '0', '0', '#joy', '#interest', 'positive', '0.787', 'energy', 'intense', 'acrobatic', 'mainly', 'hardly']
senticnet['vigorously_enforce'] = ['-0.88', '0', '0.977', '-0.81', '#sadness', '#anger', 'negative', '-0.89', 'execute', 'run', 'compel', 'oblige', 'obligate']
senticnet['viking_armoured_vehicle'] = ['-0.56', '0', '0.714', '-0.89', '#anger', '#disgust', 'negative', '-0.72', 'tank', 'tank_destroyer', 'armored_car', 'armoured_car', 'armored_personnel_carrier']
senticnet['viking_helmet'] = ['0.793', '0.847', '0', '0', '#joy', '#interest', 'positive', '0.82', 'cross_bun', 'hot_cross_bun', 'bear_paw', 'coffee_roll', 'bun']
senticnet['viking_lander'] = ['0.052', '0.119', '0', '0.141', '#interest', '#admiration', 'positive', '0.104', 'neigh', 'domestic_animal', 'animal_type', 'animal', 'home_animal']
senticnet['viking_stove'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'kitchen_cabinet', 'kitchen_cupboard', 'dishwasher', 'cupboard', 'own_kitchen']
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senticnet['villa'] = ['0.893', '0.683', '0', '0.789', '#joy', '#admiration', 'positive', '0.788', 'house', 'expensive_house', 'fabulous', 'fancy_house', 'vip']
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senticnet['villainous'] = ['0', '0', '0.861', '-0.74', '#anger', '#disgust', 'negative', '-0.80', 'reprehensible', 'nature', 'offensive', 'villain', 'obnoxious']
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senticnet['vincible'] = ['0.699', '-0.94', '0', '0.578', '#joy', '#surprise', 'positive', '0.741', 'beatable', 'vanquishable', 'conquerable', 'subduable', 'subjugable']
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senticnet['vindicated'] = ['0.912', '-0.74', '0', '0.695', '#joy', '#surprise', 'positive', '0.785', 'absolved', 'exculpated', 'exonerated', 'guiltless', 'clear']
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senticnet['vinegar'] = ['-0.81', '0', '-0.83', '0', '#sadness', '#fear', 'negative', '-0.82', 'peanut', 'leave_over', 'floppy', 'circular_motion', 'store_vegetable']
senticnet['vinegariness'] = ['-0.99', '0', '0.744', '-0.74', '#sadness', '#anger', 'negative', '-0.82', 'acerbity', 'acidity', 'vinegarishness', 'tartness', 'taste_property']
senticnet['vinegarishness'] = ['-0.99', '0', '0.744', '-0.74', '#sadness', '#anger', 'negative', '-0.82', 'acerbity', 'vinegariness', 'acidity', 'tartness', 'taste_property']
senticnet['vinification'] = ['0.826', '-0.51', '0', '0.517', '#joy', '#admiration', 'positive', '0.62', 'zymosis', 'top_fermentation', 'bottom_fermentation', 'chemical_process', 'chemical_change']
senticnet['vinnie'] = ['0.17', '0.125', '-0.04', '0.196', '#joy', '#admiration', 'positive', '0.111', 'pal', 'single_person', 'comrade', 'good', 'companion']
senticnet['vinoba_bhave'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['vintage'] = ['0', '0.726', '0', '0.768', '#interest', '#admiration', 'positive', '0.747', 'wine', 'year', 'harvesting', 'juice', 'vintager']
senticnet['vintage_champagne'] = ['0.331', '0.119', '0.022', '0.229', '#joy', '#admiration', 'positive', '0.164', 'spice_life', 'red_light_district', 'fresh_produce', 'enough_food', 'knowledgeable']
senticnet['vintage_clothing'] = ['0.048', '0', '0', '0.092', '#joy', '#admiration', 'positive', '0.07', 'gym_locker', 'observatory', 'prison_cell', 'luggage_store', 'luggage_compartment']
senticnet['vintage_fender'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'lever']
senticnet['vintage_furniture'] = ['-0.93', '0', '0.607', '-0.67', '#sadness', '#disgust', 'negative', '-0.73', 'miss', 'girl', 'missy', 'young_lady', 'young_woman']
senticnet['vintage_jewelry'] = ['-0.93', '0', '0.607', '-0.67', '#sadness', '#disgust', 'negative', '-0.73', 'miss', 'girl', 'missy', 'young_lady', 'young_woman']
senticnet['vintage_wine'] = ['0.331', '0.119', '0.022', '0.229', '#joy', '#admiration', 'positive', '0.164', 'spice_life', 'red_light_district', 'fresh_produce', 'enough_food', 'knowledgeable']
senticnet['vintager'] = ['0', '0.723', '0', '0.762', '#interest', '#admiration', 'positive', '0.742', 'wine', 'year', 'harvesting', 'juice', 'vintage']
senticnet['vintner'] = ['0.962', '0.754', '0', '0', '#joy', '#interest', 'positive', '0.858', 'shopper', 'clerk', 'patron', 'customer', 'hairdresser']
senticnet['vinyasa'] = ['0', '0', '0', '0.93', '#admiration', '#admiration', 'positive', '0.93', 'lift_weight', 'release_stress', 'move_body', 'take_hike', 'change_color']
senticnet['vinyasa_yoga'] = ['0', '0', '0', '0.93', '#admiration', '#admiration', 'positive', '0.93', 'lift_weight', 'release_stress', 'move_body', 'take_hike', 'change_color']
senticnet['vinyl'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['vinyl_album'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['vinyl_disc'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['vinyl_ep'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['vinyl_lp'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['vinyl_record'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['vinyl_release'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['vinyl_version'] = ['0.705', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.734', 'compact_disc', 'music_storage', 'store_music', 'cd-rom', 'seventy']
senticnet['viol'] = ['0.033', '0', '0.009', '0.057', '#joy', '#admiration', 'positive', '0.027', 'triangle', 'baton', 'bugle', 'type_of_bow', 'horn']
senticnet['violance'] = ['-1', '0', '0.341', '-0.98', '#sadness', '#disgust', 'negative', '-0.77', 'kill', 'murder', 'death', 'kill_person', 'stab_to_death']
senticnet['violate'] = ['-0.63', '0', '0.796', '-0.77', '#anger', '#disgust', 'negative', '-0.73', 'blindside', 'profane', 'assault', 'savage', 'assail']
senticnet['violated_wp'] = ['0.533', '0.818', '0', '0.683', '#interest', '#admiration', 'positive', '0.678', 'gain_information', 'report_news', 'solve', 'seek_answer', 'gather_information']
senticnet['violation'] = ['-0.69', '0', '0.752', '-0.82', '#anger', '#disgust', 'negative', '-0.75', 'usurpation', 'trespass', 'encroachment', 'intrusion', 'inroad']
senticnet['violator'] = ['-0.89', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.93', 'felon', 'crook', 'malefactor', 'outlaw', 'criminal']
senticnet['violence'] = ['-0.97', '0', '-0.98', '-0.82', '#sadness', '#fear', 'negative', '-0.92', 'siege', 'conflict', 'combat', 'big_fight', 'battle']
senticnet['violent'] = ['0', '0', '-0.75', '-0.74', '#fear', '#disgust', 'negative', '-0.75', 'threat', 'hazardous', 'deadly', 'funny_look', 'rival']
senticnet['violent_action'] = ['-0.76', '0', '0.85', '-0.77', '#anger', '#disgust', 'negative', '-0.79', 'bad_action', 'bad_behavior', 'consequence', 'judge_decision', 'badness']
senticnet['violent_agitation'] = ['-0.71', '0', '0.928', '-0.74', '#anger', '#disgust', 'negative', '-0.79', 'set_off', 'shake_up', 'stir_up', 'instigate', 'inflame']
senticnet['violent_altercation'] = ['-0.60', '0', '0.777', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'batrachomyomachia', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['violent_backlash'] = ['-0.51', '0', '0.946', '-0.99', '#anger', '#disgust', 'negative', '-0.82', 'trammel', 'limit', 'restrict', 'bound', 'confine']
senticnet['violent_behavior'] = ['-0.75', '0', '0.886', '-0.72', '#sadness', '#anger', 'negative', '-0.78', 'harass', 'move', 'check', 'fork', 'pin']
senticnet['violent_brawl'] = ['-0.60', '0', '0.777', '-0.51', '#sadness', '#anger', 'negative', '-0.62', 'batrachomyomachia', 'wrangle', 'dustup', 'quarrel', 'row']
senticnet['violent_clash'] = ['-0.2', '-0.03', '0', '-0.23', '#sadness', '#disgust', 'negative', '-0.13', 'beat_up', 'fight', 'conflict', 'violence', 'fight_against']
senticnet['violent_collision'] = ['-0.12', '0', '-0.09', '-0.04', '#sadness', '#fear', 'negative', '-0.08', 'hold_two_together', 'install_window', 'float_through_air', 'spring_up', 'collision']
senticnet['violent_confrontation'] = ['-0.2', '-0.03', '0', '-0.23', '#sadness', '#disgust', 'negative', '-0.13', 'beat_up', 'fight', 'conflict', 'violence', 'fight_against']
senticnet['violent_crackdown'] = ['-0.51', '0', '0.946', '-0.99', '#anger', '#disgust', 'negative', '-0.82', 'trammel', 'limit', 'restrict', 'bound', 'confine']
senticnet['violent_crime'] = ['-0.47', '0.174', '-0.44', '0.105', '#sadness', '#fear', 'negative', '-0.16', 'wrong', 'wrongdoing', 'bad_choice', 'harmful_human', 'bad_behavior']
senticnet['violent_crime_rate'] = ['-0.47', '0.174', '-0.44', '0.105', '#sadness', '#fear', 'negative', '-0.16', 'wrong', 'wrongdoing', 'bad_choice', 'harmful_human', 'bad_behavior']
senticnet['violent_death'] = ['-0.71', '0', '0.748', '-0.55', '#sadness', '#anger', 'negative', '-0.67', 'casualty', 'fatal_accident', 'killing', 'fatality', 'death']
senticnet['violent_felony'] = ['-0.84', '0', '0.745', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'crime', 'against_law', 'revenge', 'vengeance', 'theft']
senticnet['violent_gang'] = ['0.867', '-0.84', '0', '0.876', '#joy', '#admiration', 'positive', '0.863', 'meet', 'team', 'team_up', 'embed', 'aggroup']
senticnet['violent_juvenile_crime'] = ['-0.47', '0.174', '-0.44', '0.105', '#sadness', '#fear', 'negative', '-0.16', 'wrong', 'wrongdoing', 'bad_choice', 'harmful_human', 'bad_behavior']
senticnet['violent_outburst'] = ['0', '0', '0.781', '-0.82', '#anger', '#disgust', 'negative', '-0.80', 'cry', 'acting_out', 'explosion', 'halloo', 'yell']
senticnet['violent_overthrow'] = ['-0.84', '0', '0.744', '-0.58', '#sadness', '#anger', 'negative', '-0.72', 'subversion', 'subversive_activity', 'termination', 'ending', 'conclusion']
senticnet['violent_person'] = ['-0.83', '0', '-0.7', '-0.79', '#sadness', '#disgust', 'negative', '-0.77', 'death_destruction', 'brutal', 'destroy_live', 'kill_enemy', 'murder']
senticnet['violent_rage'] = ['0', '0', '1', '0', '#anger', '#anger', 'negative', '-1', 'furious', 'raging', 'stormy', 'roaring', 'wrathful']
senticnet['violent_reaction'] = ['0', '0', '0.781', '-0.82', '#anger', '#disgust', 'negative', '-0.80', 'cry', 'acting_out', 'explosion', 'halloo', 'yell']
senticnet['violent_retaliation'] = ['-0.88', '0', '0.733', '-0.60', '#sadness', '#anger', 'negative', '-0.74', 'vengeance', 'payback', 'retribution', 'reprisal', 'return']
senticnet['violent_retribution'] = ['-0.88', '0', '0.733', '-0.60', '#sadness', '#anger', 'negative', '-0.74', 'vengeance', 'payback', 'retribution', 'reprisal', 'return']
senticnet['violent_sport'] = ['0', '-0.09', '0', '0.102', '#surprise', '#admiration', 'positive', '0.097', 'type_of_sport', 'rough_sport', 'rough_game', 'team_sport', 'contact_sport']
senticnet['violent_storm'] = ['0', '-0.99', '-0.99', '0', '#surprise', '#fear', 'positive', '0.004', 'blizzard', 'snow_storm', 'storm', 'danger', 'rainy']
senticnet['violent_streak'] = ['-0.57', '0', '0.711', '-0.99', '#anger', '#disgust', 'negative', '-0.76', 'grouch', 'grump', 'crosspatch', 'churl', 'crank']
senticnet['violent_temper'] = ['-0.12', '0.13', '0.646', '-0.64', '#anger', '#disgust', 'negative', '-0.32', 'irascibility', 'quick_temper', 'short_temper', 'irascible', 'ill_temper']
senticnet['violent_thunderstorm'] = ['0', '-0.41', '-0.95', '0', '#surprise', '#fear', 'negative', '-0.26', 'blizzard', 'snow_storm', 'storm', 'danger', 'rainy']
senticnet['violent_victimization'] = ['-0.47', '0.174', '-0.44', '0.105', '#sadness', '#fear', 'negative', '-0.16', 'wrong', 'wrongdoing', 'bad_choice', 'harmful_human', 'bad_behavior']
senticnet['violently'] = ['-0.98', '0', '0', '-0.72', '#sadness', '#disgust', 'negative', '-0.85', 'gushing', 'precipitate', 'shatter', 'spit', 'violent']
senticnet['violently_ill'] = ['-0.71', '0', '-0.96', '-0.94', '#fear', '#disgust', 'negative', '-0.87', 'ill', 'unconscious', 'sick', 'feel_sick', 'sleep_apnea']
senticnet['violet'] = ['-0.08', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.08', 'cyan', 'yellow_blue', 'cold_color', 'color', 'cool_color']
senticnet['violin'] = ['0.041', '-0.66', '0', '0.356', '#surprise', '#admiration', 'positive', '0.353', 'instrumental', 'symphony', 'concert', 'music', 'sound_beautiful']
senticnet['violin_bow'] = ['0.109', '0.129', '0', '0.098', '#joy', '#interest', 'positive', '0.112', 'take_lesson', 'take_music_lesson', 'a_lot_of_practice', 'tune_instrument', 'learn_read_music']
senticnet['violin_prodigy'] = ['0.592', '-0.93', '0', '0.935', '#surprise', '#admiration', 'positive', '0.819', 'child', 'small_fry', 'tyke', 'tike', 'kid']
senticnet['violin_teacher'] = ['0.536', '-0.56', '0', '0.567', '#surprise', '#admiration', 'positive', '0.557', 'recreation', 'diversion', 'move', 'motility', 'movement']
senticnet['violin_virtuoso'] = ['0.592', '-0.93', '0', '0.935', '#surprise', '#admiration', 'positive', '0.819', 'child', 'small_fry', 'tyke', 'tike', 'kid']
senticnet['violinist'] = ['0.877', '0.899', '0', '0', '#joy', '#interest', 'positive', '0.888', 'songwriter', 'vip', 'rock_group', 'guitar_player', 'singer']
senticnet['vip'] = ['0.88', '0.894', '0', '0', '#joy', '#interest', 'positive', '0.887', 'songwriter', 'singer', 'rock_group', 'guitar_player', 'violinist']
senticnet['vip_reception'] = ['0.962', '0', '0', '0', '#joy', '#joy', 'positive', '0.962', 'interest_person', 'social_occasion', 'become_intoxicated', 'network', 'get_sex']
senticnet['vip_room'] = ['0.841', '0', '0', '0.71', '#joy', '#admiration', 'positive', '0.775', 'waiter', 'bar_service', 'beverage_service', 'concierge', 'minibar']
senticnet['vip_terminal'] = ['0', '0.147', '0', '0.099', '#interest', '#admiration', 'positive', '0.123', 'airliner', 'airport', 'baggage_compartment', 'aircraft', 'cable_car']
senticnet['vipassan'] = ['0.088', '0.135', '0', '0', '#joy', '#interest', 'positive', '0.112', 'watch_others', 'meditate', 'imagine', 'mantra', 'dance_music']
senticnet['vipassana_meditation'] = ['-0.13', '-0.07', '0.05', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'try_best', 'loose', 'try_hard', 'strategize', 'sore_knee']
senticnet['vipassana_nana'] = ['-0.85', '0', '0.968', '-0.75', '#sadness', '#anger', 'negative', '-0.86', 'temporariness', 'transience', 'transiency', 'transitoriness', 'mortality']
senticnet['viper'] = ['-0.96', '0', '0.865', '-0.75', '#sadness', '#anger', 'negative', '-0.85', 'gaboon_viper', 'ophidian', 'serpent', 'snake', 'adder']
senticnet['viper_room'] = ['0.763', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.75', 'pub', 'disco', 'dance_club', 'cafe', 'tavern']
senticnet['viraemia'] = ['-0.53', '0', '0.608', '-0.93', '#anger', '#disgust', 'negative', '-0.69', 'reprieve', 'respite', 'hiatus', 'abatement', 'suspension']
senticnet['viral'] = ['0', '0.331', '-0.09', '0.242', '#interest', '#admiration', 'positive', '0.158', 'spread_idea', 'importance', 'tenet', 'culture', 'idea']
senticnet['viral_disease'] = ['-0.55', '0', '0.757', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'rubor', 'redness', 'cephalitis', 'sleepy_sickness', 'epidemic_encephalitis']
senticnet['viral_hemorrhagic_fever'] = ['-0.95', '0', '0.876', '-0.89', '#sadness', '#disgust', 'negative', '-0.90', 'bubonic_plague', 'pestis_bubonica', 'glandular_plague', 'septicemic_plague', 'pestilence']
senticnet['viral_hepatitis'] = ['-0.88', '0', '0.864', '-0.79', '#sadness', '#anger', 'negative', '-0.84', 'hepatitis', 'disease', 'feel_sick', 'liver_disease', 'infectious_disease']
senticnet['viral_inactivation'] = ['-0.62', '0', '0.987', '-0.74', '#anger', '#disgust', 'negative', '-0.78', 'medical_aid', 'treatment', 'hospice', 'digitalization', 'bloodletting']
senticnet['viral_infection'] = ['-0.92', '0', '0.824', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'bronchopneumonia', 'bronchial_pneumonia', 'pneumonia', 'pleuropneumonia', 'pneumococcal_pneumonia']
senticnet['viral_life_cycle'] = ['0.085', '0', '0', '0.089', '#joy', '#admiration', 'positive', '0.087', 'steam', 'haze', 'mist', 'misty', 'summer_weather']
senticnet['viral_load'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'protease_inhibitor', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['viral_marketing'] = ['0', '0.331', '-0.09', '0.242', '#interest', '#admiration', 'positive', '0.158', 'spread_idea', 'importance', 'tenet', 'culture', 'idea']
senticnet['viral_phenomenon'] = ['0', '0.331', '-0.09', '0.242', '#interest', '#admiration', 'positive', '0.158', 'spread_idea', 'importance', 'tenet', 'culture', 'idea']
senticnet['viral_pneumonia'] = ['-0.92', '0', '0.824', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'bronchopneumonia', 'bronchial_pneumonia', 'pneumonia', 'pleuropneumonia', 'pneumococcal_pneumonia']
senticnet['viral_strain'] = ['0', '0', '-0.98', '-0.85', '#fear', '#disgust', 'negative', '-0.92', 'contagious_disease', 'infectious_disease', 'sexually_transmit_disease', 'disease', 'malady']
senticnet['viral_video'] = ['0', '0.331', '-0.09', '0.242', '#interest', '#admiration', 'positive', '0.158', 'spread_idea', 'importance', 'tenet', 'culture', 'idea']
senticnet['virally'] = ['0', '0.331', '-0.09', '0.242', '#interest', '#admiration', 'positive', '0.158', 'spread_idea', 'importance', 'tenet', 'culture', 'idea']
senticnet['viread'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'indinavir', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['virgin'] = ['0.794', '-0.52', '0', '0.966', '#joy', '#admiration', 'positive', '0.762', 'naif', 'boy_scout', 'dear', 'inexperienced_person', 'mortal']
senticnet['virgin_birth'] = ['-0.83', '0', '0.648', '-0.60', '#sadness', '#anger', 'negative', '-0.69', 'infallibility', 'dependability', 'dependableness', 'reliability', 'reliableness']
senticnet['virgin_fiber'] = ['0.024', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'stump', 'plank', 'timber', 'paper', 'mahogany']
senticnet['virgin_killer'] = ['0.542', '-0.83', '0', '0.927', '#surprise', '#admiration', 'positive', '0.769', 'incest', 'extramarital_sex', 'free_love', 'sexual_intercourse', 'intercourse']
senticnet['virgin_martyr'] = ['0', '0.248', '-0.08', '0.173', '#interest', '#admiration', 'positive', '0.113', 'god', 'minister', 'evangelist', 'rector', 'goddess']
senticnet['virgin_pulp'] = ['0.024', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'stump', 'plank', 'timber', 'paper', 'mahogany']
senticnet['virgin_womb'] = ['0.706', '0', '0', '0', '#joy', '#joy', 'positive', '0.706', 'birth', 'new_baby', 'born', 'birthday', 'thanksgiving']
senticnet['virginal'] = ['0.829', '-0.70', '0', '0.924', '#joy', '#admiration', 'positive', '0.82', 'virtuous', 'celibate', 'pure', 'chaste', 'continent']
senticnet['virginiana'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'intrepidly', 'fearlessness', 'fearlessly', 'unafraid', 'unfearing']
senticnet['virginity'] = ['0.758', '0.818', '0', '0', '#joy', '#interest', 'positive', '0.788', 'little_boy', 'youngster', 'youth', 'brat', 'lad']
senticnet['virile'] = ['0', '0.996', '0', '0.85', '#interest', '#admiration', 'positive', '0.923', 'strength', 'strong', 'physical_strength', 'athlete', 'muscle']
senticnet['virilio'] = ['0.654', '-0.71', '0', '0.624', '#joy', '#surprise', 'positive', '0.664', 'viewable', 'gross', 'telescopic', 'seeable', 'visual']
senticnet['virility'] = ['0.982', '-0.72', '0', '0.615', '#joy', '#surprise', 'positive', '0.772', 'sexual_desire', 'concupiscence', 'physical_attraction', 'prurience', 'pruriency']
senticnet['virility_drug'] = ['-0.59', '0', '0.537', '-0.57', '#sadness', '#disgust', 'negative', '-0.57', 'phentolamine', 'sildenafil', 'sildenafil_citrate', 'tadalafil', 'vardenafil']
senticnet['virility_drug_citrate'] = ['-0.60', '0', '0.528', '-0.58', '#sadness', '#disgust', 'negative', '-0.57', 'phentolamine', 'virility_drug', 'sildenafil', 'tadalafil', 'vardenafil']
senticnet['virilization'] = ['0.995', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.918', 'tell_everyone', 'reason', 'clarify', 'forethought', 'opinion']
senticnet['virilocal'] = ['0.903', '0.684', '0', '0.791', '#joy', '#admiration', 'positive', '0.793', 'relative', 'family_member', 'relationship', 'family', 'mother']
senticnet['virologic_failure'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'protease_inhibitor', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['virologic_response'] = ['-0.81', '0', '0.767', '-0.93', '#sadness', '#disgust', 'negative', '-0.84', 'antiviral', 'protease_inhibitor', 'nelfinavir', 'ritonavir', 'saquinavir']
senticnet['virtua_cop'] = ['-0.86', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.79', 'gun', 'firearm', 'handgun', 'rifle', 'pistol']
senticnet['virtual_blank_check'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'address_book', 'get_rich', 'ladle', 'swiss_army_knife', 'whisk']
senticnet['virtual_circuit'] = ['0.078', '0.103', '-0.09', '0.191', '#interest', '#admiration', 'positive', '0.069', 'connect', 'aunt_uncle', 'short_form', 'relationship', 'kinship']
senticnet['virtual_disappearance'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'absolutism', 'autocracy', 'ascendancy', 'tyrant']
senticnet['virtual_environment'] = ['0.026', '0.14', '-0.13', '0.31', '#interest', '#admiration', 'positive', '0.085', 'teach_new', 'share_idea', 'gesture', 'start_talk', 'exchange_idea']
senticnet['virtual_house_arrest'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicative', 'unpronounceable', 'impassive', 'inexpressive', 'silent']
senticnet['virtual_keyboard'] = ['0.893', '0.979', '0', '0', '#joy', '#interest', 'positive', '0.936', 'find_phone_number', 'dial_phone_number', 'pick_up_telephone_receiver', 'dial', 'dial_telephone']
senticnet['virtual_lan'] = ['0.63', '-0.55', '0', '0.946', '#joy', '#admiration', 'positive', '0.711', 'complect', 'connect', 'link', 'link_up', 'join']
senticnet['virtual_machine'] = ['0.069', '0.14', '-0.06', '0.108', '#interest', '#admiration', 'positive', '0.063', 'biological_machine', 'social_creature', 'live_creature', 'powerful', 'alive']
senticnet['virtual_monopoly'] = ['0.998', '-0.54', '0', '0.856', '#joy', '#admiration', 'positive', '0.798', 'profitable', 'moneymaking', 'gainful', 'paying', 'paid']
senticnet['virtual_reality'] = ['0.026', '0.14', '-0.13', '0.31', '#interest', '#admiration', 'positive', '0.085', 'teach_new', 'share_idea', 'gesture', 'start_talk', 'exchange_idea']
senticnet['virtual_reality_vr'] = ['0.026', '0.14', '-0.13', '0.31', '#interest', '#admiration', 'positive', '0.085', 'teach_new', 'share_idea', 'gesture', 'start_talk', 'exchange_idea']
senticnet['virtuality'] = ['-0.63', '0', '0.694', '-0.66', '#anger', '#disgust', 'negative', '-0.66', 'corporality', 'tangibleness', 'palpability', 'reality', 'solidness']
senticnet['virtualization'] = ['0.637', '-0.91', '0', '0.757', '#surprise', '#admiration', 'positive', '0.768', 'tractability', 'manageableness', 'docility', 'obedience', 'amenability']
senticnet['virtually'] = ['0.758', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.776', 'unheard-of', 'unnoticed', 'unheard', 'unimaginable', 'unseen']
senticnet['virtually_empty'] = ['-0.02', '-0.02', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.02', 'empty_glass', 'empty_space', 'worthless', 'empty_stomach', 'universe']
senticnet['virtually_every'] = ['0.956', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.935', 'aggregate', 'total', 'whole', 'complete', 'integral']
senticnet['virtually_everywhere'] = ['0', '0.093', '0', '0.053', '#interest', '#admiration', 'positive', '0.073', 'underground', 'under_ground', 'environment', 'outpost', 'dark_place']
senticnet['virtually_identical'] = ['0.14', '0.345', '-0.20', '0.378', '#interest', '#admiration', 'positive', '0.165', 'good_etiquette', 'same', 'similarity', 'band_together', 'niceness']
senticnet['virtually_immortal'] = ['0', '0', '0', '0.816', '#admiration', '#admiration', 'positive', '0.816', 'immortality', 'live_forever', 'deathless', 'eternal_life', 'everlasting_life']
senticnet['virtually_impossible'] = ['-0.80', '0', '-0.80', '-0.96', '#sadness', '#disgust', 'negative', '-0.85', 'unable', 'absurd', 'difficult', 'impractical', 'unimaginable']
senticnet['virtually_inaccessible'] = ['0', '0', '0.921', '-0.87', '#anger', '#disgust', 'negative', '-0.9', 'outback', 'trackless', 'pathless', 'roadless', 'untracked']
senticnet['virtually_indestructible'] = ['0', '0.839', '0', '0.963', '#interest', '#admiration', 'positive', '0.901', 'imputrescible', 'undestroyable', 'imperishable', 'durable', 'perdurable']
senticnet['virtually_unrecognizable'] = ['-0.82', '0', '0.758', '-0.70', '#sadness', '#anger', 'negative', '-0.76', 'unclassifiable', 'unrecognizable', 'unidentifiable', 'intangible', 'undiagnosable']
senticnet['virtue'] = ['0.193', '0.298', '-0.18', '0.369', '#interest', '#admiration', 'positive', '0.168', 'goodness', 'good_quality', 'honest', 'best', 'honor']
senticnet['virtuosic'] = ['0.76', '-0.59', '0', '0.713', '#joy', '#admiration', 'positive', '0.689', 'spare_enemy', 'magnanimous', 'generous', 'forgiving', 'deal_gently']
senticnet['virtuosity'] = ['0', '0.803', '0', '0.71', '#interest', '#admiration', 'positive', '0.756', 'novelty', 'innovativeness', 'freshness', 'ability', 'power']
senticnet['virtuoso'] = ['0.755', '0.866', '0', '0', '#joy', '#interest', 'positive', '0.81', 'sensation', 'champion', 'adept', 'ace', 'maven']
senticnet['virtuoso_pianist'] = ['0.592', '-0.93', '0', '0.935', '#surprise', '#admiration', 'positive', '0.819', 'child', 'small_fry', 'tyke', 'tike', 'kid']
senticnet['virtuoso_technique'] = ['0.76', '-0.59', '0', '0.713', '#joy', '#admiration', 'positive', '0.689', 'spare_enemy', 'magnanimous', 'generous', 'forgiving', 'deal_gently']
senticnet['virtuous'] = ['0.838', '-0.71', '0', '0.926', '#joy', '#admiration', 'positive', '0.825', 'continent', 'celibate', 'pure', 'chaste', 'virginal']
senticnet['virtuous_disposition'] = ['0.657', '-0.56', '0', '0.749', '#joy', '#admiration', 'positive', '0.655', 'naturalness', 'condescension', 'condescendingness', 'mellowness', 'friendliness']
senticnet['virtuous_prince'] = ['0.702', '-0.94', '0', '0.582', '#joy', '#surprise', 'positive', '0.743', 'beatable', 'vanquishable', 'vincible', 'subduable', 'subjugable']
senticnet['virtuous_woman'] = ['0.539', '-0.96', '0', '0.745', '#surprise', '#admiration', 'positive', '0.751', 'spouse', 'partner', 'married_person', 'mate', 'better_half']
senticnet['virtuously'] = ['-0.74', '0', '0.585', '-0.58', '#sadness', '#anger', 'negative', '-0.63', 'lack_of_quality', 'meagreness', 'poorness', 'scantiness', 'scantness']
senticnet['virulence'] = ['-0.78', '0', '0.6', '-0.61', '#sadness', '#disgust', 'negative', '-0.66', 'suggestibility', 'sensitivity', 'capacity', 'reactivity', 'predisposition']
senticnet['virulent'] = ['0', '0', '-0.94', '-0.86', '#fear', '#disgust', 'negative', '-0.90', 'deadly', 'acerbic', 'blistering', 'acid', 'venomous']
senticnet['virulent_anti_semitism'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'racism', 'intolerant', 'xenophobic', 'racist', 'nazism']
senticnet['virulent_disease'] = ['-0.11', '0', '-0.15', '-0.24', '#fear', '#disgust', 'negative', '-0.17', 'disease', 'infectious_disease', 'polio', 'malady', 'sickness']
senticnet['virulent_strain'] = ['0', '0', '-0.98', '-0.85', '#fear', '#disgust', 'negative', '-0.92', 'contagious_disease', 'infectious_disease', 'sexually_transmit_disease', 'disease', 'malady']
senticnet['virulently_anti_semitic'] = ['0', '-0.00', '-0.09', '-0.06', '#fear', '#disgust', 'negative', '-0.05', 'hot_chick', 'atheist', 'computer_user', 'fastener_binder', 'monitor']
senticnet['virus'] = ['0', '0', '-0.98', '-0.85', '#fear', '#disgust', 'negative', '-0.92', 'contagious_disease', 'infectious_disease', 'sexually_transmit_disease', 'disease', 'malady']
senticnet['virus_infect'] = ['-0.83', '0', '-0.82', '-0.86', '#sadness', '#disgust', 'negative', '-0.84', 'cause_cold', 'tourism', 'reproduce', 'gland', 'procreate']
senticnet['virus_infection'] = ['0', '0', '-0.98', '-0.85', '#fear', '#disgust', 'negative', '-0.92', 'contagious_disease', 'infectious_disease', 'sexually_transmit_disease', 'disease', 'malady']
senticnet['virus_replicate'] = ['-0.83', '0', '-0.82', '-0.86', '#sadness', '#disgust', 'negative', '-0.84', 'cause_cold', 'tourism', 'reproduce', 'gland', 'procreate']
senticnet['virus_strain'] = ['0', '0', '-0.98', '-0.85', '#fear', '#disgust', 'negative', '-0.92', 'contagious_disease', 'infectious_disease', 'sexually_transmit_disease', 'disease', 'malady']
senticnet['virus_vaccine'] = ['-0.07', '0', '-0.05', '-0.18', '#sadness', '#disgust', 'negative', '-0.10', 'medical_therapy', 'vaccination', 'doctor', 'medicine', 'hospital']
senticnet['visa'] = ['0.029', '0.177', '-0.10', '0.188', '#interest', '#admiration', 'positive', '0.073', 'mastercard', 'invoice', 'american_express', 'bill', 'pay_debt']
senticnet['visa_card'] = ['-0.02', '0.119', '0', '0.054', '#interest', '#admiration', 'positive', '0.048', 'credit_card_company', 'debit_card', 'check', 'credit_card', 'cash_coin']
senticnet['visa_credit_card'] = ['-0.05', '0.14', '0.015', '0.082', '#interest', '#admiration', 'positive', '0.039', 'cash', 'cash_pay', 'need_earn_money', 'open_wallet', 'earn_wage']
senticnet['visa_debit'] = ['-0.05', '0.14', '0.015', '0.082', '#interest', '#admiration', 'positive', '0.039', 'cash', 'cash_pay', 'need_earn_money', 'open_wallet', 'earn_wage']
senticnet['visa_debit_card'] = ['-0.05', '0.14', '0.015', '0.082', '#interest', '#admiration', 'positive', '0.039', 'cash', 'cash_pay', 'need_earn_money', 'open_wallet', 'earn_wage']
senticnet['visa_mastercard'] = ['-0.05', '0.14', '0.015', '0.082', '#interest', '#admiration', 'positive', '0.039', 'cash', 'cash_pay', 'need_earn_money', 'open_wallet', 'earn_wage']
senticnet['visage'] = ['-0.94', '0', '0.636', '-0.74', '#sadness', '#disgust', 'negative', '-0.77', 'incommunicado', 'unpronounceable', 'incommunicative', 'inexpressive', 'silent']
senticnet['visarga'] = ['-0.61', '0', '0.721', '-0.68', '#anger', '#disgust', 'negative', '-0.67', 'character', 'logogram', 'logograph', 'graph', 'ideogram']
senticnet['viscacha'] = ['-0.12', '-0.05', '-0.05', '-0.19', '#sadness', '#disgust', 'negative', '-0.07', 'flora', 'eukaryote', 'endangered_species', 'carbon_monoxide', 'tree_animal']
senticnet['visceral'] = ['0.785', '-0.86', '0', '0.881', '#surprise', '#admiration', 'positive', '0.845', 'absurd', 'inconsequential', 'intuitive', 'unlogical', 'nonrational']
senticnet['visceral_layer'] = ['-0.53', '0', '0.982', '-0.67', '#anger', '#disgust', 'negative', '-0.73', 'meniscus', 'semilunar_cartilage', 'gristle', 'hyaline_cartilage', 'arytenoid']
senticnet['visceral_pericardium'] = ['-0.03', '0', '0', '-0.11', '#sadness', '#disgust', 'negative', '-0.07', 'store_cloth', 'clothes_factory', 'stock', 'sword', 'gun_store']
senticnet['visceral_reaction'] = ['-0.06', '0.123', '0.712', '-0.67', '#anger', '#disgust', 'negative', '-0.33', 'repulsive', 'repugnant', 'detestably', 'abominably', 'odiously']
senticnet['viscid'] = ['0', '0', '0', '-0.96', '#disgust', '#disgust', 'negative', '-0.96', 'thick_substance', 'viscous_substance', 'mucous_substance', 'disgusting', 'disgust']
senticnet['visco_elastic'] = ['0.808', '0', '0', '0.943', '#joy', '#admiration', 'positive', '0.875', 'a_lot_of_practice', 'flexibility', 'take_lesson', 'coordination', 'air_tank']
senticnet['viscoelastic'] = ['0.808', '0', '0', '0.943', '#joy', '#admiration', 'positive', '0.875', 'a_lot_of_practice', 'flexibility', 'take_lesson', 'coordination', 'air_tank']
senticnet['viscoelastic_behavior'] = ['0.808', '0', '0', '0.943', '#joy', '#admiration', 'positive', '0.875', 'a_lot_of_practice', 'flexibility', 'take_lesson', 'coordination', 'air_tank']
senticnet['viscoelastic_material'] = ['0.808', '0', '0', '0.943', '#joy', '#admiration', 'positive', '0.875', 'a_lot_of_practice', 'flexibility', 'take_lesson', 'coordination', 'air_tank']
senticnet['viscoelasticity'] = ['0.808', '0', '0', '0.943', '#joy', '#admiration', 'positive', '0.875', 'a_lot_of_practice', 'flexibility', 'take_lesson', 'coordination', 'air_tank']
senticnet['viscom'] = ['-0.02', '0', '-0.04', '-0.03', '#fear', '#disgust', 'negative', '-0.03', 'studio', 'art_piece', 'art_gallery', 'artist', 'museum']
senticnet['viscose_rayon'] = ['0.024', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'stump', 'plank', 'timber', 'paper', 'mahogany']
senticnet['viscosity'] = ['-0.03', '0.178', '0', '0.168', '#interest', '#admiration', 'positive', '0.104', 'speed', 'fast', 'fast_and_furious', 'speedy', 'fleet']
senticnet['viscount'] = ['0.066', '0.163', '-0.06', '0.163', '#interest', '#admiration', 'positive', '0.083', 'lord', 'royal_title', 'almighty', 'duchess', 'omniscient']
senticnet['viscount_hereford'] = ['0.066', '0.163', '-0.06', '0.163', '#interest', '#admiration', 'positive', '0.083', 'lord', 'royal_title', 'almighty', 'duchess', 'omniscient']
senticnet['viscount_leverhulme'] = ['-0.99', '0', '0.66', '-0.50', '#sadness', '#anger', 'negative', '-0.72', 'bar', 'treadle', 'pry', 'key', 'peavey']
senticnet['viscount_lisle'] = ['0.066', '0.163', '-0.06', '0.163', '#interest', '#admiration', 'positive', '0.083', 'lord', 'royal_title', 'almighty', 'duchess', 'omniscient']
senticnet['viscountcy'] = ['0.066', '0.163', '-0.06', '0.163', '#interest', '#admiration', 'positive', '0.083', 'lord', 'royal_title', 'almighty', 'duchess', 'omniscient']
senticnet['viscous'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'wet', 'sticky', 'gluey', 'tacky', 'gooey']
senticnet['viscous_drag'] = ['0', '0', '-0.11', '0', '#fear', '#fear', 'negative', '-0.11', 'chemical_reaction', 'set_fire', 'grow_yard', 'heat_light', 'burn_down_house']
senticnet['viscous_flow'] = ['-0.98', '0', '0.516', '-0.90', '#sadness', '#disgust', 'negative', '-0.80', 'devastation', 'annihilation', 'pulverization', 'vaporization', 'destruction']
senticnet['viscous_fluid'] = ['0.676', '0.63', '-0.23', '0.31', '#joy', '#interest', 'positive', '0.345', 'perkiness', 'walk_on_air', 'exultingly', 'exultantly', 'jubilance']
senticnet['viscous_lidocaine'] = ['-0.53', '0', '0.954', '-0.54', '#anger', '#disgust', 'negative', '-0.67', 'ranitidine', 'histamine_blocker', 'gastric_ranitidine', 'alkalizer', 'antiacid']
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senticnet['viscous_substance'] = ['0', '0', '0', '-0.96', '#disgust', '#disgust', 'negative', '-0.96', 'thick_substance', 'phlegm', 'mucous_substance', 'disgusting', 'disgust']
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senticnet['visha'] = ['0', '0', '0.737', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'annoy', 'skin_rash', 'bother', 'irritation', 'great_dane']
senticnet['vishay_intertechnology'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'builder']
senticnet['visibility'] = ['0.68', '-0.91', '0', '0.564', '#joy', '#surprise', 'positive', '0.719', 'audibility', 'perceptibility', 'visibleness', 'audibleness', 'physical_property']
senticnet['visible'] = ['0', '0.875', '0', '0.868', '#interest', '#admiration', 'positive', '0.871', 'blue_green', 'red_green', 'hue_tint', 'colorful', 'yellow_green']
senticnet['visible_light'] = ['-0.92', '0', '0.734', '-0.51', '#sadness', '#anger', 'negative', '-0.72', 'chemiluminescence', 'light', 'bioluminescence', 'luminescence', 'visible_radiation']
senticnet['visible_light_spectrum'] = ['-0.07', '0.078', '0', '0.078', '#interest', '#admiration', 'positive', '0.028', 'red_blue', 'red_orange', 'green_yellow', 'hue_tint', 'yellow_green']
senticnet['visible_mold'] = ['0.718', '0', '0', '0', '#joy', '#joy', 'positive', '0.718', 'water_container', 'still_water', 'clean_water', 'water', 'cool_water']
senticnet['visible_radiation'] = ['-0.92', '0', '0.734', '-0.51', '#sadness', '#anger', 'negative', '-0.72', 'chemiluminescence', 'light', 'bioluminescence', 'visible_light', 'luminescence']
senticnet['visible_spectrum'] = ['-0.07', '0.078', '0', '0.078', '#interest', '#admiration', 'positive', '0.028', 'red_blue', 'red_orange', 'green_yellow', 'hue_tint', 'yellow_green']
senticnet['visibleness'] = ['0.677', '-0.92', '0', '0.565', '#joy', '#surprise', 'positive', '0.721', 'audibility', 'perceptibility', 'audibleness', 'visibility', 'physical_property']
senticnet['visibly_emotional'] = ['-0.71', '0.312', '-0.4', '-0.08', '#sadness', '#fear', 'negative', '-0.22', 'lachrymose', 'weeping', 'dolorous', 'tearfulness', 'weepiness']
senticnet['visibly_upset'] = ['-0.71', '0.312', '-0.4', '-0.08', '#sadness', '#fear', 'negative', '-0.22', 'lachrymose', 'weeping', 'dolorous', 'tearfulness', 'weepiness']
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senticnet['vision_defect'] = ['-0.79', '0', '0.927', '-0.95', '#anger', '#disgust', 'negative', '-0.89', 'disability', 'disablement', 'aphakia', 'color_blindness', 'color_vision_deficiency']
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senticnet['vision_loss'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'walking_stick', 'sunglasses', 'disabled', 'poor_visual_perception', 'blind_person']
senticnet['visionary'] = ['0.836', '0', '0', '0.94', '#joy', '#admiration', 'positive', '0.888', 'idealist', 'inspired', 'imaginative', 'creative', 'inventive']
senticnet['visionary_leader'] = ['0.053', '0.136', '0', '0.057', '#interest', '#admiration', 'positive', '0.082', 'boss', 'supervisor', 'leader', 'head_person', 'head_guy']
senticnet['visionary_leadership'] = ['-0.03', '0.081', '0', '0.095', '#interest', '#admiration', 'positive', '0.047', 'sight', 'eye_function', 'eye_sense', 'see_ability', 'aim']
senticnet['visit'] = ['0.065', '0', '-0.05', '0.085', '#joy', '#admiration', 'positive', '0.032', 'join_club', 'social_skill', 'go_abroad', 'drive_around', 'ride_train']
senticnet['visit_barber'] = ['0.123', '0.29', '-0.19', '0.278', '#interest', '#admiration', 'positive', '0.125', 'go_barber', 'haircut', 'go_hair_salon', 'makeover', 'get_hair_colored']
senticnet['visit_doctor'] = ['0', '0', '-0.88', '0', '#fear', '#fear', 'negative', '-0.88', 'blood_test', 'go_doctor', 'go_see_doctor', 'see_doctor', 'eat_balance_diet']
senticnet['visit_family'] = ['0', '0.074', '0.051', '0.105', '#interest', '#admiration', 'positive', '0.043', 'visit_parent', 'christmas_holiday', 'visit', 'visit_mother', 'visit_father']
senticnet['visit_father'] = ['0', '0.074', '0.051', '0.105', '#interest', '#admiration', 'positive', '0.043', 'visit_family', 'christmas_holiday', 'visit', 'visit_mother', 'visit_parent']
senticnet['visit_friend'] = ['0.719', '0.988', '0', '0', '#joy', '#interest', 'positive', '0.853', 'socialize', 'bring_gift', 'talk_friend', 'meet_new_person', 'surprise']
senticnet['visit_grandmother'] = ['0.941', '0.929', '0', '0', '#joy', '#interest', 'positive', '0.935', 'explain_everything', 'forward_email', 'deal_card_player', 'stand_alone', 'bridge_difference']
senticnet['visit_grandparent'] = ['0.788', '0.592', '-0.20', '0.342', '#joy', '#interest', 'positive', '0.379', 'family_nest', 'thank_parent', 'home_sweet_home', 'family', 'head_home']
senticnet['visit_library'] = ['0', '0.074', '0.051', '0.105', '#interest', '#admiration', 'positive', '0.043', 'buy_book', 'study_history', 'open_book', 'go_to_library', 'buy_textbook']
senticnet['visit_mother'] = ['0', '0.074', '0.051', '0.105', '#interest', '#admiration', 'positive', '0.043', 'visit_family', 'christmas_holiday', 'visit', 'visit_parent', 'visit_father']
senticnet['visit_new_place'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'globe_trotter', 'travel', 'wanderlust', 'explore', 'discover']
senticnet['visit_office'] = ['0.869', '0.836', '0', '0.862', '#joy', '#admiration', 'positive', '0.856', 'arrive_work', 'finish_task', 'plan_retirement', 'floss_tooth', 'erase_blackboard']
senticnet['visit_parent'] = ['0', '0.074', '0.051', '0.105', '#interest', '#admiration', 'positive', '0.043', 'visit_family', 'christmas_holiday', 'visit', 'visit_mother', 'visit_father']
senticnet['visit_place'] = ['0.095', '-0.04', '0.031', '0.052', '#joy', '#admiration', 'positive', '0.04', 'get_place', 'travel', 'move', 'get_to_destination', 'go_to_place']
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senticnet['voivodeship_governor_wojewoda'] = ['0.033', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.036', 'chair', 'bench', 'couch', 'stool', 'coach_seat']
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senticnet['vokrri'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'white_animal', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
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senticnet['wait_table'] = ['0.974', '0.789', '0', '0.828', '#joy', '#admiration', 'positive', '0.864', 'get_out_of_bed', 'start_new_day', 'buy_sandwich', 'eat_breakfast', 'get_coffee']
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senticnet['waiting_game'] = ['0.681', '-0.85', '0', '0.605', '#joy', '#surprise', 'positive', '0.712', 'contrivance', 'bubble', 'house_of_cards', 'stratagem', 'strategy']
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senticnet['waitress'] = ['0', '0.771', '0', '0.835', '#interest', '#admiration', 'positive', '0.803', 'server', 'manager', 'wait_staff', 'public_servant', 'waiter']
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senticnet['waive_immunity'] = ['0', '0', '0', '0.816', '#admiration', '#admiration', 'positive', '0.816', 'resistance', 'protection', 'untouchable', 'invulnerable', 'sacrosanct']
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senticnet['wake'] = ['0', '0.909', '0', '0.948', '#interest', '#admiration', 'positive', '0.928', 'set_off', 'shake_up', 'scandalmongering', 'sensationalistic', 'sensational']
senticnet['wake_morning'] = ['0.882', '0.778', '0', '0', '#joy', '#interest', 'positive', '0.83', 'make_music', 'produce_sound', 'communicator', 'clear_throat', 'mouth_noise']
senticnet['wake_up'] = ['0', '0.967', '0', '0.763', '#interest', '#admiration', 'positive', '0.865', 'awake', 'get_up_early', 'drink_coffee', 'drink_milk', 'have_breakfast']
senticnet['wake_up_call'] = ['-0.13', '-0.08', '0.015', '-0.21', '#sadness', '#disgust', 'negative', '-0.07', 'wake_up_early', 'set_alarm', 'lack_of_sleep', 'alarm', 'set_timer']
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senticnet['waking'] = ['-0.56', '0', '0.568', '-0.71', '#anger', '#disgust', 'negative', '-0.61', 'vigil', 'arousal', 'wakefulness', 'physical_condition', 'physiological_state']
senticnet['waking_dream'] = ['-0.59', '0', '0.799', '-0.69', '#anger', '#disgust', 'negative', '-0.69', 'untroubled', 'undisturbed', 'unmolested', 'unworried', 'unconcerned']
senticnet['waking_up'] = ['0', '0.08', '0', '0.179', '#interest', '#admiration', 'positive', '0.13', 'wake_up_morning', 'up_early', 'get_up_early', 'gather_energy_tomorrow', 'sit_chair']
senticnet['wakizashi'] = ['-0.04', '0.138', '-0.08', '0.111', '#interest', '#admiration', 'positive', '0.031', 'geometry', 'craft', 'blade_geometry', 'blade', 'collect_item']
senticnet['wal'] = ['-0.05', '0.082', '0.028', '0.16', '#interest', '#admiration', 'positive', '0.04', 'limp', 'stand_upon', 'walk_device', 'walk_part', 'foot_action']
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senticnet['walia_ibex'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheep', 'argal', 'urial', 'white_sheep', 'mountain_sheep']
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senticnet['walk_across_street'] = ['0.981', '0.735', '0', '0', '#joy', '#interest', 'positive', '0.858', 'date_woman', 'marry_man', 'service_car', 'date_man', 'oil_hinge']
senticnet['walk_along'] = ['0', '0', '0', '0.165', '#admiration', '#admiration', 'positive', '0.165', 'get_around', 'passenger_car', 'fun_ride', 'car_go', 'go_elsewhere']
senticnet['walk_along_beach'] = ['0.093', '0.087', '-0.05', '0.136', '#joy', '#admiration', 'positive', '0.067', 'have_fun', 'wear_necklace', 'sing_microphone', 'eat_together', 'gain_respect']
senticnet['walk_around'] = ['0.839', '0.926', '0', '0', '#joy', '#interest', 'positive', '0.883', 'walk', 'find_garden', 'enter_place', 'walk_down', 'pass_through']
senticnet['walk_away'] = ['-0.02', '0.087', '0', '0.107', '#interest', '#admiration', 'positive', '0.056', 'say_good_bye', 'step_aside', 'step_out', 'resign', 'open_mailbox']
senticnet['walk_barefoot'] = ['-0.84', '0', '0.827', '-0.81', '#sadness', '#anger', 'negative', '-0.83', 'sop', 'douse', 'dowse', 'drench', 'soak']
senticnet['walk_beach'] = ['0.827', '0.794', '0', '0', '#joy', '#interest', 'positive', '0.81', 'bible', 'popular_hobby', 'big_boat', 'song_lyric', 'bible_story']
senticnet['walk_closet'] = ['0.961', '0.707', '0', '0', '#joy', '#interest', 'positive', '0.834', 'rug', 'carpet', 'linoleum', 'mat', 'floor']
senticnet['walk_device'] = ['0', '0.069', '0.031', '0.149', '#interest', '#admiration', 'positive', '0.062', 'walk_part', 'extension', 'foot_action', 'limp', 'stand_upon']
senticnet['walk_dog'] = ['-0.18', '0', '-0.14', '-0.11', '#sadness', '#fear', 'negative', '-0.14', 'fly_kite', 'ride_roller_coaster', 'skateboard', 'rake_leave', 'jump_up_down']
senticnet['walk_down'] = ['0', '0.757', '0', '0.835', '#interest', '#admiration', 'positive', '0.796', 'find_garden', 'enter_place', 'walk', 'walk_through', 'progress']
senticnet['walk_drive'] = ['0', '0', '0', '0.104', '#admiration', '#admiration', 'positive', '0.104', 'use_vehicle', 'turn_car', 'transportation', 'car_travel', 'car_go']
senticnet['walk_exercise'] = ['-0.81', '0', '0.71', '-0.80', '#sadness', '#disgust', 'negative', '-0.77', 'push_cart', 'lose_wallet', 'go_debt', 'pay_dinner', 'spend_much_money']
senticnet['walk_fast'] = ['0', '0.722', '0', '0.787', '#interest', '#admiration', 'positive', '0.754', 'fast_walk', 'move_fast', 'walk_quickly', 'run_slowly', 'human_movement']
senticnet['walk_on_air'] = ['0.673', '0.631', '-0.23', '0.309', '#joy', '#interest', 'positive', '0.345', 'exultingly', 'on_cloud_nine', 'exultation', 'jubilance', 'happiness']
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senticnet['walk_out_dog'] = ['0.867', '0.714', '0', '0', '#joy', '#interest', 'positive', '0.79', 'laugh_loudly', 'walk_out', 'laugh_out_loud', 'entertain', 'feel_emotion']
senticnet['walk_over'] = ['0.53', '-0.65', '0', '0.652', '#surprise', '#admiration', 'positive', '0.613', 'vanquish', 'beat', 'beat_out', 'trounce', 'crush']
senticnet['walk_part'] = ['-0.04', '0.086', '0.036', '0.166', '#interest', '#admiration', 'positive', '0.043', 'limp', 'stand_upon', 'walk_device', 'wal', 'foot_action']
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senticnet['walk_school'] = ['-0.11', '0', '-0.07', '-0.04', '#sadness', '#fear', 'negative', '-0.07', 'study', 'throw_fit', 'walk', 'shoulder_responsibility', 'stroll']
senticnet['walk_slowly'] = ['0', '0.781', '0', '0', '#interest', '#interest', 'positive', '0.781', 'dirty_kitchen', 'comfortable_position', 'hot_dog_bun', 'get_tired', 'collapse']
senticnet['walk_stick'] = ['0.071', '-0.33', '0.01', '0.127', '#surprise', '#admiration', 'positive', '0.132', 'stick', 'sharp_nail', 'wand', 'cue', 'long_stick']
senticnet['walk_style'] = ['-0.13', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.09', 'walk', 'find_garden', 'walk_down', 'go_for_stroll', 'enter_place']
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senticnet['walk_through_door'] = ['-0.03', '0', '0.033', '-0.06', '#sadness', '#disgust', 'negative', '-0.04', 'carry_luggage', 'pass_by', 'meet_friend', 'pay_dinner', 'increase_value']
senticnet['walk_through_wall'] = ['-0.08', '0', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.09', 'magic', 'travel_fast_light', 'live_forever', 'drink_sea_water', 'live_ocean']
senticnet['walk_up'] = ['-0.04', '0.087', '0', '0.133', '#interest', '#admiration', 'positive', '0.058', 'stroll', 'go_up_down', 'tread', 'go_up', 'step']
senticnet['walk_upon'] = ['-0.07', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.05', 'stand_upon', 'keep_foot_warm', 'stand', 'walk', 'find_garden']
senticnet['walk_wood'] = ['0.117', '0', '0.04', '0.147', '#joy', '#admiration', 'positive', '0.075', 'comfortable_place_sleep', 'sleep_furniture', 'use_bed', 'time_rest', 'cozy']
senticnet['walked_away'] = ['-0.02', '0.087', '0', '0.107', '#interest', '#admiration', 'positive', '0.056', 'say_good_bye', 'step_aside', 'step_out', 'resign', 'open_mailbox']
senticnet['walked_barefoot'] = ['-0.84', '0', '0.827', '-0.81', '#sadness', '#anger', 'negative', '-0.83', 'sop', 'douse', 'dowse', 'drench', 'soak']
senticnet['walked_onstage'] = ['0.773', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.844', 'favorite', 'crowd', 'acclamation', 'bunch', 'large_crowd']
senticnet['walked_upright'] = ['-0.93', '0', '0.508', '-0.58', '#sadness', '#disgust', 'negative', '-0.67', 'inhuman', 'bloodless', 'apelike', 'nonhuman', 'nonhumanal']
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senticnet['walkie_talkie'] = ['0.035', '-0.01', '0.027', '0.085', '#joy', '#admiration', 'positive', '0.026', 'telephone', 'find_phone_number', 'telephone_room', 'phone', 'use_telephone']
senticnet['walking_around'] = ['0', '0.075', '0.028', '0.173', '#interest', '#admiration', 'positive', '0.073', 'friend', 'spend_time_with_friend', 'party', 'fun', 'hang_out_with_friend']
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senticnet['walking_cane'] = ['0.609', '-0.74', '0', '0.716', '#surprise', '#admiration', 'positive', '0.69', 'malacca', 'sword', 'swagger_stick', 'sword_cane', 'sword_stick']
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senticnet['walking_gait'] = ['-0.15', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.09', 'use_leg', 'walk_quickly', 'casual', 'take_step', 'strut']
senticnet['walking_meditation'] = ['-0.13', '-0.07', '0.05', '-0.13', '#sadness', '#disgust', 'negative', '-0.06', 'try_best', 'loose', 'try_hard', 'strategize', 'sore_knee']
senticnet['walking_pneumonia'] = ['-0.92', '0', '0.824', '-0.52', '#sadness', '#anger', 'negative', '-0.75', 'bronchopneumonia', 'bronchial_pneumonia', 'pneumonia', 'pleuropneumonia', 'pneumococcal_pneumonia']
senticnet['walking_stick'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'blind_person', 'sunglasses', 'disabled', 'poor_visual_perception', 'vision_loss']
senticnet['walkman'] = ['0.304', '0.339', '-0.19', '0.333', '#interest', '#admiration', 'positive', '0.195', 'careful', 'reinforce', 'load_bear', 'go_outdoors', 'allowance']
senticnet['walkout'] = ['-0.77', '0', '0.505', '-0.50', '#sadness', '#anger', 'negative', '-0.59', 'wildcat_strike', 'sympathy_strike', 'sympathetic_strike', 'work_stoppage', 'job_action']
senticnet['walkover'] = ['-0.68', '0', '0.545', '-0.68', '#sadness', '#disgust', 'negative', '-0.64', 'stunt', 'tumble', 'split', 'acrobatic_feat', 'backbend']
senticnet['walkway'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'street_city', 'small_street', 'pave', 'black_pavement', 'dark_road']
senticnet['wall'] = ['0.024', '0', '0', '0.075', '#joy', '#admiration', 'positive', '0.05', 'door', 'door_lock', 'window_door', 'frame', 'doorway']
senticnet['wall-like'] = ['0', '0.835', '0', '0.888', '#interest', '#admiration', 'positive', '0.861', 'ringlike', 'reinforced-concrete', 'ring-like', 'trapezoidal-shaped', 'prism-shaped']
senticnet['wall-to-wall'] = ['0', '0', '0.972', '-0.74', '#anger', '#disgust', 'negative', '-0.85', 'pervasively', 'pervading', 'pervasive', 'affecting', 'colored']
senticnet['wall_art'] = ['0.165', '0.069', '0.028', '0.057', '#joy', '#interest', 'positive', '0.066', 'make_paint', 'stir_paint_brush', 'buy_paint', 'stir_paint', 'pick_up_brush']
senticnet['wall_clock'] = ['0', '0', '0.95', '-0.86', '#anger', '#disgust', 'negative', '-0.90', 'watch', 'stopwatch', 'timepiece', 'wristwatch', 'find_pawnshop']
senticnet['wall_cover'] = ['0', '0', '0', '0.094', '#admiration', '#admiration', 'positive', '0.094', 'cover_wall', 'latex', 'two-dimensional', 'decorator', 'dye']
senticnet['wall_crawler'] = ['-0.98', '0', '0.714', '-0.90', '#sadness', '#disgust', 'negative', '-0.86', 'stupid', 'stupid_person', 'stupe', 'dullard', 'dolt']
senticnet['wall_decoration'] = ['-0.04', '0.135', '-0.05', '0.162', '#interest', '#admiration', 'positive', '0.05', 'wall_hang', 'image', 'artwork', 'embellish', 'illustration']
senticnet['wall_germander'] = ['-0.56', '0', '0.91', '-0.80', '#anger', '#disgust', 'negative', '-0.76', 'subshrub', 'suffrutex', 'wood_sage', 'germander', 'cat_thyme']
senticnet['wall_hang'] = ['0', '0.112', '0', '0.125', '#interest', '#admiration', 'positive', '0.118', 'depiction', 'wall_decoration', 'representation', 'artwork', 'painting']
senticnet['wall_street'] = ['0', '0.119', '0', '0.106', '#interest', '#admiration', 'positive', '0.112', 'stock_market', 'stock', 'market', 'retail_store', 'corner_store']
senticnet['wallaby_camp'] = ['-0.69', '0', '0.768', '-0.67', '#sadness', '#anger', 'negative', '-0.71', 'dither', 'pother', 'worry', 'flap', 'scruple']
senticnet['wallboard'] = ['0', '0.127', '-0.04', '0.194', '#interest', '#admiration', 'positive', '0.092', 'concrete', 'tar', 'pavement', 'big_city_life', 'popular_building_material']
senticnet['walled_cemetery'] = ['-0.50', '0', '0.762', '-0.89', '#anger', '#disgust', 'negative', '-0.71', 'cemetery', 'graveyard', 'burial_site', 'grassland', 'burying_ground']
senticnet['walled_town'] = ['0.021', '0.119', '0', '0.129', '#interest', '#admiration', 'positive', '0.09', 'manor', 'palace', 'medieval', 'tower', 'chess_game']
senticnet['wallet'] = ['0', '0.142', '0', '0', '#interest', '#interest', 'positive', '0.142', 'coffer', 'billfold', 'bank_vault', 'bank', 'piggy_bank']
senticnet['wallet_purse'] = ['0', '0.115', '0', '0', '#interest', '#interest', 'positive', '0.115', 'pocket', 'purse', 'coin_purse', 'bank_vault', 'bank_account']
senticnet['walleye'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'divergent_strabismus', 'exotropia', 'squint', 'abnormality', 'abnormalcy']
senticnet['walleye_action'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'divergent_strabismus', 'exotropia', 'squint', 'abnormality', 'abnormalcy']
senticnet['walleye_fishing'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'divergent_strabismus', 'exotropia', 'squint', 'abnormality', 'abnormalcy']
senticnet['wallop'] = ['-0.85', '0', '0.758', '-0.88', '#sadness', '#disgust', 'negative', '-0.83', 'effect', 'strike', 'consequence', 'outcome', 'result']
senticnet['walloper'] = ['0.675', '-0.86', '0', '0.718', '#surprise', '#admiration', 'positive', '0.754', 'whopper', 'white_lie', 'jactitation', 'prevarication', 'fib']
senticnet['wallow'] = ['0.704', '0.623', '-0.23', '0.321', '#joy', '#interest', 'positive', '0.354', 'jubilant', 'exuberant', 'jubilate', 'exult', 'carefreeness']
senticnet['wallpaper'] = ['0', '0.236', '0', '0.126', '#interest', '#admiration', 'positive', '0.181', 'paper_clip', 'decorate_wall', 'intern', 'glue', 'paper_fastener']
senticnet['wallpaper_factory'] = ['-0.22', '-0.03', '0.026', '-0.13', '#sadness', '#disgust', 'negative', '-0.08', 'blind_man', 'blind_person', 'shade', 'need_help', 'shadow']
senticnet['wallpaper_paste'] = ['0', '-0.12', '0', '0', '#surprise', '#surprise', 'positive', '0.127', 'chop', 'glue', 'carve', 'bandage', 'split']
senticnet['wally'] = ['-0.73', '0', '0.601', '-0.99', '#sadness', '#disgust', 'negative', '-0.77', 'sap', 'fool', 'saphead', 'muggins', 'tomfool']
senticnet['wally_backman'] = ['0.663', '-0.68', '0', '0.892', '#surprise', '#admiration', 'positive', '0.747', 'saphead', 'muggins', 'tomfool', 'fool', 'sap']
senticnet['wally_cleaver'] = ['0.941', '0.987', '0', '0', '#joy', '#interest', 'positive', '0.964', 'monkey', 'marmoset', 'lemur', 'fiance', 'koala']
senticnet['wally_wood'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'encyclopaedia', 'child_story', 'comic', 'disney_cartoon']
senticnet['wallyball'] = ['0.059', '-0.04', '0.014', '0', '#joy', '#surprise', 'positive', '0.031', 'golf', 'rugby', 'soccer', 'football', 'play_golf']
senticnet['walmart'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'newsstand', 'bookshop', 'soda_machine', 'shoe_store', 'hooker']
senticnet['walmer_castle'] = ['0.582', '-0.97', '0', '0.802', '#surprise', '#admiration', 'positive', '0.788', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['walnut'] = ['0.02', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.038', 'many_fruits', 'peach', 'buy_fruit', 'pineapple', 'tomato']
senticnet['walnut_creek'] = ['-0.06', '0.21', '0', '0.076', '#interest', '#admiration', 'positive', '0.074', 'new_think', 'figure_out', 'brain_activity', 'idea', 'come_up_idea']
senticnet['walnut_salad'] = ['0.859', '0.917', '0', '0', '#joy', '#interest', 'positive', '0.888', 'green_apple', 'apricot', 'lime', 'peach', 'cherry']
senticnet['walnut_street'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'boulevard', 'lane', 'thoroughfare', 'intersection', 'curb']
senticnet['walnut_wood'] = ['0', '0.773', '0', '0.895', '#interest', '#admiration', 'positive', '0.834', 'wooden_board', 'make_wood', 'wooden_box', 'wooden_floor', 'long_wood']
senticnet['walrus'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'seal', 'otter', 'mussel', 'alligator', 'whale']
senticnet['walrus_ivory'] = ['0.063', '0.089', '0.036', '0.146', '#interest', '#admiration', 'positive', '0.066', 'horn', 'jew_harp', 'brass_instrument', 'trumpet', 'crash_cymbal']
senticnet['walrus_meat'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'seal', 'otter', 'mussel', 'alligator', 'whale']
senticnet['walschaerts_valve_gear'] = ['0.069', '0.097', '0', '0.172', '#interest', '#admiration', 'positive', '0.113', 'type_of_engine', 'liquid_gas', 'warm_air', 'hot_air', 'steamy']
senticnet['walsh'] = ['-0.85', '0', '0.676', '-0.82', '#sadness', '#disgust', 'negative', '-0.78', 'erythroderma', 'erythema_nodosum', 'erythema_multiforme', 'windburn', 'erythema']
senticnet['walt_disney_animated_film'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'child_movie', 'child_story', 'funny_moment', 'childhood']
senticnet['walther_ppk'] = ['0', '0', '-0.87', '-0.82', '#fear', '#disgust', 'negative', '-0.84', 'pistol', 'gun', 'hydrogen_bomb', 'rifle', 'shotgun']
senticnet['waltz'] = ['0.702', '0', '0', '0.703', '#joy', '#admiration', 'positive', '0.702', 'swirl', 'relate_music', 'dance', 'dance_step', 'dance_move']
senticnet['wampus_cat'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'unofficial', 'informal', 'loose', 'drumhead']
senticnet['wan'] = ['-0.96', '0', '-0.99', '0', '#sadness', '#fear', 'negative', '-0.98', 'ailing', 'qualm', 'frequently', 'sickness', 'sickly']
senticnet['wan_connection'] = ['0.078', '0.103', '-0.09', '0.191', '#interest', '#admiration', 'positive', '0.069', 'connect', 'aunt_uncle', 'short_form', 'relationship', 'kinship']
senticnet['wanamaker_department'] = ['0', '0.081', '0', '0.071', '#interest', '#admiration', 'positive', '0.076', 'thrift_store', 'type_of_store', 'record_store', 'many_shops', 'clothes_store']
senticnet['wand'] = ['0.105', '-0.17', '0.057', '0.079', '#joy', '#surprise', 'positive', '0.076', 'cue', 'twig', 'long_stick', 'stick', 'adhere']
senticnet['wande_coal'] = ['0', '0', '0.876', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'untune', 'discompose', 'discomfit', 'disconcert', 'upset']
senticnet['wander'] = ['-0.61', '0', '0.538', '-0.67', '#sadness', '#disgust', 'negative', '-0.60', 'gad', 'move', 'gallivant', 'jazz_around', 'travel']
senticnet['wander_aimlessly'] = ['0.047', '-0.01', '0.033', '0.071', '#joy', '#admiration', 'positive', '0.024', 'get_place', 'dawdle_around', 'get_around_town', 'go_to_place', 'stroll']
senticnet['wander_around'] = ['0.047', '-0.01', '0.033', '0.071', '#joy', '#admiration', 'positive', '0.024', 'get_place', 'dawdle_around', 'get_around_town', 'go_to_place', 'stroll']
senticnet['wander_off'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'annoy', 'irritate', 'trouble', 'nag', 'bother']
senticnet['wander_through'] = ['0.876', '0.883', '0', '0', '#joy', '#interest', 'positive', '0.879', 'walk_along', 'gait', 'take_step', 'run_slowly', 'locomotion']
senticnet['wandered_around'] = ['0.047', '-0.01', '0.033', '0.071', '#joy', '#admiration', 'positive', '0.024', 'get_place', 'dawdle_around', 'get_around_town', 'go_to_place', 'stroll']
senticnet['wanderer'] = ['0.756', '0', '0', '0', '#joy', '#joy', 'positive', '0.756', 'companionship', 'cherish', 'lover', 'bond', 'buddy']
senticnet['wandering'] = ['0', '0', '-0.97', '-0.91', '#fear', '#disgust', 'negative', '-0.94', 'fickle', 'erratic', 'planetary', 'quicksilver', 'mercurial']
senticnet['wandering_aimlessly'] = ['0.047', '-0.01', '0.033', '0.071', '#joy', '#admiration', 'positive', '0.024', 'get_place', 'dawdle_around', 'get_around_town', 'go_to_place', 'stroll']
senticnet['wandering_aimlessly_around'] = ['-0.03', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.04', 'rabbit', 'squirrel', 'mole', 'chipmunk', 'nymph']
senticnet['wandering_around'] = ['0.047', '-0.01', '0.033', '0.071', '#joy', '#admiration', 'positive', '0.024', 'get_place', 'dawdle_around', 'get_around_town', 'go_to_place', 'stroll']
senticnet['wanderlust'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'globe_trotter', 'travel', 'visit_new_place', 'explore', 'discover']
senticnet['wanders_around'] = ['0.047', '-0.01', '0.033', '0.071', '#joy', '#admiration', 'positive', '0.024', 'get_place', 'dawdle_around', 'get_around_town', 'go_to_place', 'stroll']
senticnet['wandsworth_prison'] = ['0', '0', '0.995', '-0.89', '#anger', '#disgust', 'negative', '-0.94', 'shoe_store', 'chess_tournament', 'dress_room', 'front_desk', 'locker_room']
senticnet['wane'] = ['-0.95', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.83', 'house', 'dim', 'surface', 'child', 'period']
senticnet['wangle'] = ['-0.89', '0', '0.783', '-0.89', '#sadness', '#disgust', 'negative', '-0.85', 'dodge', 'plant', 'scheme', 'strategy', 'wangling']
senticnet['wangler'] = ['-0.57', '0', '0.839', '-0.87', '#anger', '#disgust', 'negative', '-0.76', 'beguiler', 'slicker', 'deceiver', 'cheat', 'cheater']
senticnet['wangling'] = ['-0.89', '0', '0.783', '-0.89', '#sadness', '#disgust', 'negative', '-0.85', 'wangle', 'plant', 'scheme', 'strategy', 'dodge']
senticnet['waning_crescent'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['waning_influence'] = ['-0.83', '0', '0.858', '-0.79', '#sadness', '#anger', 'negative', '-0.83', 'dominance', 'absolutism', 'autocracy', 'ascendancy', 'tyrant']
senticnet['waning_moon'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['wank'] = ['-0.05', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.05', 'masturbate', 'jerk', 'jerk_off', 'watch_porn', 'porn']
senticnet['wanker'] = ['-0.05', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.05', 'masturbate', 'jerk', 'jerk_off', 'watch_porn', 'porn']
senticnet['wanna'] = ['0', '0', '0.741', '-0.8', '#anger', '#disgust', 'negative', '-0.77', 'sex', 'make_love', 'safe_sex', 'sexual_intercourse', 'become_aroused']
senticnet['wanna-be'] = ['-0.96', '0', '0', '-0.96', '#sadness', '#disgust', 'negative', '-0.96', 'washed-up', 'has-been', 'talentless', 'druggie', 'has-been']
senticnet['wannabe'] = ['-0.19', '0', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.15', 'bad_child', 'misbehavior', 'bad_kid', 'bad_action', 'difficulty']
senticnet['wanness'] = ['-0.66', '0', '0.782', '-0.52', '#sadness', '#anger', 'negative', '-0.65', 'lividness', 'pallidness', 'pallor', 'complexion', 'achromasia']
senticnet['wanted'] = ['0.586', '-0.81', '0', '0.564', '#joy', '#surprise', 'positive', '0.656', 'preferred', 'darling', 'admired', 'loved', 'precious']
senticnet['wanting'] = ['-0.58', '0', '0.996', '-0.84', '#anger', '#disgust', 'negative', '-0.81', 'undermanned', 'understaffed', 'deficient', 'lacking', 'inadequate']
senticnet['wanton'] = ['0', '0', '-0.77', '-0.90', '#fear', '#disgust', 'negative', '-0.83', 'dalliance', 'piddle', 'wanton_away', 'trifling', 'dawdling']
senticnet['wanton_away'] = ['0', '0', '-0.77', '-0.91', '#fear', '#disgust', 'negative', '-0.84', 'dalliance', 'piddle', 'trifling', 'wanton', 'dawdling']
senticnet['wanton_conduct'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'negligence', 'evasion', 'dereliction', 'contributory_negligence']
senticnet['wanton_cruelty'] = ['-0.89', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.85', 'unkind', 'ruthlessness', 'cruelly', 'pitilessness', 'unassertively']
senticnet['wanton_destruction'] = ['-0.14', '-0.00', '-0.12', '-0.21', '#sadness', '#disgust', 'negative', '-0.11', 'torture', 'demolish', 'maim', 'terminate', 'hurt_person']
senticnet['wanton_disregard'] = ['-0.92', '0', '0.682', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'contrariness', 'wildness', 'intractability', 'intractableness', 'perversity']
senticnet['wanton_misconduct'] = ['-0.93', '0', '0.624', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'carelessness', 'neglectfulness', 'laxness', 'laxity', 'remissness']
senticnet['wantonly'] = ['-0.04', '0.164', '0.685', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maliciousness', 'spitefulness', 'maleficence', 'malefic', 'malign']
senticnet['wantonness'] = ['-0.69', '0', '0.89', '-0.98', '#anger', '#disgust', 'negative', '-0.85', 'evil', 'wrongness', 'putrefaction', 'immorality', 'corruptibility']
senticnet['wanttoinvalidate'] = ['0.935', '-0.95', '0', '0.949', '#surprise', '#admiration', 'positive', '0.947', 'identifiable', 'specifiable', 'recognizable', 'diagnosable', 'classifiable']
senticnet['wapentake'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'bodyguard', 'outrider', 'bodyguard_of_guard', 'beefeater', 'defender']
senticnet['wapo'] = ['0.783', '-0.63', '0', '0.829', '#joy', '#admiration', 'positive', '0.749', 'leg_of_lamb', 'lamb_roast', 'joint', 'roast', 'rack_of_lamb']
senticnet['war'] = ['-0.05', '0', '-0.11', '-0.19', '#fear', '#disgust', 'negative', '-0.12', 'arsenal', 'gun_store', 'battle', 'big_fight', 'combat']
senticnet['war-monger'] = ['-0.87', '0', '0', '-1.0', '#sadness', '#disgust', 'negative', '-0.93', 'sycophant', 'suck-up', 'lapdog', 'yes-man', 'flunky']
senticnet['war_advocacy'] = ['0.729', '-0.56', '0', '0.785', '#joy', '#admiration', 'positive', '0.692', 'ideology', 'militarism', 'warmongering', 'hawkishness', 'political_orientation']
senticnet['war_chest'] = ['-0.94', '0', '0.863', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'poke', 'knockout_punch', 'lick', 'punch', 'biff']
senticnet['war_correspondent'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'adventurer', 'humankind']
senticnet['war_crime'] = ['-0.94', '0', '0.904', '-0.94', '#sadness', '#disgust', 'negative', '-0.93', 'war', 'battle', 'fight', 'crime', 'fight_war']
senticnet['war_machine'] = ['-0.59', '0', '0.665', '-0.52', '#sadness', '#anger', 'negative', '-0.59', 'military', 'armed_forces', 'armed_service', 'military_machine', 'military_reserve']
senticnet['war_materiel'] = ['0.814', '-0.87', '0', '0.918', '#surprise', '#admiration', 'positive', '0.87', 'ship_company', 'company', 'work_force', 'workforce', 'full_complement']
senticnet['war_memorial'] = ['0.072', '0.126', '0', '0', '#joy', '#interest', 'positive', '0.099', 'subway_station', 'memorial', 'utopia', 'mortuary', 'hero']
senticnet['war_monger'] = ['-0.52', '0', '0.95', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'skeptic', 'sceptic', 'doubter', 'pessimist', 'negativist']
senticnet['war_museum'] = ['-0.03', '0', '-0.1', '-0.19', '#fear', '#disgust', 'negative', '-0.10', 'underworld', 'battlefield', 'prisoner_war_camp', 'shoot_range', 'war_zone']
senticnet['war_paint'] = ['-0.97', '0', '0.51', '-0.89', '#sadness', '#disgust', 'negative', '-0.79', 'wear', 'raiment', 'wearable', 'clothing', 'article_of_clothing']
senticnet['war_pow'] = ['-0.78', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.87', 'jailbird', 'captive', 'imprisoned', 'convict', 'inmate']
senticnet['war_ravaged_country'] = ['0.798', '-0.51', '0', '0.519', '#joy', '#admiration', 'positive', '0.611', 'averageness', 'commonness', 'expectedness', 'typicality', 'condition']
senticnet['war_ravaged_nation'] = ['0.798', '-0.51', '0', '0.519', '#joy', '#admiration', 'positive', '0.611', 'averageness', 'commonness', 'expectedness', 'typicality', 'condition']
senticnet['war_resister'] = ['-0.97', '0', '0.62', '-0.72', '#sadness', '#disgust', 'negative', '-0.77', 'obstructor', 'obstructer', 'technophobe', 'obstructionist', 'naysayer']
senticnet['war_torn'] = ['0.798', '-0.51', '0', '0.519', '#joy', '#admiration', 'positive', '0.611', 'averageness', 'commonness', 'expectedness', 'typicality', 'condition']
senticnet['war_torn_country'] = ['-0.03', '0', '-0.08', '-0.20', '#fear', '#disgust', 'negative', '-0.10', 'gun_shop', 'arsenal', 'holster', 'armory', 'gun_store']
senticnet['war_whoop'] = ['0.041', '0', '0', '0.064', '#joy', '#admiration', 'positive', '0.053', 'squeak', 'clang', 'musical_note', 'bad_music', 'roar']
senticnet['war_zone'] = ['-0.03', '0', '-0.08', '-0.20', '#fear', '#disgust', 'negative', '-0.10', 'gun_shop', 'arsenal', 'holster', 'armory', 'gun_store']
senticnet['warband'] = ['-0.80', '0', '0.826', '-0.62', '#sadness', '#anger', 'negative', '-0.75', 'liege', 'liege_lord', 'master', 'feudal_lord', 'lord']
senticnet['warble'] = ['0', '0', '0.802', '-0.75', '#anger', '#disgust', 'negative', '-0.78', 'cry', 'yell', 'scream', 'hollo', 'call']
senticnet['warbling_vireo'] = ['-0.55', '0', '0.955', '-0.96', '#anger', '#disgust', 'negative', '-0.82', 'house_mouse', 'mouse', 'rat', 'fieldmouse', 'nude_mouse']
senticnet['warbly'] = ['-0.89', '0', '0.722', '-0.94', '#sadness', '#disgust', 'negative', '-0.85', 'reflex', 'reflex_response', 'reflex_action', 'instinctive_reflex', 'innate_reflex']
senticnet['warchief'] = ['0.02', '0.131', '0.749', '-0.67', '#anger', '#disgust', 'negative', '-0.31', 'belligerence', 'belligerency', 'belligerent', 'war', 'rancor']
senticnet['ward'] = ['-0.87', '0', '-0.86', '0', '#sadness', '#fear', 'negative', '-0.86', 'ward', 'smith', 'walker', 'miller', 'wright']
senticnet['ward_off'] = ['-0.74', '0', '0.966', '0', '#sadness', '#anger', 'negative', '-0.85', 'move', 'displace', 'fire', 'chase_away', 'ostracise']
senticnet['warded_off'] = ['0', '0', '-0.85', '-0.95', '#fear', '#disgust', 'negative', '-0.90', 'scarily', 'scared', 'scare_away', 'frighten_off', 'scare_off']
senticnet['warden'] = ['-0.78', '0', '-0.96', '0', '#sadness', '#fear', 'negative', '-0.87', 'jailbird', 'captive', 'imprisoned', 'convict', 'inmate']
senticnet['warding_off'] = ['0', '0', '-0.85', '-0.95', '#fear', '#disgust', 'negative', '-0.90', 'scarily', 'scared', 'scare_away', 'frighten_off', 'scare_off']
senticnet['wardrobe'] = ['-0.76', '0', '0.672', '-0.88', '#sadness', '#disgust', 'negative', '-0.77', 'attire', 'garb', 'dress', 'fancy_dress', 'masquerade']
senticnet['wardrobe_closet'] = ['0', '0.171', '-0.05', '0.102', '#interest', '#admiration', 'positive', '0.072', 'bed', 'mattress', 'bead', 'bed_cover', 'seduce']
senticnet['ware'] = ['0', '0.179', '-0.05', '0.214', '#interest', '#admiration', 'positive', '0.113', 'product', 'goods', 'merchandise', 'grocery', 'groceries']
senticnet['warehouse'] = ['0', '0.07', '0', '0.067', '#interest', '#admiration', 'positive', '0.069', 'car_trunk', 'walmart', 'recycle_bin', 'store', 'storage_place']
senticnet['warehouse_worker'] = ['0.969', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.936', 'attendant', 'manager', 'public_servant', 'specialist', 'accomplice']
senticnet['warfare'] = ['-0.05', '0', '-0.07', '-0.17', '#fear', '#disgust', 'negative', '-0.10', 'violence', 'combat', 'conflict', 'big_fight', 'join_military']
senticnet['warhawks_lost'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'home', 'position', 'time_limit', 'monthly', 'modern_home']
senticnet['warhead'] = ['0', '0', '0.853', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'kiloton', 'megaton', 'megaton', 'thermonuclear', 'nuke']
senticnet['warily'] = ['-0.79', '0', '-0.70', '0', '#sadness', '#fear', 'negative', '-0.75', 'hesitance', 'hesitancy', 'hesitatingly', 'unassertive', 'unassertively']
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senticnet['warlike_spirit'] = ['0.646', '-0.9', '0', '0.684', '#surprise', '#admiration', 'positive', '0.743', 'acquirement', 'skill', 'accomplishment', 'acquisition', 'attainment']
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senticnet['warm_affection'] = ['0.608', '0.647', '-0.26', '0.325', '#joy', '#interest', 'positive', '0.328', 'amicably', 'amicable', 'amicableness', 'brotherly', 'friendly']
senticnet['warm_air'] = ['0.039', '0', '0', '0.052', '#joy', '#admiration', 'positive', '0.045', 'hot_air', 'liquid_gas', 'type_of_engine', 'fire_hazard', 'fire_starter']
senticnet['warm_bath'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
senticnet['warm_bed'] = ['0.149', '0', '0', '0.153', '#joy', '#admiration', 'positive', '0.151', 'alleyway', 'comfortable_chair', 'home', 'animal_shelter', 'veterinarian_office']
senticnet['warm_blanket'] = ['0.808', '0', '0', '0', '#joy', '#joy', 'positive', '0.808', 'fall_asleep', 'relax_mind', 'curl_up', 'make_out', 'bed_time']
senticnet['warm_blood'] = ['0.059', '0', '0.042', '0.055', '#joy', '#admiration', 'positive', '0.024', 'mammary_gland', 'hairy', 'hairy_animal', 'nipple', 'woodland_creature']
senticnet['warm_blooded'] = ['0.037', '0.078', '0', '0', '#joy', '#interest', 'positive', '0.057', 'leopard', 'only_mammal', 'lynx', 'mane', 'hare']
senticnet['warm_breeze'] = ['0.981', '0.891', '0', '0', '#joy', '#interest', 'positive', '0.936', 'bitterness', 'nip', 'bite', 'sting', 'sharpness']
senticnet['warm_chocolate_cake'] = ['0.076', '0', '0.032', '0.106', '#joy', '#admiration', 'positive', '0.05', 'delicious', 'good_eat', 'yummy', 'edible', 'eatable']
senticnet['warm_clothes'] = ['0', '-0.06', '0.082', '-0.06', '#anger', '#disgust', 'negative', '-0.02', 'fabric_shop', 'sweater', 'garment', 'clothes', 'winter_clothes']
senticnet['warm_color'] = ['-0.04', '0', '0.059', '-0.03', '#sadness', '#anger', 'negative', '-0.04', 'secondary_color', 'color', 'color_rainbow', 'happiness', 'primary_color']
senticnet['warm_down'] = ['0.135', '0.073', '0', '0', '#joy', '#interest', 'positive', '0.104', 'rest_relaxation', 'sex_with_stranger', 'go_bed', 'go_sleep', 'stay_overnight']
senticnet['warm_feeling'] = ['0', '0.764', '0', '0.886', '#interest', '#admiration', 'positive', '0.825', 'show_love', 'sentiment', 'lust', 'feel_love', 'demand']
senticnet['warm_food'] = ['0.822', '0.86', '0', '0.751', '#joy', '#interest', 'positive', '0.811', 'get_ready', 'quick_movement', 'speedy', 'quickly', 'fry_egg']
senticnet['warm_foot'] = ['0', '0.704', '0', '0.773', '#interest', '#admiration', 'positive', '0.738', 'foot', 'winter_boot', 'footwear', 'winter_sock', 'warm']
senticnet['warm_fuzzy_feeling'] = ['0.933', '0.868', '0', '0', '#joy', '#interest', 'positive', '0.901', 'love_back', 'sexual_intercourse', 'find_mate', 'show_empathy', 'arousal']
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senticnet['warm_hospitality'] = ['0.58', '0.651', '-0.26', '0.308', '#joy', '#interest', 'positive', '0.319', 'well-disposed', 'approving', 'approve', 'sanctioned', 'approved']
senticnet['warm_house'] = ['0.608', '0.647', '-0.26', '0.325', '#joy', '#interest', 'positive', '0.328', 'heat_home', 'heat_house', 'produce_heat', 'warm_room', 'light_dark']
senticnet['warm_place'] = ['0.608', '0.647', '-0.26', '0.325', '#joy', '#interest', 'positive', '0.328', 'warm_house', 'produce_heat', 'heat_house', 'warm_room', 'heat_home']
senticnet['warm_room'] = ['0.608', '0.647', '-0.26', '0.325', '#joy', '#interest', 'positive', '0.328', 'heat_house', 'warm_house', 'light_dark', 'produce_heat', 'heat_home']
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senticnet['warm_smile'] = ['0.58', '0.651', '-0.26', '0.308', '#joy', '#interest', 'positive', '0.319', 'well-disposed', 'approving', 'approve', 'sanctioned', 'approved']
senticnet['warm_soapy_water'] = ['-0.60', '0', '0.992', '-0.94', '#anger', '#disgust', 'negative', '-0.84', 'cleanse', 'lather', 'floss', 'decontaminate', 'bathe']
senticnet['warm_sunny'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'weather', 'four_seasons', 'fall_spring', 'rain_snow', 'climate']
senticnet['warm_sunshine'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'weather', 'four_seasons', 'fall_spring', 'rain_snow', 'climate']
senticnet['warm_to'] = ['0.777', '-0.85', '0', '0.615', '#joy', '#surprise', 'positive', '0.748', 'academic', 'play', 'trifle', 'abstract', 'look_at']
senticnet['warm_up'] = ['0.095', '0.204', '-0.12', '0.174', '#interest', '#admiration', 'positive', '0.087', 'go_outdoors', 'sleep_tent', 'keep_dry', 'pitch_tent', 'careful']
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senticnet['warm_welcome'] = ['0.561', '-0.79', '0', '0.89', '#surprise', '#admiration', 'positive', '0.748', 'superlative', 'compliment', 'testimonial', 'praise', 'approval']
senticnet['warmer'] = ['0.805', '0', '0', '0.711', '#joy', '#admiration', 'positive', '0.758', 'caring', 'benevolent', 'benign', 'fatherly', 'motherly']
senticnet['warmer_air'] = ['0', '0.094', '0', '0', '#interest', '#interest', 'positive', '0.094', 'cold', 'cavern', 'under_umbrella', 'parasol', 'stick_wall']
senticnet['warmer_weather'] = ['0.15', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.087', 'winter', 'winter_time', 'snow_fall', 'snowy', 'put_warm_clothes']
senticnet['warmest_welcome'] = ['0', '0.852', '0', '0', '#interest', '#interest', 'positive', '0.852', 'streamer', 'tribute', 'salutation', 'gift', 'anniversary_gift']
senticnet['warmhearted'] = ['0.551', '0.67', '-0.27', '0.285', '#joy', '#interest', 'positive', '0.307', 'caring', 'lovingness', 'warmheartedness', 'benevolently', 'benevolence']
senticnet['warmheartedness'] = ['0.512', '0.674', '-0.27', '0.269', '#joy', '#interest', 'positive', '0.296', 'caring', 'lovingness', 'infatuation', 'calf_love', 'puppy_love']
senticnet['warming_rack'] = ['0.061', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.055', 'cook', 'stick_butter', 'find_recipe', 'bake_oven', 'food_bake']
senticnet['warming_trend'] = ['0.05', '0.151', '0', '0.159', '#interest', '#admiration', 'positive', '0.12', 'glamour', 'stylish', 'style', 'clothes_style', 'fancy_clothes']
senticnet['warmly'] = ['0', '0.764', '0', '0.845', '#interest', '#admiration', 'positive', '0.804', 'empathetically', 'empathic', 'empathetic', 'kind-heartedness', 'kindheartedness']
senticnet['warmly_welcome'] = ['0.819', '0', '0', '0.746', '#joy', '#admiration', 'positive', '0.782', 'proudly', 'smug', 'congratulation', 'self-satisfied', 'applause']
senticnet['warmness'] = ['0.461', '0.674', '-0.26', '0.247', '#joy', '#interest', 'positive', '0.28', 'affectionateness', 'philia', 'affectional', 'affective', 'emotive']
senticnet['warmonger'] = ['-0.02', '0', '-0.13', '-0.07', '#fear', '#disgust', 'negative', '-0.07', 'settle_disagreement', 'defeat_enemy', 'gain_territory', 'overpower', 'conquer_enemy']
senticnet['warmongering'] = ['0.72', '-0.56', '0', '0.779', '#joy', '#admiration', 'positive', '0.688', 'ideology', 'militarism', 'political_orientation', 'war_advocacy', 'hawkishness']
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senticnet['warmwater'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'dirtiness', 'befoulment', 'defilement', 'dinginess', 'dinge']
senticnet['warn'] = ['-0.22', '0.107', '-0.25', '0.054', '#sadness', '#fear', 'negative', '-0.08', 'caution', 'catalyst', 'unsafe', 'signal', 'problem']
senticnet['warn_sign'] = ['-0.25', '0', '-0.19', '-0.04', '#sadness', '#fear', 'negative', '-0.16', 'warm_color', 'secondary_color', 'color', 'primary_color', 'cool_color']
senticnet['warner_chilcott'] = ['0.559', '0.656', '-0.26', '0.285', '#joy', '#interest', 'positive', '0.31', 'zealous', 'avidity', 'eagerness', 'avidness', 'thirstily']
senticnet['warning'] = ['-0.11', '-0.01', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.06', 'beep', 'primary_color', 'color_rainbow', 'color', 'light_red']
senticnet['warning_center_jtwc'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'wildcat', 'informal', 'loose', 'drumhead']
senticnet['warning_coloration'] = ['0.62', '-0.59', '0', '0.967', '#joy', '#admiration', 'positive', '0.728', 'aposematic_coloration', 'protective_coloration', 'apatetic_coloration', 'cryptic_coloration', 'coloration']
senticnet['warp'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'deflection', 'aberrance', 'chromosomal_aberration']
senticnet['warpaint'] = ['0.946', '-0.91', '0', '0.664', '#joy', '#surprise', 'positive', '0.841', 'pretender', 'phony', 'phoney', 'hypocrite', 'dissembler']
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senticnet['warrant'] = ['0.787', '-0.95', '0', '0.947', '#surprise', '#admiration', 'positive', '0.896', 'okay', 'plump_for', 'sanction', 'approve', 'champion']
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senticnet['warranty_deed'] = ['0.621', '-0.60', '0', '0.545', '#joy', '#surprise', 'positive', '0.59', 'privilege', 'perquisite', 'public_easement', 'right_of_way', 'prerogative']
senticnet['warre'] = ['0.621', '0.648', '-0.25', '0.296', '#joy', '#interest', 'positive', '0.328', 'pacifically', 'repose', 'peace-loving', 'peaceable', 'peace_of_mind']
senticnet['warrior'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'soldier', 'samurai', 'army', 'fighter', 'infantry']
senticnet['warsaw'] = ['0', '0', '0', '0.1', '#admiration', '#admiration', 'positive', '0.1', 'find_earth', 'wax', 'plutonium', 'shine', 'melt']
senticnet['warship'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'submarine', 'lifeboat', 'titanic', 'large_ship', 'canoe']
senticnet['wart'] = ['-0.07', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.06', 'sharp_tooth', 'pointy_ear', 'tail', 'big_tooth', 'fang']
senticnet['warthen'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'dicky', 'impaired', 'lessened', 'diminished']
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senticnet['wartime_airfield'] = ['-0.70', '0', '0.621', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'flare_path', 'airfield', 'grassland', 'runway', 'road']
senticnet['warty'] = ['-0.98', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.90', 'hoary', 'scabby', 'scaly', 'slimy', 'leathery']
senticnet['warty_frog'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'cold-blooded', 'reptile', 'scaly', 'amphibian', 'aquatic']
senticnet['wary'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'alert', 'watchful', 'guard', 'vigilant', 'on_alert']
senticnet['warzone'] = ['-0.03', '0', '-0.08', '-0.20', '#fear', '#disgust', 'negative', '-0.10', 'gun_shop', 'arsenal', 'holster', 'armory', 'gun_store']
senticnet['wasabi'] = ['0.077', '0', '0', '0', '#joy', '#joy', 'positive', '0.077', 'dip', 'garlic', 'oat', 'savory', 'rye']
senticnet['wasat_party'] = ['0.648', '0.627', '-0.26', '0.329', '#joy', '#interest', 'positive', '0.334', 'brotherly', 'friendly', 'fraternal', 'amicability', 'amicable']
senticnet['wash'] = ['-0.60', '0', '0.992', '-0.94', '#anger', '#disgust', 'negative', '-0.84', 'cleanse', 'lather', 'floss', 'decontaminate', 'bathe']
senticnet['wash_away'] = ['0', '-0.01', '0.046', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'varnish', 'brush', 'dye', 'pigment', 'canvas']
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senticnet['wash_basin'] = ['0.778', '-0.93', '0', '0.578', '#joy', '#surprise', 'positive', '0.763', 'flushless_toilet', 'flush_toilet', 'commode', 'potty_seat', 'potty_chair']
senticnet['wash_behind_ear'] = ['0', '0', '0', '0.09', '#admiration', '#admiration', 'positive', '0.09', 'remove_dirt', 'clean_hand', 'cleanse_body', 'clean_skin', 'cleaner']
senticnet['wash_body'] = ['0.805', '0', '0', '0', '#joy', '#joy', 'positive', '0.805', 'use_washcloth', 'wash_dog', 'lather', 'clean', 'stay_fresh']
senticnet['wash_cloth'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'toilet_paper', 'beach_towel', 'face_towel', 'hand_towel', 'tissue']
senticnet['wash_clothes'] = ['0', '0.716', '0', '0.741', '#interest', '#admiration', 'positive', '0.728', 'clean_clothes', 'get_clean', 'take_shower', 'clean_body', 'become_clean']
senticnet['wash_dish'] = ['0.786', '0', '0', '0', '#joy', '#joy', 'positive', '0.786', 'wash_dog', 'eat_dessert', 'clean', 'camp_out', 'mop_floor']
senticnet['wash_dog'] = ['0.813', '0', '0', '0', '#joy', '#joy', 'positive', '0.813', 'wash_body', 'clean', 'lather', 'use_washcloth', 'maintain_hygiene']
senticnet['wash_first'] = ['-0.04', '-0.01', '0.04', '-0.02', '#sadness', '#anger', 'negative', '-0.02', 'take_bite', 'use_tooth', 'buy_market', 'chew', 'put_napkin_lap']
senticnet['wash_hair'] = ['0.855', '0.849', '0', '0.767', '#joy', '#interest', 'positive', '0.824', 'keep_clothes_clean', 'smell_good', 'smell_nice', 'well_dress', 'smell_clean']
senticnet['wash_hand'] = ['0.053', '0.078', '0', '0', '#joy', '#interest', 'positive', '0.066', 'wash_clothes', 'hygiene', 'clean_clothes', 'get_clean', 'cologne']
senticnet['wash_laundry'] = ['0', '0.799', '0', '0', '#interest', '#interest', 'positive', '0.799', 'iron_shirt', 'sit_on_beach', 'dry_clothes', 'iron_clothes', 'absorb_moisture']
senticnet['wash_mouth_soap'] = ['0.029', '0', '-0.09', '-0.12', '#fear', '#disgust', 'negative', '-0.06', 'cigarette_smoke', 'smoke_pipe', 'cigarette', 'smoke', 'cigar']
senticnet['wash_out'] = ['0.847', '-0.80', '0', '0.711', '#joy', '#surprise', 'positive', '0.789', 'mollify', 'enervate', 'stultify', 'weaken', 'cripple']
senticnet['wash_person'] = ['0.095', '-0.04', '0.04', '0', '#joy', '#surprise', 'positive', '0.033', 'cleanse_body', 'feel_clean', 'smell_clean', 'moisturize_skin', 'feel_fresh']
senticnet['wash_self'] = ['0.994', '0', '0', '0.826', '#joy', '#admiration', 'positive', '0.91', 'clean_agent', 'foam', 'lather', 'bubbly', 'cleanser']
senticnet['wash_thoroughly'] = ['-0.60', '0', '0.992', '-0.94', '#anger', '#disgust', 'negative', '-0.84', 'cleanse', 'lather', 'floss', 'decontaminate', 'bathe']
senticnet['wash_tooth'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'smart_technology', 'electric_shaver', 'hair_dryer', 'stay_healthy', 'electric_toothbrush']
senticnet['wash_up'] = ['0.785', '0', '0', '0.841', '#joy', '#admiration', 'positive', '0.813', 'cleanse', 'clean_dish', 'hygiene', 'wash_body', 'clean_self']
senticnet['wash_utensil'] = ['0', '0.779', '0', '0.813', '#interest', '#admiration', 'positive', '0.796', 'clean_dish', 'wash_up', 'hygiene', 'wash_body', 'clean_self']
senticnet['wash_window'] = ['0', '0', '0', '-0.97', '#disgust', '#disgust', 'negative', '-0.97', 'sit_on_beach', 'pet_cat', 'absorb_moisture', 'mow_grass', 'clean_sink']
senticnet['washable'] = ['-0.81', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.85', 'bandage', 'painkiller', 'gauze', 'aspirin', 'contraceptive_device']
senticnet['washbasin'] = ['0', '-0.76', '0', '0.966', '#surprise', '#admiration', 'positive', '0.865', 'pedestal', 'sink', 'vanity', 'pedestal', 'mounted']
senticnet['washcloth'] = ['0', '0', '0.865', '-0.95', '#anger', '#disgust', 'negative', '-0.90', 'faucet_overflow', 'hot_faucet', 'hot_tap', 'faucet', 'dirty_dish']
senticnet['washed-up'] = ['-0.72', '0', '0', '-0.93', '#sadness', '#disgust', 'negative', '-0.83', 'has-been', 'down-and-out', 'down-on-his-luck', 'over-the-hill', 'untalented']
senticnet['washed_overboard'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'lifebelt', 'save_life', 'lifeguard', 'life_jacket', 'safety_equipment']
senticnet['washer'] = ['0', '0.07', '0', '0', '#interest', '#interest', 'positive', '0.07', 'purse', 'pocket', 'clothes_washer', 'wallet_purse', 'woman_purse']
senticnet['washer_drier'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'kitchen_cabinet', 'kitchen_cupboard', 'dishwasher', 'cupboard', 'own_kitchen']
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senticnet['watchword'] = ['0.022', '0.123', '0.038', '0.129', '#interest', '#admiration', 'positive', '0.059', 'hymn', 'written_music', 'walk_beach', 'lyric', 'piece_of_music']
senticnet['water'] = ['-0.18', '-0.07', '0', '-0.20', '#sadness', '#disgust', 'negative', '-0.10', 'bad_drug_spill', 'bad_drug', 'atmosphere', 'cool_bad_drug', 'obtain_bad_drug']
senticnet['water-based'] = ['0', '0', '0.959', '-0.76', '#anger', '#disgust', 'negative', '-0.86', 'non-toxic', 'odorless', 'nontoxic', 'non-flammable', 'biodegradable']
senticnet['water-impermeable'] = ['-0.82', '0', '0', '-0.83', '#sadness', '#disgust', 'negative', '-0.83', 'sheet-like', 'cap-like', 'semi-conducting', 'sheetlike', 'screen-like']
senticnet['water-loving'] = ['0', '0', '0.76', '-0.88', '#anger', '#disgust', 'negative', '-0.82', 'moisture-loving', 'heat-loving', 'sun-loving', 'shade-loving', 'hydrophilic']
senticnet['water-safety'] = ['0', '0', '0.931', '-0.73', '#anger', '#disgust', 'negative', '-0.83', 'surf-fishing', 'school-improvement', 'summer-friendly', 'action-planning', 'soil-building']
senticnet['water-soluble'] = ['0', '0', '-0.79', '-0.95', '#fear', '#disgust', 'negative', '-0.87', 'soluble', 'insoluble', 'indigestible', 'aqueous', 'precipitate']
senticnet['water-tight'] = ['-0.71', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.76', 'air-tight', 'watertight', 'refrigerate', 'airtight', 'hermetically']
senticnet['water_action'] = ['0', '0.082', '0', '0.214', '#interest', '#admiration', 'positive', '0.148', 'water_motion', 'water_movement', 'arm_movement', 'water_relate', 'running_water']
senticnet['water_activity'] = ['0.891', '0.833', '0', '0', '#joy', '#interest', 'positive', '0.862', 'water_transport', 'travel_over_water', 'backstroke', 'water_sport', 'propulsion']
senticnet['water_aerodrome'] = ['-0.70', '0', '0.621', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'flare_path', 'airfield', 'grassland', 'runway', 'road']
senticnet['water_air'] = ['0.038', '0', '0.01', '0.059', '#joy', '#admiration', 'positive', '0.029', 'humid', 'water_molecule', 'water_vapor', 'water', 'water_mass']
senticnet['water_ballast'] = ['0.676', '0.63', '-0.23', '0.31', '#joy', '#interest', 'positive', '0.345', 'perkiness', 'walk_on_air', 'exultingly', 'exultantly', 'jubilance']
senticnet['water_beach'] = ['0', '0', '0', '0.09', '#admiration', '#admiration', 'positive', '0.09', 'aquarium', 'seashore', 'ocean_water', 'chowder', 'pacific']
senticnet['water_bird'] = ['-0.03', '-0.02', '0.027', '-0.10', '#sadness', '#disgust', 'negative', '-0.03', 'crow', 'dove', 'bird_type', 'bird_prey', 'raven']
senticnet['water_biscuit'] = ['-0.95', '0', '0.762', '-0.64', '#sadness', '#anger', 'negative', '-0.78', 'saltine', 'soda_cracker', 'oyster_cracker', 'cracker', 'graham_cracker']
senticnet['water_blister'] = ['-0.78', '0', '0.855', '-0.99', '#anger', '#disgust', 'negative', '-0.87', 'blood_blister', 'pustule', 'bleb', 'vesicle', 'cyst']
senticnet['water_body'] = ['0.057', '0.118', '-0.04', '0.151', '#interest', '#admiration', 'positive', '0.069', 'small_river', 'lake', 'water_feature', 'fresh_water', 'large_body']
senticnet['water_bottle'] = ['0', '-0.02', '0.027', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'shoulder_bag', 'satchel', 'satchel_bag', 'tote_bag', 'carry']
senticnet['water_bubble'] = ['-0.22', '-0.03', '0.026', '-0.13', '#sadness', '#disgust', 'negative', '-0.08', 'burn_toast', 'bad_eyesight', 'blemish', 'dig_tunnel', 'vermin']
senticnet['water_bug'] = ['-0.77', '0', '0.576', '-0.92', '#sadness', '#disgust', 'negative', '-0.75', 'insect', 'firebug', 'heteropterous_insect', 'assassin_bug', 'reduviid']
senticnet['water_caltrop'] = ['0.83', '-0.80', '0', '0.969', '#joy', '#admiration', 'positive', '0.867', 'water_chestnut', 'ling', 'ling_ko', 'aquatic_plant', 'water_plant']
senticnet['water_carpet'] = ['-0.83', '0', '0.768', '-0.69', '#sadness', '#anger', 'negative', '-0.76', 'golden_saxifrage', 'water_mat', 'aquatic_plant', 'water_plant', 'hydrophyte']
senticnet['water_chestnut'] = ['0.83', '-0.80', '0', '0.969', '#joy', '#admiration', 'positive', '0.867', 'water_caltrop', 'ling', 'ling_ko', 'aquatic_plant', 'water_plant']
senticnet['water_chestnut_ko'] = ['0.824', '-0.80', '0', '0.962', '#joy', '#admiration', 'positive', '0.864', 'water_caltrop', 'water_chestnut', 'ling', 'aquatic_plant', 'water_plant']
senticnet['water_chlorination'] = ['-0.62', '0', '0.987', '-0.74', '#anger', '#disgust', 'negative', '-0.78', 'medical_aid', 'treatment', 'hospice', 'digitalization', 'bloodletting']
senticnet['water_color'] = ['0.833', '0.927', '0', '0', '#joy', '#interest', 'positive', '0.88', 'beautiful', 'transparency', 'sunny_weather', 'intelligent', 'clarity']
senticnet['water_column'] = ['0.88', '0.788', '0', '0', '#joy', '#interest', 'positive', '0.834', 'carbon', 'sediment', 'southern_ocean', 'rate', 'measurement']
senticnet['water_container'] = ['0.718', '0', '0', '0', '#joy', '#joy', 'positive', '0.718', 'clean_water', 'still_water', 'dampness', 'water', 'cool_water']
senticnet['water_crop'] = ['-0.05', '0.094', '0', '0.058', '#interest', '#admiration', 'positive', '0.032', 'farm_vegetable', 'cultivation', 'grassland', 'grow_stuff', 'agricultural']
senticnet['water_crowfoot'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'cattail', 'nailrod', 'reed_mace', 'reedmace']
senticnet['water_disinfection'] = ['-0.62', '0', '0.987', '-0.74', '#anger', '#disgust', 'negative', '-0.78', 'medical_aid', 'treatment', 'hospice', 'digitalization', 'bloodletting']
senticnet['water_dispenser'] = ['0.939', '0.939', '0', '0', '#joy', '#interest', 'positive', '0.939', 'puddle', 'sop', 'liquid_water', 'finger_paint', 'bread']
senticnet['water_drip'] = ['-0.06', '0', '0.043', '0', '#sadness', '#anger', 'negative', '-0.05', 'downward', 'angle', 'incline_plane', 'decline', 'mountain_side']
senticnet['water_drop'] = ['0.925', '0.744', '0', '0', '#joy', '#interest', 'positive', '0.835', 'sky', 'come_down', 'water_fall', 'cloud_sky', 'cloud_skylet']
senticnet['water_droplet'] = ['0.73', '0.719', '0', '0', '#joy', '#interest', 'positive', '0.724', 'sky', 'come_down', 'water_fall', 'water_drop', 'cloud_sky']
senticnet['water_dropwort'] = ['-0.79', '0', '0.906', '-0.67', '#sadness', '#anger', 'negative', '-0.79', 'poisonous_plant', 'hemlock_poisonous_plant', 'water_hemlock', 'plant_life', 'poison_sumac']
senticnet['water_ear'] = ['0.894', '0.903', '0', '0', '#joy', '#interest', 'positive', '0.899', 'drown', 'get_wet', 'become_relaxed', 'wet_clothes', 'wrinkled_skin']
senticnet['water_eye'] = ['-0.22', '-0.01', '-0.13', '-0.07', '#sadness', '#fear', 'negative', '-0.10', 'shed_tear', 'eye_tear', 'weep', 'bawl', 'sob']
senticnet['water_fall'] = ['0.755', '0.754', '0', '0', '#joy', '#interest', 'positive', '0.754', 'sky', 'come_down', 'cloud_sky', 'water_drop', 'water_droplet']
senticnet['water_faucet'] = ['0.06', '-0.02', '0', '0.051', '#joy', '#admiration', 'positive', '0.047', 'knock', 'pour_water', 'dance_well', 'scuba_gear', 'go_water']
senticnet['water_feature'] = ['-0.03', '0.072', '0', '0.113', '#interest', '#admiration', 'positive', '0.051', 'water_body', 'flowing_water', 'small_stream', 'big_lake', 'water_mass']
senticnet['water_float'] = ['-0.02', '-0.01', '0.069', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'harbor', 'water_body', 'water_transport', 'ship_boat', 'cabin']
senticnet['water_flow'] = ['0', '0.923', '0', '0.742', '#interest', '#admiration', 'positive', '0.833', 'flowing_water', 'water_stream', 'water_movement', 'ebb', 'fast_water']
senticnet['water_flower'] = ['0.067', '0.153', '-0.05', '0.111', '#interest', '#admiration', 'positive', '0.069', 'small_flower', 'plant_yard', 'grassland', 'plant_tree', 'garden_plant']
senticnet['water_form'] = ['0', '0.067', '0', '0.218', '#interest', '#admiration', 'positive', '0.143', 'water_formation', 'water_relate', 'arm_movement', 'water_action', 'hand_gesture']
senticnet['water_formation'] = ['0', '0.074', '0', '0.195', '#interest', '#admiration', 'positive', '0.135', 'water_form', 'water_relate', 'wind_water', 'small_stream', 'big_lake']
senticnet['water_fountain'] = ['0.06', '0.067', '0', '-0.05', '#joy', '#interest', 'positive', '0.024', 'flea_market', 'winery', 'brewery', 'load_dock', 'wish_well']
senticnet['water_freeze'] = ['0', '0', '0', '-0.09', '#disgust', '#disgust', 'negative', '-0.09', 'stand_queue', 'go_post_office', 'wait_line', 'stand_in_line', 'get_line']
senticnet['water_garden'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'adjust_water_temperature', 'water_lawn', 'heater', 'shower', 'cook']
senticnet['water_gun'] = ['0.947', '0.7', '0', '0', '#joy', '#interest', 'positive', '0.823', 'baseball_bat', 'hockey_stick', 'goalie', 'hockey_puck', 'bullseye']
senticnet['water_heater'] = ['0.02', '0.076', '0', '0', '#joy', '#interest', 'positive', '0.048', 'land_earth', 'tidal_wave', 'puddle', 'brine', 'spurt']
senticnet['water_hemlock'] = ['-0.79', '0', '0.906', '-0.67', '#sadness', '#anger', 'negative', '-0.79', 'water_dropwort', 'hemlock_water_dropwort', 'poisonous_plant', 'plant_life', 'poison_sumac']
senticnet['water_hole'] = ['0.113', '0', '0', '0.118', '#joy', '#admiration', 'positive', '0.115', 'place_to_drink', 'metal_stick', 'drinking_place', 'hangout', 'meet_place']
senticnet['water_hose'] = ['-0.03', '-0.05', '0.06', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'prevent_conception', 'tired', 'lose', 'increase_price', 'gyroscope']
senticnet['water_inside'] = ['0', '0', '0', '0.09', '#admiration', '#admiration', 'positive', '0.09', 'water_spray', 'liquid_water', 'rise_water', 'humid', 'water_relate']
senticnet['water_intoxication'] = ['-0.72', '0', '0.762', '-0.79', '#anger', '#disgust', 'negative', '-0.75', 'heat_hyperpyrexia', 'heat_prostration', 'prostration', 'sunstroke', 'thermic_fever']
senticnet['water_jug'] = ['0', '-0.02', '0.027', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'shoulder_bag', 'satchel', 'satchel_bag', 'tote_bag', 'carry']
senticnet['water_lawn'] = ['-0.09', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.09', 'water_garden', 'shower', 'heater', 'adjust_water_temperature', 'cook']
senticnet['water_locust'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'locust_tree', 'swamp_locust', 'honey_locust', 'black_locust']
senticnet['water_mammal'] = ['0', '-0.10', '0', '0', '#surprise', '#surprise', 'positive', '0.101', 'mammal', 'type_of_animal', 'animal', 'sea_mammal', 'baby_deer']
senticnet['water_mass'] = ['-0.05', '0.068', '0', '0.102', '#interest', '#admiration', 'positive', '0.038', 'small_ocean', 'much_water', 'water_molecule', 'water_body', 'water_feature']
senticnet['water_mat'] = ['-0.83', '0', '0.768', '-0.69', '#sadness', '#anger', 'negative', '-0.76', 'water_carpet', 'golden_saxifrage', 'aquatic_plant', 'water_plant', 'hydrophyte']
senticnet['water_milfoil'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'cattail', 'nailrod', 'reed_mace', 'reedmace']
senticnet['water_moccasin'] = ['-0.51', '0', '0.625', '-0.98', '#anger', '#disgust', 'negative', '-0.70', 'common_water_snake', 'banded_water_snake', 'water_snake', 'grass_snake', 'ring_snake']
senticnet['water_molecule'] = ['0.038', '0', '0.01', '0.059', '#joy', '#admiration', 'positive', '0.029', 'humid', 'water_air', 'water_vapor', 'water', 'water_mass']
senticnet['water_motion'] = ['0', '0.072', '-0.06', '0.222', '#interest', '#admiration', 'positive', '0.077', 'water_movement', 'sea_movement', 'flowing_water', 'move_liquid', 'ebb']
senticnet['water_movement'] = ['0', '0', '0', '0.162', '#admiration', '#admiration', 'positive', '0.162', 'sea_movement', 'ebb', 'water_motion', 'water_flow', 'fast_water']
senticnet['water_parsnip'] = ['-0.79', '0', '0.906', '-0.67', '#sadness', '#anger', 'negative', '-0.79', 'poisonous_plant', 'hemlock_poisonous_plant', 'water_hemlock', 'plant_life', 'poison_sumac']
senticnet['water_particle'] = ['-0.04', '0.078', '0', '0.174', '#interest', '#admiration', 'positive', '0.069', 'high_up', 'block_sun', 'crystal_clear', 'sun_cover', 'up_high']
senticnet['water_passage'] = ['0', '0.074', '0', '0.139', '#interest', '#admiration', 'positive', '0.107', 'waterway', 'small_stream', 'flowing_water', 'water_flow', 'water_stream']
senticnet['water_pik'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'smart_technology', 'electric_shaver', 'hair_dryer', 'stay_healthy', 'wash_tooth']
senticnet['water_plant'] = ['-0.83', '0', '0.768', '-0.69', '#sadness', '#anger', 'negative', '-0.76', 'water_carpet', 'water_mat', 'aquatic_plant', 'golden_saxifrage', 'hydrophyte']
senticnet['water_plantain'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'cattail', 'nailrod', 'reed_mace', 'reedmace']
senticnet['water_pollution'] = ['0', '0', '0.735', '-0.70', '#anger', '#disgust', 'negative', '-0.72', 'swing_set', 'roadway_square', 'fresh_air', 'dog_poop', 'wildflower']
senticnet['water_polo_team'] = ['0.035', '0.155', '0', '0', '#joy', '#interest', 'positive', '0.095', 'basketball_court', 'basketball_game', 'basketball', 'ball', 'sport']
senticnet['water_quality'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'bottled_water', 'sugar_water']
senticnet['water_reabsorption'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'keep_alive', 'body_system', 'body_working', 'blood_pressure', 'heart']
senticnet['water_relate'] = ['0.026', '0.072', '0', '0.204', '#interest', '#admiration', 'positive', '0.101', 'water_action', 'water_form', 'arm_movement', 'water_motion', 'water_formation']
senticnet['water_repellent'] = ['-0.02', '0.078', '0', '0.08', '#interest', '#admiration', 'positive', '0.045', 'rigid', 'fragile', 'opaque', 'delicate', 'versatile']
senticnet['water_resistance'] = ['0', '0.952', '0', '0', '#interest', '#interest', 'positive', '0.952', 'tile', 'hair', 'design', 'bathroom_wall', 'algae']
senticnet['water_sea'] = ['-0.03', '-0.00', '0.024', '-0.05', '#sadness', '#disgust', 'negative', '-0.02', 'under_water', 'underwater', 'bottom_sea', 'ocean', 'sea']
senticnet['water_sky'] = ['-0.03', '0', '0.048', '0', '#sadness', '#anger', 'negative', '-0.04', 'crystal_clear', 'sun_cover', 'water_droplet', 'water', 'wet_air']
senticnet['water_snake'] = ['-0.51', '0', '0.625', '-0.98', '#anger', '#disgust', 'negative', '-0.70', 'common_water_snake', 'banded_water_snake', 'water_moccasin', 'grass_snake', 'ring_snake']
senticnet['water_soap'] = ['0', '0.095', '0', '0.074', '#interest', '#admiration', 'positive', '0.084', 'laundry_detergent', 'buy_wash_powder', 'clothes_dirty', 'laundry', 'use_water']
senticnet['water_solubility'] = ['-0.52', '0', '0.596', '-0.76', '#anger', '#disgust', 'negative', '-0.62', 'retention', 'retentiveness', 'retentivity', 'solidness', 'nonabsorbency']
senticnet['water_source'] = ['0', '0.097', '0', '0.16', '#interest', '#admiration', 'positive', '0.129', 'water_mass', 'water_body', 'much_water', 'small_ocean', 'water_feature']
senticnet['water_spill'] = ['-0.18', '-0.07', '0', '-0.20', '#sadness', '#disgust', 'negative', '-0.10', 'bad_drug', 'water', 'atmosphere', 'cool_water', 'obtain_water']
senticnet['water_splash'] = ['0', '0', '0', '0.169', '#admiration', '#admiration', 'positive', '0.169', 'arm_movement', 'ocean_movement', 'hand_gesture', 'sea_movement', 'goodbye']
senticnet['water_sport'] = ['0.928', '0.923', '0', '0', '#joy', '#interest', 'positive', '0.925', 'swim', 'water_activity', 'movement_of_water', 'backstroke', 'swimming_pool']
senticnet['water_spot'] = ['0.101', '0', '0', '0', '#joy', '#joy', 'positive', '0.101', 'get_cold', 'wet_clothes', 'wetness', 'become_cold', 'filthy']
senticnet['water_spray'] = ['0.095', '0', '-0.04', '0.062', '#joy', '#admiration', 'positive', '0.037', 'liquid_water', 'photographer', 'moisture', 'rise_water', 'droplet']
senticnet['water_stream'] = ['0', '0.879', '0', '0.926', '#interest', '#admiration', 'positive', '0.903', 'water_flow', 'flowing_water', 'small_stream', 'nile', 'big_lake']
senticnet['water_supply'] = ['0.728', '-0.51', '0', '0.811', '#joy', '#admiration', 'positive', '0.685', 'beverage', 'drinkable', 'potable', 'bottled_water', 'sugar_water']
senticnet['water_transport'] = ['0.034', '0', '0', '0.128', '#joy', '#admiration', 'positive', '0.081', 'water_movement', 'movement_of_water', 'water_motion', 'move_water', 'fast_water']
senticnet['water_transportation'] = ['0.153', '0', '0', '0.149', '#joy', '#admiration', 'positive', '0.151', 'paddle', 'raft', 'canoe', 'water_vehicle', 'ship']
senticnet['water_vapor'] = ['0', '0.073', '-0.05', '0.093', '#interest', '#admiration', 'positive', '0.038', 'vapor', 'condense_water', 'evaporate', 'gaseous', 'bubble']
senticnet['water_vapor_imagery'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'hurricane', 'natural_disaster', 'flood', 'atmospheric_pressure', 'tropical_storm']
senticnet['water_vapour'] = ['0.095', '0', '-0.04', '0.062', '#joy', '#admiration', 'positive', '0.037', 'photographer', 'water_spray', 'moisture', 'rise_water', 'droplet']
senticnet['water_vehicle'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'big_boat', 'boat', 'large_boat', 'yacht']
senticnet['waterboarding_technique'] = ['-0.65', '0', '0.718', '-0.73', '#anger', '#disgust', 'negative', '-0.70', 'inquirer', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['watercolor'] = ['0', '0.067', '0', '0.077', '#interest', '#admiration', 'positive', '0.072', 'pencil_mark', 'use_ink', 'script', 'pencil_paper', 'written_text']
senticnet['watercolor_paint'] = ['0', '0.067', '0', '0.077', '#interest', '#admiration', 'positive', '0.072', 'pencil_mark', 'use_ink', 'script', 'pencil_paper', 'written_text']
senticnet['watercolor_paper'] = ['0', '0.068', '0', '0', '#interest', '#interest', 'positive', '0.068', 'artist', 'oil_paint', 'painter', 'creative_person', 'make_paint']
senticnet['watercolorist'] = ['0.845', '0', '0', '0.957', '#joy', '#admiration', 'positive', '0.901', 'portraitist', 'miniaturist', 'printmaker', 'lutenist', 'horologist']
senticnet['watercooler'] = ['-0.86', '0', '0', '-0.76', '#sadness', '#disgust', 'negative', '-0.81', 'sympathize', 'commiseration', 'feel_sorry', 'ruth', 'pathos']
senticnet['watercourse'] = ['0.933', '0.795', '0', '0', '#joy', '#interest', 'positive', '0.864', 'water_passage', 'small_stream', 'water_flow', 'flowing_water', 'water_stream']
senticnet['watercraft'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'yacht', 'military_ship', 'boat', 'lifeboat']
senticnet['watercress'] = ['0.758', '-0.97', '0', '0.851', '#surprise', '#admiration', 'positive', '0.861', 'marsh_cress', 'yellow_watercress', 'mountain_watercress', 'common_watercress', 'great_yellowcress']
senticnet['watercress_salad'] = ['0.52', '-0.69', '0', '0.639', '#surprise', '#admiration', 'positive', '0.617', 'black_bread', 'pumpernickel', 'limpa', 'bread', 'rye_bread']
senticnet['watereth'] = ['0.969', '-0.86', '0', '0.631', '#joy', '#surprise', 'positive', '0.823', 'assuage', 'perfect', 'make_look_better', 'relieve', 'sublimate']
senticnet['waterfall'] = ['0', '0', '0.015', '-0.08', '#anger', '#disgust', 'negative', '-0.04', 'riverbed', 'bottom_ocean', 'surface_earth', 'pond', 'arctic']
senticnet['waterfront'] = ['0.021', '0.108', '0', '0.083', '#interest', '#admiration', 'positive', '0.071', 'beach', 'ocean_beach', 'ocean', 'coast', 'sea']
senticnet['waterfront_restaurant'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'drifter', 'bum', 'sundowner']
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senticnet['watering_hole'] = ['0.165', '-0.07', '0.033', '0', '#joy', '#surprise', 'positive', '0.068', 'metal_stick', 'drinking_place', 'bart', 'meet_place', 'water_hole']
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senticnet['watermark'] = ['0.81', '0.763', '0', '0', '#joy', '#interest', 'positive', '0.786', 'dollar_bill', 'franc', 'euro', 'dollar', 'cent']
senticnet['watermelon'] = ['-0.98', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.90', 'raisin', 'seedless', 'apricot', 'plum', 'grape']
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senticnet['waterpark'] = ['0.056', '-0.01', '0.036', '0.073', '#joy', '#admiration', 'positive', '0.028', 'thrill_ride', 'have_fun', 'drag_race', 'enjoyment', 'roller_coaster']
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senticnet['waterproof_mascara'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'sexual_excitement', 'sexy', 'wet_lip', 'kiss', 'clean_up']
senticnet['watershed_management'] = ['0', '0', '0', '0.857', '#admiration', '#admiration', 'positive', '0.857', 'say', 'beckon', 'call', 'conserve', 'retort']
senticnet['waterslide'] = ['0', '0', '0', '0.964', '#admiration', '#admiration', 'positive', '0.964', 'swimming_pool', 'swim', 'swimsuit', 'lake', 'have_fun']
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senticnet['waterton_glacier_international'] = ['0.497', '0.453', '-0.23', '0.189', '#joy', '#interest', 'positive', '0.226', 'calm', 'calmness', 'stillness', 'truce', 'love']
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senticnet['waterwheel'] = ['-0.78', '0', '0.699', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'battery', 'stamp_battery', 'quartz_battery', 'quartz_mill', 'factory']
senticnet['watery'] = ['0.952', '0.716', '0', '0', '#joy', '#interest', 'positive', '0.834', 'wetness', 'water_spray', 'rain_water', 'wet_cold', 'essential_human_life']
senticnet['watery_diarrhea'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'inflammatory_disease', 'stomach_flu', 'intestinal_flu', 'pelvic_inflammatory_disease']
senticnet['watery_diarrhoea'] = ['-0.75', '0', '0.84', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'angina', 'inflammatory_disease', 'stomach_flu', 'intestinal_flu', 'pelvic_inflammatory_disease']
senticnet['watery_fluid'] = ['0', '0', '0', '-0.96', '#disgust', '#disgust', 'negative', '-0.96', 'thick_substance', 'viscous_substance', 'mucous_substance', 'disgusting', 'disgust']
senticnet['watery_soup'] = ['0', '0', '0.919', '-0.78', '#anger', '#disgust', 'negative', '-0.85', 'green_bread', 'rotten', 'disgusting', 'taste_awful', 'mouldy']
senticnet['watmough'] = ['0', '0.996', '0', '0.85', '#interest', '#admiration', 'positive', '0.923', 'strength', 'strong', 'physical_strength', 'athlete', 'muscle']
senticnet['watt'] = ['0', '0', '-1', '-1', '#fear', '#disgust', 'negative', '-1.0', 'moon_gravity', 'force', 'volt', 'coercion', 'power_source']
senticnet['watt_amplifier'] = ['0.036', '-0.04', '0.027', '0.063', '#surprise', '#admiration', 'positive', '0.028', 'radio', 'speaker', 'channel_selector', 'power_switch', 'female_television_connector']
senticnet['watt_bulb'] = ['-0.63', '0', '0.897', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'cd-rom', 'international_candle', 'candle', 'candlepower_unit', 'standard_candle']
senticnet['wattle'] = ['-0.86', '0', '0.829', '0', '#sadness', '#anger', 'negative', '-0.84', 'gun_carriage', 'clotheshorse', 'undercarriage', 'frame', 'framework']
senticnet['wattle_acacia'] = ['0', '0', '-0.14', '-0.03', '#fear', '#disgust', 'negative', '-0.09', 'pine_cone', 'fallen_leaf', 'poison_ivy', 'detritus', 'tress']
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senticnet['wav_format'] = ['0.055', '0', '0.03', '0.136', '#joy', '#admiration', 'positive', '0.054', 'loud_soft', 'decibel', 'loudness', 'sound', 'melodic_noise']
senticnet['wave'] = ['0.031', '0.158', '-0.16', '0.194', '#interest', '#admiration', 'positive', '0.055', 'farewell', 'flow', 'salute', 'multiple_purposes', 'wave_wind']
senticnet['wave_amplitude'] = ['-0.93', '0', '0.698', '-0.85', '#sadness', '#disgust', 'negative', '-0.83', 'candlepower', 'light_intensity', 'acoustic_power', 'sound_pressure_level', 'threshold_level']
senticnet['wave_arm'] = ['0.733', '0.789', '0', '0', '#joy', '#interest', 'positive', '0.761', 'greet', 'salute', 'smile', 'say_goodbye_to', 'attract_attention']
senticnet['wave_crowd'] = ['0.887', '0', '0', '0.881', '#joy', '#admiration', 'positive', '0.884', 'hide_body', 'quicken', 'run_away', 'grab_ball', 'run_president']
senticnet['wave_equation'] = ['-0.65', '0', '0.65', '-0.98', '#sadness', '#disgust', 'negative', '-0.76', 'assumption', 'precondition', 'provision', 'constraint', 'premiss']
senticnet['wave_flag'] = ['0.995', '0.747', '0', '0', '#joy', '#interest', 'positive', '0.871', 'age_gracefully', 'hurt_animal', 'kill_specific_group', 'feel_proud', 'lose_key']
senticnet['wave_friend'] = ['0.706', '0.796', '0', '0', '#joy', '#interest', 'positive', '0.751', 'naive', 'welcome_friend', 'greet', 'needy', 'captain_boat']
senticnet['wave_good_bye'] = ['-0.12', '0', '-0.05', '-0.02', '#sadness', '#fear', 'negative', '-0.06', 'throw_fit', 'bye', 'greet', 'battle_illness', 'honor_parent']
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senticnet['wave_hand'] = ['0.895', '0.765', '0', '0', '#joy', '#interest', 'positive', '0.83', 'position_furniture', 'speak_english', 'deal_card_player', 'forward_email', 'explain_everything']
senticnet['wave_hello'] = ['0.743', '0.865', '0', '0', '#joy', '#interest', 'positive', '0.804', 'position_furniture', 'deal_card_player', 'mind_store', 'rush_job', 'explain_everything']
senticnet['wave_impedance_transformer'] = ['0.834', '0.932', '0', '0', '#joy', '#interest', 'positive', '0.883', 'dime', 'nickel', 'metal_money', 'coin', 'money']
senticnet['wave_off'] = ['0.739', '-0.76', '0', '0.977', '#surprise', '#admiration', 'positive', '0.827', 'accent', 'stress', 'play_down', 'emphasize', 'punctuate']
senticnet['wave_velocity'] = ['-0.03', '0.178', '0', '0.168', '#interest', '#admiration', 'positive', '0.104', 'speed', 'fast', 'fast_and_furious', 'speedy', 'fleet']
senticnet['wave_wind'] = ['0.037', '0.161', '-0.15', '0.203', '#interest', '#admiration', 'positive', '0.062', 'wave', 'flow', 'salute', 'multiple_purposes', 'farewell']
senticnet['waved_goodbye'] = ['-0.12', '0', '-0.05', '-0.02', '#sadness', '#fear', 'negative', '-0.06', 'throw_fit', 'bye', 'greet', 'battle_illness', 'honor_parent']
senticnet['waveform'] = ['0', '0.098', '-0.06', '0.15', '#interest', '#admiration', 'positive', '0.061', 'make_book', 'use_ink', 'make_newspaper', 'pencil_mark', 'computer_command']
senticnet['waveguide'] = ['-0.05', '0.068', '-0.10', '-0.08', '#fear', '#disgust', 'negative', '-0.04', 'tooth_decay', 'body_ache', 'symptom', 'pierce', 'tooth']
senticnet['waveland'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
senticnet['wavelength'] = ['-0.04', '0.096', '-0.04', '0.084', '#interest', '#admiration', 'positive', '0.023', 'meaning', 'green_orange', 'yellow_green', 'red_green', 'red_orange']
senticnet['wavelength_range'] = ['-0.07', '0.078', '0', '0.078', '#interest', '#admiration', 'positive', '0.028', 'red_blue', 'red_orange', 'green_yellow', 'hue_tint', 'yellow_green']
senticnet['wavelike'] = ['0', '0', '0.95', '-0.94', '#anger', '#disgust', 'negative', '-0.94', 'undulation', 'undulating', 'undulated', 'rippling', 'undulation']
senticnet['wavenumber'] = ['0.807', '-0.88', '0', '0.718', '#joy', '#surprise', 'positive', '0.802', 'sex', 'gayness', 'sexual_gayness', 'lesbianism', 'sapphism']
senticnet['waver'] = ['-0.88', '0', '0.925', '-0.55', '#sadness', '#anger', 'negative', '-0.78', 'falter', 'hesitate', 'linger', 'boggle', 'dwell_on']
senticnet['wavering'] = ['0.924', '-0.83', '0', '0.654', '#joy', '#surprise', 'positive', '0.805', 'indecisiveness', 'doubt', 'incertitude', 'dubiety', 'doubtfulness']
senticnet['waverley_abbey'] = ['0.049', '0.16', '0', '0.155', '#interest', '#admiration', 'positive', '0.121', 'castle', 'palace', 'medieval', 'aristocrat', 'throne']
senticnet['waving_goodbye'] = ['0', '0.145', '0', '0.2', '#interest', '#admiration', 'positive', '0.172', 'hand_gesture', 'arm_movement', 'water_splash', 'hand_motion', 'water_form']
senticnet['waving_hello'] = ['-0.12', '0', '-0.05', '-0.02', '#sadness', '#fear', 'negative', '-0.06', 'throw_fit', 'bye', 'greet', 'battle_illness', 'honor_parent']
senticnet['wavy'] = ['-0.96', '0', '-0.70', '0', '#sadness', '#fear', 'negative', '-0.83', 'curly', 'frizzy', 'hair', 'hairstyle', 'undulating']
senticnet['wavy_black'] = ['-0.60', '0', '0.895', '-0.54', '#sadness', '#anger', 'negative', '-0.68', 'coal_black', 'black', 'pitch_black', 'soot_black', 'achromatic_color']
senticnet['wavy_hair'] = ['0', '0', '0.841', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'ancient', 'many_years', 'year', 'old', 'teen']
senticnet['wavy_line'] = ['0.03', '0', '0.017', '0.138', '#joy', '#admiration', 'positive', '0.05', 'up_down', 'curl', 'bump', 'hand_gesture', 'goodbye']
senticnet['wawl'] = ['-0.87', '0', '0.591', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'howl', 'wail', 'ululate', 'roar', 'yawl']
senticnet['wax'] = ['0', '0', '0', '0.1', '#admiration', '#admiration', 'positive', '0.1', 'find_earth', 'polish', 'plutonium', 'shine', 'melt']
senticnet['wax_bean'] = ['0.861', '-0.54', '0', '0.976', '#joy', '#admiration', 'positive', '0.795', 'kidney_bean', 'frijol', 'frijole', 'green_bean', 'common_bean']
senticnet['wax_candle'] = ['0', '0.232', '-0.10', '0.156', '#interest', '#admiration', 'positive', '0.094', 'light', 'eat_cake', 'birthday_party', 'candle_light', 'romantic_atmosphere']
senticnet['wax_dental_floss'] = ['0.973', '0.963', '0', '0', '#joy', '#interest', 'positive', '0.968', 'fluoride_toothpaste', 'tartar_control_toothpaste', 'dental_floss', 'gel_toothpaste', 'disposable_razor']
senticnet['wax_eloquent'] = ['0.909', '-0.85', '0', '0.846', '#joy', '#surprise', 'positive', '0.87', 'respectability', 'reputability', 'venerability', 'venerableness', 'righteousness']
senticnet['wax_palm'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'carnauba_palm', 'carnauba', 'fan_palm', 'palmetto', 'palmyra']
senticnet['wax_paper'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'gift_wrap', 'kraft', 'kraft_paper', 'wrapping_paper', 'paper']
senticnet['wax_seal'] = ['-0.98', '0', '0.664', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'unfortunate', 'unfortunate_person', 'weeper', 'pallgriever', 'griever']
senticnet['wax_tablet'] = ['0.719', '0.757', '0', '0', '#joy', '#interest', 'positive', '0.738', 'matriculate', 'enroll_class', 'enroll_school', 'enroll', 'register']
senticnet['wax_tadpole'] = ['0.876', '0.948', '0', '0', '#joy', '#interest', 'positive', '0.912', 'sting', 'tidbit', 'nip', 'tiny_amount', 'piece']
senticnet['waxed_paper'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'foil', 'tin_foil', 'gold_foil']
senticnet['waxen'] = ['-0.66', '0', '0.782', '-0.52', '#sadness', '#anger', 'negative', '-0.65', 'lividness', 'pallidness', 'pallor', 'complexion', 'achromasia']
senticnet['waxing_gibbous_moon'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['waxing_moon'] = ['0.037', '0.09', '0.049', '0.223', '#interest', '#admiration', 'positive', '0.075', 'kick_ass', 'full_moon_party', 'wave_crowd', 'special_day', 'calendar']
senticnet['waxwork'] = ['-0.82', '0', '0.758', '-0.70', '#sadness', '#anger', 'negative', '-0.76', 'unclassifiable', 'unrecognizable', 'unidentifiable', 'intangible', 'undiagnosable']
senticnet['waxy'] = ['0', '0.769', '0', '0.707', '#interest', '#admiration', 'positive', '0.738', 'yellowish', 'brownish', 'rubbery', 'leathery', 'whitish']
senticnet['waxy_cuticle'] = ['0', '0.097', '0', '0.085', '#interest', '#admiration', 'positive', '0.091', 'lunula', 'arm_part', 'leave_hand', 'ten_fingers', 'five_fingers']
senticnet['waxy_layer'] = ['0.025', '-0.00', '0', '0.125', '#joy', '#admiration', 'positive', '0.053', 'goo', 'adhesive', 'flour_water', 'insert', 'thick_liquid']
senticnet['waxy_substance'] = ['0.025', '-0.00', '0', '0.125', '#joy', '#admiration', 'positive', '0.053', 'goo', 'adhesive', 'flour_water', 'insert', 'thick_liquid']
senticnet['way'] = ['-0.04', '0.136', '0', '0.105', '#interest', '#admiration', 'positive', '0.067', 'street', 'path', 'long_road', 'small_street', 'road']
senticnet['way_of_acting'] = ['0', '0.847', '0', '0.793', '#interest', '#admiration', 'positive', '0.82', 'behavior', 'good_etiquette', 'manner', 'niceness', 'good_behavior']
senticnet['wayfarer'] = ['0.756', '0', '0', '0', '#joy', '#joy', 'positive', '0.756', 'companionship', 'cherish', 'lover', 'bond', 'buddy']
senticnet['wayfaring_stranger'] = ['0', '0.176', '0', '0.216', '#interest', '#admiration', 'positive', '0.196', 'choir_stall', 'worshiper', 'pew', 'fundamentalist', 'pulpit']
senticnet['waylay'] = ['0', '0', '-0.89', '-0.87', '#fear', '#disgust', 'negative', '-0.88', 'ambuscade', 'lurk', 'ambush', 'scupper', 'bushwhack']
senticnet['waynhim'] = ['-0.04', '0.166', '0.684', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'malign', 'malefic', 'maliciously', 'spitefulness', 'maliciousness']
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senticnet['waziristan'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'webpage', 'store_record', 'ideal']
senticnet['wb_network'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'kit_fox', 'prairie_fox', 'white_fox']
senticnet['wba_heavyweight_title'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['wbc_light_heavyweight_title'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['wbc_lightweight_title'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['wbc_youth'] = ['0', '0', '0', '0.834', '#admiration', '#admiration', 'positive', '0.834', 'royal_person', 'royal_title', 'top', 'master', 'rank']
senticnet['wbo_champion'] = ['0.944', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.967', 'meeting', 'meet', 'match', 'encounter', 'round']
senticnet['wbo_cruiserweight_title'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['wbo_heavyweight_title'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['wchar'] = ['-0.04', '0', '-0.11', '-0.03', '#sadness', '#fear', 'negative', '-0.06', 'chemical_reaction', 'burn', 'chemical_process', 'get_burnt', 'set_on_fire']
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senticnet['weak_acid'] = ['0.972', '0.861', '0', '0', '#joy', '#interest', 'positive', '0.916', 'pacifism', 'communication', 'verbal_contract', 'good_fortune', 'sincere']
senticnet['weak_body'] = ['-0.85', '0', '0.949', '-0.83', '#sadness', '#anger', 'negative', '-0.88', 'disease', 'cancer', 'leukemia', 'death', 'feel_sick']
senticnet['weak_demand'] = ['0', '0.285', '-0.07', '0.163', '#interest', '#admiration', 'positive', '0.124', 'plus', 'importance', 'a_lot', 'bonus', 'extra']
senticnet['weak_pulse'] = ['-0.72', '0', '0.762', '-0.79', '#anger', '#disgust', 'negative', '-0.75', 'heat_hyperpyrexia', 'heat_prostration', 'prostration', 'sunstroke', 'thermic_fever']
senticnet['weak_tropical_storm'] = ['-0.02', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.05', 'atmospheric_phenomenon', 'natural_disaster', 'flood', 'destruction', 'tropical_storm']
senticnet['weaken'] = ['0.847', '-0.80', '0', '0.711', '#joy', '#surprise', 'positive', '0.789', 'mollify', 'enervate', 'stultify', 'wash_out', 'cripple']
senticnet['weakened'] = ['-0.98', '0', '0.583', '-0.79', '#sadness', '#disgust', 'negative', '-0.78', 'decreased', 'diminished', 'remittent', 'faded', 'small']
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senticnet['weaker_euro'] = ['0.771', '0.846', '0', '0', '#joy', '#interest', 'positive', '0.808', 'dollar_bill', 'franc', 'banknote', 'dollar', 'cent']
senticnet['weakland'] = ['-0.95', '0', '0.673', '-0.85', '#sadness', '#disgust', 'negative', '-0.82', 'discharge', 'free', 'dethrone', 'disinvest', 'unfrock']
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senticnet['wealthy_widow'] = ['0.976', '0.811', '0', '0', '#joy', '#interest', 'positive', '0.893', 'deposit', 'bank_loan', 'wealth', 'finance', 'account']
senticnet['wean'] = ['0', '0.811', '0', '0.7', '#interest', '#admiration', 'positive', '0.756', 'weaning', 'weaned', 'weening', 'breastfeed', 'medicate']
senticnet['weaned'] = ['0', '0.804', '0', '0.697', '#interest', '#admiration', 'positive', '0.75', 'weaning', 'wean', 'weening', 'breastfeed', 'medicate']
senticnet['weaning'] = ['0', '0.809', '0', '0.691', '#interest', '#admiration', 'positive', '0.75', 'wean', 'weaned', 'weening', 'breastfeed', 'medicate']
senticnet['weaning_food'] = ['0.053', '0.085', '0.019', '0', '#joy', '#interest', 'positive', '0.04', 'farm', 'countryside', 'farm_animal', 'cow', 'healthy']
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senticnet['weapon_grade_uranium'] = ['-0.03', '-0.03', '-0.05', '-0.20', '#fear', '#disgust', 'negative', '-0.06', 'nuclear_bomb', 'bomb', 'explosive', 'atomic_bomb', 'grenade']
senticnet['weapon_omega'] = ['-0.88', '0', '0.882', '-0.58', '#sadness', '#anger', 'negative', '-0.78', 'mortal', 'behemoth', 'unusual_person', 'aberrant', 'giant']
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senticnet['weapon_platoon'] = ['0', '0', '-0.06', '-0.12', '#fear', '#disgust', 'negative', '-0.09', 'army', 'attacker', 'military', 'soldier', 'infantry']
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senticnet['weaponized_anthrax'] = ['-0.90', '0', '0.529', '-0.84', '#sadness', '#disgust', 'negative', '-0.75', 'poison_gas', 'nerve_agent', 'mustard_gas', 'mustard_agent', 'blistering_agent']
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senticnet['wear_bath_suit'] = ['-0.07', '0', '0.009', '0', '#sadness', '#anger', 'negative', '-0.04', 'put_swimsuit', 'go_pool', 'put_bath_suit', 'wear_swim_suit', 'go_lake']
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senticnet['wear_clean_clothes'] = ['0', '0.842', '0', '0', '#interest', '#interest', 'positive', '0.842', 'smell_nice', 'personal_hygiene', 'remove_dirt_body', 'clean_clothes', 'become_clean']
senticnet['wear_clean_underwear'] = ['0.973', '0', '0', '0', '#joy', '#joy', 'positive', '0.973', 'make_doctor_appointment', 'schedule_appointment_doctor', 'doctor_appointment', 'call_doctor', 'make_appointment_with_doctor']
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senticnet['wear_dress'] = ['0', '0.842', '0', '0', '#interest', '#interest', 'positive', '0.842', 'wear_clothes', 'go_outside', 'fancy_dress', 'dress', 'wear_good_shoes']
senticnet['wear_glass'] = ['0.837', '0', '0', '0', '#joy', '#joy', 'positive', '0.837', 'ability_see', 'near_tv', 'open_eye', 'turn_on_tv', 'eyesight']
senticnet['wear_glasses'] = ['-0.96', '0', '0', '-0.71', '#sadness', '#disgust', 'negative', '-0.83', 'nerd', 'big_glasses', 'computer_lover', 'geek', 'blind']
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senticnet['wear_hat'] = ['0.028', '0.149', '-0.06', '0.121', '#interest', '#admiration', 'positive', '0.059', 'serve_beer', 'raise_hell', 'drink_tea', 'drink_juice', 'drink_much_beer']
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senticnet['wear_jacket'] = ['0.069', '0', '0', '0', '#joy', '#joy', 'positive', '0.069', 'knit_sweater', 'make_drape', 'weave', 'weave_cloth', 'cover_body']
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senticnet['wear_lipstick'] = ['0.824', '0.86', '0', '0.983', '#interest', '#admiration', 'positive', '0.889', 'look_smart', 'social_acceptance', 'improve_appearance', 'improve_look', 'look_good']
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senticnet['wear_mask'] = ['-0.91', '0', '0.429', '-0.96', '#sadness', '#disgust', 'negative', '-0.76', 'hide_identity', 'hide_face', 'bad_person', 'thief', 'robbery']
senticnet['wear_necklace'] = ['0.789', '0', '0', '0.878', '#joy', '#admiration', 'positive', '0.834', 'look_good', 'world_peace', 'gain_respect', 'experience_life', 'strong_relationship']
senticnet['wear_nice_clothes'] = ['0', '0.913', '0', '0.852', '#interest', '#admiration', 'positive', '0.883', 'little_hair', 'improve_look', 'short_hair', 'look_different', 'increase_self-confidence']
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senticnet['wear_old_clothes'] = ['0', '0.995', '0', '0', '#interest', '#interest', 'positive', '0.995', 'use_soap', 'use_water', 'launder', 'sort_clothes', 'clean_product']
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senticnet['wear_out_shoes'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'become_physically_fit', 'physical_exertion', 'exercise_dog', 'much_exercise', 'vigorous_exercise']
senticnet['wear_pantyhose'] = ['0', '-0.07', '0.099', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'prescription_medication', 'lingerie', 'sachet', 'shave_kit', 'panties']
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senticnet['wear_protective_clothing'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'change_tire', 'fix_flat_tire', 'play_rugby', 'wear_silly_hat', 'become_adult']
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senticnet['wear_raincoat'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'carry_umbrella', 'rain_parade', 'turn_heat', 'turn_aircon_on', 'read_bedtime_story']
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senticnet['wear_robe'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'fashion', 'become_judge', 'supreme_court', 'arbitrator', 'judge_jury']
senticnet['wear_running_shoes'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'put_running_shoes', 'wear_good_shoes', 'put_shoes', 'use_leg', 'put_jogging_shoes']
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senticnet['wear_shirt'] = ['0.979', '0.948', '0', '0', '#joy', '#interest', 'positive', '0.964', 'clothes', 'piece_of_clothing', 'wear_clothes', 'fabric_shop', 'erode']
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senticnet['wear_skirt'] = ['0.925', '0.724', '0', '0', '#joy', '#interest', 'positive', '0.825', 'dig_grave', 'bicycle_across_town', 'service_car', 'look_good', 'marry_man']
senticnet['wear_sock'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'wear_clothes', 'eat_dessert', 'keep_warm', 'floss_tooth', 'put_on_sock']
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senticnet['wear_swim_suit'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'put_swimsuit', 'go_pool', 'put_bath_suit', 'wear_bath_suit', 'go_lake']
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senticnet['wear_underwear'] = ['0.824', '0.86', '0', '0.983', '#interest', '#admiration', 'positive', '0.889', 'look_smart', 'social_acceptance', 'improve_appearance', 'improve_look', 'look_good']
senticnet['wear_uniform'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'travel_work', 'fly_plane', 'push_limit', 'lock_up', 'carelessness']
senticnet['wear_upon'] = ['0.589', '-0.54', '0', '0.755', '#joy', '#admiration', 'positive', '0.629', 'tucker', 'tucker_out', 'exhaust', 'beat', 'overtire']
senticnet['wearability'] = ['0.99', '-0.88', '0', '0.831', '#joy', '#surprise', 'positive', '0.9', 'functionality', 'sensibleness', 'viability', 'usefulness', 'realism']
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senticnet['wearing_sunscreen'] = ['0', '0', '0', '0.968', '#admiration', '#admiration', 'positive', '0.968', 'change_tire', 'fix_flat_tire', 'play_rugby', 'wear_silly_hat', 'become_adult']
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senticnet['weening'] = ['0', '0.809', '0', '0.69', '#interest', '#admiration', 'positive', '0.75', 'weaning', 'weaned', 'wean', 'breastfeed', 'medicate']
senticnet['weep'] = ['-0.70', '0', '0', '-0.90', '#sadness', '#disgust', 'negative', '-0.80', 'bawl', 'sob', 'shed_tear', 'cry_hard', 'wail']
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senticnet['weeper'] = ['-0.98', '0', '0.664', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'unfortunate', 'unfortunate_person', 'griever', 'pallbearer', 'bearer']
senticnet['weepie'] = ['0.85', '-0.71', '0', '0.617', '#joy', '#surprise', 'positive', '0.728', 'pretender', 'phony', 'phoney', 'hypocrite', 'dissembler']
senticnet['weepiness'] = ['-0.71', '0.312', '-0.4', '-0.08', '#sadness', '#fear', 'negative', '-0.22', 'lachrymose', 'tearful', 'weeping', 'dolorous', 'tearfulness']
senticnet['weeping'] = ['-0.71', '0.312', '-0.4', '-0.08', '#sadness', '#fear', 'negative', '-0.22', 'lachrymose', 'tearful', 'dolorous', 'tearfulness', 'weepiness']
senticnet['weeping_bitterly'] = ['-0.85', '0.21', '-0.56', '0', '#sadness', '#fear', 'negative', '-0.40', 'doleful', 'sadly', 'dolefully', 'ruefulness', 'wretched']
senticnet['weetabix'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'veal', 'beef', 'food_group', 'animal_flesh', 'curry']
senticnet['weft'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_cloth']
senticnet['wehrmacht'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'healthy_person', 'man', 'pervert', 'compatriot']
senticnet['weibull_distribution'] = ['-0.59', '0', '0.672', '-0.83', '#anger', '#disgust', 'negative', '-0.7', 'geometrical_irregularity', 'radial_asymmetry', 'lopsidedness', 'asymmetry', 'irregularity']
senticnet['weigh'] = ['0.939', '0.864', '0', '0', '#joy', '#interest', 'positive', '0.901', 'proportion', 'weight_measurement', 'weight', 'measurement_device', 'measure_pound']
senticnet['weigh_consequence'] = ['-0.17', '0.111', '-0.11', '-0.14', '#sadness', '#disgust', 'negative', '-0.07', 'good_plan', 'overpower', 'commit_crime', 'fight_battle', 'get_revenge']
senticnet['weigh_much'] = ['0', '0.085', '0.013', '0.212', '#interest', '#admiration', 'positive', '0.095', 'heavy', 'dense', 'smooth_surface', 'magnetic', 'precious']
senticnet['weighed_against'] = ['0.975', '-0.99', '0', '0.644', '#joy', '#surprise', 'positive', '0.871', 'demesne', 'barony', 'seigneury', 'seigniory', 'immovable']
senticnet['weighed_down'] = ['-0.82', '0.226', '-0.46', '-0.05', '#sadness', '#fear', 'negative', '-0.27', 'heaviness', 'heavy_weight', 'heavy_person', 'heavy_breathe', 'heavy_air']
senticnet['weighs_approximately'] = ['0.764', '0.985', '0', '0', '#joy', '#interest', 'positive', '0.875', 'heavy_weight', 'weigh', 'weight_measurement', 'heaviness', 'proportion']
senticnet['weight'] = ['0.764', '0.985', '0', '0', '#joy', '#interest', 'positive', '0.875', 'heavy_weight', 'weigh', 'weight_measurement', 'heaviness', 'proportion']
senticnet['weight_bearing_exercise'] = ['-0.06', '-0.02', '0.021', '-0.06', '#sadness', '#disgust', 'negative', '-0.03', 'clean_window', 'vacuum_carpet', 'repetitive_task', 'run_long_time', 'exercise_daily']
senticnet['weight_gain'] = ['0.964', '0', '0', '0', '#joy', '#joy', 'positive', '0.964', 'become_little_hungry', 'impress_girlfriend', 'eat_pizza', 'eat', 'satisfy_crave']
senticnet['weight_lift'] = ['0.076', '0', '0', '0', '#joy', '#joy', 'positive', '0.076', 'increase_metabolism', 'feel_fit', 'become_healthy', 'exercise', 'big_muscle']
senticnet['weight_loss'] = ['0.076', '0', '0', '0', '#joy', '#joy', 'positive', '0.076', 'increase_metabolism', 'big_muscle', 'feel_fit', 'diet', 'become_healthy']
senticnet['weight_loss_diet'] = ['0.076', '0', '0', '0', '#joy', '#joy', 'positive', '0.076', 'increase_metabolism', 'big_muscle', 'feel_fit', 'diet', 'become_healthy']
senticnet['weight_loss_medication'] = ['-0.72', '0', '0.973', '-0.57', '#sadness', '#anger', 'negative', '-0.76', 'corpulency', 'blubber', 'fat', 'embonpoint', 'stoutness']
senticnet['weight_loss_regimen'] = ['0.076', '0', '0', '0', '#joy', '#joy', 'positive', '0.076', 'increase_metabolism', 'big_muscle', 'feel_fit', 'diet', 'become_healthy']
senticnet['weight_loss_supplement'] = ['0.076', '0', '0', '0', '#joy', '#joy', 'positive', '0.076', 'increase_metabolism', 'big_muscle', 'feel_fit', 'diet', 'become_healthy']
senticnet['weight_machine'] = ['0', '0.142', '0', '0.125', '#interest', '#admiration', 'positive', '0.134', 'pulley', 'tool', 'exercise_equipment', 'wrench', 'clamp']
senticnet['weight_measurement'] = ['-0.03', '0.099', '0.056', '0.154', '#interest', '#admiration', 'positive', '0.041', 'heavy_weight', 'measure_pound', 'measurement_device', 'proportion', 'heaviness']
senticnet['weight_range'] = ['0', '0.254', '-0.13', '0.298', '#interest', '#admiration', 'positive', '0.138', 'sport', 'tennis', 'set_point', 'violent_action', 'match_point']
senticnet['weight_ratio'] = ['-0.53', '0', '0.765', '-0.94', '#anger', '#disgust', 'negative', '-0.74', 'jaggedness', 'unregularity', 'unevenness', 'irregularity', 'patchiness']
senticnet['weight_reduction'] = ['0.076', '0', '0', '0', '#joy', '#joy', 'positive', '0.076', 'increase_metabolism', 'big_muscle', 'feel_fit', 'diet', 'become_healthy']
senticnet['weighted_sum'] = ['0', '0.203', '0.036', '0.204', '#interest', '#admiration', 'positive', '0.124', 'amount', 'count', 'total', 'quantity', 'total_amount']
senticnet['weightlift'] = ['-0.75', '0', '0.794', '-0.58', '#sadness', '#anger', 'negative', '-0.71', 'jerk', 'snatch', 'military_press', 'clean', 'bench_press']
senticnet['weightlifting'] = ['-0.60', '0', '0.814', '-0.89', '#anger', '#disgust', 'negative', '-0.76', 'jerk', 'snatch', 'military_press', 'clean', 'bench_press']
senticnet['weighty'] = ['0', '0', '-0.84', '-0.84', '#fear', '#disgust', 'negative', '-0.84', 'grave', 'telling', 'heavy', 'cogent', 'grievous']
senticnet['weimaraner'] = ['0.039', '0', '0', '0.059', '#joy', '#admiration', 'positive', '0.049', 'big_dog', 'dog_cat', 'smart_dog', 'pet', 'domesticated_animal']
senticnet['weinmannia'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['weird'] = ['-0.84', '0', '-0.96', '-0.82', '#sadness', '#fear', 'negative', '-0.87', 'strange', 'uneducated', 'ignorant', 'precise', 'contrary']
senticnet['weird_behavior'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'antisocial', 'attitude', 'disorder', 'sociopathic', 'unfriendly']
senticnet['weird_feeling'] = ['-0.80', '0', '-0.8', '0', '#sadness', '#fear', 'negative', '-0.80', 'sinking', 'misgiving', 'qualm', 'scruple', 'insecurely']
senticnet['weird_stuff'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['weirdly'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['weirdo'] = ['-0.64', '0', '0.781', '-0.74', '#anger', '#disgust', 'negative', '-0.72', 'lunatic', 'crazy', 'loony', 'looney', 'nutcase']
senticnet['welche'] = ['0.764', '-0.61', '0', '0.878', '#joy', '#admiration', 'positive', '0.754', 'feculence', 'befoulment', 'defilement', 'dinginess', 'dirtiness']
senticnet['welcher'] = ['-0.52', '0', '0.506', '-0.94', '#sadness', '#disgust', 'negative', '-0.65', 'gouger', 'swindler', 'defrauder', 'fraud', 'chiseler']
senticnet['welcome'] = ['0', '0.779', '0', '0', '#interest', '#interest', 'positive', '0.779', 'salutation', 'aye', 'please', 'nod', 'give']
senticnet['welcome_boost'] = ['0.66', '-0.85', '0', '0.744', '#surprise', '#admiration', 'positive', '0.752', 'further', 'promote', 'encourage', 'conduce', 'advance']
senticnet['welcome_friend'] = ['-0.10', '0.075', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.03', 'captain_boat', 'greet', 'needy', 'come_house', 'agree_disagree']
senticnet['welcome_guest'] = ['0', '0.755', '0', '0', '#interest', '#interest', 'positive', '0.755', 'eat_good_food', 'wipe_foot', 'greet', 'eat_well', 'eat_sushi']
senticnet['welcome_relief'] = ['0', '0', '0.954', '-0.76', '#anger', '#disgust', 'negative', '-0.85', 'fervid', 'ardent', 'impassioned', 'fervent', 'fiery']
senticnet['welcome_respite_from'] = ['0', '0', '0', '-0.76', '#disgust', '#disgust', 'negative', '-0.76', 'fevered', 'feverish', 'feverishly', 'buck_fever', 'expectantly']
senticnet['welcoming_atmosphere'] = ['0.777', '0.972', '0', '0.913', '#interest', '#admiration', 'positive', '0.887', 'serene', 'easy', 'adoration', 'togetherness', 'console']
senticnet['welcoming_attitude'] = ['0.58', '0.651', '-0.26', '0.308', '#joy', '#interest', 'positive', '0.319', 'well-disposed', 'approving', 'approve', 'sanctioned', 'approved']
senticnet['welcoming_ceremony'] = ['0.065', '0', '-0.05', '0.085', '#joy', '#admiration', 'positive', '0.032', 'join_club', 'social_skill', 'go_abroad', 'drive_around', 'ride_train']
senticnet['welcoming_environment'] = ['0.777', '0.972', '0', '0.913', '#interest', '#admiration', 'positive', '0.887', 'serene', 'easy', 'adoration', 'togetherness', 'console']
senticnet['weld'] = ['0', '0', '0', '0.114', '#admiration', '#admiration', 'positive', '0.114', 'move_part', 'make_go', 'mode_transportation', 'form_of_transportation', 'car_steer']
senticnet['welded_steel'] = ['0.72', '0', '0', '0.733', '#joy', '#admiration', 'positive', '0.726', 'spanner', 'gold', 'strong_material', 'platinum', 'durable']
senticnet['welded_together'] = ['-0.02', '0.103', '0.069', '0.151', '#interest', '#admiration', 'positive', '0.039', 'single', 'unit_of_measurement', 'do_it_yourself', 'enumeration', 'alone']
senticnet['welder'] = ['0.969', '0.904', '0', '0', '#joy', '#interest', 'positive', '0.936', 'attendant', 'manager', 'public_servant', 'specialist', 'accomplice']
senticnet['welding_equipment'] = ['-0.02', '-0.05', '0', '0.069', '#surprise', '#admiration', 'positive', '0.032', 'fixture', 'endowment', 'material', 'stuff', 'kit']
senticnet['welfare'] = ['0.915', '0', '0.705', '0.912', '#joy', '#admiration', 'positive', '0.374', 'reward', 'bribe', 'charity', 'profit', 'mastercard']
senticnet['well'] = ['0.728', '0.713', '0', '0.715', '#joy', '#admiration', 'positive', '0.719', 'brook', 'stream', 'well_done', 'creek', 'pond']
senticnet['well-adjusted'] = ['0.951', '0', '0', '0.857', '#joy', '#admiration', 'positive', '0.904', 'self-sufficient', 'self-confident', 'resourceful', 'self-sustaining', 'self-reliant']
senticnet['well-aimed'] = ['0', '0', '0.754', '-0.97', '#anger', '#disgust', 'negative', '-0.86', 'barrage', 'fusillade', 'withering', 'musketry', 'machine-gun']
senticnet['well-built'] = ['0', '-0.74', '0', '0.891', '#surprise', '#admiration', 'positive', '0.819', 'well-constructed', 'well-crafted', 'well-made', 'well-designed', 'well-priced']
senticnet['well-cast'] = ['-0.93', '0', '0', '-0.79', '#sadness', '#disgust', 'negative', '-0.86', 'woefully', 'mis-cast', 'overacting', 'miscast', 'snubbed']
senticnet['well-chosen'] = ['0', '-0.89', '0', '0.991', '#surprise', '#admiration', 'positive', '0.945', 'carefully-chosen', 'well-selected', 'well-crafted', 'well-placed', 'well-constructed']
senticnet['well-constructed'] = ['0', '-0.75', '0', '0.886', '#surprise', '#admiration', 'positive', '0.819', 'well-made', 'well-crafted', 'well-built', 'well-designed', 'well-priced']
senticnet['well-crafted'] = ['0', '-0.75', '0', '0.888', '#surprise', '#admiration', 'positive', '0.82', 'well-constructed', 'well-made', 'well-built', 'well-designed', 'well-priced']
senticnet['well-designed'] = ['0', '-0.75', '0', '0.888', '#surprise', '#admiration', 'positive', '0.82', 'well-constructed', 'well-crafted', 'well-built', 'well-made', 'well-priced']
senticnet['well-disposed'] = ['0.58', '0.644', '-0.27', '0.298', '#joy', '#interest', 'positive', '0.313', 'friendliness', 'approving', 'approve', 'sanctioned', 'approved']
senticnet['well-educated'] = ['0', '0.831', '0', '0.831', '#interest', '#admiration', 'positive', '0.831', 'well-read', 'well-spoken', 'erudite', 'well-traveled', 'well-informed']
senticnet['well-favored'] = ['0.909', '0.727', '0', '0', '#joy', '#interest', 'positive', '0.818', 'big', 'handsome', 'good-looking', 'better-looking', 'bighearted']
senticnet['well-fitting'] = ['0', '-0.99', '0', '-0.84', '#surprise', '#disgust', 'positive', '0.076', 'too-tight', 'worn-out', 'too-small', 'ill-fitting', 'ratty']
senticnet['well-fixed'] = ['0.752', '0.967', '0', '0', '#joy', '#interest', 'positive', '0.859', 'well-heeled', 'prosperous', 'well-to-do', 'comfortable', 'easy']
senticnet['well-heeled'] = ['0.75', '0.965', '0', '0', '#joy', '#interest', 'positive', '0.857', 'well-to-do', 'prosperous', 'well-fixed', 'comfortable', 'easy']
senticnet['well-informed'] = ['0', '0.836', '0', '0.833', '#interest', '#admiration', 'positive', '0.835', 'well-educated', 'well-spoken', 'erudite', 'well-traveled', 'well-read']
senticnet['well-intended'] = ['0', '-0.78', '0', '0.844', '#surprise', '#admiration', 'positive', '0.815', 'well-meant', 'well-intentioned', 'well-meaning', 'intentioned', 'halfhearted']
senticnet['well-intentioned'] = ['0', '-0.78', '0', '0.843', '#surprise', '#admiration', 'positive', '0.814', 'well-intended', 'well-meant', 'well-meaning', 'intentioned', 'halfhearted']
senticnet['well-kept'] = ['0', '-0.81', '0', '0.853', '#surprise', '#admiration', 'positive', '0.832', 'well-maintained', 'well-tended', 'manicured', 'spotlessly', 'landscaped']
senticnet['well-known'] = ['-0.72', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.77', 'best-loved', 'best-known', 'lesser-known', 'famous', 'renowned']
senticnet['well-made'] = ['0', '-0.74', '0', '0.895', '#surprise', '#admiration', 'positive', '0.82', 'well-constructed', 'well-crafted', 'well-built', 'well-designed', 'well-priced']
senticnet['well-maintained'] = ['0', '-0.81', '0', '0.849', '#surprise', '#admiration', 'positive', '0.833', 'well-kept', 'well-tended', 'manicured', 'spotlessly', 'landscaped']
senticnet['well-meaning'] = ['-0.79', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.89', 'well-intentioned', 'intentioned', 'good-hearted', 'well-intended', 'misguided']
senticnet['well-meant'] = ['0', '-0.77', '0', '0.847', '#surprise', '#admiration', 'positive', '0.813', 'well-intended', 'well-intentioned', 'well-meaning', 'intentioned', 'halfhearted']
senticnet['well-off'] = ['0', '0.902', '0', '0.935', '#interest', '#admiration', 'positive', '0.919', 'well-to-do', 'affluent', 'wealthier', 'better-off', 'middle-class']
senticnet['well-paid'] = ['-0.78', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.78', 'sinecure', 'sinecure', 'cushy', 'low-paying', 'high-paid']
senticnet['well-placed'] = ['0', '-0.90', '0', '0.989', '#surprise', '#admiration', 'positive', '0.949', 'carefully-chosen', 'well-selected', 'well-crafted', 'well-chosen', 'well-constructed']
senticnet['well-priced'] = ['0', '-0.74', '0', '0.893', '#surprise', '#admiration', 'positive', '0.82', 'well-constructed', 'well-crafted', 'well-built', 'well-designed', 'well-made']
senticnet['well-read'] = ['0', '0.839', '0', '0.838', '#interest', '#admiration', 'positive', '0.839', 'well-educated', 'well-spoken', 'erudite', 'well-traveled', 'well-informed']
senticnet['well-selected'] = ['0', '-0.90', '0', '0.982', '#surprise', '#admiration', 'positive', '0.943', 'carefully-chosen', 'well-chosen', 'well-crafted', 'well-placed', 'well-constructed']
senticnet['well-spoken'] = ['0', '0.833', '0', '0.832', '#interest', '#admiration', 'positive', '0.833', 'well-educated', 'well-read', 'erudite', 'well-traveled', 'well-informed']
senticnet['well-tended'] = ['0', '-0.81', '0', '0.851', '#surprise', '#admiration', 'positive', '0.835', 'well-maintained', 'well-kept', 'manicured', 'spotlessly', 'landscaped']
senticnet['well-thought-of'] = ['0', '-0.85', '0', '0.823', '#surprise', '#admiration', 'positive', '0.839', 'generic-looking', 'well-visited', 'tower-style', 'high-profiled', 'clever-clever']
senticnet['well-to-do'] = ['0.757', '0.973', '0', '0', '#joy', '#interest', 'positive', '0.865', 'well-heeled', 'prosperous', 'well-fixed', 'comfortable', 'easy']
senticnet['well-traveled'] = ['0', '0.837', '0', '0.834', '#interest', '#admiration', 'positive', '0.835', 'well-educated', 'well-spoken', 'erudite', 'well-read', 'well-informed']
senticnet['well-visited'] = ['0', '-0.85', '0', '0.818', '#surprise', '#admiration', 'positive', '0.838', 'generic-looking', 'well-thought-of', 'tower-style', 'high-profiled', 'clever-clever']
senticnet['well-worn'] = ['0', '0', '-0.87', '-0.73', '#fear', '#disgust', 'negative', '-0.80', 'banal', 'commonplace', 'old-hat', 'hackneyed', 'shopworn']
senticnet['well_balanced_diet'] = ['-0.06', '-0.02', '0.021', '-0.06', '#sadness', '#disgust', 'negative', '-0.03', 'clean_window', 'vacuum_carpet', 'repetitive_task', 'run_long_time', 'exercise_daily']
senticnet['well_calcified_dermal'] = ['0.969', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.772', 'aspheric', 'aspherical', 'prolate', 'rounded', 'crescent']
senticnet['well_deigned'] = ['0.035', '0.224', '-0.06', '0.185', '#interest', '#admiration', 'positive', '0.096', 'prevent_stress', 'comfortable', 'useful', 'ergonomic_chair', 'ergonomic_pillow']
senticnet['well_deserved_recognition'] = ['0.561', '-0.79', '0', '0.89', '#surprise', '#admiration', 'positive', '0.748', 'superlative', 'compliment', 'testimonial', 'praise', 'approval']
senticnet['well_deserved_reputation'] = ['0.769', '0', '0', '0.879', '#joy', '#admiration', 'positive', '0.824', 'enviousness', 'enviably', 'preferred', 'lovable', 'desired']
senticnet['well_done'] = ['0', '0.757', '0', '0.949', '#interest', '#admiration', 'positive', '0.853', 'high_quality', 'really_good', 'excellence', 'superlative', 'high_standard']
senticnet['well_dress'] = ['0.855', '0.849', '0', '0.767', '#joy', '#interest', 'positive', '0.824', 'keep_clothes_clean', 'smell_good', 'smell_nice', 'wash_hair', 'smell_clean']
senticnet['well_educated_workforce'] = ['0.837', '0', '0', '0', '#joy', '#joy', 'positive', '0.837', 'a_lot_of_sex', 'desirable', 'give_birth_child', 'sexual_intercourse', 'diversity']
senticnet['well_fixed'] = ['0.769', '0', '0', '0.782', '#joy', '#admiration', 'positive', '0.776', 'easy', 'well_off', 'prosperous', 'well-heeled', 'comfortable']
senticnet['well_groomed'] = ['0.824', '0.86', '0', '0.983', '#interest', '#admiration', 'positive', '0.889', 'look_smart', 'social_acceptance', 'improve_appearance', 'improve_look', 'look_good']
senticnet['well_known'] = ['0.983', '0.915', '0', '0.737', '#joy', '#interest', 'positive', '0.878', 'famous', 'everyone_know', 'collection', 'favorite', 'clique']
senticnet['well_light'] = ['0.05', '0.239', '0', '0.159', '#interest', '#admiration', 'positive', '0.149', 'hold_coin', 'money_collector', 'money_keeper', 'money_hold', 'save_money']
senticnet['well_off'] = ['0.775', '0', '0', '0.783', '#joy', '#admiration', 'positive', '0.779', 'easy', 'well_fixed', 'prosperous', 'well-heeled', 'comfortable']
senticnet['well_rotted_manure'] = ['-0.12', '0', '-0.10', '-0.03', '#sadness', '#fear', 'negative', '-0.08', 'dog_poop', 'picnic', 'hike_trail', 'detritus', 'wildlife']
senticnet['well_test'] = ['0.774', '0.728', '0', '0.932', '#joy', '#admiration', 'positive', '0.811', 'accumulate_knowledge', 'wish_learn', 'learn_history', 'become_knowledgeable', 'eye_strain']
senticnet['wellbeing'] = ['0.963', '-0.72', '0', '0.654', '#joy', '#surprise', 'positive', '0.781', 'eudaimonia', 'human_flourishing', 'successfulness', 'upbeat', 'strength']
senticnet['wellhead'] = ['-0.71', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.84', 'shut-in', 'house-bound', 'home-bound', 'housebound', 'shut-in']
senticnet['wellington_regiment'] = ['0.033', '0.088', '-0.06', '0.068', '#interest', '#admiration', 'positive', '0.032', 'earl', 'noble', 'king', 'sultan', 'royal_title']
senticnet['wellington_rugby_league'] = ['-0.68', '0', '0.923', '-0.68', '#sadness', '#anger', 'negative', '-0.76', 'negative', 'veto', 'refute', 'rebut', 'controvert']
senticnet['wellness'] = ['0.252', '0.204', '-0.13', '0.246', '#joy', '#admiration', 'positive', '0.141', 'medical_care', 'good_parent', 'health', 'keep_well', 'medical']
senticnet['wellness_lifestyle'] = ['0', '0.095', '0', '0.05', '#interest', '#admiration', 'positive', '0.073', 'need_earn_money', 'earn_money', 'responsible', 'perform_service', 'go_work_to_earn_money']
senticnet['wellnigh'] = ['-0.71', '0', '0.836', '0', '#sadness', '#anger', 'negative', '-0.77', 'awkward', 'unenviable', 'embarrassingly', 'embarrassing', 'sheepish']
senticnet['wellspring'] = ['-0.99', '0', '0.835', '-0.58', '#sadness', '#anger', 'negative', '-0.80', 'beginning', 'origin', 'root', 'rootage', 'source']
senticnet['wels_catfish'] = ['0', '-0.02', '0.044', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'haddock', 'mermaid', 'fish_rod', 'tidal_wave', 'dryad']
senticnet['welsh_nationalism'] = ['0.526', '-0.63', '0', '0.59', '#surprise', '#admiration', 'positive', '0.583', 'nonconformity', 'iconoclasm', 'heterodoxy', 'nonconformance', 'orientation']
senticnet['welsh_post_hardcore'] = ['-0.98', '0', '0.664', '-0.52', '#sadness', '#anger', 'negative', '-0.72', 'unfortunate', 'unfortunate_person', 'weeper', 'griever', 'bearer']
senticnet['welsher'] = ['-0.66', '0', '0.602', '-0.57', '#sadness', '#anger', 'negative', '-0.61', 'gouger', 'swindler', 'defrauder', 'fraud', 'chiseler']
senticnet['welt'] = ['-0.08', '0', '-0.05', '-0.06', '#sadness', '#disgust', 'negative', '-0.06', 'heal_wind', 'wind', 'cut', 'abrasion', 'ache']
senticnet['welte_mignon'] = ['-0.75', '0', '-0.85', '0', '#sadness', '#fear', 'negative', '-0.80', 'high-strung', 'uptight', 'restive', 'nervy', 'jumpy']
senticnet['welter'] = ['-0.85', '0', '0.583', '-0.71', '#sadness', '#disgust', 'negative', '-0.71', 'muddle', 'fuddle', 'mare_nest', 'rummage', 'clutter']
senticnet['welterweight'] = ['-0.80', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.78', 'bantamweight', 'featherweight', 'middleweight', 'flyweight', 'heavyweight']
senticnet['welterweight_bout'] = ['0.944', '0.99', '0', '0', '#joy', '#interest', 'positive', '0.967', 'meeting', 'meet', 'match', 'encounter', 'round']
senticnet['welterweight_champion'] = ['0', '0', '0', '0.834', '#admiration', '#admiration', 'positive', '0.834', 'royal_person', 'royal_title', 'top', 'master', 'rank']
senticnet['welterweight_crown'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['welterweight_title_against'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['weltschmerz'] = ['-0.77', '0.277', '-0.44', '-0.05', '#sadness', '#fear', 'negative', '-0.24', 'heavyheartedness', 'bored', 'world-weariness', 'heavyhearted', 'world-weary']
senticnet['welwitschia'] = ['-0.77', '0', '0.585', '-0.83', '#sadness', '#disgust', 'negative', '-0.73', 'progymnosperm', 'gymnosperm', 'cycad', 'seed_fern', 'pteridosperm']
senticnet['wemple'] = ['-0.62', '0', '0.632', '-0.78', '#anger', '#disgust', 'negative', '-0.67', 'inquisitor', 'enquirer', 'questioner', 'querier', 'asker']
senticnet['wench'] = ['0', '0.915', '0', '0.981', '#interest', '#admiration', 'positive', '0.948', 'lady', 'female', 'mother', 'woman', 'mom']
senticnet['wendigo'] = ['0.02', '0.131', '0.749', '-0.67', '#anger', '#disgust', 'negative', '-0.31', 'belligerence', 'belligerency', 'belligerent', 'war', 'rancor']
senticnet['wendish_mythology'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'jawless_fish', 'osteostracan', 'cephalaspid', 'ostracoderm', 'heterostracan']
senticnet['wepf'] = ['-0.68', '0', '0.923', '-0.68', '#sadness', '#anger', 'negative', '-0.76', 'negative', 'veto', 'refute', 'rebut', 'controvert']
senticnet['were_bryce_elder'] = ['-0.51', '0', '0.545', '-0.91', '#anger', '#disgust', 'negative', '-0.65', 'gossip', 'chitchat', 'chit_chat', 'gab', 'small_talk']
senticnet['were_burnt_alive'] = ['-0.73', '0', '0.641', '-0.74', '#sadness', '#disgust', 'negative', '-0.70', 'treated', 'burned', 'proofed', 'doped', 'activated']
senticnet['were_partly_offset'] = ['0.702', '-0.54', '0', '0.614', '#joy', '#admiration', 'positive', '0.62', 'point_in_time', 'threshold', 'birth', 'incipiency', 'incipience']
senticnet['wererat'] = ['0.526', '-0.99', '0', '0.673', '#surprise', '#admiration', 'positive', '0.732', 'faery', 'faerie', 'fairy', 'leprechaun', 'sandman']
senticnet['weretiger'] = ['-0.79', '0', '0.92', '-0.75', '#sadness', '#anger', 'negative', '-0.82', 'evil_spirit', 'incubus', 'daemon', 'succuba', 'dybbuk']
senticnet['werewolf'] = ['-0.79', '0', '0.92', '-0.75', '#sadness', '#anger', 'negative', '-0.82', 'evil_spirit', 'incubus', 'daemon', 'succuba', 'dybbuk']
senticnet['wergo'] = ['0.805', '-0.58', '0', '0.525', '#joy', '#surprise', 'positive', '0.637', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['wernicke_aphasia'] = ['-0.82', '0', '0.953', '-0.87', '#anger', '#disgust', 'negative', '-0.88', 'auditory_aphasia', 'acoustic_aphasia', 'brain_damage', 'nominal_aphasia', 'anomic_aphasia']
senticnet['wernicke_encephalopathy'] = ['-0.94', '0', '0.821', '-0.75', '#sadness', '#anger', 'negative', '-0.84', 'predementedness', 'insanity', 'dementedness', 'senile_psychosis', 'alcoholic_dementia']
senticnet['wernickekorsakoff_syndrome'] = ['0.753', '-0.74', '0', '0.587', '#joy', '#surprise', 'positive', '0.695', 'draining', 'exhausting', 'debilitating', 'enervating', 'enfeebling']
senticnet['wernickes_aphasia'] = ['-0.56', '0', '0.509', '-0.83', '#sadness', '#disgust', 'negative', '-0.63', 'inarticulate', 'aphonic', 'speechless', 'unarticulated', 'dumb']
senticnet['wes_anderson_fantastic'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'kit_fox', 'prairie_fox', 'white_fox']
senticnet['wes_craven'] = ['-0.08', '-0.01', '-0.16', '-0.13', '#fear', '#disgust', 'negative', '-0.09', 'hockey_game', 'movie', 'scary_movie', 'cinema', 'cctv']
senticnet['wes_newton'] = ['0.823', '0.982', '0', '0', '#joy', '#interest', 'positive', '0.902', 'spherical', 'three_dimensions', 'round_orb', 'coil', 'fill_with_sand']
senticnet['wesabe'] = ['-0.03', '0.112', '-0.08', '0.164', '#interest', '#admiration', 'positive', '0.04', 'run', 'jump_over', 'quick', 'sprint', 'flee']
senticnet['wesleyan'] = ['0.782', '-0.76', '0', '0', '#joy', '#surprise', 'positive', '0.772', 'methodist', 'edu', 'news', 'article', 'edu_date']
senticnet['west'] = ['0', '0', '-0.71', '-0.98', '#fear', '#disgust', 'negative', '-0.84', 'droop', 'tiring', 'fatigue', 'tire', 'overtire']
senticnet['west-central'] = ['-0.83', '0', '-0.88', '0', '#sadness', '#fear', 'negative', '-0.85', 'north-central', 'south-eastern', 'south-central', 'north-western', 'north-eastern']
senticnet['west_brom_defender'] = ['-0.71', '0', '0.83', '-0.68', '#sadness', '#anger', 'negative', '-0.74', 'gag', 'jest', 'jape', 'joke', 'laugh']
senticnet['west_coast_hip_hop'] = ['0.081', '-0.05', '0.052', '0.072', '#joy', '#admiration', 'positive', '0.039', 'rhythm', 'type_of_music', 'instrumental', 'lyric', 'melody']
senticnet['west_coast_rap'] = ['0.081', '-0.05', '0.052', '0.072', '#joy', '#admiration', 'positive', '0.039', 'rhythm', 'type_of_music', 'instrumental', 'lyric', 'melody']
senticnet['west_coast_rapper'] = ['0', '0.712', '0', '0.844', '#interest', '#admiration', 'positive', '0.778', 'dollar_change', 'small_currency', 'currency_unit', 'metal_money', 'american_money']
senticnet['west_coast_rugby_league'] = ['-0.68', '0', '0.923', '-0.68', '#sadness', '#anger', 'negative', '-0.76', 'negative', 'veto', 'refute', 'rebut', 'controvert']
senticnet['west_elm'] = ['0.041', '0.098', '0', '0', '#joy', '#interest', 'positive', '0.07', 'utopia', 'java', 'memorial', 'nowhere', 'coffee']
senticnet['west_heslerton'] = ['0.099', '-0.07', '-0.08', '0.195', '#joy', '#admiration', 'positive', '0.071', 'art', 'exhibit', 'craft', 'fine_art', 'artwork']
senticnet['west_indiaman'] = ['0.582', '-0.97', '0', '0.802', '#surprise', '#admiration', 'positive', '0.788', 'jimdandy', 'jimhickey', 'crackerjack', 'class_act', 'humdinger']
senticnet['west_mercia'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'law_enforcement_agency', 'law', 'agency']
senticnet['west_midland_constabulary'] = ['0.632', '-0.96', '0', '0.632', '#joy', '#surprise', 'positive', '0.743', 'police', 'police_force', 'law_enforcement_agency', 'law', 'agency']
senticnet['west_mifflin'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
senticnet['west_nile_disease'] = ['-0.85', '0', '0.549', '-0.93', '#sadness', '#disgust', 'negative', '-0.78', 'coccidioidomycosis', 'coccidiomycosis', 'mycosis', 'desert_rheumatism', 'cryptococcosis']
senticnet['west_nile_virus_wnv'] = ['-0.77', '0', '0.871', '-0.64', '#sadness', '#anger', 'negative', '-0.76', 'leptomeningitis', 'meningitis', 'epidemic_meningitis', 'brain_fever', 'cerebrospinal_fever']
senticnet['west_showground'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
senticnet['west_thoroughfare'] = ['0.042', '-0.06', '0', '-0.03', '#joy', '#surprise', 'positive', '0.025', 'artery', 'skeletal_system', 'groin', 'neck', 'muscle']
senticnet['west_yellowstone'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['westair'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['westbound_direction'] = ['0.032', '0.116', '0.016', '0', '#joy', '#interest', 'positive', '0.044', 'curb', 'crossroad', 'ring_road', 'lane', 'interstate']
senticnet['westbound_train'] = ['0.861', '-0.52', '0', '0.598', '#joy', '#admiration', 'positive', '0.663', 'exit', 'get_off', 'deplane', 'go_out', 'get_out']
senticnet['westerly_flow'] = ['-0.69', '0', '0.835', '-0.56', '#sadness', '#anger', 'negative', '-0.69', 'roughness', 'inclemency', 'bad_weather', 'inclementness', 'windiness']
senticnet['western'] = ['0.106', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.062', 'circus_act', 'sumo', 'rodeo', 'grand_canyon', 'have_fun']
senticnet['western_arctic_caribou'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'cheetah', 'giraffe', 'kangaroo', 'mule', 'horse']
senticnet['western_atlantic_ocean'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'pacific_ocean', 'small_lake', 'mere']
senticnet['western_boundary'] = ['0.034', '0.09', '0.039', '0.061', '#interest', '#admiration', 'positive', '0.036', 'border', 'limit', 'edge', 'indoor', 'side']
senticnet['western_capercaillie'] = ['-0.53', '0', '0.648', '-0.70', '#anger', '#disgust', 'negative', '-0.63', 'bird', 'hawk', 'eagle', 'vulture', 'secretary_bird']
senticnet['western_civilisation'] = ['0', '0.08', '0', '0.117', '#interest', '#admiration', 'positive', '0.099', 'stand_still', 'go_home', 'pull_out', 'stretch_out', 'advance_civilization']
senticnet['western_civilization'] = ['0', '0.08', '0', '0.117', '#interest', '#admiration', 'positive', '0.099', 'stand_still', 'go_home', 'pull_out', 'stretch_out', 'advance_civilization']
senticnet['western_classical_music'] = ['0.136', '-0.11', '0.031', '0', '#joy', '#surprise', 'positive', '0.075', 'rap', 'popular_music', 'music', 'band_play', 'rock_roll']
senticnet['western_coast'] = ['0', '0.087', '0', '0.071', '#interest', '#admiration', 'positive', '0.079', 'pond', 'lake', 'pacific_ocean', 'small_lake', 'mere']
senticnet['western_colonialism'] = ['-0.65', '0', '0.691', '-0.54', '#sadness', '#anger', 'negative', '-0.63', 'moronity', 'mental_deficiency', 'imbecility', 'stupidity', 'mental_defectiveness']
senticnet['western_europe'] = ['0', '0.717', '0', '0.987', '#interest', '#admiration', 'positive', '0.852', 'university', 'collaboration_expertise', 'research', 'french_expertise', 'project']
senticnet['western_ganga_dynasty'] = ['0.913', '0.944', '0', '0', '#joy', '#interest', 'positive', '0.928', 'kingdom', 'rule_government', 'aboriginal', 'queen_of_england', 'imperial']
senticnet['western_gray_squirrel'] = ['-0.55', '0', '0.955', '-0.96', '#anger', '#disgust', 'negative', '-0.82', 'house_mouse', 'mouse', 'rat', 'fieldmouse', 'nude_mouse']
senticnet['western_hemisphere'] = ['0.838', '0.707', '0', '0.904', '#joy', '#admiration', 'positive', '0.816', 'natural_order', 'universe', 'existence', 'creation', 'cosmos']
senticnet['western_hemispheric'] = ['0', '0', '0', '0.119', '#admiration', '#admiration', 'positive', '0.119', 'planet', 'earth', 'spherical', 'planet_solar_system', 'terrain']
senticnet['western_hemlock'] = ['0.045', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.356', 'ponderosa', 'bull_pine', 'western_yellow_pine', 'yellow_pine', 'longleaf_pine']
senticnet['western_honey_bee'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'yellow_jacket', 'hornet', 'vespid', 'vespid_wasp']
senticnet['western_ideography'] = ['0.042', '-0.00', '0.027', '0.099', '#joy', '#admiration', 'positive', '0.031', 'chess_knight', 'chess_queen', 'chess_pawn', 'pawn', 'chess_bishop']
senticnet['western_imperialism'] = ['-0.65', '0', '0.691', '-0.54', '#sadness', '#anger', 'negative', '-0.63', 'moronity', 'mental_deficiency', 'imbecility', 'stupidity', 'mental_defectiveness']
senticnet['western_juniper'] = ['0.045', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.356', 'ponderosa', 'bull_pine', 'western_yellow_pine', 'yellow_pine', 'longleaf_pine']
senticnet['western_poison_oak'] = ['-0.92', '0', '0.912', '-0.94', '#sadness', '#disgust', 'negative', '-0.92', 'true_blackberry', 'sand_blackberry', 'blackberry_bush', 'blackberry', 'running_blackberry']
senticnet['western_red_cedar'] = ['-0.69', '0', '0.725', '-0.89', '#anger', '#disgust', 'negative', '-0.77', 'ponderosa', 'ponderosa_pine', 'western_yellow_pine', 'bull_pine', 'longleaf_pine']
senticnet['western_redcedar'] = ['-0.69', '0', '0.725', '-0.89', '#anger', '#disgust', 'negative', '-0.77', 'ponderosa', 'ponderosa_pine', 'western_yellow_pine', 'bull_pine', 'longleaf_pine']
senticnet['western_saxifrage'] = ['-0.56', '0', '0.848', '-0.80', '#anger', '#disgust', 'negative', '-0.73', 'herb', 'yellow_mountain_saxifrage', 'meadow_saxifrage', 'mossy_saxifrage', 'breakstone']
senticnet['western_spotted_skunk'] = ['-0.54', '0', '0.768', '-0.50', '#sadness', '#anger', 'negative', '-0.60', 'polecat', 'hooded_skunk', 'hognosed_skunk', 'badger_skunk', 'rooter_skunk']
senticnet['western_yellow_pine'] = ['-0.69', '0', '0.725', '-0.89', '#anger', '#disgust', 'negative', '-0.77', 'ponderosa', 'ponderosa_pine', 'yellow_pine', 'bull_pine', 'longleaf_pine']
senticnet['western_yunnan_province'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['westerner'] = ['-0.57', '0', '0.803', '-0.77', '#anger', '#disgust', 'negative', '-0.71', 'au_pair', 'exile', 'deportee', 'gringo', 'import']
senticnet['westernization'] = ['-0.65', '0', '0.691', '-0.54', '#sadness', '#anger', 'negative', '-0.63', 'moronity', 'mental_deficiency', 'imbecility', 'stupidity', 'mental_defectiveness']
senticnet['westernmost'] = ['0.595', '-0.61', '0', '0.826', '#surprise', '#admiration', 'positive', '0.679', 'completeness', 'work', 'whole_shebang', 'absolutely', 'wholly']
senticnet['westernmost_part'] = ['0', '0.08', '0.032', '0.096', '#interest', '#admiration', 'positive', '0.048', 'land_form', 'land_formation', 'land_feature', 'geographic_feature', 'landscape_feature']
senticnet['wet'] = ['-0.02', '-0.02', '0.076', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'wear_raincoat', 'carry_umbrella', 'feel_cold', 'camp_out', 'splash_water']
senticnet['wet_air'] = ['0.058', '0', '0.023', '0.058', '#joy', '#admiration', 'positive', '0.031', 'fluid', 'white_water', 'liquid', 'heavy_air', 'potable_liquid']
senticnet['wet_bed'] = ['-0.14', '-0.01', '-0.05', '-0.12', '#sadness', '#disgust', 'negative', '-0.07', 'quiet_mind', 'miss_appointment', 'sleep_bed', 'lower_standard', 'dress_doll']
senticnet['wet_blanket'] = ['-0.51', '0', '0.906', '-0.92', '#anger', '#disgust', 'negative', '-0.78', 'worrier', 'fusspot', 'worrywart', 'unwelcome_person', 'killjoy']
senticnet['wet_cleaning_machine'] = ['0', '-0.06', '0', '0', '#surprise', '#surprise', 'positive', '0.068', 'kitchen_cabinet', 'kitchen_cupboard', 'kitchen', 'cupboard', 'own_kitchen']
senticnet['wet_cloth'] = ['0.024', '0.157', '0.018', '0.145', '#interest', '#admiration', 'positive', '0.077', 'toilet_paper', 'beach_towel', 'face_towel', 'hand_towel', 'tissue']
senticnet['wet_clothes'] = ['0.955', '0.955', '0', '0', '#joy', '#interest', 'positive', '0.955', 'clothes_shrink', 'become_cleaner', 'dry_skin', 'become_wet', 'run_out_soap']
senticnet['wet_cold'] = ['-0.18', '-0.07', '0', '-0.20', '#sadness', '#disgust', 'negative', '-0.10', 'uncomfortable', 'frozen_water', 'wetness', 'coldness', 'frozen']
senticnet['wet_dream'] = ['-0.05', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.05', 'masturbate', 'jerk', 'jerk_off', 'watch_porn', 'porn']
senticnet['wet_floor'] = ['-0.03', '-0.00', '0.066', '0', '#sadness', '#anger', 'negative', '-0.03', 'dirt', 'allergy', 'dust', 'dirt_floor', 'insect']
senticnet['wet_hair'] = ['0', '0', '0', '-0.80', '#disgust', '#disgust', 'negative', '-0.80', 'wet_clothes', 'get_cold', 'sunburned', 'hurt_back', 'sore_back']
senticnet['wet_lease'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['wet_lip'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'sexual_excitement', 'sexy', 'lip_stick', 'kiss', 'clean_up']
senticnet['wet_meadow'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'bulrush', 'nailrod', 'reed_mace', 'reedmace']
senticnet['wet_mud'] = ['-0.06', '-0.01', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'low', 'ground', 'look_down', 'ground_level', 'under_house']
senticnet['wet_paint'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'wall_decoration', 'renovation', 'painting', 'artwork', 'new_house']
senticnet['wet_pant'] = ['0', '0', '0.742', '-0.78', '#anger', '#disgust', 'negative', '-0.76', 'listen_story', 'trade_stock', 'become_depressed', 'act_silly', 'correct_mistake']
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senticnet['wet_prairie'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'bulrush', 'nailrod', 'reed_mace', 'reedmace']
senticnet['wet_rot'] = ['0.718', '0', '0', '0', '#joy', '#joy', 'positive', '0.718', 'water_container', 'still_water', 'clean_water', 'water', 'cool_water']
senticnet['wet_sand'] = ['-0.06', '-0.01', '0.031', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'low', 'ground', 'look_down', 'ground_level', 'under_house']
senticnet['wet_snow'] = ['0', '0', '-0.93', '-0.82', '#fear', '#disgust', 'negative', '-0.88', 'thunderstorm', 'snow_storm', 'storm', 'weather', 'rainy']
senticnet['wet_soil'] = ['-0.02', '-0.02', '0.076', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'wear_raincoat', 'carry_umbrella', 'feel_cold', 'camp_out', 'splash_water']
senticnet['wet_suit'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'lifebelt', 'save_life', 'lifeguard', 'life_jacket', 'safety_equipment']
senticnet['wet_tissue'] = ['0.159', '0.068', '-0.07', '0.118', '#joy', '#admiration', 'positive', '0.067', 'clean_mouth', 'wipe_mouth_napkin', 'refresh', 'wipe_dirty', 'paper_towel']
senticnet['wet_towel'] = ['-0.06', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.06', 'bathroom_wall', 'fallen_tree', 'relax', 'under_rock', 'compost_pile']
senticnet['wet_tundra'] = ['-0.04', '0', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.04', 'bad_luck', 'damn', 'artic', 'mammal_family', 'bad_mood']
senticnet['wet_washcloth'] = ['-0.75', '0', '-0.75', '-0.99', '#sadness', '#disgust', 'negative', '-0.83', 'tease', 'upset', 'lubricant', 'upset_person', 'piston']
senticnet['wet_weather'] = ['-0.02', '-0.02', '0.076', '-0.05', '#anger', '#disgust', 'negative', '-0.03', 'wear_raincoat', 'carry_umbrella', 'feel_cold', 'camp_out', 'splash_water']
senticnet['wet_wipe'] = ['0.986', '0', '0', '0', '#joy', '#joy', 'positive', '0.986', 'personal_cleanliness', 'cleanness', 'wipe_up_spill', 'smell_good', 'clean_carpet']
senticnet['wet_woodland'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'biome', 'timberland']
senticnet['wether'] = ['0.651', '-0.82', '0', '0.907', '#surprise', '#admiration', 'positive', '0.793', 'black_sheep', 'ewe', 'ram', 'tup', 'sheep']
senticnet['wetherly'] = ['0.025', '0.27', '-0.11', '0.243', '#interest', '#admiration', 'positive', '0.105', 'keep_safe', 'store_dry_goods', 'store_stuff', 'secure', 'save']
senticnet['wetland_conservation'] = ['0', '0', '0', '0.857', '#admiration', '#admiration', 'positive', '0.857', 'say', 'beckon', 'call', 'conserve', 'retort']
senticnet['wetland_wildlife'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'habitat', 'mom_house', 'windsor', 'pacific_northwest', 'wilderness']
senticnet['wetness'] = ['-0.03', '0', '0.014', '-0.03', '#sadness', '#disgust', 'negative', '-0.02', 'ice_crystal', 'frozen_water', 'wet_cold', 'coldness', 'frozen']
senticnet['wettability'] = ['-0.50', '0', '0.552', '-0.93', '#anger', '#disgust', 'negative', '-0.66', 'nubbiness', 'tweediness', 'harshness', 'graininess', 'granularity']
senticnet['wetter'] = ['-0.53', '0', '0.508', '-0.74', '#sadness', '#disgust', 'negative', '-0.59', 'bedpisser', 'bed_pisser', 'mortal', 'pisser', 'person']
senticnet['wetter_weather'] = ['0', '0.094', '0', '0', '#interest', '#interest', 'positive', '0.094', 'cold', 'cavern', 'under_umbrella', 'parasol', 'stick_wall']
senticnet['wetting_agent'] = ['-0.76', '0', '0', '-0.78', '#sadness', '#disgust', 'negative', '-0.77', 'surface-active_agent', 'wetter', 'surfactant', 'effect', 'water']
senticnet['weve'] = ['0', '0.078', '0', '0.096', '#interest', '#admiration', 'positive', '0.087', 'ancient_rome', 'ancient', 'age', 'ancient_artifact', 'century']
senticnet['wgc_american'] = ['0', '0.161', '-0.08', '0.219', '#interest', '#admiration', 'positive', '0.098', 'deliver', 'send', 'mention', 'reply', 'remark']
senticnet['whack'] = ['0', '0', '0.72', '-0.90', '#anger', '#disgust', 'negative', '-0.81', 'torture', 'impair', 'abuse', 'kick', 'excruciate']
senticnet['whack_job'] = ['-0.61', '0', '0.981', '-0.98', '#anger', '#disgust', 'negative', '-0.85', 'pious_platitude', 'nothingness', 'jazz', 'talk', 'malarky']
senticnet['whacked_out'] = ['-0.76', '0', '0.904', '-0.80', '#anger', '#disgust', 'negative', '-0.82', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['whacking'] = ['-0.84', '0', '0.557', '-0.79', '#sadness', '#disgust', 'negative', '-0.73', 'corporal_punishment', 'lashing', 'whipping', 'tanning', 'flogging']
senticnet['whaddon'] = ['0.623', '-0.56', '0', '0.974', '#joy', '#admiration', 'positive', '0.721', 'alterable', 'convertible', 'translatable', 'transformable', 'transmutable']
senticnet['whale'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'seal', 'walrus', 'mussel', 'alligator', 'otter']
senticnet['whale_bone'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'seal', 'otter', 'mussel', 'alligator', 'whale']
senticnet['whale_louse'] = ['0.96', '-0.77', '0', '0.929', '#joy', '#admiration', 'positive', '0.887', 'amphipod', 'beach_flea', 'sand_hopper', 'sandhopper', 'sand_flea']
senticnet['whale_tail'] = ['-0.20', '-0.10', '0.07', '-0.24', '#sadness', '#disgust', 'negative', '-0.10', 'gill', 'sea_life', 'seafood', 'swim_animal', 'fillet']
senticnet['whaleboat'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'driftwood', 'sea_shell', 'scenic_view', 'naked_woman', 'tidal_wave']
senticnet['whaling_ship'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'succourer', 'recoverer', 'salvor', 'mortal']
senticnet['whaling_vessel'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'succourer', 'recoverer', 'salvor', 'mortal']
senticnet['wham'] = ['0', '0', '-0.73', '-0.70', '#fear', '#disgust', 'negative', '-0.72', 'sock', 'bop', 'whack', 'whopping', 'wallop']
senticnet['whammy'] = ['-0.97', '0', '0.597', '-0.65', '#sadness', '#disgust', 'negative', '-0.74', 'blow', 'black_eye', 'setback', 'reversal', 'reverse']
senticnet['whammy_bar'] = ['0.748', '-0.90', '0', '0.99', '#surprise', '#admiration', 'positive', '0.882', 'shakiness', 'quivering', 'shaking', 'vibration', 'quiver']
senticnet['whar'] = ['-0.85', '0', '0.583', '-0.71', '#sadness', '#disgust', 'negative', '-0.71', 'muddle', 'rummage', 'mare_nest', 'welter', 'clutter']
senticnet['wharf'] = ['0', '0', '0.027', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'port', 'sea', 'boat', 'ocean', 'ship']
senticnet['whatever'] = ['0', '0', '-0.70', '-0.84', '#fear', '#disgust', 'negative', '-0.77', 'whim', 'capricious', 'dictated', 'dictate', 'whim']
senticnet['whatever_else'] = ['0', '0.074', '0.026', '0.105', '#interest', '#admiration', 'positive', '0.051', 'handwritten', 'scribble', 'music_part', 'written_message', 'calligraphy']
senticnet['whatsis'] = ['0.972', '-0.83', '0', '0', '#joy', '#surprise', 'positive', '0.902', 'widget', 'doodad', 'thingumajig', 'thingamabob', 'gimmick']
senticnet['whatsoever_hath'] = ['-0.79', '0', '0.607', '-0.92', '#sadness', '#disgust', 'negative', '-0.77', 'stubbornness', 'obstinacy', 'obstinance', 'bullheadedness', 'pigheadedness']
senticnet['wheat'] = ['0', '0.071', '0.027', '0.053', '#interest', '#admiration', 'positive', '0.032', 'grain', 'rye', 'corn', 'oat', 'white_rice']
senticnet['wheat_allergy'] = ['-0.86', '0', '0.76', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['wheat_beer'] = ['0', '0.091', '-0.05', '0.073', '#interest', '#admiration', 'positive', '0.037', 'privately', 'hold_liquid', 'make_wish', 'rinse_dish', 'find_toilet']
senticnet['wheat_bran'] = ['0.52', '-0.69', '0', '0.639', '#surprise', '#admiration', 'positive', '0.617', 'black_bread', 'pumpernickel', 'limpa', 'bread', 'rye_bread']
senticnet['wheat_bread'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'veal', 'beef', 'food_group', 'animal_flesh', 'curry']
senticnet['wheat_corn'] = ['0', '0.072', '0.036', '0.059', '#interest', '#admiration', 'positive', '0.032', 'wheat', 'farm_plant', 'plant_food', 'wheat_rice', 'farm_vegetable']
senticnet['wheat_farmer'] = ['-0.08', '0', '-0.14', '0', '#sadness', '#fear', 'negative', '-0.11', 'serf', 'peasant', 'servant', 'poor', 'villein']
senticnet['wheat_flour'] = ['0.03', '0', '0', '0.076', '#joy', '#admiration', 'positive', '0.053', 'food_ingredient', 'rice', 'granulate', 'noodel', 'white_grain']
senticnet['wheat_grain'] = ['0.061', '0.093', '0.037', '0.114', '#interest', '#admiration', 'positive', '0.058', 'whole_grain', 'food_ingredient', 'staple_food', 'starchy_food', 'food_grain']
senticnet['wheat_paste'] = ['0.528', '-0.60', '0', '0.724', '#surprise', '#admiration', 'positive', '0.619', 'gastronomy', 'building', 'carving', 'artistic_creation', 'ceramic']
senticnet['wheat_rice'] = ['0', '0.085', '0.045', '0.069', '#interest', '#admiration', 'positive', '0.036', 'farm_plant', 'wheat_corn', 'plant_food', 'wheat', 'farm_vegetable']
senticnet['wheat_triticum'] = ['0.878', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.883', 'wheat', 'grain', 'rye', 'white_rice', 'rice']
senticnet['wheaten_bread'] = ['0', '-0.13', '0.058', '0.059', '#surprise', '#admiration', 'positive', '0.047', 'cook_meat', 'cook_hamburger', 'cook_food', 'cook_rice', 'brown_meat']
senticnet['wheatgerm'] = ['0.026', '0.08', '0.043', '0.064', '#interest', '#admiration', 'positive', '0.032', 'eat_rice', 'white_brown', 'asian', 'eat_pasta', 'staple_food']
senticnet['wheaties_box'] = ['0', '0', '0', '0.97', '#admiration', '#admiration', 'positive', '0.97', 'increase_strength', 'keep_fit', 'fit_person', 'exercise', 'strengthen_heart']
senticnet['wheatsheaf'] = ['0.853', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.916', 'tavern', 'cafe', 'nightclub', 'bar', 'liquor_store']
senticnet['wheedle'] = ['0', '0', '-0.83', '-0.74', '#fear', '#disgust', 'negative', '-0.79', 'inveigle', 'palaver', 'cajole', 'coax', 'blarney']
senticnet['wheel'] = ['0', '-0.09', '0.044', '0.055', '#surprise', '#admiration', 'positive', '0.036', 'two_wheels', 'gear', 'axle', 'engine', 'steer_wheel']
senticnet['wheel-like'] = ['0.759', '-0.95', '0', '0', '#joy', '#surprise', 'positive', '0.857', 'mandala-like', 'turbine-like', 'cross-like', 'donut-like', 'propeller-like']
senticnet['wheel_arrangement'] = ['0.069', '0.097', '0', '0.172', '#interest', '#admiration', 'positive', '0.113', 'type_of_engine', 'liquid_gas', 'warm_air', 'hot_air', 'steamy']
senticnet['wheel_axle'] = ['0', '-0.09', '0.044', '0.055', '#surprise', '#admiration', 'positive', '0.036', 'two_wheels', 'gear', 'axle', 'engine', 'steer_wheel']
senticnet['wheel_hub'] = ['0', '-0.09', '0.044', '0.055', '#surprise', '#admiration', 'positive', '0.036', 'two_wheels', 'gear', 'axle', 'engine', 'steer_wheel']
senticnet['wheel_rim'] = ['0', '-0.09', '0.044', '0.055', '#surprise', '#admiration', 'positive', '0.036', 'two_wheels', 'gear', 'axle', 'engine', 'steer_wheel']
senticnet['wheelchair'] = ['-0.04', '0', '-0.08', '-0.07', '#fear', '#disgust', 'negative', '-0.06', 'dirty_laundry', 'old_photograph', 'wife', 'hot_chick', 'mommy']
senticnet['wheelchair_athlete'] = ['-0.04', '-0.03', '0', '-0.16', '#sadness', '#disgust', 'negative', '-0.05', 'crazy', 'damage_body', 'crazy_person', 'threat', 'max']
senticnet['wheelchair_lift'] = ['0', '0', '0', '0.109', '#admiration', '#admiration', 'positive', '0.109', 'knob', 'door_knob', 'door_handle', 'handle', 'hinge']
senticnet['wheelchair_racer'] = ['-0.04', '-0.03', '0', '-0.16', '#sadness', '#disgust', 'negative', '-0.05', 'crazy', 'damage_body', 'crazy_person', 'threat', 'max']
senticnet['wheelchair_ramp'] = ['0', '0', '0', '0.109', '#admiration', '#admiration', 'positive', '0.109', 'knob', 'door_knob', 'door_handle', 'handle', 'hinge']
senticnet['wheeled_armored_personnel'] = ['0', '0.089', '0.01', '0.118', '#interest', '#admiration', 'positive', '0.066', 'crate', 'mom_house', 'sack', 'poker_game', 'safekeep']
senticnet['wheeler_dealer'] = ['-0.53', '0', '0.591', '-0.59', '#anger', '#disgust', 'negative', '-0.57', 'mug', 'sitting_duck', 'easy_mark', 'dupe', 'mortal']
senticnet['wheelie_bin'] = ['-0.70', '0', '0.611', '-0.69', '#sadness', '#disgust', 'negative', '-0.67', 'dump', 'garbage_dump', 'trash_dump', 'garbage_heap', 'wasteyard']
senticnet['wheelwright_shop'] = ['-0.07', '0.228', '0.019', '0.089', '#interest', '#admiration', 'positive', '0.056', 'home_depot', 'lumberyard', 'furniture_store', 'front_porch', 'carpet_store']
senticnet['wheeze'] = ['-0.02', '-0.05', '0.092', '-0.06', '#anger', '#disgust', 'negative', '-0.03', 'gasp', 'puff', 'pant', 'blow', 'heave']
senticnet['wheezing'] = ['-0.75', '0', '0.773', '0', '#sadness', '#anger', 'negative', '-0.76', 'asthmatic', 'bronchitis', 'asthma', 'asthmatic', 'allergic']
senticnet['wheezy'] = ['-0.08', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.07', 'bicycle_store', 'enter_door', 'cost_a_lot_of_money', 'open', 'hang_door']
senticnet['whelk'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'seal', 'walrus', 'otter', 'alligator', 'whale']
senticnet['whelm'] = ['0.621', '-0.65', '0', '0.96', '#surprise', '#admiration', 'positive', '0.745', 'overwhelm', 'overpower', 'sweep_over', 'benight', 'overcome']
senticnet['whelp'] = ['-0.08', '0.158', '0.659', '-0.65', '#anger', '#disgust', 'negative', '-0.31', 'disgustedly', 'revolting', 'disgusting', 'fed_up', 'sick_of']
senticnet['whenever'] = ['0.921', '0.884', '0', '0', '#joy', '#interest', 'positive', '0.903', 'mortal', 'soul', 'individual', 'human_being', 'creature']
senticnet['whensoever'] = ['0.621', '0.648', '-0.25', '0.296', '#joy', '#interest', 'positive', '0.328', 'pacifically', 'repose', 'peace-loving', 'peaceable', 'peace_of_mind']
senticnet['whenu'] = ['-0.85', '0', '0.685', '-0.83', '#sadness', '#disgust', 'negative', '-0.79', 'allicrocodilian', 'crocodile', 'false_gavial', 'crocodilian', 'caiman']
senticnet['whereinto'] = ['0.774', '-0.82', '0', '0.63', '#joy', '#surprise', 'positive', '0.744', 'quality', 'perishability', 'perishableness', 'unsatisfactoriness', 'inadequacy']
senticnet['wheresoever'] = ['0.621', '0.648', '-0.25', '0.296', '#joy', '#interest', 'positive', '0.328', 'pacifically', 'repose', 'peace-loving', 'peaceable', 'peace_of_mind']
senticnet['wherewithal'] = ['-0.94', '0', '0.863', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'poke', 'knockout_punch', 'lick', 'punch', 'biff']
senticnet['whet'] = ['0', '0.778', '0', '0.694', '#interest', '#admiration', 'positive', '0.736', 'sharpen', 'hone', 'file', 'grind', 'skill']
senticnet['whew'] = ['-0.80', '0', '-0.8', '0', '#sadness', '#fear', 'negative', '-0.80', 'sinking', 'misgiving', 'qualm', 'scruple', 'insecurely']
senticnet['whey_powder'] = ['0.057', '0', '0.018', '0.058', '#joy', '#admiration', 'positive', '0.032', 'porridge', 'food', 'milk', 'healthy', 'morning_food']
senticnet['whey_protein'] = ['0.057', '0', '0.018', '0.058', '#joy', '#admiration', 'positive', '0.032', 'porridge', 'food', 'milk', 'healthy', 'morning_food']
senticnet['whey_protein_concentrate'] = ['0.057', '0', '0.018', '0.058', '#joy', '#admiration', 'positive', '0.032', 'porridge', 'food', 'milk', 'healthy', 'morning_food']
senticnet['whiche_thyng'] = ['-0.79', '0', '0.607', '-0.92', '#sadness', '#disgust', 'negative', '-0.77', 'stubbornness', 'obstinacy', 'obstinance', 'bullheadedness', 'pigheadedness']
senticnet['whichever'] = ['-0.61', '0', '0.856', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'unfixed', 'indefinite', 'nebulous', 'indecisive', 'coy']
senticnet['whiff'] = ['0.773', '0', '0', '0.853', '#joy', '#admiration', 'positive', '0.813', 'gasp', 'wind', 'respite', 'blow', 'breath']
senticnet['whig_ministry'] = ['-0.98', '0', '0.937', '-0.68', '#sadness', '#anger', 'negative', '-0.86', 'amateur', 'dabbler', 'mortal', 'dilettante', 'bird_watcher']
senticnet['whigham_funeral_home'] = ['-0.70', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.70', 'death_row', 'sad', 'loose_end', 'car_race', 'mortuary']
senticnet['whim'] = ['0', '0', '-0.70', '-0.83', '#fear', '#disgust', 'negative', '-0.77', 'whim', 'capricious', 'dictated', 'dictate', 'whatever']
senticnet['whim-wham'] = ['0.771', '0', '0', '0.932', '#joy', '#admiration', 'positive', '0.852', 'queer', 'whimsical', 'playful', 'humorous', 'monster']
senticnet['whimper'] = ['-0.85', '0', '-0.82', '0', '#sadness', '#fear', 'negative', '-0.83', 'mewl', 'pule', 'sniffle', 'cry', 'intermittent']
senticnet['whimsical'] = ['0.773', '0', '0', '0.942', '#joy', '#admiration', 'positive', '0.857', 'queer', 'whim-wham', 'playful', 'humorous', 'monster']
senticnet['whimsicality'] = ['0.695', '0.623', '-0.23', '0.319', '#joy', '#interest', 'positive', '0.351', 'uproarious', 'hilariously', 'hilarious', 'screaming', 'uproariously']
senticnet['whimsy'] = ['0.918', '0.476', '-0.19', '0.417', '#joy', '#interest', 'positive', '0.404', 'merriment', 'cheery', 'gaiety', 'festal', 'plume']
senticnet['whine'] = ['-0.26', '0', '-0.21', '0', '#sadness', '#fear', 'negative', '-0.23', 'shed_tear', 'bawl', 'eye_tear', 'say_prayer', 'show_emotion']
senticnet['whiner'] = ['-0.74', '0', '0.68', '-0.58', '#sadness', '#anger', 'negative', '-0.67', 'kvetch', 'complainer', 'moaner', 'sniveller', 'crybaby']
senticnet['whiney'] = ['-0.67', '0', '0.78', '-0.81', '#anger', '#disgust', 'negative', '-0.75', 'querulous', 'complaining', 'whiny', 'fretful', 'protestant']
senticnet['whinge'] = ['-0.52', '0', '0.607', '-0.54', '#anger', '#disgust', 'negative', '-0.56', 'bewail', 'bemoan', 'mutter', 'scold', 'quetch']
senticnet['whining'] = ['0', '0', '-0.73', '-0.84', '#fear', '#disgust', 'negative', '-0.78', 'peevish', 'teeny', 'crotched', 'fretful', 'harshen']
senticnet['whinny'] = ['-0.81', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.86', 'impassiveness', 'impassivity', 'emotionlessness', 'unemotionality', 'stolidity']
senticnet['whinstone'] = ['0', '0', '-0.85', '-0.71', '#fear', '#disgust', 'negative', '-0.78', 'fair_game', 'pit', 'stone_pit', 'target', 'prey']
senticnet['whiny'] = ['-0.67', '0', '0.78', '-0.81', '#anger', '#disgust', 'negative', '-0.75', 'querulous', 'whiney', 'complaining', 'fretful', 'protestant']
senticnet['whip'] = ['0.787', '0.933', '0', '0', '#joy', '#interest', 'positive', '0.86', 'type_of_bow', 'chisel', 'baton', 'arrow', 'bow']
senticnet['whip-like'] = ['0.79', '0', '0', '0.992', '#joy', '#admiration', 'positive', '0.891', 'undescended', 'rope-like', 'blade-like', 'retractile', 'dwarfish']
senticnet['whip_cream'] = ['0.055', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'chocolate_pudding', 'green_bean', 'sweetness', 'side_dish', 'sushi']
senticnet['whip_hand'] = ['0.761', '-0.50', '0', '0.896', '#joy', '#admiration', 'positive', '0.72', 'advantage', 'vantage', 'upper_hand', 'favorable_position', 'edge']
senticnet['whip_smart'] = ['-0.80', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.8', 'scared', 'frighten_away', 'scare_away', 'frighten_off', 'scare_off']
senticnet['whipcord'] = ['0.92', '-0.57', '0', '0.758', '#joy', '#admiration', 'positive', '0.751', 'bail_out', 'unspell', 'bail', 'unchain', 'run']
senticnet['whipped_cream'] = ['0.028', '0.138', '-0.04', '0.215', '#interest', '#admiration', 'positive', '0.083', 'water', 'dense', 'thick', 'milk', 'dense_water']
senticnet['whippersnapper'] = ['-0.64', '0', '0.667', '-0.82', '#anger', '#disgust', 'negative', '-0.71', 'novice', 'beginner', 'tyro', 'tiro', 'initiate']
senticnet['whipping'] = ['-0.84', '0', '0.557', '-0.79', '#sadness', '#disgust', 'negative', '-0.73', 'corporal_punishment', 'lashing', 'whacking', 'tanning', 'flogging']
senticnet['whipping_cream'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'dainty', 'delicacy', 'treat', 'jelly', 'aspic']
senticnet['whipple_disease'] = ['-0.75', '0', '0.868', '-0.99', '#anger', '#disgust', 'negative', '-0.87', 'regional_enteritis', 'regional_ileitis', 'ulcerative_colitis', 'rubor', 'redness']
senticnet['whipsaw'] = ['0.941', '0.85', '0', '0', '#joy', '#interest', 'positive', '0.895', 'potentiality', 'ground', 'ability', 'potential', 'brainwave']
senticnet['whipworm'] = ['-0.74', '0', '0.94', '-0.87', '#anger', '#disgust', 'negative', '-0.85', 'fungal_infection', 'mycosis', 'kerion', 'tinea_pedis', 'athlete_foot']
senticnet['whir'] = ['0.098', '0', '-0.04', '0.112', '#joy', '#admiration', 'positive', '0.054', 'squeak', 'clang', 'vibration', 'loud_yell', 'tremor']
senticnet['whirl'] = ['0', '0', '-0.75', '-0.84', '#fear', '#disgust', 'negative', '-0.8', 'whirling', 'twirl', 'swirl', 'swirling', 'whirlwind']
senticnet['whirling'] = ['0', '0', '-0.76', '-0.85', '#fear', '#disgust', 'negative', '-0.80', 'whirl', 'twirl', 'swirl', 'swirling', 'whirlwind']
senticnet['whirlpool_tub'] = ['0', '-0.02', '-0.08', '-0.05', '#fear', '#disgust', 'negative', '-0.03', 'mattress', 'sleep_furniture', 'cradle', 'small_bed', 'pillow']
senticnet['whirlwind'] = ['0', '0', '-0.76', '-0.85', '#fear', '#disgust', 'negative', '-0.80', 'whirling', 'twirl', 'swirl', 'swirling', 'whirl']
senticnet['whirlwind_courtship'] = ['0.878', '0.84', '0', '0.91', '#joy', '#admiration', 'positive', '0.876', 'stumble', 'slip', 'fall', 'trip', 'tumble']
senticnet['whirlwind_romance'] = ['0.878', '0.84', '0', '0.91', '#joy', '#admiration', 'positive', '0.876', 'stumble', 'slip', 'fall', 'trip', 'tumble']
senticnet['whirlwind_trip'] = ['0.954', '-0.87', '0', '0.583', '#joy', '#surprise', 'positive', '0.802', 'penetration', 'air_raid', 'air_attack', 'swoop', 'incursion']
senticnet['whirr'] = ['0.098', '0', '-0.04', '0.112', '#joy', '#admiration', 'positive', '0.054', 'squeak', 'clang', 'vibration', 'loud_yell', 'tremor']
senticnet['whisk'] = ['0.038', '-0.66', '0', '0.355', '#surprise', '#admiration', 'positive', '0.354', 'address_book', 'get_rich', 'ladle', 'swiss_army_knife', 'blank_check']
senticnet['whisk_attachment'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'salad_shaker', 'store_shelf', 'kitchen_tool', 'pliers', 'ladle']
senticnet['whisker'] = ['0', '-0.04', '0.085', '-0.07', '#anger', '#disgust', 'negative', '-0.03', 'claw', 'fur_keep_warm', 'tic', 'bristle', 'muzzle']
senticnet['whisker_plot'] = ['0', '-0.02', '0', '0.051', '#surprise', '#admiration', 'positive', '0.037', 'cardboard_box', 'bag', 'package', 'box_cereal', 'bin']
senticnet['whiskered'] = ['-0.65', '0', '0.626', '-0.61', '#sadness', '#anger', 'negative', '-0.63', 'bearded', 'barbate', 'bewhiskered', 'unshaven', 'whiskery']
senticnet['whiskery'] = ['-0.94', '0', '0.84', '-0.91', '#sadness', '#disgust', 'negative', '-0.90', 'bearded', 'barbate', 'bewhiskered', 'whiskered', 'unshaved']
senticnet['whiskey'] = ['0.899', '0.991', '0', '0', '#joy', '#interest', 'positive', '0.945', 'bourbon', 'scotch', 'vodka', 'brandy', 'wine']
senticnet['whiskey_au'] = ['0.057', '0', '-0.07', '-0.07', '#fear', '#disgust', 'negative', '-0.03', 'huge_fire', 'fire', 'rapid_oxidation', 'heat_light', 'flame']
senticnet['whisky'] = ['0.078', '-0.02', '0.017', '0', '#joy', '#surprise', 'positive', '0.029', 'wine', 'lemonade', 'juice', 'cider', 'soft_drink']
senticnet['whisky_mist'] = ['0.763', '-0.73', '0', '0', '#joy', '#surprise', 'positive', '0.75', 'pub', 'disco', 'dance_club', 'cafe', 'tavern']
senticnet['whisper'] = ['0.96', '0.762', '0', '0', '#joy', '#interest', 'positive', '0.861', 'clear_throat', 'mouth_move', 'wake_morning', 'hear', 'mouth_noise']
senticnet['whispering'] = ['0.943', '-0.69', '0', '0.819', '#joy', '#admiration', 'positive', '0.818', 'speech_production', 'voicelessness', 'whisper', 'susurration', 'speech']
senticnet['whispering_campaign'] = ['-0.79', '0', '0.525', '-0.63', '#sadness', '#disgust', 'negative', '-0.65', 'campaigning', 'candidacy', 'candidature', 'electioneering', 'political_campaign']
senticnet['whistle'] = ['-0.08', '-0.03', '-0.09', '-0.06', '#sadness', '#fear', 'negative', '-0.05', 'say_hello', 'shake_hand', 'feel_strange', 'look_eye', 'hump_leg']
senticnet['whistle_happy_tune'] = ['0.736', '0.719', '0', '0.794', '#joy', '#admiration', 'positive', '0.75', 'whistle', 'smoke_cigar', 'cross_bridge', 'deal_card', 'bounce_ball']
senticnet['whistleblower_lawsuit'] = ['-0.91', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.85', 'lawyer', 'take_court', 'call_lawyer', 'determine_guilt', 'go_to_court']
senticnet['whistler_pachycephala'] = ['-0.03', '-0.01', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'shit', 'wildflower', 'poop', 'wild_flower', 'dog_poop']
senticnet['whistling_duck'] = ['-0.50', '0', '0.764', '-0.61', '#anger', '#disgust', 'negative', '-0.62', 'doctor', 'mountebank', 'charlatan', 'quack', 'physician']
senticnet['whiston'] = ['-0.64', '0', '0.678', '-0.70', '#anger', '#disgust', 'negative', '-0.67', 'pound', 'dyne', 'force_unit', 'sthene', 'poundal']
senticnet['whit'] = ['0', '0.074', '0.026', '0.105', '#interest', '#admiration', 'positive', '0.051', 'handwritten', 'scribble', 'music_part', 'written_message', 'calligraphy']
senticnet['white'] = ['-0.55', '0', '0.561', '-0.51', '#sadness', '#anger', 'negative', '-0.54', 'whitey', 'honky', 'honkey', 'honkie', 'mortal']
senticnet['white-and-black'] = ['0.929', '-0.81', '0', '0', '#joy', '#surprise', 'positive', '0.872', 'post-summit', 'six-voice', 'full-dress', 'company-run', 'post-victory']
senticnet['white-fleshed'] = ['0', '0', '-0.78', '-0.98', '#fear', '#disgust', 'negative', '-0.88', 'scaleless', 'trophy-sized', 'small-mouth', 'algae-eating', 'reddish-black']
senticnet['white-haired'] = ['0', '0', '-0.81', '-0.86', '#fear', '#disgust', 'negative', '-0.83', 'limbless', 'armless', 'legless', 'lanky', 'lizard']
senticnet['white-hot'] = ['0.899', '0.863', '0', '0', '#joy', '#interest', 'positive', '0.881', 'red-hot', 'glowed', 'searing', 'blazing', 'smoldering']
senticnet['white-out'] = ['0', '-0.05', '0.065', '-0.07', '#anger', '#disgust', 'negative', '-0.02', 'tape_dispenser', 'letter_scale', 'intern', 'paper_punch', 'post_note']
senticnet['white_angel'] = ['0.673', '-0.64', '0', '0.669', '#joy', '#admiration', 'positive', '0.661', 'eudemon', 'guardian_angel', 'good_spirit', 'genius_locus', 'angel']
senticnet['white_animal'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'baji', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
senticnet['white_apartment_house'] = ['0.865', '0.768', '0', '0', '#joy', '#interest', 'positive', '0.817', 'capitol', 'government_building', 'apartment_house', 'house', 'government']
senticnet['white_baneberry'] = ['0.773', '-0.55', '0', '0.949', '#joy', '#admiration', 'positive', '0.757', 'cornel', 'dwarf_cornel', 'crackerberry', 'pudding_berry', 'common_white_dogwood']
senticnet['white_baseball_cap'] = ['-0.04', '0.1', '0', '0.152', '#interest', '#admiration', 'positive', '0.068', 'pants', 'jacket', 'clothes', 'robe', 'garment']
senticnet['white_beaked_dolphin'] = ['0', '0', '-0.07', '0', '#fear', '#fear', 'negative', '-0.07', 'many_fishes', 'seahorse', 'starfish', 'plankton', 'sea_wee']
senticnet['white_beard'] = ['0', '0', '0.841', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'ancient', 'many_years', 'year', 'old', 'teen']
senticnet['white_bellied_sea'] = ['0', '0.105', '0', '0', '#interest', '#interest', 'positive', '0.105', 'beak', 'many_feathers', 'big_eye', 'bird', 'bird_mouth']
senticnet['white_belly'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'neck', 'body_part', 'henchman', 'shoulder', 'head']
senticnet['white_birch_betula'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'bottlebrush_buckeye', 'red_buckeye', 'particolored_buckeye']
senticnet['white_bird'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'glaucous_gull', 'dove', 'egret', 'white_swan', 'eismowe']
senticnet['white_blood_cell'] = ['0.602', '-0.73', '0', '0.942', '#surprise', '#admiration', 'positive', '0.76', 'leukocyte', 'leucocyte', 'eosinophil', 'white_cell', 'white_blood_corpuscle']
senticnet['white_blood_corpuscle'] = ['0.602', '-0.73', '0', '0.942', '#surprise', '#admiration', 'positive', '0.76', 'leukocyte', 'leucocyte', 'white_blood_cell', 'white_cell', 'eosinophil']
senticnet['white_bow_tie'] = ['-0.84', '0', '0.633', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'grizzle', 'postiche', 'wig', 'periwig', 'peruke']
senticnet['white_breasted_kingfisher'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'amoeba', 'single_celled']
senticnet['white_breasted_sea'] = ['0', '0.105', '0', '0', '#interest', '#interest', 'positive', '0.105', 'beak', 'many_feathers', 'big_eye', 'bird', 'bird_mouth']
senticnet['white_breasted_waterhen'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'amoeba', 'single_celled']
senticnet['white_brown'] = ['0.874', '0.973', '0', '0', '#joy', '#interest', 'positive', '0.923', 'tasty', 'flavor', 'sweetness', 'edible', 'a_lot_of_fat']
senticnet['white_cane'] = ['-0.22', '-0.03', '0.026', '-0.13', '#sadness', '#disgust', 'negative', '-0.08', 'blind_man', 'blind_person', 'shade', 'need_help', 'shadow']
senticnet['white_cell'] = ['0.602', '-0.73', '0', '0.942', '#surprise', '#admiration', 'positive', '0.76', 'leukocyte', 'leucocyte', 'white_blood_cell', 'eosinophil', 'white_blood_corpuscle']
senticnet['white_checkerboard'] = ['-0.45', '0.081', '-0.25', '0', '#sadness', '#fear', 'negative', '-0.20', 'green_yellow', 'color', 'blue_green', 'cyan', 'purple']
senticnet['white_chocolate'] = ['0.028', '0.138', '-0.04', '0.215', '#interest', '#admiration', 'positive', '0.083', 'water', 'dense', 'thick', 'milk', 'dense_water']
senticnet['white_cinnamon'] = ['-0.95', '0', '0.892', '-0.77', '#sadness', '#anger', 'negative', '-0.87', 'cork', 'cassia_bark', 'canella', 'canella_bark', 'bark']
senticnet['white_clapboard'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'condominium', 'rent_apartment', 'high_rise', 'condo', 'row_house']
senticnet['white_cloth'] = ['0.024', '0', '0', '0.074', '#joy', '#admiration', 'positive', '0.049', 'sheet', 'cover', 'quilt', 'comforter', 'modesty']
senticnet['white_cockatoo'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet']
senticnet['white_color'] = ['0.044', '0', '0.04', '0.101', '#joy', '#admiration', 'positive', '0.035', 'tasty', 'white_stuff', 'fluid', 'flavor', 'crystalline_solid']
senticnet['white_color_scheme'] = ['-0.45', '0.081', '-0.25', '0', '#sadness', '#fear', 'negative', '-0.20', 'green_yellow', 'color', 'blue_green', 'cyan', 'purple']
senticnet['white_colour_scheme'] = ['-0.45', '0.081', '-0.25', '0', '#sadness', '#fear', 'negative', '-0.20', 'green_yellow', 'color', 'blue_green', 'cyan', 'purple']
senticnet['white_costal_streak'] = ['-0.95', '0', '0.892', '-0.81', '#sadness', '#anger', 'negative', '-0.88', 'blurry', 'foggy', 'hazy', 'muzzy', 'blurred']
senticnet['white_cotton'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'fabric', 'underwear', 'pajama', 'shirt', 'pants']
senticnet['white_crappie'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'divergent_strabismus', 'exotropia', 'squint', 'abnormality', 'abnormalcy']
senticnet['white_crystalline_powder'] = ['-0.91', '0', '0.65', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'absolute_alcohol', 'spirits_of_wine', 'denatured_alcohol', 'alcohol', 'plant_product']
senticnet['white_crystalline_solid'] = ['-0.81', '0', '0.774', '-0.77', '#sadness', '#disgust', 'negative', '-0.78', 'acid', 'hypophosphoric_acid', 'periodic_acid', 'oxyacid', 'phosphorous_acid']
senticnet['white_cube'] = ['0.023', '0.085', '-0.04', '0.088', '#interest', '#admiration', 'positive', '0.038', 'thigh', 'hip', 'joint', 'above_leg', 'knee']
senticnet['white_dove'] = ['0.026', '0.18', '-0.11', '0.18', '#interest', '#admiration', 'positive', '0.069', 'deity', 'god', 'angel', 'goddess', 'rabbi']
senticnet['white_dress_shirt'] = ['-0.02', '0.256', '0', '0.166', '#interest', '#admiration', 'positive', '0.132', 'coat', 'shirt', 'garment', 'dress', 'vest']
senticnet['white_dwarf_star'] = ['-0.55', '0', '0.869', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'air_bubble', 'foam', 'froth', 'soap_bubble', 'bubble']
senticnet['white_egret'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'amoeba', 'single_celled']
senticnet['white_enamel'] = ['0.586', '-0.65', '0', '0.503', '#joy', '#surprise', 'positive', '0.58', 'decoration', 'distinguished_service_cross', 'medal', 'medallion', 'palm']
senticnet['white_ensign'] = ['0.053', '0.068', '-0.05', '0.06', '#interest', '#admiration', 'positive', '0.032', 'linen', 'star_stripe', 'velvet', 'nation', 'burlap']
senticnet['white_face'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'afraid', 'scared', 'terrified', 'faint', 'sick']
senticnet['white_faced_saki'] = ['0.921', '0.728', '0', '0', '#joy', '#interest', 'positive', '0.825', 'monkey', 'fiance', 'lemur', 'beaver', 'koala']
senticnet['white_feather'] = ['0.041', '0', '0', '0.058', '#joy', '#admiration', 'positive', '0.05', 'bird_animal', 'wing', 'farm_bird', 'barnyard_animal', 'hen_baby']
senticnet['white_fir'] = ['0.045', '-0.66', '0', '0.361', '#surprise', '#admiration', 'positive', '0.356', 'ponderosa', 'bull_pine', 'western_yellow_pine', 'yellow_pine', 'longleaf_pine']
senticnet['white_footed_mouse'] = ['-0.55', '0', '0.955', '-0.96', '#anger', '#disgust', 'negative', '-0.82', 'house_mouse', 'mouse', 'rat', 'fieldmouse', 'nude_mouse']
senticnet['white_forelock'] = ['0', '0.073', '-0.05', '0.067', '#interest', '#admiration', 'positive', '0.027', 'blonde', 'purple', 'violet', 'dark_light', 'hue']
senticnet['white_fox'] = ['-0.66', '0', '0.992', '-0.72', '#anger', '#disgust', 'negative', '-0.79', 'vixen', 'red_fox', 'kit_fox', 'prairie_fox', 'fox']
senticnet['white_fronted_goose'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['white_fur'] = ['-0.58', '0', '0.66', '-0.8', '#anger', '#disgust', 'negative', '-0.68', 'bald', 'depilous', 'glabrescent', 'glabrous', 'balding']
senticnet['white_gown'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'pinafore', 'pinny', 'dirndl', 'dress', 'muumuu']
senticnet['white_grain'] = ['0.03', '0', '0', '0.076', '#joy', '#admiration', 'positive', '0.053', 'food_ingredient', 'rice', 'granulate', 'noodel', 'wheat_flour']
senticnet['white_grape_juice'] = ['0.557', '-0.53', '0', '0.704', '#joy', '#admiration', 'positive', '0.598', 'lemon_juice', 'chocolate_pudding', 'grape_juice', 'orange_juice', 'juice']
senticnet['white_grub'] = ['-0.02', '-0.09', '0.11', '-0.13', '#anger', '#disgust', 'negative', '-0.04', 'beetle', 'gypsy_moth', 'mole_cricket', 'grasshopper', 'many_insects']
senticnet['white_haired_man'] = ['0.895', '0.712', '0', '0', '#joy', '#interest', 'positive', '0.803', 'timorousness', 'timidness', 'timidity', 'shyly', 'timidly']
senticnet['white_handkerchief'] = ['0.028', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.047', 'cowhide', 'kerchief', 'hanky', 'facial_tissue', 'zipper']
senticnet['white_hart'] = ['0.853', '-0.97', '0', '0', '#joy', '#surprise', 'positive', '0.916', 'tavern', 'cafe', 'nightclub', 'bar', 'liquor_store']
senticnet['white_heron'] = ['0', '0.065', '0.021', '0.052', '#interest', '#admiration', 'positive', '0.032', 'microscopic', 'tiny_organism', 'microbe', 'amoeba', 'single_celled']
senticnet['white_house'] = ['0', '0.709', '0', '0.933', '#interest', '#admiration', 'positive', '0.821', 'capitol', 'government_building', 'president', 'apartment_house', 'government']
senticnet['white_house_adviser'] = ['0', '0.709', '0', '0.933', '#interest', '#admiration', 'positive', '0.821', 'capitol', 'government_building', 'president', 'apartment_house', 'government']
senticnet['white_house_aide'] = ['0', '0', '0', '0.876', '#admiration', '#admiration', 'positive', '0.876', 'good_manner', 'courteous', 'good_behavior', 'handle_carefully', 'politeness']
senticnet['white_house_announcement'] = ['0', '0.724', '0', '0.77', '#interest', '#admiration', 'positive', '0.747', 'qas', 'text', 'edu', 'distribution', 'messageid']
senticnet['white_house_christmas_tree'] = ['0.856', '0', '0', '0', '#joy', '#joy', 'positive', '0.856', 'tree', 'evergreen_tree', 'big_tree', 'santa_claus', 'christmas']
senticnet['white_house_correspondent'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'adventurer', 'humankind']
senticnet['white_house_deputy_chief'] = ['0', '0', '0', '0.876', '#admiration', '#admiration', 'positive', '0.876', 'good_manner', 'courteous', 'good_behavior', 'handle_carefully', 'politeness']
senticnet['white_house_easter_egg'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'poppy_seed', 'custard', 'bread', 'biscuit']
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senticnet['white_lipped_deer'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheep', 'argal', 'urial', 'white_sheep', 'mountain_sheep']
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senticnet['white_male_suffrage'] = ['0.196', '0.129', '-0.06', '0.184', '#joy', '#admiration', 'positive', '0.111', 'pleasant', 'pleasurable', 'bourgeois', 'important_human', 'good']
senticnet['white_marble_statue'] = ['0.085', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.079', 'sculpture', 'creative', 'yellow_ribbon', 'artwork', 'monument']
senticnet['white_muslin'] = ['-0.53', '0', '0.569', '-0.77', '#anger', '#disgust', 'negative', '-0.62', 'pinafore', 'pinny', 'dirndl', 'dress', 'muumuu']
senticnet['white_mustache'] = ['0', '0', '0.841', '-0.70', '#anger', '#disgust', 'negative', '-0.77', 'ancient', 'many_years', 'year', 'old', 'teen']
senticnet['white_necked_stork'] = ['-0.02', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.04', 'wren', 'bird', 'quail', 'sparrow', 'chick']
senticnet['white_nile'] = ['0', '0.068', '0', '0.142', '#interest', '#admiration', 'positive', '0.105', 'water_stream', 'water_formation', 'small_stream', 'water_flow', 'flow_lake']
senticnet['white_paper'] = ['0.819', '0.976', '0', '0', '#joy', '#interest', 'positive', '0.897', 'phto_copy', 'writing_paper', 'printing_paper', 'note_book', 'color_paper']
senticnet['white_pickup_truck'] = ['0', '0', '0', '0.069', '#admiration', '#admiration', 'positive', '0.069', 'trailer', 'trail_car', 'camion', 'wagon', 'towboat']
senticnet['white_pigment'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['white_pine_blister'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['white_pith'] = ['0', '0.141', '0.01', '0.115', '#interest', '#admiration', 'positive', '0.082', 'sepal', 'achene', 'funiculus', 'seed', 'pip']
senticnet['white_plague'] = ['0.633', '-0.63', '0', '0.507', '#joy', '#surprise', 'positive', '0.591', 'drug_addiction', 'dependance', 'physical_condition', 'physiological_state', 'physiological_condition']
senticnet['white_porcelain'] = ['-0.06', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.04', 'polish', 'magnify', 'see_out', 'sill', 'pane']
senticnet['white_potato'] = ['0.532', '-0.68', '0', '0.984', '#surprise', '#admiration', 'positive', '0.733', 'potato', 'chips', 'murphy', 'spud', 'tater']
senticnet['white_powder'] = ['0.913', '0.977', '0', '0', '#joy', '#interest', 'positive', '0.945', 'white_substance', 'food_ingredient', 'white_grain', 'make_up_powder', 'granulate']
senticnet['white_powdery_substance'] = ['0.029', '-0.03', '0.066', '-0.08', '#anger', '#disgust', 'negative', '-0.02', 'sheet', 'drawer', 'file_box', 'desk_tray', 'letter_scale']
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senticnet['white_soft'] = ['0.203', '0.077', '0', '0.189', '#joy', '#admiration', 'positive', '0.156', 'soft', 'skin', 'striped', 'waterproof', 'fuzzy']
senticnet['white_spruce_picea_glauca'] = ['0.776', '-0.6', '0', '0.568', '#joy', '#surprise', 'positive', '0.648', 'swamp_chestnut_oak', 'chinquapin_oak', 'chinkapin_oak', 'yellow_chestnut_oak', 'basket_oak']
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senticnet['white_stork'] = ['-0.02', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.04', 'wren', 'bird', 'quail', 'sparrow', 'chick']
senticnet['white_stripe'] = ['0.942', '-0.61', '0', '0.908', '#joy', '#admiration', 'positive', '0.82', 'adornment', 'cigar_band', 'hatband', 'neckband', 'pinstripe']
senticnet['white_stripe_down'] = ['0.942', '-0.61', '0', '0.908', '#joy', '#admiration', 'positive', '0.82', 'adornment', 'cigar_band', 'hatband', 'neckband', 'pinstripe']
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senticnet['white_stuff'] = ['0.044', '0', '0.04', '0.101', '#joy', '#admiration', 'positive', '0.035', 'tasty', 'white_color', 'fluid', 'flavor', 'crystalline_solid']
senticnet['white_substance'] = ['0.808', '0.902', '0', '0', '#joy', '#interest', 'positive', '0.855', 'white_powder', 'white', 'granulate', 'smelly', 'food_flavor']
senticnet['white_sucker'] = ['0', '-0.02', '0.044', '-0.08', '#anger', '#disgust', 'negative', '-0.03', 'haddock', 'mermaid', 'fish_rod', 'tidal_wave', 'dryad']
senticnet['white_supercilium'] = ['-0.59', '0', '0.887', '-0.71', '#anger', '#disgust', 'negative', '-0.73', 'armet', 'basinet', 'helmet', 'heaume', 'morion']
senticnet['white_swan'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'glaucous_gull', 'dove', 'egret', 'white_bird', 'eismowe']
senticnet['white_sweet_clover'] = ['0', '0.086', '0', '0', '#interest', '#interest', 'positive', '0.086', 'swim_ocean', 'weight_lift', 'sit_in_shade', 'cut_grass', 'trust_friend']
senticnet['white_tailed_mongoose'] = ['0', '0', '-0.95', '-0.81', '#fear', '#disgust', 'negative', '-0.88', 'herb', 'plant', 'poisonous_plant', 'yellow_flower', 'lichen']
senticnet['white_tailed_ptarmigan'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'argali', 'argal', 'urial', 'white_sheep', 'wild_sheep']
senticnet['white_tailed_sea'] = ['0', '0.105', '0', '0', '#interest', '#interest', 'positive', '0.105', 'beak', 'many_feathers', 'big_eye', 'bird', 'bird_mouth']
senticnet['white_tin_glaze'] = ['-0.06', '0', '0.028', '0', '#sadness', '#anger', 'negative', '-0.04', 'polish', 'magnify', 'see_out', 'sill', 'pane']
senticnet['white_tooth'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'teeth_whitening', 'smile', 'clean_teeth', 'beautiful_smile', 'dentist']
senticnet['white_tunic'] = ['-0.53', '0', '0.682', '-0.74', '#anger', '#disgust', 'negative', '-0.65', 'girdle', 'cummerbund', 'sash', 'waistband', 'waistcloth']
senticnet['white_tuxedo'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'clothes_style', 'style', 'glamour', 'model', 'formal_clothes']
senticnet['white_wagtail'] = ['-0.84', '0', '0.655', '-0.85', '#sadness', '#disgust', 'negative', '-0.78', 'piping_crow', 'butcherbird', 'currawong', 'bell_magpie', 'oscine']
senticnet['white_waistcoat'] = ['-0.84', '0', '0.633', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'grizzle', 'postiche', 'horsehair_wig', 'wig', 'peruke']
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senticnet['white_whale'] = ['-0.93', '0', '0.544', '-0.77', '#sadness', '#disgust', 'negative', '-0.75', 'mutant', 'variation', 'sport', 'lusus_naturae', 'mutation']
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senticnet['white_wine_vinegar'] = ['-0.95', '0', '0.845', '-0.67', '#sadness', '#anger', 'negative', '-0.82', 'grapefruit_peel', 'candied_citrus_peel', 'orange_peel', 'candied_fruit', 'succade']
senticnet['white_wingbar'] = ['-0.59', '0', '0.887', '-0.71', '#anger', '#disgust', 'negative', '-0.73', 'armet', 'basinet', 'helmet', 'heaume', 'morion']
senticnet['white_winged_scoter'] = ['0.022', '-0.11', '0.014', '0.057', '#surprise', '#admiration', 'positive', '0.045', 'wood', 'many_steps', 'franchise', 'stump', 'timber']
senticnet['white_witch'] = ['-0.63', '0', '0.674', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'baneful', 'evil', 'baleful', 'malevolent', 'malign']
senticnet['white_zombie'] = ['-0.79', '0', '0.885', '-0.76', '#sadness', '#anger', 'negative', '-0.81', 'infernal', 'mortal', 'dead_soul', 'dead_soule', 'living_dead']
senticnet['whitebark_pine'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'rust_fungus', 'apple_rust', 'fungus']
senticnet['whiteboard'] = ['0.044', '-0.02', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'sheet_paper', 'piece_of_paper', 'staple_remover', 'board', 'paperwork']
senticnet['whitechapel_bell_foundry'] = ['0.034', '0', '0.01', '0.087', '#joy', '#admiration', 'positive', '0.037', 'chime', 'bell_noise', 'alto', 'piano', 'ring_loudly']
senticnet['whited_sepulcher'] = ['0.978', '-0.99', '0', '0.69', '#joy', '#surprise', 'positive', '0.886', 'pretender', 'phony', 'phoney', 'hypocrite', 'dissembler']
senticnet['whitefish_point_underwater'] = ['0', '0.243', '-0.11', '0.201', '#interest', '#admiration', 'positive', '0.108', 'upkeep', 'contain', 'keep_safe', 'keep', 'prevent']
senticnet['whitefly'] = ['0', '-0.09', '0.123', '-0.11', '#anger', '#disgust', 'negative', '-0.04', 'gypsy_moth', 'japanese_beetle', 'cucumber_beetle', 'mosquito', 'earwig']
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senticnet['whitehaven_beach'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'beachcomber', 'bum', 'drifter']
senticnet['whiten_teeth'] = ['-0.93', '0', '-0.77', '0', '#sadness', '#fear', 'negative', '-0.85', 'protect_from_sun', 'shade', 'meaning', 'corrector', 'sunglasses']
senticnet['whiteness'] = ['0', '0', '0', '0.092', '#admiration', '#admiration', 'positive', '0.092', 'sun_cover', 'crystal_clear', 'water_sky', 'block_sun', 'weather_forecast']
senticnet['whitening'] = ['0.99', '-0.53', '0', '0.977', '#joy', '#admiration', 'positive', '0.835', 'change_of_state', 'discoloration', 'faded', 'change_of_color', 'lightening']
senticnet['whiteout'] = ['0', '0', '-0.93', '-0.82', '#fear', '#disgust', 'negative', '-0.88', 'thunderstorm', 'snow_storm', 'storm', 'weather', 'rainy']
senticnet['whiter_teeth'] = ['0.824', '0.86', '0', '0.983', '#interest', '#admiration', 'positive', '0.889', 'look_smart', 'social_acceptance', 'improve_appearance', 'improve_look', 'look_good']
senticnet['whitetail'] = ['-0.03', '0', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.06', 'lion', 'leopard', 'mountain_lion', 'craven', 'wolf']
senticnet['whitetail_deer'] = ['-0.03', '0', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.06', 'lion', 'leopard', 'mountain_lion', 'craven', 'wolf']
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senticnet['whithersoever'] = ['0', '0.152', '-0.08', '0.208', '#interest', '#admiration', 'positive', '0.091', 'departure', 'leave', 'colored_green', 'green_color', 'make_big']
senticnet['whiti'] = ['-0.91', '0', '0.769', '-0.84', '#sadness', '#disgust', 'negative', '-0.84', 'break', 'good_luck', 'chance_eventpy_chance', 'chance_event', 'coincidence']
senticnet['whitish'] = ['0', '0.764', '0', '0.699', '#interest', '#admiration', 'positive', '0.732', 'yellowish', 'brownish', 'rubbery', 'leathery', 'waxy']
senticnet['whitish_belly'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'neck', 'body_part', 'henchman', 'shoulder', 'head']
senticnet['whitish_flesh'] = ['0', '0.141', '0.01', '0.115', '#interest', '#admiration', 'positive', '0.082', 'sepal', 'achene', 'funiculus', 'seed', 'pip']
senticnet['whitish_throat'] = ['0.044', '-0.03', '0.037', '0.089', '#joy', '#admiration', 'positive', '0.032', 'neck', 'body_part', 'henchman', 'shoulder', 'head']
senticnet['whitlowwort'] = ['-0.72', '0', '0.64', '-0.58', '#sadness', '#anger', 'negative', '-0.65', 'pearlwort', 'pearlweed', 'flora', 'rupturewort', 'groundcover']
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senticnet['whitsuntide'] = ['0.726', '0.925', '0', '0', '#joy', '#interest', 'positive', '0.826', 'friday', 'monday', 'midweek', 'weekend', 'day_of_week']
senticnet['whittle'] = ['0', '-0.09', '0', '0', '#surprise', '#surprise', 'positive', '0.098', 'price_product', 'fillet_fish', 'photography', 'demo', 'pottery']
senticnet['whiz_bang'] = ['0.995', '0.801', '0', '0.968', '#joy', '#admiration', 'positive', '0.921', 'acquire_knowledge', 'comprehend', 'great', 'accumulate_knowledge', 'increase_knowledge']
senticnet['whiz_past'] = ['0.691', '-0.97', '0', '0.771', '#surprise', '#admiration', 'positive', '0.812', 'squeal', 'blate', 'churr', 'let_loose', 'sibilate']
senticnet['whizz'] = ['-0.18', '-0.00', '-0.18', '0', '#sadness', '#fear', 'negative', '-0.11', 'pulverize', 'violent_action', 'smash', 'hit_hard', 'melt_away']
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senticnet['whl_west'] = ['0.977', '-0.93', '0', '0.583', '#joy', '#surprise', 'positive', '0.831', 'giant', 'shinny', 'supernova', 'supergiant', 'binary_star']
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senticnet['whodunit'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'anxiety', 'apprehension', 'insecurity', 'tension', 'horror_movie']
senticnet['whodunnit'] = ['-0.89', '0', '-0.71', '0', '#sadness', '#fear', 'negative', '-0.80', 'riddle', 'obituary', 'editorial', 'news_story', 'article']
senticnet['whole'] = ['0.749', '0.837', '0', '0', '#joy', '#interest', 'positive', '0.793', 'unhurt', 'unscathed', 'hale', 'unharmed', 'unit']
senticnet['whole_affair'] = ['0', '-0.81', '0', '0.817', '#surprise', '#admiration', 'positive', '0.814', 'behave', 'act', 'perpetrator', 'portray', 'stage_play']
senticnet['whole_bunch'] = ['0.057', '0.141', '-0.06', '0.162', '#interest', '#admiration', 'positive', '0.074', 'belongings', 'material', 'fabric', 'guarantee', 'madonna']
senticnet['whole_cow_milk'] = ['-0.03', '0.086', '0', '0.076', '#interest', '#admiration', 'positive', '0.042', 'drink_water', 'drink_espresso', 'drink', 'drink_coffee', 'drink_hot_chocolate']
senticnet['whole_edifice'] = ['0.026', '0.219', '0', '0.16', '#interest', '#admiration', 'positive', '0.135', 'large_build', 'palace', 'high_rise', 'structure', 'fortification']
senticnet['whole_grain'] = ['0.061', '0.093', '0.037', '0.114', '#interest', '#admiration', 'positive', '0.058', 'cereal_grain', 'food_ingredient', 'staple_food', 'starchy_food', 'food_grain']
senticnet['whole_grain_bread'] = ['-0.92', '0', '0.609', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'puffed_rice', 'granola', 'raisin_bran', 'corn_flake', 'bran_flake']
senticnet['whole_grain_cereal'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'veal', 'beef', 'food_group', 'animal_flesh', 'curry']
senticnet['whole_grain_flour'] = ['0.026', '0.08', '0.043', '0.064', '#interest', '#admiration', 'positive', '0.032', 'eat_rice', 'white_brown', 'asian', 'eat_pasta', 'staple_food']
senticnet['whole_grain_pasta'] = ['-0.92', '0', '0.609', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'puffed_rice', 'granola', 'raisin_bran', 'corn_flake', 'bran_flake']
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senticnet['whole_lotta_love'] = ['0.773', '0', '0', '0.914', '#joy', '#admiration', 'positive', '0.844', 'favorite', 'crowd', 'acclamation', 'bunch', 'large_crowd']
senticnet['whole_lotta_rosie'] = ['0.542', '-0.83', '0', '0.927', '#surprise', '#admiration', 'positive', '0.769', 'incest', 'extramarital_sex', 'free_love', 'sexual_intercourse', 'intercourse']
senticnet['whole_milk_powder'] = ['0.057', '0', '0.018', '0.058', '#joy', '#admiration', 'positive', '0.032', 'porridge', 'food', 'milk', 'healthy', 'morning_food']
senticnet['whole_number'] = ['0.89', '0', '0', '0.824', '#joy', '#admiration', 'positive', '0.857', 'integer', 'number_theory', 'study', 'mathematics', 'oldest']
senticnet['whole_ordeal'] = ['-0.80', '0', '-0.8', '0', '#sadness', '#fear', 'negative', '-0.80', 'sinking', 'misgiving', 'qualm', 'scruple', 'insecurely']
senticnet['whole_shebang'] = ['0.639', '-0.82', '0', '0.997', '#surprise', '#admiration', 'positive', '0.821', 'completeness', 'work', 'entireness', 'operation', 'whole']
senticnet['whole_step'] = ['-0.65', '0', '0.681', '-0.94', '#anger', '#disgust', 'negative', '-0.75', 'tone', 'whole_tone', 'step', 'musical_interval', 'semitone']
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senticnet['whole_wheat_bread'] = ['0.056', '0', '0', '0.05', '#joy', '#admiration', 'positive', '0.053', 'leftover', 'dusty', 'mayonnaise', 'leave_over', 'savory']
senticnet['whole_wheat_pasta'] = ['-0.92', '0', '0.609', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'puffed_rice', 'granola', 'raisin_bran', 'corn_flake', 'bran_flake']
senticnet['whole_wheat_toast'] = ['-0.92', '0', '0.609', '-0.74', '#sadness', '#disgust', 'negative', '-0.75', 'puffed_rice', 'granola', 'raisin_bran', 'corn_flake', 'bran_flake']
senticnet['wholegrain'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'veal', 'beef', 'food_group', 'animal_flesh', 'curry']
senticnet['wholeheartedly'] = ['0.755', '0.972', '0', '0.775', '#interest', '#admiration', 'positive', '0.834', 'grasp', 'grab', 'hold', 'sensual', 'grip']
senticnet['wholemeal'] = ['-0.75', '0', '0', '-0.81', '#sadness', '#disgust', 'negative', '-0.78', 'week-old', 'month-old', 'cubed', 'good-quality', 'day-old']
senticnet['wholemeal_bread'] = ['0.52', '-0.69', '0', '0.639', '#surprise', '#admiration', 'positive', '0.617', 'black_bread', 'pumpernickel', 'limpa', 'bread', 'breadstuff']
senticnet['wholemeal_flour'] = ['0.026', '0.08', '0.043', '0.064', '#interest', '#admiration', 'positive', '0.032', 'eat_rice', 'white_brown', 'asian', 'eat_pasta', 'staple_food']
senticnet['wholeness'] = ['0.937', '-0.74', '0', '0.562', '#joy', '#surprise', 'positive', '0.749', 'haleness', 'healthiness', 'rosiness', 'health', 'vitality']
senticnet['wholesale'] = ['-0.73', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.82', 'indiscriminate', 'equal-opportunity', 'catch-all', 'indistinguishably', 'indiscriminately']
senticnet['wholesale_bakery'] = ['0.914', '-0.56', '0', '0.869', '#joy', '#admiration', 'positive', '0.781', 'tabula_rasa', 'ego', 'subconscious', 'unconscious', 'psyche']
senticnet['wholesale_condemnation'] = ['-0.51', '0', '0.607', '-0.76', '#anger', '#disgust', 'negative', '-0.63', 'offensiveness', 'odiousness', 'blatancy', 'patency', 'obviousness']
senticnet['wholesale_demolition'] = ['-0.08', '0', '-0.08', '-0.14', '#sadness', '#disgust', 'negative', '-0.10', 'violence', 'assault', 'combat', 'siege', 'use_military']
senticnet['wholesale_destruction'] = ['-0.87', '0', '0.889', '-0.62', '#sadness', '#anger', 'negative', '-0.79', 'ruination', 'ruin', 'deterioration', 'blight', 'impairment']
senticnet['wholesale_electricity'] = ['0.986', '0.897', '0', '0', '#joy', '#interest', 'positive', '0.942', 'potential_energy', 'watt', 'electrical', 'power_source', 'volt']
senticnet['wholesale_extermination'] = ['-0.08', '0', '-0.15', '-0.20', '#fear', '#disgust', 'negative', '-0.14', 'casualty', 'reaper', 'wound', 'cause_death', 'gunshot']
senticnet['wholesale_grocer'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'pharmaceutical_chemist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['wholesale_grocery'] = ['0.654', '-0.57', '0', '0.685', '#joy', '#admiration', 'positive', '0.639', 'pharmacologist', 'pharmaceutical_chemist', 'health_professional', 'primary_care_provider', 'health_care_provider']
senticnet['wholesale_price_index'] = ['0.538', '-0.80', '0', '0.517', '#joy', '#surprise', 'positive', '0.619', 'indicator', 'retail_price_index', 'producer_price_index', 'price_index', 'consumer_price_index']
senticnet['wholesale_slaughter'] = ['-0.75', '0', '0', '-0.89', '#sadness', '#disgust', 'negative', '-0.82', 'oppression', 'caged_lion', 'slave', 'captivity', 'misery']
senticnet['wholesome'] = ['0.656', '-0.73', '0', '0.736', '#surprise', '#admiration', 'positive', '0.708', 'strong', 'salubrious', 'healthy', 'solid', 'alimentary']
senticnet['wholesome_entertainment'] = ['0.656', '-0.73', '0', '0.736', '#surprise', '#admiration', 'positive', '0.708', 'strong', 'salubrious', 'healthy', 'solid', 'alimentary']
senticnet['wholesome_food'] = ['0.656', '-0.73', '0', '0.736', '#surprise', '#admiration', 'positive', '0.708', 'strong', 'salubrious', 'healthy', 'solid', 'alimentary']
senticnet['wholesomely'] = ['0', '0.999', '0', '0.917', '#interest', '#admiration', 'positive', '0.958', 'unassumingly', 'readably', 'incongruously', 'cozily', 'fetchingly']
senticnet['wholly'] = ['0.588', '-0.62', '0', '0.825', '#surprise', '#admiration', 'positive', '0.679', 'completeness', 'work', 'whole_shebang', 'absolutely', 'entirety']
senticnet['wholly_ignorant'] = ['-0.96', '0', '0.91', '-0.61', '#sadness', '#anger', 'negative', '-0.82', 'unpersuadable', 'unsuasible', 'unresponsive', 'resistant', 'incapable']
senticnet['wholly_irrelevant'] = ['-0.50', '0', '0.89', '-0.78', '#anger', '#disgust', 'negative', '-0.72', 'excess', 'degree', 'grade', 'level', 'immoderateness']
senticnet['wholly_owned_subsidiary'] = ['0.111', '0.18', '0', '0.128', '#interest', '#admiration', 'positive', '0.14', 'corporation', 'partnership', 'large_business', 'business_organization', 'enterprise']
senticnet['whomsoever'] = ['0.621', '0.648', '-0.25', '0.296', '#joy', '#interest', 'positive', '0.328', 'pacifically', 'repose', 'peace-loving', 'peaceable', 'peace_of_mind']
senticnet['whoop'] = ['0.041', '0', '0', '0.064', '#joy', '#admiration', 'positive', '0.053', 'squeak', 'clang', 'musical_note', 'bad_music', 'roar']
senticnet['whoopee'] = ['-0.70', '0', '0.704', '-0.55', '#sadness', '#anger', 'negative', '-0.65', 'revel', 'merrymaking', 'jollification', 'binge', 'riot']
senticnet['whoopee_cushion'] = ['0', '0', '0', '-0.76', '#disgust', '#disgust', 'negative', '-0.76', 'burp', 'rude', 'bad_manner', 'shame', 'get_angry']
senticnet['whooper'] = ['0.042', '0.088', '0', '0', '#joy', '#interest', 'positive', '0.065', 'jump_over_obstacle', 'attempt_fly', 'aircraft', 'pilot', 'wing']
senticnet['whoosh'] = ['0.098', '0', '-0.04', '0.112', '#joy', '#admiration', 'positive', '0.054', 'squeak', 'clang', 'vibration', 'loud_yell', 'tremor']
senticnet['whopper'] = ['0.671', '-0.86', '0', '0.717', '#surprise', '#admiration', 'positive', '0.752', 'prevarication', 'white_lie', 'jactitation', 'walloper', 'fib']
senticnet['whopping'] = ['0', '0', '-0.73', '-0.70', '#fear', '#disgust', 'negative', '-0.71', 'sock', 'bop', 'whack', 'wham', 'wallop']
senticnet['whore'] = ['-0.83', '0', '0.885', '-0.52', '#sadness', '#anger', 'negative', '-0.74', 'slattern', 'camp_follower', 'call_girl', 'comfort_woman', 'ianfu']
senticnet['whoredom'] = ['0', '0', '0.753', '-0.72', '#anger', '#disgust', 'negative', '-0.73', 'sinful', 'human_emotion', 'call', 'demand', 'yearn']
senticnet['whorehouse'] = ['0.023', '0.085', '0', '0', '#joy', '#interest', 'positive', '0.054', 'public_place', 'front_build', 'rest_area', 'memorial', 'nuclear_power_plant']
senticnet['whoremonger'] = ['-0.54', '0', '0.691', '-0.96', '#anger', '#disgust', 'negative', '-0.73', 'slut', 'trollop', 'libertine', 'adulteress', 'fornicatress']
senticnet['whoreson'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'holy_terror', 'rapscallion', 'scalawag', 'scallywag', 'monkey']
senticnet['whoso'] = ['-0.84', '0', '-0.97', '0', '#sadness', '#fear', 'negative', '-0.90', 'mortify', 'embarrassed', 'self-hatred', 'self-disgust', 'humiliated']
senticnet['whosoever'] = ['0.038', '0', '0', '0.097', '#joy', '#admiration', 'positive', '0.068', 'rule', 'decision', 'follow', 'discipline', 'acceptance']
senticnet['why_re_invent'] = ['0', '-0.09', '0.044', '0.055', '#surprise', '#admiration', 'positive', '0.036', 'two_wheels', 'gear', 'axle', 'engine', 'steer_wheel']
senticnet['wi_aye'] = ['0.027', '0', '0.012', '0.063', '#joy', '#admiration', 'positive', '0.026', 'vegetation', 'tree_flower', 'green_growth', 'small', 'greenery']
senticnet['wic_program'] = ['0.063', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.065', 'look_catalog', 'make_selection', 'good_credit', 'menu', 'save_money']
senticnet['wick'] = ['0.999', '0.72', '0', '0', '#joy', '#interest', 'positive', '0.859', 'cake', 'brownie', 'mousse', 'custard', 'birthday_cake']
senticnet['wick_moisture'] = ['0.706', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.806', 'shoe', 'flip_flop', 'slipper', 'casual_shoes', 'footwear']
senticnet['wicked'] = ['-0.74', '0', '0.823', '0', '#sadness', '#anger', 'negative', '-0.78', 'very', 'wickedly', 'ill', 'impiously', 'male']
senticnet['wicked_men'] = ['-0.52', '0', '0.542', '-0.93', '#anger', '#disgust', 'negative', '-0.67', 'intoxication', 'drunkenness', 'inebriation', 'inebriety', 'tipsiness']
senticnet['wicked_priest'] = ['-0.52', '0', '0.701', '-0.50', '#sadness', '#anger', 'negative', '-0.57', 'disturber', 'provoker', 'inciter', 'instigant', 'hellhound']
senticnet['wicked_queen'] = ['-0.63', '0', '0.674', '-0.75', '#anger', '#disgust', 'negative', '-0.68', 'baneful', 'evil', 'baleful', 'malevolent', 'malign']
senticnet['wicked_stepmother'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'mythical', 'tale', 'dictionary', 'magic', 'perfect_world']
senticnet['wicked_witch'] = ['0.931', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.855', 'crystal_clear', 'blue_sky', 'imaginary', 'water_sky', 'blue_color']
senticnet['wickedest'] = ['-0.55', '0', '0.947', '-0.77', '#anger', '#disgust', 'negative', '-0.75', 'bemock', 'deride', 'twit', 'tease', 'razz']
senticnet['wickedly'] = ['-0.74', '0', '0.827', '0', '#sadness', '#anger', 'negative', '-0.78', 'very', 'wicked', 'ill', 'impiously', 'male']
senticnet['wickedly_funny'] = ['0.807', '-0.83', '0', '0.688', '#joy', '#surprise', 'positive', '0.776', 'folly', 'lunacy', 'craziness', 'indulgence', 'foolery']
senticnet['wickedness'] = ['0.952', '-0.78', '0', '0.56', '#joy', '#surprise', 'positive', '0.764', 'sexual_immorality', 'foul_play', 'irreverence', 'deviltry', 'devilry']
senticnet['wicker'] = ['-0.11', '0', '-0.16', '0.062', '#sadness', '#fear', 'negative', '-0.07', 'tile_floor', 'cunt', 'miss', 'short_form', 'happy_together']
senticnet['wicker_basket'] = ['-0.71', '0', '0.954', '-0.56', '#sadness', '#anger', 'negative', '-0.74', 'jackknife', 'pocket_knife', 'penknife', 'switchblade', 'switchblade_knife']
senticnet['wickerwork'] = ['0', '0.773', '0', '0.895', '#interest', '#admiration', 'positive', '0.834', 'wooden_board', 'make_wood', 'wooden_box', 'wooden_floor', 'long_wood']
senticnet['wicket'] = ['0', '0', '0', '0.19', '#admiration', '#admiration', 'positive', '0.19', 'file_bill', 'record', 'webpage', 'note', 'store_record']
senticnet['wicket_maiden'] = ['-0.62', '0', '0.713', '-0.98', '#anger', '#disgust', 'negative', '-0.77', 'muddy_up', 'muck_up', 'foul', 'blemish', 'smear']
senticnet['wicking'] = ['0', '0.854', '0', '0.938', '#interest', '#admiration', 'positive', '0.896', 'fast-drying', 'quick-dry', 'moisture-wicking', 'quick-drying', 'breathable']
senticnet['wid_hae'] = ['0.947', '0.914', '0', '0', '#joy', '#interest', 'positive', '0.93', 'ball_bounce', 'bicycle_tire', 'tire', 'increase_price', 'gyroscope']
senticnet['widcombe'] = ['0.042', '-0.05', '0', '0', '#joy', '#surprise', 'positive', '0.049', 'toilet', 'bathroom_drawerroom', 'beauty_parlor', 'hotel_bathroom_drawerroom', 'bathroom_drawer']
senticnet['widdow'] = ['-0.69', '0', '0.883', '-0.91', '#anger', '#disgust', 'negative', '-0.82', 'stick_in', 'enclose', 'insert', 'feed_in', 'put_in']
senticnet['widdy'] = ['-0.85', '0', '0.583', '-0.71', '#sadness', '#disgust', 'negative', '-0.71', 'muddle', 'rummage', 'mare_nest', 'welter', 'clutter']
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senticnet['wide-awake'] = ['0.773', '-0.88', '0', '0', '#joy', '#surprise', 'positive', '0.831', 'groggy', 'jet-lagged', 'clear-headed', 'bleary-eyed', 'hung-over']
senticnet['wide-eyed'] = ['0', '-0.66', '0', '0', '#surprise', '#surprise', 'positive', '0.665', 'surprised', 'dazed', 'surprise', 'goggle-eyed', 'open-mouthed']
senticnet['wide-ranging'] = ['0', '0.762', '0', '0.917', '#interest', '#admiration', 'positive', '0.84', 'far-reaching', 'broad-ranging', 'far-ranging', 'extensive', 'diverse']
senticnet['wide_acclaim'] = ['0.853', '0.828', '0', '0.98', '#joy', '#admiration', 'positive', '0.887', 'endorse', 'recommend', 'stimulate_mind', 'acculturation', 'support']
senticnet['wide_angle'] = ['0', '-0.02', '0.048', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'telescope', 'read_glass', 'eyeglass', 'binoculars', 'monocle']
senticnet['wide_angle_camera'] = ['0', '-0.02', '0.048', '-0.05', '#anger', '#disgust', 'negative', '-0.02', 'telescope', 'read_glass', 'eyeglass', 'binoculars', 'monocle']
senticnet['wide_angle_shot'] = ['0.034', '0.075', '0', '0.143', '#interest', '#admiration', 'positive', '0.084', 'photograph', 'art', 'painting', 'artwork', 'mural']
senticnet['wide_angle_view'] = ['0.838', '0.828', '0', '0', '#joy', '#interest', 'positive', '0.833', 'vista', 'spectacular', 'perspective', 'image', 'scenic']
senticnet['wide_applicability'] = ['0.637', '-0.91', '0', '0.757', '#surprise', '#admiration', 'positive', '0.768', 'manageability', 'manageableness', 'docility', 'obedience', 'tractability']
senticnet['wide_array'] = ['0.057', '0', '0', '0.153', '#joy', '#admiration', 'positive', '0.105', 'multiple', 'cute', 'variety', 'hairy', 'large_breadbox']
senticnet['wide_awake'] = ['0', '0.08', '0', '0.179', '#interest', '#admiration', 'positive', '0.13', 'wake_up_morning', 'up_early', 'get_up_early', 'gather_energy_tomorrow', 'sit_chair']
senticnet['wide_brim'] = ['0.08', '0.106', '-0.04', '0.112', '#interest', '#admiration', 'positive', '0.063', 'helmet', 'head_gear', 'cap', 'head_wear', 'head_protection']
senticnet['wide_brimmed_hat'] = ['0', '-0.08', '0.105', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'underwear', 'shirt', 'panties', 'robe', 'slack']
senticnet['wide_circulation'] = ['0.751', '-0.98', '0', '0.989', '#surprise', '#admiration', 'positive', '0.909', 'okay', 'approval', 'commendation', 'okey', 'okeh']
senticnet['wide_crater'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'firework', 'bright_flash', 'nuclear_weapon', 'earthquake', 'nuclear_bomb']
senticnet['wide_critical_acclaim'] = ['0.853', '0.828', '0', '0.98', '#joy', '#admiration', 'positive', '0.887', 'endorse', 'recommend', 'stimulate_mind', 'acculturation', 'support']
senticnet['wide_discretion'] = ['-0.52', '0', '0.561', '-0.90', '#anger', '#disgust', 'negative', '-0.66', 'difference', 'tolerance', 'allowance', 'discrepancy', 'margin']
senticnet['wide_eyed_wonder'] = ['-0.83', '0', '0.703', '-0.85', '#sadness', '#disgust', 'negative', '-0.79', 'hurly_burly', 'kerfuffle', 'commotion', 'disruption', 'disturbance']
senticnet['wide_geographical'] = ['0.828', '-0.89', '0', '0.693', '#joy', '#surprise', 'positive', '0.806', 'confines', 'expanse', 'view', 'internationality', 'purview']
senticnet['wide_latitude'] = ['-0.52', '0', '0.561', '-0.90', '#anger', '#disgust', 'negative', '-0.66', 'difference', 'tolerance', 'allowance', 'discrepancy', 'margin']
senticnet['wide_leeway'] = ['-0.52', '0', '0.561', '-0.90', '#anger', '#disgust', 'negative', '-0.66', 'difference', 'tolerance', 'allowance', 'discrepancy', 'margin']
senticnet['wide_of_mark'] = ['-0.51', '0', '0.502', '-0.58', '#sadness', '#disgust', 'negative', '-0.53', 'wrong', 'incorrect', 'inaccurate', 'unfaithful', 'faulty']
senticnet['wide_open'] = ['0', '0.088', '0.022', '0.098', '#interest', '#admiration', 'positive', '0.055', 'ground', 'outer', 'around_earth', 'star_planet', 'nothingness']
senticnet['wide_range'] = ['0', '0.945', '0', '0.709', '#interest', '#admiration', 'positive', '0.827', 'study', 'research', 'project', 'well', 'student']
senticnet['wide_ranging_discussion'] = ['0', '0', '0', '0.082', '#admiration', '#admiration', 'positive', '0.082', 'discuss', 'communication', 'communicate_person', 'share', 'autograph']
senticnet['wide_ranging_inquiry'] = ['0.845', '0.916', '0', '0', '#joy', '#interest', 'positive', '0.881', 'question', 'answer', 'query', 'question_start', 'question_answering']
senticnet['wide_ranging_investigation'] = ['0', '0.825', '0', '0', '#interest', '#interest', 'positive', '0.825', 'query', 'interrogation', 'inquire', 'response', 'solution']
senticnet['wide_ranging_probe'] = ['0', '0.825', '0', '0', '#interest', '#interest', 'positive', '0.825', 'query', 'interrogation', 'inquire', 'response', 'solution']
senticnet['wide_renown'] = ['0.852', '-0.75', '0', '0.763', '#joy', '#admiration', 'positive', '0.789', 'honor', 'laurel', 'fame', 'stock', 'character']
senticnet['wide_spectrum'] = ['0.057', '0', '0', '0.153', '#joy', '#admiration', 'positive', '0.105', 'multiple', 'cute', 'variety', 'hairy', 'large_breadbox']
senticnet['wide_street'] = ['0', '0.088', '0.022', '0.098', '#interest', '#admiration', 'positive', '0.055', 'street', 'wide', 'country_road', 'freeway', 'highway']
senticnet['wide_variation'] = ['0.295', '0.072', '0', '0.136', '#joy', '#admiration', 'positive', '0.168', 'live_environment', 'beautiful', 'lovely', 'important_man', 'peaceful']
senticnet['wide_variety'] = ['0', '0.809', '0', '0.853', '#interest', '#admiration', 'positive', '0.831', 'research', 'study', 'project', 'student', 'well']
senticnet['wide_viewing_angle'] = ['0.057', '0', '0.012', '0.08', '#joy', '#admiration', 'positive', '0.042', 'see_better', 'glasses', 'become_gigantic', 'magnify', 'contact_lens']
senticnet['wide_web'] = ['-0.09', '-0.03', '0.061', '0', '#sadness', '#anger', 'negative', '-0.04', 'bad_reception', 'wifi', 'bad_connection', 'phone_call', 'underground']
senticnet['widely'] = ['0', '0.758', '0', '0.793', '#interest', '#admiration', 'positive', '0.776', 'wide', 'project', 'research', 'study', 'different_application']
senticnet['widely-used'] = ['0', '0', '-0.93', '-0.85', '#fear', '#disgust', 'negative', '-0.89', 'special-purpose', 'general-purpose', 'high-performance', 'microprocessor', 'programmable']
senticnet['widely_accessible'] = ['0.81', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.774', 'available', 'public', 'population', 'crowd', 'majority']
senticnet['widely_available'] = ['0.81', '0.739', '0', '0', '#joy', '#interest', 'positive', '0.774', 'available', 'public', 'population', 'crowd', 'majority']
senticnet['widely_discredited'] = ['-0.72', '0', '0.755', '0', '#sadness', '#anger', 'negative', '-0.73', 'disgrace', 'shameful', 'dishonorably', 'inglorious', 'dishonored']
senticnet['widely_read'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'important', 'universal', 'vip', 'prestige', 'status']
senticnet['widely_respected'] = ['0.865', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.887', 'manager', 'colleague', 'office_worker', 'coworker', 'secretary']
senticnet['widely_umbilicate'] = ['0.969', '-0.54', '0', '0.806', '#joy', '#admiration', 'positive', '0.772', 'aspheric', 'aspherical', 'prolate', 'rounded', 'crescent']
senticnet['widen'] = ['-0.02', '0.167', '-0.04', '0.259', '#interest', '#admiration', 'positive', '0.09', 'grow_long', 'elongate', 'rubbery', 'elasticity', 'make_big']
senticnet['wider_community'] = ['0.025', '0.073', '-0.05', '0.1', '#interest', '#admiration', 'positive', '0.037', 'young_age', 'energy', 'hope', 'young_life', 'healthy']
senticnet['wider_context'] = ['0', '0.139', '0', '0.18', '#interest', '#admiration', 'positive', '0.16', 'aspect', 'vista', 'scenic', 'spectacular', 'view']
senticnet['wider_crackdown'] = ['-0.51', '0', '0.946', '-0.99', '#anger', '#disgust', 'negative', '-0.82', 'trammel', 'limit', 'restrict', 'bound', 'confine']
senticnet['wider_range'] = ['0.828', '-0.89', '0', '0.693', '#joy', '#surprise', 'positive', '0.806', 'confines', 'expanse', 'view', 'internationality', 'purview']
senticnet['widere'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['widere_norsk_air'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'goffer', 'gopher', 'actor', 'doer', 'worker']
senticnet['widerstand'] = ['-0.81', '-0.71', '0.843', '-0.72', '#sadness', '#anger', 'negative', '-0.41', 'lack_of_rules', 'fascism', 'despot', 'tyrant', 'anarchism']
senticnet['widespread'] = ['-0.97', '0', '-0.70', '0', '#sadness', '#fear', 'negative', '-0.83', 'doomsday', 'far-reaching', 'seismic', 'affect', 'pandemic']
senticnet['widespread_acclaim'] = ['0.853', '0.828', '0', '0.98', '#joy', '#admiration', 'positive', '0.887', 'endorse', 'recommend', 'stimulate_mind', 'acculturation', 'support']
senticnet['widespread_anger'] = ['-0.86', '0', '0.516', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'bronx_cheer', 'call', 'cry', 'yell', 'shout']
senticnet['widespread_condemnation'] = ['-0.86', '0', '0.516', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'bronx_cheer', 'call', 'cry', 'yell', 'shout']
senticnet['widespread_corruption'] = ['-0.50', '0', '0.807', '-0.76', '#anger', '#disgust', 'negative', '-0.69', 'powerlessness', 'impotence', 'impotency', 'inefficacy', 'inefficaciousness']
senticnet['widespread_critical_acclaim'] = ['0.853', '0.828', '0', '0.98', '#joy', '#admiration', 'positive', '0.887', 'endorse', 'recommend', 'stimulate_mind', 'acculturation', 'support']
senticnet['widespread_criticism'] = ['-0.16', '0.179', '0.568', '-0.61', '#anger', '#disgust', 'negative', '-0.29', 'angrily', 'choler', 'see_red', 'dander', 'hackle']
senticnet['widespread_damage'] = ['-0.87', '0', '0.889', '-0.62', '#sadness', '#anger', 'negative', '-0.79', 'ruination', 'ruin', 'deterioration', 'blight', 'impairment']
senticnet['widespread_deforestation'] = ['0', '-0.02', '0.085', '-0.09', '#anger', '#disgust', 'negative', '-0.05', 'tree_wood', 'forest', 'sideways', 'dead_tree', 'wood']
senticnet['widespread_destruction'] = ['-0.87', '0', '0.889', '-0.62', '#sadness', '#anger', 'negative', '-0.79', 'ruination', 'ruin', 'deterioration', 'blight', 'impairment']
senticnet['widespread_devastation'] = ['-0.87', '0', '0.889', '-0.62', '#sadness', '#anger', 'negative', '-0.79', 'ruination', 'ruin', 'deterioration', 'blight', 'impairment']
senticnet['widespread_discontent'] = ['-0.77', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.84', 'jumpiness', 'jitteriness', 'restive', 'nervy', 'jumpy']
senticnet['widespread_disease'] = ['0', '0', '-0.80', '-0.88', '#fear', '#disgust', 'negative', '-0.84', 'doom', 'destruction', 'universal_death', 'doomsday', 'death']
senticnet['widespread_drought'] = ['-0.98', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.89', 'dry', 'lose_team', 'leave_out', 'disrespect', 'cause_suffer']
senticnet['widespread_graft'] = ['0', '0', '-0.77', '-0.86', '#fear', '#disgust', 'negative', '-0.82', 'abduction', 'punishment', 'emergency', 'illiteracy', 'heal']
senticnet['widespread_hunger'] = ['-0.61', '0', '0.712', '-0.86', '#anger', '#disgust', 'negative', '-0.73', 'impoverishment', 'deprivation', 'indigence', 'penury', 'pauperism']
senticnet['widespread_illiteracy'] = ['-0.90', '0', '0.76', '-0.87', '#sadness', '#disgust', 'negative', '-0.84', 'analphabet', 'analphabetic', 'functional_illiterate', 'ignoramus', 'know_nothing']
senticnet['widespread_occurrence'] = ['0', '0.184', '-0.05', '0.179', '#interest', '#admiration', 'positive', '0.104', 'rich', 'a_lot', 'abundant', 'wealth', 'plentiful']
senticnet['widespread_outrage'] = ['-0.86', '0', '0.516', '-0.76', '#sadness', '#disgust', 'negative', '-0.71', 'bronx_cheer', 'call', 'cry', 'yell', 'shout']
senticnet['widespread_popularity'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'important', 'universal', 'vip', 'prestige', 'status']
senticnet['widespread_poverty'] = ['-0.84', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.82', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecure']
senticnet['widespread_praise'] = ['0.853', '0.828', '0', '0.98', '#joy', '#admiration', 'positive', '0.887', 'endorse', 'recommend', 'stimulate_mind', 'acculturation', 'support']
senticnet['widespread_recognition'] = ['0.258', '0.35', '-0.23', '0.318', '#interest', '#admiration', 'positive', '0.173', 'brownie_point', 'honor', 'reinforcement', 'positive_reinforcement', 'fame']
senticnet['widespread_resentment'] = ['-0.77', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.84', 'jumpiness', 'jitteriness', 'restive', 'nervy', 'jumpy']
senticnet['widespread_skepticism'] = ['0', '0', '0.928', '-0.70', '#anger', '#disgust', 'negative', '-0.81', 'doubt', 'incertitude', 'dubiety', 'doubtfulness', 'dubiousness']
senticnet['widespread_unemployment'] = ['-0.84', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.82', 'misgiving', 'qualm', 'scruple', 'insecurely', 'insecure']
senticnet['widespread_unrest'] = ['-0.77', '0', '-0.91', '0', '#sadness', '#fear', 'negative', '-0.84', 'jumpiness', 'jitteriness', 'restive', 'nervy', 'jumpy']
senticnet['widespread_vaccination'] = ['0.722', '0.865', '0', '0.748', '#interest', '#admiration', 'positive', '0.778', 'medical_therapy', 'vaccine', 'doctor', 'medicine', 'hospital']
senticnet['widest'] = ['0', '0.075', '0', '0.159', '#interest', '#admiration', 'positive', '0.117', 'maximum', 'max', 'utmost', 'top', 'extreme']
senticnet['widest_point'] = ['0.707', '0.959', '0', '0', '#joy', '#interest', 'positive', '0.833', 'length_measurement', 'measurement', 'imperial_measurement', 'measure', 'spatial_dimension']
senticnet['widest_variety'] = ['0.135', '0.225', '-0.12', '0.432', '#interest', '#admiration', 'positive', '0.167', 'excellent', 'goodness', 'well_done', 'great', 'good_quality']
senticnet['widget'] = ['0.979', '-0.82', '0', '0', '#joy', '#surprise', 'positive', '0.902', 'whatsis', 'doodad', 'thingumajig', 'thingamabob', 'gimmick']
senticnet['widow'] = ['0', '0', '0.832', '-0.78', '#anger', '#disgust', 'negative', '-0.80', 'motherless', 'destitute', 'fatherless', 'single-parent', 'friendless']
senticnet['widowed'] = ['-0.73', '0', '0.721', '-0.98', '#sadness', '#disgust', 'negative', '-0.81', 'unwed', 'unwedded', 'divorced', 'unmarried', 'mateless']
senticnet['widowed_mother'] = ['0.02', '0.209', '-0.14', '0.202', '#interest', '#admiration', 'positive', '0.071', 'familiar', 'mum', 'familiarity_uncle', 'care_giver', 'familiarity']
senticnet['widowed_queen'] = ['0.051', '0.097', '0', '0.065', '#interest', '#admiration', 'positive', '0.071', 'king', 'queen', 'duchess', 'throne', 'lord']
senticnet['widowed_sister'] = ['0.707', '0', '0', '0.734', '#joy', '#admiration', 'positive', '0.72', 'husband', 'mum', 'parent', 'mother', 'woman_child']
senticnet['widower'] = ['-0.70', '0', '0.919', '-0.73', '#anger', '#disgust', 'negative', '-0.78', 'unfruitful', 'unsuccessful', 'acarpous', 'abortive', 'stillborn']
senticnet['widowhood'] = ['-0.64', '0', '0.752', '-0.68', '#anger', '#disgust', 'negative', '-0.69', 'marital_status', 'marriage', 'matrimony', 'union', 'spousal_relationship']
senticnet['width'] = ['0.734', '0.884', '0', '0', '#joy', '#interest', 'positive', '0.809', 'dimension', 'size', 'mass_measure', 'length_dimension', 'gauge']
senticnet['width_ratio'] = ['0.707', '0.959', '0', '0', '#joy', '#interest', 'positive', '0.833', 'length_measurement', 'measurement', 'imperial_measurement', 'measure', 'spatial_dimension']
senticnet['wie_eine'] = ['-0.11', '0', '-0.09', '0.082', '#sadness', '#fear', 'negative', '-0.04', 'stare', 'ear', 'facial_expression', 'mouth', 'dimple']
senticnet['wiege'] = ['0', '-0.02', '-0.08', '-0.05', '#fear', '#disgust', 'negative', '-0.03', 'mattress', 'sleep_furniture', 'master_bedroom', 'small_bed', 'pillow']
senticnet['wield'] = ['-0.94', '0', '0.863', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'poke', 'knockout_punch', 'lick', 'punch', 'biff']
senticnet['wield_considerable'] = ['-0.94', '0', '0.863', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'poke', 'knockout_punch', 'lick', 'punch', 'biff']
senticnet['wield_influence'] = ['-0.94', '0', '0.863', '-0.98', '#sadness', '#disgust', 'negative', '-0.92', 'poke', 'knockout_punch', 'lick', 'punch', 'biff']
senticnet['wield_power'] = ['0.54', '-0.97', '0', '0.569', '#surprise', '#admiration', 'positive', '0.695', 'overstate', 'hyperbolize', 'glorify', 'exaggerate', 'overdraw']
senticnet['wielder'] = ['0.105', '-0.17', '0.057', '0.079', '#joy', '#surprise', 'positive', '0.076', 'cue', 'twig', 'long_stick', 'stick', 'adhere']
senticnet['wielkopolska'] = ['0', '0', '0', '0.1', '#admiration', '#admiration', 'positive', '0.1', 'find_earth', 'wax', 'plutonium', 'shine', 'melt']
senticnet['wiener_dog'] = ['-0.06', '0', '-0.04', '0', '#sadness', '#fear', 'negative', '-0.05', 'dog', 'red-bellied_black_snake', 'eastern_brown_snake', 'small_dog', 'puma']
senticnet['wife'] = ['0.707', '0', '0', '0.734', '#joy', '#admiration', 'positive', '0.72', 'husband', 'mum', 'parent', 'mother', 'woman_child']
senticnet['wife_sally_bercow'] = ['0.034', '-0.02', '0', '0.057', '#joy', '#admiration', 'positive', '0.039', 'female_television_connector', 'channel_selector', 'power_switch', 'tuner', 'radio']
senticnet['wifehood'] = ['0.531', '0.673', '-0.27', '0.275', '#joy', '#interest', 'positive', '0.301', 'amorous', 'amorousness', 'amatory', 'enamoredness', 'puppy_love']
senticnet['wifely'] = ['0.585', '-0.88', '0', '0.615', '#surprise', '#admiration', 'positive', '0.695', 'parental_quality', 'maternal_quality', 'quality', 'maternalism', 'fatherliness']
senticnet['wifi'] = ['-0.09', '-0.03', '0.061', '0', '#sadness', '#anger', 'negative', '-0.04', 'bad_reception', 'bad_connection', 'internet', 'phone_call', 'underground']
senticnet['wifi_connection'] = ['-0.09', '-0.03', '0.061', '0', '#sadness', '#anger', 'negative', '-0.04', 'bad_reception', 'bad_connection', 'internet', 'phone_call', 'underground']
senticnet['wifi_hotspot'] = ['-0.87', '0', '0.818', '0', '#sadness', '#anger', 'negative', '-0.84', 'slow_speed', 'wifi', 'computer_internet_connection', 'internet_connection', 'boot_up_computer']
senticnet['wifi_network'] = ['-0.09', '-0.03', '0.061', '0', '#sadness', '#anger', 'negative', '-0.04', 'bad_reception', 'bad_connection', 'internet', 'phone_call', 'underground']
senticnet['wig'] = ['-0.84', '0', '0.633', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'grizzle', 'postiche', 'horsehair_wig', 'periwig', 'peruke']
senticnet['wigan_leigh'] = ['-0.02', '0.1', '-0.07', '0.089', '#interest', '#admiration', 'positive', '0.023', 'functionary', 'employment', 'labor', 'employ', 'toil']
senticnet['wigeon'] = ['-0.50', '0', '0.764', '-0.61', '#anger', '#disgust', 'negative', '-0.62', 'doctor', 'mountebank', 'charlatan', 'quack', 'physician']
senticnet['wiger'] = ['0.746', '-0.88', '0', '0.723', '#joy', '#surprise', 'positive', '0.783', 'strike', 'run_into', 'hit', 'impinge_on', 'collide_with']
senticnet['wigging'] = ['-0.99', '0', '0.709', '-0.60', '#sadness', '#anger', 'negative', '-0.76', 'reprimand', 'chiding', 'rebuke', 'reproof', 'reproval']
senticnet['wiggle'] = ['0.94', '0.938', '0', '0', '#joy', '#interest', 'positive', '0.939', 'wrap_around', 'shrug', 'shake', 'rub', 'wiggly']
senticnet['wiggle_room'] = ['-0.52', '0', '0.561', '-0.90', '#anger', '#disgust', 'negative', '-0.66', 'difference', 'tolerance', 'allowance', 'discrepancy', 'margin']
senticnet['wiggly'] = ['0.935', '0.931', '0', '0', '#joy', '#interest', 'positive', '0.933', 'wrap_around', 'shrug', 'shake', 'rub', 'wiggle']
senticnet['wightiana'] = ['0.704', '-0.92', '0', '0.517', '#joy', '#surprise', 'positive', '0.715', 'plant', 'flora', 'plant_life', 'rock_plant', 'lithophytic_plant']
senticnet['wigraine'] = ['-0.72', '0', '0.973', '-0.57', '#sadness', '#anger', 'negative', '-0.76', 'corpulency', 'blubber', 'fat', 'embonpoint', 'stoutness']
senticnet['wigwam'] = ['0.03', '0', '0', '0.094', '#joy', '#admiration', 'positive', '0.062', 'place', 'address', 'abide', 'post', 'property']
senticnet['wiibroe'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'concupiscence', 'lustfulness', 'amorousness']
senticnet['wiimote'] = ['0.105', '-0.17', '0.057', '0.079', '#joy', '#surprise', 'positive', '0.076', 'cue', 'twig', 'long_stick', 'stick', 'adhere']
senticnet['wikala'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'hostel', 'caravanserai', 'khan', 'caravan_inn', 'imaret']
senticnet['wikepedia'] = ['-0.02', '0', '-0.10', '0', '#sadness', '#fear', 'negative', '-0.06', 'ads', 'boring', 'bother', 'stop_watch', 'annoying']
senticnet['wiki'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'comic_book', 'child_book', 'child_story', 'comic', 'disney_cartoon']
senticnet['wiki_article'] = ['-0.02', '0', '-0.10', '0', '#sadness', '#fear', 'negative', '-0.06', 'ads', 'boring', 'bother', 'stop_watch', 'annoying']
senticnet['wiki_page'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'swat', 'store_record', 'ideal']
senticnet['wikidemon'] = ['0.533', '0.818', '0', '0.683', '#interest', '#admiration', 'positive', '0.678', 'gain_information', 'report_news', 'solve', 'seek_answer', 'gather_information']
senticnet['wikipage'] = ['-0.02', '0', '-0.10', '0', '#sadness', '#fear', 'negative', '-0.06', 'ads', 'boring', 'bother', 'stop_watch', 'annoying']
senticnet['wikipedia'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'comic_book', 'child_book', 'child_story', 'comic', 'disney_cartoon']
senticnet['wikipedia_arbitration'] = ['0.533', '0.818', '0', '0.683', '#interest', '#admiration', 'positive', '0.678', 'gain_information', 'report_news', 'solve', 'seek_answer', 'gather_information']
senticnet['wikipedia_article'] = ['-0.02', '0', '-0.10', '0', '#sadness', '#fear', 'negative', '-0.06', 'ads', 'boring', 'bother', 'stop_watch', 'annoying']
senticnet['wikipedia_page'] = ['-0.02', '0', '0.077', '0', '#sadness', '#anger', 'negative', '-0.05', 'record', 'file_bill', 'swat', 'store_record', 'ideal']
senticnet['wikipedia_userpage'] = ['-0.02', '0', '-0.10', '0', '#sadness', '#fear', 'negative', '-0.06', 'ads', 'boring', 'bother', 'stop_watch', 'annoying']
senticnet['wikipeida'] = ['-0.02', '0', '-0.10', '0', '#sadness', '#fear', 'negative', '-0.06', 'ads', 'boring', 'bother', 'stop_watch', 'annoying']
senticnet['wikpedia'] = ['-0.02', '0', '-0.10', '0', '#sadness', '#fear', 'negative', '-0.06', 'ads', 'boring', 'bother', 'stop_watch', 'annoying']
senticnet['wilcock'] = ['-0.20', '-0.10', '0.07', '-0.24', '#sadness', '#disgust', 'negative', '-0.10', 'flipper', 'sea_life', 'seafood', 'swim_animal', 'fillet']
senticnet['wilcoxon_signed_rank'] = ['0.928', '-0.87', '0', '0.679', '#joy', '#surprise', 'positive', '0.829', 'examine', 'experiment', 'alpha_test', 'beta_test', 'road_test']
senticnet['wild'] = ['-0.80', '0', '0.856', '-0.84', '#anger', '#disgust', 'negative', '-0.83', 'primitiveness', 'crudeness', 'crudity', 'primitivism', 'rudeness']
senticnet['wild_ancestor'] = ['0.735', '-0.71', '0', '0.566', '#joy', '#surprise', 'positive', '0.672', 'improvement', 'betterment', 'habituation', 'adjustment', 'shakedown']
senticnet['wild_animal'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'bear', 'need_protect', 'lion', 'nature', 'wild_life']
senticnet['wild_beast'] = ['0.118', '0.28', '-0.20', '0.216', '#interest', '#admiration', 'positive', '0.103', 'friend_person', 'sense_fear', 'mark_territory', 'trip_person', 'care_deeply']
senticnet['wild_bergamot'] = ['-0.52', '0', '0.527', '-0.78', '#anger', '#disgust', 'negative', '-0.61', 'herb', 'black_snakeroot', 'heartleaf', 'asarabacca', 'herbaceous_plant']
senticnet['wild_bird'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'hold_egg', 'baby_bird', 'hatch', 'lay_egg', 'bird_home']
senticnet['wild_bison'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['wild_boar_sus_scrofa'] = ['-0.55', '0', '0.955', '-0.96', '#anger', '#disgust', 'negative', '-0.82', 'house_mouse', 'mouse', 'rat', 'fieldmouse', 'nude_mouse']
senticnet['wild_brawl'] = ['0.915', '-0.84', '0', '0.778', '#joy', '#surprise', 'positive', '0.847', 'fall_out', 'debate', 'wrangle', 'fight', 'altercate']
senticnet['wild_carrot'] = ['0.632', '-0.93', '0', '0.928', '#surprise', '#admiration', 'positive', '0.831', 'herb', 'oregano', 'marjoram', 'pot_marjoram', 'origanum']
senticnet['wild_cat'] = ['0', '0', '0.029', '-0.05', '#anger', '#disgust', 'negative', '-0.04', 'large_cat', 'big_cat', 'carnivore', 'feline', 'canine']
senticnet['wild_duck'] = ['0.039', '0.089', '-0.08', '0.143', '#interest', '#admiration', 'positive', '0.048', 'bird', 'nightingale', 'stork', 'eagle', 'rooster']
senticnet['wild_flower'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'tough', 'wild_plant', 'vitality', 'bionergy', 'nature']
senticnet['wild_forehand'] = ['0', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.91', 'umpire', 'referee', 'legal_proceed', 'clang', 'clamor']
senticnet['wild_garlic'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['wild_geranium'] = ['-0.52', '0', '0.527', '-0.78', '#anger', '#disgust', 'negative', '-0.61', 'herb', 'black_snakeroot', 'heartleaf', 'asarabacca', 'herbaceous_plant']
senticnet['wild_ginger'] = ['-0.52', '0', '0.527', '-0.78', '#anger', '#disgust', 'negative', '-0.61', 'herb', 'black_snakeroot', 'heartleaf', 'asarabacca', 'herbaceous_plant']
senticnet['wild_goat'] = ['-0.51', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.78', 'bezoar_goat', 'pasang', 'markhor', 'goat', 'ibex']
senticnet['wild_grape'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'black_locust']
senticnet['wild_horse'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'lost_pet', 'find_animal', 'animal', 'doghouse', 'find_dog']
senticnet['wild_hyacinth'] = ['0.598', '-0.72', '0', '0.941', '#surprise', '#admiration', 'positive', '0.753', 'sicklepod', 'tower_rock_cress', 'tower_mustard', 'rock_cress', 'rock_cress_plant']
senticnet['wild_licorice'] = ['-0.94', '0', '0.608', '-0.95', '#sadness', '#disgust', 'negative', '-0.83', 'milk_vetch', 'wild_liquorice', 'alpine_milk_vetch', 'purple_milk_vetch', 'woody_plant']
senticnet['wild_life'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'bear', 'need_protect', 'lion', 'nature', 'wild_animal']
senticnet['wild_liquorice'] = ['-0.94', '0', '0.608', '-0.95', '#sadness', '#disgust', 'negative', '-0.83', 'wild_licorice', 'milk_vetch', 'alpine_milk_vetch', 'purple_milk_vetch', 'woody_plant']
senticnet['wild_man'] = ['0', '0', '0.96', '-0.80', '#anger', '#disgust', 'negative', '-0.88', 'monster', 'abominable_snowman', 'orang_pendek', 'yeti', 'abominable']
senticnet['wild_marjoram'] = ['0.632', '-0.93', '0', '0.928', '#surprise', '#admiration', 'positive', '0.831', 'herb', 'oregano', 'marjoram', 'pot_marjoram', 'origanum']
senticnet['wild_mushroom'] = ['0.046', '0', '0.041', '0.102', '#joy', '#admiration', 'positive', '0.036', 'good_eat', 'delicious', 'yummy', 'tasty', 'mouth_watering']
senticnet['wild_nature'] = ['0', '0.852', '0', '0.711', '#interest', '#admiration', 'positive', '0.781', 'plato', 'brown_hair', 'natural_world', 'feature', 'stick_out']
senticnet['wild_olive'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'black_locust']
senticnet['wild_origanum'] = ['0.63', '-0.94', '0', '0.919', '#surprise', '#admiration', 'positive', '0.83', 'herb', 'oregano', 'origanum', 'pot_origanum', 'marjoram']
senticnet['wild_parsnip'] = ['0.861', '-0.87', '0', '0.597', '#joy', '#surprise', 'positive', '0.779', 'herb', 'cultivated_parsnip', 'pastinaca_sativa', 'madnep', 'herbaceous_plant']
senticnet['wild_pig'] = ['-0.97', '0', '0.677', '-0.95', '#sadness', '#disgust', 'negative', '-0.86', 'stunt', 'game', 'mutant', 'invertebrate', 'animate_being']
senticnet['wild_plant'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'tough', 'wild_flower', 'vitality', 'bionergy', 'nature']
senticnet['wild_plum'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'black_locust']
senticnet['wild_reindeer'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'argali', 'argal', 'urial', 'white_sheep', 'wild_sheep']
senticnet['wild_ride'] = ['0.086', '-0.03', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'carny', 'fortune_teller', 'midway', 'ferris_wheel', 'roller-coaster']
senticnet['wild_rye'] = ['0.656', '-0.63', '0', '0.844', '#joy', '#admiration', 'positive', '0.711', 'bullrush', 'cattail', 'nailrod', 'reed_mace', 'reedmace']
senticnet['wild_sarsaparilla'] = ['-0.52', '0', '0.527', '-0.78', '#anger', '#disgust', 'negative', '-0.61', 'herb', 'black_snakeroot', 'heartleaf', 'asarabacca', 'herbaceous_plant']
senticnet['wild_sheep'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'argali', 'argal', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['wild_sheepi'] = ['-0.80', '0', '0.665', '-0.67', '#sadness', '#disgust', 'negative', '-0.71', 'argal', 'wild_sheep', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['wild_thyme'] = ['-0.86', '0', '0.665', '-0.61', '#sadness', '#anger', 'negative', '-0.71', 'brambly', 'braky', 'wooded', 'scrubby', 'forested']
senticnet['wild_turkey'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['wild_waterfowl'] = ['0', '-0.06', '0.022', '0.054', '#surprise', '#admiration', 'positive', '0.031', 'hold_egg', 'baby_bird', 'hatch', 'lay_egg', 'bird_home']
senticnet['wild_west'] = ['0', '0', '-0.11', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'hunting_season', 'hound', 'cougar', 'hunter', 'dragon']
senticnet['wild_wolf'] = ['0.731', '0', '0', '0.843', '#joy', '#admiration', 'positive', '0.787', 'breed', 'domestic_pet', 'smart_dog', 'big_dog', 'fierce']
senticnet['wild_yak'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheep', 'argal', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['wild_yeast'] = ['0', '0.104', '-0.09', '0.146', '#interest', '#admiration', 'positive', '0.052', 'thaw', 'slush', 'water_inside', 'give_water', 'obtain_water']
senticnet['wildcat'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'unofficial', 'informal', 'loose', 'drumhead']
senticnet['wildcat_offense'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'unofficial', 'informal', 'loose', 'drumhead']
senticnet['wildcat_strike'] = ['-0.77', '0', '0.505', '-0.50', '#sadness', '#anger', 'negative', '-0.59', 'work_stoppage', 'sympathy_strike', 'sympathetic_strike', 'walkout', 'job_action']
senticnet['wildenhain'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['wilderness'] = ['0', '0.089', '0', '0.1', '#interest', '#admiration', 'positive', '0.095', 'habitat', 'mom_house', 'windsor', 'pacific_northwest', 'refuge']
senticnet['wilderness_adventure'] = ['0.704', '-0.80', '0', '0', '#joy', '#surprise', 'positive', '0.755', 'quest', 'stunt', 'illusion', 'journey', 'activity']
senticnet['wilderness_lodge'] = ['0.024', '0.152', '-0.06', '0.236', '#interest', '#admiration', 'positive', '0.087', 'vacation', 'exit', 'stance', 'take_break', 'holiday']
senticnet['wilderness_survival'] = ['0.93', '-0.95', '0', '0.909', '#joy', '#surprise', 'positive', '0.93', 'acquirement', 'skill', 'accomplishment', 'acquisition', 'attainment']
senticnet['wildest'] = ['-0.56', '0', '0.746', '-0.62', '#anger', '#disgust', 'negative', '-0.64', 'contrariness', 'fractiousness', 'intractability', 'intractableness', 'perversity']
senticnet['wildflower'] = ['-0.79', '0', '0.837', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'sunray', 'wilding', 'sagebrush_buttercup', 'pasqueflower', 'pasque_flower']
senticnet['wildflower_bloom'] = ['-0.79', '0', '0.837', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'sunray', 'wilding', 'sagebrush_buttercup', 'pasqueflower', 'pasque_flower']
senticnet['wildflower_garden'] = ['-0.79', '0', '0.837', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'sunray', 'wilding', 'sagebrush_buttercup', 'pasqueflower', 'pasque_flower']
senticnet['wildfowl'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'grouse', 'quail', 'partridge', 'pheasant']
senticnet['wilding'] = ['0.771', '-0.63', '0', '0.875', '#joy', '#admiration', 'positive', '0.761', 'plant', 'flora', 'wildflower', 'wild_flower', 'plant_life']
senticnet['wildlife'] = ['-0.12', '0', '-0.10', '-0.04', '#sadness', '#fear', 'negative', '-0.09', 'dog_poop', 'picnic', 'hike_trail', 'detritus', 'compost']
senticnet['wildlife_biology'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'study_person', 'subject_study', 'zoo', 'zoologist', 'geology']
senticnet['wildlife_conservation'] = ['0', '0', '0', '0.857', '#admiration', '#admiration', 'positive', '0.857', 'say', 'beckon', 'call', 'conserve', 'retort']
senticnet['wildlife_ecology'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'study_person', 'subject_study', 'zoo', 'zoologist', 'geology']
senticnet['wildlife_management'] = ['0', '0', '0', '0.857', '#admiration', '#admiration', 'positive', '0.857', 'say', 'beckon', 'call', 'conserve', 'retort']
senticnet['wildlife_preservation'] = ['0', '0', '0', '0.857', '#admiration', '#admiration', 'positive', '0.857', 'say', 'beckon', 'call', 'conserve', 'retort']
senticnet['wildlife_refuge'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'habitat', 'mom_house', 'windsor', 'pacific_northwest', 'wilderness']
senticnet['wildlife_refuge_complex'] = ['0', '0.091', '0', '0.101', '#interest', '#admiration', 'positive', '0.096', 'habitat', 'mom_house', 'windsor', 'pacific_northwest', 'wilderness']
senticnet['wildly'] = ['-0.72', '0', '0.715', '0', '#sadness', '#anger', 'negative', '-0.72', 'reason', 'madly', 'manner', 'understanding', 'mad']
senticnet['wildly_popular'] = ['0', '0', '0', '0.882', '#admiration', '#admiration', 'positive', '0.882', 'important', 'universal', 'vip', 'prestige', 'status']
senticnet['wildly_uneven'] = ['-0.62', '0', '0.988', '-0.59', '#sadness', '#anger', 'negative', '-0.73', 'motley', 'mixture', 'variety', 'assortment', 'alphabet_soup']
senticnet['wildness'] = ['-0.56', '0', '0.746', '-0.62', '#anger', '#disgust', 'negative', '-0.64', 'contrariness', 'fractiousness', 'intractability', 'intractableness', 'perversity']
senticnet['wildwater_kingdom'] = ['0.089', '-0.18', '0.037', '-0.06', '#joy', '#surprise', 'positive', '0.043', 'balloon', 'ballon', 'fielder', 'kite', 'blimp']
senticnet['wildwood_flower'] = ['0', '0.176', '0', '0.216', '#interest', '#admiration', 'positive', '0.196', 'choir_stall', 'worshiper', 'pew', 'fundamentalist', 'pulpit']
senticnet['wile'] = ['-0.97', '0', '0.713', '-0.65', '#sadness', '#anger', 'negative', '-0.77', 'humbug', 'deceit', 'deception', 'fraudulence', 'dissembling']
senticnet['wilfred_thesiger'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'correspondent', 'humankind']
senticnet['wilful'] = ['-0.61', '0', '0.643', '-0.67', '#anger', '#disgust', 'negative', '-0.64', 'obstinate', 'hang_in', 'ask_for_trouble', 'preserve', 'plug']
senticnet['wilful_misconduct'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'negligence', 'evasion', 'dereliction', 'contributory_negligence']
senticnet['wilful_neglect'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'negligence', 'evasion', 'dereliction', 'contributory_negligence']
senticnet['wilfully'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'negligence', 'evasion', 'dereliction', 'contributory_negligence']
senticnet['wilhelm_weinberg'] = ['0.481', '0.663', '-0.25', '0.24', '#joy', '#interest', 'positive', '0.283', 'intrepidly', 'fearlessness', 'fearlessly', 'unafraid', 'unfearing']
senticnet['wiliness'] = ['0.62', '-0.97', '0', '0.781', '#surprise', '#admiration', 'positive', '0.79', 'foxiness', 'slyness', 'perspicacity', 'craftiness', 'cunning']
senticnet['wilkes_barre'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
senticnet['wilkinsburg'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
senticnet['wilkinson'] = ['-0.20', '-0.10', '0.07', '-0.24', '#sadness', '#disgust', 'negative', '-0.10', 'flipper', 'sea_life', 'seafood', 'swim_animal', 'fillet']
senticnet['wilkinson_eyre'] = ['-0.81', '0', '0.543', '-0.63', '#sadness', '#disgust', 'negative', '-0.66', 'caper', 'pickle', 'relish', 'dill_pickle', 'sweet_pickle']
senticnet['will_expedition'] = ['-0.69', '0', '0.89', '-0.70', '#anger', '#disgust', 'negative', '-0.76', 'still', 'suppress', 'moderate', 'hush_up', 'quell']
senticnet['will_power'] = ['0.937', '-0.79', '0', '0.634', '#joy', '#surprise', 'positive', '0.79', 'resoluteness', 'firmness_of_purpose', 'firmness', 'resolve', 'presence_of_mind']
senticnet['willenken'] = ['0.022', '-0.11', '0.014', '0.057', '#surprise', '#admiration', 'positive', '0.045', 'wood', 'many_steps', 'franchise', 'stump', 'timber']
senticnet['willful'] = ['0.761', '0.978', '0', '0', '#joy', '#interest', 'positive', '0.869', 'intended', 'vandalism', 'willfully', 'intentional', 'willfulness']
senticnet['willful_conduct'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'negligence', 'evasion', 'dereliction', 'contributory_negligence']
senticnet['willful_disregard'] = ['-0.61', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.69', 'irresponsibility', 'untrustiness', 'undependability', 'undependableness', 'unreliability']
senticnet['willful_failure'] = ['-0.93', '0', '0.624', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'carelessness', 'neglectfulness', 'laxness', 'laxity', 'remissness']
senticnet['willful_ignorance'] = ['-0.59', '0', '0.834', '-0.72', '#anger', '#disgust', 'negative', '-0.71', 'simple_mindedness', 'simpleness', 'ingenuousness', 'artlessness', 'simplicity']
senticnet['willful_misconduct'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'negligence', 'evasion', 'dereliction', 'contributory_negligence']
senticnet['willful_neglect'] = ['-0.93', '0', '0.624', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'carelessness', 'neglectfulness', 'laxness', 'laxity', 'remissness']
senticnet['willful_violation'] = ['-0.93', '0', '0.624', '-0.59', '#sadness', '#anger', 'negative', '-0.72', 'carelessness', 'neglectfulness', 'laxness', 'laxity', 'remissness']
senticnet['willfully'] = ['0.757', '0.976', '0', '0', '#joy', '#interest', 'positive', '0.867', 'intended', 'vandalism', 'willful', 'intentional', 'willfulness']
senticnet['willfully_ignorant'] = ['-0.63', '0', '0.88', '-0.94', '#anger', '#disgust', 'negative', '-0.81', 'beguiler', 'slicker', 'perjurer', 'false_witness', 'storyteller']
senticnet['willfulness'] = ['-0.92', '0', '0.682', '-0.63', '#sadness', '#anger', 'negative', '-0.74', 'contrariness', 'wildness', 'intractability', 'intractableness', 'perversity']
senticnet['willi_unsoeld'] = ['0', '0.076', '0', '0.063', '#interest', '#admiration', 'positive', '0.07', 'up_hill', 'elevation', 'up_mountain', 'ascend', 'upwards']
senticnet['williams'] = ['0.921', '-0.72', '0', '0', '#joy', '#surprise', 'positive', '0.822', 'star', 'length', 'bottom', 'award', 'asterisk']
senticnet['williams_sonoma'] = ['0.912', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.917', 'carpet_store', 'lumberyard', 'furniture_store', 'front_porch', 'shoe_shop']
senticnet['williamsburg_bridge'] = ['0.706', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.768', 'train_station', 'subway_stop', 'parking_lot', 'subway_station', 'park_structure']
senticnet['williamsport'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
senticnet['willing'] = ['0.835', '0.761', '0', '0', '#joy', '#interest', 'positive', '0.798', 'unforced', 'leave', 'bequeath', 'volition', 'uncoerced']
senticnet['willingly'] = ['0.917', '0.773', '0', '0', '#joy', '#interest', 'positive', '0.845', 'voluntarily', 'knowingly', 'consented', 'obliged', 'refusing']
senticnet['willingness'] = ['0.877', '-0.51', '0', '0.624', '#joy', '#admiration', 'positive', '0.671', 'readiness', 'eagerness', 'zeal', 'disposition', 'openness']
senticnet['willow'] = ['0.041', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.042', 'birch', 'cedar', 'oak', 'elm', 'lumber']
senticnet['willow_carr'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'biome', 'timberland']
senticnet['willow_grouse'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['willow_ptarmigan'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['willow_salix'] = ['-0.94', '0', '0.608', '-0.95', '#sadness', '#disgust', 'negative', '-0.83', 'wild_licorice', 'wild_liquorice', 'alpine_milk_vetch', 'purple_milk_vetch', 'milk_vetch']
senticnet['willow_salix_spp'] = ['-0.94', '0', '0.608', '-0.95', '#sadness', '#disgust', 'negative', '-0.83', 'wild_licorice', 'wild_liquorice', 'alpine_milk_vetch', 'purple_milk_vetch', 'milk_vetch']
senticnet['willow_street'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'boulevard', 'lane', 'thoroughfare', 'intersection', 'curb']
senticnet['willow_tree'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'black_locust']
senticnet['willowy'] = ['0.727', '-0.53', '0', '0.912', '#joy', '#admiration', 'positive', '0.725', 'gainly', 'gracile', 'graceful', 'elegant', 'smooth']
senticnet['willpower'] = ['0.804', '-0.67', '0', '0.658', '#joy', '#surprise', 'positive', '0.711', 'resoluteness', 'firmness_of_purpose', 'firmness', 'resolve', 'presence_of_mind']
senticnet['willy_mason'] = ['0.75', '-0.88', '0', '0.989', '#surprise', '#admiration', 'positive', '0.875', 'dog', 'cur', 'fice', 'pariah_dog', 'domestic_dog']
senticnet['willy_nilly'] = ['0.653', '0.635', '-0.24', '0.294', '#joy', '#interest', 'positive', '0.335', 'merrily', 'jubilantly', 'happily', 'mirthfully', 'gayly']
senticnet['wilno'] = ['0', '0', '0', '0.1', '#admiration', '#admiration', 'positive', '0.1', 'find_earth', 'wax', 'plutonium', 'shine', 'melt']
senticnet['wilshire_boulevard_temple'] = ['0', '0.209', '0.026', '0.184', '#interest', '#admiration', 'positive', '0.122', 'church', 'confession', 'temple', 'cathedral', 'monastery']
senticnet['wilson'] = ['-0.69', '0', '0.89', '-0.70', '#anger', '#disgust', 'negative', '-0.76', 'still', 'suppress', 'moderate', 'hush_up', 'quell']
senticnet['wilsonian'] = ['0.946', '-0.54', '0', '0.774', '#joy', '#admiration', 'positive', '0.754', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['wilt'] = ['-0.08', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.08', 'step_aside', 'meander', 'step_out', 'open_mailbox', 'stay_still']
senticnet['wilt_disease'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'apple_rust', 'fungus']
senticnet['wily'] = ['0', '-0.97', '0', '0.745', '#surprise', '#admiration', 'positive', '0.862', 'shrewd', 'clever', 'sharp', 'astute', 'canny']
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senticnet['winter_clothes'] = ['0.053', '-0.01', '-0.05', '0.121', '#joy', '#admiration', 'positive', '0.034', 'coat', 'keep_warm', 'thick_jacket', 'sweater', 'winter_glove']
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senticnet['without_hindrance'] = ['0.621', '0.648', '-0.25', '0.296', '#joy', '#interest', 'positive', '0.328', 'pacifically', 'repose', 'peace-loving', 'peaceable', 'peace_of_mind']
senticnet['without_human_habitation'] = ['-0.71', '0', '0', '-0.82', '#sadness', '#disgust', 'negative', '-0.77', 'cold_weather', 'snow', 'ice', 'cold', 'arctic']
senticnet['without_leave_awol'] = ['-0.56', '0', '-0.49', '-0.09', '#sadness', '#fear', 'negative', '-0.38', 'lack', 'poor', 'poverty', 'emptiness', 'hunger']
senticnet['without_light'] = ['-0.72', '0', '-0.89', '0', '#sadness', '#fear', 'negative', '-0.80', 'dim', 'darkness', 'dusky', 'unseen', 'blurred_vision']
senticnet['without_merit'] = ['-0.86', '0', '0.897', '-0.81', '#sadness', '#anger', 'negative', '-0.85', 'disregard', 'ignore', 'brush_off', 'push_aside', 'flout']
senticnet['without_metabolic_activation'] = ['-0.85', '0', '0.936', '-0.80', '#sadness', '#anger', 'negative', '-0.86', 'abnormalcy', 'abnormality', 'deflection', 'warp', 'aberrance']
senticnet['without_molestation'] = ['0.621', '0.648', '-0.25', '0.296', '#joy', '#interest', 'positive', '0.328', 'pacifically', 'repose', 'peace-loving', 'peaceably', 'peace_of_mind']
senticnet['without_money'] = ['-0.95', '0', '0', '-0.81', '#sadness', '#disgust', 'negative', '-0.88', 'lose_credibility', 'broke', 'abuse', 'feel_stressed', 'go_bankrupt']
senticnet['without_notice'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'without_warning', 'unexpected', 'unpredicted', 'unanticipated', 'out_of_blue']
senticnet['without_scruple'] = ['-0.56', '0', '0.505', '-0.95', '#sadness', '#disgust', 'negative', '-0.67', 'unnaturalness', 'artificiality', 'pretension', 'staginess', 'theatricality']
senticnet['without_space'] = ['0', '0', '-0.81', '-0.71', '#fear', '#disgust', 'negative', '-0.76', 'breathless', 'tight', 'narrow', 'immovale', 'small']
senticnet['without_support'] = ['0', '0', '-0.94', '-0.77', '#fear', '#disgust', 'negative', '-0.85', 'break', 'broken_shelf', 'damage', 'need_fix', 'destroy']
senticnet['without_warning'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'out_of_blue', 'unexpected', 'unpredicted', 'unanticipated', 'without_notice']
senticnet['withstand'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'brave_out', 'endure', 'weather', 'undamaged', 'stand_firm']
senticnet['withstand_scrutiny'] = ['0.631', '-0.58', '0', '0.914', '#joy', '#admiration', 'positive', '0.711', 'defendable', 'invulnerable', 'impregnable', 'secure', 'bombproof']
senticnet['witness'] = ['-0.05', '0.173', '-0.09', '0.111', '#interest', '#admiration', 'positive', '0.035', 'trial', 'call_witness', 'judgment', 'become_lawyer', 'justice']
senticnet['witness_stand'] = ['-0.21', '0.16', '-0.19', '0.057', '#sadness', '#fear', 'negative', '-0.04', 'murder', 'make_judgment', 'call_police', 'arrest_criminal', 'tell_truth']
senticnet['witness_testify'] = ['-0.21', '0.16', '-0.19', '0.057', '#sadness', '#fear', 'negative', '-0.04', 'murder', 'make_judgment', 'call_police', 'arrest_criminal', 'tell_truth']
senticnet['witness_testimony'] = ['-0.21', '0.16', '-0.19', '0.057', '#sadness', '#fear', 'negative', '-0.04', 'murder', 'make_judgment', 'call_police', 'arrest_criminal', 'tell_truth']
senticnet['witnessing_domestic_violence'] = ['-0.8', '0', '0.875', '-0.74', '#sadness', '#anger', 'negative', '-0.80', 'inhuman_treatment', 'cruelty', 'child_neglect', 'mistreatment', 'child_abuse']
senticnet['witnessing_violence'] = ['-0.8', '0', '0.875', '-0.74', '#sadness', '#anger', 'negative', '-0.80', 'inhuman_treatment', 'cruelty', 'child_neglect', 'mistreatment', 'child_abuse']
senticnet['witticism'] = ['0.863', '0.949', '0', '0', '#joy', '#interest', 'positive', '0.906', 'tell_funny_joke', 'tell_joke', 'funny_joke', 'jest', 'joke']
senticnet['wittily'] = ['0.512', '0.658', '-0.25', '0.256', '#joy', '#interest', 'positive', '0.293', 'riotously', 'expansively', 'ebulliently', 'zestfulness', 'gusto']
senticnet['wittingly'] = ['-0.04', '0.164', '0.685', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maliciousness', 'spitefulness', 'maleficence', 'malefic', 'malign']
senticnet['witty'] = ['0', '0.709', '0', '0.891', '#interest', '#admiration', 'positive', '0.8', 'writer', 'write_book', 'write_poem', 'write_down_idea', 'write_story']
senticnet['witty_banter'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'witty_remark', 'cross_talking', 'swordplay', 'wordplay', 'jester']
senticnet['witty_dialogue'] = ['-0.07', '0', '-0.06', '0', '#sadness', '#fear', 'negative', '-0.06', 'anxiety', 'apprehension', 'insecurity', 'tension', 'horror_movie']
senticnet['witty_remark'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'banter', 'cross_talking', 'swordplay', 'wordplay', 'jester']
senticnet['wiv'] = ['-0.05', '0', '-0.06', '-0.04', '#sadness', '#fear', 'negative', '-0.05', 'masturbate', 'jerk', 'jerk_off', 'watch_porn', 'porn']
senticnet['wizard'] = ['0', '-0.00', '0', '0.097', '#surprise', '#admiration', 'positive', '0.051', 'heavy_paper', 'lightweight_metal', 'rusty_metal', 'metal_type', 'soft_metal']
senticnet['wizardry'] = ['0.793', '-0.61', '0', '0.673', '#joy', '#admiration', 'positive', '0.694', 'fruitfulness', 'genius', 'fecundity', 'ability', 'creativeness']
senticnet['wizened'] = ['0.806', '0', '0', '0.71', '#joy', '#admiration', 'positive', '0.758', 'weather-worn', 'weatherbeaten', 'weather-beaten', 'beaten-up', 'grizzled']
senticnet['wizkid'] = ['0', '0', '0.876', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'untune', 'discompose', 'discomfit', 'disconcert', 'upset']
senticnet['wizzle'] = ['0', '0', '0.748', '-0.81', '#anger', '#disgust', 'negative', '-0.78', 'search_information', 'look_for_information', 'check_email', 'phone_line_busy', 'distract']
senticnet['wladyslaw'] = ['0', '0', '0', '0.1', '#admiration', '#admiration', 'positive', '0.1', 'find_earth', 'wax', 'plutonium', 'shine', 'melt']
senticnet['wlan'] = ['-0.09', '-0.03', '0.061', '0', '#sadness', '#anger', 'negative', '-0.04', 'bad_reception', 'bad_connection', 'internet', 'phone_call', 'underground']
senticnet['wlii_dt'] = ['-0.84', '0', '0.579', '-0.74', '#sadness', '#disgust', 'negative', '-0.72', 'squiggle', 'hand', 'chicken_scratch', 'handwriting', 'script']
senticnet['wmik'] = ['-0.56', '0', '0.714', '-0.89', '#anger', '#disgust', 'negative', '-0.72', 'tank', 'tank_destroyer', 'armored_car', 'armoured_car', 'armored_personnel_carrier']
senticnet['wmv_file'] = ['-0.55', '0', '0.912', '-0.50', '#sadness', '#anger', 'negative', '-0.65', 'wrangle', 'dustup', 'quarrel', 'row', 'squabble']
senticnet['woah'] = ['0.902', '0.995', '0', '0', '#joy', '#interest', 'positive', '0.949', 'beautiful_girl', 'hen_baby', 'pretty_woman', 'hen_child', 'bird']
senticnet['wobble'] = ['0.038', '0', '0.031', '0.124', '#joy', '#admiration', 'positive', '0.044', 'rotate', 'rotate_movement', 'change_direction', 'move', 'go_around']
senticnet['wobbly'] = ['-0.89', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.85', 'high-strung', 'uptight', 'restive', 'overstrung', 'nervy']
senticnet['wodehousian'] = ['0.806', '-0.70', '0', '0.687', '#joy', '#surprise', 'positive', '0.734', 'saphead', 'muggins', 'tomfool', 'fool', 'sap']
senticnet['wodge'] = ['-0.77', '0', '0.874', '-0.80', '#anger', '#disgust', 'negative', '-0.81', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['wodzimierz'] = ['0', '0', '0', '0.1', '#admiration', '#admiration', 'positive', '0.1', 'find_earth', 'wax', 'plutonium', 'shine', 'melt']
senticnet['woe'] = ['-0.96', '0', '-0.74', '-0.91', '#sadness', '#disgust', 'negative', '-0.87', 'heartbreak', 'repent', 'repentance', 'guilty_conscience', 'penitentially']
senticnet['woe_unto'] = ['-0.52', '0', '0.542', '-0.93', '#anger', '#disgust', 'negative', '-0.67', 'intoxication', 'drunkenness', 'inebriation', 'inebriety', 'tipsiness']
senticnet['woebegone'] = ['-0.90', '0.138', '-0.63', '0.071', '#sadness', '#fear', 'negative', '-0.33', 'woeful', 'execrable', 'deplorable', 'woefulness', 'plaintiveness']
senticnet['woeful'] = ['-0.90', '0.138', '-0.63', '0.071', '#sadness', '#fear', 'negative', '-0.33', 'execrable', 'deplorable', 'woebegone', 'woefulness', 'plaintiveness']
senticnet['woefully'] = ['-0.88', '0.174', '-0.59', '0', '#sadness', '#fear', 'negative', '-0.43', 'lamentably', 'deplorably', 'bereaved', 'bereft', 'grief-stricken']
senticnet['woefulness'] = ['-0.90', '0.138', '-0.63', '0.071', '#sadness', '#fear', 'negative', '-0.33', 'woeful', 'execrable', 'deplorable', 'woebegone', 'plaintiveness']
senticnet['woful'] = ['-0.73', '0', '0.732', '-0.53', '#sadness', '#anger', 'negative', '-0.66', 'insurmountable', 'insuperable', 'inexpugnable', 'indomitable', 'unsubduable']
senticnet['wog'] = ['-0.35', '-0.13', '-0.05', '-0.38', '#sadness', '#disgust', 'negative', '-0.16', 'dick', 'idiot', 'asshole', 'fuck', 'motherfucker']
senticnet['woggle_bug'] = ['0.931', '-0.77', '0', '0', '#joy', '#surprise', 'positive', '0.855', 'crystal_clear', 'blue_sky', 'imaginary', 'water_sky', 'blue_color']
senticnet['wolesi_jirga_house'] = ['-0.57', '0', '0.576', '-0.78', '#anger', '#disgust', 'negative', '-0.64', 'reprieve', 'respite', 'suspend', 'call', 'delay']
senticnet['wolf'] = ['-0.03', '0', '-0.08', '0', '#sadness', '#fear', 'negative', '-0.06', 'lion', 'leopard', 'mountain_lion', 'bobcat', 'craven']
senticnet['wolf_clan'] = ['0.989', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.97', 'committee', 'council', 'caste', 'association', 'organization']
senticnet['wolf_pack'] = ['-0.90', '0', '0.569', '-0.96', '#sadness', '#disgust', 'negative', '-0.81', 'unauthorized', 'unofficial', 'informal', 'loose', 'drumhead']
senticnet['wolf_pup'] = ['0', '-0.97', '0', '0', '#surprise', '#surprise', 'positive', '0.977', 'cougar', 'skunk', 'lion', 'tiger', 'lynx']
senticnet['wolf_rayet'] = ['0.931', '-0.82', '0', '0.709', '#joy', '#surprise', 'positive', '0.822', 'light', 'aureole', 'radiance', 'visible_light', 'visible_radiation']
senticnet['wolf_rayet_star'] = ['-0.55', '0', '0.869', '-0.83', '#anger', '#disgust', 'negative', '-0.75', 'air_bubble', 'foam', 'froth', 'soap_bubble', 'bubble']
senticnet['wolf_reintroduction'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['wolf_snake'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'death_adder', 'ophidian', 'serpent', 'elapid', 'taipan']
senticnet['wolf_spider'] = ['0', '0', '-0.92', '-0.97', '#fear', '#disgust', 'negative', '-0.95', 'annoy_bug', 'insect', 'bug', 'hare', 'nymph']
senticnet['wolfed_down'] = ['0.866', '-0.91', '0', '0.893', '#surprise', '#admiration', 'positive', '0.892', 'mooch', 'cadge', 'freeload', 'sponge', 'bum']
senticnet['wolfi_landstreicher'] = ['0.95', '-0.77', '0', '0.578', '#joy', '#surprise', 'positive', '0.767', 'philosophy', 'doctrine', 'philosophical_system', 'school_of_thought', 'ism']
senticnet['wolfing_down'] = ['0.867', '0', '0', '0', '#joy', '#joy', 'positive', '0.867', 'relax', 'eat_food', 'eat_snack', 'eat_sweet', 'childhood']
senticnet['wolfish'] = ['0', '0', '-0.96', '-0.84', '#fear', '#disgust', 'negative', '-0.90', 'voracious', 'rapacious', 'esurient', 'ravenous', 'edacious']
senticnet['wolfram_research'] = ['0', '0', '0.737', '-0.86', '#anger', '#disgust', 'negative', '-0.8', 'annoy', 'skin_rash', 'bother', 'irritation', 'great_dane']
senticnet['wolfsbane'] = ['0.02', '0.131', '0.749', '-0.67', '#anger', '#disgust', 'negative', '-0.31', 'belligerence', 'belligerency', 'belligerent', 'war', 'rancor']
senticnet['wollaston_prism'] = ['-0.50', '0', '0.75', '-0.95', '#anger', '#disgust', 'negative', '-0.73', 'beam', 'beam_of_light', 'sunbeam', 'ray', 'ray_of_light']
senticnet['wolong'] = ['0.02', '0.071', '0', '0.069', '#interest', '#admiration', 'positive', '0.053', 'many_animals', 'donkey', 'chimpanzee', 'cute_animal', 'creature']
senticnet['wolong_giant_panda'] = ['0.02', '0.071', '0', '0.069', '#interest', '#admiration', 'positive', '0.053', 'many_animals', 'donkey', 'chimpanzee', 'cute_animal', 'creature']
senticnet['wolong_nature_reserve'] = ['0.02', '0.071', '0', '0.069', '#interest', '#admiration', 'positive', '0.053', 'many_animals', 'donkey', 'chimpanzee', 'cute_animal', 'creature']
senticnet['wolverine'] = ['0.02', '0.131', '0.749', '-0.67', '#anger', '#disgust', 'negative', '-0.31', 'belligerence', 'belligerency', 'belligerent', 'war', 'rancor']
senticnet['wom_girl'] = ['0.874', '0', '0', '0.889', '#joy', '#admiration', 'positive', '0.881', 'sem', 'act', 'education', 'science', 'different_application']
senticnet['woman'] = ['0.632', '0.645', '-0.24', '0.295', '#joy', '#interest', 'positive', '0.333', 'human_being', 'bloke', 'adult', 'male', 'sexy_woman']
senticnet['woman_basketball_team'] = ['0.035', '0.155', '0', '0', '#joy', '#interest', 'positive', '0.095', 'basketball_court', 'basketball_game', 'basketball', 'ball', 'sport']
senticnet['woman_becomes_pregnant'] = ['0.97', '0.729', '0', '0.946', '#joy', '#admiration', 'positive', '0.882', 'make_baby', 'give_birth_child', 'sexual_intercourse', 'find_partner', 'make_love']
senticnet['woman_breast'] = ['-0.03', '-0.01', '0.058', '0', '#sadness', '#anger', 'negative', '-0.02', 'dartboard', 'loop', 'disk', 'puck', 'disc']
senticnet['woman_child'] = ['0.707', '0', '0', '0.731', '#joy', '#admiration', 'positive', '0.719', 'husband', 'mum', 'parent', 'mother', 'wife']
senticnet['woman_closet'] = ['-0.02', '-0.07', '0.079', '-0.08', '#anger', '#disgust', 'negative', '-0.02', 'clothes_store', 'store_shoes', 'fabric_store', 'keep_clothes', 'fabric_shop']
senticnet['woman_cloth'] = ['0.053', '-0.06', '0.047', '0.087', '#surprise', '#admiration', 'positive', '0.04', 'formal_cloth', 'woman_garment', 'woman_suit', 'woman_pant', 'woman_shirt']
senticnet['woman_clothes'] = ['0.053', '-0.06', '0.047', '0.087', '#surprise', '#admiration', 'positive', '0.04', 'woman_dress', 'woman_garment', 'stocking', 'woman_culotte', 'tutu']
senticnet['woman_clothing_store'] = ['0.026', '0.082', '0', '0', '#joy', '#interest', 'positive', '0.054', 'account_department', 'office', 'accountant_office', 'shirt_pocket', 'desk_drawer']
senticnet['woman_culotte'] = ['0.053', '-0.06', '0.047', '0.087', '#surprise', '#admiration', 'positive', '0.04', 'woman_dress', 'woman_garment', 'stocking', 'woman_clothes', 'tutu']
senticnet['woman_dress'] = ['0.053', '-0.06', '0.047', '0.087', '#surprise', '#admiration', 'positive', '0.04', 'woman_garment', 'skirt', 'female_cloth', 'frilly_design', 'robe']
senticnet['woman_engineer_swe'] = ['0.856', '0', '0', '0.963', '#joy', '#admiration', 'positive', '0.909', 'progress', 'get_better_job', 'further_career', 'graduate_school', 'get_diploma']
senticnet['woman_garment'] = ['0.053', '-0.06', '0.047', '0.087', '#surprise', '#admiration', 'positive', '0.04', 'formal_cloth', 'woman_cloth', 'woman_suit', 'woman_pant', 'woman_shirt']
senticnet['woman_giving_birth'] = ['0', '0', '0', '0.939', '#admiration', '#admiration', 'positive', '0.939', 'large_family', 'small_child', 'youth', 'quiet_baby', 'deliver_baby']
senticnet['woman_hater'] = ['0', '0', '0.733', '-0.98', '#anger', '#disgust', 'negative', '-0.85', 'fetishist', 'degenerate', 'lech', 'letch', 'pervert']
senticnet['woman_pant'] = ['0.053', '-0.06', '0.047', '0.087', '#surprise', '#admiration', 'positive', '0.04', 'formal_cloth', 'woman_cloth', 'woman_suit', 'woman_garment', 'woman_shirt']
senticnet['woman_purse'] = ['0', '0.08', '0', '0', '#interest', '#interest', 'positive', '0.08', 'purse', 'pocket', 'wallet', 'man_purse', 'bag']
senticnet['woman_right_activist'] = ['-0.73', '0', '0.778', '-0.71', '#sadness', '#anger', 'negative', '-0.74', 'dry', 'insurgent', 'militant', 'passive_resister', 'demonstrator']
senticnet['woman_scientist'] = ['0.868', '0.987', '0', '0', '#joy', '#interest', 'positive', '0.927', 'woman', 'science', 'engineering', 'engineer', 'award']
senticnet['woman_shirt'] = ['0.053', '-0.06', '0.047', '0.087', '#surprise', '#admiration', 'positive', '0.04', 'formal_cloth', 'woman_cloth', 'woman_suit', 'woman_pant', 'woman_garment']
senticnet['woman_suit'] = ['0.053', '-0.06', '0.047', '0.087', '#surprise', '#admiration', 'positive', '0.04', 'formal_cloth', 'woman_cloth', 'woman_garment', 'woman_pant', 'woman_shirt']
senticnet['woman_volleyball_team'] = ['0.035', '0.155', '0', '0', '#joy', '#interest', 'positive', '0.095', 'basketball_court', 'basketball_game', 'basketball', 'ball', 'sport']
senticnet['woman_womb'] = ['0.706', '0', '0', '0', '#joy', '#joy', 'positive', '0.706', 'birth', 'new_baby', 'born', 'birthday', 'thanksgiving']
senticnet['womanhood'] = ['0.693', '-0.52', '0', '0.82', '#joy', '#admiration', 'positive', '0.681', 'concrete_representation', 'shape', 'representation', 'mental_representation', 'internal_representation']
senticnet['womaniser'] = ['0.814', '-0.50', '0', '0.622', '#joy', '#admiration', 'positive', '0.648', 'friend', 'champion', 'supporter', 'protagonist', 'endorser']
senticnet['womanish'] = ['-0.63', '0', '0.986', '-0.94', '#anger', '#disgust', 'negative', '-0.85', 'unmanful', 'effeminate', 'emasculate', 'cissy', 'sissified']
senticnet['womanizer'] = ['-0.19', '0', '-0.12', '0', '#sadness', '#fear', 'negative', '-0.15', 'bad_child', 'misbehavior', 'bad_kid', 'bad_action', 'difficulty']
senticnet['womankind'] = ['0.539', '-0.96', '0', '0.745', '#surprise', '#admiration', 'positive', '0.751', 'spouse', 'partner', 'married_person', 'mate', 'better_half']
senticnet['womanly'] = ['0.656', '-0.73', '0', '0.736', '#surprise', '#admiration', 'positive', '0.708', 'strong', 'salubrious', 'healthy', 'solid', 'alimentary']
senticnet['womb'] = ['0.159', '0.068', '-0.07', '0.118', '#joy', '#admiration', 'positive', '0.067', 'stomach', 'uterus', 'pregnant', 'lap', 'gravid']
senticnet['wombat'] = ['-0.95', '0', '0.624', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['womens_basketball_team'] = ['0.035', '0.155', '0', '0', '#joy', '#interest', 'positive', '0.095', 'basketball_court', 'basketball_game', 'basketball', 'ball', 'sport']
senticnet['womenswear'] = ['0.053', '-0.01', '-0.05', '0.121', '#joy', '#admiration', 'positive', '0.034', 'coat', 'winter_clothes', 'stylish', 'adorn', 'protective_cover']
senticnet['wonder'] = ['0.957', '0.894', '0', '0.921', '#joy', '#admiration', 'positive', '0.924', 'awe', 'amazement', 'astonishment', 'perplex', 'marvel']
senticnet['wonder_child'] = ['0.664', '-0.66', '0', '0.987', '#joy', '#admiration', 'positive', '0.771', 'child', 'small_fry', 'tyke', 'tike', 'kid']
senticnet['wonder_why'] = ['-0.70', '0', '-0.72', '0', '#sadness', '#fear', 'negative', '-0.71', 'devout', 'earnestness', 'earnestly', 'seriousness', 'in_earnest']
senticnet['wonder_woman'] = ['0.036', '0.127', '0.019', '0.225', '#interest', '#admiration', 'positive', '0.092', 'bird', 'sing_bird', 'hatch', 'vulture', 'only_bird']
senticnet['wonderful'] = ['0.722', '0.798', '0', '0.896', '#interest', '#admiration', 'positive', '0.805', 'fantastic', 'amaze', 'grand', 'pose', 'marvel']
senticnet['wonderful_feeling'] = ['0.536', '0.44', '-0.39', '0.41', '#joy', '#interest', 'positive', '0.249', 'good_emotion', 'affection', 'pleasant_emotion', 'happy_emotion', 'ecstatic']
senticnet['wonderful_place_visit'] = ['0.029', '0.1', '0.027', '0.054', '#interest', '#admiration', 'positive', '0.039', 'institution', 'bachelor_degree', 'place_learn', 'educational_facility', 'educational_institution']
senticnet['wonderfully'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'terrific', 'wondrously', 'tremendous', 'marvelous', 'wondrous']
senticnet['wonderfully_diverse'] = ['-0.05', '0.096', '0', '0.066', '#interest', '#admiration', 'positive', '0.037', 'hue_tint', 'spectrum', 'red_blue', 'green_yellow', 'tint']
senticnet['wonderfully_rich'] = ['0.843', '0.977', '0', '0', '#joy', '#interest', 'positive', '0.91', 'business', 'finance', 'revenue', 'wealth', 'income']
senticnet['wondering_why'] = ['-0.52', '0', '0.607', '-0.54', '#anger', '#disgust', 'negative', '-0.56', 'bewail', 'bemoan', 'mutter', 'scold', 'quetch']
senticnet['wonderland'] = ['0.703', '-0.57', '0', '0.918', '#joy', '#admiration', 'positive', '0.733', 'land', 'dry_land', 'earth', 'ground', 'solid_ground']
senticnet['wonderland_amusement_park'] = ['0.089', '-0.18', '0.037', '-0.06', '#joy', '#surprise', 'positive', '0.043', 'balloon', 'ballon', 'fielder', 'kite', 'blimp']
senticnet['wonderlic_intelligence_test'] = ['0.926', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.916', 'score_goal', 'accomplish', 'finalize', 'pot', 'make_point']
senticnet['wonderment'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'terrific', 'wondrously', 'tremendous', 'marvelous', 'wondrous']
senticnet['wonderworker'] = ['0', '0.248', '-0.08', '0.173', '#interest', '#admiration', 'positive', '0.113', 'god', 'minister', 'evangelist', 'rector', 'goddess']
senticnet['wondrous'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'terrific', 'wondrously', 'tremendous', 'amazing', 'marvelous']
senticnet['wondrously'] = ['0.046', '-0.66', '0', '0.365', '#surprise', '#admiration', 'positive', '0.357', 'terrific', 'tremendous', 'marvelous', 'wondrous', 'amazing']
senticnet['wonkette'] = ['0.73', '0.972', '0', '0', '#joy', '#interest', 'positive', '0.851', 'sting', 'nip', 'bite', 'flea', 'least']
senticnet['wonko'] = ['0.203', '0.077', '0', '0.189', '#joy', '#admiration', 'positive', '0.156', 'sea_shell', 'coast', 'seaside', 'sediment', 'shell']
senticnet['wonsa'] = ['-0.53', '0', '0.977', '-0.54', '#anger', '#disgust', 'negative', '-0.68', 'rank', 'surpass', 'shine_at', 'excel_at', 'exceed']
senticnet['woo'] = ['0.85', '0', '0', '0', '#joy', '#joy', 'positive', '0.85', 'date', 'accuracy', 'sexy_woman', 'seduction', 'enjoy_person']
senticnet['wood'] = ['0', '-0.02', '0.085', '-0.09', '#anger', '#disgust', 'negative', '-0.05', 'tree_dead_tree', 'forest', 'sideways', 'deforestation', 'dead_tree']
senticnet['wood_ash'] = ['0.037', '0.084', '0', '0', '#joy', '#interest', 'positive', '0.06', 'leave_over', 'leftover', 'cellulose', 'cane', 'remainder']
senticnet['wood_bird'] = ['0.062', '0.072', '0', '0', '#joy', '#interest', 'positive', '0.067', 'sculptor', 'carving', 'wood_duck', 'art_craft', 'wood_product']
senticnet['wood_board'] = ['-0.06', '-0.02', '0.049', '0', '#sadness', '#anger', 'negative', '-0.03', 'wooden_board', 'hardwood', 'piece_of_wood', 'build_ground', 'cover_ground']
senticnet['wood_burn'] = ['0', '0', '-0.08', '0', '#fear', '#fear', 'negative', '-0.08', 'rapid_oxidation', 'heat_light', 'chemical_reaction', 'huge_fire', 'blow_out']
senticnet['wood_burning_stove'] = ['0.088', '0.076', '0.016', '0.094', '#joy', '#admiration', 'positive', '0.06', 'copper_wire', 'stove', 'several_types', 'oven', 'suspect']
senticnet['wood_carver'] = ['-0.52', '0', '0.506', '-0.94', '#sadness', '#disgust', 'negative', '-0.65', 'gouger', 'swindler', 'defrauder', 'fraud', 'welcher']
senticnet['wood_charcoal'] = ['0.037', '0.084', '0', '0', '#joy', '#interest', 'positive', '0.06', 'leave_over', 'leftover', 'cellulose', 'cane', 'remainder']
senticnet['wood_chopstick'] = ['0.803', '0.87', '0', '0', '#joy', '#interest', 'positive', '0.837', 'tree', 'rectangular', 'pencil', 'wooden_chair', 'wood_product']
senticnet['wood_cutter'] = ['-0.18', '-0.12', '0.049', '-0.24', '#sadness', '#disgust', 'negative', '-0.09', 'tree_trunk', 'plank', 'artificial_leg', 'hardwood', 'tree_wood']
senticnet['wood_duck'] = ['0.062', '0.072', '0', '0', '#joy', '#interest', 'positive', '0.067', 'sculptor', 'carving', 'wood_bird', 'art_craft', 'wood_product']
senticnet['wood_elf'] = ['0', '-0.03', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'gnome', 'elf', 'goblin', 'undersized', 'small_person']
senticnet['wood_fiber'] = ['0.024', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'stump', 'plank', 'timber', 'paper', 'mahogany']
senticnet['wood_fibre'] = ['0.024', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'stump', 'plank', 'timber', 'paper', 'mahogany']
senticnet['wood_glue'] = ['0', '0.078', '0.027', '0.196', '#interest', '#admiration', 'positive', '0.082', 'heel', 'ankle', 'toe', 'foot_massage', 'foot']
senticnet['wood_ibis'] = ['0.797', '-0.71', '0', '0.736', '#joy', '#admiration', 'positive', '0.751', 'ibis', 'wood_stork', 'sacred_ibis', 'wading_bird', 'wader']
senticnet['wood_material'] = ['0.071', '0', '0.031', '0.064', '#joy', '#admiration', 'positive', '0.035', 'comfortable', 'wooden_chair', 'tile_floor', 'wooden_bed', 'wooden_floor']
senticnet['wood_meadowgrass'] = ['-0.76', '0', '0.513', '-0.72', '#sadness', '#disgust', 'negative', '-0.66', 'foxtail', 'foxtail_grass', 'manna_grass', 'sweet_grass', 'meadow_grass']
senticnet['wood_panel'] = ['0', '0.087', '0', '0.118', '#interest', '#admiration', 'positive', '0.102', 'wooden_structure', 'entrance_room', 'build_open', 'home_entrance', 'hinge']
senticnet['wood_pigeon'] = ['0.567', '-0.93', '0', '0.659', '#surprise', '#admiration', 'positive', '0.72', 'bird', 'wildfowl', 'quail', 'partridge', 'pheasant']
senticnet['wood_pile'] = ['-0.84', '0', '0.987', '-0.77', '#sadness', '#anger', 'negative', '-0.87', 'thorn', 'sticker', 'glochid', 'prickle', 'spikelet']
senticnet['wood_plank'] = ['0', '0.087', '0', '0.118', '#interest', '#admiration', 'positive', '0.102', 'wood_structure', 'wooden_house', 'wood_panel', 'plank', 'wood']
senticnet['wood_product'] = ['0', '0.821', '0', '0.988', '#interest', '#admiration', 'positive', '0.904', 'tree', 'rectangular', 'pencil', 'wooden_chair', 'wood_chopstick']
senticnet['wood_pulp'] = ['0.024', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'stump', 'plank', 'timber', 'paper', 'mahogany']
senticnet['wood_pulp_paper'] = ['0.024', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'stump', 'plank', 'timber', 'paper', 'mahogany']
senticnet['wood_rot'] = ['0.718', '0', '0', '0', '#joy', '#joy', 'positive', '0.718', 'water_container', 'still_water', 'clean_water', 'water', 'cool_water']
senticnet['wood_rush_luzula'] = ['-0.59', '0', '0.942', '-0.75', '#anger', '#disgust', 'negative', '-0.76', 'sedum', 'herb', 'stonecrop', 'orpine', 'orpin']
senticnet['wood_sage'] = ['-0.56', '0', '0.91', '-0.80', '#anger', '#disgust', 'negative', '-0.76', 'subshrub', 'suffrutex', 'germander', 'wall_germander', 'cat_thyme']
senticnet['wood_sandpiper'] = ['0.797', '-0.71', '0', '0.736', '#joy', '#admiration', 'positive', '0.751', 'wood_ibis', 'wood_stork', 'ibis', 'wading_bird', 'wader']
senticnet['wood_smoke'] = ['0.047', '0', '-0.10', '-0.06', '#fear', '#disgust', 'negative', '-0.03', 'smoke', 'tobacco_product', 'smoke_tobacco', 'lung_cancer', 'cigar']
senticnet['wood_sorrel'] = ['-0.14', '0.098', '0.662', '-0.65', '#anger', '#disgust', 'negative', '-0.34', 'pesky', 'miffed', 'galling', 'annoying', 'vexatious']
senticnet['wood_stork'] = ['0.797', '-0.71', '0', '0.736', '#joy', '#admiration', 'positive', '0.751', 'wood_ibis', 'ibis', 'sacred_ibis', 'wading_bird', 'wader']
senticnet['wood_stove'] = ['0.02', '0.076', '0', '0', '#joy', '#interest', 'positive', '0.048', 'land_earth', 'tidal_wave', 'puddle', 'brine', 'spurt']
senticnet['wood_structure'] = ['0', '0.132', '0', '0.09', '#interest', '#admiration', 'positive', '0.111', 'wooden_house', 'wooden', 'religious_significance', 'wood_panel', 'wood']
senticnet['wood_trim'] = ['0', '0.128', '-0.12', '0.183', '#interest', '#admiration', 'positive', '0.063', 'support_roof', 'furnish', 'ceiling', 'window_door', 'frame']
senticnet['wood_worktop'] = ['0.025', '0.084', '0', '0.062', '#interest', '#admiration', 'positive', '0.057', 'kitchen_worktop', 'worktop', 'surface', 'work', 'work_surface']
senticnet['wooded'] = ['-0.87', '0', '0.664', '-0.62', '#sadness', '#anger', 'negative', '-0.71', 'brambly', 'braky', 'rushy', 'scrubby', 'forested']
senticnet['wooded_hill'] = ['0', '0.093', '0', '0.078', '#interest', '#admiration', 'positive', '0.085', 'steep', 'landscape_feature', 'knoll', 'hill_mountain', 'incline']
senticnet['wooded_hillside'] = ['0', '0.093', '0', '0.078', '#interest', '#admiration', 'positive', '0.085', 'steep', 'landscape_feature', 'knoll', 'hill_mountain', 'incline']
senticnet['wooded_ridge'] = ['0', '0.093', '0', '0.078', '#interest', '#admiration', 'positive', '0.085', 'steep', 'landscape_feature', 'knoll', 'hill_mountain', 'incline']
senticnet['wooded_slope'] = ['0', '0.093', '0', '0.078', '#interest', '#admiration', 'positive', '0.085', 'steep', 'landscape_feature', 'knoll', 'hill_mountain', 'incline']
senticnet['wooded_valley'] = ['-0.91', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.85', 'play', 'perpetrator', 'role_play', 'act_part', 'terrible_event']
senticnet['wooden'] = ['0', '0.773', '0', '0.895', '#interest', '#admiration', 'positive', '0.834', 'wooden_board', 'make_wood', 'wooden_box', 'wooden_floor', 'long_wood']
senticnet['wooden_balcony'] = ['0', '0.117', '-0.07', '0.094', '#interest', '#admiration', 'positive', '0.045', 'stage_border', 'row_seat', 'stage_entrance', 'theater_seat', 'theater_box']
senticnet['wooden_beam'] = ['0', '-0.00', '0', '0.088', '#surprise', '#admiration', 'positive', '0.048', 'heavy_paper', 'lightweight_metal', 'wizard', 'metal_type', 'soft_metal']
senticnet['wooden_bed'] = ['0.071', '0', '0.031', '0.064', '#joy', '#admiration', 'positive', '0.035', 'comfortable', 'wooden_chair', 'tile_floor', 'wooden_floor', 'wood_material']
senticnet['wooden_bench'] = ['0.071', '0.127', '0', '0.197', '#interest', '#admiration', 'positive', '0.132', 'place_work', 'workplace', 'office', 'work', 'hang_wall']
senticnet['wooden_board'] = ['0.071', '0', '0.031', '0.064', '#joy', '#admiration', 'positive', '0.035', 'wooden_floor', 'plank', 'wooden_table', 'hardwood', 'cover_ground']
senticnet['wooden_boat'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'boat', 'yacht', 'big_boat', 'large_boat']
senticnet['wooden_bowl'] = ['0.035', '-0.01', '0.009', '0.063', '#joy', '#admiration', 'positive', '0.026', 'container', 'carton', 'contain', 'hold_cupcake', 'store_dish']
senticnet['wooden_box'] = ['-0.12', '0.074', '-0.12', '-0.05', '#sadness', '#fear', 'negative', '-0.05', 'bury_dead', 'in_person', 'death_place', 'corpse', 'morgue']
senticnet['wooden_bridge'] = ['0', '0.069', '0', '0', '#interest', '#interest', 'positive', '0.069', 'suspension_bridge', 'under_bridge', 'roadway', 'county_highway', 'long_road']
senticnet['wooden_cane'] = ['0.609', '-0.74', '0', '0.716', '#surprise', '#admiration', 'positive', '0.69', 'malacca', 'sword', 'swagger_stick', 'sword_cane', 'sword_stick']
senticnet['wooden_casket'] = ['0', '0.082', '-0.09', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'coffin', 'headstone', 'morgue', 'bury_body', 'casket']
senticnet['wooden_chair'] = ['0', '-0.12', '0', '0', '#surprise', '#surprise', 'positive', '0.122', 'rest', 'bench_sit', 'park', 'garden', 'outdoor']
senticnet['wooden_chest'] = ['0', '0.149', '0', '0', '#interest', '#interest', 'positive', '0.149', 'wallet', 'billfold', 'bank', 'piggy_bank', 'bank_vault']
senticnet['wooden_coaster'] = ['0.089', '-0.18', '0.037', '-0.06', '#joy', '#surprise', 'positive', '0.043', 'balloon', 'ballon', 'fielder', 'kite', 'blimp']
senticnet['wooden_coffin'] = ['-0.88', '0', '-0.93', '0', '#sadness', '#fear', 'negative', '-0.90', 'coffin', 'casket', 'morgue', 'headstone', 'burial']
senticnet['wooden_comb'] = ['0.022', '-0.01', '0.062', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'electric_toothbrush', 'moistener', 'hairbrush', 'shampoo_conditioner', 'shave_kit']
senticnet['wooden_cradle'] = ['0', '-0.02', '-0.08', '-0.05', '#fear', '#disgust', 'negative', '-0.03', 'mattress', 'sleep_furniture', 'master_bedroom', 'small_bed', 'pillow']
senticnet['wooden_deck'] = ['0.074', '0', '0', '0', '#joy', '#joy', 'positive', '0.074', 'hike_trail', 'ant_hill', 'compost', 'picnic', 'soccer_field']
senticnet['wooden_doll'] = ['0.793', '0.847', '0', '0', '#joy', '#interest', 'positive', '0.82', 'cross_bun', 'hot_cross_bun', 'bear_paw', 'coffee_roll', 'bun']
senticnet['wooden_door'] = ['0', '0', '0', '0.077', '#admiration', '#admiration', 'positive', '0.077', 'glass_front', 'gate', 'doorway', 'use_farm', 'entranceway']
senticnet['wooden_dowel'] = ['0', '0.089', '0', '0', '#interest', '#interest', 'positive', '0.089', 'ground', 'basement', 'base', 'foundation', 'basis']
senticnet['wooden_floor'] = ['0.071', '0', '0.031', '0.064', '#joy', '#admiration', 'positive', '0.035', 'comfortable', 'wooden_chair', 'tile_floor', 'wooden_bed', 'wood_material']
senticnet['wooden_flute'] = ['0.821', '-0.53', '0', '0.785', '#joy', '#admiration', 'positive', '0.712', 'fife', 'nose_flute', 'piccolo', 'woodwind', 'flute']
senticnet['wooden_frame'] = ['0', '0.773', '0', '0.895', '#interest', '#admiration', 'positive', '0.834', 'wooden_board', 'make_wood', 'wooden_box', 'wooden_floor', 'long_wood']
senticnet['wooden_furniture'] = ['0.022', '0.069', '0.027', '0.059', '#interest', '#admiration', 'positive', '0.031', 'perfect_surface', 'flat_land', 'flat_surface', 'bowl', 'plate']
senticnet['wooden_grandstand'] = ['0.953', '-0.90', '0', '0', '#joy', '#surprise', 'positive', '0.929', 'hockey_fan', 'goalie', 'competitor', 'spectator', 'hockey_stick']
senticnet['wooden_handle'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'useful_tool']
senticnet['wooden_house'] = ['0', '0.087', '0', '0.118', '#interest', '#admiration', 'positive', '0.102', 'wood_structure', 'wood_plank', 'wood_panel', 'plank', 'wood']
senticnet['wooden_jetty'] = ['0.84', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.798', 'big_breadbox', 'water_vehicle', 'military_ship', 'ship', 'small_boat']
senticnet['wooden_leg'] = ['-0.18', '-0.12', '0.049', '-0.24', '#sadness', '#disgust', 'negative', '-0.09', 'tree_trunk', 'plank', 'artificial_leg', 'hardwood', 'tree_wood']
senticnet['wooden_mallet'] = ['0.609', '-0.74', '0', '0.716', '#surprise', '#admiration', 'positive', '0.69', 'malacca', 'sword', 'swagger_stick', 'sword_cane', 'sword_stick']
senticnet['wooden_mortar'] = ['0', '0', '-0.95', '-0.72', '#fear', '#disgust', 'negative', '-0.83', 'cannon', 'explosive', 'firearm', 'pistol', 'machine_gun']
senticnet['wooden_paddle'] = ['0.609', '-0.74', '0', '0.716', '#surprise', '#admiration', 'positive', '0.69', 'malacca', 'sword', 'swagger_stick', 'sword_cane', 'sword_stick']
senticnet['wooden_peg'] = ['-0.71', '0', '0.932', '-0.50', '#sadness', '#anger', 'negative', '-0.71', 'refuse', 'subvert', 'decline', 'balk', 'disobey']
senticnet['wooden_pier'] = ['0.84', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.798', 'big_breadbox', 'water_vehicle', 'military_ship', 'ship', 'small_boat']
senticnet['wooden_plank'] = ['0', '-0.00', '0', '0.088', '#surprise', '#admiration', 'positive', '0.048', 'heavy_paper', 'lightweight_metal', 'wizard', 'metal_type', 'soft_metal']
senticnet['wooden_plaque'] = ['-0.81', '0', '0.741', '-0.76', '#sadness', '#disgust', 'negative', '-0.77', 'spot', 'clog', 'dentist', 'arterial_plaque', 'dental_plaque']
senticnet['wooden_platform'] = ['0', '0.773', '0', '0.895', '#interest', '#admiration', 'positive', '0.834', 'wooden_board', 'make_wood', 'wooden_box', 'wooden_floor', 'long_wood']
senticnet['wooden_plow'] = ['-0.99', '0', '0.827', '-0.54', '#sadness', '#anger', 'negative', '-0.78', 'treat', 'cover', 'handle', 'theologize', 'deal']
senticnet['wooden_pole'] = ['0.609', '-0.74', '0', '0.716', '#surprise', '#admiration', 'positive', '0.69', 'malacca', 'sword', 'swagger_stick', 'sword_cane', 'sword_stick']
senticnet['wooden_porch'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'vestibule', 'door', 'central_passage', 'gate', 'door_handle']
senticnet['wooden_pulpit'] = ['0.023', '0.218', '0.01', '0.235', '#interest', '#admiration', 'positive', '0.116', 'confessional', 'altar', 'chapel', 'temple', 'christianity']
senticnet['wooden_racket'] = ['0', '0', '0.845', '-0.99', '#anger', '#disgust', 'negative', '-0.91', 'umpire', 'referee', 'legal_proceed', 'clang', 'clamor']
senticnet['wooden_rod'] = ['0.928', '0.835', '0', '0', '#joy', '#interest', 'positive', '0.881', 'adhere', 'hockey_equipment', 'cue', 'glue', 'brick']
senticnet['wooden_roller_coaster'] = ['0.089', '-0.18', '0.037', '-0.06', '#joy', '#surprise', 'positive', '0.043', 'balloon', 'ballon', 'fielder', 'kite', 'blimp']
senticnet['wooden_rollercoaster'] = ['0.089', '-0.18', '0.037', '-0.06', '#joy', '#surprise', 'positive', '0.043', 'balloon', 'ballon', 'fielder', 'kite', 'blimp']
senticnet['wooden_schooner'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'boat', 'yacht', 'big_boat', 'large_boat']
senticnet['wooden_shack'] = ['-0.97', '0', '0.84', '-0.52', '#sadness', '#anger', 'negative', '-0.78', 'pis_aller', 'last_resort', 'temporary_expedient', 'expedient', 'stopgap']
senticnet['wooden_shaft'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'bayonet', 'spear', 'shotgun', 'bullet', 'hydrogen_bomb']
senticnet['wooden_spoon'] = ['0.719', '0.773', '0', '0', '#joy', '#interest', 'positive', '0.746', 'teaspoon', 'kitchen_tool', 'spoon', 'wooden_tool', 'wood']
senticnet['wooden_staircase'] = ['0', '0', '0', '0.128', '#admiration', '#admiration', 'positive', '0.128', 'vestibule', 'door', 'central_passage', 'gate', 'door_handle']
senticnet['wooden_statue'] = ['0.085', '0', '0', '0.073', '#joy', '#admiration', 'positive', '0.079', 'sculpture', 'creative', 'yellow_ribbon', 'artwork', 'monument']
senticnet['wooden_stick'] = ['0.071', '-0.10', '0.063', '0.134', '#surprise', '#admiration', 'positive', '0.061', 'big_stick', 'nocturnal', 'long_stick', 'tree_animal', 'same_again']
senticnet['wooden_stool'] = ['0.024', '0', '0', '0.074', '#joy', '#admiration', 'positive', '0.049', 'sheet', 'cover', 'quilt', 'comforter', 'modesty']
senticnet['wooden_structure'] = ['0', '0.082', '0.013', '0.132', '#interest', '#admiration', 'positive', '0.067', 'wood_panel', 'entrance_room', 'threshold', 'house_open', 'rectangular_shape']
senticnet['wooden_sword'] = ['0.93', '-0.78', '0', '0.873', '#joy', '#admiration', 'positive', '0.861', 'blade', 'brand', 'steel', 'claymore', 'sword']
senticnet['wooden_synagogue'] = ['0', '0.209', '0.026', '0.184', '#interest', '#admiration', 'positive', '0.122', 'church', 'confession', 'temple', 'cathedral', 'monastery']
senticnet['wooden_table'] = ['0.071', '0', '0.031', '0.064', '#joy', '#admiration', 'positive', '0.035', 'wooden_floor', 'plank', 'wooden_board', 'hardwood', 'cover_ground']
senticnet['wooden_tool'] = ['0.983', '0.931', '0', '0', '#joy', '#interest', 'positive', '0.957', 'teaspoon', 'kitchen_tool', 'spoon', 'wooden_spoon', 'wood']
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senticnet['woodgrain'] = ['0.02', '0.085', '-0.05', '0.085', '#interest', '#admiration', 'positive', '0.034', 'gold', 'silver', 'medal', 'carat', 'medallion']
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senticnet['woodland_animal'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'life', 'bear', 'woodland_creature', 'deer', 'woodland_mushroom']
senticnet['woodland_avenue'] = ['0', '0', '0.033', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'boulevard', 'lane', 'thoroughfare', 'intersection', 'curb']
senticnet['woodland_caribou'] = ['0', '0.161', '-0.08', '0.09', '#interest', '#admiration', 'positive', '0.054', 'cheetah', 'giraffe', 'kangaroo', 'mule', 'horse']
senticnet['woodland_creature'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'life', 'bear', 'woodland_animal', 'deer', 'woodland_mushroom']
senticnet['woodland_habitat'] = ['-0.73', '0', '0.59', '-0.89', '#sadness', '#disgust', 'negative', '-0.74', 'desert', 'grassland', 'forest', 'biome', 'timberland']
senticnet['woodland_mushroom'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'life', 'bear', 'woodland_animal', 'deer', 'woodland_creature']
senticnet['woodlawn_cemetery'] = ['-0.70', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.70', 'death_row', 'sad', 'loose_end', 'car_race', 'mortuary']
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senticnet['woodpulp'] = ['0.024', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'stump', 'plank', 'timber', 'paper', 'mahogany']
senticnet['woodside_park'] = ['0.623', '-0.56', '0', '0.974', '#joy', '#admiration', 'positive', '0.721', 'alterable', 'convertible', 'translatable', 'transformable', 'transmutable']
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senticnet['woodsmoke'] = ['0.786', '0', '0', '0', '#joy', '#joy', 'positive', '0.786', 'camping', 'outdoor', 'tent', 'bonfire', 'camp_out']
senticnet['woodstove'] = ['0.088', '0.076', '0.016', '0.094', '#joy', '#admiration', 'positive', '0.06', 'copper_wire', 'stove', 'several_types', 'oven', 'suspect']
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senticnet['woodward_lothrop'] = ['0', '0.081', '0', '0.071', '#interest', '#admiration', 'positive', '0.076', 'thrift_store', 'type_of_store', 'record_store', 'many_shops', 'clothes_store']
senticnet['woodwind'] = ['0.816', '-0.53', '0', '0.784', '#joy', '#admiration', 'positive', '0.712', 'fife', 'nose_flute', 'piccolo', 'woodwind_instrument', 'flute']
senticnet['woodwind_instrument'] = ['0.821', '-0.53', '0', '0.785', '#joy', '#admiration', 'positive', '0.712', 'fife', 'nose_flute', 'piccolo', 'woodwind', 'flute']
senticnet['woodwind_quintet'] = ['0.681', '-0.57', '0', '0.632', '#joy', '#admiration', 'positive', '0.628', 'classical_music', 'make_beautiful_music', 'create_music', 'part_of_band', 'make_music']
senticnet['woodworker'] = ['0', '0.233', '-0.07', '0.113', '#interest', '#admiration', 'positive', '0.089', 'structure', 'construction_worker', 'peasant', 'functionary', 'maker']
senticnet['woodworking'] = ['0.035', '0.083', '0', '0.128', '#interest', '#admiration', 'positive', '0.082', 'build_boat', 'animation', 'bring_food', 'repair', 'clean_often']
senticnet['woodworking_machinery'] = ['0.966', '0.799', '0', '0', '#joy', '#interest', 'positive', '0.883', 'mechanical_device', 'move_part', 'machine', 'work_well', 'make_car']
senticnet['woodworking_tool'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'useful_tool']
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senticnet['woody_shrub'] = ['-0.80', '0', '0.555', '-0.68', '#sadness', '#disgust', 'negative', '-0.67', 'bush', 'wormwood', 'milkwort', 'germander', 'centaury']
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senticnet['woofer'] = ['0.056', '-0.01', '0.036', '0.073', '#joy', '#admiration', 'positive', '0.028', 'loud_sound', 'stereo', 'tuner', 'speaker', 'steam_engine']
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senticnet['wool_blanket'] = ['0', '0.092', '0.016', '0.074', '#interest', '#admiration', 'positive', '0.05', 'armoire', 'pillowcase', 'bed_sheet', 'duvet_cover', 'comforter']
senticnet['wool_cap'] = ['0.08', '0.106', '-0.04', '0.112', '#interest', '#admiration', 'positive', '0.063', 'helmet', 'head_gear', 'cap', 'head_wear', 'head_protection']
senticnet['wool_cloth'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_fabric']
senticnet['wool_coat'] = ['-0.02', '0.256', '0', '0.166', '#interest', '#admiration', 'positive', '0.132', 'coat', 'shirt', 'garment', 'dress', 'vest']
senticnet['wool_fabric'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'white_cotton', 'sheepskin_material', 'woven_sheepskin', 'sheepskin', 'clothes_type']
senticnet['wool_merchant'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'bodyguard', 'outrider', 'bodyguard_of_guard', 'beefeater', 'defender']
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senticnet['wool_yarn'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_fabric']
senticnet['woolco'] = ['0', '0.081', '0', '0.071', '#interest', '#admiration', 'positive', '0.076', 'thrift_store', 'type_of_store', 'record_store', 'many_shops', 'clothes_store']
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senticnet['woolen'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_fabric']
senticnet['woolen_cloth'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_fabric']
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senticnet['woolgatherer'] = ['-0.69', '0', '0.878', '-0.62', '#sadness', '#anger', 'negative', '-0.73', 'bum', 'dreamer', 'stargazer', 'escapist', 'wishful_thinker']
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senticnet['woollen_cloth'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_fabric']
senticnet['woollen_hat'] = ['0', '-0.08', '0.105', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'underwear', 'shirt', 'panties', 'robe', 'slack']
senticnet['woollen_manufacture'] = ['-0.54', '0', '0.7', '-0.64', '#anger', '#disgust', 'negative', '-0.62', 'flourish', 'rant', 'bombast', 'ornateness', 'claptrap']
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senticnet['woolly_hat'] = ['-0.02', '0.256', '0', '0.166', '#interest', '#admiration', 'positive', '0.132', 'coat', 'shirt', 'garment', 'dress', 'vest']
senticnet['woolly_mammoth'] = ['0.969', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.96', 'extinct', 'animal', 'ice', 'elephant', 'giant']
senticnet['woolly_rhino'] = ['0.969', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.96', 'extinct', 'animal', 'ice', 'elephant', 'giant']
senticnet['woolworth'] = ['0', '0.081', '0', '0.071', '#interest', '#admiration', 'positive', '0.076', 'thrift_store', 'type_of_store', 'record_store', 'many_shops', 'clothes_store']
senticnet['wooly_mammoth'] = ['0.969', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.96', 'extinct', 'animal', 'ice', 'elephant', 'giant']
senticnet['wooster_street'] = ['0.591', '-0.74', '0', '0.672', '#surprise', '#admiration', 'positive', '0.669', 'room', 'chillax', 'hotel_room', 'furniture_store', 'living_room']
senticnet['woozy'] = ['-0.89', '0', '-0.81', '0', '#sadness', '#fear', 'negative', '-0.85', 'high-strung', 'uptight', 'restive', 'overstrung', 'nervy']
senticnet['worcestershire_sauce'] = ['-0.08', '-0.07', '0.029', '-0.17', '#sadness', '#disgust', 'negative', '-0.05', 'sour_milk', 'moldy_food', 'horseradish', 'box_cereal', 'bottle_water']
senticnet['word'] = ['0.068', '-0.01', '0.047', '0.078', '#joy', '#admiration', 'positive', '0.028', 'lyrics', 'song_lyric', 'text', 'piece_of_music', 'melody']
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senticnet['wordnet'] = ['-0.05', '0.084', '0', '0.062', '#interest', '#admiration', 'positive', '0.03', 'vocabulary', 'translation', 'book_library', 'book_poetry', 'book_genre']
senticnet['wordpad'] = ['0', '-0.12', '0', '0', '#surprise', '#surprise', 'positive', '0.127', 'chop', 'glue', 'carve', 'bandage', 'split']
senticnet['wordperfect'] = ['-0.03', '0.112', '-0.08', '0.164', '#interest', '#admiration', 'positive', '0.04', 'run', 'jump_over', 'quick', 'sprint', 'flee']
senticnet['wordperfect_format'] = ['0', '0.083', '0', '0', '#interest', '#interest', 'positive', '0.083', 'slot', 'box', 'load_dock', 'safe_deposit_box', 'emporium']
senticnet['wordplay'] = ['-0.91', '0', '-0.72', '-0.99', '#sadness', '#disgust', 'negative', '-0.87', 'witty_remark', 'cross_talking', 'sbanter', 'banter', 'jester']
senticnet['wordscraper'] = ['0.801', '-0.79', '0', '0', '#joy', '#surprise', 'positive', '0.796', 'verbosity', 'sport_play', 'excite_game', 'contest', 'fun_game']
senticnet['wordsworthian'] = ['-0.77', '0', '0.885', '-0.73', '#sadness', '#anger', 'negative', '-0.8', 'fallacy', 'misconception', 'paralogism', 'logical_fallacy', 'pseudoscience']
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senticnet['work_break'] = ['0.158', '0.125', '-0.04', '0.15', '#joy', '#admiration', 'positive', '0.097', 'coffee_break', 'inactivity', 'take_break', 'middle_day', 'rest_night']
senticnet['work_car'] = ['0', '0', '0', '0.076', '#admiration', '#admiration', 'positive', '0.076', 'travel', 'public_transportation', 'stay_sober', 'find_job', 'settle']
senticnet['work_closely'] = ['0', '0.983', '0', '0', '#interest', '#interest', 'positive', '0.983', 'find_problem', 'analyze', 'expert_report', 'examination', 'check']
senticnet['work_collaboratively'] = ['0', '0.276', '-0.12', '0.317', '#interest', '#admiration', 'positive', '0.157', 'make', 'manufacture', 'intend', 'correct', 'design']
senticnet['work_collaboratively_with'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'sexual_relationship', 'affair', 'involvement']
senticnet['work_cooperatively'] = ['0', '0.276', '-0.12', '0.317', '#interest', '#admiration', 'positive', '0.157', 'make', 'manufacture', 'intend', 'correct', 'design']
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senticnet['work_done'] = ['-0.04', '0.15', '-0.06', '0.077', '#interest', '#admiration', 'positive', '0.028', 'support_family', 'provide_service', 'earn_money', 'need_earn_money', 'complete']
senticnet['work_ethic'] = ['0.701', '0.874', '0', '0.752', '#interest', '#admiration', 'positive', '0.776', 'self_satisfaction', 'enjoy_life', 'good_feeling', 'good_attitude', 'enjoy']
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senticnet['work_force'] = ['0.814', '-0.87', '0', '0.918', '#surprise', '#admiration', 'positive', '0.87', 'ship_company', 'company', 'full_complement', 'workforce', 'manpower']
senticnet['work_group'] = ['0.056', '0.107', '0', '0.156', '#interest', '#admiration', 'positive', '0.106', 'partnership', 'business_firm', 'business_group', 'large_business', 'business_organization']
senticnet['work_hard'] = ['0.056', '0.107', '0', '0.156', '#interest', '#admiration', 'positive', '0.106', 'increase_efficiency', 'earn_paycheck', 'receive_paycheck', 'finish_work', 'earn_money']
senticnet['work_home'] = ['-0.99', '0', '-0.83', '-0.80', '#sadness', '#fear', 'negative', '-0.87', 'finish_work', 'move_furniture', 'floss_tooth', 'quit_work', 'work_team']
senticnet['work_in_garden'] = ['-0.04', '0.15', '-0.06', '0.077', '#interest', '#admiration', 'positive', '0.028', 'green_thumb', 'prune', 'water', 'sow', 'rake']
senticnet['work_msw_degree'] = ['0.036', '0.111', '-0.05', '0.054', '#interest', '#admiration', 'positive', '0.038', 'despot', 'supervisor', 'chief', 'commander', 'earl']
senticnet['work_much'] = ['-0.24', '-0.07', '0', '-0.24', '#sadness', '#disgust', 'negative', '-0.14', 'stress', 'stressful_job', 'dig_ground', 'hate_boss', 'work_hard']
senticnet['work_office'] = ['0.962', '0.967', '0', '0', '#joy', '#interest', 'positive', '0.964', 'find_book_library', 'charge_credit_card', 'sell_car', 'price_goods', 'point_out']
senticnet['work_out'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'top_model', 'exercise', 'eat_properly', 'fitness', 'go_gym']
senticnet['work_overseas'] = ['0.071', '0', '0.031', '0.064', '#joy', '#admiration', 'positive', '0.035', 'far_from_home', 'work_abroad', 'live_abroad', 'expat', 'abroad']
senticnet['work_overtime'] = ['-0.25', '0', '-0.07', '-0.2', '#sadness', '#disgust', 'negative', '-0.17', 'become_fatigued', 'muscle_ache', 'stress', 'feel_exhausted', 'get_done']
senticnet['work_person'] = ['0', '0.092', '0', '0.096', '#interest', '#admiration', 'positive', '0.094', 'blue_collar', 'laborer', 'worker', 'occupation', 'employment']
senticnet['work_pile_up'] = ['0.894', '0.791', '0', '0', '#joy', '#interest', 'positive', '0.843', 'snowed_in', 'miss_work', 'restless', 'feel_little_tired', 'take_nap']
senticnet['work_restaurant'] = ['0.025', '0.084', '0', '0.062', '#interest', '#admiration', 'positive', '0.057', 'seat_customer', 'serve', 'chore', 'cook_breakfast', 'take_order']
senticnet['work_stoppage'] = ['-0.77', '0', '0.505', '-0.50', '#sadness', '#anger', 'negative', '-0.59', 'wildcat_strike', 'sympathy_strike', 'sympathetic_strike', 'walkout', 'job_action']
senticnet['work_surface'] = ['0.025', '0.084', '0', '0.062', '#interest', '#admiration', 'positive', '0.057', 'kitchen_worktop', 'worktop', 'surface', 'work', 'wood_worktop']
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senticnet['work_to_earn_money'] = ['0', '0.146', '0', '0', '#interest', '#interest', 'positive', '0.146', 'life-long_mate', 'volume_control', 'belly_button', 'money_wallet', 'two_ears']
senticnet['work_together'] = ['0.904', '0.862', '0', '0', '#joy', '#interest', 'positive', '0.883', 'masturbation', 'intercourse', 'fuck', 'masturbate', 'anarchy']
senticnet['work_up_sweat'] = ['0.744', '0.845', '0', '0', '#joy', '#interest', 'positive', '0.794', 'stiffness', 'sweaty', 'get_tired', 'cramp', 'knee_injury']
senticnet['work_well'] = ['0', '-0.00', '0', '0.069', '#surprise', '#admiration', 'positive', '0.039', 'transportation', 'car_part', 'car_engine', 'motor_vehicle', 'mode_transportation']
senticnet['workability'] = ['0.99', '-0.88', '0', '0.831', '#joy', '#surprise', 'positive', '0.9', 'functionality', 'sensibleness', 'viability', 'usefulness', 'realism']
senticnet['workable'] = ['0.782', '-0.81', '0', '0', '#joy', '#surprise', 'positive', '0.797', 'work', 'unworkable', 'workableness', 'workably', 'smith']
senticnet['workableness'] = ['0.778', '-0.81', '0', '0', '#joy', '#surprise', 'positive', '0.795', 'work', 'unworkable', 'workable', 'workably', 'smith']
senticnet['workably'] = ['0.782', '-0.81', '0', '0', '#joy', '#surprise', 'positive', '0.8', 'work', 'unworkable', 'workableness', 'workable', 'smith']
senticnet['workaday'] = ['0', '0', '-0.81', '-0.80', '#fear', '#disgust', 'negative', '-0.81', 'quotidian', 'unremarkable', 'routine', 'everyday', 'mundane']
senticnet['workaholic'] = ['0.785', '0.821', '0', '0', '#joy', '#interest', 'positive', '0.803', 'celebrate_anniversary', 'shed_tear', 'bring_happiness', 'have_fun', 'cat_purr']
senticnet['workaholism'] = ['0.681', '-0.93', '0', '0.78', '#surprise', '#admiration', 'positive', '0.799', 'behavior', 'state', 'play', 'agency', 'activeness']
senticnet['workaround'] = ['-0.95', '0', '0.721', '-0.75', '#sadness', '#disgust', 'negative', '-0.81', 'cook_up', 'ready', 'make', 'cook', 'prepare']
senticnet['workbench'] = ['0.868', '0.717', '0', '0', '#joy', '#interest', 'positive', '0.792', 'tool_shed', 'tool', 'garage', 'tool_belt', 'hardware_store']
senticnet['workbook'] = ['-0.03', '0.168', '-0.07', '0.139', '#interest', '#admiration', 'positive', '0.049', 'command', 'direct', 'follow', 'take_charge', 'govern']
senticnet['workday'] = ['0.1', '0.085', '0', '0.093', '#joy', '#admiration', 'positive', '0.093', 'watch', 'clock', 'hourglass', 'minute', 'stopwatch']
senticnet['worked_collaboratively'] = ['0.979', '0.93', '0', '0.723', '#joy', '#interest', 'positive', '0.877', 'information_transfer', 'tranfer', 'inform_person', 'inform_others', 'information']
senticnet['worker'] = ['0.03', '0.09', '-0.05', '0.062', '#interest', '#admiration', 'positive', '0.032', 'laborer', 'occupation', 'profession', 'work_person', 'career']
senticnet['worker_bee'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'lair', 'habitat', 'animal_home']
senticnet['worker_safety'] = ['0.082', '0.319', '-0.10', '0.147', '#interest', '#admiration', 'positive', '0.11', 'protect', 'defend', 'secure', 'guard', 'watch_over']
senticnet['worker_shortage'] = ['-0.83', '0', '-0.88', '-0.78', '#sadness', '#fear', 'negative', '-0.83', 'little', 'sadness', 'small_amount', 'lack', 'absence']
senticnet['worker_thread'] = ['-0.15', '0', '0.029', '-0.11', '#sadness', '#disgust', 'negative', '-0.1', 'start_over', 'front_line', 'find_line', 'take_turn', 'long_line']
senticnet['worketh'] = ['0.673', '-0.66', '0', '0.566', '#joy', '#surprise', 'positive', '0.634', 'wickedness', 'impiety', 'impiousness', 'unjustness', 'dishonor']
senticnet['workfighter'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'army_fighter', 'army_person', 'sailor', 'fighter', 'man']
senticnet['workflow'] = ['-0.03', '0.2', '-0.10', '0.298', '#interest', '#admiration', 'positive', '0.09', 'procedure', 'action', 'create_form', 'transform', 'posture']
senticnet['workforce'] = ['0.814', '-0.88', '0', '0.914', '#surprise', '#admiration', 'positive', '0.871', 'ship_company', 'company', 'work_force', 'manpower', 'full_complement']
senticnet['workforce_diversity'] = ['0.032', '0.11', '0', '0.105', '#interest', '#admiration', 'positive', '0.082', 'work', 'place_work', 'implementation', 'disorganize', 'employment']
senticnet['workfunction'] = ['0.965', '-0.84', '0', '0.599', '#joy', '#surprise', 'positive', '0.802', 'mental_faculty', 'faculty', 'module', 'remembrance', 'anamnesis']
senticnet['workgroup'] = ['0.065', '0.113', '0', '0.1', '#interest', '#admiration', 'positive', '0.093', 'big_group', 'clique', 'group_of_people', 'multitude', 'large_group']
senticnet['workhorse'] = ['0.085', '0.218', '-0.07', '0.308', '#interest', '#admiration', 'positive', '0.135', 'delicate', 'useful', 'good', 'light_colored', 'beautiful']
senticnet['workhouse'] = ['0.051', '0.126', '-0.07', '0.164', '#interest', '#admiration', 'positive', '0.067', 'edifice', 'palace', 'high_rise', 'current', 'bell_tower']
senticnet['working-class'] = ['-0.85', '0', '-0.9', '0', '#sadness', '#fear', 'negative', '-0.87', 'disenfranchised', 'impoverished', 'ghetto', 'under-class', 'underclass']
senticnet['working_closely'] = ['0.692', '-0.99', '0', '0.614', '#joy', '#surprise', 'positive', '0.766', 'intimacy', 'affaire', 'sexual_relationship', 'affair', 'involvement']
senticnet['working_collaboratively'] = ['0.979', '0.93', '0', '0.723', '#joy', '#interest', 'positive', '0.877', 'information_transfer', 'tranfer', 'inform_person', 'inform_others', 'information']
senticnet['working_girl'] = ['-0.97', '0', '0.8', '-0.84', '#sadness', '#disgust', 'negative', '-0.87', 'hooker', 'prostitute', 'bad_girl', 'mistress', 'pay_for_sex']
senticnet['working_holidaymaker'] = ['0.875', '0', '0', '0.802', '#joy', '#admiration', 'positive', '0.839', 'foreigner', 'exile', 'deportee', 'gringo', 'import']
senticnet['working_together'] = ['0.979', '0.93', '0', '0.723', '#joy', '#interest', 'positive', '0.877', 'information_transfer', 'tranfer', 'inform_person', 'inform_others', 'information']
senticnet['working_undercover'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'yeoman', 'outrider', 'yeoman_of_guard', 'beefeater', 'defender']
senticnet['workingman'] = ['-0.67', '0', '0.85', '-0.59', '#sadness', '#anger', 'negative', '-0.70', 'rack', 'boot', 'whip', 'iron_boot', 'instrument_of_torture']
senticnet['worklife'] = ['0.032', '0.11', '0', '0.105', '#interest', '#admiration', 'positive', '0.082', 'work', 'place_work', 'implementation', 'disorganize', 'employment']
senticnet['workload'] = ['-0.03', '0.136', '-0.09', '0.105', '#interest', '#admiration', 'positive', '0.028', 'chore', 'hire_help', 'police_work', 'career', 'job']
senticnet['workman'] = ['0', '0', '0', '-0.07', '#disgust', '#disgust', 'negative', '-0.07', 'army_man', 'army_person', 'sailor', 'man', 'fighter']
senticnet['workmanlike'] = ['0.98', '-0.67', '0', '0.676', '#joy', '#admiration', 'positive', '0.778', 'effective', 'competent', 'efficient', 'able', 'capable']
senticnet['workmanship'] = ['0.876', '-0.76', '0', '0.963', '#joy', '#admiration', 'positive', '0.867', 'housecraft', 'tradecraft', 'watercraft', 'acquirement', 'skill']
senticnet['workout'] = ['-0.06', '0', '-0.05', '0', '#sadness', '#fear', 'negative', '-0.06', 'go_gym', 'join_gym', 'check_doctor', 'lift_weight', 'eat_balance_diet']
senticnet['workout_routine'] = ['-0.04', '-0.04', '0.056', '-0.1', '#anger', '#disgust', 'negative', '-0.03', 'exercise', 'exercise_regularly', 'need_fresh_air', 'car_repair', 'strong_muscle']
senticnet['workpackage'] = ['0', '0.097', '0', '0.092', '#interest', '#admiration', 'positive', '0.095', 'urgent', 'classify_ad', 'important', 'review', 'repetition']
senticnet['workpiece'] = ['0.022', '0.069', '0.027', '0.059', '#interest', '#admiration', 'positive', '0.031', 'perfect_surface', 'plane', 'flat_land', 'flat', 'floor']
senticnet['workplace'] = ['0.032', '0.11', '0', '0.105', '#interest', '#admiration', 'positive', '0.082', 'work', 'place_work', 'implementation', 'disorganize', 'employment']
senticnet['workplace_discrimination'] = ['0.032', '0.11', '0', '0.105', '#interest', '#admiration', 'positive', '0.082', 'work', 'place_work', 'implementation', 'disorganize', 'employment']
senticnet['workplace_diversity'] = ['0.032', '0.11', '0', '0.105', '#interest', '#admiration', 'positive', '0.082', 'work', 'place_work', 'implementation', 'disorganize', 'employment']
senticnet['workplace_fatality'] = ['-0.71', '0', '0.748', '-0.55', '#sadness', '#anger', 'negative', '-0.67', 'casualty', 'fatal_accident', 'killing', 'violent_death', 'death']
senticnet['workplace_flexibility'] = ['0.032', '0.11', '0', '0.105', '#interest', '#admiration', 'positive', '0.082', 'work', 'place_work', 'implementation', 'disorganize', 'employment']
senticnet['workplace_harassment'] = ['0.032', '0.11', '0', '0.105', '#interest', '#admiration', 'positive', '0.082', 'work', 'place_work', 'implementation', 'disorganize', 'employment']
senticnet['workplace_literacy'] = ['0.032', '0.11', '0', '0.105', '#interest', '#admiration', 'positive', '0.082', 'work', 'place_work', 'implementation', 'disorganize', 'employment']
senticnet['workplace_safety'] = ['0.032', '0.11', '0', '0.105', '#interest', '#admiration', 'positive', '0.082', 'work', 'place_work', 'implementation', 'disorganize', 'employment']
senticnet['workplace_violence'] = ['0.032', '0.11', '0', '0.105', '#interest', '#admiration', 'positive', '0.082', 'work', 'place_work', 'implementation', 'disorganize', 'employment']
senticnet['workplace_violence_prevention'] = ['0.032', '0.11', '0', '0.105', '#interest', '#admiration', 'positive', '0.082', 'work', 'place_work', 'implementation', 'disorganize', 'employment']
senticnet['workplan'] = ['0', '0.097', '0', '0.092', '#interest', '#admiration', 'positive', '0.095', 'urgent', 'classify_ad', 'important', 'review', 'repetition']
senticnet['workrate'] = ['0.608', '-0.55', '0', '0.726', '#joy', '#admiration', 'positive', '0.628', 'genius', 'endowment', 'flair', 'gift', 'knack']
senticnet['works_closely'] = ['0.735', '0.759', '0', '0.706', '#joy', '#interest', 'positive', '0.733', 'permission', 'let', 'take', 'make_right', 'go_ahead']
senticnet['workshop'] = ['0', '0.94', '0', '0.903', '#interest', '#admiration', 'positive', '0.921', 'academe', 'industry', 'research', 'career', 'academia']
senticnet['worksite'] = ['0.032', '0.11', '0', '0.105', '#interest', '#admiration', 'positive', '0.082', 'work', 'place_work', 'implementation', 'disorganize', 'employment']
senticnet['workstation'] = ['0', '0.754', '0', '0.855', '#interest', '#admiration', 'positive', '0.804', 'different_application', 'network', 'project', 'student', 'standard_grant']
senticnet['worktable'] = ['0', '0.068', '0', '0', '#interest', '#interest', 'positive', '0.068', 'artist', 'oil_paint', 'painter', 'creative_person', 'make_paint']
senticnet['worktop'] = ['0.023', '0.081', '0', '0.059', '#interest', '#admiration', 'positive', '0.054', 'kitchen_worktop', 'wood_worktop', 'surface', 'work', 'work_surface']
senticnet['workwear'] = ['-0.76', '0', '0.58', '-0.50', '#sadness', '#anger', 'negative', '-0.61', 'attire', 'garb', 'dress', 'gym_suit', 'running_suit']
senticnet['workweek'] = ['0.762', '-0.61', '0', '0.745', '#joy', '#admiration', 'positive', '0.708', 'day', 'period', 'hour', 'face_time', 'work_time']
senticnet['world'] = ['0.838', '0.707', '0', '0.904', '#joy', '#admiration', 'positive', '0.816', 'natural_order', 'universe', 'existence', 'creation', 'cosmos']
senticnet['world-altering'] = ['0.872', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.917', 'world-shaking', 'life-shattering', 'earth-shaking', 'world-shattering', 'earthshaking']
senticnet['world-class'] = ['0', '0.857', '0', '0.711', '#interest', '#admiration', 'positive', '0.784', 'world-renowned', 'top-notch', 'renowned', 'state-of-the-art', 'unparalleled']
senticnet['world-renowned'] = ['0', '0.852', '0', '0.705', '#interest', '#admiration', 'positive', '0.778', 'world-class', 'top-notch', 'renowned', 'state-of-the-art', 'unparalleled']
senticnet['world-shaking'] = ['0.877', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.92', 'world-shattering', 'life-shattering', 'earth-shaking', 'world-altering', 'earthshaking']
senticnet['world-shattering'] = ['0.88', '-0.96', '0', '0', '#joy', '#surprise', 'positive', '0.92', 'world-shaking', 'life-shattering', 'earth-shaking', 'world-altering', 'earthshaking']
senticnet['world-weariness'] = ['-0.77', '0.27', '-0.45', '-0.04', '#sadness', '#fear', 'negative', '-0.25', 'world-weary', 'bored', 'weltschmerz', 'depressed', 'downcast']
senticnet['world-weary'] = ['-0.77', '0.27', '-0.45', '-0.04', '#sadness', '#fear', 'negative', '-0.25', 'bored', 'world-weariness', 'weltschmerz', 'depressed', 'downcast']
senticnet['world_archery_federation'] = ['0', '-0.07', '0.091', '-0.19', '#anger', '#disgust', 'negative', '-0.07', 'projectile', 'use_hunt', 'weapon', 'ammunition', 'firearm']
senticnet['world_champion'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'nazi', 'man', 'pervert', 'healthy_person']
senticnet['world_championship'] = ['0', '0', '0', '0.834', '#admiration', '#admiration', 'positive', '0.834', 'royal_person', 'royal_title', 'top', 'master', 'rank']
senticnet['world_colored_middleweight'] = ['0', '0', '0', '0.834', '#admiration', '#admiration', 'positive', '0.834', 'royal_person', 'royal_title', 'top', 'master', 'rank']
senticnet['world_domination'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'dictatorship', 'dictator', 'colony', 'world_war', 'oppression']
senticnet['world_event'] = ['0.915', '0', '0.724', '0.952', '#joy', '#admiration', 'positive', '0.381', 'influence_worldwide', 'celebrate', 'olympic_game', 'international_affair', 'magnificent']
senticnet['world_featherweight_champion'] = ['0', '0', '0', '0.834', '#admiration', '#admiration', 'positive', '0.834', 'royal_person', 'royal_title', 'top', 'master', 'rank']
senticnet['world_greatest'] = ['0.522', '-0.88', '0', '0.965', '#surprise', '#admiration', 'positive', '0.789', 'overage', 'asset', 'security', 'business', 'hole_card']
senticnet['world_hunger'] = ['-0.07', '-0.06', '0.013', '-0.14', '#sadness', '#disgust', 'negative', '-0.04', 'weight_gain', 'clog_artery', 'salivate', 'consume', 'taste_flavor']
senticnet['world_largest'] = ['0.522', '-0.88', '0', '0.965', '#surprise', '#admiration', 'positive', '0.789', 'overage', 'asset', 'security', 'business', 'hole_card']
senticnet['world_largest_producer'] = ['0', '0.164', '-0.08', '0.215', '#interest', '#admiration', 'positive', '0.098', 'manufacture', 'factory_output', 'creation', 'assembly_line', 'make']
senticnet['world_largest_retailer'] = ['0.912', '-0.92', '0', '0', '#joy', '#surprise', 'positive', '0.917', 'carpet_store', 'lumberyard', 'furniture_store', 'front_porch', 'shoe_shop']
senticnet['world_light_heavyweight'] = ['0', '0', '0', '0.834', '#admiration', '#admiration', 'positive', '0.834', 'royal_person', 'royal_title', 'top', 'master', 'rank']
senticnet['world_middleweight_title'] = ['0.85', '-0.89', '0', '0.634', '#joy', '#surprise', 'positive', '0.793', 'rival', 'competition', 'comer', 'competitor', 'contender']
senticnet['world_peace'] = ['0.838', '0.707', '0', '0.904', '#joy', '#admiration', 'positive', '0.816', 'love_parent', 'strong_relationship', 'run_business', 'complement', 'good_fortune']
senticnet['world_pool_billiard'] = ['-0.68', '0', '0.923', '-0.68', '#sadness', '#anger', 'negative', '-0.76', 'negative', 'veto', 'refute', 'rebut', 'controvert']
senticnet['world_record_holder'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'nazi', 'man', 'pervert', 'healthy_person']
senticnet['world_religion'] = ['0', '0.29', '0', '0.263', '#interest', '#admiration', 'positive', '0.276', 'religion', 'prayer', 'philosophy', 'religious_purpose', 'gospel']
senticnet['world_silver_medallist'] = ['0.052', '0', '0', '0.069', '#joy', '#admiration', 'positive', '0.06', 'philanthropist', 'nazi', 'man', 'pervert', 'healthy_person']
senticnet['world_snooker'] = ['-0.66', '0', '0.771', '-0.95', '#anger', '#disgust', 'negative', '-0.79', 'delude', 'deceive', 'cozen', 'play_trick_on', 'lead_on']
senticnet['world_snooker_champion'] = ['-0.66', '0', '0.771', '-0.95', '#anger', '#disgust', 'negative', '-0.79', 'delude', 'deceive', 'cozen', 'play_trick_on', 'lead_on']
senticnet['world_snooker_championship'] = ['-0.66', '0', '0.771', '-0.95', '#anger', '#disgust', 'negative', '-0.79', 'delude', 'deceive', 'cozen', 'play_trick_on', 'lead_on']
senticnet['world_war'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'dictatorship', 'dictator', 'colony', 'world_domination', 'oppression']
senticnet['world_warii'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'dictatorship', 'dictator', 'colony', 'world_domination', 'oppression']
senticnet['world_wide'] = ['0.021', '0.067', '0', '0', '#joy', '#interest', 'positive', '0.044', 'fish', 'urchin', 'bait', 'land_fish', 'mussel']
senticnet['world_wide_web'] = ['0.955', '0.712', '0', '0', '#joy', '#interest', 'positive', '0.833', 'computer_network', 'web', 'www', 'project', 'student']
senticnet['worldand'] = ['0.838', '0.707', '0', '0.904', '#joy', '#admiration', 'positive', '0.816', 'natural_order', 'universe', 'existence', 'creation', 'cosmos']
senticnet['worldly'] = ['0.913', '-0.63', '0', '0.961', '#joy', '#admiration', 'positive', '0.836', 'materialistic', 'mercenary', 'mundane', 'material', 'economic']
senticnet['worldly_existence'] = ['-0.73', '0', '0.732', '-0.53', '#sadness', '#anger', 'negative', '-0.66', 'insurmountable', 'insuperable', 'unconquerable', 'indomitable', 'unsubduable']
senticnet['worldly_wisdom'] = ['0.889', '-0.69', '0', '0.908', '#joy', '#admiration', 'positive', '0.83', 'pharisaic', 'pious', 'sanctimonious', 'reverent', 'worshipful']
senticnet['worldly_wise'] = ['-0.94', '0', '0.779', '-0.85', '#sadness', '#disgust', 'negative', '-0.86', 'eremitic', 'eremitical', 'cloistered', 'anchoritic', 'hermitic']
senticnet['worldthe'] = ['0.838', '0.707', '0', '0.904', '#joy', '#admiration', 'positive', '0.816', 'natural_order', 'universe', 'existence', 'creation', 'cosmos']
senticnet['worldview'] = ['0', '0', '0', '0.986', '#admiration', '#admiration', 'positive', '0.986', 'idea', 'decision', 'consider', 'polarize', 'make_right_decision']
senticnet['worldwide'] = ['0.838', '0.707', '0', '0.904', '#joy', '#admiration', 'positive', '0.816', 'natural_order', 'universe', 'existence', 'creation', 'cosmos']
senticnet['worldwide_acclaim'] = ['0.853', '0.828', '0', '0.98', '#joy', '#admiration', 'positive', '0.887', 'endorse', 'recommend', 'stimulate_mind', 'acculturation', 'support']
senticnet['worldwide_condemnation'] = ['-0.16', '0.179', '0.568', '-0.61', '#anger', '#disgust', 'negative', '-0.29', 'angrily', 'choler', 'see_red', 'dander', 'hackle']
senticnet['worm'] = ['0', '-0.00', '0.038', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'earthworm', 'compost', 'fallen_leaf', 'detritus', 'mammoth_ice']
senticnet['worm_gear'] = ['0', '0.142', '0', '0.125', '#interest', '#admiration', 'positive', '0.134', 'weight_machine', 'power_drill', 'screw', 'screwdriver', 'air_pump']
senticnet['worm_intestine'] = ['-0.08', '-0.06', '0.028', '-0.12', '#sadness', '#disgust', 'negative', '-0.04', 'slender', 'fur_keep_warm', 'two_ears', 'two_eyes', 'claw']
senticnet['worm_larva'] = ['-0.83', '0', '-0.82', '-0.86', '#sadness', '#disgust', 'negative', '-0.84', 'cause_cold', 'tourism', 'reproduce', 'gland', 'procreate']
senticnet['worm_lizard'] = ['-0.03', '-0.01', '-0.05', '-0.12', '#fear', '#disgust', 'negative', '-0.04', 'shit', 'wildflower', 'poop', 'wild_flower', 'dog_poop']
senticnet['worm_snake'] = ['-0.57', '0', '0.571', '-0.97', '#sadness', '#disgust', 'negative', '-0.70', 'viper', 'elapid', 'elapid_snake', 'ophidian', 'blind_snake']
senticnet['wormlike'] = ['0.674', '-0.94', '0', '0.655', '#joy', '#surprise', 'positive', '0.759', 'bowing', 'submissive', 'groveling', 'sub', 'cringing']
senticnet['wormtail'] = ['0.105', '-0.17', '0.057', '0.079', '#joy', '#surprise', 'positive', '0.076', 'cue', 'twig', 'long_stick', 'stick', 'adhere']
senticnet['wormwood'] = ['-0.66', '0', '0.811', '-0.78', '#anger', '#disgust', 'negative', '-0.75', 'subshrub', 'suffrutex', 'old_woman', 'mugwort', 'southernwood']
senticnet['worn-down'] = ['-0.85', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.80', 'well-worn', 'ill-fitting', 'worn-out', 'ratty', 'beat-up']
senticnet['worn-out'] = ['-0.84', '0', '0', '-0.75', '#sadness', '#disgust', 'negative', '-0.80', 'well-worn', 'ill-fitting', 'worn-down', 'ratty', 'beat-up']
senticnet['worried'] = ['-0.95', '0', '-0.78', '0', '#sadness', '#fear', 'negative', '-0.87', 'uneasily', 'apprehensively', 'apprehensive', 'anxiously', 'solicitousness']
senticnet['worriedly'] = ['0', '0', '-0.74', '-0.77', '#fear', '#disgust', 'negative', '-0.75', 'furtively', 'distractedly', 'disconsolately', 'interestedly', 'shiftily']
senticnet['worrier'] = ['-0.82', '0', '0.556', '-0.86', '#sadness', '#disgust', 'negative', '-0.75', 'killjoy', 'fusspot', 'worrywart', 'unwelcome_person', 'party_pooper']
senticnet['worriment'] = ['-0.80', '0', '0.605', '-0.76', '#sadness', '#disgust', 'negative', '-0.72', 'difficulty', 'awkwardness', 'flea_bite', 'difficultness', 'cumbersomeness']
senticnet['worrisome'] = ['0.953', '0.831', '0', '0', '#joy', '#interest', 'positive', '0.892', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['worry'] = ['-0.87', '0', '0.936', '-0.98', '#anger', '#disgust', 'negative', '-0.93', 'nervousness', 'shame', 'embarrassment', 'panic', 'upset']
senticnet['worry-free'] = ['-0.72', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.76', 'hassle-free', 'trouble-free', 'problem-free', 'stress-free', 'effortless']
senticnet['worry_about'] = ['-0.99', '0', '0', '-0.88', '#sadness', '#disgust', 'negative', '-0.93', 'dispose', 'ditch', 'exchange_goods', 'discard', 'delete']
senticnet['worry_bout'] = ['0.591', '-0.76', '0', '0.66', '#surprise', '#admiration', 'positive', '0.673', 'darling', 'pet', 'favorite', 'first_choice', 'macushla']
senticnet['worrying'] = ['0.806', '0.866', '0', '0', '#joy', '#interest', 'positive', '0.836', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['worrying_sign'] = ['0.706', '0.945', '0', '0', '#joy', '#interest', 'positive', '0.825', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['worrying_trend'] = ['0.706', '0.945', '0', '0', '#joy', '#interest', 'positive', '0.825', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['worryingly'] = ['0.706', '0.945', '0', '0', '#joy', '#interest', 'positive', '0.825', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['worryingly_high'] = ['0.706', '0.945', '0', '0', '#joy', '#interest', 'positive', '0.825', 'disquieted', 'distressing', 'disturbing', 'perturbing', 'troubling']
senticnet['worrywart'] = ['-0.82', '0', '0.556', '-0.86', '#sadness', '#disgust', 'negative', '-0.75', 'worrier', 'fusspot', 'killjoy', 'unwelcome_person', 'party_pooper']
senticnet['worse'] = ['-0.08', '0', '-0.09', '-0.05', '#sadness', '#fear', 'negative', '-0.07', 'hard_find', 'bad_quality', 'bad', 'precise', 'awful']
senticnet['worse_economic_condition'] = ['-0.97', '0', '0.936', '-0.98', '#sadness', '#disgust', 'negative', '-0.96', 'worse', 'minus_sign', 'negative', 'worse_health_condition', 'get_worse']
senticnet['worse_health_condition'] = ['-0.97', '0', '0.936', '-0.98', '#sadness', '#disgust', 'negative', '-0.96', 'worse', 'minus_sign', 'negative', 'get_worse', 'worse_economic_condition']
senticnet['worsen'] = ['-0.74', '0', '0.749', '-0.82', '#anger', '#disgust', 'negative', '-0.77', 'inflame', 'drop_away', 'lose', 'fall_away', 'deteriorate']
senticnet['worsening'] = ['-0.69', '0', '0.941', '-0.50', '#sadness', '#anger', 'negative', '-0.71', 'decline', 'slack', 'slump', 'falloff', 'falling_off']
senticnet['worsening_drought'] = ['-0.98', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.89', 'dry', 'lose_team', 'leave_out', 'disrespect', 'cause_suffer']
senticnet['worship'] = ['0.265', '0.601', '-0.20', '0.373', '#interest', '#admiration', 'positive', '0.258', 'hope', 'religious_purpose', 'trust', 'devotion', 'religious']
senticnet['worship_girl'] = ['0.193', '0.298', '-0.18', '0.369', '#interest', '#admiration', 'positive', '0.168', 'respect_woman', 'feel_love', 'care_deeply', 'find_woman', 'pregnant']
senticnet['worship_god'] = ['0.817', '0', '0', '0.839', '#joy', '#admiration', 'positive', '0.828', 'religious_purpose', 'belief_god', 'prayer', 'pray_god', 'religious']
senticnet['worship_house'] = ['0.798', '0.975', '0', '0.755', '#joy', '#interest', 'positive', '0.843', 'church_part', 'worship_place', 'religious_build', 'holy_place', 'place_of_worship']
senticnet['worship_place'] = ['0', '0.235', '0.027', '0.247', '#interest', '#admiration', 'positive', '0.152', 'worship_house', 'church_part', 'religious_build', 'holy_place', 'place_of_worship']
senticnet['worshiper'] = ['0.037', '0.243', '0', '0.207', '#interest', '#admiration', 'positive', '0.162', 'believer', 'cleric', 'vicar', 'christian', 'fundamentalist']
senticnet['worshipful'] = ['0.524', '0.681', '-0.27', '0.281', '#joy', '#interest', 'positive', '0.302', 'adoring', 'devotedness', 'devoted', 'amorousness', 'amorous']
senticnet['worshipful_company'] = ['0.967', '-0.98', '0', '0.64', '#joy', '#surprise', 'positive', '0.863', 'club', 'social_club', 'society', 'jockey_club', 'gild']
senticnet['worshipper'] = ['0.065', '0.17', '-0.05', '0.143', '#interest', '#admiration', 'positive', '0.081', 'supervisor', 'native_american', 'ambassador', 'master', 'general']
senticnet['worst'] = ['-0.12', '-0.08', '-0.05', '-0.26', '#sadness', '#disgust', 'negative', '-0.08', 'car_race', 'mortuary', 'dry_cleaner', 'loose_end', 'death_row']
senticnet['worst_downturn'] = ['0', '0.078', '0', '0.096', '#interest', '#admiration', 'positive', '0.087', 'ancient_rome', 'ancient', 'age', 'ancient_artifact', 'century']
senticnet['worst_drought'] = ['-0.98', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.89', 'dry', 'lose_team', 'leave_out', 'disrespect', 'cause_suffer']
senticnet['worst_housing_slump'] = ['0', '0.078', '0', '0.096', '#interest', '#admiration', 'positive', '0.087', 'ancient_rome', 'ancient', 'age', 'ancient_artifact', 'century']
senticnet['worst_losing_streak'] = ['0.033', '0.158', '-0.06', '0.145', '#interest', '#admiration', 'positive', '0.067', 'finish', 'end', 'finish_complete', 'end_point', 'finalize']
senticnet['worst_nightmare'] = ['-0.08', '0', '-0.04', '-0.15', '#sadness', '#disgust', 'negative', '-0.09', 'unsettle', 'sleepwalk', 'hallucinate', 'get_rest', 'reflect']
senticnet['worst_traffic_congestion'] = ['-0.56', '0', '0.655', '-0.85', '#anger', '#disgust', 'negative', '-0.69', 'slovenly_woman', 'slut', 'trollop', 'slattern', 'slob']
senticnet['worsted_yarn'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_cloth']
senticnet['worth'] = ['0', '0.249', '0.012', '0.154', '#interest', '#admiration', 'positive', '0.13', 'utility', 'money_value', 'monetary_value', 'money_amount', 'sticker']
senticnet['worthier'] = ['0.586', '-0.96', '0', '0.9', '#surprise', '#admiration', 'positive', '0.818', 'kick_upstairs', 'gentle', 'prefer', 'spot_promote', 'favor']
senticnet['worthiest'] = ['0.586', '-0.96', '0', '0.9', '#surprise', '#admiration', 'positive', '0.818', 'kick_upstairs', 'gentle', 'prefer', 'spot_promote', 'favor']
senticnet['worthily'] = ['0.693', '-0.69', '0', '0.714', '#surprise', '#admiration', 'positive', '0.702', 'vow', 'dedicate', 'apply', 'rededicate', 'use']
senticnet['worthiness'] = ['0.76', '-0.95', '0', '0.506', '#joy', '#surprise', 'positive', '0.741', 'goodness', 'praiseworthiness', 'laudability', 'laudableness', 'quotability']
senticnet['worthington_pump'] = ['0.966', '0.799', '0', '0', '#joy', '#interest', 'positive', '0.883', 'mechanical_device', 'move_part', 'machine', 'work_well', 'make_car']
senticnet['worthless'] = ['-0.78', '0', '0.651', '-0.56', '#sadness', '#anger', 'negative', '-0.66', 'rubbishy', 'sorry', 'trashy', 'otiose', 'superfluous']
senticnet['worthless_piece'] = ['-0.58', '0', '0.714', '-0.61', '#anger', '#disgust', 'negative', '-0.63', 'diversionist', 'sleeper', 'destroyer', 'ruiner', 'undoer']
senticnet['worthlessness'] = ['-0.77', '0', '0.599', '-0.52', '#sadness', '#anger', 'negative', '-0.63', 'vanity', 'trashiness', 'damn', 'shoddiness', 'shit']
senticnet['worthwhile'] = ['0', '0.249', '0.012', '0.154', '#interest', '#admiration', 'positive', '0.13', 'utility', 'money_value', 'monetary_value', 'money_amount', 'sticker']
senticnet['worthwhile_investment'] = ['0.808', '-0.83', '0', '0.548', '#joy', '#surprise', 'positive', '0.73', 'successfulness', 'luckiness', 'prosperity', 'circumstance', 'blessing']
senticnet['worthy'] = ['0.864', '-0.53', '0', '0.522', '#joy', '#admiration', 'positive', '0.639', 'bailable', 'desirable', 'suitable', 'eligible', 'entitled']
senticnet['worthy_foe'] = ['-0.09', '0', '-0.05', '-0.24', '#sadness', '#disgust', 'negative', '-0.13', 'war', 'enemy', 'arsenal', 'big_fight', 'conflict']
senticnet['worthy_gentleman'] = ['-0.69', '0', '0.883', '-0.91', '#anger', '#disgust', 'negative', '-0.82', 'stick_in', 'enclose', 'insert', 'feed_in', 'put_in']
senticnet['worthy_opponent'] = ['0.833', '-0.97', '0', '0.91', '#surprise', '#admiration', 'positive', '0.906', 'combatant', 'fencer', 'belligerent', 'fighter', 'scrapper']
senticnet['wouldst'] = ['0.887', '-0.84', '0', '0.819', '#joy', '#surprise', 'positive', '0.851', 'arouse', 'elicit', 'enkindle', 'kindle', 'evoke']
senticnet['wound'] = ['-0.89', '0', '-0.92', '-0.81', '#sadness', '#fear', 'negative', '-0.87', 'gunshot', 'damage', 'vaccination', 'harm', 'fire_bullet']
senticnet['wound_infection'] = ['-0.53', '0', '0.671', '-0.55', '#anger', '#disgust', 'negative', '-0.58', 'dirty', 'putrefactive', 'putrefacient', 'abscessed', 'contaminative']
senticnet['wounded'] = ['-0.64', '0', '0.497', '-0.73', '#sadness', '#disgust', 'negative', '-0.62', 'eviscerate', 'mangled', 'torn', 'hurt', 'injured']
senticnet['wounded_pride'] = ['-0.64', '0', '0.941', '-0.92', '#anger', '#disgust', 'negative', '-0.83', 'unction', 'carron_oil', 'ointment', 'unguent', 'balm']
senticnet['woundwort'] = ['-0.66', '0', '0.859', '-0.82', '#anger', '#disgust', 'negative', '-0.78', 'pain_in_ass', 'pain', 'pain_in_neck', 'annoyance', 'bother']
senticnet['woven'] = ['0', '-0.04', '0.064', '-0.09', '#anger', '#disgust', 'negative', '-0.04', 'cut_fabric', 'cover_wind', 'braid', 'use_needle', 'seam']
senticnet['woven_bamboo'] = ['0', '0.773', '0', '0.895', '#interest', '#admiration', 'positive', '0.834', 'wooden_board', 'make_wood', 'wooden_box', 'wooden_floor', 'long_wood']
senticnet['woven_cloth'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_fabric']
senticnet['woven_cotton'] = ['0.028', '0.066', '0', '0', '#joy', '#interest', 'positive', '0.047', 'handkerchief', 'kerchief', 'hanky', 'facial_tissue', 'zipper']
senticnet['woven_fabric'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_cloth']
senticnet['woven_into'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'white_cotton', 'sheepskin_material', 'woven_sheepskin', 'sheepskin', 'clothes_type']
senticnet['woven_sheepskin'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'weave_cloth', 'sheepskin_material', 'woven_fabric', 'embroidery', 'cowhide']
senticnet['woven_silk'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_fabric']
senticnet['woven_wool'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_fabric']
senticnet['wow'] = ['0', '-0.75', '0', '0.934', '#surprise', '#admiration', 'positive', '0.846', 'suddenly', 'unexpected', 'sudden', 'type_of_party', 'special']
senticnet['wp_ae'] = ['0.533', '0.818', '0', '0.683', '#interest', '#admiration', 'positive', '0.678', 'gain_information', 'report_news', 'solve', 'seek_answer', 'gather_information']
senticnet['wp_boomerang'] = ['0.533', '0.818', '0', '0.683', '#interest', '#admiration', 'positive', '0.678', 'gain_information', 'report_news', 'solve', 'seek_answer', 'gather_information']
senticnet['wp_dick'] = ['-0.69', '0', '0.792', '-0.98', '#anger', '#disgust', 'negative', '-0.82', 'outcry', 'call', 'cry', 'yell', 'shout']
senticnet['wp_vand'] = ['0.792', '-0.86', '0', '0.888', '#surprise', '#admiration', 'positive', '0.849', 'absurd', 'inconsequential', 'intuitive', 'visceral', 'nonrational']
senticnet['wpbsa'] = ['-0.66', '0', '0.771', '-0.95', '#anger', '#disgust', 'negative', '-0.79', 'delude', 'deceive', 'cozen', 'play_trick_on', 'lead_on']
senticnet['wpra'] = ['0.938', '0.779', '0', '0', '#joy', '#interest', 'positive', '0.859', 'carnival', 'circus', 'fairground', 'fair', 'disneyland']
senticnet['wpw_syndrome'] = ['-0.83', '0', '0.987', '-0.50', '#sadness', '#anger', 'negative', '-0.77', 'heart_block', 'atrioventricular_block', 'bradycardia', 'ventricular_fibrillation', 'tachycardia']
senticnet['wraith'] = ['0', '0.076', '0.024', '0.137', '#interest', '#admiration', 'positive', '0.063', 'kennel', 'hive', 'animal_home', 'habitat', 'beehive']
senticnet['wraithlike'] = ['-0.96', '0', '0.939', '-0.60', '#sadness', '#anger', 'negative', '-0.83', 'airy', 'shadowy', 'stringy', 'insubstantial', 'ethereal']
senticnet['wrangle'] = ['-0.67', '0', '0.838', '-0.73', '#anger', '#disgust', 'negative', '-0.75', 'altercation', 'fracas', 'difference', 'affray', 'bickering']
senticnet['wrangle_over'] = ['-0.67', '0', '0.838', '-0.73', '#anger', '#disgust', 'negative', '-0.75', 'altercation', 'fracas', 'difference', 'affray', 'bickering']
senticnet['wrangles_over'] = ['-0.67', '0', '0.838', '-0.73', '#anger', '#disgust', 'negative', '-0.75', 'altercation', 'fracas', 'difference', 'affray', 'bickering']
senticnet['wrangling_over'] = ['-0.67', '0', '0.838', '-0.73', '#anger', '#disgust', 'negative', '-0.75', 'altercation', 'fracas', 'difference', 'affray', 'bickering']
senticnet['wrap'] = ['0.741', '0.993', '0', '0', '#joy', '#interest', 'positive', '0.867', 'give', 'gift_wrap', 'give_away', 'donate', 'wrap_gift']
senticnet['wrap_around'] = ['0.067', '0.088', '-0.07', '0.246', '#interest', '#admiration', 'positive', '0.081', 'multiple_purposes', 'revolve', 'start_run', 'wiggle', 'twist']
senticnet['wrap_gift'] = ['0.915', '0', '0.724', '0.952', '#joy', '#admiration', 'positive', '0.381', 'festival', 'give_present', 'birthday', 'celebrate_event', 'wrap_present']
senticnet['wrap_present'] = ['0', '0.996', '0', '0.82', '#interest', '#admiration', 'positive', '0.908', 'drive_home', 'gift_wrap', 'wrap_gift', 'carry_home', 'use_credit_card']
senticnet['wrap_tightly'] = ['0.716', '-0.79', '0', '0.543', '#joy', '#surprise', 'positive', '0.683', 'chaff', 'wrap', 'aluminium_foil', 'tin_foil', 'gold_foil']
senticnet['wrap_up'] = ['0', '0.106', '-0.08', '0.115', '#interest', '#admiration', 'positive', '0.045', 'donate', 'gift_wrap', 'donate_charity', 'out_money', 'subscribe_magazine']
senticnet['wraparound'] = ['0.067', '0.088', '-0.07', '0.246', '#interest', '#admiration', 'positive', '0.081', 'multiple_purposes', 'revolve', 'start_run', 'wiggle', 'twist']
senticnet['wrapped_around'] = ['0.067', '0.088', '-0.07', '0.246', '#interest', '#admiration', 'positive', '0.081', 'multiple_purposes', 'revolve', 'start_run', 'wiggle', 'twist']
senticnet['wrapped_tightly'] = ['0', '0', '0', '-0.17', '#disgust', '#disgust', 'negative', '-0.17', 'medicine', 'aspirin', 'blank_check', 'painkiller', 'video_tape']
senticnet['wrapped_up'] = ['0.906', '0.957', '0', '0', '#joy', '#interest', 'positive', '0.931', 'ended', 'completed', 'finish_job', 'complete_job', 'complete_task']
senticnet['wrapper'] = ['-0.95', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.84', 'hold_pencil', 'food_scrap', 'paper_product', 'waste_paper', 'cover']
senticnet['wrapper_around'] = ['-0.95', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.84', 'hold_pencil', 'food_scrap', 'paper_product', 'waste_paper', 'cover']
senticnet['wrappers_around'] = ['-0.95', '0', '0', '-0.73', '#sadness', '#disgust', 'negative', '-0.84', 'hold_pencil', 'food_scrap', 'paper_product', 'waste_paper', 'cover']
senticnet['wrapping_around'] = ['0.067', '0.088', '-0.07', '0.246', '#interest', '#admiration', 'positive', '0.081', 'multiple_purposes', 'revolve', 'start_run', 'wiggle', 'twist']
senticnet['wrapping_paper'] = ['-0.74', '0', '0.907', '-0.86', '#anger', '#disgust', 'negative', '-0.83', 'gift_wrap', 'kraft', 'kraft_paper', 'butcher_paper', 'paper']
senticnet['wrasse'] = ['-0.54', '0', '0.829', '-0.72', '#anger', '#disgust', 'negative', '-0.7', 'puffer', 'pufferfish', 'blowfish', 'triggerfish', 'plectognath']
senticnet['wrath'] = ['-0.73', '0', '0.899', '0', '#sadness', '#anger', 'negative', '-0.81', 'temper', 'madness', 'scorn', 'despise', 'disgust']
senticnet['wrathful'] = ['-0.11', '0.112', '0.674', '-0.66', '#anger', '#disgust', 'negative', '-0.33', 'wrathfully', 'wrothful', 'wroth', 'lividly', 'lividity']
senticnet['wrathfully'] = ['-0.11', '0.112', '0.674', '-0.66', '#anger', '#disgust', 'negative', '-0.33', 'wrathful', 'wrothful', 'wroth', 'lividly', 'lividity']
senticnet['wreak'] = ['-0.71', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.82', 'ravager', 'ravage', 'devastation', 'rapid', 'depredation']
senticnet['wreak_havoc'] = ['0', '0', '-0.71', '0', '#fear', '#fear', 'negative', '-0.71', 'hazardous', 'violent', 'take_life', 'threat', 'lethal']
senticnet['wreak_vengeance'] = ['-0.78', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.85', 'revenge', 'retribution', 'go_jail', 'sin', 'incarceration']
senticnet['wreaking'] = ['0', '0.906', '0', '0.779', '#interest', '#admiration', 'positive', '0.843', 'hellbent', 'dead-set', 'fixated', 'hell-bent', 'bloodthirsty']
senticnet['wreaks_havoc'] = ['0', '0', '-0.71', '0', '#fear', '#fear', 'negative', '-0.71', 'hazardous', 'violent', 'take_life', 'threat', 'lethal']
senticnet['wreaks_vengeance'] = ['-0.04', '0.166', '0.684', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'malign', 'malefic', 'maliciously', 'spitefulness', 'maliciousness']
senticnet['wreath'] = ['0.586', '-0.65', '0', '0.503', '#joy', '#surprise', 'positive', '0.58', 'decoration', 'distinguished_service_cross', 'medal', 'medallion', 'palm']
senticnet['wreath_argent'] = ['0.03', '0', '0.017', '0.138', '#joy', '#admiration', 'positive', '0.05', 'up_down', 'curl', 'bump', 'hand_gesture', 'goodbye']
senticnet['wreath_laying_ceremony'] = ['0.82', '0', '0', '0.885', '#joy', '#admiration', 'positive', '0.853', 'religious_event', 'sunday', 'happy_event', 'tradition', 'wedding']
senticnet['wreathed_hornbill'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'death_adder', 'ophidian', 'serpent', 'elapid', 'taipan']
senticnet['wreck'] = ['-0.10', '-0.01', '-0.09', '-0.10', '#sadness', '#disgust', 'negative', '-0.07', 'broken_bone', 'tear_apart', 'smash', 'damage', 'painful_injury']
senticnet['wreck_site'] = ['-0.73', '0', '0.822', '-0.73', '#sadness', '#anger', 'negative', '-0.76', 'succorer', 'succourer', 'recoverer', 'salvor', 'mortal']
senticnet['wrecked'] = ['-0.62', '0', '0.856', '-0.99', '#anger', '#disgust', 'negative', '-0.82', 'destroyed', 'spoilt', 'impoverished', 'blighted', 'extinguished']
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senticnet['wren'] = ['-0.10', '0', '-0.09', '0', '#sadness', '#fear', 'negative', '-0.1', 'quail', 'chicken_egg', 'sparrow', 'chicken', 'stork']
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senticnet['wrest_control'] = ['-0.53', '0', '0.752', '-0.89', '#anger', '#disgust', 'negative', '-0.72', 'fall', 'keel_over', 'descend', 'go_down', 'come_down']
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senticnet['wring_out'] = ['-0.91', '0', '0.656', '-0.81', '#sadness', '#disgust', 'negative', '-0.79', 'withdraw', 'squeeze_out', 'take', 'rip_out', 'demodulate']
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senticnet['wrinkle_free'] = ['0.824', '0.86', '0', '0.983', '#interest', '#admiration', 'positive', '0.889', 'look_smart', 'social_acceptance', 'improve_appearance', 'improve_look', 'look_good']
senticnet['wrinkle_resistant'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'white_cotton', 'sheepskin_material', 'woven_sheepskin', 'sheepskin', 'clothes_type']
senticnet['wrinkled_forehead'] = ['-0.66', '0', '0.782', '-0.52', '#sadness', '#anger', 'negative', '-0.65', 'lividness', 'pallidness', 'pallor', 'complexion', 'achromasia']
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senticnet['wrist_bone'] = ['-0.60', '0', '0.701', '-0.72', '#anger', '#disgust', 'negative', '-0.67', 'lunate_bone', 'semilunar_bone', 'carpal_bone', 'triquetral', 'triquetral_bone']
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senticnet['wrist_strap'] = ['0.783', '-0.75', '0', '0', '#joy', '#surprise', 'positive', '0.766', 'baseball_bat', 'water_gun', 'goalie', 'hockey_stick', 'bullseye']
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senticnet['write'] = ['0.95', '0.712', '0', '0', '#joy', '#interest', 'positive', '0.831', 'hold_document', 'bereavement_note', 'continue_story', 'make_paper_airplane', 'bereavement']
senticnet['write-off'] = ['0', '0.873', '0', '0.808', '#interest', '#admiration', 'positive', '0.841', 'close-out', 'write-off', 'delinquent', 'non-tax', 'receivables']
senticnet['write_assignment'] = ['0.022', '0', '0.015', '0.096', '#joy', '#admiration', 'positive', '0.034', 'write_book', 'study', 'exam', 'write_paper', 'research']
senticnet['write_back'] = ['0', '0.743', '0', '0', '#interest', '#interest', 'positive', '0.743', 'retort', 'answer_back', 'response', 'send_back', 'question_answer']
senticnet['write_book'] = ['0', '0.743', '0', '0', '#interest', '#interest', 'positive', '0.743', 'write_novel', 'write_poem', 'write_story', 'write', 'sign_autograph']
senticnet['write_brush'] = ['0.021', '-0.04', '0.051', '-0.09', '#anger', '#disgust', 'negative', '-0.01', 'mechanical_pencil', 'marker', 'quill_brush', 'pen', 'write_instrument']
senticnet['write_check'] = ['0.86', '0.89', '0', '0', '#joy', '#interest', 'positive', '0.875', 'open_wallet', 'take_out_wallet', 'go_bank', 'save_money', 'money_bank']
senticnet['write_code'] = ['0', '0.85', '0', '0.847', '#interest', '#admiration', 'positive', '0.849', 'learn_program_language', 'learn_computer_language', 'turn_computer_on', 'access_computer', 'a_lot_of_time']
senticnet['write_computer_program'] = ['-0.10', '0', '0', '-0.08', '#sadness', '#disgust', 'negative', '-0.09', 'master_skill', 'lose_key', 'erase_blackboard', 'wave_hello', 'code']
senticnet['write_date'] = ['0.463', '0.561', '-0.29', '0.281', '#joy', '#interest', 'positive', '0.253', 'impress_date', 'digitate_date', 'use_computer', 'use_pen', 'date']
senticnet['write_device'] = ['0.022', '0', '0.015', '0.096', '#joy', '#admiration', 'positive', '0.034', 'write_tool', 'write_implement', 'device', 'equipment', 'write_paper']
senticnet['write_down'] = ['0.745', '0.942', '0', '0', '#joy', '#interest', 'positive', '0.843', 'write_down_idea', 'record_story', 'take_note', 'remember', 'use_pen']
senticnet['write_down_idea'] = ['0.953', '0.704', '0', '0', '#joy', '#interest', 'positive', '0.829', 'write_down', 'read_aloud', 'concentrate', 'visualize', 'misspell']
senticnet['write_email'] = ['0.94', '0.859', '0', '0', '#joy', '#interest', 'positive', '0.899', 'use_technology', 'use_computer', 'write', 'email', 'surf_internet']
senticnet['write_equipment'] = ['0.022', '0', '0.015', '0.096', '#joy', '#admiration', 'positive', '0.034', 'write_tool', 'write_instrument', 'equipment', 'write_device', 'write_implement']
senticnet['write_essay'] = ['0.945', '0.783', '0', '0', '#joy', '#interest', 'positive', '0.864', 'chew_pencil', 'get_nervous', 'paragraph', 'write_down', 'take_note_class']
senticnet['write_exam'] = ['-0.03', '0.085', '0.001', '0.083', '#interest', '#admiration', 'positive', '0.032', 'take_exam', 'flash_card', 'take_final', 'flash_cardination', 'take_midterm']
senticnet['write_friend'] = ['0.983', '0', '0', '0.945', '#joy', '#admiration', 'positive', '0.964', 'write_novel', 'book_reservation', 'wedge_door_open', 'receive_mail', 'draw_diagram']
senticnet['write_implement'] = ['0.022', '0', '0.015', '0.096', '#joy', '#admiration', 'positive', '0.034', 'write_tool', 'write_instrument', 'equipment', 'write_device', 'write_equipment']
senticnet['write_instrument'] = ['0.022', '0', '0.015', '0.096', '#joy', '#admiration', 'positive', '0.034', 'pencil', 'pen', 'highlighter', 'pencil_sharpener', 'marking_pen']
senticnet['write_law'] = ['0', '0.887', '0', '0.91', '#interest', '#admiration', 'positive', '0.899', 'president', 'chief', 'respect', 'act_wisely', 'major']
senticnet['write_letter'] = ['0.024', '-0.07', '0', '0', '#joy', '#surprise', 'positive', '0.05', 'stuff_pillow', 'write_suicide_note', 'write', 'girdle', 'become_dull']
senticnet['write_list'] = ['-0.05', '0', '0.024', '0', '#sadness', '#anger', 'negative', '-0.03', 'decide_buy', 'make_list', 'go_shopping', 'drive_to_store', 'look_price']
senticnet['write_lyric'] = ['0.563', '0.781', '0', '0.787', '#interest', '#admiration', 'positive', '0.71', 'song_lyric', 'lyrics', 'rhyme', 'stanza', 'refrain']
senticnet['write_material'] = ['0.098', '0.078', '0', '0.061', '#joy', '#interest', 'positive', '0.079', 'book_page', 'thin_wood', 'paper', 'write_device', 'book_content']
senticnet['write_message'] = ['-0.02', '0.094', '0', '0.097', '#interest', '#admiration', 'positive', '0.054', 'write_text', 'find_computer', 'body_language', 'check_spelling', 'put_envelope']
senticnet['write_note'] = ['-0.02', '0.094', '0', '0.097', '#interest', '#admiration', 'positive', '0.054', 'make_note', 'write', 'take_note', 'note', 'notebook']
senticnet['write_novel'] = ['0.022', '0', '0.015', '0.096', '#joy', '#admiration', 'positive', '0.034', 'write', 'novel', 'write_poem', 'story', 'write_book']
senticnet['write_number'] = ['0.995', '0.722', '0', '0.773', '#joy', '#admiration', 'positive', '0.83', 'use_phone', 'call_number', 'take_number', 'contact', 'add_number']
senticnet['write_on_notebook'] = ['-0.02', '0.094', '0', '0.097', '#interest', '#admiration', 'positive', '0.054', 'remember_information', 'make_note', 'take_note', 'note', 'notebook']
senticnet['write_on_paper'] = ['0.963', '0.91', '0', '0', '#joy', '#interest', 'positive', '0.936', 'proofread', 'think_write', 'pick_up_pen', 'learn_write', 'run_out_ink']
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senticnet['write_poem'] = ['0', '0.82', '0', '0.925', '#interest', '#admiration', 'positive', '0.873', 'write_book', 'write_story', 'poet', 'good_poem', 'writing']
senticnet['write_poetry'] = ['0', '0', '0', '0.077', '#admiration', '#admiration', 'positive', '0.077', 'write_novel', 'take_message', 'fact_right', 'body_language', 'draw_picture']
senticnet['write_program'] = ['0.983', '0', '0', '0.945', '#joy', '#admiration', 'positive', '0.964', 'program_computer', 'design_software', 'fix_computer', 'add_up_column_number', 'program']
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senticnet['write_resume'] = ['0', '0.068', '0', '0', '#interest', '#interest', 'positive', '0.068', 'look_job', 'change_work', 'certificate', 'go_interview', 'new_job']
senticnet['write_review'] = ['0.826', '0.773', '0', '0', '#joy', '#interest', 'positive', '0.799', 'review', 'book', 'read_book', 'review_book', 'analyze_book']
senticnet['write_story'] = ['0', '0.719', '0', '0.895', '#interest', '#admiration', 'positive', '0.807', 'writer', 'write_book', 'write_poem', 'write_down_idea', 'witty']
senticnet['write_suicide_note'] = ['-0.06', '-0.02', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'commit_suicide', 'suicide', 'kill', 'suicide_note', 'execution']
senticnet['write_text'] = ['0.608', '0.7', '0', '0.637', '#interest', '#admiration', 'positive', '0.648', 'create_document', 'access_internet', 'enter_information', 'write_message', 'type']
senticnet['write_thesis'] = ['-0.03', '0.086', '0.01', '0.091', '#interest', '#admiration', 'positive', '0.034', 'assignment', 'take_exam', 'research', 'school_test', 'study']
senticnet['write_tool'] = ['0.022', '0', '0.015', '0.096', '#joy', '#admiration', 'positive', '0.034', 'write_equipment', 'write_implement', 'tool', 'write_device', 'write_instrument']
senticnet['write_with_style'] = ['0', '0.991', '0', '0.984', '#interest', '#admiration', 'positive', '0.988', 'write_book', 'artist', 'publish_book', 'life_style', 'fashion_style']
senticnet['writer'] = ['0', '0.982', '0', '0.974', '#interest', '#admiration', 'positive', '0.978', 'literature', 'poetry', 'fiction', 'literary', 'prose']
senticnet['writer_cramp'] = ['-0.24', '0.113', '-0.18', '0', '#sadness', '#fear', 'negative', '-0.10', 'hand_cramp', 'use_brain', 'think_story', 'use_mind', 'use_imagination']
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senticnet['writer_ned_parker'] = ['-0.75', '0.288', '-0.43', '-0.05', '#sadness', '#fear', 'negative', '-0.23', 'dejectedness', 'low-spiritedness', 'lowness', 'downheartedness', 'cheerless']
senticnet['writer_strike'] = ['-0.77', '0', '0.505', '-0.50', '#sadness', '#anger', 'negative', '-0.59', 'wildcat_strike', 'sympathy_strike', 'sympathetic_strike', 'walkout', 'job_action']
senticnet['writer_zane_grey'] = ['-0.94', '0', '0.839', '-0.53', '#sadness', '#anger', 'negative', '-0.77', 'hobo', 'tramp', 'drifter', 'bum', 'sundowner']
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senticnet['writhe'] = ['0', '0', '0.921', '-0.77', '#anger', '#disgust', 'negative', '-0.84', 'mangle', 'deteriorate', 'lie', 'misrepresent', 'contort']
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senticnet['writhing_around'] = ['0', '-0.08', '0', '0', '#surprise', '#surprise', 'positive', '0.082', 'yo-yo', 'playroom', 'backgammon', 'action_figure', 'board_game']
senticnet['writing'] = ['-0.99', '0', '0.964', '-0.96', '#sadness', '#anger', 'negative', '-0.97', 'face', 'font', 'fount', 'typeface', 'monospaced_font']
senticnet['writing_paper'] = ['0.829', '0.892', '0', '0', '#joy', '#interest', 'positive', '0.861', 'photo_copy', 'color_paper', 'printing_paper', 'note_book', 'white_paper']
senticnet['writing_style'] = ['-0.64', '0', '0.784', '-0.72', '#anger', '#disgust', 'negative', '-0.71', 'poetry', 'verse', 'drama', 'prose', 'form']
senticnet['written_communication'] = ['0.021', '0.107', '0', '0.156', '#interest', '#admiration', 'positive', '0.095', 'correspondence', 'note_down', 'document', 'handwritten', 'communication_method']
senticnet['written_language'] = ['0', '0', '0', '0.133', '#admiration', '#admiration', 'positive', '0.133', 'information_source', 'grammar_part', 'give_info', 'communication_method', 'part_of_speech']
senticnet['written_message'] = ['0', '0.098', '0.024', '0.162', '#interest', '#admiration', 'positive', '0.079', 'correspondence', 'handwritten', 'musical_symbol', 'note', 'written_communication']
senticnet['written_music'] = ['0.852', '0.907', '0', '0.805', '#joy', '#interest', 'positive', '0.855', 'tune', 'melody', 'lyric', 'harmony', 'music']
senticnet['written_paper'] = ['-0.16', '0', '0', '0', '#sadness', '#sadness', 'negative', '-0.16', 'print_paper', 'lack_of_sleep', 'lose', 'tired', 'tedium']
senticnet['written_text'] = ['-0.03', '0', '-0.04', '0', '#sadness', '#fear', 'negative', '-0.04', 'make_book', 'readable', 'essay', 'pencil_mark', 'read_material']
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senticnet['wrong_answer'] = ['-0.94', '-0.70', '0.726', '0', '#sadness', '#anger', 'negative', '-0.32', 'excuse', 'result', 'problem', 'deliberate', 'get_answer']
senticnet['wrong_direction'] = ['0', '0.21', '-0.11', '0.345', '#interest', '#admiration', 'positive', '0.147', 'direction', 'unto', 'forehead', 'upon', 'go_toward']
senticnet['wrong_doer'] = ['0.511', '-0.59', '0', '0.576', '#surprise', '#admiration', 'positive', '0.561', 'due', 'traceable', 'ascribable', 'attributable', 'referable']
senticnet['wrong_food'] = ['-0.94', '0', '0.891', '-0.93', '#sadness', '#disgust', 'negative', '-0.92', 'peanut', 'seafood', 'cause_allergy', 'sick', 'allergy_symptom']
senticnet['wrong_person'] = ['-0.97', '0', '0.893', '-0.99', '#sadness', '#disgust', 'negative', '-0.95', 'heart_broken', 'bad_couple', 'fake_love', 'bad_person', 'bad_marriage']
senticnet['wrong_place'] = ['0', '0', '-0.04', '-0.04', '#fear', '#disgust', 'negative', '-0.04', 'bad_luck', 'wrong', 'hockey_rink', 'cyberspace', 'mistake']
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senticnet['wrongful'] = ['0', '0', '-0.83', '-0.98', '#fear', '#disgust', 'negative', '-0.91', 'illegitimate', 'unconventional', 'illicit', 'unlawful', 'improper']
senticnet['wrongful_abdication'] = ['-0.64', '0', '0.625', '-0.88', '#sadness', '#disgust', 'negative', '-0.71', 'difficulty', 'awkwardness', 'flea_bite', 'difficultness', 'cumbersomeness']
senticnet['wrongful_act'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'negligence', 'evasion', 'dereliction', 'contributory_negligence']
senticnet['wrongful_conduct'] = ['-0.63', '0', '0.728', '-0.58', '#sadness', '#anger', 'negative', '-0.65', 'misconduct', 'inroad', 'wrongdoing', 'usurpation', 'actus_reus']
senticnet['wrongful_death'] = ['-0.71', '0', '-0.75', '-0.90', '#fear', '#disgust', 'negative', '-0.79', 'convent', 'monastery', 'capital_build', 'confession', 'synagogue']
senticnet['wrongful_death_action'] = ['-0.69', '0', '0.516', '-0.96', '#sadness', '#disgust', 'negative', '-0.72', 'criminal_negligence', 'culpable_negligence', 'evasion', 'dereliction', 'negligence']
senticnet['wrongful_death_suit'] = ['-0.91', '0', '-0.80', '0', '#sadness', '#fear', 'negative', '-0.85', 'lawyer', 'take_court', 'call_lawyer', 'determine_guilt', 'go_to_court']
senticnet['wrongfully'] = ['-0.04', '0.164', '0.685', '-0.65', '#anger', '#disgust', 'negative', '-0.30', 'maliciousness', 'spitefulness', 'maleficence', 'malefic', 'malign']
senticnet['wrongfulness'] = ['0.862', '-0.75', '0', '0.977', '#joy', '#admiration', 'positive', '0.865', 'unrighteousness', 'unjustness', 'unfairness', 'wrong', 'inequity']
senticnet['wrongheaded'] = ['0', '0', '0.832', '-0.91', '#anger', '#disgust', 'negative', '-0.87', 'persistent', 'opinionated', 'rock-ribbed', 'obstinately', 'obstinate']
senticnet['wrongly'] = ['0', '0', '0.769', '-0.89', '#anger', '#disgust', 'negative', '-0.83', 'unjust', 'tyrant', 'libel', 'victimize', 'unjustly']
senticnet['wrongness'] = ['-0.69', '0', '0.89', '-0.98', '#anger', '#disgust', 'negative', '-0.85', 'evil', 'immorality', 'putrefaction', 'wantonness', 'corruptibility']
senticnet['wroth'] = ['-0.11', '0.112', '0.674', '-0.66', '#anger', '#disgust', 'negative', '-0.33', 'wrathfully', 'wrathful', 'wrothful', 'lividly', 'lividity']
senticnet['wrothful'] = ['-0.12', '0.107', '0.668', '-0.65', '#anger', '#disgust', 'negative', '-0.33', 'lividity', 'livid', 'lividly', 'wrathfully', 'exasperate']
senticnet['wrought_iron'] = ['0', '0.773', '0', '0.895', '#interest', '#admiration', 'positive', '0.834', 'wooden_board', 'make_wood', 'wooden_box', 'wooden_floor', 'long_wood']
senticnet['wrought_iron_balustrade'] = ['0', '0', '0.095', '0', '#anger', '#anger', 'negative', '-0.09', 'build_part', 'flat', 'step', 'large_rock', 'hard_surface']
senticnet['wrought_iron_gate'] = ['0.028', '0', '0', '0.051', '#joy', '#admiration', 'positive', '0.04', 'entranceway', 'doorway', 'entrance', 'entryway', 'corridor']
senticnet['wrought_upon'] = ['-0.96', '0', '0.91', '-0.61', '#sadness', '#anger', 'negative', '-0.82', 'unpersuadable', 'unsuasible', 'unresponsive', 'resistant', 'incapable']
senticnet['wry_humor'] = ['0.918', '0.476', '-0.19', '0.417', '#joy', '#interest', 'positive', '0.404', 'merriment', 'cheery', 'gaiety', 'festal', 'plume']
senticnet['wry_smile'] = ['0.791', '0.934', '0', '0.85', '#interest', '#admiration', 'positive', '0.858', 'simper', 'please', 'content', 'satisfy', 'gratify']
senticnet['wry_wit'] = ['-0.69', '0', '0.597', '-0.56', '#sadness', '#anger', 'negative', '-0.62', 'improperness', 'impropriety', 'indelicacy', 'spiciness', 'gaminess']
senticnet['wti_contract'] = ['0.868', '-0.84', '0', '0.673', '#joy', '#surprise', 'positive', '0.796', 'instrument', 'option', 'derivative_instrument', 'legal_document', 'legal_instrument']
senticnet['wud'] = ['0.104', '0', '0', '0.088', '#joy', '#admiration', 'positive', '0.096', 'baby_flower', 'wee', 'bud', 'part_of_plant', 'leafy']
senticnet['wujud'] = ['0.9', '-0.51', '0', '0.836', '#joy', '#admiration', 'positive', '0.75', 'villager', 'inhabitant', 'habitant', 'dweller', 'denizen']
senticnet['wulfhere'] = ['-0.92', '0', '0.659', '-0.81', '#sadness', '#disgust', 'negative', '-0.8', 'unripe', 'napping', 'off_guard', 'off_one_guard', 'unprepared']
senticnet['wulfnoth'] = ['-0.92', '0', '0.659', '-0.81', '#sadness', '#disgust', 'negative', '-0.8', 'unripe', 'napping', 'off_guard', 'off_one_guard', 'unprepared']
senticnet['wullie'] = ['0.027', '0', '0.012', '0.063', '#joy', '#admiration', 'positive', '0.026', 'vegetation', 'tree_flower', 'green_growth', 'small', 'greenery']
senticnet['wundagore'] = ['-0.51', '0', '0.582', '-0.97', '#anger', '#disgust', 'negative', '-0.69', 'elapid', 'ophidian', 'serpent', 'krait', 'taipan']
senticnet['wunderkind'] = ['-0.61', '0', '0.738', '-0.80', '#anger', '#disgust', 'negative', '-0.71', 'dissident', 'beat', 'bohemian', 'maverick', 'recusant']
senticnet['wuqiu'] = ['-0.65', '0', '0.505', '-0.91', '#sadness', '#disgust', 'negative', '-0.69', 'hellhound', 'mythical_being', 'dragon', 'mythical_monster', 'amphisbaena']
senticnet['wushan_man'] = ['0.537', '-0.51', '0', '0.807', '#joy', '#admiration', 'positive', '0.619', 'lesbian', 'fairy', 'fagot', 'faggot', 'fag']
senticnet['wuss'] = ['-0.79', '0', '0.744', '-0.98', '#sadness', '#disgust', 'negative', '-0.84', 'chicken', 'softy', 'softie', 'crybaby', 'wimp']
senticnet['wwi'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'dictatorship', 'dictator', 'colony', 'world_domination', 'oppression']
senticnet['wwii'] = ['-0.70', '0', '0.925', '0', '#sadness', '#anger', 'negative', '-0.81', 'dictatorship', 'dictator', 'colony', 'world_domination', 'oppression']
senticnet['www'] = ['0.12', '0', '0.032', '0.08', '#joy', '#admiration', 'positive', '0.056', 'world_wide', 'web', 'internet', 'ass', 'web_browser']
senticnet['wyatt_wingfoot'] = ['0.02', '0.131', '0.749', '-0.67', '#anger', '#disgust', 'negative', '-0.31', 'belligerence', 'belligerency', 'belligerent', 'war', 'rancor']
senticnet['wych_elm_ulmus_glabra'] = ['-0.57', '0', '0.582', '-0.65', '#anger', '#disgust', 'negative', '-0.60', 'sweet_buckeye', 'dwarf_buckeye', 'bottlebrush_buckeye', 'red_buckeye', 'particolored_buckeye']
senticnet['wye_intersection'] = ['0.032', '0.116', '0.016', '0', '#joy', '#interest', 'positive', '0.044', 'curb', 'crossroad', 'ring_road', 'lane', 'interstate']
senticnet['wyeth'] = ['0.559', '0.656', '-0.26', '0.285', '#joy', '#interest', 'positive', '0.31', 'zealous', 'avidity', 'eagerness', 'avidness', 'thirstily']
senticnet['wyfe'] = ['-0.76', '0', '0.995', '-0.94', '#anger', '#disgust', 'negative', '-0.90', 'enuresis', 'urinary_incontinence', 'elimination', 'evacuation', 'excretion']
senticnet['wyner'] = ['-0.51', '0', '0.562', '-0.69', '#anger', '#disgust', 'negative', '-0.59', 'fishbowl', 'fish_bowl', 'goldfish_bowl', 'jorum', 'bowl']
senticnet['wynken'] = ['0.021', '0.179', '-0.13', '0.164', '#interest', '#admiration', 'positive', '0.058', 'nod_head', 'point_finger', 'upgrade', 'ask', 'put_bid']
senticnet['wynn_encore'] = ['0.057', '0.13', '0', '0.119', '#interest', '#admiration', 'positive', '0.102', 'friend_wedding', 'circular_route', 'spanish_restaurant', 'altar', 'monastery']
senticnet['wynona_big'] = ['0.941', '0.987', '0', '0', '#joy', '#interest', 'positive', '0.964', 'monkey', 'marmoset', 'lemur', 'fiance', 'koala']
senticnet['wythoff_symbol'] = ['0.042', '-0.00', '0.027', '0.099', '#joy', '#admiration', 'positive', '0.031', 'chess_knight', 'chess_queen', 'chess_pawn', 'pawn', 'chess_bishop']
senticnet['wyvern'] = ['-0.75', '0', '0.708', '0', '#sadness', '#anger', 'negative', '-0.73', 'hound', 'cage_animal', 'kind_animal', 'hunter', 'animal']
senticnet['x-ray'] = ['0', '0', '-0.07', '-0.16', '#fear', '#disgust', 'negative', '-0.12', 'radio_wave', 'sun', 'solar_cell', 'cosmic_ray', 'gamma_ray']
senticnet['x-ray_machine'] = ['-0.03', '0', '-0.10', '-0.22', '#fear', '#disgust', 'negative', '-0.11', 'syringe', 'latex_glove', 'check_body', 'wait_room', 'novocaine']
senticnet['x_ray'] = ['-0.87', '-0.79', '0.729', '-0.97', '#sadness', '#disgust', 'negative', '-0.44', 'radiation', 'cancer', 'sickness', 'potential_danger', 'electromagnetic_radiation']
senticnet['xanthippe'] = ['-0.62', '0', '0.645', '-0.67', '#anger', '#disgust', 'negative', '-0.65', 'lady_maid', 'handmaiden', 'chambermaid', 'maid', 'parlormaid']
senticnet['xanthium_strumarium'] = ['0.281', '0.203', '-0.07', '0.184', '#joy', '#interest', 'positive', '0.147', 'clematis', 'legume', 'leguminous_plant', 'climber', 'root_climber']
senticnet['xanthomonas_campestris_pv'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'apple_rust', 'fungus']
senticnet['xanthosoma'] = ['0.035', '0.104', '0', '0.12', '#interest', '#admiration', 'positive', '0.086', 'fruit_vegetable', 'vegetable', 'vegetable_fruit', 'fresh_vegetable', 'vegetarian_food']
senticnet['xenomorph'] = ['-0.07', '0.192', '0.634', '-0.63', '#anger', '#disgust', 'negative', '-0.28', 'murderously', 'amuck', 'murderousness', 'misocainea', 'misogyny']
senticnet['xenon_arc_lamp'] = ['-0.63', '0', '0.897', '-0.91', '#anger', '#disgust', 'negative', '-0.81', 'cd-rom', 'international_candle', 'candle', 'candlepower_unit', 'standard_candle']
senticnet['xenophobe'] = ['-0.21', '0', '-0.19', '-0.18', '#sadness', '#fear', 'negative', '-0.2', 'censorship', 'bad_word', 'kidnap', 'intolerance', 'sectarian']
senticnet['xenophobia'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'racism', 'intolerant', 'xenophobic', 'racist', 'nazism']
senticnet['xenophobic'] = ['-0.72', '0', '-0.82', '-0.84', '#fear', '#disgust', 'negative', '-0.79', 'racism', 'intolerant', 'racist', 'antisemitism', 'nazism']
senticnet['xeroderma_pigmentosum_xp'] = ['-0.57', '0', '0.973', '-0.84', '#anger', '#disgust', 'negative', '-0.79', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect', 'congenital_disease']
senticnet['xerographic'] = ['0.022', '-0.05', '0.079', '-0.11', '#anger', '#disgust', 'negative', '-0.02', 'stationery', 'ink', 'ball_point_pen', 'refill', 'note']
senticnet['xerography'] = ['-0.03', '0.201', '-0.13', '0.171', '#interest', '#admiration', 'positive', '0.049', 'scientific_statement', 'hypothetical', 'conjecture', 'theory', 'fact']
senticnet['xerophthalmia'] = ['-0.82', '0', '0.996', '-0.61', '#sadness', '#anger', 'negative', '-0.81', 'visual_impairment', 'visual_defect', 'vision_defect', 'visual_disorder', 'avitaminosis']
senticnet['xerostomia'] = ['-0.73', '0', '0.931', '-0.82', '#anger', '#disgust', 'negative', '-0.83', 'aridness', 'thirstiness', 'dryness', 'dry_mouth', 'sereness']
senticnet['xhem_hasa'] = ['-0.88', '0', '0.903', '-0.50', '#sadness', '#anger', 'negative', '-0.76', 'send_down', 'rusticate', 'expel', 'throw_out', 'kick_out']
senticnet['xhtml_mobile'] = ['0', '0.189', '0.04', '0.091', '#interest', '#admiration', 'positive', '0.08', 'combination', 'margin', 'teller', 'merge', 'finite']
senticnet['xiangqi'] = ['0', '0', '0', '0.115', '#admiration', '#admiration', 'positive', '0.115', 'chess_knight', 'strategy', 'pawn', 'chess_queen', 'chess_rook']
senticnet['xiao_liwu'] = ['0.02', '0.071', '0', '0.069', '#interest', '#admiration', 'positive', '0.053', 'many_animals', 'donkey', 'chimpanzee', 'cute_animal', 'creature']
senticnet['xii'] = ['0.055', '0.189', '-0.09', '0.113', '#interest', '#admiration', 'positive', '0.065', 'tame', 'crack', 'sense_fear', 'friend_person', 'good_manner']
senticnet['xiphosura'] = ['-0.54', '0', '0.992', '-0.87', '#anger', '#disgust', 'negative', '-0.80', 'hag', 'osteostracan', 'cephalaspid', 'ostracoderm', 'jawless_fish']
senticnet['xiv'] = ['0.055', '0.189', '-0.09', '0.113', '#interest', '#admiration', 'positive', '0.065', 'tame', 'crack', 'sense_fear', 'friend_person', 'good_manner']
senticnet['xix'] = ['0.055', '0.189', '-0.09', '0.113', '#interest', '#admiration', 'positive', '0.065', 'tame', 'crack', 'sense_fear', 'friend_person', 'good_manner']
senticnet['xlabel'] = ['0.777', '-0.85', '0', '0.615', '#joy', '#surprise', 'positive', '0.748', 'think', 'play', 'trifle', 'abstract', 'warm_to']
senticnet['xliv'] = ['0.055', '0.189', '-0.09', '0.113', '#interest', '#admiration', 'positive', '0.065', 'tame', 'crack', 'sense_fear', 'friend_person', 'good_manner']
senticnet['xlix'] = ['0.055', '0.189', '-0.09', '0.113', '#interest', '#admiration', 'positive', '0.065', 'tame', 'crack', 'sense_fear', 'friend_person', 'good_manner']
senticnet['xlvi'] = ['0.055', '0.189', '-0.09', '0.113', '#interest', '#admiration', 'positive', '0.065', 'tame', 'crack', 'sense_fear', 'friend_person', 'good_manner']
senticnet['xoloitzcuintli'] = ['0.039', '0', '0', '0.059', '#joy', '#admiration', 'positive', '0.049', 'big_dog', 'dog_cat', 'smart_dog', 'pet', 'domesticated_animal']
senticnet['xstrata_plc_xta_lse'] = ['0.025', '0.115', '0.06', '0.183', '#interest', '#admiration', 'positive', '0.066', 'volume_measurement', 'liquid_measurement', 'mass_measure', 'geometry', 'size']
senticnet['xu_shen'] = ['-0.86', '0', '0.619', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'character', 'logogram', 'logograph', 'graph', 'grapheme']
senticnet['xung'] = ['-0.63', '0', '0.791', '-0.68', '#anger', '#disgust', 'negative', '-0.70', 'little', 'pinch', 'modicum', 'small_indefinite_amount', 'indefinite_quantity']
senticnet['xury'] = ['0.054', '0.237', '-0.15', '0.33', '#interest', '#admiration', 'positive', '0.117', 'retrieve', 'bring', 'tote', 'take', 'go_get']
senticnet['xvi'] = ['0.055', '0.189', '-0.09', '0.113', '#interest', '#admiration', 'positive', '0.065', 'tame', 'crack', 'sense_fear', 'friend_person', 'good_manner']
senticnet['xxii'] = ['0.055', '0.189', '-0.09', '0.113', '#interest', '#admiration', 'positive', '0.065', 'tame', 'crack', 'sense_fear', 'friend_person', 'good_manner']
senticnet['xxiii'] = ['0.055', '0.189', '-0.09', '0.113', '#interest', '#admiration', 'positive', '0.065', 'tame', 'crack', 'sense_fear', 'friend_person', 'good_manner']
senticnet['xxiv'] = ['0.055', '0.189', '-0.09', '0.113', '#interest', '#admiration', 'positive', '0.065', 'tame', 'crack', 'sense_fear', 'friend_person', 'good_manner']
senticnet['xxix'] = ['0.055', '0.189', '-0.09', '0.113', '#interest', '#admiration', 'positive', '0.065', 'tame', 'crack', 'sense_fear', 'friend_person', 'good_manner']
senticnet['xxvii'] = ['0.055', '0.189', '-0.09', '0.113', '#interest', '#admiration', 'positive', '0.065', 'tame', 'crack', 'sense_fear', 'friend_person', 'good_manner']
senticnet['xy_karyotype'] = ['0.718', '-0.72', '0', '0.952', '#surprise', '#admiration', 'positive', '0.799', 'love', 'aphrodisia', 'concupiscence', 'lustfulness', 'amorousness']
senticnet['xylella_fastidiosa'] = ['-0.51', '0', '0.781', '-0.85', '#anger', '#disgust', 'negative', '-0.71', 'flax_rust', 'flax_rust_fungus', 'blister_rust', 'apple_rust', 'fungus']
senticnet['xylitol'] = ['0', '0', '0.813', '-0.95', '#anger', '#disgust', 'negative', '-0.88', 'ice_cream_melt', 'allergy', 'toothache', 'gonorrhea', 'tastebud']
senticnet['xylophone'] = ['0.155', '-0.11', '0.009', '-0.05', '#joy', '#surprise', 'positive', '0.05', 'dance_well', 'relate_music', 'enter_contest', 'jump_around', 'cheer_up']
senticnet['xz_plane'] = ['-0.97', '0', '0.865', '-0.54', '#sadness', '#anger', 'negative', '-0.79', 'piping', 'lace', 'lacing', 'cord', 'slack']
senticnet['ya'] = ['0.104', '0', '0', '0.088', '#joy', '#admiration', 'positive', '0.096', 'baby_flower', 'wee', 'bud', 'part_of_plant', 'leafy']
senticnet['ya_know'] = ['-0.89', '0', '0.739', '-0.97', '#sadness', '#disgust', 'negative', '-0.86', 'rot', 'bunk', 'bunkum', 'buncombe', 'guff']
senticnet['yabusame'] = ['0', '-0.07', '0.091', '-0.19', '#anger', '#disgust', 'negative', '-0.07', 'projectile', 'use_hunt', 'weapon', 'ammunition', 'firearm']
senticnet['yacc_parser_generator'] = ['0.756', '0', '0', '0', '#joy', '#joy', 'positive', '0.756', 'companionship', 'cherish', 'lover', 'bond', 'buddy']
senticnet['yacht'] = ['-0.09', '0.192', '-0.08', '0.106', '#interest', '#admiration', 'positive', '0.029', 'ship', 'speed_boat', 'water_vehicle', 'sailboat', 'boat']
senticnet['yacht_basin'] = ['0', '0', '0.027', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'port', 'sea', 'boat', 'ocean', 'ship']
senticnet['yacht_harbor'] = ['0', '0', '0.027', '-0.04', '#anger', '#disgust', 'negative', '-0.03', 'port', 'sea', 'boat', 'ocean', 'ship']
senticnet['yachtsman'] = ['0.703', '0.913', '0', '0.701', '#joy', '#interest', 'positive', '0.772', 'discovery', 'egalitarian', 'pioneer', 'correspondent', 'humankind']
senticnet['yacn'] = ['0.035', '0.104', '0', '0.12', '#interest', '#admiration', 'positive', '0.086', 'fruit_vegetable', 'vegetable', 'vegetable_fruit', 'fresh_vegetable', 'vegetarian_food']
senticnet['yadong'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['yahoo_senior_vice'] = ['-0.63', '0', '0.862', '-0.52', '#sadness', '#anger', 'negative', '-0.67', 'knot', 'bunch', 'tuft', 'tussock', 'agglomeration']
senticnet['yahtzee'] = ['0', '0.133', '0', '0', '#interest', '#interest', 'positive', '0.133', 'jewelry_store', 'hold_change', 'bank_account', 'coffer', 'wallet']
senticnet['yahwistic'] = ['-0.81', '0', '0', '-0.77', '#sadness', '#disgust', 'negative', '-0.79', 'stifle', 'forgotten', 'old', 'touch_ground', 'seldom']
senticnet['yak'] = ['0.751', '0.769', '0', '0', '#joy', '#interest', 'positive', '0.76', 'yak', 'sheep', 'cow', 'camel', 'boar']
senticnet['yak_butter'] = ['0.827', '0', '0', '0', '#joy', '#joy', 'positive', '0.827', 'wrathful', 'wrathfully', 'wroth', 'wrothful', 'lividity']
senticnet['yak_wool'] = ['-0.53', '0', '0.751', '-0.97', '#anger', '#disgust', 'negative', '-0.75', 'guanaco', 'domestic_llama', 'llama', 'artiodactyl', 'artiodactyl_mammal']
senticnet['yaller'] = ['0.591', '-0.76', '0', '0.66', '#surprise', '#admiration', 'positive', '0.673', 'darling', 'pet', 'favorite', 'first_choice', 'macushla']
senticnet['yamachiche'] = ['-0.71', '0', '0.917', '-0.51', '#sadness', '#anger', 'negative', '-0.71', 'demesne', 'estate', 'land', 'landed_estate', 'acre']
senticnet['yamaha_motorbike'] = ['0.21', '0.131', '-0.05', '0.084', '#joy', '#interest', 'positive', '0.093', 'fuel', 'car', 'fossil_fuel', 'transport', 'natural_resource']
senticnet['yammer'] = ['-0.93', '0', '0', '-0.97', '#sadness', '#disgust', 'negative', '-0.95', 'peevish', 'way', 'peevishly', 'manner', 'pettishly']
senticnet['yanan'] = ['-0.76', '0', '0.692', '-0.84', '#sadness', '#disgust', 'negative', '-0.76', 'iron_curtain', 'ideological_barrier', 'color_bar', 'color_line', 'barrier']
senticnet['yang_mill_theory'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'general_relativity', 'general_theory_of_relativity', 'relativity', 'special_relativity', 'special_theory_of_relativity']
senticnet['yangmills_theory'] = ['0.708', '-0.93', '0', '0.764', '#surprise', '#admiration', 'positive', '0.802', 'general_relativity', 'general_theory_of_relativity', 'relativity', 'special_relativity', 'special_theory_of_relativity']
senticnet['yank'] = ['0.988', '0.787', '0', '0', '#joy', '#interest', 'positive', '0.887', 'tug', 'carry', 'tow', 'extend', 'drag']
senticnet['yankee_pitcher'] = ['0.663', '-0.68', '0', '0.892', '#surprise', '#admiration', 'positive', '0.747', 'saphead', 'muggins', 'tomfool', 'fool', 'sap']
senticnet['yankee_reliever'] = ['-0.96', '0', '0.624', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'vitiated', 'dicky', 'impaired', 'lessened', 'diminished']
senticnet['yanomam'] = ['-0.17', '0', '-0.16', '-0.09', '#sadness', '#fear', 'negative', '-0.14', 'murder', 'hurt_others', 'death_penalty', 'death_destruction', 'end_live']
senticnet['yard'] = ['0.843', '0', '0', '0', '#joy', '#joy', 'positive', '0.843', 'outside_house', 'garden', 'back_house', 'backyard', 'outdoor']
senticnet['yard_gain'] = ['0.855', '0.741', '0', '0.862', '#joy', '#admiration', 'positive', '0.819', 'further_goal', 'pass_ball', 'pass_glass', 'help', 'pass_exam']
senticnet['yard_sale'] = ['0.048', '0', '0', '0.092', '#joy', '#admiration', 'positive', '0.07', 'gym_locker', 'observatory', 'prison_cell', 'luggage_store', 'luggage_compartment']
senticnet['yard_waste'] = ['-0.12', '0', '-0.10', '-0.03', '#sadness', '#fear', 'negative', '-0.08', 'dog_poop', 'picnic', 'hike_trail', 'detritus', 'wildlife']
senticnet['yardstick'] = ['0.906', '0.86', '0', '0', '#joy', '#interest', 'positive', '0.883', 'measure_inch', 'measure_tool', 'measurement_tool', 'measure', 'size_up']
senticnet['yarn'] = ['0', '0.081', '0.015', '0.052', '#interest', '#admiration', 'positive', '0.039', 'cotton', 'fabric_material', 'sheepskin', 'spin_silk', 'woven_cloth']
senticnet['yarnwinder'] = ['0.056', '0.139', '-0.07', '0.154', '#interest', '#admiration', 'positive', '0.07', 'belongings', 'material', 'fabric', 'guarantee', 'stuff']
senticnet['yarrow_achillea_millefolium'] = ['-0.79', '0', '0.906', '-0.67', '#sadness', '#anger', 'negative', '-0.79', 'poisonous_plant', 'hemlock_poisonous_plant', 'water_hemlock', 'plant_life', 'poison_sumac']
senticnet['yasht'] = ['-0.97', '0', '0.772', '-0.67', '#sadness', '#anger', 'negative', '-0.80', 'expiation', 'atonement', 'deliverance', 'redemption', 'rescue']
senticnet['yasna'] = ['-0.97', '0', '0.772', '-0.67', '#sadness', '#anger', 'negative', '-0.80', 'expiation', 'atonement', 'deliverance', 'redemption', 'rescue']
senticnet['yasuko_kobayashi'] = ['-0.67', '0', '0.931', '-0.77', '#anger', '#disgust', 'negative', '-0.79', 'asylum', 'mental_hospital', 'psychiatric_hospital', 'mental_institution', 'mental_home']
senticnet['yaupon'] = ['-0.85', '0', '0.5', '-0.70', '#sadness', '#disgust', 'negative', '-0.68', 'courbaril', 'water_locust', 'swamp_locust', 'honey_locust', 'black_locust']
senticnet['yaw'] = ['-0.92', '0', '0.883', '0', '#sadness', '#anger', 'negative', '-0.90', 'unsteadiness', 'falter', 'fluctuation', 'giddy', 'unsteady']
senticnet['yawl'] = ['-0.50', '0', '0.611', '-0.75', '#anger', '#disgust', 'negative', '-0.62', 'howl', 'wail', 'ululate', 'roar', 'waul']
senticnet['yawn'] = ['-0.1', '-0.06', '0', '-0.12', '#sadness', '#disgust', 'negative', '-0.05', 'fall_out_bed', 'take_breath', 'shut_off_alarm_clock', 'wind_down', 'put_feet_up']
senticnet['yawn-inducing'] = ['0.748', '0', '0', '0.704', '#joy', '#admiration', 'positive', '0.726', 'conjugal', 'connubial', 'blamelessness', 'nuptial', 'alone-time']
senticnet['yawning'] = ['0', '0', '-0.98', '-0.9', '#fear', '#disgust', 'negative', '-0.94', 'oscitancy', 'oscitance', 'gape', 'oscitant', 'drowsy']
senticnet['yazoo_city'] = ['0', '0', '-0.75', '-0.80', '#fear', '#disgust', 'negative', '-0.78', 'footling', 'lilliputian', 'little', 'fiddling', 'niggling']
senticnet['yba'] = ['-0.61', '0', '0.738', '-0.80', '#anger', '#disgust', 'negative', '-0.71', 'dissident', 'beat', 'bohemian', 'maverick', 'recusant']
senticnet['yd_touchdown_run'] = ['0.926', '0.906', '0', '0', '#joy', '#interest', 'positive', '0.916', 'score_goal', 'accomplish', 'finalize', 'pot', 'make_point']
senticnet['ye'] = ['-0.04', '0.268', '-0.06', '0.227', '#interest', '#admiration', 'positive', '0.097', 'proposition', 'abstract', 'get_rid', 'theoretical', 'business_transaction']
senticnet['ye_dinna'] = ['-0.63', '0', '0.683', '-0.96', '#anger', '#disgust', 'negative', '-0.76', 'slovenly_woman', 'slut', 'trollop', 'slattern', 'litterer']
senticnet['ye_olde_trip'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['ye_ve'] = ['-0.85', '0', '0.583', '-0.71', '#sadness', '#disgust', 'negative', '-0.71', 'muddle', 'rummage', 'mare_nest', 'welter', 'clutter']
senticnet['yea'] = ['-0.04', '0.268', '-0.06', '0.227', '#interest', '#admiration', 'positive', '0.097', 'proposition', 'abstract', 'get_rid', 'theoretical', 'business_transaction']
senticnet['yeah'] = ['0.765', '-1', '0', '0', '#joy', '#surprise', 'positive', '0.883', 'okay', 'alright', 'gonna', 'gotta', 'hey']
senticnet['year'] = ['-0.85', '0', '0.584', '-0.79', '#sadness', '#disgust', 'negative', '-0.74', 'age', 'old_age', 'second_childhood', 'eld', 'geezerhood']
senticnet['year_contract_extension'] = ['0.865', '-0.84', '0', '0.915', '#joy', '#admiration', 'positive', '0.875', 'grant', 'sale_in_gross', 'contract_of_hazard', 'adhesion_contract', 'contract_of_adhesion']
senticnet['year_old_altar_boy'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'acolyte', 'reverend', 'thurifer', 'clergyman', 'man_of_cloth']
senticnet['year_old_boy'] = ['0.575', '-0.62', '0', '0.596', '#surprise', '#admiration', 'positive', '0.598', 'altar_boy', 'cub', 'ball_boy', 'bat_boy', 'lad']
senticnet['year_old_daughter'] = ['-0.06', '0', '0.01', '0', '#sadness', '#anger', 'negative', '-0.03', 'youth', 'young_child', 'small_child', 'lad', 'tiny']
senticnet['year_old_girl'] = ['-0.06', '0', '0.01', '0', '#sadness', '#anger', 'negative', '-0.03', 'youth', 'young_child', 'small_child', 'lad', 'tiny']
senticnet['year_old_grandmother'] = ['0.826', '0.862', '0', '0', '#joy', '#interest', 'positive', '0.844', 'aunt_uncle', 'picture_of_family', 'aunt', 'family_photo', 'relative']
senticnet['year_old_mare'] = ['-0.76', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.78', 'strangle', 'bloodshed', 'cripple', 'hear_bad_news', 'sexually_transmit_disease']
senticnet['year_old_son'] = ['-0.06', '0', '0.01', '0', '#sadness', '#anger', 'negative', '-0.03', 'youth', 'young_child', 'small_child', 'lad', 'tiny']
senticnet['year_old_woolly_mammoth'] = ['0.969', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.96', 'extinct', 'animal', 'ice', 'elephant', 'giant']
senticnet['year_shirt_sponsorship'] = ['-0.04', '0.274', '-0.15', '0.278', '#interest', '#admiration', 'positive', '0.089', 'business_deal', 'trade', 'exchange', 'bargain', 'offer']
senticnet['yearbook'] = ['-0.51', '0', '0.762', '-0.86', '#anger', '#disgust', 'negative', '-0.71', 'lowerclassman', 'underclassman', 'senior', 'college_girl', 'undergrad']
senticnet['yearly'] = ['-0.85', '0', '0.584', '-0.79', '#sadness', '#disgust', 'negative', '-0.74', 'age', 'old_age', 'second_childhood', 'eld', 'geezerhood']
senticnet['yearly_celebration'] = ['0.877', '0.952', '0', '0.905', '#interest', '#admiration', 'positive', '0.911', 'special_day', 'annual_celebration', 'party_time', 'cake_candle', 'celebrate']
senticnet['yearly_rainfall'] = ['0', '0', '0.073', '0', '#anger', '#anger', 'negative', '-0.07', 'condensation', 'water_fall', 'falling_water', 'frozen_water', 'water_drop']
senticnet['yearly_tribute'] = ['0.73', '-0.56', '0', '0.621', '#joy', '#admiration', 'positive', '0.64', 'amnesty', 'unsusceptibility', 'diplomatic_immunity', 'exemption', 'impunity']
senticnet['yearn'] = ['0', '0', '0', '0.864', '#admiration', '#admiration', 'positive', '0.864', 'call', 'strong_desire', 'ache', 'demand', 'need']
senticnet['yearning'] = ['0.994', '-0.78', '0', '0.5', '#joy', '#surprise', 'positive', '0.76', 'dissatisfaction', 'disgruntlement', 'dysphoria', 'longing', 'discontentment']
senticnet['yeasayer'] = ['0.304', '0.339', '-0.19', '0.333', '#interest', '#admiration', 'positive', '0.195', 'careful', 'reinforce', 'load_bear', 'go_outdoors', 'allowance']
senticnet['yeast'] = ['0.829', '0.896', '0', '0', '#joy', '#interest', 'positive', '0.863', 'starchy_food', 'staple_food', 'whole_grain', 'crust', 'basic_food']
senticnet['yeast-risen'] = ['-0.84', '0', '0', '-0.85', '#sadness', '#disgust', 'negative', '-0.84', 'pizza-like', 'bread-like', 'crepe-like', 'donut-like', 'pancake-like']
senticnet['yeast_dough'] = ['0', '-0.06', '0', '0.065', '#surprise', '#admiration', 'positive', '0.063', 'doughnut', 'roll', 'custard', 'bread', 'biscuit']
senticnet['yeast_fermentation'] = ['0', '0.104', '-0.09', '0.146', '#interest', '#admiration', 'positive', '0.052', 'thaw', 'slush', 'water_inside', 'give_water', 'obtain_water']
senticnet['yeast_infection'] = ['-0.73', '-0.72', '-0.86', '-0.79', '#fear', '#disgust', 'negative', '-0.41', 'venereal_disease', 'overpopulation', 'std', 'loss_offreedom', 'lower_standard_live']
senticnet['yeast_mixture'] = ['0.053', '-0.06', '0', '0', '#joy', '#surprise', 'positive', '0.06', 'salad_shaker', 'store_shelf', 'kitchen_tool', 'pliers', 'ladle']
senticnet['yeast_overgrowth'] = ['0', '0', '0.955', '-0.78', '#anger', '#disgust', 'negative', '-0.87', 'worm_intestine', 'fur_keep_warm', 'unpleasant-smelling', 'halitosis', 'asthama']
senticnet['yehud'] = ['0', '0', '0', '0.081', '#admiration', '#admiration', 'positive', '0.081', 'vatican', 'jewish_community', 'large_city', 'big_city', 'major_city']
senticnet['yell'] = ['-0.82', '0', '0', '-0.92', '#sadness', '#disgust', 'negative', '-0.87', 'shout', 'yell_loudly', 'scream', 'loud_shout', 'loud_yell']
senticnet['yell_loudly'] = ['-0.83', '0', '0', '-0.80', '#sadness', '#disgust', 'negative', '-0.81', 'shout', 'yell', 'scream', 'high_volume', 'noise']
senticnet['yelling'] = ['-0.96', '0', '0.533', '-0.95', '#sadness', '#disgust', 'negative', '-0.81', 'outcry', 'call', 'cry', 'yell', 'shout']
senticnet['yellow'] = ['0', '0.19', '-0.09', '0.175', '#interest', '#admiration', 'positive', '0.089', 'red', 'green_black', 'green_red', 'peelable', 'precious_jewel']
senticnet['yellow_animal'] = ['-0.85', '0', '0.749', '-0.99', '#sadness', '#disgust', 'negative', '-0.86', 'bird', 'eat_worm', 'farm_bird', 'mistreat', 'biped']
senticnet['yellow_arrow'] = ['0.059', '0', '0', '0.049', '#joy', '#admiration', 'positive', '0.054', 'bayonet', 'spear', 'shotgun', 'bullet', 'hydrogen_bomb']
senticnet['yellow_backed_duiker'] = ['0', '0', '-0.95', '-0.81', '#fear', '#disgust', 'negative', '-0.88', 'herb', 'plant', 'poisonous_plant', 'yellow_flower', 'lichen']
senticnet['yellow_bile'] = ['-0.12', '0.13', '0.646', '-0.64', '#anger', '#disgust', 'negative', '-0.32', 'irascibility', 'quick_temper', 'short_temper', 'irascible', 'ill_temper']
senticnet['yellow_blue'] = ['-0.05', '0', '0.031', '0', '#sadness', '#anger', 'negative', '-0.04', 'blue_yellow', 'hue', 'violet', 'cool_color', 'tint']
senticnet['yellow_bone_marrow'] = ['0.73', '-0.72', '0', '0.653', '#joy', '#surprise', 'positive', '0.703', 'red_marrow', 'red_bone_marrow', 'yellow_marrow', 'bone_marrow', 'connective_tissue']
senticnet['yellow_brick'] = ['0', '0.943', '0', '0', '#interest', '#interest', 'positive', '0.943', 'condominium', 'rent_apartment', 'high_rise', 'condo', 'row_house']
senticnet['yellow_cab'] = ['0.04', '0.144', '-0.07', '0.122', '#interest', '#admiration', 'positive', '0.058', 'travel', 'abroad', 'foreign_country', 'transit', 'elsewhere']
senticnet['yellow_card'] = ['-0.98', '0', '0.334', '-0.91', '#sadness', '#disgust', 'negative', '-0.74', 'punishment', 'fault', 'probabilty_of_losing', 'penalty_shoot', 'red_card']
senticnet['yellow_chestnut_oak'] = ['0.776', '-0.6', '0', '0.568', '#joy', '#surprise', 'positive', '0.648', 'swamp_chestnut_oak', 'chinquapin_oak', 'chinkapin_oak', 'chestnut_oak', 'basket_oak']
senticnet['yellow_color'] = ['0', '0.121', '0.041', '0.12', '#interest', '#admiration', 'positive', '0.067', 'little_grain', 'nutritious', 'cereal_crop', 'small_grain', 'food_grain']
senticnet['yellow_coneflower'] = ['-0.79', '0', '0.837', '-0.88', '#anger', '#disgust', 'negative', '-0.83', 'sunray', 'wilding', 'sagebrush_buttercup', 'pasqueflower', 'pasque_flower']
senticnet['yellow_discoloration'] = ['-0.56', '0', '0.906', '-0.80', '#anger', '#disgust', 'negative', '-0.76', 'jaundice_of_newborn', 'disease', 'icterus_neonatorum', 'kernicterus', 'symptom']
senticnet['yellow_dye'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['yellow_fever'] = ['-0.86', '0', '0.825', '-0.54', '#sadness', '#anger', 'negative', '-0.74', 'pestis', 'plague', 'pestilence', 'pest', 'ambulant_plague']
senticnet['yellow_fever_epidemic'] = ['0.579', '-0.81', '0', '0.833', '#surprise', '#admiration', 'positive', '0.741', 'murine_typhus', 'rat_typhus', 'urban_typhus', 'endemic_typhus', 'rickettsial_disease']
senticnet['yellow_fin_tuna'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'crab', 'swimming_crab', 'rock_crab', 'fiddler_crab', 'pea_crab']
senticnet['yellow_flower'] = ['0.864', '0.823', '0', '0', '#joy', '#interest', 'positive', '0.843', 'flower', 'plant', 'flower_bloom', 'yellow_rose', 'good_smell']
senticnet['yellow_foliage'] = ['-0.89', '0', '0', '-0.98', '#sadness', '#disgust', 'negative', '-0.94', 'tropical_fish', 'wood_duck', 'disney', 'pink_flower', 'beau']
senticnet['yellow_fronted_canary'] = ['0.971', '-0.59', '0', '0.768', '#joy', '#admiration', 'positive', '0.779', 'parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet']
senticnet['yellow_fruit'] = ['0.057', '0', '0.038', '0.073', '#joy', '#admiration', 'positive', '0.031', 'delicious_fruit', 'small_fruit', 'tree_fruit', 'vine_fruit', 'domesticate_plant']
senticnet['yellow_green'] = ['-0.06', '0.081', '0', '0.082', '#interest', '#admiration', 'positive', '0.034', 'green_yellow', 'red_orange', 'red_green', 'green_orange', 'red_blue']
senticnet['yellow_highlighter'] = ['0', '-0.04', '0.061', '-0.09', '#anger', '#disgust', 'negative', '-0.03', 'index_card', 'ring_binder', 'highlighter_pen', 'memo_pad', 'notepad']
senticnet['yellow_hornet'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'hornet', 'yellow_jacket', 'giant_hornet', 'vespid', 'vespid_wasp']
senticnet['yellow_jacket'] = ['-0.66', '0', '0.946', '-0.55', '#sadness', '#anger', 'negative', '-0.72', 'yellow_hornet', 'hornet', 'giant_hornet', 'vespid', 'vespid_wasp']
senticnet['yellow_jaundice'] = ['0', '0', '0', '-0.96', '#disgust', '#disgust', 'negative', '-0.96', 'thick_substance', 'viscous_substance', 'mucous_substance', 'disgusting', 'disgust']
senticnet['yellow_journalism'] = ['0', '-0.92', '0', '0.944', '#surprise', '#admiration', 'positive', '0.934', 'scandalmongering', 'sensationalistic', 'sensationally', 'inflame', 'fire_up']
senticnet['yellow_lady_slipper'] = ['0.277', '0.161', '0', '0.337', '#joy', '#admiration', 'positive', '0.258', 'moccasin_flower', 'nerveroot', 'showy_lady_slipper', 'lady_slipper', 'clustered_lady_slipper']
senticnet['yellow_line'] = ['0.972', '0.918', '0', '0', '#joy', '#interest', 'positive', '0.945', 'traffic_rule', 'develop_city', 'bicycle_riding', 'drive_car', 'control_road']
senticnet['yellow_loosestrife'] = ['-0.92', '0', '0.912', '-0.94', '#sadness', '#disgust', 'negative', '-0.92', 'true_blackberry', 'sand_blackberry', 'blackberry_bush', 'blackberry', 'running_blackberry']
senticnet['yellow_marrow'] = ['0.73', '-0.72', '0', '0.653', '#joy', '#surprise', 'positive', '0.703', 'red_marrow', 'red_bone_marrow', 'bone_marrow', 'yellow_bone_marrow', 'connective_tissue']
senticnet['yellow_milkwort'] = ['-0.70', '0', '0.769', '-0.70', '#sadness', '#anger', 'negative', '-0.72', 'subshrub', 'suffrutex', 'senega', 'orange_milkwort', 'milkwort']
senticnet['yellow_mountain_saxifrage'] = ['-0.56', '0', '0.848', '-0.80', '#anger', '#disgust', 'negative', '-0.73', 'herb', 'breakstone', 'meadow_saxifrage', 'mossy_saxifrage', 'western_saxifrage']
senticnet['yellow_ochre'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['yellow_orange'] = ['0', '0.123', '-0.06', '0.11', '#interest', '#admiration', 'positive', '0.055', 'red_orange', 'reflect_light', 'blue_green', 'red_green', 'black_green']
senticnet['yellow_paint'] = ['0.996', '0', '0', '0', '#joy', '#joy', 'positive', '0.996', 'wall_decoration', 'renovation', 'painting', 'artwork', 'new_house']
senticnet['yellow_pea'] = ['0', '-0.00', '0.053', '-0.03', '#anger', '#disgust', 'negative', '-0.02', 'kale', 'cucumber', 'cabbage', 'lettuce', 'broccoli']
senticnet['yellow_perch'] = ['-0.89', '0', '0.709', '-0.64', '#sadness', '#anger', 'negative', '-0.74', 'divergent_strabismus', 'exotropia', 'squint', 'abnormality', 'abnormalcy']
senticnet['yellow_pigment'] = ['0', '0', '0', '0.098', '#admiration', '#admiration', 'positive', '0.098', 'cobalt', 'many_shades', 'cyan', 'green_yellow', 'tint']
senticnet['yellow_pine'] = ['-0.69', '0', '0.725', '-0.89', '#anger', '#disgust', 'negative', '-0.77', 'ponderosa', 'ponderosa_pine', 'western_yellow_pine', 'bull_pine', 'longleaf_pine']
senticnet['yellow_poplar'] = ['-0.69', '0', '0.725', '-0.89', '#anger', '#disgust', 'negative', '-0.77', 'ponderosa', 'ponderosa_pine', 'western_yellow_pine', 'bull_pine', 'longleaf_pine']
senticnet['yellow_rattle'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
senticnet['yellow_rattle_rhinanthus_minor'] = ['-0.71', '0', '0.983', '-0.62', '#sadness', '#anger', 'negative', '-0.77', 'bush', 'mallow', 'mu_rose', 'common_mallow', 'tall_mallow']
senticnet['yellow_ribbon'] = ['0.065', '-0.01', '0', '0', '#joy', '#surprise', 'positive', '0.04', 'oil_painting', 'tapestry', 'wall_art', 'use_oil_paint', 'artist_tool']
senticnet['yellow_rose'] = ['0', '0.71', '0', '0.701', '#interest', '#admiration', 'positive', '0.706', 'flower', 'plant', 'flower_bloom', 'yellow_flower', 'good_smell']
senticnet['yellow_sash'] = ['-0.53', '0', '0.682', '-0.74', '#anger', '#disgust', 'negative', '-0.65', 'girdle', 'cincture', 'cummerbund', 'waistband', 'waistcloth']
senticnet['yellow_silk'] = ['0.035', '0', '0', '0.116', '#joy', '#admiration', 'positive', '0.076', 'maroon', 'color', 'bright_color', 'hair_colored', 'cool_color']
senticnet['yellow_slicker'] = ['0', '-0.08', '0.105', '-0.10', '#anger', '#disgust', 'negative', '-0.04', 'underwear', 'shirt', 'panties', 'robe', 'slack']
senticnet['yellow_stripe'] = ['0.942', '-0.61', '0', '0.908', '#joy', '#admiration', 'positive', '0.82', 'adornment', 'cigar_band', 'hatband', 'neckband', 'pinstripe']
senticnet['yellow_supergiant'] = ['0.509', '-0.72', '0', '0.657', '#surprise', '#admiration', 'positive', '0.629', 'giant', 'huge', 'supernova', 'supergiant', 'star']
senticnet['yellow_taxi'] = ['0.04', '0.144', '-0.07', '0.122', '#interest', '#admiration', 'positive', '0.058', 'travel', 'abroad', 'foreign_country', 'transit', 'elsewhere']
senticnet['yellow_throated_marten'] = ['-0.79', '0', '0.667', '-0.66', '#sadness', '#disgust', 'negative', '-0.71', 'wild_sheep', 'argal', 'urial', 'white_sheep', 'mountain_sheep']
senticnet['yellow_tooth'] = ['-0.09', '0', '0', '-0.11', '#sadness', '#disgust', 'negative', '-0.10', 'farm_crop', 'bad_taste', 'starch', 'meat', 'cereal_grain']
senticnet['yellow_waistcoat'] = ['-0.84', '0', '0.633', '-0.53', '#sadness', '#anger', 'negative', '-0.67', 'grizzle', 'postiche', 'horsehair_wig', 'wig', 'peruke']
senticnet['yellow_watercress'] = ['0.758', '-0.97', '0', '0.851', '#surprise', '#admiration', 'positive', '0.861', 'marsh_cress', 'watercress', 'mountain_watercress', 'common_watercress', 'great_yellowcress']
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senticnet['yellowbelly'] = ['-0.95', '0', '0.624', '-0.57', '#sadness', '#anger', 'negative', '-0.71', 'fry', 'kid', 'minor', 'tike', 'small_fry']
senticnet['yellowish'] = ['0', '0.761', '0', '0.704', '#interest', '#admiration', 'positive', '0.732', 'waxy', 'brownish', 'rubbery', 'leathery', 'whitish']
senticnet['yellowish_skin'] = ['0.352', '0.142', '0', '0.281', '#joy', '#admiration', 'positive', '0.258', 'knowledgeable', 'complement', 'much_need', 'see_ocean', 'good_sense_of_humor']
senticnet['yellowish_white'] = ['-0.7', '0', '0.617', '-0.52', '#sadness', '#anger', 'negative', '-0.61', 'silvery', 'achromatic', 'bluish_black', 'hueless', 'pitchy']
senticnet['yellowjacket'] = ['0.748', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.873', 'butterfly', 'moth', 'parrot', 'beetle', 'falcon']
senticnet['yellowness'] = ['-0.51', '0', '0.626', '-0.54', '#anger', '#disgust', 'negative', '-0.56', 'brownish_yellow', 'canary_yellow', 'old_gold', 'gold', 'canary']
senticnet['yellowness_yellow'] = ['-0.52', '0', '0.621', '-0.55', '#anger', '#disgust', 'negative', '-0.56', 'brownish_yellow', 'canary', 'old_gold', 'gold', 'yellowness']
senticnet['yellowstone'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['yellowstone_bison'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['yellowstone_ecosystem'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['yellowstone_hotspot'] = ['-0.87', '0', '0.818', '0', '#sadness', '#anger', 'negative', '-0.84', 'slow_speed', 'wifi', 'computer_internet_connection', 'internet_connection', 'boot_up_computer']
senticnet['yellowstone_lake'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['yellowstone_national_park'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['yellowstone_park'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['yellowstone_river'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['yellowtail'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'crab', 'swimming_crab', 'rock_crab', 'fiddler_crab', 'pea_crab']
senticnet['yellowtail_snapper'] = ['-0.69', '0', '0.766', '-0.82', '#anger', '#disgust', 'negative', '-0.76', 'crab', 'swimming_crab', 'rock_crab', 'fiddler_crab', 'pea_crab']
senticnet['yen'] = ['-0.99', '0', '-0.75', '0', '#sadness', '#fear', 'negative', '-0.87', 'waste', 'ache', 'yearn', 'languish', 'pine_away']
senticnet['yeni_mosque'] = ['0.931', '-0.74', '0', '0.729', '#joy', '#surprise', 'positive', '0.802', 'caravansary', 'caravanserai', 'khan', 'caravan_inn', 'hostel']
senticnet['yeobright'] = ['0.579', '0.652', '-0.24', '0.276', '#joy', '#interest', 'positive', '0.315', 'tranquilly', 'tranquility', 'quietness', 'tranquil', 'unagitated']
senticnet['yeoman'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'bodyguard', 'outrider', 'bodyguard_of_guard', 'beefeater', 'defender']
senticnet['yeoman_farmer'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'bodyguard', 'outrider', 'bodyguard_of_guard', 'beefeater', 'defender']
senticnet['yeoman_of_guard'] = ['-0.99', '0', '0.877', '-0.77', '#sadness', '#anger', 'negative', '-0.88', 'yeoman', 'outrider', 'bodyguard', 'beefeater', 'defender']
senticnet['yes'] = ['0.712', '0', '0', '0.858', '#joy', '#admiration', 'positive', '0.785', 'well', 'think', 'really', 'people', 'time']
senticnet['yes-man'] = ['-0.87', '0', '0', '-0.99', '#sadness', '#disgust', 'negative', '-0.93', 'sycophant', 'suck-up', 'lapdog', 'war-monger', 'flunky']
senticnet['yesterday'] = ['0', '0', '-0.97', '-0.93', '#fear', '#disgust', 'negative', '-0.95', 'reckon', 'yore', 'courtship', 'year', 'time']
senticnet['yesterday_morning'] = ['0.02', '0.067', '0', '0', '#joy', '#interest', 'positive', '0.044', 'sunrise', 'begin_day', 'bright_sky', 'sunny', 'part_of_day']
senticnet['yeti'] = ['0.848', '-0.51', '0', '0.841', '#joy', '#admiration', 'positive', '0.734', 'mythical_monster', 'mythical_creature', 'abominable_snowman', 'doppelganger', 'legendary_creature']
senticnet['yew_wood'] = ['-0.06', '0', '0.026', '0', '#sadness', '#anger', 'negative', '-0.04', 'woodworking', 'tool', 'garden_tool', 'garden_implement', 'useful_tool']
senticnet['yewbarrow'] = ['0.911', '-0.82', '0', '0.844', '#joy', '#admiration', 'positive', '0.86', 'autobahn', 'autostrada', 'turnpike', 'toll_road', 'highway']
senticnet['yiannakis_omirou'] = ['0.911', '-0.71', '0', '0.552', '#joy', '#surprise', 'positive', '0.726', 'deliver_goods', 'succeed', 'win', 'bring_home_bacon', 'come_through']
senticnet['yield'] = ['-0.75', '0', '-1', '0', '#sadness', '#fear', 'negative', '-0.87', 'give_in', 'submit', 'accept', 'defer', 'succumb']
senticnet['yieldeth'] = ['-0.16', '0.179', '0.568', '-0.61', '#anger', '#disgust', 'negative', '-0.29', 'angrily', 'ire', 'see_red', 'dander', 'hackle']
senticnet['yielding'] = ['0.624', '-0.50', '0', '0.816', '#joy', '#admiration', 'positive', '0.648', 'docile', 'tame', 'meek', 'sheeplike', 'sheepish']
senticnet['ylabel'] = ['0.777', '-0.85', '0', '0.615', '#joy', '#surprise', 'positive', '0.748', 'think', 'play', 'trifle', 'abstract', 'warm_to']
senticnet['ymca_camp'] = ['0', '0.156', '-0.06', '0.198', '#interest', '#admiration', 'positive', '0.098', 'grassland', 'camping', 'camp_out', 'campsite', 'campground']
senticnet['yo-yo'] = ['0.978', '0.983', '0', '0', '#joy', '#interest', 'positive', '0.98', 'toy_soldier', 'toy', 'action_figure', 'toy_car', 'toy_marble']
senticnet['yob'] = ['-0.77', '0', '0.692', '-0.72', '#sadness', '#disgust', 'negative', '-0.73', 'bullyboy', 'muscleman', 'muscle', 'skinhead', 'skin']
senticnet['yobbo'] = ['-0.69', '0', '0.876', '-0.62', '#sadness', '#anger', 'negative', '-0.72', 'bullyboy', 'muscleman', 'muscle', 'skinhead', 'skin']
senticnet['yobo'] = ['-0.95', '0', '0.886', '-0.51', '#sadness', '#anger', 'negative', '-0.78', 'bullyboy', 'muscleman', 'muscle', 'skinhead', 'skin']
senticnet['yoga'] = ['0', '0', '0', '0.93', '#admiration', '#admiration', 'positive', '0.93', 'lift_weight', 'release_stress', 'move_body', 'take_hike', 'change_color']
senticnet['yoga_instructor'] = ['0', '0', '0', '0.93', '#admiration', '#admiration', 'positive', '0.93', 'lift_weight', 'release_stress', 'move_body', 'take_hike', 'change_color']
senticnet['yoga_meditation'] = ['0', '0', '0', '0.93', '#admiration', '#admiration', 'positive', '0.93', 'lift_weight', 'release_stress', 'move_body', 'take_hike', 'change_color']
senticnet['yoga_studio'] = ['0', '0', '0', '0.93', '#admiration', '#admiration', 'positive', '0.93', 'lift_weight', 'release_stress', 'move_body', 'take_hike', 'change_color']
senticnet['yoga_teacher'] = ['0', '0', '0', '0.93', '#admiration', '#admiration', 'positive', '0.93', 'lift_weight', 'release_stress', 'move_body', 'take_hike', 'change_color']
senticnet['yoga_therapy'] = ['0', '0', '0', '0.93', '#admiration', '#admiration', 'positive', '0.93', 'lift_weight', 'release_stress', 'move_body', 'take_hike', 'change_color']
senticnet['yoghurt'] = ['0.053', '0.085', '0.019', '0', '#joy', '#interest', 'positive', '0.04', 'farm', 'countryside', 'farm_animal', 'cow', 'healthy']
senticnet['yogi_bear'] = ['0', '-0.17', '-0.04', '0.091', '#surprise', '#admiration', 'positive', '0.072', 'child_book', 'child_movie', 'child_story', 'funny_moment', 'childhood']
senticnet['yogic'] = ['0', '0', '0', '0.93', '#admiration', '#admiration', 'positive', '0.93', 'lift_weight', 'release_stress', 'move_body', 'take_hike', 'change_color']
senticnet['yogurt'] = ['0', '0', '0', '0.93', '#admiration', '#admiration', 'positive', '0.93', 'lunch_box', 'ferment_beverage', 'healthy_drink', 'ice_cream', 'healthy_snack']
senticnet['yogurt_container'] = ['0', '0', '0', '0.93', '#admiration', '#admiration', 'positive', '0.93', 'yogurt', 'ferment_beverage', 'healthy_drink', 'ice_cream', 'healthy_snack']
senticnet['yohimbine'] = ['0.627', '-0.72', '0', '0.974', '#surprise', '#admiration', 'positive', '0.774', 'analgesic', 'morphia', 'anodyne', 'painkiller', 'pain_pill']
senticnet['yok'] = ['0.973', '-0.50', '0', '0.612', '#joy', '#admiration', 'positive', '0.698', 'chic', 'chicness', 'jauntiness', 'modishness', 'stylishness']
senticnet['yokel'] = ['-0.96', '0', '0.924', '-0.86', '#sadness', '#anger', 'negative', '-0.91', 'redneck', 'rustic', 'rube', 'hick', 'hayseed']
senticnet['yokozuna'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['yokozuna_asashoryu'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['yokut'] = ['0.989', '0.952', '0', '0', '#joy', '#interest', 'positive', '0.97', 'committee', 'council', 'caste', 'association', 'organization']
senticnet['yolo'] = ['0.715', '0.75', '0', '0', '#joy', '#interest', 'positive', '0.732', 'butter_toast', 'taste_food', 'eat_animal', 'feel_grateful', 'live_apartment']
senticnet['yonder'] = ['0.949', '-0.84', '0', '0', '#joy', '#surprise', 'positive', '0.895', 'over', 'heaven', 'beyond', 'hither', 'away']
senticnet['yoof'] = ['-0.62', '0', '0.768', '-0.58', '#sadness', '#anger', 'negative', '-0.66', 'rascal', 'rapscallion', 'scalawag', 'holy_terror', 'monkey']
senticnet['yore'] = ['0.797', '0.997', '0', '0', '#joy', '#interest', 'positive', '0.897', 'reckon', 'yesterday', 'courtship', 'age', 'year']
senticnet['yorick_skull'] = ['-0.91', '0', '-0.79', '0', '#sadness', '#fear', 'negative', '-0.85', 'play', 'perpetrator', 'role_play', 'act_part', 'terrible_event']
senticnet['york_theatre'] = ['0.033', '0.088', '-0.06', '0.068', '#interest', '#admiration', 'positive', '0.032', 'earl', 'noble', 'king', 'sultan', 'royal_title']
senticnet['yorkshireman'] = ['-0.79', '0', '0.992', '-0.93', '#anger', '#disgust', 'negative', '-0.90', 'peddler', 'pedlar', 'packman', 'hawker', 'pitchman']
senticnet['yorktown'] = ['0.084', '-0.05', '0.013', '0', '#joy', '#surprise', 'positive', '0.043', 'carnival_ride', 'carny', 'fortune_teller', 'horseback', 'ferris_wheel']
senticnet['yosemite'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['yosemite_national_park'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['yosemite_valley'] = ['-0.03', '0.224', '-0.06', '0.067', '#interest', '#admiration', 'positive', '0.05', 'bill', 'invoice', 'expense', 'mastercard', 'payment']
senticnet['yoshiaki_fujiwara'] = ['0', '0.128', '0.01', '0.093', '#interest', '#admiration', 'positive', '0.07', 'giant', 'capitol_build', 'tall_man', 'egret', 'capital_build']
senticnet['yoshihiro_togashi'] = ['0.916', '0.882', '0', '0', '#joy', '#interest', 'positive', '0.899', 'anime', 'cartoon', 'radio-control_helicopter', 'beat_off', 'comic_book']
senticnet['yost_ice'] = ['0.026', '-0.03', '0', '0.091', '#surprise', '#admiration', 'positive', '0.051', 'stadium', 'auditorium', 'television_station', 'music_hall', 'movie_theater']
senticnet['youanmi'] = ['-0.78', '0', '0.699', '-0.80', '#sadness', '#disgust', 'negative', '-0.76', 'battery', 'stamp_battery', 'quartz_battery', 'quartz_mill', 'factory']
senticnet['young'] = ['0.966', '0', '0', '0.802', '#joy', '#admiration', 'positive', '0.884', 'new', 'untested', 'youthful', 'offspring', 'immature']
senticnet['young_adulthood'] = ['0.995', '0.841', '0', '0', '#joy', '#interest', 'positive', '0.918', 'tell_everyone', 'reason', 'clarify', 'forethought', 'opinion']
senticnet['young_age'] = ['0.025', '0.073', '-0.05', '0.1', '#interest', '#admiration', 'positive', '0.037', 'young_people', 'energy', 'hope', 'young_life', 'healthy']
senticnet['young_animal'] = ['0.024', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'new_life', 'baby_dog', 'little', 'cute', 'baby_cat']
senticnet['young_aristocrat'] = ['0.814', '-0.50', '0', '0.622', '#joy', '#admiration', 'positive', '0.648', 'friend', 'champion', 'supporter', 'protagonist', 'endorser']
senticnet['young_boy'] = ['0', '0', '0', '0.101', '#admiration', '#admiration', 'positive', '0.101', 'mailman', 'package', 'mail', 'parcel', 'letter_slot']
senticnet['young_child'] = ['0.024', '-0.04', '0', '0', '#joy', '#surprise', 'positive', '0.035', 'child', 'youth', 'child_smile', 'kid', 'childhood']
senticnet['young_gentleman'] = ['0.525', '-0.85', '0', '0.599', '#surprise', '#admiration', 'positive', '0.66', 'frolic', 'gambol', 'romp', 'play', 'caper']
senticnet['young_girl'] = ['0', '0.871', '0', '0.861', '#interest', '#admiration', 'positive', '0.866', 'female', 'lady', 'girl', 'alive', 'human_being']
senticnet['young_horse'] = ['0.831', '0.888', '0', '0', '#joy', '#interest', 'positive', '0.859', 'horse', 'sprinter', 'great_dane', 'mule', 'ride_horse']
senticnet['young_kid'] = ['0.054', '0', '0', '0.061', '#joy', '#admiration', 'positive', '0.057', 'small_boy', 'small_adult', 'young_people', 'little_person', 'helpless']
senticnet['young_lad'] = ['0.575', '-0.62', '0', '0.596', '#surprise', '#admiration', 'positive', '0.598', 'altar_boy', 'cub', 'ball_boy', 'bat_boy', 'lad']
senticnet['young_lady'] = ['0', '0.848', '0', '0.723', '#interest', '#admiration', 'positive', '0.785', 'young_woman', 'sexy', 'beauty', 'human_being', 'miss']
senticnet['young_life'] = ['0.025', '0.073', '-0.05', '0.1', '#interest', '#admiration', 'positive', '0.037', 'young_age', 'energy', 'hope', 'young_people', 'healthy']
senticnet['young_man'] = ['0', '0.076', '0', '0', '#interest', '#interest', 'positive', '0.076', 'person', 'alive', 'investor', 'human_being', 'gardener']
senticnet['young_men'] = ['0.025', '0.073', '-0.05', '0.1', '#interest', '#admiration', 'positive', '0.037', 'young_age', 'energy', 'hope', 'young_life', 'healthy']
senticnet['young_mody'] = ['0.504', '-0.93', '0', '0.797', '#surprise', '#admiration', 'positive', '0.747', 'diabetes', 'genetic_disease', 'genetic_disorder', 'genetic_abnormality', 'genetic_defect']
senticnet['young_people'] = ['0.025', '0.073', '-0.05', '0.1', '#interest', '#admiration', 'positive', '0.037', 'young_age', 'energy', 'hope', 'young_life', 'healthy']
senticnet['young_scholar'] = ['0', '0.842', '0', '0.735', '#interest', '#admiration', 'positive', '0.788', 'manpower_training', 'mathematics', 'education', 'elem_secondary_informal_sci', 'continue_grant']
senticnet['young_scientist'] = ['0', '0.983', '0', '0.807', '#interest', '#admiration', 'positive', '0.895', 'training', 'research', 'fellowship', 'biological_informatics', 'conference']
senticnet['young_social_scientist'] = ['0', '0.884', '0', '0.742', '#interest', '#admiration', 'positive', '0.813', 'advance', 'training', 'project', 'addition', 'knowledge']
senticnet['young_woman'] = ['0', '0.851', '0', '0.966', '#interest', '#admiration', 'positive', '0.908', 'sexy', 'young_lady', 'beauty', 'young_age', 'attractive']
senticnet['younger'] = ['0.03', '0.109', '-0.07', '0.084', '#interest', '#admiration', 'positive', '0.036', 'plural', 'kin', 'relate_person', 'calve', 'young_people']
senticnet['younger_avestan'] = ['-0.22', '0', '-0.09', '-0.16', '#sadness', '#disgust', 'negative', '-0.16', 'inequality', 'reject', 'discard', 'difference', 'gap']
senticnet['younger_brother'] = ['0.948', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.888', 'relation', 'mom', 'kin', 'cousin', 'granddaughter']
senticnet['younger_cousin'] = ['0', '0.732', '0', '0.994', '#interest', '#admiration', 'positive', '0.863', 'foster_kid', 'daughter', 'relation', 'sister', 'adopt']
senticnet['younger_daughter'] = ['0.707', '0', '0', '0.734', '#joy', '#admiration', 'positive', '0.72', 'husband', 'mum', 'parent', 'mother', 'woman_child']
senticnet['younger_generation'] = ['0.025', '0.073', '-0.05', '0.1', '#interest', '#admiration', 'positive', '0.037', 'young_age', 'energy', 'hope', 'young_life', 'healthy']
senticnet['younger_sister'] = ['0.02', '0.209', '-0.14', '0.202', '#interest', '#admiration', 'positive', '0.071', 'familiar', 'mum', 'familiarity_uncle', 'care_giver', 'familiarity']
senticnet['younger_son'] = ['0.948', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.888', 'relation', 'mom', 'kin', 'cousin', 'granddaughter']
senticnet['youngest_brother'] = ['0.948', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.888', 'relation', 'mom', 'kin', 'cousin', 'granddaughter']
senticnet['youngest_child'] = ['0.706', '0', '0', '0', '#joy', '#joy', 'positive', '0.706', 'birth', 'new_baby', 'mother_womb', 'birthday', 'thanksgiving']
senticnet['youngest_contestant'] = ['-0.08', '-0.03', '0', '-0.05', '#sadness', '#disgust', 'negative', '-0.03', 'graduate_student', 'student', 'serious_student', 'competition', 'hard_worker']
senticnet['youngest_daughter'] = ['0', '0.732', '0', '0.994', '#interest', '#admiration', 'positive', '0.863', 'foster_kid', 'daughter', 'relation', 'sister', 'adopt']
senticnet['youngest_sister'] = ['0.02', '0.209', '-0.14', '0.202', '#interest', '#admiration', 'positive', '0.071', 'familiar', 'mum', 'familiarity_uncle', 'care_giver', 'familiarity']
senticnet['youngest_son'] = ['0.948', '0.829', '0', '0', '#joy', '#interest', 'positive', '0.888', 'relation', 'mom', 'kin', 'cousin', 'granddaughter']
senticnet['youngster'] = ['0.754', '0.816', '0', '0', '#joy', '#interest', 'positive', '0.785', 'little_boy', 'innocence', 'youth', 'brat', 'lad']
senticnet['youngstown'] = ['0', '0', '0.04', '-0.04', '#anger', '#disgust', 'negative', '-0.04', 'ore', 'alloy', 'titanium', 'iron', 'strong_material']
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senticnet['zac_purchase'] = ['0', '0', '-0.11', '-0.12', '#fear', '#disgust', 'negative', '-0.11', 'hunting_season', 'hound', 'cougar', 'outlaw', 'dragon']
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senticnet['zdzisaw'] = ['0', '0', '0', '0.1', '#admiration', '#admiration', 'positive', '0.1', 'find_earth', 'wax', 'plutonium', 'shine', 'melt']
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senticnet['zebra_crossing'] = ['0.908', '-0.56', '0', '0.878', '#joy', '#admiration', 'positive', '0.783', 'crosswalk', 'pelican_crossing', 'school_crossing', 'crossing', 'crossover']
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senticnet['zune_marketplace'] = ['0.101', '-0.04', '0.036', '0', '#joy', '#surprise', 'positive', '0.038', 'song', 'rap', 'jingle', 'chord', 'lyric']
senticnet['zune_software'] = ['0.101', '-0.04', '0.036', '0', '#joy', '#surprise', 'positive', '0.038', 'song', 'rap', 'jingle', 'chord', 'lyric']
senticnet['zuni'] = ['0.052', '0.099', '0', '0.067', '#interest', '#admiration', 'positive', '0.073', 'police_rank', 'head_guy', 'chief', 'leader', 'commander']
senticnet['zurna'] = ['0.821', '-0.53', '0', '0.785', '#joy', '#admiration', 'positive', '0.712', 'fife', 'nose_flute', 'piccolo', 'woodwind', 'flute']
senticnet['zvi_goffer'] = ['0.991', '-0.73', '0', '0.668', '#joy', '#surprise', 'positive', '0.797', 'busy_bee', 'gopher', 'actor', 'doer', 'worker']
senticnet['zweihnder'] = ['0.93', '-0.78', '0', '0.873', '#joy', '#admiration', 'positive', '0.861', 'blade', 'brand', 'steel', 'claymore', 'sword']
senticnet['zygmunt'] = ['0', '0', '0', '0.1', '#admiration', '#admiration', 'positive', '0.1', 'find_earth', 'wax', 'plutonium', 'shine', 'melt']
senticnet['zygomatic'] = ['0.719', '-0.57', '0', '0.651', '#joy', '#admiration', 'positive', '0.647', 'chap', 'break', 'slit', 'gap', 'shift']
senticnet['zygomatic_arch'] = ['0.681', '-0.63', '0', '0.647', '#joy', '#admiration', 'positive', '0.653', 'hip_socket', 'tooth_socket', 'alveolus', 'socket', 'cotyloid_cavity']
senticnet['zygomatic_notch'] = ['0.886', '-0.99', '0', '0.542', '#joy', '#surprise', 'positive', '0.808', 'jowl', 'lower_jaw', 'mandible', 'lantern_jaw', 'lantern_jawr_bone']
senticnet['zygomatic_plate'] = ['0.1', '-0.12', '0.05', '-0.03', '#joy', '#surprise', 'positive', '0.034', 'fancy_dress', 'blouse', 'fashion', 'dress', 'skirt']
senticnet['zygomycota'] = ['0.807', '-0.86', '0', '0.611', '#joy', '#surprise', 'positive', '0.762', 'costia', 'zoomastigote', 'zooflagellate', 'dinoflagellate', 'hypermastigote']
senticnet['zygote'] = ['0', '0.067', '0', '0.07', '#interest', '#admiration', 'positive', '0.069', 'peck', 'bushel', 'chicken_egg', 'ovum', 'egg']
senticnet['zygotic_transcription'] = ['0.085', '0', '0', '0.089', '#joy', '#admiration', 'positive', '0.087', 'steam', 'haze', 'mist', 'misty', 'summer_weather']
senticnet['zymberi'] = ['0.284', '0.208', '-0.09', '0.196', '#joy', '#interest', 'positive', '0.148', 'polar_bear', 'white_animal', 'arctic_fox', 'white_rabbit', 'fuzzy_animal']
senticnet['zymosis'] = ['0.833', '-0.50', '0', '0.519', '#joy', '#admiration', 'positive', '0.62', 'bottom_fermentation', 'top_fermentation', 'vinification', 'chemical_process', 'chemical_change']
